[ { "path": ".gitattributes", "content": "*.sh eol=lf\ngs_quant/_version.py export-subst\n" }, { "path": ".github/ISSUE_TEMPLATE/bug_report.md", "content": "---\nname: Bug report\nabout: Create a report to help us improve\ntitle: \"[BUG]\"\nlabels: ''\nassignees: andyphillipsgs\n\n---\n\n**Describe the bug**\nA clear and concise description of what the bug is.\n\n**To Reproduce**\nSteps to reproduce the behavior with code example.\n\n**Expected behavior**\nA clear and concise description of what you expected to happen.\n\n**Screenshots**\nIf applicable, add screenshots to help explain your problem.\n\n**Systems setup:**\n - OS: [e.g. Windows 10]\n - Python version\n - GS-Quant version [e.g. 0.7]\n\n**Additional context**\nAdd any other context about the problem here.\n" }, { "path": ".github/ISSUE_TEMPLATE/feature_request.md", "content": "---\nname: Feature request\nabout: Suggest an idea for this project\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Describe the problem.**\nA clear and concise description of the problem.\n\n**Describe the solution you'd like**\nA clear and concise description of what you want to happen.\n\n**Describe alternatives you've considered**\nA clear and concise description of any alternative solutions or features you've considered.\n\n**Are you willing to contribute**\nYes/No\n\n**Additional context**\nAdd any other context or screenshots about the feature request here\n" }, { "path": ".github/workflows/python-publish.yml", "content": "# This workflows will upload a Python Package using Twine when a release is created\n# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries\n\nname: Upload Python Package\n\non:\n release:\n types: [created]\n\njobs:\n deploy:\n\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v3\n - name: Set up Python\n uses: actions/setup-python@v3\n with:\n python-version: '3.10'\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install build twine\n - name: Build and publish\n env:\n TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}\n TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}\n run: |\n python -m build\n twine upload dist/*\n" }, { "path": ".gitignore", "content": "*.pyc\ngs_quant/.idea/\n" }, { "path": ".gitlab-ci.yml", "content": "include:\n - project: 'fp/open-source/os-ci'\n file:\n - '.gitlab-ci.yml'\n\nvariables:\n CURL: /sw/gs/git-prod/dist/linux/bin/\n GITLAB_SERVICES_HOME: /gns/area/certified/com/gs/gitlab/gitlab-services/gitlab-services-4.0\n JAVA_HOME: /sw/external/jdk-11/\n NODE: /gns/area/certified/external/org/nodejs/node.js/node_v12.21.0_linux_x64-12.21.0.gns/node_v12.21.0_linux_x64/node-v12.21.0-linux-x64/bin\n GS_PYPI_URL: \"http://pypi.site.gs.com/simple\"\n GS_PYPI_HOST: \"pypi.site.gs.com\"\n \nstages:\n - .pre\n - test\n - clmscan\n - etch_upload\n - .post\n \ncloud_etch_upload:\n stage: etch_upload\n tags:\n - linux\n - default\n - kubernetes\n image: registry.aws.site.gs.com:443/dx/ete/cloudetch-uploader:current\n script:\n - \"$CLOUD_ETCH_UPLOAD\"\n \ntest:py39:\n image: \"registry.aws.site.gs.com:443/dsml/python/python-raw-image/python3.9-rhel8:current\"\n stage: test \n tags: [ linux, default, kubernetes ]\n before_script:\n - export PYTHONPATH=.\n - export PATH=$NODE:$CURL:$JAVA_HOME/bin:$GITLAB_SERVICES_HOME/scripts:$PATH\n - python3 -V\n - pip3 install -i $GS_PYPI_URL --trusted-host $GS_PYPI_HOST --upgrade pip\n - pip3 install -i $GS_PYPI_URL --trusted-host $GS_PYPI_HOST .[test]\n script:\n - pytest -rsx gs_quant/test --junitxml=pytest.xml\n artifacts:\n when: always\n paths:\n - \"pytest.xml\"\n" }, { "path": ".gs-project.yml", "content": "productGuid: \"product::820010\"\n\n\n#etch section\netch:\n testResultPaths:\n - type: \"JUNIT\"\n paths:\n - \"pytest.xml\"\n\n" }, { "path": ".pre-commit-config.yaml", "content": "# run pip install .[develop] to install dependencies\nrepos:\n - repo: local\n hooks:\n - id: ruff-lint\n name: Run ruff lint\n language: system\n types: [python]\n files: 'gs_quant/.*py$'\n entry: ruff check --fix\n - id: ruff-format\n name: Run ruff format\n language: system\n files: 'gs_quant/.*py$'\n entry: ruff format" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to making participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, gender identity and expression, level of experience,\nnationality, personal appearance, race, religion, or sexual identity and\norientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or\nadvances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\n address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces\nwhen an individual is representing the project or its community. Examples of\nrepresenting a project or community include using an official project e-mail\naddress, posting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event. Representation of a project may be\nfurther defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at gs-oss-abuse@gs.com. All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at [http://contributor-covenant.org/version/1/4][version]\n\n[homepage]: http://contributor-covenant.org\n[version]: http://contributor-covenant.org/version/1/4/" }, { "path": "CONTRIBUTING.md", "content": "# Contributing to gs-quant\n\nThis file contains information about reporting issues as well as contributing code. Make sure\nyou read our [Contributor Covenant Code of Conduct](CODE_OF_CONDUCT.md) before you start participating.\n\n\n# Development and Build Environment\n\ngs-quant's development environment relies on the following:\n\n### 1) Python 3.9+\nAvailable at [Python.org](https://www.python.org/downloads/) or from your OS's package manager.\n\n\n### 2) IDE\nThe main developers use [PyCharm](https://www.jetbrains.com/pycharm/) but you can use another IDE or editor.\n\nPlease do not commit IDE files.\n\n\n# Issues\nSearch the issue tracker for a relevant issue or create a new one.\n\n\n# Making changes\nFork the repository in GitHub and make changes in your fork.\n\nBefore you submit your first pull request, please first submit a DCO, per the instructions in the last section on this page.\n\nFinally, submit a pull request. In your pull requests:\n* Make sure you [rebase your fork](https://github.com/edx/edx-platform/wiki/How-to-Rebase-a-Pull-Request) so that pull requests can be fast-forward merges.\n* We generally prefer squashed commits, unless multi-commits add clarity or are required for mixed copyright commits.\n* Your commit message for your code must contain a `covered by: ` line. See above.\n* Every file you modify should contain a single line with copyright information after the Apache header:\n```\n//Portions copyright . Licensed under Apache 2.0 license\n```\n* New files must contain the standard Apache 2.0 header with appropriate copyright holder.\n* If you're going to contribute code from other open source projects, commit that code first with `covered by: `\nwhere `` is license of the code being committed. Ensure the file retains its original copyright notice and add an appropriate line to\nNOTICE.txt in the same commit. You can then modify that code in subsequent commits with a reference to your DCO and copyright.\n\n# Licensing\n\nPlease make sure that any new dependency licenses are listed in the following list of pre-approved licenses list:\n\n```\nMIT\nASL (all versions)\nBSD\nBSD-like\n``` \n\n\n# Coding Style\nPlease see [PEP 8 - Style Guide for Python Code](https://www.python.org/dev/peps/pep-0008/).\n\n\n# Appendix: Contribution Prerequisite: Submitting a DCO\n\nIf you have never contributed to gs-quant, or your copyright ownership has changed, you must first create a pull request that has\na developer certificate of origin (DCO) in it. To create this file, follow these steps:\n\nFor code you write, determine who the copyright owner is. If you are employed in the US, it's likely that your\nemployer can exert copyright ownership over your work, even if the work was not done during regular working hours or\nusing the employer's equipment. Copyright law is highly variable from jurisdiction to jurisdiction. Consult your\nemployer or a lawyer if you are not sure.\n\nIf you've determined that the copyright holder for the code you write is yourself, \nplease fill out the following (replace all `<>` terms); place it in a file under `dco/.dco`. \n\n```\n1) I, , certify that all work committed with the commit message \n\"covered by: .dco\" is my original work and I own the copyright \nto this work. I agree to contribute this code under the Apache 2.0 license.\n\n2) I understand and agree all contribution including all personal \ninformation I submit with it is maintained indefinitely and may be \nredistributed consistent with the open source license(s) involved. \n\nThis certification is effective for all code contributed from to 9999-01-01.\n```\n\nIf you've determined that the copyright holder for the code you write is some other entity (e.g. your employer), \nyou must ensure that you are authorized by the copyright holder to be able to license this code under the \nApache 2.0 license for the purpose of contribution to gs-quant. Negotiating such authorization and administering \nthe terms is entirely between you and the copyright holder. Please fill out the following (replace all\n`<>` terms); place it in a file under `dco/-.dco`. \n\n```\n1) I, , certify that all work committed with the commit message \n\"covered by: -.dco\" is copyright \n and that I am authorized by \nto contribute this code under the Apache 2.0 license.\n\n2) I understand and agree all contribution including all personal \ninformation I submit with it is maintained indefinitely and may be \nredistributed consistent with the open source license(s) involved. \n\nThis certification is effective for all code contributed from to 9999-01-01.\n```\n\n`` must reference your real name; we will not accept aliases, pseudonyms or anonymous contributions.\nIssue a pull request with the appropriate DCO and a change to NOTICE.txt with\none line `This product contains code copyright , licensed under Apache 2.0 license`." }, { "path": "LICENSE", "content": "\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. 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Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n\n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n\n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n\n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n\n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n\n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n\n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. The contents\n of the NOTICE file are for informational purposes only and\n do not modify the License. You may add Your own attribution\n notices within Derivative Works that You distribute, alongside\n or as an addendum to the NOTICE text from the Work, provided\n that such additional attribution notices cannot be construed\n as modifying the License.\n\n You may add Your own copyright statement to Your modifications and\n may provide additional or different license terms and conditions\n for use, reproduction, or distribution of Your modifications, or\n for any such Derivative Works as a whole, provided Your use,\n reproduction, and distribution of the Work otherwise complies with\n the conditions stated in this License.\n\n 5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n\n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n\n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n\n 8. Limitation of Liability. In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n\n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n\n END OF TERMS AND CONDITIONS\n\n APPENDIX: How to apply the Apache License to your work.\n\n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License." }, { "path": "MANIFEST.in", "content": "include gs_quant/config.ini\ninclude gs_quant/resources/asset-service.json\ninclude gs_quant/resources/data-service.json\ninclude gs_quant/resources/risk-service.json\ninclude gs_quant/resources/certificates/*\ninclude gs_quant/examples/*\ninclude gs_quant/guides/*\ninclude gs_quant/tutorials/*\ninclude gs_quant/docs/*\ninclude versioneer.py\ninclude gs_quant/_version.py" }, { "path": "NOTICE", "content": "gs-quant\nCopyright 2018 Goldman Sachs\n" }, { "path": "NOTICE.txt", "content": "gs-quant\nCopyright 2018-2019 Goldman Sachs\nThis product contains code copyright Scott Weinstein, licensed under Apache 2.0 license\n" }, { "path": "README.md", "content": "# GS Quant\n\n**GS Quant** is a Python toolkit for quantitative finance, created on top of one of the world’s most powerful risk transfer platforms. Designed to accelerate development of quantitative trading strategies and risk management solutions, crafted over 25 years of experience navigating global markets.\n\nIt is created and maintained by quantitative developers (quants) at Goldman Sachs to enable the development of trading strategies and analysis of derivative products. GS Quant can be used to facilitate derivative structuring, trading, and risk management, or as a set of statistical packages for data analytics applications.\n\nIn order to access the APIs you will need a client id and secret. These are available to institutional clients of Goldman Sachs. Please speak to your sales coverage or Marquee Sales for further information. \n\nPlease refer to [Goldman Sachs Developer](https://developer.gs.com/docs/gsquant/) for additional information.\n\n## Requirements\n\n* Python 3.9 or greater\n* Access to PIP package manager\n\n## Installation\n\n```\npip install gs-quant\n```\n\n## Examples\n\nYou can find examples, guides and tutorials in the respective folders on [Goldman Sachs Developer](https://developer.gs.com/docs/gsquant/).\n\n\n## Help\n\nPlease reach out to `gs-quant@gs.com` with any questions, comments or feedback.\n" }, { "path": "SKILL.md", "content": "---\nname: gs-quant\ndescription: This document covers the core workflows for using the `gs_quant` library: establishing a session, constructing and resolving instruments, building portfolios, pricing historically, and extracting results. For backtesting, see the dedicated guide at `gs_quant/backtests/SKILL.md`.\n\n# SKILL.md — gs_quant Usage Guide\n\nThis document covers the core workflows for using the `gs_quant` library: establishing a session, constructing and resolving instruments, building portfolios, pricing historically, and extracting results.\n\n> **Backtesting:** For a comprehensive guide to the backtesting framework (engines, triggers, actions, strategies, and result extraction), see [`gs_quant/backtests/SKILL.md`](gs_quant/backtests/SKILL.md).\n\n---\n\n## 1. Creating a Session with `GsSession.use`\n\nAll API communication in `gs_quant` flows through an authenticated `GsSession`. Before making any pricing or data calls you must initialise a session with `GsSession.use()`.\n\n### OAuth2 (Application Credentials)\n\n```python\nfrom gs_quant.session import GsSession, Environment\n\nGsSession.use(\n environment_or_domain=Environment.PROD,\n client_id='my_client_id',\n client_secret='my_client_secret',\n scopes=('run_analytics',)\n)\n```\n\n- **`environment_or_domain`** — `Environment.PROD` (default), `Environment.QA`, or `Environment.DEV`. You can also pass a raw URL string.\n- **`client_id` / `client_secret`** — OAuth2 application credentials. When both are provided the library creates an `OAuth2Session`.\n- **`scopes`** — Optional iterable of `GsSession.Scopes` values. Usually when pricing trade you will need `RUN_ANALYTICS`.\n\n### Kerberos / SSO (Internal GS)\n\nIf no `client_id` is supplied, the library will attempt Kerberos or pass-through authentication automatically:\nThis is for internal GS users only and requires appropriate network access and installation of gs_quant_internal.\n\n```python\nGsSession.use(Environment.PROD)\n```\n\n### Using as a Context Manager\n\n`GsSession` can also be used as a context manager so the session is cleaned up on exit:\n\n```python\nwith GsSession.get(Environment.PROD, client_id='...', client_secret='...') as session:\n # session is active inside this block\n ...\n```\n\n### Verifying the Session\n\nAfter calling `GsSession.use(...)`, the active session is accessible as:\n\n```python\nGsSession.current # the currently active GsSession instance\n```\n\nAll subsequent API calls (instrument resolution, pricing, data queries) will use this session automatically.\n\n---\n\n## 2. Constructing Trades with `gs_quant.instrument`\n\nInstruments are the building blocks of gs_quant. Every tradeable product is represented as a dataclass in `gs_quant.instrument`. Construct an instrument by importing its class and supplying the key economic parameters — any parameter you omit will be resolved by the server later.\n### Common Instrument Examples\n\n#### Interest Rate Swap\n\n```python\nfrom gs_quant.instrument import IRSwap\n\nswap = IRSwap(\n pay_or_receive='Pay', # 'Pay' or 'Receive' the fixed leg\n termination_date='10y', # tenor or explicit date\n notional_currency='USD', # currency\n fixed_rate=0.03, # optional — leave None to resolve at market\n)\n```\n\n#### Interest Rate Swaption\n\n```python\nfrom gs_quant.instrument import IRSwaption\n\nswaption = IRSwaption(\n pay_or_receive='Receive',\n termination_date='10y',\n notional_currency='EUR',\n expiration_date='1y',\n strike='ATM',\n)\n```\n\n#### Interest Rate Cap\n\n```python\nfrom gs_quant.instrument import IRCap\n\ncap = IRCap(\n termination_date='1y',\n notional_currency='USD',\n)\n```\n\n#### FX Option\n\n```python\nfrom gs_quant.instrument import FXOption\n\noption = FXOption(\n pair='EURUSD',\n expiration_date='3m',\n option_type='Call',\n strike_price='ATMF',\n notional_amount=10e6,\n)\n```\n\n#### FX Forward\n\n```python\nfrom gs_quant.instrument import FXForward\n\nfwd = FXForward(\n pair='USDJPY',\n settlement_date='6m',\n notional_amount=10e6,\n)\n```\n\n### Important: FX Instrument Pitfalls\n\nThere are two common mistakes when working with FX options that can lead to confusing results:\n\n#### 1. Always Set `premium=0` on FX Options\n\nWhen constructing FX options (FXOption, FXBinary, FXMultiCrossBinary, etc.), if you don't specify a `premium`, the instrument resolution will automatically set a premium such that the `DollarPrice` becomes zero. This is by design — it represents a \"fair value\" trade where the premium exactly offsets the option value.\n\n**Problem:** If you want to know the cost/value of the option, you'll always get 0.\n\n**Solution:** Always set `premium=0` explicitly when you want `DollarPrice` to return the actual option value:\n\n```python\nfrom gs_quant.instrument import FXOption, FXBinary\n\n# WRONG - DollarPrice will be ~0 after resolution\noption_wrong = FXOption(\n pair='EURUSD',\n expiration_date='3m',\n option_type='Call',\n strike_price='ATMF',\n notional_amount=10e6,\n)\n\n# CORRECT - DollarPrice will be the option value\noption_correct = FXOption(\n pair='EURUSD',\n expiration_date='3m',\n option_type='Call',\n strike_price='ATMF',\n notional_amount=10e6,\n premium=0, # <-- Important!\n)\n\n# Same applies to FXBinary\nbinary = FXBinary(\n pair='EURUSD',\n buy_sell=BuySell.Buy,\n option_type=OptionType.Call,\n strike_price='s',\n notional_amount=1e6,\n notional_currency=Currency.USD,\n expiration_date='3m',\n premium=0, # <-- Important!\n)\n```\n\n#### 2. FXMultiCrossBinaryLeg Uses Different OptionType Values\n\nWhen constructing `FXMultiCrossBinaryLeg` objects (used within `FXMultiCrossBinary` for dual digital options), you must use `OptionType.Binary_Call` or `OptionType.Binary_Put` — **not** `OptionType.Call` or `OptionType.Put`.\n\nThis is different from `FXBinary` which uses `OptionType.Call` / `OptionType.Put`.\n\n```python\nfrom gs_quant.instrument import FXBinary, FXMultiCrossBinary, FXMultiCrossBinaryLeg\nfrom gs_quant.common import BuySell, OptionType, Currency\n\n# FXBinary uses OptionType.Call / OptionType.Put\nsingle_digital = FXBinary(\n pair='EURUSD',\n buy_sell=BuySell.Buy,\n option_type=OptionType.Call, # <-- Call or Put\n strike_price='s',\n notional_amount=1e6,\n notional_currency=Currency.USD,\n expiration_date='3m',\n premium=0,\n)\n\n# FXMultiCrossBinaryLeg uses OptionType.Binary_Call / OptionType.Binary_Put\ndual_digital = FXMultiCrossBinary(\n legs=(\n FXMultiCrossBinaryLeg(\n pair='EURUSD',\n option_type=OptionType.Binary_Call, # <-- Binary_Call or Binary_Put\n strike_price='s',\n ),\n FXMultiCrossBinaryLeg(\n pair='USDJPY',\n option_type=OptionType.Binary_Call, # <-- Binary_Call or Binary_Put\n strike_price='s',\n ),\n ),\n buy_sell=BuySell.Buy,\n notional_amount=1e6,\n notional_currency=Currency.USD,\n expiration_date='3m',\n premium=0, # <-- Don't forget this too!\n)\n```\n\n#### Equity Option\n\n```python\nfrom gs_quant.instrument import EqOption\n\neq_opt = EqOption(\n underlier='.SPX',\n expiration_date='3m',\n strike_price='ATMF',\n option_type='Call',\n option_style='European',\n)\n```\n\nYou can set the instrument `name` property for easy identification later:\n\n```python\nswap.name = 'USD 10y Payer'\n```\n\n---\n\n## 3. Resolving an Instrument\n\nWhen you construct an instrument you typically only specify a subset of its parameters. **Resolving** fills in all remaining fields by sending the instrument to the GS pricing service, which returns a fully specified version based on current market data.\n\n```python\nfrom gs_quant.instrument import IRSwap\n\nswap = IRSwap('Pay', '10y', 'USD')\n\n# Before resolve: swap.fixed_rate is None\nswap.resolve()\n# After resolve: swap.fixed_rate is now populated with the current par rate\n\nprint(swap.fixed_rate) # e.g. 0.0345\n```\n\n### What `resolve()` Does\n\n1. Sends the instrument to the GS analytics service along with the current `PricingContext` (pricing date and market).\n2. The service computes any missing parameters — for example the fixed rate of a par swap, the premium of an option, or the forward points of an FX forward.\n3. By default (`in_place=True`), the instrument is updated in place. Pass `in_place=False` to receive a new resolved copy instead.\n\n### Resolve Under a Specific Pricing Date\n\n```python\nfrom gs_quant.markets import PricingContext\nimport datetime as dt\n\nwith PricingContext(pricing_date=dt.date(2025, 1, 15)):\n resolved = swap.resolve(in_place=False)\n resolved_swap = resolved.result()\n```\n\n### Historical Resolution\n\nResolution can be done across multiple dates via `HistoricalPricingContext`, but `in_place` must be `False`:\n\n```python\nfrom gs_quant.markets import HistoricalPricingContext\nimport datetime as dt\n\nwith HistoricalPricingContext(dt.date(2025, 1, 1), dt.date(2025, 1, 31)):\n resolved_by_date = swap.resolve(in_place=False)\n\n# resolved_by_date is a dict of {date: resolved_instrument}\n```\n\n---\n\n## 4. Combining Instruments in Portfolios\n\nThe `Portfolio` class groups instruments together so you can price, resolve, and analyse them as a single unit.\n\n### Creating a Portfolio\n\n```python\nfrom gs_quant.instrument import IRSwap, IRSwaption\nfrom gs_quant.markets.portfolio import Portfolio\n\nswap = IRSwap('Pay', '10y', 'USD', name='USD 10y Payer')\nswaption = IRSwaption('Receive', '10y', 'EUR', expiration_date='1y', name='EUR 1y10y Receiver')\n\nportfolio = Portfolio([swap, swaption], name='My Portfolio')\n```\n\nYou can also construct a portfolio from a dictionary (keys become instrument names):\n\n```python\nportfolio = Portfolio({\n 'USD 10y Payer': IRSwap('Pay', '10y', 'USD'),\n 'EUR 5y Receiver': IRSwap('Receive', '5y', 'EUR'),\n})\n```\n\n### Nesting Portfolios\n\nPortfolios can contain other portfolios, creating a hierarchy:\n\n```python\nusd_book = Portfolio([IRSwap('Pay', '5y', 'USD'), IRSwap('Receive', '10y', 'USD')], name='USD Book')\neur_book = Portfolio([IRSwap('Pay', '5y', 'EUR')], name='EUR Book')\n\nmaster = Portfolio([usd_book, eur_book], name='Master Book')\n```\n\n### Portfolio Operations\n\n```python\n# Add instruments\nportfolio.append(IRSwap('Pay', '2y', 'GBP'))\n\n# Iterate\nfor instrument in portfolio:\n print(instrument)\n\n# Access by index\nfirst = portfolio[0]\n\n# Access by name\nusd_swap = portfolio['USD 10y Payer']\n\n# Number of top-level priceables\nlen(portfolio)\n\n# All instruments across nested portfolios\nportfolio.all_instruments\n```\n\n### Resolving a Portfolio\n\n```python\nportfolio.resolve() # resolves all instruments in place\n```\n\n### Pricing a Portfolio\n\n```python\nfrom gs_quant.risk import DollarPrice, IRDelta\n\n# Single risk measure\nprices = portfolio.calc(DollarPrice)\n\n# Multiple risk measures at once\nresults = portfolio.calc([DollarPrice, IRDelta])\n```\n\nThe result is a `PortfolioRiskResult` which can be sliced by instrument, risk measure, or date (see section 6).\n\n---\n\n## 5. Historical Pricing with `HistoricalPricingContext`\n\n`HistoricalPricingContext` lets you compute risk measures across a range of dates using the close-of-business market for each date.\n\n### Basic Usage\n\n```python\nimport datetime as dt\nfrom gs_quant.instrument import IRSwap\nfrom gs_quant.markets import HistoricalPricingContext\nfrom gs_quant.risk import DollarPrice\n\nswap = IRSwap('Pay', '10y', 'USD')\n\nwith HistoricalPricingContext(dt.date(2025, 1, 2), dt.date(2025, 1, 31)):\n price_f = swap.price()\n\nprice_series = price_f.result() # a pandas Series indexed by date\n```\n\n### By Number of Business Days\n\nPass an integer to price over the last N business days:\n\n```python\nwith HistoricalPricingContext(10):\n price_f = swap.price()\n\nprice_series = price_f.result()\n```\n\n### With Custom Date List\n\n```python\ndates = [dt.date(2025, 1, 2), dt.date(2025, 3, 15), dt.date(2025, 6, 30)]\n\nwith HistoricalPricingContext(dates=dates):\n price_f = swap.price()\n```\n\n### Key Parameters\n\n| Parameter | Description |\n|---|---|\n| `start` | Start date (or number of business days back from today) |\n| `end` | End date (defaults to today) |\n| `calendars` | Holiday calendar(s) for date generation |\n| `dates` | Explicit iterable of dates (mutually exclusive with `start`) |\n| `is_batch` | Use batch mode for long-running calculations (avoids timeouts) |\n| `is_async` | Return immediately without blocking |\n| `show_progress` | Display a tqdm progress bar |\n| `market_data_location` | `'NYC'`, `'LDN'`, or `'HKG'` (defaults to `'LDN'`) |\n| `csa_term` | CSA term for discounting |\n\n### Historical Pricing with Portfolios\n\n```python\nwith HistoricalPricingContext(dt.date(2025, 1, 2), dt.date(2025, 1, 31)):\n result = portfolio.calc(DollarPrice)\n\n# result is a PortfolioRiskResult with a date dimension\n```\n\n## 6. Result Extraction\n\nCalculation results in gs_quant are rich typed objects that carry metadata (risk key, unit, error info) alongside the actual values. Understanding the result types and how to extract data from them is essential.\n\n### Result Types\n\n| Type | Description |\n|---|---|\n| `FloatWithInfo` | Scalar result (e.g. present value). Behaves like a `float` but carries a `risk_key` and `unit`. |\n| `SeriesWithInfo` | Time series result (historical pricing). A `pandas.Series` with metadata. |\n| `DataFrameWithInfo` | Structured/bucketed result (e.g. delta ladder). A `pandas.DataFrame` with metadata. |\n| `ErrorValue` | Indicates a calculation error. Check `.error` for the message. |\n| `MultipleRiskMeasureResult` | Dict-like mapping of `RiskMeasure → result` when multiple measures are requested on a single instrument. |\n| `PortfolioRiskResult` | Result for a portfolio — can be sliced by instrument, risk measure, or date. |\n\n### Single Instrument Results\n\n```python\nfrom gs_quant.instrument import IRSwap\nfrom gs_quant.risk import DollarPrice, IRDelta\n\nswap = IRSwap('Pay', '10y', 'USD')\n\n# Scalar result\nprice = swap.dollar_price() # FloatWithInfo\nprint(float(price)) # the numeric value\n\n# Local currency price\nlocal_price = swap.price() # FloatWithInfo\n\n# Structured result\ndelta = swap.calc(IRDelta) # DataFrameWithInfo — a bucketed delta ladder\nprint(delta) # displays as a DataFrame with columns like mkt_type, mkt_asset, etc.\n```\n\n### Using Futures (Async / Batched)\n\nInside an entered `PricingContext`, calculations return `PricingFuture` objects. Call `.result()` after exiting the context:\n\n```python\nfrom gs_quant.markets import PricingContext\n\nwith PricingContext():\n price_f = swap.dollar_price()\n delta_f = swap.calc(IRDelta)\n\nprice = price_f.result() # FloatWithInfo\ndelta = delta_f.result() # DataFrameWithInfo\n```\n\n### Multiple Risk Measures on a Single Instrument\n\n```python\nfrom gs_quant.risk import DollarPrice, IRDelta, IRVega\n\nresult = swap.calc([DollarPrice, IRDelta, IRVega]) # MultipleRiskMeasureResult\n\nprice = result[DollarPrice] # FloatWithInfo\ndelta = result[IRDelta] # DataFrameWithInfo\nvega = result[IRVega] # DataFrameWithInfo\n```\n\n### Portfolio Results\n\n`portfolio.calc()` returns a `PortfolioRiskResult` which supports flexible slicing:\n\n```python\nfrom gs_quant.risk import DollarPrice, IRDelta\n\nresult = portfolio.calc([DollarPrice, IRDelta])\n\n# Slice by risk measure\nprices = result[DollarPrice] # PortfolioRiskResult for DollarPrice only\n\n# Slice by instrument (name or object)\nswap_result = result['USD 10y Payer'] # MultipleRiskMeasureResult for that instrument\nswap_price = swap_result[DollarPrice] # FloatWithInfo\n\n# Slice by index\nfirst_result = result[0]\n\n# Iterate over instruments\nfor instrument_result in result:\n print(instrument_result)\n\n# Aggregate across all instruments\ntotal_price = result[DollarPrice].aggregate() # sums all instrument prices\n```\n\n### Historical Results\n\nWhen using `HistoricalPricingContext`, scalar results become time series:\n\n```python\nimport datetime as dt\nfrom gs_quant.markets import HistoricalPricingContext\n\nwith HistoricalPricingContext(dt.date(2025, 1, 2), dt.date(2025, 1, 31)):\n price_f = swap.price()\n\nprice_series = price_f.result() # SeriesWithInfo indexed by date\nprint(price_series)\n\n# Access value for a specific date\njan_15_price = price_series[dt.date(2025, 1, 15)]\n```\n\nHistorical portfolio results can also be sliced by date:\n\n```python\nwith HistoricalPricingContext(dt.date(2025, 1, 2), dt.date(2025, 1, 31)):\n result = portfolio.calc(DollarPrice)\n\n# Slice by date\njan_15 = result[dt.date(2025, 1, 15)] # PortfolioRiskResult for that single date\n\n# Available dates\nresult.dates # tuple of dt.date\n```\n\n### Converting to DataFrames\n\nAll result types support conversion to pandas DataFrames:\n\n```python\n# Portfolio result to DataFrame (pivoted)\ndf = result.to_frame()\n\n# Unpivoted (raw records)\ndf_raw = result.to_frame(values=None, index=None, columns=None)\n\n# Custom pivoting\ndf_custom = result.to_frame(values='value', index='dates', columns='instrument_name')\n```\n\n`MultipleRiskMeasureResult` also supports `.to_frame()`:\n\n```python\nmulti_result = swap.calc([DollarPrice, IRDelta])\ndf = multi_result.to_frame()\n```\n\n### Checking for Errors\n\n```python\nfrom gs_quant.risk import ErrorValue\n\nprice = swap.dollar_price()\nif isinstance(price, ErrorValue):\n print(f'Calculation failed: {price.error}')\nelse:\n print(f'Price: {float(price)}')\n```\n\n### Arithmetic on Results\n\nResults support arithmetic operations, which is useful for computing P&L or scaling:\n\n```python\n# Multiply portfolio result by a scalar\nscaled = result * 1000\n\n# Add results from different portfolios\ncombined = result_a + result_b\n```\n\n---\n\n## 7. Backtesting\n\ngs_quant includes a full backtesting framework under `gs_quant.backtests`. It lets you define trading strategies as combinations of **triggers** (when to act) and **actions** (what to do), then simulate them historically using one of several engines.\n\nFor the complete backtesting guide — including all trigger types, action types, engine selection, and result extraction — see:\n\n📄 **[`gs_quant/backtests/SKILL.md`](gs_quant/backtests/SKILL.md)**\n\n### Quick Example — Monthly FX Option Roll\n\n```python\nfrom datetime import date\nfrom gs_quant.instrument import FXOption\nfrom gs_quant.common import BuySell, OptionType\nfrom gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\nfrom gs_quant.backtests.actions import AddTradeAction\nfrom gs_quant.backtests.generic_engine import GenericEngine\nfrom gs_quant.backtests.strategy import Strategy\nfrom gs_quant.risk import Price\n\nstart_date = date(2023, 1, 3)\nend_date = date(2024, 12, 31)\n\ncall = FXOption(\n buy_sell=BuySell.Buy, option_type=OptionType.Call,\n pair='USDJPY', strike_price='ATMF', expiration_date='2y',\n name='2y_call', premium=0,\n)\n\ntrig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m')\naction = AddTradeAction(call, '1m')\ntrigger = PeriodicTrigger(trig_req, action)\n\nstrategy = Strategy(None, trigger)\nGE = GenericEngine()\nbacktest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n# View results\nbacktest.result_summary['Total'].plot(title='Performance')\n```\n\n" }, { "path": "conftest.py", "content": "# This import is needed to register the magic pylint hooks to make it skip the integration tests\n# flake8: noqa\n# pylint: disable=wildcard-import, unused-wildcard-import\nfrom gs_quant.test.mock_data_test_utils import *\n" }, { "path": "dco/Bryan Galindo.dco", "content": "1) I, Bryan Galindo, certify that all work committed with the commit message\n\"covered by: Bryan Galindo.dco\" is my original work and I own the copyright\nto this work. I agree to contribute this code under the Apache 2.0 license.\n\n2) I understand and agree all contribution including all personal\ninformation I submit with it is maintained indefinitely and may be\nredistributed consistent with the open source license(s) involved.\n\nThis certification is effective for all code contributed from 2022-08-30 to 9999-01-01." }, { "path": "dco/Grant McGovern.dco", "content": "1) I, Grant McGovern, certify that all work committed with the commit message\n\"covered by: Grant McGovern.dco\" is my original work and I own the copyright\nto this work. I agree to contribute this code under the Apache 2.0 license.\n\n2) I understand and agree all contribution including all personal\ninformation I submit with it is maintained indefinitely and may be\nredistributed consistent with the open source license(s) involved.\n\nThis certification is effective for all code contributed from 2019-05-14 to 9999-01-01." }, { "path": "dco/Maverick Lin.dco", "content": "1) I, Maverick Lin, certify that all work committed with the commit message\n\"covered by: Maverick Lin.dco\" is my original work and I own the copyright\nto this work. I agree to contribute this code under the Apache 2.0 license.\n\n2) I understand and agree all contribution including all personal\ninformation I submit with it is maintained indefinitely and may be\nredistributed consistent with the open source license(s) involved.\n\nThis certification is effective for all code contributed from 2019-05-14 to 9999-01-01.\n" }, { "path": "dco/Scott Weinstein.dco", "content": "1) I, Scott Weinstein, certify that all work committed with the commit message\n\"covered by: Scott Weinstein.dco\" is my original work and I own the copyright\nto this work. I agree to contribute this code under the Apache 2.0 license.\n\n2) I understand and agree all contribution including all personal\ninformation I submit with it is maintained indefinitely and may be\nredistributed consistent with the open source license(s) involved.\n\nThis certification is effective for all code contributed from 2019-05-08 to 9999-01-01." }, { "path": "docs/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS =\nSPHINXBUILD = sphinx-build\nSOURCEDIR = .\nBUILDDIR = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)" }, { "path": "docs/README.md", "content": "# Overview\n\nSphinx-generated documentation for gs-quant codebase\n\nIf you're looking for tutorials and other developer docs, check out [the developer-site repo](https://gitlab.gs.com/marquee/aurora/developer-site/).\n\n# Important Links\n\n- [GS Developer Marketing](https://developer.gs.com/discover/products/gs-quant/)\n- [GS Developer Docs](https://developer.gs.com/docs/gsquant/)\n\n# Contacts\n\n- Symphony room: gs_quant, GS Developer Site\n- Distribution list: gs-quant@gs.com\n" }, { "path": "docs/_build/html/.buildinfo", "content": "# Sphinx build info version 1\n# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.\nconfig: a31b8105bdf16238d68e5a89b0da3f8a\ntags: 645f666f9bcd5a90fca523b33c5a78b7\n" }, { "path": "docs/_build/html/_modules/aenum.html", "content": "\n\n\n\n \n\n \n \n \n \n \n aenum — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n\n\n\n
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Source code for aenum

\n"""Python Advanced Enumerations & NameTuples"""\n\nimport sys as _sys\npyver = float('%s.%s' % _sys.version_info[:2])\n\nimport re\n\ntry:\n    from collections import OrderedDict\nexcept ImportError:\n    OrderedDict = dict\nfrom collections import defaultdict\ntry:\n    import sqlite3\nexcept ImportError:\n    sqlite3 = None\n\nif pyver >= 3:\n    from functools import reduce\n\nfrom operator import or_ as _or_, and_ as _and_, xor as _xor_, inv as _inv_\nfrom operator import abs as _abs_, add as _add_, floordiv as _floordiv_\nfrom operator import lshift as _lshift_, rshift as _rshift_, mod as _mod_\nfrom operator import mul as _mul_, neg as _neg_, pos as _pos_, pow as _pow_\nfrom operator import truediv as _truediv_, sub as _sub_\nif pyver < 3:\n    from operator import div as _div_\n\nif pyver >= 3:\n    from inspect import getfullargspec\n    def getargspec(method):\n        args, varargs, keywords, defaults, _, _, _ = getfullargspec(method)\n        return args, varargs, keywords, defaults\nelse:\n    from inspect import getargspec\n\n\n__all__ = [\n        'NamedConstant', 'constant', 'skip', 'nonmember', 'member', 'no_arg',\n        'Enum', 'IntEnum', 'AutoNumberEnum', 'OrderedEnum', 'UniqueEnum',\n        'StrEnum', 'UpperStrEnum', 'LowerStrEnum',\n        'Flag', 'IntFlag',\n        'AutoNumber', 'MultiValue', 'NoAlias', 'Unique',\n        'enum', 'extend_enum', 'unique', 'enum_property',\n        'NamedTuple', 'SqliteEnum',\n        ]\nif sqlite3 is None:\n    __all__.remove('SqliteEnum')\n\nversion = 2, 2, 4\n\ntry:\n    any\nexcept NameError:\n    def any(iterable):\n        for element in iterable:\n            if element:\n                return True\n        return False\n\ntry:\n    basestring\nexcept NameError:\n    # In Python 2 basestring is the ancestor of both str and unicode\n    # in Python 3 it's just str, but was missing in 3.1\n    basestring = str\n\ntry:\n    unicode\nexcept NameError:\n    # In Python 3 unicode no longer exists (it's just str)\n    unicode = str\n\ntry:\n    long\n    baseinteger = int, long\nexcept NameError:\n    baseinteger = int\n# deprecated\nbaseint = baseinteger\n\ntry:\n    NoneType\nexcept NameError:\n    NoneType = type(None)\n\ntry:\n    # derive from stdlib enum if possible\n    import enum\n    if hasattr(enum, 'version'):\n        StdlibEnumMeta = StdlibEnum = None\n    else:\n        from enum import EnumMeta as StdlibEnumMeta, Enum as StdlibEnum\n    del enum\nexcept ImportError:\n    StdlibEnumMeta = StdlibEnum = None\n\n# will be exported later\nAutoValue = AutoNumber = MultiValue = NoAlias = Unique = None\n\nclass enum_property(object):\n    """\n    This is a descriptor, used to define attributes that act differently\n    when accessed through an enum member and through an enum class.\n    Instance access is the same as property(), but access to an attribute\n    through the enum class will look in the class' _member_map_.\n    """\n\n    def __init__(self, fget=None, doc=None, name=None):\n        self.fget = fget\n        self.__doc__ = doc or fget.__doc__\n        self.name = name\n\n    def __call__(self, func, doc=None):\n        self.fget = func\n        self.__doc__ = self.__doc__ or doc or func.__doc__\n\n    def __get__(self, instance, ownerclass=None):\n        if instance is None:\n            try:\n                return ownerclass._member_map_[self.name]\n            except KeyError:\n                raise AttributeError('%r not found in %r' % (self.name, ownerclass.__name__))\n        else:\n            if self.fget is not None:\n                return self.fget(instance)\n            else:\n                # search through mro\n                for base in ownerclass.__mro__[1:]:\n                    if self.name in base.__dict__:\n                        attr = base.__dict__[self.name]\n                        break\n                else:\n                    raise AttributeError('%r not found in %r' % (self.name, instance))\n                if isinstance(attr, classmethod):\n                    attr = attr.__func__\n                    return lambda *args, **kwds: attr(ownerclass, *args, **kwds)\n                elif isinstance(attr, staticmethod):\n                    return attr.__func__\n                elif isinstance(attr, (property, enum_property)):\n                    return attr.__get__(instance, ownerclass)\n                elif callable(attr):\n                    return lambda *arg, **kwds: attr(instance, *arg, **kwds)\n                else:\n                    return attr\n\n    def __set__(self, instance, value):\n        ownerclass = instance.__class__\n        for base in ownerclass.__mro__[1:]:\n            if self.name in base.__dict__:\n                attr = base.__dict__[self.name]\n                if isinstance(attr, property):\n                    setter = attr.__set__\n                    if setter is not None:\n                        return setter(instance, value)\n        else:\n            raise AttributeError("can't set attribute %r" % (self.name, ))\n\n    def __delete__(self, instance):\n        raise AttributeError("can't delete attribute %r" % (self.name, ))\n\n_RouteClassAttributeToGetattr = enum_property\n\nclass NonMember(object):\n    """\n    Protects item from becaming an Enum member during class creation.\n    """\n    def __init__(self, value):\n        self.value = value\n\n    def __get__(self, instance, ownerclass=None):\n        return self.value\nskip = nonmember = NonMember\n\nclass Member(object):\n    """\n    Forces item to became an Enum member during class creation.\n    """\n    def __init__(self, value):\n        self.value = value\nmember = Member\n\n\ndef _is_descriptor(obj):\n    """Returns True if obj is a descriptor, False otherwise."""\n    return (\n            hasattr(obj, '__get__') or\n            hasattr(obj, '__set__') or\n            hasattr(obj, '__delete__'))\n\n\ndef _is_dunder(name):\n    """Returns True if a __dunder__ name, False otherwise."""\n    return (len(name) > 4 and\n            name[:2] == name[-2:] == '__' and\n            name[2] != '_' and\n            name[-3] != '_')\n\n\ndef _is_sunder(name):\n    """Returns True if a _sunder_ name, False otherwise."""\n    return (len(name) > 2 and\n            name[0] == name[-1] == '_' and\n            name[1] != '_' and\n            name[-2] != '_')\n\ndef _is_internal_class(cls_name, obj):\n    # only 3.3 and up, always return False in 3.2 and below\n    if pyver < 3.3:\n        return False\n    else:\n        qualname = getattr(obj, '__qualname__', False)\n        return not _is_descriptor(obj) and qualname and re.search(r"\\.?%s\\.\\w+$" % cls_name, qualname)\n\ndef _make_class_unpicklable(cls):\n    """Make the given class un-picklable."""\n    def _break_on_call_reduce(self, protocol=None):\n        raise TypeError('%r cannot be pickled' % (self, ))\n    cls.__reduce_ex__ = _break_on_call_reduce\n    cls.__module__ = '<unknown>'\n\ndef _check_auto_args(method):\n    """check if new generate method supports *args and **kwds"""\n    if isinstance(method, staticmethod):\n        method = method.__get__(type)\n    method = getattr(method, 'im_func', method)\n    args, varargs, keywords, defaults = getargspec(method)\n    return varargs is not None and keywords is not None\n\ndef _get_attr_from_chain(cls, attr):\n    sentinel = object()\n    for basecls in cls.mro():\n        obj = basecls.__dict__.get(attr, sentinel)\n        if obj is not sentinel:\n            return obj\n\ndef _value(obj):\n    if isinstance(obj, (auto, constant)):\n        return obj.value\n    else:\n        return obj\n\n################\n# Constant stuff\n################\n\n# metaclass and class dict for NamedConstant\n\nclass constant(object):\n    '''\n    Simple constant descriptor for NamedConstant and Enum use.\n    '''\n    def __init__(self, value, doc=None):\n        self.value = value\n        self.__doc__ = doc\n\n    def __get__(self, *args):\n        return self.value\n\n    def __repr__(self):\n        return '%s(%r)' % (self.__class__.__name__, self.value)\n\n    def __and__(self, other):\n        return _and_(self.value, _value(other))\n\n    def __rand__(self, other):\n        return _and_(_value(other), self.value)\n\n    def __invert__(self):\n        return _inv_(self.value)\n\n    def __or__(self, other):\n        return _or_(self.value, _value(other))\n\n    def __ror__(self, other):\n        return _or_(_value(other), self.value)\n\n    def __xor__(self, other):\n        return _xor_(self.value, _value(other))\n\n    def __rxor__(self, other):\n        return _xor_(_value(other), self.value)\n\n    def __abs__(self):\n        return _abs_(self.value)\n\n    def __add__(self, other):\n        return _add_(self.value, _value(other))\n\n    def __radd__(self, other):\n        return _add_(_value(other), self.value)\n\n    def __neg__(self):\n        return _neg_(self.value)\n\n    def __pos__(self):\n        return _pos_(self.value)\n\n    if pyver < 3:\n        def __div__(self, other):\n            return _div_(self.value, _value(other))\n\n    def __rdiv__(self, other):\n        return _div_(_value(other), (self.value))\n\n    def __floordiv__(self, other):\n        return _floordiv_(self.value, _value(other))\n\n    def __rfloordiv__(self, other):\n        return _floordiv_(_value(other), self.value)\n\n    def __truediv__(self, other):\n        return _truediv_(self.value, _value(other))\n\n    def __rtruediv__(self, other):\n        return _truediv_(_value(other), self.value)\n\n    def __lshift__(self, other):\n        return _lshift_(self.value, _value(other))\n\n    def __rlshift__(self, other):\n        return _lshift_(_value(other), self.value)\n\n    def __rshift__(self, other):\n        return _rshift_(self.value, _value(other))\n\n    def __rrshift__(self, other):\n        return _rshift_(_value(other), self.value)\n\n    def __mod__(self, other):\n        return _mod_(self.value, _value(other))\n\n    def __rmod__(self, other):\n        return _mod_(_value(other), self.value)\n\n    def __mul__(self, other):\n        return _mul_(self.value, _value(other))\n\n    def __rmul__(self, other):\n        return _mul_(_value(other), self.value)\n\n    def __pow__(self, other):\n        return _pow_(self.value, _value(other))\n\n    def __rpow__(self, other):\n        return _pow_(_value(other), self.value)\n\n    def __sub__(self, other):\n        return _sub_(self.value, _value(other))\n\n    def __rsub__(self, other):\n        return _sub_(_value(other), self.value)\n\n\n\nNamedConstant = None\n\nclass _NamedConstantDict(dict):\n    """Track constant order and ensure names are not reused.\n\n    NamedConstantMeta will use the names found in self._names as the\n    Constant names.\n    """\n    def __init__(self):\n        super(_NamedConstantDict, self).__init__()\n        self._names = []\n\n    def __setitem__(self, key, value):\n        """Changes anything not dundered or not a constant descriptor.\n\n        If an constant name is used twice, an error is raised; duplicate\n        values are not checked for.\n\n        Single underscore (sunder) names are reserved.\n        """\n        if _is_sunder(key):\n            raise ValueError('_names_ are reserved for future NamedConstant use')\n        elif _is_dunder(key):\n            pass\n        elif key in self._names:\n            # overwriting an existing constant?\n            raise TypeError('attempt to reuse name: %r' % (key, ))\n        elif isinstance(value, constant) or not _is_descriptor(value):\n            if key in self:\n                # overwriting a descriptor?\n                raise TypeError('%s already defined as: %r' % (key, self[key]))\n            self._names.append(key)\n        super(_NamedConstantDict, self).__setitem__(key, value)\n\n\nclass NamedConstantMeta(type):\n    """\n    Block attempts to reassign NamedConstant attributes.\n    """\n\n    def __new__(metacls, cls, bases, clsdict):\n        if type(clsdict) is dict:\n            original_dict = clsdict\n            clsdict = _NamedConstantDict()\n            for k, v in original_dict.items():\n                clsdict[k] = v\n        newdict = {}\n        constants = {}\n        for name, obj in clsdict.items():\n            if name in clsdict._names:\n                constants[name] = obj\n                continue\n            elif isinstance(obj, nonmember):\n                obj = obj.value\n            newdict[name] = obj\n        newcls = super(NamedConstantMeta, metacls).__new__(metacls, cls, bases, newdict)\n        newcls._named_constant_cache_ = {}\n        for name, obj in constants.items():\n            newcls.__new__(newcls, name, obj)\n        return newcls\n\n    def __delattr__(cls, attr):\n        cur_obj = cls.__dict__.get(attr)\n        if NamedConstant is not None and isinstance(cur_obj, NamedConstant):\n            raise AttributeError('cannot delete constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))\n        super(NamedConstantMeta, cls).__delattr__(attr)\n\n    def __setattr__(cls, name, value):\n        """Block attempts to reassign NamedConstants.\n        """\n        cur_obj = cls.__dict__.get(name)\n        if NamedConstant is not None and isinstance(cur_obj, NamedConstant):\n            raise AttributeError('cannot rebind constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))\n        super(NamedConstantMeta, cls).__setattr__(name, value)\n\ntemp_constant_dict = {}\ntemp_constant_dict['__doc__'] = "NamedConstants protection.\\n\\n    Derive from this class to lock NamedConstants.\\n\\n"\n\ndef __new__(cls, name, value=None, doc=None):\n    if value is None:\n        # lookup, name is value\n        value = name\n        for name, obj in cls.__dict__.items():\n            if isinstance(obj, cls) and obj._value_ == value:\n                return obj\n        else:\n            raise ValueError('%r does not exist in %r' % (value, cls.__name__))\n    cur_obj = cls.__dict__.get(name)\n    if isinstance(cur_obj, NamedConstant):\n        raise AttributeError('cannot rebind constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))\n    elif isinstance(value, constant):\n        doc = doc or value.__doc__\n        value = value.value\n    metacls = cls.__class__\n    if isinstance(value, NamedConstant):\n        # constants from other classes are reduced to their actual value\n        value = value._value_\n    actual_type = type(value)\n    value_type = cls._named_constant_cache_.get(actual_type)\n    if value_type is None:\n        value_type = type(cls.__name__, (cls, type(value)), {})\n        cls._named_constant_cache_[type(value)] = value_type\n    obj = actual_type.__new__(value_type, value)\n    obj._name_ = name\n    obj._value_ = value\n    obj.__doc__ = doc\n    metacls.__setattr__(cls, name, obj)\n    return obj\ntemp_constant_dict['__new__'] = __new__\ndel __new__\n\ndef __repr__(self):\n    return "<%s.%s: %r>" % (\n            self.__class__.__name__, self._name_, self._value_)\ntemp_constant_dict['__repr__'] = __repr__\ndel __repr__\n\ndef __reduce_ex__(self, proto):\n    return getattr, (self.__class__, self._name_)\ntemp_constant_dict['__reduce_ex__'] = __reduce_ex__\ndel __reduce_ex__\n\n\nNamedConstant = NamedConstantMeta('NamedConstant', (object, ), temp_constant_dict)\nConstant = NamedConstant\ndel temp_constant_dict\n\n# now for a NamedTuple\n\nclass _NamedTupleDict(OrderedDict):\n    """Track field order and ensure field names are not reused.\n\n    NamedTupleMeta will use the names found in self._field_names to translate\n    to indices.\n    """\n    def __init__(self, *args, **kwds):\n        self._field_names = []\n        super(_NamedTupleDict, self).__init__(*args, **kwds)\n\n    def __setitem__(self, key, value):\n        """Records anything not dundered or not a descriptor.\n\n        If a field name is used twice, an error is raised.\n\n        Single underscore (sunder) names are reserved.\n        """\n        if _is_sunder(key):\n            if key not in ('_size_', '_order_'):\n                raise ValueError('_names_ are reserved for future NamedTuple use')\n        elif _is_dunder(key):\n            if key == '__order__':\n                key = '_order_'\n        elif key in self._field_names:\n            # overwriting a field?\n            raise TypeError('attempt to reuse field name: %r' % (key, ))\n        elif not _is_descriptor(value):\n            if key in self:\n                # field overwriting a descriptor?\n                raise TypeError('%s already defined as: %r' % (key, self[key]))\n            self._field_names.append(key)\n        super(_NamedTupleDict, self).__setitem__(key, value)\n\n\nclass _TupleAttributeAtIndex(object):\n\n    def __init__(self, name, index, doc, default):\n        self.name = name\n        self.index = index\n        if doc is undefined:\n            doc = None\n        self.__doc__ = doc\n        self.default = default\n\n    def __get__(self, instance, owner):\n        if instance is None:\n            return self\n        if len(instance) <= self.index:\n            raise AttributeError('%s instance has no value for %s' % (instance.__class__.__name__, self.name))\n        return instance[self.index]\n\n    def __repr__(self):\n        return '%s(%d)' % (self.__class__.__name__, self.index)\n\n\nclass undefined(object):\n    def __repr__(self):\n        return 'undefined'\n    def __bool__(self):\n        return False\n    __nonzero__ = __bool__\nundefined = undefined()\n\n\nclass TupleSize(NamedConstant):\n    fixed = constant('fixed', 'tuple length is static')\n    minimum = constant('minimum', 'tuple must be at least x long (x is calculated during creation')\n    variable = constant('variable', 'tuple length can be anything')\n\nclass NamedTupleMeta(type):\n    """Metaclass for NamedTuple"""\n\n    @classmethod\n    def __prepare__(metacls, cls, bases, size=undefined):\n        return _NamedTupleDict()\n\n    def __init__(cls, *args , **kwds):\n        super(NamedTupleMeta, cls).__init__(*args)\n\n    def __new__(metacls, cls, bases, clsdict, size=undefined):\n        if bases == (object, ):\n            bases = (tuple, object)\n        elif tuple not in bases:\n            if object in bases:\n                index = bases.index(object)\n                bases = bases[:index] + (tuple, ) + bases[index:]\n            else:\n                bases = bases + (tuple, )\n        # include any fields from base classes\n        base_dict = _NamedTupleDict()\n        namedtuple_bases = []\n        for base in bases:\n            if isinstance(base, NamedTupleMeta):\n                namedtuple_bases.append(base)\n        i = 0\n        if namedtuple_bases:\n            for name, index, doc, default in metacls._convert_fields(*namedtuple_bases):\n                base_dict[name] = index, doc, default\n                i = max(i, index)\n        # construct properly ordered dict with normalized indexes\n        for k, v in clsdict.items():\n            base_dict[k] = v\n        original_dict = base_dict\n        if size is not undefined and '_size_' in original_dict:\n            raise TypeError('_size_ cannot be set if "size" is passed in header')\n        add_order = isinstance(clsdict, _NamedTupleDict)\n        clsdict = _NamedTupleDict()\n        clsdict.setdefault('_size_', size or TupleSize.fixed)\n        unnumbered = OrderedDict()\n        numbered = OrderedDict()\n        _order_ = original_dict.pop('_order_', [])\n        if _order_ :\n            _order_ = _order_.replace(',',' ').split()\n            add_order = False\n        # and process this class\n        for k, v in original_dict.items():\n            if k not in original_dict._field_names:\n                clsdict[k] = v\n            else:\n                # TODO:normalize v here\n                if isinstance(v, baseinteger):\n                    # assume an offset\n                    v = v, undefined, undefined\n                    i = v[0] + 1\n                    target = numbered\n                elif isinstance(v, basestring):\n                    # assume a docstring\n                    if add_order:\n                        v = i, v, undefined\n                        i += 1\n                        target = numbered\n                    else:\n                        v = undefined, v, undefined\n                        target = unnumbered\n                elif isinstance(v, tuple) and len(v) in (2, 3) and isinstance(v[0], baseinteger) and isinstance(v[1], (basestring, NoneType)):\n                    # assume an offset, a docstring, and (maybe) a default\n                    if len(v) == 2:\n                        v = v + (undefined, )\n                    v = v\n                    i = v[0] + 1\n                    target = numbered\n                elif isinstance(v, tuple) and len(v) in (1, 2) and isinstance(v[0], (basestring, NoneType)):\n                    # assume a docstring, and (maybe) a default\n                    if len(v) == 1:\n                        v = v + (undefined, )\n                    if add_order:\n                        v = (i, ) + v\n                        i += 1\n                        target = numbered\n                    else:\n                        v = (undefined, ) + v\n                        target = unnumbered\n                else:\n                    # refuse to guess further\n                    raise ValueError('not sure what to do with %s=%r (should be OFFSET [, DOC [, DEFAULT]])' % (k, v))\n                target[k] = v\n        # all index values have been normalized\n        # deal with _order_ (or lack thereof)\n        fields = []\n        aliases = []\n        seen = set()\n        max_len = 0\n        if not _order_:\n            if unnumbered:\n                raise ValueError("_order_ not specified and OFFSETs not declared for %r" % (unnumbered.keys(), ))\n            for name, (index, doc, default) in sorted(numbered.items(), key=lambda nv: (nv[1][0], nv[0])):\n                if index in seen:\n                    aliases.append(name)\n                else:\n                    fields.append(name)\n                    seen.add(index)\n                    max_len = max(max_len, index + 1)\n            offsets = numbered\n        else:\n            # check if any unnumbered not in _order_\n            missing = set(unnumbered) - set(_order_)\n            if missing:\n                raise ValueError("unable to order fields: %s (use _order_ or specify OFFSET" % missing)\n            offsets = OrderedDict()\n            # if any unnumbered, number them from their position in _order_\n            i = 0\n            for k in _order_:\n                try:\n                    index, doc, default = unnumbered.pop(k, None) or numbered.pop(k)\n                except IndexError:\n                    raise ValueError('%s (from _order_) not found in %s' % (k, cls))\n                if index is not undefined:\n                    i = index\n                if i in seen:\n                    aliases.append(k)\n                else:\n                    fields.append(k)\n                    seen.add(i)\n                offsets[k] = i, doc, default\n                i += 1\n                max_len = max(max_len, i)\n            # now handle anything in numbered\n            for k, (index, doc, default) in sorted(numbered.items(), key=lambda nv: (nv[1][0], nv[0])):\n                if index in seen:\n                    aliases.append(k)\n                else:\n                    fields.append(k)\n                    seen.add(index)\n                offsets[k] = index, doc, default\n                max_len = max(max_len, index+1)\n\n        # at this point fields and aliases should be ordered lists, offsets should be an\n        # OrdededDict with each value an int, str or None or undefined, default or None or undefined\n        assert len(fields) + len(aliases) == len(offsets), "number of fields + aliases != number of offsets"\n        assert set(fields) & set(offsets) == set(fields), "some fields are not in offsets: %s" % set(fields) & set(offsets)\n        assert set(aliases) & set(offsets) == set(aliases), "some aliases are not in offsets: %s" % set(aliases) & set(offsets)\n        for name, (index, doc, default) in offsets.items():\n            assert isinstance(index, baseinteger), "index for %s is not an int (%s:%r)" % (name, type(index), index)\n            assert isinstance(doc, (basestring, NoneType)) or doc is undefined, "doc is not a str, None, nor undefined (%s:%r)" % (name, type(doc), doc)\n\n        # create descriptors for fields\n        for name, (index, doc, default) in offsets.items():\n            clsdict[name] = _TupleAttributeAtIndex(name, index, doc, default)\n        clsdict['__slots__'] = ()\n\n        # create our new NamedTuple type\n        namedtuple_class = super(NamedTupleMeta, metacls).__new__(metacls, cls, bases, clsdict)\n        namedtuple_class._fields_ = fields\n        namedtuple_class._aliases_ = aliases\n        namedtuple_class._defined_len_ = max_len\n        return namedtuple_class\n\n    @staticmethod\n    def _convert_fields(*namedtuples):\n        "create list of index, doc, default triplets for cls in namedtuples"\n        all_fields = []\n        for cls in namedtuples:\n            base = len(all_fields)\n            for field in cls._fields_:\n                desc = getattr(cls, field)\n                all_fields.append((field, base+desc.index, desc.__doc__, desc.default))\n        return all_fields\n\n    def __add__(cls, other):\n        "A new NamedTuple is created by concatenating the _fields_ and adjusting the descriptors"\n        if not isinstance(other, NamedTupleMeta):\n            return NotImplemented\n        return NamedTupleMeta('%s%s' % (cls.__name__, other.__name__), (cls, other), {})\n\n    def __call__(cls, *args, **kwds):\n        """Creates a new NamedTuple class or an instance of a NamedTuple subclass.\n\n        NamedTuple should have args of (class_name, names, module)\n\n            `names` can be:\n\n                * A string containing member names, separated either with spaces or\n                  commas.  Values are auto-numbered from 1.\n                * An iterable of member names.  Values are auto-numbered from 1.\n                * An iterable of (member name, value) pairs.\n                * A mapping of member name -> value.\n\n                `module`, if set, will be stored in the new class' __module__ attribute;\n\n                Note: if `module` is not set this routine will attempt to discover the\n                calling module by walking the frame stack; if this is unsuccessful\n                the resulting class will not be pickleable.\n\n        subclass should have whatever arguments and/or keywords will be used to create an\n        instance of the subclass\n        """\n        if cls is NamedTuple:\n            original_args = args\n            original_kwds = kwds.copy()\n            # create a new subclass\n            try:\n                if 'class_name' in kwds:\n                    class_name = kwds.pop('class_name')\n                else:\n                    class_name, args = args[0], args[1:]\n                if 'names' in kwds:\n                    names = kwds.pop('names')\n                else:\n                    names, args = args[0], args[1:]\n                if 'module' in kwds:\n                    module = kwds.pop('module')\n                elif args:\n                    module, args = args[0], args[1:]\n                else:\n                    module = None\n                if 'type' in kwds:\n                    type = kwds.pop('type')\n                elif args:\n                    type, args = args[0], args[1:]\n                else:\n                    type = None\n\n            except IndexError:\n                raise TypeError('too few arguments to NamedTuple: %s, %s' % (original_args, original_kwds))\n            if args or kwds:\n                raise TypeError('too many arguments to NamedTuple: %s, %s' % (original_args, original_kwds))\n            if pyver < 3.0:\n                # if class_name is unicode, attempt a conversion to ASCII\n                if isinstance(class_name, unicode):\n                    try:\n                        class_name = class_name.encode('ascii')\n                    except UnicodeEncodeError:\n                        raise TypeError('%r is not representable in ASCII' % (class_name, ))\n            # quick exit if names is a NamedTuple\n            if isinstance(names, NamedTupleMeta):\n                names.__name__ = class_name\n                if type is not None and type not in names.__bases__:\n                    names.__bases__ = (type, ) + names.__bases__\n                return names\n\n            metacls = cls.__class__\n            bases = (cls, )\n            clsdict = metacls.__prepare__(class_name, bases)\n\n            # special processing needed for names?\n            if isinstance(names, basestring):\n                names = names.replace(',', ' ').split()\n            if isinstance(names, (tuple, list)) and isinstance(names[0], basestring):\n                names = [(e, i) for (i, e) in enumerate(names)]\n            # Here, names is either an iterable of (name, index) or (name, index, doc, default) or a mapping.\n            item = None  # in case names is empty\n            for item in names:\n                if isinstance(item, basestring):\n                    # mapping\n                    field_name, field_index = item, names[item]\n                else:\n                    # non-mapping\n                    if len(item) == 2:\n                        field_name, field_index = item\n                    else:\n                        field_name, field_index = item[0], item[1:]\n                clsdict[field_name] = field_index\n            if type is not None:\n                if not isinstance(type, tuple):\n                    type = (type, )\n                bases = type + bases\n            namedtuple_class = metacls.__new__(metacls, class_name, bases, clsdict)\n\n            # TODO: replace the frame hack if a blessed way to know the calling\n            # module is ever developed\n            if module is None:\n                try:\n                    module = _sys._getframe(1).f_globals['__name__']\n                except (AttributeError, ValueError, KeyError):\n                    pass\n            if module is None:\n                _make_class_unpicklable(namedtuple_class)\n            else:\n                namedtuple_class.__module__ = module\n\n            return namedtuple_class\n        else:\n            # instantiate a subclass\n            namedtuple_instance = cls.__new__(cls, *args, **kwds)\n            if isinstance(namedtuple_instance, cls):\n                namedtuple_instance.__init__(*args, **kwds)\n            return namedtuple_instance\n\n    @property\n    def __fields__(cls):\n        return list(cls._fields_)\n    # collections.namedtuple compatibility\n    _fields = __fields__\n\n    @property\n    def __aliases__(cls):\n        return list(cls._aliases_)\n\n    def __repr__(cls):\n        return "<NamedTuple %r>" % (cls.__name__, )\n\ntemp_namedtuple_dict = {}\ntemp_namedtuple_dict['__doc__'] = "NamedTuple base class.\\n\\n    Derive from this class to define new NamedTuples.\\n\\n"\n\ndef __new__(cls, *args, **kwds):\n    if cls._size_ is TupleSize.fixed and len(args) > cls._defined_len_:\n        raise TypeError('%d fields expected, %d received' % (cls._defined_len_, len(args)))\n    unknown = set(kwds) - set(cls._fields_) - set(cls._aliases_)\n    if unknown:\n        raise TypeError('unknown fields: %r' % (unknown, ))\n    final_args = list(args) + [undefined] * (len(cls.__fields__) - len(args))\n    for field, value in kwds.items():\n        index = getattr(cls, field).index\n        if final_args[index] != undefined:\n            raise TypeError('field %s specified more than once' % field)\n        final_args[index] = value\n    missing = []\n    for index, value in enumerate(final_args):\n        if value is undefined:\n            # look for default values\n            name = cls.__fields__[index]\n            default = getattr(cls, name).default\n            if default is undefined:\n                missing.append(name)\n            else:\n                final_args[index] = default\n    if missing:\n        if cls._size_ in (TupleSize.fixed, TupleSize.minimum):\n            raise TypeError('values not provided for field(s): %s' % ', '.join(missing))\n        while final_args and final_args[-1] is undefined:\n            final_args.pop()\n            missing.pop()\n        if cls._size_ is not TupleSize.variable or undefined in final_args:\n            raise TypeError('values not provided for field(s): %s' % ', '.join(missing))\n    return tuple.__new__(cls, tuple(final_args))\n\ntemp_namedtuple_dict['__new__'] = __new__\ndel __new__\n\ndef __reduce_ex__(self, proto):\n    return self.__class__, tuple(getattr(self, f) for f in self._fields_)\ntemp_namedtuple_dict['__reduce_ex__'] = __reduce_ex__\ndel __reduce_ex__\n\ndef __repr__(self):\n    if len(self) == len(self._fields_):\n        return "%s(%s)" % (\n                self.__class__.__name__, ', '.join(['%s=%r' % (f, o) for f, o in zip(self._fields_, self)])\n                )\n    else:\n        return '%s(%s)' % (self.__class__.__name__, ', '.join([repr(o) for o in self]))\ntemp_namedtuple_dict['__repr__'] = __repr__\ndel __repr__\n\ndef __str__(self):\n    return "%s(%s)" % (\n            self.__class__.__name__, ', '.join(['%r' % (getattr(self, f), ) for f in self._fields_])\n            )\ntemp_namedtuple_dict['__str__'] = __str__\ndel __str__\n\n## compatibility methods with stdlib namedtuple\n@property\ndef __aliases__(self):\n    return list(self.__class__._aliases_)\ntemp_namedtuple_dict['__aliases__'] = __aliases__\ndel __aliases__\n\n@property\ndef __fields__(self):\n    return list(self.__class__._fields_)\ntemp_namedtuple_dict['__fields__'] = __fields__\ntemp_namedtuple_dict['_fields'] = __fields__\ndel __fields__\n\ndef _make(cls, iterable, new=None, len=None):\n    return cls.__new__(cls, *iterable)\ntemp_namedtuple_dict['_make'] = classmethod(_make)\ndel _make\n\ndef _asdict(self):\n    return OrderedDict(zip(self._fields_, self))\ntemp_namedtuple_dict['_asdict'] = _asdict\ndel _asdict\n\ndef _replace(self, **kwds):\n    current = self._asdict()\n    current.update(kwds)\n    return self.__class__(**current)\ntemp_namedtuple_dict['_replace'] = _replace\ndel _replace\n\nNamedTuple = NamedTupleMeta('NamedTuple', (object, ), temp_namedtuple_dict)\ndel temp_namedtuple_dict\n\n# defined now for immediate use\n\ndef enumsort(things):\n    """\n    sorts things by value if all same type; otherwise by name\n    """\n    if not things:\n        return things\n    sort_type = type(things[0])\n    if not issubclass(sort_type, tuple):\n        # direct sort or type error\n        if not all((type(v) is sort_type) for v in things[1:]):\n            raise TypeError('cannot sort items of different types')\n        return sorted(things)\n    else:\n        # expecting list of (name, value) tuples\n        sort_type = type(things[0][1])\n        try:\n            if all((type(v[1]) is sort_type) for v in things[1:]):\n                return sorted(things, key=lambda i: i[1])\n            else:\n                raise TypeError('try name sort instead')\n        except TypeError:\n            return sorted(things, key=lambda i: i[0])\n\ndef export(collection, namespace=None):\n    """\n    export([collection,] namespace) -> Export members to target namespace.\n\n    If collection is not given, act as a decorator.\n    """\n    if namespace is None:\n        namespace = collection\n        def export_decorator(collection):\n            return export(collection, namespace)\n        return export_decorator\n    elif issubclass(collection, NamedConstant):\n        for n, c in collection.__dict__.items():\n            if isinstance(c, NamedConstant):\n                namespace[n] = c\n    elif issubclass(collection, Enum):\n        data = collection.__members__.items()\n        for n, m in data:\n            namespace[n] = m\n    else:\n        raise TypeError('%r is not a supported collection' % (collection,) )\n    return collection\n\n# Constants used in Enum\n\n@export(globals())\nclass EnumConstants(NamedConstant):\n    AutoValue = constant('autovalue', 'values are automatically created from _generate_next_value_')\n    AutoNumber = constant('autonumber', 'integer value is prepended to members, beginning from START')\n    MultiValue = constant('multivalue', 'each member can have several values')\n    NoAlias = constant('noalias', 'duplicate valued members are distinct, not aliased')\n    Unique = constant('unique', 'duplicate valued members are not allowed')\n\n\n############\n# Enum stuff\n############\n\n# Dummy value for Enum as EnumMeta explicity checks for it, but of course until\n# EnumMeta finishes running the first time the Enum class doesn't exist.  This\n# is also why there are checks in EnumMeta like `if Enum is not None`\nEnum = Flag = None\n\nclass enum(object):\n    """\n    Helper class to track args, kwds.\n    """\n    def __init__(self, *args, **kwds):\n        self._args = args\n        self._kwds = kwds.items()\n        self._hash = hash(args)\n        self.name = None\n\n    @property\n    def args(self):\n        return self._args\n\n    @property\n    def kwds(self):\n        return dict([(k, v) for k, v in self._kwds])\n\n    def __hash__(self):\n        return self._hash\n\n    def __eq__(self, other):\n        if not isinstance(other, self.__class__):\n            return NotImplemented\n        return self.args == other.args and self.kwds == other.kwds\n\n    def __ne__(self, other):\n        if not isinstance(other, self.__class__):\n            return NotImplemented\n        return self.args != other.args or self.kwds != other.kwds\n\n    def __repr__(self):\n        final = []\n        args = ', '.join(['%r' % (a, ) for a in self.args])\n        if args:\n            final.append(args)\n        kwds = ', '.join([('%s=%r') % (k, v) for k, v in enumsort(list(self.kwds.items()))])\n        if kwds:\n            final.append(kwds)\n        return '%s(%s)' % (self.__class__.__name__, ', '.join(final))\n\n_auto_null = object()\nclass auto(enum):\n    """\n    Instances are replaced with an appropriate value in Enum class suites.\n    """\n    _value = _auto_null\n    _operations = []\n\n    def __and__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_and_, (self, other)))\n        return new_auto\n\n    def __rand__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_and_, (other, self)))\n        return new_auto\n\n    def __invert__(self):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_inv_, (self,)))\n        return new_auto\n\n    def __or__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_or_, (self, other)))\n        return new_auto\n\n    def __ror__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_or_, (other, self)))\n        return new_auto\n\n    def __xor__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_xor_, (self, other)))\n        return new_auto\n\n    def __rxor__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_xor_, (other, self)))\n        return new_auto\n\n    def __abs__(self):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_abs_, (self, )))\n        return new_auto\n\n    def __add__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_add_, (self, other)))\n        return new_auto\n\n    def __radd__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_add_, (other, self)))\n        return new_auto\n\n    def __neg__(self):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_neg_, (self, )))\n        return new_auto\n\n    def __pos__(self):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_pos_, (self, )))\n        return new_auto\n\n    if pyver < 3:\n        def __div__(self, other):\n            new_auto = self.__class__()\n            new_auto._operations = self._operations[:]\n            new_auto._operations.append((_div_, (self, other)))\n            return new_auto\n\n    def __rdiv__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_div_, (other, self)))\n        return new_auto\n\n    def __floordiv__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_floordiv_, (self, other)))\n        return new_auto\n\n    def __rfloordiv__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_floordiv_, (other, self)))\n        return new_auto\n\n    def __truediv__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_truediv_, (self, other)))\n        return new_auto\n\n    def __rtruediv__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_truediv_, (other, self)))\n        return new_auto\n\n    def __lshift__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_lshift_, (self, other)))\n        return new_auto\n\n    def __rlshift__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_lshift_, (other, self)))\n        return new_auto\n\n    def __rshift__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_rshift_, (self, other)))\n        return new_auto\n\n    def __rrshift__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_rshift_, (other, self)))\n        return new_auto\n\n    def __mod__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_mod_, (self, other)))\n        return new_auto\n\n    def __rmod__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_mod_, (other, self)))\n        return new_auto\n\n    def __mul__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_mul_, (self, other)))\n        return new_auto\n\n    def __rmul__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_mul_, (other, self)))\n        return new_auto\n\n    def __pow__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_pow_, (self, other)))\n        return new_auto\n\n    def __rpow__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_pow_, (other, self)))\n        return new_auto\n\n    def __sub__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_sub_, (self, other)))\n        return new_auto\n\n    def __rsub__(self, other):\n        new_auto = self.__class__()\n        new_auto._operations = self._operations[:]\n        new_auto._operations.append((_sub_, (other, self)))\n        return new_auto\n\n\n\n    @property\n    def value(self):\n        if self._value is not _auto_null and self._operations:\n            raise TypeError('auto() object out of sync')\n        elif self._value is _auto_null and not self._operations:\n            return self._value\n        elif self._value is not _auto_null:\n            return self._value\n        else:\n            return self._resolve()\n\n    @value.setter\n    def value(self, value):\n        if self._operations:\n            value = self._resolve(value)\n        self._value = value\n\n    def _resolve(self, base_value=None):\n            cls = self.__class__\n            for op, params in self._operations:\n                values = []\n                for param in params:\n                    if isinstance(param, cls):\n                        if param.value is _auto_null:\n                            if base_value is None:\n                                return _auto_null\n                            else:\n                                values.append(base_value)\n                        else:\n                            values.append(param.value)\n                    else:\n                        values.append(param)\n                value = op(*values)\n            self._operations[:] = []\n            self._value = value\n            return value\n\nclass _EnumDict(dict):\n    """Track enum member order and ensure member names are not reused.\n\n    EnumMeta will use the names found in self._member_names as the\n    enumeration member names.\n    """\n    def __init__(self, cls_name, settings, start, constructor_init, constructor_start):\n        super(_EnumDict, self).__init__()\n        self._cls_name = cls_name\n        self._constructor_init = constructor_init\n        self._constructor_start = constructor_start\n        # for Flag enumerations, we may need to get the _init_ from __new__\n        self._new_to_init = False\n        # list of enum members\n        self._member_names = []\n        self._settings = settings\n        autonumber = AutoNumber in settings\n        autovalue = AutoValue in settings\n        multivalue = MultiValue in settings\n        if autonumber and start is None:\n            # starting value for AutoNumber\n            start = 1\n        elif start is not None and not autonumber:\n            autonumber = True\n        if start is not None:\n            self._value = start - 1\n        else:\n            self._value = None\n        # when the magic turns off\n        self._locked = not (autovalue or autonumber)\n        # if auto or autonumber\n        self._autovalue = autovalue\n        self._autonumber = autonumber\n        # if multiple values are allowed\n        self._multivalue = multivalue\n        # if init fields are specified\n        self._init = None\n        # list of temporary names\n        self._ignore = []\n        self._ignore_init_done = False\n        # if _sunder_ values can be changed via the class body\n        self._allow_init = True\n        self._last_values = []\n\n    def __getitem__(self, key):\n        if key == self._cls_name and self._cls_name not in self:\n            return enum\n        elif key == '_auto_on_':\n            self._locked = False\n            if not self._autonumber:\n                self._autovalue = True\n            return None\n        elif key == '_auto_off_':\n            self._locked = True\n            return None\n        elif (\n                self._locked\n                or key in self\n                or key in self._ignore\n                or _is_sunder(key)\n                or _is_dunder(key)\n                ):\n            return super(_EnumDict, self).__getitem__(key)\n        elif self._autonumber:\n            try:\n                # try to generate the next value\n                value = self._value + 1\n                self._value += 1\n            except:\n                # couldn't work the magic, report error\n                raise KeyError('%s not found' % (key,))\n        elif self._autovalue:\n            value = self._generate_next_value(key, 1, len(self._member_names), self._last_values[:])\n        else:\n            raise Exception('neither AutoNumber nor AutoValue set -- why am I here?')\n        self.__setitem__(key, value)\n        return value\n\n    def __setitem__(self, key, value):\n        """Changes anything not sundured, dundered, nor a descriptor.\n\n        If an enum member name is used twice, an error is raised; duplicate\n        values are not checked for.\n\n        Single underscore (sunder) names are reserved.\n        """\n        if _is_internal_class(self._cls_name, value):\n            pass\n        elif _is_sunder(key):\n            if key not in (\n                    '_init_', '_settings_', '_order_', '_ignore_', '_start_',\n                    '_create_pseudo_member_', '_create_pseudo_member_values_',\n                    '_generate_next_value_',\n                    '_missing_', '_missing_value_', '_missing_name_',\n                    ):\n                raise ValueError('_names_ are reserved for  Enum use')\n            elif not self._allow_init and key not in (\n                    'create_pseudo_member_', '_missing_', '_missing_value_', '_missing_name_',\n                ):\n                # sunder is used during creation, must be specified first\n                raise ValueError('cannot set %r after init phase' % (key, ))\n            elif key == '_ignore_':\n                if self._ignore_init_done:\n                    raise TypeError('ignore can only be specified once')\n                if isinstance(value, basestring):\n                    value = value.split()\n                else:\n                    value = list(value)\n                self._ignore = value\n                already = set(value) & set(self._member_names)\n                if already:\n                    raise ValueError('_ignore_ cannot specify already set names: %r' % (already, ))\n                self._ignore_init_done = True\n            elif key == '_start_':\n                if self._constructor_start:\n                    raise TypeError('start specified in constructor and class body')\n                if value is None:\n                    self._value = None\n                    self._autonumber = False\n                    if not self._autovalue:\n                        self._locked = True\n                else:\n                    self._value = value - 1\n                    self._locked = False\n                    self._autonumber = True\n            elif key == '_settings_':\n                if not isinstance(value, (set, tuple)):\n                    value = (value, )\n                if not isinstance(value, set):\n                    value = set(value)\n                self._settings |= value\n                if NoAlias in value and Unique in value:\n                    raise TypeError('cannot specify both NoAlias and Unique')\n                elif MultiValue in value and NoAlias in value:\n                    raise TypeError('cannot specify both MultiValue and NoAlias')\n                elif AutoValue in value and AutoNumber in value:\n                    raise TypeError('cannot specify both AutoValue and AutoNumber')\n                allowed_settings = dict.fromkeys(['autovalue', 'autonumber', 'noalias', 'unique', 'multivalue'])\n                for arg in value:\n                    if arg not in allowed_settings:\n                        raise TypeError('unknown qualifier: %r (from %r)' % (arg, value))\n                    allowed_settings[arg] = True\n                self._multivalue = allowed_settings['multivalue']\n                self._autovalue = allowed_settings['autovalue']\n                self._autonumber = allowed_settings['autonumber']\n                self._locked = not (self._autonumber or self._autovalue)\n                if (self._autovalue or self._autonumber) and not self._ignore_init_done:\n                    self._ignore = ['property', 'classmethod', 'staticmethod', 'aenum', 'auto']\n                if self._autonumber and self._value is None:\n                    self._value = 0\n                if self._autonumber and self._init and self._init[0:1] == ['value']:\n                    self._init.pop(0)\n                value = tuple(self._settings)\n            elif key == '_init_':\n                if self._constructor_init:\n                    raise TypeError('init specified in constructor and in class body')\n                _init_ = value\n                if isinstance(_init_, basestring):\n                    _init_ = _init_.replace(',',' ').split()\n                if _init_[0:1] == ['value'] and self._autonumber:\n                    _init_.pop(0)\n                self._init = _init_\n            elif key == '_generate_next_value_':\n                if isinstance(value, staticmethod):\n                    gnv = value.__func__\n                elif isinstance(value, classmethod):\n                    raise TypeError('_generate_next_value should be a staticmethod, not a classmethod')\n                else:\n                    gnv = value\n                    value = staticmethod(value)\n                setattr(self, '_generate_next_value', gnv)\n                self._auto_args = _check_auto_args(value)\n        elif _is_dunder(key):\n            if key == '__order__':\n                key = '_order_'\n                if not self._allow_init:\n                    # _order_ is used during creation, must be specified first\n                    raise ValueError('cannot set %r after init phase' % (key, ))\n            elif key == '__new__' and self._new_to_init:\n                # ArgSpec(args=[...], varargs=[...], keywords=[...], defaults=[...]\n                if isinstance(value, staticmethod):\n                    value = value.__func__\n                new_args = getargspec(value)[0][1:]\n                self._init = new_args\n            if _is_descriptor(value):\n                self._locked = True\n        elif key in self._member_names:\n            # descriptor overwriting an enum?\n            raise TypeError('attempt to reuse name: %r' % (key, ))\n        elif key in self._ignore:\n            pass\n        elif not _is_descriptor(value):\n            self._allow_init = False\n            if key in self:\n                # enum overwriting a descriptor?\n                raise TypeError('%s already defined as: %r' % (key, self[key]))\n            if self._multivalue:\n                # make sure it's a tuple\n                if not isinstance(value, tuple):\n                    value = (value, )\n                # do we need to calculate the next value?\n                if self._autonumber:\n                    if self._init:\n                        target_length = len(self._init)\n                        if self._init[0] != 'value':\n                            target_length += 1\n                        if len(value) != target_length:\n                            value = (self._value + 1, ) + value\n                        if isinstance(value[0], auto):\n                            value = (self._value + 1, ) + value[1:]\n                    else:\n                        try:\n                            value = (self._value + 1, ) + value\n                        except TypeError:\n                            pass\n                    self._value = value[0]\n            elif self._autovalue and self._init and not isinstance(value, auto):\n                # call generate iff init is specified and calls for more values than are present\n                target_values = len(self._init)\n                if not isinstance(value, tuple):\n                    value = (value, )\n                source_values = len(value)\n                if target_values != source_values:\n                    gnv = self._generate_next_value\n                    if self._auto_args:\n                        value = gnv(\n                                key, 1,\n                                len(self._member_names),\n                                self._last_values[:],\n                                *value\n                                )\n                    else:\n                        value = gnv(\n                                key,\n                                1,\n                                len(self._member_names),\n                                self._last_values[:],\n                                )\n\n            elif self._autonumber and not self._locked:\n                # convert any auto instances to integers\n                if isinstance(value, auto):\n                    value = self._value + 1\n                elif isinstance(value, basestring):\n                    pass\n                else:\n                    try:\n                        new_value = []\n                        for v in value:\n                            if isinstance(v, auto):\n                                new_value.append(self._value + 1)\n                            else:\n                                new_value.append(v)\n                        value = tuple(new_value)\n                    except TypeError:\n                        # value wasn't iterable\n                        pass\n                if isinstance(value, int):\n                    self._value = value\n                elif isinstance(value, tuple):\n                    if self._init is None:\n                        # old behavior -> if first item is int, use it as value\n                        #                 otherwise, generate a value and prepend it\n                        if value and isinstance(value[0], baseinteger):\n                            self._value = value[0]\n                        else:\n                            self._value += 1\n                            value = (self._value, ) + value\n                    elif len(value) == len(self._init):\n                        # provide actual value for member\n                        self._value += 1\n                        value = (self._value, ) + value\n                    elif 'value'  not in self._init and len(value) == len(self._init) + 1:\n                        # actual value for member is provided\n                        self._value = value[0]\n                    elif 'value' in self._init and len(value) == len(self._init) - 1:\n                        count = self._value + 1\n                        value = count, value\n                        self._value = count\n                    else:\n                        # mismatch\n                        raise TypeError('%s: number of fields provided do not match init' % key)\n                else:\n                    if self._init is not None and (len(self._init) != 1 or 'value' in self._init):\n                        raise TypeError('%s: number of fields provided do not match init' % key)\n                    count = self._value + 1\n                    value = count, value\n                    self._value = count\n            elif isinstance(value, auto):\n                # if AutoNumber set use built-in value, not _generate_next_value_\n                if self._autonumber:\n                    value = self._value + 1\n                    self._value = value\n                else:\n                    if value.value == _auto_null:\n                        gnv = self._generate_next_value\n                        prev_values = []\n                        for v in self._last_values:\n                            if isinstance(v, auto):\n                                prev_values.append(v.value)\n                            else:\n                                prev_values.append(v)\n                        if isinstance(gnv, staticmethod):\n                            gnv = gnv.__func__\n                        if self._auto_args:\n                            value.value = gnv(\n                                    key,\n                                    1,\n                                    len(self._member_names),\n                                    prev_values,\n                                    *value.args,\n                                    **value.kwds\n                                    )\n                        else:\n                            value.value = gnv(\n                                    key,\n                                    1,\n                                    len(self._member_names),\n                                    prev_values,\n                                    )\n            elif isinstance(value, enum):\n                value.name = key\n            else:\n                pass\n            self._member_names.append(key)\n        else:\n            # not a new member, turn off the autoassign magic\n            self._locked = True\n            self._allow_init = False\n        if not _is_sunder(key) and not _is_dunder(key) and not _is_descriptor(value):\n            if (self._autonumber or self._multivalue) and isinstance(value, tuple):\n                self._last_values.append(value[0])\n            else:\n                self._last_values.append(value)\n        super(_EnumDict, self).__setitem__(key, value)\n\n\nno_arg = object()\nclass EnumMeta(StdlibEnumMeta or type):\n    """Metaclass for Enum"""\n    @classmethod\n    def __prepare__(metacls, cls, bases, init=None, start=None, settings=()):\n        # settings are a combination of current and all past settings\n        constructor_init = init is not None\n        constructor_start = start is not None\n        if not isinstance(settings, tuple):\n            settings = settings,\n        settings = set(settings)\n        generate = None\n        order = None\n        # inherit previous flags\n        member_type, first_enum = metacls._get_mixins_(bases)\n        if first_enum is not None:\n            settings |= first_enum._settings_\n            init = init or first_enum._auto_init_\n            order = first_enum._order_function_\n            if start is None:\n                start = first_enum._start_\n            generate = getattr(first_enum, '_generate_next_value_', None)\n            generate = getattr(generate, 'im_func', generate)\n        # check for custom settings\n        if NoAlias in settings and Unique in settings:\n            raise TypeError('cannot specify both NoAlias and Unique')\n        elif MultiValue in settings and NoAlias in settings:\n            raise TypeError('cannot specify both MultiValue and NoAlias')\n        elif AutoValue in settings and AutoNumber in settings:\n            raise TypeError('cannot specify both AutoValue and AutoNumber')\n        allowed_settings = dict.fromkeys(['autovalue', 'autonumber', 'noalias', 'unique', 'multivalue'])\n        for arg in settings:\n            if arg not in allowed_settings:\n                raise TypeError('unknown qualifier: %r' % (arg, ))\n            allowed_settings[arg] = True\n        enum_dict = _EnumDict(cls_name=cls, settings=settings, start=start, constructor_init=constructor_init, constructor_start=constructor_start)\n        if settings & set([AutoValue, AutoNumber]) or start is not None:\n            enum_dict['_ignore_'] = ['property', 'classmethod', 'staticmethod']\n            enum_dict._ignore_init_done = False\n        if generate:\n            enum_dict['_generate_next_value_'] = generate\n        if init is not None:\n            if isinstance(init, basestring):\n                init = init.replace(',',' ').split()\n            if init[0:1] == ['value'] and AutoNumber in settings:\n                init.pop(0)\n            enum_dict._init = init\n        elif Flag is not None and any(issubclass(b, Flag) for b in bases) and member_type not in (int, object):\n            enum_dict._new_to_init = True\n            if Flag in bases:\n                # only happens on first mixin with Flag\n                def _generate_next_value_(name, start, count, values, *args, **kwds):\n                    return (2 ** count, ) + args\n                enum_dict['_generate_next_value_'] = staticmethod(_generate_next_value_)\n                def __new__(cls, flag_value, type_value):\n                    obj = member_type.__new__(cls, type_value)\n                    obj._value_ = flag_value\n                    return obj\n                enum_dict['__new__'] = __new__\n            else:\n                try:\n                    new_args = getargspec(first_enum.__new_member__)[0][1:]\n                    enum_dict._init = new_args\n                except TypeError:\n                    pass\n        if order is not None:\n            enum_dict['_order_'] = staticmethod(order)\n        return enum_dict\n\n    def __init__(cls, *args , **kwds):\n        super(EnumMeta, cls).__init__(*args)\n\n    def __new__(metacls, cls, bases, clsdict, init=None, start=None, settings=()):\n        # handle py2 case first\n        if type(clsdict) is not _EnumDict:\n            # py2 ard/or functional API gyrations\n            init = clsdict.pop('_init_', None)\n            start = clsdict.pop('_start_', None)\n            settings = clsdict.pop('_settings_', ())\n            _order_ = clsdict.pop('_order_', clsdict.pop('__order__', None))\n            _ignore_ = clsdict.pop('_ignore_', None)\n            _create_pseudo_member_ = clsdict.pop('_create_pseudo_member_', None)\n            _create_pseudo_member_values_ = clsdict.pop('_create_pseudo_member_values_', None)\n            _generate_next_value_ = clsdict.pop('_generate_next_value_', None)\n            _missing_ = clsdict.pop('_missing_', None)\n            _missing_value_ = clsdict.pop('_missing_value_', None)\n            _missing_name_ = clsdict.pop('_missing_name_', None)\n            __new__ = clsdict.pop('__new__', None)\n            enum_members = dict([\n                    (k, v) for (k, v) in clsdict.items()\n                    if not (_is_sunder(k) or _is_dunder(k) or _is_descriptor(v))\n                    ])\n            original_dict = clsdict\n            clsdict = metacls.__prepare__(cls, bases, init=init, start=start, settings=settings)\n            init = init or clsdict._init\n            if _order_ is None:\n                _order_ = clsdict.get('_order_')\n                if _order_ is not None:\n                    _order_ = _order_.__get__(cls)\n            if isinstance(original_dict, OrderedDict):\n                calced_order = original_dict\n            elif _order_ is None:\n                calced_order = [name for (name, value) in enumsort(list(enum_members.items()))]\n            elif isinstance(_order_, basestring):\n                calced_order = _order_ = _order_.replace(',', ' ').split()\n            elif callable(_order_):\n                if init:\n                    if not isinstance(init, basestring):\n                        init = ' '.join(init)\n                member = NamedTuple('member', init and 'name ' + init or ['name', 'value'])\n                calced_order = []\n                for name, value in enum_members.items():\n                    if init:\n                        if not isinstance(value, tuple):\n                            value = (value, )\n                        name_value = (name, ) + value\n                    else:\n                        name_value = tuple((name, value))\n                    if member._defined_len_ != len(name_value):\n                        raise TypeError('%d values expected (%s), %d received (%s)' % (\n                            member._defined_len_,\n                            ', '.join(member._fields_),\n                            len(name_value),\n                            ', '.join([repr(v) for v in name_value]),\n                            ))\n                    calced_order.append(member(*name_value))\n                calced_order = [m.name for m in sorted(calced_order, key=_order_)]\n            else:\n                calced_order = _order_\n            for name in (\n                    '_ignore_', '_create_pseudo_member_', '_create_pseudo_member_values_',\n                    '_generate_next_value_', '_order_', '__new__',\n                    '_missing_', '_missing_value_', '_missing_name_',\n                ):\n                attr = locals()[name]\n                if attr is not None:\n                    clsdict[name] = attr\n            # now add members\n            for k in calced_order:\n                clsdict[k] = original_dict[k]\n            for k, v in original_dict.items():\n                if k not in calced_order:\n                    clsdict[k] = v\n            del _order_, _ignore_, _create_pseudo_member_, _create_pseudo_member_values_,\n            del _generate_next_value_, _missing_, _missing_value_, _missing_name_\n\n        # resume normal path\n        if clsdict._new_to_init:\n            # remove calculated _init_ as it's no longer needed\n            clsdict._init = None\n        clsdict._locked = True\n        member_type, first_enum = metacls._get_mixins_(bases)\n        _order_ = clsdict.pop('_order_', None)\n        if isinstance(_order_, basestring):\n            _order_ = _order_.replace(',',' ').split()\n        init = clsdict._init\n        start = clsdict._value\n        settings = clsdict._settings\n        if start is not None:\n            start += 1\n        creating_init = []\n        auto_init = False\n        if init is None and (AutoNumber in settings or start is not None):\n                creating_init = ['value']\n        elif init is not None:\n            auto_init = True\n            if (AutoNumber in settings or start is not None) and 'value' not in  init:\n                creating_init = ['value'] + init\n            else:\n                creating_init = init[:]\n        autonumber = AutoNumber in settings\n        autovalue = AutoValue in settings\n        multivalue = MultiValue in settings\n        noalias = NoAlias in settings\n        unique = Unique in settings\n        # an Enum class cannot be mixed with other types (int, float, etc.) if\n        #   it has an inherited __new__ unless a new __new__ is defined (or\n        #   the resulting class will fail).\n        # an Enum class is final once enumeration items have been defined;\n        #\n        # remove any keys listed in _ignore_\n        clsdict.setdefault('_ignore_', []).append('_ignore_')\n        ignore = clsdict['_ignore_']\n        for key in ignore:\n            clsdict.pop(key, None)\n        # get the method to create enum members\n        __new__, save_new, new_uses_args = metacls._find_new_(\n                clsdict,\n                member_type,\n                first_enum,\n                )\n        # save enum items into separate mapping so they don't get baked into\n        # the new class\n        enum_members = dict((k, clsdict[k]) for k in clsdict._member_names)\n        for name in clsdict._member_names:\n            del clsdict[name]\n        # move skipped values out of the descriptor, and add names to DynamicAttributes\n        for name, obj in clsdict.items():\n            if isinstance(obj, nonmember):\n                dict.__setitem__(clsdict, name, obj.value)\n            elif isinstance(obj, enum_property):\n                obj.name = name\n        # check for illegal enum names (any others?)\n        invalid_names = set(enum_members) & set(['mro', ''])\n        if invalid_names:\n            raise ValueError('Invalid enum member name(s): %s' % (\n                ', '.join(invalid_names), ))\n        # create our new Enum type\n        enum_class = type.__new__(metacls, cls, bases, clsdict)\n        enum_class._member_names_ = []               # names in random order\n        enum_class._member_map_ = OrderedDict()\n        enum_class._member_type_ = member_type\n        # save current flags for subclasses\n        enum_class._settings_ = settings\n        enum_class._start_ = start\n        enum_class._auto_init_ = _auto_init_ = init\n        enum_class._order_function_ = None\n        if 'value' in creating_init and creating_init[0] != 'value':\n            raise TypeError("'value', if specified, must be the first item in 'init'")\n        # save attributes from super classes so we know if we can take\n        # the shortcut of storing members in the class dict\n        base_attributes = set([a for b in enum_class.mro() for a in b.__dict__])\n        # Reverse value->name map for hashable values.\n        enum_class._value2member_map_ = {}\n        enum_class._value2member_seq_ = ()\n        # instantiate them, checking for duplicates as we go\n        # we instantiate first instead of checking for duplicates first in case\n        # a custom __new__ is doing something funky with the values -- such as\n        # auto-numbering ;)\n        if __new__ is None:\n            __new__ = enum_class.__new__\n        for member_name in clsdict._member_names:\n            value = enum_members[member_name]\n            if isinstance(value, auto):\n                value = value.value\n            kwds = {}\n            new_args = ()\n            init_args = ()\n            extra_mv_args = ()\n            if isinstance(value, enum):\n                args = value.args\n                kwds = value.kwds\n            elif isinstance(value, Member):\n                value = value.value\n                args = (value, )\n            elif not isinstance(value, tuple):\n                args = (value, )\n            else:\n                args = value\n            # possibilities\n            #\n            # - no init, multivalue  -> __new__[0], __init__(*[:]), extra=[1:]\n            # - init w/o value, multivalue  -> __new__[0], __init__(*[:]), extra=[1:]\n            #\n            # - init w/value, multivalue  -> __new__[0], __init__(*[1:]),  extra=[1:]\n            #\n            # - init w/value, no multivalue  -> __new__[0], __init__(*[1:]), extra=[]\n            #\n            # - init w/o value, no multivalue  -> __new__[:], __init__(*[:]), extra=[]\n            # - no init, no multivalue  ->  __new__[:], __init__(*[:]), extra=[]\n            if multivalue or 'value' in creating_init:\n                if multivalue:\n                    # when multivalue is True, creating_init can be anything\n                    new_args = args[0:1]\n                    extra_mv_args = args[1:]\n                    if 'value' in creating_init:\n                        init_args = args[1:]\n                    else:\n                        init_args = args\n                else:\n                    # 'value' is definitely in creating_init\n                    new_args = args[0:1]\n                    if auto_init:\n                        # don't pass in value\n                        init_args = args[1:]\n                    else:\n                        # keep the all args for user-defined __init__\n                        init_args = args\n                value = new_args[0]\n            else:\n                # either no creating_init, or it doesn't have 'value'\n                new_args = args\n                init_args = args\n            if member_type is tuple:   # special case for tuple enums\n                new_args = (new_args, )     # wrap it one more time\n            if not new_uses_args:\n                enum_member = __new__(enum_class)\n                if not hasattr(enum_member, '_value_'):\n                    enum_member._value_ = value\n            else:\n                enum_member = __new__(enum_class, *new_args, **kwds)\n                if not hasattr(enum_member, '_value_'):\n                    enum_member._value_ = member_type(*new_args, **kwds)\n            value = enum_member._value_\n            enum_member._name_ = member_name\n            enum_member.__objclass__ = enum_class\n            enum_member.__init__(*init_args, **kwds)\n            # If another member with the same value was already defined, the\n            # new member becomes an alias to the existing one.\n            if noalias:\n                # unless NoAlias was specified\n                enum_class._member_names_.append(member_name)\n            else:\n                nonunique = defaultdict(list)\n                for name, canonical_member in enum_class._member_map_.items():\n                    if canonical_member.value == enum_member._value_:\n                        if unique:\n                            nonunique[name].append(member_name)\n                            continue\n                        enum_member = canonical_member\n                        break\n                else:\n                    # Aliases don't appear in member names (only in __members__).\n                    enum_class._member_names_.append(member_name)\n                if nonunique:\n                    # duplicates not allowed if Unique specified\n                    message = []\n                    for name, aliases in nonunique.items():\n                        bad_aliases = ','.join(aliases)\n                        message.append('%s --> %s [%r]' % (name, bad_aliases, enum_class[name].value))\n                    raise ValueError(\n                            'duplicate names found in %r: %s' %\n                                (cls, ';  '.join(message))\n                            )\n            # members are added as enum_property's\n            setattr(enum_class, member_name, enum_property(name=member_name))\n            # now add to _member_map_\n            enum_class._member_map_[member_name] = enum_member\n            values = (value, ) + extra_mv_args\n            enum_member._values_ = values\n            for value in values:\n                # first check if value has already been used\n                if multivalue and (\n                        value in enum_class._value2member_map_\n                        or any(v == value for (v, m) in enum_class._value2member_seq_)\n                        ):\n                    raise ValueError('%r has already been used' % (value, ))\n                try:\n                    # This may fail if value is not hashable. We can't add the value\n                    # to the map, and by-value lookups for this value will be\n                    # linear.\n                    if noalias:\n                        raise TypeError('cannot use dict to store value')\n                    enum_class._value2member_map_[value] = enum_member\n                except TypeError:\n                    enum_class._value2member_seq_ += ((value, enum_member), )\n        # check for constants with auto() values\n        for k, v in enum_class.__dict__.items():\n            if isinstance(v, constant) and isinstance(v.value, auto):\n                v.value = enum_class(v.value.value)\n        # If a custom type is mixed into the Enum, and it does not know how\n        # to pickle itself, pickle.dumps will succeed but pickle.loads will\n        # fail.  Rather than have the error show up later and possibly far\n        # from the source, sabotage the pickle protocol for this class so\n        # that pickle.dumps also fails.\n        #\n        # However, if the new class implements its own __reduce_ex__, do not\n        # sabotage -- it's on them to make sure it works correctly.  We use\n        # __reduce_ex__ instead of any of the others as it is preferred by\n        # pickle over __reduce__, and it handles all pickle protocols.\n        unpicklable = False\n        if '__reduce_ex__' not in clsdict:\n            if member_type is not object:\n                methods = ('__getnewargs_ex__', '__getnewargs__',\n                        '__reduce_ex__', '__reduce__')\n                if not any(m in member_type.__dict__ for m in methods):\n                    _make_class_unpicklable(enum_class)\n                    unpicklable = True\n\n        # double check that repr and friends are not the mixin's or various\n        # things break (such as pickle)\n\n        for name in ('__repr__', '__str__', '__format__', '__reduce_ex__'):\n            enum_class_method = enum_class.__dict__.get(name, None)\n            if enum_class_method:\n                # class has defined/imported/copied the method\n                continue\n            class_method = getattr(enum_class, name)\n            obj_method = getattr(member_type, name, None)\n            enum_method = getattr(first_enum, name, None)\n            if obj_method is not None and obj_method == class_method:\n                if name == '__reduce_ex__' and unpicklable:\n                    continue\n                setattr(enum_class, name, enum_method)\n\n        # method resolution and int's are not playing nice\n        # Python's less than 2.6 use __cmp__\n\n        if pyver < 2.6:\n\n            if issubclass(enum_class, int):\n                setattr(enum_class, '__cmp__', getattr(int, '__cmp__'))\n\n        elif pyver < 3.0:\n\n            if issubclass(enum_class, int):\n                for method in (\n                        '__le__',\n                        '__lt__',\n                        '__gt__',\n                        '__ge__',\n                        '__eq__',\n                        '__ne__',\n                        '__hash__',\n                        ):\n                    setattr(enum_class, method, getattr(int, method))\n\n        # replace any other __new__ with our own (as long as Enum is not None,\n        # anyway) -- again, this is to support pickle\n        if Enum is not None:\n            # if the user defined their own __new__, save it before it gets\n            # clobbered in case they subclass later\n            if save_new:\n                setattr(enum_class, '__new_member__', enum_class.__dict__['__new__'])\n            setattr(enum_class, '__new__', Enum.__dict__['__new__'])\n\n        # py3 support for definition order (helps keep py2/py3 code in sync)\n        if _order_:\n            if isinstance(_order_, staticmethod):\n                # _order_ = staticmethod.__get__(enum_class)\n                # _order_ = getattr(_order_, 'im_func', _order_)\n                _order_ = _order_.__func__\n            if callable(_order_):\n                # save order for future subclasses\n                enum_class._order_function_ = staticmethod(_order_)\n                # create ordered list for comparison\n                _order_ = [m.name for m in sorted(enum_class, key=_order_)]\n            if _order_ != enum_class._member_names_:\n                raise TypeError('member order does not match _order_: %r %r' % (enum_class._member_names_, enum_class._member_map_.items()))\n        return enum_class\n\n    def __bool__(cls):\n        """\n        classes/types should always be True.\n        """\n        return True\n\n    def __call__(cls, value=no_arg, names=None, module=None, type=None, start=1):\n        """Either returns an existing member, or creates a new enum class.\n\n        This method is used both when an enum class is given a value to match\n        to an enumeration member (i.e. Color(3)) and for the functional API\n        (i.e. Color = Enum('Color', names='red green blue')).\n\n        When used for the functional API: `module`, if set, will be stored in\n        the new class' __module__ attribute; `type`, if set, will be mixed in\n        as the first base class.\n\n        Note: if `module` is not set this routine will attempt to discover the\n        calling module by walking the frame stack; if this is unsuccessful\n        the resulting class will not be pickleable.\n        """\n        if names is None:  # simple value lookup\n            return cls.__new__(cls, value)\n        # otherwise, functional API: we're creating a new Enum type\n        return cls._create_(value, names, module=module, type=type, start=start)\n\n    def __contains__(cls, member):\n        if not isinstance(member, Enum):\n            raise TypeError("%r (%r) is not an <aenum 'Enum'>" % (member, type(member)))\n        if not isinstance(member, cls):\n            return False\n        return True\n\n    def __delattr__(cls, attr):\n        # nicer error message when someone tries to delete an attribute\n        # (see issue19025).\n        if attr in cls._member_map_:\n            raise AttributeError(\n                    "%s: cannot delete Enum member %r." % (cls.__name__, attr),\n                    )\n        if isinstance(_get_attr_from_chain(cls, attr), constant):\n            raise AttributeError(\n                    "%s: cannot delete constant %r" % (cls.__name__, attr),\n                    )\n        super(EnumMeta, cls).__delattr__(attr)\n\n    def __dir__(self):\n        return (['__class__', '__doc__', '__members__', '__module__'] +\n                self._member_names_)\n\n    @property\n    def __members__(cls):\n        """Returns a mapping of member name->value.\n\n        This mapping lists all enum members, including aliases. Note that this\n        is a copy of the internal mapping.\n        """\n        return cls._member_map_.copy()\n\n    def __getitem__(cls, name):\n        try:\n            return cls._member_map_[name]\n        except KeyError:\n            exc = _sys.exc_info()[1]\n        if issubclass(cls, Flag) and '|' in name:\n            try:\n                # may be an __or__ed name\n                result = cls(0)\n                for n in name.split('|'):\n                    result |= cls[n]\n                return result\n            except KeyError:\n                raise exc\n        result = cls._missing_name_(name)\n        if isinstance(result, cls):\n            return result\n        else:\n            raise exc\n\n    def __iter__(cls):\n        return (cls._member_map_[name] for name in cls._member_names_)\n\n    def __reversed__(cls):\n        return (cls._member_map_[name] for name in reversed(cls._member_names_))\n\n    def __len__(cls):\n        return len(cls._member_names_)\n\n    __nonzero__ = __bool__\n\n    def __repr__(cls):\n        return "<aenum %r>" % (cls.__name__, )\n\n    def __setattr__(cls, name, value):\n        """Block attempts to reassign Enum members/constants.\n\n        A simple assignment to the class namespace only changes one of the\n        several possible ways to get an Enum member from the Enum class,\n        resulting in an inconsistent Enumeration.\n        """\n        member_map = cls.__dict__.get('_member_map_', {})\n        if name in member_map:\n            raise AttributeError(\n                    '%s: cannot rebind member %r.' % (cls.__name__, name),\n                    )\n        cur_obj = cls.__dict__.get(name)\n        if isinstance(cur_obj, constant):\n            raise AttributeError(\n                    '%s: cannot rebind constant %r' % (cls.__name__, name),\n                    )\n        super(EnumMeta, cls).__setattr__(name, value)\n\n    def _create_(cls, class_name, names, module=None, type=None, start=1):\n        """Convenience method to create a new Enum class.\n\n        `names` can be:\n\n        * A string containing member names, separated either with spaces or\n          commas.  Values are auto-numbered from 1.\n        * An iterable of member names.  Values are auto-numbered from 1.\n        * An iterable of (member name, value) pairs.\n        * A mapping of member name -> value.\n        """\n        if pyver < 3.0:\n            # if class_name is unicode, attempt a conversion to ASCII\n            if isinstance(class_name, unicode):\n                try:\n                    class_name = class_name.encode('ascii')\n                except UnicodeEncodeError:\n                    raise TypeError('%r is not representable in ASCII' % (class_name, ))\n        metacls = cls.__class__\n        if type is None:\n            bases = (cls, )\n        else:\n            bases = (type, cls)\n        _, first_enum = cls._get_mixins_(bases)\n        generate = getattr(first_enum, '_generate_next_value_', None)\n        generate = getattr(generate, 'im_func', generate)\n        # special processing needed for names?\n        if isinstance(names, basestring):\n            names = names.replace(',', ' ').split()\n        if isinstance(names, (tuple, list)) and names and isinstance(names[0], basestring):\n            original_names, names = names, []\n            last_values = []\n            for count, name in enumerate(original_names):\n                value = generate(name, start, count, last_values[:])\n                last_values.append(value)\n                names.append((name, value))\n        # Here, names is either an iterable of (name, value) or a mapping.\n        item = None  # in case names is empty\n        clsdict = None\n        for item in names:\n            if clsdict is None:\n                # first time initialization\n                if isinstance(item, basestring):\n                    clsdict = {}\n                else:\n                    # remember the order\n                    clsdict = metacls.__prepare__(class_name, bases)\n            if isinstance(item, basestring):\n                member_name, member_value = item, names[item]\n            else:\n                member_name, member_value = item\n            clsdict[member_name] = member_value\n        if clsdict is None:\n            # in case names was empty\n            clsdict = metacls.__prepare__(class_name, bases)\n        enum_class = metacls.__new__(metacls, class_name, bases, clsdict)\n        # TODO: replace the frame hack if a blessed way to know the calling\n        # module is ever developed\n        if module is None:\n            try:\n                module = _sys._getframe(2).f_globals['__name__']\n            except (AttributeError, KeyError):\n                pass\n        if module is None:\n            _make_class_unpicklable(enum_class)\n        else:\n            enum_class.__module__ = module\n        return enum_class\n\n    @staticmethod\n    def _get_mixins_(bases):\n        """Returns the type for creating enum members, and the first inherited\n        enum class.\n\n        bases: the tuple of bases that was given to __new__\n        """\n        if not bases or Enum is None:\n            return object, Enum\n        def _find_data_type(bases):\n            for chain in bases:\n                for base in chain.__mro__:\n                    if base is object or base is StdlibEnum:\n                        continue\n                    elif '__new__' in base.__dict__:\n                        if issubclass(base, Enum):\n                            continue\n                        return base\n\n        # ensure final parent class is an Enum derivative, find any concrete\n        # data type, and check that Enum has no members\n        first_enum = bases[-1]\n        if not issubclass(first_enum, Enum):\n            raise TypeError("new enumerations should be created as "\n                    "`EnumName([mixin_type, ...] [data_type,] enum_type)`")\n        member_type = _find_data_type(bases) or object\n        if first_enum._member_names_:\n            raise TypeError("cannot extend enumerations via subclassing")\n\n        return member_type, first_enum\n\n    if pyver < 3.0:\n        @staticmethod\n        def _find_new_(clsdict, member_type, first_enum):\n            """Returns the __new__ to be used for creating the enum members.\n\n            clsdict: the class dictionary given to __new__\n            member_type: the data type whose __new__ will be used by default\n            first_enum: enumeration to check for an overriding __new__\n            """\n            # now find the correct __new__, checking to see of one was defined\n            # by the user; also check earlier enum classes in case a __new__ was\n            # saved as __new_member__\n            __new__ = clsdict.get('__new__', None)\n            if __new__:\n                return None, True, True      # __new__, save_new, new_uses_args\n\n            N__new__ = getattr(None, '__new__')\n            O__new__ = getattr(object, '__new__')\n            if Enum is None:\n                E__new__ = N__new__\n            else:\n                E__new__ = Enum.__dict__['__new__']\n            # check all possibles for __new_member__ before falling back to\n            # __new__\n            for method in ('__new_member__', '__new__'):\n                for possible in (member_type, first_enum):\n                    try:\n                        target = possible.__dict__[method]\n                    except (AttributeError, KeyError):\n                        target = getattr(possible, method, None)\n                    if target not in [\n                            None,\n                            N__new__,\n                            O__new__,\n                            E__new__,\n                            ]:\n                        if method == '__new_member__':\n                            clsdict['__new__'] = target\n                            return None, False, True\n                        if isinstance(target, staticmethod):\n                            target = target.__get__(member_type)\n                        __new__ = target\n                        break\n                if __new__ is not None:\n                    break\n            else:\n                __new__ = object.__new__\n\n            # if a non-object.__new__ is used then whatever value/tuple was\n            # assigned to the enum member name will be passed to __new__ and to the\n            # new enum member's __init__\n            if __new__ is object.__new__:\n                new_uses_args = False\n            else:\n                new_uses_args = True\n\n            return __new__, False, new_uses_args\n    else:\n        @staticmethod\n        def _find_new_(clsdict, member_type, first_enum):\n            """Returns the __new__ to be used for creating the enum members.\n\n            clsdict: the class dictionary given to __new__\n            member_type: the data type whose __new__ will be used by default\n            first_enum: enumeration to check for an overriding __new__\n            """\n            # now find the correct __new__, checking to see of one was defined\n            # by the user; also check earlier enum classes in case a __new__ was\n            # saved as __new_member__\n            __new__ = clsdict.get('__new__', None)\n\n            # should __new__ be saved as __new_member__ later?\n            save_new = __new__ is not None\n\n            if __new__ is None:\n                # check all possibles for __new_member__ before falling back to\n                # __new__\n                for method in ('__new_member__', '__new__'):\n                    for possible in (member_type, first_enum):\n                        target = getattr(possible, method, None)\n                        if target not in (\n                                None,\n                                None.__new__,\n                                object.__new__,\n                                Enum.__new__,\n                                StdlibEnum.__new__\n                                ):\n                            __new__ = target\n                            break\n                    if __new__ is not None:\n                        break\n                else:\n                    __new__ = object.__new__\n            # if a non-object.__new__ is used then whatever value/tuple was\n            # assigned to the enum member name will be passed to __new__ and to the\n            # new enum member's __init__\n            if __new__ is object.__new__:\n                new_uses_args = False\n            else:\n                new_uses_args = True\n\n            return __new__, save_new, new_uses_args\n\n\n########################################################\n# In order to support Python 2 and 3 with a single\n# codebase we have to create the Enum methods separately\n# and then use the `type(name, bases, dict)` method to\n# create the class.\n########################################################\ntemp_enum_dict = EnumMeta.__prepare__('Enum', (object, ))\ntemp_enum_dict['__doc__'] = "Generic enumeration.\\n\\n    Derive from this class to define new enumerations.\\n\\n"\n\ndef __init__(self, *args, **kwds):\n    # auto-init method\n    _auto_init_ = self._auto_init_\n    if _auto_init_ is None:\n        return\n    if 'value' in _auto_init_:\n        # remove 'value' from _auto_init_ as it has already been handled\n        _auto_init_ = _auto_init_[1:]\n    if _auto_init_:\n        if len(_auto_init_) < len(args):\n            raise TypeError('%d arguments expected (%s), %d received (%s)'\n                    % (len(_auto_init_), _auto_init_, len(args), args))\n        for name, arg in zip(_auto_init_, args):\n            setattr(self, name, arg)\n        if len(args) < len(_auto_init_):\n            remaining_args = _auto_init_[len(args):]\n            for name in remaining_args:\n                value = kwds.pop(name, undefined)\n                if value is undefined:\n                    raise TypeError('missing value for: %r' % (name, ))\n                setattr(self, name, value)\n            if kwds:\n                # too many keyword arguments\n                raise TypeError('invalid keyword(s): %s' % ', '.join(kwds.keys()))\ntemp_enum_dict['__init__'] = __init__\ndel __init__\n\ndef __new__(cls, value):\n    # all enum instances are actually created during class construction\n    # without calling this method; this method is called by the metaclass'\n    # __call__ (i.e. Color(3) ), and by pickle\n    if NoAlias in cls._settings_:\n        raise TypeError('NoAlias enumerations cannot be looked up by value')\n    if type(value) is cls:\n        # For lookups like Color(Color.red)\n        # value = value.value\n        return value\n    # by-value search for a matching enum member\n    # see if it's in the reverse mapping (for hashable values)\n    try:\n        if value in cls._value2member_map_:\n            return cls._value2member_map_[value]\n    except TypeError:\n        # not there, now do long search -- O(n) behavior\n        for name, member in cls._value2member_seq_:\n            if name == value:\n                return member\n    # still not found -- try _missing_ hook\n    try:\n        exc = None\n        result = cls._missing_value_(value)\n    except Exception as e:\n        exc = e\n        result = None\n    if isinstance(result, cls):\n        return result\n    else:\n        if value is no_arg:\n            ve_exc = ValueError('%s() should be called with a value' % (cls.__name__, ))\n        else:\n            ve_exc = ValueError("%r is not a valid %s" % (value, cls.__name__))\n        if result is None and exc is None:\n            raise ve_exc\n        elif exc is None:\n            exc = TypeError(\n                    'error in %s._missing_: returned %r instead of None or a valid member'\n                    % (cls.__name__, result)\n                    )\n        exc.__context__ = ve_exc\n        raise exc\ntemp_enum_dict['__new__'] = __new__\ndel __new__\n\n@staticmethod\ndef _generate_next_value_(name, start, count, last_values, *args, **kwds):\n    for last_value in reversed(last_values):\n        try:\n            return last_value + 1\n        except TypeError:\n            pass\n    else:\n        return start\ntemp_enum_dict['_generate_next_value_'] = _generate_next_value_\ndel _generate_next_value_\n\n@classmethod\ndef _missing_(cls, value):\n    "deprecated, use _missing_value_ instead"\n    return None\ntemp_enum_dict['_missing_'] = _missing_\ndel _missing_\n\n@classmethod\ndef _missing_value_(cls, value):\n    "used for failed value access"\n    return cls._missing_(value)\ntemp_enum_dict['_missing_value_'] = _missing_value_\ndel _missing_value_\n\n@classmethod\ndef _missing_name_(cls, name):\n    "used for failed item access"\n    return None\ntemp_enum_dict['_missing_name_'] = _missing_name_\ndel _missing_name_\n\ndef __repr__(self):\n    return "<%s.%s: %r>" % (\n            self.__class__.__name__, self._name_, self._value_)\ntemp_enum_dict['__repr__'] = __repr__\ndel __repr__\n\ndef __str__(self):\n    return "%s.%s" % (self.__class__.__name__, self._name_)\ntemp_enum_dict['__str__'] = __str__\ndel __str__\n\nif pyver >= 3.0:\n    def __dir__(self):\n        added_behavior = [\n                m\n                for cls in self.__class__.mro()\n                for m in cls.__dict__\n                if m[0] != '_' and m not in self._member_map_\n                ]\n        return (['__class__', '__doc__', '__module__', ] + added_behavior)\n    temp_enum_dict['__dir__'] = __dir__\n    del __dir__\n\ndef __format__(self, format_spec):\n    # mixed-in Enums should use the mixed-in type's __format__, otherwise\n    # we can get strange results with the Enum name showing up instead of\n    # the value\n\n    # pure Enum branch / overridden __str__ branch\n    overridden_str = self.__class__.__str__ != Enum.__str__\n    if self._member_type_ is object or overridden_str:\n        cls = str\n        val = str(self)\n    # mix-in branch\n    else:\n        cls = self._member_type_\n        val = self.value\n    return cls.__format__(val, format_spec)\ntemp_enum_dict['__format__'] = __format__\ndel __format__\n\ndef __hash__(self):\n    return hash(self._name_)\ntemp_enum_dict['__hash__'] = __hash__\ndel __hash__\n\ndef __reduce_ex__(self, proto):\n    return self.__class__, (self._value_, )\ntemp_enum_dict['__reduce_ex__'] = __reduce_ex__\ndel __reduce_ex__\n\n\n####################################\n# Python's less than 2.6 use __cmp__\n\nif pyver < 2.6:\n\n    def __cmp__(self, other):\n        if type(other) is self.__class__:\n            if self is other:\n                return 0\n            return -1\n        return NotImplemented\n        raise TypeError("unorderable types: %s() and %s()" % (self.__class__.__name__, other.__class__.__name__))\n    temp_enum_dict['__cmp__'] = __cmp__\n    del __cmp__\n\nelse:\n\n    def __le__(self, other):\n        raise TypeError("unorderable types: %s() <= %s()" % (self.__class__.__name__, other.__class__.__name__))\n    temp_enum_dict['__le__'] = __le__\n    del __le__\n\n    def __lt__(self, other):\n        raise TypeError("unorderable types: %s() < %s()" % (self.__class__.__name__, other.__class__.__name__))\n    temp_enum_dict['__lt__'] = __lt__\n    del __lt__\n\n    def __ge__(self, other):\n        raise TypeError("unorderable types: %s() >= %s()" % (self.__class__.__name__, other.__class__.__name__))\n    temp_enum_dict['__ge__'] = __ge__\n    del __ge__\n\n    def __gt__(self, other):\n        raise TypeError("unorderable types: %s() > %s()" % (self.__class__.__name__, other.__class__.__name__))\n    temp_enum_dict['__gt__'] = __gt__\n    del __gt__\n\n\ndef __eq__(self, other):\n    if type(other) is self.__class__:\n        return self is other\n    return NotImplemented\ntemp_enum_dict['__eq__'] = __eq__\ndel __eq__\n\ndef __ne__(self, other):\n    if type(other) is self.__class__:\n        return self is not other\n    return NotImplemented\ntemp_enum_dict['__ne__'] = __ne__\ndel __ne__\n\ndef __hash__(self):\n    return hash(self._name_)\ntemp_enum_dict['__hash__'] = __hash__\ndel __hash__\n\ndef __reduce_ex__(self, proto):\n    return self.__class__, (self._value_, )\ntemp_enum_dict['__reduce_ex__'] = __reduce_ex__\ndel __reduce_ex__\n\ndef _convert(cls, name, module, filter, source=None):\n    """\n    Create a new Enum subclass that replaces a collection of global constants\n    """\n    # convert all constants from source (or module) that pass filter() to\n    # a new Enum called name, and export the enum and its members back to\n    # module;\n    # also, replace the __reduce_ex__ method so unpickling works in\n    # previous Python versions\n    module_globals = vars(_sys.modules[module])\n    if source:\n        source = vars(source)\n    else:\n        source = module_globals\n    members = [(key, source[key]) for key in source.keys() if filter(key)]\n    try:\n        # sort by value, name\n        members.sort(key=lambda t: (t[1], t[0]))\n    except TypeError:\n        # unless some values aren't comparable, in which case sort by just name\n        members.sort(key=lambda t: t[0])\n    cls = cls(name, members, module=module)\n    cls.__reduce_ex__ = _reduce_ex_by_name\n    module_globals.update(cls.__members__)\n    module_globals[name] = cls\n    return cls\ntemp_enum_dict['_convert'] = classmethod(_convert)\ndel _convert\n\n# enum_property is used to provide access to the `name`, `value', etc.,\n# properties of enum members while keeping some measure of protection\n# from modification, while still allowing for an enumeration to have\n# members named `name`, `value`, etc..  This works because enumeration\n# members are not set directly on the enum class -- enum_property will\n# look them up in _member_map_.\n\n@enum_property\ndef name(self):\n    return self._name_\ntemp_enum_dict['name'] = name\ndel name\n\n@enum_property\ndef value(self):\n    return self._value_\ntemp_enum_dict['value'] = value\ndel value\n\n@enum_property\ndef values(self):\n    return self._values_\ntemp_enum_dict['values'] = values\ndel values\n\ndef _reduce_ex_by_name(self, proto):\n    return self.name\n\nif StdlibEnum is not None:\n    Enum = EnumMeta('Enum', (StdlibEnum, ), temp_enum_dict)\nelse:\n    Enum = EnumMeta('Enum', (object, ), temp_enum_dict)\ndel temp_enum_dict\n\n# Enum has now been created\n###########################\n\nclass IntEnum(int, Enum):\n    """Enum where members are also (and must be) ints"""\n\nclass StrEnum(str, Enum): \n    """Enum where members are also (and must already be) strings\n    \n        default value is member name\n    """\n    def __new__(cls, value, *args, **kwds):\n        if args or kwds:\n            raise TypeError('only a single string value may be specified')\n        if not isinstance(value, str):\n            raise TypeError('values for StrEnum must be strings, not %r' % type(value))\n        obj = str.__new__(cls, value)\n        obj._value_ = value\n        return obj\n    def _generate_next_value_(name, start, count, last_values, *args, **kwds):\n        return name\n\nclass LowerStrEnum(StrEnum):\n    """Enum where members are also (and must already be) lower-case strings\n    \n        default value is member name, lower-cased\n    """\n    def __new__(cls, value, *args, **kwds):\n        obj = StrEnum.__new_member__(cls, value, *args, **kwds)\n        if value != value.lower():\n            raise ValueError('%r is not lower-case' % value)\n        return obj\n    def _generate_next_value_(name, start, count, last_values, *args, **kwds):\n        return name.lower()\n\nclass UpperStrEnum(StrEnum):\n    """Enum where members are also (and must already be) upper-case strings\n    \n        default value is member name, upper-cased\n    """\n    def __new__(cls, value, *args, **kwds):\n        obj = StrEnum.__new_member__(cls, value, *args, **kwds)\n        if value != value.upper():\n            raise ValueError('%r is not upper-case' % value)\n        return obj\n    def _generate_next_value_(name, start, count, last_values, *args, **kwds):\n        return name.upper()\n\nif pyver >= 3:\n    class AutoEnum(Enum):\n        """\n        automatically use _generate_next_value_ when values are missing (Python 3 only)\n        """\n        _settings_ = AutoValue\n\nclass AutoNumberEnum(Enum):\n    """\n    Automatically assign increasing values to members.\n\n    Py3: numbers match creation order\n    Py2: numbers are assigned alphabetically by member name\n    """\n    def __new__(cls, *args, **kwds):\n        value = len(cls.__members__) + 1\n        obj = object.__new__(cls)\n        obj._value_ = value\n        return obj\n\nclass MultiValueEnum(Enum):\n    """\n    Multiple values can map to each member.\n    """\n    _settings_ = MultiValue\n\nclass NoAliasEnum(Enum):\n    """\n    Duplicate value members are distinct, but cannot be looked up by value.\n    """\n    _settings_ = NoAlias\n\nclass OrderedEnum(Enum):\n    """\n    Add ordering based on values of Enum members.\n    """\n    def __ge__(self, other):\n        if self.__class__ is other.__class__:\n            return self._value_ >= other._value_\n        return NotImplemented\n\n    def __gt__(self, other):\n        if self.__class__ is other.__class__:\n            return self._value_ > other._value_\n        return NotImplemented\n\n    def __le__(self, other):\n        if self.__class__ is other.__class__:\n            return self._value_ <= other._value_\n        return NotImplemented\n\n    def __lt__(self, other):\n        if self.__class__ is other.__class__:\n            return self._value_ < other._value_\n        return NotImplemented\n\nif sqlite3:\n    class SqliteEnum(Enum):\n        def __conform__(self, protocol):\n            if protocol is sqlite3.PrepareProtocol:\n                return self.name\n\nclass UniqueEnum(Enum):\n    """\n    Ensure no duplicate values exist.\n    """\n    _settings_ = Unique\n\n\ndef convert(enum, name, module, filter, source=None):\n    """\n    Create a new Enum subclass that replaces a collection of global constants\n\n    enum: Enum, IntEnum, ...\n    name: name of new Enum\n    module: name of module (__name__ in global context)\n    filter: function that returns True if name should be converted to Enum member\n    source: namespace to check (defaults to 'module')\n    """\n    # convert all constants from source (or module) that pass filter() to\n    # a new Enum called name, and export the enum and its members back to\n    # module;\n    # also, replace the __reduce_ex__ method so unpickling works in\n    # previous Python versions\n    module_globals = vars(_sys.modules[module])\n    if source:\n        source = vars(source)\n    else:\n        source = module_globals\n    members = dict((name, value) for name, value in source.items() if filter(name))\n    enum = enum(name, members, module=module)\n    enum.__reduce_ex__ = _reduce_ex_by_name\n    module_globals.update(enum.__members__)\n    module_globals[name] = enum\n\ndef extend_enum(enumeration, name, *args, **_private_kwds):\n    """\n    Add a new member to an existing Enum.\n    """\n    try:\n        _member_map_ = enumeration._member_map_\n        _member_names_ = enumeration._member_names_\n        _member_type_ = enumeration._member_type_\n        _value2member_map_ = enumeration._value2member_map_\n        base_attributes = set([a for b in enumeration.mro() for a in b.__dict__])\n    except AttributeError:\n        raise TypeError('%r is not a supported Enum' % (enumeration, ))\n    try:\n        _value2member_seq_ = enumeration._value2member_seq_\n        # _auto_number_ = enumeration._auto_number_\n        _multi_value_ = MultiValue in enumeration._settings_\n        _no_alias_ = NoAlias in enumeration._settings_\n        _unique_ = Unique in enumeration._settings_\n        # _unique_ = Unique in enumeration._settings_\n        _auto_init_ = enumeration._auto_init_ or []\n    except AttributeError:\n        # standard Enum\n        _value2member_seq_ = []\n        # _auto_number_ = False\n        _multi_value_ = False\n        _no_alias_ = False\n        # _unique_ = False\n        _auto_init_ = []\n    mt_new = _member_type_.__new__\n    _new = getattr(enumeration, '__new_member__', mt_new)\n    if not args:\n        _gnv = getattr(enumeration, '_generate_next_value_')\n        if _gnv is None:\n            raise TypeError('value not provided and _generate_next_value_ missing')\n        last_values = [m.value for m in enumeration]\n        count = len(enumeration)\n        start = getattr(enumeration, '_start_')\n        if start is None:\n            start = last_values and last_values[0] or 1\n        args = ( _gnv(name, start, count, last_values), )\n    if _new is object.__new__:\n        new_uses_args = False\n    else:\n        new_uses_args = True\n    if len(args) == 1:\n        [value] = args\n    else:\n        value = args\n    more_values = ()\n    kwds = {}\n    if isinstance(value, enum):\n        args = value.args\n        kwds = value.kwds\n    if not isinstance(value, tuple):\n        args = (value, )\n    else:\n        args = value\n    # tease value out of auto-init if specified\n    if 'value' in _auto_init_:\n        if 'value' in kwds:\n            value = kwds.pop('value')\n        else:\n            value, args = args[0], args[1:]\n    elif _multi_value_:\n        value, more_values, args = args[0], args[1:], ()\n    if _member_type_ is tuple:\n        args = (args, )\n    if not new_uses_args:\n        new_member = _new(enumeration)\n        if not hasattr(new_member, '_value_'):\n            new_member._value_ = value\n    else:\n        new_member = _new(enumeration, *args, **kwds)\n        if not hasattr(new_member, '_value_'):\n            new_member._value_ = _member_type_(*args)\n    value = new_member._value_\n    new_member._name_ = name\n    new_member.__objclass__ = enumeration.__class__\n    new_member.__init__(*args)\n    if _private_kwds.get('create_only'):\n        return new_member\n    # If another member with the same value was already defined, the\n    # new member becomes an alias to the existing one.\n    is_alias = False\n    if _no_alias_:\n        # unless NoAlias was specified\n        _member_names_.append(name)\n        _member_map_[name] = new_member\n    else:\n        for canonical_member in _member_map_.values():\n            _values_ = getattr(canonical_member, '_values_', [canonical_member._value_])\n            for canonical_value in _values_:\n                if canonical_value == new_member._value_:\n                    # name is an alias\n                    if _unique_ or _multi_value_:\n                        # aliases not allowed if Unique specified\n                        raise ValueError('%s is a duplicate of %s' % (name, canonical_member.name))\n                    if name not in base_attributes:\n                        setattr(enumeration, name, canonical_member)\n                    else:\n                        # check type of name\n                        for parent in enumeration.mro()[1:]:\n                            if name in parent.__dict__:\n                                obj = parent.__dict__[name]\n                                if not isinstance(obj, enum_property):\n                                    raise TypeError('%r already used: %r' % (name, obj))\n                                break\n                    # Aliases don't appear in member names (only in __members__ and _member_map_).\n                    _member_map_[new_member._name_] = canonical_member\n                    new_member = canonical_member\n                    is_alias = True\n                    break\n            if is_alias:\n                break\n        else:\n            # not an alias\n            values = (value, ) + more_values\n            new_member._values_ = values\n            for value in (value, ) + more_values:\n                # first check if value has already been used\n                if _multi_value_ and (\n                        value in _value2member_map_\n                        or any(v == value for (v, m) in _value2member_seq_)\n                        ):\n                    raise ValueError('%r has already been used' % (value, ))\n                try:\n                    # This may fail if value is not hashable. We can't add the value\n                    # to the map, and by-value lookups for this value will be\n                    # linear.\n                    if _no_alias_:\n                        raise TypeError('cannot use dict to store value')\n                    _value2member_map_[value] = new_member\n                except TypeError:\n                    _value2member_seq_ += ((value, new_member), )\n            if name not in base_attributes:\n                setattr(enumeration, name, new_member)\n            else:\n                # check type of name\n                for parent in enumeration.mro()[1:]:\n                    if name in parent.__dict__:\n                        obj = parent.__dict__[name]\n                        if not isinstance(obj, enum_property):\n                            raise TypeError('%r already used: %r' % (name, obj))\n                        break\n            _member_names_.append(name)\n            _member_map_[name] = new_member\n            try:\n                _value2member_map_[value] = new_member\n            except TypeError:\n                pass\n\ndef unique(enumeration):\n    """\n    Class decorator that ensures only unique members exist in an enumeration.\n    """\n    duplicates = []\n    for name, member in enumeration.__members__.items():\n        if name != member.name:\n            duplicates.append((name, member.name))\n    if duplicates:\n        duplicate_names = ', '.join(\n                ["%s -> %s" % (alias, name) for (alias, name) in duplicates]\n                )\n        raise ValueError('duplicate names found in %r: %s' %\n                (enumeration, duplicate_names)\n                )\n    return enumeration\n\nclass Flag(Enum):\n    """Support for flags"""\n\n    def _generate_next_value_(name, start, count, last_values):\n        """\n        Generate the next value when not given.\n\n        name: the name of the member\n        start: the initital start value or None\n        count: the number of existing members\n        last_value: the last value assigned or None\n        """\n        if not count:\n            return (1, start)[start is not None]\n        error = False\n        for last_value in reversed(last_values):\n            if isinstance(last_value, auto):\n                last_value = last_value.value\n            try:\n                high_bit = _high_bit(last_value)\n                break\n            except Exception:\n                error = True\n                break\n        if error:\n            raise TypeError('invalid Flag value: %r' % (last_value, ))\n        return 2 ** (high_bit+1)\n\n    @classmethod\n    def _missing_(cls, value):\n        original_value = value\n        if value < 0:\n            value = ~value\n        possible_member = cls._create_pseudo_member_(value)\n        if original_value < 0:\n            possible_member = ~possible_member\n        return possible_member\n\n    @classmethod\n    def _create_pseudo_member_(cls, *values):\n        """\n        Create a composite member iff value contains only members.\n        """\n        value = values[0]\n        pseudo_member = cls._value2member_map_.get(value, None)\n        if pseudo_member is None:\n            # verify all bits are accounted for\n            members, extra_flags = _decompose(cls, value)\n            if extra_flags:\n                raise ValueError("%r is not a valid %s" % (value, cls.__name__))\n            # give subclasses a chance to modify values for new pseudo-member\n            values = cls._create_pseudo_member_values_(members, *values)\n            # construct a singleton enum pseudo-member\n            pseudo_member = extend_enum(cls, None, *values, create_only=True)\n            # use setdefault in case another thread already created a composite\n            # with this value\n            pseudo_member = cls._value2member_map_.setdefault(value, pseudo_member)\n        return pseudo_member\n\n    @classmethod\n    def _create_pseudo_member_values_(cls, members, *values):\n        return values\n\n    def __contains__(self, other):\n        if not isinstance(other, Flag):\n            raise TypeError("%r (%r) is not an <aenum 'Flag'>" % (other, type(other)))\n        if not isinstance(other, self.__class__):\n            return False\n        return other._value_ & self._value_ == other._value_\n\n    def __repr__(self):\n        cls = self.__class__\n        if self._name_ is not None:\n            return '<%s.%s: %r>' % (cls.__name__, self._name_, self._value_)\n        members, uncovered = _decompose(cls, self._value_)\n        return '<%s.%s: %r>' % (\n                cls.__name__,\n                '|'.join([str(m._name_ or m._value_) for m in members]),\n                self._value_,\n                )\n\n    def __str__(self):\n        cls = self.__class__\n        if self._name_ is not None:\n            return '%s.%s' % (cls.__name__, self._name_)\n        members, uncovered = _decompose(cls, self._value_)\n        if len(members) == 1 and members[0]._name_ is None:\n            return '%s.%r' % (cls.__name__, members[0]._value_)\n        else:\n            return '%s.%s' % (\n                    cls.__name__,\n                    '|'.join([str(m._name_ or m._value_) for m in members]),\n                    )\n\n    def __bool__(self):\n        return bool(self._value_)\n    if pyver < 3:\n        __nonzero__ = __bool__\n        del __bool__\n\n    def __or__(self, other):\n        if not isinstance(other, self.__class__):\n            return NotImplemented\n        return self.__class__(self._value_ | other._value_)\n\n    def __and__(self, other):\n        if not isinstance(other, self.__class__):\n            return NotImplemented\n        return self.__class__(self._value_ & other._value_)\n\n    def __xor__(self, other):\n        if not isinstance(other, self.__class__):\n            return NotImplemented\n        return self.__class__(self._value_ ^ other._value_)\n\n    def __invert__(self):\n        members, uncovered = _decompose(self.__class__, self._value_)\n        inverted_members = [\n                m for m in self.__class__\n                if m not in members and not m._value_ & self._value_\n                ]\n        inverted = reduce(_or_, inverted_members, self.__class__(0))\n        return self.__class__(inverted)\n\n    def __iter__(self):\n        members, extra_flags = _decompose(self.__class__, self.value)\n        return (m for m in members if m._value_ != 0)\n\n\nclass IntFlag(int, Flag):\n    """Support for integer-based Flags"""\n\n    @classmethod\n    def _missing_(cls, value):\n        if not isinstance(value, int):\n            raise ValueError("%r is not a valid %s" % (value, cls.__name__))\n        new_member = cls._create_pseudo_member_(value)\n        return new_member\n\n    @classmethod\n    def _create_pseudo_member_(cls, value):\n        pseudo_member = cls._value2member_map_.get(value, None)\n        if pseudo_member is None:\n            need_to_create = [value]\n            # get unaccounted for bits\n            _, extra_flags = _decompose(cls, value)\n            while extra_flags:\n                bit = _high_bit(extra_flags)\n                flag_value = 2 ** bit\n                if (flag_value not in cls._value2member_map_ and\n                        flag_value not in need_to_create\n                        ):\n                    need_to_create.append(flag_value)\n                if extra_flags == -flag_value:\n                    extra_flags = 0\n                else:\n                    extra_flags ^= flag_value\n            for value in reversed(need_to_create):\n                # construct singleton pseudo-members\n                pseudo_member = int.__new__(cls, value)\n                pseudo_member._name_ = None\n                pseudo_member._value_ = value\n                # use setdefault in case another thread already created a composite\n                # with this value\n                pseudo_member = cls._value2member_map_.setdefault(value, pseudo_member)\n        return pseudo_member\n\n    def __or__(self, other):\n        if not isinstance(other, (self.__class__, int)):\n            return NotImplemented\n        result = self.__class__(self._value_ | self.__class__(other)._value_)\n        return result\n\n    def __and__(self, other):\n        if not isinstance(other, (self.__class__, int)):\n            return NotImplemented\n        return self.__class__(self._value_ & self.__class__(other)._value_)\n\n    def __xor__(self, other):\n        if not isinstance(other, (self.__class__, int)):\n            return NotImplemented\n        return self.__class__(self._value_ ^ self.__class__(other)._value_)\n\n    __ror__ = __or__\n    __rand__ = __and__\n    __rxor__ = __xor__\n\n    def __invert__(self):\n        result = self.__class__(~self._value_)\n        return result\n\n\ndef _high_bit(value):\n    """returns index of highest bit, or -1 if value is zero or negative"""\n    return value.bit_length() - 1\n\ndef _decompose(flag, value):\n    """Extract all members from the value."""\n    # _decompose is only called if the value is not named\n    not_covered = value\n    negative = value < 0\n    # issue29167: wrap accesses to _value2member_map_ in a list to avoid race\n    #             conditions between iterating over it and having more psuedo-\n    #             members added to it\n    if negative:\n        # only check for named flags\n        flags_to_check = [\n                (m, v)\n                for v, m in list(flag._value2member_map_.items())\n                if m.name is not None\n                ]\n    else:\n        # check for named flags and powers-of-two flags\n        flags_to_check = [\n                (m, v)\n                for v, m in list(flag._value2member_map_.items())\n                if m.name is not None or _power_of_two(v)\n                ]\n    members = []\n    for member, member_value in flags_to_check:\n        if member_value and member_value & value == member_value:\n            members.append(member)\n            not_covered &= ~member_value\n    if not members and value in flag._value2member_map_:\n        members.append(flag._value2member_map_[value])\n    members.sort(key=lambda m: m._value_, reverse=True)\n    if len(members) > 1 and members[0].value == value:\n        # we have the breakdown, don't need the value member itself\n        members.pop(0)\n    return members, not_covered\n\ndef _power_of_two(value):\n    if value < 1:\n        return False\n    return value == 2 ** _high_bit(value)\n\n\nclass module(object):\n\n    def __init__(self, cls, *args):\n        self.__name__ = cls.__name__\n        self._parent_module = cls.__module__\n        self.__all__ = []\n        all_objects = cls.__dict__\n        if not args:\n            args = [k for k, v in all_objects.items() if isinstance(v, (NamedConstant, Enum))]\n        for name in args:\n            self.__dict__[name] = all_objects[name]\n            self.__all__.append(name)\n\n    def register(self):\n        _sys.modules["%s.%s" % (self._parent_module, self.__name__)] = self\n\n\n
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Source code for gs_quant.base

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nimport builtins\nimport copy\nimport datetime as dt\nimport itertools\nimport keyword\nimport logging\nfrom abc import ABCMeta, abstractmethod\nfrom collections import namedtuple\nfrom enum import EnumMeta\nfrom functools import wraps\nfrom inspect import signature, Parameter\nfrom typing import Optional, Union, get_type_hints\n\nimport inflection\nfrom dateutil.parser import isoparse\nimport numpy as np\n\nfrom gs_quant.context_base import ContextBase, ContextMeta, do_not_serialise\n\n_logger = logging.getLogger(__name__)\n\n_valid_date_formats = ('%Y-%m-%d',  # '2020-07-28'\n                       '%d%b%y',    # '28Jul20'\n                       '%d-%b-%y',  # '28-Jul-20'\n                       '%d/%m/%Y')  # '28/07/2020\n\n\ndef _normalise_arg(arg: str) -> str:\n    if keyword.iskeyword(arg) or arg in dir(builtins):\n        return arg + '_'\n    else:\n        return arg\n\n\ndef camel_case_translate(f):\n    @wraps(f)\n    def wrapper(*args, **kwargs):\n        normalised_kwargs = {}\n        for arg, value in kwargs.items():\n            arg = _normalise_arg(arg)\n\n            if not arg.isupper():\n                snake_case_arg = inflection.underscore(arg)\n                if snake_case_arg != arg:\n                    if snake_case_arg in kwargs:\n                        raise ValueError('{} and {} both specified'.format(arg, snake_case_arg))\n\n                    normalised_kwargs[snake_case_arg] = value\n                else:\n                    normalised_kwargs[arg] = value\n            else:\n                normalised_kwargs[arg] = value\n\n        return f(*args, **normalised_kwargs)\n\n    return wrapper\n\n\ndef do_not_resolve(func):\n    func.do_not_resolve = True\n    return func\n\n\nclass RiskKey(namedtuple('RiskKey', ('provider', 'date', 'market', 'params', 'scenario', 'risk_measure'))):\n\n    @property\n    def ex_date_and_market(self):\n        return RiskKey(self.provider, None, None, self.params, self.scenario, self.risk_measure)\n\n    @property\n    def ex_measure(self):\n        return RiskKey(self.provider, self.date, self.market, self.params, self.scenario, None)\n\n    @property\n    def fields(self):\n        return self._fields\n\n\nclass EnumBase:\n\n    @classmethod\n    def _missing_(cls: EnumMeta, key):\n        return next((m for m in cls.__members__.values() if m.value.lower() == key.lower()), None)\n\n    def __reduce_ex__(self, protocol):\n        return self.__class__, (self.value,)\n\n    def __lt__(self: EnumMeta, other):\n        return self.value < other.value\n\n\nclass Base(metaclass=ABCMeta):\n    """The base class for all generated classes"""\n\n    __properties = set()\n\n    def __init__(self, **kwargs):\n        self.__calced_hash: Optional[int] = None\n        self.__as_dict = {False: {}, True: {}}\n\n        try:\n            self.name: Optional[str] = None\n        except AttributeError:\n            # Regrettably, read-only properties called name exist\n            pass\n\n    def __getattr__(self, item):\n        snake_case_item = inflection.underscore(item)\n        if snake_case_item in super().__getattribute__('properties')():\n            return super().__getattribute__(snake_case_item)\n        else:\n            return super().__getattribute__(item)\n\n    def __setattr__(self, key, value):\n        properties = super().__getattribute__('properties')()\n        snake_case_key = inflection.underscore(key)\n        key_is_property = key in properties\n        snake_case_key_is_property = snake_case_key in properties\n\n        if snake_case_key_is_property and not key_is_property:\n            return super().__setattr__(snake_case_key, value)\n        else:\n            return super().__setattr__(key, value)\n\n    def __repr__(self):\n        if self.name is not None:\n            return '{} ({})'.format(self.name, self.__class__.__name__)\n\n        return super().__repr__()\n\n    def _property_changed(self, prop: str):\n        self.__calced_hash = None\n        self.__as_dict = {False: {}, True: {}}\n\n    @property\n    def _hash_is_calced(self) -> bool:\n        return self.__calced_hash is not None\n\n    def __hash__(self) -> int:\n        if not self._hash_is_calced:\n            calced_hash = hash(self.name)\n            for prop in self.properties():\n                value = super().__getattribute__(prop)\n                if isinstance(value, dict):\n                    value = tuple(value.items())\n                elif isinstance(value, list):\n                    value = tuple(value)\n                calced_hash ^= hash(value)\n\n            self.__calced_hash = calced_hash\n\n        return self.__calced_hash\n\n    def __eq__(self, other) -> bool:\n        return \\\n            type(self) == type(other) and (self.name is None or other.name is None or self.name == other.name) and \\\n            (self.__calced_hash is None or other.__calced_hash is None or self.__calced_hash == other.__calced_hash) \\\n            and all(super(Base, self).__getattribute__(p) == super(Base, other).__getattribute__(p)\n                    for p in self.properties())\n\n    def __ne__(self, other) -> bool:\n        return not self.__eq__(other)\n\n    def __lt__(self, other):\n        if not isinstance(other, type(self)):\n            return type(self).__name__ < type(other).__name__\n\n        for prop in itertools.chain(('name',), sorted(self.properties())):\n            val = super(Base, self).__getattribute__(prop)\n            other_val = super(Base, other).__getattribute__(prop)\n\n            if val != other_val:\n                if val is None:\n                    return False\n                return val < other_val\n\n        return False\n\n    def clone(self, **kwargs):\n        """\n            Clone this object, overriding specified values\n\n            :param kwargs: property names and values, e.g. swap.clone(fixed_rate=0.01)\n\n            **Examples**\n\n            To change the market data location of the default context:\n\n            >>> from gs_quant.instrument import IRCap\n            >>> cap = IRCap('5y', 'GBP')\n            >>>\n            >>> new_cap = cap.clone(cap_rate=0.01)\n        """\n        clone = copy.copy(self)\n        properties = self.properties()\n        for key, value in kwargs.items():\n            if key in properties:\n                setattr(clone, key, value)\n            else:\n                raise ValueError('Only properties may be passed as kwargs')\n\n        return clone\n\n    @classmethod\n    def properties(cls) -> set:\n        """The public property names of this class"""\n        if not cls.__properties:\n            cls.__properties = set(i for i in dir(cls) if isinstance(getattr(cls, i), property)\n                                   and not i.startswith('_') and not\n                                   getattr(getattr(cls, i).fget, 'do_not_serialise', False))\n        return cls.__properties\n\n    def as_dict(self, as_camel_case: bool = False) -> dict:\n        """Dictionary of the public, non-null properties and values"""\n        if not self.__as_dict[as_camel_case]:\n            raw_properties = self.properties()\n            properties = (inflection.camelize(p, uppercase_first_letter=False) for p in raw_properties) \\\n                if as_camel_case else raw_properties\n            values = (super(Base, self).__getattribute__(p) for p in raw_properties)\n            self.__as_dict[as_camel_case] = dict((p, v) for p, v in zip(properties, values) if v is not None)\n\n        return copy.copy(self.__as_dict[as_camel_case])\n\n    @classmethod\n    def prop_type(cls, prop: str, additional: Optional[list] = None) -> type:\n        return_hints = get_type_hints(getattr(cls, prop).fget).get('return')\n        if hasattr(return_hints, '__origin__'):\n            prop_type = return_hints.__origin__\n        else:\n            prop_type = return_hints\n\n        if prop_type == Union:\n            prop_type = next((a for a in return_hints.__args__ if issubclass(a, (Base, EnumBase))), None)\n            if prop_type is None:\n                for typ in (dt.datetime, dt.date):\n                    if typ in return_hints.__args__:\n                        prop_type = typ\n\n                        if additional is not None:\n                            additional.extend([a for a in return_hints.__args__ if a != prop_type])\n                        break\n\n            if prop_type is None and additional is not None:\n                prop_type = return_hints.__args__[-1]\n                additional.extend(return_hints.__args__[:-1])\n\n        if prop_type is InstrumentBase:\n            # TODO Fix this\n            from .instrument import Instrument\n            prop_type = Instrument\n\n        return prop_type\n\n    @classmethod\n    def prop_item_type(cls, prop: str) -> type:\n        return_hints = get_type_hints(getattr(cls, prop).fget).get('return')\n        item_type = return_hints.__args__[0]\n\n        item_args = [i for i in getattr(item_type, '__args__', ()) if isinstance(i, type)]\n        if item_args:\n            item_type = next((a for a in item_args if issubclass(a, (Base, EnumBase))), item_args[-1])\n\n        return item_type\n\n    def __from_dict(self, values: dict):\n        for prop in self.properties():\n            if getattr(type(self), prop).fset is None:\n                continue\n\n            prop_value = values.get(prop, values.get(inflection.camelize(prop, uppercase_first_letter=False)))\n\n            if prop_value is not None:\n                if isinstance(prop_value, np.generic):\n                    prop_value = prop_value.item()\n\n                additional_types = []\n                prop_type = self.prop_type(prop, additional=additional_types)\n\n                if prop_type is None:\n                    # This shouldn't happen\n                    setattr(self, prop, prop_value)\n                elif issubclass(prop_type, dt.datetime):\n                    if isinstance(prop_value, int):\n                        setattr(self, prop, dt.datetime.fromtimestamp(prop_value / 1000).isoformat())\n                    else:\n                        import re\n                        matcher = re.search('\\\\.([0-9]*)Z$', prop_value)\n                        if matcher:\n                            sub_seconds = matcher.group(1)\n                            if len(sub_seconds) > 6:\n                                prop_value = re.sub(matcher.re, '.{}Z'.format(sub_seconds[:6]), prop_value)\n\n                        try:\n                            setattr(self, prop, isoparse(prop_value))\n                        except ValueError:\n                            if str in additional_types:\n                                setattr(self, prop, prop_value)\n                elif issubclass(prop_type, dt.date) and type(prop_value) is not dt.date:\n                    date_value = None\n\n                    if isinstance(prop_value, float):\n                        # Assume it's an Excel date\n                        if prop_value > 59:\n                            prop_value -= 1  # Excel leap year bug, 1900 is not a leap year!\n                        date_value = dt.datetime(1899, 12, 31) + dt.timedelta(days=prop_value).date()\n                    elif isinstance(prop_value, str):\n                        for format in _valid_date_formats:\n                            try:\n                                date_value = dt.datetime.strptime(prop_value, format).date()\n                                break\n                            except ValueError:\n                                pass\n\n                    setattr(self, prop, date_value or prop_value)\n                elif issubclass(prop_type, float) and isinstance(prop_value, str):\n                    if prop_value.endswith('%'):\n                        setattr(self, prop, float(prop_value[:-1]) / 100)\n                    else:\n                        setattr(self, prop, float(prop_value))\n                elif issubclass(prop_type, EnumBase):\n                    setattr(self, prop, get_enum_value(prop_type, prop_value))\n                elif issubclass(prop_type, Base):\n                    if isinstance(prop_value, Base):\n                        setattr(self, prop, prop_value)\n                    else:\n                        setattr(self, prop, prop_type.from_dict(prop_value))\n                elif issubclass(prop_type, (list, tuple)):\n                    item_type = self.prop_item_type(prop)\n                    if issubclass(item_type, Base):\n                        item_values = tuple(v if isinstance(v, (Base, EnumBase)) else item_type.from_dict(v)\n                                            for v in prop_value)\n                    elif issubclass(item_type, EnumBase):\n                        item_values = tuple(get_enum_value(item_type, v) for v in prop_value)\n                    else:\n                        item_values = tuple(prop_value)\n                    setattr(self, prop, item_values)\n                else:\n                    setattr(self, prop, prop_value)\n\n    @classmethod\n    def _from_dict(cls, values: dict):\n        args = [k for k, v in signature(cls.__init__).parameters.items() if k not in ('kwargs', '_kwargs')\n                and v.default == Parameter.empty][1:]\n        required = {}\n\n        for arg in args:\n            prop_name = arg[:-1] if arg.endswith('_') and not keyword.iskeyword(arg) else arg\n            prop_type = cls.prop_type(prop_name)\n            value = values.pop(arg, None)\n\n            if prop_type:\n                if issubclass(prop_type, Base) and isinstance(value, dict):\n                    value = prop_type.from_dict(value)\n                elif issubclass(prop_type, (list, tuple)) and isinstance(value, (list, tuple)):\n                    item_type = cls.prop_item_type(prop_name)\n                    if issubclass(item_type, Base):\n                        value = tuple(v if isinstance(v, (Base, EnumBase)) else item_type.from_dict(v) for v in value)\n                elif issubclass(prop_type, EnumBase):\n                    value = get_enum_value(prop_type, value)\n\n            required[arg] = value\n\n        instance = cls(**required)\n        instance.__from_dict(values)\n        return instance\n\n    @classmethod\n    def from_dict(cls, values: dict):\n        """\n        Construct an instance of this type from a dictionary\n\n        :param values: a dictionary (potentially nested)\n        :return: an instance of this type, populated with values\n        """\n        return cls._from_dict(values)\n\n    @classmethod\n    def default_instance(cls):\n        """\n        Construct a default instance of this type\n        """\n        args = [k for k, v in signature(cls.__init__).parameters.items() if v.default == Parameter.empty][1:]\n        required = {a: None for a in args}\n        return cls(**required)\n\n    def from_instance(self, instance):\n        """\n        Copy the values from an existing instance of the same type to our self\n        :param instance: from which to copy:\n        :return:\n        """\n        if not isinstance(instance, type(self)):\n            raise ValueError('Can only use from_instance with an object of the same type')\n\n        for prop in self.properties():\n            attr = getattr(super().__getattribute__('__class__'), prop)\n            if attr.fset:\n                super(Base, self).__setattr__(prop, super(Base, instance).__getattribute__(prop))\n\n\n
[docs]class Priceable(Base, metaclass=ABCMeta):\n\n    def resolve(self, in_place: bool = True):\n        """\n        Resolve non-supplied properties of an instrument\n\n        **Examples**\n\n        >>> from gs_quant.instrument import IRSwap\n        >>>\n        >>> swap = IRSwap('Pay', '10y', 'USD')\n        >>> rate = swap.fixedRate\n\n        rate is None\n\n        >>> swap.resolve()\n        >>> rate = swap.fixedRate\n\n        rates is now the solved fixed rate\n        """\n        raise NotImplementedError\n\n    def dollar_price(self):\n        """\n        Present value in USD\n\n        :return:  a float or a future, depending on whether the current PricingContext is async, or has been entered\n\n        **Examples**\n\n        >>> from gs_quant.instrument import IRCap\n        >>>\n        >>> cap = IRCap('1y', 'EUR')\n        >>> price = cap.dollar_price()\n\n        price is the present value in USD (a float)\n\n        >>> cap_usd = IRCap('1y', 'USD')\n        >>> cap_eur = IRCap('1y', 'EUR')\n        >>>\n        >>> from gs_quant.markets import PricingContext\n        >>>\n        >>> with PricingContext():\n        >>>     price_usd_f = cap_usd.dollar_price()\n        >>>     price_eur_f = cap_eur.dollar_price()\n        >>>\n        >>> price_usd = price_usd_f.result()\n        >>> price_eur = price_eur_f.result()\n\n        price_usd_f and price_eur_f are futures, price_usd and price_eur are floats\n        """\n        raise NotImplementedError\n\n    def price(self):\n        """\n        Present value in local currency. Note that this is not yet supported on all instruments\n\n        ***Examples**\n\n        >>> from gs_quant.instrument import IRSwap\n        >>>\n        >>> swap = IRSwap('Pay', '10y', 'EUR')\n        >>> price = swap.price()\n\n        price is the present value in EUR (a float)\n        """\n        raise NotImplementedError\n\n    def calc(self, risk_measure, fn=None):\n        """\n        Calculate the value of the risk_measure\n\n        :param risk_measure: the risk measure to compute, e.g. IRDelta (from gs_quant.risk)\n        :param fn: a function for post-processing results\n        :return: a float or dataframe, depending on whether the value is scalar or structured, or a future thereof\n        (depending on how PricingContext is being used)\n\n        **Examples**\n\n        >>> from gs_quant.instrument import IRCap\n        >>> from gs_quant.risk import IRDelta\n        >>>\n        >>> cap = IRCap('1y', 'USD')\n        >>> delta = cap.calc(IRDelta)\n\n        delta is a dataframe\n\n        >>> from gs_quant.instrument import EqOption\n        >>> from gs_quant.risk import EqDelta\n        >>>\n        >>> option = EqOption('.SPX', '3m', 'ATMF', 'Call', 'European')\n        >>> delta = option.calc(EqDelta)\n\n        delta is a float\n\n        >>> from gs_quant.markets import PricingContext\n        >>>\n        >>> cap_usd = IRCap('1y', 'USD')\n        >>> cap_eur = IRCap('1y', 'EUR')\n\n        >>> with PricingContext():\n        >>>     usd_delta_f = cap_usd.calc(IRDelta)\n        >>>     eur_delta_f = cap_eur.calc(IRDelta)\n        >>>\n        >>> usd_delta = usd_delta_f.result()\n        >>> eur_delta = eur_delta_f.result()\n\n        usd_delta_f and eur_delta_f are futures, usd_delta and eur_delta are dataframes\n        """\n        raise NotImplementedError
\n\n\nclass __ScenarioMeta(ABCMeta, ContextMeta):\n    pass\n\n\nclass Scenario(Base, ContextBase, metaclass=__ScenarioMeta):\n    pass\n\n\nclass InstrumentBase(Base):\n\n    def __init__(self, quantity: Optional[float] = 1):\n        super().__init__()\n        self.__instrument_quantity = quantity\n        self.__resolution_key: Optional[RiskKey] = None\n        self.__unresolved: Optional[InstrumentBase] = None\n\n    @property\n    @abstractmethod\n    @do_not_serialise\n    def provider(self):\n        ...\n\n    @property\n    @do_not_serialise\n    def instrument_quantity(self) -> float:\n        return self.__instrument_quantity\n\n    @property\n    @do_not_serialise\n    def resolution_key(self) -> RiskKey:\n        return self.__resolution_key\n\n    @property\n    @do_not_serialise\n    def unresolved(self):\n        return self.__unresolved\n\n    def _property_changed(self, prop: str):\n        if self.__resolution_key:\n            self.__resolution_key = None\n            self.unresolve()\n\n        super()._property_changed(prop)\n\n    def from_instance(self, instance):\n        self.__resolution_key = None\n        super().from_instance(instance)\n        self.__unresolved = instance.__unresolved\n        self.__resolution_key = instance.__resolution_key\n\n    def resolved(self, values: dict, resolution_key: RiskKey):\n        new_instrument = self.from_dict(values)\n        new_instrument.name = self.name\n        new_instrument.__unresolved = copy.copy(self)\n        new_instrument.__resolution_key = resolution_key\n        return new_instrument\n\n    def unresolve(self):\n        if self.__resolution_key and self.__unresolved:\n            self.from_instance(self.__unresolved)\n            self.__resolution_key = None\n            self.__unresolved = None\n\n\nclass QuotableBuilder(Base, metaclass=ABCMeta):\n\n    def __init__(self, valuation_overrides: Optional[dict] = None):\n        super().__init__()\n        self.valuation_overrides = valuation_overrides\n\n    @property\n    @do_not_resolve\n    def valuation_overrides(self) -> Optional[dict]:\n        return self.__valuation_overrides\n\n    @valuation_overrides.setter\n    def valuation_overrides(self, value: dict):\n        self.__valuation_overrides = value\n\n\nclass Market(Base):\n\n    @property\n    @abstractmethod\n    def location(self):\n        ...\n\n\nclass Sentinel:\n\n    def __init__(self, name: str):\n        self.__name = name\n\n    def __eq__(self, other):\n        return self.__name == other.__name\n\n\ndef get_enum_value(enum_type: EnumMeta, value: Union[EnumBase, str]):\n    if value in (None,):\n        return None\n\n    if isinstance(value, enum_type):\n        return value\n\n    try:\n        enum_value = enum_type(value)\n    except ValueError:\n        _logger.warning('Setting value to {}, which is not a valid entry in {}'.format(value, enum_type))\n        enum_value = value\n\n    return enum_value\n\n\ndef as_tuple(f):\n    def wrap(*args):\n        value = args[1]\n        if value is not None and not isinstance(value, str):\n            try:\n                iter(value)\n            except TypeError:\n                value = (value,)\n\n        return f(args[0], value)\n\n    return wrap\n
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Source code for gs_quant.data.core

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nimport datetime\nfrom enum import Enum\n\nfrom gs_quant.context_base import ContextBaseWithDefault\n\n\ndef _now():\n    return datetime.datetime.now(datetime.timezone.utc)\n\n\nclass DataFrequency(Enum):\n    """Data Frequency enumeration\n\n    Enumeration of different data frequencies for field subscription\n\n    """\n\n    #: Data subscription for series updating daily\n    DAILY = 'daily'\n\n    #: Data subscription for real-time or intraday series\n    REAL_TIME = 'realTime'\n\n    #: Data subscription for real-time or daily series\n    ANY = 'any'\n\n\n
[docs]class DataContext(ContextBaseWithDefault):\n
[docs]    def __init__(self, start=None, end=None):\n        super().__init__()\n        self.__start = start\n        self.__end = end
\n\n    @staticmethod\n    def _get_date(o, default):\n        if o is None:\n            return default\n        elif isinstance(o, datetime.datetime):\n            # note that datetime objects are also instances of date\n            return o.date()\n        elif isinstance(o, datetime.date):\n            return o\n        elif isinstance(o, str):\n            loc = o.find('T')\n            ds = o[:loc] if loc != -1 else o\n            return datetime.datetime.strptime(ds, '%Y-%m-%d').date()\n        else:\n            raise ValueError(f'{o} is not a valid date')\n\n    @staticmethod\n    def _get_datetime(o, default):\n        if o is None:\n            return default\n        elif isinstance(o, datetime.datetime):\n            return o\n        elif isinstance(o, datetime.date):\n            return datetime.datetime.combine(o, datetime.time(tzinfo=datetime.timezone.utc))\n        elif isinstance(o, str):\n            tmp = datetime.datetime.strptime(o, '%Y-%m-%dT%H:%M:%SZ')\n            return tmp.replace(tzinfo=datetime.timezone.utc)\n        else:\n            raise ValueError(f'{o} is not a valid date')\n\n    @property\n    def start_date(self):\n        return self._get_date(self.__start, datetime.date.today() - datetime.timedelta(days=30))\n\n    @property\n    def end_date(self):\n        return self._get_date(self.__end, datetime.date.today())\n\n    @property\n    def start_time(self):\n        return self._get_datetime(self.__start, _now() - datetime.timedelta(days=1))\n\n    @property\n    def end_time(self):\n        return self._get_datetime(self.__end, _now())
\n\n\nif __name__ == '__main__':\n    with DataContext(datetime.date(2019, 1, 1), datetime.datetime(2019, 2, 1, tzinfo=datetime.timezone.utc)) as dc:\n        print(f'{dc.start_date}, {dc.end_date}')\n        print(f'{dc.start_time}, {dc.end_time}')\n    with DataContext(None, '2019-01-01T00:00:00Z') as dc2:\n        print(f'{dc2.start_date}, {dc2.end_date}')\n        print(f'{dc2.start_time}, {dc2.end_time}')\n
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Source code for gs_quant.data.dataset

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nimport datetime as dt\nfrom enum import Enum\nfrom typing import Iterable, Optional, Union, List\n\nimport pandas as pd\n\nfrom gs_quant.api.data import DataApi\nfrom gs_quant.data.fields import Fields\nfrom gs_quant.errors import MqValueError\n\n\n
[docs]class Dataset:\n    """A collection of related data"""\n\n    class Vendor(Enum):\n        pass\n\n    class GS(Vendor):\n        HOLIDAY = 'HOLIDAY'\n        EDRVOL_PERCENT_INTRADAY = 'EDRVOL_PERCENT_INTRADAY'\n        EDRVOL_PERCENT_STANDARD = 'EDRVOL_PERCENT_STANDARD'\n        MA_RANK = 'MA_RANK'\n        EDRVS_INDEX_SHORT = 'EDRVS_INDEX_SHORT'\n        EDRVS_INDEX_LONG = 'EDRVS_INDEX_LONG'\n\n        # Baskets\n        CBGSSI = 'CBGSSI'\n        CB = 'CB'\n\n        # STS\n        STSLEVELS = 'STSLEVELS'\n\n        # Test Datasets\n        WEATHER = 'WEATHER'\n\n    class TR(Vendor):\n        TREOD = 'TREOD'\n        TR = 'TR'\n        TR_FXSPOT = 'TR_FXSPOT'\n\n    class FRED(Vendor):\n        GDP = 'GDP'\n\n
[docs]    def __init__(self, dataset_id: Union[str, Vendor], provider: DataApi = None):\n        """\n\n        :param dataset_id: The dataset's identifier\n        :param provider: The data provider\n        """\n        self.__id = self._get_dataset_id_str(dataset_id)\n        self.__provider = provider
\n\n    def _get_dataset_id_str(self, dataset_id):\n        return dataset_id.value if isinstance(dataset_id, Dataset.Vendor) else dataset_id\n\n    @property\n    def id(self) -> str:\n        """\n        The dataset's identifier\n        """\n        return self.__id\n\n    @property\n    def name(self):\n        pass\n\n    @property\n    def provider(self):\n        from gs_quant.api.gs.data import GsDataApi\n        return self.__provider or GsDataApi\n\n    def get_data(\n            self,\n            start: Optional[Union[dt.date, dt.datetime]] = None,\n            end: Optional[Union[dt.date, dt.datetime]] = None,\n            as_of: Optional[dt.datetime] = None,\n            since: Optional[dt.datetime] = None,\n            fields: Optional[Iterable[Union[str, Fields]]] = None,\n            asset_id_type: str = None,\n            **kwargs\n    ) -> pd.DataFrame:\n        """\n        Get data for the given range and parameters\n\n        :param start: Requested start date/datetime for data\n        :param end: Requested end date/datetime for data\n        :param as_of: Request data as_of\n        :param since: Request data since\n        :param fields: DataSet fields to include\n        :param kwargs: Extra query arguments, e.g. ticker='EDZ19'\n        :return: A Dataframe of the requested data\n\n        **Examples**\n\n        >>> from gs_quant.data import Dataset\n        >>> import datetime as dt\n        >>>\n        >>> weather = Dataset('WEATHER')\n        >>> weather_data = weather.get_data(dt.date(2016, 1, 15), dt.date(2016, 1, 16), city=('Boston', 'Austin'))\n        """\n\n        field_names = None if fields is None else list(map(lambda f: f if isinstance(f, str) else f.value, fields))\n\n        query = self.provider.build_query(\n            start=start,\n            end=end,\n            as_of=as_of,\n            since=since,\n            fields=field_names,\n            **kwargs\n        )\n        data = self.provider.query_data(query, self.id, asset_id_type=asset_id_type)\n\n        return self.provider.construct_dataframe_with_types(self.id, data)\n\n    def get_data_series(\n            self,\n            field: Union[str, Fields],\n            start: Optional[Union[dt.date, dt.datetime]] = None,\n            end: Optional[Union[dt.date, dt.datetime]] = None,\n            as_of: Optional[dt.datetime] = None,\n            since: Optional[dt.datetime] = None,\n            **kwargs\n    ) -> pd.Series:\n        """\n        Get a time series of data for a field of a dataset\n\n        :param field: The DataSet field to use\n        :param start: Requested start date/datetime for data\n        :param end: Requested end date/datetime for data\n        :param as_of: Request data as_of\n        :param since: Request data since\n        :param kwargs: Extra query arguments, e.g. ticker='EDZ19'\n        :return: A Series of the requested data, indexed by date or time, depending on the DataSet\n\n        **Examples**\n\n        >>> from gs_quant.data import Dataset\n        >>> import datetime as dt\n        >>>\n        >>> weather = Dataset('WEATHER')\n        >>> dew_point = weather\n        >>>>    .get_data_series('dewPoint', dt.date(2016, 1, 15), dt.date(2016, 1, 16), city=('Boston', 'Austin'))\n        """\n\n        field_value = field if isinstance(field, str) else field.value\n\n        query = self.provider.build_query(\n            start=start,\n            end=end,\n            as_of=as_of,\n            since=since,\n            fields=(field_value,),\n            **kwargs\n        )\n\n        symbol_dimensions = self.provider.symbol_dimensions(self.id)\n        if len(symbol_dimensions) != 1:\n            raise MqValueError('get_data_series only valid for symbol_dimensions of length 1')\n\n        symbol_dimension = symbol_dimensions[0]\n        data = self.provider.query_data(query, self.id)\n        df = self.provider.construct_dataframe_with_types(self.id, data)\n\n        from gs_quant.api.gs.data import GsDataApi\n\n        if isinstance(self.provider, GsDataApi):\n            gb = df.groupby(symbol_dimension)\n            if len(gb.groups) > 1:\n                raise MqValueError('Not a series for a single {}'.format(symbol_dimension))\n\n        if df.empty:\n            return pd.Series()\n        return pd.Series(index=df.index, data=df.loc[:, field_value].values)\n\n    def get_data_last(\n            self,\n            as_of: Optional[Union[dt.date, dt.datetime]],\n            start: Optional[Union[dt.date, dt.datetime]] = None,\n            fields: Optional[Iterable[str]] = None,\n            **kwargs\n    ) -> pd.DataFrame:\n        """\n        Get the last point for this DataSet, at or before as_of\n\n        :param as_of: The date or time as of which to query\n        :param start: The start of the range to query\n        :param fields: The fields for which to query\n        :param kwargs: Additional query parameters, e.g., city='Boston'\n        :return: A Dataframe of values\n\n        **Examples**\n\n        >>> from gs_quant.data import Dataset\n        >>> import datetime as dt\n        >>>\n        >>> weather = Dataset('WEATHER')\n        >>> last = weather.get_data_last(dt.datetime.now())\n        """\n        query = self.provider.build_query(\n            start=start,\n            end=as_of,\n            fields=fields,\n            format='JSON',\n            **kwargs\n        )\n        query.format = None  # "last" endpoint does not support MessagePack\n\n        data = self.provider.last_data(query, self.id)\n        return self.provider.construct_dataframe_with_types(self.id, data)\n\n    def get_coverage(\n            self,\n            limit: int = None,\n            offset: int = None,\n            fields: List[str] = None,\n            include_history: bool = False,\n            **kwargs\n    ) -> pd.DataFrame:\n        """\n        Get the assets covered by this DataSet\n\n        :param limit: The maximum number of assets to return\n        :param offset: The offset\n        :param fields: The fields to return, e.g. assetId\n        :param include_history: Return column for historyStartDate\n        :return: A Dataframe of the assets covered\n\n        **Examples**\n\n        >>> from gs_quant.data import Dataset\n        >>>\n        >>> weather = Dataset('WEATHER')\n        >>> cities = weather.get_coverage()\n        """\n        coverage = self.provider.get_coverage(\n            self.id,\n            limit=limit,\n            offset=offset,\n            fields=fields,\n            include_history=include_history,\n            **kwargs\n        )\n\n        return pd.DataFrame(coverage)
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Source code for gs_quant.data.fields

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\n\nfrom aenum import Enum, extend_enum\nfrom typing import Optional\n\n\nclass DataMeasure(Enum):\n    """Data measure definitions\n\n    Enumeration of measures available through data APIs. Measures are facts that are usually quantities and that can\n    be aggregated over a defined period of time. For example: tradePrice and volume are measures\n\n    """\n    ASK_PRICE = 'askPrice'\n    BID_PRICE = 'bidPrice'\n    HIGH_PRICE = 'highPrice'\n    MID_PRICE = 'midPrice'\n    LOW_PRICE = 'lowPrice'\n    OPEN_PRICE = 'openPrice'\n    CLOSE_PRICE = 'closePrice'\n    TRADE_PRICE = 'tradePrice'\n    SPOT_PRICE = 'spot'\n    VOLUME = 'volume'\n    ADJUSTED_ASK_PRICE = 'adjustedAskPrice'\n    ADJUSTED_BID_PRICE = 'adjustedBidPrice'\n    ADJUSTED_HIGH_PRICE = 'adjustedHighPrice'\n    ADJUSTED_LOW_PRICE = 'adjustedLowPrice'\n    ADJUSTED_OPEN_PRICE = 'adjustedBidPrice'\n    ADJUSTED_TRADE_PRICE = 'adjustedTradePrice'\n    ADJUSTED_VOLUME = 'adjustedVolume'\n    IMPLIED_VOLATILITY = 'impliedVolatility'\n    VAR_SWAP = 'varSwap'\n\n\nclass DataDimension(Enum):\n    """Data dimension definitions\n\n    Enumeration of dimensions available through data APIs. Dimensions describe or provide context to measures, and can\n    be used to select or group data. For example: ticker and exchange are dimensions\n\n    """\n    ASSET_ID = 'assetId'\n    NAME = 'name'\n    RIC = 'ric'\n    TENOR = 'tenor'\n    STRIKE_REFERENCE = 'strikeReference'\n    RELATIVE_STRIKE = 'relativeStrike'\n    EXPIRATION_DATE = 'expirationDate'\n    UPDATE_TIME = 'updateTime'\n\n\n
[docs]class Fields(Enum):\n    """Data field enumeration\n\n    Enumeration of fields available through data APIs\n\n    """\n\n    @property\n    def unit(self) -> Optional[str]:\n        # TODO: Define units and look up appropriate unit for self\n        return None
\n\n\nfor enum in DataMeasure:\n    extend_enum(Fields, enum.name, enum.value)\n\nfor enum in DataDimension:\n    extend_enum(Fields, enum.name, enum.value)\n
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Source code for gs_quant.datetime.date

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\n\nimport datetime as dt\nimport numpy as np\nimport calendar as cal\nfrom enum import Enum, IntEnum\nfrom typing import Iterable, Optional, Tuple, Union\nfrom gs_quant.datetime.gscalendar import GsCalendar\n\nDateOrDates = Union[dt.date, Iterable[dt.date]]\n\n\nclass PaymentFrequency(IntEnum):\n    """Payment frequency enumeration\n\n    Provides an enumeration of different payment frequencies used to to discount cashflows and accrue interest\n\n    """\n    DAILY = 252\n    WEEKLY = 52\n    SEMI_MONTHLY = 26\n    MONTHLY = 12\n    SEMI_QUARTERLY = 6\n    QUARTERLY = 4\n    TRI_ANNUALLY = 3\n    SEMI_ANNUALLY = 2\n    ANNUALLY = 1\n\n\nclass DayCountConvention(Enum):\n    """Day Count Convention enumeration\n\n    Provides an enumeration of different day count conventions for determining how interest accrues over payment periods\n    for financial securities\n\n    """\n\n    # Actual/360: Number of days between dates divided by 360\n    ACTUAL_360 = "ACTUAL_360"\n\n    # Actual/364: Number of days between dates divided by 364\n    ACTUAL_364 = "ACTUAL_364"\n\n    # Actual/365_25: Number of days between dates divided by 365.25\n    ACTUAL_365_25 = "ACTUAL_365_25"\n\n    # Actual/365 FIXED: Number of days between dates divided by 365\n    ACTUAL_365F = "ACTUAL_365F"\n\n    # Actual/365 LEAP: Number of days between dates divided by 365 or 366 in leap years\n    ACTUAL_365L = "ACTUAL_365L"\n\n    # ONE_ONE: Always returns a day count fraction of 1\n    ONE_ONE = "ONE_ONE"\n\n\n
[docs]def is_business_day(dates: DateOrDates, calendars: Union[str, Tuple[str, ...]] = (), week_mask: Optional[str] = None)\\\n        -> Union[bool, Tuple[bool]]:\n    """\n    Determine whether each date in dates is a business day\n\n    :param dates: The input date or dates\n    :param calendars: Calendars to use for holidays\n    :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n    :return: True/False if dates is a single date. A tuple indicating True/False for each date if dates is an iterable\n\n    **Examples**\n\n    >>> import datetime as dt\n    >>> is_business_day(dt.date.today())\n    >>> is_business_day(dt.date(2019, 7, 4), calendars=('NYSE',))\n    """\n    calendar = GsCalendar.get(calendars)\n    res = np.is_busday(dates, busdaycal=calendar.business_day_calendar(week_mask))\n    return tuple(res) if isinstance(res, np.ndarray) else res
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[docs]def business_day_offset(\n        dates: DateOrDates, offsets: Union[int, Iterable[int]],\n        roll: str = 'raise', calendars: Union[str, Tuple[str, ...]] = (),\n        week_mask: Optional[str] = None) -> DateOrDates:\n    """\n    Apply offsets to the dates and move to the nearest business date\n\n    :param dates: The input date or dates\n    :param offsets: The number of days by which to adjust the dates\n    :param roll: Which direction to roll, in order to get to the nearest business date\n    :param calendars: Calendars to use for holidays\n    :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n    :return: A date (if dates is a single date) or tuple of dates, adjusted by the offsets\n\n    **Examples**\n\n    >>> import datetime as dt\n    >>> prev_bus_date = business_day_offset(dt.date.today(), -1, roll='forward')\n    """\n    calendar = GsCalendar.get(calendars)\n    res = np.busday_offset(dates, offsets, roll, busdaycal=calendar.business_day_calendar(week_mask)).astype(dt.date)\n    return tuple(res) if isinstance(res, np.ndarray) else res
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[docs]def prev_business_date(\n        dates: DateOrDates = dt.date.today(),\n        calendars: Union[str, Tuple[str, ...]] = (),\n        week_mask: Optional[str] = None) -> DateOrDates:\n    """\n    Returns the previous business date for a given date or date series, defaulting to today.\n\n    :param dates: The input date or dates, defaults to today\n    :param calendars: Calendars to use for holidays\n    :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n    :return: A date (if dates is a single date) or tuple of dates, adjusted by the offset of one day.\n\n    **Example**\n\n    >>> import datetime as dt\n    >>> prev_bus_date = prev_business_date()\n    """\n    return business_day_offset(dates, -1, roll='forward', calendars=calendars, week_mask=week_mask)
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[docs]def business_day_count(begin_dates: DateOrDates, end_dates: DateOrDates, calendars: Union[str, Tuple[str, ...]] = (\n), week_mask: Optional[str] = None) -> Union[int, Tuple[int]]:\n    """\n    Determine the number of business days between begin_dates and end_dates\n\n    :param begin_dates: A date or collection of beginning dates\n    :param end_dates: A date or collection of end dates\n    :param calendars: Calendars to use for holidays\n    :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n    :return: An int or tuple of ints, representing the number of business days between begin_dates and end_dates\n\n    **Examples**\n\n    >>> import datetime as dt\n    >>> today = dt.date.today()\n    >>> bus_days = business_day_count(today, today + dt.timedelta(days=7))\n    """\n    calendar = GsCalendar.get(calendars)\n    res = np.busday_count(begin_dates, end_dates, busdaycal=calendar.business_day_calendar(week_mask))\n    return tuple(res) if isinstance(res, np.ndarray) else res
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[docs]def date_range(begin: Union[int, dt.date],\n               end: Union[int, dt.date],\n               calendars: Union[str, Tuple[str, ...]] = (),\n               week_mask: Optional[str] = None) -> Iterable[dt.date]:\n    """\n    Construct a range of dates\n\n    :param begin: Beginning date or int. An int will be interpreted as the number of business days before end\n        (which must be a date)\n    :param end: End date or int. An int will be interpreted as the number of business days after begin\n        (which must be a date)\n    :param calendars: Calendars to use for holidays\n    :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n    :return: A generator of dates\n\n    >>> import datetime as dt\n    >>> today = dt.date.today()\n    >>> dates = tuple(date_range(5, today))\n    >>>\n    >>> for date in date_range(dt.date(2019, 1, 1), dt.date(2019, 2, 1)):\n    >>>     print(date)\n    """\n    if isinstance(begin, dt.date):\n        if isinstance(end, dt.date):\n            def f():\n                prev = begin\n                if prev > end:\n                    raise ValueError('begin must be <= end')\n\n                while prev <= end:\n                    yield prev\n                    prev = business_day_offset(prev, 1, calendars=calendars, week_mask=week_mask)\n\n            return (d for d in f())\n        elif isinstance(end, int):\n            return (business_day_offset(begin, i, calendars=calendars, week_mask=week_mask) for i in range(end))\n        else:\n            raise ValueError('end must be a date or int')\n    elif isinstance(begin, int):\n        if isinstance(end, dt.date):\n            return (business_day_offset(end, -i, roll='preceding', calendars=calendars, week_mask=week_mask)\n                    for i in range(begin))\n        else:\n            raise ValueError('end must be a date if begin is an int')\n    else:\n        raise ValueError('begin must be a date or int')
\n\n\ndef has_feb_29(start: dt.date, end: dt.date):\n    """\n    Determine if date range has a leap day (29Feb)\n\n    :param start: first date\n    :param end: second date\n\n    **Usage**\n\n    Determine if a given date range contains a leap day (Feb 29). Used for various day count convention calculations\n    which alter behaviour for leap years. Start date is exclusive and end date is inclusive\n\n    **Examples**\n\n    Determine if a given date range contains 29Feb\n\n    >>> start = date(2020, 1, 1)\n    >>> end = date(2020, 3, 15)\n    >>> has_feb_29(start, end)\n    """\n    feb_29 = False\n    for x in range(1, (end - start).days + 1):\n        date = start + dt.timedelta(days=x)\n        feb_29 = feb_29 | (date.month == 2 and date.day == 29)\n\n    return feb_29\n\n\ndef day_count_fraction(\n        start: dt.date,         # First payment date\n        end: dt.date,           # Second payment date\n        convention: DayCountConvention = DayCountConvention.ACTUAL_360,\n        frequency: PaymentFrequency = PaymentFrequency.MONTHLY\n):\n    """\n    Compute day count fraction between dates\n\n    :param start: first date\n    :param end: second date\n    :param convention: day count convention\n    :param frequency: payment frequency of instrument\n    :return: day count fraction between dates per convention\n\n    **Usage**\n\n    Compute day count fraction between dates, based on the value of *convention*. For more information on the available\n    day count conventions, see the\n    `Day Count Conventions <https://developer.gs.com/docs/gsquant/guides/Dates/1-day-count-conventions>`_ guide.\n\n    **Examples**\n\n    Compute day count fraction between two dates using Actual/360 convention:\n\n    >>> start = date(2015, 11, 12)\n    >>> end = date(2017, 12, 15)\n    >>> day_count_fraction(start, end, DayCountConvention.ACTUAL_360)\n\n    """\n\n    if convention == DayCountConvention.ACTUAL_360:\n        return (end - start).days / 360\n    elif convention == DayCountConvention.ACTUAL_364:\n        return (end - start).days / 364\n    elif convention == DayCountConvention.ACTUAL_365F:\n        return (end - start).days / 365\n    elif convention == DayCountConvention.ACTUAL_365L:\n        if frequency == PaymentFrequency.ANNUALLY:\n            days_in_year = 366 if has_feb_29(start, end) else 365\n        else:\n            days_in_year = 366 if cal.isleap(end.year) else 365\n        return (end - start).days / days_in_year\n    elif convention == DayCountConvention.ACTUAL_365_25:\n        return (end - start).days / 365.25\n    elif convention == DayCountConvention.ONE_ONE:\n        return 1\n    else:\n        raise ValueError('Unknown day count convention: ' + convention.value)\n
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Source code for gs_quant.datetime.point

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nimport datetime as dt\nimport re\nimport string\n\nConstPoints = {\n    "O/N": 0,\n    "T/N": 0.1,\n    "OIS FIX": 1,\n    "CASH STUB": 1.1,\n    "CASHSTUB": 1.1,\n    "DEFAULT": 0,\n    "IN": 0.1,\n    "OUT": 0.2\n}\n\n# Regular expression for different types of market data coordinate points\nEuroOrFraReg = \\\n    r"^(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec|JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)+([0-9][0-9])$"\nNumberReg = r"^([0-9]*)$"\nMMMYYYYReg = r"^([a-zA-Z]{3}[0-9]{4})$"\nDDMMMYYYYReg = r"^([1-3]*[0-9]{1}[a-zA-Z]{3}[0-9]{4})$"\nSpikeQEReg = r"^(QE[0-9])-([0-9]{4})$"\nFRAxReg = r"^([0-9]+)x([0-9]+)$"\nRDatePartReg = r"^([-]*[0-9]+[mydwbfMYDWBF])([-]*[0-9]+[mydwbfMYDWBF])?$"\nCashFXReg = r"^([-]*[0-9]+[mydwbfMYDWBF])([-]*[0-9]+[mydwbfMYDWBF])? XC$"\nPricerCoordRegI = r"^(No )([0-9]*)$"\nPricerCoordRegII = r"^(Pricer )([0-9]*)$"\nPricerBFReg = r"^([-]*[0-9]+[mydwbfMYDWBFM])([-]*[0-9]+[mydwbfMYDWBF])([-]*[0-9]+[mydwbfMYDWBF])?$"\nPricerBondSpreadReg = r"^[0-9][SQHT]([0-9]{2})[/][0-9][SQHT]([0-9]{2})"\nSeasonalFrontReg = r"(Front|Back)"\ninfl_volReg = r"(Caplet|ZCCap|Swaption|ZCSwo)"\nMMMReg = r"^(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)$"\nMMMYYReg = r"^(JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC) ([0-9]{2})"\nDatePairReg = r"^([0-9]{8})/([0-9]{8})$"\nDatePairReg2 = r"^([0-9]{8}) ([0-9]{8})$"\nFXVolAddonParmsReg = \\\n    r"(Spread Addon|Spread Init|Spread Final|Rho Addon|Rho Init|Rho Final|Vol Addon|Vol Init|Vol Final|HL|Addon HL)"\nCopulaReg = r"(Rho$|Rho Rate|Rho Vol|RR|BF|Alpha|Beta|KO$|K0=L|K0=S)"\nBondCoordReg = r"^[0-9]* ([0-9.]*) ([0-9]{2}/[0-9]{2}/[0-9]{4})$"\nBondFutReg = r"^[A-Z]{3}([FGHJKMNQUVXZ])([0-9])$"\nFFFutReg = r"^FF([FGHJKMNQUVXZ])([0-9])$"\nRepoGCReg = r"^(ON|SN|TN|[0-9]+) (|Month |Week |Year |Day )GC$"\nFloatingYear = r"^([0-9]*\\.[0-9])[yY]$"\nRelativeReg = r"^([0-9]+) (day|week|month|year|DAY|WEEK|MONTH|YEAR)$"\n\nLYYReg = r"([FGHJKMNQUVXZ])([0-9]{2})"\nDateRuleReg = r"^([-]*[0-9]+[mydwbfMYDWBFM])+$"\nDDMMMYYReg = r"^([0-3]*[0-9]{1}[a-zA-Z]{3}[0-9]{2})$"\n\nFutMonth = r"FGHJKMNQUVXZ"\n\nDictDayRule = {\n    'Month': 30,\n    'MONTH': 30,\n    'Week': 7,\n    'WEEK': 7,\n    'Year': 365,\n    'YEAR': 365,\n    'Day': 1,\n    'DAY': 1,\n    'd': 1,\n    'D': 1,\n    'w': 7,\n    'W': 7,\n    'b': 1,\n    'B': 1,\n    'f': 30,\n    'F': 30,\n    'm': 30,\n    'M': 30,\n    'y': 365,\n    'Y': 365\n}\n\n\ndef relative_date_add(date_rule: str, strict: bool = False) -> float:\n    """Change the string in date rule format to the number of days. E.g 1d to 1, 1y to 365, 1m to 30, -1w to -7"""\n    days = ''\n\n    if re.search(DateRuleReg, date_rule) is not None:\n        res = re.search(DateRuleReg, date_rule)\n        date_str = res.group(1)\n        if date_str[0] == '-':\n            num = float(date_str[1:-1])\n            days = '-'\n        else:\n            num = float(date_str[:-1])\n        rule = date_str[-1:]\n        if rule in DictDayRule:\n            scale = DictDayRule[rule]\n            days = days + str(num * scale)\n            d = float(days)\n            return d\n        else:\n            raise ValueError('There are no valid day rule for the point provided.')\n\n    if strict:\n        raise ValueError(f'invalid date rule {date_rule}')\n    return 0\n\n\n
[docs]def point_sort_order(point: str, ref_date: dt.date = dt.date.today()) -> float:\n    """\n    Calculates a number that can be used to sort Mkt Points by it.\n\n    :param point: The point string from MarketDataCoordinate.\n    :param ref_date: Reference date, normally the pricing date.\n    :return: The number of days from the reference date to the date specified by the point string\n\n    **Examples**\n\n    >>> import datetime as dt\n    >>> days = point_sort_order(point = 'Dec20', ref_date=dt.date.today())\n    """\n\n    if not point or not isinstance(point, str):\n        return 0\n\n    const_value = ConstPoints.get(point.upper())\n    if const_value is not None:\n        return const_value\n\n    parts = point.split(';')\n    if len(parts) > 1:\n        first = point_sort_order(parts[0])\n        if not first:\n            return 0\n\n        return first + (0.1 * sum(point_sort_order(p, ref_date) for p in parts[1:]) / first)\n\n    days = None\n\n    if point == 'o/n':\n        days = 0\n    elif point == 't/n':\n        days = 0.1\n    elif point == 'Cash Stub':\n        days = 1.1\n    elif point == 'CashStub':\n        days = 1.1\n    elif point == 'Default':\n        days = 0\n    elif point == 'In':\n        days = 0.1\n    elif point == 'Out':\n        days = 0.2\n    elif re.search(infl_volReg, point) is not None:\n        res = re.search(infl_volReg, point)\n        infl_vol = res.group(1)\n        if infl_vol == 'Caplet':\n            days = 0\n        elif infl_vol == 'ZCCap':\n            days = 1\n        elif infl_vol == 'Swaption':\n            days = 2\n        elif infl_vol == 'ZCSwo':\n            days = 3\n    elif re.search(CopulaReg, point) is not None:\n        pass\n    elif re.search(SeasonalFrontReg, point) is not None:\n        res = re.search(SeasonalFrontReg, point)\n        if res.group(1) == 'Front':\n            days = 0\n        else:\n            days = 1\n    elif re.search(MMMReg, point) is not None:\n        res = re.search(MMMReg, point)\n        date_str = '1' + res.group(1) + '2000'\n        format_str = '%d%b%Y'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(EuroOrFraReg, point) is not None:\n        res = re.search(EuroOrFraReg, point)\n        date_str = '15' + res.group(1) + res.group(2)\n        format_str = '%d%b%y'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(RDatePartReg, point) is not None:\n        res = re.search(RDatePartReg, point)\n        date_str = res.group(1)\n        days = relative_date_add(date_str)\n    elif re.search(CashFXReg, point) is not None:\n        res = re.search(CashFXReg, point)\n        date_str = res.group(1)\n        days = relative_date_add(date_str)\n    elif re.search(PricerBFReg, point) is not None:\n        res = re.search(PricerBFReg, point)\n        date_str = res.group(1)\n        days = relative_date_add(date_str)\n    elif re.search(FRAxReg, point) is not None:\n        res = re.search(FRAxReg, point)\n        date_str = res.group(1) + 'm'\n        days = relative_date_add(date_str)\n    elif re.search(SpikeQEReg, point) is not None:\n        res = re.search(SpikeQEReg, point)\n        qe = res.group(1)\n        if qe == 'QE1':\n            month = "Mar"\n        elif qe == 'QE2':\n            month = "Jun"\n        elif qe == 'QE3':\n            month = "Sep"\n        elif qe == 'QE4':\n            month = 'Dec'\n        else:\n            month = 'Dec'\n        date_str = "1" + month + res.group(2)\n        format_str = '%d%b%Y'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(MMMYYYYReg, point) is not None:\n        res = re.search(MMMYYYYReg, point)\n        date_str = '1' + res.group(1)\n        format_str = '%d%b%Y'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(DDMMMYYYYReg, point) is not None:\n        res = re.search(DDMMMYYYYReg, point)\n        date_str = res.group(1)\n        format_str = '%d%b%Y'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(NumberReg, point) is not None:\n        res = re.search(NumberReg, point)\n        days = float(res.group(1))\n    elif re.search(FloatingYear, point) is not None:\n        res = re.search(FloatingYear, point)\n        year = float(res.group(1))\n        days = 365 * year\n    elif re.search(PricerCoordRegI, point) is not None:\n        res = re.search(PricerCoordRegI, point)\n        days = float(res.group(2))\n    elif re.search(PricerCoordRegII, point) is not None:\n        res = re.search(PricerCoordRegII, point)\n        days = float(res.group(2))\n    elif re.search(PricerBondSpreadReg, point) is not None:\n        pass\n    elif re.search(LYYReg, point) is not None:\n        res = re.search(LYYReg, point)\n        month = FutMonth.find(res.group(1)) + 1\n        year = res.group(2)\n        date_str = year + '-' + str(month) + '-1'\n        format_str = '%y-%m-%d'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(DatePairReg, point) is not None:\n        res = re.search(DatePairReg, point)\n        date_str = res.group(2)\n        format_str = '%Y%m%d'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(DatePairReg2, point) is not None:\n        res = re.search(DatePairReg2, point)\n        date_str = res.group(2)\n        format_str = '%Y%m%d'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(MMMYYReg, point) is not None:\n        res = re.search(MMMYYReg, point)\n        date_str = '1' + res.group(1) + res.group(2)\n        format_str = '%d%b%y'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(FXVolAddonParmsReg, point) is not None:\n        pass\n    elif re.search(BondCoordReg, point) is not None:\n        res = re.search(BondCoordReg, point)\n        date_str = res.group(2)\n        format_str = '%d/%m/%Y'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(BondFutReg, point) is not None:\n        res = re.search(BondFutReg, point)\n        month = FutMonth.find(res.group(1)) + 1\n        date_str = str(ref_date.year) + '-' + str(month) + '-1'\n        format_str = '%Y-%m-%d'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(FFFutReg, point) is not None:\n        res = re.search(FFFutReg, point)\n        month = FutMonth.find(res.group(1)) + 1\n        date_str = str(ref_date.year) + '-' + str(month) + '-1'\n        format_str = '%Y-%m-%d'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    elif re.search(RepoGCReg, point) is not None:\n        res = re.search(RepoGCReg, point)\n        if point == 'ON GC':\n            days = 0\n        elif point == 'TN GC':\n            days = 1\n        elif point == 'SN GC':\n            days = 2\n        elif res.group(2).strip() in DictDayRule:\n            scale = DictDayRule[res.group(2).strip()]\n            num = float(res.group(1))\n            days = num * scale\n    elif re.search(RelativeReg, point) is not None:\n        res = re.search(RelativeReg, point)\n        rule = string.capwords(res.group(2))\n        if rule in DictDayRule:\n            scale = DictDayRule[rule]\n            num = float(res.group(1))\n            days = num * scale\n    elif re.search(DDMMMYYReg, point) is not None:\n        res = re.search(DDMMMYYReg, point)\n        date_str = res.group(1)\n        format_str = '%d%b%y'\n        days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n    return days
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Source code for gs_quant.instrument.core

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nfrom gs_quant.api.gs.parser import GsParserApi\nfrom gs_quant.api.gs.risk import GsRiskApi\nfrom gs_quant.base import get_enum_value, InstrumentBase, QuotableBuilder\nfrom gs_quant.common import AssetClass, AssetType, XRef\nfrom gs_quant.context_base import do_not_serialise\nfrom gs_quant.markets import HistoricalPricingContext, MarketDataCoordinate, PricingCache, PricingContext\nfrom gs_quant.priceable import PriceableImpl\nfrom gs_quant.risk import FloatWithInfo, DataFrameWithInfo, SeriesWithInfo, ResolvedInstrumentValues, RiskMeasure\nfrom gs_quant.risk.results import ErrorValue, MultipleRiskMeasureFuture, PricingFuture\nfrom gs_quant.session import GsSession\n\nfrom abc import ABCMeta\nimport datetime as dt\nimport logging\nfrom typing import Iterable, Optional, Tuple, Union\nimport inspect\n\n_logger = logging.getLogger(__name__)\n\n\nclass Instrument(PriceableImpl, InstrumentBase, metaclass=ABCMeta):\n    PROVIDER = GsRiskApi\n    __instrument_mappings = {}\n\n    def __getattribute__(self, name):\n        ret = super().__getattribute__(name)\n        if ret is not None or name not in self.properties():\n            return ret\n\n        attr = getattr(super().__getattribute__('__class__'), name, None)\n        if getattr(attr.fget, 'do_not_resolve', False):\n            return ret\n\n        resolved = False\n\n        try:\n            resolved = super().__getattribute__('resolution_key') is not None\n        except AttributeError:\n            pass\n\n        if GsSession.current_is_set and not resolved:\n            if attr and isinstance(attr, property):\n                resolved_inst = self.resolve(in_place=False)\n                if isinstance(resolved_inst, PricingFuture):\n                    ret = PricingFuture()\n                    resolved_inst.add_done_callback(lambda inst_f: ret.set_result(\n                        object.__getattribute__(inst_f.result(), name)))\n                else:\n                    ret = object.__getattribute__(resolved_inst, name)\n\n        return ret\n\n    def _property_changed(self, prop: str):\n        if self._hash_is_calced:\n            PricingCache.drop(self)\n\n        super()._property_changed(prop)\n\n    @classmethod\n    def __asset_class_and_type_to_instrument(cls):\n        if not cls.__instrument_mappings:\n            import gs_quant.target.instrument as instrument_\n            instrument_classes = [c for _, c in inspect.getmembers(instrument_, inspect.isclass) if\n                                  issubclass(c, Instrument) and c is not Instrument]\n\n            cls.__instrument_mappings[(AssetClass.Cash, AssetType.Currency)] = instrument_.Forward\n\n            for clazz in instrument_classes:\n                instrument = clazz.default_instance()\n                cls.__instrument_mappings[(instrument.asset_class, instrument.type)] = clazz\n\n        return cls.__instrument_mappings\n\n    @property\n    @do_not_serialise\n    def provider(self):\n        return self.PROVIDER\n\n    def resolve(self, in_place: bool = True) -> Optional[Union[PriceableImpl, PricingFuture, dict]]:\n        """\n        Resolve non-supplied properties of an instrument\n\n        **Examples**\n\n        >>> from gs_quant.instrument import IRSwap\n        >>>\n        >>> swap = IRSwap('Pay', '10y', 'USD')\n        >>> rate = swap.fixedRate\n\n        rate is None\n\n        >>> swap.resolve()\n        >>> rate = swap.fixedRate\n\n        rates is now the solved fixed rate\n        """\n\n        is_historical = isinstance(PricingContext.current, HistoricalPricingContext)\n\n        def handle_result(result: Optional[Union[ErrorValue, InstrumentBase]]) -> Optional[PriceableImpl]:\n            ret = None if in_place else result\n            if isinstance(result, ErrorValue):\n                _logger.error('Failed to resolve instrument fields: ' + result.error)\n                ret = {result.risk_key.date: self} if is_historical else self\n            elif result is None:\n                _logger.error('Unknown error resolving instrument fields')\n                ret = {dt.date.today(): self} if is_historical else self\n            elif in_place:\n                self.from_instance(result)\n\n            return ret\n\n        if in_place and is_historical:\n            raise RuntimeError('Cannot resolve in place under a HistoricalPricingContext')\n\n        return self.calc(ResolvedInstrumentValues, fn=handle_result)\n\n    def calc(self, risk_measure: Union[RiskMeasure, Iterable[RiskMeasure]], fn=None) \\\n            -> Union[DataFrameWithInfo, ErrorValue, FloatWithInfo, PriceableImpl, PricingFuture,\n                     SeriesWithInfo, Tuple[MarketDataCoordinate, ...]]:\n        """\n        Calculate the value of the risk_measure\n\n        :param risk_measure: the risk measure to compute, e.g. IRDelta (from gs_quant.risk)\n        :param fn: post-processing function (optional)\n        :return: a float or dataframe, depending on whether the value is scalar or structured, or a future thereof\n        (depending on how PricingContext is being used)\n\n        **Examples**\n\n        >>> from gs_quant.instrument import IRCap\n        >>> from gs_quant.risk import IRDelta\n        >>>\n        >>> cap = IRCap('1y', 'USD')\n        >>> delta = cap.calc(IRDelta)\n\n        delta is a dataframe\n\n        >>> from gs_quant.instrument import EqOption\n        >>> from gs_quant.risk import EqDelta\n        >>>\n        >>> option = EqOption('.SPX', '3m', 'ATMF', 'Call', 'European')\n        >>> delta = option.calc(EqDelta)\n\n        delta is a float\n\n        >>> from gs_quant.markets import PricingContext\n        >>>\n        >>> cap_usd = IRCap('1y', 'USD')\n        >>> cap_eur = IRCap('1y', 'EUR')\n\n        >>> with PricingContext():\n        >>>     usd_delta_f = cap_usd.calc(IRDelta)\n        >>>     eur_delta_f = cap_eur.calc(IRDelta)\n        >>>\n        >>> usd_delta = usd_delta_f.result()\n        >>> eur_delta = eur_delta_f.result()\n\n        usd_delta_f and eur_delta_f are futures, usd_delta and eur_delta are dataframes\n        """\n        futures = {r: r.pricing_context.calc(self, r)\n                   for r in ((risk_measure,) if isinstance(risk_measure, RiskMeasure) else risk_measure)}\n        future = MultipleRiskMeasureFuture(self, futures) if len(futures) > 1 else futures[\n            risk_measure if isinstance(risk_measure, RiskMeasure) else risk_measure[0]]\n\n        if fn is not None:\n            ret = PricingFuture()\n\n            def cb(f):\n                try:\n                    ret.set_result(fn(f.result()))\n                except Exception as e:\n                    ret.set_exception(e)\n\n            future.add_done_callback(cb)\n            future = ret\n\n        return future.result() if not (PricingContext.current.is_entered or PricingContext.current.is_async) else future\n\n    @classmethod\n    def from_dict(cls, values: dict):\n        if values:\n            if issubclass(cls, QuotableBuilder):\n                if 'properties' in values:\n                    values.update(values.pop('properties'))\n                return cls._from_dict(values)\n            elif hasattr(cls, 'asset_class'):\n                return cls._from_dict(values)\n            else:\n                if '$type' in values:\n                    from gs_quant_internal import tdapi\n                    tdapi_cls = getattr(tdapi, values['$type'].replace('Defn', 'Builder'))\n                    if not tdapi_cls:\n                        raise RuntimeError('Cannot resolve TDAPI type {}'.format(tdapi_cls))\n\n                    return tdapi_cls.from_dict(values)\n\n                asset_class_field = next((f for f in ('asset_class', 'assetClass') if f in values), None)\n                if not asset_class_field:\n                    raise ValueError('assetClass/asset_class not specified')\n                if 'type' not in values:\n                    raise ValueError('type not specified')\n\n                asset_type = values.pop('type')\n                asset_class = values.pop(asset_class_field)\n                default_type = Security if asset_type in [None, "", "Security"] and asset_class in [None, "",\n                                                                                                    "Security"] \\\n                    else None\n\n                instrument = cls.__asset_class_and_type_to_instrument().get((\n                    get_enum_value(AssetClass, asset_class),\n                    get_enum_value(AssetType, asset_type)), default_type)\n\n                if instrument is None:\n                    raise ValueError('unable to build instrument')\n\n                return instrument._from_dict(values)\n\n    @classmethod\n    def from_quick_entry(cls, text: str, asset_class: Optional[AssetClass] = None):\n        if not asset_class:\n            try:\n                inst = cls.default_instance()\n                asset_class = inst.asset_class\n            except AttributeError:\n                pass\n\n        if not asset_class:\n            res = GsParserApi.get_instrument_from_text(text)\n            if len(res):  # multiple instruments returned\n                instrument = res.pop(0)\n            else:\n                raise ValueError('Could not resolve instrument')\n        else:\n            instrument = GsParserApi.get_instrument_from_text_asset_class(text, asset_class.value)\n        try:\n            return cls.from_dict(instrument)\n        except AttributeError:\n            raise ValueError('Invalid instrument specification')\n\n    @classmethod\n    def from_asset_ids(cls, asset_ids: Tuple[str, ...]) -> Tuple[InstrumentBase, ...]:\n        from gs_quant.api.gs.assets import GsAssetApi\n        instruments = GsAssetApi.get_instruments_for_asset_ids(asset_ids)\n\n        try:\n            inst = cls.default_instance()\n            asset_class = inst.asset_class\n            asset_type = inst.type\n\n            if not all(i.asset_class == asset_class and i.type == asset_type for i in instruments):\n                raise ValueError(f'Instrument(s) not all of type {cls.__name__}')\n        except AttributeError:\n            pass\n\n        return instruments\n\n    @classmethod\n    def from_asset_id(cls, asset_id: str) -> InstrumentBase:\n        return cls.from_asset_ids((asset_id,))[0]\n\n    @staticmethod\n    def compose(components: Iterable):\n        return {c.risk_key.date if isinstance(c, ErrorValue) else c.resolution_key.date: c for c in components}\n\n\n
[docs]class Security(XRef, Instrument):\n    """A security, specified by a well-known identifier"""\n\n    def __init__(self,\n                 ticker: str = None,\n                 bbid: str = None,\n                 ric: str = None,\n                 isin: str = None,\n                 cusip: str = None,\n                 prime_id: str = None,\n                 quantity: float = 1):\n        """\n        Create a security by passing one identifier only and, optionally, a quantity\n\n        :param ticker: Exchange ticker\n        :param bbid: Bloomberg identifier\n        :param isin: International Security Number\n        :param cusip: CUSIP\n        :param prime_id: Prime (GS internal) identifier\n        :param quantity: Quantity (number of contracts for exchange-traded instruments, notional for bonds)\n        """\n        if len(tuple(filter(None, (f is not None for f in (ticker, bbid, isin, cusip, prime_id))))) > 1:\n            raise ValueError('Only specify one identifier')\n\n        XRef.__init__(self, ticker=ticker, bbid=bbid, ric=ric, isin=isin, cusip=cusip, prime_id=prime_id)\n        Instrument.__init__(self, quantity)
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Source code for gs_quant.markets.core

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nimport asyncio\nimport datetime as dt\nimport logging\nimport weakref\nfrom abc import ABCMeta\nfrom concurrent.futures import ThreadPoolExecutor\nfrom inspect import signature\nfrom itertools import zip_longest\nfrom tqdm import tqdm\nfrom typing import Optional, Union\n\nfrom .markets import CloseMarket, LiveMarket, Market, close_market_date\nfrom gs_quant.base import InstrumentBase, RiskKey, Scenario, get_enum_value\nfrom gs_quant.common import PricingLocation\nfrom gs_quant.context_base import ContextBaseWithDefault\nfrom gs_quant.datetime.date import business_day_offset\nfrom gs_quant.risk import CompositeScenario, DataFrameWithInfo, ErrorValue, FloatWithInfo, MarketDataScenario, \\\n    RiskMeasure, StringWithInfo\nfrom gs_quant.risk.results import PricingFuture\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.common import PricingDateAndMarketDataAsOf\nfrom gs_quant.target.risk import RiskPosition, RiskRequest, RiskRequestParameters\n\n\n_logger = logging.getLogger(__name__)\n\nCacheResult = Union[DataFrameWithInfo, FloatWithInfo, StringWithInfo]\n\n\nclass PricingCache(metaclass=ABCMeta):\n    """\n    Weakref cache for instrument calcs\n    """\n    __cache = weakref.WeakKeyDictionary()\n\n    @classmethod\n    def clear(cls):\n        __cache = weakref.WeakKeyDictionary()\n\n    @classmethod\n    def get(cls, risk_key: RiskKey, instrument: InstrumentBase) -> Optional[CacheResult]:\n        return cls.__cache.get(instrument, {}).get(risk_key)\n\n    @classmethod\n    def put(cls, risk_key: RiskKey, instrument: InstrumentBase, result: CacheResult):\n        if not isinstance(result, ErrorValue) and not isinstance(risk_key.market, LiveMarket):\n            cls.__cache.setdefault(instrument, {})[risk_key] = result\n\n    @classmethod\n    def drop(cls, instrument: InstrumentBase):\n        if instrument in cls.__cache:\n            cls.__cache.pop(instrument)\n\n\n
[docs]class PricingContext(ContextBaseWithDefault):\n\n    """\n    A context for controlling pricing and market data behaviour\n    """\n\n
[docs]    def __init__(self,\n                 pricing_date: Optional[dt.date] = None,\n                 market_data_location: Optional[Union[PricingLocation, str]] = None,\n                 is_async: bool = False,\n                 is_batch: bool = False,\n                 use_cache: bool = False,\n                 visible_to_gs: Optional[bool] = None,\n                 csa_term: Optional[str] = None,\n                 timeout: Optional[int] = None,\n                 market: Optional[Market] = None,\n                 show_progress: Optional[bool] = False):\n        """\n        The methods on this class should not be called directly. Instead, use the methods on the instruments,\n        as per the examples\n\n        :param pricing_date: the date for pricing calculations. Default is today\n        :param market_data_location: the location for sourcing market data ('NYC', 'LDN' or 'HKG' (defaults to LDN)\n        :param is_async: if True, return (a future) immediately. If False, block (defaults to False)\n        :param is_batch: use for calculations expected to run longer than 3 mins, to avoid timeouts.\n            It can be used with is_async=True|False (defaults to False)\n        :param use_cache: store results in the pricing cache (defaults to False)\n        :param visible_to_gs: are the contents of risk requests visible to GS (defaults to False)\n        :param csa_term: the csa under which the calculations are made. Default is local ccy ois index\n\n        **Examples**\n\n        To change the market data location of the default context:\n\n        >>> from gs_quant.markets import PricingContext\n        >>> import datetime as dt\n        >>>\n        >>> PricingContext.current = PricingContext(market_data_location='LDN')\n\n        For a blocking, synchronous request:\n\n        >>> from gs_quant.instrument import IRCap\n        >>> cap = IRCap('5y', 'GBP')\n        >>>\n        >>> with PricingContext():\n        >>>     price_f = cap.dollar_price()\n        >>>\n        >>> price = price_f.result()\n\n        For an asynchronous request:\n\n        >>> with PricingContext(is_async=True):\n        >>>     price_f = cap.dollar_price()\n        >>>\n        >>> while not price_f.done:\n        >>>     ...\n        """\n        super().__init__()\n\n        if market and market_data_location and market.location.value is not market_data_location:\n            raise ValueError('market.location and market_data_location cannot be different')\n\n        if not market_data_location:\n            if not market:\n                # use parent context's market_data_location\n                if self != self.active_context:\n                    market_data_location = self.active_context.market_data_location\n                # if no parent but there was a context set previously, use that\n                elif self.prior_context:\n                    market_data_location = self.prior_context.market_data_location\n            else:\n                market_data_location = market.location\n\n        self.__pricing_date = pricing_date or (self.prior_context.pricing_date if self.prior_context else\n                                               business_day_offset(dt.date.today(), 0, roll='preceding'))\n        self.__csa_term = csa_term\n        self.__is_async = is_async\n        self.__is_batch = is_batch\n        self.__timeout = timeout\n        self.__use_cache = use_cache\n        self.__visible_to_gs = visible_to_gs\n        self.__market_data_location = get_enum_value(PricingLocation, market_data_location)\n        self.__market = market or CloseMarket(\n            date=close_market_date(self.__market_data_location, self.__pricing_date) if pricing_date else None,\n            location=self.__market_data_location if market_data_location else None)\n        self.__pending = {}\n        self.__show_progress = show_progress
\n\n    def _on_exit(self, exc_type, exc_val, exc_tb):\n        if exc_val:\n            raise exc_val\n        else:\n            self.__calc()\n\n    def __calc(self):\n        def run_requests(requests_: list, provider_, create_event_loop: bool):\n            if create_event_loop:\n                asyncio.set_event_loop(asyncio.new_event_loop())\n\n            results = {}\n\n            try:\n                with session:\n                    results = provider_.run(requests_, 1000, progress_bar, timeout=self.__timeout)\n            except Exception as e:\n                results = {k: e for k in self.__pending.keys()}\n            finally:\n                while self.__pending:\n                    (risk_key_, priceable_), future = self.__pending.popitem()\n                    result = results.get((risk_key_, priceable_))\n\n                    if result is not None:\n                        if self.__use_cache:\n                            PricingCache.put(risk_key_, priceable_, result)\n                    else:\n                        result = ErrorValue(risk_key_, 'No result returned')\n\n                    future.set_result(result)\n\n        # Group requests optimally\n        requests_by_provider = {}\n        for (key, instrument) in self.__pending.keys():\n            requests_by_provider.setdefault(key.provider, {})\\\n                .setdefault((key.params, key.scenario, key.date, key.market), {})\\\n                .setdefault(instrument, set())\\\n                .add(key.risk_measure)\n\n        requests_for_provider = {}\n        if requests_by_provider:\n            session = GsSession.current\n            request_visible_to_gs = session.is_internal() if self.__visible_to_gs is None else self.__visible_to_gs\n\n            for provider, by_params_scenario_date_market in requests_by_provider.items():\n                grouped_requests = {}\n\n                for (params, scenario, date, market), positions_by_measures in by_params_scenario_date_market.items():\n                    for instrument, risk_measures in positions_by_measures.items():\n                        grouped_requests.setdefault((params, scenario, date, market, tuple(sorted(risk_measures))),\n                                                    []).append(instrument)\n\n                requests = []\n\n                for (params, scenario, date, market, risk_measures), instruments in grouped_requests.items():\n                    for chunk in [tuple(filter(None, i)) for i in zip_longest(*[iter(instruments)] * 1000)]:\n                        requests.append(RiskRequest(\n                            tuple(RiskPosition(instrument=i, quantity=i.instrument_quantity) for i in chunk),\n                            risk_measures,\n                            parameters=self._parameters,\n                            wait_for_results=not self.__is_batch,\n                            scenario=scenario,\n                            pricing_and_market_data_as_of=(PricingDateAndMarketDataAsOf(pricing_date=date,\n                                                                                        market=market),),\n                            request_visible_to_gs=request_visible_to_gs,\n                        ))\n\n                requests_for_provider[provider] = requests\n            \n            show_status = self.__show_progress and\\\n                (len(requests_for_provider) > 1 or len(next(iter(requests_for_provider.values()))) > 1)\n            request_pool = ThreadPoolExecutor(len(requests_for_provider))\\\n                if len(requests_for_provider) > 1 or self.__is_async else None\n            progress_bar = tqdm(total=len(self.__pending), position=0, maxinterval=1) if show_status else None\n            completion_futures = []\n\n            for provider, requests in requests_for_provider.items():\n                if request_pool:\n                    completion_future = request_pool.submit(run_requests, requests, provider, True)\n                    if not self.__is_async:\n                        completion_futures.append(completion_future)\n                else:\n                    run_requests(requests, provider, False)\n\n            # Wait on results if not async, so exceptions are surfaced\n            if request_pool:\n                request_pool.shutdown(False)\n                all(f.result() for f in completion_futures)\n\n    def __risk_key(self, risk_measure: RiskMeasure, provider: type) -> RiskKey:\n        return RiskKey(provider, self.__pricing_date, self.__market, self._parameters, self._scenario, risk_measure)\n\n    @property\n    def _parameters(self) -> RiskRequestParameters:\n        return RiskRequestParameters(csa_term=self.__csa_term, raw_results=True)\n\n    @property\n    def _scenario(self) -> Optional[MarketDataScenario]:\n        scenarios = Scenario.path\n        if not scenarios:\n            return None\n\n        return MarketDataScenario(scenario=scenarios[0] if len(scenarios) == 1 else\n                                  CompositeScenario(scenarios=tuple(reversed(scenarios))))\n\n    @property\n    def active_context(self):\n        return next((c for c in reversed(PricingContext.path) if c.is_entered), self)\n\n    @property\n    def prior_context(self):\n        return PricingContext.path[-1] if len(PricingContext.path) else None\n\n    @property\n    def is_current(self) -> bool:\n        return self == PricingContext.current\n\n    @property\n    def is_async(self) -> bool:\n        return self.__is_async\n\n    @property\n    def is_batch(self) -> bool:\n        return self.__is_batch\n\n    @property\n    def market(self) -> Market:\n        return self.__market\n\n    @property\n    def market_data_location(self) -> PricingLocation:\n        return self.__market_data_location\n\n    @property\n    def csa_term(self) -> str:\n        return self._parameters.csa_term\n\n    @property\n    def pricing_date(self) -> dt.date:\n        """Pricing date"""\n        return self.__pricing_date\n\n    @property\n    def use_cache(self) -> bool:\n        """Cache results"""\n        return self.__use_cache\n\n    @property\n    def visible_to_gs(self) -> Optional[bool]:\n        """Request contents visible to GS"""\n        return self.__visible_to_gs\n\n    def clone(self, **kwargs):\n        clone_kwargs = {k: getattr(self, k, None) for k in signature(self.__init__).parameters.keys()}\n        clone_kwargs.update(kwargs)\n        return self.__class__(**clone_kwargs)\n\n    def _calc(self, instrument: InstrumentBase, risk_key: RiskKey) -> PricingFuture:\n        future = self.active_context.__pending.get((risk_key, instrument))\n\n        if future is None:\n            future = PricingFuture()\n            cached_result = PricingCache.get(risk_key, instrument) if self.use_cache else None\n\n            if cached_result is not None:\n                future.set_result(cached_result)\n            else:\n                self.active_context.__pending[(risk_key, instrument)] = future\n\n        if not (self.is_entered or self.is_async):\n            self.__calc()\n\n        return future\n    \n    def calc(self, instrument: InstrumentBase, risk_measure: RiskMeasure) -> PricingFuture:\n        """\n        Calculate the risk measure for the instrument. Do not use directly, use via instruments\n\n        :param instrument: The instrument\n        :param risk_measure: The measure we wish to calculate\n        :return: A PricingFuture whose result will be the calculation result\n\n        **Examples**\n\n        >>> from gs_quant.instrument import IRSwap\n        >>> from gs_quant.risk import IRDelta\n        >>>\n        >>> swap = IRSwap('Pay', '10y', 'USD', fixed_rate=0.01)\n        >>> delta = swap.calc(IRDelta)\n        """\n        return self._calc(instrument, self.__risk_key(risk_measure, instrument.provider))
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Source code for gs_quant.markets.historical

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nimport datetime as dt\nfrom typing import Iterable, Optional, Tuple, Union\n\nfrom gs_quant.base import InstrumentBase, RiskKey\nfrom gs_quant.datetime.date import date_range\nfrom gs_quant.risk import RiskMeasure, RollFwd, MarketDataScenario\nfrom gs_quant.risk.results import HistoricalPricingFuture, PricingFuture\n\nfrom .core import PricingContext\nfrom .markets import CloseMarket, close_market_date\n\n\n
[docs]class HistoricalPricingContext(PricingContext):\n    """\n    A context for producing valuations over multiple dates\n    """\n\n
[docs]    def __init__(\n            self,\n            start: Optional[Union[int, dt.date]] = None,\n            end: Optional[Union[int, dt.date]] = None,\n            calendars: Union[str, Tuple] = (),\n            dates: Optional[Iterable[dt.date]] = None,\n            is_async: bool = False,\n            is_batch: bool = False,\n            use_cache: bool = False,\n            visible_to_gs: bool = False,\n            csa_term: str = None,\n            market_data_location: Optional[str] = None,\n            timeout: Optional[int] = None,\n            show_progress: Optional[bool] = False):\n        """\n        A context for producing valuations over multiple dates\n\n        :param start: start date\n        :param end: end date (defaults to today)\n        :param calendars: holiday calendars\n        :param dates: a custom iterable of dates\n        :param is_async: return immediately (True) or wait for results (False) (defaults to False)\n        :param is_batch: use for calculations expected to run longer than 3 mins, to avoid timeouts.\n            It can be used with is_async=True|False (defaults to False)\n        :param use_cache: store results in the pricing cache (defaults to False)\n        :param visible_to_gs: are the contents of risk requests visible to GS (defaults to False)\n        :param csa_term: the csa under which the calculations are made. Default is local ccy ois index\n        :param market_data_location: the location for sourcing market data ('NYC', 'LDN' or 'HKG' (defaults to LDN)\n\n        **Examples**\n\n        >>> from gs_quant.instrument import IRSwap\n        >>>\n        >>> ir_swap = IRSwap('Pay', '10y', 'DKK')\n        >>> with HistoricalPricingContext(10):\n        >>>     price_f = ir_swap.price()\n        >>>\n        >>> price_series = price_f.result()\n        """\n        super().__init__(is_async=is_async, is_batch=is_batch, use_cache=use_cache, visible_to_gs=visible_to_gs,\n                         csa_term=csa_term, market_data_location=market_data_location,\n                         timeout=timeout, show_progress=show_progress)\n        if start is not None:\n            if dates is not None:\n                raise ValueError('Must supply start or dates, not both')\n\n            if end is None:\n                end = dt.date.today()\n\n            self.__date_range = tuple(date_range(start, end, calendars=calendars))\n        elif dates is not None:\n            self.__date_range = tuple(dates)\n        else:\n            raise ValueError('Must supply start or dates')
\n\n    def calc(self, instrument: InstrumentBase, risk_measure: RiskMeasure) -> PricingFuture:\n        futures = []\n\n        provider = instrument.provider\n        scenario = self._scenario\n        parameters = self._parameters\n        location = self.market.location\n\n        for date in self.__date_range:\n            market = CloseMarket(location=location, date=close_market_date(location, date))\n            risk_key = RiskKey(provider, date, market, parameters, scenario, risk_measure)\n            futures.append(self._calc(instrument, risk_key))\n\n        return HistoricalPricingFuture(futures)
\n\n\nclass BackToTheFuturePricingContext(HistoricalPricingContext):\n    """\n    A context for producing valuations over multiple dates both in the past and into the future\n    """\n\n    def __init__(\n            self,\n            start: Optional[Union[int, dt.date]] = None,\n            end: Optional[Union[int, dt.date]] = None,\n            calendars: Union[str, Tuple] = (),\n            dates: Optional[Iterable[dt.date]] = None,\n            roll_to_fwds: bool = True,\n            is_async: bool = False,\n            is_batch: bool = False,\n            use_cache: bool = False,\n            visible_to_gs: bool = False,\n            csa_term: str = None,\n            market_data_location: Optional[str] = None,\n            timeout: Optional[int] = None,\n            show_progress: Optional[bool] = False):\n        """\n        A context for producing valuations over multiple dates\n\n        :param start: start date\n        :param end: end date (defaults to today)\n        :param calendars: holiday calendars\n        :param dates: a custom iterable of dates\n        :param roll_to_fwds: if True then for future dates assume fwd curve is realised.  If False assume spot rates\n            are realised.  (defaults to True)\n        :param is_async: return immediately (True) or wait for results (False) (defaults to False)\n        :param is_batch: use for calculations expected to run longer than 3 mins, to avoid timeouts.\n            It can be used with is_async=True|False (defaults to False)\n        :param use_cache: store results in the pricing cache (defaults to False)\n        :param visible_to_gs: are the contents of risk requests visible to GS (defaults to False)\n        :param csa_term: the csa under which the calculations are made. Default is local ccy ois index\n        :param market_data_location: the location for sourcing market data ('NYC', 'LDN' or 'HKG' (defaults to LDN)\n\n        **Examples** assuming today is between dt.date(2020, 7, 6) and dt.date(2020, 7, 14)\n\n        >>> from gs_quant.instrument import IRSwap\n        >>>\n        >>> ir_swap = IRSwap('Pay', '10y', 'DKK')\n        >>> with BackToTheFuturePricingContext(dt.date(2020, 7, 6), dt.date(2020, 7, 14), roll_to_fwds=True):\n        >>>     price_f = ir_swap.price()\n        >>>\n        >>> price_series = price_f.result()\n        """\n        super().__init__(start=start, end=end, calendars=calendars, dates=dates,\n                         is_async=is_async, is_batch=is_batch, use_cache=use_cache, visible_to_gs=visible_to_gs,\n                         csa_term=csa_term, market_data_location=market_data_location,\n                         timeout=timeout, show_progress=show_progress)\n        self._roll_to_fwds = roll_to_fwds\n        if start is not None:\n            if dates is not None:\n                raise ValueError('Must supply start or dates, not both')\n\n            if end is None:\n                end = dt.date.today()\n\n            self.__date_range = tuple(date_range(start, end, calendars=calendars))\n        elif dates is not None:\n            self.__date_range = tuple(dates)\n        else:\n            raise ValueError('Must supply start or dates')\n\n    def calc(self, instrument: InstrumentBase, risk_measure: RiskMeasure) -> PricingFuture:\n        futures = []\n\n        provider = instrument.provider\n        base_scenario = self._scenario\n        parameters = self._parameters\n        location = self.market.location\n        base_market = self.market\n        for date in self.__date_range:\n            if date > self.pricing_date:\n                scenario = MarketDataScenario(RollFwd(date=date, realise_fwd=self._roll_to_fwds))\n                risk_key = RiskKey(provider, date, base_market, parameters, scenario, risk_measure)\n                futures.append(self._calc(instrument, risk_key))\n            else:\n                market = CloseMarket(location=location, date=close_market_date(location, date))\n                risk_key = RiskKey(provider, date, market, parameters, base_scenario, risk_measure)\n                futures.append(self._calc(instrument, risk_key))\n\n        return HistoricalPricingFuture(futures)\n
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Source code for gs_quant.markets.portfolio

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicablNe law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nimport copy\nimport datetime as dt\nimport logging\nfrom typing import Iterable, Optional, Tuple, Union\n\nimport numpy as np\nimport pandas as pd\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.portfolios import GsPortfolioApi\nfrom gs_quant.context_base import nullcontext\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.markets import HistoricalPricingContext, OverlayMarket, PricingContext\nfrom gs_quant.priceable import PriceableImpl\nfrom gs_quant.risk import RiskMeasure, ResolvedInstrumentValues\nfrom gs_quant.risk.results import CompositeResultFuture, PortfolioRiskResult, PortfolioPath, PricingFuture\nfrom gs_quant.target.common import RiskPosition\nfrom gs_quant.target.portfolios import Position, PositionSet, RiskRequest, PricingDateAndMarketDataAsOf\nfrom more_itertools import unique_everseen\nfrom itertools import chain\n\n_logger = logging.getLogger(__name__)\n\n\n
[docs]class Portfolio(PriceableImpl):\n    """A collection of instruments\n\n    Portfolio holds a collection of instruments in order to run pricing and risk scenarios\n\n    """\n\n
[docs]    def __init__(self,\n                 priceables: Optional[Union[PriceableImpl, Iterable[PriceableImpl], dict]] = (),\n                 name: Optional[str] = None):\n        """\n        Creates a portfolio object which can be used to hold instruments\n\n        :param priceables: constructed with an instrument, portfolio, iterable of either, or a dictionary where\n            key is name and value is a priceable\n        """\n        super().__init__()\n        if isinstance(priceables, dict):\n            priceables_list = []\n            for name, priceable in priceables.items():\n                priceable.name = name\n                priceables_list.append(priceable)\n            self.priceables = priceables_list\n        else:\n            self.priceables = priceables\n\n        self.__name = name\n        self.__id = None
\n\n    def __getitem__(self, item):\n        if isinstance(item, (int, slice)):\n            return self.__priceables[item]\n        elif isinstance(item, PortfolioPath):\n            return item(self, rename_to_parent=True)\n        else:\n            values = tuple(self[p] for it in item for p in self.paths(it)) if isinstance(item, list) else tuple(\n                self[p] for p in self.paths(item))\n            return values[0] if len(values) == 1 else values\n\n    def __contains__(self, item):\n        if isinstance(item, PriceableImpl):\n            return any(item in p.__priceables_lookup for p in self.all_portfolios + (self,))\n        elif isinstance(item, str):\n            return any(item in p.__priceables_by_name for p in self.all_portfolios + (self,))\n        else:\n            return False\n\n    def __len__(self):\n        return len(self.__priceables)\n\n    def __iter__(self):\n        return iter(self.__priceables)\n\n    def __hash__(self):\n        hash_code = hash(self.__name) ^ hash(self.__id)\n        for priceable in self.__priceables:\n            hash_code ^= hash(priceable)\n\n        return hash_code\n\n    def __eq__(self, other):\n        if not isinstance(other, Portfolio):\n            return False\n\n        for path in self.all_paths:\n            try:\n                if path(self) != path(other):\n                    return False\n            except IndexError:\n                return False\n\n        return True\n\n    def __add__(self, other):\n        if not isinstance(other, Portfolio):\n            raise ValueError('Can only add instances of Portfolio')\n\n        return Portfolio(self.__priceables + other.__priceables)\n\n    @property\n    def __pricing_context(self) -> PricingContext:\n        return PricingContext.current if not PricingContext.current.is_entered else nullcontext()\n\n    @property\n    def id(self) -> str:\n        return self.__id\n\n    @property\n    def name(self) -> str:\n        return self.__name\n\n    @name.setter\n    def name(self, name):\n        self.__name = name\n\n    @property\n    def priceables(self) -> Tuple[PriceableImpl, ...]:\n        return self.__priceables\n\n    @priceables.setter\n    def priceables(self, priceables: Union[PriceableImpl, Iterable[PriceableImpl]]):\n        self.__priceables = (priceables,) if isinstance(priceables, PriceableImpl) else tuple(priceables)\n        self.__priceables_lookup = {}\n        self.__priceables_by_name = {}\n\n        for idx, i in enumerate(self.__priceables):\n            self.__priceables_lookup.setdefault(copy.copy(i), []).append(idx)\n            if i and i.name:\n                self.__priceables_by_name.setdefault(i.name, []).append(idx)\n\n    @priceables.deleter\n    def priceables(self):\n        self.__priceables = None\n        self.__priceables_lookup = None\n        self.__priceables_by_name = None\n\n    @property\n    def instruments(self) -> Tuple[Instrument, ...]:\n        return tuple(unique_everseen(i for i in self.__priceables if isinstance(i, Instrument)))\n\n    @property\n    def all_instruments(self) -> Tuple[Instrument, ...]:\n        instr = chain(self.instruments, chain.from_iterable(p.all_instruments for p in self.all_portfolios))\n        return tuple(unique_everseen(instr))\n\n    @property\n    def portfolios(self) -> Tuple[PriceableImpl, ...]:\n        return tuple(i for i in self.__priceables if isinstance(i, Portfolio))\n\n    @property\n    def all_portfolios(self) -> Tuple[PriceableImpl, ...]:\n        stack = list(self.portfolios)\n        portfolios = list(unique_everseen(stack))\n\n        while stack:\n            portfolio = stack.pop()\n            if portfolio in portfolios:\n                continue\n\n            sub_portfolios = portfolio.portfolios\n            portfolios.extend(sub_portfolios)\n            stack.extend(sub_portfolios)\n\n        return tuple(unique_everseen(portfolios))\n\n    def subset(self, paths: Iterable[PortfolioPath], name=None):\n        return Portfolio(tuple(self[p] for p in paths), name=name)\n\n    @staticmethod\n    def __from_internal_positions(id_type: str, positions_id):\n        instruments = GsPortfolioApi.get_instruments_by_position_type(id_type, positions_id)\n        return Portfolio(instruments, name=positions_id)\n\n    @staticmethod\n    def from_eti(eti: str):\n        return Portfolio.__from_internal_positions('ETI', eti.replace(',', '%2C'))\n\n    @staticmethod\n    def from_book(book: str, book_type: str = 'risk'):\n        return Portfolio.__from_internal_positions(book_type, book)\n\n    @staticmethod\n    def from_asset_id(asset_id: str, date=None):\n        asset = GsAssetApi.get_asset(asset_id)\n        response = GsAssetApi.get_asset_positions_for_date(asset_id, date) if date else \\\n            GsAssetApi.get_latest_positions(asset_id)\n        response = response[0] if isinstance(response, tuple) else response\n        positions = response.positions if isinstance(response, PositionSet) else response['positions']\n        instruments = GsAssetApi.get_instruments_for_positions(positions)\n        ret = Portfolio(instruments, name=asset.name)\n        ret.__id = asset_id\n        return ret\n\n    @staticmethod\n    def from_asset_name(name: str):\n        asset = GsAssetApi.get_asset_by_name(name)\n        return Portfolio.load_from_portfolio_id(asset.id)\n\n    @staticmethod\n    def from_portfolio_id(portfolio_id: str, date=None):\n        portfolio = GsPortfolioApi.get_portfolio(portfolio_id)\n        response = GsPortfolioApi.get_positions_for_date(portfolio_id, date) if date else \\\n            GsPortfolioApi.get_latest_positions(portfolio_id)\n        response = response[0] if isinstance(response, tuple) else response\n        positions = response.positions if isinstance(response, PositionSet) else response['positions']\n        instruments = GsAssetApi.get_instruments_for_positions(positions)\n        ret = Portfolio(instruments, name=portfolio.name)\n        ret.__id = portfolio_id\n        return ret\n\n    @staticmethod\n    def from_portfolio_name(name: str):\n        portfolio = GsPortfolioApi.get_portfolio_by_name(name)\n        return Portfolio.load_from_portfolio_id(portfolio.id)\n\n    @staticmethod\n    def from_quote(quote_id: str):\n        instruments = GsPortfolioApi.get_instruments_by_workflow_id(quote_id)\n        return Portfolio(instruments, name=quote_id)\n\n    def save(self, overwrite: Optional[bool] = False):\n        if self.portfolios:\n            raise ValueError('Cannot save portfolios with nested portfolios')\n\n        if self.__id:\n            if not overwrite:\n                raise ValueError(f'Portfolio with id {id} already exists. Use overwrite=True to overwrite')\n        else:\n            if not self.__name:\n                raise ValueError('name not set')\n\n            try:\n                self.__id = GsPortfolioApi.get_portfolio_by_name(self.__name).id\n                if not overwrite:\n                    raise RuntimeError(\n                        f'Portfolio {self.__name} with id {self.__id} already exists. Use overwrite=True to overwrite')\n            except ValueError:\n                from gs_quant.target.portfolios import Portfolio as MarqueePortfolio\n                self.__id = GsPortfolioApi.create_portfolio(MarqueePortfolio('USD', self.__name)).id\n                _logger.info(f'Created Marquee portfolio {self.__name} with id {self.__id}')\n\n        position_set = PositionSet(\n            position_date=dt.date.today(),\n            positions=tuple(Position(asset_id=GsAssetApi.get_or_create_asset_from_instrument(i))\n                            for i in self.instruments))\n\n        GsPortfolioApi.update_positions(self.__id, (position_set,))\n\n    def save_as_quote(self, overwrite: Optional[bool] = False):\n        if self.portfolios:\n            raise ValueError('Cannot save portfolios with nested portfolios')\n\n        pricing_context = self.__pricing_context\n\n        request = RiskRequest(\n            tuple(RiskPosition(instrument=i, quantity=i.instrument_quantity) for i in self.instruments),\n            (ResolvedInstrumentValues,),\n            pricing_and_market_data_as_of=(PricingDateAndMarketDataAsOf(pricing_date=pricing_context.pricing_date,\n                                                                        market=pricing_context.market),)\n        )\n\n        if self.__id:\n            if not overwrite:\n                raise ValueError(f'Portfolio with id {id} already exists. Use overwrite=True to overwrite')\n            else:\n                GsPortfolioApi.update_quote(self.__id, request)\n                _logger.info(f'Updated Structuring with id {self.__id}')\n        else:\n            self.__id = GsPortfolioApi.save_quote(request)\n            _logger.info(f'Created Structuring with id {self.__id}')\n\n    @classmethod\n    def from_frame(cls, data: pd.DataFrame, mappings: dict = None):\n        def get_value(this_row: pd.Series, attribute: str):\n            value = mappings.get(attribute, attribute)\n            return value(this_row) if callable(value) else this_row.get(value)\n\n        instruments = []\n        mappings = mappings or {}\n        data = data.replace({np.nan: None})\n\n        for row in (r for _, r in data.iterrows() if any(v for v in r.values if v is not None)):\n            instrument = None\n            for init_keys in (('asset_class', 'type'), ('$type',)):\n                init_values = tuple(filter(None, (get_value(row, k) for k in init_keys)))\n                if len(init_keys) == len(init_values):\n                    instrument = Instrument.from_dict(dict(zip(init_keys, init_values)))\n                    instrument = instrument.from_dict({p: get_value(row, p) for p in instrument.properties()})\n                    break\n\n            if instrument:\n                instruments.append(instrument)\n            else:\n                raise ValueError('Neither asset_class/type nor $type specified')\n\n        return cls(instruments)\n\n    @classmethod\n    def from_csv(\n            cls,\n            csv_file: str,\n            mappings: Optional[dict] = None\n    ):\n        data = pd.read_csv(csv_file, skip_blank_lines=True).replace({np.nan: None})\n        return cls.from_frame(data, mappings)\n\n    def append(self, priceables: Union[PriceableImpl, Iterable[PriceableImpl]]):\n        self.priceables += ((priceables,) if isinstance(priceables, PriceableImpl) else tuple(priceables))\n\n    def pop(self, item) -> PriceableImpl:\n        priceable = self[item]\n        self.priceables = [inst for inst in self.instruments if inst != priceable]\n        return priceable\n\n    def to_frame(self, mappings: Optional[dict] = None) -> pd.DataFrame:\n        def to_records(portfolio: Portfolio) -> list:\n            records = []\n\n            for priceable in portfolio.priceables:\n                if isinstance(priceable, Portfolio):\n                    records.extend(to_records(priceable))\n                else:\n                    as_dict = priceable.as_dict()\n                    if hasattr(priceable, '_type'):\n                        as_dict['$type'] = priceable._type\n\n                    records.append(dict(chain(as_dict.items(),\n                                              (('instrument', priceable), ('portfolio', portfolio.name)))))\n\n            return records\n\n        df = pd.DataFrame.from_records(to_records(self)).set_index(['portfolio', 'instrument'])\n        all_columns = df.columns.to_list()\n        columns = sorted(c for c in all_columns if c not in ('asset_class', 'type', '$type'))\n\n        for asset_column in ('$type', 'type', 'asset_class'):\n            if asset_column in all_columns:\n                columns = [asset_column] + columns\n\n        df = df[columns]\n        mappings = mappings or {}\n\n        for key, value in mappings.items():\n            if isinstance(value, str):\n                df[key] = df[value]\n            elif callable(value):\n                df[key] = len(df) * [None]\n                df[key] = df.apply(value, axis=1)\n\n        return df\n\n    def to_csv(self, csv_file: str, mappings: Optional[dict] = None, ignored_cols: Optional[list] = None):\n        port_df = self.to_frame(mappings or {})\n        port_df = port_df[np.setdiff1d(port_df.columns, ignored_cols or [])]\n        port_df.reset_index(drop=True, inplace=True)\n\n        port_df.to_csv(csv_file)\n\n    @property\n    def all_paths(self) -> Tuple[PortfolioPath, ...]:\n        paths = ()\n        stack = [(None, self)]\n        while stack:\n            parent, portfolio = stack.pop()\n\n            for idx, priceable in enumerate(portfolio.__priceables):\n                path = parent + PortfolioPath(idx) if parent is not None else PortfolioPath(idx)\n                if isinstance(priceable, Portfolio):\n                    stack.insert(0, (path, priceable))\n                else:\n                    paths += (path,)\n\n        return paths\n\n    def paths(self, key: Union[str, PriceableImpl]) -> Tuple[PortfolioPath, ...]:\n        if not isinstance(key, (str, Instrument, Portfolio)):\n            raise ValueError('key must be a name or Instrument or Portfolio')\n\n        idx = self.__priceables_by_name.get(key) if isinstance(key, str) else self.__priceables_lookup.get(key)\n        paths = tuple(PortfolioPath(i) for i in idx) if idx else ()\n\n        for path, porfolio in ((PortfolioPath(i), p)\n                               for i, p in enumerate(self.__priceables) if isinstance(p, Portfolio)):\n            paths += tuple(path + sub_path for sub_path in porfolio.paths(key))\n\n        return paths\n\n    def resolve(self, in_place: bool = True) -> Optional[Union[PricingFuture, PriceableImpl, dict]]:\n        pricing_context = self.__pricing_context\n        with pricing_context:\n            futures = [p.resolve(in_place) for p in self.__priceables]\n\n        if not in_place:\n            ret = {} if isinstance(PricingContext.current, HistoricalPricingContext) else Portfolio(name=self.name)\n            result_future = PricingFuture() if not isinstance(\n                pricing_context, PricingContext) or pricing_context.is_async or pricing_context.is_entered else None\n\n            def cb(future: CompositeResultFuture):\n                if isinstance(ret, Portfolio):\n                    ret.priceables = [f.result() for f in future.futures]\n                else:\n                    priceables_by_date = {}\n                    for future in futures:\n                        for date, priceable in future.result().items():\n                            priceables_by_date.setdefault(date, []).append(priceable)\n\n                    for date, priceables in priceables_by_date.items():\n                        if any(p for p in priceables if not isinstance(p, PriceableImpl)):\n                            _logger.error(f'Error resolving on {date}, skipping that date')\n                        else:\n                            ret[date] = Portfolio(priceables, name=self.name)\n\n                if result_future:\n                    result_future.set_result(ret)\n\n            CompositeResultFuture(futures).add_done_callback(cb)\n            return result_future or ret\n\n    def market(self) -> Union[OverlayMarket, PricingFuture, dict]:\n        """\n        Market Data map of coordinates and values. Note that this is not yet supported on all instruments\n\n        ***Examples**\n\n        >>> from gs_quant.markets.portfolio import Portfolio\n        >>>\n        >>> portfolio = Portfolio(...)\n        >>> market = portfolio.market()\n\n        """\n        pricing_context = self.__pricing_context\n        with pricing_context:\n            futures = [i.market() for i in self.all_instruments]\n\n        result_future = PricingFuture()\n        return_future = not isinstance(pricing_context,\n                                       PricingContext) or pricing_context.is_async or pricing_context.is_entered\n\n        def cb(future: CompositeResultFuture):\n            def update_market_data(all_market_data, this_market_data):\n                for item in this_market_data:\n                    existing_value = all_market_data.setdefault(item.coordinate, item.value)\n                    if abs(existing_value - item.value) > 1e-6:\n                        raise ValueError(\n                            f'Conflicting values for {item.coordinate}: {existing_value} vs {item.value}')\n\n            results = [f.result() for f in future.futures]\n            is_historical = isinstance(results[0], dict)\n            market_data = None if is_historical else {}\n            overlay_markets = {} if is_historical else None\n\n            for result in results:\n                if market_data is not None:\n                    update_market_data(market_data, result.market_data)\n                else:\n                    for market in result.values():\n                        update_market_data(overlay_markets.setdefault(market.base_market, {}), market.market_data)\n\n            if market_data:\n                ret = OverlayMarket(base_market=results[0].base_market, market_data=market_data)\n            else:\n                ret = {base_market.date: OverlayMarket(base_market=base_market, market_data=market_data)\n                       for base_market, market_data in overlay_markets.items()}\n\n            if result_future:\n                result_future.set_result(ret)\n\n        CompositeResultFuture(futures).add_done_callback(cb)\n        return result_future if return_future else result_future.result()\n\n    def calc(self, risk_measure: Union[RiskMeasure, Iterable[RiskMeasure]], fn=None) -> PortfolioRiskResult:\n        with self.__pricing_context:\n            return PortfolioRiskResult(self,\n                                       (risk_measure,) if isinstance(risk_measure, RiskMeasure) else risk_measure,\n                                       [p.calc(risk_measure, fn=fn) for p in self.__priceables])
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Source code for gs_quant.markets.securities

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\n\nimport datetime as dt\nimport json\nimport threading\nfrom abc import ABCMeta, abstractmethod\nfrom enum import Enum\nfrom typing import Optional, Tuple, Union, Dict\n\nimport cachetools\nimport pandas as pd\nimport pytz\nfrom pydash import get\n\nfrom gs_quant.api.gs.assets import GsAssetApi, GsAsset, AssetClass, AssetParameters, \\\n    AssetType as GsAssetType, PositionSet, Currency\nfrom gs_quant.base import get_enum_value\nfrom gs_quant.common import PositionType\nfrom gs_quant.data import DataMeasure, DataFrequency\nfrom gs_quant.data.coordinate import DataDimensions\nfrom gs_quant.data.coordinate import DateOrDatetime\nfrom gs_quant.entities.entity import Entity, EntityIdentifier, EntityType\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.markets import PricingContext\n\n\nclass ExchangeCode(Enum):\n    """Exchange enumeration\n\n    Exchange codes representing global venues where Securities are listed and traded\n\n    """\n\n    NASDAQ = "NASD"  # Nasdaq Global Stock Market\n    NYSE = "NYSE"  # New York Stock Exchange\n\n\n
[docs]class AssetType(Enum):\n    """Asset type enumeration\n\n    Enumeration of different types of asset or security.\n\n    """\n\n    #: Index which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets\n    INDEX = "Index"\n\n    #: Exchange traded fund which tracks an evolving portfolio of securities and is listed on an exchange to be\n    #: traded as a security\n    ETF = "ETF"\n\n    #: Bespoke basket which provides exposure to a customized collection of assets with levels published daily; can be\n    #: traded on swap and rebalanced programmatically\n    BASKET = "Custom Basket"\n\n    #: Listed equities which provide access to equity holding in a company and participation in dividends and other\n    #: distributions in common, preferred or other variants which provide different investor rights\n    STOCK = "Single Stock"\n\n    #: Standardized listed contract which provides delivery of an asset at a pre-defined forward date and can be\n    #: settled in cash or physical form\n    FUTURE = "Future"\n\n    #: FX cross or currency pair\n    CROSS = "Cross"\n\n    #: Currency\n    CURRENCY = "Currency"\n\n    #: Rate\n    RATE = "Rate"\n\n    #: Cash\n    CASH = "Cash"\n\n    #: Weather Index\n    WEATHER_INDEX = "Weather Index"\n\n    #: Swap\n    SWAP = "Swap"\n\n    #: Option\n    OPTION = "Option"\n\n    #: Commodity Reference Price\n    COMMODITY_REFERENCE_PRICE = "Commodity Reference Price"\n\n    # COMMODITY NATURAL GAS Hub\n    COMMODITY_NATURAL_GAS_HUB = "Commodity Natural Gas Hub"\n\n    # COMMODITY EU NATURAL GAS Hub\n    COMMODITY_EU_NATURAL_GAS_HUB = "Commodity EU Natural Gas Hub"\n\n    #: Commodity Power Node\n    COMMODITY_POWER_NODE = "Commodity Power Node"\n\n    #: Commodity Power Aggregated Nodes\n    COMMODITY_POWER_AGGREGATED_NODES = "Commodity Power Aggregated Nodes"\n\n    #: Bond\n    BOND = "Bond"\n\n    #: Future Market\n    FUTURE_MARKET = "Future Market"\n\n    #: Future Contract\n    FUTURE_CONTRACT = "Future Contract"\n\n    #: Commodity\n    COMMODITY = "Commodity"\n\n    #: Crypto\n    CRYPTO_CURRENCY = "Crypto Currency"
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[docs]class AssetIdentifier(EntityIdentifier):\n    """Asset type enumeration\n\n    Enumeration of different security identifiers\n\n    """\n\n    MARQUEE_ID = "MQID"  #: Goldman Sachs Marquee identifier code (MA4B66MW5E27UAHKG34)\n    REUTERS_ID = "RIC"  #: Thompson Reuters Instrument Code (RIC), (GS.N)\n    BLOOMBERG_ID = "BBID"  #: Bloomberg identifier and exchange code (GS UN)\n    BLOOMBERG_COMPOSITE_ID = "BCID"  #: Bloomberg composite identifier and exchange code (GS US)\n    CUSIP = "CUSIP"  #: Committee on Uniform Security Identification Procedures code (38141G104)\n    ISIN = "ISIN"  #: International Securities Identification Number (US38141G1040)\n    SEDOL = "SEDOL"  #: LSE Stock Exchange Daily Official List code (2407966)\n    TICKER = "TICKER"  #: Exchange ticker (GS)
\n\n\nclass ReturnType(Enum):\n    """Index return type\n\n    Represents different index return types or funding models\n\n    """\n\n    EXCESS_RETURN = "Excess Return"  # Returns are excess of funding rate in denominated currency\n    TOTAL_RETURN = "Total Return"  # Returns are inclusive of funding rate in denominated currency\n\n\n
[docs]class Asset(Entity, metaclass=ABCMeta):\n
[docs]    def __init__(self,\n                 id_: str,\n                 asset_class: AssetClass,\n                 name: str,\n                 exchange: Optional[str] = None,\n                 currency: Optional[str] = None,\n                 parameters: AssetParameters = None,\n                 entity: Optional[Dict] = None):\n        super().__init__(id_, EntityType.ASSET, entity=entity)\n        self.__id = id_\n        self.asset_class = asset_class\n        self.name = name\n        self.exchange = exchange\n        self.currency = currency\n        self.parameters = parameters
\n\n    def get_marquee_id(self):\n        return self.__id\n\n    def get_identifiers(self, as_of: dt.date = None) -> dict:\n        """\n        Get asset identifiers\n\n        :param as_of: As of date for query\n        :return: dict of identifiers\n\n        **Usage**\n\n        Get asset identifiers as of a given date. Where the identifiers are temporal (and can change over time), this\n        function will return the identifiers as of that point in time. If no date is provided as a parameter, will use\n        the current PricingContext.\n\n        **Examples**\n\n        Get current asset identifiers:\n\n        >>> gs = SecurityMaster.get_asset("GS", AssetIdentifier.TICKER)\n        >>> gs.get_identifiers()\n\n        Get identifiers as of 1Jan18:\n\n        >>> gs.get_identifiers(dt.date(2018,1,1))\n\n        Use PricingContext to determine as of date:\n\n        >>> with PricingContext(dt.date(2018,1,1)) as ctx:\n        >>>     gs.get_identifiers()\n\n        **See also**\n\n        :class:`AssetIdentifier`\n        :func:`get_asset`\n        """\n        if not as_of:\n            as_of = PricingContext.current.pricing_date\n\n            if isinstance(as_of, dt.datetime):\n                as_of = as_of.date()\n\n        valid_ids = set(item.value for item in AssetIdentifier)\n        xrefs = GsAssetApi.get_asset_xrefs(self.__id)\n        identifiers = {}\n\n        for xref in xrefs:\n            start_date = xref.startDate\n            end_date = xref.endDate\n\n            if start_date <= as_of <= end_date:\n                identifiers = {k.upper(): v for k, v in xref.identifiers.as_dict().items() if k.upper() in valid_ids}\n\n        return identifiers\n\n    @cachetools.cached(cachetools.TTLCache(256, 600),\n                       lambda s, id_type, as_of=None: cachetools.keys.hashkey(s.get_marquee_id(), id_type, as_of),\n                       threading.RLock())\n    def get_identifier(self, id_type: AssetIdentifier, as_of: dt.date = None):\n        """\n        Get asset identifier\n\n        :param as_of: As of date for query\n        :param id_type: requested id type\n        :return: identifier value\n\n        **Usage**\n\n        Get asset identifier as of a given date. Where the identifiers are temporal (and can change over time), this\n        function will return the identifier as of that point in time. If no date is provided as a parameter, will use\n        the current PricingContext.\n\n        **Examples**\n\n        Get current SEDOL:\n\n        >>> import datetime as dt\n        >>>\n        >>> gs = SecurityMaster.get_asset("GS", AssetIdentifier.TICKER)\n        >>> gs.get_identifier(AssetIdentifier.SEDOL)\n\n        Get SEDOL as of 1Jan18:\n\n        >>> gs.get_identifier(AssetIdentifier.SEDOL, as_of=dt.date(2018,1,1))\n\n        Use PricingContext to determine as of date:\n\n        >>> with PricingContext(dt.date(2018,1,1)) as ctx:\n        >>>     gs.get_identifier(AssetIdentifier.SEDOL)\n\n        **See also**\n\n        :class:`AssetIdentifier`\n        :func:`get_asset_identifiers`\n\n        """\n        if id_type == AssetIdentifier.MARQUEE_ID:\n            return self.__id\n\n        ids = self.get_identifiers(as_of=as_of)\n        return ids.get(id_type.value)\n\n    def get_data_series(self,\n                        measure: DataMeasure,\n                        dimensions: Optional[DataDimensions] = None,\n                        frequency: Optional[DataFrequency] = None,\n                        start: Optional[DateOrDatetime] = None,\n                        end: Optional[DateOrDatetime] = None) -> pd.Series:\n        """\n        Get asset series\n\n        :param measure: measure to get as series\n        :param dimensions: dimensions to query (e.g. tenor)\n        :param frequency: data frequency to query\n        :param start: start of the series\n        :param end: end of the series\n        :return: timeseries of given measure\n\n        **Usage**\n\n        Get a given timeseries for the asset\n\n        **Examples**\n\n        Get close price series:\n\n        >>> from gs_quant.markets.securities import SecurityMaster\n        >>> from gs_quant.data import DataMeasure\n        >>>\n        >>> gs = SecurityMaster.get_asset("GS", AssetIdentifier.TICKER)\n        >>> gs.get_data_series(DataMeasure.CLOSE_PRICE)\n\n        **See also**\n\n        :class:`DataMeasure`\n\n        """\n\n        coordinate = self.get_data_coordinate(measure, dimensions, frequency)\n        if coordinate is None:\n            raise MqValueError(f"No data co-ordinate found for these parameters: {measure, dimensions, frequency}")\n        return coordinate.get_series(start=start, end=end)\n\n    def get_close_prices(self,\n                         start: Optional[dt.date] = None,\n                         end: Optional[dt.date] = None) -> pd.Series:\n        """\n        Get close price series\n\n        :return: timeseries of close prices\n\n        **Usage**\n\n        Get close prices for an asset\n\n        **Examples**\n\n        Get close price series:\n\n        >>> from gs_quant.markets.securities import SecurityMaster\n        >>>\n        >>> gs = SecurityMaster.get_asset("GS", AssetIdentifier.TICKER)\n        >>> gs.get_close_prices()\n\n        **See also**\n\n        :class:`DataMeasure`\n        :func:`get_data_series`\n\n        """\n\n        return self.get_data_series(DataMeasure.CLOSE_PRICE, None, DataFrequency.DAILY, start, end)\n\n    @abstractmethod\n    def get_type(self) -> AssetType:\n        """Overridden by sub-classes to return security type"""\n\n    @property\n    def data_dimension(self) -> str:\n        return 'assetId'\n\n    @classmethod\n    def entity_type(cls) -> EntityType:\n        return EntityType.ASSET\n\n    @classmethod\n    def get(cls,\n            id_value: str,\n            id_type: AssetIdentifier,\n            as_of: Union[dt.date, dt.datetime] = None,\n            exchange_code: ExchangeCode = None,\n            asset_type: AssetType = None,\n            sort_by_rank: bool = False) -> Optional['Asset']:\n        asset = SecurityMaster.get_asset(id_value, id_type, as_of, exchange_code, asset_type, sort_by_rank)\n        return asset
\n\n\n
[docs]class Stock(Asset):\n    """Base Security Type\n\n    Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n    referenced in a derivative transaction\n\n    """\n\n
[docs]    def __init__(self,\n                 id_: str,\n                 name: str,\n                 exchange: Optional[str] = None,\n                 currency: Optional[Currency] = None,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Equity, name, exchange, currency, entity=entity)
\n\n    def get_type(self) -> AssetType:\n        return AssetType.STOCK\n\n    def get_currency(self) -> Optional[Currency]:\n        return self.currency
\n\n\nclass Cross(Asset):\n    """Base Security Type\n\n    Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n    referenced in a derivative transaction\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.FX, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.CROSS\n\n\nclass Future(Asset):\n    """Future Security Type\n\n    Represents a standardized listed contract which provides delivery of an asset at a pre-defined forward date and can\n    be settled in cash or physical form\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 asset_class: Union[AssetClass, str],\n                 name: str,\n                 currency: Optional[Currency] = None,\n                 entity: Optional[Dict] = None):\n        if isinstance(asset_class, str):\n            asset_class = get_enum_value(AssetClass, asset_class)\n\n        Asset.__init__(self, id_, asset_class, name, currency=currency, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.FUTURE\n\n    def get_currency(self) -> Optional[Currency]:\n        return self.currency\n\n\nclass Currency(Asset):\n    """Base Security Type\n\n    Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n    referenced in a derivative transaction\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Cash, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.CURRENCY\n\n\nclass Rate(Asset):\n    """Base Security Type\n\n    Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n    referenced in a derivative transaction\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Rates, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.RATE\n\n\nclass Cash(Asset):\n    """Cash Security Type\n\n    Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n    referenced in a derivative transaction\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Cash, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.CASH\n\n\nclass WeatherIndex(Asset):\n    """Weather Index Type\n\n    Represents an underlying index on a weather derivative, including where the data (e.g. CPD) has been collected,\n    an actual physical reference point (weather station) and various fall back arrangements.\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.WEATHER_INDEX\n\n\nclass CommodityReferencePrice(Asset):\n    """Commodity Reference Price\n\n    Represents an underlying index for commodities in the event that no ISDA Commodity Reference Price exists.\n    Includes base, details, unit, currency and exchange id or publication etc.\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.COMMODITY_REFERENCE_PRICE\n\n\nclass CommodityNaturalGasHub(Asset):\n    """Commodity Natural Gas Hub\n\n    Represents a distinct location in commodity Natural Gas markets\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.COMMODITY_NATURAL_GAS_HUB\n\n\nclass CommodityEUNaturalGasHub(Asset):\n    """Commodity EU Natural Gas Hub\n\n    Represents a virtual/physical hub in EU Natural Gas markets\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.COMMODITY_EU_NATURAL_GAS_HUB\n\n\nclass CryptoCurrency(Asset):\n    """Crypto Currency\n\n    Represents a cryptocurrency\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 asset_class: Union[AssetClass, str],\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.CRYPTO_CURRENCY\n\n\nclass CommodityPowerNode(Asset):\n    """Commodity Power Node\n\n    Represents a distinct location in commodity power markets\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.COMMODITY_POWER_NODE\n\n\nclass CommodityPowerAggregatedNodes(Asset):\n    """Commodity Power Aggregated Nodes\n\n    Represents a group of locations in commodity power markets\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.COMMODITY_POWER_AGGREGATED_NODES\n\n\nclass Commodity(Asset):\n    """Commodity\n\n    Represents a commodity.\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.COMMODITY\n\n\nclass Bond(Asset):\n    """Bond\n\n    Represents a bond.\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 name: str,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, AssetClass.Credit, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.BOND\n\n\nclass FutureMarket(Asset):\n    """Future Market\n\n    Represents a future market\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 asset_class: Union[AssetClass, str],\n                 name: str,\n                 entity: Optional[Dict] = None):\n        if isinstance(asset_class, str):\n            asset_class = get_enum_value(AssetClass, asset_class)\n        Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.FUTURE_MARKET\n\n\nclass FutureContract(Asset):\n    """Future Contract\n\n    Represents a future contract\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 asset_class: Union[AssetClass, str],\n                 name: Union[AssetClass, str],\n                 entity: Optional[Dict] = None):\n        if isinstance(asset_class, str):\n            asset_class = get_enum_value(AssetClass, asset_class)\n        Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.FUTURE_CONTRACT\n\n\nclass Swap(Asset):\n    """Swap Instrument Type\n\n    Represents a Swap Instrument\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 asset_class: Union[AssetClass, str],\n                 name: str,\n                 entity: Optional[Dict] = None):\n        if isinstance(asset_class, str):\n            asset_class = get_enum_value(AssetClass, asset_class)\n\n        Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.SWAP\n\n\nclass Option(Asset):\n    """Option Instrument Type\n\n    Represents an Option Instrument\n\n    """\n\n    def __init__(self,\n                 id_: str,\n                 asset_class: Union[AssetClass, str],\n                 name: str,\n                 entity: Optional[Dict] = None):\n        if isinstance(asset_class, str):\n            asset_class = get_enum_value(AssetClass, asset_class)\n\n        Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n    def get_type(self) -> AssetType:\n        return AssetType.OPTION\n\n\nclass IndexConstituentProvider(metaclass=ABCMeta):\n    def __init__(self, id_: str):\n        self.__id = id_\n\n    def get_constituents(self, as_of: dt.date = None, position_type: PositionType = PositionType.CLOSE) -> Tuple[\n            PositionSet, ...]:\n        """\n        Get asset constituents\n\n        :param as_of: As of date for query\n        :param position_type:\n        :return: dict of identifiers\n\n        **Usage**\n\n        Get index constituents as of a given date. If no date is provided as a parameter, will use the current\n        PricingContext.\n\n        **Examples**\n\n        Get current index constituents (defaults to close):\n\n        >>> import datetime as dt\n        >>>\n        >>> gs = SecurityMaster.get_asset('GSTHHVIP', AssetIdentifier.TICKER)\n        >>> gs.get_constituents()\n\n        Get constituents as of market open on 3Jan18:\n\n        >>> gs.get_constituents(dt.date(2018,1,3), PositionType.OPEN)\n\n        Use PricingContext to determine as of date:\n\n        >>> with PricingContext(dt.date(2018,1,1)) as ctx:\n        >>>     gs.get_constituents()\n\n        **See also**\n\n        :class:`AssetIdentifier`\n        :func:`get_asset`\n\n        """\n        if not as_of:\n            as_of = PricingContext.current.pricing_date\n\n            if isinstance(as_of, dt.datetime):\n                as_of = as_of.date()\n\n        return GsAssetApi.get_asset_positions_for_date(self.__id, as_of, position_type.value)\n\n\n
[docs]class Index(Asset, IndexConstituentProvider):\n    """Index Asset\n\n    Index which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets\n    """\n\n
[docs]    def __init__(self,\n                 id_: str,\n                 asset_class: AssetClass,\n                 name: str,\n                 exchange: Optional[str] = None,\n                 currency: Optional[Currency] = None,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, asset_class, name, exchange, currency, entity=entity)\n        IndexConstituentProvider.__init__(self, id_)
\n\n    def get_type(self) -> AssetType:\n        return AssetType.INDEX\n\n    def get_currency(self) -> Optional[Currency]:\n        return self.currency\n\n    def get_return_type(self) -> ReturnType:\n        if self.parameters is None or self.parameters.index_return_type is None:\n            return ReturnType.TOTAL_RETURN\n\n        return ReturnType(self.parameters.index_return_type)
\n\n\nclass ETF(Asset, IndexConstituentProvider):\n    """ETF Asset\n\n    ETF which tracks an evolving portfolio of securities, and can be traded on exchange\n    """\n\n    def __init__(self,\n                 id_: str,\n                 asset_class: AssetClass,\n                 name: str,\n                 exchange: Optional[str] = None,\n                 currency: Optional[Currency] = None,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, asset_class, name, exchange, currency, entity=entity)\n        IndexConstituentProvider.__init__(self, id_)\n\n    def get_type(self) -> AssetType:\n        return AssetType.ETF\n\n    def get_currency(self) -> Optional[Currency]:\n        return self.currency\n\n\nclass Basket(Asset, IndexConstituentProvider):\n    """Basket Asset\n\n    Basket which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets\n    """\n\n    def __init__(self,\n                 id_: str,\n                 asset_class: AssetClass,\n                 name: str,\n                 currency: Optional[Currency] = None,\n                 entity: Optional[Dict] = None):\n        Asset.__init__(self, id_, asset_class, name, currency=currency, entity=entity)\n        IndexConstituentProvider.__init__(self, id_)\n\n    def get_type(self) -> AssetType:\n        return AssetType.BASKET\n\n    def get_currency(self) -> Optional[Currency]:\n        return self.currency\n\n\n
[docs]class SecurityMaster:\n    """Security Master\n\n     The SecurityMaster class provides an interface to security lookup functions. This allows querying and retrieval of\n     different security types (assets) based on a variety of different identifiers through point-in-time lookups.\n\n     Uses the current PricingContext to provide as of dates if optional arguments are not provided. Will return\n     the relevant asset subclass depending on the type of the security\n\n    **See also**\n\n    :class:`Asset`\n\n     """\n\n    @classmethod\n    def __gs_asset_to_asset(cls, gs_asset: GsAsset) -> Asset:\n        asset_type = gs_asset.type.value\n        asset_entity: Dict = json.loads(json.dumps(gs_asset.as_dict(), cls=JSONEncoder))\n\n        if asset_type in (GsAssetType.Single_Stock.value,):\n            return Stock(gs_asset.id, gs_asset.name, gs_asset.exchange, gs_asset.currency, entity=asset_entity)\n\n        if asset_type in (GsAssetType.ETF.value,):\n            return ETF(gs_asset.id, gs_asset.assetClass, gs_asset.name, gs_asset.exchange, gs_asset.currency,\n                       entity=asset_entity)\n\n        if asset_type in (\n                GsAssetType.Index.value,\n                GsAssetType.Risk_Premia.value,\n                GsAssetType.Access.value,\n                GsAssetType.Multi_Asset_Allocation.value):\n            return Index(gs_asset.id, gs_asset.assetClass, gs_asset.name, gs_asset.exchange, gs_asset.currency,\n                         entity=asset_entity)\n\n        if asset_type in (\n                GsAssetType.Custom_Basket.value,\n                GsAssetType.Research_Basket.value):\n            return Basket(gs_asset.id, gs_asset.assetClass, gs_asset.name, gs_asset.currency, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Future.value,):\n            return Future(gs_asset.id, gs_asset.assetClass, gs_asset.name, gs_asset.currency, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Cross.value,):\n            return Cross(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Currency.value,):\n            return Currency(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Rate.value,):\n            return Rate(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Cash.value,):\n            return Cash(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.WeatherIndex.value,):\n            return WeatherIndex(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Swap.value,):\n            return Swap(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Option.value,):\n            return Option(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.CommodityReferencePrice.value,):\n            return CommodityReferencePrice(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.CommodityNaturalGasHub.value,):\n            return CommodityNaturalGasHub(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.CommodityEUNaturalGasHub.value,):\n            return CommodityEUNaturalGasHub(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.CommodityPowerNode.value,):\n            return CommodityPowerNode(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.CommodityPowerAggregatedNodes.value,):\n            return CommodityPowerAggregatedNodes(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Bond.value,):\n            return Bond(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Commodity.value,):\n            return Commodity(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.FutureMarket.value,):\n            return FutureMarket(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.FutureContract.value,):\n            return FutureContract(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n        if asset_type in (GsAssetType.Crypto_Currency.value,):\n            return CryptoCurrency(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n        raise TypeError(f'unsupported asset type {asset_type}')\n\n    @classmethod\n    def __asset_type_to_gs_types(cls, asset_type: AssetType) -> Tuple[GsAssetType, ...]:\n        asset_map = {\n            AssetType.STOCK: (GsAssetType.Single_Stock,),\n            AssetType.INDEX: (\n                GsAssetType.Index, GsAssetType.Multi_Asset_Allocation, GsAssetType.Risk_Premia, GsAssetType.Access),\n            AssetType.ETF: (GsAssetType.ETF, GsAssetType.ETN),\n            AssetType.BASKET: (GsAssetType.Custom_Basket, GsAssetType.Research_Basket),\n            AssetType.FUTURE: (GsAssetType.Future,),\n            AssetType.RATE: (GsAssetType.Rate,),\n        }\n\n        return asset_map.get(asset_type)\n\n    @classmethod\n    def get_asset(cls,\n                  id_value: str,\n                  id_type: AssetIdentifier,\n                  as_of: Union[dt.date, dt.datetime] = None,\n                  exchange_code: ExchangeCode = None,\n                  asset_type: AssetType = None,\n                  sort_by_rank: bool = False\n                  ) -> Asset:\n        """\n        Get an asset by identifier and identifier type\n\n        :param id_value: identifier value\n        :param id_type: identifier type\n        :param exchange_code: exchange code\n        :param asset_type: asset type\n        :param as_of: As of date for query\n        :param sort_by_rank: whether to sort assets by rank.\n        :return: Asset object or None\n\n        **Usage**\n\n        Get asset object using a specified identifier and identifier type. Where the identifiers are temporal (and can\n        change over time), will use the current MarketContext to evaluate based on the specified date.\n\n        **Examples**\n\n        Get asset by bloomberg id:\n\n        >>> gs = SecurityMaster.get_asset("GS UN", AssetIdentifier.BLOOMBERG_ID)\n\n        Get asset by ticker and exchange code:\n\n        >>> gs = SecurityMaster.get_asset("GS", AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE)\n\n        Get asset by ticker and asset type:\n\n        >>> spx = SecurityMaster.get_asset("SPX", AssetIdentifier.TICKER, asset_type=AssetType.INDEX)\n\n        **See also**\n\n        :class:`AssetIdentifier`\n        :func:`get_many_assets`\n\n        """\n\n        if not as_of:\n            as_of = PricingContext.current.pricing_date\n\n        if isinstance(as_of, dt.date):\n            as_of = dt.datetime.combine(as_of, dt.time(0, 0), pytz.utc)\n\n        if id_type is AssetIdentifier.MARQUEE_ID:\n            gs_asset = GsAssetApi.get_asset(id_value)\n            return cls.__gs_asset_to_asset(gs_asset)\n\n        query = {id_type.value.lower(): id_value}\n\n        if exchange_code is not None:\n            query['exchange'] = exchange_code.value\n\n        if asset_type is not None:\n            query['type'] = [t.value for t in cls.__asset_type_to_gs_types(asset_type)]\n\n        if sort_by_rank:\n            results = GsAssetApi.get_many_assets(as_of=as_of, return_type=dict, order_by=['>rank'], **query)\n            result = get(results, '0')\n\n            if result:\n                result = GsAsset.from_dict(result)\n        else:\n            results = GsAssetApi.get_many_assets(as_of=as_of, **query)\n            result = next(iter(results), None)\n\n        if result:\n            return cls.__gs_asset_to_asset(result)
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Source code for gs_quant.models.epidemiology

\n"""\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nfrom abc import ABC, abstractmethod\nfrom typing import Type\n\nimport numpy as np\nfrom scipy.integrate import odeint\nfrom lmfit import minimize, Parameters, report_fit\n\n"""\nStatistical models for the transmission of infectious diseases\n"""\n\n\nclass CompartmentalModel(ABC):\n\n    @classmethod\n    @abstractmethod\n    def calibrate(cls, xs, t, parameters) -> tuple:\n        """ Should be of form callable(y, t, ...)\n            See https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.odeint.html """\n        raise NotImplementedError\n\n    @classmethod\n    @abstractmethod\n    def get_parameters(cls, *args, **kwargs) -> tuple:\n        raise NotImplementedError\n\n\n
[docs]class SIR(CompartmentalModel):\n    """SIR Model"""\n\n    @classmethod\n    def calibrate(cls, xs: tuple, t: float, parameters: [Parameters, tuple]) -> tuple:\n        """\n        SIR model derivatives at t.\n\n        :param xs: variables that we are solving for, i.e. [S]usceptible, [I]nfected, [R]emoved\n        :param t: time parameter, inactive for this model\n        :param parameters: parameters of the model (not including initial conditions), i.e. beta, gamma, N\n        :return: tuple, the derivatives dSdt, dIdt, dRdt of each of the S, I, R variables\n        """\n        s, i, r = xs\n\n        if isinstance(parameters, Parameters):\n            beta = parameters['beta'].value\n            gamma = parameters['gamma'].value\n            N = parameters['N'].value\n        elif isinstance(parameters, tuple):\n            beta, gamma, N = parameters\n        else:\n            raise ValueError("Cannot recognize parameter input")\n\n        dSdt = - beta * s * i / N\n        dIdt = beta * s * i / N - gamma * i\n        dRdt = gamma * i\n\n        return dSdt, dIdt, dRdt\n\n    @classmethod\n    def get_parameters(cls, S0: float, I0: float, R0: float, N: float, beta: float = 0.2, gamma: float = 0.1,\n                       beta_max: float = 10, gamma_max: float = 1, S0_fixed: bool = True, S0_max: float = 1e6,\n                       beta_fixed: bool = False, gamma_fixed: bool = False, R0_fixed: bool = True, R0_max: float = 1e6,\n                       I0_fixed: bool = True, I0_max: float = 1e6)\\\n            -> tuple:\n        """\n        Produce a set of parameters for the SIR model.\n\n        :param S0: initial number of susceptible in the population\n        :param I0: initial number of infected in the population, usually set to 1\n        :param R0: initial number of recovered/removed in the population, usually set to 0\n        :param N: size of the population\n        :param beta: transmission rate parameter\n        :param gamma: recovery rate parameter\n        :param beta_max: maximum value to consider for beta during parameter fitting\n        :param gamma_max: maximum value of gamma to consider during parameter fitting\n        :param S0_fixed: whether to keep S0 fixed during fitting\n        :param S0_max: maximum value of S0 to consider during parameter fitting\n        :param R0_fixed: whether to keep R0 fixed during fitting\n        :param R0_max: maximum value of R0 to consider during parameter fitting\n        :param I0_fixed: whether to keep I0 fixed during fitting\n        :param I0_max: maximum value of I0 to consider during parameter fitting\n        :return: tuple[Parameters, list]: (parameters, a list of the names of the variables for initial conditions)\n        """\n        parameters = Parameters()\n        parameters.add('N', value=N, min=0, max=N, vary=False)\n        parameters.add('S0', value=S0, min=0, max=S0_max, vary=not S0_fixed)\n        parameters.add('I0', value=I0, min=0, max=I0_max, vary=not I0_fixed)\n        parameters.add('R0', value=R0, min=0, max=R0_max, vary=not R0_fixed)\n        parameters.add('beta', value=beta, min=0, max=beta_max, vary=not beta_fixed)\n        parameters.add('gamma', value=gamma, min=0, max=gamma_max, vary=not gamma_fixed)\n        initial_conditions = ['S0', 'I0', 'R0']\n\n        return parameters, initial_conditions
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[docs]class SEIR(CompartmentalModel):\n    """SEIR Model"""\n\n    @classmethod\n    def calibrate(cls, xs: tuple, t: float, parameters: [Parameters, tuple]) -> tuple:\n        """\n        SEIR model derivatives at t.\n\n        :param xs: variables that we are solving for, i.e. [S]usceptible, [E]xposed, [I]nfected, [R]emoved\n        :param t: time parameter, inactive for this model\n        :param parameters: parameters of the model (not including initial conditions), i.e. beta, gamma, sigma, N\n        :return: tuple, the derivatives dSdt, dEdt, dIdt, dRdt of each of the S, E, I, R variables\n        """\n        s, e, i, r = xs\n\n        if isinstance(parameters, Parameters):\n            beta = parameters['beta'].value\n            gamma = parameters['gamma'].value\n            sigma = parameters['sigma'].value\n            N = parameters['N'].value\n        elif isinstance(parameters, tuple):\n            beta, gamma, sigma, N = parameters\n        else:\n            raise ValueError("Cannot recognize parameter input")\n\n        dSdt = -beta * s * i / N\n        dEdt = beta * s * i / N - sigma * e\n        dIdt = sigma * e - gamma * i\n        dRdt = gamma * i\n\n        return dSdt, dEdt, dIdt, dRdt\n\n    @classmethod\n    def get_parameters(cls, S0: float, E0: float, I0: float, R0: float, N: float, beta: float = 0.2, gamma: float = 0.1,\n                       sigma: float = 0.2, beta_max: float = 10, gamma_max: float = 1, sigma_max: float = 1,\n                       beta_fixed: bool = False, gamma_fixed: bool = False, sigma_fixed: bool = False,\n                       S0_fixed: bool = True, S0_max: float = 1e6, R0_fixed: bool = True, R0_max: float = 1e6,\n                       I0_fixed: bool = True, I0_max: float = 1e6, E0_fixed: bool = True, E0_max: float = 1e6) -> tuple:\n        """\n        Produce a set of parameters for the SIR model.\n\n        :param S0: initial number of susceptible in the population\n        :param E0: initial number of exposed in the population\n        :param I0: initial number of infected in the population, usually set to 1\n        :param R0: initial number of recovered/removed in the population, usually set to 0\n        :param N: size of the population\n        :param beta: transmission rate parameter\n        :param gamma: recovery rate parameter\n        :param sigma: parameter controlling transition from exposed to infectious\n        :param beta_max: maximum value to consider for beta during parameter fitting\n        :param gamma_max: maximum value of gamma to consider during parameter fitting\n        :param sigma_max: maximum value of gamma to consider during parameter fitting\n        :param S0_fixed: whether to keep S0 fixed during fitting\n        :param S0_max: maximum value of S0 to consider during parameter fitting\n        :param E0_fixed: whether to keep E0 fixed during fitting\n        :param E0_max: maximum value of E0 to consider during parameter fitting\n        :param R0_fixed: whether to keep R0 fixed during fitting\n        :param R0_max: maximum value of R0 to consider during parameter fitting\n        :param I0_fixed: whether to keep I0 fixed during fitting\n        :param I0_max: maximum value of I0 to consider during parameter fitting\n        :return: tuple[Parameters, list]: (parameters, a list of the names of the variables for initial conditions)\n        """\n        parameters = Parameters()\n        parameters.add('N', value=N, min=0, max=N, vary=False)\n        parameters.add('S0', value=S0, min=0, max=S0_max, vary=not S0_fixed)\n        parameters.add('E0', value=E0, min=0, max=E0_max, vary=not E0_fixed)\n        parameters.add('I0', value=I0, min=0, max=I0_max, vary=not I0_fixed)\n        parameters.add('R0', value=R0, min=0, max=R0_max, vary=not R0_fixed)\n        parameters.add('beta', value=beta, min=0, max=beta_max, vary=not beta_fixed)\n        parameters.add('gamma', value=gamma, min=0, max=gamma_max, vary=not gamma_fixed)\n        parameters.add('sigma', value=sigma, min=0, max=sigma_max, vary=not sigma_fixed)\n        initial_conditions = ['S0', 'E0', 'I0', 'R0']\n\n        return parameters, initial_conditions
\n\n\ndef switch(t: float, T: float, eta: float = 0, xi: float = 0.1, nu: float = 0) -> float:\n    """\n    Return a time-dependent factor that decreases exponentially, to scale parameters that are affected at some\n    time point T.\n\n    Ex. quarantine control measures were instated at some point T > t0, where t0 is the first day in available data.\n    After this point, transmission rate was effectively reduced. This is modeled as a time-varying exponential decrease\n    from 1 (no effect) to some fixed coefficient eta (the full effect) (ie. at time t0 it is 1 * beta, and by\n    time T + nu, it is around eta * beta). If quarantine reduces transmission rate by 60%, eta would be 0.4. The\n    paramater xi controls the steepness of this decrease, the nu controls the shift relative to time T at which the\n    decrease peaks (ie. effects from quarantine measures at time T are visible at T+5 so nu=5). If nu=0, steepest\n    point is at time T.\n\n    To disable the 'switch', set eta = 1.0\n\n    :param t: current time step\n    :param T: some fixed time step at which the regime 'switches' - T is relative to t0, which is positioned at 0\n              NOTE: this is not necessarily the steepest point of decrease. That is controlled by nu.\n    :param eta: optional - the proportional reduction in transmission rate after control measures (ie. quarantine)\n    :param xi: optional - the slope of the exponential decrease\n    :param nu: optional - the shift of the exponential decrease\n    :return:\n    """\n    return eta + (1 - eta) / (1 + np.exp(xi * (t - T - nu)))\n\n\nclass SEIRCM(CompartmentalModel):\n    """ SEIR Model from https://www.medrxiv.org/content/10.1101/2020.03.04.20031104v1.full.pdf\n        with cumulative cases (C) and cumulative fatalities (M) """\n\n    @classmethod\n    def calibrate(cls, xs: tuple, t: float, parameters: [Parameters, tuple]) -> tuple:\n        """\n        SEIRCM model derivatives at t.\n\n        :param xs: variables that we are solving for\n                   i.e. [S]usceptible, [E]xposed, [I]nfected, [R]emoved, [C]ases, [M]ortality\n        :param t: time parameter\n        :param parameters: parameters of the model (not including initial conditions)\n                           i.e. beta, gamma, sigma, eta, epsilon\n        :return: the derivatives of each of the S, E, I, R, C, M variables\n        """\n        s, e, i, r, c, m = xs\n\n        if isinstance(parameters, Parameters):\n            beta = parameters['beta'].value  # transmission rate\n            gamma = parameters['gamma'].value  # removal rate\n            sigma = parameters['sigma'].value  # infection rate\n            eta = parameters['eta'].value  # control measure redux\n            epsilon = parameters['sigma'].value  # case fatality rate\n            T_quarantine = parameters['T'].value  # time of quarantine policy, relative to t=0 (first reported case)\n        elif isinstance(parameters, tuple):\n            beta, gamma, sigma, eta, epsilon, T_quarantine = parameters\n        else:\n            raise ValueError("Cannot recognize parameter input")\n\n        # total population\n        N = s + e + i + r\n\n        # if T_quarantine is 0, we are not considering effect of quarantine policy so scale factor is fixed at 1\n        quarantine_factor = switch(t, T_quarantine, eta=eta) if T_quarantine else 1\n\n        dSdt = -(quarantine_factor * beta) * s * i / N  # susceptible -> exposed\n        dEdt = (quarantine_factor * beta) * s * i / N - sigma * e  # exposed -> infected AND recorded cases\n        dIdt = sigma * e - gamma * i  # infected -> removed (recovered AND dead)\n        dRdt = (1 - epsilon) * gamma * i  # recovered (from removed)\n        dCdt = sigma * e  # recorded cases (from infected)\n        dMdt = epsilon * gamma * i  # dead (from removed)\n\n        return dSdt, dEdt, dIdt, dRdt, dCdt, dMdt\n\n    @classmethod\n    def get_parameters(cls,\n                       S0: float, E0: float, I0: float, R0: float, C0: float, M0: float,\n                       T_quarantine: float = 0,\n                       beta: float = 0.2, gamma: float = 0.1, sigma: float = 0.2,\n                       eta: float = 0.6, epsilon: float = 0.02,\n                       beta_max: float = 10, gamma_max: float = 1, sigma_max: float = 1,\n                       eta_max: float = 1, epsilon_max: float = 1,\n                       beta_fixed: bool = False, gamma_fixed: bool = False, sigma_fixed: bool = False,\n                       eta_fixed: bool = False, epsilon_fixed: bool = False,\n                       S0_fixed: bool = True, S0_max: float = 1e6,\n                       R0_fixed: bool = True, R0_max: float = 1e6,\n                       I0_fixed: bool = True, I0_max: float = 1e6,\n                       E0_fixed: bool = True, E0_max: float = 1e6,\n                       C0_fixed: bool = True, C0_max: float = 1e6,\n                       M0_fixed: bool = True, M0_max: float = 1e6) -> tuple:\n        """\n        Produce a set of parameters for the SIERCM model.\n\n        :param S0: initial number of susceptible in the population\n        :param E0: initial number of exposed in the population\n        :param I0: initial number of infected in the population, usually set to 1\n        :param R0: initial number of recovered/removed in the population, usually set to 0\n        :param C0: initial number of cumulative infected cases\n        :param M0: initial number of cumulative fatalities\n        :param T_quarantine: relative time at which quarantine policy goes in effect. If 0, not used.\n        :param beta: transmission rate parameter\n        :param gamma: recovery rate parameter\n        :param sigma: parameter controlling transition from exposed to infectious\n        :param eta: parameter controlling proportion by which quarantine measure reduces rate of transmission\n        :param epsilon: parameter controlling rate of death\n        :param beta_max: maximum value to consider for beta during parameter fitting\n        :param gamma_max: maximum value of gamma to consider during parameter fitting\n        :param sigma_max: maximum value to consider for sigma during parameter fitting\n        :param eta_max: maximum value to consider for eta during parameter fitting\n        :param epsilon_max: maximum value to consider for epsilon during parameter fitting\n        :param beta_fixed: whether to keep beta fixed during fitting\n        :param gamma_fixed: whether to keep gamma fixed during fitting\n        :param sigma_fixed: whether to keep sigma fixed during fitting\n        :param eta_fixed: whether to keep eta fixed during fitting\n        :param epsilon_fixed: whether to keep epsilon fixed during fitting\n        :param S0_fixed: whether to keep S0 fixed during fitting\n        :param S0_max: maximum value of S0 to consider during parameter fitting\n        :param E0_fixed: whether to keep E0 fixed during fitting\n        :param E0_max: maximum value of E0 to consider during parameter fitting\n        :param R0_fixed: whether to keep R0 fixed during fitting\n        :param R0_max: maximum value of R0 to consider during parameter fitting\n        :param I0_fixed: whether to keep I0 fixed during fitting\n        :param I0_max: maximum value of I0 to consider during parameter fitting\n        :param C0_fixed: whether to keep C0 fixed during fitting\n        :param C0_max: maximum value of C0 to consider during parameter fitting\n        :param M0_fixed: whether to keep M0 fixed during fitting\n        :param M0_max: maximum value of M0 to consider during parameter fitting\n        :return: tuple[Parameters, list]: (parameters, a list of the names of the variables for initial conditions)\n        """\n        parameters = Parameters()\n        parameters.add('T', value=T_quarantine, min=0, max=T_quarantine, vary=False)\n        parameters.add('S0', value=S0, min=0, max=S0_max, vary=not S0_fixed)\n        parameters.add('E0', value=E0, min=0, max=E0_max, vary=not E0_fixed)\n        parameters.add('I0', value=I0, min=0, max=I0_max, vary=not I0_fixed)\n        parameters.add('R0', value=R0, min=0, max=R0_max, vary=not R0_fixed)\n        parameters.add('C0', value=C0, min=0, max=C0_max, vary=not C0_fixed)\n        parameters.add('M0', value=M0, min=0, max=M0_max, vary=not M0_fixed)\n        parameters.add('beta', value=beta, min=0, max=beta_max, vary=not beta_fixed)\n        parameters.add('gamma', value=gamma, min=0, max=gamma_max, vary=not gamma_fixed)\n        parameters.add('sigma', value=sigma, min=0, max=sigma_max, vary=not sigma_fixed)\n        parameters.add('eta', value=eta, min=0, max=eta_max, vary=not eta_fixed)\n        parameters.add('epsilon', value=epsilon, min=0, max=epsilon_max, vary=not epsilon_fixed)\n\n        initial_conditions = ['S0', 'E0', 'I0', 'R0', 'C0', 'M0']\n\n        return parameters, initial_conditions\n\n\nclass SEIRCMAgeStratified(CompartmentalModel):\n    """ Age-structured SEIRCM Model from https://www.medrxiv.org/content/10.1101/2020.03.04.20031104v1.full.pdf """\n\n    @classmethod\n    def calibrate(cls, y: list, t: float, parameters: Parameters) -> list:\n        """\n        SEIR model derivatives at t.\n\n        :param y: variables that we are solving for\n                  i.e. [S]usceptible, [E]xposed, [I]nfected, [R]emoved, [C]ases, [M]ortality\n        :param t: time parameter\n        :param parameters: parameters of the model (not including initial conditions)\n                           i.e. beta, gamma, sigma, eta, epsilon\n        :return: the derivatives dydt of each of the variable in y\n        """\n        beta = parameters['beta'].value  # transmission rate\n        gamma = parameters['gamma'].value  # removal rate\n        sigma = parameters['sigma'].value  # infection rate\n        eta = parameters['eta'].value  # control measure redux\n        T_quarantine = parameters['T'].value  # time of quarantine policy, relative to t=0 (first reported case)\n        K = parameters['K'].value  # number of age groups\n\n        # y is of dimension (6 * K) x 1, where the first K are 'S', next K are 'E', and so on for each age group...\n        assert len(y) == 6 * K, f'Error: SEIRCM states not organized into {K} age groups!'\n        dydt = [0] * len(y)\n\n        def epsilon(k):  # case fatality rate per age group\n            return parameters[f'epsilon_{k}'].value\n\n        N = sum(y[:4 * K])  # sum(S) + sum(E) + sum(I) + sum(R) where sum is over age groups\n        I_total = sum(y[2 * K:3 * K])  # total number of infectious people across age groups\n        s, e, i = lambda k: y[k], lambda k: y[K + k], lambda k: y[2 * K + k]\n\n        # if T_quarantine is 0, we are not considering effect of quarantine policy so scale factor is fixed at 1\n        quarantine_factor = switch(t, T_quarantine, eta=eta) if T_quarantine else 1\n\n        for k in range(K):\n            dydt[k] = -(quarantine_factor * beta) * s(k) * I_total / N  # susceptible -> exposed\n            dydt[K + k] = (quarantine_factor * beta) * s(k) * I_total / N - sigma * e(k)  # exposed -> infected\n            dydt[2 * K + k] = sigma * e(k) - gamma * i(k)  # infected -> removed\n            dydt[3 * K + k] = (1 - epsilon(k)) * gamma * i(k)  # -> cumulative recovered (from removed)\n            dydt[4 * K + k] = sigma * e(k)  # -> cumulative recorded cases (from infected)\n            dydt[5 * K + k] = epsilon(k) * gamma * i(k)  # -> cumulative fatalities (from removed)\n\n        return dydt\n\n    @classmethod\n    def get_parameters(cls,\n                       S0: np.ndarray, E0: np.ndarray, I0: np.ndarray, R0: np.ndarray, C0: np.ndarray, M0: np.ndarray,\n                       K: int, T_quarantine: float = 0,\n                       beta: float = 0.2, gamma: float = 0.1, sigma: float = 0.2,\n                       eta: float = 0.6, epsilon: np.ndarray = None,\n                       beta_max: float = 10, gamma_max: float = 1, sigma_max: float = 1,\n                       eta_max: float = 1, epsilon_max: float = 1,\n                       beta_fixed: bool = False, gamma_fixed: bool = False, sigma_fixed: bool = False,\n                       eta_fixed: bool = False, epsilon_fixed: bool = False,\n                       S0_fixed: bool = True, S0_max: float = 1e6,\n                       R0_fixed: bool = True, R0_max: float = 1e6,\n                       I0_fixed: bool = True, I0_max: float = 1e6,\n                       E0_fixed: bool = True, E0_max: float = 1e6,\n                       C0_fixed: bool = True, C0_max: float = 1e6,\n                       M0_fixed: bool = True, M0_max: float = 1e6) -> tuple:\n        """\n        Produce a set of parameters for the age-stratified SIERCM model.\n\n        :param S0: initial number of susceptible in the population per age group\n        :param E0: initial number of exposed in the population per age group\n        :param I0: initial number of infected in the population per age group\n        :param R0: initial number of recovered/removed in the population  per age group\n        :param C0: initial number of cumulative infected cases per age group\n        :param M0: initial number of cumulative fatalities per age group\n        :param K: number of age groups\n        :param T_quarantine: relative time at which quarantine policy goes in effect. If 0, not used.\n        :param beta: transmission rate parameter\n        :param gamma: removal rate parameter\n        :param sigma: parameter controlling transition from exposed to infectious\n        :param eta: parameter controlling proportion by which quarantine measure reduces rate of transmission\n        :param epsilon: parameter controlling rate of death per age group. Recovery rate == 1 - epsilon\n        :param beta_max: maximum value to consider for beta during parameter fitting\n        :param gamma_max: maximum value of gamma to consider during parameter fitting\n        :param sigma_max: maximum value to consider for sigma during parameter fitting\n        :param eta_max: maximum value to consider for eta during parameter fitting\n        :param epsilon_max: maximum value to consider for epsilon during parameter fitting\n        :param beta_fixed: whether to keep beta fixed during fitting\n        :param gamma_fixed: whether to keep gamma fixed during fitting\n        :param sigma_fixed: whether to keep sigma fixed during fitting\n        :param eta_fixed: whether to keep eta fixed during fitting\n        :param epsilon_fixed: whether to keep epsilon fixed during fitting\n        :param S0_fixed: whether to keep S0 fixed during fitting\n        :param S0_max: maximum value of S0 to consider during parameter fitting\n        :param E0_fixed: whether to keep E0 fixed during fitting\n        :param E0_max: maximum value of E0 to consider during parameter fitting\n        :param R0_fixed: whether to keep R0 fixed during fitting\n        :param R0_max: maximum value of R0 to consider during parameter fitting\n        :param I0_fixed: whether to keep I0 fixed during fitting\n        :param I0_max: maximum value of I0 to consider during parameter fitting\n        :param C0_fixed: whether to keep C0 fixed during fitting\n        :param C0_max: maximum value of C0 to consider during parameter fitting\n        :param M0_fixed: whether to keep M0 fixed during fitting\n        :param M0_max: maximum value of M0 to consider during parameter fitting\n        :return: tuple[Parameters, list]: (parameters, a list of the names of the variables for initial conditions)\n        """\n        parameters = Parameters()\n        parameters.add('K', value=K, min=0, max=K, vary=False)\n        parameters.add('T', value=T_quarantine, min=-1, max=T_quarantine, vary=False)\n        parameters.add('beta', value=beta, min=0, max=beta_max, vary=not beta_fixed)\n        parameters.add('gamma', value=gamma, min=0, max=gamma_max, vary=not gamma_fixed)\n        parameters.add('sigma', value=sigma, min=0, max=sigma_max, vary=not sigma_fixed)\n        parameters.add('eta', value=eta, min=0, max=eta_max, vary=not eta_fixed)\n\n        # add parameters that vary by age group\n        for k in range(K):\n            parameters.add(f'epsilon_{k}', value=epsilon[k], min=0, max=epsilon_max, vary=not epsilon_fixed)\n\n        # add initial state conditions that vary by age group\n        initial_conditions = []\n        for param in ['S0', 'E0', 'I0', 'R0', 'C0', 'M0']:\n            for k in range(K):\n                parameters.add(f'{param}_{k}', value=eval(param)[k], min=0, max=eval(f'{param}_max'),\n                               vary=not eval(f'{param}_fixed'))\n                initial_conditions.append(f'{param}_{k}')\n\n        return parameters, initial_conditions\n\n\n
[docs]class EpidemicModel:\n\n    """Class to perform solutions and parameter-fitting of epidemic models"""\n\n
[docs]    def __init__(self, model: Type[CompartmentalModel], parameters: tuple = None, data: np.array = None,\n                 initial_conditions: list = None, fit_method: str = 'leastsq', error: callable = None,\n                 fit_period: float = None):\n        """\n        A class to standardize fitting and solving epidemiological models.\n\n        :param model: the model to use, currently a class in the form of SIR, SEIR above\n        :param parameters: tuple, parameters to use for the model, defaults to the output of [model].get_parameters\n        :param data: np.array, data that can be used to calibrate the model\n        :param initial_conditions: list, initial conditions for the model\n        :param fit_method: str, the method to use to minimize the (given) error. Available methods are those in the\n                           lmfit.minimizer.minimize function. Default is Levenberg-Marquardt least squares minimization.\n        :param error: callable, control which residuals (and in what form) to minimize for fitting.\n        :param fit_period: float, how far back to fit the data, defaults to fitting all data\n        """\n        self.model = model\n        self.parameters = parameters\n        self.data = data\n        self.initial_conditions = initial_conditions\n        self.fit_method = fit_method\n        self.error = error\n        self.fit_period = fit_period\n        self.result = None\n        self.fitted_parameters = None
\n\n    def solve(self, time_range: np.ndarray, initial_conditions: [list, tuple], parameters) -> np.ndarray:\n        """\n        Integrate the model ODEs to get a solution.\n\n        :param time_range: the time range to solve for\n        :param initial_conditions: the initial conditions for the solution\n        :param parameters: the parameters for the solution\n        :return:\n        """\n        x = odeint(self.model.calibrate, initial_conditions, time_range, args=(parameters,))\n        x = np.array(x)\n        return x\n\n    def residual(self, parameters: Parameters, time_range: np.arange, data: np.ndarray) -> np.ndarray:\n        """\n        Obtain fit error (to minimize).\n\n        :param parameters: parameters to use (which we are usually minimizing the residual for)\n        :param time_range: time range for solution (over which we obtain the residual)\n        :param data: data to fit the models too (i.e. compute residuals in terms of)\n        :return:\n        """\n        initial_conditions = []\n        for variable in self.initial_conditions:\n            initial_conditions.append(parameters[variable].value)\n\n        # obtain solution given current initial conditions and parameters\n        solution = self.solve(time_range, initial_conditions, parameters)\n\n        # compute residual, using custom error function if it has been passed in\n        residual = solution - data if self.error is None else self.error(solution, data, parameters)\n\n        if self.fit_period is not None:\n            residual = residual[-self.fit_period:]\n\n        return residual.ravel()\n\n    def fit(self, time_range: np.arange = None, parameters: [Parameters, tuple] = None, initial_conditions: list = None,\n            residual=None, verbose: bool = False, data: np.array = None, fit_period: float = None):\n        """\n        Fit the model based on data in the form np.array([X1,...,Xn])\n        """\n        if data is None:\n            if self.data is None:\n                raise ValueError("No data to fit the model on!")\n            data = self.data\n        if initial_conditions is not None:\n            self.initial_conditions = initial_conditions\n        if self.initial_conditions is None:\n            raise ValueError("No initial conditions to fit the model with!")\n        if parameters is None:\n            if self.parameters is None:\n                raise ValueError("No parameters to fit the model with!")\n            parameters = self.parameters\n        if time_range is None:\n            time_range = np.arange(data.shape[0])\n        if fit_period is not None:\n            self.fit_period = fit_period\n        if residual is None:\n            residual = self.residual\n\n        result = minimize(residual, parameters, args=(time_range, data), method=self.fit_method)\n        self.result = result\n        self.fitted_parameters = result.params.valuesdict()\n\n        if verbose:\n            report_fit(result)\n\n        return result
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Source code for gs_quant.risk.core

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nfrom abc import ABCMeta, abstractmethod\nfrom concurrent.futures import Future\nfrom copy import copy\nimport itertools\nimport datetime as dt\nfrom typing import Iterable, Optional, Tuple, Union, Any, Callable\n\nimport pandas as pd\n\nfrom .measures import RiskMeasure\nfrom gs_quant.base import RiskKey\nfrom gs_quant.datetime import point_sort_order\n\n__column_sort_fns = {\n    'label1': point_sort_order,\n    'mkt_point': point_sort_order,\n    'point': point_sort_order\n}\n__risk_columns = ('date', 'time', 'mkt_type', 'mkt_asset', 'mkt_class', 'mkt_point')\n\n\nclass ResultInfo(metaclass=ABCMeta):\n\n    def __init__(\n            self,\n            risk_key: RiskKey,\n            unit: Optional[dict] = None,\n            error: Optional[Union[str, dict]] = None\n    ):\n        self.__risk_key = risk_key\n        self.__unit = unit\n        self.__error = error\n\n    @property\n    @abstractmethod\n    def raw_value(self):\n        ...\n\n    @property\n    def risk_key(self) -> RiskKey:\n        return self.__risk_key\n\n    @property\n    def unit(self) -> dict:\n        """The units of this result"""\n        return self.__unit\n\n    @property\n    def error(self) -> Union[str, dict]:\n        """Any error associated with this result"""\n        return self.__error\n\n    @staticmethod\n    def composition_info(components: Iterable):\n        dates = []\n        values = []\n        errors = {}\n        risk_key = None\n        unit = None\n\n        for component in components:\n            date = component.risk_key.date\n            risk_key = component.risk_key.ex_date_and_market if risk_key is None else risk_key\n\n            if isinstance(component, (ErrorValue, Exception)):\n                errors[date] = component\n            else:\n                values.append(component.raw_value)\n                dates.append(date)\n                unit = unit or component.unit\n\n        return dates, values, errors, risk_key, unit\n\n\nclass ErrorValue(ResultInfo):\n\n    def __init__(self, risk_key: RiskKey, error: Union[str, dict]):\n        super().__init__(risk_key, error=error)\n\n    def __repr__(self):\n        return self.error\n\n    @property\n    def raw_value(self):\n        return None\n\n\nclass ScalarWithInfo(ResultInfo, metaclass=ABCMeta):\n\n    def __init__(\n            self,\n            risk_key: RiskKey,\n            value: Union[float, str],\n            unit: Optional[dict] = None,\n            error: Optional[Union[str, dict]] = None):\n        float.__init__(value)\n        ResultInfo.__init__(self, risk_key, unit=unit, error=error)\n\n    @property\n    @abstractmethod\n    def raw_value(self):\n        ...\n\n    @staticmethod\n    def compose(components: Iterable):\n        dates, values, errors, risk_key, unit = ResultInfo.composition_info(components)\n        return SeriesWithInfo(pd.Series(index=pd.DatetimeIndex(dates).date, data=values),\n                              risk_key=risk_key,\n                              unit=unit,\n                              error=errors)\n\n\nclass FloatWithInfo(ScalarWithInfo, float):\n\n    def __new__(cls,\n                risk_key: RiskKey,\n                value: Union[float, str],\n                unit: Optional[str] = None,\n                error: Optional[str] = None):\n        return float.__new__(cls, value)\n\n    @property\n    def raw_value(self) -> float:\n        return float(self)\n\n    def __repr__(self):\n\n        return self.error if self.error else float.__repr__(self)\n\n    def __add__(self, other):\n        if isinstance(other, FloatWithInfo):\n            if self.unit == other.unit:\n                return FloatWithInfo(combine_risk_key(self.risk_key, other.risk_key), self.raw_value + other.raw_value,\n                                     self.unit)\n            else:\n                raise ValueError('FloatWithInfo unit mismatch')\n        return super(FloatWithInfo, self).__add__(other)\n\n    def to_frame(self):\n        return self\n\n\nclass StringWithInfo(ScalarWithInfo, str):\n\n    def __new__(cls,\n                risk_key: RiskKey,\n                value: Union[float, str],\n                unit: Optional[dict] = None,\n                error: Optional[str] = None):\n        return str.__new__(cls, value)\n\n    @property\n    def raw_value(self) -> str:\n        return str(self)\n\n    def __repr__(self):\n        return self.error if self.error else str.__repr__(self)\n\n\nclass SeriesWithInfo(pd.Series, ResultInfo):\n    _internal_names = pd.DataFrame._internal_names + \\\n                      ['_ResultInfo__' + i for i in dir(ResultInfo) if isinstance(getattr(ResultInfo, i), property)]\n    _internal_names_set = set(_internal_names)\n\n    def __init__(\n            self,\n            *args,\n            risk_key: Optional[RiskKey] = None,\n            unit: Optional[dict] = None,\n            error: Optional[Union[str, dict]] = None,\n            **kwargs\n    ):\n        pd.Series.__init__(self, *args, **kwargs)\n        ResultInfo.__init__(self, risk_key, unit=unit, error=error)\n\n    def __repr__(self):\n        if self.error:\n            return pd.Series.__repr__(self) + "\\nErrors: " + str(self.error)\n        return pd.Series.__repr__(self)\n\n    @property\n    def _constructor(self):\n        return SeriesWithInfo\n\n    @property\n    def _constructor_expanddim(self):\n        return DataFrameWithInfo\n\n    @property\n    def raw_value(self) -> pd.Series:\n        return pd.Series(self)\n\n\nclass DataFrameWithInfo(pd.DataFrame, ResultInfo):\n    _internal_names = pd.DataFrame._internal_names + \\\n                      ['_ResultInfo__' + i for i in dir(ResultInfo) if isinstance(getattr(ResultInfo, i), property)]\n    _internal_names_set = set(_internal_names)\n\n    def __init__(\n            self,\n            *args,\n            risk_key: Optional[RiskKey] = None,\n            unit: Optional[dict] = None,\n            error: Optional[Union[str, dict]] = None,\n            **kwargs\n    ):\n        pd.DataFrame.__init__(self, *args, **kwargs)\n        ResultInfo.__init__(self, risk_key, unit=unit, error=error)\n\n    def __repr__(self):\n        if self.error:\n            return pd.DataFrame.__repr__(self) + "\\nErrors: " + str(self.errors)\n        return pd.DataFrame.__repr__(self)\n\n    @property\n    def _constructor(self):\n        return DataFrameWithInfo\n\n    @property\n    def _constructor_sliced(self):\n        return SeriesWithInfo\n\n    @property\n    def raw_value(self) -> pd.DataFrame:\n        return pd.DataFrame(self)\n\n    @staticmethod\n    def compose(components: Iterable):\n        dates, values, errors, risk_key, unit = ResultInfo.composition_info(components)\n        df = pd.concat(v.assign(date=d) for d, v in zip(dates, values)).set_index('date')\n\n        return DataFrameWithInfo(df, risk_key=risk_key, unit=unit, error=errors)\n\n    def to_frame(self):\n        return self\n\n\n
[docs]def aggregate_risk(results: Iterable[Union[DataFrameWithInfo, Future]], threshold: Optional[float] = None) \\\n        -> pd.DataFrame:\n    """\n    Combine the results of multiple InstrumentBase.calc() calls, into a single result\n\n    :param results: An iterable of Dataframes and/or Futures (returned by InstrumentBase.calc())\n    :param threshold: exclude values whose absolute value falls below this threshold\n    :return: A Dataframe with the aggregated results\n\n    **Examples**\n\n    >>> from gs_quant.instrument import IRCap, IRFloor\n    >>> from gs_quant.markets import PricingContext\n    >>> from gs_quant.risk import IRDelta, IRVega\n    >>>\n    >>> cap = IRCap('5y', 'GBP')\n    >>> floor = IRFloor('5y', 'GBP')\n    >>> instruments = (cap, floor)\n    >>>\n    >>> with PricingContext():\n    >>>     delta_f = [inst.calc(IRDelta) for inst in instruments]\n    >>>     vega_f = [inst.calc(IRVega) for inst in (cap, floor)]\n    >>>\n    >>> delta = aggregate_risk(delta_f, threshold=0.1)\n    >>> vega = aggregate_risk(vega_f)\n\n    delta_f and vega_f are lists of futures, where the result will be a Dataframe\n    delta and vega are Dataframes, representing the merged risk of the individual instruments\n    """\n    dfs = [r.result().raw_value if isinstance(r, Future) else r.raw_value for r in results]\n    result = pd.concat(df.reset_index(level=0) if df.index.name == 'date' else df for df in dfs)\n    result = result.groupby([c for c in result.columns if c != 'value'], as_index=False).sum()\n\n    if threshold is not None:\n        result = result[result.value.abs() > threshold]\n\n    return sort_risk(result)
\n\n\nResultType = Union[None, dict, tuple, DataFrameWithInfo, FloatWithInfo, SeriesWithInfo]\n\n\ndef aggregate_results(results: Iterable[ResultType]) -> ResultType:\n    unit = None\n    risk_key = None\n    results = tuple(results)\n\n    if not len(results):\n        return None\n\n    for result in results:\n        if isinstance(result, Exception):\n            raise Exception\n\n        if result.error:\n            raise ValueError('Cannot aggregate results in error')\n\n        if not isinstance(result, type(results[0])):\n            raise ValueError('Cannot aggregate heterogeneous types')\n\n        if result.unit:\n            if unit and unit != result.unit:\n                raise ValueError('Cannot aggregate results with different units')\n\n            unit = unit or result.unit\n\n        if risk_key and risk_key != result.risk_key:\n            raise ValueError('Cannot aggregate results with different pricing keys')\n\n        risk_key = risk_key or result.risk_key\n\n    inst = next(iter(results))\n    if isinstance(inst, dict):\n        return dict((k, aggregate_results([r[k] for r in results])) for k in inst.keys())\n    elif isinstance(inst, tuple):\n        return tuple(set(itertools.chain.from_iterable(results)))\n    elif isinstance(inst, FloatWithInfo):\n        return FloatWithInfo(risk_key, sum(results), unit=unit)\n    elif isinstance(inst, SeriesWithInfo):\n        return SeriesWithInfo(sum(results), risk_key=risk_key, unit=unit)\n    elif isinstance(inst, DataFrameWithInfo):\n        return DataFrameWithInfo(aggregate_risk(results), risk_key=risk_key, unit=unit)\n\n\n
[docs]def subtract_risk(left: DataFrameWithInfo, right: DataFrameWithInfo) -> pd.DataFrame:\n    """Subtract bucketed risk. Dimensions must be identical\n\n    :param left: Results to substract from\n    :param right: Results to substract\n\n    **Examples**\n\n    >>> from gs_quant.datetime.date import business_day_offset\n    >>> from gs_quant.instrument IRSwap\n    >>> from gs_quant.markets import PricingContext\n    >>> from gs_quant.risk import IRDelta\n    >>> import datetime as dt\n    >>>\n    >>> ir_swap = IRSwap('Pay', '10y', 'USD')\n    >>> delta_today = ir_swap.calc(IRDelta)\n    >>>\n    >>> with PricingContext(pricing_date=business_day_offset(dt.date.today(), -1, roll='preceding')):\n    >>>     delta_yday_f = ir_swap.calc(IRDelta)\n    >>>\n    >>> delta_diff = subtract_risk(delta_today, delta_yday_f.result())\n    """\n    assert (left.columns.names == right.columns.names)\n    assert ('value' in left.columns.names)\n\n    right_negated = copy(right)\n    right_negated.value *= -1\n\n    return aggregate_risk((left, right_negated))
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[docs]def sort_risk(df: pd.DataFrame, by: Tuple[str, ...] = __risk_columns) -> pd.DataFrame:\n    """\n    Sort bucketed risk\n\n    :param df: Input Dataframe\n    :param by: Columns to sort by\n    :return: A sorted Dataframe\n    """\n    columns = tuple(df.columns)\n    indices = [columns.index(c) if c in columns else -1 for c in by]\n    fns = [__column_sort_fns.get(c) for c in columns]\n\n    def cmp(row) -> tuple:\n        return tuple(\n            (fns[i](row[i]) or 0) if fns[i] else row[i] for i in indices if i != -1)\n\n    data = sorted((row for _, row in df.iterrows()), key=cmp)\n    fields = [f for f in by if f in columns]\n    fields.extend(f for f in columns if f not in fields)\n\n    result = pd.DataFrame.from_records(data, columns=columns)[fields]\n    if 'date' in result:\n        result = result.set_index('date')\n\n    return result
\n\n\ndef combine_risk_key(key_1: RiskKey, key_2: RiskKey) -> RiskKey:\n    """\n    Combine two risk keys (key_1, key_2) into a new RiskKey\n\n    :type key_1: RiskKey\n    :type key_2: RiskKey\n    """\n\n    def get_field_value(field_name: str): getattr(key_1, field_name) \\\n        if getattr(key_1, field_name) == getattr(key_2, field_name) else None\n\n    return RiskKey(get_field_value("provider"), get_field_value("date"), get_field_value("market"),\n                   get_field_value("params"), get_field_value("scenario"), get_field_value("risk_measure"))\n
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Source code for gs_quant.risk.measures

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nfrom typing import Optional, Union\n\nfrom gs_quant.base import Market\nfrom gs_quant.common import AssetClass\nfrom gs_quant.context_base import do_not_serialise\nfrom gs_quant.target.risk import RiskMeasure as __RiskMeasure, RiskMeasureType, RiskMeasureUnit\n\n\nclass RiskMeasure(__RiskMeasure):\n\n    def __repr__(self):\n        return self.name or self.measure_type.name\n\n    @property\n    @do_not_serialise\n    def pricing_context(self):\n        from gs_quant.markets import PricingContext\n        return PricingContext.current\n\n\nclass __RelativeRiskMeasure(RiskMeasure):\n\n    def __init__(self,\n                 to_market: Market,\n                 asset_class: Union[AssetClass, str] = None,\n                 measure_type: Union[RiskMeasureType, str] = None,\n                 unit: Union[RiskMeasureUnit, str] = None,\n                 value: Union[float, str] = None,\n                 name: str = None):\n        super().__init__(asset_class=asset_class, measure_type=measure_type, unit=unit, value=value, name=name)\n        self.__to_market = to_market\n\n    @property\n    @do_not_serialise\n    def pricing_context(self):\n        from gs_quant.markets import PricingContext, RelativeMarket\n        current = PricingContext.current\n        return current.clone(market=RelativeMarket(from_market=current.market, to_market=self.__to_market))\n\n\nclass PnlExplain(__RelativeRiskMeasure):\n\n    """ Pnl Explained """\n\n    def __init__(self, to_market: Market):\n        super().__init__(to_market, measure_type=RiskMeasureType.PnlExplain, name=RiskMeasureType.PnlExplain.value)\n\n\nclass PnlExplainClose(PnlExplain):\n\n    def __init__(self):\n        from gs_quant.markets import CloseMarket\n        super().__init__(CloseMarket())\n\n\nclass PnlExplainLive(PnlExplain):\n\n    def __init__(self):\n        from gs_quant.markets import LiveMarket\n        super().__init__(LiveMarket())\n\n\nclass PnlPredictLive(__RelativeRiskMeasure):\n\n    """ Pnl Predicted """\n\n    def __init__(self):\n        from gs_quant.markets import LiveMarket\n        super().__init__(LiveMarket(), measure_type=RiskMeasureType.PnlPredict, name=RiskMeasureType.PnlPredict.value)\n\n\ndef __risk_measure_with_doc_string(name: str,\n                                   doc: str,\n                                   measure_type: RiskMeasureType,\n                                   asset_class: Optional[AssetClass] = None,\n                                   unit: Optional[RiskMeasureUnit] = None\n                                   ) -> RiskMeasure:\n    measure = RiskMeasure(measure_type=measure_type, asset_class=asset_class, unit=unit, name=name)\n    measure.__doc__ = doc\n    return measure\n\n\nDollarPrice = __risk_measure_with_doc_string('DollarPrice', 'Present value in USD', RiskMeasureType.Dollar_Price)\nPrice = __risk_measure_with_doc_string('Price', 'Present value in local currency', RiskMeasureType.PV)\nForwardPrice = __risk_measure_with_doc_string(\n    'ForwardPrice',\n    'Forward price',\n    RiskMeasureType.Forward_Price,\n    unit=RiskMeasureUnit.BPS)\nBaseCPI = __risk_measure_with_doc_string('BaseCPI', 'Base CPI level', RiskMeasureType.BaseCPI)\nTheta = __risk_measure_with_doc_string('Theta', '1 day Theta', RiskMeasureType.Theta)\nEqDelta = __risk_measure_with_doc_string(\n    'EqDelta',\n    'Equity Delta',\n    RiskMeasureType.Delta,\n    asset_class=AssetClass.Equity)\nEqGamma = __risk_measure_with_doc_string(\n    'EqGamma',\n    'Equity Gamma',\n    RiskMeasureType.Gamma,\n    asset_class=AssetClass.Equity)\nEqVega = __risk_measure_with_doc_string('EqVega', 'Equity Vega', RiskMeasureType.Vega, asset_class=AssetClass.Equity)\nEqSpot = __risk_measure_with_doc_string(\n    'EqSpot',\n    'Equity Spot Level',\n    RiskMeasureType.Spot, asset_class=AssetClass.Equity)\nEqAnnualImpliedVol = __risk_measure_with_doc_string(\n    'EqAnnualImpliedVol',\n    'Equity Annual Implied Volatility (%)',\n    RiskMeasureType.Annual_Implied_Volatility,\n    asset_class=AssetClass.Equity,\n    unit=RiskMeasureUnit.Percent)\nCommodDelta = __risk_measure_with_doc_string(\n    'CommodDelta',\n    'Commodity Delta',\n    RiskMeasureType.Delta,\n    asset_class=AssetClass.Commod)\nCommodTheta = __risk_measure_with_doc_string(\n    'CommodTheta',\n    'Commodity Theta',\n    RiskMeasureType.Theta,\n    asset_class=AssetClass.Commod)\nCommodVega = __risk_measure_with_doc_string(\n    'CommodVega',\n    'Commodity Vega',\n    RiskMeasureType.Vega,\n    asset_class=AssetClass.Commod)\nFairVolStrike = __risk_measure_with_doc_string(\n    'FairVolStrike',\n    'Fair Volatility Strike Value of a Variance Swap',\n    RiskMeasureType.FairVolStrike)\nFairVarStrike = __risk_measure_with_doc_string(\n    'FairVarStrike',\n    'Fair Variance Strike Value of a Variance Swap',\n    RiskMeasureType.FairVarStrike)\nFXDelta = __risk_measure_with_doc_string('FXDelta', 'FX Delta', RiskMeasureType.Delta, asset_class=AssetClass.FX)\nFXGamma = __risk_measure_with_doc_string('FXGamma', 'FX Gamma', RiskMeasureType.Gamma, asset_class=AssetClass.FX)\nFXVega = __risk_measure_with_doc_string('FXVega', 'FX Vega', RiskMeasureType.Vega, asset_class=AssetClass.FX)\nFXSpot = __risk_measure_with_doc_string('FXSpot', 'FX Spot Rate', RiskMeasureType.Spot, asset_class=AssetClass.FX)\nIRBasis = __risk_measure_with_doc_string(\n    'IRBasis',\n    'Interest Rate Basis',\n    RiskMeasureType.Basis,\n    asset_class=AssetClass.Rates)\nInflationDelta = __risk_measure_with_doc_string(\n    'InflationDelta',\n    'Inflation Delta',\n    RiskMeasureType.InflationDelta,\n    asset_class=AssetClass.Rates)\nInflationDeltaParallel = __risk_measure_with_doc_string(\n    'InflationDeltaParallel',\n    'Inflation Parallel Delta',\n    RiskMeasureType.ParallelInflationDelta,\n    asset_class=AssetClass.Rates)\nInflationDeltaParallelLocalCcy = __risk_measure_with_doc_string(\n    'InflationDeltaParallelLocalCcy',\n    'Inflation Parallel Delta (Local Ccy)',\n    RiskMeasureType.ParallelInflationDeltaLocalCcy,\n    asset_class=AssetClass.Rates)\nIRDelta = __risk_measure_with_doc_string(\n    'IRDelta',\n    'Interest Rate Delta',\n    RiskMeasureType.Delta,\n    asset_class=AssetClass.Rates)\nIRDeltaParallel = __risk_measure_with_doc_string(\n    'IRDeltaParallel',\n    'Interest Rate Parallel Delta',\n    RiskMeasureType.ParallelDelta,\n    asset_class=AssetClass.Rates)\nIRDeltaLocalCcy = __risk_measure_with_doc_string(\n    'IRDeltaLocalCcy',\n    'Interest Rate Delta (Local Ccy)',\n    RiskMeasureType.DeltaLocalCcy,\n    asset_class=AssetClass.Rates)\nIRDeltaParallelLocalCcy = __risk_measure_with_doc_string(\n    'IRDeltaParallelLocalCcy',\n    'Interest Rate Parallel Delta (Local Ccy)',\n    RiskMeasureType.ParallelDeltaLocalCcy,\n    asset_class=AssetClass.Rates)\nIRDiscountDeltaParallel = __risk_measure_with_doc_string(\n    'IRDiscountDeltaParallel',\n    'Parallel Discount Delta',\n    RiskMeasureType.ParallelDiscountDelta,\n    asset_class=AssetClass.Rates)\nIRDiscountDeltaParallelLocalCcy = __risk_measure_with_doc_string(\n    'IRDiscountDeltaParallelLocalCcy',\n    'Parallel Discount Delta (Local Ccy)',\n    RiskMeasureType.ParallelDiscountDeltaLocalCcy,\n    asset_class=AssetClass.Rates)\nIRXccyDelta = __risk_measure_with_doc_string(\n    'IRXccyDelta',\n    'Cross-ccy Delta',\n    RiskMeasureType.XccyDelta,\n    asset_class=AssetClass.Rates)\nIRXccyDeltaParallel = __risk_measure_with_doc_string(\n    'IRXccyDeltaParallel',\n    'Cross-ccy Parallel Delta',\n    RiskMeasureType.ParallelXccyDelta,\n    asset_class=AssetClass.Rates)\nIRXccyDeltaParallelLocalCurrency = __risk_measure_with_doc_string(\n    'IRXccyDeltaParallelLocalCurrency',\n    'Cross-ccy Parallel Delta (Local Ccy)',\n    RiskMeasureType.ParallelXccyDeltaLocalCcy,\n    asset_class=AssetClass.Rates)\nIRGammaParallel = __risk_measure_with_doc_string(\n    'IRGammaParallel',\n    'Interest Rate Parallel Gamma',\n    RiskMeasureType.ParallelGamma,\n    asset_class=AssetClass.Rates)\nIRGammaParallelLocalCcy = __risk_measure_with_doc_string(\n    'IRGammaParallelLocalCcy',\n    'Interest Rate Parallel Gamma (Local Ccy)',\n    RiskMeasureType.ParallelGammaLocalCcy,\n    asset_class=AssetClass.Rates)\nIRVega = __risk_measure_with_doc_string(\n    'IRVega',\n    'Interest Rate Vega',\n    RiskMeasureType.Vega,\n    asset_class=AssetClass.Rates)\nIRVegaParallel = __risk_measure_with_doc_string(\n    'IRVegaParallel',\n    'Interest Rate Parallel Vega',\n    RiskMeasureType.ParallelVega,\n    asset_class=AssetClass.Rates)\nIRVegaLocalCcy = __risk_measure_with_doc_string(\n    'IRVegaLocalCcy',\n    'Interest Rate Vega (Local Ccy)',\n    RiskMeasureType.VegaLocalCcy,\n    asset_class=AssetClass.Rates)\nIRVegaParallelLocalCcy = __risk_measure_with_doc_string(\n    'IRVegaParallelLocalCcy',\n    'Interest Rate Parallel Vega (Local Ccy)',\n    RiskMeasureType.ParallelVegaLocalCcy,\n    asset_class=AssetClass.Rates)\nIRAnnualImpliedVol = __risk_measure_with_doc_string(\n    'IRAnnualImpliedVol',\n    'Interest Rate Annual Implied Volatility (%)',\n    RiskMeasureType.Annual_Implied_Volatility,\n    asset_class=AssetClass.Rates,\n    unit=RiskMeasureUnit.Percent)\nIRAnnualATMImpliedVol = __risk_measure_with_doc_string(\n    'IRAnnualATMImpliedVol',\n    'Interest Rate Annual Implied At-The-Money Volatility (%)',\n    RiskMeasureType.Annual_ATMF_Implied_Volatility,\n    asset_class=AssetClass.Rates,\n    unit=RiskMeasureUnit.Percent)\nIRDailyImpliedVol = __risk_measure_with_doc_string(\n    'IRDailyImpliedVol',\n    'Interest Rate Daily Implied Volatility (bps)',\n    RiskMeasureType.Daily_Implied_Volatility,\n    asset_class=AssetClass.Rates,\n    unit=RiskMeasureUnit.BPS)\nIRSpotRate = __risk_measure_with_doc_string(\n    'IRSpotRate',\n    'At-The-Money Spot Rate (%)',\n    RiskMeasureType.Spot_Rate,\n    asset_class=AssetClass.Rates,\n    unit=RiskMeasureUnit.Percent)\nIRFwdRate = __risk_measure_with_doc_string(\n    'IRFwdRate',\n    'Par Rate (%)',\n    RiskMeasureType.Forward_Rate,\n    asset_class=AssetClass.Rates,\n    unit=RiskMeasureUnit.Percent)\nCRIFIRCurve = __risk_measure_with_doc_string(\n    'CRIFIRCurve',\n    'CRIF IR Curve',\n    RiskMeasureType.CRIF_IRCurve)\nResolvedInstrumentValues = __risk_measure_with_doc_string(\n    'ResolvedInstrumentBaseValues',\n    'Resolved InstrumentBase Values',\n    RiskMeasureType.Resolved_Instrument_Values\n)\nDescription = __risk_measure_with_doc_string(\n    'Description',\n    'Description',\n    RiskMeasureType.Description\n)\nCashflows = __risk_measure_with_doc_string(\n    'Cashflows',\n    'Cashflows',\n    RiskMeasureType.Cashflows\n)\nMarketDataAssets = __risk_measure_with_doc_string(\n    'MarketDataAssets',\n    'MarketDataAssets',\n    RiskMeasureType.Market_Data_Assets\n)\nMarketData = __risk_measure_with_doc_string(\n    'Market Data',\n    'Market Data map of coordinates and values',\n    RiskMeasureType.Market_Data\n)\n
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Source code for gs_quant.target.common

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\n\nimport datetime\nfrom typing import Tuple, Union\nfrom enum import Enum\nfrom gs_quant.base import Base, EnumBase, InstrumentBase, Priceable, Scenario, camel_case_translate, get_enum_value\n\n\n
[docs]class AssetClass(EnumBase, Enum):    \n    \n    """Asset classification of security. Assets are classified into broad groups which\n       exhibit similar characteristics and behave in a consistent way under\n       different market conditions"""\n\n    Cash = 'Cash'\n    Commod = 'Commod'\n    Credit = 'Credit'\n    Cross_Asset = 'Cross Asset'\n    Econ = 'Econ'\n    Equity = 'Equity'\n    Fund = 'Fund'\n    FX = 'FX'\n    Mortgage = 'Mortgage'\n    Rates = 'Rates'\n    Loan = 'Loan'\n    Social = 'Social'\n    Cryptocurrency = 'Cryptocurrency'\n    \n    def __repr__(self):\n        return self.value
\n\n\nclass AssetType(EnumBase, Enum):    \n    \n    """Asset type differentiates the product categorization or contract type"""\n\n    Access = 'Access'\n    Any = 'Any'\n    AveragePriceOption = 'AveragePriceOption'\n    Basis = 'Basis'\n    BasisSwap = 'BasisSwap'\n    Benchmark = 'Benchmark'\n    Benchmark_Rate = 'Benchmark Rate'\n    Binary = 'Binary'\n    Bond = 'Bond'\n    BondFuture = 'BondFuture'\n    BondFutureOption = 'BondFutureOption'\n    BondOption = 'BondOption'\n    Calendar_Spread = 'Calendar Spread'\n    Cap = 'Cap'\n    Cash = 'Cash'\n    Certificate = 'Certificate'\n    CD = 'CD'\n    Cliquet = 'Cliquet'\n    CMSOption = 'CMSOption'\n    CMSOptionStrip = 'CMSOptionStrip'\n    CMSSpreadOption = 'CMSSpreadOption'\n    CMSSpreadOptionStrip = 'CMSSpreadOptionStrip'\n    Commodity = 'Commodity'\n    CommodityReferencePrice = 'CommodityReferencePrice'\n    CommodVarianceSwap = 'CommodVarianceSwap'\n    CommodityPowerNode = 'CommodityPowerNode'\n    CommodityPowerAggregatedNodes = 'CommodityPowerAggregatedNodes'\n    CommodityNaturalGasHub = 'CommodityNaturalGasHub'\n    CommodityEUNaturalGasHub = 'CommodityEUNaturalGasHub'\n    Company = 'Company'\n    Convertible = 'Convertible'\n    Credit_Basket = 'Credit Basket'\n    Cross = 'Cross'\n    Crypto_Currency = 'Crypto Currency'\n    CSL = 'CSL'\n    Currency = 'Currency'\n    Custom_Basket = 'Custom Basket'\n    Default_Swap = 'Default Swap'\n    Economic = 'Economic'\n    Endowment = 'Endowment'\n    Equity_Basket = 'Equity Basket'\n    ETF = 'ETF'\n    ETN = 'ETN'\n    Event = 'Event'\n    FRA = 'FRA'\n    Fixing = 'Fixing'\n    FixedLeg = 'FixedLeg'\n    FloatLeg = 'FloatLeg'\n    Floor = 'Floor'\n    Forward = 'Forward'\n    Fund = 'Fund'\n    Future = 'Future'\n    FutureContract = 'FutureContract'\n    FutureMarket = 'FutureMarket'\n    FutureOption = 'FutureOption'\n    FutureStrategy = 'FutureStrategy'\n    Hedge_Fund = 'Hedge Fund'\n    Index = 'Index'\n    InflationSwap = 'InflationSwap'\n    Inter_Commodity_Spread = 'Inter-Commodity Spread'\n    InvoiceSpread = 'InvoiceSpread'\n    Market_Location = 'Market Location'\n    MLF = 'MLF'\n    Multi_Asset_Allocation = 'Multi-Asset Allocation'\n    MultiCrossBinary = 'MultiCrossBinary'\n    MultiCrossBinaryLeg = 'MultiCrossBinaryLeg'\n    Mutual_Fund = 'Mutual Fund'\n    Note = 'Note'\n    Option = 'Option'\n    OptionLeg = 'OptionLeg'\n    OptionStrategy = 'OptionStrategy'\n    Pension_Fund = 'Pension Fund'\n    Preferred_Stock = 'Preferred Stock'\n    Physical = 'Physical'\n    Precious_Metal = 'Precious Metal'\n    Precious_Metal_Swap = 'Precious Metal Swap'\n    Precious_Metal_RFQ = 'Precious Metal RFQ'\n    Reference_Entity = 'Reference Entity'\n    Research_Basket = 'Research Basket'\n    Rate = 'Rate'\n    Risk_Premia = 'Risk Premia'\n    Roll = 'Roll'\n    Securities_Lending_Loan = 'Securities Lending Loan'\n    Share_Class = 'Share Class'\n    Single_Stock = 'Single Stock'\n    Swap = 'Swap'\n    SwapLeg = 'SwapLeg'\n    SwapStrategy = 'SwapStrategy'\n    Swaption = 'Swaption'\n    Synthetic = 'Synthetic'\n    Systematic_Hedging = 'Systematic Hedging'\n    VarianceSwap = 'VarianceSwap'\n    VolatilitySwap = 'VolatilitySwap'\n    WeatherIndex = 'WeatherIndex'\n    XccySwap = 'XccySwap'\n    XccySwapFixFix = 'XccySwapFixFix'\n    XccySwapFixFlt = 'XccySwapFixFlt'\n    XccySwapMTM = 'XccySwapMTM'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass BasketAction(EnumBase, Enum):    \n    \n    """Indicates what was the action taken on basket - create/edit/rebalance"""\n\n    Create = 'Create'\n    Edit = 'Edit'\n    Rebalance = 'Rebalance'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass BondStrikeType(EnumBase, Enum):    \n    \n    """The type of the bond strike - price, yield etc"""\n\n    Price = 'Price'\n    Yield = 'Yield'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass BusinessDayConvention(EnumBase, Enum):    \n    \n    """Business Day Convention"""\n\n    Following = 'Following'\n    Modified_Following = 'Modified Following'\n    Previous = 'Previous'\n    Unadjusted = 'Unadjusted'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass BuySell(EnumBase, Enum):    \n    \n    """Buy or Sell side of contract"""\n\n    Buy = 'Buy'\n    Sell = 'Sell'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass CommodMeanRule(EnumBase, Enum):    \n    \n    """Commodity mean rule"""\n\n    Do_Not_Remove = 'Do Not Remove'\n    Remove_Calculated = 'Remove Calculated'\n    Remove_Fixed = 'Remove Fixed'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass CommodUnit(EnumBase, Enum):    \n    \n    """A coding scheme value to identify the unit of measure (e.g. Therms) in which the\n       undelryer is denominated."""\n\n    Lot = 'Lot'\n    MegaWattHour = 'MegaWattHour'\n    Metric_Ton = 'Metric Ton'\n    Million_British_Thermal_Units = 'Million British Thermal Units'\n    Oil_Barrel = 'Oil Barrel'\n    Troy_Pound = 'Troy Pound'\n    US_Gallon = 'US Gallon'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass CountryCode(EnumBase, Enum):    \n    \n    """ISO Country code"""\n\n    AU = 'AU'\n    CX = 'CX'\n    CC = 'CC'\n    HM = 'HM'\n    NF = 'NF'\n    NZ = 'NZ'\n    CK = 'CK'\n    NU = 'NU'\n    TK = 'TK'\n    JP = 'JP'\n    JN = 'JN'\n    EU = 'EU'\n    ER = 'ER'\n    EZ = 'EZ'\n    AT = 'AT'\n    BE = 'BE'\n    FI = 'FI'\n    FR = 'FR'\n    GF = 'GF'\n    PF = 'PF'\n    TF = 'TF'\n    GP = 'GP'\n    MQ = 'MQ'\n    YT = 'YT'\n    NC = 'NC'\n    RE = 'RE'\n    SH = 'SH'\n    PM = 'PM'\n    WF = 'WF'\n    DE = 'DE'\n    GE = 'GE'\n    GR = 'GR'\n    IE = 'IE'\n    IT = 'IT'\n    LU = 'LU'\n    NL = 'NL'\n    AW = 'AW'\n    AN = 'AN'\n    PT = 'PT'\n    ES = 'ES'\n    BY = 'BY'\n    CH = 'CH'\n    SE = 'SE'\n    SW = 'SW'\n    DK = 'DK'\n    FO = 'FO'\n    NO = 'NO'\n    BV = 'BV'\n    SJ = 'SJ'\n    LI = 'LI'\n    GB = 'GB'\n    UK = 'UK'\n    AI = 'AI'\n    IO = 'IO'\n    KY = 'KY'\n    FK = 'FK'\n    GI = 'GI'\n    MS = 'MS'\n    PN = 'PN'\n    GS = 'GS'\n    TC = 'TC'\n    VG = 'VG'\n    JE = 'JE'\n    _02 = '02'\n    US = 'US'\n    AS = 'AS'\n    GU = 'GU'\n    MP = 'MP'\n    PR = 'PR'\n    UM = 'UM'\n    VI = 'VI'\n    CA = 'CA'\n    AR = 'AR'\n    BA = 'BA'\n    BD = 'BD'\n    BG = 'BG'\n    BS = 'BS'\n    BM = 'BM'\n    BO = 'BO'\n    BR = 'BR'\n    CL = 'CL'\n    CN = 'CN'\n    CO = 'CO'\n    CR = 'CR'\n    CZ = 'CZ'\n    DO = 'DO'\n    EC = 'EC'\n    EG = 'EG'\n    GA = 'GA'\n    GT = 'GT'\n    HK = 'HK'\n    HR = 'HR'\n    HU = 'HU'\n    IL = 'IL'\n    IM = 'IM'\n    IR = 'IR'\n    IS = 'IS'\n    JO = 'JO'\n    KE = 'KE'\n    KR = 'KR'\n    KZ = 'KZ'\n    LB = 'LB'\n    LK = 'LK'\n    LT = 'LT'\n    MA = 'MA'\n    MH = 'MH'\n    ML = 'ML'\n    MO = 'MO'\n    MT = 'MT'\n    MX = 'MX'\n    MY = 'MY'\n    NI = 'NI'\n    OM = 'OM'\n    PA = 'PA'\n    PD = 'PD'\n    PE = 'PE'\n    PH = 'PH'\n    PK = 'PK'\n    PL = 'PL'\n    QA = 'QA'\n    RO = 'RO'\n    RU = 'RU'\n    SA = 'SA'\n    SG = 'SG'\n    SI = 'SI'\n    SK = 'SK'\n    SV = 'SV'\n    TH = 'TH'\n    TN = 'TN'\n    TP = 'TP'\n    TR = 'TR'\n    TW = 'TW'\n    UA = 'UA'\n    UY = 'UY'\n    VE = 'VE'\n    VN = 'VN'\n    ZA = 'ZA'\n    BH = 'BH'\n    EE = 'EE'\n    GH = 'GH'\n    ME = 'ME'\n    RS = 'RS'\n    ZM = 'ZM'\n    ZW = 'ZW'\n    TT = 'TT'\n    AE = 'AE'\n    KW = 'KW'\n    BB = 'BB'\n    LV = 'LV'\n    GG = 'GG'\n    CY = 'CY'\n    CI = 'CI'\n    MU = 'MU'\n    PY = 'PY'\n    HN = 'HN'\n    BZ = 'BZ'\n    NA = 'NA'\n    FJ = 'FJ'\n    BW = 'BW'\n    DZ = 'DZ'\n    MN = 'MN'\n    SN = 'SN'\n    TZ = 'TZ'\n    AD = 'AD'\n    AG = 'AG'\n    AL = 'AL'\n    AM = 'AM'\n    AO = 'AO'\n    AZ = 'AZ'\n    BF = 'BF'\n    BI = 'BI'\n    BJ = 'BJ'\n    BN = 'BN'\n    BT = 'BT'\n    CD = 'CD'\n    CF = 'CF'\n    CG = 'CG'\n    CM = 'CM'\n    CU = 'CU'\n    CV = 'CV'\n    CS = 'CS'\n    DJ = 'DJ'\n    DM = 'DM'\n    EH = 'EH'\n    ET = 'ET'\n    FM = 'FM'\n    GD = 'GD'\n    GL = 'GL'\n    GM = 'GM'\n    GN = 'GN'\n    GQ = 'GQ'\n    GW = 'GW'\n    GY = 'GY'\n    HT = 'HT'\n    ID = 'ID'\n    IN = 'IN'\n    IQ = 'IQ'\n    JM = 'JM'\n    KG = 'KG'\n    KH = 'KH'\n    KI = 'KI'\n    KM = 'KM'\n    KN = 'KN'\n    KP = 'KP'\n    LA = 'LA'\n    LC = 'LC'\n    LR = 'LR'\n    LS = 'LS'\n    LY = 'LY'\n    MC = 'MC'\n    MD = 'MD'\n    MG = 'MG'\n    MK = 'MK'\n    MM = 'MM'\n    MR = 'MR'\n    MV = 'MV'\n    MW = 'MW'\n    MZ = 'MZ'\n    NE = 'NE'\n    NG = 'NG'\n    NP = 'NP'\n    NR = 'NR'\n    PG = 'PG'\n    PW = 'PW'\n    RW = 'RW'\n    SB = 'SB'\n    SC = 'SC'\n    SD = 'SD'\n    SL = 'SL'\n    SM = 'SM'\n    SO = 'SO'\n    SR = 'SR'\n    ST = 'ST'\n    SY = 'SY'\n    SZ = 'SZ'\n    TD = 'TD'\n    TG = 'TG'\n    TJ = 'TJ'\n    TL = 'TL'\n    TM = 'TM'\n    TO = 'TO'\n    TV = 'TV'\n    UG = 'UG'\n    UZ = 'UZ'\n    VA = 'VA'\n    VC = 'VC'\n    VU = 'VU'\n    WS = 'WS'\n    YE = 'YE'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass CreditOptionStrikeType(EnumBase, Enum):    \n    \n    Spread_Adj = 'Spread Adj'\n    Delta = 'Delta'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass CreditOptionType(EnumBase, Enum):    \n    \n    Payer = 'Payer'\n    Receiver = 'Receiver'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass Currency(EnumBase, Enum):    \n    \n    """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n\n    _ = ''\n    ACU = 'ACU'\n    ADP = 'ADP'\n    AED = 'AED'\n    AFA = 'AFA'\n    ALL = 'ALL'\n    AMD = 'AMD'\n    ANG = 'ANG'\n    AOA = 'AOA'\n    AOK = 'AOK'\n    AON = 'AON'\n    ARA = 'ARA'\n    ARS = 'ARS'\n    ARZ = 'ARZ'\n    ATS = 'ATS'\n    AUD = 'AUD'\n    AUZ = 'AUZ'\n    AZM = 'AZM'\n    B03 = 'B03'\n    BAD = 'BAD'\n    BAK = 'BAK'\n    BAM = 'BAM'\n    BBD = 'BBD'\n    BDN = 'BDN'\n    BDT = 'BDT'\n    BEF = 'BEF'\n    BGL = 'BGL'\n    BGN = 'BGN'\n    BHD = 'BHD'\n    BIF = 'BIF'\n    BMD = 'BMD'\n    BND = 'BND'\n    BR6 = 'BR6'\n    BRE = 'BRE'\n    BRF = 'BRF'\n    BRL = 'BRL'\n    BRR = 'BRR'\n    BSD = 'BSD'\n    BTC = 'BTC'\n    BTN = 'BTN'\n    BTR = 'BTR'\n    BWP = 'BWP'\n    BYR = 'BYR'\n    BZD = 'BZD'\n    C23 = 'C23'\n    CAC = 'CAC'\n    CAD = 'CAD'\n    CAZ = 'CAZ'\n    CCI = 'CCI'\n    CDF = 'CDF'\n    CFA = 'CFA'\n    CHF = 'CHF'\n    CHZ = 'CHZ'\n    CLF = 'CLF'\n    CLP = 'CLP'\n    CLZ = 'CLZ'\n    CNH = 'CNH'\n    CNO = 'CNO'\n    CNY = 'CNY'\n    CNZ = 'CNZ'\n    COP = 'COP'\n    COZ = 'COZ'\n    CPB = 'CPB'\n    CPI = 'CPI'\n    CRC = 'CRC'\n    CUP = 'CUP'\n    CVE = 'CVE'\n    CYP = 'CYP'\n    CZH = 'CZH'\n    CZK = 'CZK'\n    DAX = 'DAX'\n    DEM = 'DEM'\n    DIJ = 'DIJ'\n    DJF = 'DJF'\n    DKK = 'DKK'\n    DOP = 'DOP'\n    DZD = 'DZD'\n    E51 = 'E51'\n    E52 = 'E52'\n    E53 = 'E53'\n    E54 = 'E54'\n    ECI = 'ECI'\n    ECS = 'ECS'\n    ECU = 'ECU'\n    EEK = 'EEK'\n    EF0 = 'EF0'\n    EGP = 'EGP'\n    ESP = 'ESP'\n    ETB = 'ETB'\n    EUR = 'EUR'\n    EUZ = 'EUZ'\n    F06 = 'F06'\n    FED = 'FED'\n    FIM = 'FIM'\n    FJD = 'FJD'\n    FKP = 'FKP'\n    FRF = 'FRF'\n    FT1 = 'FT1'\n    GBP = 'GBP'\n    GBZ = 'GBZ'\n    GEK = 'GEK'\n    GHC = 'GHC'\n    GHS = 'GHS'\n    GHY = 'GHY'\n    GIP = 'GIP'\n    GLR = 'GLR'\n    GMD = 'GMD'\n    GNF = 'GNF'\n    GQE = 'GQE'\n    GRD = 'GRD'\n    GTQ = 'GTQ'\n    GWP = 'GWP'\n    GYD = 'GYD'\n    HKB = 'HKB'\n    HKD = 'HKD'\n    HNL = 'HNL'\n    HRK = 'HRK'\n    HSI = 'HSI'\n    HTG = 'HTG'\n    HUF = 'HUF'\n    IDB = 'IDB'\n    IDO = 'IDO'\n    IDR = 'IDR'\n    IEP = 'IEP'\n    IGP = 'IGP'\n    ILS = 'ILS'\n    INO = 'INO'\n    INP = 'INP'\n    INR = 'INR'\n    IPA = 'IPA'\n    IPX = 'IPX'\n    IQD = 'IQD'\n    IRR = 'IRR'\n    IRS = 'IRS'\n    ISI = 'ISI'\n    ISK = 'ISK'\n    ISO = 'ISO'\n    ITL = 'ITL'\n    J05 = 'J05'\n    JMD = 'JMD'\n    JNI = 'JNI'\n    JOD = 'JOD'\n    JPY = 'JPY'\n    JPZ = 'JPZ'\n    JZ9 = 'JZ9'\n    KES = 'KES'\n    KGS = 'KGS'\n    KHR = 'KHR'\n    KMF = 'KMF'\n    KOR = 'KOR'\n    KPW = 'KPW'\n    KRW = 'KRW'\n    KWD = 'KWD'\n    KYD = 'KYD'\n    KZT = 'KZT'\n    LAK = 'LAK'\n    LBA = 'LBA'\n    LBP = 'LBP'\n    LHY = 'LHY'\n    LKR = 'LKR'\n    LRD = 'LRD'\n    LSL = 'LSL'\n    LSM = 'LSM'\n    LTL = 'LTL'\n    LUF = 'LUF'\n    LVL = 'LVL'\n    LYD = 'LYD'\n    MAD = 'MAD'\n    MDL = 'MDL'\n    MGF = 'MGF'\n    MKD = 'MKD'\n    MMK = 'MMK'\n    MNT = 'MNT'\n    MOP = 'MOP'\n    MRO = 'MRO'\n    MTP = 'MTP'\n    MUR = 'MUR'\n    MVR = 'MVR'\n    MWK = 'MWK'\n    MXB = 'MXB'\n    MXN = 'MXN'\n    MXP = 'MXP'\n    MXW = 'MXW'\n    MXZ = 'MXZ'\n    MYO = 'MYO'\n    MYR = 'MYR'\n    MZM = 'MZM'\n    MZN = 'MZN'\n    NAD = 'NAD'\n    ND3 = 'ND3'\n    NGF = 'NGF'\n    NGI = 'NGI'\n    NGN = 'NGN'\n    NIC = 'NIC'\n    NLG = 'NLG'\n    NOK = 'NOK'\n    NOZ = 'NOZ'\n    NPR = 'NPR'\n    NZD = 'NZD'\n    NZZ = 'NZZ'\n    O08 = 'O08'\n    OMR = 'OMR'\n    PAB = 'PAB'\n    PEI = 'PEI'\n    PEN = 'PEN'\n    PEZ = 'PEZ'\n    PGK = 'PGK'\n    PHP = 'PHP'\n    PKR = 'PKR'\n    PLN = 'PLN'\n    PLZ = 'PLZ'\n    PSI = 'PSI'\n    PTE = 'PTE'\n    PYG = 'PYG'\n    QAR = 'QAR'\n    R2K = 'R2K'\n    ROL = 'ROL'\n    RON = 'RON'\n    RSD = 'RSD'\n    RUB = 'RUB'\n    RUF = 'RUF'\n    RUR = 'RUR'\n    RWF = 'RWF'\n    SAR = 'SAR'\n    SBD = 'SBD'\n    SCR = 'SCR'\n    SDP = 'SDP'\n    SDR = 'SDR'\n    SEK = 'SEK'\n    SET = 'SET'\n    SGD = 'SGD'\n    SGS = 'SGS'\n    SHP = 'SHP'\n    SKK = 'SKK'\n    SLL = 'SLL'\n    SRG = 'SRG'\n    SSI = 'SSI'\n    STD = 'STD'\n    SUR = 'SUR'\n    SVC = 'SVC'\n    SVT = 'SVT'\n    SYP = 'SYP'\n    SZL = 'SZL'\n    T21 = 'T21'\n    T51 = 'T51'\n    T52 = 'T52'\n    T53 = 'T53'\n    T54 = 'T54'\n    T55 = 'T55'\n    T71 = 'T71'\n    TE0 = 'TE0'\n    TED = 'TED'\n    TF9 = 'TF9'\n    THB = 'THB'\n    THO = 'THO'\n    TMM = 'TMM'\n    TND = 'TND'\n    TNT = 'TNT'\n    TOP = 'TOP'\n    TPE = 'TPE'\n    TPX = 'TPX'\n    TRB = 'TRB'\n    TRL = 'TRL'\n    TRY = 'TRY'\n    TRZ = 'TRZ'\n    TTD = 'TTD'\n    TWD = 'TWD'\n    TZS = 'TZS'\n    UAH = 'UAH'\n    UCB = 'UCB'\n    UDI = 'UDI'\n    UFC = 'UFC'\n    UFZ = 'UFZ'\n    UGS = 'UGS'\n    UGX = 'UGX'\n    USB = 'USB'\n    USD = 'USD'\n    UVR = 'UVR'\n    UYP = 'UYP'\n    UYU = 'UYU'\n    VAC = 'VAC'\n    VEB = 'VEB'\n    VEF = 'VEF'\n    VES = 'VES'\n    VND = 'VND'\n    VUV = 'VUV'\n    WST = 'WST'\n    XAF = 'XAF'\n    XAG = 'XAG'\n    XAU = 'XAU'\n    XPD = 'XPD'\n    XPT = 'XPT'\n    XCD = 'XCD'\n    XDR = 'XDR'\n    XEU = 'XEU'\n    XOF = 'XOF'\n    XPF = 'XPF'\n    YDD = 'YDD'\n    YER = 'YER'\n    YUD = 'YUD'\n    YUN = 'YUN'\n    ZAL = 'ZAL'\n    ZAR = 'ZAR'\n    ZAZ = 'ZAZ'\n    ZMK = 'ZMK'\n    ZMW = 'ZMW'\n    ZRN = 'ZRN'\n    ZRZ = 'ZRZ'\n    ZWD = 'ZWD'\n    AUd = 'AUd'\n    BWp = 'BWp'\n    EUr = 'EUr'\n    GBp = 'GBp'\n    ILs = 'ILs'\n    KWd = 'KWd'\n    MWk = 'MWk'\n    SGd = 'SGd'\n    SZl = 'SZl'\n    USd = 'USd'\n    ZAr = 'ZAr'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass CurrencyName(EnumBase, Enum):    \n    \n    """Currency Names"""\n\n    _ = ''\n    United_States_Dollar = 'United States Dollar'\n    Australian_Dollar = 'Australian Dollar'\n    Canadian_Dollar = 'Canadian Dollar'\n    Swiss_Franc = 'Swiss Franc'\n    Yuan_Renminbi_Hong_Kong = 'Yuan Renminbi (Hong Kong)'\n    Czech_Republic_Koruna = 'Czech Republic Koruna'\n    Euro = 'Euro'\n    Pound_Sterling = 'Pound Sterling'\n    Japanese_Yen = 'Japanese Yen'\n    South_Korean_Won = 'South Korean Won'\n    Malasyan_Ringgit = 'Malasyan Ringgit'\n    Norwegian_Krone = 'Norwegian Krone'\n    New_Zealand_Dollar = 'New Zealand Dollar'\n    Polish_Zloty = 'Polish Zloty'\n    Russian_Rouble = 'Russian Rouble'\n    Swedish_Krona = 'Swedish Krona'\n    South_African_Rand = 'South African Rand'\n    Yuan_Renminbi_Onshore = 'Yuan Renminbi (Onshore)'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass DayCountFraction(EnumBase, Enum):    \n    \n    """Day Count Fraction"""\n\n    ACT_OVER_360 = 'ACT/360'\n    ACT_OVER_365_Fixed = 'ACT/365 (Fixed)'\n    ACT_OVER_365_ISDA = 'ACT/365 ISDA'\n    ACT_OVER_ACT_ISDA = 'ACT/ACT ISDA'\n    _30_OVER_360 = '30/360'\n    _30E_OVER_360 = '30E/360'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass Field(EnumBase, Enum):    \n    \n    """Field to be returned"""\n\n    investmentRate = 'investmentRate'\n    startingEmmaLegalEntityId = 'startingEmmaLegalEntityId'\n    mdapiClass = 'mdapiClass'\n    totalNotionalUSD = 'totalNotionalUSD'\n    bidUnadjusted = 'bidUnadjusted'\n    aggressiveFillsPercentage = 'aggressiveFillsPercentage'\n    vehicleType = 'vehicleType'\n    totalFatalitiesByState = 'totalFatalitiesByState'\n    newActive = 'newActive'\n    dailyRisk = 'dailyRisk'\n    energy = 'energy'\n    sunshineDailyForecast = 'sunshineDailyForecast'\n    sentimentScore = 'sentimentScore'\n    _0 = '0'\n    _1 = '1'\n    _2 = '2'\n    _3 = '3'\n    correlation = 'correlation'\n    exposure = 'exposure'\n    size = 'size'\n    _4 = '4'\n    _5 = '5'\n    _6 = '6'\n    _7 = '7'\n    marketDataAsset = 'marketDataAsset'\n    _8 = '8'\n    _9 = '9'\n    buy75cents = 'buy75cents'\n    unadjustedHigh = 'unadjustedHigh'\n    sourceImportance = 'sourceImportance'\n    closingYield = 'closingYield'\n    wind = 'wind'\n    sc16 = 'sc16'\n    sc15 = 'sc15'\n    sc12 = 'sc12'\n    sc11 = 'sc11'\n    primaryVwapInLimitUnrealizedBps = 'primaryVwapInLimitUnrealizedBps'\n    displayName = 'displayName'\n    minutesToTrade100Pct = 'minutesToTrade100Pct'\n    sc14 = 'sc14'\n    cumulativeVolumeInShares = 'cumulativeVolumeInShares'\n    sc13 = 'sc13'\n    newFatalities = 'newFatalities'\n    buy50bps = 'buy50bps'\n    numStaffedBeds = 'numStaffedBeds'\n    upfrontPayment = 'upfrontPayment'\n    arrivalMidRealizedCash = 'arrivalMidRealizedCash'\n    sc10 = 'sc10'\n    sc05 = 'sc05'\n    a = 'a'\n    sc04 = 'sc04'\n    b = 'b'\n    sc07 = 'sc07'\n    c = 'c'\n    yieldToMaturity = 'yieldToMaturity'\n    sc06 = 'sc06'\n    address = 'address'\n    sc01 = 'sc01'\n    leg2PaymentFrequency = 'leg2PaymentFrequency'\n    sc03 = 'sc03'\n    sc02 = 'sc02'\n    geographyName = 'geographyName'\n    borrower = 'borrower'\n    settlePrice = 'settlePrice'\n    performanceContribution = 'performanceContribution'\n    sc09 = 'sc09'\n    mktClass = 'mktClass'\n    sc08 = 'sc08'\n    collateralization = 'collateralization'\n    futureMonthU26 = 'futureMonthU26'\n    futureMonthU25 = 'futureMonthU25'\n    futureMonthU24 = 'futureMonthU24'\n    futureMonthU23 = 'futureMonthU23'\n    futureMonthU22 = 'futureMonthU22'\n    statementId = 'statementId'\n    futureMonthU21 = 'futureMonthU21'\n    modifiedDuration = 'modifiedDuration'\n    shortRatesContribution = 'shortRatesContribution'\n    impliedNormalVolatility = 'impliedNormalVolatility'\n    solarGeneration = 'solarGeneration'\n    mtmPrice = 'mtmPrice'\n    swapSpreadChange = 'swapSpreadChange'\n    realizedArrivalPerformanceUSD = 'realizedArrivalPerformanceUSD'\n    portfolioAssets = 'portfolioAssets'\n    pricingdate = 'pricingdate'\n    tcmCostHorizon3Hour = 'tcmCostHorizon3Hour'\n    exchangeRate = 'exchangeRate'\n    potentialBedCapInc = 'potentialBedCapInc'\n    numberCovered = 'numberCovered'\n    numberOfPositions = 'numberOfPositions'\n    openUnadjusted = 'openUnadjusted'\n    strikeTime = 'strikeTime'\n    askPrice = 'askPrice'\n    eventId = 'eventId'\n    sectors = 'sectors'\n    additionalPriceNotationType = 'additionalPriceNotationType'\n    grossInvestmentQtd = 'grossInvestmentQtd'\n    annualizedRisk = 'annualizedRisk'\n    estimatedHoldingTimeShort = 'estimatedHoldingTimeShort'\n    midcurvePremium = 'midcurvePremium'\n    volumeComposite = 'volumeComposite'\n    sharpeQtd = 'sharpeQtd'\n    estimatedHoldingTimeLong = 'estimatedHoldingTimeLong'\n    external = 'external'\n    trackerName = 'trackerName'\n    sell50cents = 'sell50cents'\n    tradePrice = 'tradePrice'\n    cleared = 'cleared'\n    primeIdNumeric = 'primeIdNumeric'\n    buy8bps = 'buy8bps'\n    totalNotionalLocal = 'totalNotionalLocal'\n    cid = 'cid'\n    totalConfirmedSeniorHome = 'totalConfirmedSeniorHome'\n    ctdFwdPrice = 'ctdFwdPrice'\n    sinkFactor = 'sinkFactor'\n    temperatureForecast = 'temperatureForecast'\n    bidHigh = 'bidHigh'\n    pnlQtd = 'pnlQtd'\n    buy50cents = 'buy50cents'\n    sell4bps = 'sell4bps'\n    receiverDayCountFraction = 'receiverDayCountFraction'\n    auctionClosePercentage = 'auctionClosePercentage'\n    targetPrice = 'targetPrice'\n    bosInBpsDescription = 'bosInBpsDescription'\n    lowPrice = 'lowPrice'\n    adv22DayPct = 'adv22DayPct'\n    matchedMaturitySwapSpread12m = 'matchedMaturitySwapSpread12m'\n    priceRangeInTicksLabel = 'priceRangeInTicksLabel'\n    ticker = 'ticker'\n    notionalUnit = 'notionalUnit'\n    tcmCostHorizon1Day = 'tcmCostHorizon1Day'\n    approval = 'approval'\n    testMeasure = 'testMeasure'\n    optionLockOutPeriod = 'optionLockOutPeriod'\n    executionTime = 'executionTime'\n    sourceValueForecast = 'sourceValueForecast'\n    leg2Spread = 'leg2Spread'\n    shortConvictionLarge = 'shortConvictionLarge'\n    ccgName = 'ccgName'\n    dollarExcessReturn = 'dollarExcessReturn'\n    gsn = 'gsn'\n    tradeEndDate = 'tradeEndDate'\n    receiverRateOption = 'receiverRateOption'\n    gss = 'gss'\n    percentOfMediandv1m = 'percentOfMediandv1m'\n    lendables = 'lendables'\n    sell75cents = 'sell75cents'\n    optionAdjustedSpread = 'optionAdjustedSpread'\n    optionAdjustedSwapSpread = 'optionAdjustedSwapSpread'\n    bosInTicksLabel = 'bosInTicksLabel'\n    positionSourceId = 'positionSourceId'\n    buy1bps = 'buy1bps'\n    buy3point5bps = 'buy3point5bps'\n    gsSustainRegion = 'gsSustainRegion'\n    absoluteReturnWtd = 'absoluteReturnWtd'\n    deploymentId = 'deploymentId'\n    assetParametersSeniority = 'assetParametersSeniority'\n    askSpread = 'askSpread'\n    flow = 'flow'\n    futureMonthH26 = 'futureMonthH26'\n    loanRebate = 'loanRebate'\n    futureMonthH25 = 'futureMonthH25'\n    period = 'period'\n    indexCreateSource = 'indexCreateSource'\n    futureMonthH24 = 'futureMonthH24'\n    futureMonthH23 = 'futureMonthH23'\n    futureMonthH22 = 'futureMonthH22'\n    futureMonthH21 = 'futureMonthH21'\n    nonUsdOis = 'nonUsdOis'\n    realTWIContribution = 'realTWIContribution'\n    mktAsset = 'mktAsset'\n    leg2IndexLocation = 'leg2IndexLocation'\n    twapUnrealizedBps = 'twapUnrealizedBps'\n    lastUpdatedMessage = 'lastUpdatedMessage'\n    loanValue = 'loanValue'\n    optionAdjustedOISSpread = 'optionAdjustedOISSpread'\n    totalReturnPrice = 'totalReturnPrice'\n    weightedPercentInModel = 'weightedPercentInModel'\n    initLoanSpreadRequired = 'initLoanSpreadRequired'\n    electionPeriod = 'electionPeriod'\n    fundingAskPrice = 'fundingAskPrice'\n    historicalBeta = 'historicalBeta'\n    bondRiskPremiumIndex = 'bondRiskPremiumIndex'\n    hitRateYtd = 'hitRateYtd'\n    girGsdeerGsfeer = 'girGsdeerGsfeer'\n    numUnits = 'numUnits'\n    assetParametersReceiverFrequency = 'assetParametersReceiverFrequency'\n    expenseRatioGrossBps = 'expenseRatioGrossBps'\n    relativePayoffWtd = 'relativePayoffWtd'\n    ctdPrice = 'ctdPrice'\n    paceOfRollNow = 'paceOfRollNow'\n    product = 'product'\n    leg2ReturnType = 'leg2ReturnType'\n    agentLenderFee = 'agentLenderFee'\n    disseminationId = 'disseminationId'\n    optionStrikePrice = 'optionStrikePrice'\n    precipitationType = 'precipitationType'\n    lowerBound = 'lowerBound'\n    arrivalMidNormalized = 'arrivalMidNormalized'\n    underlyingAsset2 = 'underlyingAsset2'\n    underlyingAsset1 = 'underlyingAsset1'\n    legalEntity = 'legalEntity'\n    performanceFee = 'performanceFee'\n    orderState = 'orderState'\n    actualDataQuality = 'actualDataQuality'\n    indexRatio = 'indexRatio'\n    queueInLotsLabel = 'queueInLotsLabel'\n    adv10DayPct = 'adv10DayPct'\n    longConvictionMedium = 'longConvictionMedium'\n    relativeHitRateWtd = 'relativeHitRateWtd'\n    dailyTrackingError = 'dailyTrackingError'\n    sell140cents = 'sell140cents'\n    sell10bps = 'sell10bps'\n    aggressiveOffsetFromLast = 'aggressiveOffsetFromLast'\n    longitude = 'longitude'\n    newIcu = 'newIcu'\n    marketCap = 'marketCap'\n    weightedAverageMid = 'weightedAverageMid'\n    clusterRegion = 'clusterRegion'\n    valoren = 'valoren'\n    averageExecutionPrice = 'averageExecutionPrice'\n    proceedsAssetOISSwapSpread1m = 'proceedsAssetOISSwapSpread1m'\n    payoffWtd = 'payoffWtd'\n    basis = 'basis'\n    investmentRateTrend = 'investmentRateTrend'\n    grossInvestmentMtd = 'grossInvestmentMtd'\n    _200 = '200'\n    hedgeId = 'hedgeId'\n    _201 = '201'\n    sharpeMtd = 'sharpeMtd'\n    _202 = '202'\n    _203 = '203'\n    tcmCostHorizon8Day = 'tcmCostHorizon8Day'\n    _204 = '204'\n    residualVariance = 'residualVariance'\n    _205 = '205'\n    restrictInternalDerivedData = 'restrictInternalDerivedData'\n    _206 = '206'\n    _207 = '207'\n    _208 = '208'\n    adv5DayPct = 'adv5DayPct'\n    _209 = '209'\n    midpointFillsPercentage = 'midpointFillsPercentage'\n    openInterest = 'openInterest'\n    turnoverCompositeUnadjusted = 'turnoverCompositeUnadjusted'\n    fwdPoints = 'fwdPoints'\n    relativeReturnWtd = 'relativeReturnWtd'\n    units = 'units'\n    payerRateOption = 'payerRateOption'\n    assetClassificationsRiskCountryName = 'assetClassificationsRiskCountryName'\n    extMktPoint3 = 'extMktPoint3'\n    _210 = '210'\n    _211 = '211'\n    matchedMaturitySwapSpread = 'matchedMaturitySwapSpread'\n    _212 = '212'\n    cityName = 'cityName'\n    _213 = '213'\n    hourlyBucket = 'hourlyBucket'\n    _214 = '214'\n    averageImpliedVolatility = 'averageImpliedVolatility'\n    totalHospitalizedWithSymptoms = 'totalHospitalizedWithSymptoms'\n    _215 = '215'\n    _216 = '216'\n    _217 = '217'\n    daysOpenRealizedCash = 'daysOpenRealizedCash'\n    _218 = '218'\n    _219 = '219'\n    adjustedHighPrice = 'adjustedHighPrice'\n    proceedsAssetOISSwapSpread = 'proceedsAssetOISSwapSpread'\n    extMktPoint1 = 'extMktPoint1'\n    direction = 'direction'\n    extMktPoint2 = 'extMktPoint2'\n    subRegionCode = 'subRegionCode'\n    assetParametersFixedRate = 'assetParametersFixedRate'\n    isEstimatedReturn = 'isEstimatedReturn'\n    valueForecast = 'valueForecast'\n    totalIcu = 'totalIcu'\n    positionSourceType = 'positionSourceType'\n    previousCloseUnrealizedCash = 'previousCloseUnrealizedCash'\n    minimumDenomination = 'minimumDenomination'\n    futureValueNotional = 'futureValueNotional'\n    participationRate = 'participationRate'\n    obfr = 'obfr'\n    _220 = '220'\n    _221 = '221'\n    _222 = '222'\n    buy9point5bps = 'buy9point5bps'\n    _223 = '223'\n    _224 = '224'\n    _225 = '225'\n    optionLockPeriod = 'optionLockPeriod'\n    _226 = '226'\n    esMomentumPercentile = 'esMomentumPercentile'\n    _227 = '227'\n    _228 = '228'\n    advPercentage = 'advPercentage'\n    _229 = '229'\n    leg1AveragingMethod = 'leg1AveragingMethod'\n    turnoverComposite = 'turnoverComposite'\n    forecastDate = 'forecastDate'\n    internalIndexCalcRegion = 'internalIndexCalcRegion'\n    positionType = 'positionType'\n    subAssetClass = 'subAssetClass'\n    shortInterest = 'shortInterest'\n    referencePeriod = 'referencePeriod'\n    adjustedVolume = 'adjustedVolume'\n    ctdFwdYield = 'ctdFwdYield'\n    secDB = 'secDB'\n    memoryUsed = 'memoryUsed'\n    bpeQualityStars = 'bpeQualityStars'\n    _230 = '230'\n    _231 = '231'\n    _232 = '232'\n    ctd = 'ctd'\n    _233 = '233'\n    _234 = '234'\n    _235 = '235'\n    _236 = '236'\n    _237 = '237'\n    _238 = '238'\n    _239 = '239'\n    intendedParticipationRate = 'intendedParticipationRate'\n    leg1PaymentType = 'leg1PaymentType'\n    tradingPnl = 'tradingPnl'\n    collateralValueRequired = 'collateralValueRequired'\n    buy45bps = 'buy45bps'\n    priceToEarningsPositive = 'priceToEarningsPositive'\n    forecast = 'forecast'\n    forecastValue = 'forecastValue'\n    _240 = '240'\n    pnl = 'pnl'\n    _241 = '241'\n    _242 = '242'\n    _243 = '243'\n    volumeInLimit = 'volumeInLimit'\n    _244 = '244'\n    isTerritory = 'isTerritory'\n    _245 = '245'\n    leg2DeliveryPoint = 'leg2DeliveryPoint'\n    _246 = '246'\n    _247 = '247'\n    _248 = '248'\n    _249 = '249'\n    tcmCostHorizon4Day = 'tcmCostHorizon4Day'\n    styles = 'styles'\n    shortName = 'shortName'\n    resetFrequency1 = 'resetFrequency1'\n    buy4bps = 'buy4bps'\n    resetFrequency2 = 'resetFrequency2'\n    otherPriceTerm = 'otherPriceTerm'\n    bidGspread = 'bidGspread'\n    openPrice = 'openPrice'\n    psId = 'psId'\n    hitRateMtd = 'hitRateMtd'\n    fairVolatility = 'fairVolatility'\n    dollarCross = 'dollarCross'\n    portfolioType = 'portfolioType'\n    currency = 'currency'\n    clusterClass = 'clusterClass'\n    sell50bps = 'sell50bps'\n    futureMonthM21 = 'futureMonthM21'\n    bidSize = 'bidSize'\n    arrivalMid = 'arrivalMid'\n    assetParametersExchangeCurrency = 'assetParametersExchangeCurrency'\n    candidateName = 'candidateName'\n    impliedLognormalVolatility = 'impliedLognormalVolatility'\n    vwapInLimitUnrealizedCash = 'vwapInLimitUnrealizedCash'\n    ratingMoodys = 'ratingMoodys'\n    futureMonthM26 = 'futureMonthM26'\n    futureMonthM25 = 'futureMonthM25'\n    futureMonthM24 = 'futureMonthM24'\n    futureMonthM23 = 'futureMonthM23'\n    futureMonthM22 = 'futureMonthM22'\n    flowPct = 'flowPct'\n    source = 'source'\n    assetClassificationsCountryCode = 'assetClassificationsCountryCode'\n    settleDrop = 'settleDrop'\n    dataSetSubCategory = 'dataSetSubCategory'\n    sell9point5bps = 'sell9point5bps'\n    quantityBucket = 'quantityBucket'\n    optionStyleSDR = 'optionStyleSDR'\n    oeName = 'oeName'\n    given = 'given'\n    leg2DayCountConvention = 'leg2DayCountConvention'\n    liquidityScoreSell = 'liquidityScoreSell'\n    delistingDate = 'delistingDate'\n    weight = 'weight'\n    accruedInterest = 'accruedInterest'\n    businessScope = 'businessScope'\n    wtdDegreeDays = 'wtdDegreeDays'\n    absoluteWeight = 'absoluteWeight'\n    measure = 'measure'\n    temperatureHourlyForecast = 'temperatureHourlyForecast'\n    icebergTipRateType = 'icebergTipRateType'\n    sharpeYtd = 'sharpeYtd'\n    windSpeedForecast = 'windSpeedForecast'\n    grossInvestmentYtd = 'grossInvestmentYtd'\n    yieldPrice = 'yieldPrice'\n    leg1TotalNotionalUnit = 'leg1TotalNotionalUnit'\n    issuePrice = 'issuePrice'\n    askHigh = 'askHigh'\n    expectedDataQuality = 'expectedDataQuality'\n    regionName = 'regionName'\n    valueRevised = 'valueRevised'\n    discretionUpperBound = 'discretionUpperBound'\n    adjustedTradePrice = 'adjustedTradePrice'\n    forecastTime = 'forecastTime'\n    isoSubdivisionCodeAlpha2 = 'isoSubdivisionCodeAlpha2'\n    ctdConversionFactor = 'ctdConversionFactor'\n    proceedsAssetSwapSpread = 'proceedsAssetSwapSpread'\n    isADR = 'isADR'\n    issueDate = 'issueDate'\n    serviceId = 'serviceId'\n    yes = 'yes'\n    gScore = 'gScore'\n    marketValue = 'marketValue'\n    entityId = 'entityId'\n    notionalCurrency1 = 'notionalCurrency1'\n    netDebtToEbitda = 'netDebtToEbitda'\n    numUnitsUpper = 'numUnitsUpper'\n    notionalCurrency2 = 'notionalCurrency2'\n    inLimitParticipationRate = 'inLimitParticipationRate'\n    pressureForecast = 'pressureForecast'\n    paid = 'paid'\n    fixedRate = 'fixedRate'\n    short = 'short'\n    time = 'time'\n    buy4point5bps = 'buy4point5bps'\n    sell30cents = 'sell30cents'\n    eventEndDateTime = 'eventEndDateTime'\n    leg1PaymentFrequency = 'leg1PaymentFrequency'\n    cmId = 'cmId'\n    taxonomy = 'taxonomy'\n    buy45cents = 'buy45cents'\n    measures = 'measures'\n    seasonalAdjustment = 'seasonalAdjustment'\n    rankWtd = 'rankWtd'\n    underlyer = 'underlyer'\n    createdTime = 'createdTime'\n    identifier = 'identifier'\n    priceUnit = 'priceUnit'\n    tradeReportRefId = 'tradeReportRefId'\n    subdivisionId = 'subdivisionId'\n    unadjustedLow = 'unadjustedLow'\n    buy160cents = 'buy160cents'\n    portfolioId = 'portfolioId'\n    zSpread = 'zSpread'\n    capFloorAtmFwdRate = 'capFloorAtmFwdRate'\n    esPercentile = 'esPercentile'\n    tdapi = 'tdapi'\n    locationCode = 'locationCode'\n    rcic = 'rcic'\n    nameRaw = 'nameRaw'\n    simonAssetTags = 'simonAssetTags'\n    hitRateQtd = 'hitRateQtd'\n    primaryVolumeInLimit = 'primaryVolumeInLimit'\n    precipitationDailyForecastPercent = 'precipitationDailyForecastPercent'\n    aumEnd = 'aumEnd'\n    premium = 'premium'\n    low = 'low'\n    crossGroup = 'crossGroup'\n    reportRunTime = 'reportRunTime'\n    fiveDayPriceChangeBps = 'fiveDayPriceChangeBps'\n    holdings = 'holdings'\n    precipitationDailyForecast = 'precipitationDailyForecast'\n    priceMethod = 'priceMethod'\n    assetParametersFixedRateFrequency = 'assetParametersFixedRateFrequency'\n    oisXccy = 'oisXccy'\n    daysOpen = 'daysOpen'\n    buy110cents = 'buy110cents'\n    averageSpreadBps = 'averageSpreadBps'\n    buy55cents = 'buy55cents'\n    futureMonthQ26 = 'futureMonthQ26'\n    issueSize = 'issueSize'\n    futureMonthQ25 = 'futureMonthQ25'\n    futureMonthQ24 = 'futureMonthQ24'\n    futureMonthQ23 = 'futureMonthQ23'\n    futureMonthQ22 = 'futureMonthQ22'\n    pendingLoanCount = 'pendingLoanCount'\n    futureMonthQ21 = 'futureMonthQ21'\n    priceSpotStopLossUnit = 'priceSpotStopLossUnit'\n    priceRangeInTicksDescription = 'priceRangeInTicksDescription'\n    tradeVolume = 'tradeVolume'\n    primaryCountryRic = 'primaryCountryRic'\n    optionExpirationFrequency = 'optionExpirationFrequency'\n    isActive = 'isActive'\n    useMachineLearning = 'useMachineLearning'\n    growthScore = 'growthScore'\n    bufferThreshold = 'bufferThreshold'\n    buy120cents = 'buy120cents'\n    matchedMaturitySwapRate = 'matchedMaturitySwapRate'\n    primaryVwap = 'primaryVwap'\n    exchangeTypeId = 'exchangeTypeId'\n    basisSwapRate = 'basisSwapRate'\n    exchangeCode = 'exchangeCode'\n    group = 'group'\n    assetParametersTerminationDate = 'assetParametersTerminationDate'\n    estimatedSpread = 'estimatedSpread'\n    yieldChangeOnDay = 'yieldChangeOnDay'\n    created = 'created'\n    autoTags = 'autoTags'\n    tcmCost = 'tcmCost'\n    sustainJapan = 'sustainJapan'\n    historyStartDate = 'historyStartDate'\n    bidSpread = 'bidSpread'\n    percentageComplete = 'percentageComplete'\n    hedgeTrackingError = 'hedgeTrackingError'\n    windSpeedType = 'windSpeedType'\n    strikePrice = 'strikePrice'\n    parAssetSwapSpread12m = 'parAssetSwapSpread12m'\n    tradeReportId = 'tradeReportId'\n    adjustedOpenPrice = 'adjustedOpenPrice'\n    countryId = 'countryId'\n    point = 'point'\n    pnlMtd = 'pnlMtd'\n    totalReturns = 'totalReturns'\n    lender = 'lender'\n    annReturn1Year = 'annReturn1Year'\n    ctdFwdDv01 = 'ctdFwdDv01'\n    effYield7Day = 'effYield7Day'\n    meetingDate = 'meetingDate'\n    calendarSpreadMispricing = 'calendarSpreadMispricing'\n    buy140cents = 'buy140cents'\n    priceNotation2Type = 'priceNotation2Type'\n    fundFocus = 'fundFocus'\n    relativeStrike = 'relativeStrike'\n    flagship = 'flagship'\n    additionalPriceNotation = 'additionalPriceNotation'\n    factorCategory = 'factorCategory'\n    equityDelta = 'equityDelta'\n    grossWeight = 'grossWeight'\n    listed = 'listed'\n    sell7bps = 'sell7bps'\n    earningsRecordType = 'earningsRecordType'\n    mean = 'mean'\n    askYield = 'askYield'\n    shockStyle = 'shockStyle'\n    methodology = 'methodology'\n    buy25cents = 'buy25cents'\n    amountOutstanding = 'amountOutstanding'\n    marketPnl = 'marketPnl'\n    sustainAsiaExJapan = 'sustainAsiaExJapan'\n    sell6point5bps = 'sell6point5bps'\n    neighbourAssetId = 'neighbourAssetId'\n    countIdeasYtd = 'countIdeasYtd'\n    simonIntlAssetTags = 'simonIntlAssetTags'\n    path = 'path'\n    vwapUnrealizedCash = 'vwapUnrealizedCash'\n    payoffMtd = 'payoffMtd'\n    bosInBpsLabel = 'bosInBpsLabel'\n    bosInBps = 'bosInBps'\n    pointClass = 'pointClass'\n    fxSpot = 'fxSpot'\n    restrictNamedIndividuals = 'restrictNamedIndividuals'\n    hedgeVolatility = 'hedgeVolatility'\n    tags = 'tags'\n    population = 'population'\n    underlyingAssetId = 'underlyingAssetId'\n    realLongRatesContribution = 'realLongRatesContribution'\n    pctprices_return = 'pctprices_return'\n    domain = 'domain'\n    buy80cents = 'buy80cents'\n    forwardTenor = 'forwardTenor'\n    averagePrice = 'averagePrice'\n    targetPriceRealizedBps = 'targetPriceRealizedBps'\n    leg2FixedRate = 'leg2FixedRate'\n    shareClassAssets = 'shareClassAssets'\n    annuity = 'annuity'\n    totalCount = 'totalCount'\n    quoteType = 'quoteType'\n    corporateActionStatus = 'corporateActionStatus'\n    peggedTipSize = 'peggedTipSize'\n    uid = 'uid'\n    esPolicyPercentile = 'esPolicyPercentile'\n    usdOis = 'usdOis'\n    term = 'term'\n    restrictInternalGsNtk = 'restrictInternalGsNtk'\n    tcmCostParticipationRate100Pct = 'tcmCostParticipationRate100Pct'\n    relativeUniverse = 'relativeUniverse'\n    measureIdx = 'measureIdx'\n    fredId = 'fredId'\n    twiContribution = 'twiContribution'\n    cloudCoverType = 'cloudCoverType'\n    delisted = 'delisted'\n    regionalFocus = 'regionalFocus'\n    volumePrimary = 'volumePrimary'\n    assetParametersPayerDesignatedMaturity = 'assetParametersPayerDesignatedMaturity'\n    buy30cents = 'buy30cents'\n    fundingBidPrice = 'fundingBidPrice'\n    series = 'series'\n    sell3bps = 'sell3bps'\n    settlementPrice = 'settlementPrice'\n    quarter = 'quarter'\n    sell18bps = 'sell18bps'\n    assetParametersFloatingRateOption = 'assetParametersFloatingRateOption'\n    realizedVwapPerformanceBps = 'realizedVwapPerformanceBps'\n    voteShare = 'voteShare'\n    servicingCostShortPnl = 'servicingCostShortPnl'\n    totalConfirmed = 'totalConfirmed'\n    economicForecast = 'economicForecast'\n    plotId = 'plotId'\n    clusterDescription = 'clusterDescription'\n    concentrationLimit = 'concentrationLimit'\n    windSpeed = 'windSpeed'\n    observationHour = 'observationHour'\n    signal = 'signal'\n    borrowerId = 'borrowerId'\n    dataProduct = 'dataProduct'\n    buy7point5bps = 'buy7point5bps'\n    limitPrice = 'limitPrice'\n    bmPrimeId = 'bmPrimeId'\n    dataType = 'dataType'\n    count = 'count'\n    conviction = 'conviction'\n    rfqstate = 'rfqstate'\n    benchmarkMaturity = 'benchmarkMaturity'\n    grossFlowNormalized = 'grossFlowNormalized'\n    buy14bps = 'buy14bps'\n    factorId = 'factorId'\n    futureMonthV26 = 'futureMonthV26'\n    stsFxCurrency = 'stsFxCurrency'\n    futureMonthV25 = 'futureMonthV25'\n    bidChange = 'bidChange'\n    month = 'month'\n    futureMonthV24 = 'futureMonthV24'\n    investmentWtd = 'investmentWtd'\n    futureMonthV23 = 'futureMonthV23'\n    futureMonthV22 = 'futureMonthV22'\n    futureMonthV21 = 'futureMonthV21'\n    expiration = 'expiration'\n    leg2ResetFrequency = 'leg2ResetFrequency'\n    controversyScore = 'controversyScore'\n    proceedAssetSwapSpread = 'proceedAssetSwapSpread'\n    concentrationLevel = 'concentrationLevel'\n    importance = 'importance'\n    assetClassificationsGicsSector = 'assetClassificationsGicsSector'\n    stsAssetName = 'stsAssetName'\n    netExposureClassification = 'netExposureClassification'\n    settlementMethod = 'settlementMethod'\n    receiverDesignatedMaturity = 'receiverDesignatedMaturity'\n    title = 'title'\n    xRefTypeId = 'xRefTypeId'\n    duration = 'duration'\n    load = 'load'\n    alpha = 'alpha'\n    company = 'company'\n    settlementFrequency = 'settlementFrequency'\n    distAvg7Day = 'distAvg7Day'\n    inRiskModel = 'inRiskModel'\n    dailyNetShareholderFlowsPercent = 'dailyNetShareholderFlowsPercent'\n    filledNotionalLocal = 'filledNotionalLocal'\n    everHospitalized = 'everHospitalized'\n    meetingNumber = 'meetingNumber'\n    midGspread = 'midGspread'\n    daysOpenUnrealizedBps = 'daysOpenUnrealizedBps'\n    longLevel = 'longLevel'\n    dataDescription = 'dataDescription'\n    temperatureType = 'temperatureType'\n    gsideid = 'gsideid'\n    repoRate = 'repoRate'\n    division = 'division'\n    cloudCoverDailyForecast = 'cloudCoverDailyForecast'\n    windSpeedDailyForecast = 'windSpeedDailyForecast'\n    assetParametersFloatingRateDayCountFraction = 'assetParametersFloatingRateDayCountFraction'\n    tradeAction = 'tradeAction'\n    action = 'action'\n    ctdYield = 'ctdYield'\n    arrivalHaircutVwapNormalized = 'arrivalHaircutVwapNormalized'\n    priceComponent = 'priceComponent'\n    queueClockTimeDescription = 'queueClockTimeDescription'\n    assetParametersReceiverDayCountFraction = 'assetParametersReceiverDayCountFraction'\n    percentMidExecutionQuantity = 'percentMidExecutionQuantity'\n    deltaStrike = 'deltaStrike'\n    cloudCover = 'cloudCover'\n    assetParametersNotionalCurrency = 'assetParametersNotionalCurrency'\n    buy18bps = 'buy18bps'\n    valueActual = 'valueActual'\n    upi = 'upi'\n    collateralCurrency = 'collateralCurrency'\n    originalCountry = 'originalCountry'\n    field = 'field'\n    geographicFocus = 'geographicFocus'\n    daysOpenRealizedBps = 'daysOpenRealizedBps'\n    fxRiskPremiumIndex = 'fxRiskPremiumIndex'\n    skew = 'skew'\n    status = 'status'\n    notionalCurrency = 'notionalCurrency'\n    sustainEmergingMarkets = 'sustainEmergingMarkets'\n    eventDateTime = 'eventDateTime'\n    leg1DesignatedMaturity = 'leg1DesignatedMaturity'\n    totalPrice = 'totalPrice'\n    onBehalfOf = 'onBehalfOf'\n    testType = 'testType'\n    accruedInterestStandard = 'accruedInterestStandard'\n    futureMonthZ26 = 'futureMonthZ26'\n    futureMonthZ25 = 'futureMonthZ25'\n    ccgCode = 'ccgCode'\n    shortExposure = 'shortExposure'\n    leg1FixedPaymentCurrency = 'leg1FixedPaymentCurrency'\n    arrivalHaircutVwap = 'arrivalHaircutVwap'\n    executionDays = 'executionDays'\n    recallDueDate = 'recallDueDate'\n    forward = 'forward'\n    strike = 'strike'\n    spreadLimit = 'spreadLimit'\n    productScope = 'productScope'\n    assetParametersIssuerType = 'assetParametersIssuerType'\n    currency1 = 'currency1'\n    currency2 = 'currency2'\n    previousCloseRealizedBps = 'previousCloseRealizedBps'\n    daysSinceReported = 'daysSinceReported'\n    eventStatus = 'eventStatus'\n    vwapInLimit = 'vwapInLimit'\n    fwdDuration = 'fwdDuration'\n    _return = 'return'\n    isPairBasket = 'isPairBasket'\n    notionalAmount = 'notionalAmount'\n    payOrReceive = 'payOrReceive'\n    totalSevere = 'totalSevere'\n    unexecutedNotionalUSD = 'unexecutedNotionalUSD'\n    expectedResidualPercentage = 'expectedResidualPercentage'\n    maturityDate = 'maturityDate'\n    traceAdvSell = 'traceAdvSell'\n    eventName = 'eventName'\n    addressLine2 = 'addressLine2'\n    indicationOfOtherPriceAffectingTerm = 'indicationOfOtherPriceAffectingTerm'\n    unadjustedBid = 'unadjustedBid'\n    backtestType = 'backtestType'\n    gsdeer = 'gsdeer'\n    assetParametersIssuer = 'assetParametersIssuer'\n    gRegionalPercentile = 'gRegionalPercentile'\n    coverageChecked = 'coverageChecked'\n    oisXccyExSpike = 'oisXccyExSpike'\n    totalRisk = 'totalRisk'\n    mnav = 'mnav'\n    marketVolume = 'marketVolume'\n    swapAnnuity = 'swapAnnuity'\n    parAssetSwapSpread = 'parAssetSwapSpread'\n    currYield7Day = 'currYield7Day'\n    pressure = 'pressure'\n    shortDescription = 'shortDescription'\n    futureMonthZ24 = 'futureMonthZ24'\n    feed = 'feed'\n    futureMonthZ23 = 'futureMonthZ23'\n    mktPoint1 = 'mktPoint1'\n    futureMonthZ22 = 'futureMonthZ22'\n    futureMonthZ21 = 'futureMonthZ21'\n    futureMonthZ20 = 'futureMonthZ20'\n    assetParametersCommoditySector = 'assetParametersCommoditySector'\n    priceNotation2 = 'priceNotation2'\n    marketBufferThreshold = 'marketBufferThreshold'\n    priceNotation3 = 'priceNotation3'\n    mktPoint3 = 'mktPoint3'\n    mktPoint2 = 'mktPoint2'\n    leg2Type = 'leg2Type'\n    mktPoint4 = 'mktPoint4'\n    degreeDaysType = 'degreeDaysType'\n    sentiment = 'sentiment'\n    investmentIncome = 'investmentIncome'\n    groupType = 'groupType'\n    forwardPointImm = 'forwardPointImm'\n    twap = 'twap'\n    clientShortName = 'clientShortName'\n    groupCategory = 'groupCategory'\n    bidPlusAsk = 'bidPlusAsk'\n    foreignCcyRate = 'foreignCcyRate'\n    electionOdds = 'electionOdds'\n    windDirectionForecast = 'windDirectionForecast'\n    requireAnonClientName = 'requireAnonClientName'\n    pricingLocation = 'pricingLocation'\n    beta = 'beta'\n    lastReturnsEndDate = 'lastReturnsEndDate'\n    upfrontPaymentDate = 'upfrontPaymentDate'\n    sell1point5bps = 'sell1point5bps'\n    longExposure = 'longExposure'\n    sell4point5bps = 'sell4point5bps'\n    tcmCostParticipationRate20Pct = 'tcmCostParticipationRate20Pct'\n    venueType = 'venueType'\n    multiAssetClassSwap = 'multiAssetClassSwap'\n    deltaChangeId = 'deltaChangeId'\n    implementationId = 'implementationId'\n    leg1FixedPayment = 'leg1FixedPayment'\n    esNumericScore = 'esNumericScore'\n    inBenchmark = 'inBenchmark'\n    actionSDR = 'actionSDR'\n    countIdeasQtd = 'countIdeasQtd'\n    knockOutPrice = 'knockOutPrice'\n    ctdAssetId = 'ctdAssetId'\n    buy10bps = 'buy10bps'\n    precipitation = 'precipitation'\n    valueType = 'valueType'\n    betaAdjustedNetExposure = 'betaAdjustedNetExposure'\n    estimatedRodVolume = 'estimatedRodVolume'\n    sell14bps = 'sell14bps'\n    _10 = '10'\n    _11 = '11'\n    _12 = '12'\n    _13 = '13'\n    excessReturnPrice = 'excessReturnPrice'\n    _14 = '14'\n    _15 = '15'\n    _16 = '16'\n    _17 = '17'\n    _18 = '18'\n    _19 = '19'\n    fxPnl = 'fxPnl'\n    assetClassificationsGicsIndustryGroup = 'assetClassificationsGicsIndustryGroup'\n    lendingSecId = 'lendingSecId'\n    dollarDuration = 'dollarDuration'\n    equityTheta = 'equityTheta'\n    dv01 = 'dv01'\n    startDate = 'startDate'\n    _20 = '20'\n    _21 = '21'\n    _22 = '22'\n    _23 = '23'\n    mixedSwap = 'mixedSwap'\n    swaptionPremium = 'swaptionPremium'\n    _24 = '24'\n    _25 = '25'\n    _26 = '26'\n    snowfall = 'snowfall'\n    liquidityBucketBuy = 'liquidityBucketBuy'\n    _27 = '27'\n    mic = 'mic'\n    _28 = '28'\n    latitude = 'latitude'\n    _29 = '29'\n    mid = 'mid'\n    impliedRepo = 'impliedRepo'\n    long = 'long'\n    firstExecutionTime = 'firstExecutionTime'\n    coveredBond = 'coveredBond'\n    regionCode = 'regionCode'\n    buy20cents = 'buy20cents'\n    longWeight = 'longWeight'\n    calculationTime = 'calculationTime'\n    liquidityBucketSell = 'liquidityBucketSell'\n    daysOpenUnrealizedCash = 'daysOpenUnrealizedCash'\n    temperature = 'temperature'\n    averageRealizedVariance = 'averageRealizedVariance'\n    ratingFitch = 'ratingFitch'\n    financialReturnsScore = 'financialReturnsScore'\n    yearOrQuarter = 'yearOrQuarter'\n    _30 = '30'\n    _31 = '31'\n    _32 = '32'\n    nonSymbolDimensions = 'nonSymbolDimensions'\n    _33 = '33'\n    commoditiesForecast = 'commoditiesForecast'\n    _34 = '34'\n    _35 = '35'\n    covid19ByState = 'covid19ByState'\n    _36 = '36'\n    _37 = '37'\n    _38 = '38'\n    _39 = '39'\n    percentageExpectedResidual = 'percentageExpectedResidual'\n    hospitalName = 'hospitalName'\n    buy90cents = 'buy90cents'\n    periodType = 'periodType'\n    assetClassificationsCountryName = 'assetClassificationsCountryName'\n    totalHospitalized = 'totalHospitalized'\n    peggedRefillInterval = 'peggedRefillInterval'\n    fatalitiesProbable = 'fatalitiesProbable'\n    _40 = '40'\n    administrativeRegion = 'administrativeRegion'\n    _41 = '41'\n    open = 'open'\n    _42 = '42'\n    _43 = '43'\n    _44 = '44'\n    _45 = '45'\n    cusip = 'cusip'\n    totalConfirmedByState = 'totalConfirmedByState'\n    _46 = '46'\n    ideaActivityTime = 'ideaActivityTime'\n    _47 = '47'\n    _48 = '48'\n    _49 = '49'\n    windAttribute = 'windAttribute'\n    spreadOptionAtmFwdRate = 'spreadOptionAtmFwdRate'\n    netExposure = 'netExposure'\n    isLegacyPairBasket = 'isLegacyPairBasket'\n    issuerType = 'issuerType'\n    buy70cents = 'buy70cents'\n    strikeReference = 'strikeReference'\n    assetCount = 'assetCount'\n    _50 = '50'\n    _51 = '51'\n    isOrderInLimit = 'isOrderInLimit'\n    _52 = '52'\n    _53 = '53'\n    _54 = '54'\n    fundamentalMetric = 'fundamentalMetric'\n    _55 = '55'\n    _56 = '56'\n    quoteStatusId = 'quoteStatusId'\n    _57 = '57'\n    absoluteValue = 'absoluteValue'\n    closingReport = 'closingReport'\n    _58 = '58'\n    previousTotalConfirmed = 'previousTotalConfirmed'\n    _59 = '59'\n    longTenor = 'longTenor'\n    multiplier = 'multiplier'\n    buy40cents = 'buy40cents'\n    assetCountPriced = 'assetCountPriced'\n    voteDirection = 'voteDirection'\n    impliedRepoRate = 'impliedRepoRate'\n    settlementCurrency = 'settlementCurrency'\n    wtdDegreeDaysForecast = 'wtdDegreeDaysForecast'\n    indicationOfCollateralization = 'indicationOfCollateralization'\n    futureMonthN26 = 'futureMonthN26'\n    _60 = '60'\n    lendingPartnerFee = 'lendingPartnerFee'\n    futureMonthN25 = 'futureMonthN25'\n    _61 = '61'\n    futureMonthN24 = 'futureMonthN24'\n    _62 = '62'\n    primaryVwapRealizedBps = 'primaryVwapRealizedBps'\n    futureMonthN23 = 'futureMonthN23'\n    _63 = '63'\n    futureMonthN22 = 'futureMonthN22'\n    _64 = '64'\n    futureMonthN21 = 'futureMonthN21'\n    _65 = '65'\n    _66 = '66'\n    _67 = '67'\n    _68 = '68'\n    _69 = '69'\n    breakEvenInflation = 'breakEvenInflation'\n    pnlYtd = 'pnlYtd'\n    leg1ReturnType = 'leg1ReturnType'\n    tenor2 = 'tenor2'\n    resetFrequency = 'resetFrequency'\n    assetParametersPayerFrequency = 'assetParametersPayerFrequency'\n    degreeDaysForecast = 'degreeDaysForecast'\n    isManuallySilenced = 'isManuallySilenced'\n    buy3bps = 'buy3bps'\n    lastUpdatedById = 'lastUpdatedById'\n    legalEntityAcct = 'legalEntityAcct'\n    targetShareholderMeetingDate = 'targetShareholderMeetingDate'\n    _70 = '70'\n    _71 = '71'\n    _72 = '72'\n    paceOfRollp0 = 'paceOfRollp0'\n    _73 = '73'\n    _74 = '74'\n    controversyPercentile = 'controversyPercentile'\n    leg1NotionalCurrency = 'leg1NotionalCurrency'\n    _75 = '75'\n    complianceEffectiveTime = 'complianceEffectiveTime'\n    expirationDate = 'expirationDate'\n    _76 = '76'\n    _77 = '77'\n    _78 = '78'\n    _79 = '79'\n    floatingRateDayCountFraction = 'floatingRateDayCountFraction'\n    callLastDate = 'callLastDate'\n    factorReturn = 'factorReturn'\n    passiveFlowRatio = 'passiveFlowRatio'\n    composite5DayAdv = 'composite5DayAdv'\n    marginalContributionToRisk = 'marginalContributionToRisk'\n    closeDate = 'closeDate'\n    temperatureHourForecast = 'temperatureHourForecast'\n    newIdeasWtd = 'newIdeasWtd'\n    assetClassSDR = 'assetClassSDR'\n    yieldToWorst = 'yieldToWorst'\n    _80 = '80'\n    closingPrice = 'closingPrice'\n    _81 = '81'\n    turnoverCompositeAdjusted = 'turnoverCompositeAdjusted'\n    comment = 'comment'\n    sourceSymbol = 'sourceSymbol'\n    _82 = '82'\n    _83 = '83'\n    _84 = '84'\n    askUnadjusted = 'askUnadjusted'\n    _85 = '85'\n    _86 = '86'\n    restrictExternalDerivedData = 'restrictExternalDerivedData'\n    _87 = '87'\n    _88 = '88'\n    _89 = '89'\n    askChange = 'askChange'\n    countIdeasMtd = 'countIdeasMtd'\n    endDate = 'endDate'\n    sunshine = 'sunshine'\n    contractType = 'contractType'\n    momentumType = 'momentumType'\n    specificRisk = 'specificRisk'\n    mdapi = 'mdapi'\n    payoffQtd = 'payoffQtd'\n    loss = 'loss'\n    midcurveVol = 'midcurveVol'\n    sell6bps = 'sell6bps'\n    tradingCostPnl = 'tradingCostPnl'\n    priceNotationType = 'priceNotationType'\n    price = 'price'\n    paymentQuantity = 'paymentQuantity'\n    _90 = '90'\n    _91 = '91'\n    _92 = '92'\n    _93 = '93'\n    _94 = '94'\n    _95 = '95'\n    _96 = '96'\n    _97 = '97'\n    _98 = '98'\n    redemptionDate = 'redemptionDate'\n    _99 = '99'\n    leg2NotionalCurrency = 'leg2NotionalCurrency'\n    subRegion = 'subRegion'\n    benchmark = 'benchmark'\n    tcmCostParticipationRate15Pct = 'tcmCostParticipationRate15Pct'\n    fiscalYear = 'fiscalYear'\n    recallDate = 'recallDate'\n    esgMetricValue = 'esgMetricValue'\n    internal = 'internal'\n    gender = 'gender'\n    assetClassificationsGicsIndustry = 'assetClassificationsGicsIndustry'\n    adjustedBidPrice = 'adjustedBidPrice'\n    lowUnadjusted = 'lowUnadjusted'\n    MACSSecondaryAssetClass = 'MACSSecondaryAssetClass'\n    confirmedPerMillion = 'confirmedPerMillion'\n    dataSourceId = 'dataSourceId'\n    integratedScore = 'integratedScore'\n    buy7bps = 'buy7bps'\n    arrivalMidUnrealizedCash = 'arrivalMidUnrealizedCash'\n    knockInPrice = 'knockInPrice'\n    event = 'event'\n    isIntradayAuction = 'isIntradayAuction'\n    locationName = 'locationName'\n    coupon = 'coupon'\n    percentageAuctionExecutedQuantity = 'percentageAuctionExecutedQuantity'\n    avgYield7Day = 'avgYield7Day'\n    originalDisseminationId = 'originalDisseminationId'\n    totalOnVent = 'totalOnVent'\n    twapUnrealizedCash = 'twapUnrealizedCash'\n    stsCreditMarket = 'stsCreditMarket'\n    onsCode = 'onsCode'\n    passiveTouchFillsPercentage = 'passiveTouchFillsPercentage'\n    seniority = 'seniority'\n    leg1Index = 'leg1Index'\n    highUnadjusted = 'highUnadjusted'\n    submissionEvent = 'submissionEvent'\n    TVProductMnemonic = 'TVProductMnemonic'\n    avgTradeRateLabel = 'avgTradeRateLabel'\n    lastActivityDate = 'lastActivityDate'\n    disseminationTime = 'disseminationTime'\n    priceToCash = 'priceToCash'\n    buy10cents = 'buy10cents'\n    navSpread = 'navSpread'\n    venueMIC = 'venueMIC'\n    dollarTotalReturn = 'dollarTotalReturn'\n    blockUnit = 'blockUnit'\n    midSpread = 'midSpread'\n    istatProvinceCode = 'istatProvinceCode'\n    totalRecoveredByState = 'totalRecoveredByState'\n    repurchaseRate = 'repurchaseRate'\n    dataSource = 'dataSource'\n    totalBeingTested = 'totalBeingTested'\n    clearedOrBilateral = 'clearedOrBilateral'\n    metricName = 'metricName'\n    askGspread = 'askGspread'\n    forecastHour = 'forecastHour'\n    leg2PaymentType = 'leg2PaymentType'\n    calSpreadMisPricing = 'calSpreadMisPricing'\n    totalTestedNegative = 'totalTestedNegative'\n    rate366 = 'rate366'\n    platform = 'platform'\n    rate365 = 'rate365'\n    fixedRateFrequency = 'fixedRateFrequency'\n    rate360 = 'rate360'\n    isContinuous = 'isContinuous'\n    value = 'value'\n    payerDesignatedMaturity = 'payerDesignatedMaturity'\n    productType = 'productType'\n    mdv22Day = 'mdv22Day'\n    twapRealizedBps = 'twapRealizedBps'\n    testMeasureLabel = 'testMeasureLabel'\n    quantity = 'quantity'\n    reportId = 'reportId'\n    indexWeight = 'indexWeight'\n    MACSPrimaryAssetClass = 'MACSPrimaryAssetClass'\n    trader = 'trader'\n    leg2PriceType = 'leg2PriceType'\n    totalActive = 'totalActive'\n    gsid2 = 'gsid2'\n    matchedMaturityOISSwapSpread = 'matchedMaturityOISSwapSpread'\n    valuationDate = 'valuationDate'\n    restrictGsFederation = 'restrictGsFederation'\n    positionSource = 'positionSource'\n    tcmCostHorizon6Hour = 'tcmCostHorizon6Hour'\n    buy200cents = 'buy200cents'\n    vwapUnrealizedBps = 'vwapUnrealizedBps'\n    priceToBook = 'priceToBook'\n    isin = 'isin'\n    plId = 'plId'\n    lastReturnsStartDate = 'lastReturnsStartDate'\n    collateralValueVariance = 'collateralValueVariance'\n    year = 'year'\n    forecastPeriod = 'forecastPeriod'\n    callFirstDate = 'callFirstDate'\n    dataSetIds = 'dataSetIds'\n    economicTermsHash = 'economicTermsHash'\n    numBeds = 'numBeds'\n    sell20bps = 'sell20bps'\n    clientType = 'clientType'\n    percentageCloseExecutedQuantity = 'percentageCloseExecutedQuantity'\n    macaulayDuration = 'macaulayDuration'\n    availableInventory = 'availableInventory'\n    est1DayCompletePct = 'est1DayCompletePct'\n    relativeHitRateYtd = 'relativeHitRateYtd'\n    createdById = 'createdById'\n    marketDataType = 'marketDataType'\n    realShortRatesContribution = 'realShortRatesContribution'\n    metricCategory = 'metricCategory'\n    annualizedCarry = 'annualizedCarry'\n    valuePrevious = 'valuePrevious'\n    transmissionClassification = 'transmissionClassification'\n    avgTradeRate = 'avgTradeRate'\n    shortLevel = 'shortLevel'\n    version = 'version'\n    categoryType = 'categoryType'\n    policyRateExpectation = 'policyRateExpectation'\n    uploadDate = 'uploadDate'\n    blockOffFacility = 'blockOffFacility'\n    unrealizedVwapPerformanceUSD = 'unrealizedVwapPerformanceUSD'\n    paceOfRollp75 = 'paceOfRollp75'\n    earningsPerSharePositive = 'earningsPerSharePositive'\n    numIcuBeds = 'numIcuBeds'\n    bucketVolumeInPercentage = 'bucketVolumeInPercentage'\n    estimatedTradingCost = 'estimatedTradingCost'\n    eid = 'eid'\n    relativeReturnQtd = 'relativeReturnQtd'\n    assessedTestMeasure = 'assessedTestMeasure'\n    mktQuotingStyle = 'mktQuotingStyle'\n    expirationTenor = 'expirationTenor'\n    priceLimit = 'priceLimit'\n    marketModelId = 'marketModelId'\n    receiverFrequency = 'receiverFrequency'\n    realizedCorrelation = 'realizedCorrelation'\n    issueStatus = 'issueStatus'\n    collateralValueActual = 'collateralValueActual'\n    atmFwdRate = 'atmFwdRate'\n    tcmCostParticipationRate75Pct = 'tcmCostParticipationRate75Pct'\n    close = 'close'\n    esProductImpactScore = 'esProductImpactScore'\n    equityVega = 'equityVega'\n    executedFillQuantity = 'executedFillQuantity'\n    lenderPayment = 'lenderPayment'\n    fiveDayMove = 'fiveDayMove'\n    valueFormat = 'valueFormat'\n    windChillForecast = 'windChillForecast'\n    targetNotional = 'targetNotional'\n    fillLegId = 'fillLegId'\n    rationale = 'rationale'\n    realizedTwapPerformanceBps = 'realizedTwapPerformanceBps'\n    lastUpdatedSince = 'lastUpdatedSince'\n    totalTests = 'totalTests'\n    equitiesContribution = 'equitiesContribution'\n    simonId = 'simonId'\n    congestion = 'congestion'\n    notes = 'notes'\n    totalProbableSeniorHome = 'totalProbableSeniorHome'\n    eventCategory = 'eventCategory'\n    averageFillRate = 'averageFillRate'\n    unadjustedOpen = 'unadjustedOpen'\n    criticality = 'criticality'\n    bidAskSpread = 'bidAskSpread'\n    arrivalMidUnrealizedBps = 'arrivalMidUnrealizedBps'\n    optionType = 'optionType'\n    terminationDate = 'terminationDate'\n    queriesPerSecond = 'queriesPerSecond'\n    liquidityType = 'liquidityType'\n    creditLimit = 'creditLimit'\n    rankQtd = 'rankQtd'\n    combinedKey = 'combinedKey'\n    girFxForecast = 'girFxForecast'\n    effectiveTenor = 'effectiveTenor'\n    girCommoditiesForecast = 'girCommoditiesForecast'\n    relativeHumidityDailyForecast = 'relativeHumidityDailyForecast'\n    std30DaysSubsidizedYield = 'std30DaysSubsidizedYield'\n    annualizedTrackingError = 'annualizedTrackingError'\n    futureMonthF26 = 'futureMonthF26'\n    futureMonthF25 = 'futureMonthF25'\n    volSwap = 'volSwap'\n    futureMonthF24 = 'futureMonthF24'\n    heatIndexDailyForecast = 'heatIndexDailyForecast'\n    futureMonthF23 = 'futureMonthF23'\n    realFCI = 'realFCI'\n    blockTradesAndLargeNotionalOffFacilitySwaps = 'blockTradesAndLargeNotionalOffFacilitySwaps'\n    futureMonthF22 = 'futureMonthF22'\n    buy1point5bps = 'buy1point5bps'\n    futureMonthF21 = 'futureMonthF21'\n    expirationSettlementDate = 'expirationSettlementDate'\n    absoluteReturnQtd = 'absoluteReturnQtd'\n    grossExposure = 'grossExposure'\n    volume = 'volume'\n    adv = 'adv'\n    shortConvictionMedium = 'shortConvictionMedium'\n    completeTestMeasure = 'completeTestMeasure'\n    exchange = 'exchange'\n    esPolicyScore = 'esPolicyScore'\n    rollVolumeStd = 'rollVolumeStd'\n    temperatureDailyForecast = 'temperatureDailyForecast'\n    relativePayoffQtd = 'relativePayoffQtd'\n    onLoanPercentage = 'onLoanPercentage'\n    twapRemainingSlices = 'twapRemainingSlices'\n    fairVariance = 'fairVariance'\n    hitRateWtd = 'hitRateWtd'\n    previousCloseRealizedCash = 'previousCloseRealizedCash'\n    realizedVolatility = 'realizedVolatility'\n    unexecutedQuantity = 'unexecutedQuantity'\n    proceedsAssetSwapSpread1m = 'proceedsAssetSwapSpread1m'\n    cloneParentId = 'cloneParentId'\n    windSpeedHourlyForecast = 'windSpeedHourlyForecast'\n    etfFlowRatio = 'etfFlowRatio'\n    assetParametersReceiverRateOption = 'assetParametersReceiverRateOption'\n    buy60cents = 'buy60cents'\n    securitySubTypeId = 'securitySubTypeId'\n    message = 'message'\n    stsRatesCountry = 'stsRatesCountry'\n    sell65cents = 'sell65cents'\n    horizon = 'horizon'\n    wouldIfGoodLevel = 'wouldIfGoodLevel'\n    bufferThresholdRequired = 'bufferThresholdRequired'\n    faceValue = 'faceValue'\n    rollVolumeHist = 'rollVolumeHist'\n    counterPartyStatus = 'counterPartyStatus'\n    composite22DayAdv = 'composite22DayAdv'\n    percentageFarExecutedQuantity = 'percentageFarExecutedQuantity'\n    loanSpreadRequired = 'loanSpreadRequired'\n    assetClass = 'assetClass'\n    sovereignSpreadContribution = 'sovereignSpreadContribution'\n    ric = 'ric'\n    bucketEndTime = 'bucketEndTime'\n    rateType = 'rateType'\n    totalFatalitiesSeniorHome = 'totalFatalitiesSeniorHome'\n    loanStatus = 'loanStatus'\n    shortWeight = 'shortWeight'\n    geographyId = 'geographyId'\n    sell7point5bps = 'sell7point5bps'\n    nav = 'nav'\n    fiscalQuarter = 'fiscalQuarter'\n    versionString = 'versionString'\n    payoffYtd = 'payoffYtd'\n    marketImpact = 'marketImpact'\n    eventType = 'eventType'\n    fillPrice = 'fillPrice'\n    assetCountLong = 'assetCountLong'\n    sell180cents = 'sell180cents'\n    spot = 'spot'\n    applicationId = 'applicationId'\n    indicativeClosePrice = 'indicativeClosePrice'\n    swapSpread = 'swapSpread'\n    tradingRestriction = 'tradingRestriction'\n    assetParametersPayOrReceive = 'assetParametersPayOrReceive'\n    priceSpotEntryUnit = 'priceSpotEntryUnit'\n    unrealizedArrivalPerformanceBps = 'unrealizedArrivalPerformanceBps'\n    city = 'city'\n    pnlWtd = 'pnlWtd'\n    covariance = 'covariance'\n    bucketVolumeInShares = 'bucketVolumeInShares'\n    commodityForecast = 'commodityForecast'\n    valid = 'valid'\n    stsCommodity = 'stsCommodity'\n    initialPricingDate = 'initialPricingDate'\n    indicationOfEndUserException = 'indicationOfEndUserException'\n    windDirectionHourlyForecast = 'windDirectionHourlyForecast'\n    esScore = 'esScore'\n    _yield = 'yield'\n    fatalitiesUnderlyingConditionsPresent = 'fatalitiesUnderlyingConditionsPresent'\n    priceRangeInTicks = 'priceRangeInTicks'\n    paceOfRollp25 = 'paceOfRollp25'\n    dayCloseRealizedUSD = 'dayCloseRealizedUSD'\n    pctChange = 'pctChange'\n    brightnessType = 'brightnessType'\n    futureMonth3M = 'futureMonth3M'\n    numberOfRolls = 'numberOfRolls'\n    isoCountryCodeNumeric = 'isoCountryCodeNumeric'\n    priceType = 'priceType'\n    realizedVwapPerformanceUSD = 'realizedVwapPerformanceUSD'\n    fuelType = 'fuelType'\n    bbid = 'bbid'\n    vegaNotionalAmount = 'vegaNotionalAmount'\n    fatalitiesUnderlyingConditionsAbsent = 'fatalitiesUnderlyingConditionsAbsent'\n    effectiveDate = 'effectiveDate'\n    capped = 'capped'\n    rating = 'rating'\n    optionCurrency = 'optionCurrency'\n    isCloseAuction = 'isCloseAuction'\n    volatility = 'volatility'\n    avgVentUtil = 'avgVentUtil'\n    underlyingAssetIds = 'underlyingAssetIds'\n    buy6point5bps = 'buy6point5bps'\n    vwapInLimitRealizedCash = 'vwapInLimitRealizedCash'\n    estimatedClosingAuctionVolume = 'estimatedClosingAuctionVolume'\n    sell2bps = 'sell2bps'\n    annualRisk = 'annualRisk'\n    eti = 'eti'\n    vwapInLimitRealizedBps = 'vwapInLimitRealizedBps'\n    rankMtd = 'rankMtd'\n    marketBuffer = 'marketBuffer'\n    futureMonthJ24 = 'futureMonthJ24'\n    lastUploadedTime = 'lastUploadedTime'\n    futureMonthJ23 = 'futureMonthJ23'\n    oeId = 'oeId'\n    futureMonthJ22 = 'futureMonthJ22'\n    futureMonthJ21 = 'futureMonthJ21'\n    bbidEquivalent = 'bbidEquivalent'\n    initBufferThresholdRequired = 'initBufferThresholdRequired'\n    leg2DesignatedMaturity = 'leg2DesignatedMaturity'\n    matchedMaturityOISSwapRate = 'matchedMaturityOISSwapRate'\n    fairPrice = 'fairPrice'\n    participationRateInLimit = 'participationRateInLimit'\n    extMktClass = 'extMktClass'\n    priceCurrency = 'priceCurrency'\n    failedCount = 'failedCount'\n    leg1IndexLocation = 'leg1IndexLocation'\n    supraStrategy = 'supraStrategy'\n    dayCountConvention = 'dayCountConvention'\n    roundedNotionalAmount1 = 'roundedNotionalAmount1'\n    roundedNotionalAmount2 = 'roundedNotionalAmount2'\n    factorSource = 'factorSource'\n    futureMonthJ26 = 'futureMonthJ26'\n    lendingSecType = 'lendingSecType'\n    futureMonthJ25 = 'futureMonthJ25'\n    leverage = 'leverage'\n    forecastDay = 'forecastDay'\n    optionFamily = 'optionFamily'\n    generatorOutput = 'generatorOutput'\n    priceSpotStopLossValue = 'priceSpotStopLossValue'\n    kpiId = 'kpiId'\n    windGeneration = 'windGeneration'\n    percentageMidExecutedQuantity = 'percentageMidExecutedQuantity'\n    borrowCost = 'borrowCost'\n    knockOutDirection = 'knockOutDirection'\n    riskModel = 'riskModel'\n    assetParametersVendor = 'assetParametersVendor'\n    fairValue = 'fairValue'\n    openTime = 'openTime'\n    pressureHourlyForecast = 'pressureHourlyForecast'\n    localCcyRate = 'localCcyRate'\n    endUserException = 'endUserException'\n    sell90cents = 'sell90cents'\n    executionVenue = 'executionVenue'\n    primaryVwapInLimitRealizedBps = 'primaryVwapInLimitRealizedBps'\n    approveRebalance = 'approveRebalance'\n    adjustedClosePrice = 'adjustedClosePrice'\n    lmsId = 'lmsId'\n    rebateRate = 'rebateRate'\n    sell130cents = 'sell130cents'\n    sell32bps = 'sell32bps'\n    paceOfRollp50 = 'paceOfRollp50'\n    priceMoveVsArrival = 'priceMoveVsArrival'\n    strikeRelative = 'strikeRelative'\n    pressureType = 'pressureType'\n    buy40bps = 'buy40bps'\n    priceNotation = 'priceNotation'\n    strategy = 'strategy'\n    issueStatusDate = 'issueStatusDate'\n    lenderIncome = 'lenderIncome'\n    pbClientId = 'pbClientId'\n    istatRegionCode = 'istatRegionCode'\n    sell9bps = 'sell9bps'\n    ownerId = 'ownerId'\n    composite10DayAdv = 'composite10DayAdv'\n    maxLoanBalance = 'maxLoanBalance'\n    ideaActivityType = 'ideaActivityType'\n    sell60cents = 'sell60cents'\n    ideaSource = 'ideaSource'\n    everOnVent = 'everOnVent'\n    buy15cents = 'buy15cents'\n    unadjustedAsk = 'unadjustedAsk'\n    contributionName = 'contributionName'\n    givenPlusPaid = 'givenPlusPaid'\n    lastFillPrice = 'lastFillPrice'\n    shortConvictionSmall = 'shortConvictionSmall'\n    upfrontPaymentCurrency = 'upfrontPaymentCurrency'\n    spotSettlementDate = 'spotSettlementDate'\n    matrixOrder = 'matrixOrder'\n    dateIndex = 'dateIndex'\n    payerDayCountFraction = 'payerDayCountFraction'\n    assetClassificationsIsPrimary = 'assetClassificationsIsPrimary'\n    breakEvenInflationChange = 'breakEvenInflationChange'\n    buy130cents = 'buy130cents'\n    dwiContribution = 'dwiContribution'\n    asset2Id = 'asset2Id'\n    averageFillPrice = 'averageFillPrice'\n    depthSpreadScore = 'depthSpreadScore'\n    sell10cents = 'sell10cents'\n    subAccount = 'subAccount'\n    buy65cents = 'buy65cents'\n    bondCdsBasis = 'bondCdsBasis'\n    vendor = 'vendor'\n    dataSet = 'dataSet'\n    notionalAmount2 = 'notionalAmount2'\n    notionalAmount1 = 'notionalAmount1'\n    queueingTime = 'queueingTime'\n    annReturn5Year = 'annReturn5Year'\n    volumeStartOfDay = 'volumeStartOfDay'\n    priceNotation3Type = 'priceNotation3Type'\n    assetParametersFloatingRateDesignatedMaturity = 'assetParametersFloatingRateDesignatedMaturity'\n    executedNotionalLocal = 'executedNotionalLocal'\n    businessSponsor = 'businessSponsor'\n    unexplained = 'unexplained'\n    seasonalAdjustmentShort = 'seasonalAdjustmentShort'\n    metric = 'metric'\n    ask = 'ask'\n    closePrice = 'closePrice'\n    endTime = 'endTime'\n    sell100cents = 'sell100cents'\n    executionTimestamp = 'executionTimestamp'\n    buy180cents = 'buy180cents'\n    absoluteStrike = 'absoluteStrike'\n    sell3point5bps = 'sell3point5bps'\n    liquidityScoreBuy = 'liquidityScoreBuy'\n    paymentFrequency = 'paymentFrequency'\n    expenseRatioNetBps = 'expenseRatioNetBps'\n    metricType = 'metricType'\n    rankYtd = 'rankYtd'\n    leg1Spread = 'leg1Spread'\n    coverageRegion = 'coverageRegion'\n    absoluteReturnYtd = 'absoluteReturnYtd'\n    dayCountConvention2 = 'dayCountConvention2'\n    fwdtier = 'fwdtier'\n    degreeDays = 'degreeDays'\n    turnoverAdjusted = 'turnoverAdjusted'\n    priceSpotTargetValue = 'priceSpotTargetValue'\n    marketDataPoint = 'marketDataPoint'\n    numOfFunds = 'numOfFunds'\n    tradeTime = 'tradeTime'\n    executionId = 'executionId'\n    turnoverUnadjusted = 'turnoverUnadjusted'\n    leg1FloatingIndex = 'leg1FloatingIndex'\n    hedgeAnnualizedVolatility = 'hedgeAnnualizedVolatility'\n    benchmarkCurrency = 'benchmarkCurrency'\n    futuresContract = 'futuresContract'\n    name = 'name'\n    aum = 'aum'\n    leg1DayCountConvention = 'leg1DayCountConvention'\n    cbsCode = 'cbsCode'\n    folderName = 'folderName'\n    apiUsage = 'apiUsage'\n    twapInterval = 'twapInterval'\n    uniqueId = 'uniqueId'\n    optionExpirationDate = 'optionExpirationDate'\n    swaptionAtmFwdRate = 'swaptionAtmFwdRate'\n    liveDate = 'liveDate'\n    corporateActionType = 'corporateActionType'\n    primeId = 'primeId'\n    description = 'description'\n    assetClassificationsIsCountryPrimary = 'assetClassificationsIsCountryPrimary'\n    rebateRateLimit = 'rebateRateLimit'\n    factor = 'factor'\n    daysOnLoan = 'daysOnLoan'\n    longConvictionSmall = 'longConvictionSmall'\n    sell40cents = 'sell40cents'\n    relativePayoffYtd = 'relativePayoffYtd'\n    gsfeer = 'gsfeer'\n    relativeHitRateQtd = 'relativeHitRateQtd'\n    wam = 'wam'\n    wal = 'wal'\n    quantityccy = 'quantityccy'\n    backtestId = 'backtestId'\n    dirtyPrice = 'dirtyPrice'\n    corporateSpreadContribution = 'corporateSpreadContribution'\n    relativeHumidityHourlyForecast = 'relativeHumidityHourlyForecast'\n    multipleScore = 'multipleScore'\n    betaAdjustedExposure = 'betaAdjustedExposure'\n    dividendPoints = 'dividendPoints'\n    brightness = 'brightness'\n    assetParametersReceiverDesignatedMaturity = 'assetParametersReceiverDesignatedMaturity'\n    bosInTicksDescription = 'bosInTicksDescription'\n    testId = 'testId'\n    impliedCorrelation = 'impliedCorrelation'\n    normalizedPerformance = 'normalizedPerformance'\n    overnightNewsEndTime = 'overnightNewsEndTime'\n    bytesConsumed = 'bytesConsumed'\n    swaptionVol = 'swaptionVol'\n    estimatedClosingVolume = 'estimatedClosingVolume'\n    issuer = 'issuer'\n    dividendYield = 'dividendYield'\n    marketType = 'marketType'\n    numUnitsLower = 'numUnitsLower'\n    sourceOrigin = 'sourceOrigin'\n    proceedsAssetSwapSpread3m = 'proceedsAssetSwapSpread3m'\n    totalQuantity = 'totalQuantity'\n    internalUser = 'internalUser'\n    sell40bps = 'sell40bps'\n    redemptionOption = 'redemptionOption'\n    notionalUnit2 = 'notionalUnit2'\n    notionalUnit1 = 'notionalUnit1'\n    sedol = 'sedol'\n    roundingCostPnl = 'roundingCostPnl'\n    midYield = 'midYield'\n    unexecutedNotionalLocal = 'unexecutedNotionalLocal'\n    sustainGlobal = 'sustainGlobal'\n    endingDate = 'endingDate'\n    proceedsAssetSwapSpread12m = 'proceedsAssetSwapSpread12m'\n    grossInvestmentWtd = 'grossInvestmentWtd'\n    annReturn3Year = 'annReturn3Year'\n    sharpeWtd = 'sharpeWtd'\n    discountFactor = 'discountFactor'\n    relativeReturnMtd = 'relativeReturnMtd'\n    priceChangeOnDay = 'priceChangeOnDay'\n    buy100cents = 'buy100cents'\n    forwardPoint = 'forwardPoint'\n    fci = 'fci'\n    recallQuantity = 'recallQuantity'\n    fxPositioning = 'fxPositioning'\n    gsidEquivalent = 'gsidEquivalent'\n    categories = 'categories'\n    extMktAsset = 'extMktAsset'\n    quotingStyle = 'quotingStyle'\n    errorMessage = 'errorMessage'\n    midPrice = 'midPrice'\n    proceedsAssetSwapSpread6m = 'proceedsAssetSwapSpread6m'\n    stsEmDm = 'stsEmDm'\n    embeddedOption = 'embeddedOption'\n    tcmCostHorizon2Day = 'tcmCostHorizon2Day'\n    ageBand = 'ageBand'\n    returnsEnabled = 'returnsEnabled'\n    runId = 'runId'\n    queueInLots = 'queueInLots'\n    tenderOfferExpirationDate = 'tenderOfferExpirationDate'\n    midcurveAnnuity = 'midcurveAnnuity'\n    lendingFundNavTrend = 'lendingFundNavTrend'\n    cloudCoverForecast = 'cloudCoverForecast'\n    tcmCostParticipationRate5Pct = 'tcmCostParticipationRate5Pct'\n    defaultBackcast = 'defaultBackcast'\n    newsOnIntensity = 'newsOnIntensity'\n    priceFormingContinuationData = 'priceFormingContinuationData'\n    adjustedShortInterest = 'adjustedShortInterest'\n    newHospitalized = 'newHospitalized'\n    assetParametersStrike = 'assetParametersStrike'\n    buy35cents = 'buy35cents'\n    leg2TotalNotional = 'leg2TotalNotional'\n    assetParametersEffectiveDate = 'assetParametersEffectiveDate'\n    annReturn10Year = 'annReturn10Year'\n    numAdultIcuBeds = 'numAdultIcuBeds'\n    daysToExpiration = 'daysToExpiration'\n    continuationEvent = 'continuationEvent'\n    wiId = 'wiId'\n    marketCapCategory = 'marketCapCategory'\n    historicalVolume = 'historicalVolume'\n    buy5cents = 'buy5cents'\n    eventStartDate = 'eventStartDate'\n    leg1FixedRate = 'leg1FixedRate'\n    equityGamma = 'equityGamma'\n    rptId = 'rptId'\n    grossIncome = 'grossIncome'\n    emId = 'emId'\n    assetCountInModel = 'assetCountInModel'\n    stsCreditRegion = 'stsCreditRegion'\n    minTemperature = 'minTemperature'\n    bucketStartTime = 'bucketStartTime'\n    fillType = 'fillType'\n    closeTime = 'closeTime'\n    failPct = 'failPct'\n    isoCountryCodeAlpha2 = 'isoCountryCodeAlpha2'\n    isoCountryCodeAlpha3 = 'isoCountryCodeAlpha3'\n    amount = 'amount'\n    lendingFundAcct = 'lendingFundAcct'\n    rebate = 'rebate'\n    electionType = 'electionType'\n    relativeHitRateMtd = 'relativeHitRateMtd'\n    impliedVolatility = 'impliedVolatility'\n    spread = 'spread'\n    variance = 'variance'\n    wtdDegreeDaysDailyForecast = 'wtdDegreeDaysDailyForecast'\n    swaptionAnnuity = 'swaptionAnnuity'\n    buy6bps = 'buy6bps'\n    g10Currency = 'g10Currency'\n    humidityForecast = 'humidityForecast'\n    relativePeriod = 'relativePeriod'\n    user = 'user'\n    customer = 'customer'\n    leg1ResetFrequency = 'leg1ResetFrequency'\n    queueClockTimeLabel = 'queueClockTimeLabel'\n    paceOfRollp100 = 'paceOfRollp100'\n    assetClassificationsGicsSubIndustry = 'assetClassificationsGicsSubIndustry'\n    dewPointHourlyForecast = 'dewPointHourlyForecast'\n    locationType = 'locationType'\n    facetDivisionalReportingGroupId = 'facetDivisionalReportingGroupId'\n    realizedTwapPerformanceUSD = 'realizedTwapPerformanceUSD'\n    swapRate = 'swapRate'\n    algoExecutionStyle = 'algoExecutionStyle'\n    clientContact = 'clientContact'\n    minTemperatureHour = 'minTemperatureHour'\n    tradingCurrency = 'tradingCurrency'\n    totalByOnset = 'totalByOnset'\n    agencySwapSpread = 'agencySwapSpread'\n    rank = 'rank'\n    mixedSwapOtherReportedSDR = 'mixedSwapOtherReportedSDR'\n    humidity = 'humidity'\n    dataSetCategory = 'dataSetCategory'\n    vwapRealizedBps = 'vwapRealizedBps'\n    buy9bps = 'buy9bps'\n    totalTested = 'totalTested'\n    fatalitiesConfirmed = 'fatalitiesConfirmed'\n    universeId1 = 'universeId1'\n    assetParametersPayerDayCountFraction = 'assetParametersPayerDayCountFraction'\n    universeId2 = 'universeId2'\n    bidLow = 'bidLow'\n    bucketizePrice = 'bucketizePrice'\n    fairVarianceVolatility = 'fairVarianceVolatility'\n    covid19 = 'covid19'\n    clientExposure = 'clientExposure'\n    leg2TotalNotionalUnit = 'leg2TotalNotionalUnit'\n    sell45cents = 'sell45cents'\n    gsSustainSubSector = 'gsSustainSubSector'\n    sinkable = 'sinkable'\n    isReal = 'isReal'\n    maxTemperatureHour = 'maxTemperatureHour'\n    leg2AveragingMethod = 'leg2AveragingMethod'\n    jsn = 'jsn'\n    sell160cents = 'sell160cents'\n    knockInDirection = 'knockInDirection'\n    dayCloseUnrealizedUSD = 'dayCloseUnrealizedUSD'\n    tenor = 'tenor'\n    pricingConvention = 'pricingConvention'\n    popularity = 'popularity'\n    floatingRateOption = 'floatingRateOption'\n    hedgeValueType = 'hedgeValueType'\n    assetParametersClearingHouse = 'assetParametersClearingHouse'\n    disclaimer = 'disclaimer'\n    payerFrequency = 'payerFrequency'\n    loanFee = 'loanFee'\n    deploymentVersion = 'deploymentVersion'\n    buy16bps = 'buy16bps'\n    tradeDayCount = 'tradeDayCount'\n    priceToSales = 'priceToSales'\n    newIdeasQtd = 'newIdeasQtd'\n    subdivisionName = 'subdivisionName'\n    adjustedAskPrice = 'adjustedAskPrice'\n    factorUniverse = 'factorUniverse'\n    arrivalRt = 'arrivalRt'\n    internalIndexCalcAgent = 'internalIndexCalcAgent'\n    excessMarginValue = 'excessMarginValue'\n    transactionCost = 'transactionCost'\n    centralBankSwapRate = 'centralBankSwapRate'\n    previousNewConfirmed = 'previousNewConfirmed'\n    unrealizedVwapPerformanceBps = 'unrealizedVwapPerformanceBps'\n    degreeDaysDailyForecast = 'degreeDaysDailyForecast'\n    positionAmount = 'positionAmount'\n    heatIndexHourlyForecast = 'heatIndexHourlyForecast'\n    maRank = 'maRank'\n    fxPositioningSource = 'fxPositioningSource'\n    eventStartDateTime = 'eventStartDateTime'\n    impliedVolatilityByDeltaStrike = 'impliedVolatilityByDeltaStrike'\n    mqSymbol = 'mqSymbol'\n    numTotalUnits = 'numTotalUnits'\n    corporateAction = 'corporateAction'\n    leg1PriceType = 'leg1PriceType'\n    assetParametersPayerRateOption = 'assetParametersPayerRateOption'\n    sell20cents = 'sell20cents'\n    leg2FixedPaymentCurrency = 'leg2FixedPaymentCurrency'\n    gRegionalScore = 'gRegionalScore'\n    hardToBorrow = 'hardToBorrow'\n    sell5bps = 'sell5bps'\n    rollVwap = 'rollVwap'\n    wpk = 'wpk'\n    bespokeSwap = 'bespokeSwap'\n    assetParametersExpirationDate = 'assetParametersExpirationDate'\n    countryName = 'countryName'\n    carry = 'carry'\n    startingDate = 'startingDate'\n    loanId = 'loanId'\n    onboarded = 'onboarded'\n    liquidityScore = 'liquidityScore'\n    longRatesContribution = 'longRatesContribution'\n    sourceDateSpan = 'sourceDateSpan'\n    annYield6Month = 'annYield6Month'\n    underlyingDataSetId = 'underlyingDataSetId'\n    closeUnadjusted = 'closeUnadjusted'\n    valueUnit = 'valueUnit'\n    quantityUnit = 'quantityUnit'\n    adjustedLowPrice = 'adjustedLowPrice'\n    isMomentum = 'isMomentum'\n    longConvictionLarge = 'longConvictionLarge'\n    oad = 'oad'\n    rate = 'rate'\n    couponType = 'couponType'\n    client = 'client'\n    convictionList = 'convictionList'\n    passiveEtfRatio = 'passiveEtfRatio'\n    futureMonthG26 = 'futureMonthG26'\n    futureMonthG25 = 'futureMonthG25'\n    futureMonthG24 = 'futureMonthG24'\n    futureMonthG23 = 'futureMonthG23'\n    typeOfReturn = 'typeOfReturn'\n    futureMonthG22 = 'futureMonthG22'\n    servicingCostLongPnl = 'servicingCostLongPnl'\n    excessMarginPercentage = 'excessMarginPercentage'\n    futureMonthG21 = 'futureMonthG21'\n    totalMild = 'totalMild'\n    realizedArrivalPerformanceBps = 'realizedArrivalPerformanceBps'\n    precipitationDailyForecastInches = 'precipitationDailyForecastInches'\n    exchangeId = 'exchangeId'\n    leg2FixedPayment = 'leg2FixedPayment'\n    tcmCostHorizon20Day = 'tcmCostHorizon20Day'\n    realm = 'realm'\n    bid = 'bid'\n    hedgeValue = 'hedgeValue'\n    orderStartTime = 'orderStartTime'\n    isAggressive = 'isAggressive'\n    floatingRateDesignatedMaturity = 'floatingRateDesignatedMaturity'\n    percentageNearExecutedQuantity = 'percentageNearExecutedQuantity'\n    orderId = 'orderId'\n    hospitalType = 'hospitalType'\n    dayCloseRealizedBps = 'dayCloseRealizedBps'\n    precipitationHourlyForecast = 'precipitationHourlyForecast'\n    marketCapUSD = 'marketCapUSD'\n    auctionFillsPercentage = 'auctionFillsPercentage'\n    highPrice = 'highPrice'\n    absoluteShares = 'absoluteShares'\n    fixedRateDayCountFraction = 'fixedRateDayCountFraction'\n    model = 'model'\n    unrealizedTwapPerformanceUSD = 'unrealizedTwapPerformanceUSD'\n    id = 'id'\n    maturity = 'maturity'\n    deltaChange = 'deltaChange'\n    index = 'index'\n    unrealizedArrivalPerformanceUSD = 'unrealizedArrivalPerformanceUSD'\n    icebergSlippage = 'icebergSlippage'\n    sell120cents = 'sell120cents'\n    futureMonthX26 = 'futureMonthX26'\n    assetTypes = 'assetTypes'\n    futureMonthX25 = 'futureMonthX25'\n    bcid = 'bcid'\n    mktPoint = 'mktPoint'\n    futureMonthX24 = 'futureMonthX24'\n    restrictionStartDate = 'restrictionStartDate'\n    touchLiquidityScore = 'touchLiquidityScore'\n    futureMonthX23 = 'futureMonthX23'\n    futureMonthX22 = 'futureMonthX22'\n    factorCategoryId = 'factorCategoryId'\n    securityTypeId = 'securityTypeId'\n    futureMonthX21 = 'futureMonthX21'\n    investmentYtd = 'investmentYtd'\n    leg2Notional = 'leg2Notional'\n    sell1bps = 'sell1bps'\n    sell200cents = 'sell200cents'\n    expectedCompletionDate = 'expectedCompletionDate'\n    spreadOptionVol = 'spreadOptionVol'\n    sell80cents = 'sell80cents'\n    inflationSwapRate = 'inflationSwapRate'\n    activeQueries = 'activeQueries'\n    sell45bps = 'sell45bps'\n    embededOption = 'embededOption'\n    eventSource = 'eventSource'\n    qisPermNo = 'qisPermNo'\n    settlement = 'settlement'\n    shareclassId = 'shareclassId'\n    feature2 = 'feature2'\n    feature3 = 'feature3'\n    stsCommoditySector = 'stsCommoditySector'\n    exceptionStatus = 'exceptionStatus'\n    overnightNewsIntensity = 'overnightNewsIntensity'\n    salesCoverage = 'salesCoverage'\n    feature1 = 'feature1'\n    tcmCostParticipationRate10Pct = 'tcmCostParticipationRate10Pct'\n    eventTime = 'eventTime'\n    positionSourceName = 'positionSourceName'\n    deliveryDate = 'deliveryDate'\n    interestRate = 'interestRate'\n    side = 'side'\n    dynamicHybridAggressiveStyle = 'dynamicHybridAggressiveStyle'\n    complianceRestrictedStatus = 'complianceRestrictedStatus'\n    borrowFee = 'borrowFee'\n    everIcu = 'everIcu'\n    noWorseThanLevel = 'noWorseThanLevel'\n    updateTime = 'updateTime'\n    loanSpread = 'loanSpread'\n    tcmCostHorizon12Hour = 'tcmCostHorizon12Hour'\n    dewPoint = 'dewPoint'\n    researchCommission = 'researchCommission'\n    buy2bps = 'buy2bps'\n    assetClassificationsRiskCountryCode = 'assetClassificationsRiskCountryCode'\n    newIdeasMtd = 'newIdeasMtd'\n    varSwapByExpiry = 'varSwapByExpiry'\n    sellDate = 'sellDate'\n    aumStart = 'aumStart'\n    assetParametersSettlement = 'assetParametersSettlement'\n    maxTemperature = 'maxTemperature'\n    acquirerShareholderMeetingDate = 'acquirerShareholderMeetingDate'\n    countIdeasWtd = 'countIdeasWtd'\n    arrivalRtNormalized = 'arrivalRtNormalized'\n    reportType = 'reportType'\n    sourceURL = 'sourceURL'\n    estimatedReturn = 'estimatedReturn'\n    high = 'high'\n    sourceLastUpdate = 'sourceLastUpdate'\n    sunshineForecast = 'sunshineForecast'\n    quantityMW = 'quantityMW'\n    sell70cents = 'sell70cents'\n    sell110cents = 'sell110cents'\n    pnodeId = 'pnodeId'\n    humidityType = 'humidityType'\n    prevCloseAsk = 'prevCloseAsk'\n    level = 'level'\n    impliedVolatilityByExpiration = 'impliedVolatilityByExpiration'\n    assetParametersFixedRateDayCountFraction = 'assetParametersFixedRateDayCountFraction'\n    esMomentumScore = 'esMomentumScore'\n    leg2Index = 'leg2Index'\n    netWeight = 'netWeight'\n    portfolioManagers = 'portfolioManagers'\n    bosInTicks = 'bosInTicks'\n    assetParametersCouponType = 'assetParametersCouponType'\n    expectedResidualQuantity = 'expectedResidualQuantity'\n    rollDate = 'rollDate'\n    dynamicHybridSpeed = 'dynamicHybridSpeed'\n    capFloorVol = 'capFloorVol'\n    targetQuantity = 'targetQuantity'\n    submitter = 'submitter'\n    no = 'no'\n    notional = 'notional'\n    esDisclosurePercentage = 'esDisclosurePercentage'\n    closeExecutedQuantityPercentage = 'closeExecutedQuantityPercentage'\n    twapRealizedCash = 'twapRealizedCash'\n    isOpenAuction = 'isOpenAuction'\n    leg1Type = 'leg1Type'\n    wetBulbTempHourlyForecast = 'wetBulbTempHourlyForecast'\n    cleanupPrice = 'cleanupPrice'\n    total = 'total'\n    filledNotionalUSD = 'filledNotionalUSD'\n    assetId = 'assetId'\n    testStatus = 'testStatus'\n    mktType = 'mktType'\n    lastUpdatedTime = 'lastUpdatedTime'\n    yield30Day = 'yield30Day'\n    buy28bps = 'buy28bps'\n    proportionOfRisk = 'proportionOfRisk'\n    futureMonthK23 = 'futureMonthK23'\n    futureMonthK22 = 'futureMonthK22'\n    futureMonthK21 = 'futureMonthK21'\n    primaryEntityId = 'primaryEntityId'\n    cross = 'cross'\n    ideaStatus = 'ideaStatus'\n    contractSubtype = 'contractSubtype'\n    sri = 'sri'\n    fxForecast = 'fxForecast'\n    fixingTimeLabel = 'fixingTimeLabel'\n    isETF = 'isETF'\n    _100 = '100'\n    _101 = '101'\n    _102 = '102'\n    _103 = '103'\n    _104 = '104'\n    fillId = 'fillId'\n    excessReturns = 'excessReturns'\n    _105 = '105'\n    _106 = '106'\n    dollarReturn = 'dollarReturn'\n    orderInLimit = 'orderInLimit'\n    expiryTime = 'expiryTime'\n    _107 = '107'\n    returnOnEquity = 'returnOnEquity'\n    _108 = '108'\n    _109 = '109'\n    futureMonthK26 = 'futureMonthK26'\n    futureMonthK25 = 'futureMonthK25'\n    futureMonthK24 = 'futureMonthK24'\n    restrictionEndDate = 'restrictionEndDate'\n    queueInLotsDescription = 'queueInLotsDescription'\n    volumeLimit = 'volumeLimit'\n    objective = 'objective'\n    navPrice = 'navPrice'\n    leg1UnderlyingAsset = 'leg1UnderlyingAsset'\n    _110 = '110'\n    _111 = '111'\n    _112 = '112'\n    _113 = '113'\n    privatePlacementType = 'privatePlacementType'\n    _114 = '114'\n    hedgeNotional = 'hedgeNotional'\n    _115 = '115'\n    _116 = '116'\n    askLow = 'askLow'\n    intendedPRate = 'intendedPRate'\n    _117 = '117'\n    _118 = '118'\n    _119 = '119'\n    expiry = 'expiry'\n    avgMonthlyYield = 'avgMonthlyYield'\n    periodDirection = 'periodDirection'\n    prevRptId = 'prevRptId'\n    earningsPerShare = 'earningsPerShare'\n    strikePercentage = 'strikePercentage'\n    esProductImpactPercentile = 'esProductImpactPercentile'\n    vwapRealizedCash = 'vwapRealizedCash'\n    parAssetSwapSpread1m = 'parAssetSwapSpread1m'\n    prevCloseBid = 'prevCloseBid'\n    minimumIncrement = 'minimumIncrement'\n    tcmCostHorizon16Day = 'tcmCostHorizon16Day'\n    investmentMtd = 'investmentMtd'\n    settlementDate = 'settlementDate'\n    weightedAverageMidNormalized = 'weightedAverageMidNormalized'\n    _120 = '120'\n    _121 = '121'\n    _122 = '122'\n    salesPerShare = 'salesPerShare'\n    _123 = '123'\n    _124 = '124'\n    _125 = '125'\n    unadjustedClose = 'unadjustedClose'\n    _126 = '126'\n    _127 = '127'\n    _128 = '128'\n    _129 = '129'\n    loanDate = 'loanDate'\n    matchedMaturitySwapSpread1m = 'matchedMaturitySwapSpread1m'\n    collateralPercentageActual = 'collateralPercentageActual'\n    vwapInLimitUnrealizedBps = 'vwapInLimitUnrealizedBps'\n    metricValue = 'metricValue'\n    autoExecState = 'autoExecState'\n    totalRecovered = 'totalRecovered'\n    relativeReturnYtd = 'relativeReturnYtd'\n    _130 = '130'\n    tickServer = 'tickServer'\n    _131 = '131'\n    _132 = '132'\n    _133 = '133'\n    _134 = '134'\n    cumulativeVolumeInPercentage = 'cumulativeVolumeInPercentage'\n    _135 = '135'\n    _136 = '136'\n    _137 = '137'\n    _138 = '138'\n    _139 = '139'\n    realTimeRestrictionStatus = 'realTimeRestrictionStatus'\n    tradeType = 'tradeType'\n    settlementType = 'settlementType'\n    netChange = 'netChange'\n    numberOfUnderliers = 'numberOfUnderliers'\n    swapType = 'swapType'\n    forecastType = 'forecastType'\n    leg1Notional = 'leg1Notional'\n    sellSettleDate = 'sellSettleDate'\n    _140 = '140'\n    _141 = '141'\n    _142 = '142'\n    _143 = '143'\n    _144 = '144'\n    _145 = '145'\n    _146 = '146'\n    _147 = '147'\n    newIdeasYtd = 'newIdeasYtd'\n    managementFee = 'managementFee'\n    _148 = '148'\n    _149 = '149'\n    parAssetSwapSpread3m = 'parAssetSwapSpread3m'\n    sell36bps = 'sell36bps'\n    matchedMaturitySwapSpread3m = 'matchedMaturitySwapSpread3m'\n    sourceId = 'sourceId'\n    country = 'country'\n    vwap = 'vwap'\n    touchSpreadScore = 'touchSpreadScore'\n    ratingSecondHighest = 'ratingSecondHighest'\n    sell24bps = 'sell24bps'\n    _150 = '150'\n    _151 = '151'\n    _152 = '152'\n    frequency = 'frequency'\n    _153 = '153'\n    _154 = '154'\n    activityId = 'activityId'\n    _155 = '155'\n    estimatedImpact = 'estimatedImpact'\n    sell35cents = 'sell35cents'\n    _156 = '156'\n    loanSpreadBucket = 'loanSpreadBucket'\n    _157 = '157'\n    _158 = '158'\n    coronavirusGlobalActivityTracker = 'coronavirusGlobalActivityTracker'\n    _159 = '159'\n    underlyers = 'underlyers'\n    assetParametersPricingLocation = 'assetParametersPricingLocation'\n    eventDescription = 'eventDescription'\n    icebergMaxSize = 'icebergMaxSize'\n    assetParametersCoupon = 'assetParametersCoupon'\n    details = 'details'\n    sector = 'sector'\n    avgBedUtilRate = 'avgBedUtilRate'\n    buy20bps = 'buy20bps'\n    epidemic = 'epidemic'\n    mctr = 'mctr'\n    exchangeTime = 'exchangeTime'\n    historicalClose = 'historicalClose'\n    fipsCode = 'fipsCode'\n    _160 = '160'\n    _161 = '161'\n    buy32bps = 'buy32bps'\n    _162 = '162'\n    _163 = '163'\n    ideaId = 'ideaId'\n    commentStatus = 'commentStatus'\n    marginalCost = 'marginalCost'\n    _164 = '164'\n    _165 = '165'\n    _166 = '166'\n    _167 = '167'\n    _168 = '168'\n    clientWeight = 'clientWeight'\n    _169 = '169'\n    leg1DeliveryPoint = 'leg1DeliveryPoint'\n    sell5cents = 'sell5cents'\n    liqWkly = 'liqWkly'\n    unrealizedTwapPerformanceBps = 'unrealizedTwapPerformanceBps'\n    region = 'region'\n    temperatureHour = 'temperatureHour'\n    upperBound = 'upperBound'\n    sell55cents = 'sell55cents'\n    _170 = '170'\n    _171 = '171'\n    numPediIcuBeds = 'numPediIcuBeds'\n    _172 = '172'\n    bidYield = 'bidYield'\n    _173 = '173'\n    _174 = '174'\n    expectedResidual = 'expectedResidual'\n    _175 = '175'\n    _176 = '176'\n    optionPremium = 'optionPremium'\n    _177 = '177'\n    _178 = '178'\n    _179 = '179'\n    ownerName = 'ownerName'\n    parAssetSwapSpread6m = 'parAssetSwapSpread6m'\n    zScore = 'zScore'\n    sell12bps = 'sell12bps'\n    eventStartTime = 'eventStartTime'\n    matchedMaturitySwapSpread6m = 'matchedMaturitySwapSpread6m'\n    turnover = 'turnover'\n    priceSpotTargetUnit = 'priceSpotTargetUnit'\n    coverage = 'coverage'\n    gPercentile = 'gPercentile'\n    _180 = '180'\n    _181 = '181'\n    _182 = '182'\n    cloudCoverHourlyForecast = 'cloudCoverHourlyForecast'\n    _183 = '183'\n    _184 = '184'\n    lendingFundNav = 'lendingFundNav'\n    sourceOriginalCategory = 'sourceOriginalCategory'\n    percentCloseExecutionQuantity = 'percentCloseExecutionQuantity'\n    _185 = '185'\n    latestExecutionTime = 'latestExecutionTime'\n    _186 = '186'\n    _187 = '187'\n    arrivalMidRealizedBps = 'arrivalMidRealizedBps'\n    _188 = '188'\n    _189 = '189'\n    location = 'location'\n    scenarioId = 'scenarioId'\n    terminationTenor = 'terminationTenor'\n    queueClockTime = 'queueClockTime'\n    discretionLowerBound = 'discretionLowerBound'\n    tcmCostParticipationRate50Pct = 'tcmCostParticipationRate50Pct'\n    ratingLinear = 'ratingLinear'\n    previousCloseUnrealizedBps = 'previousCloseUnrealizedBps'\n    _190 = '190'\n    _191 = '191'\n    subAssetClassForOtherCommodity = 'subAssetClassForOtherCommodity'\n    _192 = '192'\n    forwardPrice = 'forwardPrice'\n    _193 = '193'\n    type = 'type'\n    _194 = '194'\n    strikeRef = 'strikeRef'\n    _195 = '195'\n    _196 = '196'\n    _197 = '197'\n    cumulativePnl = 'cumulativePnl'\n    _198 = '198'\n    shortTenor = 'shortTenor'\n    sell28bps = 'sell28bps'\n    fundClass = 'fundClass'\n    _199 = '199'\n    unadjustedVolume = 'unadjustedVolume'\n    buy36bps = 'buy36bps'\n    positionIdx = 'positionIdx'\n    windChillHourlyForecast = 'windChillHourlyForecast'\n    secName = 'secName'\n    impliedVolatilityByRelativeStrike = 'impliedVolatilityByRelativeStrike'\n    percentADV = 'percentADV'\n    leg1TotalNotional = 'leg1TotalNotional'\n    contract = 'contract'\n    paymentFrequency1 = 'paymentFrequency1'\n    paymentFrequency2 = 'paymentFrequency2'\n    bespoke = 'bespoke'\n    repoTenor = 'repoTenor'\n    sell15cents = 'sell15cents'\n    investmentQtd = 'investmentQtd'\n    heatIndexForecast = 'heatIndexForecast'\n    ratingStandardAndPoors = 'ratingStandardAndPoors'\n    qualityStars = 'qualityStars'\n    leg2FloatingIndex = 'leg2FloatingIndex'\n    sourceTicker = 'sourceTicker'\n    primaryVwapUnrealizedBps = 'primaryVwapUnrealizedBps'\n    gsid = 'gsid'\n    lendingFund = 'lendingFund'\n    sensitivity = 'sensitivity'\n    dayCount = 'dayCount'\n    sell16bps = 'sell16bps'\n    relativeBreakEvenInflationChange = 'relativeBreakEvenInflationChange'\n    sell25cents = 'sell25cents'\n    varSwap = 'varSwap'\n    buy5point5bps = 'buy5point5bps'\n    blockLargeNotional = 'blockLargeNotional'\n    sell2point5bps = 'sell2point5bps'\n    capacity = 'capacity'\n    sectorsRaw = 'sectorsRaw'\n    primaryVwapInLimit = 'primaryVwapInLimit'\n    shareclassPrice = 'shareclassPrice'\n    tradeSize = 'tradeSize'\n    priceSpotEntryValue = 'priceSpotEntryValue'\n    buy8point5bps = 'buy8point5bps'\n    symbolDimensions = 'symbolDimensions'\n    buy24bps = 'buy24bps'\n    observation = 'observation'\n    optionTypeSDR = 'optionTypeSDR'\n    scenarioGroupId = 'scenarioGroupId'\n    averageImpliedVariance = 'averageImpliedVariance'\n    avgTradeRateDescription = 'avgTradeRateDescription'\n    fraction = 'fraction'\n    assetCountShort = 'assetCountShort'\n    collateralPercentageRequired = 'collateralPercentageRequired'\n    sell5point5bps = 'sell5point5bps'\n    date = 'date'\n    zipCode = 'zipCode'\n    totalStdReturnSinceInception = 'totalStdReturnSinceInception'\n    sourceCategory = 'sourceCategory'\n    volumeUnadjusted = 'volumeUnadjusted'\n    passiveRatio = 'passiveRatio'\n    priceToEarnings = 'priceToEarnings'\n    orderDepth = 'orderDepth'\n    annYield3Month = 'annYield3Month'\n    netFlowStd = 'netFlowStd'\n    encodedStats = 'encodedStats'\n    buy5bps = 'buy5bps'\n    runTime = 'runTime'\n    askSize = 'askSize'\n    absoluteReturnMtd = 'absoluteReturnMtd'\n    std30DaysUnsubsidizedYield = 'std30DaysUnsubsidizedYield'\n    resource = 'resource'\n    averageRealizedVolatility = 'averageRealizedVolatility'\n    traceAdvBuy = 'traceAdvBuy'\n    newConfirmed = 'newConfirmed'\n    sell8bps = 'sell8bps'\n    bidPrice = 'bidPrice'\n    sell8point5bps = 'sell8point5bps'\n    targetPriceUnrealizedBps = 'targetPriceUnrealizedBps'\n    esNumericPercentile = 'esNumericPercentile'\n    leg2UnderlyingAsset = 'leg2UnderlyingAsset'\n    csaTerms = 'csaTerms'\n    relativePayoffMtd = 'relativePayoffMtd'\n    dailyNetShareholderFlows = 'dailyNetShareholderFlows'\n    buy2point5bps = 'buy2point5bps'\n    cai = 'cai'\n    executedNotionalUSD = 'executedNotionalUSD'\n    systemTime = 'systemTime'\n    totalHomeIsolation = 'totalHomeIsolation'\n    stationName = 'stationName'\n    passPct = 'passPct'\n    openingReport = 'openingReport'\n    midcurveAtmFwdRate = 'midcurveAtmFwdRate'\n    precipitationForecast = 'precipitationForecast'\n    equityRiskPremiumIndex = 'equityRiskPremiumIndex'\n    fatalitiesUnderlyingConditionsUnknown = 'fatalitiesUnderlyingConditionsUnknown'\n    buy12bps = 'buy12bps'\n    clearingHouse = 'clearingHouse'\n    dayCloseUnrealizedBps = 'dayCloseUnrealizedBps'\n    stsRatesMaturity = 'stsRatesMaturity'\n    liqDly = 'liqDly'\n    contributorRole = 'contributorRole'\n    totalFatalities = 'totalFatalities'\n    adjustedClose = 'adjustedClose'\n    averageValue = 'averageValue'\n    avgInterestRate = 'avgInterestRate'\n    basisDuration = 'basisDuration'\n    bestMonthDate = 'bestMonthDate'\n    bloombergTicker = 'bloombergTicker'\n    capexDepreciation = 'capexDepreciation'\n    capexSales = 'capexSales'\n    cashConversion = 'cashConversion'\n    category = 'category'\n    convexity = 'convexity'\n    countryCode = 'countryCode'\n    croci = 'croci'\n    currentValue = 'currentValue'\n    dacf = 'dacf'\n    dailyVolatility = 'dailyVolatility'\n    divYield = 'divYield'\n    dpsGrowth = 'dpsGrowth'\n    drawdownOverReturn = 'drawdownOverReturn'\n    ebitdaGrowth = 'ebitdaGrowth'\n    ebitdaMargin = 'ebitdaMargin'\n    ebitGrowth = 'ebitGrowth'\n    ebitMargin = 'ebitMargin'\n    evGci = 'evGci'\n    fcfConversion = 'fcfConversion'\n    fcfYield = 'fcfYield'\n    gci = 'gci'\n    grossProfTotAssets = 'grossProfTotAssets'\n    historicCPR = 'historicCPR'\n    incrementalMargin = 'incrementalMargin'\n    industry = 'industry'\n    informationRatio = 'informationRatio'\n    interestCover = 'interestCover'\n    lastChange = 'lastChange'\n    lastChangePct = 'lastChangePct'\n    lastDate = 'lastDate'\n    lastValue = 'lastValue'\n    liborMatchedMaturitySwap = 'liborMatchedMaturitySwap'\n    liborOAS = 'liborOAS'\n    liborProceedsASW = 'liborProceedsASW'\n    liborzSpread = 'liborzSpread'\n    manEarningGrowthMeas = 'manEarningGrowthMeas'\n    marginalRiskContribution = 'marginalRiskContribution'\n    maxDrawdown = 'maxDrawdown'\n    netDebtEbitda = 'netDebtEbitda'\n    netDebtEquity = 'netDebtEquity'\n    niGrowth = 'niGrowth'\n    niMargin = 'niMargin'\n    oisMatchedMaturitySwap = 'oisMatchedMaturitySwap'\n    oisProceedsASW = 'oisProceedsASW'\n    oiszSpread = 'oiszSpread'\n    optionStyle = 'optionStyle'\n    payup = 'payup'\n    positionDate = 'positionDate'\n    preTaxProfitGrowth = 'preTaxProfitGrowth'\n    riskPremiaStyles = 'riskPremiaStyles'\n    roce = 'roce'\n    rolldown = 'rolldown'\n    salesGrowth = 'salesGrowth'\n    sharpeRatio = 'sharpeRatio'\n    totalDebtCapital = 'totalDebtCapital'\n    totalDebtTotalAsset = 'totalDebtTotalAsset'\n    totalReturn = 'totalReturn'\n    unleveredFcfYield = 'unleveredFcfYield'\n    worstMonthDate = 'worstMonthDate'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass Format(EnumBase, Enum):    \n    \n    """Alternative format for data to be returned in"""\n\n    Json = 'Json'\n    Excel = 'Excel'\n    MessagePack = 'MessagePack'\n    Pdf = 'Pdf'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass IndexNotTradingReasons(EnumBase, Enum):    \n    \n    """Reasons the index was not traded"""\n\n    Cost = 'Cost'\n    Client_does_not_like_the_construction = 'Client does not like the construction'\n    Basket_created_prematurely = 'Basket created prematurely'\n    Economics_of_the_basket_changed__client_no_longer_interested_in_trading = 'Economics of the basket changed: client no longer interested in trading'\n    GS_booking_OVER_operational_issues = 'GS booking/operational issues'\n    _ = ''\n    \n    def __repr__(self):\n        return self.value\n\n\nclass LiquidityMeasure(EnumBase, Enum):    \n    \n    """A list of the different liquidity measures to choose from."""\n\n    Summary = 'Summary'\n    Constituent_Transaction_Costs = 'Constituent Transaction Costs'\n    Constituents = 'Constituents'\n    Largest_Holdings_By_Weight = 'Largest Holdings By Weight'\n    Least_Liquid_Holdings = 'Least Liquid Holdings'\n    ADV_Percent_Buckets = 'ADV Percent Buckets'\n    Market_Cap_Buckets = 'Market Cap Buckets'\n    Region_Buckets = 'Region Buckets'\n    Country_Buckets = 'Country Buckets'\n    Sector_Buckets = 'Sector Buckets'\n    Industry_Buckets = 'Industry Buckets'\n    Risk_Buckets = 'Risk Buckets'\n    Factor_Risk_Buckets = 'Factor Risk Buckets'\n    Exposure_Buckets = 'Exposure Buckets'\n    Factor_Exposure_Buckets = 'Factor Exposure Buckets'\n    Percent_Of_Trade_Complete_Over_Time = 'Percent Of Trade Complete Over Time'\n    Execution_Cost_With_Different_Time_Horizons = 'Execution Cost With Different Time Horizons'\n    Participation_Rate_With_Different_Time_Horizons = 'Participation Rate With Different Time Horizons'\n    Risk_With_Different_Time_Horizons = 'Risk With Different Time Horizons'\n    Historical_ADV_Percent_Curve = 'Historical ADV Percent Curve'\n    Time_Series_Data = 'Time Series Data'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass MarketDataFrequency(EnumBase, Enum):    \n    \n    Real_Time = 'Real Time'\n    End_Of_Day = 'End Of Day'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass MarketDataShockType(EnumBase, Enum):    \n    \n    """Market data shock type"""\n\n    Absolute = 'Absolute'\n    Proportional = 'Proportional'\n    Invalid = 'Invalid'\n    Override = 'Override'\n    StdDev = 'StdDev'\n    AutoDefault = 'AutoDefault'\n    CSWFFR = 'CSWFFR'\n    StdVolFactor = 'StdVolFactor'\n    StdVolFactorProportional = 'StdVolFactorProportional'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass MarketDataVendor(EnumBase, Enum):    \n    \n    Goldman_Sachs = 'Goldman Sachs'\n    Thomson_Reuters = 'Thomson Reuters'\n    Solactive = 'Solactive'\n    Bloomberg = 'Bloomberg'\n    Axioma = 'Axioma'\n    Goldman_Sachs_Prime_Services = 'Goldman Sachs Prime Services'\n    Goldman_Sachs_Global_Investment_Research = 'Goldman Sachs Global Investment Research'\n    National_Weather_Service = 'National Weather Service'\n    WM = 'WM'\n    Hedge_Fund_Research__Inc_ = 'Hedge Fund Research, Inc.'\n    London_Stock_Exchange = 'London Stock Exchange'\n    Goldman_Sachs_MDFarm = 'Goldman Sachs MDFarm'\n    PredictIt = 'PredictIt'\n    Iowa_Electronic_Markets = 'Iowa Electronic Markets'\n    RealClearPolitics = 'RealClearPolitics'\n    _538 = '538'\n    FiveThirtyEight = 'FiveThirtyEight'\n    Opinium = 'Opinium'\n    YouGov = 'YouGov'\n    MorningStar = 'MorningStar'\n    Survation = 'Survation'\n    Survation__YouGov = 'Survation, YouGov'\n    European_Centre_for_Disease_Prevention_and_Control = 'European Centre for Disease Prevention and Control'\n    Centers_for_Disease_Control_and_Prevention = 'Centers for Disease Control and Prevention'\n    Johns_Hopkins_University = 'Johns Hopkins University'\n    Google = 'Google'\n    National_Health_Service = 'National Health Service'\n    World_Health_Organization = 'World Health Organization'\n    Wikipedia = 'Wikipedia'\n    StarSchema = 'StarSchema'\n    Covid_Working_Group = 'Covid Working Group'\n    CovidTracking = 'CovidTracking'\n    Bing = 'Bing'\n    FRED = 'FRED'\n    Institute_for_Health_Metrics_and_Evaluation = 'Institute for Health Metrics and Evaluation'\n    Refinitiv = 'Refinitiv'\n    Goldman_Sachs_Global_Investment_Research__Refinitiv = 'Goldman Sachs Global Investment Research, Refinitiv'\n    EPFR = 'EPFR'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass OptionExpiryType(EnumBase, Enum):    \n    \n    _1m = '1m'\n    _2m = '2m'\n    _3m = '3m'\n    _4m = '4m'\n    _5m = '5m'\n    _6m = '6m'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass OptionSettlementMethod(EnumBase, Enum):    \n    \n    """How the option is settled (e.g. Cash, Physical)"""\n\n    Cash = 'Cash'\n    Physical = 'Physical'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass OptionStrikeType(EnumBase, Enum):    \n    \n    Relative = 'Relative'\n    Delta = 'Delta'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass OptionStyle(EnumBase, Enum):    \n    \n    """Option Exercise Style"""\n\n    European = 'European'\n    American = 'American'\n    Bermudan = 'Bermudan'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass OptionType(EnumBase, Enum):    \n    \n    """Option Type"""\n\n    Call = 'Call'\n    Put = 'Put'\n    Binary_Call = 'Binary Call'\n    Binary_Put = 'Binary Put'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass PCOCurrencyType(EnumBase, Enum):    \n    \n    """Currency Type Options for PCO"""\n\n    Exposure = 'Exposure'\n    Base = 'Base'\n    Local = 'Local'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass PCOFundCalendar(EnumBase, Enum):    \n    \n    """Fund Calendar Options for PCO"""\n\n    New_York = 'New York'\n    London = 'London'\n    Hong_Kong = 'Hong Kong'\n    Singapore = 'Singapore'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass PayReceive(EnumBase, Enum):    \n    \n    """Pay or receive fixed"""\n\n    Pay = 'Pay'\n    Receive = 'Receive'\n    Straddle = 'Straddle'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass Period(EnumBase, Enum):    \n    \n    """A coding scheme to define a period corresponding to a quantity amount"""\n\n    Month = 'Month'\n    Quarter = 'Quarter'\n    Hour = 'Hour'\n    Day = 'Day'\n    BusinessDay = 'BusinessDay'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass PricingLocation(EnumBase, Enum):    \n    \n    """Based on the location of the exchange. Called 'Native Region' in SecDB"""\n\n    NYC = 'NYC'\n    LDN = 'LDN'\n    TKO = 'TKO'\n    HKG = 'HKG'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass PrincipalExchange(EnumBase, Enum):    \n    \n    """How principal is exchanged"""\n\n    _None = 'None'\n    Both = 'Both'\n    First = 'First'\n    Last = 'Last'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass ProductCode(EnumBase, Enum):    \n    \n    """Override the clearing destination/symbol"""\n\n    CME__BB = 'CME::BB'\n    CME__BK = 'CME::BK'\n    CME__BY = 'CME::BY'\n    CME__BZ = 'CME::BZ'\n    CME__CL = 'CME::CL'\n    CME__CL_BZ = 'CME::CL-BZ'\n    CME__CS = 'CME::CS'\n    CME__CY = 'CME::CY'\n    CME__HK = 'CME::HK'\n    CME__HO = 'CME::HO'\n    CME__HOB = 'CME::HOB'\n    CME__HO_CL = 'CME::HO-CL'\n    CME__MP = 'CME::MP'\n    CME__NG = 'CME::NG'\n    CME__NLS = 'CME::NLS'\n    CME__RB = 'CME::RB'\n    CME__RBB = 'CME::RBB'\n    CME__RB_BZ = 'CME::RB-BZ'\n    CME__RB_CL = 'CME::RB-CL'\n    CME__RH = 'CME::RH'\n    CME__RL = 'CME::RL'\n    CME__RM = 'CME::RM'\n    CME__WS = 'CME::WS'\n    CME_ICE__RB_B = 'CME-ICE::RB-B'\n    ICE__B = 'ICE::B'\n    ICE__BNB = 'ICE::BNB'\n    ICE__BTD = 'ICE::BTD'\n    ICE__G = 'ICE::G'\n    ICE__G_B = 'ICE::G-B'\n    ICE__HBT = 'ICE::HBT'\n    ICE__HNG = 'ICE::HNG'\n    ICE__HOF = 'ICE::HOF'\n    ICE__I = 'ICE::I'\n    ICE__N = 'ICE::N'\n    ICE__N_B = 'ICE::N-B'\n    ICE__O = 'ICE::O'\n    ICE__O_B = 'ICE::O-B'\n    ICE__R = 'ICE::R'\n    ICE__RBR = 'ICE::RBR'\n    ICE__T = 'ICE::T'\n    ICE__T_B = 'ICE::T-B'\n    ICE__ULA = 'ICE::ULA'\n    ICE__ULC = 'ICE::ULC'\n    ICE__ULD = 'ICE::ULD'\n    ICE__ULM = 'ICE::ULM'\n    ICE__WTB = 'ICE::WTB'\n    LME__MAL = 'LME::MAL'\n    LME__MNI = 'LME::MNI'\n    LME__MPB = 'LME::MPB'\n    LME__MZN = 'LME::MZN'\n    OTC__BRT = 'OTC::BRT'\n    OTC__GO = 'OTC::GO'\n    OTC__GO_BRT = 'OTC::GO-BRT'\n    OTC__HO = 'OTC::HO'\n    OTC__HO_BRT = 'OTC::HO-BRT'\n    OTC__HO_GO = 'OTC::HO-GO'\n    OTC__HO_WTI = 'OTC::HO-WTI'\n    OTC__MAL = 'OTC::MAL'\n    OTC__MNI = 'OTC::MNI'\n    OTC__MPB = 'OTC::MPB'\n    OTC__MZN = 'OTC::MZN'\n    OTC__NG = 'OTC::NG'\n    OTC__RB = 'OTC::RB'\n    OTC__RB_BRT = 'OTC::RB-BRT'\n    OTC__RB_HO = 'OTC::RB-HO'\n    OTC__RB_WTI = 'OTC::RB-WTI'\n    OTC__WTI = 'OTC::WTI'\n    OTC__WTI_BRT = 'OTC::WTI-BRT'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass Region(EnumBase, Enum):    \n    \n    """Regional classification for the asset"""\n\n    _ = ''\n    Americas = 'Americas'\n    Asia = 'Asia'\n    EM = 'EM'\n    Europe = 'Europe'\n    Global = 'Global'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass ReportJobPriority(EnumBase, Enum):    \n    \n    """Report job priority."""\n\n    High = 'High'\n    Normal = 'Normal'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass RiskMeasureType(EnumBase, Enum):    \n    \n    """The type of measure to perform risk on. e.g. Greeks"""\n\n    Annual_ATM_Implied_Volatility = 'Annual ATM Implied Volatility'\n    Annual_ATMF_Implied_Volatility = 'Annual ATMF Implied Volatility'\n    Annual_Implied_Volatility = 'Annual Implied Volatility'\n    AnnuityLocalCcy = 'AnnuityLocalCcy'\n    BaseCPI = 'BaseCPI'\n    Basis = 'Basis'\n    BSPrice = 'BSPrice'\n    CRIF_IRCurve = 'CRIF IRCurve'\n    Cashflows = 'Cashflows'\n    Daily_Implied_Volatility = 'Daily Implied Volatility'\n    Delta = 'Delta'\n    DeltaLocalCcy = 'DeltaLocalCcy'\n    Description = 'Description'\n    Dollar_Price = 'Dollar Price'\n    DV01 = 'DV01'\n    Fair_Price = 'Fair Price'\n    FairVarStrike = 'FairVarStrike'\n    FairVolStrike = 'FairVolStrike'\n    Forward_Price = 'Forward Price'\n    Forward_Rate = 'Forward Rate'\n    Price = 'Price'\n    Gamma = 'Gamma'\n    GammaLocalCcy = 'GammaLocalCcy'\n    InflationDelta = 'InflationDelta'\n    Local_Currency_Accrual_in_Cents = 'Local Currency Accrual in Cents'\n    Local_Currency_Annuity = 'Local Currency Annuity'\n    Market_Data = 'Market Data'\n    Market_Data_Assets = 'Market Data Assets'\n    MV = 'MV'\n    OAS = 'OAS'\n    ParallelBasis = 'ParallelBasis'\n    ParallelDelta = 'ParallelDelta'\n    ParallelDeltaLocalCcy = 'ParallelDeltaLocalCcy'\n    ParallelDiscountDelta = 'ParallelDiscountDelta'\n    ParallelDiscountDeltaLocalCcy = 'ParallelDiscountDeltaLocalCcy'\n    ParallelInflationDelta = 'ParallelInflationDelta'\n    ParallelIndexDelta = 'ParallelIndexDelta'\n    ParallelIndexDeltaLocalCcy = 'ParallelIndexDeltaLocalCcy'\n    ParallelInflationDeltaLocalCcy = 'ParallelInflationDeltaLocalCcy'\n    ParallelXccyDelta = 'ParallelXccyDelta'\n    ParallelXccyDeltaLocalCcy = 'ParallelXccyDeltaLocalCcy'\n    ParallelGamma = 'ParallelGamma'\n    ParallelGammaLocalCcy = 'ParallelGammaLocalCcy'\n    ParallelVega = 'ParallelVega'\n    ParallelVegaLocalCcy = 'ParallelVegaLocalCcy'\n    Premium_In_Cents = 'Premium In Cents'\n    Premium = 'Premium'\n    Resolved_Instrument_Values = 'Resolved Instrument Values'\n    PNL = 'PNL'\n    PnlExplain = 'PnlExplain'\n    PnlExplainLocalCcy = 'PnlExplainLocalCcy'\n    PnlPredict = 'PnlPredict'\n    PV = 'PV'\n    QuotedDelta = 'QuotedDelta'\n    Spot = 'Spot'\n    Spot_Rate = 'Spot Rate'\n    Strike = 'Strike'\n    Theta = 'Theta'\n    Vanna = 'Vanna'\n    Vega = 'Vega'\n    VegaLocalCcy = 'VegaLocalCcy'\n    Volga = 'Volga'\n    Volatility = 'Volatility'\n    XccyDelta = 'XccyDelta'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass RiskMeasureUnit(EnumBase, Enum):    \n    \n    """The unit of change of underlying in the risk computation."""\n\n    Percent = 'Percent'\n    Dollar = 'Dollar'\n    BPS = 'BPS'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass ScenarioType(EnumBase, Enum):    \n    \n    """Type of Scenario"""\n\n    Spot_Vol = 'Spot Vol'\n    Greeks = 'Greeks'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass SettlementType(EnumBase, Enum):    \n    \n    """Settlement Type"""\n\n    Cash = 'Cash'\n    Physical = 'Physical'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass StrikeMethodType(EnumBase, Enum):    \n    \n    Spread = 'Spread'\n    Delta = 'Delta'\n    Percentage_of_Price = 'Percentage of Price'\n    Fixed = 'Fixed'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass SwapClearingHouse(EnumBase, Enum):    \n    \n    """Swap Clearing House"""\n\n    LCH = 'LCH'\n    EUREX = 'EUREX'\n    JSCC = 'JSCC'\n    CME = 'CME'\n    NONE = 'NONE'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass SwapSettlement(EnumBase, Enum):    \n    \n    """Swap Settlement Type"""\n\n    Phys_CLEARED = 'Phys.CLEARED'\n    Physical = 'Physical'\n    Cash_CollatCash = 'Cash.CollatCash'\n    Cash_PYU = 'Cash.PYU'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass TradeAs(EnumBase, Enum):    \n    \n    """Option trade as (i.e. listed, otc, lookalike etc)"""\n\n    Listed = 'Listed'\n    Listed_Look_alike_OTC = 'Listed Look alike OTC'\n    Flex = 'Flex'\n    OTC = 'OTC'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass TradeType(EnumBase, Enum):    \n    \n    """Direction"""\n\n    Buy = 'Buy'\n    Sell = 'Sell'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass UnderlierType(EnumBase, Enum):    \n    \n    """Type of underlyer"""\n\n    BBID = 'BBID'\n    CUSIP = 'CUSIP'\n    ISIN = 'ISIN'\n    SEDOL = 'SEDOL'\n    RIC = 'RIC'\n    Ticker = 'Ticker'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass ValuationTime(EnumBase, Enum):    \n    \n    """The time of valuation, e.g. for an option"""\n\n    MktClose = 'MktClose'\n    MktOpen = 'MktOpen'\n    SQ = 'SQ'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass VarianceConvention(EnumBase, Enum):    \n    \n    """Specifies whether the variance is Annualized or Total"""\n\n    Annualized = 'Annualized'\n    Total = 'Total'\n    \n    def __repr__(self):\n        return self.value\n\n\nclass AssetIdPriceable(Priceable):\n        \n    """An object to hold assetId when it can't be passed as a string."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        asset_id: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.asset_id = asset_id\n        self.name = name\n\n    @property\n    def asset_id(self) -> str:\n        """Marquee unique asset identifier."""\n        return self.__asset_id\n\n    @asset_id.setter\n    def asset_id(self, value: str):\n        self._property_changed('asset_id')\n        self.__asset_id = value        \n\n\nclass CSLDate(Base):\n        \n    """A date"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        date_value: datetime.date = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.date_value = date_value\n        self.name = name\n\n    @property\n    def date_value(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__date_value\n\n    @date_value.setter\n    def date_value(self, value: datetime.date):\n        self._property_changed('date_value')\n        self.__date_value = value        \n\n\nclass CSLDouble(Base):\n        \n    """A double"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        double_value: float = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.double_value = double_value\n        self.name = name\n\n    @property\n    def double_value(self) -> float:\n        """The value"""\n        return self.__double_value\n\n    @double_value.setter\n    def double_value(self, value: float):\n        self._property_changed('double_value')\n        self.__double_value = value        \n\n\nclass CSLFXCross(Base):\n        \n    """An FX cross"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        string_value: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.string_value = string_value\n        self.name = name\n\n    @property\n    def string_value(self) -> str:\n        """Currency pair"""\n        return self.__string_value\n\n    @string_value.setter\n    def string_value(self, value: str):\n        self._property_changed('string_value')\n        self.__string_value = value        \n\n\nclass CSLIndex(Base):\n        \n    """An index"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        string_value: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.string_value = string_value\n        self.name = name\n\n    @property\n    def string_value(self) -> str:\n        """Display name of the asset"""\n        return self.__string_value\n\n    @string_value.setter\n    def string_value(self, value: str):\n        self._property_changed('string_value')\n        self.__string_value = value        \n\n\nclass CSLSimpleSchedule(Base):\n        \n    """A fixing date, settlement date pair"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        fixing_date: datetime.date = None,\n        settlement_date: datetime.date = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.fixing_date = fixing_date\n        self.settlement_date = settlement_date\n        self.name = name\n\n    @property\n    def fixing_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__fixing_date\n\n    @fixing_date.setter\n    def fixing_date(self, value: datetime.date):\n        self._property_changed('fixing_date')\n        self.__fixing_date = value        \n\n    @property\n    def settlement_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: datetime.date):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n\nclass CSLStock(Base):\n        \n    """A stock"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        string_value: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.string_value = string_value\n        self.name = name\n\n    @property\n    def string_value(self) -> str:\n        """Display name of the asset"""\n        return self.__string_value\n\n    @string_value.setter\n    def string_value(self, value: str):\n        self._property_changed('string_value')\n        self.__string_value = value        \n\n\nclass CSLString(Base):\n        \n    """A string"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        string_value: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.string_value = string_value\n        self.name = name\n\n    @property\n    def string_value(self) -> str:\n        """The value"""\n        return self.__string_value\n\n    @string_value.setter\n    def string_value(self, value: str):\n        self._property_changed('string_value')\n        self.__string_value = value        \n\n\nclass CSLSymCaseNamedParam(Base):\n        \n    """A named case-sensitive string."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        sym_case_value: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.sym_case_value = sym_case_value\n        self.name = name\n\n    @property\n    def sym_case_value(self) -> str:\n        """A case-sensitive string"""\n        return self.__sym_case_value\n\n    @sym_case_value.setter\n    def sym_case_value(self, value: str):\n        self._property_changed('sym_case_value')\n        self.__sym_case_value = value        \n\n    @property\n    def name(self) -> str:\n        """A name for the symbol"""\n        return self.__name\n\n    @name.setter\n    def name(self, value: str):\n        self._property_changed('name')\n        self.__name = value        \n\n\nclass CompositeScenario(Base):\n        \n    """A scenario for composing scenarios"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        scenarios: tuple = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.scenarios = scenarios\n        self.name = name\n\n    @property\n    def scenario_type(self) -> str:\n        """CompositeScenario"""\n        return 'CompositeScenario'        \n\n    @property\n    def scenarios(self) -> tuple:\n        """The scenarios, in order"""\n        return self.__scenarios\n\n    @scenarios.setter\n    def scenarios(self, value: tuple):\n        self._property_changed('scenarios')\n        self.__scenarios = value        \n\n\nclass EntitlementExclusions(Base):\n        \n    """Defines the exclusion entitlements of a given resource."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        view: Tuple[Tuple[str, ...], ...] = None,\n        edit: Tuple[Tuple[str, ...], ...] = None,\n        admin: Tuple[Tuple[str, ...], ...] = None,\n        rebalance: Tuple[Tuple[str, ...], ...] = None,\n        execute: Tuple[Tuple[str, ...], ...] = None,\n        trade: Tuple[Tuple[str, ...], ...] = None,\n        upload: Tuple[Tuple[str, ...], ...] = None,\n        query: Tuple[Tuple[str, ...], ...] = None,\n        performance_details: Tuple[Tuple[str, ...], ...] = None,\n        plot: Tuple[Tuple[str, ...], ...] = None,\n        delete: Tuple[Tuple[str, ...], ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.view = view\n        self.edit = edit\n        self.admin = admin\n        self.rebalance = rebalance\n        self.execute = execute\n        self.trade = trade\n        self.upload = upload\n        self.query = query\n        self.performance_details = performance_details\n        self.plot = plot\n        self.delete = delete\n        self.name = name\n\n    @property\n    def view(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__view\n\n    @view.setter\n    def view(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('view')\n        self.__view = value        \n\n    @property\n    def edit(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__edit\n\n    @edit.setter\n    def edit(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('edit')\n        self.__edit = value        \n\n    @property\n    def admin(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__admin\n\n    @admin.setter\n    def admin(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('admin')\n        self.__admin = value        \n\n    @property\n    def rebalance(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__rebalance\n\n    @rebalance.setter\n    def rebalance(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('rebalance')\n        self.__rebalance = value        \n\n    @property\n    def execute(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__execute\n\n    @execute.setter\n    def execute(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('execute')\n        self.__execute = value        \n\n    @property\n    def trade(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__trade\n\n    @trade.setter\n    def trade(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('trade')\n        self.__trade = value        \n\n    @property\n    def upload(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__upload\n\n    @upload.setter\n    def upload(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('upload')\n        self.__upload = value        \n\n    @property\n    def query(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__query\n\n    @query.setter\n    def query(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('query')\n        self.__query = value        \n\n    @property\n    def performance_details(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__performance_details\n\n    @performance_details.setter\n    def performance_details(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('performance_details')\n        self.__performance_details = value        \n\n    @property\n    def plot(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__plot\n\n    @plot.setter\n    def plot(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('plot')\n        self.__plot = value        \n\n    @property\n    def delete(self) -> Tuple[Tuple[str, ...], ...]:\n        return self.__delete\n\n    @delete.setter\n    def delete(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('delete')\n        self.__delete = value        \n\n\nclass Entitlements(Base):\n        \n    """Defines the entitlements of a given resource."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        view: Tuple[str, ...] = None,\n        edit: Tuple[str, ...] = None,\n        admin: Tuple[str, ...] = None,\n        rebalance: Tuple[str, ...] = None,\n        execute: Tuple[str, ...] = None,\n        trade: Tuple[str, ...] = None,\n        upload: Tuple[str, ...] = None,\n        query: Tuple[str, ...] = None,\n        performance_details: Tuple[str, ...] = None,\n        plot: Tuple[str, ...] = None,\n        delete: Tuple[str, ...] = None,\n        display: Tuple[str, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.view = view\n        self.edit = edit\n        self.admin = admin\n        self.rebalance = rebalance\n        self.execute = execute\n        self.trade = trade\n        self.upload = upload\n        self.query = query\n        self.performance_details = performance_details\n        self.plot = plot\n        self.delete = delete\n        self.display = display\n        self.name = name\n\n    @property\n    def view(self) -> Tuple[str, ...]:\n        """Permission to view the resource and its contents"""\n        return self.__view\n\n    @view.setter\n    def view(self, value: Tuple[str, ...]):\n        self._property_changed('view')\n        self.__view = value        \n\n    @property\n    def edit(self) -> Tuple[str, ...]:\n        """Permission to edit details about the resource content, excluding entitlements.\n           Can also delete the resource"""\n        return self.__edit\n\n    @edit.setter\n    def edit(self, value: Tuple[str, ...]):\n        self._property_changed('edit')\n        self.__edit = value        \n\n    @property\n    def admin(self) -> Tuple[str, ...]:\n        """Permission to edit all details of the resource, including entitlements. Can also\n           delete the resource"""\n        return self.__admin\n\n    @admin.setter\n    def admin(self, value: Tuple[str, ...]):\n        self._property_changed('admin')\n        self.__admin = value        \n\n    @property\n    def rebalance(self) -> Tuple[str, ...]:\n        """Permission to rebalance the constituent weights of the resource"""\n        return self.__rebalance\n\n    @rebalance.setter\n    def rebalance(self, value: Tuple[str, ...]):\n        self._property_changed('rebalance')\n        self.__rebalance = value        \n\n    @property\n    def execute(self) -> Tuple[str, ...]:\n        """Permission to execute functions and/or reports with the resource"""\n        return self.__execute\n\n    @execute.setter\n    def execute(self, value: Tuple[str, ...]):\n        self._property_changed('execute')\n        self.__execute = value        \n\n    @property\n    def trade(self) -> Tuple[str, ...]:\n        """Permission to trade the resource"""\n        return self.__trade\n\n    @trade.setter\n    def trade(self, value: Tuple[str, ...]):\n        self._property_changed('trade')\n        self.__trade = value        \n\n    @property\n    def upload(self) -> Tuple[str, ...]:\n        """Permission to upload data to the given resource"""\n        return self.__upload\n\n    @upload.setter\n    def upload(self, value: Tuple[str, ...]):\n        self._property_changed('upload')\n        self.__upload = value        \n\n    @property\n    def query(self) -> Tuple[str, ...]:\n        """Permission to query data from the given resource"""\n        return self.__query\n\n    @query.setter\n    def query(self, value: Tuple[str, ...]):\n        self._property_changed('query')\n        self.__query = value        \n\n    @property\n    def performance_details(self) -> Tuple[str, ...]:\n        """Permission to view the resource, it's entire contents, and related data"""\n        return self.__performance_details\n\n    @performance_details.setter\n    def performance_details(self, value: Tuple[str, ...]):\n        self._property_changed('performance_details')\n        self.__performance_details = value        \n\n    @property\n    def plot(self) -> Tuple[str, ...]:\n        """Permission to plot data from the given resource"""\n        return self.__plot\n\n    @plot.setter\n    def plot(self, value: Tuple[str, ...]):\n        self._property_changed('plot')\n        self.__plot = value        \n\n    @property\n    def delete(self) -> Tuple[str, ...]:\n        """Permission to delete the resource"""\n        return self.__delete\n\n    @delete.setter\n    def delete(self, value: Tuple[str, ...]):\n        self._property_changed('delete')\n        self.__delete = value        \n\n    @property\n    def display(self) -> Tuple[str, ...]:\n        """Permission to query data for web router request so that it can prevent\n           programmatic access (api access) to the licensed data."""\n        return self.__display\n\n    @display.setter\n    def display(self, value: Tuple[str, ...]):\n        self._property_changed('display')\n        self.__display = value        \n\n\nclass ISelectNewUnit(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        id_: str,\n        new_units: float = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.__id = id_\n        self.new_units = new_units\n        self.name = name\n\n    @property\n    def id(self) -> str:\n        return self.__id\n\n    @id.setter\n    def id(self, value: str):\n        self._property_changed('id')\n        self.__id = value        \n\n    @property\n    def new_units(self) -> float:\n        return self.__new_units\n\n    @new_units.setter\n    def new_units(self, value: float):\n        self._property_changed('new_units')\n        self.__new_units = value        \n\n\nclass ISelectNewWeight(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        id_: str,\n        new_weight: float = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.__id = id_\n        self.new_weight = new_weight\n        self.name = name\n\n    @property\n    def id(self) -> str:\n        return self.__id\n\n    @id.setter\n    def id(self, value: str):\n        self._property_changed('id')\n        self.__id = value        \n\n    @property\n    def new_weight(self) -> float:\n        return self.__new_weight\n\n    @new_weight.setter\n    def new_weight(self, value: float):\n        self._property_changed('new_weight')\n        self.__new_weight = value        \n\n\nclass Identifier(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        type_: str = None,\n        value: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.__type = type_\n        self.value = value\n        self.name = name\n\n    @property\n    def type(self) -> str:\n        """Identifier type code"""\n        return self.__type\n\n    @type.setter\n    def type(self, value: str):\n        self._property_changed('type')\n        self.__type = value        \n\n    @property\n    def value(self) -> str:\n        """Identifier value"""\n        return self.__value\n\n    @value.setter\n    def value(self, value: str):\n        self._property_changed('value')\n        self.__value = value        \n\n\nclass LiborFallbackScenario(Scenario):\n        \n    """A scenario to change the libor rate to RFR + Spread after Dec2021 in the index\n       curve"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        date: datetime.date = None,\n        fallback_type: str = 'RFR',\n        discounting: bool = False,\n        cash_flows: bool = True,\n        name: str = None\n    ):        \n        super().__init__()\n        self.date = date\n        self.fallback_type = fallback_type\n        self.discounting = discounting\n        self.cash_flows = cash_flows\n        self.name = name\n\n    @property\n    def scenario_type(self) -> str:\n        """LiborFallbackScenario"""\n        return 'LiborFallbackScenario'        \n\n    @property\n    def date(self) -> datetime.date:\n        """Announce Date For the Libor Fallback"""\n        return self.__date\n\n    @date.setter\n    def date(self, value: datetime.date):\n        self._property_changed('date')\n        self.__date = value        \n\n    @property\n    def fallback_type(self) -> str:\n        """Different Rules for Libor Fallback"""\n        return self.__fallback_type\n\n    @fallback_type.setter\n    def fallback_type(self, value: str):\n        self._property_changed('fallback_type')\n        self.__fallback_type = value        \n\n    @property\n    def discounting(self) -> bool:\n        """If True, use RFR Discounting, otherwise continue to use libor discounting"""\n        return self.__discounting\n\n    @discounting.setter\n    def discounting(self, value: bool):\n        self._property_changed('discounting')\n        self.__discounting = value        \n\n    @property\n    def cash_flows(self) -> bool:\n        """If True, use RFR + Spread when projecting cash flows, otherwise continue to use\n           libor"""\n        return self.__cash_flows\n\n    @cash_flows.setter\n    def cash_flows(self, value: bool):\n        self._property_changed('cash_flows')\n        self.__cash_flows = value        \n\n\nclass Link(Base):\n        \n    """Hyperlink"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        title: str = None,\n        source: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.title = title\n        self.source = source\n        self.name = name\n\n    @property\n    def title(self) -> str:\n        """display text"""\n        return self.__title\n\n    @title.setter\n    def title(self, value: str):\n        self._property_changed('title')\n        self.__title = value        \n\n    @property\n    def source(self) -> str:\n        """link"""\n        return self.__source\n\n    @source.setter\n    def source(self, value: str):\n        self._property_changed('source')\n        self.__source = value        \n\n\nclass LiquidityReportParameters(Base):\n        \n    """Parameters to be used on liquidity reports"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        title: str = None,\n        email: str = None,\n        trading_desk: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.title = title\n        self.email = email\n        self.trading_desk = trading_desk\n        self.name = name\n\n    @property\n    def title(self) -> str:\n        """Report title"""\n        return self.__title\n\n    @title.setter\n    def title(self, value: str):\n        self._property_changed('title')\n        self.__title = value        \n\n    @property\n    def email(self) -> str:\n        return self.__email\n\n    @email.setter\n    def email(self, value: str):\n        self._property_changed('email')\n        self.__email = value        \n\n    @property\n    def trading_desk(self) -> str:\n        return self.__trading_desk\n\n    @trading_desk.setter\n    def trading_desk(self, value: str):\n        self._property_changed('trading_desk')\n        self.__trading_desk = value        \n\n\nclass MarketDataCoordinate(Base):\n        \n    """Object representation of a market data coordinate"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        mkt_type: str = None,\n        mkt_asset: str = None,\n        mkt_class: str = None,\n        mkt_point: Tuple[str, ...] = None,\n        mkt_quoting_style: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.mkt_type = mkt_type\n        self.mkt_asset = mkt_asset\n        self.mkt_class = mkt_class\n        self.mkt_point = mkt_point\n        self.mkt_quoting_style = mkt_quoting_style\n        self.name = name\n\n    @property\n    def mkt_type(self) -> str:\n        """The MDAPI Type, e.g. IR, IR BASIS, FX, FX Vol"""\n        return self.__mkt_type\n\n    @mkt_type.setter\n    def mkt_type(self, value: str):\n        self._property_changed('mkt_type')\n        self.__mkt_type = value        \n\n    @property\n    def mkt_asset(self) -> str:\n        """The MDAPI Asset, e.g. USD, EUR-EURIBOR-Telerate, WTI"""\n        return self.__mkt_asset\n\n    @mkt_asset.setter\n    def mkt_asset(self, value: str):\n        self._property_changed('mkt_asset')\n        self.__mkt_asset = value        \n\n    @property\n    def mkt_class(self) -> str:\n        """The MDAPI Class, e.g. Swap, Cash."""\n        return self.__mkt_class\n\n    @mkt_class.setter\n    def mkt_class(self, value: str):\n        self._property_changed('mkt_class')\n        self.__mkt_class = value        \n\n    @property\n    def mkt_point(self) -> Tuple[str, ...]:\n        """The MDAPI Point, e.g. 3m, 10y, 11y, Dec19"""\n        return self.__mkt_point\n\n    @mkt_point.setter\n    def mkt_point(self, value: Tuple[str, ...]):\n        self._property_changed('mkt_point')\n        self.__mkt_point = value        \n\n    @property\n    def mkt_quoting_style(self) -> str:\n        return self.__mkt_quoting_style\n\n    @mkt_quoting_style.setter\n    def mkt_quoting_style(self, value: str):\n        self._property_changed('mkt_quoting_style')\n        self.__mkt_quoting_style = value        \n\n\nclass Op(Base):\n        \n    """Operations for searches."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        gte: Union[datetime.date, float] = None,\n        lte: Union[datetime.date, float] = None,\n        lt: Union[datetime.date, float] = None,\n        gt: Union[datetime.date, float] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.gte = gte\n        self.lte = lte\n        self.lt = lt\n        self.gt = gt\n        self.name = name\n\n    @property\n    def gte(self) -> Union[datetime.date, float]:\n        """search for values greater than or equal."""\n        return self.__gte\n\n    @gte.setter\n    def gte(self, value: Union[datetime.date, float]):\n        self._property_changed('gte')\n        self.__gte = value        \n\n    @property\n    def lte(self) -> Union[datetime.date, float]:\n        """search for values less than or equal to."""\n        return self.__lte\n\n    @lte.setter\n    def lte(self, value: Union[datetime.date, float]):\n        self._property_changed('lte')\n        self.__lte = value        \n\n    @property\n    def lt(self) -> Union[datetime.date, float]:\n        """search for values less than."""\n        return self.__lt\n\n    @lt.setter\n    def lt(self, value: Union[datetime.date, float]):\n        self._property_changed('lt')\n        self.__lt = value        \n\n    @property\n    def gt(self) -> Union[datetime.date, float]:\n        """search for values greater than."""\n        return self.__gt\n\n    @gt.setter\n    def gt(self, value: Union[datetime.date, float]):\n        self._property_changed('gt')\n        self.__gt = value        \n\n\nclass PCOBenchmarkOptions(Base):\n        \n    """Parameters required for PCO Benchmark"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        name: str = None,\n        target_ratio: str = None\n    ):        \n        super().__init__()\n        self.name = name\n        self.target_ratio = target_ratio\n\n    @property\n    def name(self) -> str:\n        """Benchmark name"""\n        return self.__name\n\n    @name.setter\n    def name(self, value: str):\n        self._property_changed('name')\n        self.__name = value        \n\n    @property\n    def target_ratio(self) -> str:\n        return self.__target_ratio\n\n    @target_ratio.setter\n    def target_ratio(self, value: str):\n        self._property_changed('target_ratio')\n        self.__target_ratio = value        \n\n\nclass PCOExposureAdjustments(Base):\n        \n    """Parameters required for PCO Exposure Adjustments"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pco_number_as_string: str = None,\n        net_subscription_redemption: str = None,\n        net_subscription_redemption_limits: Tuple[str, ...] = None,\n        adjustment_vs_subscription_redemption: str = None,\n        adjustment_vs_subscription_redemption_limits: Tuple[str, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pco_number_as_string = pco_number_as_string\n        self.net_subscription_redemption = net_subscription_redemption\n        self.net_subscription_redemption_limits = net_subscription_redemption_limits\n        self.adjustment_vs_subscription_redemption = adjustment_vs_subscription_redemption\n        self.adjustment_vs_subscription_redemption_limits = adjustment_vs_subscription_redemption_limits\n        self.name = name\n\n    @property\n    def pco_number_as_string(self) -> str:\n        return self.__pco_number_as_string\n\n    @pco_number_as_string.setter\n    def pco_number_as_string(self, value: str):\n        self._property_changed('pco_number_as_string')\n        self.__pco_number_as_string = value        \n\n    @property\n    def net_subscription_redemption(self) -> str:\n        """net subscription and redemption"""\n        return self.__net_subscription_redemption\n\n    @net_subscription_redemption.setter\n    def net_subscription_redemption(self, value: str):\n        self._property_changed('net_subscription_redemption')\n        self.__net_subscription_redemption = value        \n\n    @property\n    def net_subscription_redemption_limits(self) -> Tuple[str, ...]:\n        """Upper and lower limit of subscription and redemption adjustment"""\n        return self.__net_subscription_redemption_limits\n\n    @net_subscription_redemption_limits.setter\n    def net_subscription_redemption_limits(self, value: Tuple[str, ...]):\n        self._property_changed('net_subscription_redemption_limits')\n        self.__net_subscription_redemption_limits = value        \n\n    @property\n    def adjustment_vs_subscription_redemption(self) -> str:\n        """subscription and redemption adjustment"""\n        return self.__adjustment_vs_subscription_redemption\n\n    @adjustment_vs_subscription_redemption.setter\n    def adjustment_vs_subscription_redemption(self, value: str):\n        self._property_changed('adjustment_vs_subscription_redemption')\n        self.__adjustment_vs_subscription_redemption = value        \n\n    @property\n    def adjustment_vs_subscription_redemption_limits(self) -> Tuple[str, ...]:\n        """Upper and lower limit of subscription and redemption adjustment"""\n        return self.__adjustment_vs_subscription_redemption_limits\n\n    @adjustment_vs_subscription_redemption_limits.setter\n    def adjustment_vs_subscription_redemption_limits(self, value: Tuple[str, ...]):\n        self._property_changed('adjustment_vs_subscription_redemption_limits')\n        self.__adjustment_vs_subscription_redemption_limits = value        \n\n\nclass PCOMtMHistoricalData(Base):\n        \n    """Parameters required for PCO Unrealised Mark to Market Historical Data"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        value: str = None,\n        timestamp: datetime.datetime = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.value = value\n        self.timestamp = timestamp\n        self.name = name\n\n    @property\n    def value(self) -> str:\n        """Indicative unrealised mark to market value"""\n        return self.__value\n\n    @value.setter\n    def value(self, value: str):\n        self._property_changed('value')\n        self.__value = value        \n\n    @property\n    def timestamp(self) -> datetime.datetime:\n        """Timestamp of unrealised mark to market of open trade for a currency"""\n        return self.__timestamp\n\n    @timestamp.setter\n    def timestamp(self, value: datetime.datetime):\n        self._property_changed('timestamp')\n        self.__timestamp = value        \n\n\nclass PCOSettlementsData(Base):\n        \n    """Parameters required for a PCO Settlement"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        timestamp: datetime.datetime = None,\n        settlement: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.timestamp = timestamp\n        self.settlement = settlement\n        self.name = name\n\n    @property\n    def timestamp(self) -> datetime.datetime:\n        """Timestamp of settlement"""\n        return self.__timestamp\n\n    @timestamp.setter\n    def timestamp(self, value: datetime.datetime):\n        self._property_changed('timestamp')\n        self.__timestamp = value        \n\n    @property\n    def settlement(self) -> str:\n        """Settlement value"""\n        return self.__settlement\n\n    @settlement.setter\n    def settlement(self, value: str):\n        self._property_changed('settlement')\n        self.__settlement = value        \n\n\nclass PCOTargetDeviationData(Base):\n        \n    """Parameters required for a Target Deviation data"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        value: str = None,\n        timestamp: datetime.datetime = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.value = value\n        self.timestamp = timestamp\n        self.name = name\n\n    @property\n    def value(self) -> str:\n        """Target deviation  value"""\n        return self.__value\n\n    @value.setter\n    def value(self, value: str):\n        self._property_changed('value')\n        self.__value = value        \n\n    @property\n    def timestamp(self) -> datetime.datetime:\n        """Timestamp"""\n        return self.__timestamp\n\n    @timestamp.setter\n    def timestamp(self, value: datetime.datetime):\n        self._property_changed('timestamp')\n        self.__timestamp = value        \n\n\nclass PerformanceStats(Base):\n        \n    """Performance statistics."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        alpha: float = None,\n        annualized_return: float = None,\n        annualized_volatility: float = None,\n        average_return: float = None,\n        average_value: float = None,\n        average_volume_last_month: float = None,\n        best_month: float = None,\n        best_month_date: datetime.date = None,\n        beta: float = None,\n        close_price: float = None,\n        correlation: float = None,\n        current_value: float = None,\n        drawdown_over_return: float = None,\n        high: float = None,\n        high_eod: float = None,\n        last_change: float = None,\n        last_change_pct: float = None,\n        last_date: datetime.date = None,\n        last_value: float = None,\n        low: float = None,\n        low_eod: float = None,\n        max_draw_down: float = None,\n        max_draw_down_duration: int = None,\n        open_price: float = None,\n        positive_months: float = None,\n        sharpe_ratio: float = None,\n        sortino_ratio: float = None,\n        worst_month: float = None,\n        worst_month_date: datetime.date = None,\n        total_return: float = None,\n        volume: float = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.alpha = alpha\n        self.annualized_return = annualized_return\n        self.annualized_volatility = annualized_volatility\n        self.average_return = average_return\n        self.average_value = average_value\n        self.average_volume_last_month = average_volume_last_month\n        self.best_month = best_month\n        self.best_month_date = best_month_date\n        self.beta = beta\n        self.close_price = close_price\n        self.correlation = correlation\n        self.current_value = current_value\n        self.drawdown_over_return = drawdown_over_return\n        self.high = high\n        self.high_eod = high_eod\n        self.last_change = last_change\n        self.last_change_pct = last_change_pct\n        self.last_date = last_date\n        self.last_value = last_value\n        self.low = low\n        self.low_eod = low_eod\n        self.max_draw_down = max_draw_down\n        self.max_draw_down_duration = max_draw_down_duration\n        self.open_price = open_price\n        self.positive_months = positive_months\n        self.sharpe_ratio = sharpe_ratio\n        self.sortino_ratio = sortino_ratio\n        self.worst_month = worst_month\n        self.worst_month_date = worst_month_date\n        self.total_return = total_return\n        self.volume = volume\n        self.name = name\n\n    @property\n    def alpha(self) -> float:\n        """Measure of performance compared to a market benchmark."""\n        return self.__alpha\n\n    @alpha.setter\n    def alpha(self, value: float):\n        self._property_changed('alpha')\n        self.__alpha = value        \n\n    @property\n    def annualized_return(self) -> float:\n        """Compounded Annual Growth Rate (CAGR)."""\n        return self.__annualized_return\n\n    @annualized_return.setter\n    def annualized_return(self, value: float):\n        self._property_changed('annualized_return')\n        self.__annualized_return = value        \n\n    @property\n    def annualized_volatility(self) -> float:\n        """Standard deviation of daily returns, annualized."""\n        return self.__annualized_volatility\n\n    @annualized_volatility.setter\n    def annualized_volatility(self, value: float):\n        self._property_changed('annualized_volatility')\n        self.__annualized_volatility = value        \n\n    @property\n    def average_return(self) -> float:\n        """Average of the performance returns."""\n        return self.__average_return\n\n    @average_return.setter\n    def average_return(self, value: float):\n        self._property_changed('average_return')\n        self.__average_return = value        \n\n    @property\n    def average_value(self) -> float:\n        """Average value."""\n        return self.__average_value\n\n    @average_value.setter\n    def average_value(self, value: float):\n        self._property_changed('average_value')\n        self.__average_value = value        \n\n    @property\n    def average_volume_last_month(self) -> float:\n        """30 day average volume."""\n        return self.__average_volume_last_month\n\n    @average_volume_last_month.setter\n    def average_volume_last_month(self, value: float):\n        self._property_changed('average_volume_last_month')\n        self.__average_volume_last_month = value        \n\n    @property\n    def best_month(self) -> float:\n        """Best monthly return (first to last day of month)."""\n        return self.__best_month\n\n    @best_month.setter\n    def best_month(self, value: float):\n        self._property_changed('best_month')\n        self.__best_month = value        \n\n    @property\n    def best_month_date(self) -> datetime.date:\n        """Best monthly return date (first to last day of month)."""\n        return self.__best_month_date\n\n    @best_month_date.setter\n    def best_month_date(self, value: datetime.date):\n        self._property_changed('best_month_date')\n        self.__best_month_date = value        \n\n    @property\n    def beta(self) -> float:\n        """Measure of volatility compared to a market benchmark."""\n        return self.__beta\n\n    @beta.setter\n    def beta(self, value: float):\n        self._property_changed('beta')\n        self.__beta = value        \n\n    @property\n    def close_price(self) -> float:\n        """previous close price."""\n        return self.__close_price\n\n    @close_price.setter\n    def close_price(self, value: float):\n        self._property_changed('close_price')\n        self.__close_price = value        \n\n    @property\n    def correlation(self) -> float:\n        """Pearson correlation."""\n        return self.__correlation\n\n    @correlation.setter\n    def correlation(self, value: float):\n        self._property_changed('correlation')\n        self.__correlation = value        \n\n    @property\n    def current_value(self) -> float:\n        """Current value."""\n        return self.__current_value\n\n    @current_value.setter\n    def current_value(self, value: float):\n        self._property_changed('current_value')\n        self.__current_value = value        \n\n    @property\n    def drawdown_over_return(self) -> float:\n        """Maximum drawdown divided by annualized return."""\n        return self.__drawdown_over_return\n\n    @drawdown_over_return.setter\n    def drawdown_over_return(self, value: float):\n        self._property_changed('drawdown_over_return')\n        self.__drawdown_over_return = value        \n\n    @property\n    def high(self) -> float:\n        """Highest real time price for the previous 24 hours."""\n        return self.__high\n\n    @high.setter\n    def high(self, value: float):\n        self._property_changed('high')\n        self.__high = value        \n\n    @property\n    def high_eod(self) -> float:\n        """Highest end of day price."""\n        return self.__high_eod\n\n    @high_eod.setter\n    def high_eod(self, value: float):\n        self._property_changed('high_eod')\n        self.__high_eod = value        \n\n    @property\n    def last_change(self) -> float:\n        """Last published value."""\n        return self.__last_change\n\n    @last_change.setter\n    def last_change(self, value: float):\n        self._property_changed('last_change')\n        self.__last_change = value        \n\n    @property\n    def last_change_pct(self) -> float:\n        """Last change in percent."""\n        return self.__last_change_pct\n\n    @last_change_pct.setter\n    def last_change_pct(self, value: float):\n        self._property_changed('last_change_pct')\n        self.__last_change_pct = value        \n\n    @property\n    def last_date(self) -> datetime.date:\n        """Last publication date."""\n        return self.__last_date\n\n    @last_date.setter\n    def last_date(self, value: datetime.date):\n        self._property_changed('last_date')\n        self.__last_date = value        \n\n    @property\n    def last_value(self) -> float:\n        """Last published value."""\n        return self.__last_value\n\n    @last_value.setter\n    def last_value(self, value: float):\n        self._property_changed('last_value')\n        self.__last_value = value        \n\n    @property\n    def low(self) -> float:\n        """Lowest real time price for the previous 24 hours."""\n        return self.__low\n\n    @low.setter\n    def low(self, value: float):\n        self._property_changed('low')\n        self.__low = value        \n\n    @property\n    def low_eod(self) -> float:\n        """Lowest end of day price."""\n        return self.__low_eod\n\n    @low_eod.setter\n    def low_eod(self, value: float):\n        self._property_changed('low_eod')\n        self.__low_eod = value        \n\n    @property\n    def max_draw_down(self) -> float:\n        """Maximum peak to trough percentage drawdown."""\n        return self.__max_draw_down\n\n    @max_draw_down.setter\n    def max_draw_down(self, value: float):\n        self._property_changed('max_draw_down')\n        self.__max_draw_down = value        \n\n    @property\n    def max_draw_down_duration(self) -> int:\n        """Amount of time in days between beginning and end of drawdown."""\n        return self.__max_draw_down_duration\n\n    @max_draw_down_duration.setter\n    def max_draw_down_duration(self, value: int):\n        self._property_changed('max_draw_down_duration')\n        self.__max_draw_down_duration = value        \n\n    @property\n    def open_price(self) -> float:\n        """Open price."""\n        return self.__open_price\n\n    @open_price.setter\n    def open_price(self, value: float):\n        self._property_changed('open_price')\n        self.__open_price = value        \n\n    @property\n    def positive_months(self) -> float:\n        """Percentage of months that performed positively."""\n        return self.__positive_months\n\n    @positive_months.setter\n    def positive_months(self, value: float):\n        self._property_changed('positive_months')\n        self.__positive_months = value        \n\n    @property\n    def sharpe_ratio(self) -> float:\n        """Annualized return of the series minus risk free rate (accrued daily) divided by\n           annual volatility."""\n        return self.__sharpe_ratio\n\n    @sharpe_ratio.setter\n    def sharpe_ratio(self, value: float):\n        self._property_changed('sharpe_ratio')\n        self.__sharpe_ratio = value        \n\n    @property\n    def sortino_ratio(self) -> float:\n        """Annualized return of the series minus risk free rate (accrued daily) divided by\n           annual volatility of negative returns."""\n        return self.__sortino_ratio\n\n    @sortino_ratio.setter\n    def sortino_ratio(self, value: float):\n        self._property_changed('sortino_ratio')\n        self.__sortino_ratio = value        \n\n    @property\n    def worst_month(self) -> float:\n        """Worst monthly return (first to last day of month)."""\n        return self.__worst_month\n\n    @worst_month.setter\n    def worst_month(self, value: float):\n        self._property_changed('worst_month')\n        self.__worst_month = value        \n\n    @property\n    def worst_month_date(self) -> datetime.date:\n        """Worst monthly return date (first to last day of month)."""\n        return self.__worst_month_date\n\n    @worst_month_date.setter\n    def worst_month_date(self, value: datetime.date):\n        self._property_changed('worst_month_date')\n        self.__worst_month_date = value        \n\n    @property\n    def total_return(self) -> float:\n        """Total return."""\n        return self.__total_return\n\n    @total_return.setter\n    def total_return(self, value: float):\n        self._property_changed('total_return')\n        self.__total_return = value        \n\n    @property\n    def volume(self) -> float:\n        """volume."""\n        return self.__volume\n\n    @volume.setter\n    def volume(self, value: float):\n        self._property_changed('volume')\n        self.__volume = value        \n\n\nclass RiskPosition(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        instrument: Priceable,\n        quantity: float = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.instrument = instrument\n        self.quantity = quantity\n        self.name = name\n\n    @property\n    def instrument(self) -> Priceable:\n        """Instrument or Id   To specify a Marquee asset use the asset Id. For listed\n           products use an XRef, e.g. { 'bid': 'NGZ19 Comdty' }, { 'isin':\n           'US912810SD19' }. To specify an instrument use one of the listed\n           types"""\n        return self.__instrument\n\n    @instrument.setter\n    def instrument(self, value: Priceable):\n        self._property_changed('instrument')\n        self.__instrument = value        \n\n    @property\n    def quantity(self) -> float:\n        """Quantity of instrument"""\n        return self.__quantity\n\n    @quantity.setter\n    def quantity(self, value: float):\n        self._property_changed('quantity')\n        self.__quantity = value        \n\n\nclass RiskRequestParameters(Base):\n        \n    """Parameters for the risk request"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        csa_term: str = None,\n        raw_results: bool = False,\n        use_historical_diddles_only: bool = False,\n        name: str = None\n    ):        \n        super().__init__()\n        self.csa_term = csa_term\n        self.raw_results = raw_results\n        self.use_historical_diddles_only = use_historical_diddles_only\n        self.name = name\n\n    @property\n    def csa_term(self) -> str:\n        """The CSA Term for CSA specific discounting, e.g. EUR-1"""\n        return self.__csa_term\n\n    @csa_term.setter\n    def csa_term(self, value: str):\n        self._property_changed('csa_term')\n        self.__csa_term = value        \n\n    @property\n    def raw_results(self) -> bool:\n        return self.__raw_results\n\n    @raw_results.setter\n    def raw_results(self, value: bool):\n        self._property_changed('raw_results')\n        self.__raw_results = value        \n\n    @property\n    def use_historical_diddles_only(self) -> bool:\n        """Enables a backward compatible pricing mode"""\n        return self.__use_historical_diddles_only\n\n    @use_historical_diddles_only.setter\n    def use_historical_diddles_only(self, value: bool):\n        self._property_changed('use_historical_diddles_only')\n        self.__use_historical_diddles_only = value        \n\n\nclass SimpleParty(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        party_type: str = None,\n        party_name: str = None,\n        party_book: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.party_type = party_type\n        self.party_name = party_name\n        self.party_book = party_book\n        self.name = name\n\n    @property\n    def party_type(self) -> str:\n        return self.__party_type\n\n    @party_type.setter\n    def party_type(self, value: str):\n        self._property_changed('party_type')\n        self.__party_type = value        \n\n    @property\n    def party_name(self) -> str:\n        return self.__party_name\n\n    @party_name.setter\n    def party_name(self, value: str):\n        self._property_changed('party_name')\n        self.__party_name = value        \n\n    @property\n    def party_book(self) -> str:\n        return self.__party_book\n\n    @party_book.setter\n    def party_book(self, value: str):\n        self._property_changed('party_book')\n        self.__party_book = value        \n\n\nclass SocialDomain(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        onboarded: dict,\n        returns_enabled: bool = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.onboarded = onboarded\n        self.returns_enabled = returns_enabled\n        self.name = name\n\n    @property\n    def onboarded(self) -> dict:\n        return self.__onboarded\n\n    @onboarded.setter\n    def onboarded(self, value: dict):\n        self._property_changed('onboarded')\n        self.__onboarded = value        \n\n    @property\n    def returns_enabled(self) -> bool:\n        """True if the fund has returns enabled"""\n        return self.__returns_enabled\n\n    @returns_enabled.setter\n    def returns_enabled(self, value: bool):\n        self._property_changed('returns_enabled')\n        self.__returns_enabled = value        \n\n\nclass TimeFilter(Base):\n        \n    """Filter to restrict data to a range of hours per day."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        start_hours: str,\n        end_hours: str,\n        time_zone: str,\n        name: str = None\n    ):        \n        super().__init__()\n        self.start_hours = start_hours\n        self.end_hours = end_hours\n        self.time_zone = time_zone\n        self.name = name\n\n    @property\n    def start_hours(self) -> str:\n        """Start hours in the format HH::MM::SS after which the data will be shown. Data is\n           inclusive of the startHours value."""\n        return self.__start_hours\n\n    @start_hours.setter\n    def start_hours(self, value: str):\n        self._property_changed('start_hours')\n        self.__start_hours = value        \n\n    @property\n    def end_hours(self) -> str:\n        """End hours in the format HH::MM::SS up to which the data will be shown. Data is\n           exclusive of the endHours value with a precision of 1 second."""\n        return self.__end_hours\n\n    @end_hours.setter\n    def end_hours(self, value: str):\n        self._property_changed('end_hours')\n        self.__end_hours = value        \n\n    @property\n    def time_zone(self) -> str:\n        """The time zone with respect to which the start and end hours will be applied\n           (must be a valid IANA TimeZone identifier)."""\n        return self.__time_zone\n\n    @time_zone.setter\n    def time_zone(self, value: str):\n        self._property_changed('time_zone')\n        self.__time_zone = value        \n\n\nclass WeightedPosition(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        asset_id: str,\n        weight: float,\n        name: str = None\n    ):        \n        super().__init__()\n        self.asset_id = asset_id\n        self.weight = weight\n        self.name = name\n\n    @property\n    def asset_id(self) -> str:\n        """Marquee unique identifier"""\n        return self.__asset_id\n\n    @asset_id.setter\n    def asset_id(self, value: str):\n        self._property_changed('asset_id')\n        self.__asset_id = value        \n\n    @property\n    def weight(self) -> float:\n        """Relative net weight of the given position"""\n        return self.__weight\n\n    @weight.setter\n    def weight(self, value: float):\n        self._property_changed('weight')\n        self.__weight = value        \n\n\nclass XRef(Priceable):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        ric: str = None,\n        rcic: str = None,\n        eid: str = None,\n        gsideid: str = None,\n        gsid: str = None,\n        gsid_equivalent: str = None,\n        cid: str = None,\n        bbid: str = None,\n        bcid: str = None,\n        delisted: str = None,\n        bbid_equivalent: str = None,\n        cusip: str = None,\n        gss: str = None,\n        isin: str = None,\n        jsn: str = None,\n        prime_id: str = None,\n        sedol: str = None,\n        ticker: str = None,\n        valoren: str = None,\n        wpk: str = None,\n        gsn: str = None,\n        sec_name: str = None,\n        cross: str = None,\n        simon_id: str = None,\n        em_id: str = None,\n        cm_id: str = None,\n        lms_id: str = None,\n        tdapi: str = None,\n        mdapi: str = None,\n        mdapi_class: str = None,\n        mic: str = None,\n        sf_id: str = None,\n        dollar_cross: str = None,\n        mq_symbol: str = None,\n        primary_country_ric: str = None,\n        pnode_id: str = None,\n        wi_id: str = None,\n        ps_id: str = None,\n        pl_id: str = None,\n        exchange_code: str = None,\n        plot_id: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.ric = ric\n        self.rcic = rcic\n        self.eid = eid\n        self.gsideid = gsideid\n        self.gsid = gsid\n        self.gsid_equivalent = gsid_equivalent\n        self.cid = cid\n        self.bbid = bbid\n        self.bcid = bcid\n        self.delisted = delisted\n        self.bbid_equivalent = bbid_equivalent\n        self.cusip = cusip\n        self.gss = gss\n        self.isin = isin\n        self.jsn = jsn\n        self.prime_id = prime_id\n        self.sedol = sedol\n        self.ticker = ticker\n        self.valoren = valoren\n        self.wpk = wpk\n        self.gsn = gsn\n        self.sec_name = sec_name\n        self.cross = cross\n        self.simon_id = simon_id\n        self.em_id = em_id\n        self.cm_id = cm_id\n        self.lms_id = lms_id\n        self.tdapi = tdapi\n        self.mdapi = mdapi\n        self.mdapi_class = mdapi_class\n        self.mic = mic\n        self.sf_id = sf_id\n        self.dollar_cross = dollar_cross\n        self.mq_symbol = mq_symbol\n        self.primary_country_ric = primary_country_ric\n        self.pnode_id = pnode_id\n        self.wi_id = wi_id\n        self.ps_id = ps_id\n        self.pl_id = pl_id\n        self.exchange_code = exchange_code\n        self.plot_id = plot_id\n        self.name = name\n\n    @property\n    def ric(self) -> str:\n        """Reuters Instrument Code identifier"""\n        return self.__ric\n\n    @ric.setter\n    def ric(self, value: str):\n        self._property_changed('ric')\n        self.__ric = value        \n\n    @property\n    def rcic(self) -> str:\n        """Reuters Composite Instrument Code Identifier"""\n        return self.__rcic\n\n    @rcic.setter\n    def rcic(self, value: str):\n        self._property_changed('rcic')\n        self.__rcic = value        \n\n    @property\n    def eid(self) -> str:\n        """EID Identifier"""\n        return self.__eid\n\n    @eid.setter\n    def eid(self, value: str):\n        self._property_changed('eid')\n        self.__eid = value        \n\n    @property\n    def gsideid(self) -> str:\n        """GSID_EID Identifier"""\n        return self.__gsideid\n\n    @gsideid.setter\n    def gsideid(self, value: str):\n        self._property_changed('gsideid')\n        self.__gsideid = value        \n\n    @property\n    def gsid(self) -> str:\n        """GSID Identifier"""\n        return self.__gsid\n\n    @gsid.setter\n    def gsid(self, value: str):\n        self._property_changed('gsid')\n        self.__gsid = value        \n\n    @property\n    def gsid_equivalent(self) -> str:\n        """GSID Equivalent Identifier"""\n        return self.__gsid_equivalent\n\n    @gsid_equivalent.setter\n    def gsid_equivalent(self, value: str):\n        self._property_changed('gsid_equivalent')\n        self.__gsid_equivalent = value        \n\n    @property\n    def cid(self) -> str:\n        """Company Id Identifier"""\n        return self.__cid\n\n    @cid.setter\n    def cid(self, value: str):\n        self._property_changed('cid')\n        self.__cid = value        \n\n    @property\n    def bbid(self) -> str:\n        """Bloomberg Id Identifier"""\n        return self.__bbid\n\n    @bbid.setter\n    def bbid(self, value: str):\n        self._property_changed('bbid')\n        self.__bbid = value        \n\n    @property\n    def bcid(self) -> str:\n        """Bloomberg Composite Identifier"""\n        return self.__bcid\n\n    @bcid.setter\n    def bcid(self, value: str):\n        self._property_changed('bcid')\n        self.__bcid = value        \n\n    @property\n    def delisted(self) -> str:\n        """Whether an asset has been delisted"""\n        return self.__delisted\n\n    @delisted.setter\n    def delisted(self, value: str):\n        self._property_changed('delisted')\n        self.__delisted = value        \n\n    @property\n    def bbid_equivalent(self) -> str:\n        """Bloomberg Equivalent Identifier"""\n        return self.__bbid_equivalent\n\n    @bbid_equivalent.setter\n    def bbid_equivalent(self, value: str):\n        self._property_changed('bbid_equivalent')\n        self.__bbid_equivalent = value        \n\n    @property\n    def cusip(self) -> str:\n        """Cusip Identifier"""\n        return self.__cusip\n\n    @cusip.setter\n    def cusip(self, value: str):\n        self._property_changed('cusip')\n        self.__cusip = value        \n\n    @property\n    def gss(self) -> str:\n        """GS Symbol identifier"""\n        return self.__gss\n\n    @gss.setter\n    def gss(self, value: str):\n        self._property_changed('gss')\n        self.__gss = value        \n\n    @property\n    def isin(self) -> str:\n        """International Security Number"""\n        return self.__isin\n\n    @isin.setter\n    def isin(self, value: str):\n        self._property_changed('isin')\n        self.__isin = value        \n\n    @property\n    def jsn(self) -> str:\n        """Japan Security Number"""\n        return self.__jsn\n\n    @jsn.setter\n    def jsn(self, value: str):\n        self._property_changed('jsn')\n        self.__jsn = value        \n\n    @property\n    def prime_id(self) -> str:\n        """PrimeID Identifier"""\n        return self.__prime_id\n\n    @prime_id.setter\n    def prime_id(self, value: str):\n        self._property_changed('prime_id')\n        self.__prime_id = value        \n\n    @property\n    def sedol(self) -> str:\n        """Sedol Identifier"""\n        return self.__sedol\n\n    @sedol.setter\n    def sedol(self, value: str):\n        self._property_changed('sedol')\n        self.__sedol = value        \n\n    @property\n    def ticker(self) -> str:\n        """Ticker Identifier"""\n        return self.__ticker\n\n    @ticker.setter\n    def ticker(self, value: str):\n        self._property_changed('ticker')\n        self.__ticker = value        \n\n    @property\n    def valoren(self) -> str:\n        """Valoren Identifier"""\n        return self.__valoren\n\n    @valoren.setter\n    def valoren(self, value: str):\n        self._property_changed('valoren')\n        self.__valoren = value        \n\n    @property\n    def wpk(self) -> str:\n        """Wertpapier Kenn-Nummer"""\n        return self.__wpk\n\n    @wpk.setter\n    def wpk(self, value: str):\n        self._property_changed('wpk')\n        self.__wpk = value        \n\n    @property\n    def gsn(self) -> str:\n        """Goldman Sachs internal product number"""\n        return self.__gsn\n\n    @gsn.setter\n    def gsn(self, value: str):\n        self._property_changed('gsn')\n        self.__gsn = value        \n\n    @property\n    def sec_name(self) -> str:\n        """Internal Goldman Sachs security name"""\n        return self.__sec_name\n\n    @sec_name.setter\n    def sec_name(self, value: str):\n        self._property_changed('sec_name')\n        self.__sec_name = value        \n\n    @property\n    def cross(self) -> str:\n        """Cross identifier"""\n        return self.__cross\n\n    @cross.setter\n    def cross(self, value: str):\n        self._property_changed('cross')\n        self.__cross = value        \n\n    @property\n    def simon_id(self) -> str:\n        """SIMON product identifier"""\n        return self.__simon_id\n\n    @simon_id.setter\n    def simon_id(self, value: str):\n        self._property_changed('simon_id')\n        self.__simon_id = value        \n\n    @property\n    def em_id(self) -> str:\n        """Entity Master Identifier"""\n        return self.__em_id\n\n    @em_id.setter\n    def em_id(self, value: str):\n        self._property_changed('em_id')\n        self.__em_id = value        \n\n    @property\n    def cm_id(self) -> str:\n        """Client Master Party Id"""\n        return self.__cm_id\n\n    @cm_id.setter\n    def cm_id(self, value: str):\n        self._property_changed('cm_id')\n        self.__cm_id = value        \n\n    @property\n    def lms_id(self) -> str:\n        """Listed Market Symbol"""\n        return self.__lms_id\n\n    @lms_id.setter\n    def lms_id(self, value: str):\n        self._property_changed('lms_id')\n        self.__lms_id = value        \n\n    @property\n    def tdapi(self) -> str:\n        """TDAPI Description"""\n        return self.__tdapi\n\n    @tdapi.setter\n    def tdapi(self, value: str):\n        self._property_changed('tdapi')\n        self.__tdapi = value        \n\n    @property\n    def mdapi(self) -> str:\n        """MDAPI Asset"""\n        return self.__mdapi\n\n    @mdapi.setter\n    def mdapi(self, value: str):\n        self._property_changed('mdapi')\n        self.__mdapi = value        \n\n    @property\n    def mdapi_class(self) -> str:\n        """MDAPI Asset Class"""\n        return self.__mdapi_class\n\n    @mdapi_class.setter\n    def mdapi_class(self, value: str):\n        self._property_changed('mdapi_class')\n        self.__mdapi_class = value        \n\n    @property\n    def mic(self) -> str:\n        """Market Identifier Code"""\n        return self.__mic\n\n    @mic.setter\n    def mic(self, value: str):\n        self._property_changed('mic')\n        self.__mic = value        \n\n    @property\n    def sf_id(self) -> str:\n        """SalesForce ID"""\n        return self.__sf_id\n\n    @sf_id.setter\n    def sf_id(self, value: str):\n        self._property_changed('sf_id')\n        self.__sf_id = value        \n\n    @property\n    def dollar_cross(self) -> str:\n        """USD cross identifier for a particular currency"""\n        return self.__dollar_cross\n\n    @dollar_cross.setter\n    def dollar_cross(self, value: str):\n        self._property_changed('dollar_cross')\n        self.__dollar_cross = value        \n\n    @property\n    def mq_symbol(self) -> str:\n        """Marquee Symbol for generic MQ entities"""\n        return self.__mq_symbol\n\n    @mq_symbol.setter\n    def mq_symbol(self, value: str):\n        self._property_changed('mq_symbol')\n        self.__mq_symbol = value        \n\n    @property\n    def primary_country_ric(self) -> str:\n        """Reuters Primary Country Instrument Code Identifier"""\n        return self.__primary_country_ric\n\n    @primary_country_ric.setter\n    def primary_country_ric(self, value: str):\n        self._property_changed('primary_country_ric')\n        self.__primary_country_ric = value        \n\n    @property\n    def pnode_id(self) -> str:\n        """Pricing node identifier sourced from Morningstar"""\n        return self.__pnode_id\n\n    @pnode_id.setter\n    def pnode_id(self, value: str):\n        self._property_changed('pnode_id')\n        self.__pnode_id = value        \n\n    @property\n    def wi_id(self) -> str:\n        """Weather Index Identifier"""\n        return self.__wi_id\n\n    @wi_id.setter\n    def wi_id(self, value: str):\n        self._property_changed('wi_id')\n        self.__wi_id = value        \n\n    @property\n    def ps_id(self) -> str:\n        """Platts Symbol"""\n        return self.__ps_id\n\n    @ps_id.setter\n    def ps_id(self, value: str):\n        self._property_changed('ps_id')\n        self.__ps_id = value        \n\n    @property\n    def pl_id(self) -> str:\n        """Platts Symbol Name"""\n        return self.__pl_id\n\n    @pl_id.setter\n    def pl_id(self, value: str):\n        self._property_changed('pl_id')\n        self.__pl_id = value        \n\n    @property\n    def exchange_code(self) -> str:\n        """EEX Exchange Code"""\n        return self.__exchange_code\n\n    @exchange_code.setter\n    def exchange_code(self, value: str):\n        self._property_changed('exchange_code')\n        self.__exchange_code = value        \n\n    @property\n    def plot_id(self) -> str:\n        """Plot Identifier"""\n        return self.__plot_id\n\n    @plot_id.setter\n    def plot_id(self, value: str):\n        self._property_changed('plot_id')\n        self.__plot_id = value        \n\n\nclass CSLCurrency(Base):\n        \n    """A currency"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        string_value: Union[Currency, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.string_value = string_value\n        self.name = name\n\n    @property\n    def string_value(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__string_value\n\n    @string_value.setter\n    def string_value(self, value: Union[Currency, str]):\n        self._property_changed('string_value')\n        self.__string_value = get_enum_value(Currency, value)        \n\n\nclass CSLDateArray(Base):\n        \n    """An array of dates"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        date_values: Tuple[CSLDate, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.date_values = date_values\n        self.name = name\n\n    @property\n    def date_values(self) -> Tuple[CSLDate, ...]:\n        """A date"""\n        return self.__date_values\n\n    @date_values.setter\n    def date_values(self, value: Tuple[CSLDate, ...]):\n        self._property_changed('date_values')\n        self.__date_values = value        \n\n\nclass CSLDateArrayNamedParam(Base):\n        \n    """A named array of dates"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        date_values: Tuple[CSLDate, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.date_values = date_values\n        self.name = name\n\n    @property\n    def date_values(self) -> Tuple[CSLDate, ...]:\n        """A date"""\n        return self.__date_values\n\n    @date_values.setter\n    def date_values(self, value: Tuple[CSLDate, ...]):\n        self._property_changed('date_values')\n        self.__date_values = value        \n\n    @property\n    def name(self) -> str:\n        """A name for the array"""\n        return self.__name\n\n    @name.setter\n    def name(self, value: str):\n        self._property_changed('name')\n        self.__name = value        \n\n\nclass CSLDoubleArray(Base):\n        \n    """An array of doubles"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        double_values: Tuple[CSLDouble, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.double_values = double_values\n        self.name = name\n\n    @property\n    def double_values(self) -> Tuple[CSLDouble, ...]:\n        """A double"""\n        return self.__double_values\n\n    @double_values.setter\n    def double_values(self, value: Tuple[CSLDouble, ...]):\n        self._property_changed('double_values')\n        self.__double_values = value        \n\n\nclass CSLFXCrossArray(Base):\n        \n    """An array of FX crosses"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        fx_cross_values: Tuple[CSLFXCross, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.fx_cross_values = fx_cross_values\n        self.name = name\n\n    @property\n    def fx_cross_values(self) -> Tuple[CSLFXCross, ...]:\n        """An FX cross"""\n        return self.__fx_cross_values\n\n    @fx_cross_values.setter\n    def fx_cross_values(self, value: Tuple[CSLFXCross, ...]):\n        self._property_changed('fx_cross_values')\n        self.__fx_cross_values = value        \n\n\nclass CSLIndexArray(Base):\n        \n    """An array of indices"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        index_values: Tuple[CSLIndex, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.index_values = index_values\n        self.name = name\n\n    @property\n    def index_values(self) -> Tuple[CSLIndex, ...]:\n        """An index"""\n        return self.__index_values\n\n    @index_values.setter\n    def index_values(self, value: Tuple[CSLIndex, ...]):\n        self._property_changed('index_values')\n        self.__index_values = value        \n\n\nclass CSLSimpleScheduleArray(Base):\n        \n    """An array of simple schedules"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        simple_schedule_values: Tuple[CSLSimpleSchedule, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.simple_schedule_values = simple_schedule_values\n        self.name = name\n\n    @property\n    def simple_schedule_values(self) -> Tuple[CSLSimpleSchedule, ...]:\n        """A fixing date, settlement date pair"""\n        return self.__simple_schedule_values\n\n    @simple_schedule_values.setter\n    def simple_schedule_values(self, value: Tuple[CSLSimpleSchedule, ...]):\n        self._property_changed('simple_schedule_values')\n        self.__simple_schedule_values = value        \n\n\nclass CSLStockArray(Base):\n        \n    """An array of stocks"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        stock_values: Tuple[CSLStock, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.stock_values = stock_values\n        self.name = name\n\n    @property\n    def stock_values(self) -> Tuple[CSLStock, ...]:\n        """A stock"""\n        return self.__stock_values\n\n    @stock_values.setter\n    def stock_values(self, value: Tuple[CSLStock, ...]):\n        self._property_changed('stock_values')\n        self.__stock_values = value        \n\n\nclass CSLStringArray(Base):\n        \n    """An array of strings"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        string_values: Tuple[CSLString, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.string_values = string_values\n        self.name = name\n\n    @property\n    def string_values(self) -> Tuple[CSLString, ...]:\n        """A string"""\n        return self.__string_values\n\n    @string_values.setter\n    def string_values(self, value: Tuple[CSLString, ...]):\n        self._property_changed('string_values')\n        self.__string_values = value        \n\n\nclass CarryScenario(Scenario):\n        \n    """A scenario to manipulate time along the forward curve"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        date: Union[datetime.date, str] = None,\n        time_shift: int = None,\n        roll_to_fwds: bool = True,\n        holiday_calendar: Union[PricingLocation, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.date = date\n        self.time_shift = time_shift\n        self.roll_to_fwds = roll_to_fwds\n        self.holiday_calendar = holiday_calendar\n        self.name = name\n\n    @property\n    def scenario_type(self) -> str:\n        """CarryScenario"""\n        return 'CarryScenario'        \n\n    @property\n    def date(self) -> Union[datetime.date, str]:\n        """Date to shift markets to (absolute or relative)"""\n        return self.__date\n\n    @date.setter\n    def date(self, value: Union[datetime.date, str]):\n        self._property_changed('date')\n        self.__date = value        \n\n    @property\n    def time_shift(self) -> int:\n        """Number of days to shift market (in days)"""\n        return self.__time_shift\n\n    @time_shift.setter\n    def time_shift(self, value: int):\n        self._property_changed('time_shift')\n        self.__time_shift = value        \n\n    @property\n    def roll_to_fwds(self) -> bool:\n        """Roll along the forward curve or roll in spot space"""\n        return self.__roll_to_fwds\n\n    @roll_to_fwds.setter\n    def roll_to_fwds(self, value: bool):\n        self._property_changed('roll_to_fwds')\n        self.__roll_to_fwds = value        \n\n    @property\n    def holiday_calendar(self) -> Union[PricingLocation, str]:\n        """Calendar to use for relative dates"""\n        return self.__holiday_calendar\n\n    @holiday_calendar.setter\n    def holiday_calendar(self, value: Union[PricingLocation, str]):\n        self._property_changed('holiday_calendar')\n        self.__holiday_calendar = get_enum_value(PricingLocation, value)        \n\n\nclass CloseMarket(Base):\n        \n    """Close market"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        date: datetime.date,\n        location: Union[PricingLocation, str],\n        name: str = None\n    ):        \n        super().__init__()\n        self.date = date\n        self.location = location\n        self.name = name\n\n    @property\n    def market_type(self) -> str:\n        """CloseMarket"""\n        return 'CloseMarket'        \n\n    @property\n    def date(self) -> datetime.date:\n        """Date for the market data"""\n        return self.__date\n\n    @date.setter\n    def date(self, value: datetime.date):\n        self._property_changed('date')\n        self.__date = value        \n\n    @property\n    def location(self) -> Union[PricingLocation, str]:\n        """Location for the market data"""\n        return self.__location\n\n    @location.setter\n    def location(self, value: Union[PricingLocation, str]):\n        self._property_changed('location')\n        self.__location = get_enum_value(PricingLocation, value)        \n\n\nclass CommodPrice(Base):\n        \n    """Commodity price in units and currency, used for quoting strike, premium, fixed\n       price"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        price: Union[float, str] = None,\n        unit: Union[CommodUnit, str] = None,\n        currency: Union[CurrencyName, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.price = price\n        self.unit = unit\n        self.currency = currency\n        self.name = name\n\n    @property\n    def price(self) -> Union[float, str]:\n        """price"""\n        return self.__price\n\n    @price.setter\n    def price(self, value: Union[float, str]):\n        self._property_changed('price')\n        self.__price = value        \n\n    @property\n    def unit(self) -> Union[CommodUnit, str]:\n        """A coding scheme value to identify the unit of measure (e.g. Therms) in which the\n           undelryer is denominated."""\n        return self.__unit\n\n    @unit.setter\n    def unit(self, value: Union[CommodUnit, str]):\n        self._property_changed('unit')\n        self.__unit = get_enum_value(CommodUnit, value)        \n\n    @property\n    def currency(self) -> Union[CurrencyName, str]:\n        """Currency Names"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[CurrencyName, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(CurrencyName, value)        \n\n\nclass GIRDomain(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        document_links: Tuple[Link, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.document_links = document_links\n        self.name = name\n\n    @property\n    def document_links(self) -> Tuple[Link, ...]:\n        """Documents related to this asset"""\n        return self.__document_links\n\n    @document_links.setter\n    def document_links(self, value: Tuple[Link, ...]):\n        self._property_changed('document_links')\n        self.__document_links = value        \n\n\nclass ISelectNewParameter(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        early_unwind_after: float = None,\n        early_unwind_applicable: str = None,\n        expiry_date_rule: str = None,\n        option_target_expiry_parameter: float = None,\n        option_early_unwind_days: float = None,\n        in_alpha: bool = None,\n        is_fsr_target_factor: bool = None,\n        fsr_max_ratio: float = None,\n        fsr_min_ratio: float = None,\n        module_enabled: bool = None,\n        module_name: str = None,\n        target_strike: float = None,\n        strike_method: Union[StrikeMethodType, str] = None,\n        option_expiry: Union[OptionExpiryType, str] = None,\n        bloomberg_id: str = None,\n        stock_id: str = None,\n        ric: str = None,\n        new_weight: float = None,\n        notional: float = None,\n        leverage: float = None,\n        option_type: Union[OptionType, str] = None,\n        option_strike_type: Union[OptionStrikeType, str] = None,\n        credit_option_type: Union[CreditOptionType, str] = None,\n        credit_option_strike_type: Union[CreditOptionStrikeType, str] = None,\n        strike_relative: float = None,\n        trade_type: Union[TradeType, str] = None,\n        signal: float = None,\n        new_signal: float = None,\n        new_min_weight: float = None,\n        new_max_weight: float = None,\n        min_weight: float = None,\n        max_weight: float = None,\n        election: str = None,\n        base_date: str = None,\n        commodity: str = None,\n        component_weight: float = None,\n        contract_nearby_number: float = None,\n        expiration_schedule: str = None,\n        fixing_type: str = None,\n        last_eligible_date: float = None,\n        num_roll_days: float = None,\n        roll_end: float = None,\n        roll_start: float = None,\n        roll_type: str = None,\n        valid_contract_expiry: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.early_unwind_after = early_unwind_after\n        self.early_unwind_applicable = early_unwind_applicable\n        self.expiry_date_rule = expiry_date_rule\n        self.option_target_expiry_parameter = option_target_expiry_parameter\n        self.option_early_unwind_days = option_early_unwind_days\n        self.in_alpha = in_alpha\n        self.is_fsr_target_factor = is_fsr_target_factor\n        self.fsr_max_ratio = fsr_max_ratio\n        self.fsr_min_ratio = fsr_min_ratio\n        self.module_enabled = module_enabled\n        self.module_name = module_name\n        self.target_strike = target_strike\n        self.strike_method = strike_method\n        self.option_expiry = option_expiry\n        self.bloomberg_id = bloomberg_id\n        self.stock_id = stock_id\n        self.ric = ric\n        self.new_weight = new_weight\n        self.notional = notional\n        self.leverage = leverage\n        self.option_type = option_type\n        self.option_strike_type = option_strike_type\n        self.credit_option_type = credit_option_type\n        self.credit_option_strike_type = credit_option_strike_type\n        self.strike_relative = strike_relative\n        self.trade_type = trade_type\n        self.signal = signal\n        self.new_signal = new_signal\n        self.new_min_weight = new_min_weight\n        self.new_max_weight = new_max_weight\n        self.min_weight = min_weight\n        self.max_weight = max_weight\n        self.election = election\n        self.base_date = base_date\n        self.commodity = commodity\n        self.component_weight = component_weight\n        self.contract_nearby_number = contract_nearby_number\n        self.expiration_schedule = expiration_schedule\n        self.fixing_type = fixing_type\n        self.last_eligible_date = last_eligible_date\n        self.num_roll_days = num_roll_days\n        self.roll_end = roll_end\n        self.roll_start = roll_start\n        self.roll_type = roll_type\n        self.valid_contract_expiry = valid_contract_expiry\n        self.name = name\n\n    @property\n    def early_unwind_after(self) -> float:\n        return self.__early_unwind_after\n\n    @early_unwind_after.setter\n    def early_unwind_after(self, value: float):\n        self._property_changed('early_unwind_after')\n        self.__early_unwind_after = value        \n\n    @property\n    def early_unwind_applicable(self) -> str:\n        """Indicates whether the module can be unwinded early"""\n        return self.__early_unwind_applicable\n\n    @early_unwind_applicable.setter\n    def early_unwind_applicable(self, value: str):\n        self._property_changed('early_unwind_applicable')\n        self.__early_unwind_applicable = value        \n\n    @property\n    def expiry_date_rule(self) -> str:\n        """Free text description of asset. Description provided will be indexed in the\n           search service for free text relevance match"""\n        return self.__expiry_date_rule\n\n    @expiry_date_rule.setter\n    def expiry_date_rule(self, value: str):\n        self._property_changed('expiry_date_rule')\n        self.__expiry_date_rule = value        \n\n    @property\n    def option_target_expiry_parameter(self) -> float:\n        return self.__option_target_expiry_parameter\n\n    @option_target_expiry_parameter.setter\n    def option_target_expiry_parameter(self, value: float):\n        self._property_changed('option_target_expiry_parameter')\n        self.__option_target_expiry_parameter = value        \n\n    @property\n    def option_early_unwind_days(self) -> float:\n        return self.__option_early_unwind_days\n\n    @option_early_unwind_days.setter\n    def option_early_unwind_days(self, value: float):\n        self._property_changed('option_early_unwind_days')\n        self.__option_early_unwind_days = value        \n\n    @property\n    def in_alpha(self) -> bool:\n        return self.__in_alpha\n\n    @in_alpha.setter\n    def in_alpha(self, value: bool):\n        self._property_changed('in_alpha')\n        self.__in_alpha = value        \n\n    @property\n    def is_fsr_target_factor(self) -> bool:\n        return self.__is_fsr_target_factor\n\n    @is_fsr_target_factor.setter\n    def is_fsr_target_factor(self, value: bool):\n        self._property_changed('is_fsr_target_factor')\n        self.__is_fsr_target_factor = value        \n\n    @property\n    def fsr_max_ratio(self) -> float:\n        return self.__fsr_max_ratio\n\n    @fsr_max_ratio.setter\n    def fsr_max_ratio(self, value: float):\n        self._property_changed('fsr_max_ratio')\n        self.__fsr_max_ratio = value        \n\n    @property\n    def fsr_min_ratio(self) -> float:\n        return self.__fsr_min_ratio\n\n    @fsr_min_ratio.setter\n    def fsr_min_ratio(self, value: float):\n        self._property_changed('fsr_min_ratio')\n        self.__fsr_min_ratio = value        \n\n    @property\n    def module_enabled(self) -> bool:\n        """Enable to disable the module"""\n        return self.__module_enabled\n\n    @module_enabled.setter\n    def module_enabled(self, value: bool):\n        self._property_changed('module_enabled')\n        self.__module_enabled = value        \n\n    @property\n    def module_name(self) -> str:\n        """Free text description of asset. Description provided will be indexed in the\n           search service for free text relevance match"""\n        return self.__module_name\n\n    @module_name.setter\n    def module_name(self, value: str):\n        self._property_changed('module_name')\n        self.__module_name = value        \n\n    @property\n    def target_strike(self) -> float:\n        return self.__target_strike\n\n    @target_strike.setter\n    def target_strike(self, value: float):\n        self._property_changed('target_strike')\n        self.__target_strike = value        \n\n    @property\n    def strike_method(self) -> Union[StrikeMethodType, str]:\n        return self.__strike_method\n\n    @strike_method.setter\n    def strike_method(self, value: Union[StrikeMethodType, str]):\n        self._property_changed('strike_method')\n        self.__strike_method = get_enum_value(StrikeMethodType, value)        \n\n    @property\n    def option_expiry(self) -> Union[OptionExpiryType, str]:\n        return self.__option_expiry\n\n    @option_expiry.setter\n    def option_expiry(self, value: Union[OptionExpiryType, str]):\n        self._property_changed('option_expiry')\n        self.__option_expiry = get_enum_value(OptionExpiryType, value)        \n\n    @property\n    def bloomberg_id(self) -> str:\n        return self.__bloomberg_id\n\n    @bloomberg_id.setter\n    def bloomberg_id(self, value: str):\n        self._property_changed('bloomberg_id')\n        self.__bloomberg_id = value        \n\n    @property\n    def stock_id(self) -> str:\n        return self.__stock_id\n\n    @stock_id.setter\n    def stock_id(self, value: str):\n        self._property_changed('stock_id')\n        self.__stock_id = value        \n\n    @property\n    def ric(self) -> str:\n        return self.__ric\n\n    @ric.setter\n    def ric(self, value: str):\n        self._property_changed('ric')\n        self.__ric = value        \n\n    @property\n    def new_weight(self) -> float:\n        return self.__new_weight\n\n    @new_weight.setter\n    def new_weight(self, value: float):\n        self._property_changed('new_weight')\n        self.__new_weight = value        \n\n    @property\n    def notional(self) -> float:\n        return self.__notional\n\n    @notional.setter\n    def notional(self, value: float):\n        self._property_changed('notional')\n        self.__notional = value        \n\n    @property\n    def leverage(self) -> float:\n        return self.__leverage\n\n    @leverage.setter\n    def leverage(self, value: float):\n        self._property_changed('leverage')\n        self.__leverage = value        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def option_strike_type(self) -> Union[OptionStrikeType, str]:\n        return self.__option_strike_type\n\n    @option_strike_type.setter\n    def option_strike_type(self, value: Union[OptionStrikeType, str]):\n        self._property_changed('option_strike_type')\n        self.__option_strike_type = get_enum_value(OptionStrikeType, value)        \n\n    @property\n    def credit_option_type(self) -> Union[CreditOptionType, str]:\n        return self.__credit_option_type\n\n    @credit_option_type.setter\n    def credit_option_type(self, value: Union[CreditOptionType, str]):\n        self._property_changed('credit_option_type')\n        self.__credit_option_type = get_enum_value(CreditOptionType, value)        \n\n    @property\n    def credit_option_strike_type(self) -> Union[CreditOptionStrikeType, str]:\n        return self.__credit_option_strike_type\n\n    @credit_option_strike_type.setter\n    def credit_option_strike_type(self, value: Union[CreditOptionStrikeType, str]):\n        self._property_changed('credit_option_strike_type')\n        self.__credit_option_strike_type = get_enum_value(CreditOptionStrikeType, value)        \n\n    @property\n    def strike_relative(self) -> float:\n        return self.__strike_relative\n\n    @strike_relative.setter\n    def strike_relative(self, value: float):\n        self._property_changed('strike_relative')\n        self.__strike_relative = value        \n\n    @property\n    def trade_type(self) -> Union[TradeType, str]:\n        """Direction"""\n        return self.__trade_type\n\n    @trade_type.setter\n    def trade_type(self, value: Union[TradeType, str]):\n        self._property_changed('trade_type')\n        self.__trade_type = get_enum_value(TradeType, value)        \n\n    @property\n    def signal(self) -> float:\n        return self.__signal\n\n    @signal.setter\n    def signal(self, value: float):\n        self._property_changed('signal')\n        self.__signal = value        \n\n    @property\n    def new_signal(self) -> float:\n        return self.__new_signal\n\n    @new_signal.setter\n    def new_signal(self, value: float):\n        self._property_changed('new_signal')\n        self.__new_signal = value        \n\n    @property\n    def new_min_weight(self) -> float:\n        return self.__new_min_weight\n\n    @new_min_weight.setter\n    def new_min_weight(self, value: float):\n        self._property_changed('new_min_weight')\n        self.__new_min_weight = value        \n\n    @property\n    def new_max_weight(self) -> float:\n        return self.__new_max_weight\n\n    @new_max_weight.setter\n    def new_max_weight(self, value: float):\n        self._property_changed('new_max_weight')\n        self.__new_max_weight = value        \n\n    @property\n    def min_weight(self) -> float:\n        return self.__min_weight\n\n    @min_weight.setter\n    def min_weight(self, value: float):\n        self._property_changed('min_weight')\n        self.__min_weight = value        \n\n    @property\n    def max_weight(self) -> float:\n        return self.__max_weight\n\n    @max_weight.setter\n    def max_weight(self, value: float):\n        self._property_changed('max_weight')\n        self.__max_weight = value        \n\n    @property\n    def election(self) -> str:\n        return self.__election\n\n    @election.setter\n    def election(self, value: str):\n        self._property_changed('election')\n        self.__election = value        \n\n    @property\n    def base_date(self) -> str:\n        """The base date type to use for the nearby contract roll"""\n        return self.__base_date\n\n    @base_date.setter\n    def base_date(self, value: str):\n        self._property_changed('base_date')\n        self.__base_date = value        \n\n    @property\n    def commodity(self) -> str:\n        """The commodity symbol for the module"""\n        return self.__commodity\n\n    @commodity.setter\n    def commodity(self, value: str):\n        self._property_changed('commodity')\n        self.__commodity = value        \n\n    @property\n    def component_weight(self) -> float:\n        """The weight allocated to the specified module"""\n        return self.__component_weight\n\n    @component_weight.setter\n    def component_weight(self, value: float):\n        self._property_changed('component_weight')\n        self.__component_weight = value        \n\n    @property\n    def contract_nearby_number(self) -> float:\n        """The nearby contract to roll into"""\n        return self.__contract_nearby_number\n\n    @contract_nearby_number.setter\n    def contract_nearby_number(self, value: float):\n        self._property_changed('contract_nearby_number')\n        self.__contract_nearby_number = value        \n\n    @property\n    def expiration_schedule(self) -> str:\n        """The contract expiration schedule to be used"""\n        return self.__expiration_schedule\n\n    @expiration_schedule.setter\n    def expiration_schedule(self, value: str):\n        self._property_changed('expiration_schedule')\n        self.__expiration_schedule = value        \n\n    @property\n    def fixing_type(self) -> str:\n        """Type of fixing used to determine the price"""\n        return self.__fixing_type\n\n    @fixing_type.setter\n    def fixing_type(self, value: str):\n        self._property_changed('fixing_type')\n        self.__fixing_type = value        \n\n    @property\n    def last_eligible_date(self) -> float:\n        """The last eligible date for the roll"""\n        return self.__last_eligible_date\n\n    @last_eligible_date.setter\n    def last_eligible_date(self, value: float):\n        self._property_changed('last_eligible_date')\n        self.__last_eligible_date = value        \n\n    @property\n    def num_roll_days(self) -> float:\n        """The number of days over which the roll is spread"""\n        return self.__num_roll_days\n\n    @num_roll_days.setter\n    def num_roll_days(self, value: float):\n        self._property_changed('num_roll_days')\n        self.__num_roll_days = value        \n\n    @property\n    def roll_end(self) -> float:\n        """Day on which to end the roll"""\n        return self.__roll_end\n\n    @roll_end.setter\n    def roll_end(self, value: float):\n        self._property_changed('roll_end')\n        self.__roll_end = value        \n\n    @property\n    def roll_start(self) -> float:\n        """Day on which to start the roll"""\n        return self.__roll_start\n\n    @roll_start.setter\n    def roll_start(self, value: float):\n        self._property_changed('roll_start')\n        self.__roll_start = value        \n\n    @property\n    def roll_type(self) -> str:\n        """Type of contract roll"""\n        return self.__roll_type\n\n    @roll_type.setter\n    def roll_type(self, value: str):\n        self._property_changed('roll_type')\n        self.__roll_type = value        \n\n    @property\n    def valid_contract_expiry(self) -> str:\n        """The valid contract expiry months"""\n        return self.__valid_contract_expiry\n\n    @valid_contract_expiry.setter\n    def valid_contract_expiry(self, value: str):\n        self._property_changed('valid_contract_expiry')\n        self.__valid_contract_expiry = value        \n\n\nclass LiveMarket(Base):\n        \n    """Live market"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        location: Union[PricingLocation, str],\n        name: str = None\n    ):        \n        super().__init__()\n        self.location = location\n        self.name = name\n\n    @property\n    def market_type(self) -> str:\n        """LiveMarket"""\n        return 'LiveMarket'        \n\n    @property\n    def location(self) -> Union[PricingLocation, str]:\n        """Location for the market data"""\n        return self.__location\n\n    @location.setter\n    def location(self, value: Union[PricingLocation, str]):\n        self._property_changed('location')\n        self.__location = get_enum_value(PricingLocation, value)        \n\n\nclass MarketDataCoordinateValue(Base):\n        \n    """Market data coordinate and value"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        coordinate: MarketDataCoordinate,\n        value: float,\n        name: str = None\n    ):        \n        super().__init__()\n        self.coordinate = coordinate\n        self.value = value\n        self.name = name\n\n    @property\n    def coordinate(self) -> MarketDataCoordinate:\n        """Market data coordinate"""\n        return self.__coordinate\n\n    @coordinate.setter\n    def coordinate(self, value: MarketDataCoordinate):\n        self._property_changed('coordinate')\n        self.__coordinate = value        \n\n    @property\n    def value(self) -> float:\n        """Value for the coordinate"""\n        return self.__value\n\n    @value.setter\n    def value(self, value: float):\n        self._property_changed('value')\n        self.__value = value        \n\n\nclass MarketDataPattern(Base):\n        \n    """A pattern used to match market coordinates"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        mkt_type: str = None,\n        mkt_asset: str = None,\n        mkt_class: str = None,\n        mkt_point: Tuple[str, ...] = None,\n        mkt_quoting_style: str = None,\n        is_active: bool = None,\n        is_investment_grade: bool = None,\n        currency: Union[Currency, str] = None,\n        country_code: Union[CountryCode, str] = None,\n        gics_sector: str = None,\n        gics_industry_group: str = None,\n        gics_industry: str = None,\n        gics_sub_industry: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.mkt_type = mkt_type\n        self.mkt_asset = mkt_asset\n        self.mkt_class = mkt_class\n        self.mkt_point = mkt_point\n        self.mkt_quoting_style = mkt_quoting_style\n        self.is_active = is_active\n        self.is_investment_grade = is_investment_grade\n        self.currency = currency\n        self.country_code = country_code\n        self.gics_sector = gics_sector\n        self.gics_industry_group = gics_industry_group\n        self.gics_industry = gics_industry\n        self.gics_sub_industry = gics_sub_industry\n        self.name = name\n\n    @property\n    def mkt_type(self) -> str:\n        """The Market Data Type, e.g. IR, IR_BASIS, FX, FX_Vol"""\n        return self.__mkt_type\n\n    @mkt_type.setter\n    def mkt_type(self, value: str):\n        self._property_changed('mkt_type')\n        self.__mkt_type = value        \n\n    @property\n    def mkt_asset(self) -> str:\n        """The specific point, e.g. 3m, 10y, 11y, Dec19"""\n        return self.__mkt_asset\n\n    @mkt_asset.setter\n    def mkt_asset(self, value: str):\n        self._property_changed('mkt_asset')\n        self.__mkt_asset = value        \n\n    @property\n    def mkt_class(self) -> str:\n        """The market data pointClass, e.g. Swap, Cash."""\n        return self.__mkt_class\n\n    @mkt_class.setter\n    def mkt_class(self, value: str):\n        self._property_changed('mkt_class')\n        self.__mkt_class = value        \n\n    @property\n    def mkt_point(self) -> Tuple[str, ...]:\n        """The specific point, e.g. 3m, 10y, 11y, Dec19"""\n        return self.__mkt_point\n\n    @mkt_point.setter\n    def mkt_point(self, value: Tuple[str, ...]):\n        self._property_changed('mkt_point')\n        self.__mkt_point = value        \n\n    @property\n    def mkt_quoting_style(self) -> str:\n        return self.__mkt_quoting_style\n\n    @mkt_quoting_style.setter\n    def mkt_quoting_style(self, value: str):\n        self._property_changed('mkt_quoting_style')\n        self.__mkt_quoting_style = value        \n\n    @property\n    def is_active(self) -> bool:\n        """Is the asset active"""\n        return self.__is_active\n\n    @is_active.setter\n    def is_active(self, value: bool):\n        self._property_changed('is_active')\n        self.__is_active = value        \n\n    @property\n    def is_investment_grade(self) -> bool:\n        """Is the asset investment grade"""\n        return self.__is_investment_grade\n\n    @is_investment_grade.setter\n    def is_investment_grade(self, value: bool):\n        self._property_changed('is_investment_grade')\n        self.__is_investment_grade = value        \n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def country_code(self) -> Union[CountryCode, str]:\n        """ISO Country code"""\n        return self.__country_code\n\n    @country_code.setter\n    def country_code(self, value: Union[CountryCode, str]):\n        self._property_changed('country_code')\n        self.__country_code = get_enum_value(CountryCode, value)        \n\n    @property\n    def gics_sector(self) -> str:\n        """GICS Sector classification (level 1)"""\n        return self.__gics_sector\n\n    @gics_sector.setter\n    def gics_sector(self, value: str):\n        self._property_changed('gics_sector')\n        self.__gics_sector = value        \n\n    @property\n    def gics_industry_group(self) -> str:\n        """GICS Industry Group classification (level 2)"""\n        return self.__gics_industry_group\n\n    @gics_industry_group.setter\n    def gics_industry_group(self, value: str):\n        self._property_changed('gics_industry_group')\n        self.__gics_industry_group = value        \n\n    @property\n    def gics_industry(self) -> str:\n        """GICS Industry classification (level 3)"""\n        return self.__gics_industry\n\n    @gics_industry.setter\n    def gics_industry(self, value: str):\n        self._property_changed('gics_industry')\n        self.__gics_industry = value        \n\n    @property\n    def gics_sub_industry(self) -> str:\n        """GICS Sub Industry classification (level 4)"""\n        return self.__gics_sub_industry\n\n    @gics_sub_industry.setter\n    def gics_sub_industry(self, value: str):\n        self._property_changed('gics_sub_industry')\n        self.__gics_sub_industry = value        \n\n\nclass MarketDataScenario(Base):\n        \n    """A market data scenario to apply to the calculation"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        scenario: dict,\n        subtract_base: bool = False,\n        name: str = None\n    ):        \n        super().__init__()\n        self.scenario = scenario\n        self.subtract_base = subtract_base\n        self.name = name\n\n    @property\n    def scenario(self) -> dict:\n        """The scenario"""\n        return self.__scenario\n\n    @scenario.setter\n    def scenario(self, value: dict):\n        self._property_changed('scenario')\n        self.__scenario = value        \n\n    @property\n    def subtract_base(self) -> bool:\n        """Subtract values computed under the base market data state, to return a diff, if\n           true"""\n        return self.__subtract_base\n\n    @subtract_base.setter\n    def subtract_base(self, value: bool):\n        self._property_changed('subtract_base')\n        self.__subtract_base = value        \n\n\nclass MarketDataShock(Base):\n        \n    """A shock to apply to market coordinate values"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        shock_type: Union[MarketDataShockType, str],\n        value: float,\n        precision: float = None,\n        cap: float = None,\n        floor: float = None,\n        coordinate_cap: float = None,\n        coordinate_floor: float = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.shock_type = shock_type\n        self.value = value\n        self.precision = precision\n        self.cap = cap\n        self.floor = floor\n        self.coordinate_cap = coordinate_cap\n        self.coordinate_floor = coordinate_floor\n        self.name = name\n\n    @property\n    def shock_type(self) -> Union[MarketDataShockType, str]:\n        """Market data shock type"""\n        return self.__shock_type\n\n    @shock_type.setter\n    def shock_type(self, value: Union[MarketDataShockType, str]):\n        self._property_changed('shock_type')\n        self.__shock_type = get_enum_value(MarketDataShockType, value)        \n\n    @property\n    def value(self) -> float:\n        """The amount by which to shock matching coordinates"""\n        return self.__value\n\n    @value.setter\n    def value(self, value: float):\n        self._property_changed('value')\n        self.__value = value        \n\n    @property\n    def precision(self) -> float:\n        """The precision to which the shock will be rounded"""\n        return self.__precision\n\n    @precision.setter\n    def precision(self, value: float):\n        self._property_changed('precision')\n        self.__precision = value        \n\n    @property\n    def cap(self) -> float:\n        """Upper bound on the shocked value"""\n        return self.__cap\n\n    @cap.setter\n    def cap(self, value: float):\n        self._property_changed('cap')\n        self.__cap = value        \n\n    @property\n    def floor(self) -> float:\n        """Lower bound on the shocked value"""\n        return self.__floor\n\n    @floor.setter\n    def floor(self, value: float):\n        self._property_changed('floor')\n        self.__floor = value        \n\n    @property\n    def coordinate_cap(self) -> float:\n        """Upper bound on the pre-shocked value of matching coordinates"""\n        return self.__coordinate_cap\n\n    @coordinate_cap.setter\n    def coordinate_cap(self, value: float):\n        self._property_changed('coordinate_cap')\n        self.__coordinate_cap = value        \n\n    @property\n    def coordinate_floor(self) -> float:\n        """Lower bound on the pre-shocked value of matching coordinates"""\n        return self.__coordinate_floor\n\n    @coordinate_floor.setter\n    def coordinate_floor(self, value: float):\n        self._property_changed('coordinate_floor')\n        self.__coordinate_floor = value        \n\n\nclass PCOBenchmark(Base):\n        \n    """Parameters required for PCO Benchmark"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        selected: str = None,\n        options: Tuple[PCOBenchmarkOptions, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.selected = selected\n        self.options = options\n        self.name = name\n\n    @property\n    def selected(self) -> str:\n        return self.__selected\n\n    @selected.setter\n    def selected(self, value: str):\n        self._property_changed('selected')\n        self.__selected = value        \n\n    @property\n    def options(self) -> Tuple[PCOBenchmarkOptions, ...]:\n        """Parameters required for PCO Benchmark"""\n        return self.__options\n\n    @options.setter\n    def options(self, value: Tuple[PCOBenchmarkOptions, ...]):\n        self._property_changed('options')\n        self.__options = value        \n\n\nclass PCOCashBalance(Base):\n        \n    """Parameters required for PCO Cash Balance"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        local_currency: Union[Currency, str] = None,\n        cash_balance_limits: Tuple[str, ...] = None,\n        cash_reserve: str = None,\n        long_threshold: str = None,\n        short_threshold: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.local_currency = local_currency\n        self.cash_balance_limits = cash_balance_limits\n        self.cash_reserve = cash_reserve\n        self.long_threshold = long_threshold\n        self.short_threshold = short_threshold\n        self.name = name\n\n    @property\n    def local_currency(self) -> Union[Currency, str]:\n        """Local currency"""\n        return self.__local_currency\n\n    @local_currency.setter\n    def local_currency(self, value: Union[Currency, str]):\n        self._property_changed('local_currency')\n        self.__local_currency = get_enum_value(Currency, value)        \n\n    @property\n    def cash_balance_limits(self) -> Tuple[str, ...]:\n        """Upper and lower limits for cash balance"""\n        return self.__cash_balance_limits\n\n    @cash_balance_limits.setter\n    def cash_balance_limits(self, value: Tuple[str, ...]):\n        self._property_changed('cash_balance_limits')\n        self.__cash_balance_limits = value        \n\n    @property\n    def cash_reserve(self) -> str:\n        """Target cash reserve"""\n        return self.__cash_reserve\n\n    @cash_reserve.setter\n    def cash_reserve(self, value: str):\n        self._property_changed('cash_reserve')\n        self.__cash_reserve = value        \n\n    @property\n    def long_threshold(self) -> str:\n        """Long cash threshold"""\n        return self.__long_threshold\n\n    @long_threshold.setter\n    def long_threshold(self, value: str):\n        self._property_changed('long_threshold')\n        self.__long_threshold = value        \n\n    @property\n    def short_threshold(self) -> str:\n        """Short cash threshold"""\n        return self.__short_threshold\n\n    @short_threshold.setter\n    def short_threshold(self, value: str):\n        self._property_changed('short_threshold')\n        self.__short_threshold = value        \n\n\nclass PCOParameterValues(Base):\n        \n    """Parameters required for a PCO Generic Value"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        currency: Union[Currency, str] = None,\n        value: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.currency = currency\n        self.value = value\n        self.name = name\n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def value(self) -> Union[float, str]:\n        """Generic value for a PCO parameter"""\n        return self.__value\n\n    @value.setter\n    def value(self, value: Union[float, str]):\n        self._property_changed('value')\n        self.__value = value        \n\n\nclass PCOSettlements(Base):\n        \n    """Parameters required for a PCO Settlement"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        currency: Union[Currency, str] = None,\n        data: Tuple[PCOSettlementsData, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.currency = currency\n        self.data = data\n        self.name = name\n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def data(self) -> Tuple[PCOSettlementsData, ...]:\n        """Parameters required for a PCO Settlement"""\n        return self.__data\n\n    @data.setter\n    def data(self, value: Tuple[PCOSettlementsData, ...]):\n        self._property_changed('data')\n        self.__data = value        \n\n\nclass PCOTargetDeviation(Base):\n        \n    """Parameters required for a Target Deviation"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        currency: Union[Currency, str] = None,\n        data: Tuple[PCOTargetDeviationData, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.currency = currency\n        self.data = data\n        self.name = name\n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def data(self) -> Tuple[PCOTargetDeviationData, ...]:\n        """Parameters required for a Target Deviation data"""\n        return self.__data\n\n    @data.setter\n    def data(self, value: Tuple[PCOTargetDeviationData, ...]):\n        self._property_changed('data')\n        self.__data = value        \n\n\nclass PCOUnrealisedMarkToMarket(Base):\n        \n    """Parameters required for a Unrealised mark to market."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        total: str = None,\n        next_settlement_date: datetime.datetime = None,\n        next_settlement: str = None,\n        next_roll_date: datetime.datetime = None,\n        historical_data: Tuple[PCOMtMHistoricalData, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.total = total\n        self.next_settlement_date = next_settlement_date\n        self.next_settlement = next_settlement\n        self.next_roll_date = next_roll_date\n        self.historical_data = historical_data\n        self.name = name\n\n    @property\n    def total(self) -> str:\n        """Total indicative unrealised mark to market value"""\n        return self.__total\n\n    @total.setter\n    def total(self, value: str):\n        self._property_changed('total')\n        self.__total = value        \n\n    @property\n    def next_settlement_date(self) -> datetime.datetime:\n        """Next settlement date"""\n        return self.__next_settlement_date\n\n    @next_settlement_date.setter\n    def next_settlement_date(self, value: datetime.datetime):\n        self._property_changed('next_settlement_date')\n        self.__next_settlement_date = value        \n\n    @property\n    def next_settlement(self) -> str:\n        """Next settlement value"""\n        return self.__next_settlement\n\n    @next_settlement.setter\n    def next_settlement(self, value: str):\n        self._property_changed('next_settlement')\n        self.__next_settlement = value        \n\n    @property\n    def next_roll_date(self) -> datetime.datetime:\n        """Next roll date"""\n        return self.__next_roll_date\n\n    @next_roll_date.setter\n    def next_roll_date(self, value: datetime.datetime):\n        self._property_changed('next_roll_date')\n        self.__next_roll_date = value        \n\n    @property\n    def historical_data(self) -> Tuple[PCOMtMHistoricalData, ...]:\n        """History of unrealised mark to market of open trades"""\n        return self.__historical_data\n\n    @historical_data.setter\n    def historical_data(self, value: Tuple[PCOMtMHistoricalData, ...]):\n        self._property_changed('historical_data')\n        self.__historical_data = value        \n\n\nclass Position(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        asset_id: str = None,\n        quantity: float = None,\n        notional: float = None,\n        party_to: SimpleParty = None,\n        party_from: SimpleParty = None,\n        external_ids: Tuple[dict, ...] = None,\n        margin_ids: Tuple[dict, ...] = None,\n        instrument: InstrumentBase = None,\n        description: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.asset_id = asset_id\n        self.quantity = quantity\n        self.notional = notional\n        self.party_to = party_to\n        self.party_from = party_from\n        self.external_ids = external_ids\n        self.margin_ids = margin_ids\n        self.instrument = instrument\n        self.description = description\n        self.name = name\n\n    @property\n    def asset_id(self) -> str:\n        """Marquee unique asset identifier."""\n        return self.__asset_id\n\n    @asset_id.setter\n    def asset_id(self, value: str):\n        self._property_changed('asset_id')\n        self.__asset_id = value        \n\n    @property\n    def quantity(self) -> float:\n        """Quantity of position"""\n        return self.__quantity\n\n    @quantity.setter\n    def quantity(self, value: float):\n        self._property_changed('quantity')\n        self.__quantity = value        \n\n    @property\n    def notional(self) -> float:\n        """Notional of position"""\n        return self.__notional\n\n    @notional.setter\n    def notional(self, value: float):\n        self._property_changed('notional')\n        self.__notional = value        \n\n    @property\n    def party_to(self) -> SimpleParty:\n        return self.__party_to\n\n    @party_to.setter\n    def party_to(self, value: SimpleParty):\n        self._property_changed('party_to')\n        self.__party_to = value        \n\n    @property\n    def party_from(self) -> SimpleParty:\n        return self.__party_from\n\n    @party_from.setter\n    def party_from(self, value: SimpleParty):\n        self._property_changed('party_from')\n        self.__party_from = value        \n\n    @property\n    def external_ids(self) -> Tuple[dict, ...]:\n        """A list of identifiers (external to Marquee) for this position"""\n        return self.__external_ids\n\n    @external_ids.setter\n    def external_ids(self, value: Tuple[dict, ...]):\n        self._property_changed('external_ids')\n        self.__external_ids = value        \n\n    @property\n    def margin_ids(self) -> Tuple[dict, ...]:\n        """A list of margin identifiers (e.g. CSA) for this position"""\n        return self.__margin_ids\n\n    @margin_ids.setter\n    def margin_ids(self, value: Tuple[dict, ...]):\n        self._property_changed('margin_ids')\n        self.__margin_ids = value        \n\n    @property\n    def instrument(self) -> InstrumentBase:\n        """Valid Instruments"""\n        return self.__instrument\n\n    @instrument.setter\n    def instrument(self, value: InstrumentBase):\n        self._property_changed('instrument')\n        self.__instrument = value        \n\n    @property\n    def description(self) -> str:\n        """Description of a particular trade or position."""\n        return self.__description\n\n    @description.setter\n    def description(self, value: str):\n        self._property_changed('description')\n        self.__description = value        \n\n\nclass RiskMeasure(Base):\n        \n    """The measure to perform risk on. Each risk measure consists of an asset class, a\n       measure type, and a unit."""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        asset_class: Union[AssetClass, str] = None,\n        measure_type: Union[RiskMeasureType, str] = None,\n        unit: Union[RiskMeasureUnit, str] = None,\n        value: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.asset_class = asset_class\n        self.measure_type = measure_type\n        self.unit = unit\n        self.value = value\n        self.name = name\n\n    @property\n    def asset_class(self) -> Union[AssetClass, str]:\n        """Asset classification of security. Assets are classified into broad groups which\n           exhibit similar characteristics and behave in a consistent way under\n           different market conditions"""\n        return self.__asset_class\n\n    @asset_class.setter\n    def asset_class(self, value: Union[AssetClass, str]):\n        self._property_changed('asset_class')\n        self.__asset_class = get_enum_value(AssetClass, value)        \n\n    @property\n    def measure_type(self) -> Union[RiskMeasureType, str]:\n        """The type of measure to perform risk on. e.g. Greeks"""\n        return self.__measure_type\n\n    @measure_type.setter\n    def measure_type(self, value: Union[RiskMeasureType, str]):\n        self._property_changed('measure_type')\n        self.__measure_type = get_enum_value(RiskMeasureType, value)        \n\n    @property\n    def unit(self) -> Union[RiskMeasureUnit, str]:\n        """The unit of change of underlying in the risk computation."""\n        return self.__unit\n\n    @unit.setter\n    def unit(self, value: Union[RiskMeasureUnit, str]):\n        self._property_changed('unit')\n        self.__unit = get_enum_value(RiskMeasureUnit, value)        \n\n    @property\n    def value(self) -> Union[float, str]:\n        """Value of this measure"""\n        return self.__value\n\n    @value.setter\n    def value(self, value: Union[float, str]):\n        self._property_changed('value')\n        self.__value = value        \n\n\nclass RollFwd(Scenario):\n        \n    """A scenario to manipulate time along the forward curve"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        date: Union[datetime.date, str] = None,\n        realise_fwd: bool = True,\n        holiday_calendar: Union[PricingLocation, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.date = date\n        self.realise_fwd = realise_fwd\n        self.holiday_calendar = holiday_calendar\n        self.name = name\n\n    @property\n    def scenario_type(self) -> str:\n        """RollFwd"""\n        return 'RollFwd'        \n\n    @property\n    def date(self) -> Union[datetime.date, str]:\n        """Absolute or Relative Date to shift markets to"""\n        return self.__date\n\n    @date.setter\n    def date(self, value: Union[datetime.date, str]):\n        self._property_changed('date')\n        self.__date = value        \n\n    @property\n    def realise_fwd(self) -> bool:\n        """Roll along the forward curve or roll in spot space"""\n        return self.__realise_fwd\n\n    @realise_fwd.setter\n    def realise_fwd(self, value: bool):\n        self._property_changed('realise_fwd')\n        self.__realise_fwd = value        \n\n    @property\n    def holiday_calendar(self) -> Union[PricingLocation, str]:\n        """Calendar to use for relative dates"""\n        return self.__holiday_calendar\n\n    @holiday_calendar.setter\n    def holiday_calendar(self, value: Union[PricingLocation, str]):\n        self._property_changed('holiday_calendar')\n        self.__holiday_calendar = get_enum_value(PricingLocation, value)        \n\n\nclass TimestampedMarket(Base):\n        \n    """Timestamped market"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        location: Union[PricingLocation, str],\n        timestamp: datetime.datetime = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.timestamp = timestamp\n        self.location = location\n        self.name = name\n\n    @property\n    def market_type(self) -> str:\n        """TimestampedMarket"""\n        return 'TimestampedMarket'        \n\n    @property\n    def timestamp(self) -> datetime.datetime:\n        """Timestamp for the market data"""\n        return self.__timestamp\n\n    @timestamp.setter\n    def timestamp(self, value: datetime.datetime):\n        self._property_changed('timestamp')\n        self.__timestamp = value        \n\n    @property\n    def location(self) -> Union[PricingLocation, str]:\n        """Location for the market data"""\n        return self.__location\n\n    @location.setter\n    def location(self, value: Union[PricingLocation, str]):\n        self._property_changed('location')\n        self.__location = get_enum_value(PricingLocation, value)        \n\n\nclass CSLCurrencyArray(Base):\n        \n    """An array of currencies"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        currency_values: Tuple[CSLCurrency, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.currency_values = currency_values\n        self.name = name\n\n    @property\n    def currency_values(self) -> Tuple[CSLCurrency, ...]:\n        """A currency"""\n        return self.__currency_values\n\n    @currency_values.setter\n    def currency_values(self, value: Tuple[CSLCurrency, ...]):\n        self._property_changed('currency_values')\n        self.__currency_values = value        \n\n\nclass CSLSchedule(Base):\n        \n    """A schedule"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        first_date: datetime.date = None,\n        last_date: datetime.date = None,\n        calendar_name: str = None,\n        period: str = None,\n        delay: str = None,\n        business_day_convention: str = None,\n        day_count_convention: str = None,\n        days_per_term: str = None,\n        delay_business_day_convention: str = None,\n        delay_calendar_name: str = None,\n        has_reset_date: bool = None,\n        term_formula: str = None,\n        extra_dates: Tuple[CSLDateArrayNamedParam, ...] = None,\n        extra_dates_by_offset: Tuple[CSLSymCaseNamedParam, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.first_date = first_date\n        self.last_date = last_date\n        self.calendar_name = calendar_name\n        self.period = period\n        self.delay = delay\n        self.business_day_convention = business_day_convention\n        self.day_count_convention = day_count_convention\n        self.days_per_term = days_per_term\n        self.delay_business_day_convention = delay_business_day_convention\n        self.delay_calendar_name = delay_calendar_name\n        self.has_reset_date = has_reset_date\n        self.term_formula = term_formula\n        self.extra_dates = extra_dates\n        self.extra_dates_by_offset = extra_dates_by_offset\n        self.name = name\n\n    @property\n    def first_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__first_date\n\n    @first_date.setter\n    def first_date(self, value: datetime.date):\n        self._property_changed('first_date')\n        self.__first_date = value        \n\n    @property\n    def last_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__last_date\n\n    @last_date.setter\n    def last_date(self, value: datetime.date):\n        self._property_changed('last_date')\n        self.__last_date = value        \n\n    @property\n    def calendar_name(self) -> str:\n        """The name of the holiday calendar"""\n        return self.__calendar_name\n\n    @calendar_name.setter\n    def calendar_name(self, value: str):\n        self._property_changed('calendar_name')\n        self.__calendar_name = value        \n\n    @property\n    def period(self) -> str:\n        """Tenor"""\n        return self.__period\n\n    @period.setter\n    def period(self, value: str):\n        self._property_changed('period')\n        self.__period = value        \n\n    @property\n    def delay(self) -> str:\n        """The delay"""\n        return self.__delay\n\n    @delay.setter\n    def delay(self, value: str):\n        self._property_changed('delay')\n        self.__delay = value        \n\n    @property\n    def business_day_convention(self) -> str:\n        return self.__business_day_convention\n\n    @business_day_convention.setter\n    def business_day_convention(self, value: str):\n        self._property_changed('business_day_convention')\n        self.__business_day_convention = value        \n\n    @property\n    def day_count_convention(self) -> str:\n        return self.__day_count_convention\n\n    @day_count_convention.setter\n    def day_count_convention(self, value: str):\n        self._property_changed('day_count_convention')\n        self.__day_count_convention = value        \n\n    @property\n    def days_per_term(self) -> str:\n        return self.__days_per_term\n\n    @days_per_term.setter\n    def days_per_term(self, value: str):\n        self._property_changed('days_per_term')\n        self.__days_per_term = value        \n\n    @property\n    def delay_business_day_convention(self) -> str:\n        return self.__delay_business_day_convention\n\n    @delay_business_day_convention.setter\n    def delay_business_day_convention(self, value: str):\n        self._property_changed('delay_business_day_convention')\n        self.__delay_business_day_convention = value        \n\n    @property\n    def delay_calendar_name(self) -> str:\n        """The name of the holiday calendar"""\n        return self.__delay_calendar_name\n\n    @delay_calendar_name.setter\n    def delay_calendar_name(self, value: str):\n        self._property_changed('delay_calendar_name')\n        self.__delay_calendar_name = value        \n\n    @property\n    def has_reset_date(self) -> bool:\n        return self.__has_reset_date\n\n    @has_reset_date.setter\n    def has_reset_date(self, value: bool):\n        self._property_changed('has_reset_date')\n        self.__has_reset_date = value        \n\n    @property\n    def term_formula(self) -> str:\n        return self.__term_formula\n\n    @term_formula.setter\n    def term_formula(self, value: str):\n        self._property_changed('term_formula')\n        self.__term_formula = value        \n\n    @property\n    def extra_dates(self) -> Tuple[CSLDateArrayNamedParam, ...]:\n        """A named array of dates"""\n        return self.__extra_dates\n\n    @extra_dates.setter\n    def extra_dates(self, value: Tuple[CSLDateArrayNamedParam, ...]):\n        self._property_changed('extra_dates')\n        self.__extra_dates = value        \n\n    @property\n    def extra_dates_by_offset(self) -> Tuple[CSLSymCaseNamedParam, ...]:\n        """A named case-sensitive string."""\n        return self.__extra_dates_by_offset\n\n    @extra_dates_by_offset.setter\n    def extra_dates_by_offset(self, value: Tuple[CSLSymCaseNamedParam, ...]):\n        self._property_changed('extra_dates_by_offset')\n        self.__extra_dates_by_offset = value        \n\n\nclass CurveScenario(Scenario):\n        \n    """A scenario to manipulate curve shape"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        market_data_pattern: MarketDataPattern = None,\n        annualised_parallel_shift: float = None,\n        annualised_slope_shift: float = None,\n        pivot_point: float = None,\n        cutoff: float = None,\n        floor: float = None,\n        denominated: str = None,\n        tenor: str = None,\n        rate_option: str = None,\n        bucket_shift: float = None,\n        bucket_start: str = None,\n        bucket_end: str = None,\n        style: str = 'MarketData',\n        name: str = None\n    ):        \n        super().__init__()\n        self.market_data_pattern = market_data_pattern\n        self.annualised_parallel_shift = annualised_parallel_shift\n        self.annualised_slope_shift = annualised_slope_shift\n        self.pivot_point = pivot_point\n        self.cutoff = cutoff\n        self.floor = floor\n        self.denominated = denominated\n        self.tenor = tenor\n        self.rate_option = rate_option\n        self.bucket_shift = bucket_shift\n        self.bucket_start = bucket_start\n        self.bucket_end = bucket_end\n        self.style = style\n        self.name = name\n\n    @property\n    def scenario_type(self) -> str:\n        """CurveScenario"""\n        return 'CurveScenario'        \n\n    @property\n    def market_data_pattern(self) -> MarketDataPattern:\n        """Market pattern for matching curve assets"""\n        return self.__market_data_pattern\n\n    @market_data_pattern.setter\n    def market_data_pattern(self, value: MarketDataPattern):\n        self._property_changed('market_data_pattern')\n        self.__market_data_pattern = value        \n\n    @property\n    def annualised_parallel_shift(self) -> float:\n        """Size of the parallel shift (in bps/year)"""\n        return self.__annualised_parallel_shift\n\n    @annualised_parallel_shift.setter\n    def annualised_parallel_shift(self, value: float):\n        self._property_changed('annualised_parallel_shift')\n        self.__annualised_parallel_shift = value        \n\n    @property\n    def annualised_slope_shift(self) -> float:\n        """Size of the slope shift (in bps/year)"""\n        return self.__annualised_slope_shift\n\n    @annualised_slope_shift.setter\n    def annualised_slope_shift(self, value: float):\n        self._property_changed('annualised_slope_shift')\n        self.__annualised_slope_shift = value        \n\n    @property\n    def pivot_point(self) -> float:\n        """The pivot point (in years)"""\n        return self.__pivot_point\n\n    @pivot_point.setter\n    def pivot_point(self, value: float):\n        self._property_changed('pivot_point')\n        self.__pivot_point = value        \n\n    @property\n    def cutoff(self) -> float:\n        """The cutoff point (in years)"""\n        return self.__cutoff\n\n    @cutoff.setter\n    def cutoff(self, value: float):\n        self._property_changed('cutoff')\n        self.__cutoff = value        \n\n    @property\n    def floor(self) -> float:\n        """The floor value (in bps)"""\n        return self.__floor\n\n    @floor.setter\n    def floor(self, value: float):\n        self._property_changed('floor')\n        self.__floor = value        \n\n    @property\n    def denominated(self) -> str:\n        """Currency to which shock is applied"""\n        return self.__denominated\n\n    @denominated.setter\n    def denominated(self, value: str):\n        self._property_changed('denominated')\n        self.__denominated = value        \n\n    @property\n    def tenor(self) -> str:\n        """Tenor of rate option to which shock is applied"""\n        return self.__tenor\n\n    @tenor.setter\n    def tenor(self, value: str):\n        self._property_changed('tenor')\n        self.__tenor = value        \n\n    @property\n    def rate_option(self) -> str:\n        """Rate option to which shock is applied"""\n        return self.__rate_option\n\n    @rate_option.setter\n    def rate_option(self, value: str):\n        self._property_changed('rate_option')\n        self.__rate_option = value        \n\n    @property\n    def bucket_shift(self) -> float:\n        """Size of the bucket shift (in bps)"""\n        return self.__bucket_shift\n\n    @bucket_shift.setter\n    def bucket_shift(self, value: float):\n        self._property_changed('bucket_shift')\n        self.__bucket_shift = value        \n\n    @property\n    def bucket_start(self) -> str:\n        """The start date of the custom bucket"""\n        return self.__bucket_start\n\n    @bucket_start.setter\n    def bucket_start(self, value: str):\n        self._property_changed('bucket_start')\n        self.__bucket_start = value        \n\n    @property\n    def bucket_end(self) -> str:\n        """The end date of the custom bucket"""\n        return self.__bucket_end\n\n    @bucket_end.setter\n    def bucket_end(self, value: str):\n        self._property_changed('bucket_end')\n        self.__bucket_end = value        \n\n    @property\n    def style(self) -> str:\n        """Different styles for risk calculation"""\n        return self.__style\n\n    @style.setter\n    def style(self, value: str):\n        self._property_changed('style')\n        self.__style = value        \n\n\nclass DataSetFieldMap(Base):\n        \n    """The mapping between data set field and risk measure type"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        data_set_id: str,\n        field: str,\n        results_field: str,\n        risk_measure: RiskMeasure,\n        name: str = None\n    ):        \n        super().__init__()\n        self.data_set_id = data_set_id\n        self.field = field\n        self.results_field = results_field\n        self.risk_measure = risk_measure\n        self.name = name\n\n    @property\n    def data_set_id(self) -> str:\n        """Unique id of dataset."""\n        return self.__data_set_id\n\n    @data_set_id.setter\n    def data_set_id(self, value: str):\n        self._property_changed('data_set_id')\n        self.__data_set_id = value        \n\n    @property\n    def field(self) -> str:\n        """The field for data set, e.g. rate"""\n        return self.__field\n\n    @field.setter\n    def field(self, value: str):\n        self._property_changed('field')\n        self.__field = value        \n\n    @property\n    def results_field(self) -> str:\n        """The source field in the results, e.g. value or fixedRate"""\n        return self.__results_field\n\n    @results_field.setter\n    def results_field(self, value: str):\n        self._property_changed('results_field')\n        self.__results_field = value        \n\n    @property\n    def risk_measure(self) -> RiskMeasure:\n        """The measure to perform risk on. Each risk measure consists of an asset class, a\n           measure type, and a unit."""\n        return self.__risk_measure\n\n    @risk_measure.setter\n    def risk_measure(self, value: RiskMeasure):\n        self._property_changed('risk_measure')\n        self.__risk_measure = value        \n\n\nclass FieldValueMap(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        **kwargs\n    ):        \n        super().__init__()\n        self.investment_rate = kwargs.get('investment_rate')\n        self.starting_emma_legal_entity_id = kwargs.get('starting_emma_legal_entity_id')\n        self.mdapi_class = kwargs.get('mdapi_class')\n        self.total_notional_usd = kwargs.get('total_notional_usd')\n        self.bid_unadjusted = kwargs.get('bid_unadjusted')\n        self.aggressive_fills_percentage = kwargs.get('aggressive_fills_percentage')\n        self.vehicle_type = kwargs.get('vehicle_type')\n        self.total_fatalities_by_state = kwargs.get('total_fatalities_by_state')\n        self.new_active = kwargs.get('new_active')\n        self.daily_risk = kwargs.get('daily_risk')\n        self.energy = kwargs.get('energy')\n        self.sunshine_daily_forecast = kwargs.get('sunshine_daily_forecast')\n        self.sentiment_score = kwargs.get('sentiment_score')\n        self.correlation = kwargs.get('correlation')\n        self.exposure = kwargs.get('exposure')\n        self.size = kwargs.get('size')\n        self.market_data_asset = kwargs.get('market_data_asset')\n        self.buy75cents = kwargs.get('buy75cents')\n        self.unadjusted_high = kwargs.get('unadjusted_high')\n        self.source_importance = kwargs.get('source_importance')\n        self.closing_yield = kwargs.get('closing_yield')\n        self.wind = kwargs.get('wind')\n        self.sc16 = kwargs.get('sc16')\n        self.sc15 = kwargs.get('sc15')\n        self.sc12 = kwargs.get('sc12')\n        self.sc11 = kwargs.get('sc11')\n        self.primary_vwap_in_limit_unrealized_bps = kwargs.get('primary_vwap_in_limit_unrealized_bps')\n        self.display_name = kwargs.get('display_name')\n        self.minutes_to_trade100_pct = kwargs.get('minutes_to_trade100_pct')\n        self.sc14 = kwargs.get('sc14')\n        self.cumulative_volume_in_shares = kwargs.get('cumulative_volume_in_shares')\n        self.sc13 = kwargs.get('sc13')\n        self.new_fatalities = kwargs.get('new_fatalities')\n        self.buy50bps = kwargs.get('buy50bps')\n        self.num_staffed_beds = kwargs.get('num_staffed_beds')\n        self.upfront_payment = kwargs.get('upfront_payment')\n        self.arrival_mid_realized_cash = kwargs.get('arrival_mid_realized_cash')\n        self.sc10 = kwargs.get('sc10')\n        self.sc05 = kwargs.get('sc05')\n        self.a = kwargs.get('a')\n        self.sc04 = kwargs.get('sc04')\n        self.b = kwargs.get('b')\n        self.sc07 = kwargs.get('sc07')\n        self.c = kwargs.get('c')\n        self.yield_to_maturity = kwargs.get('yield_to_maturity')\n        self.sc06 = kwargs.get('sc06')\n        self.address = kwargs.get('address')\n        self.sc01 = kwargs.get('sc01')\n        self.leg2_payment_frequency = kwargs.get('leg2_payment_frequency')\n        self.sc03 = kwargs.get('sc03')\n        self.sc02 = kwargs.get('sc02')\n        self.geography_name = kwargs.get('geography_name')\n        self.borrower = kwargs.get('borrower')\n        self.settle_price = kwargs.get('settle_price')\n        self.performance_contribution = kwargs.get('performance_contribution')\n        self.sc09 = kwargs.get('sc09')\n        self.mkt_class = kwargs.get('mkt_class')\n        self.sc08 = kwargs.get('sc08')\n        self.collateralization = kwargs.get('collateralization')\n        self.future_month_u26 = kwargs.get('future_month_u26')\n        self.future_month_u25 = kwargs.get('future_month_u25')\n        self.future_month_u24 = kwargs.get('future_month_u24')\n        self.future_month_u23 = kwargs.get('future_month_u23')\n        self.future_month_u22 = kwargs.get('future_month_u22')\n        self.statement_id = kwargs.get('statement_id')\n        self.future_month_u21 = kwargs.get('future_month_u21')\n        self.modified_duration = kwargs.get('modified_duration')\n        self.short_rates_contribution = kwargs.get('short_rates_contribution')\n        self.implied_normal_volatility = kwargs.get('implied_normal_volatility')\n        self.solar_generation = kwargs.get('solar_generation')\n        self.mtm_price = kwargs.get('mtm_price')\n        self.swap_spread_change = kwargs.get('swap_spread_change')\n        self.realized_arrival_performance_usd = kwargs.get('realized_arrival_performance_usd')\n        self.portfolio_assets = kwargs.get('portfolio_assets')\n        self.pricingdate = kwargs.get('pricingdate')\n        self.tcm_cost_horizon3_hour = kwargs.get('tcm_cost_horizon3_hour')\n        self.exchange_rate = kwargs.get('exchange_rate')\n        self.potential_bed_cap_inc = kwargs.get('potential_bed_cap_inc')\n        self.number_covered = kwargs.get('number_covered')\n        self.number_of_positions = kwargs.get('number_of_positions')\n        self.open_unadjusted = kwargs.get('open_unadjusted')\n        self.strike_time = kwargs.get('strike_time')\n        self.ask_price = kwargs.get('ask_price')\n        self.event_id = kwargs.get('event_id')\n        self.sectors = kwargs.get('sectors')\n        self.additional_price_notation_type = kwargs.get('additional_price_notation_type')\n        self.gross_investment_qtd = kwargs.get('gross_investment_qtd')\n        self.annualized_risk = kwargs.get('annualized_risk')\n        self.estimated_holding_time_short = kwargs.get('estimated_holding_time_short')\n        self.midcurve_premium = kwargs.get('midcurve_premium')\n        self.volume_composite = kwargs.get('volume_composite')\n        self.sharpe_qtd = kwargs.get('sharpe_qtd')\n        self.estimated_holding_time_long = kwargs.get('estimated_holding_time_long')\n        self.external = kwargs.get('external')\n        self.tracker_name = kwargs.get('tracker_name')\n        self.sell50cents = kwargs.get('sell50cents')\n        self.trade_price = kwargs.get('trade_price')\n        self.cleared = kwargs.get('cleared')\n        self.prime_id_numeric = kwargs.get('prime_id_numeric')\n        self.buy8bps = kwargs.get('buy8bps')\n        self.total_notional_local = kwargs.get('total_notional_local')\n        self.cid = kwargs.get('cid')\n        self.total_confirmed_senior_home = kwargs.get('total_confirmed_senior_home')\n        self.ctd_fwd_price = kwargs.get('ctd_fwd_price')\n        self.sink_factor = kwargs.get('sink_factor')\n        self.temperature_forecast = kwargs.get('temperature_forecast')\n        self.bid_high = kwargs.get('bid_high')\n        self.pnl_qtd = kwargs.get('pnl_qtd')\n        self.buy50cents = kwargs.get('buy50cents')\n        self.sell4bps = kwargs.get('sell4bps')\n        self.receiver_day_count_fraction = kwargs.get('receiver_day_count_fraction')\n        self.auction_close_percentage = kwargs.get('auction_close_percentage')\n        self.target_price = kwargs.get('target_price')\n        self.bos_in_bps_description = kwargs.get('bos_in_bps_description')\n        self.low_price = kwargs.get('low_price')\n        self.adv22_day_pct = kwargs.get('adv22_day_pct')\n        self.matched_maturity_swap_spread12m = kwargs.get('matched_maturity_swap_spread12m')\n        self.price_range_in_ticks_label = kwargs.get('price_range_in_ticks_label')\n        self.ticker = kwargs.get('ticker')\n        self.notional_unit = kwargs.get('notional_unit')\n        self.tcm_cost_horizon1_day = kwargs.get('tcm_cost_horizon1_day')\n        self.approval = kwargs.get('approval')\n        self.test_measure = kwargs.get('test_measure')\n        self.option_lock_out_period = kwargs.get('option_lock_out_period')\n        self.execution_time = kwargs.get('execution_time')\n        self.source_value_forecast = kwargs.get('source_value_forecast')\n        self.leg2_spread = kwargs.get('leg2_spread')\n        self.short_conviction_large = kwargs.get('short_conviction_large')\n        self.ccg_name = kwargs.get('ccg_name')\n        self.dollar_excess_return = kwargs.get('dollar_excess_return')\n        self.gsn = kwargs.get('gsn')\n        self.trade_end_date = kwargs.get('trade_end_date')\n        self.receiver_rate_option = kwargs.get('receiver_rate_option')\n        self.gss = kwargs.get('gss')\n        self.percent_of_mediandv1m = kwargs.get('percent_of_mediandv1m')\n        self.lendables = kwargs.get('lendables')\n        self.sell75cents = kwargs.get('sell75cents')\n        self.option_adjusted_spread = kwargs.get('option_adjusted_spread')\n        self.option_adjusted_swap_spread = kwargs.get('option_adjusted_swap_spread')\n        self.bos_in_ticks_label = kwargs.get('bos_in_ticks_label')\n        self.position_source_id = kwargs.get('position_source_id')\n        self.buy1bps = kwargs.get('buy1bps')\n        self.buy3point5bps = kwargs.get('buy3point5bps')\n        self.gs_sustain_region = kwargs.get('gs_sustain_region')\n        self.absolute_return_wtd = kwargs.get('absolute_return_wtd')\n        self.deployment_id = kwargs.get('deployment_id')\n        self.asset_parameters_seniority = kwargs.get('asset_parameters_seniority')\n        self.ask_spread = kwargs.get('ask_spread')\n        self.flow = kwargs.get('flow')\n        self.future_month_h26 = kwargs.get('future_month_h26')\n        self.loan_rebate = kwargs.get('loan_rebate')\n        self.future_month_h25 = kwargs.get('future_month_h25')\n        self.period = kwargs.get('period')\n        self.index_create_source = kwargs.get('index_create_source')\n        self.future_month_h24 = kwargs.get('future_month_h24')\n        self.future_month_h23 = kwargs.get('future_month_h23')\n        self.future_month_h22 = kwargs.get('future_month_h22')\n        self.future_month_h21 = kwargs.get('future_month_h21')\n        self.non_usd_ois = kwargs.get('non_usd_ois')\n        self.real_twi_contribution = kwargs.get('real_twi_contribution')\n        self.mkt_asset = kwargs.get('mkt_asset')\n        self.leg2_index_location = kwargs.get('leg2_index_location')\n        self.twap_unrealized_bps = kwargs.get('twap_unrealized_bps')\n        self.last_updated_message = kwargs.get('last_updated_message')\n        self.loan_value = kwargs.get('loan_value')\n        self.option_adjusted_ois_spread = kwargs.get('option_adjusted_ois_spread')\n        self.total_return_price = kwargs.get('total_return_price')\n        self.weighted_percent_in_model = kwargs.get('weighted_percent_in_model')\n        self.init_loan_spread_required = kwargs.get('init_loan_spread_required')\n        self.election_period = kwargs.get('election_period')\n        self.funding_ask_price = kwargs.get('funding_ask_price')\n        self.historical_beta = kwargs.get('historical_beta')\n        self.bond_risk_premium_index = kwargs.get('bond_risk_premium_index')\n        self.hit_rate_ytd = kwargs.get('hit_rate_ytd')\n        self.gir_gsdeer_gsfeer = kwargs.get('gir_gsdeer_gsfeer')\n        self.num_units = kwargs.get('num_units')\n        self.asset_parameters_receiver_frequency = kwargs.get('asset_parameters_receiver_frequency')\n        self.expense_ratio_gross_bps = kwargs.get('expense_ratio_gross_bps')\n        self.relative_payoff_wtd = kwargs.get('relative_payoff_wtd')\n        self.ctd_price = kwargs.get('ctd_price')\n        self.pace_of_roll_now = kwargs.get('pace_of_roll_now')\n        self.product = kwargs.get('product')\n        self.leg2_return_type = kwargs.get('leg2_return_type')\n        self.agent_lender_fee = kwargs.get('agent_lender_fee')\n        self.dissemination_id = kwargs.get('dissemination_id')\n        self.option_strike_price = kwargs.get('option_strike_price')\n        self.precipitation_type = kwargs.get('precipitation_type')\n        self.lower_bound = kwargs.get('lower_bound')\n        self.arrival_mid_normalized = kwargs.get('arrival_mid_normalized')\n        self.underlying_asset2 = kwargs.get('underlying_asset2')\n        self.underlying_asset1 = kwargs.get('underlying_asset1')\n        self.legal_entity = kwargs.get('legal_entity')\n        self.performance_fee = kwargs.get('performance_fee')\n        self.order_state = kwargs.get('order_state')\n        self.actual_data_quality = kwargs.get('actual_data_quality')\n        self.index_ratio = kwargs.get('index_ratio')\n        self.queue_in_lots_label = kwargs.get('queue_in_lots_label')\n        self.adv10_day_pct = kwargs.get('adv10_day_pct')\n        self.long_conviction_medium = kwargs.get('long_conviction_medium')\n        self.relative_hit_rate_wtd = kwargs.get('relative_hit_rate_wtd')\n        self.daily_tracking_error = kwargs.get('daily_tracking_error')\n        self.sell140cents = kwargs.get('sell140cents')\n        self.sell10bps = kwargs.get('sell10bps')\n        self.aggressive_offset_from_last = kwargs.get('aggressive_offset_from_last')\n        self.longitude = kwargs.get('longitude')\n        self.new_icu = kwargs.get('new_icu')\n        self.market_cap = kwargs.get('market_cap')\n        self.weighted_average_mid = kwargs.get('weighted_average_mid')\n        self.cluster_region = kwargs.get('cluster_region')\n        self.valoren = kwargs.get('valoren')\n        self.average_execution_price = kwargs.get('average_execution_price')\n        self.proceeds_asset_ois_swap_spread1m = kwargs.get('proceeds_asset_ois_swap_spread1m')\n        self.payoff_wtd = kwargs.get('payoff_wtd')\n        self.basis = kwargs.get('basis')\n        self.investment_rate_trend = kwargs.get('investment_rate_trend')\n        self.gross_investment_mtd = kwargs.get('gross_investment_mtd')\n        self.hedge_id = kwargs.get('hedge_id')\n        self.sharpe_mtd = kwargs.get('sharpe_mtd')\n        self.tcm_cost_horizon8_day = kwargs.get('tcm_cost_horizon8_day')\n        self.residual_variance = kwargs.get('residual_variance')\n        self.restrict_internal_derived_data = kwargs.get('restrict_internal_derived_data')\n        self.adv5_day_pct = kwargs.get('adv5_day_pct')\n        self.midpoint_fills_percentage = kwargs.get('midpoint_fills_percentage')\n        self.open_interest = kwargs.get('open_interest')\n        self.turnover_composite_unadjusted = kwargs.get('turnover_composite_unadjusted')\n        self.fwd_points = kwargs.get('fwd_points')\n        self.relative_return_wtd = kwargs.get('relative_return_wtd')\n        self.units = kwargs.get('units')\n        self.payer_rate_option = kwargs.get('payer_rate_option')\n        self.asset_classifications_risk_country_name = kwargs.get('asset_classifications_risk_country_name')\n        self.ext_mkt_point3 = kwargs.get('ext_mkt_point3')\n        self.matched_maturity_swap_spread = kwargs.get('matched_maturity_swap_spread')\n        self.city_name = kwargs.get('city_name')\n        self.hourly_bucket = kwargs.get('hourly_bucket')\n        self.average_implied_volatility = kwargs.get('average_implied_volatility')\n        self.total_hospitalized_with_symptoms = kwargs.get('total_hospitalized_with_symptoms')\n        self.days_open_realized_cash = kwargs.get('days_open_realized_cash')\n        self.adjusted_high_price = kwargs.get('adjusted_high_price')\n        self.proceeds_asset_ois_swap_spread = kwargs.get('proceeds_asset_ois_swap_spread')\n        self.ext_mkt_point1 = kwargs.get('ext_mkt_point1')\n        self.direction = kwargs.get('direction')\n        self.ext_mkt_point2 = kwargs.get('ext_mkt_point2')\n        self.sub_region_code = kwargs.get('sub_region_code')\n        self.asset_parameters_fixed_rate = kwargs.get('asset_parameters_fixed_rate')\n        self.is_estimated_return = kwargs.get('is_estimated_return')\n        self.value_forecast = kwargs.get('value_forecast')\n        self.total_icu = kwargs.get('total_icu')\n        self.position_source_type = kwargs.get('position_source_type')\n        self.previous_close_unrealized_cash = kwargs.get('previous_close_unrealized_cash')\n        self.minimum_denomination = kwargs.get('minimum_denomination')\n        self.future_value_notional = kwargs.get('future_value_notional')\n        self.participation_rate = kwargs.get('participation_rate')\n        self.obfr = kwargs.get('obfr')\n        self.buy9point5bps = kwargs.get('buy9point5bps')\n        self.option_lock_period = kwargs.get('option_lock_period')\n        self.es_momentum_percentile = kwargs.get('es_momentum_percentile')\n        self.adv_percentage = kwargs.get('adv_percentage')\n        self.leg1_averaging_method = kwargs.get('leg1_averaging_method')\n        self.turnover_composite = kwargs.get('turnover_composite')\n        self.forecast_date = kwargs.get('forecast_date')\n        self.internal_index_calc_region = kwargs.get('internal_index_calc_region')\n        self.position_type = kwargs.get('position_type')\n        self.sub_asset_class = kwargs.get('sub_asset_class')\n        self.short_interest = kwargs.get('short_interest')\n        self.reference_period = kwargs.get('reference_period')\n        self.adjusted_volume = kwargs.get('adjusted_volume')\n        self.ctd_fwd_yield = kwargs.get('ctd_fwd_yield')\n        self.sec_db = kwargs.get('sec_db')\n        self.memory_used = kwargs.get('memory_used')\n        self.bpe_quality_stars = kwargs.get('bpe_quality_stars')\n        self.ctd = kwargs.get('ctd')\n        self.intended_participation_rate = kwargs.get('intended_participation_rate')\n        self.leg1_payment_type = kwargs.get('leg1_payment_type')\n        self.trading_pnl = kwargs.get('trading_pnl')\n        self.collateral_value_required = kwargs.get('collateral_value_required')\n        self.buy45bps = kwargs.get('buy45bps')\n        self.price_to_earnings_positive = kwargs.get('price_to_earnings_positive')\n        self.forecast = kwargs.get('forecast')\n        self.forecast_value = kwargs.get('forecast_value')\n        self.pnl = kwargs.get('pnl')\n        self.volume_in_limit = kwargs.get('volume_in_limit')\n        self.is_territory = kwargs.get('is_territory')\n        self.leg2_delivery_point = kwargs.get('leg2_delivery_point')\n        self.tcm_cost_horizon4_day = kwargs.get('tcm_cost_horizon4_day')\n        self.styles = kwargs.get('styles')\n        self.short_name = kwargs.get('short_name')\n        self.reset_frequency1 = kwargs.get('reset_frequency1')\n        self.buy4bps = kwargs.get('buy4bps')\n        self.reset_frequency2 = kwargs.get('reset_frequency2')\n        self.other_price_term = kwargs.get('other_price_term')\n        self.bid_gspread = kwargs.get('bid_gspread')\n        self.open_price = kwargs.get('open_price')\n        self.ps_id = kwargs.get('ps_id')\n        self.hit_rate_mtd = kwargs.get('hit_rate_mtd')\n        self.fair_volatility = kwargs.get('fair_volatility')\n        self.dollar_cross = kwargs.get('dollar_cross')\n        self.portfolio_type = kwargs.get('portfolio_type')\n        self.currency = kwargs.get('currency')\n        self.cluster_class = kwargs.get('cluster_class')\n        self.sell50bps = kwargs.get('sell50bps')\n        self.future_month_m21 = kwargs.get('future_month_m21')\n        self.bid_size = kwargs.get('bid_size')\n        self.arrival_mid = kwargs.get('arrival_mid')\n        self.asset_parameters_exchange_currency = kwargs.get('asset_parameters_exchange_currency')\n        self.candidate_name = kwargs.get('candidate_name')\n        self.implied_lognormal_volatility = kwargs.get('implied_lognormal_volatility')\n        self.vwap_in_limit_unrealized_cash = kwargs.get('vwap_in_limit_unrealized_cash')\n        self.rating_moodys = kwargs.get('rating_moodys')\n        self.future_month_m26 = kwargs.get('future_month_m26')\n        self.future_month_m25 = kwargs.get('future_month_m25')\n        self.future_month_m24 = kwargs.get('future_month_m24')\n        self.future_month_m23 = kwargs.get('future_month_m23')\n        self.future_month_m22 = kwargs.get('future_month_m22')\n        self.flow_pct = kwargs.get('flow_pct')\n        self.source = kwargs.get('source')\n        self.asset_classifications_country_code = kwargs.get('asset_classifications_country_code')\n        self.settle_drop = kwargs.get('settle_drop')\n        self.data_set_sub_category = kwargs.get('data_set_sub_category')\n        self.sell9point5bps = kwargs.get('sell9point5bps')\n        self.quantity_bucket = kwargs.get('quantity_bucket')\n        self.option_style_sdr = kwargs.get('option_style_sdr')\n        self.oe_name = kwargs.get('oe_name')\n        self.given = kwargs.get('given')\n        self.leg2_day_count_convention = kwargs.get('leg2_day_count_convention')\n        self.liquidity_score_sell = kwargs.get('liquidity_score_sell')\n        self.delisting_date = kwargs.get('delisting_date')\n        self.weight = kwargs.get('weight')\n        self.accrued_interest = kwargs.get('accrued_interest')\n        self.business_scope = kwargs.get('business_scope')\n        self.wtd_degree_days = kwargs.get('wtd_degree_days')\n        self.absolute_weight = kwargs.get('absolute_weight')\n        self.measure = kwargs.get('measure')\n        self.temperature_hourly_forecast = kwargs.get('temperature_hourly_forecast')\n        self.iceberg_tip_rate_type = kwargs.get('iceberg_tip_rate_type')\n        self.sharpe_ytd = kwargs.get('sharpe_ytd')\n        self.wind_speed_forecast = kwargs.get('wind_speed_forecast')\n        self.gross_investment_ytd = kwargs.get('gross_investment_ytd')\n        self.yield_price = kwargs.get('yield_price')\n        self.leg1_total_notional_unit = kwargs.get('leg1_total_notional_unit')\n        self.issue_price = kwargs.get('issue_price')\n        self.ask_high = kwargs.get('ask_high')\n        self.expected_data_quality = kwargs.get('expected_data_quality')\n        self.region_name = kwargs.get('region_name')\n        self.value_revised = kwargs.get('value_revised')\n        self.discretion_upper_bound = kwargs.get('discretion_upper_bound')\n        self.adjusted_trade_price = kwargs.get('adjusted_trade_price')\n        self.forecast_time = kwargs.get('forecast_time')\n        self.iso_subdivision_code_alpha2 = kwargs.get('iso_subdivision_code_alpha2')\n        self.ctd_conversion_factor = kwargs.get('ctd_conversion_factor')\n        self.proceeds_asset_swap_spread = kwargs.get('proceeds_asset_swap_spread')\n        self.is_adr = kwargs.get('is_adr')\n        self.issue_date = kwargs.get('issue_date')\n        self.service_id = kwargs.get('service_id')\n        self.yes = kwargs.get('yes')\n        self.g_score = kwargs.get('g_score')\n        self.market_value = kwargs.get('market_value')\n        self.entity_id = kwargs.get('entity_id')\n        self.notional_currency1 = kwargs.get('notional_currency1')\n        self.net_debt_to_ebitda = kwargs.get('net_debt_to_ebitda')\n        self.num_units_upper = kwargs.get('num_units_upper')\n        self.notional_currency2 = kwargs.get('notional_currency2')\n        self.in_limit_participation_rate = kwargs.get('in_limit_participation_rate')\n        self.pressure_forecast = kwargs.get('pressure_forecast')\n        self.paid = kwargs.get('paid')\n        self.fixed_rate = kwargs.get('fixed_rate')\n        self.short = kwargs.get('short')\n        self.time = kwargs.get('time')\n        self.buy4point5bps = kwargs.get('buy4point5bps')\n        self.sell30cents = kwargs.get('sell30cents')\n        self.event_end_date_time = kwargs.get('event_end_date_time')\n        self.leg1_payment_frequency = kwargs.get('leg1_payment_frequency')\n        self.cm_id = kwargs.get('cm_id')\n        self.taxonomy = kwargs.get('taxonomy')\n        self.buy45cents = kwargs.get('buy45cents')\n        self.measures = kwargs.get('measures')\n        self.seasonal_adjustment = kwargs.get('seasonal_adjustment')\n        self.rank_wtd = kwargs.get('rank_wtd')\n        self.underlyer = kwargs.get('underlyer')\n        self.created_time = kwargs.get('created_time')\n        self.identifier = kwargs.get('identifier')\n        self.price_unit = kwargs.get('price_unit')\n        self.trade_report_ref_id = kwargs.get('trade_report_ref_id')\n        self.subdivision_id = kwargs.get('subdivision_id')\n        self.unadjusted_low = kwargs.get('unadjusted_low')\n        self.buy160cents = kwargs.get('buy160cents')\n        self.portfolio_id = kwargs.get('portfolio_id')\n        self.z_spread = kwargs.get('z_spread')\n        self.cap_floor_atm_fwd_rate = kwargs.get('cap_floor_atm_fwd_rate')\n        self.es_percentile = kwargs.get('es_percentile')\n        self.tdapi = kwargs.get('tdapi')\n        self.location_code = kwargs.get('location_code')\n        self.rcic = kwargs.get('rcic')\n        self.name_raw = kwargs.get('name_raw')\n        self.simon_asset_tags = kwargs.get('simon_asset_tags')\n        self.hit_rate_qtd = kwargs.get('hit_rate_qtd')\n        self.primary_volume_in_limit = kwargs.get('primary_volume_in_limit')\n        self.precipitation_daily_forecast_percent = kwargs.get('precipitation_daily_forecast_percent')\n        self.aum_end = kwargs.get('aum_end')\n        self.premium = kwargs.get('premium')\n        self.low = kwargs.get('low')\n        self.cross_group = kwargs.get('cross_group')\n        self.report_run_time = kwargs.get('report_run_time')\n        self.five_day_price_change_bps = kwargs.get('five_day_price_change_bps')\n        self.holdings = kwargs.get('holdings')\n        self.precipitation_daily_forecast = kwargs.get('precipitation_daily_forecast')\n        self.price_method = kwargs.get('price_method')\n        self.asset_parameters_fixed_rate_frequency = kwargs.get('asset_parameters_fixed_rate_frequency')\n        self.ois_xccy = kwargs.get('ois_xccy')\n        self.days_open = kwargs.get('days_open')\n        self.buy110cents = kwargs.get('buy110cents')\n        self.average_spread_bps = kwargs.get('average_spread_bps')\n        self.buy55cents = kwargs.get('buy55cents')\n        self.future_month_q26 = kwargs.get('future_month_q26')\n        self.issue_size = kwargs.get('issue_size')\n        self.future_month_q25 = kwargs.get('future_month_q25')\n        self.future_month_q24 = kwargs.get('future_month_q24')\n        self.future_month_q23 = kwargs.get('future_month_q23')\n        self.future_month_q22 = kwargs.get('future_month_q22')\n        self.pending_loan_count = kwargs.get('pending_loan_count')\n        self.future_month_q21 = kwargs.get('future_month_q21')\n        self.price_spot_stop_loss_unit = kwargs.get('price_spot_stop_loss_unit')\n        self.price_range_in_ticks_description = kwargs.get('price_range_in_ticks_description')\n        self.trade_volume = kwargs.get('trade_volume')\n        self.primary_country_ric = kwargs.get('primary_country_ric')\n        self.option_expiration_frequency = kwargs.get('option_expiration_frequency')\n        self.is_active = kwargs.get('is_active')\n        self.use_machine_learning = kwargs.get('use_machine_learning')\n        self.growth_score = kwargs.get('growth_score')\n        self.buffer_threshold = kwargs.get('buffer_threshold')\n        self.buy120cents = kwargs.get('buy120cents')\n        self.matched_maturity_swap_rate = kwargs.get('matched_maturity_swap_rate')\n        self.primary_vwap = kwargs.get('primary_vwap')\n        self.exchange_type_id = kwargs.get('exchange_type_id')\n        self.basis_swap_rate = kwargs.get('basis_swap_rate')\n        self.exchange_code = kwargs.get('exchange_code')\n        self.group = kwargs.get('group')\n        self.asset_parameters_termination_date = kwargs.get('asset_parameters_termination_date')\n        self.estimated_spread = kwargs.get('estimated_spread')\n        self.yield_change_on_day = kwargs.get('yield_change_on_day')\n        self.created = kwargs.get('created')\n        self.auto_tags = kwargs.get('auto_tags')\n        self.tcm_cost = kwargs.get('tcm_cost')\n        self.sustain_japan = kwargs.get('sustain_japan')\n        self.history_start_date = kwargs.get('history_start_date')\n        self.bid_spread = kwargs.get('bid_spread')\n        self.percentage_complete = kwargs.get('percentage_complete')\n        self.hedge_tracking_error = kwargs.get('hedge_tracking_error')\n        self.wind_speed_type = kwargs.get('wind_speed_type')\n        self.strike_price = kwargs.get('strike_price')\n        self.par_asset_swap_spread12m = kwargs.get('par_asset_swap_spread12m')\n        self.trade_report_id = kwargs.get('trade_report_id')\n        self.adjusted_open_price = kwargs.get('adjusted_open_price')\n        self.country_id = kwargs.get('country_id')\n        self.point = kwargs.get('point')\n        self.pnl_mtd = kwargs.get('pnl_mtd')\n        self.total_returns = kwargs.get('total_returns')\n        self.lender = kwargs.get('lender')\n        self.ann_return1_year = kwargs.get('ann_return1_year')\n        self.ctd_fwd_dv01 = kwargs.get('ctd_fwd_dv01')\n        self.eff_yield7_day = kwargs.get('eff_yield7_day')\n        self.meeting_date = kwargs.get('meeting_date')\n        self.calendar_spread_mispricing = kwargs.get('calendar_spread_mispricing')\n        self.buy140cents = kwargs.get('buy140cents')\n        self.price_notation2_type = kwargs.get('price_notation2_type')\n        self.fund_focus = kwargs.get('fund_focus')\n        self.relative_strike = kwargs.get('relative_strike')\n        self.flagship = kwargs.get('flagship')\n        self.additional_price_notation = kwargs.get('additional_price_notation')\n        self.factor_category = kwargs.get('factor_category')\n        self.equity_delta = kwargs.get('equity_delta')\n        self.gross_weight = kwargs.get('gross_weight')\n        self.listed = kwargs.get('listed')\n        self.sell7bps = kwargs.get('sell7bps')\n        self.earnings_record_type = kwargs.get('earnings_record_type')\n        self.mean = kwargs.get('mean')\n        self.ask_yield = kwargs.get('ask_yield')\n        self.shock_style = kwargs.get('shock_style')\n        self.methodology = kwargs.get('methodology')\n        self.buy25cents = kwargs.get('buy25cents')\n        self.amount_outstanding = kwargs.get('amount_outstanding')\n        self.market_pnl = kwargs.get('market_pnl')\n        self.sustain_asia_ex_japan = kwargs.get('sustain_asia_ex_japan')\n        self.sell6point5bps = kwargs.get('sell6point5bps')\n        self.neighbour_asset_id = kwargs.get('neighbour_asset_id')\n        self.count_ideas_ytd = kwargs.get('count_ideas_ytd')\n        self.simon_intl_asset_tags = kwargs.get('simon_intl_asset_tags')\n        self.path = kwargs.get('path')\n        self.vwap_unrealized_cash = kwargs.get('vwap_unrealized_cash')\n        self.payoff_mtd = kwargs.get('payoff_mtd')\n        self.bos_in_bps_label = kwargs.get('bos_in_bps_label')\n        self.bos_in_bps = kwargs.get('bos_in_bps')\n        self.point_class = kwargs.get('point_class')\n        self.fx_spot = kwargs.get('fx_spot')\n        self.restrict_named_individuals = kwargs.get('restrict_named_individuals')\n        self.hedge_volatility = kwargs.get('hedge_volatility')\n        self.tags = kwargs.get('tags')\n        self.population = kwargs.get('population')\n        self.underlying_asset_id = kwargs.get('underlying_asset_id')\n        self.real_long_rates_contribution = kwargs.get('real_long_rates_contribution')\n        self.pctprices_return = kwargs.get('pctprices_return')\n        self.domain = kwargs.get('domain')\n        self.buy80cents = kwargs.get('buy80cents')\n        self.forward_tenor = kwargs.get('forward_tenor')\n        self.average_price = kwargs.get('average_price')\n        self.target_price_realized_bps = kwargs.get('target_price_realized_bps')\n        self.leg2_fixed_rate = kwargs.get('leg2_fixed_rate')\n        self.share_class_assets = kwargs.get('share_class_assets')\n        self.annuity = kwargs.get('annuity')\n        self.total_count = kwargs.get('total_count')\n        self.quote_type = kwargs.get('quote_type')\n        self.corporate_action_status = kwargs.get('corporate_action_status')\n        self.pegged_tip_size = kwargs.get('pegged_tip_size')\n        self.uid = kwargs.get('uid')\n        self.es_policy_percentile = kwargs.get('es_policy_percentile')\n        self.usd_ois = kwargs.get('usd_ois')\n        self.term = kwargs.get('term')\n        self.restrict_internal_gs_ntk = kwargs.get('restrict_internal_gs_ntk')\n        self.tcm_cost_participation_rate100_pct = kwargs.get('tcm_cost_participation_rate100_pct')\n        self.relative_universe = kwargs.get('relative_universe')\n        self.measure_idx = kwargs.get('measure_idx')\n        self.fred_id = kwargs.get('fred_id')\n        self.twi_contribution = kwargs.get('twi_contribution')\n        self.cloud_cover_type = kwargs.get('cloud_cover_type')\n        self.delisted = kwargs.get('delisted')\n        self.regional_focus = kwargs.get('regional_focus')\n        self.volume_primary = kwargs.get('volume_primary')\n        self.asset_parameters_payer_designated_maturity = kwargs.get('asset_parameters_payer_designated_maturity')\n        self.buy30cents = kwargs.get('buy30cents')\n        self.funding_bid_price = kwargs.get('funding_bid_price')\n        self.series = kwargs.get('series')\n        self.sell3bps = kwargs.get('sell3bps')\n        self.settlement_price = kwargs.get('settlement_price')\n        self.quarter = kwargs.get('quarter')\n        self.sell18bps = kwargs.get('sell18bps')\n        self.asset_parameters_floating_rate_option = kwargs.get('asset_parameters_floating_rate_option')\n        self.realized_vwap_performance_bps = kwargs.get('realized_vwap_performance_bps')\n        self.vote_share = kwargs.get('vote_share')\n        self.servicing_cost_short_pnl = kwargs.get('servicing_cost_short_pnl')\n        self.total_confirmed = kwargs.get('total_confirmed')\n        self.economic_forecast = kwargs.get('economic_forecast')\n        self.plot_id = kwargs.get('plot_id')\n        self.cluster_description = kwargs.get('cluster_description')\n        self.concentration_limit = kwargs.get('concentration_limit')\n        self.wind_speed = kwargs.get('wind_speed')\n        self.observation_hour = kwargs.get('observation_hour')\n        self.signal = kwargs.get('signal')\n        self.borrower_id = kwargs.get('borrower_id')\n        self.data_product = kwargs.get('data_product')\n        self.buy7point5bps = kwargs.get('buy7point5bps')\n        self.limit_price = kwargs.get('limit_price')\n        self.bm_prime_id = kwargs.get('bm_prime_id')\n        self.data_type = kwargs.get('data_type')\n        self.count = kwargs.get('count')\n        self.conviction = kwargs.get('conviction')\n        self.rfqstate = kwargs.get('rfqstate')\n        self.benchmark_maturity = kwargs.get('benchmark_maturity')\n        self.gross_flow_normalized = kwargs.get('gross_flow_normalized')\n        self.buy14bps = kwargs.get('buy14bps')\n        self.factor_id = kwargs.get('factor_id')\n        self.future_month_v26 = kwargs.get('future_month_v26')\n        self.sts_fx_currency = kwargs.get('sts_fx_currency')\n        self.future_month_v25 = kwargs.get('future_month_v25')\n        self.bid_change = kwargs.get('bid_change')\n        self.month = kwargs.get('month')\n        self.future_month_v24 = kwargs.get('future_month_v24')\n        self.investment_wtd = kwargs.get('investment_wtd')\n        self.future_month_v23 = kwargs.get('future_month_v23')\n        self.future_month_v22 = kwargs.get('future_month_v22')\n        self.future_month_v21 = kwargs.get('future_month_v21')\n        self.expiration = kwargs.get('expiration')\n        self.leg2_reset_frequency = kwargs.get('leg2_reset_frequency')\n        self.controversy_score = kwargs.get('controversy_score')\n        self.proceed_asset_swap_spread = kwargs.get('proceed_asset_swap_spread')\n        self.concentration_level = kwargs.get('concentration_level')\n        self.importance = kwargs.get('importance')\n        self.asset_classifications_gics_sector = kwargs.get('asset_classifications_gics_sector')\n        self.sts_asset_name = kwargs.get('sts_asset_name')\n        self.net_exposure_classification = kwargs.get('net_exposure_classification')\n        self.settlement_method = kwargs.get('settlement_method')\n        self.receiver_designated_maturity = kwargs.get('receiver_designated_maturity')\n        self.title = kwargs.get('title')\n        self.x_ref_type_id = kwargs.get('x_ref_type_id')\n        self.duration = kwargs.get('duration')\n        self.load = kwargs.get('load')\n        self.alpha = kwargs.get('alpha')\n        self.company = kwargs.get('company')\n        self.settlement_frequency = kwargs.get('settlement_frequency')\n        self.dist_avg7_day = kwargs.get('dist_avg7_day')\n        self.in_risk_model = kwargs.get('in_risk_model')\n        self.daily_net_shareholder_flows_percent = kwargs.get('daily_net_shareholder_flows_percent')\n        self.filled_notional_local = kwargs.get('filled_notional_local')\n        self.ever_hospitalized = kwargs.get('ever_hospitalized')\n        self.meeting_number = kwargs.get('meeting_number')\n        self.mid_gspread = kwargs.get('mid_gspread')\n        self.days_open_unrealized_bps = kwargs.get('days_open_unrealized_bps')\n        self.long_level = kwargs.get('long_level')\n        self.data_description = kwargs.get('data_description')\n        self.temperature_type = kwargs.get('temperature_type')\n        self.gsideid = kwargs.get('gsideid')\n        self.repo_rate = kwargs.get('repo_rate')\n        self.division = kwargs.get('division')\n        self.cloud_cover_daily_forecast = kwargs.get('cloud_cover_daily_forecast')\n        self.wind_speed_daily_forecast = kwargs.get('wind_speed_daily_forecast')\n        self.asset_parameters_floating_rate_day_count_fraction = kwargs.get(\n            'asset_parameters_floating_rate_day_count_fraction')\n        self.trade_action = kwargs.get('trade_action')\n        self.action = kwargs.get('action')\n        self.ctd_yield = kwargs.get('ctd_yield')\n        self.arrival_haircut_vwap_normalized = kwargs.get('arrival_haircut_vwap_normalized')\n        self.price_component = kwargs.get('price_component')\n        self.queue_clock_time_description = kwargs.get('queue_clock_time_description')\n        self.asset_parameters_receiver_day_count_fraction = kwargs.get('asset_parameters_receiver_day_count_fraction')\n        self.percent_mid_execution_quantity = kwargs.get('percent_mid_execution_quantity')\n        self.delta_strike = kwargs.get('delta_strike')\n        self.cloud_cover = kwargs.get('cloud_cover')\n        self.asset_parameters_notional_currency = kwargs.get('asset_parameters_notional_currency')\n        self.buy18bps = kwargs.get('buy18bps')\n        self.value_actual = kwargs.get('value_actual')\n        self.upi = kwargs.get('upi')\n        self.collateral_currency = kwargs.get('collateral_currency')\n        self.original_country = kwargs.get('original_country')\n        self.field = kwargs.get('field')\n        self.geographic_focus = kwargs.get('geographic_focus')\n        self.days_open_realized_bps = kwargs.get('days_open_realized_bps')\n        self.fx_risk_premium_index = kwargs.get('fx_risk_premium_index')\n        self.skew = kwargs.get('skew')\n        self.status = kwargs.get('status')\n        self.notional_currency = kwargs.get('notional_currency')\n        self.sustain_emerging_markets = kwargs.get('sustain_emerging_markets')\n        self.event_date_time = kwargs.get('event_date_time')\n        self.leg1_designated_maturity = kwargs.get('leg1_designated_maturity')\n        self.total_price = kwargs.get('total_price')\n        self.on_behalf_of = kwargs.get('on_behalf_of')\n        self.test_type = kwargs.get('test_type')\n        self.accrued_interest_standard = kwargs.get('accrued_interest_standard')\n        self.future_month_z26 = kwargs.get('future_month_z26')\n        self.future_month_z25 = kwargs.get('future_month_z25')\n        self.ccg_code = kwargs.get('ccg_code')\n        self.short_exposure = kwargs.get('short_exposure')\n        self.leg1_fixed_payment_currency = kwargs.get('leg1_fixed_payment_currency')\n        self.arrival_haircut_vwap = kwargs.get('arrival_haircut_vwap')\n        self.execution_days = kwargs.get('execution_days')\n        self.recall_due_date = kwargs.get('recall_due_date')\n        self.forward = kwargs.get('forward')\n        self.strike = kwargs.get('strike')\n        self.spread_limit = kwargs.get('spread_limit')\n        self.product_scope = kwargs.get('product_scope')\n        self.asset_parameters_issuer_type = kwargs.get('asset_parameters_issuer_type')\n        self.currency1 = kwargs.get('currency1')\n        self.currency2 = kwargs.get('currency2')\n        self.previous_close_realized_bps = kwargs.get('previous_close_realized_bps')\n        self.days_since_reported = kwargs.get('days_since_reported')\n        self.event_status = kwargs.get('event_status')\n        self.vwap_in_limit = kwargs.get('vwap_in_limit')\n        self.fwd_duration = kwargs.get('fwd_duration')\n        self.__return = kwargs.get('return_')\n        self.is_pair_basket = kwargs.get('is_pair_basket')\n        self.notional_amount = kwargs.get('notional_amount')\n        self.pay_or_receive = kwargs.get('pay_or_receive')\n        self.total_severe = kwargs.get('total_severe')\n        self.unexecuted_notional_usd = kwargs.get('unexecuted_notional_usd')\n        self.expected_residual_percentage = kwargs.get('expected_residual_percentage')\n        self.maturity_date = kwargs.get('maturity_date')\n        self.trace_adv_sell = kwargs.get('trace_adv_sell')\n        self.event_name = kwargs.get('event_name')\n        self.address_line2 = kwargs.get('address_line2')\n        self.indication_of_other_price_affecting_term = kwargs.get('indication_of_other_price_affecting_term')\n        self.unadjusted_bid = kwargs.get('unadjusted_bid')\n        self.backtest_type = kwargs.get('backtest_type')\n        self.gsdeer = kwargs.get('gsdeer')\n        self.asset_parameters_issuer = kwargs.get('asset_parameters_issuer')\n        self.g_regional_percentile = kwargs.get('g_regional_percentile')\n        self.coverage_checked = kwargs.get('coverage_checked')\n        self.ois_xccy_ex_spike = kwargs.get('ois_xccy_ex_spike')\n        self.total_risk = kwargs.get('total_risk')\n        self.mnav = kwargs.get('mnav')\n        self.market_volume = kwargs.get('market_volume')\n        self.swap_annuity = kwargs.get('swap_annuity')\n        self.par_asset_swap_spread = kwargs.get('par_asset_swap_spread')\n        self.curr_yield7_day = kwargs.get('curr_yield7_day')\n        self.pressure = kwargs.get('pressure')\n        self.short_description = kwargs.get('short_description')\n        self.future_month_z24 = kwargs.get('future_month_z24')\n        self.feed = kwargs.get('feed')\n        self.future_month_z23 = kwargs.get('future_month_z23')\n        self.mkt_point1 = kwargs.get('mkt_point1')\n        self.future_month_z22 = kwargs.get('future_month_z22')\n        self.future_month_z21 = kwargs.get('future_month_z21')\n        self.future_month_z20 = kwargs.get('future_month_z20')\n        self.asset_parameters_commodity_sector = kwargs.get('asset_parameters_commodity_sector')\n        self.price_notation2 = kwargs.get('price_notation2')\n        self.market_buffer_threshold = kwargs.get('market_buffer_threshold')\n        self.price_notation3 = kwargs.get('price_notation3')\n        self.mkt_point3 = kwargs.get('mkt_point3')\n        self.mkt_point2 = kwargs.get('mkt_point2')\n        self.leg2_type = kwargs.get('leg2_type')\n        self.mkt_point4 = kwargs.get('mkt_point4')\n        self.degree_days_type = kwargs.get('degree_days_type')\n        self.sentiment = kwargs.get('sentiment')\n        self.investment_income = kwargs.get('investment_income')\n        self.group_type = kwargs.get('group_type')\n        self.forward_point_imm = kwargs.get('forward_point_imm')\n        self.twap = kwargs.get('twap')\n        self.client_short_name = kwargs.get('client_short_name')\n        self.group_category = kwargs.get('group_category')\n        self.bid_plus_ask = kwargs.get('bid_plus_ask')\n        self.foreign_ccy_rate = kwargs.get('foreign_ccy_rate')\n        self.election_odds = kwargs.get('election_odds')\n        self.wind_direction_forecast = kwargs.get('wind_direction_forecast')\n        self.require_anon_client_name = kwargs.get('require_anon_client_name')\n        self.pricing_location = kwargs.get('pricing_location')\n        self.beta = kwargs.get('beta')\n        self.last_returns_end_date = kwargs.get('last_returns_end_date')\n        self.upfront_payment_date = kwargs.get('upfront_payment_date')\n        self.sell1point5bps = kwargs.get('sell1point5bps')\n        self.long_exposure = kwargs.get('long_exposure')\n        self.sell4point5bps = kwargs.get('sell4point5bps')\n        self.tcm_cost_participation_rate20_pct = kwargs.get('tcm_cost_participation_rate20_pct')\n        self.venue_type = kwargs.get('venue_type')\n        self.multi_asset_class_swap = kwargs.get('multi_asset_class_swap')\n        self.delta_change_id = kwargs.get('delta_change_id')\n        self.implementation_id = kwargs.get('implementation_id')\n        self.leg1_fixed_payment = kwargs.get('leg1_fixed_payment')\n        self.es_numeric_score = kwargs.get('es_numeric_score')\n        self.in_benchmark = kwargs.get('in_benchmark')\n        self.action_sdr = kwargs.get('action_sdr')\n        self.count_ideas_qtd = kwargs.get('count_ideas_qtd')\n        self.knock_out_price = kwargs.get('knock_out_price')\n        self.ctd_asset_id = kwargs.get('ctd_asset_id')\n        self.buy10bps = kwargs.get('buy10bps')\n        self.precipitation = kwargs.get('precipitation')\n        self.value_type = kwargs.get('value_type')\n        self.beta_adjusted_net_exposure = kwargs.get('beta_adjusted_net_exposure')\n        self.estimated_rod_volume = kwargs.get('estimated_rod_volume')\n        self.sell14bps = kwargs.get('sell14bps')\n        self.excess_return_price = kwargs.get('excess_return_price')\n        self.fx_pnl = kwargs.get('fx_pnl')\n        self.asset_classifications_gics_industry_group = kwargs.get('asset_classifications_gics_industry_group')\n        self.lending_sec_id = kwargs.get('lending_sec_id')\n        self.dollar_duration = kwargs.get('dollar_duration')\n        self.equity_theta = kwargs.get('equity_theta')\n        self.dv01 = kwargs.get('dv01')\n        self.start_date = kwargs.get('start_date')\n        self.mixed_swap = kwargs.get('mixed_swap')\n        self.swaption_premium = kwargs.get('swaption_premium')\n        self.snowfall = kwargs.get('snowfall')\n        self.liquidity_bucket_buy = kwargs.get('liquidity_bucket_buy')\n        self.mic = kwargs.get('mic')\n        self.latitude = kwargs.get('latitude')\n        self.mid = kwargs.get('mid')\n        self.implied_repo = kwargs.get('implied_repo')\n        self.long = kwargs.get('long')\n        self.first_execution_time = kwargs.get('first_execution_time')\n        self.covered_bond = kwargs.get('covered_bond')\n        self.region_code = kwargs.get('region_code')\n        self.buy20cents = kwargs.get('buy20cents')\n        self.long_weight = kwargs.get('long_weight')\n        self.calculation_time = kwargs.get('calculation_time')\n        self.liquidity_bucket_sell = kwargs.get('liquidity_bucket_sell')\n        self.days_open_unrealized_cash = kwargs.get('days_open_unrealized_cash')\n        self.temperature = kwargs.get('temperature')\n        self.average_realized_variance = kwargs.get('average_realized_variance')\n        self.rating_fitch = kwargs.get('rating_fitch')\n        self.financial_returns_score = kwargs.get('financial_returns_score')\n        self.year_or_quarter = kwargs.get('year_or_quarter')\n        self.non_symbol_dimensions = kwargs.get('non_symbol_dimensions')\n        self.commodities_forecast = kwargs.get('commodities_forecast')\n        self.covid19_by_state = kwargs.get('covid19_by_state')\n        self.percentage_expected_residual = kwargs.get('percentage_expected_residual')\n        self.hospital_name = kwargs.get('hospital_name')\n        self.buy90cents = kwargs.get('buy90cents')\n        self.period_type = kwargs.get('period_type')\n        self.asset_classifications_country_name = kwargs.get('asset_classifications_country_name')\n        self.total_hospitalized = kwargs.get('total_hospitalized')\n        self.pegged_refill_interval = kwargs.get('pegged_refill_interval')\n        self.fatalities_probable = kwargs.get('fatalities_probable')\n        self.administrative_region = kwargs.get('administrative_region')\n        self.__open = kwargs.get('open_')\n        self.cusip = kwargs.get('cusip')\n        self.total_confirmed_by_state = kwargs.get('total_confirmed_by_state')\n        self.idea_activity_time = kwargs.get('idea_activity_time')\n        self.wind_attribute = kwargs.get('wind_attribute')\n        self.spread_option_atm_fwd_rate = kwargs.get('spread_option_atm_fwd_rate')\n        self.net_exposure = kwargs.get('net_exposure')\n        self.is_legacy_pair_basket = kwargs.get('is_legacy_pair_basket')\n        self.issuer_type = kwargs.get('issuer_type')\n        self.buy70cents = kwargs.get('buy70cents')\n        self.strike_reference = kwargs.get('strike_reference')\n        self.asset_count = kwargs.get('asset_count')\n        self.is_order_in_limit = kwargs.get('is_order_in_limit')\n        self.fundamental_metric = kwargs.get('fundamental_metric')\n        self.quote_status_id = kwargs.get('quote_status_id')\n        self.absolute_value = kwargs.get('absolute_value')\n        self.closing_report = kwargs.get('closing_report')\n        self.previous_total_confirmed = kwargs.get('previous_total_confirmed')\n        self.long_tenor = kwargs.get('long_tenor')\n        self.multiplier = kwargs.get('multiplier')\n        self.buy40cents = kwargs.get('buy40cents')\n        self.asset_count_priced = kwargs.get('asset_count_priced')\n        self.vote_direction = kwargs.get('vote_direction')\n        self.implied_repo_rate = kwargs.get('implied_repo_rate')\n        self.settlement_currency = kwargs.get('settlement_currency')\n        self.wtd_degree_days_forecast = kwargs.get('wtd_degree_days_forecast')\n        self.indication_of_collateralization = kwargs.get('indication_of_collateralization')\n        self.future_month_n26 = kwargs.get('future_month_n26')\n        self.lending_partner_fee = kwargs.get('lending_partner_fee')\n        self.future_month_n25 = kwargs.get('future_month_n25')\n        self.future_month_n24 = kwargs.get('future_month_n24')\n        self.primary_vwap_realized_bps = kwargs.get('primary_vwap_realized_bps')\n        self.future_month_n23 = kwargs.get('future_month_n23')\n        self.future_month_n22 = kwargs.get('future_month_n22')\n        self.future_month_n21 = kwargs.get('future_month_n21')\n        self.break_even_inflation = kwargs.get('break_even_inflation')\n        self.pnl_ytd = kwargs.get('pnl_ytd')\n        self.leg1_return_type = kwargs.get('leg1_return_type')\n        self.tenor2 = kwargs.get('tenor2')\n        self.reset_frequency = kwargs.get('reset_frequency')\n        self.asset_parameters_payer_frequency = kwargs.get('asset_parameters_payer_frequency')\n        self.degree_days_forecast = kwargs.get('degree_days_forecast')\n        self.is_manually_silenced = kwargs.get('is_manually_silenced')\n        self.buy3bps = kwargs.get('buy3bps')\n        self.last_updated_by_id = kwargs.get('last_updated_by_id')\n        self.legal_entity_acct = kwargs.get('legal_entity_acct')\n        self.target_shareholder_meeting_date = kwargs.get('target_shareholder_meeting_date')\n        self.pace_of_rollp0 = kwargs.get('pace_of_rollp0')\n        self.controversy_percentile = kwargs.get('controversy_percentile')\n        self.leg1_notional_currency = kwargs.get('leg1_notional_currency')\n        self.compliance_effective_time = kwargs.get('compliance_effective_time')\n        self.expiration_date = kwargs.get('expiration_date')\n        self.floating_rate_day_count_fraction = kwargs.get('floating_rate_day_count_fraction')\n        self.call_last_date = kwargs.get('call_last_date')\n        self.factor_return = kwargs.get('factor_return')\n        self.passive_flow_ratio = kwargs.get('passive_flow_ratio')\n        self.composite5_day_adv = kwargs.get('composite5_day_adv')\n        self.marginal_contribution_to_risk = kwargs.get('marginal_contribution_to_risk')\n        self.close_date = kwargs.get('close_date')\n        self.temperature_hour_forecast = kwargs.get('temperature_hour_forecast')\n        self.new_ideas_wtd = kwargs.get('new_ideas_wtd')\n        self.asset_class_sdr = kwargs.get('asset_class_sdr')\n        self.yield_to_worst = kwargs.get('yield_to_worst')\n        self.closing_price = kwargs.get('closing_price')\n        self.turnover_composite_adjusted = kwargs.get('turnover_composite_adjusted')\n        self.comment = kwargs.get('comment')\n        self.source_symbol = kwargs.get('source_symbol')\n        self.ask_unadjusted = kwargs.get('ask_unadjusted')\n        self.restrict_external_derived_data = kwargs.get('restrict_external_derived_data')\n        self.ask_change = kwargs.get('ask_change')\n        self.count_ideas_mtd = kwargs.get('count_ideas_mtd')\n        self.end_date = kwargs.get('end_date')\n        self.sunshine = kwargs.get('sunshine')\n        self.contract_type = kwargs.get('contract_type')\n        self.momentum_type = kwargs.get('momentum_type')\n        self.specific_risk = kwargs.get('specific_risk')\n        self.mdapi = kwargs.get('mdapi')\n        self.payoff_qtd = kwargs.get('payoff_qtd')\n        self.loss = kwargs.get('loss')\n        self.midcurve_vol = kwargs.get('midcurve_vol')\n        self.sell6bps = kwargs.get('sell6bps')\n        self.trading_cost_pnl = kwargs.get('trading_cost_pnl')\n        self.price_notation_type = kwargs.get('price_notation_type')\n        self.price = kwargs.get('price')\n        self.payment_quantity = kwargs.get('payment_quantity')\n        self.redemption_date = kwargs.get('redemption_date')\n        self.leg2_notional_currency = kwargs.get('leg2_notional_currency')\n        self.sub_region = kwargs.get('sub_region')\n        self.benchmark = kwargs.get('benchmark')\n        self.tcm_cost_participation_rate15_pct = kwargs.get('tcm_cost_participation_rate15_pct')\n        self.fiscal_year = kwargs.get('fiscal_year')\n        self.recall_date = kwargs.get('recall_date')\n        self.esg_metric_value = kwargs.get('esg_metric_value')\n        self.internal = kwargs.get('internal')\n        self.gender = kwargs.get('gender')\n        self.asset_classifications_gics_industry = kwargs.get('asset_classifications_gics_industry')\n        self.adjusted_bid_price = kwargs.get('adjusted_bid_price')\n        self.low_unadjusted = kwargs.get('low_unadjusted')\n        self.macs_secondary_asset_class = kwargs.get('macs_secondary_asset_class')\n        self.confirmed_per_million = kwargs.get('confirmed_per_million')\n        self.data_source_id = kwargs.get('data_source_id')\n        self.integrated_score = kwargs.get('integrated_score')\n        self.buy7bps = kwargs.get('buy7bps')\n        self.arrival_mid_unrealized_cash = kwargs.get('arrival_mid_unrealized_cash')\n        self.knock_in_price = kwargs.get('knock_in_price')\n        self.event = kwargs.get('event')\n        self.is_intraday_auction = kwargs.get('is_intraday_auction')\n        self.location_name = kwargs.get('location_name')\n        self.coupon = kwargs.get('coupon')\n        self.percentage_auction_executed_quantity = kwargs.get('percentage_auction_executed_quantity')\n        self.avg_yield7_day = kwargs.get('avg_yield7_day')\n        self.original_dissemination_id = kwargs.get('original_dissemination_id')\n        self.total_on_vent = kwargs.get('total_on_vent')\n        self.twap_unrealized_cash = kwargs.get('twap_unrealized_cash')\n        self.sts_credit_market = kwargs.get('sts_credit_market')\n        self.ons_code = kwargs.get('ons_code')\n        self.passive_touch_fills_percentage = kwargs.get('passive_touch_fills_percentage')\n        self.seniority = kwargs.get('seniority')\n        self.leg1_index = kwargs.get('leg1_index')\n        self.high_unadjusted = kwargs.get('high_unadjusted')\n        self.submission_event = kwargs.get('submission_event')\n        self.tv_product_mnemonic = kwargs.get('tv_product_mnemonic')\n        self.avg_trade_rate_label = kwargs.get('avg_trade_rate_label')\n        self.last_activity_date = kwargs.get('last_activity_date')\n        self.dissemination_time = kwargs.get('dissemination_time')\n        self.price_to_cash = kwargs.get('price_to_cash')\n        self.buy10cents = kwargs.get('buy10cents')\n        self.nav_spread = kwargs.get('nav_spread')\n        self.venue_mic = kwargs.get('venue_mic')\n        self.dollar_total_return = kwargs.get('dollar_total_return')\n        self.block_unit = kwargs.get('block_unit')\n        self.mid_spread = kwargs.get('mid_spread')\n        self.istat_province_code = kwargs.get('istat_province_code')\n        self.total_recovered_by_state = kwargs.get('total_recovered_by_state')\n        self.repurchase_rate = kwargs.get('repurchase_rate')\n        self.data_source = kwargs.get('data_source')\n        self.total_being_tested = kwargs.get('total_being_tested')\n        self.cleared_or_bilateral = kwargs.get('cleared_or_bilateral')\n        self.metric_name = kwargs.get('metric_name')\n        self.ask_gspread = kwargs.get('ask_gspread')\n        self.forecast_hour = kwargs.get('forecast_hour')\n        self.leg2_payment_type = kwargs.get('leg2_payment_type')\n        self.cal_spread_mis_pricing = kwargs.get('cal_spread_mis_pricing')\n        self.total_tested_negative = kwargs.get('total_tested_negative')\n        self.rate366 = kwargs.get('rate366')\n        self.platform = kwargs.get('platform')\n        self.rate365 = kwargs.get('rate365')\n        self.fixed_rate_frequency = kwargs.get('fixed_rate_frequency')\n        self.rate360 = kwargs.get('rate360')\n        self.is_continuous = kwargs.get('is_continuous')\n        self.value = kwargs.get('value')\n        self.payer_designated_maturity = kwargs.get('payer_designated_maturity')\n        self.product_type = kwargs.get('product_type')\n        self.mdv22_day = kwargs.get('mdv22_day')\n        self.twap_realized_bps = kwargs.get('twap_realized_bps')\n        self.test_measure_label = kwargs.get('test_measure_label')\n        self.quantity = kwargs.get('quantity')\n        self.report_id = kwargs.get('report_id')\n        self.index_weight = kwargs.get('index_weight')\n        self.macs_primary_asset_class = kwargs.get('macs_primary_asset_class')\n        self.trader = kwargs.get('trader')\n        self.leg2_price_type = kwargs.get('leg2_price_type')\n        self.total_active = kwargs.get('total_active')\n        self.gsid2 = kwargs.get('gsid2')\n        self.matched_maturity_ois_swap_spread = kwargs.get('matched_maturity_ois_swap_spread')\n        self.valuation_date = kwargs.get('valuation_date')\n        self.restrict_gs_federation = kwargs.get('restrict_gs_federation')\n        self.position_source = kwargs.get('position_source')\n        self.tcm_cost_horizon6_hour = kwargs.get('tcm_cost_horizon6_hour')\n        self.buy200cents = kwargs.get('buy200cents')\n        self.vwap_unrealized_bps = kwargs.get('vwap_unrealized_bps')\n        self.price_to_book = kwargs.get('price_to_book')\n        self.isin = kwargs.get('isin')\n        self.pl_id = kwargs.get('pl_id')\n        self.last_returns_start_date = kwargs.get('last_returns_start_date')\n        self.collateral_value_variance = kwargs.get('collateral_value_variance')\n        self.year = kwargs.get('year')\n        self.forecast_period = kwargs.get('forecast_period')\n        self.call_first_date = kwargs.get('call_first_date')\n        self.data_set_ids = kwargs.get('data_set_ids')\n        self.economic_terms_hash = kwargs.get('economic_terms_hash')\n        self.num_beds = kwargs.get('num_beds')\n        self.sell20bps = kwargs.get('sell20bps')\n        self.client_type = kwargs.get('client_type')\n        self.percentage_close_executed_quantity = kwargs.get('percentage_close_executed_quantity')\n        self.macaulay_duration = kwargs.get('macaulay_duration')\n        self.available_inventory = kwargs.get('available_inventory')\n        self.est1_day_complete_pct = kwargs.get('est1_day_complete_pct')\n        self.relative_hit_rate_ytd = kwargs.get('relative_hit_rate_ytd')\n        self.created_by_id = kwargs.get('created_by_id')\n        self.market_data_type = kwargs.get('market_data_type')\n        self.real_short_rates_contribution = kwargs.get('real_short_rates_contribution')\n        self.metric_category = kwargs.get('metric_category')\n        self.annualized_carry = kwargs.get('annualized_carry')\n        self.value_previous = kwargs.get('value_previous')\n        self.transmission_classification = kwargs.get('transmission_classification')\n        self.avg_trade_rate = kwargs.get('avg_trade_rate')\n        self.short_level = kwargs.get('short_level')\n        self.version = kwargs.get('version')\n        self.category_type = kwargs.get('category_type')\n        self.policy_rate_expectation = kwargs.get('policy_rate_expectation')\n        self.upload_date = kwargs.get('upload_date')\n        self.block_off_facility = kwargs.get('block_off_facility')\n        self.unrealized_vwap_performance_usd = kwargs.get('unrealized_vwap_performance_usd')\n        self.pace_of_rollp75 = kwargs.get('pace_of_rollp75')\n        self.earnings_per_share_positive = kwargs.get('earnings_per_share_positive')\n        self.num_icu_beds = kwargs.get('num_icu_beds')\n        self.bucket_volume_in_percentage = kwargs.get('bucket_volume_in_percentage')\n        self.estimated_trading_cost = kwargs.get('estimated_trading_cost')\n        self.eid = kwargs.get('eid')\n        self.relative_return_qtd = kwargs.get('relative_return_qtd')\n        self.assessed_test_measure = kwargs.get('assessed_test_measure')\n        self.mkt_quoting_style = kwargs.get('mkt_quoting_style')\n        self.expiration_tenor = kwargs.get('expiration_tenor')\n        self.price_limit = kwargs.get('price_limit')\n        self.market_model_id = kwargs.get('market_model_id')\n        self.receiver_frequency = kwargs.get('receiver_frequency')\n        self.realized_correlation = kwargs.get('realized_correlation')\n        self.issue_status = kwargs.get('issue_status')\n        self.collateral_value_actual = kwargs.get('collateral_value_actual')\n        self.atm_fwd_rate = kwargs.get('atm_fwd_rate')\n        self.tcm_cost_participation_rate75_pct = kwargs.get('tcm_cost_participation_rate75_pct')\n        self.close = kwargs.get('close')\n        self.es_product_impact_score = kwargs.get('es_product_impact_score')\n        self.equity_vega = kwargs.get('equity_vega')\n        self.executed_fill_quantity = kwargs.get('executed_fill_quantity')\n        self.lender_payment = kwargs.get('lender_payment')\n        self.five_day_move = kwargs.get('five_day_move')\n        self.value_format = kwargs.get('value_format')\n        self.wind_chill_forecast = kwargs.get('wind_chill_forecast')\n        self.target_notional = kwargs.get('target_notional')\n        self.fill_leg_id = kwargs.get('fill_leg_id')\n        self.rationale = kwargs.get('rationale')\n        self.realized_twap_performance_bps = kwargs.get('realized_twap_performance_bps')\n        self.last_updated_since = kwargs.get('last_updated_since')\n        self.total_tests = kwargs.get('total_tests')\n        self.equities_contribution = kwargs.get('equities_contribution')\n        self.simon_id = kwargs.get('simon_id')\n        self.congestion = kwargs.get('congestion')\n        self.notes = kwargs.get('notes')\n        self.total_probable_senior_home = kwargs.get('total_probable_senior_home')\n        self.event_category = kwargs.get('event_category')\n        self.average_fill_rate = kwargs.get('average_fill_rate')\n        self.unadjusted_open = kwargs.get('unadjusted_open')\n        self.criticality = kwargs.get('criticality')\n        self.bid_ask_spread = kwargs.get('bid_ask_spread')\n        self.arrival_mid_unrealized_bps = kwargs.get('arrival_mid_unrealized_bps')\n        self.option_type = kwargs.get('option_type')\n        self.termination_date = kwargs.get('termination_date')\n        self.queries_per_second = kwargs.get('queries_per_second')\n        self.liquidity_type = kwargs.get('liquidity_type')\n        self.credit_limit = kwargs.get('credit_limit')\n        self.rank_qtd = kwargs.get('rank_qtd')\n        self.combined_key = kwargs.get('combined_key')\n        self.gir_fx_forecast = kwargs.get('gir_fx_forecast')\n        self.effective_tenor = kwargs.get('effective_tenor')\n        self.gir_commodities_forecast = kwargs.get('gir_commodities_forecast')\n        self.relative_humidity_daily_forecast = kwargs.get('relative_humidity_daily_forecast')\n        self.std30_days_subsidized_yield = kwargs.get('std30_days_subsidized_yield')\n        self.annualized_tracking_error = kwargs.get('annualized_tracking_error')\n        self.future_month_f26 = kwargs.get('future_month_f26')\n        self.future_month_f25 = kwargs.get('future_month_f25')\n        self.vol_swap = kwargs.get('vol_swap')\n        self.future_month_f24 = kwargs.get('future_month_f24')\n        self.heat_index_daily_forecast = kwargs.get('heat_index_daily_forecast')\n        self.future_month_f23 = kwargs.get('future_month_f23')\n        self.real_fci = kwargs.get('real_fci')\n        self.block_trades_and_large_notional_off_facility_swaps = kwargs.get(\n            'block_trades_and_large_notional_off_facility_swaps')\n        self.future_month_f22 = kwargs.get('future_month_f22')\n        self.buy1point5bps = kwargs.get('buy1point5bps')\n        self.future_month_f21 = kwargs.get('future_month_f21')\n        self.expiration_settlement_date = kwargs.get('expiration_settlement_date')\n        self.absolute_return_qtd = kwargs.get('absolute_return_qtd')\n        self.gross_exposure = kwargs.get('gross_exposure')\n        self.volume = kwargs.get('volume')\n        self.adv = kwargs.get('adv')\n        self.short_conviction_medium = kwargs.get('short_conviction_medium')\n        self.complete_test_measure = kwargs.get('complete_test_measure')\n        self.exchange = kwargs.get('exchange')\n        self.es_policy_score = kwargs.get('es_policy_score')\n        self.roll_volume_std = kwargs.get('roll_volume_std')\n        self.temperature_daily_forecast = kwargs.get('temperature_daily_forecast')\n        self.relative_payoff_qtd = kwargs.get('relative_payoff_qtd')\n        self.on_loan_percentage = kwargs.get('on_loan_percentage')\n        self.twap_remaining_slices = kwargs.get('twap_remaining_slices')\n        self.fair_variance = kwargs.get('fair_variance')\n        self.hit_rate_wtd = kwargs.get('hit_rate_wtd')\n        self.previous_close_realized_cash = kwargs.get('previous_close_realized_cash')\n        self.realized_volatility = kwargs.get('realized_volatility')\n        self.unexecuted_quantity = kwargs.get('unexecuted_quantity')\n        self.proceeds_asset_swap_spread1m = kwargs.get('proceeds_asset_swap_spread1m')\n        self.clone_parent_id = kwargs.get('clone_parent_id')\n        self.wind_speed_hourly_forecast = kwargs.get('wind_speed_hourly_forecast')\n        self.etf_flow_ratio = kwargs.get('etf_flow_ratio')\n        self.asset_parameters_receiver_rate_option = kwargs.get('asset_parameters_receiver_rate_option')\n        self.buy60cents = kwargs.get('buy60cents')\n        self.security_sub_type_id = kwargs.get('security_sub_type_id')\n        self.message = kwargs.get('message')\n        self.sts_rates_country = kwargs.get('sts_rates_country')\n        self.sell65cents = kwargs.get('sell65cents')\n        self.horizon = kwargs.get('horizon')\n        self.would_if_good_level = kwargs.get('would_if_good_level')\n        self.buffer_threshold_required = kwargs.get('buffer_threshold_required')\n        self.face_value = kwargs.get('face_value')\n        self.roll_volume_hist = kwargs.get('roll_volume_hist')\n        self.counter_party_status = kwargs.get('counter_party_status')\n        self.composite22_day_adv = kwargs.get('composite22_day_adv')\n        self.percentage_far_executed_quantity = kwargs.get('percentage_far_executed_quantity')\n        self.loan_spread_required = kwargs.get('loan_spread_required')\n        self.asset_class = kwargs.get('asset_class')\n        self.sovereign_spread_contribution = kwargs.get('sovereign_spread_contribution')\n        self.ric = kwargs.get('ric')\n        self.bucket_end_time = kwargs.get('bucket_end_time')\n        self.rate_type = kwargs.get('rate_type')\n        self.total_fatalities_senior_home = kwargs.get('total_fatalities_senior_home')\n        self.loan_status = kwargs.get('loan_status')\n        self.short_weight = kwargs.get('short_weight')\n        self.geography_id = kwargs.get('geography_id')\n        self.sell7point5bps = kwargs.get('sell7point5bps')\n        self.nav = kwargs.get('nav')\n        self.fiscal_quarter = kwargs.get('fiscal_quarter')\n        self.version_string = kwargs.get('version_string')\n        self.payoff_ytd = kwargs.get('payoff_ytd')\n        self.market_impact = kwargs.get('market_impact')\n        self.event_type = kwargs.get('event_type')\n        self.fill_price = kwargs.get('fill_price')\n        self.asset_count_long = kwargs.get('asset_count_long')\n        self.sell180cents = kwargs.get('sell180cents')\n        self.spot = kwargs.get('spot')\n        self.application_id = kwargs.get('application_id')\n        self.indicative_close_price = kwargs.get('indicative_close_price')\n        self.swap_spread = kwargs.get('swap_spread')\n        self.trading_restriction = kwargs.get('trading_restriction')\n        self.asset_parameters_pay_or_receive = kwargs.get('asset_parameters_pay_or_receive')\n        self.price_spot_entry_unit = kwargs.get('price_spot_entry_unit')\n        self.unrealized_arrival_performance_bps = kwargs.get('unrealized_arrival_performance_bps')\n        self.city = kwargs.get('city')\n        self.pnl_wtd = kwargs.get('pnl_wtd')\n        self.covariance = kwargs.get('covariance')\n        self.bucket_volume_in_shares = kwargs.get('bucket_volume_in_shares')\n        self.commodity_forecast = kwargs.get('commodity_forecast')\n        self.valid = kwargs.get('valid')\n        self.sts_commodity = kwargs.get('sts_commodity')\n        self.initial_pricing_date = kwargs.get('initial_pricing_date')\n        self.indication_of_end_user_exception = kwargs.get('indication_of_end_user_exception')\n        self.wind_direction_hourly_forecast = kwargs.get('wind_direction_hourly_forecast')\n        self.es_score = kwargs.get('es_score')\n        self.__yield = kwargs.get('yield_')\n        self.fatalities_underlying_conditions_present = kwargs.get('fatalities_underlying_conditions_present')\n        self.price_range_in_ticks = kwargs.get('price_range_in_ticks')\n        self.pace_of_rollp25 = kwargs.get('pace_of_rollp25')\n        self.day_close_realized_usd = kwargs.get('day_close_realized_usd')\n        self.pct_change = kwargs.get('pct_change')\n        self.brightness_type = kwargs.get('brightness_type')\n        self.future_month3_m = kwargs.get('future_month3_m')\n        self.number_of_rolls = kwargs.get('number_of_rolls')\n        self.iso_country_code_numeric = kwargs.get('iso_country_code_numeric')\n        self.price_type = kwargs.get('price_type')\n        self.realized_vwap_performance_usd = kwargs.get('realized_vwap_performance_usd')\n        self.fuel_type = kwargs.get('fuel_type')\n        self.bbid = kwargs.get('bbid')\n        self.vega_notional_amount = kwargs.get('vega_notional_amount')\n        self.fatalities_underlying_conditions_absent = kwargs.get('fatalities_underlying_conditions_absent')\n        self.effective_date = kwargs.get('effective_date')\n        self.capped = kwargs.get('capped')\n        self.rating = kwargs.get('rating')\n        self.option_currency = kwargs.get('option_currency')\n        self.is_close_auction = kwargs.get('is_close_auction')\n        self.volatility = kwargs.get('volatility')\n        self.avg_vent_util = kwargs.get('avg_vent_util')\n        self.underlying_asset_ids = kwargs.get('underlying_asset_ids')\n        self.buy6point5bps = kwargs.get('buy6point5bps')\n        self.vwap_in_limit_realized_cash = kwargs.get('vwap_in_limit_realized_cash')\n        self.estimated_closing_auction_volume = kwargs.get('estimated_closing_auction_volume')\n        self.sell2bps = kwargs.get('sell2bps')\n        self.annual_risk = kwargs.get('annual_risk')\n        self.eti = kwargs.get('eti')\n        self.vwap_in_limit_realized_bps = kwargs.get('vwap_in_limit_realized_bps')\n        self.rank_mtd = kwargs.get('rank_mtd')\n        self.market_buffer = kwargs.get('market_buffer')\n        self.future_month_j24 = kwargs.get('future_month_j24')\n        self.last_uploaded_time = kwargs.get('last_uploaded_time')\n        self.future_month_j23 = kwargs.get('future_month_j23')\n        self.oe_id = kwargs.get('oe_id')\n        self.future_month_j22 = kwargs.get('future_month_j22')\n        self.future_month_j21 = kwargs.get('future_month_j21')\n        self.bbid_equivalent = kwargs.get('bbid_equivalent')\n        self.init_buffer_threshold_required = kwargs.get('init_buffer_threshold_required')\n        self.leg2_designated_maturity = kwargs.get('leg2_designated_maturity')\n        self.matched_maturity_ois_swap_rate = kwargs.get('matched_maturity_ois_swap_rate')\n        self.fair_price = kwargs.get('fair_price')\n        self.participation_rate_in_limit = kwargs.get('participation_rate_in_limit')\n        self.ext_mkt_class = kwargs.get('ext_mkt_class')\n        self.price_currency = kwargs.get('price_currency')\n        self.failed_count = kwargs.get('failed_count')\n        self.leg1_index_location = kwargs.get('leg1_index_location')\n        self.supra_strategy = kwargs.get('supra_strategy')\n        self.day_count_convention = kwargs.get('day_count_convention')\n        self.rounded_notional_amount1 = kwargs.get('rounded_notional_amount1')\n        self.rounded_notional_amount2 = kwargs.get('rounded_notional_amount2')\n        self.factor_source = kwargs.get('factor_source')\n        self.future_month_j26 = kwargs.get('future_month_j26')\n        self.lending_sec_type = kwargs.get('lending_sec_type')\n        self.future_month_j25 = kwargs.get('future_month_j25')\n        self.leverage = kwargs.get('leverage')\n        self.forecast_day = kwargs.get('forecast_day')\n        self.option_family = kwargs.get('option_family')\n        self.generator_output = kwargs.get('generator_output')\n        self.price_spot_stop_loss_value = kwargs.get('price_spot_stop_loss_value')\n        self.kpi_id = kwargs.get('kpi_id')\n        self.wind_generation = kwargs.get('wind_generation')\n        self.percentage_mid_executed_quantity = kwargs.get('percentage_mid_executed_quantity')\n        self.borrow_cost = kwargs.get('borrow_cost')\n        self.knock_out_direction = kwargs.get('knock_out_direction')\n        self.risk_model = kwargs.get('risk_model')\n        self.asset_parameters_vendor = kwargs.get('asset_parameters_vendor')\n        self.fair_value = kwargs.get('fair_value')\n        self.open_time = kwargs.get('open_time')\n        self.pressure_hourly_forecast = kwargs.get('pressure_hourly_forecast')\n        self.local_ccy_rate = kwargs.get('local_ccy_rate')\n        self.end_user_exception = kwargs.get('end_user_exception')\n        self.sell90cents = kwargs.get('sell90cents')\n        self.execution_venue = kwargs.get('execution_venue')\n        self.primary_vwap_in_limit_realized_bps = kwargs.get('primary_vwap_in_limit_realized_bps')\n        self.approve_rebalance = kwargs.get('approve_rebalance')\n        self.adjusted_close_price = kwargs.get('adjusted_close_price')\n        self.lms_id = kwargs.get('lms_id')\n        self.rebate_rate = kwargs.get('rebate_rate')\n        self.sell130cents = kwargs.get('sell130cents')\n        self.sell32bps = kwargs.get('sell32bps')\n        self.pace_of_rollp50 = kwargs.get('pace_of_rollp50')\n        self.price_move_vs_arrival = kwargs.get('price_move_vs_arrival')\n        self.strike_relative = kwargs.get('strike_relative')\n        self.pressure_type = kwargs.get('pressure_type')\n        self.buy40bps = kwargs.get('buy40bps')\n        self.price_notation = kwargs.get('price_notation')\n        self.strategy = kwargs.get('strategy')\n        self.issue_status_date = kwargs.get('issue_status_date')\n        self.lender_income = kwargs.get('lender_income')\n        self.pb_client_id = kwargs.get('pb_client_id')\n        self.istat_region_code = kwargs.get('istat_region_code')\n        self.sell9bps = kwargs.get('sell9bps')\n        self.owner_id = kwargs.get('owner_id')\n        self.composite10_day_adv = kwargs.get('composite10_day_adv')\n        self.max_loan_balance = kwargs.get('max_loan_balance')\n        self.idea_activity_type = kwargs.get('idea_activity_type')\n        self.sell60cents = kwargs.get('sell60cents')\n        self.idea_source = kwargs.get('idea_source')\n        self.ever_on_vent = kwargs.get('ever_on_vent')\n        self.buy15cents = kwargs.get('buy15cents')\n        self.unadjusted_ask = kwargs.get('unadjusted_ask')\n        self.contribution_name = kwargs.get('contribution_name')\n        self.given_plus_paid = kwargs.get('given_plus_paid')\n        self.last_fill_price = kwargs.get('last_fill_price')\n        self.short_conviction_small = kwargs.get('short_conviction_small')\n        self.upfront_payment_currency = kwargs.get('upfront_payment_currency')\n        self.spot_settlement_date = kwargs.get('spot_settlement_date')\n        self.matrix_order = kwargs.get('matrix_order')\n        self.date_index = kwargs.get('date_index')\n        self.payer_day_count_fraction = kwargs.get('payer_day_count_fraction')\n        self.asset_classifications_is_primary = kwargs.get('asset_classifications_is_primary')\n        self.break_even_inflation_change = kwargs.get('break_even_inflation_change')\n        self.buy130cents = kwargs.get('buy130cents')\n        self.dwi_contribution = kwargs.get('dwi_contribution')\n        self.asset2_id = kwargs.get('asset2_id')\n        self.average_fill_price = kwargs.get('average_fill_price')\n        self.depth_spread_score = kwargs.get('depth_spread_score')\n        self.sell10cents = kwargs.get('sell10cents')\n        self.sub_account = kwargs.get('sub_account')\n        self.buy65cents = kwargs.get('buy65cents')\n        self.bond_cds_basis = kwargs.get('bond_cds_basis')\n        self.vendor = kwargs.get('vendor')\n        self.data_set = kwargs.get('data_set')\n        self.notional_amount2 = kwargs.get('notional_amount2')\n        self.notional_amount1 = kwargs.get('notional_amount1')\n        self.queueing_time = kwargs.get('queueing_time')\n        self.ann_return5_year = kwargs.get('ann_return5_year')\n        self.volume_start_of_day = kwargs.get('volume_start_of_day')\n        self.price_notation3_type = kwargs.get('price_notation3_type')\n        self.asset_parameters_floating_rate_designated_maturity = kwargs.get(\n            'asset_parameters_floating_rate_designated_maturity')\n        self.executed_notional_local = kwargs.get('executed_notional_local')\n        self.business_sponsor = kwargs.get('business_sponsor')\n        self.unexplained = kwargs.get('unexplained')\n        self.seasonal_adjustment_short = kwargs.get('seasonal_adjustment_short')\n        self.metric = kwargs.get('metric')\n        self.ask = kwargs.get('ask')\n        self.close_price = kwargs.get('close_price')\n        self.end_time = kwargs.get('end_time')\n        self.sell100cents = kwargs.get('sell100cents')\n        self.execution_timestamp = kwargs.get('execution_timestamp')\n        self.buy180cents = kwargs.get('buy180cents')\n        self.absolute_strike = kwargs.get('absolute_strike')\n        self.sell3point5bps = kwargs.get('sell3point5bps')\n        self.liquidity_score_buy = kwargs.get('liquidity_score_buy')\n        self.payment_frequency = kwargs.get('payment_frequency')\n        self.expense_ratio_net_bps = kwargs.get('expense_ratio_net_bps')\n        self.metric_type = kwargs.get('metric_type')\n        self.rank_ytd = kwargs.get('rank_ytd')\n        self.leg1_spread = kwargs.get('leg1_spread')\n        self.coverage_region = kwargs.get('coverage_region')\n        self.absolute_return_ytd = kwargs.get('absolute_return_ytd')\n        self.day_count_convention2 = kwargs.get('day_count_convention2')\n        self.fwdtier = kwargs.get('fwdtier')\n        self.degree_days = kwargs.get('degree_days')\n        self.turnover_adjusted = kwargs.get('turnover_adjusted')\n        self.price_spot_target_value = kwargs.get('price_spot_target_value')\n        self.market_data_point = kwargs.get('market_data_point')\n        self.num_of_funds = kwargs.get('num_of_funds')\n        self.trade_time = kwargs.get('trade_time')\n        self.execution_id = kwargs.get('execution_id')\n        self.turnover_unadjusted = kwargs.get('turnover_unadjusted')\n        self.leg1_floating_index = kwargs.get('leg1_floating_index')\n        self.hedge_annualized_volatility = kwargs.get('hedge_annualized_volatility')\n        self.benchmark_currency = kwargs.get('benchmark_currency')\n        self.futures_contract = kwargs.get('futures_contract')\n        self.name = kwargs.get('name')\n        self.aum = kwargs.get('aum')\n        self.leg1_day_count_convention = kwargs.get('leg1_day_count_convention')\n        self.cbs_code = kwargs.get('cbs_code')\n        self.folder_name = kwargs.get('folder_name')\n        self.api_usage = kwargs.get('api_usage')\n        self.twap_interval = kwargs.get('twap_interval')\n        self.unique_id = kwargs.get('unique_id')\n        self.option_expiration_date = kwargs.get('option_expiration_date')\n        self.swaption_atm_fwd_rate = kwargs.get('swaption_atm_fwd_rate')\n        self.live_date = kwargs.get('live_date')\n        self.corporate_action_type = kwargs.get('corporate_action_type')\n        self.prime_id = kwargs.get('prime_id')\n        self.description = kwargs.get('description')\n        self.asset_classifications_is_country_primary = kwargs.get('asset_classifications_is_country_primary')\n        self.rebate_rate_limit = kwargs.get('rebate_rate_limit')\n        self.factor = kwargs.get('factor')\n        self.days_on_loan = kwargs.get('days_on_loan')\n        self.long_conviction_small = kwargs.get('long_conviction_small')\n        self.sell40cents = kwargs.get('sell40cents')\n        self.relative_payoff_ytd = kwargs.get('relative_payoff_ytd')\n        self.gsfeer = kwargs.get('gsfeer')\n        self.relative_hit_rate_qtd = kwargs.get('relative_hit_rate_qtd')\n        self.wam = kwargs.get('wam')\n        self.wal = kwargs.get('wal')\n        self.quantityccy = kwargs.get('quantityccy')\n        self.backtest_id = kwargs.get('backtest_id')\n        self.dirty_price = kwargs.get('dirty_price')\n        self.corporate_spread_contribution = kwargs.get('corporate_spread_contribution')\n        self.relative_humidity_hourly_forecast = kwargs.get('relative_humidity_hourly_forecast')\n        self.multiple_score = kwargs.get('multiple_score')\n        self.beta_adjusted_exposure = kwargs.get('beta_adjusted_exposure')\n        self.dividend_points = kwargs.get('dividend_points')\n        self.brightness = kwargs.get('brightness')\n        self.asset_parameters_receiver_designated_maturity = kwargs.get(\n            'asset_parameters_receiver_designated_maturity')\n        self.bos_in_ticks_description = kwargs.get('bos_in_ticks_description')\n        self.test_id = kwargs.get('test_id')\n        self.implied_correlation = kwargs.get('implied_correlation')\n        self.normalized_performance = kwargs.get('normalized_performance')\n        self.overnight_news_end_time = kwargs.get('overnight_news_end_time')\n        self.bytes_consumed = kwargs.get('bytes_consumed')\n        self.swaption_vol = kwargs.get('swaption_vol')\n        self.estimated_closing_volume = kwargs.get('estimated_closing_volume')\n        self.issuer = kwargs.get('issuer')\n        self.dividend_yield = kwargs.get('dividend_yield')\n        self.market_type = kwargs.get('market_type')\n        self.num_units_lower = kwargs.get('num_units_lower')\n        self.source_origin = kwargs.get('source_origin')\n        self.proceeds_asset_swap_spread3m = kwargs.get('proceeds_asset_swap_spread3m')\n        self.total_quantity = kwargs.get('total_quantity')\n        self.internal_user = kwargs.get('internal_user')\n        self.sell40bps = kwargs.get('sell40bps')\n        self.redemption_option = kwargs.get('redemption_option')\n        self.notional_unit2 = kwargs.get('notional_unit2')\n        self.notional_unit1 = kwargs.get('notional_unit1')\n        self.sedol = kwargs.get('sedol')\n        self.rounding_cost_pnl = kwargs.get('rounding_cost_pnl')\n        self.mid_yield = kwargs.get('mid_yield')\n        self.unexecuted_notional_local = kwargs.get('unexecuted_notional_local')\n        self.sustain_global = kwargs.get('sustain_global')\n        self.ending_date = kwargs.get('ending_date')\n        self.proceeds_asset_swap_spread12m = kwargs.get('proceeds_asset_swap_spread12m')\n        self.gross_investment_wtd = kwargs.get('gross_investment_wtd')\n        self.ann_return3_year = kwargs.get('ann_return3_year')\n        self.sharpe_wtd = kwargs.get('sharpe_wtd')\n        self.discount_factor = kwargs.get('discount_factor')\n        self.relative_return_mtd = kwargs.get('relative_return_mtd')\n        self.price_change_on_day = kwargs.get('price_change_on_day')\n        self.buy100cents = kwargs.get('buy100cents')\n        self.forward_point = kwargs.get('forward_point')\n        self.fci = kwargs.get('fci')\n        self.recall_quantity = kwargs.get('recall_quantity')\n        self.fx_positioning = kwargs.get('fx_positioning')\n        self.gsid_equivalent = kwargs.get('gsid_equivalent')\n        self.categories = kwargs.get('categories')\n        self.ext_mkt_asset = kwargs.get('ext_mkt_asset')\n        self.quoting_style = kwargs.get('quoting_style')\n        self.error_message = kwargs.get('error_message')\n        self.mid_price = kwargs.get('mid_price')\n        self.proceeds_asset_swap_spread6m = kwargs.get('proceeds_asset_swap_spread6m')\n        self.sts_em_dm = kwargs.get('sts_em_dm')\n        self.embedded_option = kwargs.get('embedded_option')\n        self.tcm_cost_horizon2_day = kwargs.get('tcm_cost_horizon2_day')\n        self.age_band = kwargs.get('age_band')\n        self.returns_enabled = kwargs.get('returns_enabled')\n        self.run_id = kwargs.get('run_id')\n        self.queue_in_lots = kwargs.get('queue_in_lots')\n        self.tender_offer_expiration_date = kwargs.get('tender_offer_expiration_date')\n        self.midcurve_annuity = kwargs.get('midcurve_annuity')\n        self.lending_fund_nav_trend = kwargs.get('lending_fund_nav_trend')\n        self.cloud_cover_forecast = kwargs.get('cloud_cover_forecast')\n        self.tcm_cost_participation_rate5_pct = kwargs.get('tcm_cost_participation_rate5_pct')\n        self.default_backcast = kwargs.get('default_backcast')\n        self.news_on_intensity = kwargs.get('news_on_intensity')\n        self.price_forming_continuation_data = kwargs.get('price_forming_continuation_data')\n        self.adjusted_short_interest = kwargs.get('adjusted_short_interest')\n        self.new_hospitalized = kwargs.get('new_hospitalized')\n        self.asset_parameters_strike = kwargs.get('asset_parameters_strike')\n        self.buy35cents = kwargs.get('buy35cents')\n        self.leg2_total_notional = kwargs.get('leg2_total_notional')\n        self.asset_parameters_effective_date = kwargs.get('asset_parameters_effective_date')\n        self.ann_return10_year = kwargs.get('ann_return10_year')\n        self.num_adult_icu_beds = kwargs.get('num_adult_icu_beds')\n        self.days_to_expiration = kwargs.get('days_to_expiration')\n        self.continuation_event = kwargs.get('continuation_event')\n        self.wi_id = kwargs.get('wi_id')\n        self.market_cap_category = kwargs.get('market_cap_category')\n        self.historical_volume = kwargs.get('historical_volume')\n        self.buy5cents = kwargs.get('buy5cents')\n        self.event_start_date = kwargs.get('event_start_date')\n        self.leg1_fixed_rate = kwargs.get('leg1_fixed_rate')\n        self.equity_gamma = kwargs.get('equity_gamma')\n        self.rpt_id = kwargs.get('rpt_id')\n        self.gross_income = kwargs.get('gross_income')\n        self.em_id = kwargs.get('em_id')\n        self.asset_count_in_model = kwargs.get('asset_count_in_model')\n        self.sts_credit_region = kwargs.get('sts_credit_region')\n        self.min_temperature = kwargs.get('min_temperature')\n        self.bucket_start_time = kwargs.get('bucket_start_time')\n        self.fill_type = kwargs.get('fill_type')\n        self.close_time = kwargs.get('close_time')\n        self.fail_pct = kwargs.get('fail_pct')\n        self.iso_country_code_alpha2 = kwargs.get('iso_country_code_alpha2')\n        self.iso_country_code_alpha3 = kwargs.get('iso_country_code_alpha3')\n        self.amount = kwargs.get('amount')\n        self.lending_fund_acct = kwargs.get('lending_fund_acct')\n        self.rebate = kwargs.get('rebate')\n        self.election_type = kwargs.get('election_type')\n        self.relative_hit_rate_mtd = kwargs.get('relative_hit_rate_mtd')\n        self.implied_volatility = kwargs.get('implied_volatility')\n        self.spread = kwargs.get('spread')\n        self.variance = kwargs.get('variance')\n        self.wtd_degree_days_daily_forecast = kwargs.get('wtd_degree_days_daily_forecast')\n        self.swaption_annuity = kwargs.get('swaption_annuity')\n        self.buy6bps = kwargs.get('buy6bps')\n        self.g10_currency = kwargs.get('g10_currency')\n        self.humidity_forecast = kwargs.get('humidity_forecast')\n        self.relative_period = kwargs.get('relative_period')\n        self.user = kwargs.get('user')\n        self.customer = kwargs.get('customer')\n        self.leg1_reset_frequency = kwargs.get('leg1_reset_frequency')\n        self.queue_clock_time_label = kwargs.get('queue_clock_time_label')\n        self.pace_of_rollp100 = kwargs.get('pace_of_rollp100')\n        self.asset_classifications_gics_sub_industry = kwargs.get('asset_classifications_gics_sub_industry')\n        self.dew_point_hourly_forecast = kwargs.get('dew_point_hourly_forecast')\n        self.location_type = kwargs.get('location_type')\n        self.facet_divisional_reporting_group_id = kwargs.get('facet_divisional_reporting_group_id')\n        self.realized_twap_performance_usd = kwargs.get('realized_twap_performance_usd')\n        self.swap_rate = kwargs.get('swap_rate')\n        self.algo_execution_style = kwargs.get('algo_execution_style')\n        self.client_contact = kwargs.get('client_contact')\n        self.min_temperature_hour = kwargs.get('min_temperature_hour')\n        self.trading_currency = kwargs.get('trading_currency')\n        self.total_by_onset = kwargs.get('total_by_onset')\n        self.agency_swap_spread = kwargs.get('agency_swap_spread')\n        self.rank = kwargs.get('rank')\n        self.mixed_swap_other_reported_sdr = kwargs.get('mixed_swap_other_reported_sdr')\n        self.humidity = kwargs.get('humidity')\n        self.data_set_category = kwargs.get('data_set_category')\n        self.vwap_realized_bps = kwargs.get('vwap_realized_bps')\n        self.buy9bps = kwargs.get('buy9bps')\n        self.total_tested = kwargs.get('total_tested')\n        self.fatalities_confirmed = kwargs.get('fatalities_confirmed')\n        self.universe_id1 = kwargs.get('universe_id1')\n        self.asset_parameters_payer_day_count_fraction = kwargs.get('asset_parameters_payer_day_count_fraction')\n        self.universe_id2 = kwargs.get('universe_id2')\n        self.bid_low = kwargs.get('bid_low')\n        self.bucketize_price = kwargs.get('bucketize_price')\n        self.fair_variance_volatility = kwargs.get('fair_variance_volatility')\n        self.covid19 = kwargs.get('covid19')\n        self.client_exposure = kwargs.get('client_exposure')\n        self.leg2_total_notional_unit = kwargs.get('leg2_total_notional_unit')\n        self.sell45cents = kwargs.get('sell45cents')\n        self.gs_sustain_sub_sector = kwargs.get('gs_sustain_sub_sector')\n        self.sinkable = kwargs.get('sinkable')\n        self.is_real = kwargs.get('is_real')\n        self.max_temperature_hour = kwargs.get('max_temperature_hour')\n        self.leg2_averaging_method = kwargs.get('leg2_averaging_method')\n        self.jsn = kwargs.get('jsn')\n        self.sell160cents = kwargs.get('sell160cents')\n        self.knock_in_direction = kwargs.get('knock_in_direction')\n        self.day_close_unrealized_usd = kwargs.get('day_close_unrealized_usd')\n        self.tenor = kwargs.get('tenor')\n        self.pricing_convention = kwargs.get('pricing_convention')\n        self.popularity = kwargs.get('popularity')\n        self.floating_rate_option = kwargs.get('floating_rate_option')\n        self.hedge_value_type = kwargs.get('hedge_value_type')\n        self.asset_parameters_clearing_house = kwargs.get('asset_parameters_clearing_house')\n        self.disclaimer = kwargs.get('disclaimer')\n        self.payer_frequency = kwargs.get('payer_frequency')\n        self.loan_fee = kwargs.get('loan_fee')\n        self.deployment_version = kwargs.get('deployment_version')\n        self.buy16bps = kwargs.get('buy16bps')\n        self.trade_day_count = kwargs.get('trade_day_count')\n        self.price_to_sales = kwargs.get('price_to_sales')\n        self.new_ideas_qtd = kwargs.get('new_ideas_qtd')\n        self.subdivision_name = kwargs.get('subdivision_name')\n        self.adjusted_ask_price = kwargs.get('adjusted_ask_price')\n        self.factor_universe = kwargs.get('factor_universe')\n        self.arrival_rt = kwargs.get('arrival_rt')\n        self.internal_index_calc_agent = kwargs.get('internal_index_calc_agent')\n        self.excess_margin_value = kwargs.get('excess_margin_value')\n        self.transaction_cost = kwargs.get('transaction_cost')\n        self.central_bank_swap_rate = kwargs.get('central_bank_swap_rate')\n        self.previous_new_confirmed = kwargs.get('previous_new_confirmed')\n        self.unrealized_vwap_performance_bps = kwargs.get('unrealized_vwap_performance_bps')\n        self.degree_days_daily_forecast = kwargs.get('degree_days_daily_forecast')\n        self.position_amount = kwargs.get('position_amount')\n        self.heat_index_hourly_forecast = kwargs.get('heat_index_hourly_forecast')\n        self.ma_rank = kwargs.get('ma_rank')\n        self.fx_positioning_source = kwargs.get('fx_positioning_source')\n        self.event_start_date_time = kwargs.get('event_start_date_time')\n        self.implied_volatility_by_delta_strike = kwargs.get('implied_volatility_by_delta_strike')\n        self.mq_symbol = kwargs.get('mq_symbol')\n        self.num_total_units = kwargs.get('num_total_units')\n        self.corporate_action = kwargs.get('corporate_action')\n        self.leg1_price_type = kwargs.get('leg1_price_type')\n        self.asset_parameters_payer_rate_option = kwargs.get('asset_parameters_payer_rate_option')\n        self.sell20cents = kwargs.get('sell20cents')\n        self.leg2_fixed_payment_currency = kwargs.get('leg2_fixed_payment_currency')\n        self.g_regional_score = kwargs.get('g_regional_score')\n        self.hard_to_borrow = kwargs.get('hard_to_borrow')\n        self.sell5bps = kwargs.get('sell5bps')\n        self.roll_vwap = kwargs.get('roll_vwap')\n        self.wpk = kwargs.get('wpk')\n        self.bespoke_swap = kwargs.get('bespoke_swap')\n        self.asset_parameters_expiration_date = kwargs.get('asset_parameters_expiration_date')\n        self.country_name = kwargs.get('country_name')\n        self.carry = kwargs.get('carry')\n        self.starting_date = kwargs.get('starting_date')\n        self.loan_id = kwargs.get('loan_id')\n        self.onboarded = kwargs.get('onboarded')\n        self.liquidity_score = kwargs.get('liquidity_score')\n        self.long_rates_contribution = kwargs.get('long_rates_contribution')\n        self.source_date_span = kwargs.get('source_date_span')\n        self.ann_yield6_month = kwargs.get('ann_yield6_month')\n        self.underlying_data_set_id = kwargs.get('underlying_data_set_id')\n        self.close_unadjusted = kwargs.get('close_unadjusted')\n        self.value_unit = kwargs.get('value_unit')\n        self.quantity_unit = kwargs.get('quantity_unit')\n        self.adjusted_low_price = kwargs.get('adjusted_low_price')\n        self.is_momentum = kwargs.get('is_momentum')\n        self.long_conviction_large = kwargs.get('long_conviction_large')\n        self.oad = kwargs.get('oad')\n        self.rate = kwargs.get('rate')\n        self.coupon_type = kwargs.get('coupon_type')\n        self.client = kwargs.get('client')\n        self.conviction_list = kwargs.get('conviction_list')\n        self.passive_etf_ratio = kwargs.get('passive_etf_ratio')\n        self.future_month_g26 = kwargs.get('future_month_g26')\n        self.future_month_g25 = kwargs.get('future_month_g25')\n        self.future_month_g24 = kwargs.get('future_month_g24')\n        self.future_month_g23 = kwargs.get('future_month_g23')\n        self.type_of_return = kwargs.get('type_of_return')\n        self.future_month_g22 = kwargs.get('future_month_g22')\n        self.servicing_cost_long_pnl = kwargs.get('servicing_cost_long_pnl')\n        self.excess_margin_percentage = kwargs.get('excess_margin_percentage')\n        self.future_month_g21 = kwargs.get('future_month_g21')\n        self.total_mild = kwargs.get('total_mild')\n        self.realized_arrival_performance_bps = kwargs.get('realized_arrival_performance_bps')\n        self.precipitation_daily_forecast_inches = kwargs.get('precipitation_daily_forecast_inches')\n        self.exchange_id = kwargs.get('exchange_id')\n        self.leg2_fixed_payment = kwargs.get('leg2_fixed_payment')\n        self.tcm_cost_horizon20_day = kwargs.get('tcm_cost_horizon20_day')\n        self.realm = kwargs.get('realm')\n        self.bid = kwargs.get('bid')\n        self.hedge_value = kwargs.get('hedge_value')\n        self.order_start_time = kwargs.get('order_start_time')\n        self.is_aggressive = kwargs.get('is_aggressive')\n        self.floating_rate_designated_maturity = kwargs.get('floating_rate_designated_maturity')\n        self.percentage_near_executed_quantity = kwargs.get('percentage_near_executed_quantity')\n        self.order_id = kwargs.get('order_id')\n        self.hospital_type = kwargs.get('hospital_type')\n        self.day_close_realized_bps = kwargs.get('day_close_realized_bps')\n        self.precipitation_hourly_forecast = kwargs.get('precipitation_hourly_forecast')\n        self.market_cap_usd = kwargs.get('market_cap_usd')\n        self.auction_fills_percentage = kwargs.get('auction_fills_percentage')\n        self.high_price = kwargs.get('high_price')\n        self.absolute_shares = kwargs.get('absolute_shares')\n        self.fixed_rate_day_count_fraction = kwargs.get('fixed_rate_day_count_fraction')\n        self.model = kwargs.get('model')\n        self.unrealized_twap_performance_usd = kwargs.get('unrealized_twap_performance_usd')\n        self.__id = kwargs.get('id_')\n        self.maturity = kwargs.get('maturity')\n        self.delta_change = kwargs.get('delta_change')\n        self.index = kwargs.get('index')\n        self.unrealized_arrival_performance_usd = kwargs.get('unrealized_arrival_performance_usd')\n        self.iceberg_slippage = kwargs.get('iceberg_slippage')\n        self.sell120cents = kwargs.get('sell120cents')\n        self.future_month_x26 = kwargs.get('future_month_x26')\n        self.asset_types = kwargs.get('asset_types')\n        self.future_month_x25 = kwargs.get('future_month_x25')\n        self.bcid = kwargs.get('bcid')\n        self.mkt_point = kwargs.get('mkt_point')\n        self.future_month_x24 = kwargs.get('future_month_x24')\n        self.restriction_start_date = kwargs.get('restriction_start_date')\n        self.touch_liquidity_score = kwargs.get('touch_liquidity_score')\n        self.future_month_x23 = kwargs.get('future_month_x23')\n        self.future_month_x22 = kwargs.get('future_month_x22')\n        self.factor_category_id = kwargs.get('factor_category_id')\n        self.security_type_id = kwargs.get('security_type_id')\n        self.future_month_x21 = kwargs.get('future_month_x21')\n        self.investment_ytd = kwargs.get('investment_ytd')\n        self.leg2_notional = kwargs.get('leg2_notional')\n        self.sell1bps = kwargs.get('sell1bps')\n        self.sell200cents = kwargs.get('sell200cents')\n        self.expected_completion_date = kwargs.get('expected_completion_date')\n        self.spread_option_vol = kwargs.get('spread_option_vol')\n        self.sell80cents = kwargs.get('sell80cents')\n        self.inflation_swap_rate = kwargs.get('inflation_swap_rate')\n        self.active_queries = kwargs.get('active_queries')\n        self.sell45bps = kwargs.get('sell45bps')\n        self.embeded_option = kwargs.get('embeded_option')\n        self.event_source = kwargs.get('event_source')\n        self.qis_perm_no = kwargs.get('qis_perm_no')\n        self.settlement = kwargs.get('settlement')\n        self.shareclass_id = kwargs.get('shareclass_id')\n        self.feature2 = kwargs.get('feature2')\n        self.feature3 = kwargs.get('feature3')\n        self.sts_commodity_sector = kwargs.get('sts_commodity_sector')\n        self.exception_status = kwargs.get('exception_status')\n        self.overnight_news_intensity = kwargs.get('overnight_news_intensity')\n        self.sales_coverage = kwargs.get('sales_coverage')\n        self.feature1 = kwargs.get('feature1')\n        self.tcm_cost_participation_rate10_pct = kwargs.get('tcm_cost_participation_rate10_pct')\n        self.event_time = kwargs.get('event_time')\n        self.position_source_name = kwargs.get('position_source_name')\n        self.delivery_date = kwargs.get('delivery_date')\n        self.interest_rate = kwargs.get('interest_rate')\n        self.side = kwargs.get('side')\n        self.dynamic_hybrid_aggressive_style = kwargs.get('dynamic_hybrid_aggressive_style')\n        self.compliance_restricted_status = kwargs.get('compliance_restricted_status')\n        self.borrow_fee = kwargs.get('borrow_fee')\n        self.ever_icu = kwargs.get('ever_icu')\n        self.no_worse_than_level = kwargs.get('no_worse_than_level')\n        self.update_time = kwargs.get('update_time')\n        self.loan_spread = kwargs.get('loan_spread')\n        self.tcm_cost_horizon12_hour = kwargs.get('tcm_cost_horizon12_hour')\n        self.dew_point = kwargs.get('dew_point')\n        self.research_commission = kwargs.get('research_commission')\n        self.buy2bps = kwargs.get('buy2bps')\n        self.asset_classifications_risk_country_code = kwargs.get('asset_classifications_risk_country_code')\n        self.new_ideas_mtd = kwargs.get('new_ideas_mtd')\n        self.var_swap_by_expiry = kwargs.get('var_swap_by_expiry')\n        self.sell_date = kwargs.get('sell_date')\n        self.aum_start = kwargs.get('aum_start')\n        self.asset_parameters_settlement = kwargs.get('asset_parameters_settlement')\n        self.max_temperature = kwargs.get('max_temperature')\n        self.acquirer_shareholder_meeting_date = kwargs.get('acquirer_shareholder_meeting_date')\n        self.count_ideas_wtd = kwargs.get('count_ideas_wtd')\n        self.arrival_rt_normalized = kwargs.get('arrival_rt_normalized')\n        self.report_type = kwargs.get('report_type')\n        self.source_url = kwargs.get('source_url')\n        self.estimated_return = kwargs.get('estimated_return')\n        self.high = kwargs.get('high')\n        self.source_last_update = kwargs.get('source_last_update')\n        self.sunshine_forecast = kwargs.get('sunshine_forecast')\n        self.quantity_mw = kwargs.get('quantity_mw')\n        self.sell70cents = kwargs.get('sell70cents')\n        self.sell110cents = kwargs.get('sell110cents')\n        self.pnode_id = kwargs.get('pnode_id')\n        self.humidity_type = kwargs.get('humidity_type')\n        self.prev_close_ask = kwargs.get('prev_close_ask')\n        self.level = kwargs.get('level')\n        self.implied_volatility_by_expiration = kwargs.get('implied_volatility_by_expiration')\n        self.asset_parameters_fixed_rate_day_count_fraction = kwargs.get(\n            'asset_parameters_fixed_rate_day_count_fraction')\n        self.es_momentum_score = kwargs.get('es_momentum_score')\n        self.leg2_index = kwargs.get('leg2_index')\n        self.net_weight = kwargs.get('net_weight')\n        self.portfolio_managers = kwargs.get('portfolio_managers')\n        self.bos_in_ticks = kwargs.get('bos_in_ticks')\n        self.asset_parameters_coupon_type = kwargs.get('asset_parameters_coupon_type')\n        self.expected_residual_quantity = kwargs.get('expected_residual_quantity')\n        self.roll_date = kwargs.get('roll_date')\n        self.dynamic_hybrid_speed = kwargs.get('dynamic_hybrid_speed')\n        self.cap_floor_vol = kwargs.get('cap_floor_vol')\n        self.target_quantity = kwargs.get('target_quantity')\n        self.submitter = kwargs.get('submitter')\n        self.no = kwargs.get('no')\n        self.notional = kwargs.get('notional')\n        self.es_disclosure_percentage = kwargs.get('es_disclosure_percentage')\n        self.close_executed_quantity_percentage = kwargs.get('close_executed_quantity_percentage')\n        self.twap_realized_cash = kwargs.get('twap_realized_cash')\n        self.is_open_auction = kwargs.get('is_open_auction')\n        self.leg1_type = kwargs.get('leg1_type')\n        self.wet_bulb_temp_hourly_forecast = kwargs.get('wet_bulb_temp_hourly_forecast')\n        self.cleanup_price = kwargs.get('cleanup_price')\n        self.total = kwargs.get('total')\n        self.filled_notional_usd = kwargs.get('filled_notional_usd')\n        self.asset_id = kwargs.get('asset_id')\n        self.test_status = kwargs.get('test_status')\n        self.mkt_type = kwargs.get('mkt_type')\n        self.last_updated_time = kwargs.get('last_updated_time')\n        self.yield30_day = kwargs.get('yield30_day')\n        self.buy28bps = kwargs.get('buy28bps')\n        self.proportion_of_risk = kwargs.get('proportion_of_risk')\n        self.future_month_k23 = kwargs.get('future_month_k23')\n        self.future_month_k22 = kwargs.get('future_month_k22')\n        self.future_month_k21 = kwargs.get('future_month_k21')\n        self.primary_entity_id = kwargs.get('primary_entity_id')\n        self.cross = kwargs.get('cross')\n        self.idea_status = kwargs.get('idea_status')\n        self.contract_subtype = kwargs.get('contract_subtype')\n        self.sri = kwargs.get('sri')\n        self.fx_forecast = kwargs.get('fx_forecast')\n        self.fixing_time_label = kwargs.get('fixing_time_label')\n        self.is_etf = kwargs.get('is_etf')\n        self.fill_id = kwargs.get('fill_id')\n        self.excess_returns = kwargs.get('excess_returns')\n        self.dollar_return = kwargs.get('dollar_return')\n        self.order_in_limit = kwargs.get('order_in_limit')\n        self.expiry_time = kwargs.get('expiry_time')\n        self.return_on_equity = kwargs.get('return_on_equity')\n        self.future_month_k26 = kwargs.get('future_month_k26')\n        self.future_month_k25 = kwargs.get('future_month_k25')\n        self.future_month_k24 = kwargs.get('future_month_k24')\n        self.restriction_end_date = kwargs.get('restriction_end_date')\n        self.queue_in_lots_description = kwargs.get('queue_in_lots_description')\n        self.volume_limit = kwargs.get('volume_limit')\n        self.objective = kwargs.get('objective')\n        self.nav_price = kwargs.get('nav_price')\n        self.leg1_underlying_asset = kwargs.get('leg1_underlying_asset')\n        self.private_placement_type = kwargs.get('private_placement_type')\n        self.hedge_notional = kwargs.get('hedge_notional')\n        self.ask_low = kwargs.get('ask_low')\n        self.intended_p_rate = kwargs.get('intended_p_rate')\n        self.expiry = kwargs.get('expiry')\n        self.avg_monthly_yield = kwargs.get('avg_monthly_yield')\n        self.period_direction = kwargs.get('period_direction')\n        self.prev_rpt_id = kwargs.get('prev_rpt_id')\n        self.earnings_per_share = kwargs.get('earnings_per_share')\n        self.strike_percentage = kwargs.get('strike_percentage')\n        self.es_product_impact_percentile = kwargs.get('es_product_impact_percentile')\n        self.vwap_realized_cash = kwargs.get('vwap_realized_cash')\n        self.par_asset_swap_spread1m = kwargs.get('par_asset_swap_spread1m')\n        self.prev_close_bid = kwargs.get('prev_close_bid')\n        self.minimum_increment = kwargs.get('minimum_increment')\n        self.tcm_cost_horizon16_day = kwargs.get('tcm_cost_horizon16_day')\n        self.investment_mtd = kwargs.get('investment_mtd')\n        self.settlement_date = kwargs.get('settlement_date')\n        self.weighted_average_mid_normalized = kwargs.get('weighted_average_mid_normalized')\n        self.sales_per_share = kwargs.get('sales_per_share')\n        self.unadjusted_close = kwargs.get('unadjusted_close')\n        self.loan_date = kwargs.get('loan_date')\n        self.matched_maturity_swap_spread1m = kwargs.get('matched_maturity_swap_spread1m')\n        self.collateral_percentage_actual = kwargs.get('collateral_percentage_actual')\n        self.vwap_in_limit_unrealized_bps = kwargs.get('vwap_in_limit_unrealized_bps')\n        self.metric_value = kwargs.get('metric_value')\n        self.auto_exec_state = kwargs.get('auto_exec_state')\n        self.total_recovered = kwargs.get('total_recovered')\n        self.relative_return_ytd = kwargs.get('relative_return_ytd')\n        self.tick_server = kwargs.get('tick_server')\n        self.cumulative_volume_in_percentage = kwargs.get('cumulative_volume_in_percentage')\n        self.real_time_restriction_status = kwargs.get('real_time_restriction_status')\n        self.trade_type = kwargs.get('trade_type')\n        self.settlement_type = kwargs.get('settlement_type')\n        self.net_change = kwargs.get('net_change')\n        self.number_of_underliers = kwargs.get('number_of_underliers')\n        self.swap_type = kwargs.get('swap_type')\n        self.forecast_type = kwargs.get('forecast_type')\n        self.leg1_notional = kwargs.get('leg1_notional')\n        self.sell_settle_date = kwargs.get('sell_settle_date')\n        self.new_ideas_ytd = kwargs.get('new_ideas_ytd')\n        self.management_fee = kwargs.get('management_fee')\n        self.par_asset_swap_spread3m = kwargs.get('par_asset_swap_spread3m')\n        self.sell36bps = kwargs.get('sell36bps')\n        self.matched_maturity_swap_spread3m = kwargs.get('matched_maturity_swap_spread3m')\n        self.source_id = kwargs.get('source_id')\n        self.country = kwargs.get('country')\n        self.vwap = kwargs.get('vwap')\n        self.touch_spread_score = kwargs.get('touch_spread_score')\n        self.rating_second_highest = kwargs.get('rating_second_highest')\n        self.sell24bps = kwargs.get('sell24bps')\n        self.frequency = kwargs.get('frequency')\n        self.activity_id = kwargs.get('activity_id')\n        self.estimated_impact = kwargs.get('estimated_impact')\n        self.sell35cents = kwargs.get('sell35cents')\n        self.loan_spread_bucket = kwargs.get('loan_spread_bucket')\n        self.coronavirus_global_activity_tracker = kwargs.get('coronavirus_global_activity_tracker')\n        self.underlyers = kwargs.get('underlyers')\n        self.asset_parameters_pricing_location = kwargs.get('asset_parameters_pricing_location')\n        self.event_description = kwargs.get('event_description')\n        self.iceberg_max_size = kwargs.get('iceberg_max_size')\n        self.asset_parameters_coupon = kwargs.get('asset_parameters_coupon')\n        self.details = kwargs.get('details')\n        self.sector = kwargs.get('sector')\n        self.avg_bed_util_rate = kwargs.get('avg_bed_util_rate')\n        self.buy20bps = kwargs.get('buy20bps')\n        self.epidemic = kwargs.get('epidemic')\n        self.mctr = kwargs.get('mctr')\n        self.exchange_time = kwargs.get('exchange_time')\n        self.historical_close = kwargs.get('historical_close')\n        self.fips_code = kwargs.get('fips_code')\n        self.buy32bps = kwargs.get('buy32bps')\n        self.idea_id = kwargs.get('idea_id')\n        self.comment_status = kwargs.get('comment_status')\n        self.marginal_cost = kwargs.get('marginal_cost')\n        self.client_weight = kwargs.get('client_weight')\n        self.leg1_delivery_point = kwargs.get('leg1_delivery_point')\n        self.sell5cents = kwargs.get('sell5cents')\n        self.liq_wkly = kwargs.get('liq_wkly')\n        self.unrealized_twap_performance_bps = kwargs.get('unrealized_twap_performance_bps')\n        self.region = kwargs.get('region')\n        self.temperature_hour = kwargs.get('temperature_hour')\n        self.upper_bound = kwargs.get('upper_bound')\n        self.sell55cents = kwargs.get('sell55cents')\n        self.num_pedi_icu_beds = kwargs.get('num_pedi_icu_beds')\n        self.bid_yield = kwargs.get('bid_yield')\n        self.expected_residual = kwargs.get('expected_residual')\n        self.option_premium = kwargs.get('option_premium')\n        self.owner_name = kwargs.get('owner_name')\n        self.par_asset_swap_spread6m = kwargs.get('par_asset_swap_spread6m')\n        self.z_score = kwargs.get('z_score')\n        self.sell12bps = kwargs.get('sell12bps')\n        self.event_start_time = kwargs.get('event_start_time')\n        self.matched_maturity_swap_spread6m = kwargs.get('matched_maturity_swap_spread6m')\n        self.turnover = kwargs.get('turnover')\n        self.price_spot_target_unit = kwargs.get('price_spot_target_unit')\n        self.coverage = kwargs.get('coverage')\n        self.g_percentile = kwargs.get('g_percentile')\n        self.cloud_cover_hourly_forecast = kwargs.get('cloud_cover_hourly_forecast')\n        self.lending_fund_nav = kwargs.get('lending_fund_nav')\n        self.source_original_category = kwargs.get('source_original_category')\n        self.percent_close_execution_quantity = kwargs.get('percent_close_execution_quantity')\n        self.latest_execution_time = kwargs.get('latest_execution_time')\n        self.arrival_mid_realized_bps = kwargs.get('arrival_mid_realized_bps')\n        self.location = kwargs.get('location')\n        self.scenario_id = kwargs.get('scenario_id')\n        self.termination_tenor = kwargs.get('termination_tenor')\n        self.queue_clock_time = kwargs.get('queue_clock_time')\n        self.discretion_lower_bound = kwargs.get('discretion_lower_bound')\n        self.tcm_cost_participation_rate50_pct = kwargs.get('tcm_cost_participation_rate50_pct')\n        self.rating_linear = kwargs.get('rating_linear')\n        self.previous_close_unrealized_bps = kwargs.get('previous_close_unrealized_bps')\n        self.sub_asset_class_for_other_commodity = kwargs.get('sub_asset_class_for_other_commodity')\n        self.forward_price = kwargs.get('forward_price')\n        self.__type = kwargs.get('type_')\n        self.strike_ref = kwargs.get('strike_ref')\n        self.cumulative_pnl = kwargs.get('cumulative_pnl')\n        self.short_tenor = kwargs.get('short_tenor')\n        self.sell28bps = kwargs.get('sell28bps')\n        self.fund_class = kwargs.get('fund_class')\n        self.unadjusted_volume = kwargs.get('unadjusted_volume')\n        self.buy36bps = kwargs.get('buy36bps')\n        self.position_idx = kwargs.get('position_idx')\n        self.wind_chill_hourly_forecast = kwargs.get('wind_chill_hourly_forecast')\n        self.sec_name = kwargs.get('sec_name')\n        self.implied_volatility_by_relative_strike = kwargs.get('implied_volatility_by_relative_strike')\n        self.percent_adv = kwargs.get('percent_adv')\n        self.leg1_total_notional = kwargs.get('leg1_total_notional')\n        self.contract = kwargs.get('contract')\n        self.payment_frequency1 = kwargs.get('payment_frequency1')\n        self.payment_frequency2 = kwargs.get('payment_frequency2')\n        self.bespoke = kwargs.get('bespoke')\n        self.repo_tenor = kwargs.get('repo_tenor')\n        self.sell15cents = kwargs.get('sell15cents')\n        self.investment_qtd = kwargs.get('investment_qtd')\n        self.heat_index_forecast = kwargs.get('heat_index_forecast')\n        self.rating_standard_and_poors = kwargs.get('rating_standard_and_poors')\n        self.quality_stars = kwargs.get('quality_stars')\n        self.leg2_floating_index = kwargs.get('leg2_floating_index')\n        self.source_ticker = kwargs.get('source_ticker')\n        self.primary_vwap_unrealized_bps = kwargs.get('primary_vwap_unrealized_bps')\n        self.gsid = kwargs.get('gsid')\n        self.lending_fund = kwargs.get('lending_fund')\n        self.sensitivity = kwargs.get('sensitivity')\n        self.day_count = kwargs.get('day_count')\n        self.sell16bps = kwargs.get('sell16bps')\n        self.relative_break_even_inflation_change = kwargs.get('relative_break_even_inflation_change')\n        self.sell25cents = kwargs.get('sell25cents')\n        self.var_swap = kwargs.get('var_swap')\n        self.buy5point5bps = kwargs.get('buy5point5bps')\n        self.block_large_notional = kwargs.get('block_large_notional')\n        self.sell2point5bps = kwargs.get('sell2point5bps')\n        self.capacity = kwargs.get('capacity')\n        self.sectors_raw = kwargs.get('sectors_raw')\n        self.primary_vwap_in_limit = kwargs.get('primary_vwap_in_limit')\n        self.shareclass_price = kwargs.get('shareclass_price')\n        self.trade_size = kwargs.get('trade_size')\n        self.price_spot_entry_value = kwargs.get('price_spot_entry_value')\n        self.buy8point5bps = kwargs.get('buy8point5bps')\n        self.symbol_dimensions = kwargs.get('symbol_dimensions')\n        self.buy24bps = kwargs.get('buy24bps')\n        self.observation = kwargs.get('observation')\n        self.option_type_sdr = kwargs.get('option_type_sdr')\n        self.scenario_group_id = kwargs.get('scenario_group_id')\n        self.average_implied_variance = kwargs.get('average_implied_variance')\n        self.avg_trade_rate_description = kwargs.get('avg_trade_rate_description')\n        self.fraction = kwargs.get('fraction')\n        self.asset_count_short = kwargs.get('asset_count_short')\n        self.collateral_percentage_required = kwargs.get('collateral_percentage_required')\n        self.sell5point5bps = kwargs.get('sell5point5bps')\n        self.date = kwargs.get('date')\n        self.zip_code = kwargs.get('zip_code')\n        self.total_std_return_since_inception = kwargs.get('total_std_return_since_inception')\n        self.source_category = kwargs.get('source_category')\n        self.volume_unadjusted = kwargs.get('volume_unadjusted')\n        self.passive_ratio = kwargs.get('passive_ratio')\n        self.price_to_earnings = kwargs.get('price_to_earnings')\n        self.order_depth = kwargs.get('order_depth')\n        self.ann_yield3_month = kwargs.get('ann_yield3_month')\n        self.net_flow_std = kwargs.get('net_flow_std')\n        self.encoded_stats = kwargs.get('encoded_stats')\n        self.buy5bps = kwargs.get('buy5bps')\n        self.run_time = kwargs.get('run_time')\n        self.ask_size = kwargs.get('ask_size')\n        self.absolute_return_mtd = kwargs.get('absolute_return_mtd')\n        self.std30_days_unsubsidized_yield = kwargs.get('std30_days_unsubsidized_yield')\n        self.resource = kwargs.get('resource')\n        self.average_realized_volatility = kwargs.get('average_realized_volatility')\n        self.trace_adv_buy = kwargs.get('trace_adv_buy')\n        self.new_confirmed = kwargs.get('new_confirmed')\n        self.sell8bps = kwargs.get('sell8bps')\n        self.bid_price = kwargs.get('bid_price')\n        self.sell8point5bps = kwargs.get('sell8point5bps')\n        self.target_price_unrealized_bps = kwargs.get('target_price_unrealized_bps')\n        self.es_numeric_percentile = kwargs.get('es_numeric_percentile')\n        self.leg2_underlying_asset = kwargs.get('leg2_underlying_asset')\n        self.csa_terms = kwargs.get('csa_terms')\n        self.relative_payoff_mtd = kwargs.get('relative_payoff_mtd')\n        self.daily_net_shareholder_flows = kwargs.get('daily_net_shareholder_flows')\n        self.buy2point5bps = kwargs.get('buy2point5bps')\n        self.cai = kwargs.get('cai')\n        self.executed_notional_usd = kwargs.get('executed_notional_usd')\n        self.system_time = kwargs.get('system_time')\n        self.total_home_isolation = kwargs.get('total_home_isolation')\n        self.station_name = kwargs.get('station_name')\n        self.pass_pct = kwargs.get('pass_pct')\n        self.opening_report = kwargs.get('opening_report')\n        self.midcurve_atm_fwd_rate = kwargs.get('midcurve_atm_fwd_rate')\n        self.precipitation_forecast = kwargs.get('precipitation_forecast')\n        self.equity_risk_premium_index = kwargs.get('equity_risk_premium_index')\n        self.fatalities_underlying_conditions_unknown = kwargs.get('fatalities_underlying_conditions_unknown')\n        self.buy12bps = kwargs.get('buy12bps')\n        self.clearing_house = kwargs.get('clearing_house')\n        self.day_close_unrealized_bps = kwargs.get('day_close_unrealized_bps')\n        self.sts_rates_maturity = kwargs.get('sts_rates_maturity')\n        self.liq_dly = kwargs.get('liq_dly')\n        self.contributor_role = kwargs.get('contributor_role')\n        self.total_fatalities = kwargs.get('total_fatalities')\n\n    @property\n    def investment_rate(self) -> float:\n        """The rate of return on an investment.  In the context of securities lending, it\n           is the rate being earned on the reinvested collateral received from\n           the borrower."""\n        return self.__investment_rate\n\n    @investment_rate.setter\n    def investment_rate(self, value: float):\n        self._property_changed('investment_rate')\n        self.__investment_rate = value        \n\n    @property\n    def starting_emma_legal_entity_id(self) -> str:\n        """Starting EMMA Legal Entity ID (LE) for which the SCR client agreement was\n           signed."""\n        return self.__starting_emma_legal_entity_id\n\n    @starting_emma_legal_entity_id.setter\n    def starting_emma_legal_entity_id(self, value: str):\n        self._property_changed('starting_emma_legal_entity_id')\n        self.__starting_emma_legal_entity_id = value        \n\n    @property\n    def mdapi_class(self) -> str:\n        """MDAPI Asset Class."""\n        return self.__mdapi_class\n\n    @mdapi_class.setter\n    def mdapi_class(self, value: str):\n        self._property_changed('mdapi_class')\n        self.__mdapi_class = value        \n\n    @property\n    def total_notional_usd(self) -> float:\n        """Total notional on order in dollars."""\n        return self.__total_notional_usd\n\n    @total_notional_usd.setter\n    def total_notional_usd(self, value: float):\n        self._property_changed('total_notional_usd')\n        self.__total_notional_usd = value        \n\n    @property\n    def bid_unadjusted(self) -> float:\n        """Unadjusted bid level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__bid_unadjusted\n\n    @bid_unadjusted.setter\n    def bid_unadjusted(self, value: float):\n        self._property_changed('bid_unadjusted')\n        self.__bid_unadjusted = value        \n\n    @property\n    def aggressive_fills_percentage(self) -> float:\n        """Percent of total order filled at aggressive touch."""\n        return self.__aggressive_fills_percentage\n\n    @aggressive_fills_percentage.setter\n    def aggressive_fills_percentage(self, value: float):\n        self._property_changed('aggressive_fills_percentage')\n        self.__aggressive_fills_percentage = value        \n\n    @property\n    def vehicle_type(self) -> str:\n        """Type of investment vehicle. Only viewable after having been granted additional\n           access to asset information."""\n        return self.__vehicle_type\n\n    @vehicle_type.setter\n    def vehicle_type(self, value: str):\n        self._property_changed('vehicle_type')\n        self.__vehicle_type = value        \n\n    @property\n    def total_fatalities_by_state(self) -> float:\n        """Total number of fatalities by state."""\n        return self.__total_fatalities_by_state\n\n    @total_fatalities_by_state.setter\n    def total_fatalities_by_state(self, value: float):\n        self._property_changed('total_fatalities_by_state')\n        self.__total_fatalities_by_state = value        \n\n    @property\n    def new_active(self) -> float:\n        """Change in number of active cases from previous day."""\n        return self.__new_active\n\n    @new_active.setter\n    def new_active(self, value: float):\n        self._property_changed('new_active')\n        self.__new_active = value        \n\n    @property\n    def daily_risk(self) -> float:\n        """Daily Risk Value."""\n        return self.__daily_risk\n\n    @daily_risk.setter\n    def daily_risk(self, value: float):\n        self._property_changed('daily_risk')\n        self.__daily_risk = value        \n\n    @property\n    def energy(self) -> float:\n        """Energy price component."""\n        return self.__energy\n\n    @energy.setter\n    def energy(self, value: float):\n        self._property_changed('energy')\n        self.__energy = value        \n\n    @property\n    def sunshine_daily_forecast(self) -> float:\n        """The forecast value for sunshine in percent."""\n        return self.__sunshine_daily_forecast\n\n    @sunshine_daily_forecast.setter\n    def sunshine_daily_forecast(self, value: float):\n        self._property_changed('sunshine_daily_forecast')\n        self.__sunshine_daily_forecast = value        \n\n    @property\n    def sentiment_score(self) -> float:\n        """A value representing a sentiment indicator."""\n        return self.__sentiment_score\n\n    @sentiment_score.setter\n    def sentiment_score(self, value: float):\n        self._property_changed('sentiment_score')\n        self.__sentiment_score = value        \n\n    @property\n    def correlation(self) -> float:\n        """Market implied correlation between two tenors."""\n        return self.__correlation\n\n    @correlation.setter\n    def correlation(self, value: float):\n        self._property_changed('correlation')\n        self.__correlation = value        \n\n    @property\n    def exposure(self) -> float:\n        """Exposure of a given asset or portfolio in the denominated currency of the asset\n           or portfolio."""\n        return self.__exposure\n\n    @exposure.setter\n    def exposure(self, value: float):\n        self._property_changed('exposure')\n        self.__exposure = value        \n\n    @property\n    def size(self) -> float:\n        """Size of market execution."""\n        return self.__size\n\n    @size.setter\n    def size(self, value: float):\n        self._property_changed('size')\n        self.__size = value        \n\n    @property\n    def market_data_asset(self) -> str:\n        """The market data asset (e.g. USD, USD/EUR)."""\n        return self.__market_data_asset\n\n    @market_data_asset.setter\n    def market_data_asset(self, value: str):\n        self._property_changed('market_data_asset')\n        self.__market_data_asset = value        \n\n    @property\n    def buy75cents(self) -> float:\n        """The amount GS would buy for 75 cents charge."""\n        return self.__buy75cents\n\n    @buy75cents.setter\n    def buy75cents(self, value: float):\n        self._property_changed('buy75cents')\n        self.__buy75cents = value        \n\n    @property\n    def unadjusted_high(self) -> float:\n        """Unadjusted high level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__unadjusted_high\n\n    @unadjusted_high.setter\n    def unadjusted_high(self, value: float):\n        self._property_changed('unadjusted_high')\n        self.__unadjusted_high = value        \n\n    @property\n    def source_importance(self) -> float:\n        """Source importance."""\n        return self.__source_importance\n\n    @source_importance.setter\n    def source_importance(self, value: float):\n        self._property_changed('source_importance')\n        self.__source_importance = value        \n\n    @property\n    def closing_yield(self) -> float:\n        """Closing yield."""\n        return self.__closing_yield\n\n    @closing_yield.setter\n    def closing_yield(self, value: float):\n        self._property_changed('closing_yield')\n        self.__closing_yield = value        \n\n    @property\n    def wind(self) -> float:\n        """Value of the wind attribute selected."""\n        return self.__wind\n\n    @wind.setter\n    def wind(self, value: float):\n        self._property_changed('wind')\n        self.__wind = value        \n\n    @property\n    def sc16(self) -> bool:\n        """Document Ongoing SCR Compliance."""\n        return self.__sc16\n\n    @sc16.setter\n    def sc16(self, value: bool):\n        self._property_changed('sc16')\n        self.__sc16 = value        \n\n    @property\n    def sc15(self) -> bool:\n        """Restrict Access To Specified Named Personnel."""\n        return self.__sc15\n\n    @sc15.setter\n    def sc15(self, value: bool):\n        self._property_changed('sc15')\n        self.__sc15 = value        \n\n    @property\n    def sc12(self) -> bool:\n        """Exclude From Indications Of Interest."""\n        return self.__sc12\n\n    @sc12.setter\n    def sc12(self, value: bool):\n        self._property_changed('sc12')\n        self.__sc12 = value        \n\n    @property\n    def sc11(self) -> bool:\n        """Replace Client True Name With Alias Name."""\n        return self.__sc11\n\n    @sc11.setter\n    def sc11(self, value: bool):\n        self._property_changed('sc11')\n        self.__sc11 = value        \n\n    @property\n    def primary_vwap_in_limit_unrealized_bps(self) -> float:\n        """Unrealised performance vs Primary VWAP In Limit, in bps."""\n        return self.__primary_vwap_in_limit_unrealized_bps\n\n    @primary_vwap_in_limit_unrealized_bps.setter\n    def primary_vwap_in_limit_unrealized_bps(self, value: float):\n        self._property_changed('primary_vwap_in_limit_unrealized_bps')\n        self.__primary_vwap_in_limit_unrealized_bps = value        \n\n    @property\n    def display_name(self) -> str:\n        """Display Name."""\n        return self.__display_name\n\n    @display_name.setter\n    def display_name(self, value: str):\n        self._property_changed('display_name')\n        self.__display_name = value        \n\n    @property\n    def minutes_to_trade100_pct(self) -> float:\n        """Minutes to trade 100 percent."""\n        return self.__minutes_to_trade100_pct\n\n    @minutes_to_trade100_pct.setter\n    def minutes_to_trade100_pct(self, value: float):\n        self._property_changed('minutes_to_trade100_pct')\n        self.__minutes_to_trade100_pct = value        \n\n    @property\n    def sc14(self) -> bool:\n        """Notify Strat Supervisors Of Personnel Assignment Limitation."""\n        return self.__sc14\n\n    @sc14.setter\n    def sc14(self, value: bool):\n        self._property_changed('sc14')\n        self.__sc14 = value        \n\n    @property\n    def cumulative_volume_in_shares(self) -> float:\n        """Forecast of the cumulative volume of shares from start of day up to the end of\n           the bucket interval."""\n        return self.__cumulative_volume_in_shares\n\n    @cumulative_volume_in_shares.setter\n    def cumulative_volume_in_shares(self, value: float):\n        self._property_changed('cumulative_volume_in_shares')\n        self.__cumulative_volume_in_shares = value        \n\n    @property\n    def sc13(self) -> bool:\n        """Restrict Access In Client Relationship Management Platforms To Sales Personnel\n           Covering Client."""\n        return self.__sc13\n\n    @sc13.setter\n    def sc13(self, value: bool):\n        self._property_changed('sc13')\n        self.__sc13 = value        \n\n    @property\n    def new_fatalities(self) -> float:\n        """New fatal cases."""\n        return self.__new_fatalities\n\n    @new_fatalities.setter\n    def new_fatalities(self, value: float):\n        self._property_changed('new_fatalities')\n        self.__new_fatalities = value        \n\n    @property\n    def buy50bps(self) -> float:\n        """The amount GS would buy for 50 bps charge."""\n        return self.__buy50bps\n\n    @buy50bps.setter\n    def buy50bps(self, value: float):\n        self._property_changed('buy50bps')\n        self.__buy50bps = value        \n\n    @property\n    def num_staffed_beds(self) -> float:\n        """Number of adult bed, pediatric bed, birthing room, or newborn ICU bed (excluding\n           newborn bassinets) maintained in a patient care area for lodging\n           patients in acute, long term, or domiciliary areas of the hospital."""\n        return self.__num_staffed_beds\n\n    @num_staffed_beds.setter\n    def num_staffed_beds(self, value: float):\n        self._property_changed('num_staffed_beds')\n        self.__num_staffed_beds = value        \n\n    @property\n    def upfront_payment(self) -> float:\n        """Upfront payment fee."""\n        return self.__upfront_payment\n\n    @upfront_payment.setter\n    def upfront_payment(self, value: float):\n        self._property_changed('upfront_payment')\n        self.__upfront_payment = value        \n\n    @property\n    def arrival_mid_realized_cash(self) -> float:\n        """Consolidated realised performance vs Arrival Mid, in USD."""\n        return self.__arrival_mid_realized_cash\n\n    @arrival_mid_realized_cash.setter\n    def arrival_mid_realized_cash(self, value: float):\n        self._property_changed('arrival_mid_realized_cash')\n        self.__arrival_mid_realized_cash = value        \n\n    @property\n    def sc10(self) -> bool:\n        """Exclude From Calibration And Research."""\n        return self.__sc10\n\n    @sc10.setter\n    def sc10(self, value: bool):\n        self._property_changed('sc10')\n        self.__sc10 = value        \n\n    @property\n    def sc05(self) -> bool:\n        """Restrict Access To Trade Execution Mandate Personnel."""\n        return self.__sc05\n\n    @sc05.setter\n    def sc05(self, value: bool):\n        self._property_changed('sc05')\n        self.__sc05 = value        \n\n    @property\n    def a(self) -> float:\n        """Stock specific coefficient."""\n        return self.__a\n\n    @a.setter\n    def a(self, value: float):\n        self._property_changed('a')\n        self.__a = value        \n\n    @property\n    def sc04(self) -> bool:\n        """Restrict Access To Sales Personnel Covering Both Client And Executed Product."""\n        return self.__sc04\n\n    @sc04.setter\n    def sc04(self, value: bool):\n        self._property_changed('sc04')\n        self.__sc04 = value        \n\n    @property\n    def b(self) -> float:\n        """Stock specific coefficient."""\n        return self.__b\n\n    @b.setter\n    def b(self, value: float):\n        self._property_changed('b')\n        self.__b = value        \n\n    @property\n    def sc07(self) -> bool:\n        """Exclude From Market Share Survey Submissions."""\n        return self.__sc07\n\n    @sc07.setter\n    def sc07(self, value: bool):\n        self._property_changed('sc07')\n        self.__sc07 = value        \n\n    @property\n    def c(self) -> float:\n        """Stock specific coefficient."""\n        return self.__c\n\n    @c.setter\n    def c(self, value: float):\n        self._property_changed('c')\n        self.__c = value        \n\n    @property\n    def yield_to_maturity(self) -> float:\n        """The return an investor realizes on a bond sold at the mid price at maturity."""\n        return self.__yield_to_maturity\n\n    @yield_to_maturity.setter\n    def yield_to_maturity(self, value: float):\n        self._property_changed('yield_to_maturity')\n        self.__yield_to_maturity = value        \n\n    @property\n    def sc06(self) -> bool:\n        """Restrict Access To Agency Personnel."""\n        return self.__sc06\n\n    @sc06.setter\n    def sc06(self, value: bool):\n        self._property_changed('sc06')\n        self.__sc06 = value        \n\n    @property\n    def address(self) -> str:\n        """Street address."""\n        return self.__address\n\n    @address.setter\n    def address(self, value: str):\n        self._property_changed('address')\n        self.__address = value        \n\n    @property\n    def sc01(self) -> bool:\n        """Define Information Barriers Policy."""\n        return self.__sc01\n\n    @sc01.setter\n    def sc01(self, value: bool):\n        self._property_changed('sc01')\n        self.__sc01 = value        \n\n    @property\n    def leg2_payment_frequency(self) -> str:\n        """Same as Leg 1 Payment Frequency."""\n        return self.__leg2_payment_frequency\n\n    @leg2_payment_frequency.setter\n    def leg2_payment_frequency(self, value: str):\n        self._property_changed('leg2_payment_frequency')\n        self.__leg2_payment_frequency = value        \n\n    @property\n    def sc03(self) -> bool:\n        """Educate Sales And Traders About SCR Agreements."""\n        return self.__sc03\n\n    @sc03.setter\n    def sc03(self, value: bool):\n        self._property_changed('sc03')\n        self.__sc03 = value        \n\n    @property\n    def sc02(self) -> bool:\n        """Train On Information Barriers Policy."""\n        return self.__sc02\n\n    @sc02.setter\n    def sc02(self, value: bool):\n        self._property_changed('sc02')\n        self.__sc02 = value        \n\n    @property\n    def geography_name(self) -> str:\n        """Name of the country or region for which metric is calculated."""\n        return self.__geography_name\n\n    @geography_name.setter\n    def geography_name(self, value: str):\n        self._property_changed('geography_name')\n        self.__geography_name = value        \n\n    @property\n    def borrower(self) -> str:\n        """Name of the borrowing entity on a securities lending agreement."""\n        return self.__borrower\n\n    @borrower.setter\n    def borrower(self, value: str):\n        self._property_changed('borrower')\n        self.__borrower = value        \n\n    @property\n    def settle_price(self) -> float:\n        """Settling level of an asset based on official exchange fixing or calculation\n           agent marked level."""\n        return self.__settle_price\n\n    @settle_price.setter\n    def settle_price(self, value: float):\n        self._property_changed('settle_price')\n        self.__settle_price = value        \n\n    @property\n    def performance_contribution(self) -> float:\n        """The contribution of an underlying asset to the overall performance."""\n        return self.__performance_contribution\n\n    @performance_contribution.setter\n    def performance_contribution(self, value: float):\n        self._property_changed('performance_contribution')\n        self.__performance_contribution = value        \n\n    @property\n    def sc09(self) -> bool:\n        """Exclude From Market Color Commentary By Sales And Trading."""\n        return self.__sc09\n\n    @sc09.setter\n    def sc09(self, value: bool):\n        self._property_changed('sc09')\n        self.__sc09 = value        \n\n    @property\n    def mkt_class(self) -> str:\n        """The MDAPI Class (e.g. Swap, Cash)."""\n        return self.__mkt_class\n\n    @mkt_class.setter\n    def mkt_class(self, value: str):\n        self._property_changed('mkt_class')\n        self.__mkt_class = value        \n\n    @property\n    def sc08(self) -> bool:\n        """Exclude From Systematic Market Color Reporting And Advertised Volumes."""\n        return self.__sc08\n\n    @sc08.setter\n    def sc08(self, value: bool):\n        self._property_changed('sc08')\n        self.__sc08 = value        \n\n    @property\n    def collateralization(self) -> str:\n        """If an SB swap is not cleared, an indication of whether a swap is\n           Uncollateralized (UC), Partially Collateralized (PC), One-Way\n           Collateralized (OC), or Fully Collateralized (FC)."""\n        return self.__collateralization\n\n    @collateralization.setter\n    def collateralization(self, value: str):\n        self._property_changed('collateralization')\n        self.__collateralization = value        \n\n    @property\n    def future_month_u26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_u26\n\n    @future_month_u26.setter\n    def future_month_u26(self, value: float):\n        self._property_changed('future_month_u26')\n        self.__future_month_u26 = value        \n\n    @property\n    def future_month_u25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_u25\n\n    @future_month_u25.setter\n    def future_month_u25(self, value: float):\n        self._property_changed('future_month_u25')\n        self.__future_month_u25 = value        \n\n    @property\n    def future_month_u24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_u24\n\n    @future_month_u24.setter\n    def future_month_u24(self, value: float):\n        self._property_changed('future_month_u24')\n        self.__future_month_u24 = value        \n\n    @property\n    def future_month_u23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_u23\n\n    @future_month_u23.setter\n    def future_month_u23(self, value: float):\n        self._property_changed('future_month_u23')\n        self.__future_month_u23 = value        \n\n    @property\n    def future_month_u22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_u22\n\n    @future_month_u22.setter\n    def future_month_u22(self, value: float):\n        self._property_changed('future_month_u22')\n        self.__future_month_u22 = value        \n\n    @property\n    def statement_id(self) -> str:\n        """Statement UUID."""\n        return self.__statement_id\n\n    @statement_id.setter\n    def statement_id(self, value: str):\n        self._property_changed('statement_id')\n        self.__statement_id = value        \n\n    @property\n    def future_month_u21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_u21\n\n    @future_month_u21.setter\n    def future_month_u21(self, value: float):\n        self._property_changed('future_month_u21')\n        self.__future_month_u21 = value        \n\n    @property\n    def modified_duration(self) -> float:\n        """Measure of a bond's price sensitivity to changes in interest rates."""\n        return self.__modified_duration\n\n    @modified_duration.setter\n    def modified_duration(self, value: float):\n        self._property_changed('modified_duration')\n        self.__modified_duration = value        \n\n    @property\n    def short_rates_contribution(self) -> float:\n        """Contribution of short rate component to FCI."""\n        return self.__short_rates_contribution\n\n    @short_rates_contribution.setter\n    def short_rates_contribution(self, value: float):\n        self._property_changed('short_rates_contribution')\n        self.__short_rates_contribution = value        \n\n    @property\n    def implied_normal_volatility(self) -> float:\n        """Market implied volatility measured using a normal model in bps/day."""\n        return self.__implied_normal_volatility\n\n    @implied_normal_volatility.setter\n    def implied_normal_volatility(self, value: float):\n        self._property_changed('implied_normal_volatility')\n        self.__implied_normal_volatility = value        \n\n    @property\n    def solar_generation(self) -> float:\n        """Solar generation value provided by source."""\n        return self.__solar_generation\n\n    @solar_generation.setter\n    def solar_generation(self, value: float):\n        self._property_changed('solar_generation')\n        self.__solar_generation = value        \n\n    @property\n    def mtm_price(self) -> float:\n        """Amount of profit or loss realized over statement period."""\n        return self.__mtm_price\n\n    @mtm_price.setter\n    def mtm_price(self, value: float):\n        self._property_changed('mtm_price')\n        self.__mtm_price = value        \n\n    @property\n    def swap_spread_change(self) -> float:\n        """Swap spread change."""\n        return self.__swap_spread_change\n\n    @swap_spread_change.setter\n    def swap_spread_change(self, value: float):\n        self._property_changed('swap_spread_change')\n        self.__swap_spread_change = value        \n\n    @property\n    def realized_arrival_performance_usd(self) -> float:\n        """Average execution price vs Consolidated Arrival (in limit) since order inception\n           ??? realized in $."""\n        return self.__realized_arrival_performance_usd\n\n    @realized_arrival_performance_usd.setter\n    def realized_arrival_performance_usd(self, value: float):\n        self._property_changed('realized_arrival_performance_usd')\n        self.__realized_arrival_performance_usd = value        \n\n    @property\n    def portfolio_assets(self) -> float:\n        """Total amount of assets under management across all share classes."""\n        return self.__portfolio_assets\n\n    @portfolio_assets.setter\n    def portfolio_assets(self, value: float):\n        self._property_changed('portfolio_assets')\n        self.__portfolio_assets = value        \n\n    @property\n    def pricingdate(self) -> str:\n        """Pricing Date."""\n        return self.__pricingdate\n\n    @pricingdate.setter\n    def pricingdate(self, value: str):\n        self._property_changed('pricingdate')\n        self.__pricingdate = value        \n\n    @property\n    def tcm_cost_horizon3_hour(self) -> float:\n        """TCM cost with a 3 hour time horizon."""\n        return self.__tcm_cost_horizon3_hour\n\n    @tcm_cost_horizon3_hour.setter\n    def tcm_cost_horizon3_hour(self, value: float):\n        self._property_changed('tcm_cost_horizon3_hour')\n        self.__tcm_cost_horizon3_hour = value        \n\n    @property\n    def exchange_rate(self) -> float:\n        """Amount that an exchange charges in fees, in mils. Negative numbers denote\n           rebates."""\n        return self.__exchange_rate\n\n    @exchange_rate.setter\n    def exchange_rate(self, value: float):\n        self._property_changed('exchange_rate')\n        self.__exchange_rate = value        \n\n    @property\n    def potential_bed_cap_inc(self) -> float:\n        """The number of beds by which a hospital can increase bed capacity, derived from\n           the difference between number of allowed licensed beds and number of\n           currently staffed beds."""\n        return self.__potential_bed_cap_inc\n\n    @potential_bed_cap_inc.setter\n    def potential_bed_cap_inc(self, value: float):\n        self._property_changed('potential_bed_cap_inc')\n        self.__potential_bed_cap_inc = value        \n\n    @property\n    def number_covered(self) -> float:\n        """Number of underlyers covered by risk model."""\n        return self.__number_covered\n\n    @number_covered.setter\n    def number_covered(self, value: float):\n        self._property_changed('number_covered')\n        self.__number_covered = value        \n\n    @property\n    def number_of_positions(self) -> float:\n        """Number of positions."""\n        return self.__number_of_positions\n\n    @number_of_positions.setter\n    def number_of_positions(self, value: float):\n        self._property_changed('number_of_positions')\n        self.__number_of_positions = value        \n\n    @property\n    def open_unadjusted(self) -> float:\n        """Unadjusted open level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__open_unadjusted\n\n    @open_unadjusted.setter\n    def open_unadjusted(self, value: float):\n        self._property_changed('open_unadjusted')\n        self.__open_unadjusted = value        \n\n    @property\n    def strike_time(self) -> datetime.datetime:\n        """Time at which order began executing."""\n        return self.__strike_time\n\n    @strike_time.setter\n    def strike_time(self, value: datetime.datetime):\n        self._property_changed('strike_time')\n        self.__strike_time = value        \n\n    @property\n    def ask_price(self) -> float:\n        """Latest Ask Price (price offering to sell)."""\n        return self.__ask_price\n\n    @ask_price.setter\n    def ask_price(self, value: float):\n        self._property_changed('ask_price')\n        self.__ask_price = value        \n\n    @property\n    def event_id(self) -> str:\n        """Goldman Sachs internal event identifier."""\n        return self.__event_id\n\n    @event_id.setter\n    def event_id(self, value: str):\n        self._property_changed('event_id')\n        self.__event_id = value        \n\n    @property\n    def sectors(self) -> Tuple[str, ...]:\n        """Sector classifications of an asset."""\n        return self.__sectors\n\n    @sectors.setter\n    def sectors(self, value: Tuple[str, ...]):\n        self._property_changed('sectors')\n        self.__sectors = value        \n\n    @property\n    def additional_price_notation_type(self) -> str:\n        """Basis points, Price, Yield, Spread, Coupon, etc., depending on the type of SB\n           swap, which is calculated at affirmation."""\n        return self.__additional_price_notation_type\n\n    @additional_price_notation_type.setter\n    def additional_price_notation_type(self, value: str):\n        self._property_changed('additional_price_notation_type')\n        self.__additional_price_notation_type = value        \n\n    @property\n    def gross_investment_qtd(self) -> float:\n        """Total gross investment Quarter to date."""\n        return self.__gross_investment_qtd\n\n    @gross_investment_qtd.setter\n    def gross_investment_qtd(self, value: float):\n        self._property_changed('gross_investment_qtd')\n        self.__gross_investment_qtd = value        \n\n    @property\n    def annualized_risk(self) -> float:\n        """Annualized risk."""\n        return self.__annualized_risk\n\n    @annualized_risk.setter\n    def annualized_risk(self, value: float):\n        self._property_changed('annualized_risk')\n        self.__annualized_risk = value        \n\n    @property\n    def estimated_holding_time_short(self) -> float:\n        """The estimated holding time for a short position."""\n        return self.__estimated_holding_time_short\n\n    @estimated_holding_time_short.setter\n    def estimated_holding_time_short(self, value: float):\n        self._property_changed('estimated_holding_time_short')\n        self.__estimated_holding_time_short = value        \n\n    @property\n    def midcurve_premium(self) -> float:\n        """Midcurve premium."""\n        return self.__midcurve_premium\n\n    @midcurve_premium.setter\n    def midcurve_premium(self, value: float):\n        self._property_changed('midcurve_premium')\n        self.__midcurve_premium = value        \n\n    @property\n    def volume_composite(self) -> float:\n        """Accumulated number of shares, lots or contracts traded according to the market\n           convention at all exchanges."""\n        return self.__volume_composite\n\n    @volume_composite.setter\n    def volume_composite(self, value: float):\n        self._property_changed('volume_composite')\n        self.__volume_composite = value        \n\n    @property\n    def sharpe_qtd(self) -> float:\n        """Sharpe ratio Quarter to date."""\n        return self.__sharpe_qtd\n\n    @sharpe_qtd.setter\n    def sharpe_qtd(self, value: float):\n        self._property_changed('sharpe_qtd')\n        self.__sharpe_qtd = value        \n\n    @property\n    def estimated_holding_time_long(self) -> float:\n        """The estimated holding time for a long position."""\n        return self.__estimated_holding_time_long\n\n    @estimated_holding_time_long.setter\n    def estimated_holding_time_long(self, value: float):\n        self._property_changed('estimated_holding_time_long')\n        self.__estimated_holding_time_long = value        \n\n    @property\n    def external(self) -> bool:\n        """Whether entity was created by an external user."""\n        return self.__external\n\n    @external.setter\n    def external(self, value: bool):\n        self._property_changed('external')\n        self.__external = value        \n\n    @property\n    def tracker_name(self) -> str:\n        """Name of the tracker."""\n        return self.__tracker_name\n\n    @tracker_name.setter\n    def tracker_name(self, value: str):\n        self._property_changed('tracker_name')\n        self.__tracker_name = value        \n\n    @property\n    def sell50cents(self) -> float:\n        """The amount GS would sell for 50 cents charge."""\n        return self.__sell50cents\n\n    @sell50cents.setter\n    def sell50cents(self, value: float):\n        self._property_changed('sell50cents')\n        self.__sell50cents = value        \n\n    @property\n    def trade_price(self) -> float:\n        """Last trade price or value."""\n        return self.__trade_price\n\n    @trade_price.setter\n    def trade_price(self, value: float):\n        self._property_changed('trade_price')\n        self.__trade_price = value        \n\n    @property\n    def cleared(self) -> str:\n        """An indication of whether or not an SB swap transaction is going to be cleared by\n           a derivatives clearing organization."""\n        return self.__cleared\n\n    @cleared.setter\n    def cleared(self, value: str):\n        self._property_changed('cleared')\n        self.__cleared = value        \n\n    @property\n    def prime_id_numeric(self) -> float:\n        """Prime ID as a number."""\n        return self.__prime_id_numeric\n\n    @prime_id_numeric.setter\n    def prime_id_numeric(self, value: float):\n        self._property_changed('prime_id_numeric')\n        self.__prime_id_numeric = value        \n\n    @property\n    def buy8bps(self) -> float:\n        """The amount GS would buy for 8 bps charge."""\n        return self.__buy8bps\n\n    @buy8bps.setter\n    def buy8bps(self, value: float):\n        self._property_changed('buy8bps')\n        self.__buy8bps = value        \n\n    @property\n    def total_notional_local(self) -> float:\n        """Total notional on order in local currency."""\n        return self.__total_notional_local\n\n    @total_notional_local.setter\n    def total_notional_local(self, value: float):\n        self._property_changed('total_notional_local')\n        self.__total_notional_local = value        \n\n    @property\n    def cid(self) -> str:\n        """Goldman Sachs internal company identifier."""\n        return self.__cid\n\n    @cid.setter\n    def cid(self, value: str):\n        self._property_changed('cid')\n        self.__cid = value        \n\n    @property\n    def total_confirmed_senior_home(self) -> float:\n        """Confirmed cases in Senior homes and care facilities."""\n        return self.__total_confirmed_senior_home\n\n    @total_confirmed_senior_home.setter\n    def total_confirmed_senior_home(self, value: float):\n        self._property_changed('total_confirmed_senior_home')\n        self.__total_confirmed_senior_home = value        \n\n    @property\n    def ctd_fwd_price(self) -> float:\n        """Cheapest to deliver bond fwd price."""\n        return self.__ctd_fwd_price\n\n    @ctd_fwd_price.setter\n    def ctd_fwd_price(self, value: float):\n        self._property_changed('ctd_fwd_price')\n        self.__ctd_fwd_price = value        \n\n    @property\n    def sink_factor(self) -> float:\n        """The level to which a sinkable bond has currently sunk."""\n        return self.__sink_factor\n\n    @sink_factor.setter\n    def sink_factor(self, value: float):\n        self._property_changed('sink_factor')\n        self.__sink_factor = value        \n\n    @property\n    def temperature_forecast(self) -> float:\n        """The forecast temperature of diff types of given units."""\n        return self.__temperature_forecast\n\n    @temperature_forecast.setter\n    def temperature_forecast(self, value: float):\n        self._property_changed('temperature_forecast')\n        self.__temperature_forecast = value        \n\n    @property\n    def bid_high(self) -> float:\n        """The highest bid (price willing to buy)."""\n        return self.__bid_high\n\n    @bid_high.setter\n    def bid_high(self, value: float):\n        self._property_changed('bid_high')\n        self.__bid_high = value        \n\n    @property\n    def pnl_qtd(self) -> float:\n        """Total PnL Quarter to date."""\n        return self.__pnl_qtd\n\n    @pnl_qtd.setter\n    def pnl_qtd(self, value: float):\n        self._property_changed('pnl_qtd')\n        self.__pnl_qtd = value        \n\n    @property\n    def buy50cents(self) -> float:\n        """The amount GS would buy for 50 cents charge."""\n        return self.__buy50cents\n\n    @buy50cents.setter\n    def buy50cents(self, value: float):\n        self._property_changed('buy50cents')\n        self.__buy50cents = value        \n\n    @property\n    def sell4bps(self) -> float:\n        """The amount GS would sell for 4 bps charge."""\n        return self.__sell4bps\n\n    @sell4bps.setter\n    def sell4bps(self, value: float):\n        self._property_changed('sell4bps')\n        self.__sell4bps = value        \n\n    @property\n    def receiver_day_count_fraction(self) -> str:\n        """Day Count Fraction"""\n        return self.__receiver_day_count_fraction\n\n    @receiver_day_count_fraction.setter\n    def receiver_day_count_fraction(self, value: str):\n        self._property_changed('receiver_day_count_fraction')\n        self.__receiver_day_count_fraction = value        \n\n    @property\n    def auction_close_percentage(self) -> float:\n        """Percentage of total order being reserved for the Closing Auction."""\n        return self.__auction_close_percentage\n\n    @auction_close_percentage.setter\n    def auction_close_percentage(self, value: float):\n        self._property_changed('auction_close_percentage')\n        self.__auction_close_percentage = value        \n\n    @property\n    def target_price(self) -> float:\n        """Target Price."""\n        return self.__target_price\n\n    @target_price.setter\n    def target_price(self, value: float):\n        self._property_changed('target_price')\n        self.__target_price = value        \n\n    @property\n    def bos_in_bps_description(self) -> str:\n        """Description of the Stock's Bid-Offer Spread in Basis points on the particular\n           date."""\n        return self.__bos_in_bps_description\n\n    @bos_in_bps_description.setter\n    def bos_in_bps_description(self, value: str):\n        self._property_changed('bos_in_bps_description')\n        self.__bos_in_bps_description = value        \n\n    @property\n    def low_price(self) -> float:\n        """Low level of an asset based on official exchange fixing or calculation agent\n           marked level."""\n        return self.__low_price\n\n    @low_price.setter\n    def low_price(self, value: float):\n        self._property_changed('low_price')\n        self.__low_price = value        \n\n    @property\n    def adv22_day_pct(self) -> float:\n        """Median number of shares or units of a given asset traded over a 21 day period."""\n        return self.__adv22_day_pct\n\n    @adv22_day_pct.setter\n    def adv22_day_pct(self, value: float):\n        self._property_changed('adv22_day_pct')\n        self.__adv22_day_pct = value        \n\n    @property\n    def matched_maturity_swap_spread12m(self) -> float:\n        """Matched maturity swap spread vs 12m tenor."""\n        return self.__matched_maturity_swap_spread12m\n\n    @matched_maturity_swap_spread12m.setter\n    def matched_maturity_swap_spread12m(self, value: float):\n        self._property_changed('matched_maturity_swap_spread12m')\n        self.__matched_maturity_swap_spread12m = value        \n\n    @property\n    def price_range_in_ticks_label(self):\n        return self.__price_range_in_ticks_label\n\n    @price_range_in_ticks_label.setter\n    def price_range_in_ticks_label(self, value):\n        self._property_changed('price_range_in_ticks_label')\n        self.__price_range_in_ticks_label = value        \n\n    @property\n    def ticker(self) -> str:\n        """Ticker."""\n        return self.__ticker\n\n    @ticker.setter\n    def ticker(self, value: str):\n        self._property_changed('ticker')\n        self.__ticker = value        \n\n    @property\n    def notional_unit(self) -> str:\n        """Unit of reported notional price."""\n        return self.__notional_unit\n\n    @notional_unit.setter\n    def notional_unit(self, value: str):\n        self._property_changed('notional_unit')\n        self.__notional_unit = value        \n\n    @property\n    def tcm_cost_horizon1_day(self) -> float:\n        """TCM cost with a 1 day time horizon."""\n        return self.__tcm_cost_horizon1_day\n\n    @tcm_cost_horizon1_day.setter\n    def tcm_cost_horizon1_day(self, value: float):\n        self._property_changed('tcm_cost_horizon1_day')\n        self.__tcm_cost_horizon1_day = value        \n\n    @property\n    def approval(self) -> float:\n        """Approval rating."""\n        return self.__approval\n\n    @approval.setter\n    def approval(self, value: float):\n        self._property_changed('approval')\n        self.__approval = value        \n\n    @property\n    def test_measure(self) -> float:\n        """Generic field to hold aggregatable numeric measure for test result. For e.g\n           delay for timeliness or variance for correctness."""\n        return self.__test_measure\n\n    @test_measure.setter\n    def test_measure(self, value: float):\n        self._property_changed('test_measure')\n        self.__test_measure = value        \n\n    @property\n    def option_lock_out_period(self) -> str:\n        """An indication of the first allowable exercise date of the option."""\n        return self.__option_lock_out_period\n\n    @option_lock_out_period.setter\n    def option_lock_out_period(self, value: str):\n        self._property_changed('option_lock_out_period')\n        self.__option_lock_out_period = value        \n\n    @property\n    def execution_time(self) -> datetime.datetime:\n        """Arrival execution time in seconds from the epoch."""\n        return self.__execution_time\n\n    @execution_time.setter\n    def execution_time(self, value: datetime.datetime):\n        self._property_changed('execution_time')\n        self.__execution_time = value        \n\n    @property\n    def source_value_forecast(self) -> str:\n        """TE own projections."""\n        return self.__source_value_forecast\n\n    @source_value_forecast.setter\n    def source_value_forecast(self, value: str):\n        self._property_changed('source_value_forecast')\n        self.__source_value_forecast = value        \n\n    @property\n    def leg2_spread(self) -> float:\n        """Spread of leg."""\n        return self.__leg2_spread\n\n    @leg2_spread.setter\n    def leg2_spread(self, value: float):\n        self._property_changed('leg2_spread')\n        self.__leg2_spread = value        \n\n    @property\n    def short_conviction_large(self) -> float:\n        """The count of short ideas with large conviction."""\n        return self.__short_conviction_large\n\n    @short_conviction_large.setter\n    def short_conviction_large(self, value: float):\n        self._property_changed('short_conviction_large')\n        self.__short_conviction_large = value        \n\n    @property\n    def ccg_name(self) -> str:\n        """Name of the clinical commissioning group."""\n        return self.__ccg_name\n\n    @ccg_name.setter\n    def ccg_name(self, value: str):\n        self._property_changed('ccg_name')\n        self.__ccg_name = value        \n\n    @property\n    def dollar_excess_return(self) -> float:\n        """The dollar excess return of an instrument."""\n        return self.__dollar_excess_return\n\n    @dollar_excess_return.setter\n    def dollar_excess_return(self, value: float):\n        self._property_changed('dollar_excess_return')\n        self.__dollar_excess_return = value        \n\n    @property\n    def gsn(self) -> str:\n        """Goldman Sachs internal product number."""\n        return self.__gsn\n\n    @gsn.setter\n    def gsn(self, value: str):\n        self._property_changed('gsn')\n        self.__gsn = value        \n\n    @property\n    def trade_end_date(self) -> datetime.date:\n        """End date of the trade."""\n        return self.__trade_end_date\n\n    @trade_end_date.setter\n    def trade_end_date(self, value: datetime.date):\n        self._property_changed('trade_end_date')\n        self.__trade_end_date = value        \n\n    @property\n    def receiver_rate_option(self) -> str:\n        """The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE."""\n        return self.__receiver_rate_option\n\n    @receiver_rate_option.setter\n    def receiver_rate_option(self, value: str):\n        self._property_changed('receiver_rate_option')\n        self.__receiver_rate_option = value        \n\n    @property\n    def gss(self) -> str:\n        """Goldman Sachs internal product symbol."""\n        return self.__gss\n\n    @gss.setter\n    def gss(self, value: str):\n        self._property_changed('gss')\n        self.__gss = value        \n\n    @property\n    def percent_of_mediandv1m(self) -> float:\n        """Percentage of median daily volume calculated using 1 month period (last 22\n           trading days)."""\n        return self.__percent_of_mediandv1m\n\n    @percent_of_mediandv1m.setter\n    def percent_of_mediandv1m(self, value: float):\n        self._property_changed('percent_of_mediandv1m')\n        self.__percent_of_mediandv1m = value        \n\n    @property\n    def lendables(self) -> float:\n        """Market value of holdings available to a securities lending program for lending."""\n        return self.__lendables\n\n    @lendables.setter\n    def lendables(self, value: float):\n        self._property_changed('lendables')\n        self.__lendables = value        \n\n    @property\n    def sell75cents(self) -> float:\n        """The amount GS would sell for 75 cents charge."""\n        return self.__sell75cents\n\n    @sell75cents.setter\n    def sell75cents(self, value: float):\n        self._property_changed('sell75cents')\n        self.__sell75cents = value        \n\n    @property\n    def option_adjusted_spread(self) -> float:\n        """Spread of a fixed-income security rate and the risk-free rate of return, which\n           is then adjusted to take into account an embedded option."""\n        return self.__option_adjusted_spread\n\n    @option_adjusted_spread.setter\n    def option_adjusted_spread(self, value: float):\n        self._property_changed('option_adjusted_spread')\n        self.__option_adjusted_spread = value        \n\n    @property\n    def option_adjusted_swap_spread(self) -> float:\n        """Callability Option Adjusted Spread to yield curve."""\n        return self.__option_adjusted_swap_spread\n\n    @option_adjusted_swap_spread.setter\n    def option_adjusted_swap_spread(self, value: float):\n        self._property_changed('option_adjusted_swap_spread')\n        self.__option_adjusted_swap_spread = value        \n\n    @property\n    def bos_in_ticks_label(self):\n        return self.__bos_in_ticks_label\n\n    @bos_in_ticks_label.setter\n    def bos_in_ticks_label(self, value):\n        self._property_changed('bos_in_ticks_label')\n        self.__bos_in_ticks_label = value        \n\n    @property\n    def position_source_id(self) -> str:\n        """Marquee unique identifier"""\n        return self.__position_source_id\n\n    @position_source_id.setter\n    def position_source_id(self, value: str):\n        self._property_changed('position_source_id')\n        self.__position_source_id = value        \n\n    @property\n    def buy1bps(self) -> float:\n        """The amount GS would buy for 1 bps charge."""\n        return self.__buy1bps\n\n    @buy1bps.setter\n    def buy1bps(self, value: float):\n        self._property_changed('buy1bps')\n        self.__buy1bps = value        \n\n    @property\n    def buy3point5bps(self) -> float:\n        """The amount GS would buy for 3.5 bps charge."""\n        return self.__buy3point5bps\n\n    @buy3point5bps.setter\n    def buy3point5bps(self, value: float):\n        self._property_changed('buy3point5bps')\n        self.__buy3point5bps = value        \n\n    @property\n    def gs_sustain_region(self) -> str:\n        """Region assigned by GIR ESG SUSTAIN team."""\n        return self.__gs_sustain_region\n\n    @gs_sustain_region.setter\n    def gs_sustain_region(self, value: str):\n        self._property_changed('gs_sustain_region')\n        self.__gs_sustain_region = value        \n\n    @property\n    def absolute_return_wtd(self) -> float:\n        """Absolute Return Week to Date."""\n        return self.__absolute_return_wtd\n\n    @absolute_return_wtd.setter\n    def absolute_return_wtd(self, value: float):\n        self._property_changed('absolute_return_wtd')\n        self.__absolute_return_wtd = value        \n\n    @property\n    def deployment_id(self) -> float:\n        """Deployment ID."""\n        return self.__deployment_id\n\n    @deployment_id.setter\n    def deployment_id(self, value: float):\n        self._property_changed('deployment_id')\n        self.__deployment_id = value        \n\n    @property\n    def asset_parameters_seniority(self) -> str:\n        """The seniority of the bond."""\n        return self.__asset_parameters_seniority\n\n    @asset_parameters_seniority.setter\n    def asset_parameters_seniority(self, value: str):\n        self._property_changed('asset_parameters_seniority')\n        self.__asset_parameters_seniority = value        \n\n    @property\n    def ask_spread(self) -> float:\n        """Spread between the yields of a debt security and its benchmark when both are\n           purchased at ask price."""\n        return self.__ask_spread\n\n    @ask_spread.setter\n    def ask_spread(self, value: float):\n        self._property_changed('ask_spread')\n        self.__ask_spread = value        \n\n    @property\n    def flow(self) -> float:\n        """Flow of the fund."""\n        return self.__flow\n\n    @flow.setter\n    def flow(self, value: float):\n        self._property_changed('flow')\n        self.__flow = value        \n\n    @property\n    def future_month_h26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_h26\n\n    @future_month_h26.setter\n    def future_month_h26(self, value: float):\n        self._property_changed('future_month_h26')\n        self.__future_month_h26 = value        \n\n    @property\n    def loan_rebate(self) -> float:\n        """Rebate paid back to a securities lending borrower."""\n        return self.__loan_rebate\n\n    @loan_rebate.setter\n    def loan_rebate(self, value: float):\n        self._property_changed('loan_rebate')\n        self.__loan_rebate = value        \n\n    @property\n    def future_month_h25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_h25\n\n    @future_month_h25.setter\n    def future_month_h25(self, value: float):\n        self._property_changed('future_month_h25')\n        self.__future_month_h25 = value        \n\n    @property\n    def period(self) -> str:\n        """Period for the relevant metric, such as 1y (1 year)."""\n        return self.__period\n\n    @period.setter\n    def period(self, value: str):\n        self._property_changed('period')\n        self.__period = value        \n\n    @property\n    def index_create_source(self) -> str:\n        """Source of basket creation"""\n        return self.__index_create_source\n\n    @index_create_source.setter\n    def index_create_source(self, value: str):\n        self._property_changed('index_create_source')\n        self.__index_create_source = value        \n\n    @property\n    def future_month_h24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_h24\n\n    @future_month_h24.setter\n    def future_month_h24(self, value: float):\n        self._property_changed('future_month_h24')\n        self.__future_month_h24 = value        \n\n    @property\n    def future_month_h23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_h23\n\n    @future_month_h23.setter\n    def future_month_h23(self, value: float):\n        self._property_changed('future_month_h23')\n        self.__future_month_h23 = value        \n\n    @property\n    def future_month_h22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_h22\n\n    @future_month_h22.setter\n    def future_month_h22(self, value: float):\n        self._property_changed('future_month_h22')\n        self.__future_month_h22 = value        \n\n    @property\n    def future_month_h21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_h21\n\n    @future_month_h21.setter\n    def future_month_h21(self, value: float):\n        self._property_changed('future_month_h21')\n        self.__future_month_h21 = value        \n\n    @property\n    def non_usd_ois(self) -> float:\n        """Non USD Currency Ois rate. Feature applied only to Xccy curves."""\n        return self.__non_usd_ois\n\n    @non_usd_ois.setter\n    def non_usd_ois(self, value: float):\n        self._property_changed('non_usd_ois')\n        self.__non_usd_ois = value        \n\n    @property\n    def real_twi_contribution(self) -> float:\n        """Contribution of real trade weighted exchange rate index component to real FCI."""\n        return self.__real_twi_contribution\n\n    @real_twi_contribution.setter\n    def real_twi_contribution(self, value: float):\n        self._property_changed('real_twi_contribution')\n        self.__real_twi_contribution = value        \n\n    @property\n    def mkt_asset(self) -> str:\n        """The MDAPI Asset (e.g. USD, USD/EUR)."""\n        return self.__mkt_asset\n\n    @mkt_asset.setter\n    def mkt_asset(self, value: str):\n        self._property_changed('mkt_asset')\n        self.__mkt_asset = value        \n\n    @property\n    def leg2_index_location(self) -> str:\n        """Location of leg."""\n        return self.__leg2_index_location\n\n    @leg2_index_location.setter\n    def leg2_index_location(self, value: str):\n        self._property_changed('leg2_index_location')\n        self.__leg2_index_location = value        \n\n    @property\n    def twap_unrealized_bps(self) -> float:\n        """Consolidated unrealised performance vs TWAP In Limit, in bps."""\n        return self.__twap_unrealized_bps\n\n    @twap_unrealized_bps.setter\n    def twap_unrealized_bps(self, value: float):\n        self._property_changed('twap_unrealized_bps')\n        self.__twap_unrealized_bps = value        \n\n    @property\n    def last_updated_message(self) -> str:\n        """Last Updated Message."""\n        return self.__last_updated_message\n\n    @last_updated_message.setter\n    def last_updated_message(self, value: str):\n        self._property_changed('last_updated_message')\n        self.__last_updated_message = value        \n\n    @property\n    def loan_value(self) -> float:\n        """The value of the securities or cash delivered by a borrower to a lender to\n           support a loan of securities."""\n        return self.__loan_value\n\n    @loan_value.setter\n    def loan_value(self, value: float):\n        self._property_changed('loan_value')\n        self.__loan_value = value        \n\n    @property\n    def option_adjusted_ois_spread(self) -> float:\n        """Option adjusted OIS spread."""\n        return self.__option_adjusted_ois_spread\n\n    @option_adjusted_ois_spread.setter\n    def option_adjusted_ois_spread(self, value: float):\n        self._property_changed('option_adjusted_ois_spread')\n        self.__option_adjusted_ois_spread = value        \n\n    @property\n    def total_return_price(self) -> float:\n        """The total return price of an instrument."""\n        return self.__total_return_price\n\n    @total_return_price.setter\n    def total_return_price(self, value: float):\n        self._property_changed('total_return_price')\n        self.__total_return_price = value        \n\n    @property\n    def weighted_percent_in_model(self) -> float:\n        """Weighted percent of constituent in risk model."""\n        return self.__weighted_percent_in_model\n\n    @weighted_percent_in_model.setter\n    def weighted_percent_in_model(self, value: float):\n        self._property_changed('weighted_percent_in_model')\n        self.__weighted_percent_in_model = value        \n\n    @property\n    def init_loan_spread_required(self) -> float:\n        """The minimum spread requirement for a securities lending loan on the day of loan\n           initiation."""\n        return self.__init_loan_spread_required\n\n    @init_loan_spread_required.setter\n    def init_loan_spread_required(self, value: float):\n        self._property_changed('init_loan_spread_required')\n        self.__init_loan_spread_required = value        \n\n    @property\n    def election_period(self) -> str:\n        """Period of election."""\n        return self.__election_period\n\n    @election_period.setter\n    def election_period(self, value: str):\n        self._property_changed('election_period')\n        self.__election_period = value        \n\n    @property\n    def funding_ask_price(self) -> float:\n        """Latest Ask Price (price offering to sell)."""\n        return self.__funding_ask_price\n\n    @funding_ask_price.setter\n    def funding_ask_price(self, value: float):\n        self._property_changed('funding_ask_price')\n        self.__funding_ask_price = value        \n\n    @property\n    def historical_beta(self) -> float:\n        """Historical beta."""\n        return self.__historical_beta\n\n    @historical_beta.setter\n    def historical_beta(self, value: float):\n        self._property_changed('historical_beta')\n        self.__historical_beta = value        \n\n    @property\n    def bond_risk_premium_index(self) -> float:\n        """Bond risk premium index: difference between growth rate forecast and 10y\n           treasury yield."""\n        return self.__bond_risk_premium_index\n\n    @bond_risk_premium_index.setter\n    def bond_risk_premium_index(self, value: float):\n        self._property_changed('bond_risk_premium_index')\n        self.__bond_risk_premium_index = value        \n\n    @property\n    def hit_rate_ytd(self) -> float:\n        """Hit Rate Ratio Year to Date."""\n        return self.__hit_rate_ytd\n\n    @hit_rate_ytd.setter\n    def hit_rate_ytd(self, value: float):\n        self._property_changed('hit_rate_ytd')\n        self.__hit_rate_ytd = value        \n\n    @property\n    def gir_gsdeer_gsfeer(self) -> float:\n        """GSDEER and GSFEER quarterly estimates for currency fair values made by Global\n           Investment Research (GIR) macro analysts."""\n        return self.__gir_gsdeer_gsfeer\n\n    @gir_gsdeer_gsfeer.setter\n    def gir_gsdeer_gsfeer(self, value: float):\n        self._property_changed('gir_gsdeer_gsfeer')\n        self.__gir_gsdeer_gsfeer = value        \n\n    @property\n    def num_units(self) -> float:\n        """Units."""\n        return self.__num_units\n\n    @num_units.setter\n    def num_units(self, value: float):\n        self._property_changed('num_units')\n        self.__num_units = value        \n\n    @property\n    def asset_parameters_receiver_frequency(self) -> str:\n        """Tenor"""\n        return self.__asset_parameters_receiver_frequency\n\n    @asset_parameters_receiver_frequency.setter\n    def asset_parameters_receiver_frequency(self, value: str):\n        self._property_changed('asset_parameters_receiver_frequency')\n        self.__asset_parameters_receiver_frequency = value        \n\n    @property\n    def expense_ratio_gross_bps(self) -> float:\n        """Gives basis point measure of management fee."""\n        return self.__expense_ratio_gross_bps\n\n    @expense_ratio_gross_bps.setter\n    def expense_ratio_gross_bps(self, value: float):\n        self._property_changed('expense_ratio_gross_bps')\n        self.__expense_ratio_gross_bps = value        \n\n    @property\n    def relative_payoff_wtd(self) -> float:\n        """Total win divided by total loss Week to date."""\n        return self.__relative_payoff_wtd\n\n    @relative_payoff_wtd.setter\n    def relative_payoff_wtd(self, value: float):\n        self._property_changed('relative_payoff_wtd')\n        self.__relative_payoff_wtd = value        \n\n    @property\n    def ctd_price(self) -> float:\n        """Price of cheapest to deliver bond."""\n        return self.__ctd_price\n\n    @ctd_price.setter\n    def ctd_price(self, value: float):\n        self._property_changed('ctd_price')\n        self.__ctd_price = value        \n\n    @property\n    def pace_of_roll_now(self) -> float:\n        """The pace of the roll as of the pricing date."""\n        return self.__pace_of_roll_now\n\n    @pace_of_roll_now.setter\n    def pace_of_roll_now(self, value: float):\n        self._property_changed('pace_of_roll_now')\n        self.__pace_of_roll_now = value        \n\n    @property\n    def product(self) -> str:\n        """The asset, reference asset, or reference obligation for payments of a party???s\n           obligations under the SB swap transaction reference."""\n        return self.__product\n\n    @product.setter\n    def product(self, value: str):\n        self._property_changed('product')\n        self.__product = value        \n\n    @property\n    def leg2_return_type(self) -> str:\n        """Indication if return of leg 2 is total."""\n        return self.__leg2_return_type\n\n    @leg2_return_type.setter\n    def leg2_return_type(self, value: str):\n        self._property_changed('leg2_return_type')\n        self.__leg2_return_type = value        \n\n    @property\n    def agent_lender_fee(self) -> float:\n        """Fee earned by the Agent Lender for facilitating a securities lending agreement."""\n        return self.__agent_lender_fee\n\n    @agent_lender_fee.setter\n    def agent_lender_fee(self, value: float):\n        self._property_changed('agent_lender_fee')\n        self.__agent_lender_fee = value        \n\n    @property\n    def dissemination_id(self) -> str:\n        """DDR generated unique and random ID for reconciliation purpose."""\n        return self.__dissemination_id\n\n    @dissemination_id.setter\n    def dissemination_id(self, value: str):\n        self._property_changed('dissemination_id')\n        self.__dissemination_id = value        \n\n    @property\n    def option_strike_price(self) -> float:\n        """Strike price of the option. Also called option level."""\n        return self.__option_strike_price\n\n    @option_strike_price.setter\n    def option_strike_price(self, value: float):\n        self._property_changed('option_strike_price')\n        self.__option_strike_price = value        \n\n    @property\n    def precipitation_type(self) -> str:\n        """The type of precipitation required: Rain or snow etc."""\n        return self.__precipitation_type\n\n    @precipitation_type.setter\n    def precipitation_type(self, value: str):\n        self._property_changed('precipitation_type')\n        self.__precipitation_type = value        \n\n    @property\n    def lower_bound(self) -> float:\n        """Lower bound value."""\n        return self.__lower_bound\n\n    @lower_bound.setter\n    def lower_bound(self, value: float):\n        self._property_changed('lower_bound')\n        self.__lower_bound = value        \n\n    @property\n    def arrival_mid_normalized(self) -> float:\n        """Performance against Benchmark in pip."""\n        return self.__arrival_mid_normalized\n\n    @arrival_mid_normalized.setter\n    def arrival_mid_normalized(self, value: float):\n        self._property_changed('arrival_mid_normalized')\n        self.__arrival_mid_normalized = value        \n\n    @property\n    def underlying_asset2(self) -> str:\n        """Same as Underlying Asset 1 if populated."""\n        return self.__underlying_asset2\n\n    @underlying_asset2.setter\n    def underlying_asset2(self, value: str):\n        self._property_changed('underlying_asset2')\n        self.__underlying_asset2 = value        \n\n    @property\n    def underlying_asset1(self) -> str:\n        """The asset, reference asset, or reference obligation for payments of a party???s\n           obligations under the SB swap transaction reference."""\n        return self.__underlying_asset1\n\n    @underlying_asset1.setter\n    def underlying_asset1(self, value: str):\n        self._property_changed('underlying_asset1')\n        self.__underlying_asset1 = value        \n\n    @property\n    def legal_entity(self) -> str:\n        """Entity that has legal rights to the fund."""\n        return self.__legal_entity\n\n    @legal_entity.setter\n    def legal_entity(self, value: str):\n        self._property_changed('legal_entity')\n        self.__legal_entity = value        \n\n    @property\n    def performance_fee(self) -> Union[Op, float]:\n        return self.__performance_fee\n\n    @performance_fee.setter\n    def performance_fee(self, value: Union[Op, float]):\n        self._property_changed('performance_fee')\n        self.__performance_fee = value        \n\n    @property\n    def order_state(self) -> str:\n        """State of an order."""\n        return self.__order_state\n\n    @order_state.setter\n    def order_state(self, value: str):\n        self._property_changed('order_state')\n        self.__order_state = value        \n\n    @property\n    def actual_data_quality(self) -> float:\n        """Actual data quality level."""\n        return self.__actual_data_quality\n\n    @actual_data_quality.setter\n    def actual_data_quality(self, value: float):\n        self._property_changed('actual_data_quality')\n        self.__actual_data_quality = value        \n\n    @property\n    def index_ratio(self) -> float:\n        """Latest Bid Price (price willing to buy)."""\n        return self.__index_ratio\n\n    @index_ratio.setter\n    def index_ratio(self, value: float):\n        self._property_changed('index_ratio')\n        self.__index_ratio = value        \n\n    @property\n    def queue_in_lots_label(self):\n        return self.__queue_in_lots_label\n\n    @queue_in_lots_label.setter\n    def queue_in_lots_label(self, value):\n        self._property_changed('queue_in_lots_label')\n        self.__queue_in_lots_label = value        \n\n    @property\n    def adv10_day_pct(self) -> float:\n        """Median number of shares or units of a given asset traded over a 10 day period."""\n        return self.__adv10_day_pct\n\n    @adv10_day_pct.setter\n    def adv10_day_pct(self, value: float):\n        self._property_changed('adv10_day_pct')\n        self.__adv10_day_pct = value        \n\n    @property\n    def long_conviction_medium(self) -> float:\n        """The count of long ideas with medium conviction."""\n        return self.__long_conviction_medium\n\n    @long_conviction_medium.setter\n    def long_conviction_medium(self, value: float):\n        self._property_changed('long_conviction_medium')\n        self.__long_conviction_medium = value        \n\n    @property\n    def relative_hit_rate_wtd(self) -> float:\n        """Hit Rate Ratio Week to Date."""\n        return self.__relative_hit_rate_wtd\n\n    @relative_hit_rate_wtd.setter\n    def relative_hit_rate_wtd(self, value: float):\n        self._property_changed('relative_hit_rate_wtd')\n        self.__relative_hit_rate_wtd = value        \n\n    @property\n    def daily_tracking_error(self) -> float:\n        """Daily tracking error."""\n        return self.__daily_tracking_error\n\n    @daily_tracking_error.setter\n    def daily_tracking_error(self, value: float):\n        self._property_changed('daily_tracking_error')\n        self.__daily_tracking_error = value        \n\n    @property\n    def sell140cents(self) -> float:\n        """The amount GS would sell for 140 cents charge."""\n        return self.__sell140cents\n\n    @sell140cents.setter\n    def sell140cents(self, value: float):\n        self._property_changed('sell140cents')\n        self.__sell140cents = value        \n\n    @property\n    def sell10bps(self) -> float:\n        """The amount GS would sell for 10 bps charge."""\n        return self.__sell10bps\n\n    @sell10bps.setter\n    def sell10bps(self, value: float):\n        self._property_changed('sell10bps')\n        self.__sell10bps = value        \n\n    @property\n    def aggressive_offset_from_last(self) -> float:\n        """TBD."""\n        return self.__aggressive_offset_from_last\n\n    @aggressive_offset_from_last.setter\n    def aggressive_offset_from_last(self, value: float):\n        self._property_changed('aggressive_offset_from_last')\n        self.__aggressive_offset_from_last = value        \n\n    @property\n    def longitude(self) -> float:\n        """Longitude."""\n        return self.__longitude\n\n    @longitude.setter\n    def longitude(self, value: float):\n        self._property_changed('longitude')\n        self.__longitude = value        \n\n    @property\n    def new_icu(self) -> float:\n        """Number of new patients hospitalized in ICU."""\n        return self.__new_icu\n\n    @new_icu.setter\n    def new_icu(self, value: float):\n        self._property_changed('new_icu')\n        self.__new_icu = value        \n\n    @property\n    def market_cap(self) -> float:\n        """Market capitalization of a given asset in denominated currency."""\n        return self.__market_cap\n\n    @market_cap.setter\n    def market_cap(self, value: float):\n        self._property_changed('market_cap')\n        self.__market_cap = value        \n\n    @property\n    def weighted_average_mid(self) -> float:\n        """Weighted Average Mid."""\n        return self.__weighted_average_mid\n\n    @weighted_average_mid.setter\n    def weighted_average_mid(self, value: float):\n        self._property_changed('weighted_average_mid')\n        self.__weighted_average_mid = value        \n\n    @property\n    def cluster_region(self):\n        return self.__cluster_region\n\n    @cluster_region.setter\n    def cluster_region(self, value):\n        self._property_changed('cluster_region')\n        self.__cluster_region = value        \n\n    @property\n    def valoren(self) -> str:\n        """Valoren or VALOR number, Swiss primary security identifier (subject to\n           licensing)."""\n        return self.__valoren\n\n    @valoren.setter\n    def valoren(self, value: str):\n        self._property_changed('valoren')\n        self.__valoren = value        \n\n    @property\n    def average_execution_price(self) -> float:\n        """The average execution price of the order."""\n        return self.__average_execution_price\n\n    @average_execution_price.setter\n    def average_execution_price(self, value: float):\n        self._property_changed('average_execution_price')\n        self.__average_execution_price = value        \n\n    @property\n    def proceeds_asset_ois_swap_spread1m(self) -> float:\n        """Proceeds Asset OIS Swap Spread 1m."""\n        return self.__proceeds_asset_ois_swap_spread1m\n\n    @proceeds_asset_ois_swap_spread1m.setter\n    def proceeds_asset_ois_swap_spread1m(self, value: float):\n        self._property_changed('proceeds_asset_ois_swap_spread1m')\n        self.__proceeds_asset_ois_swap_spread1m = value        \n\n    @property\n    def payoff_wtd(self) -> float:\n        """Total win divided by total loss Week to date."""\n        return self.__payoff_wtd\n\n    @payoff_wtd.setter\n    def payoff_wtd(self, value: float):\n        self._property_changed('payoff_wtd')\n        self.__payoff_wtd = value        \n\n    @property\n    def basis(self) -> float:\n        """Spread to be added to the shorter tenor leg for the swap to be ATM."""\n        return self.__basis\n\n    @basis.setter\n    def basis(self, value: float):\n        self._property_changed('basis')\n        self.__basis = value        \n\n    @property\n    def investment_rate_trend(self) -> float:\n        """The day over day trend of the rate of return on an investment."""\n        return self.__investment_rate_trend\n\n    @investment_rate_trend.setter\n    def investment_rate_trend(self, value: float):\n        self._property_changed('investment_rate_trend')\n        self.__investment_rate_trend = value        \n\n    @property\n    def gross_investment_mtd(self) -> float:\n        """Total gross investment Month to date."""\n        return self.__gross_investment_mtd\n\n    @gross_investment_mtd.setter\n    def gross_investment_mtd(self, value: float):\n        self._property_changed('gross_investment_mtd')\n        self.__gross_investment_mtd = value        \n\n    @property\n    def hedge_id(self) -> str:\n        """Marquee unique identifier for a hedge."""\n        return self.__hedge_id\n\n    @hedge_id.setter\n    def hedge_id(self, value: str):\n        self._property_changed('hedge_id')\n        self.__hedge_id = value        \n\n    @property\n    def sharpe_mtd(self) -> float:\n        """Sharpe ratio Month to date."""\n        return self.__sharpe_mtd\n\n    @sharpe_mtd.setter\n    def sharpe_mtd(self, value: float):\n        self._property_changed('sharpe_mtd')\n        self.__sharpe_mtd = value        \n\n    @property\n    def tcm_cost_horizon8_day(self) -> float:\n        """TCM cost with a 8 day time horizon."""\n        return self.__tcm_cost_horizon8_day\n\n    @tcm_cost_horizon8_day.setter\n    def tcm_cost_horizon8_day(self, value: float):\n        self._property_changed('tcm_cost_horizon8_day')\n        self.__tcm_cost_horizon8_day = value        \n\n    @property\n    def residual_variance(self) -> float:\n        """Residual variance."""\n        return self.__residual_variance\n\n    @residual_variance.setter\n    def residual_variance(self, value: float):\n        self._property_changed('residual_variance')\n        self.__residual_variance = value        \n\n    @property\n    def restrict_internal_derived_data(self) -> bool:\n        """Restricts Ability to Use Internally as Part of Derived Data."""\n        return self.__restrict_internal_derived_data\n\n    @restrict_internal_derived_data.setter\n    def restrict_internal_derived_data(self, value: bool):\n        self._property_changed('restrict_internal_derived_data')\n        self.__restrict_internal_derived_data = value        \n\n    @property\n    def adv5_day_pct(self) -> float:\n        """Median number of shares or units of a given asset traded over a 5 day period."""\n        return self.__adv5_day_pct\n\n    @adv5_day_pct.setter\n    def adv5_day_pct(self, value: float):\n        self._property_changed('adv5_day_pct')\n        self.__adv5_day_pct = value        \n\n    @property\n    def midpoint_fills_percentage(self) -> float:\n        """Percent of total order filled at midpoint ."""\n        return self.__midpoint_fills_percentage\n\n    @midpoint_fills_percentage.setter\n    def midpoint_fills_percentage(self, value: float):\n        self._property_changed('midpoint_fills_percentage')\n        self.__midpoint_fills_percentage = value        \n\n    @property\n    def open_interest(self) -> float:\n        """Measure of level of activity in market."""\n        return self.__open_interest\n\n    @open_interest.setter\n    def open_interest(self, value: float):\n        self._property_changed('open_interest')\n        self.__open_interest = value        \n\n    @property\n    def turnover_composite_unadjusted(self) -> float:\n        """Turnover composite not adjusted for corporate actions."""\n        return self.__turnover_composite_unadjusted\n\n    @turnover_composite_unadjusted.setter\n    def turnover_composite_unadjusted(self, value: float):\n        self._property_changed('turnover_composite_unadjusted')\n        self.__turnover_composite_unadjusted = value        \n\n    @property\n    def fwd_points(self) -> float:\n        """Forward points."""\n        return self.__fwd_points\n\n    @fwd_points.setter\n    def fwd_points(self, value: float):\n        self._property_changed('fwd_points')\n        self.__fwd_points = value        \n\n    @property\n    def relative_return_wtd(self) -> float:\n        """Relative Return Week to Date."""\n        return self.__relative_return_wtd\n\n    @relative_return_wtd.setter\n    def relative_return_wtd(self, value: float):\n        self._property_changed('relative_return_wtd')\n        self.__relative_return_wtd = value        \n\n    @property\n    def units(self) -> str:\n        """Units for series."""\n        return self.__units\n\n    @units.setter\n    def units(self, value: str):\n        self._property_changed('units')\n        self.__units = value        \n\n    @property\n    def payer_rate_option(self) -> str:\n        """The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE."""\n        return self.__payer_rate_option\n\n    @payer_rate_option.setter\n    def payer_rate_option(self, value: str):\n        self._property_changed('payer_rate_option')\n        self.__payer_rate_option = value        \n\n    @property\n    def asset_classifications_risk_country_name(self) -> str:\n        """Risk country."""\n        return self.__asset_classifications_risk_country_name\n\n    @asset_classifications_risk_country_name.setter\n    def asset_classifications_risk_country_name(self, value: str):\n        self._property_changed('asset_classifications_risk_country_name')\n        self.__asset_classifications_risk_country_name = value        \n\n    @property\n    def ext_mkt_point3(self) -> str:\n        """Third dimension of external MDAPI Point."""\n        return self.__ext_mkt_point3\n\n    @ext_mkt_point3.setter\n    def ext_mkt_point3(self, value: str):\n        self._property_changed('ext_mkt_point3')\n        self.__ext_mkt_point3 = value        \n\n    @property\n    def matched_maturity_swap_spread(self) -> float:\n        """Spread between a Matched Maturity Swap Rate and the yield of the bond."""\n        return self.__matched_maturity_swap_spread\n\n    @matched_maturity_swap_spread.setter\n    def matched_maturity_swap_spread(self, value: float):\n        self._property_changed('matched_maturity_swap_spread')\n        self.__matched_maturity_swap_spread = value        \n\n    @property\n    def city_name(self) -> str:\n        """Name of city."""\n        return self.__city_name\n\n    @city_name.setter\n    def city_name(self, value: str):\n        self._property_changed('city_name')\n        self.__city_name = value        \n\n    @property\n    def hourly_bucket(self) -> str:\n        """Bucket denoting hour of the day."""\n        return self.__hourly_bucket\n\n    @hourly_bucket.setter\n    def hourly_bucket(self, value: str):\n        self._property_changed('hourly_bucket')\n        self.__hourly_bucket = value        \n\n    @property\n    def average_implied_volatility(self) -> float:\n        """Average volatility of an asset implied by observations of market prices."""\n        return self.__average_implied_volatility\n\n    @average_implied_volatility.setter\n    def average_implied_volatility(self, value: float):\n        self._property_changed('average_implied_volatility')\n        self.__average_implied_volatility = value        \n\n    @property\n    def total_hospitalized_with_symptoms(self) -> float:\n        """Total number of hospitalized cases due to symptoms, but not in ICU."""\n        return self.__total_hospitalized_with_symptoms\n\n    @total_hospitalized_with_symptoms.setter\n    def total_hospitalized_with_symptoms(self, value: float):\n        self._property_changed('total_hospitalized_with_symptoms')\n        self.__total_hospitalized_with_symptoms = value        \n\n    @property\n    def days_open_realized_cash(self) -> float:\n        """Realised performance vs Day Open, in USD."""\n        return self.__days_open_realized_cash\n\n    @days_open_realized_cash.setter\n    def days_open_realized_cash(self, value: float):\n        self._property_changed('days_open_realized_cash')\n        self.__days_open_realized_cash = value        \n\n    @property\n    def adjusted_high_price(self) -> float:\n        """Adjusted high level of an asset based on official exchange fixing or calculation\n           agent marked level."""\n        return self.__adjusted_high_price\n\n    @adjusted_high_price.setter\n    def adjusted_high_price(self, value: float):\n        self._property_changed('adjusted_high_price')\n        self.__adjusted_high_price = value        \n\n    @property\n    def proceeds_asset_ois_swap_spread(self) -> float:\n        """Proceeds asset OIS swap spread."""\n        return self.__proceeds_asset_ois_swap_spread\n\n    @proceeds_asset_ois_swap_spread.setter\n    def proceeds_asset_ois_swap_spread(self, value: float):\n        self._property_changed('proceeds_asset_ois_swap_spread')\n        self.__proceeds_asset_ois_swap_spread = value        \n\n    @property\n    def ext_mkt_point1(self) -> str:\n        """First dimension of external MDAPI Point."""\n        return self.__ext_mkt_point1\n\n    @ext_mkt_point1.setter\n    def ext_mkt_point1(self, value: str):\n        self._property_changed('ext_mkt_point1')\n        self.__ext_mkt_point1 = value        \n\n    @property\n    def direction(self) -> str:\n        """Indicates whether exposure of a given position is long or short."""\n        return self.__direction\n\n    @direction.setter\n    def direction(self, value: str):\n        self._property_changed('direction')\n        self.__direction = value        \n\n    @property\n    def ext_mkt_point2(self) -> str:\n        """Second dimension of external MDAPI Point."""\n        return self.__ext_mkt_point2\n\n    @ext_mkt_point2.setter\n    def ext_mkt_point2(self, value: str):\n        self._property_changed('ext_mkt_point2')\n        self.__ext_mkt_point2 = value        \n\n    @property\n    def sub_region_code(self) -> str:\n        """ISO 3166 Sub Region Code."""\n        return self.__sub_region_code\n\n    @sub_region_code.setter\n    def sub_region_code(self, value: str):\n        self._property_changed('sub_region_code')\n        self.__sub_region_code = value        \n\n    @property\n    def asset_parameters_fixed_rate(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__asset_parameters_fixed_rate\n\n    @asset_parameters_fixed_rate.setter\n    def asset_parameters_fixed_rate(self, value: Union[float, str]):\n        self._property_changed('asset_parameters_fixed_rate')\n        self.__asset_parameters_fixed_rate = value        \n\n    @property\n    def is_estimated_return(self) -> bool:\n        """Whether the return of asset over a given period is an estimate or not."""\n        return self.__is_estimated_return\n\n    @is_estimated_return.setter\n    def is_estimated_return(self, value: bool):\n        self._property_changed('is_estimated_return')\n        self.__is_estimated_return = value        \n\n    @property\n    def value_forecast(self) -> str:\n        """Average forecast among a representative group of economists."""\n        return self.__value_forecast\n\n    @value_forecast.setter\n    def value_forecast(self, value: str):\n        self._property_changed('value_forecast')\n        self.__value_forecast = value        \n\n    @property\n    def total_icu(self) -> float:\n        """Total number of cases in intensive care."""\n        return self.__total_icu\n\n    @total_icu.setter\n    def total_icu(self, value: float):\n        self._property_changed('total_icu')\n        self.__total_icu = value        \n\n    @property\n    def position_source_type(self) -> str:\n        """Source object for position data"""\n        return self.__position_source_type\n\n    @position_source_type.setter\n    def position_source_type(self, value: str):\n        self._property_changed('position_source_type')\n        self.__position_source_type = value        \n\n    @property\n    def previous_close_unrealized_cash(self) -> float:\n        """Unrealised performance vs Previous Close, in USD."""\n        return self.__previous_close_unrealized_cash\n\n    @previous_close_unrealized_cash.setter\n    def previous_close_unrealized_cash(self, value: float):\n        self._property_changed('previous_close_unrealized_cash')\n        self.__previous_close_unrealized_cash = value        \n\n    @property\n    def minimum_denomination(self) -> float:\n        """The lowest denomination of an issue that can be purchased as authorized by the\n           bond documents."""\n        return self.__minimum_denomination\n\n    @minimum_denomination.setter\n    def minimum_denomination(self, value: float):\n        self._property_changed('minimum_denomination')\n        self.__minimum_denomination = value        \n\n    @property\n    def future_value_notional(self) -> float:\n        """The future notional value of the asset."""\n        return self.__future_value_notional\n\n    @future_value_notional.setter\n    def future_value_notional(self, value: float):\n        self._property_changed('future_value_notional')\n        self.__future_value_notional = value        \n\n    @property\n    def participation_rate(self) -> float:\n        """Executed quantity over market volume (e.g. 5, 10, 20)."""\n        return self.__participation_rate\n\n    @participation_rate.setter\n    def participation_rate(self, value: float):\n        self._property_changed('participation_rate')\n        self.__participation_rate = value        \n\n    @property\n    def obfr(self) -> float:\n        """The overnight bank funding rate."""\n        return self.__obfr\n\n    @obfr.setter\n    def obfr(self, value: float):\n        self._property_changed('obfr')\n        self.__obfr = value        \n\n    @property\n    def buy9point5bps(self) -> float:\n        """The amount GS would buy for 9.5 bps charge."""\n        return self.__buy9point5bps\n\n    @buy9point5bps.setter\n    def buy9point5bps(self, value: float):\n        self._property_changed('buy9point5bps')\n        self.__buy9point5bps = value        \n\n    @property\n    def option_lock_period(self) -> str:\n        """An indication of the first allowable exercise date of the option."""\n        return self.__option_lock_period\n\n    @option_lock_period.setter\n    def option_lock_period(self, value: str):\n        self._property_changed('option_lock_period')\n        self.__option_lock_period = value        \n\n    @property\n    def es_momentum_percentile(self) -> float:\n        """A percentile that captures a company's E&S momentum ranking within its\n           subsector."""\n        return self.__es_momentum_percentile\n\n    @es_momentum_percentile.setter\n    def es_momentum_percentile(self, value: float):\n        self._property_changed('es_momentum_percentile')\n        self.__es_momentum_percentile = value        \n\n    @property\n    def adv_percentage(self) -> float:\n        """Percentage of ADV."""\n        return self.__adv_percentage\n\n    @adv_percentage.setter\n    def adv_percentage(self, value: float):\n        self._property_changed('adv_percentage')\n        self.__adv_percentage = value        \n\n    @property\n    def leg1_averaging_method(self) -> str:\n        """Averaging method of leg."""\n        return self.__leg1_averaging_method\n\n    @leg1_averaging_method.setter\n    def leg1_averaging_method(self, value: str):\n        self._property_changed('leg1_averaging_method')\n        self.__leg1_averaging_method = value        \n\n    @property\n    def turnover_composite(self) -> float:\n        """Turnover composite."""\n        return self.__turnover_composite\n\n    @turnover_composite.setter\n    def turnover_composite(self, value: float):\n        self._property_changed('turnover_composite')\n        self.__turnover_composite = value        \n\n    @property\n    def forecast_date(self) -> datetime.date:\n        """Date of forecasted electricity loads."""\n        return self.__forecast_date\n\n    @forecast_date.setter\n    def forecast_date(self, value: datetime.date):\n        self._property_changed('forecast_date')\n        self.__forecast_date = value        \n\n    @property\n    def internal_index_calc_region(self) -> str:\n        """Classification for the asset based on index calculation region"""\n        return self.__internal_index_calc_region\n\n    @internal_index_calc_region.setter\n    def internal_index_calc_region(self, value: str):\n        self._property_changed('internal_index_calc_region')\n        self.__internal_index_calc_region = value        \n\n    @property\n    def position_type(self) -> str:\n        """Type of positions."""\n        return self.__position_type\n\n    @position_type.setter\n    def position_type(self, value: str):\n        self._property_changed('position_type')\n        self.__position_type = value        \n\n    @property\n    def sub_asset_class(self) -> str:\n        """An indication of the sub asset class."""\n        return self.__sub_asset_class\n\n    @sub_asset_class.setter\n    def sub_asset_class(self, value: str):\n        self._property_changed('sub_asset_class')\n        self.__sub_asset_class = value        \n\n    @property\n    def short_interest(self) -> float:\n        """Short interest value."""\n        return self.__short_interest\n\n    @short_interest.setter\n    def short_interest(self, value: float):\n        self._property_changed('short_interest')\n        self.__short_interest = value        \n\n    @property\n    def reference_period(self) -> str:\n        """The period for which released data refers to."""\n        return self.__reference_period\n\n    @reference_period.setter\n    def reference_period(self, value: str):\n        self._property_changed('reference_period')\n        self.__reference_period = value        \n\n    @property\n    def adjusted_volume(self) -> float:\n        """Accumulated number of shares, lots or contracts traded according to the market\n           convention adjusted for corporate actions."""\n        return self.__adjusted_volume\n\n    @adjusted_volume.setter\n    def adjusted_volume(self, value: float):\n        self._property_changed('adjusted_volume')\n        self.__adjusted_volume = value        \n\n    @property\n    def ctd_fwd_yield(self) -> float:\n        """Cheapest to deliver bond fwd yield."""\n        return self.__ctd_fwd_yield\n\n    @ctd_fwd_yield.setter\n    def ctd_fwd_yield(self, value: float):\n        self._property_changed('ctd_fwd_yield')\n        self.__ctd_fwd_yield = value        \n\n    @property\n    def sec_db(self) -> str:\n        """Internal Goldman Sachs security database location for the asset."""\n        return self.__sec_db\n\n    @sec_db.setter\n    def sec_db(self, value: str):\n        self._property_changed('sec_db')\n        self.__sec_db = value        \n\n    @property\n    def memory_used(self) -> float:\n        """Total memory used by tickserver."""\n        return self.__memory_used\n\n    @memory_used.setter\n    def memory_used(self, value: float):\n        self._property_changed('memory_used')\n        self.__memory_used = value        \n\n    @property\n    def bpe_quality_stars(self) -> float:\n        """Confidence in the BPE."""\n        return self.__bpe_quality_stars\n\n    @bpe_quality_stars.setter\n    def bpe_quality_stars(self, value: float):\n        self._property_changed('bpe_quality_stars')\n        self.__bpe_quality_stars = value        \n\n    @property\n    def ctd(self) -> str:\n        """Description of cheapest to deliver bond."""\n        return self.__ctd\n\n    @ctd.setter\n    def ctd(self, value: str):\n        self._property_changed('ctd')\n        self.__ctd = value        \n\n    @property\n    def intended_participation_rate(self) -> float:\n        """Intended participation rate."""\n        return self.__intended_participation_rate\n\n    @intended_participation_rate.setter\n    def intended_participation_rate(self, value: float):\n        self._property_changed('intended_participation_rate')\n        self.__intended_participation_rate = value        \n\n    @property\n    def leg1_payment_type(self) -> str:\n        """Type of payment stream on leg 1."""\n        return self.__leg1_payment_type\n\n    @leg1_payment_type.setter\n    def leg1_payment_type(self, value: str):\n        self._property_changed('leg1_payment_type')\n        self.__leg1_payment_type = value        \n\n    @property\n    def trading_pnl(self) -> float:\n        """Trading Profit and Loss (PNL)."""\n        return self.__trading_pnl\n\n    @trading_pnl.setter\n    def trading_pnl(self, value: float):\n        self._property_changed('trading_pnl')\n        self.__trading_pnl = value        \n\n    @property\n    def collateral_value_required(self) -> float:\n        """Value of collateral required to cover a given position."""\n        return self.__collateral_value_required\n\n    @collateral_value_required.setter\n    def collateral_value_required(self, value: float):\n        self._property_changed('collateral_value_required')\n        self.__collateral_value_required = value        \n\n    @property\n    def buy45bps(self) -> float:\n        """The amount GS would buy for 45 bps charge."""\n        return self.__buy45bps\n\n    @buy45bps.setter\n    def buy45bps(self, value: float):\n        self._property_changed('buy45bps')\n        self.__buy45bps = value        \n\n    @property\n    def price_to_earnings_positive(self) -> float:\n        """Price to earnings positive."""\n        return self.__price_to_earnings_positive\n\n    @price_to_earnings_positive.setter\n    def price_to_earnings_positive(self, value: float):\n        self._property_changed('price_to_earnings_positive')\n        self.__price_to_earnings_positive = value        \n\n    @property\n    def forecast(self) -> float:\n        """Forward FX forecast."""\n        return self.__forecast\n\n    @forecast.setter\n    def forecast(self, value: float):\n        self._property_changed('forecast')\n        self.__forecast = value        \n\n    @property\n    def forecast_value(self) -> float:\n        """Forecast value."""\n        return self.__forecast_value\n\n    @forecast_value.setter\n    def forecast_value(self, value: float):\n        self._property_changed('forecast_value')\n        self.__forecast_value = value        \n\n    @property\n    def pnl(self) -> float:\n        """Profit and Loss."""\n        return self.__pnl\n\n    @pnl.setter\n    def pnl(self, value: float):\n        self._property_changed('pnl')\n        self.__pnl = value        \n\n    @property\n    def volume_in_limit(self) -> float:\n        """Consolidated Volume In Limit."""\n        return self.__volume_in_limit\n\n    @volume_in_limit.setter\n    def volume_in_limit(self, value: float):\n        self._property_changed('volume_in_limit')\n        self.__volume_in_limit = value        \n\n    @property\n    def is_territory(self) -> bool:\n        """Whether or not a territory."""\n        return self.__is_territory\n\n    @is_territory.setter\n    def is_territory(self, value: bool):\n        self._property_changed('is_territory')\n        self.__is_territory = value        \n\n    @property\n    def leg2_delivery_point(self) -> str:\n        """Delivery point of leg."""\n        return self.__leg2_delivery_point\n\n    @leg2_delivery_point.setter\n    def leg2_delivery_point(self, value: str):\n        self._property_changed('leg2_delivery_point')\n        self.__leg2_delivery_point = value        \n\n    @property\n    def tcm_cost_horizon4_day(self) -> float:\n        """TCM cost with a 4 day time horizon."""\n        return self.__tcm_cost_horizon4_day\n\n    @tcm_cost_horizon4_day.setter\n    def tcm_cost_horizon4_day(self, value: float):\n        self._property_changed('tcm_cost_horizon4_day')\n        self.__tcm_cost_horizon4_day = value        \n\n    @property\n    def styles(self) -> Tuple[Tuple[str, ...], ...]:\n        """Styles or themes associated with the asset (max 50)"""\n        return self.__styles\n\n    @styles.setter\n    def styles(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('styles')\n        self.__styles = value        \n\n    @property\n    def short_name(self) -> str:\n        """Short name."""\n        return self.__short_name\n\n    @short_name.setter\n    def short_name(self, value: str):\n        self._property_changed('short_name')\n        self.__short_name = value        \n\n    @property\n    def reset_frequency1(self) -> str:\n        """An integer multiplier of a period describing how often the parties to an SB swap\n           transaction shall evaluate and, when applicable, change the price\n           used for the underlying assets of the swap transaction. Such reset\n           frequency may be described as one letter preceded by an integer."""\n        return self.__reset_frequency1\n\n    @reset_frequency1.setter\n    def reset_frequency1(self, value: str):\n        self._property_changed('reset_frequency1')\n        self.__reset_frequency1 = value        \n\n    @property\n    def buy4bps(self) -> float:\n        """The amount GS would buy for 4 bps charge."""\n        return self.__buy4bps\n\n    @buy4bps.setter\n    def buy4bps(self, value: float):\n        self._property_changed('buy4bps')\n        self.__buy4bps = value        \n\n    @property\n    def reset_frequency2(self) -> str:\n        """Same as Reset Frequency 1."""\n        return self.__reset_frequency2\n\n    @reset_frequency2.setter\n    def reset_frequency2(self, value: str):\n        self._property_changed('reset_frequency2')\n        self.__reset_frequency2 = value        \n\n    @property\n    def other_price_term(self) -> str:\n        """An indication that the publicly reportable SB swap transaction has one or more\n           additional term(s) or provision(s), other than those listed in the\n           required real-time data fields, that materially affect(s) the price\n           of the swap transaction."""\n        return self.__other_price_term\n\n    @other_price_term.setter\n    def other_price_term(self, value: str):\n        self._property_changed('other_price_term')\n        self.__other_price_term = value        \n\n    @property\n    def bid_gspread(self) -> float:\n        """Bid G spread."""\n        return self.__bid_gspread\n\n    @bid_gspread.setter\n    def bid_gspread(self, value: float):\n        self._property_changed('bid_gspread')\n        self.__bid_gspread = value        \n\n    @property\n    def open_price(self) -> float:\n        """Opening level of an asset based on official exchange fixing or calculation agent\n           marked level."""\n        return self.__open_price\n\n    @open_price.setter\n    def open_price(self, value: float):\n        self._property_changed('open_price')\n        self.__open_price = value        \n\n    @property\n    def ps_id(self) -> str:\n        """Platts Symbol."""\n        return self.__ps_id\n\n    @ps_id.setter\n    def ps_id(self, value: str):\n        self._property_changed('ps_id')\n        self.__ps_id = value        \n\n    @property\n    def hit_rate_mtd(self) -> float:\n        """Hit Rate Ratio Month to Date."""\n        return self.__hit_rate_mtd\n\n    @hit_rate_mtd.setter\n    def hit_rate_mtd(self, value: float):\n        self._property_changed('hit_rate_mtd')\n        self.__hit_rate_mtd = value        \n\n    @property\n    def fair_volatility(self) -> float:\n        """Strike in volatility terms, calculated as square root of fair variance."""\n        return self.__fair_volatility\n\n    @fair_volatility.setter\n    def fair_volatility(self, value: float):\n        self._property_changed('fair_volatility')\n        self.__fair_volatility = value        \n\n    @property\n    def dollar_cross(self) -> str:\n        """USD cross symbol for a particular currency."""\n        return self.__dollar_cross\n\n    @dollar_cross.setter\n    def dollar_cross(self, value: str):\n        self._property_changed('dollar_cross')\n        self.__dollar_cross = value        \n\n    @property\n    def portfolio_type(self) -> str:\n        """Portfolio type differentiates the portfolio categorization"""\n        return self.__portfolio_type\n\n    @portfolio_type.setter\n    def portfolio_type(self, value: str):\n        self._property_changed('portfolio_type')\n        self.__portfolio_type = value        \n\n    @property\n    def currency(self) -> str:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: str):\n        self._property_changed('currency')\n        self.__currency = value        \n\n    @property\n    def cluster_class(self) -> str:\n        """The Cluster the stock belongs to on the particular date. The cluster class will\n           be assigned to a value between 1 and 13 (inclusive)."""\n        return self.__cluster_class\n\n    @cluster_class.setter\n    def cluster_class(self, value: str):\n        self._property_changed('cluster_class')\n        self.__cluster_class = value        \n\n    @property\n    def sell50bps(self) -> float:\n        """The amount GS would sell for 50 bps charge."""\n        return self.__sell50bps\n\n    @sell50bps.setter\n    def sell50bps(self, value: float):\n        self._property_changed('sell50bps')\n        self.__sell50bps = value        \n\n    @property\n    def future_month_m21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_m21\n\n    @future_month_m21.setter\n    def future_month_m21(self, value: float):\n        self._property_changed('future_month_m21')\n        self.__future_month_m21 = value        \n\n    @property\n    def bid_size(self) -> float:\n        """The number of shares, lots, or contracts willing to buy at the Bid price."""\n        return self.__bid_size\n\n    @bid_size.setter\n    def bid_size(self, value: float):\n        self._property_changed('bid_size')\n        self.__bid_size = value        \n\n    @property\n    def arrival_mid(self) -> float:\n        """Arrival Mid Price."""\n        return self.__arrival_mid\n\n    @arrival_mid.setter\n    def arrival_mid(self, value: float):\n        self._property_changed('arrival_mid')\n        self.__arrival_mid = value        \n\n    @property\n    def asset_parameters_exchange_currency(self) -> str:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__asset_parameters_exchange_currency\n\n    @asset_parameters_exchange_currency.setter\n    def asset_parameters_exchange_currency(self, value: str):\n        self._property_changed('asset_parameters_exchange_currency')\n        self.__asset_parameters_exchange_currency = value        \n\n    @property\n    def candidate_name(self) -> str:\n        """Name of candidate in election."""\n        return self.__candidate_name\n\n    @candidate_name.setter\n    def candidate_name(self, value: str):\n        self._property_changed('candidate_name')\n        self.__candidate_name = value        \n\n    @property\n    def implied_lognormal_volatility(self) -> float:\n        """Market implied volatility measured using a lognormal model in percent/year."""\n        return self.__implied_lognormal_volatility\n\n    @implied_lognormal_volatility.setter\n    def implied_lognormal_volatility(self, value: float):\n        self._property_changed('implied_lognormal_volatility')\n        self.__implied_lognormal_volatility = value        \n\n    @property\n    def vwap_in_limit_unrealized_cash(self) -> float:\n        """Consolidated unrealised performance vs VWAP In Limit, in USD."""\n        return self.__vwap_in_limit_unrealized_cash\n\n    @vwap_in_limit_unrealized_cash.setter\n    def vwap_in_limit_unrealized_cash(self, value: float):\n        self._property_changed('vwap_in_limit_unrealized_cash')\n        self.__vwap_in_limit_unrealized_cash = value        \n\n    @property\n    def rating_moodys(self) -> str:\n        """Bond rating from Moody's."""\n        return self.__rating_moodys\n\n    @rating_moodys.setter\n    def rating_moodys(self, value: str):\n        self._property_changed('rating_moodys')\n        self.__rating_moodys = value        \n\n    @property\n    def future_month_m26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_m26\n\n    @future_month_m26.setter\n    def future_month_m26(self, value: float):\n        self._property_changed('future_month_m26')\n        self.__future_month_m26 = value        \n\n    @property\n    def future_month_m25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_m25\n\n    @future_month_m25.setter\n    def future_month_m25(self, value: float):\n        self._property_changed('future_month_m25')\n        self.__future_month_m25 = value        \n\n    @property\n    def future_month_m24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_m24\n\n    @future_month_m24.setter\n    def future_month_m24(self, value: float):\n        self._property_changed('future_month_m24')\n        self.__future_month_m24 = value        \n\n    @property\n    def future_month_m23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_m23\n\n    @future_month_m23.setter\n    def future_month_m23(self, value: float):\n        self._property_changed('future_month_m23')\n        self.__future_month_m23 = value        \n\n    @property\n    def future_month_m22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_m22\n\n    @future_month_m22.setter\n    def future_month_m22(self, value: float):\n        self._property_changed('future_month_m22')\n        self.__future_month_m22 = value        \n\n    @property\n    def flow_pct(self) -> float:\n        """Percent of flow of the fund."""\n        return self.__flow_pct\n\n    @flow_pct.setter\n    def flow_pct(self, value: float):\n        self._property_changed('flow_pct')\n        self.__flow_pct = value        \n\n    @property\n    def source(self) -> str:\n        """Source of data."""\n        return self.__source\n\n    @source.setter\n    def source(self, value: str):\n        self._property_changed('source')\n        self.__source = value        \n\n    @property\n    def asset_classifications_country_code(self) -> str:\n        """Country code (ISO 3166)."""\n        return self.__asset_classifications_country_code\n\n    @asset_classifications_country_code.setter\n    def asset_classifications_country_code(self, value: str):\n        self._property_changed('asset_classifications_country_code')\n        self.__asset_classifications_country_code = value        \n\n    @property\n    def settle_drop(self) -> float:\n        """Latest Bid Price (price willing to buy)."""\n        return self.__settle_drop\n\n    @settle_drop.setter\n    def settle_drop(self, value: float):\n        self._property_changed('settle_drop')\n        self.__settle_drop = value        \n\n    @property\n    def data_set_sub_category(self) -> str:\n        """Second level grouping of dataset."""\n        return self.__data_set_sub_category\n\n    @data_set_sub_category.setter\n    def data_set_sub_category(self, value: str):\n        self._property_changed('data_set_sub_category')\n        self.__data_set_sub_category = value        \n\n    @property\n    def sell9point5bps(self) -> float:\n        """The amount GS would sell for 9.5 bps charge."""\n        return self.__sell9point5bps\n\n    @sell9point5bps.setter\n    def sell9point5bps(self, value: float):\n        self._property_changed('sell9point5bps')\n        self.__sell9point5bps = value        \n\n    @property\n    def quantity_bucket(self) -> str:\n        """Range of pricing hours."""\n        return self.__quantity_bucket\n\n    @quantity_bucket.setter\n    def quantity_bucket(self, value: str):\n        self._property_changed('quantity_bucket')\n        self.__quantity_bucket = value        \n\n    @property\n    def option_style_sdr(self) -> str:\n        """Style of the option."""\n        return self.__option_style_sdr\n\n    @option_style_sdr.setter\n    def option_style_sdr(self, value: str):\n        self._property_changed('option_style_sdr')\n        self.__option_style_sdr = value        \n\n    @property\n    def oe_name(self) -> str:\n        """Name of user's organization."""\n        return self.__oe_name\n\n    @oe_name.setter\n    def oe_name(self, value: str):\n        self._property_changed('oe_name')\n        self.__oe_name = value        \n\n    @property\n    def given(self) -> float:\n        """Number of trades given."""\n        return self.__given\n\n    @given.setter\n    def given(self, value: float):\n        self._property_changed('given')\n        self.__given = value        \n\n    @property\n    def leg2_day_count_convention(self) -> str:\n        """The determination of how interest accrues over time for the SB swap."""\n        return self.__leg2_day_count_convention\n\n    @leg2_day_count_convention.setter\n    def leg2_day_count_convention(self, value: str):\n        self._property_changed('leg2_day_count_convention')\n        self.__leg2_day_count_convention = value        \n\n    @property\n    def liquidity_score_sell(self) -> float:\n        """The liquidity score assigned to selling the bond."""\n        return self.__liquidity_score_sell\n\n    @liquidity_score_sell.setter\n    def liquidity_score_sell(self, value: float):\n        self._property_changed('liquidity_score_sell')\n        self.__liquidity_score_sell = value        \n\n    @property\n    def delisting_date(self) -> str:\n        """Date at which the entity is delisted."""\n        return self.__delisting_date\n\n    @delisting_date.setter\n    def delisting_date(self, value: str):\n        self._property_changed('delisting_date')\n        self.__delisting_date = value        \n\n    @property\n    def weight(self) -> float:\n        """Weight of a given position within a portfolio, by default calcualted as\n           netWeight."""\n        return self.__weight\n\n    @weight.setter\n    def weight(self, value: float):\n        self._property_changed('weight')\n        self.__weight = value        \n\n    @property\n    def accrued_interest(self) -> float:\n        """The accrued interest paid on the bond."""\n        return self.__accrued_interest\n\n    @accrued_interest.setter\n    def accrued_interest(self, value: float):\n        self._property_changed('accrued_interest')\n        self.__accrued_interest = value        \n\n    @property\n    def business_scope(self) -> str:\n        """Business/Product Scope."""\n        return self.__business_scope\n\n    @business_scope.setter\n    def business_scope(self, value: str):\n        self._property_changed('business_scope')\n        self.__business_scope = value        \n\n    @property\n    def wtd_degree_days(self) -> float:\n        """The actual/forecast value for weighted degree days."""\n        return self.__wtd_degree_days\n\n    @wtd_degree_days.setter\n    def wtd_degree_days(self, value: float):\n        self._property_changed('wtd_degree_days')\n        self.__wtd_degree_days = value        \n\n    @property\n    def absolute_weight(self) -> float:\n        """Weight in terms of absolute notional."""\n        return self.__absolute_weight\n\n    @absolute_weight.setter\n    def absolute_weight(self, value: float):\n        self._property_changed('absolute_weight')\n        self.__absolute_weight = value        \n\n    @property\n    def measure(self) -> str:\n        """A calculated metric in the risk scenario."""\n        return self.__measure\n\n    @measure.setter\n    def measure(self, value: str):\n        self._property_changed('measure')\n        self.__measure = value        \n\n    @property\n    def temperature_hourly_forecast(self) -> float:\n        """The hourly forecast value for temperature in Fahrenheit."""\n        return self.__temperature_hourly_forecast\n\n    @temperature_hourly_forecast.setter\n    def temperature_hourly_forecast(self, value: float):\n        self._property_changed('temperature_hourly_forecast')\n        self.__temperature_hourly_forecast = value        \n\n    @property\n    def iceberg_tip_rate_type(self) -> str:\n        """The unit associated with an Iceberg tip rate."""\n        return self.__iceberg_tip_rate_type\n\n    @iceberg_tip_rate_type.setter\n    def iceberg_tip_rate_type(self, value: str):\n        self._property_changed('iceberg_tip_rate_type')\n        self.__iceberg_tip_rate_type = value        \n\n    @property\n    def sharpe_ytd(self) -> float:\n        """Sharpe ratio Year to date."""\n        return self.__sharpe_ytd\n\n    @sharpe_ytd.setter\n    def sharpe_ytd(self, value: float):\n        self._property_changed('sharpe_ytd')\n        self.__sharpe_ytd = value        \n\n    @property\n    def wind_speed_forecast(self) -> float:\n        """The forecast value for wind speed."""\n        return self.__wind_speed_forecast\n\n    @wind_speed_forecast.setter\n    def wind_speed_forecast(self, value: float):\n        self._property_changed('wind_speed_forecast')\n        self.__wind_speed_forecast = value        \n\n    @property\n    def gross_investment_ytd(self) -> float:\n        """Total gross investment Year to date."""\n        return self.__gross_investment_ytd\n\n    @gross_investment_ytd.setter\n    def gross_investment_ytd(self, value: float):\n        self._property_changed('gross_investment_ytd')\n        self.__gross_investment_ytd = value        \n\n    @property\n    def yield_price(self) -> float:\n        """Yield price."""\n        return self.__yield_price\n\n    @yield_price.setter\n    def yield_price(self, value: float):\n        self._property_changed('yield_price')\n        self.__yield_price = value        \n\n    @property\n    def leg1_total_notional_unit(self) -> str:\n        """Unit of reported notional price."""\n        return self.__leg1_total_notional_unit\n\n    @leg1_total_notional_unit.setter\n    def leg1_total_notional_unit(self, value: str):\n        self._property_changed('leg1_total_notional_unit')\n        self.__leg1_total_notional_unit = value        \n\n    @property\n    def issue_price(self) -> float:\n        """The price for which the instrument is issued."""\n        return self.__issue_price\n\n    @issue_price.setter\n    def issue_price(self, value: float):\n        self._property_changed('issue_price')\n        self.__issue_price = value        \n\n    @property\n    def ask_high(self) -> float:\n        """The highest Ask Price (price offering to sell)."""\n        return self.__ask_high\n\n    @ask_high.setter\n    def ask_high(self, value: float):\n        self._property_changed('ask_high')\n        self.__ask_high = value        \n\n    @property\n    def expected_data_quality(self) -> float:\n        """Expected data quality level."""\n        return self.__expected_data_quality\n\n    @expected_data_quality.setter\n    def expected_data_quality(self, value: float):\n        self._property_changed('expected_data_quality')\n        self.__expected_data_quality = value        \n\n    @property\n    def region_name(self) -> str:\n        """Name of the region for which FCI is calculated ??? Developed Markets, Emerging\n           Markets, Euro Area, Global."""\n        return self.__region_name\n\n    @region_name.setter\n    def region_name(self, value: str):\n        self._property_changed('region_name')\n        self.__region_name = value        \n\n    @property\n    def value_revised(self) -> str:\n        """Revised value."""\n        return self.__value_revised\n\n    @value_revised.setter\n    def value_revised(self, value: str):\n        self._property_changed('value_revised')\n        self.__value_revised = value        \n\n    @property\n    def discretion_upper_bound(self) -> float:\n        """TThe upper bound of the discretion band as published from the algo."""\n        return self.__discretion_upper_bound\n\n    @discretion_upper_bound.setter\n    def discretion_upper_bound(self, value: float):\n        self._property_changed('discretion_upper_bound')\n        self.__discretion_upper_bound = value        \n\n    @property\n    def adjusted_trade_price(self) -> float:\n        """Last trade price or value adjusted for corporate actions."""\n        return self.__adjusted_trade_price\n\n    @adjusted_trade_price.setter\n    def adjusted_trade_price(self, value: float):\n        self._property_changed('adjusted_trade_price')\n        self.__adjusted_trade_price = value        \n\n    @property\n    def forecast_time(self) -> datetime.datetime:\n        """Time of forecasted electricity loads (in UTC)."""\n        return self.__forecast_time\n\n    @forecast_time.setter\n    def forecast_time(self, value: datetime.datetime):\n        self._property_changed('forecast_time')\n        self.__forecast_time = value        \n\n    @property\n    def iso_subdivision_code_alpha2(self) -> str:\n        """ISO 3166-2 code for identifying the principal subdivisions of all countries\n           coded in ISO 3166-1 (up to three alphanumeric characters)."""\n        return self.__iso_subdivision_code_alpha2\n\n    @iso_subdivision_code_alpha2.setter\n    def iso_subdivision_code_alpha2(self, value: str):\n        self._property_changed('iso_subdivision_code_alpha2')\n        self.__iso_subdivision_code_alpha2 = value        \n\n    @property\n    def ctd_conversion_factor(self) -> float:\n        """Cheapest to deliver bond's conversion factor."""\n        return self.__ctd_conversion_factor\n\n    @ctd_conversion_factor.setter\n    def ctd_conversion_factor(self, value: float):\n        self._property_changed('ctd_conversion_factor')\n        self.__ctd_conversion_factor = value        \n\n    @property\n    def proceeds_asset_swap_spread(self) -> float:\n        """Proceeds asset swap spread."""\n        return self.__proceeds_asset_swap_spread\n\n    @proceeds_asset_swap_spread.setter\n    def proceeds_asset_swap_spread(self, value: float):\n        self._property_changed('proceeds_asset_swap_spread')\n        self.__proceeds_asset_swap_spread = value        \n\n    @property\n    def is_adr(self) -> bool:\n        """Is ADR or not."""\n        return self.__is_adr\n\n    @is_adr.setter\n    def is_adr(self, value: bool):\n        self._property_changed('is_adr')\n        self.__is_adr = value        \n\n    @property\n    def issue_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__issue_date\n\n    @issue_date.setter\n    def issue_date(self, value: datetime.date):\n        self._property_changed('issue_date')\n        self.__issue_date = value        \n\n    @property\n    def service_id(self) -> str:\n        """Service ID."""\n        return self.__service_id\n\n    @service_id.setter\n    def service_id(self, value: str):\n        self._property_changed('service_id')\n        self.__service_id = value        \n\n    @property\n    def yes(self) -> float:\n        """Price of yes contract."""\n        return self.__yes\n\n    @yes.setter\n    def yes(self, value: float):\n        self._property_changed('yes')\n        self.__yes = value        \n\n    @property\n    def g_score(self) -> float:\n        """A company's score for G metrics relative to the entire ESG universe."""\n        return self.__g_score\n\n    @g_score.setter\n    def g_score(self, value: float):\n        self._property_changed('g_score')\n        self.__g_score = value        \n\n    @property\n    def market_value(self) -> float:\n        """Marketable value of a given position, generally the market price for a given\n           date."""\n        return self.__market_value\n\n    @market_value.setter\n    def market_value(self, value: float):\n        self._property_changed('market_value')\n        self.__market_value = value        \n\n    @property\n    def entity_id(self) -> str:\n        """Identifies an entity uniquely; in use for Data Quality Checker."""\n        return self.__entity_id\n\n    @entity_id.setter\n    def entity_id(self, value: str):\n        self._property_changed('entity_id')\n        self.__entity_id = value        \n\n    @property\n    def notional_currency1(self) -> str:\n        """An indication of the type of currency of the notional or principal amount."""\n        return self.__notional_currency1\n\n    @notional_currency1.setter\n    def notional_currency1(self, value: str):\n        self._property_changed('notional_currency1')\n        self.__notional_currency1 = value        \n\n    @property\n    def net_debt_to_ebitda(self) -> float:\n        """Net Debt to EBITDA."""\n        return self.__net_debt_to_ebitda\n\n    @net_debt_to_ebitda.setter\n    def net_debt_to_ebitda(self, value: float):\n        self._property_changed('net_debt_to_ebitda')\n        self.__net_debt_to_ebitda = value        \n\n    @property\n    def num_units_upper(self) -> float:\n        """Units Upper."""\n        return self.__num_units_upper\n\n    @num_units_upper.setter\n    def num_units_upper(self, value: float):\n        self._property_changed('num_units_upper')\n        self.__num_units_upper = value        \n\n    @property\n    def notional_currency2(self) -> str:\n        """Same as Notional Currency 1."""\n        return self.__notional_currency2\n\n    @notional_currency2.setter\n    def notional_currency2(self, value: str):\n        self._property_changed('notional_currency2')\n        self.__notional_currency2 = value        \n\n    @property\n    def in_limit_participation_rate(self) -> float:\n        """Average in-limit participation rate of the order, including principal fills."""\n        return self.__in_limit_participation_rate\n\n    @in_limit_participation_rate.setter\n    def in_limit_participation_rate(self, value: float):\n        self._property_changed('in_limit_participation_rate')\n        self.__in_limit_participation_rate = value        \n\n    @property\n    def pressure_forecast(self) -> float:\n        """The pressure forecast of a given unit."""\n        return self.__pressure_forecast\n\n    @pressure_forecast.setter\n    def pressure_forecast(self, value: float):\n        self._property_changed('pressure_forecast')\n        self.__pressure_forecast = value        \n\n    @property\n    def paid(self) -> float:\n        """Number of trades paid."""\n        return self.__paid\n\n    @paid.setter\n    def paid(self, value: float):\n        self._property_changed('paid')\n        self.__paid = value        \n\n    @property\n    def fixed_rate(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__fixed_rate\n\n    @fixed_rate.setter\n    def fixed_rate(self, value: Union[float, str]):\n        self._property_changed('fixed_rate')\n        self.__fixed_rate = value        \n\n    @property\n    def short(self) -> float:\n        """Short exposure."""\n        return self.__short\n\n    @short.setter\n    def short(self, value: float):\n        self._property_changed('short')\n        self.__short = value        \n\n    @property\n    def time(self) -> datetime.datetime:\n        """ISO 8601 formatted date and time."""\n        return self.__time\n\n    @time.setter\n    def time(self, value: datetime.datetime):\n        self._property_changed('time')\n        self.__time = value        \n\n    @property\n    def buy4point5bps(self) -> float:\n        """The amount GS would buy for 4.5 bps charge."""\n        return self.__buy4point5bps\n\n    @buy4point5bps.setter\n    def buy4point5bps(self, value: float):\n        self._property_changed('buy4point5bps')\n        self.__buy4point5bps = value        \n\n    @property\n    def sell30cents(self) -> float:\n        """The amount GS would sell for 30 cents charge."""\n        return self.__sell30cents\n\n    @sell30cents.setter\n    def sell30cents(self, value: float):\n        self._property_changed('sell30cents')\n        self.__sell30cents = value        \n\n    @property\n    def event_end_date_time(self) -> datetime.datetime:\n        """The end time of the event if the event occurs during a time window and the event\n           has a specific end time."""\n        return self.__event_end_date_time\n\n    @event_end_date_time.setter\n    def event_end_date_time(self, value: datetime.datetime):\n        self._property_changed('event_end_date_time')\n        self.__event_end_date_time = value        \n\n    @property\n    def leg1_payment_frequency(self) -> str:\n        """An integer multiplier of a time period describing how often the parties to the\n           SB swap transaction exchange payments associated with each party???s\n           obligation. Such payment frequency may be described as one letter\n           preceded by an integer."""\n        return self.__leg1_payment_frequency\n\n    @leg1_payment_frequency.setter\n    def leg1_payment_frequency(self, value: str):\n        self._property_changed('leg1_payment_frequency')\n        self.__leg1_payment_frequency = value        \n\n    @property\n    def cm_id(self) -> str:\n        """Prime Client Master Party Id."""\n        return self.__cm_id\n\n    @cm_id.setter\n    def cm_id(self, value: str):\n        self._property_changed('cm_id')\n        self.__cm_id = value        \n\n    @property\n    def taxonomy(self) -> str:\n        """An indication of the product taxonomy."""\n        return self.__taxonomy\n\n    @taxonomy.setter\n    def taxonomy(self, value: str):\n        self._property_changed('taxonomy')\n        self.__taxonomy = value        \n\n    @property\n    def buy45cents(self) -> float:\n        """The amount GS would buy for 45 cents charge."""\n        return self.__buy45cents\n\n    @buy45cents.setter\n    def buy45cents(self, value: float):\n        self._property_changed('buy45cents')\n        self.__buy45cents = value        \n\n    @property\n    def measures(self) -> Tuple[str, ...]:\n        """Fields that are nullable."""\n        return self.__measures\n\n    @measures.setter\n    def measures(self, value: Tuple[str, ...]):\n        self._property_changed('measures')\n        self.__measures = value        \n\n    @property\n    def seasonal_adjustment(self) -> str:\n        """Indicates if and how the forecast is seasonally adjusted."""\n        return self.__seasonal_adjustment\n\n    @seasonal_adjustment.setter\n    def seasonal_adjustment(self, value: str):\n        self._property_changed('seasonal_adjustment')\n        self.__seasonal_adjustment = value        \n\n    @property\n    def rank_wtd(self) -> float:\n        """Rank of the Contributor Week to Date."""\n        return self.__rank_wtd\n\n    @rank_wtd.setter\n    def rank_wtd(self, value: float):\n        self._property_changed('rank_wtd')\n        self.__rank_wtd = value        \n\n    @property\n    def underlyer(self) -> str:\n        """The underlyer of the security. The cross for FX forwards, for example."""\n        return self.__underlyer\n\n    @underlyer.setter\n    def underlyer(self, value: str):\n        self._property_changed('underlyer')\n        self.__underlyer = value        \n\n    @property\n    def created_time(self) -> datetime.datetime:\n        """Time created. ISO 8601 formatted string."""\n        return self.__created_time\n\n    @created_time.setter\n    def created_time(self, value: datetime.datetime):\n        self._property_changed('created_time')\n        self.__created_time = value        \n\n    @property\n    def identifier(self) -> str:\n        """Filter by any identifier of an asset like ticker, bloomberg id etc."""\n        return self.__identifier\n\n    @identifier.setter\n    def identifier(self, value: str):\n        self._property_changed('identifier')\n        self.__identifier = value        \n\n    @property\n    def price_unit(self) -> str:\n        """Unit of reported price."""\n        return self.__price_unit\n\n    @price_unit.setter\n    def price_unit(self, value: str):\n        self._property_changed('price_unit')\n        self.__price_unit = value        \n\n    @property\n    def trade_report_ref_id(self) -> str:\n        """On cancellations and corrections, this ID will hold the Dissemination ID of the\n           originally published real-time message."""\n        return self.__trade_report_ref_id\n\n    @trade_report_ref_id.setter\n    def trade_report_ref_id(self, value: str):\n        self._property_changed('trade_report_ref_id')\n        self.__trade_report_ref_id = value        \n\n    @property\n    def subdivision_id(self) -> str:\n        """Marquee subdivision object identifier."""\n        return self.__subdivision_id\n\n    @subdivision_id.setter\n    def subdivision_id(self, value: str):\n        self._property_changed('subdivision_id')\n        self.__subdivision_id = value        \n\n    @property\n    def unadjusted_low(self) -> float:\n        """Unadjusted low level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__unadjusted_low\n\n    @unadjusted_low.setter\n    def unadjusted_low(self, value: float):\n        self._property_changed('unadjusted_low')\n        self.__unadjusted_low = value        \n\n    @property\n    def buy160cents(self) -> float:\n        """The amount GS would buy for 160 cents charge."""\n        return self.__buy160cents\n\n    @buy160cents.setter\n    def buy160cents(self, value: float):\n        self._property_changed('buy160cents')\n        self.__buy160cents = value        \n\n    @property\n    def portfolio_id(self) -> str:\n        """Marquee unique identifier for a portfolio."""\n        return self.__portfolio_id\n\n    @portfolio_id.setter\n    def portfolio_id(self, value: str):\n        self._property_changed('portfolio_id')\n        self.__portfolio_id = value        \n\n    @property\n    def z_spread(self) -> float:\n        """Zero Volatility Spread to yield curve."""\n        return self.__z_spread\n\n    @z_spread.setter\n    def z_spread(self, value: float):\n        self._property_changed('z_spread')\n        self.__z_spread = value        \n\n    @property\n    def cap_floor_atm_fwd_rate(self) -> float:\n        """Cap Floor ATM forward rate."""\n        return self.__cap_floor_atm_fwd_rate\n\n    @cap_floor_atm_fwd_rate.setter\n    def cap_floor_atm_fwd_rate(self, value: float):\n        self._property_changed('cap_floor_atm_fwd_rate')\n        self.__cap_floor_atm_fwd_rate = value        \n\n    @property\n    def es_percentile(self) -> float:\n        """An aggregate percentile that captures a company's E&S ranking within its\n           subsector."""\n        return self.__es_percentile\n\n    @es_percentile.setter\n    def es_percentile(self, value: float):\n        self._property_changed('es_percentile')\n        self.__es_percentile = value        \n\n    @property\n    def tdapi(self) -> str:\n        """TDAPI Description."""\n        return self.__tdapi\n\n    @tdapi.setter\n    def tdapi(self, value: str):\n        self._property_changed('tdapi')\n        self.__tdapi = value        \n\n    @property\n    def location_code(self) -> str:\n        """The unique government-designated code for locations (of varying granularity)."""\n        return self.__location_code\n\n    @location_code.setter\n    def location_code(self, value: str):\n        self._property_changed('location_code')\n        self.__location_code = value        \n\n    @property\n    def rcic(self) -> str:\n        """Reuters composite instrument code (subject to licensing)."""\n        return self.__rcic\n\n    @rcic.setter\n    def rcic(self, value: str):\n        self._property_changed('rcic')\n        self.__rcic = value        \n\n    @property\n    def name_raw(self) -> str:\n        """Legal or published name for the asset."""\n        return self.__name_raw\n\n    @name_raw.setter\n    def name_raw(self, value: str):\n        self._property_changed('name_raw')\n        self.__name_raw = value        \n\n    @property\n    def simon_asset_tags(self) -> Tuple[str, ...]:\n        """SIMON Asset Tags."""\n        return self.__simon_asset_tags\n\n    @simon_asset_tags.setter\n    def simon_asset_tags(self, value: Tuple[str, ...]):\n        self._property_changed('simon_asset_tags')\n        self.__simon_asset_tags = value        \n\n    @property\n    def hit_rate_qtd(self) -> float:\n        """Hit Rate Ratio Quarter to Date."""\n        return self.__hit_rate_qtd\n\n    @hit_rate_qtd.setter\n    def hit_rate_qtd(self, value: float):\n        self._property_changed('hit_rate_qtd')\n        self.__hit_rate_qtd = value        \n\n    @property\n    def primary_volume_in_limit(self) -> float:\n        """Primary volume in limit as specified on the order."""\n        return self.__primary_volume_in_limit\n\n    @primary_volume_in_limit.setter\n    def primary_volume_in_limit(self, value: float):\n        self._property_changed('primary_volume_in_limit')\n        self.__primary_volume_in_limit = value        \n\n    @property\n    def precipitation_daily_forecast_percent(self) -> float:\n        """The forecast value for precipitation in Percent."""\n        return self.__precipitation_daily_forecast_percent\n\n    @precipitation_daily_forecast_percent.setter\n    def precipitation_daily_forecast_percent(self, value: float):\n        self._property_changed('precipitation_daily_forecast_percent')\n        self.__precipitation_daily_forecast_percent = value        \n\n    @property\n    def aum_end(self) -> float:\n        """Assets under management at the end of the period."""\n        return self.__aum_end\n\n    @aum_end.setter\n    def aum_end(self, value: float):\n        self._property_changed('aum_end')\n        self.__aum_end = value        \n\n    @property\n    def premium(self) -> float:\n        """An indication of the market value at the time of execution."""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: float):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def low(self) -> float:\n        """Low level of an asset based on official exchange fixing or calculation agent\n           marked level."""\n        return self.__low\n\n    @low.setter\n    def low(self, value: float):\n        self._property_changed('low')\n        self.__low = value        \n\n    @property\n    def cross_group(self) -> str:\n        """Economic cross groupings."""\n        return self.__cross_group\n\n    @cross_group.setter\n    def cross_group(self, value: str):\n        self._property_changed('cross_group')\n        self.__cross_group = value        \n\n    @property\n    def report_run_time(self) -> datetime.datetime:\n        """Time that the report was run."""\n        return self.__report_run_time\n\n    @report_run_time.setter\n    def report_run_time(self, value: datetime.datetime):\n        self._property_changed('report_run_time')\n        self.__report_run_time = value        \n\n    @property\n    def five_day_price_change_bps(self) -> float:\n        """The five day movement in price measured in basis points."""\n        return self.__five_day_price_change_bps\n\n    @five_day_price_change_bps.setter\n    def five_day_price_change_bps(self, value: float):\n        self._property_changed('five_day_price_change_bps')\n        self.__five_day_price_change_bps = value        \n\n    @property\n    def holdings(self) -> float:\n        """Number of units of a given asset held within a portfolio."""\n        return self.__holdings\n\n    @holdings.setter\n    def holdings(self, value: float):\n        self._property_changed('holdings')\n        self.__holdings = value        \n\n    @property\n    def precipitation_daily_forecast(self) -> float:\n        """The forecast value for precipitation."""\n        return self.__precipitation_daily_forecast\n\n    @precipitation_daily_forecast.setter\n    def precipitation_daily_forecast(self, value: float):\n        self._property_changed('precipitation_daily_forecast')\n        self.__precipitation_daily_forecast = value        \n\n    @property\n    def price_method(self) -> str:\n        """Method used to calculate net price."""\n        return self.__price_method\n\n    @price_method.setter\n    def price_method(self, value: str):\n        self._property_changed('price_method')\n        self.__price_method = value        \n\n    @property\n    def asset_parameters_fixed_rate_frequency(self) -> str:\n        """Tenor"""\n        return self.__asset_parameters_fixed_rate_frequency\n\n    @asset_parameters_fixed_rate_frequency.setter\n    def asset_parameters_fixed_rate_frequency(self, value: str):\n        self._property_changed('asset_parameters_fixed_rate_frequency')\n        self.__asset_parameters_fixed_rate_frequency = value        \n\n    @property\n    def ois_xccy(self) -> float:\n        """Xccy OIS Spread rate."""\n        return self.__ois_xccy\n\n    @ois_xccy.setter\n    def ois_xccy(self, value: float):\n        self._property_changed('ois_xccy')\n        self.__ois_xccy = value        \n\n    @property\n    def days_open(self) -> float:\n        """Day Open price."""\n        return self.__days_open\n\n    @days_open.setter\n    def days_open(self, value: float):\n        self._property_changed('days_open')\n        self.__days_open = value        \n\n    @property\n    def buy110cents(self) -> float:\n        """The amount GS would buy for 110 cents charge."""\n        return self.__buy110cents\n\n    @buy110cents.setter\n    def buy110cents(self, value: float):\n        self._property_changed('buy110cents')\n        self.__buy110cents = value        \n\n    @property\n    def average_spread_bps(self) -> float:\n        """Time-weighted average bid/ask spread during the life of the order."""\n        return self.__average_spread_bps\n\n    @average_spread_bps.setter\n    def average_spread_bps(self, value: float):\n        self._property_changed('average_spread_bps')\n        self.__average_spread_bps = value        \n\n    @property\n    def buy55cents(self) -> float:\n        """The amount GS would buy for 55 cents charge."""\n        return self.__buy55cents\n\n    @buy55cents.setter\n    def buy55cents(self, value: float):\n        self._property_changed('buy55cents')\n        self.__buy55cents = value        \n\n    @property\n    def future_month_q26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_q26\n\n    @future_month_q26.setter\n    def future_month_q26(self, value: float):\n        self._property_changed('future_month_q26')\n        self.__future_month_q26 = value        \n\n    @property\n    def issue_size(self) -> float:\n        """The notional issue size of the bond."""\n        return self.__issue_size\n\n    @issue_size.setter\n    def issue_size(self, value: float):\n        self._property_changed('issue_size')\n        self.__issue_size = value        \n\n    @property\n    def future_month_q25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_q25\n\n    @future_month_q25.setter\n    def future_month_q25(self, value: float):\n        self._property_changed('future_month_q25')\n        self.__future_month_q25 = value        \n\n    @property\n    def future_month_q24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_q24\n\n    @future_month_q24.setter\n    def future_month_q24(self, value: float):\n        self._property_changed('future_month_q24')\n        self.__future_month_q24 = value        \n\n    @property\n    def future_month_q23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_q23\n\n    @future_month_q23.setter\n    def future_month_q23(self, value: float):\n        self._property_changed('future_month_q23')\n        self.__future_month_q23 = value        \n\n    @property\n    def future_month_q22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_q22\n\n    @future_month_q22.setter\n    def future_month_q22(self, value: float):\n        self._property_changed('future_month_q22')\n        self.__future_month_q22 = value        \n\n    @property\n    def pending_loan_count(self) -> float:\n        """The number of pending loans that exist on a given date."""\n        return self.__pending_loan_count\n\n    @pending_loan_count.setter\n    def pending_loan_count(self, value: float):\n        self._property_changed('pending_loan_count')\n        self.__pending_loan_count = value        \n\n    @property\n    def future_month_q21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_q21\n\n    @future_month_q21.setter\n    def future_month_q21(self, value: float):\n        self._property_changed('future_month_q21')\n        self.__future_month_q21 = value        \n\n    @property\n    def price_spot_stop_loss_unit(self) -> str:\n        """Unit in which the stop loss price is reported."""\n        return self.__price_spot_stop_loss_unit\n\n    @price_spot_stop_loss_unit.setter\n    def price_spot_stop_loss_unit(self, value: str):\n        self._property_changed('price_spot_stop_loss_unit')\n        self.__price_spot_stop_loss_unit = value        \n\n    @property\n    def price_range_in_ticks_description(self) -> str:\n        """Description of the Stock's Price Range in Ticks on the particular date."""\n        return self.__price_range_in_ticks_description\n\n    @price_range_in_ticks_description.setter\n    def price_range_in_ticks_description(self, value: str):\n        self._property_changed('price_range_in_ticks_description')\n        self.__price_range_in_ticks_description = value        \n\n    @property\n    def trade_volume(self) -> float:\n        """Market trade volume."""\n        return self.__trade_volume\n\n    @trade_volume.setter\n    def trade_volume(self, value: float):\n        self._property_changed('trade_volume')\n        self.__trade_volume = value        \n\n    @property\n    def primary_country_ric(self) -> str:\n        """Reuters primary country instrument code (subject to licensing)."""\n        return self.__primary_country_ric\n\n    @primary_country_ric.setter\n    def primary_country_ric(self, value: str):\n        self._property_changed('primary_country_ric')\n        self.__primary_country_ric = value        \n\n    @property\n    def option_expiration_frequency(self) -> str:\n        """Option Expiration Frequency provided by Participant (e.g., Daily, Monthly)."""\n        return self.__option_expiration_frequency\n\n    @option_expiration_frequency.setter\n    def option_expiration_frequency(self, value: str):\n        self._property_changed('option_expiration_frequency')\n        self.__option_expiration_frequency = value        \n\n    @property\n    def is_active(self) -> bool:\n        """Whether this entry is active."""\n        return self.__is_active\n\n    @is_active.setter\n    def is_active(self, value: bool):\n        self._property_changed('is_active')\n        self.__is_active = value        \n\n    @property\n    def use_machine_learning(self) -> bool:\n        """Use Machine learning."""\n        return self.__use_machine_learning\n\n    @use_machine_learning.setter\n    def use_machine_learning(self, value: bool):\n        self._property_changed('use_machine_learning')\n        self.__use_machine_learning = value        \n\n    @property\n    def growth_score(self) -> float:\n        """Growth percentile relative to Americas coverage universe (a higher score means\n           faster growth)."""\n        return self.__growth_score\n\n    @growth_score.setter\n    def growth_score(self, value: float):\n        self._property_changed('growth_score')\n        self.__growth_score = value        \n\n    @property\n    def buffer_threshold(self) -> float:\n        """The buffer between holdings and on loan quantity for an asset."""\n        return self.__buffer_threshold\n\n    @buffer_threshold.setter\n    def buffer_threshold(self, value: float):\n        self._property_changed('buffer_threshold')\n        self.__buffer_threshold = value        \n\n    @property\n    def buy120cents(self) -> float:\n        """The amount GS would buy for 120 cents charge."""\n        return self.__buy120cents\n\n    @buy120cents.setter\n    def buy120cents(self, value: float):\n        self._property_changed('buy120cents')\n        self.__buy120cents = value        \n\n    @property\n    def matched_maturity_swap_rate(self) -> float:\n        """Matched maturity swap rate."""\n        return self.__matched_maturity_swap_rate\n\n    @matched_maturity_swap_rate.setter\n    def matched_maturity_swap_rate(self, value: float):\n        self._property_changed('matched_maturity_swap_rate')\n        self.__matched_maturity_swap_rate = value        \n\n    @property\n    def primary_vwap(self) -> float:\n        """Primary VWAP benchmark price."""\n        return self.__primary_vwap\n\n    @primary_vwap.setter\n    def primary_vwap(self, value: float):\n        self._property_changed('primary_vwap')\n        self.__primary_vwap = value        \n\n    @property\n    def exchange_type_id(self) -> str:\n        """PadMaster unique exchange type identifier."""\n        return self.__exchange_type_id\n\n    @exchange_type_id.setter\n    def exchange_type_id(self, value: str):\n        self._property_changed('exchange_type_id')\n        self.__exchange_type_id = value        \n\n    @property\n    def basis_swap_rate(self) -> float:\n        """Fixed spread or basis that equates the two floating legs of a basis swap at the\n           time of the trade."""\n        return self.__basis_swap_rate\n\n    @basis_swap_rate.setter\n    def basis_swap_rate(self, value: float):\n        self._property_changed('basis_swap_rate')\n        self.__basis_swap_rate = value        \n\n    @property\n    def exchange_code(self) -> str:\n        """EEX Exchange Code."""\n        return self.__exchange_code\n\n    @exchange_code.setter\n    def exchange_code(self, value: str):\n        self._property_changed('exchange_code')\n        self.__exchange_code = value        \n\n    @property\n    def group(self) -> str:\n        """Region or sector following the MSCI Global Industry Classification Standard\n           (GICS)."""\n        return self.__group\n\n    @group.setter\n    def group(self, value: str):\n        self._property_changed('group')\n        self.__group = value        \n\n    @property\n    def asset_parameters_termination_date(self) -> str:\n        """Relative termination date."""\n        return self.__asset_parameters_termination_date\n\n    @asset_parameters_termination_date.setter\n    def asset_parameters_termination_date(self, value: str):\n        self._property_changed('asset_parameters_termination_date')\n        self.__asset_parameters_termination_date = value        \n\n    @property\n    def estimated_spread(self) -> float:\n        """Average bid-ask quoted spread of the stock (bps) over the execution horizon (1\n           day)."""\n        return self.__estimated_spread\n\n    @estimated_spread.setter\n    def estimated_spread(self, value: float):\n        self._property_changed('estimated_spread')\n        self.__estimated_spread = value        \n\n    @property\n    def yield_change_on_day(self) -> float:\n        """Yield change on day."""\n        return self.__yield_change_on_day\n\n    @yield_change_on_day.setter\n    def yield_change_on_day(self, value: float):\n        self._property_changed('yield_change_on_day')\n        self.__yield_change_on_day = value        \n\n    @property\n    def created(self) -> datetime.datetime:\n        """Created time."""\n        return self.__created\n\n    @created.setter\n    def created(self, value: datetime.datetime):\n        self._property_changed('created')\n        self.__created = value        \n\n    @property\n    def auto_tags(self) -> Tuple[str, ...]:\n        """Metadata associated with the object"""\n        return self.__auto_tags\n\n    @auto_tags.setter\n    def auto_tags(self, value: Tuple[str, ...]):\n        self._property_changed('auto_tags')\n        self.__auto_tags = value        \n\n    @property\n    def tcm_cost(self) -> float:\n        """Pretrade computation of trading out cost."""\n        return self.__tcm_cost\n\n    @tcm_cost.setter\n    def tcm_cost(self, value: float):\n        self._property_changed('tcm_cost')\n        self.__tcm_cost = value        \n\n    @property\n    def sustain_japan(self) -> bool:\n        """True if the stock is on the SUSTAIN Japan list as of the corresponding date.\n           False if the stock is removed from the SUSTAIN Japan list on the\n           corresponding date."""\n        return self.__sustain_japan\n\n    @sustain_japan.setter\n    def sustain_japan(self, value: bool):\n        self._property_changed('sustain_japan')\n        self.__sustain_japan = value        \n\n    @property\n    def history_start_date(self) -> datetime.date:\n        """Asset history start date."""\n        return self.__history_start_date\n\n    @history_start_date.setter\n    def history_start_date(self, value: datetime.date):\n        self._property_changed('history_start_date')\n        self.__history_start_date = value        \n\n    @property\n    def bid_spread(self) -> float:\n        """Spread between the yields of a debt security and its benchmark when both are\n           purchased at bid price."""\n        return self.__bid_spread\n\n    @bid_spread.setter\n    def bid_spread(self, value: float):\n        self._property_changed('bid_spread')\n        self.__bid_spread = value        \n\n    @property\n    def percentage_complete(self) -> float:\n        """Percent Complete."""\n        return self.__percentage_complete\n\n    @percentage_complete.setter\n    def percentage_complete(self, value: float):\n        self._property_changed('percentage_complete')\n        self.__percentage_complete = value        \n\n    @property\n    def hedge_tracking_error(self) -> float:\n        """Standard deviation of the difference in the portfolio and benchmark returns over\n           time."""\n        return self.__hedge_tracking_error\n\n    @hedge_tracking_error.setter\n    def hedge_tracking_error(self, value: float):\n        self._property_changed('hedge_tracking_error')\n        self.__hedge_tracking_error = value        \n\n    @property\n    def wind_speed_type(self) -> str:\n        """The hourly or average speed of wind."""\n        return self.__wind_speed_type\n\n    @wind_speed_type.setter\n    def wind_speed_type(self, value: str):\n        self._property_changed('wind_speed_type')\n        self.__wind_speed_type = value        \n\n    @property\n    def strike_price(self) -> float:\n        """Strike price."""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: float):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def par_asset_swap_spread12m(self) -> float:\n        """Par asset swap spread vs 12m tenor."""\n        return self.__par_asset_swap_spread12m\n\n    @par_asset_swap_spread12m.setter\n    def par_asset_swap_spread12m(self, value: float):\n        self._property_changed('par_asset_swap_spread12m')\n        self.__par_asset_swap_spread12m = value        \n\n    @property\n    def trade_report_id(self) -> str:\n        """DDR generated unique and random ID for reconciliation purpose."""\n        return self.__trade_report_id\n\n    @trade_report_id.setter\n    def trade_report_id(self, value: str):\n        self._property_changed('trade_report_id')\n        self.__trade_report_id = value        \n\n    @property\n    def adjusted_open_price(self) -> float:\n        """Opening level of an asset based on official exchange fixing or calculation agent\n           marked level adjusted for corporate actions."""\n        return self.__adjusted_open_price\n\n    @adjusted_open_price.setter\n    def adjusted_open_price(self, value: float):\n        self._property_changed('adjusted_open_price')\n        self.__adjusted_open_price = value        \n\n    @property\n    def country_id(self) -> str:\n        """Marquee country object identifier."""\n        return self.__country_id\n\n    @country_id.setter\n    def country_id(self, value: str):\n        self._property_changed('country_id')\n        self.__country_id = value        \n\n    @property\n    def point(self) -> str:\n        """MDAPI point."""\n        return self.__point\n\n    @point.setter\n    def point(self, value: str):\n        self._property_changed('point')\n        self.__point = value        \n\n    @property\n    def pnl_mtd(self) -> float:\n        """Total PnL Month to date."""\n        return self.__pnl_mtd\n\n    @pnl_mtd.setter\n    def pnl_mtd(self, value: float):\n        self._property_changed('pnl_mtd')\n        self.__pnl_mtd = value        \n\n    @property\n    def total_returns(self) -> float:\n        """Total returns for backtest."""\n        return self.__total_returns\n\n    @total_returns.setter\n    def total_returns(self, value: float):\n        self._property_changed('total_returns')\n        self.__total_returns = value        \n\n    @property\n    def lender(self) -> str:\n        """Name of the lending entity on a securities lending agreement."""\n        return self.__lender\n\n    @lender.setter\n    def lender(self, value: str):\n        self._property_changed('lender')\n        self.__lender = value        \n\n    @property\n    def ann_return1_year(self) -> float:\n        """Total return representing past performance, used for GS Money Market onshore\n           funds over one year."""\n        return self.__ann_return1_year\n\n    @ann_return1_year.setter\n    def ann_return1_year(self, value: float):\n        self._property_changed('ann_return1_year')\n        self.__ann_return1_year = value        \n\n    @property\n    def ctd_fwd_dv01(self) -> float:\n        """DV01 of cheapest to deliver bond."""\n        return self.__ctd_fwd_dv01\n\n    @ctd_fwd_dv01.setter\n    def ctd_fwd_dv01(self, value: float):\n        self._property_changed('ctd_fwd_dv01')\n        self.__ctd_fwd_dv01 = value        \n\n    @property\n    def eff_yield7_day(self) -> float:\n        """Average income return over the previous 7 days reduced by any capital gains that\n           may have been included in rate calculation, assuming the rate stays\n           the same for one year and that dividends are reinvested."""\n        return self.__eff_yield7_day\n\n    @eff_yield7_day.setter\n    def eff_yield7_day(self, value: float):\n        self._property_changed('eff_yield7_day')\n        self.__eff_yield7_day = value        \n\n    @property\n    def meeting_date(self) -> str:\n        """Date the Central bank Meeting took place."""\n        return self.__meeting_date\n\n    @meeting_date.setter\n    def meeting_date(self, value: str):\n        self._property_changed('meeting_date')\n        self.__meeting_date = value        \n\n    @property\n    def calendar_spread_mispricing(self) -> float:\n        """The richness/cheapness of the calendar spread relative to Libor."""\n        return self.__calendar_spread_mispricing\n\n    @calendar_spread_mispricing.setter\n    def calendar_spread_mispricing(self, value: float):\n        self._property_changed('calendar_spread_mispricing')\n        self.__calendar_spread_mispricing = value        \n\n    @property\n    def buy140cents(self) -> float:\n        """The amount GS would buy for 140 cents charge."""\n        return self.__buy140cents\n\n    @buy140cents.setter\n    def buy140cents(self, value: float):\n        self._property_changed('buy140cents')\n        self.__buy140cents = value        \n\n    @property\n    def price_notation2_type(self) -> str:\n        """Basis points, Price, Yield, Spread, Coupon, etc., depending on the type of SB\n           swap, which is calculated at affirmation."""\n        return self.__price_notation2_type\n\n    @price_notation2_type.setter\n    def price_notation2_type(self, value: str):\n        self._property_changed('price_notation2_type')\n        self.__price_notation2_type = value        \n\n    @property\n    def fund_focus(self) -> str:\n        """Focus of the fund. For example whether it is ESG or Non-ESG."""\n        return self.__fund_focus\n\n    @fund_focus.setter\n    def fund_focus(self, value: str):\n        self._property_changed('fund_focus')\n        self.__fund_focus = value        \n\n    @property\n    def relative_strike(self) -> float:\n        """Strike relative to spot or forward level in terms of percent of either spot or\n           forward level."""\n        return self.__relative_strike\n\n    @relative_strike.setter\n    def relative_strike(self, value: float):\n        self._property_changed('relative_strike')\n        self.__relative_strike = value        \n\n    @property\n    def flagship(self) -> bool:\n        """Whether or not it is a flagship basket."""\n        return self.__flagship\n\n    @flagship.setter\n    def flagship(self, value: bool):\n        self._property_changed('flagship')\n        self.__flagship = value        \n\n    @property\n    def additional_price_notation(self) -> float:\n        """The additional price notation value includes execution events, the presence of\n           collateral, frontend payments, back-end payments, or other\n           noneconomic characteristics (e.g. counterparty credit risk) not\n           illustrated in the reporting field for pricing characteristic."""\n        return self.__additional_price_notation\n\n    @additional_price_notation.setter\n    def additional_price_notation(self, value: float):\n        self._property_changed('additional_price_notation')\n        self.__additional_price_notation = value        \n\n    @property\n    def factor_category(self) -> str:\n        """The text to display when representing a factor category. Must match the regex\n           ^[\\w -]{0,32}$."""\n        return self.__factor_category\n\n    @factor_category.setter\n    def factor_category(self, value: str):\n        self._property_changed('factor_category')\n        self.__factor_category = value        \n\n    @property\n    def equity_delta(self) -> float:\n        """Delta exposure to equity products."""\n        return self.__equity_delta\n\n    @equity_delta.setter\n    def equity_delta(self, value: float):\n        self._property_changed('equity_delta')\n        self.__equity_delta = value        \n\n    @property\n    def gross_weight(self) -> float:\n        """Sum of the absolute weight values, which equals the sum of absolute long and\n           short weights. If you have IBM stock with shortWeight 0.2 and also\n           IBM stock with longWeight 0.4, then the grossWeight would be 0.6\n           (0.2+0.4)."""\n        return self.__gross_weight\n\n    @gross_weight.setter\n    def gross_weight(self, value: float):\n        self._property_changed('gross_weight')\n        self.__gross_weight = value        \n\n    @property\n    def listed(self) -> bool:\n        """Whether the asset is listed or not."""\n        return self.__listed\n\n    @listed.setter\n    def listed(self, value: bool):\n        self._property_changed('listed')\n        self.__listed = value        \n\n    @property\n    def sell7bps(self) -> float:\n        """The amount GS would sell for 7 bps charge."""\n        return self.__sell7bps\n\n    @sell7bps.setter\n    def sell7bps(self, value: float):\n        self._property_changed('sell7bps')\n        self.__sell7bps = value        \n\n    @property\n    def earnings_record_type(self) -> str:\n        """The violation status for this particular line item."""\n        return self.__earnings_record_type\n\n    @earnings_record_type.setter\n    def earnings_record_type(self, value: str):\n        self._property_changed('earnings_record_type')\n        self.__earnings_record_type = value        \n\n    @property\n    def mean(self) -> float:\n        """Calculated average."""\n        return self.__mean\n\n    @mean.setter\n    def mean(self, value: float):\n        self._property_changed('mean')\n        self.__mean = value        \n\n    @property\n    def ask_yield(self) -> float:\n        """The return an investor realizes on a bond sold at the ask price."""\n        return self.__ask_yield\n\n    @ask_yield.setter\n    def ask_yield(self, value: float):\n        self._property_changed('ask_yield')\n        self.__ask_yield = value        \n\n    @property\n    def shock_style(self) -> str:\n        """Style of shocks to be used."""\n        return self.__shock_style\n\n    @shock_style.setter\n    def shock_style(self, value: str):\n        self._property_changed('shock_style')\n        self.__shock_style = value        \n\n    @property\n    def methodology(self) -> str:\n        """Methodology of dataset."""\n        return self.__methodology\n\n    @methodology.setter\n    def methodology(self, value: str):\n        self._property_changed('methodology')\n        self.__methodology = value        \n\n    @property\n    def buy25cents(self) -> float:\n        """The amount GS would buy for 25 cents charge."""\n        return self.__buy25cents\n\n    @buy25cents.setter\n    def buy25cents(self, value: float):\n        self._property_changed('buy25cents')\n        self.__buy25cents = value        \n\n    @property\n    def amount_outstanding(self) -> float:\n        """The aggregate principal amount of the total number of bonds not redeemed or\n           otherwise discharged."""\n        return self.__amount_outstanding\n\n    @amount_outstanding.setter\n    def amount_outstanding(self, value: float):\n        self._property_changed('amount_outstanding')\n        self.__amount_outstanding = value        \n\n    @property\n    def market_pnl(self) -> float:\n        """Market Profit and Loss (PNL)."""\n        return self.__market_pnl\n\n    @market_pnl.setter\n    def market_pnl(self, value: float):\n        self._property_changed('market_pnl')\n        self.__market_pnl = value        \n\n    @property\n    def sustain_asia_ex_japan(self) -> bool:\n        """True if the stock is on the SUSTAIN Asia Ex Japan list as of the corresponding\n           date. False if the stock is removed from the SUSTAIN Asia Ex Japan\n           list on the corresponding date."""\n        return self.__sustain_asia_ex_japan\n\n    @sustain_asia_ex_japan.setter\n    def sustain_asia_ex_japan(self, value: bool):\n        self._property_changed('sustain_asia_ex_japan')\n        self.__sustain_asia_ex_japan = value        \n\n    @property\n    def sell6point5bps(self) -> float:\n        """The amount GS would sell for 6.5 bps charge."""\n        return self.__sell6point5bps\n\n    @sell6point5bps.setter\n    def sell6point5bps(self, value: float):\n        self._property_changed('sell6point5bps')\n        self.__sell6point5bps = value        \n\n    @property\n    def neighbour_asset_id(self) -> str:\n        """Marquee identifier for the corresponding neighbour."""\n        return self.__neighbour_asset_id\n\n    @neighbour_asset_id.setter\n    def neighbour_asset_id(self, value: str):\n        self._property_changed('neighbour_asset_id')\n        self.__neighbour_asset_id = value        \n\n    @property\n    def count_ideas_ytd(self) -> float:\n        """Ideas alive at a time Year to date."""\n        return self.__count_ideas_ytd\n\n    @count_ideas_ytd.setter\n    def count_ideas_ytd(self, value: float):\n        self._property_changed('count_ideas_ytd')\n        self.__count_ideas_ytd = value        \n\n    @property\n    def simon_intl_asset_tags(self) -> Tuple[str, ...]:\n        """SIMON International Asset Tags."""\n        return self.__simon_intl_asset_tags\n\n    @simon_intl_asset_tags.setter\n    def simon_intl_asset_tags(self, value: Tuple[str, ...]):\n        self._property_changed('simon_intl_asset_tags')\n        self.__simon_intl_asset_tags = value        \n\n    @property\n    def path(self) -> str:\n        """Path to value."""\n        return self.__path\n\n    @path.setter\n    def path(self, value: str):\n        self._property_changed('path')\n        self.__path = value        \n\n    @property\n    def vwap_unrealized_cash(self) -> float:\n        """Consolidated unrealised performance vs VWAP, in USD."""\n        return self.__vwap_unrealized_cash\n\n    @vwap_unrealized_cash.setter\n    def vwap_unrealized_cash(self, value: float):\n        self._property_changed('vwap_unrealized_cash')\n        self.__vwap_unrealized_cash = value        \n\n    @property\n    def payoff_mtd(self) -> float:\n        """Total win divided by total loss Month to date."""\n        return self.__payoff_mtd\n\n    @payoff_mtd.setter\n    def payoff_mtd(self, value: float):\n        self._property_changed('payoff_mtd')\n        self.__payoff_mtd = value        \n\n    @property\n    def bos_in_bps_label(self):\n        return self.__bos_in_bps_label\n\n    @bos_in_bps_label.setter\n    def bos_in_bps_label(self, value):\n        self._property_changed('bos_in_bps_label')\n        self.__bos_in_bps_label = value        \n\n    @property\n    def bos_in_bps(self) -> float:\n        """The Bid-Offer Spread of the stock in Basis points on the particular date."""\n        return self.__bos_in_bps\n\n    @bos_in_bps.setter\n    def bos_in_bps(self, value: float):\n        self._property_changed('bos_in_bps')\n        self.__bos_in_bps = value        \n\n    @property\n    def point_class(self) -> str:\n        """MDAPI Class."""\n        return self.__point_class\n\n    @point_class.setter\n    def point_class(self, value: str):\n        self._property_changed('point_class')\n        self.__point_class = value        \n\n    @property\n    def fx_spot(self) -> float:\n        """FX spot rate as determined by fixing source."""\n        return self.__fx_spot\n\n    @fx_spot.setter\n    def fx_spot(self, value: float):\n        self._property_changed('fx_spot')\n        self.__fx_spot = value        \n\n    @property\n    def restrict_named_individuals(self) -> bool:\n        """Restricts Visbility to Named Individuals."""\n        return self.__restrict_named_individuals\n\n    @restrict_named_individuals.setter\n    def restrict_named_individuals(self, value: bool):\n        self._property_changed('restrict_named_individuals')\n        self.__restrict_named_individuals = value        \n\n    @property\n    def hedge_volatility(self) -> float:\n        """Standard deviation of the annualized returns."""\n        return self.__hedge_volatility\n\n    @hedge_volatility.setter\n    def hedge_volatility(self, value: float):\n        self._property_changed('hedge_volatility')\n        self.__hedge_volatility = value        \n\n    @property\n    def tags(self) -> Tuple[str, ...]:\n        """Metadata associated with the object"""\n        return self.__tags\n\n    @tags.setter\n    def tags(self, value: Tuple[str, ...]):\n        self._property_changed('tags')\n        self.__tags = value        \n\n    @property\n    def population(self) -> float:\n        """Total population of a country, state or subdivision."""\n        return self.__population\n\n    @population.setter\n    def population(self, value: float):\n        self._property_changed('population')\n        self.__population = value        \n\n    @property\n    def underlying_asset_id(self) -> str:\n        """Marquee identifier for constituents of an index or portfolio."""\n        return self.__underlying_asset_id\n\n    @underlying_asset_id.setter\n    def underlying_asset_id(self, value: str):\n        self._property_changed('underlying_asset_id')\n        self.__underlying_asset_id = value        \n\n    @property\n    def real_long_rates_contribution(self) -> float:\n        """Contribution of long rate component to real FCI."""\n        return self.__real_long_rates_contribution\n\n    @real_long_rates_contribution.setter\n    def real_long_rates_contribution(self, value: float):\n        self._property_changed('real_long_rates_contribution')\n        self.__real_long_rates_contribution = value        \n\n    @property\n    def pctprices_return(self) -> float:\n        """The pctprices(return) function."""\n        return self.__pctprices_return\n\n    @pctprices_return.setter\n    def pctprices_return(self, value: float):\n        self._property_changed('pctprices_return')\n        self.__pctprices_return = value        \n\n    @property\n    def domain(self) -> str:\n        """Domain that request came from."""\n        return self.__domain\n\n    @domain.setter\n    def domain(self, value: str):\n        self._property_changed('domain')\n        self.__domain = value        \n\n    @property\n    def buy80cents(self) -> float:\n        """The amount GS would buy for 80 cents charge."""\n        return self.__buy80cents\n\n    @buy80cents.setter\n    def buy80cents(self, value: float):\n        self._property_changed('buy80cents')\n        self.__buy80cents = value        \n\n    @property\n    def forward_tenor(self) -> str:\n        """Start of swap after option expiry."""\n        return self.__forward_tenor\n\n    @forward_tenor.setter\n    def forward_tenor(self, value: str):\n        self._property_changed('forward_tenor')\n        self.__forward_tenor = value        \n\n    @property\n    def average_price(self) -> float:\n        """Average execution price."""\n        return self.__average_price\n\n    @average_price.setter\n    def average_price(self, value: float):\n        self._property_changed('average_price')\n        self.__average_price = value        \n\n    @property\n    def target_price_realized_bps(self) -> float:\n        """Realised performance vs Target Price, in bps."""\n        return self.__target_price_realized_bps\n\n    @target_price_realized_bps.setter\n    def target_price_realized_bps(self, value: float):\n        self._property_changed('target_price_realized_bps')\n        self.__target_price_realized_bps = value        \n\n    @property\n    def leg2_fixed_rate(self) -> float:\n        """If fixed rate leg, the fixed rate of leg 2."""\n        return self.__leg2_fixed_rate\n\n    @leg2_fixed_rate.setter\n    def leg2_fixed_rate(self, value: float):\n        self._property_changed('leg2_fixed_rate')\n        self.__leg2_fixed_rate = value        \n\n    @property\n    def share_class_assets(self) -> float:\n        """Total amount of assets under management in this shareclass."""\n        return self.__share_class_assets\n\n    @share_class_assets.setter\n    def share_class_assets(self, value: float):\n        self._property_changed('share_class_assets')\n        self.__share_class_assets = value        \n\n    @property\n    def annuity(self) -> float:\n        """Local currency annuity."""\n        return self.__annuity\n\n    @annuity.setter\n    def annuity(self, value: float):\n        self._property_changed('annuity')\n        self.__annuity = value        \n\n    @property\n    def total_count(self) -> float:\n        """Number of entities in the test."""\n        return self.__total_count\n\n    @total_count.setter\n    def total_count(self, value: float):\n        self._property_changed('total_count')\n        self.__total_count = value        \n\n    @property\n    def quote_type(self) -> str:\n        """Quote Type."""\n        return self.__quote_type\n\n    @quote_type.setter\n    def quote_type(self, value: str):\n        self._property_changed('quote_type')\n        self.__quote_type = value        \n\n    @property\n    def corporate_action_status(self) -> str:\n        """Different statuses for corporate actions from solactive"""\n        return self.__corporate_action_status\n\n    @corporate_action_status.setter\n    def corporate_action_status(self, value: str):\n        self._property_changed('corporate_action_status')\n        self.__corporate_action_status = value        \n\n    @property\n    def pegged_tip_size(self) -> float:\n        """Pegged order tip size in terms of the quantity unit."""\n        return self.__pegged_tip_size\n\n    @pegged_tip_size.setter\n    def pegged_tip_size(self, value: float):\n        self._property_changed('pegged_tip_size')\n        self.__pegged_tip_size = value        \n\n    @property\n    def uid(self) -> str:\n        """Two-digit code for countries and regions for which FCI numbers are represented.\n           For countries it is the ISO 3166 2-digit country code. Regions are\n           denoted as DM (Developed Markets), EM (Emerging Markets), EA (Euro\n           Area) and GL (Global)."""\n        return self.__uid\n\n    @uid.setter\n    def uid(self, value: str):\n        self._property_changed('uid')\n        self.__uid = value        \n\n    @property\n    def es_policy_percentile(self) -> float:\n        """A percentile that captures a company's E&S policy ranking within its subsector."""\n        return self.__es_policy_percentile\n\n    @es_policy_percentile.setter\n    def es_policy_percentile(self, value: float):\n        self._property_changed('es_policy_percentile')\n        self.__es_policy_percentile = value        \n\n    @property\n    def usd_ois(self) -> float:\n        """USD Ois rate."""\n        return self.__usd_ois\n\n    @usd_ois.setter\n    def usd_ois(self, value: float):\n        self._property_changed('usd_ois')\n        self.__usd_ois = value        \n\n    @property\n    def term(self) -> str:\n        """Allowed risk model terms"""\n        return self.__term\n\n    @term.setter\n    def term(self, value: str):\n        self._property_changed('term')\n        self.__term = value        \n\n    @property\n    def restrict_internal_gs_ntk(self) -> bool:\n        """Restricts Internal Visibility to Data Beyond GS NTK."""\n        return self.__restrict_internal_gs_ntk\n\n    @restrict_internal_gs_ntk.setter\n    def restrict_internal_gs_ntk(self, value: bool):\n        self._property_changed('restrict_internal_gs_ntk')\n        self.__restrict_internal_gs_ntk = value        \n\n    @property\n    def tcm_cost_participation_rate100_pct(self) -> float:\n        """TCM cost with a 100 percent participation rate."""\n        return self.__tcm_cost_participation_rate100_pct\n\n    @tcm_cost_participation_rate100_pct.setter\n    def tcm_cost_participation_rate100_pct(self, value: float):\n        self._property_changed('tcm_cost_participation_rate100_pct')\n        self.__tcm_cost_participation_rate100_pct = value        \n\n    @property\n    def relative_universe(self) -> str:\n        """Universe relative to which the metric is computed."""\n        return self.__relative_universe\n\n    @relative_universe.setter\n    def relative_universe(self, value: str):\n        self._property_changed('relative_universe')\n        self.__relative_universe = value        \n\n    @property\n    def measure_idx(self) -> int:\n        """The index of the corresponding measure in the risk request."""\n        return self.__measure_idx\n\n    @measure_idx.setter\n    def measure_idx(self, value: int):\n        self._property_changed('measure_idx')\n        self.__measure_idx = value        \n\n    @property\n    def fred_id(self) -> str:\n        """FRED series identifier."""\n        return self.__fred_id\n\n    @fred_id.setter\n    def fred_id(self, value: str):\n        self._property_changed('fred_id')\n        self.__fred_id = value        \n\n    @property\n    def twi_contribution(self) -> float:\n        """Contribution of trade weighted exchange rate index component to FCI."""\n        return self.__twi_contribution\n\n    @twi_contribution.setter\n    def twi_contribution(self, value: float):\n        self._property_changed('twi_contribution')\n        self.__twi_contribution = value        \n\n    @property\n    def cloud_cover_type(self) -> str:\n        """The type of cloud cover: Average etc."""\n        return self.__cloud_cover_type\n\n    @cloud_cover_type.setter\n    def cloud_cover_type(self, value: str):\n        self._property_changed('cloud_cover_type')\n        self.__cloud_cover_type = value        \n\n    @property\n    def delisted(self) -> str:\n        """Whether the security has been delisted."""\n        return self.__delisted\n\n    @delisted.setter\n    def delisted(self, value: str):\n        self._property_changed('delisted')\n        self.__delisted = value        \n\n    @property\n    def regional_focus(self) -> str:\n        """Section of the world a fund is focused on from an investment perspective. Same\n           view permissions as the asset."""\n        return self.__regional_focus\n\n    @regional_focus.setter\n    def regional_focus(self, value: str):\n        self._property_changed('regional_focus')\n        self.__regional_focus = value        \n\n    @property\n    def volume_primary(self) -> float:\n        """Accumulated number of shares, lots or contracts traded according to the market\n           convention at the primary exchange."""\n        return self.__volume_primary\n\n    @volume_primary.setter\n    def volume_primary(self, value: float):\n        self._property_changed('volume_primary')\n        self.__volume_primary = value        \n\n    @property\n    def asset_parameters_payer_designated_maturity(self) -> str:\n        """Tenor"""\n        return self.__asset_parameters_payer_designated_maturity\n\n    @asset_parameters_payer_designated_maturity.setter\n    def asset_parameters_payer_designated_maturity(self, value: str):\n        self._property_changed('asset_parameters_payer_designated_maturity')\n        self.__asset_parameters_payer_designated_maturity = value        \n\n    @property\n    def buy30cents(self) -> float:\n        """The amount GS would buy for 30 cents charge."""\n        return self.__buy30cents\n\n    @buy30cents.setter\n    def buy30cents(self, value: float):\n        self._property_changed('buy30cents')\n        self.__buy30cents = value        \n\n    @property\n    def funding_bid_price(self) -> float:\n        """Latest Bid Price (price willing to buy)."""\n        return self.__funding_bid_price\n\n    @funding_bid_price.setter\n    def funding_bid_price(self, value: float):\n        self._property_changed('funding_bid_price')\n        self.__funding_bid_price = value        \n\n    @property\n    def series(self) -> str:\n        """Series."""\n        return self.__series\n\n    @series.setter\n    def series(self, value: str):\n        self._property_changed('series')\n        self.__series = value        \n\n    @property\n    def sell3bps(self) -> float:\n        """The amount GS would sell for 3 bps charge."""\n        return self.__sell3bps\n\n    @sell3bps.setter\n    def sell3bps(self, value: float):\n        self._property_changed('sell3bps')\n        self.__sell3bps = value        \n\n    @property\n    def settlement_price(self) -> float:\n        """Settlement Price."""\n        return self.__settlement_price\n\n    @settlement_price.setter\n    def settlement_price(self, value: float):\n        self._property_changed('settlement_price')\n        self.__settlement_price = value        \n\n    @property\n    def quarter(self) -> str:\n        """Quarter of forecast."""\n        return self.__quarter\n\n    @quarter.setter\n    def quarter(self, value: str):\n        self._property_changed('quarter')\n        self.__quarter = value        \n\n    @property\n    def sell18bps(self) -> float:\n        """The amount GS would sell for 18 bps charge."""\n        return self.__sell18bps\n\n    @sell18bps.setter\n    def sell18bps(self, value: float):\n        self._property_changed('sell18bps')\n        self.__sell18bps = value        \n\n    @property\n    def asset_parameters_floating_rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE."""\n        return self.__asset_parameters_floating_rate_option\n\n    @asset_parameters_floating_rate_option.setter\n    def asset_parameters_floating_rate_option(self, value: str):\n        self._property_changed('asset_parameters_floating_rate_option')\n        self.__asset_parameters_floating_rate_option = value        \n\n    @property\n    def realized_vwap_performance_bps(self) -> float:\n        """Average execution price vs Consolidated VWAP (in limit) since order inception\n           ??? realized."""\n        return self.__realized_vwap_performance_bps\n\n    @realized_vwap_performance_bps.setter\n    def realized_vwap_performance_bps(self, value: float):\n        self._property_changed('realized_vwap_performance_bps')\n        self.__realized_vwap_performance_bps = value        \n\n    @property\n    def vote_share(self) -> float:\n        """Vote share (predicted) in election."""\n        return self.__vote_share\n\n    @vote_share.setter\n    def vote_share(self, value: float):\n        self._property_changed('vote_share')\n        self.__vote_share = value        \n\n    @property\n    def servicing_cost_short_pnl(self) -> float:\n        """Servicing Cost Short Profit and Loss."""\n        return self.__servicing_cost_short_pnl\n\n    @servicing_cost_short_pnl.setter\n    def servicing_cost_short_pnl(self, value: float):\n        self._property_changed('servicing_cost_short_pnl')\n        self.__servicing_cost_short_pnl = value        \n\n    @property\n    def total_confirmed(self) -> float:\n        """Total number of confirmed cases."""\n        return self.__total_confirmed\n\n    @total_confirmed.setter\n    def total_confirmed(self, value: float):\n        self._property_changed('total_confirmed')\n        self.__total_confirmed = value        \n\n    @property\n    def economic_forecast(self) -> float:\n        """Forecast value."""\n        return self.__economic_forecast\n\n    @economic_forecast.setter\n    def economic_forecast(self, value: float):\n        self._property_changed('economic_forecast')\n        self.__economic_forecast = value        \n\n    @property\n    def plot_id(self) -> str:\n        """Plot Identifier."""\n        return self.__plot_id\n\n    @plot_id.setter\n    def plot_id(self, value: str):\n        self._property_changed('plot_id')\n        self.__plot_id = value        \n\n    @property\n    def cluster_description(self) -> str:\n        """Description of the Cluster characteristics."""\n        return self.__cluster_description\n\n    @cluster_description.setter\n    def cluster_description(self, value: str):\n        self._property_changed('cluster_description')\n        self.__cluster_description = value        \n\n    @property\n    def concentration_limit(self) -> float:\n        """The allowable percentage of the outstanding loan balance of a lenders book that\n           can be lent to/in a particular borrower/market/etc."""\n        return self.__concentration_limit\n\n    @concentration_limit.setter\n    def concentration_limit(self, value: float):\n        self._property_changed('concentration_limit')\n        self.__concentration_limit = value        \n\n    @property\n    def wind_speed(self) -> float:\n        """Average wind speed in knots."""\n        return self.__wind_speed\n\n    @wind_speed.setter\n    def wind_speed(self, value: float):\n        self._property_changed('wind_speed')\n        self.__wind_speed = value        \n\n    @property\n    def observation_hour(self) -> str:\n        """The observation hour for hourly weather forecast."""\n        return self.__observation_hour\n\n    @observation_hour.setter\n    def observation_hour(self, value: str):\n        self._property_changed('observation_hour')\n        self.__observation_hour = value        \n\n    @property\n    def signal(self) -> float:\n        """Signal."""\n        return self.__signal\n\n    @signal.setter\n    def signal(self, value: float):\n        self._property_changed('signal')\n        self.__signal = value        \n\n    @property\n    def borrower_id(self) -> str:\n        """Id of the borrowing entity on a securities lending agreement."""\n        return self.__borrower_id\n\n    @borrower_id.setter\n    def borrower_id(self, value: str):\n        self._property_changed('borrower_id')\n        self.__borrower_id = value        \n\n    @property\n    def data_product(self) -> str:\n        """Product that dataset belongs to."""\n        return self.__data_product\n\n    @data_product.setter\n    def data_product(self, value: str):\n        self._property_changed('data_product')\n        self.__data_product = value        \n\n    @property\n    def buy7point5bps(self) -> float:\n        """The amount GS would buy for 7.5 bps charge."""\n        return self.__buy7point5bps\n\n    @buy7point5bps.setter\n    def buy7point5bps(self, value: float):\n        self._property_changed('buy7point5bps')\n        self.__buy7point5bps = value        \n\n    @property\n    def limit_price(self) -> float:\n        """Limit price of the order."""\n        return self.__limit_price\n\n    @limit_price.setter\n    def limit_price(self, value: float):\n        self._property_changed('limit_price')\n        self.__limit_price = value        \n\n    @property\n    def bm_prime_id(self) -> float:\n        """Benchmark prime ID of the treasury."""\n        return self.__bm_prime_id\n\n    @bm_prime_id.setter\n    def bm_prime_id(self, value: float):\n        self._property_changed('bm_prime_id')\n        self.__bm_prime_id = value        \n\n    @property\n    def data_type(self) -> str:\n        """The type of data for Power or Load. Values may include Actual, Forecast."""\n        return self.__data_type\n\n    @data_type.setter\n    def data_type(self, value: str):\n        self._property_changed('data_type')\n        self.__data_type = value        \n\n    @property\n    def count(self) -> float:\n        """Total number of a collection."""\n        return self.__count\n\n    @count.setter\n    def count(self, value: float):\n        self._property_changed('count')\n        self.__count = value        \n\n    @property\n    def conviction(self) -> str:\n        """Confidence level in the trade idea."""\n        return self.__conviction\n\n    @conviction.setter\n    def conviction(self, value: str):\n        self._property_changed('conviction')\n        self.__conviction = value        \n\n    @property\n    def rfqstate(self) -> str:\n        """RFQ State."""\n        return self.__rfqstate\n\n    @rfqstate.setter\n    def rfqstate(self, value: str):\n        self._property_changed('rfqstate')\n        self.__rfqstate = value        \n\n    @property\n    def benchmark_maturity(self) -> str:\n        """The benchmark tenor."""\n        return self.__benchmark_maturity\n\n    @benchmark_maturity.setter\n    def benchmark_maturity(self, value: str):\n        self._property_changed('benchmark_maturity')\n        self.__benchmark_maturity = value        \n\n    @property\n    def gross_flow_normalized(self) -> float:\n        """Gross flow for the asset divided by average in defined history."""\n        return self.__gross_flow_normalized\n\n    @gross_flow_normalized.setter\n    def gross_flow_normalized(self, value: float):\n        self._property_changed('gross_flow_normalized')\n        self.__gross_flow_normalized = value        \n\n    @property\n    def buy14bps(self) -> float:\n        """The amount GS would buy for 14 bps charge."""\n        return self.__buy14bps\n\n    @buy14bps.setter\n    def buy14bps(self, value: float):\n        self._property_changed('buy14bps')\n        self.__buy14bps = value        \n\n    @property\n    def factor_id(self) -> str:\n        """Id for Factors."""\n        return self.__factor_id\n\n    @factor_id.setter\n    def factor_id(self, value: str):\n        self._property_changed('factor_id')\n        self.__factor_id = value        \n\n    @property\n    def future_month_v26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_v26\n\n    @future_month_v26.setter\n    def future_month_v26(self, value: float):\n        self._property_changed('future_month_v26')\n        self.__future_month_v26 = value        \n\n    @property\n    def sts_fx_currency(self) -> str:\n        """Currency of underlying FX risk for STS assets."""\n        return self.__sts_fx_currency\n\n    @sts_fx_currency.setter\n    def sts_fx_currency(self, value: str):\n        self._property_changed('sts_fx_currency')\n        self.__sts_fx_currency = value        \n\n    @property\n    def future_month_v25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_v25\n\n    @future_month_v25.setter\n    def future_month_v25(self, value: float):\n        self._property_changed('future_month_v25')\n        self.__future_month_v25 = value        \n\n    @property\n    def bid_change(self) -> float:\n        """Change in BID price."""\n        return self.__bid_change\n\n    @bid_change.setter\n    def bid_change(self, value: float):\n        self._property_changed('bid_change')\n        self.__bid_change = value        \n\n    @property\n    def month(self) -> str:\n        """Month in YYYYMM format."""\n        return self.__month\n\n    @month.setter\n    def month(self, value: str):\n        self._property_changed('month')\n        self.__month = value        \n\n    @property\n    def future_month_v24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_v24\n\n    @future_month_v24.setter\n    def future_month_v24(self, value: float):\n        self._property_changed('future_month_v24')\n        self.__future_month_v24 = value        \n\n    @property\n    def investment_wtd(self) -> float:\n        """Total net investment Week to date."""\n        return self.__investment_wtd\n\n    @investment_wtd.setter\n    def investment_wtd(self, value: float):\n        self._property_changed('investment_wtd')\n        self.__investment_wtd = value        \n\n    @property\n    def future_month_v23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_v23\n\n    @future_month_v23.setter\n    def future_month_v23(self, value: float):\n        self._property_changed('future_month_v23')\n        self.__future_month_v23 = value        \n\n    @property\n    def future_month_v22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_v22\n\n    @future_month_v22.setter\n    def future_month_v22(self, value: float):\n        self._property_changed('future_month_v22')\n        self.__future_month_v22 = value        \n\n    @property\n    def future_month_v21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_v21\n\n    @future_month_v21.setter\n    def future_month_v21(self, value: float):\n        self._property_changed('future_month_v21')\n        self.__future_month_v21 = value        \n\n    @property\n    def expiration(self) -> str:\n        """The expiration date of the associated contract and the last date it trades."""\n        return self.__expiration\n\n    @expiration.setter\n    def expiration(self, value: str):\n        self._property_changed('expiration')\n        self.__expiration = value        \n\n    @property\n    def leg2_reset_frequency(self) -> str:\n        """An integer multiplier of a period describing how often the parties to an SB swap\n           transaction shall evaluate and, when applicable, change the price\n           used for the underlying assets of the swap transaction (leg 2). Such\n           reset frequency may be described as one letter preceded by an\n           integer."""\n        return self.__leg2_reset_frequency\n\n    @leg2_reset_frequency.setter\n    def leg2_reset_frequency(self, value: str):\n        self._property_changed('leg2_reset_frequency')\n        self.__leg2_reset_frequency = value        \n\n    @property\n    def controversy_score(self) -> float:\n        """A company's score for controversy metrics."""\n        return self.__controversy_score\n\n    @controversy_score.setter\n    def controversy_score(self, value: float):\n        self._property_changed('controversy_score')\n        self.__controversy_score = value        \n\n    @property\n    def proceed_asset_swap_spread(self) -> float:\n        """Spread between a Proceed Swap Rate and the yield of the bond."""\n        return self.__proceed_asset_swap_spread\n\n    @proceed_asset_swap_spread.setter\n    def proceed_asset_swap_spread(self, value: float):\n        self._property_changed('proceed_asset_swap_spread')\n        self.__proceed_asset_swap_spread = value        \n\n    @property\n    def concentration_level(self) -> float:\n        """The percentage of the outstanding loan balance of a lenders book that is being\n           lent to/in a particular borrower/market/etc."""\n        return self.__concentration_level\n\n    @concentration_level.setter\n    def concentration_level(self, value: float):\n        self._property_changed('concentration_level')\n        self.__concentration_level = value        \n\n    @property\n    def importance(self) -> float:\n        """Importance."""\n        return self.__importance\n\n    @importance.setter\n    def importance(self, value: float):\n        self._property_changed('importance')\n        self.__importance = value        \n\n    @property\n    def asset_classifications_gics_sector(self) -> str:\n        """GICS Sector classification (level 1)."""\n        return self.__asset_classifications_gics_sector\n\n    @asset_classifications_gics_sector.setter\n    def asset_classifications_gics_sector(self, value: str):\n        self._property_changed('asset_classifications_gics_sector')\n        self.__asset_classifications_gics_sector = value        \n\n    @property\n    def sts_asset_name(self) -> str:\n        """Name of risk asset for STS underliers."""\n        return self.__sts_asset_name\n\n    @sts_asset_name.setter\n    def sts_asset_name(self, value: str):\n        self._property_changed('sts_asset_name')\n        self.__sts_asset_name = value        \n\n    @property\n    def net_exposure_classification(self) -> str:\n        """Classification for net exposure of fund."""\n        return self.__net_exposure_classification\n\n    @net_exposure_classification.setter\n    def net_exposure_classification(self, value: str):\n        self._property_changed('net_exposure_classification')\n        self.__net_exposure_classification = value        \n\n    @property\n    def settlement_method(self) -> str:\n        """Settlement method of the swap."""\n        return self.__settlement_method\n\n    @settlement_method.setter\n    def settlement_method(self, value: str):\n        self._property_changed('settlement_method')\n        self.__settlement_method = value        \n\n    @property\n    def receiver_designated_maturity(self) -> str:\n        """Tenor"""\n        return self.__receiver_designated_maturity\n\n    @receiver_designated_maturity.setter\n    def receiver_designated_maturity(self, value: str):\n        self._property_changed('receiver_designated_maturity')\n        self.__receiver_designated_maturity = value        \n\n    @property\n    def title(self) -> str:\n        """Title of series."""\n        return self.__title\n\n    @title.setter\n    def title(self, value: str):\n        self._property_changed('title')\n        self.__title = value        \n\n    @property\n    def x_ref_type_id(self) -> str:\n        """PadMaster unique xref type identifier."""\n        return self.__x_ref_type_id\n\n    @x_ref_type_id.setter\n    def x_ref_type_id(self, value: str):\n        self._property_changed('x_ref_type_id')\n        self.__x_ref_type_id = value        \n\n    @property\n    def duration(self) -> str:\n        """Candle duration. Supported values are??1m, 1h, 1d etc."""\n        return self.__duration\n\n    @duration.setter\n    def duration(self, value: str):\n        self._property_changed('duration')\n        self.__duration = value        \n\n    @property\n    def load(self) -> float:\n        """Quantity of power in the electrical grid."""\n        return self.__load\n\n    @load.setter\n    def load(self, value: float):\n        self._property_changed('load')\n        self.__load = value        \n\n    @property\n    def alpha(self) -> float:\n        """Alpha."""\n        return self.__alpha\n\n    @alpha.setter\n    def alpha(self, value: float):\n        self._property_changed('alpha')\n        self.__alpha = value        \n\n    @property\n    def company(self) -> str:\n        """Activity user company."""\n        return self.__company\n\n    @company.setter\n    def company(self, value: str):\n        self._property_changed('company')\n        self.__company = value        \n\n    @property\n    def settlement_frequency(self) -> str:\n        """Settlement Frequency provided by Participant (e.g., Monthly, Daily)."""\n        return self.__settlement_frequency\n\n    @settlement_frequency.setter\n    def settlement_frequency(self, value: str):\n        self._property_changed('settlement_frequency')\n        self.__settlement_frequency = value        \n\n    @property\n    def dist_avg7_day(self) -> float:\n        """Goldman custom calculated value, only used for GS onshore Money Market Funds,\n           assumes sum of the past 7 days divided by 7 and expressed as a\n           percent."""\n        return self.__dist_avg7_day\n\n    @dist_avg7_day.setter\n    def dist_avg7_day(self, value: float):\n        self._property_changed('dist_avg7_day')\n        self.__dist_avg7_day = value        \n\n    @property\n    def in_risk_model(self) -> bool:\n        """Whether or not the asset is in the risk model universe."""\n        return self.__in_risk_model\n\n    @in_risk_model.setter\n    def in_risk_model(self, value: bool):\n        self._property_changed('in_risk_model')\n        self.__in_risk_model = value        \n\n    @property\n    def daily_net_shareholder_flows_percent(self) -> float:\n        """Percent of assets paid daily."""\n        return self.__daily_net_shareholder_flows_percent\n\n    @daily_net_shareholder_flows_percent.setter\n    def daily_net_shareholder_flows_percent(self, value: float):\n        self._property_changed('daily_net_shareholder_flows_percent')\n        self.__daily_net_shareholder_flows_percent = value        \n\n    @property\n    def filled_notional_local(self) -> float:\n        """Executed notional in Local."""\n        return self.__filled_notional_local\n\n    @filled_notional_local.setter\n    def filled_notional_local(self, value: float):\n        self._property_changed('filled_notional_local')\n        self.__filled_notional_local = value        \n\n    @property\n    def ever_hospitalized(self) -> float:\n        """The total number of patients ever hospitalized."""\n        return self.__ever_hospitalized\n\n    @ever_hospitalized.setter\n    def ever_hospitalized(self, value: float):\n        self._property_changed('ever_hospitalized')\n        self.__ever_hospitalized = value        \n\n    @property\n    def meeting_number(self) -> float:\n        """Central bank meeting number."""\n        return self.__meeting_number\n\n    @meeting_number.setter\n    def meeting_number(self, value: float):\n        self._property_changed('meeting_number')\n        self.__meeting_number = value        \n\n    @property\n    def mid_gspread(self) -> float:\n        """Mid G spread."""\n        return self.__mid_gspread\n\n    @mid_gspread.setter\n    def mid_gspread(self, value: float):\n        self._property_changed('mid_gspread')\n        self.__mid_gspread = value        \n\n    @property\n    def days_open_unrealized_bps(self) -> float:\n        """Unrealised performance vs Day Open, in BPS."""\n        return self.__days_open_unrealized_bps\n\n    @days_open_unrealized_bps.setter\n    def days_open_unrealized_bps(self, value: float):\n        self._property_changed('days_open_unrealized_bps')\n        self.__days_open_unrealized_bps = value        \n\n    @property\n    def long_level(self) -> float:\n        """Level of the 5-day normalized flow for long selling/buying."""\n        return self.__long_level\n\n    @long_level.setter\n    def long_level(self, value: float):\n        self._property_changed('long_level')\n        self.__long_level = value        \n\n    @property\n    def data_description(self) -> str:\n        """Description of data that client is requesting."""\n        return self.__data_description\n\n    @data_description.setter\n    def data_description(self, value: str):\n        self._property_changed('data_description')\n        self.__data_description = value        \n\n    @property\n    def temperature_type(self) -> str:\n        """The type of temperature required: max or min or average etc."""\n        return self.__temperature_type\n\n    @temperature_type.setter\n    def temperature_type(self, value: str):\n        self._property_changed('temperature_type')\n        self.__temperature_type = value        \n\n    @property\n    def gsideid(self) -> str:\n        """Goldman Sachs internal composite equity and exchange identifier."""\n        return self.__gsideid\n\n    @gsideid.setter\n    def gsideid(self, value: str):\n        self._property_changed('gsideid')\n        self.__gsideid = value        \n\n    @property\n    def repo_rate(self) -> float:\n        """Repurchase Rate."""\n        return self.__repo_rate\n\n    @repo_rate.setter\n    def repo_rate(self, value: float):\n        self._property_changed('repo_rate')\n        self.__repo_rate = value        \n\n    @property\n    def division(self) -> str:\n        """Division that owns the data."""\n        return self.__division\n\n    @division.setter\n    def division(self, value: str):\n        self._property_changed('division')\n        self.__division = value        \n\n    @property\n    def cloud_cover_daily_forecast(self) -> float:\n        """The forecast value for cloud cover in percentage."""\n        return self.__cloud_cover_daily_forecast\n\n    @cloud_cover_daily_forecast.setter\n    def cloud_cover_daily_forecast(self, value: float):\n        self._property_changed('cloud_cover_daily_forecast')\n        self.__cloud_cover_daily_forecast = value        \n\n    @property\n    def wind_speed_daily_forecast(self) -> float:\n        """The forecast value for wind speed in Miles per hour."""\n        return self.__wind_speed_daily_forecast\n\n    @wind_speed_daily_forecast.setter\n    def wind_speed_daily_forecast(self, value: float):\n        self._property_changed('wind_speed_daily_forecast')\n        self.__wind_speed_daily_forecast = value        \n\n    @property\n    def asset_parameters_floating_rate_day_count_fraction(self) -> str:\n        """Day Count Fraction"""\n        return self.__asset_parameters_floating_rate_day_count_fraction\n\n    @asset_parameters_floating_rate_day_count_fraction.setter\n    def asset_parameters_floating_rate_day_count_fraction(self, value: str):\n        self._property_changed('asset_parameters_floating_rate_day_count_fraction')\n        self.__asset_parameters_floating_rate_day_count_fraction = value        \n\n    @property\n    def trade_action(self) -> str:\n        """An indication that a publicly reportable securitybased (SB) swap transaction has\n           been incorrectly or erroneously publicly disseminated and is canceled\n           or corrected or a new transaction."""\n        return self.__trade_action\n\n    @trade_action.setter\n    def trade_action(self, value: str):\n        self._property_changed('trade_action')\n        self.__trade_action = value        \n\n    @property\n    def action(self) -> str:\n        """The activity action. For example: Viewed"""\n        return self.__action\n\n    @action.setter\n    def action(self, value: str):\n        self._property_changed('action')\n        self.__action = value        \n\n    @property\n    def ctd_yield(self) -> float:\n        """Yield of cheapest to deliver bond."""\n        return self.__ctd_yield\n\n    @ctd_yield.setter\n    def ctd_yield(self, value: float):\n        self._property_changed('ctd_yield')\n        self.__ctd_yield = value        \n\n    @property\n    def arrival_haircut_vwap_normalized(self) -> float:\n        """Performance against Benchmark in pip."""\n        return self.__arrival_haircut_vwap_normalized\n\n    @arrival_haircut_vwap_normalized.setter\n    def arrival_haircut_vwap_normalized(self, value: float):\n        self._property_changed('arrival_haircut_vwap_normalized')\n        self.__arrival_haircut_vwap_normalized = value        \n\n    @property\n    def price_component(self) -> str:\n        """Component of total price."""\n        return self.__price_component\n\n    @price_component.setter\n    def price_component(self, value: str):\n        self._property_changed('price_component')\n        self.__price_component = value        \n\n    @property\n    def queue_clock_time_description(self) -> str:\n        """Description of the Stock's Queue Clock Time on the particular date."""\n        return self.__queue_clock_time_description\n\n    @queue_clock_time_description.setter\n    def queue_clock_time_description(self, value: str):\n        self._property_changed('queue_clock_time_description')\n        self.__queue_clock_time_description = value        \n\n    @property\n    def asset_parameters_receiver_day_count_fraction(self) -> str:\n        """Day Count Fraction"""\n        return self.__asset_parameters_receiver_day_count_fraction\n\n    @asset_parameters_receiver_day_count_fraction.setter\n    def asset_parameters_receiver_day_count_fraction(self, value: str):\n        self._property_changed('asset_parameters_receiver_day_count_fraction')\n        self.__asset_parameters_receiver_day_count_fraction = value        \n\n    @property\n    def percent_mid_execution_quantity(self) -> float:\n        """Percentage of order filled at mid-touch."""\n        return self.__percent_mid_execution_quantity\n\n    @percent_mid_execution_quantity.setter\n    def percent_mid_execution_quantity(self, value: float):\n        self._property_changed('percent_mid_execution_quantity')\n        self.__percent_mid_execution_quantity = value        \n\n    @property\n    def delta_strike(self) -> str:\n        """Option strike price expressed in terms of delta * 100."""\n        return self.__delta_strike\n\n    @delta_strike.setter\n    def delta_strike(self, value: str):\n        self._property_changed('delta_strike')\n        self.__delta_strike = value        \n\n    @property\n    def cloud_cover(self) -> float:\n        """Value of the cloud cover for a given unit."""\n        return self.__cloud_cover\n\n    @cloud_cover.setter\n    def cloud_cover(self, value: float):\n        self._property_changed('cloud_cover')\n        self.__cloud_cover = value        \n\n    @property\n    def asset_parameters_notional_currency(self) -> str:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__asset_parameters_notional_currency\n\n    @asset_parameters_notional_currency.setter\n    def asset_parameters_notional_currency(self, value: str):\n        self._property_changed('asset_parameters_notional_currency')\n        self.__asset_parameters_notional_currency = value        \n\n    @property\n    def buy18bps(self) -> float:\n        """The amount GS would buy for 18 bps charge."""\n        return self.__buy18bps\n\n    @buy18bps.setter\n    def buy18bps(self, value: float):\n        self._property_changed('buy18bps')\n        self.__buy18bps = value        \n\n    @property\n    def value_actual(self) -> str:\n        """Latest released value."""\n        return self.__value_actual\n\n    @value_actual.setter\n    def value_actual(self, value: str):\n        self._property_changed('value_actual')\n        self.__value_actual = value        \n\n    @property\n    def upi(self) -> str:\n        """Unique product identifier for product."""\n        return self.__upi\n\n    @upi.setter\n    def upi(self, value: str):\n        self._property_changed('upi')\n        self.__upi = value        \n\n    @property\n    def collateral_currency(self) -> str:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__collateral_currency\n\n    @collateral_currency.setter\n    def collateral_currency(self, value: str):\n        self._property_changed('collateral_currency')\n        self.__collateral_currency = value        \n\n    @property\n    def original_country(self) -> str:\n        """Country in source dataset."""\n        return self.__original_country\n\n    @original_country.setter\n    def original_country(self, value: str):\n        self._property_changed('original_country')\n        self.__original_country = value        \n\n    @property\n    def field(self) -> str:\n        """The market data field (e.g. rate, price). This can be resolved into a dataset\n           when combined with vendor and intraday=true/false."""\n        return self.__field\n\n    @field.setter\n    def field(self, value: str):\n        self._property_changed('field')\n        self.__field = value        \n\n    @property\n    def geographic_focus(self) -> str:\n        """Geographic region of focus."""\n        return self.__geographic_focus\n\n    @geographic_focus.setter\n    def geographic_focus(self, value: str):\n        self._property_changed('geographic_focus')\n        self.__geographic_focus = value        \n\n    @property\n    def days_open_realized_bps(self) -> float:\n        """Realised performance vs Day Open, in BPS."""\n        return self.__days_open_realized_bps\n\n    @days_open_realized_bps.setter\n    def days_open_realized_bps(self, value: float):\n        self._property_changed('days_open_realized_bps')\n        self.__days_open_realized_bps = value        \n\n    @property\n    def fx_risk_premium_index(self) -> float:\n        """FX risk premium index: percentage difference between average spot rate and OECD\n           PPP exchange rate."""\n        return self.__fx_risk_premium_index\n\n    @fx_risk_premium_index.setter\n    def fx_risk_premium_index(self, value: float):\n        self._property_changed('fx_risk_premium_index')\n        self.__fx_risk_premium_index = value        \n\n    @property\n    def skew(self) -> float:\n        """Volatility skew."""\n        return self.__skew\n\n    @skew.setter\n    def skew(self, value: float):\n        self._property_changed('skew')\n        self.__skew = value        \n\n    @property\n    def status(self) -> str:\n        """Status of report run"""\n        return self.__status\n\n    @status.setter\n    def status(self, value: str):\n        self._property_changed('status')\n        self.__status = value        \n\n    @property\n    def notional_currency(self) -> str:\n        """An indication of the type of currency of the notional or principal amount."""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: str):\n        self._property_changed('notional_currency')\n        self.__notional_currency = value        \n\n    @property\n    def sustain_emerging_markets(self) -> bool:\n        """True if the stock is on the SUSTAIN Emerging Markets list as of the\n           corresponding date. False if the stock is removed from the SUSTAIN\n           Emerging Markets list on the corresponding date."""\n        return self.__sustain_emerging_markets\n\n    @sustain_emerging_markets.setter\n    def sustain_emerging_markets(self, value: bool):\n        self._property_changed('sustain_emerging_markets')\n        self.__sustain_emerging_markets = value        \n\n    @property\n    def event_date_time(self) -> datetime.datetime:\n        """The time of the event if the event has a specific time, using UTC convention, or\n           the end time of the event if the event occurs during a time window\n           (optional)."""\n        return self.__event_date_time\n\n    @event_date_time.setter\n    def event_date_time(self, value: datetime.datetime):\n        self._property_changed('event_date_time')\n        self.__event_date_time = value        \n\n    @property\n    def leg1_designated_maturity(self) -> str:\n        """Floating rate index designated maturity period of leg 1."""\n        return self.__leg1_designated_maturity\n\n    @leg1_designated_maturity.setter\n    def leg1_designated_maturity(self, value: str):\n        self._property_changed('leg1_designated_maturity')\n        self.__leg1_designated_maturity = value        \n\n    @property\n    def total_price(self) -> float:\n        """Net price of the asset."""\n        return self.__total_price\n\n    @total_price.setter\n    def total_price(self, value: float):\n        self._property_changed('total_price')\n        self.__total_price = value        \n\n    @property\n    def on_behalf_of(self) -> str:\n        """Marquee unique identifier"""\n        return self.__on_behalf_of\n\n    @on_behalf_of.setter\n    def on_behalf_of(self, value: str):\n        self._property_changed('on_behalf_of')\n        self.__on_behalf_of = value        \n\n    @property\n    def test_type(self) -> str:\n        """Semantic category that test belongs to e.g. Timeliness, Completeness etc."""\n        return self.__test_type\n\n    @test_type.setter\n    def test_type(self, value: str):\n        self._property_changed('test_type')\n        self.__test_type = value        \n\n    @property\n    def accrued_interest_standard(self) -> float:\n        """The accrued interest paid on the bond if it is settled two business days after\n           the trade date."""\n        return self.__accrued_interest_standard\n\n    @accrued_interest_standard.setter\n    def accrued_interest_standard(self, value: float):\n        self._property_changed('accrued_interest_standard')\n        self.__accrued_interest_standard = value        \n\n    @property\n    def future_month_z26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_z26\n\n    @future_month_z26.setter\n    def future_month_z26(self, value: float):\n        self._property_changed('future_month_z26')\n        self.__future_month_z26 = value        \n\n    @property\n    def future_month_z25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_z25\n\n    @future_month_z25.setter\n    def future_month_z25(self, value: float):\n        self._property_changed('future_month_z25')\n        self.__future_month_z25 = value        \n\n    @property\n    def ccg_code(self) -> str:\n        """Derived CCG code based on location of the patient at the time of triage."""\n        return self.__ccg_code\n\n    @ccg_code.setter\n    def ccg_code(self, value: str):\n        self._property_changed('ccg_code')\n        self.__ccg_code = value        \n\n    @property\n    def short_exposure(self) -> float:\n        """Exposure of a given portfolio to securities which are short in direction. If you\n           are $60 short and $40 long, shortExposure would be $60."""\n        return self.__short_exposure\n\n    @short_exposure.setter\n    def short_exposure(self, value: float):\n        self._property_changed('short_exposure')\n        self.__short_exposure = value        \n\n    @property\n    def leg1_fixed_payment_currency(self) -> str:\n        """If fixed payment leg, the unit of fixed payment."""\n        return self.__leg1_fixed_payment_currency\n\n    @leg1_fixed_payment_currency.setter\n    def leg1_fixed_payment_currency(self, value: str):\n        self._property_changed('leg1_fixed_payment_currency')\n        self.__leg1_fixed_payment_currency = value        \n\n    @property\n    def arrival_haircut_vwap(self) -> float:\n        """Arrival Haircut VWAP."""\n        return self.__arrival_haircut_vwap\n\n    @arrival_haircut_vwap.setter\n    def arrival_haircut_vwap(self, value: float):\n        self._property_changed('arrival_haircut_vwap')\n        self.__arrival_haircut_vwap = value        \n\n    @property\n    def execution_days(self) -> float:\n        """Number of days to used to execute."""\n        return self.__execution_days\n\n    @execution_days.setter\n    def execution_days(self, value: float):\n        self._property_changed('execution_days')\n        self.__execution_days = value        \n\n    @property\n    def recall_due_date(self) -> datetime.date:\n        """Date in which the recall of securities in a stock loan recall activity must be\n           complete."""\n        return self.__recall_due_date\n\n    @recall_due_date.setter\n    def recall_due_date(self, value: datetime.date):\n        self._property_changed('recall_due_date')\n        self.__recall_due_date = value        \n\n    @property\n    def forward(self) -> float:\n        """Forward value."""\n        return self.__forward\n\n    @forward.setter\n    def forward(self, value: float):\n        self._property_changed('forward')\n        self.__forward = value        \n\n    @property\n    def strike(self) -> float:\n        """Strike level relative to at the money in basis points."""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: float):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def spread_limit(self) -> float:\n        """Spread limit (in pips) for which order slices will be skipped."""\n        return self.__spread_limit\n\n    @spread_limit.setter\n    def spread_limit(self, value: float):\n        self._property_changed('spread_limit')\n        self.__spread_limit = value        \n\n    @property\n    def product_scope(self) -> str:\n        """Asset Class(es) for which the restriction is required."""\n        return self.__product_scope\n\n    @product_scope.setter\n    def product_scope(self, value: str):\n        self._property_changed('product_scope')\n        self.__product_scope = value        \n\n    @property\n    def asset_parameters_issuer_type(self) -> str:\n        """The type of the bond issuer."""\n        return self.__asset_parameters_issuer_type\n\n    @asset_parameters_issuer_type.setter\n    def asset_parameters_issuer_type(self, value: str):\n        self._property_changed('asset_parameters_issuer_type')\n        self.__asset_parameters_issuer_type = value        \n\n    @property\n    def currency1(self) -> str:\n        """An indication of the type of currency of the notional or principal amount."""\n        return self.__currency1\n\n    @currency1.setter\n    def currency1(self, value: str):\n        self._property_changed('currency1')\n        self.__currency1 = value        \n\n    @property\n    def currency2(self) -> str:\n        """Same as currency 1."""\n        return self.__currency2\n\n    @currency2.setter\n    def currency2(self, value: str):\n        self._property_changed('currency2')\n        self.__currency2 = value        \n\n    @property\n    def previous_close_realized_bps(self) -> float:\n        """Realised performance vs Previous Close, in bps."""\n        return self.__previous_close_realized_bps\n\n    @previous_close_realized_bps.setter\n    def previous_close_realized_bps(self, value: float):\n        self._property_changed('previous_close_realized_bps')\n        self.__previous_close_realized_bps = value        \n\n    @property\n    def days_since_reported(self) -> float:\n        """Days since last reported."""\n        return self.__days_since_reported\n\n    @days_since_reported.setter\n    def days_since_reported(self, value: float):\n        self._property_changed('days_since_reported')\n        self.__days_since_reported = value        \n\n    @property\n    def event_status(self) -> str:\n        """Included if there is additional information about an event, such as the event\n           being cancelled."""\n        return self.__event_status\n\n    @event_status.setter\n    def event_status(self, value: str):\n        self._property_changed('event_status')\n        self.__event_status = value        \n\n    @property\n    def vwap_in_limit(self) -> float:\n        """VWAP In Limit benchmark price."""\n        return self.__vwap_in_limit\n\n    @vwap_in_limit.setter\n    def vwap_in_limit(self, value: float):\n        self._property_changed('vwap_in_limit')\n        self.__vwap_in_limit = value        \n\n    @property\n    def fwd_duration(self) -> float:\n        """Duration of the forward."""\n        return self.__fwd_duration\n\n    @fwd_duration.setter\n    def fwd_duration(self, value: float):\n        self._property_changed('fwd_duration')\n        self.__fwd_duration = value        \n\n    @property\n    def return_(self) -> float:\n        """Return of asset over a given period (e.g. close-to-close)."""\n        return self.__return\n\n    @return_.setter\n    def return_(self, value: float):\n        self._property_changed('return_')\n        self.__return = value        \n\n    @property\n    def is_pair_basket(self) -> bool:\n        """Whether or not it is a pair basket."""\n        return self.__is_pair_basket\n\n    @is_pair_basket.setter\n    def is_pair_basket(self, value: bool):\n        self._property_changed('is_pair_basket')\n        self.__is_pair_basket = value        \n\n    @property\n    def notional_amount(self) -> float:\n        """Only applicable on Commodity Index products."""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: float):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def pay_or_receive(self) -> str:\n        """Pay or receive fixed"""\n        return self.__pay_or_receive\n\n    @pay_or_receive.setter\n    def pay_or_receive(self, value: str):\n        self._property_changed('pay_or_receive')\n        self.__pay_or_receive = value        \n\n    @property\n    def total_severe(self) -> float:\n        """Total number of active cases with severe symptoms."""\n        return self.__total_severe\n\n    @total_severe.setter\n    def total_severe(self, value: float):\n        self._property_changed('total_severe')\n        self.__total_severe = value        \n\n    @property\n    def unexecuted_notional_usd(self) -> float:\n        """Notional that has not bee executed, in dollars."""\n        return self.__unexecuted_notional_usd\n\n    @unexecuted_notional_usd.setter\n    def unexecuted_notional_usd(self, value: float):\n        self._property_changed('unexecuted_notional_usd')\n        self.__unexecuted_notional_usd = value        \n\n    @property\n    def expected_residual_percentage(self) -> float:\n        """Percent of order that will be left as residual."""\n        return self.__expected_residual_percentage\n\n    @expected_residual_percentage.setter\n    def expected_residual_percentage(self, value: float):\n        self._property_changed('expected_residual_percentage')\n        self.__expected_residual_percentage = value        \n\n    @property\n    def maturity_date(self) -> datetime.date:\n        """The maturity, termination, or end date of the reportable SB swap transaction."""\n        return self.__maturity_date\n\n    @maturity_date.setter\n    def maturity_date(self, value: datetime.date):\n        self._property_changed('maturity_date')\n        self.__maturity_date = value        \n\n    @property\n    def trace_adv_sell(self) -> float:\n        """TRACE ADV for the sell side."""\n        return self.__trace_adv_sell\n\n    @trace_adv_sell.setter\n    def trace_adv_sell(self, value: float):\n        self._property_changed('trace_adv_sell')\n        self.__trace_adv_sell = value        \n\n    @property\n    def event_name(self) -> str:\n        """Event name."""\n        return self.__event_name\n\n    @event_name.setter\n    def event_name(self, value: str):\n        self._property_changed('event_name')\n        self.__event_name = value        \n\n    @property\n    def address_line2(self) -> str:\n        """Second line of the street address, typically reserved for apartment, suite or\n           space number."""\n        return self.__address_line2\n\n    @address_line2.setter\n    def address_line2(self, value: str):\n        self._property_changed('address_line2')\n        self.__address_line2 = value        \n\n    @property\n    def indication_of_other_price_affecting_term(self) -> str:\n        """An indication that the publicly reportable SB swap transaction has one or more\n           additional term(s) or provision(s), other than those listed in the\n           required real-time data fields, that materially affect(s) the price\n           of the swap transaction."""\n        return self.__indication_of_other_price_affecting_term\n\n    @indication_of_other_price_affecting_term.setter\n    def indication_of_other_price_affecting_term(self, value: str):\n        self._property_changed('indication_of_other_price_affecting_term')\n        self.__indication_of_other_price_affecting_term = value        \n\n    @property\n    def unadjusted_bid(self) -> float:\n        """Unadjusted bid level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__unadjusted_bid\n\n    @unadjusted_bid.setter\n    def unadjusted_bid(self, value: float):\n        self._property_changed('unadjusted_bid')\n        self.__unadjusted_bid = value        \n\n    @property\n    def backtest_type(self) -> str:\n        """Backtest type differentiates the backtest type."""\n        return self.__backtest_type\n\n    @backtest_type.setter\n    def backtest_type(self, value: str):\n        self._property_changed('backtest_type')\n        self.__backtest_type = value        \n\n    @property\n    def gsdeer(self) -> float:\n        """Goldman Sachs Dynamic Equilibrium Exchange Rate."""\n        return self.__gsdeer\n\n    @gsdeer.setter\n    def gsdeer(self, value: float):\n        self._property_changed('gsdeer')\n        self.__gsdeer = value        \n\n    @property\n    def asset_parameters_issuer(self) -> str:\n        """The issuer of this bond."""\n        return self.__asset_parameters_issuer\n\n    @asset_parameters_issuer.setter\n    def asset_parameters_issuer(self, value: str):\n        self._property_changed('asset_parameters_issuer')\n        self.__asset_parameters_issuer = value        \n\n    @property\n    def g_regional_percentile(self) -> float:\n        """A percentile that captures a company's G ranking relative to its region."""\n        return self.__g_regional_percentile\n\n    @g_regional_percentile.setter\n    def g_regional_percentile(self, value: float):\n        self._property_changed('g_regional_percentile')\n        self.__g_regional_percentile = value        \n\n    @property\n    def coverage_checked(self) -> float:\n        """Number of entities checked in a run of the checker."""\n        return self.__coverage_checked\n\n    @coverage_checked.setter\n    def coverage_checked(self, value: float):\n        self._property_changed('coverage_checked')\n        self.__coverage_checked = value        \n\n    @property\n    def ois_xccy_ex_spike(self) -> float:\n        """Xccy OIS Spread rate excluding Xccy Spikes impacts."""\n        return self.__ois_xccy_ex_spike\n\n    @ois_xccy_ex_spike.setter\n    def ois_xccy_ex_spike(self, value: float):\n        self._property_changed('ois_xccy_ex_spike')\n        self.__ois_xccy_ex_spike = value        \n\n    @property\n    def total_risk(self) -> float:\n        """Total risk."""\n        return self.__total_risk\n\n    @total_risk.setter\n    def total_risk(self, value: float):\n        self._property_changed('total_risk')\n        self.__total_risk = value        \n\n    @property\n    def mnav(self) -> float:\n        """Net asset value, assets of a fund (ex dividend) divided by total number of\n           shares."""\n        return self.__mnav\n\n    @mnav.setter\n    def mnav(self, value: float):\n        self._property_changed('mnav')\n        self.__mnav = value        \n\n    @property\n    def market_volume(self) -> float:\n        """Volume of the stock throughout the whole day across primaries and MTFs."""\n        return self.__market_volume\n\n    @market_volume.setter\n    def market_volume(self, value: float):\n        self._property_changed('market_volume')\n        self.__market_volume = value        \n\n    @property\n    def swap_annuity(self) -> float:\n        """Annuity for a benchmark tenor on a currency's fixed-floating swap curve."""\n        return self.__swap_annuity\n\n    @swap_annuity.setter\n    def swap_annuity(self, value: float):\n        self._property_changed('swap_annuity')\n        self.__swap_annuity = value        \n\n    @property\n    def par_asset_swap_spread(self) -> float:\n        """Spread between a Par Swap Rate and the yield of the bond."""\n        return self.__par_asset_swap_spread\n\n    @par_asset_swap_spread.setter\n    def par_asset_swap_spread(self, value: float):\n        self._property_changed('par_asset_swap_spread')\n        self.__par_asset_swap_spread = value        \n\n    @property\n    def curr_yield7_day(self) -> float:\n        """Average income return over previous 7 days reduced by any capital gains that may\n           have been included in rate calculation, according to the current\n           amount."""\n        return self.__curr_yield7_day\n\n    @curr_yield7_day.setter\n    def curr_yield7_day(self, value: float):\n        self._property_changed('curr_yield7_day')\n        self.__curr_yield7_day = value        \n\n    @property\n    def pressure(self) -> float:\n        """Average barometric pressure on a given day in inches of mercury."""\n        return self.__pressure\n\n    @pressure.setter\n    def pressure(self, value: float):\n        self._property_changed('pressure')\n        self.__pressure = value        \n\n    @property\n    def short_description(self) -> str:\n        """Short description of dataset."""\n        return self.__short_description\n\n    @short_description.setter\n    def short_description(self, value: str):\n        self._property_changed('short_description')\n        self.__short_description = value        \n\n    @property\n    def future_month_z24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_z24\n\n    @future_month_z24.setter\n    def future_month_z24(self, value: float):\n        self._property_changed('future_month_z24')\n        self.__future_month_z24 = value        \n\n    @property\n    def feed(self) -> str:\n        """Indicates the source feed of the data."""\n        return self.__feed\n\n    @feed.setter\n    def feed(self, value: str):\n        self._property_changed('feed')\n        self.__feed = value        \n\n    @property\n    def future_month_z23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_z23\n\n    @future_month_z23.setter\n    def future_month_z23(self, value: float):\n        self._property_changed('future_month_z23')\n        self.__future_month_z23 = value        \n\n    @property\n    def mkt_point1(self) -> str:\n        """The first dimension of Mkt Point, e.g. 3M, 11Y, DEC19."""\n        return self.__mkt_point1\n\n    @mkt_point1.setter\n    def mkt_point1(self, value: str):\n        self._property_changed('mkt_point1')\n        self.__mkt_point1 = value        \n\n    @property\n    def future_month_z22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_z22\n\n    @future_month_z22.setter\n    def future_month_z22(self, value: float):\n        self._property_changed('future_month_z22')\n        self.__future_month_z22 = value        \n\n    @property\n    def future_month_z21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_z21\n\n    @future_month_z21.setter\n    def future_month_z21(self, value: float):\n        self._property_changed('future_month_z21')\n        self.__future_month_z21 = value        \n\n    @property\n    def future_month_z20(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_z20\n\n    @future_month_z20.setter\n    def future_month_z20(self, value: float):\n        self._property_changed('future_month_z20')\n        self.__future_month_z20 = value        \n\n    @property\n    def asset_parameters_commodity_sector(self) -> str:\n        """The sector of the commodity"""\n        return self.__asset_parameters_commodity_sector\n\n    @asset_parameters_commodity_sector.setter\n    def asset_parameters_commodity_sector(self, value: str):\n        self._property_changed('asset_parameters_commodity_sector')\n        self.__asset_parameters_commodity_sector = value        \n\n    @property\n    def price_notation2(self) -> float:\n        """The Basis points, Price, Yield, Spread, Coupon, etc., value depending on the\n           type of SB swap, which is calculated at affirmation."""\n        return self.__price_notation2\n\n    @price_notation2.setter\n    def price_notation2(self, value: float):\n        self._property_changed('price_notation2')\n        self.__price_notation2 = value        \n\n    @property\n    def market_buffer_threshold(self) -> float:\n        """The required buffer between holdings and on loan quantity for a market."""\n        return self.__market_buffer_threshold\n\n    @market_buffer_threshold.setter\n    def market_buffer_threshold(self, value: float):\n        self._property_changed('market_buffer_threshold')\n        self.__market_buffer_threshold = value        \n\n    @property\n    def price_notation3(self) -> float:\n        """The Basis points, Price, Yield, Spread, Coupon, etc., value depending on the\n           type of SB swap, which is calculated at affirmation."""\n        return self.__price_notation3\n\n    @price_notation3.setter\n    def price_notation3(self, value: float):\n        self._property_changed('price_notation3')\n        self.__price_notation3 = value        \n\n    @property\n    def mkt_point3(self) -> str:\n        """The third dimension of Mkt Point, e.g. 3M, 11Y, DEC19."""\n        return self.__mkt_point3\n\n    @mkt_point3.setter\n    def mkt_point3(self, value: str):\n        self._property_changed('mkt_point3')\n        self.__mkt_point3 = value        \n\n    @property\n    def mkt_point2(self) -> str:\n        """The second dimension of Mkt Point, e.g. 3M, 11Y, DEC19."""\n        return self.__mkt_point2\n\n    @mkt_point2.setter\n    def mkt_point2(self, value: str):\n        self._property_changed('mkt_point2')\n        self.__mkt_point2 = value        \n\n    @property\n    def leg2_type(self) -> str:\n        """Indication if leg 2 is fixed or floating or Physical."""\n        return self.__leg2_type\n\n    @leg2_type.setter\n    def leg2_type(self, value: str):\n        self._property_changed('leg2_type')\n        self.__leg2_type = value        \n\n    @property\n    def mkt_point4(self) -> str:\n        """The fourth dimension of Mkt Point, e.g. 3M, 11Y, DEC19."""\n        return self.__mkt_point4\n\n    @mkt_point4.setter\n    def mkt_point4(self, value: str):\n        self._property_changed('mkt_point4')\n        self.__mkt_point4 = value        \n\n    @property\n    def degree_days_type(self) -> str:\n        """The type of degree days required: Heating or Cooling etc."""\n        return self.__degree_days_type\n\n    @degree_days_type.setter\n    def degree_days_type(self, value: str):\n        self._property_changed('degree_days_type')\n        self.__degree_days_type = value        \n\n    @property\n    def sentiment(self) -> float:\n        """A measure of the overall sentiment of overnight news."""\n        return self.__sentiment\n\n    @sentiment.setter\n    def sentiment(self, value: float):\n        self._property_changed('sentiment')\n        self.__sentiment = value        \n\n    @property\n    def investment_income(self) -> float:\n        """The income earned by the reinvested collateral."""\n        return self.__investment_income\n\n    @investment_income.setter\n    def investment_income(self, value: float):\n        self._property_changed('investment_income')\n        self.__investment_income = value        \n\n    @property\n    def group_type(self) -> str:\n        """Type of the group."""\n        return self.__group_type\n\n    @group_type.setter\n    def group_type(self, value: str):\n        self._property_changed('group_type')\n        self.__group_type = value        \n\n    @property\n    def forward_point_imm(self) -> float:\n        """Immutable outright forward minus spot."""\n        return self.__forward_point_imm\n\n    @forward_point_imm.setter\n    def forward_point_imm(self, value: float):\n        self._property_changed('forward_point_imm')\n        self.__forward_point_imm = value        \n\n    @property\n    def twap(self) -> float:\n        """Time weighted average price."""\n        return self.__twap\n\n    @twap.setter\n    def twap(self, value: float):\n        self._property_changed('twap')\n        self.__twap = value        \n\n    @property\n    def client_short_name(self) -> str:\n        """The short name of a client."""\n        return self.__client_short_name\n\n    @client_short_name.setter\n    def client_short_name(self, value: str):\n        self._property_changed('client_short_name')\n        self.__client_short_name = value        \n\n    @property\n    def group_category(self) -> str:\n        """The type of group: region or sector."""\n        return self.__group_category\n\n    @group_category.setter\n    def group_category(self, value: str):\n        self._property_changed('group_category')\n        self.__group_category = value        \n\n    @property\n    def bid_plus_ask(self) -> float:\n        """Sum of bid & ask."""\n        return self.__bid_plus_ask\n\n    @bid_plus_ask.setter\n    def bid_plus_ask(self, value: float):\n        self._property_changed('bid_plus_ask')\n        self.__bid_plus_ask = value        \n\n    @property\n    def foreign_ccy_rate(self) -> float:\n        """The interest rate of the foreign currency of the associated FX contract."""\n        return self.__foreign_ccy_rate\n\n    @foreign_ccy_rate.setter\n    def foreign_ccy_rate(self, value: float):\n        self._property_changed('foreign_ccy_rate')\n        self.__foreign_ccy_rate = value        \n\n    @property\n    def election_odds(self) -> float:\n        """Odds of winning election."""\n        return self.__election_odds\n\n    @election_odds.setter\n    def election_odds(self, value: float):\n        self._property_changed('election_odds')\n        self.__election_odds = value        \n\n    @property\n    def wind_direction_forecast(self) -> float:\n        """The forecast wind direction of given units."""\n        return self.__wind_direction_forecast\n\n    @wind_direction_forecast.setter\n    def wind_direction_forecast(self, value: float):\n        self._property_changed('wind_direction_forecast')\n        self.__wind_direction_forecast = value        \n\n    @property\n    def require_anon_client_name(self) -> bool:\n        """Requires Anonymization of Client Name."""\n        return self.__require_anon_client_name\n\n    @require_anon_client_name.setter\n    def require_anon_client_name(self, value: bool):\n        self._property_changed('require_anon_client_name')\n        self.__require_anon_client_name = value        \n\n    @property\n    def pricing_location(self) -> str:\n        """Quill pricing location."""\n        return self.__pricing_location\n\n    @pricing_location.setter\n    def pricing_location(self, value: str):\n        self._property_changed('pricing_location')\n        self.__pricing_location = value        \n\n    @property\n    def beta(self) -> float:\n        """Beta."""\n        return self.__beta\n\n    @beta.setter\n    def beta(self, value: float):\n        self._property_changed('beta')\n        self.__beta = value        \n\n    @property\n    def last_returns_end_date(self) -> datetime.date:\n        """Hedge fund last returns before end date."""\n        return self.__last_returns_end_date\n\n    @last_returns_end_date.setter\n    def last_returns_end_date(self, value: datetime.date):\n        self._property_changed('last_returns_end_date')\n        self.__last_returns_end_date = value        \n\n    @property\n    def upfront_payment_date(self) -> datetime.date:\n        """Date of upront payment."""\n        return self.__upfront_payment_date\n\n    @upfront_payment_date.setter\n    def upfront_payment_date(self, value: datetime.date):\n        self._property_changed('upfront_payment_date')\n        self.__upfront_payment_date = value        \n\n    @property\n    def sell1point5bps(self) -> float:\n        """The amount GS would sell for 1.5 bps charge."""\n        return self.__sell1point5bps\n\n    @sell1point5bps.setter\n    def sell1point5bps(self, value: float):\n        self._property_changed('sell1point5bps')\n        self.__sell1point5bps = value        \n\n    @property\n    def long_exposure(self) -> float:\n        """Exposure of a given portfolio to securities which are long in direction. If you\n           are $60 short and $40 long, longExposure would be $40."""\n        return self.__long_exposure\n\n    @long_exposure.setter\n    def long_exposure(self, value: float):\n        self._property_changed('long_exposure')\n        self.__long_exposure = value        \n\n    @property\n    def sell4point5bps(self) -> float:\n        """The amount GS would sell for 4.5 bps charge."""\n        return self.__sell4point5bps\n\n    @sell4point5bps.setter\n    def sell4point5bps(self, value: float):\n        self._property_changed('sell4point5bps')\n        self.__sell4point5bps = value        \n\n    @property\n    def tcm_cost_participation_rate20_pct(self) -> float:\n        """TCM cost with a 20 percent participation rate."""\n        return self.__tcm_cost_participation_rate20_pct\n\n    @tcm_cost_participation_rate20_pct.setter\n    def tcm_cost_participation_rate20_pct(self, value: float):\n        self._property_changed('tcm_cost_participation_rate20_pct')\n        self.__tcm_cost_participation_rate20_pct = value        \n\n    @property\n    def venue_type(self) -> str:\n        """The type of venue (Exchange, BD, Dark Pool)."""\n        return self.__venue_type\n\n    @venue_type.setter\n    def venue_type(self, value: str):\n        self._property_changed('venue_type')\n        self.__venue_type = value        \n\n    @property\n    def multi_asset_class_swap(self) -> str:\n        """Indicates if the swap falls under multiple asset classes."""\n        return self.__multi_asset_class_swap\n\n    @multi_asset_class_swap.setter\n    def multi_asset_class_swap(self, value: str):\n        self._property_changed('multi_asset_class_swap')\n        self.__multi_asset_class_swap = value        \n\n    @property\n    def delta_change_id(self) -> float:\n        """PadMaster unique delta change identifier."""\n        return self.__delta_change_id\n\n    @delta_change_id.setter\n    def delta_change_id(self, value: float):\n        self._property_changed('delta_change_id')\n        self.__delta_change_id = value        \n\n    @property\n    def implementation_id(self) -> str:\n        """Marquee unique Implementation identifier."""\n        return self.__implementation_id\n\n    @implementation_id.setter\n    def implementation_id(self, value: str):\n        self._property_changed('implementation_id')\n        self.__implementation_id = value        \n\n    @property\n    def leg1_fixed_payment(self) -> float:\n        """If fixed payment leg, the fixed payment amount, which is price*number of\n           contracts bought*contract unit."""\n        return self.__leg1_fixed_payment\n\n    @leg1_fixed_payment.setter\n    def leg1_fixed_payment(self, value: float):\n        self._property_changed('leg1_fixed_payment')\n        self.__leg1_fixed_payment = value        \n\n    @property\n    def es_numeric_score(self) -> float:\n        """A company's score for E&S subsector-relative numeric metrics."""\n        return self.__es_numeric_score\n\n    @es_numeric_score.setter\n    def es_numeric_score(self, value: float):\n        self._property_changed('es_numeric_score')\n        self.__es_numeric_score = value        \n\n    @property\n    def in_benchmark(self) -> bool:\n        """Whether or not the asset is in the benchmark."""\n        return self.__in_benchmark\n\n    @in_benchmark.setter\n    def in_benchmark(self, value: bool):\n        self._property_changed('in_benchmark')\n        self.__in_benchmark = value        \n\n    @property\n    def action_sdr(self) -> str:\n        """An indication that a publicly reportable securitybased (SB) swap transaction has\n           been incorrectly or erroneously publicly disseminated and is canceled\n           or corrected or a new transaction."""\n        return self.__action_sdr\n\n    @action_sdr.setter\n    def action_sdr(self, value: str):\n        self._property_changed('action_sdr')\n        self.__action_sdr = value        \n\n    @property\n    def count_ideas_qtd(self) -> float:\n        """Ideas alive at a time Quarter to date."""\n        return self.__count_ideas_qtd\n\n    @count_ideas_qtd.setter\n    def count_ideas_qtd(self, value: float):\n        self._property_changed('count_ideas_qtd')\n        self.__count_ideas_qtd = value        \n\n    @property\n    def knock_out_price(self) -> float:\n        """Market level required for an order to knock out."""\n        return self.__knock_out_price\n\n    @knock_out_price.setter\n    def knock_out_price(self, value: float):\n        self._property_changed('knock_out_price')\n        self.__knock_out_price = value        \n\n    @property\n    def ctd_asset_id(self) -> str:\n        """Id of cheapest to deliver bond."""\n        return self.__ctd_asset_id\n\n    @ctd_asset_id.setter\n    def ctd_asset_id(self, value: str):\n        self._property_changed('ctd_asset_id')\n        self.__ctd_asset_id = value        \n\n    @property\n    def buy10bps(self) -> float:\n        """The amount GS would buy for 10 bps charge."""\n        return self.__buy10bps\n\n    @buy10bps.setter\n    def buy10bps(self, value: float):\n        self._property_changed('buy10bps')\n        self.__buy10bps = value        \n\n    @property\n    def precipitation(self) -> float:\n        """Amount of rainfall in inches."""\n        return self.__precipitation\n\n    @precipitation.setter\n    def precipitation(self, value: float):\n        self._property_changed('precipitation')\n        self.__precipitation = value        \n\n    @property\n    def value_type(self) -> str:\n        """Value type."""\n        return self.__value_type\n\n    @value_type.setter\n    def value_type(self, value: str):\n        self._property_changed('value_type')\n        self.__value_type = value        \n\n    @property\n    def beta_adjusted_net_exposure(self) -> float:\n        """Beta adjusted net exposure."""\n        return self.__beta_adjusted_net_exposure\n\n    @beta_adjusted_net_exposure.setter\n    def beta_adjusted_net_exposure(self, value: float):\n        self._property_changed('beta_adjusted_net_exposure')\n        self.__beta_adjusted_net_exposure = value        \n\n    @property\n    def estimated_rod_volume(self) -> float:\n        """Consolidated rest of day volume in continuous trading and closing auction."""\n        return self.__estimated_rod_volume\n\n    @estimated_rod_volume.setter\n    def estimated_rod_volume(self, value: float):\n        self._property_changed('estimated_rod_volume')\n        self.__estimated_rod_volume = value        \n\n    @property\n    def sell14bps(self) -> float:\n        """The amount GS would sell for 14 bps charge."""\n        return self.__sell14bps\n\n    @sell14bps.setter\n    def sell14bps(self, value: float):\n        self._property_changed('sell14bps')\n        self.__sell14bps = value        \n\n    @property\n    def excess_return_price(self) -> float:\n        """The excess return price of an instrument."""\n        return self.__excess_return_price\n\n    @excess_return_price.setter\n    def excess_return_price(self, value: float):\n        self._property_changed('excess_return_price')\n        self.__excess_return_price = value        \n\n    @property\n    def fx_pnl(self) -> float:\n        """Foreign Exchange Profit and Loss (PNL)."""\n        return self.__fx_pnl\n\n    @fx_pnl.setter\n    def fx_pnl(self, value: float):\n        self._property_changed('fx_pnl')\n        self.__fx_pnl = value        \n\n    @property\n    def asset_classifications_gics_industry_group(self) -> str:\n        """GICS Industry Group classification (level 2)."""\n        return self.__asset_classifications_gics_industry_group\n\n    @asset_classifications_gics_industry_group.setter\n    def asset_classifications_gics_industry_group(self, value: str):\n        self._property_changed('asset_classifications_gics_industry_group')\n        self.__asset_classifications_gics_industry_group = value        \n\n    @property\n    def lending_sec_id(self) -> str:\n        """Securities lending identifiter for the security on loan."""\n        return self.__lending_sec_id\n\n    @lending_sec_id.setter\n    def lending_sec_id(self, value: str):\n        self._property_changed('lending_sec_id')\n        self.__lending_sec_id = value        \n\n    @property\n    def dollar_duration(self) -> float:\n        """Measure of a bond's price sensitivity to changes in interest rates."""\n        return self.__dollar_duration\n\n    @dollar_duration.setter\n    def dollar_duration(self, value: float):\n        self._property_changed('dollar_duration')\n        self.__dollar_duration = value        \n\n    @property\n    def equity_theta(self) -> float:\n        """Theta exposure to equity products."""\n        return self.__equity_theta\n\n    @equity_theta.setter\n    def equity_theta(self, value: float):\n        self._property_changed('equity_theta')\n        self.__equity_theta = value        \n\n    @property\n    def dv01(self) -> float:\n        """Measure of the dollar change in a bond's value to a change in the market\n           interest rate."""\n        return self.__dv01\n\n    @dv01.setter\n    def dv01(self, value: float):\n        self._property_changed('dv01')\n        self.__dv01 = value        \n\n    @property\n    def start_date(self) -> datetime.date:\n        """Start date specific to an asset. For example start date for the swap."""\n        return self.__start_date\n\n    @start_date.setter\n    def start_date(self, value: datetime.date):\n        self._property_changed('start_date')\n        self.__start_date = value        \n\n    @property\n    def mixed_swap(self) -> str:\n        """Indicates if the swap falls under both the CFTC and SEC jurisdictions."""\n        return self.__mixed_swap\n\n    @mixed_swap.setter\n    def mixed_swap(self, value: str):\n        self._property_changed('mixed_swap')\n        self.__mixed_swap = value        \n\n    @property\n    def swaption_premium(self) -> float:\n        """Swaption premium."""\n        return self.__swaption_premium\n\n    @swaption_premium.setter\n    def swaption_premium(self, value: float):\n        self._property_changed('swaption_premium')\n        self.__swaption_premium = value        \n\n    @property\n    def snowfall(self) -> float:\n        """Amount of snowfall in inches."""\n        return self.__snowfall\n\n    @snowfall.setter\n    def snowfall(self, value: float):\n        self._property_changed('snowfall')\n        self.__snowfall = value        \n\n    @property\n    def liquidity_bucket_buy(self) -> float:\n        """Liquidity bucket for buying the bond."""\n        return self.__liquidity_bucket_buy\n\n    @liquidity_bucket_buy.setter\n    def liquidity_bucket_buy(self, value: float):\n        self._property_changed('liquidity_bucket_buy')\n        self.__liquidity_bucket_buy = value        \n\n    @property\n    def mic(self) -> str:\n        """Market identifier code."""\n        return self.__mic\n\n    @mic.setter\n    def mic(self, value: str):\n        self._property_changed('mic')\n        self.__mic = value        \n\n    @property\n    def latitude(self) -> float:\n        """Latitude."""\n        return self.__latitude\n\n    @latitude.setter\n    def latitude(self, value: float):\n        self._property_changed('latitude')\n        self.__latitude = value        \n\n    @property\n    def mid(self) -> float:\n        """Mid."""\n        return self.__mid\n\n    @mid.setter\n    def mid(self, value: float):\n        self._property_changed('mid')\n        self.__mid = value        \n\n    @property\n    def implied_repo(self) -> float:\n        """Rate of return that can be earned by simultaneously selling a bond futures and\n           then buying that actual bond of equal amount in the cash market using\n           borrowed money."""\n        return self.__implied_repo\n\n    @implied_repo.setter\n    def implied_repo(self, value: float):\n        self._property_changed('implied_repo')\n        self.__implied_repo = value        \n\n    @property\n    def long(self) -> float:\n        """Long exposure."""\n        return self.__long\n\n    @long.setter\n    def long(self, value: float):\n        self._property_changed('long')\n        self.__long = value        \n\n    @property\n    def first_execution_time(self) -> datetime.datetime:\n        """Arrival execution time."""\n        return self.__first_execution_time\n\n    @first_execution_time.setter\n    def first_execution_time(self, value: datetime.datetime):\n        self._property_changed('first_execution_time')\n        self.__first_execution_time = value        \n\n    @property\n    def covered_bond(self) -> bool:\n        """Whether the debt security is collateralized against a pool of assets that, in\n           case of failure of the issuer, can cover claims at any point of time."""\n        return self.__covered_bond\n\n    @covered_bond.setter\n    def covered_bond(self, value: bool):\n        self._property_changed('covered_bond')\n        self.__covered_bond = value        \n\n    @property\n    def region_code(self) -> str:\n        """ISO 3166 Region Code. Generally a three digit code."""\n        return self.__region_code\n\n    @region_code.setter\n    def region_code(self, value: str):\n        self._property_changed('region_code')\n        self.__region_code = value        \n\n    @property\n    def buy20cents(self) -> float:\n        """The amount GS would buy for 20 cents charge."""\n        return self.__buy20cents\n\n    @buy20cents.setter\n    def buy20cents(self, value: float):\n        self._property_changed('buy20cents')\n        self.__buy20cents = value        \n\n    @property\n    def long_weight(self) -> float:\n        """Long weight of a position in a given portfolio. Equivalent to position long\n           exposure / total long exposure. If you have a position with a\n           longExposure of $20, and your portfolio longExposure is $100,\n           longWeight would be 0.2 (20/100)."""\n        return self.__long_weight\n\n    @long_weight.setter\n    def long_weight(self, value: float):\n        self._property_changed('long_weight')\n        self.__long_weight = value        \n\n    @property\n    def calculation_time(self) -> int:\n        """Time taken to calculate risk metric (ms)."""\n        return self.__calculation_time\n\n    @calculation_time.setter\n    def calculation_time(self, value: int):\n        self._property_changed('calculation_time')\n        self.__calculation_time = value        \n\n    @property\n    def liquidity_bucket_sell(self) -> float:\n        """Liquidity bucket for selling the bond."""\n        return self.__liquidity_bucket_sell\n\n    @liquidity_bucket_sell.setter\n    def liquidity_bucket_sell(self, value: float):\n        self._property_changed('liquidity_bucket_sell')\n        self.__liquidity_bucket_sell = value        \n\n    @property\n    def days_open_unrealized_cash(self) -> float:\n        """Unrealised performance vs Day Open, in USD."""\n        return self.__days_open_unrealized_cash\n\n    @days_open_unrealized_cash.setter\n    def days_open_unrealized_cash(self, value: float):\n        self._property_changed('days_open_unrealized_cash')\n        self.__days_open_unrealized_cash = value        \n\n    @property\n    def temperature(self) -> float:\n        """Value of the temperature of given type."""\n        return self.__temperature\n\n    @temperature.setter\n    def temperature(self, value: float):\n        self._property_changed('temperature')\n        self.__temperature = value        \n\n    @property\n    def average_realized_variance(self) -> float:\n        """Average variance of an asset realized by observations of market prices."""\n        return self.__average_realized_variance\n\n    @average_realized_variance.setter\n    def average_realized_variance(self, value: float):\n        self._property_changed('average_realized_variance')\n        self.__average_realized_variance = value        \n\n    @property\n    def rating_fitch(self) -> str:\n        """Bond rating from Fitch."""\n        return self.__rating_fitch\n\n    @rating_fitch.setter\n    def rating_fitch(self, value: str):\n        self._property_changed('rating_fitch')\n        self.__rating_fitch = value        \n\n    @property\n    def financial_returns_score(self) -> float:\n        """Financial Returns percentile relative to Americas coverage universe (a higher\n           score means stronger financial returns)."""\n        return self.__financial_returns_score\n\n    @financial_returns_score.setter\n    def financial_returns_score(self, value: float):\n        self._property_changed('financial_returns_score')\n        self.__financial_returns_score = value        \n\n    @property\n    def year_or_quarter(self) -> str:\n        """Year or quarter for which the forecast holds."""\n        return self.__year_or_quarter\n\n    @year_or_quarter.setter\n    def year_or_quarter(self, value: str):\n        self._property_changed('year_or_quarter')\n        self.__year_or_quarter = value        \n\n    @property\n    def non_symbol_dimensions(self) -> Tuple[str, ...]:\n        """Fields that are not nullable."""\n        return self.__non_symbol_dimensions\n\n    @non_symbol_dimensions.setter\n    def non_symbol_dimensions(self, value: Tuple[str, ...]):\n        self._property_changed('non_symbol_dimensions')\n        self.__non_symbol_dimensions = value        \n\n    @property\n    def commodities_forecast(self) -> float:\n        """Forecast value."""\n        return self.__commodities_forecast\n\n    @commodities_forecast.setter\n    def commodities_forecast(self, value: float):\n        self._property_changed('commodities_forecast')\n        self.__commodities_forecast = value        \n\n    @property\n    def covid19_by_state(self) -> float:\n        """Total number of people affected by covid19 for a given state country."""\n        return self.__covid19_by_state\n\n    @covid19_by_state.setter\n    def covid19_by_state(self, value: float):\n        self._property_changed('covid19_by_state')\n        self.__covid19_by_state = value        \n\n    @property\n    def percentage_expected_residual(self) -> float:\n        """Percentage expected residual quantity."""\n        return self.__percentage_expected_residual\n\n    @percentage_expected_residual.setter\n    def percentage_expected_residual(self, value: float):\n        self._property_changed('percentage_expected_residual')\n        self.__percentage_expected_residual = value        \n\n    @property\n    def hospital_name(self) -> str:\n        """Name of hospital, where hospital is defined as a healthcare institution\n           providing at inpatient, therapeutic, and/or rehabilitation services\n           under the supervision of physicians."""\n        return self.__hospital_name\n\n    @hospital_name.setter\n    def hospital_name(self, value: str):\n        self._property_changed('hospital_name')\n        self.__hospital_name = value        \n\n    @property\n    def buy90cents(self) -> float:\n        """The amount GS would buy for 90 cents charge."""\n        return self.__buy90cents\n\n    @buy90cents.setter\n    def buy90cents(self, value: float):\n        self._property_changed('buy90cents')\n        self.__buy90cents = value        \n\n    @property\n    def period_type(self) -> str:\n        """Period type."""\n        return self.__period_type\n\n    @period_type.setter\n    def period_type(self, value: str):\n        self._property_changed('period_type')\n        self.__period_type = value        \n\n    @property\n    def asset_classifications_country_name(self) -> str:\n        """Country name of asset."""\n        return self.__asset_classifications_country_name\n\n    @asset_classifications_country_name.setter\n    def asset_classifications_country_name(self, value: str):\n        self._property_changed('asset_classifications_country_name')\n        self.__asset_classifications_country_name = value        \n\n    @property\n    def total_hospitalized(self) -> float:\n        """Total number of hospitalized cases."""\n        return self.__total_hospitalized\n\n    @total_hospitalized.setter\n    def total_hospitalized(self, value: float):\n        self._property_changed('total_hospitalized')\n        self.__total_hospitalized = value        \n\n    @property\n    def pegged_refill_interval(self) -> float:\n        """Time interval (in seconds) between a tranche getting filled and the next tranche\n           being shown on the order book."""\n        return self.__pegged_refill_interval\n\n    @pegged_refill_interval.setter\n    def pegged_refill_interval(self, value: float):\n        self._property_changed('pegged_refill_interval')\n        self.__pegged_refill_interval = value        \n\n    @property\n    def fatalities_probable(self) -> float:\n        """Total number of probable fatalities."""\n        return self.__fatalities_probable\n\n    @fatalities_probable.setter\n    def fatalities_probable(self, value: float):\n        self._property_changed('fatalities_probable')\n        self.__fatalities_probable = value        \n\n    @property\n    def administrative_region(self) -> str:\n        """Name of the county or province."""\n        return self.__administrative_region\n\n    @administrative_region.setter\n    def administrative_region(self, value: str):\n        self._property_changed('administrative_region')\n        self.__administrative_region = value        \n\n    @property\n    def open(self) -> float:\n        """Opening level of an asset based on official exchange fixing or calculation agent\n           marked level."""\n        return self.__open\n\n    @open.setter\n    def open(self, value: float):\n        self._property_changed('open')\n        self.__open = value        \n\n    @property\n    def cusip(self) -> str:\n        """CUSIP - Committee on Uniform Securities Identification Procedures number\n           (subject to licensing)."""\n        return self.__cusip\n\n    @cusip.setter\n    def cusip(self, value: str):\n        self._property_changed('cusip')\n        self.__cusip = value        \n\n    @property\n    def total_confirmed_by_state(self) -> float:\n        """Total number of confirmed cases by state."""\n        return self.__total_confirmed_by_state\n\n    @total_confirmed_by_state.setter\n    def total_confirmed_by_state(self, value: float):\n        self._property_changed('total_confirmed_by_state')\n        self.__total_confirmed_by_state = value        \n\n    @property\n    def idea_activity_time(self) -> datetime.datetime:\n        """The time the idea activity took place. If ideaStatus is open, the time reflects\n           the Idea creation time. If ideaStatus is closed, the time reflects\n           the time the idea was closed."""\n        return self.__idea_activity_time\n\n    @idea_activity_time.setter\n    def idea_activity_time(self, value: datetime.datetime):\n        self._property_changed('idea_activity_time')\n        self.__idea_activity_time = value        \n\n    @property\n    def wind_attribute(self) -> str:\n        """The attribute of wind required: direction or speed etc."""\n        return self.__wind_attribute\n\n    @wind_attribute.setter\n    def wind_attribute(self, value: str):\n        self._property_changed('wind_attribute')\n        self.__wind_attribute = value        \n\n    @property\n    def spread_option_atm_fwd_rate(self) -> float:\n        """Spread Option ATM forward rate."""\n        return self.__spread_option_atm_fwd_rate\n\n    @spread_option_atm_fwd_rate.setter\n    def spread_option_atm_fwd_rate(self, value: float):\n        self._property_changed('spread_option_atm_fwd_rate')\n        self.__spread_option_atm_fwd_rate = value        \n\n    @property\n    def net_exposure(self) -> float:\n        """The difference between long and short exposure in the portfolio. If you are $60\n           short and $40 long, then the netExposure would be -$20 (-60+40)."""\n        return self.__net_exposure\n\n    @net_exposure.setter\n    def net_exposure(self, value: float):\n        self._property_changed('net_exposure')\n        self.__net_exposure = value        \n\n    @property\n    def is_legacy_pair_basket(self) -> bool:\n        """Whether or not it is a legacy pair baskets which are not tradabale as don't have\n           secdb asset."""\n        return self.__is_legacy_pair_basket\n\n    @is_legacy_pair_basket.setter\n    def is_legacy_pair_basket(self, value: bool):\n        self._property_changed('is_legacy_pair_basket')\n        self.__is_legacy_pair_basket = value        \n\n    @property\n    def issuer_type(self) -> str:\n        """The type of the bond issuer."""\n        return self.__issuer_type\n\n    @issuer_type.setter\n    def issuer_type(self, value: str):\n        self._property_changed('issuer_type')\n        self.__issuer_type = value        \n\n    @property\n    def buy70cents(self) -> float:\n        """The amount GS would buy for 70 cents charge."""\n        return self.__buy70cents\n\n    @buy70cents.setter\n    def buy70cents(self, value: float):\n        self._property_changed('buy70cents')\n        self.__buy70cents = value        \n\n    @property\n    def strike_reference(self) -> str:\n        """Reference for strike level (enum: spot, forward)."""\n        return self.__strike_reference\n\n    @strike_reference.setter\n    def strike_reference(self, value: str):\n        self._property_changed('strike_reference')\n        self.__strike_reference = value        \n\n    @property\n    def asset_count(self) -> float:\n        """Number of assets in a portfolio or index."""\n        return self.__asset_count\n\n    @asset_count.setter\n    def asset_count(self, value: float):\n        self._property_changed('asset_count')\n        self.__asset_count = value        \n\n    @property\n    def is_order_in_limit(self) -> bool:\n        """Is the order???s passive leg in limit."""\n        return self.__is_order_in_limit\n\n    @is_order_in_limit.setter\n    def is_order_in_limit(self, value: bool):\n        self._property_changed('is_order_in_limit')\n        self.__is_order_in_limit = value        \n\n    @property\n    def fundamental_metric(self) -> float:\n        """Fundamental metric value."""\n        return self.__fundamental_metric\n\n    @fundamental_metric.setter\n    def fundamental_metric(self, value: float):\n        self._property_changed('fundamental_metric')\n        self.__fundamental_metric = value        \n\n    @property\n    def quote_status_id(self) -> str:\n        """PadMaster unique quote status identifier."""\n        return self.__quote_status_id\n\n    @quote_status_id.setter\n    def quote_status_id(self, value: str):\n        self._property_changed('quote_status_id')\n        self.__quote_status_id = value        \n\n    @property\n    def absolute_value(self) -> float:\n        """The notional value of the asset."""\n        return self.__absolute_value\n\n    @absolute_value.setter\n    def absolute_value(self, value: float):\n        self._property_changed('absolute_value')\n        self.__absolute_value = value        \n\n    @property\n    def closing_report(self) -> str:\n        """Report that was published when the trade idea was closed."""\n        return self.__closing_report\n\n    @closing_report.setter\n    def closing_report(self, value: str):\n        self._property_changed('closing_report')\n        self.__closing_report = value        \n\n    @property\n    def previous_total_confirmed(self) -> float:\n        """The previous day's number of total confirmed cases."""\n        return self.__previous_total_confirmed\n\n    @previous_total_confirmed.setter\n    def previous_total_confirmed(self, value: float):\n        self._property_changed('previous_total_confirmed')\n        self.__previous_total_confirmed = value        \n\n    @property\n    def long_tenor(self) -> str:\n        """Tenor of instrument."""\n        return self.__long_tenor\n\n    @long_tenor.setter\n    def long_tenor(self, value: str):\n        self._property_changed('long_tenor')\n        self.__long_tenor = value        \n\n    @property\n    def multiplier(self) -> float:\n        """Underlying unit per asset multiplier."""\n        return self.__multiplier\n\n    @multiplier.setter\n    def multiplier(self, value: float):\n        self._property_changed('multiplier')\n        self.__multiplier = value        \n\n    @property\n    def buy40cents(self) -> float:\n        """The amount GS would buy for 40 cents charge."""\n        return self.__buy40cents\n\n    @buy40cents.setter\n    def buy40cents(self, value: float):\n        self._property_changed('buy40cents')\n        self.__buy40cents = value        \n\n    @property\n    def asset_count_priced(self) -> float:\n        """Number of assets in a portfolio which could be priced."""\n        return self.__asset_count_priced\n\n    @asset_count_priced.setter\n    def asset_count_priced(self, value: float):\n        self._property_changed('asset_count_priced')\n        self.__asset_count_priced = value        \n\n    @property\n    def vote_direction(self) -> float:\n        """Direction of vote."""\n        return self.__vote_direction\n\n    @vote_direction.setter\n    def vote_direction(self, value: float):\n        self._property_changed('vote_direction')\n        self.__vote_direction = value        \n\n    @property\n    def implied_repo_rate(self) -> float:\n        """Rate of return that can be earned by simultaneously selling a bond futures or\n           forward contract, and then buying that actual bond of equal amount in\n           the cash market using borrowed money."""\n        return self.__implied_repo_rate\n\n    @implied_repo_rate.setter\n    def implied_repo_rate(self, value: float):\n        self._property_changed('implied_repo_rate')\n        self.__implied_repo_rate = value        \n\n    @property\n    def settlement_currency(self) -> str:\n        """The settlement currency type for SB swap transactions in the FX asset class."""\n        return self.__settlement_currency\n\n    @settlement_currency.setter\n    def settlement_currency(self, value: str):\n        self._property_changed('settlement_currency')\n        self.__settlement_currency = value        \n\n    @property\n    def wtd_degree_days_forecast(self) -> float:\n        """The forecast for weighted degree days of diff types of given units."""\n        return self.__wtd_degree_days_forecast\n\n    @wtd_degree_days_forecast.setter\n    def wtd_degree_days_forecast(self, value: float):\n        self._property_changed('wtd_degree_days_forecast')\n        self.__wtd_degree_days_forecast = value        \n\n    @property\n    def indication_of_collateralization(self) -> str:\n        """If an SB swap is not cleared, an indication of whether a swap is\n           Uncollateralized (UC), Partially Collateralized (PC), One-Way\n           Collateralized (OC), or Fully Collateralized (FC)."""\n        return self.__indication_of_collateralization\n\n    @indication_of_collateralization.setter\n    def indication_of_collateralization(self, value: str):\n        self._property_changed('indication_of_collateralization')\n        self.__indication_of_collateralization = value        \n\n    @property\n    def future_month_n26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_n26\n\n    @future_month_n26.setter\n    def future_month_n26(self, value: float):\n        self._property_changed('future_month_n26')\n        self.__future_month_n26 = value        \n\n    @property\n    def lending_partner_fee(self) -> float:\n        """Fee earned by the Lending Partner in a securities lending agreement."""\n        return self.__lending_partner_fee\n\n    @lending_partner_fee.setter\n    def lending_partner_fee(self, value: float):\n        self._property_changed('lending_partner_fee')\n        self.__lending_partner_fee = value        \n\n    @property\n    def future_month_n25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_n25\n\n    @future_month_n25.setter\n    def future_month_n25(self, value: float):\n        self._property_changed('future_month_n25')\n        self.__future_month_n25 = value        \n\n    @property\n    def future_month_n24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_n24\n\n    @future_month_n24.setter\n    def future_month_n24(self, value: float):\n        self._property_changed('future_month_n24')\n        self.__future_month_n24 = value        \n\n    @property\n    def primary_vwap_realized_bps(self) -> float:\n        """Realised performance vs Primary VWAP, in bps."""\n        return self.__primary_vwap_realized_bps\n\n    @primary_vwap_realized_bps.setter\n    def primary_vwap_realized_bps(self, value: float):\n        self._property_changed('primary_vwap_realized_bps')\n        self.__primary_vwap_realized_bps = value        \n\n    @property\n    def future_month_n23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_n23\n\n    @future_month_n23.setter\n    def future_month_n23(self, value: float):\n        self._property_changed('future_month_n23')\n        self.__future_month_n23 = value        \n\n    @property\n    def future_month_n22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_n22\n\n    @future_month_n22.setter\n    def future_month_n22(self, value: float):\n        self._property_changed('future_month_n22')\n        self.__future_month_n22 = value        \n\n    @property\n    def future_month_n21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_n21\n\n    @future_month_n21.setter\n    def future_month_n21(self, value: float):\n        self._property_changed('future_month_n21')\n        self.__future_month_n21 = value        \n\n    @property\n    def break_even_inflation(self) -> float:\n        """Break even inflation."""\n        return self.__break_even_inflation\n\n    @break_even_inflation.setter\n    def break_even_inflation(self, value: float):\n        self._property_changed('break_even_inflation')\n        self.__break_even_inflation = value        \n\n    @property\n    def pnl_ytd(self) -> float:\n        """Total PnL Year to date."""\n        return self.__pnl_ytd\n\n    @pnl_ytd.setter\n    def pnl_ytd(self, value: float):\n        self._property_changed('pnl_ytd')\n        self.__pnl_ytd = value        \n\n    @property\n    def leg1_return_type(self) -> str:\n        """Indication if return of leg 1 is total."""\n        return self.__leg1_return_type\n\n    @leg1_return_type.setter\n    def leg1_return_type(self, value: str):\n        self._property_changed('leg1_return_type')\n        self.__leg1_return_type = value        \n\n    @property\n    def tenor2(self) -> str:\n        """Tenor of instrument."""\n        return self.__tenor2\n\n    @tenor2.setter\n    def tenor2(self, value: str):\n        self._property_changed('tenor2')\n        self.__tenor2 = value        \n\n    @property\n    def reset_frequency(self) -> str:\n        """An integer multiplier of a period describing how often the parties to an SB swap\n           transaction shall evaluate and, when applicable, change the price\n           used for the underlying assets of the swap transaction. Such reset\n           frequency may be described as one letter preceded by an integer."""\n        return self.__reset_frequency\n\n    @reset_frequency.setter\n    def reset_frequency(self, value: str):\n        self._property_changed('reset_frequency')\n        self.__reset_frequency = value        \n\n    @property\n    def asset_parameters_payer_frequency(self) -> str:\n        """Tenor"""\n        return self.__asset_parameters_payer_frequency\n\n    @asset_parameters_payer_frequency.setter\n    def asset_parameters_payer_frequency(self, value: str):\n        self._property_changed('asset_parameters_payer_frequency')\n        self.__asset_parameters_payer_frequency = value        \n\n    @property\n    def degree_days_forecast(self) -> float:\n        """The forecast value for degree days."""\n        return self.__degree_days_forecast\n\n    @degree_days_forecast.setter\n    def degree_days_forecast(self, value: float):\n        self._property_changed('degree_days_forecast')\n        self.__degree_days_forecast = value        \n\n    @property\n    def is_manually_silenced(self) -> str:\n        """Indicates if an entity was silenced at time of check."""\n        return self.__is_manually_silenced\n\n    @is_manually_silenced.setter\n    def is_manually_silenced(self, value: str):\n        self._property_changed('is_manually_silenced')\n        self.__is_manually_silenced = value        \n\n    @property\n    def buy3bps(self) -> float:\n        """The amount GS would buy for 3 bps charge."""\n        return self.__buy3bps\n\n    @buy3bps.setter\n    def buy3bps(self, value: float):\n        self._property_changed('buy3bps')\n        self.__buy3bps = value        \n\n    @property\n    def last_updated_by_id(self) -> str:\n        """Unique identifier of user who last updated the object."""\n        return self.__last_updated_by_id\n\n    @last_updated_by_id.setter\n    def last_updated_by_id(self, value: str):\n        self._property_changed('last_updated_by_id')\n        self.__last_updated_by_id = value        \n\n    @property\n    def legal_entity_acct(self) -> str:\n        """Account assoicated with the entity that has legal rights to the fund."""\n        return self.__legal_entity_acct\n\n    @legal_entity_acct.setter\n    def legal_entity_acct(self, value: str):\n        self._property_changed('legal_entity_acct')\n        self.__legal_entity_acct = value        \n\n    @property\n    def target_shareholder_meeting_date(self) -> str:\n        """Target acquisition entity shareholder meeting date."""\n        return self.__target_shareholder_meeting_date\n\n    @target_shareholder_meeting_date.setter\n    def target_shareholder_meeting_date(self, value: str):\n        self._property_changed('target_shareholder_meeting_date')\n        self.__target_shareholder_meeting_date = value        \n\n    @property\n    def pace_of_rollp0(self) -> float:\n        """The 0th-percentile of the pace of roll compared to previous quarters (over the\n           last 5 years) at the same point in time, i.e. cross-sectional."""\n        return self.__pace_of_rollp0\n\n    @pace_of_rollp0.setter\n    def pace_of_rollp0(self, value: float):\n        self._property_changed('pace_of_rollp0')\n        self.__pace_of_rollp0 = value        \n\n    @property\n    def controversy_percentile(self) -> float:\n        """A percentile that captures a company's controversy ranking."""\n        return self.__controversy_percentile\n\n    @controversy_percentile.setter\n    def controversy_percentile(self, value: float):\n        self._property_changed('controversy_percentile')\n        self.__controversy_percentile = value        \n\n    @property\n    def leg1_notional_currency(self) -> str:\n        """An indication of the type of currency of the notional or principal amount."""\n        return self.__leg1_notional_currency\n\n    @leg1_notional_currency.setter\n    def leg1_notional_currency(self, value: str):\n        self._property_changed('leg1_notional_currency')\n        self.__leg1_notional_currency = value        \n\n    @property\n    def compliance_effective_time(self) -> datetime.datetime:\n        """Time that the compliance status became effective."""\n        return self.__compliance_effective_time\n\n    @compliance_effective_time.setter\n    def compliance_effective_time(self, value: datetime.datetime):\n        self._property_changed('compliance_effective_time')\n        self.__compliance_effective_time = value        \n\n    @property\n    def expiration_date(self) -> datetime.date:\n        """The expiration date of the associated contract and the last date it trades."""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: datetime.date):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> str:\n        """Day Count Fraction"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: str):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = value        \n\n    @property\n    def call_last_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__call_last_date\n\n    @call_last_date.setter\n    def call_last_date(self, value: datetime.date):\n        self._property_changed('call_last_date')\n        self.__call_last_date = value        \n\n    @property\n    def factor_return(self) -> float:\n        """Factor return."""\n        return self.__factor_return\n\n    @factor_return.setter\n    def factor_return(self, value: float):\n        self._property_changed('factor_return')\n        self.__factor_return = value        \n\n    @property\n    def passive_flow_ratio(self) -> float:\n        """Ratio of signed passive dollar flow to market cap of global stocks, i.e.\n           proportion of company's market cap that is part of passive fund flows\n           observed weekly. Normalised between -3 (low level) and +3 (high\n           level)."""\n        return self.__passive_flow_ratio\n\n    @passive_flow_ratio.setter\n    def passive_flow_ratio(self, value: float):\n        self._property_changed('passive_flow_ratio')\n        self.__passive_flow_ratio = value        \n\n    @property\n    def composite5_day_adv(self) -> float:\n        """Composite 5 day ADV."""\n        return self.__composite5_day_adv\n\n    @composite5_day_adv.setter\n    def composite5_day_adv(self, value: float):\n        self._property_changed('composite5_day_adv')\n        self.__composite5_day_adv = value        \n\n    @property\n    def marginal_contribution_to_risk(self) -> float:\n        """Marginal contribution of a given asset to portfolio variance, is dependent on\n           covariance matrix."""\n        return self.__marginal_contribution_to_risk\n\n    @marginal_contribution_to_risk.setter\n    def marginal_contribution_to_risk(self, value: float):\n        self._property_changed('marginal_contribution_to_risk')\n        self.__marginal_contribution_to_risk = value        \n\n    @property\n    def close_date(self) -> datetime.date:\n        """Date the trade idea was closed."""\n        return self.__close_date\n\n    @close_date.setter\n    def close_date(self, value: datetime.date):\n        self._property_changed('close_date')\n        self.__close_date = value        \n\n    @property\n    def temperature_hour_forecast(self) -> float:\n        """The hour forecast of max/min temperature of a given day."""\n        return self.__temperature_hour_forecast\n\n    @temperature_hour_forecast.setter\n    def temperature_hour_forecast(self, value: float):\n        self._property_changed('temperature_hour_forecast')\n        self.__temperature_hour_forecast = value        \n\n    @property\n    def new_ideas_wtd(self) -> float:\n        """Ideas received by clients Week to date."""\n        return self.__new_ideas_wtd\n\n    @new_ideas_wtd.setter\n    def new_ideas_wtd(self, value: float):\n        self._property_changed('new_ideas_wtd')\n        self.__new_ideas_wtd = value        \n\n    @property\n    def asset_class_sdr(self) -> str:\n        """An indication of one of the broad categories. For our use case will typically be\n           CO."""\n        return self.__asset_class_sdr\n\n    @asset_class_sdr.setter\n    def asset_class_sdr(self, value: str):\n        self._property_changed('asset_class_sdr')\n        self.__asset_class_sdr = value        \n\n    @property\n    def yield_to_worst(self) -> float:\n        """The return an investor realizes on a bond sold at the mid price at the worst\n           call date."""\n        return self.__yield_to_worst\n\n    @yield_to_worst.setter\n    def yield_to_worst(self, value: float):\n        self._property_changed('yield_to_worst')\n        self.__yield_to_worst = value        \n\n    @property\n    def closing_price(self) -> float:\n        """Closing price."""\n        return self.__closing_price\n\n    @closing_price.setter\n    def closing_price(self, value: float):\n        self._property_changed('closing_price')\n        self.__closing_price = value        \n\n    @property\n    def turnover_composite_adjusted(self) -> float:\n        """Turnover composite adjusted for corporate actions."""\n        return self.__turnover_composite_adjusted\n\n    @turnover_composite_adjusted.setter\n    def turnover_composite_adjusted(self, value: float):\n        self._property_changed('turnover_composite_adjusted')\n        self.__turnover_composite_adjusted = value        \n\n    @property\n    def comment(self) -> str:\n        """The comment associated with the trade idea in URL encoded format."""\n        return self.__comment\n\n    @comment.setter\n    def comment(self, value: str):\n        self._property_changed('comment')\n        self.__comment = value        \n\n    @property\n    def source_symbol(self) -> str:\n        """Source symbol."""\n        return self.__source_symbol\n\n    @source_symbol.setter\n    def source_symbol(self, value: str):\n        self._property_changed('source_symbol')\n        self.__source_symbol = value        \n\n    @property\n    def ask_unadjusted(self) -> float:\n        """Unadjusted ask level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__ask_unadjusted\n\n    @ask_unadjusted.setter\n    def ask_unadjusted(self, value: float):\n        self._property_changed('ask_unadjusted')\n        self.__ask_unadjusted = value        \n\n    @property\n    def restrict_external_derived_data(self) -> bool:\n        """Restricts Ability to Use Externally as Part of Derived Data."""\n        return self.__restrict_external_derived_data\n\n    @restrict_external_derived_data.setter\n    def restrict_external_derived_data(self, value: bool):\n        self._property_changed('restrict_external_derived_data')\n        self.__restrict_external_derived_data = value        \n\n    @property\n    def ask_change(self) -> float:\n        """Change in the ask price."""\n        return self.__ask_change\n\n    @ask_change.setter\n    def ask_change(self, value: float):\n        self._property_changed('ask_change')\n        self.__ask_change = value        \n\n    @property\n    def count_ideas_mtd(self) -> float:\n        """Ideas alive at a time Month to date."""\n        return self.__count_ideas_mtd\n\n    @count_ideas_mtd.setter\n    def count_ideas_mtd(self, value: float):\n        self._property_changed('count_ideas_mtd')\n        self.__count_ideas_mtd = value        \n\n    @property\n    def end_date(self) -> datetime.date:\n        """The maturity, termination, or end date of the reportable SB swap transaction."""\n        return self.__end_date\n\n    @end_date.setter\n    def end_date(self, value: datetime.date):\n        self._property_changed('end_date')\n        self.__end_date = value        \n\n    @property\n    def sunshine(self) -> float:\n        """The percent of sunshine for a given location."""\n        return self.__sunshine\n\n    @sunshine.setter\n    def sunshine(self, value: float):\n        self._property_changed('sunshine')\n        self.__sunshine = value        \n\n    @property\n    def contract_type(self) -> str:\n        """Contract type."""\n        return self.__contract_type\n\n    @contract_type.setter\n    def contract_type(self, value: str):\n        self._property_changed('contract_type')\n        self.__contract_type = value        \n\n    @property\n    def momentum_type(self) -> str:\n        """The mode/gear of an algo order running in range bound or directional mode."""\n        return self.__momentum_type\n\n    @momentum_type.setter\n    def momentum_type(self, value: str):\n        self._property_changed('momentum_type')\n        self.__momentum_type = value        \n\n    @property\n    def specific_risk(self) -> float:\n        """Specific Risk."""\n        return self.__specific_risk\n\n    @specific_risk.setter\n    def specific_risk(self, value: float):\n        self._property_changed('specific_risk')\n        self.__specific_risk = value        \n\n    @property\n    def mdapi(self) -> str:\n        """MDAPI Asset."""\n        return self.__mdapi\n\n    @mdapi.setter\n    def mdapi(self, value: str):\n        self._property_changed('mdapi')\n        self.__mdapi = value        \n\n    @property\n    def payoff_qtd(self) -> float:\n        """Total win divided by total loss Quarter to date."""\n        return self.__payoff_qtd\n\n    @payoff_qtd.setter\n    def payoff_qtd(self, value: float):\n        self._property_changed('payoff_qtd')\n        self.__payoff_qtd = value        \n\n    @property\n    def loss(self) -> float:\n        """Loss price component."""\n        return self.__loss\n\n    @loss.setter\n    def loss(self, value: float):\n        self._property_changed('loss')\n        self.__loss = value        \n\n    @property\n    def midcurve_vol(self) -> float:\n        """Historical implied normal volatility for a liquid point on midcurve vol surface."""\n        return self.__midcurve_vol\n\n    @midcurve_vol.setter\n    def midcurve_vol(self, value: float):\n        self._property_changed('midcurve_vol')\n        self.__midcurve_vol = value        \n\n    @property\n    def sell6bps(self) -> float:\n        """The amount GS would sell for 6 bps charge."""\n        return self.__sell6bps\n\n    @sell6bps.setter\n    def sell6bps(self, value: float):\n        self._property_changed('sell6bps')\n        self.__sell6bps = value        \n\n    @property\n    def trading_cost_pnl(self) -> float:\n        """Trading cost profit and loss (PNL)."""\n        return self.__trading_cost_pnl\n\n    @trading_cost_pnl.setter\n    def trading_cost_pnl(self, value: float):\n        self._property_changed('trading_cost_pnl')\n        self.__trading_cost_pnl = value        \n\n    @property\n    def price_notation_type(self) -> str:\n        """Basis points, Price, Yield, Spread, Coupon, etc., depending on the type of SB\n           swap, which is calculated at affirmation."""\n        return self.__price_notation_type\n\n    @price_notation_type.setter\n    def price_notation_type(self, value: str):\n        self._property_changed('price_notation_type')\n        self.__price_notation_type = value        \n\n    @property\n    def price(self) -> float:\n        """Price of instrument."""\n        return self.__price\n\n    @price.setter\n    def price(self, value: float):\n        self._property_changed('price')\n        self.__price = value        \n\n    @property\n    def payment_quantity(self) -> float:\n        """Quantity in the payment currency."""\n        return self.__payment_quantity\n\n    @payment_quantity.setter\n    def payment_quantity(self, value: float):\n        self._property_changed('payment_quantity')\n        self.__payment_quantity = value        \n\n    @property\n    def redemption_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__redemption_date\n\n    @redemption_date.setter\n    def redemption_date(self, value: datetime.date):\n        self._property_changed('redemption_date')\n        self.__redemption_date = value        \n\n    @property\n    def leg2_notional_currency(self) -> str:\n        """An indication of the type of currency of the notional or principal amount."""\n        return self.__leg2_notional_currency\n\n    @leg2_notional_currency.setter\n    def leg2_notional_currency(self, value: str):\n        self._property_changed('leg2_notional_currency')\n        self.__leg2_notional_currency = value        \n\n    @property\n    def sub_region(self) -> str:\n        """Filter by any identifier of an asset like ticker, bloomberg id etc."""\n        return self.__sub_region\n\n    @sub_region.setter\n    def sub_region(self, value: str):\n        self._property_changed('sub_region')\n        self.__sub_region = value        \n\n    @property\n    def benchmark(self) -> str:\n        """Benchmark index of a contributor."""\n        return self.__benchmark\n\n    @benchmark.setter\n    def benchmark(self, value: str):\n        self._property_changed('benchmark')\n        self.__benchmark = value        \n\n    @property\n    def tcm_cost_participation_rate15_pct(self) -> float:\n        """TCM cost with a 15 percent participation rate."""\n        return self.__tcm_cost_participation_rate15_pct\n\n    @tcm_cost_participation_rate15_pct.setter\n    def tcm_cost_participation_rate15_pct(self, value: float):\n        self._property_changed('tcm_cost_participation_rate15_pct')\n        self.__tcm_cost_participation_rate15_pct = value        \n\n    @property\n    def fiscal_year(self) -> str:\n        """The Calendar Year."""\n        return self.__fiscal_year\n\n    @fiscal_year.setter\n    def fiscal_year(self, value: str):\n        self._property_changed('fiscal_year')\n        self.__fiscal_year = value        \n\n    @property\n    def recall_date(self) -> datetime.date:\n        """The date at which the securities on loan were recalled."""\n        return self.__recall_date\n\n    @recall_date.setter\n    def recall_date(self, value: datetime.date):\n        self._property_changed('recall_date')\n        self.__recall_date = value        \n\n    @property\n    def esg_metric_value(self) -> float:\n        """Value of the metric corresponding to the parameter metricName."""\n        return self.__esg_metric_value\n\n    @esg_metric_value.setter\n    def esg_metric_value(self, value: float):\n        self._property_changed('esg_metric_value')\n        self.__esg_metric_value = value        \n\n    @property\n    def internal(self) -> bool:\n        """Whether request came from internal or external."""\n        return self.__internal\n\n    @internal.setter\n    def internal(self, value: bool):\n        self._property_changed('internal')\n        self.__internal = value        \n\n    @property\n    def gender(self) -> str:\n        """Gender of the patient."""\n        return self.__gender\n\n    @gender.setter\n    def gender(self, value: str):\n        self._property_changed('gender')\n        self.__gender = value        \n\n    @property\n    def asset_classifications_gics_industry(self) -> str:\n        """GICS Industry classification (level 3)."""\n        return self.__asset_classifications_gics_industry\n\n    @asset_classifications_gics_industry.setter\n    def asset_classifications_gics_industry(self, value: str):\n        self._property_changed('asset_classifications_gics_industry')\n        self.__asset_classifications_gics_industry = value        \n\n    @property\n    def adjusted_bid_price(self) -> float:\n        """Latest Bid Price (price willing to buy) adjusted for corporate actions."""\n        return self.__adjusted_bid_price\n\n    @adjusted_bid_price.setter\n    def adjusted_bid_price(self, value: float):\n        self._property_changed('adjusted_bid_price')\n        self.__adjusted_bid_price = value        \n\n    @property\n    def low_unadjusted(self) -> float:\n        """Unadjusted low level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__low_unadjusted\n\n    @low_unadjusted.setter\n    def low_unadjusted(self, value: float):\n        self._property_changed('low_unadjusted')\n        self.__low_unadjusted = value        \n\n    @property\n    def macs_secondary_asset_class(self) -> str:\n        """Indicates the secondary asset class the multi asset class swap falls under."""\n        return self.__macs_secondary_asset_class\n\n    @macs_secondary_asset_class.setter\n    def macs_secondary_asset_class(self, value: str):\n        self._property_changed('macs_secondary_asset_class')\n        self.__macs_secondary_asset_class = value        \n\n    @property\n    def confirmed_per_million(self) -> float:\n        """Total number of confirmed cases per population of one million."""\n        return self.__confirmed_per_million\n\n    @confirmed_per_million.setter\n    def confirmed_per_million(self, value: float):\n        self._property_changed('confirmed_per_million')\n        self.__confirmed_per_million = value        \n\n    @property\n    def data_source_id(self) -> str:\n        """PadMaster unique data source identifier."""\n        return self.__data_source_id\n\n    @data_source_id.setter\n    def data_source_id(self, value: str):\n        self._property_changed('data_source_id')\n        self.__data_source_id = value        \n\n    @property\n    def integrated_score(self) -> float:\n        """Average of the Growth, Financial Returns and (1-Multiple) percentile (a higher\n           score means more attractive)."""\n        return self.__integrated_score\n\n    @integrated_score.setter\n    def integrated_score(self, value: float):\n        self._property_changed('integrated_score')\n        self.__integrated_score = value        \n\n    @property\n    def buy7bps(self) -> float:\n        """The amount GS would buy for 7 bps charge."""\n        return self.__buy7bps\n\n    @buy7bps.setter\n    def buy7bps(self, value: float):\n        self._property_changed('buy7bps')\n        self.__buy7bps = value        \n\n    @property\n    def arrival_mid_unrealized_cash(self) -> float:\n        """Consolidated unrealised performance vs Arrival Mid, in USD."""\n        return self.__arrival_mid_unrealized_cash\n\n    @arrival_mid_unrealized_cash.setter\n    def arrival_mid_unrealized_cash(self, value: float):\n        self._property_changed('arrival_mid_unrealized_cash')\n        self.__arrival_mid_unrealized_cash = value        \n\n    @property\n    def knock_in_price(self) -> float:\n        """Market level required for an order to knock in."""\n        return self.__knock_in_price\n\n    @knock_in_price.setter\n    def knock_in_price(self, value: float):\n        self._property_changed('knock_in_price')\n        self.__knock_in_price = value        \n\n    @property\n    def event(self) -> str:\n        """An indication of whether an SB swap transaction is a post-execution event that\n           affects the price of the swap transaction, e.g. terminations,\n           assignments, novations, exchanges, transfers, amendments, conveyances\n           or extinguishing of rights that change the price of the SB swap."""\n        return self.__event\n\n    @event.setter\n    def event(self, value: str):\n        self._property_changed('event')\n        self.__event = value        \n\n    @property\n    def is_intraday_auction(self) -> bool:\n        """Flag indicating whether the bucket is within the intraday auction or not."""\n        return self.__is_intraday_auction\n\n    @is_intraday_auction.setter\n    def is_intraday_auction(self, value: bool):\n        self._property_changed('is_intraday_auction')\n        self.__is_intraday_auction = value        \n\n    @property\n    def location_name(self) -> str:\n        """Name of geographical region (of varying granularity)."""\n        return self.__location_name\n\n    @location_name.setter\n    def location_name(self, value: str):\n        self._property_changed('location_name')\n        self.__location_name = value        \n\n    @property\n    def coupon(self) -> float:\n        """The fixed coupon for this bond."""\n        return self.__coupon\n\n    @coupon.setter\n    def coupon(self, value: float):\n        self._property_changed('coupon')\n        self.__coupon = value        \n\n    @property\n    def percentage_auction_executed_quantity(self) -> float:\n        """Percentage of total order filled at auction."""\n        return self.__percentage_auction_executed_quantity\n\n    @percentage_auction_executed_quantity.setter\n    def percentage_auction_executed_quantity(self, value: float):\n        self._property_changed('percentage_auction_executed_quantity')\n        self.__percentage_auction_executed_quantity = value        \n\n    @property\n    def avg_yield7_day(self) -> float:\n        """Only used for GS Money Market funds, assumes sum of the past 7 days, divided by\n           7, and expressed as a percent."""\n        return self.__avg_yield7_day\n\n    @avg_yield7_day.setter\n    def avg_yield7_day(self, value: float):\n        self._property_changed('avg_yield7_day')\n        self.__avg_yield7_day = value        \n\n    @property\n    def original_dissemination_id(self) -> str:\n        """On cancellations and corrections, this ID will hold the Dissemination ID of the\n           originally published real-time message."""\n        return self.__original_dissemination_id\n\n    @original_dissemination_id.setter\n    def original_dissemination_id(self, value: str):\n        self._property_changed('original_dissemination_id')\n        self.__original_dissemination_id = value        \n\n    @property\n    def total_on_vent(self) -> float:\n        """Total number of patients on ventilator for COVID."""\n        return self.__total_on_vent\n\n    @total_on_vent.setter\n    def total_on_vent(self, value: float):\n        self._property_changed('total_on_vent')\n        self.__total_on_vent = value        \n\n    @property\n    def twap_unrealized_cash(self) -> float:\n        """Consolidated unrealised performance vs TWAP In Limit, in USD."""\n        return self.__twap_unrealized_cash\n\n    @twap_unrealized_cash.setter\n    def twap_unrealized_cash(self, value: float):\n        self._property_changed('twap_unrealized_cash')\n        self.__twap_unrealized_cash = value        \n\n    @property\n    def sts_credit_market(self) -> str:\n        """Credit risk market."""\n        return self.__sts_credit_market\n\n    @sts_credit_market.setter\n    def sts_credit_market(self, value: str):\n        self._property_changed('sts_credit_market')\n        self.__sts_credit_market = value        \n\n    @property\n    def ons_code(self) -> str:\n        """ONS Region code in the UK."""\n        return self.__ons_code\n\n    @ons_code.setter\n    def ons_code(self, value: str):\n        self._property_changed('ons_code')\n        self.__ons_code = value        \n\n    @property\n    def passive_touch_fills_percentage(self) -> float:\n        """Percent of total order filled at passive touch."""\n        return self.__passive_touch_fills_percentage\n\n    @passive_touch_fills_percentage.setter\n    def passive_touch_fills_percentage(self, value: float):\n        self._property_changed('passive_touch_fills_percentage')\n        self.__passive_touch_fills_percentage = value        \n\n    @property\n    def seniority(self) -> str:\n        """The seniority of the bond."""\n        return self.__seniority\n\n    @seniority.setter\n    def seniority(self, value: str):\n        self._property_changed('seniority')\n        self.__seniority = value        \n\n    @property\n    def leg1_index(self) -> str:\n        """If floating index leg, the index."""\n        return self.__leg1_index\n\n    @leg1_index.setter\n    def leg1_index(self, value: str):\n        self._property_changed('leg1_index')\n        self.__leg1_index = value        \n\n    @property\n    def high_unadjusted(self) -> float:\n        """Unadjusted high level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__high_unadjusted\n\n    @high_unadjusted.setter\n    def high_unadjusted(self, value: float):\n        self._property_changed('high_unadjusted')\n        self.__high_unadjusted = value        \n\n    @property\n    def submission_event(self) -> str:\n        """Describes the status of the submission."""\n        return self.__submission_event\n\n    @submission_event.setter\n    def submission_event(self, value: str):\n        self._property_changed('submission_event')\n        self.__submission_event = value        \n\n    @property\n    def tv_product_mnemonic(self) -> str:\n        """Unique by Trade Vault Product based on Product Taxonomy."""\n        return self.__tv_product_mnemonic\n\n    @tv_product_mnemonic.setter\n    def tv_product_mnemonic(self, value: str):\n        self._property_changed('tv_product_mnemonic')\n        self.__tv_product_mnemonic = value        \n\n    @property\n    def avg_trade_rate_label(self):\n        return self.__avg_trade_rate_label\n\n    @avg_trade_rate_label.setter\n    def avg_trade_rate_label(self, value):\n        self._property_changed('avg_trade_rate_label')\n        self.__avg_trade_rate_label = value        \n\n    @property\n    def last_activity_date(self) -> datetime.date:\n        """Last date an idea was entered in Alpha Capture."""\n        return self.__last_activity_date\n\n    @last_activity_date.setter\n    def last_activity_date(self, value: datetime.date):\n        self._property_changed('last_activity_date')\n        self.__last_activity_date = value        \n\n    @property\n    def dissemination_time(self) -> datetime.datetime:\n        """Time of dissemination."""\n        return self.__dissemination_time\n\n    @dissemination_time.setter\n    def dissemination_time(self, value: datetime.datetime):\n        self._property_changed('dissemination_time')\n        self.__dissemination_time = value        \n\n    @property\n    def price_to_cash(self) -> float:\n        """Price to cash."""\n        return self.__price_to_cash\n\n    @price_to_cash.setter\n    def price_to_cash(self, value: float):\n        self._property_changed('price_to_cash')\n        self.__price_to_cash = value        \n\n    @property\n    def buy10cents(self) -> float:\n        """The amount GS would buy for 10 cents charge."""\n        return self.__buy10cents\n\n    @buy10cents.setter\n    def buy10cents(self, value: float):\n        self._property_changed('buy10cents')\n        self.__buy10cents = value        \n\n    @property\n    def nav_spread(self) -> float:\n        """Net asset value spread. Quoted (running) spread (mid) of the underlying basket\n           of single name CDS. (Theoretical Index value). In basis points."""\n        return self.__nav_spread\n\n    @nav_spread.setter\n    def nav_spread(self, value: float):\n        self._property_changed('nav_spread')\n        self.__nav_spread = value        \n\n    @property\n    def venue_mic(self) -> str:\n        """MIC code of the venue."""\n        return self.__venue_mic\n\n    @venue_mic.setter\n    def venue_mic(self, value: str):\n        self._property_changed('venue_mic')\n        self.__venue_mic = value        \n\n    @property\n    def dollar_total_return(self) -> float:\n        """The dollar total return of an instrument."""\n        return self.__dollar_total_return\n\n    @dollar_total_return.setter\n    def dollar_total_return(self, value: float):\n        self._property_changed('dollar_total_return')\n        self.__dollar_total_return = value        \n\n    @property\n    def block_unit(self) -> str:\n        """Unit of measure used for Block trades."""\n        return self.__block_unit\n\n    @block_unit.setter\n    def block_unit(self, value: str):\n        self._property_changed('block_unit')\n        self.__block_unit = value        \n\n    @property\n    def mid_spread(self) -> float:\n        """Spread between the yields of a debt security and its benchmark when both are\n           purchased at mid price."""\n        return self.__mid_spread\n\n    @mid_spread.setter\n    def mid_spread(self, value: float):\n        self._property_changed('mid_spread')\n        self.__mid_spread = value        \n\n    @property\n    def istat_province_code(self) -> float:\n        """Italian province code (ISTAT 2019)."""\n        return self.__istat_province_code\n\n    @istat_province_code.setter\n    def istat_province_code(self, value: float):\n        self._property_changed('istat_province_code')\n        self.__istat_province_code = value        \n\n    @property\n    def total_recovered_by_state(self) -> float:\n        """Total number of recovered cases by state."""\n        return self.__total_recovered_by_state\n\n    @total_recovered_by_state.setter\n    def total_recovered_by_state(self, value: float):\n        self._property_changed('total_recovered_by_state')\n        self.__total_recovered_by_state = value        \n\n    @property\n    def repurchase_rate(self) -> float:\n        """Repurchase Rate."""\n        return self.__repurchase_rate\n\n    @repurchase_rate.setter\n    def repurchase_rate(self, value: float):\n        self._property_changed('repurchase_rate')\n        self.__repurchase_rate = value        \n\n    @property\n    def data_source(self) -> str:\n        """The source of data from the feeds."""\n        return self.__data_source\n\n    @data_source.setter\n    def data_source(self, value: str):\n        self._property_changed('data_source')\n        self.__data_source = value        \n\n    @property\n    def total_being_tested(self) -> float:\n        """Total number of cases waiting for test results."""\n        return self.__total_being_tested\n\n    @total_being_tested.setter\n    def total_being_tested(self, value: float):\n        self._property_changed('total_being_tested')\n        self.__total_being_tested = value        \n\n    @property\n    def cleared_or_bilateral(self) -> str:\n        """An indication of whether or not an SB swap transaction is going to be cleared by\n           a derivatives clearing organization."""\n        return self.__cleared_or_bilateral\n\n    @cleared_or_bilateral.setter\n    def cleared_or_bilateral(self, value: str):\n        self._property_changed('cleared_or_bilateral')\n        self.__cleared_or_bilateral = value        \n\n    @property\n    def metric_name(self) -> str:\n        """Name of the metric."""\n        return self.__metric_name\n\n    @metric_name.setter\n    def metric_name(self, value: str):\n        self._property_changed('metric_name')\n        self.__metric_name = value        \n\n    @property\n    def ask_gspread(self) -> float:\n        """Ask G spread."""\n        return self.__ask_gspread\n\n    @ask_gspread.setter\n    def ask_gspread(self, value: float):\n        self._property_changed('ask_gspread')\n        self.__ask_gspread = value        \n\n    @property\n    def forecast_hour(self) -> float:\n        """The hour forecast for maximum or minumum temperature of given location."""\n        return self.__forecast_hour\n\n    @forecast_hour.setter\n    def forecast_hour(self, value: float):\n        self._property_changed('forecast_hour')\n        self.__forecast_hour = value        \n\n    @property\n    def leg2_payment_type(self) -> str:\n        """Type of payment stream on leg 2."""\n        return self.__leg2_payment_type\n\n    @leg2_payment_type.setter\n    def leg2_payment_type(self, value: str):\n        self._property_changed('leg2_payment_type')\n        self.__leg2_payment_type = value        \n\n    @property\n    def cal_spread_mis_pricing(self) -> float:\n        """Futures implied funding rate relative to interest rate benchmark (usually Libor-\n           based). Represents dividend-adjusted rate at which investor is\n           borrowing (lending) when long (short) future."""\n        return self.__cal_spread_mis_pricing\n\n    @cal_spread_mis_pricing.setter\n    def cal_spread_mis_pricing(self, value: float):\n        self._property_changed('cal_spread_mis_pricing')\n        self.__cal_spread_mis_pricing = value        \n\n    @property\n    def total_tested_negative(self) -> float:\n        """Total number of test cases that are negative."""\n        return self.__total_tested_negative\n\n    @total_tested_negative.setter\n    def total_tested_negative(self, value: float):\n        self._property_changed('total_tested_negative')\n        self.__total_tested_negative = value        \n\n    @property\n    def rate366(self) -> float:\n        """Rate with interest calculated according to the number of days in a leap year,\n           366."""\n        return self.__rate366\n\n    @rate366.setter\n    def rate366(self, value: float):\n        self._property_changed('rate366')\n        self.__rate366 = value        \n\n    @property\n    def platform(self) -> str:\n        """Originating platform."""\n        return self.__platform\n\n    @platform.setter\n    def platform(self, value: str):\n        self._property_changed('platform')\n        self.__platform = value        \n\n    @property\n    def rate365(self) -> float:\n        """Rate with interest calculated according to a normal number of days in the total\n           year, 365."""\n        return self.__rate365\n\n    @rate365.setter\n    def rate365(self, value: float):\n        self._property_changed('rate365')\n        self.__rate365 = value        \n\n    @property\n    def fixed_rate_frequency(self) -> str:\n        """Tenor"""\n        return self.__fixed_rate_frequency\n\n    @fixed_rate_frequency.setter\n    def fixed_rate_frequency(self, value: str):\n        self._property_changed('fixed_rate_frequency')\n        self.__fixed_rate_frequency = value        \n\n    @property\n    def rate360(self) -> float:\n        """Rate with interest calculated according to the discount method, using the number\n           of days used by banks, 360."""\n        return self.__rate360\n\n    @rate360.setter\n    def rate360(self, value: float):\n        self._property_changed('rate360')\n        self.__rate360 = value        \n\n    @property\n    def is_continuous(self) -> bool:\n        """Flag indicating whether the bucket is within continuous or not."""\n        return self.__is_continuous\n\n    @is_continuous.setter\n    def is_continuous(self, value: bool):\n        self._property_changed('is_continuous')\n        self.__is_continuous = value        \n\n    @property\n    def value(self) -> float:\n        """The given value."""\n        return self.__value\n\n    @value.setter\n    def value(self, value: float):\n        self._property_changed('value')\n        self.__value = value        \n\n    @property\n    def payer_designated_maturity(self) -> str:\n        """Tenor"""\n        return self.__payer_designated_maturity\n\n    @payer_designated_maturity.setter\n    def payer_designated_maturity(self, value: str):\n        self._property_changed('payer_designated_maturity')\n        self.__payer_designated_maturity = value        \n\n    @property\n    def product_type(self) -> str:\n        """Product type of basket"""\n        return self.__product_type\n\n    @product_type.setter\n    def product_type(self, value: str):\n        self._property_changed('product_type')\n        self.__product_type = value        \n\n    @property\n    def mdv22_day(self) -> float:\n        """Twenty two day median of shares traded per day."""\n        return self.__mdv22_day\n\n    @mdv22_day.setter\n    def mdv22_day(self, value: float):\n        self._property_changed('mdv22_day')\n        self.__mdv22_day = value        \n\n    @property\n    def twap_realized_bps(self) -> float:\n        """Consolidated realised performance vs TWAP In Limit, in bps."""\n        return self.__twap_realized_bps\n\n    @twap_realized_bps.setter\n    def twap_realized_bps(self, value: float):\n        self._property_changed('twap_realized_bps')\n        self.__twap_realized_bps = value        \n\n    @property\n    def test_measure_label(self) -> str:\n        """Semantic label for the test measure."""\n        return self.__test_measure_label\n\n    @test_measure_label.setter\n    def test_measure_label(self, value: str):\n        self._property_changed('test_measure_label')\n        self.__test_measure_label = value        \n\n    @property\n    def quantity(self) -> float:\n        """Number of units of a given asset held within a portfolio."""\n        return self.__quantity\n\n    @quantity.setter\n    def quantity(self, value: float):\n        self._property_changed('quantity')\n        self.__quantity = value        \n\n    @property\n    def report_id(self) -> str:\n        """Report Identifier."""\n        return self.__report_id\n\n    @report_id.setter\n    def report_id(self, value: str):\n        self._property_changed('report_id')\n        self.__report_id = value        \n\n    @property\n    def index_weight(self) -> float:\n        """Weight of MSCI World positions in the region or sector (%)."""\n        return self.__index_weight\n\n    @index_weight.setter\n    def index_weight(self, value: float):\n        self._property_changed('index_weight')\n        self.__index_weight = value        \n\n    @property\n    def macs_primary_asset_class(self) -> str:\n        """Indicates the primary asset class the multi asset class swap falls under."""\n        return self.__macs_primary_asset_class\n\n    @macs_primary_asset_class.setter\n    def macs_primary_asset_class(self, value: str):\n        self._property_changed('macs_primary_asset_class')\n        self.__macs_primary_asset_class = value        \n\n    @property\n    def trader(self) -> str:\n        """Trader name."""\n        return self.__trader\n\n    @trader.setter\n    def trader(self, value: str):\n        self._property_changed('trader')\n        self.__trader = value        \n\n    @property\n    def leg2_price_type(self) -> str:\n        """Price denomination and unit of leg 2."""\n        return self.__leg2_price_type\n\n    @leg2_price_type.setter\n    def leg2_price_type(self, value: str):\n        self._property_changed('leg2_price_type')\n        self.__leg2_price_type = value        \n\n    @property\n    def total_active(self) -> float:\n        """Total number of confirmed cases minus recovered and fatalities."""\n        return self.__total_active\n\n    @total_active.setter\n    def total_active(self, value: float):\n        self._property_changed('total_active')\n        self.__total_active = value        \n\n    @property\n    def gsid2(self) -> str:\n        """GSID identifier for datasets which have two gsids in the measures."""\n        return self.__gsid2\n\n    @gsid2.setter\n    def gsid2(self, value: str):\n        self._property_changed('gsid2')\n        self.__gsid2 = value        \n\n    @property\n    def matched_maturity_ois_swap_spread(self) -> float:\n        """Spread between a Matched Maturity OIS Swap Rate and the yield of the bond."""\n        return self.__matched_maturity_ois_swap_spread\n\n    @matched_maturity_ois_swap_spread.setter\n    def matched_maturity_ois_swap_spread(self, value: float):\n        self._property_changed('matched_maturity_ois_swap_spread')\n        self.__matched_maturity_ois_swap_spread = value        \n\n    @property\n    def valuation_date(self) -> str:\n        """Specific to rates, the date a valuation is recorded."""\n        return self.__valuation_date\n\n    @valuation_date.setter\n    def valuation_date(self, value: str):\n        self._property_changed('valuation_date')\n        self.__valuation_date = value        \n\n    @property\n    def restrict_gs_federation(self) -> bool:\n        """Restricts GS Federation Visibility."""\n        return self.__restrict_gs_federation\n\n    @restrict_gs_federation.setter\n    def restrict_gs_federation(self, value: bool):\n        self._property_changed('restrict_gs_federation')\n        self.__restrict_gs_federation = value        \n\n    @property\n    def position_source(self) -> str:\n        """Specifies position data location for the portfolio"""\n        return self.__position_source\n\n    @position_source.setter\n    def position_source(self, value: str):\n        self._property_changed('position_source')\n        self.__position_source = value        \n\n    @property\n    def tcm_cost_horizon6_hour(self) -> float:\n        """TCM cost with a 6 hour time horizon."""\n        return self.__tcm_cost_horizon6_hour\n\n    @tcm_cost_horizon6_hour.setter\n    def tcm_cost_horizon6_hour(self, value: float):\n        self._property_changed('tcm_cost_horizon6_hour')\n        self.__tcm_cost_horizon6_hour = value        \n\n    @property\n    def buy200cents(self) -> float:\n        """The amount GS would buy for 200 cents charge."""\n        return self.__buy200cents\n\n    @buy200cents.setter\n    def buy200cents(self, value: float):\n        self._property_changed('buy200cents')\n        self.__buy200cents = value        \n\n    @property\n    def vwap_unrealized_bps(self) -> float:\n        """Consolidated unrealised performance vs VWAP, in bps."""\n        return self.__vwap_unrealized_bps\n\n    @vwap_unrealized_bps.setter\n    def vwap_unrealized_bps(self, value: float):\n        self._property_changed('vwap_unrealized_bps')\n        self.__vwap_unrealized_bps = value        \n\n    @property\n    def price_to_book(self) -> float:\n        """Price to book."""\n        return self.__price_to_book\n\n    @price_to_book.setter\n    def price_to_book(self, value: float):\n        self._property_changed('price_to_book')\n        self.__price_to_book = value        \n\n    @property\n    def isin(self) -> str:\n        """ISIN - International securities identifier number (subect to licensing)."""\n        return self.__isin\n\n    @isin.setter\n    def isin(self, value: str):\n        self._property_changed('isin')\n        self.__isin = value        \n\n    @property\n    def pl_id(self) -> str:\n        """Platts Symbol Name or description."""\n        return self.__pl_id\n\n    @pl_id.setter\n    def pl_id(self, value: str):\n        self._property_changed('pl_id')\n        self.__pl_id = value        \n\n    @property\n    def last_returns_start_date(self) -> datetime.date:\n        """Hedge fund last returns after start date."""\n        return self.__last_returns_start_date\n\n    @last_returns_start_date.setter\n    def last_returns_start_date(self, value: datetime.date):\n        self._property_changed('last_returns_start_date')\n        self.__last_returns_start_date = value        \n\n    @property\n    def collateral_value_variance(self) -> float:\n        """Diffrence between actual and required collateral levels."""\n        return self.__collateral_value_variance\n\n    @collateral_value_variance.setter\n    def collateral_value_variance(self, value: float):\n        self._property_changed('collateral_value_variance')\n        self.__collateral_value_variance = value        \n\n    @property\n    def year(self) -> str:\n        """Year of forecast."""\n        return self.__year\n\n    @year.setter\n    def year(self, value: str):\n        self._property_changed('year')\n        self.__year = value        \n\n    @property\n    def forecast_period(self) -> str:\n        """Year, quarter or horizon for which the forecast holds."""\n        return self.__forecast_period\n\n    @forecast_period.setter\n    def forecast_period(self, value: str):\n        self._property_changed('forecast_period')\n        self.__forecast_period = value        \n\n    @property\n    def call_first_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__call_first_date\n\n    @call_first_date.setter\n    def call_first_date(self, value: datetime.date):\n        self._property_changed('call_first_date')\n        self.__call_first_date = value        \n\n    @property\n    def data_set_ids(self) -> Tuple[Tuple[str, ...], ...]:\n        """The dataset Ids."""\n        return self.__data_set_ids\n\n    @data_set_ids.setter\n    def data_set_ids(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('data_set_ids')\n        self.__data_set_ids = value        \n\n    @property\n    def economic_terms_hash(self) -> str:\n        """Hash code for an asset."""\n        return self.__economic_terms_hash\n\n    @economic_terms_hash.setter\n    def economic_terms_hash(self, value: str):\n        self._property_changed('economic_terms_hash')\n        self.__economic_terms_hash = value        \n\n    @property\n    def num_beds(self) -> float:\n        """Maximum number of beds for which a hospital holds a license to operate; however,\n           many hospitals do not operate all the beds for which they are\n           licensed."""\n        return self.__num_beds\n\n    @num_beds.setter\n    def num_beds(self, value: float):\n        self._property_changed('num_beds')\n        self.__num_beds = value        \n\n    @property\n    def sell20bps(self) -> float:\n        """The amount GS would sell for 20 bps charge."""\n        return self.__sell20bps\n\n    @sell20bps.setter\n    def sell20bps(self, value: float):\n        self._property_changed('sell20bps')\n        self.__sell20bps = value        \n\n    @property\n    def client_type(self) -> str:\n        """Client type, ex: Hedge Fund, Real Money, Corporate, etc."""\n        return self.__client_type\n\n    @client_type.setter\n    def client_type(self, value: str):\n        self._property_changed('client_type')\n        self.__client_type = value        \n\n    @property\n    def percentage_close_executed_quantity(self) -> float:\n        """Percent of order filled in closing Auction."""\n        return self.__percentage_close_executed_quantity\n\n    @percentage_close_executed_quantity.setter\n    def percentage_close_executed_quantity(self, value: float):\n        self._property_changed('percentage_close_executed_quantity')\n        self.__percentage_close_executed_quantity = value        \n\n    @property\n    def macaulay_duration(self) -> float:\n        """Measure of a bond's price sensitivity to changes in interest rates."""\n        return self.__macaulay_duration\n\n    @macaulay_duration.setter\n    def macaulay_duration(self, value: float):\n        self._property_changed('macaulay_duration')\n        self.__macaulay_duration = value        \n\n    @property\n    def available_inventory(self) -> float:\n        """An estimated indication of the share quantity potentially available to borrow in\n           the relevant asset."""\n        return self.__available_inventory\n\n    @available_inventory.setter\n    def available_inventory(self, value: float):\n        self._property_changed('available_inventory')\n        self.__available_inventory = value        \n\n    @property\n    def est1_day_complete_pct(self) -> float:\n        """Estimated 1 day completion percentage."""\n        return self.__est1_day_complete_pct\n\n    @est1_day_complete_pct.setter\n    def est1_day_complete_pct(self, value: float):\n        self._property_changed('est1_day_complete_pct')\n        self.__est1_day_complete_pct = value        \n\n    @property\n    def relative_hit_rate_ytd(self) -> float:\n        """Hit Rate Ratio Year to Date."""\n        return self.__relative_hit_rate_ytd\n\n    @relative_hit_rate_ytd.setter\n    def relative_hit_rate_ytd(self, value: float):\n        self._property_changed('relative_hit_rate_ytd')\n        self.__relative_hit_rate_ytd = value        \n\n    @property\n    def created_by_id(self) -> str:\n        """Unique identifier of user who created the object."""\n        return self.__created_by_id\n\n    @created_by_id.setter\n    def created_by_id(self, value: str):\n        self._property_changed('created_by_id')\n        self.__created_by_id = value        \n\n    @property\n    def market_data_type(self) -> str:\n        """The market data type (e.g. IR BASIS, FX Vol). This can be resolved into a\n           dataset when combined with vendor and intraday=true/false."""\n        return self.__market_data_type\n\n    @market_data_type.setter\n    def market_data_type(self, value: str):\n        self._property_changed('market_data_type')\n        self.__market_data_type = value        \n\n    @property\n    def real_short_rates_contribution(self) -> float:\n        """Contribution of short rate component to real FCI."""\n        return self.__real_short_rates_contribution\n\n    @real_short_rates_contribution.setter\n    def real_short_rates_contribution(self, value: float):\n        self._property_changed('real_short_rates_contribution')\n        self.__real_short_rates_contribution = value        \n\n    @property\n    def metric_category(self) -> str:\n        """Category of the metric."""\n        return self.__metric_category\n\n    @metric_category.setter\n    def metric_category(self, value: str):\n        self._property_changed('metric_category')\n        self.__metric_category = value        \n\n    @property\n    def annualized_carry(self) -> float:\n        """Annualized carry of the bond."""\n        return self.__annualized_carry\n\n    @annualized_carry.setter\n    def annualized_carry(self, value: float):\n        self._property_changed('annualized_carry')\n        self.__annualized_carry = value        \n\n    @property\n    def value_previous(self) -> str:\n        """Value for the previous period after the revision (if revision is applicable)."""\n        return self.__value_previous\n\n    @value_previous.setter\n    def value_previous(self, value: str):\n        self._property_changed('value_previous')\n        self.__value_previous = value        \n\n    @property\n    def transmission_classification(self) -> str:\n        """Classification of the transmission."""\n        return self.__transmission_classification\n\n    @transmission_classification.setter\n    def transmission_classification(self, value: str):\n        self._property_changed('transmission_classification')\n        self.__transmission_classification = value        \n\n    @property\n    def avg_trade_rate(self) -> float:\n        """The Average Trading Rate of the stock on the particular date."""\n        return self.__avg_trade_rate\n\n    @avg_trade_rate.setter\n    def avg_trade_rate(self, value: float):\n        self._property_changed('avg_trade_rate')\n        self.__avg_trade_rate = value        \n\n    @property\n    def short_level(self) -> float:\n        """Level of the 5-day normalized flow for short selling/covering."""\n        return self.__short_level\n\n    @short_level.setter\n    def short_level(self, value: float):\n        self._property_changed('short_level')\n        self.__short_level = value        \n\n    @property\n    def version(self) -> float:\n        """Version number."""\n        return self.__version\n\n    @version.setter\n    def version(self, value: float):\n        self._property_changed('version')\n        self.__version = value        \n\n    @property\n    def category_type(self) -> str:\n        """Type of the category."""\n        return self.__category_type\n\n    @category_type.setter\n    def category_type(self, value: str):\n        self._property_changed('category_type')\n        self.__category_type = value        \n\n    @property\n    def policy_rate_expectation(self) -> float:\n        """Returns the historical policy rate expectations for a given meeting date."""\n        return self.__policy_rate_expectation\n\n    @policy_rate_expectation.setter\n    def policy_rate_expectation(self, value: float):\n        self._property_changed('policy_rate_expectation')\n        self.__policy_rate_expectation = value        \n\n    @property\n    def upload_date(self) -> datetime.date:\n        """Index Position Upload Date."""\n        return self.__upload_date\n\n    @upload_date.setter\n    def upload_date(self, value: datetime.date):\n        self._property_changed('upload_date')\n        self.__upload_date = value        \n\n    @property\n    def block_off_facility(self) -> str:\n        """An indication of whether this is a block trade or off-facility swap."""\n        return self.__block_off_facility\n\n    @block_off_facility.setter\n    def block_off_facility(self, value: str):\n        self._property_changed('block_off_facility')\n        self.__block_off_facility = value        \n\n    @property\n    def unrealized_vwap_performance_usd(self) -> float:\n        """Average execution price vs Consolidated VWAP (in limit) since order inception\n           ??? unrealized in $."""\n        return self.__unrealized_vwap_performance_usd\n\n    @unrealized_vwap_performance_usd.setter\n    def unrealized_vwap_performance_usd(self, value: float):\n        self._property_changed('unrealized_vwap_performance_usd')\n        self.__unrealized_vwap_performance_usd = value        \n\n    @property\n    def pace_of_rollp75(self) -> float:\n        """The 75th-percentile of the pace of roll compared to previous quarters (over the\n           last 5 years) at the same point in time, i.e. cross-sectional.."""\n        return self.__pace_of_rollp75\n\n    @pace_of_rollp75.setter\n    def pace_of_rollp75(self, value: float):\n        self._property_changed('pace_of_rollp75')\n        self.__pace_of_rollp75 = value        \n\n    @property\n    def earnings_per_share_positive(self) -> float:\n        """Earnings per share positive."""\n        return self.__earnings_per_share_positive\n\n    @earnings_per_share_positive.setter\n    def earnings_per_share_positive(self, value: float):\n        self._property_changed('earnings_per_share_positive')\n        self.__earnings_per_share_positive = value        \n\n    @property\n    def num_icu_beds(self) -> float:\n        """All qualified Intensive Care Unit beds, including psychiatric ICU beds and Detox\n           ICU beds."""\n        return self.__num_icu_beds\n\n    @num_icu_beds.setter\n    def num_icu_beds(self, value: float):\n        self._property_changed('num_icu_beds')\n        self.__num_icu_beds = value        \n\n    @property\n    def bucket_volume_in_percentage(self) -> float:\n        """Forecast of the percentage volume of shares in the bucket relative to the total\n           volume forecasted for the day."""\n        return self.__bucket_volume_in_percentage\n\n    @bucket_volume_in_percentage.setter\n    def bucket_volume_in_percentage(self, value: float):\n        self._property_changed('bucket_volume_in_percentage')\n        self.__bucket_volume_in_percentage = value        \n\n    @property\n    def estimated_trading_cost(self) -> float:\n        """The estimated transaction cost of the order."""\n        return self.__estimated_trading_cost\n\n    @estimated_trading_cost.setter\n    def estimated_trading_cost(self, value: float):\n        self._property_changed('estimated_trading_cost')\n        self.__estimated_trading_cost = value        \n\n    @property\n    def eid(self) -> str:\n        """Goldman Sachs internal exchange identifier."""\n        return self.__eid\n\n    @eid.setter\n    def eid(self, value: str):\n        self._property_changed('eid')\n        self.__eid = value        \n\n    @property\n    def relative_return_qtd(self) -> float:\n        """Relative Return Quarter to Date."""\n        return self.__relative_return_qtd\n\n    @relative_return_qtd.setter\n    def relative_return_qtd(self, value: float):\n        self._property_changed('relative_return_qtd')\n        self.__relative_return_qtd = value        \n\n    @property\n    def assessed_test_measure(self) -> float:\n        """Generic field to hold aggregated assessed numeric measure for test result. This\n           field stores the aggregated test measures of entities which are not\n           temporarily ignored by silencing (like max delay across unsilenced\n           assets). This is further used to perform any follow up actions like\n           alerting."""\n        return self.__assessed_test_measure\n\n    @assessed_test_measure.setter\n    def assessed_test_measure(self, value: float):\n        self._property_changed('assessed_test_measure')\n        self.__assessed_test_measure = value        \n\n    @property\n    def mkt_quoting_style(self) -> str:\n        """Quoting style."""\n        return self.__mkt_quoting_style\n\n    @mkt_quoting_style.setter\n    def mkt_quoting_style(self, value: str):\n        self._property_changed('mkt_quoting_style')\n        self.__mkt_quoting_style = value        \n\n    @property\n    def expiration_tenor(self) -> str:\n        """Tenor"""\n        return self.__expiration_tenor\n\n    @expiration_tenor.setter\n    def expiration_tenor(self, value: str):\n        self._property_changed('expiration_tenor')\n        self.__expiration_tenor = value        \n\n    @property\n    def price_limit(self) -> float:\n        """Limit price of the order."""\n        return self.__price_limit\n\n    @price_limit.setter\n    def price_limit(self, value: float):\n        self._property_changed('price_limit')\n        self.__price_limit = value        \n\n    @property\n    def market_model_id(self) -> str:\n        """Marquee unique market model identifier"""\n        return self.__market_model_id\n\n    @market_model_id.setter\n    def market_model_id(self, value: str):\n        self._property_changed('market_model_id')\n        self.__market_model_id = value        \n\n    @property\n    def receiver_frequency(self) -> str:\n        """Tenor"""\n        return self.__receiver_frequency\n\n    @receiver_frequency.setter\n    def receiver_frequency(self, value: str):\n        self._property_changed('receiver_frequency')\n        self.__receiver_frequency = value        \n\n    @property\n    def realized_correlation(self) -> float:\n        """Correlation of an asset realized by observations of market prices."""\n        return self.__realized_correlation\n\n    @realized_correlation.setter\n    def realized_correlation(self, value: float):\n        self._property_changed('realized_correlation')\n        self.__realized_correlation = value        \n\n    @property\n    def issue_status(self) -> str:\n        """Status of the issue."""\n        return self.__issue_status\n\n    @issue_status.setter\n    def issue_status(self, value: str):\n        self._property_changed('issue_status')\n        self.__issue_status = value        \n\n    @property\n    def collateral_value_actual(self) -> float:\n        """Value of collateral covering the given position."""\n        return self.__collateral_value_actual\n\n    @collateral_value_actual.setter\n    def collateral_value_actual(self, value: float):\n        self._property_changed('collateral_value_actual')\n        self.__collateral_value_actual = value        \n\n    @property\n    def atm_fwd_rate(self) -> float:\n        """ATM forward rate."""\n        return self.__atm_fwd_rate\n\n    @atm_fwd_rate.setter\n    def atm_fwd_rate(self, value: float):\n        self._property_changed('atm_fwd_rate')\n        self.__atm_fwd_rate = value        \n\n    @property\n    def tcm_cost_participation_rate75_pct(self) -> float:\n        """TCM cost with a 75 percent participation rate."""\n        return self.__tcm_cost_participation_rate75_pct\n\n    @tcm_cost_participation_rate75_pct.setter\n    def tcm_cost_participation_rate75_pct(self, value: float):\n        self._property_changed('tcm_cost_participation_rate75_pct')\n        self.__tcm_cost_participation_rate75_pct = value        \n\n    @property\n    def close(self) -> float:\n        """Closing level of an asset based on official exchange fixing or calculation agent\n           marked level."""\n        return self.__close\n\n    @close.setter\n    def close(self, value: float):\n        self._property_changed('close')\n        self.__close = value        \n\n    @property\n    def es_product_impact_score(self) -> float:\n        """A company's score for E&S subsector-relative product impact metrics."""\n        return self.__es_product_impact_score\n\n    @es_product_impact_score.setter\n    def es_product_impact_score(self, value: float):\n        self._property_changed('es_product_impact_score')\n        self.__es_product_impact_score = value        \n\n    @property\n    def equity_vega(self) -> float:\n        """Vega exposure to equity products."""\n        return self.__equity_vega\n\n    @equity_vega.setter\n    def equity_vega(self, value: float):\n        self._property_changed('equity_vega')\n        self.__equity_vega = value        \n\n    @property\n    def executed_fill_quantity(self) -> float:\n        """Executed quantity - on executions."""\n        return self.__executed_fill_quantity\n\n    @executed_fill_quantity.setter\n    def executed_fill_quantity(self, value: float):\n        self._property_changed('executed_fill_quantity')\n        self.__executed_fill_quantity = value        \n\n    @property\n    def lender_payment(self) -> float:\n        """Payment made to lender's bank in support of the income accrued from securities\n           lending."""\n        return self.__lender_payment\n\n    @lender_payment.setter\n    def lender_payment(self, value: float):\n        self._property_changed('lender_payment')\n        self.__lender_payment = value        \n\n    @property\n    def five_day_move(self) -> float:\n        """Five day move in the price."""\n        return self.__five_day_move\n\n    @five_day_move.setter\n    def five_day_move(self, value: float):\n        self._property_changed('five_day_move')\n        self.__five_day_move = value        \n\n    @property\n    def value_format(self) -> str:\n        """Value format."""\n        return self.__value_format\n\n    @value_format.setter\n    def value_format(self, value: str):\n        self._property_changed('value_format')\n        self.__value_format = value        \n\n    @property\n    def wind_chill_forecast(self) -> float:\n        """The forecast wind chill of given units."""\n        return self.__wind_chill_forecast\n\n    @wind_chill_forecast.setter\n    def wind_chill_forecast(self, value: float):\n        self._property_changed('wind_chill_forecast')\n        self.__wind_chill_forecast = value        \n\n    @property\n    def target_notional(self) -> float:\n        """Notional value of the hedge target."""\n        return self.__target_notional\n\n    @target_notional.setter\n    def target_notional(self, value: float):\n        self._property_changed('target_notional')\n        self.__target_notional = value        \n\n    @property\n    def fill_leg_id(self) -> str:\n        """Unique identifier for the leg on which the fill executed."""\n        return self.__fill_leg_id\n\n    @fill_leg_id.setter\n    def fill_leg_id(self, value: str):\n        self._property_changed('fill_leg_id')\n        self.__fill_leg_id = value        \n\n    @property\n    def rationale(self) -> str:\n        """Reason for changing the status of a trade idea."""\n        return self.__rationale\n\n    @rationale.setter\n    def rationale(self, value: str):\n        self._property_changed('rationale')\n        self.__rationale = value        \n\n    @property\n    def realized_twap_performance_bps(self) -> float:\n        """Average execution price vs Consolidated TWAP since order inception ??? realized."""\n        return self.__realized_twap_performance_bps\n\n    @realized_twap_performance_bps.setter\n    def realized_twap_performance_bps(self, value: float):\n        self._property_changed('realized_twap_performance_bps')\n        self.__realized_twap_performance_bps = value        \n\n    @property\n    def last_updated_since(self) -> datetime.datetime:\n        """ISO 8601-formatted timestamp"""\n        return self.__last_updated_since\n\n    @last_updated_since.setter\n    def last_updated_since(self, value: datetime.datetime):\n        self._property_changed('last_updated_since')\n        self.__last_updated_since = value        \n\n    @property\n    def total_tests(self) -> float:\n        """Total number of tests administered."""\n        return self.__total_tests\n\n    @total_tests.setter\n    def total_tests(self, value: float):\n        self._property_changed('total_tests')\n        self.__total_tests = value        \n\n    @property\n    def equities_contribution(self) -> float:\n        """Contribution of equity component to FCI."""\n        return self.__equities_contribution\n\n    @equities_contribution.setter\n    def equities_contribution(self, value: float):\n        self._property_changed('equities_contribution')\n        self.__equities_contribution = value        \n\n    @property\n    def simon_id(self) -> str:\n        """SIMON application asset identifier."""\n        return self.__simon_id\n\n    @simon_id.setter\n    def simon_id(self, value: str):\n        self._property_changed('simon_id')\n        self.__simon_id = value        \n\n    @property\n    def congestion(self) -> float:\n        """Congestion price component."""\n        return self.__congestion\n\n    @congestion.setter\n    def congestion(self, value: float):\n        self._property_changed('congestion')\n        self.__congestion = value        \n\n    @property\n    def notes(self) -> str:\n        """Notes for series."""\n        return self.__notes\n\n    @notes.setter\n    def notes(self, value: str):\n        self._property_changed('notes')\n        self.__notes = value        \n\n    @property\n    def total_probable_senior_home(self) -> float:\n        """Probable cases in Senior homes and care facilities."""\n        return self.__total_probable_senior_home\n\n    @total_probable_senior_home.setter\n    def total_probable_senior_home(self, value: float):\n        self._property_changed('total_probable_senior_home')\n        self.__total_probable_senior_home = value        \n\n    @property\n    def event_category(self) -> str:\n        """Category."""\n        return self.__event_category\n\n    @event_category.setter\n    def event_category(self, value: str):\n        self._property_changed('event_category')\n        self.__event_category = value        \n\n    @property\n    def average_fill_rate(self) -> float:\n        """Percent complete."""\n        return self.__average_fill_rate\n\n    @average_fill_rate.setter\n    def average_fill_rate(self, value: float):\n        self._property_changed('average_fill_rate')\n        self.__average_fill_rate = value        \n\n    @property\n    def unadjusted_open(self) -> float:\n        """Unadjusted open level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__unadjusted_open\n\n    @unadjusted_open.setter\n    def unadjusted_open(self, value: float):\n        self._property_changed('unadjusted_open')\n        self.__unadjusted_open = value        \n\n    @property\n    def criticality(self) -> float:\n        """The upgrade criticality of a deployment."""\n        return self.__criticality\n\n    @criticality.setter\n    def criticality(self, value: float):\n        self._property_changed('criticality')\n        self.__criticality = value        \n\n    @property\n    def bid_ask_spread(self) -> float:\n        """Bid ask spread."""\n        return self.__bid_ask_spread\n\n    @bid_ask_spread.setter\n    def bid_ask_spread(self, value: float):\n        self._property_changed('bid_ask_spread')\n        self.__bid_ask_spread = value        \n\n    @property\n    def arrival_mid_unrealized_bps(self) -> float:\n        """Consolidated unrealised performance vs Arrival Mid, in bps."""\n        return self.__arrival_mid_unrealized_bps\n\n    @arrival_mid_unrealized_bps.setter\n    def arrival_mid_unrealized_bps(self, value: float):\n        self._property_changed('arrival_mid_unrealized_bps')\n        self.__arrival_mid_unrealized_bps = value        \n\n    @property\n    def option_type(self) -> str:\n        """One of two option types."""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: str):\n        self._property_changed('option_type')\n        self.__option_type = value        \n\n    @property\n    def termination_date(self) -> datetime.date:\n        """The date at which the measure becomes terminated."""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: datetime.date):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def queries_per_second(self) -> float:\n        """Queries per second."""\n        return self.__queries_per_second\n\n    @queries_per_second.setter\n    def queries_per_second(self, value: float):\n        self._property_changed('queries_per_second')\n        self.__queries_per_second = value        \n\n    @property\n    def liquidity_type(self) -> str:\n        """Taking/Providing."""\n        return self.__liquidity_type\n\n    @liquidity_type.setter\n    def liquidity_type(self, value: str):\n        self._property_changed('liquidity_type')\n        self.__liquidity_type = value        \n\n    @property\n    def credit_limit(self) -> float:\n        """The allowed credit limit."""\n        return self.__credit_limit\n\n    @credit_limit.setter\n    def credit_limit(self, value: float):\n        self._property_changed('credit_limit')\n        self.__credit_limit = value        \n\n    @property\n    def rank_qtd(self) -> float:\n        """Rank of the Contributor Quarter to Date."""\n        return self.__rank_qtd\n\n    @rank_qtd.setter\n    def rank_qtd(self, value: float):\n        self._property_changed('rank_qtd')\n        self.__rank_qtd = value        \n\n    @property\n    def combined_key(self) -> str:\n        """Key for epidemic data."""\n        return self.__combined_key\n\n    @combined_key.setter\n    def combined_key(self, value: str):\n        self._property_changed('combined_key')\n        self.__combined_key = value        \n\n    @property\n    def gir_fx_forecast(self) -> float:\n        """FX forecast value for the relative period."""\n        return self.__gir_fx_forecast\n\n    @gir_fx_forecast.setter\n    def gir_fx_forecast(self, value: float):\n        self._property_changed('gir_fx_forecast')\n        self.__gir_fx_forecast = value        \n\n    @property\n    def effective_tenor(self) -> str:\n        """Tenor"""\n        return self.__effective_tenor\n\n    @effective_tenor.setter\n    def effective_tenor(self, value: str):\n        self._property_changed('effective_tenor')\n        self.__effective_tenor = value        \n\n    @property\n    def gir_commodities_forecast(self) -> float:\n        """Forecast value."""\n        return self.__gir_commodities_forecast\n\n    @gir_commodities_forecast.setter\n    def gir_commodities_forecast(self, value: float):\n        self._property_changed('gir_commodities_forecast')\n        self.__gir_commodities_forecast = value        \n\n    @property\n    def relative_humidity_daily_forecast(self) -> float:\n        """The forecast value for humidity in Percentage."""\n        return self.__relative_humidity_daily_forecast\n\n    @relative_humidity_daily_forecast.setter\n    def relative_humidity_daily_forecast(self, value: float):\n        self._property_changed('relative_humidity_daily_forecast')\n        self.__relative_humidity_daily_forecast = value        \n\n    @property\n    def std30_days_subsidized_yield(self) -> float:\n        """Average annual total returns as of most recent calendar quarter-end, does not\n           account for any fee waivers or expense reimbursements."""\n        return self.__std30_days_subsidized_yield\n\n    @std30_days_subsidized_yield.setter\n    def std30_days_subsidized_yield(self, value: float):\n        self._property_changed('std30_days_subsidized_yield')\n        self.__std30_days_subsidized_yield = value        \n\n    @property\n    def annualized_tracking_error(self) -> float:\n        """Annualized tracking error."""\n        return self.__annualized_tracking_error\n\n    @annualized_tracking_error.setter\n    def annualized_tracking_error(self, value: float):\n        self._property_changed('annualized_tracking_error')\n        self.__annualized_tracking_error = value        \n\n    @property\n    def future_month_f26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_f26\n\n    @future_month_f26.setter\n    def future_month_f26(self, value: float):\n        self._property_changed('future_month_f26')\n        self.__future_month_f26 = value        \n\n    @property\n    def future_month_f25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_f25\n\n    @future_month_f25.setter\n    def future_month_f25(self, value: float):\n        self._property_changed('future_month_f25')\n        self.__future_month_f25 = value        \n\n    @property\n    def vol_swap(self) -> float:\n        """The strike in volatility terms, calculated as square root of fair variance."""\n        return self.__vol_swap\n\n    @vol_swap.setter\n    def vol_swap(self, value: float):\n        self._property_changed('vol_swap')\n        self.__vol_swap = value        \n\n    @property\n    def future_month_f24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_f24\n\n    @future_month_f24.setter\n    def future_month_f24(self, value: float):\n        self._property_changed('future_month_f24')\n        self.__future_month_f24 = value        \n\n    @property\n    def heat_index_daily_forecast(self) -> float:\n        """The daily forecast value for heat index in Fahrenheit."""\n        return self.__heat_index_daily_forecast\n\n    @heat_index_daily_forecast.setter\n    def heat_index_daily_forecast(self, value: float):\n        self._property_changed('heat_index_daily_forecast')\n        self.__heat_index_daily_forecast = value        \n\n    @property\n    def future_month_f23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_f23\n\n    @future_month_f23.setter\n    def future_month_f23(self, value: float):\n        self._property_changed('future_month_f23')\n        self.__future_month_f23 = value        \n\n    @property\n    def real_fci(self) -> float:\n        """Real FCI value."""\n        return self.__real_fci\n\n    @real_fci.setter\n    def real_fci(self, value: float):\n        self._property_changed('real_fci')\n        self.__real_fci = value        \n\n    @property\n    def block_trades_and_large_notional_off_facility_swaps(self) -> str:\n        """An indication of whether this is a block trade or off-facility swap."""\n        return self.__block_trades_and_large_notional_off_facility_swaps\n\n    @block_trades_and_large_notional_off_facility_swaps.setter\n    def block_trades_and_large_notional_off_facility_swaps(self, value: str):\n        self._property_changed('block_trades_and_large_notional_off_facility_swaps')\n        self.__block_trades_and_large_notional_off_facility_swaps = value        \n\n    @property\n    def future_month_f22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_f22\n\n    @future_month_f22.setter\n    def future_month_f22(self, value: float):\n        self._property_changed('future_month_f22')\n        self.__future_month_f22 = value        \n\n    @property\n    def buy1point5bps(self) -> float:\n        """The amount GS would buy for 1.5 bps charge."""\n        return self.__buy1point5bps\n\n    @buy1point5bps.setter\n    def buy1point5bps(self, value: float):\n        self._property_changed('buy1point5bps')\n        self.__buy1point5bps = value        \n\n    @property\n    def future_month_f21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_f21\n\n    @future_month_f21.setter\n    def future_month_f21(self, value: float):\n        self._property_changed('future_month_f21')\n        self.__future_month_f21 = value        \n\n    @property\n    def expiration_settlement_date(self) -> datetime.date:\n        """Date on which cash/stocks are settled after expiration date."""\n        return self.__expiration_settlement_date\n\n    @expiration_settlement_date.setter\n    def expiration_settlement_date(self, value: datetime.date):\n        self._property_changed('expiration_settlement_date')\n        self.__expiration_settlement_date = value        \n\n    @property\n    def absolute_return_qtd(self) -> float:\n        """Absolute Return Quarter to Date."""\n        return self.__absolute_return_qtd\n\n    @absolute_return_qtd.setter\n    def absolute_return_qtd(self, value: float):\n        self._property_changed('absolute_return_qtd')\n        self.__absolute_return_qtd = value        \n\n    @property\n    def gross_exposure(self) -> float:\n        """Sum of absolute long and short exposures in the portfolio. If you are $60 short\n           and $40 long, then the grossExposure would be $100 (60+40)."""\n        return self.__gross_exposure\n\n    @gross_exposure.setter\n    def gross_exposure(self, value: float):\n        self._property_changed('gross_exposure')\n        self.__gross_exposure = value        \n\n    @property\n    def volume(self) -> float:\n        """Accumulated number of shares, lots or contracts traded according to the market\n           convention."""\n        return self.__volume\n\n    @volume.setter\n    def volume(self, value: float):\n        self._property_changed('volume')\n        self.__volume = value        \n\n    @property\n    def adv(self) -> float:\n        """Average number of shares or units of a given asset traded over a defined period."""\n        return self.__adv\n\n    @adv.setter\n    def adv(self, value: float):\n        self._property_changed('adv')\n        self.__adv = value        \n\n    @property\n    def short_conviction_medium(self) -> float:\n        """The count of short ideas with medium conviction."""\n        return self.__short_conviction_medium\n\n    @short_conviction_medium.setter\n    def short_conviction_medium(self, value: float):\n        self._property_changed('short_conviction_medium')\n        self.__short_conviction_medium = value        \n\n    @property\n    def complete_test_measure(self) -> float:\n        """Generic field to hold aggregated complete numeric measure for test result. This\n           field stores the aggregated test measures of all the entities (like\n           max delay across all the assets, either silenced or unsilenced)."""\n        return self.__complete_test_measure\n\n    @complete_test_measure.setter\n    def complete_test_measure(self, value: float):\n        self._property_changed('complete_test_measure')\n        self.__complete_test_measure = value        \n\n    @property\n    def exchange(self) -> str:\n        """Name of marketplace where security, derivative or other instrument is traded"""\n        return self.__exchange\n\n    @exchange.setter\n    def exchange(self, value: str):\n        self._property_changed('exchange')\n        self.__exchange = value        \n\n    @property\n    def es_policy_score(self) -> float:\n        """A company's score for E&S subsector-relative policy metrics."""\n        return self.__es_policy_score\n\n    @es_policy_score.setter\n    def es_policy_score(self, value: float):\n        self._property_changed('es_policy_score')\n        self.__es_policy_score = value        \n\n    @property\n    def roll_volume_std(self) -> float:\n        """Cross-sectional roll volume in standard deviations."""\n        return self.__roll_volume_std\n\n    @roll_volume_std.setter\n    def roll_volume_std(self, value: float):\n        self._property_changed('roll_volume_std')\n        self.__roll_volume_std = value        \n\n    @property\n    def temperature_daily_forecast(self) -> float:\n        """The forecast temperature of diff types in Fahrenheit."""\n        return self.__temperature_daily_forecast\n\n    @temperature_daily_forecast.setter\n    def temperature_daily_forecast(self, value: float):\n        self._property_changed('temperature_daily_forecast')\n        self.__temperature_daily_forecast = value        \n\n    @property\n    def relative_payoff_qtd(self) -> float:\n        """Total win divided by total loss Quarter to date."""\n        return self.__relative_payoff_qtd\n\n    @relative_payoff_qtd.setter\n    def relative_payoff_qtd(self, value: float):\n        self._property_changed('relative_payoff_qtd')\n        self.__relative_payoff_qtd = value        \n\n    @property\n    def on_loan_percentage(self) -> float:\n        """On loan ratio as compared net asset value of lending fund."""\n        return self.__on_loan_percentage\n\n    @on_loan_percentage.setter\n    def on_loan_percentage(self, value: float):\n        self._property_changed('on_loan_percentage')\n        self.__on_loan_percentage = value        \n\n    @property\n    def twap_remaining_slices(self) -> float:\n        """The number of slices remaining on a TWAP."""\n        return self.__twap_remaining_slices\n\n    @twap_remaining_slices.setter\n    def twap_remaining_slices(self, value: float):\n        self._property_changed('twap_remaining_slices')\n        self.__twap_remaining_slices = value        \n\n    @property\n    def fair_variance(self) -> float:\n        """Strike such that the price of an uncapped variance swap on the underlying index\n           is zero at inception."""\n        return self.__fair_variance\n\n    @fair_variance.setter\n    def fair_variance(self, value: float):\n        self._property_changed('fair_variance')\n        self.__fair_variance = value        \n\n    @property\n    def hit_rate_wtd(self) -> float:\n        """Hit Rate Ratio Week to Date."""\n        return self.__hit_rate_wtd\n\n    @hit_rate_wtd.setter\n    def hit_rate_wtd(self, value: float):\n        self._property_changed('hit_rate_wtd')\n        self.__hit_rate_wtd = value        \n\n    @property\n    def previous_close_realized_cash(self) -> float:\n        """Realised performance vs Previous Close, in USD."""\n        return self.__previous_close_realized_cash\n\n    @previous_close_realized_cash.setter\n    def previous_close_realized_cash(self, value: float):\n        self._property_changed('previous_close_realized_cash')\n        self.__previous_close_realized_cash = value        \n\n    @property\n    def realized_volatility(self) -> float:\n        """Volatility of an asset realized by observations of market prices."""\n        return self.__realized_volatility\n\n    @realized_volatility.setter\n    def realized_volatility(self, value: float):\n        self._property_changed('realized_volatility')\n        self.__realized_volatility = value        \n\n    @property\n    def unexecuted_quantity(self) -> float:\n        """Un-executed quantity, in shares."""\n        return self.__unexecuted_quantity\n\n    @unexecuted_quantity.setter\n    def unexecuted_quantity(self, value: float):\n        self._property_changed('unexecuted_quantity')\n        self.__unexecuted_quantity = value        \n\n    @property\n    def proceeds_asset_swap_spread1m(self) -> float:\n        """Proceeds asset swap spread vs 1m tenor."""\n        return self.__proceeds_asset_swap_spread1m\n\n    @proceeds_asset_swap_spread1m.setter\n    def proceeds_asset_swap_spread1m(self, value: float):\n        self._property_changed('proceeds_asset_swap_spread1m')\n        self.__proceeds_asset_swap_spread1m = value        \n\n    @property\n    def clone_parent_id(self) -> str:\n        """Marquee unique identifier"""\n        return self.__clone_parent_id\n\n    @clone_parent_id.setter\n    def clone_parent_id(self, value: str):\n        self._property_changed('clone_parent_id')\n        self.__clone_parent_id = value        \n\n    @property\n    def wind_speed_hourly_forecast(self) -> float:\n        """The hourly forecast value for wind speed in Mph."""\n        return self.__wind_speed_hourly_forecast\n\n    @wind_speed_hourly_forecast.setter\n    def wind_speed_hourly_forecast(self, value: float):\n        self._property_changed('wind_speed_hourly_forecast')\n        self.__wind_speed_hourly_forecast = value        \n\n    @property\n    def etf_flow_ratio(self) -> float:\n        """Ratio of signed ETF dollar flow to market cap of global stocks, i.e. proportion\n           of company's market cap that is part of ETF flows observed weekly.\n           Normalised between -3 (low level) and +3 (high level)."""\n        return self.__etf_flow_ratio\n\n    @etf_flow_ratio.setter\n    def etf_flow_ratio(self, value: float):\n        self._property_changed('etf_flow_ratio')\n        self.__etf_flow_ratio = value        \n\n    @property\n    def asset_parameters_receiver_rate_option(self) -> str:\n        """The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE."""\n        return self.__asset_parameters_receiver_rate_option\n\n    @asset_parameters_receiver_rate_option.setter\n    def asset_parameters_receiver_rate_option(self, value: str):\n        self._property_changed('asset_parameters_receiver_rate_option')\n        self.__asset_parameters_receiver_rate_option = value        \n\n    @property\n    def buy60cents(self) -> float:\n        """The amount GS would buy for 60 cents charge."""\n        return self.__buy60cents\n\n    @buy60cents.setter\n    def buy60cents(self, value: float):\n        self._property_changed('buy60cents')\n        self.__buy60cents = value        \n\n    @property\n    def security_sub_type_id(self) -> str:\n        """PadMaster unique security sub type identifier."""\n        return self.__security_sub_type_id\n\n    @security_sub_type_id.setter\n    def security_sub_type_id(self, value: str):\n        self._property_changed('security_sub_type_id')\n        self.__security_sub_type_id = value        \n\n    @property\n    def message(self) -> str:\n        """Miscellaneous remarks."""\n        return self.__message\n\n    @message.setter\n    def message(self, value: str):\n        self._property_changed('message')\n        self.__message = value        \n\n    @property\n    def sts_rates_country(self) -> str:\n        """Country of interest rate risk for STS assets."""\n        return self.__sts_rates_country\n\n    @sts_rates_country.setter\n    def sts_rates_country(self, value: str):\n        self._property_changed('sts_rates_country')\n        self.__sts_rates_country = value        \n\n    @property\n    def sell65cents(self) -> float:\n        """The amount GS would sell for 65 cents charge."""\n        return self.__sell65cents\n\n    @sell65cents.setter\n    def sell65cents(self, value: float):\n        self._property_changed('sell65cents')\n        self.__sell65cents = value        \n\n    @property\n    def horizon(self) -> str:\n        """Time period indicating the validity of the idea. Eg. 2d (2 days), 1w (1 week),\n           3m (3 months), 1y (1 year)."""\n        return self.__horizon\n\n    @horizon.setter\n    def horizon(self, value: str):\n        self._property_changed('horizon')\n        self.__horizon = value        \n\n    @property\n    def would_if_good_level(self) -> float:\n        """If specified, allows the remaining quantity of an algo to be filled like a limit\n           order."""\n        return self.__would_if_good_level\n\n    @would_if_good_level.setter\n    def would_if_good_level(self, value: float):\n        self._property_changed('would_if_good_level')\n        self.__would_if_good_level = value        \n\n    @property\n    def buffer_threshold_required(self) -> float:\n        """The required buffer between holdings and on loan quantity for an asset."""\n        return self.__buffer_threshold_required\n\n    @buffer_threshold_required.setter\n    def buffer_threshold_required(self, value: float):\n        self._property_changed('buffer_threshold_required')\n        self.__buffer_threshold_required = value        \n\n    @property\n    def face_value(self) -> float:\n        """The face value of the instrument."""\n        return self.__face_value\n\n    @face_value.setter\n    def face_value(self, value: float):\n        self._property_changed('face_value')\n        self.__face_value = value        \n\n    @property\n    def roll_volume_hist(self) -> float:\n        """Five Year historical roll volume."""\n        return self.__roll_volume_hist\n\n    @roll_volume_hist.setter\n    def roll_volume_hist(self, value: float):\n        self._property_changed('roll_volume_hist')\n        self.__roll_volume_hist = value        \n\n    @property\n    def counter_party_status(self) -> str:\n        """The lending status of a counterparty for a particular portfolio."""\n        return self.__counter_party_status\n\n    @counter_party_status.setter\n    def counter_party_status(self, value: str):\n        self._property_changed('counter_party_status')\n        self.__counter_party_status = value        \n\n    @property\n    def composite22_day_adv(self) -> float:\n        """Composite 22 day ADV."""\n        return self.__composite22_day_adv\n\n    @composite22_day_adv.setter\n    def composite22_day_adv(self, value: float):\n        self._property_changed('composite22_day_adv')\n        self.__composite22_day_adv = value        \n\n    @property\n    def percentage_far_executed_quantity(self) -> float:\n        """Percentage of total order filled at aggressive touch."""\n        return self.__percentage_far_executed_quantity\n\n    @percentage_far_executed_quantity.setter\n    def percentage_far_executed_quantity(self, value: float):\n        self._property_changed('percentage_far_executed_quantity')\n        self.__percentage_far_executed_quantity = value        \n\n    @property\n    def loan_spread_required(self) -> float:\n        """The minimum spread requirement for a securities lending loan."""\n        return self.__loan_spread_required\n\n    @loan_spread_required.setter\n    def loan_spread_required(self, value: float):\n        self._property_changed('loan_spread_required')\n        self.__loan_spread_required = value        \n\n    @property\n    def asset_class(self) -> str:\n        """Asset classification of security. Assets are classified into broad groups which\n           exhibit similar characteristics and behave in a consistent way under\n           different market conditions"""\n        return self.__asset_class\n\n    @asset_class.setter\n    def asset_class(self, value: str):\n        self._property_changed('asset_class')\n        self.__asset_class = value        \n\n    @property\n    def sovereign_spread_contribution(self) -> float:\n        """Contribution of sovereign spread component to FCI. Only applicable to Euro\n           countries."""\n        return self.__sovereign_spread_contribution\n\n    @sovereign_spread_contribution.setter\n    def sovereign_spread_contribution(self, value: float):\n        self._property_changed('sovereign_spread_contribution')\n        self.__sovereign_spread_contribution = value        \n\n    @property\n    def ric(self) -> str:\n        """Reuters instrument code (subject to licensing)."""\n        return self.__ric\n\n    @ric.setter\n    def ric(self, value: str):\n        self._property_changed('ric')\n        self.__ric = value        \n\n    @property\n    def bucket_end_time(self) -> datetime.datetime:\n        """The end time of the bucket in ISO 8601 formatted date and time."""\n        return self.__bucket_end_time\n\n    @bucket_end_time.setter\n    def bucket_end_time(self, value: datetime.datetime):\n        self._property_changed('bucket_end_time')\n        self.__bucket_end_time = value        \n\n    @property\n    def rate_type(self) -> str:\n        """Type of swap structured for a Central Bank swap: rate type = Meeting Forward,\n           swaps structured trough End of Year, rateType = EOY Forward and for\n           Spot OIS value rateType = Spot."""\n        return self.__rate_type\n\n    @rate_type.setter\n    def rate_type(self, value: str):\n        self._property_changed('rate_type')\n        self.__rate_type = value        \n\n    @property\n    def total_fatalities_senior_home(self) -> float:\n        """Confirmed fatalities in Senior homes and care facilities."""\n        return self.__total_fatalities_senior_home\n\n    @total_fatalities_senior_home.setter\n    def total_fatalities_senior_home(self, value: float):\n        self._property_changed('total_fatalities_senior_home')\n        self.__total_fatalities_senior_home = value        \n\n    @property\n    def loan_status(self) -> str:\n        """Notes which point of the lifecyle a securities lending loan is in."""\n        return self.__loan_status\n\n    @loan_status.setter\n    def loan_status(self, value: str):\n        self._property_changed('loan_status')\n        self.__loan_status = value        \n\n    @property\n    def short_weight(self) -> float:\n        """Short weight of a position in a given portfolio. Equivalent to position short\n           exposure / total short exposure. If you have a position with a\n           shortExposure of $20, and your portfolio shortExposure is $100, then\n           your asset shortWeight would be 0.2 (20/100)."""\n        return self.__short_weight\n\n    @short_weight.setter\n    def short_weight(self, value: float):\n        self._property_changed('short_weight')\n        self.__short_weight = value        \n\n    @property\n    def geography_id(self) -> str:\n        """Region or Country code for which the numbers are represented. For countries, it\n           is the ISO 3166 2-digit country code."""\n        return self.__geography_id\n\n    @geography_id.setter\n    def geography_id(self, value: str):\n        self._property_changed('geography_id')\n        self.__geography_id = value        \n\n    @property\n    def sell7point5bps(self) -> float:\n        """The amount GS would sell for 7.5 bps charge."""\n        return self.__sell7point5bps\n\n    @sell7point5bps.setter\n    def sell7point5bps(self, value: float):\n        self._property_changed('sell7point5bps')\n        self.__sell7point5bps = value        \n\n    @property\n    def nav(self) -> float:\n        """Net asset value."""\n        return self.__nav\n\n    @nav.setter\n    def nav(self, value: float):\n        self._property_changed('nav')\n        self.__nav = value        \n\n    @property\n    def fiscal_quarter(self) -> str:\n        """One of the four three-month periods that make up the fiscal year."""\n        return self.__fiscal_quarter\n\n    @fiscal_quarter.setter\n    def fiscal_quarter(self, value: str):\n        self._property_changed('fiscal_quarter')\n        self.__fiscal_quarter = value        \n\n    @property\n    def version_string(self) -> str:\n        """String representing the version number."""\n        return self.__version_string\n\n    @version_string.setter\n    def version_string(self, value: str):\n        self._property_changed('version_string')\n        self.__version_string = value        \n\n    @property\n    def payoff_ytd(self) -> float:\n        """Total win divided by total loss Year to date."""\n        return self.__payoff_ytd\n\n    @payoff_ytd.setter\n    def payoff_ytd(self, value: float):\n        self._property_changed('payoff_ytd')\n        self.__payoff_ytd = value        \n\n    @property\n    def market_impact(self) -> float:\n        """Market impact is based on the Goldman Sachs Shortfall Model where available\n           alongside best estimates from the desk."""\n        return self.__market_impact\n\n    @market_impact.setter\n    def market_impact(self, value: float):\n        self._property_changed('market_impact')\n        self.__market_impact = value        \n\n    @property\n    def event_type(self) -> str:\n        """Equals Analyst Meeting if the event indicates an analyst meeting. Equals\n           Earnings Release if the event indicates an earnings release. Equals\n           Sales Release when the event indicates a sales release. Indicates\n           Drug Data when the event indicates an event related to drugs data.\n           Equals Other for any other events."""\n        return self.__event_type\n\n    @event_type.setter\n    def event_type(self, value: str):\n        self._property_changed('event_type')\n        self.__event_type = value        \n\n    @property\n    def fill_price(self) -> float:\n        """Last execution price."""\n        return self.__fill_price\n\n    @fill_price.setter\n    def fill_price(self, value: float):\n        self._property_changed('fill_price')\n        self.__fill_price = value        \n\n    @property\n    def asset_count_long(self) -> float:\n        """Number of assets in a portfolio with long exposure."""\n        return self.__asset_count_long\n\n    @asset_count_long.setter\n    def asset_count_long(self, value: float):\n        self._property_changed('asset_count_long')\n        self.__asset_count_long = value        \n\n    @property\n    def sell180cents(self) -> float:\n        """The amount GS would sell for 180 cents charge."""\n        return self.__sell180cents\n\n    @sell180cents.setter\n    def sell180cents(self, value: float):\n        self._property_changed('sell180cents')\n        self.__sell180cents = value        \n\n    @property\n    def spot(self) -> float:\n        """Spot price."""\n        return self.__spot\n\n    @spot.setter\n    def spot(self, value: float):\n        self._property_changed('spot')\n        self.__spot = value        \n\n    @property\n    def application_id(self) -> str:\n        """Client application identifier."""\n        return self.__application_id\n\n    @application_id.setter\n    def application_id(self, value: str):\n        self._property_changed('application_id')\n        self.__application_id = value        \n\n    @property\n    def indicative_close_price(self) -> float:\n        """Indicative close price of an asset forseen at end of the day."""\n        return self.__indicative_close_price\n\n    @indicative_close_price.setter\n    def indicative_close_price(self, value: float):\n        self._property_changed('indicative_close_price')\n        self.__indicative_close_price = value        \n\n    @property\n    def swap_spread(self) -> float:\n        """Swap spread."""\n        return self.__swap_spread\n\n    @swap_spread.setter\n    def swap_spread(self, value: float):\n        self._property_changed('swap_spread')\n        self.__swap_spread = value        \n\n    @property\n    def trading_restriction(self) -> bool:\n        """Whether or not the asset has trading restrictions."""\n        return self.__trading_restriction\n\n    @trading_restriction.setter\n    def trading_restriction(self, value: bool):\n        self._property_changed('trading_restriction')\n        self.__trading_restriction = value        \n\n    @property\n    def asset_parameters_pay_or_receive(self) -> str:\n        """Pay or receive fixed"""\n        return self.__asset_parameters_pay_or_receive\n\n    @asset_parameters_pay_or_receive.setter\n    def asset_parameters_pay_or_receive(self, value: str):\n        self._property_changed('asset_parameters_pay_or_receive')\n        self.__asset_parameters_pay_or_receive = value        \n\n    @property\n    def price_spot_entry_unit(self) -> str:\n        """Unit in which the opening price is reported."""\n        return self.__price_spot_entry_unit\n\n    @price_spot_entry_unit.setter\n    def price_spot_entry_unit(self, value: str):\n        self._property_changed('price_spot_entry_unit')\n        self.__price_spot_entry_unit = value        \n\n    @property\n    def unrealized_arrival_performance_bps(self) -> float:\n        """Average execution price vs Consolidated Arrival (in limit) since order inception\n           ??? unrealized."""\n        return self.__unrealized_arrival_performance_bps\n\n    @unrealized_arrival_performance_bps.setter\n    def unrealized_arrival_performance_bps(self, value: float):\n        self._property_changed('unrealized_arrival_performance_bps')\n        self.__unrealized_arrival_performance_bps = value        \n\n    @property\n    def city(self) -> str:\n        """City for which the weather data was gathered."""\n        return self.__city\n\n    @city.setter\n    def city(self, value: str):\n        self._property_changed('city')\n        self.__city = value        \n\n    @property\n    def pnl_wtd(self) -> float:\n        """Total PnL Week to date."""\n        return self.__pnl_wtd\n\n    @pnl_wtd.setter\n    def pnl_wtd(self, value: float):\n        self._property_changed('pnl_wtd')\n        self.__pnl_wtd = value        \n\n    @property\n    def covariance(self) -> float:\n        """Covariance between two ."""\n        return self.__covariance\n\n    @covariance.setter\n    def covariance(self, value: float):\n        self._property_changed('covariance')\n        self.__covariance = value        \n\n    @property\n    def bucket_volume_in_shares(self) -> float:\n        """Forecast of the total volume of shares in the bucket."""\n        return self.__bucket_volume_in_shares\n\n    @bucket_volume_in_shares.setter\n    def bucket_volume_in_shares(self, value: float):\n        self._property_changed('bucket_volume_in_shares')\n        self.__bucket_volume_in_shares = value        \n\n    @property\n    def commodity_forecast(self) -> float:\n        """Commodity forecast value for the tenor, year or quarter."""\n        return self.__commodity_forecast\n\n    @commodity_forecast.setter\n    def commodity_forecast(self, value: float):\n        self._property_changed('commodity_forecast')\n        self.__commodity_forecast = value        \n\n    @property\n    def valid(self) -> float:\n        """Valid."""\n        return self.__valid\n\n    @valid.setter\n    def valid(self, value: float):\n        self._property_changed('valid')\n        self.__valid = value        \n\n    @property\n    def sts_commodity(self) -> str:\n        """Commodity name for STS assets."""\n        return self.__sts_commodity\n\n    @sts_commodity.setter\n    def sts_commodity(self, value: str):\n        self._property_changed('sts_commodity')\n        self.__sts_commodity = value        \n\n    @property\n    def initial_pricing_date(self) -> datetime.date:\n        """Initial pricing date for basket actions."""\n        return self.__initial_pricing_date\n\n    @initial_pricing_date.setter\n    def initial_pricing_date(self, value: datetime.date):\n        self._property_changed('initial_pricing_date')\n        self.__initial_pricing_date = value        \n\n    @property\n    def indication_of_end_user_exception(self) -> str:\n        """If buyer or seller or both is electing the End User Exception."""\n        return self.__indication_of_end_user_exception\n\n    @indication_of_end_user_exception.setter\n    def indication_of_end_user_exception(self, value: str):\n        self._property_changed('indication_of_end_user_exception')\n        self.__indication_of_end_user_exception = value        \n\n    @property\n    def wind_direction_hourly_forecast(self) -> float:\n        """The hourly forecast value for wind direction."""\n        return self.__wind_direction_hourly_forecast\n\n    @wind_direction_hourly_forecast.setter\n    def wind_direction_hourly_forecast(self, value: float):\n        self._property_changed('wind_direction_hourly_forecast')\n        self.__wind_direction_hourly_forecast = value        \n\n    @property\n    def es_score(self) -> float:\n        """A company's score for E&S subsector-relative metrics."""\n        return self.__es_score\n\n    @es_score.setter\n    def es_score(self, value: float):\n        self._property_changed('es_score')\n        self.__es_score = value        \n\n    @property\n    def yield_(self) -> float:\n        """The return an investor realizes on a bond sold at the mid price."""\n        return self.__yield\n\n    @yield_.setter\n    def yield_(self, value: float):\n        self._property_changed('yield_')\n        self.__yield = value        \n\n    @property\n    def fatalities_underlying_conditions_present(self) -> float:\n        """Total number of fatalities of people with underlying conditions."""\n        return self.__fatalities_underlying_conditions_present\n\n    @fatalities_underlying_conditions_present.setter\n    def fatalities_underlying_conditions_present(self, value: float):\n        self._property_changed('fatalities_underlying_conditions_present')\n        self.__fatalities_underlying_conditions_present = value        \n\n    @property\n    def price_range_in_ticks(self) -> float:\n        """The Price Range of the stock in Ticks on the particular date."""\n        return self.__price_range_in_ticks\n\n    @price_range_in_ticks.setter\n    def price_range_in_ticks(self, value: float):\n        self._property_changed('price_range_in_ticks')\n        self.__price_range_in_ticks = value        \n\n    @property\n    def pace_of_rollp25(self) -> float:\n        """The 25th-percentile of the pace of roll compared to previous quarters (over the\n           last 5 years) at the same point in time, i.e. cross-sectional."""\n        return self.__pace_of_rollp25\n\n    @pace_of_rollp25.setter\n    def pace_of_rollp25(self, value: float):\n        self._property_changed('pace_of_rollp25')\n        self.__pace_of_rollp25 = value        \n\n    @property\n    def day_close_realized_usd(self) -> float:\n        """Realised performance vs Previous Close, in $."""\n        return self.__day_close_realized_usd\n\n    @day_close_realized_usd.setter\n    def day_close_realized_usd(self, value: float):\n        self._property_changed('day_close_realized_usd')\n        self.__day_close_realized_usd = value        \n\n    @property\n    def pct_change(self) -> float:\n        """Percentage change of the latest trade price or value from the adjusted\n           historical close."""\n        return self.__pct_change\n\n    @pct_change.setter\n    def pct_change(self, value: float):\n        self._property_changed('pct_change')\n        self.__pct_change = value        \n\n    @property\n    def brightness_type(self) -> str:\n        """The type of measure required like visibility or sunshine."""\n        return self.__brightness_type\n\n    @brightness_type.setter\n    def brightness_type(self, value: str):\n        self._property_changed('brightness_type')\n        self.__brightness_type = value        \n\n    @property\n    def future_month3_m(self) -> float:\n        """Commods future month code."""\n        return self.__future_month3_m\n\n    @future_month3_m.setter\n    def future_month3_m(self, value: float):\n        self._property_changed('future_month3_m')\n        self.__future_month3_m = value        \n\n    @property\n    def number_of_rolls(self) -> int:\n        """Contract's number of rolls per year."""\n        return self.__number_of_rolls\n\n    @number_of_rolls.setter\n    def number_of_rolls(self, value: int):\n        self._property_changed('number_of_rolls')\n        self.__number_of_rolls = value        \n\n    @property\n    def iso_country_code_numeric(self) -> float:\n        """Iso numeric code."""\n        return self.__iso_country_code_numeric\n\n    @iso_country_code_numeric.setter\n    def iso_country_code_numeric(self, value: float):\n        self._property_changed('iso_country_code_numeric')\n        self.__iso_country_code_numeric = value        \n\n    @property\n    def price_type(self) -> str:\n        """Price denomination and unit."""\n        return self.__price_type\n\n    @price_type.setter\n    def price_type(self, value: str):\n        self._property_changed('price_type')\n        self.__price_type = value        \n\n    @property\n    def realized_vwap_performance_usd(self) -> float:\n        """Average execution price vs Consolidated VWAP (in limit) since order inception\n           ??? realized in $."""\n        return self.__realized_vwap_performance_usd\n\n    @realized_vwap_performance_usd.setter\n    def realized_vwap_performance_usd(self, value: float):\n        self._property_changed('realized_vwap_performance_usd')\n        self.__realized_vwap_performance_usd = value        \n\n    @property\n    def fuel_type(self) -> str:\n        """The type of fuel used to generate electricity"""\n        return self.__fuel_type\n\n    @fuel_type.setter\n    def fuel_type(self, value: str):\n        self._property_changed('fuel_type')\n        self.__fuel_type = value        \n\n    @property\n    def bbid(self) -> str:\n        """Bloomberg identifier (ticker and exchange code)."""\n        return self.__bbid\n\n    @bbid.setter\n    def bbid(self, value: str):\n        self._property_changed('bbid')\n        self.__bbid = value        \n\n    @property\n    def vega_notional_amount(self) -> float:\n        """The notional for a variance swap."""\n        return self.__vega_notional_amount\n\n    @vega_notional_amount.setter\n    def vega_notional_amount(self, value: float):\n        self._property_changed('vega_notional_amount')\n        self.__vega_notional_amount = value        \n\n    @property\n    def fatalities_underlying_conditions_absent(self) -> float:\n        """Total number of fatalities of people with no underlying conditions."""\n        return self.__fatalities_underlying_conditions_absent\n\n    @fatalities_underlying_conditions_absent.setter\n    def fatalities_underlying_conditions_absent(self, value: float):\n        self._property_changed('fatalities_underlying_conditions_absent')\n        self.__fatalities_underlying_conditions_absent = value        \n\n    @property\n    def effective_date(self) -> datetime.date:\n        """The date at which the measure becomes effective."""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: datetime.date):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def capped(self) -> str:\n        """Whether a trade is capped at the block notional threshold."""\n        return self.__capped\n\n    @capped.setter\n    def capped(self, value: str):\n        self._property_changed('capped')\n        self.__capped = value        \n\n    @property\n    def rating(self) -> str:\n        """Analyst Rating, which may take on the following values."""\n        return self.__rating\n\n    @rating.setter\n    def rating(self, value: str):\n        self._property_changed('rating')\n        self.__rating = value        \n\n    @property\n    def option_currency(self) -> str:\n        """An indication of type of currency on the option premium."""\n        return self.__option_currency\n\n    @option_currency.setter\n    def option_currency(self, value: str):\n        self._property_changed('option_currency')\n        self.__option_currency = value        \n\n    @property\n    def is_close_auction(self) -> bool:\n        """Flag indicating whether the bucket is within the close auction or not."""\n        return self.__is_close_auction\n\n    @is_close_auction.setter\n    def is_close_auction(self, value: bool):\n        self._property_changed('is_close_auction')\n        self.__is_close_auction = value        \n\n    @property\n    def volatility(self) -> float:\n        """Market implied correlation between two tenors."""\n        return self.__volatility\n\n    @volatility.setter\n    def volatility(self, value: float):\n        self._property_changed('volatility')\n        self.__volatility = value        \n\n    @property\n    def avg_vent_util(self) -> float:\n        """Average number of patients on a ventilator per week."""\n        return self.__avg_vent_util\n\n    @avg_vent_util.setter\n    def avg_vent_util(self, value: float):\n        self._property_changed('avg_vent_util')\n        self.__avg_vent_util = value        \n\n    @property\n    def underlying_asset_ids(self) -> Tuple[str, ...]:\n        """Marquee IDs of the underlying assets."""\n        return self.__underlying_asset_ids\n\n    @underlying_asset_ids.setter\n    def underlying_asset_ids(self, value: Tuple[str, ...]):\n        self._property_changed('underlying_asset_ids')\n        self.__underlying_asset_ids = value        \n\n    @property\n    def buy6point5bps(self) -> float:\n        """The amount GS would buy for 6.5 bps charge."""\n        return self.__buy6point5bps\n\n    @buy6point5bps.setter\n    def buy6point5bps(self, value: float):\n        self._property_changed('buy6point5bps')\n        self.__buy6point5bps = value        \n\n    @property\n    def vwap_in_limit_realized_cash(self) -> float:\n        """Consolidated realised performance vs VWAP In Limit, in USD."""\n        return self.__vwap_in_limit_realized_cash\n\n    @vwap_in_limit_realized_cash.setter\n    def vwap_in_limit_realized_cash(self, value: float):\n        self._property_changed('vwap_in_limit_realized_cash')\n        self.__vwap_in_limit_realized_cash = value        \n\n    @property\n    def estimated_closing_auction_volume(self) -> float:\n        """Estimated closing auction volume."""\n        return self.__estimated_closing_auction_volume\n\n    @estimated_closing_auction_volume.setter\n    def estimated_closing_auction_volume(self, value: float):\n        self._property_changed('estimated_closing_auction_volume')\n        self.__estimated_closing_auction_volume = value        \n\n    @property\n    def sell2bps(self) -> float:\n        """The amount GS would sell for 2 bps charge."""\n        return self.__sell2bps\n\n    @sell2bps.setter\n    def sell2bps(self, value: float):\n        self._property_changed('sell2bps')\n        self.__sell2bps = value        \n\n    @property\n    def annual_risk(self) -> float:\n        """Annualized risk of a given portfolio, position or asset. Generally computed as\n           annualized daily standard deviation of returns."""\n        return self.__annual_risk\n\n    @annual_risk.setter\n    def annual_risk(self, value: float):\n        self._property_changed('annual_risk')\n        self.__annual_risk = value        \n\n    @property\n    def eti(self) -> str:\n        """External Trade Identifier."""\n        return self.__eti\n\n    @eti.setter\n    def eti(self, value: str):\n        self._property_changed('eti')\n        self.__eti = value        \n\n    @property\n    def vwap_in_limit_realized_bps(self) -> float:\n        """Consolidated realised performance vs VWAP In Limit, in bps."""\n        return self.__vwap_in_limit_realized_bps\n\n    @vwap_in_limit_realized_bps.setter\n    def vwap_in_limit_realized_bps(self, value: float):\n        self._property_changed('vwap_in_limit_realized_bps')\n        self.__vwap_in_limit_realized_bps = value        \n\n    @property\n    def rank_mtd(self) -> float:\n        """Rank of the Contributor Month to Date."""\n        return self.__rank_mtd\n\n    @rank_mtd.setter\n    def rank_mtd(self, value: float):\n        self._property_changed('rank_mtd')\n        self.__rank_mtd = value        \n\n    @property\n    def market_buffer(self) -> float:\n        """The actual buffer between holdings and on loan quantity for a market."""\n        return self.__market_buffer\n\n    @market_buffer.setter\n    def market_buffer(self, value: float):\n        self._property_changed('market_buffer')\n        self.__market_buffer = value        \n\n    @property\n    def future_month_j24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_j24\n\n    @future_month_j24.setter\n    def future_month_j24(self, value: float):\n        self._property_changed('future_month_j24')\n        self.__future_month_j24 = value        \n\n    @property\n    def last_uploaded_time(self) -> datetime.datetime:\n        """Timestamp noting last uploaded datapoint."""\n        return self.__last_uploaded_time\n\n    @last_uploaded_time.setter\n    def last_uploaded_time(self, value: datetime.datetime):\n        self._property_changed('last_uploaded_time')\n        self.__last_uploaded_time = value        \n\n    @property\n    def future_month_j23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_j23\n\n    @future_month_j23.setter\n    def future_month_j23(self, value: float):\n        self._property_changed('future_month_j23')\n        self.__future_month_j23 = value        \n\n    @property\n    def oe_id(self) -> str:\n        """Marquee unique identifier"""\n        return self.__oe_id\n\n    @oe_id.setter\n    def oe_id(self, value: str):\n        self._property_changed('oe_id')\n        self.__oe_id = value        \n\n    @property\n    def future_month_j22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_j22\n\n    @future_month_j22.setter\n    def future_month_j22(self, value: float):\n        self._property_changed('future_month_j22')\n        self.__future_month_j22 = value        \n\n    @property\n    def future_month_j21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_j21\n\n    @future_month_j21.setter\n    def future_month_j21(self, value: float):\n        self._property_changed('future_month_j21')\n        self.__future_month_j21 = value        \n\n    @property\n    def bbid_equivalent(self) -> str:\n        """Bloomberg identifier (ticker and country code) equivalent - i.e. for OTCs\n           options, the equivalent BBID on exchange."""\n        return self.__bbid_equivalent\n\n    @bbid_equivalent.setter\n    def bbid_equivalent(self, value: str):\n        self._property_changed('bbid_equivalent')\n        self.__bbid_equivalent = value        \n\n    @property\n    def init_buffer_threshold_required(self) -> float:\n        """The required buffer between holdings and on loan quantity for an asset on the\n           date of loan initiation."""\n        return self.__init_buffer_threshold_required\n\n    @init_buffer_threshold_required.setter\n    def init_buffer_threshold_required(self, value: float):\n        self._property_changed('init_buffer_threshold_required')\n        self.__init_buffer_threshold_required = value        \n\n    @property\n    def leg2_designated_maturity(self) -> str:\n        """Floating rate index designated maturity period of leg 2."""\n        return self.__leg2_designated_maturity\n\n    @leg2_designated_maturity.setter\n    def leg2_designated_maturity(self, value: str):\n        self._property_changed('leg2_designated_maturity')\n        self.__leg2_designated_maturity = value        \n\n    @property\n    def matched_maturity_ois_swap_rate(self) -> float:\n        """Matched Maturity OIS swap rate."""\n        return self.__matched_maturity_ois_swap_rate\n\n    @matched_maturity_ois_swap_rate.setter\n    def matched_maturity_ois_swap_rate(self, value: float):\n        self._property_changed('matched_maturity_ois_swap_rate')\n        self.__matched_maturity_ois_swap_rate = value        \n\n    @property\n    def fair_price(self) -> float:\n        """Fair Price for a swap."""\n        return self.__fair_price\n\n    @fair_price.setter\n    def fair_price(self, value: float):\n        self._property_changed('fair_price')\n        self.__fair_price = value        \n\n    @property\n    def participation_rate_in_limit(self) -> float:\n        """Average in limit participation rate of the order, including principal fills."""\n        return self.__participation_rate_in_limit\n\n    @participation_rate_in_limit.setter\n    def participation_rate_in_limit(self, value: float):\n        self._property_changed('participation_rate_in_limit')\n        self.__participation_rate_in_limit = value        \n\n    @property\n    def ext_mkt_class(self) -> str:\n        """External MDAPI Class."""\n        return self.__ext_mkt_class\n\n    @ext_mkt_class.setter\n    def ext_mkt_class(self, value: str):\n        self._property_changed('ext_mkt_class')\n        self.__ext_mkt_class = value        \n\n    @property\n    def price_currency(self) -> str:\n        """Denominated pricing currency."""\n        return self.__price_currency\n\n    @price_currency.setter\n    def price_currency(self, value: str):\n        self._property_changed('price_currency')\n        self.__price_currency = value        \n\n    @property\n    def failed_count(self) -> float:\n        """Number of entities that failed the test."""\n        return self.__failed_count\n\n    @failed_count.setter\n    def failed_count(self, value: float):\n        self._property_changed('failed_count')\n        self.__failed_count = value        \n\n    @property\n    def leg1_index_location(self) -> str:\n        """Location of leg."""\n        return self.__leg1_index_location\n\n    @leg1_index_location.setter\n    def leg1_index_location(self, value: str):\n        self._property_changed('leg1_index_location')\n        self.__leg1_index_location = value        \n\n    @property\n    def supra_strategy(self) -> str:\n        """Broad descriptor of a fund's investment approach. Same view permissions as the\n           asset"""\n        return self.__supra_strategy\n\n    @supra_strategy.setter\n    def supra_strategy(self, value: str):\n        self._property_changed('supra_strategy')\n        self.__supra_strategy = value        \n\n    @property\n    def day_count_convention(self) -> str:\n        """The determination of how interest accrues over time for the SB swap."""\n        return self.__day_count_convention\n\n    @day_count_convention.setter\n    def day_count_convention(self, value: str):\n        self._property_changed('day_count_convention')\n        self.__day_count_convention = value        \n\n    @property\n    def rounded_notional_amount1(self) -> float:\n        """The total Notional amount or quantity of units of the underlying asset."""\n        return self.__rounded_notional_amount1\n\n    @rounded_notional_amount1.setter\n    def rounded_notional_amount1(self, value: float):\n        self._property_changed('rounded_notional_amount1')\n        self.__rounded_notional_amount1 = value        \n\n    @property\n    def rounded_notional_amount2(self) -> float:\n        """Same as Rounded Notional Amount 1."""\n        return self.__rounded_notional_amount2\n\n    @rounded_notional_amount2.setter\n    def rounded_notional_amount2(self, value: float):\n        self._property_changed('rounded_notional_amount2')\n        self.__rounded_notional_amount2 = value        \n\n    @property\n    def factor_source(self) -> str:\n        """Factor source. One of: Axioma, Prime."""\n        return self.__factor_source\n\n    @factor_source.setter\n    def factor_source(self, value: str):\n        self._property_changed('factor_source')\n        self.__factor_source = value        \n\n    @property\n    def future_month_j26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_j26\n\n    @future_month_j26.setter\n    def future_month_j26(self, value: float):\n        self._property_changed('future_month_j26')\n        self.__future_month_j26 = value        \n\n    @property\n    def lending_sec_type(self) -> str:\n        """Securities lending identifiter for the security on loan."""\n        return self.__lending_sec_type\n\n    @lending_sec_type.setter\n    def lending_sec_type(self, value: str):\n        self._property_changed('lending_sec_type')\n        self.__lending_sec_type = value        \n\n    @property\n    def future_month_j25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_j25\n\n    @future_month_j25.setter\n    def future_month_j25(self, value: float):\n        self._property_changed('future_month_j25')\n        self.__future_month_j25 = value        \n\n    @property\n    def leverage(self) -> float:\n        """Leverage."""\n        return self.__leverage\n\n    @leverage.setter\n    def leverage(self, value: float):\n        self._property_changed('leverage')\n        self.__leverage = value        \n\n    @property\n    def forecast_day(self) -> str:\n        """The offset for forecast day for weather from today."""\n        return self.__forecast_day\n\n    @forecast_day.setter\n    def forecast_day(self, value: str):\n        self._property_changed('forecast_day')\n        self.__forecast_day = value        \n\n    @property\n    def option_family(self) -> str:\n        """Style of the option."""\n        return self.__option_family\n\n    @option_family.setter\n    def option_family(self, value: str):\n        self._property_changed('option_family')\n        self.__option_family = value        \n\n    @property\n    def generator_output(self) -> float:\n        """The output of an electricity generator in MW."""\n        return self.__generator_output\n\n    @generator_output.setter\n    def generator_output(self, value: float):\n        self._property_changed('generator_output')\n        self.__generator_output = value        \n\n    @property\n    def price_spot_stop_loss_value(self) -> float:\n        """Stop loss price value of the trade idea."""\n        return self.__price_spot_stop_loss_value\n\n    @price_spot_stop_loss_value.setter\n    def price_spot_stop_loss_value(self, value: float):\n        self._property_changed('price_spot_stop_loss_value')\n        self.__price_spot_stop_loss_value = value        \n\n    @property\n    def kpi_id(self) -> str:\n        """Marquee unique KPI identifier."""\n        return self.__kpi_id\n\n    @kpi_id.setter\n    def kpi_id(self, value: str):\n        self._property_changed('kpi_id')\n        self.__kpi_id = value        \n\n    @property\n    def wind_generation(self) -> float:\n        """Wind generation value provided by source."""\n        return self.__wind_generation\n\n    @wind_generation.setter\n    def wind_generation(self, value: float):\n        self._property_changed('wind_generation')\n        self.__wind_generation = value        \n\n    @property\n    def percentage_mid_executed_quantity(self) -> float:\n        """Percentage of total order filled at midpoint."""\n        return self.__percentage_mid_executed_quantity\n\n    @percentage_mid_executed_quantity.setter\n    def percentage_mid_executed_quantity(self, value: float):\n        self._property_changed('percentage_mid_executed_quantity')\n        self.__percentage_mid_executed_quantity = value        \n\n    @property\n    def borrow_cost(self) -> float:\n        """An indication of the rate one would be charged for borrowing/shorting the\n           relevant asset on that day, expressed in bps. Rates may change daily."""\n        return self.__borrow_cost\n\n    @borrow_cost.setter\n    def borrow_cost(self, value: float):\n        self._property_changed('borrow_cost')\n        self.__borrow_cost = value        \n\n    @property\n    def knock_out_direction(self) -> str:\n        """The side of the knock-out price the market level must reach to trigger the knock\n           out."""\n        return self.__knock_out_direction\n\n    @knock_out_direction.setter\n    def knock_out_direction(self, value: str):\n        self._property_changed('knock_out_direction')\n        self.__knock_out_direction = value        \n\n    @property\n    def risk_model(self) -> str:\n        """Model used to compute risk or performance attribution. Defines universe,\n           factors, calibration period etc."""\n        return self.__risk_model\n\n    @risk_model.setter\n    def risk_model(self, value: str):\n        self._property_changed('risk_model')\n        self.__risk_model = value        \n\n    @property\n    def asset_parameters_vendor(self) -> str:\n        """Marquee unique identifier"""\n        return self.__asset_parameters_vendor\n\n    @asset_parameters_vendor.setter\n    def asset_parameters_vendor(self, value: str):\n        self._property_changed('asset_parameters_vendor')\n        self.__asset_parameters_vendor = value        \n\n    @property\n    def fair_value(self) -> float:\n        """Fair Value."""\n        return self.__fair_value\n\n    @fair_value.setter\n    def fair_value(self, value: float):\n        self._property_changed('fair_value')\n        self.__fair_value = value        \n\n    @property\n    def open_time(self) -> datetime.datetime:\n        """Time opened. ISO 8601 formatted string."""\n        return self.__open_time\n\n    @open_time.setter\n    def open_time(self, value: datetime.datetime):\n        self._property_changed('open_time')\n        self.__open_time = value        \n\n    @property\n    def pressure_hourly_forecast(self) -> float:\n        """The hourly forecast value for pressure in Inch."""\n        return self.__pressure_hourly_forecast\n\n    @pressure_hourly_forecast.setter\n    def pressure_hourly_forecast(self, value: float):\n        self._property_changed('pressure_hourly_forecast')\n        self.__pressure_hourly_forecast = value        \n\n    @property\n    def local_ccy_rate(self) -> float:\n        """The interest rate of the local currency of the associated FX contract."""\n        return self.__local_ccy_rate\n\n    @local_ccy_rate.setter\n    def local_ccy_rate(self, value: float):\n        self._property_changed('local_ccy_rate')\n        self.__local_ccy_rate = value        \n\n    @property\n    def end_user_exception(self) -> str:\n        """If buyer or seller or both is electing the End User Exception."""\n        return self.__end_user_exception\n\n    @end_user_exception.setter\n    def end_user_exception(self, value: str):\n        self._property_changed('end_user_exception')\n        self.__end_user_exception = value        \n\n    @property\n    def sell90cents(self) -> float:\n        """The amount GS would sell for 90 cents charge."""\n        return self.__sell90cents\n\n    @sell90cents.setter\n    def sell90cents(self, value: float):\n        self._property_changed('sell90cents')\n        self.__sell90cents = value        \n\n    @property\n    def execution_venue(self) -> str:\n        """An indication of whether the SB swap transaction was executed on a registered\n           swap execution facility or designated contract market or was executed\n           as an off-facility swap."""\n        return self.__execution_venue\n\n    @execution_venue.setter\n    def execution_venue(self, value: str):\n        self._property_changed('execution_venue')\n        self.__execution_venue = value        \n\n    @property\n    def primary_vwap_in_limit_realized_bps(self) -> float:\n        """Realised performance vs Primary VWAP In Limit, in bps."""\n        return self.__primary_vwap_in_limit_realized_bps\n\n    @primary_vwap_in_limit_realized_bps.setter\n    def primary_vwap_in_limit_realized_bps(self, value: float):\n        self._property_changed('primary_vwap_in_limit_realized_bps')\n        self.__primary_vwap_in_limit_realized_bps = value        \n\n    @property\n    def approve_rebalance(self) -> bool:\n        """An approved basket."""\n        return self.__approve_rebalance\n\n    @approve_rebalance.setter\n    def approve_rebalance(self, value: bool):\n        self._property_changed('approve_rebalance')\n        self.__approve_rebalance = value        \n\n    @property\n    def adjusted_close_price(self) -> float:\n        """Closing Price adjusted for corporate actions."""\n        return self.__adjusted_close_price\n\n    @adjusted_close_price.setter\n    def adjusted_close_price(self, value: float):\n        self._property_changed('adjusted_close_price')\n        self.__adjusted_close_price = value        \n\n    @property\n    def lms_id(self) -> str:\n        """Market identifier code."""\n        return self.__lms_id\n\n    @lms_id.setter\n    def lms_id(self, value: str):\n        self._property_changed('lms_id')\n        self.__lms_id = value        \n\n    @property\n    def rebate_rate(self) -> float:\n        """Defines the rate of the cash-back payment to an investor who puts up collateral\n           in borrowing a stock. A rebate rate of interest implies a fee for the\n           loan of securities."""\n        return self.__rebate_rate\n\n    @rebate_rate.setter\n    def rebate_rate(self, value: float):\n        self._property_changed('rebate_rate')\n        self.__rebate_rate = value        \n\n    @property\n    def sell130cents(self) -> float:\n        """The amount GS would sell for 130 cents charge."""\n        return self.__sell130cents\n\n    @sell130cents.setter\n    def sell130cents(self, value: float):\n        self._property_changed('sell130cents')\n        self.__sell130cents = value        \n\n    @property\n    def sell32bps(self) -> float:\n        """The amount GS would sell for 32 bps charge."""\n        return self.__sell32bps\n\n    @sell32bps.setter\n    def sell32bps(self, value: float):\n        self._property_changed('sell32bps')\n        self.__sell32bps = value        \n\n    @property\n    def pace_of_rollp50(self) -> float:\n        """The 50th-percentile of the pace of roll compared to previous quarters (over the\n           last 5 years) at the same point in time, i.e. cross-sectional."""\n        return self.__pace_of_rollp50\n\n    @pace_of_rollp50.setter\n    def pace_of_rollp50(self, value: float):\n        self._property_changed('pace_of_rollp50')\n        self.__pace_of_rollp50 = value        \n\n    @property\n    def price_move_vs_arrival(self) -> float:\n        """Stock price return versus arrival price return (in bps)."""\n        return self.__price_move_vs_arrival\n\n    @price_move_vs_arrival.setter\n    def price_move_vs_arrival(self, value: float):\n        self._property_changed('price_move_vs_arrival')\n        self.__price_move_vs_arrival = value        \n\n    @property\n    def strike_relative(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_relative\n\n    @strike_relative.setter\n    def strike_relative(self, value: Union[float, str]):\n        self._property_changed('strike_relative')\n        self.__strike_relative = value        \n\n    @property\n    def pressure_type(self) -> str:\n        """The type of pressure required like sea level or barometric."""\n        return self.__pressure_type\n\n    @pressure_type.setter\n    def pressure_type(self, value: str):\n        self._property_changed('pressure_type')\n        self.__pressure_type = value        \n\n    @property\n    def buy40bps(self) -> float:\n        """The amount GS would buy for 40 bps charge."""\n        return self.__buy40bps\n\n    @buy40bps.setter\n    def buy40bps(self, value: float):\n        self._property_changed('buy40bps')\n        self.__buy40bps = value        \n\n    @property\n    def price_notation(self) -> float:\n        """The Basis points, Price, Yield, Spread, Coupon, etc., value depending on the\n           type of SB swap, which is calculated at affirmation."""\n        return self.__price_notation\n\n    @price_notation.setter\n    def price_notation(self, value: float):\n        self._property_changed('price_notation')\n        self.__price_notation = value        \n\n    @property\n    def strategy(self) -> str:\n        """More specific descriptor of a fund's investment approach. Same view permissions\n           as the asset."""\n        return self.__strategy\n\n    @strategy.setter\n    def strategy(self, value: str):\n        self._property_changed('strategy')\n        self.__strategy = value        \n\n    @property\n    def issue_status_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__issue_status_date\n\n    @issue_status_date.setter\n    def issue_status_date(self, value: datetime.date):\n        self._property_changed('issue_status_date')\n        self.__issue_status_date = value        \n\n    @property\n    def lender_income(self) -> float:\n        """Income earned by the Lender for the loan of securities to a borrower."""\n        return self.__lender_income\n\n    @lender_income.setter\n    def lender_income(self, value: float):\n        self._property_changed('lender_income')\n        self.__lender_income = value        \n\n    @property\n    def pb_client_id(self) -> str:\n        """Prime Brokerage client identifier."""\n        return self.__pb_client_id\n\n    @pb_client_id.setter\n    def pb_client_id(self, value: str):\n        self._property_changed('pb_client_id')\n        self.__pb_client_id = value        \n\n    @property\n    def istat_region_code(self) -> float:\n        """Italian region code (ISTAT 2019)."""\n        return self.__istat_region_code\n\n    @istat_region_code.setter\n    def istat_region_code(self, value: float):\n        self._property_changed('istat_region_code')\n        self.__istat_region_code = value        \n\n    @property\n    def sell9bps(self) -> float:\n        """The amount GS would sell for 9 bps charge."""\n        return self.__sell9bps\n\n    @sell9bps.setter\n    def sell9bps(self, value: float):\n        self._property_changed('sell9bps')\n        self.__sell9bps = value        \n\n    @property\n    def owner_id(self) -> str:\n        """Marquee unique identifier for user who owns the object."""\n        return self.__owner_id\n\n    @owner_id.setter\n    def owner_id(self, value: str):\n        self._property_changed('owner_id')\n        self.__owner_id = value        \n\n    @property\n    def composite10_day_adv(self) -> float:\n        """Composite 10 day ADV."""\n        return self.__composite10_day_adv\n\n    @composite10_day_adv.setter\n    def composite10_day_adv(self, value: float):\n        self._property_changed('composite10_day_adv')\n        self.__composite10_day_adv = value        \n\n    @property\n    def max_loan_balance(self) -> float:\n        """The maximum allowable balance that can be lent out for a lender."""\n        return self.__max_loan_balance\n\n    @max_loan_balance.setter\n    def max_loan_balance(self, value: float):\n        self._property_changed('max_loan_balance')\n        self.__max_loan_balance = value        \n\n    @property\n    def idea_activity_type(self) -> str:\n        """Equals CorporateAction if the activity originates as a result of a corporate\n           action. Equals GovernanceAction if the activity originates as a\n           result of a control measure. Equals UserAction if the activity is\n           user driven."""\n        return self.__idea_activity_type\n\n    @idea_activity_type.setter\n    def idea_activity_type(self, value: str):\n        self._property_changed('idea_activity_type')\n        self.__idea_activity_type = value        \n\n    @property\n    def sell60cents(self) -> float:\n        """The amount GS would sell for 60 cents charge."""\n        return self.__sell60cents\n\n    @sell60cents.setter\n    def sell60cents(self, value: float):\n        self._property_changed('sell60cents')\n        self.__sell60cents = value        \n\n    @property\n    def idea_source(self) -> str:\n        """Equals User if the idea activity originates from a sales person. Equals System\n           if the idea activity is system generated."""\n        return self.__idea_source\n\n    @idea_source.setter\n    def idea_source(self, value: str):\n        self._property_changed('idea_source')\n        self.__idea_source = value        \n\n    @property\n    def ever_on_vent(self) -> float:\n        """Total number of patients ever on ventilator for COVID."""\n        return self.__ever_on_vent\n\n    @ever_on_vent.setter\n    def ever_on_vent(self, value: float):\n        self._property_changed('ever_on_vent')\n        self.__ever_on_vent = value        \n\n    @property\n    def buy15cents(self) -> float:\n        """The amount GS would buy for 15 cents charge."""\n        return self.__buy15cents\n\n    @buy15cents.setter\n    def buy15cents(self, value: float):\n        self._property_changed('buy15cents')\n        self.__buy15cents = value        \n\n    @property\n    def unadjusted_ask(self) -> float:\n        """Unadjusted ask level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__unadjusted_ask\n\n    @unadjusted_ask.setter\n    def unadjusted_ask(self, value: float):\n        self._property_changed('unadjusted_ask')\n        self.__unadjusted_ask = value        \n\n    @property\n    def contribution_name(self) -> str:\n        """Sector or type group name for the corresponding geography when metricName is\n           either CAI_CONTRIBUTION_SECTOR or CAI_CONTRIBUTION_TYPE."""\n        return self.__contribution_name\n\n    @contribution_name.setter\n    def contribution_name(self, value: str):\n        self._property_changed('contribution_name')\n        self.__contribution_name = value        \n\n    @property\n    def given_plus_paid(self) -> float:\n        """Total of given & paid."""\n        return self.__given_plus_paid\n\n    @given_plus_paid.setter\n    def given_plus_paid(self, value: float):\n        self._property_changed('given_plus_paid')\n        self.__given_plus_paid = value        \n\n    @property\n    def last_fill_price(self) -> float:\n        """Last execution price."""\n        return self.__last_fill_price\n\n    @last_fill_price.setter\n    def last_fill_price(self, value: float):\n        self._property_changed('last_fill_price')\n        self.__last_fill_price = value        \n\n    @property\n    def short_conviction_small(self) -> float:\n        """The count of short ideas with small conviction."""\n        return self.__short_conviction_small\n\n    @short_conviction_small.setter\n    def short_conviction_small(self, value: float):\n        self._property_changed('short_conviction_small')\n        self.__short_conviction_small = value        \n\n    @property\n    def upfront_payment_currency(self) -> str:\n        """Currency of upfront payment."""\n        return self.__upfront_payment_currency\n\n    @upfront_payment_currency.setter\n    def upfront_payment_currency(self, value: str):\n        self._property_changed('upfront_payment_currency')\n        self.__upfront_payment_currency = value        \n\n    @property\n    def spot_settlement_date(self) -> datetime.date:\n        """The settlement date for a spot FX trade."""\n        return self.__spot_settlement_date\n\n    @spot_settlement_date.setter\n    def spot_settlement_date(self, value: datetime.date):\n        self._property_changed('spot_settlement_date')\n        self.__spot_settlement_date = value        \n\n    @property\n    def matrix_order(self) -> float:\n        """The matrix col/row order. Number from 0-249."""\n        return self.__matrix_order\n\n    @matrix_order.setter\n    def matrix_order(self, value: float):\n        self._property_changed('matrix_order')\n        self.__matrix_order = value        \n\n    @property\n    def date_index(self) -> float:\n        """For a rates asset, represents the proxy for a meeting number."""\n        return self.__date_index\n\n    @date_index.setter\n    def date_index(self, value: float):\n        self._property_changed('date_index')\n        self.__date_index = value        \n\n    @property\n    def payer_day_count_fraction(self) -> str:\n        """Day Count Fraction"""\n        return self.__payer_day_count_fraction\n\n    @payer_day_count_fraction.setter\n    def payer_day_count_fraction(self, value: str):\n        self._property_changed('payer_day_count_fraction')\n        self.__payer_day_count_fraction = value        \n\n    @property\n    def asset_classifications_is_primary(self) -> bool:\n        """Whether or not it is the primary exchange asset."""\n        return self.__asset_classifications_is_primary\n\n    @asset_classifications_is_primary.setter\n    def asset_classifications_is_primary(self, value: bool):\n        self._property_changed('asset_classifications_is_primary')\n        self.__asset_classifications_is_primary = value        \n\n    @property\n    def break_even_inflation_change(self) -> float:\n        """Break even inflation change."""\n        return self.__break_even_inflation_change\n\n    @break_even_inflation_change.setter\n    def break_even_inflation_change(self, value: float):\n        self._property_changed('break_even_inflation_change')\n        self.__break_even_inflation_change = value        \n\n    @property\n    def buy130cents(self) -> float:\n        """The amount GS would buy for 130 cents charge."""\n        return self.__buy130cents\n\n    @buy130cents.setter\n    def buy130cents(self, value: float):\n        self._property_changed('buy130cents')\n        self.__buy130cents = value        \n\n    @property\n    def dwi_contribution(self) -> float:\n        """Contribution of debt weighted exchange rate index to FCI. Only applicable to EM\n           countries."""\n        return self.__dwi_contribution\n\n    @dwi_contribution.setter\n    def dwi_contribution(self, value: float):\n        self._property_changed('dwi_contribution')\n        self.__dwi_contribution = value        \n\n    @property\n    def asset2_id(self) -> str:\n        """Marquee unique asset identifier."""\n        return self.__asset2_id\n\n    @asset2_id.setter\n    def asset2_id(self, value: str):\n        self._property_changed('asset2_id')\n        self.__asset2_id = value        \n\n    @property\n    def average_fill_price(self) -> float:\n        """Average fill price for the order since it started."""\n        return self.__average_fill_price\n\n    @average_fill_price.setter\n    def average_fill_price(self, value: float):\n        self._property_changed('average_fill_price')\n        self.__average_fill_price = value        \n\n    @property\n    def depth_spread_score(self) -> float:\n        """Z-score of the difference between the mid price and the best price an order to\n           buy or sell a specific notional can be filled at."""\n        return self.__depth_spread_score\n\n    @depth_spread_score.setter\n    def depth_spread_score(self, value: float):\n        self._property_changed('depth_spread_score')\n        self.__depth_spread_score = value        \n\n    @property\n    def sell10cents(self) -> float:\n        """The amount GS would sell for 10 cents charge."""\n        return self.__sell10cents\n\n    @sell10cents.setter\n    def sell10cents(self, value: float):\n        self._property_changed('sell10cents')\n        self.__sell10cents = value        \n\n    @property\n    def sub_account(self) -> str:\n        """Subaccount."""\n        return self.__sub_account\n\n    @sub_account.setter\n    def sub_account(self, value: str):\n        self._property_changed('sub_account')\n        self.__sub_account = value        \n\n    @property\n    def buy65cents(self) -> float:\n        """The amount GS would buy for 65 cents charge."""\n        return self.__buy65cents\n\n    @buy65cents.setter\n    def buy65cents(self, value: float):\n        self._property_changed('buy65cents')\n        self.__buy65cents = value        \n\n    @property\n    def bond_cds_basis(self) -> float:\n        """Difference between bond spread and underlying cds of the bond."""\n        return self.__bond_cds_basis\n\n    @bond_cds_basis.setter\n    def bond_cds_basis(self, value: float):\n        self._property_changed('bond_cds_basis')\n        self.__bond_cds_basis = value        \n\n    @property\n    def vendor(self) -> Union[Union[MarketDataVendor, str], str]:\n        return self.__vendor\n\n    @vendor.setter\n    def vendor(self, value: Union[Union[MarketDataVendor, str], str]):\n        self._property_changed('vendor')\n        self.__vendor = get_enum_value(MarketDataVendor, value)        \n\n    @property\n    def data_set(self) -> str:\n        """Unique id of dataset."""\n        return self.__data_set\n\n    @data_set.setter\n    def data_set(self, value: str):\n        self._property_changed('data_set')\n        self.__data_set = value        \n\n    @property\n    def notional_amount2(self) -> float:\n        """The total Notional amount or quantity of units of the underlying asset."""\n        return self.__notional_amount2\n\n    @notional_amount2.setter\n    def notional_amount2(self, value: float):\n        self._property_changed('notional_amount2')\n        self.__notional_amount2 = value        \n\n    @property\n    def notional_amount1(self) -> float:\n        """The total Notional amount or quantity of units of the underlying asset."""\n        return self.__notional_amount1\n\n    @notional_amount1.setter\n    def notional_amount1(self, value: float):\n        self._property_changed('notional_amount1')\n        self.__notional_amount1 = value        \n\n    @property\n    def queueing_time(self) -> int:\n        """Time for which risk calculation was queued (ms)."""\n        return self.__queueing_time\n\n    @queueing_time.setter\n    def queueing_time(self, value: int):\n        self._property_changed('queueing_time')\n        self.__queueing_time = value        \n\n    @property\n    def ann_return5_year(self) -> float:\n        """Total return representing past performance, used for GS Money Market onshore\n           funds, over five years."""\n        return self.__ann_return5_year\n\n    @ann_return5_year.setter\n    def ann_return5_year(self, value: float):\n        self._property_changed('ann_return5_year')\n        self.__ann_return5_year = value        \n\n    @property\n    def volume_start_of_day(self) -> float:\n        """Start of day forecast of daily volume."""\n        return self.__volume_start_of_day\n\n    @volume_start_of_day.setter\n    def volume_start_of_day(self, value: float):\n        self._property_changed('volume_start_of_day')\n        self.__volume_start_of_day = value        \n\n    @property\n    def price_notation3_type(self) -> str:\n        """Basis points, Price, Yield, Spread, Coupon, etc., depending on the type of SB\n           swap, which is calculated at affirmation."""\n        return self.__price_notation3_type\n\n    @price_notation3_type.setter\n    def price_notation3_type(self, value: str):\n        self._property_changed('price_notation3_type')\n        self.__price_notation3_type = value        \n\n    @property\n    def asset_parameters_floating_rate_designated_maturity(self) -> str:\n        """Tenor"""\n        return self.__asset_parameters_floating_rate_designated_maturity\n\n    @asset_parameters_floating_rate_designated_maturity.setter\n    def asset_parameters_floating_rate_designated_maturity(self, value: str):\n        self._property_changed('asset_parameters_floating_rate_designated_maturity')\n        self.__asset_parameters_floating_rate_designated_maturity = value        \n\n    @property\n    def executed_notional_local(self) -> float:\n        """Executed notional in local currency."""\n        return self.__executed_notional_local\n\n    @executed_notional_local.setter\n    def executed_notional_local(self, value: float):\n        self._property_changed('executed_notional_local')\n        self.__executed_notional_local = value        \n\n    @property\n    def business_sponsor(self) -> str:\n        """Business Sponsor."""\n        return self.__business_sponsor\n\n    @business_sponsor.setter\n    def business_sponsor(self, value: str):\n        self._property_changed('business_sponsor')\n        self.__business_sponsor = value        \n\n    @property\n    def unexplained(self) -> float:\n        """PNL unexplained by risk model."""\n        return self.__unexplained\n\n    @unexplained.setter\n    def unexplained(self, value: float):\n        self._property_changed('unexplained')\n        self.__unexplained = value        \n\n    @property\n    def seasonal_adjustment_short(self) -> str:\n        """Seasonal adjustment of series."""\n        return self.__seasonal_adjustment_short\n\n    @seasonal_adjustment_short.setter\n    def seasonal_adjustment_short(self, value: str):\n        self._property_changed('seasonal_adjustment_short')\n        self.__seasonal_adjustment_short = value        \n\n    @property\n    def metric(self) -> str:\n        """Metric for the associated asset."""\n        return self.__metric\n\n    @metric.setter\n    def metric(self, value: str):\n        self._property_changed('metric')\n        self.__metric = value        \n\n    @property\n    def ask(self) -> float:\n        """Latest Ask Price (price offering to sell)."""\n        return self.__ask\n\n    @ask.setter\n    def ask(self, value: float):\n        self._property_changed('ask')\n        self.__ask = value        \n\n    @property\n    def close_price(self) -> float:\n        """Closing level of an asset based on official exchange fixing or calculation agent\n           marked level."""\n        return self.__close_price\n\n    @close_price.setter\n    def close_price(self, value: float):\n        self._property_changed('close_price')\n        self.__close_price = value        \n\n    @property\n    def end_time(self) -> datetime.datetime:\n        """End time."""\n        return self.__end_time\n\n    @end_time.setter\n    def end_time(self, value: datetime.datetime):\n        self._property_changed('end_time')\n        self.__end_time = value        \n\n    @property\n    def sell100cents(self) -> float:\n        """The amount GS would sell for 100 cents charge."""\n        return self.__sell100cents\n\n    @sell100cents.setter\n    def sell100cents(self, value: float):\n        self._property_changed('sell100cents')\n        self.__sell100cents = value        \n\n    @property\n    def execution_timestamp(self) -> datetime.datetime:\n        """The time and date of execution of the publicly reportable swap transaction in\n           Coordinated Universal Time (UTC - CCYY-MMDDThh:mm:ss)."""\n        return self.__execution_timestamp\n\n    @execution_timestamp.setter\n    def execution_timestamp(self, value: datetime.datetime):\n        self._property_changed('execution_timestamp')\n        self.__execution_timestamp = value        \n\n    @property\n    def buy180cents(self) -> float:\n        """The amount GS would buy for 180 cents charge."""\n        return self.__buy180cents\n\n    @buy180cents.setter\n    def buy180cents(self, value: float):\n        self._property_changed('buy180cents')\n        self.__buy180cents = value        \n\n    @property\n    def absolute_strike(self) -> float:\n        """Actual strike value specified in outright terms."""\n        return self.__absolute_strike\n\n    @absolute_strike.setter\n    def absolute_strike(self, value: float):\n        self._property_changed('absolute_strike')\n        self.__absolute_strike = value        \n\n    @property\n    def sell3point5bps(self) -> float:\n        """The amount GS would sell for 3.5 bps charge."""\n        return self.__sell3point5bps\n\n    @sell3point5bps.setter\n    def sell3point5bps(self, value: float):\n        self._property_changed('sell3point5bps')\n        self.__sell3point5bps = value        \n\n    @property\n    def liquidity_score_buy(self) -> float:\n        """The liquidity score assigned to buying the bond."""\n        return self.__liquidity_score_buy\n\n    @liquidity_score_buy.setter\n    def liquidity_score_buy(self, value: float):\n        self._property_changed('liquidity_score_buy')\n        self.__liquidity_score_buy = value        \n\n    @property\n    def payment_frequency(self) -> str:\n        """An integer multiplier of a time period describing how often the parties to the\n           SB swap transaction exchange payments associated with each party???s\n           obligation. Such payment frequency may be described as one letter\n           preceded by an integer."""\n        return self.__payment_frequency\n\n    @payment_frequency.setter\n    def payment_frequency(self, value: str):\n        self._property_changed('payment_frequency')\n        self.__payment_frequency = value        \n\n    @property\n    def expense_ratio_net_bps(self) -> float:\n        """Gives basis point measure of management fee, net."""\n        return self.__expense_ratio_net_bps\n\n    @expense_ratio_net_bps.setter\n    def expense_ratio_net_bps(self, value: float):\n        self._property_changed('expense_ratio_net_bps')\n        self.__expense_ratio_net_bps = value        \n\n    @property\n    def metric_type(self) -> str:\n        """Type of the metric."""\n        return self.__metric_type\n\n    @metric_type.setter\n    def metric_type(self, value: str):\n        self._property_changed('metric_type')\n        self.__metric_type = value        \n\n    @property\n    def rank_ytd(self) -> float:\n        """Rank of the Contributor Year to Date."""\n        return self.__rank_ytd\n\n    @rank_ytd.setter\n    def rank_ytd(self, value: float):\n        self._property_changed('rank_ytd')\n        self.__rank_ytd = value        \n\n    @property\n    def leg1_spread(self) -> float:\n        """Spread of leg."""\n        return self.__leg1_spread\n\n    @leg1_spread.setter\n    def leg1_spread(self, value: float):\n        self._property_changed('leg1_spread')\n        self.__leg1_spread = value        \n\n    @property\n    def coverage_region(self) -> str:\n        """Coverage Region of a contributor."""\n        return self.__coverage_region\n\n    @coverage_region.setter\n    def coverage_region(self, value: str):\n        self._property_changed('coverage_region')\n        self.__coverage_region = value        \n\n    @property\n    def absolute_return_ytd(self) -> float:\n        """Absolute Return Year to Date."""\n        return self.__absolute_return_ytd\n\n    @absolute_return_ytd.setter\n    def absolute_return_ytd(self, value: float):\n        self._property_changed('absolute_return_ytd')\n        self.__absolute_return_ytd = value        \n\n    @property\n    def day_count_convention2(self) -> str:\n        """Day count convention for leg 2."""\n        return self.__day_count_convention2\n\n    @day_count_convention2.setter\n    def day_count_convention2(self, value: str):\n        self._property_changed('day_count_convention2')\n        self.__day_count_convention2 = value        \n\n    @property\n    def fwdtier(self) -> str:\n        """Forward tier."""\n        return self.__fwdtier\n\n    @fwdtier.setter\n    def fwdtier(self, value: str):\n        self._property_changed('fwdtier')\n        self.__fwdtier = value        \n\n    @property\n    def degree_days(self) -> float:\n        """Value of the temperature of given type."""\n        return self.__degree_days\n\n    @degree_days.setter\n    def degree_days(self, value: float):\n        self._property_changed('degree_days')\n        self.__degree_days = value        \n\n    @property\n    def turnover_adjusted(self) -> float:\n        """Turnover adjusted for corporate actions."""\n        return self.__turnover_adjusted\n\n    @turnover_adjusted.setter\n    def turnover_adjusted(self, value: float):\n        self._property_changed('turnover_adjusted')\n        self.__turnover_adjusted = value        \n\n    @property\n    def price_spot_target_value(self) -> float:\n        """Target price value of the trade idea."""\n        return self.__price_spot_target_value\n\n    @price_spot_target_value.setter\n    def price_spot_target_value(self, value: float):\n        self._property_changed('price_spot_target_value')\n        self.__price_spot_target_value = value        \n\n    @property\n    def market_data_point(self) -> Tuple[Tuple[str, ...], ...]:\n        """The market data point (e.g. 3m, 10y, 11y, Dec19)."""\n        return self.__market_data_point\n\n    @market_data_point.setter\n    def market_data_point(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('market_data_point')\n        self.__market_data_point = value        \n\n    @property\n    def num_of_funds(self) -> float:\n        """Number of funds used for aggregation for a given period."""\n        return self.__num_of_funds\n\n    @num_of_funds.setter\n    def num_of_funds(self, value: float):\n        self._property_changed('num_of_funds')\n        self.__num_of_funds = value        \n\n    @property\n    def trade_time(self) -> datetime.datetime:\n        """Trade Time."""\n        return self.__trade_time\n\n    @trade_time.setter\n    def trade_time(self, value: datetime.datetime):\n        self._property_changed('trade_time')\n        self.__trade_time = value        \n\n    @property\n    def execution_id(self) -> str:\n        """Execution ID."""\n        return self.__execution_id\n\n    @execution_id.setter\n    def execution_id(self, value: str):\n        self._property_changed('execution_id')\n        self.__execution_id = value        \n\n    @property\n    def turnover_unadjusted(self) -> float:\n        """Turnover not adjusted for corporate actions."""\n        return self.__turnover_unadjusted\n\n    @turnover_unadjusted.setter\n    def turnover_unadjusted(self, value: float):\n        self._property_changed('turnover_unadjusted')\n        self.__turnover_unadjusted = value        \n\n    @property\n    def leg1_floating_index(self) -> str:\n        """If floating index leg, the index."""\n        return self.__leg1_floating_index\n\n    @leg1_floating_index.setter\n    def leg1_floating_index(self, value: str):\n        self._property_changed('leg1_floating_index')\n        self.__leg1_floating_index = value        \n\n    @property\n    def hedge_annualized_volatility(self) -> float:\n        """Standard deviation of the annualized returns."""\n        return self.__hedge_annualized_volatility\n\n    @hedge_annualized_volatility.setter\n    def hedge_annualized_volatility(self, value: float):\n        self._property_changed('hedge_annualized_volatility')\n        self.__hedge_annualized_volatility = value        \n\n    @property\n    def benchmark_currency(self) -> str:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__benchmark_currency\n\n    @benchmark_currency.setter\n    def benchmark_currency(self, value: str):\n        self._property_changed('benchmark_currency')\n        self.__benchmark_currency = value        \n\n    @property\n    def futures_contract(self) -> str:\n        """The related futures contract code if applicable."""\n        return self.__futures_contract\n\n    @futures_contract.setter\n    def futures_contract(self, value: str):\n        self._property_changed('futures_contract')\n        self.__futures_contract = value        \n\n    @property\n    def name(self) -> str:\n        """Legal or published name for the asset."""\n        return self.__name\n\n    @name.setter\n    def name(self, value: str):\n        self._property_changed('name')\n        self.__name = value        \n\n    @property\n    def aum(self) -> Union[Op, float]:\n        return self.__aum\n\n    @aum.setter\n    def aum(self, value: Union[Op, float]):\n        self._property_changed('aum')\n        self.__aum = value        \n\n    @property\n    def leg1_day_count_convention(self) -> str:\n        """The determination of how interest accrues over time for the SB swap."""\n        return self.__leg1_day_count_convention\n\n    @leg1_day_count_convention.setter\n    def leg1_day_count_convention(self, value: str):\n        self._property_changed('leg1_day_count_convention')\n        self.__leg1_day_count_convention = value        \n\n    @property\n    def cbs_code(self) -> str:\n        """CBS Code for Municipality in Netherlands."""\n        return self.__cbs_code\n\n    @cbs_code.setter\n    def cbs_code(self, value: str):\n        self._property_changed('cbs_code')\n        self.__cbs_code = value        \n\n    @property\n    def folder_name(self) -> str:\n        """Folder Name of a chart."""\n        return self.__folder_name\n\n    @folder_name.setter\n    def folder_name(self, value: str):\n        self._property_changed('folder_name')\n        self.__folder_name = value        \n\n    @property\n    def api_usage(self) -> float:\n        """Usage Metrics for a given client entity based on specified service and metrics\n           feature."""\n        return self.__api_usage\n\n    @api_usage.setter\n    def api_usage(self, value: float):\n        self._property_changed('api_usage')\n        self.__api_usage = value        \n\n    @property\n    def twap_interval(self) -> float:\n        """Time interval (in milliseconds) at which a TWAP attempts to fill."""\n        return self.__twap_interval\n\n    @twap_interval.setter\n    def twap_interval(self, value: float):\n        self._property_changed('twap_interval')\n        self.__twap_interval = value        \n\n    @property\n    def unique_id(self) -> float:\n        """A unique identifier."""\n        return self.__unique_id\n\n    @unique_id.setter\n    def unique_id(self, value: float):\n        self._property_changed('unique_id')\n        self.__unique_id = value        \n\n    @property\n    def option_expiration_date(self) -> datetime.date:\n        """An indication of the date that the option is no longer available for exercise."""\n        return self.__option_expiration_date\n\n    @option_expiration_date.setter\n    def option_expiration_date(self, value: datetime.date):\n        self._property_changed('option_expiration_date')\n        self.__option_expiration_date = value        \n\n    @property\n    def swaption_atm_fwd_rate(self) -> float:\n        """Swaption ATM forward rate."""\n        return self.__swaption_atm_fwd_rate\n\n    @swaption_atm_fwd_rate.setter\n    def swaption_atm_fwd_rate(self, value: float):\n        self._property_changed('swaption_atm_fwd_rate')\n        self.__swaption_atm_fwd_rate = value        \n\n    @property\n    def live_date(self) -> Union[Op, datetime.date]:\n        return self.__live_date\n\n    @live_date.setter\n    def live_date(self, value: Union[Op, datetime.date]):\n        self._property_changed('live_date')\n        self.__live_date = value        \n\n    @property\n    def corporate_action_type(self) -> str:\n        """Different types of corporate actions from solactive"""\n        return self.__corporate_action_type\n\n    @corporate_action_type.setter\n    def corporate_action_type(self, value: str):\n        self._property_changed('corporate_action_type')\n        self.__corporate_action_type = value        \n\n    @property\n    def prime_id(self) -> str:\n        """Prime Id."""\n        return self.__prime_id\n\n    @prime_id.setter\n    def prime_id(self, value: str):\n        self._property_changed('prime_id')\n        self.__prime_id = value        \n\n    @property\n    def description(self) -> str:\n        """Description of asset."""\n        return self.__description\n\n    @description.setter\n    def description(self, value: str):\n        self._property_changed('description')\n        self.__description = value        \n\n    @property\n    def asset_classifications_is_country_primary(self) -> bool:\n        """Whether or not it is the primary exchange asset for the exchange country."""\n        return self.__asset_classifications_is_country_primary\n\n    @asset_classifications_is_country_primary.setter\n    def asset_classifications_is_country_primary(self, value: bool):\n        self._property_changed('asset_classifications_is_country_primary')\n        self.__asset_classifications_is_country_primary = value        \n\n    @property\n    def rebate_rate_limit(self) -> float:\n        """The maximum allowable rebate rate that can be paid out to the borrower of a\n           stock."""\n        return self.__rebate_rate_limit\n\n    @rebate_rate_limit.setter\n    def rebate_rate_limit(self, value: float):\n        self._property_changed('rebate_rate_limit')\n        self.__rebate_rate_limit = value        \n\n    @property\n    def factor(self) -> str:\n        """Name of the factor. i.e. Momentum"""\n        return self.__factor\n\n    @factor.setter\n    def factor(self, value: str):\n        self._property_changed('factor')\n        self.__factor = value        \n\n    @property\n    def days_on_loan(self) -> float:\n        """The number of days this loan as been on our books."""\n        return self.__days_on_loan\n\n    @days_on_loan.setter\n    def days_on_loan(self, value: float):\n        self._property_changed('days_on_loan')\n        self.__days_on_loan = value        \n\n    @property\n    def long_conviction_small(self) -> float:\n        """The count of long ideas with small conviction."""\n        return self.__long_conviction_small\n\n    @long_conviction_small.setter\n    def long_conviction_small(self, value: float):\n        self._property_changed('long_conviction_small')\n        self.__long_conviction_small = value        \n\n    @property\n    def sell40cents(self) -> float:\n        """The amount GS would sell for 40 cents charge."""\n        return self.__sell40cents\n\n    @sell40cents.setter\n    def sell40cents(self, value: float):\n        self._property_changed('sell40cents')\n        self.__sell40cents = value        \n\n    @property\n    def relative_payoff_ytd(self) -> float:\n        """Total win divided by total loss Year to date."""\n        return self.__relative_payoff_ytd\n\n    @relative_payoff_ytd.setter\n    def relative_payoff_ytd(self, value: float):\n        self._property_changed('relative_payoff_ytd')\n        self.__relative_payoff_ytd = value        \n\n    @property\n    def gsfeer(self) -> float:\n        """Goldman Sachs Fundamental Equilibrium Exchange Rate."""\n        return self.__gsfeer\n\n    @gsfeer.setter\n    def gsfeer(self, value: float):\n        self._property_changed('gsfeer')\n        self.__gsfeer = value        \n\n    @property\n    def relative_hit_rate_qtd(self) -> float:\n        """Hit Rate Ratio Quarter to Date."""\n        return self.__relative_hit_rate_qtd\n\n    @relative_hit_rate_qtd.setter\n    def relative_hit_rate_qtd(self, value: float):\n        self._property_changed('relative_hit_rate_qtd')\n        self.__relative_hit_rate_qtd = value        \n\n    @property\n    def wam(self) -> float:\n        """Weighted average maturity, average of effective maturities of all securities\n           held in portfolio, weighted."""\n        return self.__wam\n\n    @wam.setter\n    def wam(self, value: float):\n        self._property_changed('wam')\n        self.__wam = value        \n\n    @property\n    def wal(self) -> float:\n        """Weighted average life, measures sensitivity to changes in liquidity."""\n        return self.__wal\n\n    @wal.setter\n    def wal(self, value: float):\n        self._property_changed('wal')\n        self.__wal = value        \n\n    @property\n    def quantityccy(self) -> str:\n        """Quantity unit."""\n        return self.__quantityccy\n\n    @quantityccy.setter\n    def quantityccy(self, value: str):\n        self._property_changed('quantityccy')\n        self.__quantityccy = value        \n\n    @property\n    def backtest_id(self) -> str:\n        """Marquee unique backtest identifier."""\n        return self.__backtest_id\n\n    @backtest_id.setter\n    def backtest_id(self, value: str):\n        self._property_changed('backtest_id')\n        self.__backtest_id = value        \n\n    @property\n    def dirty_price(self) -> float:\n        """Price of the Bond including accrued interest."""\n        return self.__dirty_price\n\n    @dirty_price.setter\n    def dirty_price(self, value: float):\n        self._property_changed('dirty_price')\n        self.__dirty_price = value        \n\n    @property\n    def corporate_spread_contribution(self) -> float:\n        """Contribution of corporate spread component to FCI."""\n        return self.__corporate_spread_contribution\n\n    @corporate_spread_contribution.setter\n    def corporate_spread_contribution(self, value: float):\n        self._property_changed('corporate_spread_contribution')\n        self.__corporate_spread_contribution = value        \n\n    @property\n    def relative_humidity_hourly_forecast(self) -> float:\n        """The hourly forecast value for relative humidity in percentage."""\n        return self.__relative_humidity_hourly_forecast\n\n    @relative_humidity_hourly_forecast.setter\n    def relative_humidity_hourly_forecast(self, value: float):\n        self._property_changed('relative_humidity_hourly_forecast')\n        self.__relative_humidity_hourly_forecast = value        \n\n    @property\n    def multiple_score(self) -> float:\n        """Multiple percentile relative to Americas coverage universe (a higher score means\n           more expensive)."""\n        return self.__multiple_score\n\n    @multiple_score.setter\n    def multiple_score(self, value: float):\n        self._property_changed('multiple_score')\n        self.__multiple_score = value        \n\n    @property\n    def beta_adjusted_exposure(self) -> float:\n        """Beta adjusted exposure."""\n        return self.__beta_adjusted_exposure\n\n    @beta_adjusted_exposure.setter\n    def beta_adjusted_exposure(self, value: float):\n        self._property_changed('beta_adjusted_exposure')\n        self.__beta_adjusted_exposure = value        \n\n    @property\n    def dividend_points(self) -> float:\n        """Expected Dividend in index points."""\n        return self.__dividend_points\n\n    @dividend_points.setter\n    def dividend_points(self, value: float):\n        self._property_changed('dividend_points')\n        self.__dividend_points = value        \n\n    @property\n    def brightness(self) -> float:\n        """Value of the measure type selected."""\n        return self.__brightness\n\n    @brightness.setter\n    def brightness(self, value: float):\n        self._property_changed('brightness')\n        self.__brightness = value        \n\n    @property\n    def asset_parameters_receiver_designated_maturity(self) -> str:\n        """Tenor"""\n        return self.__asset_parameters_receiver_designated_maturity\n\n    @asset_parameters_receiver_designated_maturity.setter\n    def asset_parameters_receiver_designated_maturity(self, value: str):\n        self._property_changed('asset_parameters_receiver_designated_maturity')\n        self.__asset_parameters_receiver_designated_maturity = value        \n\n    @property\n    def bos_in_ticks_description(self) -> str:\n        """Description of the Stock's Bid-Offer Spread in Ticks on the particular date."""\n        return self.__bos_in_ticks_description\n\n    @bos_in_ticks_description.setter\n    def bos_in_ticks_description(self, value: str):\n        self._property_changed('bos_in_ticks_description')\n        self.__bos_in_ticks_description = value        \n\n    @property\n    def test_id(self) -> str:\n        """Unique ID for test configuration."""\n        return self.__test_id\n\n    @test_id.setter\n    def test_id(self, value: str):\n        self._property_changed('test_id')\n        self.__test_id = value        \n\n    @property\n    def implied_correlation(self) -> float:\n        """Correlation of an asset implied by observations of market prices."""\n        return self.__implied_correlation\n\n    @implied_correlation.setter\n    def implied_correlation(self, value: float):\n        self._property_changed('implied_correlation')\n        self.__implied_correlation = value        \n\n    @property\n    def normalized_performance(self) -> float:\n        """Performance that is normalized to 1."""\n        return self.__normalized_performance\n\n    @normalized_performance.setter\n    def normalized_performance(self, value: float):\n        self._property_changed('normalized_performance')\n        self.__normalized_performance = value        \n\n    @property\n    def overnight_news_end_time(self) -> datetime.datetime:\n        """The overnight news end time."""\n        return self.__overnight_news_end_time\n\n    @overnight_news_end_time.setter\n    def overnight_news_end_time(self, value: datetime.datetime):\n        self._property_changed('overnight_news_end_time')\n        self.__overnight_news_end_time = value        \n\n    @property\n    def bytes_consumed(self) -> float:\n        """Bytes of raw data fetched in a run of the checker."""\n        return self.__bytes_consumed\n\n    @bytes_consumed.setter\n    def bytes_consumed(self, value: float):\n        self._property_changed('bytes_consumed')\n        self.__bytes_consumed = value        \n\n    @property\n    def swaption_vol(self) -> float:\n        """Historical implied normal volatility for a liquid point on swaption vol surface."""\n        return self.__swaption_vol\n\n    @swaption_vol.setter\n    def swaption_vol(self, value: float):\n        self._property_changed('swaption_vol')\n        self.__swaption_vol = value        \n\n    @property\n    def estimated_closing_volume(self) -> float:\n        """Estimated closing auction volume (in shares)."""\n        return self.__estimated_closing_volume\n\n    @estimated_closing_volume.setter\n    def estimated_closing_volume(self, value: float):\n        self._property_changed('estimated_closing_volume')\n        self.__estimated_closing_volume = value        \n\n    @property\n    def issuer(self) -> str:\n        """The issuer of this bond."""\n        return self.__issuer\n\n    @issuer.setter\n    def issuer(self, value: str):\n        self._property_changed('issuer')\n        self.__issuer = value        \n\n    @property\n    def dividend_yield(self) -> float:\n        """Annualized Dividend Yield."""\n        return self.__dividend_yield\n\n    @dividend_yield.setter\n    def dividend_yield(self, value: float):\n        self._property_changed('dividend_yield')\n        self.__dividend_yield = value        \n\n    @property\n    def market_type(self) -> str:\n        """The type of market for Power. Values may include Physical Bilateral, Day Ahead\n           or Real Time."""\n        return self.__market_type\n\n    @market_type.setter\n    def market_type(self, value: str):\n        self._property_changed('market_type')\n        self.__market_type = value        \n\n    @property\n    def num_units_lower(self) -> float:\n        """Units Lower."""\n        return self.__num_units_lower\n\n    @num_units_lower.setter\n    def num_units_lower(self, value: float):\n        self._property_changed('num_units_lower')\n        self.__num_units_lower = value        \n\n    @property\n    def source_origin(self) -> str:\n        """Source origin."""\n        return self.__source_origin\n\n    @source_origin.setter\n    def source_origin(self, value: str):\n        self._property_changed('source_origin')\n        self.__source_origin = value        \n\n    @property\n    def proceeds_asset_swap_spread3m(self) -> float:\n        """Proceeds asset swap spread vs 3m tenor."""\n        return self.__proceeds_asset_swap_spread3m\n\n    @proceeds_asset_swap_spread3m.setter\n    def proceeds_asset_swap_spread3m(self, value: float):\n        self._property_changed('proceeds_asset_swap_spread3m')\n        self.__proceeds_asset_swap_spread3m = value        \n\n    @property\n    def total_quantity(self) -> float:\n        """Rounded total quantity."""\n        return self.__total_quantity\n\n    @total_quantity.setter\n    def total_quantity(self, value: float):\n        self._property_changed('total_quantity')\n        self.__total_quantity = value        \n\n    @property\n    def internal_user(self) -> bool:\n        """Whether user is internal or not."""\n        return self.__internal_user\n\n    @internal_user.setter\n    def internal_user(self, value: bool):\n        self._property_changed('internal_user')\n        self.__internal_user = value        \n\n    @property\n    def sell40bps(self) -> float:\n        """The amount GS would sell for 40 bps charge."""\n        return self.__sell40bps\n\n    @sell40bps.setter\n    def sell40bps(self, value: float):\n        self._property_changed('sell40bps')\n        self.__sell40bps = value        \n\n    @property\n    def redemption_option(self) -> str:\n        """Indicates the calculation convention for callable instruments."""\n        return self.__redemption_option\n\n    @redemption_option.setter\n    def redemption_option(self, value: str):\n        self._property_changed('redemption_option')\n        self.__redemption_option = value        \n\n    @property\n    def notional_unit2(self) -> str:\n        """Unit of reported notional price."""\n        return self.__notional_unit2\n\n    @notional_unit2.setter\n    def notional_unit2(self, value: str):\n        self._property_changed('notional_unit2')\n        self.__notional_unit2 = value        \n\n    @property\n    def notional_unit1(self) -> str:\n        """Unit of reported notional price."""\n        return self.__notional_unit1\n\n    @notional_unit1.setter\n    def notional_unit1(self, value: str):\n        self._property_changed('notional_unit1')\n        self.__notional_unit1 = value        \n\n    @property\n    def sedol(self) -> str:\n        """SEDOL - Stock Exchange Daily Official List (subject to licensing)."""\n        return self.__sedol\n\n    @sedol.setter\n    def sedol(self, value: str):\n        self._property_changed('sedol')\n        self.__sedol = value        \n\n    @property\n    def rounding_cost_pnl(self) -> float:\n        """Rounding Cost Profit and Loss."""\n        return self.__rounding_cost_pnl\n\n    @rounding_cost_pnl.setter\n    def rounding_cost_pnl(self, value: float):\n        self._property_changed('rounding_cost_pnl')\n        self.__rounding_cost_pnl = value        \n\n    @property\n    def mid_yield(self) -> float:\n        """The return an investor realizes on a bond sold at the mid price."""\n        return self.__mid_yield\n\n    @mid_yield.setter\n    def mid_yield(self, value: float):\n        self._property_changed('mid_yield')\n        self.__mid_yield = value        \n\n    @property\n    def unexecuted_notional_local(self) -> float:\n        """Total un-executed notional in local currency."""\n        return self.__unexecuted_notional_local\n\n    @unexecuted_notional_local.setter\n    def unexecuted_notional_local(self, value: float):\n        self._property_changed('unexecuted_notional_local')\n        self.__unexecuted_notional_local = value        \n\n    @property\n    def sustain_global(self) -> bool:\n        """True if the stock is on the SUSTAIN (Global) 50 list as of the corresponding\n           date. False if the stock is removed from the SUSTAIN (Global) 50 list\n           on the corresponding date."""\n        return self.__sustain_global\n\n    @sustain_global.setter\n    def sustain_global(self, value: bool):\n        self._property_changed('sustain_global')\n        self.__sustain_global = value        \n\n    @property\n    def ending_date(self) -> str:\n        """End date of the period the valuation refers to."""\n        return self.__ending_date\n\n    @ending_date.setter\n    def ending_date(self, value: str):\n        self._property_changed('ending_date')\n        self.__ending_date = value        \n\n    @property\n    def proceeds_asset_swap_spread12m(self) -> float:\n        """Proceeds asset swap spread vs 12m tenor."""\n        return self.__proceeds_asset_swap_spread12m\n\n    @proceeds_asset_swap_spread12m.setter\n    def proceeds_asset_swap_spread12m(self, value: float):\n        self._property_changed('proceeds_asset_swap_spread12m')\n        self.__proceeds_asset_swap_spread12m = value        \n\n    @property\n    def gross_investment_wtd(self) -> float:\n        """Total gross investment Week to date."""\n        return self.__gross_investment_wtd\n\n    @gross_investment_wtd.setter\n    def gross_investment_wtd(self, value: float):\n        self._property_changed('gross_investment_wtd')\n        self.__gross_investment_wtd = value        \n\n    @property\n    def ann_return3_year(self) -> float:\n        """Total return representing past performance, used for GS Money Market onshore\n           funds, over three years."""\n        return self.__ann_return3_year\n\n    @ann_return3_year.setter\n    def ann_return3_year(self, value: float):\n        self._property_changed('ann_return3_year')\n        self.__ann_return3_year = value        \n\n    @property\n    def sharpe_wtd(self) -> float:\n        """Sharpe ratio Week to date."""\n        return self.__sharpe_wtd\n\n    @sharpe_wtd.setter\n    def sharpe_wtd(self, value: float):\n        self._property_changed('sharpe_wtd')\n        self.__sharpe_wtd = value        \n\n    @property\n    def discount_factor(self) -> float:\n        """Present Value of 1 unit of currency on an earlier date."""\n        return self.__discount_factor\n\n    @discount_factor.setter\n    def discount_factor(self, value: float):\n        self._property_changed('discount_factor')\n        self.__discount_factor = value        \n\n    @property\n    def relative_return_mtd(self) -> float:\n        """Relative Return Month to Date."""\n        return self.__relative_return_mtd\n\n    @relative_return_mtd.setter\n    def relative_return_mtd(self, value: float):\n        self._property_changed('relative_return_mtd')\n        self.__relative_return_mtd = value        \n\n    @property\n    def price_change_on_day(self) -> float:\n        """Price change on day."""\n        return self.__price_change_on_day\n\n    @price_change_on_day.setter\n    def price_change_on_day(self, value: float):\n        self._property_changed('price_change_on_day')\n        self.__price_change_on_day = value        \n\n    @property\n    def buy100cents(self) -> float:\n        """The amount GS would buy for 100 cents charge."""\n        return self.__buy100cents\n\n    @buy100cents.setter\n    def buy100cents(self, value: float):\n        self._property_changed('buy100cents')\n        self.__buy100cents = value        \n\n    @property\n    def forward_point(self) -> float:\n        """Outright forward minus spot."""\n        return self.__forward_point\n\n    @forward_point.setter\n    def forward_point(self, value: float):\n        self._property_changed('forward_point')\n        self.__forward_point = value        \n\n    @property\n    def fci(self) -> float:\n        """Nominal FCI value."""\n        return self.__fci\n\n    @fci.setter\n    def fci(self, value: float):\n        self._property_changed('fci')\n        self.__fci = value        \n\n    @property\n    def recall_quantity(self) -> float:\n        """Defines the amount of shares being recalled in a stock loan recall activity."""\n        return self.__recall_quantity\n\n    @recall_quantity.setter\n    def recall_quantity(self, value: float):\n        self._property_changed('recall_quantity')\n        self.__recall_quantity = value        \n\n    @property\n    def fx_positioning(self) -> float:\n        """Positioning data in the FX market."""\n        return self.__fx_positioning\n\n    @fx_positioning.setter\n    def fx_positioning(self, value: float):\n        self._property_changed('fx_positioning')\n        self.__fx_positioning = value        \n\n    @property\n    def gsid_equivalent(self) -> str:\n        """Goldman Sachs internal equity identifier equivalent - i.e. - for Corporate\n           Bonds, the equivalent GSID of the underlying security."""\n        return self.__gsid_equivalent\n\n    @gsid_equivalent.setter\n    def gsid_equivalent(self, value: str):\n        self._property_changed('gsid_equivalent')\n        self.__gsid_equivalent = value        \n\n    @property\n    def categories(self) -> Tuple[str, ...]:\n        """Categories for series."""\n        return self.__categories\n\n    @categories.setter\n    def categories(self, value: Tuple[str, ...]):\n        self._property_changed('categories')\n        self.__categories = value        \n\n    @property\n    def ext_mkt_asset(self) -> str:\n        """External MDAPI Asset."""\n        return self.__ext_mkt_asset\n\n    @ext_mkt_asset.setter\n    def ext_mkt_asset(self, value: str):\n        self._property_changed('ext_mkt_asset')\n        self.__ext_mkt_asset = value        \n\n    @property\n    def quoting_style(self) -> str:\n        """Quoting style."""\n        return self.__quoting_style\n\n    @quoting_style.setter\n    def quoting_style(self, value: str):\n        self._property_changed('quoting_style')\n        self.__quoting_style = value        \n\n    @property\n    def error_message(self) -> str:\n        """Error message to correspond to error in factor field."""\n        return self.__error_message\n\n    @error_message.setter\n    def error_message(self, value: str):\n        self._property_changed('error_message')\n        self.__error_message = value        \n\n    @property\n    def mid_price(self) -> float:\n        """The mid price."""\n        return self.__mid_price\n\n    @mid_price.setter\n    def mid_price(self, value: float):\n        self._property_changed('mid_price')\n        self.__mid_price = value        \n\n    @property\n    def proceeds_asset_swap_spread6m(self) -> float:\n        """Proceeds asset swap spread vs 6m tenor."""\n        return self.__proceeds_asset_swap_spread6m\n\n    @proceeds_asset_swap_spread6m.setter\n    def proceeds_asset_swap_spread6m(self, value: float):\n        self._property_changed('proceeds_asset_swap_spread6m')\n        self.__proceeds_asset_swap_spread6m = value        \n\n    @property\n    def sts_em_dm(self) -> str:\n        """Emerging or developed market classification."""\n        return self.__sts_em_dm\n\n    @sts_em_dm.setter\n    def sts_em_dm(self, value: str):\n        self._property_changed('sts_em_dm')\n        self.__sts_em_dm = value        \n\n    @property\n    def embedded_option(self) -> str:\n        """An indication of whether or not the option fields are for an embedded option."""\n        return self.__embedded_option\n\n    @embedded_option.setter\n    def embedded_option(self, value: str):\n        self._property_changed('embedded_option')\n        self.__embedded_option = value        \n\n    @property\n    def tcm_cost_horizon2_day(self) -> float:\n        """TCM cost with a 2 day time horizon."""\n        return self.__tcm_cost_horizon2_day\n\n    @tcm_cost_horizon2_day.setter\n    def tcm_cost_horizon2_day(self, value: float):\n        self._property_changed('tcm_cost_horizon2_day')\n        self.__tcm_cost_horizon2_day = value        \n\n    @property\n    def age_band(self) -> str:\n        """The age band of the caller based on the date of birth provided."""\n        return self.__age_band\n\n    @age_band.setter\n    def age_band(self, value: str):\n        self._property_changed('age_band')\n        self.__age_band = value        \n\n    @property\n    def returns_enabled(self) -> bool:\n        """Whether or not returns are enabled for the Hedge Fund."""\n        return self.__returns_enabled\n\n    @returns_enabled.setter\n    def returns_enabled(self, value: bool):\n        self._property_changed('returns_enabled')\n        self.__returns_enabled = value        \n\n    @property\n    def run_id(self) -> str:\n        """Unique id for check runs, used to query entity data corresponding to dataset\n           level data."""\n        return self.__run_id\n\n    @run_id.setter\n    def run_id(self, value: str):\n        self._property_changed('run_id')\n        self.__run_id = value        \n\n    @property\n    def queue_in_lots(self) -> float:\n        """The Queue size in Lots (if applicable) of the stock  on the particular date."""\n        return self.__queue_in_lots\n\n    @queue_in_lots.setter\n    def queue_in_lots(self, value: float):\n        self._property_changed('queue_in_lots')\n        self.__queue_in_lots = value        \n\n    @property\n    def tender_offer_expiration_date(self) -> str:\n        """Expiration date of the tender offer."""\n        return self.__tender_offer_expiration_date\n\n    @tender_offer_expiration_date.setter\n    def tender_offer_expiration_date(self, value: str):\n        self._property_changed('tender_offer_expiration_date')\n        self.__tender_offer_expiration_date = value        \n\n    @property\n    def midcurve_annuity(self) -> float:\n        """Midcurve annuity."""\n        return self.__midcurve_annuity\n\n    @midcurve_annuity.setter\n    def midcurve_annuity(self, value: float):\n        self._property_changed('midcurve_annuity')\n        self.__midcurve_annuity = value        \n\n    @property\n    def lending_fund_nav_trend(self) -> float:\n        """Net Asset Value trend of a securities lending fund."""\n        return self.__lending_fund_nav_trend\n\n    @lending_fund_nav_trend.setter\n    def lending_fund_nav_trend(self, value: float):\n        self._property_changed('lending_fund_nav_trend')\n        self.__lending_fund_nav_trend = value        \n\n    @property\n    def cloud_cover_forecast(self) -> float:\n        """The forecast value for cloud cover."""\n        return self.__cloud_cover_forecast\n\n    @cloud_cover_forecast.setter\n    def cloud_cover_forecast(self, value: float):\n        self._property_changed('cloud_cover_forecast')\n        self.__cloud_cover_forecast = value        \n\n    @property\n    def tcm_cost_participation_rate5_pct(self) -> float:\n        """TCM cost with a 5 percent participation rate."""\n        return self.__tcm_cost_participation_rate5_pct\n\n    @tcm_cost_participation_rate5_pct.setter\n    def tcm_cost_participation_rate5_pct(self, value: float):\n        self._property_changed('tcm_cost_participation_rate5_pct')\n        self.__tcm_cost_participation_rate5_pct = value        \n\n    @property\n    def default_backcast(self) -> bool:\n        """Is basket backcasted using initial positions."""\n        return self.__default_backcast\n\n    @default_backcast.setter\n    def default_backcast(self, value: bool):\n        self._property_changed('default_backcast')\n        self.__default_backcast = value        \n\n    @property\n    def news_on_intensity(self) -> float:\n        """A measure of the intensity of media attention to each stock compared with other\n           stocks in the index."""\n        return self.__news_on_intensity\n\n    @news_on_intensity.setter\n    def news_on_intensity(self, value: float):\n        self._property_changed('news_on_intensity')\n        self.__news_on_intensity = value        \n\n    @property\n    def price_forming_continuation_data(self) -> str:\n        """An indication of whether an SB swap transaction is a post-execution event that\n           affects the price of the swap transaction, e.g. terminations,\n           assignments, novations, exchanges, transfers, amendments, conveyances\n           or extinguishing of rights that change the price of the SB swap."""\n        return self.__price_forming_continuation_data\n\n    @price_forming_continuation_data.setter\n    def price_forming_continuation_data(self, value: str):\n        self._property_changed('price_forming_continuation_data')\n        self.__price_forming_continuation_data = value        \n\n    @property\n    def adjusted_short_interest(self) -> float:\n        """Adjusted Short Interest rate."""\n        return self.__adjusted_short_interest\n\n    @adjusted_short_interest.setter\n    def adjusted_short_interest(self, value: float):\n        self._property_changed('adjusted_short_interest')\n        self.__adjusted_short_interest = value        \n\n    @property\n    def new_hospitalized(self) -> float:\n        """Number of new patients hospitalized."""\n        return self.__new_hospitalized\n\n    @new_hospitalized.setter\n    def new_hospitalized(self, value: float):\n        self._property_changed('new_hospitalized')\n        self.__new_hospitalized = value        \n\n    @property\n    def asset_parameters_strike(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__asset_parameters_strike\n\n    @asset_parameters_strike.setter\n    def asset_parameters_strike(self, value: Union[float, str]):\n        self._property_changed('asset_parameters_strike')\n        self.__asset_parameters_strike = value        \n\n    @property\n    def buy35cents(self) -> float:\n        """The amount GS would buy for 35 cents charge."""\n        return self.__buy35cents\n\n    @buy35cents.setter\n    def buy35cents(self, value: float):\n        self._property_changed('buy35cents')\n        self.__buy35cents = value        \n\n    @property\n    def leg2_total_notional(self) -> float:\n        """The total Notional amount or quantity of units of the leg 2 underlying asset."""\n        return self.__leg2_total_notional\n\n    @leg2_total_notional.setter\n    def leg2_total_notional(self, value: float):\n        self._property_changed('leg2_total_notional')\n        self.__leg2_total_notional = value        \n\n    @property\n    def asset_parameters_effective_date(self) -> str:\n        """Relative effective date."""\n        return self.__asset_parameters_effective_date\n\n    @asset_parameters_effective_date.setter\n    def asset_parameters_effective_date(self, value: str):\n        self._property_changed('asset_parameters_effective_date')\n        self.__asset_parameters_effective_date = value        \n\n    @property\n    def ann_return10_year(self) -> float:\n        """Total return representing past performance, used for GS Money Market onshore\n           funds, over ten years."""\n        return self.__ann_return10_year\n\n    @ann_return10_year.setter\n    def ann_return10_year(self, value: float):\n        self._property_changed('ann_return10_year')\n        self.__ann_return10_year = value        \n\n    @property\n    def num_adult_icu_beds(self) -> float:\n        """All ICU beds, burn ICU beds, surgical ICU beds, or trauma ICU beds meant for\n           emergency situations where hospitals may use additional intensive\n           care beds to supplement an influx of patients. Does not include\n           neonatal, pediatric or premature ICU beds."""\n        return self.__num_adult_icu_beds\n\n    @num_adult_icu_beds.setter\n    def num_adult_icu_beds(self, value: float):\n        self._property_changed('num_adult_icu_beds')\n        self.__num_adult_icu_beds = value        \n\n    @property\n    def days_to_expiration(self) -> float:\n        """Days to Expiration for the contract."""\n        return self.__days_to_expiration\n\n    @days_to_expiration.setter\n    def days_to_expiration(self, value: float):\n        self._property_changed('days_to_expiration')\n        self.__days_to_expiration = value        \n\n    @property\n    def continuation_event(self) -> str:\n        """An indication of whether an SB swap transaction is a post-execution event that\n           affects the price of the swap transaction, e.g. terminations,\n           assignments, novations, exchanges, transfers, amendments, conveyances\n           or extinguishing of rights that change the price of the SB swap."""\n        return self.__continuation_event\n\n    @continuation_event.setter\n    def continuation_event(self, value: str):\n        self._property_changed('continuation_event')\n        self.__continuation_event = value        \n\n    @property\n    def wi_id(self) -> str:\n        """Weather Index Identifier."""\n        return self.__wi_id\n\n    @wi_id.setter\n    def wi_id(self, value: str):\n        self._property_changed('wi_id')\n        self.__wi_id = value        \n\n    @property\n    def market_cap_category(self) -> str:\n        """Category of market capitalizations a fund is focused on from an investment\n           perspective. Same view permissions as the asset."""\n        return self.__market_cap_category\n\n    @market_cap_category.setter\n    def market_cap_category(self, value: str):\n        self._property_changed('market_cap_category')\n        self.__market_cap_category = value        \n\n    @property\n    def historical_volume(self) -> float:\n        """One month rolling average."""\n        return self.__historical_volume\n\n    @historical_volume.setter\n    def historical_volume(self, value: float):\n        self._property_changed('historical_volume')\n        self.__historical_volume = value        \n\n    @property\n    def buy5cents(self) -> float:\n        """The amount GS would buy for 5 cents charge."""\n        return self.__buy5cents\n\n    @buy5cents.setter\n    def buy5cents(self, value: float):\n        self._property_changed('buy5cents')\n        self.__buy5cents = value        \n\n    @property\n    def event_start_date(self) -> datetime.date:\n        """The start date of the event if the event occurs during a time window, in the\n           time zone of the exchange where the company is listed (optional)."""\n        return self.__event_start_date\n\n    @event_start_date.setter\n    def event_start_date(self, value: datetime.date):\n        self._property_changed('event_start_date')\n        self.__event_start_date = value        \n\n    @property\n    def leg1_fixed_rate(self) -> float:\n        """If fixed rate leg, the fixed rate of leg 1."""\n        return self.__leg1_fixed_rate\n\n    @leg1_fixed_rate.setter\n    def leg1_fixed_rate(self, value: float):\n        self._property_changed('leg1_fixed_rate')\n        self.__leg1_fixed_rate = value        \n\n    @property\n    def equity_gamma(self) -> float:\n        """Gamma exposure to equity products."""\n        return self.__equity_gamma\n\n    @equity_gamma.setter\n    def equity_gamma(self, value: float):\n        self._property_changed('equity_gamma')\n        self.__equity_gamma = value        \n\n    @property\n    def rpt_id(self) -> str:\n        """DDR generated unique and random ID for reconciliation purpose."""\n        return self.__rpt_id\n\n    @rpt_id.setter\n    def rpt_id(self, value: str):\n        self._property_changed('rpt_id')\n        self.__rpt_id = value        \n\n    @property\n    def gross_income(self) -> float:\n        """The income earned by the reinvested collateral including the rebate or fee,\n           excluding lender or partner fees."""\n        return self.__gross_income\n\n    @gross_income.setter\n    def gross_income(self, value: float):\n        self._property_changed('gross_income')\n        self.__gross_income = value        \n\n    @property\n    def em_id(self) -> str:\n        """Entity Master identifier."""\n        return self.__em_id\n\n    @em_id.setter\n    def em_id(self, value: str):\n        self._property_changed('em_id')\n        self.__em_id = value        \n\n    @property\n    def asset_count_in_model(self) -> float:\n        """Number of assets in a portfolio in a given risk model."""\n        return self.__asset_count_in_model\n\n    @asset_count_in_model.setter\n    def asset_count_in_model(self, value: float):\n        self._property_changed('asset_count_in_model')\n        self.__asset_count_in_model = value        \n\n    @property\n    def sts_credit_region(self) -> str:\n        """Credit risk region."""\n        return self.__sts_credit_region\n\n    @sts_credit_region.setter\n    def sts_credit_region(self, value: str):\n        self._property_changed('sts_credit_region')\n        self.__sts_credit_region = value        \n\n    @property\n    def min_temperature(self) -> float:\n        """Minimum temperature observed on a given day in fahrenheit."""\n        return self.__min_temperature\n\n    @min_temperature.setter\n    def min_temperature(self, value: float):\n        self._property_changed('min_temperature')\n        self.__min_temperature = value        \n\n    @property\n    def bucket_start_time(self) -> datetime.datetime:\n        """The start time of the bucket in ISO 8601 formatted date and time."""\n        return self.__bucket_start_time\n\n    @bucket_start_time.setter\n    def bucket_start_time(self, value: datetime.datetime):\n        self._property_changed('bucket_start_time')\n        self.__bucket_start_time = value        \n\n    @property\n    def fill_type(self) -> str:\n        """Dark/Lit etc."""\n        return self.__fill_type\n\n    @fill_type.setter\n    def fill_type(self, value: str):\n        self._property_changed('fill_type')\n        self.__fill_type = value        \n\n    @property\n    def close_time(self) -> datetime.datetime:\n        """Time closed. ISO 8601 formatted string."""\n        return self.__close_time\n\n    @close_time.setter\n    def close_time(self, value: datetime.datetime):\n        self._property_changed('close_time')\n        self.__close_time = value        \n\n    @property\n    def fail_pct(self) -> float:\n        """Fail percentage."""\n        return self.__fail_pct\n\n    @fail_pct.setter\n    def fail_pct(self, value: float):\n        self._property_changed('fail_pct')\n        self.__fail_pct = value        \n\n    @property\n    def iso_country_code_alpha2(self) -> str:\n        """Iso2 country codes (2-digit)."""\n        return self.__iso_country_code_alpha2\n\n    @iso_country_code_alpha2.setter\n    def iso_country_code_alpha2(self, value: str):\n        self._property_changed('iso_country_code_alpha2')\n        self.__iso_country_code_alpha2 = value        \n\n    @property\n    def iso_country_code_alpha3(self) -> str:\n        """Iso3 country codes (3-digit)."""\n        return self.__iso_country_code_alpha3\n\n    @iso_country_code_alpha3.setter\n    def iso_country_code_alpha3(self, value: str):\n        self._property_changed('iso_country_code_alpha3')\n        self.__iso_country_code_alpha3 = value        \n\n    @property\n    def amount(self) -> float:\n        """Amount corporate actions pay out."""\n        return self.__amount\n\n    @amount.setter\n    def amount(self, value: float):\n        self._property_changed('amount')\n        self.__amount = value        \n\n    @property\n    def lending_fund_acct(self) -> str:\n        """Account associated with the securities lending fund."""\n        return self.__lending_fund_acct\n\n    @lending_fund_acct.setter\n    def lending_fund_acct(self, value: str):\n        self._property_changed('lending_fund_acct')\n        self.__lending_fund_acct = value        \n\n    @property\n    def rebate(self) -> float:\n        """Amount of the payment to an investor who puts up collateral in borrowing a\n           stock."""\n        return self.__rebate\n\n    @rebate.setter\n    def rebate(self, value: float):\n        self._property_changed('rebate')\n        self.__rebate = value        \n\n    @property\n    def election_type(self) -> str:\n        """Type of election e.g. presidential."""\n        return self.__election_type\n\n    @election_type.setter\n    def election_type(self, value: str):\n        self._property_changed('election_type')\n        self.__election_type = value        \n\n    @property\n    def relative_hit_rate_mtd(self) -> float:\n        """Hit Rate Ratio Month to Date."""\n        return self.__relative_hit_rate_mtd\n\n    @relative_hit_rate_mtd.setter\n    def relative_hit_rate_mtd(self, value: float):\n        self._property_changed('relative_hit_rate_mtd')\n        self.__relative_hit_rate_mtd = value        \n\n    @property\n    def implied_volatility(self) -> float:\n        """Volatility of an asset implied by observations of market prices."""\n        return self.__implied_volatility\n\n    @implied_volatility.setter\n    def implied_volatility(self, value: float):\n        self._property_changed('implied_volatility')\n        self.__implied_volatility = value        \n\n    @property\n    def spread(self) -> float:\n        """Quoted (running) spread (mid) of buying / selling protection on an index.\n           (Equally weighted CDS basket). In basis points."""\n        return self.__spread\n\n    @spread.setter\n    def spread(self, value: float):\n        self._property_changed('spread')\n        self.__spread = value        \n\n    @property\n    def variance(self) -> float:\n        """Market implied variance between two tenors."""\n        return self.__variance\n\n    @variance.setter\n    def variance(self, value: float):\n        self._property_changed('variance')\n        self.__variance = value        \n\n    @property\n    def wtd_degree_days_daily_forecast(self) -> float:\n        """The forecast value for weighted degree day in Fahrenheit."""\n        return self.__wtd_degree_days_daily_forecast\n\n    @wtd_degree_days_daily_forecast.setter\n    def wtd_degree_days_daily_forecast(self, value: float):\n        self._property_changed('wtd_degree_days_daily_forecast')\n        self.__wtd_degree_days_daily_forecast = value        \n\n    @property\n    def swaption_annuity(self) -> float:\n        """Swaption annuity."""\n        return self.__swaption_annuity\n\n    @swaption_annuity.setter\n    def swaption_annuity(self, value: float):\n        self._property_changed('swaption_annuity')\n        self.__swaption_annuity = value        \n\n    @property\n    def buy6bps(self) -> float:\n        """The amount GS would buy for 6 bps charge."""\n        return self.__buy6bps\n\n    @buy6bps.setter\n    def buy6bps(self, value: float):\n        self._property_changed('buy6bps')\n        self.__buy6bps = value        \n\n    @property\n    def g10_currency(self) -> bool:\n        """Is a G10 asset."""\n        return self.__g10_currency\n\n    @g10_currency.setter\n    def g10_currency(self, value: bool):\n        self._property_changed('g10_currency')\n        self.__g10_currency = value        \n\n    @property\n    def humidity_forecast(self) -> float:\n        """The forecast value for humidity."""\n        return self.__humidity_forecast\n\n    @humidity_forecast.setter\n    def humidity_forecast(self, value: float):\n        self._property_changed('humidity_forecast')\n        self.__humidity_forecast = value        \n\n    @property\n    def relative_period(self) -> str:\n        """The relative period forward for which the forecast is available."""\n        return self.__relative_period\n\n    @relative_period.setter\n    def relative_period(self, value: str):\n        self._property_changed('relative_period')\n        self.__relative_period = value        \n\n    @property\n    def user(self) -> str:\n        """Originating platform."""\n        return self.__user\n\n    @user.setter\n    def user(self, value: str):\n        self._property_changed('user')\n        self.__user = value        \n\n    @property\n    def customer(self) -> str:\n        """Customer kerberos."""\n        return self.__customer\n\n    @customer.setter\n    def customer(self, value: str):\n        self._property_changed('customer')\n        self.__customer = value        \n\n    @property\n    def leg1_reset_frequency(self) -> str:\n        """An integer multiplier of a period describing how often the parties to an SB swap\n           transaction shall evaluate and, when applicable, change the price\n           used for the underlying assets of the swap transaction (leg 1). Such\n           reset frequency may be described as one letter preceded by an\n           integer."""\n        return self.__leg1_reset_frequency\n\n    @leg1_reset_frequency.setter\n    def leg1_reset_frequency(self, value: str):\n        self._property_changed('leg1_reset_frequency')\n        self.__leg1_reset_frequency = value        \n\n    @property\n    def queue_clock_time_label(self):\n        return self.__queue_clock_time_label\n\n    @queue_clock_time_label.setter\n    def queue_clock_time_label(self, value):\n        self._property_changed('queue_clock_time_label')\n        self.__queue_clock_time_label = value        \n\n    @property\n    def pace_of_rollp100(self) -> float:\n        """The 100th-percentile of the pace of roll compared to previous quarters (over the\n           last 5 years) at the same point in time, i.e. cross-sectional."""\n        return self.__pace_of_rollp100\n\n    @pace_of_rollp100.setter\n    def pace_of_rollp100(self, value: float):\n        self._property_changed('pace_of_rollp100')\n        self.__pace_of_rollp100 = value        \n\n    @property\n    def asset_classifications_gics_sub_industry(self) -> str:\n        """GICS Sub Industry classification (level 4)."""\n        return self.__asset_classifications_gics_sub_industry\n\n    @asset_classifications_gics_sub_industry.setter\n    def asset_classifications_gics_sub_industry(self, value: str):\n        self._property_changed('asset_classifications_gics_sub_industry')\n        self.__asset_classifications_gics_sub_industry = value        \n\n    @property\n    def dew_point_hourly_forecast(self) -> float:\n        """The hourly forecast value for dew point in Fahrenheit."""\n        return self.__dew_point_hourly_forecast\n\n    @dew_point_hourly_forecast.setter\n    def dew_point_hourly_forecast(self, value: float):\n        self._property_changed('dew_point_hourly_forecast')\n        self.__dew_point_hourly_forecast = value        \n\n    @property\n    def location_type(self) -> str:\n        """The type of location (of varying granularity), e.g. Nation, Region, etc."""\n        return self.__location_type\n\n    @location_type.setter\n    def location_type(self, value: str):\n        self._property_changed('location_type')\n        self.__location_type = value        \n\n    @property\n    def facet_divisional_reporting_group_id(self) -> str:\n        """FACET Divisional Reporting Group ID (DRG) most relevant to the Starting EMMA LE\n           ID which contains all associated OE IDs."""\n        return self.__facet_divisional_reporting_group_id\n\n    @facet_divisional_reporting_group_id.setter\n    def facet_divisional_reporting_group_id(self, value: str):\n        self._property_changed('facet_divisional_reporting_group_id')\n        self.__facet_divisional_reporting_group_id = value        \n\n    @property\n    def realized_twap_performance_usd(self) -> float:\n        """Average execution price vs Consolidated TWAP since order inception ??? realized\n           in $."""\n        return self.__realized_twap_performance_usd\n\n    @realized_twap_performance_usd.setter\n    def realized_twap_performance_usd(self, value: float):\n        self._property_changed('realized_twap_performance_usd')\n        self.__realized_twap_performance_usd = value        \n\n    @property\n    def swap_rate(self) -> float:\n        """ATM fixed rate for a benchmark tenor on a currency's fixed-floating swap curve."""\n        return self.__swap_rate\n\n    @swap_rate.setter\n    def swap_rate(self, value: float):\n        self._property_changed('swap_rate')\n        self.__swap_rate = value        \n\n    @property\n    def algo_execution_style(self) -> str:\n        """Algo execution style. Dictates how an algo order is being filled."""\n        return self.__algo_execution_style\n\n    @algo_execution_style.setter\n    def algo_execution_style(self, value: str):\n        self._property_changed('algo_execution_style')\n        self.__algo_execution_style = value        \n\n    @property\n    def client_contact(self) -> str:\n        """Name of client(s) requesting data."""\n        return self.__client_contact\n\n    @client_contact.setter\n    def client_contact(self, value: str):\n        self._property_changed('client_contact')\n        self.__client_contact = value        \n\n    @property\n    def min_temperature_hour(self) -> float:\n        """The forecast value of hour with minimum temperature in a day."""\n        return self.__min_temperature_hour\n\n    @min_temperature_hour.setter\n    def min_temperature_hour(self, value: float):\n        self._property_changed('min_temperature_hour')\n        self.__min_temperature_hour = value        \n\n    @property\n    def trading_currency(self) -> str:\n        """Execution currency."""\n        return self.__trading_currency\n\n    @trading_currency.setter\n    def trading_currency(self, value: str):\n        self._property_changed('trading_currency')\n        self.__trading_currency = value        \n\n    @property\n    def total_by_onset(self) -> float:\n        """Total number, by onset of infection, as opposed to confirmation. The date of\n           onset is calculated as illness onset date if known. If not, an\n           estimated illness onset date was calculated using specimen collection\n           date."""\n        return self.__total_by_onset\n\n    @total_by_onset.setter\n    def total_by_onset(self, value: float):\n        self._property_changed('total_by_onset')\n        self.__total_by_onset = value        \n\n    @property\n    def agency_swap_spread(self) -> float:\n        """Agency swap spread."""\n        return self.__agency_swap_spread\n\n    @agency_swap_spread.setter\n    def agency_swap_spread(self, value: float):\n        self._property_changed('agency_swap_spread')\n        self.__agency_swap_spread = value        \n\n    @property\n    def rank(self) -> float:\n        """Rank to determine most relevant asset."""\n        return self.__rank\n\n    @rank.setter\n    def rank(self, value: float):\n        self._property_changed('rank')\n        self.__rank = value        \n\n    @property\n    def mixed_swap_other_reported_sdr(self) -> str:\n        """Indicates the other SDR to which a mixed swap is reported."""\n        return self.__mixed_swap_other_reported_sdr\n\n    @mixed_swap_other_reported_sdr.setter\n    def mixed_swap_other_reported_sdr(self, value: str):\n        self._property_changed('mixed_swap_other_reported_sdr')\n        self.__mixed_swap_other_reported_sdr = value        \n\n    @property\n    def humidity(self) -> float:\n        """The humidity forecast for given location."""\n        return self.__humidity\n\n    @humidity.setter\n    def humidity(self, value: float):\n        self._property_changed('humidity')\n        self.__humidity = value        \n\n    @property\n    def data_set_category(self) -> str:\n        """Top level grouping of dataset."""\n        return self.__data_set_category\n\n    @data_set_category.setter\n    def data_set_category(self, value: str):\n        self._property_changed('data_set_category')\n        self.__data_set_category = value        \n\n    @property\n    def vwap_realized_bps(self) -> float:\n        """Consolidated realised performance vs VWAP, in bps."""\n        return self.__vwap_realized_bps\n\n    @vwap_realized_bps.setter\n    def vwap_realized_bps(self, value: float):\n        self._property_changed('vwap_realized_bps')\n        self.__vwap_realized_bps = value        \n\n    @property\n    def buy9bps(self) -> float:\n        """The amount GS would buy for 9 bps charge."""\n        return self.__buy9bps\n\n    @buy9bps.setter\n    def buy9bps(self, value: float):\n        self._property_changed('buy9bps')\n        self.__buy9bps = value        \n\n    @property\n    def total_tested(self) -> float:\n        """Total number of tested cases."""\n        return self.__total_tested\n\n    @total_tested.setter\n    def total_tested(self, value: float):\n        self._property_changed('total_tested')\n        self.__total_tested = value        \n\n    @property\n    def fatalities_confirmed(self) -> float:\n        """Total number of confirmed fatalities."""\n        return self.__fatalities_confirmed\n\n    @fatalities_confirmed.setter\n    def fatalities_confirmed(self, value: float):\n        self._property_changed('fatalities_confirmed')\n        self.__fatalities_confirmed = value        \n\n    @property\n    def universe_id1(self) -> str:\n        """The risk model universe identifier value."""\n        return self.__universe_id1\n\n    @universe_id1.setter\n    def universe_id1(self, value: str):\n        self._property_changed('universe_id1')\n        self.__universe_id1 = value        \n\n    @property\n    def asset_parameters_payer_day_count_fraction(self) -> str:\n        """Day Count Fraction"""\n        return self.__asset_parameters_payer_day_count_fraction\n\n    @asset_parameters_payer_day_count_fraction.setter\n    def asset_parameters_payer_day_count_fraction(self, value: str):\n        self._property_changed('asset_parameters_payer_day_count_fraction')\n        self.__asset_parameters_payer_day_count_fraction = value        \n\n    @property\n    def universe_id2(self) -> str:\n        """The risk model universe identifier value."""\n        return self.__universe_id2\n\n    @universe_id2.setter\n    def universe_id2(self, value: str):\n        self._property_changed('universe_id2')\n        self.__universe_id2 = value        \n\n    @property\n    def bid_low(self) -> float:\n        """Lowest Bid Price (price willing to buy)."""\n        return self.__bid_low\n\n    @bid_low.setter\n    def bid_low(self, value: float):\n        self._property_changed('bid_low')\n        self.__bid_low = value        \n\n    @property\n    def bucketize_price(self) -> float:\n        """Aggregated price of power for given quantity bucket."""\n        return self.__bucketize_price\n\n    @bucketize_price.setter\n    def bucketize_price(self, value: float):\n        self._property_changed('bucketize_price')\n        self.__bucketize_price = value        \n\n    @property\n    def fair_variance_volatility(self) -> float:\n        """The strike in volatility terms, calculated as square root of fair variance."""\n        return self.__fair_variance_volatility\n\n    @fair_variance_volatility.setter\n    def fair_variance_volatility(self, value: float):\n        self._property_changed('fair_variance_volatility')\n        self.__fair_variance_volatility = value        \n\n    @property\n    def covid19(self) -> float:\n        """Total number of people affected by covid19 for agiven country."""\n        return self.__covid19\n\n    @covid19.setter\n    def covid19(self, value: float):\n        self._property_changed('covid19')\n        self.__covid19 = value        \n\n    @property\n    def client_exposure(self) -> float:\n        """Exposure of client positions to the factor in percent of equity."""\n        return self.__client_exposure\n\n    @client_exposure.setter\n    def client_exposure(self, value: float):\n        self._property_changed('client_exposure')\n        self.__client_exposure = value        \n\n    @property\n    def leg2_total_notional_unit(self) -> str:\n        """Unit of reported notional price."""\n        return self.__leg2_total_notional_unit\n\n    @leg2_total_notional_unit.setter\n    def leg2_total_notional_unit(self, value: str):\n        self._property_changed('leg2_total_notional_unit')\n        self.__leg2_total_notional_unit = value        \n\n    @property\n    def sell45cents(self) -> float:\n        """The amount GS would sell for 45 cents charge."""\n        return self.__sell45cents\n\n    @sell45cents.setter\n    def sell45cents(self, value: float):\n        self._property_changed('sell45cents')\n        self.__sell45cents = value        \n\n    @property\n    def gs_sustain_sub_sector(self) -> str:\n        """GS SUSTAIN sector."""\n        return self.__gs_sustain_sub_sector\n\n    @gs_sustain_sub_sector.setter\n    def gs_sustain_sub_sector(self, value: str):\n        self._property_changed('gs_sustain_sub_sector')\n        self.__gs_sustain_sub_sector = value        \n\n    @property\n    def sinkable(self) -> bool:\n        """A bond that is protected by a fund (called a sinking fund) that sets aside money\n           to ensure principal and interest payments are made by the issuer as\n           promised."""\n        return self.__sinkable\n\n    @sinkable.setter\n    def sinkable(self, value: bool):\n        self._property_changed('sinkable')\n        self.__sinkable = value        \n\n    @property\n    def is_real(self) -> bool:\n        """Flag indicating whether the value is real or nominal."""\n        return self.__is_real\n\n    @is_real.setter\n    def is_real(self, value: bool):\n        self._property_changed('is_real')\n        self.__is_real = value        \n\n    @property\n    def max_temperature_hour(self) -> float:\n        """The forecast value of hour with max temperature in a day."""\n        return self.__max_temperature_hour\n\n    @max_temperature_hour.setter\n    def max_temperature_hour(self, value: float):\n        self._property_changed('max_temperature_hour')\n        self.__max_temperature_hour = value        \n\n    @property\n    def leg2_averaging_method(self) -> str:\n        """Averaging method of leg."""\n        return self.__leg2_averaging_method\n\n    @leg2_averaging_method.setter\n    def leg2_averaging_method(self, value: str):\n        self._property_changed('leg2_averaging_method')\n        self.__leg2_averaging_method = value        \n\n    @property\n    def jsn(self) -> str:\n        """Japan security number (subject to licensing)."""\n        return self.__jsn\n\n    @jsn.setter\n    def jsn(self, value: str):\n        self._property_changed('jsn')\n        self.__jsn = value        \n\n    @property\n    def sell160cents(self) -> float:\n        """The amount GS would sell for 160 cents charge."""\n        return self.__sell160cents\n\n    @sell160cents.setter\n    def sell160cents(self, value: float):\n        self._property_changed('sell160cents')\n        self.__sell160cents = value        \n\n    @property\n    def knock_in_direction(self) -> str:\n        """The side of the knock-in price the market level must reach to trigger the knock\n           in."""\n        return self.__knock_in_direction\n\n    @knock_in_direction.setter\n    def knock_in_direction(self, value: str):\n        self._property_changed('knock_in_direction')\n        self.__knock_in_direction = value        \n\n    @property\n    def day_close_unrealized_usd(self) -> float:\n        """Unrealized performance vs Close, in $."""\n        return self.__day_close_unrealized_usd\n\n    @day_close_unrealized_usd.setter\n    def day_close_unrealized_usd(self, value: float):\n        self._property_changed('day_close_unrealized_usd')\n        self.__day_close_unrealized_usd = value        \n\n    @property\n    def tenor(self) -> str:\n        """Tenor of instrument."""\n        return self.__tenor\n\n    @tenor.setter\n    def tenor(self, value: str):\n        self._property_changed('tenor')\n        self.__tenor = value        \n\n    @property\n    def pricing_convention(self) -> str:\n        """Pricing convention that is used."""\n        return self.__pricing_convention\n\n    @pricing_convention.setter\n    def pricing_convention(self, value: str):\n        self._property_changed('pricing_convention')\n        self.__pricing_convention = value        \n\n    @property\n    def popularity(self) -> float:\n        """Popularity of series."""\n        return self.__popularity\n\n    @popularity.setter\n    def popularity(self, value: float):\n        self._property_changed('popularity')\n        self.__popularity = value        \n\n    @property\n    def floating_rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE."""\n        return self.__floating_rate_option\n\n    @floating_rate_option.setter\n    def floating_rate_option(self, value: str):\n        self._property_changed('floating_rate_option')\n        self.__floating_rate_option = value        \n\n    @property\n    def hedge_value_type(self) -> str:\n        """The type of value for which is applied to value the hedge in the comparison."""\n        return self.__hedge_value_type\n\n    @hedge_value_type.setter\n    def hedge_value_type(self, value: str):\n        self._property_changed('hedge_value_type')\n        self.__hedge_value_type = value        \n\n    @property\n    def asset_parameters_clearing_house(self) -> str:\n        """Clearing house."""\n        return self.__asset_parameters_clearing_house\n\n    @asset_parameters_clearing_house.setter\n    def asset_parameters_clearing_house(self, value: str):\n        self._property_changed('asset_parameters_clearing_house')\n        self.__asset_parameters_clearing_house = value        \n\n    @property\n    def disclaimer(self) -> str:\n        """The legal disclaimer associated with the record."""\n        return self.__disclaimer\n\n    @disclaimer.setter\n    def disclaimer(self, value: str):\n        self._property_changed('disclaimer')\n        self.__disclaimer = value        \n\n    @property\n    def payer_frequency(self) -> str:\n        """Tenor"""\n        return self.__payer_frequency\n\n    @payer_frequency.setter\n    def payer_frequency(self, value: str):\n        self._property_changed('payer_frequency')\n        self.__payer_frequency = value        \n\n    @property\n    def loan_fee(self) -> float:\n        """Fee charged for the loan of securities to a borrower in a securities lending\n           agreement."""\n        return self.__loan_fee\n\n    @loan_fee.setter\n    def loan_fee(self, value: float):\n        self._property_changed('loan_fee')\n        self.__loan_fee = value        \n\n    @property\n    def deployment_version(self) -> str:\n        """Deployment version."""\n        return self.__deployment_version\n\n    @deployment_version.setter\n    def deployment_version(self, value: str):\n        self._property_changed('deployment_version')\n        self.__deployment_version = value        \n\n    @property\n    def buy16bps(self) -> float:\n        """The amount GS would buy for 16 bps charge."""\n        return self.__buy16bps\n\n    @buy16bps.setter\n    def buy16bps(self, value: float):\n        self._property_changed('buy16bps')\n        self.__buy16bps = value        \n\n    @property\n    def trade_day_count(self) -> str:\n        """The determination of how interest accrues over time for the SB swap."""\n        return self.__trade_day_count\n\n    @trade_day_count.setter\n    def trade_day_count(self, value: str):\n        self._property_changed('trade_day_count')\n        self.__trade_day_count = value        \n\n    @property\n    def price_to_sales(self) -> float:\n        """Price to sales."""\n        return self.__price_to_sales\n\n    @price_to_sales.setter\n    def price_to_sales(self, value: float):\n        self._property_changed('price_to_sales')\n        self.__price_to_sales = value        \n\n    @property\n    def new_ideas_qtd(self) -> float:\n        """Ideas received by clients Quarter to date."""\n        return self.__new_ideas_qtd\n\n    @new_ideas_qtd.setter\n    def new_ideas_qtd(self, value: float):\n        self._property_changed('new_ideas_qtd')\n        self.__new_ideas_qtd = value        \n\n    @property\n    def subdivision_name(self) -> str:\n        """Name of the region or subdivision."""\n        return self.__subdivision_name\n\n    @subdivision_name.setter\n    def subdivision_name(self, value: str):\n        self._property_changed('subdivision_name')\n        self.__subdivision_name = value        \n\n    @property\n    def adjusted_ask_price(self) -> float:\n        """Latest Ask Price (price offering to sell) adjusted for corporate actions."""\n        return self.__adjusted_ask_price\n\n    @adjusted_ask_price.setter\n    def adjusted_ask_price(self, value: float):\n        self._property_changed('adjusted_ask_price')\n        self.__adjusted_ask_price = value        \n\n    @property\n    def factor_universe(self) -> str:\n        """Factor universe."""\n        return self.__factor_universe\n\n    @factor_universe.setter\n    def factor_universe(self, value: str):\n        self._property_changed('factor_universe')\n        self.__factor_universe = value        \n\n    @property\n    def arrival_rt(self) -> float:\n        """Arrival Realtime."""\n        return self.__arrival_rt\n\n    @arrival_rt.setter\n    def arrival_rt(self, value: float):\n        self._property_changed('arrival_rt')\n        self.__arrival_rt = value        \n\n    @property\n    def internal_index_calc_agent(self) -> bool:\n        """Calculation agent of the index."""\n        return self.__internal_index_calc_agent\n\n    @internal_index_calc_agent.setter\n    def internal_index_calc_agent(self, value: bool):\n        self._property_changed('internal_index_calc_agent')\n        self.__internal_index_calc_agent = value        \n\n    @property\n    def excess_margin_value(self) -> float:\n        """Available credit value."""\n        return self.__excess_margin_value\n\n    @excess_margin_value.setter\n    def excess_margin_value(self, value: float):\n        self._property_changed('excess_margin_value')\n        self.__excess_margin_value = value        \n\n    @property\n    def transaction_cost(self) -> float:\n        """Transaction cost."""\n        return self.__transaction_cost\n\n    @transaction_cost.setter\n    def transaction_cost(self, value: float):\n        self._property_changed('transaction_cost')\n        self.__transaction_cost = value        \n\n    @property\n    def central_bank_swap_rate(self) -> float:\n        """Returns the OIS swap rate for a swap structured between consecutive meeting\n           dates."""\n        return self.__central_bank_swap_rate\n\n    @central_bank_swap_rate.setter\n    def central_bank_swap_rate(self, value: float):\n        self._property_changed('central_bank_swap_rate')\n        self.__central_bank_swap_rate = value        \n\n    @property\n    def previous_new_confirmed(self) -> float:\n        """The previous day's number of new confirmed cases."""\n        return self.__previous_new_confirmed\n\n    @previous_new_confirmed.setter\n    def previous_new_confirmed(self, value: float):\n        self._property_changed('previous_new_confirmed')\n        self.__previous_new_confirmed = value        \n\n    @property\n    def unrealized_vwap_performance_bps(self) -> float:\n        """Average execution price vs Consolidated VWAP (in limit) since order inception\n           ??? unrealized."""\n        return self.__unrealized_vwap_performance_bps\n\n    @unrealized_vwap_performance_bps.setter\n    def unrealized_vwap_performance_bps(self, value: float):\n        self._property_changed('unrealized_vwap_performance_bps')\n        self.__unrealized_vwap_performance_bps = value        \n\n    @property\n    def degree_days_daily_forecast(self) -> float:\n        """The forecast value for degree days in Fahrenheit."""\n        return self.__degree_days_daily_forecast\n\n    @degree_days_daily_forecast.setter\n    def degree_days_daily_forecast(self, value: float):\n        self._property_changed('degree_days_daily_forecast')\n        self.__degree_days_daily_forecast = value        \n\n    @property\n    def position_amount(self) -> float:\n        """Corporate actions amount * shares."""\n        return self.__position_amount\n\n    @position_amount.setter\n    def position_amount(self, value: float):\n        self._property_changed('position_amount')\n        self.__position_amount = value        \n\n    @property\n    def heat_index_hourly_forecast(self) -> float:\n        """The hourly forecast value for heat index in Fahrenheit."""\n        return self.__heat_index_hourly_forecast\n\n    @heat_index_hourly_forecast.setter\n    def heat_index_hourly_forecast(self, value: float):\n        self._property_changed('heat_index_hourly_forecast')\n        self.__heat_index_hourly_forecast = value        \n\n    @property\n    def ma_rank(self) -> float:\n        """M&A Rank, which may take on the following values: 1 represents high (at least\n           30%, but less than 50%) probability of the company becoming an\n           acquisition target, 2 represents medium (at least 15%, but less than\n           30%) probability and 3 represents low (less than 15%) probability."""\n        return self.__ma_rank\n\n    @ma_rank.setter\n    def ma_rank(self, value: float):\n        self._property_changed('ma_rank')\n        self.__ma_rank = value        \n\n    @property\n    def fx_positioning_source(self) -> str:\n        """Source of positioning data in the FX market."""\n        return self.__fx_positioning_source\n\n    @fx_positioning_source.setter\n    def fx_positioning_source(self, value: str):\n        self._property_changed('fx_positioning_source')\n        self.__fx_positioning_source = value        \n\n    @property\n    def event_start_date_time(self) -> datetime.datetime:\n        """The start time of the event if the event occurs during a time window and the\n           event has a specific start time, using UTC convention (optional)."""\n        return self.__event_start_date_time\n\n    @event_start_date_time.setter\n    def event_start_date_time(self, value: datetime.datetime):\n        self._property_changed('event_start_date_time')\n        self.__event_start_date_time = value        \n\n    @property\n    def implied_volatility_by_delta_strike(self) -> float:\n        """Volatility of an asset implied by observations of market prices."""\n        return self.__implied_volatility_by_delta_strike\n\n    @implied_volatility_by_delta_strike.setter\n    def implied_volatility_by_delta_strike(self, value: float):\n        self._property_changed('implied_volatility_by_delta_strike')\n        self.__implied_volatility_by_delta_strike = value        \n\n    @property\n    def mq_symbol(self) -> str:\n        """Goldman Sachs Marquee Symbol applied to entities such as Backtester."""\n        return self.__mq_symbol\n\n    @mq_symbol.setter\n    def mq_symbol(self, value: str):\n        self._property_changed('mq_symbol')\n        self.__mq_symbol = value        \n\n    @property\n    def num_total_units(self) -> float:\n        """Total Units."""\n        return self.__num_total_units\n\n    @num_total_units.setter\n    def num_total_units(self, value: float):\n        self._property_changed('num_total_units')\n        self.__num_total_units = value        \n\n    @property\n    def corporate_action(self) -> bool:\n        """Whether or not it is a corporate action."""\n        return self.__corporate_action\n\n    @corporate_action.setter\n    def corporate_action(self, value: bool):\n        self._property_changed('corporate_action')\n        self.__corporate_action = value        \n\n    @property\n    def leg1_price_type(self) -> str:\n        """Price denomination and unit of leg 1."""\n        return self.__leg1_price_type\n\n    @leg1_price_type.setter\n    def leg1_price_type(self, value: str):\n        self._property_changed('leg1_price_type')\n        self.__leg1_price_type = value        \n\n    @property\n    def asset_parameters_payer_rate_option(self) -> str:\n        """The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE."""\n        return self.__asset_parameters_payer_rate_option\n\n    @asset_parameters_payer_rate_option.setter\n    def asset_parameters_payer_rate_option(self, value: str):\n        self._property_changed('asset_parameters_payer_rate_option')\n        self.__asset_parameters_payer_rate_option = value        \n\n    @property\n    def sell20cents(self) -> float:\n        """The amount GS would sell for 20 cents charge."""\n        return self.__sell20cents\n\n    @sell20cents.setter\n    def sell20cents(self, value: float):\n        self._property_changed('sell20cents')\n        self.__sell20cents = value        \n\n    @property\n    def leg2_fixed_payment_currency(self) -> str:\n        """If fixed payment leg, the unit of fixed payment."""\n        return self.__leg2_fixed_payment_currency\n\n    @leg2_fixed_payment_currency.setter\n    def leg2_fixed_payment_currency(self, value: str):\n        self._property_changed('leg2_fixed_payment_currency')\n        self.__leg2_fixed_payment_currency = value        \n\n    @property\n    def g_regional_score(self) -> float:\n        """A company's score for G metrics within its region."""\n        return self.__g_regional_score\n\n    @g_regional_score.setter\n    def g_regional_score(self, value: float):\n        self._property_changed('g_regional_score')\n        self.__g_regional_score = value        \n\n    @property\n    def hard_to_borrow(self) -> bool:\n        """Whether or not an asset is hard to borrow."""\n        return self.__hard_to_borrow\n\n    @hard_to_borrow.setter\n    def hard_to_borrow(self, value: bool):\n        self._property_changed('hard_to_borrow')\n        self.__hard_to_borrow = value        \n\n    @property\n    def sell5bps(self) -> float:\n        """The amount GS would sell for 5 bps charge."""\n        return self.__sell5bps\n\n    @sell5bps.setter\n    def sell5bps(self, value: float):\n        self._property_changed('sell5bps')\n        self.__sell5bps = value        \n\n    @property\n    def roll_vwap(self) -> float:\n        """Calendar spread vwap."""\n        return self.__roll_vwap\n\n    @roll_vwap.setter\n    def roll_vwap(self, value: float):\n        self._property_changed('roll_vwap')\n        self.__roll_vwap = value        \n\n    @property\n    def wpk(self) -> str:\n        """Wertpapierkennnummer (WKN, WPKN, Wert), German security identifier code (subject\n           to licensing)."""\n        return self.__wpk\n\n    @wpk.setter\n    def wpk(self, value: str):\n        self._property_changed('wpk')\n        self.__wpk = value        \n\n    @property\n    def bespoke_swap(self) -> str:\n        """Indication if the trade is bespoke."""\n        return self.__bespoke_swap\n\n    @bespoke_swap.setter\n    def bespoke_swap(self, value: str):\n        self._property_changed('bespoke_swap')\n        self.__bespoke_swap = value        \n\n    @property\n    def asset_parameters_expiration_date(self) -> str:\n        """Relative expiration date."""\n        return self.__asset_parameters_expiration_date\n\n    @asset_parameters_expiration_date.setter\n    def asset_parameters_expiration_date(self, value: str):\n        self._property_changed('asset_parameters_expiration_date')\n        self.__asset_parameters_expiration_date = value        \n\n    @property\n    def country_name(self) -> str:\n        """Country name for which FCI is calculated."""\n        return self.__country_name\n\n    @country_name.setter\n    def country_name(self, value: str):\n        self._property_changed('country_name')\n        self.__country_name = value        \n\n    @property\n    def carry(self) -> float:\n        """Carry."""\n        return self.__carry\n\n    @carry.setter\n    def carry(self, value: float):\n        self._property_changed('carry')\n        self.__carry = value        \n\n    @property\n    def starting_date(self) -> str:\n        """Start date of the period the valuation refers to."""\n        return self.__starting_date\n\n    @starting_date.setter\n    def starting_date(self, value: str):\n        self._property_changed('starting_date')\n        self.__starting_date = value        \n\n    @property\n    def loan_id(self) -> str:\n        """Loan reference for a securities lending loan."""\n        return self.__loan_id\n\n    @loan_id.setter\n    def loan_id(self, value: str):\n        self._property_changed('loan_id')\n        self.__loan_id = value        \n\n    @property\n    def onboarded(self) -> bool:\n        """Whether or not social domain has been onboarded."""\n        return self.__onboarded\n\n    @onboarded.setter\n    def onboarded(self, value: bool):\n        self._property_changed('onboarded')\n        self.__onboarded = value        \n\n    @property\n    def liquidity_score(self) -> float:\n        """Liquidity conditions in the aggregate market, calculated as the average of touch\n           liquidity score, touch spread score, and depth spread score."""\n        return self.__liquidity_score\n\n    @liquidity_score.setter\n    def liquidity_score(self, value: float):\n        self._property_changed('liquidity_score')\n        self.__liquidity_score = value        \n\n    @property\n    def long_rates_contribution(self) -> float:\n        """Contribution of long rate component to FCI."""\n        return self.__long_rates_contribution\n\n    @long_rates_contribution.setter\n    def long_rates_contribution(self, value: float):\n        self._property_changed('long_rates_contribution')\n        self.__long_rates_contribution = value        \n\n    @property\n    def source_date_span(self) -> float:\n        """Date span for event in days."""\n        return self.__source_date_span\n\n    @source_date_span.setter\n    def source_date_span(self, value: float):\n        self._property_changed('source_date_span')\n        self.__source_date_span = value        \n\n    @property\n    def ann_yield6_month(self) -> float:\n        """Calculates the total return for 6 months, representing past performance."""\n        return self.__ann_yield6_month\n\n    @ann_yield6_month.setter\n    def ann_yield6_month(self, value: float):\n        self._property_changed('ann_yield6_month')\n        self.__ann_yield6_month = value        \n\n    @property\n    def underlying_data_set_id(self) -> str:\n        """Dataset on which this (virtual) dataset is based."""\n        return self.__underlying_data_set_id\n\n    @underlying_data_set_id.setter\n    def underlying_data_set_id(self, value: str):\n        self._property_changed('underlying_data_set_id')\n        self.__underlying_data_set_id = value        \n\n    @property\n    def close_unadjusted(self) -> float:\n        """Unadjusted Close level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__close_unadjusted\n\n    @close_unadjusted.setter\n    def close_unadjusted(self, value: float):\n        self._property_changed('close_unadjusted')\n        self.__close_unadjusted = value        \n\n    @property\n    def value_unit(self) -> str:\n        """Value unit."""\n        return self.__value_unit\n\n    @value_unit.setter\n    def value_unit(self, value: str):\n        self._property_changed('value_unit')\n        self.__value_unit = value        \n\n    @property\n    def quantity_unit(self) -> str:\n        """Unit of measure for trade quantity."""\n        return self.__quantity_unit\n\n    @quantity_unit.setter\n    def quantity_unit(self, value: str):\n        self._property_changed('quantity_unit')\n        self.__quantity_unit = value        \n\n    @property\n    def adjusted_low_price(self) -> float:\n        """Adjusted low level of an asset based on official exchange fixing or calculation\n           agent marked level."""\n        return self.__adjusted_low_price\n\n    @adjusted_low_price.setter\n    def adjusted_low_price(self, value: float):\n        self._property_changed('adjusted_low_price')\n        self.__adjusted_low_price = value        \n\n    @property\n    def is_momentum(self) -> bool:\n        """Flag indicating whether the value is a momentum value or not."""\n        return self.__is_momentum\n\n    @is_momentum.setter\n    def is_momentum(self, value: bool):\n        self._property_changed('is_momentum')\n        self.__is_momentum = value        \n\n    @property\n    def long_conviction_large(self) -> float:\n        """The count of long ideas with large conviction."""\n        return self.__long_conviction_large\n\n    @long_conviction_large.setter\n    def long_conviction_large(self, value: float):\n        self._property_changed('long_conviction_large')\n        self.__long_conviction_large = value        \n\n    @property\n    def oad(self) -> float:\n        """Option-adjusted duration."""\n        return self.__oad\n\n    @oad.setter\n    def oad(self, value: float):\n        self._property_changed('oad')\n        self.__oad = value        \n\n    @property\n    def rate(self) -> float:\n        """Rate of the asset for the time period in percent."""\n        return self.__rate\n\n    @rate.setter\n    def rate(self, value: float):\n        self._property_changed('rate')\n        self.__rate = value        \n\n    @property\n    def coupon_type(self) -> str:\n        """The coupon type of the bond."""\n        return self.__coupon_type\n\n    @coupon_type.setter\n    def coupon_type(self, value: str):\n        self._property_changed('coupon_type')\n        self.__coupon_type = value        \n\n    @property\n    def client(self) -> str:\n        """Entity name."""\n        return self.__client\n\n    @client.setter\n    def client(self, value: str):\n        self._property_changed('client')\n        self.__client = value        \n\n    @property\n    def conviction_list(self) -> bool:\n        """Conviction List, which is true if the security is on the Conviction Buy List or\n           false otherwise. Securities with a convictionList value equal to true\n           are by definition a subset of the securities with a rating equal to\n           Buy."""\n        return self.__conviction_list\n\n    @conviction_list.setter\n    def conviction_list(self, value: bool):\n        self._property_changed('conviction_list')\n        self.__conviction_list = value        \n\n    @property\n    def passive_etf_ratio(self) -> float:\n        """Ratio of passive ETF ownership, i.e the proportion of a company's marketcap that\n           is held by passive ETFs. Normalised between -3 (low level) and +3\n           (high level)."""\n        return self.__passive_etf_ratio\n\n    @passive_etf_ratio.setter\n    def passive_etf_ratio(self, value: float):\n        self._property_changed('passive_etf_ratio')\n        self.__passive_etf_ratio = value        \n\n    @property\n    def future_month_g26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_g26\n\n    @future_month_g26.setter\n    def future_month_g26(self, value: float):\n        self._property_changed('future_month_g26')\n        self.__future_month_g26 = value        \n\n    @property\n    def future_month_g25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_g25\n\n    @future_month_g25.setter\n    def future_month_g25(self, value: float):\n        self._property_changed('future_month_g25')\n        self.__future_month_g25 = value        \n\n    @property\n    def future_month_g24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_g24\n\n    @future_month_g24.setter\n    def future_month_g24(self, value: float):\n        self._property_changed('future_month_g24')\n        self.__future_month_g24 = value        \n\n    @property\n    def future_month_g23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_g23\n\n    @future_month_g23.setter\n    def future_month_g23(self, value: float):\n        self._property_changed('future_month_g23')\n        self.__future_month_g23 = value        \n\n    @property\n    def type_of_return(self) -> str:\n        """The type of return for the commodity index. Only applicable for commodity\n           indices."""\n        return self.__type_of_return\n\n    @type_of_return.setter\n    def type_of_return(self, value: str):\n        self._property_changed('type_of_return')\n        self.__type_of_return = value        \n\n    @property\n    def future_month_g22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_g22\n\n    @future_month_g22.setter\n    def future_month_g22(self, value: float):\n        self._property_changed('future_month_g22')\n        self.__future_month_g22 = value        \n\n    @property\n    def servicing_cost_long_pnl(self) -> float:\n        """Servicing Cost Long Profit and Loss."""\n        return self.__servicing_cost_long_pnl\n\n    @servicing_cost_long_pnl.setter\n    def servicing_cost_long_pnl(self, value: float):\n        self._property_changed('servicing_cost_long_pnl')\n        self.__servicing_cost_long_pnl = value        \n\n    @property\n    def excess_margin_percentage(self) -> float:\n        """Available credit percentage."""\n        return self.__excess_margin_percentage\n\n    @excess_margin_percentage.setter\n    def excess_margin_percentage(self, value: float):\n        self._property_changed('excess_margin_percentage')\n        self.__excess_margin_percentage = value        \n\n    @property\n    def future_month_g21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_g21\n\n    @future_month_g21.setter\n    def future_month_g21(self, value: float):\n        self._property_changed('future_month_g21')\n        self.__future_month_g21 = value        \n\n    @property\n    def total_mild(self) -> float:\n        """Total number of active cases with mild symptoms."""\n        return self.__total_mild\n\n    @total_mild.setter\n    def total_mild(self, value: float):\n        self._property_changed('total_mild')\n        self.__total_mild = value        \n\n    @property\n    def realized_arrival_performance_bps(self) -> float:\n        """Average execution price vs Consolidated Arrival (in limit) since order inception\n           ??? realized."""\n        return self.__realized_arrival_performance_bps\n\n    @realized_arrival_performance_bps.setter\n    def realized_arrival_performance_bps(self, value: float):\n        self._property_changed('realized_arrival_performance_bps')\n        self.__realized_arrival_performance_bps = value        \n\n    @property\n    def precipitation_daily_forecast_inches(self) -> float:\n        """The forecast value for precipitation in Inch."""\n        return self.__precipitation_daily_forecast_inches\n\n    @precipitation_daily_forecast_inches.setter\n    def precipitation_daily_forecast_inches(self, value: float):\n        self._property_changed('precipitation_daily_forecast_inches')\n        self.__precipitation_daily_forecast_inches = value        \n\n    @property\n    def exchange_id(self) -> str:\n        """Unique identifier for an exchange."""\n        return self.__exchange_id\n\n    @exchange_id.setter\n    def exchange_id(self, value: str):\n        self._property_changed('exchange_id')\n        self.__exchange_id = value        \n\n    @property\n    def leg2_fixed_payment(self) -> float:\n        """If fixed payment leg, the fixed payment amount, which is price*number of\n           contracts bought*contract unit."""\n        return self.__leg2_fixed_payment\n\n    @leg2_fixed_payment.setter\n    def leg2_fixed_payment(self, value: float):\n        self._property_changed('leg2_fixed_payment')\n        self.__leg2_fixed_payment = value        \n\n    @property\n    def tcm_cost_horizon20_day(self) -> float:\n        """TCM cost with a 20 day time horizon."""\n        return self.__tcm_cost_horizon20_day\n\n    @tcm_cost_horizon20_day.setter\n    def tcm_cost_horizon20_day(self, value: float):\n        self._property_changed('tcm_cost_horizon20_day')\n        self.__tcm_cost_horizon20_day = value        \n\n    @property\n    def realm(self) -> str:\n        """Realm."""\n        return self.__realm\n\n    @realm.setter\n    def realm(self, value: str):\n        self._property_changed('realm')\n        self.__realm = value        \n\n    @property\n    def bid(self) -> float:\n        """Latest Bid Price (price willing to buy)."""\n        return self.__bid\n\n    @bid.setter\n    def bid(self, value: float):\n        self._property_changed('bid')\n        self.__bid = value        \n\n    @property\n    def hedge_value(self) -> float:\n        return self.__hedge_value\n\n    @hedge_value.setter\n    def hedge_value(self, value: float):\n        self._property_changed('hedge_value')\n        self.__hedge_value = value        \n\n    @property\n    def order_start_time(self) -> datetime.datetime:\n        """Order start time."""\n        return self.__order_start_time\n\n    @order_start_time.setter\n    def order_start_time(self, value: datetime.datetime):\n        self._property_changed('order_start_time')\n        self.__order_start_time = value        \n\n    @property\n    def is_aggressive(self) -> float:\n        """Indicates if the fill was aggressive or passive."""\n        return self.__is_aggressive\n\n    @is_aggressive.setter\n    def is_aggressive(self, value: float):\n        self._property_changed('is_aggressive')\n        self.__is_aggressive = value        \n\n    @property\n    def floating_rate_designated_maturity(self) -> str:\n        """Tenor"""\n        return self.__floating_rate_designated_maturity\n\n    @floating_rate_designated_maturity.setter\n    def floating_rate_designated_maturity(self, value: str):\n        self._property_changed('floating_rate_designated_maturity')\n        self.__floating_rate_designated_maturity = value        \n\n    @property\n    def percentage_near_executed_quantity(self) -> float:\n        """Percentage of total order filled at passive touch."""\n        return self.__percentage_near_executed_quantity\n\n    @percentage_near_executed_quantity.setter\n    def percentage_near_executed_quantity(self, value: float):\n        self._property_changed('percentage_near_executed_quantity')\n        self.__percentage_near_executed_quantity = value        \n\n    @property\n    def order_id(self) -> str:\n        """The unique ID of the order."""\n        return self.__order_id\n\n    @order_id.setter\n    def order_id(self, value: str):\n        self._property_changed('order_id')\n        self.__order_id = value        \n\n    @property\n    def hospital_type(self) -> str:\n        """Categorical type of hospital as defined by the last four digits of the\n           hospital???s Medicare Provider Number, ie. Critical Access Hospital,\n           Short Term Acute Care Hospital, etc."""\n        return self.__hospital_type\n\n    @hospital_type.setter\n    def hospital_type(self, value: str):\n        self._property_changed('hospital_type')\n        self.__hospital_type = value        \n\n    @property\n    def day_close_realized_bps(self) -> float:\n        """Realised performance vs Previous Close, in bps."""\n        return self.__day_close_realized_bps\n\n    @day_close_realized_bps.setter\n    def day_close_realized_bps(self, value: float):\n        self._property_changed('day_close_realized_bps')\n        self.__day_close_realized_bps = value        \n\n    @property\n    def precipitation_hourly_forecast(self) -> float:\n        """The hourly forecast value for precipitation in Inch."""\n        return self.__precipitation_hourly_forecast\n\n    @precipitation_hourly_forecast.setter\n    def precipitation_hourly_forecast(self, value: float):\n        self._property_changed('precipitation_hourly_forecast')\n        self.__precipitation_hourly_forecast = value        \n\n    @property\n    def market_cap_usd(self) -> float:\n        """Market capitalization of a given asset denominated in USD."""\n        return self.__market_cap_usd\n\n    @market_cap_usd.setter\n    def market_cap_usd(self, value: float):\n        self._property_changed('market_cap_usd')\n        self.__market_cap_usd = value        \n\n    @property\n    def auction_fills_percentage(self) -> float:\n        """Percent of total order filled in auction."""\n        return self.__auction_fills_percentage\n\n    @auction_fills_percentage.setter\n    def auction_fills_percentage(self, value: float):\n        self._property_changed('auction_fills_percentage')\n        self.__auction_fills_percentage = value        \n\n    @property\n    def high_price(self) -> float:\n        """High level of an asset based on official exchange fixing or calculation agent\n           marked level."""\n        return self.__high_price\n\n    @high_price.setter\n    def high_price(self, value: float):\n        self._property_changed('high_price')\n        self.__high_price = value        \n\n    @property\n    def absolute_shares(self) -> float:\n        """The number of shares without adjusting for side."""\n        return self.__absolute_shares\n\n    @absolute_shares.setter\n    def absolute_shares(self, value: float):\n        self._property_changed('absolute_shares')\n        self.__absolute_shares = value        \n\n    @property\n    def fixed_rate_day_count_fraction(self) -> str:\n        """Day Count Fraction"""\n        return self.__fixed_rate_day_count_fraction\n\n    @fixed_rate_day_count_fraction.setter\n    def fixed_rate_day_count_fraction(self, value: str):\n        self._property_changed('fixed_rate_day_count_fraction')\n        self.__fixed_rate_day_count_fraction = value        \n\n    @property\n    def model(self) -> str:\n        """Model."""\n        return self.__model\n\n    @model.setter\n    def model(self, value: str):\n        self._property_changed('model')\n        self.__model = value        \n\n    @property\n    def unrealized_twap_performance_usd(self) -> float:\n        """Average execution price vs Consolidated TWAP since order inception ???\n           unrealized in $."""\n        return self.__unrealized_twap_performance_usd\n\n    @unrealized_twap_performance_usd.setter\n    def unrealized_twap_performance_usd(self, value: float):\n        self._property_changed('unrealized_twap_performance_usd')\n        self.__unrealized_twap_performance_usd = value        \n\n    @property\n    def id(self) -> str:\n        """Marquee unique identifier"""\n        return self.__id\n\n    @id.setter\n    def id(self, value: str):\n        self._property_changed('id')\n        self.__id = value        \n\n    @property\n    def maturity(self) -> str:\n        """Maturity of the instrument."""\n        return self.__maturity\n\n    @maturity.setter\n    def maturity(self, value: str):\n        self._property_changed('maturity')\n        self.__maturity = value        \n\n    @property\n    def delta_change(self) -> str:\n        """PadMaster delta changes."""\n        return self.__delta_change\n\n    @delta_change.setter\n    def delta_change(self, value: str):\n        self._property_changed('delta_change')\n        self.__delta_change = value        \n\n    @property\n    def index(self) -> float:\n        """Mid point or volume weighted average of all transactions."""\n        return self.__index\n\n    @index.setter\n    def index(self, value: float):\n        self._property_changed('index')\n        self.__index = value        \n\n    @property\n    def unrealized_arrival_performance_usd(self) -> float:\n        """Average execution price vs Consolidated Arrival (in limit) since order inception\n           ??? unrealized in $."""\n        return self.__unrealized_arrival_performance_usd\n\n    @unrealized_arrival_performance_usd.setter\n    def unrealized_arrival_performance_usd(self, value: float):\n        self._property_changed('unrealized_arrival_performance_usd')\n        self.__unrealized_arrival_performance_usd = value        \n\n    @property\n    def iceberg_slippage(self) -> float:\n        """Iceberg slippage represented as pips."""\n        return self.__iceberg_slippage\n\n    @iceberg_slippage.setter\n    def iceberg_slippage(self, value: float):\n        self._property_changed('iceberg_slippage')\n        self.__iceberg_slippage = value        \n\n    @property\n    def sell120cents(self) -> float:\n        """The amount GS would sell for 120 cents charge."""\n        return self.__sell120cents\n\n    @sell120cents.setter\n    def sell120cents(self, value: float):\n        self._property_changed('sell120cents')\n        self.__sell120cents = value        \n\n    @property\n    def future_month_x26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_x26\n\n    @future_month_x26.setter\n    def future_month_x26(self, value: float):\n        self._property_changed('future_month_x26')\n        self.__future_month_x26 = value        \n\n    @property\n    def asset_types(self) -> Tuple[Tuple[Union[AssetType, str], ...], ...]:\n        """Asset Types supported."""\n        return self.__asset_types\n\n    @asset_types.setter\n    def asset_types(self, value: Tuple[Tuple[Union[AssetType, str], ...], ...]):\n        self._property_changed('asset_types')\n        self.__asset_types = value        \n\n    @property\n    def future_month_x25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_x25\n\n    @future_month_x25.setter\n    def future_month_x25(self, value: float):\n        self._property_changed('future_month_x25')\n        self.__future_month_x25 = value        \n\n    @property\n    def bcid(self) -> str:\n        """Bloomberg composite identifier (ticker and country code)."""\n        return self.__bcid\n\n    @bcid.setter\n    def bcid(self, value: str):\n        self._property_changed('bcid')\n        self.__bcid = value        \n\n    @property\n    def mkt_point(self) -> Tuple[Tuple[str, ...], ...]:\n        """The MDAPI Point (e.g. 3m, 10y, 11y, Dec19)."""\n        return self.__mkt_point\n\n    @mkt_point.setter\n    def mkt_point(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('mkt_point')\n        self.__mkt_point = value        \n\n    @property\n    def future_month_x24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_x24\n\n    @future_month_x24.setter\n    def future_month_x24(self, value: float):\n        self._property_changed('future_month_x24')\n        self.__future_month_x24 = value        \n\n    @property\n    def restriction_start_date(self) -> datetime.date:\n        """The date at which the security restriction was enacted."""\n        return self.__restriction_start_date\n\n    @restriction_start_date.setter\n    def restriction_start_date(self, value: datetime.date):\n        self._property_changed('restriction_start_date')\n        self.__restriction_start_date = value        \n\n    @property\n    def touch_liquidity_score(self) -> float:\n        """Z-score of the amount available to trade at the top of the aggregated order\n           book."""\n        return self.__touch_liquidity_score\n\n    @touch_liquidity_score.setter\n    def touch_liquidity_score(self, value: float):\n        self._property_changed('touch_liquidity_score')\n        self.__touch_liquidity_score = value        \n\n    @property\n    def future_month_x23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_x23\n\n    @future_month_x23.setter\n    def future_month_x23(self, value: float):\n        self._property_changed('future_month_x23')\n        self.__future_month_x23 = value        \n\n    @property\n    def future_month_x22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_x22\n\n    @future_month_x22.setter\n    def future_month_x22(self, value: float):\n        self._property_changed('future_month_x22')\n        self.__future_month_x22 = value        \n\n    @property\n    def factor_category_id(self) -> str:\n        """Id for Factor Categories. Note that 'CUR' should be used to represent the\n           currency category. Must match regex pattern ^[a-zA-Z0-9]{0,20}$."""\n        return self.__factor_category_id\n\n    @factor_category_id.setter\n    def factor_category_id(self, value: str):\n        self._property_changed('factor_category_id')\n        self.__factor_category_id = value        \n\n    @property\n    def security_type_id(self) -> str:\n        """PadMaster unique security type identifier."""\n        return self.__security_type_id\n\n    @security_type_id.setter\n    def security_type_id(self, value: str):\n        self._property_changed('security_type_id')\n        self.__security_type_id = value        \n\n    @property\n    def future_month_x21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_x21\n\n    @future_month_x21.setter\n    def future_month_x21(self, value: float):\n        self._property_changed('future_month_x21')\n        self.__future_month_x21 = value        \n\n    @property\n    def investment_ytd(self) -> float:\n        """Total net investment Year to date."""\n        return self.__investment_ytd\n\n    @investment_ytd.setter\n    def investment_ytd(self, value: float):\n        self._property_changed('investment_ytd')\n        self.__investment_ytd = value        \n\n    @property\n    def leg2_notional(self) -> float:\n        """The total Notional amount or quantity of units of the leg 2 underlying asset."""\n        return self.__leg2_notional\n\n    @leg2_notional.setter\n    def leg2_notional(self, value: float):\n        self._property_changed('leg2_notional')\n        self.__leg2_notional = value        \n\n    @property\n    def sell1bps(self) -> float:\n        """The amount GS would sell for 1 bps charge."""\n        return self.__sell1bps\n\n    @sell1bps.setter\n    def sell1bps(self, value: float):\n        self._property_changed('sell1bps')\n        self.__sell1bps = value        \n\n    @property\n    def sell200cents(self) -> float:\n        """The amount GS would sell for 200 cents charge."""\n        return self.__sell200cents\n\n    @sell200cents.setter\n    def sell200cents(self, value: float):\n        self._property_changed('sell200cents')\n        self.__sell200cents = value        \n\n    @property\n    def expected_completion_date(self) -> str:\n        """Expected day of acquisition completion."""\n        return self.__expected_completion_date\n\n    @expected_completion_date.setter\n    def expected_completion_date(self, value: str):\n        self._property_changed('expected_completion_date')\n        self.__expected_completion_date = value        \n\n    @property\n    def spread_option_vol(self) -> float:\n        """Historical implied normal volatility for a liquid point on spread option vol\n           surface."""\n        return self.__spread_option_vol\n\n    @spread_option_vol.setter\n    def spread_option_vol(self, value: float):\n        self._property_changed('spread_option_vol')\n        self.__spread_option_vol = value        \n\n    @property\n    def sell80cents(self) -> float:\n        """The amount GS would sell for 80 cents charge."""\n        return self.__sell80cents\n\n    @sell80cents.setter\n    def sell80cents(self, value: float):\n        self._property_changed('sell80cents')\n        self.__sell80cents = value        \n\n    @property\n    def inflation_swap_rate(self) -> float:\n        """Zero coupon inflation swap break-even rate for a given currency."""\n        return self.__inflation_swap_rate\n\n    @inflation_swap_rate.setter\n    def inflation_swap_rate(self, value: float):\n        self._property_changed('inflation_swap_rate')\n        self.__inflation_swap_rate = value        \n\n    @property\n    def active_queries(self) -> float:\n        """Active Queries."""\n        return self.__active_queries\n\n    @active_queries.setter\n    def active_queries(self, value: float):\n        self._property_changed('active_queries')\n        self.__active_queries = value        \n\n    @property\n    def sell45bps(self) -> float:\n        """The amount GS would sell for 45 bps charge."""\n        return self.__sell45bps\n\n    @sell45bps.setter\n    def sell45bps(self, value: float):\n        self._property_changed('sell45bps')\n        self.__sell45bps = value        \n\n    @property\n    def embeded_option(self) -> str:\n        """An indication of whether or not the option fields are for an embedded option."""\n        return self.__embeded_option\n\n    @embeded_option.setter\n    def embeded_option(self, value: str):\n        self._property_changed('embeded_option')\n        self.__embeded_option = value        \n\n    @property\n    def event_source(self) -> str:\n        """Equals GS if the event is sourced from Goldman Sachs Global Investment Research\n           analysts. Equals TR if the event is sourced from Refinitive\n           StreetEvents."""\n        return self.__event_source\n\n    @event_source.setter\n    def event_source(self, value: str):\n        self._property_changed('event_source')\n        self.__event_source = value        \n\n    @property\n    def qis_perm_no(self) -> str:\n        """QIS Permanent Security Number."""\n        return self.__qis_perm_no\n\n    @qis_perm_no.setter\n    def qis_perm_no(self, value: str):\n        self._property_changed('qis_perm_no')\n        self.__qis_perm_no = value        \n\n    @property\n    def settlement(self) -> str:\n        """Swap Settlement Type"""\n        return self.__settlement\n\n    @settlement.setter\n    def settlement(self, value: str):\n        self._property_changed('settlement')\n        self.__settlement = value        \n\n    @property\n    def shareclass_id(self) -> str:\n        """Identifies shareclass with a unique code."""\n        return self.__shareclass_id\n\n    @shareclass_id.setter\n    def shareclass_id(self, value: str):\n        self._property_changed('shareclass_id')\n        self.__shareclass_id = value        \n\n    @property\n    def feature2(self) -> str:\n        """Second metrics feature."""\n        return self.__feature2\n\n    @feature2.setter\n    def feature2(self, value: str):\n        self._property_changed('feature2')\n        self.__feature2 = value        \n\n    @property\n    def feature3(self) -> str:\n        """Third metrics feature."""\n        return self.__feature3\n\n    @feature3.setter\n    def feature3(self, value: str):\n        self._property_changed('feature3')\n        self.__feature3 = value        \n\n    @property\n    def sts_commodity_sector(self) -> str:\n        """Commodity sector for STS assets."""\n        return self.__sts_commodity_sector\n\n    @sts_commodity_sector.setter\n    def sts_commodity_sector(self, value: str):\n        self._property_changed('sts_commodity_sector')\n        self.__sts_commodity_sector = value        \n\n    @property\n    def exception_status(self) -> str:\n        """The violation status for this particular line item."""\n        return self.__exception_status\n\n    @exception_status.setter\n    def exception_status(self, value: str):\n        self._property_changed('exception_status')\n        self.__exception_status = value        \n\n    @property\n    def overnight_news_intensity(self) -> float:\n        """A measure of the intensity/extent of media attention to each stock compared with\n           other stocks in the index. It is based on the number of overnight\n           news articles that are highly relevant for each stock. Overnight is\n           between previous day???s close and current day???s open."""\n        return self.__overnight_news_intensity\n\n    @overnight_news_intensity.setter\n    def overnight_news_intensity(self, value: float):\n        self._property_changed('overnight_news_intensity')\n        self.__overnight_news_intensity = value        \n\n    @property\n    def sales_coverage(self) -> str:\n        """Primary or secondary sales coverage."""\n        return self.__sales_coverage\n\n    @sales_coverage.setter\n    def sales_coverage(self, value: str):\n        self._property_changed('sales_coverage')\n        self.__sales_coverage = value        \n\n    @property\n    def feature1(self) -> str:\n        """First metrics feature."""\n        return self.__feature1\n\n    @feature1.setter\n    def feature1(self, value: str):\n        self._property_changed('feature1')\n        self.__feature1 = value        \n\n    @property\n    def tcm_cost_participation_rate10_pct(self) -> float:\n        """TCM cost with a 10 percent participation rate."""\n        return self.__tcm_cost_participation_rate10_pct\n\n    @tcm_cost_participation_rate10_pct.setter\n    def tcm_cost_participation_rate10_pct(self, value: float):\n        self._property_changed('tcm_cost_participation_rate10_pct')\n        self.__tcm_cost_participation_rate10_pct = value        \n\n    @property\n    def event_time(self) -> str:\n        """The time of the event if the event has a specific time or the end time of the\n           event if the event occurs during a time window (optional). It is\n           represented in HH:MM 24 hour format in the time zone of the exchange\n           where the company is listed."""\n        return self.__event_time\n\n    @event_time.setter\n    def event_time(self, value: str):\n        self._property_changed('event_time')\n        self.__event_time = value        \n\n    @property\n    def position_source_name(self) -> str:\n        """Position source name for quick access."""\n        return self.__position_source_name\n\n    @position_source_name.setter\n    def position_source_name(self, value: str):\n        self._property_changed('position_source_name')\n        self.__position_source_name = value        \n\n    @property\n    def delivery_date(self) -> datetime.date:\n        """The final date by which the underlying commodity for a futures contract must be\n           delivered in order for the terms of the contract to be fulfilled."""\n        return self.__delivery_date\n\n    @delivery_date.setter\n    def delivery_date(self, value: datetime.date):\n        self._property_changed('delivery_date')\n        self.__delivery_date = value        \n\n    @property\n    def interest_rate(self) -> float:\n        """Interest rate."""\n        return self.__interest_rate\n\n    @interest_rate.setter\n    def interest_rate(self, value: float):\n        self._property_changed('interest_rate')\n        self.__interest_rate = value        \n\n    @property\n    def side(self) -> str:\n        """Long or short."""\n        return self.__side\n\n    @side.setter\n    def side(self, value: str):\n        self._property_changed('side')\n        self.__side = value        \n\n    @property\n    def dynamic_hybrid_aggressive_style(self) -> str:\n        """Aggressive style of a Dynamic Hybrid order."""\n        return self.__dynamic_hybrid_aggressive_style\n\n    @dynamic_hybrid_aggressive_style.setter\n    def dynamic_hybrid_aggressive_style(self, value: str):\n        self._property_changed('dynamic_hybrid_aggressive_style')\n        self.__dynamic_hybrid_aggressive_style = value        \n\n    @property\n    def compliance_restricted_status(self) -> str:\n        """Restricted status as set by compliance."""\n        return self.__compliance_restricted_status\n\n    @compliance_restricted_status.setter\n    def compliance_restricted_status(self, value: str):\n        self._property_changed('compliance_restricted_status')\n        self.__compliance_restricted_status = value        \n\n    @property\n    def borrow_fee(self) -> float:\n        """An indication of the rate one would be charged for borrowing/shorting the\n           relevant asset on that day, expressed in annualized percent terms.\n           Rates may change daily."""\n        return self.__borrow_fee\n\n    @borrow_fee.setter\n    def borrow_fee(self, value: float):\n        self._property_changed('borrow_fee')\n        self.__borrow_fee = value        \n\n    @property\n    def ever_icu(self) -> float:\n        """Total number of patients ever in intensive care for COVID."""\n        return self.__ever_icu\n\n    @ever_icu.setter\n    def ever_icu(self, value: float):\n        self._property_changed('ever_icu')\n        self.__ever_icu = value        \n\n    @property\n    def no_worse_than_level(self) -> float:\n        """Prevents execution if the market level is worse than the specified level."""\n        return self.__no_worse_than_level\n\n    @no_worse_than_level.setter\n    def no_worse_than_level(self, value: float):\n        self._property_changed('no_worse_than_level')\n        self.__no_worse_than_level = value        \n\n    @property\n    def update_time(self) -> datetime.datetime:\n        """Update time of the data element, which allows historical as-of query."""\n        return self.__update_time\n\n    @update_time.setter\n    def update_time(self, value: datetime.datetime):\n        self._property_changed('update_time')\n        self.__update_time = value        \n\n    @property\n    def loan_spread(self) -> float:\n        """The difference between the investment rate on cash collateral and the rebate\n           rate of a loan."""\n        return self.__loan_spread\n\n    @loan_spread.setter\n    def loan_spread(self, value: float):\n        self._property_changed('loan_spread')\n        self.__loan_spread = value        \n\n    @property\n    def tcm_cost_horizon12_hour(self) -> float:\n        """TCM cost with a 12 hour time horizon."""\n        return self.__tcm_cost_horizon12_hour\n\n    @tcm_cost_horizon12_hour.setter\n    def tcm_cost_horizon12_hour(self, value: float):\n        self._property_changed('tcm_cost_horizon12_hour')\n        self.__tcm_cost_horizon12_hour = value        \n\n    @property\n    def dew_point(self) -> float:\n        """Temperature in fahrenheit below which water condenses."""\n        return self.__dew_point\n\n    @dew_point.setter\n    def dew_point(self, value: float):\n        self._property_changed('dew_point')\n        self.__dew_point = value        \n\n    @property\n    def research_commission(self) -> float:\n        """The dollar amount of commissions received from clients."""\n        return self.__research_commission\n\n    @research_commission.setter\n    def research_commission(self, value: float):\n        self._property_changed('research_commission')\n        self.__research_commission = value        \n\n    @property\n    def buy2bps(self) -> float:\n        """The amount GS would buy for 2 bps charge."""\n        return self.__buy2bps\n\n    @buy2bps.setter\n    def buy2bps(self, value: float):\n        self._property_changed('buy2bps')\n        self.__buy2bps = value        \n\n    @property\n    def asset_classifications_risk_country_code(self) -> str:\n        """Risk Country code (ISO 3166)."""\n        return self.__asset_classifications_risk_country_code\n\n    @asset_classifications_risk_country_code.setter\n    def asset_classifications_risk_country_code(self, value: str):\n        self._property_changed('asset_classifications_risk_country_code')\n        self.__asset_classifications_risk_country_code = value        \n\n    @property\n    def new_ideas_mtd(self) -> float:\n        """Ideas received by clients Month to date."""\n        return self.__new_ideas_mtd\n\n    @new_ideas_mtd.setter\n    def new_ideas_mtd(self, value: float):\n        self._property_changed('new_ideas_mtd')\n        self.__new_ideas_mtd = value        \n\n    @property\n    def var_swap_by_expiry(self) -> float:\n        """Strike such that the price of an uncapped variance swap on the underlying index\n           is zero at inception."""\n        return self.__var_swap_by_expiry\n\n    @var_swap_by_expiry.setter\n    def var_swap_by_expiry(self, value: float):\n        self._property_changed('var_swap_by_expiry')\n        self.__var_swap_by_expiry = value        \n\n    @property\n    def sell_date(self) -> datetime.date:\n        """Sell date of the securities triggering the stock loan recall activity."""\n        return self.__sell_date\n\n    @sell_date.setter\n    def sell_date(self, value: datetime.date):\n        self._property_changed('sell_date')\n        self.__sell_date = value        \n\n    @property\n    def aum_start(self) -> float:\n        """Assets under management at the start of the period."""\n        return self.__aum_start\n\n    @aum_start.setter\n    def aum_start(self, value: float):\n        self._property_changed('aum_start')\n        self.__aum_start = value        \n\n    @property\n    def asset_parameters_settlement(self) -> str:\n        """Settlement type."""\n        return self.__asset_parameters_settlement\n\n    @asset_parameters_settlement.setter\n    def asset_parameters_settlement(self, value: str):\n        self._property_changed('asset_parameters_settlement')\n        self.__asset_parameters_settlement = value        \n\n    @property\n    def max_temperature(self) -> float:\n        """Maximum temperature observed on a given day in fahrenheit."""\n        return self.__max_temperature\n\n    @max_temperature.setter\n    def max_temperature(self, value: float):\n        self._property_changed('max_temperature')\n        self.__max_temperature = value        \n\n    @property\n    def acquirer_shareholder_meeting_date(self) -> str:\n        """Shareholders meeting date for acquiring entity."""\n        return self.__acquirer_shareholder_meeting_date\n\n    @acquirer_shareholder_meeting_date.setter\n    def acquirer_shareholder_meeting_date(self, value: str):\n        self._property_changed('acquirer_shareholder_meeting_date')\n        self.__acquirer_shareholder_meeting_date = value        \n\n    @property\n    def count_ideas_wtd(self) -> float:\n        """Ideas alive at a time Week to date."""\n        return self.__count_ideas_wtd\n\n    @count_ideas_wtd.setter\n    def count_ideas_wtd(self, value: float):\n        self._property_changed('count_ideas_wtd')\n        self.__count_ideas_wtd = value        \n\n    @property\n    def arrival_rt_normalized(self) -> float:\n        """Performance against Benchmark in pip."""\n        return self.__arrival_rt_normalized\n\n    @arrival_rt_normalized.setter\n    def arrival_rt_normalized(self, value: float):\n        self._property_changed('arrival_rt_normalized')\n        self.__arrival_rt_normalized = value        \n\n    @property\n    def report_type(self) -> str:\n        """Type of report to execute"""\n        return self.__report_type\n\n    @report_type.setter\n    def report_type(self, value: str):\n        self._property_changed('report_type')\n        self.__report_type = value        \n\n    @property\n    def source_url(self) -> str:\n        """Source URL."""\n        return self.__source_url\n\n    @source_url.setter\n    def source_url(self, value: str):\n        self._property_changed('source_url')\n        self.__source_url = value        \n\n    @property\n    def estimated_return(self) -> float:\n        """Estimated return of asset over a given period (e.g. close-to-close)."""\n        return self.__estimated_return\n\n    @estimated_return.setter\n    def estimated_return(self, value: float):\n        self._property_changed('estimated_return')\n        self.__estimated_return = value        \n\n    @property\n    def high(self) -> float:\n        """High level of an asset based on official exchange fixing or calculation agent\n           marked level."""\n        return self.__high\n\n    @high.setter\n    def high(self, value: float):\n        self._property_changed('high')\n        self.__high = value        \n\n    @property\n    def source_last_update(self) -> str:\n        """Source last update."""\n        return self.__source_last_update\n\n    @source_last_update.setter\n    def source_last_update(self, value: str):\n        self._property_changed('source_last_update')\n        self.__source_last_update = value        \n\n    @property\n    def sunshine_forecast(self) -> float:\n        """The forecast value for sunshine."""\n        return self.__sunshine_forecast\n\n    @sunshine_forecast.setter\n    def sunshine_forecast(self, value: float):\n        self._property_changed('sunshine_forecast')\n        self.__sunshine_forecast = value        \n\n    @property\n    def quantity_mw(self) -> float:\n        """Quantity of electricity in megawatts."""\n        return self.__quantity_mw\n\n    @quantity_mw.setter\n    def quantity_mw(self, value: float):\n        self._property_changed('quantity_mw')\n        self.__quantity_mw = value        \n\n    @property\n    def sell70cents(self) -> float:\n        """The amount GS would sell for 70 cents charge."""\n        return self.__sell70cents\n\n    @sell70cents.setter\n    def sell70cents(self, value: float):\n        self._property_changed('sell70cents')\n        self.__sell70cents = value        \n\n    @property\n    def sell110cents(self) -> float:\n        """The amount GS would sell for 110 cents charge."""\n        return self.__sell110cents\n\n    @sell110cents.setter\n    def sell110cents(self, value: float):\n        self._property_changed('sell110cents')\n        self.__sell110cents = value        \n\n    @property\n    def pnode_id(self) -> str:\n        """Pricing node identifier sourced from Morningstar."""\n        return self.__pnode_id\n\n    @pnode_id.setter\n    def pnode_id(self, value: str):\n        self._property_changed('pnode_id')\n        self.__pnode_id = value        \n\n    @property\n    def humidity_type(self) -> str:\n        """The humidity type: Relative, average etc."""\n        return self.__humidity_type\n\n    @humidity_type.setter\n    def humidity_type(self, value: str):\n        self._property_changed('humidity_type')\n        self.__humidity_type = value        \n\n    @property\n    def prev_close_ask(self) -> float:\n        """Previous business day's close ask price."""\n        return self.__prev_close_ask\n\n    @prev_close_ask.setter\n    def prev_close_ask(self, value: float):\n        self._property_changed('prev_close_ask')\n        self.__prev_close_ask = value        \n\n    @property\n    def level(self) -> float:\n        """Level of the 5-day normalized flow in a given factor."""\n        return self.__level\n\n    @level.setter\n    def level(self, value: float):\n        self._property_changed('level')\n        self.__level = value        \n\n    @property\n    def implied_volatility_by_expiration(self) -> float:\n        """Volatility of an asset implied by observations of market prices."""\n        return self.__implied_volatility_by_expiration\n\n    @implied_volatility_by_expiration.setter\n    def implied_volatility_by_expiration(self, value: float):\n        self._property_changed('implied_volatility_by_expiration')\n        self.__implied_volatility_by_expiration = value        \n\n    @property\n    def asset_parameters_fixed_rate_day_count_fraction(self) -> str:\n        """Day Count Fraction"""\n        return self.__asset_parameters_fixed_rate_day_count_fraction\n\n    @asset_parameters_fixed_rate_day_count_fraction.setter\n    def asset_parameters_fixed_rate_day_count_fraction(self, value: str):\n        self._property_changed('asset_parameters_fixed_rate_day_count_fraction')\n        self.__asset_parameters_fixed_rate_day_count_fraction = value        \n\n    @property\n    def es_momentum_score(self) -> float:\n        """A company's score for E&S subsector-relative momentum."""\n        return self.__es_momentum_score\n\n    @es_momentum_score.setter\n    def es_momentum_score(self, value: float):\n        self._property_changed('es_momentum_score')\n        self.__es_momentum_score = value        \n\n    @property\n    def leg2_index(self) -> str:\n        """If floating index leg, the index."""\n        return self.__leg2_index\n\n    @leg2_index.setter\n    def leg2_index(self, value: str):\n        self._property_changed('leg2_index')\n        self.__leg2_index = value        \n\n    @property\n    def net_weight(self) -> float:\n        """Difference between the longWeight and shortWeight. If you have IBM stock with\n           shortWeight 0.2 and also IBM stock with longWeight 0.4, then the\n           netWeight would be 0.2 (-0.2+0.4)."""\n        return self.__net_weight\n\n    @net_weight.setter\n    def net_weight(self, value: float):\n        self._property_changed('net_weight')\n        self.__net_weight = value        \n\n    @property\n    def portfolio_managers(self) -> Tuple[str, ...]:\n        """Portfolio managers of asset."""\n        return self.__portfolio_managers\n\n    @portfolio_managers.setter\n    def portfolio_managers(self, value: Tuple[str, ...]):\n        self._property_changed('portfolio_managers')\n        self.__portfolio_managers = value        \n\n    @property\n    def bos_in_ticks(self) -> float:\n        """The Bid-Offer Spread of the stock in Ticks on the particular date."""\n        return self.__bos_in_ticks\n\n    @bos_in_ticks.setter\n    def bos_in_ticks(self, value: float):\n        self._property_changed('bos_in_ticks')\n        self.__bos_in_ticks = value        \n\n    @property\n    def asset_parameters_coupon_type(self) -> str:\n        """The coupon type of the bond."""\n        return self.__asset_parameters_coupon_type\n\n    @asset_parameters_coupon_type.setter\n    def asset_parameters_coupon_type(self, value: str):\n        self._property_changed('asset_parameters_coupon_type')\n        self.__asset_parameters_coupon_type = value        \n\n    @property\n    def expected_residual_quantity(self) -> float:\n        """Expected Residual Quantity."""\n        return self.__expected_residual_quantity\n\n    @expected_residual_quantity.setter\n    def expected_residual_quantity(self, value: float):\n        self._property_changed('expected_residual_quantity')\n        self.__expected_residual_quantity = value        \n\n    @property\n    def roll_date(self) -> datetime.date:\n        """Roll Dates for Equity Index Quarterly Futures."""\n        return self.__roll_date\n\n    @roll_date.setter\n    def roll_date(self, value: datetime.date):\n        self._property_changed('roll_date')\n        self.__roll_date = value        \n\n    @property\n    def dynamic_hybrid_speed(self) -> str:\n        """Execution speed of a Dynamic Hybrid order."""\n        return self.__dynamic_hybrid_speed\n\n    @dynamic_hybrid_speed.setter\n    def dynamic_hybrid_speed(self, value: str):\n        self._property_changed('dynamic_hybrid_speed')\n        self.__dynamic_hybrid_speed = value        \n\n    @property\n    def cap_floor_vol(self) -> float:\n        """Historical implied normal volatility for a liquid point on cap and floor vol\n           surface."""\n        return self.__cap_floor_vol\n\n    @cap_floor_vol.setter\n    def cap_floor_vol(self, value: float):\n        self._property_changed('cap_floor_vol')\n        self.__cap_floor_vol = value        \n\n    @property\n    def target_quantity(self) -> float:\n        """The target executed quantity as published from the algo."""\n        return self.__target_quantity\n\n    @target_quantity.setter\n    def target_quantity(self, value: float):\n        self._property_changed('target_quantity')\n        self.__target_quantity = value        \n\n    @property\n    def submitter(self) -> str:\n        """Name of person submitting request."""\n        return self.__submitter\n\n    @submitter.setter\n    def submitter(self, value: str):\n        self._property_changed('submitter')\n        self.__submitter = value        \n\n    @property\n    def no(self) -> float:\n        """Price of no contract."""\n        return self.__no\n\n    @no.setter\n    def no(self, value: float):\n        self._property_changed('no')\n        self.__no = value        \n\n    @property\n    def notional(self) -> float:\n        """Notional."""\n        return self.__notional\n\n    @notional.setter\n    def notional(self, value: float):\n        self._property_changed('notional')\n        self.__notional = value        \n\n    @property\n    def es_disclosure_percentage(self) -> float:\n        """The percentage of E&S metrics the company discloses relative to the number of\n           E&S metrics relevant for its subsector."""\n        return self.__es_disclosure_percentage\n\n    @es_disclosure_percentage.setter\n    def es_disclosure_percentage(self, value: float):\n        self._property_changed('es_disclosure_percentage')\n        self.__es_disclosure_percentage = value        \n\n    @property\n    def close_executed_quantity_percentage(self) -> float:\n        """Percent of order executed in closing Auction."""\n        return self.__close_executed_quantity_percentage\n\n    @close_executed_quantity_percentage.setter\n    def close_executed_quantity_percentage(self, value: float):\n        self._property_changed('close_executed_quantity_percentage')\n        self.__close_executed_quantity_percentage = value        \n\n    @property\n    def twap_realized_cash(self) -> float:\n        """Consolidated realised performance vs TWAP In Limit, in USD."""\n        return self.__twap_realized_cash\n\n    @twap_realized_cash.setter\n    def twap_realized_cash(self, value: float):\n        self._property_changed('twap_realized_cash')\n        self.__twap_realized_cash = value        \n\n    @property\n    def is_open_auction(self) -> bool:\n        """Flag indicating whether the bucket is within the open auction or not."""\n        return self.__is_open_auction\n\n    @is_open_auction.setter\n    def is_open_auction(self, value: bool):\n        self._property_changed('is_open_auction')\n        self.__is_open_auction = value        \n\n    @property\n    def leg1_type(self) -> str:\n        """Indication if leg 1 is fixed or floating or Physical."""\n        return self.__leg1_type\n\n    @leg1_type.setter\n    def leg1_type(self, value: str):\n        self._property_changed('leg1_type')\n        self.__leg1_type = value        \n\n    @property\n    def wet_bulb_temp_hourly_forecast(self) -> float:\n        """The hourly forecast value for wet bulb temperature in Fahrenheit."""\n        return self.__wet_bulb_temp_hourly_forecast\n\n    @wet_bulb_temp_hourly_forecast.setter\n    def wet_bulb_temp_hourly_forecast(self, value: float):\n        self._property_changed('wet_bulb_temp_hourly_forecast')\n        self.__wet_bulb_temp_hourly_forecast = value        \n\n    @property\n    def cleanup_price(self) -> float:\n        """The WIG price used by the algo."""\n        return self.__cleanup_price\n\n    @cleanup_price.setter\n    def cleanup_price(self, value: float):\n        self._property_changed('cleanup_price')\n        self.__cleanup_price = value        \n\n    @property\n    def total(self) -> float:\n        """Total exposure."""\n        return self.__total\n\n    @total.setter\n    def total(self, value: float):\n        self._property_changed('total')\n        self.__total = value        \n\n    @property\n    def filled_notional_usd(self) -> float:\n        """Executed Notional in USD."""\n        return self.__filled_notional_usd\n\n    @filled_notional_usd.setter\n    def filled_notional_usd(self, value: float):\n        self._property_changed('filled_notional_usd')\n        self.__filled_notional_usd = value        \n\n    @property\n    def asset_id(self) -> str:\n        """Marquee unique asset identifier."""\n        return self.__asset_id\n\n    @asset_id.setter\n    def asset_id(self, value: str):\n        self._property_changed('asset_id')\n        self.__asset_id = value        \n\n    @property\n    def test_status(self) -> str:\n        """Result of the quality test."""\n        return self.__test_status\n\n    @test_status.setter\n    def test_status(self, value: str):\n        self._property_changed('test_status')\n        self.__test_status = value        \n\n    @property\n    def mkt_type(self) -> str:\n        """The MDAPI Type (e.g. IR BASIS, FX Vol)."""\n        return self.__mkt_type\n\n    @mkt_type.setter\n    def mkt_type(self, value: str):\n        self._property_changed('mkt_type')\n        self.__mkt_type = value        \n\n    @property\n    def last_updated_time(self) -> datetime.datetime:\n        """Timestamp of when the object was last updated."""\n        return self.__last_updated_time\n\n    @last_updated_time.setter\n    def last_updated_time(self, value: datetime.datetime):\n        self._property_changed('last_updated_time')\n        self.__last_updated_time = value        \n\n    @property\n    def yield30_day(self) -> float:\n        """Net income per share for last 30 days/NAV."""\n        return self.__yield30_day\n\n    @yield30_day.setter\n    def yield30_day(self, value: float):\n        self._property_changed('yield30_day')\n        self.__yield30_day = value        \n\n    @property\n    def buy28bps(self) -> float:\n        """The amount GS would buy for 28 bps charge."""\n        return self.__buy28bps\n\n    @buy28bps.setter\n    def buy28bps(self, value: float):\n        self._property_changed('buy28bps')\n        self.__buy28bps = value        \n\n    @property\n    def proportion_of_risk(self) -> float:\n        """Proportion of risk with respect to the portfolio's total risk."""\n        return self.__proportion_of_risk\n\n    @proportion_of_risk.setter\n    def proportion_of_risk(self, value: float):\n        self._property_changed('proportion_of_risk')\n        self.__proportion_of_risk = value        \n\n    @property\n    def future_month_k23(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_k23\n\n    @future_month_k23.setter\n    def future_month_k23(self, value: float):\n        self._property_changed('future_month_k23')\n        self.__future_month_k23 = value        \n\n    @property\n    def future_month_k22(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_k22\n\n    @future_month_k22.setter\n    def future_month_k22(self, value: float):\n        self._property_changed('future_month_k22')\n        self.__future_month_k22 = value        \n\n    @property\n    def future_month_k21(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_k21\n\n    @future_month_k21.setter\n    def future_month_k21(self, value: float):\n        self._property_changed('future_month_k21')\n        self.__future_month_k21 = value        \n\n    @property\n    def primary_entity_id(self) -> str:\n        """Primary Key for an entity; in use for Data Quality Checker."""\n        return self.__primary_entity_id\n\n    @primary_entity_id.setter\n    def primary_entity_id(self, value: str):\n        self._property_changed('primary_entity_id')\n        self.__primary_entity_id = value        \n\n    @property\n    def cross(self) -> str:\n        """FX cross symbol."""\n        return self.__cross\n\n    @cross.setter\n    def cross(self, value: str):\n        self._property_changed('cross')\n        self.__cross = value        \n\n    @property\n    def idea_status(self) -> str:\n        """The activity status of the idea."""\n        return self.__idea_status\n\n    @idea_status.setter\n    def idea_status(self, value: str):\n        self._property_changed('idea_status')\n        self.__idea_status = value        \n\n    @property\n    def contract_subtype(self) -> str:\n        """Contract subtype."""\n        return self.__contract_subtype\n\n    @contract_subtype.setter\n    def contract_subtype(self, value: str):\n        self._property_changed('contract_subtype')\n        self.__contract_subtype = value        \n\n    @property\n    def sri(self) -> bool:\n        """Whether or not an asset is classified as socially responsible investing."""\n        return self.__sri\n\n    @sri.setter\n    def sri(self, value: bool):\n        self._property_changed('sri')\n        self.__sri = value        \n\n    @property\n    def fx_forecast(self) -> float:\n        """FX forecast value for the relative period."""\n        return self.__fx_forecast\n\n    @fx_forecast.setter\n    def fx_forecast(self, value: float):\n        self._property_changed('fx_forecast')\n        self.__fx_forecast = value        \n\n    @property\n    def fixing_time_label(self) -> str:\n        """Time at which the fixing was taken."""\n        return self.__fixing_time_label\n\n    @fixing_time_label.setter\n    def fixing_time_label(self, value: str):\n        self._property_changed('fixing_time_label')\n        self.__fixing_time_label = value        \n\n    @property\n    def is_etf(self) -> bool:\n        """Whether or not assetType is an ETF."""\n        return self.__is_etf\n\n    @is_etf.setter\n    def is_etf(self, value: bool):\n        self._property_changed('is_etf')\n        self.__is_etf = value        \n\n    @property\n    def fill_id(self) -> str:\n        """Unique identifier for a fill."""\n        return self.__fill_id\n\n    @fill_id.setter\n    def fill_id(self, value: str):\n        self._property_changed('fill_id')\n        self.__fill_id = value        \n\n    @property\n    def excess_returns(self) -> float:\n        """Excess returns for backtest."""\n        return self.__excess_returns\n\n    @excess_returns.setter\n    def excess_returns(self, value: float):\n        self._property_changed('excess_returns')\n        self.__excess_returns = value        \n\n    @property\n    def dollar_return(self) -> float:\n        """Dollar return of asset over a given period (e.g. close-to-close)."""\n        return self.__dollar_return\n\n    @dollar_return.setter\n    def dollar_return(self, value: float):\n        self._property_changed('dollar_return')\n        self.__dollar_return = value        \n\n    @property\n    def order_in_limit(self) -> bool:\n        """Is the order???s passive leg in limit."""\n        return self.__order_in_limit\n\n    @order_in_limit.setter\n    def order_in_limit(self, value: bool):\n        self._property_changed('order_in_limit')\n        self.__order_in_limit = value        \n\n    @property\n    def expiry_time(self) -> datetime.datetime:\n        """Order expiration date."""\n        return self.__expiry_time\n\n    @expiry_time.setter\n    def expiry_time(self, value: datetime.datetime):\n        self._property_changed('expiry_time')\n        self.__expiry_time = value        \n\n    @property\n    def return_on_equity(self) -> float:\n        """Return on equity."""\n        return self.__return_on_equity\n\n    @return_on_equity.setter\n    def return_on_equity(self, value: float):\n        self._property_changed('return_on_equity')\n        self.__return_on_equity = value        \n\n    @property\n    def future_month_k26(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_k26\n\n    @future_month_k26.setter\n    def future_month_k26(self, value: float):\n        self._property_changed('future_month_k26')\n        self.__future_month_k26 = value        \n\n    @property\n    def future_month_k25(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_k25\n\n    @future_month_k25.setter\n    def future_month_k25(self, value: float):\n        self._property_changed('future_month_k25')\n        self.__future_month_k25 = value        \n\n    @property\n    def future_month_k24(self) -> float:\n        """Commods future month code."""\n        return self.__future_month_k24\n\n    @future_month_k24.setter\n    def future_month_k24(self, value: float):\n        self._property_changed('future_month_k24')\n        self.__future_month_k24 = value        \n\n    @property\n    def restriction_end_date(self) -> datetime.date:\n        """The date at which the security restriction was lifted."""\n        return self.__restriction_end_date\n\n    @restriction_end_date.setter\n    def restriction_end_date(self, value: datetime.date):\n        self._property_changed('restriction_end_date')\n        self.__restriction_end_date = value        \n\n    @property\n    def queue_in_lots_description(self) -> str:\n        """Description of the Stock's Queue size in Lots (if applicable) on the particular\n           date."""\n        return self.__queue_in_lots_description\n\n    @queue_in_lots_description.setter\n    def queue_in_lots_description(self, value: str):\n        self._property_changed('queue_in_lots_description')\n        self.__queue_in_lots_description = value        \n\n    @property\n    def volume_limit(self) -> float:\n        """The allowed percentage of ATV that can be placed on loan for a lender."""\n        return self.__volume_limit\n\n    @volume_limit.setter\n    def volume_limit(self, value: float):\n        self._property_changed('volume_limit')\n        self.__volume_limit = value        \n\n    @property\n    def objective(self) -> str:\n        """The objective of the hedge."""\n        return self.__objective\n\n    @objective.setter\n    def objective(self, value: str):\n        self._property_changed('objective')\n        self.__objective = value        \n\n    @property\n    def nav_price(self) -> float:\n        """Net asset value price. Quoted price (mid, 100 ??? Upfront) of the underlying\n           basket of single name CDS. (Theoretical Index value). In percent."""\n        return self.__nav_price\n\n    @nav_price.setter\n    def nav_price(self, value: float):\n        self._property_changed('nav_price')\n        self.__nav_price = value        \n\n    @property\n    def leg1_underlying_asset(self) -> str:\n        """The asset, reference asset, or reference obligation for payments of a party???s\n           obligations under the SB swap transaction reference."""\n        return self.__leg1_underlying_asset\n\n    @leg1_underlying_asset.setter\n    def leg1_underlying_asset(self, value: str):\n        self._property_changed('leg1_underlying_asset')\n        self.__leg1_underlying_asset = value        \n\n    @property\n    def private_placement_type(self) -> str:\n        """Regulation that applies to a bond."""\n        return self.__private_placement_type\n\n    @private_placement_type.setter\n    def private_placement_type(self, value: str):\n        self._property_changed('private_placement_type')\n        self.__private_placement_type = value        \n\n    @property\n    def hedge_notional(self) -> float:\n        """Notional value of the hedge."""\n        return self.__hedge_notional\n\n    @hedge_notional.setter\n    def hedge_notional(self, value: float):\n        self._property_changed('hedge_notional')\n        self.__hedge_notional = value        \n\n    @property\n    def ask_low(self) -> float:\n        """The lowest ask Price (price offering to sell)."""\n        return self.__ask_low\n\n    @ask_low.setter\n    def ask_low(self, value: float):\n        self._property_changed('ask_low')\n        self.__ask_low = value        \n\n    @property\n    def intended_p_rate(self) -> float:\n        """The intended participation rate from the algo."""\n        return self.__intended_p_rate\n\n    @intended_p_rate.setter\n    def intended_p_rate(self, value: float):\n        self._property_changed('intended_p_rate')\n        self.__intended_p_rate = value        \n\n    @property\n    def expiry(self) -> str:\n        """The time period before the option expires."""\n        return self.__expiry\n\n    @expiry.setter\n    def expiry(self, value: str):\n        self._property_changed('expiry')\n        self.__expiry = value        \n\n    @property\n    def avg_monthly_yield(self) -> float:\n        """Only used for GS Money Market funds, assumes sum of the past 30 days, divided by\n           30, and expressed as a percent."""\n        return self.__avg_monthly_yield\n\n    @avg_monthly_yield.setter\n    def avg_monthly_yield(self, value: float):\n        self._property_changed('avg_monthly_yield')\n        self.__avg_monthly_yield = value        \n\n    @property\n    def period_direction(self) -> str:\n        """Direction of the outlook period."""\n        return self.__period_direction\n\n    @period_direction.setter\n    def period_direction(self, value: str):\n        self._property_changed('period_direction')\n        self.__period_direction = value        \n\n    @property\n    def prev_rpt_id(self) -> str:\n        """On cancellations and corrections, this ID will hold the Dissemination ID of the\n           originally published real-time message."""\n        return self.__prev_rpt_id\n\n    @prev_rpt_id.setter\n    def prev_rpt_id(self, value: str):\n        self._property_changed('prev_rpt_id')\n        self.__prev_rpt_id = value        \n\n    @property\n    def earnings_per_share(self) -> float:\n        """Earnings per share."""\n        return self.__earnings_per_share\n\n    @earnings_per_share.setter\n    def earnings_per_share(self, value: float):\n        self._property_changed('earnings_per_share')\n        self.__earnings_per_share = value        \n\n    @property\n    def strike_percentage(self) -> float:\n        """Strike compared to market value."""\n        return self.__strike_percentage\n\n    @strike_percentage.setter\n    def strike_percentage(self, value: float):\n        self._property_changed('strike_percentage')\n        self.__strike_percentage = value        \n\n    @property\n    def es_product_impact_percentile(self) -> float:\n        """A percentile that captures a company's E&S product impact ranking within its\n           subsector."""\n        return self.__es_product_impact_percentile\n\n    @es_product_impact_percentile.setter\n    def es_product_impact_percentile(self, value: float):\n        self._property_changed('es_product_impact_percentile')\n        self.__es_product_impact_percentile = value        \n\n    @property\n    def vwap_realized_cash(self) -> float:\n        """Consolidated realised performance vs VWAP, in USD."""\n        return self.__vwap_realized_cash\n\n    @vwap_realized_cash.setter\n    def vwap_realized_cash(self, value: float):\n        self._property_changed('vwap_realized_cash')\n        self.__vwap_realized_cash = value        \n\n    @property\n    def par_asset_swap_spread1m(self) -> float:\n        """Par asset swap spread vs 1m tenor."""\n        return self.__par_asset_swap_spread1m\n\n    @par_asset_swap_spread1m.setter\n    def par_asset_swap_spread1m(self, value: float):\n        self._property_changed('par_asset_swap_spread1m')\n        self.__par_asset_swap_spread1m = value        \n\n    @property\n    def prev_close_bid(self) -> float:\n        """Previous close BID price."""\n        return self.__prev_close_bid\n\n    @prev_close_bid.setter\n    def prev_close_bid(self, value: float):\n        self._property_changed('prev_close_bid')\n        self.__prev_close_bid = value        \n\n    @property\n    def minimum_increment(self) -> float:\n        """The minimum increment size of the bond purchase allowed above the minimum\n           denomination as authorized by the bond documents."""\n        return self.__minimum_increment\n\n    @minimum_increment.setter\n    def minimum_increment(self, value: float):\n        self._property_changed('minimum_increment')\n        self.__minimum_increment = value        \n\n    @property\n    def tcm_cost_horizon16_day(self) -> float:\n        """TCM cost with a 16 day time horizon."""\n        return self.__tcm_cost_horizon16_day\n\n    @tcm_cost_horizon16_day.setter\n    def tcm_cost_horizon16_day(self, value: float):\n        self._property_changed('tcm_cost_horizon16_day')\n        self.__tcm_cost_horizon16_day = value        \n\n    @property\n    def investment_mtd(self) -> float:\n        """Total net investment Month to date."""\n        return self.__investment_mtd\n\n    @investment_mtd.setter\n    def investment_mtd(self, value: float):\n        self._property_changed('investment_mtd')\n        self.__investment_mtd = value        \n\n    @property\n    def settlement_date(self) -> datetime.date:\n        """The settlement date of the associated FX contract."""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: datetime.date):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def weighted_average_mid_normalized(self) -> float:\n        """Performance against Benchmark in pip."""\n        return self.__weighted_average_mid_normalized\n\n    @weighted_average_mid_normalized.setter\n    def weighted_average_mid_normalized(self, value: float):\n        self._property_changed('weighted_average_mid_normalized')\n        self.__weighted_average_mid_normalized = value        \n\n    @property\n    def sales_per_share(self) -> float:\n        """Sales per share."""\n        return self.__sales_per_share\n\n    @sales_per_share.setter\n    def sales_per_share(self, value: float):\n        self._property_changed('sales_per_share')\n        self.__sales_per_share = value        \n\n    @property\n    def unadjusted_close(self) -> float:\n        """Unadjusted Close level of an asset based on official exchange fixing or\n           calculation agent marked level."""\n        return self.__unadjusted_close\n\n    @unadjusted_close.setter\n    def unadjusted_close(self, value: float):\n        self._property_changed('unadjusted_close')\n        self.__unadjusted_close = value        \n\n    @property\n    def loan_date(self) -> datetime.date:\n        """The date at which the securities loan was enacted."""\n        return self.__loan_date\n\n    @loan_date.setter\n    def loan_date(self, value: datetime.date):\n        self._property_changed('loan_date')\n        self.__loan_date = value        \n\n    @property\n    def matched_maturity_swap_spread1m(self) -> float:\n        """Matched maturity swap spread vs 1m tenor."""\n        return self.__matched_maturity_swap_spread1m\n\n    @matched_maturity_swap_spread1m.setter\n    def matched_maturity_swap_spread1m(self, value: float):\n        self._property_changed('matched_maturity_swap_spread1m')\n        self.__matched_maturity_swap_spread1m = value        \n\n    @property\n    def collateral_percentage_actual(self) -> float:\n        """Collateral percentage covering contractual the given position."""\n        return self.__collateral_percentage_actual\n\n    @collateral_percentage_actual.setter\n    def collateral_percentage_actual(self, value: float):\n        self._property_changed('collateral_percentage_actual')\n        self.__collateral_percentage_actual = value        \n\n    @property\n    def vwap_in_limit_unrealized_bps(self) -> float:\n        """Consolidated unrealised performance vs VWAP In Limit, in bps."""\n        return self.__vwap_in_limit_unrealized_bps\n\n    @vwap_in_limit_unrealized_bps.setter\n    def vwap_in_limit_unrealized_bps(self, value: float):\n        self._property_changed('vwap_in_limit_unrealized_bps')\n        self.__vwap_in_limit_unrealized_bps = value        \n\n    @property\n    def metric_value(self) -> float:\n        """Value of the metric calculated for the given geographyName and date."""\n        return self.__metric_value\n\n    @metric_value.setter\n    def metric_value(self, value: float):\n        self._property_changed('metric_value')\n        self.__metric_value = value        \n\n    @property\n    def auto_exec_state(self) -> str:\n        """Auto Execution State."""\n        return self.__auto_exec_state\n\n    @auto_exec_state.setter\n    def auto_exec_state(self, value: str):\n        self._property_changed('auto_exec_state')\n        self.__auto_exec_state = value        \n\n    @property\n    def total_recovered(self) -> float:\n        """Total number of recovered cases."""\n        return self.__total_recovered\n\n    @total_recovered.setter\n    def total_recovered(self, value: float):\n        self._property_changed('total_recovered')\n        self.__total_recovered = value        \n\n    @property\n    def relative_return_ytd(self) -> float:\n        """Relative Return Year to Date."""\n        return self.__relative_return_ytd\n\n    @relative_return_ytd.setter\n    def relative_return_ytd(self, value: float):\n        self._property_changed('relative_return_ytd')\n        self.__relative_return_ytd = value        \n\n    @property\n    def tick_server(self) -> str:\n        """Tickserver Symbol."""\n        return self.__tick_server\n\n    @tick_server.setter\n    def tick_server(self, value: str):\n        self._property_changed('tick_server')\n        self.__tick_server = value        \n\n    @property\n    def cumulative_volume_in_percentage(self) -> float:\n        """Forecast of the percentage of the cumulative volume of shares from start of day\n           to the end of the bucket interval, relative to the total daily\n           volume."""\n        return self.__cumulative_volume_in_percentage\n\n    @cumulative_volume_in_percentage.setter\n    def cumulative_volume_in_percentage(self, value: float):\n        self._property_changed('cumulative_volume_in_percentage')\n        self.__cumulative_volume_in_percentage = value        \n\n    @property\n    def real_time_restriction_status(self) -> Tuple[Tuple[str, ...], ...]:\n        """Real Time Restricted status as set by compliance."""\n        return self.__real_time_restriction_status\n\n    @real_time_restriction_status.setter\n    def real_time_restriction_status(self, value: Tuple[Tuple[str, ...], ...]):\n        self._property_changed('real_time_restriction_status')\n        self.__real_time_restriction_status = value        \n\n    @property\n    def trade_type(self) -> str:\n        """Trade type."""\n        return self.__trade_type\n\n    @trade_type.setter\n    def trade_type(self, value: str):\n        self._property_changed('trade_type')\n        self.__trade_type = value        \n\n    @property\n    def settlement_type(self) -> str:\n        """Swap Settlement Type"""\n        return self.__settlement_type\n\n    @settlement_type.setter\n    def settlement_type(self, value: str):\n        self._property_changed('settlement_type')\n        self.__settlement_type = value        \n\n    @property\n    def net_change(self) -> float:\n        """Difference between the lastest trading price or value and the adjusted\n           historical closing value or settlement price."""\n        return self.__net_change\n\n    @net_change.setter\n    def net_change(self, value: float):\n        self._property_changed('net_change')\n        self.__net_change = value        \n\n    @property\n    def number_of_underliers(self) -> float:\n        """Total number of underliers."""\n        return self.__number_of_underliers\n\n    @number_of_underliers.setter\n    def number_of_underliers(self, value: float):\n        self._property_changed('number_of_underliers')\n        self.__number_of_underliers = value        \n\n    @property\n    def swap_type(self) -> str:\n        """Swap type of position."""\n        return self.__swap_type\n\n    @swap_type.setter\n    def swap_type(self, value: str):\n        self._property_changed('swap_type')\n        self.__swap_type = value        \n\n    @property\n    def forecast_type(self) -> str:\n        """Type of return for commodity indices. Spot for individual commodities."""\n        return self.__forecast_type\n\n    @forecast_type.setter\n    def forecast_type(self, value: str):\n        self._property_changed('forecast_type')\n        self.__forecast_type = value        \n\n    @property\n    def leg1_notional(self) -> float:\n        """The total Notional amount or quantity of units of the leg 1 underlying asset."""\n        return self.__leg1_notional\n\n    @leg1_notional.setter\n    def leg1_notional(self, value: float):\n        self._property_changed('leg1_notional')\n        self.__leg1_notional = value        \n\n    @property\n    def sell_settle_date(self) -> datetime.date:\n        """Data that the sell of securities will settle."""\n        return self.__sell_settle_date\n\n    @sell_settle_date.setter\n    def sell_settle_date(self, value: datetime.date):\n        self._property_changed('sell_settle_date')\n        self.__sell_settle_date = value        \n\n    @property\n    def new_ideas_ytd(self) -> float:\n        """Ideas received by clients Year to date."""\n        return self.__new_ideas_ytd\n\n    @new_ideas_ytd.setter\n    def new_ideas_ytd(self, value: float):\n        self._property_changed('new_ideas_ytd')\n        self.__new_ideas_ytd = value        \n\n    @property\n    def management_fee(self) -> Union[Op, float]:\n        return self.__management_fee\n\n    @management_fee.setter\n    def management_fee(self, value: Union[Op, float]):\n        self._property_changed('management_fee')\n        self.__management_fee = value        \n\n    @property\n    def par_asset_swap_spread3m(self) -> float:\n        """Par asset swap spread vs 3m tenor."""\n        return self.__par_asset_swap_spread3m\n\n    @par_asset_swap_spread3m.setter\n    def par_asset_swap_spread3m(self, value: float):\n        self._property_changed('par_asset_swap_spread3m')\n        self.__par_asset_swap_spread3m = value        \n\n    @property\n    def sell36bps(self) -> float:\n        """The amount GS would sell for 36 bps charge."""\n        return self.__sell36bps\n\n    @sell36bps.setter\n    def sell36bps(self, value: float):\n        self._property_changed('sell36bps')\n        self.__sell36bps = value        \n\n    @property\n    def matched_maturity_swap_spread3m(self) -> float:\n        """Matched maturity swap spread vs 3m tenor."""\n        return self.__matched_maturity_swap_spread3m\n\n    @matched_maturity_swap_spread3m.setter\n    def matched_maturity_swap_spread3m(self, value: float):\n        self._property_changed('matched_maturity_swap_spread3m')\n        self.__matched_maturity_swap_spread3m = value        \n\n    @property\n    def source_id(self) -> str:\n        """Unique id of data provider."""\n        return self.__source_id\n\n    @source_id.setter\n    def source_id(self, value: str):\n        self._property_changed('source_id')\n        self.__source_id = value        \n\n    @property\n    def country(self) -> str:\n        """Country of incorporation of asset."""\n        return self.__country\n\n    @country.setter\n    def country(self, value: str):\n        self._property_changed('country')\n        self.__country = value        \n\n    @property\n    def vwap(self) -> float:\n        """VWAP benchmark price."""\n        return self.__vwap\n\n    @vwap.setter\n    def vwap(self, value: float):\n        self._property_changed('vwap')\n        self.__vwap = value        \n\n    @property\n    def touch_spread_score(self) -> float:\n        """Z-score of the difference between highest bid and lowest offer."""\n        return self.__touch_spread_score\n\n    @touch_spread_score.setter\n    def touch_spread_score(self, value: float):\n        self._property_changed('touch_spread_score')\n        self.__touch_spread_score = value        \n\n    @property\n    def rating_second_highest(self) -> str:\n        """Second highest bond rating between Moody's, Fitch, and Standard and Poor's."""\n        return self.__rating_second_highest\n\n    @rating_second_highest.setter\n    def rating_second_highest(self, value: str):\n        self._property_changed('rating_second_highest')\n        self.__rating_second_highest = value        \n\n    @property\n    def sell24bps(self) -> float:\n        """The amount GS would sell for 24 bps charge."""\n        return self.__sell24bps\n\n    @sell24bps.setter\n    def sell24bps(self, value: float):\n        self._property_changed('sell24bps')\n        self.__sell24bps = value        \n\n    @property\n    def frequency(self) -> str:\n        """Requested frequency of data delivery."""\n        return self.__frequency\n\n    @frequency.setter\n    def frequency(self, value: str):\n        self._property_changed('frequency')\n        self.__frequency = value        \n\n    @property\n    def activity_id(self) -> str:\n        """Marquee unique Activity identifier."""\n        return self.__activity_id\n\n    @activity_id.setter\n    def activity_id(self, value: str):\n        self._property_changed('activity_id')\n        self.__activity_id = value        \n\n    @property\n    def estimated_impact(self) -> float:\n        """Likely impact of a proposed trade on the price of an asset (bps). The model's\n           shortfall estimates reflect how much it cost to execute similar\n           trades in the past, as opposed to providing a hypothetical cost\n           derived using tick data."""\n        return self.__estimated_impact\n\n    @estimated_impact.setter\n    def estimated_impact(self, value: float):\n        self._property_changed('estimated_impact')\n        self.__estimated_impact = value        \n\n    @property\n    def sell35cents(self) -> float:\n        """The amount GS would sell for 35 cents charge."""\n        return self.__sell35cents\n\n    @sell35cents.setter\n    def sell35cents(self, value: float):\n        self._property_changed('sell35cents')\n        self.__sell35cents = value        \n\n    @property\n    def loan_spread_bucket(self) -> str:\n        """The difference between the investment rate on cash collateral and the rebate\n           rate of a loan."""\n        return self.__loan_spread_bucket\n\n    @loan_spread_bucket.setter\n    def loan_spread_bucket(self, value: str):\n        self._property_changed('loan_spread_bucket')\n        self.__loan_spread_bucket = value        \n\n    @property\n    def coronavirus_global_activity_tracker(self) -> float:\n        """Value for the global activity tracker."""\n        return self.__coronavirus_global_activity_tracker\n\n    @coronavirus_global_activity_tracker.setter\n    def coronavirus_global_activity_tracker(self, value: float):\n        self._property_changed('coronavirus_global_activity_tracker')\n        self.__coronavirus_global_activity_tracker = value        \n\n    @property\n    def underlyers(self) -> str:\n        """Top underlyers. Supported values are 'all' or 'top_10', 'top_15', etc.."""\n        return self.__underlyers\n\n    @underlyers.setter\n    def underlyers(self, value: str):\n        self._property_changed('underlyers')\n        self.__underlyers = value        \n\n    @property\n    def asset_parameters_pricing_location(self) -> str:\n        """The location in which the asset was priced."""\n        return self.__asset_parameters_pricing_location\n\n    @asset_parameters_pricing_location.setter\n    def asset_parameters_pricing_location(self, value: str):\n        self._property_changed('asset_parameters_pricing_location')\n        self.__asset_parameters_pricing_location = value        \n\n    @property\n    def event_description(self) -> str:\n        """Short description of the event, providing additional information beyond\n           eventType."""\n        return self.__event_description\n\n    @event_description.setter\n    def event_description(self, value: str):\n        self._property_changed('event_description')\n        self.__event_description = value        \n\n    @property\n    def iceberg_max_size(self) -> float:\n        """Iceberg max size in terms of the quantity unit."""\n        return self.__iceberg_max_size\n\n    @iceberg_max_size.setter\n    def iceberg_max_size(self, value: float):\n        self._property_changed('iceberg_max_size')\n        self.__iceberg_max_size = value        \n\n    @property\n    def asset_parameters_coupon(self) -> float:\n        """The fixed coupon for this bond."""\n        return self.__asset_parameters_coupon\n\n    @asset_parameters_coupon.setter\n    def asset_parameters_coupon(self, value: float):\n        self._property_changed('asset_parameters_coupon')\n        self.__asset_parameters_coupon = value        \n\n    @property\n    def details(self) -> str:\n        """Corporate action details."""\n        return self.__details\n\n    @details.setter\n    def details(self, value: str):\n        self._property_changed('details')\n        self.__details = value        \n\n    @property\n    def sector(self) -> str:\n        """The risk model sector of the stock."""\n        return self.__sector\n\n    @sector.setter\n    def sector(self, value: str):\n        self._property_changed('sector')\n        self.__sector = value        \n\n    @property\n    def avg_bed_util_rate(self) -> float:\n        """Average rate of bed utilization, computed as Total Patient Days (excluding\n           nursery days) / Bed Days Available."""\n        return self.__avg_bed_util_rate\n\n    @avg_bed_util_rate.setter\n    def avg_bed_util_rate(self, value: float):\n        self._property_changed('avg_bed_util_rate')\n        self.__avg_bed_util_rate = value        \n\n    @property\n    def buy20bps(self) -> float:\n        """The amount GS would buy for 20 bps charge."""\n        return self.__buy20bps\n\n    @buy20bps.setter\n    def buy20bps(self, value: float):\n        self._property_changed('buy20bps')\n        self.__buy20bps = value        \n\n    @property\n    def epidemic(self) -> float:\n        """Total number of people affected by an epidemic."""\n        return self.__epidemic\n\n    @epidemic.setter\n    def epidemic(self, value: float):\n        self._property_changed('epidemic')\n        self.__epidemic = value        \n\n    @property\n    def mctr(self) -> float:\n        """Marginal contribution of a given asset to portfolio variance, is dependent on\n           covariance matrix."""\n        return self.__mctr\n\n    @mctr.setter\n    def mctr(self, value: float):\n        self._property_changed('mctr')\n        self.__mctr = value        \n\n    @property\n    def exchange_time(self) -> datetime.datetime:\n        """Local time at the executing venue."""\n        return self.__exchange_time\n\n    @exchange_time.setter\n    def exchange_time(self, value: datetime.datetime):\n        self._property_changed('exchange_time')\n        self.__exchange_time = value        \n\n    @property\n    def historical_close(self) -> float:\n        """Historical Close Price."""\n        return self.__historical_close\n\n    @historical_close.setter\n    def historical_close(self, value: float):\n        self._property_changed('historical_close')\n        self.__historical_close = value        \n\n    @property\n    def fips_code(self) -> float:\n        """County FIPS code."""\n        return self.__fips_code\n\n    @fips_code.setter\n    def fips_code(self, value: float):\n        self._property_changed('fips_code')\n        self.__fips_code = value        \n\n    @property\n    def buy32bps(self) -> float:\n        """The amount GS would buy for 32 bps charge."""\n        return self.__buy32bps\n\n    @buy32bps.setter\n    def buy32bps(self, value: float):\n        self._property_changed('buy32bps')\n        self.__buy32bps = value        \n\n    @property\n    def idea_id(self) -> str:\n        """Marquee unique trade idea identifier."""\n        return self.__idea_id\n\n    @idea_id.setter\n    def idea_id(self, value: str):\n        self._property_changed('idea_id')\n        self.__idea_id = value        \n\n    @property\n    def comment_status(self) -> str:\n        """Corporate action comment status."""\n        return self.__comment_status\n\n    @comment_status.setter\n    def comment_status(self, value: str):\n        self._property_changed('comment_status')\n        self.__comment_status = value        \n\n    @property\n    def marginal_cost(self) -> float:\n        """Marginal cost."""\n        return self.__marginal_cost\n\n    @marginal_cost.setter\n    def marginal_cost(self, value: float):\n        self._property_changed('marginal_cost')\n        self.__marginal_cost = value        \n\n    @property\n    def client_weight(self) -> float:\n        """Weight of client positions in the region or sector (%)."""\n        return self.__client_weight\n\n    @client_weight.setter\n    def client_weight(self, value: float):\n        self._property_changed('client_weight')\n        self.__client_weight = value        \n\n    @property\n    def leg1_delivery_point(self) -> str:\n        """Delivery point of leg."""\n        return self.__leg1_delivery_point\n\n    @leg1_delivery_point.setter\n    def leg1_delivery_point(self, value: str):\n        self._property_changed('leg1_delivery_point')\n        self.__leg1_delivery_point = value        \n\n    @property\n    def sell5cents(self) -> float:\n        """The amount GS would sell for 5 cents charge."""\n        return self.__sell5cents\n\n    @sell5cents.setter\n    def sell5cents(self, value: float):\n        self._property_changed('sell5cents')\n        self.__sell5cents = value        \n\n    @property\n    def liq_wkly(self) -> float:\n        """Percent of assets that could be quickly and easily converted into investable\n           cash without loss of value within a week."""\n        return self.__liq_wkly\n\n    @liq_wkly.setter\n    def liq_wkly(self, value: float):\n        self._property_changed('liq_wkly')\n        self.__liq_wkly = value        \n\n    @property\n    def unrealized_twap_performance_bps(self) -> float:\n        """Average execution price vs Consolidated TWAP since order inception ???\n           unrealized."""\n        return self.__unrealized_twap_performance_bps\n\n    @unrealized_twap_performance_bps.setter\n    def unrealized_twap_performance_bps(self, value: float):\n        self._property_changed('unrealized_twap_performance_bps')\n        self.__unrealized_twap_performance_bps = value        \n\n    @property\n    def region(self) -> str:\n        """Regional classification for the asset"""\n        return self.__region\n\n    @region.setter\n    def region(self, value: str):\n        self._property_changed('region')\n        self.__region = value        \n\n    @property\n    def temperature_hour(self) -> float:\n        """The forecast value of hour with min/max temperature in a day."""\n        return self.__temperature_hour\n\n    @temperature_hour.setter\n    def temperature_hour(self, value: float):\n        self._property_changed('temperature_hour')\n        self.__temperature_hour = value        \n\n    @property\n    def upper_bound(self) -> float:\n        """Upper bound value."""\n        return self.__upper_bound\n\n    @upper_bound.setter\n    def upper_bound(self, value: float):\n        self._property_changed('upper_bound')\n        self.__upper_bound = value        \n\n    @property\n    def sell55cents(self) -> float:\n        """The amount GS would sell for 55 cents charge."""\n        return self.__sell55cents\n\n    @sell55cents.setter\n    def sell55cents(self, value: float):\n        self._property_changed('sell55cents')\n        self.__sell55cents = value        \n\n    @property\n    def num_pedi_icu_beds(self) -> float:\n        """All neonatal, pediatric and premature ICU beds."""\n        return self.__num_pedi_icu_beds\n\n    @num_pedi_icu_beds.setter\n    def num_pedi_icu_beds(self, value: float):\n        self._property_changed('num_pedi_icu_beds')\n        self.__num_pedi_icu_beds = value        \n\n    @property\n    def bid_yield(self) -> float:\n        """The return an investor realizes on a bond sold at the bid price."""\n        return self.__bid_yield\n\n    @bid_yield.setter\n    def bid_yield(self, value: float):\n        self._property_changed('bid_yield')\n        self.__bid_yield = value        \n\n    @property\n    def expected_residual(self) -> float:\n        """Expected residual quantity."""\n        return self.__expected_residual\n\n    @expected_residual.setter\n    def expected_residual(self, value: float):\n        self._property_changed('expected_residual')\n        self.__expected_residual = value        \n\n    @property\n    def option_premium(self) -> float:\n        """An indication of the market value of the option at the time of execution."""\n        return self.__option_premium\n\n    @option_premium.setter\n    def option_premium(self, value: float):\n        self._property_changed('option_premium')\n        self.__option_premium = value        \n\n    @property\n    def owner_name(self) -> str:\n        """Name of person submitting request."""\n        return self.__owner_name\n\n    @owner_name.setter\n    def owner_name(self, value: str):\n        self._property_changed('owner_name')\n        self.__owner_name = value        \n\n    @property\n    def par_asset_swap_spread6m(self) -> float:\n        """Par asset swap spread vs 6m tenor."""\n        return self.__par_asset_swap_spread6m\n\n    @par_asset_swap_spread6m.setter\n    def par_asset_swap_spread6m(self, value: float):\n        self._property_changed('par_asset_swap_spread6m')\n        self.__par_asset_swap_spread6m = value        \n\n    @property\n    def z_score(self) -> float:\n        """Z Score."""\n        return self.__z_score\n\n    @z_score.setter\n    def z_score(self, value: float):\n        self._property_changed('z_score')\n        self.__z_score = value        \n\n    @property\n    def sell12bps(self) -> float:\n        """The amount GS would sell for 12 bps charge."""\n        return self.__sell12bps\n\n    @sell12bps.setter\n    def sell12bps(self, value: float):\n        self._property_changed('sell12bps')\n        self.__sell12bps = value        \n\n    @property\n    def event_start_time(self) -> str:\n        """The start time of the event if the event occurs during a time window and the\n           event has a specific start time. It is represented in HH:MM 24 hour\n           format in the time zone of the exchange where the company is listed."""\n        return self.__event_start_time\n\n    @event_start_time.setter\n    def event_start_time(self, value: str):\n        self._property_changed('event_start_time')\n        self.__event_start_time = value        \n\n    @property\n    def matched_maturity_swap_spread6m(self) -> float:\n        """Matched maturity swap spread vs 6m tenor."""\n        return self.__matched_maturity_swap_spread6m\n\n    @matched_maturity_swap_spread6m.setter\n    def matched_maturity_swap_spread6m(self, value: float):\n        self._property_changed('matched_maturity_swap_spread6m')\n        self.__matched_maturity_swap_spread6m = value        \n\n    @property\n    def turnover(self) -> float:\n        """Turnover."""\n        return self.__turnover\n\n    @turnover.setter\n    def turnover(self, value: float):\n        self._property_changed('turnover')\n        self.__turnover = value        \n\n    @property\n    def price_spot_target_unit(self) -> str:\n        """Unit in which the target price is reported."""\n        return self.__price_spot_target_unit\n\n    @price_spot_target_unit.setter\n    def price_spot_target_unit(self, value: str):\n        self._property_changed('price_spot_target_unit')\n        self.__price_spot_target_unit = value        \n\n    @property\n    def coverage(self) -> str:\n        """Coverage of dataset."""\n        return self.__coverage\n\n    @coverage.setter\n    def coverage(self, value: str):\n        self._property_changed('coverage')\n        self.__coverage = value        \n\n    @property\n    def g_percentile(self) -> float:\n        """A percentile that captures a company's G ranking relative to the entire ESG\n           universe."""\n        return self.__g_percentile\n\n    @g_percentile.setter\n    def g_percentile(self, value: float):\n        self._property_changed('g_percentile')\n        self.__g_percentile = value        \n\n    @property\n    def cloud_cover_hourly_forecast(self) -> float:\n        """The hourly forecast value for cloud cover."""\n        return self.__cloud_cover_hourly_forecast\n\n    @cloud_cover_hourly_forecast.setter\n    def cloud_cover_hourly_forecast(self, value: float):\n        self._property_changed('cloud_cover_hourly_forecast')\n        self.__cloud_cover_hourly_forecast = value        \n\n    @property\n    def lending_fund_nav(self) -> float:\n        """Net Asset Value of a securities lending fund."""\n        return self.__lending_fund_nav\n\n    @lending_fund_nav.setter\n    def lending_fund_nav(self, value: float):\n        self._property_changed('lending_fund_nav')\n        self.__lending_fund_nav = value        \n\n    @property\n    def source_original_category(self) -> str:\n        """Source category's original name."""\n        return self.__source_original_category\n\n    @source_original_category.setter\n    def source_original_category(self, value: str):\n        self._property_changed('source_original_category')\n        self.__source_original_category = value        \n\n    @property\n    def percent_close_execution_quantity(self) -> float:\n        """Percentage of order filled at auction close."""\n        return self.__percent_close_execution_quantity\n\n    @percent_close_execution_quantity.setter\n    def percent_close_execution_quantity(self, value: float):\n        self._property_changed('percent_close_execution_quantity')\n        self.__percent_close_execution_quantity = value        \n\n    @property\n    def latest_execution_time(self) -> datetime.datetime:\n        """ISO 8601-formatted timestamp"""\n        return self.__latest_execution_time\n\n    @latest_execution_time.setter\n    def latest_execution_time(self, value: datetime.datetime):\n        self._property_changed('latest_execution_time')\n        self.__latest_execution_time = value        \n\n    @property\n    def arrival_mid_realized_bps(self) -> float:\n        """Consolidated realised performance vs Arrival Mid, in bps."""\n        return self.__arrival_mid_realized_bps\n\n    @arrival_mid_realized_bps.setter\n    def arrival_mid_realized_bps(self, value: float):\n        self._property_changed('arrival_mid_realized_bps')\n        self.__arrival_mid_realized_bps = value        \n\n    @property\n    def location(self) -> str:\n        """The location at which a price fixing has been taken."""\n        return self.__location\n\n    @location.setter\n    def location(self, value: str):\n        self._property_changed('location')\n        self.__location = value        \n\n    @property\n    def scenario_id(self) -> str:\n        """Marquee unique scenario identifier"""\n        return self.__scenario_id\n\n    @scenario_id.setter\n    def scenario_id(self, value: str):\n        self._property_changed('scenario_id')\n        self.__scenario_id = value        \n\n    @property\n    def termination_tenor(self) -> str:\n        """Tenor"""\n        return self.__termination_tenor\n\n    @termination_tenor.setter\n    def termination_tenor(self, value: str):\n        self._property_changed('termination_tenor')\n        self.__termination_tenor = value        \n\n    @property\n    def queue_clock_time(self) -> float:\n        """The Queue Clock Time of the stock  on the particular date."""\n        return self.__queue_clock_time\n\n    @queue_clock_time.setter\n    def queue_clock_time(self, value: float):\n        self._property_changed('queue_clock_time')\n        self.__queue_clock_time = value        \n\n    @property\n    def discretion_lower_bound(self) -> float:\n        """The lower bound of the discretion band as published from the algo."""\n        return self.__discretion_lower_bound\n\n    @discretion_lower_bound.setter\n    def discretion_lower_bound(self, value: float):\n        self._property_changed('discretion_lower_bound')\n        self.__discretion_lower_bound = value        \n\n    @property\n    def tcm_cost_participation_rate50_pct(self) -> float:\n        """TCM cost with a 50 percent participation rate."""\n        return self.__tcm_cost_participation_rate50_pct\n\n    @tcm_cost_participation_rate50_pct.setter\n    def tcm_cost_participation_rate50_pct(self, value: float):\n        self._property_changed('tcm_cost_participation_rate50_pct')\n        self.__tcm_cost_participation_rate50_pct = value        \n\n    @property\n    def rating_linear(self) -> float:\n        """Rating of the bond in linear form."""\n        return self.__rating_linear\n\n    @rating_linear.setter\n    def rating_linear(self, value: float):\n        self._property_changed('rating_linear')\n        self.__rating_linear = value        \n\n    @property\n    def previous_close_unrealized_bps(self) -> float:\n        """Unrealised performance vs Previous Close, in bps."""\n        return self.__previous_close_unrealized_bps\n\n    @previous_close_unrealized_bps.setter\n    def previous_close_unrealized_bps(self, value: float):\n        self._property_changed('previous_close_unrealized_bps')\n        self.__previous_close_unrealized_bps = value        \n\n    @property\n    def sub_asset_class_for_other_commodity(self) -> str:\n        """An indication of the sub asset class."""\n        return self.__sub_asset_class_for_other_commodity\n\n    @sub_asset_class_for_other_commodity.setter\n    def sub_asset_class_for_other_commodity(self, value: str):\n        self._property_changed('sub_asset_class_for_other_commodity')\n        self.__sub_asset_class_for_other_commodity = value        \n\n    @property\n    def forward_price(self) -> float:\n        """Trader's estimate for the price of power in MWh."""\n        return self.__forward_price\n\n    @forward_price.setter\n    def forward_price(self, value: float):\n        self._property_changed('forward_price')\n        self.__forward_price = value        \n\n    @property\n    def type(self) -> str:\n        """Asset type differentiates the product categorization or contract type"""\n        return self.__type\n\n    @type.setter\n    def type(self, value: str):\n        self._property_changed('type')\n        self.__type = value        \n\n    @property\n    def strike_ref(self) -> str:\n        """Reference for strike level (enum: spot, forward,delta_call, delta_put,\n           delta_neutral)."""\n        return self.__strike_ref\n\n    @strike_ref.setter\n    def strike_ref(self, value: str):\n        self._property_changed('strike_ref')\n        self.__strike_ref = value        \n\n    @property\n    def cumulative_pnl(self) -> float:\n        """Cumulative PnL from the start date to the current date."""\n        return self.__cumulative_pnl\n\n    @cumulative_pnl.setter\n    def cumulative_pnl(self, value: float):\n        self._property_changed('cumulative_pnl')\n        self.__cumulative_pnl = value        \n\n    @property\n    def short_tenor(self) -> str:\n        """Tenor of instrument."""\n        return self.__short_tenor\n\n    @short_tenor.setter\n    def short_tenor(self, value: str):\n        self._property_changed('short_tenor')\n        self.__short_tenor = value        \n\n    @property\n    def sell28bps(self) -> float:\n        """The amount GS would sell for 28 bps charge."""\n        return self.__sell28bps\n\n    @sell28bps.setter\n    def sell28bps(self, value: float):\n        self._property_changed('sell28bps')\n        self.__sell28bps = value        \n\n    @property\n    def fund_class(self) -> str:\n        """Class of the fund."""\n        return self.__fund_class\n\n    @fund_class.setter\n    def fund_class(self, value: str):\n        self._property_changed('fund_class')\n        self.__fund_class = value        \n\n    @property\n    def unadjusted_volume(self) -> float:\n        """Unadjusted volume traded."""\n        return self.__unadjusted_volume\n\n    @unadjusted_volume.setter\n    def unadjusted_volume(self, value: float):\n        self._property_changed('unadjusted_volume')\n        self.__unadjusted_volume = value        \n\n    @property\n    def buy36bps(self) -> float:\n        """The amount GS would buy for 36 bps charge."""\n        return self.__buy36bps\n\n    @buy36bps.setter\n    def buy36bps(self, value: float):\n        self._property_changed('buy36bps')\n        self.__buy36bps = value        \n\n    @property\n    def position_idx(self) -> int:\n        """The index of the corresponding position in the risk request."""\n        return self.__position_idx\n\n    @position_idx.setter\n    def position_idx(self, value: int):\n        self._property_changed('position_idx')\n        self.__position_idx = value        \n\n    @property\n    def wind_chill_hourly_forecast(self) -> float:\n        """The hourly forecast value for wind chill."""\n        return self.__wind_chill_hourly_forecast\n\n    @wind_chill_hourly_forecast.setter\n    def wind_chill_hourly_forecast(self, value: float):\n        self._property_changed('wind_chill_hourly_forecast')\n        self.__wind_chill_hourly_forecast = value        \n\n    @property\n    def sec_name(self) -> str:\n        """Internal Goldman Sachs security name."""\n        return self.__sec_name\n\n    @sec_name.setter\n    def sec_name(self, value: str):\n        self._property_changed('sec_name')\n        self.__sec_name = value        \n\n    @property\n    def implied_volatility_by_relative_strike(self) -> float:\n        """Volatility of an asset implied by observations of market prices."""\n        return self.__implied_volatility_by_relative_strike\n\n    @implied_volatility_by_relative_strike.setter\n    def implied_volatility_by_relative_strike(self, value: float):\n        self._property_changed('implied_volatility_by_relative_strike')\n        self.__implied_volatility_by_relative_strike = value        \n\n    @property\n    def percent_adv(self) -> float:\n        """Size of trade as percentage of average daily volume (e.g. .05, 1, 2, ..., 20)."""\n        return self.__percent_adv\n\n    @percent_adv.setter\n    def percent_adv(self, value: float):\n        self._property_changed('percent_adv')\n        self.__percent_adv = value        \n\n    @property\n    def leg1_total_notional(self) -> float:\n        """The total Notional amount or quantity of units of the leg 1 underlying asset."""\n        return self.__leg1_total_notional\n\n    @leg1_total_notional.setter\n    def leg1_total_notional(self, value: float):\n        self._property_changed('leg1_total_notional')\n        self.__leg1_total_notional = value        \n\n    @property\n    def contract(self) -> str:\n        """Contract month code and year (e.g. F18)."""\n        return self.__contract\n\n    @contract.setter\n    def contract(self, value: str):\n        self._property_changed('contract')\n        self.__contract = value        \n\n    @property\n    def payment_frequency1(self) -> str:\n        """An integer multiplier of a time period describing how often the parties to the\n           SB swap transaction exchange payments associated with each party???s\n           obligation. Such payment frequency may be described as one letter\n           preceded by an integer."""\n        return self.__payment_frequency1\n\n    @payment_frequency1.setter\n    def payment_frequency1(self, value: str):\n        self._property_changed('payment_frequency1')\n        self.__payment_frequency1 = value        \n\n    @property\n    def payment_frequency2(self) -> str:\n        """Same as Payment Frequency 1."""\n        return self.__payment_frequency2\n\n    @payment_frequency2.setter\n    def payment_frequency2(self, value: str):\n        self._property_changed('payment_frequency2')\n        self.__payment_frequency2 = value        \n\n    @property\n    def bespoke(self) -> str:\n        """Indication if the trade is bespoke."""\n        return self.__bespoke\n\n    @bespoke.setter\n    def bespoke(self, value: str):\n        self._property_changed('bespoke')\n        self.__bespoke = value        \n\n    @property\n    def repo_tenor(self) -> str:\n        """Maturity of repurchase agreement."""\n        return self.__repo_tenor\n\n    @repo_tenor.setter\n    def repo_tenor(self, value: str):\n        self._property_changed('repo_tenor')\n        self.__repo_tenor = value        \n\n    @property\n    def sell15cents(self) -> float:\n        """The amount GS would sell for 15 cents charge."""\n        return self.__sell15cents\n\n    @sell15cents.setter\n    def sell15cents(self, value: float):\n        self._property_changed('sell15cents')\n        self.__sell15cents = value        \n\n    @property\n    def investment_qtd(self) -> float:\n        """Total net investment Quarter to date."""\n        return self.__investment_qtd\n\n    @investment_qtd.setter\n    def investment_qtd(self, value: float):\n        self._property_changed('investment_qtd')\n        self.__investment_qtd = value        \n\n    @property\n    def heat_index_forecast(self) -> float:\n        """The heat index forecast in a given unit."""\n        return self.__heat_index_forecast\n\n    @heat_index_forecast.setter\n    def heat_index_forecast(self, value: float):\n        self._property_changed('heat_index_forecast')\n        self.__heat_index_forecast = value        \n\n    @property\n    def rating_standard_and_poors(self) -> str:\n        """Bond rating from Standard And Poor's."""\n        return self.__rating_standard_and_poors\n\n    @rating_standard_and_poors.setter\n    def rating_standard_and_poors(self, value: str):\n        self._property_changed('rating_standard_and_poors')\n        self.__rating_standard_and_poors = value        \n\n    @property\n    def quality_stars(self) -> float:\n        """Confidence in the BPE."""\n        return self.__quality_stars\n\n    @quality_stars.setter\n    def quality_stars(self, value: float):\n        self._property_changed('quality_stars')\n        self.__quality_stars = value        \n\n    @property\n    def leg2_floating_index(self) -> str:\n        """If floating index leg, the index."""\n        return self.__leg2_floating_index\n\n    @leg2_floating_index.setter\n    def leg2_floating_index(self, value: str):\n        self._property_changed('leg2_floating_index')\n        self.__leg2_floating_index = value        \n\n    @property\n    def source_ticker(self) -> str:\n        """Source ticker."""\n        return self.__source_ticker\n\n    @source_ticker.setter\n    def source_ticker(self, value: str):\n        self._property_changed('source_ticker')\n        self.__source_ticker = value        \n\n    @property\n    def primary_vwap_unrealized_bps(self) -> float:\n        """Unrealised performance vs Primary VWAP, in bps."""\n        return self.__primary_vwap_unrealized_bps\n\n    @primary_vwap_unrealized_bps.setter\n    def primary_vwap_unrealized_bps(self, value: float):\n        self._property_changed('primary_vwap_unrealized_bps')\n        self.__primary_vwap_unrealized_bps = value        \n\n    @property\n    def gsid(self) -> str:\n        """Goldman Sachs internal equity identifier."""\n        return self.__gsid\n\n    @gsid.setter\n    def gsid(self, value: str):\n        self._property_changed('gsid')\n        self.__gsid = value        \n\n    @property\n    def lending_fund(self) -> str:\n        """Name of the lending fund on a securities lending agreement."""\n        return self.__lending_fund\n\n    @lending_fund.setter\n    def lending_fund(self, value: str):\n        self._property_changed('lending_fund')\n        self.__lending_fund = value        \n\n    @property\n    def sensitivity(self) -> float:\n        """Sensitivity."""\n        return self.__sensitivity\n\n    @sensitivity.setter\n    def sensitivity(self, value: float):\n        self._property_changed('sensitivity')\n        self.__sensitivity = value        \n\n    @property\n    def day_count(self) -> str:\n        """The determination of how interest accrues over time for the SB swap."""\n        return self.__day_count\n\n    @day_count.setter\n    def day_count(self, value: str):\n        self._property_changed('day_count')\n        self.__day_count = value        \n\n    @property\n    def sell16bps(self) -> float:\n        """The amount GS would sell for 16 bps charge."""\n        return self.__sell16bps\n\n    @sell16bps.setter\n    def sell16bps(self, value: float):\n        self._property_changed('sell16bps')\n        self.__sell16bps = value        \n\n    @property\n    def relative_break_even_inflation_change(self) -> float:\n        """Relative break even inflation change."""\n        return self.__relative_break_even_inflation_change\n\n    @relative_break_even_inflation_change.setter\n    def relative_break_even_inflation_change(self, value: float):\n        self._property_changed('relative_break_even_inflation_change')\n        self.__relative_break_even_inflation_change = value        \n\n    @property\n    def sell25cents(self) -> float:\n        """The amount GS would sell for 25 cents charge."""\n        return self.__sell25cents\n\n    @sell25cents.setter\n    def sell25cents(self, value: float):\n        self._property_changed('sell25cents')\n        self.__sell25cents = value        \n\n    @property\n    def var_swap(self) -> float:\n        """Strike such that the price of an uncapped variance swap on the underlying index\n           is zero at inception."""\n        return self.__var_swap\n\n    @var_swap.setter\n    def var_swap(self, value: float):\n        self._property_changed('var_swap')\n        self.__var_swap = value        \n\n    @property\n    def buy5point5bps(self) -> float:\n        """The amount GS would buy for 5.5 bps charge."""\n        return self.__buy5point5bps\n\n    @buy5point5bps.setter\n    def buy5point5bps(self, value: float):\n        self._property_changed('buy5point5bps')\n        self.__buy5point5bps = value        \n\n    @property\n    def block_large_notional(self) -> str:\n        """An indication of whether this is a block trade or off-facility swap."""\n        return self.__block_large_notional\n\n    @block_large_notional.setter\n    def block_large_notional(self, value: str):\n        self._property_changed('block_large_notional')\n        self.__block_large_notional = value        \n\n    @property\n    def sell2point5bps(self) -> float:\n        """The amount GS would sell for 2.5 bps charge."""\n        return self.__sell2point5bps\n\n    @sell2point5bps.setter\n    def sell2point5bps(self, value: float):\n        self._property_changed('sell2point5bps')\n        self.__sell2point5bps = value        \n\n    @property\n    def capacity(self) -> str:\n        """Risk/agency execution."""\n        return self.__capacity\n\n    @capacity.setter\n    def capacity(self, value: str):\n        self._property_changed('capacity')\n        self.__capacity = value        \n\n    @property\n    def sectors_raw(self) -> Tuple[str, ...]:\n        """Sector classifications of an asset."""\n        return self.__sectors_raw\n\n    @sectors_raw.setter\n    def sectors_raw(self, value: Tuple[str, ...]):\n        self._property_changed('sectors_raw')\n        self.__sectors_raw = value        \n\n    @property\n    def primary_vwap_in_limit(self) -> float:\n        """Primary VWAP In Limit benchmark price."""\n        return self.__primary_vwap_in_limit\n\n    @primary_vwap_in_limit.setter\n    def primary_vwap_in_limit(self, value: float):\n        self._property_changed('primary_vwap_in_limit')\n        self.__primary_vwap_in_limit = value        \n\n    @property\n    def shareclass_price(self) -> float:\n        """Price of the shareclass on a certain day."""\n        return self.__shareclass_price\n\n    @shareclass_price.setter\n    def shareclass_price(self, value: float):\n        self._property_changed('shareclass_price')\n        self.__shareclass_price = value        \n\n    @property\n    def trade_size(self) -> float:\n        """Size of trade ($mm)."""\n        return self.__trade_size\n\n    @trade_size.setter\n    def trade_size(self, value: float):\n        self._property_changed('trade_size')\n        self.__trade_size = value        \n\n    @property\n    def price_spot_entry_value(self) -> float:\n        """Opening price value of the trade idea."""\n        return self.__price_spot_entry_value\n\n    @price_spot_entry_value.setter\n    def price_spot_entry_value(self, value: float):\n        self._property_changed('price_spot_entry_value')\n        self.__price_spot_entry_value = value        \n\n    @property\n    def buy8point5bps(self) -> float:\n        """The amount GS would buy for 8.5 bps charge."""\n        return self.__buy8point5bps\n\n    @buy8point5bps.setter\n    def buy8point5bps(self, value: float):\n        self._property_changed('buy8point5bps')\n        self.__buy8point5bps = value        \n\n    @property\n    def symbol_dimensions(self) -> Tuple[str, ...]:\n        """Set of fields that determine database table name."""\n        return self.__symbol_dimensions\n\n    @symbol_dimensions.setter\n    def symbol_dimensions(self, value: Tuple[str, ...]):\n        self._property_changed('symbol_dimensions')\n        self.__symbol_dimensions = value        \n\n    @property\n    def buy24bps(self) -> float:\n        """The amount GS would buy for 24 bps charge."""\n        return self.__buy24bps\n\n    @buy24bps.setter\n    def buy24bps(self, value: float):\n        self._property_changed('buy24bps')\n        self.__buy24bps = value        \n\n    @property\n    def observation(self) -> str:\n        """Value of observation."""\n        return self.__observation\n\n    @observation.setter\n    def observation(self, value: str):\n        self._property_changed('observation')\n        self.__observation = value        \n\n    @property\n    def option_type_sdr(self) -> str:\n        """An indication of the type of the option."""\n        return self.__option_type_sdr\n\n    @option_type_sdr.setter\n    def option_type_sdr(self, value: str):\n        self._property_changed('option_type_sdr')\n        self.__option_type_sdr = value        \n\n    @property\n    def scenario_group_id(self) -> str:\n        """Marquee unique scenario group identifier"""\n        return self.__scenario_group_id\n\n    @scenario_group_id.setter\n    def scenario_group_id(self, value: str):\n        self._property_changed('scenario_group_id')\n        self.__scenario_group_id = value        \n\n    @property\n    def average_implied_variance(self) -> float:\n        """Average variance of an asset implied by observations of market prices."""\n        return self.__average_implied_variance\n\n    @average_implied_variance.setter\n    def average_implied_variance(self, value: float):\n        self._property_changed('average_implied_variance')\n        self.__average_implied_variance = value        \n\n    @property\n    def avg_trade_rate_description(self) -> str:\n        """Description of the Stock's Average Trading Rate on the particular date."""\n        return self.__avg_trade_rate_description\n\n    @avg_trade_rate_description.setter\n    def avg_trade_rate_description(self, value: str):\n        self._property_changed('avg_trade_rate_description')\n        self.__avg_trade_rate_description = value        \n\n    @property\n    def fraction(self) -> float:\n        """Fraction."""\n        return self.__fraction\n\n    @fraction.setter\n    def fraction(self, value: float):\n        self._property_changed('fraction')\n        self.__fraction = value        \n\n    @property\n    def asset_count_short(self) -> float:\n        """Number of assets in a portfolio with short exposure."""\n        return self.__asset_count_short\n\n    @asset_count_short.setter\n    def asset_count_short(self, value: float):\n        self._property_changed('asset_count_short')\n        self.__asset_count_short = value        \n\n    @property\n    def collateral_percentage_required(self) -> float:\n        """Collateral percentage requied to cover the given position."""\n        return self.__collateral_percentage_required\n\n    @collateral_percentage_required.setter\n    def collateral_percentage_required(self, value: float):\n        self._property_changed('collateral_percentage_required')\n        self.__collateral_percentage_required = value        \n\n    @property\n    def sell5point5bps(self) -> float:\n        """The amount GS would sell for 5.5 bps charge."""\n        return self.__sell5point5bps\n\n    @sell5point5bps.setter\n    def sell5point5bps(self, value: float):\n        self._property_changed('sell5point5bps')\n        self.__sell5point5bps = value        \n\n    @property\n    def date(self) -> datetime.date:\n        """ISO 8601 formatted date."""\n        return self.__date\n\n    @date.setter\n    def date(self, value: datetime.date):\n        self._property_changed('date')\n        self.__date = value        \n\n    @property\n    def zip_code(self) -> float:\n        """Postal code."""\n        return self.__zip_code\n\n    @zip_code.setter\n    def zip_code(self, value: float):\n        self._property_changed('zip_code')\n        self.__zip_code = value        \n\n    @property\n    def total_std_return_since_inception(self) -> float:\n        """Average annual total returns as of most recent calendar quarter-end."""\n        return self.__total_std_return_since_inception\n\n    @total_std_return_since_inception.setter\n    def total_std_return_since_inception(self, value: float):\n        self._property_changed('total_std_return_since_inception')\n        self.__total_std_return_since_inception = value        \n\n    @property\n    def source_category(self) -> str:\n        """Source category of event."""\n        return self.__source_category\n\n    @source_category.setter\n    def source_category(self, value: str):\n        self._property_changed('source_category')\n        self.__source_category = value        \n\n    @property\n    def volume_unadjusted(self) -> float:\n        """Unadjusted volume traded."""\n        return self.__volume_unadjusted\n\n    @volume_unadjusted.setter\n    def volume_unadjusted(self, value: float):\n        self._property_changed('volume_unadjusted')\n        self.__volume_unadjusted = value        \n\n    @property\n    def passive_ratio(self) -> float:\n        """Ratio of passive ownership (including ETFs and mutual funds), i.e the proportion\n           of a company's marketcap that is held by passive ETFs and mutual\n           funds. Normalised between -3 (low level) and +3 (high level)."""\n        return self.__passive_ratio\n\n    @passive_ratio.setter\n    def passive_ratio(self, value: float):\n        self._property_changed('passive_ratio')\n        self.__passive_ratio = value        \n\n    @property\n    def price_to_earnings(self) -> float:\n        """Price to earnings."""\n        return self.__price_to_earnings\n\n    @price_to_earnings.setter\n    def price_to_earnings(self, value: float):\n        self._property_changed('price_to_earnings')\n        self.__price_to_earnings = value        \n\n    @property\n    def order_depth(self) -> float:\n        """Order book depth level of the corresponding ask or bid."""\n        return self.__order_depth\n\n    @order_depth.setter\n    def order_depth(self, value: float):\n        self._property_changed('order_depth')\n        self.__order_depth = value        \n\n    @property\n    def ann_yield3_month(self) -> float:\n        """Calculates the total return for 3 months, representing past performance."""\n        return self.__ann_yield3_month\n\n    @ann_yield3_month.setter\n    def ann_yield3_month(self, value: float):\n        self._property_changed('ann_yield3_month')\n        self.__ann_yield3_month = value        \n\n    @property\n    def net_flow_std(self) -> float:\n        """Net flow for the asset in standard deviations."""\n        return self.__net_flow_std\n\n    @net_flow_std.setter\n    def net_flow_std(self, value: float):\n        self._property_changed('net_flow_std')\n        self.__net_flow_std = value        \n\n    @property\n    def encoded_stats(self) -> str:\n        """Asset stats object in json format."""\n        return self.__encoded_stats\n\n    @encoded_stats.setter\n    def encoded_stats(self, value: str):\n        self._property_changed('encoded_stats')\n        self.__encoded_stats = value        \n\n    @property\n    def buy5bps(self) -> float:\n        """The amount GS would buy for 5 bps charge."""\n        return self.__buy5bps\n\n    @buy5bps.setter\n    def buy5bps(self, value: float):\n        self._property_changed('buy5bps')\n        self.__buy5bps = value        \n\n    @property\n    def run_time(self) -> float:\n        """Time that a run of the QA checker took."""\n        return self.__run_time\n\n    @run_time.setter\n    def run_time(self, value: float):\n        self._property_changed('run_time')\n        self.__run_time = value        \n\n    @property\n    def ask_size(self) -> float:\n        """The number of shares, lots, or contracts willing to sell at the Ask price."""\n        return self.__ask_size\n\n    @ask_size.setter\n    def ask_size(self, value: float):\n        self._property_changed('ask_size')\n        self.__ask_size = value        \n\n    @property\n    def absolute_return_mtd(self) -> float:\n        """Absolute Return Month to Date."""\n        return self.__absolute_return_mtd\n\n    @absolute_return_mtd.setter\n    def absolute_return_mtd(self, value: float):\n        self._property_changed('absolute_return_mtd')\n        self.__absolute_return_mtd = value        \n\n    @property\n    def std30_days_unsubsidized_yield(self) -> float:\n        """Average annual total returns as of most recent calendar quarter-end."""\n        return self.__std30_days_unsubsidized_yield\n\n    @std30_days_unsubsidized_yield.setter\n    def std30_days_unsubsidized_yield(self, value: float):\n        self._property_changed('std30_days_unsubsidized_yield')\n        self.__std30_days_unsubsidized_yield = value        \n\n    @property\n    def resource(self) -> str:\n        """The event resource. For example: Asset"""\n        return self.__resource\n\n    @resource.setter\n    def resource(self, value: str):\n        self._property_changed('resource')\n        self.__resource = value        \n\n    @property\n    def average_realized_volatility(self) -> float:\n        """Average volatility of an asset realized by observations of market prices."""\n        return self.__average_realized_volatility\n\n    @average_realized_volatility.setter\n    def average_realized_volatility(self, value: float):\n        self._property_changed('average_realized_volatility')\n        self.__average_realized_volatility = value        \n\n    @property\n    def trace_adv_buy(self) -> float:\n        """TRACE ADV for the buy side."""\n        return self.__trace_adv_buy\n\n    @trace_adv_buy.setter\n    def trace_adv_buy(self, value: float):\n        self._property_changed('trace_adv_buy')\n        self.__trace_adv_buy = value        \n\n    @property\n    def new_confirmed(self) -> float:\n        """New cofirmed cases."""\n        return self.__new_confirmed\n\n    @new_confirmed.setter\n    def new_confirmed(self, value: float):\n        self._property_changed('new_confirmed')\n        self.__new_confirmed = value        \n\n    @property\n    def sell8bps(self) -> float:\n        """The amount GS would sell for 8 bps charge."""\n        return self.__sell8bps\n\n    @sell8bps.setter\n    def sell8bps(self, value: float):\n        self._property_changed('sell8bps')\n        self.__sell8bps = value        \n\n    @property\n    def bid_price(self) -> float:\n        """Latest Bid Price (price willing to buy)."""\n        return self.__bid_price\n\n    @bid_price.setter\n    def bid_price(self, value: float):\n        self._property_changed('bid_price')\n        self.__bid_price = value        \n\n    @property\n    def sell8point5bps(self) -> float:\n        """The amount GS would sell for 8.5 bps charge."""\n        return self.__sell8point5bps\n\n    @sell8point5bps.setter\n    def sell8point5bps(self, value: float):\n        self._property_changed('sell8point5bps')\n        self.__sell8point5bps = value        \n\n    @property\n    def target_price_unrealized_bps(self) -> float:\n        """Unrealised performance vs Target Price, in bps."""\n        return self.__target_price_unrealized_bps\n\n    @target_price_unrealized_bps.setter\n    def target_price_unrealized_bps(self, value: float):\n        self._property_changed('target_price_unrealized_bps')\n        self.__target_price_unrealized_bps = value        \n\n    @property\n    def es_numeric_percentile(self) -> float:\n        """A percentile that captures a company's E&S numeric ranking within its subsector."""\n        return self.__es_numeric_percentile\n\n    @es_numeric_percentile.setter\n    def es_numeric_percentile(self, value: float):\n        self._property_changed('es_numeric_percentile')\n        self.__es_numeric_percentile = value        \n\n    @property\n    def leg2_underlying_asset(self) -> str:\n        """The asset, reference asset, or reference obligation for payments of a party???s\n           obligations under the SB swap transaction reference."""\n        return self.__leg2_underlying_asset\n\n    @leg2_underlying_asset.setter\n    def leg2_underlying_asset(self, value: str):\n        self._property_changed('leg2_underlying_asset')\n        self.__leg2_underlying_asset = value        \n\n    @property\n    def csa_terms(self) -> str:\n        """Identifier of terms or rules under which collateral is posted or transferred\n           between swap counterparties (e.g. ccy-1 means LCH clearing with\n           collateral in currency ccy)."""\n        return self.__csa_terms\n\n    @csa_terms.setter\n    def csa_terms(self, value: str):\n        self._property_changed('csa_terms')\n        self.__csa_terms = value        \n\n    @property\n    def relative_payoff_mtd(self) -> float:\n        """Total win divided by total loss Month to date."""\n        return self.__relative_payoff_mtd\n\n    @relative_payoff_mtd.setter\n    def relative_payoff_mtd(self, value: float):\n        self._property_changed('relative_payoff_mtd')\n        self.__relative_payoff_mtd = value        \n\n    @property\n    def daily_net_shareholder_flows(self) -> float:\n        """Cash dividends paid daily."""\n        return self.__daily_net_shareholder_flows\n\n    @daily_net_shareholder_flows.setter\n    def daily_net_shareholder_flows(self, value: float):\n        self._property_changed('daily_net_shareholder_flows')\n        self.__daily_net_shareholder_flows = value        \n\n    @property\n    def buy2point5bps(self) -> float:\n        """The amount GS would buy for 2.5 bps charge."""\n        return self.__buy2point5bps\n\n    @buy2point5bps.setter\n    def buy2point5bps(self, value: float):\n        self._property_changed('buy2point5bps')\n        self.__buy2point5bps = value        \n\n    @property\n    def cai(self) -> float:\n        """Current Activity Indicator (CAI) and Current Activity Indicator Innovations\n           (CAII) for each of the world's large economies and many smaller ones,\n           as well as aggregate CAIs and CAIIs for regions."""\n        return self.__cai\n\n    @cai.setter\n    def cai(self, value: float):\n        self._property_changed('cai')\n        self.__cai = value        \n\n    @property\n    def executed_notional_usd(self) -> float:\n        """Executed notional in USD."""\n        return self.__executed_notional_usd\n\n    @executed_notional_usd.setter\n    def executed_notional_usd(self, value: float):\n        self._property_changed('executed_notional_usd')\n        self.__executed_notional_usd = value        \n\n    @property\n    def system_time(self) -> datetime.datetime:\n        """Time at which an event took place in the system. ISO 8601 formatted string."""\n        return self.__system_time\n\n    @system_time.setter\n    def system_time(self, value: datetime.datetime):\n        self._property_changed('system_time')\n        self.__system_time = value        \n\n    @property\n    def total_home_isolation(self) -> float:\n        """Total number of cases in home isolation."""\n        return self.__total_home_isolation\n\n    @total_home_isolation.setter\n    def total_home_isolation(self, value: float):\n        self._property_changed('total_home_isolation')\n        self.__total_home_isolation = value        \n\n    @property\n    def station_name(self) -> str:\n        """The name of weather station where data is recorded/forecast."""\n        return self.__station_name\n\n    @station_name.setter\n    def station_name(self, value: str):\n        self._property_changed('station_name')\n        self.__station_name = value        \n\n    @property\n    def pass_pct(self) -> float:\n        """Pass percentage."""\n        return self.__pass_pct\n\n    @pass_pct.setter\n    def pass_pct(self, value: float):\n        self._property_changed('pass_pct')\n        self.__pass_pct = value        \n\n    @property\n    def opening_report(self) -> str:\n        """Report that was published when the trade idea was opened."""\n        return self.__opening_report\n\n    @opening_report.setter\n    def opening_report(self, value: str):\n        self._property_changed('opening_report')\n        self.__opening_report = value        \n\n    @property\n    def midcurve_atm_fwd_rate(self) -> float:\n        """Midcurve ATM forward rate."""\n        return self.__midcurve_atm_fwd_rate\n\n    @midcurve_atm_fwd_rate.setter\n    def midcurve_atm_fwd_rate(self, value: float):\n        self._property_changed('midcurve_atm_fwd_rate')\n        self.__midcurve_atm_fwd_rate = value        \n\n    @property\n    def precipitation_forecast(self) -> float:\n        """The forecast value for precipitation."""\n        return self.__precipitation_forecast\n\n    @precipitation_forecast.setter\n    def precipitation_forecast(self, value: float):\n        self._property_changed('precipitation_forecast')\n        self.__precipitation_forecast = value        \n\n    @property\n    def equity_risk_premium_index(self) -> float:\n        """Equity risk premium index: difference between cost of equity and 10y treasury\n           yield."""\n        return self.__equity_risk_premium_index\n\n    @equity_risk_premium_index.setter\n    def equity_risk_premium_index(self, value: float):\n        self._property_changed('equity_risk_premium_index')\n        self.__equity_risk_premium_index = value        \n\n    @property\n    def fatalities_underlying_conditions_unknown(self) -> float:\n        """Total number of fatalities of people that it is unknown whether they had\n           underlying conditions."""\n        return self.__fatalities_underlying_conditions_unknown\n\n    @fatalities_underlying_conditions_unknown.setter\n    def fatalities_underlying_conditions_unknown(self, value: float):\n        self._property_changed('fatalities_underlying_conditions_unknown')\n        self.__fatalities_underlying_conditions_unknown = value        \n\n    @property\n    def buy12bps(self) -> float:\n        """The amount GS would buy for 12 bps charge."""\n        return self.__buy12bps\n\n    @buy12bps.setter\n    def buy12bps(self, value: float):\n        self._property_changed('buy12bps')\n        self.__buy12bps = value        \n\n    @property\n    def clearing_house(self) -> str:\n        """Swap Clearing House"""\n        return self.__clearing_house\n\n    @clearing_house.setter\n    def clearing_house(self, value: str):\n        self._property_changed('clearing_house')\n        self.__clearing_house = value        \n\n    @property\n    def day_close_unrealized_bps(self) -> float:\n        """Unrealized performance vs Close, in bps."""\n        return self.__day_close_unrealized_bps\n\n    @day_close_unrealized_bps.setter\n    def day_close_unrealized_bps(self, value: float):\n        self._property_changed('day_close_unrealized_bps')\n        self.__day_close_unrealized_bps = value        \n\n    @property\n    def sts_rates_maturity(self) -> str:\n        """Risk maturity bucket for STS assets."""\n        return self.__sts_rates_maturity\n\n    @sts_rates_maturity.setter\n    def sts_rates_maturity(self, value: str):\n        self._property_changed('sts_rates_maturity')\n        self.__sts_rates_maturity = value        \n\n    @property\n    def liq_dly(self) -> float:\n        """Percent of assets that could be quickly and easily converted into investable\n           cash without loss of value within a day."""\n        return self.__liq_dly\n\n    @liq_dly.setter\n    def liq_dly(self, value: float):\n        self._property_changed('liq_dly')\n        self.__liq_dly = value        \n\n    @property\n    def contributor_role(self) -> str:\n        """Role (specialist / generalist..) of a contributor."""\n        return self.__contributor_role\n\n    @contributor_role.setter\n    def contributor_role(self, value: str):\n        self._property_changed('contributor_role')\n        self.__contributor_role = value        \n\n    @property\n    def total_fatalities(self) -> float:\n        """Total number of fatalities."""\n        return self.__total_fatalities\n\n    @total_fatalities.setter\n    def total_fatalities(self, value: float):\n        self._property_changed('total_fatalities')\n        self.__total_fatalities = value        \n\n\nclass IndexCurveShift(Scenario):\n        \n    """A scenario to manipulate index curve shape"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        market_data_pattern: MarketDataPattern = None,\n        annualised_parallel_shift: float = None,\n        annualised_slope_shift: float = None,\n        cutoff: float = None,\n        floor: float = None,\n        tenor: str = None,\n        rate_option: str = None,\n        bucket_shift: float = None,\n        bucket_start: datetime.date = None,\n        bucket_end: datetime.date = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.market_data_pattern = market_data_pattern\n        self.annualised_parallel_shift = annualised_parallel_shift\n        self.annualised_slope_shift = annualised_slope_shift\n        self.cutoff = cutoff\n        self.floor = floor\n        self.tenor = tenor\n        self.rate_option = rate_option\n        self.bucket_shift = bucket_shift\n        self.bucket_start = bucket_start\n        self.bucket_end = bucket_end\n        self.name = name\n\n    @property\n    def scenario_type(self) -> str:\n        """IndexCurveShift"""\n        return 'IndexCurveShift'        \n\n    @property\n    def market_data_pattern(self) -> MarketDataPattern:\n        """Market pattern for matching curve assets"""\n        return self.__market_data_pattern\n\n    @market_data_pattern.setter\n    def market_data_pattern(self, value: MarketDataPattern):\n        self._property_changed('market_data_pattern')\n        self.__market_data_pattern = value        \n\n    @property\n    def annualised_parallel_shift(self) -> float:\n        """Size of the parallel shift (in bps/year)"""\n        return self.__annualised_parallel_shift\n\n    @annualised_parallel_shift.setter\n    def annualised_parallel_shift(self, value: float):\n        self._property_changed('annualised_parallel_shift')\n        self.__annualised_parallel_shift = value        \n\n    @property\n    def annualised_slope_shift(self) -> float:\n        """Size of the slope shift (in bps/year)"""\n        return self.__annualised_slope_shift\n\n    @annualised_slope_shift.setter\n    def annualised_slope_shift(self, value: float):\n        self._property_changed('annualised_slope_shift')\n        self.__annualised_slope_shift = value        \n\n    @property\n    def cutoff(self) -> float:\n        """The cutoff point (in years)"""\n        return self.__cutoff\n\n    @cutoff.setter\n    def cutoff(self, value: float):\n        self._property_changed('cutoff')\n        self.__cutoff = value        \n\n    @property\n    def floor(self) -> float:\n        """The floor value (in bps)"""\n        return self.__floor\n\n    @floor.setter\n    def floor(self, value: float):\n        self._property_changed('floor')\n        self.__floor = value        \n\n    @property\n    def tenor(self) -> str:\n        """Tenor of rate option to which shock is applied"""\n        return self.__tenor\n\n    @tenor.setter\n    def tenor(self, value: str):\n        self._property_changed('tenor')\n        self.__tenor = value        \n\n    @property\n    def rate_option(self) -> str:\n        """Rate option to which shock is applied"""\n        return self.__rate_option\n\n    @rate_option.setter\n    def rate_option(self, value: str):\n        self._property_changed('rate_option')\n        self.__rate_option = value        \n\n    @property\n    def bucket_shift(self) -> float:\n        """Size of the bucket shift (in bps)"""\n        return self.__bucket_shift\n\n    @bucket_shift.setter\n    def bucket_shift(self, value: float):\n        self._property_changed('bucket_shift')\n        self.__bucket_shift = value        \n\n    @property\n    def bucket_start(self) -> datetime.date:\n        """The start date of the custom bucket"""\n        return self.__bucket_start\n\n    @bucket_start.setter\n    def bucket_start(self, value: datetime.date):\n        self._property_changed('bucket_start')\n        self.__bucket_start = value        \n\n    @property\n    def bucket_end(self) -> datetime.date:\n        """The end date of the custom bucket"""\n        return self.__bucket_end\n\n    @bucket_end.setter\n    def bucket_end(self, value: datetime.date):\n        self._property_changed('bucket_end')\n        self.__bucket_end = value        \n\n\nclass MarketDataPatternAndShock(Base):\n        \n    """A shock to apply to market coordinate values matching the supplied pattern"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pattern: MarketDataPattern,\n        shock: MarketDataShock,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pattern = pattern\n        self.shock = shock\n        self.name = name\n\n    @property\n    def pattern(self) -> MarketDataPattern:\n        """A pattern used to match market coordinates"""\n        return self.__pattern\n\n    @pattern.setter\n    def pattern(self, value: MarketDataPattern):\n        self._property_changed('pattern')\n        self.__pattern = value        \n\n    @property\n    def shock(self) -> MarketDataShock:\n        """A shock to apply to market coordinate values"""\n        return self.__shock\n\n    @shock.setter\n    def shock(self, value: MarketDataShock):\n        self._property_changed('shock')\n        self.__shock = value        \n\n\nclass PCOExposureLeg(Base):\n        \n    """Parameters required for PCO Exposure Leg"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        local_to_base_rate: str = None,\n        local_nav_limits: Tuple[str, ...] = None,\n        base_nav_limits: Tuple[str, ...] = None,\n        all_approved_hedge_ratio: str = None,\n        show_all_approved_hedge_ratio: bool = None,\n        hedge_ratio: str = None,\n        exposure_ratio: str = None,\n        local_currency: Union[Currency, str] = None,\n        target_ratio: str = None,\n        benchmark: PCOBenchmark = None,\n        long_rebalance_threshold: str = None,\n        short_rebalance_threshold: str = None,\n        auto_roll: bool = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.local_to_base_rate = local_to_base_rate\n        self.local_nav_limits = local_nav_limits\n        self.base_nav_limits = base_nav_limits\n        self.all_approved_hedge_ratio = all_approved_hedge_ratio\n        self.show_all_approved_hedge_ratio = show_all_approved_hedge_ratio\n        self.hedge_ratio = hedge_ratio\n        self.exposure_ratio = exposure_ratio\n        self.local_currency = local_currency\n        self.target_ratio = target_ratio\n        self.benchmark = benchmark\n        self.long_rebalance_threshold = long_rebalance_threshold\n        self.short_rebalance_threshold = short_rebalance_threshold\n        self.auto_roll = auto_roll\n        self.name = name\n\n    @property\n    def local_to_base_rate(self) -> str:\n        """Previously day FX spot rates for each currency pair"""\n        return self.__local_to_base_rate\n\n    @local_to_base_rate.setter\n    def local_to_base_rate(self, value: str):\n        self._property_changed('local_to_base_rate')\n        self.__local_to_base_rate = value        \n\n    @property\n    def local_nav_limits(self) -> Tuple[str, ...]:\n        """Net Asset Value limits for local currency"""\n        return self.__local_nav_limits\n\n    @local_nav_limits.setter\n    def local_nav_limits(self, value: Tuple[str, ...]):\n        self._property_changed('local_nav_limits')\n        self.__local_nav_limits = value        \n\n    @property\n    def base_nav_limits(self) -> Tuple[str, ...]:\n        """Net Asset Value limits for base currency"""\n        return self.__base_nav_limits\n\n    @base_nav_limits.setter\n    def base_nav_limits(self, value: Tuple[str, ...]):\n        self._property_changed('base_nav_limits')\n        self.__base_nav_limits = value        \n\n    @property\n    def all_approved_hedge_ratio(self) -> str:\n        """Projected hedge ratio"""\n        return self.__all_approved_hedge_ratio\n\n    @all_approved_hedge_ratio.setter\n    def all_approved_hedge_ratio(self, value: str):\n        self._property_changed('all_approved_hedge_ratio')\n        self.__all_approved_hedge_ratio = value        \n\n    @property\n    def show_all_approved_hedge_ratio(self) -> bool:\n        """If UI displays projected hedge ratio"""\n        return self.__show_all_approved_hedge_ratio\n\n    @show_all_approved_hedge_ratio.setter\n    def show_all_approved_hedge_ratio(self, value: bool):\n        self._property_changed('show_all_approved_hedge_ratio')\n        self.__show_all_approved_hedge_ratio = value        \n\n    @property\n    def hedge_ratio(self) -> str:\n        """Ratio of target exposure that is intended to be hedged"""\n        return self.__hedge_ratio\n\n    @hedge_ratio.setter\n    def hedge_ratio(self, value: str):\n        self._property_changed('hedge_ratio')\n        self.__hedge_ratio = value        \n\n    @property\n    def exposure_ratio(self) -> str:\n        """Exposure ratio"""\n        return self.__exposure_ratio\n\n    @exposure_ratio.setter\n    def exposure_ratio(self, value: str):\n        self._property_changed('exposure_ratio')\n        self.__exposure_ratio = value        \n\n    @property\n    def local_currency(self) -> Union[Currency, str]:\n        """Local currency"""\n        return self.__local_currency\n\n    @local_currency.setter\n    def local_currency(self, value: Union[Currency, str]):\n        self._property_changed('local_currency')\n        self.__local_currency = get_enum_value(Currency, value)        \n\n    @property\n    def target_ratio(self) -> str:\n        """Target hedge ratio for each currency"""\n        return self.__target_ratio\n\n    @target_ratio.setter\n    def target_ratio(self, value: str):\n        self._property_changed('target_ratio')\n        self.__target_ratio = value        \n\n    @property\n    def benchmark(self) -> PCOBenchmark:\n        """Benchmark used for each currency"""\n        return self.__benchmark\n\n    @benchmark.setter\n    def benchmark(self, value: PCOBenchmark):\n        self._property_changed('benchmark')\n        self.__benchmark = value        \n\n    @property\n    def long_rebalance_threshold(self) -> str:\n        """Long threshold for TNA adjustment for each currency"""\n        return self.__long_rebalance_threshold\n\n    @long_rebalance_threshold.setter\n    def long_rebalance_threshold(self, value: str):\n        self._property_changed('long_rebalance_threshold')\n        self.__long_rebalance_threshold = value        \n\n    @property\n    def short_rebalance_threshold(self) -> str:\n        """Short threshold for TNA adjustment for each currency"""\n        return self.__short_rebalance_threshold\n\n    @short_rebalance_threshold.setter\n    def short_rebalance_threshold(self, value: str):\n        self._property_changed('short_rebalance_threshold')\n        self.__short_rebalance_threshold = value        \n\n    @property\n    def auto_roll(self) -> bool:\n        """Whether roll orders will be automatically generated for each currency"""\n        return self.__auto_roll\n\n    @auto_roll.setter\n    def auto_roll(self, value: bool):\n        self._property_changed('auto_roll')\n        self.__auto_roll = value        \n\n\nclass CSLScheduleArray(Base):\n        \n    """An array of schedules"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        schedule_values: Tuple[CSLSchedule, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.schedule_values = schedule_values\n        self.name = name\n\n    @property\n    def schedule_values(self) -> Tuple[CSLSchedule, ...]:\n        """A schedule"""\n        return self.__schedule_values\n\n    @schedule_values.setter\n    def schedule_values(self, value: Tuple[CSLSchedule, ...]):\n        self._property_changed('schedule_values')\n        self.__schedule_values = value        \n\n\nclass LiquidityRequest(Base):\n        \n    """Required parameters in order to get liquidity information on a set of positions"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        notional: float = None,\n        positions: dict = None,\n        risk_model: str = None,\n        date: datetime.date = None,\n        currency: Union[Currency, str] = None,\n        participation_rate: float = None,\n        execution_horizon: float = None,\n        execution_start_time: datetime.datetime = None,\n        execution_end_time: datetime.datetime = None,\n        benchmark_id: str = None,\n        measures: Tuple[Union[LiquidityMeasure, str], ...] = None,\n        time_series_benchmark_ids: Tuple[str, ...] = None,\n        time_series_start_date: datetime.date = None,\n        time_series_end_date: datetime.date = None,\n        format_: Union[Format, str] = None,\n        report_parameters: LiquidityReportParameters = None,\n        explode_positions: bool = False,\n        name: str = None\n    ):        \n        super().__init__()\n        self.notional = notional\n        self.positions = positions\n        self.risk_model = risk_model\n        self.date = date\n        self.currency = currency\n        self.participation_rate = participation_rate\n        self.execution_horizon = execution_horizon\n        self.execution_start_time = execution_start_time\n        self.execution_end_time = execution_end_time\n        self.benchmark_id = benchmark_id\n        self.measures = measures\n        self.time_series_benchmark_ids = time_series_benchmark_ids\n        self.time_series_start_date = time_series_start_date\n        self.time_series_end_date = time_series_end_date\n        self.__format = get_enum_value(Format, format_)\n        self.report_parameters = report_parameters\n        self.explode_positions = explode_positions\n        self.name = name\n\n    @property\n    def notional(self) -> float:\n        """Notional value of the positions."""\n        return self.__notional\n\n    @notional.setter\n    def notional(self, value: float):\n        self._property_changed('notional')\n        self.__notional = value        \n\n    @property\n    def positions(self) -> dict:\n        """A set of quantity or weighted positions."""\n        return self.__positions\n\n    @positions.setter\n    def positions(self, value: dict):\n        self._property_changed('positions')\n        self.__positions = value        \n\n    @property\n    def risk_model(self) -> str:\n        """Marquee unique risk model identifier"""\n        return self.__risk_model\n\n    @risk_model.setter\n    def risk_model(self, value: str):\n        self._property_changed('risk_model')\n        self.__risk_model = value        \n\n    @property\n    def date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__date\n\n    @date.setter\n    def date(self, value: datetime.date):\n        self._property_changed('date')\n        self.__date = value        \n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def participation_rate(self) -> float:\n        return self.__participation_rate\n\n    @participation_rate.setter\n    def participation_rate(self, value: float):\n        self._property_changed('participation_rate')\n        self.__participation_rate = value        \n\n    @property\n    def execution_horizon(self) -> float:\n        return self.__execution_horizon\n\n    @execution_horizon.setter\n    def execution_horizon(self, value: float):\n        self._property_changed('execution_horizon')\n        self.__execution_horizon = value        \n\n    @property\n    def execution_start_time(self) -> datetime.datetime:\n        """ISO 8601-formatted timestamp"""\n        return self.__execution_start_time\n\n    @execution_start_time.setter\n    def execution_start_time(self, value: datetime.datetime):\n        self._property_changed('execution_start_time')\n        self.__execution_start_time = value        \n\n    @property\n    def execution_end_time(self) -> datetime.datetime:\n        """ISO 8601-formatted timestamp"""\n        return self.__execution_end_time\n\n    @execution_end_time.setter\n    def execution_end_time(self, value: datetime.datetime):\n        self._property_changed('execution_end_time')\n        self.__execution_end_time = value        \n\n    @property\n    def benchmark_id(self) -> str:\n        """Marquee unique asset identifier of the benchmark."""\n        return self.__benchmark_id\n\n    @benchmark_id.setter\n    def benchmark_id(self, value: str):\n        self._property_changed('benchmark_id')\n        self.__benchmark_id = value        \n\n    @property\n    def measures(self) -> Tuple[Union[LiquidityMeasure, str], ...]:\n        return self.__measures\n\n    @measures.setter\n    def measures(self, value: Tuple[Union[LiquidityMeasure, str], ...]):\n        self._property_changed('measures')\n        self.__measures = value        \n\n    @property\n    def time_series_benchmark_ids(self) -> Tuple[str, ...]:\n        """Marquee unique identifiers of assets to be used as benchmarks."""\n        return self.__time_series_benchmark_ids\n\n    @time_series_benchmark_ids.setter\n    def time_series_benchmark_ids(self, value: Tuple[str, ...]):\n        self._property_changed('time_series_benchmark_ids')\n        self.__time_series_benchmark_ids = value        \n\n    @property\n    def time_series_start_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__time_series_start_date\n\n    @time_series_start_date.setter\n    def time_series_start_date(self, value: datetime.date):\n        self._property_changed('time_series_start_date')\n        self.__time_series_start_date = value        \n\n    @property\n    def time_series_end_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__time_series_end_date\n\n    @time_series_end_date.setter\n    def time_series_end_date(self, value: datetime.date):\n        self._property_changed('time_series_end_date')\n        self.__time_series_end_date = value        \n\n    @property\n    def format(self) -> Union[Format, str]:\n        """Alternative format for data to be returned in"""\n        return self.__format\n\n    @format.setter\n    def format(self, value: Union[Format, str]):\n        self._property_changed('format')\n        self.__format = get_enum_value(Format, value)        \n\n    @property\n    def report_parameters(self) -> LiquidityReportParameters:\n        """Parameters to be used on liquidity reports"""\n        return self.__report_parameters\n\n    @report_parameters.setter\n    def report_parameters(self, value: LiquidityReportParameters):\n        self._property_changed('report_parameters')\n        self.__report_parameters = value        \n\n    @property\n    def explode_positions(self) -> bool:\n        """Flag determining whether the positions should be exploded before doing\n           calculations."""\n        return self.__explode_positions\n\n    @explode_positions.setter\n    def explode_positions(self, value: bool):\n        self._property_changed('explode_positions')\n        self.__explode_positions = value        \n\n\nclass MarketDataShockBasedScenario(Scenario):\n        \n    """A scenario comprised of user-defined market data shocks"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        shocks: Tuple[MarketDataPatternAndShock, ...],\n        name: str = None\n    ):        \n        super().__init__()\n        self.shocks = shocks\n        self.name = name\n\n    @property\n    def scenario_type(self) -> str:\n        """MarketDataShockBasedScenario"""\n        return 'MarketDataShockBasedScenario'        \n\n    @property\n    def shocks(self) -> Tuple[MarketDataPatternAndShock, ...]:\n        """A shock to apply to market coordinate values matching the supplied pattern"""\n        return self.__shocks\n\n    @shocks.setter\n    def shocks(self, value: Tuple[MarketDataPatternAndShock, ...]):\n        self._property_changed('shocks')\n        self.__shocks = value        \n\n\nclass OverlayMarket(Base):\n        \n    """A market with explicit coordinate values overlayed over a base market"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        base_market: Union[CloseMarket, LiveMarket, TimestampedMarket],\n        market_data: Tuple[MarketDataCoordinateValue, ...],\n        name: str = None\n    ):        \n        super().__init__()\n        self.base_market = base_market\n        self.market_data = market_data\n        self.name = name\n\n    @property\n    def market_type(self) -> str:\n        """OverlayMarket"""\n        return 'OverlayMarket'        \n\n    @property\n    def base_market(self) -> Union[CloseMarket, LiveMarket, TimestampedMarket]:\n        """The base market"""\n        return self.__base_market\n\n    @base_market.setter\n    def base_market(self, value: Union[CloseMarket, LiveMarket, TimestampedMarket]):\n        self._property_changed('base_market')\n        self.__base_market = value        \n\n    @property\n    def market_data(self) -> Tuple[MarketDataCoordinateValue, ...]:\n        """Market data to overlay over the base market"""\n        return self.__market_data\n\n    @market_data.setter\n    def market_data(self, value: Tuple[MarketDataCoordinateValue, ...]):\n        self._property_changed('market_data')\n        self.__market_data = value        \n\n\nclass PCOExposure(Base):\n        \n    """Parameters required for PCO Exposure"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        last_data_updated_date_time: datetime.datetime = None,\n        nav_includes_fx_hedges: bool = None,\n        use_fx_rate_on_base_fx_forward: bool = None,\n        last_generate_orders_date_time: datetime.datetime = None,\n        legs: Tuple[PCOExposureLeg, ...] = None,\n        adjustments: PCOExposureAdjustments = None,\n        ratio_mode: str = None,\n        hedge_calc_currency: Union[PCOCurrencyType, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.last_data_updated_date_time = last_data_updated_date_time\n        self.nav_includes_fx_hedges = nav_includes_fx_hedges\n        self.use_fx_rate_on_base_fx_forward = use_fx_rate_on_base_fx_forward\n        self.last_generate_orders_date_time = last_generate_orders_date_time\n        self.legs = legs\n        self.adjustments = adjustments\n        self.ratio_mode = ratio_mode\n        self.hedge_calc_currency = hedge_calc_currency\n        self.name = name\n\n    @property\n    def last_data_updated_date_time(self) -> datetime.datetime:\n        """Last time when data was updated"""\n        return self.__last_data_updated_date_time\n\n    @last_data_updated_date_time.setter\n    def last_data_updated_date_time(self, value: datetime.datetime):\n        self._property_changed('last_data_updated_date_time')\n        self.__last_data_updated_date_time = value        \n\n    @property\n    def nav_includes_fx_hedges(self) -> bool:\n        """Whether Net Asset Value includes FX hedges"""\n        return self.__nav_includes_fx_hedges\n\n    @nav_includes_fx_hedges.setter\n    def nav_includes_fx_hedges(self, value: bool):\n        self._property_changed('nav_includes_fx_hedges')\n        self.__nav_includes_fx_hedges = value        \n\n    @property\n    def use_fx_rate_on_base_fx_forward(self) -> bool:\n        """Use open hedge in notional of base currency or local currency"""\n        return self.__use_fx_rate_on_base_fx_forward\n\n    @use_fx_rate_on_base_fx_forward.setter\n    def use_fx_rate_on_base_fx_forward(self, value: bool):\n        self._property_changed('use_fx_rate_on_base_fx_forward')\n        self.__use_fx_rate_on_base_fx_forward = value        \n\n    @property\n    def last_generate_orders_date_time(self) -> datetime.datetime:\n        """Last time when orders are generated"""\n        return self.__last_generate_orders_date_time\n\n    @last_generate_orders_date_time.setter\n    def last_generate_orders_date_time(self, value: datetime.datetime):\n        self._property_changed('last_generate_orders_date_time')\n        self.__last_generate_orders_date_time = value        \n\n    @property\n    def legs(self) -> Tuple[PCOExposureLeg, ...]:\n        """Exposure details for each leg"""\n        return self.__legs\n\n    @legs.setter\n    def legs(self, value: Tuple[PCOExposureLeg, ...]):\n        self._property_changed('legs')\n        self.__legs = value        \n\n    @property\n    def adjustments(self) -> PCOExposureAdjustments:\n        """Exposure adjustments"""\n        return self.__adjustments\n\n    @adjustments.setter\n    def adjustments(self, value: PCOExposureAdjustments):\n        self._property_changed('adjustments')\n        self.__adjustments = value        \n\n    @property\n    def ratio_mode(self) -> str:\n        """One of hedge ratio or exposure ratio"""\n        return self.__ratio_mode\n\n    @ratio_mode.setter\n    def ratio_mode(self, value: str):\n        self._property_changed('ratio_mode')\n        self.__ratio_mode = value        \n\n    @property\n    def hedge_calc_currency(self) -> Union[PCOCurrencyType, str]:\n        """One of Local and Base"""\n        return self.__hedge_calc_currency\n\n    @hedge_calc_currency.setter\n    def hedge_calc_currency(self, value: Union[PCOCurrencyType, str]):\n        self._property_changed('hedge_calc_currency')\n        self.__hedge_calc_currency = get_enum_value(PCOCurrencyType, value)        \n\n\nclass PositionSet(Base):\n        \n    @camel_case_translate\n    def __init__(\n        self,\n        positions: Tuple[Position, ...],\n        position_date: datetime.date,\n        id_: str = None,\n        last_update_time: datetime.datetime = None,\n        type_: str = None,\n        divisor: float = None,\n        last_updated_time: datetime.datetime = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.__id = id_\n        self.position_date = position_date\n        self.last_update_time = last_update_time\n        self.positions = positions\n        self.__type = type_\n        self.divisor = divisor\n        self.last_updated_time = last_updated_time\n        self.name = name\n\n    @property\n    def id(self) -> str:\n        """Unique identifier"""\n        return self.__id\n\n    @id.setter\n    def id(self, value: str):\n        self._property_changed('id')\n        self.__id = value        \n\n    @property\n    def position_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__position_date\n\n    @position_date.setter\n    def position_date(self, value: datetime.date):\n        self._property_changed('position_date')\n        self.__position_date = value        \n\n    @property\n    def last_update_time(self) -> datetime.datetime:\n        """ISO 8601-formatted timestamp"""\n        return self.__last_update_time\n\n    @last_update_time.setter\n    def last_update_time(self, value: datetime.datetime):\n        self._property_changed('last_update_time')\n        self.__last_update_time = value        \n\n    @property\n    def positions(self) -> Tuple[Position, ...]:\n        """Array of quantity position objects."""\n        return self.__positions\n\n    @positions.setter\n    def positions(self, value: Tuple[Position, ...]):\n        self._property_changed('positions')\n        self.__positions = value        \n\n    @property\n    def type(self) -> str:\n        """The composition type of a Portfolio"""\n        return self.__type\n\n    @type.setter\n    def type(self, value: str):\n        self._property_changed('type')\n        self.__type = value        \n\n    @property\n    def divisor(self) -> float:\n        """optional index divisor for a position set"""\n        return self.__divisor\n\n    @divisor.setter\n    def divisor(self, value: float):\n        self._property_changed('divisor')\n        self.__divisor = value        \n\n    @property\n    def last_updated_time(self) -> datetime.datetime:\n        """Timestamp of when the object was last updated."""\n        return self.__last_updated_time\n\n    @last_updated_time.setter\n    def last_updated_time(self, value: datetime.datetime):\n        self._property_changed('last_updated_time')\n        self.__last_updated_time = value        \n\n\nclass RelativeMarket(Base):\n        \n    """Market for pricing between two states (e.g. for PnlExplain)"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        from_market: Union[OverlayMarket, Union[CloseMarket, LiveMarket, TimestampedMarket]],\n        to_market: Union[OverlayMarket, Union[CloseMarket, LiveMarket, TimestampedMarket]],\n        name: str = None\n    ):        \n        super().__init__()\n        self.from_market = from_market\n        self.to_market = to_market\n        self.name = name\n\n    @property\n    def market_type(self) -> str:\n        """RelativeMarket"""\n        return 'RelativeMarket'        \n\n    @property\n    def from_market(self) -> Union[OverlayMarket, Union[CloseMarket, LiveMarket, TimestampedMarket]]:\n        """The base market"""\n        return self.__from_market\n\n    @from_market.setter\n    def from_market(self, value: Union[OverlayMarket, Union[CloseMarket, LiveMarket, TimestampedMarket]]):\n        self._property_changed('from_market')\n        self.__from_market = value        \n\n    @property\n    def to_market(self) -> Union[OverlayMarket, Union[CloseMarket, LiveMarket, TimestampedMarket]]:\n        """The target market"""\n        return self.__to_market\n\n    @to_market.setter\n    def to_market(self, value: Union[OverlayMarket, Union[CloseMarket, LiveMarket, TimestampedMarket]]):\n        self._property_changed('to_market')\n        self.__to_market = value        \n\n\nclass PricingDateAndMarketDataAsOf(Base):\n        \n    """Pricing date and market data as of (date or time)"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pricing_date: datetime.date,\n        market_data_as_of: Union[datetime.date, datetime.datetime] = None,\n        market: dict = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pricing_date = pricing_date\n        self.market_data_as_of = market_data_as_of\n        self.market = market\n        self.name = name\n\n    @property\n    def pricing_date(self) -> datetime.date:\n        """The date for which to perform the calculation"""\n        return self.__pricing_date\n\n    @pricing_date.setter\n    def pricing_date(self, value: datetime.date):\n        self._property_changed('pricing_date')\n        self.__pricing_date = value        \n\n    @property\n    def market_data_as_of(self) -> Union[datetime.date, datetime.datetime]:\n        """The date or time to source market data"""\n        return self.__market_data_as_of\n\n    @market_data_as_of.setter\n    def market_data_as_of(self, value: Union[datetime.date, datetime.datetime]):\n        self._property_changed('market_data_as_of')\n        self.__market_data_as_of = value        \n\n    @property\n    def market(self) -> dict:\n        """The market used for pricing"""\n        return self.__market\n\n    @market.setter\n    def market(self, value: dict):\n        self._property_changed('market')\n        self.__market = value        \n\n\nclass RiskRequest(Base):\n        \n    """Object representation of a risk calculation request"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        positions: Tuple[RiskPosition, ...],\n        measures: Tuple[RiskMeasure, ...],\n        pricing_and_market_data_as_of: Tuple[PricingDateAndMarketDataAsOf, ...] = None,\n        pricing_location: Union[PricingLocation, str] = None,\n        wait_for_results: bool = False,\n        scenario: MarketDataScenario = None,\n        parameters: RiskRequestParameters = None,\n        request_visible_to_gs: bool = False,\n        name: str = None\n    ):        \n        super().__init__()\n        self.positions = positions\n        self.measures = measures\n        self.pricing_and_market_data_as_of = pricing_and_market_data_as_of\n        self.pricing_location = pricing_location\n        self.wait_for_results = wait_for_results\n        self.scenario = scenario\n        self.parameters = parameters\n        self.request_visible_to_gs = request_visible_to_gs\n        self.name = name\n\n    @property\n    def positions(self) -> Tuple[RiskPosition, ...]:\n        """The positions on which to run the risk calculation"""\n        return self.__positions\n\n    @positions.setter\n    def positions(self, value: Tuple[RiskPosition, ...]):\n        self._property_changed('positions')\n        self.__positions = value        \n\n    @property\n    def measures(self) -> Tuple[RiskMeasure, ...]:\n        """A collection of risk measures to compute. E.g. { 'measureType': 'Delta',\n           'assetClass': 'Equity'"""\n        return self.__measures\n\n    @measures.setter\n    def measures(self, value: Tuple[RiskMeasure, ...]):\n        self._property_changed('measures')\n        self.__measures = value        \n\n    @property\n    def pricing_and_market_data_as_of(self) -> Tuple[PricingDateAndMarketDataAsOf, ...]:\n        """Pricing date and market data as of (date or time)"""\n        return self.__pricing_and_market_data_as_of\n\n    @pricing_and_market_data_as_of.setter\n    def pricing_and_market_data_as_of(self, value: Tuple[PricingDateAndMarketDataAsOf, ...]):\n        self._property_changed('pricing_and_market_data_as_of')\n        self.__pricing_and_market_data_as_of = value        \n\n    @property\n    def pricing_location(self) -> Union[PricingLocation, str]:\n        """The location for pricing and market data"""\n        return self.__pricing_location\n\n    @pricing_location.setter\n    def pricing_location(self, value: Union[PricingLocation, str]):\n        self._property_changed('pricing_location')\n        self.__pricing_location = get_enum_value(PricingLocation, value)        \n\n    @property\n    def wait_for_results(self) -> bool:\n        """For short-running requests this may be set to true and the results will be\n           returned directly. If false, the response will contain the Id to\n           retrieve the results"""\n        return self.__wait_for_results\n\n    @wait_for_results.setter\n    def wait_for_results(self, value: bool):\n        self._property_changed('wait_for_results')\n        self.__wait_for_results = value        \n\n    @property\n    def scenario(self) -> MarketDataScenario:\n        """A market data scenario to apply to the calculation"""\n        return self.__scenario\n\n    @scenario.setter\n    def scenario(self, value: MarketDataScenario):\n        self._property_changed('scenario')\n        self.__scenario = value        \n\n    @property\n    def parameters(self) -> RiskRequestParameters:\n        """Parameters for the risk request"""\n        return self.__parameters\n\n    @parameters.setter\n    def parameters(self, value: RiskRequestParameters):\n        self._property_changed('parameters')\n        self.__parameters = value        \n\n    @property\n    def request_visible_to_gs(self) -> bool:\n        return self.__request_visible_to_gs\n\n    @request_visible_to_gs.setter\n    def request_visible_to_gs(self, value: bool):\n        self._property_changed('request_visible_to_gs')\n        self.__request_visible_to_gs = value        \n\n\nclass ReportParameters(Base):\n        \n    """Parameters specific to the report type"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        asset_class: Union[AssetClass, str] = None,\n        transaction_cost_model: str = None,\n        trading_cost: float = None,\n        servicing_cost_long: float = None,\n        servicing_cost_short: float = None,\n        region: str = None,\n        risk_model: str = None,\n        fx_hedged: bool = None,\n        publish_to_bloomberg: bool = None,\n        publish_to_reuters: bool = None,\n        publish_to_factset: bool = None,\n        include_price_history: bool = None,\n        index_update: bool = None,\n        index_rebalance: bool = None,\n        basket_action: Union[BasketAction, str] = None,\n        api_domain: bool = None,\n        initial_price: float = None,\n        stock_level_exposures: bool = None,\n        explode_positions: bool = None,\n        scenario_id: str = None,\n        scenario_ids: Tuple[str, ...] = None,\n        scenario_group_id: str = None,\n        scenario_type: Union[ScenarioType, str] = None,\n        market_model_id: str = None,\n        risk_measures: Tuple[RiskMeasure, ...] = None,\n        initial_pricing_date: datetime.date = None,\n        backcast: bool = None,\n        risk_request: RiskRequest = None,\n        participation_rate: float = None,\n        approve_rebalance: bool = None,\n        use_risk_request_batch_mode: bool = False,\n        limited_access_assets: Tuple[str, ...] = None,\n        corporate_action_restricted_assets: Tuple[str, ...] = None,\n        backcast_dates: Tuple[datetime.date, ...] = None,\n        base_currency: Union[Currency, str] = None,\n        local_currency: Union[Currency, str] = None,\n        fund_calendar: Union[PCOFundCalendar, str] = None,\n        calculation_currency: Union[PCOCurrencyType, str] = None,\n        hedge_settlement_interval: Tuple[PCOParameterValues, ...] = None,\n        hedge_settlement_day: Tuple[PCOParameterValues, ...] = None,\n        roll_horizon: Tuple[PCOParameterValues, ...] = None,\n        pnl_currency: Tuple[PCOParameterValues, ...] = None,\n        nav_publication_period: Tuple[PCOParameterValues, ...] = None,\n        roll_date_zero_threshold: bool = None,\n        unrealised_mark_to_market: PCOUnrealisedMarkToMarket = None,\n        target_deviation: Tuple[PCOTargetDeviation, ...] = None,\n        cash_balances: Tuple[PCOCashBalance, ...] = None,\n        exposure: PCOExposure = None,\n        settlements: Tuple[PCOSettlements, ...] = None,\n        show_cash: bool = None,\n        show_exposure: bool = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.asset_class = asset_class\n        self.transaction_cost_model = transaction_cost_model\n        self.trading_cost = trading_cost\n        self.servicing_cost_long = servicing_cost_long\n        self.servicing_cost_short = servicing_cost_short\n        self.region = region\n        self.risk_model = risk_model\n        self.fx_hedged = fx_hedged\n        self.publish_to_bloomberg = publish_to_bloomberg\n        self.publish_to_reuters = publish_to_reuters\n        self.publish_to_factset = publish_to_factset\n        self.include_price_history = include_price_history\n        self.index_update = index_update\n        self.index_rebalance = index_rebalance\n        self.basket_action = basket_action\n        self.api_domain = api_domain\n        self.initial_price = initial_price\n        self.stock_level_exposures = stock_level_exposures\n        self.explode_positions = explode_positions\n        self.scenario_id = scenario_id\n        self.scenario_ids = scenario_ids\n        self.scenario_group_id = scenario_group_id\n        self.scenario_type = scenario_type\n        self.market_model_id = market_model_id\n        self.risk_measures = risk_measures\n        self.initial_pricing_date = initial_pricing_date\n        self.backcast = backcast\n        self.risk_request = risk_request\n        self.participation_rate = participation_rate\n        self.approve_rebalance = approve_rebalance\n        self.use_risk_request_batch_mode = use_risk_request_batch_mode\n        self.limited_access_assets = limited_access_assets\n        self.corporate_action_restricted_assets = corporate_action_restricted_assets\n        self.backcast_dates = backcast_dates\n        self.base_currency = base_currency\n        self.local_currency = local_currency\n        self.fund_calendar = fund_calendar\n        self.calculation_currency = calculation_currency\n        self.hedge_settlement_interval = hedge_settlement_interval\n        self.hedge_settlement_day = hedge_settlement_day\n        self.roll_horizon = roll_horizon\n        self.pnl_currency = pnl_currency\n        self.nav_publication_period = nav_publication_period\n        self.roll_date_zero_threshold = roll_date_zero_threshold\n        self.unrealised_mark_to_market = unrealised_mark_to_market\n        self.target_deviation = target_deviation\n        self.cash_balances = cash_balances\n        self.exposure = exposure\n        self.settlements = settlements\n        self.show_cash = show_cash\n        self.show_exposure = show_exposure\n        self.name = name\n\n    @property\n    def asset_class(self) -> Union[AssetClass, str]:\n        """Asset classification of security. Assets are classified into broad groups which\n           exhibit similar characteristics and behave in a consistent way under\n           different market conditions"""\n        return self.__asset_class\n\n    @asset_class.setter\n    def asset_class(self, value: Union[AssetClass, str]):\n        self._property_changed('asset_class')\n        self.__asset_class = get_enum_value(AssetClass, value)        \n\n    @property\n    def transaction_cost_model(self) -> str:\n        """Determines which model to use"""\n        return self.__transaction_cost_model\n\n    @transaction_cost_model.setter\n    def transaction_cost_model(self, value: str):\n        self._property_changed('transaction_cost_model')\n        self.__transaction_cost_model = value        \n\n    @property\n    def trading_cost(self) -> float:\n        """bps cost to execute delta"""\n        return self.__trading_cost\n\n    @trading_cost.setter\n    def trading_cost(self, value: float):\n        self._property_changed('trading_cost')\n        self.__trading_cost = value        \n\n    @property\n    def servicing_cost_long(self) -> float:\n        """bps cost to fund long positions"""\n        return self.__servicing_cost_long\n\n    @servicing_cost_long.setter\n    def servicing_cost_long(self, value: float):\n        self._property_changed('servicing_cost_long')\n        self.__servicing_cost_long = value        \n\n    @property\n    def servicing_cost_short(self) -> float:\n        """bps cost to fund short positions"""\n        return self.__servicing_cost_short\n\n    @servicing_cost_short.setter\n    def servicing_cost_short(self, value: float):\n        self._property_changed('servicing_cost_short')\n        self.__servicing_cost_short = value        \n\n    @property\n    def region(self) -> str:\n        """The region of the report"""\n        return self.__region\n\n    @region.setter\n    def region(self, value: str):\n        self._property_changed('region')\n        self.__region = value        \n\n    @property\n    def risk_model(self) -> str:\n        """Marquee unique risk model identifier"""\n        return self.__risk_model\n\n    @risk_model.setter\n    def risk_model(self, value: str):\n        self._property_changed('risk_model')\n        self.__risk_model = value        \n\n    @property\n    def fx_hedged(self) -> bool:\n        """Assume portfolio is FX Hedged"""\n        return self.__fx_hedged\n\n    @fx_hedged.setter\n    def fx_hedged(self, value: bool):\n        self._property_changed('fx_hedged')\n        self.__fx_hedged = value        \n\n    @property\n    def publish_to_bloomberg(self) -> bool:\n        """Publish Basket to Bloomberg"""\n        return self.__publish_to_bloomberg\n\n    @publish_to_bloomberg.setter\n    def publish_to_bloomberg(self, value: bool):\n        self._property_changed('publish_to_bloomberg')\n        self.__publish_to_bloomberg = value        \n\n    @property\n    def publish_to_reuters(self) -> bool:\n        """Publish Basket to Reuters"""\n        return self.__publish_to_reuters\n\n    @publish_to_reuters.setter\n    def publish_to_reuters(self, value: bool):\n        self._property_changed('publish_to_reuters')\n        self.__publish_to_reuters = value        \n\n    @property\n    def publish_to_factset(self) -> bool:\n        """Publish Basket to Factset"""\n        return self.__publish_to_factset\n\n    @publish_to_factset.setter\n    def publish_to_factset(self, value: bool):\n        self._property_changed('publish_to_factset')\n        self.__publish_to_factset = value        \n\n    @property\n    def include_price_history(self) -> bool:\n        """Include full price history"""\n        return self.__include_price_history\n\n    @include_price_history.setter\n    def include_price_history(self, value: bool):\n        self._property_changed('include_price_history')\n        self.__include_price_history = value        \n\n    @property\n    def index_update(self) -> bool:\n        """Update the basket"""\n        return self.__index_update\n\n    @index_update.setter\n    def index_update(self, value: bool):\n        self._property_changed('index_update')\n        self.__index_update = value        \n\n    @property\n    def index_rebalance(self) -> bool:\n        """Rebalance the basket"""\n        return self.__index_rebalance\n\n    @index_rebalance.setter\n    def index_rebalance(self, value: bool):\n        self._property_changed('index_rebalance')\n        self.__index_rebalance = value        \n\n    @property\n    def basket_action(self) -> Union[BasketAction, str]:\n        """Indicates which basket action triggered the report"""\n        return self.__basket_action\n\n    @basket_action.setter\n    def basket_action(self, value: Union[BasketAction, str]):\n        self._property_changed('basket_action')\n        self.__basket_action = get_enum_value(BasketAction, value)        \n\n    @property\n    def api_domain(self) -> bool:\n        """Indicates if report is triggered from ui/api call"""\n        return self.__api_domain\n\n    @api_domain.setter\n    def api_domain(self, value: bool):\n        self._property_changed('api_domain')\n        self.__api_domain = value        \n\n    @property\n    def initial_price(self) -> float:\n        """Initial price for the position set"""\n        return self.__initial_price\n\n    @initial_price.setter\n    def initial_price(self, value: float):\n        self._property_changed('initial_price')\n        self.__initial_price = value        \n\n    @property\n    def stock_level_exposures(self) -> bool:\n        """Publish stock level exposures"""\n        return self.__stock_level_exposures\n\n    @stock_level_exposures.setter\n    def stock_level_exposures(self, value: bool):\n        self._property_changed('stock_level_exposures')\n        self.__stock_level_exposures = value        \n\n    @property\n    def explode_positions(self) -> bool:\n        """Whether to explode positions during risk run"""\n        return self.__explode_positions\n\n    @explode_positions.setter\n    def explode_positions(self, value: bool):\n        self._property_changed('explode_positions')\n        self.__explode_positions = value        \n\n    @property\n    def scenario_id(self) -> str:\n        """Marquee unique scenario identifier"""\n        return self.__scenario_id\n\n    @scenario_id.setter\n    def scenario_id(self, value: str):\n        self._property_changed('scenario_id')\n        self.__scenario_id = value        \n\n    @property\n    def scenario_ids(self) -> Tuple[str, ...]:\n        """Array of scenario identifiers related to the object"""\n        return self.__scenario_ids\n\n    @scenario_ids.setter\n    def scenario_ids(self, value: Tuple[str, ...]):\n        self._property_changed('scenario_ids')\n        self.__scenario_ids = value        \n\n    @property\n    def scenario_group_id(self) -> str:\n        """Marquee unique scenario group identifier"""\n        return self.__scenario_group_id\n\n    @scenario_group_id.setter\n    def scenario_group_id(self, value: str):\n        self._property_changed('scenario_group_id')\n        self.__scenario_group_id = value        \n\n    @property\n    def scenario_type(self) -> Union[ScenarioType, str]:\n        """Type of Scenario"""\n        return self.__scenario_type\n\n    @scenario_type.setter\n    def scenario_type(self, value: Union[ScenarioType, str]):\n        self._property_changed('scenario_type')\n        self.__scenario_type = get_enum_value(ScenarioType, value)        \n\n    @property\n    def market_model_id(self) -> str:\n        """Marquee unique market model identifier"""\n        return self.__market_model_id\n\n    @market_model_id.setter\n    def market_model_id(self, value: str):\n        self._property_changed('market_model_id')\n        self.__market_model_id = value        \n\n    @property\n    def risk_measures(self) -> Tuple[RiskMeasure, ...]:\n        """An array of risk measures to get from the risk calculation."""\n        return self.__risk_measures\n\n    @risk_measures.setter\n    def risk_measures(self, value: Tuple[RiskMeasure, ...]):\n        self._property_changed('risk_measures')\n        self.__risk_measures = value        \n\n    @property\n    def initial_pricing_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__initial_pricing_date\n\n    @initial_pricing_date.setter\n    def initial_pricing_date(self, value: datetime.date):\n        self._property_changed('initial_pricing_date')\n        self.__initial_pricing_date = value        \n\n    @property\n    def backcast(self) -> bool:\n        """Use backcasted portfolio derived from positions on the end date."""\n        return self.__backcast\n\n    @backcast.setter\n    def backcast(self, value: bool):\n        self._property_changed('backcast')\n        self.__backcast = value        \n\n    @property\n    def risk_request(self) -> RiskRequest:\n        """A request for a risk calculation"""\n        return self.__risk_request\n\n    @risk_request.setter\n    def risk_request(self, value: RiskRequest):\n        self._property_changed('risk_request')\n        self.__risk_request = value        \n\n    @property\n    def participation_rate(self) -> float:\n        """Liquidity analytics participation rate."""\n        return self.__participation_rate\n\n    @participation_rate.setter\n    def participation_rate(self, value: float):\n        self._property_changed('participation_rate')\n        self.__participation_rate = value        \n\n    @property\n    def approve_rebalance(self) -> bool:\n        """An approved basket"""\n        return self.__approve_rebalance\n\n    @approve_rebalance.setter\n    def approve_rebalance(self, value: bool):\n        self._property_changed('approve_rebalance')\n        self.__approve_rebalance = value        \n\n    @property\n    def use_risk_request_batch_mode(self) -> bool:\n        """Switch to enable RiskRequest batching"""\n        return self.__use_risk_request_batch_mode\n\n    @use_risk_request_batch_mode.setter\n    def use_risk_request_batch_mode(self, value: bool):\n        self._property_changed('use_risk_request_batch_mode')\n        self.__use_risk_request_batch_mode = value        \n\n    @property\n    def limited_access_assets(self) -> Tuple[str, ...]:\n        """List of constituents in the basket that GS has limited access to"""\n        return self.__limited_access_assets\n\n    @limited_access_assets.setter\n    def limited_access_assets(self, value: Tuple[str, ...]):\n        self._property_changed('limited_access_assets')\n        self.__limited_access_assets = value        \n\n    @property\n    def corporate_action_restricted_assets(self) -> Tuple[str, ...]:\n        """List of constituents in the basket that will not be adjusted for corporate\n           actions in the future"""\n        return self.__corporate_action_restricted_assets\n\n    @corporate_action_restricted_assets.setter\n    def corporate_action_restricted_assets(self, value: Tuple[str, ...]):\n        self._property_changed('corporate_action_restricted_assets')\n        self.__corporate_action_restricted_assets = value        \n\n    @property\n    def backcast_dates(self) -> Tuple[datetime.date, ...]:\n        """List of dates user upload to backcast basket"""\n        return self.__backcast_dates\n\n    @backcast_dates.setter\n    def backcast_dates(self, value: Tuple[datetime.date, ...]):\n        self._property_changed('backcast_dates')\n        self.__backcast_dates = value        \n\n    @property\n    def base_currency(self) -> Union[Currency, str]:\n        """Base currency"""\n        return self.__base_currency\n\n    @base_currency.setter\n    def base_currency(self, value: Union[Currency, str]):\n        self._property_changed('base_currency')\n        self.__base_currency = get_enum_value(Currency, value)        \n\n    @property\n    def local_currency(self) -> Union[Currency, str]:\n        """Local currency"""\n        return self.__local_currency\n\n    @local_currency.setter\n    def local_currency(self, value: Union[Currency, str]):\n        self._property_changed('local_currency')\n        self.__local_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fund_calendar(self) -> Union[PCOFundCalendar, str]:\n        """Holiday Calendar of Fund"""\n        return self.__fund_calendar\n\n    @fund_calendar.setter\n    def fund_calendar(self, value: Union[PCOFundCalendar, str]):\n        self._property_changed('fund_calendar')\n        self.__fund_calendar = get_enum_value(PCOFundCalendar, value)        \n\n    @property\n    def calculation_currency(self) -> Union[PCOCurrencyType, str]:\n        """Calculation currency type"""\n        return self.__calculation_currency\n\n    @calculation_currency.setter\n    def calculation_currency(self, value: Union[PCOCurrencyType, str]):\n        self._property_changed('calculation_currency')\n        self.__calculation_currency = get_enum_value(PCOCurrencyType, value)        \n\n    @property\n    def hedge_settlement_interval(self) -> Tuple[PCOParameterValues, ...]:\n        """Default tenor of hedging for each currency"""\n        return self.__hedge_settlement_interval\n\n    @hedge_settlement_interval.setter\n    def hedge_settlement_interval(self, value: Tuple[PCOParameterValues, ...]):\n        self._property_changed('hedge_settlement_interval')\n        self.__hedge_settlement_interval = value        \n\n    @property\n    def hedge_settlement_day(self) -> Tuple[PCOParameterValues, ...]:\n        """Settlement date of each currency"""\n        return self.__hedge_settlement_day\n\n    @hedge_settlement_day.setter\n    def hedge_settlement_day(self, value: Tuple[PCOParameterValues, ...]):\n        self._property_changed('hedge_settlement_day')\n        self.__hedge_settlement_day = value        \n\n    @property\n    def roll_horizon(self) -> Tuple[PCOParameterValues, ...]:\n        """Number of days to roll before settlement for each currency"""\n        return self.__roll_horizon\n\n    @roll_horizon.setter\n    def roll_horizon(self, value: Tuple[PCOParameterValues, ...]):\n        self._property_changed('roll_horizon')\n        self.__roll_horizon = value        \n\n    @property\n    def pnl_currency(self) -> Tuple[PCOParameterValues, ...]:\n        """One of Local and Base"""\n        return self.__pnl_currency\n\n    @pnl_currency.setter\n    def pnl_currency(self, value: Tuple[PCOParameterValues, ...]):\n        self._property_changed('pnl_currency')\n        self.__pnl_currency = value        \n\n    @property\n    def nav_publication_period(self) -> Tuple[PCOParameterValues, ...]:\n        """Days it takes for a subscription or redemption show up in NAV after it happens"""\n        return self.__nav_publication_period\n\n    @nav_publication_period.setter\n    def nav_publication_period(self, value: Tuple[PCOParameterValues, ...]):\n        self._property_changed('nav_publication_period')\n        self.__nav_publication_period = value        \n\n    @property\n    def roll_date_zero_threshold(self) -> bool:\n        """If true, rebalance this program when rolling"""\n        return self.__roll_date_zero_threshold\n\n    @roll_date_zero_threshold.setter\n    def roll_date_zero_threshold(self, value: bool):\n        self._property_changed('roll_date_zero_threshold')\n        self.__roll_date_zero_threshold = value        \n\n    @property\n    def unrealised_mark_to_market(self) -> PCOUnrealisedMarkToMarket:\n        """History of unrealised mark to market of open trades for each currency"""\n        return self.__unrealised_mark_to_market\n\n    @unrealised_mark_to_market.setter\n    def unrealised_mark_to_market(self, value: PCOUnrealisedMarkToMarket):\n        self._property_changed('unrealised_mark_to_market')\n        self.__unrealised_mark_to_market = value        \n\n    @property\n    def target_deviation(self) -> Tuple[PCOTargetDeviation, ...]:\n        """History of target deviation for each currency"""\n        return self.__target_deviation\n\n    @target_deviation.setter\n    def target_deviation(self, value: Tuple[PCOTargetDeviation, ...]):\n        self._property_changed('target_deviation')\n        self.__target_deviation = value        \n\n    @property\n    def cash_balances(self) -> Tuple[PCOCashBalance, ...]:\n        """Cash flows for each currency"""\n        return self.__cash_balances\n\n    @cash_balances.setter\n    def cash_balances(self, value: Tuple[PCOCashBalance, ...]):\n        self._property_changed('cash_balances')\n        self.__cash_balances = value        \n\n    @property\n    def exposure(self) -> PCOExposure:\n        """Total exposure for portfolio"""\n        return self.__exposure\n\n    @exposure.setter\n    def exposure(self, value: PCOExposure):\n        self._property_changed('exposure')\n        self.__exposure = value        \n\n    @property\n    def settlements(self) -> Tuple[PCOSettlements, ...]:\n        """History of settlements for each currency"""\n        return self.__settlements\n\n    @settlements.setter\n    def settlements(self, value: Tuple[PCOSettlements, ...]):\n        self._property_changed('settlements')\n        self.__settlements = value        \n\n    @property\n    def show_cash(self) -> bool:\n        """If cash table is shown in UI"""\n        return self.__show_cash\n\n    @show_cash.setter\n    def show_cash(self, value: bool):\n        self._property_changed('show_cash')\n        self.__show_cash = value        \n\n    @property\n    def show_exposure(self) -> bool:\n        """If exposure table is shown in UI"""\n        return self.__show_exposure\n\n    @show_exposure.setter\n    def show_exposure(self, value: bool):\n        self._property_changed('show_exposure')\n        self.__show_exposure = value        \n\n\nclass ReportScheduleRequest(Base):\n        \n    """Parameters in order to schedule a report"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        parameters: ReportParameters = None,\n        end_date: datetime.date = None,\n        start_date: datetime.date = None,\n        priority: Union[ReportJobPriority, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.parameters = parameters\n        self.end_date = end_date\n        self.start_date = start_date\n        self.priority = priority\n        self.name = name\n\n    @property\n    def parameters(self) -> ReportParameters:\n        """Parameters specific to the report type"""\n        return self.__parameters\n\n    @parameters.setter\n    def parameters(self, value: ReportParameters):\n        self._property_changed('parameters')\n        self.__parameters = value        \n\n    @property\n    def end_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__end_date\n\n    @end_date.setter\n    def end_date(self, value: datetime.date):\n        self._property_changed('end_date')\n        self.__end_date = value        \n\n    @property\n    def start_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__start_date\n\n    @start_date.setter\n    def start_date(self, value: datetime.date):\n        self._property_changed('start_date')\n        self.__start_date = value        \n\n    @property\n    def priority(self) -> Union[ReportJobPriority, str]:\n        """Report job priority."""\n        return self.__priority\n\n    @priority.setter\n    def priority(self, value: Union[ReportJobPriority, str]):\n        self._property_changed('priority')\n        self.__priority = get_enum_value(ReportJobPriority, value)        \n
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Source code for gs_quant.target.instrument

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\n\nfrom gs_quant.target.common import *\nimport datetime\nfrom typing import Tuple, Union\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.base import InstrumentBase, camel_case_translate, get_enum_value\n\n\nclass CSLPython(Instrument):\n        \n    """Object representation of an arbitrary payoff defined in Python"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        class_name: str = None,\n        denominated: Union[Currency, str] = None,\n        double_params: Tuple[CSLDouble, ...] = None,\n        date_params: Tuple[CSLDate, ...] = None,\n        string_params: Tuple[CSLString, ...] = None,\n        simple_schedule_params: Tuple[CSLSimpleSchedule, ...] = None,\n        schedule_params: Tuple[CSLSchedule, ...] = None,\n        currency_params: Tuple[CSLCurrency, ...] = None,\n        stock_params: Tuple[CSLStock, ...] = None,\n        index_params: Tuple[CSLIndex, ...] = None,\n        fx_cross_params: Tuple[CSLFXCross, ...] = None,\n        double_array_params: Tuple[CSLDoubleArray, ...] = None,\n        date_array_params: Tuple[CSLDateArray, ...] = None,\n        string_array_params: Tuple[CSLStringArray, ...] = None,\n        simple_schedule_array_params: Tuple[CSLSimpleScheduleArray, ...] = None,\n        schedule_array_params: Tuple[CSLScheduleArray, ...] = None,\n        currency_array_params: Tuple[CSLCurrencyArray, ...] = None,\n        stock_array_params: Tuple[CSLStockArray, ...] = None,\n        index_array_params: Tuple[CSLIndexArray, ...] = None,\n        fx_cross_array_params: Tuple[CSLFXCrossArray, ...] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.class_name = class_name\n        self.denominated = denominated\n        self.double_params = double_params\n        self.date_params = date_params\n        self.string_params = string_params\n        self.simple_schedule_params = simple_schedule_params\n        self.schedule_params = schedule_params\n        self.currency_params = currency_params\n        self.stock_params = stock_params\n        self.index_params = index_params\n        self.fx_cross_params = fx_cross_params\n        self.double_array_params = double_array_params\n        self.date_array_params = date_array_params\n        self.string_array_params = string_array_params\n        self.simple_schedule_array_params = simple_schedule_array_params\n        self.schedule_array_params = schedule_array_params\n        self.currency_array_params = currency_array_params\n        self.stock_array_params = stock_array_params\n        self.index_array_params = index_array_params\n        self.fx_cross_array_params = fx_cross_array_params\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Cross Asset"""\n        return AssetClass.Cross_Asset        \n\n    @property\n    def type(self) -> AssetType:\n        """CSL"""\n        return AssetType.CSL        \n\n    @property\n    def class_name(self) -> str:\n        """A reference to the Python script defining this payoff class"""\n        return self.__class_name\n\n    @class_name.setter\n    def class_name(self, value: str):\n        self._property_changed('class_name')\n        self.__class_name = value        \n\n    @property\n    def denominated(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__denominated\n\n    @denominated.setter\n    def denominated(self, value: Union[Currency, str]):\n        self._property_changed('denominated')\n        self.__denominated = get_enum_value(Currency, value)        \n\n    @property\n    def double_params(self) -> Tuple[CSLDouble, ...]:\n        """A double"""\n        return self.__double_params\n\n    @double_params.setter\n    def double_params(self, value: Tuple[CSLDouble, ...]):\n        self._property_changed('double_params')\n        self.__double_params = value        \n\n    @property\n    def date_params(self) -> Tuple[CSLDate, ...]:\n        """A date"""\n        return self.__date_params\n\n    @date_params.setter\n    def date_params(self, value: Tuple[CSLDate, ...]):\n        self._property_changed('date_params')\n        self.__date_params = value        \n\n    @property\n    def string_params(self) -> Tuple[CSLString, ...]:\n        """A string"""\n        return self.__string_params\n\n    @string_params.setter\n    def string_params(self, value: Tuple[CSLString, ...]):\n        self._property_changed('string_params')\n        self.__string_params = value        \n\n    @property\n    def simple_schedule_params(self) -> Tuple[CSLSimpleSchedule, ...]:\n        """A fixing date, settlement date pair"""\n        return self.__simple_schedule_params\n\n    @simple_schedule_params.setter\n    def simple_schedule_params(self, value: Tuple[CSLSimpleSchedule, ...]):\n        self._property_changed('simple_schedule_params')\n        self.__simple_schedule_params = value        \n\n    @property\n    def schedule_params(self) -> Tuple[CSLSchedule, ...]:\n        """A schedule"""\n        return self.__schedule_params\n\n    @schedule_params.setter\n    def schedule_params(self, value: Tuple[CSLSchedule, ...]):\n        self._property_changed('schedule_params')\n        self.__schedule_params = value        \n\n    @property\n    def currency_params(self) -> Tuple[CSLCurrency, ...]:\n        """A currency"""\n        return self.__currency_params\n\n    @currency_params.setter\n    def currency_params(self, value: Tuple[CSLCurrency, ...]):\n        self._property_changed('currency_params')\n        self.__currency_params = value        \n\n    @property\n    def stock_params(self) -> Tuple[CSLStock, ...]:\n        """A stock"""\n        return self.__stock_params\n\n    @stock_params.setter\n    def stock_params(self, value: Tuple[CSLStock, ...]):\n        self._property_changed('stock_params')\n        self.__stock_params = value        \n\n    @property\n    def index_params(self) -> Tuple[CSLIndex, ...]:\n        """An index"""\n        return self.__index_params\n\n    @index_params.setter\n    def index_params(self, value: Tuple[CSLIndex, ...]):\n        self._property_changed('index_params')\n        self.__index_params = value        \n\n    @property\n    def fx_cross_params(self) -> Tuple[CSLFXCross, ...]:\n        """An FX cross"""\n        return self.__fx_cross_params\n\n    @fx_cross_params.setter\n    def fx_cross_params(self, value: Tuple[CSLFXCross, ...]):\n        self._property_changed('fx_cross_params')\n        self.__fx_cross_params = value        \n\n    @property\n    def double_array_params(self) -> Tuple[CSLDoubleArray, ...]:\n        """An array of doubles"""\n        return self.__double_array_params\n\n    @double_array_params.setter\n    def double_array_params(self, value: Tuple[CSLDoubleArray, ...]):\n        self._property_changed('double_array_params')\n        self.__double_array_params = value        \n\n    @property\n    def date_array_params(self) -> Tuple[CSLDateArray, ...]:\n        """An array of dates"""\n        return self.__date_array_params\n\n    @date_array_params.setter\n    def date_array_params(self, value: Tuple[CSLDateArray, ...]):\n        self._property_changed('date_array_params')\n        self.__date_array_params = value        \n\n    @property\n    def string_array_params(self) -> Tuple[CSLStringArray, ...]:\n        """An array of strings"""\n        return self.__string_array_params\n\n    @string_array_params.setter\n    def string_array_params(self, value: Tuple[CSLStringArray, ...]):\n        self._property_changed('string_array_params')\n        self.__string_array_params = value        \n\n    @property\n    def simple_schedule_array_params(self) -> Tuple[CSLSimpleScheduleArray, ...]:\n        """An array of simple schedules"""\n        return self.__simple_schedule_array_params\n\n    @simple_schedule_array_params.setter\n    def simple_schedule_array_params(self, value: Tuple[CSLSimpleScheduleArray, ...]):\n        self._property_changed('simple_schedule_array_params')\n        self.__simple_schedule_array_params = value        \n\n    @property\n    def schedule_array_params(self) -> Tuple[CSLScheduleArray, ...]:\n        """An array of schedules"""\n        return self.__schedule_array_params\n\n    @schedule_array_params.setter\n    def schedule_array_params(self, value: Tuple[CSLScheduleArray, ...]):\n        self._property_changed('schedule_array_params')\n        self.__schedule_array_params = value        \n\n    @property\n    def currency_array_params(self) -> Tuple[CSLCurrencyArray, ...]:\n        """An array of currencies"""\n        return self.__currency_array_params\n\n    @currency_array_params.setter\n    def currency_array_params(self, value: Tuple[CSLCurrencyArray, ...]):\n        self._property_changed('currency_array_params')\n        self.__currency_array_params = value        \n\n    @property\n    def stock_array_params(self) -> Tuple[CSLStockArray, ...]:\n        """An array of stocks"""\n        return self.__stock_array_params\n\n    @stock_array_params.setter\n    def stock_array_params(self, value: Tuple[CSLStockArray, ...]):\n        self._property_changed('stock_array_params')\n        self.__stock_array_params = value        \n\n    @property\n    def index_array_params(self) -> Tuple[CSLIndexArray, ...]:\n        """An array of indices"""\n        return self.__index_array_params\n\n    @index_array_params.setter\n    def index_array_params(self, value: Tuple[CSLIndexArray, ...]):\n        self._property_changed('index_array_params')\n        self.__index_array_params = value        \n\n    @property\n    def fx_cross_array_params(self) -> Tuple[CSLFXCrossArray, ...]:\n        """An array of FX crosses"""\n        return self.__fx_cross_array_params\n\n    @fx_cross_array_params.setter\n    def fx_cross_array_params(self, value: Tuple[CSLFXCrossArray, ...]):\n        self._property_changed('fx_cross_array_params')\n        self.__fx_cross_array_params = value        \n\n\nclass Cash(Instrument):\n        \n    """Cash payment"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        currency: Union[Currency, str] = None,\n        payment_date: datetime.date = None,\n        notional_amount: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.currency = currency\n        self.payment_date = payment_date\n        self.notional_amount = notional_amount\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Cash"""\n        return AssetClass.Cash        \n\n    @property\n    def type(self) -> AssetType:\n        """Cash"""\n        return AssetType.Cash        \n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def payment_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__payment_date\n\n    @payment_date.setter\n    def payment_date(self, value: datetime.date):\n        self._property_changed('payment_date')\n        self.__payment_date = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n\nclass CommodOTCOptionLeg(Instrument):\n        \n    """Commodities OTC option leg"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: str = None,\n        contract: str = None,\n        leg_description: str = None,\n        fixing_currency: Union[CurrencyName, str] = None,\n        fixing_currency_source: str = None,\n        option_type: Union[OptionType, str] = None,\n        quantity_multiplier: int = None,\n        premium: CommodPrice = None,\n        premium_settlement: str = None,\n        strike: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.contract = contract\n        self.leg_description = leg_description\n        self.fixing_currency = fixing_currency\n        self.fixing_currency_source = fixing_currency_source\n        self.option_type = option_type\n        self.quantity_multiplier = quantity_multiplier\n        self.premium = premium\n        self.premium_settlement = premium_settlement\n        self.strike = strike\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Commod"""\n        return AssetClass.Commod        \n\n    @property\n    def type(self) -> AssetType:\n        """OptionLeg"""\n        return AssetType.OptionLeg        \n\n    @property\n    def underlier(self) -> str:\n        """Commodity asset"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: str):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def contract(self) -> str:\n        """The observed contract at each pricing date e.g First Nearby, Second Nearby"""\n        return self.__contract\n\n    @contract.setter\n    def contract(self, value: str):\n        self._property_changed('contract')\n        self.__contract = value        \n\n    @property\n    def leg_description(self) -> str:\n        """The description of the averaging style"""\n        return self.__leg_description\n\n    @leg_description.setter\n    def leg_description(self, value: str):\n        self._property_changed('leg_description')\n        self.__leg_description = value        \n\n    @property\n    def fixing_currency(self) -> Union[CurrencyName, str]:\n        """Currency Names"""\n        return self.__fixing_currency\n\n    @fixing_currency.setter\n    def fixing_currency(self, value: Union[CurrencyName, str]):\n        self._property_changed('fixing_currency')\n        self.__fixing_currency = get_enum_value(CurrencyName, value)        \n\n    @property\n    def fixing_currency_source(self) -> str:\n        """fixing currency conversion rate source"""\n        return self.__fixing_currency_source\n\n    @fixing_currency_source.setter\n    def fixing_currency_source(self, value: str):\n        self._property_changed('fixing_currency_source')\n        self.__fixing_currency_source = value        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def quantity_multiplier(self) -> int:\n        """quantity multiplier for driving the long/short direction of the leg"""\n        return self.__quantity_multiplier\n\n    @quantity_multiplier.setter\n    def quantity_multiplier(self, value: int):\n        self._property_changed('quantity_multiplier')\n        self.__quantity_multiplier = value        \n\n    @property\n    def premium(self) -> CommodPrice:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: CommodPrice):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_settlement(self) -> str:\n        """read only description in plain English of settlement terms"""\n        return self.__premium_settlement\n\n    @premium_settlement.setter\n    def premium_settlement(self, value: str):\n        self._property_changed('premium_settlement')\n        self.__premium_settlement = value        \n\n    @property\n    def strike(self) -> Union[float, str]:\n        """Size of some value, i.e. notional like 1.3b, 1.5, 1000"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: Union[float, str]):\n        self._property_changed('strike')\n        self.__strike = value        \n\n\nclass CommodOTCSwapLeg(Instrument):\n        \n    """Commodities OTC swap leg"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: str = None,\n        contract: str = None,\n        leg_description: str = None,\n        fixing_currency: Union[CurrencyName, str] = None,\n        fixing_currency_source: str = None,\n        quantity_multiplier: int = None,\n        fixed_price: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.contract = contract\n        self.leg_description = leg_description\n        self.fixing_currency = fixing_currency\n        self.fixing_currency_source = fixing_currency_source\n        self.quantity_multiplier = quantity_multiplier\n        self.fixed_price = fixed_price\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Commod"""\n        return AssetClass.Commod        \n\n    @property\n    def type(self) -> AssetType:\n        """SwapLeg"""\n        return AssetType.SwapLeg        \n\n    @property\n    def underlier(self) -> str:\n        """Commodity asset"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: str):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def contract(self) -> str:\n        """The observed contract at each pricing date e.g First Nearby, Second Nearby"""\n        return self.__contract\n\n    @contract.setter\n    def contract(self, value: str):\n        self._property_changed('contract')\n        self.__contract = value        \n\n    @property\n    def leg_description(self) -> str:\n        """The description of the averaging style"""\n        return self.__leg_description\n\n    @leg_description.setter\n    def leg_description(self, value: str):\n        self._property_changed('leg_description')\n        self.__leg_description = value        \n\n    @property\n    def fixing_currency(self) -> Union[CurrencyName, str]:\n        """Currency Names"""\n        return self.__fixing_currency\n\n    @fixing_currency.setter\n    def fixing_currency(self, value: Union[CurrencyName, str]):\n        self._property_changed('fixing_currency')\n        self.__fixing_currency = get_enum_value(CurrencyName, value)        \n\n    @property\n    def fixing_currency_source(self) -> str:\n        """fixing currency conversion rate source"""\n        return self.__fixing_currency_source\n\n    @fixing_currency_source.setter\n    def fixing_currency_source(self, value: str):\n        self._property_changed('fixing_currency_source')\n        self.__fixing_currency_source = value        \n\n    @property\n    def quantity_multiplier(self) -> int:\n        """quantity multiplier for driving the long/short direction of the leg"""\n        return self.__quantity_multiplier\n\n    @quantity_multiplier.setter\n    def quantity_multiplier(self, value: int):\n        self._property_changed('quantity_multiplier')\n        self.__quantity_multiplier = value        \n\n    @property\n    def fixed_price(self) -> Union[float, str]:\n        """Size of some value, i.e. notional like 1.3b, 1.5, 1000"""\n        return self.__fixed_price\n\n    @fixed_price.setter\n    def fixed_price(self, value: Union[float, str]):\n        self._property_changed('fixed_price')\n        self.__fixed_price = value        \n\n\nclass CommodOption(Instrument):\n        \n    """Flat object representation of a commodities option"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        commodity: str = None,\n        commodity_reference_price: str = None,\n        underlier_short_name: str = None,\n        start: Union[datetime.date, str] = None,\n        end: Union[datetime.date, str] = None,\n        number_of_periods: int = None,\n        strategy: str = None,\n        quantity: Union[float, str] = None,\n        quantity_unit: str = None,\n        quantity_period: str = None,\n        settlement: str = None,\n        contract: str = None,\n        floating_type: str = None,\n        fixing_currency: Union[CurrencyName, str] = None,\n        fixing_currency_source: str = None,\n        strike: str = None,\n        strike_currency: Union[CurrencyName, str] = None,\n        currency_summary: Union[CurrencyName, str] = None,\n        strike_unit: str = None,\n        option_type: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.commodity = commodity\n        self.commodity_reference_price = commodity_reference_price\n        self.underlier_short_name = underlier_short_name\n        self.start = start\n        self.end = end\n        self.number_of_periods = number_of_periods\n        self.strategy = strategy\n        self.quantity = quantity\n        self.quantity_unit = quantity_unit\n        self.quantity_period = quantity_period\n        self.settlement = settlement\n        self.contract = contract\n        self.floating_type = floating_type\n        self.fixing_currency = fixing_currency\n        self.fixing_currency_source = fixing_currency_source\n        self.strike = strike\n        self.strike_currency = strike_currency\n        self.currency_summary = currency_summary\n        self.strike_unit = strike_unit\n        self.option_type = option_type\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Commod"""\n        return AssetClass.Commod        \n\n    @property\n    def type(self) -> AssetType:\n        """Option"""\n        return AssetType.Option        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def commodity(self) -> str:\n        """Commodity asset"""\n        return self.__commodity\n\n    @commodity.setter\n    def commodity(self, value: str):\n        self._property_changed('commodity')\n        self.__commodity = value        \n\n    @property\n    def commodity_reference_price(self) -> str:\n        """The ISDA reference price"""\n        return self.__commodity_reference_price\n\n    @commodity_reference_price.setter\n    def commodity_reference_price(self, value: str):\n        self._property_changed('commodity_reference_price')\n        self.__commodity_reference_price = value        \n\n    @property\n    def underlier_short_name(self) -> str:\n        """Plain-English underlier short name"""\n        return self.__underlier_short_name\n\n    @underlier_short_name.setter\n    def underlier_short_name(self, value: str):\n        self._property_changed('underlier_short_name')\n        self.__underlier_short_name = value        \n\n    @property\n    def start(self) -> Union[datetime.date, str]:\n        """Date or Contract Month"""\n        return self.__start\n\n    @start.setter\n    def start(self, value: Union[datetime.date, str]):\n        self._property_changed('start')\n        self.__start = value        \n\n    @property\n    def end(self) -> Union[datetime.date, str]:\n        """Date or Contract Month"""\n        return self.__end\n\n    @end.setter\n    def end(self, value: Union[datetime.date, str]):\n        self._property_changed('end')\n        self.__end = value        \n\n    @property\n    def number_of_periods(self) -> int:\n        """The number of settlement periods"""\n        return self.__number_of_periods\n\n    @number_of_periods.setter\n    def number_of_periods(self, value: int):\n        self._property_changed('number_of_periods')\n        self.__number_of_periods = value        \n\n    @property\n    def strategy(self) -> str:\n        """Option Strategy"""\n        return self.__strategy\n\n    @strategy.setter\n    def strategy(self, value: str):\n        self._property_changed('strategy')\n        self.__strategy = value        \n\n    @property\n    def quantity(self) -> Union[float, str]:\n        """Size of some value, i.e. notional like 1.3b, 1.5, 1000"""\n        return self.__quantity\n\n    @quantity.setter\n    def quantity(self, value: Union[float, str]):\n        self._property_changed('quantity')\n        self.__quantity = value        \n\n    @property\n    def quantity_unit(self) -> str:\n        """Commodity asset"""\n        return self.__quantity_unit\n\n    @quantity_unit.setter\n    def quantity_unit(self, value: str):\n        self._property_changed('quantity_unit')\n        self.__quantity_unit = value        \n\n    @property\n    def quantity_period(self) -> str:\n        """period corresponding to a quantity amount"""\n        return self.__quantity_period\n\n    @quantity_period.setter\n    def quantity_period(self, value: str):\n        self._property_changed('quantity_period')\n        self.__quantity_period = value        \n\n    @property\n    def settlement(self) -> str:\n        """read only description in plain English of settlement terms"""\n        return self.__settlement\n\n    @settlement.setter\n    def settlement(self, value: str):\n        self._property_changed('settlement')\n        self.__settlement = value        \n\n    @property\n    def contract(self) -> str:\n        """The observed contract at each pricing date e.g First Nearby, Second Nearby"""\n        return self.__contract\n\n    @contract.setter\n    def contract(self, value: str):\n        self._property_changed('contract')\n        self.__contract = value        \n\n    @property\n    def floating_type(self) -> str:\n        """The description of the averaging style"""\n        return self.__floating_type\n\n    @floating_type.setter\n    def floating_type(self, value: str):\n        self._property_changed('floating_type')\n        self.__floating_type = value        \n\n    @property\n    def fixing_currency(self) -> Union[CurrencyName, str]:\n        """Currency Names"""\n        return self.__fixing_currency\n\n    @fixing_currency.setter\n    def fixing_currency(self, value: Union[CurrencyName, str]):\n        self._property_changed('fixing_currency')\n        self.__fixing_currency = get_enum_value(CurrencyName, value)        \n\n    @property\n    def fixing_currency_source(self) -> str:\n        """fixing currency conversion rate source"""\n        return self.__fixing_currency_source\n\n    @fixing_currency_source.setter\n    def fixing_currency_source(self, value: str):\n        self._property_changed('fixing_currency_source')\n        self.__fixing_currency_source = value        \n\n    @property\n    def strike(self) -> str:\n        """strike price (e.g. 50 or 50qd)"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: str):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def strike_currency(self) -> Union[CurrencyName, str]:\n        """Currency Names"""\n        return self.__strike_currency\n\n    @strike_currency.setter\n    def strike_currency(self, value: Union[CurrencyName, str]):\n        self._property_changed('strike_currency')\n        self.__strike_currency = get_enum_value(CurrencyName, value)        \n\n    @property\n    def currency_summary(self) -> Union[CurrencyName, str]:\n        """Currency Names"""\n        return self.__currency_summary\n\n    @currency_summary.setter\n    def currency_summary(self, value: Union[CurrencyName, str]):\n        self._property_changed('currency_summary')\n        self.__currency_summary = get_enum_value(CurrencyName, value)        \n\n    @property\n    def strike_unit(self) -> str:\n        """Commodity asset"""\n        return self.__strike_unit\n\n    @strike_unit.setter\n    def strike_unit(self, value: str):\n        self._property_changed('strike_unit')\n        self.__strike_unit = value        \n\n    @property\n    def option_type(self) -> str:\n        """e.g. call or put"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: str):\n        self._property_changed('option_type')\n        self.__option_type = value        \n\n\nclass CommodSwap(Instrument):\n        \n    """Flat representation of a commodities swap"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        commodity: str = None,\n        commodity_reference_price: str = None,\n        start: Union[datetime.date, str] = None,\n        end: Union[datetime.date, str] = None,\n        contract: str = None,\n        number_of_periods: int = None,\n        strategy: str = None,\n        quantity: Union[float, str] = None,\n        quantity_unit: str = None,\n        quantity_period: Union[Period, str] = None,\n        settlement: str = None,\n        floating_type: str = None,\n        fixing_currency: Union[CurrencyName, str] = None,\n        fixing_currency_source: str = None,\n        fixed_price: Union[float, str] = None,\n        fixed_price_unit: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.commodity = commodity\n        self.commodity_reference_price = commodity_reference_price\n        self.start = start\n        self.end = end\n        self.contract = contract\n        self.number_of_periods = number_of_periods\n        self.strategy = strategy\n        self.quantity = quantity\n        self.quantity_unit = quantity_unit\n        self.quantity_period = quantity_period\n        self.settlement = settlement\n        self.floating_type = floating_type\n        self.fixing_currency = fixing_currency\n        self.fixing_currency_source = fixing_currency_source\n        self.fixed_price = fixed_price\n        self.fixed_price_unit = fixed_price_unit\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Commod"""\n        return AssetClass.Commod        \n\n    @property\n    def type(self) -> AssetType:\n        """Swap"""\n        return AssetType.Swap        \n\n    @property\n    def commodity(self) -> str:\n        """Commodity asset"""\n        return self.__commodity\n\n    @commodity.setter\n    def commodity(self, value: str):\n        self._property_changed('commodity')\n        self.__commodity = value        \n\n    @property\n    def commodity_reference_price(self) -> str:\n        """The ISDA reference price"""\n        return self.__commodity_reference_price\n\n    @commodity_reference_price.setter\n    def commodity_reference_price(self, value: str):\n        self._property_changed('commodity_reference_price')\n        self.__commodity_reference_price = value        \n\n    @property\n    def start(self) -> Union[datetime.date, str]:\n        """Date or Contract Month"""\n        return self.__start\n\n    @start.setter\n    def start(self, value: Union[datetime.date, str]):\n        self._property_changed('start')\n        self.__start = value        \n\n    @property\n    def end(self) -> Union[datetime.date, str]:\n        """Date or Contract Month"""\n        return self.__end\n\n    @end.setter\n    def end(self, value: Union[datetime.date, str]):\n        self._property_changed('end')\n        self.__end = value        \n\n    @property\n    def contract(self) -> str:\n        """The observed contract at each pricing date e.g First Nearby, Second Nearby"""\n        return self.__contract\n\n    @contract.setter\n    def contract(self, value: str):\n        self._property_changed('contract')\n        self.__contract = value        \n\n    @property\n    def number_of_periods(self) -> int:\n        """The number of settlement periods"""\n        return self.__number_of_periods\n\n    @number_of_periods.setter\n    def number_of_periods(self, value: int):\n        self._property_changed('number_of_periods')\n        self.__number_of_periods = value        \n\n    @property\n    def strategy(self) -> str:\n        """Swap Strategy : Strip and Commodity Spread"""\n        return self.__strategy\n\n    @strategy.setter\n    def strategy(self, value: str):\n        self._property_changed('strategy')\n        self.__strategy = value        \n\n    @property\n    def quantity(self) -> Union[float, str]:\n        """Size of some value, i.e. notional like 1.3b, 1.5, 1000"""\n        return self.__quantity\n\n    @quantity.setter\n    def quantity(self, value: Union[float, str]):\n        self._property_changed('quantity')\n        self.__quantity = value        \n\n    @property\n    def quantity_unit(self) -> str:\n        """Commodity asset"""\n        return self.__quantity_unit\n\n    @quantity_unit.setter\n    def quantity_unit(self, value: str):\n        self._property_changed('quantity_unit')\n        self.__quantity_unit = value        \n\n    @property\n    def quantity_period(self) -> Union[Period, str]:\n        """A coding scheme to define a period corresponding to a quantity amount"""\n        return self.__quantity_period\n\n    @quantity_period.setter\n    def quantity_period(self, value: Union[Period, str]):\n        self._property_changed('quantity_period')\n        self.__quantity_period = get_enum_value(Period, value)        \n\n    @property\n    def settlement(self) -> str:\n        """read only description in plain English of settlement terms"""\n        return self.__settlement\n\n    @settlement.setter\n    def settlement(self, value: str):\n        self._property_changed('settlement')\n        self.__settlement = value        \n\n    @property\n    def floating_type(self) -> str:\n        """The description of the averaging style"""\n        return self.__floating_type\n\n    @floating_type.setter\n    def floating_type(self, value: str):\n        self._property_changed('floating_type')\n        self.__floating_type = value        \n\n    @property\n    def fixing_currency(self) -> Union[CurrencyName, str]:\n        """Currency Names"""\n        return self.__fixing_currency\n\n    @fixing_currency.setter\n    def fixing_currency(self, value: Union[CurrencyName, str]):\n        self._property_changed('fixing_currency')\n        self.__fixing_currency = get_enum_value(CurrencyName, value)        \n\n    @property\n    def fixing_currency_source(self) -> str:\n        """fixing currency conversion rate source"""\n        return self.__fixing_currency_source\n\n    @fixing_currency_source.setter\n    def fixing_currency_source(self, value: str):\n        self._property_changed('fixing_currency_source')\n        self.__fixing_currency_source = value        \n\n    @property\n    def fixed_price(self) -> Union[float, str]:\n        """Size of some value, i.e. notional like 1.3b, 1.5, 1000"""\n        return self.__fixed_price\n\n    @fixed_price.setter\n    def fixed_price(self, value: Union[float, str]):\n        self._property_changed('fixed_price')\n        self.__fixed_price = value        \n\n    @property\n    def fixed_price_unit(self) -> str:\n        """Commodity asset"""\n        return self.__fixed_price_unit\n\n    @fixed_price_unit.setter\n    def fixed_price_unit(self, value: str):\n        self._property_changed('fixed_price_unit')\n        self.__fixed_price_unit = value        \n\n\nclass CommodVarianceSwap(Instrument):\n        \n    """Object representation of a commodities volitility / variance swap"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        side: Union[BuySell, str] = None,\n        notional: float = 1,\n        notional_currency: Union[Currency, str] = None,\n        asset: str = None,\n        asset_fixing_source: str = None,\n        contract: str = None,\n        fixing_currency: Union[Currency, str] = None,\n        fx_fixing_source: str = None,\n        settlement_date: Union[datetime.date, str] = None,\n        strike: Union[float, str] = None,\n        variance_convention: Union[VarianceConvention, str] = None,\n        annualization_factor: float = None,\n        divisor: str = None,\n        start_date: datetime.date = None,\n        end_date: Union[datetime.date, str] = None,\n        mean_rule: Union[CommodMeanRule, str] = None,\n        fixed_mean: float = None,\n        first_fixing: float = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.side = side\n        self.notional = notional\n        self.notional_currency = notional_currency\n        self.asset = asset\n        self.asset_fixing_source = asset_fixing_source\n        self.contract = contract\n        self.fixing_currency = fixing_currency\n        self.fx_fixing_source = fx_fixing_source\n        self.settlement_date = settlement_date\n        self.strike = strike\n        self.variance_convention = variance_convention\n        self.annualization_factor = annualization_factor\n        self.divisor = divisor\n        self.start_date = start_date\n        self.end_date = end_date\n        self.mean_rule = mean_rule\n        self.fixed_mean = fixed_mean\n        self.first_fixing = first_fixing\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Commod"""\n        return AssetClass.Commod        \n\n    @property\n    def type(self) -> AssetType:\n        """VarianceSwap"""\n        return AssetType.VarianceSwap        \n\n    @property\n    def side(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__side\n\n    @side.setter\n    def side(self, value: Union[BuySell, str]):\n        self._property_changed('side')\n        self.__side = get_enum_value(BuySell, value)        \n\n    @property\n    def notional(self) -> float:\n        """The notional amount of the variance swap"""\n        return self.__notional\n\n    @notional.setter\n    def notional(self, value: float):\n        self._property_changed('notional')\n        self.__notional = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """The currency of the notional amount"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def asset(self) -> str:\n        """Commodity asset"""\n        return self.__asset\n\n    @asset.setter\n    def asset(self, value: str):\n        self._property_changed('asset')\n        self.__asset = value        \n\n    @property\n    def asset_fixing_source(self) -> str:\n        return self.__asset_fixing_source\n\n    @asset_fixing_source.setter\n    def asset_fixing_source(self, value: str):\n        self._property_changed('asset_fixing_source')\n        self.__asset_fixing_source = value        \n\n    @property\n    def contract(self) -> str:\n        """The contract we are observing (e.g. Z24)"""\n        return self.__contract\n\n    @contract.setter\n    def contract(self, value: str):\n        self._property_changed('contract')\n        self.__contract = value        \n\n    @property\n    def fixing_currency(self) -> Union[Currency, str]:\n        """The currency in which we observe the fix"""\n        return self.__fixing_currency\n\n    @fixing_currency.setter\n    def fixing_currency(self, value: Union[Currency, str]):\n        self._property_changed('fixing_currency')\n        self.__fixing_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fx_fixing_source(self) -> str:\n        """The source to use in the condition that the fixing currency is different from\n           the underlying currency"""\n        return self.__fx_fixing_source\n\n    @fx_fixing_source.setter\n    def fx_fixing_source(self, value: str):\n        self._property_changed('fx_fixing_source')\n        self.__fx_fixing_source = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Settlement date of the trade"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def strike(self) -> Union[float, str]:\n        """The strike in variance, default to ATM"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: Union[float, str]):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def variance_convention(self) -> Union[VarianceConvention, str]:\n        """'Annualized' to annualize the variance (using Annualization Factor) or 'total'\n           for no annualization'"""\n        return self.__variance_convention\n\n    @variance_convention.setter\n    def variance_convention(self, value: Union[VarianceConvention, str]):\n        self._property_changed('variance_convention')\n        self.__variance_convention = get_enum_value(VarianceConvention, value)        \n\n    @property\n    def annualization_factor(self) -> float:\n        """Annualization factor used to compute variance, defaults to 252"""\n        return self.__annualization_factor\n\n    @annualization_factor.setter\n    def annualization_factor(self, value: float):\n        self._property_changed('annualization_factor')\n        self.__annualization_factor = value        \n\n    @property\n    def divisor(self) -> str:\n        """Number of returns or Number of returns - 1"""\n        return self.__divisor\n\n    @divisor.setter\n    def divisor(self, value: str):\n        self._property_changed('divisor')\n        self.__divisor = value        \n\n    @property\n    def start_date(self) -> datetime.date:\n        """The start date of the observation"""\n        return self.__start_date\n\n    @start_date.setter\n    def start_date(self, value: datetime.date):\n        self._property_changed('start_date')\n        self.__start_date = value        \n\n    @property\n    def end_date(self) -> Union[datetime.date, str]:\n        """The end date of the observation"""\n        return self.__end_date\n\n    @end_date.setter\n    def end_date(self, value: Union[datetime.date, str]):\n        self._property_changed('end_date')\n        self.__end_date = value        \n\n    @property\n    def mean_rule(self) -> Union[CommodMeanRule, str]:\n        """Commodity mean rule"""\n        return self.__mean_rule\n\n    @mean_rule.setter\n    def mean_rule(self, value: Union[CommodMeanRule, str]):\n        self._property_changed('mean_rule')\n        self.__mean_rule = get_enum_value(CommodMeanRule, value)        \n\n    @property\n    def fixed_mean(self) -> float:\n        """True if we want to specify the mean to be used in variance computation"""\n        return self.__fixed_mean\n\n    @fixed_mean.setter\n    def fixed_mean(self, value: float):\n        self._property_changed('fixed_mean')\n        self.__fixed_mean = value        \n\n    @property\n    def first_fixing(self) -> float:\n        """By default there is none, if populate would use it as the first fixing"""\n        return self.__first_fixing\n\n    @first_fixing.setter\n    def first_fixing(self, value: float):\n        self._property_changed('first_fixing')\n        self.__first_fixing = value        \n\n\n
[docs]class EqCliquet(Instrument):\n        \n    """Object representation of an Equity Cliquet"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: Union[float, str] = None,\n        underlier_type: Union[UnderlierType, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        strike_price: float = None,\n        currency: Union[Currency, str] = None,\n        first_valuation_date: datetime.date = None,\n        global_floor: float = -1000000,\n        global_cap: float = 1000000,\n        last_valuation_date: datetime.date = None,\n        notional_amount: Union[float, str] = None,\n        payment_frequency: str = 'Maturity',\n        return_style: str = 'Rate of Return',\n        return_type: str = 'Sum',\n        valuation_period: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.underlier_type = underlier_type\n        self.expiration_date = expiration_date\n        self.strike_price = strike_price\n        self.currency = currency\n        self.first_valuation_date = first_valuation_date\n        self.global_floor = global_floor\n        self.global_cap = global_cap\n        self.last_valuation_date = last_valuation_date\n        self.notional_amount = notional_amount\n        self.payment_frequency = payment_frequency\n        self.return_style = return_style\n        self.return_type = return_type\n        self.valuation_period = valuation_period\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Equity"""\n        return AssetClass.Equity        \n\n    @property\n    def type(self) -> AssetType:\n        """Cliquet"""\n        return AssetType.Cliquet        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier security identifier"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def underlier_type(self) -> Union[UnderlierType, str]:\n        """Type of underlyer"""\n        return self.__underlier_type\n\n    @underlier_type.setter\n    def underlier_type(self, value: Union[UnderlierType, str]):\n        self._property_changed('underlier_type')\n        self.__underlier_type = get_enum_value(UnderlierType, value)        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def strike_price(self) -> float:\n        """Strike price as value"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: float):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def first_valuation_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__first_valuation_date\n\n    @first_valuation_date.setter\n    def first_valuation_date(self, value: datetime.date):\n        self._property_changed('first_valuation_date')\n        self.__first_valuation_date = value        \n\n    @property\n    def global_floor(self) -> float:\n        """Global Floor of return, relevant only if paying at maturity"""\n        return self.__global_floor\n\n    @global_floor.setter\n    def global_floor(self, value: float):\n        self._property_changed('global_floor')\n        self.__global_floor = value        \n\n    @property\n    def global_cap(self) -> float:\n        """Global Cap of return, relevant only if paying at maturity"""\n        return self.__global_cap\n\n    @global_cap.setter\n    def global_cap(self, value: float):\n        self._property_changed('global_cap')\n        self.__global_cap = value        \n\n    @property\n    def last_valuation_date(self) -> datetime.date:\n        """ISO 8601-formatted date"""\n        return self.__last_valuation_date\n\n    @last_valuation_date.setter\n    def last_valuation_date(self, value: datetime.date):\n        self._property_changed('last_valuation_date')\n        self.__last_valuation_date = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional of this position"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def payment_frequency(self) -> str:\n        return self.__payment_frequency\n\n    @payment_frequency.setter\n    def payment_frequency(self, value: str):\n        self._property_changed('payment_frequency')\n        self.__payment_frequency = value        \n\n    @property\n    def return_style(self) -> str:\n        """Return calculation style"""\n        return self.__return_style\n\n    @return_style.setter\n    def return_style(self, value: str):\n        self._property_changed('return_style')\n        self.__return_style = value        \n\n    @property\n    def return_type(self) -> str:\n        """Sum or Product of periodic return, relevant only if paying at maturity"""\n        return self.__return_type\n\n    @return_type.setter\n    def return_type(self, value: str):\n        self._property_changed('return_type')\n        self.__return_type = value        \n\n    @property\n    def valuation_period(self) -> str:\n        """Tenor"""\n        return self.__valuation_period\n\n    @valuation_period.setter\n    def valuation_period(self, value: str):\n        self._property_changed('valuation_period')\n        self.__valuation_period = value        
\n\n\n
[docs]class EqForward(Instrument):\n        \n    """Object representation of an equity forward"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: Union[float, str] = None,\n        underlier_type: Union[UnderlierType, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        forward_price: float = None,\n        number_of_shares: int = 1,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.underlier_type = underlier_type\n        self.expiration_date = expiration_date\n        self.forward_price = forward_price\n        self.number_of_shares = number_of_shares\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Equity"""\n        return AssetClass.Equity        \n\n    @property\n    def type(self) -> AssetType:\n        """Forward"""\n        return AssetType.Forward        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier security identifier"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def underlier_type(self) -> Union[UnderlierType, str]:\n        """Type of underlyer"""\n        return self.__underlier_type\n\n    @underlier_type.setter\n    def underlier_type(self, value: Union[UnderlierType, str]):\n        self._property_changed('underlier_type')\n        self.__underlier_type = get_enum_value(UnderlierType, value)        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def forward_price(self) -> float:\n        """Forward price"""\n        return self.__forward_price\n\n    @forward_price.setter\n    def forward_price(self, value: float):\n        self._property_changed('forward_price')\n        self.__forward_price = value        \n\n    @property\n    def number_of_shares(self) -> int:\n        """Number of shares"""\n        return self.__number_of_shares\n\n    @number_of_shares.setter\n    def number_of_shares(self, value: int):\n        self._property_changed('number_of_shares')\n        self.__number_of_shares = value        
\n\n\n
[docs]class EqOption(Instrument):\n        \n    """Instrument definition for equity option"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: Union[float, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        strike_price: Union[float, str] = None,\n        option_type: Union[OptionType, str] = None,\n        option_style: Union[OptionStyle, str] = None,\n        number_of_options: float = None,\n        exchange: str = None,\n        multiplier: float = None,\n        settlement_date: Union[datetime.date, str] = None,\n        settlement_currency: Union[Currency, str] = None,\n        premium: float = 0,\n        premium_payment_date: Union[datetime.date, str] = None,\n        valuation_time: Union[ValuationTime, str] = None,\n        method_of_settlement: Union[OptionSettlementMethod, str] = None,\n        underlier_type: Union[UnderlierType, str] = None,\n        buy_sell: Union[BuySell, str] = None,\n        premium_currency: Union[Currency, str] = None,\n        trade_as: Union[TradeAs, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.expiration_date = expiration_date\n        self.strike_price = strike_price\n        self.option_type = option_type\n        self.option_style = option_style\n        self.number_of_options = number_of_options\n        self.exchange = exchange\n        self.multiplier = multiplier\n        self.settlement_date = settlement_date\n        self.settlement_currency = settlement_currency\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.valuation_time = valuation_time\n        self.method_of_settlement = method_of_settlement\n        self.underlier_type = underlier_type\n        self.buy_sell = buy_sell\n        self.premium_currency = premium_currency\n        self.trade_as = trade_as\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Equity"""\n        return AssetClass.Equity        \n\n    @property\n    def type(self) -> AssetType:\n        """Option"""\n        return AssetType.Option        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier security identifier"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def option_style(self) -> Union[OptionStyle, str]:\n        """Option Exercise Style"""\n        return self.__option_style\n\n    @option_style.setter\n    def option_style(self, value: Union[OptionStyle, str]):\n        self._property_changed('option_style')\n        self.__option_style = get_enum_value(OptionStyle, value)        \n\n    @property\n    def number_of_options(self) -> float:\n        """Number of options"""\n        return self.__number_of_options\n\n    @number_of_options.setter\n    def number_of_options(self, value: float):\n        self._property_changed('number_of_options')\n        self.__number_of_options = value        \n\n    @property\n    def exchange(self) -> str:\n        """Name of marketplace where security, derivative or other instrument is traded"""\n        return self.__exchange\n\n    @exchange.setter\n    def exchange(self, value: str):\n        self._property_changed('exchange')\n        self.__exchange = value        \n\n    @property\n    def multiplier(self) -> float:\n        """Number of stock units per option contract"""\n        return self.__multiplier\n\n    @multiplier.setter\n    def multiplier(self, value: float):\n        self._property_changed('multiplier')\n        self.__multiplier = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def settlement_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__settlement_currency\n\n    @settlement_currency.setter\n    def settlement_currency(self, value: Union[Currency, str]):\n        self._property_changed('settlement_currency')\n        self.__settlement_currency = get_enum_value(Currency, value)        \n\n    @property\n    def premium(self) -> float:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: float):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Option premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def valuation_time(self) -> Union[ValuationTime, str]:\n        """Valuation time (e.g. MktClose, MktOpen) of the underlying level for exercise"""\n        return self.__valuation_time\n\n    @valuation_time.setter\n    def valuation_time(self, value: Union[ValuationTime, str]):\n        self._property_changed('valuation_time')\n        self.__valuation_time = get_enum_value(ValuationTime, value)        \n\n    @property\n    def method_of_settlement(self) -> Union[OptionSettlementMethod, str]:\n        """How the option is settled (e.g. Cash, Physical)"""\n        return self.__method_of_settlement\n\n    @method_of_settlement.setter\n    def method_of_settlement(self, value: Union[OptionSettlementMethod, str]):\n        self._property_changed('method_of_settlement')\n        self.__method_of_settlement = get_enum_value(OptionSettlementMethod, value)        \n\n    @property\n    def underlier_type(self) -> Union[UnderlierType, str]:\n        """Type of underlyer"""\n        return self.__underlier_type\n\n    @underlier_type.setter\n    def underlier_type(self, value: Union[UnderlierType, str]):\n        self._property_changed('underlier_type')\n        self.__underlier_type = get_enum_value(UnderlierType, value)        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def premium_currency(self) -> Union[Currency, str]:\n        """Currency of the option premium"""\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: Union[Currency, str]):\n        self._property_changed('premium_currency')\n        self.__premium_currency = get_enum_value(Currency, value)        \n\n    @property\n    def trade_as(self) -> Union[TradeAs, str]:\n        """Option trade as (i.e. listed, otc, lookalike etc)"""\n        return self.__trade_as\n\n    @trade_as.setter\n    def trade_as(self, value: Union[TradeAs, str]):\n        self._property_changed('trade_as')\n        self.__trade_as = get_enum_value(TradeAs, value)        
\n\n\nclass EqOptionLeg(Instrument):\n        \n    """Instrument definition for equity option leg"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        expiration_date: Union[datetime.date, str] = None,\n        strike_price: Union[float, str] = None,\n        option_type: Union[OptionType, str] = None,\n        option_style: Union[OptionStyle, str] = None,\n        number_of_options: float = None,\n        exchange: str = None,\n        multiplier: float = None,\n        settlement_date: Union[datetime.date, str] = None,\n        settlement_currency: Union[Currency, str] = None,\n        premium: float = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        valuation_time: Union[ValuationTime, str] = None,\n        method_of_settlement: Union[OptionSettlementMethod, str] = None,\n        buy_sell: Union[BuySell, str] = None,\n        premium_currency: Union[Currency, str] = None,\n        trade_as: Union[TradeAs, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.expiration_date = expiration_date\n        self.strike_price = strike_price\n        self.option_type = option_type\n        self.option_style = option_style\n        self.number_of_options = number_of_options\n        self.exchange = exchange\n        self.multiplier = multiplier\n        self.settlement_date = settlement_date\n        self.settlement_currency = settlement_currency\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.valuation_time = valuation_time\n        self.method_of_settlement = method_of_settlement\n        self.buy_sell = buy_sell\n        self.premium_currency = premium_currency\n        self.trade_as = trade_as\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Equity"""\n        return AssetClass.Equity        \n\n    @property\n    def type(self) -> AssetType:\n        """OptionLeg"""\n        return AssetType.OptionLeg        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def option_style(self) -> Union[OptionStyle, str]:\n        """Option Exercise Style"""\n        return self.__option_style\n\n    @option_style.setter\n    def option_style(self, value: Union[OptionStyle, str]):\n        self._property_changed('option_style')\n        self.__option_style = get_enum_value(OptionStyle, value)        \n\n    @property\n    def number_of_options(self) -> float:\n        """Number of options"""\n        return self.__number_of_options\n\n    @number_of_options.setter\n    def number_of_options(self, value: float):\n        self._property_changed('number_of_options')\n        self.__number_of_options = value        \n\n    @property\n    def exchange(self) -> str:\n        """Name of marketplace where security, derivative or other instrument is traded"""\n        return self.__exchange\n\n    @exchange.setter\n    def exchange(self, value: str):\n        self._property_changed('exchange')\n        self.__exchange = value        \n\n    @property\n    def multiplier(self) -> float:\n        """Number of stock units per option contract"""\n        return self.__multiplier\n\n    @multiplier.setter\n    def multiplier(self, value: float):\n        self._property_changed('multiplier')\n        self.__multiplier = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def settlement_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__settlement_currency\n\n    @settlement_currency.setter\n    def settlement_currency(self, value: Union[Currency, str]):\n        self._property_changed('settlement_currency')\n        self.__settlement_currency = get_enum_value(Currency, value)        \n\n    @property\n    def premium(self) -> float:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: float):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Option premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def valuation_time(self) -> Union[ValuationTime, str]:\n        """Valuation time (e.g. MktClose, MktOpen) of the underlying level for exercise"""\n        return self.__valuation_time\n\n    @valuation_time.setter\n    def valuation_time(self, value: Union[ValuationTime, str]):\n        self._property_changed('valuation_time')\n        self.__valuation_time = get_enum_value(ValuationTime, value)        \n\n    @property\n    def method_of_settlement(self) -> Union[OptionSettlementMethod, str]:\n        """How the option is settled (e.g. Cash, Physical)"""\n        return self.__method_of_settlement\n\n    @method_of_settlement.setter\n    def method_of_settlement(self, value: Union[OptionSettlementMethod, str]):\n        self._property_changed('method_of_settlement')\n        self.__method_of_settlement = get_enum_value(OptionSettlementMethod, value)        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def premium_currency(self) -> Union[Currency, str]:\n        """Currency of the option premium"""\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: Union[Currency, str]):\n        self._property_changed('premium_currency')\n        self.__premium_currency = get_enum_value(Currency, value)        \n\n    @property\n    def trade_as(self) -> Union[TradeAs, str]:\n        """Option trade as (i.e. listed, otc, lookalike etc)"""\n        return self.__trade_as\n\n    @trade_as.setter\n    def trade_as(self, value: Union[TradeAs, str]):\n        self._property_changed('trade_as')\n        self.__trade_as = get_enum_value(TradeAs, value)        \n\n\nclass EqStock(Instrument):\n        \n    """Instrument definition for equities"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        asset_name: str = None,\n        buy_sell: Union[BuySell, str] = None,\n        premium: float = None,\n        premium_currency: str = None,\n        quantity: float = None,\n        settlement_date: datetime.date = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.asset_name = asset_name\n        self.buy_sell = buy_sell\n        self.premium = premium\n        self.premium_currency = premium_currency\n        self.quantity = quantity\n        self.settlement_date = settlement_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Equity"""\n        return AssetClass.Equity        \n\n    @property\n    def type(self) -> AssetType:\n        """Single Stock"""\n        return AssetType.Single_Stock        \n\n    @property\n    def asset_name(self) -> str:\n        return self.__asset_name\n\n    @asset_name.setter\n    def asset_name(self, value: str):\n        self._property_changed('asset_name')\n        self.__asset_name = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def premium(self) -> float:\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: float):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_currency(self) -> str:\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: str):\n        self._property_changed('premium_currency')\n        self.__premium_currency = value        \n\n    @property\n    def quantity(self) -> float:\n        return self.__quantity\n\n    @quantity.setter\n    def quantity(self, value: float):\n        self._property_changed('quantity')\n        self.__quantity = value        \n\n    @property\n    def settlement_date(self) -> datetime.date:\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: datetime.date):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n\n
[docs]class EqSynthetic(Instrument):\n        \n    """Instrument definition for equity synthetics"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: Union[float, str],\n        expiry: str,\n        currency: Union[Currency, str] = None,\n        swap_type: str = 'Eq Swap',\n        buy_sell: Union[BuySell, str] = None,\n        underlier_type: Union[UnderlierType, str] = None,\n        effective_date: datetime.date = None,\n        num_of_underlyers: float = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.currency = currency\n        self.swap_type = swap_type\n        self.buy_sell = buy_sell\n        self.underlier_type = underlier_type\n        self.effective_date = effective_date\n        self.num_of_underlyers = num_of_underlyers\n        self.expiry = expiry\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Equity"""\n        return AssetClass.Equity        \n\n    @property\n    def type(self) -> AssetType:\n        """Synthetic"""\n        return AssetType.Synthetic        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier security identifier"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def swap_type(self) -> str:\n        return self.__swap_type\n\n    @swap_type.setter\n    def swap_type(self, value: str):\n        self._property_changed('swap_type')\n        self.__swap_type = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def underlier_type(self) -> Union[UnderlierType, str]:\n        """Type of underlyer"""\n        return self.__underlier_type\n\n    @underlier_type.setter\n    def underlier_type(self, value: Union[UnderlierType, str]):\n        self._property_changed('underlier_type')\n        self.__underlier_type = get_enum_value(UnderlierType, value)        \n\n    @property\n    def effective_date(self) -> datetime.date:\n        """The date on which the synthetic becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: datetime.date):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def num_of_underlyers(self) -> float:\n        """number of underlyers referenced in synthetic contract"""\n        return self.__num_of_underlyers\n\n    @num_of_underlyers.setter\n    def num_of_underlyers(self, value: float):\n        self._property_changed('num_of_underlyers')\n        self.__num_of_underlyers = value        \n\n    @property\n    def expiry(self) -> str:\n        """Tenor"""\n        return self.__expiry\n\n    @expiry.setter\n    def expiry(self, value: str):\n        self._property_changed('expiry')\n        self.__expiry = value        
\n\n\n
[docs]class EqVarianceSwap(Instrument):\n        \n    """Instrument definition for equity variance swap"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: Union[float, str] = None,\n        underlier_type: Union[UnderlierType, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        strike_price: Union[float, str] = None,\n        variance_cap: float = None,\n        settlement_date: Union[datetime.date, str] = None,\n        premium: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.underlier_type = underlier_type\n        self.expiration_date = expiration_date\n        self.strike_price = strike_price\n        self.variance_cap = variance_cap\n        self.settlement_date = settlement_date\n        self.premium = premium\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Equity"""\n        return AssetClass.Equity        \n\n    @property\n    def type(self) -> AssetType:\n        """VarianceSwap"""\n        return AssetType.VarianceSwap        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier security identifier"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def underlier_type(self) -> Union[UnderlierType, str]:\n        """Type of underlyer"""\n        return self.__underlier_type\n\n    @underlier_type.setter\n    def underlier_type(self, value: Union[UnderlierType, str]):\n        self._property_changed('underlier_type')\n        self.__underlier_type = get_enum_value(UnderlierType, value)        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Variance strike as value or percentage string e.g. 62.5, 95%"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def variance_cap(self) -> float:\n        """Variance Cap as absolute value"""\n        return self.__variance_cap\n\n    @variance_cap.setter\n    def variance_cap(self, value: float):\n        self._property_changed('variance_cap')\n        self.__variance_cap = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Settlement date"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """VarSwap premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        
\n\n\nclass FRA(Instrument):\n        \n    """A forward rate agreement"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        clearing_house: Union[SwapClearingHouse, str] = None,\n        clearing_legally_binding: float = None,\n        day_count_fraction: Union[DayCountFraction, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        fixed_rate: Union[float, str] = None,\n        frequency: str = None,\n        calendar: str = None,\n        rate_option: str = None,\n        maturity: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        payment_delay: str = None,\n        roll_convention: str = None,\n        notional_amount: Union[float, str] = None,\n        spread: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.clearing_house = clearing_house\n        self.clearing_legally_binding = clearing_legally_binding\n        self.day_count_fraction = day_count_fraction\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.fixed_rate = fixed_rate\n        self.frequency = frequency\n        self.calendar = calendar\n        self.rate_option = rate_option\n        self.maturity = maturity\n        self.notional_currency = notional_currency\n        self.payment_delay = payment_delay\n        self.roll_convention = roll_convention\n        self.notional_amount = notional_amount\n        self.spread = spread\n        self.effective_date = effective_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """FRA"""\n        return AssetType.FRA        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def clearing_house(self) -> Union[SwapClearingHouse, str]:\n        """Swap Clearing House"""\n        return self.__clearing_house\n\n    @clearing_house.setter\n    def clearing_house(self, value: Union[SwapClearingHouse, str]):\n        self._property_changed('clearing_house')\n        self.__clearing_house = get_enum_value(SwapClearingHouse, value)        \n\n    @property\n    def clearing_legally_binding(self) -> float:\n        return self.__clearing_legally_binding\n\n    @clearing_legally_binding.setter\n    def clearing_legally_binding(self, value: float):\n        self._property_changed('clearing_legally_binding')\n        self.__clearing_legally_binding = value        \n\n    @property\n    def day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction"""\n        return self.__day_count_fraction\n\n    @day_count_fraction.setter\n    def day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('day_count_fraction')\n        self.__day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def fixed_rate(self) -> Union[float, str]:\n        """The forward rate"""\n        return self.__fixed_rate\n\n    @fixed_rate.setter\n    def fixed_rate(self, value: Union[float, str]):\n        self._property_changed('fixed_rate')\n        self.__fixed_rate = value        \n\n    @property\n    def frequency(self) -> str:\n        """The frequency of floating payments, e.g. 3m"""\n        return self.__frequency\n\n    @frequency.setter\n    def frequency(self, value: str):\n        self._property_changed('frequency')\n        self.__frequency = value        \n\n    @property\n    def calendar(self) -> str:\n        """The calendar"""\n        return self.__calendar\n\n    @calendar.setter\n    def calendar(self, value: str):\n        self._property_changed('calendar')\n        self.__calendar = value        \n\n    @property\n    def rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__rate_option\n\n    @rate_option.setter\n    def rate_option(self, value: str):\n        self._property_changed('rate_option')\n        self.__rate_option = value        \n\n    @property\n    def maturity(self) -> Union[Union[datetime.date, str], str]:\n        """The maturity of the FRA, e.g. 2050-04-01, 10y"""\n        return self.__maturity\n\n    @maturity.setter\n    def maturity(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('maturity')\n        self.__maturity = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def payment_delay(self) -> str:\n        """The delay of payments"""\n        return self.__payment_delay\n\n    @payment_delay.setter\n    def payment_delay(self, value: str):\n        self._property_changed('payment_delay')\n        self.__payment_delay = value        \n\n    @property\n    def roll_convention(self) -> str:\n        """The roll convention"""\n        return self.__roll_convention\n\n    @roll_convention.setter\n    def roll_convention(self, value: str):\n        self._property_changed('roll_convention')\n        self.__roll_convention = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def spread(self) -> Union[float, str]:\n        """The spread over the floating rate"""\n        return self.__spread\n\n    @spread.setter\n    def spread(self, value: Union[float, str]):\n        self._property_changed('spread')\n        self.__spread = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the FRA becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n\n
[docs]class FXBinary(Instrument):\n        \n    """Object representation of a FX binary option"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pair: str = None,\n        buy_sell: Union[BuySell, str] = None,\n        option_type: Union[OptionType, str] = None,\n        notional_amount: Union[float, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        strike_price: Union[float, str] = None,\n        settlement_date: Union[datetime.date, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        expiration_time: str = None,\n        premium: Union[float, str] = None,\n        premium_currency: Union[Currency, str] = None,\n        premium_payment_date: str = None,\n        fixing_source: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pair = pair\n        self.buy_sell = buy_sell\n        self.option_type = option_type\n        self.notional_amount = notional_amount\n        self.notional_currency = notional_currency\n        self.strike_price = strike_price\n        self.settlement_date = settlement_date\n        self.expiration_date = expiration_date\n        self.expiration_time = expiration_time\n        self.premium = premium\n        self.premium_currency = premium_currency\n        self.premium_payment_date = premium_payment_date\n        self.fixing_source = fixing_source\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """FX"""\n        return AssetClass.FX        \n\n    @property\n    def type(self) -> AssetType:\n        """Binary"""\n        return AssetType.Binary        \n\n    @property\n    def pair(self) -> str:\n        """A currency pair, e.g.: EURUSD or EUR USD"""\n        return self.__pair\n\n    @pair.setter\n    def pair(self, value: str):\n        self._property_changed('pair')\n        self.__pair = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Settlement date of the option, after expiration"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def expiration_time(self) -> str:\n        """The location and (optionally) time of spot for expiration"""\n        return self.__expiration_time\n\n    @expiration_time.setter\n    def expiration_time(self, value: str):\n        self._property_changed('expiration_time')\n        self.__expiration_time = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_currency(self) -> Union[Currency, str]:\n        """Currency of the option premium"""\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: Union[Currency, str]):\n        self._property_changed('premium_currency')\n        self.__premium_currency = get_enum_value(Currency, value)        \n\n    @property\n    def premium_payment_date(self) -> str:\n        """Payment date of the option premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: str):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fixing_source(self) -> str:\n        """The data source to be used for observation of FX spot on the fixing date"""\n        return self.__fixing_source\n\n    @fixing_source.setter\n    def fixing_source(self, value: str):\n        self._property_changed('fixing_source')\n        self.__fixing_source = value        
\n\n\n
[docs]class FXForward(Instrument):\n        \n    """Object representation of an FX forward"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pair: str = None,\n        settlement_date: Union[datetime.date, str] = None,\n        forward_rate: Union[float, str] = None,\n        notional_amount: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pair = pair\n        self.settlement_date = settlement_date\n        self.forward_rate = forward_rate\n        self.notional_amount = notional_amount\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """FX"""\n        return AssetClass.FX        \n\n    @property\n    def type(self) -> AssetType:\n        """Forward"""\n        return AssetType.Forward        \n\n    @property\n    def pair(self) -> str:\n        """A currency pair, e.g.: EURUSD or EUR USD"""\n        return self.__pair\n\n    @pair.setter\n    def pair(self, value: str):\n        self._property_changed('pair')\n        self.__pair = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def forward_rate(self) -> Union[float, str]:\n        """Forward FX rate"""\n        return self.__forward_rate\n\n    @forward_rate.setter\n    def forward_rate(self, value: Union[float, str]):\n        self._property_changed('forward_rate')\n        self.__forward_rate = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        
\n\n\n
[docs]class FXMultiCrossBinaryLeg(Instrument):\n        \n    """Object representation of a single leg of a multi-cross binary option"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pair: str = None,\n        option_type: Union[OptionType, str] = None,\n        strike_price: Union[float, str] = None,\n        fixing_source: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pair = pair\n        self.option_type = option_type\n        self.strike_price = strike_price\n        self.fixing_source = fixing_source\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """FX"""\n        return AssetClass.FX        \n\n    @property\n    def type(self) -> AssetType:\n        """MultiCrossBinaryLeg"""\n        return AssetType.MultiCrossBinaryLeg        \n\n    @property\n    def pair(self) -> str:\n        """A currency pair, e.g.: EURUSD or EUR USD"""\n        return self.__pair\n\n    @pair.setter\n    def pair(self, value: str):\n        self._property_changed('pair')\n        self.__pair = value        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def fixing_source(self) -> str:\n        """The data source to be used for observation of FX spot on the fixing date"""\n        return self.__fixing_source\n\n    @fixing_source.setter\n    def fixing_source(self, value: str):\n        self._property_changed('fixing_source')\n        self.__fixing_source = value        
\n\n\n
[docs]class FXOption(Instrument):\n        \n    """Object representation of an FX option"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pair: str = None,\n        buy_sell: Union[BuySell, str] = None,\n        option_type: Union[OptionType, str] = None,\n        notional_amount: Union[float, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount_other_currency: Union[float, str] = None,\n        strike_price: Union[float, str] = None,\n        settlement_date: Union[datetime.date, str] = None,\n        settlement_currency: Union[Currency, str] = None,\n        settlement_rate_option: str = None,\n        method_of_settlement: Union[OptionSettlementMethod, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        expiration_time: str = None,\n        premium: Union[float, str] = None,\n        premium_currency: Union[Currency, str] = None,\n        premium_payment_date: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pair = pair\n        self.buy_sell = buy_sell\n        self.option_type = option_type\n        self.notional_amount = notional_amount\n        self.notional_currency = notional_currency\n        self.notional_amount_other_currency = notional_amount_other_currency\n        self.strike_price = strike_price\n        self.settlement_date = settlement_date\n        self.settlement_currency = settlement_currency\n        self.settlement_rate_option = settlement_rate_option\n        self.method_of_settlement = method_of_settlement\n        self.expiration_date = expiration_date\n        self.expiration_time = expiration_time\n        self.premium = premium\n        self.premium_currency = premium_currency\n        self.premium_payment_date = premium_payment_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """FX"""\n        return AssetClass.FX        \n\n    @property\n    def type(self) -> AssetType:\n        """Option"""\n        return AssetType.Option        \n\n    @property\n    def pair(self) -> str:\n        """A currency pair, e.g.: EURUSD or EUR USD"""\n        return self.__pair\n\n    @pair.setter\n    def pair(self, value: str):\n        self._property_changed('pair')\n        self.__pair = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount_other_currency(self) -> Union[float, str]:\n        """Notional amount in currency other than NotionalCurrency from the pair"""\n        return self.__notional_amount_other_currency\n\n    @notional_amount_other_currency.setter\n    def notional_amount_other_currency(self, value: Union[float, str]):\n        self._property_changed('notional_amount_other_currency')\n        self.__notional_amount_other_currency = value        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Settlement date of the option, after expiration"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def settlement_currency(self) -> Union[Currency, str]:\n        """Currency of settlement"""\n        return self.__settlement_currency\n\n    @settlement_currency.setter\n    def settlement_currency(self, value: Union[Currency, str]):\n        self._property_changed('settlement_currency')\n        self.__settlement_currency = get_enum_value(Currency, value)        \n\n    @property\n    def settlement_rate_option(self) -> str:\n        """The source of spot for settlement"""\n        return self.__settlement_rate_option\n\n    @settlement_rate_option.setter\n    def settlement_rate_option(self, value: str):\n        self._property_changed('settlement_rate_option')\n        self.__settlement_rate_option = value        \n\n    @property\n    def method_of_settlement(self) -> Union[OptionSettlementMethod, str]:\n        """How the option is settled (e.g. Cash, Physical)"""\n        return self.__method_of_settlement\n\n    @method_of_settlement.setter\n    def method_of_settlement(self, value: Union[OptionSettlementMethod, str]):\n        self._property_changed('method_of_settlement')\n        self.__method_of_settlement = get_enum_value(OptionSettlementMethod, value)        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def expiration_time(self) -> str:\n        """The location and (optionally) time of spot for expiration"""\n        return self.__expiration_time\n\n    @expiration_time.setter\n    def expiration_time(self, value: str):\n        self._property_changed('expiration_time')\n        self.__expiration_time = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_currency(self) -> Union[Currency, str]:\n        """Currency of the option premium"""\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: Union[Currency, str]):\n        self._property_changed('premium_currency')\n        self.__premium_currency = get_enum_value(Currency, value)        \n\n    @property\n    def premium_payment_date(self) -> str:\n        """Payment date of the option premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: str):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        
\n\n\nclass FXOptionLeg(Instrument):\n        \n    """Object representation of a FX option leg used in FXOptionStrategy"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        option_type: Union[OptionType, str] = None,\n        notional_amount: Union[float, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount_other_currency: Union[float, str] = None,\n        strike_price: Union[float, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        settlement_date: Union[datetime.date, str] = None,\n        premium: Union[float, str] = None,\n        premium_currency: Union[Currency, str] = None,\n        premium_payment_date: str = None,\n        settlement_currency: Union[Currency, str] = None,\n        settlement_rate_option: str = None,\n        method_of_settlement: Union[OptionSettlementMethod, str] = None,\n        expiration_time: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.option_type = option_type\n        self.notional_amount = notional_amount\n        self.notional_currency = notional_currency\n        self.notional_amount_other_currency = notional_amount_other_currency\n        self.strike_price = strike_price\n        self.expiration_date = expiration_date\n        self.settlement_date = settlement_date\n        self.premium = premium\n        self.premium_currency = premium_currency\n        self.premium_payment_date = premium_payment_date\n        self.settlement_currency = settlement_currency\n        self.settlement_rate_option = settlement_rate_option\n        self.method_of_settlement = method_of_settlement\n        self.expiration_time = expiration_time\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """FX"""\n        return AssetClass.FX        \n\n    @property\n    def type(self) -> AssetType:\n        """OptionLeg"""\n        return AssetType.OptionLeg        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount_other_currency(self) -> Union[float, str]:\n        """Notional amount in currency other than NotionalCurrency from the pair"""\n        return self.__notional_amount_other_currency\n\n    @notional_amount_other_currency.setter\n    def notional_amount_other_currency(self, value: Union[float, str]):\n        self._property_changed('notional_amount_other_currency')\n        self.__notional_amount_other_currency = value        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Settlement date of the option, after expiration"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_currency(self) -> Union[Currency, str]:\n        """Currency of the option premium"""\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: Union[Currency, str]):\n        self._property_changed('premium_currency')\n        self.__premium_currency = get_enum_value(Currency, value)        \n\n    @property\n    def premium_payment_date(self) -> str:\n        """Payment date of the option premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: str):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def settlement_currency(self) -> Union[Currency, str]:\n        """Currency of settlement"""\n        return self.__settlement_currency\n\n    @settlement_currency.setter\n    def settlement_currency(self, value: Union[Currency, str]):\n        self._property_changed('settlement_currency')\n        self.__settlement_currency = get_enum_value(Currency, value)        \n\n    @property\n    def settlement_rate_option(self) -> str:\n        """The source of spot for settlement"""\n        return self.__settlement_rate_option\n\n    @settlement_rate_option.setter\n    def settlement_rate_option(self, value: str):\n        self._property_changed('settlement_rate_option')\n        self.__settlement_rate_option = value        \n\n    @property\n    def method_of_settlement(self) -> Union[OptionSettlementMethod, str]:\n        """How the option is settled (e.g. Cash, Physical)"""\n        return self.__method_of_settlement\n\n    @method_of_settlement.setter\n    def method_of_settlement(self, value: Union[OptionSettlementMethod, str]):\n        self._property_changed('method_of_settlement')\n        self.__method_of_settlement = get_enum_value(OptionSettlementMethod, value)        \n\n    @property\n    def expiration_time(self) -> str:\n        """The location and (optionally) time of spot for expiration"""\n        return self.__expiration_time\n\n    @expiration_time.setter\n    def expiration_time(self, value: str):\n        self._property_changed('expiration_time')\n        self.__expiration_time = value        \n\n\n
[docs]class FXVolatilitySwap(Instrument):\n        \n    """Object representation of an FX Vol Swap"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pair: str = None,\n        buy_sell: Union[BuySell, str] = None,\n        strike_vol: Union[float, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        first_fixing_date: Union[datetime.date, str] = None,\n        last_fixing_date: Union[datetime.date, str] = None,\n        settlement_date: Union[datetime.date, str] = None,\n        fixing_source: str = None,\n        fixing_frequency: str = None,\n        annualization_factor: float = None,\n        calculate_mean_return: float = 0,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pair = pair\n        self.buy_sell = buy_sell\n        self.strike_vol = strike_vol\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.first_fixing_date = first_fixing_date\n        self.last_fixing_date = last_fixing_date\n        self.settlement_date = settlement_date\n        self.fixing_source = fixing_source\n        self.fixing_frequency = fixing_frequency\n        self.annualization_factor = annualization_factor\n        self.calculate_mean_return = calculate_mean_return\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """FX"""\n        return AssetClass.FX        \n\n    @property\n    def type(self) -> AssetType:\n        """VolatilitySwap"""\n        return AssetType.VolatilitySwap        \n\n    @property\n    def pair(self) -> str:\n        """A currency pair, e.g.: EURUSD or EUR USD"""\n        return self.__pair\n\n    @pair.setter\n    def pair(self, value: str):\n        self._property_changed('pair')\n        self.__pair = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def strike_vol(self) -> Union[float, str]:\n        """Volatility strike"""\n        return self.__strike_vol\n\n    @strike_vol.setter\n    def strike_vol(self, value: Union[float, str]):\n        self._property_changed('strike_vol')\n        self.__strike_vol = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """ Notional amount in dollar terms"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def first_fixing_date(self) -> Union[datetime.date, str]:\n        """First averaging date or observation date"""\n        return self.__first_fixing_date\n\n    @first_fixing_date.setter\n    def first_fixing_date(self, value: Union[datetime.date, str]):\n        self._property_changed('first_fixing_date')\n        self.__first_fixing_date = value        \n\n    @property\n    def last_fixing_date(self) -> Union[datetime.date, str]:\n        """Last averaging date or valuation date"""\n        return self.__last_fixing_date\n\n    @last_fixing_date.setter\n    def last_fixing_date(self, value: Union[datetime.date, str]):\n        self._property_changed('last_fixing_date')\n        self.__last_fixing_date = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def fixing_source(self) -> str:\n        """The data source to be used for observations of FX spot on each fixing"""\n        return self.__fixing_source\n\n    @fixing_source.setter\n    def fixing_source(self, value: str):\n        self._property_changed('fixing_source')\n        self.__fixing_source = value        \n\n    @property\n    def fixing_frequency(self) -> str:\n        """Fixing frequency (ex. Daily / Business Days)"""\n        return self.__fixing_frequency\n\n    @fixing_frequency.setter\n    def fixing_frequency(self, value: str):\n        self._property_changed('fixing_frequency')\n        self.__fixing_frequency = value        \n\n    @property\n    def annualization_factor(self) -> float:\n        """Annualization factor is the number of days used per year to compute volatility"""\n        return self.__annualization_factor\n\n    @annualization_factor.setter\n    def annualization_factor(self, value: float):\n        self._property_changed('annualization_factor')\n        self.__annualization_factor = value        \n\n    @property\n    def calculate_mean_return(self) -> float:\n        """Indicates whether the mean return is calculated (true) or taken as zero (false)\n           in the realized volatility computation"""\n        return self.__calculate_mean_return\n\n    @calculate_mean_return.setter\n    def calculate_mean_return(self, value: float):\n        self._property_changed('calculate_mean_return')\n        self.__calculate_mean_return = value        
\n\n\n
[docs]class Forward(Instrument):\n        \n    """Forward cash payment"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        currency: Union[Currency, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        notional_amount: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.currency = currency\n        self.expiration_date = expiration_date\n        self.notional_amount = notional_amount\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Cash"""\n        return AssetClass.Cash        \n\n    @property\n    def type(self) -> AssetType:\n        """Forward"""\n        return AssetType.Forward        \n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        
\n\n\n
[docs]class IRBasisSwap(Instrument):\n        \n    """A single currency exchange of cashflows from different interest rate indices"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        principal_exchange: Union[PrincipalExchange, str] = None,\n        payer_spread: Union[float, str] = None,\n        payer_rate_option: str = None,\n        payer_designated_maturity: str = None,\n        payer_frequency: str = None,\n        payer_day_count_fraction: Union[DayCountFraction, str] = None,\n        payer_business_day_convention: Union[BusinessDayConvention, str] = None,\n        receiver_spread: Union[float, str] = None,\n        receiver_rate_option: str = None,\n        receiver_designated_maturity: str = None,\n        receiver_frequency: str = None,\n        receiver_day_count_fraction: Union[DayCountFraction, str] = None,\n        receiver_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        clearing_house: Union[SwapClearingHouse, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.principal_exchange = principal_exchange\n        self.payer_spread = payer_spread\n        self.payer_rate_option = payer_rate_option\n        self.payer_designated_maturity = payer_designated_maturity\n        self.payer_frequency = payer_frequency\n        self.payer_day_count_fraction = payer_day_count_fraction\n        self.payer_business_day_convention = payer_business_day_convention\n        self.receiver_spread = receiver_spread\n        self.receiver_rate_option = receiver_rate_option\n        self.receiver_designated_maturity = receiver_designated_maturity\n        self.receiver_frequency = receiver_frequency\n        self.receiver_day_count_fraction = receiver_day_count_fraction\n        self.receiver_business_day_convention = receiver_business_day_convention\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.clearing_house = clearing_house\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """BasisSwap"""\n        return AssetType.BasisSwap        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """The termination of the swap, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the swap becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def principal_exchange(self) -> Union[PrincipalExchange, str]:\n        """The date on which the swap becomes effective"""\n        return self.__principal_exchange\n\n    @principal_exchange.setter\n    def principal_exchange(self, value: Union[PrincipalExchange, str]):\n        self._property_changed('principal_exchange')\n        self.__principal_exchange = get_enum_value(PrincipalExchange, value)        \n\n    @property\n    def payer_spread(self) -> Union[float, str]:\n        """Spread over the payer rate"""\n        return self.__payer_spread\n\n    @payer_spread.setter\n    def payer_spread(self, value: Union[float, str]):\n        self._property_changed('payer_spread')\n        self.__payer_spread = value        \n\n    @property\n    def payer_rate_option(self) -> str:\n        """The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-TELERATE"""\n        return self.__payer_rate_option\n\n    @payer_rate_option.setter\n    def payer_rate_option(self, value: str):\n        self._property_changed('payer_rate_option')\n        self.__payer_rate_option = value        \n\n    @property\n    def payer_designated_maturity(self) -> str:\n        """Tenor of the payerRateOption, e.g. 3m, 6m"""\n        return self.__payer_designated_maturity\n\n    @payer_designated_maturity.setter\n    def payer_designated_maturity(self, value: str):\n        self._property_changed('payer_designated_maturity')\n        self.__payer_designated_maturity = value        \n\n    @property\n    def payer_frequency(self) -> str:\n        """The frequency of payer payments, e.g. 6m"""\n        return self.__payer_frequency\n\n    @payer_frequency.setter\n    def payer_frequency(self, value: str):\n        self._property_changed('payer_frequency')\n        self.__payer_frequency = value        \n\n    @property\n    def payer_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the payer"""\n        return self.__payer_day_count_fraction\n\n    @payer_day_count_fraction.setter\n    def payer_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('payer_day_count_fraction')\n        self.__payer_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def payer_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the payer"""\n        return self.__payer_business_day_convention\n\n    @payer_business_day_convention.setter\n    def payer_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('payer_business_day_convention')\n        self.__payer_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def receiver_spread(self) -> Union[float, str]:\n        """Spread over the receiver rate"""\n        return self.__receiver_spread\n\n    @receiver_spread.setter\n    def receiver_spread(self, value: Union[float, str]):\n        self._property_changed('receiver_spread')\n        self.__receiver_spread = value        \n\n    @property\n    def receiver_rate_option(self) -> str:\n        """The underlying benchmark for the receiver, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__receiver_rate_option\n\n    @receiver_rate_option.setter\n    def receiver_rate_option(self, value: str):\n        self._property_changed('receiver_rate_option')\n        self.__receiver_rate_option = value        \n\n    @property\n    def receiver_designated_maturity(self) -> str:\n        """Tenor of the receiverRateOption, e.g. 3m, 6m"""\n        return self.__receiver_designated_maturity\n\n    @receiver_designated_maturity.setter\n    def receiver_designated_maturity(self, value: str):\n        self._property_changed('receiver_designated_maturity')\n        self.__receiver_designated_maturity = value        \n\n    @property\n    def receiver_frequency(self) -> str:\n        """The frequency of receiver payments, e.g. 6m"""\n        return self.__receiver_frequency\n\n    @receiver_frequency.setter\n    def receiver_frequency(self, value: str):\n        self._property_changed('receiver_frequency')\n        self.__receiver_frequency = value        \n\n    @property\n    def receiver_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the receiver"""\n        return self.__receiver_day_count_fraction\n\n    @receiver_day_count_fraction.setter\n    def receiver_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('receiver_day_count_fraction')\n        self.__receiver_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def receiver_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the receiver"""\n        return self.__receiver_business_day_convention\n\n    @receiver_business_day_convention.setter\n    def receiver_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('receiver_business_day_convention')\n        self.__receiver_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def clearing_house(self) -> Union[SwapClearingHouse, str]:\n        """Swap Clearing House"""\n        return self.__clearing_house\n\n    @clearing_house.setter\n    def clearing_house(self, value: Union[SwapClearingHouse, str]):\n        self._property_changed('clearing_house')\n        self.__clearing_house = get_enum_value(SwapClearingHouse, value)        
\n\n\nclass IRBondFuture(Instrument):\n        \n    """A future on a (treasury) bond"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        notional_amount: Union[float, str] = None,\n        underlier: Union[float, str] = None,\n        currency: Union[Currency, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        exchange: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.notional_amount = notional_amount\n        self.underlier = underlier\n        self.currency = currency\n        self.expiration_date = expiration_date\n        self.exchange = exchange\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """BondFuture"""\n        return AssetType.BondFuture        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier security identifier"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__currency\n\n    @currency.setter\n    def currency(self, value: Union[Currency, str]):\n        self._property_changed('currency')\n        self.__currency = get_enum_value(Currency, value)        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def exchange(self) -> str:\n        """Name of marketplace where security, derivative or other instrument is traded"""\n        return self.__exchange\n\n    @exchange.setter\n    def exchange(self, value: str):\n        self._property_changed('exchange')\n        self.__exchange = value        \n\n\nclass IRBondOption(Instrument):\n        \n    """Object representation of a bond option"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: Union[float, str] = None,\n        notional_amount: Union[float, str] = None,\n        expiration_date: Union[Union[datetime.date, str], str] = None,\n        option_type: Union[OptionType, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        strike: Union[float, str] = None,\n        strike_type: Union[BondStrikeType, str] = None,\n        premium: Union[float, str] = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        settlement: Union[SettlementType, str] = None,\n        underlier_type: Union[UnderlierType, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.notional_amount = notional_amount\n        self.expiration_date = expiration_date\n        self.option_type = option_type\n        self.effective_date = effective_date\n        self.strike = strike\n        self.strike_type = strike_type\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.settlement = settlement\n        self.underlier_type = underlier_type\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """BondOption"""\n        return AssetType.BondOption        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier security identifier"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def expiration_date(self) -> Union[Union[datetime.date, str], str]:\n        """Bond option expiration date, 2020-05-01, 3m"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """Bond option effective date, e.g. 2019-01-01, 10y"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def strike(self) -> Union[float, str]:\n        """The strike of the option"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: Union[float, str]):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def strike_type(self) -> Union[BondStrikeType, str]:\n        """The type of the bond strike - price, yield etc"""\n        return self.__strike_type\n\n    @strike_type.setter\n    def strike_type(self, value: Union[BondStrikeType, str]):\n        self._property_changed('strike_type')\n        self.__strike_type = get_enum_value(BondStrikeType, value)        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """The premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def settlement(self) -> Union[SettlementType, str]:\n        """Settlement Type"""\n        return self.__settlement\n\n    @settlement.setter\n    def settlement(self, value: Union[SettlementType, str]):\n        self._property_changed('settlement')\n        self.__settlement = get_enum_value(SettlementType, value)        \n\n    @property\n    def underlier_type(self) -> Union[UnderlierType, str]:\n        """Type of underlyer"""\n        return self.__underlier_type\n\n    @underlier_type.setter\n    def underlier_type(self, value: Union[UnderlierType, str]):\n        self._property_changed('underlier_type')\n        self.__underlier_type = get_enum_value(UnderlierType, value)        \n\n\n
[docs]class IRCMSOption(Instrument):\n        \n    """Object representation of a constant maturity option (cap, floor, straddle)"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        cap_floor: str = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        strike: Union[float, str] = None,\n        index: str = None,\n        multiplier: float = None,\n        premium: Union[float, str] = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        buy_sell: Union[BuySell, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.cap_floor = cap_floor\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.strike = strike\n        self.index = index\n        self.multiplier = multiplier\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.buy_sell = buy_sell\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """CMSOption"""\n        return AssetType.CMSOption        \n\n    @property\n    def cap_floor(self) -> str:\n        """Structure type, e.g. Cap, Floor, Straddle, Binary Cap"""\n        return self.__cap_floor\n\n    @cap_floor.setter\n    def cap_floor(self, value: str):\n        self._property_changed('cap_floor')\n        self.__cap_floor = value        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """Swap termination date, e.g. 2030-05-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """CMS option effective date, e.g. 2019-01-01, 10y"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def strike(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: Union[float, str]):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def index(self) -> str:\n        """The underlying benchmark i.e. 30yUSD"""\n        return self.__index\n\n    @index.setter\n    def index(self, value: str):\n        self._property_changed('index')\n        self.__index = value        \n\n    @property\n    def multiplier(self) -> float:\n        """Multiplier"""\n        return self.__multiplier\n\n    @multiplier.setter\n    def multiplier(self, value: float):\n        self._property_changed('multiplier')\n        self.__multiplier = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """The premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        
\n\n\n
[docs]class IRCMSOptionStrip(Instrument):\n        \n    """Object representation of a constant maturity option strip (cap, floor, straddle)"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        cap_floor: str = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        strike: Union[float, str] = None,\n        index: str = None,\n        floating_rate_frequency: str = None,\n        floating_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        reset_delay: str = None,\n        multiplier: float = None,\n        premium: Union[float, str] = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        buy_sell: Union[BuySell, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.cap_floor = cap_floor\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.strike = strike\n        self.index = index\n        self.floating_rate_frequency = floating_rate_frequency\n        self.floating_rate_day_count_fraction = floating_rate_day_count_fraction\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.reset_delay = reset_delay\n        self.multiplier = multiplier\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.buy_sell = buy_sell\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """CMSOptionStrip"""\n        return AssetType.CMSOptionStrip        \n\n    @property\n    def cap_floor(self) -> str:\n        """Structure type, e.g. Cap, Floor, Straddle, Binary Cap"""\n        return self.__cap_floor\n\n    @cap_floor.setter\n    def cap_floor(self, value: str):\n        self._property_changed('cap_floor')\n        self.__cap_floor = value        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """Swap termination date, e.g. 2030-05-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """CMS option effective date, e.g. 2019-01-01, 10y"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def strike(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: Union[float, str]):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def index(self) -> str:\n        """The underlying benchmark i.e. 30yUSD"""\n        return self.__index\n\n    @index.setter\n    def index(self, value: str):\n        self._property_changed('index')\n        self.__index = value        \n\n    @property\n    def floating_rate_frequency(self) -> str:\n        """Period e.g. 3m, 1y"""\n        return self.__floating_rate_frequency\n\n    @floating_rate_frequency.setter\n    def floating_rate_frequency(self, value: str):\n        self._property_changed('floating_rate_frequency')\n        self.__floating_rate_frequency = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the floating rate"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention of the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def reset_delay(self) -> str:\n        """Delay of the reset e.g. 2d"""\n        return self.__reset_delay\n\n    @reset_delay.setter\n    def reset_delay(self, value: str):\n        self._property_changed('reset_delay')\n        self.__reset_delay = value        \n\n    @property\n    def multiplier(self) -> float:\n        """Multiplier"""\n        return self.__multiplier\n\n    @multiplier.setter\n    def multiplier(self, value: float):\n        self._property_changed('multiplier')\n        self.__multiplier = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """The premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        
\n\n\n
[docs]class IRCMSSpreadOption(Instrument):\n        \n    """Object representation of a constant maturity spread option (cap, floor,\n       straddle)"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        cap_floor: str = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        strike: Union[float, str] = None,\n        index1_tenor: str = None,\n        index2_tenor: str = None,\n        premium: Union[float, str] = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        buy_sell: Union[BuySell, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.cap_floor = cap_floor\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.strike = strike\n        self.index1_tenor = index1_tenor\n        self.index2_tenor = index2_tenor\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.buy_sell = buy_sell\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """CMSSpreadOption"""\n        return AssetType.CMSSpreadOption        \n\n    @property\n    def cap_floor(self) -> str:\n        """Structure type, e.g. Cap, Floor, Straddle"""\n        return self.__cap_floor\n\n    @cap_floor.setter\n    def cap_floor(self, value: str):\n        self._property_changed('cap_floor')\n        self.__cap_floor = value        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """Swap termination date, e.g. 2030-05-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """CMS option effective date, e.g. 2019-01-01, 10y"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def strike(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: Union[float, str]):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def index1_tenor(self) -> str:\n        """The tenor of the underlying benchmark to be the first element i.e. 30y"""\n        return self.__index1_tenor\n\n    @index1_tenor.setter\n    def index1_tenor(self, value: str):\n        self._property_changed('index1_tenor')\n        self.__index1_tenor = value        \n\n    @property\n    def index2_tenor(self) -> str:\n        """The  tenor of the underlying benchmark to be the second element i.e. 5y"""\n        return self.__index2_tenor\n\n    @index2_tenor.setter\n    def index2_tenor(self, value: str):\n        self._property_changed('index2_tenor')\n        self.__index2_tenor = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """The premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        
\n\n\n
[docs]class IRCMSSpreadOptionStrip(Instrument):\n        \n    """Object representation of a constant maturity spread option strip (cap, floor,\n       straddle)"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        cap_floor: str = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        strike: Union[float, str] = None,\n        index1: str = None,\n        index2: str = None,\n        floating_rate_frequency: str = None,\n        floating_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        reset_delay: str = None,\n        premium: Union[float, str] = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        buy_sell: Union[BuySell, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.cap_floor = cap_floor\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.strike = strike\n        self.index1 = index1\n        self.index2 = index2\n        self.floating_rate_frequency = floating_rate_frequency\n        self.floating_rate_day_count_fraction = floating_rate_day_count_fraction\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.reset_delay = reset_delay\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.buy_sell = buy_sell\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """CMSSpreadOptionStrip"""\n        return AssetType.CMSSpreadOptionStrip        \n\n    @property\n    def cap_floor(self) -> str:\n        """Structure type, e.g. Cap, Floor, Straddle, Binary Cap"""\n        return self.__cap_floor\n\n    @cap_floor.setter\n    def cap_floor(self, value: str):\n        self._property_changed('cap_floor')\n        self.__cap_floor = value        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """Swap termination date, e.g. 2030-05-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """CMS option effective date, e.g. 2019-01-01, 10y"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def strike(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: Union[float, str]):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def index1(self) -> str:\n        """The underlying benchmark to be the first element from i.e. 30yUSD"""\n        return self.__index1\n\n    @index1.setter\n    def index1(self, value: str):\n        self._property_changed('index1')\n        self.__index1 = value        \n\n    @property\n    def index2(self) -> str:\n        """The underlying benchmark to be the second element from i.e. 5yUSD"""\n        return self.__index2\n\n    @index2.setter\n    def index2(self, value: str):\n        self._property_changed('index2')\n        self.__index2 = value        \n\n    @property\n    def floating_rate_frequency(self) -> str:\n        """Period e.g. 3m, 1y"""\n        return self.__floating_rate_frequency\n\n    @floating_rate_frequency.setter\n    def floating_rate_frequency(self, value: str):\n        self._property_changed('floating_rate_frequency')\n        self.__floating_rate_frequency = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the floating rate"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention of the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def reset_delay(self) -> str:\n        """Delay of the reset e.g. 2d"""\n        return self.__reset_delay\n\n    @reset_delay.setter\n    def reset_delay(self, value: str):\n        self._property_changed('reset_delay')\n        self.__reset_delay = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """The premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        
\n\n\n
[docs]class IRCap(Instrument):\n        \n    """Object representation of an interest rate cap"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        termination_date: Union[datetime.date, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[datetime.date, str] = None,\n        floating_rate_option: str = None,\n        floating_rate_designated_maturity: str = None,\n        floating_rate_frequency: str = None,\n        floating_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        cap_rate: Union[float, str] = None,\n        premium: Union[float, str] = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.floating_rate_option = floating_rate_option\n        self.floating_rate_designated_maturity = floating_rate_designated_maturity\n        self.floating_rate_frequency = floating_rate_frequency\n        self.floating_rate_day_count_fraction = floating_rate_day_count_fraction\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.cap_rate = cap_rate\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """Cap"""\n        return AssetType.Cap        \n\n    @property\n    def termination_date(self) -> Union[datetime.date, str]:\n        """The termination of the cap, e.g. 2025-04-01, 2y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[datetime.date, str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[datetime.date, str]:\n        """The date on which the cap becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[datetime.date, str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def floating_rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__floating_rate_option\n\n    @floating_rate_option.setter\n    def floating_rate_option(self, value: str):\n        self._property_changed('floating_rate_option')\n        self.__floating_rate_option = value        \n\n    @property\n    def floating_rate_designated_maturity(self) -> str:\n        """Tenor of the floatingRateOption, e.g. 3m, 6m"""\n        return self.__floating_rate_designated_maturity\n\n    @floating_rate_designated_maturity.setter\n    def floating_rate_designated_maturity(self, value: str):\n        self._property_changed('floating_rate_designated_maturity')\n        self.__floating_rate_designated_maturity = value        \n\n    @property\n    def floating_rate_frequency(self) -> str:\n        """The frequency of floating payments, e.g. 3m"""\n        return self.__floating_rate_frequency\n\n    @floating_rate_frequency.setter\n    def floating_rate_frequency(self, value: str):\n        self._property_changed('floating_rate_frequency')\n        self.__floating_rate_frequency = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the floating rate"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention of the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def cap_rate(self) -> Union[float, str]:\n        """The rate of this cap, as value, percent or at-the-money e.g. 62.5, 95%, ATM-25,\n           ATMF"""\n        return self.__cap_rate\n\n    @cap_rate.setter\n    def cap_rate(self, value: Union[float, str]):\n        self._property_changed('cap_rate')\n        self.__cap_rate = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """The premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        
\n\n\nclass IRFixedLeg(Instrument):\n        \n    """A strip of vanilla fixed rate cashflows"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        fixed_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        fixed_first_stub: Union[Union[datetime.date, str], str] = None,\n        fixed_rate_frequency: str = None,\n        fixed_holidays: str = None,\n        fixed_last_stub: Union[Union[datetime.date, str], str] = None,\n        fixed_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fixed_rate: Union[float, str] = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        principal_exchange: Union[PrincipalExchange, str] = None,\n        roll_convention: str = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.fixed_rate_day_count_fraction = fixed_rate_day_count_fraction\n        self.fixed_first_stub = fixed_first_stub\n        self.fixed_rate_frequency = fixed_rate_frequency\n        self.fixed_holidays = fixed_holidays\n        self.fixed_last_stub = fixed_last_stub\n        self.fixed_rate_business_day_convention = fixed_rate_business_day_convention\n        self.fixed_rate = fixed_rate\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.principal_exchange = principal_exchange\n        self.roll_convention = roll_convention\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """FixedLeg"""\n        return AssetType.FixedLeg        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def fixed_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the fixed rate"""\n        return self.__fixed_rate_day_count_fraction\n\n    @fixed_rate_day_count_fraction.setter\n    def fixed_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('fixed_rate_day_count_fraction')\n        self.__fixed_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def fixed_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for fixed leg"""\n        return self.__fixed_first_stub\n\n    @fixed_first_stub.setter\n    def fixed_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('fixed_first_stub')\n        self.__fixed_first_stub = value        \n\n    @property\n    def fixed_rate_frequency(self) -> str:\n        """The frequency of fixed payments, e.g. 6m"""\n        return self.__fixed_rate_frequency\n\n    @fixed_rate_frequency.setter\n    def fixed_rate_frequency(self, value: str):\n        self._property_changed('fixed_rate_frequency')\n        self.__fixed_rate_frequency = value        \n\n    @property\n    def fixed_holidays(self) -> str:\n        """The accrual calendar for fixed leg"""\n        return self.__fixed_holidays\n\n    @fixed_holidays.setter\n    def fixed_holidays(self, value: str):\n        self._property_changed('fixed_holidays')\n        self.__fixed_holidays = value        \n\n    @property\n    def fixed_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for fixed leg"""\n        return self.__fixed_last_stub\n\n    @fixed_last_stub.setter\n    def fixed_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('fixed_last_stub')\n        self.__fixed_last_stub = value        \n\n    @property\n    def fixed_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the fixed rate"""\n        return self.__fixed_rate_business_day_convention\n\n    @fixed_rate_business_day_convention.setter\n    def fixed_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('fixed_rate_business_day_convention')\n        self.__fixed_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fixed_rate(self) -> Union[float, str]:\n        """The coupon of the fixed leg"""\n        return self.__fixed_rate\n\n    @fixed_rate.setter\n    def fixed_rate(self, value: Union[float, str]):\n        self._property_changed('fixed_rate')\n        self.__fixed_rate = value        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """The termination of the leg, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def principal_exchange(self) -> Union[PrincipalExchange, str]:\n        """When the exchange of principal is done"""\n        return self.__principal_exchange\n\n    @principal_exchange.setter\n    def principal_exchange(self, value: Union[PrincipalExchange, str]):\n        self._property_changed('principal_exchange')\n        self.__principal_exchange = get_enum_value(PrincipalExchange, value)        \n\n    @property\n    def roll_convention(self) -> str:\n        """The roll convention"""\n        return self.__roll_convention\n\n    @roll_convention.setter\n    def roll_convention(self, value: str):\n        self._property_changed('roll_convention')\n        self.__roll_convention = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the instrument becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n\nclass IRFloatLeg(Instrument):\n        \n    """A strip of vanilla floating rate cashflows"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        floating_rate_for_the_initial_calculation_period: float = None,\n        floating_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        floating_first_stub: Union[Union[datetime.date, str], str] = None,\n        floating_rate_frequency: str = None,\n        floating_holidays: str = None,\n        floating_last_stub: Union[Union[datetime.date, str], str] = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        floating_rate_option: str = None,\n        floating_rate_designated_maturity: str = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        principal_exchange: Union[PrincipalExchange, str] = None,\n        roll_convention: str = None,\n        notional_amount: Union[float, str] = None,\n        floating_rate_spread: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.floating_rate_for_the_initial_calculation_period = floating_rate_for_the_initial_calculation_period\n        self.floating_rate_day_count_fraction = floating_rate_day_count_fraction\n        self.floating_first_stub = floating_first_stub\n        self.floating_rate_frequency = floating_rate_frequency\n        self.floating_holidays = floating_holidays\n        self.floating_last_stub = floating_last_stub\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.floating_rate_option = floating_rate_option\n        self.floating_rate_designated_maturity = floating_rate_designated_maturity\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.principal_exchange = principal_exchange\n        self.roll_convention = roll_convention\n        self.notional_amount = notional_amount\n        self.floating_rate_spread = floating_rate_spread\n        self.effective_date = effective_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """FloatLeg"""\n        return AssetType.FloatLeg        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def floating_rate_for_the_initial_calculation_period(self) -> float:\n        """First fixing"""\n        return self.__floating_rate_for_the_initial_calculation_period\n\n    @floating_rate_for_the_initial_calculation_period.setter\n    def floating_rate_for_the_initial_calculation_period(self, value: float):\n        self._property_changed('floating_rate_for_the_initial_calculation_period')\n        self.__floating_rate_for_the_initial_calculation_period = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the floating rate"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def floating_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for floating leg"""\n        return self.__floating_first_stub\n\n    @floating_first_stub.setter\n    def floating_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('floating_first_stub')\n        self.__floating_first_stub = value        \n\n    @property\n    def floating_rate_frequency(self) -> str:\n        """The frequency of floating payments, e.g. 6m"""\n        return self.__floating_rate_frequency\n\n    @floating_rate_frequency.setter\n    def floating_rate_frequency(self, value: str):\n        self._property_changed('floating_rate_frequency')\n        self.__floating_rate_frequency = value        \n\n    @property\n    def floating_holidays(self) -> str:\n        """The accrual calendar for floating leg"""\n        return self.__floating_holidays\n\n    @floating_holidays.setter\n    def floating_holidays(self, value: str):\n        self._property_changed('floating_holidays')\n        self.__floating_holidays = value        \n\n    @property\n    def floating_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for floating leg"""\n        return self.__floating_last_stub\n\n    @floating_last_stub.setter\n    def floating_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('floating_last_stub')\n        self.__floating_last_stub = value        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def floating_rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__floating_rate_option\n\n    @floating_rate_option.setter\n    def floating_rate_option(self, value: str):\n        self._property_changed('floating_rate_option')\n        self.__floating_rate_option = value        \n\n    @property\n    def floating_rate_designated_maturity(self) -> str:\n        """Tenor of the floatingRateOption, e.g. 3m, 6m"""\n        return self.__floating_rate_designated_maturity\n\n    @floating_rate_designated_maturity.setter\n    def floating_rate_designated_maturity(self, value: str):\n        self._property_changed('floating_rate_designated_maturity')\n        self.__floating_rate_designated_maturity = value        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """The termination of the leg, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def principal_exchange(self) -> Union[PrincipalExchange, str]:\n        """When the exchange of principals is done"""\n        return self.__principal_exchange\n\n    @principal_exchange.setter\n    def principal_exchange(self, value: Union[PrincipalExchange, str]):\n        self._property_changed('principal_exchange')\n        self.__principal_exchange = get_enum_value(PrincipalExchange, value)        \n\n    @property\n    def roll_convention(self) -> str:\n        """The roll convention"""\n        return self.__roll_convention\n\n    @roll_convention.setter\n    def roll_convention(self, value: str):\n        self._property_changed('roll_convention')\n        self.__roll_convention = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def floating_rate_spread(self) -> Union[float, str]:\n        """The spread over the floating rate"""\n        return self.__floating_rate_spread\n\n    @floating_rate_spread.setter\n    def floating_rate_spread(self, value: Union[float, str]):\n        self._property_changed('floating_rate_spread')\n        self.__floating_rate_spread = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the instrument becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n\n
[docs]class IRFloor(Instrument):\n        \n    """Object representation of an interest rate floor"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        termination_date: Union[datetime.date, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[datetime.date, str] = None,\n        floating_rate_option: str = None,\n        floating_rate_designated_maturity: str = None,\n        floating_rate_frequency: str = None,\n        floating_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        floor_rate: Union[float, str] = None,\n        premium: Union[float, str] = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.floating_rate_option = floating_rate_option\n        self.floating_rate_designated_maturity = floating_rate_designated_maturity\n        self.floating_rate_frequency = floating_rate_frequency\n        self.floating_rate_day_count_fraction = floating_rate_day_count_fraction\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.floor_rate = floor_rate\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """Floor"""\n        return AssetType.Floor        \n\n    @property\n    def termination_date(self) -> Union[datetime.date, str]:\n        """The termination of the floor, e.g. 2025-04-01, 2y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[datetime.date, str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[datetime.date, str]:\n        """The date on which the floor becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[datetime.date, str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def floating_rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__floating_rate_option\n\n    @floating_rate_option.setter\n    def floating_rate_option(self, value: str):\n        self._property_changed('floating_rate_option')\n        self.__floating_rate_option = value        \n\n    @property\n    def floating_rate_designated_maturity(self) -> str:\n        """Tenor of the floatingRateOption, e.g. 3m, 6m"""\n        return self.__floating_rate_designated_maturity\n\n    @floating_rate_designated_maturity.setter\n    def floating_rate_designated_maturity(self, value: str):\n        self._property_changed('floating_rate_designated_maturity')\n        self.__floating_rate_designated_maturity = value        \n\n    @property\n    def floating_rate_frequency(self) -> str:\n        """The frequency of floating payments, e.g. 3m"""\n        return self.__floating_rate_frequency\n\n    @floating_rate_frequency.setter\n    def floating_rate_frequency(self, value: str):\n        self._property_changed('floating_rate_frequency')\n        self.__floating_rate_frequency = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the floating rate"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention of the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def floor_rate(self) -> Union[float, str]:\n        """The rate of this floor, as value, percent or at-the-money e.g. 62.5, 95%,\n           ATM-25, ATMF"""\n        return self.__floor_rate\n\n    @floor_rate.setter\n    def floor_rate(self, value: Union[float, str]):\n        self._property_changed('floor_rate')\n        self.__floor_rate = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """The premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        
\n\n\n
[docs]class IRSwap(Instrument):\n        \n    """A vanilla interest rate swap of fixed vs floating cashflows"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pay_or_receive: Union[PayReceive, str] = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        principal_exchange: Union[PrincipalExchange, str] = None,\n        floating_rate_for_the_initial_calculation_period: float = None,\n        floating_rate_option: str = None,\n        floating_rate_designated_maturity: str = None,\n        floating_rate_spread: Union[float, str] = None,\n        floating_rate_frequency: str = None,\n        floating_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fixed_rate: Union[float, str] = None,\n        fixed_rate_frequency: str = None,\n        fixed_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        fixed_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        clearing_house: Union[SwapClearingHouse, str] = None,\n        fixed_first_stub: Union[Union[datetime.date, str], str] = None,\n        floating_first_stub: Union[Union[datetime.date, str], str] = None,\n        fixed_last_stub: Union[Union[datetime.date, str], str] = None,\n        floating_last_stub: Union[Union[datetime.date, str], str] = None,\n        fixed_holidays: str = None,\n        floating_holidays: str = None,\n        roll_convention: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pay_or_receive = pay_or_receive\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.principal_exchange = principal_exchange\n        self.floating_rate_for_the_initial_calculation_period = floating_rate_for_the_initial_calculation_period\n        self.floating_rate_option = floating_rate_option\n        self.floating_rate_designated_maturity = floating_rate_designated_maturity\n        self.floating_rate_spread = floating_rate_spread\n        self.floating_rate_frequency = floating_rate_frequency\n        self.floating_rate_day_count_fraction = floating_rate_day_count_fraction\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.fixed_rate = fixed_rate\n        self.fixed_rate_frequency = fixed_rate_frequency\n        self.fixed_rate_day_count_fraction = fixed_rate_day_count_fraction\n        self.fixed_rate_business_day_convention = fixed_rate_business_day_convention\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.clearing_house = clearing_house\n        self.fixed_first_stub = fixed_first_stub\n        self.floating_first_stub = floating_first_stub\n        self.fixed_last_stub = fixed_last_stub\n        self.floating_last_stub = floating_last_stub\n        self.fixed_holidays = fixed_holidays\n        self.floating_holidays = floating_holidays\n        self.roll_convention = roll_convention\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """Swap"""\n        return AssetType.Swap        \n\n    @property\n    def pay_or_receive(self) -> Union[PayReceive, str]:\n        """Pay or receive fixed"""\n        return self.__pay_or_receive\n\n    @pay_or_receive.setter\n    def pay_or_receive(self, value: Union[PayReceive, str]):\n        self._property_changed('pay_or_receive')\n        self.__pay_or_receive = get_enum_value(PayReceive, value)        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """The termination of the swap, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the swap becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def principal_exchange(self) -> Union[PrincipalExchange, str]:\n        """The date on which the swap becomes effective"""\n        return self.__principal_exchange\n\n    @principal_exchange.setter\n    def principal_exchange(self, value: Union[PrincipalExchange, str]):\n        self._property_changed('principal_exchange')\n        self.__principal_exchange = get_enum_value(PrincipalExchange, value)        \n\n    @property\n    def floating_rate_for_the_initial_calculation_period(self) -> float:\n        """First fixing"""\n        return self.__floating_rate_for_the_initial_calculation_period\n\n    @floating_rate_for_the_initial_calculation_period.setter\n    def floating_rate_for_the_initial_calculation_period(self, value: float):\n        self._property_changed('floating_rate_for_the_initial_calculation_period')\n        self.__floating_rate_for_the_initial_calculation_period = value        \n\n    @property\n    def floating_rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__floating_rate_option\n\n    @floating_rate_option.setter\n    def floating_rate_option(self, value: str):\n        self._property_changed('floating_rate_option')\n        self.__floating_rate_option = value        \n\n    @property\n    def floating_rate_designated_maturity(self) -> str:\n        """Tenor of the floatingRateOption, e.g. 3m, 6m"""\n        return self.__floating_rate_designated_maturity\n\n    @floating_rate_designated_maturity.setter\n    def floating_rate_designated_maturity(self, value: str):\n        self._property_changed('floating_rate_designated_maturity')\n        self.__floating_rate_designated_maturity = value        \n\n    @property\n    def floating_rate_spread(self) -> Union[float, str]:\n        """The spread over the floating rate"""\n        return self.__floating_rate_spread\n\n    @floating_rate_spread.setter\n    def floating_rate_spread(self, value: Union[float, str]):\n        self._property_changed('floating_rate_spread')\n        self.__floating_rate_spread = value        \n\n    @property\n    def floating_rate_frequency(self) -> str:\n        """The frequency of floating payments, e.g. 3m"""\n        return self.__floating_rate_frequency\n\n    @floating_rate_frequency.setter\n    def floating_rate_frequency(self, value: str):\n        self._property_changed('floating_rate_frequency')\n        self.__floating_rate_frequency = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the floating rate"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention of the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fixed_rate(self) -> Union[float, str]:\n        """The coupon of the fixed leg"""\n        return self.__fixed_rate\n\n    @fixed_rate.setter\n    def fixed_rate(self, value: Union[float, str]):\n        self._property_changed('fixed_rate')\n        self.__fixed_rate = value        \n\n    @property\n    def fixed_rate_frequency(self) -> str:\n        """The frequency of fixed payments, e.g. 6m"""\n        return self.__fixed_rate_frequency\n\n    @fixed_rate_frequency.setter\n    def fixed_rate_frequency(self, value: str):\n        self._property_changed('fixed_rate_frequency')\n        self.__fixed_rate_frequency = value        \n\n    @property\n    def fixed_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the fixed rate"""\n        return self.__fixed_rate_day_count_fraction\n\n    @fixed_rate_day_count_fraction.setter\n    def fixed_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('fixed_rate_day_count_fraction')\n        self.__fixed_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def fixed_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the fixed rate"""\n        return self.__fixed_rate_business_day_convention\n\n    @fixed_rate_business_day_convention.setter\n    def fixed_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('fixed_rate_business_day_convention')\n        self.__fixed_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def clearing_house(self) -> Union[SwapClearingHouse, str]:\n        """Swap Clearing House"""\n        return self.__clearing_house\n\n    @clearing_house.setter\n    def clearing_house(self, value: Union[SwapClearingHouse, str]):\n        self._property_changed('clearing_house')\n        self.__clearing_house = get_enum_value(SwapClearingHouse, value)        \n\n    @property\n    def fixed_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for fixed leg"""\n        return self.__fixed_first_stub\n\n    @fixed_first_stub.setter\n    def fixed_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('fixed_first_stub')\n        self.__fixed_first_stub = value        \n\n    @property\n    def floating_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for floating leg"""\n        return self.__floating_first_stub\n\n    @floating_first_stub.setter\n    def floating_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('floating_first_stub')\n        self.__floating_first_stub = value        \n\n    @property\n    def fixed_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for fixed leg"""\n        return self.__fixed_last_stub\n\n    @fixed_last_stub.setter\n    def fixed_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('fixed_last_stub')\n        self.__fixed_last_stub = value        \n\n    @property\n    def floating_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for floating leg"""\n        return self.__floating_last_stub\n\n    @floating_last_stub.setter\n    def floating_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('floating_last_stub')\n        self.__floating_last_stub = value        \n\n    @property\n    def fixed_holidays(self) -> str:\n        """The accrual calendar for fixed leg"""\n        return self.__fixed_holidays\n\n    @fixed_holidays.setter\n    def fixed_holidays(self, value: str):\n        self._property_changed('fixed_holidays')\n        self.__fixed_holidays = value        \n\n    @property\n    def floating_holidays(self) -> str:\n        """The accrual calendar for floating leg"""\n        return self.__floating_holidays\n\n    @floating_holidays.setter\n    def floating_holidays(self, value: str):\n        self._property_changed('floating_holidays')\n        self.__floating_holidays = value        \n\n    @property\n    def roll_convention(self) -> str:\n        """The roll convention"""\n        return self.__roll_convention\n\n    @roll_convention.setter\n    def roll_convention(self, value: str):\n        self._property_changed('roll_convention')\n        self.__roll_convention = value        
\n\n\n
[docs]class IRSwaption(Instrument):\n        \n    """Object representation of a swaption"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pay_or_receive: str = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_currency: Union[Currency, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        notional_amount: Union[float, str] = None,\n        expiration_date: Union[Union[datetime.date, str], str] = None,\n        floating_rate_option: str = None,\n        floating_rate_designated_maturity: str = None,\n        floating_rate_spread: float = None,\n        floating_rate_frequency: str = None,\n        floating_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fixed_rate_frequency: str = None,\n        fixed_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        fixed_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        strike: Union[float, str] = None,\n        premium: Union[float, str] = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        clearing_house: Union[SwapClearingHouse, str] = None,\n        settlement: Union[SwapSettlement, str] = None,\n        buy_sell: Union[BuySell, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pay_or_receive = pay_or_receive\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.effective_date = effective_date\n        self.notional_amount = notional_amount\n        self.expiration_date = expiration_date\n        self.floating_rate_option = floating_rate_option\n        self.floating_rate_designated_maturity = floating_rate_designated_maturity\n        self.floating_rate_spread = floating_rate_spread\n        self.floating_rate_frequency = floating_rate_frequency\n        self.floating_rate_day_count_fraction = floating_rate_day_count_fraction\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.fixed_rate_frequency = fixed_rate_frequency\n        self.fixed_rate_day_count_fraction = fixed_rate_day_count_fraction\n        self.fixed_rate_business_day_convention = fixed_rate_business_day_convention\n        self.strike = strike\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.clearing_house = clearing_house\n        self.settlement = settlement\n        self.buy_sell = buy_sell\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """Swaption"""\n        return AssetType.Swaption        \n\n    @property\n    def pay_or_receive(self) -> str:\n        """Pay or receive fixed"""\n        return self.__pay_or_receive\n\n    @pay_or_receive.setter\n    def pay_or_receive(self, value: str):\n        self._property_changed('pay_or_receive')\n        self.__pay_or_receive = value        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """Swaption termination date, e.g. 2030-05-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """Swaption effective date, e.g. 2019-01-01, 10y"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def expiration_date(self) -> Union[Union[datetime.date, str], str]:\n        """Swaption expiration date, 2020-05-01, 3m"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def floating_rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__floating_rate_option\n\n    @floating_rate_option.setter\n    def floating_rate_option(self, value: str):\n        self._property_changed('floating_rate_option')\n        self.__floating_rate_option = value        \n\n    @property\n    def floating_rate_designated_maturity(self) -> str:\n        """Tenor"""\n        return self.__floating_rate_designated_maturity\n\n    @floating_rate_designated_maturity.setter\n    def floating_rate_designated_maturity(self, value: str):\n        self._property_changed('floating_rate_designated_maturity')\n        self.__floating_rate_designated_maturity = value        \n\n    @property\n    def floating_rate_spread(self) -> float:\n        """The spread over the floating rate"""\n        return self.__floating_rate_spread\n\n    @floating_rate_spread.setter\n    def floating_rate_spread(self, value: float):\n        self._property_changed('floating_rate_spread')\n        self.__floating_rate_spread = value        \n\n    @property\n    def floating_rate_frequency(self) -> str:\n        """The frequency of floating payments, e.g. 3m"""\n        return self.__floating_rate_frequency\n\n    @floating_rate_frequency.setter\n    def floating_rate_frequency(self, value: str):\n        self._property_changed('floating_rate_frequency')\n        self.__floating_rate_frequency = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the floating rate"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention of the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fixed_rate_frequency(self) -> str:\n        """The frequency of fixed payments, e.g. 6m"""\n        return self.__fixed_rate_frequency\n\n    @fixed_rate_frequency.setter\n    def fixed_rate_frequency(self, value: str):\n        self._property_changed('fixed_rate_frequency')\n        self.__fixed_rate_frequency = value        \n\n    @property\n    def fixed_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the fixed rate"""\n        return self.__fixed_rate_day_count_fraction\n\n    @fixed_rate_day_count_fraction.setter\n    def fixed_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('fixed_rate_day_count_fraction')\n        self.__fixed_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def fixed_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the fixed rate"""\n        return self.__fixed_rate_business_day_convention\n\n    @fixed_rate_business_day_convention.setter\n    def fixed_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('fixed_rate_business_day_convention')\n        self.__fixed_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def strike(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike\n\n    @strike.setter\n    def strike(self, value: Union[float, str]):\n        self._property_changed('strike')\n        self.__strike = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """The premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def clearing_house(self) -> Union[SwapClearingHouse, str]:\n        """Swap Clearing House"""\n        return self.__clearing_house\n\n    @clearing_house.setter\n    def clearing_house(self, value: Union[SwapClearingHouse, str]):\n        self._property_changed('clearing_house')\n        self.__clearing_house = get_enum_value(SwapClearingHouse, value)        \n\n    @property\n    def settlement(self) -> Union[SwapSettlement, str]:\n        """Swap Settlement Type"""\n        return self.__settlement\n\n    @settlement.setter\n    def settlement(self, value: Union[SwapSettlement, str]):\n        self._property_changed('settlement')\n        self.__settlement = get_enum_value(SwapSettlement, value)        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        
\n\n\n
[docs]class IRXccySwap(Instrument):\n        \n    """An exchange of cashflows from different interest rate indices"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_amount: float = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        principal_exchange: Union[PrincipalExchange, str] = None,\n        payer_currency: Union[Currency, str] = None,\n        payer_spread: Union[float, str] = None,\n        payer_rate_option: str = None,\n        payer_designated_maturity: str = None,\n        payer_frequency: str = None,\n        payer_day_count_fraction: Union[DayCountFraction, str] = None,\n        payer_business_day_convention: Union[BusinessDayConvention, str] = None,\n        receiver_currency: Union[Currency, str] = None,\n        receiver_spread: Union[float, str] = None,\n        receiver_rate_option: str = None,\n        receiver_designated_maturity: str = None,\n        receiver_frequency: str = None,\n        receiver_day_count_fraction: Union[DayCountFraction, str] = None,\n        receiver_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        initial_fx_rate: float = None,\n        payer_first_stub: Union[Union[datetime.date, str], str] = None,\n        receiver_first_stub: Union[Union[datetime.date, str], str] = None,\n        payer_last_stub: Union[Union[datetime.date, str], str] = None,\n        receiver_last_stub: Union[Union[datetime.date, str], str] = None,\n        payer_holidays: str = None,\n        receiver_holidays: str = None,\n        notional_reset_side: Union[PayReceive, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.termination_date = termination_date\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.principal_exchange = principal_exchange\n        self.payer_currency = payer_currency\n        self.payer_spread = payer_spread\n        self.payer_rate_option = payer_rate_option\n        self.payer_designated_maturity = payer_designated_maturity\n        self.payer_frequency = payer_frequency\n        self.payer_day_count_fraction = payer_day_count_fraction\n        self.payer_business_day_convention = payer_business_day_convention\n        self.receiver_currency = receiver_currency\n        self.receiver_spread = receiver_spread\n        self.receiver_rate_option = receiver_rate_option\n        self.receiver_designated_maturity = receiver_designated_maturity\n        self.receiver_frequency = receiver_frequency\n        self.receiver_day_count_fraction = receiver_day_count_fraction\n        self.receiver_business_day_convention = receiver_business_day_convention\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.initial_fx_rate = initial_fx_rate\n        self.payer_first_stub = payer_first_stub\n        self.receiver_first_stub = receiver_first_stub\n        self.payer_last_stub = payer_last_stub\n        self.receiver_last_stub = receiver_last_stub\n        self.payer_holidays = payer_holidays\n        self.receiver_holidays = receiver_holidays\n        self.notional_reset_side = notional_reset_side\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """XccySwapMTM"""\n        return AssetType.XccySwapMTM        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """The termination of the swap, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_amount(self) -> float:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: float):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the swap becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def principal_exchange(self) -> Union[PrincipalExchange, str]:\n        """The date on which the swap becomes effective"""\n        return self.__principal_exchange\n\n    @principal_exchange.setter\n    def principal_exchange(self, value: Union[PrincipalExchange, str]):\n        self._property_changed('principal_exchange')\n        self.__principal_exchange = get_enum_value(PrincipalExchange, value)        \n\n    @property\n    def payer_currency(self) -> Union[Currency, str]:\n        """Payer currency"""\n        return self.__payer_currency\n\n    @payer_currency.setter\n    def payer_currency(self, value: Union[Currency, str]):\n        self._property_changed('payer_currency')\n        self.__payer_currency = get_enum_value(Currency, value)        \n\n    @property\n    def payer_spread(self) -> Union[float, str]:\n        """Spread over the payer rate"""\n        return self.__payer_spread\n\n    @payer_spread.setter\n    def payer_spread(self, value: Union[float, str]):\n        self._property_changed('payer_spread')\n        self.__payer_spread = value        \n\n    @property\n    def payer_rate_option(self) -> str:\n        """The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-TELERATE"""\n        return self.__payer_rate_option\n\n    @payer_rate_option.setter\n    def payer_rate_option(self, value: str):\n        self._property_changed('payer_rate_option')\n        self.__payer_rate_option = value        \n\n    @property\n    def payer_designated_maturity(self) -> str:\n        """Tenor of the payerRateOption, e.g. 3m, 6m"""\n        return self.__payer_designated_maturity\n\n    @payer_designated_maturity.setter\n    def payer_designated_maturity(self, value: str):\n        self._property_changed('payer_designated_maturity')\n        self.__payer_designated_maturity = value        \n\n    @property\n    def payer_frequency(self) -> str:\n        """The frequency of payer payments, e.g. 6m"""\n        return self.__payer_frequency\n\n    @payer_frequency.setter\n    def payer_frequency(self, value: str):\n        self._property_changed('payer_frequency')\n        self.__payer_frequency = value        \n\n    @property\n    def payer_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the payer"""\n        return self.__payer_day_count_fraction\n\n    @payer_day_count_fraction.setter\n    def payer_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('payer_day_count_fraction')\n        self.__payer_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def payer_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the payer"""\n        return self.__payer_business_day_convention\n\n    @payer_business_day_convention.setter\n    def payer_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('payer_business_day_convention')\n        self.__payer_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def receiver_currency(self) -> Union[Currency, str]:\n        """Receiver currency"""\n        return self.__receiver_currency\n\n    @receiver_currency.setter\n    def receiver_currency(self, value: Union[Currency, str]):\n        self._property_changed('receiver_currency')\n        self.__receiver_currency = get_enum_value(Currency, value)        \n\n    @property\n    def receiver_spread(self) -> Union[float, str]:\n        """Spread over the receiver rate"""\n        return self.__receiver_spread\n\n    @receiver_spread.setter\n    def receiver_spread(self, value: Union[float, str]):\n        self._property_changed('receiver_spread')\n        self.__receiver_spread = value        \n\n    @property\n    def receiver_rate_option(self) -> str:\n        """The underlying benchmark for the receiver, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__receiver_rate_option\n\n    @receiver_rate_option.setter\n    def receiver_rate_option(self, value: str):\n        self._property_changed('receiver_rate_option')\n        self.__receiver_rate_option = value        \n\n    @property\n    def receiver_designated_maturity(self) -> str:\n        """Tenor of the receiverRateOption, e.g. 3m, 6m"""\n        return self.__receiver_designated_maturity\n\n    @receiver_designated_maturity.setter\n    def receiver_designated_maturity(self, value: str):\n        self._property_changed('receiver_designated_maturity')\n        self.__receiver_designated_maturity = value        \n\n    @property\n    def receiver_frequency(self) -> str:\n        """The frequency of receiver payments, e.g. 6m"""\n        return self.__receiver_frequency\n\n    @receiver_frequency.setter\n    def receiver_frequency(self, value: str):\n        self._property_changed('receiver_frequency')\n        self.__receiver_frequency = value        \n\n    @property\n    def receiver_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the receiver"""\n        return self.__receiver_day_count_fraction\n\n    @receiver_day_count_fraction.setter\n    def receiver_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('receiver_day_count_fraction')\n        self.__receiver_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def receiver_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the receiver"""\n        return self.__receiver_business_day_convention\n\n    @receiver_business_day_convention.setter\n    def receiver_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('receiver_business_day_convention')\n        self.__receiver_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def initial_fx_rate(self) -> float:\n        """Payment date of the fee"""\n        return self.__initial_fx_rate\n\n    @initial_fx_rate.setter\n    def initial_fx_rate(self, value: float):\n        self._property_changed('initial_fx_rate')\n        self.__initial_fx_rate = value        \n\n    @property\n    def payer_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for payer leg"""\n        return self.__payer_first_stub\n\n    @payer_first_stub.setter\n    def payer_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('payer_first_stub')\n        self.__payer_first_stub = value        \n\n    @property\n    def receiver_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for receiver leg"""\n        return self.__receiver_first_stub\n\n    @receiver_first_stub.setter\n    def receiver_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('receiver_first_stub')\n        self.__receiver_first_stub = value        \n\n    @property\n    def payer_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for payer leg"""\n        return self.__payer_last_stub\n\n    @payer_last_stub.setter\n    def payer_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('payer_last_stub')\n        self.__payer_last_stub = value        \n\n    @property\n    def receiver_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for receiver leg"""\n        return self.__receiver_last_stub\n\n    @receiver_last_stub.setter\n    def receiver_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('receiver_last_stub')\n        self.__receiver_last_stub = value        \n\n    @property\n    def payer_holidays(self) -> str:\n        """The accrual calendar for payer leg"""\n        return self.__payer_holidays\n\n    @payer_holidays.setter\n    def payer_holidays(self, value: str):\n        self._property_changed('payer_holidays')\n        self.__payer_holidays = value        \n\n    @property\n    def receiver_holidays(self) -> str:\n        """The accrual calendar for receiver leg"""\n        return self.__receiver_holidays\n\n    @receiver_holidays.setter\n    def receiver_holidays(self, value: str):\n        self._property_changed('receiver_holidays')\n        self.__receiver_holidays = value        \n\n    @property\n    def notional_reset_side(self) -> Union[PayReceive, str]:\n        """Pay or Rec leg resetting"""\n        return self.__notional_reset_side\n\n    @notional_reset_side.setter\n    def notional_reset_side(self, value: Union[PayReceive, str]):\n        self._property_changed('notional_reset_side')\n        self.__notional_reset_side = get_enum_value(PayReceive, value)        
\n\n\n
[docs]class IRXccySwapFixFix(Instrument):\n        \n    """An exchange of fixed cashflows in different currencies"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_amount: float = None,\n        receiver_notional_amount: float = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        principal_exchange: Union[PrincipalExchange, str] = None,\n        payer_currency: Union[Currency, str] = None,\n        payer_rate: Union[float, str] = None,\n        payer_frequency: str = None,\n        payer_day_count_fraction: Union[DayCountFraction, str] = None,\n        payer_business_day_convention: Union[BusinessDayConvention, str] = None,\n        receiver_currency: Union[Currency, str] = None,\n        receiver_rate: Union[float, str] = None,\n        receiver_frequency: str = None,\n        receiver_day_count_fraction: Union[DayCountFraction, str] = None,\n        receiver_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.termination_date = termination_date\n        self.notional_amount = notional_amount\n        self.receiver_notional_amount = receiver_notional_amount\n        self.effective_date = effective_date\n        self.principal_exchange = principal_exchange\n        self.payer_currency = payer_currency\n        self.payer_rate = payer_rate\n        self.payer_frequency = payer_frequency\n        self.payer_day_count_fraction = payer_day_count_fraction\n        self.payer_business_day_convention = payer_business_day_convention\n        self.receiver_currency = receiver_currency\n        self.receiver_rate = receiver_rate\n        self.receiver_frequency = receiver_frequency\n        self.receiver_day_count_fraction = receiver_day_count_fraction\n        self.receiver_business_day_convention = receiver_business_day_convention\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """XccySwapFixFix"""\n        return AssetType.XccySwapFixFix        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """The termination of the swap, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_amount(self) -> float:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: float):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def receiver_notional_amount(self) -> float:\n        """Receiver notional amount"""\n        return self.__receiver_notional_amount\n\n    @receiver_notional_amount.setter\n    def receiver_notional_amount(self, value: float):\n        self._property_changed('receiver_notional_amount')\n        self.__receiver_notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the swap becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def principal_exchange(self) -> Union[PrincipalExchange, str]:\n        """The date on which the swap becomes effective"""\n        return self.__principal_exchange\n\n    @principal_exchange.setter\n    def principal_exchange(self, value: Union[PrincipalExchange, str]):\n        self._property_changed('principal_exchange')\n        self.__principal_exchange = get_enum_value(PrincipalExchange, value)        \n\n    @property\n    def payer_currency(self) -> Union[Currency, str]:\n        """Payer currency"""\n        return self.__payer_currency\n\n    @payer_currency.setter\n    def payer_currency(self, value: Union[Currency, str]):\n        self._property_changed('payer_currency')\n        self.__payer_currency = get_enum_value(Currency, value)        \n\n    @property\n    def payer_rate(self) -> Union[float, str]:\n        """Payer rate"""\n        return self.__payer_rate\n\n    @payer_rate.setter\n    def payer_rate(self, value: Union[float, str]):\n        self._property_changed('payer_rate')\n        self.__payer_rate = value        \n\n    @property\n    def payer_frequency(self) -> str:\n        """The frequency of payer payments, e.g. 6m"""\n        return self.__payer_frequency\n\n    @payer_frequency.setter\n    def payer_frequency(self, value: str):\n        self._property_changed('payer_frequency')\n        self.__payer_frequency = value        \n\n    @property\n    def payer_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the payer"""\n        return self.__payer_day_count_fraction\n\n    @payer_day_count_fraction.setter\n    def payer_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('payer_day_count_fraction')\n        self.__payer_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def payer_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the payer"""\n        return self.__payer_business_day_convention\n\n    @payer_business_day_convention.setter\n    def payer_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('payer_business_day_convention')\n        self.__payer_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def receiver_currency(self) -> Union[Currency, str]:\n        """Receiver currency"""\n        return self.__receiver_currency\n\n    @receiver_currency.setter\n    def receiver_currency(self, value: Union[Currency, str]):\n        self._property_changed('receiver_currency')\n        self.__receiver_currency = get_enum_value(Currency, value)        \n\n    @property\n    def receiver_rate(self) -> Union[float, str]:\n        """Receiver rate"""\n        return self.__receiver_rate\n\n    @receiver_rate.setter\n    def receiver_rate(self, value: Union[float, str]):\n        self._property_changed('receiver_rate')\n        self.__receiver_rate = value        \n\n    @property\n    def receiver_frequency(self) -> str:\n        """The frequency of receiver payments, e.g. 6m"""\n        return self.__receiver_frequency\n\n    @receiver_frequency.setter\n    def receiver_frequency(self, value: str):\n        self._property_changed('receiver_frequency')\n        self.__receiver_frequency = value        \n\n    @property\n    def receiver_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the receiver"""\n        return self.__receiver_day_count_fraction\n\n    @receiver_day_count_fraction.setter\n    def receiver_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('receiver_day_count_fraction')\n        self.__receiver_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def receiver_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the receiver"""\n        return self.__receiver_business_day_convention\n\n    @receiver_business_day_convention.setter\n    def receiver_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('receiver_business_day_convention')\n        self.__receiver_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        
\n\n\n
[docs]class IRXccySwapFixFlt(Instrument):\n        \n    """An exchange of fixed vs floating cashflows in different currencies"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pay_or_receive: Union[PayReceive, str] = None,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        principal_exchange: Union[PrincipalExchange, str] = None,\n        floating_rate_currency: Union[Currency, str] = None,\n        floating_rate_for_the_initial_calculation_period: float = None,\n        floating_rate_option: str = None,\n        floating_rate_designated_maturity: str = None,\n        floating_rate_spread: Union[float, str] = None,\n        floating_rate_frequency: str = None,\n        floating_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fixed_rate_currency: Union[Currency, str] = None,\n        fixed_rate: Union[float, str] = None,\n        fixed_rate_frequency: str = None,\n        fixed_rate_day_count_fraction: Union[DayCountFraction, str] = None,\n        fixed_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        fixed_first_stub: Union[Union[datetime.date, str], str] = None,\n        floating_first_stub: Union[Union[datetime.date, str], str] = None,\n        fixed_last_stub: Union[Union[datetime.date, str], str] = None,\n        floating_last_stub: Union[Union[datetime.date, str], str] = None,\n        fixed_holidays: str = None,\n        floating_holidays: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pay_or_receive = pay_or_receive\n        self.termination_date = termination_date\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.principal_exchange = principal_exchange\n        self.floating_rate_currency = floating_rate_currency\n        self.floating_rate_for_the_initial_calculation_period = floating_rate_for_the_initial_calculation_period\n        self.floating_rate_option = floating_rate_option\n        self.floating_rate_designated_maturity = floating_rate_designated_maturity\n        self.floating_rate_spread = floating_rate_spread\n        self.floating_rate_frequency = floating_rate_frequency\n        self.floating_rate_day_count_fraction = floating_rate_day_count_fraction\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.fixed_rate_currency = fixed_rate_currency\n        self.fixed_rate = fixed_rate\n        self.fixed_rate_frequency = fixed_rate_frequency\n        self.fixed_rate_day_count_fraction = fixed_rate_day_count_fraction\n        self.fixed_rate_business_day_convention = fixed_rate_business_day_convention\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.fixed_first_stub = fixed_first_stub\n        self.floating_first_stub = floating_first_stub\n        self.fixed_last_stub = fixed_last_stub\n        self.floating_last_stub = floating_last_stub\n        self.fixed_holidays = fixed_holidays\n        self.floating_holidays = floating_holidays\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """XccySwapFixFlt"""\n        return AssetType.XccySwapFixFlt        \n\n    @property\n    def pay_or_receive(self) -> Union[PayReceive, str]:\n        """Pay or receive fixed"""\n        return self.__pay_or_receive\n\n    @pay_or_receive.setter\n    def pay_or_receive(self, value: Union[PayReceive, str]):\n        self._property_changed('pay_or_receive')\n        self.__pay_or_receive = get_enum_value(PayReceive, value)        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """The termination of the swap, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the swap becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def principal_exchange(self) -> Union[PrincipalExchange, str]:\n        """The date on which the swap becomes effective"""\n        return self.__principal_exchange\n\n    @principal_exchange.setter\n    def principal_exchange(self, value: Union[PrincipalExchange, str]):\n        self._property_changed('principal_exchange')\n        self.__principal_exchange = get_enum_value(PrincipalExchange, value)        \n\n    @property\n    def floating_rate_currency(self) -> Union[Currency, str]:\n        """Floating rate currency"""\n        return self.__floating_rate_currency\n\n    @floating_rate_currency.setter\n    def floating_rate_currency(self, value: Union[Currency, str]):\n        self._property_changed('floating_rate_currency')\n        self.__floating_rate_currency = get_enum_value(Currency, value)        \n\n    @property\n    def floating_rate_for_the_initial_calculation_period(self) -> float:\n        """First fixing"""\n        return self.__floating_rate_for_the_initial_calculation_period\n\n    @floating_rate_for_the_initial_calculation_period.setter\n    def floating_rate_for_the_initial_calculation_period(self, value: float):\n        self._property_changed('floating_rate_for_the_initial_calculation_period')\n        self.__floating_rate_for_the_initial_calculation_period = value        \n\n    @property\n    def floating_rate_option(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__floating_rate_option\n\n    @floating_rate_option.setter\n    def floating_rate_option(self, value: str):\n        self._property_changed('floating_rate_option')\n        self.__floating_rate_option = value        \n\n    @property\n    def floating_rate_designated_maturity(self) -> str:\n        """Tenor of the floatingRateOption, e.g. 3m, 6m"""\n        return self.__floating_rate_designated_maturity\n\n    @floating_rate_designated_maturity.setter\n    def floating_rate_designated_maturity(self, value: str):\n        self._property_changed('floating_rate_designated_maturity')\n        self.__floating_rate_designated_maturity = value        \n\n    @property\n    def floating_rate_spread(self) -> Union[float, str]:\n        """The spread over the floating rate"""\n        return self.__floating_rate_spread\n\n    @floating_rate_spread.setter\n    def floating_rate_spread(self, value: Union[float, str]):\n        self._property_changed('floating_rate_spread')\n        self.__floating_rate_spread = value        \n\n    @property\n    def floating_rate_frequency(self) -> str:\n        """The frequency of floating payments, e.g. 3m"""\n        return self.__floating_rate_frequency\n\n    @floating_rate_frequency.setter\n    def floating_rate_frequency(self, value: str):\n        self._property_changed('floating_rate_frequency')\n        self.__floating_rate_frequency = value        \n\n    @property\n    def floating_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction of the floating rate"""\n        return self.__floating_rate_day_count_fraction\n\n    @floating_rate_day_count_fraction.setter\n    def floating_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('floating_rate_day_count_fraction')\n        self.__floating_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention of the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fixed_rate_currency(self) -> Union[Currency, str]:\n        """Fixed rate currency"""\n        return self.__fixed_rate_currency\n\n    @fixed_rate_currency.setter\n    def fixed_rate_currency(self, value: Union[Currency, str]):\n        self._property_changed('fixed_rate_currency')\n        self.__fixed_rate_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fixed_rate(self) -> Union[float, str]:\n        """The coupon of the fixed leg"""\n        return self.__fixed_rate\n\n    @fixed_rate.setter\n    def fixed_rate(self, value: Union[float, str]):\n        self._property_changed('fixed_rate')\n        self.__fixed_rate = value        \n\n    @property\n    def fixed_rate_frequency(self) -> str:\n        """The frequency of fixed payments, e.g. 6m"""\n        return self.__fixed_rate_frequency\n\n    @fixed_rate_frequency.setter\n    def fixed_rate_frequency(self, value: str):\n        self._property_changed('fixed_rate_frequency')\n        self.__fixed_rate_frequency = value        \n\n    @property\n    def fixed_rate_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the fixed rate"""\n        return self.__fixed_rate_day_count_fraction\n\n    @fixed_rate_day_count_fraction.setter\n    def fixed_rate_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('fixed_rate_day_count_fraction')\n        self.__fixed_rate_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def fixed_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the fixed rate"""\n        return self.__fixed_rate_business_day_convention\n\n    @fixed_rate_business_day_convention.setter\n    def fixed_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('fixed_rate_business_day_convention')\n        self.__fixed_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def fixed_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for fixed leg"""\n        return self.__fixed_first_stub\n\n    @fixed_first_stub.setter\n    def fixed_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('fixed_first_stub')\n        self.__fixed_first_stub = value        \n\n    @property\n    def floating_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for floating leg"""\n        return self.__floating_first_stub\n\n    @floating_first_stub.setter\n    def floating_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('floating_first_stub')\n        self.__floating_first_stub = value        \n\n    @property\n    def fixed_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for fixed leg"""\n        return self.__fixed_last_stub\n\n    @fixed_last_stub.setter\n    def fixed_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('fixed_last_stub')\n        self.__fixed_last_stub = value        \n\n    @property\n    def floating_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for floating leg"""\n        return self.__floating_last_stub\n\n    @floating_last_stub.setter\n    def floating_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('floating_last_stub')\n        self.__floating_last_stub = value        \n\n    @property\n    def fixed_holidays(self) -> str:\n        """The accrual calendar for fixed leg"""\n        return self.__fixed_holidays\n\n    @fixed_holidays.setter\n    def fixed_holidays(self, value: str):\n        self._property_changed('fixed_holidays')\n        self.__fixed_holidays = value        \n\n    @property\n    def floating_holidays(self) -> str:\n        """The accrual calendar for floating leg"""\n        return self.__floating_holidays\n\n    @floating_holidays.setter\n    def floating_holidays(self, value: str):\n        self._property_changed('floating_holidays')\n        self.__floating_holidays = value        
\n\n\nclass IRXccySwapFltFlt(Instrument):\n        \n    """An exchange of cashflows from different interest rate indices, non-resetting"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        termination_date: Union[Union[datetime.date, str], str] = None,\n        notional_amount: Union[float, str] = None,\n        effective_date: Union[Union[datetime.date, str], str] = None,\n        principal_exchange: Union[PrincipalExchange, str] = None,\n        payer_currency: Union[Currency, str] = None,\n        payer_spread: Union[float, str] = None,\n        payer_rate_option: str = None,\n        payer_designated_maturity: str = None,\n        payer_frequency: str = None,\n        payer_day_count_fraction: Union[DayCountFraction, str] = None,\n        payer_business_day_convention: Union[BusinessDayConvention, str] = None,\n        receiver_currency: Union[Currency, str] = None,\n        receiver_spread: Union[float, str] = None,\n        receiver_rate_option: str = None,\n        receiver_designated_maturity: str = None,\n        receiver_frequency: str = None,\n        receiver_day_count_fraction: Union[DayCountFraction, str] = None,\n        receiver_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fee: float = 0,\n        fee_currency: Union[Currency, str] = None,\n        fee_payment_date: Union[datetime.date, str] = None,\n        payer_first_stub: Union[Union[datetime.date, str], str] = None,\n        receiver_first_stub: Union[Union[datetime.date, str], str] = None,\n        payer_last_stub: Union[Union[datetime.date, str], str] = None,\n        receiver_last_stub: Union[Union[datetime.date, str], str] = None,\n        payer_holidays: str = None,\n        receiver_holidays: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.termination_date = termination_date\n        self.notional_amount = notional_amount\n        self.effective_date = effective_date\n        self.principal_exchange = principal_exchange\n        self.payer_currency = payer_currency\n        self.payer_spread = payer_spread\n        self.payer_rate_option = payer_rate_option\n        self.payer_designated_maturity = payer_designated_maturity\n        self.payer_frequency = payer_frequency\n        self.payer_day_count_fraction = payer_day_count_fraction\n        self.payer_business_day_convention = payer_business_day_convention\n        self.receiver_currency = receiver_currency\n        self.receiver_spread = receiver_spread\n        self.receiver_rate_option = receiver_rate_option\n        self.receiver_designated_maturity = receiver_designated_maturity\n        self.receiver_frequency = receiver_frequency\n        self.receiver_day_count_fraction = receiver_day_count_fraction\n        self.receiver_business_day_convention = receiver_business_day_convention\n        self.fee = fee\n        self.fee_currency = fee_currency\n        self.fee_payment_date = fee_payment_date\n        self.payer_first_stub = payer_first_stub\n        self.receiver_first_stub = receiver_first_stub\n        self.payer_last_stub = payer_last_stub\n        self.receiver_last_stub = receiver_last_stub\n        self.payer_holidays = payer_holidays\n        self.receiver_holidays = receiver_holidays\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """XccySwap"""\n        return AssetType.XccySwap        \n\n    @property\n    def termination_date(self) -> Union[Union[datetime.date, str], str]:\n        """The termination of the swap, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def effective_date(self) -> Union[Union[datetime.date, str], str]:\n        """The date on which the swap becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def principal_exchange(self) -> Union[PrincipalExchange, str]:\n        """Principal exchanges at inception, termination or both"""\n        return self.__principal_exchange\n\n    @principal_exchange.setter\n    def principal_exchange(self, value: Union[PrincipalExchange, str]):\n        self._property_changed('principal_exchange')\n        self.__principal_exchange = get_enum_value(PrincipalExchange, value)        \n\n    @property\n    def payer_currency(self) -> Union[Currency, str]:\n        """Payer currency"""\n        return self.__payer_currency\n\n    @payer_currency.setter\n    def payer_currency(self, value: Union[Currency, str]):\n        self._property_changed('payer_currency')\n        self.__payer_currency = get_enum_value(Currency, value)        \n\n    @property\n    def payer_spread(self) -> Union[float, str]:\n        """Spread over the payer rate"""\n        return self.__payer_spread\n\n    @payer_spread.setter\n    def payer_spread(self, value: Union[float, str]):\n        self._property_changed('payer_spread')\n        self.__payer_spread = value        \n\n    @property\n    def payer_rate_option(self) -> str:\n        """The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-TELERATE"""\n        return self.__payer_rate_option\n\n    @payer_rate_option.setter\n    def payer_rate_option(self, value: str):\n        self._property_changed('payer_rate_option')\n        self.__payer_rate_option = value        \n\n    @property\n    def payer_designated_maturity(self) -> str:\n        """Tenor of the payerRateOption, e.g. 3m, 6m"""\n        return self.__payer_designated_maturity\n\n    @payer_designated_maturity.setter\n    def payer_designated_maturity(self, value: str):\n        self._property_changed('payer_designated_maturity')\n        self.__payer_designated_maturity = value        \n\n    @property\n    def payer_frequency(self) -> str:\n        """The frequency of payer payments, e.g. 6m"""\n        return self.__payer_frequency\n\n    @payer_frequency.setter\n    def payer_frequency(self, value: str):\n        self._property_changed('payer_frequency')\n        self.__payer_frequency = value        \n\n    @property\n    def payer_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the payer"""\n        return self.__payer_day_count_fraction\n\n    @payer_day_count_fraction.setter\n    def payer_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('payer_day_count_fraction')\n        self.__payer_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def payer_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the payer"""\n        return self.__payer_business_day_convention\n\n    @payer_business_day_convention.setter\n    def payer_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('payer_business_day_convention')\n        self.__payer_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def receiver_currency(self) -> Union[Currency, str]:\n        """Receiver currency"""\n        return self.__receiver_currency\n\n    @receiver_currency.setter\n    def receiver_currency(self, value: Union[Currency, str]):\n        self._property_changed('receiver_currency')\n        self.__receiver_currency = get_enum_value(Currency, value)        \n\n    @property\n    def receiver_spread(self) -> Union[float, str]:\n        """Spread over the receiver rate"""\n        return self.__receiver_spread\n\n    @receiver_spread.setter\n    def receiver_spread(self, value: Union[float, str]):\n        self._property_changed('receiver_spread')\n        self.__receiver_spread = value        \n\n    @property\n    def receiver_rate_option(self) -> str:\n        """The underlying benchmark for the receiver, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\n           TELERATE"""\n        return self.__receiver_rate_option\n\n    @receiver_rate_option.setter\n    def receiver_rate_option(self, value: str):\n        self._property_changed('receiver_rate_option')\n        self.__receiver_rate_option = value        \n\n    @property\n    def receiver_designated_maturity(self) -> str:\n        """Tenor of the receiverRateOption, e.g. 3m, 6m"""\n        return self.__receiver_designated_maturity\n\n    @receiver_designated_maturity.setter\n    def receiver_designated_maturity(self, value: str):\n        self._property_changed('receiver_designated_maturity')\n        self.__receiver_designated_maturity = value        \n\n    @property\n    def receiver_frequency(self) -> str:\n        """The frequency of receiver payments, e.g. 6m"""\n        return self.__receiver_frequency\n\n    @receiver_frequency.setter\n    def receiver_frequency(self, value: str):\n        self._property_changed('receiver_frequency')\n        self.__receiver_frequency = value        \n\n    @property\n    def receiver_day_count_fraction(self) -> Union[DayCountFraction, str]:\n        """The day count fraction for the receiver"""\n        return self.__receiver_day_count_fraction\n\n    @receiver_day_count_fraction.setter\n    def receiver_day_count_fraction(self, value: Union[DayCountFraction, str]):\n        self._property_changed('receiver_day_count_fraction')\n        self.__receiver_day_count_fraction = get_enum_value(DayCountFraction, value)        \n\n    @property\n    def receiver_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the receiver"""\n        return self.__receiver_business_day_convention\n\n    @receiver_business_day_convention.setter\n    def receiver_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('receiver_business_day_convention')\n        self.__receiver_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def fee_currency(self) -> Union[Currency, str]:\n        """Currency of the fee"""\n        return self.__fee_currency\n\n    @fee_currency.setter\n    def fee_currency(self, value: Union[Currency, str]):\n        self._property_changed('fee_currency')\n        self.__fee_currency = get_enum_value(Currency, value)        \n\n    @property\n    def fee_payment_date(self) -> Union[datetime.date, str]:\n        """Payment date of the fee"""\n        return self.__fee_payment_date\n\n    @fee_payment_date.setter\n    def fee_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('fee_payment_date')\n        self.__fee_payment_date = value        \n\n    @property\n    def payer_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for payer leg"""\n        return self.__payer_first_stub\n\n    @payer_first_stub.setter\n    def payer_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('payer_first_stub')\n        self.__payer_first_stub = value        \n\n    @property\n    def receiver_first_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the first stub for receiver leg"""\n        return self.__receiver_first_stub\n\n    @receiver_first_stub.setter\n    def receiver_first_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('receiver_first_stub')\n        self.__receiver_first_stub = value        \n\n    @property\n    def payer_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for payer leg"""\n        return self.__payer_last_stub\n\n    @payer_last_stub.setter\n    def payer_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('payer_last_stub')\n        self.__payer_last_stub = value        \n\n    @property\n    def receiver_last_stub(self) -> Union[Union[datetime.date, str], str]:\n        """The date of the last stub for receiver leg"""\n        return self.__receiver_last_stub\n\n    @receiver_last_stub.setter\n    def receiver_last_stub(self, value: Union[Union[datetime.date, str], str]):\n        self._property_changed('receiver_last_stub')\n        self.__receiver_last_stub = value        \n\n    @property\n    def payer_holidays(self) -> str:\n        """The accrual calendar for payer leg"""\n        return self.__payer_holidays\n\n    @payer_holidays.setter\n    def payer_holidays(self, value: str):\n        self._property_changed('payer_holidays')\n        self.__payer_holidays = value        \n\n    @property\n    def receiver_holidays(self) -> str:\n        """The accrual calendar for receiver leg"""\n        return self.__receiver_holidays\n\n    @receiver_holidays.setter\n    def receiver_holidays(self, value: str):\n        self._property_changed('receiver_holidays')\n        self.__receiver_holidays = value        \n\n\n
[docs]class InflationSwap(Instrument):\n        \n    """A vanilla inflation swap of fixed vs floating cashflows adjusted to an inflation\n       rate"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pay_or_receive: Union[PayReceive, str] = None,\n        termination_date: Union[datetime.date, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        effective_date: Union[datetime.date, str] = None,\n        notional_amount: Union[float, str] = None,\n        index: str = None,\n        floating_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fixed_rate: Union[float, str] = None,\n        fixed_rate_business_day_convention: Union[BusinessDayConvention, str] = None,\n        fee: float = 0,\n        base_cpi: float = None,\n        clearing_house: Union[SwapClearingHouse, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pay_or_receive = pay_or_receive\n        self.termination_date = termination_date\n        self.notional_currency = notional_currency\n        self.effective_date = effective_date\n        self.notional_amount = notional_amount\n        self.index = index\n        self.floating_rate_business_day_convention = floating_rate_business_day_convention\n        self.fixed_rate = fixed_rate\n        self.fixed_rate_business_day_convention = fixed_rate_business_day_convention\n        self.fee = fee\n        self.base_cpi = base_cpi\n        self.clearing_house = clearing_house\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """InflationSwap"""\n        return AssetType.InflationSwap        \n\n    @property\n    def pay_or_receive(self) -> Union[PayReceive, str]:\n        """Pay or receive fixed"""\n        return self.__pay_or_receive\n\n    @pay_or_receive.setter\n    def pay_or_receive(self, value: Union[PayReceive, str]):\n        self._property_changed('pay_or_receive')\n        self.__pay_or_receive = get_enum_value(PayReceive, value)        \n\n    @property\n    def termination_date(self) -> Union[datetime.date, str]:\n        """The termination of the swap, e.g. 2050-04-01, 10y"""\n        return self.__termination_date\n\n    @termination_date.setter\n    def termination_date(self, value: Union[datetime.date, str]):\n        self._property_changed('termination_date')\n        self.__termination_date = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Notional currency"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def effective_date(self) -> Union[datetime.date, str]:\n        """The date on which the swap becomes effective"""\n        return self.__effective_date\n\n    @effective_date.setter\n    def effective_date(self, value: Union[datetime.date, str]):\n        self._property_changed('effective_date')\n        self.__effective_date = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def index(self) -> str:\n        """The underlying benchmark for the floating rate, e.g. CPI-U"""\n        return self.__index\n\n    @index.setter\n    def index(self, value: str):\n        self._property_changed('index')\n        self.__index = value        \n\n    @property\n    def floating_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention of the floating rate"""\n        return self.__floating_rate_business_day_convention\n\n    @floating_rate_business_day_convention.setter\n    def floating_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('floating_rate_business_day_convention')\n        self.__floating_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fixed_rate(self) -> Union[float, str]:\n        """The coupon of the fixed leg"""\n        return self.__fixed_rate\n\n    @fixed_rate.setter\n    def fixed_rate(self, value: Union[float, str]):\n        self._property_changed('fixed_rate')\n        self.__fixed_rate = value        \n\n    @property\n    def fixed_rate_business_day_convention(self) -> Union[BusinessDayConvention, str]:\n        """The business day convention for the fixed rate"""\n        return self.__fixed_rate_business_day_convention\n\n    @fixed_rate_business_day_convention.setter\n    def fixed_rate_business_day_convention(self, value: Union[BusinessDayConvention, str]):\n        self._property_changed('fixed_rate_business_day_convention')\n        self.__fixed_rate_business_day_convention = get_enum_value(BusinessDayConvention, value)        \n\n    @property\n    def fee(self) -> float:\n        """The fee"""\n        return self.__fee\n\n    @fee.setter\n    def fee(self, value: float):\n        self._property_changed('fee')\n        self.__fee = value        \n\n    @property\n    def base_cpi(self) -> float:\n        """Base CPI level"""\n        return self.__base_cpi\n\n    @base_cpi.setter\n    def base_cpi(self, value: float):\n        self._property_changed('base_cpi')\n        self.__base_cpi = value        \n\n    @property\n    def clearing_house(self) -> Union[SwapClearingHouse, str]:\n        """Swap Clearing House"""\n        return self.__clearing_house\n\n    @clearing_house.setter\n    def clearing_house(self, value: Union[SwapClearingHouse, str]):\n        self._property_changed('clearing_house')\n        self.__clearing_house = get_enum_value(SwapClearingHouse, value)        
\n\n\nclass InstrumentsAnyAssetRef(Instrument):\n        \n    """An instrument that references an asset ID (from the asset service)"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        asset_id: str = None,\n        buy_sell: Union[BuySell, str] = None,\n        size: float = None,\n        product_code: Union[ProductCode, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.asset_id = asset_id\n        self.buy_sell = buy_sell\n        self.size = size\n        self.product_code = product_code\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Cross Asset"""\n        return AssetClass.Cross_Asset        \n\n    @property\n    def type(self) -> AssetType:\n        """Any"""\n        return AssetType.Any        \n\n    @property\n    def asset_id(self) -> str:\n        return self.__asset_id\n\n    @asset_id.setter\n    def asset_id(self, value: str):\n        self._property_changed('asset_id')\n        self.__asset_id = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def size(self) -> float:\n        return self.__size\n\n    @size.setter\n    def size(self, value: float):\n        self._property_changed('size')\n        self.__size = value        \n\n    @property\n    def product_code(self) -> Union[ProductCode, str]:\n        """Override the clearing destination/symbol"""\n        return self.__product_code\n\n    @product_code.setter\n    def product_code(self, value: Union[ProductCode, str]):\n        self._property_changed('product_code')\n        self.__product_code = get_enum_value(ProductCode, value)        \n\n\nclass InstrumentsXassetBond(Instrument):\n        \n    """A bond"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        identifier: str = None,\n        identifier_type: Union[UnderlierType, str] = None,\n        size: float = None,\n        settlement_date: Union[datetime.date, str] = None,\n        settlement_currency: Union[Currency, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.identifier = identifier\n        self.identifier_type = identifier_type\n        self.size = size\n        self.settlement_date = settlement_date\n        self.settlement_currency = settlement_currency\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Cross Asset"""\n        return AssetClass.Cross_Asset        \n\n    @property\n    def type(self) -> AssetType:\n        """Bond"""\n        return AssetType.Bond        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def identifier(self) -> str:\n        return self.__identifier\n\n    @identifier.setter\n    def identifier(self, value: str):\n        self._property_changed('identifier')\n        self.__identifier = value        \n\n    @property\n    def identifier_type(self) -> Union[UnderlierType, str]:\n        """Type of underlyer"""\n        return self.__identifier_type\n\n    @identifier_type.setter\n    def identifier_type(self, value: Union[UnderlierType, str]):\n        self._property_changed('identifier_type')\n        self.__identifier_type = get_enum_value(UnderlierType, value)        \n\n    @property\n    def size(self) -> float:\n        return self.__size\n\n    @size.setter\n    def size(self, value: float):\n        self._property_changed('size')\n        self.__size = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def settlement_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__settlement_currency\n\n    @settlement_currency.setter\n    def settlement_currency(self, value: Union[Currency, str]):\n        self._property_changed('settlement_currency')\n        self.__settlement_currency = get_enum_value(Currency, value)        \n\n\nclass CommodOTCOption(Instrument):\n        \n    """Object representation of a commodities OTC option strategies"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        start: Union[datetime.date, str] = None,\n        end: Union[datetime.date, str] = None,\n        number_of_periods: int = None,\n        strategy: str = None,\n        quantity: Union[float, str] = None,\n        quantity_unit: str = None,\n        quantity_period: Union[Period, str] = None,\n        legs: Tuple[CommodOTCOptionLeg, ...] = None,\n        settlement: str = None,\n        premium_summary: Union[float, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.start = start\n        self.end = end\n        self.number_of_periods = number_of_periods\n        self.strategy = strategy\n        self.quantity = quantity\n        self.quantity_unit = quantity_unit\n        self.quantity_period = quantity_period\n        self.legs = legs\n        self.settlement = settlement\n        self.premium_summary = premium_summary\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Commod"""\n        return AssetClass.Commod        \n\n    @property\n    def type(self) -> AssetType:\n        """OptionStrategy"""\n        return AssetType.OptionStrategy        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def start(self) -> Union[datetime.date, str]:\n        """Date or Contract Month"""\n        return self.__start\n\n    @start.setter\n    def start(self, value: Union[datetime.date, str]):\n        self._property_changed('start')\n        self.__start = value        \n\n    @property\n    def end(self) -> Union[datetime.date, str]:\n        """Date or Contract Month"""\n        return self.__end\n\n    @end.setter\n    def end(self, value: Union[datetime.date, str]):\n        self._property_changed('end')\n        self.__end = value        \n\n    @property\n    def number_of_periods(self) -> int:\n        """The number of settlement periods"""\n        return self.__number_of_periods\n\n    @number_of_periods.setter\n    def number_of_periods(self, value: int):\n        self._property_changed('number_of_periods')\n        self.__number_of_periods = value        \n\n    @property\n    def strategy(self) -> str:\n        """Option Strategy"""\n        return self.__strategy\n\n    @strategy.setter\n    def strategy(self, value: str):\n        self._property_changed('strategy')\n        self.__strategy = value        \n\n    @property\n    def quantity(self) -> Union[float, str]:\n        """Size of some value, i.e. notional like 1.3b, 1.5, 1000"""\n        return self.__quantity\n\n    @quantity.setter\n    def quantity(self, value: Union[float, str]):\n        self._property_changed('quantity')\n        self.__quantity = value        \n\n    @property\n    def quantity_unit(self) -> str:\n        """Commodity asset"""\n        return self.__quantity_unit\n\n    @quantity_unit.setter\n    def quantity_unit(self, value: str):\n        self._property_changed('quantity_unit')\n        self.__quantity_unit = value        \n\n    @property\n    def quantity_period(self) -> Union[Period, str]:\n        """A coding scheme to define a period corresponding to a quantity amount"""\n        return self.__quantity_period\n\n    @quantity_period.setter\n    def quantity_period(self, value: Union[Period, str]):\n        self._property_changed('quantity_period')\n        self.__quantity_period = get_enum_value(Period, value)        \n\n    @property\n    def legs(self) -> Tuple[CommodOTCOptionLeg, ...]:\n        """Commodities OTC option leg"""\n        return self.__legs\n\n    @legs.setter\n    def legs(self, value: Tuple[CommodOTCOptionLeg, ...]):\n        self._property_changed('legs')\n        self.__legs = value        \n\n    @property\n    def settlement(self) -> str:\n        """read only description in plain English of settlement terms"""\n        return self.__settlement\n\n    @settlement.setter\n    def settlement(self, value: str):\n        self._property_changed('settlement')\n        self.__settlement = value        \n\n    @property\n    def premium_summary(self) -> Union[float, str]:\n        """TBD : Overall Option premium for all Legs"""\n        return self.__premium_summary\n\n    @premium_summary.setter\n    def premium_summary(self, value: Union[float, str]):\n        self._property_changed('premium_summary')\n        self.__premium_summary = value        \n\n\n
[docs]class CommodOTCSwap(Instrument):\n        \n    """Object representation of a commodities swap"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        start: Union[datetime.date, str] = None,\n        end: Union[datetime.date, str] = None,\n        number_of_periods: int = None,\n        strategy: str = None,\n        quantity: Union[float, str] = None,\n        quantity_unit: str = None,\n        quantity_period: Union[Period, str] = None,\n        legs: Tuple[CommodOTCSwapLeg, ...] = None,\n        settlement: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.start = start\n        self.end = end\n        self.number_of_periods = number_of_periods\n        self.strategy = strategy\n        self.quantity = quantity\n        self.quantity_unit = quantity_unit\n        self.quantity_period = quantity_period\n        self.legs = legs\n        self.settlement = settlement\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Commod"""\n        return AssetClass.Commod        \n\n    @property\n    def type(self) -> AssetType:\n        """SwapStrategy"""\n        return AssetType.SwapStrategy        \n\n    @property\n    def start(self) -> Union[datetime.date, str]:\n        """Date or Contract Month"""\n        return self.__start\n\n    @start.setter\n    def start(self, value: Union[datetime.date, str]):\n        self._property_changed('start')\n        self.__start = value        \n\n    @property\n    def end(self) -> Union[datetime.date, str]:\n        """Date or Contract Month"""\n        return self.__end\n\n    @end.setter\n    def end(self, value: Union[datetime.date, str]):\n        self._property_changed('end')\n        self.__end = value        \n\n    @property\n    def number_of_periods(self) -> int:\n        """The number of settlement periods"""\n        return self.__number_of_periods\n\n    @number_of_periods.setter\n    def number_of_periods(self, value: int):\n        self._property_changed('number_of_periods')\n        self.__number_of_periods = value        \n\n    @property\n    def strategy(self) -> str:\n        """Swap Strategy : Strip and Commodity Spread"""\n        return self.__strategy\n\n    @strategy.setter\n    def strategy(self, value: str):\n        self._property_changed('strategy')\n        self.__strategy = value        \n\n    @property\n    def quantity(self) -> Union[float, str]:\n        """Size of some value, i.e. notional like 1.3b, 1.5, 1000"""\n        return self.__quantity\n\n    @quantity.setter\n    def quantity(self, value: Union[float, str]):\n        self._property_changed('quantity')\n        self.__quantity = value        \n\n    @property\n    def quantity_unit(self) -> str:\n        """Commodity asset"""\n        return self.__quantity_unit\n\n    @quantity_unit.setter\n    def quantity_unit(self, value: str):\n        self._property_changed('quantity_unit')\n        self.__quantity_unit = value        \n\n    @property\n    def quantity_period(self) -> Union[Period, str]:\n        """A coding scheme to define a period corresponding to a quantity amount"""\n        return self.__quantity_period\n\n    @quantity_period.setter\n    def quantity_period(self, value: Union[Period, str]):\n        self._property_changed('quantity_period')\n        self.__quantity_period = get_enum_value(Period, value)        \n\n    @property\n    def legs(self) -> Tuple[CommodOTCSwapLeg, ...]:\n        """Commodities OTC swap leg"""\n        return self.__legs\n\n    @legs.setter\n    def legs(self, value: Tuple[CommodOTCSwapLeg, ...]):\n        self._property_changed('legs')\n        self.__legs = value        \n\n    @property\n    def settlement(self) -> str:\n        """read only description in plain English of settlement terms"""\n        return self.__settlement\n\n    @settlement.setter\n    def settlement(self, value: str):\n        self._property_changed('settlement')\n        self.__settlement = value        
\n\n\nclass EqOptionStrategy(Instrument):\n        \n    """Instrument definition for equity option strategy"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        underlier: Union[float, str],\n        strategy: str,\n        legs: Tuple[EqOptionLeg, ...],\n        underlier_type: Union[UnderlierType, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        strike_price: Union[float, str] = None,\n        option_type: Union[OptionType, str] = None,\n        option_style: Union[OptionStyle, str] = None,\n        number_of_options: float = None,\n        multiplier: float = None,\n        settlement_date: Union[datetime.date, str] = None,\n        settlement_currency: Union[Currency, str] = None,\n        premium: float = None,\n        premium_payment_date: Union[datetime.date, str] = None,\n        valuation_time: Union[ValuationTime, str] = None,\n        method_of_settlement: Union[OptionSettlementMethod, str] = None,\n        buy_sell: Union[BuySell, str] = None,\n        premium_currency: Union[Currency, str] = None,\n        exchange: str = None,\n        trade_as: Union[TradeAs, str] = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.underlier = underlier\n        self.underlier_type = underlier_type\n        self.strategy = strategy\n        self.legs = legs\n        self.expiration_date = expiration_date\n        self.strike_price = strike_price\n        self.option_type = option_type\n        self.option_style = option_style\n        self.number_of_options = number_of_options\n        self.multiplier = multiplier\n        self.settlement_date = settlement_date\n        self.settlement_currency = settlement_currency\n        self.premium = premium\n        self.premium_payment_date = premium_payment_date\n        self.valuation_time = valuation_time\n        self.method_of_settlement = method_of_settlement\n        self.buy_sell = buy_sell\n        self.premium_currency = premium_currency\n        self.exchange = exchange\n        self.trade_as = trade_as\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Equity"""\n        return AssetClass.Equity        \n\n    @property\n    def type(self) -> AssetType:\n        """OptionStrategy"""\n        return AssetType.OptionStrategy        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier security identifier"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def underlier_type(self) -> Union[UnderlierType, str]:\n        """Type of underlyer"""\n        return self.__underlier_type\n\n    @underlier_type.setter\n    def underlier_type(self, value: Union[UnderlierType, str]):\n        self._property_changed('underlier_type')\n        self.__underlier_type = get_enum_value(UnderlierType, value)        \n\n    @property\n    def strategy(self) -> str:\n        """Option Strategy"""\n        return self.__strategy\n\n    @strategy.setter\n    def strategy(self, value: str):\n        self._property_changed('strategy')\n        self.__strategy = value        \n\n    @property\n    def legs(self) -> Tuple[EqOptionLeg, ...]:\n        """Instrument definition for equity option leg"""\n        return self.__legs\n\n    @legs.setter\n    def legs(self, value: Tuple[EqOptionLeg, ...]):\n        self._property_changed('legs')\n        self.__legs = value        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def option_style(self) -> Union[OptionStyle, str]:\n        """Option Exercise Style"""\n        return self.__option_style\n\n    @option_style.setter\n    def option_style(self, value: Union[OptionStyle, str]):\n        self._property_changed('option_style')\n        self.__option_style = get_enum_value(OptionStyle, value)        \n\n    @property\n    def number_of_options(self) -> float:\n        """Number of options"""\n        return self.__number_of_options\n\n    @number_of_options.setter\n    def number_of_options(self, value: float):\n        self._property_changed('number_of_options')\n        self.__number_of_options = value        \n\n    @property\n    def multiplier(self) -> float:\n        """Number of stock units per option contract"""\n        return self.__multiplier\n\n    @multiplier.setter\n    def multiplier(self, value: float):\n        self._property_changed('multiplier')\n        self.__multiplier = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def settlement_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__settlement_currency\n\n    @settlement_currency.setter\n    def settlement_currency(self, value: Union[Currency, str]):\n        self._property_changed('settlement_currency')\n        self.__settlement_currency = get_enum_value(Currency, value)        \n\n    @property\n    def premium(self) -> float:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: float):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_payment_date(self) -> Union[datetime.date, str]:\n        """Option premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: Union[datetime.date, str]):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n    @property\n    def valuation_time(self) -> Union[ValuationTime, str]:\n        """Valuation time (e.g. MktClose, MktOpen) of the underlying level for exercise"""\n        return self.__valuation_time\n\n    @valuation_time.setter\n    def valuation_time(self, value: Union[ValuationTime, str]):\n        self._property_changed('valuation_time')\n        self.__valuation_time = get_enum_value(ValuationTime, value)        \n\n    @property\n    def method_of_settlement(self) -> Union[OptionSettlementMethod, str]:\n        """How the option is settled (e.g. Cash, Physical)"""\n        return self.__method_of_settlement\n\n    @method_of_settlement.setter\n    def method_of_settlement(self, value: Union[OptionSettlementMethod, str]):\n        self._property_changed('method_of_settlement')\n        self.__method_of_settlement = get_enum_value(OptionSettlementMethod, value)        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def premium_currency(self) -> Union[Currency, str]:\n        """Currency of the option premium"""\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: Union[Currency, str]):\n        self._property_changed('premium_currency')\n        self.__premium_currency = get_enum_value(Currency, value)        \n\n    @property\n    def exchange(self) -> str:\n        """Name of marketplace where security, derivative or other instrument is traded"""\n        return self.__exchange\n\n    @exchange.setter\n    def exchange(self, value: str):\n        self._property_changed('exchange')\n        self.__exchange = value        \n\n    @property\n    def trade_as(self) -> Union[TradeAs, str]:\n        """Option trade as (i.e. listed, otc, lookalike etc)"""\n        return self.__trade_as\n\n    @trade_as.setter\n    def trade_as(self, value: Union[TradeAs, str]):\n        self._property_changed('trade_as')\n        self.__trade_as = get_enum_value(TradeAs, value)        \n\n\n
[docs]class FXMultiCrossBinary(Instrument):\n        \n    """Object representation of an FX multi-cross binary"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        legs: Tuple[FXMultiCrossBinaryLeg, ...] = None,\n        notional_amount: Union[float, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        settlement_date: Union[datetime.date, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        expiration_time: str = None,\n        premium: Union[float, str] = None,\n        premium_currency: Union[Currency, str] = None,\n        premium_payment_date: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.legs = legs\n        self.notional_amount = notional_amount\n        self.notional_currency = notional_currency\n        self.settlement_date = settlement_date\n        self.expiration_date = expiration_date\n        self.expiration_time = expiration_time\n        self.premium = premium\n        self.premium_currency = premium_currency\n        self.premium_payment_date = premium_payment_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """FX"""\n        return AssetClass.FX        \n\n    @property\n    def type(self) -> AssetType:\n        """MultiCrossBinary"""\n        return AssetType.MultiCrossBinary        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def legs(self) -> Tuple[FXMultiCrossBinaryLeg, ...]:\n        """Object representation of a single leg of a multi-cross binary option"""\n        return self.__legs\n\n    @legs.setter\n    def legs(self, value: Tuple[FXMultiCrossBinaryLeg, ...]):\n        self._property_changed('legs')\n        self.__legs = value        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Settlement date of the option, after expiration"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def expiration_time(self) -> str:\n        """The location and (optionally) time of spot for expiration"""\n        return self.__expiration_time\n\n    @expiration_time.setter\n    def expiration_time(self, value: str):\n        self._property_changed('expiration_time')\n        self.__expiration_time = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_currency(self) -> Union[Currency, str]:\n        """Currency of the option premium"""\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: Union[Currency, str]):\n        self._property_changed('premium_currency')\n        self.__premium_currency = get_enum_value(Currency, value)        \n\n    @property\n    def premium_payment_date(self) -> str:\n        """Payment date of the option premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: str):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        
\n\n\nclass FXOptionStrategy(Instrument):\n        \n    """Object representation of a FX option Strategy"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        pair: str = None,\n        buy_sell: Union[BuySell, str] = None,\n        strategy_name: str = None,\n        legs: Tuple[FXOptionLeg, ...] = None,\n        option_type: Union[OptionType, str] = None,\n        notional_amount: Union[float, str] = None,\n        notional_currency: Union[Currency, str] = None,\n        notional_amount_other_currency: Union[float, str] = None,\n        strike_price: Union[float, str] = None,\n        settlement_date: Union[datetime.date, str] = None,\n        settlement_currency: Union[Currency, str] = None,\n        settlement_rate_option: str = None,\n        method_of_settlement: Union[OptionSettlementMethod, str] = None,\n        expiration_date: Union[datetime.date, str] = None,\n        expiration_time: str = None,\n        premium: Union[float, str] = None,\n        premium_currency: Union[Currency, str] = None,\n        premium_payment_date: str = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.pair = pair\n        self.buy_sell = buy_sell\n        self.strategy_name = strategy_name\n        self.legs = legs\n        self.option_type = option_type\n        self.notional_amount = notional_amount\n        self.notional_currency = notional_currency\n        self.notional_amount_other_currency = notional_amount_other_currency\n        self.strike_price = strike_price\n        self.settlement_date = settlement_date\n        self.settlement_currency = settlement_currency\n        self.settlement_rate_option = settlement_rate_option\n        self.method_of_settlement = method_of_settlement\n        self.expiration_date = expiration_date\n        self.expiration_time = expiration_time\n        self.premium = premium\n        self.premium_currency = premium_currency\n        self.premium_payment_date = premium_payment_date\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """FX"""\n        return AssetClass.FX        \n\n    @property\n    def type(self) -> AssetType:\n        """OptionStrategy"""\n        return AssetType.OptionStrategy        \n\n    @property\n    def pair(self) -> str:\n        """A currency pair, e.g.: EURUSD or EUR USD"""\n        return self.__pair\n\n    @pair.setter\n    def pair(self, value: str):\n        self._property_changed('pair')\n        self.__pair = value        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def strategy_name(self) -> str:\n        return self.__strategy_name\n\n    @strategy_name.setter\n    def strategy_name(self, value: str):\n        self._property_changed('strategy_name')\n        self.__strategy_name = value        \n\n    @property\n    def legs(self) -> Tuple[FXOptionLeg, ...]:\n        """Object representation of a FX option leg used in FXOptionStrategy"""\n        return self.__legs\n\n    @legs.setter\n    def legs(self, value: Tuple[FXOptionLeg, ...]):\n        self._property_changed('legs')\n        self.__legs = value        \n\n    @property\n    def option_type(self) -> Union[OptionType, str]:\n        """Option Type"""\n        return self.__option_type\n\n    @option_type.setter\n    def option_type(self, value: Union[OptionType, str]):\n        self._property_changed('option_type')\n        self.__option_type = get_enum_value(OptionType, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def notional_currency(self) -> Union[Currency, str]:\n        """Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)"""\n        return self.__notional_currency\n\n    @notional_currency.setter\n    def notional_currency(self, value: Union[Currency, str]):\n        self._property_changed('notional_currency')\n        self.__notional_currency = get_enum_value(Currency, value)        \n\n    @property\n    def notional_amount_other_currency(self) -> Union[float, str]:\n        """Notional amount in currency other than NotionalCurrency from the pair"""\n        return self.__notional_amount_other_currency\n\n    @notional_amount_other_currency.setter\n    def notional_amount_other_currency(self, value: Union[float, str]):\n        self._property_changed('notional_amount_other_currency')\n        self.__notional_amount_other_currency = value        \n\n    @property\n    def strike_price(self) -> Union[float, str]:\n        """Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n           100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n           65.4/-45.8"""\n        return self.__strike_price\n\n    @strike_price.setter\n    def strike_price(self, value: Union[float, str]):\n        self._property_changed('strike_price')\n        self.__strike_price = value        \n\n    @property\n    def settlement_date(self) -> Union[datetime.date, str]:\n        """Settlement date of the option, after expiration"""\n        return self.__settlement_date\n\n    @settlement_date.setter\n    def settlement_date(self, value: Union[datetime.date, str]):\n        self._property_changed('settlement_date')\n        self.__settlement_date = value        \n\n    @property\n    def settlement_currency(self) -> Union[Currency, str]:\n        """Currency of settlement"""\n        return self.__settlement_currency\n\n    @settlement_currency.setter\n    def settlement_currency(self, value: Union[Currency, str]):\n        self._property_changed('settlement_currency')\n        self.__settlement_currency = get_enum_value(Currency, value)        \n\n    @property\n    def settlement_rate_option(self) -> str:\n        """The source of spot for settlement"""\n        return self.__settlement_rate_option\n\n    @settlement_rate_option.setter\n    def settlement_rate_option(self, value: str):\n        self._property_changed('settlement_rate_option')\n        self.__settlement_rate_option = value        \n\n    @property\n    def method_of_settlement(self) -> Union[OptionSettlementMethod, str]:\n        """How the option is settled (e.g. Cash, Physical)"""\n        return self.__method_of_settlement\n\n    @method_of_settlement.setter\n    def method_of_settlement(self, value: Union[OptionSettlementMethod, str]):\n        self._property_changed('method_of_settlement')\n        self.__method_of_settlement = get_enum_value(OptionSettlementMethod, value)        \n\n    @property\n    def expiration_date(self) -> Union[datetime.date, str]:\n        """Date or tenor, e.g. 2018-09-03, 3m, Dec21"""\n        return self.__expiration_date\n\n    @expiration_date.setter\n    def expiration_date(self, value: Union[datetime.date, str]):\n        self._property_changed('expiration_date')\n        self.__expiration_date = value        \n\n    @property\n    def expiration_time(self) -> str:\n        """The location and (optionally) time of spot for expiration"""\n        return self.__expiration_time\n\n    @expiration_time.setter\n    def expiration_time(self, value: str):\n        self._property_changed('expiration_time')\n        self.__expiration_time = value        \n\n    @property\n    def premium(self) -> Union[float, str]:\n        """Option premium"""\n        return self.__premium\n\n    @premium.setter\n    def premium(self, value: Union[float, str]):\n        self._property_changed('premium')\n        self.__premium = value        \n\n    @property\n    def premium_currency(self) -> Union[Currency, str]:\n        """Currency of the option premium"""\n        return self.__premium_currency\n\n    @premium_currency.setter\n    def premium_currency(self, value: Union[Currency, str]):\n        self._property_changed('premium_currency')\n        self.__premium_currency = get_enum_value(Currency, value)        \n\n    @property\n    def premium_payment_date(self) -> str:\n        """Payment date of the option premium"""\n        return self.__premium_payment_date\n\n    @premium_payment_date.setter\n    def premium_payment_date(self, value: str):\n        self._property_changed('premium_payment_date')\n        self.__premium_payment_date = value        \n\n\nclass InvoiceSpread(Instrument):\n        \n    """An interest swap vs a bond future"""\n\n    @camel_case_translate\n    def __init__(\n        self,\n        buy_sell: Union[BuySell, str] = None,\n        notional_amount: Union[float, str] = None,\n        underlier: Union[float, str] = None,\n        swap: IRSwap = None,\n        future: IRBondFuture = None,\n        name: str = None\n    ):        \n        super().__init__()\n        self.buy_sell = buy_sell\n        self.notional_amount = notional_amount\n        self.underlier = underlier\n        self.swap = swap\n        self.future = future\n        self.name = name\n\n    @property\n    def asset_class(self) -> AssetClass:\n        """Rates"""\n        return AssetClass.Rates        \n\n    @property\n    def type(self) -> AssetType:\n        """InvoiceSpread"""\n        return AssetType.InvoiceSpread        \n\n    @property\n    def buy_sell(self) -> Union[BuySell, str]:\n        """Buy or Sell side of contract"""\n        return self.__buy_sell\n\n    @buy_sell.setter\n    def buy_sell(self, value: Union[BuySell, str]):\n        self._property_changed('buy_sell')\n        self.__buy_sell = get_enum_value(BuySell, value)        \n\n    @property\n    def notional_amount(self) -> Union[float, str]:\n        """Notional amount"""\n        return self.__notional_amount\n\n    @notional_amount.setter\n    def notional_amount(self, value: Union[float, str]):\n        self._property_changed('notional_amount')\n        self.__notional_amount = value        \n\n    @property\n    def underlier(self) -> Union[float, str]:\n        """Underlier of the bond future"""\n        return self.__underlier\n\n    @underlier.setter\n    def underlier(self, value: Union[float, str]):\n        self._property_changed('underlier')\n        self.__underlier = value        \n\n    @property\n    def swap(self) -> IRSwap:\n        """A vanilla interest rate swap of fixed vs floating cashflows"""\n        return self.__swap\n\n    @swap.setter\n    def swap(self, value: IRSwap):\n        self._property_changed('swap')\n        self.__swap = value        \n\n    @property\n    def future(self) -> IRBondFuture:\n        """A future on a (treasury) bond"""\n        return self.__future\n\n    @future.setter\n    def future(self, value: IRBondFuture):\n        self._property_changed('future')\n        self.__future = value        \n
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Source code for gs_quant.timeseries.algebra

\n# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the "License");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#   http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Chart Service will attempt to make public functions (not prefixed with _) from this module available. Such functions\n# should be fully documented: docstrings should describe parameters and the return value, and provide a 1-line\n# description. Type annotations should be provided for parameters.\n\n\nimport math\n\nfrom .datetime import *\nfrom .helper import plot_function\nfrom functools import reduce\nfrom gs_quant.errors import MqTypeError\n\n"""\nAlgebra library contains basic numerical and algebraic operations, including addition, division, multiplication,\ndivision and other functions on timeseries\n"""\n\n\nclass FilterOperator(Enum):\n    LESS = 'less_than'\n    GREATER = 'greater_than'\n    L_EQUALS = 'l_equals'\n    G_EQUALS = 'g_equals'\n    EQUALS = 'equals'\n    N_EQUALS = 'not_equals'\n\n\n
[docs]@plot_function\ndef add(x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP) \\\n        -> Union[pd.Series, Real]:\n    """\n    Add two series or scalars\n\n    :param x: timeseries or scalar\n    :param y: timeseries or scalar\n    :param method: interpolation method (default: step). Only used when both x and y are timeseries\n    :return: timeseries of x + y or sum of the given real numbers\n\n    **Usage**\n\n    Add two series or scalar variables with the given interpolation method\n\n    :math:`R_t =  X_t + Y_t`\n\n    Alignment operators:\n\n    =========   ========================================================================\n    Method      Behavior\n    =========   ========================================================================\n    intersect   Resultant series only has values on the intersection of dates. Values\n                for dates present in only one series will be ignored\n    nan         Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as nan in the\n                other series, and therefore in the resultant series\n    zero        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as zero in the\n                other series\n    step        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be interpolated via step\n                function in the other series\n    time        Resultant series have values on the union of dates / times. Missing\n                values surrounded by valid values will be interpolated given length of\n                interval. Input series must use DateTimeIndex.\n    =========   ========================================================================\n\n    **Examples**\n\n    Add two series:\n\n    >>> a = generate_series(100)\n    >>> b = generate_series(100)\n    >>> add(a, b, Interpolate.STEP)\n\n    **See also**\n\n    :func:`subtract`\n    """\n\n    if isinstance(x, Real) and isinstance(y, Real):\n        return x + y\n\n    [x_align, y_align] = align(x, y, method)\n    return x_align.add(y_align)
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[docs]@plot_function\ndef subtract(x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP) \\\n        -> Union[pd.Series, Real]:\n    """\n    Add two series or scalars\n\n    :param x: timeseries or scalar\n    :param y: timeseries or scalar\n    :param method: index alignment operator (default: intersect). Only used when both x and y are timeseries\n    :return: timeseries of x - y or difference between the given real numbers\n\n    **Usage**\n\n    Subtracts one series or scalar from another applying the given interpolation method\n\n    :math:`R_t =  X_t - Y_t`\n\n    Alignment operators:\n\n    =========   ========================================================================\n    Method      Behavior\n    =========   ========================================================================\n    intersect   Resultant series only has values on the intersection of dates.\n                Values for dates present in only one series will be ignored\n    nan         Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as nan in the\n                other series, and therefore in the resultant series\n    zero        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as zero in the\n                other series\n    step        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be interpolated via step\n                function in the other series\n    time        Resultant series have values on the union of dates / times. Missing\n                values surrounded by valid values will be interpolated given length of\n                interval. Input series must use DateTimeIndex.\n    =========   ========================================================================\n\n    **Examples**\n\n    Subtract one series from another:\n\n    >>> a = generate_series(100)\n    >>> b = generate_series(100)\n    >>> subtract(a, b, Interpolate.STEP)\n\n    **See also**\n\n    :func:`add`\n    """\n\n    # Determine how we want to handle observations prior to start date\n\n    if isinstance(x, Real) and isinstance(y, Real):\n        return x - y\n\n    [x_align, y_align] = align(x, y, method)\n    return x_align.subtract(y_align)
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[docs]@plot_function\ndef multiply(x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP) \\\n        -> Union[pd.Series, Real]:\n    """\n    Multiply two series or scalars\n\n    :param x: timeseries or scalar\n    :param y: timeseries or scalar\n    :param method: interpolation method (default: step). Only used when both x and y are timeseries\n    :return: timeseries of x * y or product of the given real numbers\n\n    **Usage**\n\n    Multiply two series or scalar variables applying the given interpolation method\n\n    :math:`R_t =  X_t \\\\times Y_t`\n\n    Alignment operators:\n\n    =========   ========================================================================\n    Method      Behavior\n    =========   ========================================================================\n    intersect   Resultant series only has values on the intersection of dates. Values\n                for dates present in only one series will be ignored\n    nan         Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as nan in the\n                other series, and therefore in the resultant series\n    zero        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as zero in the\n                other series\n    step        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be interpolated via step\n                function in the other series\n    time        Resultant series have values on the union of dates / times. Missing\n                values surrounded by valid values will be interpolated given length of\n                interval. Input series must use DateTimeIndex.\n    =========   ========================================================================\n\n    **Examples**\n\n    Multiply two series:\n\n    >>> a = generate_series(100)\n    >>> b = generate_series(100)\n    >>> multiply(a, b, Interpolate.STEP)\n\n    **See also**\n\n    :func:`divide`\n    """\n\n    if isinstance(x, Real) and isinstance(y, Real):\n        return x * y\n\n    [x_align, y_align] = align(x, y, method)\n    return x_align.multiply(y_align)
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[docs]@plot_function\ndef divide(x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP) \\\n        -> Union[pd.Series, Real]:\n    """\n    Divide two series or scalars\n\n    :param x: timeseries or scalar\n    :param y: timeseries or scalar\n    :param method: interpolation method (default: step). Only used when both x and y are timeseries\n    :return: timeseries of x / y or quotient of the given real numbers\n\n    **Usage**\n\n    Divide two series or scalar variables applying the given interpolation method\n\n    :math:`R_t =  X_t / Y_t`\n\n    Alignment operators:\n\n    =========   ========================================================================\n    Method      Behavior\n    =========   ========================================================================\n    intersect   Resultant series only has values on the intersection of dates.\n                Values for dates present in only one series will be ignored\n    nan         Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as nan in the\n                other series, and therefore in the resultant series\n    zero        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as zero in the\n                other series\n    step        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be interpolated via step\n                function in the other series\n    time        Resultant series have values on the union of dates / times. Missing\n                values surrounded by valid values will be interpolated given length of\n                interval. Input series must use DateTimeIndex.\n    =========   ========================================================================\n\n    **Examples**\n\n    Divide two series:\n\n    >>> a = generate_series(100)\n    >>> b = generate_series(100)\n    >>> divide(a, b, Interpolate.STEP)\n\n    **See also**\n\n    :func:`multiply`\n    """\n\n    if isinstance(x, Real) and isinstance(y, Real):\n        return x / y\n\n    [x_align, y_align] = align(x, y, method)\n    return x_align.divide(y_align)
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[docs]@plot_function\ndef floordiv(x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP) \\\n        -> Union[pd.Series, Real]:\n    """\n    Floor divide two series or scalars\n\n    :param x: timeseries or scalar\n    :param y: timeseries or scalar\n    :param method: interpolation method (default: step). Only used for operating two series\n    :return: timeseries of x // y or quotient of the floor division of the given real numbers\n\n    **Usage**\n\n    Divide two series or scalar variables applying the given interpolation method\n\n    :math:`R_t =  X_t / Y_t`\n\n    Alignment operators:\n\n    =========   ========================================================================\n    Method      Behavior\n    =========   ========================================================================\n    intersect   Resultant series only has values on the intersection of dates.\n                Values for dates present in only one series will be ignored\n    nan         Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as nan in the\n                other series, and therefore in the resultant series\n    zero        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be treated as zero in the\n                other series\n    step        Resultant series has values on the union of dates in both series. Values\n                for dates only available in one series will be interpolated via step\n                function in the other series\n    =========   ========================================================================\n\n    **Examples**\n\n    Floor divide two series:\n\n    >>> a = generate_series(100)\n    >>> b = generate_series(100)\n    >>> floordiv(a, b, Interpolate.STEP)\n\n    **See also**\n\n    :func:`divide`\n    """\n\n    if isinstance(x, Real) and isinstance(y, Real):\n        return x // y\n\n    [x_align, y_align] = align(x, y, method)\n    return x_align.floordiv(y_align)
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[docs]@plot_function\ndef exp(x: pd.Series) -> pd.Series:\n    """\n    Exponential of series\n\n    :param x: timeseries\n    :return: exponential of each element\n\n    **Usage**\n\n    For each element in the series, :math:`X_t`, raise :math:`e` (Euler's number) to the power of :math:`X_t`.\n    Euler's number is the base of the natural logarithm, :math:`ln`.\n\n    :math:`R_t = e^{X_t}`\n\n    **Examples**\n\n    Raise :math:`e` to the power :math:`1`. Returns Euler's number, approximately 2.71828\n\n    >>> exp(1)\n\n    **See also**\n\n    :func:`log`\n\n    """\n    return np.exp(x)
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[docs]@plot_function\ndef log(x: pd.Series) -> pd.Series:\n    """\n    Natural logarithm of series\n\n    :param x: timeseries\n    :return: series with exponential of each element\n\n    **Usage**\n\n    For each element in the series, :math:`X_t`, return the natural logarithm :math:`ln` of :math:`X_t`\n    The natural logarithm is the logarithm in base :math:`e`.\n\n    :math:`R_t = log(X_t)`\n\n    This function is the inverse of the exponential function.\n\n    More information on `logarithms <https://en.wikipedia.org/wiki/Logarithm>`_\n\n    **Examples**\n\n    Take natural logarithm of 3\n\n    >>> log(3)\n\n    **See also**\n\n    :func:`exp`\n\n    """\n    return np.log(x)
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[docs]@plot_function\ndef power(x: pd.Series, y: float = 1) -> pd.Series:\n    """\n    Raise each element in series to power\n\n    :param x: timeseries\n    :param y: value\n    :return: date-based time series of square roots\n\n    **Usage**\n\n    Raise each value in time series :math:`X_t` to the power :math:`y`:\n\n    :math:`R_t = X_t^{y}`\n\n    **Examples**\n\n    Generate price series and raise each value to the power 2:\n\n    >>> prices = generate_series(100)\n    >>> power(prices, 2)\n\n    **See also**\n\n    :func:`sqrt`\n\n    """\n    return np.power(x, y)
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[docs]@plot_function\ndef sqrt(x: Union[Real, pd.Series]) -> Union[Real, pd.Series]:\n    """\n    Square root of (a) each element in a series or (b) a real number\n\n    :param x: date-based time series of prices or real number\n    :return: date-based time series of square roots or square root of given number\n\n    **Usage**\n\n    Return the square root of each value in time series :math:`X_t`:\n\n    :math:`R_t = \\\\sqrt{X_t}`\n\n    **Examples**\n\n    Generate price series and take square root of each value:\n\n    >>> prices = generate_series(100)\n    >>> sqrt(prices)\n\n    **See also**\n\n    :func:`pow`\n\n    """\n    if isinstance(x, pd.Series):\n        return np.sqrt(x)\n\n    result = math.sqrt(x)\n    # return int if result is integral (should work for values up to 2**53)\n    return round(result) if round(result) == result else result
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[docs]@plot_function\ndef abs_(x: pd.Series) -> pd.Series:\n    """\n    Absolute value of each element in series\n\n    :param x: date-based time series of prices\n    :return: date-based time series of absolute value\n\n    **Usage**\n\n    Return the absolute value of :math:`X`. For each value in time series :math:`X_t`, return :math:`X_t` if :math:`X_t`\n    is greater than or equal to 0; otherwise return :math:`-X_t`:\n\n    :math:`R_t = |X_t|`\n\n    Equivalent to :math:`R_t = \\sqrt{X_t^2}`\n\n    **Examples**\n\n    Generate price series and take absolute value of :math:`X_t-100`\n\n    >>> prices = generate_series(100) - 100\n    >>> abs_(prices)\n\n    **See also**\n\n    :func:`exp` :func:`sqrt`\n\n    """\n    return abs(x)
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[docs]@plot_function\ndef floor(x: pd.Series, value: float = 0) -> pd.Series:\n    """\n    Floor series at minimum value\n\n    :param x: date-based time series of prices\n    :param value: minimum value\n    :return: date-based time series of maximum value\n\n    **Usage**\n\n    Returns series where all values are greater than or equal to the minimum value.\n\n    :math:`R_t = max(X_t, value)`\n\n    See `Floor and Ceil functions <https://en.wikipedia.org/wiki/Floor_and_ceiling_functions>`_ for more details\n\n    **Examples**\n\n    Generate price series and floor all values at 100\n\n    >>> prices = generate_series(100)\n    >>> floor(prices, 100)\n\n    **See also**\n\n    :func:`ceil`\n\n    """\n    assert x.index.is_monotonic_increasing\n    return x.apply(lambda y: max(y, value))
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[docs]@plot_function\ndef ceil(x: pd.Series, value: float = 0) -> pd.Series:\n    """\n    Cap series at maximum value\n\n    :param x: date-based time series of prices\n    :param value: maximum value\n    :return: date-based time series of maximum value\n\n    **Usage**\n\n    Returns series where all values are less than or equal to the maximum value.\n\n    :math:`R_t = min(X_t, value)`\n\n    See `Floor and Ceil functions <https://en.wikipedia.org/wiki/Floor_and_ceiling_functions>`_ for more details\n\n    **Examples**\n\n    Generate price series and floor all values at 100\n\n    >>> prices = generate_series(100)\n    >>> floor(prices, 100)\n\n    **See also**\n\n    :func:`floor`\n\n    """\n    assert x.index.is_monotonic_increasing\n    return x.apply(lambda y: min(y, value))
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[docs]@plot_function\ndef filter_(x: pd.Series, operator: Optional[FilterOperator] = None, value: Optional[Real] = None) -> pd.Series:\n    """\n    Removes values where comparison with the operator and value combination results in true, defaults to removing\n    missing values from the series\n\n    :param x: timeseries\n    :param operator: FilterOperator describing logic for value removal, e.g 'less_than'\n    :param value: number indicating value(s) to remove from the series\n    :return: timeseries with specified values removed\n\n\n    **Usage**\n\n    Remove each value determined by operator and value from timeseries where that expression yields true\n\n    **Examples**\n\n    Remove 0 from time series\n\n    >>> prices = generate_series(100)\n    >>> filter_(prices, FilterOperator.EQUALS, 0)\n\n    Remove positive numbers from time series\n\n    >>> prices = generate_series(100)\n    >>> filter_(prices, FilterOperator.GREATER, 0)\n\n    Remove missing values from time series\n\n    >>> prices = generate_series(100)\n    >>> filter_(prices)\n\n    """\n\n    if value is None and operator is None:\n        x = x.dropna(axis=0, how='any')\n    elif value is None:\n        raise MqValueError('No value is specified for the operator')\n    else:\n        if operator == FilterOperator.EQUALS:\n            remove = x == value\n        elif operator == FilterOperator.GREATER:\n            remove = x > value\n        elif operator == FilterOperator.LESS:\n            remove = x < value\n        elif operator == FilterOperator.L_EQUALS:\n            remove = x <= value\n        elif operator == FilterOperator.G_EQUALS:\n            remove = x >= value\n        elif operator == FilterOperator.N_EQUALS:\n            remove = x != value\n        else:\n            if not isinstance(operator, str):\n                operator = str(operator)\n            raise MqValueError('Unexpected operator: ' + operator)\n        x = x.drop(x[remove].index)\n    return x
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[docs]@plot_function\ndef smooth_spikes(x: pd.Series, threshold: float) -> pd.Series:\n    """\n    Smooth out the spikes of a series. If a point is larger/smaller than (1 +/- threshold) times both neighbors, replace\n    it with the average of those neighbours. Note: the first and last points in the input series are dropped.\n\n    :param x: timeseries\n    :param threshold: minimum increment to trigger filter\n    :return: smoothed timeseries\n\n    **Usage**\n\n    Returns series where values that exceed the threshold relative to both neighbors are replaced.\n\n    **Examples**\n\n    Generate price series and smooth spikes over a threshold of 0.5.\n\n    >>> prices = generate_series(100)\n    >>> smooth_spikes(prices, 0.5)\n\n    **See also**\n\n    :func:`exponential_moving_average`\n    """\n\n    if len(x) < 3:\n        return pd.Series()\n\n    result = x.copy()\n    multiplier = (1 + threshold)\n    current, next_ = x.iloc[0:2]\n    for i in range(1, len(x) - 1):\n        previous = current\n        current = next_\n        next_ = x.iloc[i + 1]\n\n        scaled = current * multiplier\n        if (current > previous * multiplier and current > next_ * multiplier) or (previous > scaled and next_ > scaled):\n            result.iloc[i] = (previous + next_) / 2\n\n    return result[1:-1]
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[docs]@plot_function\ndef repeat(x: pd.Series, n: int = 1) -> pd.Series:\n    """\n    Repeats values for days where data is missing. For any date with missing data, the last recorded value is used.\n    Optionally downsamples the result such that there are data points every n days.\n\n    :param x: date-based timeseries\n    :param n: desired frequency of output\n    :return: a timeseries that has been forward-filled, and optionally downsampled\n\n    **Usage**\n\n    Fill missing values with last seen value e.g. to combine daily with weekly or monthly data.\n    """\n    if not 0 < n < 367:\n        raise MqValueError('n must be between 0 and 367')\n    index = pd.date_range(freq=f'{n}D', start=x.index[0], end=x.index[-1])\n    return x.reindex(index, method='ffill')
\n\n\ndef _sum_boolean_series(*series):\n    if not 2 <= len(series) <= 100:\n        raise MqValueError('expected between 2 and 100 arguments')\n\n    for s in series:\n        if not isinstance(s, pd.Series):\n            raise MqTypeError('all arguments must be series')\n        if not all(map(lambda a: a in (0, 1), s.values)):\n            raise MqValueError(f'cannot perform operation on series with value(s) other than 1 and 0: {s.values}')\n\n    current = series[0].add(series[1], fill_value=0)\n    for s in series[2:]:\n        current = current.add(s, fill_value=0)\n    return current\n\n\n
[docs]@plot_function\ndef and_(*series: pd.Series) -> pd.Series:\n    """\n    Logical "and" of two or more boolean series.\n\n    :param series: input series\n    :return: result series (of numeric type, with booleans represented as 1s and 0s)\n    """\n    s = _sum_boolean_series(*series)\n    return (s == len(series)).astype(int)
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[docs]@plot_function\ndef or_(*series: pd.Series) -> pd.Series:\n    """\n    Logical "or" of two or more boolean series.\n\n    :param series: input series\n    :return: result series (of numeric type, with booleans represented as 1s and 0s)\n    """\n    s = _sum_boolean_series(*series)\n    return (s > 0).astype(int)
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[docs]@plot_function\ndef not_(series: pd.Series) -> pd.Series:\n    """\n    Logical negation of a single boolean series.\n\n    :param series: single input series\n    :return: result series (of numeric type, with booleans represented as 1s and 0s)\n    """\n    if not all(map(lambda a: a in (0, 1), series.values)):\n        raise MqValueError(f'cannot negate series with value(s) other than 1 and 0: {series.values}')\n    return series.replace([0, 1], [1, 0])
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[docs]@plot_function\ndef if_(flags: pd.Series, x: Union[pd.Series, float], y: Union[pd.Series, float]) -> pd.Series:\n    """\n    Returns a series s. For i in the index of flags, s[i] = x[i] if flags[i] == 1 else y[i].\n\n    :param flags: series of 1s and 0s\n    :param x: values to use when flag is 1\n    :param y: values to use when flag is 0\n    :return: result series\n    """\n    if not all(map(lambda a: a in (0, 1), flags.values)):\n        raise MqValueError(f'cannot perform "if" on series with value(s) other than 1 and 0: {flags.values}')\n\n    def ensure_series(s):\n        if isinstance(s, (float, int)):\n            return flags, pd.Series([s] * flags.shape[0], index=flags.index)\n        elif isinstance(s, pd.Series):\n            return flags.align(s)\n        else:\n            raise MqTypeError('expected a number or series')\n\n    x_flags, x = ensure_series(x)\n    y_flags, y = ensure_series(y)\n    return pd.concat([x[x_flags == 1], y[y_flags == 0]]).sort_index()
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[docs]@plot_function\ndef weighted_sum(series: List[pd.Series], weights: list) -> pd.Series:\n    """\n    Calculate a weighted sum.\n\n    :param series: list of time series\n    :param weights: list of weights\n    :return: time series of weighted average\n\n    **Usage**\n\n    Calculate a weighted sum e.g. for a basket.\n\n    **Examples**\n\n    Generate price series and get a sum (weights 70%/30%).\n\n    >>> prices1 = generate_series(100)\n    >>> prices2 = generate_series(100)\n    >>> mybasket = weighted_sum([prices1, prices2], [0.7, 0.3])\n\n    **See also**\n\n    :func:`basket`\n    """\n    if not all(isinstance(x, pd.Series) for x in series):\n        raise MqTypeError("expected a list of time series")\n    if not all(isinstance(y, (float, int)) for y in weights):\n        raise MqTypeError("expected a list of number for weights")\n    if len(weights) != len(series):\n        raise MqValueError("must have one weight for each time series")\n\n    # for input series, get the intersection of their calendars\n    cal = pd.DatetimeIndex(\n        reduce(\n            np.intersect1d,\n            (\n                curve.index\n                for curve in series\n            ),\n        )\n    )\n\n    # reindex inputs and calculate\n    series = [s.reindex(cal) for s in series]\n    weights = [pd.Series(w, index=cal) for w in weights]\n    return sum(series[i] * weights[i] for i in range(len(series))) / sum(weights)
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Source code for gs_quant.timeseries.analysis

\n# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the "License");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#   http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Chart Service will attempt to make public functions (not prefixed with _) from this module available. Such functions\n# should be fully documented: docstrings should describe parameters and the return value, and provide a 1-line\n# description. Type annotations should be provided for parameters.\nimport re\n\nfrom gs_quant.datetime import relative_date_add\nfrom gs_quant.timeseries.datetime import *\nfrom .helper import plot_function\n\n"""\nTimeseries analysis library contains functions used to analyze properties of timeseries, including laging, differencing,\nautocorrelation, co-integration and other operations\n"""\n\n\n
[docs]@plot_function\ndef first(x: pd.Series) -> pd.Series:\n    """\n    First value of series\n\n    :param x: time series\n    :return: time series of first value\n\n    **Usage**\n\n    Return series with first value of `X` for all dates:\n\n    :math:`R_t = X_0`\n\n    where :math:`X_0` is the first value in the series\n\n    **Examples**\n\n    Last value of series:\n\n    >>> series = generate_series(100)\n    >>> returns = first(series)\n\n    **See also**\n\n    :func:`last`\n\n    """\n    return pd.Series(x[0], x.index)
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[docs]@plot_function\ndef last(x: pd.Series) -> pd.Series:\n    """\n    Last value of series (as a series)\n\n    :param x: time series\n    :return: time series of last value\n\n    **Usage**\n\n    Return series with last value of `X` for all dates:\n\n    :math:`R_t = X_T`\n\n    where :math:`X_T` is the last value in the series\n\n    **Examples**\n\n    Last value of series:\n\n    >>> series = generate_series(100)\n    >>> returns = last(series)\n\n    **See also**\n\n    :func:`first`\n\n    """\n    return pd.Series(x[-1], x.index)
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[docs]@plot_function\ndef last_value(x: pd.Series) -> Union[int, float]:\n    """\n    Last value of series (as a scalar)\n\n    :param x: time series\n    :return: last value\n\n    **Usage**\n\n    Return a scalar value :math:`X_T` where T is the last index value in the series.\n\n    **Examples**\n\n    Last value of series:\n\n    >>> series = generate_series(100)\n    >>> lv = last_value(series)\n\n    **See also**\n\n    :func:`last`\n\n    """\n    if x.empty:\n        raise MqValueError("cannot get last value of an empty series")\n    return x.iloc[-1]
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[docs]@plot_function\ndef count(x: pd.Series) -> pd.Series:\n    """\n    Count observations in series\n\n    :param x: time series\n    :return: number of observations\n\n    **Usage**\n\n    Count the number of valid observations in a series:\n\n    :math:`R_t = R_{t-1} + 1`\n\n    if :math:`X_t` is not NaN, and\n\n    :math:`R_t = R_{t-1} + 0`\n\n    if :math:`X_t` is NaN\n\n    **Examples**\n\n    Count observations in series:\n\n    >>> series = generate_series(100)\n    >>> count = count(series)\n\n    **See also**\n\n    :func:`sum`\n\n    """\n    return x.rolling(x.size, 0).count()
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[docs]@plot_function\ndef diff(x: pd.Series, obs: int = 1) -> pd.Series:\n    """\n    Diff observations with given lag\n\n    :param x: time series of prices\n    :param obs: number of observations to lag\n    :return: date-based time series of return\n\n    **Usage**\n\n    Compute the difference in series values over a given lag:\n\n    :math:`R_t = X_t - X_{t-obs}`\n\n    where :math:`obs` is the number of observations to lag series in diff function\n\n    **Examples**\n\n    Diff prices levels:\n\n    >>> series = generate_series(100)\n    >>> returns = diff(series)\n\n    **See also**\n\n    :func:`lag`\n\n    """\n\n    if x.size < 1:\n        return x\n\n    ret_series = x - x.shift(obs)\n\n    return ret_series
\n\n\nclass LagMode(Enum):\n    TRUNCATE = "truncate"\n    EXTEND = "extend"\n\n\n
[docs]@plot_function\ndef lag(x: pd.Series, obs: Union[Window, int, str] = 1, mode: LagMode = LagMode.EXTEND) -> pd.Series:\n    """\n    Lag timeseries by a number of observations or a relative date.\n\n    :param x: timeseries of prices\n    :param obs: non-zero integer (number of observations) or relative date e.g. "-90d", "1d", "1m", "1y"\n    :param mode: whether to extend series index (into the future)\n    :return: date-based time series of return\n\n    **Usage**\n\n    Shift the series backwards by a specified number of observations:\n\n    :math:`R_t =  X_{t-obs}`\n\n    where :math:`obs` is the number of observations to lag series\n\n    **Examples**\n\n    Lag series by 2 observations:\n\n    >>> prices = generate_series(100)\n    >>> lagged = lag(prices, 2)\n\n    Lag series by 1 year:\n\n    >>> prices = generate_series(100)\n    >>> lagged = lag(prices, '1y')\n\n    **See also**\n\n    :func:`diff`\n    """\n    if isinstance(obs, str):\n        end = x.index[-1]\n        y = x.copy()  # avoid mutating the provided series\n\n        match = re.fullmatch('(\\\\d+)y', obs)\n        if match:\n            y.index += pd.DateOffset(years=int(match.group(1)))\n            y = y.groupby(y.index).first()\n        else:\n            y.index += pd.DateOffset(relative_date_add(obs))\n\n        if mode == LagMode.EXTEND:\n            return y\n        return y[:end]\n\n    obs = getattr(obs, 'w', obs)\n    # Determine how we want to handle observations prior to start date\n    if mode == LagMode.EXTEND:\n        if x.index.resolution != 'day':\n            raise MqValueError(f'unable to extend index with resolution {x.index.resolution}')\n        kwargs = {'periods': abs(obs) + 1, 'freq': 'D'}\n        if obs > 0:\n            kwargs['start'] = x.index[-1]\n        else:\n            kwargs['end'] = x.index[0]\n        x = x.reindex(x.index.union(pd.date_range(**kwargs)))\n    return x.shift(obs)
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Source code for gs_quant.timeseries.backtesting

\n"""\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\n\nfrom dateutil.relativedelta import relativedelta as rdelta\nfrom functools import reduce\n\nfrom gs_quant.timeseries.helper import _create_enum\nfrom .statistics import *\n\nRebalFreq = _create_enum('RebalFreq', ['Daily', 'Monthly'])\nReturnType = _create_enum('ReturnType', ['excess_return'])\n\n\n
[docs]@plot_function\ndef basket(\n    series: list,\n    weights: list,\n    costs: list = None,\n    rebal_freq: RebalFreq = RebalFreq.DAILY,\n    return_type: ReturnType = ReturnType.EXCESS_RETURN,\n) -> pd.Series:\n    """\n    Calculates a basket return series.\n\n    :param series: list of time series of instrument prices\n    :param weights: list of weights\n    :param costs: list of execution costs in decimal; defaults to costs of 0\n    :param rebal_freq: rebalancing frequency - Daily or Monthly\n    :param return_type: return type of underlying instruments - only excess return is supported\n    :return: time series of the resulting basket\n\n    **Usage**\n\n    Calculates a basket return series.\n\n    **Examples**\n\n    Generate price series and combine them in a basket (weights 70%/30%) which rebalances monthly and assumes\n    execution costs 5bps and 10bps each time the constituents are traded.\n\n    >>> prices1 = generate_series(100)\n    >>> prices2 = generate_series(100)\n    >>> mybasket = basket([prices1, prices2], [0.7, 0.3], [0.0005, 0.001], monthly)\n\n    **See also**\n\n    :func:`prices`\n    """\n\n    num_assets = len(series)\n    costs = costs or [0] * num_assets\n\n    if not all(isinstance(x, pd.Series) for x in series):\n        raise MqTypeError("expected a list of series")\n\n    if len(weights) != num_assets or len(weights) != len(costs):\n        raise MqValueError("series, weights, and cost lists must have the same length")\n\n    # For all inputs which are Pandas series, get the intersection of their calendars\n    cal = pd.DatetimeIndex(\n        reduce(\n            np.intersect1d,\n            (\n                curve.index\n                for curve in series + weights + costs\n                if isinstance(curve, pd.Series)\n            ),\n        )\n    )\n\n    # Reindex inputs and convert to pandas dataframes\n    series = pd.concat([curve.reindex(cal) for curve in series], axis=1)\n    weights = pd.concat([pd.Series(w, index=cal) for w in weights], axis=1)\n    costs = pd.concat([pd.Series(c, index=cal) for c in costs], axis=1)\n\n    if rebal_freq == RebalFreq.DAILY:\n        rebal_dates = cal\n    else:\n        # Get hypothetical monthly rebalances\n        num_rebals = (cal[-1].year - cal[0].year) * 12 + cal[-1].month - cal[0].month\n        rebal_dates = [cal[0] + i * rdelta(months=1) for i in range(num_rebals + 1)]\n        # Convert these to actual calendar days\n        rebal_dates = [d for d in rebal_dates if d < max(cal)]\n        rebal_dates = [min(cal[cal >= date]) for date in rebal_dates]\n\n    # Create Units dataframe\n    units = pd.DataFrame(index=cal, columns=series.columns)\n    output = pd.Series(index=cal)\n\n    # Initialize backtest\n    output.values[0] = 100\n    units.values[0, ] = (\n        output.values[0] * weights.values[0, ] / series.values[0, ]\n    )\n\n    # Run backtest\n    prev_rebal = 0\n    for i, date in enumerate(cal[1:], 1):\n        # Update performance\n        output.values[i] = output.values[i - 1] + np.dot(\n            units.values[i - 1, ], series.values[i, ] - series.values[i - 1, ]\n        )\n\n        # Rebalance on rebal_dates\n        if date in rebal_dates:\n            # Compute costs\n            actual_weights = (\n                weights.values[prev_rebal, ] *\n                (series.values[i, ] / series.values[prev_rebal, ]) *\n                (output.values[prev_rebal] / output.values[i])\n            )\n            output.values[i] -= (\n                np.dot(costs.values[i, ], np.abs(weights.values[i, ] - actual_weights)) *\n                output.values[i]\n            )\n\n            # Rebalance\n            units.values[i, ] = (\n                output.values[i] * weights.values[i, ] / series.values[i, ]\n            )\n            prev_rebal = i\n        else:\n            units.values[i, ] = units.values[\n                i - 1,\n            ]\n\n    return output
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Source code for gs_quant.timeseries.datetime

\n# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the "License");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#   http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Chart Service will attempt to make public functions (not prefixed with _) from this module available. Such functions\n# should be fully documented: docstrings should describe parameters and the return value, and provide a 1-line\n# description. Type annotations should be provided for parameters.\nfrom datetime import date, time, timedelta\nfrom numbers import Real\nimport numpy as np\nfrom .helper import *\nfrom ..errors import MqValueError\nfrom ..datetime.date import DayCountConvention, PaymentFrequency, day_count_fraction\nfrom ..datetime.date import date_range as _date_range\n\n"""\nDate and time manipulation for timeseries, including date or time shifting, calendar operations, curve alignment and\ninterpolation operations. Includes sampling operations based on daif dates[0]te or time manipulation\n"""\n\n\ndef __interpolate_step(x: pd.Series, dates: pd.Series = None) -> pd.Series:\n    if x.empty:\n        raise MqValueError('Cannot perform step interpolation on an empty series')\n\n    first_date = pd.Timestamp(dates.index[0]) if isinstance(x.index[0], pd.Timestamp) else dates.index[0]\n\n    # locate previous valid date or take first value from series\n    prev = x.index[0] if first_date < x.index[0] else x.index[x.index.get_loc(first_date, 'pad')]\n\n    current = x[prev]\n\n    curve = x.align(dates, 'right', )[0]                  # only need values from dates\n\n    for knot in curve.iteritems():\n        if np.isnan(knot[1]):\n            curve[knot[0]] = current\n        else:\n            current = knot[1]\n    return curve\n\n\n
[docs]@plot_function\ndef align(x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.INTERSECT) -> \\\n        Union[List[pd.Series], List[Real]]:\n    """\n    Align dates of two series or scalars\n\n    :param x: first timeseries or scalar\n    :param y: second timeseries or scalar\n    :param method: interpolation method (default: intersect). Only used when both x and y are timeseries\n    :return: timeseries with specified dates or two scalars from the input\n\n    **Usage**\n\n    Align the dates of two series using the specified interpolation method. Returns two series with dates based on the\n    method of interpolation, for example, can be used to intersect the dates of two series, union dates with a defined\n    manner to compute missing values.\n\n    Interpolation methods:\n\n    =========   ========================================================================\n    Type        Behavior\n    =========   ========================================================================\n    intersect   Resultant series only have values on the intersection of dates /times.\n    nan         Resultant series have values on the union of dates /times. Values will\n                be NaN for dates or times only present in the other series\n    zero        Resultant series have values on the union of  dates / times. Values will\n                be zero for dates or times only present in the other series\n    step        Resultant series have values on the union of  dates / times. Each series\n                will use the value of the previous valid point if requested date does\n                not exist. Values prior to the first date will be equivalent to the\n                first available value\n    time        Resultant series have values on the union of dates / times. Missing\n                values surrounded by valid values will be interpolated given length of\n                interval. Input series must use DateTimeIndex.\n    =========   ========================================================================\n\n    **Examples**\n\n    Stepwize interpolation of series based on dates in second series:\n\n    >>> a = generate_series(100)\n    >>> b = generate_series(100)\n    >>> align(a, b)\n\n    **See also**\n\n    :func:`sub`\n    """\n    if isinstance(x, Real) and isinstance(y, Real):\n        return [x, y]\n    if isinstance(x, Real):\n        return [pd.Series(x, index=y.index), y]\n    if isinstance(y, Real):\n        return [x, pd.Series(y, index=x.index)]\n\n    if method == Interpolate.INTERSECT:\n        return x.align(y, 'inner')\n    if method == Interpolate.NAN:\n        return x.align(y, 'outer')\n    if method == Interpolate.ZERO:\n        return x.align(y, 'outer', fill_value=0)\n    if method == Interpolate.TIME:\n        new_x, new_y = x.align(y, 'outer')\n        new_x.interpolate('time', limit_area='inside', inplace=True)\n        new_y.interpolate('time', limit_area='inside', inplace=True)\n        return [new_x, new_y]\n    if method == Interpolate.STEP:\n        new_x, new_y = x.align(y, 'outer')\n        new_x.fillna(method='ffill', inplace=True)\n        new_y.fillna(method='ffill', inplace=True)\n        new_x.fillna(method='bfill', inplace=True)\n        new_y.fillna(method='bfill', inplace=True)\n        return [new_x, new_y]\n    else:\n        raise MqValueError('Unknown intersection type: ' + method)
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[docs]@plot_function\ndef interpolate(x: pd.Series, dates: Union[List[date], List[time], pd.Series] = None,\n                method: Interpolate = Interpolate.INTERSECT) -> pd.Series:\n    """\n    Interpolate over specified dates or times\n\n    :param x: timeseries to interpolate\n    :param dates: array of dates/times or another series to interpolate\n    :param method: interpolation method (default: intersect)\n    :return: timeseries with specified dates\n\n    **Usage**\n\n    Interpolate the series X over the dates specified by the dates parameter. This can be an array of dates or another\n    series, in which case the index of the series will be used to specify dates\n\n    Interpolation methods:\n\n    =========   ========================================================================\n    Type        Behavior\n    =========   ========================================================================\n    intersect   Resultant series only has values on the intersection of dates /times.\n                Will only contain intersection of valid dates / times in the series\n    nan         Resultant series only has values on the intersection of dates /times.\n                Value will be NaN for dates not present in the series\n    zero        Resultant series has values on all requested dates / times. The series\n                will have a value of zero where the requested date or time was not\n                present in the series\n    step        Resultant series has values on all requested dates / times. The series\n                will use the value of the previous valid point if requested date does\n                not exist. Values prior to the first date will be equivalent to the\n                first available value\n    =========   ========================================================================\n\n    **Examples**\n\n    Stepwize interpolation of series based on dates in second series:\n\n    >>> a = generate_series(100)\n    >>> b = generate_series(100)\n    >>> interpolate(a, b, Interpolate.INTERSECT)\n\n    **See also**\n\n    :func:`sub`\n    """\n    if dates is None:\n        dates = x\n\n    if isinstance(dates, pd.Series):\n        align_series = dates\n    else:\n        align_series = pd.Series(np.nan, dates)\n\n    if method == Interpolate.INTERSECT:\n        return x.align(align_series, 'inner')[0]\n    if method == Interpolate.NAN:\n        return x.align(align_series, 'right')[0]\n    if method == Interpolate.ZERO:\n        align_series = pd.Series(0.0, dates)\n        return x.align(align_series, 'right', fill_value=0)[0]\n    if method == Interpolate.STEP:\n        return __interpolate_step(x, align_series)\n    else:\n        raise MqValueError('Unknown intersection type: ' + method)
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[docs]@plot_function\ndef value(x: pd.Series, date: Union[date, time], method: Interpolate = Interpolate.STEP) -> pd.Series:\n    """\n    Value at specified date or time\n\n    :param x: timeseries\n    :param date: requested date or time\n    :param method: interpolation method (default: step)\n    :return: value at specified date or time\n\n    **Usage**\n\n    Returns the value of series X at the specified date:\n\n    :math:`Y_t = X_{date}`\n\n    If the requested date or time is not present in the series, the value function will return the value from the\n    previous available date or time by default. Caller can specify other interpolation styles via the method param:\n\n    Interpolation methods:\n\n    =========   ========================================================================\n    Type        Behavior\n    =========   ========================================================================\n    intersect   Only returns a value for valid dates\n    nan         Value will be NaN for dates not present in the series\n    zero        Value will be zero for dates not present in the series\n    step        Value of the previous valid point if requested date does not exist.\n                Values prior to the first date will be equivalent to the first available\n                value\n    =========   ========================================================================\n\n    **Examples**\n\n    Value of series on 5Mar18:\n\n    >>> a = generate_series(100)\n    >>> value(a, date(2019, 1, 3)\n\n    **See also**\n\n    :func:`interpolate`\n    """\n\n    values = interpolate(x, [date], method)\n    return values.get(0)
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[docs]@plot_function\ndef day(x: pd.Series) -> pd.Series:\n    """\n    Day of each value in series\n\n    :param x: time series\n    :return: day of observations\n\n    **Usage**\n\n    Returns the day as a numeric value for each observation in the series:\n\n    :math:`Y_t = day(t)`\n\n    Day of the time or date is the integer day number within the month, e.g. 1-31\n\n    **Examples**\n\n    Day for observations in series:\n\n    >>> series = generate_series(100)\n    >>> days = day(series)\n\n    **See also**\n\n    :func:`month` :func:`year`\n\n    """\n    return pd.to_datetime(x.index.to_series()).dt.day
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[docs]@plot_function\ndef month(x: pd.Series) -> pd.Series:\n    """\n    Month of each value in series\n\n    :param x: time series\n    :return: month of observations\n\n    **Usage**\n\n    Returns the month as a numeric value for each observation in the series:\n\n    :math:`Y_t = month(t)`\n\n    Month of the time or date is the integer month number, e.g. 1-12\n\n    **Examples**\n\n    Day for observations in series:\n\n    >>> series = generate_series(100)\n    >>> days = month(series)\n\n    **See also**\n\n    :func:`day` :func:`year`\n\n    """\n    return pd.to_datetime(x.index.to_series()).dt.month
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[docs]@plot_function\ndef year(x: pd.Series) -> pd.Series:\n    """\n    Year of each value in series\n\n    :param x: time series\n    :return: year of observations\n\n    **Usage**\n\n    Returns the year as a numeric value for each observation in the series:\n\n    :math:`Y_t = year(t)`\n\n    Year of the time or date is the integer year number, e.g. 2019, 2020\n\n    **Examples**\n\n    Year for observations in series:\n\n    >>> series = generate_series(100)\n    >>> days = year(series)\n\n    **See also**\n\n    :func:`day` :func:`month`\n\n    """\n    return pd.to_datetime(x.index.to_series()).dt.year
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[docs]@plot_function\ndef quarter(x: pd.Series) -> pd.Series:\n    """\n    Quarter of each value in series\n\n    :param x: time series\n    :return: quarter of observations\n\n    **Usage**\n\n    Returns the quarter as a numeric value for each observation in the series:\n\n    :math:`Y_t = quarter(t)`\n\n    Quarter of the time or date is the integer quarter number, e.g. 1, 2, 3, 4\n\n    **Examples**\n\n    Quarter for observations in series:\n\n    >>> series = generate_series(100)\n    >>> days = quarter(series)\n\n    **See also**\n\n    :func:`day` :func:`month`\n\n    """\n    return pd.to_datetime(x.index.to_series()).dt.quarter
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[docs]@plot_function\ndef weekday(x: pd.Series) -> pd.Series:\n    """\n    Weekday of each value in series\n\n    :param x: time series\n    :return: weekday of observations\n\n    **Usage**\n\n    Returns the weekday as a numeric value for each observation in the series:\n\n    :math:`Y_t = weekday(t)`\n\n    Weekday of the time or date is the integer day of the week, e.g. 0-6, where 0 represents Monday\n\n    **Examples**\n\n    Weekday for observations in series:\n\n    >>> series = generate_series(100)\n    >>> days = weekday(series)\n\n    **See also**\n\n    :func:`day` :func:`month`\n\n    """\n    return pd.to_datetime(x.index.to_series()).dt.weekday
\n\n\n@plot_function\ndef day_count_fractions(\n    dates: Union[List[date], pd.Series],\n    convention: DayCountConvention = DayCountConvention.ACTUAL_360,\n    frequency: PaymentFrequency = PaymentFrequency.MONTHLY\n) -> pd.Series:\n    """\n    Day count fractions between dates in series\n\n    :param dates: time series or array of dates\n    :param convention: day count convention (default: Actual/360 ISDA)\n    :param frequency: payment frequency of instrument (optional)\n    :return: series of day count fractions\n\n    **Usage**\n\n    Returns the day count fraction between dates in the series\n\n    :math:`Y_t = DCF(t_{-1}, t)`\n\n    Default is Actual/360 per ISDA specification:\n\n    :math:`Y_t = \\frac{Days(t_{-1}, t)}{360}`\n\n    For a full list of available conventions, see\n    `Day Count Conventions <https://developer.gs.com/docs/gsquant/guides/Dates/1-day-count-conventions>`_.\n    For more information on day count conventions, see the\n    `day count conventions <https://en.wikipedia.org/wiki/Day_count_convention>`_ page on Wikipedia\n\n    **Examples**\n\n    Weekday for observations in series:\n\n    >>> series = generate_series(100)\n    >>> days = day_count_fractions(series)\n\n    **See also**\n\n    :func:`day` :func:`month` :func:`year`\n\n    """\n    if isinstance(dates, pd.Series):\n        date_list = list(dates.index)\n    else:\n        date_list = dates\n\n    if len(date_list) < 2:\n        return pd.Series([])\n\n    start_dates = date_list[0:-1]\n    end_dates = date_list[1:len(date_list)]\n\n    dcfs = map(lambda a, b: day_count_fraction(a, b, convention, frequency), start_dates, end_dates)\n    return pd.Series(data=[np.NaN] + list(dcfs), index=date_list[0:len(date_list)])\n\n\n
[docs]@plot_function\ndef date_range(x: pd.Series, start_date: Union[date, int], end_date: Union[date, int],\n               weekdays_only: bool = False) -> pd.Series:\n    """\n    Create a time series from a (sub-)range of dates in an existing time series.\n\n    :param x: time series\n    :param start_date: start date for the sliced time series. If integer, number of observations after the first\n    :param end_date: end date for the sliced time series. If integer, number of observations before the last\n    :param weekdays_only: whether to include only weekdays in the sliced ranges\n    :return: sliced time series\n\n    **Usage**\n\n    Returns a restricted ("sliced") time series based on start and end dates:\n\n    :math:`Y_t = R_t |_{start < t < end}`\n\n    **Examples**\n\n    Slice the first and last week of a time series:\n\n    >>> series = generate_series(100)\n    >>> sliced_series = date_range(series,7,7)\n\n    **See also**\n\n    :func:`day` :func: `lag`\n\n    """\n    if not (x.index.is_all_dates or all(map(lambda a: isinstance(a, date), x.index.values))):\n        raise MqValueError('input is not a time series')\n\n    if isinstance(start_date, int):\n        start_date = x.index[start_date]\n    if isinstance(end_date, int):\n        end_date = x.index[- (1 + end_date)]\n\n    try:\n        start_date = start_date.date()\n        end_date = end_date.date()\n    except AttributeError:\n        pass\n\n    if weekdays_only:\n        week_mask = None\n        wd = start_date.weekday()\n        if wd > 4:\n            start_date += timedelta(days=7 - wd)\n    else:\n        week_mask = tuple([True] * 7)\n\n    return x.loc[x.index.intersection(list(_date_range(start_date, end_date, week_mask=week_mask)))]
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[docs]@plot_function\ndef prepend(x: List[pd.Series]) -> pd.Series:\n    """\n    Prepend data series\n\n    :param x: an array of timeseries\n    :return: concatenated timeseries\n\n    **Usage**\n\n    For input series [:math:`x_1`, :math:`x_2`, ... , :math:`X_n`], takes data from series :math:`X_i` until\n    the first date for which :math:`X_{i+1}` has data, useful when a higher quality series has a shorter history\n    than a lower quality series.\n\n    **Examples**\n\n    Prepend two series:\n\n    >>> x = generate_series(100)\n    >>> y = generate_series(100)\n    >>> prepend([x, y])\n\n    **See also**\n\n    :func:`union`\n\n    """\n    res = pd.Series(dtype='float64')\n    for i in range(len(x)):\n        this = x[i]\n        if i == len(x) - 1:\n            return res.append(this)\n        end = x[i + 1].index[0]\n        res = res.append(this.loc[this.index < end])\n    return res
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[docs]@plot_function\ndef union(x: List[pd.Series]) -> pd.Series:\n    """\n    Fill in missing dates or times of one series with another\n\n    :param x: an array of timeseries\n    :return: combined series\n\n    **Usage**\n\n    Starting from :math:`i=1`, takes points from series :math:`x_i`. Where points are missing from :math:`x_i`,\n    returns points from :math:`x_{i+1}`.\n\n    **Examples**\n\n    Union of two series:\n\n    >>> x = generate_series(100)\n    >>> y = generate_series(100)\n    >>> union([x, y])\n\n    **See also**\n\n    :func:`prepend`\n\n    """\n    res = pd.Series(dtype='float64')\n    for series in x:\n        res = res.combine_first(series)\n    return res
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Source code for gs_quant.timeseries.econometrics

\n# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the "License");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#   http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Chart Service will attempt to make public functions (not prefixed with _) from this module available. Such functions\n# should be fully documented: docstrings should describe parameters and the return value, and provide a 1-line\n# description. Type annotations should be provided for parameters.\nfrom .statistics import *\nfrom ..errors import *\nimport itertools\nfrom dataclasses import dataclass\nfrom typing import Union, Tuple\nimport numpy as np\nimport pandas as pd\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom statsmodels.tools.eval_measures import mse\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.data import DataContext\nfrom gs_quant.datetime.date import DayCountConvention\nfrom gs_quant.markets.securities import Asset\nfrom gs_quant.target.common import Currency\nfrom gs_quant.timeseries.datetime import align\n\n"""\nEconometrics timeseries library is for standard economic and time series analytics operations, including returns,\ndiffs, lags, volatilities and other numerical operations which are generally finance-oriented\n"""\n\n\nclass AnnualizationFactor(IntEnum):\n    DAILY = 252\n    WEEKLY = 52\n    SEMI_MONTHLY = 26\n    MONTHLY = 12\n    QUARTERLY = 4\n    ANNUALLY = 1\n\n\nclass SharpeAssets(Enum):\n    USD = 'MAP35DA6K5B1YXGX'\n    AUD = 'MAFRZWJ790MQY0EW'\n    CHF = 'MAS0NN4ZX7NYXB36'\n    EUR = 'MA95W0N1214395N8'\n    GBP = 'MA41ZEFTWR8Q7HBM'\n    JPY = 'MA8GXV3SJ0TXH1JV'\n    SEK = 'MAGNZZY0GJ4TATNG'\n\n\ndef excess_returns_pure(price_series: pd.Series, spot_curve: pd.Series) -> pd.Series:\n    curve, bench_curve = align(price_series, spot_curve, Interpolate.INTERSECT)\n\n    e_returns = [curve.iloc[0]]\n    for i in range(1, len(curve)):\n        multiplier = 1 + curve.iloc[i] / curve.iloc[i - 1] - bench_curve.iloc[i] / bench_curve.iloc[i - 1]\n        e_returns.append(e_returns[-1] * multiplier)\n    return pd.Series(e_returns, index=curve.index)\n\n\ndef excess_returns(price_series: pd.Series, benchmark_or_rate: Union[Asset, Currency, float], *,\n                   day_count_convention=DayCountConvention.ACTUAL_360) -> pd.Series:\n    if isinstance(benchmark_or_rate, float):\n        er = [price_series.iloc[0]]\n        for j in range(1, len(price_series)):\n            fraction = day_count_fraction(price_series.index[j - 1], price_series.index[j], day_count_convention)\n            er.append(er[-1] + price_series.iloc[j] - price_series.iloc[j - 1] * (1 + benchmark_or_rate * fraction))\n        return pd.Series(er, index=price_series.index)\n\n    if isinstance(benchmark_or_rate, Currency):\n        try:\n            marquee_id = SharpeAssets[benchmark_or_rate.value].value\n        except KeyError:\n            raise MqValueError(f"unsupported currency {benchmark_or_rate}")\n    else:\n        marquee_id = benchmark_or_rate.get_marquee_id()\n\n    with DataContext(price_series.index[0], price_series.index[-1]):\n        q = GsDataApi.build_market_data_query([marquee_id], QueryType.SPOT)\n        df = GsDataApi.get_market_data(q)\n    if df.empty:\n        raise MqValueError(f'could not retrieve risk-free rate {marquee_id}')\n\n    return excess_returns_pure(price_series, df['spot'])\n\n\ndef _annualized_return(levels: pd.Series, rolling: int) -> pd.Series:\n    starting = [0] * rolling\n    starting.extend([a for a in range(1, len(levels) - rolling + 1)])\n    points = list(\n        map(lambda d, v, i: pow(v / levels[i], 365.25 / (d - levels.index[i]).days) - 1, levels.index[1:],\n            levels.values[1:], starting[1:]))\n    points.insert(0, 0)\n    return pd.Series(points, index=levels.index)\n\n\ndef get_ratio_pure(er: pd.Series, w: Union[Window, int, str]) -> pd.Series:\n    w = normalize_window(er, w or None)  # continue to support 0 as an input for window\n    ann_return = _annualized_return(er, w.w)\n    ann_vol = volatility(er, w.w).iloc[1:] if w.w < len(er) else volatility(er)\n    result = ann_return / ann_vol * 100\n    return apply_ramp(result, w)\n\n\ndef _get_ratio(input_series: pd.Series, benchmark_or_rate: Union[Asset, float, str], w: Union[Window, int, str], *,\n               day_count_convention: DayCountConvention, curve_type: CurveType = CurveType.PRICES) -> pd.Series:\n    if curve_type == CurveType.PRICES:\n        er = excess_returns(input_series, benchmark_or_rate, day_count_convention=day_count_convention)\n    else:\n        assert curve_type == CurveType.EXCESS_RETURNS\n        er = input_series\n\n    return get_ratio_pure(er, w)\n\n\nclass RiskFreeRateCurrency(Enum):\n    USD = "USD"\n    AUD = "AUD"\n    CHF = "CFH"\n    EUR = "EUR"\n    GBP = "GBP"\n    JPY = "JPY"\n    SEK = "SEK"\n\n\n
[docs]@plot_session_function\ndef excess_returns_(price_series: pd.Series, currency: RiskFreeRateCurrency = RiskFreeRateCurrency.USD) -> pd.Series:\n    """\n    Calculate excess returns\n\n    :param price_series: price series\n    :param currency: currency for risk-free rate, defaults to USD\n    :return: excess returns\n\n    **Usage**\n\n    Given a price series P and risk-free rate R, excess returns E are defined as:\n\n    :math:`E_t = E_{t-1} + P_t - P_{t-1} * (1 + R * (D_t - D_{t-1}) / 360)`\n\n    The `Actual/360 <https://en.wikipedia.org/wiki/Day_count_convention#Actual/360>`_ day count convention is used.\n\n    **Examples**\n\n    Get excess returns from a price series.\n\n    >>> er = excess_returns(generate_series(100), USD)\n    """\n    return excess_returns(price_series, Currency(currency.value), day_count_convention=DayCountConvention.ACTUAL_360)
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[docs]@plot_session_function\ndef sharpe_ratio(series: pd.Series, currency: RiskFreeRateCurrency = RiskFreeRateCurrency.USD,\n                 w: Union[Window, int] = None, curve_type: CurveType = CurveType.PRICES) -> pd.Series:\n    """\n    Calculate Sharpe ratio\n\n    :param series: series of prices or excess returns for an asset\n    :param currency: currency for risk-free rate, defaults to USD\n    :param curve_type: whether input series is of prices or excess returns, defaults to prices\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.\n    :return: Sharpe ratio\n\n    **Usage**\n\n    Given a price series P, risk-free rate R, and window of size w returns the rolling\n    `Sharpe ratio <https://en.wikipedia.org/wiki/Sharpe_ratio>`_ S:\n\n    :math:`S_t = \\\\frac{(E_t / E_{t-w+1})^{365.25 / (D_t - D_{t-w} - 1)}}{volatility(E, w)_t}`\n\n    Excess returns E are defined as:\n\n    :math:`E_t = E_{t-1} + P_t - P_{t-1} * (1 + R * (D_t - D_{t-1}) / 360)`\n\n    where D is the date for a data point. The\n    `Actual/360 <https://en.wikipedia.org/wiki/Day_count_convention#Actual/360>`_ day count convention is used.\n\n    **Examples**\n\n    Get rolling sharpe ratio of a price series (with window of 252).\n\n    >>> sr = sharpe_ratio(generate_series(100), USD, 252, CurveType.PRICES)\n\n    **See also**\n\n    :func:`volatility`\n    """\n    return _get_ratio(series, Currency(currency.value), w, day_count_convention=DayCountConvention.ACTUAL_360,\n                      curve_type=curve_type)
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[docs]@plot_function\ndef returns(series: pd.Series, obs: int = 1, type: Returns = Returns.SIMPLE) -> pd.Series:\n    """\n    Calculate returns from price series\n\n    :param series: time series of prices\n    :param obs: number of observations\n    :param type: returns type: simple, logarithmic or absolute\n    :return: date-based time series of return\n\n    **Usage**\n\n    Compute returns series from price levels, based on the value of *type*:\n\n    ===========   =============================\n    Type          Description\n    ===========   =============================\n    simple        Simple arithmetic returns\n    logarithmic   Logarithmic returns\n    absolute      Absolute returns\n    ===========   =============================\n\n    *Simple*\n\n    Simple geometric change in asset prices, which can be aggregated across assets\n\n    :math:`Y_t = \\\\frac{X_t}{X_{t-obs}} - 1`\n\n    where :math:`X_t` is the asset price at time :math:`t`\n\n    *Logarithmic*\n\n    Natural logarithm of asset price changes, which can be aggregated through time\n\n    :math:`Y_t = log(X_t) - log(X_{t-obs})`\n\n    where :math:`X_t` is the asset price at time :math:`t`\n\n    *Absolute*\n\n    Absolute change in asset prices\n\n    :math:`Y_t = X_t - X_{t-obs}`\n\n    where :math:`X_t` is the asset price at time :math:`t`\n\n    **Examples**\n\n    Generate price series and take compute returns\n\n    >>> prices = generate_series(100)\n    >>> returns = returns(prices)\n\n    **See also**\n\n    :func:`prices`\n    """\n\n    if series.size < 1:\n        return series\n\n    if type == Returns.SIMPLE:\n        ret_series = series / series.shift(obs) - 1\n    elif type == Returns.LOGARITHMIC:\n        log_s = series.apply(math.log)\n        ret_series = log_s - log_s.shift(obs)\n    elif type == Returns.ABSOLUTE:\n        ret_series = series - series.shift(obs)\n    else:\n        raise MqValueError('Unknown returns type (use simple / logarithmic / absolute)')\n\n    return ret_series
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[docs]@plot_function\ndef prices(series: pd.Series, initial: int = 1, type: Returns = Returns.SIMPLE) -> pd.Series:\n    """\n    Calculate price levels from returns series\n\n    :param series: time series of returns\n    :param initial: initial price level\n    :param type: returns type: simple, logarithmic or absolute\n    :return: date-based time series of return\n\n    **Usage**\n\n    Compute price levels from returns series, based on the value of *type*:\n\n    ===========   =============================\n    Type          Description\n    ===========   =============================\n    simple        Simple arithmetic returns\n    logarithmic   Logarithmic returns\n    absolute      Absolute returns\n    ===========   =============================\n\n    *Simple*\n\n    Compute asset price series from simple returns:\n\n    :math:`Y_t = (1 + X_{t-1}) Y_{t-1}`\n\n    where :math:`X_t` is the asset price at time :math:`t` and :math:`Y_0 = initial`\n\n    *Logarithmic*\n\n    Compute asset price series from logarithmic returns:\n\n    :math:`Y_t = e^{X_{t-1}} Y_{t-1}`\n\n    where :math:`X_t` is the asset price at time :math:`t` and :math:`Y_0 = initial`\n\n    *Absolute*\n\n    Compute asset price series from absolute returns:\n\n    :math:`Y_t = X_{t-1} + Y_{t-1}`\n\n    where :math:`X_t` is the asset price at time :math:`t` and :math:`Y_0 = initial`\n\n    **Examples**\n\n    Generate price series and take compute returns\n\n    >>> series = generate_series(100)\n    >>> returns = prices(returns(series))\n\n    **See also**\n\n    :func:`returns` :func:`product` :func:`exp`\n    """\n\n    if series.size < 1:\n        return series\n\n    if type == Returns.SIMPLE:\n        return product(1 + series) * initial\n    elif type == Returns.LOGARITHMIC:\n        return product(series.apply(math.exp)) * initial\n    elif type == Returns.ABSOLUTE:\n        return sum_(series) + initial\n    else:\n        raise MqValueError('Unknown returns type (use simple / Logarithmic / absolute)')
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[docs]@plot_function\ndef index(x: pd.Series, initial: int = 1) -> pd.Series:\n    """\n    Geometric series normalization\n\n    :param x: time series\n    :param initial: initial value\n    :return: normalized time series\n\n    **Usage**\n\n    Divides every value in x by the initial value of x:\n\n    :math:`Y_t = initial * X_t / X_0`\n\n    where :math:`X_0` is the first value in the series\n\n    **Examples**\n\n    Normalize series to 1:\n\n    >>> series = generate_series(100)\n    >>> returns = index(series)\n\n    **See also**\n\n    :func:`returns`\n\n    """\n    i = x.first_valid_index()\n    return pd.Series() if i is None else initial * x / x[i]
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[docs]@plot_function\ndef change(x: pd.Series) -> pd.Series:\n    """\n    Arithmetic series normalization\n\n    :param x: time series\n    :return: normalized time series\n\n    **Usage**\n\n    Compute difference of every value from the initial value of x:\n\n    :math:`Y_t = X_t - X_0`\n\n    where :math:`X_0` is the first value in the series\n\n    **Examples**\n\n    Change in level from initial value:\n\n    >>> series = generate_series(100)\n    >>> returns = change(series)\n\n    **See also**\n\n    :func:`index`\n\n    """\n    return x - x[0]
\n\n\ndef _get_annualization_factor(x):\n    prev_idx = x.index[0]\n    distances = []\n\n    for idx, value in x.iloc[1:].iteritems():\n        d = (idx - prev_idx).days\n        if d == 0:\n            raise MqValueError('multiple data points on same date')\n        distances.append(d)\n        prev_idx = idx\n\n    average_distance = numpy.average(distances)\n    if average_distance < 2.1:\n        factor = AnnualizationFactor.DAILY\n    elif 6 <= average_distance < 8:\n        factor = AnnualizationFactor.WEEKLY\n    elif 14 <= average_distance < 17:\n        factor = AnnualizationFactor.SEMI_MONTHLY\n    elif 25 <= average_distance < 35:\n        factor = AnnualizationFactor.MONTHLY\n    elif 85 <= average_distance < 97:\n        factor = AnnualizationFactor.QUARTERLY\n    elif 360 <= average_distance < 386:\n        factor = AnnualizationFactor.ANNUALLY\n    else:\n        raise MqValueError('Cannot infer annualization factor, average distance: ' + str(average_distance))\n    return factor\n\n\n
[docs]@plot_function\ndef annualize(x: pd.Series) -> pd.Series:\n    """\n    Annualize series based on sample observation frequency\n\n    :param x: time series of prices\n    :return: date-based time series of annualized values\n\n    **Usage**\n\n    Based on number of days between observations, will determine an annualization factor and then adjust values\n    accordingly. Useful for annualizing daily or monthly returns\n\n    :math:`Y_t = X_t * \\sqrt{F}`\n\n    Annualization factors as follows, based on period implied by observations:\n\n    =========   =============================\n    Period      Annualization Factor (F)\n    =========   =============================\n    Daily       :math:`252`\n    Weekly      :math:`52`\n    Bi-Weekly   :math:`26`\n    Monthly     :math:`12`\n    Quarterly   :math:`4`\n    Annually    :math:`1`\n    =========   =============================\n\n    **Examples**\n\n    Annualize daily returns series:\n\n    >>> prices = generate_series(100)\n    >>> ann = annualize(returns(prices))\n\n    **See also**\n\n    :func:`returns`\n    """\n\n    factor: int = _get_annualization_factor(x)\n    return x * math.sqrt(factor)
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[docs]@plot_function\ndef volatility(x: pd.Series, w: Union[Window, int, str] = Window(None, 0),\n               returns_type: Returns = Returns.SIMPLE) -> pd.Series:\n    """\n    Realized volatility of price series\n\n    :param x: time series of prices\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :param returns_type: returns type: simple, logarithmic or absolute\n    :return: date-based time series of return\n\n    **Usage**\n\n    Calculate rolling annualized realized volatility of a price series over a given window. Annual volatility of 20% is\n    returned as 20.0:\n\n    :math:`Y_t = \\sqrt{\\\\frac{1}{N-1} \\sum_{i=t-w+1}^t (R_t - \\overline{R_t})^2} * \\sqrt{252} * 100`\n\n    where N is the number of observations in each rolling window :math:`w`, :math:`R_t` is the return on time\n    :math:`t` based on *returns_type*\n\n    ===========   =======================================================\n    Type          Description\n    ===========   =======================================================\n    simple        Simple geometric change in asset prices:\n                  :math:`R_t = \\\\frac{X_t}{X_{t-1}} - 1`\n                  where :math:`X_t` is the asset price at time :math:`t`\n    logarithmic   Natural logarithm of asset price changes:\n                  :math:`R_t = log(X_t) - log(X_{t-1})`\n                  where :math:`X_t` is the asset price at time :math:`t`\n    absolute      Absolute change in asset prices:\n                  :math:`Y_t = X_t - X_{t-obs}`\n                  where :math:`X_t` is the asset price at time :math:`t`\n    ===========   =======================================================\n\n    and :math:`\\overline{R_t}` is the mean value over the same window:\n\n    :math:`\\overline{R_t} = \\\\frac{\\sum_{i=t-w+1}^{t} R_t}{N}`\n\n    If window is not provided, computes realized volatility over the full series\n\n    **Examples**\n\n    Compute rolling :math:`1` month (:math:`22` business day) annualized volatility of price series\n\n    >>> series = generate_series(100)\n    >>> vol_series = volatility(series, 22)\n    >>> vol_series = volatility(series, Window(22, 30))\n\n    **See also**\n\n    :func:`std` :func:`annualize` :func:`returns`\n\n    """\n    w = normalize_window(x, w)\n\n    if x.size < 1:\n        return x\n\n    return apply_ramp(annualize(std(returns(x, type=returns_type), Window(w.w, 0))).mul(100), w)
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[docs]@plot_function\ndef correlation(x: pd.Series, y: pd.Series,\n                w: Union[Window, int, str] = Window(None, 0), type_: SeriesType = SeriesType.PRICES) -> pd.Series:\n    """\n    Rolling correlation of two price series\n\n    :param x: price series\n    :param y: price series\n    :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :param type_: type of both input series: prices or returns\n    :return: date-based time series of correlation\n\n    **Usage**\n\n    Calculate rolling `realized correlation <https://en.wikipedia.org/wiki/Correlation_and_dependence>`_,\n    :math:`\\\\rho_t` of two price series over a given window:\n\n    :math:`\\\\rho_t = \\\\frac{\\sum_{i=t-w+1}^t (R_t - \\overline{R_t})(Y_t - \\overline{S_t})}{(N-1)\\sigma R_t\\sigma S_t}`\n\n    where N is the number of observations in each rolling window, :math:`w`, and :math:`R_t` and :math:`S_t` are the\n    simple returns for each series on time :math:`t`\n\n    If prices are provided:\n\n    :math:`R_t = \\\\frac{X_t}{X_{t-1}} - 1` and :math:`S_t = \\\\frac{Y_t}{Y_{t-1}} - 1`\n\n    If returns are provided:\n\n    :math:`R_t = X_t` and :math:`S_t = Y_t`\n\n    :math:`\\overline{R_t}`, :math:`\\overline{S_t}` are the mean values, and :math:`\\sigma R_{t}` and\n    :math:`\\sigma S_{t}` are the sample standard deviations, of  series\n    :math:`R_t` and :math:`S_t` over the same window\n\n    If window is not provided, computes realized correlation over the full series\n\n    **Examples**\n\n    Compute rolling :math:`1` month (:math:`22` business day) correlation of price series\n\n    >>> series1 = generate_series(100)\n    >>> series2 = generate_series(100)\n    >>> corr = correlation(series1, series2, 22)\n\n    **See also**\n\n    :func:`std` :func:`returns`\n\n    """\n    w = normalize_window(x, w)\n\n    if x.size < 1:\n        return x\n\n    given_prices = type_ == SeriesType.PRICES\n    ret_1 = returns(x) if given_prices else x\n    ret_2 = returns(y) if given_prices else y\n\n    clean_ret1 = ret_1.dropna()\n    clean_ret2 = ret_2.dropna()\n\n    if isinstance(w.w, pd.DateOffset):\n        values = [clean_ret1.loc[(clean_ret1.index > idx - w.w) & (clean_ret1.index <= idx)].corr(clean_ret2)\n                  for idx in clean_ret1.index]\n        corr = pd.Series(values, index=clean_ret1.index)\n    else:\n        corr = clean_ret1.rolling(w.w, 0).corr(clean_ret2)\n\n    return apply_ramp(interpolate(corr, x, Interpolate.NAN), w)
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[docs]@plot_function\ndef beta(x: pd.Series, b: pd.Series, w: Union[Window, int, str] = Window(None, 0), prices: bool = True) -> pd.Series:\n    """\n    Rolling beta of price series and benchmark\n\n    :param x: time series of prices\n    :param b: time series of benchmark prices\n    :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :param prices: True if input series are prices, False if they are returns\n    :return: date-based time series of beta\n\n    **Usage**\n\n    Calculate rolling `beta <https://en.wikipedia.org/wiki/Beta_(finance)>`_,\n    :math:`\\\\beta_t` of a series to a benchmark over a given window:\n\n    :math:`R_t = \\\\alpha_t + \\\\beta S_t + \\epsilon_t`\n\n    Calculated as:\n\n    :math:`\\\\beta_t = \\\\frac{\\sum_{i=t-w+1}^t Cov(R_t, S_t)}{Var(S_t)}`\n\n    where N is the number of observations in each rolling window, :math:`w`, and :math:`R_t` and :math:`S_t` are the\n    simple returns for each series on time :math:`t`:\n\n    :math:`R_t = \\\\frac{X_t}{X_{t-1}} - 1` and :math:`S_t = \\\\frac{b_t}{b_{t-1}} - 1`\n\n    If prices = False, assumes returns are provided:\n\n    :math:`R_t = X_t` and :math:`S_t = b_t`\n\n    :math:`Cov(R_t, S_t)` and :math:`Var(S_t)` are the mean and variance of  series\n    :math:`R_t` and :math:`S_t` over the same window\n\n    If window is not provided, computes beta over the full series\n\n    **Examples**\n\n    Compute rolling :math:`1` month (:math:`22` business day) beta of two price series\n\n    >>> series = generate_series(100)\n    >>> benchmark = generate_series(100)\n    >>> b = beta(series, benchmark, 22)\n\n    **See also**\n\n    :func:`var` :func:`cov` :func:`correlation` :func:`returns`\n    """\n    w = normalize_window(x, w)\n\n    ret_series = returns(x) if prices else x\n    ret_benchmark = returns(b) if prices else b\n\n    if isinstance(w.w, pd.DateOffset):\n        result = pd.Series([ret_series.loc[(ret_series.index > idx - w.w) & (ret_series.index <= idx)].cov(\n            ret_benchmark.loc[(ret_benchmark.index > idx - w.w) & (ret_benchmark.index <= idx)]\n        ) / ret_benchmark.loc[(ret_benchmark.index > idx - w.w) & (ret_benchmark.index <= idx)].var()\n            for idx in ret_series.index], index=ret_series.index)\n    else:\n        cov = ret_series.rolling(w.w, 0).cov(ret_benchmark.rolling(w.w, 0))\n        result = cov / ret_benchmark.rolling(w.w, 0).var()\n\n    # do not compute initial values as they may be extreme when sample size is small\n\n    result[0:3] = np.nan\n\n    return apply_ramp(interpolate(result, x, Interpolate.NAN), w)
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[docs]@plot_function\ndef max_drawdown(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Compute the maximum peak to trough drawdown over a rolling window.\n\n    :param x: time series\n    :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: time series of rolling maximum drawdown\n\n    **Examples**\n\n    Compute the maximum peak to trough `drawdown <https://en.wikipedia.org/wiki/Drawdown_(economics)>`_\n\n    >>> series = generate_series(100)\n    >>> max_drawdown(series)\n\n    **See also**\n\n    :func:`returns`\n\n    """\n    w = normalize_window(x, w)\n    if isinstance(w.w, pd.DateOffset):\n        scores = pd.Series([x[idx] / x.loc[(x.index > idx - w.w) & (x.index <= idx)].max() - 1 for idx in x.index],\n                           index=x.index)\n        result = pd.Series([scores.loc[(scores.index > idx - w.w) & (scores.index <= idx)].min()\n                            for idx in scores.index], index=scores.index)\n    else:\n        rolling_max = x.rolling(w.w, 0).max()\n        result = (x / rolling_max - 1).rolling(w.w, 0).min()\n    return apply_ramp(result, w)
\n\n\n@dataclass\nclass ARIMABestParams:\n    freq: str = ''\n    p: int = None\n    d: int = None\n    q: int = None\n    const: float = None\n    ar_coef: List[float] = None\n    ma_coef: List[float] = None\n    resid: List[float] = None\n    series: pd.Series = None\n\n\nclass Arima:\n    """\n    ARIMA is the Autoregressive Integrated Moving Average Model and is used\n    to normalize and forecast time series data. ARIMA has 3 parameters:\n    (p, d, q) where:\n        :p is the number of autoregressive terms\n        :d is the number of nonseasonal differences\n        :q is the number of lagged forecast errors in the prediction equation\n    An ARIMA model is selected from the Cartesian product of sets p, q, and d.\n    The time series is split into train and test sets and an ARIMA model is fit\n    for every combination on the training set. The model with the lowest\n    mean-squared error (MSE) on the test set is selected as the best model. The\n    original times series can then be transformed by the best model.\n    **Examples**\n    >>> series = generate_series(100)\n    >>> ar = econometrics.Arima()\n    >>> ar.fit(series, train_size=0.8)\n    >>> transformed_time_series = ar.transform(series)\n    """\n\n    def __init__(self):\n        self.best_params = {}\n\n    @staticmethod\n    def _evaluate_arima_model(X: Union[pd.Series, pd.DataFrame], arima_order: Tuple[int, int, int],\n                              train_size: Union[float, int, None], freq: str) -> Tuple[float, dict]:\n        train_size = int(len(X) * 0.75) if train_size is None else int(len(X) * train_size) \\\n            if isinstance(train_size, float) else train_size\n        train, test = X[:train_size].astype(float), X[train_size:].astype(float)\n\n        model = ARIMA(train, order=arima_order, freq=freq)\n        model_fit = model.fit(disp=False, method='css', trend='nc')\n\n        # calculate test error\n        yhat = model_fit.forecast(len(test))[0]\n        error = mse(test, yhat)\n\n        return error, model_fit\n\n    def fit(self, X: Union[pd.Series, pd.DataFrame], train_size: Union[float, int, None] = None,\n            p_vals: list = (0, 1, 2), d_vals: list = (0, 1, 2), q_vals: list = (0, 1, 2), freq: str = None) -> 'arima':\n        """\n        Train a combination of ARIMA models. If pandas DataFrame, finds the\n        best arima model parameters for each column. If pandas Series, finds\n        the best arima model parameters for the series.\n        :param X: time series to be operated on; required parameter\n        :param train_size: if float, should be between 0.0 and 1.0 and\n        represent the proportion of the dataset to include in the train split.\n        If int, represents the absolute number of train samples. If None,\n        the value is automatically set 0.75\n        :p_vals: number of autoregressive terms to search; default is [0,1,2]\n        :d_vals: number of differences to search; default is [0,1,2]\n        :q_vals: number of lagged forecast to search; always [0,1,2]\n        :freq: frequency of time series, default is None\n        :return: self\n        """\n        if isinstance(X, pd.Series):\n            X = X.to_frame()\n\n        for series_id in X.columns:\n            series = X[series_id]\n            best_score = float('inf')\n            best_order = None\n            best_const = None\n            best_ar_coef = None\n            best_ma_coef = None\n            best_resid = None\n            for order in list(itertools.product(*[p_vals, d_vals, q_vals])):\n                try:\n                    error, model_fit = self._evaluate_arima_model(series, order, train_size, freq)\n                    if error < best_score:\n                        best_score = error\n                        best_order = order\n                        best_const = model_fit.params.to_dict().get('const', 0)\n                        best_ar_coef = model_fit.arparams\n                        best_ma_coef = model_fit.maparams\n                        best_resid = model_fit.resid\n                except Exception as e:\n                    print('   {}'.format(e))\n                    continue\n\n            p, d, q = best_order\n            self.best_params[series_id] = ARIMABestParams(freq, p, d, q, best_const, best_ar_coef, best_ma_coef,\n                                                          best_resid, series)\n        return self\n\n    def _difference(self, X: pd.Series, d: int):\n        """Helper Function to Difference Time Series n Times"""\n\n        return X if d == 0 else X.diff() if d == 1 else self._difference(X.diff(), d - 1)\n\n    @staticmethod\n    def _lagged_values(X: pd.Series, p: int, ar_coef: list):\n        """Helper Function to Calculate AutoRegressive(AR) Component"""\n\n        return X if p == 0 else pd.concat([X.copy().shift(periods=i) for i in range(1, p + 1)], axis=1).dot(ar_coef)\n\n    @staticmethod\n    def _calculate_residuals(X_ar: pd.Series, X_diff: pd.Series, p: int, d: int, q: int, ma_coef: list):\n        """Helper Function to Calculate Residuals/MA Component"""\n\n        ma_coef = ma_coef[::-1]\n        resid = X_ar.copy(deep=True)\n        resid[:] = 0\n\n        X_ma = X_ar.copy(deep=True)\n        X_ma[:] = np.nan\n\n        for x in range(p + d, len(X_ar)):\n            ma_component = resid[x - q: x].dot(ma_coef)\n            prediction = X_ar[x] + ma_component\n            residual = X_diff[x] - prediction\n            resid[x] = residual\n            X_ma[x] = prediction\n\n        return resid, X_ma\n\n    def _arima_transform_series(self, s: ARIMABestParams) -> pd.Series:\n        """Helper Function to Transform Series"""\n\n        # Difference first\n        X_diff = self._difference(s.series, s.d)\n\n        # Calculate Autoregressive Component\n        X_diff_ar = self._lagged_values(X_diff, s.p, s.ar_coef)\n\n        # Calculate Residuals and Moving Average Component\n        _, X_diff_ar_ma = self._calculate_residuals(X_diff_ar, X_diff, s.p, s.d, s.q, s.ma_coef)\n        return X_diff_ar_ma\n\n    def transform(self, X: Union[pd.Series, pd.DataFrame]) -> pd.DataFrame:\n        """\n        Transform a series based on the best ARIMA found from fit().\n        Does not support tranformation using MA components.\n        :param X: time series to be operated on; required parameter\n        :return: DataFrame\n        """\n        X = X.to_frame() if isinstance(X, pd.Series) else X\n        return pd.DataFrame({s_id: self._arima_transform_series(self.best_params[s_id]) for s_id in X.columns})\n
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Source code for gs_quant.timeseries.helper

\n"""\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the "License");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n  http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied.  See the License for the\nspecific language governing permissions and limitations\nunder the License.\n"""\nimport inspect\nimport logging\nfrom enum import Enum, IntEnum\nfrom functools import wraps\nfrom typing import Optional, Union, List\n\nimport pandas as pd\n\nfrom gs_quant.api.gs.data import QueryType\n\n\ndef _create_enum(name, members):\n    return Enum(name, {n.upper(): n.lower() for n in members}, module=__name__)\n\n\ndef _create_int_enum(name, mappings):\n    return IntEnum(name, {k.upper(): v for k, v in mappings.items()})\n\n\ndef _to_offset(tenor: str) -> pd.DateOffset:\n    import re\n    matcher = re.fullmatch('(\\\\d+)([dwmy])', tenor)\n    if not matcher:\n        raise ValueError('invalid tenor ' + tenor)\n\n    ab = matcher.group(2)\n    if ab == 'd':\n        name = 'days'\n    elif ab == 'w':\n        name = 'weeks'\n    elif ab == 'm':\n        name = 'months'\n    else:\n        assert ab == 'y'\n        name = 'years'\n\n    kwarg = {name: int(matcher.group(1))}\n    return pd.DateOffset(**kwarg)\n\n\nInterpolate = _create_enum('Interpolate', ['intersect', 'step', 'nan', 'zero', 'time'])\nReturns = _create_enum('Returns', ['simple', 'logarithmic', 'absolute'])\nSeriesType = _create_enum('SeriesType', ['prices', 'returns'])\nCurveType = _create_enum('CurveType', ['prices', 'excess_returns'])\n\n\n
[docs]class Window:\n    """\n    Create a Window with size and ramp up to use.\n\n    :param w: window size\n    :param r: ramp up value. Defaults to the window size.\n    :return: new window object\n\n    **Usage**\n\n    The window size and ramp up value can either the number of observations or a string representation of the time\n    period.\n\n    **Examples**\n\n    Window size is :math:`22` obversations and the ramp up value is :math:`10`:\n\n    >>> Window(22, 10)\n\n    Window size is one month and the ramp up size is one week:\n\n    >>> Window('1m', '1w')\n\n    """\n\n    def __init__(self, w: Union[int, str, None] = None, r: Union[int, str, None] = None):\n        self.w = w\n        self.r = w if r is None else r\n\n    def as_dict(self):\n        return {\n            'w': self.w,\n            'r': self.r\n        }\n\n    @classmethod\n    def from_dict(cls, obj):\n        return Window(w=obj.get('w'), r=obj.get('r'))
\n\n\ndef _check_window(series_length: int, window: Window):\n    if series_length > 0 and isinstance(window.w, int) and isinstance(window.r, int):\n        if window.w <= 0:\n            raise ValueError('Window value must be greater than zero.')\n        if window.r > series_length or window.r < 0:\n            raise ValueError('Ramp value must be less than the length of the series and greater than zero.')\n\n\ndef apply_ramp(x: pd.Series, window: Window) -> pd.Series:\n    _check_window(len(x), window)\n    if isinstance(window.w, int) and window.w > len(x):  # does not restrict window size when it is a DataOffset\n        return pd.Series([])\n    if isinstance(window.r, pd.DateOffset):\n        return x.loc[x.index[0] + window.r:]\n    else:\n        return x[window.r:]\n\n\ndef normalize_window(x: Union[pd.Series, pd.DataFrame], window: Union[Window, int, str, None],\n                     default_window: int = None) -> Window:\n    if default_window is None:\n        default_window = len(x)\n\n    if isinstance(window, int):\n        window = Window(window, window)\n    elif isinstance(window, str):\n        window = Window(_to_offset(window), _to_offset(window))\n    else:\n        if window is None:\n            window = Window(default_window, 0)\n        else:\n            if isinstance(window.w, str):\n                window = Window(_to_offset(window.w), window.r)\n            if isinstance(window.r, str):\n                window = Window(window.w, _to_offset(window.r))\n            if window.w is None:\n                window = Window(default_window, window.r)\n\n    _check_window(default_window, window)\n    return window\n\n\ndef plot_function(fn):\n    # Indicates that fn should be exported to plottool as a pure function.\n    fn.plot_function = True\n    return fn\n\n\ndef plot_session_function(fn):\n    fn.plot_function = True\n    fn.requires_session = True\n    return fn\n\n\ndef plot_measure(asset_class: Optional[tuple] = None, asset_type: Optional[tuple] = None,\n                 dependencies: Optional[List[QueryType]] = []):\n    # Indicates that fn should be exported to plottool as a member function / pseudo-measure.\n    # Set category to None for no restrictions, else provide a tuple of allowed values.\n    def decorator(fn):\n        assert asset_class is None or isinstance(asset_class, tuple)\n        assert asset_type is None or isinstance(asset_type, tuple)\n\n        fn.plot_measure = True\n        fn.asset_class = asset_class\n        fn.asset_type = asset_type\n        fn.dependencies = dependencies\n\n        return fn\n\n    return decorator\n\n\ndef plot_method(fn):\n    # Indicates that fn should be exported to plottool as a method.\n    fn.plot_method = True\n\n    # Allows fn to accept and ignore real_time argument even if it is not defined in the signature\n    @wraps(fn)\n    def ignore_extra_argument(*args, **kwargs):\n        for arg in ('real_time', 'interval', 'time_filter'):\n            if arg not in inspect.signature(fn).parameters:\n                kwargs.pop(arg, None)\n        return fn(*args, **kwargs)\n\n    return ignore_extra_argument\n\n\ndef log_return(logger: logging.Logger, message):\n    def outer(fn):\n        @wraps(fn)\n        def inner(*args, **kwargs):\n            response = fn(*args, **kwargs)\n            logger.debug('%s: %s', message, response)\n            return response\n\n        return inner\n\n    return outer\n
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Source code for gs_quant.timeseries.statistics

\n# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the "License");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#  http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Marquee Plot Service will attempt to make public functions (not prefixed with _) from this module available.\n# Such functions should be fully documented: docstrings should describe parameters and the return value, and provide\n# a 1-line description. Type annotations should be provided for parameters.\n\nimport datetime\nimport numpy\nimport scipy.stats.mstats as stats\nfrom scipy.stats import percentileofscore\nfrom statsmodels.regression.rolling import RollingOLS\nfrom .algebra import *\nimport statsmodels.api as sm\nfrom ..models.epidemiology import SIR, SEIR, EpidemicModel\nfrom ..data import DataContext\n\n\n"""\nStats library is for basic arithmetic and statistical operations on timeseries.\nThese include basic algebraic operations, probability and distribution analysis.\nGenerally not finance-specific routines.\n"""\n\n\ndef _concat_series(series: List[pd.Series]):\n    curves = []\n    constants = {}\n    k = 0\n    for s in series:\n        if s.min() != s.max():\n            curves.append(s)\n        else:\n            constants[f'temp{k}'] = s.min()\n            k += 1\n    return pd.concat(curves, axis=1).assign(**constants)\n\n\n
[docs]@plot_function\ndef min_(x: Union[pd.Series, List[pd.Series]], w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Minimum value of series over given window\n\n    :param x: series: a timeseries or an array of timeseries\n\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of minimum value\n\n    **Usage**\n\n    Returns the minimum value of the series over each window.\n\n    If :math:`x` is a series:\n\n    :math:`R_t = min(X_{t-w+1}:X_t)`\n\n    where :math:`w` is the size of the rolling window.\n\n\n    If :math:`x` is an array of series:\n\n    :math:`R_t = min(X_{1, t-w+1}:X_{n, t})`\n\n    where :math:`w` is the size of the rolling window, and :math:`n` is the number of series.\n\n    If window is not provided, returns the minimum value over the\n    full series. If the window size is greater than the available data, will return minimum of available values.\n\n    **Examples**\n\n    Minimum value of price series over the last :math:`22` observations:\n\n    >>> prices = generate_series(100)\n    >>> min_(prices, 22)\n\n    **See also**\n\n    :func:`max_`\n\n    """\n    if isinstance(x, list):\n        x = _concat_series(x).min(axis=1)\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n    if isinstance(w.w, pd.DateOffset):\n        values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].min() for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).min(), w)
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[docs]@plot_function\ndef max_(x: Union[pd.Series, List[pd.Series]], w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Maximum value of series over given window\n\n    :param x: series: a timeseries or an array of timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of maximum value\n\n    **Usage**\n\n    Returns the maximum value of the series over each window.\n\n    If :math:`x` is a series:\n\n    :math:`R_t = max(X_{t-w+1}:X_t)`\n\n    where :math:`w` is the size of the rolling window.\n\n    If :math:`x` is an array of series:\n\n    :math:`R_t = max(X_{1, t-w+1}:X_{n, t})`\n\n    where :math:`w` is the size of the rolling window, and :math:`n` is the number of series.\n\n    If window is not provided, returns the maximum value over the full series. If the window size is greater than the\n    available data, will return maximum of available values.\n\n    **Examples**\n\n    Maximum value of price series over the last :math:`22` observations:\n\n    >>> prices = generate_series(100)\n    >>> max_(prices, 22)\n\n    **See also**\n\n    :func:`min_`\n\n    """\n    if isinstance(x, list):\n        x = _concat_series(x).max(axis=1)\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n    if isinstance(w.w, pd.DateOffset):\n        values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].max() for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).max(), w)
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[docs]@plot_function\ndef range_(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Range of series over given window\n\n    :param x: series: timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of range\n\n    **Usage**\n\n    Returns the range of the series (max - min) over rolling window:\n\n    :math:`R_t = max(X_{t-w+1}:X_t) - min(X_{t-w+1}:X_t)`\n\n    where :math:`w` is the size of the rolling window. If window is not provided, returns the range over the\n    full series. If the window size is greater than the available data, will return range of all available values.\n\n    **Examples**\n\n    Range of price series over the last :math:`22` observations:\n\n    >>> prices = generate_series(100)\n    >>> range_(prices, 22)\n\n    **See also**\n\n    :func:`min_` :func:`max_`\n\n    """\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n\n    max = max_(x, Window(w.w, 0))\n    min = min_(x, Window(w.w, 0))\n\n    return apply_ramp(max - min, w)
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[docs]@plot_function\ndef mean(x: Union[pd.Series, List[pd.Series]], w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Arithmetic mean of series over given window\n\n    :param x: series: a timeseries or an array of timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of mean value\n\n    **Usage**\n\n    Calculates `arithmetic mean <https://en.wikipedia.org/wiki/Arithmetic_mean>`_ of the series over a rolling window\n\n    If a timeseries is provided:\n\n    :math:`R_t = \\\\frac{\\sum_{i=t-w+1}^{t} X_i}{N}`\n\n    where :math:`N` is the number of observations in each rolling window, :math:`w`.\n\n    If an array of timeseries is provided:\n\n    :math:`R_t = \\\\frac{\\sum_{i=t-w+1}^{t} {\\sum_{j=1}^{n}} X_{ij}}{N}`\n\n    where :math:`n` is the number of series, and :math:`N` is the number of observations in each rolling window,\n    :math:`w`.\n\n    If window is not provided, computes rolling mean over the full series. If the window size is greater than the\n    available data, will return mean of available values.\n\n    **Examples**\n\n    Generate price series and compute mean over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> mean(prices, 22)\n\n    **See also**\n\n    :func:`median` :func:`mode`\n\n    """\n    if isinstance(x, list):\n        x = pd.concat(x, axis=1)\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n    if isinstance(w.w, pd.DateOffset):\n        values = [np.nanmean(x.loc[(x.index > idx - w.w) & (x.index <= idx)]) for idx in x.index]\n    else:\n        values = [np.nanmean(x.iloc[max(idx - w.w + 1, 0): idx + 1]) for idx in range(0, len(x))]\n    return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)
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[docs]@plot_function\ndef median(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Median value of series over given window\n\n    :param x: series: timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of median value\n\n    **Usage**\n\n    Computes the `median <https://en.wikipedia.org/wiki/Median>`_ value over a given window. For each window, this\n    function will return the middle value when all elements in the window are sorted. If the number of observations in\n    the window is even, will return the average of the middle two values. If the window size is greater than the\n    available data, will return median of available values:\n\n    :math:`d = \\\\frac{w-1}{2}`\n\n    :math:`R_t = \\\\frac{X_{\\lfloor t-d \\\\rfloor} + X_{\\lceil t-d \\\\rceil}}{2}`\n\n    where :math:`w` is the size of the rolling window. If window is not provided, computes median over the full series\n\n    **Examples**\n\n    Generate price series and compute median over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> median(prices, 22)\n\n    **See also**\n\n    :func:`mean` :func:`mode`\n    """\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n    if isinstance(w.w, pd.DateOffset):\n        values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].median() for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).median(), w)
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[docs]@plot_function\ndef mode(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Most common value in series over given window\n\n    :param x: series: timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of mode value\n\n    **Usage**\n\n    Computes the `mode <https://en.wikipedia.org/wiki/Mode_(statistics)>`_ over a given window. For each window, this\n    function will return the most common value of all elements in the window. If there are multiple values with the same\n    frequency of occurrence, will return the smallest value.\n\n    If window is not provided, computes mode over the full series.\n\n    **Examples**\n\n    Generate price series and compute mode over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> mode(prices, 22)\n\n    **See also**\n\n    :func:`mean` :func:`median`\n    """\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n    if isinstance(w.w, pd.DateOffset):\n        values = [stats.mode(x.loc[(x.index > idx - w.w) & (x.index <= idx)]).mode[0] for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).apply(lambda y: stats.mode(y).mode, raw=True), w)
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[docs]@plot_function\ndef sum_(x: Union[pd.Series, List[pd.Series]], w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Rolling sum of series over given window\n\n    :param x: series: a timeseries or an array of timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of rolling sum\n\n    **Usage**\n\n    Calculate the sum of observations over a given rolling window.\n\n    If :math:`x` is a series:\n\n    :math:`R_t = \\sum_{i=t-w+1}^{t} X_i`\n\n    where :math:`w` is the size of the rolling window.\n\n    If :math:`x` is an array of series:\n\n    :math:`R_t = \\sum_{i=t-w+1}^{t} \\sum_{j=1}^{n} X_{ij}`\n\n    where :math:`w` is the size of the rolling window and :math:`n` is the number of series\n\n    If window is not provided, computes sum over the full series. If the window size is greater than the available data,\n    will return sum of available values.\n\n    **Examples**\n\n    Generate price series and compute rolling sum over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> sum_(prices, 22)\n\n    **See also**\n\n    :func:`product`\n\n    """\n    if isinstance(x, list):\n        x = pd.concat(x, axis=1).sum(axis=1)\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing\n    if isinstance(w.w, pd.DateOffset):\n        values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].sum() for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).sum(), w)
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[docs]@plot_function\ndef product(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Rolling product of series over given window\n\n    :param x: series: timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of rolling product\n\n    **Usage**\n\n    Calculate the product of observations over a given rolling window. For each time, :math:`t`, returns the value\n    of all observations from :math:`t-w+1` to :math:`t` multiplied together:\n\n    :math:`R_t = \\prod_{i=t-w+1}^{t} X_i`\n\n    where :math:`w` is the size of the rolling window. If window is not provided, computes product over the full series\n\n    **Examples**\n\n    Generate price series and compute rolling sum over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> product(1+returns(prices))\n\n    **See also**\n\n    :func:`sum_`\n    """\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing\n    if isinstance(w.w, pd.DateOffset):\n        values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].agg(pd.Series.prod) for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).agg(pd.Series.prod), w)
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[docs]@plot_function\ndef std(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Rolling standard deviation of series over given window\n\n    :param x: series: timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of standard deviation\n\n    **Usage**\n\n    Provides `unbiased estimator <https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation>`_ of sample\n    `standard deviation <https://en.wikipedia.org/wiki/Standard_deviation>`_ over a rolling window:\n\n    :math:`R_t = \\sqrt{\\\\frac{1}{N-1} \\sum_{i=t-w+1}^t (X_i - \\overline{X_t})^2}`\n\n    where :math:`N` is the number of observations in each rolling window, :math:`w`, and :math:`\\overline{X_t}` is the\n    mean value over the same window:\n\n    :math:`\\overline{X_t} = \\\\frac{\\sum_{i=t-w+1}^{t} X_i}{N}`\n\n    If window is not provided, computes standard deviation over the full series\n\n    **Examples**\n\n    Generate price series and compute standard deviation of returns over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> std(returns(prices), 22)\n\n    **See also**\n\n    :func:`sum` :func:`mean` :func:`var`\n\n    """\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n    if isinstance(w.w, pd.DateOffset):\n        values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].std() for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).std(), w)
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[docs]@plot_function\ndef exponential_std(x: pd.Series, beta: float = 0.75) -> pd.Series:\n    """\n    Exponentially weighted standard deviation\n\n    :param x: time series\n    :param beta: how much to weigh the previous price in the time series, thus controlling how much importance we\n                  place on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)\n    :return: time series of standard deviation of the input series\n\n    **Usage**\n\n    Provides an unbiased estimator of `exponentially weighted standard deviation\n    <https://en.wikipedia.org/wiki/Moving_average#Exponentially_weighted_moving_variance_and_standard_deviation>`_ of\n    a series [:math:`X_0`, :math:`X_1`, :math:`X_2`, ...]:\n\n    :math:`S_t = \\\\sqrt{[EWMA(X_t^2) - EWMA(X_t)^2] * DF_t}`\n\n    where :math:`EWMA(X_t)` is the `exponential moving average\n    <https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average>`_ at :math:`t` (see function\n    :func:`exponential_moving_average`), :math:`DF_t` is the debiasing factor (see\n    `Weighted sample variance <https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_variance>`_\n    for further details):\n\n    :math:`DF_t = \\\\frac{(\\sum_{i=0}^t w_i)^2} {(\\sum_{i=0}^t w_i)^2 - \\sum_{i=0}^t w_i^2}`\n\n    where :math:`w_i` is the weight assigned to :math:`i` th observation:\n\n    :math:`w_i = (1-\\\\beta)\\\\beta^i` for i<t; :math:`\\\\beta^i` for i=t\n\n    **Examples**\n\n    Generate price series and compute exponentially weighted standard deviation of returns\n\n    >>> prices = generate_series(100)\n    >>> exponential_std(returns(prices), 0.9)\n\n    **See also**\n\n    :func:`std` :func:`var` :func:`exponential_moving_average`\n\n    """\n    return x.ewm(alpha=1 - beta, adjust=False).std()
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[docs]@plot_function\ndef var(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Rolling variance of series over given window\n\n    :param x: series: timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of variance\n\n    **Usage**\n\n    Provides `unbiased estimator <https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation>`_ of sample\n    `variance <https://en.wikipedia.org/wiki/Variance>`_ over a rolling window:\n\n    :math:`R_t = \\\\frac{1}{N-1} \\sum_{i=t-w+1}^t (X_i - \\overline{X_t})^2`\n\n    where :math:`N` is the number of observations in each rolling window, :math:`w`, and :math:`\\overline{X_t}` is the\n    mean value over the same window:\n\n    :math:`\\overline{X_t} = \\\\frac{\\sum_{i=t-w+1}^{t} X_i}{N}`\n\n    If window is not provided, computes variance over the full series\n\n    **Examples**\n\n    Generate price series and compute variance of returns over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> var(returns(prices), 22)\n\n    **See also**\n\n    :func:`var` :func:`mean` :func:`std`\n\n    """\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n    if isinstance(w.w, pd.DateOffset):\n        values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].var() for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).var(), w)
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[docs]@plot_function\ndef cov(x: pd.Series, y: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Rolling co-variance of series over given window\n\n    :param x: series: timeseries\n    :param y: series: timeseries\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of covariance\n\n    **Usage**\n\n    Provides `unbiased estimator <https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation>`_ of sample\n    `co-variance <https://en.wikipedia.org/wiki/Covariance>`_ over a rolling window:\n\n    :math:`R_t = \\\\frac{1}{N-1} \\sum_{i=t-w+1}^t (X_i - \\overline{X_t}) (Y_i - \\overline{Y_t})`\n\n    where :math:`N` is the number of observations in each rolling window, :math:`w`, and :math:`\\overline{X_t}` and\n    :math:`\\overline{Y_t}` represent the sample mean of series :math:`X_t` and :math:`Y_t` over the same window:\n\n    :math:`\\overline{X_t} = \\\\frac{\\sum_{i=t-w+1}^{t} X_i}{N}` and\n    :math:`\\overline{Y_t} = \\\\frac{\\sum_{i=t-w+1}^{t} Y_i}{N}`\n\n    If window is not provided, computes variance over the full series\n\n    **Examples**\n\n    Generate price series and compute variance of returns over :math:`22` observations\n\n    >>> prices_x = generate_series(100)\n    >>> prices_y = generate_series(100)\n    >>> cov(returns(prices_x) returns(prices_y), 22)\n\n    **See also**\n\n    :func:`sum` :func:`mean` :func:`var`\n    """\n    w = normalize_window(x, w)\n    assert x.index.is_monotonic_increasing, "series index is monotonic increasing"\n    if isinstance(w.w, pd.DateOffset):\n        values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].cov(y) for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).cov(y), w)
\n\n\ndef _zscore(x):\n    if x.size == 1:\n        return 0\n\n    return stats.zscore(x, ddof=1)[-1]\n\n\n
[docs]@plot_function\ndef zscores(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Rolling z-scores over a given window\n\n    :param x: time series of prices\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of z-scores\n\n    **Usage**\n\n    Calculate `standard score <https://en.wikipedia.org/wiki/Standard_score>`_ of each value in series over given\n    window. Standard deviation and sample mean are computed over the specified rolling window, then element is\n    normalized to provide a rolling z-score:\n\n    :math:`R_t = \\\\frac { X_t - \\mu }{ \\sigma }`\n\n    Where :math:`\\mu` and :math:`\\sigma` are sample mean and standard deviation over the given window\n\n    If window is not provided, computes z-score relative to mean and standard deviation over the full series\n\n    **Examples**\n\n    Generate price series and compute z-score of returns over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> zscores(returns(prices), 22)\n\n    **See also**\n\n    :func:`mean` :func:`std`\n\n    """\n    if x.size < 1:\n        return x\n\n    if isinstance(w, int):\n        w = normalize_window(x, w)\n    elif isinstance(w, str):\n        if not (isinstance(x.index, pd.DatetimeIndex) or isinstance(x.index[0], datetime.date)):\n            raise MqValueError("When string is passed window index must be a DatetimeIndex or of type datetime.date")\n        w = normalize_window(x, w)\n    if not w.w:\n        if x.size == 1:\n            return pd.Series([0.0], index=x.index, dtype=np.dtype(float))\n\n        clean_series = x.dropna()\n        zscore_series = pd.Series(stats.zscore(clean_series, ddof=1), clean_series.index, dtype=np.dtype(float))\n        return interpolate(zscore_series, x, Interpolate.NAN)\n    if not isinstance(w.w, int):\n        w = normalize_window(x, w)\n        values = [_zscore(x.loc[(x.index > idx - w.w) & (x.index <= idx)]) for idx in x.index]\n        return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n    else:\n        return apply_ramp(x.rolling(w.w, 0).apply(_zscore, raw=False), w)
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[docs]@plot_function\ndef winsorize(x: pd.Series, limit: float = 2.5, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Limit extreme values in series\n\n    :param x: time series of prices\n    :param limit: max z-score of values\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of winsorized values\n\n    **Usage**\n\n    Cap and floor values in the series which have a z-score greater or less than provided value. This function will\n    restrict the distribution of values. Calculates the sample standard deviation and adjusts values which\n    fall outside the specified range to be equal to the upper or lower limits\n\n    Lower and upper limits are defined as:\n\n    :math:`upper = \\mu + \\sigma \\\\times limit`\n\n    :math:`lower = \\mu - \\sigma \\\\times limit`\n\n    Where :math:`\\mu` and :math:`\\sigma` are sample mean and standard deviation. The series is restricted by:\n\n    :math:`R_t = max( min( X_t, upper), lower )`\n\n    See `winsorising <https://en.wikipedia.org/wiki/Winsorizing>`_ for additional information\n\n    **Examples**\n\n    Generate price series and winsorize z-score of returns over :math:`22` observations\n\n    >>> prices = generate_series(100)\n    >>> winsorize(zscore(returns(prices), 22))\n\n    **See also**\n\n    :func:`zscore` :func:`mean` :func:`std`\n\n    """\n    w = normalize_window(x, w)\n\n    if x.size < 1:\n        return x\n\n    assert w.w, "window is not 0"\n\n    mu = x.mean()\n    sigma = x.std()\n\n    high = mu + sigma * limit\n    low = mu - sigma * limit\n\n    ret = ceil(x, high)\n    ret = floor(ret, low)\n\n    return apply_ramp(ret, w)
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[docs]@plot_function\ndef generate_series(length: int) -> pd.Series:\n    """\n    Generate sample timeseries\n\n    :param length: number of observations\n    :return: date-based time series of randomly generated prices\n\n    **Usage**\n\n    Create timeseries from returns generated from a normally distributed random variables (IDD). Length determines the\n    number of observations to be generated.\n\n    Assume random variables :math:`R` which follow a normal distribution with mean :math:`0` and standard deviation\n    of :math:`1`\n\n    :math:`R \\sim N(0, 1)`\n\n    The timeseries is generated from these random numbers through:\n\n    :math:`X_t = (1 + R)X_{t-1}`\n\n    **Examples**\n\n    Generate price series with 100 observations starting from today's date:\n\n    >>> prices = generate_series(100)\n\n    **See also**\n\n    :func:`numpy.random.normal()`\n\n    """\n    levels = [100]\n    dates = [datetime.date.today()]\n\n    for i in range(length - 1):\n        levels.append(levels[i] * 1 + numpy.random.normal())\n        dates.append(datetime.date.fromordinal(dates[i].toordinal() + 1))\n\n    return pd.Series(data=levels, index=dates, dtype=np.dtype(float))
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[docs]@plot_function\ndef percentiles(x: pd.Series, y: pd.Series = None, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Rolling percentiles over given window\n\n    :param x: value series\n    :param y: distribution series\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: timeseries of percentiles\n\n    **Usage**\n\n    Calculate `percentile rank <https://en.wikipedia.org/wiki/Percentile_rank>`_ of :math:`y` in the sample distribution\n    of :math:`x` over a rolling window of length :math:`w`:\n\n    :math:`R_t = \\\\frac{\\sum_{i=t-N+1}^{t}{[X_i<{Y_t}]}+0.5\\sum_{i=t-N+1}^{t}{[X_i={Y_t}]}}{N}\\\\times100\\%`\n\n    Where :math:`N` is the number of observations in a rolling window. If :math:`y` is not provided, calculates\n    percentiles of :math:`x` over its historical values. If window length :math:`w` is not provided, uses an\n    ever-growing history of values. If :math:`w` is greater than the available data size, returns empty.\n\n    **Examples**\n\n    Compute percentile ranks of a series in the sample distribution of a second series over :math:`22` observations\n\n    >>> a = generate_series(100)\n    >>> b = generate_series(100)\n    >>> percentiles(a, b, 22)\n\n    **See also**\n\n    :func:`zscores`\n\n    """\n    w = normalize_window(x, w)\n    if x.empty:\n        return x\n\n    if y is None:\n        y = x.copy()\n\n    if isinstance(w.r, int) and w.r > len(y):\n        raise ValueError('Ramp value must be less than the length of the series y.')\n\n    if isinstance(w.w, int) and w.w > len(x):\n        return pd.Series()\n\n    res = pd.Series(dtype=np.dtype(float))\n    for idx, val in y.iteritems():\n        sample = x.loc[(x.index > idx - w.w) & (x.index <= idx)] if isinstance(w.w, pd.DateOffset) else x[:idx][-w.w:]\n        res.loc[idx] = percentileofscore(sample, val, kind='mean')\n\n    if isinstance(w.r, pd.DateOffset):\n        return res.loc[res.index[0] + w.r:]\n    else:\n        return res[w.r:]
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[docs]@plot_function\ndef percentile(x: pd.Series, n: float, w: Union[Window, int, str] = None) -> Union[pd.Series, float]:\n    """\n    Returns the nth percentile of a series.\n\n    :param x: series\n    :param n: percentile\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n    :return: nth percentile\n\n    **Usage**\n\n    Calculates the `nth percentile rank <https://en.wikipedia.org/wiki/Percentile_rank>`_ of :math:`x`. Rolling nth\n    percentile is returned if a window is specified, else a scalar for nth percentile over the entire series.\n\n    **Example**\n\n    Compute the 90th percentile of a series.\n\n    >>> a = generate_series(100)\n    >>> percentile(a, 90)\n\n    """\n    if not 0 <= n <= 100:\n        raise MqValueError('percentile must be in range [0, 100]')\n    x = x.dropna()\n    if x.size < 1:\n        return x\n    if w is None:\n        return numpy.percentile(x.values, n)\n\n    n /= 100\n    w = normalize_window(x, w)\n    if isinstance(w.w, pd.DateOffset):\n        try:\n            values = [x.loc[(x.index > idx - w.w) & (x.index <= idx)].quantile(n) for idx in x.index]\n        except TypeError:\n            raise MqTypeError(f'cannot use relative dates with index {x.index}')\n        res = pd.Series(values, index=x.index, dtype=np.dtype(float))\n    else:\n        res = x.rolling(w.w, 0).quantile(n)\n    return apply_ramp(res, w)
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[docs]class LinearRegression:\n\n    """\n    Fit an Ordinary least squares (OLS) linear regression model.\n\n    :param X: observations of the explanatory variable(s)\n    :param y: observations of the dependant variable\n    :param fit_intercept: whether to calculate intercept in the model\n\n    **Usage**\n\n    Fit `OLS Model <https://en.wikipedia.org/wiki/Ordinary_least_squares>`_ based on observations of the explanatory\n    variables(s) X and the dependant variable y. If X and y are not aligned, only use the intersection of dates/times\n\n    **Examples**\n\n    R Squared of an OLS model:\n\n    >>> x = generate_series(100)\n    >>> y = generate_series(100)\n    >>> r = LinearRegression(x, y)\n    >>> r.r_squared()\n\n    """\n\n    def __init__(self, X: Union[pd.Series, List[pd.Series]], y: pd.Series, fit_intercept: bool = True):\n        df = pd.concat(X, axis=1) if isinstance(X, list) else X.to_frame()\n        df = sm.add_constant(df) if fit_intercept else df\n        df.columns = range(len(df.columns)) if fit_intercept else range(1, len(df.columns) + 1)\n\n        df = df[~df.isin([np.nan, np.inf, -np.inf]).any(1)]  # filter out nan and inf\n        y = y[~y.isin([np.nan, np.inf, -np.inf])]\n        df_aligned, y_aligned = df.align(y, 'inner', axis=0)  # align series\n\n        self._index_scope = range(0, len(df.columns)) if fit_intercept else range(1, len(df.columns) + 1)\n        self._res = sm.OLS(y_aligned, df_aligned).fit()\n        self._fit_intercept = fit_intercept\n\n    @plot_method\n    def coefficient(self, i: int) -> float:\n        """\n        Estimated coefficient\n\n        :param i: coefficient of which predictor to get. If intercept is used, start from 0, else start from 1\n        :return: estimated coefficient of the i-th predictor\n        """\n        return self._res.params[i]\n\n    @plot_method\n    def r_squared(self) -> float:\n        """\n        Coefficient of determination (R Squared)\n\n        :return: R Squared\n        """\n        return self._res.rsquared\n\n    @plot_method\n    def fitted_values(self) -> pd.Series:\n        """\n        Fitted values\n\n        :return: fitted values\n        """\n        return self._res.fittedvalues\n\n    @plot_method\n    def predict(self, X_predict: Union[pd.Series, List[pd.Series]]) -> pd.Series:\n        """\n        Use the model for prediction\n\n        :param X_predict: the values for which to predict\n        :return: predicted values\n        """\n        df = pd.concat(X_predict, axis=1) if isinstance(X_predict, list) else X_predict.to_frame()\n        return self._res.predict(sm.add_constant(df) if self._fit_intercept else df)\n\n    @plot_method\n    def standard_deviation_of_errors(self) -> float:\n        """\n        Standard deviation of the error term\n\n        :return: standard deviation of the error term\n        """\n        return np.sqrt(self._res.mse_resid)
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[docs]class RollingLinearRegression:\n\n    """\n    Fit a rolling ordinary least squares (OLS) linear regression model.\n\n    :param X: observations of the explanatory variable(s)\n    :param y: observations of the dependant variable\n    :param w: number of observations in each rolling window. Must be larger than the number of observations or\n              explanatory variables\n    :param fit_intercept: whether to calculate intercept in the model\n\n    **Usage**\n\n    Fit `OLS Model <https://en.wikipedia.org/wiki/Ordinary_least_squares>`_ based on observations of the explanatory\n    variables(s) X and the dependant variable y across a rolling window with fixed number of observations.\n    The parameters of each rolling window are stored at the end of each window.\n    If X and y are not aligned, only use the intersection of dates/times\n\n    **Examples**\n\n    R Squared of a rolling OLS model:\n\n    >>> x = generate_series(100)\n    >>> y = generate_series(100)\n    >>> r = RollingLinearRegression(x, y, 5)\n    >>> r.r_squared()\n\n    """\n\n    def __init__(self, X: Union[pd.Series, List[pd.Series]], y: pd.Series, w: int, fit_intercept: bool = True):\n        df = pd.concat(X, axis=1) if isinstance(X, list) else X.to_frame()\n        df = sm.add_constant(df) if fit_intercept else df\n        df.columns = range(len(df.columns)) if fit_intercept else range(1, len(df.columns) + 1)\n\n        if w <= len(df.columns):\n            raise MqValueError('Window length must be larger than the number of explanatory variables')\n\n        df = df[~df.isin([np.nan, np.inf, -np.inf]).any(1)]  # filter out nan and inf\n        y = y[~y.isin([np.nan, np.inf, -np.inf])]\n        df_aligned, y_aligned = df.align(y, 'inner', axis=0)  # align series\n\n        self._X = df_aligned.copy()\n        self._res = RollingOLS(y_aligned, df_aligned, w).fit()\n\n    @plot_method\n    def coefficient(self, i: int) -> pd.Series:\n        """\n        Estimated coefficients\n\n        :param i: coefficients of which predictor to get. If intercept is used, start from 0, else start from 1\n        :return: estimated coefficients of the i-th predictor\n        """\n        return self._res.params[i]\n\n    @plot_method\n    def r_squared(self) -> pd.Series:\n        """\n        Coefficients of determination (R Squared) of rolling regressions\n\n        :return: R Squared\n        """\n        return self._res.rsquared\n\n    @plot_method\n    def fitted_values(self) -> pd.Series:\n        """\n        Fitted values at the end of each rolling window\n\n        :return: fitted values\n        """\n        comp = self._X.mul(self._res.params.values)\n        return comp.sum(axis=1, min_count=len(comp.columns))\n\n    @plot_method\n    def standard_deviation_of_errors(self) -> pd.Series:\n        """\n        Standard deviations of the error terms\n\n        :return: standard deviations of the error terms\n        """\n        return np.sqrt(self._res.mse_resid)
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[docs]class SIRModel:\n\n    """SIR Compartmental model for transmission of infectious disease\n\n    :param beta: transmission rate of the infection\n    :param gamma: recovery rate of the infection\n    :param s: number of susceptible individuals in population\n    :param i: number of infectious individuals in population\n    :param r: number of recovered individuals in population\n    :param n: total population size\n    :param end_date: end date for the evolution of the model\n    :param fit: whether to fit the model to the data\n    :param fit_period: on how many days back to fit the model\n\n    **Usage**\n\n    Fit `SIR Model <https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model>`_ based on the\n    population in each compartment over a given time period.\n\n    The SIR models the movement of individuals between three compartments: susceptible (S), infected (I), and resistant\n    (R). The model calibrates parameters :\n\n    ===========   =======================================================\n    Parameter     Description\n    ===========   =======================================================\n    S0            initial susceptible individuals\n    I0            initial infected individuals\n    R0            initial recovered individuals\n    beta          Transmission rate from susceptible to infected\n    gamma         Immunity rate from infected to resistant\n    ===========   =======================================================\n\n    The parameters beta and gamma model how fast people move from being susceptible to infected (beta), and\n    subsequently from infected to resistant (gamma). This model can be used to forecast the populations of each\n    compartment once calibrated\n\n    """\n    def __init__(self, beta: float = None, gamma: float = None, s: Union[pd.Series, float] = None,\n                 i: Union[pd.Series, float] = None, r: Union[pd.Series, float] = None,\n                 n: Union[pd.Series, float] = None, fit: bool = True,\n                 fit_period: int = None):\n        n = n.dropna()[0] if isinstance(n, pd.Series) else n\n        n = 100 if n is None else n\n        fit = False if s is None and i is None and r is None else fit\n        s = n if s is None else s\n        i = 1 if i is None else i\n        r = 0 if r is None else r\n\n        data_start = [ts.index.min().date() for ts in [s, i, r] if isinstance(ts, pd.Series)]\n        data_start.append(DataContext.current.start_date)\n        start_date = max(data_start)\n\n        data_end = [ts.index.max().date() for ts in [s, i, r] if isinstance(ts, pd.Series)]\n        data_end.append(DataContext.current.end_date)\n        end_date = max(data_end)\n\n        self.s = s if isinstance(s, pd.Series) else [s]\n        self.i = i if isinstance(i, pd.Series) else [i]\n        self.r = r if isinstance(r, pd.Series) else [r]\n        self.n = n\n        self.beta_init = beta\n        self.gamma_init = gamma\n        self.fit = fit\n        self.fit_period = fit_period\n        self.beta_fixed = not (self.fit or (self.beta_init is None))\n        self.gamma_fixed = not (self.fit or (self.gamma_init is None))\n\n        data = np.array([self.s, self.i, self.r]).T\n\n        beta_init = self.beta_init if self.beta_init is not None else 0.9\n        gamma_init = self.gamma_init if self.gamma_init is not None else 0.01\n\n        parameters, initial_conditions = SIR.get_parameters(self.s[0], self.i[0], self.r[0], n, beta=beta_init,\n                                                            gamma=gamma_init, beta_fixed=self.beta_fixed,\n                                                            gamma_fixed=self.gamma_fixed, S0_fixed=True, I0_fixed=True,\n                                                            R0_fixed=True)\n        self.parameters = parameters\n\n        self._model = EpidemicModel(SIR, parameters=parameters, data=data, initial_conditions=initial_conditions,\n                                    fit_period=self.fit_period)\n        if self.fit:\n            self._model.fit(verbose=False)\n\n        t = np.arange((end_date - start_date).days + 1)\n        predict = self._model.solve(t, (self.s0(), self.i0(), self.r0()), (self.beta(), self.gamma(), n))\n\n        predict_dates = pd.date_range(start_date, end_date)\n\n        self._model.s_predict = pd.Series(predict[:, 0], predict_dates)\n        self._model.i_predict = pd.Series(predict[:, 1], predict_dates)\n        self._model.r_predict = pd.Series(predict[:, 2], predict_dates)\n\n    @plot_method\n    def s0(self) -> float:\n        """\n        Model calibration for initial susceptible individuals\n\n        :return: initial susceptible individuals\n        """\n        if self.fit:\n            return self._model.fitted_parameters['S0']\n        return self.parameters['S0'].value\n\n    @plot_method\n    def i0(self) -> float:\n        """\n        Model calibration for initial infectious individuals\n\n        :return: initial infectious individuals\n        """\n        if self.fit:\n            return self._model.fitted_parameters['I0']\n        return self.parameters['I0'].value\n\n    @plot_method\n    def r0(self) -> float:\n        """\n        Model calibration for initial recovered individuals\n\n        :return: initial recovered individuals\n        """\n        if self.fit:\n            return self._model.fitted_parameters['R0']\n        return self.parameters['R0'].value\n\n    @plot_method\n    def beta(self) -> float:\n        """\n        Model calibration for transmission rate (susceptible to infected)\n\n        :return: beta\n        """\n        if self.fit:\n            return self._model.fitted_parameters['beta']\n        return self.parameters['beta'].value\n\n    @plot_method\n    def gamma(self) -> float:\n        """\n        Model calibration for immunity (infected to resistant)\n\n        :return: beta\n        """\n        if self.fit:\n            return self._model.fitted_parameters['gamma']\n        return self.parameters['gamma'].value\n\n    @plot_method\n    def predict_s(self) -> pd.Series:\n        """\n        Model calibration for susceptible individuals through time\n\n        :return: susceptible predict\n        """\n        return self._model.s_predict\n\n    @plot_method\n    def predict_i(self) -> pd.Series:\n        """\n        Model calibration for infected individuals through time\n\n        :return: infected predict\n        """\n        return self._model.i_predict\n\n    @plot_method\n    def predict_r(self) -> pd.Series:\n        """\n        Model calibration for recovered individuals through time\n\n        :return: infected predict\n        """\n        return self._model.r_predict
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[docs]class SEIRModel(SIRModel):\n\n    """SEIR Compartmental model for transmission of infectious disease\n\n    :param beta: transmission rate of the infection\n    :param gamma: recovery rate of the infection\n    :param sigma: immunity rate from exposed to infected\n    :param s: number of susceptible individuals in population\n    :param e: number of exposed individuals in population\n    :param i: number of infectious individuals in population\n    :param r: number of recovered individuals in population\n    :param n: total population size\n    :param end_date: end date for the evolution of the model\n    :param fit: whether to fit the model to the data\n    :param fit_period: on how many days back to fit the model\n\n    **Usage**\n\n    Fit `SEIR Model <https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model>`_ based on the\n    population in each compartment over a given time period.\n\n    The SEIR models the movement of individuals between four compartments: susceptible (S), exposed (E), infected (I),\n    and resistant (R). The model calibrates parameters :\n\n    ===========   =======================================================\n    Parameter     Description\n    ===========   =======================================================\n    S0            initial susceptible individuals\n    E0            initial exposed individuals\n    I0            initial infected individuals\n    R0            initial recovered individuals\n    beta          Transmission rate from susceptible to exposed\n    gamma         Immunity rate from infected to resistant\n    sigma         Immunity rate from exposed to infected\n    ===========   =======================================================\n\n    The parameters beta, gamma, and sigma, model how fast people move from being susceptible to exposed (beta),\n    from exposed to infected (sigma), and subsequently from infected to resistant (gamma). This model can be used to\n    predict the populations of each compartment once calibrated.\n\n    """\n    def __init__(self, beta: float = None, gamma: float = None, sigma: float = None, s: Union[pd.Series, float] = None,\n                 e: Union[pd.Series, float] = None, i: Union[pd.Series, float] = None,\n                 r: Union[pd.Series, float] = None, n: Union[pd.Series, float] = None,\n                 fit: bool = True, fit_period: int = None):\n        n = n.dropna()[0] if isinstance(n, pd.Series) else n\n        n = 100 if n is None else n\n        fit = False if all(state is None for state in (s, e, i, r)) else fit\n        s = n if s is None else s\n        e = 1 if e is None else e\n        i = 1 if i is None else i\n        r = 0 if r is None else r\n\n        data_start = [ts.index.min().date() for ts in [s, i, r] if isinstance(ts, pd.Series)]\n        data_start.append(DataContext.current.start_date)\n        start_date = max(data_start)\n\n        data_end = [ts.index.max().date() for ts in [s, i, r] if isinstance(ts, pd.Series)]\n        data_end.append(DataContext.current.end_date)\n        end_date = max(data_end)\n\n        self.s = s if isinstance(s, pd.Series) else [s]\n        self.e = e if isinstance(e, pd.Series) else [e]\n        self.i = i if isinstance(i, pd.Series) else [i]\n        self.r = r if isinstance(r, pd.Series) else [r]\n        self.n = n\n        self.beta_init = beta\n        self.gamma_init = gamma\n        self.sigma_init = sigma\n        self.fit = fit\n        self.fit_period = fit_period\n        self.beta_fixed = not (self.fit or (self.beta is None))\n        self.gamma_fixed = not (self.fit or (self.gamma is None))\n        self.sigma_fixed = not (self.fit or (self.sigma is None))\n\n        data = np.array([self.s, self.e, self.i, self.r]).T\n\n        beta_init = self.beta_init if self.beta_init is not None else 0.9\n        gamma_init = self.gamma_init if self.gamma_init is not None else 0.01\n        sigma_init = self.sigma_init if self.sigma_init is not None else 0.2\n\n        parameters, initial_conditions = SEIR.get_parameters(self.s[0], self.e[0], self.i[0], self.r[0], n,\n                                                             beta=beta_init, gamma=gamma_init, sigma=sigma_init,\n                                                             beta_fixed=self.beta_fixed,\n                                                             gamma_fixed=self.gamma_fixed,\n                                                             sigma_fixed=self.sigma_fixed,\n                                                             S0_fixed=True, I0_fixed=True,\n                                                             R0_fixed=True, E0_fixed=True, S0_max=5e6, I0_max=5e6,\n                                                             E0_max=10e6, R0_max=10e6)\n        self.parameters = parameters\n\n        self._model = EpidemicModel(SEIR, parameters=parameters, data=data, initial_conditions=initial_conditions,\n                                    fit_period=self.fit_period)\n        if self.fit:\n            self._model.fit(verbose=False)\n\n        t = np.arange((end_date - start_date).days + 1)\n        predict = self._model.solve(t, (self.s0(), self.e0(), self.i0(), self.r0()),\n                                    (self.beta(), self.gamma(), self.sigma(), n))\n\n        predict_dates = pd.date_range(start_date, end_date)\n\n        self._model.s_predict = pd.Series(predict[:, 0], predict_dates)\n        self._model.e_predict = pd.Series(predict[:, 1], predict_dates)\n        self._model.i_predict = pd.Series(predict[:, 2], predict_dates)\n        self._model.r_predict = pd.Series(predict[:, 3], predict_dates)\n\n    @plot_method\n    def e0(self) -> float:\n        """\n        Model calibration for initial exposed individuals\n\n        :return: initial exposed individuals\n        """\n        if self.fit:\n            return self._model.fitted_parameters['E0']\n        return self.parameters['E0'].value\n\n    @plot_method\n    def beta(self) -> float:\n        """\n        Model calibration for transmission rate (susceptible to exposed)\n\n        :return: beta\n        """\n        if self.fit:\n            return self._model.fitted_parameters['beta']\n        return self.parameters['beta'].value\n\n    @plot_method\n    def gamma(self) -> float:\n        """\n        Model calibration for immunity (infected to resistant)\n\n        :return: gamma\n        """\n        if self.fit:\n            return self._model.fitted_parameters['gamma']\n        return self.parameters['gamma'].value\n\n    @plot_method\n    def sigma(self) -> float:\n        """\n        Model calibration for infection rate (exposed to infected)\n\n        :return: sigma\n        """\n        if self.fit:\n            return self._model.fitted_parameters['sigma']\n        return self.parameters['sigma'].value\n\n    @plot_method\n    def predict_e(self) -> pd.Series:\n        """\n        Model calibration for exposed individuals through time\n\n        :return: exposed predict\n        """\n        return self._model.e_predict
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Source code for gs_quant.timeseries.technicals

\n# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the "License");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#  http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Marquee Plot Service will attempt to make public functions (not prefixed with _) from this module available.\n# Such functions should be fully documented: docstrings should describe parameters and the return value, and provide\n# a 1-line description. Type annotations should be provided for parameters.\nfrom gs_quant.timeseries import diff, annualize, returns\nfrom .statistics import *\n\n"""\nTechnicals library is for technical analysis functions on timeseries, including moving averages,\nvolatility indicators and and other numerical operations which are finance-oriented for analyzing\nstatistical properties of trading activity, such as price movement and volume changes\n"""\n\n\n
[docs]@plot_function\ndef moving_average(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Moving average over specified window\n\n    :param x: time series of prices\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: date-based time series of return\n\n    **Usage**\n\n    Simple arithmetic moving average over the specified window (number of observations). Shorter windows will be more\n    reactive to changes in the asset price, but more volatile. Larger windows will be smoother but less reactive to\n    near term changes in asset prices.\n\n    :math:`R_t = \\\\frac{\\sum_{i=t-w+1}^{t} X_t}{N}`\n\n    where N is the number of observations in each rolling window, :math:`w`. If window is not provided, computes\n    rolling mean over the full series\n\n    Equivalent to ``mean``\n\n    **Examples**\n\n    Generate price series with 100 observations starting from today's date:\n\n    >>> prices = generate_series(100)\n    >>> moving_average(prices, 22)\n\n    **See also**\n\n    :func:`mean`\n\n    """\n    w = normalize_window(x, w)\n    return apply_ramp(mean(x, Window(w.w, 0)), w)
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[docs]@plot_function\ndef bollinger_bands(x: pd.Series, w: Union[Window, int, str] = Window(None, 0), k: float = 2) -> pd.DataFrame:\n    """\n    Bollinger bands with given window and width\n\n    :param x: time series of prices\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :param k: band width in standard deviations (default: 2)\n    :return: date-based time series of return\n\n    **Usage**\n\n    Standard deviation bands around the moving average of asset price level. Bollinger bands can be used to determine\n    a range around the price level which responds to local volatility changes. Returns two series,\n    upper, :math:`u_t` and lower, :math:`l_t`\n\n    :math:`u_t = \\\\bar{X_t} + k\\sigma_t`\n\n    :math:`l_t = \\\\bar{X_t} - k\\sigma_t`\n\n    where :math:`\\\\bar{X_t}` is the moving average over specified window, and :math:`\\\\sigma_t` is the rolling\n    standard deviation over the specified window\n\n    See `Bollinger Bands <https://en.wikipedia.org/wiki/Bollinger_Bands>`_ for more information\n\n    **Examples**\n\n    Compute bollinger bands around :math:`20` day moving average at :math:`2` standard deviations:\n\n    >>> prices = generate_series(100)\n    >>> bollinger_bands(prices, 20, 2)\n\n    **See also**\n\n    :func:`moving_average` :func:`std`\n    """\n    w = normalize_window(x, w)\n    avg = moving_average(x, w)\n    sigma_t = std(x, w)\n\n    upper = avg + k * sigma_t\n    lower = avg - k * sigma_t\n\n    return pd.concat([lower, upper], axis=1)
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[docs]@plot_function\ndef smoothed_moving_average(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n    """\n    Smoothed moving average over specified window\n\n    :param x: time series of prices\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: date-based time series of return\n\n    **Usage**\n\n    A modified moving average (MMA), running moving average (RMA), or smoothed moving average (SMMA) is defined as:\n\n    :math:`P_{MM,today} = \\\\frac{(N-1)P_{MM,yesterday} + P_today}{N}`\n\n    where N is the number of observations in each rolling window, :math:`w`. If window is not provided, computes\n    rolling mean over the full series\n\n    See `Modified moving average <https://en.wikipedia.org/wiki/Moving_average#Modified_moving_average>`_ for more\n    information\n\n\n    **Examples**\n\n    Generate price series with 100 observations starting from today's date:\n\n    >>> prices = generate_series(100)\n    >>> smoothed_moving_average(prices, 22)\n\n    **See also**\n\n    :func:`mean` :func:'moving_average'\n\n    """\n    w = normalize_window(x, w)\n    window_size = w.w\n    ramp = w.r\n    means = apply_ramp(mean(x, Window(window_size, 0)), w)\n    if means.size < 1:\n        return pd.Series()\n    initial_moving_average = means[0]\n    if (isinstance(ramp, int) and ramp > 0) or isinstance(ramp, pd.DateOffset):\n        x = apply_ramp(x, w)\n\n    smoothed_moving_averages = x.copy()\n    smoothed_moving_averages *= 0\n    smoothed_moving_averages[0] = initial_moving_average\n    for i in range(1, len(x)):\n        if isinstance(window_size, int):\n            window_num_elem = window_size\n        else:\n            window_num_elem = len(x[(x.index > (x.index[i] - window_size)) & (x.index <= x.index[i])])\n        smoothed_moving_averages[i] = ((window_num_elem - 1) * smoothed_moving_averages[i - 1] + x[i]) / window_num_elem\n    return smoothed_moving_averages
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[docs]@plot_function\ndef relative_strength_index(x: pd.Series, w: Union[Window, int, str] = 14) -> pd.DataFrame:\n    """\n    Relative Strength Index\n\n    :param x: time series of prices\n    :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n              and 10 the ramp up value.  If w is a string, it should be a relative date like '1m', '1d', etc.\n              Window size defaults to length of series.\n    :return: date-based time series of RSI\n\n    **Usage**\n\n    The RSI computes momentum as the ratio of higher closes to lower closes: stocks which have had more or stronger\n    positive changes have a higher RSI than stocks which have had more or stronger negative changes.\n\n    See `RSI <https://en.wikipedia.org/wiki/Relative_strength_index>`_ for more information\n\n    **Examples**\n\n    Compute relative strength index over a :math:`14` day window:\n\n    >>> prices = generate_series(100)\n    >>> relative_strength_index(prices, 14)\n\n    **See also**\n\n    :func:`moving_average` :func:`std` :func:`smoothed_moving_average`\n    """\n    w = normalize_window(x, w)\n    one_period_change = diff(x, 1)[1:]\n    gains = one_period_change.copy()\n    losses = one_period_change.copy()\n    gains[gains < 0] = 0\n    losses[losses > 0] = 0\n    losses[losses < 0] *= -1\n\n    moving_avg_gains = smoothed_moving_average(gains, w)\n    moving_avg_losses = smoothed_moving_average(losses, w)\n\n    rsi_len = len(moving_avg_gains)\n    rsi = moving_avg_gains.copy()\n    rsi *= 0\n\n    for index in range(0, rsi_len):\n        if moving_avg_losses[index] == 0:\n            rsi[index] = 100\n        else:\n            relative_strength = moving_avg_gains[index] / moving_avg_losses[index]\n            rsi[index] = 100 - (100 / (1 + relative_strength))\n\n    return rsi
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[docs]@plot_function\ndef exponential_moving_average(x: pd.Series, beta: float = 0.75) -> pd.Series:\n    """\n    Exponentially weighted moving average\n\n    :param x: time series of prices\n    :param beta: how much to weigh the previous observations in the time series, thus controlling how much importance we\n        place on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)\n    :return: date-based time series of return\n\n    **Usage**\n\n    The exponential(ly weighted) moving average (EMA) of a series [:math:`X_0`, :math:`X_1`, :math:`X_2`, ...],\n    is defined as:\n\n    :math:`Y_0 = X_0`\n\n    :math:`Y_t = \\\\beta \\cdot Y_{t-1} + (1 - \\\\beta) \\cdot X_t`\n\n    where :math:`\\\\beta` is the weight we place on the previous average.\n\n    See `Exponential moving average <https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average>`_ for\n    more information\n\n    **Examples**\n\n    Generate price series with 100 observations starting from today's date:\n\n    >>> prices = generate_series(100)\n    >>> exponential_moving_average(prices, 0.9)\n\n    **See also**\n\n    :func:`mean` :func:`moving_average` :func:`smoothed_moving_average`\n\n    """\n    return x.ewm(alpha=1 - beta, adjust=False).mean()
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[docs]@plot_function\ndef exponential_volatility(x: pd.Series, beta: float = 0.75) -> pd.Series:\n    """\n    Exponentially weighted volatility\n\n    :param x: time series of prices\n    :param beta: how much to weigh the previous price in the time series, thus controlling how much importance we\n                  place on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)\n    :return: date-based time series of exponential volatility of the input series\n\n    **Usage**\n\n    Calculates the exponentially weighted standard deviation of the return of the input series, and annualizes the\n    standard deviation\n\n    **Examples**\n\n    Generate price series and compute exponentially weighted standard deviation of returns\n\n    >>> prices = generate_series(100)\n    >>> exponential_volatility(prices, 0.9)\n\n    The above is equivalent to\n\n    >>> annualize(exponential_std(returns(prices), 0.9)) * 100\n\n    **See also**\n\n    :func:`volatility` :func:`exponential_std` :func:`exponential_spread_volatility`\n\n    """\n    return annualize(exponential_std(returns(x), beta)).mul(100)
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[docs]@plot_function\ndef exponential_spread_volatility(x: pd.Series, beta: float = 0.75) -> pd.Series:\n    """\n    Exponentially weighted spread volatility\n\n    :param x: time series of prices\n    :param beta: how much to weigh the previous price in the time series, thus controlling how much importance we\n                  place on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)\n    :return: date-based time series of exponential spread volatility of the input series\n\n    **Usage**\n\n    Exponentially weights the daily differences of the input series, calculates the annualized standard deviation\n\n    **Examples**\n\n    Generate price series and compute exponentially weighted standard deviation of returns\n\n    >>> prices = generate_series(100)\n    >>> exponential_volatility(prices, 0.9)\n\n    The above is equivalent to\n\n    >>> annualize(exponential_std(diff(prices, 1), 0.9))\n\n    **See also**\n\n    :func:`volatility` :func:`exponential_std` :func:`exponential_volatility`\n\n    """\n    return annualize(exponential_std(diff(x, 1), beta))
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\n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~DataContext.end_date\n ~DataContext.end_time\n ~DataContext.is_entered\n ~DataContext.start_date\n ~DataContext.start_time\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.data.Dataset.rst.txt", "content": "gs\\_quant.data.Dataset\n======================\n\n.. currentmodule:: gs_quant.data\n\n.. autoclass:: Dataset\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Dataset.__init__\n ~Dataset.get_coverage\n ~Dataset.get_data\n ~Dataset.get_data_last\n ~Dataset.get_data_series\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Dataset.id\n ~Dataset.name\n ~Dataset.provider\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.data.Fields.rst.txt", "content": "gs\\_quant.data.Fields\n=====================\n\n.. currentmodule:: gs_quant.data\n\n.. autoclass:: Fields\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Fields.ADJUSTED_ASK_PRICE\n ~Fields.ADJUSTED_BID_PRICE\n ~Fields.ADJUSTED_HIGH_PRICE\n ~Fields.ADJUSTED_LOW_PRICE\n ~Fields.ADJUSTED_TRADE_PRICE\n ~Fields.ADJUSTED_VOLUME\n ~Fields.ASK_PRICE\n ~Fields.ASSET_ID\n ~Fields.BID_PRICE\n ~Fields.CLOSE_PRICE\n ~Fields.EXPIRATION_DATE\n ~Fields.HIGH_PRICE\n ~Fields.IMPLIED_VOLATILITY\n ~Fields.LOW_PRICE\n ~Fields.MID_PRICE\n ~Fields.NAME\n ~Fields.OPEN_PRICE\n ~Fields.RELATIVE_STRIKE\n ~Fields.RIC\n ~Fields.SPOT_PRICE\n ~Fields.STRIKE_REFERENCE\n ~Fields.TENOR\n ~Fields.TRADE_PRICE\n ~Fields.UPDATE_TIME\n ~Fields.VAR_SWAP\n ~Fields.VOLUME\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.CommodOTCSwap.rst.txt", "content": "CommodOTCSwap\n=============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: CommodOTCSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: end\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: number_of_periods\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity\n \n \n .. autoattribute:: quantity_period\n \n \n .. autoattribute:: quantity_unit\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement\n \n \n .. autoattribute:: start\n \n \n .. autoattribute:: strategy\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.CommodSwap.rst.txt", "content": "CommodSwap\n==========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: CommodSwap\n\n \n .. rubric:: Properties\n \n \n \n \n \n .. autoattribute:: calculation_period_frequency\n \n \n .. autoattribute:: calculation_periods\n \n \n .. autoattribute:: commodity\n \n \n .. autoattribute:: commodity_reference_price\n \n \n .. autoattribute:: currency\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: start\n \n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.EqCliquet.rst.txt", "content": "EqCliquet\n=========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqCliquet\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: currency\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: first_valuation_date\n \n \n .. autoattribute:: global_cap\n \n \n .. autoattribute:: global_floor\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: last_valuation_date\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: payment_frequency\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: return_style\n \n \n .. autoattribute:: return_type\n \n \n .. autoattribute:: strike_price\n \n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: valuation_period\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.EqForward.rst.txt", "content": "EqForward\n=========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqForward\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: forward_price\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: number_of_shares\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.EqOption.rst.txt", "content": "EqOption\n========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqOption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: exchange\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: multiplier\n \n \n .. autoattribute:: number_of_options\n \n \n .. autoattribute:: option_style\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: trade_as\n \n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: valuation_time\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.EqSynthetic.rst.txt", "content": "EqSynthetic\n===========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqSynthetic\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: currency\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: expiry\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: num_of_underlyers\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: swap_type\n \n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.EqVarianceSwap.rst.txt", "content": "EqVarianceSwap\n==============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqVarianceSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_price\n \n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: variance_cap\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.FXBinary.rst.txt", "content": "FXBinary\n========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXBinary\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: fixing_source\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_price\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.FXForward.rst.txt", "content": "FXForward\n=========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXForward\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: forward_rate\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.FXMultiCrossBinary.rst.txt", "content": "FXMultiCrossBinary\n==================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXMultiCrossBinary\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.FXMultiCrossBinaryLeg.rst.txt", "content": "FXMultiCrossBinaryLeg\n=====================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXMultiCrossBinaryLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: fixing_source\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike_price\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.FXOption.rst.txt", "content": "FXOption\n========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXOption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_amount_other_currency\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.FXVolatilitySwap.rst.txt", "content": "FXVolatilitySwap\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXVolatilitySwap\n\n \n .. rubric:: Properties\n \n \n \n .. autoattribute:: annualization_factor\n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: calculate_mean_return\n \n \n .. autoattribute:: first_fixing_date\n \n \n .. autoattribute:: fixing_frequency\n \n \n .. autoattribute:: fixing_source\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: last_fixing_date\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_vol\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.Forward.rst.txt", "content": "Forward\n=======\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: Forward\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: currency\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRBasisSwap.rst.txt", "content": "IRBasisSwap\n===========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRBasisSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: clearing_house\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: payer_business_day_convention\n \n \n .. autoattribute:: payer_day_count_fraction\n \n \n .. autoattribute:: payer_designated_maturity\n \n \n .. autoattribute:: payer_frequency\n \n \n .. autoattribute:: payer_rate_option\n \n \n .. autoattribute:: payer_spread\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: receiver_business_day_convention\n \n \n .. autoattribute:: receiver_day_count_fraction\n \n \n .. autoattribute:: receiver_designated_maturity\n \n \n .. autoattribute:: receiver_frequency\n \n \n .. autoattribute:: receiver_rate_option\n \n \n .. autoattribute:: receiver_spread\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRCMSOption.rst.txt", "content": "IRCMSOption\n===========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCMSOption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: cap_floor\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: index\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: multiplier\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRCMSOptionStrip.rst.txt", "content": "IRCMSOptionStrip\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCMSOptionStrip\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: cap_floor\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: index\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: multiplier\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: reset_delay\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRCMSSpreadOption.rst.txt", "content": "IRCMSSpreadOption\n=================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCMSSpreadOption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: cap_floor\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: index1_tenor\n \n \n .. autoattribute:: index2_tenor\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRCMSSpreadOptionStrip.rst.txt", "content": "IRCMSSpreadOptionStrip\n======================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCMSSpreadOptionStrip\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: cap_floor\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: index1\n \n \n .. autoattribute:: index2\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: reset_delay\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRCap.rst.txt", "content": "IRCap\n=====\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: cap_rate\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRFloor.rst.txt", "content": "IRFloor\n=======\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRFloor\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: floor_rate\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRSwap.rst.txt", "content": "IRSwap\n======\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: clearing_house\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: fixed_first_stub\n \n \n .. autoattribute:: fixed_holidays\n \n \n .. autoattribute:: fixed_last_stub\n \n \n .. autoattribute:: fixed_rate\n \n \n .. autoattribute:: fixed_rate_business_day_convention\n \n \n .. autoattribute:: fixed_rate_day_count_fraction\n \n \n .. autoattribute:: fixed_rate_frequency\n \n \n .. autoattribute:: floating_first_stub\n \n \n .. autoattribute:: floating_holidays\n \n \n .. autoattribute:: floating_last_stub\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_for_the_initial_calculation_period\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: floating_rate_spread\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pay_or_receive\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: roll_convention\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRSwaption.rst.txt", "content": "IRSwaption\n==========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRSwaption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: clearing_house\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: fixed_rate_business_day_convention\n \n \n .. autoattribute:: fixed_rate_day_count_fraction\n \n \n .. autoattribute:: fixed_rate_frequency\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: floating_rate_spread\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pay_or_receive\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRXccySwap.rst.txt", "content": "IRXccySwap\n==========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRXccySwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: initial_fx_rate\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_reset_side\n \n \n .. autoattribute:: payer_business_day_convention\n \n \n .. autoattribute:: payer_currency\n \n \n .. autoattribute:: payer_day_count_fraction\n \n \n .. autoattribute:: payer_designated_maturity\n \n \n .. autoattribute:: payer_first_stub\n \n \n .. autoattribute:: payer_frequency\n \n \n .. autoattribute:: payer_holidays\n \n \n .. autoattribute:: payer_last_stub\n \n \n .. autoattribute:: payer_rate_option\n \n \n .. autoattribute:: payer_spread\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: receiver_business_day_convention\n \n \n .. autoattribute:: receiver_currency\n \n \n .. autoattribute:: receiver_day_count_fraction\n \n \n .. autoattribute:: receiver_designated_maturity\n \n \n .. autoattribute:: receiver_first_stub\n \n \n .. autoattribute:: receiver_frequency\n \n \n .. autoattribute:: receiver_holidays\n \n \n .. autoattribute:: receiver_last_stub\n \n \n .. autoattribute:: receiver_rate_option\n \n \n .. autoattribute:: receiver_spread\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRXccySwapFixFix.rst.txt", "content": "IRXccySwapFixFix\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRXccySwapFixFix\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: payer_business_day_convention\n \n \n .. autoattribute:: payer_currency\n \n \n .. autoattribute:: payer_day_count_fraction\n \n \n .. autoattribute:: payer_frequency\n \n \n .. autoattribute:: payer_rate\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: receiver_business_day_convention\n \n \n .. autoattribute:: receiver_currency\n \n \n .. autoattribute:: receiver_day_count_fraction\n \n \n .. autoattribute:: receiver_frequency\n \n \n .. autoattribute:: receiver_notional_amount\n \n \n .. autoattribute:: receiver_rate\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.IRXccySwapFixFlt.rst.txt", "content": "IRXccySwapFixFlt\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRXccySwapFixFlt\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: fixed_first_stub\n \n \n .. autoattribute:: fixed_holidays\n \n \n .. autoattribute:: fixed_last_stub\n \n \n .. autoattribute:: fixed_rate\n \n \n .. autoattribute:: fixed_rate_business_day_convention\n \n \n .. autoattribute:: fixed_rate_currency\n \n \n .. autoattribute:: fixed_rate_day_count_fraction\n \n \n .. autoattribute:: fixed_rate_frequency\n \n \n .. autoattribute:: floating_first_stub\n \n \n .. autoattribute:: floating_holidays\n \n \n .. autoattribute:: floating_last_stub\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_currency\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_for_the_initial_calculation_period\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: floating_rate_spread\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: pay_or_receive\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.InflationSwap.rst.txt", "content": "InflationSwap\n=============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: InflationSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: base_cpi\n \n \n .. autoattribute:: clearing_house\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fixed_rate\n \n \n .. autoattribute:: fixed_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: index\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pay_or_receive\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.instrument.Security.rst.txt", "content": "Security\n========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: Security\n\n \n .. rubric:: Properties\n \n \n \n .. autoattribute:: bbid\n \n \n .. autoattribute:: bbid_equivalent\n \n \n .. autoattribute:: bcid\n \n \n .. autoattribute:: cid\n \n \n .. autoattribute:: cm_id\n \n \n .. autoattribute:: cross\n \n \n .. autoattribute:: cusip\n \n \n .. autoattribute:: delisted\n \n \n .. autoattribute:: dollar_cross\n \n \n .. autoattribute:: eid\n \n \n .. autoattribute:: em_id\n \n \n .. autoattribute:: exchange_code\n \n \n .. autoattribute:: gsid\n \n \n .. autoattribute:: gsid_equivalent\n \n \n .. autoattribute:: gsideid\n \n \n .. autoattribute:: gsn\n \n \n .. autoattribute:: gss\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: isin\n \n \n .. autoattribute:: jsn\n \n \n .. autoattribute:: lms_id\n \n \n .. autoattribute:: mdapi\n \n \n .. autoattribute:: mdapi_class\n \n \n .. autoattribute:: mic\n \n \n .. autoattribute:: mq_symbol\n \n \n .. autoattribute:: pl_id\n \n \n .. autoattribute:: plot_id\n \n \n .. autoattribute:: pnode_id\n \n \n .. autoattribute:: primary_country_ric\n \n \n .. autoattribute:: prime_id\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: ps_id\n \n \n .. autoattribute:: rcic\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: ric\n \n \n .. autoattribute:: sec_name\n \n \n .. autoattribute:: sedol\n \n \n .. autoattribute:: sf_id\n \n \n .. autoattribute:: simon_id\n \n \n .. autoattribute:: tdapi\n \n \n .. autoattribute:: ticker\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: valoren\n \n \n .. autoattribute:: wi_id\n \n \n .. autoattribute:: wpk\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.HistoricalPricingContext.rst.txt", "content": "gs\\_quant.markets.HistoricalPricingContext\n==========================================\n\n.. currentmodule:: gs_quant.markets\n\n.. autoclass:: HistoricalPricingContext\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~HistoricalPricingContext.__init__\n ~HistoricalPricingContext.calc\n ~HistoricalPricingContext.clone\n ~HistoricalPricingContext.default_value\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~HistoricalPricingContext.active_context\n ~HistoricalPricingContext.csa_term\n ~HistoricalPricingContext.is_async\n ~HistoricalPricingContext.is_batch\n ~HistoricalPricingContext.is_current\n ~HistoricalPricingContext.is_entered\n ~HistoricalPricingContext.market\n ~HistoricalPricingContext.market_data_location\n ~HistoricalPricingContext.pricing_date\n ~HistoricalPricingContext.prior_context\n ~HistoricalPricingContext.use_cache\n ~HistoricalPricingContext.visible_to_gs\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.PricingContext.rst.txt", "content": "gs\\_quant.markets.PricingContext\n================================\n\n.. currentmodule:: gs_quant.markets\n\n.. autoclass:: PricingContext\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~PricingContext.__init__\n ~PricingContext.calc\n ~PricingContext.clone\n ~PricingContext.default_value\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~PricingContext.active_context\n ~PricingContext.csa_term\n ~PricingContext.is_async\n ~PricingContext.is_batch\n ~PricingContext.is_current\n ~PricingContext.is_entered\n ~PricingContext.market\n ~PricingContext.market_data_location\n ~PricingContext.pricing_date\n ~PricingContext.prior_context\n ~PricingContext.use_cache\n ~PricingContext.visible_to_gs\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.portfolio.Portfolio.rst.txt", "content": "gs\\_quant.markets.portfolio.Portfolio\n=====================================\n\n.. currentmodule:: gs_quant.markets.portfolio\n\n.. autoclass:: Portfolio\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Portfolio.__init__\n ~Portfolio.append\n ~Portfolio.as_dict\n ~Portfolio.calc\n ~Portfolio.clone\n ~Portfolio.default_instance\n ~Portfolio.dollar_price\n ~Portfolio.from_asset_id\n ~Portfolio.from_asset_name\n ~Portfolio.from_book\n ~Portfolio.from_csv\n ~Portfolio.from_dict\n ~Portfolio.from_eti\n ~Portfolio.from_frame\n ~Portfolio.from_instance\n ~Portfolio.from_portfolio_id\n ~Portfolio.from_portfolio_name\n ~Portfolio.from_quote\n ~Portfolio.market\n ~Portfolio.paths\n ~Portfolio.pop\n ~Portfolio.price\n ~Portfolio.prop_item_type\n ~Portfolio.prop_type\n ~Portfolio.properties\n ~Portfolio.resolve\n ~Portfolio.save\n ~Portfolio.save_as_quote\n ~Portfolio.subset\n ~Portfolio.to_csv\n ~Portfolio.to_frame\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Portfolio.all_instruments\n ~Portfolio.all_paths\n ~Portfolio.all_portfolios\n ~Portfolio.id\n ~Portfolio.instruments\n ~Portfolio.name\n ~Portfolio.portfolios\n ~Portfolio.priceables\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.securities.Asset.rst.txt", "content": "gs\\_quant.markets.securities.Asset\n==================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: Asset\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Asset.__init__\n ~Asset.entity_type\n ~Asset.get\n ~Asset.get_close_prices\n ~Asset.get_data_coordinate\n ~Asset.get_data_series\n ~Asset.get_entity\n ~Asset.get_identifier\n ~Asset.get_identifiers\n ~Asset.get_marquee_id\n ~Asset.get_type\n ~Asset.get_unique_entity_key\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Asset.data_dimension\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.securities.AssetClass.rst.txt", "content": "gs\\_quant.markets.securities.AssetClass\n=======================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: AssetClass\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~AssetClass.Cash\n ~AssetClass.Commod\n ~AssetClass.Credit\n ~AssetClass.Cross_Asset\n ~AssetClass.Cryptocurrency\n ~AssetClass.Econ\n ~AssetClass.Equity\n ~AssetClass.FX\n ~AssetClass.Fund\n ~AssetClass.Loan\n ~AssetClass.Mortgage\n ~AssetClass.Rates\n ~AssetClass.Social\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.securities.AssetIdentifier.rst.txt", "content": "gs\\_quant.markets.securities.AssetIdentifier\n============================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: AssetIdentifier\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~AssetIdentifier.BLOOMBERG_COMPOSITE_ID\n ~AssetIdentifier.BLOOMBERG_ID\n ~AssetIdentifier.CUSIP\n ~AssetIdentifier.ISIN\n ~AssetIdentifier.MARQUEE_ID\n ~AssetIdentifier.REUTERS_ID\n ~AssetIdentifier.SEDOL\n ~AssetIdentifier.TICKER\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.securities.AssetType.rst.txt", "content": "gs\\_quant.markets.securities.AssetType\n======================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: AssetType\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~AssetType.BASKET\n ~AssetType.BOND\n ~AssetType.CASH\n ~AssetType.COMMODITY\n ~AssetType.COMMODITY_EU_NATURAL_GAS_HUB\n ~AssetType.COMMODITY_NATURAL_GAS_HUB\n ~AssetType.COMMODITY_POWER_AGGREGATED_NODES\n ~AssetType.COMMODITY_POWER_NODE\n ~AssetType.COMMODITY_REFERENCE_PRICE\n ~AssetType.CROSS\n ~AssetType.CRYPTO_CURRENCY\n ~AssetType.CURRENCY\n ~AssetType.ETF\n ~AssetType.FUTURE\n ~AssetType.FUTURE_CONTRACT\n ~AssetType.FUTURE_MARKET\n ~AssetType.INDEX\n ~AssetType.OPTION\n ~AssetType.RATE\n ~AssetType.STOCK\n ~AssetType.SWAP\n ~AssetType.WEATHER_INDEX\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.securities.Index.rst.txt", "content": "gs\\_quant.markets.securities.Index\n==================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: Index\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Index.__init__\n ~Index.entity_type\n ~Index.get\n ~Index.get_close_prices\n ~Index.get_constituents\n ~Index.get_currency\n ~Index.get_data_coordinate\n ~Index.get_data_series\n ~Index.get_entity\n ~Index.get_identifier\n ~Index.get_identifiers\n ~Index.get_marquee_id\n ~Index.get_return_type\n ~Index.get_type\n ~Index.get_unique_entity_key\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Index.data_dimension\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.securities.SecurityMaster.rst.txt", "content": "gs\\_quant.markets.securities.SecurityMaster\n===========================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: SecurityMaster\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~SecurityMaster.__init__\n ~SecurityMaster.get_asset\n \n \n\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.markets.securities.Stock.rst.txt", "content": "gs\\_quant.markets.securities.Stock\n==================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: Stock\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Stock.__init__\n ~Stock.entity_type\n ~Stock.get\n ~Stock.get_close_prices\n ~Stock.get_currency\n ~Stock.get_data_coordinate\n ~Stock.get_data_series\n ~Stock.get_entity\n ~Stock.get_identifier\n ~Stock.get_identifiers\n ~Stock.get_marquee_id\n ~Stock.get_type\n ~Stock.get_unique_entity_key\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Stock.data_dimension\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.models.epidemiology.EpidemicModel.rst.txt", "content": "gs\\_quant.models.epidemiology.EpidemicModel\n===========================================\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autoclass:: EpidemicModel\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~EpidemicModel.__init__\n ~EpidemicModel.fit\n ~EpidemicModel.residual\n ~EpidemicModel.solve\n \n \n\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.models.epidemiology.SEIR.rst.txt", "content": "gs\\_quant.models.epidemiology.SEIR\n==================================\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autoclass:: SEIR\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~SEIR.__init__\n ~SEIR.calibrate\n ~SEIR.get_parameters\n \n \n\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.models.epidemiology.SIR.rst.txt", "content": "gs\\_quant.models.epidemiology.SIR\n=================================\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autoclass:: SIR\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~SIR.__init__\n ~SIR.calibrate\n ~SIR.get_parameters\n \n \n\n \n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.timeseries.datetime.Window.rst.txt", "content": "gs\\_quant.timeseries.datetime.Window\n====================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autoclass:: Window\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~Window.as_dict\n \n ~Window.from_dict\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.timeseries.statistics.LinearRegression.rst.txt", "content": "gs\\_quant.timeseries.statistics.LinearRegression\n================================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autoclass:: LinearRegression\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~LinearRegression.coefficient\n \n ~LinearRegression.fitted_values\n \n ~LinearRegression.predict\n \n ~LinearRegression.r_squared\n \n ~LinearRegression.standard_deviation_of_errors\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.timeseries.statistics.RollingLinearRegression.rst.txt", "content": "gs\\_quant.timeseries.statistics.RollingLinearRegression\n=======================================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autoclass:: RollingLinearRegression\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~RollingLinearRegression.coefficient\n \n ~RollingLinearRegression.fitted_values\n \n ~RollingLinearRegression.r_squared\n \n ~RollingLinearRegression.standard_deviation_of_errors\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.timeseries.statistics.SEIRModel.rst.txt", "content": "gs\\_quant.timeseries.statistics.SEIRModel\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autoclass:: SEIRModel\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~SEIRModel.beta\n \n ~SEIRModel.e0\n \n ~SEIRModel.gamma\n \n ~SEIRModel.i0\n \n ~SEIRModel.predict_e\n \n ~SEIRModel.predict_i\n \n ~SEIRModel.predict_r\n \n ~SEIRModel.predict_s\n \n ~SEIRModel.r0\n \n ~SEIRModel.s0\n \n ~SEIRModel.sigma\n \n " }, { "path": "docs/_build/html/_sources/classes/gs_quant.timeseries.statistics.SIRModel.rst.txt", "content": "gs\\_quant.timeseries.statistics.SIRModel\n========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autoclass:: SIRModel\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~SIRModel.beta\n \n ~SIRModel.gamma\n \n ~SIRModel.i0\n \n ~SIRModel.predict_i\n \n ~SIRModel.predict_r\n \n ~SIRModel.predict_s\n \n ~SIRModel.r0\n \n ~SIRModel.s0\n \n " }, { "path": "docs/_build/html/_sources/data.rst.txt", "content": "Data Package\n============\n\n.. currentmodule:: gs_quant.data\n\n.. autosummary::\n :toctree: classes\n\n DataContext\n Dataset\n Fields" }, { "path": "docs/_build/html/_sources/datetime.rst.txt", "content": "Datetime Package\n================\n\nDate\n----\n.. currentmodule:: gs_quant.datetime.date\n\n.. autosummary::\n :toctree: functions\n\n business_day_count\n business_day_offset\n date_range\n is_business_day\n prev_business_date\n\nPoint\n-----\n.. currentmodule:: gs_quant.datetime.point\n\n.. autosummary::\n :toctree: functions\n\n point_sort_order" }, { "path": "docs/_build/html/_sources/functions/gs_quant.datetime.date.business_day_count.rst.txt", "content": "gs\\_quant.datetime.date.business\\_day\\_count\n============================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: business_day_count" }, { "path": "docs/_build/html/_sources/functions/gs_quant.datetime.date.business_day_offset.rst.txt", "content": "gs\\_quant.datetime.date.business\\_day\\_offset\n=============================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: business_day_offset" }, { "path": "docs/_build/html/_sources/functions/gs_quant.datetime.date.date_range.rst.txt", "content": "gs\\_quant.datetime.date.date\\_range\n===================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: date_range" }, { "path": "docs/_build/html/_sources/functions/gs_quant.datetime.date.is_business_day.rst.txt", "content": "gs\\_quant.datetime.date.is\\_business\\_day\n=========================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: is_business_day" }, { "path": "docs/_build/html/_sources/functions/gs_quant.datetime.date.prev_business_date.rst.txt", "content": "gs\\_quant.datetime.date.prev\\_business\\_date\n============================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: prev_business_date" }, { "path": "docs/_build/html/_sources/functions/gs_quant.datetime.point.point_sort_order.rst.txt", "content": "gs\\_quant.datetime.point.point\\_sort\\_order\n===========================================\n\n.. currentmodule:: gs_quant.datetime.point\n\n.. autofunction:: point_sort_order" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.abs_.rst.txt", "content": "gs\\_quant.timeseries.algebra.abs\\_\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: abs_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.add.rst.txt", "content": "gs\\_quant.timeseries.algebra.add\n================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: add" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.and_.rst.txt", "content": "gs\\_quant.timeseries.algebra.and\\_\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: and_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.ceil.rst.txt", "content": "gs\\_quant.timeseries.algebra.ceil\n=================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: ceil" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.divide.rst.txt", "content": "gs\\_quant.timeseries.algebra.divide\n===================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: divide" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.exp.rst.txt", "content": "gs\\_quant.timeseries.algebra.exp\n================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: exp" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.filter_.rst.txt", "content": "gs\\_quant.timeseries.algebra.filter\\_\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: filter_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.floor.rst.txt", "content": "gs\\_quant.timeseries.algebra.floor\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: floor" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.floordiv.rst.txt", "content": "gs\\_quant.timeseries.algebra.floordiv\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: floordiv" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.if_.rst.txt", "content": "gs\\_quant.timeseries.algebra.if\\_\n=================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: if_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.log.rst.txt", "content": "gs\\_quant.timeseries.algebra.log\n================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: log" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.multiply.rst.txt", "content": "gs\\_quant.timeseries.algebra.multiply\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: multiply" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.not_.rst.txt", "content": "gs\\_quant.timeseries.algebra.not\\_\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: not_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.or_.rst.txt", "content": "gs\\_quant.timeseries.algebra.or\\_\n=================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: or_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.power.rst.txt", "content": "gs\\_quant.timeseries.algebra.power\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: power" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.repeat.rst.txt", "content": "gs\\_quant.timeseries.algebra.repeat\n===================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: repeat" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.smooth_spikes.rst.txt", "content": "gs\\_quant.timeseries.algebra.smooth\\_spikes\n===========================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: smooth_spikes" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.sqrt.rst.txt", "content": "gs\\_quant.timeseries.algebra.sqrt\n=================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: sqrt" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.subtract.rst.txt", "content": "gs\\_quant.timeseries.algebra.subtract\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: subtract" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.algebra.weighted_sum.rst.txt", "content": "gs\\_quant.timeseries.algebra.weighted\\_sum\n==========================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: weighted_sum" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.analysis.count.rst.txt", "content": "gs\\_quant.timeseries.analysis.count\n===================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: count" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.analysis.diff.rst.txt", "content": "gs\\_quant.timeseries.analysis.diff\n==================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: diff" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.analysis.first.rst.txt", "content": "gs\\_quant.timeseries.analysis.first\n===================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: first" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.analysis.lag.rst.txt", "content": "gs\\_quant.timeseries.analysis.lag\n=================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: lag" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.analysis.last.rst.txt", "content": "gs\\_quant.timeseries.analysis.last\n==================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: last" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.analysis.last_value.rst.txt", "content": "gs\\_quant.timeseries.analysis.last\\_value\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: last_value" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.analysis.weighted_sum.rst.txt", "content": "weighted_sum\n==================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: weighted_sum" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.backtesting.basket.rst.txt", "content": "gs\\_quant.timeseries.backtesting.basket\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.backtesting\n\n.. autofunction:: basket" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.align.rst.txt", "content": "gs\\_quant.timeseries.datetime.align\n===================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: align" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.date_range.rst.txt", "content": "gs\\_quant.timeseries.datetime.date\\_range\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: date_range" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.day.rst.txt", "content": "gs\\_quant.timeseries.datetime.day\n=================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: day" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.interpolate.rst.txt", "content": "gs\\_quant.timeseries.datetime.interpolate\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: interpolate" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.month.rst.txt", "content": "gs\\_quant.timeseries.datetime.month\n===================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: month" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.prepend.rst.txt", "content": "gs\\_quant.timeseries.datetime.prepend\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: prepend" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.quarter.rst.txt", "content": "gs\\_quant.timeseries.datetime.quarter\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: quarter" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.union.rst.txt", "content": "gs\\_quant.timeseries.datetime.union\n===================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: union" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.value.rst.txt", "content": "gs\\_quant.timeseries.datetime.value\n===================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: value" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.weekday.rst.txt", "content": "gs\\_quant.timeseries.datetime.weekday\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: weekday" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.datetime.year.rst.txt", "content": "gs\\_quant.timeseries.datetime.year\n==================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: year" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.annualize.rst.txt", "content": "gs\\_quant.timeseries.econometrics.annualize\n===========================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: annualize" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.beta.rst.txt", "content": "gs\\_quant.timeseries.econometrics.beta\n======================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: beta" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.change.rst.txt", "content": "gs\\_quant.timeseries.econometrics.change\n========================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: change" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.correlation.rst.txt", "content": "gs\\_quant.timeseries.econometrics.correlation\n=============================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: correlation" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.excess_returns_.rst.txt", "content": "gs\\_quant.timeseries.econometrics.excess\\_returns\\_\n===================================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: excess_returns_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.index.rst.txt", "content": "gs\\_quant.timeseries.econometrics.index\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: index" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.max_drawdown.rst.txt", "content": "gs\\_quant.timeseries.econometrics.max\\_drawdown\n===============================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: max_drawdown" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.prices.rst.txt", "content": "gs\\_quant.timeseries.econometrics.prices\n========================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: prices" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.returns.rst.txt", "content": "gs\\_quant.timeseries.econometrics.returns\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: returns" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.sharpe_ratio.rst.txt", "content": "gs\\_quant.timeseries.econometrics.sharpe\\_ratio\n===============================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: sharpe_ratio" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.econometrics.volatility.rst.txt", "content": "gs\\_quant.timeseries.econometrics.volatility\n============================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: volatility" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.cov.rst.txt", "content": "gs\\_quant.timeseries.statistics.cov\n===================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: cov" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.exponential_std.rst.txt", "content": "gs\\_quant.timeseries.statistics.exponential\\_std\n================================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: exponential_std" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.generate_series.rst.txt", "content": "gs\\_quant.timeseries.statistics.generate\\_series\n================================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: generate_series" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.max_.rst.txt", "content": "gs\\_quant.timeseries.statistics.max\\_\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: max_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.mean.rst.txt", "content": "gs\\_quant.timeseries.statistics.mean\n====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: mean" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.median.rst.txt", "content": "gs\\_quant.timeseries.statistics.median\n======================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: median" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.min_.rst.txt", "content": "gs\\_quant.timeseries.statistics.min\\_\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: min_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.mode.rst.txt", "content": "gs\\_quant.timeseries.statistics.mode\n====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: mode" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.percentile.rst.txt", "content": "gs\\_quant.timeseries.statistics.percentile\n==========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: percentile" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.percentiles.rst.txt", "content": "gs\\_quant.timeseries.statistics.percentiles\n===========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: percentiles" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.product.rst.txt", "content": "gs\\_quant.timeseries.statistics.product\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: product" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.range_.rst.txt", "content": "gs\\_quant.timeseries.statistics.range\\_\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: range_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.std.rst.txt", "content": "gs\\_quant.timeseries.statistics.std\n===================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: std" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.sum_.rst.txt", "content": "gs\\_quant.timeseries.statistics.sum\\_\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: sum_" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.var.rst.txt", "content": "gs\\_quant.timeseries.statistics.var\n===================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: var" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.winsorize.rst.txt", "content": "gs\\_quant.timeseries.statistics.winsorize\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: winsorize" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.statistics.zscores.rst.txt", "content": "gs\\_quant.timeseries.statistics.zscores\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: zscores" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.technicals.bollinger_bands.rst.txt", "content": "gs\\_quant.timeseries.technicals.bollinger\\_bands\n================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: bollinger_bands" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.technicals.exponential_moving_average.rst.txt", "content": "gs\\_quant.timeseries.technicals.exponential\\_moving\\_average\n============================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: exponential_moving_average" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.technicals.exponential_spread_volatility.rst.txt", "content": "gs\\_quant.timeseries.technicals.exponential\\_spread\\_volatility\n===============================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: exponential_spread_volatility" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.technicals.exponential_volatility.rst.txt", "content": "gs\\_quant.timeseries.technicals.exponential\\_volatility\n=======================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: exponential_volatility" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.technicals.moving_average.rst.txt", "content": "gs\\_quant.timeseries.technicals.moving\\_average\n===============================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: moving_average" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.technicals.relative_strength_index.rst.txt", "content": "gs\\_quant.timeseries.technicals.relative\\_strength\\_index\n=========================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: relative_strength_index" }, { "path": "docs/_build/html/_sources/functions/gs_quant.timeseries.technicals.smoothed_moving_average.rst.txt", "content": "gs\\_quant.timeseries.technicals.smoothed\\_moving\\_average\n=========================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: smoothed_moving_average" }, { "path": "docs/_build/html/_sources/index.rst.txt", "content": ".. gs_quant documentation master file, created by\n sphinx-quickstart on Sat Jan 5 20:53:00 2019.\n You can adapt this file completely to your liking, but it should at least\n contain the root `toctree` directive.\n\nGS Quant API\n============\n\n.. toctree::\n :maxdepth: 2\n :caption: Packages\n\n data\n datetime\n instrument \n markets\n models\n risk\n timeseries\n\n:ref:`genindex`\n\n" }, { "path": "docs/_build/html/_sources/instrument.rst.txt", "content": "\nInstrument Package\n==================\n\n.. currentmodule:: gs_quant.base\n\n.. autosummary::\n :toctree: classes\n\n Priceable\n\n\nInstruments\n-----------\n\n.. currentmodule:: gs_quant.instrument\n\n.. autosummary::\n :toctree: classes\n :template: instruments.rst\n\n CommodOTCSwap\n EqCliquet\n EqForward\n EqOption\n EqSynthetic\n EqVarianceSwap\n Forward\n FXBinary\n FXForward\n FXMultiCrossBinary\n FXMultiCrossBinaryLeg\n FXOption\n FXVolatilitySwap\n InflationSwap\n IRBasisSwap\n IRCap\n IRCMSOption\n IRCMSOptionStrip\n IRCMSSpreadOption\n IRCMSSpreadOptionStrip\n IRFloor\n IRSwap\n IRSwaption\n IRXccySwap\n IRXccySwapFixFix\n IRXccySwapFixFlt\n Security\n" }, { "path": "docs/_build/html/_sources/market.rst.txt", "content": "\nMarket Package\n==============\n\n.. currentmodule:: gs_quant.markets\n\n.. autosummary::\n :toctree: classes\n\n PricingContext\n HistoricalPricingContext\n\n\nSecurities\n----------\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autosummary::\n :toctree: classes\n\n Asset\n AssetClass\n AssetIdentifier\n AssetType\n Index\n Stock\n SecurityMaster\n\n\n\n\n" }, { "path": "docs/_build/html/_sources/markets.rst.txt", "content": "\nMarkets Package\n===============\n\n.. currentmodule:: gs_quant.markets\n\n.. autosummary::\n :toctree: classes\n\n PricingContext\n HistoricalPricingContext\n\n\nPortfolio\n---------\n\n.. currentmodule:: gs_quant.markets.portfolio\n\n.. autosummary::\n :toctree: classes\n\n Portfolio\n\n\nSecurities\n----------\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autosummary::\n :toctree: classes\n\n Asset\n AssetClass\n AssetIdentifier\n AssetType\n Index\n Stock\n SecurityMaster\n\n\n\n\n" }, { "path": "docs/_build/html/_sources/models.rst.txt", "content": "\nModels Package\n==============\n\nEpidemiology\n------------\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autosummary::\n :toctree: classes\n\n SIR\n SEIR\n EpidemicModel\n\n\n\n\n" }, { "path": "docs/_build/html/_sources/risk.rst.txt", "content": "\nRisk Package\n============\n\nFunctions\n---------\n.. currentmodule:: gs_quant.risk\n\n.. autofunction:: aggregate_risk\n.. autofunction:: subtract_risk\n.. autofunction:: sort_risk\n\nMeasures\n--------\n.. autodata:: DollarPrice\n.. autodata:: Price\n.. autodata:: ForwardPrice\n.. autodata:: Theta\n.. autodata:: BaseCPI\n.. autodata:: CommodDelta\n.. autodata:: CommodTheta\n.. autodata:: CommodVega\n.. autodata:: EqDelta\n.. autodata:: EqGamma\n.. autodata:: EqVega\n.. autodata:: EqSpot\n.. autodata:: EqAnnualImpliedVol\n.. autodata:: FairVarStrike\n.. autodata:: FairVolStrike\n.. autodata:: FXDelta\n.. autodata:: FXGamma\n.. autodata:: FXVega\n.. autodata:: FXSpot\n.. autodata:: InflationDelta\n.. autodata:: InflationDeltaParallel\n.. autodata:: InflationDeltaParallelLocalCcy\n.. autodata:: IRBasis\n.. autodata:: IRDelta\n.. autodata:: IRDeltaParallel\n.. autodata:: IRDeltaLocalCcy\n.. autodata:: IRDeltaParallelLocalCcy\n.. autodata:: IRXccyDelta\n.. autodata:: IRXccyDeltaParallel\n.. autodata:: IRXccyDeltaParallelLocalCurrency\n.. autodata:: IRGammaParallel\n.. autodata:: IRGammaParallelLocalCcy\n.. autodata:: IRVega\n.. autodata:: IRVegaParallel\n.. autodata:: IRVegaLocalCcy\n.. autodata:: IRVegaParallelLocalCcy\n.. autodata:: IRAnnualImpliedVol\n.. autodata:: IRAnnualATMImpliedVol\n.. autodata:: IRDailyImpliedVol\n.. autodata:: IRSpotRate\n.. autodata:: IRFwdRate\n" }, { "path": "docs/_build/html/_sources/timeseries.rst.txt", "content": "\nTimeseries Package\n==================\n\nAlgebra\n-------\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autosummary::\n :toctree: functions\n\n abs_\n add\n and_\n ceil\n divide\n exp\n filter_\n floor\n floordiv\n if_\n log\n multiply\n not_\n or_\n power\n repeat\n smooth_spikes\n sqrt\n subtract\n weighted_sum\n\n\nAnalysis\n--------\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autosummary::\n :toctree: functions\n\n diff\n first\n last\n last_value\n count\n lag\n\n\nBacktesting\n-----------\n\n.. currentmodule:: gs_quant.timeseries.backtesting\n\n.. autosummary::\n :toctree: functions\n\n basket\n\n\nDate / Time\n-----------\n\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autosummary::\n :toctree: functions\n\n align\n interpolate\n value\n day\n weekday\n month\n year\n quarter\n date_range\n prepend\n union\n\n.. autosummary::\n :toctree: classes\n :template: timeseries_class.rst\n\n Window\n\n\nEconometrics\n------------\n\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autosummary::\n :toctree: functions\n\n annualize\n beta\n change\n correlation\n excess_returns_\n index\n max_drawdown\n prices\n returns\n sharpe_ratio\n volatility\n\n\nStatistics\n----------\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autosummary::\n :toctree: functions\n\n cov\n exponential_std\n generate_series\n max_ \n mean\n median \n min_\n mode\n percentile\n percentiles\n product\n range_\n std\n sum_\n var\n winsorize\n zscores\n\n.. autosummary::\n :toctree: classes\n :template: timeseries_class.rst\n\n LinearRegression\n RollingLinearRegression\n SIRModel\n SEIRModel\n\nTechnical Analysis\n------------------\n\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autosummary::\n :toctree: functions\n\n bollinger_bands\n moving_average\n exponential_moving_average\n exponential_volatility\n exponential_spread_volatility\n smoothed_moving_average\n 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*vertical-align: middle;\n}\nbutton,\ninput {\n line-height: normal;\n}\nbutton,\ninput[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n cursor: pointer;\n -webkit-appearance: button;\n *overflow: visible;\n}\nbutton[disabled],\ninput[disabled] {\n cursor: default;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box;\n padding: 0;\n *width: 13px;\n *height: 13px;\n}\ninput[type=\"search\"] {\n -webkit-appearance: textfield;\n -moz-box-sizing: content-box;\n -webkit-box-sizing: content-box;\n box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-decoration,\ninput[type=\"search\"]::-webkit-search-cancel-button {\n -webkit-appearance: none;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\ntextarea {\n overflow: auto;\n vertical-align: top;\n resize: vertical;\n}\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\ntd {\n vertical-align: top;\n}\n.chromeframe {\n margin: 0.2em 0;\n background: #ccc;\n color: #000;\n padding: 0.2em 0;\n}\n.ir {\n display: block;\n border: 0;\n text-indent: -999em;\n overflow: hidden;\n background-color: transparent;\n background-repeat: no-repeat;\n text-align: left;\n direction: ltr;\n *line-height: 0;\n}\n.ir br {\n display: none;\n}\n.hidden {\n display: none !important;\n visibility: hidden;\n}\n.visuallyhidden {\n border: 0;\n clip: rect(0 0 0 0);\n height: 1px;\n margin: -1px;\n overflow: hidden;\n padding: 0;\n position: absolute;\n width: 1px;\n}\n.visuallyhidden.focusable:active,\n.visuallyhidden.focusable:focus {\n clip: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n position: static;\n width: auto;\n}\n.invisible {\n visibility: hidden;\n}\n.relative {\n position: relative;\n}\nbig,\nsmall {\n font-size: 100%;\n}\n@media print {\n html,\n body,\n section {\n background: none !important;\n }\n * {\n box-shadow: none !important;\n text-shadow: none !important;\n filter: none !important;\n -ms-filter: none !important;\n }\n a,\n a:visited {\n text-decoration: underline;\n }\n .ir a:after,\n a[href^=\"javascript:\"]:after,\n a[href^=\"#\"]:after {\n content: \"\";\n }\n pre,\n blockquote {\n page-break-inside: avoid;\n }\n thead {\n display: table-header-group;\n }\n tr,\n img {\n page-break-inside: avoid;\n }\n img {\n max-width: 100% !important;\n }\n @page {\n margin: 0.5cm;\n }\n p,\n h2,\n .rst-content .toctree-wrapper p.caption,\n h3 {\n orphans: 3;\n widows: 3;\n }\n h2,\n .rst-content .toctree-wrapper p.caption,\n h3 {\n page-break-after: avoid;\n }\n}\n.fa:before,\n.wy-menu-vertical li span.toctree-expand:before,\n.wy-menu-vertical li.on a span.toctree-expand:before,\n.wy-menu-vertical li.current > a span.toctree-expand:before,\n.rst-content .admonition-title:before,\n.rst-content h1 .headerlink:before,\n.rst-content h2 .headerlink:before,\n.rst-content h3 .headerlink:before,\n.rst-content h4 .headerlink:before,\n.rst-content h5 .headerlink:before,\n.rst-content h6 .headerlink:before,\n.rst-content dl dt .headerlink:before,\n.rst-content p.caption .headerlink:before,\n.rst-content table > caption .headerlink:before,\n.rst-content .code-block-caption .headerlink:before,\n.rst-content tt.download span:first-child:before,\n.rst-content code.download span:first-child:before,\n.icon:before,\n.wy-dropdown .caret:before,\n.wy-inline-validate.wy-inline-validate-success .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-danger .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-warning .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-info .wy-input-context:before,\n.wy-alert,\n.rst-content .note,\n.rst-content .attention,\n.rst-content .caution,\n.rst-content .danger,\n.rst-content .error,\n.rst-content .hint,\n.rst-content .important,\n.rst-content .tip,\n.rst-content .warning,\n.rst-content .seealso,\n.rst-content .admonition-todo,\n.rst-content .admonition,\n.btn,\ninput[type=\"text\"],\ninput[type=\"password\"],\ninput[type=\"email\"],\ninput[type=\"url\"],\ninput[type=\"date\"],\ninput[type=\"month\"],\ninput[type=\"time\"],\ninput[type=\"datetime\"],\ninput[type=\"datetime-local\"],\ninput[type=\"week\"],\ninput[type=\"number\"],\ninput[type=\"search\"],\ninput[type=\"tel\"],\ninput[type=\"color\"],\nselect,\ntextarea,\n.wy-menu-vertical li.on a,\n.wy-menu-vertical li.current > a,\n.wy-side-nav-search > a,\n.wy-side-nav-search .wy-dropdown > a,\n.wy-nav-top a {\n -webkit-font-smoothing: antialiased;\n}\n.clearfix {\n *zoom: 1;\n}\n.clearfix:before,\n.clearfix:after {\n display: table;\n content: \"\";\n}\n.clearfix:after {\n clear: both;\n}\n\n.fa-border {\n padding: 0.2em 0.25em 0.15em;\n border: solid 0.08em #eee;\n border-radius: 0.1em;\n}\n.fa-pull-left {\n float: left;\n}\n.fa-pull-right {\n float: right;\n}\n.fa.fa-pull-left,\n.wy-menu-vertical li span.fa-pull-left.toctree-expand,\n.wy-menu-vertical li.on a 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span.fa-pull-right.toctree-expand,\n.rst-content .fa-pull-right.admonition-title,\n.rst-content h1 .fa-pull-right.headerlink,\n.rst-content h2 .fa-pull-right.headerlink,\n.rst-content h3 .fa-pull-right.headerlink,\n.rst-content h4 .fa-pull-right.headerlink,\n.rst-content h5 .fa-pull-right.headerlink,\n.rst-content h6 .fa-pull-right.headerlink,\n.rst-content dl dt .fa-pull-right.headerlink,\n.rst-content p.caption .fa-pull-right.headerlink,\n.rst-content table > caption .fa-pull-right.headerlink,\n.rst-content .code-block-caption .fa-pull-right.headerlink,\n.rst-content tt.download span.fa-pull-right:first-child,\n.rst-content code.download span.fa-pull-right:first-child,\n.fa-pull-right.icon {\n margin-left: 0.3em;\n}\n.pull-right {\n float: right;\n}\n.pull-left {\n float: left;\n}\n.fa.pull-left,\n.wy-menu-vertical li span.pull-left.toctree-expand,\n.wy-menu-vertical li.on a span.pull-left.toctree-expand,\n.wy-menu-vertical li.current > a span.pull-left.toctree-expand,\n.rst-content 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h4 .pull-right.headerlink,\n.rst-content h5 .pull-right.headerlink,\n.rst-content h6 .pull-right.headerlink,\n.rst-content dl dt .pull-right.headerlink,\n.rst-content p.caption .pull-right.headerlink,\n.rst-content table > caption .pull-right.headerlink,\n.rst-content .code-block-caption .pull-right.headerlink,\n.rst-content tt.download span.pull-right:first-child,\n.rst-content code.download span.pull-right:first-child,\n.pull-right.icon {\n margin-left: 0.3em;\n}\n.fa-arrow-circle-left:before,\n.icon-circle-arrow-left:before {\n content: url(../images/gs-arrow-left.svg);\n position: relative;\n top: 5px;\n right: 4px;\n}\n.fa-arrow-circle-right:before,\n.icon-circle-arrow-right:before {\n content: url(../images/gs-arrow-right.svg);\n position: relative;\n top: 5px;\n left: 0px;\n}\n.fa-minus-square:before {\n content: url(../images/gs-menu-collapse.svg);\n}\n.fa-minus-square-o:before,\n.wy-menu-vertical li.on a span.toctree-expand:before,\n.wy-menu-vertical li.current > a 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.headerlink,\n.rst-content .code-block-caption .headerlink,\n.rst-content tt.download span:first-child,\n.rst-content code.download span:first-child,\n.icon,\n.wy-dropdown .caret,\n.wy-inline-validate.wy-inline-validate-success .wy-input-context,\n.wy-inline-validate.wy-inline-validate-danger .wy-input-context,\n.wy-inline-validate.wy-inline-validate-warning .wy-input-context,\n.wy-inline-validate.wy-inline-validate-info .wy-input-context {\n font-family: inherit;\n}\n.fa:before,\n.wy-menu-vertical li span.toctree-expand:before,\n.wy-menu-vertical li.on a span.toctree-expand:before,\n.wy-menu-vertical li.current > a span.toctree-expand:before,\n.rst-content .admonition-title:before,\n.rst-content h1 .headerlink:before,\n.rst-content h2 .headerlink:before,\n.rst-content h3 .headerlink:before,\n.rst-content h4 .headerlink:before,\n.rst-content h5 .headerlink:before,\n.rst-content h6 .headerlink:before,\n.rst-content dl dt .headerlink:before,\n.rst-content p.caption .headerlink:before,\n.rst-content table > caption .headerlink:before,\n.rst-content .code-block-caption .headerlink:before,\n.rst-content tt.download span:first-child:before,\n.rst-content code.download span:first-child:before,\n.icon:before,\n.wy-dropdown .caret:before,\n.wy-inline-validate.wy-inline-validate-success .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-danger .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-warning .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-info .wy-input-context:before {\n display: inline-block;\n font-style: normal;\n font-weight: normal;\n line-height: 1;\n text-decoration: inherit;\n width: 12px;\n height: 16px;\n margin-top: -2px;\n margin-left: -8px;\n}\na .fa,\na .wy-menu-vertical li span.toctree-expand,\n.wy-menu-vertical li a span.toctree-expand,\n.wy-menu-vertical li.on a span.toctree-expand,\n.wy-menu-vertical li.current > a span.toctree-expand,\na .rst-content .admonition-title,\n.rst-content a .admonition-title,\na .rst-content h1 .headerlink,\n.rst-content h1 a .headerlink,\na .rst-content h2 .headerlink,\n.rst-content h2 a .headerlink,\na .rst-content h3 .headerlink,\n.rst-content h3 a .headerlink,\na .rst-content h4 .headerlink,\n.rst-content h4 a .headerlink,\na .rst-content h5 .headerlink,\n.rst-content h5 a .headerlink,\na .rst-content h6 .headerlink,\n.rst-content h6 a .headerlink,\na .rst-content dl dt .headerlink,\n.rst-content dl dt a .headerlink,\na .rst-content p.caption .headerlink,\n.rst-content p.caption a .headerlink,\na .rst-content table > caption .headerlink,\n.rst-content table > caption a .headerlink,\na .rst-content .code-block-caption .headerlink,\n.rst-content .code-block-caption a .headerlink,\na .rst-content tt.download span:first-child,\n.rst-content tt.download a span:first-child,\na .rst-content code.download span:first-child,\n.rst-content code.download a span:first-child,\na .icon {\n display: inline-block;\n text-decoration: inherit;\n}\n.btn .fa,\n.btn .wy-menu-vertical li span.toctree-expand,\n.wy-menu-vertical li .btn span.toctree-expand,\n.btn .wy-menu-vertical li.on a span.toctree-expand,\n.wy-menu-vertical li.on a .btn span.toctree-expand,\n.btn .wy-menu-vertical li.current > a span.toctree-expand,\n.wy-menu-vertical li.current > a .btn span.toctree-expand,\n.btn .rst-content .admonition-title,\n.rst-content .btn .admonition-title,\n.btn .rst-content h1 .headerlink,\n.rst-content h1 .btn .headerlink,\n.btn .rst-content h2 .headerlink,\n.rst-content h2 .btn .headerlink,\n.btn .rst-content h3 .headerlink,\n.rst-content h3 .btn .headerlink,\n.btn .rst-content h4 .headerlink,\n.rst-content h4 .btn .headerlink,\n.btn .rst-content h5 .headerlink,\n.rst-content h5 .btn .headerlink,\n.btn .rst-content h6 .headerlink,\n.rst-content h6 .btn .headerlink,\n.btn .rst-content dl dt .headerlink,\n.rst-content dl dt .btn .headerlink,\n.btn .rst-content p.caption .headerlink,\n.rst-content p.caption .btn .headerlink,\n.btn .rst-content table > caption .headerlink,\n.rst-content table > caption .btn .headerlink,\n.btn .rst-content .code-block-caption .headerlink,\n.rst-content .code-block-caption .btn .headerlink,\n.btn .rst-content tt.download span:first-child,\n.rst-content tt.download .btn span:first-child,\n.btn .rst-content code.download span:first-child,\n.rst-content code.download .btn span:first-child,\n.btn .icon,\n.nav .fa,\n.nav .wy-menu-vertical li span.toctree-expand,\n.wy-menu-vertical li .nav span.toctree-expand,\n.nav .wy-menu-vertical li.on a span.toctree-expand,\n.wy-menu-vertical li.on a .nav span.toctree-expand,\n.nav .wy-menu-vertical li.current > a span.toctree-expand,\n.wy-menu-vertical li.current > a .nav span.toctree-expand,\n.nav .rst-content .admonition-title,\n.rst-content .nav .admonition-title,\n.nav .rst-content h1 .headerlink,\n.rst-content h1 .nav .headerlink,\n.nav .rst-content h2 .headerlink,\n.rst-content h2 .nav .headerlink,\n.nav .rst-content h3 .headerlink,\n.rst-content h3 .nav .headerlink,\n.nav .rst-content h4 .headerlink,\n.rst-content h4 .nav .headerlink,\n.nav .rst-content h5 .headerlink,\n.rst-content h5 .nav .headerlink,\n.nav .rst-content h6 .headerlink,\n.rst-content h6 .nav .headerlink,\n.nav .rst-content dl dt .headerlink,\n.rst-content dl dt .nav .headerlink,\n.nav .rst-content p.caption .headerlink,\n.rst-content p.caption .nav .headerlink,\n.nav .rst-content table > caption .headerlink,\n.rst-content table > caption .nav .headerlink,\n.nav .rst-content .code-block-caption .headerlink,\n.rst-content .code-block-caption .nav .headerlink,\n.nav .rst-content tt.download span:first-child,\n.rst-content tt.download .nav span:first-child,\n.nav .rst-content code.download span:first-child,\n.rst-content code.download .nav span:first-child,\n.nav .icon {\n display: inline;\n}\n.btn .fa.fa-large,\n.btn .wy-menu-vertical li span.fa-large.toctree-expand,\n.wy-menu-vertical li .btn span.fa-large.toctree-expand,\n.btn .rst-content .fa-large.admonition-title,\n.rst-content .btn .fa-large.admonition-title,\n.btn .rst-content h1 .fa-large.headerlink,\n.rst-content h1 .btn .fa-large.headerlink,\n.btn .rst-content h2 .fa-large.headerlink,\n.rst-content h2 .btn .fa-large.headerlink,\n.btn .rst-content h3 .fa-large.headerlink,\n.rst-content h3 .btn .fa-large.headerlink,\n.btn .rst-content h4 .fa-large.headerlink,\n.rst-content h4 .btn .fa-large.headerlink,\n.btn .rst-content h5 .fa-large.headerlink,\n.rst-content h5 .btn .fa-large.headerlink,\n.btn .rst-content h6 .fa-large.headerlink,\n.rst-content h6 .btn .fa-large.headerlink,\n.btn .rst-content dl dt .fa-large.headerlink,\n.rst-content dl dt .btn .fa-large.headerlink,\n.btn .rst-content p.caption .fa-large.headerlink,\n.rst-content p.caption .btn .fa-large.headerlink,\n.btn .rst-content table > caption .fa-large.headerlink,\n.rst-content table > caption .btn .fa-large.headerlink,\n.btn .rst-content .code-block-caption .fa-large.headerlink,\n.rst-content .code-block-caption .btn .fa-large.headerlink,\n.btn .rst-content tt.download span.fa-large:first-child,\n.rst-content tt.download .btn span.fa-large:first-child,\n.btn .rst-content code.download span.fa-large:first-child,\n.rst-content code.download .btn span.fa-large:first-child,\n.btn .fa-large.icon,\n.nav .fa.fa-large,\n.nav .wy-menu-vertical li span.fa-large.toctree-expand,\n.wy-menu-vertical li .nav span.fa-large.toctree-expand,\n.nav .rst-content .fa-large.admonition-title,\n.rst-content .nav .fa-large.admonition-title,\n.nav .rst-content h1 .fa-large.headerlink,\n.rst-content h1 .nav .fa-large.headerlink,\n.nav .rst-content h2 .fa-large.headerlink,\n.rst-content h2 .nav .fa-large.headerlink,\n.nav .rst-content h3 .fa-large.headerlink,\n.rst-content h3 .nav .fa-large.headerlink,\n.nav .rst-content h4 .fa-large.headerlink,\n.rst-content h4 .nav .fa-large.headerlink,\n.nav .rst-content h5 .fa-large.headerlink,\n.rst-content h5 .nav .fa-large.headerlink,\n.nav .rst-content h6 .fa-large.headerlink,\n.rst-content h6 .nav .fa-large.headerlink,\n.nav .rst-content dl dt .fa-large.headerlink,\n.rst-content dl dt .nav .fa-large.headerlink,\n.nav .rst-content p.caption .fa-large.headerlink,\n.rst-content p.caption .nav .fa-large.headerlink,\n.nav .rst-content table > caption .fa-large.headerlink,\n.rst-content table > caption .nav .fa-large.headerlink,\n.nav .rst-content .code-block-caption .fa-large.headerlink,\n.rst-content .code-block-caption .nav .fa-large.headerlink,\n.nav .rst-content tt.download span.fa-large:first-child,\n.rst-content tt.download .nav span.fa-large:first-child,\n.nav .rst-content code.download span.fa-large:first-child,\n.rst-content code.download .nav span.fa-large:first-child,\n.nav .fa-large.icon {\n line-height: 0.9em;\n}\n.btn .fa.fa-spin,\n.btn .wy-menu-vertical li span.fa-spin.toctree-expand,\n.wy-menu-vertical li .btn span.fa-spin.toctree-expand,\n.btn .rst-content .fa-spin.admonition-title,\n.rst-content .btn .fa-spin.admonition-title,\n.btn .rst-content h1 .fa-spin.headerlink,\n.rst-content h1 .btn .fa-spin.headerlink,\n.btn .rst-content h2 .fa-spin.headerlink,\n.rst-content h2 .btn .fa-spin.headerlink,\n.btn .rst-content h3 .fa-spin.headerlink,\n.rst-content h3 .btn .fa-spin.headerlink,\n.btn .rst-content h4 .fa-spin.headerlink,\n.rst-content h4 .btn .fa-spin.headerlink,\n.btn .rst-content h5 .fa-spin.headerlink,\n.rst-content h5 .btn .fa-spin.headerlink,\n.btn .rst-content h6 .fa-spin.headerlink,\n.rst-content h6 .btn .fa-spin.headerlink,\n.btn .rst-content dl dt .fa-spin.headerlink,\n.rst-content dl dt .btn .fa-spin.headerlink,\n.btn .rst-content p.caption .fa-spin.headerlink,\n.rst-content p.caption .btn .fa-spin.headerlink,\n.btn .rst-content table > caption .fa-spin.headerlink,\n.rst-content table > caption .btn .fa-spin.headerlink,\n.btn .rst-content .code-block-caption .fa-spin.headerlink,\n.rst-content .code-block-caption .btn .fa-spin.headerlink,\n.btn .rst-content tt.download span.fa-spin:first-child,\n.rst-content tt.download .btn span.fa-spin:first-child,\n.btn .rst-content code.download span.fa-spin:first-child,\n.rst-content code.download .btn span.fa-spin:first-child,\n.btn .fa-spin.icon,\n.nav .fa.fa-spin,\n.nav .wy-menu-vertical li span.fa-spin.toctree-expand,\n.wy-menu-vertical li .nav span.fa-spin.toctree-expand,\n.nav .rst-content .fa-spin.admonition-title,\n.rst-content .nav .fa-spin.admonition-title,\n.nav .rst-content h1 .fa-spin.headerlink,\n.rst-content h1 .nav .fa-spin.headerlink,\n.nav .rst-content h2 .fa-spin.headerlink,\n.rst-content h2 .nav .fa-spin.headerlink,\n.nav .rst-content h3 .fa-spin.headerlink,\n.rst-content h3 .nav .fa-spin.headerlink,\n.nav .rst-content h4 .fa-spin.headerlink,\n.rst-content h4 .nav .fa-spin.headerlink,\n.nav .rst-content h5 .fa-spin.headerlink,\n.rst-content h5 .nav .fa-spin.headerlink,\n.nav .rst-content h6 .fa-spin.headerlink,\n.rst-content h6 .nav .fa-spin.headerlink,\n.nav .rst-content dl dt .fa-spin.headerlink,\n.rst-content dl dt .nav .fa-spin.headerlink,\n.nav .rst-content p.caption .fa-spin.headerlink,\n.rst-content p.caption .nav .fa-spin.headerlink,\n.nav .rst-content table > caption .fa-spin.headerlink,\n.rst-content table > caption .nav .fa-spin.headerlink,\n.nav .rst-content .code-block-caption .fa-spin.headerlink,\n.rst-content .code-block-caption .nav .fa-spin.headerlink,\n.nav .rst-content tt.download span.fa-spin:first-child,\n.rst-content tt.download .nav span.fa-spin:first-child,\n.nav .rst-content code.download span.fa-spin:first-child,\n.rst-content code.download .nav span.fa-spin:first-child,\n.nav .fa-spin.icon {\n display: inline-block;\n}\n.btn.fa:before,\n.wy-menu-vertical li span.btn.toctree-expand:before,\n.rst-content .btn.admonition-title:before,\n.rst-content h1 .btn.headerlink:before,\n.rst-content h2 .btn.headerlink:before,\n.rst-content h3 .btn.headerlink:before,\n.rst-content h4 .btn.headerlink:before,\n.rst-content h5 .btn.headerlink:before,\n.rst-content h6 .btn.headerlink:before,\n.rst-content dl dt .btn.headerlink:before,\n.rst-content p.caption .btn.headerlink:before,\n.rst-content table > caption .btn.headerlink:before,\n.rst-content .code-block-caption .btn.headerlink:before,\n.rst-content tt.download span.btn:first-child:before,\n.rst-content code.download span.btn:first-child:before,\n.btn.icon:before {\n opacity: 0.5;\n -webkit-transition: opacity 0.05s ease-in;\n -moz-transition: opacity 0.05s ease-in;\n transition: opacity 0.05s ease-in;\n}\n.btn.fa:hover:before,\n.wy-menu-vertical li span.btn.toctree-expand:hover:before,\n.rst-content .btn.admonition-title:hover:before,\n.rst-content h1 .btn.headerlink:hover:before,\n.rst-content h2 .btn.headerlink:hover:before,\n.rst-content h3 .btn.headerlink:hover:before,\n.rst-content h4 .btn.headerlink:hover:before,\n.rst-content h5 .btn.headerlink:hover:before,\n.rst-content h6 .btn.headerlink:hover:before,\n.rst-content dl dt .btn.headerlink:hover:before,\n.rst-content p.caption .btn.headerlink:hover:before,\n.rst-content table > caption .btn.headerlink:hover:before,\n.rst-content .code-block-caption .btn.headerlink:hover:before,\n.rst-content tt.download span.btn:first-child:hover:before,\n.rst-content code.download span.btn:first-child:hover:before,\n.btn.icon:hover:before {\n opacity: 1;\n}\n.btn-mini .fa:before,\n.btn-mini .wy-menu-vertical li span.toctree-expand:before,\n.wy-menu-vertical li .btn-mini span.toctree-expand:before,\n.btn-mini .rst-content .admonition-title:before,\n.rst-content .btn-mini .admonition-title:before,\n.btn-mini .rst-content h1 .headerlink:before,\n.rst-content h1 .btn-mini .headerlink:before,\n.btn-mini .rst-content h2 .headerlink:before,\n.rst-content h2 .btn-mini .headerlink:before,\n.btn-mini .rst-content h3 .headerlink:before,\n.rst-content h3 .btn-mini .headerlink:before,\n.btn-mini .rst-content h4 .headerlink:before,\n.rst-content h4 .btn-mini .headerlink:before,\n.btn-mini .rst-content h5 .headerlink:before,\n.rst-content h5 .btn-mini .headerlink:before,\n.btn-mini .rst-content h6 .headerlink:before,\n.rst-content h6 .btn-mini .headerlink:before,\n.btn-mini .rst-content dl dt .headerlink:before,\n.rst-content dl dt .btn-mini .headerlink:before,\n.btn-mini .rst-content p.caption .headerlink:before,\n.rst-content p.caption .btn-mini .headerlink:before,\n.btn-mini .rst-content table > caption .headerlink:before,\n.rst-content table > caption .btn-mini .headerlink:before,\n.btn-mini .rst-content .code-block-caption .headerlink:before,\n.rst-content .code-block-caption .btn-mini .headerlink:before,\n.btn-mini .rst-content tt.download span:first-child:before,\n.rst-content tt.download .btn-mini span:first-child:before,\n.btn-mini .rst-content code.download span:first-child:before,\n.rst-content code.download .btn-mini span:first-child:before,\n.btn-mini .icon:before {\n font-size: 14px;\n vertical-align: -15%;\n}\n.wy-alert,\n.rst-content .note,\n.rst-content .attention,\n.rst-content .caution,\n.rst-content .danger,\n.rst-content .error,\n.rst-content .hint,\n.rst-content .important,\n.rst-content .tip,\n.rst-content .warning,\n.rst-content .seealso,\n.rst-content .admonition-todo,\n.rst-content .admonition {\n padding: 12px;\n line-height: 24px;\n margin-bottom: 24px;\n background: #e7f2fa;\n}\n.wy-alert-title,\n.rst-content .admonition-title {\n color: #fff;\n font-weight: bold;\n display: block;\n color: #fff;\n background: #6ab0de;\n margin: -12px;\n padding: 6px 12px;\n margin-bottom: 12px;\n}\n.wy-alert.wy-alert-danger,\n.rst-content .wy-alert-danger.note,\n.rst-content .wy-alert-danger.attention,\n.rst-content .wy-alert-danger.caution,\n.rst-content .danger,\n.rst-content .error,\n.rst-content .wy-alert-danger.hint,\n.rst-content .wy-alert-danger.important,\n.rst-content .wy-alert-danger.tip,\n.rst-content .wy-alert-danger.warning,\n.rst-content .wy-alert-danger.seealso,\n.rst-content .wy-alert-danger.admonition-todo,\n.rst-content .wy-alert-danger.admonition {\n background: #fdf3f2;\n}\n.wy-alert.wy-alert-danger .wy-alert-title,\n.rst-content .wy-alert-danger.note .wy-alert-title,\n.rst-content .wy-alert-danger.attention .wy-alert-title,\n.rst-content .wy-alert-danger.caution .wy-alert-title,\n.rst-content .danger .wy-alert-title,\n.rst-content .error .wy-alert-title,\n.rst-content .wy-alert-danger.hint .wy-alert-title,\n.rst-content .wy-alert-danger.important .wy-alert-title,\n.rst-content .wy-alert-danger.tip .wy-alert-title,\n.rst-content .wy-alert-danger.warning .wy-alert-title,\n.rst-content .wy-alert-danger.seealso .wy-alert-title,\n.rst-content .wy-alert-danger.admonition-todo .wy-alert-title,\n.rst-content .wy-alert-danger.admonition .wy-alert-title,\n.wy-alert.wy-alert-danger .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-danger .admonition-title,\n.rst-content .wy-alert-danger.note .admonition-title,\n.rst-content .wy-alert-danger.attention .admonition-title,\n.rst-content .wy-alert-danger.caution .admonition-title,\n.rst-content .danger .admonition-title,\n.rst-content .error .admonition-title,\n.rst-content .wy-alert-danger.hint .admonition-title,\n.rst-content .wy-alert-danger.important .admonition-title,\n.rst-content .wy-alert-danger.tip .admonition-title,\n.rst-content .wy-alert-danger.warning .admonition-title,\n.rst-content .wy-alert-danger.seealso .admonition-title,\n.rst-content .wy-alert-danger.admonition-todo .admonition-title,\n.rst-content .wy-alert-danger.admonition .admonition-title {\n background: #f29f97;\n}\n.wy-alert.wy-alert-warning,\n.rst-content .wy-alert-warning.note,\n.rst-content .attention,\n.rst-content .caution,\n.rst-content .wy-alert-warning.danger,\n.rst-content .wy-alert-warning.error,\n.rst-content .wy-alert-warning.hint,\n.rst-content .wy-alert-warning.important,\n.rst-content .wy-alert-warning.tip,\n.rst-content .warning,\n.rst-content .wy-alert-warning.seealso,\n.rst-content .admonition-todo,\n.rst-content .wy-alert-warning.admonition {\n background: #ffedcc;\n}\n.wy-alert.wy-alert-warning .wy-alert-title,\n.rst-content .wy-alert-warning.note .wy-alert-title,\n.rst-content .attention .wy-alert-title,\n.rst-content .caution .wy-alert-title,\n.rst-content .wy-alert-warning.danger .wy-alert-title,\n.rst-content .wy-alert-warning.error .wy-alert-title,\n.rst-content .wy-alert-warning.hint .wy-alert-title,\n.rst-content .wy-alert-warning.important .wy-alert-title,\n.rst-content .wy-alert-warning.tip .wy-alert-title,\n.rst-content .warning .wy-alert-title,\n.rst-content .wy-alert-warning.seealso .wy-alert-title,\n.rst-content .admonition-todo .wy-alert-title,\n.rst-content .wy-alert-warning.admonition .wy-alert-title,\n.wy-alert.wy-alert-warning .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-warning .admonition-title,\n.rst-content .wy-alert-warning.note .admonition-title,\n.rst-content .attention .admonition-title,\n.rst-content .caution .admonition-title,\n.rst-content .wy-alert-warning.danger .admonition-title,\n.rst-content .wy-alert-warning.error .admonition-title,\n.rst-content .wy-alert-warning.hint .admonition-title,\n.rst-content .wy-alert-warning.important .admonition-title,\n.rst-content .wy-alert-warning.tip .admonition-title,\n.rst-content .warning .admonition-title,\n.rst-content .wy-alert-warning.seealso .admonition-title,\n.rst-content .admonition-todo .admonition-title,\n.rst-content .wy-alert-warning.admonition .admonition-title {\n background: #f0b37e;\n}\n.wy-alert.wy-alert-info,\n.rst-content .note,\n.rst-content .wy-alert-info.attention,\n.rst-content .wy-alert-info.caution,\n.rst-content .wy-alert-info.danger,\n.rst-content .wy-alert-info.error,\n.rst-content .wy-alert-info.hint,\n.rst-content .wy-alert-info.important,\n.rst-content .wy-alert-info.tip,\n.rst-content .wy-alert-info.warning,\n.rst-content .seealso,\n.rst-content .wy-alert-info.admonition-todo,\n.rst-content .wy-alert-info.admonition {\n background: #e7f2fa;\n}\n.wy-alert.wy-alert-info .wy-alert-title,\n.rst-content .note .wy-alert-title,\n.rst-content .wy-alert-info.attention .wy-alert-title,\n.rst-content .wy-alert-info.caution .wy-alert-title,\n.rst-content .wy-alert-info.danger .wy-alert-title,\n.rst-content .wy-alert-info.error .wy-alert-title,\n.rst-content .wy-alert-info.hint .wy-alert-title,\n.rst-content .wy-alert-info.important .wy-alert-title,\n.rst-content .wy-alert-info.tip .wy-alert-title,\n.rst-content .wy-alert-info.warning .wy-alert-title,\n.rst-content .seealso .wy-alert-title,\n.rst-content .wy-alert-info.admonition-todo .wy-alert-title,\n.rst-content .wy-alert-info.admonition .wy-alert-title,\n.wy-alert.wy-alert-info .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-info .admonition-title,\n.rst-content .note .admonition-title,\n.rst-content .wy-alert-info.attention .admonition-title,\n.rst-content .wy-alert-info.caution .admonition-title,\n.rst-content .wy-alert-info.danger .admonition-title,\n.rst-content .wy-alert-info.error .admonition-title,\n.rst-content .wy-alert-info.hint .admonition-title,\n.rst-content .wy-alert-info.important .admonition-title,\n.rst-content .wy-alert-info.tip .admonition-title,\n.rst-content .wy-alert-info.warning .admonition-title,\n.rst-content .seealso .admonition-title,\n.rst-content .wy-alert-info.admonition-todo .admonition-title,\n.rst-content .wy-alert-info.admonition .admonition-title {\n background: #6ab0de;\n}\n.wy-alert.wy-alert-success,\n.rst-content .wy-alert-success.note,\n.rst-content .wy-alert-success.attention,\n.rst-content .wy-alert-success.caution,\n.rst-content .wy-alert-success.danger,\n.rst-content .wy-alert-success.error,\n.rst-content .hint,\n.rst-content .important,\n.rst-content .tip,\n.rst-content .wy-alert-success.warning,\n.rst-content .wy-alert-success.seealso,\n.rst-content .wy-alert-success.admonition-todo,\n.rst-content .wy-alert-success.admonition {\n background: #dbfaf4;\n}\n.wy-alert.wy-alert-success .wy-alert-title,\n.rst-content .wy-alert-success.note .wy-alert-title,\n.rst-content .wy-alert-success.attention .wy-alert-title,\n.rst-content .wy-alert-success.caution .wy-alert-title,\n.rst-content .wy-alert-success.danger .wy-alert-title,\n.rst-content .wy-alert-success.error .wy-alert-title,\n.rst-content .hint .wy-alert-title,\n.rst-content .important .wy-alert-title,\n.rst-content .tip .wy-alert-title,\n.rst-content .wy-alert-success.warning .wy-alert-title,\n.rst-content .wy-alert-success.seealso .wy-alert-title,\n.rst-content .wy-alert-success.admonition-todo .wy-alert-title,\n.rst-content .wy-alert-success.admonition .wy-alert-title,\n.wy-alert.wy-alert-success .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-success .admonition-title,\n.rst-content .wy-alert-success.note .admonition-title,\n.rst-content .wy-alert-success.attention .admonition-title,\n.rst-content .wy-alert-success.caution .admonition-title,\n.rst-content .wy-alert-success.danger .admonition-title,\n.rst-content .wy-alert-success.error .admonition-title,\n.rst-content .hint .admonition-title,\n.rst-content .important .admonition-title,\n.rst-content .tip .admonition-title,\n.rst-content .wy-alert-success.warning .admonition-title,\n.rst-content .wy-alert-success.seealso .admonition-title,\n.rst-content .wy-alert-success.admonition-todo .admonition-title,\n.rst-content .wy-alert-success.admonition .admonition-title {\n background: #1abc9c;\n}\n.wy-alert.wy-alert-neutral,\n.rst-content .wy-alert-neutral.note,\n.rst-content .wy-alert-neutral.attention,\n.rst-content .wy-alert-neutral.caution,\n.rst-content .wy-alert-neutral.danger,\n.rst-content .wy-alert-neutral.error,\n.rst-content .wy-alert-neutral.hint,\n.rst-content .wy-alert-neutral.important,\n.rst-content .wy-alert-neutral.tip,\n.rst-content .wy-alert-neutral.warning,\n.rst-content .wy-alert-neutral.seealso,\n.rst-content .wy-alert-neutral.admonition-todo,\n.rst-content .wy-alert-neutral.admonition {\n background: #f3f6f6;\n}\n.wy-alert.wy-alert-neutral .wy-alert-title,\n.rst-content .wy-alert-neutral.note .wy-alert-title,\n.rst-content .wy-alert-neutral.attention .wy-alert-title,\n.rst-content .wy-alert-neutral.caution .wy-alert-title,\n.rst-content .wy-alert-neutral.danger .wy-alert-title,\n.rst-content .wy-alert-neutral.error .wy-alert-title,\n.rst-content .wy-alert-neutral.hint .wy-alert-title,\n.rst-content .wy-alert-neutral.important .wy-alert-title,\n.rst-content .wy-alert-neutral.tip .wy-alert-title,\n.rst-content .wy-alert-neutral.warning .wy-alert-title,\n.rst-content .wy-alert-neutral.seealso .wy-alert-title,\n.rst-content .wy-alert-neutral.admonition-todo .wy-alert-title,\n.rst-content .wy-alert-neutral.admonition .wy-alert-title,\n.wy-alert.wy-alert-neutral .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-neutral .admonition-title,\n.rst-content .wy-alert-neutral.note .admonition-title,\n.rst-content .wy-alert-neutral.attention .admonition-title,\n.rst-content .wy-alert-neutral.caution .admonition-title,\n.rst-content .wy-alert-neutral.danger .admonition-title,\n.rst-content .wy-alert-neutral.error .admonition-title,\n.rst-content .wy-alert-neutral.hint .admonition-title,\n.rst-content .wy-alert-neutral.important .admonition-title,\n.rst-content .wy-alert-neutral.tip .admonition-title,\n.rst-content .wy-alert-neutral.warning .admonition-title,\n.rst-content .wy-alert-neutral.seealso .admonition-title,\n.rst-content .wy-alert-neutral.admonition-todo .admonition-title,\n.rst-content .wy-alert-neutral.admonition .admonition-title {\n color: #404040;\n background: #e1e4e5;\n}\n.wy-alert.wy-alert-neutral a,\n.rst-content .wy-alert-neutral.note a,\n.rst-content .wy-alert-neutral.attention a,\n.rst-content .wy-alert-neutral.caution a,\n.rst-content .wy-alert-neutral.danger a,\n.rst-content .wy-alert-neutral.error a,\n.rst-content .wy-alert-neutral.hint a,\n.rst-content .wy-alert-neutral.important a,\n.rst-content .wy-alert-neutral.tip a,\n.rst-content .wy-alert-neutral.warning a,\n.rst-content .wy-alert-neutral.seealso a,\n.rst-content .wy-alert-neutral.admonition-todo a,\n.rst-content .wy-alert-neutral.admonition a {\n color: #2980b9;\n}\n.wy-alert p:last-child,\n.rst-content .note p:last-child,\n.rst-content .attention p:last-child,\n.rst-content .caution p:last-child,\n.rst-content .danger p:last-child,\n.rst-content .error p:last-child,\n.rst-content .hint p:last-child,\n.rst-content .important p:last-child,\n.rst-content .tip p:last-child,\n.rst-content .warning p:last-child,\n.rst-content .seealso p:last-child,\n.rst-content .admonition-todo p:last-child,\n.rst-content .admonition p:last-child {\n margin-bottom: 0;\n}\n.wy-tray-container {\n position: fixed;\n bottom: 0px;\n left: 0;\n z-index: 600;\n}\n.wy-tray-container li {\n display: block;\n width: 300px;\n background: transparent;\n color: #fff;\n text-align: center;\n box-shadow: 0 5px 5px 0 rgba(0, 0, 0, 0.1);\n padding: 0 24px;\n min-width: 20%;\n opacity: 0;\n height: 0;\n line-height: 56px;\n overflow: hidden;\n -webkit-transition: all 0.3s ease-in;\n -moz-transition: all 0.3s ease-in;\n transition: all 0.3s ease-in;\n}\n.wy-tray-container li.wy-tray-item-success {\n background: #27ae60;\n}\n.wy-tray-container li.wy-tray-item-info {\n background: #2980b9;\n}\n.wy-tray-container li.wy-tray-item-warning {\n background: #e67e22;\n}\n.wy-tray-container li.wy-tray-item-danger {\n background: #e74c3c;\n}\n.wy-tray-container li.on {\n opacity: 1;\n height: 56px;\n}\n@media screen and (max-width: 768px) {\n .wy-tray-container {\n bottom: auto;\n top: 0;\n width: 100%;\n }\n .wy-tray-container li {\n width: 100%;\n }\n}\nbutton {\n font-size: 100%;\n margin: 0;\n vertical-align: baseline;\n *vertical-align: middle;\n cursor: pointer;\n line-height: normal;\n -webkit-appearance: button;\n *overflow: visible;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\nbutton[disabled] {\n cursor: default;\n}\n.btn {\n display: inline-block;\n line-height: normal;\n white-space: nowrap;\n text-align: center;\n cursor: pointer;\n font-size: 16px;\n padding: 24px;\n color: #fff;\n background-color: #fafafa;\n text-decoration: none;\n font-weight: normal;\n font-family: \"Roboto-Regular\", sans-serif;\n outline-none: false;\n vertical-align: middle;\n *display: inline;\n zoom: 1;\n -webkit-user-drag: none;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n width: 50%;\n}\n.btn-hover {\n background: #2e8ece;\n color: #fff;\n}\n.btn:hover {\n background: #2cc36b;\n color: #fff;\n}\n.btn:focus {\n background: #2cc36b;\n outline: 0;\n}\n.btn:active {\n box-shadow: 0px -1px 0px 0px rgba(0, 0, 0, 0.05) inset,\n 0px 2px 0px 0px rgba(0, 0, 0, 0.1) inset;\n padding: 8px 12px 6px 12px;\n}\n.btn:visited {\n color: #fff;\n}\n.btn:disabled {\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n filter: alpha(opacity=40);\n opacity: 0.4;\n cursor: not-allowed;\n box-shadow: none;\n}\n.btn-disabled {\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n filter: alpha(opacity=40);\n opacity: 0.4;\n cursor: not-allowed;\n box-shadow: none;\n}\n.btn-disabled:hover,\n.btn-disabled:focus,\n.btn-disabled:active {\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n filter: alpha(opacity=40);\n opacity: 0.4;\n cursor: not-allowed;\n box-shadow: none;\n}\n.btn::-moz-focus-inner {\n padding: 0;\n border: 0;\n}\n.btn-small {\n font-size: 80%;\n}\n.btn-info {\n background-color: #2980b9 !important;\n}\n.btn-info:hover {\n background-color: #2e8ece !important;\n}\n\n.btn-neutral {\n background-color: #fafafa !important;\n color: #404040 !important;\n}\n\n.btn-neutral:hover {\n background-color: #fafafa !important;\n color: #404040;\n}\n\n.btn-neutral:visited {\n color: #404040 !important;\n}\n\n.btn-success {\n background-color: #27ae60 !important;\n}\n.btn-success:hover {\n background-color: #295 !important;\n}\n.btn-danger {\n background-color: #e74c3c !important;\n}\n.btn-danger:hover {\n background-color: #ea6153 !important;\n}\n.btn-warning {\n background-color: #e67e22 !important;\n}\n.btn-warning:hover {\n background-color: #e98b39 !important;\n}\n.btn-invert {\n background-color: #222;\n}\n.btn-invert:hover {\n background-color: #2f2f2f !important;\n}\n.btn-link {\n background-color: transparent !important;\n color: #2980b9;\n box-shadow: none;\n border-color: transparent !important;\n}\n.btn-link:hover {\n background-color: transparent !important;\n color: #409ad5 !important;\n box-shadow: none;\n}\n.btn-link:active {\n background-color: transparent !important;\n color: #409ad5 !important;\n box-shadow: none;\n}\n.btn-link:visited {\n color: #9b59b6;\n}\n.wy-btn-group .btn,\n.wy-control .btn {\n vertical-align: middle;\n}\n.wy-btn-group {\n margin-bottom: 24px;\n *zoom: 1;\n}\n.wy-btn-group:before,\n.wy-btn-group:after {\n display: table;\n content: \"\";\n}\n.wy-btn-group:after {\n clear: both;\n}\n.wy-dropdown {\n position: relative;\n display: inline-block;\n}\n.wy-dropdown-active .wy-dropdown-menu {\n display: block;\n}\n.wy-dropdown-menu {\n position: absolute;\n left: 0;\n display: none;\n float: left;\n top: 100%;\n min-width: 100%;\n background: #fcfcfc;\n z-index: 100;\n border: solid 1px #cfd7dd;\n box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.1);\n padding: 12px;\n}\n.wy-dropdown-menu > dd > a {\n display: block;\n clear: both;\n color: #404040;\n white-space: nowrap;\n font-size: 90%;\n padding: 0 12px;\n cursor: pointer;\n}\n.wy-dropdown-menu > dd > a:hover {\n background: #2980b9;\n color: #fff;\n}\n.wy-dropdown-menu > dd.divider {\n border-top: solid 1px #cfd7dd;\n margin: 6px 0;\n}\n.wy-dropdown-menu > dd.search {\n padding-bottom: 12px;\n}\n.wy-dropdown-menu > dd.search input[type=\"search\"] {\n width: 100%;\n}\n.wy-dropdown-menu > dd.call-to-action {\n background: #e3e3e3;\n text-transform: uppercase;\n font-weight: 500;\n font-size: 80%;\n}\n.wy-dropdown-menu > dd.call-to-action:hover {\n background: #e3e3e3;\n}\n.wy-dropdown-menu > dd.call-to-action .btn {\n color: #fff;\n}\n.wy-dropdown.wy-dropdown-up .wy-dropdown-menu {\n bottom: 100%;\n top: auto;\n left: auto;\n right: 0;\n}\n.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu {\n background: #fcfcfc;\n margin-top: 2px;\n}\n.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu a {\n padding: 6px 12px;\n}\n.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu a:hover {\n background: #2980b9;\n color: #fff;\n}\n.wy-dropdown.wy-dropdown-left .wy-dropdown-menu {\n right: 0;\n left: auto;\n text-align: right;\n}\n.wy-dropdown-arrow:before {\n content: \" \";\n border-bottom: 5px solid #f5f5f5;\n border-left: 5px solid transparent;\n border-right: 5px solid transparent;\n position: absolute;\n display: block;\n top: -4px;\n left: 50%;\n margin-left: -3px;\n}\n.wy-dropdown-arrow.wy-dropdown-arrow-left:before {\n left: 11px;\n}\n.wy-form-stacked select {\n display: block;\n}\n.wy-form-aligned input,\n.wy-form-aligned textarea,\n.wy-form-aligned select,\n.wy-form-aligned .wy-help-inline,\n.wy-form-aligned label {\n display: inline-block;\n *display: inline;\n *zoom: 1;\n vertical-align: middle;\n}\n.wy-form-aligned .wy-control-group > label {\n display: inline-block;\n vertical-align: middle;\n width: 10em;\n margin: 6px 12px 0 0;\n float: left;\n}\n.wy-form-aligned .wy-control {\n float: left;\n}\n.wy-form-aligned .wy-control label {\n display: block;\n}\n.wy-form-aligned .wy-control select {\n margin-top: 6px;\n}\nfieldset {\n border: 0;\n margin: 0;\n padding: 0;\n}\nlegend {\n display: block;\n width: 100%;\n border: 0;\n padding: 0;\n white-space: normal;\n margin-bottom: 24px;\n font-size: 150%;\n *margin-left: -7px;\n}\nlabel {\n display: block;\n margin: 0 0 0.3125em 0;\n color: #333;\n font-size: 90%;\n}\ninput,\nselect,\ntextarea {\n font-size: 100%;\n margin: 0;\n vertical-align: baseline;\n *vertical-align: middle;\n}\n.wy-control-group {\n margin-bottom: 24px;\n *zoom: 1;\n max-width: 68em;\n margin-left: auto;\n margin-right: auto;\n *zoom: 1;\n}\n.wy-control-group:before,\n.wy-control-group:after {\n display: table;\n content: \"\";\n}\n.wy-control-group:after {\n clear: both;\n}\n.wy-control-group:before,\n.wy-control-group:after {\n display: table;\n content: \"\";\n}\n.wy-control-group:after {\n clear: both;\n}\n.wy-control-group.wy-control-group-required > label:after {\n content: \" *\";\n color: #e74c3c;\n}\n.wy-control-group .wy-form-full,\n.wy-control-group .wy-form-halves,\n.wy-control-group .wy-form-thirds {\n padding-bottom: 12px;\n}\n.wy-control-group .wy-form-full select,\n.wy-control-group .wy-form-halves select,\n.wy-control-group .wy-form-thirds select {\n width: 100%;\n}\n.wy-control-group .wy-form-full input[type=\"text\"],\n.wy-control-group .wy-form-full input[type=\"password\"],\n.wy-control-group .wy-form-full input[type=\"email\"],\n.wy-control-group .wy-form-full input[type=\"url\"],\n.wy-control-group .wy-form-full input[type=\"date\"],\n.wy-control-group .wy-form-full input[type=\"month\"],\n.wy-control-group .wy-form-full input[type=\"time\"],\n.wy-control-group .wy-form-full input[type=\"datetime\"],\n.wy-control-group .wy-form-full input[type=\"datetime-local\"],\n.wy-control-group .wy-form-full input[type=\"week\"],\n.wy-control-group .wy-form-full input[type=\"number\"],\n.wy-control-group .wy-form-full input[type=\"search\"],\n.wy-control-group .wy-form-full input[type=\"tel\"],\n.wy-control-group .wy-form-full input[type=\"color\"],\n.wy-control-group .wy-form-halves input[type=\"text\"],\n.wy-control-group .wy-form-halves input[type=\"password\"],\n.wy-control-group .wy-form-halves input[type=\"email\"],\n.wy-control-group .wy-form-halves input[type=\"url\"],\n.wy-control-group .wy-form-halves input[type=\"date\"],\n.wy-control-group .wy-form-halves input[type=\"month\"],\n.wy-control-group .wy-form-halves input[type=\"time\"],\n.wy-control-group .wy-form-halves input[type=\"datetime\"],\n.wy-control-group .wy-form-halves input[type=\"datetime-local\"],\n.wy-control-group .wy-form-halves input[type=\"week\"],\n.wy-control-group .wy-form-halves input[type=\"number\"],\n.wy-control-group .wy-form-halves input[type=\"search\"],\n.wy-control-group .wy-form-halves input[type=\"tel\"],\n.wy-control-group .wy-form-halves input[type=\"color\"],\n.wy-control-group .wy-form-thirds input[type=\"text\"],\n.wy-control-group .wy-form-thirds input[type=\"password\"],\n.wy-control-group .wy-form-thirds input[type=\"email\"],\n.wy-control-group .wy-form-thirds input[type=\"url\"],\n.wy-control-group .wy-form-thirds input[type=\"date\"],\n.wy-control-group .wy-form-thirds input[type=\"month\"],\n.wy-control-group .wy-form-thirds input[type=\"time\"],\n.wy-control-group .wy-form-thirds input[type=\"datetime\"],\n.wy-control-group .wy-form-thirds input[type=\"datetime-local\"],\n.wy-control-group .wy-form-thirds input[type=\"week\"],\n.wy-control-group .wy-form-thirds input[type=\"number\"],\n.wy-control-group .wy-form-thirds input[type=\"search\"],\n.wy-control-group .wy-form-thirds input[type=\"tel\"],\n.wy-control-group .wy-form-thirds input[type=\"color\"] {\n width: 100%;\n}\n.wy-control-group .wy-form-full {\n float: left;\n display: block;\n margin-right: 2.3576515979%;\n width: 100%;\n margin-right: 0;\n}\n.wy-control-group .wy-form-full:last-child {\n margin-right: 0;\n}\n.wy-control-group .wy-form-halves {\n float: left;\n display: block;\n margin-right: 2.3576515979%;\n width: 48.821174201%;\n}\n.wy-control-group .wy-form-halves:last-child {\n margin-right: 0;\n}\n.wy-control-group .wy-form-halves:nth-of-type(2n) {\n margin-right: 0;\n}\n.wy-control-group .wy-form-halves:nth-of-type(2n + 1) {\n clear: left;\n}\n.wy-control-group .wy-form-thirds {\n float: left;\n display: block;\n margin-right: 2.3576515979%;\n width: 31.7615656014%;\n}\n.wy-control-group .wy-form-thirds:last-child {\n margin-right: 0;\n}\n.wy-control-group .wy-form-thirds:nth-of-type(3n) {\n margin-right: 0;\n}\n.wy-control-group .wy-form-thirds:nth-of-type(3n + 1) {\n clear: left;\n}\n.wy-control-group.wy-control-group-no-input .wy-control {\n margin: 6px 0 0 0;\n font-size: 90%;\n}\n.wy-control-no-input {\n display: inline-block;\n margin: 6px 0 0 0;\n font-size: 90%;\n}\n.wy-control-group.fluid-input input[type=\"text\"],\n.wy-control-group.fluid-input input[type=\"password\"],\n.wy-control-group.fluid-input input[type=\"email\"],\n.wy-control-group.fluid-input input[type=\"url\"],\n.wy-control-group.fluid-input input[type=\"date\"],\n.wy-control-group.fluid-input input[type=\"month\"],\n.wy-control-group.fluid-input input[type=\"time\"],\n.wy-control-group.fluid-input input[type=\"datetime\"],\n.wy-control-group.fluid-input input[type=\"datetime-local\"],\n.wy-control-group.fluid-input input[type=\"week\"],\n.wy-control-group.fluid-input input[type=\"number\"],\n.wy-control-group.fluid-input input[type=\"search\"],\n.wy-control-group.fluid-input input[type=\"tel\"],\n.wy-control-group.fluid-input input[type=\"color\"] {\n width: 100%;\n}\n.wy-form-message-inline {\n display: inline-block;\n padding-left: 0.3em;\n color: #666;\n vertical-align: middle;\n font-size: 90%;\n}\n.wy-form-message {\n display: block;\n color: #999;\n font-size: 70%;\n margin-top: 0.3125em;\n font-style: italic;\n}\n.wy-form-message p {\n font-size: inherit;\n font-style: italic;\n margin-bottom: 6px;\n}\n.wy-form-message p:last-child {\n margin-bottom: 0;\n}\ninput {\n line-height: normal;\n}\ninput[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button;\n cursor: pointer;\n font-family: \"Roboto-Regular\", sans-serif;\n *overflow: visible;\n}\ninput[type=\"text\"],\ninput[type=\"password\"],\ninput[type=\"email\"],\ninput[type=\"url\"],\ninput[type=\"date\"],\ninput[type=\"month\"],\ninput[type=\"time\"],\ninput[type=\"datetime\"],\ninput[type=\"datetime-local\"],\ninput[type=\"week\"],\ninput[type=\"number\"],\ninput[type=\"search\"],\ninput[type=\"tel\"],\ninput[type=\"color\"] {\n -webkit-appearance: none;\n padding: 6px;\n display: inline-block;\n border: 1px solid #ccc;\n font-size: 80%;\n font-family: \"Roboto-Regular\", sans-serif;\n box-shadow: inset 0 1px 3px #ddd;\n border-radius: 0;\n -webkit-transition: border 0.3s linear;\n -moz-transition: border 0.3s linear;\n transition: border 0.3s linear;\n}\ninput[type=\"datetime-local\"] {\n padding: 0.34375em 0.625em;\n}\ninput[disabled] {\n cursor: default;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n padding: 0;\n margin-right: 0.3125em;\n *height: 13px;\n *width: 13px;\n}\ninput[type=\"search\"] {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\ninput[type=\"text\"]:focus,\ninput[type=\"password\"]:focus,\ninput[type=\"email\"]:focus,\ninput[type=\"url\"]:focus,\ninput[type=\"date\"]:focus,\ninput[type=\"month\"]:focus,\ninput[type=\"time\"]:focus,\ninput[type=\"datetime\"]:focus,\ninput[type=\"datetime-local\"]:focus,\ninput[type=\"week\"]:focus,\ninput[type=\"number\"]:focus,\ninput[type=\"search\"]:focus,\ninput[type=\"tel\"]:focus,\ninput[type=\"color\"]:focus {\n outline: 0;\n outline: thin dotted \\9;\n border-color: #333;\n}\ninput.no-focus:focus {\n border-color: #ccc !important;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n outline: thin dotted #333;\n outline: 1px auto #129fea;\n}\ninput[type=\"text\"][disabled],\ninput[type=\"password\"][disabled],\ninput[type=\"email\"][disabled],\ninput[type=\"url\"][disabled],\ninput[type=\"date\"][disabled],\ninput[type=\"month\"][disabled],\ninput[type=\"time\"][disabled],\ninput[type=\"datetime\"][disabled],\ninput[type=\"datetime-local\"][disabled],\ninput[type=\"week\"][disabled],\ninput[type=\"number\"][disabled],\ninput[type=\"search\"][disabled],\ninput[type=\"tel\"][disabled],\ninput[type=\"color\"][disabled] {\n cursor: not-allowed;\n background-color: #fafafa;\n}\ninput:focus:invalid,\ntextarea:focus:invalid,\nselect:focus:invalid {\n color: #e74c3c;\n border: 1px solid #e74c3c;\n}\ninput:focus:invalid:focus,\ntextarea:focus:invalid:focus,\nselect:focus:invalid:focus {\n border-color: #e74c3c;\n}\ninput[type=\"file\"]:focus:invalid:focus,\ninput[type=\"radio\"]:focus:invalid:focus,\ninput[type=\"checkbox\"]:focus:invalid:focus {\n outline-color: #e74c3c;\n}\ninput.wy-input-large {\n padding: 12px;\n font-size: 100%;\n}\ntextarea {\n overflow: auto;\n vertical-align: top;\n width: 100%;\n font-family: \"Roboto-Regular\", sans-serif;\n}\nselect,\ntextarea {\n padding: 0.5em 0.625em;\n display: inline-block;\n border: 1px solid #ccc;\n font-size: 80%;\n box-shadow: inset 0 1px 3px #ddd;\n -webkit-transition: border 0.3s linear;\n -moz-transition: border 0.3s linear;\n transition: border 0.3s linear;\n}\nselect {\n border: 1px solid #ccc;\n background-color: #fff;\n}\nselect[multiple] {\n height: auto;\n}\nselect:focus,\ntextarea:focus {\n outline: 0;\n}\nselect[disabled],\ntextarea[disabled],\ninput[readonly],\nselect[readonly],\ntextarea[readonly] {\n cursor: not-allowed;\n background-color: #fafafa;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled] {\n cursor: not-allowed;\n}\n.wy-checkbox,\n.wy-radio {\n margin: 6px 0;\n color: #404040;\n display: block;\n}\n.wy-checkbox input,\n.wy-radio input {\n vertical-align: baseline;\n}\n.wy-form-message-inline {\n display: inline-block;\n *display: inline;\n *zoom: 1;\n vertical-align: middle;\n}\n.wy-input-prefix,\n.wy-input-suffix {\n white-space: nowrap;\n padding: 6px;\n}\n.wy-input-prefix .wy-input-context,\n.wy-input-suffix .wy-input-context {\n line-height: 27px;\n padding: 0 8px;\n display: inline-block;\n font-size: 80%;\n\n background: #fafafa;\n border: solid 1px #ccc;\n color: #999;\n}\n.wy-input-suffix .wy-input-context {\n border-left: 0;\n}\n.wy-input-prefix .wy-input-context {\n border-right: 0;\n}\n.wy-switch {\n position: relative;\n display: block;\n height: 24px;\n margin-top: 12px;\n cursor: pointer;\n}\n.wy-switch:before {\n position: absolute;\n content: \"\";\n display: block;\n left: 0;\n top: 0;\n width: 36px;\n height: 12px;\n border-radius: 4px;\n background: #ccc;\n -webkit-transition: all 0.2s ease-in-out;\n -moz-transition: all 0.2s ease-in-out;\n transition: all 0.2s ease-in-out;\n}\n.wy-switch:after {\n position: absolute;\n content: \"\";\n display: block;\n width: 18px;\n height: 18px;\n border-radius: 4px;\n background: #999;\n left: -3px;\n top: -3px;\n -webkit-transition: all 0.2s ease-in-out;\n -moz-transition: all 0.2s ease-in-out;\n transition: all 0.2s ease-in-out;\n}\n.wy-switch span {\n position: absolute;\n left: 48px;\n display: block;\n font-size: 12px;\n color: #ccc;\n line-height: 1;\n}\n.wy-switch.active:before {\n background: #1e8449;\n}\n.wy-switch.active:after {\n left: 24px;\n background: #27ae60;\n}\n.wy-switch.disabled {\n cursor: not-allowed;\n opacity: 0.8;\n}\n.wy-control-group.wy-control-group-error .wy-form-message,\n.wy-control-group.wy-control-group-error > label {\n color: #e74c3c;\n}\n.wy-control-group.wy-control-group-error input[type=\"text\"],\n.wy-control-group.wy-control-group-error input[type=\"password\"],\n.wy-control-group.wy-control-group-error input[type=\"email\"],\n.wy-control-group.wy-control-group-error input[type=\"url\"],\n.wy-control-group.wy-control-group-error input[type=\"date\"],\n.wy-control-group.wy-control-group-error input[type=\"month\"],\n.wy-control-group.wy-control-group-error input[type=\"time\"],\n.wy-control-group.wy-control-group-error input[type=\"datetime\"],\n.wy-control-group.wy-control-group-error input[type=\"datetime-local\"],\n.wy-control-group.wy-control-group-error input[type=\"week\"],\n.wy-control-group.wy-control-group-error input[type=\"number\"],\n.wy-control-group.wy-control-group-error input[type=\"search\"],\n.wy-control-group.wy-control-group-error input[type=\"tel\"],\n.wy-control-group.wy-control-group-error input[type=\"color\"] {\n border: solid 1px #e74c3c;\n}\n.wy-control-group.wy-control-group-error textarea {\n border: solid 1px #e74c3c;\n}\n.wy-inline-validate {\n white-space: nowrap;\n}\n.wy-inline-validate .wy-input-context {\n padding: 0.5em 0.625em;\n display: inline-block;\n font-size: 80%;\n}\n.wy-inline-validate.wy-inline-validate-success .wy-input-context {\n color: #27ae60;\n}\n.wy-inline-validate.wy-inline-validate-danger .wy-input-context {\n color: #e74c3c;\n}\n.wy-inline-validate.wy-inline-validate-warning .wy-input-context {\n color: #e67e22;\n}\n.wy-inline-validate.wy-inline-validate-info .wy-input-context {\n color: #2980b9;\n}\n.rotate-90 {\n -webkit-transform: rotate(90deg);\n -moz-transform: rotate(90deg);\n -ms-transform: rotate(90deg);\n -o-transform: rotate(90deg);\n transform: rotate(90deg);\n}\n.rotate-180 {\n -webkit-transform: rotate(180deg);\n -moz-transform: rotate(180deg);\n -ms-transform: rotate(180deg);\n -o-transform: rotate(180deg);\n transform: rotate(180deg);\n}\n.rotate-270 {\n -webkit-transform: rotate(270deg);\n -moz-transform: rotate(270deg);\n -ms-transform: rotate(270deg);\n -o-transform: rotate(270deg);\n transform: rotate(270deg);\n}\n.mirror {\n -webkit-transform: scaleX(-1);\n -moz-transform: scaleX(-1);\n -ms-transform: scaleX(-1);\n -o-transform: scaleX(-1);\n transform: scaleX(-1);\n}\n.mirror.rotate-90 {\n -webkit-transform: scaleX(-1) rotate(90deg);\n -moz-transform: scaleX(-1) rotate(90deg);\n -ms-transform: scaleX(-1) rotate(90deg);\n -o-transform: scaleX(-1) rotate(90deg);\n transform: scaleX(-1) rotate(90deg);\n}\n.mirror.rotate-180 {\n -webkit-transform: scaleX(-1) rotate(180deg);\n -moz-transform: scaleX(-1) rotate(180deg);\n -ms-transform: scaleX(-1) rotate(180deg);\n -o-transform: scaleX(-1) rotate(180deg);\n transform: scaleX(-1) rotate(180deg);\n}\n.mirror.rotate-270 {\n -webkit-transform: scaleX(-1) rotate(270deg);\n -moz-transform: scaleX(-1) rotate(270deg);\n -ms-transform: scaleX(-1) rotate(270deg);\n -o-transform: scaleX(-1) rotate(270deg);\n transform: scaleX(-1) rotate(270deg);\n}\n@media only screen and (max-width: 480px) {\n .wy-form button[type=\"submit\"] {\n margin: 0.7em 0 0;\n }\n .wy-form input[type=\"text\"],\n .wy-form input[type=\"password\"],\n .wy-form input[type=\"email\"],\n .wy-form input[type=\"url\"],\n .wy-form input[type=\"date\"],\n .wy-form input[type=\"month\"],\n .wy-form input[type=\"time\"],\n .wy-form input[type=\"datetime\"],\n .wy-form input[type=\"datetime-local\"],\n .wy-form input[type=\"week\"],\n .wy-form input[type=\"number\"],\n .wy-form input[type=\"search\"],\n .wy-form input[type=\"tel\"],\n .wy-form input[type=\"color\"] {\n margin-bottom: 0.3em;\n display: block;\n }\n .wy-form label {\n margin-bottom: 0.3em;\n display: block;\n }\n .wy-form input[type=\"password\"],\n .wy-form input[type=\"email\"],\n .wy-form input[type=\"url\"],\n .wy-form input[type=\"date\"],\n .wy-form input[type=\"month\"],\n .wy-form input[type=\"time\"],\n .wy-form input[type=\"datetime\"],\n .wy-form input[type=\"datetime-local\"],\n .wy-form input[type=\"week\"],\n .wy-form input[type=\"number\"],\n .wy-form input[type=\"search\"],\n .wy-form input[type=\"tel\"],\n .wy-form input[type=\"color\"] {\n margin-bottom: 0;\n }\n .wy-form-aligned .wy-control-group label {\n margin-bottom: 0.3em;\n text-align: left;\n display: block;\n width: 100%;\n }\n .wy-form-aligned .wy-control {\n margin: 1.5em 0 0 0;\n }\n .wy-form .wy-help-inline,\n .wy-form-message-inline,\n .wy-form-message {\n display: block;\n font-size: 80%;\n padding: 6px 0;\n }\n}\n@media screen and (max-width: 768px) {\n .tablet-hide {\n display: none;\n }\n}\n@media screen and (max-width: 480px) {\n .mobile-hide {\n display: none;\n }\n}\n.float-left {\n float: left;\n}\n.float-right {\n float: right;\n}\n.full-width {\n width: 100%;\n}\n.wy-table,\n.rst-content table.docutils,\n.rst-content table.field-list {\n border-collapse: collapse;\n border-spacing: 0;\n empty-cells: show;\n margin-bottom: 24px;\n}\n.wy-table caption,\n.rst-content table.docutils caption,\n.rst-content table.field-list caption {\n color: #000;\n font: italic 85%/1 arial, sans-serif;\n padding: 1em 0;\n text-align: center;\n}\n.wy-table td,\n.rst-content table.docutils td,\n.rst-content table.field-list td,\n.wy-table th,\n.rst-content table.docutils th,\n.rst-content table.field-list th {\n font-size: 14px;\n margin: 0;\n overflow: visible;\n /* padding: 8px 16px; */\n}\n\n.wy-table-responsive .wy-table td:first-child,\n.rst-content table.docutils td:first-child,\n.rst-content table.field-list td:first-child,\n.wy-table th:first-child,\n.rst-content table.docutils th:first-child,\n.rst-content table.field-list th:first-child {\n border: none;\n}\n.wy-table thead,\n.rst-content table.docutils thead,\n.rst-content table.field-list thead {\n color: #000;\n text-align: left;\n vertical-align: bottom;\n white-space: nowrap;\n}\n.wy-table thead th,\n.rst-content table.docutils thead th,\n.rst-content table.field-list thead th {\n font-weight: bold;\n border-bottom: solid 2px #e1e4e5;\n}\n.wy-table td,\n.rst-content table.docutils td,\n.rst-content table.field-list td {\n background-color: transparent;\n vertical-align: middle;\n}\n.wy-table td p,\n.rst-content table.docutils td p,\n.rst-content table.field-list td p {\n line-height: 18px;\n}\n.wy-table td p:last-child,\n.rst-content table.docutils td p:last-child,\n.rst-content table.field-list td p:last-child {\n margin-bottom: 0;\n}\n.wy-table .wy-table-cell-min,\n.rst-content table.docutils .wy-table-cell-min,\n.rst-content table.field-list .wy-table-cell-min {\n width: 1%;\n padding-right: 0;\n}\n.wy-table .wy-table-cell-min input[type=\"checkbox\"],\n.rst-content table.docutils .wy-table-cell-min input[type=\"checkbox\"],\n.rst-content table.field-list .wy-table-cell-min input[type=\"checkbox\"],\n.wy-table .wy-table-cell-min input[type=\"checkbox\"],\n.rst-content table.docutils .wy-table-cell-min input[type=\"checkbox\"],\n.rst-content table.field-list .wy-table-cell-min input[type=\"checkbox\"] {\n margin: 0;\n}\n.wy-table-secondary {\n color: gray;\n font-size: 90%;\n}\n.wy-table-tertiary {\n color: gray;\n font-size: 80%;\n}\n.wy-table-odd td,\n.wy-table-striped tr:nth-child(2n-1) td,\n.rst-content table.docutils:not(.field-list) tr:nth-child(2n-1) td {\n border: none;\n}\n.wy-table-backed {\n background-color: #f3f6f6;\n}\n\ntr.row-odd {\n background: #fafafa;\n /* border: 1px solid #e6e6e6; */\n border-radius: 8px;\n}\n\n.wy-table-bordered-all,\n.rst-content table.docutils {\n border: none;\n}\n.wy-table-bordered-all td,\n.rst-content table.docutils td {\n padding: 24px 16px;\n border: none;\n}\n.wy-table-bordered-all tbody > tr:last-child td,\n.rst-content table.docutils tbody > tr:last-child td {\n border-bottom-width: 0;\n}\n.wy-table-bordered {\n border: 1px solid #e1e4e5;\n}\n.wy-table-bordered-rows td {\n border-bottom: 1px solid #e1e4e5;\n}\n.wy-table-bordered-rows tbody > tr:last-child td {\n border-bottom-width: 0;\n}\n.wy-table-horizontal tbody > tr:last-child td {\n border-bottom-width: 0;\n}\n.wy-table-horizontal td,\n.wy-table-horizontal th {\n border-width: 0 0 1px 0;\n border-bottom: 1px solid #e1e4e5;\n}\n.wy-table-horizontal tbody > tr:last-child td {\n border-bottom-width: 0;\n}\n.wy-table-responsive {\n margin-bottom: 24px;\n max-width: 100%;\n overflow: auto;\n}\n.wy-table-responsive table {\n margin-bottom: 0 !important;\n}\n.wy-table-responsive table td,\n.wy-table-responsive table th {\n white-space: nowrap;\n}\na {\n color: #0f6dee;\n text-decoration: none;\n cursor: pointer;\n}\na:hover {\n color: #0f6dee;\n}\nhtml {\n height: 100%;\n overflow-x: hidden;\n}\nbody {\n font-family: \"Roboto-Regular\", sans-serif;\n font-weight: normal;\n color: #404040;\n min-height: 100%;\n background: #edf0f2;\n}\n.wy-text-left {\n text-align: left;\n}\n.wy-text-center {\n text-align: center;\n}\n.wy-text-right {\n text-align: right;\n}\n.wy-text-large {\n font-size: 120%;\n}\n.wy-text-normal {\n font-size: 100%;\n}\n.wy-text-small,\nsmall {\n font-size: 80%;\n}\n.wy-text-strike {\n text-decoration: line-through;\n}\n.wy-text-warning {\n color: #e67e22 !important;\n}\na.wy-text-warning:hover {\n color: #eb9950 !important;\n}\n.wy-text-info {\n color: #2980b9 !important;\n}\na.wy-text-info:hover {\n color: #409ad5 !important;\n}\n.wy-text-success {\n color: #27ae60 !important;\n}\na.wy-text-success:hover {\n color: #36d278 !important;\n}\n.wy-text-danger {\n color: #e74c3c !important;\n}\na.wy-text-danger:hover {\n color: #ed7669 !important;\n}\n.wy-text-neutral {\n color: #404040 !important;\n}\na.wy-text-neutral:hover {\n color: #595959 !important;\n}\nh1,\nh2,\n.rst-content .toctree-wrapper p.caption,\nh3,\nh4,\nh5,\nh6,\nlegend {\n margin-top: 0;\n font-weight: normal;\n font-family: \"Roboto-Regular\", sans-serif;\n}\np {\n line-height: 20px;\n margin: 0;\n font-size: 14px;\n margin-bottom: 24px;\n}\nh1 {\n font-family: \"Roboto Mono\", monospace;\n font-size: 34px;\n line-height: 54px;\n margin-bottom: 16px;\n font-weight: 700;\n}\n\nh2 {\n font-family: \"Roboto Mono\", monospace;\n font-size: 24px;\n line-height: 36px;\n font-weight: 700;\n}\n\n.rst-content .toctree-wrapper p.caption {\n font-family: Roboto-Medium;\n font-weight: 400;\n font-size: 20px;\n color: rgba(0, 0, 0, 0.87);\n line-height: 32px;\n margin-bottom: 8px;\n}\nh3 {\n font-size: 125%;\n}\nh4 {\n font-family: \"Roboto Mono\", monospace;\n font-size: 16px;\n line-height: 24px;\n color: rgba(0, 0, 0, 0.8);\n font-weight: 700;\n}\nh5 {\n font-size: 110%;\n}\nh6 {\n font-size: 100%;\n}\nhr {\n margin-left: 0;\n margin-right: 0;\n margin-top: 3rem;\n margin-bottom: 3rem;\n padding-bottom: 0;\n padding-left: 0;\n padding-right: 0;\n padding-top: 0;\n background: hsla(0, 0%, 0%, 0.2);\n border: none;\n height: 1px;\n}\ncode,\n.rst-content tt,\n.rst-content code {\n white-space: nowrap;\n max-width: 100%;\n background: #fafafa;\n border: 1px solid #e6e6e6;\n border-radius: 8px;\n /* font-size: 75%; */\n padding: 0 5px;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n color: #e74c3c;\n overflow-x: auto;\n}\ncode.code-large,\n.rst-content tt.code-large {\n font-size: 90%;\n}\n.wy-plain-list-disc,\n.rst-content .section ul,\n.rst-content .toctree-wrapper ul,\narticle ul {\n list-style: disc;\n font-size: 14px;\n line-height: 24px;\n margin-bottom: 24px;\n}\n.wy-plain-list-disc li,\n.rst-content .section ul li,\n.rst-content .toctree-wrapper ul li,\narticle ul li {\n /* list-style: disc;\n margin-left: 24px; */\n}\n.wy-plain-list-disc li p:last-child,\n.rst-content .section ul li p:last-child,\n.rst-content .toctree-wrapper ul li p:last-child,\narticle ul li p:last-child {\n margin-bottom: 0;\n}\n.wy-plain-list-disc li ul,\n.rst-content .section ul li ul,\n.rst-content .toctree-wrapper ul li ul,\narticle ul li ul {\n margin-bottom: 0;\n margin-left: 24px;\n}\n.wy-plain-list-disc li li,\n.rst-content .section ul li li,\n.rst-content .toctree-wrapper ul li li,\narticle ul li li {\n list-style: none;\n}\n.wy-plain-list-disc li li li,\n.rst-content .section ul li li li,\n.rst-content .toctree-wrapper ul li li li,\narticle ul li li li {\n list-style: none;\n}\n.wy-plain-list-disc li ol li,\n.rst-content .section ul li ol li,\n.rst-content .toctree-wrapper ul li ol li,\narticle ul li ol li {\n list-style: decimal;\n}\n.wy-plain-list-decimal,\n.rst-content .section ol,\n.rst-content ol.arabic,\narticle ol {\n list-style: decimal;\n line-height: 24px;\n margin-bottom: 24px;\n}\n.wy-plain-list-decimal li,\n.rst-content .section ol li,\n.rst-content ol.arabic li,\narticle ol li {\n list-style: decimal;\n margin-left: 24px;\n}\n.wy-plain-list-decimal li p:last-child,\n.rst-content .section ol li p:last-child,\n.rst-content ol.arabic li p:last-child,\narticle ol li p:last-child {\n margin-bottom: 0;\n}\n.wy-plain-list-decimal li ul,\n.rst-content .section ol li ul,\n.rst-content ol.arabic li ul,\narticle ol li ul {\n margin-bottom: 0;\n}\n.wy-plain-list-decimal li ul li,\n.rst-content .section ol li ul li,\n.rst-content ol.arabic li ul li,\narticle ol li ul li {\n list-style: disc;\n}\n.wy-breadcrumbs {\n *zoom: 1;\n font-size: 12px;\n text-transform: uppercase;\n color: rgba(0, 0, 0, 0.54);\n line-height: 16px;\n margin-bottom: 4px;\n font-weight: 700;\n}\n.wy-breadcrumbs:before,\n.wy-breadcrumbs:after {\n display: table;\n content: \"\";\n}\n.wy-breadcrumbs:after {\n clear: both;\n}\n.wy-breadcrumbs li {\n display: inline-block;\n}\n.wy-breadcrumbs li.wy-breadcrumbs-aside {\n float: right;\n}\n.wy-breadcrumbs li a {\n display: inline-block;\n color: rgba(0, 0, 0, 0.54);\n}\n.wy-breadcrumbs li a:hover {\n display: inline-block;\n color: rgba(15, 109, 238, 0.87);\n text-decoration: underline;\n}\n.wy-breadcrumbs li a:first-child {\n padding-left: 0;\n}\n.wy-breadcrumbs li code,\n.wy-breadcrumbs li .rst-content tt,\n.rst-content .wy-breadcrumbs li tt {\n /* padding: 5px; */\n border: none;\n background: none;\n}\n.wy-breadcrumbs li code.literal,\n.wy-breadcrumbs li .rst-content tt.literal,\n.rst-content .wy-breadcrumbs li tt.literal {\n color: #404040;\n}\n.wy-breadcrumbs-extra {\n margin-bottom: 0;\n color: #b3b3b3;\n font-size: 80%;\n display: inline-block;\n}\n@media screen and (max-width: 480px) {\n .wy-breadcrumbs-extra {\n display: none;\n }\n .wy-breadcrumbs li.wy-breadcrumbs-aside {\n display: none;\n }\n}\n@media print {\n .wy-breadcrumbs li.wy-breadcrumbs-aside {\n display: none;\n }\n}\nhtml {\n font: 112.5%/1.45em \"Roboto-Regular\";\n -webkit-font-smoothing: antialiased;\n}\n.wy-affix {\n position: fixed;\n top: 1.618em;\n}\n.wy-menu a:hover {\n text-decoration: none;\n}\n.wy-menu-horiz {\n *zoom: 1;\n}\n.wy-menu-horiz:before,\n.wy-menu-horiz:after {\n display: table;\n content: \"\";\n}\n.wy-menu-horiz:after {\n clear: both;\n}\n.wy-menu-horiz ul,\n.wy-menu-horiz li {\n display: inline-block;\n}\n.wy-menu-horiz li:hover {\n background: rgba(255, 255, 255, 0.1);\n}\n.wy-menu-horiz li.divide-left {\n border-left: solid 1px #404040;\n}\n.wy-menu-horiz li.divide-right {\n border-right: solid 1px #404040;\n}\n.wy-menu-horiz a {\n height: 32px;\n display: inline-block;\n line-height: 32px;\n padding: 0 16px;\n}\n.wy-menu-vertical {\n width: 100%;\n}\n.wy-menu-vertical header,\n.wy-menu-vertical p.caption {\n font-family: \"Roboto-Regular\", sans-serif;\n font-size: 12px;\n line-height: 16px;\n color: rgba(0, 0, 0, 0.54);\n text-transform: uppercase;\n margin-bottom: 4px;\n color: rgba(0, 0, 0, 0.54);\n height: 32px;\n display: inline-block;\n line-height: 32px;\n padding: 0 32px;\n margin: 48px 0 0 0;\n white-space: nowrap;\n font-weight: bold;\n}\n.wy-menu-vertical ul {\n margin-bottom: 48px;\n}\n.wy-menu-vertical li.divide-top {\n border-top: solid 1px #404040;\n}\n.wy-menu-vertical li.divide-bottom {\n border-bottom: solid 1px #404040;\n}\n.wy-menu-vertical li.current {\n /* background: #e3e3e3; */\n}\n.wy-menu-vertical li.current a {\n /* color: gray; */\n /* border-right: solid 1px #c9c9c9; */\n padding: 0.4045em 2.427em;\n padding-left: 48px;\n}\n.wy-menu-vertical li.current a:hover {\n /* background: #d6d6d6; */\n}\n.wy-menu-vertical li code,\n.wy-menu-vertical li .rst-content tt,\n.rst-content .wy-menu-vertical li tt {\n border: none;\n background: inherit;\n color: inherit;\n padding-left: 0;\n padding-right: 0;\n}\n.wy-menu-vertical li span.toctree-expand {\n display: block;\n float: left;\n margin-left: -1.2em;\n font-size: 0.8em;\n line-height: 1.6em;\n color: #4d4d4d;\n}\n.wy-menu-vertical li.on a,\n.wy-menu-vertical li.current > a {\n padding: 0.4045em 32px;\n font-weight: bold;\n position: relative;\n /* background: #fcfcfc; */\n border: none;\n padding-left: 1.618em -4px;\n}\n.wy-menu-vertical li.on a:hover,\n.wy-menu-vertical li.current > a:hover {\n /* background: #fcfcfc; */\n}\n.wy-menu-vertical li.on a:hover span.toctree-expand,\n.wy-menu-vertical li.current > a:hover span.toctree-expand {\n /* color: gray; */\n}\n.wy-menu-vertical li.on a span.toctree-expand,\n.wy-menu-vertical li.current > a span.toctree-expand {\n display: block;\n font-size: 0.8em;\n line-height: 1.6em;\n /* color: #333; */\n}\n.wy-menu-vertical li.toctree-l1.current > a {\n /* border-bottom: solid 1px #c9c9c9; */\n /* border-top: solid 1px #c9c9c9; */\n}\n.wy-menu-vertical li.toctree-l2 a,\n.wy-menu-vertical li.toctree-l3 a,\n.wy-menu-vertical li.toctree-l4 a {\n color: rgba(15, 109, 238, 0.87);\n}\n.wy-menu-vertical li.toctree-l1.current li.toctree-l2 > ul,\n.wy-menu-vertical li.toctree-l2.current li.toctree-l3 > ul {\n display: none;\n}\n.wy-menu-vertical li.toctree-l1.current li.toctree-l2.current > ul,\n.wy-menu-vertical li.toctree-l2.current li.toctree-l3.current > ul {\n display: block;\n}\n.wy-menu-vertical li.toctree-l2.current > a {\n /* background: #c9c9c9; */\n padding: 0.4045em 2.427em;\n padding-left: 48px;\n}\n.wy-menu-vertical li.toctree-l2.current li.toctree-l3 > a {\n display: block;\n /* background: #c9c9c9; */\n padding: 0.4045em 4.045em;\n}\n.wy-menu-vertical li.toctree-l2 a:hover span.toctree-expand {\n /* color: gray; */\n}\n.wy-menu-vertical li.toctree-l2 span.toctree-expand {\n /* color: #a3a3a3; */\n}\n.wy-menu-vertical li.toctree-l3 {\n font-size: 0.9em;\n}\n.wy-menu-vertical li.toctree-l3.current > a {\n /* background: #bdbdbd; */\n padding: 0.4045em 32px;\n}\n.wy-menu-vertical li.toctree-l3.current li.toctree-l4 > a {\n display: block;\n background: #bdbdbd;\n padding: 0.4045em 5.663em;\n}\n.wy-menu-vertical li.toctree-l3 a:hover span.toctree-expand {\n color: gray;\n}\n.wy-menu-vertical li.toctree-l3 span.toctree-expand {\n color: #969696;\n}\n.wy-menu-vertical li.toctree-l4 {\n font-size: 0.9em;\n}\n.wy-menu-vertical li.current ul {\n display: block;\n}\n.wy-menu-vertical li ul {\n margin-bottom: 0;\n display: none;\n}\n.wy-menu-vertical li ul li a {\n margin-bottom: 0;\n /* color: #d9d9d9; */\n font-weight: normal;\n}\n.wy-menu-vertical a {\n display: inline-block;\n line-height: 18px;\n padding: 0.4045em 32px;\n display: block;\n position: relative;\n font-size: 14px;\n color: rgba(15, 109, 238, 0.87);\n}\n.wy-menu-vertical a:hover {\n /* background-color: #4e4a4a; */\n cursor: pointer;\n}\n.wy-menu-vertical a:hover span.toctree-expand {\n /* color: #d9d9d9; */\n}\n.wy-menu-vertical a:active {\n /* background-color: #2980b9; */\n cursor: pointer;\n /* color: #fff; */\n}\n.wy-menu-vertical a:active span.toctree-expand {\n /* color: #fff; */\n}\n.wy-side-nav-search {\n display: none;\n width: 100%;\n padding: 0.809em;\n margin-bottom: 0.809em;\n /* z-index: 200; */\n background-color: #2980b9;\n text-align: center;\n padding: 0.809em;\n color: #fcfcfc;\n margin-bottom: 0.809em;\n}\n.wy-side-nav-search input[type=\"text\"] {\n width: 100%;\n border-radius: 50px;\n padding: 6px 12px;\n border-color: #2472a4;\n}\n.wy-side-nav-search img {\n display: block;\n margin: auto auto 0.809em auto;\n height: 45px;\n width: 45px;\n background-color: #2980b9;\n padding: 5px;\n border-radius: 100%;\n}\n.wy-side-nav-search > a,\n.wy-side-nav-search .wy-dropdown > a {\n color: #fcfcfc;\n font-size: 100%;\n font-weight: bold;\n display: inline-block;\n padding: 4px 6px;\n margin-bottom: 0.809em;\n}\n.wy-side-nav-search > a:hover,\n.wy-side-nav-search .wy-dropdown > a:hover {\n background: rgba(255, 255, 255, 0.1);\n}\n.wy-side-nav-search > a img.logo,\n.wy-side-nav-search .wy-dropdown > a img.logo {\n display: block;\n margin: 0 auto;\n height: auto;\n width: auto;\n border-radius: 0;\n max-width: 100%;\n background: transparent;\n}\n.wy-side-nav-search > a.icon img.logo,\n.wy-side-nav-search .wy-dropdown > a.icon img.logo {\n margin-top: 0.85em;\n}\n.wy-side-nav-search > div.version {\n margin-top: -0.4045em;\n margin-bottom: 0.809em;\n font-weight: normal;\n color: rgba(255, 255, 255, 0.3);\n}\n.wy-nav .wy-menu-vertical header {\n color: #2980b9;\n}\n.wy-nav .wy-menu-vertical a {\n color: #b3b3b3;\n}\n.wy-nav .wy-menu-vertical a:hover {\n background-color: #2980b9;\n color: #fff;\n}\n[data-menu-wrap] {\n -webkit-transition: all 0.2s ease-in;\n -moz-transition: all 0.2s ease-in;\n transition: all 0.2s ease-in;\n position: absolute;\n opacity: 1;\n width: 100%;\n opacity: 0;\n}\n[data-menu-wrap].move-center {\n left: 0;\n right: auto;\n opacity: 1;\n}\n[data-menu-wrap].move-left {\n right: auto;\n left: -100%;\n opacity: 0;\n}\n[data-menu-wrap].move-right {\n right: -100%;\n left: auto;\n opacity: 0;\n}\n.wy-body-for-nav {\n background: #fff;\n}\n.wy-grid-for-nav {\n display: flex;\n width: 100%;\n height: 100%;\n min-height: 100vh;\n}\n.wy-nav-side {\n /* padding-bottom: 2em; */\n width: 320px;\n /* overflow-x: hidden;\n overflow-y: hidden; */\n min-height: 100%;\n height: 100vh;\n color: #9b9b9b;\n border-right: 0.5px solid lightgrey;\n background-color: #fafafa;\n z-index: 200;\n visibility: visible;\n /* margin-top: 111px; */\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n\n position: sticky;\n top: 0;\n}\n.wy-side-scroll {\n width: 320px;\n color: #9b9b9b;\n border-right: 0.5px solid lightgrey;\n background-color: #fafafa;\n position: relative;\n overflow-x: hidden;\n overflow-y: scroll;\n height: 100%;\n}\n.wy-nav-top {\n display: none;\n background: #2980b9;\n color: #fff;\n padding: 0.4045em 0.809em;\n position: relative;\n line-height: 50px;\n text-align: center;\n font-size: 100%;\n *zoom: 1;\n}\n.wy-nav-top:before,\n.wy-nav-top:after {\n display: table;\n content: \"\";\n}\n.wy-nav-top:after {\n clear: both;\n}\n.wy-nav-top a {\n color: #fff;\n font-weight: bold;\n}\n.wy-nav-top img {\n margin-right: 12px;\n height: 45px;\n width: 45px;\n background-color: #2980b9;\n padding: 5px;\n border-radius: 100%;\n}\n.wy-nav-top i {\n font-size: 30px;\n float: left;\n cursor: pointer;\n padding-top: inherit;\n}\n.wy-nav-content-wrap {\n width: 100%;\n min-height: 100%;\n margin: 0px 32px;\n}\n.wy-nav-content {\n padding: 64px 0px;\n height: 100%;\n max-width: 1140px;\n margin: auto;\n}\n.wy-body-mask {\n position: fixed;\n width: 100%;\n height: 100%;\n background: rgba(0, 0, 0, 0.2);\n display: none;\n z-index: 499;\n}\n.wy-body-mask.on {\n display: block;\n}\nfooter {\n color: gray;\n}\nfooter p {\n margin-bottom: 12px;\n}\nfooter span.commit code,\nfooter span.commit .rst-content tt,\n.rst-content footer span.commit tt {\n padding: 0px;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n font-size: 1em;\n background: none;\n border: none;\n color: gray;\n}\n.rst-footer-buttons {\n display: flex;\n}\n.rst-footer-buttons:before,\n.rst-footer-buttons:after {\n width: 100%;\n}\n.rst-footer-buttons:before,\n.rst-footer-buttons:after {\n display: none;\n content: \"\";\n}\n.rst-footer-buttons:after {\n clear: both;\n}\n.rst-breadcrumbs-buttons {\n margin-top: 12px;\n *zoom: 1;\n}\n.rst-breadcrumbs-buttons:before,\n.rst-breadcrumbs-buttons:after {\n display: none;\n content: \"\";\n}\n.rst-breadcrumbs-buttons:after {\n clear: both;\n}\n#search-results .search li {\n margin-bottom: 24px;\n border-bottom: solid 1px #e1e4e5;\n padding-bottom: 24px;\n}\n#search-results .search li:first-child {\n border-top: solid 1px #e1e4e5;\n padding-top: 24px;\n}\n#search-results .search li a {\n font-size: 120%;\n margin-bottom: 12px;\n display: inline-block;\n}\n#search-results .context {\n color: gray;\n font-size: 90%;\n}\n.genindextable li > ul {\n margin-left: 24px;\n}\n\n/* GS Developer Action Button */\n\n#gs-developer-action-button-container {\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-tap-highlight-color: transparent;\n user-select: none;\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n z-index: 999;\n justify-content: center;\n align-items: center;\n position: fixed;\n display: flex;\n right: 32px;\n bottom: 32px;\n background: #0f6dee;\n color: white;\n width: 64px;\n height: 64px;\n border-radius: 50%;\n visibility: hidden;\n opacity: 0;\n box-shadow: 0 10px 20px 0 rgba(48, 48, 50, 0.19),\n 0 6px 6px 0 rgba(48, 48, 50, 0.23);\n}\n\n#gs-developer-action-button-container:active {\n transform: scale(0.95);\n background: #0a5cce;\n}\n\n#gs-nav-action {\n margin-top: 4px;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-tap-highlight-color: transparent;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n\n#gs-nav-action:before {\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-tap-highlight-color: transparent;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n content: url(../images/menu-open.svg);\n}\n\n#gs-nav-action.active:before {\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-tap-highlight-color: transparent;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n content: url(../images/menu-close.svg);\n}\n\n@media screen and (max-width: 80rem) {\n .wy-body-for-nav {\n background: #fff;\n }\n\n #gs-developer-action-button-container {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n opacity: 1;\n visibility: visible;\n }\n\n .wy-nav-top {\n display: block;\n }\n .wy-nav-side {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n visibility: hidden;\n max-width: 0px;\n height: 100vh;\n }\n .wy-nav-side.shift {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n visibility: visible;\n max-width: 320px;\n height: 100vh;\n }\n .wy-side-scroll {\n width: 320px;\n }\n .wy-side-nav-search {\n width: auto;\n }\n .wy-menu.wy-menu-vertical {\n width: auto;\n }\n .wy-nav-content-wrap {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n /* margin-left: 0; */\n }\n .wy-nav-content-wrap .wy-nav-content {\n /* padding: 1.618em; */\n }\n .wy-nav-content-wrap.shift {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n /* position: fixed; */\n min-width: 100%;\n /* left: 85%; */\n top: 0;\n height: 100%;\n overflow: hidden;\n }\n}\n@media screen and (min-width: 1100px) {\n .wy-nav-content-wrap {\n background: #fff;\n }\n .wy-nav-content {\n margin: 0 auto;\n /* background: #fcfcfc; */\n }\n}\n@media print {\n .rst-versions,\n footer,\n .wy-nav-side {\n display: none;\n }\n .wy-nav-content-wrap {\n margin-left: 0;\n }\n}\n.rst-versions {\n position: fixed;\n bottom: 0;\n left: 0;\n width: 300px;\n color: #fcfcfc;\n background: #1f1d1d;\n font-family: \"Roboto-Regular\", sans-serif;\n z-index: 400;\n}\n.rst-versions a {\n color: #2980b9;\n text-decoration: none;\n}\n.rst-versions .rst-badge-small {\n display: none;\n}\n.rst-versions .rst-current-version {\n padding: 12px;\n background-color: #272525;\n display: block;\n text-align: right;\n font-size: 90%;\n cursor: pointer;\n color: #27ae60;\n *zoom: 1;\n}\n.rst-versions .rst-current-version:before,\n.rst-versions .rst-current-version:after {\n display: table;\n content: \"\";\n}\n.rst-versions .rst-current-version:after {\n clear: both;\n}\n.rst-versions .rst-current-version .fa,\n.rst-versions .rst-current-version .wy-menu-vertical li span.toctree-expand,\n.wy-menu-vertical li .rst-versions .rst-current-version span.toctree-expand,\n.rst-versions .rst-current-version .rst-content .admonition-title,\n.rst-content .rst-versions .rst-current-version .admonition-title,\n.rst-versions .rst-current-version .rst-content h1 .headerlink,\n.rst-content h1 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h2 .headerlink,\n.rst-content h2 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h3 .headerlink,\n.rst-content h3 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h4 .headerlink,\n.rst-content h4 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h5 .headerlink,\n.rst-content h5 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h6 .headerlink,\n.rst-content h6 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content dl dt .headerlink,\n.rst-content dl dt .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content p.caption .headerlink,\n.rst-content p.caption .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content table > caption .headerlink,\n.rst-content table > caption .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content .code-block-caption .headerlink,\n.rst-content .code-block-caption .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content tt.download span:first-child,\n.rst-content tt.download .rst-versions .rst-current-version span:first-child,\n.rst-versions .rst-current-version .rst-content code.download span:first-child,\n.rst-content code.download .rst-versions .rst-current-version span:first-child,\n.rst-versions .rst-current-version .icon {\n color: #fcfcfc;\n}\n.rst-versions .rst-current-version .fa-book,\n.rst-versions .rst-current-version .icon-book {\n float: left;\n}\n.rst-versions .rst-current-version .icon-book {\n float: left;\n}\n.rst-versions .rst-current-version.rst-out-of-date {\n background-color: #e74c3c;\n color: #fff;\n}\n.rst-versions .rst-current-version.rst-active-old-version {\n background-color: #f1c40f;\n color: #000;\n}\n.rst-versions.shift-up {\n height: auto;\n max-height: 100%;\n overflow-y: scroll;\n}\n.rst-versions.shift-up .rst-other-versions {\n display: block;\n}\n.rst-versions .rst-other-versions {\n font-size: 90%;\n padding: 12px;\n color: gray;\n display: none;\n}\n.rst-versions .rst-other-versions hr {\n display: block;\n height: 1px;\n border: 0;\n margin: 20px 0;\n padding: 0;\n border-top: solid 1px #413d3d;\n}\n.rst-versions .rst-other-versions dd {\n display: inline-block;\n margin: 0;\n}\n.rst-versions .rst-other-versions dd a {\n display: inline-block;\n padding: 6px;\n color: #fcfcfc;\n}\n.rst-versions.rst-badge {\n width: auto;\n bottom: 20px;\n right: 20px;\n left: auto;\n border: none;\n max-width: 300px;\n max-height: 90%;\n}\n.rst-versions.rst-badge .icon-book {\n float: none;\n}\n.rst-versions.rst-badge .fa-book,\n.rst-versions.rst-badge .icon-book {\n float: none;\n}\n.rst-versions.rst-badge.shift-up .rst-current-version {\n text-align: right;\n}\n.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,\n.rst-versions.rst-badge.shift-up .rst-current-version .icon-book {\n float: left;\n}\n.rst-versions.rst-badge.shift-up .rst-current-version .icon-book {\n float: left;\n}\n.rst-versions.rst-badge .rst-current-version {\n width: auto;\n height: 30px;\n line-height: 30px;\n padding: 0 6px;\n display: block;\n text-align: center;\n}\n@media screen and (max-width: 768px) {\n .rst-versions {\n width: 85%;\n display: none;\n }\n .rst-versions.shift {\n display: block;\n }\n}\n.rst-content img {\n max-width: 100%;\n height: auto;\n}\n.rst-content div.figure {\n margin-bottom: 24px;\n}\n.rst-content div.figure p.caption {\n font-style: italic;\n}\n.rst-content div.figure p:last-child.caption {\n margin-bottom: 0px;\n}\n.rst-content div.figure.align-center {\n text-align: center;\n}\n.rst-content .section > img,\n.rst-content .section > a > img {\n margin-bottom: 24px;\n}\n.rst-content abbr[title] {\n text-decoration: none;\n}\n.rst-content.style-external-links a.reference.external:after {\n content: \"\";\n color: #b3b3b3;\n vertical-align: super;\n font-size: 60%;\n margin: 0 0.2em;\n}\n.rst-content blockquote {\n margin-left: 24px;\n line-height: 24px;\n margin-bottom: 24px;\n}\n.rst-content pre.literal-block {\n white-space: pre;\n margin: 0;\n padding: 12px 12px;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n display: block;\n overflow: auto;\n}\n.rst-content pre.literal-block,\n.rst-content div[class^=\"highlight\"] {\n font-family: \"Roboto Mono\";\n display: flex;\n flex-wrap: wrap;\n margin: 16px 0;\n font-size: 90%;\n line-height: normal;\n background: #e7f2fa;\n color: #2980b9;\n padding: 16px 12px;\n position: relative;\n background: #fafafa;\n border: 1px solid #e6e6e6;\n border-radius: 8px;\n}\n.rst-content pre.literal-block div[class^=\"highlight\"],\n.rst-content div[class^=\"highlight\"] div[class^=\"highlight\"] {\n padding: 0px;\n border: none;\n margin: 0;\n}\n.rst-content div[class^=\"highlight\"] td.code {\n width: 100%;\n}\n.rst-content .linenodiv pre {\n border-right: solid 1px #e6e9ea;\n margin: 0;\n padding: 12px 12px;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n user-select: none;\n pointer-events: none;\n}\n.rst-content div[class^=\"highlight\"] pre {\n white-space: pre;\n margin: 0;\n /* padding: 12px 12px; */\n display: block;\n overflow: auto;\n}\n.rst-content div[class^=\"highlight\"] pre .hll {\n display: block;\n margin: 0 -12px;\n padding: 0 12px;\n}\n.rst-content pre.literal-block,\n.rst-content div[class^=\"highlight\"] pre,\n.rst-content .linenodiv pre {\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n font-size: 14px;\n line-height: 1.5;\n}\n.rst-content .code-block-caption {\n font-style: italic;\n line-height: 1;\n padding: 1em 0;\n text-align: center;\n}\n@media print {\n .rst-content .codeblock,\n .rst-content div[class^=\"highlight\"],\n .rst-content div[class^=\"highlight\"] pre {\n white-space: pre-wrap;\n }\n}\n.rst-content .note .last,\n.rst-content .attention .last,\n.rst-content .caution .last,\n.rst-content .danger .last,\n.rst-content .error .last,\n.rst-content .hint .last,\n.rst-content .important .last,\n.rst-content .tip .last,\n.rst-content .warning .last,\n.rst-content .seealso .last,\n.rst-content .admonition-todo .last,\n.rst-content .admonition .last {\n margin-bottom: 0;\n}\n.rst-content .admonition-title:before {\n margin-right: 4px;\n}\n.rst-content .admonition table {\n border-color: rgba(0, 0, 0, 0.1);\n}\n.rst-content .admonition table td,\n.rst-content .admonition table th {\n background: transparent !important;\n border-color: rgba(0, 0, 0, 0.1) !important;\n}\n.rst-content .section ol.loweralpha,\n.rst-content .section ol.loweralpha li {\n list-style: lower-alpha;\n}\n.rst-content .section ol.upperalpha,\n.rst-content .section ol.upperalpha li {\n list-style: upper-alpha;\n}\n.rst-content .section ol p,\n.rst-content .section ul p {\n margin-bottom: 12px;\n}\n.rst-content .section ol p:last-child,\n.rst-content .section ul p:last-child {\n margin-bottom: 24px;\n}\n.rst-content .line-block {\n margin-left: 0px;\n margin-bottom: 24px;\n line-height: 24px;\n}\n.rst-content .line-block .line-block {\n margin-left: 24px;\n margin-bottom: 0px;\n}\n.rst-content .topic-title {\n font-weight: bold;\n margin-bottom: 12px;\n}\n.rst-content .toc-backref {\n color: #404040;\n}\n.rst-content .align-right {\n float: right;\n margin: 0px 0px 24px 24px;\n}\n.rst-content .align-left {\n float: left;\n margin: 0px 24px 24px 0px;\n}\n.rst-content .align-center {\n margin: auto;\n}\n.rst-content .align-center:not(table) {\n display: block;\n}\n.rst-content h1 .headerlink,\n.rst-content h2 .headerlink,\n.rst-content .toctree-wrapper p.caption .headerlink,\n.rst-content h3 .headerlink,\n.rst-content h4 .headerlink,\n.rst-content h5 .headerlink,\n.rst-content h6 .headerlink,\n.rst-content dl dt .headerlink,\n.rst-content p.caption .headerlink,\n.rst-content table > caption .headerlink,\n.rst-content .code-block-caption .headerlink {\n visibility: hidden;\n font-size: 14px;\n}\n.rst-content h1 .headerlink:after,\n.rst-content h2 .headerlink:after,\n.rst-content .toctree-wrapper p.caption .headerlink:after,\n.rst-content h3 .headerlink:after,\n.rst-content h4 .headerlink:after,\n.rst-content h5 .headerlink:after,\n.rst-content h6 .headerlink:after,\n.rst-content dl dt .headerlink:after,\n.rst-content p.caption .headerlink:after,\n.rst-content table > caption .headerlink:after,\n.rst-content .code-block-caption .headerlink:after {\n content: url(../images/gs-link.svg);\n position: relative;\n top: 2px;\n}\n.rst-content h1:hover .headerlink:after,\n.rst-content h2:hover .headerlink:after,\n.rst-content .toctree-wrapper p.caption:hover .headerlink:after,\n.rst-content h3:hover .headerlink:after,\n.rst-content h4:hover .headerlink:after,\n.rst-content h5:hover .headerlink:after,\n.rst-content h6:hover .headerlink:after,\n.rst-content dl dt:hover .headerlink:after,\n.rst-content p.caption:hover .headerlink:after,\n.rst-content table > caption:hover .headerlink:after,\n.rst-content .code-block-caption:hover .headerlink:after {\n visibility: visible;\n}\n.rst-content table > caption .headerlink:after {\n font-size: 12px;\n}\n.rst-content .centered {\n text-align: center;\n}\n.rst-content .sidebar {\n float: right;\n width: 40%;\n display: block;\n margin: 0 0 24px 24px;\n padding: 24px;\n background: #f3f6f6;\n border: solid 1px #e1e4e5;\n}\n.rst-content .sidebar p,\n.rst-content .sidebar ul,\n.rst-content .sidebar dl {\n font-size: 90%;\n}\n.rst-content .sidebar .last {\n margin-bottom: 0;\n}\n.rst-content .sidebar .sidebar-title {\n display: block;\n font-family: \"Roboto-Regular\", sans-serif;\n font-weight: bold;\n background: #e1e4e5;\n padding: 6px 12px;\n margin: -24px;\n margin-bottom: 24px;\n font-size: 100%;\n}\n.rst-content .highlighted {\n background: #f1c40f;\n display: inline-block;\n font-weight: bold;\n padding: 0 6px;\n}\n.rst-content .footnote-reference,\n.rst-content .citation-reference {\n vertical-align: baseline;\n position: relative;\n top: -0.4em;\n line-height: 0;\n font-size: 90%;\n}\n.rst-content table.docutils.citation,\n.rst-content table.docutils.footnote {\n background: none;\n border: none;\n color: gray;\n}\n.rst-content table.docutils.citation td,\n.rst-content table.docutils.citation tr,\n.rst-content table.docutils.footnote td,\n.rst-content table.docutils.footnote tr {\n border: none;\n background-color: transparent !important;\n white-space: normal;\n}\n.rst-content table.docutils.citation td.label,\n.rst-content table.docutils.footnote td.label {\n padding-left: 0;\n padding-right: 0;\n vertical-align: top;\n}\n.rst-content table.docutils.citation tt,\n.rst-content table.docutils.citation code,\n.rst-content table.docutils.footnote tt,\n.rst-content table.docutils.footnote code {\n color: #555;\n}\n.rst-content .wy-table-responsive.citation,\n.rst-content .wy-table-responsive.footnote {\n margin-bottom: 0;\n}\n.rst-content .wy-table-responsive.citation + :not(.citation),\n.rst-content .wy-table-responsive.footnote + :not(.footnote) {\n margin-top: 24px;\n}\n.rst-content .wy-table-responsive.citation:last-child,\n.rst-content .wy-table-responsive.footnote:last-child {\n margin-bottom: 24px;\n}\n.rst-content table.docutils th {\n border-color: #e1e4e5;\n}\n.rst-content table.docutils td .last,\n.rst-content table.docutils td .last :last-child {\n margin-bottom: 0;\n}\n.rst-content table.field-list {\n border: none;\n}\n.rst-content table.field-list td {\n border: none;\n}\n.rst-content table.field-list td p {\n font-size: inherit;\n line-height: inherit;\n}\n.rst-content table.field-list td > strong {\n display: inline-block;\n}\n.rst-content table.field-list .field-name {\n padding-right: 10px;\n text-align: left;\n white-space: nowrap;\n}\n.rst-content table.field-list .field-body {\n text-align: left;\n}\n.rst-content tt,\n.rst-content tt,\n.rst-content code {\n color: #000;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n padding: 2px 5px;\n}\n.rst-content tt big,\n.rst-content tt em,\n.rst-content tt big,\n.rst-content code big,\n.rst-content tt em,\n.rst-content code em {\n font-size: 100% !important;\n line-height: normal;\n}\n.rst-content tt.literal,\n.rst-content tt.literal,\n.rst-content code.literal {\n color: #e74c3c;\n}\n.rst-content tt.xref,\na .rst-content tt,\n.rst-content tt.xref,\n.rst-content code.xref,\na .rst-content tt,\na .rst-content code {\n font-weight: bold;\n color: #404040;\n}\n.rst-content pre,\n.rst-content kbd,\n.rst-content samp {\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n}\n.rst-content a tt,\n.rst-content a tt,\n.rst-content a code {\n color: #2980b9;\n}\n.rst-content dl {\n margin-bottom: 24px;\n}\n.rst-content dl dt {\n font-weight: normal;\n margin-bottom: 12px;\n}\n.rst-content dl p,\n.rst-content dl table,\n.rst-content dl ul,\n.rst-content dl ol {\n margin-bottom: 12px !important;\n}\n.rst-content dl dd {\n margin: 0px;\n line-height: 24px;\n}\n.rst-content dl:not(.docutils) {\n margin-bottom: 56px;\n}\n.rst-content dl:not(.docutils) dt {\n font-family: \"Roboto Mono\";\n display: flex;\n flex-wrap: wrap;\n margin: 16px 0;\n font-size: 14px;\n background: #e7f2fa;\n color: #0f6dee;\n padding: 16px 12px;\n position: relative;\n background: #fafafa;\n border: 1px solid #e6e6e6;\n border-radius: 8px;\n}\n.rst-content dl:not(.docutils) dt:before {\n color: #6ab0de;\n}\n.rst-content dl:not(.docutils) dt .headerlink {\n color: #404040;\n font-size: 100% !important;\n}\n.rst-content dl:not(.docutils) dl dt {\n font-family: \"Roboto Mono\";\n\n display: flex;\n flex-wrap: wrap;\n align-items: center;\n\n margin: 16px 0;\n font-size: 14px;\n line-height: normal;\n background: #e7f2fa;\n\n color: #2980b9;\n padding: 16px 12px;\n position: relative;\n background: #fafafa;\n border: 1px solid #e6e6e6;\n border-radius: 8px;\n}\n.rst-content dl:not(.docutils) dl dt .headerlink {\n color: #404040;\n font-size: 100% !important;\n}\n.rst-content dl:not(.docutils) dt:first-child {\n margin-top: 0;\n}\n.rst-content dl:not(.docutils) tt,\n.rst-content dl:not(.docutils) tt,\n.rst-content dl:not(.docutils) code {\n font-weight: bold;\n}\n.rst-content dl:not(.docutils) tt.descname,\n.rst-content dl:not(.docutils) tt.descclassname,\n.rst-content dl:not(.docutils) tt.descname,\n.rst-content dl:not(.docutils) code.descname,\n.rst-content dl:not(.docutils) tt.descclassname,\n.rst-content dl:not(.docutils) code.descclassname {\n background-color: transparent;\n border: none;\n padding: 0;\n font-size: 100% !important;\n}\n.rst-content dl:not(.docutils) tt.descname,\n.rst-content dl:not(.docutils) tt.descname,\n.rst-content dl:not(.docutils) code.descname {\n font-weight: bold;\n}\n.rst-content dl:not(.docutils) .optional {\n display: inline-block;\n padding: 0 4px;\n color: #000;\n font-weight: bold;\n}\n.rst-content dl:not(.docutils) .property {\n display: inline-block;\n padding-right: 8px;\n}\n.rst-content .viewcode-link,\n.rst-content .viewcode-back {\n display: inline-block;\n color: #27ae60;\n padding-left: 24px;\n}\n.rst-content .viewcode-back {\n display: block;\n float: right;\n}\n.rst-content p.rubric {\n font-family: \"Roboto Mono\", monospace;\n font-size: 24px;\n line-height: 36px;\n margin-bottom: 12px;\n margin-top: 48px;\n font-weight: bold;\n}\n.rst-content tt.download,\n.rst-content code.download {\n background: inherit;\n padding: inherit;\n font-weight: normal;\n font-family: inherit;\n font-size: inherit;\n color: inherit;\n border: inherit;\n white-space: inherit;\n}\n.rst-content tt.download span:first-child,\n.rst-content code.download span:first-child {\n -webkit-font-smoothing: subpixel-antialiased;\n}\n.rst-content tt.download span:first-child:before,\n.rst-content code.download span:first-child:before {\n margin-right: 4px;\n}\n.rst-content .guilabel {\n border: 1px solid #7fbbe3;\n background: #e7f2fa;\n font-size: 80%;\n font-weight: 700;\n border-radius: 4px;\n padding: 2.4px 6px;\n margin: auto 2px;\n}\n.rst-content .versionmodified {\n font-style: italic;\n}\n@media screen and (max-width: 480px) {\n .rst-content .sidebar {\n width: 100%;\n }\n}\nspan[id*=\"MathJax-Span\"] {\n color: #404040;\n}\n.math {\n text-align: center;\n}\n\nem {\n font-style: normal;\n}\n\n/* GS Developer Overrides */\n\ntd.field-body {\n padding: 0 !important;\n}\n\n/* GS Developer Global */\n\n#gs-developer-global {\n background: #0c1521;\n position: fixed;\n height: auto;\n width: 100%;\n z-index: 1000;\n}\n\n#gs-developer-global p {\n color: white;\n font-size: 24px;\n margin: 0;\n}\n\n/* GS Developer Components */\n\n.container-block-module {\n display: flex;\n flex-direction: row;\n flex-wrap: wrap;\n margin: -16px;\n}\n\n.link-block-module {\n display: flex;\n flex-direction: row;\n background-color: #fafafa;\n flex-basis: 240px;\n cursor: pointer;\n flex-grow: 1;\n margin: 16px;\n}\n\n.link-block-module-container {\n display: flex;\n color: unset;\n}\n\n.link-block-module-content {\n display: flex;\n flex-direction: column;\n justify-content: center;\n margin: 24px;\n width: 100%;\n}\n\n.link-block-module-title-label {\n margin: 0;\n margin-bottom: 4px;\n}\n\n.link-block-module-body-label {\n margin: 0;\n margin-bottom: 0;\n color: rgba(0, 0, 0, 0.8);\n}\n\ni {\n font-family: \"Material-Icons\";\n}\n" }, { "path": "docs/_build/html/_static/doctools.js", "content": "/*\n * doctools.js\n * ~~~~~~~~~~~\n *\n * Sphinx JavaScript utilities for all documentation.\n *\n * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.\n * :license: BSD, see LICENSE for details.\n *\n */\n\n/**\n * select a different prefix for underscore\n */\n$u = _.noConflict();\n\n/**\n * make the code below compatible with browsers without\n * an installed firebug like debugger\nif (!window.console || !console.firebug) {\n var names = [\"log\", \"debug\", \"info\", \"warn\", \"error\", \"assert\", \"dir\",\n \"dirxml\", \"group\", \"groupEnd\", \"time\", \"timeEnd\", \"count\", \"trace\",\n \"profile\", \"profileEnd\"];\n window.console = {};\n for (var i = 0; i < names.length; ++i)\n window.console[names[i]] = function() {};\n}\n */\n\n/**\n * small helper function to urldecode strings\n */\njQuery.urldecode = function(x) {\n return decodeURIComponent(x).replace(/\\+/g, ' ');\n};\n\n/**\n * small helper function to urlencode strings\n */\njQuery.urlencode = encodeURIComponent;\n\n/**\n * This function returns the parsed url parameters of the\n * current request. Multiple values per key are supported,\n * it will always return arrays of strings for the value parts.\n */\njQuery.getQueryParameters = function(s) {\n if (typeof s === 'undefined')\n s = document.location.search;\n var parts = s.substr(s.indexOf('?') + 1).split('&');\n var result = {};\n for (var i = 0; i < parts.length; i++) {\n var tmp = parts[i].split('=', 2);\n var key = jQuery.urldecode(tmp[0]);\n var value = jQuery.urldecode(tmp[1]);\n if (key in result)\n result[key].push(value);\n else\n result[key] = [value];\n }\n return result;\n};\n\n/**\n * highlight a given string on a jquery object by wrapping it in\n * span elements with the given class name.\n */\njQuery.fn.highlightText = function(text, className) {\n function highlight(node, addItems) {\n if (node.nodeType === 3) {\n var val = node.nodeValue;\n var pos = val.toLowerCase().indexOf(text);\n if (pos >= 0 &&\n !jQuery(node.parentNode).hasClass(className) &&\n !jQuery(node.parentNode).hasClass(\"nohighlight\")) {\n var span;\n var isInSVG = jQuery(node).closest(\"body, svg, foreignObject\").is(\"svg\");\n if (isInSVG) {\n span = document.createElementNS(\"http://www.w3.org/2000/svg\", \"tspan\");\n } else {\n span = document.createElement(\"span\");\n span.className = className;\n }\n span.appendChild(document.createTextNode(val.substr(pos, text.length)));\n node.parentNode.insertBefore(span, node.parentNode.insertBefore(\n document.createTextNode(val.substr(pos + text.length)),\n node.nextSibling));\n node.nodeValue = val.substr(0, pos);\n if (isInSVG) {\n var rect = document.createElementNS(\"http://www.w3.org/2000/svg\", \"rect\");\n var bbox = node.parentElement.getBBox();\n rect.x.baseVal.value = bbox.x;\n rect.y.baseVal.value = bbox.y;\n rect.width.baseVal.value = bbox.width;\n rect.height.baseVal.value = bbox.height;\n rect.setAttribute('class', className);\n addItems.push({\n \"parent\": node.parentNode,\n \"target\": rect});\n }\n }\n }\n else if (!jQuery(node).is(\"button, select, textarea\")) {\n jQuery.each(node.childNodes, function() {\n highlight(this, addItems);\n });\n }\n }\n var addItems = [];\n var result = this.each(function() {\n highlight(this, addItems);\n });\n for (var i = 0; i < addItems.length; ++i) {\n jQuery(addItems[i].parent).before(addItems[i].target);\n }\n return result;\n};\n\n/*\n * backward compatibility for jQuery.browser\n * This will be supported until firefox bug is fixed.\n */\nif (!jQuery.browser) {\n jQuery.uaMatch = function(ua) {\n ua = ua.toLowerCase();\n\n var match = /(chrome)[ \\/]([\\w.]+)/.exec(ua) ||\n /(webkit)[ \\/]([\\w.]+)/.exec(ua) ||\n /(opera)(?:.*version|)[ \\/]([\\w.]+)/.exec(ua) ||\n /(msie) ([\\w.]+)/.exec(ua) ||\n ua.indexOf(\"compatible\") < 0 && /(mozilla)(?:.*? rv:([\\w.]+)|)/.exec(ua) ||\n [];\n\n return {\n browser: match[ 1 ] || \"\",\n version: match[ 2 ] || \"0\"\n };\n };\n jQuery.browser = {};\n jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;\n}\n\n/**\n * Small JavaScript module for the documentation.\n */\nvar Documentation = {\n\n init : function() {\n this.fixFirefoxAnchorBug();\n this.highlightSearchWords();\n this.initIndexTable();\n if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {\n this.initOnKeyListeners();\n }\n },\n\n /**\n * i18n support\n */\n TRANSLATIONS : {},\n PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },\n LOCALE : 'unknown',\n\n // gettext and ngettext don't access this so that the functions\n // can safely bound to a different name (_ = Documentation.gettext)\n gettext : function(string) {\n var translated = Documentation.TRANSLATIONS[string];\n if (typeof translated === 'undefined')\n return string;\n return (typeof translated === 'string') ? translated : translated[0];\n },\n\n ngettext : function(singular, plural, n) {\n var translated = Documentation.TRANSLATIONS[singular];\n if (typeof translated === 'undefined')\n return (n == 1) ? singular : plural;\n return translated[Documentation.PLURALEXPR(n)];\n },\n\n addTranslations : function(catalog) {\n for (var key in catalog.messages)\n this.TRANSLATIONS[key] = catalog.messages[key];\n this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');\n this.LOCALE = catalog.locale;\n },\n\n /**\n * add context elements like header anchor links\n */\n addContextElements : function() {\n $('div[id] > :header:first').each(function() {\n $('\\u00B6').\n attr('href', '#' + this.id).\n attr('title', _('Permalink to this headline')).\n appendTo(this);\n });\n $('dt[id]').each(function() {\n $('\\u00B6').\n attr('href', '#' + this.id).\n attr('title', _('Permalink to this definition')).\n appendTo(this);\n });\n },\n\n /**\n * workaround a firefox stupidity\n * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075\n */\n fixFirefoxAnchorBug : function() {\n if (document.location.hash && $.browser.mozilla)\n window.setTimeout(function() {\n document.location.href += '';\n }, 10);\n },\n\n /**\n * highlight the search words provided in the url in the text\n */\n highlightSearchWords : function() {\n var params = $.getQueryParameters();\n var terms = (params.highlight) ? params.highlight[0].split(/\\s+/) : [];\n if (terms.length) {\n var body = $('div.body');\n if (!body.length) {\n body = $('body');\n }\n window.setTimeout(function() {\n $.each(terms, function() {\n body.highlightText(this.toLowerCase(), 'highlighted');\n });\n }, 10);\n $('

' + _('Hide Search Matches') + '

')\n .appendTo($('#searchbox'));\n }\n },\n\n /**\n * init the domain index toggle buttons\n */\n initIndexTable : function() {\n var togglers = $('img.toggler').click(function() {\n var src = $(this).attr('src');\n var idnum = $(this).attr('id').substr(7);\n $('tr.cg-' + idnum).toggle();\n if (src.substr(-9) === 'minus.png')\n $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');\n else\n $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');\n }).css('display', '');\n if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {\n togglers.click();\n }\n },\n\n /**\n * helper function to hide the search marks again\n */\n hideSearchWords : function() {\n $('#searchbox .highlight-link').fadeOut(300);\n $('span.highlighted').removeClass('highlighted');\n },\n\n /**\n * make the url absolute\n */\n makeURL : function(relativeURL) {\n return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;\n },\n\n /**\n * get the current relative url\n */\n getCurrentURL : function() {\n var path = document.location.pathname;\n var parts = path.split(/\\//);\n $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\\//), function() {\n if (this === '..')\n parts.pop();\n });\n var url = parts.join('/');\n return path.substring(url.lastIndexOf('/') + 1, path.length - 1);\n },\n\n initOnKeyListeners: function() {\n $(document).keydown(function(event) {\n var activeElementType = document.activeElement.tagName;\n // don't navigate when in search box, textarea, dropdown or button\n if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'\n && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey\n && !event.shiftKey) {\n switch (event.keyCode) {\n case 37: // left\n var prevHref = $('link[rel=\"prev\"]').prop('href');\n if (prevHref) {\n window.location.href = prevHref;\n return false;\n }\n case 39: // right\n var nextHref = $('link[rel=\"next\"]').prop('href');\n if (nextHref) {\n window.location.href = nextHref;\n return false;\n }\n }\n }\n });\n }\n};\n\n// quick alias for translations\n_ = Documentation.gettext;\n\n$(document).ready(function() {\n Documentation.init();\n});\n" }, { "path": "docs/_build/html/_static/documentation_options.js", "content": "var DOCUMENTATION_OPTIONS = {\n URL_ROOT: document.getElementById(\"documentation_options\").getAttribute('data-url_root'),\n VERSION: '0.1',\n LANGUAGE: 'None',\n COLLAPSE_INDEX: false,\n BUILDER: 'html',\n FILE_SUFFIX: '.html',\n LINK_SUFFIX: '.html',\n HAS_SOURCE: true,\n SOURCELINK_SUFFIX: '.txt',\n NAVIGATION_WITH_KEYS: false\n};" }, { "path": "docs/_build/html/_static/jquery-3.5.1.js", "content": "/*!\n * jQuery JavaScript Library v3.5.1\n * https://jquery.com/\n *\n * Includes Sizzle.js\n * https://sizzlejs.com/\n *\n * Copyright JS Foundation and other contributors\n * Released under the MIT license\n * https://jquery.org/license\n *\n * Date: 2020-05-04T22:49Z\n */\n( function( global, factory ) {\n\n\t\"use strict\";\n\n\tif ( typeof module === \"object\" && typeof module.exports === \"object\" ) {\n\n\t\t// For CommonJS and CommonJS-like environments where a proper `window`\n\t\t// is present, execute the factory and get jQuery.\n\t\t// For environments that do not have a `window` with a `document`\n\t\t// (such as Node.js), expose a factory as module.exports.\n\t\t// This accentuates the need for the creation of a real `window`.\n\t\t// e.g. var jQuery = require(\"jquery\")(window);\n\t\t// See ticket #14549 for more info.\n\t\tmodule.exports = global.document ?\n\t\t\tfactory( global, true ) :\n\t\t\tfunction( w ) {\n\t\t\t\tif ( !w.document ) {\n\t\t\t\t\tthrow new Error( \"jQuery requires a window with a document\" );\n\t\t\t\t}\n\t\t\t\treturn factory( w );\n\t\t\t};\n\t} else {\n\t\tfactory( global );\n\t}\n\n// Pass this if window is not defined yet\n} )( typeof window !== \"undefined\" ? window : this, function( window, noGlobal ) {\n\n// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1\n// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode\n// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common\n// enough that all such attempts are guarded in a try block.\n\"use strict\";\n\nvar arr = [];\n\nvar getProto = Object.getPrototypeOf;\n\nvar slice = arr.slice;\n\nvar flat = arr.flat ? function( array ) {\n\treturn arr.flat.call( array );\n} : function( array ) {\n\treturn arr.concat.apply( [], array );\n};\n\n\nvar push = arr.push;\n\nvar indexOf = arr.indexOf;\n\nvar class2type = {};\n\nvar toString = class2type.toString;\n\nvar hasOwn = class2type.hasOwnProperty;\n\nvar fnToString = hasOwn.toString;\n\nvar ObjectFunctionString = fnToString.call( Object );\n\nvar support = {};\n\nvar isFunction = function isFunction( obj ) {\n\n // Support: Chrome <=57, Firefox <=52\n // In some browsers, typeof returns \"function\" for HTML elements\n // (i.e., `typeof document.createElement( \"object\" ) === \"function\"`).\n // We don't want to classify *any* DOM node as a function.\n return typeof obj === \"function\" && typeof obj.nodeType !== \"number\";\n };\n\n\nvar isWindow = function isWindow( obj ) {\n\t\treturn obj != null && obj === obj.window;\n\t};\n\n\nvar document = window.document;\n\n\n\n\tvar preservedScriptAttributes = {\n\t\ttype: true,\n\t\tsrc: true,\n\t\tnonce: true,\n\t\tnoModule: true\n\t};\n\n\tfunction DOMEval( code, node, doc ) {\n\t\tdoc = doc || document;\n\n\t\tvar i, val,\n\t\t\tscript = doc.createElement( \"script\" );\n\n\t\tscript.text = code;\n\t\tif ( node ) {\n\t\t\tfor ( i in preservedScriptAttributes ) {\n\n\t\t\t\t// Support: Firefox 64+, Edge 18+\n\t\t\t\t// Some browsers don't support the \"nonce\" property on scripts.\n\t\t\t\t// On the other hand, just using `getAttribute` is not enough as\n\t\t\t\t// the `nonce` attribute is reset to an empty string whenever it\n\t\t\t\t// becomes browsing-context connected.\n\t\t\t\t// See https://github.com/whatwg/html/issues/2369\n\t\t\t\t// See https://html.spec.whatwg.org/#nonce-attributes\n\t\t\t\t// The `node.getAttribute` check was added for the sake of\n\t\t\t\t// `jQuery.globalEval` so that it can fake a nonce-containing node\n\t\t\t\t// via an object.\n\t\t\t\tval = node[ i ] || node.getAttribute && node.getAttribute( i );\n\t\t\t\tif ( val ) {\n\t\t\t\t\tscript.setAttribute( i, val );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tdoc.head.appendChild( script ).parentNode.removeChild( script );\n\t}\n\n\nfunction toType( obj ) {\n\tif ( obj == null ) {\n\t\treturn obj + \"\";\n\t}\n\n\t// Support: Android <=2.3 only (functionish RegExp)\n\treturn typeof obj === \"object\" || typeof obj === \"function\" ?\n\t\tclass2type[ toString.call( obj ) ] || \"object\" :\n\t\ttypeof obj;\n}\n/* global Symbol */\n// Defining this global in .eslintrc.json would create a danger of using the global\n// unguarded in another place, it seems safer to define global only for this module\n\n\n\nvar\n\tversion = \"3.5.1\",\n\n\t// Define a local copy of jQuery\n\tjQuery = function( selector, context ) {\n\n\t\t// The jQuery object is actually just the init constructor 'enhanced'\n\t\t// Need init if jQuery is called (just allow error to be thrown if not included)\n\t\treturn new jQuery.fn.init( selector, context );\n\t};\n\njQuery.fn = jQuery.prototype = {\n\n\t// The current version of jQuery being used\n\tjquery: version,\n\n\tconstructor: jQuery,\n\n\t// The default length of a jQuery object is 0\n\tlength: 0,\n\n\ttoArray: function() {\n\t\treturn slice.call( this );\n\t},\n\n\t// Get the Nth element in the matched element set OR\n\t// Get the whole matched element set as a clean array\n\tget: function( num ) {\n\n\t\t// Return all the elements in a clean array\n\t\tif ( num == null ) {\n\t\t\treturn slice.call( this );\n\t\t}\n\n\t\t// Return just the one element from the set\n\t\treturn num < 0 ? this[ num + this.length ] : this[ num ];\n\t},\n\n\t// Take an array of elements and push it onto the stack\n\t// (returning the new matched element set)\n\tpushStack: function( elems ) {\n\n\t\t// Build a new jQuery matched element set\n\t\tvar ret = jQuery.merge( this.constructor(), elems );\n\n\t\t// Add the old object onto the stack (as a reference)\n\t\tret.prevObject = this;\n\n\t\t// Return the newly-formed element set\n\t\treturn ret;\n\t},\n\n\t// Execute a callback for every element in the matched set.\n\teach: function( callback ) {\n\t\treturn jQuery.each( this, callback );\n\t},\n\n\tmap: function( callback ) {\n\t\treturn this.pushStack( jQuery.map( this, function( elem, i ) {\n\t\t\treturn callback.call( elem, i, elem );\n\t\t} ) );\n\t},\n\n\tslice: function() {\n\t\treturn this.pushStack( slice.apply( this, arguments ) );\n\t},\n\n\tfirst: function() {\n\t\treturn this.eq( 0 );\n\t},\n\n\tlast: function() {\n\t\treturn this.eq( -1 );\n\t},\n\n\teven: function() {\n\t\treturn this.pushStack( jQuery.grep( this, function( _elem, i ) {\n\t\t\treturn ( i + 1 ) % 2;\n\t\t} ) );\n\t},\n\n\todd: function() {\n\t\treturn this.pushStack( jQuery.grep( this, function( _elem, i ) {\n\t\t\treturn i % 2;\n\t\t} ) );\n\t},\n\n\teq: function( i ) {\n\t\tvar len = this.length,\n\t\t\tj = +i + ( i < 0 ? len : 0 );\n\t\treturn this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );\n\t},\n\n\tend: function() {\n\t\treturn this.prevObject || this.constructor();\n\t},\n\n\t// For internal use only.\n\t// Behaves like an Array's method, not like a jQuery method.\n\tpush: push,\n\tsort: arr.sort,\n\tsplice: arr.splice\n};\n\njQuery.extend = jQuery.fn.extend = function() {\n\tvar options, name, src, copy, copyIsArray, clone,\n\t\ttarget = arguments[ 0 ] || {},\n\t\ti = 1,\n\t\tlength = arguments.length,\n\t\tdeep = false;\n\n\t// Handle a deep copy situation\n\tif ( typeof target === \"boolean\" ) {\n\t\tdeep = target;\n\n\t\t// Skip the boolean and the target\n\t\ttarget = arguments[ i ] || {};\n\t\ti++;\n\t}\n\n\t// Handle case when target is a string or something (possible in deep copy)\n\tif ( typeof target !== \"object\" && !isFunction( target ) ) {\n\t\ttarget = {};\n\t}\n\n\t// Extend jQuery itself if only one argument is passed\n\tif ( i === length ) {\n\t\ttarget = this;\n\t\ti--;\n\t}\n\n\tfor ( ; i < length; i++ ) {\n\n\t\t// Only deal with non-null/undefined values\n\t\tif ( ( options = arguments[ i ] ) != null ) {\n\n\t\t\t// Extend the base object\n\t\t\tfor ( name in options ) {\n\t\t\t\tcopy = options[ name ];\n\n\t\t\t\t// Prevent Object.prototype pollution\n\t\t\t\t// Prevent never-ending loop\n\t\t\t\tif ( name === \"__proto__\" || target === copy ) {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\n\t\t\t\t// Recurse if we're merging plain objects or arrays\n\t\t\t\tif ( deep && copy && ( jQuery.isPlainObject( copy ) ||\n\t\t\t\t\t( copyIsArray = Array.isArray( copy ) ) ) ) {\n\t\t\t\t\tsrc = target[ name ];\n\n\t\t\t\t\t// Ensure proper type for the source value\n\t\t\t\t\tif ( copyIsArray && !Array.isArray( src ) ) {\n\t\t\t\t\t\tclone = [];\n\t\t\t\t\t} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {\n\t\t\t\t\t\tclone = {};\n\t\t\t\t\t} else {\n\t\t\t\t\t\tclone = src;\n\t\t\t\t\t}\n\t\t\t\t\tcopyIsArray = false;\n\n\t\t\t\t\t// Never move original objects, clone them\n\t\t\t\t\ttarget[ name ] = jQuery.extend( deep, clone, copy );\n\n\t\t\t\t// Don't bring in undefined values\n\t\t\t\t} else if ( copy !== undefined ) {\n\t\t\t\t\ttarget[ name ] = copy;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Return the modified object\n\treturn target;\n};\n\njQuery.extend( {\n\n\t// Unique for each copy of jQuery on the page\n\texpando: \"jQuery\" + ( version + Math.random() ).replace( /\\D/g, \"\" ),\n\n\t// Assume jQuery is ready without the ready module\n\tisReady: true,\n\n\terror: function( msg ) {\n\t\tthrow new Error( msg );\n\t},\n\n\tnoop: function() {},\n\n\tisPlainObject: function( obj ) {\n\t\tvar proto, Ctor;\n\n\t\t// Detect obvious negatives\n\t\t// Use toString instead of jQuery.type to catch host objects\n\t\tif ( !obj || toString.call( obj ) !== \"[object Object]\" ) {\n\t\t\treturn false;\n\t\t}\n\n\t\tproto = getProto( obj );\n\n\t\t// Objects with no prototype (e.g., `Object.create( null )`) are plain\n\t\tif ( !proto ) {\n\t\t\treturn true;\n\t\t}\n\n\t\t// Objects with prototype are plain iff they were constructed by a global Object function\n\t\tCtor = hasOwn.call( proto, \"constructor\" ) && proto.constructor;\n\t\treturn typeof Ctor === \"function\" && fnToString.call( Ctor ) === ObjectFunctionString;\n\t},\n\n\tisEmptyObject: function( obj ) {\n\t\tvar name;\n\n\t\tfor ( name in obj ) {\n\t\t\treturn false;\n\t\t}\n\t\treturn true;\n\t},\n\n\t// Evaluates a script in a provided context; falls back to the global one\n\t// if not specified.\n\tglobalEval: function( code, options, doc ) {\n\t\tDOMEval( code, { nonce: options && options.nonce }, doc );\n\t},\n\n\teach: function( obj, callback ) {\n\t\tvar length, i = 0;\n\n\t\tif ( isArrayLike( obj ) ) {\n\t\t\tlength = obj.length;\n\t\t\tfor ( ; i < length; i++ ) {\n\t\t\t\tif ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tfor ( i in obj ) {\n\t\t\t\tif ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn obj;\n\t},\n\n\t// results is for internal usage only\n\tmakeArray: function( arr, results ) {\n\t\tvar ret = results || [];\n\n\t\tif ( arr != null ) {\n\t\t\tif ( isArrayLike( Object( arr ) ) ) {\n\t\t\t\tjQuery.merge( ret,\n\t\t\t\t\ttypeof arr === \"string\" ?\n\t\t\t\t\t[ arr ] : arr\n\t\t\t\t);\n\t\t\t} else {\n\t\t\t\tpush.call( ret, arr );\n\t\t\t}\n\t\t}\n\n\t\treturn ret;\n\t},\n\n\tinArray: function( elem, arr, i ) {\n\t\treturn arr == null ? -1 : indexOf.call( arr, elem, i );\n\t},\n\n\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t// push.apply(_, arraylike) throws on ancient WebKit\n\tmerge: function( first, second ) {\n\t\tvar len = +second.length,\n\t\t\tj = 0,\n\t\t\ti = first.length;\n\n\t\tfor ( ; j < len; j++ ) {\n\t\t\tfirst[ i++ ] = second[ j ];\n\t\t}\n\n\t\tfirst.length = i;\n\n\t\treturn first;\n\t},\n\n\tgrep: function( elems, callback, invert ) {\n\t\tvar callbackInverse,\n\t\t\tmatches = [],\n\t\t\ti = 0,\n\t\t\tlength = elems.length,\n\t\t\tcallbackExpect = !invert;\n\n\t\t// Go through the array, only saving the items\n\t\t// that pass the validator function\n\t\tfor ( ; i < length; i++ ) {\n\t\t\tcallbackInverse = !callback( elems[ i ], i );\n\t\t\tif ( callbackInverse !== callbackExpect ) {\n\t\t\t\tmatches.push( elems[ i ] );\n\t\t\t}\n\t\t}\n\n\t\treturn matches;\n\t},\n\n\t// arg is for internal usage only\n\tmap: function( elems, callback, arg ) {\n\t\tvar length, value,\n\t\t\ti = 0,\n\t\t\tret = [];\n\n\t\t// Go through the array, translating each of the items to their new values\n\t\tif ( isArrayLike( elems ) ) {\n\t\t\tlength = elems.length;\n\t\t\tfor ( ; i < length; i++ ) {\n\t\t\t\tvalue = callback( elems[ i ], i, arg );\n\n\t\t\t\tif ( value != null ) {\n\t\t\t\t\tret.push( value );\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Go through every key on the object,\n\t\t} else {\n\t\t\tfor ( i in elems ) {\n\t\t\t\tvalue = callback( elems[ i ], i, arg );\n\n\t\t\t\tif ( value != null ) {\n\t\t\t\t\tret.push( value );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Flatten any nested arrays\n\t\treturn flat( ret );\n\t},\n\n\t// A global GUID counter for objects\n\tguid: 1,\n\n\t// jQuery.support is not used in Core but other projects attach their\n\t// properties to it so it needs to exist.\n\tsupport: support\n} );\n\nif ( typeof Symbol === \"function\" ) {\n\tjQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];\n}\n\n// Populate the class2type map\njQuery.each( \"Boolean Number String Function Array Date RegExp Object Error Symbol\".split( \" \" ),\nfunction( _i, name ) {\n\tclass2type[ \"[object \" + name + \"]\" ] = name.toLowerCase();\n} );\n\nfunction isArrayLike( obj ) {\n\n\t// Support: real iOS 8.2 only (not reproducible in simulator)\n\t// `in` check used to prevent JIT error (gh-2145)\n\t// hasOwn isn't used here due to false negatives\n\t// regarding Nodelist length in IE\n\tvar length = !!obj && \"length\" in obj && obj.length,\n\t\ttype = toType( obj );\n\n\tif ( isFunction( obj ) || isWindow( obj ) ) {\n\t\treturn false;\n\t}\n\n\treturn type === \"array\" || length === 0 ||\n\t\ttypeof length === \"number\" && length > 0 && ( length - 1 ) in obj;\n}\nvar Sizzle =\n/*!\n * Sizzle CSS Selector Engine v2.3.5\n * https://sizzlejs.com/\n *\n * Copyright JS Foundation and other contributors\n * Released under the MIT license\n * https://js.foundation/\n *\n * Date: 2020-03-14\n */\n( function( window ) {\nvar i,\n\tsupport,\n\tExpr,\n\tgetText,\n\tisXML,\n\ttokenize,\n\tcompile,\n\tselect,\n\toutermostContext,\n\tsortInput,\n\thasDuplicate,\n\n\t// Local document vars\n\tsetDocument,\n\tdocument,\n\tdocElem,\n\tdocumentIsHTML,\n\trbuggyQSA,\n\trbuggyMatches,\n\tmatches,\n\tcontains,\n\n\t// Instance-specific data\n\texpando = \"sizzle\" + 1 * new Date(),\n\tpreferredDoc = window.document,\n\tdirruns = 0,\n\tdone = 0,\n\tclassCache = createCache(),\n\ttokenCache = createCache(),\n\tcompilerCache = createCache(),\n\tnonnativeSelectorCache = createCache(),\n\tsortOrder = function( a, b ) {\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t}\n\t\treturn 0;\n\t},\n\n\t// Instance methods\n\thasOwn = ( {} ).hasOwnProperty,\n\tarr = [],\n\tpop = arr.pop,\n\tpushNative = arr.push,\n\tpush = arr.push,\n\tslice = arr.slice,\n\n\t// Use a stripped-down indexOf as it's faster than native\n\t// https://jsperf.com/thor-indexof-vs-for/5\n\tindexOf = function( list, elem ) {\n\t\tvar i = 0,\n\t\t\tlen = list.length;\n\t\tfor ( ; i < len; i++ ) {\n\t\t\tif ( list[ i ] === elem ) {\n\t\t\t\treturn i;\n\t\t\t}\n\t\t}\n\t\treturn -1;\n\t},\n\n\tbooleans = \"checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|\" +\n\t\t\"ismap|loop|multiple|open|readonly|required|scoped\",\n\n\t// Regular expressions\n\n\t// http://www.w3.org/TR/css3-selectors/#whitespace\n\twhitespace = \"[\\\\x20\\\\t\\\\r\\\\n\\\\f]\",\n\n\t// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram\n\tidentifier = \"(?:\\\\\\\\[\\\\da-fA-F]{1,6}\" + whitespace +\n\t\t\"?|\\\\\\\\[^\\\\r\\\\n\\\\f]|[\\\\w-]|[^\\0-\\\\x7f])+\",\n\n\t// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors\n\tattributes = \"\\\\[\" + whitespace + \"*(\" + identifier + \")(?:\" + whitespace +\n\n\t\t// Operator (capture 2)\n\t\t\"*([*^$|!~]?=)\" + whitespace +\n\n\t\t// \"Attribute values must be CSS identifiers [capture 5]\n\t\t// or strings [capture 3 or capture 4]\"\n\t\t\"*(?:'((?:\\\\\\\\.|[^\\\\\\\\'])*)'|\\\"((?:\\\\\\\\.|[^\\\\\\\\\\\"])*)\\\"|(\" + identifier + \"))|)\" +\n\t\twhitespace + \"*\\\\]\",\n\n\tpseudos = \":(\" + identifier + \")(?:\\\\((\" +\n\n\t\t// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:\n\t\t// 1. quoted (capture 3; capture 4 or capture 5)\n\t\t\"('((?:\\\\\\\\.|[^\\\\\\\\'])*)'|\\\"((?:\\\\\\\\.|[^\\\\\\\\\\\"])*)\\\")|\" +\n\n\t\t// 2. simple (capture 6)\n\t\t\"((?:\\\\\\\\.|[^\\\\\\\\()[\\\\]]|\" + attributes + \")*)|\" +\n\n\t\t// 3. anything else (capture 2)\n\t\t\".*\" +\n\t\t\")\\\\)|)\",\n\n\t// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter\n\trwhitespace = new RegExp( whitespace + \"+\", \"g\" ),\n\trtrim = new RegExp( \"^\" + whitespace + \"+|((?:^|[^\\\\\\\\])(?:\\\\\\\\.)*)\" +\n\t\twhitespace + \"+$\", \"g\" ),\n\n\trcomma = new RegExp( \"^\" + whitespace + \"*,\" + whitespace + \"*\" ),\n\trcombinators = new RegExp( \"^\" + whitespace + \"*([>+~]|\" + whitespace + \")\" + whitespace +\n\t\t\"*\" ),\n\trdescend = new RegExp( whitespace + \"|>\" ),\n\n\trpseudo = new RegExp( pseudos ),\n\tridentifier = new RegExp( \"^\" + identifier + \"$\" ),\n\n\tmatchExpr = {\n\t\t\"ID\": new RegExp( \"^#(\" + identifier + \")\" ),\n\t\t\"CLASS\": new RegExp( \"^\\\\.(\" + identifier + \")\" ),\n\t\t\"TAG\": new RegExp( \"^(\" + identifier + \"|[*])\" ),\n\t\t\"ATTR\": new RegExp( \"^\" + attributes ),\n\t\t\"PSEUDO\": new RegExp( \"^\" + pseudos ),\n\t\t\"CHILD\": new RegExp( \"^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\\\(\" +\n\t\t\twhitespace + \"*(even|odd|(([+-]|)(\\\\d*)n|)\" + whitespace + \"*(?:([+-]|)\" +\n\t\t\twhitespace + \"*(\\\\d+)|))\" + whitespace + \"*\\\\)|)\", \"i\" ),\n\t\t\"bool\": new RegExp( \"^(?:\" + booleans + \")$\", \"i\" ),\n\n\t\t// For use in libraries implementing .is()\n\t\t// We use this for POS matching in `select`\n\t\t\"needsContext\": new RegExp( \"^\" + whitespace +\n\t\t\t\"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\\\(\" + whitespace +\n\t\t\t\"*((?:-\\\\d)?\\\\d*)\" + whitespace + \"*\\\\)|)(?=[^-]|$)\", \"i\" )\n\t},\n\n\trhtml = /HTML$/i,\n\trinputs = /^(?:input|select|textarea|button)$/i,\n\trheader = /^h\\d$/i,\n\n\trnative = /^[^{]+\\{\\s*\\[native \\w/,\n\n\t// Easily-parseable/retrievable ID or TAG or CLASS selectors\n\trquickExpr = /^(?:#([\\w-]+)|(\\w+)|\\.([\\w-]+))$/,\n\n\trsibling = /[+~]/,\n\n\t// CSS escapes\n\t// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters\n\trunescape = new RegExp( \"\\\\\\\\[\\\\da-fA-F]{1,6}\" + whitespace + \"?|\\\\\\\\([^\\\\r\\\\n\\\\f])\", \"g\" ),\n\tfunescape = function( escape, nonHex ) {\n\t\tvar high = \"0x\" + escape.slice( 1 ) - 0x10000;\n\n\t\treturn nonHex ?\n\n\t\t\t// Strip the backslash prefix from a non-hex escape sequence\n\t\t\tnonHex :\n\n\t\t\t// Replace a hexadecimal escape sequence with the encoded Unicode code point\n\t\t\t// Support: IE <=11+\n\t\t\t// For values outside the Basic Multilingual Plane (BMP), manually construct a\n\t\t\t// surrogate pair\n\t\t\thigh < 0 ?\n\t\t\t\tString.fromCharCode( high + 0x10000 ) :\n\t\t\t\tString.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );\n\t},\n\n\t// CSS string/identifier serialization\n\t// https://drafts.csswg.org/cssom/#common-serializing-idioms\n\trcssescape = /([\\0-\\x1f\\x7f]|^-?\\d)|^-$|[^\\0-\\x1f\\x7f-\\uFFFF\\w-]/g,\n\tfcssescape = function( ch, asCodePoint ) {\n\t\tif ( asCodePoint ) {\n\n\t\t\t// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER\n\t\t\tif ( ch === \"\\0\" ) {\n\t\t\t\treturn \"\\uFFFD\";\n\t\t\t}\n\n\t\t\t// Control characters and (dependent upon position) numbers get escaped as code points\n\t\t\treturn ch.slice( 0, -1 ) + \"\\\\\" +\n\t\t\t\tch.charCodeAt( ch.length - 1 ).toString( 16 ) + \" \";\n\t\t}\n\n\t\t// Other potentially-special ASCII characters get backslash-escaped\n\t\treturn \"\\\\\" + ch;\n\t},\n\n\t// Used for iframes\n\t// See setDocument()\n\t// Removing the function wrapper causes a \"Permission Denied\"\n\t// error in IE\n\tunloadHandler = function() {\n\t\tsetDocument();\n\t},\n\n\tinDisabledFieldset = addCombinator(\n\t\tfunction( elem ) {\n\t\t\treturn elem.disabled === true && elem.nodeName.toLowerCase() === \"fieldset\";\n\t\t},\n\t\t{ dir: \"parentNode\", next: \"legend\" }\n\t);\n\n// Optimize for push.apply( _, NodeList )\ntry {\n\tpush.apply(\n\t\t( arr = slice.call( preferredDoc.childNodes ) ),\n\t\tpreferredDoc.childNodes\n\t);\n\n\t// Support: Android<4.0\n\t// Detect silently failing push.apply\n\t// eslint-disable-next-line no-unused-expressions\n\tarr[ preferredDoc.childNodes.length ].nodeType;\n} catch ( e ) {\n\tpush = { apply: arr.length ?\n\n\t\t// Leverage slice if possible\n\t\tfunction( target, els ) {\n\t\t\tpushNative.apply( target, slice.call( els ) );\n\t\t} :\n\n\t\t// Support: IE<9\n\t\t// Otherwise append directly\n\t\tfunction( target, els ) {\n\t\t\tvar j = target.length,\n\t\t\t\ti = 0;\n\n\t\t\t// Can't trust NodeList.length\n\t\t\twhile ( ( target[ j++ ] = els[ i++ ] ) ) {}\n\t\t\ttarget.length = j - 1;\n\t\t}\n\t};\n}\n\nfunction Sizzle( selector, context, results, seed ) {\n\tvar m, i, elem, nid, match, groups, newSelector,\n\t\tnewContext = context && context.ownerDocument,\n\n\t\t// nodeType defaults to 9, since context defaults to document\n\t\tnodeType = context ? context.nodeType : 9;\n\n\tresults = results || [];\n\n\t// Return early from calls with invalid selector or context\n\tif ( typeof selector !== \"string\" || !selector ||\n\t\tnodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {\n\n\t\treturn results;\n\t}\n\n\t// Try to shortcut find operations (as opposed to filters) in HTML documents\n\tif ( !seed ) {\n\t\tsetDocument( context );\n\t\tcontext = context || document;\n\n\t\tif ( documentIsHTML ) {\n\n\t\t\t// If the selector is sufficiently simple, try using a \"get*By*\" DOM method\n\t\t\t// (excepting DocumentFragment context, where the methods don't exist)\n\t\t\tif ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {\n\n\t\t\t\t// ID selector\n\t\t\t\tif ( ( m = match[ 1 ] ) ) {\n\n\t\t\t\t\t// Document context\n\t\t\t\t\tif ( nodeType === 9 ) {\n\t\t\t\t\t\tif ( ( elem = context.getElementById( m ) ) ) {\n\n\t\t\t\t\t\t\t// Support: IE, Opera, Webkit\n\t\t\t\t\t\t\t// TODO: identify versions\n\t\t\t\t\t\t\t// getElementById can match elements by name instead of ID\n\t\t\t\t\t\t\tif ( elem.id === m ) {\n\t\t\t\t\t\t\t\tresults.push( elem );\n\t\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t// Element context\n\t\t\t\t\t} else {\n\n\t\t\t\t\t\t// Support: IE, Opera, Webkit\n\t\t\t\t\t\t// TODO: identify versions\n\t\t\t\t\t\t// getElementById can match elements by name instead of ID\n\t\t\t\t\t\tif ( newContext && ( elem = newContext.getElementById( m ) ) &&\n\t\t\t\t\t\t\tcontains( context, elem ) &&\n\t\t\t\t\t\t\telem.id === m ) {\n\n\t\t\t\t\t\t\tresults.push( elem );\n\t\t\t\t\t\t\treturn results;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t// Type selector\n\t\t\t\t} else if ( match[ 2 ] ) {\n\t\t\t\t\tpush.apply( results, context.getElementsByTagName( selector ) );\n\t\t\t\t\treturn results;\n\n\t\t\t\t// Class selector\n\t\t\t\t} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&\n\t\t\t\t\tcontext.getElementsByClassName ) {\n\n\t\t\t\t\tpush.apply( results, context.getElementsByClassName( m ) );\n\t\t\t\t\treturn results;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Take advantage of querySelectorAll\n\t\t\tif ( support.qsa &&\n\t\t\t\t!nonnativeSelectorCache[ selector + \" \" ] &&\n\t\t\t\t( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&\n\n\t\t\t\t// Support: IE 8 only\n\t\t\t\t// Exclude object elements\n\t\t\t\t( nodeType !== 1 || context.nodeName.toLowerCase() !== \"object\" ) ) {\n\n\t\t\t\tnewSelector = selector;\n\t\t\t\tnewContext = context;\n\n\t\t\t\t// qSA considers elements outside a scoping root when evaluating child or\n\t\t\t\t// descendant combinators, which is not what we want.\n\t\t\t\t// In such cases, we work around the behavior by prefixing every selector in the\n\t\t\t\t// list with an ID selector referencing the scope context.\n\t\t\t\t// The technique has to be used as well when a leading combinator is used\n\t\t\t\t// as such selectors are not recognized by querySelectorAll.\n\t\t\t\t// Thanks to Andrew Dupont for this technique.\n\t\t\t\tif ( nodeType === 1 &&\n\t\t\t\t\t( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {\n\n\t\t\t\t\t// Expand context for sibling selectors\n\t\t\t\t\tnewContext = rsibling.test( selector ) && testContext( context.parentNode ) ||\n\t\t\t\t\t\tcontext;\n\n\t\t\t\t\t// We can use :scope instead of the ID hack if the browser\n\t\t\t\t\t// supports it & if we're not changing the context.\n\t\t\t\t\tif ( newContext !== context || !support.scope ) {\n\n\t\t\t\t\t\t// Capture the context ID, setting it first if necessary\n\t\t\t\t\t\tif ( ( nid = context.getAttribute( \"id\" ) ) ) {\n\t\t\t\t\t\t\tnid = nid.replace( rcssescape, fcssescape );\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tcontext.setAttribute( \"id\", ( nid = expando ) );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Prefix every selector in the list\n\t\t\t\t\tgroups = tokenize( selector );\n\t\t\t\t\ti = groups.length;\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tgroups[ i ] = ( nid ? \"#\" + nid : \":scope\" ) + \" \" +\n\t\t\t\t\t\t\ttoSelector( groups[ i ] );\n\t\t\t\t\t}\n\t\t\t\t\tnewSelector = groups.join( \",\" );\n\t\t\t\t}\n\n\t\t\t\ttry {\n\t\t\t\t\tpush.apply( results,\n\t\t\t\t\t\tnewContext.querySelectorAll( newSelector )\n\t\t\t\t\t);\n\t\t\t\t\treturn results;\n\t\t\t\t} catch ( qsaError ) {\n\t\t\t\t\tnonnativeSelectorCache( selector, true );\n\t\t\t\t} finally {\n\t\t\t\t\tif ( nid === expando ) {\n\t\t\t\t\t\tcontext.removeAttribute( \"id\" );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// All others\n\treturn select( selector.replace( rtrim, \"$1\" ), context, results, seed );\n}\n\n/**\n * Create key-value caches of limited size\n * @returns {function(string, object)} Returns the Object data after storing it on itself with\n *\tproperty name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)\n *\tdeleting the oldest entry\n */\nfunction createCache() {\n\tvar keys = [];\n\n\tfunction cache( key, value ) {\n\n\t\t// Use (key + \" \") to avoid collision with native prototype properties (see Issue #157)\n\t\tif ( keys.push( key + \" \" ) > Expr.cacheLength ) {\n\n\t\t\t// Only keep the most recent entries\n\t\t\tdelete cache[ keys.shift() ];\n\t\t}\n\t\treturn ( cache[ key + \" \" ] = value );\n\t}\n\treturn cache;\n}\n\n/**\n * Mark a function for special use by Sizzle\n * @param {Function} fn The function to mark\n */\nfunction markFunction( fn ) {\n\tfn[ expando ] = true;\n\treturn fn;\n}\n\n/**\n * Support testing using an element\n * @param {Function} fn Passed the created element and returns a boolean result\n */\nfunction assert( fn ) {\n\tvar el = document.createElement( \"fieldset\" );\n\n\ttry {\n\t\treturn !!fn( el );\n\t} catch ( e ) {\n\t\treturn false;\n\t} finally {\n\n\t\t// Remove from its parent by default\n\t\tif ( el.parentNode ) {\n\t\t\tel.parentNode.removeChild( el );\n\t\t}\n\n\t\t// release memory in IE\n\t\tel = null;\n\t}\n}\n\n/**\n * Adds the same handler for all of the specified attrs\n * @param {String} attrs Pipe-separated list of attributes\n * @param {Function} handler The method that will be applied\n */\nfunction addHandle( attrs, handler ) {\n\tvar arr = attrs.split( \"|\" ),\n\t\ti = arr.length;\n\n\twhile ( i-- ) {\n\t\tExpr.attrHandle[ arr[ i ] ] = handler;\n\t}\n}\n\n/**\n * Checks document order of two siblings\n * @param {Element} a\n * @param {Element} b\n * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b\n */\nfunction siblingCheck( a, b ) {\n\tvar cur = b && a,\n\t\tdiff = cur && a.nodeType === 1 && b.nodeType === 1 &&\n\t\t\ta.sourceIndex - b.sourceIndex;\n\n\t// Use IE sourceIndex if available on both nodes\n\tif ( diff ) {\n\t\treturn diff;\n\t}\n\n\t// Check if b follows a\n\tif ( cur ) {\n\t\twhile ( ( cur = cur.nextSibling ) ) {\n\t\t\tif ( cur === b ) {\n\t\t\t\treturn -1;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn a ? 1 : -1;\n}\n\n/**\n * Returns a function to use in pseudos for input types\n * @param {String} type\n */\nfunction createInputPseudo( type ) {\n\treturn function( elem ) {\n\t\tvar name = elem.nodeName.toLowerCase();\n\t\treturn name === \"input\" && elem.type === type;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for buttons\n * @param {String} type\n */\nfunction createButtonPseudo( type ) {\n\treturn function( elem ) {\n\t\tvar name = elem.nodeName.toLowerCase();\n\t\treturn ( name === \"input\" || name === \"button\" ) && elem.type === type;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for :enabled/:disabled\n * @param {Boolean} disabled true for :disabled; false for :enabled\n */\nfunction createDisabledPseudo( disabled ) {\n\n\t// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable\n\treturn function( elem ) {\n\n\t\t// Only certain elements can match :enabled or :disabled\n\t\t// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled\n\t\t// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled\n\t\tif ( \"form\" in elem ) {\n\n\t\t\t// Check for inherited disabledness on relevant non-disabled elements:\n\t\t\t// * listed form-associated elements in a disabled fieldset\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#category-listed\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled\n\t\t\t// * option elements in a disabled optgroup\n\t\t\t// https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled\n\t\t\t// All such elements have a \"form\" property.\n\t\t\tif ( elem.parentNode && elem.disabled === false ) {\n\n\t\t\t\t// Option elements defer to a parent optgroup if present\n\t\t\t\tif ( \"label\" in elem ) {\n\t\t\t\t\tif ( \"label\" in elem.parentNode ) {\n\t\t\t\t\t\treturn elem.parentNode.disabled === disabled;\n\t\t\t\t\t} else {\n\t\t\t\t\t\treturn elem.disabled === disabled;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Support: IE 6 - 11\n\t\t\t\t// Use the isDisabled shortcut property to check for disabled fieldset ancestors\n\t\t\t\treturn elem.isDisabled === disabled ||\n\n\t\t\t\t\t// Where there is no isDisabled, check manually\n\t\t\t\t\t/* jshint -W018 */\n\t\t\t\t\telem.isDisabled !== !disabled &&\n\t\t\t\t\tinDisabledFieldset( elem ) === disabled;\n\t\t\t}\n\n\t\t\treturn elem.disabled === disabled;\n\n\t\t// Try to winnow out elements that can't be disabled before trusting the disabled property.\n\t\t// Some victims get caught in our net (label, legend, menu, track), but it shouldn't\n\t\t// even exist on them, let alone have a boolean value.\n\t\t} else if ( \"label\" in elem ) {\n\t\t\treturn elem.disabled === disabled;\n\t\t}\n\n\t\t// Remaining elements are neither :enabled nor :disabled\n\t\treturn false;\n\t};\n}\n\n/**\n * Returns a function to use in pseudos for positionals\n * @param {Function} fn\n */\nfunction createPositionalPseudo( fn ) {\n\treturn markFunction( function( argument ) {\n\t\targument = +argument;\n\t\treturn markFunction( function( seed, matches ) {\n\t\t\tvar j,\n\t\t\t\tmatchIndexes = fn( [], seed.length, argument ),\n\t\t\t\ti = matchIndexes.length;\n\n\t\t\t// Match elements found at the specified indexes\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( seed[ ( j = matchIndexes[ i ] ) ] ) {\n\t\t\t\t\tseed[ j ] = !( matches[ j ] = seed[ j ] );\n\t\t\t\t}\n\t\t\t}\n\t\t} );\n\t} );\n}\n\n/**\n * Checks a node for validity as a Sizzle context\n * @param {Element|Object=} context\n * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value\n */\nfunction testContext( context ) {\n\treturn context && typeof context.getElementsByTagName !== \"undefined\" && context;\n}\n\n// Expose support vars for convenience\nsupport = Sizzle.support = {};\n\n/**\n * Detects XML nodes\n * @param {Element|Object} elem An element or a document\n * @returns {Boolean} True iff elem is a non-HTML XML node\n */\nisXML = Sizzle.isXML = function( elem ) {\n\tvar namespace = elem.namespaceURI,\n\t\tdocElem = ( elem.ownerDocument || elem ).documentElement;\n\n\t// Support: IE <=8\n\t// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes\n\t// https://bugs.jquery.com/ticket/4833\n\treturn !rhtml.test( namespace || docElem && docElem.nodeName || \"HTML\" );\n};\n\n/**\n * Sets document-related variables once based on the current document\n * @param {Element|Object} [doc] An element or document object to use to set the document\n * @returns {Object} Returns the current document\n */\nsetDocument = Sizzle.setDocument = function( node ) {\n\tvar hasCompare, subWindow,\n\t\tdoc = node ? node.ownerDocument || node : preferredDoc;\n\n\t// Return early if doc is invalid or already selected\n\t// Support: IE 11+, Edge 17 - 18+\n\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t// two documents; shallow comparisons work.\n\t// eslint-disable-next-line eqeqeq\n\tif ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {\n\t\treturn document;\n\t}\n\n\t// Update global variables\n\tdocument = doc;\n\tdocElem = document.documentElement;\n\tdocumentIsHTML = !isXML( document );\n\n\t// Support: IE 9 - 11+, Edge 12 - 18+\n\t// Accessing iframe documents after unload throws \"permission denied\" errors (jQuery #13936)\n\t// Support: IE 11+, Edge 17 - 18+\n\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t// two documents; shallow comparisons work.\n\t// eslint-disable-next-line eqeqeq\n\tif ( preferredDoc != document &&\n\t\t( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {\n\n\t\t// Support: IE 11, Edge\n\t\tif ( subWindow.addEventListener ) {\n\t\t\tsubWindow.addEventListener( \"unload\", unloadHandler, false );\n\n\t\t// Support: IE 9 - 10 only\n\t\t} else if ( subWindow.attachEvent ) {\n\t\t\tsubWindow.attachEvent( \"onunload\", unloadHandler );\n\t\t}\n\t}\n\n\t// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,\n\t// Safari 4 - 5 only, Opera <=11.6 - 12.x only\n\t// IE/Edge & older browsers don't support the :scope pseudo-class.\n\t// Support: Safari 6.0 only\n\t// Safari 6.0 supports :scope but it's an alias of :root there.\n\tsupport.scope = assert( function( el ) {\n\t\tdocElem.appendChild( el ).appendChild( document.createElement( \"div\" ) );\n\t\treturn typeof el.querySelectorAll !== \"undefined\" &&\n\t\t\t!el.querySelectorAll( \":scope fieldset div\" ).length;\n\t} );\n\n\t/* Attributes\n\t---------------------------------------------------------------------- */\n\n\t// Support: IE<8\n\t// Verify that getAttribute really returns attributes and not properties\n\t// (excepting IE8 booleans)\n\tsupport.attributes = assert( function( el ) {\n\t\tel.className = \"i\";\n\t\treturn !el.getAttribute( \"className\" );\n\t} );\n\n\t/* getElement(s)By*\n\t---------------------------------------------------------------------- */\n\n\t// Check if getElementsByTagName(\"*\") returns only elements\n\tsupport.getElementsByTagName = assert( function( el ) {\n\t\tel.appendChild( document.createComment( \"\" ) );\n\t\treturn !el.getElementsByTagName( \"*\" ).length;\n\t} );\n\n\t// Support: IE<9\n\tsupport.getElementsByClassName = rnative.test( document.getElementsByClassName );\n\n\t// Support: IE<10\n\t// Check if getElementById returns elements by name\n\t// The broken getElementById methods don't pick up programmatically-set names,\n\t// so use a roundabout getElementsByName test\n\tsupport.getById = assert( function( el ) {\n\t\tdocElem.appendChild( el ).id = expando;\n\t\treturn !document.getElementsByName || !document.getElementsByName( expando ).length;\n\t} );\n\n\t// ID filter and find\n\tif ( support.getById ) {\n\t\tExpr.filter[ \"ID\" ] = function( id ) {\n\t\t\tvar attrId = id.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\treturn elem.getAttribute( \"id\" ) === attrId;\n\t\t\t};\n\t\t};\n\t\tExpr.find[ \"ID\" ] = function( id, context ) {\n\t\t\tif ( typeof context.getElementById !== \"undefined\" && documentIsHTML ) {\n\t\t\t\tvar elem = context.getElementById( id );\n\t\t\t\treturn elem ? [ elem ] : [];\n\t\t\t}\n\t\t};\n\t} else {\n\t\tExpr.filter[ \"ID\" ] = function( id ) {\n\t\t\tvar attrId = id.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\tvar node = typeof elem.getAttributeNode !== \"undefined\" &&\n\t\t\t\t\telem.getAttributeNode( \"id\" );\n\t\t\t\treturn node && node.value === attrId;\n\t\t\t};\n\t\t};\n\n\t\t// Support: IE 6 - 7 only\n\t\t// getElementById is not reliable as a find shortcut\n\t\tExpr.find[ \"ID\" ] = function( id, context ) {\n\t\t\tif ( typeof context.getElementById !== \"undefined\" && documentIsHTML ) {\n\t\t\t\tvar node, i, elems,\n\t\t\t\t\telem = context.getElementById( id );\n\n\t\t\t\tif ( elem ) {\n\n\t\t\t\t\t// Verify the id attribute\n\t\t\t\t\tnode = elem.getAttributeNode( \"id\" );\n\t\t\t\t\tif ( node && node.value === id ) {\n\t\t\t\t\t\treturn [ elem ];\n\t\t\t\t\t}\n\n\t\t\t\t\t// Fall back on getElementsByName\n\t\t\t\t\telems = context.getElementsByName( id );\n\t\t\t\t\ti = 0;\n\t\t\t\t\twhile ( ( elem = elems[ i++ ] ) ) {\n\t\t\t\t\t\tnode = elem.getAttributeNode( \"id\" );\n\t\t\t\t\t\tif ( node && node.value === id ) {\n\t\t\t\t\t\t\treturn [ elem ];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\treturn [];\n\t\t\t}\n\t\t};\n\t}\n\n\t// Tag\n\tExpr.find[ \"TAG\" ] = support.getElementsByTagName ?\n\t\tfunction( tag, context ) {\n\t\t\tif ( typeof context.getElementsByTagName !== \"undefined\" ) {\n\t\t\t\treturn context.getElementsByTagName( tag );\n\n\t\t\t// DocumentFragment nodes don't have gEBTN\n\t\t\t} else if ( support.qsa ) {\n\t\t\t\treturn context.querySelectorAll( tag );\n\t\t\t}\n\t\t} :\n\n\t\tfunction( tag, context ) {\n\t\t\tvar elem,\n\t\t\t\ttmp = [],\n\t\t\t\ti = 0,\n\n\t\t\t\t// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too\n\t\t\t\tresults = context.getElementsByTagName( tag );\n\n\t\t\t// Filter out possible comments\n\t\t\tif ( tag === \"*\" ) {\n\t\t\t\twhile ( ( elem = results[ i++ ] ) ) {\n\t\t\t\t\tif ( elem.nodeType === 1 ) {\n\t\t\t\t\t\ttmp.push( elem );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\treturn tmp;\n\t\t\t}\n\t\t\treturn results;\n\t\t};\n\n\t// Class\n\tExpr.find[ \"CLASS\" ] = support.getElementsByClassName && function( className, context ) {\n\t\tif ( typeof context.getElementsByClassName !== \"undefined\" && documentIsHTML ) {\n\t\t\treturn context.getElementsByClassName( className );\n\t\t}\n\t};\n\n\t/* QSA/matchesSelector\n\t---------------------------------------------------------------------- */\n\n\t// QSA and matchesSelector support\n\n\t// matchesSelector(:active) reports false when true (IE9/Opera 11.5)\n\trbuggyMatches = [];\n\n\t// qSa(:focus) reports false when true (Chrome 21)\n\t// We allow this because of a bug in IE8/9 that throws an error\n\t// whenever `document.activeElement` is accessed on an iframe\n\t// So, we allow :focus to pass through QSA all the time to avoid the IE error\n\t// See https://bugs.jquery.com/ticket/13378\n\trbuggyQSA = [];\n\n\tif ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {\n\n\t\t// Build QSA regex\n\t\t// Regex strategy adopted from Diego Perini\n\t\tassert( function( el ) {\n\n\t\t\tvar input;\n\n\t\t\t// Select is set to empty string on purpose\n\t\t\t// This is to test IE's treatment of not explicitly\n\t\t\t// setting a boolean content attribute,\n\t\t\t// since its presence should be enough\n\t\t\t// https://bugs.jquery.com/ticket/12359\n\t\t\tdocElem.appendChild( el ).innerHTML = \"\" +\n\t\t\t\t\"\";\n\n\t\t\t// Support: IE8, Opera 11-12.16\n\t\t\t// Nothing should be selected when empty strings follow ^= or $= or *=\n\t\t\t// The test attribute must be unknown in Opera but \"safe\" for WinRT\n\t\t\t// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section\n\t\t\tif ( el.querySelectorAll( \"[msallowcapture^='']\" ).length ) {\n\t\t\t\trbuggyQSA.push( \"[*^$]=\" + whitespace + \"*(?:''|\\\"\\\")\" );\n\t\t\t}\n\n\t\t\t// Support: IE8\n\t\t\t// Boolean attributes and \"value\" are not treated correctly\n\t\t\tif ( !el.querySelectorAll( \"[selected]\" ).length ) {\n\t\t\t\trbuggyQSA.push( \"\\\\[\" + whitespace + \"*(?:value|\" + booleans + \")\" );\n\t\t\t}\n\n\t\t\t// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+\n\t\t\tif ( !el.querySelectorAll( \"[id~=\" + expando + \"-]\" ).length ) {\n\t\t\t\trbuggyQSA.push( \"~=\" );\n\t\t\t}\n\n\t\t\t// Support: IE 11+, Edge 15 - 18+\n\t\t\t// IE 11/Edge don't find elements on a `[name='']` query in some cases.\n\t\t\t// Adding a temporary attribute to the document before the selection works\n\t\t\t// around the issue.\n\t\t\t// Interestingly, IE 10 & older don't seem to have the issue.\n\t\t\tinput = document.createElement( \"input\" );\n\t\t\tinput.setAttribute( \"name\", \"\" );\n\t\t\tel.appendChild( input );\n\t\t\tif ( !el.querySelectorAll( \"[name='']\" ).length ) {\n\t\t\t\trbuggyQSA.push( \"\\\\[\" + whitespace + \"*name\" + whitespace + \"*=\" +\n\t\t\t\t\twhitespace + \"*(?:''|\\\"\\\")\" );\n\t\t\t}\n\n\t\t\t// Webkit/Opera - :checked should return selected option elements\n\t\t\t// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked\n\t\t\t// IE8 throws error here and will not see later tests\n\t\t\tif ( !el.querySelectorAll( \":checked\" ).length ) {\n\t\t\t\trbuggyQSA.push( \":checked\" );\n\t\t\t}\n\n\t\t\t// Support: Safari 8+, iOS 8+\n\t\t\t// https://bugs.webkit.org/show_bug.cgi?id=136851\n\t\t\t// In-page `selector#id sibling-combinator selector` fails\n\t\t\tif ( !el.querySelectorAll( \"a#\" + expando + \"+*\" ).length ) {\n\t\t\t\trbuggyQSA.push( \".#.+[+~]\" );\n\t\t\t}\n\n\t\t\t// Support: Firefox <=3.6 - 5 only\n\t\t\t// Old Firefox doesn't throw on a badly-escaped identifier.\n\t\t\tel.querySelectorAll( \"\\\\\\f\" );\n\t\t\trbuggyQSA.push( \"[\\\\r\\\\n\\\\f]\" );\n\t\t} );\n\n\t\tassert( function( el ) {\n\t\t\tel.innerHTML = \"\" +\n\t\t\t\t\"\";\n\n\t\t\t// Support: Windows 8 Native Apps\n\t\t\t// The type and name attributes are restricted during .innerHTML assignment\n\t\t\tvar input = document.createElement( \"input\" );\n\t\t\tinput.setAttribute( \"type\", \"hidden\" );\n\t\t\tel.appendChild( input ).setAttribute( \"name\", \"D\" );\n\n\t\t\t// Support: IE8\n\t\t\t// Enforce case-sensitivity of name attribute\n\t\t\tif ( el.querySelectorAll( \"[name=d]\" ).length ) {\n\t\t\t\trbuggyQSA.push( \"name\" + whitespace + \"*[*^$|!~]?=\" );\n\t\t\t}\n\n\t\t\t// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)\n\t\t\t// IE8 throws error here and will not see later tests\n\t\t\tif ( el.querySelectorAll( \":enabled\" ).length !== 2 ) {\n\t\t\t\trbuggyQSA.push( \":enabled\", \":disabled\" );\n\t\t\t}\n\n\t\t\t// Support: IE9-11+\n\t\t\t// IE's :disabled selector does not pick up the children of disabled fieldsets\n\t\t\tdocElem.appendChild( el ).disabled = true;\n\t\t\tif ( el.querySelectorAll( \":disabled\" ).length !== 2 ) {\n\t\t\t\trbuggyQSA.push( \":enabled\", \":disabled\" );\n\t\t\t}\n\n\t\t\t// Support: Opera 10 - 11 only\n\t\t\t// Opera 10-11 does not throw on post-comma invalid pseudos\n\t\t\tel.querySelectorAll( \"*,:x\" );\n\t\t\trbuggyQSA.push( \",.*:\" );\n\t\t} );\n\t}\n\n\tif ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||\n\t\tdocElem.webkitMatchesSelector ||\n\t\tdocElem.mozMatchesSelector ||\n\t\tdocElem.oMatchesSelector ||\n\t\tdocElem.msMatchesSelector ) ) ) ) {\n\n\t\tassert( function( el ) {\n\n\t\t\t// Check to see if it's possible to do matchesSelector\n\t\t\t// on a disconnected node (IE 9)\n\t\t\tsupport.disconnectedMatch = matches.call( el, \"*\" );\n\n\t\t\t// This should fail with an exception\n\t\t\t// Gecko does not error, returns false instead\n\t\t\tmatches.call( el, \"[s!='']:x\" );\n\t\t\trbuggyMatches.push( \"!=\", pseudos );\n\t\t} );\n\t}\n\n\trbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( \"|\" ) );\n\trbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( \"|\" ) );\n\n\t/* Contains\n\t---------------------------------------------------------------------- */\n\thasCompare = rnative.test( docElem.compareDocumentPosition );\n\n\t// Element contains another\n\t// Purposefully self-exclusive\n\t// As in, an element does not contain itself\n\tcontains = hasCompare || rnative.test( docElem.contains ) ?\n\t\tfunction( a, b ) {\n\t\t\tvar adown = a.nodeType === 9 ? a.documentElement : a,\n\t\t\t\tbup = b && b.parentNode;\n\t\t\treturn a === bup || !!( bup && bup.nodeType === 1 && (\n\t\t\t\tadown.contains ?\n\t\t\t\t\tadown.contains( bup ) :\n\t\t\t\t\ta.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16\n\t\t\t) );\n\t\t} :\n\t\tfunction( a, b ) {\n\t\t\tif ( b ) {\n\t\t\t\twhile ( ( b = b.parentNode ) ) {\n\t\t\t\t\tif ( b === a ) {\n\t\t\t\t\t\treturn true;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t};\n\n\t/* Sorting\n\t---------------------------------------------------------------------- */\n\n\t// Document order sorting\n\tsortOrder = hasCompare ?\n\tfunction( a, b ) {\n\n\t\t// Flag for duplicate removal\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t\treturn 0;\n\t\t}\n\n\t\t// Sort on method existence if only one input has compareDocumentPosition\n\t\tvar compare = !a.compareDocumentPosition - !b.compareDocumentPosition;\n\t\tif ( compare ) {\n\t\t\treturn compare;\n\t\t}\n\n\t\t// Calculate position if both inputs belong to the same document\n\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t// two documents; shallow comparisons work.\n\t\t// eslint-disable-next-line eqeqeq\n\t\tcompare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?\n\t\t\ta.compareDocumentPosition( b ) :\n\n\t\t\t// Otherwise we know they are disconnected\n\t\t\t1;\n\n\t\t// Disconnected nodes\n\t\tif ( compare & 1 ||\n\t\t\t( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {\n\n\t\t\t// Choose the first element that is related to our preferred document\n\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t// two documents; shallow comparisons work.\n\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\tif ( a == document || a.ownerDocument == preferredDoc &&\n\t\t\t\tcontains( preferredDoc, a ) ) {\n\t\t\t\treturn -1;\n\t\t\t}\n\n\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t// two documents; shallow comparisons work.\n\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\tif ( b == document || b.ownerDocument == preferredDoc &&\n\t\t\t\tcontains( preferredDoc, b ) ) {\n\t\t\t\treturn 1;\n\t\t\t}\n\n\t\t\t// Maintain original order\n\t\t\treturn sortInput ?\n\t\t\t\t( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :\n\t\t\t\t0;\n\t\t}\n\n\t\treturn compare & 4 ? -1 : 1;\n\t} :\n\tfunction( a, b ) {\n\n\t\t// Exit early if the nodes are identical\n\t\tif ( a === b ) {\n\t\t\thasDuplicate = true;\n\t\t\treturn 0;\n\t\t}\n\n\t\tvar cur,\n\t\t\ti = 0,\n\t\t\taup = a.parentNode,\n\t\t\tbup = b.parentNode,\n\t\t\tap = [ a ],\n\t\t\tbp = [ b ];\n\n\t\t// Parentless nodes are either documents or disconnected\n\t\tif ( !aup || !bup ) {\n\n\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t// two documents; shallow comparisons work.\n\t\t\t/* eslint-disable eqeqeq */\n\t\t\treturn a == document ? -1 :\n\t\t\t\tb == document ? 1 :\n\t\t\t\t/* eslint-enable eqeqeq */\n\t\t\t\taup ? -1 :\n\t\t\t\tbup ? 1 :\n\t\t\t\tsortInput ?\n\t\t\t\t( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :\n\t\t\t\t0;\n\n\t\t// If the nodes are siblings, we can do a quick check\n\t\t} else if ( aup === bup ) {\n\t\t\treturn siblingCheck( a, b );\n\t\t}\n\n\t\t// Otherwise we need full lists of their ancestors for comparison\n\t\tcur = a;\n\t\twhile ( ( cur = cur.parentNode ) ) {\n\t\t\tap.unshift( cur );\n\t\t}\n\t\tcur = b;\n\t\twhile ( ( cur = cur.parentNode ) ) {\n\t\t\tbp.unshift( cur );\n\t\t}\n\n\t\t// Walk down the tree looking for a discrepancy\n\t\twhile ( ap[ i ] === bp[ i ] ) {\n\t\t\ti++;\n\t\t}\n\n\t\treturn i ?\n\n\t\t\t// Do a sibling check if the nodes have a common ancestor\n\t\t\tsiblingCheck( ap[ i ], bp[ i ] ) :\n\n\t\t\t// Otherwise nodes in our document sort first\n\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t// two documents; shallow comparisons work.\n\t\t\t/* eslint-disable eqeqeq */\n\t\t\tap[ i ] == preferredDoc ? -1 :\n\t\t\tbp[ i ] == preferredDoc ? 1 :\n\t\t\t/* eslint-enable eqeqeq */\n\t\t\t0;\n\t};\n\n\treturn document;\n};\n\nSizzle.matches = function( expr, elements ) {\n\treturn Sizzle( expr, null, null, elements );\n};\n\nSizzle.matchesSelector = function( elem, expr ) {\n\tsetDocument( elem );\n\n\tif ( support.matchesSelector && documentIsHTML &&\n\t\t!nonnativeSelectorCache[ expr + \" \" ] &&\n\t\t( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&\n\t\t( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) {\n\n\t\ttry {\n\t\t\tvar ret = matches.call( elem, expr );\n\n\t\t\t// IE 9's matchesSelector returns false on disconnected nodes\n\t\t\tif ( ret || support.disconnectedMatch ||\n\n\t\t\t\t// As well, disconnected nodes are said to be in a document\n\t\t\t\t// fragment in IE 9\n\t\t\t\telem.document && elem.document.nodeType !== 11 ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\t\t} catch ( e ) {\n\t\t\tnonnativeSelectorCache( expr, true );\n\t\t}\n\t}\n\n\treturn Sizzle( expr, document, null, [ elem ] ).length > 0;\n};\n\nSizzle.contains = function( context, elem ) {\n\n\t// Set document vars if needed\n\t// Support: IE 11+, Edge 17 - 18+\n\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t// two documents; shallow comparisons work.\n\t// eslint-disable-next-line eqeqeq\n\tif ( ( context.ownerDocument || context ) != document ) {\n\t\tsetDocument( context );\n\t}\n\treturn contains( context, elem );\n};\n\nSizzle.attr = function( elem, name ) {\n\n\t// Set document vars if needed\n\t// Support: IE 11+, Edge 17 - 18+\n\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t// two documents; shallow comparisons work.\n\t// eslint-disable-next-line eqeqeq\n\tif ( ( elem.ownerDocument || elem ) != document ) {\n\t\tsetDocument( elem );\n\t}\n\n\tvar fn = Expr.attrHandle[ name.toLowerCase() ],\n\n\t\t// Don't get fooled by Object.prototype properties (jQuery #13807)\n\t\tval = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?\n\t\t\tfn( elem, name, !documentIsHTML ) :\n\t\t\tundefined;\n\n\treturn val !== undefined ?\n\t\tval :\n\t\tsupport.attributes || !documentIsHTML ?\n\t\t\telem.getAttribute( name ) :\n\t\t\t( val = elem.getAttributeNode( name ) ) && val.specified ?\n\t\t\t\tval.value :\n\t\t\t\tnull;\n};\n\nSizzle.escape = function( sel ) {\n\treturn ( sel + \"\" ).replace( rcssescape, fcssescape );\n};\n\nSizzle.error = function( msg ) {\n\tthrow new Error( \"Syntax error, unrecognized expression: \" + msg );\n};\n\n/**\n * Document sorting and removing duplicates\n * @param {ArrayLike} results\n */\nSizzle.uniqueSort = function( results ) {\n\tvar elem,\n\t\tduplicates = [],\n\t\tj = 0,\n\t\ti = 0;\n\n\t// Unless we *know* we can detect duplicates, assume their presence\n\thasDuplicate = !support.detectDuplicates;\n\tsortInput = !support.sortStable && results.slice( 0 );\n\tresults.sort( sortOrder );\n\n\tif ( hasDuplicate ) {\n\t\twhile ( ( elem = results[ i++ ] ) ) {\n\t\t\tif ( elem === results[ i ] ) {\n\t\t\t\tj = duplicates.push( i );\n\t\t\t}\n\t\t}\n\t\twhile ( j-- ) {\n\t\t\tresults.splice( duplicates[ j ], 1 );\n\t\t}\n\t}\n\n\t// Clear input after sorting to release objects\n\t// See https://github.com/jquery/sizzle/pull/225\n\tsortInput = null;\n\n\treturn results;\n};\n\n/**\n * Utility function for retrieving the text value of an array of DOM nodes\n * @param {Array|Element} elem\n */\ngetText = Sizzle.getText = function( elem ) {\n\tvar node,\n\t\tret = \"\",\n\t\ti = 0,\n\t\tnodeType = elem.nodeType;\n\n\tif ( !nodeType ) {\n\n\t\t// If no nodeType, this is expected to be an array\n\t\twhile ( ( node = elem[ i++ ] ) ) {\n\n\t\t\t// Do not traverse comment nodes\n\t\t\tret += getText( node );\n\t\t}\n\t} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {\n\n\t\t// Use textContent for elements\n\t\t// innerText usage removed for consistency of new lines (jQuery #11153)\n\t\tif ( typeof elem.textContent === \"string\" ) {\n\t\t\treturn elem.textContent;\n\t\t} else {\n\n\t\t\t// Traverse its children\n\t\t\tfor ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {\n\t\t\t\tret += getText( elem );\n\t\t\t}\n\t\t}\n\t} else if ( nodeType === 3 || nodeType === 4 ) {\n\t\treturn elem.nodeValue;\n\t}\n\n\t// Do not include comment or processing instruction nodes\n\n\treturn ret;\n};\n\nExpr = Sizzle.selectors = {\n\n\t// Can be adjusted by the user\n\tcacheLength: 50,\n\n\tcreatePseudo: markFunction,\n\n\tmatch: matchExpr,\n\n\tattrHandle: {},\n\n\tfind: {},\n\n\trelative: {\n\t\t\">\": { dir: \"parentNode\", first: true },\n\t\t\" \": { dir: \"parentNode\" },\n\t\t\"+\": { dir: \"previousSibling\", first: true },\n\t\t\"~\": { dir: \"previousSibling\" }\n\t},\n\n\tpreFilter: {\n\t\t\"ATTR\": function( match ) {\n\t\t\tmatch[ 1 ] = match[ 1 ].replace( runescape, funescape );\n\n\t\t\t// Move the given value to match[3] whether quoted or unquoted\n\t\t\tmatch[ 3 ] = ( match[ 3 ] || match[ 4 ] ||\n\t\t\t\tmatch[ 5 ] || \"\" ).replace( runescape, funescape );\n\n\t\t\tif ( match[ 2 ] === \"~=\" ) {\n\t\t\t\tmatch[ 3 ] = \" \" + match[ 3 ] + \" \";\n\t\t\t}\n\n\t\t\treturn match.slice( 0, 4 );\n\t\t},\n\n\t\t\"CHILD\": function( match ) {\n\n\t\t\t/* matches from matchExpr[\"CHILD\"]\n\t\t\t\t1 type (only|nth|...)\n\t\t\t\t2 what (child|of-type)\n\t\t\t\t3 argument (even|odd|\\d*|\\d*n([+-]\\d+)?|...)\n\t\t\t\t4 xn-component of xn+y argument ([+-]?\\d*n|)\n\t\t\t\t5 sign of xn-component\n\t\t\t\t6 x of xn-component\n\t\t\t\t7 sign of y-component\n\t\t\t\t8 y of y-component\n\t\t\t*/\n\t\t\tmatch[ 1 ] = match[ 1 ].toLowerCase();\n\n\t\t\tif ( match[ 1 ].slice( 0, 3 ) === \"nth\" ) {\n\n\t\t\t\t// nth-* requires argument\n\t\t\t\tif ( !match[ 3 ] ) {\n\t\t\t\t\tSizzle.error( match[ 0 ] );\n\t\t\t\t}\n\n\t\t\t\t// numeric x and y parameters for Expr.filter.CHILD\n\t\t\t\t// remember that false/true cast respectively to 0/1\n\t\t\t\tmatch[ 4 ] = +( match[ 4 ] ?\n\t\t\t\t\tmatch[ 5 ] + ( match[ 6 ] || 1 ) :\n\t\t\t\t\t2 * ( match[ 3 ] === \"even\" || match[ 3 ] === \"odd\" ) );\n\t\t\t\tmatch[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === \"odd\" );\n\n\t\t\t\t// other types prohibit arguments\n\t\t\t} else if ( match[ 3 ] ) {\n\t\t\t\tSizzle.error( match[ 0 ] );\n\t\t\t}\n\n\t\t\treturn match;\n\t\t},\n\n\t\t\"PSEUDO\": function( match ) {\n\t\t\tvar excess,\n\t\t\t\tunquoted = !match[ 6 ] && match[ 2 ];\n\n\t\t\tif ( matchExpr[ \"CHILD\" ].test( match[ 0 ] ) ) {\n\t\t\t\treturn null;\n\t\t\t}\n\n\t\t\t// Accept quoted arguments as-is\n\t\t\tif ( match[ 3 ] ) {\n\t\t\t\tmatch[ 2 ] = match[ 4 ] || match[ 5 ] || \"\";\n\n\t\t\t// Strip excess characters from unquoted arguments\n\t\t\t} else if ( unquoted && rpseudo.test( unquoted ) &&\n\n\t\t\t\t// Get excess from tokenize (recursively)\n\t\t\t\t( excess = tokenize( unquoted, true ) ) &&\n\n\t\t\t\t// advance to the next closing parenthesis\n\t\t\t\t( excess = unquoted.indexOf( \")\", unquoted.length - excess ) - unquoted.length ) ) {\n\n\t\t\t\t// excess is a negative index\n\t\t\t\tmatch[ 0 ] = match[ 0 ].slice( 0, excess );\n\t\t\t\tmatch[ 2 ] = unquoted.slice( 0, excess );\n\t\t\t}\n\n\t\t\t// Return only captures needed by the pseudo filter method (type and argument)\n\t\t\treturn match.slice( 0, 3 );\n\t\t}\n\t},\n\n\tfilter: {\n\n\t\t\"TAG\": function( nodeNameSelector ) {\n\t\t\tvar nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();\n\t\t\treturn nodeNameSelector === \"*\" ?\n\t\t\t\tfunction() {\n\t\t\t\t\treturn true;\n\t\t\t\t} :\n\t\t\t\tfunction( elem ) {\n\t\t\t\t\treturn elem.nodeName && elem.nodeName.toLowerCase() === nodeName;\n\t\t\t\t};\n\t\t},\n\n\t\t\"CLASS\": function( className ) {\n\t\t\tvar pattern = classCache[ className + \" \" ];\n\n\t\t\treturn pattern ||\n\t\t\t\t( pattern = new RegExp( \"(^|\" + whitespace +\n\t\t\t\t\t\")\" + className + \"(\" + whitespace + \"|$)\" ) ) && classCache(\n\t\t\t\t\t\tclassName, function( elem ) {\n\t\t\t\t\t\t\treturn pattern.test(\n\t\t\t\t\t\t\t\ttypeof elem.className === \"string\" && elem.className ||\n\t\t\t\t\t\t\t\ttypeof elem.getAttribute !== \"undefined\" &&\n\t\t\t\t\t\t\t\t\telem.getAttribute( \"class\" ) ||\n\t\t\t\t\t\t\t\t\"\"\n\t\t\t\t\t\t\t);\n\t\t\t\t} );\n\t\t},\n\n\t\t\"ATTR\": function( name, operator, check ) {\n\t\t\treturn function( elem ) {\n\t\t\t\tvar result = Sizzle.attr( elem, name );\n\n\t\t\t\tif ( result == null ) {\n\t\t\t\t\treturn operator === \"!=\";\n\t\t\t\t}\n\t\t\t\tif ( !operator ) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\n\t\t\t\tresult += \"\";\n\n\t\t\t\t/* eslint-disable max-len */\n\n\t\t\t\treturn operator === \"=\" ? result === check :\n\t\t\t\t\toperator === \"!=\" ? result !== check :\n\t\t\t\t\toperator === \"^=\" ? check && result.indexOf( check ) === 0 :\n\t\t\t\t\toperator === \"*=\" ? check && result.indexOf( check ) > -1 :\n\t\t\t\t\toperator === \"$=\" ? check && result.slice( -check.length ) === check :\n\t\t\t\t\toperator === \"~=\" ? ( \" \" + result.replace( rwhitespace, \" \" ) + \" \" ).indexOf( check ) > -1 :\n\t\t\t\t\toperator === \"|=\" ? result === check || result.slice( 0, check.length + 1 ) === check + \"-\" :\n\t\t\t\t\tfalse;\n\t\t\t\t/* eslint-enable max-len */\n\n\t\t\t};\n\t\t},\n\n\t\t\"CHILD\": function( type, what, _argument, first, last ) {\n\t\t\tvar simple = type.slice( 0, 3 ) !== \"nth\",\n\t\t\t\tforward = type.slice( -4 ) !== \"last\",\n\t\t\t\tofType = what === \"of-type\";\n\n\t\t\treturn first === 1 && last === 0 ?\n\n\t\t\t\t// Shortcut for :nth-*(n)\n\t\t\t\tfunction( elem ) {\n\t\t\t\t\treturn !!elem.parentNode;\n\t\t\t\t} :\n\n\t\t\t\tfunction( elem, _context, xml ) {\n\t\t\t\t\tvar cache, uniqueCache, outerCache, node, nodeIndex, start,\n\t\t\t\t\t\tdir = simple !== forward ? \"nextSibling\" : \"previousSibling\",\n\t\t\t\t\t\tparent = elem.parentNode,\n\t\t\t\t\t\tname = ofType && elem.nodeName.toLowerCase(),\n\t\t\t\t\t\tuseCache = !xml && !ofType,\n\t\t\t\t\t\tdiff = false;\n\n\t\t\t\t\tif ( parent ) {\n\n\t\t\t\t\t\t// :(first|last|only)-(child|of-type)\n\t\t\t\t\t\tif ( simple ) {\n\t\t\t\t\t\t\twhile ( dir ) {\n\t\t\t\t\t\t\t\tnode = elem;\n\t\t\t\t\t\t\t\twhile ( ( node = node[ dir ] ) ) {\n\t\t\t\t\t\t\t\t\tif ( ofType ?\n\t\t\t\t\t\t\t\t\t\tnode.nodeName.toLowerCase() === name :\n\t\t\t\t\t\t\t\t\t\tnode.nodeType === 1 ) {\n\n\t\t\t\t\t\t\t\t\t\treturn false;\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t// Reverse direction for :only-* (if we haven't yet done so)\n\t\t\t\t\t\t\t\tstart = dir = type === \"only\" && !start && \"nextSibling\";\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tstart = [ forward ? parent.firstChild : parent.lastChild ];\n\n\t\t\t\t\t\t// non-xml :nth-child(...) stores cache data on `parent`\n\t\t\t\t\t\tif ( forward && useCache ) {\n\n\t\t\t\t\t\t\t// Seek `elem` from a previously-cached index\n\n\t\t\t\t\t\t\t// ...in a gzip-friendly way\n\t\t\t\t\t\t\tnode = parent;\n\t\t\t\t\t\t\touterCache = node[ expando ] || ( node[ expando ] = {} );\n\n\t\t\t\t\t\t\t// Support: IE <9 only\n\t\t\t\t\t\t\t// Defend against cloned attroperties (jQuery gh-1709)\n\t\t\t\t\t\t\tuniqueCache = outerCache[ node.uniqueID ] ||\n\t\t\t\t\t\t\t\t( outerCache[ node.uniqueID ] = {} );\n\n\t\t\t\t\t\t\tcache = uniqueCache[ type ] || [];\n\t\t\t\t\t\t\tnodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];\n\t\t\t\t\t\t\tdiff = nodeIndex && cache[ 2 ];\n\t\t\t\t\t\t\tnode = nodeIndex && parent.childNodes[ nodeIndex ];\n\n\t\t\t\t\t\t\twhile ( ( node = ++nodeIndex && node && node[ dir ] ||\n\n\t\t\t\t\t\t\t\t// Fallback to seeking `elem` from the start\n\t\t\t\t\t\t\t\t( diff = nodeIndex = 0 ) || start.pop() ) ) {\n\n\t\t\t\t\t\t\t\t// When found, cache indexes on `parent` and break\n\t\t\t\t\t\t\t\tif ( node.nodeType === 1 && ++diff && node === elem ) {\n\t\t\t\t\t\t\t\t\tuniqueCache[ type ] = [ dirruns, nodeIndex, diff ];\n\t\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t// Use previously-cached element index if available\n\t\t\t\t\t\t\tif ( useCache ) {\n\n\t\t\t\t\t\t\t\t// ...in a gzip-friendly way\n\t\t\t\t\t\t\t\tnode = elem;\n\t\t\t\t\t\t\t\touterCache = node[ expando ] || ( node[ expando ] = {} );\n\n\t\t\t\t\t\t\t\t// Support: IE <9 only\n\t\t\t\t\t\t\t\t// Defend against cloned attroperties (jQuery gh-1709)\n\t\t\t\t\t\t\t\tuniqueCache = outerCache[ node.uniqueID ] ||\n\t\t\t\t\t\t\t\t\t( outerCache[ node.uniqueID ] = {} );\n\n\t\t\t\t\t\t\t\tcache = uniqueCache[ type ] || [];\n\t\t\t\t\t\t\t\tnodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];\n\t\t\t\t\t\t\t\tdiff = nodeIndex;\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t// xml :nth-child(...)\n\t\t\t\t\t\t\t// or :nth-last-child(...) or :nth(-last)?-of-type(...)\n\t\t\t\t\t\t\tif ( diff === false ) {\n\n\t\t\t\t\t\t\t\t// Use the same loop as above to seek `elem` from the start\n\t\t\t\t\t\t\t\twhile ( ( node = ++nodeIndex && node && node[ dir ] ||\n\t\t\t\t\t\t\t\t\t( diff = nodeIndex = 0 ) || start.pop() ) ) {\n\n\t\t\t\t\t\t\t\t\tif ( ( ofType ?\n\t\t\t\t\t\t\t\t\t\tnode.nodeName.toLowerCase() === name :\n\t\t\t\t\t\t\t\t\t\tnode.nodeType === 1 ) &&\n\t\t\t\t\t\t\t\t\t\t++diff ) {\n\n\t\t\t\t\t\t\t\t\t\t// Cache the index of each encountered element\n\t\t\t\t\t\t\t\t\t\tif ( useCache ) {\n\t\t\t\t\t\t\t\t\t\t\touterCache = node[ expando ] ||\n\t\t\t\t\t\t\t\t\t\t\t\t( node[ expando ] = {} );\n\n\t\t\t\t\t\t\t\t\t\t\t// Support: IE <9 only\n\t\t\t\t\t\t\t\t\t\t\t// Defend against cloned attroperties (jQuery gh-1709)\n\t\t\t\t\t\t\t\t\t\t\tuniqueCache = outerCache[ node.uniqueID ] ||\n\t\t\t\t\t\t\t\t\t\t\t\t( outerCache[ node.uniqueID ] = {} );\n\n\t\t\t\t\t\t\t\t\t\t\tuniqueCache[ type ] = [ dirruns, diff ];\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\tif ( node === elem ) {\n\t\t\t\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// Incorporate the offset, then check against cycle size\n\t\t\t\t\t\tdiff -= last;\n\t\t\t\t\t\treturn diff === first || ( diff % first === 0 && diff / first >= 0 );\n\t\t\t\t\t}\n\t\t\t\t};\n\t\t},\n\n\t\t\"PSEUDO\": function( pseudo, argument ) {\n\n\t\t\t// pseudo-class names are case-insensitive\n\t\t\t// http://www.w3.org/TR/selectors/#pseudo-classes\n\t\t\t// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters\n\t\t\t// Remember that setFilters inherits from pseudos\n\t\t\tvar args,\n\t\t\t\tfn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||\n\t\t\t\t\tSizzle.error( \"unsupported pseudo: \" + pseudo );\n\n\t\t\t// The user may use createPseudo to indicate that\n\t\t\t// arguments are needed to create the filter function\n\t\t\t// just as Sizzle does\n\t\t\tif ( fn[ expando ] ) {\n\t\t\t\treturn fn( argument );\n\t\t\t}\n\n\t\t\t// But maintain support for old signatures\n\t\t\tif ( fn.length > 1 ) {\n\t\t\t\targs = [ pseudo, pseudo, \"\", argument ];\n\t\t\t\treturn Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?\n\t\t\t\t\tmarkFunction( function( seed, matches ) {\n\t\t\t\t\t\tvar idx,\n\t\t\t\t\t\t\tmatched = fn( seed, argument ),\n\t\t\t\t\t\t\ti = matched.length;\n\t\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\t\tidx = indexOf( seed, matched[ i ] );\n\t\t\t\t\t\t\tseed[ idx ] = !( matches[ idx ] = matched[ i ] );\n\t\t\t\t\t\t}\n\t\t\t\t\t} ) :\n\t\t\t\t\tfunction( elem ) {\n\t\t\t\t\t\treturn fn( elem, 0, args );\n\t\t\t\t\t};\n\t\t\t}\n\n\t\t\treturn fn;\n\t\t}\n\t},\n\n\tpseudos: {\n\n\t\t// Potentially complex pseudos\n\t\t\"not\": markFunction( function( selector ) {\n\n\t\t\t// Trim the selector passed to compile\n\t\t\t// to avoid treating leading and trailing\n\t\t\t// spaces as combinators\n\t\t\tvar input = [],\n\t\t\t\tresults = [],\n\t\t\t\tmatcher = compile( selector.replace( rtrim, \"$1\" ) );\n\n\t\t\treturn matcher[ expando ] ?\n\t\t\t\tmarkFunction( function( seed, matches, _context, xml ) {\n\t\t\t\t\tvar elem,\n\t\t\t\t\t\tunmatched = matcher( seed, null, xml, [] ),\n\t\t\t\t\t\ti = seed.length;\n\n\t\t\t\t\t// Match elements unmatched by `matcher`\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tif ( ( elem = unmatched[ i ] ) ) {\n\t\t\t\t\t\t\tseed[ i ] = !( matches[ i ] = elem );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t} ) :\n\t\t\t\tfunction( elem, _context, xml ) {\n\t\t\t\t\tinput[ 0 ] = elem;\n\t\t\t\t\tmatcher( input, null, xml, results );\n\n\t\t\t\t\t// Don't keep the element (issue #299)\n\t\t\t\t\tinput[ 0 ] = null;\n\t\t\t\t\treturn !results.pop();\n\t\t\t\t};\n\t\t} ),\n\n\t\t\"has\": markFunction( function( selector ) {\n\t\t\treturn function( elem ) {\n\t\t\t\treturn Sizzle( selector, elem ).length > 0;\n\t\t\t};\n\t\t} ),\n\n\t\t\"contains\": markFunction( function( text ) {\n\t\t\ttext = text.replace( runescape, funescape );\n\t\t\treturn function( elem ) {\n\t\t\t\treturn ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;\n\t\t\t};\n\t\t} ),\n\n\t\t// \"Whether an element is represented by a :lang() selector\n\t\t// is based solely on the element's language value\n\t\t// being equal to the identifier C,\n\t\t// or beginning with the identifier C immediately followed by \"-\".\n\t\t// The matching of C against the element's language value is performed case-insensitively.\n\t\t// The identifier C does not have to be a valid language name.\"\n\t\t// http://www.w3.org/TR/selectors/#lang-pseudo\n\t\t\"lang\": markFunction( function( lang ) {\n\n\t\t\t// lang value must be a valid identifier\n\t\t\tif ( !ridentifier.test( lang || \"\" ) ) {\n\t\t\t\tSizzle.error( \"unsupported lang: \" + lang );\n\t\t\t}\n\t\t\tlang = lang.replace( runescape, funescape ).toLowerCase();\n\t\t\treturn function( elem ) {\n\t\t\t\tvar elemLang;\n\t\t\t\tdo {\n\t\t\t\t\tif ( ( elemLang = documentIsHTML ?\n\t\t\t\t\t\telem.lang :\n\t\t\t\t\t\telem.getAttribute( \"xml:lang\" ) || elem.getAttribute( \"lang\" ) ) ) {\n\n\t\t\t\t\t\telemLang = elemLang.toLowerCase();\n\t\t\t\t\t\treturn elemLang === lang || elemLang.indexOf( lang + \"-\" ) === 0;\n\t\t\t\t\t}\n\t\t\t\t} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );\n\t\t\t\treturn false;\n\t\t\t};\n\t\t} ),\n\n\t\t// Miscellaneous\n\t\t\"target\": function( elem ) {\n\t\t\tvar hash = window.location && window.location.hash;\n\t\t\treturn hash && hash.slice( 1 ) === elem.id;\n\t\t},\n\n\t\t\"root\": function( elem ) {\n\t\t\treturn elem === docElem;\n\t\t},\n\n\t\t\"focus\": function( elem ) {\n\t\t\treturn elem === document.activeElement &&\n\t\t\t\t( !document.hasFocus || document.hasFocus() ) &&\n\t\t\t\t!!( elem.type || elem.href || ~elem.tabIndex );\n\t\t},\n\n\t\t// Boolean properties\n\t\t\"enabled\": createDisabledPseudo( false ),\n\t\t\"disabled\": createDisabledPseudo( true ),\n\n\t\t\"checked\": function( elem ) {\n\n\t\t\t// In CSS3, :checked should return both checked and selected elements\n\t\t\t// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked\n\t\t\tvar nodeName = elem.nodeName.toLowerCase();\n\t\t\treturn ( nodeName === \"input\" && !!elem.checked ) ||\n\t\t\t\t( nodeName === \"option\" && !!elem.selected );\n\t\t},\n\n\t\t\"selected\": function( elem ) {\n\n\t\t\t// Accessing this property makes selected-by-default\n\t\t\t// options in Safari work properly\n\t\t\tif ( elem.parentNode ) {\n\t\t\t\t// eslint-disable-next-line no-unused-expressions\n\t\t\t\telem.parentNode.selectedIndex;\n\t\t\t}\n\n\t\t\treturn elem.selected === true;\n\t\t},\n\n\t\t// Contents\n\t\t\"empty\": function( elem ) {\n\n\t\t\t// http://www.w3.org/TR/selectors/#empty-pseudo\n\t\t\t// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),\n\t\t\t// but not by others (comment: 8; processing instruction: 7; etc.)\n\t\t\t// nodeType < 6 works because attributes (2) do not appear as children\n\t\t\tfor ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {\n\t\t\t\tif ( elem.nodeType < 6 ) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t},\n\n\t\t\"parent\": function( elem ) {\n\t\t\treturn !Expr.pseudos[ \"empty\" ]( elem );\n\t\t},\n\n\t\t// Element/input types\n\t\t\"header\": function( elem ) {\n\t\t\treturn rheader.test( elem.nodeName );\n\t\t},\n\n\t\t\"input\": function( elem ) {\n\t\t\treturn rinputs.test( elem.nodeName );\n\t\t},\n\n\t\t\"button\": function( elem ) {\n\t\t\tvar name = elem.nodeName.toLowerCase();\n\t\t\treturn name === \"input\" && elem.type === \"button\" || name === \"button\";\n\t\t},\n\n\t\t\"text\": function( elem ) {\n\t\t\tvar attr;\n\t\t\treturn elem.nodeName.toLowerCase() === \"input\" &&\n\t\t\t\telem.type === \"text\" &&\n\n\t\t\t\t// Support: IE<8\n\t\t\t\t// New HTML5 attribute values (e.g., \"search\") appear with elem.type === \"text\"\n\t\t\t\t( ( attr = elem.getAttribute( \"type\" ) ) == null ||\n\t\t\t\t\tattr.toLowerCase() === \"text\" );\n\t\t},\n\n\t\t// Position-in-collection\n\t\t\"first\": createPositionalPseudo( function() {\n\t\t\treturn [ 0 ];\n\t\t} ),\n\n\t\t\"last\": createPositionalPseudo( function( _matchIndexes, length ) {\n\t\t\treturn [ length - 1 ];\n\t\t} ),\n\n\t\t\"eq\": createPositionalPseudo( function( _matchIndexes, length, argument ) {\n\t\t\treturn [ argument < 0 ? argument + length : argument ];\n\t\t} ),\n\n\t\t\"even\": createPositionalPseudo( function( matchIndexes, length ) {\n\t\t\tvar i = 0;\n\t\t\tfor ( ; i < length; i += 2 ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t} ),\n\n\t\t\"odd\": createPositionalPseudo( function( matchIndexes, length ) {\n\t\t\tvar i = 1;\n\t\t\tfor ( ; i < length; i += 2 ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t} ),\n\n\t\t\"lt\": createPositionalPseudo( function( matchIndexes, length, argument ) {\n\t\t\tvar i = argument < 0 ?\n\t\t\t\targument + length :\n\t\t\t\targument > length ?\n\t\t\t\t\tlength :\n\t\t\t\t\targument;\n\t\t\tfor ( ; --i >= 0; ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t} ),\n\n\t\t\"gt\": createPositionalPseudo( function( matchIndexes, length, argument ) {\n\t\t\tvar i = argument < 0 ? argument + length : argument;\n\t\t\tfor ( ; ++i < length; ) {\n\t\t\t\tmatchIndexes.push( i );\n\t\t\t}\n\t\t\treturn matchIndexes;\n\t\t} )\n\t}\n};\n\nExpr.pseudos[ \"nth\" ] = Expr.pseudos[ \"eq\" ];\n\n// Add button/input type pseudos\nfor ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {\n\tExpr.pseudos[ i ] = createInputPseudo( i );\n}\nfor ( i in { submit: true, reset: true } ) {\n\tExpr.pseudos[ i ] = createButtonPseudo( i );\n}\n\n// Easy API for creating new setFilters\nfunction setFilters() {}\nsetFilters.prototype = Expr.filters = Expr.pseudos;\nExpr.setFilters = new setFilters();\n\ntokenize = Sizzle.tokenize = function( selector, parseOnly ) {\n\tvar matched, match, tokens, type,\n\t\tsoFar, groups, preFilters,\n\t\tcached = tokenCache[ selector + \" \" ];\n\n\tif ( cached ) {\n\t\treturn parseOnly ? 0 : cached.slice( 0 );\n\t}\n\n\tsoFar = selector;\n\tgroups = [];\n\tpreFilters = Expr.preFilter;\n\n\twhile ( soFar ) {\n\n\t\t// Comma and first run\n\t\tif ( !matched || ( match = rcomma.exec( soFar ) ) ) {\n\t\t\tif ( match ) {\n\n\t\t\t\t// Don't consume trailing commas as valid\n\t\t\t\tsoFar = soFar.slice( match[ 0 ].length ) || soFar;\n\t\t\t}\n\t\t\tgroups.push( ( tokens = [] ) );\n\t\t}\n\n\t\tmatched = false;\n\n\t\t// Combinators\n\t\tif ( ( match = rcombinators.exec( soFar ) ) ) {\n\t\t\tmatched = match.shift();\n\t\t\ttokens.push( {\n\t\t\t\tvalue: matched,\n\n\t\t\t\t// Cast descendant combinators to space\n\t\t\t\ttype: match[ 0 ].replace( rtrim, \" \" )\n\t\t\t} );\n\t\t\tsoFar = soFar.slice( matched.length );\n\t\t}\n\n\t\t// Filters\n\t\tfor ( type in Expr.filter ) {\n\t\t\tif ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||\n\t\t\t\t( match = preFilters[ type ]( match ) ) ) ) {\n\t\t\t\tmatched = match.shift();\n\t\t\t\ttokens.push( {\n\t\t\t\t\tvalue: matched,\n\t\t\t\t\ttype: type,\n\t\t\t\t\tmatches: match\n\t\t\t\t} );\n\t\t\t\tsoFar = soFar.slice( matched.length );\n\t\t\t}\n\t\t}\n\n\t\tif ( !matched ) {\n\t\t\tbreak;\n\t\t}\n\t}\n\n\t// Return the length of the invalid excess\n\t// if we're just parsing\n\t// Otherwise, throw an error or return tokens\n\treturn parseOnly ?\n\t\tsoFar.length :\n\t\tsoFar ?\n\t\t\tSizzle.error( selector ) :\n\n\t\t\t// Cache the tokens\n\t\t\ttokenCache( selector, groups ).slice( 0 );\n};\n\nfunction toSelector( tokens ) {\n\tvar i = 0,\n\t\tlen = tokens.length,\n\t\tselector = \"\";\n\tfor ( ; i < len; i++ ) {\n\t\tselector += tokens[ i ].value;\n\t}\n\treturn selector;\n}\n\nfunction addCombinator( matcher, combinator, base ) {\n\tvar dir = combinator.dir,\n\t\tskip = combinator.next,\n\t\tkey = skip || dir,\n\t\tcheckNonElements = base && key === \"parentNode\",\n\t\tdoneName = done++;\n\n\treturn combinator.first ?\n\n\t\t// Check against closest ancestor/preceding element\n\t\tfunction( elem, context, xml ) {\n\t\t\twhile ( ( elem = elem[ dir ] ) ) {\n\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\treturn matcher( elem, context, xml );\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t} :\n\n\t\t// Check against all ancestor/preceding elements\n\t\tfunction( elem, context, xml ) {\n\t\t\tvar oldCache, uniqueCache, outerCache,\n\t\t\t\tnewCache = [ dirruns, doneName ];\n\n\t\t\t// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching\n\t\t\tif ( xml ) {\n\t\t\t\twhile ( ( elem = elem[ dir ] ) ) {\n\t\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\t\tif ( matcher( elem, context, xml ) ) {\n\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\twhile ( ( elem = elem[ dir ] ) ) {\n\t\t\t\t\tif ( elem.nodeType === 1 || checkNonElements ) {\n\t\t\t\t\t\touterCache = elem[ expando ] || ( elem[ expando ] = {} );\n\n\t\t\t\t\t\t// Support: IE <9 only\n\t\t\t\t\t\t// Defend against cloned attroperties (jQuery gh-1709)\n\t\t\t\t\t\tuniqueCache = outerCache[ elem.uniqueID ] ||\n\t\t\t\t\t\t\t( outerCache[ elem.uniqueID ] = {} );\n\n\t\t\t\t\t\tif ( skip && skip === elem.nodeName.toLowerCase() ) {\n\t\t\t\t\t\t\telem = elem[ dir ] || elem;\n\t\t\t\t\t\t} else if ( ( oldCache = uniqueCache[ key ] ) &&\n\t\t\t\t\t\t\toldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {\n\n\t\t\t\t\t\t\t// Assign to newCache so results back-propagate to previous elements\n\t\t\t\t\t\t\treturn ( newCache[ 2 ] = oldCache[ 2 ] );\n\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t// Reuse newcache so results back-propagate to previous elements\n\t\t\t\t\t\t\tuniqueCache[ key ] = newCache;\n\n\t\t\t\t\t\t\t// A match means we're done; a fail means we have to keep checking\n\t\t\t\t\t\t\tif ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {\n\t\t\t\t\t\t\t\treturn true;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t};\n}\n\nfunction elementMatcher( matchers ) {\n\treturn matchers.length > 1 ?\n\t\tfunction( elem, context, xml ) {\n\t\t\tvar i = matchers.length;\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( !matchers[ i ]( elem, context, xml ) ) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn true;\n\t\t} :\n\t\tmatchers[ 0 ];\n}\n\nfunction multipleContexts( selector, contexts, results ) {\n\tvar i = 0,\n\t\tlen = contexts.length;\n\tfor ( ; i < len; i++ ) {\n\t\tSizzle( selector, contexts[ i ], results );\n\t}\n\treturn results;\n}\n\nfunction condense( unmatched, map, filter, context, xml ) {\n\tvar elem,\n\t\tnewUnmatched = [],\n\t\ti = 0,\n\t\tlen = unmatched.length,\n\t\tmapped = map != null;\n\n\tfor ( ; i < len; i++ ) {\n\t\tif ( ( elem = unmatched[ i ] ) ) {\n\t\t\tif ( !filter || filter( elem, context, xml ) ) {\n\t\t\t\tnewUnmatched.push( elem );\n\t\t\t\tif ( mapped ) {\n\t\t\t\t\tmap.push( i );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn newUnmatched;\n}\n\nfunction setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {\n\tif ( postFilter && !postFilter[ expando ] ) {\n\t\tpostFilter = setMatcher( postFilter );\n\t}\n\tif ( postFinder && !postFinder[ expando ] ) {\n\t\tpostFinder = setMatcher( postFinder, postSelector );\n\t}\n\treturn markFunction( function( seed, results, context, xml ) {\n\t\tvar temp, i, elem,\n\t\t\tpreMap = [],\n\t\t\tpostMap = [],\n\t\t\tpreexisting = results.length,\n\n\t\t\t// Get initial elements from seed or context\n\t\t\telems = seed || multipleContexts(\n\t\t\t\tselector || \"*\",\n\t\t\t\tcontext.nodeType ? [ context ] : context,\n\t\t\t\t[]\n\t\t\t),\n\n\t\t\t// Prefilter to get matcher input, preserving a map for seed-results synchronization\n\t\t\tmatcherIn = preFilter && ( seed || !selector ) ?\n\t\t\t\tcondense( elems, preMap, preFilter, context, xml ) :\n\t\t\t\telems,\n\n\t\t\tmatcherOut = matcher ?\n\n\t\t\t\t// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,\n\t\t\t\tpostFinder || ( seed ? preFilter : preexisting || postFilter ) ?\n\n\t\t\t\t\t// ...intermediate processing is necessary\n\t\t\t\t\t[] :\n\n\t\t\t\t\t// ...otherwise use results directly\n\t\t\t\t\tresults :\n\t\t\t\tmatcherIn;\n\n\t\t// Find primary matches\n\t\tif ( matcher ) {\n\t\t\tmatcher( matcherIn, matcherOut, context, xml );\n\t\t}\n\n\t\t// Apply postFilter\n\t\tif ( postFilter ) {\n\t\t\ttemp = condense( matcherOut, postMap );\n\t\t\tpostFilter( temp, [], context, xml );\n\n\t\t\t// Un-match failing elements by moving them back to matcherIn\n\t\t\ti = temp.length;\n\t\t\twhile ( i-- ) {\n\t\t\t\tif ( ( elem = temp[ i ] ) ) {\n\t\t\t\t\tmatcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif ( seed ) {\n\t\t\tif ( postFinder || preFilter ) {\n\t\t\t\tif ( postFinder ) {\n\n\t\t\t\t\t// Get the final matcherOut by condensing this intermediate into postFinder contexts\n\t\t\t\t\ttemp = [];\n\t\t\t\t\ti = matcherOut.length;\n\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\tif ( ( elem = matcherOut[ i ] ) ) {\n\n\t\t\t\t\t\t\t// Restore matcherIn since elem is not yet a final match\n\t\t\t\t\t\t\ttemp.push( ( matcherIn[ i ] = elem ) );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tpostFinder( null, ( matcherOut = [] ), temp, xml );\n\t\t\t\t}\n\n\t\t\t\t// Move matched elements from seed to results to keep them synchronized\n\t\t\t\ti = matcherOut.length;\n\t\t\t\twhile ( i-- ) {\n\t\t\t\t\tif ( ( elem = matcherOut[ i ] ) &&\n\t\t\t\t\t\t( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {\n\n\t\t\t\t\t\tseed[ temp ] = !( results[ temp ] = elem );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Add elements to results, through postFinder if defined\n\t\t} else {\n\t\t\tmatcherOut = condense(\n\t\t\t\tmatcherOut === results ?\n\t\t\t\t\tmatcherOut.splice( preexisting, matcherOut.length ) :\n\t\t\t\t\tmatcherOut\n\t\t\t);\n\t\t\tif ( postFinder ) {\n\t\t\t\tpostFinder( null, results, matcherOut, xml );\n\t\t\t} else {\n\t\t\t\tpush.apply( results, matcherOut );\n\t\t\t}\n\t\t}\n\t} );\n}\n\nfunction matcherFromTokens( tokens ) {\n\tvar checkContext, matcher, j,\n\t\tlen = tokens.length,\n\t\tleadingRelative = Expr.relative[ tokens[ 0 ].type ],\n\t\timplicitRelative = leadingRelative || Expr.relative[ \" \" ],\n\t\ti = leadingRelative ? 1 : 0,\n\n\t\t// The foundational matcher ensures that elements are reachable from top-level context(s)\n\t\tmatchContext = addCombinator( function( elem ) {\n\t\t\treturn elem === checkContext;\n\t\t}, implicitRelative, true ),\n\t\tmatchAnyContext = addCombinator( function( elem ) {\n\t\t\treturn indexOf( checkContext, elem ) > -1;\n\t\t}, implicitRelative, true ),\n\t\tmatchers = [ function( elem, context, xml ) {\n\t\t\tvar ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (\n\t\t\t\t( checkContext = context ).nodeType ?\n\t\t\t\t\tmatchContext( elem, context, xml ) :\n\t\t\t\t\tmatchAnyContext( elem, context, xml ) );\n\n\t\t\t// Avoid hanging onto element (issue #299)\n\t\t\tcheckContext = null;\n\t\t\treturn ret;\n\t\t} ];\n\n\tfor ( ; i < len; i++ ) {\n\t\tif ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {\n\t\t\tmatchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];\n\t\t} else {\n\t\t\tmatcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );\n\n\t\t\t// Return special upon seeing a positional matcher\n\t\t\tif ( matcher[ expando ] ) {\n\n\t\t\t\t// Find the next relative operator (if any) for proper handling\n\t\t\t\tj = ++i;\n\t\t\t\tfor ( ; j < len; j++ ) {\n\t\t\t\t\tif ( Expr.relative[ tokens[ j ].type ] ) {\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn setMatcher(\n\t\t\t\t\ti > 1 && elementMatcher( matchers ),\n\t\t\t\t\ti > 1 && toSelector(\n\n\t\t\t\t\t// If the preceding token was a descendant combinator, insert an implicit any-element `*`\n\t\t\t\t\ttokens\n\t\t\t\t\t\t.slice( 0, i - 1 )\n\t\t\t\t\t\t.concat( { value: tokens[ i - 2 ].type === \" \" ? \"*\" : \"\" } )\n\t\t\t\t\t).replace( rtrim, \"$1\" ),\n\t\t\t\t\tmatcher,\n\t\t\t\t\ti < j && matcherFromTokens( tokens.slice( i, j ) ),\n\t\t\t\t\tj < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),\n\t\t\t\t\tj < len && toSelector( tokens )\n\t\t\t\t);\n\t\t\t}\n\t\t\tmatchers.push( matcher );\n\t\t}\n\t}\n\n\treturn elementMatcher( matchers );\n}\n\nfunction matcherFromGroupMatchers( elementMatchers, setMatchers ) {\n\tvar bySet = setMatchers.length > 0,\n\t\tbyElement = elementMatchers.length > 0,\n\t\tsuperMatcher = function( seed, context, xml, results, outermost ) {\n\t\t\tvar elem, j, matcher,\n\t\t\t\tmatchedCount = 0,\n\t\t\t\ti = \"0\",\n\t\t\t\tunmatched = seed && [],\n\t\t\t\tsetMatched = [],\n\t\t\t\tcontextBackup = outermostContext,\n\n\t\t\t\t// We must always have either seed elements or outermost context\n\t\t\t\telems = seed || byElement && Expr.find[ \"TAG\" ]( \"*\", outermost ),\n\n\t\t\t\t// Use integer dirruns iff this is the outermost matcher\n\t\t\t\tdirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),\n\t\t\t\tlen = elems.length;\n\n\t\t\tif ( outermost ) {\n\n\t\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t\t// two documents; shallow comparisons work.\n\t\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\t\toutermostContext = context == document || context || outermost;\n\t\t\t}\n\n\t\t\t// Add elements passing elementMatchers directly to results\n\t\t\t// Support: IE<9, Safari\n\t\t\t// Tolerate NodeList properties (IE: \"length\"; Safari: ) matching elements by id\n\t\t\tfor ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {\n\t\t\t\tif ( byElement && elem ) {\n\t\t\t\t\tj = 0;\n\n\t\t\t\t\t// Support: IE 11+, Edge 17 - 18+\n\t\t\t\t\t// IE/Edge sometimes throw a \"Permission denied\" error when strict-comparing\n\t\t\t\t\t// two documents; shallow comparisons work.\n\t\t\t\t\t// eslint-disable-next-line eqeqeq\n\t\t\t\t\tif ( !context && elem.ownerDocument != document ) {\n\t\t\t\t\t\tsetDocument( elem );\n\t\t\t\t\t\txml = !documentIsHTML;\n\t\t\t\t\t}\n\t\t\t\t\twhile ( ( matcher = elementMatchers[ j++ ] ) ) {\n\t\t\t\t\t\tif ( matcher( elem, context || document, xml ) ) {\n\t\t\t\t\t\t\tresults.push( elem );\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif ( outermost ) {\n\t\t\t\t\t\tdirruns = dirrunsUnique;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Track unmatched elements for set filters\n\t\t\t\tif ( bySet ) {\n\n\t\t\t\t\t// They will have gone through all possible matchers\n\t\t\t\t\tif ( ( elem = !matcher && elem ) ) {\n\t\t\t\t\t\tmatchedCount--;\n\t\t\t\t\t}\n\n\t\t\t\t\t// Lengthen the array for every element, matched or not\n\t\t\t\t\tif ( seed ) {\n\t\t\t\t\t\tunmatched.push( elem );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// `i` is now the count of elements visited above, and adding it to `matchedCount`\n\t\t\t// makes the latter nonnegative.\n\t\t\tmatchedCount += i;\n\n\t\t\t// Apply set filters to unmatched elements\n\t\t\t// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`\n\t\t\t// equals `i`), unless we didn't visit _any_ elements in the above loop because we have\n\t\t\t// no element matchers and no seed.\n\t\t\t// Incrementing an initially-string \"0\" `i` allows `i` to remain a string only in that\n\t\t\t// case, which will result in a \"00\" `matchedCount` that differs from `i` but is also\n\t\t\t// numerically zero.\n\t\t\tif ( bySet && i !== matchedCount ) {\n\t\t\t\tj = 0;\n\t\t\t\twhile ( ( matcher = setMatchers[ j++ ] ) ) {\n\t\t\t\t\tmatcher( unmatched, setMatched, context, xml );\n\t\t\t\t}\n\n\t\t\t\tif ( seed ) {\n\n\t\t\t\t\t// Reintegrate element matches to eliminate the need for sorting\n\t\t\t\t\tif ( matchedCount > 0 ) {\n\t\t\t\t\t\twhile ( i-- ) {\n\t\t\t\t\t\t\tif ( !( unmatched[ i ] || setMatched[ i ] ) ) {\n\t\t\t\t\t\t\t\tsetMatched[ i ] = pop.call( results );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Discard index placeholder values to get only actual matches\n\t\t\t\t\tsetMatched = condense( setMatched );\n\t\t\t\t}\n\n\t\t\t\t// Add matches to results\n\t\t\t\tpush.apply( results, setMatched );\n\n\t\t\t\t// Seedless set matches succeeding multiple successful matchers stipulate sorting\n\t\t\t\tif ( outermost && !seed && setMatched.length > 0 &&\n\t\t\t\t\t( matchedCount + setMatchers.length ) > 1 ) {\n\n\t\t\t\t\tSizzle.uniqueSort( results );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Override manipulation of globals by nested matchers\n\t\t\tif ( outermost ) {\n\t\t\t\tdirruns = dirrunsUnique;\n\t\t\t\toutermostContext = contextBackup;\n\t\t\t}\n\n\t\t\treturn unmatched;\n\t\t};\n\n\treturn bySet ?\n\t\tmarkFunction( superMatcher ) :\n\t\tsuperMatcher;\n}\n\ncompile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {\n\tvar i,\n\t\tsetMatchers = [],\n\t\telementMatchers = [],\n\t\tcached = compilerCache[ selector + \" \" ];\n\n\tif ( !cached ) {\n\n\t\t// Generate a function of recursive functions that can be used to check each element\n\t\tif ( !match ) {\n\t\t\tmatch = tokenize( selector );\n\t\t}\n\t\ti = match.length;\n\t\twhile ( i-- ) {\n\t\t\tcached = matcherFromTokens( match[ i ] );\n\t\t\tif ( cached[ expando ] ) {\n\t\t\t\tsetMatchers.push( cached );\n\t\t\t} else {\n\t\t\t\telementMatchers.push( cached );\n\t\t\t}\n\t\t}\n\n\t\t// Cache the compiled function\n\t\tcached = compilerCache(\n\t\t\tselector,\n\t\t\tmatcherFromGroupMatchers( elementMatchers, setMatchers )\n\t\t);\n\n\t\t// Save selector and tokenization\n\t\tcached.selector = selector;\n\t}\n\treturn cached;\n};\n\n/**\n * A low-level selection function that works with Sizzle's compiled\n * selector functions\n * @param {String|Function} selector A selector or a pre-compiled\n * selector function built with Sizzle.compile\n * @param {Element} context\n * @param {Array} [results]\n * @param {Array} [seed] A set of elements to match against\n */\nselect = Sizzle.select = function( selector, context, results, seed ) {\n\tvar i, tokens, token, type, find,\n\t\tcompiled = typeof selector === \"function\" && selector,\n\t\tmatch = !seed && tokenize( ( selector = compiled.selector || selector ) );\n\n\tresults = results || [];\n\n\t// Try to minimize operations if there is only one selector in the list and no seed\n\t// (the latter of which guarantees us context)\n\tif ( match.length === 1 ) {\n\n\t\t// Reduce context if the leading compound selector is an ID\n\t\ttokens = match[ 0 ] = match[ 0 ].slice( 0 );\n\t\tif ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === \"ID\" &&\n\t\t\tcontext.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {\n\n\t\t\tcontext = ( Expr.find[ \"ID\" ]( token.matches[ 0 ]\n\t\t\t\t.replace( runescape, funescape ), context ) || [] )[ 0 ];\n\t\t\tif ( !context ) {\n\t\t\t\treturn results;\n\n\t\t\t// Precompiled matchers will still verify ancestry, so step up a level\n\t\t\t} else if ( compiled ) {\n\t\t\t\tcontext = context.parentNode;\n\t\t\t}\n\n\t\t\tselector = selector.slice( tokens.shift().value.length );\n\t\t}\n\n\t\t// Fetch a seed set for right-to-left matching\n\t\ti = matchExpr[ \"needsContext\" ].test( selector ) ? 0 : tokens.length;\n\t\twhile ( i-- ) {\n\t\t\ttoken = tokens[ i ];\n\n\t\t\t// Abort if we hit a combinator\n\t\t\tif ( Expr.relative[ ( type = token.type ) ] ) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif ( ( find = Expr.find[ type ] ) ) {\n\n\t\t\t\t// Search, expanding context for leading sibling combinators\n\t\t\t\tif ( ( seed = find(\n\t\t\t\t\ttoken.matches[ 0 ].replace( runescape, funescape ),\n\t\t\t\t\trsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||\n\t\t\t\t\t\tcontext\n\t\t\t\t) ) ) {\n\n\t\t\t\t\t// If seed is empty or no tokens remain, we can return early\n\t\t\t\t\ttokens.splice( i, 1 );\n\t\t\t\t\tselector = seed.length && toSelector( tokens );\n\t\t\t\t\tif ( !selector ) {\n\t\t\t\t\t\tpush.apply( results, seed );\n\t\t\t\t\t\treturn results;\n\t\t\t\t\t}\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// Compile and execute a filtering function if one is not provided\n\t// Provide `match` to avoid retokenization if we modified the selector above\n\t( compiled || compile( selector, match ) )(\n\t\tseed,\n\t\tcontext,\n\t\t!documentIsHTML,\n\t\tresults,\n\t\t!context || rsibling.test( selector ) && testContext( context.parentNode ) || context\n\t);\n\treturn results;\n};\n\n// One-time assignments\n\n// Sort stability\nsupport.sortStable = expando.split( \"\" ).sort( sortOrder ).join( \"\" ) === expando;\n\n// Support: Chrome 14-35+\n// Always assume duplicates if they aren't passed to the comparison function\nsupport.detectDuplicates = !!hasDuplicate;\n\n// Initialize against the default document\nsetDocument();\n\n// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)\n// Detached nodes confoundingly follow *each other*\nsupport.sortDetached = assert( function( el ) {\n\n\t// Should return 1, but returns 4 (following)\n\treturn el.compareDocumentPosition( document.createElement( \"fieldset\" ) ) & 1;\n} );\n\n// Support: IE<8\n// Prevent attribute/property \"interpolation\"\n// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx\nif ( !assert( function( el ) {\n\tel.innerHTML = \"\";\n\treturn el.firstChild.getAttribute( \"href\" ) === \"#\";\n} ) ) {\n\taddHandle( \"type|href|height|width\", function( elem, name, isXML ) {\n\t\tif ( !isXML ) {\n\t\t\treturn elem.getAttribute( name, name.toLowerCase() === \"type\" ? 1 : 2 );\n\t\t}\n\t} );\n}\n\n// Support: IE<9\n// Use defaultValue in place of getAttribute(\"value\")\nif ( !support.attributes || !assert( function( el ) {\n\tel.innerHTML = \"\";\n\tel.firstChild.setAttribute( \"value\", \"\" );\n\treturn el.firstChild.getAttribute( \"value\" ) === \"\";\n} ) ) {\n\taddHandle( \"value\", function( elem, _name, isXML ) {\n\t\tif ( !isXML && elem.nodeName.toLowerCase() === \"input\" ) {\n\t\t\treturn elem.defaultValue;\n\t\t}\n\t} );\n}\n\n// Support: IE<9\n// Use getAttributeNode to fetch booleans when getAttribute lies\nif ( !assert( function( el ) {\n\treturn el.getAttribute( \"disabled\" ) == null;\n} ) ) {\n\taddHandle( booleans, function( elem, name, isXML ) {\n\t\tvar val;\n\t\tif ( !isXML ) {\n\t\t\treturn elem[ name ] === true ? name.toLowerCase() :\n\t\t\t\t( val = elem.getAttributeNode( name ) ) && val.specified ?\n\t\t\t\t\tval.value :\n\t\t\t\t\tnull;\n\t\t}\n\t} );\n}\n\nreturn Sizzle;\n\n} )( window );\n\n\n\njQuery.find = Sizzle;\njQuery.expr = Sizzle.selectors;\n\n// Deprecated\njQuery.expr[ \":\" ] = jQuery.expr.pseudos;\njQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;\njQuery.text = Sizzle.getText;\njQuery.isXMLDoc = Sizzle.isXML;\njQuery.contains = Sizzle.contains;\njQuery.escapeSelector = Sizzle.escape;\n\n\n\n\nvar dir = function( elem, dir, until ) {\n\tvar matched = [],\n\t\ttruncate = until !== undefined;\n\n\twhile ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {\n\t\tif ( elem.nodeType === 1 ) {\n\t\t\tif ( truncate && jQuery( elem ).is( until ) ) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tmatched.push( elem );\n\t\t}\n\t}\n\treturn matched;\n};\n\n\nvar siblings = function( n, elem ) {\n\tvar matched = [];\n\n\tfor ( ; n; n = n.nextSibling ) {\n\t\tif ( n.nodeType === 1 && n !== elem ) {\n\t\t\tmatched.push( n );\n\t\t}\n\t}\n\n\treturn matched;\n};\n\n\nvar rneedsContext = jQuery.expr.match.needsContext;\n\n\n\nfunction nodeName( elem, name ) {\n\n return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();\n\n};\nvar rsingleTag = ( /^<([a-z][^\\/\\0>:\\x20\\t\\r\\n\\f]*)[\\x20\\t\\r\\n\\f]*\\/?>(?:<\\/\\1>|)$/i );\n\n\n\n// Implement the identical functionality for filter and not\nfunction winnow( elements, qualifier, not ) {\n\tif ( isFunction( qualifier ) ) {\n\t\treturn jQuery.grep( elements, function( elem, i ) {\n\t\t\treturn !!qualifier.call( elem, i, elem ) !== not;\n\t\t} );\n\t}\n\n\t// Single element\n\tif ( qualifier.nodeType ) {\n\t\treturn jQuery.grep( elements, function( elem ) {\n\t\t\treturn ( elem === qualifier ) !== not;\n\t\t} );\n\t}\n\n\t// Arraylike of elements (jQuery, arguments, Array)\n\tif ( typeof qualifier !== \"string\" ) {\n\t\treturn jQuery.grep( elements, function( elem ) {\n\t\t\treturn ( indexOf.call( qualifier, elem ) > -1 ) !== not;\n\t\t} );\n\t}\n\n\t// Filtered directly for both simple and complex selectors\n\treturn jQuery.filter( qualifier, elements, not );\n}\n\njQuery.filter = function( expr, elems, not ) {\n\tvar elem = elems[ 0 ];\n\n\tif ( not ) {\n\t\texpr = \":not(\" + expr + \")\";\n\t}\n\n\tif ( elems.length === 1 && elem.nodeType === 1 ) {\n\t\treturn jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];\n\t}\n\n\treturn jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {\n\t\treturn elem.nodeType === 1;\n\t} ) );\n};\n\njQuery.fn.extend( {\n\tfind: function( selector ) {\n\t\tvar i, ret,\n\t\t\tlen = this.length,\n\t\t\tself = this;\n\n\t\tif ( typeof selector !== \"string\" ) {\n\t\t\treturn this.pushStack( jQuery( selector ).filter( function() {\n\t\t\t\tfor ( i = 0; i < len; i++ ) {\n\t\t\t\t\tif ( jQuery.contains( self[ i ], this ) ) {\n\t\t\t\t\t\treturn true;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} ) );\n\t\t}\n\n\t\tret = this.pushStack( [] );\n\n\t\tfor ( i = 0; i < len; i++ ) {\n\t\t\tjQuery.find( selector, self[ i ], ret );\n\t\t}\n\n\t\treturn len > 1 ? jQuery.uniqueSort( ret ) : ret;\n\t},\n\tfilter: function( selector ) {\n\t\treturn this.pushStack( winnow( this, selector || [], false ) );\n\t},\n\tnot: function( selector ) {\n\t\treturn this.pushStack( winnow( this, selector || [], true ) );\n\t},\n\tis: function( selector ) {\n\t\treturn !!winnow(\n\t\t\tthis,\n\n\t\t\t// If this is a positional/relative selector, check membership in the returned set\n\t\t\t// so $(\"p:first\").is(\"p:last\") won't return true for a doc with two \"p\".\n\t\t\ttypeof selector === \"string\" && rneedsContext.test( selector ) ?\n\t\t\t\tjQuery( selector ) :\n\t\t\t\tselector || [],\n\t\t\tfalse\n\t\t).length;\n\t}\n} );\n\n\n// Initialize a jQuery object\n\n\n// A central reference to the root jQuery(document)\nvar rootjQuery,\n\n\t// A simple way to check for HTML strings\n\t// Prioritize #id over to avoid XSS via location.hash (#9521)\n\t// Strict HTML recognition (#11290: must start with <)\n\t// Shortcut simple #id case for speed\n\trquickExpr = /^(?:\\s*(<[\\w\\W]+>)[^>]*|#([\\w-]+))$/,\n\n\tinit = jQuery.fn.init = function( selector, context, root ) {\n\t\tvar match, elem;\n\n\t\t// HANDLE: $(\"\"), $(null), $(undefined), $(false)\n\t\tif ( !selector ) {\n\t\t\treturn this;\n\t\t}\n\n\t\t// Method init() accepts an alternate rootjQuery\n\t\t// so migrate can support jQuery.sub (gh-2101)\n\t\troot = root || rootjQuery;\n\n\t\t// Handle HTML strings\n\t\tif ( typeof selector === \"string\" ) {\n\t\t\tif ( selector[ 0 ] === \"<\" &&\n\t\t\t\tselector[ selector.length - 1 ] === \">\" &&\n\t\t\t\tselector.length >= 3 ) {\n\n\t\t\t\t// Assume that strings that start and end with <> are HTML and skip the regex check\n\t\t\t\tmatch = [ null, selector, null ];\n\n\t\t\t} else {\n\t\t\t\tmatch = rquickExpr.exec( selector );\n\t\t\t}\n\n\t\t\t// Match html or make sure no context is specified for #id\n\t\t\tif ( match && ( match[ 1 ] || !context ) ) {\n\n\t\t\t\t// HANDLE: $(html) -> $(array)\n\t\t\t\tif ( match[ 1 ] ) {\n\t\t\t\t\tcontext = context instanceof jQuery ? context[ 0 ] : context;\n\n\t\t\t\t\t// Option to run scripts is true for back-compat\n\t\t\t\t\t// Intentionally let the error be thrown if parseHTML is not present\n\t\t\t\t\tjQuery.merge( this, jQuery.parseHTML(\n\t\t\t\t\t\tmatch[ 1 ],\n\t\t\t\t\t\tcontext && context.nodeType ? context.ownerDocument || context : document,\n\t\t\t\t\t\ttrue\n\t\t\t\t\t) );\n\n\t\t\t\t\t// HANDLE: $(html, props)\n\t\t\t\t\tif ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {\n\t\t\t\t\t\tfor ( match in context ) {\n\n\t\t\t\t\t\t\t// Properties of context are called as methods if possible\n\t\t\t\t\t\t\tif ( isFunction( this[ match ] ) ) {\n\t\t\t\t\t\t\t\tthis[ match ]( context[ match ] );\n\n\t\t\t\t\t\t\t// ...and otherwise set as attributes\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tthis.attr( match, context[ match ] );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\treturn this;\n\n\t\t\t\t// HANDLE: $(#id)\n\t\t\t\t} else {\n\t\t\t\t\telem = document.getElementById( match[ 2 ] );\n\n\t\t\t\t\tif ( elem ) {\n\n\t\t\t\t\t\t// Inject the element directly into the jQuery object\n\t\t\t\t\t\tthis[ 0 ] = elem;\n\t\t\t\t\t\tthis.length = 1;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\n\t\t\t// HANDLE: $(expr, $(...))\n\t\t\t} else if ( !context || context.jquery ) {\n\t\t\t\treturn ( context || root ).find( selector );\n\n\t\t\t// HANDLE: $(expr, context)\n\t\t\t// (which is just equivalent to: $(context).find(expr)\n\t\t\t} else {\n\t\t\t\treturn this.constructor( context ).find( selector );\n\t\t\t}\n\n\t\t// HANDLE: $(DOMElement)\n\t\t} else if ( selector.nodeType ) {\n\t\t\tthis[ 0 ] = selector;\n\t\t\tthis.length = 1;\n\t\t\treturn this;\n\n\t\t// HANDLE: $(function)\n\t\t// Shortcut for document ready\n\t\t} else if ( isFunction( selector ) ) {\n\t\t\treturn root.ready !== undefined ?\n\t\t\t\troot.ready( selector ) :\n\n\t\t\t\t// Execute immediately if ready is not present\n\t\t\t\tselector( jQuery );\n\t\t}\n\n\t\treturn jQuery.makeArray( selector, this );\n\t};\n\n// Give the init function the jQuery prototype for later instantiation\ninit.prototype = jQuery.fn;\n\n// Initialize central reference\nrootjQuery = jQuery( document );\n\n\nvar rparentsprev = /^(?:parents|prev(?:Until|All))/,\n\n\t// Methods guaranteed to produce a unique set when starting from a unique set\n\tguaranteedUnique = {\n\t\tchildren: true,\n\t\tcontents: true,\n\t\tnext: true,\n\t\tprev: true\n\t};\n\njQuery.fn.extend( {\n\thas: function( target ) {\n\t\tvar targets = jQuery( target, this ),\n\t\t\tl = targets.length;\n\n\t\treturn this.filter( function() {\n\t\t\tvar i = 0;\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tif ( jQuery.contains( this, targets[ i ] ) ) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t}\n\t\t} );\n\t},\n\n\tclosest: function( selectors, context ) {\n\t\tvar cur,\n\t\t\ti = 0,\n\t\t\tl = this.length,\n\t\t\tmatched = [],\n\t\t\ttargets = typeof selectors !== \"string\" && jQuery( selectors );\n\n\t\t// Positional selectors never match, since there's no _selection_ context\n\t\tif ( !rneedsContext.test( selectors ) ) {\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tfor ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {\n\n\t\t\t\t\t// Always skip document fragments\n\t\t\t\t\tif ( cur.nodeType < 11 && ( targets ?\n\t\t\t\t\t\ttargets.index( cur ) > -1 :\n\n\t\t\t\t\t\t// Don't pass non-elements to Sizzle\n\t\t\t\t\t\tcur.nodeType === 1 &&\n\t\t\t\t\t\t\tjQuery.find.matchesSelector( cur, selectors ) ) ) {\n\n\t\t\t\t\t\tmatched.push( cur );\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );\n\t},\n\n\t// Determine the position of an element within the set\n\tindex: function( elem ) {\n\n\t\t// No argument, return index in parent\n\t\tif ( !elem ) {\n\t\t\treturn ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;\n\t\t}\n\n\t\t// Index in selector\n\t\tif ( typeof elem === \"string\" ) {\n\t\t\treturn indexOf.call( jQuery( elem ), this[ 0 ] );\n\t\t}\n\n\t\t// Locate the position of the desired element\n\t\treturn indexOf.call( this,\n\n\t\t\t// If it receives a jQuery object, the first element is used\n\t\t\telem.jquery ? elem[ 0 ] : elem\n\t\t);\n\t},\n\n\tadd: function( selector, context ) {\n\t\treturn this.pushStack(\n\t\t\tjQuery.uniqueSort(\n\t\t\t\tjQuery.merge( this.get(), jQuery( selector, context ) )\n\t\t\t)\n\t\t);\n\t},\n\n\taddBack: function( selector ) {\n\t\treturn this.add( selector == null ?\n\t\t\tthis.prevObject : this.prevObject.filter( selector )\n\t\t);\n\t}\n} );\n\nfunction sibling( cur, dir ) {\n\twhile ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}\n\treturn cur;\n}\n\njQuery.each( {\n\tparent: function( elem ) {\n\t\tvar parent = elem.parentNode;\n\t\treturn parent && parent.nodeType !== 11 ? parent : null;\n\t},\n\tparents: function( elem ) {\n\t\treturn dir( elem, \"parentNode\" );\n\t},\n\tparentsUntil: function( elem, _i, until ) {\n\t\treturn dir( elem, \"parentNode\", until );\n\t},\n\tnext: function( elem ) {\n\t\treturn sibling( elem, \"nextSibling\" );\n\t},\n\tprev: function( elem ) {\n\t\treturn sibling( elem, \"previousSibling\" );\n\t},\n\tnextAll: function( elem ) {\n\t\treturn dir( elem, \"nextSibling\" );\n\t},\n\tprevAll: function( elem ) {\n\t\treturn dir( elem, \"previousSibling\" );\n\t},\n\tnextUntil: function( elem, _i, until ) {\n\t\treturn dir( elem, \"nextSibling\", until );\n\t},\n\tprevUntil: function( elem, _i, until ) {\n\t\treturn dir( elem, \"previousSibling\", until );\n\t},\n\tsiblings: function( elem ) {\n\t\treturn siblings( ( elem.parentNode || {} ).firstChild, elem );\n\t},\n\tchildren: function( elem ) {\n\t\treturn siblings( elem.firstChild );\n\t},\n\tcontents: function( elem ) {\n\t\tif ( elem.contentDocument != null &&\n\n\t\t\t// Support: IE 11+\n\t\t\t// elements with no `data` attribute has an object\n\t\t\t// `contentDocument` with a `null` prototype.\n\t\t\tgetProto( elem.contentDocument ) ) {\n\n\t\t\treturn elem.contentDocument;\n\t\t}\n\n\t\t// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only\n\t\t// Treat the template element as a regular one in browsers that\n\t\t// don't support it.\n\t\tif ( nodeName( elem, \"template\" ) ) {\n\t\t\telem = elem.content || elem;\n\t\t}\n\n\t\treturn jQuery.merge( [], elem.childNodes );\n\t}\n}, function( name, fn ) {\n\tjQuery.fn[ name ] = function( until, selector ) {\n\t\tvar matched = jQuery.map( this, fn, until );\n\n\t\tif ( name.slice( -5 ) !== \"Until\" ) {\n\t\t\tselector = until;\n\t\t}\n\n\t\tif ( selector && typeof selector === \"string\" ) {\n\t\t\tmatched = jQuery.filter( selector, matched );\n\t\t}\n\n\t\tif ( this.length > 1 ) {\n\n\t\t\t// Remove duplicates\n\t\t\tif ( !guaranteedUnique[ name ] ) {\n\t\t\t\tjQuery.uniqueSort( matched );\n\t\t\t}\n\n\t\t\t// Reverse order for parents* and prev-derivatives\n\t\t\tif ( rparentsprev.test( name ) ) {\n\t\t\t\tmatched.reverse();\n\t\t\t}\n\t\t}\n\n\t\treturn this.pushStack( matched );\n\t};\n} );\nvar rnothtmlwhite = ( /[^\\x20\\t\\r\\n\\f]+/g );\n\n\n\n// Convert String-formatted options into Object-formatted ones\nfunction createOptions( options ) {\n\tvar object = {};\n\tjQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {\n\t\tobject[ flag ] = true;\n\t} );\n\treturn object;\n}\n\n/*\n * Create a callback list using the following parameters:\n *\n *\toptions: an optional list of space-separated options that will change how\n *\t\t\tthe callback list behaves or a more traditional option object\n *\n * By default a callback list will act like an event callback list and can be\n * \"fired\" multiple times.\n *\n * Possible options:\n *\n *\tonce:\t\t\twill ensure the callback list can only be fired once (like a Deferred)\n *\n *\tmemory:\t\t\twill keep track of previous values and will call any callback added\n *\t\t\t\t\tafter the list has been fired right away with the latest \"memorized\"\n *\t\t\t\t\tvalues (like a Deferred)\n *\n *\tunique:\t\t\twill ensure a callback can only be added once (no duplicate in the list)\n *\n *\tstopOnFalse:\tinterrupt callings when a callback returns false\n *\n */\njQuery.Callbacks = function( options ) {\n\n\t// Convert options from String-formatted to Object-formatted if needed\n\t// (we check in cache first)\n\toptions = typeof options === \"string\" ?\n\t\tcreateOptions( options ) :\n\t\tjQuery.extend( {}, options );\n\n\tvar // Flag to know if list is currently firing\n\t\tfiring,\n\n\t\t// Last fire value for non-forgettable lists\n\t\tmemory,\n\n\t\t// Flag to know if list was already fired\n\t\tfired,\n\n\t\t// Flag to prevent firing\n\t\tlocked,\n\n\t\t// Actual callback list\n\t\tlist = [],\n\n\t\t// Queue of execution data for repeatable lists\n\t\tqueue = [],\n\n\t\t// Index of currently firing callback (modified by add/remove as needed)\n\t\tfiringIndex = -1,\n\n\t\t// Fire callbacks\n\t\tfire = function() {\n\n\t\t\t// Enforce single-firing\n\t\t\tlocked = locked || options.once;\n\n\t\t\t// Execute callbacks for all pending executions,\n\t\t\t// respecting firingIndex overrides and runtime changes\n\t\t\tfired = firing = true;\n\t\t\tfor ( ; queue.length; firingIndex = -1 ) {\n\t\t\t\tmemory = queue.shift();\n\t\t\t\twhile ( ++firingIndex < list.length ) {\n\n\t\t\t\t\t// Run callback and check for early termination\n\t\t\t\t\tif ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&\n\t\t\t\t\t\toptions.stopOnFalse ) {\n\n\t\t\t\t\t\t// Jump to end and forget the data so .add doesn't re-fire\n\t\t\t\t\t\tfiringIndex = list.length;\n\t\t\t\t\t\tmemory = false;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Forget the data if we're done with it\n\t\t\tif ( !options.memory ) {\n\t\t\t\tmemory = false;\n\t\t\t}\n\n\t\t\tfiring = false;\n\n\t\t\t// Clean up if we're done firing for good\n\t\t\tif ( locked ) {\n\n\t\t\t\t// Keep an empty list if we have data for future add calls\n\t\t\t\tif ( memory ) {\n\t\t\t\t\tlist = [];\n\n\t\t\t\t// Otherwise, this object is spent\n\t\t\t\t} else {\n\t\t\t\t\tlist = \"\";\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\n\t\t// Actual Callbacks object\n\t\tself = {\n\n\t\t\t// Add a callback or a collection of callbacks to the list\n\t\t\tadd: function() {\n\t\t\t\tif ( list ) {\n\n\t\t\t\t\t// If we have memory from a past run, we should fire after adding\n\t\t\t\t\tif ( memory && !firing ) {\n\t\t\t\t\t\tfiringIndex = list.length - 1;\n\t\t\t\t\t\tqueue.push( memory );\n\t\t\t\t\t}\n\n\t\t\t\t\t( function add( args ) {\n\t\t\t\t\t\tjQuery.each( args, function( _, arg ) {\n\t\t\t\t\t\t\tif ( isFunction( arg ) ) {\n\t\t\t\t\t\t\t\tif ( !options.unique || !self.has( arg ) ) {\n\t\t\t\t\t\t\t\t\tlist.push( arg );\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} else if ( arg && arg.length && toType( arg ) !== \"string\" ) {\n\n\t\t\t\t\t\t\t\t// Inspect recursively\n\t\t\t\t\t\t\t\tadd( arg );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} );\n\t\t\t\t\t} )( arguments );\n\n\t\t\t\t\tif ( memory && !firing ) {\n\t\t\t\t\t\tfire();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Remove a callback from the list\n\t\t\tremove: function() {\n\t\t\t\tjQuery.each( arguments, function( _, arg ) {\n\t\t\t\t\tvar index;\n\t\t\t\t\twhile ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {\n\t\t\t\t\t\tlist.splice( index, 1 );\n\n\t\t\t\t\t\t// Handle firing indexes\n\t\t\t\t\t\tif ( index <= firingIndex ) {\n\t\t\t\t\t\t\tfiringIndex--;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t} );\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Check if a given callback is in the list.\n\t\t\t// If no argument is given, return whether or not list has callbacks attached.\n\t\t\thas: function( fn ) {\n\t\t\t\treturn fn ?\n\t\t\t\t\tjQuery.inArray( fn, list ) > -1 :\n\t\t\t\t\tlist.length > 0;\n\t\t\t},\n\n\t\t\t// Remove all callbacks from the list\n\t\t\tempty: function() {\n\t\t\t\tif ( list ) {\n\t\t\t\t\tlist = [];\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Disable .fire and .add\n\t\t\t// Abort any current/pending executions\n\t\t\t// Clear all callbacks and values\n\t\t\tdisable: function() {\n\t\t\t\tlocked = queue = [];\n\t\t\t\tlist = memory = \"\";\n\t\t\t\treturn this;\n\t\t\t},\n\t\t\tdisabled: function() {\n\t\t\t\treturn !list;\n\t\t\t},\n\n\t\t\t// Disable .fire\n\t\t\t// Also disable .add unless we have memory (since it would have no effect)\n\t\t\t// Abort any pending executions\n\t\t\tlock: function() {\n\t\t\t\tlocked = queue = [];\n\t\t\t\tif ( !memory && !firing ) {\n\t\t\t\t\tlist = memory = \"\";\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\t\t\tlocked: function() {\n\t\t\t\treturn !!locked;\n\t\t\t},\n\n\t\t\t// Call all callbacks with the given context and arguments\n\t\t\tfireWith: function( context, args ) {\n\t\t\t\tif ( !locked ) {\n\t\t\t\t\targs = args || [];\n\t\t\t\t\targs = [ context, args.slice ? args.slice() : args ];\n\t\t\t\t\tqueue.push( args );\n\t\t\t\t\tif ( !firing ) {\n\t\t\t\t\t\tfire();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// Call all the callbacks with the given arguments\n\t\t\tfire: function() {\n\t\t\t\tself.fireWith( this, arguments );\n\t\t\t\treturn this;\n\t\t\t},\n\n\t\t\t// To know if the callbacks have already been called at least once\n\t\t\tfired: function() {\n\t\t\t\treturn !!fired;\n\t\t\t}\n\t\t};\n\n\treturn self;\n};\n\n\nfunction Identity( v ) {\n\treturn v;\n}\nfunction Thrower( ex ) {\n\tthrow ex;\n}\n\nfunction adoptValue( value, resolve, reject, noValue ) {\n\tvar method;\n\n\ttry {\n\n\t\t// Check for promise aspect first to privilege synchronous behavior\n\t\tif ( value && isFunction( ( method = value.promise ) ) ) {\n\t\t\tmethod.call( value ).done( resolve ).fail( reject );\n\n\t\t// Other thenables\n\t\t} else if ( value && isFunction( ( method = value.then ) ) ) {\n\t\t\tmethod.call( value, resolve, reject );\n\n\t\t// Other non-thenables\n\t\t} else {\n\n\t\t\t// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:\n\t\t\t// * false: [ value ].slice( 0 ) => resolve( value )\n\t\t\t// * true: [ value ].slice( 1 ) => resolve()\n\t\t\tresolve.apply( undefined, [ value ].slice( noValue ) );\n\t\t}\n\n\t// For Promises/A+, convert exceptions into rejections\n\t// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in\n\t// Deferred#then to conditionally suppress rejection.\n\t} catch ( value ) {\n\n\t\t// Support: Android 4.0 only\n\t\t// Strict mode functions invoked without .call/.apply get global-object context\n\t\treject.apply( undefined, [ value ] );\n\t}\n}\n\njQuery.extend( {\n\n\tDeferred: function( func ) {\n\t\tvar tuples = [\n\n\t\t\t\t// action, add listener, callbacks,\n\t\t\t\t// ... .then handlers, argument index, [final state]\n\t\t\t\t[ \"notify\", \"progress\", jQuery.Callbacks( \"memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"memory\" ), 2 ],\n\t\t\t\t[ \"resolve\", \"done\", jQuery.Callbacks( \"once memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"once memory\" ), 0, \"resolved\" ],\n\t\t\t\t[ \"reject\", \"fail\", jQuery.Callbacks( \"once memory\" ),\n\t\t\t\t\tjQuery.Callbacks( \"once memory\" ), 1, \"rejected\" ]\n\t\t\t],\n\t\t\tstate = \"pending\",\n\t\t\tpromise = {\n\t\t\t\tstate: function() {\n\t\t\t\t\treturn state;\n\t\t\t\t},\n\t\t\t\talways: function() {\n\t\t\t\t\tdeferred.done( arguments ).fail( arguments );\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\t\t\t\t\"catch\": function( fn ) {\n\t\t\t\t\treturn promise.then( null, fn );\n\t\t\t\t},\n\n\t\t\t\t// Keep pipe for back-compat\n\t\t\t\tpipe: function( /* fnDone, fnFail, fnProgress */ ) {\n\t\t\t\t\tvar fns = arguments;\n\n\t\t\t\t\treturn jQuery.Deferred( function( newDefer ) {\n\t\t\t\t\t\tjQuery.each( tuples, function( _i, tuple ) {\n\n\t\t\t\t\t\t\t// Map tuples (progress, done, fail) to arguments (done, fail, progress)\n\t\t\t\t\t\t\tvar fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];\n\n\t\t\t\t\t\t\t// deferred.progress(function() { bind to newDefer or newDefer.notify })\n\t\t\t\t\t\t\t// deferred.done(function() { bind to newDefer or newDefer.resolve })\n\t\t\t\t\t\t\t// deferred.fail(function() { bind to newDefer or newDefer.reject })\n\t\t\t\t\t\t\tdeferred[ tuple[ 1 ] ]( function() {\n\t\t\t\t\t\t\t\tvar returned = fn && fn.apply( this, arguments );\n\t\t\t\t\t\t\t\tif ( returned && isFunction( returned.promise ) ) {\n\t\t\t\t\t\t\t\t\treturned.promise()\n\t\t\t\t\t\t\t\t\t\t.progress( newDefer.notify )\n\t\t\t\t\t\t\t\t\t\t.done( newDefer.resolve )\n\t\t\t\t\t\t\t\t\t\t.fail( newDefer.reject );\n\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\tnewDefer[ tuple[ 0 ] + \"With\" ](\n\t\t\t\t\t\t\t\t\t\tthis,\n\t\t\t\t\t\t\t\t\t\tfn ? [ returned ] : arguments\n\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t} );\n\t\t\t\t\t\tfns = null;\n\t\t\t\t\t} ).promise();\n\t\t\t\t},\n\t\t\t\tthen: function( onFulfilled, onRejected, onProgress ) {\n\t\t\t\t\tvar maxDepth = 0;\n\t\t\t\t\tfunction resolve( depth, deferred, handler, special ) {\n\t\t\t\t\t\treturn function() {\n\t\t\t\t\t\t\tvar that = this,\n\t\t\t\t\t\t\t\targs = arguments,\n\t\t\t\t\t\t\t\tmightThrow = function() {\n\t\t\t\t\t\t\t\t\tvar returned, then;\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.3\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-59\n\t\t\t\t\t\t\t\t\t// Ignore double-resolution attempts\n\t\t\t\t\t\t\t\t\tif ( depth < maxDepth ) {\n\t\t\t\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\treturned = handler.apply( that, args );\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.1\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-48\n\t\t\t\t\t\t\t\t\tif ( returned === deferred.promise() ) {\n\t\t\t\t\t\t\t\t\t\tthrow new TypeError( \"Thenable self-resolution\" );\n\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t// Support: Promises/A+ sections 2.3.3.1, 3.5\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-54\n\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-75\n\t\t\t\t\t\t\t\t\t// Retrieve `then` only once\n\t\t\t\t\t\t\t\t\tthen = returned &&\n\n\t\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.4\n\t\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-64\n\t\t\t\t\t\t\t\t\t\t// Only check objects and functions for thenability\n\t\t\t\t\t\t\t\t\t\t( typeof returned === \"object\" ||\n\t\t\t\t\t\t\t\t\t\t\ttypeof returned === \"function\" ) &&\n\t\t\t\t\t\t\t\t\t\treturned.then;\n\n\t\t\t\t\t\t\t\t\t// Handle a returned thenable\n\t\t\t\t\t\t\t\t\tif ( isFunction( then ) ) {\n\n\t\t\t\t\t\t\t\t\t\t// Special processors (notify) just wait for resolution\n\t\t\t\t\t\t\t\t\t\tif ( special ) {\n\t\t\t\t\t\t\t\t\t\t\tthen.call(\n\t\t\t\t\t\t\t\t\t\t\t\treturned,\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Thrower, special )\n\t\t\t\t\t\t\t\t\t\t\t);\n\n\t\t\t\t\t\t\t\t\t\t// Normal processors (resolve) also hook into progress\n\t\t\t\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t\t\t\t// ...and disregard older resolution values\n\t\t\t\t\t\t\t\t\t\t\tmaxDepth++;\n\n\t\t\t\t\t\t\t\t\t\t\tthen.call(\n\t\t\t\t\t\t\t\t\t\t\t\treturned,\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Thrower, special ),\n\t\t\t\t\t\t\t\t\t\t\t\tresolve( maxDepth, deferred, Identity,\n\t\t\t\t\t\t\t\t\t\t\t\t\tdeferred.notifyWith )\n\t\t\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t// Handle all other returned values\n\t\t\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t\t\t// Only substitute handlers pass on context\n\t\t\t\t\t\t\t\t\t\t// and multiple values (non-spec behavior)\n\t\t\t\t\t\t\t\t\t\tif ( handler !== Identity ) {\n\t\t\t\t\t\t\t\t\t\t\tthat = undefined;\n\t\t\t\t\t\t\t\t\t\t\targs = [ returned ];\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t// Process the value(s)\n\t\t\t\t\t\t\t\t\t\t// Default process is resolve\n\t\t\t\t\t\t\t\t\t\t( special || deferred.resolveWith )( that, args );\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t},\n\n\t\t\t\t\t\t\t\t// Only normal processors (resolve) catch and reject exceptions\n\t\t\t\t\t\t\t\tprocess = special ?\n\t\t\t\t\t\t\t\t\tmightThrow :\n\t\t\t\t\t\t\t\t\tfunction() {\n\t\t\t\t\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\t\t\t\t\tmightThrow();\n\t\t\t\t\t\t\t\t\t\t} catch ( e ) {\n\n\t\t\t\t\t\t\t\t\t\t\tif ( jQuery.Deferred.exceptionHook ) {\n\t\t\t\t\t\t\t\t\t\t\t\tjQuery.Deferred.exceptionHook( e,\n\t\t\t\t\t\t\t\t\t\t\t\t\tprocess.stackTrace );\n\t\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.4.1\n\t\t\t\t\t\t\t\t\t\t\t// https://promisesaplus.com/#point-61\n\t\t\t\t\t\t\t\t\t\t\t// Ignore post-resolution exceptions\n\t\t\t\t\t\t\t\t\t\t\tif ( depth + 1 >= maxDepth ) {\n\n\t\t\t\t\t\t\t\t\t\t\t\t// Only substitute handlers pass on context\n\t\t\t\t\t\t\t\t\t\t\t\t// and multiple values (non-spec behavior)\n\t\t\t\t\t\t\t\t\t\t\t\tif ( handler !== Thrower ) {\n\t\t\t\t\t\t\t\t\t\t\t\t\tthat = undefined;\n\t\t\t\t\t\t\t\t\t\t\t\t\targs = [ e ];\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t\t\tdeferred.rejectWith( that, args );\n\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t};\n\n\t\t\t\t\t\t\t// Support: Promises/A+ section 2.3.3.3.1\n\t\t\t\t\t\t\t// https://promisesaplus.com/#point-57\n\t\t\t\t\t\t\t// Re-resolve promises immediately to dodge false rejection from\n\t\t\t\t\t\t\t// subsequent errors\n\t\t\t\t\t\t\tif ( depth ) {\n\t\t\t\t\t\t\t\tprocess();\n\t\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t\t// Call an optional hook to record the stack, in case of exception\n\t\t\t\t\t\t\t\t// since it's otherwise lost when execution goes async\n\t\t\t\t\t\t\t\tif ( jQuery.Deferred.getStackHook ) {\n\t\t\t\t\t\t\t\t\tprocess.stackTrace = jQuery.Deferred.getStackHook();\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\twindow.setTimeout( process );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t};\n\t\t\t\t\t}\n\n\t\t\t\t\treturn jQuery.Deferred( function( newDefer ) {\n\n\t\t\t\t\t\t// progress_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 0 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tisFunction( onProgress ) ?\n\t\t\t\t\t\t\t\t\tonProgress :\n\t\t\t\t\t\t\t\t\tIdentity,\n\t\t\t\t\t\t\t\tnewDefer.notifyWith\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\n\t\t\t\t\t\t// fulfilled_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 1 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tisFunction( onFulfilled ) ?\n\t\t\t\t\t\t\t\t\tonFulfilled :\n\t\t\t\t\t\t\t\t\tIdentity\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\n\t\t\t\t\t\t// rejected_handlers.add( ... )\n\t\t\t\t\t\ttuples[ 2 ][ 3 ].add(\n\t\t\t\t\t\t\tresolve(\n\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\tnewDefer,\n\t\t\t\t\t\t\t\tisFunction( onRejected ) ?\n\t\t\t\t\t\t\t\t\tonRejected :\n\t\t\t\t\t\t\t\t\tThrower\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t);\n\t\t\t\t\t} ).promise();\n\t\t\t\t},\n\n\t\t\t\t// Get a promise for this deferred\n\t\t\t\t// If obj is provided, the promise aspect is added to the object\n\t\t\t\tpromise: function( obj ) {\n\t\t\t\t\treturn obj != null ? jQuery.extend( obj, promise ) : promise;\n\t\t\t\t}\n\t\t\t},\n\t\t\tdeferred = {};\n\n\t\t// Add list-specific methods\n\t\tjQuery.each( tuples, function( i, tuple ) {\n\t\t\tvar list = tuple[ 2 ],\n\t\t\t\tstateString = tuple[ 5 ];\n\n\t\t\t// promise.progress = list.add\n\t\t\t// promise.done = list.add\n\t\t\t// promise.fail = list.add\n\t\t\tpromise[ tuple[ 1 ] ] = list.add;\n\n\t\t\t// Handle state\n\t\t\tif ( stateString ) {\n\t\t\t\tlist.add(\n\t\t\t\t\tfunction() {\n\n\t\t\t\t\t\t// state = \"resolved\" (i.e., fulfilled)\n\t\t\t\t\t\t// state = \"rejected\"\n\t\t\t\t\t\tstate = stateString;\n\t\t\t\t\t},\n\n\t\t\t\t\t// rejected_callbacks.disable\n\t\t\t\t\t// fulfilled_callbacks.disable\n\t\t\t\t\ttuples[ 3 - i ][ 2 ].disable,\n\n\t\t\t\t\t// rejected_handlers.disable\n\t\t\t\t\t// fulfilled_handlers.disable\n\t\t\t\t\ttuples[ 3 - i ][ 3 ].disable,\n\n\t\t\t\t\t// progress_callbacks.lock\n\t\t\t\t\ttuples[ 0 ][ 2 ].lock,\n\n\t\t\t\t\t// progress_handlers.lock\n\t\t\t\t\ttuples[ 0 ][ 3 ].lock\n\t\t\t\t);\n\t\t\t}\n\n\t\t\t// progress_handlers.fire\n\t\t\t// fulfilled_handlers.fire\n\t\t\t// rejected_handlers.fire\n\t\t\tlist.add( tuple[ 3 ].fire );\n\n\t\t\t// deferred.notify = function() { deferred.notifyWith(...) }\n\t\t\t// deferred.resolve = function() { deferred.resolveWith(...) }\n\t\t\t// deferred.reject = function() { deferred.rejectWith(...) }\n\t\t\tdeferred[ tuple[ 0 ] ] = function() {\n\t\t\t\tdeferred[ tuple[ 0 ] + \"With\" ]( this === deferred ? undefined : this, arguments );\n\t\t\t\treturn this;\n\t\t\t};\n\n\t\t\t// deferred.notifyWith = list.fireWith\n\t\t\t// deferred.resolveWith = list.fireWith\n\t\t\t// deferred.rejectWith = list.fireWith\n\t\t\tdeferred[ tuple[ 0 ] + \"With\" ] = list.fireWith;\n\t\t} );\n\n\t\t// Make the deferred a promise\n\t\tpromise.promise( deferred );\n\n\t\t// Call given func if any\n\t\tif ( func ) {\n\t\t\tfunc.call( deferred, deferred );\n\t\t}\n\n\t\t// All done!\n\t\treturn deferred;\n\t},\n\n\t// Deferred helper\n\twhen: function( singleValue ) {\n\t\tvar\n\n\t\t\t// count of uncompleted subordinates\n\t\t\tremaining = arguments.length,\n\n\t\t\t// count of unprocessed arguments\n\t\t\ti = remaining,\n\n\t\t\t// subordinate fulfillment data\n\t\t\tresolveContexts = Array( i ),\n\t\t\tresolveValues = slice.call( arguments ),\n\n\t\t\t// the master Deferred\n\t\t\tmaster = jQuery.Deferred(),\n\n\t\t\t// subordinate callback factory\n\t\t\tupdateFunc = function( i ) {\n\t\t\t\treturn function( value ) {\n\t\t\t\t\tresolveContexts[ i ] = this;\n\t\t\t\t\tresolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;\n\t\t\t\t\tif ( !( --remaining ) ) {\n\t\t\t\t\t\tmaster.resolveWith( resolveContexts, resolveValues );\n\t\t\t\t\t}\n\t\t\t\t};\n\t\t\t};\n\n\t\t// Single- and empty arguments are adopted like Promise.resolve\n\t\tif ( remaining <= 1 ) {\n\t\t\tadoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,\n\t\t\t\t!remaining );\n\n\t\t\t// Use .then() to unwrap secondary thenables (cf. gh-3000)\n\t\t\tif ( master.state() === \"pending\" ||\n\t\t\t\tisFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {\n\n\t\t\t\treturn master.then();\n\t\t\t}\n\t\t}\n\n\t\t// Multiple arguments are aggregated like Promise.all array elements\n\t\twhile ( i-- ) {\n\t\t\tadoptValue( resolveValues[ i ], updateFunc( i ), master.reject );\n\t\t}\n\n\t\treturn master.promise();\n\t}\n} );\n\n\n// These usually indicate a programmer mistake during development,\n// warn about them ASAP rather than swallowing them by default.\nvar rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;\n\njQuery.Deferred.exceptionHook = function( error, stack ) {\n\n\t// Support: IE 8 - 9 only\n\t// Console exists when dev tools are open, which can happen at any time\n\tif ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {\n\t\twindow.console.warn( \"jQuery.Deferred exception: \" + error.message, error.stack, stack );\n\t}\n};\n\n\n\n\njQuery.readyException = function( error ) {\n\twindow.setTimeout( function() {\n\t\tthrow error;\n\t} );\n};\n\n\n\n\n// The deferred used on DOM ready\nvar readyList = jQuery.Deferred();\n\njQuery.fn.ready = function( fn ) {\n\n\treadyList\n\t\t.then( fn )\n\n\t\t// Wrap jQuery.readyException in a function so that the lookup\n\t\t// happens at the time of error handling instead of callback\n\t\t// registration.\n\t\t.catch( function( error ) {\n\t\t\tjQuery.readyException( error );\n\t\t} );\n\n\treturn this;\n};\n\njQuery.extend( {\n\n\t// Is the DOM ready to be used? Set to true once it occurs.\n\tisReady: false,\n\n\t// A counter to track how many items to wait for before\n\t// the ready event fires. See #6781\n\treadyWait: 1,\n\n\t// Handle when the DOM is ready\n\tready: function( wait ) {\n\n\t\t// Abort if there are pending holds or we're already ready\n\t\tif ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Remember that the DOM is ready\n\t\tjQuery.isReady = true;\n\n\t\t// If a normal DOM Ready event fired, decrement, and wait if need be\n\t\tif ( wait !== true && --jQuery.readyWait > 0 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// If there are functions bound, to execute\n\t\treadyList.resolveWith( document, [ jQuery ] );\n\t}\n} );\n\njQuery.ready.then = readyList.then;\n\n// The ready event handler and self cleanup method\nfunction completed() {\n\tdocument.removeEventListener( \"DOMContentLoaded\", completed );\n\twindow.removeEventListener( \"load\", completed );\n\tjQuery.ready();\n}\n\n// Catch cases where $(document).ready() is called\n// after the browser event has already occurred.\n// Support: IE <=9 - 10 only\n// Older IE sometimes signals \"interactive\" too soon\nif ( document.readyState === \"complete\" ||\n\t( document.readyState !== \"loading\" && !document.documentElement.doScroll ) ) {\n\n\t// Handle it asynchronously to allow scripts the opportunity to delay ready\n\twindow.setTimeout( jQuery.ready );\n\n} else {\n\n\t// Use the handy event callback\n\tdocument.addEventListener( \"DOMContentLoaded\", completed );\n\n\t// A fallback to window.onload, that will always work\n\twindow.addEventListener( \"load\", completed );\n}\n\n\n\n\n// Multifunctional method to get and set values of a collection\n// The value/s can optionally be executed if it's a function\nvar access = function( elems, fn, key, value, chainable, emptyGet, raw ) {\n\tvar i = 0,\n\t\tlen = elems.length,\n\t\tbulk = key == null;\n\n\t// Sets many values\n\tif ( toType( key ) === \"object\" ) {\n\t\tchainable = true;\n\t\tfor ( i in key ) {\n\t\t\taccess( elems, fn, i, key[ i ], true, emptyGet, raw );\n\t\t}\n\n\t// Sets one value\n\t} else if ( value !== undefined ) {\n\t\tchainable = true;\n\n\t\tif ( !isFunction( value ) ) {\n\t\t\traw = true;\n\t\t}\n\n\t\tif ( bulk ) {\n\n\t\t\t// Bulk operations run against the entire set\n\t\t\tif ( raw ) {\n\t\t\t\tfn.call( elems, value );\n\t\t\t\tfn = null;\n\n\t\t\t// ...except when executing function values\n\t\t\t} else {\n\t\t\t\tbulk = fn;\n\t\t\t\tfn = function( elem, _key, value ) {\n\t\t\t\t\treturn bulk.call( jQuery( elem ), value );\n\t\t\t\t};\n\t\t\t}\n\t\t}\n\n\t\tif ( fn ) {\n\t\t\tfor ( ; i < len; i++ ) {\n\t\t\t\tfn(\n\t\t\t\t\telems[ i ], key, raw ?\n\t\t\t\t\tvalue :\n\t\t\t\t\tvalue.call( elems[ i ], i, fn( elems[ i ], key ) )\n\t\t\t\t);\n\t\t\t}\n\t\t}\n\t}\n\n\tif ( chainable ) {\n\t\treturn elems;\n\t}\n\n\t// Gets\n\tif ( bulk ) {\n\t\treturn fn.call( elems );\n\t}\n\n\treturn len ? fn( elems[ 0 ], key ) : emptyGet;\n};\n\n\n// Matches dashed string for camelizing\nvar rmsPrefix = /^-ms-/,\n\trdashAlpha = /-([a-z])/g;\n\n// Used by camelCase as callback to replace()\nfunction fcamelCase( _all, letter ) {\n\treturn letter.toUpperCase();\n}\n\n// Convert dashed to camelCase; used by the css and data modules\n// Support: IE <=9 - 11, Edge 12 - 15\n// Microsoft forgot to hump their vendor prefix (#9572)\nfunction camelCase( string ) {\n\treturn string.replace( rmsPrefix, \"ms-\" ).replace( rdashAlpha, fcamelCase );\n}\nvar acceptData = function( owner ) {\n\n\t// Accepts only:\n\t// - Node\n\t// - Node.ELEMENT_NODE\n\t// - Node.DOCUMENT_NODE\n\t// - Object\n\t// - Any\n\treturn owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );\n};\n\n\n\n\nfunction Data() {\n\tthis.expando = jQuery.expando + Data.uid++;\n}\n\nData.uid = 1;\n\nData.prototype = {\n\n\tcache: function( owner ) {\n\n\t\t// Check if the owner object already has a cache\n\t\tvar value = owner[ this.expando ];\n\n\t\t// If not, create one\n\t\tif ( !value ) {\n\t\t\tvalue = {};\n\n\t\t\t// We can accept data for non-element nodes in modern browsers,\n\t\t\t// but we should not, see #8335.\n\t\t\t// Always return an empty object.\n\t\t\tif ( acceptData( owner ) ) {\n\n\t\t\t\t// If it is a node unlikely to be stringify-ed or looped over\n\t\t\t\t// use plain assignment\n\t\t\t\tif ( owner.nodeType ) {\n\t\t\t\t\towner[ this.expando ] = value;\n\n\t\t\t\t// Otherwise secure it in a non-enumerable property\n\t\t\t\t// configurable must be true to allow the property to be\n\t\t\t\t// deleted when data is removed\n\t\t\t\t} else {\n\t\t\t\t\tObject.defineProperty( owner, this.expando, {\n\t\t\t\t\t\tvalue: value,\n\t\t\t\t\t\tconfigurable: true\n\t\t\t\t\t} );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn value;\n\t},\n\tset: function( owner, data, value ) {\n\t\tvar prop,\n\t\t\tcache = this.cache( owner );\n\n\t\t// Handle: [ owner, key, value ] args\n\t\t// Always use camelCase key (gh-2257)\n\t\tif ( typeof data === \"string\" ) {\n\t\t\tcache[ camelCase( data ) ] = value;\n\n\t\t// Handle: [ owner, { properties } ] args\n\t\t} else {\n\n\t\t\t// Copy the properties one-by-one to the cache object\n\t\t\tfor ( prop in data ) {\n\t\t\t\tcache[ camelCase( prop ) ] = data[ prop ];\n\t\t\t}\n\t\t}\n\t\treturn cache;\n\t},\n\tget: function( owner, key ) {\n\t\treturn key === undefined ?\n\t\t\tthis.cache( owner ) :\n\n\t\t\t// Always use camelCase key (gh-2257)\n\t\t\towner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];\n\t},\n\taccess: function( owner, key, value ) {\n\n\t\t// In cases where either:\n\t\t//\n\t\t// 1. No key was specified\n\t\t// 2. A string key was specified, but no value provided\n\t\t//\n\t\t// Take the \"read\" path and allow the get method to determine\n\t\t// which value to return, respectively either:\n\t\t//\n\t\t// 1. The entire cache object\n\t\t// 2. The data stored at the key\n\t\t//\n\t\tif ( key === undefined ||\n\t\t\t\t( ( key && typeof key === \"string\" ) && value === undefined ) ) {\n\n\t\t\treturn this.get( owner, key );\n\t\t}\n\n\t\t// When the key is not a string, or both a key and value\n\t\t// are specified, set or extend (existing objects) with either:\n\t\t//\n\t\t// 1. An object of properties\n\t\t// 2. A key and value\n\t\t//\n\t\tthis.set( owner, key, value );\n\n\t\t// Since the \"set\" path can have two possible entry points\n\t\t// return the expected data based on which path was taken[*]\n\t\treturn value !== undefined ? value : key;\n\t},\n\tremove: function( owner, key ) {\n\t\tvar i,\n\t\t\tcache = owner[ this.expando ];\n\n\t\tif ( cache === undefined ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( key !== undefined ) {\n\n\t\t\t// Support array or space separated string of keys\n\t\t\tif ( Array.isArray( key ) ) {\n\n\t\t\t\t// If key is an array of keys...\n\t\t\t\t// We always set camelCase keys, so remove that.\n\t\t\t\tkey = key.map( camelCase );\n\t\t\t} else {\n\t\t\t\tkey = camelCase( key );\n\n\t\t\t\t// If a key with the spaces exists, use it.\n\t\t\t\t// Otherwise, create an array by matching non-whitespace\n\t\t\t\tkey = key in cache ?\n\t\t\t\t\t[ key ] :\n\t\t\t\t\t( key.match( rnothtmlwhite ) || [] );\n\t\t\t}\n\n\t\t\ti = key.length;\n\n\t\t\twhile ( i-- ) {\n\t\t\t\tdelete cache[ key[ i ] ];\n\t\t\t}\n\t\t}\n\n\t\t// Remove the expando if there's no more data\n\t\tif ( key === undefined || jQuery.isEmptyObject( cache ) ) {\n\n\t\t\t// Support: Chrome <=35 - 45\n\t\t\t// Webkit & Blink performance suffers when deleting properties\n\t\t\t// from DOM nodes, so set to undefined instead\n\t\t\t// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)\n\t\t\tif ( owner.nodeType ) {\n\t\t\t\towner[ this.expando ] = undefined;\n\t\t\t} else {\n\t\t\t\tdelete owner[ this.expando ];\n\t\t\t}\n\t\t}\n\t},\n\thasData: function( owner ) {\n\t\tvar cache = owner[ this.expando ];\n\t\treturn cache !== undefined && !jQuery.isEmptyObject( cache );\n\t}\n};\nvar dataPriv = new Data();\n\nvar dataUser = new Data();\n\n\n\n//\tImplementation Summary\n//\n//\t1. Enforce API surface and semantic compatibility with 1.9.x branch\n//\t2. Improve the module's maintainability by reducing the storage\n//\t\tpaths to a single mechanism.\n//\t3. Use the same single mechanism to support \"private\" and \"user\" data.\n//\t4. _Never_ expose \"private\" data to user code (TODO: Drop _data, _removeData)\n//\t5. Avoid exposing implementation details on user objects (eg. expando properties)\n//\t6. Provide a clear path for implementation upgrade to WeakMap in 2014\n\nvar rbrace = /^(?:\\{[\\w\\W]*\\}|\\[[\\w\\W]*\\])$/,\n\trmultiDash = /[A-Z]/g;\n\nfunction getData( data ) {\n\tif ( data === \"true\" ) {\n\t\treturn true;\n\t}\n\n\tif ( data === \"false\" ) {\n\t\treturn false;\n\t}\n\n\tif ( data === \"null\" ) {\n\t\treturn null;\n\t}\n\n\t// Only convert to a number if it doesn't change the string\n\tif ( data === +data + \"\" ) {\n\t\treturn +data;\n\t}\n\n\tif ( rbrace.test( data ) ) {\n\t\treturn JSON.parse( data );\n\t}\n\n\treturn data;\n}\n\nfunction dataAttr( elem, key, data ) {\n\tvar name;\n\n\t// If nothing was found internally, try to fetch any\n\t// data from the HTML5 data-* attribute\n\tif ( data === undefined && elem.nodeType === 1 ) {\n\t\tname = \"data-\" + key.replace( rmultiDash, \"-$&\" ).toLowerCase();\n\t\tdata = elem.getAttribute( name );\n\n\t\tif ( typeof data === \"string\" ) {\n\t\t\ttry {\n\t\t\t\tdata = getData( data );\n\t\t\t} catch ( e ) {}\n\n\t\t\t// Make sure we set the data so it isn't changed later\n\t\t\tdataUser.set( elem, key, data );\n\t\t} else {\n\t\t\tdata = undefined;\n\t\t}\n\t}\n\treturn data;\n}\n\njQuery.extend( {\n\thasData: function( elem ) {\n\t\treturn dataUser.hasData( elem ) || dataPriv.hasData( elem );\n\t},\n\n\tdata: function( elem, name, data ) {\n\t\treturn dataUser.access( elem, name, data );\n\t},\n\n\tremoveData: function( elem, name ) {\n\t\tdataUser.remove( elem, name );\n\t},\n\n\t// TODO: Now that all calls to _data and _removeData have been replaced\n\t// with direct calls to dataPriv methods, these can be deprecated.\n\t_data: function( elem, name, data ) {\n\t\treturn dataPriv.access( elem, name, data );\n\t},\n\n\t_removeData: function( elem, name ) {\n\t\tdataPriv.remove( elem, name );\n\t}\n} );\n\njQuery.fn.extend( {\n\tdata: function( key, value ) {\n\t\tvar i, name, data,\n\t\t\telem = this[ 0 ],\n\t\t\tattrs = elem && elem.attributes;\n\n\t\t// Gets all values\n\t\tif ( key === undefined ) {\n\t\t\tif ( this.length ) {\n\t\t\t\tdata = dataUser.get( elem );\n\n\t\t\t\tif ( elem.nodeType === 1 && !dataPriv.get( elem, \"hasDataAttrs\" ) ) {\n\t\t\t\t\ti = attrs.length;\n\t\t\t\t\twhile ( i-- ) {\n\n\t\t\t\t\t\t// Support: IE 11 only\n\t\t\t\t\t\t// The attrs elements can be null (#14894)\n\t\t\t\t\t\tif ( attrs[ i ] ) {\n\t\t\t\t\t\t\tname = attrs[ i ].name;\n\t\t\t\t\t\t\tif ( name.indexOf( \"data-\" ) === 0 ) {\n\t\t\t\t\t\t\t\tname = camelCase( name.slice( 5 ) );\n\t\t\t\t\t\t\t\tdataAttr( elem, name, data[ name ] );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tdataPriv.set( elem, \"hasDataAttrs\", true );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn data;\n\t\t}\n\n\t\t// Sets multiple values\n\t\tif ( typeof key === \"object\" ) {\n\t\t\treturn this.each( function() {\n\t\t\t\tdataUser.set( this, key );\n\t\t\t} );\n\t\t}\n\n\t\treturn access( this, function( value ) {\n\t\t\tvar data;\n\n\t\t\t// The calling jQuery object (element matches) is not empty\n\t\t\t// (and therefore has an element appears at this[ 0 ]) and the\n\t\t\t// `value` parameter was not undefined. An empty jQuery object\n\t\t\t// will result in `undefined` for elem = this[ 0 ] which will\n\t\t\t// throw an exception if an attempt to read a data cache is made.\n\t\t\tif ( elem && value === undefined ) {\n\n\t\t\t\t// Attempt to get data from the cache\n\t\t\t\t// The key will always be camelCased in Data\n\t\t\t\tdata = dataUser.get( elem, key );\n\t\t\t\tif ( data !== undefined ) {\n\t\t\t\t\treturn data;\n\t\t\t\t}\n\n\t\t\t\t// Attempt to \"discover\" the data in\n\t\t\t\t// HTML5 custom data-* attrs\n\t\t\t\tdata = dataAttr( elem, key );\n\t\t\t\tif ( data !== undefined ) {\n\t\t\t\t\treturn data;\n\t\t\t\t}\n\n\t\t\t\t// We tried really hard, but the data doesn't exist.\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// Set the data...\n\t\t\tthis.each( function() {\n\n\t\t\t\t// We always store the camelCased key\n\t\t\t\tdataUser.set( this, key, value );\n\t\t\t} );\n\t\t}, null, value, arguments.length > 1, null, true );\n\t},\n\n\tremoveData: function( key ) {\n\t\treturn this.each( function() {\n\t\t\tdataUser.remove( this, key );\n\t\t} );\n\t}\n} );\n\n\njQuery.extend( {\n\tqueue: function( elem, type, data ) {\n\t\tvar queue;\n\n\t\tif ( elem ) {\n\t\t\ttype = ( type || \"fx\" ) + \"queue\";\n\t\t\tqueue = dataPriv.get( elem, type );\n\n\t\t\t// Speed up dequeue by getting out quickly if this is just a lookup\n\t\t\tif ( data ) {\n\t\t\t\tif ( !queue || Array.isArray( data ) ) {\n\t\t\t\t\tqueue = dataPriv.access( elem, type, jQuery.makeArray( data ) );\n\t\t\t\t} else {\n\t\t\t\t\tqueue.push( data );\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn queue || [];\n\t\t}\n\t},\n\n\tdequeue: function( elem, type ) {\n\t\ttype = type || \"fx\";\n\n\t\tvar queue = jQuery.queue( elem, type ),\n\t\t\tstartLength = queue.length,\n\t\t\tfn = queue.shift(),\n\t\t\thooks = jQuery._queueHooks( elem, type ),\n\t\t\tnext = function() {\n\t\t\t\tjQuery.dequeue( elem, type );\n\t\t\t};\n\n\t\t// If the fx queue is dequeued, always remove the progress sentinel\n\t\tif ( fn === \"inprogress\" ) {\n\t\t\tfn = queue.shift();\n\t\t\tstartLength--;\n\t\t}\n\n\t\tif ( fn ) {\n\n\t\t\t// Add a progress sentinel to prevent the fx queue from being\n\t\t\t// automatically dequeued\n\t\t\tif ( type === \"fx\" ) {\n\t\t\t\tqueue.unshift( \"inprogress\" );\n\t\t\t}\n\n\t\t\t// Clear up the last queue stop function\n\t\t\tdelete hooks.stop;\n\t\t\tfn.call( elem, next, hooks );\n\t\t}\n\n\t\tif ( !startLength && hooks ) {\n\t\t\thooks.empty.fire();\n\t\t}\n\t},\n\n\t// Not public - generate a queueHooks object, or return the current one\n\t_queueHooks: function( elem, type ) {\n\t\tvar key = type + \"queueHooks\";\n\t\treturn dataPriv.get( elem, key ) || dataPriv.access( elem, key, {\n\t\t\tempty: jQuery.Callbacks( \"once memory\" ).add( function() {\n\t\t\t\tdataPriv.remove( elem, [ type + \"queue\", key ] );\n\t\t\t} )\n\t\t} );\n\t}\n} );\n\njQuery.fn.extend( {\n\tqueue: function( type, data ) {\n\t\tvar setter = 2;\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tdata = type;\n\t\t\ttype = \"fx\";\n\t\t\tsetter--;\n\t\t}\n\n\t\tif ( arguments.length < setter ) {\n\t\t\treturn jQuery.queue( this[ 0 ], type );\n\t\t}\n\n\t\treturn data === undefined ?\n\t\t\tthis :\n\t\t\tthis.each( function() {\n\t\t\t\tvar queue = jQuery.queue( this, type, data );\n\n\t\t\t\t// Ensure a hooks for this queue\n\t\t\t\tjQuery._queueHooks( this, type );\n\n\t\t\t\tif ( type === \"fx\" && queue[ 0 ] !== \"inprogress\" ) {\n\t\t\t\t\tjQuery.dequeue( this, type );\n\t\t\t\t}\n\t\t\t} );\n\t},\n\tdequeue: function( type ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.dequeue( this, type );\n\t\t} );\n\t},\n\tclearQueue: function( type ) {\n\t\treturn this.queue( type || \"fx\", [] );\n\t},\n\n\t// Get a promise resolved when queues of a certain type\n\t// are emptied (fx is the type by default)\n\tpromise: function( type, obj ) {\n\t\tvar tmp,\n\t\t\tcount = 1,\n\t\t\tdefer = jQuery.Deferred(),\n\t\t\telements = this,\n\t\t\ti = this.length,\n\t\t\tresolve = function() {\n\t\t\t\tif ( !( --count ) ) {\n\t\t\t\t\tdefer.resolveWith( elements, [ elements ] );\n\t\t\t\t}\n\t\t\t};\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tobj = type;\n\t\t\ttype = undefined;\n\t\t}\n\t\ttype = type || \"fx\";\n\n\t\twhile ( i-- ) {\n\t\t\ttmp = dataPriv.get( elements[ i ], type + \"queueHooks\" );\n\t\t\tif ( tmp && tmp.empty ) {\n\t\t\t\tcount++;\n\t\t\t\ttmp.empty.add( resolve );\n\t\t\t}\n\t\t}\n\t\tresolve();\n\t\treturn defer.promise( obj );\n\t}\n} );\nvar pnum = ( /[+-]?(?:\\d*\\.|)\\d+(?:[eE][+-]?\\d+|)/ ).source;\n\nvar rcssNum = new RegExp( \"^(?:([+-])=|)(\" + pnum + \")([a-z%]*)$\", \"i\" );\n\n\nvar cssExpand = [ \"Top\", \"Right\", \"Bottom\", \"Left\" ];\n\nvar documentElement = document.documentElement;\n\n\n\n\tvar isAttached = function( elem ) {\n\t\t\treturn jQuery.contains( elem.ownerDocument, elem );\n\t\t},\n\t\tcomposed = { composed: true };\n\n\t// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only\n\t// Check attachment across shadow DOM boundaries when possible (gh-3504)\n\t// Support: iOS 10.0-10.2 only\n\t// Early iOS 10 versions support `attachShadow` but not `getRootNode`,\n\t// leading to errors. We need to check for `getRootNode`.\n\tif ( documentElement.getRootNode ) {\n\t\tisAttached = function( elem ) {\n\t\t\treturn jQuery.contains( elem.ownerDocument, elem ) ||\n\t\t\t\telem.getRootNode( composed ) === elem.ownerDocument;\n\t\t};\n\t}\nvar isHiddenWithinTree = function( elem, el ) {\n\n\t\t// isHiddenWithinTree might be called from jQuery#filter function;\n\t\t// in that case, element will be second argument\n\t\telem = el || elem;\n\n\t\t// Inline style trumps all\n\t\treturn elem.style.display === \"none\" ||\n\t\t\telem.style.display === \"\" &&\n\n\t\t\t// Otherwise, check computed style\n\t\t\t// Support: Firefox <=43 - 45\n\t\t\t// Disconnected elements can have computed display: none, so first confirm that elem is\n\t\t\t// in the document.\n\t\t\tisAttached( elem ) &&\n\n\t\t\tjQuery.css( elem, \"display\" ) === \"none\";\n\t};\n\n\n\nfunction adjustCSS( elem, prop, valueParts, tween ) {\n\tvar adjusted, scale,\n\t\tmaxIterations = 20,\n\t\tcurrentValue = tween ?\n\t\t\tfunction() {\n\t\t\t\treturn tween.cur();\n\t\t\t} :\n\t\t\tfunction() {\n\t\t\t\treturn jQuery.css( elem, prop, \"\" );\n\t\t\t},\n\t\tinitial = currentValue(),\n\t\tunit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? \"\" : \"px\" ),\n\n\t\t// Starting value computation is required for potential unit mismatches\n\t\tinitialInUnit = elem.nodeType &&\n\t\t\t( jQuery.cssNumber[ prop ] || unit !== \"px\" && +initial ) &&\n\t\t\trcssNum.exec( jQuery.css( elem, prop ) );\n\n\tif ( initialInUnit && initialInUnit[ 3 ] !== unit ) {\n\n\t\t// Support: Firefox <=54\n\t\t// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)\n\t\tinitial = initial / 2;\n\n\t\t// Trust units reported by jQuery.css\n\t\tunit = unit || initialInUnit[ 3 ];\n\n\t\t// Iteratively approximate from a nonzero starting point\n\t\tinitialInUnit = +initial || 1;\n\n\t\twhile ( maxIterations-- ) {\n\n\t\t\t// Evaluate and update our best guess (doubling guesses that zero out).\n\t\t\t// Finish if the scale equals or crosses 1 (making the old*new product non-positive).\n\t\t\tjQuery.style( elem, prop, initialInUnit + unit );\n\t\t\tif ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {\n\t\t\t\tmaxIterations = 0;\n\t\t\t}\n\t\t\tinitialInUnit = initialInUnit / scale;\n\n\t\t}\n\n\t\tinitialInUnit = initialInUnit * 2;\n\t\tjQuery.style( elem, prop, initialInUnit + unit );\n\n\t\t// Make sure we update the tween properties later on\n\t\tvalueParts = valueParts || [];\n\t}\n\n\tif ( valueParts ) {\n\t\tinitialInUnit = +initialInUnit || +initial || 0;\n\n\t\t// Apply relative offset (+=/-=) if specified\n\t\tadjusted = valueParts[ 1 ] ?\n\t\t\tinitialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :\n\t\t\t+valueParts[ 2 ];\n\t\tif ( tween ) {\n\t\t\ttween.unit = unit;\n\t\t\ttween.start = initialInUnit;\n\t\t\ttween.end = adjusted;\n\t\t}\n\t}\n\treturn adjusted;\n}\n\n\nvar defaultDisplayMap = {};\n\nfunction getDefaultDisplay( elem ) {\n\tvar temp,\n\t\tdoc = elem.ownerDocument,\n\t\tnodeName = elem.nodeName,\n\t\tdisplay = defaultDisplayMap[ nodeName ];\n\n\tif ( display ) {\n\t\treturn display;\n\t}\n\n\ttemp = doc.body.appendChild( doc.createElement( nodeName ) );\n\tdisplay = jQuery.css( temp, \"display\" );\n\n\ttemp.parentNode.removeChild( temp );\n\n\tif ( display === \"none\" ) {\n\t\tdisplay = \"block\";\n\t}\n\tdefaultDisplayMap[ nodeName ] = display;\n\n\treturn display;\n}\n\nfunction showHide( elements, show ) {\n\tvar display, elem,\n\t\tvalues = [],\n\t\tindex = 0,\n\t\tlength = elements.length;\n\n\t// Determine new display value for elements that need to change\n\tfor ( ; index < length; index++ ) {\n\t\telem = elements[ index ];\n\t\tif ( !elem.style ) {\n\t\t\tcontinue;\n\t\t}\n\n\t\tdisplay = elem.style.display;\n\t\tif ( show ) {\n\n\t\t\t// Since we force visibility upon cascade-hidden elements, an immediate (and slow)\n\t\t\t// check is required in this first loop unless we have a nonempty display value (either\n\t\t\t// inline or about-to-be-restored)\n\t\t\tif ( display === \"none\" ) {\n\t\t\t\tvalues[ index ] = dataPriv.get( elem, \"display\" ) || null;\n\t\t\t\tif ( !values[ index ] ) {\n\t\t\t\t\telem.style.display = \"\";\n\t\t\t\t}\n\t\t\t}\n\t\t\tif ( elem.style.display === \"\" && isHiddenWithinTree( elem ) ) {\n\t\t\t\tvalues[ index ] = getDefaultDisplay( elem );\n\t\t\t}\n\t\t} else {\n\t\t\tif ( display !== \"none\" ) {\n\t\t\t\tvalues[ index ] = \"none\";\n\n\t\t\t\t// Remember what we're overwriting\n\t\t\t\tdataPriv.set( elem, \"display\", display );\n\t\t\t}\n\t\t}\n\t}\n\n\t// Set the display of the elements in a second loop to avoid constant reflow\n\tfor ( index = 0; index < length; index++ ) {\n\t\tif ( values[ index ] != null ) {\n\t\t\telements[ index ].style.display = values[ index ];\n\t\t}\n\t}\n\n\treturn elements;\n}\n\njQuery.fn.extend( {\n\tshow: function() {\n\t\treturn showHide( this, true );\n\t},\n\thide: function() {\n\t\treturn showHide( this );\n\t},\n\ttoggle: function( state ) {\n\t\tif ( typeof state === \"boolean\" ) {\n\t\t\treturn state ? this.show() : this.hide();\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tif ( isHiddenWithinTree( this ) ) {\n\t\t\t\tjQuery( this ).show();\n\t\t\t} else {\n\t\t\t\tjQuery( this ).hide();\n\t\t\t}\n\t\t} );\n\t}\n} );\nvar rcheckableType = ( /^(?:checkbox|radio)$/i );\n\nvar rtagName = ( /<([a-z][^\\/\\0>\\x20\\t\\r\\n\\f]*)/i );\n\nvar rscriptType = ( /^$|^module$|\\/(?:java|ecma)script/i );\n\n\n\n( function() {\n\tvar fragment = document.createDocumentFragment(),\n\t\tdiv = fragment.appendChild( document.createElement( \"div\" ) ),\n\t\tinput = document.createElement( \"input\" );\n\n\t// Support: Android 4.0 - 4.3 only\n\t// Check state lost if the name is set (#11217)\n\t// Support: Windows Web Apps (WWA)\n\t// `name` and `type` must use .setAttribute for WWA (#14901)\n\tinput.setAttribute( \"type\", \"radio\" );\n\tinput.setAttribute( \"checked\", \"checked\" );\n\tinput.setAttribute( \"name\", \"t\" );\n\n\tdiv.appendChild( input );\n\n\t// Support: Android <=4.1 only\n\t// Older WebKit doesn't clone checked state correctly in fragments\n\tsupport.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;\n\n\t// Support: IE <=11 only\n\t// Make sure textarea (and checkbox) defaultValue is properly cloned\n\tdiv.innerHTML = \"\";\n\tsupport.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;\n\n\t// Support: IE <=9 only\n\t// IE <=9 replaces \";\n\tsupport.option = !!div.lastChild;\n} )();\n\n\n// We have to close these tags to support XHTML (#13200)\nvar wrapMap = {\n\n\t// XHTML parsers do not magically insert elements in the\n\t// same way that tag soup parsers do. So we cannot shorten\n\t// this by omitting or other required elements.\n\tthead: [ 1, \"\", \"
\" ],\n\tcol: [ 2, \"\", \"
\" ],\n\ttr: [ 2, \"\", \"
\" ],\n\ttd: [ 3, \"\", \"
\" ],\n\n\t_default: [ 0, \"\", \"\" ]\n};\n\nwrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;\nwrapMap.th = wrapMap.td;\n\n// Support: IE <=9 only\nif ( !support.option ) {\n\twrapMap.optgroup = wrapMap.option = [ 1, \"\" ];\n}\n\n\nfunction getAll( context, tag ) {\n\n\t// Support: IE <=9 - 11 only\n\t// Use typeof to avoid zero-argument method invocation on host objects (#15151)\n\tvar ret;\n\n\tif ( typeof context.getElementsByTagName !== \"undefined\" ) {\n\t\tret = context.getElementsByTagName( tag || \"*\" );\n\n\t} else if ( typeof context.querySelectorAll !== \"undefined\" ) {\n\t\tret = context.querySelectorAll( tag || \"*\" );\n\n\t} else {\n\t\tret = [];\n\t}\n\n\tif ( tag === undefined || tag && nodeName( context, tag ) ) {\n\t\treturn jQuery.merge( [ context ], ret );\n\t}\n\n\treturn ret;\n}\n\n\n// Mark scripts as having already been evaluated\nfunction setGlobalEval( elems, refElements ) {\n\tvar i = 0,\n\t\tl = elems.length;\n\n\tfor ( ; i < l; i++ ) {\n\t\tdataPriv.set(\n\t\t\telems[ i ],\n\t\t\t\"globalEval\",\n\t\t\t!refElements || dataPriv.get( refElements[ i ], \"globalEval\" )\n\t\t);\n\t}\n}\n\n\nvar rhtml = /<|&#?\\w+;/;\n\nfunction buildFragment( elems, context, scripts, selection, ignored ) {\n\tvar elem, tmp, tag, wrap, attached, j,\n\t\tfragment = context.createDocumentFragment(),\n\t\tnodes = [],\n\t\ti = 0,\n\t\tl = elems.length;\n\n\tfor ( ; i < l; i++ ) {\n\t\telem = elems[ i ];\n\n\t\tif ( elem || elem === 0 ) {\n\n\t\t\t// Add nodes directly\n\t\t\tif ( toType( elem ) === \"object\" ) {\n\n\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\tjQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );\n\n\t\t\t// Convert non-html into a text node\n\t\t\t} else if ( !rhtml.test( elem ) ) {\n\t\t\t\tnodes.push( context.createTextNode( elem ) );\n\n\t\t\t// Convert html into DOM nodes\n\t\t\t} else {\n\t\t\t\ttmp = tmp || fragment.appendChild( context.createElement( \"div\" ) );\n\n\t\t\t\t// Deserialize a standard representation\n\t\t\t\ttag = ( rtagName.exec( elem ) || [ \"\", \"\" ] )[ 1 ].toLowerCase();\n\t\t\t\twrap = wrapMap[ tag ] || wrapMap._default;\n\t\t\t\ttmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];\n\n\t\t\t\t// Descend through wrappers to the right content\n\t\t\t\tj = wrap[ 0 ];\n\t\t\t\twhile ( j-- ) {\n\t\t\t\t\ttmp = tmp.lastChild;\n\t\t\t\t}\n\n\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\tjQuery.merge( nodes, tmp.childNodes );\n\n\t\t\t\t// Remember the top-level container\n\t\t\t\ttmp = fragment.firstChild;\n\n\t\t\t\t// Ensure the created nodes are orphaned (#12392)\n\t\t\t\ttmp.textContent = \"\";\n\t\t\t}\n\t\t}\n\t}\n\n\t// Remove wrapper from fragment\n\tfragment.textContent = \"\";\n\n\ti = 0;\n\twhile ( ( elem = nodes[ i++ ] ) ) {\n\n\t\t// Skip elements already in the context collection (trac-4087)\n\t\tif ( selection && jQuery.inArray( elem, selection ) > -1 ) {\n\t\t\tif ( ignored ) {\n\t\t\t\tignored.push( elem );\n\t\t\t}\n\t\t\tcontinue;\n\t\t}\n\n\t\tattached = isAttached( elem );\n\n\t\t// Append to fragment\n\t\ttmp = getAll( fragment.appendChild( elem ), \"script\" );\n\n\t\t// Preserve script evaluation history\n\t\tif ( attached ) {\n\t\t\tsetGlobalEval( tmp );\n\t\t}\n\n\t\t// Capture executables\n\t\tif ( scripts ) {\n\t\t\tj = 0;\n\t\t\twhile ( ( elem = tmp[ j++ ] ) ) {\n\t\t\t\tif ( rscriptType.test( elem.type || \"\" ) ) {\n\t\t\t\t\tscripts.push( elem );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn fragment;\n}\n\n\nvar\n\trkeyEvent = /^key/,\n\trmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,\n\trtypenamespace = /^([^.]*)(?:\\.(.+)|)/;\n\nfunction returnTrue() {\n\treturn true;\n}\n\nfunction returnFalse() {\n\treturn false;\n}\n\n// Support: IE <=9 - 11+\n// focus() and blur() are asynchronous, except when they are no-op.\n// So expect focus to be synchronous when the element is already active,\n// and blur to be synchronous when the element is not already active.\n// (focus and blur are always synchronous in other supported browsers,\n// this just defines when we can count on it).\nfunction expectSync( elem, type ) {\n\treturn ( elem === safeActiveElement() ) === ( type === \"focus\" );\n}\n\n// Support: IE <=9 only\n// Accessing document.activeElement can throw unexpectedly\n// https://bugs.jquery.com/ticket/13393\nfunction safeActiveElement() {\n\ttry {\n\t\treturn document.activeElement;\n\t} catch ( err ) { }\n}\n\nfunction on( elem, types, selector, data, fn, one ) {\n\tvar origFn, type;\n\n\t// Types can be a map of types/handlers\n\tif ( typeof types === \"object\" ) {\n\n\t\t// ( types-Object, selector, data )\n\t\tif ( typeof selector !== \"string\" ) {\n\n\t\t\t// ( types-Object, data )\n\t\t\tdata = data || selector;\n\t\t\tselector = undefined;\n\t\t}\n\t\tfor ( type in types ) {\n\t\t\ton( elem, type, selector, data, types[ type ], one );\n\t\t}\n\t\treturn elem;\n\t}\n\n\tif ( data == null && fn == null ) {\n\n\t\t// ( types, fn )\n\t\tfn = selector;\n\t\tdata = selector = undefined;\n\t} else if ( fn == null ) {\n\t\tif ( typeof selector === \"string\" ) {\n\n\t\t\t// ( types, selector, fn )\n\t\t\tfn = data;\n\t\t\tdata = undefined;\n\t\t} else {\n\n\t\t\t// ( types, data, fn )\n\t\t\tfn = data;\n\t\t\tdata = selector;\n\t\t\tselector = undefined;\n\t\t}\n\t}\n\tif ( fn === false ) {\n\t\tfn = returnFalse;\n\t} else if ( !fn ) {\n\t\treturn elem;\n\t}\n\n\tif ( one === 1 ) {\n\t\torigFn = fn;\n\t\tfn = function( event ) {\n\n\t\t\t// Can use an empty set, since event contains the info\n\t\t\tjQuery().off( event );\n\t\t\treturn origFn.apply( this, arguments );\n\t\t};\n\n\t\t// Use same guid so caller can remove using origFn\n\t\tfn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );\n\t}\n\treturn elem.each( function() {\n\t\tjQuery.event.add( this, types, fn, data, selector );\n\t} );\n}\n\n/*\n * Helper functions for managing events -- not part of the public interface.\n * Props to Dean Edwards' addEvent library for many of the ideas.\n */\njQuery.event = {\n\n\tglobal: {},\n\n\tadd: function( elem, types, handler, data, selector ) {\n\n\t\tvar handleObjIn, eventHandle, tmp,\n\t\t\tevents, t, handleObj,\n\t\t\tspecial, handlers, type, namespaces, origType,\n\t\t\telemData = dataPriv.get( elem );\n\n\t\t// Only attach events to objects that accept data\n\t\tif ( !acceptData( elem ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Caller can pass in an object of custom data in lieu of the handler\n\t\tif ( handler.handler ) {\n\t\t\thandleObjIn = handler;\n\t\t\thandler = handleObjIn.handler;\n\t\t\tselector = handleObjIn.selector;\n\t\t}\n\n\t\t// Ensure that invalid selectors throw exceptions at attach time\n\t\t// Evaluate against documentElement in case elem is a non-element node (e.g., document)\n\t\tif ( selector ) {\n\t\t\tjQuery.find.matchesSelector( documentElement, selector );\n\t\t}\n\n\t\t// Make sure that the handler has a unique ID, used to find/remove it later\n\t\tif ( !handler.guid ) {\n\t\t\thandler.guid = jQuery.guid++;\n\t\t}\n\n\t\t// Init the element's event structure and main handler, if this is the first\n\t\tif ( !( events = elemData.events ) ) {\n\t\t\tevents = elemData.events = Object.create( null );\n\t\t}\n\t\tif ( !( eventHandle = elemData.handle ) ) {\n\t\t\teventHandle = elemData.handle = function( e ) {\n\n\t\t\t\t// Discard the second event of a jQuery.event.trigger() and\n\t\t\t\t// when an event is called after a page has unloaded\n\t\t\t\treturn typeof jQuery !== \"undefined\" && jQuery.event.triggered !== e.type ?\n\t\t\t\t\tjQuery.event.dispatch.apply( elem, arguments ) : undefined;\n\t\t\t};\n\t\t}\n\n\t\t// Handle multiple events separated by a space\n\t\ttypes = ( types || \"\" ).match( rnothtmlwhite ) || [ \"\" ];\n\t\tt = types.length;\n\t\twhile ( t-- ) {\n\t\t\ttmp = rtypenamespace.exec( types[ t ] ) || [];\n\t\t\ttype = origType = tmp[ 1 ];\n\t\t\tnamespaces = ( tmp[ 2 ] || \"\" ).split( \".\" ).sort();\n\n\t\t\t// There *must* be a type, no attaching namespace-only handlers\n\t\t\tif ( !type ) {\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t// If event changes its type, use the special event handlers for the changed type\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\n\t\t\t// If selector defined, determine special event api type, otherwise given type\n\t\t\ttype = ( selector ? special.delegateType : special.bindType ) || type;\n\n\t\t\t// Update special based on newly reset type\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\n\t\t\t// handleObj is passed to all event handlers\n\t\t\thandleObj = jQuery.extend( {\n\t\t\t\ttype: type,\n\t\t\t\torigType: origType,\n\t\t\t\tdata: data,\n\t\t\t\thandler: handler,\n\t\t\t\tguid: handler.guid,\n\t\t\t\tselector: selector,\n\t\t\t\tneedsContext: selector && jQuery.expr.match.needsContext.test( selector ),\n\t\t\t\tnamespace: namespaces.join( \".\" )\n\t\t\t}, handleObjIn );\n\n\t\t\t// Init the event handler queue if we're the first\n\t\t\tif ( !( handlers = events[ type ] ) ) {\n\t\t\t\thandlers = events[ type ] = [];\n\t\t\t\thandlers.delegateCount = 0;\n\n\t\t\t\t// Only use addEventListener if the special events handler returns false\n\t\t\t\tif ( !special.setup ||\n\t\t\t\t\tspecial.setup.call( elem, data, namespaces, eventHandle ) === false ) {\n\n\t\t\t\t\tif ( elem.addEventListener ) {\n\t\t\t\t\t\telem.addEventListener( type, eventHandle );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif ( special.add ) {\n\t\t\t\tspecial.add.call( elem, handleObj );\n\n\t\t\t\tif ( !handleObj.handler.guid ) {\n\t\t\t\t\thandleObj.handler.guid = handler.guid;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Add to the element's handler list, delegates in front\n\t\t\tif ( selector ) {\n\t\t\t\thandlers.splice( handlers.delegateCount++, 0, handleObj );\n\t\t\t} else {\n\t\t\t\thandlers.push( handleObj );\n\t\t\t}\n\n\t\t\t// Keep track of which events have ever been used, for event optimization\n\t\t\tjQuery.event.global[ type ] = true;\n\t\t}\n\n\t},\n\n\t// Detach an event or set of events from an element\n\tremove: function( elem, types, handler, selector, mappedTypes ) {\n\n\t\tvar j, origCount, tmp,\n\t\t\tevents, t, handleObj,\n\t\t\tspecial, handlers, type, namespaces, origType,\n\t\t\telemData = dataPriv.hasData( elem ) && dataPriv.get( elem );\n\n\t\tif ( !elemData || !( events = elemData.events ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Once for each type.namespace in types; type may be omitted\n\t\ttypes = ( types || \"\" ).match( rnothtmlwhite ) || [ \"\" ];\n\t\tt = types.length;\n\t\twhile ( t-- ) {\n\t\t\ttmp = rtypenamespace.exec( types[ t ] ) || [];\n\t\t\ttype = origType = tmp[ 1 ];\n\t\t\tnamespaces = ( tmp[ 2 ] || \"\" ).split( \".\" ).sort();\n\n\t\t\t// Unbind all events (on this namespace, if provided) for the element\n\t\t\tif ( !type ) {\n\t\t\t\tfor ( type in events ) {\n\t\t\t\t\tjQuery.event.remove( elem, type + types[ t ], handler, selector, true );\n\t\t\t\t}\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tspecial = jQuery.event.special[ type ] || {};\n\t\t\ttype = ( selector ? special.delegateType : special.bindType ) || type;\n\t\t\thandlers = events[ type ] || [];\n\t\t\ttmp = tmp[ 2 ] &&\n\t\t\t\tnew RegExp( \"(^|\\\\.)\" + namespaces.join( \"\\\\.(?:.*\\\\.|)\" ) + \"(\\\\.|$)\" );\n\n\t\t\t// Remove matching events\n\t\t\torigCount = j = handlers.length;\n\t\t\twhile ( j-- ) {\n\t\t\t\thandleObj = handlers[ j ];\n\n\t\t\t\tif ( ( mappedTypes || origType === handleObj.origType ) &&\n\t\t\t\t\t( !handler || handler.guid === handleObj.guid ) &&\n\t\t\t\t\t( !tmp || tmp.test( handleObj.namespace ) ) &&\n\t\t\t\t\t( !selector || selector === handleObj.selector ||\n\t\t\t\t\t\tselector === \"**\" && handleObj.selector ) ) {\n\t\t\t\t\thandlers.splice( j, 1 );\n\n\t\t\t\t\tif ( handleObj.selector ) {\n\t\t\t\t\t\thandlers.delegateCount--;\n\t\t\t\t\t}\n\t\t\t\t\tif ( special.remove ) {\n\t\t\t\t\t\tspecial.remove.call( elem, handleObj );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Remove generic event handler if we removed something and no more handlers exist\n\t\t\t// (avoids potential for endless recursion during removal of special event handlers)\n\t\t\tif ( origCount && !handlers.length ) {\n\t\t\t\tif ( !special.teardown ||\n\t\t\t\t\tspecial.teardown.call( elem, namespaces, elemData.handle ) === false ) {\n\n\t\t\t\t\tjQuery.removeEvent( elem, type, elemData.handle );\n\t\t\t\t}\n\n\t\t\t\tdelete events[ type ];\n\t\t\t}\n\t\t}\n\n\t\t// Remove data and the expando if it's no longer used\n\t\tif ( jQuery.isEmptyObject( events ) ) {\n\t\t\tdataPriv.remove( elem, \"handle events\" );\n\t\t}\n\t},\n\n\tdispatch: function( nativeEvent ) {\n\n\t\tvar i, j, ret, matched, handleObj, handlerQueue,\n\t\t\targs = new Array( arguments.length ),\n\n\t\t\t// Make a writable jQuery.Event from the native event object\n\t\t\tevent = jQuery.event.fix( nativeEvent ),\n\n\t\t\thandlers = (\n\t\t\t\t\tdataPriv.get( this, \"events\" ) || Object.create( null )\n\t\t\t\t)[ event.type ] || [],\n\t\t\tspecial = jQuery.event.special[ event.type ] || {};\n\n\t\t// Use the fix-ed jQuery.Event rather than the (read-only) native event\n\t\targs[ 0 ] = event;\n\n\t\tfor ( i = 1; i < arguments.length; i++ ) {\n\t\t\targs[ i ] = arguments[ i ];\n\t\t}\n\n\t\tevent.delegateTarget = this;\n\n\t\t// Call the preDispatch hook for the mapped type, and let it bail if desired\n\t\tif ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Determine handlers\n\t\thandlerQueue = jQuery.event.handlers.call( this, event, handlers );\n\n\t\t// Run delegates first; they may want to stop propagation beneath us\n\t\ti = 0;\n\t\twhile ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {\n\t\t\tevent.currentTarget = matched.elem;\n\n\t\t\tj = 0;\n\t\t\twhile ( ( handleObj = matched.handlers[ j++ ] ) &&\n\t\t\t\t!event.isImmediatePropagationStopped() ) {\n\n\t\t\t\t// If the event is namespaced, then each handler is only invoked if it is\n\t\t\t\t// specially universal or its namespaces are a superset of the event's.\n\t\t\t\tif ( !event.rnamespace || handleObj.namespace === false ||\n\t\t\t\t\tevent.rnamespace.test( handleObj.namespace ) ) {\n\n\t\t\t\t\tevent.handleObj = handleObj;\n\t\t\t\t\tevent.data = handleObj.data;\n\n\t\t\t\t\tret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||\n\t\t\t\t\t\thandleObj.handler ).apply( matched.elem, args );\n\n\t\t\t\t\tif ( ret !== undefined ) {\n\t\t\t\t\t\tif ( ( event.result = ret ) === false ) {\n\t\t\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\t\t\tevent.stopPropagation();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Call the postDispatch hook for the mapped type\n\t\tif ( special.postDispatch ) {\n\t\t\tspecial.postDispatch.call( this, event );\n\t\t}\n\n\t\treturn event.result;\n\t},\n\n\thandlers: function( event, handlers ) {\n\t\tvar i, handleObj, sel, matchedHandlers, matchedSelectors,\n\t\t\thandlerQueue = [],\n\t\t\tdelegateCount = handlers.delegateCount,\n\t\t\tcur = event.target;\n\n\t\t// Find delegate handlers\n\t\tif ( delegateCount &&\n\n\t\t\t// Support: IE <=9\n\t\t\t// Black-hole SVG instance trees (trac-13180)\n\t\t\tcur.nodeType &&\n\n\t\t\t// Support: Firefox <=42\n\t\t\t// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)\n\t\t\t// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click\n\t\t\t// Support: IE 11 only\n\t\t\t// ...but not arrow key \"clicks\" of radio inputs, which can have `button` -1 (gh-2343)\n\t\t\t!( event.type === \"click\" && event.button >= 1 ) ) {\n\n\t\t\tfor ( ; cur !== this; cur = cur.parentNode || this ) {\n\n\t\t\t\t// Don't check non-elements (#13208)\n\t\t\t\t// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)\n\t\t\t\tif ( cur.nodeType === 1 && !( event.type === \"click\" && cur.disabled === true ) ) {\n\t\t\t\t\tmatchedHandlers = [];\n\t\t\t\t\tmatchedSelectors = {};\n\t\t\t\t\tfor ( i = 0; i < delegateCount; i++ ) {\n\t\t\t\t\t\thandleObj = handlers[ i ];\n\n\t\t\t\t\t\t// Don't conflict with Object.prototype properties (#13203)\n\t\t\t\t\t\tsel = handleObj.selector + \" \";\n\n\t\t\t\t\t\tif ( matchedSelectors[ sel ] === undefined ) {\n\t\t\t\t\t\t\tmatchedSelectors[ sel ] = handleObj.needsContext ?\n\t\t\t\t\t\t\t\tjQuery( sel, this ).index( cur ) > -1 :\n\t\t\t\t\t\t\t\tjQuery.find( sel, this, null, [ cur ] ).length;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif ( matchedSelectors[ sel ] ) {\n\t\t\t\t\t\t\tmatchedHandlers.push( handleObj );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif ( matchedHandlers.length ) {\n\t\t\t\t\t\thandlerQueue.push( { elem: cur, handlers: matchedHandlers } );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Add the remaining (directly-bound) handlers\n\t\tcur = this;\n\t\tif ( delegateCount < handlers.length ) {\n\t\t\thandlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );\n\t\t}\n\n\t\treturn handlerQueue;\n\t},\n\n\taddProp: function( name, hook ) {\n\t\tObject.defineProperty( jQuery.Event.prototype, name, {\n\t\t\tenumerable: true,\n\t\t\tconfigurable: true,\n\n\t\t\tget: isFunction( hook ) ?\n\t\t\t\tfunction() {\n\t\t\t\t\tif ( this.originalEvent ) {\n\t\t\t\t\t\t\treturn hook( this.originalEvent );\n\t\t\t\t\t}\n\t\t\t\t} :\n\t\t\t\tfunction() {\n\t\t\t\t\tif ( this.originalEvent ) {\n\t\t\t\t\t\t\treturn this.originalEvent[ name ];\n\t\t\t\t\t}\n\t\t\t\t},\n\n\t\t\tset: function( value ) {\n\t\t\t\tObject.defineProperty( this, name, {\n\t\t\t\t\tenumerable: true,\n\t\t\t\t\tconfigurable: true,\n\t\t\t\t\twritable: true,\n\t\t\t\t\tvalue: value\n\t\t\t\t} );\n\t\t\t}\n\t\t} );\n\t},\n\n\tfix: function( originalEvent ) {\n\t\treturn originalEvent[ jQuery.expando ] ?\n\t\t\toriginalEvent :\n\t\t\tnew jQuery.Event( originalEvent );\n\t},\n\n\tspecial: {\n\t\tload: {\n\n\t\t\t// Prevent triggered image.load events from bubbling to window.load\n\t\t\tnoBubble: true\n\t\t},\n\t\tclick: {\n\n\t\t\t// Utilize native event to ensure correct state for checkable inputs\n\t\t\tsetup: function( data ) {\n\n\t\t\t\t// For mutual compressibility with _default, replace `this` access with a local var.\n\t\t\t\t// `|| data` is dead code meant only to preserve the variable through minification.\n\t\t\t\tvar el = this || data;\n\n\t\t\t\t// Claim the first handler\n\t\t\t\tif ( rcheckableType.test( el.type ) &&\n\t\t\t\t\tel.click && nodeName( el, \"input\" ) ) {\n\n\t\t\t\t\t// dataPriv.set( el, \"click\", ... )\n\t\t\t\t\tleverageNative( el, \"click\", returnTrue );\n\t\t\t\t}\n\n\t\t\t\t// Return false to allow normal processing in the caller\n\t\t\t\treturn false;\n\t\t\t},\n\t\t\ttrigger: function( data ) {\n\n\t\t\t\t// For mutual compressibility with _default, replace `this` access with a local var.\n\t\t\t\t// `|| data` is dead code meant only to preserve the variable through minification.\n\t\t\t\tvar el = this || data;\n\n\t\t\t\t// Force setup before triggering a click\n\t\t\t\tif ( rcheckableType.test( el.type ) &&\n\t\t\t\t\tel.click && nodeName( el, \"input\" ) ) {\n\n\t\t\t\t\tleverageNative( el, \"click\" );\n\t\t\t\t}\n\n\t\t\t\t// Return non-false to allow normal event-path propagation\n\t\t\t\treturn true;\n\t\t\t},\n\n\t\t\t// For cross-browser consistency, suppress native .click() on links\n\t\t\t// Also prevent it if we're currently inside a leveraged native-event stack\n\t\t\t_default: function( event ) {\n\t\t\t\tvar target = event.target;\n\t\t\t\treturn rcheckableType.test( target.type ) &&\n\t\t\t\t\ttarget.click && nodeName( target, \"input\" ) &&\n\t\t\t\t\tdataPriv.get( target, \"click\" ) ||\n\t\t\t\t\tnodeName( target, \"a\" );\n\t\t\t}\n\t\t},\n\n\t\tbeforeunload: {\n\t\t\tpostDispatch: function( event ) {\n\n\t\t\t\t// Support: Firefox 20+\n\t\t\t\t// Firefox doesn't alert if the returnValue field is not set.\n\t\t\t\tif ( event.result !== undefined && event.originalEvent ) {\n\t\t\t\t\tevent.originalEvent.returnValue = event.result;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n};\n\n// Ensure the presence of an event listener that handles manually-triggered\n// synthetic events by interrupting progress until reinvoked in response to\n// *native* events that it fires directly, ensuring that state changes have\n// already occurred before other listeners are invoked.\nfunction leverageNative( el, type, expectSync ) {\n\n\t// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add\n\tif ( !expectSync ) {\n\t\tif ( dataPriv.get( el, type ) === undefined ) {\n\t\t\tjQuery.event.add( el, type, returnTrue );\n\t\t}\n\t\treturn;\n\t}\n\n\t// Register the controller as a special universal handler for all event namespaces\n\tdataPriv.set( el, type, false );\n\tjQuery.event.add( el, type, {\n\t\tnamespace: false,\n\t\thandler: function( event ) {\n\t\t\tvar notAsync, result,\n\t\t\t\tsaved = dataPriv.get( this, type );\n\n\t\t\tif ( ( event.isTrigger & 1 ) && this[ type ] ) {\n\n\t\t\t\t// Interrupt processing of the outer synthetic .trigger()ed event\n\t\t\t\t// Saved data should be false in such cases, but might be a leftover capture object\n\t\t\t\t// from an async native handler (gh-4350)\n\t\t\t\tif ( !saved.length ) {\n\n\t\t\t\t\t// Store arguments for use when handling the inner native event\n\t\t\t\t\t// There will always be at least one argument (an event object), so this array\n\t\t\t\t\t// will not be confused with a leftover capture object.\n\t\t\t\t\tsaved = slice.call( arguments );\n\t\t\t\t\tdataPriv.set( this, type, saved );\n\n\t\t\t\t\t// Trigger the native event and capture its result\n\t\t\t\t\t// Support: IE <=9 - 11+\n\t\t\t\t\t// focus() and blur() are asynchronous\n\t\t\t\t\tnotAsync = expectSync( this, type );\n\t\t\t\t\tthis[ type ]();\n\t\t\t\t\tresult = dataPriv.get( this, type );\n\t\t\t\t\tif ( saved !== result || notAsync ) {\n\t\t\t\t\t\tdataPriv.set( this, type, false );\n\t\t\t\t\t} else {\n\t\t\t\t\t\tresult = {};\n\t\t\t\t\t}\n\t\t\t\t\tif ( saved !== result ) {\n\n\t\t\t\t\t\t// Cancel the outer synthetic event\n\t\t\t\t\t\tevent.stopImmediatePropagation();\n\t\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\t\treturn result.value;\n\t\t\t\t\t}\n\n\t\t\t\t// If this is an inner synthetic event for an event with a bubbling surrogate\n\t\t\t\t// (focus or blur), assume that the surrogate already propagated from triggering the\n\t\t\t\t// native event and prevent that from happening again here.\n\t\t\t\t// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the\n\t\t\t\t// bubbling surrogate propagates *after* the non-bubbling base), but that seems\n\t\t\t\t// less bad than duplication.\n\t\t\t\t} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {\n\t\t\t\t\tevent.stopPropagation();\n\t\t\t\t}\n\n\t\t\t// If this is a native event triggered above, everything is now in order\n\t\t\t// Fire an inner synthetic event with the original arguments\n\t\t\t} else if ( saved.length ) {\n\n\t\t\t\t// ...and capture the result\n\t\t\t\tdataPriv.set( this, type, {\n\t\t\t\t\tvalue: jQuery.event.trigger(\n\n\t\t\t\t\t\t// Support: IE <=9 - 11+\n\t\t\t\t\t\t// Extend with the prototype to reset the above stopImmediatePropagation()\n\t\t\t\t\t\tjQuery.extend( saved[ 0 ], jQuery.Event.prototype ),\n\t\t\t\t\t\tsaved.slice( 1 ),\n\t\t\t\t\t\tthis\n\t\t\t\t\t)\n\t\t\t\t} );\n\n\t\t\t\t// Abort handling of the native event\n\t\t\t\tevent.stopImmediatePropagation();\n\t\t\t}\n\t\t}\n\t} );\n}\n\njQuery.removeEvent = function( elem, type, handle ) {\n\n\t// This \"if\" is needed for plain objects\n\tif ( elem.removeEventListener ) {\n\t\telem.removeEventListener( type, handle );\n\t}\n};\n\njQuery.Event = function( src, props ) {\n\n\t// Allow instantiation without the 'new' keyword\n\tif ( !( this instanceof jQuery.Event ) ) {\n\t\treturn new jQuery.Event( src, props );\n\t}\n\n\t// Event object\n\tif ( src && src.type ) {\n\t\tthis.originalEvent = src;\n\t\tthis.type = src.type;\n\n\t\t// Events bubbling up the document may have been marked as prevented\n\t\t// by a handler lower down the tree; reflect the correct value.\n\t\tthis.isDefaultPrevented = src.defaultPrevented ||\n\t\t\t\tsrc.defaultPrevented === undefined &&\n\n\t\t\t\t// Support: Android <=2.3 only\n\t\t\t\tsrc.returnValue === false ?\n\t\t\treturnTrue :\n\t\t\treturnFalse;\n\n\t\t// Create target properties\n\t\t// Support: Safari <=6 - 7 only\n\t\t// Target should not be a text node (#504, #13143)\n\t\tthis.target = ( src.target && src.target.nodeType === 3 ) ?\n\t\t\tsrc.target.parentNode :\n\t\t\tsrc.target;\n\n\t\tthis.currentTarget = src.currentTarget;\n\t\tthis.relatedTarget = src.relatedTarget;\n\n\t// Event type\n\t} else {\n\t\tthis.type = src;\n\t}\n\n\t// Put explicitly provided properties onto the event object\n\tif ( props ) {\n\t\tjQuery.extend( this, props );\n\t}\n\n\t// Create a timestamp if incoming event doesn't have one\n\tthis.timeStamp = src && src.timeStamp || Date.now();\n\n\t// Mark it as fixed\n\tthis[ jQuery.expando ] = true;\n};\n\n// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding\n// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html\njQuery.Event.prototype = {\n\tconstructor: jQuery.Event,\n\tisDefaultPrevented: returnFalse,\n\tisPropagationStopped: returnFalse,\n\tisImmediatePropagationStopped: returnFalse,\n\tisSimulated: false,\n\n\tpreventDefault: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isDefaultPrevented = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.preventDefault();\n\t\t}\n\t},\n\tstopPropagation: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isPropagationStopped = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.stopPropagation();\n\t\t}\n\t},\n\tstopImmediatePropagation: function() {\n\t\tvar e = this.originalEvent;\n\n\t\tthis.isImmediatePropagationStopped = returnTrue;\n\n\t\tif ( e && !this.isSimulated ) {\n\t\t\te.stopImmediatePropagation();\n\t\t}\n\n\t\tthis.stopPropagation();\n\t}\n};\n\n// Includes all common event props including KeyEvent and MouseEvent specific props\njQuery.each( {\n\taltKey: true,\n\tbubbles: true,\n\tcancelable: true,\n\tchangedTouches: true,\n\tctrlKey: true,\n\tdetail: true,\n\teventPhase: true,\n\tmetaKey: true,\n\tpageX: true,\n\tpageY: true,\n\tshiftKey: true,\n\tview: true,\n\t\"char\": true,\n\tcode: true,\n\tcharCode: true,\n\tkey: true,\n\tkeyCode: true,\n\tbutton: true,\n\tbuttons: true,\n\tclientX: true,\n\tclientY: true,\n\toffsetX: true,\n\toffsetY: true,\n\tpointerId: true,\n\tpointerType: true,\n\tscreenX: true,\n\tscreenY: true,\n\ttargetTouches: true,\n\ttoElement: true,\n\ttouches: true,\n\n\twhich: function( event ) {\n\t\tvar button = event.button;\n\n\t\t// Add which for key events\n\t\tif ( event.which == null && rkeyEvent.test( event.type ) ) {\n\t\t\treturn event.charCode != null ? event.charCode : event.keyCode;\n\t\t}\n\n\t\t// Add which for click: 1 === left; 2 === middle; 3 === right\n\t\tif ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {\n\t\t\tif ( button & 1 ) {\n\t\t\t\treturn 1;\n\t\t\t}\n\n\t\t\tif ( button & 2 ) {\n\t\t\t\treturn 3;\n\t\t\t}\n\n\t\t\tif ( button & 4 ) {\n\t\t\t\treturn 2;\n\t\t\t}\n\n\t\t\treturn 0;\n\t\t}\n\n\t\treturn event.which;\n\t}\n}, jQuery.event.addProp );\n\njQuery.each( { focus: \"focusin\", blur: \"focusout\" }, function( type, delegateType ) {\n\tjQuery.event.special[ type ] = {\n\n\t\t// Utilize native event if possible so blur/focus sequence is correct\n\t\tsetup: function() {\n\n\t\t\t// Claim the first handler\n\t\t\t// dataPriv.set( this, \"focus\", ... )\n\t\t\t// dataPriv.set( this, \"blur\", ... )\n\t\t\tleverageNative( this, type, expectSync );\n\n\t\t\t// Return false to allow normal processing in the caller\n\t\t\treturn false;\n\t\t},\n\t\ttrigger: function() {\n\n\t\t\t// Force setup before trigger\n\t\t\tleverageNative( this, type );\n\n\t\t\t// Return non-false to allow normal event-path propagation\n\t\t\treturn true;\n\t\t},\n\n\t\tdelegateType: delegateType\n\t};\n} );\n\n// Create mouseenter/leave events using mouseover/out and event-time checks\n// so that event delegation works in jQuery.\n// Do the same for pointerenter/pointerleave and pointerover/pointerout\n//\n// Support: Safari 7 only\n// Safari sends mouseenter too often; see:\n// https://bugs.chromium.org/p/chromium/issues/detail?id=470258\n// for the description of the bug (it existed in older Chrome versions as well).\njQuery.each( {\n\tmouseenter: \"mouseover\",\n\tmouseleave: \"mouseout\",\n\tpointerenter: \"pointerover\",\n\tpointerleave: \"pointerout\"\n}, function( orig, fix ) {\n\tjQuery.event.special[ orig ] = {\n\t\tdelegateType: fix,\n\t\tbindType: fix,\n\n\t\thandle: function( event ) {\n\t\t\tvar ret,\n\t\t\t\ttarget = this,\n\t\t\t\trelated = event.relatedTarget,\n\t\t\t\thandleObj = event.handleObj;\n\n\t\t\t// For mouseenter/leave call the handler if related is outside the target.\n\t\t\t// NB: No relatedTarget if the mouse left/entered the browser window\n\t\t\tif ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {\n\t\t\t\tevent.type = handleObj.origType;\n\t\t\t\tret = handleObj.handler.apply( this, arguments );\n\t\t\t\tevent.type = fix;\n\t\t\t}\n\t\t\treturn ret;\n\t\t}\n\t};\n} );\n\njQuery.fn.extend( {\n\n\ton: function( types, selector, data, fn ) {\n\t\treturn on( this, types, selector, data, fn );\n\t},\n\tone: function( types, selector, data, fn ) {\n\t\treturn on( this, types, selector, data, fn, 1 );\n\t},\n\toff: function( types, selector, fn ) {\n\t\tvar handleObj, type;\n\t\tif ( types && types.preventDefault && types.handleObj ) {\n\n\t\t\t// ( event ) dispatched jQuery.Event\n\t\t\thandleObj = types.handleObj;\n\t\t\tjQuery( types.delegateTarget ).off(\n\t\t\t\thandleObj.namespace ?\n\t\t\t\t\thandleObj.origType + \".\" + handleObj.namespace :\n\t\t\t\t\thandleObj.origType,\n\t\t\t\thandleObj.selector,\n\t\t\t\thandleObj.handler\n\t\t\t);\n\t\t\treturn this;\n\t\t}\n\t\tif ( typeof types === \"object\" ) {\n\n\t\t\t// ( types-object [, selector] )\n\t\t\tfor ( type in types ) {\n\t\t\t\tthis.off( type, selector, types[ type ] );\n\t\t\t}\n\t\t\treturn this;\n\t\t}\n\t\tif ( selector === false || typeof selector === \"function\" ) {\n\n\t\t\t// ( types [, fn] )\n\t\t\tfn = selector;\n\t\t\tselector = undefined;\n\t\t}\n\t\tif ( fn === false ) {\n\t\t\tfn = returnFalse;\n\t\t}\n\t\treturn this.each( function() {\n\t\t\tjQuery.event.remove( this, types, fn, selector );\n\t\t} );\n\t}\n} );\n\n\nvar\n\n\t// Support: IE <=10 - 11, Edge 12 - 13 only\n\t// In IE/Edge using regex groups here causes severe slowdowns.\n\t// See https://connect.microsoft.com/IE/feedback/details/1736512/\n\trnoInnerhtml = /\\s*$/g;\n\n// Prefer a tbody over its parent table for containing new rows\nfunction manipulationTarget( elem, content ) {\n\tif ( nodeName( elem, \"table\" ) &&\n\t\tnodeName( content.nodeType !== 11 ? content : content.firstChild, \"tr\" ) ) {\n\n\t\treturn jQuery( elem ).children( \"tbody\" )[ 0 ] || elem;\n\t}\n\n\treturn elem;\n}\n\n// Replace/restore the type attribute of script elements for safe DOM manipulation\nfunction disableScript( elem ) {\n\telem.type = ( elem.getAttribute( \"type\" ) !== null ) + \"/\" + elem.type;\n\treturn elem;\n}\nfunction restoreScript( elem ) {\n\tif ( ( elem.type || \"\" ).slice( 0, 5 ) === \"true/\" ) {\n\t\telem.type = elem.type.slice( 5 );\n\t} else {\n\t\telem.removeAttribute( \"type\" );\n\t}\n\n\treturn elem;\n}\n\nfunction cloneCopyEvent( src, dest ) {\n\tvar i, l, type, pdataOld, udataOld, udataCur, events;\n\n\tif ( dest.nodeType !== 1 ) {\n\t\treturn;\n\t}\n\n\t// 1. Copy private data: events, handlers, etc.\n\tif ( dataPriv.hasData( src ) ) {\n\t\tpdataOld = dataPriv.get( src );\n\t\tevents = pdataOld.events;\n\n\t\tif ( events ) {\n\t\t\tdataPriv.remove( dest, \"handle events\" );\n\n\t\t\tfor ( type in events ) {\n\t\t\t\tfor ( i = 0, l = events[ type ].length; i < l; i++ ) {\n\t\t\t\t\tjQuery.event.add( dest, type, events[ type ][ i ] );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\t// 2. Copy user data\n\tif ( dataUser.hasData( src ) ) {\n\t\tudataOld = dataUser.access( src );\n\t\tudataCur = jQuery.extend( {}, udataOld );\n\n\t\tdataUser.set( dest, udataCur );\n\t}\n}\n\n// Fix IE bugs, see support tests\nfunction fixInput( src, dest ) {\n\tvar nodeName = dest.nodeName.toLowerCase();\n\n\t// Fails to persist the checked state of a cloned checkbox or radio button.\n\tif ( nodeName === \"input\" && rcheckableType.test( src.type ) ) {\n\t\tdest.checked = src.checked;\n\n\t// Fails to return the selected option to the default selected state when cloning options\n\t} else if ( nodeName === \"input\" || nodeName === \"textarea\" ) {\n\t\tdest.defaultValue = src.defaultValue;\n\t}\n}\n\nfunction domManip( collection, args, callback, ignored ) {\n\n\t// Flatten any nested arrays\n\targs = flat( args );\n\n\tvar fragment, first, scripts, hasScripts, node, doc,\n\t\ti = 0,\n\t\tl = collection.length,\n\t\tiNoClone = l - 1,\n\t\tvalue = args[ 0 ],\n\t\tvalueIsFunction = isFunction( value );\n\n\t// We can't cloneNode fragments that contain checked, in WebKit\n\tif ( valueIsFunction ||\n\t\t\t( l > 1 && typeof value === \"string\" &&\n\t\t\t\t!support.checkClone && rchecked.test( value ) ) ) {\n\t\treturn collection.each( function( index ) {\n\t\t\tvar self = collection.eq( index );\n\t\t\tif ( valueIsFunction ) {\n\t\t\t\targs[ 0 ] = value.call( this, index, self.html() );\n\t\t\t}\n\t\t\tdomManip( self, args, callback, ignored );\n\t\t} );\n\t}\n\n\tif ( l ) {\n\t\tfragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );\n\t\tfirst = fragment.firstChild;\n\n\t\tif ( fragment.childNodes.length === 1 ) {\n\t\t\tfragment = first;\n\t\t}\n\n\t\t// Require either new content or an interest in ignored elements to invoke the callback\n\t\tif ( first || ignored ) {\n\t\t\tscripts = jQuery.map( getAll( fragment, \"script\" ), disableScript );\n\t\t\thasScripts = scripts.length;\n\n\t\t\t// Use the original fragment for the last item\n\t\t\t// instead of the first because it can end up\n\t\t\t// being emptied incorrectly in certain situations (#8070).\n\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\tnode = fragment;\n\n\t\t\t\tif ( i !== iNoClone ) {\n\t\t\t\t\tnode = jQuery.clone( node, true, true );\n\n\t\t\t\t\t// Keep references to cloned scripts for later restoration\n\t\t\t\t\tif ( hasScripts ) {\n\n\t\t\t\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t\t\t\t// push.apply(_, arraylike) throws on ancient WebKit\n\t\t\t\t\t\tjQuery.merge( scripts, getAll( node, \"script\" ) );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tcallback.call( collection[ i ], node, i );\n\t\t\t}\n\n\t\t\tif ( hasScripts ) {\n\t\t\t\tdoc = scripts[ scripts.length - 1 ].ownerDocument;\n\n\t\t\t\t// Reenable scripts\n\t\t\t\tjQuery.map( scripts, restoreScript );\n\n\t\t\t\t// Evaluate executable scripts on first document insertion\n\t\t\t\tfor ( i = 0; i < hasScripts; i++ ) {\n\t\t\t\t\tnode = scripts[ i ];\n\t\t\t\t\tif ( rscriptType.test( node.type || \"\" ) &&\n\t\t\t\t\t\t!dataPriv.access( node, \"globalEval\" ) &&\n\t\t\t\t\t\tjQuery.contains( doc, node ) ) {\n\n\t\t\t\t\t\tif ( node.src && ( node.type || \"\" ).toLowerCase() !== \"module\" ) {\n\n\t\t\t\t\t\t\t// Optional AJAX dependency, but won't run scripts if not present\n\t\t\t\t\t\t\tif ( jQuery._evalUrl && !node.noModule ) {\n\t\t\t\t\t\t\t\tjQuery._evalUrl( node.src, {\n\t\t\t\t\t\t\t\t\tnonce: node.nonce || node.getAttribute( \"nonce\" )\n\t\t\t\t\t\t\t\t}, doc );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tDOMEval( node.textContent.replace( rcleanScript, \"\" ), node, doc );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn collection;\n}\n\nfunction remove( elem, selector, keepData ) {\n\tvar node,\n\t\tnodes = selector ? jQuery.filter( selector, elem ) : elem,\n\t\ti = 0;\n\n\tfor ( ; ( node = nodes[ i ] ) != null; i++ ) {\n\t\tif ( !keepData && node.nodeType === 1 ) {\n\t\t\tjQuery.cleanData( getAll( node ) );\n\t\t}\n\n\t\tif ( node.parentNode ) {\n\t\t\tif ( keepData && isAttached( node ) ) {\n\t\t\t\tsetGlobalEval( getAll( node, \"script\" ) );\n\t\t\t}\n\t\t\tnode.parentNode.removeChild( node );\n\t\t}\n\t}\n\n\treturn elem;\n}\n\njQuery.extend( {\n\thtmlPrefilter: function( html ) {\n\t\treturn html;\n\t},\n\n\tclone: function( elem, dataAndEvents, deepDataAndEvents ) {\n\t\tvar i, l, srcElements, destElements,\n\t\t\tclone = elem.cloneNode( true ),\n\t\t\tinPage = isAttached( elem );\n\n\t\t// Fix IE cloning issues\n\t\tif ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&\n\t\t\t\t!jQuery.isXMLDoc( elem ) ) {\n\n\t\t\t// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2\n\t\t\tdestElements = getAll( clone );\n\t\t\tsrcElements = getAll( elem );\n\n\t\t\tfor ( i = 0, l = srcElements.length; i < l; i++ ) {\n\t\t\t\tfixInput( srcElements[ i ], destElements[ i ] );\n\t\t\t}\n\t\t}\n\n\t\t// Copy the events from the original to the clone\n\t\tif ( dataAndEvents ) {\n\t\t\tif ( deepDataAndEvents ) {\n\t\t\t\tsrcElements = srcElements || getAll( elem );\n\t\t\t\tdestElements = destElements || getAll( clone );\n\n\t\t\t\tfor ( i = 0, l = srcElements.length; i < l; i++ ) {\n\t\t\t\t\tcloneCopyEvent( srcElements[ i ], destElements[ i ] );\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tcloneCopyEvent( elem, clone );\n\t\t\t}\n\t\t}\n\n\t\t// Preserve script evaluation history\n\t\tdestElements = getAll( clone, \"script\" );\n\t\tif ( destElements.length > 0 ) {\n\t\t\tsetGlobalEval( destElements, !inPage && getAll( elem, \"script\" ) );\n\t\t}\n\n\t\t// Return the cloned set\n\t\treturn clone;\n\t},\n\n\tcleanData: function( elems ) {\n\t\tvar data, elem, type,\n\t\t\tspecial = jQuery.event.special,\n\t\t\ti = 0;\n\n\t\tfor ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {\n\t\t\tif ( acceptData( elem ) ) {\n\t\t\t\tif ( ( data = elem[ dataPriv.expando ] ) ) {\n\t\t\t\t\tif ( data.events ) {\n\t\t\t\t\t\tfor ( type in data.events ) {\n\t\t\t\t\t\t\tif ( special[ type ] ) {\n\t\t\t\t\t\t\t\tjQuery.event.remove( elem, type );\n\n\t\t\t\t\t\t\t// This is a shortcut to avoid jQuery.event.remove's overhead\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tjQuery.removeEvent( elem, type, data.handle );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Support: Chrome <=35 - 45+\n\t\t\t\t\t// Assign undefined instead of using delete, see Data#remove\n\t\t\t\t\telem[ dataPriv.expando ] = undefined;\n\t\t\t\t}\n\t\t\t\tif ( elem[ dataUser.expando ] ) {\n\n\t\t\t\t\t// Support: Chrome <=35 - 45+\n\t\t\t\t\t// Assign undefined instead of using delete, see Data#remove\n\t\t\t\t\telem[ dataUser.expando ] = undefined;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n} );\n\njQuery.fn.extend( {\n\tdetach: function( selector ) {\n\t\treturn remove( this, selector, true );\n\t},\n\n\tremove: function( selector ) {\n\t\treturn remove( this, selector );\n\t},\n\n\ttext: function( value ) {\n\t\treturn access( this, function( value ) {\n\t\t\treturn value === undefined ?\n\t\t\t\tjQuery.text( this ) :\n\t\t\t\tthis.empty().each( function() {\n\t\t\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\t\t\tthis.textContent = value;\n\t\t\t\t\t}\n\t\t\t\t} );\n\t\t}, null, value, arguments.length );\n\t},\n\n\tappend: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\tvar target = manipulationTarget( this, elem );\n\t\t\t\ttarget.appendChild( elem );\n\t\t\t}\n\t\t} );\n\t},\n\n\tprepend: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {\n\t\t\t\tvar target = manipulationTarget( this, elem );\n\t\t\t\ttarget.insertBefore( elem, target.firstChild );\n\t\t\t}\n\t\t} );\n\t},\n\n\tbefore: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.parentNode ) {\n\t\t\t\tthis.parentNode.insertBefore( elem, this );\n\t\t\t}\n\t\t} );\n\t},\n\n\tafter: function() {\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tif ( this.parentNode ) {\n\t\t\t\tthis.parentNode.insertBefore( elem, this.nextSibling );\n\t\t\t}\n\t\t} );\n\t},\n\n\tempty: function() {\n\t\tvar elem,\n\t\t\ti = 0;\n\n\t\tfor ( ; ( elem = this[ i ] ) != null; i++ ) {\n\t\t\tif ( elem.nodeType === 1 ) {\n\n\t\t\t\t// Prevent memory leaks\n\t\t\t\tjQuery.cleanData( getAll( elem, false ) );\n\n\t\t\t\t// Remove any remaining nodes\n\t\t\t\telem.textContent = \"\";\n\t\t\t}\n\t\t}\n\n\t\treturn this;\n\t},\n\n\tclone: function( dataAndEvents, deepDataAndEvents ) {\n\t\tdataAndEvents = dataAndEvents == null ? false : dataAndEvents;\n\t\tdeepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;\n\n\t\treturn this.map( function() {\n\t\t\treturn jQuery.clone( this, dataAndEvents, deepDataAndEvents );\n\t\t} );\n\t},\n\n\thtml: function( value ) {\n\t\treturn access( this, function( value ) {\n\t\t\tvar elem = this[ 0 ] || {},\n\t\t\t\ti = 0,\n\t\t\t\tl = this.length;\n\n\t\t\tif ( value === undefined && elem.nodeType === 1 ) {\n\t\t\t\treturn elem.innerHTML;\n\t\t\t}\n\n\t\t\t// See if we can take a shortcut and just use innerHTML\n\t\t\tif ( typeof value === \"string\" && !rnoInnerhtml.test( value ) &&\n\t\t\t\t!wrapMap[ ( rtagName.exec( value ) || [ \"\", \"\" ] )[ 1 ].toLowerCase() ] ) {\n\n\t\t\t\tvalue = jQuery.htmlPrefilter( value );\n\n\t\t\t\ttry {\n\t\t\t\t\tfor ( ; i < l; i++ ) {\n\t\t\t\t\t\telem = this[ i ] || {};\n\n\t\t\t\t\t\t// Remove element nodes and prevent memory leaks\n\t\t\t\t\t\tif ( elem.nodeType === 1 ) {\n\t\t\t\t\t\t\tjQuery.cleanData( getAll( elem, false ) );\n\t\t\t\t\t\t\telem.innerHTML = value;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\telem = 0;\n\n\t\t\t\t// If using innerHTML throws an exception, use the fallback method\n\t\t\t\t} catch ( e ) {}\n\t\t\t}\n\n\t\t\tif ( elem ) {\n\t\t\t\tthis.empty().append( value );\n\t\t\t}\n\t\t}, null, value, arguments.length );\n\t},\n\n\treplaceWith: function() {\n\t\tvar ignored = [];\n\n\t\t// Make the changes, replacing each non-ignored context element with the new content\n\t\treturn domManip( this, arguments, function( elem ) {\n\t\t\tvar parent = this.parentNode;\n\n\t\t\tif ( jQuery.inArray( this, ignored ) < 0 ) {\n\t\t\t\tjQuery.cleanData( getAll( this ) );\n\t\t\t\tif ( parent ) {\n\t\t\t\t\tparent.replaceChild( elem, this );\n\t\t\t\t}\n\t\t\t}\n\n\t\t// Force callback invocation\n\t\t}, ignored );\n\t}\n} );\n\njQuery.each( {\n\tappendTo: \"append\",\n\tprependTo: \"prepend\",\n\tinsertBefore: \"before\",\n\tinsertAfter: \"after\",\n\treplaceAll: \"replaceWith\"\n}, function( name, original ) {\n\tjQuery.fn[ name ] = function( selector ) {\n\t\tvar elems,\n\t\t\tret = [],\n\t\t\tinsert = jQuery( selector ),\n\t\t\tlast = insert.length - 1,\n\t\t\ti = 0;\n\n\t\tfor ( ; i <= last; i++ ) {\n\t\t\telems = i === last ? this : this.clone( true );\n\t\t\tjQuery( insert[ i ] )[ original ]( elems );\n\n\t\t\t// Support: Android <=4.0 only, PhantomJS 1 only\n\t\t\t// .get() because push.apply(_, arraylike) throws on ancient WebKit\n\t\t\tpush.apply( ret, elems.get() );\n\t\t}\n\n\t\treturn this.pushStack( ret );\n\t};\n} );\nvar rnumnonpx = new RegExp( \"^(\" + pnum + \")(?!px)[a-z%]+$\", \"i\" );\n\nvar getStyles = function( elem ) {\n\n\t\t// Support: IE <=11 only, Firefox <=30 (#15098, #14150)\n\t\t// IE throws on elements created in popups\n\t\t// FF meanwhile throws on frame elements through \"defaultView.getComputedStyle\"\n\t\tvar view = elem.ownerDocument.defaultView;\n\n\t\tif ( !view || !view.opener ) {\n\t\t\tview = window;\n\t\t}\n\n\t\treturn view.getComputedStyle( elem );\n\t};\n\nvar swap = function( elem, options, callback ) {\n\tvar ret, name,\n\t\told = {};\n\n\t// Remember the old values, and insert the new ones\n\tfor ( name in options ) {\n\t\told[ name ] = elem.style[ name ];\n\t\telem.style[ name ] = options[ name ];\n\t}\n\n\tret = callback.call( elem );\n\n\t// Revert the old values\n\tfor ( name in options ) {\n\t\telem.style[ name ] = old[ name ];\n\t}\n\n\treturn ret;\n};\n\n\nvar rboxStyle = new RegExp( cssExpand.join( \"|\" ), \"i\" );\n\n\n\n( function() {\n\n\t// Executing both pixelPosition & boxSizingReliable tests require only one layout\n\t// so they're executed at the same time to save the second computation.\n\tfunction computeStyleTests() {\n\n\t\t// This is a singleton, we need to execute it only once\n\t\tif ( !div ) {\n\t\t\treturn;\n\t\t}\n\n\t\tcontainer.style.cssText = \"position:absolute;left:-11111px;width:60px;\" +\n\t\t\t\"margin-top:1px;padding:0;border:0\";\n\t\tdiv.style.cssText =\n\t\t\t\"position:relative;display:block;box-sizing:border-box;overflow:scroll;\" +\n\t\t\t\"margin:auto;border:1px;padding:1px;\" +\n\t\t\t\"width:60%;top:1%\";\n\t\tdocumentElement.appendChild( container ).appendChild( div );\n\n\t\tvar divStyle = window.getComputedStyle( div );\n\t\tpixelPositionVal = divStyle.top !== \"1%\";\n\n\t\t// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44\n\t\treliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;\n\n\t\t// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3\n\t\t// Some styles come back with percentage values, even though they shouldn't\n\t\tdiv.style.right = \"60%\";\n\t\tpixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;\n\n\t\t// Support: IE 9 - 11 only\n\t\t// Detect misreporting of content dimensions for box-sizing:border-box elements\n\t\tboxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;\n\n\t\t// Support: IE 9 only\n\t\t// Detect overflow:scroll screwiness (gh-3699)\n\t\t// Support: Chrome <=64\n\t\t// Don't get tricked when zoom affects offsetWidth (gh-4029)\n\t\tdiv.style.position = \"absolute\";\n\t\tscrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;\n\n\t\tdocumentElement.removeChild( container );\n\n\t\t// Nullify the div so it wouldn't be stored in the memory and\n\t\t// it will also be a sign that checks already performed\n\t\tdiv = null;\n\t}\n\n\tfunction roundPixelMeasures( measure ) {\n\t\treturn Math.round( parseFloat( measure ) );\n\t}\n\n\tvar pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,\n\t\treliableTrDimensionsVal, reliableMarginLeftVal,\n\t\tcontainer = document.createElement( \"div\" ),\n\t\tdiv = document.createElement( \"div\" );\n\n\t// Finish early in limited (non-browser) environments\n\tif ( !div.style ) {\n\t\treturn;\n\t}\n\n\t// Support: IE <=9 - 11 only\n\t// Style of cloned element affects source element cloned (#8908)\n\tdiv.style.backgroundClip = \"content-box\";\n\tdiv.cloneNode( true ).style.backgroundClip = \"\";\n\tsupport.clearCloneStyle = div.style.backgroundClip === \"content-box\";\n\n\tjQuery.extend( support, {\n\t\tboxSizingReliable: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn boxSizingReliableVal;\n\t\t},\n\t\tpixelBoxStyles: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn pixelBoxStylesVal;\n\t\t},\n\t\tpixelPosition: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn pixelPositionVal;\n\t\t},\n\t\treliableMarginLeft: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn reliableMarginLeftVal;\n\t\t},\n\t\tscrollboxSize: function() {\n\t\t\tcomputeStyleTests();\n\t\t\treturn scrollboxSizeVal;\n\t\t},\n\n\t\t// Support: IE 9 - 11+, Edge 15 - 18+\n\t\t// IE/Edge misreport `getComputedStyle` of table rows with width/height\n\t\t// set in CSS while `offset*` properties report correct values.\n\t\t// Behavior in IE 9 is more subtle than in newer versions & it passes\n\t\t// some versions of this test; make sure not to make it pass there!\n\t\treliableTrDimensions: function() {\n\t\t\tvar table, tr, trChild, trStyle;\n\t\t\tif ( reliableTrDimensionsVal == null ) {\n\t\t\t\ttable = document.createElement( \"table\" );\n\t\t\t\ttr = document.createElement( \"tr\" );\n\t\t\t\ttrChild = document.createElement( \"div\" );\n\n\t\t\t\ttable.style.cssText = \"position:absolute;left:-11111px\";\n\t\t\t\ttr.style.height = \"1px\";\n\t\t\t\ttrChild.style.height = \"9px\";\n\n\t\t\t\tdocumentElement\n\t\t\t\t\t.appendChild( table )\n\t\t\t\t\t.appendChild( tr )\n\t\t\t\t\t.appendChild( trChild );\n\n\t\t\t\ttrStyle = window.getComputedStyle( tr );\n\t\t\t\treliableTrDimensionsVal = parseInt( trStyle.height ) > 3;\n\n\t\t\t\tdocumentElement.removeChild( table );\n\t\t\t}\n\t\t\treturn reliableTrDimensionsVal;\n\t\t}\n\t} );\n} )();\n\n\nfunction curCSS( elem, name, computed ) {\n\tvar width, minWidth, maxWidth, ret,\n\n\t\t// Support: Firefox 51+\n\t\t// Retrieving style before computed somehow\n\t\t// fixes an issue with getting wrong values\n\t\t// on detached elements\n\t\tstyle = elem.style;\n\n\tcomputed = computed || getStyles( elem );\n\n\t// getPropertyValue is needed for:\n\t// .css('filter') (IE 9 only, #12537)\n\t// .css('--customProperty) (#3144)\n\tif ( computed ) {\n\t\tret = computed.getPropertyValue( name ) || computed[ name ];\n\n\t\tif ( ret === \"\" && !isAttached( elem ) ) {\n\t\t\tret = jQuery.style( elem, name );\n\t\t}\n\n\t\t// A tribute to the \"awesome hack by Dean Edwards\"\n\t\t// Android Browser returns percentage for some values,\n\t\t// but width seems to be reliably pixels.\n\t\t// This is against the CSSOM draft spec:\n\t\t// https://drafts.csswg.org/cssom/#resolved-values\n\t\tif ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {\n\n\t\t\t// Remember the original values\n\t\t\twidth = style.width;\n\t\t\tminWidth = style.minWidth;\n\t\t\tmaxWidth = style.maxWidth;\n\n\t\t\t// Put in the new values to get a computed value out\n\t\t\tstyle.minWidth = style.maxWidth = style.width = ret;\n\t\t\tret = computed.width;\n\n\t\t\t// Revert the changed values\n\t\t\tstyle.width = width;\n\t\t\tstyle.minWidth = minWidth;\n\t\t\tstyle.maxWidth = maxWidth;\n\t\t}\n\t}\n\n\treturn ret !== undefined ?\n\n\t\t// Support: IE <=9 - 11 only\n\t\t// IE returns zIndex value as an integer.\n\t\tret + \"\" :\n\t\tret;\n}\n\n\nfunction addGetHookIf( conditionFn, hookFn ) {\n\n\t// Define the hook, we'll check on the first run if it's really needed.\n\treturn {\n\t\tget: function() {\n\t\t\tif ( conditionFn() ) {\n\n\t\t\t\t// Hook not needed (or it's not possible to use it due\n\t\t\t\t// to missing dependency), remove it.\n\t\t\t\tdelete this.get;\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// Hook needed; redefine it so that the support test is not executed again.\n\t\t\treturn ( this.get = hookFn ).apply( this, arguments );\n\t\t}\n\t};\n}\n\n\nvar cssPrefixes = [ \"Webkit\", \"Moz\", \"ms\" ],\n\temptyStyle = document.createElement( \"div\" ).style,\n\tvendorProps = {};\n\n// Return a vendor-prefixed property or undefined\nfunction vendorPropName( name ) {\n\n\t// Check for vendor prefixed names\n\tvar capName = name[ 0 ].toUpperCase() + name.slice( 1 ),\n\t\ti = cssPrefixes.length;\n\n\twhile ( i-- ) {\n\t\tname = cssPrefixes[ i ] + capName;\n\t\tif ( name in emptyStyle ) {\n\t\t\treturn name;\n\t\t}\n\t}\n}\n\n// Return a potentially-mapped jQuery.cssProps or vendor prefixed property\nfunction finalPropName( name ) {\n\tvar final = jQuery.cssProps[ name ] || vendorProps[ name ];\n\n\tif ( final ) {\n\t\treturn final;\n\t}\n\tif ( name in emptyStyle ) {\n\t\treturn name;\n\t}\n\treturn vendorProps[ name ] = vendorPropName( name ) || name;\n}\n\n\nvar\n\n\t// Swappable if display is none or starts with table\n\t// except \"table\", \"table-cell\", or \"table-caption\"\n\t// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display\n\trdisplayswap = /^(none|table(?!-c[ea]).+)/,\n\trcustomProp = /^--/,\n\tcssShow = { position: \"absolute\", visibility: \"hidden\", display: \"block\" },\n\tcssNormalTransform = {\n\t\tletterSpacing: \"0\",\n\t\tfontWeight: \"400\"\n\t};\n\nfunction setPositiveNumber( _elem, value, subtract ) {\n\n\t// Any relative (+/-) values have already been\n\t// normalized at this point\n\tvar matches = rcssNum.exec( value );\n\treturn matches ?\n\n\t\t// Guard against undefined \"subtract\", e.g., when used as in cssHooks\n\t\tMath.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || \"px\" ) :\n\t\tvalue;\n}\n\nfunction boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {\n\tvar i = dimension === \"width\" ? 1 : 0,\n\t\textra = 0,\n\t\tdelta = 0;\n\n\t// Adjustment may not be necessary\n\tif ( box === ( isBorderBox ? \"border\" : \"content\" ) ) {\n\t\treturn 0;\n\t}\n\n\tfor ( ; i < 4; i += 2 ) {\n\n\t\t// Both box models exclude margin\n\t\tif ( box === \"margin\" ) {\n\t\t\tdelta += jQuery.css( elem, box + cssExpand[ i ], true, styles );\n\t\t}\n\n\t\t// If we get here with a content-box, we're seeking \"padding\" or \"border\" or \"margin\"\n\t\tif ( !isBorderBox ) {\n\n\t\t\t// Add padding\n\t\t\tdelta += jQuery.css( elem, \"padding\" + cssExpand[ i ], true, styles );\n\n\t\t\t// For \"border\" or \"margin\", add border\n\t\t\tif ( box !== \"padding\" ) {\n\t\t\t\tdelta += jQuery.css( elem, \"border\" + cssExpand[ i ] + \"Width\", true, styles );\n\n\t\t\t// But still keep track of it otherwise\n\t\t\t} else {\n\t\t\t\textra += jQuery.css( elem, \"border\" + cssExpand[ i ] + \"Width\", true, styles );\n\t\t\t}\n\n\t\t// If we get here with a border-box (content + padding + border), we're seeking \"content\" or\n\t\t// \"padding\" or \"margin\"\n\t\t} else {\n\n\t\t\t// For \"content\", subtract padding\n\t\t\tif ( box === \"content\" ) {\n\t\t\t\tdelta -= jQuery.css( elem, \"padding\" + cssExpand[ i ], true, styles );\n\t\t\t}\n\n\t\t\t// For \"content\" or \"padding\", subtract border\n\t\t\tif ( box !== \"margin\" ) {\n\t\t\t\tdelta -= jQuery.css( elem, \"border\" + cssExpand[ i ] + \"Width\", true, styles );\n\t\t\t}\n\t\t}\n\t}\n\n\t// Account for positive content-box scroll gutter when requested by providing computedVal\n\tif ( !isBorderBox && computedVal >= 0 ) {\n\n\t\t// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border\n\t\t// Assuming integer scroll gutter, subtract the rest and round down\n\t\tdelta += Math.max( 0, Math.ceil(\n\t\t\telem[ \"offset\" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -\n\t\t\tcomputedVal -\n\t\t\tdelta -\n\t\t\textra -\n\t\t\t0.5\n\n\t\t// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter\n\t\t// Use an explicit zero to avoid NaN (gh-3964)\n\t\t) ) || 0;\n\t}\n\n\treturn delta;\n}\n\nfunction getWidthOrHeight( elem, dimension, extra ) {\n\n\t// Start with computed style\n\tvar styles = getStyles( elem ),\n\n\t\t// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).\n\t\t// Fake content-box until we know it's needed to know the true value.\n\t\tboxSizingNeeded = !support.boxSizingReliable() || extra,\n\t\tisBorderBox = boxSizingNeeded &&\n\t\t\tjQuery.css( elem, \"boxSizing\", false, styles ) === \"border-box\",\n\t\tvalueIsBorderBox = isBorderBox,\n\n\t\tval = curCSS( elem, dimension, styles ),\n\t\toffsetProp = \"offset\" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );\n\n\t// Support: Firefox <=54\n\t// Return a confounding non-pixel value or feign ignorance, as appropriate.\n\tif ( rnumnonpx.test( val ) ) {\n\t\tif ( !extra ) {\n\t\t\treturn val;\n\t\t}\n\t\tval = \"auto\";\n\t}\n\n\n\t// Support: IE 9 - 11 only\n\t// Use offsetWidth/offsetHeight for when box sizing is unreliable.\n\t// In those cases, the computed value can be trusted to be border-box.\n\tif ( ( !support.boxSizingReliable() && isBorderBox ||\n\n\t\t// Support: IE 10 - 11+, Edge 15 - 18+\n\t\t// IE/Edge misreport `getComputedStyle` of table rows with width/height\n\t\t// set in CSS while `offset*` properties report correct values.\n\t\t// Interestingly, in some cases IE 9 doesn't suffer from this issue.\n\t\t!support.reliableTrDimensions() && nodeName( elem, \"tr\" ) ||\n\n\t\t// Fall back to offsetWidth/offsetHeight when value is \"auto\"\n\t\t// This happens for inline elements with no explicit setting (gh-3571)\n\t\tval === \"auto\" ||\n\n\t\t// Support: Android <=4.1 - 4.3 only\n\t\t// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)\n\t\t!parseFloat( val ) && jQuery.css( elem, \"display\", false, styles ) === \"inline\" ) &&\n\n\t\t// Make sure the element is visible & connected\n\t\telem.getClientRects().length ) {\n\n\t\tisBorderBox = jQuery.css( elem, \"boxSizing\", false, styles ) === \"border-box\";\n\n\t\t// Where available, offsetWidth/offsetHeight approximate border box dimensions.\n\t\t// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the\n\t\t// retrieved value as a content box dimension.\n\t\tvalueIsBorderBox = offsetProp in elem;\n\t\tif ( valueIsBorderBox ) {\n\t\t\tval = elem[ offsetProp ];\n\t\t}\n\t}\n\n\t// Normalize \"\" and auto\n\tval = parseFloat( val ) || 0;\n\n\t// Adjust for the element's box model\n\treturn ( val +\n\t\tboxModelAdjustment(\n\t\t\telem,\n\t\t\tdimension,\n\t\t\textra || ( isBorderBox ? \"border\" : \"content\" ),\n\t\t\tvalueIsBorderBox,\n\t\t\tstyles,\n\n\t\t\t// Provide the current computed size to request scroll gutter calculation (gh-3589)\n\t\t\tval\n\t\t)\n\t) + \"px\";\n}\n\njQuery.extend( {\n\n\t// Add in style property hooks for overriding the default\n\t// behavior of getting and setting a style property\n\tcssHooks: {\n\t\topacity: {\n\t\t\tget: function( elem, computed ) {\n\t\t\t\tif ( computed ) {\n\n\t\t\t\t\t// We should always get a number back from opacity\n\t\t\t\t\tvar ret = curCSS( elem, \"opacity\" );\n\t\t\t\t\treturn ret === \"\" ? \"1\" : ret;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\t// Don't automatically add \"px\" to these possibly-unitless properties\n\tcssNumber: {\n\t\t\"animationIterationCount\": true,\n\t\t\"columnCount\": true,\n\t\t\"fillOpacity\": true,\n\t\t\"flexGrow\": true,\n\t\t\"flexShrink\": true,\n\t\t\"fontWeight\": true,\n\t\t\"gridArea\": true,\n\t\t\"gridColumn\": true,\n\t\t\"gridColumnEnd\": true,\n\t\t\"gridColumnStart\": true,\n\t\t\"gridRow\": true,\n\t\t\"gridRowEnd\": true,\n\t\t\"gridRowStart\": true,\n\t\t\"lineHeight\": true,\n\t\t\"opacity\": true,\n\t\t\"order\": true,\n\t\t\"orphans\": true,\n\t\t\"widows\": true,\n\t\t\"zIndex\": true,\n\t\t\"zoom\": true\n\t},\n\n\t// Add in properties whose names you wish to fix before\n\t// setting or getting the value\n\tcssProps: {},\n\n\t// Get and set the style property on a DOM Node\n\tstyle: function( elem, name, value, extra ) {\n\n\t\t// Don't set styles on text and comment nodes\n\t\tif ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Make sure that we're working with the right name\n\t\tvar ret, type, hooks,\n\t\t\torigName = camelCase( name ),\n\t\t\tisCustomProp = rcustomProp.test( name ),\n\t\t\tstyle = elem.style;\n\n\t\t// Make sure that we're working with the right name. We don't\n\t\t// want to query the value if it is a CSS custom property\n\t\t// since they are user-defined.\n\t\tif ( !isCustomProp ) {\n\t\t\tname = finalPropName( origName );\n\t\t}\n\n\t\t// Gets hook for the prefixed version, then unprefixed version\n\t\thooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];\n\n\t\t// Check if we're setting a value\n\t\tif ( value !== undefined ) {\n\t\t\ttype = typeof value;\n\n\t\t\t// Convert \"+=\" or \"-=\" to relative numbers (#7345)\n\t\t\tif ( type === \"string\" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {\n\t\t\t\tvalue = adjustCSS( elem, name, ret );\n\n\t\t\t\t// Fixes bug #9237\n\t\t\t\ttype = \"number\";\n\t\t\t}\n\n\t\t\t// Make sure that null and NaN values aren't set (#7116)\n\t\t\tif ( value == null || value !== value ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t// If a number was passed in, add the unit (except for certain CSS properties)\n\t\t\t// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append\n\t\t\t// \"px\" to a few hardcoded values.\n\t\t\tif ( type === \"number\" && !isCustomProp ) {\n\t\t\t\tvalue += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? \"\" : \"px\" );\n\t\t\t}\n\n\t\t\t// background-* props affect original clone's values\n\t\t\tif ( !support.clearCloneStyle && value === \"\" && name.indexOf( \"background\" ) === 0 ) {\n\t\t\t\tstyle[ name ] = \"inherit\";\n\t\t\t}\n\n\t\t\t// If a hook was provided, use that value, otherwise just set the specified value\n\t\t\tif ( !hooks || !( \"set\" in hooks ) ||\n\t\t\t\t( value = hooks.set( elem, value, extra ) ) !== undefined ) {\n\n\t\t\t\tif ( isCustomProp ) {\n\t\t\t\t\tstyle.setProperty( name, value );\n\t\t\t\t} else {\n\t\t\t\t\tstyle[ name ] = value;\n\t\t\t\t}\n\t\t\t}\n\n\t\t} else {\n\n\t\t\t// If a hook was provided get the non-computed value from there\n\t\t\tif ( hooks && \"get\" in hooks &&\n\t\t\t\t( ret = hooks.get( elem, false, extra ) ) !== undefined ) {\n\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\t// Otherwise just get the value from the style object\n\t\t\treturn style[ name ];\n\t\t}\n\t},\n\n\tcss: function( elem, name, extra, styles ) {\n\t\tvar val, num, hooks,\n\t\t\torigName = camelCase( name ),\n\t\t\tisCustomProp = rcustomProp.test( name );\n\n\t\t// Make sure that we're working with the right name. We don't\n\t\t// want to modify the value if it is a CSS custom property\n\t\t// since they are user-defined.\n\t\tif ( !isCustomProp ) {\n\t\t\tname = finalPropName( origName );\n\t\t}\n\n\t\t// Try prefixed name followed by the unprefixed name\n\t\thooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];\n\n\t\t// If a hook was provided get the computed value from there\n\t\tif ( hooks && \"get\" in hooks ) {\n\t\t\tval = hooks.get( elem, true, extra );\n\t\t}\n\n\t\t// Otherwise, if a way to get the computed value exists, use that\n\t\tif ( val === undefined ) {\n\t\t\tval = curCSS( elem, name, styles );\n\t\t}\n\n\t\t// Convert \"normal\" to computed value\n\t\tif ( val === \"normal\" && name in cssNormalTransform ) {\n\t\t\tval = cssNormalTransform[ name ];\n\t\t}\n\n\t\t// Make numeric if forced or a qualifier was provided and val looks numeric\n\t\tif ( extra === \"\" || extra ) {\n\t\t\tnum = parseFloat( val );\n\t\t\treturn extra === true || isFinite( num ) ? num || 0 : val;\n\t\t}\n\n\t\treturn val;\n\t}\n} );\n\njQuery.each( [ \"height\", \"width\" ], function( _i, dimension ) {\n\tjQuery.cssHooks[ dimension ] = {\n\t\tget: function( elem, computed, extra ) {\n\t\t\tif ( computed ) {\n\n\t\t\t\t// Certain elements can have dimension info if we invisibly show them\n\t\t\t\t// but it must have a current display style that would benefit\n\t\t\t\treturn rdisplayswap.test( jQuery.css( elem, \"display\" ) ) &&\n\n\t\t\t\t\t// Support: Safari 8+\n\t\t\t\t\t// Table columns in Safari have non-zero offsetWidth & zero\n\t\t\t\t\t// getBoundingClientRect().width unless display is changed.\n\t\t\t\t\t// Support: IE <=11 only\n\t\t\t\t\t// Running getBoundingClientRect on a disconnected node\n\t\t\t\t\t// in IE throws an error.\n\t\t\t\t\t( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?\n\t\t\t\t\t\tswap( elem, cssShow, function() {\n\t\t\t\t\t\t\treturn getWidthOrHeight( elem, dimension, extra );\n\t\t\t\t\t\t} ) :\n\t\t\t\t\t\tgetWidthOrHeight( elem, dimension, extra );\n\t\t\t}\n\t\t},\n\n\t\tset: function( elem, value, extra ) {\n\t\t\tvar matches,\n\t\t\t\tstyles = getStyles( elem ),\n\n\t\t\t\t// Only read styles.position if the test has a chance to fail\n\t\t\t\t// to avoid forcing a reflow.\n\t\t\t\tscrollboxSizeBuggy = !support.scrollboxSize() &&\n\t\t\t\t\tstyles.position === \"absolute\",\n\n\t\t\t\t// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)\n\t\t\t\tboxSizingNeeded = scrollboxSizeBuggy || extra,\n\t\t\t\tisBorderBox = boxSizingNeeded &&\n\t\t\t\t\tjQuery.css( elem, \"boxSizing\", false, styles ) === \"border-box\",\n\t\t\t\tsubtract = extra ?\n\t\t\t\t\tboxModelAdjustment(\n\t\t\t\t\t\telem,\n\t\t\t\t\t\tdimension,\n\t\t\t\t\t\textra,\n\t\t\t\t\t\tisBorderBox,\n\t\t\t\t\t\tstyles\n\t\t\t\t\t) :\n\t\t\t\t\t0;\n\n\t\t\t// Account for unreliable border-box dimensions by comparing offset* to computed and\n\t\t\t// faking a content-box to get border and padding (gh-3699)\n\t\t\tif ( isBorderBox && scrollboxSizeBuggy ) {\n\t\t\t\tsubtract -= Math.ceil(\n\t\t\t\t\telem[ \"offset\" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -\n\t\t\t\t\tparseFloat( styles[ dimension ] ) -\n\t\t\t\t\tboxModelAdjustment( elem, dimension, \"border\", false, styles ) -\n\t\t\t\t\t0.5\n\t\t\t\t);\n\t\t\t}\n\n\t\t\t// Convert to pixels if value adjustment is needed\n\t\t\tif ( subtract && ( matches = rcssNum.exec( value ) ) &&\n\t\t\t\t( matches[ 3 ] || \"px\" ) !== \"px\" ) {\n\n\t\t\t\telem.style[ dimension ] = value;\n\t\t\t\tvalue = jQuery.css( elem, dimension );\n\t\t\t}\n\n\t\t\treturn setPositiveNumber( elem, value, subtract );\n\t\t}\n\t};\n} );\n\njQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,\n\tfunction( elem, computed ) {\n\t\tif ( computed ) {\n\t\t\treturn ( parseFloat( curCSS( elem, \"marginLeft\" ) ) ||\n\t\t\t\telem.getBoundingClientRect().left -\n\t\t\t\t\tswap( elem, { marginLeft: 0 }, function() {\n\t\t\t\t\t\treturn elem.getBoundingClientRect().left;\n\t\t\t\t\t} )\n\t\t\t\t) + \"px\";\n\t\t}\n\t}\n);\n\n// These hooks are used by animate to expand properties\njQuery.each( {\n\tmargin: \"\",\n\tpadding: \"\",\n\tborder: \"Width\"\n}, function( prefix, suffix ) {\n\tjQuery.cssHooks[ prefix + suffix ] = {\n\t\texpand: function( value ) {\n\t\t\tvar i = 0,\n\t\t\t\texpanded = {},\n\n\t\t\t\t// Assumes a single number if not a string\n\t\t\t\tparts = typeof value === \"string\" ? value.split( \" \" ) : [ value ];\n\n\t\t\tfor ( ; i < 4; i++ ) {\n\t\t\t\texpanded[ prefix + cssExpand[ i ] + suffix ] =\n\t\t\t\t\tparts[ i ] || parts[ i - 2 ] || parts[ 0 ];\n\t\t\t}\n\n\t\t\treturn expanded;\n\t\t}\n\t};\n\n\tif ( prefix !== \"margin\" ) {\n\t\tjQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;\n\t}\n} );\n\njQuery.fn.extend( {\n\tcss: function( name, value ) {\n\t\treturn access( this, function( elem, name, value ) {\n\t\t\tvar styles, len,\n\t\t\t\tmap = {},\n\t\t\t\ti = 0;\n\n\t\t\tif ( Array.isArray( name ) ) {\n\t\t\t\tstyles = getStyles( elem );\n\t\t\t\tlen = name.length;\n\n\t\t\t\tfor ( ; i < len; i++ ) {\n\t\t\t\t\tmap[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );\n\t\t\t\t}\n\n\t\t\t\treturn map;\n\t\t\t}\n\n\t\t\treturn value !== undefined ?\n\t\t\t\tjQuery.style( elem, name, value ) :\n\t\t\t\tjQuery.css( elem, name );\n\t\t}, name, value, arguments.length > 1 );\n\t}\n} );\n\n\nfunction Tween( elem, options, prop, end, easing ) {\n\treturn new Tween.prototype.init( elem, options, prop, end, easing );\n}\njQuery.Tween = Tween;\n\nTween.prototype = {\n\tconstructor: Tween,\n\tinit: function( elem, options, prop, end, easing, unit ) {\n\t\tthis.elem = elem;\n\t\tthis.prop = prop;\n\t\tthis.easing = easing || jQuery.easing._default;\n\t\tthis.options = options;\n\t\tthis.start = this.now = this.cur();\n\t\tthis.end = end;\n\t\tthis.unit = unit || ( jQuery.cssNumber[ prop ] ? \"\" : \"px\" );\n\t},\n\tcur: function() {\n\t\tvar hooks = Tween.propHooks[ this.prop ];\n\n\t\treturn hooks && hooks.get ?\n\t\t\thooks.get( this ) :\n\t\t\tTween.propHooks._default.get( this );\n\t},\n\trun: function( percent ) {\n\t\tvar eased,\n\t\t\thooks = Tween.propHooks[ this.prop ];\n\n\t\tif ( this.options.duration ) {\n\t\t\tthis.pos = eased = jQuery.easing[ this.easing ](\n\t\t\t\tpercent, this.options.duration * percent, 0, 1, this.options.duration\n\t\t\t);\n\t\t} else {\n\t\t\tthis.pos = eased = percent;\n\t\t}\n\t\tthis.now = ( this.end - this.start ) * eased + this.start;\n\n\t\tif ( this.options.step ) {\n\t\t\tthis.options.step.call( this.elem, this.now, this );\n\t\t}\n\n\t\tif ( hooks && hooks.set ) {\n\t\t\thooks.set( this );\n\t\t} else {\n\t\t\tTween.propHooks._default.set( this );\n\t\t}\n\t\treturn this;\n\t}\n};\n\nTween.prototype.init.prototype = Tween.prototype;\n\nTween.propHooks = {\n\t_default: {\n\t\tget: function( tween ) {\n\t\t\tvar result;\n\n\t\t\t// Use a property on the element directly when it is not a DOM element,\n\t\t\t// or when there is no matching style property that exists.\n\t\t\tif ( tween.elem.nodeType !== 1 ||\n\t\t\t\ttween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {\n\t\t\t\treturn tween.elem[ tween.prop ];\n\t\t\t}\n\n\t\t\t// Passing an empty string as a 3rd parameter to .css will automatically\n\t\t\t// attempt a parseFloat and fallback to a string if the parse fails.\n\t\t\t// Simple values such as \"10px\" are parsed to Float;\n\t\t\t// complex values such as \"rotate(1rad)\" are returned as-is.\n\t\t\tresult = jQuery.css( tween.elem, tween.prop, \"\" );\n\n\t\t\t// Empty strings, null, undefined and \"auto\" are converted to 0.\n\t\t\treturn !result || result === \"auto\" ? 0 : result;\n\t\t},\n\t\tset: function( tween ) {\n\n\t\t\t// Use step hook for back compat.\n\t\t\t// Use cssHook if its there.\n\t\t\t// Use .style if available and use plain properties where available.\n\t\t\tif ( jQuery.fx.step[ tween.prop ] ) {\n\t\t\t\tjQuery.fx.step[ tween.prop ]( tween );\n\t\t\t} else if ( tween.elem.nodeType === 1 && (\n\t\t\t\t\tjQuery.cssHooks[ tween.prop ] ||\n\t\t\t\t\ttween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {\n\t\t\t\tjQuery.style( tween.elem, tween.prop, tween.now + tween.unit );\n\t\t\t} else {\n\t\t\t\ttween.elem[ tween.prop ] = tween.now;\n\t\t\t}\n\t\t}\n\t}\n};\n\n// Support: IE <=9 only\n// Panic based approach to setting things on disconnected nodes\nTween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {\n\tset: function( tween ) {\n\t\tif ( tween.elem.nodeType && tween.elem.parentNode ) {\n\t\t\ttween.elem[ tween.prop ] = tween.now;\n\t\t}\n\t}\n};\n\njQuery.easing = {\n\tlinear: function( p ) {\n\t\treturn p;\n\t},\n\tswing: function( p ) {\n\t\treturn 0.5 - Math.cos( p * Math.PI ) / 2;\n\t},\n\t_default: \"swing\"\n};\n\njQuery.fx = Tween.prototype.init;\n\n// Back compat <1.8 extension point\njQuery.fx.step = {};\n\n\n\n\nvar\n\tfxNow, inProgress,\n\trfxtypes = /^(?:toggle|show|hide)$/,\n\trrun = /queueHooks$/;\n\nfunction schedule() {\n\tif ( inProgress ) {\n\t\tif ( document.hidden === false && window.requestAnimationFrame ) {\n\t\t\twindow.requestAnimationFrame( schedule );\n\t\t} else {\n\t\t\twindow.setTimeout( schedule, jQuery.fx.interval );\n\t\t}\n\n\t\tjQuery.fx.tick();\n\t}\n}\n\n// Animations created synchronously will run synchronously\nfunction createFxNow() {\n\twindow.setTimeout( function() {\n\t\tfxNow = undefined;\n\t} );\n\treturn ( fxNow = Date.now() );\n}\n\n// Generate parameters to create a standard animation\nfunction genFx( type, includeWidth ) {\n\tvar which,\n\t\ti = 0,\n\t\tattrs = { height: type };\n\n\t// If we include width, step value is 1 to do all cssExpand values,\n\t// otherwise step value is 2 to skip over Left and Right\n\tincludeWidth = includeWidth ? 1 : 0;\n\tfor ( ; i < 4; i += 2 - includeWidth ) {\n\t\twhich = cssExpand[ i ];\n\t\tattrs[ \"margin\" + which ] = attrs[ \"padding\" + which ] = type;\n\t}\n\n\tif ( includeWidth ) {\n\t\tattrs.opacity = attrs.width = type;\n\t}\n\n\treturn attrs;\n}\n\nfunction createTween( value, prop, animation ) {\n\tvar tween,\n\t\tcollection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ \"*\" ] ),\n\t\tindex = 0,\n\t\tlength = collection.length;\n\tfor ( ; index < length; index++ ) {\n\t\tif ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {\n\n\t\t\t// We're done with this property\n\t\t\treturn tween;\n\t\t}\n\t}\n}\n\nfunction defaultPrefilter( elem, props, opts ) {\n\tvar prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,\n\t\tisBox = \"width\" in props || \"height\" in props,\n\t\tanim = this,\n\t\torig = {},\n\t\tstyle = elem.style,\n\t\thidden = elem.nodeType && isHiddenWithinTree( elem ),\n\t\tdataShow = dataPriv.get( elem, \"fxshow\" );\n\n\t// Queue-skipping animations hijack the fx hooks\n\tif ( !opts.queue ) {\n\t\thooks = jQuery._queueHooks( elem, \"fx\" );\n\t\tif ( hooks.unqueued == null ) {\n\t\t\thooks.unqueued = 0;\n\t\t\toldfire = hooks.empty.fire;\n\t\t\thooks.empty.fire = function() {\n\t\t\t\tif ( !hooks.unqueued ) {\n\t\t\t\t\toldfire();\n\t\t\t\t}\n\t\t\t};\n\t\t}\n\t\thooks.unqueued++;\n\n\t\tanim.always( function() {\n\n\t\t\t// Ensure the complete handler is called before this completes\n\t\t\tanim.always( function() {\n\t\t\t\thooks.unqueued--;\n\t\t\t\tif ( !jQuery.queue( elem, \"fx\" ).length ) {\n\t\t\t\t\thooks.empty.fire();\n\t\t\t\t}\n\t\t\t} );\n\t\t} );\n\t}\n\n\t// Detect show/hide animations\n\tfor ( prop in props ) {\n\t\tvalue = props[ prop ];\n\t\tif ( rfxtypes.test( value ) ) {\n\t\t\tdelete props[ prop ];\n\t\t\ttoggle = toggle || value === \"toggle\";\n\t\t\tif ( value === ( hidden ? \"hide\" : \"show\" ) ) {\n\n\t\t\t\t// Pretend to be hidden if this is a \"show\" and\n\t\t\t\t// there is still data from a stopped show/hide\n\t\t\t\tif ( value === \"show\" && dataShow && dataShow[ prop ] !== undefined ) {\n\t\t\t\t\thidden = true;\n\n\t\t\t\t// Ignore all other no-op show/hide data\n\t\t\t\t} else {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t}\n\t\t\torig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );\n\t\t}\n\t}\n\n\t// Bail out if this is a no-op like .hide().hide()\n\tpropTween = !jQuery.isEmptyObject( props );\n\tif ( !propTween && jQuery.isEmptyObject( orig ) ) {\n\t\treturn;\n\t}\n\n\t// Restrict \"overflow\" and \"display\" styles during box animations\n\tif ( isBox && elem.nodeType === 1 ) {\n\n\t\t// Support: IE <=9 - 11, Edge 12 - 15\n\t\t// Record all 3 overflow attributes because IE does not infer the shorthand\n\t\t// from identically-valued overflowX and overflowY and Edge just mirrors\n\t\t// the overflowX value there.\n\t\topts.overflow = [ style.overflow, style.overflowX, style.overflowY ];\n\n\t\t// Identify a display type, preferring old show/hide data over the CSS cascade\n\t\trestoreDisplay = dataShow && dataShow.display;\n\t\tif ( restoreDisplay == null ) {\n\t\t\trestoreDisplay = dataPriv.get( elem, \"display\" );\n\t\t}\n\t\tdisplay = jQuery.css( elem, \"display\" );\n\t\tif ( display === \"none\" ) {\n\t\t\tif ( restoreDisplay ) {\n\t\t\t\tdisplay = restoreDisplay;\n\t\t\t} else {\n\n\t\t\t\t// Get nonempty value(s) by temporarily forcing visibility\n\t\t\t\tshowHide( [ elem ], true );\n\t\t\t\trestoreDisplay = elem.style.display || restoreDisplay;\n\t\t\t\tdisplay = jQuery.css( elem, \"display\" );\n\t\t\t\tshowHide( [ elem ] );\n\t\t\t}\n\t\t}\n\n\t\t// Animate inline elements as inline-block\n\t\tif ( display === \"inline\" || display === \"inline-block\" && restoreDisplay != null ) {\n\t\t\tif ( jQuery.css( elem, \"float\" ) === \"none\" ) {\n\n\t\t\t\t// Restore the original display value at the end of pure show/hide animations\n\t\t\t\tif ( !propTween ) {\n\t\t\t\t\tanim.done( function() {\n\t\t\t\t\t\tstyle.display = restoreDisplay;\n\t\t\t\t\t} );\n\t\t\t\t\tif ( restoreDisplay == null ) {\n\t\t\t\t\t\tdisplay = style.display;\n\t\t\t\t\t\trestoreDisplay = display === \"none\" ? \"\" : display;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tstyle.display = \"inline-block\";\n\t\t\t}\n\t\t}\n\t}\n\n\tif ( opts.overflow ) {\n\t\tstyle.overflow = \"hidden\";\n\t\tanim.always( function() {\n\t\t\tstyle.overflow = opts.overflow[ 0 ];\n\t\t\tstyle.overflowX = opts.overflow[ 1 ];\n\t\t\tstyle.overflowY = opts.overflow[ 2 ];\n\t\t} );\n\t}\n\n\t// Implement show/hide animations\n\tpropTween = false;\n\tfor ( prop in orig ) {\n\n\t\t// General show/hide setup for this element animation\n\t\tif ( !propTween ) {\n\t\t\tif ( dataShow ) {\n\t\t\t\tif ( \"hidden\" in dataShow ) {\n\t\t\t\t\thidden = dataShow.hidden;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tdataShow = dataPriv.access( elem, \"fxshow\", { display: restoreDisplay } );\n\t\t\t}\n\n\t\t\t// Store hidden/visible for toggle so `.stop().toggle()` \"reverses\"\n\t\t\tif ( toggle ) {\n\t\t\t\tdataShow.hidden = !hidden;\n\t\t\t}\n\n\t\t\t// Show elements before animating them\n\t\t\tif ( hidden ) {\n\t\t\t\tshowHide( [ elem ], true );\n\t\t\t}\n\n\t\t\t/* eslint-disable no-loop-func */\n\n\t\t\tanim.done( function() {\n\n\t\t\t/* eslint-enable no-loop-func */\n\n\t\t\t\t// The final step of a \"hide\" animation is actually hiding the element\n\t\t\t\tif ( !hidden ) {\n\t\t\t\t\tshowHide( [ elem ] );\n\t\t\t\t}\n\t\t\t\tdataPriv.remove( elem, \"fxshow\" );\n\t\t\t\tfor ( prop in orig ) {\n\t\t\t\t\tjQuery.style( elem, prop, orig[ prop ] );\n\t\t\t\t}\n\t\t\t} );\n\t\t}\n\n\t\t// Per-property setup\n\t\tpropTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );\n\t\tif ( !( prop in dataShow ) ) {\n\t\t\tdataShow[ prop ] = propTween.start;\n\t\t\tif ( hidden ) {\n\t\t\t\tpropTween.end = propTween.start;\n\t\t\t\tpropTween.start = 0;\n\t\t\t}\n\t\t}\n\t}\n}\n\nfunction propFilter( props, specialEasing ) {\n\tvar index, name, easing, value, hooks;\n\n\t// camelCase, specialEasing and expand cssHook pass\n\tfor ( index in props ) {\n\t\tname = camelCase( index );\n\t\teasing = specialEasing[ name ];\n\t\tvalue = props[ index ];\n\t\tif ( Array.isArray( value ) ) {\n\t\t\teasing = value[ 1 ];\n\t\t\tvalue = props[ index ] = value[ 0 ];\n\t\t}\n\n\t\tif ( index !== name ) {\n\t\t\tprops[ name ] = value;\n\t\t\tdelete props[ index ];\n\t\t}\n\n\t\thooks = jQuery.cssHooks[ name ];\n\t\tif ( hooks && \"expand\" in hooks ) {\n\t\t\tvalue = hooks.expand( value );\n\t\t\tdelete props[ name ];\n\n\t\t\t// Not quite $.extend, this won't overwrite existing keys.\n\t\t\t// Reusing 'index' because we have the correct \"name\"\n\t\t\tfor ( index in value ) {\n\t\t\t\tif ( !( index in props ) ) {\n\t\t\t\t\tprops[ index ] = value[ index ];\n\t\t\t\t\tspecialEasing[ index ] = easing;\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tspecialEasing[ name ] = easing;\n\t\t}\n\t}\n}\n\nfunction Animation( elem, properties, options ) {\n\tvar result,\n\t\tstopped,\n\t\tindex = 0,\n\t\tlength = Animation.prefilters.length,\n\t\tdeferred = jQuery.Deferred().always( function() {\n\n\t\t\t// Don't match elem in the :animated selector\n\t\t\tdelete tick.elem;\n\t\t} ),\n\t\ttick = function() {\n\t\t\tif ( stopped ) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t\tvar currentTime = fxNow || createFxNow(),\n\t\t\t\tremaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),\n\n\t\t\t\t// Support: Android 2.3 only\n\t\t\t\t// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)\n\t\t\t\ttemp = remaining / animation.duration || 0,\n\t\t\t\tpercent = 1 - temp,\n\t\t\t\tindex = 0,\n\t\t\t\tlength = animation.tweens.length;\n\n\t\t\tfor ( ; index < length; index++ ) {\n\t\t\t\tanimation.tweens[ index ].run( percent );\n\t\t\t}\n\n\t\t\tdeferred.notifyWith( elem, [ animation, percent, remaining ] );\n\n\t\t\t// If there's more to do, yield\n\t\t\tif ( percent < 1 && length ) {\n\t\t\t\treturn remaining;\n\t\t\t}\n\n\t\t\t// If this was an empty animation, synthesize a final progress notification\n\t\t\tif ( !length ) {\n\t\t\t\tdeferred.notifyWith( elem, [ animation, 1, 0 ] );\n\t\t\t}\n\n\t\t\t// Resolve the animation and report its conclusion\n\t\t\tdeferred.resolveWith( elem, [ animation ] );\n\t\t\treturn false;\n\t\t},\n\t\tanimation = deferred.promise( {\n\t\t\telem: elem,\n\t\t\tprops: jQuery.extend( {}, properties ),\n\t\t\topts: jQuery.extend( true, {\n\t\t\t\tspecialEasing: {},\n\t\t\t\teasing: jQuery.easing._default\n\t\t\t}, options ),\n\t\t\toriginalProperties: properties,\n\t\t\toriginalOptions: options,\n\t\t\tstartTime: fxNow || createFxNow(),\n\t\t\tduration: options.duration,\n\t\t\ttweens: [],\n\t\t\tcreateTween: function( prop, end ) {\n\t\t\t\tvar tween = jQuery.Tween( elem, animation.opts, prop, end,\n\t\t\t\t\t\tanimation.opts.specialEasing[ prop ] || animation.opts.easing );\n\t\t\t\tanimation.tweens.push( tween );\n\t\t\t\treturn tween;\n\t\t\t},\n\t\t\tstop: function( gotoEnd ) {\n\t\t\t\tvar index = 0,\n\n\t\t\t\t\t// If we are going to the end, we want to run all the tweens\n\t\t\t\t\t// otherwise we skip this part\n\t\t\t\t\tlength = gotoEnd ? animation.tweens.length : 0;\n\t\t\t\tif ( stopped ) {\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\t\t\t\tstopped = true;\n\t\t\t\tfor ( ; index < length; index++ ) {\n\t\t\t\t\tanimation.tweens[ index ].run( 1 );\n\t\t\t\t}\n\n\t\t\t\t// Resolve when we played the last frame; otherwise, reject\n\t\t\t\tif ( gotoEnd ) {\n\t\t\t\t\tdeferred.notifyWith( elem, [ animation, 1, 0 ] );\n\t\t\t\t\tdeferred.resolveWith( elem, [ animation, gotoEnd ] );\n\t\t\t\t} else {\n\t\t\t\t\tdeferred.rejectWith( elem, [ animation, gotoEnd ] );\n\t\t\t\t}\n\t\t\t\treturn this;\n\t\t\t}\n\t\t} ),\n\t\tprops = animation.props;\n\n\tpropFilter( props, animation.opts.specialEasing );\n\n\tfor ( ; index < length; index++ ) {\n\t\tresult = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );\n\t\tif ( result ) {\n\t\t\tif ( isFunction( result.stop ) ) {\n\t\t\t\tjQuery._queueHooks( animation.elem, animation.opts.queue ).stop =\n\t\t\t\t\tresult.stop.bind( result );\n\t\t\t}\n\t\t\treturn result;\n\t\t}\n\t}\n\n\tjQuery.map( props, createTween, animation );\n\n\tif ( isFunction( animation.opts.start ) ) {\n\t\tanimation.opts.start.call( elem, animation );\n\t}\n\n\t// Attach callbacks from options\n\tanimation\n\t\t.progress( animation.opts.progress )\n\t\t.done( animation.opts.done, animation.opts.complete )\n\t\t.fail( animation.opts.fail )\n\t\t.always( animation.opts.always );\n\n\tjQuery.fx.timer(\n\t\tjQuery.extend( tick, {\n\t\t\telem: elem,\n\t\t\tanim: animation,\n\t\t\tqueue: animation.opts.queue\n\t\t} )\n\t);\n\n\treturn animation;\n}\n\njQuery.Animation = jQuery.extend( Animation, {\n\n\ttweeners: {\n\t\t\"*\": [ function( prop, value ) {\n\t\t\tvar tween = this.createTween( prop, value );\n\t\t\tadjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );\n\t\t\treturn tween;\n\t\t} ]\n\t},\n\n\ttweener: function( props, callback ) {\n\t\tif ( isFunction( props ) ) {\n\t\t\tcallback = props;\n\t\t\tprops = [ \"*\" ];\n\t\t} else {\n\t\t\tprops = props.match( rnothtmlwhite );\n\t\t}\n\n\t\tvar prop,\n\t\t\tindex = 0,\n\t\t\tlength = props.length;\n\n\t\tfor ( ; index < length; index++ ) {\n\t\t\tprop = props[ index ];\n\t\t\tAnimation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];\n\t\t\tAnimation.tweeners[ prop ].unshift( callback );\n\t\t}\n\t},\n\n\tprefilters: [ defaultPrefilter ],\n\n\tprefilter: function( callback, prepend ) {\n\t\tif ( prepend ) {\n\t\t\tAnimation.prefilters.unshift( callback );\n\t\t} else {\n\t\t\tAnimation.prefilters.push( callback );\n\t\t}\n\t}\n} );\n\njQuery.speed = function( speed, easing, fn ) {\n\tvar opt = speed && typeof speed === \"object\" ? jQuery.extend( {}, speed ) : {\n\t\tcomplete: fn || !fn && easing ||\n\t\t\tisFunction( speed ) && speed,\n\t\tduration: speed,\n\t\teasing: fn && easing || easing && !isFunction( easing ) && easing\n\t};\n\n\t// Go to the end state if fx are off\n\tif ( jQuery.fx.off ) {\n\t\topt.duration = 0;\n\n\t} else {\n\t\tif ( typeof opt.duration !== \"number\" ) {\n\t\t\tif ( opt.duration in jQuery.fx.speeds ) {\n\t\t\t\topt.duration = jQuery.fx.speeds[ opt.duration ];\n\n\t\t\t} else {\n\t\t\t\topt.duration = jQuery.fx.speeds._default;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Normalize opt.queue - true/undefined/null -> \"fx\"\n\tif ( opt.queue == null || opt.queue === true ) {\n\t\topt.queue = \"fx\";\n\t}\n\n\t// Queueing\n\topt.old = opt.complete;\n\n\topt.complete = function() {\n\t\tif ( isFunction( opt.old ) ) {\n\t\t\topt.old.call( this );\n\t\t}\n\n\t\tif ( opt.queue ) {\n\t\t\tjQuery.dequeue( this, opt.queue );\n\t\t}\n\t};\n\n\treturn opt;\n};\n\njQuery.fn.extend( {\n\tfadeTo: function( speed, to, easing, callback ) {\n\n\t\t// Show any hidden elements after setting opacity to 0\n\t\treturn this.filter( isHiddenWithinTree ).css( \"opacity\", 0 ).show()\n\n\t\t\t// Animate to the value specified\n\t\t\t.end().animate( { opacity: to }, speed, easing, callback );\n\t},\n\tanimate: function( prop, speed, easing, callback ) {\n\t\tvar empty = jQuery.isEmptyObject( prop ),\n\t\t\toptall = jQuery.speed( speed, easing, callback ),\n\t\t\tdoAnimation = function() {\n\n\t\t\t\t// Operate on a copy of prop so per-property easing won't be lost\n\t\t\t\tvar anim = Animation( this, jQuery.extend( {}, prop ), optall );\n\n\t\t\t\t// Empty animations, or finishing resolves immediately\n\t\t\t\tif ( empty || dataPriv.get( this, \"finish\" ) ) {\n\t\t\t\t\tanim.stop( true );\n\t\t\t\t}\n\t\t\t};\n\t\t\tdoAnimation.finish = doAnimation;\n\n\t\treturn empty || optall.queue === false ?\n\t\t\tthis.each( doAnimation ) :\n\t\t\tthis.queue( optall.queue, doAnimation );\n\t},\n\tstop: function( type, clearQueue, gotoEnd ) {\n\t\tvar stopQueue = function( hooks ) {\n\t\t\tvar stop = hooks.stop;\n\t\t\tdelete hooks.stop;\n\t\t\tstop( gotoEnd );\n\t\t};\n\n\t\tif ( typeof type !== \"string\" ) {\n\t\t\tgotoEnd = clearQueue;\n\t\t\tclearQueue = type;\n\t\t\ttype = undefined;\n\t\t}\n\t\tif ( clearQueue ) {\n\t\t\tthis.queue( type || \"fx\", [] );\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tvar dequeue = true,\n\t\t\t\tindex = type != null && type + \"queueHooks\",\n\t\t\t\ttimers = jQuery.timers,\n\t\t\t\tdata = dataPriv.get( this );\n\n\t\t\tif ( index ) {\n\t\t\t\tif ( data[ index ] && data[ index ].stop ) {\n\t\t\t\t\tstopQueue( data[ index ] );\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tfor ( index in data ) {\n\t\t\t\t\tif ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {\n\t\t\t\t\t\tstopQueue( data[ index ] );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tfor ( index = timers.length; index--; ) {\n\t\t\t\tif ( timers[ index ].elem === this &&\n\t\t\t\t\t( type == null || timers[ index ].queue === type ) ) {\n\n\t\t\t\t\ttimers[ index ].anim.stop( gotoEnd );\n\t\t\t\t\tdequeue = false;\n\t\t\t\t\ttimers.splice( index, 1 );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Start the next in the queue if the last step wasn't forced.\n\t\t\t// Timers currently will call their complete callbacks, which\n\t\t\t// will dequeue but only if they were gotoEnd.\n\t\t\tif ( dequeue || !gotoEnd ) {\n\t\t\t\tjQuery.dequeue( this, type );\n\t\t\t}\n\t\t} );\n\t},\n\tfinish: function( type ) {\n\t\tif ( type !== false ) {\n\t\t\ttype = type || \"fx\";\n\t\t}\n\t\treturn this.each( function() {\n\t\t\tvar index,\n\t\t\t\tdata = dataPriv.get( this ),\n\t\t\t\tqueue = data[ type + \"queue\" ],\n\t\t\t\thooks = data[ type + \"queueHooks\" ],\n\t\t\t\ttimers = jQuery.timers,\n\t\t\t\tlength = queue ? queue.length : 0;\n\n\t\t\t// Enable finishing flag on private data\n\t\t\tdata.finish = true;\n\n\t\t\t// Empty the queue first\n\t\t\tjQuery.queue( this, type, [] );\n\n\t\t\tif ( hooks && hooks.stop ) {\n\t\t\t\thooks.stop.call( this, true );\n\t\t\t}\n\n\t\t\t// Look for any active animations, and finish them\n\t\t\tfor ( index = timers.length; index--; ) {\n\t\t\t\tif ( timers[ index ].elem === this && timers[ index ].queue === type ) {\n\t\t\t\t\ttimers[ index ].anim.stop( true );\n\t\t\t\t\ttimers.splice( index, 1 );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Look for any animations in the old queue and finish them\n\t\t\tfor ( index = 0; index < length; index++ ) {\n\t\t\t\tif ( queue[ index ] && queue[ index ].finish ) {\n\t\t\t\t\tqueue[ index ].finish.call( this );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Turn off finishing flag\n\t\t\tdelete data.finish;\n\t\t} );\n\t}\n} );\n\njQuery.each( [ \"toggle\", \"show\", \"hide\" ], function( _i, name ) {\n\tvar cssFn = jQuery.fn[ name ];\n\tjQuery.fn[ name ] = function( speed, easing, callback ) {\n\t\treturn speed == null || typeof speed === \"boolean\" ?\n\t\t\tcssFn.apply( this, arguments ) :\n\t\t\tthis.animate( genFx( name, true ), speed, easing, callback );\n\t};\n} );\n\n// Generate shortcuts for custom animations\njQuery.each( {\n\tslideDown: genFx( \"show\" ),\n\tslideUp: genFx( \"hide\" ),\n\tslideToggle: genFx( \"toggle\" ),\n\tfadeIn: { opacity: \"show\" },\n\tfadeOut: { opacity: \"hide\" },\n\tfadeToggle: { opacity: \"toggle\" }\n}, function( name, props ) {\n\tjQuery.fn[ name ] = function( speed, easing, callback ) {\n\t\treturn this.animate( props, speed, easing, callback );\n\t};\n} );\n\njQuery.timers = [];\njQuery.fx.tick = function() {\n\tvar timer,\n\t\ti = 0,\n\t\ttimers = jQuery.timers;\n\n\tfxNow = Date.now();\n\n\tfor ( ; i < timers.length; i++ ) {\n\t\ttimer = timers[ i ];\n\n\t\t// Run the timer and safely remove it when done (allowing for external removal)\n\t\tif ( !timer() && timers[ i ] === timer ) {\n\t\t\ttimers.splice( i--, 1 );\n\t\t}\n\t}\n\n\tif ( !timers.length ) {\n\t\tjQuery.fx.stop();\n\t}\n\tfxNow = undefined;\n};\n\njQuery.fx.timer = function( timer ) {\n\tjQuery.timers.push( timer );\n\tjQuery.fx.start();\n};\n\njQuery.fx.interval = 13;\njQuery.fx.start = function() {\n\tif ( inProgress ) {\n\t\treturn;\n\t}\n\n\tinProgress = true;\n\tschedule();\n};\n\njQuery.fx.stop = function() {\n\tinProgress = null;\n};\n\njQuery.fx.speeds = {\n\tslow: 600,\n\tfast: 200,\n\n\t// Default speed\n\t_default: 400\n};\n\n\n// Based off of the plugin by Clint Helfers, with permission.\n// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/\njQuery.fn.delay = function( time, type ) {\n\ttime = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;\n\ttype = type || \"fx\";\n\n\treturn this.queue( type, function( next, hooks ) {\n\t\tvar timeout = window.setTimeout( next, time );\n\t\thooks.stop = function() {\n\t\t\twindow.clearTimeout( timeout );\n\t\t};\n\t} );\n};\n\n\n( function() {\n\tvar input = document.createElement( \"input\" ),\n\t\tselect = document.createElement( \"select\" ),\n\t\topt = select.appendChild( document.createElement( \"option\" ) );\n\n\tinput.type = \"checkbox\";\n\n\t// Support: Android <=4.3 only\n\t// Default value for a checkbox should be \"on\"\n\tsupport.checkOn = input.value !== \"\";\n\n\t// Support: IE <=11 only\n\t// Must access selectedIndex to make default options select\n\tsupport.optSelected = opt.selected;\n\n\t// Support: IE <=11 only\n\t// An input loses its value after becoming a radio\n\tinput = document.createElement( \"input\" );\n\tinput.value = \"t\";\n\tinput.type = \"radio\";\n\tsupport.radioValue = input.value === \"t\";\n} )();\n\n\nvar boolHook,\n\tattrHandle = jQuery.expr.attrHandle;\n\njQuery.fn.extend( {\n\tattr: function( name, value ) {\n\t\treturn access( this, jQuery.attr, name, value, arguments.length > 1 );\n\t},\n\n\tremoveAttr: function( name ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.removeAttr( this, name );\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tattr: function( elem, name, value ) {\n\t\tvar ret, hooks,\n\t\t\tnType = elem.nodeType;\n\n\t\t// Don't get/set attributes on text, comment and attribute nodes\n\t\tif ( nType === 3 || nType === 8 || nType === 2 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Fallback to prop when attributes are not supported\n\t\tif ( typeof elem.getAttribute === \"undefined\" ) {\n\t\t\treturn jQuery.prop( elem, name, value );\n\t\t}\n\n\t\t// Attribute hooks are determined by the lowercase version\n\t\t// Grab necessary hook if one is defined\n\t\tif ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {\n\t\t\thooks = jQuery.attrHooks[ name.toLowerCase() ] ||\n\t\t\t\t( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );\n\t\t}\n\n\t\tif ( value !== undefined ) {\n\t\t\tif ( value === null ) {\n\t\t\t\tjQuery.removeAttr( elem, name );\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif ( hooks && \"set\" in hooks &&\n\t\t\t\t( ret = hooks.set( elem, value, name ) ) !== undefined ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\telem.setAttribute( name, value + \"\" );\n\t\t\treturn value;\n\t\t}\n\n\t\tif ( hooks && \"get\" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {\n\t\t\treturn ret;\n\t\t}\n\n\t\tret = jQuery.find.attr( elem, name );\n\n\t\t// Non-existent attributes return null, we normalize to undefined\n\t\treturn ret == null ? undefined : ret;\n\t},\n\n\tattrHooks: {\n\t\ttype: {\n\t\t\tset: function( elem, value ) {\n\t\t\t\tif ( !support.radioValue && value === \"radio\" &&\n\t\t\t\t\tnodeName( elem, \"input\" ) ) {\n\t\t\t\t\tvar val = elem.value;\n\t\t\t\t\telem.setAttribute( \"type\", value );\n\t\t\t\t\tif ( val ) {\n\t\t\t\t\t\telem.value = val;\n\t\t\t\t\t}\n\t\t\t\t\treturn value;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t},\n\n\tremoveAttr: function( elem, value ) {\n\t\tvar name,\n\t\t\ti = 0,\n\n\t\t\t// Attribute names can contain non-HTML whitespace characters\n\t\t\t// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2\n\t\t\tattrNames = value && value.match( rnothtmlwhite );\n\n\t\tif ( attrNames && elem.nodeType === 1 ) {\n\t\t\twhile ( ( name = attrNames[ i++ ] ) ) {\n\t\t\t\telem.removeAttribute( name );\n\t\t\t}\n\t\t}\n\t}\n} );\n\n// Hooks for boolean attributes\nboolHook = {\n\tset: function( elem, value, name ) {\n\t\tif ( value === false ) {\n\n\t\t\t// Remove boolean attributes when set to false\n\t\t\tjQuery.removeAttr( elem, name );\n\t\t} else {\n\t\t\telem.setAttribute( name, name );\n\t\t}\n\t\treturn name;\n\t}\n};\n\njQuery.each( jQuery.expr.match.bool.source.match( /\\w+/g ), function( _i, name ) {\n\tvar getter = attrHandle[ name ] || jQuery.find.attr;\n\n\tattrHandle[ name ] = function( elem, name, isXML ) {\n\t\tvar ret, handle,\n\t\t\tlowercaseName = name.toLowerCase();\n\n\t\tif ( !isXML ) {\n\n\t\t\t// Avoid an infinite loop by temporarily removing this function from the getter\n\t\t\thandle = attrHandle[ lowercaseName ];\n\t\t\tattrHandle[ lowercaseName ] = ret;\n\t\t\tret = getter( elem, name, isXML ) != null ?\n\t\t\t\tlowercaseName :\n\t\t\t\tnull;\n\t\t\tattrHandle[ lowercaseName ] = handle;\n\t\t}\n\t\treturn ret;\n\t};\n} );\n\n\n\n\nvar rfocusable = /^(?:input|select|textarea|button)$/i,\n\trclickable = /^(?:a|area)$/i;\n\njQuery.fn.extend( {\n\tprop: function( name, value ) {\n\t\treturn access( this, jQuery.prop, name, value, arguments.length > 1 );\n\t},\n\n\tremoveProp: function( name ) {\n\t\treturn this.each( function() {\n\t\t\tdelete this[ jQuery.propFix[ name ] || name ];\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tprop: function( elem, name, value ) {\n\t\tvar ret, hooks,\n\t\t\tnType = elem.nodeType;\n\n\t\t// Don't get/set properties on text, comment and attribute nodes\n\t\tif ( nType === 3 || nType === 8 || nType === 2 ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {\n\n\t\t\t// Fix name and attach hooks\n\t\t\tname = jQuery.propFix[ name ] || name;\n\t\t\thooks = jQuery.propHooks[ name ];\n\t\t}\n\n\t\tif ( value !== undefined ) {\n\t\t\tif ( hooks && \"set\" in hooks &&\n\t\t\t\t( ret = hooks.set( elem, value, name ) ) !== undefined ) {\n\t\t\t\treturn ret;\n\t\t\t}\n\n\t\t\treturn ( elem[ name ] = value );\n\t\t}\n\n\t\tif ( hooks && \"get\" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {\n\t\t\treturn ret;\n\t\t}\n\n\t\treturn elem[ name ];\n\t},\n\n\tpropHooks: {\n\t\ttabIndex: {\n\t\t\tget: function( elem ) {\n\n\t\t\t\t// Support: IE <=9 - 11 only\n\t\t\t\t// elem.tabIndex doesn't always return the\n\t\t\t\t// correct value when it hasn't been explicitly set\n\t\t\t\t// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/\n\t\t\t\t// Use proper attribute retrieval(#12072)\n\t\t\t\tvar tabindex = jQuery.find.attr( elem, \"tabindex\" );\n\n\t\t\t\tif ( tabindex ) {\n\t\t\t\t\treturn parseInt( tabindex, 10 );\n\t\t\t\t}\n\n\t\t\t\tif (\n\t\t\t\t\trfocusable.test( elem.nodeName ) ||\n\t\t\t\t\trclickable.test( elem.nodeName ) &&\n\t\t\t\t\telem.href\n\t\t\t\t) {\n\t\t\t\t\treturn 0;\n\t\t\t\t}\n\n\t\t\t\treturn -1;\n\t\t\t}\n\t\t}\n\t},\n\n\tpropFix: {\n\t\t\"for\": \"htmlFor\",\n\t\t\"class\": \"className\"\n\t}\n} );\n\n// Support: IE <=11 only\n// Accessing the selectedIndex property\n// forces the browser to respect setting selected\n// on the option\n// The getter ensures a default option is selected\n// when in an optgroup\n// eslint rule \"no-unused-expressions\" is disabled for this code\n// since it considers such accessions noop\nif ( !support.optSelected ) {\n\tjQuery.propHooks.selected = {\n\t\tget: function( elem ) {\n\n\t\t\t/* eslint no-unused-expressions: \"off\" */\n\n\t\t\tvar parent = elem.parentNode;\n\t\t\tif ( parent && parent.parentNode ) {\n\t\t\t\tparent.parentNode.selectedIndex;\n\t\t\t}\n\t\t\treturn null;\n\t\t},\n\t\tset: function( elem ) {\n\n\t\t\t/* eslint no-unused-expressions: \"off\" */\n\n\t\t\tvar parent = elem.parentNode;\n\t\t\tif ( parent ) {\n\t\t\t\tparent.selectedIndex;\n\n\t\t\t\tif ( parent.parentNode ) {\n\t\t\t\t\tparent.parentNode.selectedIndex;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n}\n\njQuery.each( [\n\t\"tabIndex\",\n\t\"readOnly\",\n\t\"maxLength\",\n\t\"cellSpacing\",\n\t\"cellPadding\",\n\t\"rowSpan\",\n\t\"colSpan\",\n\t\"useMap\",\n\t\"frameBorder\",\n\t\"contentEditable\"\n], function() {\n\tjQuery.propFix[ this.toLowerCase() ] = this;\n} );\n\n\n\n\n\t// Strip and collapse whitespace according to HTML spec\n\t// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace\n\tfunction stripAndCollapse( value ) {\n\t\tvar tokens = value.match( rnothtmlwhite ) || [];\n\t\treturn tokens.join( \" \" );\n\t}\n\n\nfunction getClass( elem ) {\n\treturn elem.getAttribute && elem.getAttribute( \"class\" ) || \"\";\n}\n\nfunction classesToArray( value ) {\n\tif ( Array.isArray( value ) ) {\n\t\treturn value;\n\t}\n\tif ( typeof value === \"string\" ) {\n\t\treturn value.match( rnothtmlwhite ) || [];\n\t}\n\treturn [];\n}\n\njQuery.fn.extend( {\n\taddClass: function( value ) {\n\t\tvar classes, elem, cur, curValue, clazz, j, finalValue,\n\t\t\ti = 0;\n\n\t\tif ( isFunction( value ) ) {\n\t\t\treturn this.each( function( j ) {\n\t\t\t\tjQuery( this ).addClass( value.call( this, j, getClass( this ) ) );\n\t\t\t} );\n\t\t}\n\n\t\tclasses = classesToArray( value );\n\n\t\tif ( classes.length ) {\n\t\t\twhile ( ( elem = this[ i++ ] ) ) {\n\t\t\t\tcurValue = getClass( elem );\n\t\t\t\tcur = elem.nodeType === 1 && ( \" \" + stripAndCollapse( curValue ) + \" \" );\n\n\t\t\t\tif ( cur ) {\n\t\t\t\t\tj = 0;\n\t\t\t\t\twhile ( ( clazz = classes[ j++ ] ) ) {\n\t\t\t\t\t\tif ( cur.indexOf( \" \" + clazz + \" \" ) < 0 ) {\n\t\t\t\t\t\t\tcur += clazz + \" \";\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Only assign if different to avoid unneeded rendering.\n\t\t\t\t\tfinalValue = stripAndCollapse( cur );\n\t\t\t\t\tif ( curValue !== finalValue ) {\n\t\t\t\t\t\telem.setAttribute( \"class\", finalValue );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn this;\n\t},\n\n\tremoveClass: function( value ) {\n\t\tvar classes, elem, cur, curValue, clazz, j, finalValue,\n\t\t\ti = 0;\n\n\t\tif ( isFunction( value ) ) {\n\t\t\treturn this.each( function( j ) {\n\t\t\t\tjQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );\n\t\t\t} );\n\t\t}\n\n\t\tif ( !arguments.length ) {\n\t\t\treturn this.attr( \"class\", \"\" );\n\t\t}\n\n\t\tclasses = classesToArray( value );\n\n\t\tif ( classes.length ) {\n\t\t\twhile ( ( elem = this[ i++ ] ) ) {\n\t\t\t\tcurValue = getClass( elem );\n\n\t\t\t\t// This expression is here for better compressibility (see addClass)\n\t\t\t\tcur = elem.nodeType === 1 && ( \" \" + stripAndCollapse( curValue ) + \" \" );\n\n\t\t\t\tif ( cur ) {\n\t\t\t\t\tj = 0;\n\t\t\t\t\twhile ( ( clazz = classes[ j++ ] ) ) {\n\n\t\t\t\t\t\t// Remove *all* instances\n\t\t\t\t\t\twhile ( cur.indexOf( \" \" + clazz + \" \" ) > -1 ) {\n\t\t\t\t\t\t\tcur = cur.replace( \" \" + clazz + \" \", \" \" );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\t// Only assign if different to avoid unneeded rendering.\n\t\t\t\t\tfinalValue = stripAndCollapse( cur );\n\t\t\t\t\tif ( curValue !== finalValue ) {\n\t\t\t\t\t\telem.setAttribute( \"class\", finalValue );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn this;\n\t},\n\n\ttoggleClass: function( value, stateVal ) {\n\t\tvar type = typeof value,\n\t\t\tisValidValue = type === \"string\" || Array.isArray( value );\n\n\t\tif ( typeof stateVal === \"boolean\" && isValidValue ) {\n\t\t\treturn stateVal ? this.addClass( value ) : this.removeClass( value );\n\t\t}\n\n\t\tif ( isFunction( value ) ) {\n\t\t\treturn this.each( function( i ) {\n\t\t\t\tjQuery( this ).toggleClass(\n\t\t\t\t\tvalue.call( this, i, getClass( this ), stateVal ),\n\t\t\t\t\tstateVal\n\t\t\t\t);\n\t\t\t} );\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tvar className, i, self, classNames;\n\n\t\t\tif ( isValidValue ) {\n\n\t\t\t\t// Toggle individual class names\n\t\t\t\ti = 0;\n\t\t\t\tself = jQuery( this );\n\t\t\t\tclassNames = classesToArray( value );\n\n\t\t\t\twhile ( ( className = classNames[ i++ ] ) ) {\n\n\t\t\t\t\t// Check each className given, space separated list\n\t\t\t\t\tif ( self.hasClass( className ) ) {\n\t\t\t\t\t\tself.removeClass( className );\n\t\t\t\t\t} else {\n\t\t\t\t\t\tself.addClass( className );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t// Toggle whole class name\n\t\t\t} else if ( value === undefined || type === \"boolean\" ) {\n\t\t\t\tclassName = getClass( this );\n\t\t\t\tif ( className ) {\n\n\t\t\t\t\t// Store className if set\n\t\t\t\t\tdataPriv.set( this, \"__className__\", className );\n\t\t\t\t}\n\n\t\t\t\t// If the element has a class name or if we're passed `false`,\n\t\t\t\t// then remove the whole classname (if there was one, the above saved it).\n\t\t\t\t// Otherwise bring back whatever was previously saved (if anything),\n\t\t\t\t// falling back to the empty string if nothing was stored.\n\t\t\t\tif ( this.setAttribute ) {\n\t\t\t\t\tthis.setAttribute( \"class\",\n\t\t\t\t\t\tclassName || value === false ?\n\t\t\t\t\t\t\"\" :\n\t\t\t\t\t\tdataPriv.get( this, \"__className__\" ) || \"\"\n\t\t\t\t\t);\n\t\t\t\t}\n\t\t\t}\n\t\t} );\n\t},\n\n\thasClass: function( selector ) {\n\t\tvar className, elem,\n\t\t\ti = 0;\n\n\t\tclassName = \" \" + selector + \" \";\n\t\twhile ( ( elem = this[ i++ ] ) ) {\n\t\t\tif ( elem.nodeType === 1 &&\n\t\t\t\t( \" \" + stripAndCollapse( getClass( elem ) ) + \" \" ).indexOf( className ) > -1 ) {\n\t\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\n\t\treturn false;\n\t}\n} );\n\n\n\n\nvar rreturn = /\\r/g;\n\njQuery.fn.extend( {\n\tval: function( value ) {\n\t\tvar hooks, ret, valueIsFunction,\n\t\t\telem = this[ 0 ];\n\n\t\tif ( !arguments.length ) {\n\t\t\tif ( elem ) {\n\t\t\t\thooks = jQuery.valHooks[ elem.type ] ||\n\t\t\t\t\tjQuery.valHooks[ elem.nodeName.toLowerCase() ];\n\n\t\t\t\tif ( hooks &&\n\t\t\t\t\t\"get\" in hooks &&\n\t\t\t\t\t( ret = hooks.get( elem, \"value\" ) ) !== undefined\n\t\t\t\t) {\n\t\t\t\t\treturn ret;\n\t\t\t\t}\n\n\t\t\t\tret = elem.value;\n\n\t\t\t\t// Handle most common string cases\n\t\t\t\tif ( typeof ret === \"string\" ) {\n\t\t\t\t\treturn ret.replace( rreturn, \"\" );\n\t\t\t\t}\n\n\t\t\t\t// Handle cases where value is null/undef or number\n\t\t\t\treturn ret == null ? \"\" : ret;\n\t\t\t}\n\n\t\t\treturn;\n\t\t}\n\n\t\tvalueIsFunction = isFunction( value );\n\n\t\treturn this.each( function( i ) {\n\t\t\tvar val;\n\n\t\t\tif ( this.nodeType !== 1 ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif ( valueIsFunction ) {\n\t\t\t\tval = value.call( this, i, jQuery( this ).val() );\n\t\t\t} else {\n\t\t\t\tval = value;\n\t\t\t}\n\n\t\t\t// Treat null/undefined as \"\"; convert numbers to string\n\t\t\tif ( val == null ) {\n\t\t\t\tval = \"\";\n\n\t\t\t} else if ( typeof val === \"number\" ) {\n\t\t\t\tval += \"\";\n\n\t\t\t} else if ( Array.isArray( val ) ) {\n\t\t\t\tval = jQuery.map( val, function( value ) {\n\t\t\t\t\treturn value == null ? \"\" : value + \"\";\n\t\t\t\t} );\n\t\t\t}\n\n\t\t\thooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];\n\n\t\t\t// If set returns undefined, fall back to normal setting\n\t\t\tif ( !hooks || !( \"set\" in hooks ) || hooks.set( this, val, \"value\" ) === undefined ) {\n\t\t\t\tthis.value = val;\n\t\t\t}\n\t\t} );\n\t}\n} );\n\njQuery.extend( {\n\tvalHooks: {\n\t\toption: {\n\t\t\tget: function( elem ) {\n\n\t\t\t\tvar val = jQuery.find.attr( elem, \"value\" );\n\t\t\t\treturn val != null ?\n\t\t\t\t\tval :\n\n\t\t\t\t\t// Support: IE <=10 - 11 only\n\t\t\t\t\t// option.text throws exceptions (#14686, #14858)\n\t\t\t\t\t// Strip and collapse whitespace\n\t\t\t\t\t// https://html.spec.whatwg.org/#strip-and-collapse-whitespace\n\t\t\t\t\tstripAndCollapse( jQuery.text( elem ) );\n\t\t\t}\n\t\t},\n\t\tselect: {\n\t\t\tget: function( elem ) {\n\t\t\t\tvar value, option, i,\n\t\t\t\t\toptions = elem.options,\n\t\t\t\t\tindex = elem.selectedIndex,\n\t\t\t\t\tone = elem.type === \"select-one\",\n\t\t\t\t\tvalues = one ? null : [],\n\t\t\t\t\tmax = one ? index + 1 : options.length;\n\n\t\t\t\tif ( index < 0 ) {\n\t\t\t\t\ti = max;\n\n\t\t\t\t} else {\n\t\t\t\t\ti = one ? index : 0;\n\t\t\t\t}\n\n\t\t\t\t// Loop through all the selected options\n\t\t\t\tfor ( ; i < max; i++ ) {\n\t\t\t\t\toption = options[ i ];\n\n\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t// IE8-9 doesn't update selected after form reset (#2551)\n\t\t\t\t\tif ( ( option.selected || i === index ) &&\n\n\t\t\t\t\t\t\t// Don't return options that are disabled or in a disabled optgroup\n\t\t\t\t\t\t\t!option.disabled &&\n\t\t\t\t\t\t\t( !option.parentNode.disabled ||\n\t\t\t\t\t\t\t\t!nodeName( option.parentNode, \"optgroup\" ) ) ) {\n\n\t\t\t\t\t\t// Get the specific value for the option\n\t\t\t\t\t\tvalue = jQuery( option ).val();\n\n\t\t\t\t\t\t// We don't need an array for one selects\n\t\t\t\t\t\tif ( one ) {\n\t\t\t\t\t\t\treturn value;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// Multi-Selects return an array\n\t\t\t\t\t\tvalues.push( value );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\treturn values;\n\t\t\t},\n\n\t\t\tset: function( elem, value ) {\n\t\t\t\tvar optionSet, option,\n\t\t\t\t\toptions = elem.options,\n\t\t\t\t\tvalues = jQuery.makeArray( value ),\n\t\t\t\t\ti = options.length;\n\n\t\t\t\twhile ( i-- ) {\n\t\t\t\t\toption = options[ i ];\n\n\t\t\t\t\t/* eslint-disable no-cond-assign */\n\n\t\t\t\t\tif ( option.selected =\n\t\t\t\t\t\tjQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1\n\t\t\t\t\t) {\n\t\t\t\t\t\toptionSet = true;\n\t\t\t\t\t}\n\n\t\t\t\t\t/* eslint-enable no-cond-assign */\n\t\t\t\t}\n\n\t\t\t\t// Force browsers to behave consistently when non-matching value is set\n\t\t\t\tif ( !optionSet ) {\n\t\t\t\t\telem.selectedIndex = -1;\n\t\t\t\t}\n\t\t\t\treturn values;\n\t\t\t}\n\t\t}\n\t}\n} );\n\n// Radios and checkboxes getter/setter\njQuery.each( [ \"radio\", \"checkbox\" ], function() {\n\tjQuery.valHooks[ this ] = {\n\t\tset: function( elem, value ) {\n\t\t\tif ( Array.isArray( value ) ) {\n\t\t\t\treturn ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );\n\t\t\t}\n\t\t}\n\t};\n\tif ( !support.checkOn ) {\n\t\tjQuery.valHooks[ this ].get = function( elem ) {\n\t\t\treturn elem.getAttribute( \"value\" ) === null ? \"on\" : elem.value;\n\t\t};\n\t}\n} );\n\n\n\n\n// Return jQuery for attributes-only inclusion\n\n\nsupport.focusin = \"onfocusin\" in window;\n\n\nvar rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,\n\tstopPropagationCallback = function( e ) {\n\t\te.stopPropagation();\n\t};\n\njQuery.extend( jQuery.event, {\n\n\ttrigger: function( event, data, elem, onlyHandlers ) {\n\n\t\tvar i, cur, tmp, bubbleType, ontype, handle, special, lastElement,\n\t\t\teventPath = [ elem || document ],\n\t\t\ttype = hasOwn.call( event, \"type\" ) ? event.type : event,\n\t\t\tnamespaces = hasOwn.call( event, \"namespace\" ) ? event.namespace.split( \".\" ) : [];\n\n\t\tcur = lastElement = tmp = elem = elem || document;\n\n\t\t// Don't do events on text and comment nodes\n\t\tif ( elem.nodeType === 3 || elem.nodeType === 8 ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// focus/blur morphs to focusin/out; ensure we're not firing them right now\n\t\tif ( rfocusMorph.test( type + jQuery.event.triggered ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\tif ( type.indexOf( \".\" ) > -1 ) {\n\n\t\t\t// Namespaced trigger; create a regexp to match event type in handle()\n\t\t\tnamespaces = type.split( \".\" );\n\t\t\ttype = namespaces.shift();\n\t\t\tnamespaces.sort();\n\t\t}\n\t\tontype = type.indexOf( \":\" ) < 0 && \"on\" + type;\n\n\t\t// Caller can pass in a jQuery.Event object, Object, or just an event type string\n\t\tevent = event[ jQuery.expando ] ?\n\t\t\tevent :\n\t\t\tnew jQuery.Event( type, typeof event === \"object\" && event );\n\n\t\t// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)\n\t\tevent.isTrigger = onlyHandlers ? 2 : 3;\n\t\tevent.namespace = namespaces.join( \".\" );\n\t\tevent.rnamespace = event.namespace ?\n\t\t\tnew RegExp( \"(^|\\\\.)\" + namespaces.join( \"\\\\.(?:.*\\\\.|)\" ) + \"(\\\\.|$)\" ) :\n\t\t\tnull;\n\n\t\t// Clean up the event in case it is being reused\n\t\tevent.result = undefined;\n\t\tif ( !event.target ) {\n\t\t\tevent.target = elem;\n\t\t}\n\n\t\t// Clone any incoming data and prepend the event, creating the handler arg list\n\t\tdata = data == null ?\n\t\t\t[ event ] :\n\t\t\tjQuery.makeArray( data, [ event ] );\n\n\t\t// Allow special events to draw outside the lines\n\t\tspecial = jQuery.event.special[ type ] || {};\n\t\tif ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {\n\t\t\treturn;\n\t\t}\n\n\t\t// Determine event propagation path in advance, per W3C events spec (#9951)\n\t\t// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)\n\t\tif ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {\n\n\t\t\tbubbleType = special.delegateType || type;\n\t\t\tif ( !rfocusMorph.test( bubbleType + type ) ) {\n\t\t\t\tcur = cur.parentNode;\n\t\t\t}\n\t\t\tfor ( ; cur; cur = cur.parentNode ) {\n\t\t\t\teventPath.push( cur );\n\t\t\t\ttmp = cur;\n\t\t\t}\n\n\t\t\t// Only add window if we got to document (e.g., not plain obj or detached DOM)\n\t\t\tif ( tmp === ( elem.ownerDocument || document ) ) {\n\t\t\t\teventPath.push( tmp.defaultView || tmp.parentWindow || window );\n\t\t\t}\n\t\t}\n\n\t\t// Fire handlers on the event path\n\t\ti = 0;\n\t\twhile ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {\n\t\t\tlastElement = cur;\n\t\t\tevent.type = i > 1 ?\n\t\t\t\tbubbleType :\n\t\t\t\tspecial.bindType || type;\n\n\t\t\t// jQuery handler\n\t\t\thandle = (\n\t\t\t\t\tdataPriv.get( cur, \"events\" ) || Object.create( null )\n\t\t\t\t)[ event.type ] &&\n\t\t\t\tdataPriv.get( cur, \"handle\" );\n\t\t\tif ( handle ) {\n\t\t\t\thandle.apply( cur, data );\n\t\t\t}\n\n\t\t\t// Native handler\n\t\t\thandle = ontype && cur[ ontype ];\n\t\t\tif ( handle && handle.apply && acceptData( cur ) ) {\n\t\t\t\tevent.result = handle.apply( cur, data );\n\t\t\t\tif ( event.result === false ) {\n\t\t\t\t\tevent.preventDefault();\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tevent.type = type;\n\n\t\t// If nobody prevented the default action, do it now\n\t\tif ( !onlyHandlers && !event.isDefaultPrevented() ) {\n\n\t\t\tif ( ( !special._default ||\n\t\t\t\tspecial._default.apply( eventPath.pop(), data ) === false ) &&\n\t\t\t\tacceptData( elem ) ) {\n\n\t\t\t\t// Call a native DOM method on the target with the same name as the event.\n\t\t\t\t// Don't do default actions on window, that's where global variables be (#6170)\n\t\t\t\tif ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {\n\n\t\t\t\t\t// Don't re-trigger an onFOO event when we call its FOO() method\n\t\t\t\t\ttmp = elem[ ontype ];\n\n\t\t\t\t\tif ( tmp ) {\n\t\t\t\t\t\telem[ ontype ] = null;\n\t\t\t\t\t}\n\n\t\t\t\t\t// Prevent re-triggering of the same event, since we already bubbled it above\n\t\t\t\t\tjQuery.event.triggered = type;\n\n\t\t\t\t\tif ( event.isPropagationStopped() ) {\n\t\t\t\t\t\tlastElement.addEventListener( type, stopPropagationCallback );\n\t\t\t\t\t}\n\n\t\t\t\t\telem[ type ]();\n\n\t\t\t\t\tif ( event.isPropagationStopped() ) {\n\t\t\t\t\t\tlastElement.removeEventListener( type, stopPropagationCallback );\n\t\t\t\t\t}\n\n\t\t\t\t\tjQuery.event.triggered = undefined;\n\n\t\t\t\t\tif ( tmp ) {\n\t\t\t\t\t\telem[ ontype ] = tmp;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn event.result;\n\t},\n\n\t// Piggyback on a donor event to simulate a different one\n\t// Used only for `focus(in | out)` events\n\tsimulate: function( type, elem, event ) {\n\t\tvar e = jQuery.extend(\n\t\t\tnew jQuery.Event(),\n\t\t\tevent,\n\t\t\t{\n\t\t\t\ttype: type,\n\t\t\t\tisSimulated: true\n\t\t\t}\n\t\t);\n\n\t\tjQuery.event.trigger( e, null, elem );\n\t}\n\n} );\n\njQuery.fn.extend( {\n\n\ttrigger: function( type, data ) {\n\t\treturn this.each( function() {\n\t\t\tjQuery.event.trigger( type, data, this );\n\t\t} );\n\t},\n\ttriggerHandler: function( type, data ) {\n\t\tvar elem = this[ 0 ];\n\t\tif ( elem ) {\n\t\t\treturn jQuery.event.trigger( type, data, elem, true );\n\t\t}\n\t}\n} );\n\n\n// Support: Firefox <=44\n// Firefox doesn't have focus(in | out) events\n// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787\n//\n// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1\n// focus(in | out) events fire after focus & blur events,\n// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order\n// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857\nif ( !support.focusin ) {\n\tjQuery.each( { focus: \"focusin\", blur: \"focusout\" }, function( orig, fix ) {\n\n\t\t// Attach a single capturing handler on the document while someone wants focusin/focusout\n\t\tvar handler = function( event ) {\n\t\t\tjQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );\n\t\t};\n\n\t\tjQuery.event.special[ fix ] = {\n\t\t\tsetup: function() {\n\n\t\t\t\t// Handle: regular nodes (via `this.ownerDocument`), window\n\t\t\t\t// (via `this.document`) & document (via `this`).\n\t\t\t\tvar doc = this.ownerDocument || this.document || this,\n\t\t\t\t\tattaches = dataPriv.access( doc, fix );\n\n\t\t\t\tif ( !attaches ) {\n\t\t\t\t\tdoc.addEventListener( orig, handler, true );\n\t\t\t\t}\n\t\t\t\tdataPriv.access( doc, fix, ( attaches || 0 ) + 1 );\n\t\t\t},\n\t\t\tteardown: function() {\n\t\t\t\tvar doc = this.ownerDocument || this.document || this,\n\t\t\t\t\tattaches = dataPriv.access( doc, fix ) - 1;\n\n\t\t\t\tif ( !attaches ) {\n\t\t\t\t\tdoc.removeEventListener( orig, handler, true );\n\t\t\t\t\tdataPriv.remove( doc, fix );\n\n\t\t\t\t} else {\n\t\t\t\t\tdataPriv.access( doc, fix, attaches );\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t} );\n}\nvar location = window.location;\n\nvar nonce = { guid: Date.now() };\n\nvar rquery = ( /\\?/ );\n\n\n\n// Cross-browser xml parsing\njQuery.parseXML = function( data ) {\n\tvar xml;\n\tif ( !data || typeof data !== \"string\" ) {\n\t\treturn null;\n\t}\n\n\t// Support: IE 9 - 11 only\n\t// IE throws on parseFromString with invalid input.\n\ttry {\n\t\txml = ( new window.DOMParser() ).parseFromString( data, \"text/xml\" );\n\t} catch ( e ) {\n\t\txml = undefined;\n\t}\n\n\tif ( !xml || xml.getElementsByTagName( \"parsererror\" ).length ) {\n\t\tjQuery.error( \"Invalid XML: \" + data );\n\t}\n\treturn xml;\n};\n\n\nvar\n\trbracket = /\\[\\]$/,\n\trCRLF = /\\r?\\n/g,\n\trsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,\n\trsubmittable = /^(?:input|select|textarea|keygen)/i;\n\nfunction buildParams( prefix, obj, traditional, add ) {\n\tvar name;\n\n\tif ( Array.isArray( obj ) ) {\n\n\t\t// Serialize array item.\n\t\tjQuery.each( obj, function( i, v ) {\n\t\t\tif ( traditional || rbracket.test( prefix ) ) {\n\n\t\t\t\t// Treat each array item as a scalar.\n\t\t\t\tadd( prefix, v );\n\n\t\t\t} else {\n\n\t\t\t\t// Item is non-scalar (array or object), encode its numeric index.\n\t\t\t\tbuildParams(\n\t\t\t\t\tprefix + \"[\" + ( typeof v === \"object\" && v != null ? i : \"\" ) + \"]\",\n\t\t\t\t\tv,\n\t\t\t\t\ttraditional,\n\t\t\t\t\tadd\n\t\t\t\t);\n\t\t\t}\n\t\t} );\n\n\t} else if ( !traditional && toType( obj ) === \"object\" ) {\n\n\t\t// Serialize object item.\n\t\tfor ( name in obj ) {\n\t\t\tbuildParams( prefix + \"[\" + name + \"]\", obj[ name ], traditional, add );\n\t\t}\n\n\t} else {\n\n\t\t// Serialize scalar item.\n\t\tadd( prefix, obj );\n\t}\n}\n\n// Serialize an array of form elements or a set of\n// key/values into a query string\njQuery.param = function( a, traditional ) {\n\tvar prefix,\n\t\ts = [],\n\t\tadd = function( key, valueOrFunction ) {\n\n\t\t\t// If value is a function, invoke it and use its return value\n\t\t\tvar value = isFunction( valueOrFunction ) ?\n\t\t\t\tvalueOrFunction() :\n\t\t\t\tvalueOrFunction;\n\n\t\t\ts[ s.length ] = encodeURIComponent( key ) + \"=\" +\n\t\t\t\tencodeURIComponent( value == null ? \"\" : value );\n\t\t};\n\n\tif ( a == null ) {\n\t\treturn \"\";\n\t}\n\n\t// If an array was passed in, assume that it is an array of form elements.\n\tif ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {\n\n\t\t// Serialize the form elements\n\t\tjQuery.each( a, function() {\n\t\t\tadd( this.name, this.value );\n\t\t} );\n\n\t} else {\n\n\t\t// If traditional, encode the \"old\" way (the way 1.3.2 or older\n\t\t// did it), otherwise encode params recursively.\n\t\tfor ( prefix in a ) {\n\t\t\tbuildParams( prefix, a[ prefix ], traditional, add );\n\t\t}\n\t}\n\n\t// Return the resulting serialization\n\treturn s.join( \"&\" );\n};\n\njQuery.fn.extend( {\n\tserialize: function() {\n\t\treturn jQuery.param( this.serializeArray() );\n\t},\n\tserializeArray: function() {\n\t\treturn this.map( function() {\n\n\t\t\t// Can add propHook for \"elements\" to filter or add form elements\n\t\t\tvar elements = jQuery.prop( this, \"elements\" );\n\t\t\treturn elements ? jQuery.makeArray( elements ) : this;\n\t\t} )\n\t\t.filter( function() {\n\t\t\tvar type = this.type;\n\n\t\t\t// Use .is( \":disabled\" ) so that fieldset[disabled] works\n\t\t\treturn this.name && !jQuery( this ).is( \":disabled\" ) &&\n\t\t\t\trsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&\n\t\t\t\t( this.checked || !rcheckableType.test( type ) );\n\t\t} )\n\t\t.map( function( _i, elem ) {\n\t\t\tvar val = jQuery( this ).val();\n\n\t\t\tif ( val == null ) {\n\t\t\t\treturn null;\n\t\t\t}\n\n\t\t\tif ( Array.isArray( val ) ) {\n\t\t\t\treturn jQuery.map( val, function( val ) {\n\t\t\t\t\treturn { name: elem.name, value: val.replace( rCRLF, \"\\r\\n\" ) };\n\t\t\t\t} );\n\t\t\t}\n\n\t\t\treturn { name: elem.name, value: val.replace( rCRLF, \"\\r\\n\" ) };\n\t\t} ).get();\n\t}\n} );\n\n\nvar\n\tr20 = /%20/g,\n\trhash = /#.*$/,\n\trantiCache = /([?&])_=[^&]*/,\n\trheaders = /^(.*?):[ \\t]*([^\\r\\n]*)$/mg,\n\n\t// #7653, #8125, #8152: local protocol detection\n\trlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,\n\trnoContent = /^(?:GET|HEAD)$/,\n\trprotocol = /^\\/\\//,\n\n\t/* Prefilters\n\t * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)\n\t * 2) These are called:\n\t * - BEFORE asking for a transport\n\t * - AFTER param serialization (s.data is a string if s.processData is true)\n\t * 3) key is the dataType\n\t * 4) the catchall symbol \"*\" can be used\n\t * 5) execution will start with transport dataType and THEN continue down to \"*\" if needed\n\t */\n\tprefilters = {},\n\n\t/* Transports bindings\n\t * 1) key is the dataType\n\t * 2) the catchall symbol \"*\" can be used\n\t * 3) selection will start with transport dataType and THEN go to \"*\" if needed\n\t */\n\ttransports = {},\n\n\t// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression\n\tallTypes = \"*/\".concat( \"*\" ),\n\n\t// Anchor tag for parsing the document origin\n\toriginAnchor = document.createElement( \"a\" );\n\toriginAnchor.href = location.href;\n\n// Base \"constructor\" for jQuery.ajaxPrefilter and jQuery.ajaxTransport\nfunction addToPrefiltersOrTransports( structure ) {\n\n\t// dataTypeExpression is optional and defaults to \"*\"\n\treturn function( dataTypeExpression, func ) {\n\n\t\tif ( typeof dataTypeExpression !== \"string\" ) {\n\t\t\tfunc = dataTypeExpression;\n\t\t\tdataTypeExpression = \"*\";\n\t\t}\n\n\t\tvar dataType,\n\t\t\ti = 0,\n\t\t\tdataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];\n\n\t\tif ( isFunction( func ) ) {\n\n\t\t\t// For each dataType in the dataTypeExpression\n\t\t\twhile ( ( dataType = dataTypes[ i++ ] ) ) {\n\n\t\t\t\t// Prepend if requested\n\t\t\t\tif ( dataType[ 0 ] === \"+\" ) {\n\t\t\t\t\tdataType = dataType.slice( 1 ) || \"*\";\n\t\t\t\t\t( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );\n\n\t\t\t\t// Otherwise append\n\t\t\t\t} else {\n\t\t\t\t\t( structure[ dataType ] = structure[ dataType ] || [] ).push( func );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n}\n\n// Base inspection function for prefilters and transports\nfunction inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {\n\n\tvar inspected = {},\n\t\tseekingTransport = ( structure === transports );\n\n\tfunction inspect( dataType ) {\n\t\tvar selected;\n\t\tinspected[ dataType ] = true;\n\t\tjQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {\n\t\t\tvar dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );\n\t\t\tif ( typeof dataTypeOrTransport === \"string\" &&\n\t\t\t\t!seekingTransport && !inspected[ dataTypeOrTransport ] ) {\n\n\t\t\t\toptions.dataTypes.unshift( dataTypeOrTransport );\n\t\t\t\tinspect( dataTypeOrTransport );\n\t\t\t\treturn false;\n\t\t\t} else if ( seekingTransport ) {\n\t\t\t\treturn !( selected = dataTypeOrTransport );\n\t\t\t}\n\t\t} );\n\t\treturn selected;\n\t}\n\n\treturn inspect( options.dataTypes[ 0 ] ) || !inspected[ \"*\" ] && inspect( \"*\" );\n}\n\n// A special extend for ajax options\n// that takes \"flat\" options (not to be deep extended)\n// Fixes #9887\nfunction ajaxExtend( target, src ) {\n\tvar key, deep,\n\t\tflatOptions = jQuery.ajaxSettings.flatOptions || {};\n\n\tfor ( key in src ) {\n\t\tif ( src[ key ] !== undefined ) {\n\t\t\t( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];\n\t\t}\n\t}\n\tif ( deep ) {\n\t\tjQuery.extend( true, target, deep );\n\t}\n\n\treturn target;\n}\n\n/* Handles responses to an ajax request:\n * - finds the right dataType (mediates between content-type and expected dataType)\n * - returns the corresponding response\n */\nfunction ajaxHandleResponses( s, jqXHR, responses ) {\n\n\tvar ct, type, finalDataType, firstDataType,\n\t\tcontents = s.contents,\n\t\tdataTypes = s.dataTypes;\n\n\t// Remove auto dataType and get content-type in the process\n\twhile ( dataTypes[ 0 ] === \"*\" ) {\n\t\tdataTypes.shift();\n\t\tif ( ct === undefined ) {\n\t\t\tct = s.mimeType || jqXHR.getResponseHeader( \"Content-Type\" );\n\t\t}\n\t}\n\n\t// Check if we're dealing with a known content-type\n\tif ( ct ) {\n\t\tfor ( type in contents ) {\n\t\t\tif ( contents[ type ] && contents[ type ].test( ct ) ) {\n\t\t\t\tdataTypes.unshift( type );\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\t// Check to see if we have a response for the expected dataType\n\tif ( dataTypes[ 0 ] in responses ) {\n\t\tfinalDataType = dataTypes[ 0 ];\n\t} else {\n\n\t\t// Try convertible dataTypes\n\t\tfor ( type in responses ) {\n\t\t\tif ( !dataTypes[ 0 ] || s.converters[ type + \" \" + dataTypes[ 0 ] ] ) {\n\t\t\t\tfinalDataType = type;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif ( !firstDataType ) {\n\t\t\t\tfirstDataType = type;\n\t\t\t}\n\t\t}\n\n\t\t// Or just use first one\n\t\tfinalDataType = finalDataType || firstDataType;\n\t}\n\n\t// If we found a dataType\n\t// We add the dataType to the list if needed\n\t// and return the corresponding response\n\tif ( finalDataType ) {\n\t\tif ( finalDataType !== dataTypes[ 0 ] ) {\n\t\t\tdataTypes.unshift( finalDataType );\n\t\t}\n\t\treturn responses[ finalDataType ];\n\t}\n}\n\n/* Chain conversions given the request and the original response\n * Also sets the responseXXX fields on the jqXHR instance\n */\nfunction ajaxConvert( s, response, jqXHR, isSuccess ) {\n\tvar conv2, current, conv, tmp, prev,\n\t\tconverters = {},\n\n\t\t// Work with a copy of dataTypes in case we need to modify it for conversion\n\t\tdataTypes = s.dataTypes.slice();\n\n\t// Create converters map with lowercased keys\n\tif ( dataTypes[ 1 ] ) {\n\t\tfor ( conv in s.converters ) {\n\t\t\tconverters[ conv.toLowerCase() ] = s.converters[ conv ];\n\t\t}\n\t}\n\n\tcurrent = dataTypes.shift();\n\n\t// Convert to each sequential dataType\n\twhile ( current ) {\n\n\t\tif ( s.responseFields[ current ] ) {\n\t\t\tjqXHR[ s.responseFields[ current ] ] = response;\n\t\t}\n\n\t\t// Apply the dataFilter if provided\n\t\tif ( !prev && isSuccess && s.dataFilter ) {\n\t\t\tresponse = s.dataFilter( response, s.dataType );\n\t\t}\n\n\t\tprev = current;\n\t\tcurrent = dataTypes.shift();\n\n\t\tif ( current ) {\n\n\t\t\t// There's only work to do if current dataType is non-auto\n\t\t\tif ( current === \"*\" ) {\n\n\t\t\t\tcurrent = prev;\n\n\t\t\t// Convert response if prev dataType is non-auto and differs from current\n\t\t\t} else if ( prev !== \"*\" && prev !== current ) {\n\n\t\t\t\t// Seek a direct converter\n\t\t\t\tconv = converters[ prev + \" \" + current ] || converters[ \"* \" + current ];\n\n\t\t\t\t// If none found, seek a pair\n\t\t\t\tif ( !conv ) {\n\t\t\t\t\tfor ( conv2 in converters ) {\n\n\t\t\t\t\t\t// If conv2 outputs current\n\t\t\t\t\t\ttmp = conv2.split( \" \" );\n\t\t\t\t\t\tif ( tmp[ 1 ] === current ) {\n\n\t\t\t\t\t\t\t// If prev can be converted to accepted input\n\t\t\t\t\t\t\tconv = converters[ prev + \" \" + tmp[ 0 ] ] ||\n\t\t\t\t\t\t\t\tconverters[ \"* \" + tmp[ 0 ] ];\n\t\t\t\t\t\t\tif ( conv ) {\n\n\t\t\t\t\t\t\t\t// Condense equivalence converters\n\t\t\t\t\t\t\t\tif ( conv === true ) {\n\t\t\t\t\t\t\t\t\tconv = converters[ conv2 ];\n\n\t\t\t\t\t\t\t\t// Otherwise, insert the intermediate dataType\n\t\t\t\t\t\t\t\t} else if ( converters[ conv2 ] !== true ) {\n\t\t\t\t\t\t\t\t\tcurrent = tmp[ 0 ];\n\t\t\t\t\t\t\t\t\tdataTypes.unshift( tmp[ 1 ] );\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Apply converter (if not an equivalence)\n\t\t\t\tif ( conv !== true ) {\n\n\t\t\t\t\t// Unless errors are allowed to bubble, catch and return them\n\t\t\t\t\tif ( conv && s.throws ) {\n\t\t\t\t\t\tresponse = conv( response );\n\t\t\t\t\t} else {\n\t\t\t\t\t\ttry {\n\t\t\t\t\t\t\tresponse = conv( response );\n\t\t\t\t\t\t} catch ( e ) {\n\t\t\t\t\t\t\treturn {\n\t\t\t\t\t\t\t\tstate: \"parsererror\",\n\t\t\t\t\t\t\t\terror: conv ? e : \"No conversion from \" + prev + \" to \" + current\n\t\t\t\t\t\t\t};\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn { state: \"success\", data: response };\n}\n\njQuery.extend( {\n\n\t// Counter for holding the number of active queries\n\tactive: 0,\n\n\t// Last-Modified header cache for next request\n\tlastModified: {},\n\tetag: {},\n\n\tajaxSettings: {\n\t\turl: location.href,\n\t\ttype: \"GET\",\n\t\tisLocal: rlocalProtocol.test( location.protocol ),\n\t\tglobal: true,\n\t\tprocessData: true,\n\t\tasync: true,\n\t\tcontentType: \"application/x-www-form-urlencoded; charset=UTF-8\",\n\n\t\t/*\n\t\ttimeout: 0,\n\t\tdata: null,\n\t\tdataType: null,\n\t\tusername: null,\n\t\tpassword: null,\n\t\tcache: null,\n\t\tthrows: false,\n\t\ttraditional: false,\n\t\theaders: {},\n\t\t*/\n\n\t\taccepts: {\n\t\t\t\"*\": allTypes,\n\t\t\ttext: \"text/plain\",\n\t\t\thtml: \"text/html\",\n\t\t\txml: \"application/xml, text/xml\",\n\t\t\tjson: \"application/json, text/javascript\"\n\t\t},\n\n\t\tcontents: {\n\t\t\txml: /\\bxml\\b/,\n\t\t\thtml: /\\bhtml/,\n\t\t\tjson: /\\bjson\\b/\n\t\t},\n\n\t\tresponseFields: {\n\t\t\txml: \"responseXML\",\n\t\t\ttext: \"responseText\",\n\t\t\tjson: \"responseJSON\"\n\t\t},\n\n\t\t// Data converters\n\t\t// Keys separate source (or catchall \"*\") and destination types with a single space\n\t\tconverters: {\n\n\t\t\t// Convert anything to text\n\t\t\t\"* text\": String,\n\n\t\t\t// Text to html (true = no transformation)\n\t\t\t\"text html\": true,\n\n\t\t\t// Evaluate text as a json expression\n\t\t\t\"text json\": JSON.parse,\n\n\t\t\t// Parse text as xml\n\t\t\t\"text xml\": jQuery.parseXML\n\t\t},\n\n\t\t// For options that shouldn't be deep extended:\n\t\t// you can add your own custom options here if\n\t\t// and when you create one that shouldn't be\n\t\t// deep extended (see ajaxExtend)\n\t\tflatOptions: {\n\t\t\turl: true,\n\t\t\tcontext: true\n\t\t}\n\t},\n\n\t// Creates a full fledged settings object into target\n\t// with both ajaxSettings and settings fields.\n\t// If target is omitted, writes into ajaxSettings.\n\tajaxSetup: function( target, settings ) {\n\t\treturn settings ?\n\n\t\t\t// Building a settings object\n\t\t\tajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :\n\n\t\t\t// Extending ajaxSettings\n\t\t\tajaxExtend( jQuery.ajaxSettings, target );\n\t},\n\n\tajaxPrefilter: addToPrefiltersOrTransports( prefilters ),\n\tajaxTransport: addToPrefiltersOrTransports( transports ),\n\n\t// Main method\n\tajax: function( url, options ) {\n\n\t\t// If url is an object, simulate pre-1.5 signature\n\t\tif ( typeof url === \"object\" ) {\n\t\t\toptions = url;\n\t\t\turl = undefined;\n\t\t}\n\n\t\t// Force options to be an object\n\t\toptions = options || {};\n\n\t\tvar transport,\n\n\t\t\t// URL without anti-cache param\n\t\t\tcacheURL,\n\n\t\t\t// Response headers\n\t\t\tresponseHeadersString,\n\t\t\tresponseHeaders,\n\n\t\t\t// timeout handle\n\t\t\ttimeoutTimer,\n\n\t\t\t// Url cleanup var\n\t\t\turlAnchor,\n\n\t\t\t// Request state (becomes false upon send and true upon completion)\n\t\t\tcompleted,\n\n\t\t\t// To know if global events are to be dispatched\n\t\t\tfireGlobals,\n\n\t\t\t// Loop variable\n\t\t\ti,\n\n\t\t\t// uncached part of the url\n\t\t\tuncached,\n\n\t\t\t// Create the final options object\n\t\t\ts = jQuery.ajaxSetup( {}, options ),\n\n\t\t\t// Callbacks context\n\t\t\tcallbackContext = s.context || s,\n\n\t\t\t// Context for global events is callbackContext if it is a DOM node or jQuery collection\n\t\t\tglobalEventContext = s.context &&\n\t\t\t\t( callbackContext.nodeType || callbackContext.jquery ) ?\n\t\t\t\t\tjQuery( callbackContext ) :\n\t\t\t\t\tjQuery.event,\n\n\t\t\t// Deferreds\n\t\t\tdeferred = jQuery.Deferred(),\n\t\t\tcompleteDeferred = jQuery.Callbacks( \"once memory\" ),\n\n\t\t\t// Status-dependent callbacks\n\t\t\tstatusCode = s.statusCode || {},\n\n\t\t\t// Headers (they are sent all at once)\n\t\t\trequestHeaders = {},\n\t\t\trequestHeadersNames = {},\n\n\t\t\t// Default abort message\n\t\t\tstrAbort = \"canceled\",\n\n\t\t\t// Fake xhr\n\t\t\tjqXHR = {\n\t\t\t\treadyState: 0,\n\n\t\t\t\t// Builds headers hashtable if needed\n\t\t\t\tgetResponseHeader: function( key ) {\n\t\t\t\t\tvar match;\n\t\t\t\t\tif ( completed ) {\n\t\t\t\t\t\tif ( !responseHeaders ) {\n\t\t\t\t\t\t\tresponseHeaders = {};\n\t\t\t\t\t\t\twhile ( ( match = rheaders.exec( responseHeadersString ) ) ) {\n\t\t\t\t\t\t\t\tresponseHeaders[ match[ 1 ].toLowerCase() + \" \" ] =\n\t\t\t\t\t\t\t\t\t( responseHeaders[ match[ 1 ].toLowerCase() + \" \" ] || [] )\n\t\t\t\t\t\t\t\t\t\t.concat( match[ 2 ] );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t\tmatch = responseHeaders[ key.toLowerCase() + \" \" ];\n\t\t\t\t\t}\n\t\t\t\t\treturn match == null ? null : match.join( \", \" );\n\t\t\t\t},\n\n\t\t\t\t// Raw string\n\t\t\t\tgetAllResponseHeaders: function() {\n\t\t\t\t\treturn completed ? responseHeadersString : null;\n\t\t\t\t},\n\n\t\t\t\t// Caches the header\n\t\t\t\tsetRequestHeader: function( name, value ) {\n\t\t\t\t\tif ( completed == null ) {\n\t\t\t\t\t\tname = requestHeadersNames[ name.toLowerCase() ] =\n\t\t\t\t\t\t\trequestHeadersNames[ name.toLowerCase() ] || name;\n\t\t\t\t\t\trequestHeaders[ name ] = value;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Overrides response content-type header\n\t\t\t\toverrideMimeType: function( type ) {\n\t\t\t\t\tif ( completed == null ) {\n\t\t\t\t\t\ts.mimeType = type;\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Status-dependent callbacks\n\t\t\t\tstatusCode: function( map ) {\n\t\t\t\t\tvar code;\n\t\t\t\t\tif ( map ) {\n\t\t\t\t\t\tif ( completed ) {\n\n\t\t\t\t\t\t\t// Execute the appropriate callbacks\n\t\t\t\t\t\t\tjqXHR.always( map[ jqXHR.status ] );\n\t\t\t\t\t\t} else {\n\n\t\t\t\t\t\t\t// Lazy-add the new callbacks in a way that preserves old ones\n\t\t\t\t\t\t\tfor ( code in map ) {\n\t\t\t\t\t\t\t\tstatusCode[ code ] = [ statusCode[ code ], map[ code ] ];\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\treturn this;\n\t\t\t\t},\n\n\t\t\t\t// Cancel the request\n\t\t\t\tabort: function( statusText ) {\n\t\t\t\t\tvar finalText = statusText || strAbort;\n\t\t\t\t\tif ( transport ) {\n\t\t\t\t\t\ttransport.abort( finalText );\n\t\t\t\t\t}\n\t\t\t\t\tdone( 0, finalText );\n\t\t\t\t\treturn this;\n\t\t\t\t}\n\t\t\t};\n\n\t\t// Attach deferreds\n\t\tdeferred.promise( jqXHR );\n\n\t\t// Add protocol if not provided (prefilters might expect it)\n\t\t// Handle falsy url in the settings object (#10093: consistency with old signature)\n\t\t// We also use the url parameter if available\n\t\ts.url = ( ( url || s.url || location.href ) + \"\" )\n\t\t\t.replace( rprotocol, location.protocol + \"//\" );\n\n\t\t// Alias method option to type as per ticket #12004\n\t\ts.type = options.method || options.type || s.method || s.type;\n\n\t\t// Extract dataTypes list\n\t\ts.dataTypes = ( s.dataType || \"*\" ).toLowerCase().match( rnothtmlwhite ) || [ \"\" ];\n\n\t\t// A cross-domain request is in order when the origin doesn't match the current origin.\n\t\tif ( s.crossDomain == null ) {\n\t\t\turlAnchor = document.createElement( \"a\" );\n\n\t\t\t// Support: IE <=8 - 11, Edge 12 - 15\n\t\t\t// IE throws exception on accessing the href property if url is malformed,\n\t\t\t// e.g. http://example.com:80x/\n\t\t\ttry {\n\t\t\t\turlAnchor.href = s.url;\n\n\t\t\t\t// Support: IE <=8 - 11 only\n\t\t\t\t// Anchor's host property isn't correctly set when s.url is relative\n\t\t\t\turlAnchor.href = urlAnchor.href;\n\t\t\t\ts.crossDomain = originAnchor.protocol + \"//\" + originAnchor.host !==\n\t\t\t\t\turlAnchor.protocol + \"//\" + urlAnchor.host;\n\t\t\t} catch ( e ) {\n\n\t\t\t\t// If there is an error parsing the URL, assume it is crossDomain,\n\t\t\t\t// it can be rejected by the transport if it is invalid\n\t\t\t\ts.crossDomain = true;\n\t\t\t}\n\t\t}\n\n\t\t// Convert data if not already a string\n\t\tif ( s.data && s.processData && typeof s.data !== \"string\" ) {\n\t\t\ts.data = jQuery.param( s.data, s.traditional );\n\t\t}\n\n\t\t// Apply prefilters\n\t\tinspectPrefiltersOrTransports( prefilters, s, options, jqXHR );\n\n\t\t// If request was aborted inside a prefilter, stop there\n\t\tif ( completed ) {\n\t\t\treturn jqXHR;\n\t\t}\n\n\t\t// We can fire global events as of now if asked to\n\t\t// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)\n\t\tfireGlobals = jQuery.event && s.global;\n\n\t\t// Watch for a new set of requests\n\t\tif ( fireGlobals && jQuery.active++ === 0 ) {\n\t\t\tjQuery.event.trigger( \"ajaxStart\" );\n\t\t}\n\n\t\t// Uppercase the type\n\t\ts.type = s.type.toUpperCase();\n\n\t\t// Determine if request has content\n\t\ts.hasContent = !rnoContent.test( s.type );\n\n\t\t// Save the URL in case we're toying with the If-Modified-Since\n\t\t// and/or If-None-Match header later on\n\t\t// Remove hash to simplify url manipulation\n\t\tcacheURL = s.url.replace( rhash, \"\" );\n\n\t\t// More options handling for requests with no content\n\t\tif ( !s.hasContent ) {\n\n\t\t\t// Remember the hash so we can put it back\n\t\t\tuncached = s.url.slice( cacheURL.length );\n\n\t\t\t// If data is available and should be processed, append data to url\n\t\t\tif ( s.data && ( s.processData || typeof s.data === \"string\" ) ) {\n\t\t\t\tcacheURL += ( rquery.test( cacheURL ) ? \"&\" : \"?\" ) + s.data;\n\n\t\t\t\t// #9682: remove data so that it's not used in an eventual retry\n\t\t\t\tdelete s.data;\n\t\t\t}\n\n\t\t\t// Add or update anti-cache param if needed\n\t\t\tif ( s.cache === false ) {\n\t\t\t\tcacheURL = cacheURL.replace( rantiCache, \"$1\" );\n\t\t\t\tuncached = ( rquery.test( cacheURL ) ? \"&\" : \"?\" ) + \"_=\" + ( nonce.guid++ ) +\n\t\t\t\t\tuncached;\n\t\t\t}\n\n\t\t\t// Put hash and anti-cache on the URL that will be requested (gh-1732)\n\t\t\ts.url = cacheURL + uncached;\n\n\t\t// Change '%20' to '+' if this is encoded form body content (gh-2658)\n\t\t} else if ( s.data && s.processData &&\n\t\t\t( s.contentType || \"\" ).indexOf( \"application/x-www-form-urlencoded\" ) === 0 ) {\n\t\t\ts.data = s.data.replace( r20, \"+\" );\n\t\t}\n\n\t\t// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.\n\t\tif ( s.ifModified ) {\n\t\t\tif ( jQuery.lastModified[ cacheURL ] ) {\n\t\t\t\tjqXHR.setRequestHeader( \"If-Modified-Since\", jQuery.lastModified[ cacheURL ] );\n\t\t\t}\n\t\t\tif ( jQuery.etag[ cacheURL ] ) {\n\t\t\t\tjqXHR.setRequestHeader( \"If-None-Match\", jQuery.etag[ cacheURL ] );\n\t\t\t}\n\t\t}\n\n\t\t// Set the correct header, if data is being sent\n\t\tif ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {\n\t\t\tjqXHR.setRequestHeader( \"Content-Type\", s.contentType );\n\t\t}\n\n\t\t// Set the Accepts header for the server, depending on the dataType\n\t\tjqXHR.setRequestHeader(\n\t\t\t\"Accept\",\n\t\t\ts.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?\n\t\t\t\ts.accepts[ s.dataTypes[ 0 ] ] +\n\t\t\t\t\t( s.dataTypes[ 0 ] !== \"*\" ? \", \" + allTypes + \"; q=0.01\" : \"\" ) :\n\t\t\t\ts.accepts[ \"*\" ]\n\t\t);\n\n\t\t// Check for headers option\n\t\tfor ( i in s.headers ) {\n\t\t\tjqXHR.setRequestHeader( i, s.headers[ i ] );\n\t\t}\n\n\t\t// Allow custom headers/mimetypes and early abort\n\t\tif ( s.beforeSend &&\n\t\t\t( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {\n\n\t\t\t// Abort if not done already and return\n\t\t\treturn jqXHR.abort();\n\t\t}\n\n\t\t// Aborting is no longer a cancellation\n\t\tstrAbort = \"abort\";\n\n\t\t// Install callbacks on deferreds\n\t\tcompleteDeferred.add( s.complete );\n\t\tjqXHR.done( s.success );\n\t\tjqXHR.fail( s.error );\n\n\t\t// Get transport\n\t\ttransport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );\n\n\t\t// If no transport, we auto-abort\n\t\tif ( !transport ) {\n\t\t\tdone( -1, \"No Transport\" );\n\t\t} else {\n\t\t\tjqXHR.readyState = 1;\n\n\t\t\t// Send global event\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( \"ajaxSend\", [ jqXHR, s ] );\n\t\t\t}\n\n\t\t\t// If request was aborted inside ajaxSend, stop there\n\t\t\tif ( completed ) {\n\t\t\t\treturn jqXHR;\n\t\t\t}\n\n\t\t\t// Timeout\n\t\t\tif ( s.async && s.timeout > 0 ) {\n\t\t\t\ttimeoutTimer = window.setTimeout( function() {\n\t\t\t\t\tjqXHR.abort( \"timeout\" );\n\t\t\t\t}, s.timeout );\n\t\t\t}\n\n\t\t\ttry {\n\t\t\t\tcompleted = false;\n\t\t\t\ttransport.send( requestHeaders, done );\n\t\t\t} catch ( e ) {\n\n\t\t\t\t// Rethrow post-completion exceptions\n\t\t\t\tif ( completed ) {\n\t\t\t\t\tthrow e;\n\t\t\t\t}\n\n\t\t\t\t// Propagate others as results\n\t\t\t\tdone( -1, e );\n\t\t\t}\n\t\t}\n\n\t\t// Callback for when everything is done\n\t\tfunction done( status, nativeStatusText, responses, headers ) {\n\t\t\tvar isSuccess, success, error, response, modified,\n\t\t\t\tstatusText = nativeStatusText;\n\n\t\t\t// Ignore repeat invocations\n\t\t\tif ( completed ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tcompleted = true;\n\n\t\t\t// Clear timeout if it exists\n\t\t\tif ( timeoutTimer ) {\n\t\t\t\twindow.clearTimeout( timeoutTimer );\n\t\t\t}\n\n\t\t\t// Dereference transport for early garbage collection\n\t\t\t// (no matter how long the jqXHR object will be used)\n\t\t\ttransport = undefined;\n\n\t\t\t// Cache response headers\n\t\t\tresponseHeadersString = headers || \"\";\n\n\t\t\t// Set readyState\n\t\t\tjqXHR.readyState = status > 0 ? 4 : 0;\n\n\t\t\t// Determine if successful\n\t\t\tisSuccess = status >= 200 && status < 300 || status === 304;\n\n\t\t\t// Get response data\n\t\t\tif ( responses ) {\n\t\t\t\tresponse = ajaxHandleResponses( s, jqXHR, responses );\n\t\t\t}\n\n\t\t\t// Use a noop converter for missing script\n\t\t\tif ( !isSuccess && jQuery.inArray( \"script\", s.dataTypes ) > -1 ) {\n\t\t\t\ts.converters[ \"text script\" ] = function() {};\n\t\t\t}\n\n\t\t\t// Convert no matter what (that way responseXXX fields are always set)\n\t\t\tresponse = ajaxConvert( s, response, jqXHR, isSuccess );\n\n\t\t\t// If successful, handle type chaining\n\t\t\tif ( isSuccess ) {\n\n\t\t\t\t// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.\n\t\t\t\tif ( s.ifModified ) {\n\t\t\t\t\tmodified = jqXHR.getResponseHeader( \"Last-Modified\" );\n\t\t\t\t\tif ( modified ) {\n\t\t\t\t\t\tjQuery.lastModified[ cacheURL ] = modified;\n\t\t\t\t\t}\n\t\t\t\t\tmodified = jqXHR.getResponseHeader( \"etag\" );\n\t\t\t\t\tif ( modified ) {\n\t\t\t\t\t\tjQuery.etag[ cacheURL ] = modified;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// if no content\n\t\t\t\tif ( status === 204 || s.type === \"HEAD\" ) {\n\t\t\t\t\tstatusText = \"nocontent\";\n\n\t\t\t\t// if not modified\n\t\t\t\t} else if ( status === 304 ) {\n\t\t\t\t\tstatusText = \"notmodified\";\n\n\t\t\t\t// If we have data, let's convert it\n\t\t\t\t} else {\n\t\t\t\t\tstatusText = response.state;\n\t\t\t\t\tsuccess = response.data;\n\t\t\t\t\terror = response.error;\n\t\t\t\t\tisSuccess = !error;\n\t\t\t\t}\n\t\t\t} else {\n\n\t\t\t\t// Extract error from statusText and normalize for non-aborts\n\t\t\t\terror = statusText;\n\t\t\t\tif ( status || !statusText ) {\n\t\t\t\t\tstatusText = \"error\";\n\t\t\t\t\tif ( status < 0 ) {\n\t\t\t\t\t\tstatus = 0;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Set data for the fake xhr object\n\t\t\tjqXHR.status = status;\n\t\t\tjqXHR.statusText = ( nativeStatusText || statusText ) + \"\";\n\n\t\t\t// Success/Error\n\t\t\tif ( isSuccess ) {\n\t\t\t\tdeferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );\n\t\t\t} else {\n\t\t\t\tdeferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );\n\t\t\t}\n\n\t\t\t// Status-dependent callbacks\n\t\t\tjqXHR.statusCode( statusCode );\n\t\t\tstatusCode = undefined;\n\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( isSuccess ? \"ajaxSuccess\" : \"ajaxError\",\n\t\t\t\t\t[ jqXHR, s, isSuccess ? success : error ] );\n\t\t\t}\n\n\t\t\t// Complete\n\t\t\tcompleteDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );\n\n\t\t\tif ( fireGlobals ) {\n\t\t\t\tglobalEventContext.trigger( \"ajaxComplete\", [ jqXHR, s ] );\n\n\t\t\t\t// Handle the global AJAX counter\n\t\t\t\tif ( !( --jQuery.active ) ) {\n\t\t\t\t\tjQuery.event.trigger( \"ajaxStop\" );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn jqXHR;\n\t},\n\n\tgetJSON: function( url, data, callback ) {\n\t\treturn jQuery.get( url, data, callback, \"json\" );\n\t},\n\n\tgetScript: function( url, callback ) {\n\t\treturn jQuery.get( url, undefined, callback, \"script\" );\n\t}\n} );\n\njQuery.each( [ \"get\", \"post\" ], function( _i, method ) {\n\tjQuery[ method ] = function( url, data, callback, type ) {\n\n\t\t// Shift arguments if data argument was omitted\n\t\tif ( isFunction( data ) ) {\n\t\t\ttype = type || callback;\n\t\t\tcallback = data;\n\t\t\tdata = undefined;\n\t\t}\n\n\t\t// The url can be an options object (which then must have .url)\n\t\treturn jQuery.ajax( jQuery.extend( {\n\t\t\turl: url,\n\t\t\ttype: method,\n\t\t\tdataType: type,\n\t\t\tdata: data,\n\t\t\tsuccess: callback\n\t\t}, jQuery.isPlainObject( url ) && url ) );\n\t};\n} );\n\njQuery.ajaxPrefilter( function( s ) {\n\tvar i;\n\tfor ( i in s.headers ) {\n\t\tif ( i.toLowerCase() === \"content-type\" ) {\n\t\t\ts.contentType = s.headers[ i ] || \"\";\n\t\t}\n\t}\n} );\n\n\njQuery._evalUrl = function( url, options, doc ) {\n\treturn jQuery.ajax( {\n\t\turl: url,\n\n\t\t// Make this explicit, since user can override this through ajaxSetup (#11264)\n\t\ttype: \"GET\",\n\t\tdataType: \"script\",\n\t\tcache: true,\n\t\tasync: false,\n\t\tglobal: false,\n\n\t\t// Only evaluate the response if it is successful (gh-4126)\n\t\t// dataFilter is not invoked for failure responses, so using it instead\n\t\t// of the default converter is kludgy but it works.\n\t\tconverters: {\n\t\t\t\"text script\": function() {}\n\t\t},\n\t\tdataFilter: function( response ) {\n\t\t\tjQuery.globalEval( response, options, doc );\n\t\t}\n\t} );\n};\n\n\njQuery.fn.extend( {\n\twrapAll: function( html ) {\n\t\tvar wrap;\n\n\t\tif ( this[ 0 ] ) {\n\t\t\tif ( isFunction( html ) ) {\n\t\t\t\thtml = html.call( this[ 0 ] );\n\t\t\t}\n\n\t\t\t// The elements to wrap the target around\n\t\t\twrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );\n\n\t\t\tif ( this[ 0 ].parentNode ) {\n\t\t\t\twrap.insertBefore( this[ 0 ] );\n\t\t\t}\n\n\t\t\twrap.map( function() {\n\t\t\t\tvar elem = this;\n\n\t\t\t\twhile ( elem.firstElementChild ) {\n\t\t\t\t\telem = elem.firstElementChild;\n\t\t\t\t}\n\n\t\t\t\treturn elem;\n\t\t\t} ).append( this );\n\t\t}\n\n\t\treturn this;\n\t},\n\n\twrapInner: function( html ) {\n\t\tif ( isFunction( html ) ) {\n\t\t\treturn this.each( function( i ) {\n\t\t\t\tjQuery( this ).wrapInner( html.call( this, i ) );\n\t\t\t} );\n\t\t}\n\n\t\treturn this.each( function() {\n\t\t\tvar self = jQuery( this ),\n\t\t\t\tcontents = self.contents();\n\n\t\t\tif ( contents.length ) {\n\t\t\t\tcontents.wrapAll( html );\n\n\t\t\t} else {\n\t\t\t\tself.append( html );\n\t\t\t}\n\t\t} );\n\t},\n\n\twrap: function( html ) {\n\t\tvar htmlIsFunction = isFunction( html );\n\n\t\treturn this.each( function( i ) {\n\t\t\tjQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );\n\t\t} );\n\t},\n\n\tunwrap: function( selector ) {\n\t\tthis.parent( selector ).not( \"body\" ).each( function() {\n\t\t\tjQuery( this ).replaceWith( this.childNodes );\n\t\t} );\n\t\treturn this;\n\t}\n} );\n\n\njQuery.expr.pseudos.hidden = function( elem ) {\n\treturn !jQuery.expr.pseudos.visible( elem );\n};\njQuery.expr.pseudos.visible = function( elem ) {\n\treturn !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );\n};\n\n\n\n\njQuery.ajaxSettings.xhr = function() {\n\ttry {\n\t\treturn new window.XMLHttpRequest();\n\t} catch ( e ) {}\n};\n\nvar xhrSuccessStatus = {\n\n\t\t// File protocol always yields status code 0, assume 200\n\t\t0: 200,\n\n\t\t// Support: IE <=9 only\n\t\t// #1450: sometimes IE returns 1223 when it should be 204\n\t\t1223: 204\n\t},\n\txhrSupported = jQuery.ajaxSettings.xhr();\n\nsupport.cors = !!xhrSupported && ( \"withCredentials\" in xhrSupported );\nsupport.ajax = xhrSupported = !!xhrSupported;\n\njQuery.ajaxTransport( function( options ) {\n\tvar callback, errorCallback;\n\n\t// Cross domain only allowed if supported through XMLHttpRequest\n\tif ( support.cors || xhrSupported && !options.crossDomain ) {\n\t\treturn {\n\t\t\tsend: function( headers, complete ) {\n\t\t\t\tvar i,\n\t\t\t\t\txhr = options.xhr();\n\n\t\t\t\txhr.open(\n\t\t\t\t\toptions.type,\n\t\t\t\t\toptions.url,\n\t\t\t\t\toptions.async,\n\t\t\t\t\toptions.username,\n\t\t\t\t\toptions.password\n\t\t\t\t);\n\n\t\t\t\t// Apply custom fields if provided\n\t\t\t\tif ( options.xhrFields ) {\n\t\t\t\t\tfor ( i in options.xhrFields ) {\n\t\t\t\t\t\txhr[ i ] = options.xhrFields[ i ];\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Override mime type if needed\n\t\t\t\tif ( options.mimeType && xhr.overrideMimeType ) {\n\t\t\t\t\txhr.overrideMimeType( options.mimeType );\n\t\t\t\t}\n\n\t\t\t\t// X-Requested-With header\n\t\t\t\t// For cross-domain requests, seeing as conditions for a preflight are\n\t\t\t\t// akin to a jigsaw puzzle, we simply never set it to be sure.\n\t\t\t\t// (it can always be set on a per-request basis or even using ajaxSetup)\n\t\t\t\t// For same-domain requests, won't change header if already provided.\n\t\t\t\tif ( !options.crossDomain && !headers[ \"X-Requested-With\" ] ) {\n\t\t\t\t\theaders[ \"X-Requested-With\" ] = \"XMLHttpRequest\";\n\t\t\t\t}\n\n\t\t\t\t// Set headers\n\t\t\t\tfor ( i in headers ) {\n\t\t\t\t\txhr.setRequestHeader( i, headers[ i ] );\n\t\t\t\t}\n\n\t\t\t\t// Callback\n\t\t\t\tcallback = function( type ) {\n\t\t\t\t\treturn function() {\n\t\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\t\tcallback = errorCallback = xhr.onload =\n\t\t\t\t\t\t\t\txhr.onerror = xhr.onabort = xhr.ontimeout =\n\t\t\t\t\t\t\t\t\txhr.onreadystatechange = null;\n\n\t\t\t\t\t\t\tif ( type === \"abort\" ) {\n\t\t\t\t\t\t\t\txhr.abort();\n\t\t\t\t\t\t\t} else if ( type === \"error\" ) {\n\n\t\t\t\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t\t\t\t// On a manual native abort, IE9 throws\n\t\t\t\t\t\t\t\t// errors on any property access that is not readyState\n\t\t\t\t\t\t\t\tif ( typeof xhr.status !== \"number\" ) {\n\t\t\t\t\t\t\t\t\tcomplete( 0, \"error\" );\n\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\tcomplete(\n\n\t\t\t\t\t\t\t\t\t\t// File: protocol always yields status 0; see #8605, #14207\n\t\t\t\t\t\t\t\t\t\txhr.status,\n\t\t\t\t\t\t\t\t\t\txhr.statusText\n\t\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tcomplete(\n\t\t\t\t\t\t\t\t\txhrSuccessStatus[ xhr.status ] || xhr.status,\n\t\t\t\t\t\t\t\t\txhr.statusText,\n\n\t\t\t\t\t\t\t\t\t// Support: IE <=9 only\n\t\t\t\t\t\t\t\t\t// IE9 has no XHR2 but throws on binary (trac-11426)\n\t\t\t\t\t\t\t\t\t// For XHR2 non-text, let the caller handle it (gh-2498)\n\t\t\t\t\t\t\t\t\t( xhr.responseType || \"text\" ) !== \"text\" ||\n\t\t\t\t\t\t\t\t\ttypeof xhr.responseText !== \"string\" ?\n\t\t\t\t\t\t\t\t\t\t{ binary: xhr.response } :\n\t\t\t\t\t\t\t\t\t\t{ text: xhr.responseText },\n\t\t\t\t\t\t\t\t\txhr.getAllResponseHeaders()\n\t\t\t\t\t\t\t\t);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t\t};\n\n\t\t\t\t// Listen to events\n\t\t\t\txhr.onload = callback();\n\t\t\t\terrorCallback = xhr.onerror = xhr.ontimeout = callback( \"error\" );\n\n\t\t\t\t// Support: IE 9 only\n\t\t\t\t// Use onreadystatechange to replace onabort\n\t\t\t\t// to handle uncaught aborts\n\t\t\t\tif ( xhr.onabort !== undefined ) {\n\t\t\t\t\txhr.onabort = errorCallback;\n\t\t\t\t} else {\n\t\t\t\t\txhr.onreadystatechange = function() {\n\n\t\t\t\t\t\t// Check readyState before timeout as it changes\n\t\t\t\t\t\tif ( xhr.readyState === 4 ) {\n\n\t\t\t\t\t\t\t// Allow onerror to be called first,\n\t\t\t\t\t\t\t// but that will not handle a native abort\n\t\t\t\t\t\t\t// Also, save errorCallback to a variable\n\t\t\t\t\t\t\t// as xhr.onerror cannot be accessed\n\t\t\t\t\t\t\twindow.setTimeout( function() {\n\t\t\t\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\t\t\t\terrorCallback();\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t\t}\n\n\t\t\t\t// Create the abort callback\n\t\t\t\tcallback = callback( \"abort\" );\n\n\t\t\t\ttry {\n\n\t\t\t\t\t// Do send the request (this may raise an exception)\n\t\t\t\t\txhr.send( options.hasContent && options.data || null );\n\t\t\t\t} catch ( e ) {\n\n\t\t\t\t\t// #14683: Only rethrow if this hasn't been notified as an error yet\n\t\t\t\t\tif ( callback ) {\n\t\t\t\t\t\tthrow e;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t},\n\n\t\t\tabort: function() {\n\t\t\t\tif ( callback ) {\n\t\t\t\t\tcallback();\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t}\n} );\n\n\n\n\n// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)\njQuery.ajaxPrefilter( function( s ) {\n\tif ( s.crossDomain ) {\n\t\ts.contents.script = false;\n\t}\n} );\n\n// Install script dataType\njQuery.ajaxSetup( {\n\taccepts: {\n\t\tscript: \"text/javascript, application/javascript, \" +\n\t\t\t\"application/ecmascript, application/x-ecmascript\"\n\t},\n\tcontents: {\n\t\tscript: /\\b(?:java|ecma)script\\b/\n\t},\n\tconverters: {\n\t\t\"text script\": function( text ) {\n\t\t\tjQuery.globalEval( text );\n\t\t\treturn text;\n\t\t}\n\t}\n} );\n\n// Handle cache's special case and crossDomain\njQuery.ajaxPrefilter( \"script\", function( s ) {\n\tif ( s.cache === undefined ) {\n\t\ts.cache = false;\n\t}\n\tif ( s.crossDomain ) {\n\t\ts.type = \"GET\";\n\t}\n} );\n\n// Bind script tag hack transport\njQuery.ajaxTransport( \"script\", function( s ) {\n\n\t// This transport only deals with cross domain or forced-by-attrs requests\n\tif ( s.crossDomain || s.scriptAttrs ) {\n\t\tvar script, callback;\n\t\treturn {\n\t\t\tsend: function( _, complete ) {\n\t\t\t\tscript = jQuery( \"\n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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Priceable

\n
\n
\nclass Priceable(**kwargs)[source]
\n
\n
\n__init__(**kwargs)
\n

Initialize self. See help(type(self)) for accurate signature.

\n
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Methods

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__init__(**kwargs)

Initialize self.

as_dict([as_camel_case])

Dictionary of the public, non-null properties and values

calc(risk_measure[, fn])

Calculate the value of the risk_measure

clone(**kwargs)

Clone this object, overriding specified values

default_instance()

Construct a default instance of this type

dollar_price()

Present value in USD

from_dict(values)

Construct an instance of this type from a dictionary

from_instance(instance)

Copy the values from an existing instance of the same type to our self :param instance: from which to copy: :return:

price()

Present value in local currency.

prop_item_type(prop)

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rtype
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type

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prop_type(prop[, additional])

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rtype
\n

type

\n
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properties()

The public property names of this class

resolve([in_place])

Resolve non-supplied properties of an instrument

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\n Previous Topic\n

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\n Instrument Package\n

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\n CommodOTCSwap\n

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DataContext

\n
\n
\nclass DataContext(start=None, end=None)[source]
\n
\n
\n__init__(start=None, end=None)[source]
\n

Initialize self. See help(type(self)) for accurate signature.

\n
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Methods

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__init__([start, end])

Initialize self.

default_value()

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rtype
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object

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Attributes

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end_date

end_time

is_entered

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rtype
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bool

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start_date

start_time

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\n Previous Topic\n

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\n Data Package\n

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\n Next Topic\n

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\n Dataset\n

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Dataset

\n
\n
\nclass Dataset(dataset_id, provider=None)[source]
\n

A collection of related data

\n
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\n__init__(dataset_id, provider=None)[source]
\n
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Parameters
\n
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  • dataset_id (Union[str, Vendor]) – The dataset’s identifier

    • \n

  • provider (Optional[DataApi]) – The data provider

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Methods

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__init__(dataset_id[, provider])

\n
type dataset_id
\n

Union[str, Vendor]

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get_coverage([limit, offset, fields, …])

Get the assets covered by this DataSet

get_data([start, end, as_of, since, fields, …])

Get data for the given range and parameters

get_data_last(as_of[, start, fields])

Get the last point for this DataSet, at or before as_of

get_data_series(field[, start, end, as_of, …])

Get a time series of data for a field of a dataset

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Attributes

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id

The dataset’s identifier

name

provider

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\n Fields\n

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Fields

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\nclass Fields(value=<object object>, names=None, module=None, type=None, start=1)[source]
\n

Data field enumeration

\n

Enumeration of fields available through data APIs

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\n__init__(*args, **kwds)
\n

Initialize self. See help(type(self)) for accurate signature.

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Attributes

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ADJUSTED_ASK_PRICE

ADJUSTED_BID_PRICE

ADJUSTED_HIGH_PRICE

ADJUSTED_LOW_PRICE

ADJUSTED_TRADE_PRICE

ADJUSTED_VOLUME

ASK_PRICE

ASSET_ID

BID_PRICE

CLOSE_PRICE

EXPIRATION_DATE

HIGH_PRICE

IMPLIED_VOLATILITY

LOW_PRICE

MID_PRICE

NAME

OPEN_PRICE

RELATIVE_STRIKE

RIC

SPOT_PRICE

STRIKE_REFERENCE

TENOR

TRADE_PRICE

UPDATE_TIME

VAR_SWAP

VOLUME

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\n Datetime Package\n

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CommodOTCSwap

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For methods of this class, see gs_quant.base.Priceable

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\nclass CommodOTCSwap(start=None, end=None, number_of_periods=None, strategy=None, quantity=None, quantity_unit=None, quantity_period=None, legs=None, settlement=None, name=None)[source]
\n

Object representation of a commodities swap

\n

Properties

\n
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\nend
\n

Date or Contract Month

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Return type
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Union[date, str]

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\ninstrument_quantity
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Return type
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float

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\nlegs
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Commodities OTC swap leg

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Return type
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Tuple[CommodOTCSwapLeg, …]

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\nnumber_of_periods
\n

The number of settlement periods

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Return type
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int

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\nprovider
\n
\n\n
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\nquantity
\n

Size of some value, i.e. notional like 1.3b, 1.5, 1000

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
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\nquantity_period
\n

A coding scheme to define a period corresponding to a quantity amount

\n
\n
Return type
\n

Union[Period, str]

\n
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\nquantity_unit
\n

Commodity asset

\n
\n
Return type
\n

str

\n
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\n
\nresolution_key
\n
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Return type
\n

RiskKey

\n
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\nsettlement
\n

read only description in plain English of settlement terms

\n
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Return type
\n

str

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\nstart
\n

Date or Contract Month

\n
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Return type
\n

Union[date, str]

\n
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\nstrategy
\n

Strip and Commodity Spread

\n
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Type
\n

Swap Strategy

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Return type
\n

str

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\n gs_quant.base.Priceable\n

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\n EqCliquet\n

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CommodSwap

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass CommodSwap(commodity, start, commodity_reference_price=None, notional_amount=1000000.0, currency=None, calculation_periods=None, calculation_period_frequency=None)[source]
\n

Object representation of a commodities swap

\n

Properties

\n
\n
\ncalculation_period_frequency
\n

The frequency of the calculation periods

\n
\n
Return type
\n

Union[Frequency, str]

\n
\n
\n
\n\n
\n
\ncalculation_periods
\n

The number of calculation periods

\n
\n
Return type
\n

int

\n
\n
\n
\n\n
\n
\ncommodity
\n

Commodity asset

\n
\n
Return type
\n

Union[CommodityAsset, str]

\n
\n
\n
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\n
\ncommodity_reference_price
\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ncurrency
\n

Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

float

\n
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\nstart
\n
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Return type
\n

Union[date, str]

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\n Previous Topic\n

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\n gs_quant.base.Priceable\n

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\n Next Topic\n

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\n EqOption\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.EqCliquet.html", "content": "\n\n\n\n \n\n \n \n \n \n \n EqCliquet — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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EqCliquet

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass EqCliquet(underlier=None, underlier_type=None, expiration_date=None, strike_price=None, currency=None, first_valuation_date=None, global_floor=- 1000000, global_cap=1000000, last_valuation_date=None, notional_amount=None, payment_frequency='Maturity', return_style='Rate of Return', return_type='Sum', valuation_period=None, name=None)[source]
\n

Object representation of an Equity Cliquet

\n

Properties

\n
\n
\ncurrency
\n

Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nexpiration_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
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\n
\nfirst_valuation_date
\n

ISO 8601-formatted date

\n
\n
Return type
\n

date

\n
\n
\n
\n\n
\n
\nglobal_cap
\n

Global Cap of return, relevant only if paying at maturity

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nglobal_floor
\n

Global Floor of return, relevant only if paying at maturity

\n
\n
Return type
\n

float

\n
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\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
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\nlast_valuation_date
\n

ISO 8601-formatted date

\n
\n
Return type
\n

date

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional of this position

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npayment_frequency
\n
\n
Return type
\n

str

\n
\n
\n
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\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
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\n
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\nreturn_style
\n

Return calculation style

\n
\n
Return type
\n

str

\n
\n
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\n
\nreturn_type
\n

Sum or Product of periodic return, relevant only if paying at maturity

\n
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Return type
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str

\n
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\nstrike_price
\n

Strike price as value

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nunderlier
\n

Underlier security identifier

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunderlier_type
\n

Type of underlyer

\n
\n
Return type
\n

Union[UnderlierType, str]

\n
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\n\n
\n
\nunresolved
\n
\n\n
\n
\nvaluation_period
\n

Tenor

\n
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Return type
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str

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\n CommodOTCSwap\n

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\n EqForward\n

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EqForward

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass EqForward(underlier=None, underlier_type=None, expiration_date=None, forward_price=None, number_of_shares=1, name=None)[source]
\n

Object representation of an equity forward

\n

Properties

\n
\n
\nexpiration_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nforward_price
\n

Forward price

\n
\n
Return type
\n

float

\n
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\ninstrument_quantity
\n
\n
Return type
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float

\n
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\nnumber_of_shares
\n

Number of shares

\n
\n
Return type
\n

int

\n
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\nprovider
\n
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\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nunderlier
\n

Underlier security identifier

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunderlier_type
\n

Type of underlyer

\n
\n
Return type
\n

Union[UnderlierType, str]

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\nunresolved
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\n Previous Topic\n

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\n EqCliquet\n

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\n Next Topic\n

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\n EqOption\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.EqOption.html", "content": "\n\n\n\n \n\n \n \n \n \n \n EqOption — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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EqOption

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass EqOption(underlier=None, expiration_date=None, strike_price=None, option_type=None, option_style=None, number_of_options=None, exchange=None, multiplier=None, settlement_date=None, settlement_currency=None, premium=0, premium_payment_date=None, valuation_time=None, method_of_settlement=None, underlier_type=None, buy_sell=None, premium_currency=None, trade_as=None, name=None)[source]
\n

Instrument definition for equity option

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\nexchange
\n

Name of marketplace where security, derivative or other instrument is traded

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nexpiration_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nmethod_of_settlement
\n

How the option is settled (e.g. Cash, Physical)

\n
\n
Return type
\n

Union[OptionSettlementMethod, str]

\n
\n
\n
\n\n
\n
\nmultiplier
\n

Number of stock units per option contract

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnumber_of_options
\n

Number of options

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\noption_style
\n

Option Exercise Style

\n
\n
Return type
\n

Union[OptionStyle, str]

\n
\n
\n
\n\n
\n
\noption_type
\n

Option Type

\n
\n
Return type
\n

Union[OptionType, str]

\n
\n
\n
\n\n
\n
\npremium
\n

Option premium

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\npremium_currency
\n

Currency of the option premium

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Option premium

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nsettlement_currency
\n

Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nsettlement_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nstrike_price
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ntrade_as
\n

Option trade as (i.e. listed, otc, lookalike etc)

\n
\n
Return type
\n

Union[TradeAs, str]

\n
\n
\n
\n\n
\n
\nunderlier
\n

Underlier security identifier

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunderlier_type
\n

Type of underlyer

\n
\n
Return type
\n

Union[UnderlierType, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n
\nvaluation_time
\n

Valuation time (e.g. MktClose, MktOpen) of the underlying level for exercise

\n
\n
Return type
\n

Union[ValuationTime, str]

\n
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\n Previous Topic\n

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\n EqForward\n

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\n EqSynthetic\n

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EqSynthetic

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass EqSynthetic(underlier, expiry, currency=None, swap_type='Eq Swap', buy_sell=None, underlier_type=None, effective_date=None, num_of_underlyers=None, name=None)[source]
\n

Instrument definition for equity synthetics

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\ncurrency
\n

Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\neffective_date
\n

The date on which the synthetic becomes effective

\n
\n
Return type
\n

date

\n
\n
\n
\n\n
\n
\nexpiry
\n

Tenor

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnum_of_underlyers
\n

number of underlyers referenced in synthetic contract

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nswap_type
\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nunderlier
\n

Underlier security identifier

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunderlier_type
\n

Type of underlyer

\n
\n
Return type
\n

Union[UnderlierType, str]

\n
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\n
\nunresolved
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\n Previous Topic\n

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\n EqOption\n

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\n Next Topic\n

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\n EqVarianceSwap\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.EqVarianceSwap.html", "content": "\n\n\n\n \n\n \n \n \n \n \n EqVarianceSwap — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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EqVarianceSwap

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass EqVarianceSwap(underlier=None, underlier_type=None, expiration_date=None, strike_price=None, variance_cap=None, settlement_date=None, premium=None, name=None)[source]
\n

Instrument definition for equity variance swap

\n

Properties

\n
\n
\nexpiration_date
\n

Date or tenor, e.g. 2018-09-03, 3m

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\npremium
\n

VarSwap premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nsettlement_date
\n

Settlement date

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nstrike_price
\n

Variance strike as value or percentage string e.g. 62.5, 95%

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunderlier
\n

Underlier security identifier

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunderlier_type
\n

Type of underlyer

\n
\n
Return type
\n

Union[UnderlierType, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n
\nvariance_cap
\n

Variance Cap as absolute value

\n
\n
Return type
\n

float

\n
\n
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\n Previous Topic\n

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\n EqSynthetic\n

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\n Next Topic\n

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\n Forward\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.FXBinary.html", "content": "\n\n\n\n \n\n \n \n \n \n \n FXBinary — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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FXBinary

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass FXBinary(pair=None, buy_sell=None, option_type=None, notional_amount=None, notional_currency=None, strike_price=None, settlement_date=None, expiration_date=None, expiration_time=None, premium=None, premium_currency=None, premium_payment_date=None, fixing_source=None, name=None)[source]
\n

Object representation of a FX binary option

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\nexpiration_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nexpiration_time
\n

The location and (optionally) time of spot for expiration

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfixing_source
\n

The data source to be used for observation of FX spot on the fixing date

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\noption_type
\n

Option Type

\n
\n
Return type
\n

Union[OptionType, str]

\n
\n
\n
\n\n
\n
\npair
\n

EURUSD or EUR USD

\n
\n
Type
\n

A currency pair, e.g.

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npremium
\n

Option premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_currency
\n

Currency of the option premium

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the option premium

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nsettlement_date
\n

Settlement date of the option, after expiration

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nstrike_price
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n Forward\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n FXForward\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.FXForward.html", "content": "\n\n\n\n \n\n \n \n \n \n \n FXForward — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
\n\n\n\n \n
\n \n
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\n\n \n\n \n
\n
\n
\n \n
\n

FXForward

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass FXForward(pair=None, settlement_date=None, forward_rate=None, notional_amount=None, name=None)[source]
\n

Object representation of an FX forward

\n

Properties

\n
\n
\nforward_rate
\n

Forward FX rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npair
\n

EURUSD or EUR USD

\n
\n
Type
\n

A currency pair, e.g.

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nsettlement_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n FXBinary\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n FXMultiCrossBinary\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.FXMultiCrossBinary.html", "content": "\n\n\n\n \n\n \n \n \n \n \n FXMultiCrossBinary — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
\n
\n \n
\n

FXMultiCrossBinary

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass FXMultiCrossBinary(buy_sell=None, legs=None, notional_amount=None, notional_currency=None, settlement_date=None, expiration_date=None, expiration_time=None, premium=None, premium_currency=None, premium_payment_date=None, name=None)[source]
\n

Object representation of an FX multi-cross binary

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\nexpiration_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nexpiration_time
\n

The location and (optionally) time of spot for expiration

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nlegs
\n

Object representation of a single leg of a multi-cross binary option

\n
\n
Return type
\n

Tuple[FXMultiCrossBinaryLeg, …]

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium
\n

Option premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_currency
\n

Currency of the option premium

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the option premium

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nsettlement_date
\n

Settlement date of the option, after expiration

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n FXForward\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n FXMultiCrossBinaryLeg\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.FXMultiCrossBinaryLeg.html", "content": "\n\n\n\n \n\n \n \n \n \n \n FXMultiCrossBinaryLeg — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
\n\n\n\n \n
\n \n
\n \n \n \n\n
\n\n
\n \n
\n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n\n \n\n \n
\n
\n
\n \n
\n

FXMultiCrossBinaryLeg

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass FXMultiCrossBinaryLeg(pair=None, option_type=None, strike_price=None, fixing_source=None, name=None)[source]
\n

Object representation of a single leg of a multi-cross binary option

\n

Properties

\n
\n
\nfixing_source
\n

The data source to be used for observation of FX spot on the fixing date

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\noption_type
\n

Option Type

\n
\n
Return type
\n

Union[OptionType, str]

\n
\n
\n
\n\n
\n
\npair
\n

EURUSD or EUR USD

\n
\n
Type
\n

A currency pair, e.g.

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nstrike_price
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n FXMultiCrossBinary\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n FXOption\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.FXOption.html", "content": "\n\n\n\n \n\n \n \n \n \n \n FXOption — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
\n\n\n\n \n
\n \n
\n \n \n \n\n
\n\n
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\n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n\n \n\n \n
\n
\n
\n \n
\n

FXOption

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass FXOption(pair=None, buy_sell=None, option_type=None, notional_amount=None, notional_currency=None, notional_amount_other_currency=None, strike_price=None, settlement_date=None, settlement_currency=None, settlement_rate_option=None, method_of_settlement=None, expiration_date=None, expiration_time=None, premium=None, premium_currency=None, premium_payment_date=None, name=None)[source]
\n

Object representation of an FX option

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\nexpiration_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nexpiration_time
\n

The location and (optionally) time of spot for expiration

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nmethod_of_settlement
\n

How the option is settled (e.g. Cash, Physical)

\n
\n
Return type
\n

Union[OptionSettlementMethod, str]

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_amount_other_currency
\n

Notional amount in currency other than NotionalCurrency from the pair

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\noption_type
\n

Option Type

\n
\n
Return type
\n

Union[OptionType, str]

\n
\n
\n
\n\n
\n
\npair
\n

EURUSD or EUR USD

\n
\n
Type
\n

A currency pair, e.g.

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npremium
\n

Option premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_currency
\n

Currency of the option premium

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the option premium

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nsettlement_currency
\n

Currency of settlement

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nsettlement_date
\n

Settlement date of the option, after expiration

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nsettlement_rate_option
\n

The source of spot for settlement

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nstrike_price
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n FXMultiCrossBinaryLeg\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n FXVolatilitySwap\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.FXVolatilitySwap.html", "content": "\n\n\n\n \n\n \n \n \n \n \n FXVolatilitySwap — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
\n
\n \n
\n

FXVolatilitySwap

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass FXVolatilitySwap(pair=None, buy_sell=None, strike_vol=None, notional_currency=None, notional_amount=None, first_fixing_date=None, last_fixing_date=None, settlement_date=None, fixing_source=None, fixing_frequency=None, annualization_factor=None, calculate_mean_return=0, name=None)[source]
\n

Object representation of an FX Vol Swap

\n

Properties

\n
\n
\nannualization_factor
\n

Annualization factor is the number of days used per year to compute volatility

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\ncalculate_mean_return
\n

Indicates whether the mean return is calculated (true) or taken as zero (false)\nin the realized volatility computation

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfirst_fixing_date
\n

First averaging date or observation date

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfixing_frequency
\n

Fixing frequency (ex. Daily / Business Days)

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfixing_source
\n

The data source to be used for observations of FX spot on each fixing

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nlast_fixing_date
\n

Last averaging date or valuation date

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount in dollar terms

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npair
\n

EURUSD or EUR USD

\n
\n
Type
\n

A currency pair, e.g.

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nsettlement_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nstrike_vol
\n

Volatility strike

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n FXOption\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n InflationSwap\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.Forward.html", "content": "\n\n\n\n \n\n \n \n \n \n \n Forward — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
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\n

Forward

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass Forward(currency=None, expiration_date=None, notional_amount=None, name=None)[source]
\n

Forward cash payment

\n

Properties

\n
\n
\ncurrency
\n

Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nexpiration_date
\n

Date or tenor, e.g. 2018-09-03, 3m, Dec21

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
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\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
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\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
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Return type
\n

RiskKey

\n
\n
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\n
\nunresolved
\n
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\n Previous Topic\n

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\n EqVarianceSwap\n

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\n Next Topic\n

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\n FXBinary\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRBasisSwap.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRBasisSwap — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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IRBasisSwap

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRBasisSwap(termination_date=None, notional_currency=None, notional_amount=None, effective_date=None, principal_exchange=None, payer_spread=None, payer_rate_option=None, payer_designated_maturity=None, payer_frequency=None, payer_day_count_fraction=None, payer_business_day_convention=None, receiver_spread=None, receiver_rate_option=None, receiver_designated_maturity=None, receiver_frequency=None, receiver_day_count_fraction=None, receiver_business_day_convention=None, fee=0, fee_currency=None, fee_payment_date=None, clearing_house=None, name=None)[source]
\n

A single currency exchange of cashflows from different interest rate indices

\n

Properties

\n
\n
\nclearing_house
\n

Swap Clearing House

\n
\n
Return type
\n

Union[SwapClearingHouse, str]

\n
\n
\n
\n\n
\n
\neffective_date
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npayer_business_day_convention
\n

The business day convention for the payer

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\npayer_day_count_fraction
\n

The day count fraction for the payer

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\npayer_designated_maturity
\n

Tenor of the payerRateOption, e.g. 3m, 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npayer_frequency
\n

The frequency of payer payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npayer_rate_option
\n

The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-TELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npayer_spread
\n

Spread over the payer rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nprincipal_exchange
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[PrincipalExchange, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nreceiver_business_day_convention
\n

The business day convention for the receiver

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nreceiver_day_count_fraction
\n

The day count fraction for the receiver

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nreceiver_designated_maturity
\n

Tenor of the receiverRateOption, e.g. 3m, 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nreceiver_frequency
\n

The frequency of receiver payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nreceiver_rate_option
\n

The underlying benchmark for the receiver, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\nTELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nreceiver_spread
\n

Spread over the receiver rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\ntermination_date
\n

The termination of the swap, e.g. 2050-04-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
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\n Previous Topic\n

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\n InflationSwap\n

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\n Next Topic\n

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\n IRCap\n

\n
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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRCMSOption.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRCMSOption — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
\n
\n \n
\n

IRCMSOption

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRCMSOption(cap_floor=None, termination_date=None, notional_currency=None, notional_amount=None, effective_date=None, strike=None, index=None, multiplier=None, premium=None, premium_payment_date=None, fee=0, fee_currency=None, fee_payment_date=None, buy_sell=None, name=None)[source]
\n

Object representation of a constant maturity option (cap, floor, straddle)

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\ncap_floor
\n

Structure type, e.g. Cap, Floor, Straddle, Binary Cap

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\neffective_date
\n

CMS option effective date, e.g. 2019-01-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nindex
\n

The underlying benchmark i.e. 30yUSD

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nmultiplier
\n

Multiplier

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium
\n

The premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the premium

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nstrike
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ntermination_date
\n

Swap termination date, e.g. 2030-05-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
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\n\n
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\n
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\n

\n Previous Topic\n

\n

\n IRCap\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n IRCMSOptionStrip\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRCMSOptionStrip.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRCMSOptionStrip — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
\n
\n \n
\n

IRCMSOptionStrip

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRCMSOptionStrip(cap_floor=None, termination_date=None, notional_currency=None, notional_amount=None, effective_date=None, strike=None, index=None, floating_rate_frequency=None, floating_rate_day_count_fraction=None, floating_rate_business_day_convention=None, reset_delay=None, multiplier=None, premium=None, premium_payment_date=None, fee=0, fee_currency=None, fee_payment_date=None, buy_sell=None, name=None)[source]
\n

Object representation of a constant maturity option strip (cap, floor, straddle)

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\ncap_floor
\n

Structure type, e.g. Cap, Floor, Straddle, Binary Cap

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\neffective_date
\n

CMS option effective date, e.g. 2019-01-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_business_day_convention
\n

The business day convention of the floating rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_day_count_fraction
\n

The day count fraction of the floating rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_frequency
\n

Period e.g. 3m, 1y

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nindex
\n

The underlying benchmark i.e. 30yUSD

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nmultiplier
\n

Multiplier

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium
\n

The premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the premium

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nreset_delay
\n

Delay of the reset e.g. 2d

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nstrike
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ntermination_date
\n

Swap termination date, e.g. 2030-05-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRCMSOption\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n IRCMSSpreadOption\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRCMSSpreadOption.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRCMSSpreadOption — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n

IRCMSSpreadOption

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRCMSSpreadOption(cap_floor=None, termination_date=None, notional_currency=None, notional_amount=None, effective_date=None, strike=None, index1_tenor=None, index2_tenor=None, premium=None, premium_payment_date=None, fee=0, fee_currency=None, fee_payment_date=None, buy_sell=None, name=None)[source]
\n

Object representation of a constant maturity spread option (cap, floor,\nstraddle)

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\ncap_floor
\n

Structure type, e.g. Cap, Floor, Straddle

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\neffective_date
\n

CMS option effective date, e.g. 2019-01-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nindex1_tenor
\n

The tenor of the underlying benchmark to be the first element i.e. 30y

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nindex2_tenor
\n

The tenor of the underlying benchmark to be the second element i.e. 5y

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium
\n

The premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the premium

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nstrike
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ntermination_date
\n

Swap termination date, e.g. 2030-05-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRCMSOptionStrip\n

\n
\n \n
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\n Next Topic\n

\n

\n IRCMSSpreadOptionStrip\n

\n
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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRCMSSpreadOptionStrip.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRCMSSpreadOptionStrip — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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IRCMSSpreadOptionStrip

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRCMSSpreadOptionStrip(cap_floor=None, termination_date=None, notional_currency=None, notional_amount=None, effective_date=None, strike=None, index1=None, index2=None, floating_rate_frequency=None, floating_rate_day_count_fraction=None, floating_rate_business_day_convention=None, reset_delay=None, premium=None, premium_payment_date=None, fee=0, fee_currency=None, fee_payment_date=None, buy_sell=None, name=None)[source]
\n

Object representation of a constant maturity spread option strip (cap, floor,\nstraddle)

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\ncap_floor
\n

Structure type, e.g. Cap, Floor, Straddle, Binary Cap

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\neffective_date
\n

CMS option effective date, e.g. 2019-01-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_business_day_convention
\n

The business day convention of the floating rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_day_count_fraction
\n

The day count fraction of the floating rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_frequency
\n

Period e.g. 3m, 1y

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nindex1
\n

The underlying benchmark to be the first element from i.e. 30yUSD

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nindex2
\n

The underlying benchmark to be the second element from i.e. 5yUSD

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium
\n

The premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the premium

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nreset_delay
\n

Delay of the reset e.g. 2d

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nstrike
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ntermination_date
\n

Swap termination date, e.g. 2030-05-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
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\n \n
\n

\n Previous Topic\n

\n

\n IRCMSSpreadOption\n

\n
\n \n
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\n

\n Next Topic\n

\n

\n IRFloor\n

\n
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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRCap.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRCap — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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IRCap

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRCap(termination_date=None, notional_currency=None, notional_amount=None, effective_date=None, floating_rate_option=None, floating_rate_designated_maturity=None, floating_rate_frequency=None, floating_rate_day_count_fraction=None, floating_rate_business_day_convention=None, cap_rate=None, premium=None, premium_payment_date=None, fee=0, fee_currency=None, fee_payment_date=None, name=None)[source]
\n

Object representation of an interest rate cap

\n

Properties

\n
\n
\ncap_rate
\n

The rate of this cap, as value, percent or at-the-money e.g. 62.5, 95%, ATM-25,\nATMF

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\neffective_date
\n

The date on which the cap becomes effective

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_business_day_convention
\n

The business day convention of the floating rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_day_count_fraction
\n

The day count fraction of the floating rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_designated_maturity
\n

Tenor of the floatingRateOption, e.g. 3m, 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_frequency
\n

The frequency of floating payments, e.g. 3m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_option
\n

The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\nTELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium
\n

The premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the premium

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\ntermination_date
\n

The termination of the cap, e.g. 2025-04-01, 2y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRBasisSwap\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n IRCMSOption\n

\n
\n \n
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\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRFloor.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRFloor — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
\n \n
\n

IRFloor

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRFloor(termination_date=None, notional_currency=None, notional_amount=None, effective_date=None, floating_rate_option=None, floating_rate_designated_maturity=None, floating_rate_frequency=None, floating_rate_day_count_fraction=None, floating_rate_business_day_convention=None, floor_rate=None, premium=None, premium_payment_date=None, fee=0, fee_currency=None, fee_payment_date=None, name=None)[source]
\n

Object representation of an interest rate floor

\n

Properties

\n
\n
\neffective_date
\n

The date on which the floor becomes effective

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_business_day_convention
\n

The business day convention of the floating rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_day_count_fraction
\n

The day count fraction of the floating rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_designated_maturity
\n

Tenor of the floatingRateOption, e.g. 3m, 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_frequency
\n

The frequency of floating payments, e.g. 3m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_option
\n

The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\nTELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloor_rate
\n

The rate of this floor, as value, percent or at-the-money e.g. 62.5, 95%,\nATM-25, ATMF

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npremium
\n

The premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the premium

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\ntermination_date
\n

The termination of the floor, e.g. 2025-04-01, 2y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRCMSSpreadOptionStrip\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n IRSwap\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRSwap.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRSwap — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n

IRSwap

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRSwap(pay_or_receive=None, termination_date=None, notional_currency=None, notional_amount=None, effective_date=None, principal_exchange=None, floating_rate_for_the_initial_calculation_period=None, floating_rate_option=None, floating_rate_designated_maturity=None, floating_rate_spread=None, floating_rate_frequency=None, floating_rate_day_count_fraction=None, floating_rate_business_day_convention=None, fixed_rate=None, fixed_rate_frequency=None, fixed_rate_day_count_fraction=None, fixed_rate_business_day_convention=None, fee=0, fee_currency=None, fee_payment_date=None, clearing_house=None, fixed_first_stub=None, floating_first_stub=None, fixed_last_stub=None, floating_last_stub=None, fixed_holidays=None, floating_holidays=None, roll_convention=None, name=None)[source]
\n

A vanilla interest rate swap of fixed vs floating cashflows

\n

Properties

\n
\n
\nclearing_house
\n

Swap Clearing House

\n
\n
Return type
\n

Union[SwapClearingHouse, str]

\n
\n
\n
\n\n
\n
\neffective_date
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfixed_first_stub
\n

The date of the first stub for fixed leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfixed_holidays
\n

The accrual calendar for fixed leg

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfixed_last_stub
\n

The date of the last stub for fixed leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfixed_rate
\n

The coupon of the fixed leg

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_business_day_convention
\n

The business day convention for the fixed rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_day_count_fraction
\n

The day count fraction for the fixed rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_frequency
\n

The frequency of fixed payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_first_stub
\n

The date of the first stub for floating leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfloating_holidays
\n

The accrual calendar for floating leg

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_last_stub
\n

The date of the last stub for floating leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_business_day_convention
\n

The business day convention of the floating rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_day_count_fraction
\n

The day count fraction of the floating rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_designated_maturity
\n

Tenor of the floatingRateOption, e.g. 3m, 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_for_the_initial_calculation_period
\n

First fixing

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfloating_rate_frequency
\n

The frequency of floating payments, e.g. 3m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_option
\n

The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\nTELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_spread
\n

The spread over the floating rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npay_or_receive
\n

Pay or receive fixed

\n
\n
Return type
\n

Union[PayReceive, str]

\n
\n
\n
\n\n
\n
\nprincipal_exchange
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[PrincipalExchange, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nroll_convention
\n

The roll convention

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ntermination_date
\n

The termination of the swap, e.g. 2050-04-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRFloor\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n IRSwaption\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRSwaption.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRSwaption — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
\n\n\n\n \n
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\n\n \n\n \n
\n
\n
\n \n
\n

IRSwaption

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRSwaption(pay_or_receive=None, termination_date=None, notional_currency=None, effective_date=None, notional_amount=None, expiration_date=None, floating_rate_option=None, floating_rate_designated_maturity=None, floating_rate_spread=None, floating_rate_frequency=None, floating_rate_day_count_fraction=None, floating_rate_business_day_convention=None, fixed_rate_frequency=None, fixed_rate_day_count_fraction=None, fixed_rate_business_day_convention=None, strike=None, premium=None, premium_payment_date=None, fee=0, fee_currency=None, fee_payment_date=None, clearing_house=None, settlement=None, buy_sell=None, name=None)[source]
\n

Object representation of a swaption

\n

Properties

\n
\n
\nbuy_sell
\n

Buy or Sell side of contract

\n
\n
Return type
\n

Union[BuySell, str]

\n
\n
\n
\n\n
\n
\nclearing_house
\n

Swap Clearing House

\n
\n
Return type
\n

Union[SwapClearingHouse, str]

\n
\n
\n
\n\n
\n
\neffective_date
\n

Swaption effective date, e.g. 2019-01-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nexpiration_date
\n

Swaption expiration date, 2020-05-01, 3m

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_business_day_convention
\n

The business day convention for the fixed rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_day_count_fraction
\n

The day count fraction for the fixed rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_frequency
\n

The frequency of fixed payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_business_day_convention
\n

The business day convention of the floating rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_day_count_fraction
\n

The day count fraction of the floating rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_designated_maturity
\n

Tenor

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_frequency
\n

The frequency of floating payments, e.g. 3m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_option
\n

The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\nTELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_spread
\n

The spread over the floating rate

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npay_or_receive
\n

Pay or receive fixed

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npremium
\n

The premium

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npremium_payment_date
\n

Payment date of the premium

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nsettlement
\n

Swap Settlement Type

\n
\n
Return type
\n

Union[SwapSettlement, str]

\n
\n
\n
\n\n
\n
\nstrike
\n

Strike as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF, 10/vol,\n100k/pv, p=10000, p=10000USD, $200K/BP, or multiple strikes\n65.4/-45.8

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ntermination_date
\n

Swaption termination date, e.g. 2030-05-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRSwap\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n IRXccySwap\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRXccySwap.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRXccySwap — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
\n
\n \n
\n

IRXccySwap

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRXccySwap(termination_date=None, notional_amount=None, effective_date=None, principal_exchange=None, payer_currency=None, payer_spread=None, payer_rate_option=None, payer_designated_maturity=None, payer_frequency=None, payer_day_count_fraction=None, payer_business_day_convention=None, receiver_currency=None, receiver_spread=None, receiver_rate_option=None, receiver_designated_maturity=None, receiver_frequency=None, receiver_day_count_fraction=None, receiver_business_day_convention=None, fee=0, fee_currency=None, fee_payment_date=None, initial_fx_rate=None, payer_first_stub=None, receiver_first_stub=None, payer_last_stub=None, receiver_last_stub=None, payer_holidays=None, receiver_holidays=None, notional_reset_side=None, name=None)[source]
\n

An exchange of cashflows from different interest rate indices

\n

Properties

\n
\n
\neffective_date
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\ninitial_fx_rate
\n

Payment date of the fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_reset_side
\n

Pay or Rec leg resetting

\n
\n
Return type
\n

Union[PayReceive, str]

\n
\n
\n
\n\n
\n
\npayer_business_day_convention
\n

The business day convention for the payer

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\npayer_currency
\n

Payer currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npayer_day_count_fraction
\n

The day count fraction for the payer

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\npayer_designated_maturity
\n

Tenor of the payerRateOption, e.g. 3m, 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npayer_first_stub
\n

The date of the first stub for payer leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\npayer_frequency
\n

The frequency of payer payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npayer_holidays
\n

The accrual calendar for payer leg

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npayer_last_stub
\n

The date of the last stub for payer leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\npayer_rate_option
\n

The underlying benchmark for the payer, e.g. USD-LIBOR-BBA, EUR-EURIBOR-TELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npayer_spread
\n

Spread over the payer rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nprincipal_exchange
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[PrincipalExchange, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nreceiver_business_day_convention
\n

The business day convention for the receiver

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nreceiver_currency
\n

Receiver currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nreceiver_day_count_fraction
\n

The day count fraction for the receiver

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nreceiver_designated_maturity
\n

Tenor of the receiverRateOption, e.g. 3m, 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nreceiver_first_stub
\n

The date of the first stub for receiver leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nreceiver_frequency
\n

The frequency of receiver payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nreceiver_holidays
\n

The accrual calendar for receiver leg

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nreceiver_last_stub
\n

The date of the last stub for receiver leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nreceiver_rate_option
\n

The underlying benchmark for the receiver, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\nTELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nreceiver_spread
\n

Spread over the receiver rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\ntermination_date
\n

The termination of the swap, e.g. 2050-04-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRSwaption\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n IRXccySwapFixFix\n

\n
\n \n
\n \n
\n \n\n
\n
\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRXccySwapFixFix.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRXccySwapFixFix — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
\n
\n \n
\n

IRXccySwapFixFix

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRXccySwapFixFix(termination_date=None, notional_amount=None, receiver_notional_amount=None, effective_date=None, principal_exchange=None, payer_currency=None, payer_rate=None, payer_frequency=None, payer_day_count_fraction=None, payer_business_day_convention=None, receiver_currency=None, receiver_rate=None, receiver_frequency=None, receiver_day_count_fraction=None, receiver_business_day_convention=None, fee=0, fee_currency=None, fee_payment_date=None, name=None)[source]
\n

An exchange of fixed cashflows in different currencies

\n

Properties

\n
\n
\neffective_date
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\npayer_business_day_convention
\n

The business day convention for the payer

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\npayer_currency
\n

Payer currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npayer_day_count_fraction
\n

The day count fraction for the payer

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\npayer_frequency
\n

The frequency of payer payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npayer_rate
\n

Payer rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nprincipal_exchange
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[PrincipalExchange, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nreceiver_business_day_convention
\n

The business day convention for the receiver

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nreceiver_currency
\n

Receiver currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nreceiver_day_count_fraction
\n

The day count fraction for the receiver

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nreceiver_frequency
\n

The frequency of receiver payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nreceiver_notional_amount
\n

Receiver notional amount

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nreceiver_rate
\n

Receiver rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\ntermination_date
\n

The termination of the swap, e.g. 2050-04-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRXccySwap\n

\n
\n \n
\n \n
\n \n
\n

\n Next Topic\n

\n

\n IRXccySwapFixFlt\n

\n
\n \n
\n \n
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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.IRXccySwapFixFlt.html", "content": "\n\n\n\n \n\n \n \n \n \n \n IRXccySwapFixFlt — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n\n \n\n \n
\n
\n
\n \n
\n

IRXccySwapFixFlt

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass IRXccySwapFixFlt(pay_or_receive=None, termination_date=None, notional_amount=None, effective_date=None, principal_exchange=None, floating_rate_currency=None, floating_rate_for_the_initial_calculation_period=None, floating_rate_option=None, floating_rate_designated_maturity=None, floating_rate_spread=None, floating_rate_frequency=None, floating_rate_day_count_fraction=None, floating_rate_business_day_convention=None, fixed_rate_currency=None, fixed_rate=None, fixed_rate_frequency=None, fixed_rate_day_count_fraction=None, fixed_rate_business_day_convention=None, fee=0, fee_currency=None, fee_payment_date=None, fixed_first_stub=None, floating_first_stub=None, fixed_last_stub=None, floating_last_stub=None, fixed_holidays=None, floating_holidays=None, name=None)[source]
\n

An exchange of fixed vs floating cashflows in different currencies

\n

Properties

\n
\n
\neffective_date
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfee_currency
\n

Currency of the fee

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfee_payment_date
\n

Payment date of the fee

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfixed_first_stub
\n

The date of the first stub for fixed leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfixed_holidays
\n

The accrual calendar for fixed leg

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfixed_last_stub
\n

The date of the last stub for fixed leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfixed_rate
\n

The coupon of the fixed leg

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_business_day_convention
\n

The business day convention for the fixed rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_currency
\n

Fixed rate currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_day_count_fraction
\n

The day count fraction for the fixed rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_frequency
\n

The frequency of fixed payments, e.g. 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_first_stub
\n

The date of the first stub for floating leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfloating_holidays
\n

The accrual calendar for floating leg

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_last_stub
\n

The date of the last stub for floating leg

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_business_day_convention
\n

The business day convention of the floating rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_currency
\n

Floating rate currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_day_count_fraction
\n

The day count fraction of the floating rate

\n
\n
Return type
\n

Union[DayCountFraction, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_designated_maturity
\n

Tenor of the floatingRateOption, e.g. 3m, 6m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_for_the_initial_calculation_period
\n

First fixing

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfloating_rate_frequency
\n

The frequency of floating payments, e.g. 3m

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_option
\n

The underlying benchmark for the floating rate, e.g. USD-LIBOR-BBA, EUR-EURIBOR-\nTELERATE

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nfloating_rate_spread
\n

The spread over the floating rate

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\npay_or_receive
\n

Pay or receive fixed

\n
\n
Return type
\n

Union[PayReceive, str]

\n
\n
\n
\n\n
\n
\nprincipal_exchange
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[PrincipalExchange, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\ntermination_date
\n

The termination of the swap, e.g. 2050-04-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRXccySwapFixFix\n

\n
\n \n
\n \n
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\n

\n Next Topic\n

\n

\n Security\n

\n
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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.InflationSwap.html", "content": "\n\n\n\n \n\n \n \n \n \n \n InflationSwap — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n

InflationSwap

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass InflationSwap(pay_or_receive=None, termination_date=None, notional_currency=None, effective_date=None, notional_amount=None, index=None, floating_rate_business_day_convention=None, fixed_rate=None, fixed_rate_business_day_convention=None, fee=0, base_cpi=None, clearing_house=None, name=None)[source]
\n

A vanilla inflation swap of fixed vs floating cashflows adjusted to an inflation\nrate

\n

Properties

\n
\n
\nbase_cpi
\n

Base CPI level

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nclearing_house
\n

Swap Clearing House

\n
\n
Return type
\n

Union[SwapClearingHouse, str]

\n
\n
\n
\n\n
\n
\neffective_date
\n

The date on which the swap becomes effective

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nfee
\n

The fee

\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nfixed_rate
\n

The coupon of the fixed leg

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nfixed_rate_business_day_convention
\n

The business day convention for the fixed rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nfloating_rate_business_day_convention
\n

The business day convention of the floating rate

\n
\n
Return type
\n

Union[BusinessDayConvention, str]

\n
\n
\n
\n\n
\n
\nindex
\n

The underlying benchmark for the floating rate, e.g. CPI-U

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nnotional_amount
\n

Notional amount

\n
\n
Return type
\n

Union[float, str]

\n
\n
\n
\n\n
\n
\nnotional_currency
\n

Notional currency

\n
\n
Return type
\n

Union[Currency, str]

\n
\n
\n
\n\n
\n
\npay_or_receive
\n

Pay or receive fixed

\n
\n
Return type
\n

Union[PayReceive, str]

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\ntermination_date
\n

The termination of the swap, e.g. 2050-04-01, 10y

\n
\n
Return type
\n

Union[date, str]

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n\n
\n\n\n
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\n

\n Previous Topic\n

\n

\n FXVolatilitySwap\n

\n
\n \n
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\n \n
\n

\n Next Topic\n

\n

\n IRBasisSwap\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.instrument.Security.html", "content": "\n\n\n\n \n\n \n \n \n \n \n Security — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n
\n \n
\n

Security

\n

For methods of this class, see gs_quant.base.Priceable

\n
\n
\nclass Security(ticker=None, bbid=None, ric=None, isin=None, cusip=None, prime_id=None, quantity=1)[source]
\n

A security, specified by a well-known identifier

\n

Properties

\n
\n
\nbbid
\n

Bloomberg Id Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nbbid_equivalent
\n

Bloomberg Equivalent Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nbcid
\n

Bloomberg Composite Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ncid
\n

Company Id Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ncm_id
\n

Client Master Party Id

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ncross
\n

Cross identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ncusip
\n

Cusip Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ndelisted
\n

Whether an asset has been delisted

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ndollar_cross
\n

USD cross identifier for a particular currency

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\neid
\n

EID Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nem_id
\n

Entity Master Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nexchange_code
\n

EEX Exchange Code

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ngsid
\n

GSID Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ngsid_equivalent
\n

GSID Equivalent Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ngsideid
\n

GSID_EID Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ngsn
\n

Goldman Sachs internal product number

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ngss
\n

GS Symbol identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ninstrument_quantity
\n
\n
Return type
\n

float

\n
\n
\n
\n\n
\n
\nisin
\n

International Security Number

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\njsn
\n

Japan Security Number

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nlms_id
\n

Listed Market Symbol

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nmdapi
\n

MDAPI Asset

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nmdapi_class
\n

MDAPI Asset Class

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nmic
\n

Market Identifier Code

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nmq_symbol
\n

Marquee Symbol for generic MQ entities

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npl_id
\n

Platts Symbol Name

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nplot_id
\n

Plot Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\npnode_id
\n

Pricing node identifier sourced from Morningstar

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprimary_country_ric
\n

Reuters Primary Country Instrument Code Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprime_id
\n

PrimeID Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nprovider
\n
\n\n
\n
\nps_id
\n

Platts Symbol

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nrcic
\n

Reuters Composite Instrument Code Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nresolution_key
\n
\n
Return type
\n

RiskKey

\n
\n
\n
\n\n
\n
\nric
\n

Reuters Instrument Code identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nsec_name
\n

Internal Goldman Sachs security name

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nsedol
\n

Sedol Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nsf_id
\n

SalesForce ID

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nsimon_id
\n

SIMON product identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\ntdapi
\n

TDAPI Description

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nticker
\n

Ticker Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nunresolved
\n
\n\n
\n
\nvaloren
\n

Valoren Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nwi_id
\n

Weather Index Identifier

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n
\nwpk
\n

Wertpapier Kenn-Nummer

\n
\n
Return type
\n

str

\n
\n
\n
\n\n
\n\n
\n\n\n
\n \n
\n
\n
\n \n \n \n
\n \n
\n

\n Previous Topic\n

\n

\n IRXccySwapFixFlt\n

\n
\n \n
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\n Next Topic\n

\n

\n Markets Package\n

\n
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\n \n\n
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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/classes/gs_quant.markets.HistoricalPricingContext.html", "content": "\n\n\n\n \n\n \n \n \n \n \n HistoricalPricingContext — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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\n\n \n\n \n
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\n \n
\n

HistoricalPricingContext

\n
\n
\nclass HistoricalPricingContext(start=None, end=None, calendars=(), dates=None, is_async=False, is_batch=False, use_cache=False, visible_to_gs=False, csa_term=None, market_data_location=None, timeout=None, show_progress=False)[source]
\n

A context for producing valuations over multiple dates

\n
\n
\n__init__(start=None, end=None, calendars=(), dates=None, is_async=False, is_batch=False, use_cache=False, visible_to_gs=False, csa_term=None, market_data_location=None, timeout=None, show_progress=False)[source]
\n

A context for producing valuations over multiple dates

\n
\n
Parameters
\n
    \n

  • start (Union[int, date, None]) – start date

    • \n

  • end (Union[int, date, None]) – end date (defaults to today)

    • \n

  • calendars (Union[str, Tuple]) – holiday calendars

    • \n

  • dates (Optional[Iterable[date]]) – a custom iterable of dates

    • \n

  • is_async (bool) – return immediately (True) or wait for results (False) (defaults to False)

    • \n

  • is_batch (bool) – use for calculations expected to run longer than 3 mins, to avoid timeouts.\nIt can be used with is_async=True|False (defaults to False)

    • \n

  • use_cache (bool) – store results in the pricing cache (defaults to False)

    • \n

  • visible_to_gs (bool) – are the contents of risk requests visible to GS (defaults to False)

    • \n

  • csa_term (Optional[str]) – the csa under which the calculations are made. Default is local ccy ois index

    • \n

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Examples

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>>> from gs_quant.instrument import IRSwap\n>>>\n>>> ir_swap = IRSwap('Pay', '10y', 'DKK')\n>>> with HistoricalPricingContext(10):\n>>>     price_f = ir_swap.price()\n>>>\n>>> price_series = price_f.result()\n
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Methods

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__init__([start, end, calendars, dates, …])

A context for producing valuations over multiple dates

calc(instrument, risk_measure)

Calculate the risk measure for the instrument.

clone(**kwargs)

default_value()

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Attributes

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active_context

csa_term

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is_async

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bool

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is_batch

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bool

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is_current

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rtype
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bool

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is_entered

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rtype
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bool

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market

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rtype
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Market

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market_data_location

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rtype
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PricingLocation

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pricing_date

Pricing date

prior_context

use_cache

Cache results

visible_to_gs

Request contents visible to GS

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PricingContext

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\nclass PricingContext(pricing_date=None, market_data_location=None, is_async=False, is_batch=False, use_cache=False, visible_to_gs=None, csa_term=None, timeout=None, market=None, show_progress=False)[source]
\n

A context for controlling pricing and market data behaviour

\n
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\n__init__(pricing_date=None, market_data_location=None, is_async=False, is_batch=False, use_cache=False, visible_to_gs=None, csa_term=None, timeout=None, market=None, show_progress=False)[source]
\n

The methods on this class should not be called directly. Instead, use the methods on the instruments,\nas per the examples

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  • pricing_date (Optional[date]) – the date for pricing calculations. Default is today

    • \n

  • market_data_location (Union[PricingLocation, str, None]) – the location for sourcing market data (‘NYC’, ‘LDN’ or ‘HKG’ (defaults to LDN)

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  • is_async (bool) – if True, return (a future) immediately. If False, block (defaults to False)

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  • is_batch (bool) – use for calculations expected to run longer than 3 mins, to avoid timeouts.\nIt can be used with is_async=True|False (defaults to False)

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  • use_cache (bool) – store results in the pricing cache (defaults to False)

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  • visible_to_gs (Optional[bool]) – are the contents of risk requests visible to GS (defaults to False)

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  • csa_term (Optional[str]) – the csa under which the calculations are made. Default is local ccy ois index

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Examples

\n

To change the market data location of the default context:

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>>> from gs_quant.markets import PricingContext\n>>> import datetime as dt\n>>>\n>>> PricingContext.current = PricingContext(market_data_location='LDN')\n
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For a blocking, synchronous request:

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>>> from gs_quant.instrument import IRCap\n>>> cap = IRCap('5y', 'GBP')\n>>>\n>>> with PricingContext():\n>>>     price_f = cap.dollar_price()\n>>>\n>>> price = price_f.result()\n
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For an asynchronous request:

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>>> with PricingContext(is_async=True):\n>>>     price_f = cap.dollar_price()\n>>>\n>>> while not price_f.done:\n>>>     ...\n
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Methods

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__init__([pricing_date, …])

The methods on this class should not be called directly.

calc(instrument, risk_measure)

Calculate the risk measure for the instrument.

clone(**kwargs)

default_value()

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Attributes

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active_context

csa_term

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str

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is_async

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bool

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is_batch

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bool

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is_current

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bool

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is_entered

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bool

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market

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Market

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market_data_location

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rtype
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PricingLocation

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pricing_date

Pricing date

prior_context

use_cache

Cache results

visible_to_gs

Request contents visible to GS

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Portfolio

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A collection of instruments

\n

Portfolio holds a collection of instruments in order to run pricing and risk scenarios

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Creates a portfolio object which can be used to hold instruments

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Parameters
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priceables (Union[PriceableImpl, Iterable[PriceableImpl], dict, None]) – constructed with an instrument, portfolio, iterable of either, or a dictionary where\nkey is name and value is a priceable

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Methods

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__init__([priceables, name])

Creates a portfolio object which can be used to hold instruments

append(priceables)

as_dict([as_camel_case])

Dictionary of the public, non-null properties and values

calc(risk_measure[, fn])

Calculate the value of the risk_measure

clone(**kwargs)

Clone this object, overriding specified values

default_instance()

Construct a default instance of this type

dollar_price()

Present value in USD

from_asset_id(asset_id[, date])

from_asset_name(name)

from_book(book[, book_type])

from_csv(csv_file[, mappings])

from_dict(values)

Construct an instance of this type from a dictionary

from_eti(eti)

from_frame(data[, mappings])

from_instance(instance)

Copy the values from an existing instance of the same type to our self :param instance: from which to copy: :return:

from_portfolio_id(portfolio_id[, date])

from_portfolio_name(name)

from_quote(quote_id)

market()

Market Data map of coordinates and values.

paths(key)

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Tuple[PortfolioPath, …]

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pop(item)

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PriceableImpl

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price()

Present value in local currency.

prop_item_type(prop)

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prop_type(prop[, additional])

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type

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properties()

The public property names of this class

resolve([in_place])

Resolve non-supplied properties of an instrument

save([overwrite])

save_as_quote([overwrite])

subset(paths[, name])

to_csv(csv_file[, mappings, ignored_cols])

to_frame([mappings])

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DataFrame

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Attributes

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all_instruments

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Tuple[Instrument, …]

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all_paths

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Tuple[PortfolioPath, …]

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all_portfolios

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Tuple[PriceableImpl, …]

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id

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str

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instruments

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Tuple[Instrument, …]

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name

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str

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portfolios

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Tuple[PriceableImpl, …]

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priceables

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Tuple[PriceableImpl, …]

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gs_quant.markets.securities.Asset

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Initialize self. See help(type(self)) for accurate signature.

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Methods

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__init__(id_, asset_class, name[, exchange, …])

Initialize self.

entity_type()

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EntityType

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get(id_value, id_type[, as_of, …])

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rtype
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Optional[Asset]

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get_close_prices([start, end])

Get close price series

get_data_coordinate(measure[, dimensions, …])

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DataCoordinate

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get_data_series(measure[, dimensions, …])

Get asset series

get_entity()

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rtype
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Optional[Dict]

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get_identifier(id_type[, as_of])

Get asset identifier

get_identifiers([as_of])

Get asset identifiers

get_marquee_id()

get_type()

Overridden by sub-classes to return security type

get_unique_entity_key()

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EntityKey

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Attributes

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data_dimension

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gs_quant.markets.securities.AssetClass

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Asset classification of security. Assets are classified into broad groups which\nexhibit similar characteristics and behave in a consistent way under\ndifferent market conditions

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Initialize self. See help(type(self)) for accurate signature.

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Attributes

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Cash

Commod

Credit

Cross_Asset

Cryptocurrency

Econ

Equity

FX

Fund

Loan

Mortgage

Rates

Social

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gs_quant.markets.securities.AssetIdentifier

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\nclass AssetIdentifier(value)[source]
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Asset type enumeration

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Enumeration of different security identifiers

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Initialize self. See help(type(self)) for accurate signature.

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Attributes

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BLOOMBERG_COMPOSITE_ID

Bloomberg composite identifier and exchange code (GS US)

BLOOMBERG_ID

Bloomberg identifier and exchange code (GS UN)

CUSIP

Committee on Uniform Security Identification Procedures code (38141G104)

ISIN

International Securities Identification Number (US38141G1040)

MARQUEE_ID

Goldman Sachs Marquee identifier code (MA4B66MW5E27UAHKG34)

REUTERS_ID

Thompson Reuters Instrument Code (RIC), (GS.N)

SEDOL

LSE Stock Exchange Daily Official List code (2407966)

TICKER

Exchange ticker (GS)

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gs_quant.markets.securities.AssetType

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\nclass AssetType(value)[source]
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Asset type enumeration

\n

Enumeration of different types of asset or security.

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Initialize self. See help(type(self)) for accurate signature.

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Attributes

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BASKET

Bespoke basket which provides exposure to a customized collection of assets with levels published daily; can be traded on swap and rebalanced programmatically

BOND

Bond

CASH

Cash

COMMODITY

Commodity

COMMODITY_EU_NATURAL_GAS_HUB

COMMODITY_NATURAL_GAS_HUB

COMMODITY_POWER_AGGREGATED_NODES

Commodity Power Aggregated Nodes

COMMODITY_POWER_NODE

Commodity Power Node

COMMODITY_REFERENCE_PRICE

Commodity Reference Price

CROSS

FX cross or currency pair

CRYPTO_CURRENCY

Crypto

CURRENCY

Currency

ETF

Exchange traded fund which tracks an evolving portfolio of securities and is listed on an exchange to be traded as a security

FUTURE

Standardized listed contract which provides delivery of an asset at a pre-defined forward date and can be settled in cash or physical form

FUTURE_CONTRACT

Future Contract

FUTURE_MARKET

Future Market

INDEX

Index which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets

OPTION

Option

RATE

Rate

STOCK

Listed equities which provide access to equity holding in a company and participation in dividends and other distributions in common, preferred or other variants which provide different investor rights

SWAP

Swap

WEATHER_INDEX

Weather Index

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\n gs_quant.markets.securities.Index\n

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gs_quant.markets.securities.Index

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\nclass Index(id_, asset_class, name, exchange=None, currency=None, entity=None)[source]
\n

Index Asset

\n

Index which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets

\n
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\n__init__(id_, asset_class, name, exchange=None, currency=None, entity=None)[source]
\n

Initialize self. See help(type(self)) for accurate signature.

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Methods

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__init__(id_, asset_class, name[, exchange, …])

Initialize self.

entity_type()

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rtype
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EntityType

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get(id_value, id_type[, as_of, …])

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rtype
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Optional[Asset]

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get_close_prices([start, end])

Get close price series

get_constituents([as_of, position_type])

Get asset constituents

get_currency()

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rtype
\n

Optional[Currency]

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get_data_coordinate(measure[, dimensions, …])

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rtype
\n

DataCoordinate

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get_data_series(measure[, dimensions, …])

Get asset series

get_entity()

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rtype
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Optional[Dict]

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get_identifier(id_type[, as_of])

Get asset identifier

get_identifiers([as_of])

Get asset identifiers

get_marquee_id()

get_return_type()

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rtype
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ReturnType

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get_type()

Overridden by sub-classes to return security type

get_unique_entity_key()

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EntityKey

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Attributes

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data_dimension

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str

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\n gs_quant.markets.securities.AssetType\n

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\n gs_quant.markets.securities.Stock\n

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gs_quant.markets.securities.SecurityMaster

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\nclass SecurityMaster[source]
\n

Security Master

\n
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The SecurityMaster class provides an interface to security lookup functions. This allows querying and retrieval of\ndifferent security types (assets) based on a variety of different identifiers through point-in-time lookups.

\n

Uses the current PricingContext to provide as of dates if optional arguments are not provided. Will return\nthe relevant asset subclass depending on the type of the security

\n
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See also

\n

Asset

\n
\n
\n__init__()
\n

Initialize self. See help(type(self)) for accurate signature.

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Methods

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__init__()

Initialize self.

get_asset(id_value, id_type[, as_of, …])

Get an asset by identifier and identifier type

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\n Models Package\n

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gs_quant.markets.securities.Stock

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\nclass Stock(id_, name, exchange=None, currency=None, entity=None)[source]
\n

Base Security Type

\n

Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\nreferenced in a derivative transaction

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\n__init__(id_, name, exchange=None, currency=None, entity=None)[source]
\n

Initialize self. See help(type(self)) for accurate signature.

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Methods

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__init__(id_, name[, exchange, currency, entity])

Initialize self.

entity_type()

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rtype
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EntityType

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get(id_value, id_type[, as_of, …])

\n
rtype
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Optional[Asset]

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get_close_prices([start, end])

Get close price series

get_currency()

\n
rtype
\n

Optional[Currency]

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get_data_coordinate(measure[, dimensions, …])

\n
rtype
\n

DataCoordinate

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get_data_series(measure[, dimensions, …])

Get asset series

get_entity()

\n
rtype
\n

Optional[Dict]

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get_identifier(id_type[, as_of])

Get asset identifier

get_identifiers([as_of])

Get asset identifiers

get_marquee_id()

get_type()

Overridden by sub-classes to return security type

get_unique_entity_key()

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EntityKey

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Attributes

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data_dimension

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str

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gs_quant.models.epidemiology.EpidemicModel

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\nclass EpidemicModel(model, parameters=None, data=None, initial_conditions=None, fit_method='leastsq', error=None, fit_period=None)[source]
\n

Class to perform solutions and parameter-fitting of epidemic models

\n
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\n__init__(model, parameters=None, data=None, initial_conditions=None, fit_method='leastsq', error=None, fit_period=None)[source]
\n

A class to standardize fitting and solving epidemiological models.

\n
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Parameters
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  • model (Type[CompartmentalModel]) – the model to use, currently a class in the form of SIR, SEIR above

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  • parameters (Optional[tuple]) – tuple, parameters to use for the model, defaults to the output of [model].get_parameters

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  • data (Optional[array]) – np.array, data that can be used to calibrate the model

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  • initial_conditions (Optional[list]) – list, initial conditions for the model

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  • fit_method (str) – str, the method to use to minimize the (given) error. Available methods are those in the\nlmfit.minimizer.minimize function. Default is Levenberg-Marquardt least squares minimization.

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  • error (Optional[callable]) – callable, control which residuals (and in what form) to minimize for fitting.

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  • fit_period (Optional[float]) – float, how far back to fit the data, defaults to fitting all data

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Methods

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__init__(model[, parameters, data, …])

A class to standardize fitting and solving epidemiological models.

fit([time_range, parameters, …])

Fit the model based on data in the form np.array([X1,…,Xn])

residual(parameters, time_range, data)

Obtain fit error (to minimize).

solve(time_range, initial_conditions, parameters)

Integrate the model ODEs to get a solution.

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\n gs_quant.models.epidemiology.SEIR\n

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\n Risk Package\n

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gs_quant.models.epidemiology.SEIR

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\nclass SEIR[source]
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SEIR Model

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\n__init__()
\n

Initialize self. See help(type(self)) for accurate signature.

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Methods

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__init__()

Initialize self.

calibrate(xs, t, parameters)

SEIR model derivatives at t.

get_parameters(S0, E0, I0, R0, N[, beta, …])

Produce a set of parameters for the SIR model.

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\n gs_quant.models.epidemiology.SIR\n

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\n gs_quant.models.epidemiology.EpidemicModel\n

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gs_quant.models.epidemiology.SIR

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\nclass SIR[source]
\n

SIR Model

\n
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\n__init__()
\n

Initialize self. See help(type(self)) for accurate signature.

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Methods

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__init__()

Initialize self.

calibrate(xs, t, parameters)

SIR model derivatives at t.

get_parameters(S0, I0, R0, N[, beta, gamma, …])

Produce a set of parameters for the SIR model.

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\n gs_quant.models.epidemiology.SEIR\n

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gs_quant.timeseries.datetime.Window

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\nclass Window(w=None, r=None)[source]
\n

Create a Window with size and ramp up to use.

\n
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Parameters
\n
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  • w (Union[int, str, None]) – window size

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  • r (Union[int, str, None]) – ramp up value. Defaults to the window size.

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Returns
\n

new window object

\n
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Usage

\n

The window size and ramp up value can either the number of observations or a string representation of the time\nperiod.

\n

Examples

\n

Window size is \\(22\\) obversations and the ramp up value is \\(10\\):

\n
>>> Window(22, 10)\n
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Window size is one month and the ramp up size is one week:

\n
>>> Window('1m', '1w')\n
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Methods

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as_dict()

from_dict(obj)

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\n gs_quant.timeseries.datetime.union\n

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gs_quant.timeseries.statistics.LinearRegression

\n
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\nclass LinearRegression(X, y, fit_intercept=True)[source]
\n

Fit an Ordinary least squares (OLS) linear regression model.

\n
\n
Parameters
\n
    \n

  • X (Union[Series, List[Series]]) – observations of the explanatory variable(s)

    • \n

  • y (Series) – observations of the dependant variable

    • \n

  • fit_intercept (bool) – whether to calculate intercept in the model

    • \n
\n
\n
\n

Usage

\n

Fit OLS Model based on observations of the explanatory\nvariables(s) X and the dependant variable y. If X and y are not aligned, only use the intersection of dates/times

\n

Examples

\n

R Squared of an OLS model:

\n
>>> x = generate_series(100)\n>>> y = generate_series(100)\n>>> r = LinearRegression(x, y)\n>>> r.r_squared()\n
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Methods

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

coefficient(i)

Estimated coefficient

fitted_values()

Fitted values

predict(X_predict)

Use the model for prediction

r_squared()

Coefficient of determination (R Squared)

standard_deviation_of_errors()

Standard deviation of the error term

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gs_quant.timeseries.statistics.RollingLinearRegression

\n
\n
\nclass RollingLinearRegression(X, y, w, fit_intercept=True)[source]
\n

Fit a rolling ordinary least squares (OLS) linear regression model.

\n
\n
Parameters
\n
    \n

  • X (Union[Series, List[Series]]) – observations of the explanatory variable(s)

    • \n

  • y (Series) – observations of the dependant variable

    • \n

  • w (int) – number of observations in each rolling window. Must be larger than the number of observations or\nexplanatory variables

    • \n

  • fit_intercept (bool) – whether to calculate intercept in the model

    • \n
\n
\n
\n

Usage

\n

Fit OLS Model based on observations of the explanatory\nvariables(s) X and the dependant variable y across a rolling window with fixed number of observations.\nThe parameters of each rolling window are stored at the end of each window.\nIf X and y are not aligned, only use the intersection of dates/times

\n

Examples

\n

R Squared of a rolling OLS model:

\n
>>> x = generate_series(100)\n>>> y = generate_series(100)\n>>> r = RollingLinearRegression(x, y, 5)\n>>> r.r_squared()\n
\n
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Methods

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coefficient(i)

Estimated coefficients

fitted_values()

Fitted values at the end of each rolling window

r_squared()

Coefficients of determination (R Squared) of rolling regressions

standard_deviation_of_errors()

Standard deviations of the error terms

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\n gs_quant.timeseries.statistics.LinearRegression\n

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\n gs_quant.timeseries.statistics.SIRModel\n

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gs_quant.timeseries.statistics.SEIRModel

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\nclass SEIRModel(beta=None, gamma=None, sigma=None, s=None, e=None, i=None, r=None, n=None, fit=True, fit_period=None)[source]
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SEIR Compartmental model for transmission of infectious disease

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Parameters
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  • beta (Optional[float]) – transmission rate of the infection

    • \n

  • gamma (Optional[float]) – recovery rate of the infection

    • \n

  • sigma (Optional[float]) – immunity rate from exposed to infected

    • \n

  • s (Union[Series, float, None]) – number of susceptible individuals in population

    • \n

  • e (Union[Series, float, None]) – number of exposed individuals in population

    • \n

  • i (Union[Series, float, None]) – number of infectious individuals in population

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  • r (Union[Series, float, None]) – number of recovered individuals in population

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  • n (Union[Series, float, None]) – total population size

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  • end_date – end date for the evolution of the model

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  • fit (bool) – whether to fit the model to the data

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  • fit_period (Optional[int]) – on how many days back to fit the model

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Usage

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Fit SEIR Model based on the\npopulation in each compartment over a given time period.

\n

The SEIR models the movement of individuals between four compartments: susceptible (S), exposed (E), infected (I),\nand resistant (R). The model calibrates parameters :

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Parameter

Description

S0

initial susceptible individuals

E0

initial exposed individuals

I0

initial infected individuals

R0

initial recovered individuals

beta

Transmission rate from susceptible to exposed

gamma

Immunity rate from infected to resistant

sigma

Immunity rate from exposed to infected

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The parameters beta, gamma, and sigma, model how fast people move from being susceptible to exposed (beta),\nfrom exposed to infected (sigma), and subsequently from infected to resistant (gamma). This model can be used to\npredict the populations of each compartment once calibrated.

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Methods

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beta()

Model calibration for transmission rate (susceptible to exposed)

e0()

Model calibration for initial exposed individuals

gamma()

Model calibration for immunity (infected to resistant)

i0()

Model calibration for initial infectious individuals

predict_e()

Model calibration for exposed individuals through time

predict_i()

Model calibration for infected individuals through time

predict_r()

Model calibration for recovered individuals through time

predict_s()

Model calibration for susceptible individuals through time

r0()

Model calibration for initial recovered individuals

s0()

Model calibration for initial susceptible individuals

sigma()

Model calibration for infection rate (exposed to infected)

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gs_quant.timeseries.statistics.SIRModel

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\nclass SIRModel(beta=None, gamma=None, s=None, i=None, r=None, n=None, fit=True, fit_period=None)[source]
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SIR Compartmental model for transmission of infectious disease

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Parameters
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  • beta (Optional[float]) – transmission rate of the infection

    • \n

  • gamma (Optional[float]) – recovery rate of the infection

    • \n

  • s (Union[Series, float, None]) – number of susceptible individuals in population

    • \n

  • i (Union[Series, float, None]) – number of infectious individuals in population

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  • r (Union[Series, float, None]) – number of recovered individuals in population

    • \n

  • n (Union[Series, float, None]) – total population size

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  • end_date – end date for the evolution of the model

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  • fit (bool) – whether to fit the model to the data

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  • fit_period (Optional[int]) – on how many days back to fit the model

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Usage

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Fit SIR Model based on the\npopulation in each compartment over a given time period.

\n

The SIR models the movement of individuals between three compartments: susceptible (S), infected (I), and resistant\n(R). The model calibrates parameters :

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Parameter

Description

S0

initial susceptible individuals

I0

initial infected individuals

R0

initial recovered individuals

beta

Transmission rate from susceptible to infected

gamma

Immunity rate from infected to resistant

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The parameters beta and gamma model how fast people move from being susceptible to infected (beta), and\nsubsequently from infected to resistant (gamma). This model can be used to forecast the populations of each\ncompartment once calibrated

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Methods

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beta()

Model calibration for transmission rate (susceptible to infected)

gamma()

Model calibration for immunity (infected to resistant)

i0()

Model calibration for initial infectious individuals

predict_i()

Model calibration for infected individuals through time

predict_r()

Model calibration for recovered individuals through time

predict_s()

Model calibration for susceptible individuals through time

r0()

Model calibration for initial recovered individuals

s0()

Model calibration for initial susceptible individuals

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Data Package

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DataContext([start, end])

Dataset(dataset_id[, provider])

A collection of related data

Fields([value, names, module, type, start])

Data field enumeration

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Datetime Package

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Date

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business_day_count(begin_dates, end_dates[, …])

Determine the number of business days between begin_dates and end_dates

business_day_offset(dates, offsets[, roll, …])

Apply offsets to the dates and move to the nearest business date

date_range(begin, end[, calendars, week_mask])

Construct a range of dates

is_business_day(dates[, calendars, week_mask])

Determine whether each date in dates is a business day

prev_business_date([dates, calendars, week_mask])

Returns the previous business date for a given date or date series, defaulting to today.

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Point

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point_sort_order(point[, ref_date])

Calculates a number that can be used to sort Mkt Points by it.

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\n business_day_count\n

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gs_quant.datetime.date.business_day_count

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\nbusiness_day_count(begin_dates, end_dates, calendars=(), week_mask=None)[source]
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Determine the number of business days between begin_dates and end_dates

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Parameters
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  • begin_dates (Union[date, Iterable[date]]) – A date or collection of beginning dates

    • \n

  • end_dates (Union[date, Iterable[date]]) – A date or collection of end dates

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  • calendars (Union[str, Tuple[str, …]]) – Calendars to use for holidays

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  • week_mask (Optional[str]) – Which days are considered weekends (defaults to Saturday and Sunday)

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Return type
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Union[int, Tuple[int]]

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Returns
\n

An int or tuple of ints, representing the number of business days between begin_dates and end_dates

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Examples

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>>> import datetime as dt\n>>> today = dt.date.today()\n>>> bus_days = business_day_count(today, today + dt.timedelta(days=7))\n
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\n gs_quant.datetime.date.business_day_offset\n

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gs_quant.datetime.date.business_day_offset

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\nbusiness_day_offset(dates, offsets, roll='raise', calendars=(), week_mask=None)[source]
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Apply offsets to the dates and move to the nearest business date

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Parameters
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  • dates (Union[date, Iterable[date]]) – The input date or dates

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  • offsets (Union[int, Iterable[int]]) – The number of days by which to adjust the dates

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  • roll (str) – Which direction to roll, in order to get to the nearest business date

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  • calendars (Union[str, Tuple[str, …]]) – Calendars to use for holidays

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  • week_mask (Optional[str]) – Which days are considered weekends (defaults to Saturday and Sunday)

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Union[date, Iterable[date]]

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Returns
\n

A date (if dates is a single date) or tuple of dates, adjusted by the offsets

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Examples

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>>> import datetime as dt\n>>> prev_bus_date = business_day_offset(dt.date.today(), -1, roll='forward')\n
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gs_quant.datetime.date.date_range

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\ndate_range(begin, end, calendars=(), week_mask=None)[source]
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Construct a range of dates

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Parameters
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  • begin (Union[int, date]) – Beginning date or int. An int will be interpreted as the number of business days before end\n(which must be a date)

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  • end (Union[int, date]) – End date or int. An int will be interpreted as the number of business days after begin\n(which must be a date)

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Iterable[date]

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Returns
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A generator of dates

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>>> import datetime as dt\n>>> today = dt.date.today()\n>>> dates = tuple(date_range(5, today))\n>>>\n>>> for date in date_range(dt.date(2019, 1, 1), dt.date(2019, 2, 1)):\n>>>     print(date)\n
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gs_quant.datetime.date.is_business_day

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\nis_business_day(dates, calendars=(), week_mask=None)[source]
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Determine whether each date in dates is a business day

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Parameters
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  • dates (Union[date, Iterable[date]]) – The input date or dates

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  • calendars (Union[str, Tuple[str, …]]) – Calendars to use for holidays

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  • week_mask (Optional[str]) – Which days are considered weekends (defaults to Saturday and Sunday)

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Union[bool, Tuple[bool]]

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Returns
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True/False if dates is a single date. A tuple indicating True/False for each date if dates is an iterable

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Examples

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>>> import datetime as dt\n>>> is_business_day(dt.date.today())\n>>> is_business_day(dt.date(2019, 7, 4), calendars=('NYSE',))\n
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gs_quant.datetime.date.prev_business_date

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\nprev_business_date(dates=datetime.date(2021, 1, 5), calendars=(), week_mask=None)[source]
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Returns the previous business date for a given date or date series, defaulting to today.

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Parameters
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  • dates (Union[date, Iterable[date]]) – The input date or dates, defaults to today

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  • calendars (Union[str, Tuple[str, …]]) – Calendars to use for holidays

    • \n

  • week_mask (Optional[str]) – Which days are considered weekends (defaults to Saturday and Sunday)

    • \n
\n
\n
Return type
\n

Union[date, Iterable[date]]

\n
\n
Returns
\n

A date (if dates is a single date) or tuple of dates, adjusted by the offset of one day.

\n
\n
\n

Example

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>>> import datetime as dt\n>>> prev_bus_date = prev_business_date()\n
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\n gs_quant.datetime.date.is_business_day\n

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\n gs_quant.datetime.point.point_sort_order\n

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gs_quant.datetime.point.point_sort_order

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\n
\npoint_sort_order(point, ref_date=datetime.date(2021, 1, 5))[source]
\n

Calculates a number that can be used to sort Mkt Points by it.

\n
\n
Parameters
\n
    \n

  • point (str) – The point string from MarketDataCoordinate.

    • \n

  • ref_date (date) – Reference date, normally the pricing date.

    • \n
\n
\n
Return type
\n

float

\n
\n
Returns
\n

The number of days from the reference date to the date specified by the point string

\n
\n
\n

Examples

\n
>>> import datetime as dt\n>>> days = point_sort_order(point = 'Dec20', ref_date=dt.date.today())\n
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\n gs_quant.datetime.date.prev_business_date\n

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\n Instrument Package\n

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gs_quant.timeseries.algebra.abs_

\n
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\nabs_(x)[source]
\n

Absolute value of each element in series

\n
\n
Parameters
\n

x (Series) – date-based time series of prices

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of absolute value

\n
\n
\n

Usage

\n

Return the absolute value of \\(X\\). For each value in time series \\(X_t\\), return \\(X_t\\) if \\(X_t\\)\nis greater than or equal to 0; otherwise return \\(-X_t\\):

\n

\\(R_t = |X_t|\\)

\n

Equivalent to \\(R_t = \\sqrt{X_t^2}\\)

\n

Examples

\n

Generate price series and take absolute value of \\(X_t-100\\)

\n
>>> prices = generate_series(100) - 100\n>>> abs_(prices)\n
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See also

\n

exp() sqrt()

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\n Timeseries Package\n

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\n gs_quant.timeseries.algebra.add\n

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gs_quant.timeseries.algebra.add

\n
\n
\nadd(x, y, method=<Interpolate.STEP: 'step'>)[source]
\n

Add two series or scalars

\n
\n
Parameters
\n
    \n

  • x (Union[Series, Real]) – timeseries or scalar

    • \n

  • y (Union[Series, Real]) – timeseries or scalar

    • \n

  • method (Interpolate) – interpolation method (default: step). Only used when both x and y are timeseries

    • \n
\n
\n
Return type
\n

Union[Series, Real]

\n
\n
Returns
\n

timeseries of x + y or sum of the given real numbers

\n
\n
\n

Usage

\n

Add two series or scalar variables with the given interpolation method

\n

\\(R_t = X_t + Y_t\\)

\n

Alignment operators:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Method

Behavior

intersect

Resultant series only has values on the intersection of dates. Values\nfor dates present in only one series will be ignored

nan

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as nan in the\nother series, and therefore in the resultant series

zero

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as zero in the\nother series

step

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be interpolated via step\nfunction in the other series

time

Resultant series have values on the union of dates / times. Missing\nvalues surrounded by valid values will be interpolated given length of\ninterval. Input series must use DateTimeIndex.

\n

Examples

\n

Add two series:

\n
>>> a = generate_series(100)\n>>> b = generate_series(100)\n>>> add(a, b, Interpolate.STEP)\n
\n
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See also

\n

subtract()

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\n gs_quant.timeseries.algebra.abs_\n

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\n Next Topic\n

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\n gs_quant.timeseries.algebra.and_\n

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gs_quant.timeseries.algebra.and_

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Logical “and” of two or more boolean series.

\n
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Parameters
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series (Series) – input series

\n
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Return type
\n

Series

\n
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Returns
\n

result series (of numeric type, with booleans represented as 1s and 0s)

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\n gs_quant.timeseries.algebra.add\n

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\n gs_quant.timeseries.algebra.ceil\n

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gs_quant.timeseries.algebra.ceil

\n
\n
\nceil(x, value=0)[source]
\n

Cap series at maximum value

\n
\n
Parameters
\n
    \n

  • x (Series) – date-based time series of prices

    • \n

  • value (float) – maximum value

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of maximum value

\n
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\n

Usage

\n

Returns series where all values are less than or equal to the maximum value.

\n

\\(R_t = min(X_t, value)\\)

\n

See Floor and Ceil functions for more details

\n

Examples

\n

Generate price series and floor all values at 100

\n
>>> prices = generate_series(100)\n>>> floor(prices, 100)\n
\n
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See also

\n

floor()

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\n gs_quant.timeseries.algebra.divide\n

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gs_quant.timeseries.algebra.divide

\n
\n
\ndivide(x, y, method=<Interpolate.STEP: 'step'>)[source]
\n

Divide two series or scalars

\n
\n
Parameters
\n
    \n

  • x (Union[Series, Real]) – timeseries or scalar

    • \n

  • y (Union[Series, Real]) – timeseries or scalar

    • \n

  • method (Interpolate) – interpolation method (default: step). Only used when both x and y are timeseries

    • \n
\n
\n
Return type
\n

Union[Series, Real]

\n
\n
Returns
\n

timeseries of x / y or quotient of the given real numbers

\n
\n
\n

Usage

\n

Divide two series or scalar variables applying the given interpolation method

\n

\\(R_t = X_t / Y_t\\)

\n

Alignment operators:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Method

Behavior

intersect

Resultant series only has values on the intersection of dates.\nValues for dates present in only one series will be ignored

nan

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as nan in the\nother series, and therefore in the resultant series

zero

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as zero in the\nother series

step

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be interpolated via step\nfunction in the other series

time

Resultant series have values on the union of dates / times. Missing\nvalues surrounded by valid values will be interpolated given length of\ninterval. Input series must use DateTimeIndex.

\n

Examples

\n

Divide two series:

\n
>>> a = generate_series(100)\n>>> b = generate_series(100)\n>>> divide(a, b, Interpolate.STEP)\n
\n
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See also

\n

multiply()

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\n gs_quant.timeseries.algebra.exp\n

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gs_quant.timeseries.algebra.exp

\n
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\nexp(x)[source]
\n

Exponential of series

\n
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Parameters
\n

x (Series) – timeseries

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

exponential of each element

\n
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\n

Usage

\n

For each element in the series, \\(X_t\\), raise \\(e\\) (Euler’s number) to the power of \\(X_t\\).\nEuler’s number is the base of the natural logarithm, \\(ln\\).

\n

\\(R_t = e^{X_t}\\)

\n

Examples

\n

Raise \\(e\\) to the power \\(1\\). Returns Euler’s number, approximately 2.71828

\n
>>> exp(1)\n
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See also

\n

log()

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\n gs_quant.timeseries.algebra.divide\n

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\n gs_quant.timeseries.algebra.filter_\n

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gs_quant.timeseries.algebra.filter_

\n
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\nfilter_(x, operator=None, value=None)[source]
\n

Removes values where comparison with the operator and value combination results in true, defaults to removing\nmissing values from the series

\n
\n
Parameters
\n
    \n

  • x (Series) – timeseries

    • \n

  • operator (Optional[FilterOperator]) – FilterOperator describing logic for value removal, e.g ‘less_than’

    • \n

  • value (Optional[Real]) – number indicating value(s) to remove from the series

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries with specified values removed

\n
\n
\n

Usage

\n

Remove each value determined by operator and value from timeseries where that expression yields true

\n

Examples

\n

Remove 0 from time series

\n
>>> prices = generate_series(100)\n>>> filter_(prices, FilterOperator.EQUALS, 0)\n
\n
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Remove positive numbers from time series

\n
>>> prices = generate_series(100)\n>>> filter_(prices, FilterOperator.GREATER, 0)\n
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\n

Remove missing values from time series

\n
>>> prices = generate_series(100)\n>>> filter_(prices)\n
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\n gs_quant.timeseries.algebra.floor\n

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gs_quant.timeseries.algebra.floor

\n
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\nfloor(x, value=0)[source]
\n

Floor series at minimum value

\n
\n
Parameters
\n
    \n

  • x (Series) – date-based time series of prices

    • \n

  • value (float) – minimum value

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of maximum value

\n
\n
\n

Usage

\n

Returns series where all values are greater than or equal to the minimum value.

\n

\\(R_t = max(X_t, value)\\)

\n

See Floor and Ceil functions for more details

\n

Examples

\n

Generate price series and floor all values at 100

\n
>>> prices = generate_series(100)\n>>> floor(prices, 100)\n
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See also

\n

ceil()

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\n gs_quant.timeseries.algebra.floordiv\n

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gs_quant.timeseries.algebra.floordiv

\n
\n
\nfloordiv(x, y, method=<Interpolate.STEP: 'step'>)[source]
\n

Floor divide two series or scalars

\n
\n
Parameters
\n
    \n

  • x (Union[Series, Real]) – timeseries or scalar

    • \n

  • y (Union[Series, Real]) – timeseries or scalar

    • \n

  • method (Interpolate) – interpolation method (default: step). Only used for operating two series

    • \n
\n
\n
Return type
\n

Union[Series, Real]

\n
\n
Returns
\n

timeseries of x // y or quotient of the floor division of the given real numbers

\n
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\n

Usage

\n

Divide two series or scalar variables applying the given interpolation method

\n

\\(R_t = X_t / Y_t\\)

\n

Alignment operators:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Method

Behavior

intersect

Resultant series only has values on the intersection of dates.\nValues for dates present in only one series will be ignored

nan

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as nan in the\nother series, and therefore in the resultant series

zero

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as zero in the\nother series

step

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be interpolated via step\nfunction in the other series

\n

Examples

\n

Floor divide two series:

\n
>>> a = generate_series(100)\n>>> b = generate_series(100)\n>>> floordiv(a, b, Interpolate.STEP)\n
\n
\n

See also

\n

divide()

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\n gs_quant.timeseries.algebra.floor\n

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\n gs_quant.timeseries.algebra.if_\n

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gs_quant.timeseries.algebra.if_

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\nif_(flags, x, y)[source]
\n

Returns a series s. For i in the index of flags, s[i] = x[i] if flags[i] == 1 else y[i].

\n
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\n
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  • flags (Series) – series of 1s and 0s

    • \n

  • x (Union[Series, float]) – values to use when flag is 1

    • \n

  • y (Union[Series, float]) – values to use when flag is 0

    • \n
\n
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Return type
\n

Series

\n
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Returns
\n

result series

\n
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\n gs_quant.timeseries.algebra.floordiv\n

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\n gs_quant.timeseries.algebra.log\n

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gs_quant.timeseries.algebra.log

\n
\n
\nlog(x)[source]
\n

Natural logarithm of series

\n
\n
Parameters
\n

x (Series) – timeseries

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

series with exponential of each element

\n
\n
\n

Usage

\n

For each element in the series, \\(X_t\\), return the natural logarithm \\(ln\\) of \\(X_t\\)\nThe natural logarithm is the logarithm in base \\(e\\).

\n

\\(R_t = log(X_t)\\)

\n

This function is the inverse of the exponential function.

\n

More information on logarithms

\n

Examples

\n

Take natural logarithm of 3

\n
>>> log(3)\n
\n
\n

See also

\n

exp()

\n
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\n gs_quant.timeseries.algebra.if_\n

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\n gs_quant.timeseries.algebra.multiply\n

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gs_quant.timeseries.algebra.multiply

\n
\n
\nmultiply(x, y, method=<Interpolate.STEP: 'step'>)[source]
\n

Multiply two series or scalars

\n
\n
Parameters
\n
    \n

  • x (Union[Series, Real]) – timeseries or scalar

    • \n

  • y (Union[Series, Real]) – timeseries or scalar

    • \n

  • method (Interpolate) – interpolation method (default: step). Only used when both x and y are timeseries

    • \n
\n
\n
Return type
\n

Union[Series, Real]

\n
\n
Returns
\n

timeseries of x * y or product of the given real numbers

\n
\n
\n

Usage

\n

Multiply two series or scalar variables applying the given interpolation method

\n

\\(R_t = X_t \\times Y_t\\)

\n

Alignment operators:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Method

Behavior

intersect

Resultant series only has values on the intersection of dates. Values\nfor dates present in only one series will be ignored

nan

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as nan in the\nother series, and therefore in the resultant series

zero

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as zero in the\nother series

step

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be interpolated via step\nfunction in the other series

time

Resultant series have values on the union of dates / times. Missing\nvalues surrounded by valid values will be interpolated given length of\ninterval. Input series must use DateTimeIndex.

\n

Examples

\n

Multiply two series:

\n
>>> a = generate_series(100)\n>>> b = generate_series(100)\n>>> multiply(a, b, Interpolate.STEP)\n
\n
\n

See also

\n

divide()

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\n gs_quant.timeseries.algebra.log\n

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\n gs_quant.timeseries.algebra.not_\n

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gs_quant.timeseries.algebra.not_

\n
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\nnot_(series)[source]
\n

Logical negation of a single boolean series.

\n
\n
Parameters
\n

series (Series) – single input series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

result series (of numeric type, with booleans represented as 1s and 0s)

\n
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\n gs_quant.timeseries.algebra.multiply\n

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\n gs_quant.timeseries.algebra.or_\n

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gs_quant.timeseries.algebra.or_

\n
\n
\nor_(*series)[source]
\n

Logical “or” of two or more boolean series.

\n
\n
Parameters
\n

series (Series) – input series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

result series (of numeric type, with booleans represented as 1s and 0s)

\n
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\n gs_quant.timeseries.algebra.not_\n

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\n gs_quant.timeseries.algebra.power\n

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gs_quant.timeseries.algebra.power

\n
\n
\npower(x, y=1)[source]
\n

Raise each element in series to power

\n
\n
Parameters
\n
    \n

  • x (Series) – timeseries

    • \n

  • y (float) – value

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of square roots

\n
\n
\n

Usage

\n

Raise each value in time series \\(X_t\\) to the power \\(y\\):

\n

\\(R_t = X_t^{y}\\)

\n

Examples

\n

Generate price series and raise each value to the power 2:

\n
>>> prices = generate_series(100)\n>>> power(prices, 2)\n
\n
\n

See also

\n

sqrt()

\n
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\n gs_quant.timeseries.algebra.or_\n

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\n gs_quant.timeseries.algebra.repeat\n

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gs_quant.timeseries.algebra.repeat

\n
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\nrepeat(x, n=1)[source]
\n

Repeats values for days where data is missing. For any date with missing data, the last recorded value is used.\nOptionally downsamples the result such that there are data points every n days.

\n
\n
Parameters
\n
    \n

  • x (Series) – date-based timeseries

    • \n

  • n (int) – desired frequency of output

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

a timeseries that has been forward-filled, and optionally downsampled

\n
\n
\n

Usage

\n

Fill missing values with last seen value e.g. to combine daily with weekly or monthly data.

\n
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\n gs_quant.timeseries.algebra.power\n

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\n gs_quant.timeseries.algebra.smooth_spikes\n

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gs_quant.timeseries.algebra.smooth_spikes

\n
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\nsmooth_spikes(x, threshold)[source]
\n

Smooth out the spikes of a series. If a point is larger/smaller than (1 +/- threshold) times both neighbors, replace\nit with the average of those neighbours. Note: the first and last points in the input series are dropped.

\n
\n
Parameters
\n
    \n

  • x (Series) – timeseries

    • \n

  • threshold (float) – minimum increment to trigger filter

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

smoothed timeseries

\n
\n
\n

Usage

\n

Returns series where values that exceed the threshold relative to both neighbors are replaced.

\n

Examples

\n

Generate price series and smooth spikes over a threshold of 0.5.

\n
>>> prices = generate_series(100)\n>>> smooth_spikes(prices, 0.5)\n
\n
\n

See also

\n

exponential_moving_average()

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\n gs_quant.timeseries.algebra.repeat\n

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\n gs_quant.timeseries.algebra.sqrt\n

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gs_quant.timeseries.algebra.sqrt

\n
\n
\nsqrt(x)[source]
\n

Square root of (a) each element in a series or (b) a real number

\n
\n
Parameters
\n

x (Union[Real, Series]) – date-based time series of prices or real number

\n
\n
Return type
\n

Union[Real, Series]

\n
\n
Returns
\n

date-based time series of square roots or square root of given number

\n
\n
\n

Usage

\n

Return the square root of each value in time series \\(X_t\\):

\n

\\(R_t = \\sqrt{X_t}\\)

\n

Examples

\n

Generate price series and take square root of each value:

\n
>>> prices = generate_series(100)\n>>> sqrt(prices)\n
\n
\n

See also

\n

pow()

\n
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\n gs_quant.timeseries.algebra.smooth_spikes\n

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\n gs_quant.timeseries.algebra.subtract\n

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gs_quant.timeseries.algebra.subtract

\n
\n
\nsubtract(x, y, method=<Interpolate.STEP: 'step'>)[source]
\n

Add two series or scalars

\n
\n
Parameters
\n
    \n

  • x (Union[Series, Real]) – timeseries or scalar

    • \n

  • y (Union[Series, Real]) – timeseries or scalar

    • \n

  • method (Interpolate) – index alignment operator (default: intersect). Only used when both x and y are timeseries

    • \n
\n
\n
Return type
\n

Union[Series, Real]

\n
\n
Returns
\n

timeseries of x - y or difference between the given real numbers

\n
\n
\n

Usage

\n

Subtracts one series or scalar from another applying the given interpolation method

\n

\\(R_t = X_t - Y_t\\)

\n

Alignment operators:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Method

Behavior

intersect

Resultant series only has values on the intersection of dates.\nValues for dates present in only one series will be ignored

nan

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as nan in the\nother series, and therefore in the resultant series

zero

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be treated as zero in the\nother series

step

Resultant series has values on the union of dates in both series. Values\nfor dates only available in one series will be interpolated via step\nfunction in the other series

time

Resultant series have values on the union of dates / times. Missing\nvalues surrounded by valid values will be interpolated given length of\ninterval. Input series must use DateTimeIndex.

\n

Examples

\n

Subtract one series from another:

\n
>>> a = generate_series(100)\n>>> b = generate_series(100)\n>>> subtract(a, b, Interpolate.STEP)\n
\n
\n

See also

\n

add()

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\n gs_quant.timeseries.algebra.sqrt\n

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\n gs_quant.timeseries.algebra.weighted_sum\n

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gs_quant.timeseries.algebra.weighted_sum

\n
\n
\nweighted_sum(series, weights)[source]
\n

Calculate a weighted sum.

\n
\n
Parameters
\n
    \n

  • series (List[Series]) – list of time series

    • \n

  • weights (list) – list of weights

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

time series of weighted average

\n
\n
\n

Usage

\n

Calculate a weighted sum e.g. for a basket.

\n

Examples

\n

Generate price series and get a sum (weights 70%/30%).

\n
>>> prices1 = generate_series(100)\n>>> prices2 = generate_series(100)\n>>> mybasket = weighted_sum([prices1, prices2], [0.7, 0.3])\n
\n
\n

See also

\n

basket()

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\n gs_quant.timeseries.algebra.subtract\n

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\n gs_quant.timeseries.analysis.diff\n

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gs_quant.timeseries.analysis.count

\n
\n
\ncount(x)[source]
\n

Count observations in series

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

number of observations

\n
\n
\n

Usage

\n

Count the number of valid observations in a series:

\n

\\(R_t = R_{t-1} + 1\\)

\n

if \\(X_t\\) is not NaN, and

\n

\\(R_t = R_{t-1} + 0\\)

\n

if \\(X_t\\) is NaN

\n

Examples

\n

Count observations in series:

\n
>>> series = generate_series(100)\n>>> count = count(series)\n
\n
\n

See also

\n

sum()

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\n gs_quant.timeseries.analysis.last_value\n

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\n gs_quant.timeseries.analysis.lag\n

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gs_quant.timeseries.analysis.diff

\n
\n
\ndiff(x, obs=1)[source]
\n

Diff observations with given lag

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • obs (int) – number of observations to lag

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

Compute the difference in series values over a given lag:

\n

\\(R_t = X_t - X_{t-obs}\\)

\n

where \\(obs\\) is the number of observations to lag series in diff function

\n

Examples

\n

Diff prices levels:

\n
>>> series = generate_series(100)\n>>> returns = diff(series)\n
\n
\n

See also

\n

lag()

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\n gs_quant.timeseries.algebra.weighted_sum\n

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\n gs_quant.timeseries.analysis.first\n

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gs_quant.timeseries.analysis.first

\n
\n
\nfirst(x)[source]
\n

First value of series

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

time series of first value

\n
\n
\n

Usage

\n

Return series with first value of X for all dates:

\n

\\(R_t = X_0\\)

\n

where \\(X_0\\) is the first value in the series

\n

Examples

\n

Last value of series:

\n
>>> series = generate_series(100)\n>>> returns = first(series)\n
\n
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See also

\n

last()

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\n gs_quant.timeseries.analysis.diff\n

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\n gs_quant.timeseries.analysis.last\n

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gs_quant.timeseries.analysis.lag

\n
\n
\nlag(x, obs=1, mode=<LagMode.EXTEND: 'extend'>)[source]
\n

Lag timeseries by a number of observations or a relative date.

\n
\n
Parameters
\n
    \n

  • x (Series) – timeseries of prices

    • \n

  • obs (Union[Window, int, str]) – non-zero integer (number of observations) or relative date e.g. “-90d”, “1d”, “1m”, “1y”

    • \n

  • mode (LagMode) – whether to extend series index (into the future)

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

Shift the series backwards by a specified number of observations:

\n

\\(R_t = X_{t-obs}\\)

\n

where \\(obs\\) is the number of observations to lag series

\n

Examples

\n

Lag series by 2 observations:

\n
>>> prices = generate_series(100)\n>>> lagged = lag(prices, 2)\n
\n
\n

Lag series by 1 year:

\n
>>> prices = generate_series(100)\n>>> lagged = lag(prices, '1y')\n
\n
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See also

\n

diff()

\n
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\n gs_quant.timeseries.analysis.count\n

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\n gs_quant.timeseries.backtesting.basket\n

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gs_quant.timeseries.analysis.last

\n
\n
\nlast(x)[source]
\n

Last value of series (as a series)

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

time series of last value

\n
\n
\n

Usage

\n

Return series with last value of X for all dates:

\n

\\(R_t = X_T\\)

\n

where \\(X_T\\) is the last value in the series

\n

Examples

\n

Last value of series:

\n
>>> series = generate_series(100)\n>>> returns = last(series)\n
\n
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See also

\n

first()

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\n gs_quant.timeseries.analysis.first\n

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\n gs_quant.timeseries.analysis.last_value\n

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gs_quant.timeseries.analysis.last_value

\n
\n
\nlast_value(x)[source]
\n

Last value of series (as a scalar)

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Union[int, float]

\n
\n
Returns
\n

last value

\n
\n
\n

Usage

\n

Return a scalar value \\(X_T\\) where T is the last index value in the series.

\n

Examples

\n

Last value of series:

\n
>>> series = generate_series(100)\n>>> lv = last_value(series)\n
\n
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See also

\n

last()

\n
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\n Previous Topic\n

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\n gs_quant.timeseries.analysis.last\n

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\n Next Topic\n

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\n gs_quant.timeseries.analysis.count\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/functions/gs_quant.timeseries.analysis.weighted_sum.html", "content": "\n\n\n\n \n\n \n \n \n \n \n weighted_sum — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n\n\n\n
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weighted_sum

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gs_quant.timeseries.backtesting.basket

\n
\n
\nbasket(series, weights, costs=None, rebal_freq=<RebalFreq.DAILY: 'daily'>, return_type=<ReturnType.EXCESS_RETURN: 'excess_return'>)[source]
\n

Calculates a basket return series.

\n
\n
Parameters
\n
    \n

  • series (list) – list of time series of instrument prices

    • \n

  • weights (list) – list of weights

    • \n

  • costs (Optional[list]) – list of execution costs in decimal; defaults to costs of 0

    • \n

  • rebal_freq (RebalFreq) – rebalancing frequency - Daily or Monthly

    • \n

  • return_type (ReturnType) – return type of underlying instruments - only excess return is supported

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

time series of the resulting basket

\n
\n
\n

Usage

\n

Calculates a basket return series.

\n

Examples

\n

Generate price series and combine them in a basket (weights 70%/30%) which rebalances monthly and assumes\nexecution costs 5bps and 10bps each time the constituents are traded.

\n
>>> prices1 = generate_series(100)\n>>> prices2 = generate_series(100)\n>>> mybasket = basket([prices1, prices2], [0.7, 0.3], [0.0005, 0.001], monthly)\n
\n
\n

See also

\n

prices()

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\n gs_quant.timeseries.analysis.lag\n

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\n gs_quant.timeseries.datetime.align\n

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gs_quant.timeseries.datetime.align

\n
\n
\nalign(x, y, method=<Interpolate.INTERSECT: 'intersect'>)[source]
\n

Align dates of two series or scalars

\n
\n
Parameters
\n
    \n

  • x (Union[Series, Real]) – first timeseries or scalar

    • \n

  • y (Union[Series, Real]) – second timeseries or scalar

    • \n

  • method (Interpolate) – interpolation method (default: intersect). Only used when both x and y are timeseries

    • \n
\n
\n
Return type
\n

Union[List[Series], List[Real]]

\n
\n
Returns
\n

timeseries with specified dates or two scalars from the input

\n
\n
\n

Usage

\n

Align the dates of two series using the specified interpolation method. Returns two series with dates based on the\nmethod of interpolation, for example, can be used to intersect the dates of two series, union dates with a defined\nmanner to compute missing values.

\n

Interpolation methods:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Type

Behavior

intersect

Resultant series only have values on the intersection of dates /times.

nan

Resultant series have values on the union of dates /times. Values will\nbe NaN for dates or times only present in the other series

zero

Resultant series have values on the union of dates / times. Values will\nbe zero for dates or times only present in the other series

step

Resultant series have values on the union of dates / times. Each series\nwill use the value of the previous valid point if requested date does\nnot exist. Values prior to the first date will be equivalent to the\nfirst available value

time

Resultant series have values on the union of dates / times. Missing\nvalues surrounded by valid values will be interpolated given length of\ninterval. Input series must use DateTimeIndex.

\n

Examples

\n

Stepwize interpolation of series based on dates in second series:

\n
>>> a = generate_series(100)\n>>> b = generate_series(100)\n>>> align(a, b)\n
\n
\n

See also

\n

sub()

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\n gs_quant.timeseries.backtesting.basket\n

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\n gs_quant.timeseries.datetime.interpolate\n

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gs_quant.timeseries.datetime.date_range

\n
\n
\ndate_range(x, start_date, end_date, weekdays_only=False)[source]
\n

Create a time series from a (sub-)range of dates in an existing time series.

\n
\n
Parameters
\n
    \n

  • x (Series) – time series

    • \n

  • start_date (Union[date, int]) – start date for the sliced time series. If integer, number of observations after the first

    • \n

  • end_date (Union[date, int]) – end date for the sliced time series. If integer, number of observations before the last

    • \n

  • weekdays_only (bool) – whether to include only weekdays in the sliced ranges

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

sliced time series

\n
\n
\n

Usage

\n

Returns a restricted (“sliced”) time series based on start and end dates:

\n

\\(Y_t = R_t |_{start < t < end}\\)

\n

Examples

\n

Slice the first and last week of a time series:

\n
>>> series = generate_series(100)\n>>> sliced_series = date_range(series,7,7)\n
\n
\n

See also

\n

day() :func: lag

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\n gs_quant.timeseries.datetime.quarter\n

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gs_quant.timeseries.datetime.day

\n
\n
\nday(x)[source]
\n

Day of each value in series

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

day of observations

\n
\n
\n

Usage

\n

Returns the day as a numeric value for each observation in the series:

\n

\\(Y_t = day(t)\\)

\n

Day of the time or date is the integer day number within the month, e.g. 1-31

\n

Examples

\n

Day for observations in series:

\n
>>> series = generate_series(100)\n>>> days = day(series)\n
\n
\n

See also

\n

month() year()

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\n gs_quant.timeseries.datetime.value\n

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\n Next Topic\n

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\n gs_quant.timeseries.datetime.weekday\n

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gs_quant.timeseries.datetime.interpolate

\n
\n
\ninterpolate(x, dates=None, method=<Interpolate.INTERSECT: 'intersect'>)[source]
\n

Interpolate over specified dates or times

\n
\n
Parameters
\n
    \n

  • x (Series) – timeseries to interpolate

    • \n

  • dates (Union[List[date], List[time], Series, None]) – array of dates/times or another series to interpolate

    • \n

  • method (Interpolate) – interpolation method (default: intersect)

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries with specified dates

\n
\n
\n

Usage

\n

Interpolate the series X over the dates specified by the dates parameter. This can be an array of dates or another\nseries, in which case the index of the series will be used to specify dates

\n

Interpolation methods:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Type

Behavior

intersect

Resultant series only has values on the intersection of dates /times.\nWill only contain intersection of valid dates / times in the series

nan

Resultant series only has values on the intersection of dates /times.\nValue will be NaN for dates not present in the series

zero

Resultant series has values on all requested dates / times. The series\nwill have a value of zero where the requested date or time was not\npresent in the series

step

Resultant series has values on all requested dates / times. The series\nwill use the value of the previous valid point if requested date does\nnot exist. Values prior to the first date will be equivalent to the\nfirst available value

\n

Examples

\n

Stepwize interpolation of series based on dates in second series:

\n
>>> a = generate_series(100)\n>>> b = generate_series(100)\n>>> interpolate(a, b, Interpolate.INTERSECT)\n
\n
\n

See also

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sub()

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\n gs_quant.timeseries.datetime.value\n

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gs_quant.timeseries.datetime.month

\n
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\nmonth(x)[source]
\n

Month of each value in series

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

month of observations

\n
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\n

Usage

\n

Returns the month as a numeric value for each observation in the series:

\n

\\(Y_t = month(t)\\)

\n

Month of the time or date is the integer month number, e.g. 1-12

\n

Examples

\n

Day for observations in series:

\n
>>> series = generate_series(100)\n>>> days = month(series)\n
\n
\n

See also

\n

day() year()

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\n gs_quant.timeseries.datetime.weekday\n

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\n gs_quant.timeseries.datetime.year\n

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gs_quant.timeseries.datetime.prepend

\n
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\nprepend(x)[source]
\n

Prepend data series

\n
\n
Parameters
\n

x (List[Series]) – an array of timeseries

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

concatenated timeseries

\n
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Usage

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For input series [\\(x_1\\), \\(x_2\\), … , \\(X_n\\)], takes data from series \\(X_i\\) until\nthe first date for which \\(X_{i+1}\\) has data, useful when a higher quality series has a shorter history\nthan a lower quality series.

\n

Examples

\n

Prepend two series:

\n
>>> x = generate_series(100)\n>>> y = generate_series(100)\n>>> prepend([x, y])\n
\n
\n

See also

\n

union()

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\n gs_quant.timeseries.datetime.date_range\n

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\n gs_quant.timeseries.datetime.union\n

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gs_quant.timeseries.datetime.quarter

\n
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\nquarter(x)[source]
\n

Quarter of each value in series

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

quarter of observations

\n
\n
\n

Usage

\n

Returns the quarter as a numeric value for each observation in the series:

\n

\\(Y_t = quarter(t)\\)

\n

Quarter of the time or date is the integer quarter number, e.g. 1, 2, 3, 4

\n

Examples

\n

Quarter for observations in series:

\n
>>> series = generate_series(100)\n>>> days = quarter(series)\n
\n
\n

See also

\n

day() month()

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\n gs_quant.timeseries.datetime.year\n

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\n gs_quant.timeseries.datetime.date_range\n

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gs_quant.timeseries.datetime.union

\n
\n
\nunion(x)[source]
\n

Fill in missing dates or times of one series with another

\n
\n
Parameters
\n

x (List[Series]) – an array of timeseries

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

combined series

\n
\n
\n

Usage

\n

Starting from \\(i=1\\), takes points from series \\(x_i\\). Where points are missing from \\(x_i\\),\nreturns points from \\(x_{i+1}\\).

\n

Examples

\n

Union of two series:

\n
>>> x = generate_series(100)\n>>> y = generate_series(100)\n>>> union([x, y])\n
\n
\n

See also

\n

prepend()

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\n gs_quant.timeseries.datetime.Window\n

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gs_quant.timeseries.datetime.value

\n
\n
\nvalue(x, date, method=<Interpolate.STEP: 'step'>)[source]
\n

Value at specified date or time

\n
\n
Parameters
\n
    \n

  • x (Series) – timeseries

    • \n

  • date (Union[date, time]) – requested date or time

    • \n

  • method (Interpolate) – interpolation method (default: step)

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

value at specified date or time

\n
\n
\n

Usage

\n

Returns the value of series X at the specified date:

\n

\\(Y_t = X_{date}\\)

\n

If the requested date or time is not present in the series, the value function will return the value from the\nprevious available date or time by default. Caller can specify other interpolation styles via the method param:

\n

Interpolation methods:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Type

Behavior

intersect

Only returns a value for valid dates

nan

Value will be NaN for dates not present in the series

zero

Value will be zero for dates not present in the series

step

Value of the previous valid point if requested date does not exist.\nValues prior to the first date will be equivalent to the first available\nvalue

\n

Examples

\n

Value of series on 5Mar18:

\n
>>> a = generate_series(100)\n>>> value(a, date(2019, 1, 3)\n
\n
\n

See also

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interpolate()

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gs_quant.timeseries.datetime.weekday

\n
\n
\nweekday(x)[source]
\n

Weekday of each value in series

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

weekday of observations

\n
\n
\n

Usage

\n

Returns the weekday as a numeric value for each observation in the series:

\n

\\(Y_t = weekday(t)\\)

\n

Weekday of the time or date is the integer day of the week, e.g. 0-6, where 0 represents Monday

\n

Examples

\n

Weekday for observations in series:

\n
>>> series = generate_series(100)\n>>> days = weekday(series)\n
\n
\n

See also

\n

day() month()

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gs_quant.timeseries.datetime.year

\n
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\nyear(x)[source]
\n

Year of each value in series

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

year of observations

\n
\n
\n

Usage

\n

Returns the year as a numeric value for each observation in the series:

\n

\\(Y_t = year(t)\\)

\n

Year of the time or date is the integer year number, e.g. 2019, 2020

\n

Examples

\n

Year for observations in series:

\n
>>> series = generate_series(100)\n>>> days = year(series)\n
\n
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See also

\n

day() month()

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gs_quant.timeseries.econometrics.annualize

\n
\n
\nannualize(x)[source]
\n

Annualize series based on sample observation frequency

\n
\n
Parameters
\n

x (Series) – time series of prices

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of annualized values

\n
\n
\n

Usage

\n

Based on number of days between observations, will determine an annualization factor and then adjust values\naccordingly. Useful for annualizing daily or monthly returns

\n

\\(Y_t = X_t * \\sqrt{F}\\)

\n

Annualization factors as follows, based on period implied by observations:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Period

Annualization Factor (F)

Daily

\\(252\\)

Weekly

\\(52\\)

Bi-Weekly

\\(26\\)

Monthly

\\(12\\)

Quarterly

\\(4\\)

Annually

\\(1\\)

\n

Examples

\n

Annualize daily returns series:

\n
>>> prices = generate_series(100)\n>>> ann = annualize(returns(prices))\n
\n
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See also

\n

returns()

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gs_quant.timeseries.econometrics.beta

\n
\n
\nbeta(x, b, w=<gs_quant.timeseries.helper.Window object>, prices=True)[source]
\n

Rolling beta of price series and benchmark

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • b (Series) – time series of benchmark prices

    • \n

  • w (Union[Window, int, str]) – Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n

  • prices (bool) – True if input series are prices, False if they are returns

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of beta

\n
\n
\n

Usage

\n

Calculate rolling beta,\n\\(\\beta_t\\) of a series to a benchmark over a given window:

\n

\\(R_t = \\alpha_t + \\beta S_t + \\epsilon_t\\)

\n

Calculated as:

\n

\\(\\beta_t = \\frac{\\sum_{i=t-w+1}^t Cov(R_t, S_t)}{Var(S_t)}\\)

\n

where N is the number of observations in each rolling window, \\(w\\), and \\(R_t\\) and \\(S_t\\) are the\nsimple returns for each series on time \\(t\\):

\n

\\(R_t = \\frac{X_t}{X_{t-1}} - 1\\) and \\(S_t = \\frac{b_t}{b_{t-1}} - 1\\)

\n

If prices = False, assumes returns are provided:

\n

\\(R_t = X_t\\) and \\(S_t = b_t\\)

\n

\\(Cov(R_t, S_t)\\) and \\(Var(S_t)\\) are the mean and variance of series\n\\(R_t\\) and \\(S_t\\) over the same window

\n

If window is not provided, computes beta over the full series

\n

Examples

\n

Compute rolling \\(1\\) month (\\(22\\) business day) beta of two price series

\n
>>> series = generate_series(100)\n>>> benchmark = generate_series(100)\n>>> b = beta(series, benchmark, 22)\n
\n
\n

See also

\n

var() cov() correlation() returns()

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\n gs_quant.timeseries.econometrics.annualize\n

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\n gs_quant.timeseries.econometrics.change\n

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gs_quant.timeseries.econometrics.change

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\nchange(x)[source]
\n

Arithmetic series normalization

\n
\n
Parameters
\n

x (Series) – time series

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

normalized time series

\n
\n
\n

Usage

\n

Compute difference of every value from the initial value of x:

\n

\\(Y_t = X_t - X_0\\)

\n

where \\(X_0\\) is the first value in the series

\n

Examples

\n

Change in level from initial value:

\n
>>> series = generate_series(100)\n>>> returns = change(series)\n
\n
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See also

\n

index()

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\n Previous Topic\n

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\n gs_quant.timeseries.econometrics.beta\n

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\n Next Topic\n

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\n gs_quant.timeseries.econometrics.correlation\n

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gs_quant.timeseries.econometrics.correlation

\n
\n
\ncorrelation(x, y, w=<gs_quant.timeseries.helper.Window object>, type_=<SeriesType.PRICES: 'prices'>)[source]
\n

Rolling correlation of two price series

\n
\n
Parameters
\n
    \n

  • x (Series) – price series

    • \n

  • y (Series) – price series

    • \n

  • w (Union[Window, int, str]) – Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n

  • type – type of both input series: prices or returns

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of correlation

\n
\n
\n

Usage

\n

Calculate rolling realized correlation,\n\\(\\rho_t\\) of two price series over a given window:

\n

\\(\\rho_t = \\frac{\\sum_{i=t-w+1}^t (R_t - \\overline{R_t})(Y_t - \\overline{S_t})}{(N-1)\\sigma R_t\\sigma S_t}\\)

\n

where N is the number of observations in each rolling window, \\(w\\), and \\(R_t\\) and \\(S_t\\) are the\nsimple returns for each series on time \\(t\\)

\n

If prices are provided:

\n

\\(R_t = \\frac{X_t}{X_{t-1}} - 1\\) and \\(S_t = \\frac{Y_t}{Y_{t-1}} - 1\\)

\n

If returns are provided:

\n

\\(R_t = X_t\\) and \\(S_t = Y_t\\)

\n

\\(\\overline{R_t}\\), \\(\\overline{S_t}\\) are the mean values, and \\(\\sigma R_{t}\\) and\n\\(\\sigma S_{t}\\) are the sample standard deviations, of series\n\\(R_t\\) and \\(S_t\\) over the same window

\n

If window is not provided, computes realized correlation over the full series

\n

Examples

\n

Compute rolling \\(1\\) month (\\(22\\) business day) correlation of price series

\n
>>> series1 = generate_series(100)\n>>> series2 = generate_series(100)\n>>> corr = correlation(series1, series2, 22)\n
\n
\n

See also

\n

std() returns()

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\n gs_quant.timeseries.econometrics.change\n

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\n gs_quant.timeseries.econometrics.excess_returns_\n

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gs_quant.timeseries.econometrics.excess_returns_

\n
\n
\nexcess_returns_(price_series, currency=<RiskFreeRateCurrency.USD: 'USD'>)[source]
\n

Calculate excess returns

\n
\n
Parameters
\n
    \n

  • price_series (Series) – price series

    • \n

  • currency (RiskFreeRateCurrency) – currency for risk-free rate, defaults to USD

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

excess returns

\n
\n
\n

Usage

\n

Given a price series P and risk-free rate R, excess returns E are defined as:

\n

\\(E_t = E_{t-1} + P_t - P_{t-1} * (1 + R * (D_t - D_{t-1}) / 360)\\)

\n

The Actual/360 day count convention is used.

\n

Examples

\n

Get excess returns from a price series.

\n
>>> er = excess_returns(generate_series(100), USD)\n
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\n gs_quant.timeseries.econometrics.correlation\n

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\n gs_quant.timeseries.econometrics.index\n

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gs_quant.timeseries.econometrics.index

\n
\n
\nindex(x, initial=1)[source]
\n

Geometric series normalization

\n
\n
Parameters
\n
    \n

  • x (Series) – time series

    • \n

  • initial (int) – initial value

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

normalized time series

\n
\n
\n

Usage

\n

Divides every value in x by the initial value of x:

\n

\\(Y_t = initial * X_t / X_0\\)

\n

where \\(X_0\\) is the first value in the series

\n

Examples

\n

Normalize series to 1:

\n
>>> series = generate_series(100)\n>>> returns = index(series)\n
\n
\n

See also

\n

returns()

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\n gs_quant.timeseries.econometrics.excess_returns_\n

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\n gs_quant.timeseries.econometrics.max_drawdown\n

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gs_quant.timeseries.econometrics.max_drawdown

\n
\n
\nmax_drawdown(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Compute the maximum peak to trough drawdown over a rolling window.

\n
\n
Parameters
\n
    \n

  • x (Series) – time series

    • \n

  • w (Union[Window, int, str]) – Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

time series of rolling maximum drawdown

\n
\n
\n

Examples

\n

Compute the maximum peak to trough drawdown

\n
>>> series = generate_series(100)\n>>> max_drawdown(series)\n
\n
\n

See also

\n

returns()

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\n gs_quant.timeseries.econometrics.index\n

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\n gs_quant.timeseries.econometrics.prices\n

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gs_quant.timeseries.econometrics.prices

\n
\n
\nprices(series, initial=1, type=<Returns.SIMPLE: 'simple'>)[source]
\n

Calculate price levels from returns series

\n
\n
Parameters
\n
    \n

  • series (Series) – time series of returns

    • \n

  • initial (int) – initial price level

    • \n

  • type (Returns) – returns type: simple, logarithmic or absolute

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

Compute price levels from returns series, based on the value of type:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Type

Description

simple

Simple arithmetic returns

logarithmic

Logarithmic returns

absolute

Absolute returns

\n

Simple

\n

Compute asset price series from simple returns:

\n

\\(Y_t = (1 + X_{t-1}) Y_{t-1}\\)

\n

where \\(X_t\\) is the asset price at time \\(t\\) and \\(Y_0 = initial\\)

\n

Logarithmic

\n

Compute asset price series from logarithmic returns:

\n

\\(Y_t = e^{X_{t-1}} Y_{t-1}\\)

\n

where \\(X_t\\) is the asset price at time \\(t\\) and \\(Y_0 = initial\\)

\n

Absolute

\n

Compute asset price series from absolute returns:

\n

\\(Y_t = X_{t-1} + Y_{t-1}\\)

\n

where \\(X_t\\) is the asset price at time \\(t\\) and \\(Y_0 = initial\\)

\n

Examples

\n

Generate price series and take compute returns

\n
>>> series = generate_series(100)\n>>> returns = prices(returns(series))\n
\n
\n

See also

\n

returns() product() exp()

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\n gs_quant.timeseries.econometrics.max_drawdown\n

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gs_quant.timeseries.econometrics.returns

\n
\n
\nreturns(series, obs=1, type=<Returns.SIMPLE: 'simple'>)[source]
\n

Calculate returns from price series

\n
\n
Parameters
\n
    \n

  • series (Series) – time series of prices

    • \n

  • obs (int) – number of observations

    • \n

  • type (Returns) – returns type: simple, logarithmic or absolute

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

Compute returns series from price levels, based on the value of type:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Type

Description

simple

Simple arithmetic returns

logarithmic

Logarithmic returns

absolute

Absolute returns

\n

Simple

\n

Simple geometric change in asset prices, which can be aggregated across assets

\n

\\(Y_t = \\frac{X_t}{X_{t-obs}} - 1\\)

\n

where \\(X_t\\) is the asset price at time \\(t\\)

\n

Logarithmic

\n

Natural logarithm of asset price changes, which can be aggregated through time

\n

\\(Y_t = log(X_t) - log(X_{t-obs})\\)

\n

where \\(X_t\\) is the asset price at time \\(t\\)

\n

Absolute

\n

Absolute change in asset prices

\n

\\(Y_t = X_t - X_{t-obs}\\)

\n

where \\(X_t\\) is the asset price at time \\(t\\)

\n

Examples

\n

Generate price series and take compute returns

\n
>>> prices = generate_series(100)\n>>> returns = returns(prices)\n
\n
\n

See also

\n

prices()

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\n gs_quant.timeseries.econometrics.prices\n

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\n gs_quant.timeseries.econometrics.sharpe_ratio\n

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gs_quant.timeseries.econometrics.sharpe_ratio

\n
\n
\nsharpe_ratio(series, currency=<RiskFreeRateCurrency.USD: 'USD'>, w=None, curve_type=<CurveType.PRICES: 'prices'>)[source]
\n

Calculate Sharpe ratio

\n
\n
Parameters
\n
    \n

  • series (Series) – series of prices or excess returns for an asset

    • \n

  • currency (RiskFreeRateCurrency) – currency for risk-free rate, defaults to USD

    • \n

  • curve_type (CurveType) – whether input series is of prices or excess returns, defaults to prices

    • \n

  • w (Union[Window, int, None]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

Sharpe ratio

\n
\n
\n

Usage

\n

Given a price series P, risk-free rate R, and window of size w returns the rolling\nSharpe ratio S:

\n

\\(S_t = \\frac{(E_t / E_{t-w+1})^{365.25 / (D_t - D_{t-w} - 1)}}{volatility(E, w)_t}\\)

\n

Excess returns E are defined as:

\n

\\(E_t = E_{t-1} + P_t - P_{t-1} * (1 + R * (D_t - D_{t-1}) / 360)\\)

\n

where D is the date for a data point. The\nActual/360 day count convention is used.

\n

Examples

\n

Get rolling sharpe ratio of a price series (with window of 252).

\n
>>> sr = sharpe_ratio(generate_series(100), USD, 252, CurveType.PRICES)\n
\n
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See also

\n

volatility()

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\n gs_quant.timeseries.econometrics.returns\n

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\n gs_quant.timeseries.econometrics.volatility\n

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gs_quant.timeseries.econometrics.volatility

\n
\n
\nvolatility(x, w=<gs_quant.timeseries.helper.Window object>, returns_type=<Returns.SIMPLE: 'simple'>)[source]
\n

Realized volatility of price series

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n

  • returns_type (Returns) – returns type: simple, logarithmic or absolute

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

Calculate rolling annualized realized volatility of a price series over a given window. Annual volatility of 20% is\nreturned as 20.0:

\n

\\(Y_t = \\sqrt{\\frac{1}{N-1} \\sum_{i=t-w+1}^t (R_t - \\overline{R_t})^2} * \\sqrt{252} * 100\\)

\n

where N is the number of observations in each rolling window \\(w\\), \\(R_t\\) is the return on time\n\\(t\\) based on returns_type

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Type

Description

simple

Simple geometric change in asset prices:\n\\(R_t = \\frac{X_t}{X_{t-1}} - 1\\)\nwhere \\(X_t\\) is the asset price at time \\(t\\)

logarithmic

Natural logarithm of asset price changes:\n\\(R_t = log(X_t) - log(X_{t-1})\\)\nwhere \\(X_t\\) is the asset price at time \\(t\\)

absolute

Absolute change in asset prices:\n\\(Y_t = X_t - X_{t-obs}\\)\nwhere \\(X_t\\) is the asset price at time \\(t\\)

\n

and \\(\\overline{R_t}\\) is the mean value over the same window:

\n

\\(\\overline{R_t} = \\frac{\\sum_{i=t-w+1}^{t} R_t}{N}\\)

\n

If window is not provided, computes realized volatility over the full series

\n

Examples

\n

Compute rolling \\(1\\) month (\\(22\\) business day) annualized volatility of price series

\n
>>> series = generate_series(100)\n>>> vol_series = volatility(series, 22)\n>>> vol_series = volatility(series, Window(22, 30))\n
\n
\n

See also

\n

std() annualize() returns()

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\n gs_quant.timeseries.econometrics.sharpe_ratio\n

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\n gs_quant.timeseries.statistics.cov\n

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gs_quant.timeseries.statistics.cov

\n
\n
\ncov(x, y, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Rolling co-variance of series over given window

\n
\n
Parameters
\n
    \n

  • x (Series) – series: timeseries

    • \n

  • y (Series) – series: timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of covariance

\n
\n
\n

Usage

\n

Provides unbiased estimator of sample\nco-variance over a rolling window:

\n

\\(R_t = \\frac{1}{N-1} \\sum_{i=t-w+1}^t (X_i - \\overline{X_t}) (Y_i - \\overline{Y_t})\\)

\n

where \\(N\\) is the number of observations in each rolling window, \\(w\\), and \\(\\overline{X_t}\\) and\n\\(\\overline{Y_t}\\) represent the sample mean of series \\(X_t\\) and \\(Y_t\\) over the same window:

\n

\\(\\overline{X_t} = \\frac{\\sum_{i=t-w+1}^{t} X_i}{N}\\) and\n\\(\\overline{Y_t} = \\frac{\\sum_{i=t-w+1}^{t} Y_i}{N}\\)

\n

If window is not provided, computes variance over the full series

\n

Examples

\n

Generate price series and compute variance of returns over \\(22\\) observations

\n
>>> prices_x = generate_series(100)\n>>> prices_y = generate_series(100)\n>>> cov(returns(prices_x) returns(prices_y), 22)\n
\n
\n

See also

\n

sum() mean() var()

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\n gs_quant.timeseries.econometrics.volatility\n

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\n gs_quant.timeseries.statistics.exponential_std\n

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gs_quant.timeseries.statistics.exponential_std

\n
\n
\nexponential_std(x, beta=0.75)[source]
\n

Exponentially weighted standard deviation

\n
\n
Parameters
\n
    \n

  • x (Series) – time series

    • \n

  • beta (float) – how much to weigh the previous price in the time series, thus controlling how much importance we\nplace on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

time series of standard deviation of the input series

\n
\n
\n

Usage

\n

Provides an unbiased estimator of exponentially weighted standard deviation of\na series [\\(X_0\\), \\(X_1\\), \\(X_2\\), …]:

\n

\\(S_t = \\sqrt{[EWMA(X_t^2) - EWMA(X_t)^2] * DF_t}\\)

\n

where \\(EWMA(X_t)\\) is the exponential moving average at \\(t\\) (see function\nexponential_moving_average()), \\(DF_t\\) is the debiasing factor (see\nWeighted sample variance\nfor further details):

\n

\\(DF_t = \\frac{(\\sum_{i=0}^t w_i)^2} {(\\sum_{i=0}^t w_i)^2 - \\sum_{i=0}^t w_i^2}\\)

\n

where \\(w_i\\) is the weight assigned to \\(i\\) th observation:

\n

\\(w_i = (1-\\beta)\\beta^i\\) for i<t; \\(\\beta^i\\) for i=t

\n

Examples

\n

Generate price series and compute exponentially weighted standard deviation of returns

\n
>>> prices = generate_series(100)\n>>> exponential_std(returns(prices), 0.9)\n
\n
\n

See also

\n

std() var() exponential_moving_average()

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\n gs_quant.timeseries.statistics.cov\n

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\n gs_quant.timeseries.statistics.generate_series\n

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gs_quant.timeseries.statistics.generate_series

\n
\n
\ngenerate_series(length)[source]
\n

Generate sample timeseries

\n
\n
Parameters
\n

length (int) – number of observations

\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of randomly generated prices

\n
\n
\n

Usage

\n

Create timeseries from returns generated from a normally distributed random variables (IDD). Length determines the\nnumber of observations to be generated.

\n

Assume random variables \\(R\\) which follow a normal distribution with mean \\(0\\) and standard deviation\nof \\(1\\)

\n

\\(R \\sim N(0, 1)\\)

\n

The timeseries is generated from these random numbers through:

\n

\\(X_t = (1 + R)X_{t-1}\\)

\n

Examples

\n

Generate price series with 100 observations starting from today’s date:

\n
>>> prices = generate_series(100)\n
\n
\n

See also

\n

numpy.random.normal()

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\n gs_quant.timeseries.statistics.exponential_std\n

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\n gs_quant.timeseries.statistics.max_\n

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gs_quant.timeseries.statistics.max_

\n
\n
\nmax_(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Maximum value of series over given window

\n
\n
Parameters
\n
    \n

  • x (Union[Series, List[Series]]) – series: a timeseries or an array of timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of maximum value

\n
\n
\n

Usage

\n

Returns the maximum value of the series over each window.

\n

If \\(x\\) is a series:

\n

\\(R_t = max(X_{t-w+1}:X_t)\\)

\n

where \\(w\\) is the size of the rolling window.

\n

If \\(x\\) is an array of series:

\n

\\(R_t = max(X_{1, t-w+1}:X_{n, t})\\)

\n

where \\(w\\) is the size of the rolling window, and \\(n\\) is the number of series.

\n

If window is not provided, returns the maximum value over the full series. If the window size is greater than the\navailable data, will return maximum of available values.

\n

Examples

\n

Maximum value of price series over the last \\(22\\) observations:

\n
>>> prices = generate_series(100)\n>>> max_(prices, 22)\n
\n
\n

See also

\n

min_()

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\n gs_quant.timeseries.statistics.generate_series\n

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\n gs_quant.timeseries.statistics.mean\n

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gs_quant.timeseries.statistics.mean

\n
\n
\nmean(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Arithmetic mean of series over given window

\n
\n
Parameters
\n
    \n

  • x (Union[Series, List[Series]]) – series: a timeseries or an array of timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of mean value

\n
\n
\n

Usage

\n

Calculates arithmetic mean of the series over a rolling window

\n

If a timeseries is provided:

\n

\\(R_t = \\frac{\\sum_{i=t-w+1}^{t} X_i}{N}\\)

\n

where \\(N\\) is the number of observations in each rolling window, \\(w\\).

\n

If an array of timeseries is provided:

\n

\\(R_t = \\frac{\\sum_{i=t-w+1}^{t} {\\sum_{j=1}^{n}} X_{ij}}{N}\\)

\n

where \\(n\\) is the number of series, and \\(N\\) is the number of observations in each rolling window,\n\\(w\\).

\n

If window is not provided, computes rolling mean over the full series. If the window size is greater than the\navailable data, will return mean of available values.

\n

Examples

\n

Generate price series and compute mean over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> mean(prices, 22)\n
\n
\n

See also

\n

median() mode()

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\n gs_quant.timeseries.statistics.max_\n

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\n gs_quant.timeseries.statistics.median\n

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gs_quant.timeseries.statistics.median

\n
\n
\nmedian(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Median value of series over given window

\n
\n
Parameters
\n
    \n

  • x (Series) – series: timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of median value

\n
\n
\n

Usage

\n

Computes the median value over a given window. For each window, this\nfunction will return the middle value when all elements in the window are sorted. If the number of observations in\nthe window is even, will return the average of the middle two values. If the window size is greater than the\navailable data, will return median of available values:

\n

\\(d = \\frac{w-1}{2}\\)

\n

\\(R_t = \\frac{X_{\\lfloor t-d \\rfloor} + X_{\\lceil t-d \\rceil}}{2}\\)

\n

where \\(w\\) is the size of the rolling window. If window is not provided, computes median over the full series

\n

Examples

\n

Generate price series and compute median over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> median(prices, 22)\n
\n
\n

See also

\n

mean() mode()

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\n gs_quant.timeseries.statistics.mean\n

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\n gs_quant.timeseries.statistics.min_\n

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gs_quant.timeseries.statistics.min_

\n
\n
\nmin_(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Minimum value of series over given window

\n
\n
Parameters
\n
    \n

  • x (Union[Series, List[Series]]) – series: a timeseries or an array of timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of minimum value

\n
\n
\n

Usage

\n

Returns the minimum value of the series over each window.

\n

If \\(x\\) is a series:

\n

\\(R_t = min(X_{t-w+1}:X_t)\\)

\n

where \\(w\\) is the size of the rolling window.

\n

If \\(x\\) is an array of series:

\n

\\(R_t = min(X_{1, t-w+1}:X_{n, t})\\)

\n

where \\(w\\) is the size of the rolling window, and \\(n\\) is the number of series.

\n

If window is not provided, returns the minimum value over the\nfull series. If the window size is greater than the available data, will return minimum of available values.

\n

Examples

\n

Minimum value of price series over the last \\(22\\) observations:

\n
>>> prices = generate_series(100)\n>>> min_(prices, 22)\n
\n
\n

See also

\n

max_()

\n
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\n gs_quant.timeseries.statistics.median\n

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\n gs_quant.timeseries.statistics.mode\n

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gs_quant.timeseries.statistics.mode

\n
\n
\nmode(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Most common value in series over given window

\n
\n
Parameters
\n
    \n

  • x (Series) – series: timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of mode value

\n
\n
\n

Usage

\n

Computes the mode over a given window. For each window, this\nfunction will return the most common value of all elements in the window. If there are multiple values with the same\nfrequency of occurrence, will return the smallest value.

\n

If window is not provided, computes mode over the full series.

\n

Examples

\n

Generate price series and compute mode over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> mode(prices, 22)\n
\n
\n

See also

\n

mean() median()

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\n gs_quant.timeseries.statistics.min_\n

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\n gs_quant.timeseries.statistics.percentile\n

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gs_quant.timeseries.statistics.percentile

\n
\n
\npercentile(x, n, w=None)[source]
\n

Returns the nth percentile of a series.

\n
\n
Parameters
\n
    \n

  • x (Series) – series

    • \n

  • n (float) – percentile

    • \n

  • w (Union[Window, int, str, None]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.

    • \n
\n
\n
Return type
\n

Union[Series, float]

\n
\n
Returns
\n

nth percentile

\n
\n
\n

Usage

\n

Calculates the nth percentile rank of \\(x\\). Rolling nth\npercentile is returned if a window is specified, else a scalar for nth percentile over the entire series.

\n

Example

\n

Compute the 90th percentile of a series.

\n
>>> a = generate_series(100)\n>>> percentile(a, 90)\n
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\n gs_quant.timeseries.statistics.mode\n

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\n gs_quant.timeseries.statistics.percentiles\n

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gs_quant.timeseries.statistics.percentiles

\n
\n
\npercentiles(x, y=None, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Rolling percentiles over given window

\n
\n
Parameters
\n
    \n

  • x (Series) – value series

    • \n

  • y (Optional[Series]) – distribution series

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of percentiles

\n
\n
\n

Usage

\n

Calculate percentile rank of \\(y\\) in the sample distribution\nof \\(x\\) over a rolling window of length \\(w\\):

\n

\\(R_t = \\frac{\\sum_{i=t-N+1}^{t}{[X_i<{Y_t}]}+0.5\\sum_{i=t-N+1}^{t}{[X_i={Y_t}]}}{N}\\times100\\%\\)

\n

Where \\(N\\) is the number of observations in a rolling window. If \\(y\\) is not provided, calculates\npercentiles of \\(x\\) over its historical values. If window length \\(w\\) is not provided, uses an\never-growing history of values. If \\(w\\) is greater than the available data size, returns empty.

\n

Examples

\n

Compute percentile ranks of a series in the sample distribution of a second series over \\(22\\) observations

\n
>>> a = generate_series(100)\n>>> b = generate_series(100)\n>>> percentiles(a, b, 22)\n
\n
\n

See also

\n

zscores()

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\n gs_quant.timeseries.statistics.percentile\n

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\n Next Topic\n

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\n gs_quant.timeseries.statistics.product\n

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gs_quant.timeseries.statistics.product

\n
\n
\nproduct(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Rolling product of series over given window

\n
\n
Parameters
\n
    \n

  • x (Series) – series: timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of rolling product

\n
\n
\n

Usage

\n

Calculate the product of observations over a given rolling window. For each time, \\(t\\), returns the value\nof all observations from \\(t-w+1\\) to \\(t\\) multiplied together:

\n

\\(R_t = \\prod_{i=t-w+1}^{t} X_i\\)

\n

where \\(w\\) is the size of the rolling window. If window is not provided, computes product over the full series

\n

Examples

\n

Generate price series and compute rolling sum over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> product(1+returns(prices))\n
\n
\n

See also

\n

sum_()

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\n gs_quant.timeseries.statistics.percentiles\n

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\n gs_quant.timeseries.statistics.range_\n

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gs_quant.timeseries.statistics.range_

\n
\n
\nrange_(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Range of series over given window

\n
\n
Parameters
\n
    \n

  • x (Series) – series: timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of range

\n
\n
\n

Usage

\n

Returns the range of the series (max - min) over rolling window:

\n

\\(R_t = max(X_{t-w+1}:X_t) - min(X_{t-w+1}:X_t)\\)

\n

where \\(w\\) is the size of the rolling window. If window is not provided, returns the range over the\nfull series. If the window size is greater than the available data, will return range of all available values.

\n

Examples

\n

Range of price series over the last \\(22\\) observations:

\n
>>> prices = generate_series(100)\n>>> range_(prices, 22)\n
\n
\n

See also

\n

min_() max_()

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\n gs_quant.timeseries.statistics.product\n

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\n gs_quant.timeseries.statistics.std\n

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gs_quant.timeseries.statistics.std

\n
\n
\nstd(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Rolling standard deviation of series over given window

\n
\n
Parameters
\n
    \n

  • x (Series) – series: timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of standard deviation

\n
\n
\n

Usage

\n

Provides unbiased estimator of sample\nstandard deviation over a rolling window:

\n

\\(R_t = \\sqrt{\\frac{1}{N-1} \\sum_{i=t-w+1}^t (X_i - \\overline{X_t})^2}\\)

\n

where \\(N\\) is the number of observations in each rolling window, \\(w\\), and \\(\\overline{X_t}\\) is the\nmean value over the same window:

\n

\\(\\overline{X_t} = \\frac{\\sum_{i=t-w+1}^{t} X_i}{N}\\)

\n

If window is not provided, computes standard deviation over the full series

\n

Examples

\n

Generate price series and compute standard deviation of returns over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> std(returns(prices), 22)\n
\n
\n

See also

\n

sum() mean() var()

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\n gs_quant.timeseries.statistics.range_\n

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\n gs_quant.timeseries.statistics.sum_\n

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gs_quant.timeseries.statistics.sum_

\n
\n
\nsum_(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Rolling sum of series over given window

\n
\n
Parameters
\n
    \n

  • x (Union[Series, List[Series]]) – series: a timeseries or an array of timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of rolling sum

\n
\n
\n

Usage

\n

Calculate the sum of observations over a given rolling window.

\n

If \\(x\\) is a series:

\n

\\(R_t = \\sum_{i=t-w+1}^{t} X_i\\)

\n

where \\(w\\) is the size of the rolling window.

\n

If \\(x\\) is an array of series:

\n

\\(R_t = \\sum_{i=t-w+1}^{t} \\sum_{j=1}^{n} X_{ij}\\)

\n

where \\(w\\) is the size of the rolling window and \\(n\\) is the number of series

\n

If window is not provided, computes sum over the full series. If the window size is greater than the available data,\nwill return sum of available values.

\n

Examples

\n

Generate price series and compute rolling sum over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> sum_(prices, 22)\n
\n
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See also

\n

product()

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\n gs_quant.timeseries.statistics.std\n

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\n gs_quant.timeseries.statistics.var\n

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gs_quant.timeseries.statistics.var

\n
\n
\nvar(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Rolling variance of series over given window

\n
\n
Parameters
\n
    \n

  • x (Series) – series: timeseries

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of variance

\n
\n
\n

Usage

\n

Provides unbiased estimator of sample\nvariance over a rolling window:

\n

\\(R_t = \\frac{1}{N-1} \\sum_{i=t-w+1}^t (X_i - \\overline{X_t})^2\\)

\n

where \\(N\\) is the number of observations in each rolling window, \\(w\\), and \\(\\overline{X_t}\\) is the\nmean value over the same window:

\n

\\(\\overline{X_t} = \\frac{\\sum_{i=t-w+1}^{t} X_i}{N}\\)

\n

If window is not provided, computes variance over the full series

\n

Examples

\n

Generate price series and compute variance of returns over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> var(returns(prices), 22)\n
\n
\n

See also

\n

var() mean() std()

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\n gs_quant.timeseries.statistics.sum_\n

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\n gs_quant.timeseries.statistics.winsorize\n

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gs_quant.timeseries.statistics.winsorize

\n
\n
\nwinsorize(x, limit=2.5, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Limit extreme values in series

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • limit (float) – max z-score of values

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of winsorized values

\n
\n
\n

Usage

\n

Cap and floor values in the series which have a z-score greater or less than provided value. This function will\nrestrict the distribution of values. Calculates the sample standard deviation and adjusts values which\nfall outside the specified range to be equal to the upper or lower limits

\n

Lower and upper limits are defined as:

\n

\\(upper = \\mu + \\sigma \\times limit\\)

\n

\\(lower = \\mu - \\sigma \\times limit\\)

\n

Where \\(\\mu\\) and \\(\\sigma\\) are sample mean and standard deviation. The series is restricted by:

\n

\\(R_t = max( min( X_t, upper), lower )\\)

\n

See winsorising for additional information

\n

Examples

\n

Generate price series and winsorize z-score of returns over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> winsorize(zscore(returns(prices), 22))\n
\n
\n

See also

\n

zscore() mean() std()

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\n gs_quant.timeseries.statistics.var\n

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\n gs_quant.timeseries.statistics.zscores\n

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gs_quant.timeseries.statistics.zscores

\n
\n
\nzscores(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Rolling z-scores over a given window

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

timeseries of z-scores

\n
\n
\n

Usage

\n

Calculate standard score of each value in series over given\nwindow. Standard deviation and sample mean are computed over the specified rolling window, then element is\nnormalized to provide a rolling z-score:

\n

\\(R_t = \\frac { X_t - \\mu }{ \\sigma }\\)

\n

Where \\(\\mu\\) and \\(\\sigma\\) are sample mean and standard deviation over the given window

\n

If window is not provided, computes z-score relative to mean and standard deviation over the full series

\n

Examples

\n

Generate price series and compute z-score of returns over \\(22\\) observations

\n
>>> prices = generate_series(100)\n>>> zscores(returns(prices), 22)\n
\n
\n

See also

\n

mean() std()

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\n gs_quant.timeseries.statistics.winsorize\n

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\n gs_quant.timeseries.statistics.LinearRegression\n

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gs_quant.timeseries.technicals.bollinger_bands

\n
\n
\nbollinger_bands(x, w=<gs_quant.timeseries.helper.Window object>, k=2)[source]
\n

Bollinger bands with given window and width

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n

  • k (float) – band width in standard deviations (default: 2)

    • \n
\n
\n
Return type
\n

DataFrame

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

Standard deviation bands around the moving average of asset price level. Bollinger bands can be used to determine\na range around the price level which responds to local volatility changes. Returns two series,\nupper, \\(u_t\\) and lower, \\(l_t\\)

\n

\\(u_t = \\bar{X_t} + k\\sigma_t\\)

\n

\\(l_t = \\bar{X_t} - k\\sigma_t\\)

\n

where \\(\\bar{X_t}\\) is the moving average over specified window, and \\(\\sigma_t\\) is the rolling\nstandard deviation over the specified window

\n

See Bollinger Bands for more information

\n

Examples

\n

Compute bollinger bands around \\(20\\) day moving average at \\(2\\) standard deviations:

\n
>>> prices = generate_series(100)\n>>> bollinger_bands(prices, 20, 2)\n
\n
\n

See also

\n

moving_average() std()

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\n gs_quant.timeseries.statistics.SEIRModel\n

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\n gs_quant.timeseries.technicals.moving_average\n

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gs_quant.timeseries.technicals.exponential_moving_average

\n
\n
\nexponential_moving_average(x, beta=0.75)[source]
\n

Exponentially weighted moving average

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • beta (float) – how much to weigh the previous observations in the time series, thus controlling how much importance we\nplace on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

The exponential(ly weighted) moving average (EMA) of a series [\\(X_0\\), \\(X_1\\), \\(X_2\\), …],\nis defined as:

\n

\\(Y_0 = X_0\\)

\n

\\(Y_t = \\beta \\cdot Y_{t-1} + (1 - \\beta) \\cdot X_t\\)

\n

where \\(\\beta\\) is the weight we place on the previous average.

\n

See Exponential moving average for\nmore information

\n

Examples

\n

Generate price series with 100 observations starting from today’s date:

\n
>>> prices = generate_series(100)\n>>> exponential_moving_average(prices, 0.9)\n
\n
\n

See also

\n

mean() moving_average() smoothed_moving_average()

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\n gs_quant.timeseries.technicals.moving_average\n

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\n gs_quant.timeseries.technicals.exponential_volatility\n

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gs_quant.timeseries.technicals.exponential_spread_volatility

\n
\n
\nexponential_spread_volatility(x, beta=0.75)[source]
\n

Exponentially weighted spread volatility

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • beta (float) – how much to weigh the previous price in the time series, thus controlling how much importance we\nplace on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of exponential spread volatility of the input series

\n
\n
\n

Usage

\n

Exponentially weights the daily differences of the input series, calculates the annualized standard deviation

\n

Examples

\n

Generate price series and compute exponentially weighted standard deviation of returns

\n
>>> prices = generate_series(100)\n>>> exponential_volatility(prices, 0.9)\n
\n
\n

The above is equivalent to

\n
>>> annualize(exponential_std(diff(prices, 1), 0.9))\n
\n
\n

See also

\n

volatility() exponential_std() exponential_volatility()

\n
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\n gs_quant.timeseries.technicals.exponential_volatility\n

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\n gs_quant.timeseries.technicals.smoothed_moving_average\n

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gs_quant.timeseries.technicals.exponential_volatility

\n
\n
\nexponential_volatility(x, beta=0.75)[source]
\n

Exponentially weighted volatility

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • beta (float) – how much to weigh the previous price in the time series, thus controlling how much importance we\nplace on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of exponential volatility of the input series

\n
\n
\n

Usage

\n

Calculates the exponentially weighted standard deviation of the return of the input series, and annualizes the\nstandard deviation

\n

Examples

\n

Generate price series and compute exponentially weighted standard deviation of returns

\n
>>> prices = generate_series(100)\n>>> exponential_volatility(prices, 0.9)\n
\n
\n

The above is equivalent to

\n
>>> annualize(exponential_std(returns(prices), 0.9)) * 100\n
\n
\n

See also

\n

volatility() exponential_std() exponential_spread_volatility()

\n
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\n gs_quant.timeseries.technicals.exponential_moving_average\n

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\n gs_quant.timeseries.technicals.exponential_spread_volatility\n

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gs_quant.timeseries.technicals.moving_average

\n
\n
\nmoving_average(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Moving average over specified window

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

Simple arithmetic moving average over the specified window (number of observations). Shorter windows will be more\nreactive to changes in the asset price, but more volatile. Larger windows will be smoother but less reactive to\nnear term changes in asset prices.

\n

\\(R_t = \\frac{\\sum_{i=t-w+1}^{t} X_t}{N}\\)

\n

where N is the number of observations in each rolling window, \\(w\\). If window is not provided, computes\nrolling mean over the full series

\n

Equivalent to mean

\n

Examples

\n

Generate price series with 100 observations starting from today’s date:

\n
>>> prices = generate_series(100)\n>>> moving_average(prices, 22)\n
\n
\n

See also

\n

mean()

\n
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\n gs_quant.timeseries.technicals.bollinger_bands\n

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\n gs_quant.timeseries.technicals.exponential_moving_average\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/html/functions/gs_quant.timeseries.technicals.relative_strength_index.html", "content": "\n\n\n\n \n\n \n \n \n \n \n gs_quant.timeseries.technicals.relative_strength_index — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n
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gs_quant.timeseries.technicals.relative_strength_index

\n
\n
\nrelative_strength_index(x, w=14)[source]
\n

Relative Strength Index

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

DataFrame

\n
\n
Returns
\n

date-based time series of RSI

\n
\n
\n

Usage

\n

The RSI computes momentum as the ratio of higher closes to lower closes: stocks which have had more or stronger\npositive changes have a higher RSI than stocks which have had more or stronger negative changes.

\n

See RSI for more information

\n

Examples

\n

Compute relative strength index over a \\(14\\) day window:

\n
>>> prices = generate_series(100)\n>>> relative_strength_index(prices, 14)\n
\n
\n

See also

\n

moving_average() std() smoothed_moving_average()

\n
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\n gs_quant.timeseries.technicals.smoothed_moving_average\n

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gs_quant.timeseries.technicals.smoothed_moving_average

\n
\n
\nsmoothed_moving_average(x, w=<gs_quant.timeseries.helper.Window object>)[source]
\n

Smoothed moving average over specified window

\n
\n
Parameters
\n
    \n

  • x (Series) – time series of prices

    • \n

  • w (Union[Window, int, str]) – Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\nand 10 the ramp up value. If w is a string, it should be a relative date like ‘1m’, ‘1d’, etc.\nWindow size defaults to length of series.

    • \n
\n
\n
Return type
\n

Series

\n
\n
Returns
\n

date-based time series of return

\n
\n
\n

Usage

\n

A modified moving average (MMA), running moving average (RMA), or smoothed moving average (SMMA) is defined as:

\n

\\(P_{MM,today} = \\frac{(N-1)P_{MM,yesterday} + P_today}{N}\\)

\n

where N is the number of observations in each rolling window, \\(w\\). If window is not provided, computes\nrolling mean over the full series

\n

See Modified moving average for more\ninformation

\n

Examples

\n

Generate price series with 100 observations starting from today’s date:

\n
>>> prices = generate_series(100)\n>>> smoothed_moving_average(prices, 22)\n
\n
\n

See also

\n

mean() :func:’moving_average’

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\n gs_quant.timeseries.technicals.exponential_spread_volatility\n

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Index

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Instrument Package

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Priceable(**kwargs)

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Instruments

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CommodOTCSwap([start, end, …])

Object representation of a commodities swap

EqCliquet([underlier, underlier_type, …])

Object representation of an Equity Cliquet

EqForward([underlier, underlier_type, …])

Object representation of an equity forward

EqOption([underlier, expiration_date, …])

Instrument definition for equity option

EqSynthetic(underlier, expiry[, currency, …])

Instrument definition for equity synthetics

EqVarianceSwap([underlier, underlier_type, …])

Instrument definition for equity variance swap

Forward([currency, expiration_date, …])

Forward cash payment

FXBinary([pair, buy_sell, option_type, …])

Object representation of a FX binary option

FXForward([pair, settlement_date, …])

Object representation of an FX forward

FXMultiCrossBinary([buy_sell, legs, …])

Object representation of an FX multi-cross binary

FXMultiCrossBinaryLeg([pair, option_type, …])

Object representation of a single leg of a multi-cross binary option

FXOption([pair, buy_sell, option_type, …])

Object representation of an FX option

FXVolatilitySwap([pair, buy_sell, …])

Object representation of an FX Vol Swap

InflationSwap([pay_or_receive, …])

A vanilla inflation swap of fixed vs floating cashflows adjusted to an inflation rate

IRBasisSwap([termination_date, …])

A single currency exchange of cashflows from different interest rate indices

IRCap([termination_date, notional_currency, …])

Object representation of an interest rate cap

IRCMSOption([cap_floor, termination_date, …])

Object representation of a constant maturity option (cap, floor, straddle)

IRCMSOptionStrip([cap_floor, …])

Object representation of a constant maturity option strip (cap, floor, straddle)

IRCMSSpreadOption([cap_floor, …])

Object representation of a constant maturity spread option (cap, floor, straddle)

IRCMSSpreadOptionStrip([cap_floor, …])

Object representation of a constant maturity spread option strip (cap, floor, straddle)

IRFloor([termination_date, …])

Object representation of an interest rate floor

IRSwap([pay_or_receive, termination_date, …])

A vanilla interest rate swap of fixed vs floating cashflows

IRSwaption([pay_or_receive, …])

Object representation of a swaption

IRXccySwap([termination_date, …])

An exchange of cashflows from different interest rate indices

IRXccySwapFixFix([termination_date, …])

An exchange of fixed cashflows in different currencies

IRXccySwapFixFlt([pay_or_receive, …])

An exchange of fixed vs floating cashflows in different currencies

Security([ticker, bbid, ric, isin, cusip, …])

A security, specified by a well-known identifier

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Market Package

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PricingContext([pricing_date, …])

A context for controlling pricing and market data behaviour

HistoricalPricingContext([start, end, …])

A context for producing valuations over multiple dates

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Securities

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Asset(id_, asset_class, name[, exchange])

AssetClass

Asset classification of security.

AssetIdentifier

Asset type enumeration

AssetType

Asset type enumeration

Index(id_, asset_class, name[, exchange])

Index Asset

Stock(id_, name[, exchange])

Base Security Type

SecurityMaster

Security Master

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Markets Package

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PricingContext([pricing_date, …])

A context for controlling pricing and market data behaviour

HistoricalPricingContext([start, end, …])

A context for producing valuations over multiple dates

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Portfolio

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Portfolio([priceables, name])

A collection of instruments

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Securities

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Asset(id_, asset_class, name[, exchange, …])

AssetClass(value)

Asset classification of security.

AssetIdentifier(value)

Asset type enumeration

AssetType(value)

Asset type enumeration

Index(id_, asset_class, name[, exchange, …])

Index Asset

Stock(id_, name[, exchange, currency, entity])

Base Security Type

SecurityMaster()

Security Master

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Models Package

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Epidemiology

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SIR()

SIR Model

SEIR()

SEIR Model

EpidemicModel(model[, parameters, data, …])

Class to perform solutions and parameter-fitting of epidemic models

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Risk Package

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Functions

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\naggregate_risk(results, threshold=None)[source]
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Combine the results of multiple InstrumentBase.calc() calls, into a single result

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DataFrame

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Returns
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A Dataframe with the aggregated results

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Examples

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>>> from gs_quant.instrument import IRCap, IRFloor\n>>> from gs_quant.markets import PricingContext\n>>> from gs_quant.risk import IRDelta, IRVega\n>>>\n>>> cap = IRCap('5y', 'GBP')\n>>> floor = IRFloor('5y', 'GBP')\n>>> instruments = (cap, floor)\n>>>\n>>> with PricingContext():\n>>>     delta_f = [inst.calc(IRDelta) for inst in instruments]\n>>>     vega_f = [inst.calc(IRVega) for inst in (cap, floor)]\n>>>\n>>> delta = aggregate_risk(delta_f, threshold=0.1)\n>>> vega = aggregate_risk(vega_f)\n
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delta_f and vega_f are lists of futures, where the result will be a Dataframe\ndelta and vega are Dataframes, representing the merged risk of the individual instruments

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Subtract bucketed risk. Dimensions must be identical

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Examples

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>>> from gs_quant.datetime.date import business_day_offset\n>>> from gs_quant.instrument IRSwap\n>>> from gs_quant.markets import PricingContext\n>>> from gs_quant.risk import IRDelta\n>>> import datetime as dt\n>>>\n>>> ir_swap = IRSwap('Pay', '10y', 'USD')\n>>> delta_today = ir_swap.calc(IRDelta)\n>>>\n>>> with PricingContext(pricing_date=business_day_offset(dt.date.today(), -1, roll='preceding')):\n>>>     delta_yday_f = ir_swap.calc(IRDelta)\n>>>\n>>> delta_diff = subtract_risk(delta_today, delta_yday_f.result())\n
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DataFrame

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Sort bucketed risk

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DataFrame

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A sorted Dataframe

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Measures

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\nDollarPrice = DollarPrice
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Present value in USD

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\nPrice = Price
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Present value in local currency

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\nForwardPrice = ForwardPrice
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Forward price

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\nTheta = Theta
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1 day Theta

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\nBaseCPI = BaseCPI
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Base CPI level

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Commodity Delta

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\nCommodTheta = CommodTheta
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Commodity Theta

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\nCommodVega = CommodVega
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Commodity Vega

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Equity Delta

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Equity Gamma

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Equity Vega

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\nEqSpot = EqSpot
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Equity Spot Level

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\nEqAnnualImpliedVol = EqAnnualImpliedVol
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Equity Annual Implied Volatility (%)

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Fair Variance Strike Value of a Variance Swap

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Fair Volatility Strike Value of a Variance Swap

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FX Delta

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FX Gamma

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FX Vega

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FX Spot Rate

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Inflation Delta

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\nInflationDeltaParallel = InflationDeltaParallel
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Inflation Parallel Delta

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\nInflationDeltaParallelLocalCcy = InflationDeltaParallelLocalCcy
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Inflation Parallel Delta (Local Ccy)

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\nIRBasis = IRBasis
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Interest Rate Basis

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\nIRDelta = IRDelta
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Interest Rate Delta

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\nIRDeltaParallel = IRDeltaParallel
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Interest Rate Parallel Delta

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Interest Rate Delta (Local Ccy)

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Interest Rate Parallel Delta (Local Ccy)

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\nIRXccyDelta = IRXccyDelta
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Cross-ccy Delta

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\nIRXccyDeltaParallel = IRXccyDeltaParallel
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Cross-ccy Parallel Delta

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\nIRXccyDeltaParallelLocalCurrency = IRXccyDeltaParallelLocalCurrency
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Cross-ccy Parallel Delta (Local Ccy)

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\nIRGammaParallel = IRGammaParallel
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Interest Rate Parallel Gamma

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\nIRGammaParallelLocalCcy = IRGammaParallelLocalCcy
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Interest Rate Parallel Gamma (Local Ccy)

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\nIRVega = IRVega
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Interest Rate Vega

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\nIRVegaParallel = IRVegaParallel
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Interest Rate Parallel Vega

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\nIRVegaLocalCcy = IRVegaLocalCcy
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Interest Rate Vega (Local Ccy)

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Interest Rate Parallel Vega (Local Ccy)

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\nIRAnnualImpliedVol = IRAnnualImpliedVol
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Interest Rate Annual Implied Volatility (%)

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\nIRAnnualATMImpliedVol = IRAnnualATMImpliedVol
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Interest Rate Annual Implied At-The-Money Volatility (%)

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\nIRDailyImpliedVol = IRDailyImpliedVol
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Interest Rate Daily Implied Volatility (bps)

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At-The-Money Spot Rate (%)

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\nIRFwdRate = IRFwdRate
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Par Rate (%)

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t:[47,48],fee:[17,18,19,20,21,22,23,24,25,26,27,28,29],fee_curr:[17,18,19,20,21,22,23,24,25,26,27,28],fee_payment_d:[17,18,19,20,21,22,23,24,25,26,27,28],field:131,fill:[72,92],filter:73,filteroper:63,financi:40,first:[15,20,21,24,26,28,73,81,85,86,88,90,93,98,101],first_fixing_d:15,first_valuation_d:5,fit:[41,45,46,47,48],fit_intercept:[45,46],fit_method:41,fit_period:[41,47,48],fix:[10,13,15,24,25,27,28,29,40,46],fixed_first_stub:[24,28],fixed_holidai:[24,28],fixed_last_stub:[24,28],fixed_r:[24,28,29],fixed_rate_business_day_convent:[24,25,28,29],fixed_rate_curr:28,fixed_rate_day_count_fract:[24,25,28],fixed_rate_frequ:[24,25,28],fixing_frequ:15,fixing_sourc:[10,13,15],flag:66,floating_first_stub:[24,28],floating_holidai:[24,28],floating_last_stub:[24,28],floating_rate_business_day_convent:[19,21,22,23,24,25,28,29],floating_rate_curr:28,floating_rate_day_count_fract:[19,21,22,23,24,25,28],floating_rate_designated_matur:[22,23,24,25,28],floating_rate_for_the_initial_calculation_period:[24,28],floating_rate_frequ:[19,21,22,23,24,25,28],floating_rate_opt:[22,23,24,25,28],floating_rate_spread:[24,25,28],floatingrateopt:[22,23,24,28],floor:[5,18,19,20,21,23,60,65,122,135],floor_rat:23,follow:[96,109],forecast:48,form:41,format:5,forward:[6,11,52,72,135],forward_pric:6,forward_r:11,forwardpric:135,four:47,frac:[97,99,104,105,106,107,108,111,112,116,119,121,123,128,130],fraction:[17,19,21,22,23,24,25,26,27,28],free:[100,105],frequenc:[15,17,22,23,24,25,26,27,28,72,84,96,114],from:[14,17,21,26,30,31,32,47,48,56,63,75,85,86,90,92,93,98,100,103,104,109,117,125,128,130,135],full:[97,99,106,107,110,111,112,113,114,117,118,119,120,121,123,128,130],func:[86,130],further:108,futur:[32,80,135],fxdelta:135,fxgamma:135,fxmulticrossbinaryleg:12,fxspot:135,fxvega:135,gamma:[47,48,135],gbp:[5,7,8,10,12,14,16,32,135],gener:[30,53,57,60,64,71,73,74,76,84,103,104,107,108,109,111,112,114,117,119,120,121,122,123,125,126,127,128,130],generate_seri:[45,46,57,58,60,61,63,64,65,68,71,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130],geometr:[101,104,106],get:[52,76,100,105],get_paramet:41,given:[41,47,48,55,58,61,65,68,74,75,78,85,97,99,100,105,106,107,110,111,112,113,114,116,117,118,119,120,121,123,124],global:5,global_cap:5,global_floor:5,goldman:30,greater:[57,63,64,110,111,112,113,116,118,120,122],group:35,grow:116,gs_quant:[4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,131,135],gsid:30,gsid_eid:30,gsid_equival:30,gsideid:30,gsn:30,gss:30,had:129,has:[30,40,58,61,65,68,72,75,88,90],have:[58,61,68,75,85,88,122,129],held:40,help:[0,1,3,34,35,36,37,38,39,40,42,43],helper:[97,99,102,106,107,110,111,112,113,114,116,117,118,119,120,121,122,123,124,128,130],higher:[90,129],histor:116,histori:[90,116],historicalpricingcontext:131,hkg:[31,32],hold:33,holidai:[31,51,52,53,54,55],hous:[17,24,25,29],how:[7,14,41,47,48,108,125,126,127],id_:[34,38,40],idd:109,ident:135,identifi:[2,5,6,7,8,9,30,36,39],ignor:[58,61,65,68,75],immedi:[31,32],immun:[47,48],impli:[96,135],includ:86,inclus:[108,125,126,127],increment:73,index1:21,index1_tenor:20,index2:21,index2_tenor:20,index:[18,19,29,30,31,32,66,75,80,82,88,98,129,131],indic:[15,17,26,54,63],individu:[47,48,135],infect:[47,48],infecti:[47,48],inflat:[29,135],inflationdelta:135,inflationdeltaparallel:135,inflationdeltaparallellocalcci:135,inform:[67,122,124,125,129,130],initi:[0,1,3,34,35,36,37,38,39,40,41,42,43,47,48,98,101,103],initial_condit:41,initial_fx_r:26,input:[52,54,55,58,59,61,68,69,70,73,75,85,90,97,99,105,108,126,127,135],inst:135,instead:32,instrument:[7,8,9,30,31,32,33,84,131,135],instrument_quant:[4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],instrumentbas:135,integ:[80,86,87,89,91,94,95],intercept:[45,46],interest:[17,22,23,24,26,135],interfac:39,intern:30,interpol:[58,61,65,68,75,85,93],interpret:53,intersect:[45,46,58,61,65,68,75,85,88,93],interv:[58,61,68,75,85],invers:67,ir_swap:[31,135],irannualatmimpliedvol:135,irannualimpliedvol:135,irbasi:135,ircap:[32,135],irdailyimpliedvol:135,irdelta:135,irdeltalocalcci:135,irdeltaparallel:135,irdeltaparallellocalcci:135,irfloor:135,irfwdrat:135,irgammaparallel:135,irgammaparallellocalcci:135,irspotr:135,irswap:[31,135],irvega:135,irvegalocalcci:135,irvegaparallel:135,irvegaparallellocalcci:135,irxccydelta:135,irxccydeltaparallel:135,irxccydeltaparallellocalcurr:135,is_async:[31,32],is_batch:[31,32],isin:30,iso:[5,7,8,10,12,14,16],iter:[31,33,51,52,53,54,55,135],its:116,japan:30,jsn:30,kei:33,kenn:30,known:30,kwarg:0,kwd:3,l_t:124,lag:[78,86],lagmod:80,larger:[46,73,128],last:[15,24,26,28,72,73,79,82,86,110,113,118],last_fixing_d:15,last_valuation_d:5,lceil:112,ldn:[31,32],least:[41,45,46],leastsq:41,left:135,leg:[4,12,13,24,26,28,29],length:[58,61,68,75,85,97,99,102,106,107,109,110,111,112,113,114,116,117,118,119,120,121,122,123,124,128,129,130],less:[60,122,128],less_than:63,level:[7,29,78,98,103,104,124,135],levenberg:41,lfloor:112,libor:[17,22,23,24,25,26,28],like:[4,97,99,102,106,107,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,128,129,130],limit:122,linear:[45,46],list:[7,30,41,45,46,76,84,85,88,90,92,110,111,113,120,135],lmfit:41,lms_id:30,local:[31,32,124,135],locat:[10,12,14,31,32],log:[62,104,106],logarithm:[62,67,103,104,106],logic:[59,63,69,70],longer:[31,32],lookalik:7,lookup:39,lower:[90,122,124,129],made:[31,32],mani:[47,48],manner:85,market:[30,131,135],market_data_loc:[31,32],marketdatacoordin:56,marketplac:7,marquardt:41,marque:30,master:[30,39],matur:[5,18,19,20,21],max:[64,110,118,122],max_:[113,118],maximum:[60,64,102,110],mdapi:30,mdapi_class:30,mean:[15,97,99,106,107,109,112,114,119,121,122,123,125,128,130],measur:131,median:[111,114],merg:135,method:[0,1,2,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,38,39,40,41,42,43,44,45,46,47,48,58,61,65,68,75,85,88,93],method_of_settl:[7,14],mic:30,middl:112,min:[31,32,60,113,118,122],min_:[110,118],minim:41,minimum:[64,73,113],miss:[58,61,63,68,72,75,85,92],mkt:56,mkt_asset:135,mkt_class:135,mkt_point:135,mkt_type:135,mktclose:7,mktopen:7,mma:130,mode:[80,111,112],model:[45,46,47,48,131],modifi:[5,7,8,10,12,14,16,130],modul:3,momentum:129,mondai:94,monei:[7,10,13,14,18,19,20,21,22,23,25,135],month:[4,44,87,91,94,95,97,99,106],monthli:[72,84,96],more:[59,60,64,67,70,108,124,125,126,127,128,129,130],morningstar:30,most:114,move:[47,48,52,108,124,125,128,130],movement:[47,48],moving_averag:[124,125,129,130],mq_symbol:30,much:[108,125,126,127],multi:[12,13],multipl:[7,10,13,14,18,19,20,21,25,31,114,135],multipli:[7,18,19,61,117],must:[46,53,58,61,68,75,85,108,125,126,127,135],mybasket:[76,84],name:[3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,33,34,38,40],nan:[58,61,65,68,75,77,85,88,93],natur:[62,67,104,106],nearest:52,neg:129,negat:69,neighbor:73,neighbour:73,node:30,non:80,none:[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,38,40,41,44,47,48,51,52,53,54,55,63,84,88,105,115,116,135],normal:[56,98,101,109,123],note:73,notion:[4,5,10,11,12,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29],notional_amount:[5,10,11,12,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29],notional_amount_other_curr:14,notional_curr:[10,12,14,15,17,18,19,20,21,22,23,24,25,29],notional_reset_sid:26,notionalcurr:14,nth:115,num_of_underly:8,number:[4,6,7,8,15,30,44,46,47,48,51,52,53,56,58,61,62,63,65,68,74,75,77,78,80,86,87,89,91,95,96,97,99,104,106,107,109,110,111,112,113,116,119,120,121,128,130],number_of_opt:7,number_of_period:4,number_of_shar:6,numer:[59,69,70,87,89,91,94,95],nummer:30,numpi:109,nyc:[31,32],nyse:54,object:[3,4,5,6,10,11,12,13,14,15,18,19,20,21,22,23,25,33,44,97,99,102,106,107,110,111,112,113,114,116,117,118,119,120,121,122,123,124,128,130],obs:[78,80,104,106],observ:[10,13,15,40,44,45,46,77,78,80,86,87,89,91,94,95,96,97,99,104,106,107,108,109,110,111,112,113,114,116,117,118,119,120,121,122,123,125,128,130],obvers:44,occurr:114,offset:[52,55],ois:[31,32],onc:[47,48],one:[44,55,58,61,65,68,75,92],onli:[4,5,45,46,58,61,65,68,75,84,85,86,88,93],oper:[58,61,63,65,68,75],option:[2,7,10,12,13,14,18,19,20,21,31,32,39,41,47,48,51,52,53,54,55,63,72,84,116,135],option_styl:7,option_typ:[7,10,13,14],optionsettlementmethod:[7,14],optionstyl:7,optiontyp:[7,10,13,14],order:[33,52],ordinari:[45,46],otc:[4,7],other:[7,14,58,61,65,68,75,85,93],otherwis:57,out:73,output:[41,72],outsid:122,over:[17,24,25,26,28,31,47,48,73,78,88,97,99,102,106,107,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,128,129,130],overlin:[99,106,107,119,121],p_t:[100,105],p_todai:130,packag:131,pai:[5,24,25,26,28,29,31,135],pair:[10,11,13,14,15],par:135,parallel:135,param:93,paramet:[2,31,32,33,34,41,44,45,46,47,48,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,135],parti:30,particular:30,past:[108,125,126,127],pay_or_rec:[24,25,28,29],payer:[17,26,27],payer_business_day_convent:[17,26,27],payer_curr:[26,27],payer_day_count_fract:[17,26,27],payer_designated_matur:[17,26],payer_first_stub:26,payer_frequ:[17,26,27],payer_holidai:26,payer_last_stub:26,payer_r:27,payer_rate_opt:[17,26],payer_spread:[17,26],payerrateopt:[17,26],payment:[10,12,14,16,17,18,19,20,21,22,23,24,25,26,27,28],payment_frequ:5,payrec:[24,26,28,29],peak:102,peopl:[47,48],per:[7,15,32],percent:[7,10,13,14,18,19,20,21,22,23,25],percentag:9,perform:41,period:[4,5,19,21,44,47,48,96],physic:[7,14],pl_id:30,place:[108,125,126,127],plain:4,platt:30,plot:30,plot_id:30,pnode_id:30,point:[39,72,73,85,88,92,93,105,131],popul:[47,48],portfolio:[38,40,131],posit:[5,63,129],pow:74,power:62,preced:135,predict:47,pre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Package\"],titleterms:{\"function\":135,\"return\":104,\"var\":121,abs_:57,add:58,algebra:[57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,136],align:85,analysi:[77,78,79,80,81,82,136],and_:59,annual:96,api:131,asset:34,assetclass:35,assetidentifi:36,assettyp:37,backtest:[84,136],base:0,basket:84,beta:97,bollinger_band:124,business_day_count:51,business_day_offset:52,ceil:60,chang:98,commodotcswap:4,correl:99,count:77,cov:107,dai:87,data:[1,2,3,49],datacontext:1,dataset:2,date:[50,51,52,53,54,55,136],date_rang:[53,86],datetim:[44,50,51,52,53,54,55,56,85,86,87,88,89,90,91,92,93,94,95],diff:78,divid:61,econometr:[96,97,98,99,100,101,102,103,104,105,106,136],epidemicmodel:41,epidemiolog:[41,42,43,134],eqcliquet:5,eqforward:6,eqopt:7,eqsynthet:8,eqvarianceswap:9,excess_returns_:100,exp:62,exponential_moving_averag:125,exponential_spread_volatil:126,exponential_std:108,exponential_volatil:127,field:3,filter_:63,first:79,floor:64,floordiv:65,forward:16,fxbinari:10,fxforward:11,fxmulticrossbinari:12,fxmulticrossbinaryleg:13,fxoption:14,fxvolatilityswap:15,generate_seri:109,gs_quant:[0,1,2,3,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130],historicalpricingcontext:31,if_:66,index:[38,101],inflationswap:29,instrument:132,interpol:88,irbasisswap:17,ircap:22,ircmsopt:18,ircmsoptionstrip:19,ircmsspreadopt:20,ircmsspreadoptionstrip:21,irfloor:23,irswap:24,irswapt:25,irxccyswap:26,irxccyswapfixfix:27,irxccyswapfixflt:28,is_business_dai:54,lag:80,last:81,last_valu:82,linearregress:45,log:67,market:[31,32,33,34,35,36,37,38,39,40,133],max_:110,max_drawdown:102,mean:111,measur:135,median:112,min_:113,mode:114,model:[41,42,43,134],month:89,moving_averag:128,multipli:68,not_:69,or_:70,packag:[49,50,132,133,134,135,136],percentil:[115,116],point:[50,56],point_sort_ord:56,portfolio:[33,133],power:71,prepend:90,prev_business_d:55,price:103,priceabl:0,pricingcontext:32,product:117,quant:131,quarter:91,range_:118,relative_strength_index:129,repeat:72,risk:135,rollinglinearregress:46,secur:[30,34,35,36,37,38,39,40,133],securitymast:39,seir:42,seirmodel:47,sharpe_ratio:105,sir:43,sirmodel:48,smooth_spik:73,smoothed_moving_averag:130,sqrt:74,statist:[45,46,47,48,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,136],std:119,stock:40,subtract:75,sum_:120,technic:[124,125,126,127,128,129,130,136],time:136,timeseri:[44,45,46,47,48,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,136],union:92,valu:93,volatil:106,weekdai:94,weighted_sum:[76,83],window:44,winsor:122,year:95,zscore:123}})" }, { "path": "docs/_build/html/timeseries.html", "content": "\n\n\n\n \n\n \n \n \n \n \n Timeseries Package — gs_quant 0.1 documentation\n \n\n \n \n \n\n \n \n \n \n \n\n \n \n \n \n\n \n \n \n \n \n \n\n \n \n \n \n \n \n\n\n\n
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  • Packages /
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  • Timeseries Package
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Timeseries Package

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Algebra

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abs_(x)

Absolute value of each element in series

add(x, y[, method])

Add two series or scalars

and_(*series)

Logical “and” of two or more boolean series.

ceil(x[, value])

Cap series at maximum value

divide(x, y[, method])

Divide two series or scalars

exp(x)

Exponential of series

filter_(x[, operator, value])

Removes values where comparison with the operator and value combination results in true, defaults to removing missing values from the series

floor(x[, value])

Floor series at minimum value

floordiv(x, y[, method])

Floor divide two series or scalars

if_(flags, x, y)

Returns a series s.

log(x)

Natural logarithm of series

multiply(x, y[, method])

Multiply two series or scalars

not_(series)

Logical negation of a single boolean series.

or_(*series)

Logical “or” of two or more boolean series.

power(x[, y])

Raise each element in series to power

repeat(x[, n])

Repeats values for days where data is missing.

smooth_spikes(x, threshold)

Smooth out the spikes of a series.

sqrt(x)

Square root of (a) each element in a series or (b) a real number

subtract(x, y[, method])

Add two series or scalars

weighted_sum(series, weights)

Calculate a weighted sum.

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Analysis

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diff(x[, obs])

Diff observations with given lag

first(x)

First value of series

last(x)

Last value of series (as a series)

last_value(x)

Last value of series (as a scalar)

count(x)

Count observations in series

lag(x[, obs, mode])

Lag timeseries by a number of observations or a relative date.

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Backtesting

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basket(series, weights[, costs, rebal_freq, …])

Calculates a basket return series.

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Date / Time

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align(x, y[, method])

Align dates of two series or scalars

interpolate(x[, dates, method])

Interpolate over specified dates or times

value(x, date[, method])

Value at specified date or time

day(x)

Day of each value in series

weekday(x)

Weekday of each value in series

month(x)

Month of each value in series

year(x)

Year of each value in series

quarter(x)

Quarter of each value in series

date_range(x, start_date, end_date[, …])

Create a time series from a (sub-)range of dates in an existing time series.

prepend(x)

Prepend data series

union(x)

Fill in missing dates or times of one series with another

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Window([w, r])

Create a Window with size and ramp up to use.

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Econometrics

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annualize(x)

Annualize series based on sample observation frequency

beta(x, b[, w, prices])

Rolling beta of price series and benchmark

change(x)

Arithmetic series normalization

correlation(x, y[, w, type_])

Rolling correlation of two price series

excess_returns_(price_series[, currency])

Calculate excess returns

index(x[, initial])

Geometric series normalization

max_drawdown(x[, w])

Compute the maximum peak to trough drawdown over a rolling window.

prices(series[, initial, type])

Calculate price levels from returns series

returns(series[, obs, type])

Calculate returns from price series

sharpe_ratio(series[, currency, w, curve_type])

Calculate Sharpe ratio

volatility(x[, w, returns_type])

Realized volatility of price series

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Statistics

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cov(x, y[, w])

Rolling co-variance of series over given window

exponential_std(x[, beta])

Exponentially weighted standard deviation

generate_series(length)

Generate sample timeseries

max_(x[, w])

Maximum value of series over given window

mean(x[, w])

Arithmetic mean of series over given window

median(x[, w])

Median value of series over given window

min_(x[, w])

Minimum value of series over given window

mode(x[, w])

Most common value in series over given window

percentile(x, n[, w])

Returns the nth percentile of a series.

percentiles(x[, y, w])

Rolling percentiles over given window

product(x[, w])

Rolling product of series over given window

range_(x[, w])

Range of series over given window

std(x[, w])

Rolling standard deviation of series over given window

sum_(x[, w])

Rolling sum of series over given window

var(x[, w])

Rolling variance of series over given window

winsorize(x[, limit, w])

Limit extreme values in series

zscores(x[, w])

Rolling z-scores over a given window

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LinearRegression(X, y[, fit_intercept])

Fit an Ordinary least squares (OLS) linear regression model.

RollingLinearRegression(X, y, w[, fit_intercept])

Fit a rolling ordinary least squares (OLS) linear regression model.

SIRModel([beta, gamma, s, i, r, n, fit, …])

SIR Compartmental model for transmission of infectious disease

SEIRModel([beta, gamma, sigma, s, e, i, r, …])

SEIR Compartmental model for transmission of infectious disease

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Technical Analysis

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bollinger_bands(x[, w, k])

Bollinger bands with given window and width

moving_average(x[, w])

Moving average over specified window

exponential_moving_average(x[, beta])

Exponentially weighted moving average

exponential_volatility(x[, beta])

Exponentially weighted volatility

exponential_spread_volatility(x[, beta])

Exponentially weighted spread volatility

smoothed_moving_average(x[, w])

Smoothed moving average over specified window

relative_strength_index(x[, w])

Relative Strength Index

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\n gs_quant.timeseries.algebra.abs_\n

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\n\n \n\n \n \n\n \n \n \n \n\n\n" }, { "path": "docs/_build/search/search-index.json", "content": "[\n {\n \"body\": \"gs_quant base Priceable class Priceable kwargs source __init__ kwargs Initialize self See help type self for accurate signature Methods\",\n \"key\": \"classes/gs_quant.base.Priceable\",\n \"title\": \"gs_quant base Priceable\"\n },\n {\n \"body\": \"gs_quant data DataContext class DataContext start None end None source __init__ start None end None source Initialize self See help type self for accurate signature Methods Attributes\",\n \"key\": \"classes/gs_quant.data.DataContext\",\n \"title\": \"gs_quant data DataContext\"\n },\n {\n \"body\": \"gs_quant data Dataset class Dataset dataset_id provider None source A collection of related data __init__ dataset_id provider None source Parameters dataset_id Union str Vendor The dataset s identifier provider Optional DataApi The data provider Methods Attributes\",\n \"key\": \"classes/gs_quant.data.Dataset\",\n \"title\": \"gs_quant data Dataset\"\n },\n {\n \"body\": \"gs_quant data Fields class Fields value object object names None module None type None start 1 source Data field enumeration Enumeration of fields available through data APIs __init__ args kwds Initialize self See help type self for accurate signature Attributes\",\n \"key\": \"classes/gs_quant.data.Fields\",\n \"title\": \"gs_quant data Fields\"\n },\n {\n \"body\": \"CommodOTCSwap For methods of this class see gs_quant base Priceable class CommodOTCSwap start None end None number_of_periods None strategy None quantity None quantity_unit None quantity_period None legs None settlement None name None source Object representation of a commodities swap Properties end Date or Contract Month Return type Union date str instrument_quantity Return type float legs Commodities OTC swap leg Return type Tuple CommodOTCSwapLeg number_of_periods The number of settlement periods Return type int provider quantity Size of some value i e notional like 1 3b 1 5 1000 Return type Union float str quantity_period A coding scheme to define a period corresponding to a quantity amount Return type Union Period str quantity_unit Commodity asset Return type str resolution_key Return type RiskKey settlement read only description in plain English of settlement terms Return type str start Date or Contract Month Return type Union date str strategy Strip and Commodity Spread Type Swap Strategy Return type str unresolved\",\n \"key\": \"classes/gs_quant.instrument.CommodOTCSwap\",\n \"title\": \"CommodOTCSwap\"\n },\n {\n \"body\": \"EqCliquet For methods of this class see gs_quant base Priceable class EqCliquet underlier None underlier_type None expiration_date None strike_price None currency None first_valuation_date None global_floor 1000000 global_cap 1000000 last_valuation_date None notional_amount None payment_frequency Maturity return_style Rate of Return return_type Sum valuation_period None name None source Object representation of an Equity Cliquet Properties currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str expiration_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str first_valuation_date ISO 8601 formatted date Return type date global_cap Global Cap of return relevant only if paying at maturity Return type float global_floor Global Floor of return relevant only if paying at maturity Return type float instrument_quantity Return type float last_valuation_date ISO 8601 formatted date Return type date notional_amount Notional of this position Return type Union float str payment_frequency Return type str provider resolution_key Return type RiskKey return_style Return calculation style Return type str return_type Sum or Product of periodic return relevant only if paying at maturity Return type str strike_price Strike price as value Return type float underlier Underlier security identifier Return type Union float str underlier_type Type of underlyer Return type Union UnderlierType str unresolved valuation_period Tenor Return type str\",\n \"key\": \"classes/gs_quant.instrument.EqCliquet\",\n \"title\": \"EqCliquet\"\n },\n {\n \"body\": \"EqForward For methods of this class see gs_quant base Priceable class EqForward underlier None underlier_type None expiration_date None forward_price None number_of_shares 1 name None source Object representation of an equity forward Properties expiration_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str forward_price Forward price Return type float instrument_quantity Return type float number_of_shares Number of shares Return type int provider resolution_key Return type RiskKey underlier Underlier security identifier Return type Union float str underlier_type Type of underlyer Return type Union UnderlierType str unresolved\",\n \"key\": \"classes/gs_quant.instrument.EqForward\",\n \"title\": \"EqForward\"\n },\n {\n \"body\": \"EqOption For methods of this class see gs_quant base Priceable class EqOption underlier None expiration_date None strike_price None option_type None option_style None number_of_options None exchange None multiplier None settlement_date None settlement_currency None premium 0 premium_payment_date None valuation_time None method_of_settlement None underlier_type None buy_sell None premium_currency None trade_as None name None source Instrument definition for equity option Properties buy_sell Buy or Sell side of contract Return type Union BuySell str exchange Name of marketplace where security derivative or other instrument is traded Return type str expiration_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str instrument_quantity Return type float method_of_settlement How the option is settled e g Cash Physical Return type Union OptionSettlementMethod str multiplier Number of stock units per option contract Return type float number_of_options Number of options Return type float option_style Option Exercise Style Return type Union OptionStyle str option_type Option Type Return type Union OptionType str premium Option premium Return type float premium_currency Currency of the option premium Return type Union Currency str premium_payment_date Option premium Return type Union date str provider resolution_key Return type RiskKey settlement_currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str settlement_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str strike_price Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str trade_as Option trade as i e listed otc lookalike etc Return type Union TradeAs str underlier Underlier security identifier Return type Union float str underlier_type Type of underlyer Return type Union UnderlierType str unresolved valuation_time Valuation time e g MktClose MktOpen of the underlying level for exercise Return type Union ValuationTime str\",\n \"key\": \"classes/gs_quant.instrument.EqOption\",\n \"title\": \"EqOption\"\n },\n {\n \"body\": \"EqSynthetic For methods of this class see gs_quant base Priceable class EqSynthetic underlier expiry currency None swap_type Eq Swap buy_sell None underlier_type None effective_date None num_of_underlyers None name None source Instrument definition for equity synthetics Properties buy_sell Buy or Sell side of contract Return type Union BuySell str currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str effective_date The date on which the synthetic becomes effective Return type date expiry Tenor Return type str instrument_quantity Return type float num_of_underlyers number of underlyers referenced in synthetic contract Return type float provider resolution_key Return type RiskKey swap_type Return type str underlier Underlier security identifier Return type Union float str underlier_type Type of underlyer Return type Union UnderlierType str unresolved\",\n \"key\": \"classes/gs_quant.instrument.EqSynthetic\",\n \"title\": \"EqSynthetic\"\n },\n {\n \"body\": \"EqVarianceSwap For methods of this class see gs_quant base Priceable class EqVarianceSwap underlier None underlier_type None expiration_date None strike_price None variance_cap None settlement_date None premium None name None source Instrument definition for equity variance swap Properties expiration_date Date or tenor e g 2018 09 03 3m Return type Union date str instrument_quantity Return type float premium VarSwap premium Return type Union float str provider resolution_key Return type RiskKey settlement_date Settlement date Return type Union date str strike_price Variance strike as value or percentage string e g 62 5 95 Return type Union float str underlier Underlier security identifier Return type Union float str underlier_type Type of underlyer Return type Union UnderlierType str unresolved variance_cap Variance Cap as absolute value Return type float\",\n \"key\": \"classes/gs_quant.instrument.EqVarianceSwap\",\n \"title\": \"EqVarianceSwap\"\n },\n {\n \"body\": \"FXBinary For methods of this class see gs_quant base Priceable class FXBinary pair None buy_sell None option_type None notional_amount None notional_currency None strike_price None settlement_date None expiration_date None expiration_time None premium None premium_currency None premium_payment_date None fixing_source None name None source Object representation of a FX binary option Properties buy_sell Buy or Sell side of contract Return type Union BuySell str expiration_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str expiration_time The location and optionally time of spot for expiration Return type str fixing_source The data source to be used for observation of FX spot on the fixing date Return type str instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str option_type Option Type Return type Union OptionType str pair EURUSD or EUR USD Type A currency pair e g Return type str premium Option premium Return type Union float str premium_currency Currency of the option premium Return type Union Currency str premium_payment_date Payment date of the option premium Return type str provider resolution_key Return type RiskKey settlement_date Settlement date of the option after expiration Return type Union date str strike_price Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str unresolved\",\n \"key\": \"classes/gs_quant.instrument.FXBinary\",\n \"title\": \"FXBinary\"\n },\n {\n \"body\": \"FXForward For methods of this class see gs_quant base Priceable class FXForward pair None settlement_date None forward_rate None notional_amount None name None source Object representation of an FX forward Properties forward_rate Forward FX rate Return type Union float str instrument_quantity Return type float notional_amount Notional amount Return type Union float str pair EURUSD or EUR USD Type A currency pair e g Return type str provider resolution_key Return type RiskKey settlement_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.FXForward\",\n \"title\": \"FXForward\"\n },\n {\n \"body\": \"FXMultiCrossBinary For methods of this class see gs_quant base Priceable class FXMultiCrossBinary buy_sell None legs None notional_amount None notional_currency None settlement_date None expiration_date None expiration_time None premium None premium_currency None premium_payment_date None name None source Object representation of an FX multi cross binary Properties buy_sell Buy or Sell side of contract Return type Union BuySell str expiration_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str expiration_time The location and optionally time of spot for expiration Return type str instrument_quantity Return type float legs Object representation of a single leg of a multi cross binary option Return type Tuple FXMultiCrossBinaryLeg notional_amount Notional amount Return type Union float str notional_currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str premium Option premium Return type Union float str premium_currency Currency of the option premium Return type Union Currency str premium_payment_date Payment date of the option premium Return type str provider resolution_key Return type RiskKey settlement_date Settlement date of the option after expiration Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.FXMultiCrossBinary\",\n \"title\": \"FXMultiCrossBinary\"\n },\n {\n \"body\": \"FXMultiCrossBinaryLeg For methods of this class see gs_quant base Priceable class FXMultiCrossBinaryLeg pair None option_type None strike_price None fixing_source None name None source Object representation of a single leg of a multi cross binary option Properties fixing_source The data source to be used for observation of FX spot on the fixing date Return type str instrument_quantity Return type float option_type Option Type Return type Union OptionType str pair EURUSD or EUR USD Type A currency pair e g Return type str provider resolution_key Return type RiskKey strike_price Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str unresolved\",\n \"key\": \"classes/gs_quant.instrument.FXMultiCrossBinaryLeg\",\n \"title\": \"FXMultiCrossBinaryLeg\"\n },\n {\n \"body\": \"FXOption For methods of this class see gs_quant base Priceable class FXOption pair None buy_sell None option_type None notional_amount None notional_currency None notional_amount_other_currency None strike_price None settlement_date None settlement_currency None settlement_rate_option None method_of_settlement None expiration_date None expiration_time None premium None premium_currency None premium_payment_date None name None source Object representation of an FX option Properties buy_sell Buy or Sell side of contract Return type Union BuySell str expiration_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str expiration_time The location and optionally time of spot for expiration Return type str instrument_quantity Return type float method_of_settlement How the option is settled e g Cash Physical Return type Union OptionSettlementMethod str notional_amount Notional amount Return type Union float str notional_amount_other_currency Notional amount in currency other than NotionalCurrency from the pair Return type Union float str notional_currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str option_type Option Type Return type Union OptionType str pair EURUSD or EUR USD Type A currency pair e g Return type str premium Option premium Return type Union float str premium_currency Currency of the option premium Return type Union Currency str premium_payment_date Payment date of the option premium Return type str provider resolution_key Return type RiskKey settlement_currency Currency of settlement Return type Union Currency str settlement_date Settlement date of the option after expiration Return type Union date str settlement_rate_option The source of spot for settlement Return type str strike_price Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str unresolved\",\n \"key\": \"classes/gs_quant.instrument.FXOption\",\n \"title\": \"FXOption\"\n },\n {\n \"body\": \"FXVolatilitySwap For methods of this class see gs_quant base Priceable class FXVolatilitySwap pair None buy_sell None strike_vol None notional_currency None notional_amount None first_fixing_date None last_fixing_date None settlement_date None fixing_source None fixing_frequency None annualization_factor None calculate_mean_return 0 name None source Object representation of an FX Vol Swap Properties annualization_factor Annualization factor is the number of days used per year to compute volatility Return type float buy_sell Buy or Sell side of contract Return type Union BuySell str calculate_mean_return Indicates whether the mean return is calculated true or taken as zero false in the realized volatility computation Return type float first_fixing_date First averaging date or observation date Return type Union date str fixing_frequency Fixing frequency ex Daily Business Days Return type str fixing_source The data source to be used for observations of FX spot on each fixing Return type str instrument_quantity Return type float last_fixing_date Last averaging date or valuation date Return type Union date str notional_amount Notional amount in dollar terms Return type Union float str notional_currency Notional currency Return type Union Currency str pair EURUSD or EUR USD Type A currency pair e g Return type str provider resolution_key Return type RiskKey settlement_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str strike_vol Volatility strike Return type Union float str unresolved\",\n \"key\": \"classes/gs_quant.instrument.FXVolatilitySwap\",\n \"title\": \"FXVolatilitySwap\"\n },\n {\n \"body\": \"Forward For methods of this class see gs_quant base Priceable class Forward currency None expiration_date None notional_amount None name None source Forward cash payment Properties currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str expiration_date Date or tenor e g 2018 09 03 3m Dec21 Return type Union date str instrument_quantity Return type float notional_amount Notional amount Return type Union float str provider resolution_key Return type RiskKey unresolved\",\n \"key\": \"classes/gs_quant.instrument.Forward\",\n \"title\": \"Forward\"\n },\n {\n \"body\": \"IRBasisSwap For methods of this class see gs_quant base Priceable class IRBasisSwap termination_date None notional_currency None notional_amount None effective_date None principal_exchange None payer_spread None payer_rate_option None payer_designated_maturity None payer_frequency None payer_day_count_fraction None payer_business_day_convention None receiver_spread None receiver_rate_option None receiver_designated_maturity None receiver_frequency None receiver_day_count_fraction None receiver_business_day_convention None fee 0 fee_currency None fee_payment_date None clearing_house None name None source A single currency exchange of cashflows from different interest rate indices Properties clearing_house Swap Clearing House Return type Union SwapClearingHouse str effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str payer_business_day_convention The business day convention for the payer Return type Union BusinessDayConvention str payer_day_count_fraction The day count fraction for the payer Return type Union DayCountFraction str payer_designated_maturity Tenor of the payerRateOption e g 3m 6m Return type str payer_frequency The frequency of payer payments e g 6m Return type str payer_rate_option The underlying benchmark for the payer e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str payer_spread Spread over the payer rate Return type Union float str principal_exchange The date on which the swap becomes effective Return type Union PrincipalExchange str provider receiver_business_day_convention The business day convention for the receiver Return type Union BusinessDayConvention str receiver_day_count_fraction The day count fraction for the receiver Return type Union DayCountFraction str receiver_designated_maturity Tenor of the receiverRateOption e g 3m 6m Return type str receiver_frequency The frequency of receiver payments e g 6m Return type str receiver_rate_option The underlying benchmark for the receiver e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str receiver_spread Spread over the receiver rate Return type Union float str resolution_key Return type RiskKey termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRBasisSwap\",\n \"title\": \"IRBasisSwap\"\n },\n {\n \"body\": \"IRCMSOption For methods of this class see gs_quant base Priceable class IRCMSOption cap_floor None termination_date None notional_currency None notional_amount None effective_date None strike None index None multiplier None premium None premium_payment_date None fee 0 fee_currency None fee_payment_date None buy_sell None name None source Object representation of a constant maturity option cap floor straddle Properties buy_sell Buy or Sell side of contract Return type Union BuySell str cap_floor Structure type e g Cap Floor Straddle Binary Cap Return type str effective_date CMS option effective date e g 2019 01 01 10y Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str index The underlying benchmark i e 30yUSD Return type str instrument_quantity Return type float multiplier Multiplier Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str premium The premium Return type Union float str premium_payment_date Payment date of the premium Return type Union date str provider resolution_key Return type RiskKey strike Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str termination_date Swap termination date e g 2030 05 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRCMSOption\",\n \"title\": \"IRCMSOption\"\n },\n {\n \"body\": \"IRCMSOptionStrip For methods of this class see gs_quant base Priceable class IRCMSOptionStrip cap_floor None termination_date None notional_currency None notional_amount None effective_date None strike None index None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None reset_delay None multiplier None premium None premium_payment_date None fee 0 fee_currency None fee_payment_date None buy_sell None name None source Object representation of a constant maturity option strip cap floor straddle Properties buy_sell Buy or Sell side of contract Return type Union BuySell str cap_floor Structure type e g Cap Floor Straddle Binary Cap Return type str effective_date CMS option effective date e g 2019 01 01 10y Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_frequency Period e g 3m 1y Return type str index The underlying benchmark i e 30yUSD Return type str instrument_quantity Return type float multiplier Multiplier Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str premium The premium Return type Union float str premium_payment_date Payment date of the premium Return type Union date str provider reset_delay Delay of the reset e g 2d Return type str resolution_key Return type RiskKey strike Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str termination_date Swap termination date e g 2030 05 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRCMSOptionStrip\",\n \"title\": \"IRCMSOptionStrip\"\n },\n {\n \"body\": \"IRCMSSpreadOption For methods of this class see gs_quant base Priceable class IRCMSSpreadOption cap_floor None termination_date None notional_currency None notional_amount None effective_date None strike None index1_tenor None index2_tenor None premium None premium_payment_date None fee 0 fee_currency None fee_payment_date None buy_sell None name None source Object representation of a constant maturity spread option cap floor straddle Properties buy_sell Buy or Sell side of contract Return type Union BuySell str cap_floor Structure type e g Cap Floor Straddle Return type str effective_date CMS option effective date e g 2019 01 01 10y Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str index1_tenor The tenor of the underlying benchmark to be the first element i e 30y Return type str index2_tenor The tenor of the underlying benchmark to be the second element i e 5y Return type str instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str premium The premium Return type Union float str premium_payment_date Payment date of the premium Return type Union date str provider resolution_key Return type RiskKey strike Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str termination_date Swap termination date e g 2030 05 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRCMSSpreadOption\",\n \"title\": \"IRCMSSpreadOption\"\n },\n {\n \"body\": \"IRCMSSpreadOptionStrip For methods of this class see gs_quant base Priceable class IRCMSSpreadOptionStrip cap_floor None termination_date None notional_currency None notional_amount None effective_date None strike None index1 None index2 None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None reset_delay None premium None premium_payment_date None fee 0 fee_currency None fee_payment_date None buy_sell None name None source Object representation of a constant maturity spread option strip cap floor straddle Properties buy_sell Buy or Sell side of contract Return type Union BuySell str cap_floor Structure type e g Cap Floor Straddle Binary Cap Return type str effective_date CMS option effective date e g 2019 01 01 10y Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_frequency Period e g 3m 1y Return type str index1 The underlying benchmark to be the first element from i e 30yUSD Return type str index2 The underlying benchmark to be the second element from i e 5yUSD Return type str instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str premium The premium Return type Union float str premium_payment_date Payment date of the premium Return type Union date str provider reset_delay Delay of the reset e g 2d Return type str resolution_key Return type RiskKey strike Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str termination_date Swap termination date e g 2030 05 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRCMSSpreadOptionStrip\",\n \"title\": \"IRCMSSpreadOptionStrip\"\n },\n {\n \"body\": \"IRCap For methods of this class see gs_quant base Priceable class IRCap termination_date None notional_currency None notional_amount None effective_date None floating_rate_option None floating_rate_designated_maturity None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None cap_rate None premium None premium_payment_date None fee 0 fee_currency None fee_payment_date None name None source Object representation of an interest rate cap Properties cap_rate The rate of this cap as value percent or at the money e g 62 5 95 ATM 25 ATMF Return type Union float str effective_date The date on which the cap becomes effective Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor of the floatingRateOption e g 3m 6m Return type str floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str premium The premium Return type Union float str premium_payment_date Payment date of the premium Return type Union date str provider resolution_key Return type RiskKey termination_date The termination of the cap e g 2025 04 01 2y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRCap\",\n \"title\": \"IRCap\"\n },\n {\n \"body\": \"IRFloor For methods of this class see gs_quant base Priceable class IRFloor termination_date None notional_currency None notional_amount None effective_date None floating_rate_option None floating_rate_designated_maturity None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None floor_rate None premium None premium_payment_date None fee 0 fee_currency None fee_payment_date None name None source Object representation of an interest rate floor Properties effective_date The date on which the floor becomes effective Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor of the floatingRateOption e g 3m 6m Return type str floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str floor_rate The rate of this floor as value percent or at the money e g 62 5 95 ATM 25 ATMF Return type Union float str instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str premium The premium Return type Union float str premium_payment_date Payment date of the premium Return type Union date str provider resolution_key Return type RiskKey termination_date The termination of the floor e g 2025 04 01 2y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRFloor\",\n \"title\": \"IRFloor\"\n },\n {\n \"body\": \"IRSwap For methods of this class see gs_quant base Priceable class IRSwap pay_or_receive None termination_date None notional_currency None notional_amount None effective_date None principal_exchange None floating_rate_for_the_initial_calculation_period None floating_rate_option None floating_rate_designated_maturity None floating_rate_spread None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None fixed_rate None fixed_rate_frequency None fixed_rate_day_count_fraction None fixed_rate_business_day_convention None fee 0 fee_currency None fee_payment_date None clearing_house None fixed_first_stub None floating_first_stub None fixed_last_stub None floating_last_stub None fixed_holidays None floating_holidays None roll_convention None name None source A vanilla interest rate swap of fixed vs floating cashflows Properties clearing_house Swap Clearing House Return type Union SwapClearingHouse str effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str fixed_first_stub The date of the first stub for fixed leg Return type Union date str fixed_holidays The accrual calendar for fixed leg Return type str fixed_last_stub The date of the last stub for fixed leg Return type Union date str fixed_rate The coupon of the fixed leg Return type Union float str fixed_rate_business_day_convention The business day convention for the fixed rate Return type Union BusinessDayConvention str fixed_rate_day_count_fraction The day count fraction for the fixed rate Return type Union DayCountFraction str fixed_rate_frequency The frequency of fixed payments e g 6m Return type str floating_first_stub The date of the first stub for floating leg Return type Union date str floating_holidays The accrual calendar for floating leg Return type str floating_last_stub The date of the last stub for floating leg Return type Union date str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor of the floatingRateOption e g 3m 6m Return type str floating_rate_for_the_initial_calculation_period First fixing Return type float floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str floating_rate_spread The spread over the floating rate Return type Union float str instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str pay_or_receive Pay or receive fixed Return type Union PayReceive str principal_exchange The date on which the swap becomes effective Return type Union PrincipalExchange str provider resolution_key Return type RiskKey roll_convention The roll convention Return type str termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRSwap\",\n \"title\": \"IRSwap\"\n },\n {\n \"body\": \"IRSwaption For methods of this class see gs_quant base Priceable class IRSwaption pay_or_receive None termination_date None notional_currency None effective_date None notional_amount None expiration_date None floating_rate_option None floating_rate_designated_maturity None floating_rate_spread None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None fixed_rate_frequency None fixed_rate_day_count_fraction None fixed_rate_business_day_convention None strike None premium None premium_payment_date None fee 0 fee_currency None fee_payment_date None clearing_house None settlement None buy_sell None name None source Object representation of a swaption Properties buy_sell Buy or Sell side of contract Return type Union BuySell str clearing_house Swap Clearing House Return type Union SwapClearingHouse str effective_date Swaption effective date e g 2019 01 01 10y Return type Union date str expiration_date Swaption expiration date 2020 05 01 3m Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str fixed_rate_business_day_convention The business day convention for the fixed rate Return type Union BusinessDayConvention str fixed_rate_day_count_fraction The day count fraction for the fixed rate Return type Union DayCountFraction str fixed_rate_frequency The frequency of fixed payments e g 6m Return type str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor Return type str floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str floating_rate_spread The spread over the floating rate Return type float instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str pay_or_receive Pay or receive fixed Return type str premium The premium Return type Union float str premium_payment_date Payment date of the premium Return type Union date str provider resolution_key Return type RiskKey settlement Swap Settlement Type Return type Union SwapSettlement str strike Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF 10 vol 100k pv p 10000 p 10000USD 200K BP or multiple strikes 65 4 45 8 Return type Union float str termination_date Swaption termination date e g 2030 05 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRSwaption\",\n \"title\": \"IRSwaption\"\n },\n {\n \"body\": \"IRXccySwap For methods of this class see gs_quant base Priceable class IRXccySwap termination_date None notional_amount None effective_date None principal_exchange None payer_currency None payer_spread None payer_rate_option None payer_designated_maturity None payer_frequency None payer_day_count_fraction None payer_business_day_convention None receiver_currency None receiver_spread None receiver_rate_option None receiver_designated_maturity None receiver_frequency None receiver_day_count_fraction None receiver_business_day_convention None fee 0 fee_currency None fee_payment_date None initial_fx_rate None payer_first_stub None receiver_first_stub None payer_last_stub None receiver_last_stub None payer_holidays None receiver_holidays None notional_reset_side None name None source An exchange of cashflows from different interest rate indices Properties effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str initial_fx_rate Payment date of the fee Return type float instrument_quantity Return type float notional_amount Notional amount Return type float notional_reset_side Pay or Rec leg resetting Return type Union PayReceive str payer_business_day_convention The business day convention for the payer Return type Union BusinessDayConvention str payer_currency Payer currency Return type Union Currency str payer_day_count_fraction The day count fraction for the payer Return type Union DayCountFraction str payer_designated_maturity Tenor of the payerRateOption e g 3m 6m Return type str payer_first_stub The date of the first stub for payer leg Return type Union date str payer_frequency The frequency of payer payments e g 6m Return type str payer_holidays The accrual calendar for payer leg Return type str payer_last_stub The date of the last stub for payer leg Return type Union date str payer_rate_option The underlying benchmark for the payer e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str payer_spread Spread over the payer rate Return type Union float str principal_exchange The date on which the swap becomes effective Return type Union PrincipalExchange str provider receiver_business_day_convention The business day convention for the receiver Return type Union BusinessDayConvention str receiver_currency Receiver currency Return type Union Currency str receiver_day_count_fraction The day count fraction for the receiver Return type Union DayCountFraction str receiver_designated_maturity Tenor of the receiverRateOption e g 3m 6m Return type str receiver_first_stub The date of the first stub for receiver leg Return type Union date str receiver_frequency The frequency of receiver payments e g 6m Return type str receiver_holidays The accrual calendar for receiver leg Return type str receiver_last_stub The date of the last stub for receiver leg Return type Union date str receiver_rate_option The underlying benchmark for the receiver e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str receiver_spread Spread over the receiver rate Return type Union float str resolution_key Return type RiskKey termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRXccySwap\",\n \"title\": \"IRXccySwap\"\n },\n {\n \"body\": \"IRXccySwapFixFix For methods of this class see gs_quant base Priceable class IRXccySwapFixFix termination_date None notional_amount None receiver_notional_amount None effective_date None principal_exchange None payer_currency None payer_rate None payer_frequency None payer_day_count_fraction None payer_business_day_convention None receiver_currency None receiver_rate None receiver_frequency None receiver_day_count_fraction None receiver_business_day_convention None fee 0 fee_currency None fee_payment_date None name None source An exchange of fixed cashflows in different currencies Properties effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str instrument_quantity Return type float notional_amount Notional amount Return type float payer_business_day_convention The business day convention for the payer Return type Union BusinessDayConvention str payer_currency Payer currency Return type Union Currency str payer_day_count_fraction The day count fraction for the payer Return type Union DayCountFraction str payer_frequency The frequency of payer payments e g 6m Return type str payer_rate Payer rate Return type Union float str principal_exchange The date on which the swap becomes effective Return type Union PrincipalExchange str provider receiver_business_day_convention The business day convention for the receiver Return type Union BusinessDayConvention str receiver_currency Receiver currency Return type Union Currency str receiver_day_count_fraction The day count fraction for the receiver Return type Union DayCountFraction str receiver_frequency The frequency of receiver payments e g 6m Return type str receiver_notional_amount Receiver notional amount Return type float receiver_rate Receiver rate Return type Union float str resolution_key Return type RiskKey termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRXccySwapFixFix\",\n \"title\": \"IRXccySwapFixFix\"\n },\n {\n \"body\": \"IRXccySwapFixFlt For methods of this class see gs_quant base Priceable class IRXccySwapFixFlt pay_or_receive None termination_date None notional_amount None effective_date None principal_exchange None floating_rate_currency None floating_rate_for_the_initial_calculation_period None floating_rate_option None floating_rate_designated_maturity None floating_rate_spread None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None fixed_rate_currency None fixed_rate None fixed_rate_frequency None fixed_rate_day_count_fraction None fixed_rate_business_day_convention None fee 0 fee_currency None fee_payment_date None fixed_first_stub None floating_first_stub None fixed_last_stub None floating_last_stub None fixed_holidays None floating_holidays None name None source An exchange of fixed vs floating cashflows in different currencies Properties effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float fee_currency Currency of the fee Return type Union Currency str fee_payment_date Payment date of the fee Return type Union date str fixed_first_stub The date of the first stub for fixed leg Return type Union date str fixed_holidays The accrual calendar for fixed leg Return type str fixed_last_stub The date of the last stub for fixed leg Return type Union date str fixed_rate The coupon of the fixed leg Return type Union float str fixed_rate_business_day_convention The business day convention for the fixed rate Return type Union BusinessDayConvention str fixed_rate_currency Fixed rate currency Return type Union Currency str fixed_rate_day_count_fraction The day count fraction for the fixed rate Return type Union DayCountFraction str fixed_rate_frequency The frequency of fixed payments e g 6m Return type str floating_first_stub The date of the first stub for floating leg Return type Union date str floating_holidays The accrual calendar for floating leg Return type str floating_last_stub The date of the last stub for floating leg Return type Union date str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_currency Floating rate currency Return type Union Currency str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor of the floatingRateOption e g 3m 6m Return type str floating_rate_for_the_initial_calculation_period First fixing Return type float floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str floating_rate_spread The spread over the floating rate Return type Union float str instrument_quantity Return type float notional_amount Notional amount Return type Union float str pay_or_receive Pay or receive fixed Return type Union PayReceive str principal_exchange The date on which the swap becomes effective Return type Union PrincipalExchange str provider resolution_key Return type RiskKey termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.IRXccySwapFixFlt\",\n \"title\": \"IRXccySwapFixFlt\"\n },\n {\n \"body\": \"InflationSwap For methods of this class see gs_quant base Priceable class InflationSwap pay_or_receive None termination_date None notional_currency None effective_date None notional_amount None index None floating_rate_business_day_convention None fixed_rate None fixed_rate_business_day_convention None fee 0 base_cpi None clearing_house None name None source A vanilla inflation swap of fixed vs floating cashflows adjusted to an inflation rate Properties base_cpi Base CPI level Return type float clearing_house Swap Clearing House Return type Union SwapClearingHouse str effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float fixed_rate The coupon of the fixed leg Return type Union float str fixed_rate_business_day_convention The business day convention for the fixed rate Return type Union BusinessDayConvention str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str index The underlying benchmark for the floating rate e g CPI U Return type str instrument_quantity Return type float notional_amount Notional amount Return type Union float str notional_currency Notional currency Return type Union Currency str pay_or_receive Pay or receive fixed Return type Union PayReceive str provider resolution_key Return type RiskKey termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str unresolved\",\n \"key\": \"classes/gs_quant.instrument.InflationSwap\",\n \"title\": \"InflationSwap\"\n },\n {\n \"body\": \"Security For methods of this class see gs_quant base Priceable class Security ticker None bbid None ric None isin None cusip None prime_id None quantity 1 source A security specified by a well known identifier Properties bbid Bloomberg Id Identifier Return type str bbid_equivalent Bloomberg Equivalent Identifier Return type str bcid Bloomberg Composite Identifier Return type str cid Company Id Identifier Return type str cm_id Client Master Party Id Return type str cross Cross identifier Return type str cusip Cusip Identifier Return type str delisted Whether an asset has been delisted Return type str dollar_cross USD cross identifier for a particular currency Return type str eid EID Identifier Return type str em_id Entity Master Identifier Return type str exchange_code EEX Exchange Code Return type str gsid GSID Identifier Return type str gsid_equivalent GSID Equivalent Identifier Return type str gsideid GSID_EID Identifier Return type str gsn Goldman Sachs internal product number Return type str gss GS Symbol identifier Return type str instrument_quantity Return type float isin International Security Number Return type str jsn Japan Security Number Return type str lms_id Listed Market Symbol Return type str mdapi MDAPI Asset Return type str mdapi_class MDAPI Asset Class Return type str mic Market Identifier Code Return type str mq_symbol Marquee Symbol for generic MQ entities Return type str pl_id Platts Symbol Name Return type str plot_id Plot Identifier Return type str pnode_id Pricing node identifier sourced from Morningstar Return type str primary_country_ric Reuters Primary Country Instrument Code Identifier Return type str prime_id PrimeID Identifier Return type str provider ps_id Platts Symbol Return type str rcic Reuters Composite Instrument Code Identifier Return type str resolution_key Return type RiskKey ric Reuters Instrument Code identifier Return type str sec_name Internal Goldman Sachs security name Return type str sedol Sedol Identifier Return type str sf_id SalesForce ID Return type str simon_id SIMON product identifier Return type str tdapi TDAPI Description Return type str ticker Ticker Identifier Return type str unresolved valoren Valoren Identifier Return type str wi_id Weather Index Identifier Return type str wpk Wertpapier Kenn Nummer Return type str\",\n \"key\": \"classes/gs_quant.instrument.Security\",\n \"title\": \"Security\"\n },\n {\n \"body\": \"gs_quant markets HistoricalPricingContext class HistoricalPricingContext start None end None calendars dates None is_async False is_batch False use_cache False visible_to_gs False csa_term None market_data_location None timeout None show_progress False source A context for producing valuations over multiple dates __init__ start None end None calendars dates None is_async False is_batch False use_cache False visible_to_gs False csa_term None market_data_location None timeout None show_progress False source A context for producing valuations over multiple dates Parameters start Union int date None start date end Union int date None end date defaults to today calendars Union str Tuple holiday calendars dates Optional Iterable date a custom iterable of dates is_async bool return immediately True or wait for results False defaults to False is_batch bool use for calculations expected to run longer than 3 mins to avoid timeouts It can be used with is_async True False defaults to False use_cache bool store results in the pricing cache defaults to False visible_to_gs bool are the contents of risk requests visible to GS defaults to False csa_term Optional str the csa under which the calculations are made Default is local ccy ois index market_data_location Optional str the location for sourcing market data NYC LDN or HKG defaults to LDN Examples from gs_quant instrument import IRSwap ir_swap IRSwap Pay 10y DKK with HistoricalPricingContext 10 price_f ir_swap price price_series price_f result Methods Attributes\",\n \"key\": \"classes/gs_quant.markets.HistoricalPricingContext\",\n \"title\": \"gs_quant markets HistoricalPricingContext\"\n },\n {\n \"body\": \"gs_quant markets PricingContext class PricingContext pricing_date None market_data_location None is_async False is_batch False use_cache False visible_to_gs None csa_term None timeout None market None show_progress False source A context for controlling pricing and market data behaviour __init__ pricing_date None market_data_location None is_async False is_batch False use_cache False visible_to_gs None csa_term None timeout None market None show_progress False source The methods on this class should not be called directly Instead use the methods on the instruments as per the examples Parameters pricing_date Optional date the date for pricing calculations Default is today market_data_location Union PricingLocation str None the location for sourcing market data NYC LDN or HKG defaults to LDN is_async bool if True return a future immediately If False block defaults to False is_batch bool use for calculations expected to run longer than 3 mins to avoid timeouts It can be used with is_async True False defaults to False use_cache bool store results in the pricing cache defaults to False visible_to_gs Optional bool are the contents of risk requests visible to GS defaults to False csa_term Optional str the csa under which the calculations are made Default is local ccy ois index Examples To change the market data location of the default context from gs_quant markets import PricingContext import datetime as dt PricingContext current PricingContext market_data_location LDN For a blocking synchronous request from gs_quant instrument import IRCap cap IRCap 5y GBP with PricingContext price_f cap dollar_price price price_f result For an asynchronous request with PricingContext is_async True price_f cap dollar_price while not price_f done Methods Attributes\",\n \"key\": \"classes/gs_quant.markets.PricingContext\",\n \"title\": \"gs_quant markets PricingContext\"\n },\n {\n \"body\": \"gs_quant markets portfolio Portfolio class Portfolio priceables name None source A collection of instruments Portfolio holds a collection of instruments in order to run pricing and risk scenarios __init__ priceables name None source Creates a portfolio object which can be used to hold instruments Parameters priceables Union PriceableImpl Iterable PriceableImpl dict None constructed with an instrument portfolio iterable of either or a dictionary where key is name and value is a priceable Methods Attributes\",\n \"key\": \"classes/gs_quant.markets.portfolio.Portfolio\",\n \"title\": \"gs_quant markets portfolio Portfolio\"\n },\n {\n \"body\": \"gs_quant markets securities Asset class Asset id_ asset_class name exchange None currency None parameters None entity None source __init__ id_ asset_class name exchange None currency None parameters None entity None source Initialize self See help type self for accurate signature Methods Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.Asset\",\n \"title\": \"gs_quant markets securities Asset\"\n },\n {\n \"body\": \"gs_quant markets securities AssetClass class AssetClass value source Asset classification of security Assets are classified into broad groups which exhibit similar characteristics and behave in a consistent way under different market conditions __init__ Initialize self See help type self for accurate signature Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.AssetClass\",\n \"title\": \"gs_quant markets securities AssetClass\"\n },\n {\n \"body\": \"gs_quant markets securities AssetIdentifier class AssetIdentifier value source Asset type enumeration Enumeration of different security identifiers __init__ Initialize self See help type self for accurate signature Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.AssetIdentifier\",\n \"title\": \"gs_quant markets securities AssetIdentifier\"\n },\n {\n \"body\": \"gs_quant markets securities AssetType class AssetType value source Asset type enumeration Enumeration of different types of asset or security __init__ Initialize self See help type self for accurate signature Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.AssetType\",\n \"title\": \"gs_quant markets securities AssetType\"\n },\n {\n \"body\": \"gs_quant markets securities Index class Index id_ asset_class name exchange None currency None entity None source Index Asset Index which tracks an evolving portfolio of securities and can be traded through cash or derivatives markets __init__ id_ asset_class name exchange None currency None entity None source Initialize self See help type self for accurate signature Methods Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.Index\",\n \"title\": \"gs_quant markets securities Index\"\n },\n {\n \"body\": \"gs_quant markets securities SecurityMaster class SecurityMaster source Security Master The SecurityMaster class provides an interface to security lookup functions This allows querying and retrieval of different security types assets based on a variety of different identifiers through point in time lookups Uses the current PricingContext to provide as of dates if optional arguments are not provided Will return the relevant asset subclass depending on the type of the security See also Asset __init__ Initialize self See help type self for accurate signature Methods\",\n \"key\": \"classes/gs_quant.markets.securities.SecurityMaster\",\n \"title\": \"gs_quant markets securities SecurityMaster\"\n },\n {\n \"body\": \"gs_quant markets securities Stock class Stock id_ name exchange None currency None entity None source Base Security Type Represents a financial asset which can be held in a portfolio or has an observable price fixing which can be referenced in a derivative transaction __init__ id_ name exchange None currency None entity None source Initialize self See help type self for accurate signature Methods Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.Stock\",\n \"title\": \"gs_quant markets securities Stock\"\n },\n {\n \"body\": \"gs_quant models epidemiology EpidemicModel class EpidemicModel model parameters None data None initial_conditions None fit_method leastsq error None fit_period None source Class to perform solutions and parameter fitting of epidemic models __init__ model parameters None data None initial_conditions None fit_method leastsq error None fit_period None source A class to standardize fitting and solving epidemiological models Parameters model Type CompartmentalModel the model to use currently a class in the form of SIR SEIR above parameters Optional tuple tuple parameters to use for the model defaults to the output of model get_parameters data Optional array np array data that can be used to calibrate the model initial_conditions Optional list list initial conditions for the model fit_method str str the method to use to minimize the given error Available methods are those in the lmfit minimizer minimize function Default is Levenberg Marquardt least squares minimization error Optional callable callable control which residuals and in what form to minimize for fitting fit_period Optional float float how far back to fit the data defaults to fitting all data Methods\",\n \"key\": \"classes/gs_quant.models.epidemiology.EpidemicModel\",\n \"title\": \"gs_quant models epidemiology EpidemicModel\"\n },\n {\n \"body\": \"gs_quant models epidemiology SEIR class SEIR source SEIR Model __init__ Initialize self See help type self for accurate signature Methods\",\n \"key\": \"classes/gs_quant.models.epidemiology.SEIR\",\n \"title\": \"gs_quant models epidemiology SEIR\"\n },\n {\n \"body\": \"gs_quant models epidemiology SIR class SIR source SIR Model __init__ Initialize self See help type self for accurate signature Methods\",\n \"key\": \"classes/gs_quant.models.epidemiology.SIR\",\n \"title\": \"gs_quant models epidemiology SIR\"\n },\n {\n \"body\": \"gs_quant timeseries datetime Window class Window w None r None source Create a Window with size and ramp up to use Parameters w Union int str None window size r Union int str None ramp up value Defaults to the window size Returns new window object Usage The window size and ramp up value can either the number of observations or a string representation of the time period Examples Window size is 22 obversations and the ramp up value is 10 Window 22 10 Window size is one month and the ramp up size is one week Window 1m 1w Methods\",\n \"key\": \"classes/gs_quant.timeseries.datetime.Window\",\n \"title\": \"gs_quant timeseries datetime Window\"\n },\n {\n \"body\": \"gs_quant timeseries statistics LinearRegression class LinearRegression X y fit_intercept True source Fit an Ordinary least squares OLS linear regression model Parameters X Union Series List Series observations of the explanatory variable s y Series observations of the dependant variable fit_intercept bool whether to calculate intercept in the model Usage Fit OLS Model based on observations of the explanatory variables s X and the dependant variable y If X and y are not aligned only use the intersection of dates times Examples R Squared of an OLS model x generate_series 100 y generate_series 100 r LinearRegression x y r r_squared Methods\",\n \"key\": \"classes/gs_quant.timeseries.statistics.LinearRegression\",\n \"title\": \"gs_quant timeseries statistics LinearRegression\"\n },\n {\n \"body\": \"gs_quant timeseries statistics RollingLinearRegression class RollingLinearRegression X y w fit_intercept True source Fit a rolling ordinary least squares OLS linear regression model Parameters X Union Series List Series observations of the explanatory variable s y Series observations of the dependant variable w int number of observations in each rolling window Must be larger than the number of observations or explanatory variables fit_intercept bool whether to calculate intercept in the model Usage Fit OLS Model based on observations of the explanatory variables s X and the dependant variable y across a rolling window with fixed number of observations The parameters of each rolling window are stored at the end of each window If X and y are not aligned only use the intersection of dates times Examples R Squared of a rolling OLS model x generate_series 100 y generate_series 100 r RollingLinearRegression x y 5 r r_squared Methods\",\n \"key\": \"classes/gs_quant.timeseries.statistics.RollingLinearRegression\",\n \"title\": \"gs_quant timeseries statistics RollingLinearRegression\"\n },\n {\n \"body\": \"gs_quant timeseries statistics SEIRModel class SEIRModel beta None gamma None sigma None s None e None i None r None n None fit True fit_period None source SEIR Compartmental model for transmission of infectious disease Parameters beta Optional float transmission rate of the infection gamma Optional float recovery rate of the infection sigma Optional float immunity rate from exposed to infected s Union Series float None number of susceptible individuals in population e Union Series float None number of exposed individuals in population i Union Series float None number of infectious individuals in population r Union Series float None number of recovered individuals in population n Union Series float None total population size end_date end date for the evolution of the model fit bool whether to fit the model to the data fit_period Optional int on how many days back to fit the model Usage Fit SEIR Model based on the population in each compartment over a given time period The SEIR models the movement of individuals between four compartments susceptible S exposed E infected I and resistant R The model calibrates parameters Parameter Description S0 initial susceptible individuals E0 initial exposed individuals I0 initial infected individuals R0 initial recovered individuals beta Transmission rate from susceptible to exposed gamma Immunity rate from infected to resistant sigma Immunity rate from exposed to infected The parameters beta gamma and sigma model how fast people move from being susceptible to exposed beta from exposed to infected sigma and subsequently from infected to resistant gamma This model can be used to predict the populations of each compartment once calibrated Methods\",\n \"key\": \"classes/gs_quant.timeseries.statistics.SEIRModel\",\n \"title\": \"gs_quant timeseries statistics SEIRModel\"\n },\n {\n \"body\": \"gs_quant timeseries statistics SIRModel class SIRModel beta None gamma None s None i None r None n None fit True fit_period None source SIR Compartmental model for transmission of infectious disease Parameters beta Optional float transmission rate of the infection gamma Optional float recovery rate of the infection s Union Series float None number of susceptible individuals in population i Union Series float None number of infectious individuals in population r Union Series float None number of recovered individuals in population n Union Series float None total population size end_date end date for the evolution of the model fit bool whether to fit the model to the data fit_period Optional int on how many days back to fit the model Usage Fit SIR Model based on the population in each compartment over a given time period The SIR models the movement of individuals between three compartments susceptible S infected I and resistant R The model calibrates parameters Parameter Description S0 initial susceptible individuals I0 initial infected individuals R0 initial recovered individuals beta Transmission rate from susceptible to infected gamma Immunity rate from infected to resistant The parameters beta and gamma model how fast people move from being susceptible to infected beta and subsequently from infected to resistant gamma This model can be used to forecast the populations of each compartment once calibrated Methods\",\n \"key\": \"classes/gs_quant.timeseries.statistics.SIRModel\",\n \"title\": \"gs_quant timeseries statistics SIRModel\"\n },\n {\n \"body\": \"Data Package\",\n \"key\": \"data\",\n \"title\": \"Data Package\"\n },\n {\n \"body\": \"Datetime Package Date Point\",\n \"key\": \"datetime\",\n \"title\": \"Datetime Package Date Point\"\n },\n {\n \"body\": \"gs_quant datetime date business_day_count business_day_count begin_dates end_dates calendars week_mask None source Determine the number of business days between begin_dates and end_dates Parameters begin_dates Union date Iterable date A date or collection of beginning dates end_dates Union date Iterable date A date or collection of end dates calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Union int Tuple int Returns An int or tuple of ints representing the number of business days between begin_dates and end_dates Examples import datetime as dt today dt date today bus_days business_day_count today today dt timedelta days 7\",\n \"key\": \"functions/gs_quant.datetime.date.business_day_count\",\n \"title\": \"gs_quant datetime date business_day_count\"\n },\n {\n \"body\": \"gs_quant datetime date business_day_offset business_day_offset dates offsets roll raise calendars week_mask None source Apply offsets to the dates and move to the nearest business date Parameters dates Union date Iterable date The input date or dates offsets Union int Iterable int The number of days by which to adjust the dates roll str Which direction to roll in order to get to the nearest business date calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Union date Iterable date Returns A date if dates is a single date or tuple of dates adjusted by the offsets Examples import datetime as dt prev_bus_date business_day_offset dt date today 1 roll forward\",\n \"key\": \"functions/gs_quant.datetime.date.business_day_offset\",\n \"title\": \"gs_quant datetime date business_day_offset\"\n },\n {\n \"body\": \"gs_quant datetime date date_range date_range begin end calendars week_mask None source Construct a range of dates Parameters begin Union int date Beginning date or int An int will be interpreted as the number of business days before end which must be a date end Union int date End date or int An int will be interpreted as the number of business days after begin which must be a date calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Iterable date Returns A generator of dates import datetime as dt today dt date today dates tuple date_range 5 today for date in date_range dt date 2019 1 1 dt date 2019 2 1 print date\",\n \"key\": \"functions/gs_quant.datetime.date.date_range\",\n \"title\": \"gs_quant datetime date date_range\"\n },\n {\n \"body\": \"gs_quant datetime date is_business_day is_business_day dates calendars week_mask None source Determine whether each date in dates is a business day Parameters dates Union date Iterable date The input date or dates calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Union bool Tuple bool Returns True False if dates is a single date A tuple indicating True False for each date if dates is an iterable Examples import datetime as dt is_business_day dt date today is_business_day dt date 2019 7 4 calendars NYSE\",\n \"key\": \"functions/gs_quant.datetime.date.is_business_day\",\n \"title\": \"gs_quant datetime date is_business_day\"\n },\n {\n \"body\": \"gs_quant datetime date prev_business_date prev_business_date dates datetime date 2021 1 5 calendars week_mask None source Returns the previous business date for a given date or date series defaulting to today Parameters dates Union date Iterable date The input date or dates defaults to today calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Union date Iterable date Returns A date if dates is a single date or tuple of dates adjusted by the offset of one day Example import datetime as dt prev_bus_date prev_business_date\",\n \"key\": \"functions/gs_quant.datetime.date.prev_business_date\",\n \"title\": \"gs_quant datetime date prev_business_date\"\n },\n {\n \"body\": \"gs_quant datetime point point_sort_order point_sort_order point ref_date datetime date 2021 1 5 source Calculates a number that can be used to sort Mkt Points by it Parameters point str The point string from MarketDataCoordinate ref_date date Reference date normally the pricing date Return type float Returns The number of days from the reference date to the date specified by the point string Examples import datetime as dt days point_sort_order point Dec20 ref_date dt date today\",\n \"key\": \"functions/gs_quant.datetime.point.point_sort_order\",\n \"title\": \"gs_quant datetime point point_sort_order\"\n },\n {\n \"body\": \"gs_quant timeseries algebra abs_ abs_ x source Absolute value of each element in series Parameters x Series date based time series of prices Return type Series Returns date based time series of absolute value Usage Return the absolute value of X For each value in time series X_t return X_t if X_t is greater than or equal to 0 otherwise return X_t R_t X_t Equivalent to R_t sqrt X_t 2 Examples Generate price series and take absolute value of X_t 100 prices generate_series 100 100 abs_ prices See also exp sqrt\",\n \"key\": \"functions/gs_quant.timeseries.algebra.abs_\",\n \"title\": \"gs_quant timeseries algebra abs_\"\n },\n {\n \"body\": \"gs_quant timeseries algebra add add x y method Interpolate STEP step source Add two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate interpolation method default step Only used when both x and y are timeseries Return type Union Series Real Returns timeseries of x y or sum of the given real numbers Usage Add two series or scalar variables with the given interpolation method R_t X_t Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Add two series a generate_series 100 b generate_series 100 add a b Interpolate STEP See also subtract\",\n \"key\": \"functions/gs_quant.timeseries.algebra.add\",\n \"title\": \"gs_quant timeseries algebra add\"\n },\n {\n \"body\": \"gs_quant timeseries algebra and_ and_ series source Logical and of two or more boolean series Parameters series Series input series Return type Series Returns result series of numeric type with booleans represented as 1s and 0s\",\n \"key\": \"functions/gs_quant.timeseries.algebra.and_\",\n \"title\": \"gs_quant timeseries algebra and_\"\n },\n {\n \"body\": \"gs_quant timeseries algebra ceil ceil x value 0 source Cap series at maximum value Parameters x Series date based time series of prices value float maximum value Return type Series Returns date based time series of maximum value Usage Returns series where all values are less than or equal to the maximum value R_t min X_t value See Floor and Ceil functions for more details Examples Generate price series and floor all values at 100 prices generate_series 100 floor prices 100 See also floor\",\n \"key\": \"functions/gs_quant.timeseries.algebra.ceil\",\n \"title\": \"gs_quant timeseries algebra ceil\"\n },\n {\n \"body\": \"gs_quant timeseries algebra divide divide x y method Interpolate STEP step source Divide two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate interpolation method default step Only used when both x and y are timeseries Return type Union Series Real Returns timeseries of x y or quotient of the given real numbers Usage Divide two series or scalar variables applying the given interpolation method R_t X_t Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Divide two series a generate_series 100 b generate_series 100 divide a b Interpolate STEP See also multiply\",\n \"key\": \"functions/gs_quant.timeseries.algebra.divide\",\n \"title\": \"gs_quant timeseries algebra divide\"\n },\n {\n \"body\": \"gs_quant timeseries algebra exp exp x source Exponential of series Parameters x Series timeseries Return type Series Returns exponential of each element Usage For each element in the series X_t raise e Euler s number to the power of X_t Euler s number is the base of the natural logarithm ln R_t e X_t Examples Raise e to the power 1 Returns Euler s number approximately 2 71828 exp 1 See also log\",\n \"key\": \"functions/gs_quant.timeseries.algebra.exp\",\n \"title\": \"gs_quant timeseries algebra exp\"\n },\n {\n \"body\": \"gs_quant timeseries algebra filter_ filter_ x operator None value None source Removes values where comparison with the operator and value combination results in true defaults to removing missing values from the series Parameters x Series timeseries operator Optional FilterOperator FilterOperator describing logic for value removal e g less_than value Optional Real number indicating value s to remove from the series Return type Series Returns timeseries with specified values removed Usage Remove each value determined by operator and value from timeseries where that expression yields true Examples Remove 0 from time series prices generate_series 100 filter_ prices FilterOperator EQUALS 0 Remove positive numbers from time series prices generate_series 100 filter_ prices FilterOperator GREATER 0 Remove missing values from time series prices generate_series 100 filter_ prices\",\n \"key\": \"functions/gs_quant.timeseries.algebra.filter_\",\n \"title\": \"gs_quant timeseries algebra filter_\"\n },\n {\n \"body\": \"gs_quant timeseries algebra floor floor x value 0 source Floor series at minimum value Parameters x Series date based time series of prices value float minimum value Return type Series Returns date based time series of maximum value Usage Returns series where all values are greater than or equal to the minimum value R_t max X_t value See Floor and Ceil functions for more details Examples Generate price series and floor all values at 100 prices generate_series 100 floor prices 100 See also ceil\",\n \"key\": \"functions/gs_quant.timeseries.algebra.floor\",\n \"title\": \"gs_quant timeseries algebra floor\"\n },\n {\n \"body\": \"gs_quant timeseries algebra floordiv floordiv x y method Interpolate STEP step source Floor divide two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate interpolation method default step Only used for operating two series Return type Union Series Real Returns timeseries of x y or quotient of the floor division of the given real numbers Usage Divide two series or scalar variables applying the given interpolation method R_t X_t Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series Examples Floor divide two series a generate_series 100 b generate_series 100 floordiv a b Interpolate STEP See also divide\",\n \"key\": \"functions/gs_quant.timeseries.algebra.floordiv\",\n \"title\": \"gs_quant timeseries algebra floordiv\"\n },\n {\n \"body\": \"gs_quant timeseries algebra if_ if_ flags x y source Returns a series s For i in the index of flags s i x i if flags i 1 else y i Parameters flags Series series of 1s and 0s x Union Series float values to use when flag is 1 y Union Series float values to use when flag is 0 Return type Series Returns result series\",\n \"key\": \"functions/gs_quant.timeseries.algebra.if_\",\n \"title\": \"gs_quant timeseries algebra if_\"\n },\n {\n \"body\": \"gs_quant timeseries algebra log log x source Natural logarithm of series Parameters x Series timeseries Return type Series Returns series with exponential of each element Usage For each element in the series X_t return the natural logarithm ln of X_t The natural logarithm is the logarithm in base e R_t log X_t This function is the inverse of the exponential function More information on logarithms Examples Take natural logarithm of 3 log 3 See also exp\",\n \"key\": \"functions/gs_quant.timeseries.algebra.log\",\n \"title\": \"gs_quant timeseries algebra log\"\n },\n {\n \"body\": \"gs_quant timeseries algebra multiply multiply x y method Interpolate STEP step source Multiply two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate interpolation method default step Only used when both x and y are timeseries Return type Union Series Real Returns timeseries of x y or product of the given real numbers Usage Multiply two series or scalar variables applying the given interpolation method R_t X_t times Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Multiply two series a generate_series 100 b generate_series 100 multiply a b Interpolate STEP See also divide\",\n \"key\": \"functions/gs_quant.timeseries.algebra.multiply\",\n \"title\": \"gs_quant timeseries algebra multiply\"\n },\n {\n \"body\": \"gs_quant timeseries algebra not_ not_ series source Logical negation of a single boolean series Parameters series Series single input series Return type Series Returns result series of numeric type with booleans represented as 1s and 0s\",\n \"key\": \"functions/gs_quant.timeseries.algebra.not_\",\n \"title\": \"gs_quant timeseries algebra not_\"\n },\n {\n \"body\": \"gs_quant timeseries algebra or_ or_ series source Logical or of two or more boolean series Parameters series Series input series Return type Series Returns result series of numeric type with booleans represented as 1s and 0s\",\n \"key\": \"functions/gs_quant.timeseries.algebra.or_\",\n \"title\": \"gs_quant timeseries algebra or_\"\n },\n {\n \"body\": \"gs_quant timeseries algebra power power x y 1 source Raise each element in series to power Parameters x Series timeseries y float value Return type Series Returns date based time series of square roots Usage Raise each value in time series X_t to the power y R_t X_t y Examples Generate price series and raise each value to the power 2 prices generate_series 100 power prices 2 See also sqrt\",\n \"key\": \"functions/gs_quant.timeseries.algebra.power\",\n \"title\": \"gs_quant timeseries algebra power\"\n },\n {\n \"body\": \"gs_quant timeseries algebra repeat repeat x n 1 source Repeats values for days where data is missing For any date with missing data the last recorded value is used Optionally downsamples the result such that there are data points every n days Parameters x Series date based timeseries n int desired frequency of output Return type Series Returns a timeseries that has been forward filled and optionally downsampled Usage Fill missing values with last seen value e g to combine daily with weekly or monthly data\",\n \"key\": \"functions/gs_quant.timeseries.algebra.repeat\",\n \"title\": \"gs_quant timeseries algebra repeat\"\n },\n {\n \"body\": \"gs_quant timeseries algebra smooth_spikes smooth_spikes x threshold source Smooth out the spikes of a series If a point is larger smaller than 1 threshold times both neighbors replace it with the average of those neighbours Note the first and last points in the input series are dropped Parameters x Series timeseries threshold float minimum increment to trigger filter Return type Series Returns smoothed timeseries Usage Returns series where values that exceed the threshold relative to both neighbors are replaced Examples Generate price series and smooth spikes over a threshold of 0 5 prices generate_series 100 smooth_spikes prices 0 5 See also exponential_moving_average\",\n \"key\": \"functions/gs_quant.timeseries.algebra.smooth_spikes\",\n \"title\": \"gs_quant timeseries algebra smooth_spikes\"\n },\n {\n \"body\": \"gs_quant timeseries algebra sqrt sqrt x source Square root of a each element in a series or b a real number Parameters x Union Real Series date based time series of prices or real number Return type Union Real Series Returns date based time series of square roots or square root of given number Usage Return the square root of each value in time series X_t R_t sqrt X_t Examples Generate price series and take square root of each value prices generate_series 100 sqrt prices See also pow\",\n \"key\": \"functions/gs_quant.timeseries.algebra.sqrt\",\n \"title\": \"gs_quant timeseries algebra sqrt\"\n },\n {\n \"body\": \"gs_quant timeseries algebra subtract subtract x y method Interpolate STEP step source Add two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate index alignment operator default intersect Only used when both x and y are timeseries Return type Union Series Real Returns timeseries of x y or difference between the given real numbers Usage Subtracts one series or scalar from another applying the given interpolation method R_t X_t Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Subtract one series from another a generate_series 100 b generate_series 100 subtract a b Interpolate STEP See also add\",\n \"key\": \"functions/gs_quant.timeseries.algebra.subtract\",\n \"title\": \"gs_quant timeseries algebra subtract\"\n },\n {\n \"body\": \"gs_quant timeseries algebra weighted_sum weighted_sum series weights source Calculate a weighted sum Parameters series List Series list of time series weights list list of weights Return type Series Returns time series of weighted average Usage Calculate a weighted sum e g for a basket Examples Generate price series and get a sum weights 70 30 prices1 generate_series 100 prices2 generate_series 100 mybasket weighted_sum prices1 prices2 0 7 0 3 See also basket\",\n \"key\": \"functions/gs_quant.timeseries.algebra.weighted_sum\",\n \"title\": \"gs_quant timeseries algebra weighted_sum\"\n },\n {\n \"body\": \"gs_quant timeseries analysis count count x source Count observations in series Parameters x Series time series Return type Series Returns number of observations Usage Count the number of valid observations in a series R_t R_ t 1 1 if X_t is not NaN and R_t R_ t 1 0 if X_t is NaN Examples Count observations in series series generate_series 100 count count series See also sum\",\n \"key\": \"functions/gs_quant.timeseries.analysis.count\",\n \"title\": \"gs_quant timeseries analysis count\"\n },\n {\n \"body\": \"gs_quant timeseries analysis diff diff x obs 1 source Diff observations with given lag Parameters x Series time series of prices obs int number of observations to lag Return type Series Returns date based time series of return Usage Compute the difference in series values over a given lag R_t X_t X_ t obs where obs is the number of observations to lag series in diff function Examples Diff prices levels series generate_series 100 returns diff series See also lag\",\n \"key\": \"functions/gs_quant.timeseries.analysis.diff\",\n \"title\": \"gs_quant timeseries analysis diff\"\n },\n {\n \"body\": \"gs_quant timeseries analysis first first x source First value of series Parameters x Series time series Return type Series Returns time series of first value Usage Return series with first value of X for all dates R_t X_0 where X_0 is the first value in the series Examples Last value of series series generate_series 100 returns first series See also last\",\n \"key\": \"functions/gs_quant.timeseries.analysis.first\",\n \"title\": \"gs_quant timeseries analysis first\"\n },\n {\n \"body\": \"gs_quant timeseries analysis lag lag x obs 1 mode LagMode EXTEND extend source Lag timeseries by a number of observations or a relative date Parameters x Series timeseries of prices obs Union Window int str non zero integer number of observations or relative date e g 90d 1d 1m 1y mode LagMode whether to extend series index into the future Return type Series Returns date based time series of return Usage Shift the series backwards by a specified number of observations R_t X_ t obs where obs is the number of observations to lag series Examples Lag series by 2 observations prices generate_series 100 lagged lag prices 2 Lag series by 1 year prices generate_series 100 lagged lag prices 1y See also diff\",\n \"key\": \"functions/gs_quant.timeseries.analysis.lag\",\n \"title\": \"gs_quant timeseries analysis lag\"\n },\n {\n \"body\": \"gs_quant timeseries analysis last last x source Last value of series as a series Parameters x Series time series Return type Series Returns time series of last value Usage Return series with last value of X for all dates R_t X_T where X_T is the last value in the series Examples Last value of series series generate_series 100 returns last series See also first\",\n \"key\": \"functions/gs_quant.timeseries.analysis.last\",\n \"title\": \"gs_quant timeseries analysis last\"\n },\n {\n \"body\": \"gs_quant timeseries analysis last_value last_value x source Last value of series as a scalar Parameters x Series time series Return type Union int float Returns last value Usage Return a scalar value X_T where T is the last index value in the series Examples Last value of series series generate_series 100 lv last_value series See also last\",\n \"key\": \"functions/gs_quant.timeseries.analysis.last_value\",\n \"title\": \"gs_quant timeseries analysis last_value\"\n },\n {\n \"body\": \"weighted_sum\",\n \"key\": \"functions/gs_quant.timeseries.analysis.weighted_sum\",\n \"title\": \"weighted_sum\"\n },\n {\n \"body\": \"gs_quant timeseries backtesting basket basket series weights costs None rebal_freq RebalFreq DAILY daily return_type ReturnType EXCESS_RETURN excess_return source Calculates a basket return series Parameters series list list of time series of instrument prices weights list list of weights costs Optional list list of execution costs in decimal defaults to costs of 0 rebal_freq RebalFreq rebalancing frequency Daily or Monthly return_type ReturnType return type of underlying instruments only excess return is supported Return type Series Returns time series of the resulting basket Usage Calculates a basket return series Examples Generate price series and combine them in a basket weights 70 30 which rebalances monthly and assumes execution costs 5bps and 10bps each time the constituents are traded prices1 generate_series 100 prices2 generate_series 100 mybasket basket prices1 prices2 0 7 0 3 0 0005 0 001 monthly See also prices\",\n \"key\": \"functions/gs_quant.timeseries.backtesting.basket\",\n \"title\": \"gs_quant timeseries backtesting basket\"\n },\n {\n \"body\": \"gs_quant timeseries datetime align align x y method Interpolate INTERSECT intersect source Align dates of two series or scalars Parameters x Union Series Real first timeseries or scalar y Union Series Real second timeseries or scalar method Interpolate interpolation method default intersect Only used when both x and y are timeseries Return type Union List Series List Real Returns timeseries with specified dates or two scalars from the input Usage Align the dates of two series using the specified interpolation method Returns two series with dates based on the method of interpolation for example can be used to intersect the dates of two series union dates with a defined manner to compute missing values Interpolation methods Type Behavior intersect Resultant series only have values on the intersection of dates times nan Resultant series have values on the union of dates times Values will be NaN for dates or times only present in the other series zero Resultant series have values on the union of dates times Values will be zero for dates or times only present in the other series step Resultant series have values on the union of dates times Each series will use the value of the previous valid point if requested date does not exist Values prior to the first date will be equivalent to the first available value time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Stepwize interpolation of series based on dates in second series a generate_series 100 b generate_series 100 align a b See also sub\",\n \"key\": \"functions/gs_quant.timeseries.datetime.align\",\n \"title\": \"gs_quant timeseries datetime align\"\n },\n {\n \"body\": \"gs_quant timeseries datetime date_range date_range x start_date end_date weekdays_only False source Create a time series from a sub range of dates in an existing time series Parameters x Series time series start_date Union date int start date for the sliced time series If integer number of observations after the first end_date Union date int end date for the sliced time series If integer number of observations before the last weekdays_only bool whether to include only weekdays in the sliced ranges Return type Series Returns sliced time series Usage Returns a restricted sliced time series based on start and end dates Y_t R_t _ start t end Examples Slice the first and last week of a time series series generate_series 100 sliced_series date_range series 7 7 See also day func lag\",\n \"key\": \"functions/gs_quant.timeseries.datetime.date_range\",\n \"title\": \"gs_quant timeseries datetime date_range\"\n },\n {\n \"body\": \"gs_quant timeseries datetime day day x source Day of each value in series Parameters x Series time series Return type Series Returns day of observations Usage Returns the day as a numeric value for each observation in the series Y_t day t Day of the time or date is the integer day number within the month e g 1 31 Examples Day for observations in series series generate_series 100 days day series See also month year\",\n \"key\": \"functions/gs_quant.timeseries.datetime.day\",\n \"title\": \"gs_quant timeseries datetime day\"\n },\n {\n \"body\": \"gs_quant timeseries datetime interpolate interpolate x dates None method Interpolate INTERSECT intersect source Interpolate over specified dates or times Parameters x Series timeseries to interpolate dates Union List date List time Series None array of dates times or another series to interpolate method Interpolate interpolation method default intersect Return type Series Returns timeseries with specified dates Usage Interpolate the series X over the dates specified by the dates parameter This can be an array of dates or another series in which case the index of the series will be used to specify dates Interpolation methods Type Behavior intersect Resultant series only has values on the intersection of dates times Will only contain intersection of valid dates times in the series nan Resultant series only has values on the intersection of dates times Value will be NaN for dates not present in the series zero Resultant series has values on all requested dates times The series will have a value of zero where the requested date or time was not present in the series step Resultant series has values on all requested dates times The series will use the value of the previous valid point if requested date does not exist Values prior to the first date will be equivalent to the first available value Examples Stepwize interpolation of series based on dates in second series a generate_series 100 b generate_series 100 interpolate a b Interpolate INTERSECT See also sub\",\n \"key\": \"functions/gs_quant.timeseries.datetime.interpolate\",\n \"title\": \"gs_quant timeseries datetime interpolate\"\n },\n {\n \"body\": \"gs_quant timeseries datetime month month x source Month of each value in series Parameters x Series time series Return type Series Returns month of observations Usage Returns the month as a numeric value for each observation in the series Y_t month t Month of the time or date is the integer month number e g 1 12 Examples Day for observations in series series generate_series 100 days month series See also day year\",\n \"key\": \"functions/gs_quant.timeseries.datetime.month\",\n \"title\": \"gs_quant timeseries datetime month\"\n },\n {\n \"body\": \"gs_quant timeseries datetime prepend prepend x source Prepend data series Parameters x List Series an array of timeseries Return type Series Returns concatenated timeseries Usage For input series x_1 x_2 X_n takes data from series X_i until the first date for which X_ i 1 has data useful when a higher quality series has a shorter history than a lower quality series Examples Prepend two series x generate_series 100 y generate_series 100 prepend x y See also union\",\n \"key\": \"functions/gs_quant.timeseries.datetime.prepend\",\n \"title\": \"gs_quant timeseries datetime prepend\"\n },\n {\n \"body\": \"gs_quant timeseries datetime quarter quarter x source Quarter of each value in series Parameters x Series time series Return type Series Returns quarter of observations Usage Returns the quarter as a numeric value for each observation in the series Y_t quarter t Quarter of the time or date is the integer quarter number e g 1 2 3 4 Examples Quarter for observations in series series generate_series 100 days quarter series See also day month\",\n \"key\": \"functions/gs_quant.timeseries.datetime.quarter\",\n \"title\": \"gs_quant timeseries datetime quarter\"\n },\n {\n \"body\": \"gs_quant timeseries datetime union union x source Fill in missing dates or times of one series with another Parameters x List Series an array of timeseries Return type Series Returns combined series Usage Starting from i 1 takes points from series x_i Where points are missing from x_i returns points from x_ i 1 Examples Union of two series x generate_series 100 y generate_series 100 union x y See also prepend\",\n \"key\": \"functions/gs_quant.timeseries.datetime.union\",\n \"title\": \"gs_quant timeseries datetime union\"\n },\n {\n \"body\": \"gs_quant timeseries datetime value value x date method Interpolate STEP step source Value at specified date or time Parameters x Series timeseries date Union date time requested date or time method Interpolate interpolation method default step Return type Series Returns value at specified date or time Usage Returns the value of series X at the specified date Y_t X_ date If the requested date or time is not present in the series the value function will return the value from the previous available date or time by default Caller can specify other interpolation styles via the method param Interpolation methods Type Behavior intersect Only returns a value for valid dates nan Value will be NaN for dates not present in the series zero Value will be zero for dates not present in the series step Value of the previous valid point if requested date does not exist Values prior to the first date will be equivalent to the first available value Examples Value of series on 5Mar18 a generate_series 100 value a date 2019 1 3 See also interpolate\",\n \"key\": \"functions/gs_quant.timeseries.datetime.value\",\n \"title\": \"gs_quant timeseries datetime value\"\n },\n {\n \"body\": \"gs_quant timeseries datetime weekday weekday x source Weekday of each value in series Parameters x Series time series Return type Series Returns weekday of observations Usage Returns the weekday as a numeric value for each observation in the series Y_t weekday t Weekday of the time or date is the integer day of the week e g 0 6 where 0 represents Monday Examples Weekday for observations in series series generate_series 100 days weekday series See also day month\",\n \"key\": \"functions/gs_quant.timeseries.datetime.weekday\",\n \"title\": \"gs_quant timeseries datetime weekday\"\n },\n {\n \"body\": \"gs_quant timeseries datetime year year x source Year of each value in series Parameters x Series time series Return type Series Returns year of observations Usage Returns the year as a numeric value for each observation in the series Y_t year t Year of the time or date is the integer year number e g 2019 2020 Examples Year for observations in series series generate_series 100 days year series See also day month\",\n \"key\": \"functions/gs_quant.timeseries.datetime.year\",\n \"title\": \"gs_quant timeseries datetime year\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics annualize annualize x source Annualize series based on sample observation frequency Parameters x Series time series of prices Return type Series Returns date based time series of annualized values Usage Based on number of days between observations will determine an annualization factor and then adjust values accordingly Useful for annualizing daily or monthly returns Y_t X_t sqrt F Annualization factors as follows based on period implied by observations Period Annualization Factor F Daily 252 Weekly 52 Bi Weekly 26 Monthly 12 Quarterly 4 Annually 1 Examples Annualize daily returns series prices generate_series 100 ann annualize returns prices See also returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.annualize\",\n \"title\": \"gs_quant timeseries econometrics annualize\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics beta beta x b w gs_quant timeseries helper Window object prices True source Rolling beta of price series and benchmark Parameters x Series time series of prices b Series time series of benchmark prices w Union Window int str Window int or str size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series prices bool True if input series are prices False if they are returns Return type Series Returns date based time series of beta Usage Calculate rolling beta beta_t of a series to a benchmark over a given window R_t alpha_t beta S_t epsilon_t Calculated as beta_t frac sum_ i t w 1 t Cov R_t S_t Var S_t where N is the number of observations in each rolling window w and R_t and S_t are the simple returns for each series on time t R_t frac X_t X_ t 1 1 and S_t frac b_t b_ t 1 1 If prices False assumes returns are provided R_t X_t and S_t b_t Cov R_t S_t and Var S_t are the mean and variance of series R_t and S_t over the same window If window is not provided computes beta over the full series Examples Compute rolling 1 month 22 business day beta of two price series series generate_series 100 benchmark generate_series 100 b beta series benchmark 22 See also var cov correlation returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.beta\",\n \"title\": \"gs_quant timeseries econometrics beta\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics change change x source Arithmetic series normalization Parameters x Series time series Return type Series Returns normalized time series Usage Compute difference of every value from the initial value of x Y_t X_t X_0 where X_0 is the first value in the series Examples Change in level from initial value series generate_series 100 returns change series See also index\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.change\",\n \"title\": \"gs_quant timeseries econometrics change\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics correlation correlation x y w gs_quant timeseries helper Window object type_ SeriesType PRICES prices source Rolling correlation of two price series Parameters x Series price series y Series price series w Union Window int str Window int or str size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series type type of both input series prices or returns Return type Series Returns date based time series of correlation Usage Calculate rolling realized correlation rho_t of two price series over a given window rho_t frac sum_ i t w 1 t R_t overline R_t Y_t overline S_t N 1 sigma R_t sigma S_t where N is the number of observations in each rolling window w and R_t and S_t are the simple returns for each series on time t If prices are provided R_t frac X_t X_ t 1 1 and S_t frac Y_t Y_ t 1 1 If returns are provided R_t X_t and S_t Y_t overline R_t overline S_t are the mean values and sigma R_ t and sigma S_ t are the sample standard deviations of series R_t and S_t over the same window If window is not provided computes realized correlation over the full series Examples Compute rolling 1 month 22 business day correlation of price series series1 generate_series 100 series2 generate_series 100 corr correlation series1 series2 22 See also std returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.correlation\",\n \"title\": \"gs_quant timeseries econometrics correlation\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics excess_returns_ excess_returns_ price_series currency RiskFreeRateCurrency USD USD source Calculate excess returns Parameters price_series Series price series currency RiskFreeRateCurrency currency for risk free rate defaults to USD Return type Series Returns excess returns Usage Given a price series P and risk free rate R excess returns E are defined as E_t E_ t 1 P_t P_ t 1 1 R D_t D_ t 1 360 The Actual 360 day count convention is used Examples Get excess returns from a price series er excess_returns generate_series 100 USD\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.excess_returns_\",\n \"title\": \"gs_quant timeseries econometrics excess_returns_\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics index index x initial 1 source Geometric series normalization Parameters x Series time series initial int initial value Return type Series Returns normalized time series Usage Divides every value in x by the initial value of x Y_t initial X_t X_0 where X_0 is the first value in the series Examples Normalize series to 1 series generate_series 100 returns index series See also returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.index\",\n \"title\": \"gs_quant timeseries econometrics index\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics max_drawdown max_drawdown x w gs_quant timeseries helper Window object source Compute the maximum peak to trough drawdown over a rolling window Parameters x Series time series w Union Window int str Window int or str size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns time series of rolling maximum drawdown Examples Compute the maximum peak to trough drawdown series generate_series 100 max_drawdown series See also returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.max_drawdown\",\n \"title\": \"gs_quant timeseries econometrics max_drawdown\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics prices prices series initial 1 type Returns SIMPLE simple source Calculate price levels from returns series Parameters series Series time series of returns initial int initial price level type Returns returns type simple logarithmic or absolute Return type Series Returns date based time series of return Usage Compute price levels from returns series based on the value of type Type Description simple Simple arithmetic returns logarithmic Logarithmic returns absolute Absolute returns Simple Compute asset price series from simple returns Y_t 1 X_ t 1 Y_ t 1 where X_t is the asset price at time t and Y_0 initial Logarithmic Compute asset price series from logarithmic returns Y_t e X_ t 1 Y_ t 1 where X_t is the asset price at time t and Y_0 initial Absolute Compute asset price series from absolute returns Y_t X_ t 1 Y_ t 1 where X_t is the asset price at time t and Y_0 initial Examples Generate price series and take compute returns series generate_series 100 returns prices returns series See also returns product exp\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.prices\",\n \"title\": \"gs_quant timeseries econometrics prices\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics returns returns series obs 1 type Returns SIMPLE simple source Calculate returns from price series Parameters series Series time series of prices obs int number of observations type Returns returns type simple logarithmic or absolute Return type Series Returns date based time series of return Usage Compute returns series from price levels based on the value of type Type Description simple Simple arithmetic returns logarithmic Logarithmic returns absolute Absolute returns Simple Simple geometric change in asset prices which can be aggregated across assets Y_t frac X_t X_ t obs 1 where X_t is the asset price at time t Logarithmic Natural logarithm of asset price changes which can be aggregated through time Y_t log X_t log X_ t obs where X_t is the asset price at time t Absolute Absolute change in asset prices Y_t X_t X_ t obs where X_t is the asset price at time t Examples Generate price series and take compute returns prices generate_series 100 returns returns prices See also prices\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.returns\",\n \"title\": \"gs_quant timeseries econometrics returns\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics sharpe_ratio sharpe_ratio series currency RiskFreeRateCurrency USD USD w None curve_type CurveType PRICES prices source Calculate Sharpe ratio Parameters series Series series of prices or excess returns for an asset currency RiskFreeRateCurrency currency for risk free rate defaults to USD curve_type CurveType whether input series is of prices or excess returns defaults to prices w Union Window int None Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Return type Series Returns Sharpe ratio Usage Given a price series P risk free rate R and window of size w returns the rolling Sharpe ratio S S_t frac E_t E_ t w 1 365 25 D_t D_ t w 1 volatility E w _t Excess returns E are defined as E_t E_ t 1 P_t P_ t 1 1 R D_t D_ t 1 360 where D is the date for a data point The Actual 360 day count convention is used Examples Get rolling sharpe ratio of a price series with window of 252 sr sharpe_ratio generate_series 100 USD 252 CurveType PRICES See also volatility\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.sharpe_ratio\",\n \"title\": \"gs_quant timeseries econometrics sharpe_ratio\"\n },\n {\n \"body\": \"gs_quant timeseries econometrics volatility volatility x w gs_quant timeseries helper Window object returns_type Returns SIMPLE simple source Realized volatility of price series Parameters x Series time series of prices w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series returns_type Returns returns type simple logarithmic or absolute Return type Series Returns date based time series of return Usage Calculate rolling annualized realized volatility of a price series over a given window Annual volatility of 20 is returned as 20 0 Y_t sqrt frac 1 N 1 sum_ i t w 1 t R_t overline R_t 2 sqrt 252 100 where N is the number of observations in each rolling window w R_t is the return on time t based on returns_type Type Description simple Simple geometric change in asset prices R_t frac X_t X_ t 1 1 where X_t is the asset price at time t logarithmic Natural logarithm of asset price changes R_t log X_t log X_ t 1 where X_t is the asset price at time t absolute Absolute change in asset prices Y_t X_t X_ t obs where X_t is the asset price at time t and overline R_t is the mean value over the same window overline R_t frac sum_ i t w 1 t R_t N If window is not provided computes realized volatility over the full series Examples Compute rolling 1 month 22 business day annualized volatility of price series series generate_series 100 vol_series volatility series 22 vol_series volatility series Window 22 30 See also std annualize returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.volatility\",\n \"title\": \"gs_quant timeseries econometrics volatility\"\n },\n {\n \"body\": \"gs_quant timeseries statistics cov cov x y w gs_quant timeseries helper Window object source Rolling co variance of series over given window Parameters x Series series timeseries y Series series timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of covariance Usage Provides unbiased estimator of sample co variance over a rolling window R_t frac 1 N 1 sum_ i t w 1 t X_i overline X_t Y_i overline Y_t where N is the number of observations in each rolling window w and overline X_t and overline Y_t represent the sample mean of series X_t and Y_t over the same window overline X_t frac sum_ i t w 1 t X_i N and overline Y_t frac sum_ i t w 1 t Y_i N If window is not provided computes variance over the full series Examples Generate price series and compute variance of returns over 22 observations prices_x generate_series 100 prices_y generate_series 100 cov returns prices_x returns prices_y 22 See also sum mean var\",\n \"key\": \"functions/gs_quant.timeseries.statistics.cov\",\n \"title\": \"gs_quant timeseries statistics cov\"\n },\n {\n \"body\": \"gs_quant timeseries statistics exponential_std exponential_std x beta 0 75 source Exponentially weighted standard deviation Parameters x Series time series beta float how much to weigh the previous price in the time series thus controlling how much importance we place on the more distant past Must be between 0 inclusive and 1 exclusive Return type Series Returns time series of standard deviation of the input series Usage Provides an unbiased estimator of exponentially weighted standard deviation of a series X_0 X_1 X_2 S_t sqrt EWMA X_t 2 EWMA X_t 2 DF_t where EWMA X_t is the exponential moving average at t see function exponential_moving_average DF_t is the debiasing factor see Weighted sample variance for further details DF_t frac sum_ i 0 t w_i 2 sum_ i 0 t w_i 2 sum_ i 0 t w_i 2 where w_i is the weight assigned to i th observation w_i 1 beta beta i for i t beta i for i t Examples Generate price series and compute exponentially weighted standard deviation of returns prices generate_series 100 exponential_std returns prices 0 9 See also std var exponential_moving_average\",\n \"key\": \"functions/gs_quant.timeseries.statistics.exponential_std\",\n \"title\": \"gs_quant timeseries statistics exponential_std\"\n },\n {\n \"body\": \"gs_quant timeseries statistics generate_series generate_series length source Generate sample timeseries Parameters length int number of observations Return type Series Returns date based time series of randomly generated prices Usage Create timeseries from returns generated from a normally distributed random variables IDD Length determines the number of observations to be generated Assume random variables R which follow a normal distribution with mean 0 and standard deviation of 1 R sim N 0 1 The timeseries is generated from these random numbers through X_t 1 R X_ t 1 Examples Generate price series with 100 observations starting from today s date prices generate_series 100 See also numpy random normal\",\n \"key\": \"functions/gs_quant.timeseries.statistics.generate_series\",\n \"title\": \"gs_quant timeseries statistics generate_series\"\n },\n {\n \"body\": \"gs_quant timeseries statistics max_ max_ x w gs_quant timeseries helper Window object source Maximum value of series over given window Parameters x Union Series List Series series a timeseries or an array of timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of maximum value Usage Returns the maximum value of the series over each window If x is a series R_t max X_ t w 1 X_t where w is the size of the rolling window If x is an array of series R_t max X_ 1 t w 1 X_ n t where w is the size of the rolling window and n is the number of series If window is not provided returns the maximum value over the full series If the window size is greater than the available data will return maximum of available values Examples Maximum value of price series over the last 22 observations prices generate_series 100 max_ prices 22 See also min_\",\n \"key\": \"functions/gs_quant.timeseries.statistics.max_\",\n \"title\": \"gs_quant timeseries statistics max_\"\n },\n {\n \"body\": \"gs_quant timeseries statistics mean mean x w gs_quant timeseries helper Window object source Arithmetic mean of series over given window Parameters x Union Series List Series series a timeseries or an array of timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of mean value Usage Calculates arithmetic mean of the series over a rolling window If a timeseries is provided R_t frac sum_ i t w 1 t X_i N where N is the number of observations in each rolling window w If an array of timeseries is provided R_t frac sum_ i t w 1 t sum_ j 1 n X_ ij N where n is the number of series and N is the number of observations in each rolling window w If window is not provided computes rolling mean over the full series If the window size is greater than the available data will return mean of available values Examples Generate price series and compute mean over 22 observations prices generate_series 100 mean prices 22 See also median mode\",\n \"key\": \"functions/gs_quant.timeseries.statistics.mean\",\n \"title\": \"gs_quant timeseries statistics mean\"\n },\n {\n \"body\": \"gs_quant timeseries statistics median median x w gs_quant timeseries helper Window object source Median value of series over given window Parameters x Series series timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of median value Usage Computes the median value over a given window For each window this function will return the middle value when all elements in the window are sorted If the number of observations in the window is even will return the average of the middle two values If the window size is greater than the available data will return median of available values d frac w 1 2 R_t frac X_ lfloor t d rfloor X_ lceil t d rceil 2 where w is the size of the rolling window If window is not provided computes median over the full series Examples Generate price series and compute median over 22 observations prices generate_series 100 median prices 22 See also mean mode\",\n \"key\": \"functions/gs_quant.timeseries.statistics.median\",\n \"title\": \"gs_quant timeseries statistics median\"\n },\n {\n \"body\": \"gs_quant timeseries statistics min_ min_ x w gs_quant timeseries helper Window object source Minimum value of series over given window Parameters x Union Series List Series series a timeseries or an array of timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of minimum value Usage Returns the minimum value of the series over each window If x is a series R_t min X_ t w 1 X_t where w is the size of the rolling window If x is an array of series R_t min X_ 1 t w 1 X_ n t where w is the size of the rolling window and n is the number of series If window is not provided returns the minimum value over the full series If the window size is greater than the available data will return minimum of available values Examples Minimum value of price series over the last 22 observations prices generate_series 100 min_ prices 22 See also max_\",\n \"key\": \"functions/gs_quant.timeseries.statistics.min_\",\n \"title\": \"gs_quant timeseries statistics min_\"\n },\n {\n \"body\": \"gs_quant timeseries statistics mode mode x w gs_quant timeseries helper Window object source Most common value in series over given window Parameters x Series series timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of mode value Usage Computes the mode over a given window For each window this function will return the most common value of all elements in the window If there are multiple values with the same frequency of occurrence will return the smallest value If window is not provided computes mode over the full series Examples Generate price series and compute mode over 22 observations prices generate_series 100 mode prices 22 See also mean median\",\n \"key\": \"functions/gs_quant.timeseries.statistics.mode\",\n \"title\": \"gs_quant timeseries statistics mode\"\n },\n {\n \"body\": \"gs_quant timeseries statistics percentile percentile x n w None source Returns the nth percentile of a series Parameters x Series series n float percentile w Union Window int str None Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Return type Union Series float Returns nth percentile Usage Calculates the nth percentile rank of x Rolling nth percentile is returned if a window is specified else a scalar for nth percentile over the entire series Example Compute the 90th percentile of a series a generate_series 100 percentile a 90\",\n \"key\": \"functions/gs_quant.timeseries.statistics.percentile\",\n \"title\": \"gs_quant timeseries statistics percentile\"\n },\n {\n \"body\": \"gs_quant timeseries statistics percentiles percentiles x y None w gs_quant timeseries helper Window object source Rolling percentiles over given window Parameters x Series value series y Optional Series distribution series w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of percentiles Usage Calculate percentile rank of y in the sample distribution of x over a rolling window of length w R_t frac sum_ i t N 1 t X_i Y_t 0 5 sum_ i t N 1 t X_i Y_t N times100 Where N is the number of observations in a rolling window If y is not provided calculates percentiles of x over its historical values If window length w is not provided uses an ever growing history of values If w is greater than the available data size returns empty Examples Compute percentile ranks of a series in the sample distribution of a second series over 22 observations a generate_series 100 b generate_series 100 percentiles a b 22 See also zscores\",\n \"key\": \"functions/gs_quant.timeseries.statistics.percentiles\",\n \"title\": \"gs_quant timeseries statistics percentiles\"\n },\n {\n \"body\": \"gs_quant timeseries statistics product product x w gs_quant timeseries helper Window object source Rolling product of series over given window Parameters x Series series timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of rolling product Usage Calculate the product of observations over a given rolling window For each time t returns the value of all observations from t w 1 to t multiplied together R_t prod_ i t w 1 t X_i where w is the size of the rolling window If window is not provided computes product over the full series Examples Generate price series and compute rolling sum over 22 observations prices generate_series 100 product 1 returns prices See also sum_\",\n \"key\": \"functions/gs_quant.timeseries.statistics.product\",\n \"title\": \"gs_quant timeseries statistics product\"\n },\n {\n \"body\": \"gs_quant timeseries statistics range_ range_ x w gs_quant timeseries helper Window object source Range of series over given window Parameters x Series series timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of range Usage Returns the range of the series max min over rolling window R_t max X_ t w 1 X_t min X_ t w 1 X_t where w is the size of the rolling window If window is not provided returns the range over the full series If the window size is greater than the available data will return range of all available values Examples Range of price series over the last 22 observations prices generate_series 100 range_ prices 22 See also min_ max_\",\n \"key\": \"functions/gs_quant.timeseries.statistics.range_\",\n \"title\": \"gs_quant timeseries statistics range_\"\n },\n {\n \"body\": \"gs_quant timeseries statistics std std x w gs_quant timeseries helper Window object source Rolling standard deviation of series over given window Parameters x Series series timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of standard deviation Usage Provides unbiased estimator of sample standard deviation over a rolling window R_t sqrt frac 1 N 1 sum_ i t w 1 t X_i overline X_t 2 where N is the number of observations in each rolling window w and overline X_t is the mean value over the same window overline X_t frac sum_ i t w 1 t X_i N If window is not provided computes standard deviation over the full series Examples Generate price series and compute standard deviation of returns over 22 observations prices generate_series 100 std returns prices 22 See also sum mean var\",\n \"key\": \"functions/gs_quant.timeseries.statistics.std\",\n \"title\": \"gs_quant timeseries statistics std\"\n },\n {\n \"body\": \"gs_quant timeseries statistics sum_ sum_ x w gs_quant timeseries helper Window object source Rolling sum of series over given window Parameters x Union Series List Series series a timeseries or an array of timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of rolling sum Usage Calculate the sum of observations over a given rolling window If x is a series R_t sum_ i t w 1 t X_i where w is the size of the rolling window If x is an array of series R_t sum_ i t w 1 t sum_ j 1 n X_ ij where w is the size of the rolling window and n is the number of series If window is not provided computes sum over the full series If the window size is greater than the available data will return sum of available values Examples Generate price series and compute rolling sum over 22 observations prices generate_series 100 sum_ prices 22 See also product\",\n \"key\": \"functions/gs_quant.timeseries.statistics.sum_\",\n \"title\": \"gs_quant timeseries statistics sum_\"\n },\n {\n \"body\": \"gs_quant timeseries statistics var var x w gs_quant timeseries helper Window object source Rolling variance of series over given window Parameters x Series series timeseries w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of variance Usage Provides unbiased estimator of sample variance over a rolling window R_t frac 1 N 1 sum_ i t w 1 t X_i overline X_t 2 where N is the number of observations in each rolling window w and overline X_t is the mean value over the same window overline X_t frac sum_ i t w 1 t X_i N If window is not provided computes variance over the full series Examples Generate price series and compute variance of returns over 22 observations prices generate_series 100 var returns prices 22 See also var mean std\",\n \"key\": \"functions/gs_quant.timeseries.statistics.var\",\n \"title\": \"gs_quant timeseries statistics var\"\n },\n {\n \"body\": \"gs_quant timeseries statistics winsorize winsorize x limit 2 5 w gs_quant timeseries helper Window object source Limit extreme values in series Parameters x Series time series of prices limit float max z score of values w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of winsorized values Usage Cap and floor values in the series which have a z score greater or less than provided value This function will restrict the distribution of values Calculates the sample standard deviation and adjusts values which fall outside the specified range to be equal to the upper or lower limits Lower and upper limits are defined as upper mu sigma times limit lower mu sigma times limit Where mu and sigma are sample mean and standard deviation The series is restricted by R_t max min X_t upper lower See winsorising for additional information Examples Generate price series and winsorize z score of returns over 22 observations prices generate_series 100 winsorize zscore returns prices 22 See also zscore mean std\",\n \"key\": \"functions/gs_quant.timeseries.statistics.winsorize\",\n \"title\": \"gs_quant timeseries statistics winsorize\"\n },\n {\n \"body\": \"gs_quant timeseries statistics zscores zscores x w gs_quant timeseries helper Window object source Rolling z scores over a given window Parameters x Series time series of prices w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns timeseries of z scores Usage Calculate standard score of each value in series over given window Standard deviation and sample mean are computed over the specified rolling window then element is normalized to provide a rolling z score R_t frac X_t mu sigma Where mu and sigma are sample mean and standard deviation over the given window If window is not provided computes z score relative to mean and standard deviation over the full series Examples Generate price series and compute z score of returns over 22 observations prices generate_series 100 zscores returns prices 22 See also mean std\",\n \"key\": \"functions/gs_quant.timeseries.statistics.zscores\",\n \"title\": \"gs_quant timeseries statistics zscores\"\n },\n {\n \"body\": \"gs_quant timeseries technicals bollinger_bands bollinger_bands x w gs_quant timeseries helper Window object k 2 source Bollinger bands with given window and width Parameters x Series time series of prices w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series k float band width in standard deviations default 2 Return type DataFrame Returns date based time series of return Usage Standard deviation bands around the moving average of asset price level Bollinger bands can be used to determine a range around the price level which responds to local volatility changes Returns two series upper u_t and lower l_t u_t bar X_t k sigma_t l_t bar X_t k sigma_t where bar X_t is the moving average over specified window and sigma_t is the rolling standard deviation over the specified window See Bollinger Bands for more information Examples Compute bollinger bands around 20 day moving average at 2 standard deviations prices generate_series 100 bollinger_bands prices 20 2 See also moving_average std\",\n \"key\": \"functions/gs_quant.timeseries.technicals.bollinger_bands\",\n \"title\": \"gs_quant timeseries technicals bollinger_bands\"\n },\n {\n \"body\": \"gs_quant timeseries technicals exponential_moving_average exponential_moving_average x beta 0 75 source Exponentially weighted moving average Parameters x Series time series of prices beta float how much to weigh the previous observations in the time series thus controlling how much importance we place on the more distant past Must be between 0 inclusive and 1 exclusive Return type Series Returns date based time series of return Usage The exponential ly weighted moving average EMA of a series X_0 X_1 X_2 is defined as Y_0 X_0 Y_t beta cdot Y_ t 1 1 beta cdot X_t where beta is the weight we place on the previous average See Exponential moving average for more information Examples Generate price series with 100 observations starting from today s date prices generate_series 100 exponential_moving_average prices 0 9 See also mean moving_average smoothed_moving_average\",\n \"key\": \"functions/gs_quant.timeseries.technicals.exponential_moving_average\",\n \"title\": \"gs_quant timeseries technicals exponential_moving_average\"\n },\n {\n \"body\": \"gs_quant timeseries technicals exponential_spread_volatility exponential_spread_volatility x beta 0 75 source Exponentially weighted spread volatility Parameters x Series time series of prices beta float how much to weigh the previous price in the time series thus controlling how much importance we place on the more distant past Must be between 0 inclusive and 1 exclusive Return type Series Returns date based time series of exponential spread volatility of the input series Usage Exponentially weights the daily differences of the input series calculates the annualized standard deviation Examples Generate price series and compute exponentially weighted standard deviation of returns prices generate_series 100 exponential_volatility prices 0 9 The above is equivalent to annualize exponential_std diff prices 1 0 9 See also volatility exponential_std exponential_volatility\",\n \"key\": \"functions/gs_quant.timeseries.technicals.exponential_spread_volatility\",\n \"title\": \"gs_quant timeseries technicals exponential_spread_volatility\"\n },\n {\n \"body\": \"gs_quant timeseries technicals exponential_volatility exponential_volatility x beta 0 75 source Exponentially weighted volatility Parameters x Series time series of prices beta float how much to weigh the previous price in the time series thus controlling how much importance we place on the more distant past Must be between 0 inclusive and 1 exclusive Return type Series Returns date based time series of exponential volatility of the input series Usage Calculates the exponentially weighted standard deviation of the return of the input series and annualizes the standard deviation Examples Generate price series and compute exponentially weighted standard deviation of returns prices generate_series 100 exponential_volatility prices 0 9 The above is equivalent to annualize exponential_std returns prices 0 9 100 See also volatility exponential_std exponential_spread_volatility\",\n \"key\": \"functions/gs_quant.timeseries.technicals.exponential_volatility\",\n \"title\": \"gs_quant timeseries technicals exponential_volatility\"\n },\n {\n \"body\": \"gs_quant timeseries technicals moving_average moving_average x w gs_quant timeseries helper Window object source Moving average over specified window Parameters x Series time series of prices w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns date based time series of return Usage Simple arithmetic moving average over the specified window number of observations Shorter windows will be more reactive to changes in the asset price but more volatile Larger windows will be smoother but less reactive to near term changes in asset prices R_t frac sum_ i t w 1 t X_t N where N is the number of observations in each rolling window w If window is not provided computes rolling mean over the full series Equivalent to mean Examples Generate price series with 100 observations starting from today s date prices generate_series 100 moving_average prices 22 See also mean\",\n \"key\": \"functions/gs_quant.timeseries.technicals.moving_average\",\n \"title\": \"gs_quant timeseries technicals moving_average\"\n },\n {\n \"body\": \"gs_quant timeseries technicals relative_strength_index relative_strength_index x w 14 source Relative Strength Index Parameters x Series time series of prices w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type DataFrame Returns date based time series of RSI Usage The RSI computes momentum as the ratio of higher closes to lower closes stocks which have had more or stronger positive changes have a higher RSI than stocks which have had more or stronger negative changes See RSI for more information Examples Compute relative strength index over a 14 day window prices generate_series 100 relative_strength_index prices 14 See also moving_average std smoothed_moving_average\",\n \"key\": \"functions/gs_quant.timeseries.technicals.relative_strength_index\",\n \"title\": \"gs_quant timeseries technicals relative_strength_index\"\n },\n {\n \"body\": \"gs_quant timeseries technicals smoothed_moving_average smoothed_moving_average x w gs_quant timeseries helper Window object source Smoothed moving average over specified window Parameters x Series time series of prices w Union Window int str Window or int size of window and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value If w is a string it should be a relative date like 1m 1d etc Window size defaults to length of series Return type Series Returns date based time series of return Usage A modified moving average MMA running moving average RMA or smoothed moving average SMMA is defined as P_ MM today frac N 1 P_ MM yesterday P_today N where N is the number of observations in each rolling window w If window is not provided computes rolling mean over the full series See Modified moving average for more information Examples Generate price series with 100 observations starting from today s date prices generate_series 100 smoothed_moving_average prices 22 See also mean func moving_average\",\n \"key\": \"functions/gs_quant.timeseries.technicals.smoothed_moving_average\",\n \"title\": \"gs_quant timeseries technicals smoothed_moving_average\"\n },\n {\n \"body\": \"GS Quant API Packages Data Package gs_quant data DataContext gs_quant data Dataset gs_quant data Fields Datetime Package Date Point Instrument Package gs_quant base Priceable Instruments Markets Package gs_quant markets PricingContext gs_quant markets HistoricalPricingContext Portfolio Securities Models Package Epidemiology Risk Package Functions Measures Timeseries Package Algebra Analysis Backtesting Date Time Econometrics Statistics Technical Analysis Index\",\n \"key\": \"index\",\n \"title\": \"GS Quant API\"\n },\n {\n \"body\": \"Instrument Package Instruments\",\n \"key\": \"instrument\",\n \"title\": \"Instrument Package Instruments\"\n },\n {\n \"body\": \"Markets Package Portfolio Securities\",\n \"key\": \"markets\",\n \"title\": \"Markets Package Portfolio Securities\"\n },\n {\n \"body\": \"Models Package Epidemiology\",\n \"key\": \"models\",\n \"title\": \"Models Package Epidemiology\"\n },\n {\n \"body\": \"Risk Package Functions aggregate_risk results threshold None source Combine the results of multiple InstrumentBase calc calls into a single result Parameters results Iterable Union DataFrameWithInfo Future An iterable of Dataframes and or Futures returned by InstrumentBase calc threshold Optional float exclude values whose absolute value falls below this threshold Return type DataFrame Returns A Dataframe with the aggregated results Examples from gs_quant instrument import IRCap IRFloor from gs_quant markets import PricingContext from gs_quant risk import IRDelta IRVega cap IRCap 5y GBP floor IRFloor 5y GBP instruments cap floor with PricingContext delta_f inst calc IRDelta for inst in instruments vega_f inst calc IRVega for inst in cap floor delta aggregate_risk delta_f threshold 0 1 vega aggregate_risk vega_f delta_f and vega_f are lists of futures where the result will be a Dataframe delta and vega are Dataframes representing the merged risk of the individual instruments subtract_risk left right source Subtract bucketed risk Dimensions must be identical Parameters left DataFrameWithInfo Results to substract from right DataFrameWithInfo Results to substract Examples from gs_quant datetime date import business_day_offset from gs_quant instrument IRSwap from gs_quant markets import PricingContext from gs_quant risk import IRDelta import datetime as dt ir_swap IRSwap Pay 10y USD delta_today ir_swap calc IRDelta with PricingContext pricing_date business_day_offset dt date today 1 roll preceding delta_yday_f ir_swap calc IRDelta delta_diff subtract_risk delta_today delta_yday_f result Return type DataFrame sort_risk df by date time mkt_type mkt_asset mkt_class mkt_point source Sort bucketed risk Parameters df DataFrame Input Dataframe by Tuple str Columns to sort by Return type DataFrame Returns A sorted Dataframe Measures DollarPrice DollarPrice Present value in USD Price Price Present value in local currency ForwardPrice ForwardPrice Forward price Theta Theta 1 day Theta BaseCPI BaseCPI Base CPI level CommodDelta CommodDelta Commodity Delta CommodTheta CommodTheta Commodity Theta CommodVega CommodVega Commodity Vega EqDelta EqDelta Equity Delta EqGamma EqGamma Equity Gamma EqVega EqVega Equity Vega EqSpot EqSpot Equity Spot Level EqAnnualImpliedVol EqAnnualImpliedVol Equity Annual Implied Volatility FairVarStrike FairVarStrike Fair Variance Strike Value of a Variance Swap FairVolStrike FairVolStrike Fair Volatility Strike Value of a Variance Swap FXDelta FXDelta FX Delta FXGamma FXGamma FX Gamma FXVega FXVega FX Vega FXSpot FXSpot FX Spot Rate InflationDelta InflationDelta Inflation Delta InflationDeltaParallel InflationDeltaParallel Inflation Parallel Delta InflationDeltaParallelLocalCcy InflationDeltaParallelLocalCcy Inflation Parallel Delta Local Ccy IRBasis IRBasis Interest Rate Basis IRDelta IRDelta Interest Rate Delta IRDeltaParallel IRDeltaParallel Interest Rate Parallel Delta IRDeltaLocalCcy IRDeltaLocalCcy Interest Rate Delta Local Ccy IRDeltaParallelLocalCcy IRDeltaParallelLocalCcy Interest Rate Parallel Delta Local Ccy IRXccyDelta IRXccyDelta Cross ccy Delta IRXccyDeltaParallel IRXccyDeltaParallel Cross ccy Parallel Delta IRXccyDeltaParallelLocalCurrency IRXccyDeltaParallelLocalCurrency Cross ccy Parallel Delta Local Ccy IRGammaParallel IRGammaParallel Interest Rate Parallel Gamma IRGammaParallelLocalCcy IRGammaParallelLocalCcy Interest Rate Parallel Gamma Local Ccy IRVega IRVega Interest Rate Vega IRVegaParallel IRVegaParallel Interest Rate Parallel Vega IRVegaLocalCcy IRVegaLocalCcy Interest Rate Vega Local Ccy IRVegaParallelLocalCcy IRVegaParallelLocalCcy Interest Rate Parallel Vega Local Ccy IRAnnualImpliedVol IRAnnualImpliedVol Interest Rate Annual Implied Volatility IRAnnualATMImpliedVol IRAnnualATMImpliedVol Interest Rate Annual Implied At The Money Volatility IRDailyImpliedVol IRDailyImpliedVol Interest Rate Daily Implied Volatility bps IRSpotRate IRSpotRate At The Money Spot Rate IRFwdRate IRFwdRate Par Rate\",\n \"key\": \"risk\",\n \"title\": \"Risk Package Functions Measures\"\n },\n {\n \"body\": \"Timeseries Package Algebra Analysis Backtesting Date Time Econometrics Statistics Technical Analysis\",\n \"key\": \"timeseries\",\n \"title\": \"Timeseries Package Algebra Analysis Backtesting Date Time Econometrics Statistics Technical Analysis\"\n }\n]" }, { "path": "docs/_build/search/searchdata.json", "content": "[\n {\n \"body\": \"gs_quant base Priceable class Priceable source A priceable such as a derivative instrument __init__ source Initialize self See help type self for accurate signature Methods Attributes\",\n \"key\": \"classes/gs_quant.base.Priceable\",\n \"title\": \"gs_quant base Priceable\"\n },\n {\n \"body\": \"gs_quant data Dataset class Dataset dataset_id provider None source A collection of related data __init__ dataset_id provider None source Parameters dataset_id Union str Vendor The dataset s identifier provider Optional DataApi The data provider Methods Attributes\",\n \"key\": \"classes/gs_quant.data.Dataset\",\n \"title\": \"gs_quant data Dataset\"\n },\n {\n \"body\": \"CommodSwap For methods of this class see gs_quant base Priceable class CommodSwap commodity start commodity_reference_price None notional_amount 1000000 0 currency None calculation_periods None calculation_period_frequency None source Object representation of a commodities swap Properties calculation_period_frequency The frequency of the calculation periods Return type Union Frequency str calculation_periods The number of calculation periods Return type int commodity Commodity asset Return type Union CommodityAsset str commodity_reference_price Return type str currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str notional_amount Notional amount Return type float start Return type Union date str\",\n \"key\": \"classes/gs_quant.instrument.CommodSwap\",\n \"title\": \"CommodSwap\"\n },\n {\n \"body\": \"EqOption For methods of this class see gs_quant base Priceable class EqOption asset expiration_date strike_price option_type option_style number_of_options None exchange None multiplier None settlement_date None currency None premium None source Instrument definition for equity option Properties asset Ticker of the underlying stock or index Return type str currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str exchange Name of marketplace where security derivative or other instrument is traded Return type str expiration_date Date or tenor e g 2018 09 03 3m Return type Union date str multiplier Number of stock units per option contract Return type float number_of_options Number of options Return type float option_style Option Exercise Style Return type Union OptionStyle str option_type Option Type Return type Union OptionType str premium Option premium Return type float settlement_date Date or tenor e g 2018 09 03 3m Return type Union date str strike_price Strike as value percent or string e g 62 5 95 ATM ATMF 25ATM 20CallDelta 10PutDelta 10NS Return type Union float str\",\n \"key\": \"classes/gs_quant.instrument.EqOption\",\n \"title\": \"EqOption\"\n },\n {\n \"body\": \"FXForward For methods of this class see gs_quant base Priceable class FXForward pair None settlement_date None forward_rate None notional_amount None source Object representation of an FX forward Properties forward_rate Forward FX rate Return type float notional_amount Notional amount Return type float pair Currency pair Return type str settlement_date Date or tenor e g 2018 09 03 3m Return type Union date str\",\n \"key\": \"classes/gs_quant.instrument.FXForward\",\n \"title\": \"FXForward\"\n },\n {\n \"body\": \"FXOption For methods of this class see gs_quant base Priceable class FXOption call_currency put_currency expiration_date option_type call_amount 1000000 0 put_amount 1000000 0 strike None premium 0 source Object representation of a FX option Properties call_amount Amount of the call currency Return type float call_currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str expiration_date Date or tenor e g 2018 09 03 3m Return type Union date str option_type Option Type Return type Union OptionType str premium Option premium Return type float put_amount Amount of the put currency Return type float put_currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str strike Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF Return type Union float str\",\n \"key\": \"classes/gs_quant.instrument.FXOption\",\n \"title\": \"FXOption\"\n },\n {\n \"body\": \"Forward For methods of this class see gs_quant base Priceable class Forward currency expiration_date notional_amount None source Object representation of a forward Properties currency Currency ISO 4217 currency code or exchange quote modifier e g GBP vs GBp Return type Union Currency str expiration_date Date or tenor e g 2018 09 03 3m Return type Union date str notional_amount Notional amount Return type float\",\n \"key\": \"classes/gs_quant.instrument.Forward\",\n \"title\": \"Forward\"\n },\n {\n \"body\": \"IRBasisSwap For methods of this class see gs_quant base Priceable class IRBasisSwap termination_date notional_currency notional_amount 1000000 0 effective_date None payer_spread None payer_rate_option None payer_designated_maturity None payer_frequency None payer_day_count_fraction None payer_business_day_convention None receiver_spread None receiver_rate_option None receiver_designated_maturity None receiver_frequency None receiver_day_count_fraction None receiver_business_day_convention None fee 0 clearing_house None source An exchange of cashflows from different interest rate indices Properties clearing_house Swap Clearing House Return type Union SwapClearingHouse str effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float notional_amount Notional amount Return type float notional_currency Notional currency Return type Union Currency str payer_business_day_convention The business day convention for the payer Return type Union BusinessDayConvention str payer_day_count_fraction The day count fraction for the payer Return type Union DayCountFraction str payer_designated_maturity Tenor of the payerRateOption e g 3m 6m Return type str payer_frequency The frequency of payer payments e g 6m Return type str payer_rate_option The underlying benchmark for the payer e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str payer_spread Spread over the payer rate Return type float receiver_business_day_convention The business day convention for the receiver Return type Union BusinessDayConvention str receiver_day_count_fraction The day count fraction for the receiver Return type Union DayCountFraction str receiver_designated_maturity Tenor of the receiverRateOption e g 3m 6m Return type str receiver_frequency The frequency of receiver payments e g 6m Return type str receiver_rate_option The underlying benchmark for the receiver e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str receiver_spread Spread over the receiver rate Return type float termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str\",\n \"key\": \"classes/gs_quant.instrument.IRBasisSwap\",\n \"title\": \"IRBasisSwap\"\n },\n {\n \"body\": \"IRCap For methods of this class see gs_quant base Priceable class IRCap termination_date notional_currency notional_amount 1000000 0 effective_date None floating_rate_option None floating_rate_designated_maturity None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None cap_rate None premium 0 fee 0 premium_payment_date None source Object representation of an interest rate cap Properties cap_rate The rate of this cap as value percent or at the money e g 62 5 95 ATM 25 ATMF Return type Union float str effective_date The date on which the cap becomes effective Return type Union date str fee The fee Return type float floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor of the floatingRateOption e g 3m 6m Return type str floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str notional_amount Notional amount Return type float notional_currency Notional currency Return type Union Currency str premium The premium Return type float premium_payment_date Date or tenor e g 2018 09 03 3m Return type Union date str termination_date The termination of the cap e g 2025 04 01 2y Return type Union date str\",\n \"key\": \"classes/gs_quant.instrument.IRCap\",\n \"title\": \"IRCap\"\n },\n {\n \"body\": \"IRFloor For methods of this class see gs_quant base Priceable class IRFloor termination_date notional_currency notional_amount 1000000 0 effective_date None floating_rate_option None floating_rate_designated_maturity None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None floor_rate None fee 0 premium_payment_date None source Object representation of an interest rate floor Properties effective_date The date on which the floor becomes effective Return type Union date str fee The fee Return type float floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor of the floatingRateOption e g 3m 6m Return type str floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str floor_rate The rate of this floor as value percent or at the money e g 62 5 95 ATM 25 ATMF Return type Union float str notional_amount Notional amount Return type float notional_currency Notional currency Return type Union Currency str premium_payment_date Date or tenor e g 2018 09 03 3m Return type Union date str termination_date The termination of the floor e g 2025 04 01 2y Return type Union date str\",\n \"key\": \"classes/gs_quant.instrument.IRFloor\",\n \"title\": \"IRFloor\"\n },\n {\n \"body\": \"IRSwap For methods of this class see gs_quant base Priceable class IRSwap pay_or_receive termination_date notional_currency notional_amount 1000000 0 effective_date None floating_rate_for_the_initial_calculation_period None floating_rate_option None floating_rate_designated_maturity None floating_rate_spread None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None fixed_rate None fixed_rate_frequency None fixed_rate_day_count_fraction None fixed_rate_business_day_convention None fee 0 clearing_house None source A vanilla interest rate swap of fixed vs floating cashflows Properties clearing_house Swap Clearing House Return type Union SwapClearingHouse str effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float fixed_rate The coupon of the fixed leg Return type Union float str fixed_rate_business_day_convention The business day convention for the fixed rate Return type Union BusinessDayConvention str fixed_rate_day_count_fraction The day count fraction for the fixed rate Return type Union DayCountFraction str fixed_rate_frequency The frequency of fixed payments e g 6m Return type str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor of the floatingRateOption e g 3m 6m Return type str floating_rate_for_the_initial_calculation_period First fixing Return type float floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str floating_rate_spread The spread over the floating rate Return type float notional_amount Notional amount Return type float notional_currency Notional currency Return type Union Currency str pay_or_receive Pay or receive fixed Return type Union PayReceive str termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str\",\n \"key\": \"classes/gs_quant.instrument.IRSwap\",\n \"title\": \"IRSwap\"\n },\n {\n \"body\": \"IRSwaption For methods of this class see gs_quant base Priceable class IRSwaption pay_or_receive termination_date notional_currency effective_date None notional_amount 1000000 0 expiration_date None floating_rate_option None floating_rate_designated_maturity None floating_rate_spread None floating_rate_frequency None floating_rate_day_count_fraction None floating_rate_business_day_convention None fixed_rate_frequency None fixed_rate_day_count_fraction None fixed_rate_business_day_convention None strike None premium 0 fee 0 clearing_house None settlement None premium_payment_date None source Object representation of a swaption Properties clearing_house Swap Clearing House Return type Union SwapClearingHouse str effective_date Swaption effective date e g 2019 01 01 10y Return type Union date str expiration_date Swaption expiration date 2020 05 01 3m Return type Union date str fee The fee Return type float fixed_rate_business_day_convention The business day convention for the fixed rate Return type Union BusinessDayConvention str fixed_rate_day_count_fraction The day count fraction for the fixed rate Return type Union DayCountFraction str fixed_rate_frequency The frequency of fixed payments e g 6m Return type str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str floating_rate_day_count_fraction The day count fraction of the floating rate Return type Union DayCountFraction str floating_rate_designated_maturity Tenor Return type str floating_rate_frequency The frequency of floating payments e g 3m Return type str floating_rate_option The underlying benchmark for the floating rate e g USD LIBOR BBA EUR EURIBOR TELERATE Return type str floating_rate_spread The spread over the floating rate Return type float notional_amount Notional amount Return type float notional_currency Notional currency Return type Union Currency str pay_or_receive Pay or receive fixed Return type str premium The premium Return type float premium_payment_date Date or tenor e g 2018 09 03 3m Return type Union date str settlement Swap Settlement Type Return type Union SwapSettlement str strike Strike as value percent or at the money e g 62 5 95 ATM 25 ATMF Return type Union float str termination_date Swaption termination date e g 2030 05 01 10y Return type Union date str\",\n \"key\": \"classes/gs_quant.instrument.IRSwaption\",\n \"title\": \"IRSwaption\"\n },\n {\n \"body\": \"InflationSwap For methods of this class see gs_quant base Priceable class InflationSwap termination_date pay_or_receive None notional_currency None notional_amount 1000000 0 effective_date None index None floating_rate_business_day_convention None fixed_rate None fixed_rate_business_day_convention None fee 0 source A vanilla inflation swap of fixed vs floating cashflows adjusted to an inflation rate Properties effective_date The date on which the swap becomes effective Return type Union date str fee The fee Return type float fixed_rate The coupon of the fixed leg Return type Union float str fixed_rate_business_day_convention The business day convention for the fixed rate Return type Union BusinessDayConvention str floating_rate_business_day_convention The business day convention of the floating rate Return type Union BusinessDayConvention str index The underlying benchmark for the floating rate e g CPI U Return type str notional_amount Notional amount Return type float notional_currency Notional currency Return type Union Currency str pay_or_receive Pay or receive fixed Return type Union PayReceive str termination_date The termination of the swap e g 2050 04 01 10y Return type Union date str\",\n \"key\": \"classes/gs_quant.instrument.InflationSwap\",\n \"title\": \"InflationSwap\"\n },\n {\n \"body\": \"Security For methods of this class see gs_quant base Priceable class Security ticker None bbid None isin None cusip None prime_id None quantity 1 source A security specified by a well known identifier Properties bbid Bloomberg Id Identifier Return type str bbid_equivalent Bloomberg Equivalent Identifier Return type str bcid Bloomberg Composite Identifier Return type str cid Company Id Identifier Return type str cm_id Client Master Party Id Return type str cross Cross identifier Return type str cusip Cusip Identifier Return type str delisted Whether an asset has been delisted Return type str dollar_cross USD cross identifier for a particular currency Return type str eid EID Identifier Return type str em_id Entity Master Identifier Return type str gsid GSID Identifier Return type str gsideid GSID_EID Identifier Return type str gsn Goldman Sachs internal product number Return type str gss GS Symbol identifier Return type str isin International Security Number Return type str jsn Japan Security Number Return type str lms_id Listed Market Symbol Return type str mdapi MDAPI Asset Return type str mdapi_class MDAPI Asset Class Return type str mic Market Identifier Code Return type str mq_symbol Marquee Symbol for generic MQ entities Return type str prime_id PrimeID Identifier Return type str rcic Reuters Composite Instrument Code Identifier Return type str ric Reuters Instrument Code identifier Return type str sec_name Internal Goldman Sachs security name Return type str sedol Sedol Identifier Return type str sf_id SalesForce ID Return type str simon_id SIMON product identifier Return type str ticker Ticker Identifier Return type str valoren Valoren Identifier Return type str wpk Wertpapier Kenn Nummer Return type str\",\n \"key\": \"classes/gs_quant.instrument.Security\",\n \"title\": \"Security\"\n },\n {\n \"body\": \"gs_quant markets HistoricalPricingContext class HistoricalPricingContext start None end None calendars dates None is_async False is_batch False source A context for producing valuations over multiple dates __init__ start None end None calendars dates None is_async False is_batch False source A context for producing valuations over multiple dates Parameters start Union int date None start date end Union int date None end date defaults to today calendars Union str Tuple holiday calendars dates Optional Iterable date a custom iterable of dates is_async bool return immediately True or wait for results False Defaults to False is_batch bool use for calculations expected to run longer than 3 mins to avoid timeouts It can be used with is_aync True False Examples from gs_quant instrument import IRSwap ir_swap IRSwap Pay 10y DKK with HistoricalPricingContext 10 price_f ir_swap price price_series price_f result Methods Attributes\",\n \"key\": \"classes/gs_quant.markets.HistoricalPricingContext\",\n \"title\": \"gs_quant markets HistoricalPricingContext\"\n },\n {\n \"body\": \"gs_quant markets PricingContext class PricingContext pricing_date None market_data_as_of None market_data_location None is_async False is_batch False source A context for controlling pricing and market data behaviour __init__ pricing_date None market_data_as_of None market_data_location None is_async False is_batch False source The methods on this class should not be called directly Instead use the methods on the instruments as per the examples Parameters pricing_date Optional date the date for pricing calculations Default is today market_data_as_of Union date datetime None the date datetime for sourcing market data Default is 1 business day before pricing_date market_data_location Optional str the location for sourcing market data NYC LDN or HKG Default is NYC is_async bool if True return a future immediately If False block is_batch bool use for calculations expected to run longer than 3 mins to avoid timeouts It can be used with is_aync True False Examples To change the market data location of the default context from gs_quant risk import PricingContext import datetime as dt PricingContext current PricingContext market_data_location LDN For a blocking synchronous request from gs_quant instrument import IRCap cap IRCap 5y GBP with PricingContext price_f cap dollar_price price price_f result For an asynchronous request with PricingContext is_async True price_f inst dollar_price while not price_f done Methods Attributes\",\n \"key\": \"classes/gs_quant.markets.PricingContext\",\n \"title\": \"gs_quant markets PricingContext\"\n },\n {\n \"body\": \"gs_quant markets securities Asset class Asset id_ asset_class name exchange None source __init__ id_ asset_class name exchange None source Initialize self See help type self for accurate signature Methods\",\n \"key\": \"classes/gs_quant.markets.securities.Asset\",\n \"title\": \"gs_quant markets securities Asset\"\n },\n {\n \"body\": \"gs_quant markets securities AssetClass class AssetClass source Asset classification of security Assets are classified into broad groups which exhibit similar characteristics and behave in a consistent way under different market conditions __init__ Initialize self See help type self for accurate signature Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.AssetClass\",\n \"title\": \"gs_quant markets securities AssetClass\"\n },\n {\n \"body\": \"gs_quant markets securities AssetIdentifier class AssetIdentifier source Asset type enumeration Enumeration of different security identifiers __init__ Initialize self See help type self for accurate signature Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.AssetIdentifier\",\n \"title\": \"gs_quant markets securities AssetIdentifier\"\n },\n {\n \"body\": \"gs_quant markets securities AssetType class AssetType source Asset type enumeration Enumeration of different types of asset or security __init__ Initialize self See help type self for accurate signature Attributes\",\n \"key\": \"classes/gs_quant.markets.securities.AssetType\",\n \"title\": \"gs_quant markets securities AssetType\"\n },\n {\n \"body\": \"gs_quant markets securities Index class Index id_ asset_class name exchange None source Index Asset Index which tracks an evolving portfolio of securities and can be traded through cash or derivatives markets __init__ id_ asset_class name exchange None source Initialize self See help type self for accurate signature Methods\",\n \"key\": \"classes/gs_quant.markets.securities.Index\",\n \"title\": \"gs_quant markets securities Index\"\n },\n {\n \"body\": \"gs_quant markets securities SecurityMaster class SecurityMaster source Security Master The SecurityMaster class provides an interface to security lookup functions This allows querying and retrieval of different security types assets based on a variety of different identifiers through point in time lookups Uses the current PricingContext to provide as of dates if optional arguments are not provided Will return the relevant asset subclass depending on the type of the security See also Asset __init__ Initialize self See help type self for accurate signature Methods\",\n \"key\": \"classes/gs_quant.markets.securities.SecurityMaster\",\n \"title\": \"gs_quant markets securities SecurityMaster\"\n },\n {\n \"body\": \"gs_quant markets securities Stock class Stock id_ name exchange None source Base Security Type Represents a financial asset which can be held in a portfolio or has an observable price fixing which can be referenced in a derivative transaction __init__ id_ name exchange None source Initialize self See help type self for accurate signature Methods\",\n \"key\": \"classes/gs_quant.markets.securities.Stock\",\n \"title\": \"gs_quant markets securities Stock\"\n },\n {\n \"body\": \"Data Package\",\n \"key\": \"data\",\n \"title\": \"Data Package\"\n },\n {\n \"body\": \"Datetime Package Date Point\",\n \"key\": \"datetime\",\n \"title\": \"Datetime Package Date Point\"\n },\n {\n \"body\": \"business_day_count business_day_count begin_dates end_dates calendars week_mask None source Determine the number of business days between begin_dates and end_dates Parameters begin_dates Union date Iterable date A date or collection of beginning dates end_dates Union date Iterable date A date or collection of end dates calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Union int Tuple int Returns An int or tuple of ints representing the number of business days between begin_dates and end_dates Examples import datetime as dt today dt date today bus_days business_day_count today today dt timedelta days 7\",\n \"key\": \"functions/gs_quant.datetime.date.business_day_count\",\n \"title\": \"business_day_count\"\n },\n {\n \"body\": \"business_day_offset business_day_offset dates offsets roll raise calendars week_mask None source Apply offsets to the dates and move to the nearest business date Parameters dates Union date Iterable date The input date or dates offsets Union int Iterable int The number of days by which to adjust the dates roll str Which direction to roll in order to get to the nearest business date calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Union date Iterable date Returns A date if dates is a single date or tuple of dates adjusted by the offsets Examples import datetime as dt prev_bus_date business_day_offset dt date today 1 roll forward\",\n \"key\": \"functions/gs_quant.datetime.date.business_day_offset\",\n \"title\": \"business_day_offset\"\n },\n {\n \"body\": \"date_range date_range begin end calendars week_mask None source Construct a range of dates Parameters begin Union int date Beginning date or int An int will be interpreted as the number of business days before end which must be a date type end Union int date param end End date or int An int will be interpreted as the number of business days after begin which must be a date type calendars Union str Tuple str param calendars Calendars to use for holidays type week_mask Optional str param week_mask Which days are considered weekends defaults to Saturday and Sunday rtype Iterable date return A generator of dates import datetime as dt today dt date today dates tuple date_range 5 today for date in date_range dt date 2019 1 1 dt date 2019 2 1 print date\",\n \"key\": \"functions/gs_quant.datetime.date.date_range\",\n \"title\": \"date_range\"\n },\n {\n \"body\": \"is_business_day is_business_day dates calendars week_mask None source Determine whether each date in dates is a business day Parameters dates Union date Iterable date The input date or dates calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Union bool Tuple bool Returns True False if dates is a single date A tuple indicating True False for each date if dates is an iterable Examples import datetime as dt is_bus_date is_business_day dt date today is_bus_date is_business_day dt date 2019 7 4 calendars NYSE\",\n \"key\": \"functions/gs_quant.datetime.date.is_business_day\",\n \"title\": \"is_business_day\"\n },\n {\n \"body\": \"gs_quant datetime date prev_business_date prev_business_date dates datetime date 2019 11 18 calendars week_mask None source Returns the previous business date for a given date or date series defaulting to today Parameters dates Union date Iterable date The input date or dates defaults to today calendars Union str Tuple str Calendars to use for holidays week_mask Optional str Which days are considered weekends defaults to Saturday and Sunday Return type Union date Iterable date Returns A date if dates is a single date or tuple of dates adjusted by the offset of one day Example import datetime as dt prev_bus_date prev_business_date\",\n \"key\": \"functions/gs_quant.datetime.date.prev_business_date\",\n \"title\": \"gs_quant datetime date prev_business_date\"\n },\n {\n \"body\": \"point_sort_order point_sort_order point ref_date datetime date 2019 11 18 source Calculates a number that can be used to sort Mkt Points by it Parameters point str The point string from MarketDataCoordinate ref_date date Reference date normally the pricing date Return type float Returns The number of days from the reference date to the date specified by the point string Examples import datetime as dt days point_sort_order point Dec20 ref_date dt date today\",\n \"key\": \"functions/gs_quant.datetime.point.point_sort_order\",\n \"title\": \"point_sort_order\"\n },\n {\n \"body\": \"abs abs_ x source Absolute value of each element in series Parameters x Series date based time series of prices Return type Series Returns date based time series of absolute value Usage Return the absolute value of X For each value in time series X_t return X_t if X_t is greater than or equal to 0 otherwise return X_t R_t X_t Equivalent to R_t sqrt X_t 2 Examples Generate price series and take absolute value of X_t 100 prices generate_series 100 100 abs_ prices See also exp sqrt\",\n \"key\": \"functions/gs_quant.timeseries.algebra.abs_\",\n \"title\": \"abs\"\n },\n {\n \"body\": \"add add x y method Interpolate STEP step source Add two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate interpolation method default step Only used when both x and y are timeseries Return type Union Series Real Returns timeseries of x y or sum of the given real numbers Usage Add two series or scalar variables with the given interpolation method R_t X_t Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Add two series a generate_series 100 b generate_series 100 add a b Interpolate STEP See also subtract\",\n \"key\": \"functions/gs_quant.timeseries.algebra.add\",\n \"title\": \"add\"\n },\n {\n \"body\": \"ceil ceil x value 0 source Cap series at maximum value Parameters x Series date based time series of prices value float maximum value Return type Series Returns date based time series of maximum value Usage Returns series where all values are less than or equal to the maximum value R_t min X_t value See Floor and Ceil functions for more details Examples Generate price series and floor all values at 100 prices generate_series 100 floor prices 100 See also floor\",\n \"key\": \"functions/gs_quant.timeseries.algebra.ceil\",\n \"title\": \"ceil\"\n },\n {\n \"body\": \"divide divide x y method Interpolate STEP step source Divide two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate interpolation method default step Only used when both x and y are timeseries Return type Union Series Real Returns timeseries of x y or quotient of the given real numbers Usage Divide two series or scalar variables applying the given interpolation method R_t X_t Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Divide two series a generate_series 100 b generate_series 100 divide a b Interpolate STEP See also multiply\",\n \"key\": \"functions/gs_quant.timeseries.algebra.divide\",\n \"title\": \"divide\"\n },\n {\n \"body\": \"exp exp x source Exponential of series Parameters x Series timeseries Return type Series Returns exponential of each element Usage For each element in the series X_t raise e Euler s number to the power of X_t Euler s number is the base of the natural logarithm ln R_t e X_t Examples Raise e to the power 1 Returns Euler s number approximately 2 71828 exp 1 See also log\",\n \"key\": \"functions/gs_quant.timeseries.algebra.exp\",\n \"title\": \"exp\"\n },\n {\n \"body\": \"floor floor x value 0 source Floor series at minimum value Parameters x Series date based time series of prices value float minimum value Return type Series Returns date based time series of maximum value Usage Returns series where all values are greater than or equal to the minimum value R_t max X_t value See Floor and Ceil functions for more details Examples Generate price series and floor all values at 100 prices generate_series 100 floor prices 100 See also ceil\",\n \"key\": \"functions/gs_quant.timeseries.algebra.floor\",\n \"title\": \"floor\"\n },\n {\n \"body\": \"log log x source Natural logarithm of series Parameters x Series timeseries Return type Series Returns series with exponential of each element Usage For each element in the series X_t return the natural logarithm ln of X_t The natural logarithm is the logarithm in base e R_t log X_t This function is the inverse of the exponential function More information on logarithms Examples Take natural logarithm of 3 log 3 See also exp\",\n \"key\": \"functions/gs_quant.timeseries.algebra.log\",\n \"title\": \"log\"\n },\n {\n \"body\": \"multiply multiply x y method Interpolate STEP step source Multiply two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate interpolation method default step Only used when both x and y are timeseries Return type Union Series Real Returns timeseries of x y or product of the given real numbers Usage Multiply two series or scalar variables applying the given interpolation method R_t X_t times Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Multiply two series a generate_series 100 b generate_series 100 multiply a b Interpolate STEP See also divide\",\n \"key\": \"functions/gs_quant.timeseries.algebra.multiply\",\n \"title\": \"multiply\"\n },\n {\n \"body\": \"power power x y 1 source Raise each element in series to power Parameters x Series timeseries y float value Return type Series Returns date based time series of square roots Usage Raise each value in time series X_t to the power y R_t X_t y Examples Generate price series and raise each value to the power 2 prices generate_series 100 power prices 2 See also sqrt\",\n \"key\": \"functions/gs_quant.timeseries.algebra.power\",\n \"title\": \"power\"\n },\n {\n \"body\": \"sqrt sqrt x source Square root of a each element in a series or b a real number Parameters x Union Real Series date based time series of prices or real number Return type Union Real Series Returns date based time series of square roots or square root of given number Usage Return the square root of each value in time series X_t R_t sqrt X_t Examples Generate price series and take square root of each value prices generate_series 100 sqrt prices See also pow\",\n \"key\": \"functions/gs_quant.timeseries.algebra.sqrt\",\n \"title\": \"sqrt\"\n },\n {\n \"body\": \"subtract subtract x y method Interpolate STEP step source Add two series or scalars Parameters x Union Series Real timeseries or scalar y Union Series Real timeseries or scalar method Interpolate index alignment operator default intersect Only used when both x and y are timeseries Return type Union Series Real Returns timeseries of x y or difference between the given real numbers Usage Subtracts one series or scalar from another applying the given interpolation method R_t X_t Y_t Alignment operators Method Behavior intersect Resultant series only has values on the intersection of dates Values for dates present in only one series will be ignored nan Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as nan in the other series and therefore in the resultant series zero Resultant series has values on the union of dates in both series Values for dates only available in one series will be treated as zero in the other series step Resultant series has values on the union of dates in both series Values for dates only available in one series will be interpolated via step function in the other series time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Subtract one series from another a generate_series 100 b generate_series 100 subtract a b Interpolate STEP See also add\",\n \"key\": \"functions/gs_quant.timeseries.algebra.subtract\",\n \"title\": \"subtract\"\n },\n {\n \"body\": \"count count x source Count observations in series Parameters x Series time series Return type Series Returns number of observations Usage Count the number of valid observations in a series R_t R_ t 1 1 if X_t is not NaN and R_t R_ t 1 0 if X_t is NaN Examples Count observations in series series generate_series 100 count count series See also sum\",\n \"key\": \"functions/gs_quant.timeseries.analysis.count\",\n \"title\": \"count\"\n },\n {\n \"body\": \"diff diff x obs 1 source Diff observations with given lag Parameters x Series time series of prices obs int number of observations to lag Return type Series Returns date based time series of return Usage Compute the difference in series values over a given lag R_t X_t X_ t obs where obs is the number of observations to lag series in diff function Examples Diff prices levels series generate_series 100 returns diff series See also lag\",\n \"key\": \"functions/gs_quant.timeseries.analysis.diff\",\n \"title\": \"diff\"\n },\n {\n \"body\": \"first first x source First value of series Parameters x Series time series Return type Series Returns time series of first value Usage Return series with first value of X for all dates R_t X_0 where X_0 is the first value in the series Examples Last value of series series generate_series 100 returns first series See also last\",\n \"key\": \"functions/gs_quant.timeseries.analysis.first\",\n \"title\": \"first\"\n },\n {\n \"body\": \"lag lag x obs 1 source Lag timeseries by a specified number of observations Parameters x Series timeseries of prices obs int number of observations to lag series Return type Series Returns date based time series of return Usage Shift the series backwards by a specified number of observations R_t X_ t obs where obs is the number of observations to lag series Examples Lag series by 2 observations prices generate_series 100 lagged lag prices 2 See also diff\",\n \"key\": \"functions/gs_quant.timeseries.analysis.lag\",\n \"title\": \"lag\"\n },\n {\n \"body\": \"last last x source Last value of series Parameters x Series time series Return type Series Returns time series of last value Usage Return series with last value of X for all dates R_t X_T where X_T is the last value in the series Examples Last value of series series generate_series 100 returns last series See also first\",\n \"key\": \"functions/gs_quant.timeseries.analysis.last\",\n \"title\": \"last\"\n },\n {\n \"body\": \"align align x y method Interpolate INTERSECT intersect source Align dates of two series or scalars Parameters x Union Series Real first timeseries or scalar y Union Series Real second timeseries or scalar method Interpolate interpolation method default intersect Only used when both x and y are timeseries Return type Union List Series List Real Returns timeseries with specified dates or two scalars from the input Usage Align the dates of two series using the specified interpolation method Returns two series with dates based on the method of interpolation for example can be used to intersect the dates of two series union dates with a defined manner to compute missing values Interpolation methods Type Behavior intersect Resultant series only have values on the intersection of dates times nan Resultant series have values on the union of dates times Values will be NaN for dates or times only present in the other series zero Resultant series have values on the union of dates times Values will be zero for dates or times only present in the other series step Resultant series have values on the union of dates times Each series will use the value of the previous valid point if requested date does not exist Values prior to the first date will be equivalent to the first available value time Resultant series have values on the union of dates times Missing values surrounded by valid values will be interpolated given length of interval Input series must use DateTimeIndex Examples Stepwize interpolation of series based on dates in second series a generate_series 100 b generate_series 100 align a See also sub\",\n \"key\": \"functions/gs_quant.timeseries.datetime.align\",\n \"title\": \"align\"\n },\n {\n \"body\": \"day day x source Day of each value in series Parameters x Series time series Return type Series Returns day of observations Usage Returns the day as a numeric value for each observation in the series Y_t day t Day of the time or date is the integer day number within the month e g 1 31 Examples Day for observations in series series generate_series 100 days day series See also month year\",\n \"key\": \"functions/gs_quant.timeseries.datetime.day\",\n \"title\": \"day\"\n },\n {\n \"body\": \"interpolate interpolate x dates None method Interpolate INTERSECT intersect source Interpolate over specified dates or times Parameters x Series timeseries to interpolate dates Union List date List time Series None array of dates times or another series to interpolate method Interpolate interpolation method default intersect Return type Series Returns timeseries with specified dates Usage Interpolate the series X over the dates specified by the dates parameter This can be an array of dates or another series in which case the index of the series will be used to specify dates Interpolation methods Type Behavior intersect Resultant series only has values on the intersection of dates times Will only contain intersection of valid dates times in the series nan Resultant series only has values on the intersection of dates times Value will be NaN for dates not present in the series zero Resultant series has values on all requested dates times The series will have a value of zero where the requested date or time was not present in the series step Resultant series has values on all requested dates times The series will use the value of the previous valid point if requested date does not exist Values prior to the first date will be equivalent to the first available value Examples Stepwize interpolation of series based on dates in second series a generate_series 100 b generate_series 100 interpolate a b Interpolate INTERSECT See also sub\",\n \"key\": \"functions/gs_quant.timeseries.datetime.interpolate\",\n \"title\": \"interpolate\"\n },\n {\n \"body\": \"month month x source Month of each value in series Parameters x Series time series Return type Series Returns month of observations Usage Returns the month as a numeric value for each observation in the series Y_t month t Month of the time or date is the integer month number e g 1 12 Examples Day for observations in series series generate_series 100 days month series See also day year\",\n \"key\": \"functions/gs_quant.timeseries.datetime.month\",\n \"title\": \"month\"\n },\n {\n \"body\": \"quarter quarter x source Quarter of each value in series Parameters x Series time series Return type Series Returns quarter of observations Usage Returns the quarter as a numeric value for each observation in the series Y_t quarter t Quarter of the time or date is the integer quarter number e g 1 2 3 4 Examples Quarter for observations in series series generate_series 100 days quarter series See also day month\",\n \"key\": \"functions/gs_quant.timeseries.datetime.quarter\",\n \"title\": \"quarter\"\n },\n {\n \"body\": \"value value x date method Interpolate STEP step source Value at specified date or time Parameters x Series timeseries date Union date time requested date or time method Interpolate interpolation method default step Return type Series Returns value at specified date or time Usage Returns the value of series X at the specified date Y_t X_ date If the requested date or time is not present in the series the value function will return the value from the previous available date or time by default Caller can specify other interpolation styles via the method param Interpolation methods Type Behavior intersect Only returns a value for valid dates nan Value will be NaN for dates not present in the series zero Value will be zero for dates not present in the series step Value of the previous valid point if requested date does not exist Values prior to the first date will be equivalent to the first available value Examples Value of series on 5Mar18 a generate_series 100 value a date 2019 1 3 See also interpolate\",\n \"key\": \"functions/gs_quant.timeseries.datetime.value\",\n \"title\": \"value\"\n },\n {\n \"body\": \"weekday weekday x source Weekday of each value in series Parameters x Series time series Return type Series Returns weekday of observations Usage Returns the weekday as a numeric value for each observation in the series Y_t weekday t Weekday of the time or date is the integer day of the week e g 0 6 where 0 represents Monday Examples Weekday for observations in series series generate_series 100 days weekday series See also day month\",\n \"key\": \"functions/gs_quant.timeseries.datetime.weekday\",\n \"title\": \"weekday\"\n },\n {\n \"body\": \"year year x source Year of each value in series Parameters x Series time series Return type Series Returns year of observations Usage Returns the year as a numeric value for each observation in the series Y_t year t Year of the time or date is the integer year number e g 2019 2020 Examples Year for observations in series series generate_series 100 days year series See also day month\",\n \"key\": \"functions/gs_quant.timeseries.datetime.year\",\n \"title\": \"year\"\n },\n {\n \"body\": \"annualize annualize x source Annualize series based on sample observation frequency Parameters x Series time series of prices Return type Series Returns date based time series of annualized values Usage Based on number of days between observations will determine an annualization factor and then adjust values accordingly Useful for annualizing daily or monthly returns Y_t X_t sqrt F Annualization factors as follows based on period implied by observations Period Annualization Factor F Daily 252 Weekly 52 Bi Weekly 26 Monthly 12 Quarterly 4 Annually 1 Examples Annualize daily returns series prices generate_series 100 ann annualize returns prices See also returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.annualize\",\n \"title\": \"annualize\"\n },\n {\n \"body\": \"beta beta x b w Window w None r 0 prices True source Rolling beta of price series and benchmark Parameters x Series time series of prices b Series time series of benchmark prices w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series type prices bool param prices True if input series are prices False if they are returns rtype Series return date based time series of beta Usage Calculate rolling beta beta_t of a series to a benchmark over a given window R_t alpha_t beta S_t epsilon_t Calculated as beta_t frac sum_ i t w 1 t Cov R_t S_t Var S_t where N is the number of observations in each rolling window w and R_t and S_t are the simple returns for each series on time t R_t frac X_t X_ t 1 1 and S_t frac b_t b_ t 1 1 If prices False assumes returns are provided R_t X_t and S_t b_t Cov R_t S_t and Var S_t are the mean and variance of series R_t and S_t over the same window If window is not provided computes beta over the full series Examples Compute rolling 1 month 22 business day beta of two price series series generate_series 100 benchmark generate_series 100 b beta series benchmark 22 See also var cov correlation returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.beta\",\n \"title\": \"beta\"\n },\n {\n \"body\": \"change change x source Arithmetic series normalization Parameters x Series time series Return type Series Returns normalized time series Usage Compute difference of every value from the initial value of x Y_t X_t X_0 where X_0 is the first value in the series Examples Change in level from initial value series generate_series 100 returns change series See also index\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.change\",\n \"title\": \"change\"\n },\n {\n \"body\": \"correlation correlation x y w Window w None r 0 type_ SeriesType PRICES prices source Rolling correlation of two price series Parameters x Series price series y Series price series w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series param type_ type of both input series rtype Series return date based time series of correlation Usage Calculate rolling realized correlation rho_t of two price series over a given window rho_t frac sum_ i t w 1 t R_t overline R_t Y_t overline S_t N 1 sigma R_t sigma S_t where N is the number of observations in each rolling window w and R_t and S_t are the simple returns for each series on time t R_t frac X_t X_ t 1 1 and S_t frac Y_t Y_ t 1 1 If prices False assumes returns are provided R_t X_t and S_t Y_t overline R_t overline S_t are the mean values and sigma R_ t and sigma S_ t are the sample standard deviations of series R_t and S_t over the same window If window is not provided computes realized correlation over the full series Examples Compute rolling 1 month 22 business day correlation of price series series1 generate_series 100 series2 generate_series 100 corr correlation series1 series2 22 See also std returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.correlation\",\n \"title\": \"correlation\"\n },\n {\n \"body\": \"index index x initial 1 source Geometric series normalization Parameters x Series time series initial int initial value Return type Series Returns normalized time series Usage Divides every value in x by the initial value of x Y_t initial X_t X_0 where X_0 is the first value in the series Examples Normalize series to 1 series generate_series 100 returns index series See also returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.index\",\n \"title\": \"index\"\n },\n {\n \"body\": \"max_drawdown max_drawdown x w Window w None r 0 source Compute the maximum peak to trough drawdown over a rolling window Parameters x Series time series w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return time series of rolling maximum drawdown Examples Compute the maximum peak to trough drawdown series generate_series 100 max_drawdown series See also returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.max_drawdown\",\n \"title\": \"max_drawdown\"\n },\n {\n \"body\": \"prices prices series initial 1 type Returns SIMPLE simple source Calculate price levels from returns series Parameters series Series time series of returns initial int initial price level type Returns returns type simple log Return type Series Returns date based time series of return Usage Compute price levels from returns series based on the value of type Type Description simple Simple arithmetic returns log Logarithmic returns Simple Compute asset price series from simple returns Y_t 1 X_ t 1 Y_ t 1 where X_t is the asset price at time t and Y_0 initial Logarithmic Compute asset price series from logarithmic returns Y_t e X_ t 1 Y_ t 1 where X_t is the asset price at time t and Y_0 initial Examples Generate price series and take compute returns series generate_series 100 returns prices returns series See also returns product exp\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.prices\",\n \"title\": \"prices\"\n },\n {\n \"body\": \"returns returns series obs 1 type Returns SIMPLE simple source Calculate returns from price series Parameters series Series time series of prices obs int number of observations type Returns returns type Return type Series Returns date based time series of return Usage Compute returns series from price levels based on the value of type Type Description simple Simple arithmetic returns log Logarithmic returns Simple Simple geometric change in asset prices which can be aggregated across assets Y_t frac X_t X_ t obs 1 where X_t is the asset price at time t Logarithmic Natural logarithm of asset price changes which can be aggregated through time Y_t log X_t log X_ t obs where X_t is the asset price at time t Examples Generate price series and take compute returns prices generate_series 100 returns returns prices See also prices\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.returns\",\n \"title\": \"returns\"\n },\n {\n \"body\": \"volatility volatility x w Window w None r 0 returns_type Returns SIMPLE simple source Realized volatility of price series Parameters x Series time series of prices w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series type returns_type Returns param returns_type returns type rtype Series return date based time series of return Usage Calculate rolling annualized realized volatility of a price series over a given window Annual volatility of 20 is returned as 20 0 Y_t sqrt frac 1 N 1 sum_ i t w 1 t R_t overline R_t 2 sqrt 252 100 where N is the number of observations in each rolling window w R_t is the simple return on time t R_t frac X_t X_ t 1 1 and overline R_t is the mean value over the same window overline R_t frac sum_ i t w 1 t R_t N If window is not provided computes realized volatility over the full series Examples Compute rolling 1 month 22 business day annualized volatility of price series series generate_series 100 vol_series volatility series 22 vol_series volatility series Window 22 30 See also std annualize returns\",\n \"key\": \"functions/gs_quant.timeseries.econometrics.volatility\",\n \"title\": \"volatility\"\n },\n {\n \"body\": \"cov cov x y w Window w None r 0 source Rolling co variance of series over given window Parameters x Series series timeseries y Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of covariance Usage Provides unbiased estimator of sample co variance over a rolling window R_t frac 1 N 1 sum_ i t w 1 t X_i overline X_t Y_i overline Y_t where N is the number of observations in each rolling window w and overline X_t and overline Y_t represent the sample mean of series X_t and Y_t over the same window overline X_t frac sum_ i t w 1 t X_i N and overline Y_t frac sum_ i t w 1 t Y_i N If window is not provided computes variance over the full series Examples Generate price series and compute variance of returns over 22 observations prices_x generate_series 100 prices_y generate_series 100 cov returns prices_x returns prices_y 22 See also sum mean var\",\n \"key\": \"functions/gs_quant.timeseries.statistics.cov\",\n \"title\": \"cov\"\n },\n {\n \"body\": \"generate_series generate_series length source Generate sample timeseries Parameters length int number of observations Return type Series Returns date based time series of randomly generated prices Usage Create timeseries from returns generated from a normally distributed random variables IDD Length determines the number of observations to be generated Assume random variables R which follow a normal distribution with mean 0 and standard deviation of 1 R sim N 0 1 The timeseries is generated from these random numbers through X_t 1 R X_ t 1 Examples Generate price series with 100 observations starting from today s date prices generate_series 100 See also numpy random normal\",\n \"key\": \"functions/gs_quant.timeseries.statistics.generate_series\",\n \"title\": \"generate_series\"\n },\n {\n \"body\": \"max max_ x w Window w None r 0 source Maximum value of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of maximum value Usage Returns the maximum value of the series over each window R_t max X_ t w 1 X_t where w is the size of the rolling window If window is not provided returns the minimum value over the full series If the window size is greater than the available data will return minimum of available values Examples Maximum value of price series over the last 22 observations prices generate_series 100 max_ prices 22 See also min_\",\n \"key\": \"functions/gs_quant.timeseries.statistics.max_\",\n \"title\": \"max\"\n },\n {\n \"body\": \"mean mean x w Window w None r 0 source Arithmetic mean of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of mean value Usage Calculates arithmetic mean of the series over a rolling window R_t frac sum_ i t w 1 t X_i N where N is the number of observations in each rolling window w If window is not provided computes rolling mean over the full series If the window size is greater than the available data will return mean of available values Examples Generate price series and compute mean over 22 observations prices generate_series 100 mean prices 22 See also median mode\",\n \"key\": \"functions/gs_quant.timeseries.statistics.mean\",\n \"title\": \"mean\"\n },\n {\n \"body\": \"median median x w Window w None r 0 source Median value of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of median value Usage Computes the median value over a given window For each window this function will return the middle value when all elements in the window are sorted If the number of observations in the window is even will return the average of the middle two values If the window size is greater than the available data will return median of available values d frac w 1 2 R_t frac X_ lfloor t d rfloor X_ lceil t d rceil 2 where w is the size of the rolling window If window is not provided computes median over the full series Examples Generate price series and compute median over 22 observations prices generate_series 100 median prices 22 See also mean mode\",\n \"key\": \"functions/gs_quant.timeseries.statistics.median\",\n \"title\": \"median\"\n },\n {\n \"body\": \"min min_ x w Window w None r 0 source Minimum value of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of minimum value Usage Returns the minimum value of the series over each window R_t min X_ t w 1 X_t where w is the size of the rolling window If window is not provided returns the minimum value over the full series If the window size is greater than the available data will return minimum of available values Examples Minimum value of price series over the last 22 observations prices generate_series 100 min_ prices 22 See also max_\",\n \"key\": \"functions/gs_quant.timeseries.statistics.min_\",\n \"title\": \"min\"\n },\n {\n \"body\": \"mode mode x w Window w None r 0 source Most common value in series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of mode value Usage Computes the mode over a given window For each window this function will return the most common value of all elements in the window If there are multiple values with the same frequency of occurrence will return the smallest value If window is not provided computes mode over the full series Examples Generate price series and compute mode over 22 observations prices generate_series 100 mode prices 22 See also mean median\",\n \"key\": \"functions/gs_quant.timeseries.statistics.mode\",\n \"title\": \"mode\"\n },\n {\n \"body\": \"percentile percentiles x y None w Window w None r 0 source Rolling percentiles over given window Parameters x Series value series y Optional Series distribution series w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of percentiles Usage Calculate percentile rank of y in the sample distribution of x over a rolling window of length w R_t frac sum_ i t N 1 t X_i Y_t 0 5 sum_ i t N 1 t X_i Y_t N times100 Where N is the number of observations in a rolling window If y is not provided calculates percentiles of x over its historical values If window length w is not provided uses an ever growing history of values If w is greater than the available data size returns empty Examples Compute percentile ranks of a series in the sample distribution of a second series over 22 observations a generate_series 100 b generate_series 100 percentiles a b 22 See also zscores\",\n \"key\": \"functions/gs_quant.timeseries.statistics.percentiles\",\n \"title\": \"percentile\"\n },\n {\n \"body\": \"product product x w Window w None r 0 source Rolling product of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of rolling product Usage Calculate the product of observations over a given rolling window For each time t returns the value of all observations from t w 1 to t multiplied together R_t prod_ i t w 1 t X_i where w is the size of the rolling window If window is not provided computes product over the full series Examples Generate price series and compute rolling sum over 22 observations prices generate_series 100 product 1 returns prices See also sum_\",\n \"key\": \"functions/gs_quant.timeseries.statistics.product\",\n \"title\": \"product\"\n },\n {\n \"body\": \"range range_ x w Window w None r 0 source Range of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of range Usage Returns the range of the series max min over rolling window R_t max X_ t w 1 X_t min X_ t w 1 X_t where w is the size of the rolling window If window is not provided returns the range over the full series If the window size is greater than the available data will return range of all available values Examples Range of price series over the last 22 observations prices generate_series 100 range_ prices 22 See also min_ max_\",\n \"key\": \"functions/gs_quant.timeseries.statistics.range_\",\n \"title\": \"range\"\n },\n {\n \"body\": \"std std x w Window w None r 0 source Rolling standard deviation of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series Return type Series Returns timeseries of standard deviation Usage Provides unbiased estimator of sample standard deviation over a rolling window R_t sqrt frac 1 N 1 sum_ i t w 1 t X_i overline X_t 2 where N is the number of observations in each rolling window w and overline X_t is the mean value over the same window overline X_t frac sum_ i t w 1 t X_i N If window is not provided computes standard deviation over the full series Examples Generate price series and compute standard deviation of returns over 22 observations prices generate_series 100 std returns prices 22 See also sum mean var\",\n \"key\": \"functions/gs_quant.timeseries.statistics.std\",\n \"title\": \"std\"\n },\n {\n \"body\": \"sum sum_ x w Window w None r 0 source Rolling sum of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of rolling sum Usage Calculate the sum of observations over a given rolling window For each time t returns the value of all observations from t w 1 to t summed together R_t sum_ i t w 1 t X_i where w is the size of the rolling window If window is not provided computes sum over the full series Examples Generate price series and compute rolling sum over 22 observations prices generate_series 100 sum_ prices 22 See also product\",\n \"key\": \"functions/gs_quant.timeseries.statistics.sum_\",\n \"title\": \"sum\"\n },\n {\n \"body\": \"var var x w Window w None r 0 source Rolling variance of series over given window Parameters x Series series timeseries w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of variance Usage Provides unbiased estimator of sample variance over a rolling window R_t frac 1 N 1 sum_ i t w 1 t X_i overline X_t 2 where N is the number of observations in each rolling window w and overline X_t is the mean value over the same window overline X_t frac sum_ i t w 1 t X_i N If window is not provided computes variance over the full series Examples Generate price series and compute variance of returns over 22 observations prices generate_series 100 var returns prices 22 See also var mean std\",\n \"key\": \"functions/gs_quant.timeseries.statistics.var\",\n \"title\": \"var\"\n },\n {\n \"body\": \"winsorize winsorize x limit 2 5 w Window w None r 0 source Limit extreme values in series Parameters x Series time series of prices limit float max z score of values w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of winsorized values Usage Cap and floor values in the series which have a z score greater or less than provided value This function will restrict the distribution of values Calculates the sample standard deviation and adjusts values which fall outside the specified range to be equal to the upper or lower limits Lower and upper limits are defined as upper mu sigma times limit lower mu sigma times limit Where mu and sigma are sample mean and standard deviation The series is restricted by R_t max min X_t upper lower See winsorising for additional information Examples Generate price series and winsorize z score of returns over 22 observations prices generate_series 100 winsorize zscore returns prices 22 See also zscore mean std\",\n \"key\": \"functions/gs_quant.timeseries.statistics.winsorize\",\n \"title\": \"winsorize\"\n },\n {\n \"body\": \"zscores zscores x w Window w None r 0 source Rolling z scores over a given window Parameters x Series time series of prices w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series rtype Series return timeseries of z scores Usage Calculate standard score of each value in series over given window Standard deviation and sample mean are computed over the specified rolling window then element is normalized to provide a rolling z score R_t frac X_t mu sigma Where mu and sigma are sample mean and standard deviation over the given window If window is not provided computes z score relative to mean and standard deviation over the full series Examples Generate price series and compute z score of returns over 22 observations prices generate_series 100 zscores returns prices 22 See also mean std\",\n \"key\": \"functions/gs_quant.timeseries.statistics.zscores\",\n \"title\": \"zscores\"\n },\n {\n \"body\": \"bollinger_bands bollinger_bands x w Window w None r 0 k 2 source Bollinger bands with given window and width Parameters x Series time series of prices w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series type k float param k band width in standard deviations default 2 rtype DataFrame return date based time series of return Usage Standard deviation bands around the moving average of asset price level Bollinger bands can be used to determine a range around the price level which responds to local volatility changes Returns two series upper u_t and lower l_t u_t bar X_t k sigma_t l_t bar X_t k sigma_t where bar X_t is the moving average over specified window and sigma_t is the rolling standard deviation over the specified window See Bollinger Bands for more information Examples Compute bollinger bands around 20 day moving average at 2 standard deviations prices generate_series 100 bollinger_bands prices 20 2 See also moving_average std\",\n \"key\": \"functions/gs_quant.timeseries.technicals.bollinger_bands\",\n \"title\": \"bollinger_bands\"\n },\n {\n \"body\": \"moving_average moving_average x w Window w None r 0 source Moving average over specified window Parameters x Series time series of prices w Union Window int Window or int number of observations and ramp up to use e g Window 22 10 where 22 is the window size and 10 the ramp up value Window size defaults to length of series Return type Series Returns date based time series of return Usage Simple arithmetic moving average over the specified window number of observations Shorter windows will be more reactive to changes in the asset price but more volatile Larger windows will be smoother but less reactive to near term changes in asset prices R_t frac sum_ i t w 1 t X_t N where N is the number of observations in each rolling window w If window is not provided computes rolling mean over the full series Equivalent to mean Examples Generate price series with 100 observations starting from today s date prices generate_series 100 moving_average prices 22 See also mean\",\n \"key\": \"functions/gs_quant.timeseries.technicals.moving_average\",\n \"title\": \"moving_average\"\n },\n {\n \"body\": \"GS Quant API Packages Data Package gs_quant data Dataset Datetime Package Date Point Instrument Package gs_quant base Priceable Instruments Market Package gs_quant markets PricingContext gs_quant markets HistoricalPricingContext Securities Risk Package Functions Measures Timeseries Package Algebra Analysis Date Time Econometrics Statistics Technical Analysis Index\",\n \"key\": \"index\",\n \"title\": \"GS Quant API\"\n },\n {\n \"body\": \"Instrument Package Instruments\",\n \"key\": \"instrument\",\n \"title\": \"Instrument Package Instruments\"\n },\n {\n \"body\": \"Market Package Securities\",\n \"key\": \"market\",\n \"title\": \"Market Package Securities\"\n },\n {\n \"body\": \"Risk Package Functions aggregate_risk results threshold None source Combine the results of multiple Instrument calc calls into a single result Parameters results Iterable Union DataFrame Future An iterable of Dataframes and or Futures returned by Instrument calc threshold Optional float exclude values whose absolute value falls below this threshold Return type DataFrame Returns A Dataframe with the aggregated results Examples with PricingContext delta_f inst calc risk IRDelta for inst in instruments vega_f inst calc risk IRVega for inst in instruments delta aggregate_risk delta_f threshold 0 1 vega aggregate_risk vega_f delta_f and vega_f are lists of futures where the result will be a Dataframe delta and vega are Dataframes representing the merged risk of the individual instruments subtract_risk left right source Subtract bucketed risk Dimensions must be identical Parameters left DataFrame Results to substract from right DataFrame Results to substract Examples ir_swap IRSwap Pay 10y USD delta_today ir_swap calc risk IRDelta with PricingContext pricing_date business_day_offset datetime date today 1 roll preceding delta_yday_f ir_swap calc risk IRDelta delta_diff subtract_risk delta_today delta_yday_f result Return type DataFrame sort_risk df by date time marketDataType assetId point source Sort bucketed risk Parameters df DataFrame Input Dataframe by Tuple str Columns to sort by Returns A sorted Dataframe Measures DollarPrice gs_quant target common RiskMeasure object Present value in USD Price gs_quant target common RiskMeasure object Present value in local currency ForwardPrice gs_quant target common RiskMeasure object Forward price Theta gs_quant target common RiskMeasure object 1 day Theta CommodDelta gs_quant target common RiskMeasure object Commodity Delta CommodTheta gs_quant target common RiskMeasure object Commodity Theta CommodVega gs_quant target common RiskMeasure object Commodity Vega EqDelta gs_quant target common RiskMeasure object Equity Delta EqGamma gs_quant target common RiskMeasure object Equity Gamma EqVega gs_quant target common RiskMeasure object Equity Vega EqSpot gs_quant target common RiskMeasure object Equity Spot Level EqAnnualImpliedVol gs_quant target common RiskMeasure object Equity Annual Implied Volatility FXDelta gs_quant target common RiskMeasure object FX Delta FXGamma gs_quant target common RiskMeasure object FX Gamma FXVega gs_quant target common RiskMeasure object FX Vega IRDelta gs_quant target common RiskMeasure object Interest Rate Delta IRDeltaParallel gs_quant target common RiskMeasure object Interest Rate Parallel Delta IRDeltaLocalCcy gs_quant target common RiskMeasure object Interest Rate Delta Local Ccy IRDeltaParallelLocalCcy gs_quant target common RiskMeasure object Interest Rate Parallel Delta Local Ccy IRGamma gs_quant target common RiskMeasure object Interest Rate Gamma IRVega gs_quant target common RiskMeasure object Interest Rate Vega IRVegaParallel gs_quant target common RiskMeasure object Interest Rate Parallel Vega IRVegaLocalCcy gs_quant target common RiskMeasure object Interest Rate Vega Local Ccy IRVegaParallelLocalCcy gs_quant target common RiskMeasure object Interest Rate Parallel Vega Local Ccy IRAnnualImpliedVol gs_quant target common RiskMeasure object Interest Rate Annual Implied Volatility IRAnnualATMImpliedVol gs_quant target common RiskMeasure object Interest Rate Annual Implied At The Money Volatility IRDailyImpliedVol gs_quant target common RiskMeasure object Interest Rate Daily Implied Volatility bps IRSpotRate gs_quant target common RiskMeasure object At The Money Spot Rate IRFwdRate gs_quant target common RiskMeasure object Par Rate\",\n \"key\": \"risk\",\n \"title\": \"Risk Package Functions Measures\"\n },\n {\n \"body\": \"Timeseries Package Algebra Analysis Date Time Econometrics Statistics Technical Analysis\",\n \"key\": \"timeseries\",\n \"title\": \"Timeseries Package Algebra Analysis Date Time Econometrics Statistics Technical Analysis\"\n }\n]" }, { "path": "docs/_templates/contexts.rst", "content": "{{ name | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n\n {% block methods %}\n {% if methods %}\n .. rubric:: Methods\n {% for item in methods %}\n {% if item not in ('has_default',) %}\n .. automethod:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n\n\n {% block attributes %}\n .. rubric:: Properties\n {% if attributes %}\n {% for item in attributes %}\n {% if item not in () %}\n .. autoattribute:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}" }, { "path": "docs/_templates/instruments.rst", "content": "{{ name | escape | underline}}\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n\n {% block attributes %}\n .. rubric:: Properties\n {% if attributes %}\n {% for item in attributes %}\n {% if item not in ('asset_class', 'type', 'PROVIDER') %}\n .. autoattribute:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}" }, { "path": "docs/_templates/models_class.rst", "content": "{{ name | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n\n {% block attributes %}\n {% if attributes %}\n .. rubric:: Properties\n {% for item in attributes %}\n {% if item not in () %}\n .. autoattribute:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n\n\n {% block methods %}\n {% if methods %}\n .. rubric:: Methods\n {% for item in methods %}\n {% if item not in ('has_default',) %}\n .. automethod:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n" }, { "path": "docs/_templates/portfolio_manager.rst", "content": "{{ name | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n :inherited-members:\n :undoc-members:\n\n {% block methods %}\n {% if methods %}\n .. rubric:: Methods\n {% for item in methods %}\n {% if item not in ('has_default',) %}\n .. automethod:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n" }, { "path": "docs/_templates/processors.rst", "content": "{{ name | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n\n {% block methods %}\n {% if methods %}\n .. rubric:: Methods\n {% for item in methods %}\n {% if item not in ('has_default',) %}\n .. automethod:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n\n\n\n {% block attributes %}\n {% if attributes %}\n .. rubric:: Properties\n {% for item in attributes %}\n {% if item not in () %}\n .. autoattribute:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}" }, { "path": "docs/_templates/report.rst", "content": "{{ name | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n\n {% block methods %}\n {% if methods %}\n .. rubric:: Methods\n {% for item in methods %}\n {% if item not in ('has_default',) %}\n .. automethod:: {{ item }}\n {% endif %}\n {%- endfor %}\n {% endif %}\n {% endblock %}\n" }, { "path": "docs/_templates/timeseries_class.rst", "content": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n\n {% set l = methods | reject('equalto', '__init__') | list %}\n {% block methods -%}\n {% if l %}\n .. rubric:: {{ _('Methods') }}\n .. autosummary::\n {% for item in l %}\n ~{{ name }}.{{ item }}\n {% endfor %}\n {% endif %}\n {%- endblock %}" }, { "path": "docs/_themes/gs/LICENSE.txt", "content": "The MIT License (MIT)\n\nCopyright (c) 2013-2018 Dave Snider, Read the Docs, Inc. & contributors\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal in\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\nthe Software, and to permit persons to whom the Software is furnished to do so,\nsubject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS\nFOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\nCOPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\nIN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\nCONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n" }, { "path": "docs/_themes/gs/__init__.py", "content": "\"\"\"Sphinx ReadTheDocs theme.\n\nFrom https://github.com/ryan-roemer/sphinx-bootstrap-theme.\n\n\"\"\"\nfrom os import path\n\n\n__version__ = '0.4.3.dev0'\n__version_full__ = __version__\n\n\ndef get_html_theme_path():\n \"\"\"Return list of HTML theme paths.\"\"\"\n cur_dir = path.abspath(path.dirname(path.dirname(__file__)))\n return cur_dir\n\n\n# See http://www.sphinx-doc.org/en/stable/theming.html#distribute-your-theme-as-a-python-package\ndef setup(app):\n app.add_html_theme('sphinx_rtd_theme', path.abspath(path.dirname(__file__)))\n" }, { "path": "docs/_themes/gs/breadcrumbs.html", "content": "{# Support for Sphinx 1.3+ page_source_suffix, but don't break old builds. #}\n\n{% if page_source_suffix %}\n{% set suffix = page_source_suffix %}\n{% else %}\n{% set suffix = source_suffix %}\n{% endif %}\n\n{% if meta is defined and meta is not none %}\n{% set check_meta = True %}\n{% else %}\n{% set check_meta = False %}\n{% endif %}\n\n{% if check_meta and 'github_url' in meta %}\n{% set display_github = True %}\n{% endif %}\n\n{% if check_meta and 'bitbucket_url' in meta %}\n{% set display_bitbucket = True %}\n{% endif %}\n\n{% if check_meta and 'gitlab_url' in meta %}\n{% set display_gitlab = True %}\n{% endif %}\n\n
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\n\n\n\n {% block extrabody %} {% endblock %}\n
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\n {% include \"versions.html\" %}\n\n {# Do not conflict with RTD insertion of analytics script #}\n {% if not READTHEDOCS %}\n {% if theme_analytics_id %}\n \n \n\n {% endif %}\n {% endif %}\n\n {%- block footer %} {% endblock %}\n\n\n\n" }, { "path": "docs/_themes/gs/static/css/theme.css", "content": "/* sphinx_rtd_theme version 0.4.3 | MIT license */\n/* Built 20190212 16:02 */\n* {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n\n@font-face {\n font-family: \"Roboto-Regular\";\n src: url(\"/resources/fonts/Roboto-Regular.ttf\"),\n url(\"/resources/fonts/Roboto-Regular.woff\"),\n url(\"/resources/fonts/Roboto-Regular.woff2\");\n}\n\n@font-face {\n font-family: \"Roboto-Medium\";\n src: url(\"/resources/fonts/Roboto-Medium.ttf\"),\n url(\"/resources/fonts/Roboto-Medium.woff\"),\n url(\"/resources/fonts/Roboto-Medium.woff2\");\n}\n\n@font-face {\n font-family: \"Roboto Mono\";\n src: url(\"/resources/fonts/Roboto-Mono-Regular.ttf\"),\n 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{\n margin: 0;\n}\nform {\n margin: 0;\n}\nfieldset {\n border: 0;\n margin: 0;\n padding: 0;\n}\nlabel {\n cursor: pointer;\n}\nlegend {\n border: 0;\n *margin-left: -7px;\n padding: 0;\n white-space: normal;\n}\nbutton,\ninput,\nselect,\ntextarea {\n font-size: 100%;\n margin: 0;\n vertical-align: baseline;\n *vertical-align: middle;\n}\nbutton,\ninput {\n line-height: normal;\n}\nbutton,\ninput[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n cursor: pointer;\n -webkit-appearance: button;\n *overflow: visible;\n}\nbutton[disabled],\ninput[disabled] {\n cursor: default;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box;\n padding: 0;\n *width: 13px;\n *height: 13px;\n}\ninput[type=\"search\"] {\n -webkit-appearance: textfield;\n -moz-box-sizing: content-box;\n -webkit-box-sizing: content-box;\n box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-decoration,\ninput[type=\"search\"]::-webkit-search-cancel-button {\n -webkit-appearance: none;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\ntextarea {\n overflow: auto;\n vertical-align: top;\n resize: vertical;\n}\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\ntd {\n vertical-align: top;\n}\n.chromeframe {\n margin: 0.2em 0;\n background: #ccc;\n color: #000;\n padding: 0.2em 0;\n}\n.ir {\n display: block;\n border: 0;\n text-indent: -999em;\n overflow: hidden;\n background-color: transparent;\n background-repeat: no-repeat;\n text-align: left;\n direction: ltr;\n *line-height: 0;\n}\n.ir br {\n display: none;\n}\n.hidden {\n display: none !important;\n visibility: hidden;\n}\n.visuallyhidden {\n border: 0;\n clip: rect(0 0 0 0);\n height: 1px;\n margin: -1px;\n overflow: hidden;\n padding: 0;\n position: absolute;\n width: 1px;\n}\n.visuallyhidden.focusable:active,\n.visuallyhidden.focusable:focus {\n clip: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n position: static;\n width: auto;\n}\n.invisible {\n visibility: hidden;\n}\n.relative {\n position: relative;\n}\nbig,\nsmall {\n font-size: 100%;\n}\n@media print {\n html,\n body,\n section {\n background: none !important;\n }\n * {\n box-shadow: none !important;\n text-shadow: none !important;\n filter: none !important;\n -ms-filter: none !important;\n }\n a,\n a:visited {\n text-decoration: underline;\n }\n .ir a:after,\n a[href^=\"javascript:\"]:after,\n a[href^=\"#\"]:after {\n content: \"\";\n }\n pre,\n blockquote {\n page-break-inside: avoid;\n }\n thead {\n display: table-header-group;\n }\n tr,\n img {\n page-break-inside: avoid;\n }\n img {\n max-width: 100% !important;\n }\n @page {\n margin: 0.5cm;\n }\n p,\n h2,\n .rst-content .toctree-wrapper p.caption,\n h3 {\n orphans: 3;\n widows: 3;\n }\n h2,\n .rst-content .toctree-wrapper p.caption,\n h3 {\n page-break-after: avoid;\n }\n}\n.fa:before,\n.wy-menu-vertical li span.toctree-expand:before,\n.wy-menu-vertical li.on a 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.wy-input-context:before,\n.wy-alert,\n.rst-content .note,\n.rst-content .attention,\n.rst-content .caution,\n.rst-content .danger,\n.rst-content .error,\n.rst-content .hint,\n.rst-content .important,\n.rst-content .tip,\n.rst-content .warning,\n.rst-content .seealso,\n.rst-content .admonition-todo,\n.rst-content .admonition,\n.btn,\ninput[type=\"text\"],\ninput[type=\"password\"],\ninput[type=\"email\"],\ninput[type=\"url\"],\ninput[type=\"date\"],\ninput[type=\"month\"],\ninput[type=\"time\"],\ninput[type=\"datetime\"],\ninput[type=\"datetime-local\"],\ninput[type=\"week\"],\ninput[type=\"number\"],\ninput[type=\"search\"],\ninput[type=\"tel\"],\ninput[type=\"color\"],\nselect,\ntextarea,\n.wy-menu-vertical li.on a,\n.wy-menu-vertical li.current > a,\n.wy-side-nav-search > a,\n.wy-side-nav-search .wy-dropdown > a,\n.wy-nav-top a {\n -webkit-font-smoothing: antialiased;\n}\n.clearfix {\n *zoom: 1;\n}\n.clearfix:before,\n.clearfix:after {\n display: table;\n content: 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span.fa-pull-left:first-child,\n.rst-content code.download span.fa-pull-left:first-child,\n.fa-pull-left.icon {\n margin-right: 0.3em;\n}\n.fa.fa-pull-right,\n.wy-menu-vertical li span.fa-pull-right.toctree-expand,\n.wy-menu-vertical li.on a span.fa-pull-right.toctree-expand,\n.wy-menu-vertical li.current > a span.fa-pull-right.toctree-expand,\n.rst-content .fa-pull-right.admonition-title,\n.rst-content h1 .fa-pull-right.headerlink,\n.rst-content h2 .fa-pull-right.headerlink,\n.rst-content h3 .fa-pull-right.headerlink,\n.rst-content h4 .fa-pull-right.headerlink,\n.rst-content h5 .fa-pull-right.headerlink,\n.rst-content h6 .fa-pull-right.headerlink,\n.rst-content dl dt .fa-pull-right.headerlink,\n.rst-content p.caption .fa-pull-right.headerlink,\n.rst-content table > caption .fa-pull-right.headerlink,\n.rst-content .code-block-caption .fa-pull-right.headerlink,\n.rst-content tt.download span.fa-pull-right:first-child,\n.rst-content code.download span.fa-pull-right:first-child,\n.fa-pull-right.icon {\n margin-left: 0.3em;\n}\n.pull-right {\n float: right;\n}\n.pull-left {\n float: left;\n}\n.fa.pull-left,\n.wy-menu-vertical li span.pull-left.toctree-expand,\n.wy-menu-vertical li.on a span.pull-left.toctree-expand,\n.wy-menu-vertical li.current > a span.pull-left.toctree-expand,\n.rst-content .pull-left.admonition-title,\n.rst-content h1 .pull-left.headerlink,\n.rst-content h2 .pull-left.headerlink,\n.rst-content h3 .pull-left.headerlink,\n.rst-content h4 .pull-left.headerlink,\n.rst-content h5 .pull-left.headerlink,\n.rst-content h6 .pull-left.headerlink,\n.rst-content dl dt .pull-left.headerlink,\n.rst-content p.caption .pull-left.headerlink,\n.rst-content table > caption .pull-left.headerlink,\n.rst-content .code-block-caption .pull-left.headerlink,\n.rst-content tt.download span.pull-left:first-child,\n.rst-content code.download span.pull-left:first-child,\n.pull-left.icon {\n margin-right: 0.3em;\n}\n.fa.pull-right,\n.wy-menu-vertical li span.pull-right.toctree-expand,\n.wy-menu-vertical li.on a span.pull-right.toctree-expand,\n.wy-menu-vertical li.current > a span.pull-right.toctree-expand,\n.rst-content .pull-right.admonition-title,\n.rst-content h1 .pull-right.headerlink,\n.rst-content h2 .pull-right.headerlink,\n.rst-content h3 .pull-right.headerlink,\n.rst-content h4 .pull-right.headerlink,\n.rst-content h5 .pull-right.headerlink,\n.rst-content h6 .pull-right.headerlink,\n.rst-content dl dt .pull-right.headerlink,\n.rst-content p.caption .pull-right.headerlink,\n.rst-content table > caption .pull-right.headerlink,\n.rst-content .code-block-caption .pull-right.headerlink,\n.rst-content tt.download span.pull-right:first-child,\n.rst-content code.download span.pull-right:first-child,\n.pull-right.icon {\n margin-left: 0.3em;\n}\n.fa-arrow-circle-left:before,\n.icon-circle-arrow-left:before {\n content: url(../images/gs-arrow-left.svg);\n position: relative;\n top: 5px;\n right: 4px;\n}\n.fa-arrow-circle-right:before,\n.icon-circle-arrow-right:before {\n content: url(../images/gs-arrow-right.svg);\n position: relative;\n top: 5px;\n left: 0px;\n}\n.fa-minus-square:before {\n content: url(../images/gs-menu-collapse.svg);\n}\n.fa-minus-square-o:before,\n.wy-menu-vertical li.on a span.toctree-expand:before,\n.wy-menu-vertical li.current > a span.toctree-expand:before {\n content: url(../images/gs-menu-collapse.svg);\n}\n.fa-plus-square-o:before,\n.wy-menu-vertical li span.toctree-expand:before {\n content: url(../images/gs-menu-expand.svg);\n}\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n padding: 0;\n margin: -1px;\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n}\n.fa,\n.wy-menu-vertical li span.toctree-expand,\n.wy-menu-vertical li.on a span.toctree-expand,\n.wy-menu-vertical li.current > a span.toctree-expand,\n.rst-content .admonition-title,\n.rst-content h1 .headerlink,\n.rst-content h2 .headerlink,\n.rst-content h3 .headerlink,\n.rst-content h4 .headerlink,\n.rst-content h5 .headerlink,\n.rst-content h6 .headerlink,\n.rst-content dl dt .headerlink,\n.rst-content p.caption .headerlink,\n.rst-content table > caption .headerlink,\n.rst-content .code-block-caption .headerlink,\n.rst-content tt.download span:first-child,\n.rst-content code.download span:first-child,\n.icon,\n.wy-dropdown .caret,\n.wy-inline-validate.wy-inline-validate-success .wy-input-context,\n.wy-inline-validate.wy-inline-validate-danger .wy-input-context,\n.wy-inline-validate.wy-inline-validate-warning .wy-input-context,\n.wy-inline-validate.wy-inline-validate-info .wy-input-context {\n font-family: inherit;\n}\n.fa:before,\n.wy-menu-vertical li span.toctree-expand:before,\n.wy-menu-vertical li.on a span.toctree-expand:before,\n.wy-menu-vertical li.current > a span.toctree-expand:before,\n.rst-content .admonition-title:before,\n.rst-content h1 .headerlink:before,\n.rst-content h2 .headerlink:before,\n.rst-content h3 .headerlink:before,\n.rst-content h4 .headerlink:before,\n.rst-content h5 .headerlink:before,\n.rst-content h6 .headerlink:before,\n.rst-content dl dt .headerlink:before,\n.rst-content p.caption .headerlink:before,\n.rst-content table > caption .headerlink:before,\n.rst-content .code-block-caption .headerlink:before,\n.rst-content tt.download span:first-child:before,\n.rst-content code.download span:first-child:before,\n.icon:before,\n.wy-dropdown .caret:before,\n.wy-inline-validate.wy-inline-validate-success .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-danger .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-warning .wy-input-context:before,\n.wy-inline-validate.wy-inline-validate-info .wy-input-context:before {\n display: inline-block;\n font-style: normal;\n font-weight: normal;\n line-height: 1;\n text-decoration: inherit;\n width: 12px;\n height: 16px;\n margin-top: -2px;\n margin-left: -8px;\n}\na .fa,\na .wy-menu-vertical li span.toctree-expand,\n.wy-menu-vertical li a span.toctree-expand,\n.wy-menu-vertical li.on a span.toctree-expand,\n.wy-menu-vertical li.current > a span.toctree-expand,\na .rst-content .admonition-title,\n.rst-content a .admonition-title,\na .rst-content h1 .headerlink,\n.rst-content h1 a .headerlink,\na .rst-content h2 .headerlink,\n.rst-content h2 a .headerlink,\na .rst-content h3 .headerlink,\n.rst-content h3 a .headerlink,\na .rst-content h4 .headerlink,\n.rst-content h4 a .headerlink,\na .rst-content h5 .headerlink,\n.rst-content h5 a .headerlink,\na .rst-content h6 .headerlink,\n.rst-content h6 a .headerlink,\na .rst-content dl dt .headerlink,\n.rst-content dl dt a .headerlink,\na .rst-content p.caption .headerlink,\n.rst-content p.caption a .headerlink,\na .rst-content table > caption .headerlink,\n.rst-content table > caption a .headerlink,\na .rst-content .code-block-caption .headerlink,\n.rst-content .code-block-caption a .headerlink,\na .rst-content tt.download span:first-child,\n.rst-content tt.download a span:first-child,\na .rst-content code.download span:first-child,\n.rst-content code.download a span:first-child,\na .icon {\n display: inline-block;\n text-decoration: inherit;\n}\n.btn .fa,\n.btn .wy-menu-vertical li span.toctree-expand,\n.wy-menu-vertical li .btn span.toctree-expand,\n.btn .wy-menu-vertical li.on a span.toctree-expand,\n.wy-menu-vertical li.on a .btn span.toctree-expand,\n.btn .wy-menu-vertical li.current > a span.toctree-expand,\n.wy-menu-vertical li.current > a .btn span.toctree-expand,\n.btn .rst-content .admonition-title,\n.rst-content .btn .admonition-title,\n.btn .rst-content h1 .headerlink,\n.rst-content h1 .btn .headerlink,\n.btn .rst-content h2 .headerlink,\n.rst-content h2 .btn .headerlink,\n.btn .rst-content h3 .headerlink,\n.rst-content h3 .btn .headerlink,\n.btn 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.wy-menu-vertical li.current > a span.toctree-expand,\n.wy-menu-vertical li.current > a .nav span.toctree-expand,\n.nav .rst-content .admonition-title,\n.rst-content .nav .admonition-title,\n.nav .rst-content h1 .headerlink,\n.rst-content h1 .nav .headerlink,\n.nav .rst-content h2 .headerlink,\n.rst-content h2 .nav .headerlink,\n.nav .rst-content h3 .headerlink,\n.rst-content h3 .nav .headerlink,\n.nav .rst-content h4 .headerlink,\n.rst-content h4 .nav .headerlink,\n.nav .rst-content h5 .headerlink,\n.rst-content h5 .nav .headerlink,\n.nav .rst-content h6 .headerlink,\n.rst-content h6 .nav .headerlink,\n.nav .rst-content dl dt .headerlink,\n.rst-content dl dt .nav .headerlink,\n.nav .rst-content p.caption .headerlink,\n.rst-content p.caption .nav .headerlink,\n.nav .rst-content table > caption .headerlink,\n.rst-content table > caption .nav .headerlink,\n.nav .rst-content .code-block-caption .headerlink,\n.rst-content .code-block-caption .nav .headerlink,\n.nav .rst-content tt.download span:first-child,\n.rst-content tt.download .nav span:first-child,\n.nav .rst-content code.download span:first-child,\n.rst-content code.download .nav span:first-child,\n.nav .icon {\n display: inline;\n}\n.btn .fa.fa-large,\n.btn .wy-menu-vertical li span.fa-large.toctree-expand,\n.wy-menu-vertical li .btn span.fa-large.toctree-expand,\n.btn .rst-content .fa-large.admonition-title,\n.rst-content .btn .fa-large.admonition-title,\n.btn .rst-content h1 .fa-large.headerlink,\n.rst-content h1 .btn .fa-large.headerlink,\n.btn .rst-content h2 .fa-large.headerlink,\n.rst-content h2 .btn .fa-large.headerlink,\n.btn .rst-content h3 .fa-large.headerlink,\n.rst-content h3 .btn .fa-large.headerlink,\n.btn .rst-content h4 .fa-large.headerlink,\n.rst-content h4 .btn .fa-large.headerlink,\n.btn .rst-content h5 .fa-large.headerlink,\n.rst-content h5 .btn .fa-large.headerlink,\n.btn .rst-content h6 .fa-large.headerlink,\n.rst-content h6 .btn .fa-large.headerlink,\n.btn .rst-content dl dt .fa-large.headerlink,\n.rst-content dl dt .btn .fa-large.headerlink,\n.btn .rst-content p.caption .fa-large.headerlink,\n.rst-content p.caption .btn .fa-large.headerlink,\n.btn .rst-content table > caption .fa-large.headerlink,\n.rst-content table > caption .btn .fa-large.headerlink,\n.btn .rst-content .code-block-caption .fa-large.headerlink,\n.rst-content .code-block-caption .btn .fa-large.headerlink,\n.btn .rst-content tt.download span.fa-large:first-child,\n.rst-content tt.download .btn span.fa-large:first-child,\n.btn .rst-content code.download span.fa-large:first-child,\n.rst-content code.download .btn span.fa-large:first-child,\n.btn .fa-large.icon,\n.nav .fa.fa-large,\n.nav .wy-menu-vertical li span.fa-large.toctree-expand,\n.wy-menu-vertical li .nav span.fa-large.toctree-expand,\n.nav .rst-content .fa-large.admonition-title,\n.rst-content .nav .fa-large.admonition-title,\n.nav .rst-content h1 .fa-large.headerlink,\n.rst-content h1 .nav .fa-large.headerlink,\n.nav .rst-content h2 .fa-large.headerlink,\n.rst-content h2 .nav .fa-large.headerlink,\n.nav .rst-content h3 .fa-large.headerlink,\n.rst-content h3 .nav .fa-large.headerlink,\n.nav .rst-content h4 .fa-large.headerlink,\n.rst-content h4 .nav .fa-large.headerlink,\n.nav .rst-content h5 .fa-large.headerlink,\n.rst-content h5 .nav .fa-large.headerlink,\n.nav .rst-content h6 .fa-large.headerlink,\n.rst-content h6 .nav .fa-large.headerlink,\n.nav .rst-content dl dt .fa-large.headerlink,\n.rst-content dl dt .nav .fa-large.headerlink,\n.nav .rst-content p.caption .fa-large.headerlink,\n.rst-content p.caption .nav .fa-large.headerlink,\n.nav .rst-content table > caption .fa-large.headerlink,\n.rst-content table > caption .nav .fa-large.headerlink,\n.nav .rst-content .code-block-caption .fa-large.headerlink,\n.rst-content .code-block-caption .nav .fa-large.headerlink,\n.nav .rst-content tt.download span.fa-large:first-child,\n.rst-content tt.download .nav span.fa-large:first-child,\n.nav .rst-content code.download span.fa-large:first-child,\n.rst-content code.download .nav span.fa-large:first-child,\n.nav .fa-large.icon {\n line-height: 0.9em;\n}\n.btn .fa.fa-spin,\n.btn .wy-menu-vertical li span.fa-spin.toctree-expand,\n.wy-menu-vertical li .btn span.fa-spin.toctree-expand,\n.btn .rst-content .fa-spin.admonition-title,\n.rst-content .btn .fa-spin.admonition-title,\n.btn .rst-content h1 .fa-spin.headerlink,\n.rst-content h1 .btn .fa-spin.headerlink,\n.btn .rst-content h2 .fa-spin.headerlink,\n.rst-content h2 .btn .fa-spin.headerlink,\n.btn .rst-content h3 .fa-spin.headerlink,\n.rst-content h3 .btn .fa-spin.headerlink,\n.btn .rst-content h4 .fa-spin.headerlink,\n.rst-content h4 .btn .fa-spin.headerlink,\n.btn .rst-content h5 .fa-spin.headerlink,\n.rst-content h5 .btn .fa-spin.headerlink,\n.btn .rst-content h6 .fa-spin.headerlink,\n.rst-content h6 .btn .fa-spin.headerlink,\n.btn .rst-content dl dt .fa-spin.headerlink,\n.rst-content dl dt .btn .fa-spin.headerlink,\n.btn .rst-content p.caption .fa-spin.headerlink,\n.rst-content p.caption .btn .fa-spin.headerlink,\n.btn .rst-content table > caption .fa-spin.headerlink,\n.rst-content table > caption .btn .fa-spin.headerlink,\n.btn .rst-content .code-block-caption .fa-spin.headerlink,\n.rst-content .code-block-caption .btn .fa-spin.headerlink,\n.btn .rst-content tt.download span.fa-spin:first-child,\n.rst-content tt.download .btn span.fa-spin:first-child,\n.btn .rst-content code.download span.fa-spin:first-child,\n.rst-content code.download .btn span.fa-spin:first-child,\n.btn .fa-spin.icon,\n.nav .fa.fa-spin,\n.nav .wy-menu-vertical li span.fa-spin.toctree-expand,\n.wy-menu-vertical li .nav span.fa-spin.toctree-expand,\n.nav .rst-content .fa-spin.admonition-title,\n.rst-content .nav .fa-spin.admonition-title,\n.nav .rst-content h1 .fa-spin.headerlink,\n.rst-content h1 .nav .fa-spin.headerlink,\n.nav .rst-content h2 .fa-spin.headerlink,\n.rst-content h2 .nav .fa-spin.headerlink,\n.nav .rst-content h3 .fa-spin.headerlink,\n.rst-content h3 .nav .fa-spin.headerlink,\n.nav .rst-content h4 .fa-spin.headerlink,\n.rst-content h4 .nav .fa-spin.headerlink,\n.nav .rst-content h5 .fa-spin.headerlink,\n.rst-content h5 .nav .fa-spin.headerlink,\n.nav .rst-content h6 .fa-spin.headerlink,\n.rst-content h6 .nav .fa-spin.headerlink,\n.nav .rst-content dl dt .fa-spin.headerlink,\n.rst-content dl dt .nav .fa-spin.headerlink,\n.nav .rst-content p.caption .fa-spin.headerlink,\n.rst-content p.caption .nav .fa-spin.headerlink,\n.nav .rst-content table > caption .fa-spin.headerlink,\n.rst-content table > caption .nav .fa-spin.headerlink,\n.nav .rst-content .code-block-caption .fa-spin.headerlink,\n.rst-content .code-block-caption .nav .fa-spin.headerlink,\n.nav .rst-content tt.download span.fa-spin:first-child,\n.rst-content tt.download .nav span.fa-spin:first-child,\n.nav .rst-content code.download span.fa-spin:first-child,\n.rst-content code.download .nav span.fa-spin:first-child,\n.nav .fa-spin.icon {\n display: inline-block;\n}\n.btn.fa:before,\n.wy-menu-vertical li span.btn.toctree-expand:before,\n.rst-content .btn.admonition-title:before,\n.rst-content h1 .btn.headerlink:before,\n.rst-content h2 .btn.headerlink:before,\n.rst-content h3 .btn.headerlink:before,\n.rst-content h4 .btn.headerlink:before,\n.rst-content h5 .btn.headerlink:before,\n.rst-content h6 .btn.headerlink:before,\n.rst-content dl dt .btn.headerlink:before,\n.rst-content p.caption .btn.headerlink:before,\n.rst-content table > caption .btn.headerlink:before,\n.rst-content .code-block-caption .btn.headerlink:before,\n.rst-content tt.download span.btn:first-child:before,\n.rst-content code.download span.btn:first-child:before,\n.btn.icon:before {\n opacity: 0.5;\n -webkit-transition: opacity 0.05s ease-in;\n -moz-transition: opacity 0.05s ease-in;\n transition: opacity 0.05s ease-in;\n}\n.btn.fa:hover:before,\n.wy-menu-vertical li span.btn.toctree-expand:hover:before,\n.rst-content .btn.admonition-title:hover:before,\n.rst-content h1 .btn.headerlink:hover:before,\n.rst-content h2 .btn.headerlink:hover:before,\n.rst-content h3 .btn.headerlink:hover:before,\n.rst-content h4 .btn.headerlink:hover:before,\n.rst-content h5 .btn.headerlink:hover:before,\n.rst-content h6 .btn.headerlink:hover:before,\n.rst-content dl dt .btn.headerlink:hover:before,\n.rst-content p.caption .btn.headerlink:hover:before,\n.rst-content table > caption .btn.headerlink:hover:before,\n.rst-content .code-block-caption .btn.headerlink:hover:before,\n.rst-content tt.download span.btn:first-child:hover:before,\n.rst-content code.download span.btn:first-child:hover:before,\n.btn.icon:hover:before {\n opacity: 1;\n}\n.btn-mini .fa:before,\n.btn-mini .wy-menu-vertical li span.toctree-expand:before,\n.wy-menu-vertical li .btn-mini span.toctree-expand:before,\n.btn-mini .rst-content .admonition-title:before,\n.rst-content .btn-mini .admonition-title:before,\n.btn-mini .rst-content h1 .headerlink:before,\n.rst-content h1 .btn-mini .headerlink:before,\n.btn-mini .rst-content h2 .headerlink:before,\n.rst-content h2 .btn-mini .headerlink:before,\n.btn-mini .rst-content h3 .headerlink:before,\n.rst-content h3 .btn-mini .headerlink:before,\n.btn-mini .rst-content h4 .headerlink:before,\n.rst-content h4 .btn-mini .headerlink:before,\n.btn-mini .rst-content h5 .headerlink:before,\n.rst-content h5 .btn-mini .headerlink:before,\n.btn-mini .rst-content h6 .headerlink:before,\n.rst-content h6 .btn-mini .headerlink:before,\n.btn-mini .rst-content dl dt .headerlink:before,\n.rst-content dl dt .btn-mini .headerlink:before,\n.btn-mini .rst-content p.caption .headerlink:before,\n.rst-content p.caption .btn-mini .headerlink:before,\n.btn-mini .rst-content table > caption .headerlink:before,\n.rst-content table > caption .btn-mini .headerlink:before,\n.btn-mini .rst-content .code-block-caption .headerlink:before,\n.rst-content .code-block-caption .btn-mini .headerlink:before,\n.btn-mini .rst-content tt.download span:first-child:before,\n.rst-content tt.download .btn-mini span:first-child:before,\n.btn-mini .rst-content code.download span:first-child:before,\n.rst-content code.download .btn-mini span:first-child:before,\n.btn-mini .icon:before {\n font-size: 14px;\n vertical-align: -15%;\n}\n.wy-alert,\n.rst-content .note,\n.rst-content .attention,\n.rst-content .caution,\n.rst-content .danger,\n.rst-content .error,\n.rst-content .hint,\n.rst-content .important,\n.rst-content .tip,\n.rst-content .warning,\n.rst-content .seealso,\n.rst-content .admonition-todo,\n.rst-content .admonition {\n padding: 12px;\n line-height: 24px;\n margin-bottom: 24px;\n background: #e7f2fa;\n}\n.wy-alert-title,\n.rst-content .admonition-title {\n color: #fff;\n font-weight: bold;\n display: block;\n color: #fff;\n background: #6ab0de;\n margin: -12px;\n padding: 6px 12px;\n margin-bottom: 12px;\n}\n.wy-alert.wy-alert-danger,\n.rst-content .wy-alert-danger.note,\n.rst-content .wy-alert-danger.attention,\n.rst-content .wy-alert-danger.caution,\n.rst-content .danger,\n.rst-content .error,\n.rst-content .wy-alert-danger.hint,\n.rst-content .wy-alert-danger.important,\n.rst-content .wy-alert-danger.tip,\n.rst-content .wy-alert-danger.warning,\n.rst-content .wy-alert-danger.seealso,\n.rst-content .wy-alert-danger.admonition-todo,\n.rst-content .wy-alert-danger.admonition {\n background: #fdf3f2;\n}\n.wy-alert.wy-alert-danger .wy-alert-title,\n.rst-content .wy-alert-danger.note .wy-alert-title,\n.rst-content .wy-alert-danger.attention .wy-alert-title,\n.rst-content .wy-alert-danger.caution .wy-alert-title,\n.rst-content .danger .wy-alert-title,\n.rst-content .error .wy-alert-title,\n.rst-content .wy-alert-danger.hint .wy-alert-title,\n.rst-content .wy-alert-danger.important .wy-alert-title,\n.rst-content .wy-alert-danger.tip .wy-alert-title,\n.rst-content .wy-alert-danger.warning .wy-alert-title,\n.rst-content .wy-alert-danger.seealso .wy-alert-title,\n.rst-content .wy-alert-danger.admonition-todo .wy-alert-title,\n.rst-content .wy-alert-danger.admonition .wy-alert-title,\n.wy-alert.wy-alert-danger .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-danger .admonition-title,\n.rst-content .wy-alert-danger.note .admonition-title,\n.rst-content .wy-alert-danger.attention .admonition-title,\n.rst-content .wy-alert-danger.caution .admonition-title,\n.rst-content .danger .admonition-title,\n.rst-content .error .admonition-title,\n.rst-content .wy-alert-danger.hint .admonition-title,\n.rst-content .wy-alert-danger.important .admonition-title,\n.rst-content .wy-alert-danger.tip .admonition-title,\n.rst-content .wy-alert-danger.warning .admonition-title,\n.rst-content .wy-alert-danger.seealso .admonition-title,\n.rst-content .wy-alert-danger.admonition-todo .admonition-title,\n.rst-content .wy-alert-danger.admonition .admonition-title {\n background: #f29f97;\n}\n.wy-alert.wy-alert-warning,\n.rst-content .wy-alert-warning.note,\n.rst-content .attention,\n.rst-content .caution,\n.rst-content .wy-alert-warning.danger,\n.rst-content .wy-alert-warning.error,\n.rst-content .wy-alert-warning.hint,\n.rst-content .wy-alert-warning.important,\n.rst-content .wy-alert-warning.tip,\n.rst-content .warning,\n.rst-content .wy-alert-warning.seealso,\n.rst-content .admonition-todo,\n.rst-content .wy-alert-warning.admonition {\n background: #ffedcc;\n}\n.wy-alert.wy-alert-warning .wy-alert-title,\n.rst-content .wy-alert-warning.note .wy-alert-title,\n.rst-content .attention .wy-alert-title,\n.rst-content .caution .wy-alert-title,\n.rst-content .wy-alert-warning.danger .wy-alert-title,\n.rst-content .wy-alert-warning.error .wy-alert-title,\n.rst-content .wy-alert-warning.hint .wy-alert-title,\n.rst-content .wy-alert-warning.important .wy-alert-title,\n.rst-content .wy-alert-warning.tip .wy-alert-title,\n.rst-content .warning .wy-alert-title,\n.rst-content .wy-alert-warning.seealso .wy-alert-title,\n.rst-content .admonition-todo .wy-alert-title,\n.rst-content .wy-alert-warning.admonition .wy-alert-title,\n.wy-alert.wy-alert-warning .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-warning .admonition-title,\n.rst-content .wy-alert-warning.note .admonition-title,\n.rst-content .attention .admonition-title,\n.rst-content .caution .admonition-title,\n.rst-content .wy-alert-warning.danger .admonition-title,\n.rst-content .wy-alert-warning.error .admonition-title,\n.rst-content .wy-alert-warning.hint .admonition-title,\n.rst-content .wy-alert-warning.important .admonition-title,\n.rst-content .wy-alert-warning.tip .admonition-title,\n.rst-content .warning .admonition-title,\n.rst-content .wy-alert-warning.seealso .admonition-title,\n.rst-content .admonition-todo .admonition-title,\n.rst-content .wy-alert-warning.admonition .admonition-title {\n background: #f0b37e;\n}\n.wy-alert.wy-alert-info,\n.rst-content .note,\n.rst-content .wy-alert-info.attention,\n.rst-content .wy-alert-info.caution,\n.rst-content .wy-alert-info.danger,\n.rst-content .wy-alert-info.error,\n.rst-content .wy-alert-info.hint,\n.rst-content .wy-alert-info.important,\n.rst-content .wy-alert-info.tip,\n.rst-content .wy-alert-info.warning,\n.rst-content .seealso,\n.rst-content .wy-alert-info.admonition-todo,\n.rst-content .wy-alert-info.admonition {\n background: #e7f2fa;\n}\n.wy-alert.wy-alert-info .wy-alert-title,\n.rst-content .note .wy-alert-title,\n.rst-content .wy-alert-info.attention .wy-alert-title,\n.rst-content .wy-alert-info.caution .wy-alert-title,\n.rst-content .wy-alert-info.danger .wy-alert-title,\n.rst-content .wy-alert-info.error .wy-alert-title,\n.rst-content .wy-alert-info.hint .wy-alert-title,\n.rst-content .wy-alert-info.important .wy-alert-title,\n.rst-content .wy-alert-info.tip .wy-alert-title,\n.rst-content .wy-alert-info.warning .wy-alert-title,\n.rst-content .seealso .wy-alert-title,\n.rst-content .wy-alert-info.admonition-todo .wy-alert-title,\n.rst-content .wy-alert-info.admonition .wy-alert-title,\n.wy-alert.wy-alert-info .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-info .admonition-title,\n.rst-content .note .admonition-title,\n.rst-content .wy-alert-info.attention .admonition-title,\n.rst-content .wy-alert-info.caution .admonition-title,\n.rst-content .wy-alert-info.danger .admonition-title,\n.rst-content .wy-alert-info.error .admonition-title,\n.rst-content .wy-alert-info.hint .admonition-title,\n.rst-content .wy-alert-info.important .admonition-title,\n.rst-content .wy-alert-info.tip .admonition-title,\n.rst-content .wy-alert-info.warning .admonition-title,\n.rst-content .seealso .admonition-title,\n.rst-content .wy-alert-info.admonition-todo .admonition-title,\n.rst-content .wy-alert-info.admonition .admonition-title {\n background: #6ab0de;\n}\n.wy-alert.wy-alert-success,\n.rst-content .wy-alert-success.note,\n.rst-content .wy-alert-success.attention,\n.rst-content .wy-alert-success.caution,\n.rst-content .wy-alert-success.danger,\n.rst-content .wy-alert-success.error,\n.rst-content .hint,\n.rst-content .important,\n.rst-content .tip,\n.rst-content .wy-alert-success.warning,\n.rst-content .wy-alert-success.seealso,\n.rst-content .wy-alert-success.admonition-todo,\n.rst-content .wy-alert-success.admonition {\n background: #dbfaf4;\n}\n.wy-alert.wy-alert-success .wy-alert-title,\n.rst-content .wy-alert-success.note .wy-alert-title,\n.rst-content .wy-alert-success.attention .wy-alert-title,\n.rst-content .wy-alert-success.caution .wy-alert-title,\n.rst-content .wy-alert-success.danger .wy-alert-title,\n.rst-content .wy-alert-success.error .wy-alert-title,\n.rst-content .hint .wy-alert-title,\n.rst-content .important .wy-alert-title,\n.rst-content .tip .wy-alert-title,\n.rst-content .wy-alert-success.warning .wy-alert-title,\n.rst-content .wy-alert-success.seealso .wy-alert-title,\n.rst-content .wy-alert-success.admonition-todo .wy-alert-title,\n.rst-content .wy-alert-success.admonition .wy-alert-title,\n.wy-alert.wy-alert-success .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-success .admonition-title,\n.rst-content .wy-alert-success.note .admonition-title,\n.rst-content .wy-alert-success.attention .admonition-title,\n.rst-content .wy-alert-success.caution .admonition-title,\n.rst-content .wy-alert-success.danger .admonition-title,\n.rst-content .wy-alert-success.error .admonition-title,\n.rst-content .hint .admonition-title,\n.rst-content .important .admonition-title,\n.rst-content .tip .admonition-title,\n.rst-content .wy-alert-success.warning .admonition-title,\n.rst-content .wy-alert-success.seealso .admonition-title,\n.rst-content .wy-alert-success.admonition-todo .admonition-title,\n.rst-content .wy-alert-success.admonition .admonition-title {\n background: #1abc9c;\n}\n.wy-alert.wy-alert-neutral,\n.rst-content .wy-alert-neutral.note,\n.rst-content .wy-alert-neutral.attention,\n.rst-content .wy-alert-neutral.caution,\n.rst-content .wy-alert-neutral.danger,\n.rst-content .wy-alert-neutral.error,\n.rst-content .wy-alert-neutral.hint,\n.rst-content .wy-alert-neutral.important,\n.rst-content .wy-alert-neutral.tip,\n.rst-content .wy-alert-neutral.warning,\n.rst-content .wy-alert-neutral.seealso,\n.rst-content .wy-alert-neutral.admonition-todo,\n.rst-content .wy-alert-neutral.admonition {\n background: #f3f6f6;\n}\n.wy-alert.wy-alert-neutral .wy-alert-title,\n.rst-content .wy-alert-neutral.note .wy-alert-title,\n.rst-content .wy-alert-neutral.attention .wy-alert-title,\n.rst-content .wy-alert-neutral.caution .wy-alert-title,\n.rst-content .wy-alert-neutral.danger .wy-alert-title,\n.rst-content .wy-alert-neutral.error .wy-alert-title,\n.rst-content .wy-alert-neutral.hint .wy-alert-title,\n.rst-content .wy-alert-neutral.important .wy-alert-title,\n.rst-content .wy-alert-neutral.tip .wy-alert-title,\n.rst-content .wy-alert-neutral.warning .wy-alert-title,\n.rst-content .wy-alert-neutral.seealso .wy-alert-title,\n.rst-content .wy-alert-neutral.admonition-todo .wy-alert-title,\n.rst-content .wy-alert-neutral.admonition .wy-alert-title,\n.wy-alert.wy-alert-neutral .rst-content .admonition-title,\n.rst-content .wy-alert.wy-alert-neutral .admonition-title,\n.rst-content .wy-alert-neutral.note .admonition-title,\n.rst-content .wy-alert-neutral.attention .admonition-title,\n.rst-content .wy-alert-neutral.caution .admonition-title,\n.rst-content .wy-alert-neutral.danger .admonition-title,\n.rst-content .wy-alert-neutral.error .admonition-title,\n.rst-content .wy-alert-neutral.hint .admonition-title,\n.rst-content .wy-alert-neutral.important .admonition-title,\n.rst-content .wy-alert-neutral.tip .admonition-title,\n.rst-content .wy-alert-neutral.warning .admonition-title,\n.rst-content .wy-alert-neutral.seealso .admonition-title,\n.rst-content .wy-alert-neutral.admonition-todo .admonition-title,\n.rst-content .wy-alert-neutral.admonition .admonition-title {\n color: #404040;\n background: #e1e4e5;\n}\n.wy-alert.wy-alert-neutral a,\n.rst-content .wy-alert-neutral.note a,\n.rst-content .wy-alert-neutral.attention a,\n.rst-content .wy-alert-neutral.caution a,\n.rst-content .wy-alert-neutral.danger a,\n.rst-content .wy-alert-neutral.error a,\n.rst-content .wy-alert-neutral.hint a,\n.rst-content .wy-alert-neutral.important a,\n.rst-content .wy-alert-neutral.tip a,\n.rst-content .wy-alert-neutral.warning a,\n.rst-content .wy-alert-neutral.seealso a,\n.rst-content .wy-alert-neutral.admonition-todo a,\n.rst-content .wy-alert-neutral.admonition a {\n color: #2980b9;\n}\n.wy-alert p:last-child,\n.rst-content .note p:last-child,\n.rst-content .attention p:last-child,\n.rst-content .caution p:last-child,\n.rst-content .danger p:last-child,\n.rst-content .error p:last-child,\n.rst-content .hint p:last-child,\n.rst-content .important p:last-child,\n.rst-content .tip p:last-child,\n.rst-content .warning p:last-child,\n.rst-content .seealso p:last-child,\n.rst-content .admonition-todo p:last-child,\n.rst-content .admonition p:last-child {\n margin-bottom: 0;\n}\n.wy-tray-container {\n position: fixed;\n bottom: 0px;\n left: 0;\n z-index: 600;\n}\n.wy-tray-container li {\n display: block;\n width: 300px;\n background: transparent;\n color: #fff;\n text-align: center;\n box-shadow: 0 5px 5px 0 rgba(0, 0, 0, 0.1);\n padding: 0 24px;\n min-width: 20%;\n opacity: 0;\n height: 0;\n line-height: 56px;\n overflow: hidden;\n -webkit-transition: all 0.3s ease-in;\n -moz-transition: all 0.3s ease-in;\n transition: all 0.3s ease-in;\n}\n.wy-tray-container li.wy-tray-item-success {\n background: #27ae60;\n}\n.wy-tray-container li.wy-tray-item-info {\n background: #2980b9;\n}\n.wy-tray-container li.wy-tray-item-warning {\n background: #e67e22;\n}\n.wy-tray-container li.wy-tray-item-danger {\n background: #e74c3c;\n}\n.wy-tray-container li.on {\n opacity: 1;\n height: 56px;\n}\n@media screen and (max-width: 768px) {\n .wy-tray-container {\n bottom: auto;\n top: 0;\n width: 100%;\n }\n .wy-tray-container li {\n width: 100%;\n }\n}\nbutton {\n font-size: 100%;\n margin: 0;\n vertical-align: baseline;\n *vertical-align: middle;\n cursor: pointer;\n line-height: normal;\n -webkit-appearance: button;\n *overflow: visible;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\nbutton[disabled] {\n cursor: default;\n}\n.btn {\n display: inline-block;\n line-height: normal;\n white-space: nowrap;\n text-align: center;\n cursor: pointer;\n font-size: 16px;\n padding: 24px;\n color: #fff;\n background-color: #fafafa;\n text-decoration: none;\n font-weight: normal;\n font-family: \"Roboto-Regular\", sans-serif;\n outline-none: false;\n vertical-align: middle;\n *display: inline;\n zoom: 1;\n -webkit-user-drag: none;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n width: 50%;\n}\n.btn-hover {\n background: #2e8ece;\n color: #fff;\n}\n.btn:hover {\n background: #2cc36b;\n color: #fff;\n}\n.btn:focus {\n background: #2cc36b;\n outline: 0;\n}\n.btn:active {\n box-shadow: 0px -1px 0px 0px rgba(0, 0, 0, 0.05) inset,\n 0px 2px 0px 0px rgba(0, 0, 0, 0.1) inset;\n padding: 8px 12px 6px 12px;\n}\n.btn:visited {\n color: #fff;\n}\n.btn:disabled {\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n filter: alpha(opacity=40);\n opacity: 0.4;\n cursor: not-allowed;\n box-shadow: none;\n}\n.btn-disabled {\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n filter: alpha(opacity=40);\n opacity: 0.4;\n cursor: not-allowed;\n box-shadow: none;\n}\n.btn-disabled:hover,\n.btn-disabled:focus,\n.btn-disabled:active {\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n filter: alpha(opacity=40);\n opacity: 0.4;\n cursor: not-allowed;\n box-shadow: none;\n}\n.btn::-moz-focus-inner {\n padding: 0;\n border: 0;\n}\n.btn-small {\n font-size: 80%;\n}\n.btn-info {\n background-color: #2980b9 !important;\n}\n.btn-info:hover {\n background-color: #2e8ece !important;\n}\n\n.btn-neutral {\n background-color: #fafafa !important;\n color: #404040 !important;\n}\n\n.btn-neutral:hover {\n background-color: #fafafa !important;\n color: #404040;\n}\n\n.btn-neutral:visited {\n color: #404040 !important;\n}\n\n.btn-success {\n background-color: #27ae60 !important;\n}\n.btn-success:hover {\n background-color: #295 !important;\n}\n.btn-danger {\n background-color: #e74c3c !important;\n}\n.btn-danger:hover {\n background-color: #ea6153 !important;\n}\n.btn-warning {\n background-color: #e67e22 !important;\n}\n.btn-warning:hover {\n background-color: #e98b39 !important;\n}\n.btn-invert {\n background-color: #222;\n}\n.btn-invert:hover {\n background-color: #2f2f2f !important;\n}\n.btn-link {\n background-color: transparent !important;\n color: #2980b9;\n box-shadow: none;\n border-color: transparent !important;\n}\n.btn-link:hover {\n background-color: transparent !important;\n color: #409ad5 !important;\n box-shadow: none;\n}\n.btn-link:active {\n background-color: transparent !important;\n color: #409ad5 !important;\n box-shadow: none;\n}\n.btn-link:visited {\n color: #9b59b6;\n}\n.wy-btn-group .btn,\n.wy-control .btn {\n vertical-align: middle;\n}\n.wy-btn-group {\n margin-bottom: 24px;\n *zoom: 1;\n}\n.wy-btn-group:before,\n.wy-btn-group:after {\n display: table;\n content: \"\";\n}\n.wy-btn-group:after {\n clear: both;\n}\n.wy-dropdown {\n position: relative;\n display: inline-block;\n}\n.wy-dropdown-active .wy-dropdown-menu {\n display: block;\n}\n.wy-dropdown-menu {\n position: absolute;\n left: 0;\n display: none;\n float: left;\n top: 100%;\n min-width: 100%;\n background: #fcfcfc;\n z-index: 100;\n border: solid 1px #cfd7dd;\n box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.1);\n padding: 12px;\n}\n.wy-dropdown-menu > dd > a {\n display: block;\n clear: both;\n color: #404040;\n white-space: nowrap;\n font-size: 90%;\n padding: 0 12px;\n cursor: pointer;\n}\n.wy-dropdown-menu > dd > a:hover {\n background: #2980b9;\n color: #fff;\n}\n.wy-dropdown-menu > dd.divider {\n border-top: solid 1px #cfd7dd;\n margin: 6px 0;\n}\n.wy-dropdown-menu > dd.search {\n padding-bottom: 12px;\n}\n.wy-dropdown-menu > dd.search input[type=\"search\"] {\n width: 100%;\n}\n.wy-dropdown-menu > dd.call-to-action {\n background: #e3e3e3;\n text-transform: uppercase;\n font-weight: 500;\n font-size: 80%;\n}\n.wy-dropdown-menu > dd.call-to-action:hover {\n background: #e3e3e3;\n}\n.wy-dropdown-menu > dd.call-to-action .btn {\n color: #fff;\n}\n.wy-dropdown.wy-dropdown-up .wy-dropdown-menu {\n bottom: 100%;\n top: auto;\n left: auto;\n right: 0;\n}\n.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu {\n background: #fcfcfc;\n margin-top: 2px;\n}\n.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu a {\n padding: 6px 12px;\n}\n.wy-dropdown.wy-dropdown-bubble .wy-dropdown-menu a:hover {\n background: #2980b9;\n color: #fff;\n}\n.wy-dropdown.wy-dropdown-left .wy-dropdown-menu {\n right: 0;\n left: auto;\n text-align: right;\n}\n.wy-dropdown-arrow:before {\n content: \" \";\n border-bottom: 5px solid #f5f5f5;\n border-left: 5px solid transparent;\n border-right: 5px solid transparent;\n position: absolute;\n display: block;\n top: -4px;\n left: 50%;\n margin-left: -3px;\n}\n.wy-dropdown-arrow.wy-dropdown-arrow-left:before {\n left: 11px;\n}\n.wy-form-stacked select {\n display: block;\n}\n.wy-form-aligned input,\n.wy-form-aligned textarea,\n.wy-form-aligned select,\n.wy-form-aligned .wy-help-inline,\n.wy-form-aligned label {\n display: inline-block;\n *display: inline;\n *zoom: 1;\n vertical-align: middle;\n}\n.wy-form-aligned .wy-control-group > label {\n display: inline-block;\n vertical-align: middle;\n width: 10em;\n margin: 6px 12px 0 0;\n float: left;\n}\n.wy-form-aligned .wy-control {\n float: left;\n}\n.wy-form-aligned .wy-control label {\n display: block;\n}\n.wy-form-aligned .wy-control select {\n margin-top: 6px;\n}\nfieldset {\n border: 0;\n margin: 0;\n padding: 0;\n}\nlegend {\n display: block;\n width: 100%;\n border: 0;\n padding: 0;\n white-space: normal;\n margin-bottom: 24px;\n font-size: 150%;\n *margin-left: -7px;\n}\nlabel {\n display: block;\n margin: 0 0 0.3125em 0;\n color: #333;\n font-size: 90%;\n}\ninput,\nselect,\ntextarea {\n font-size: 100%;\n margin: 0;\n vertical-align: baseline;\n *vertical-align: middle;\n}\n.wy-control-group {\n margin-bottom: 24px;\n *zoom: 1;\n max-width: 68em;\n margin-left: auto;\n margin-right: auto;\n *zoom: 1;\n}\n.wy-control-group:before,\n.wy-control-group:after {\n display: table;\n content: \"\";\n}\n.wy-control-group:after {\n clear: both;\n}\n.wy-control-group:before,\n.wy-control-group:after {\n display: table;\n content: \"\";\n}\n.wy-control-group:after {\n clear: both;\n}\n.wy-control-group.wy-control-group-required > label:after {\n content: \" *\";\n color: #e74c3c;\n}\n.wy-control-group .wy-form-full,\n.wy-control-group .wy-form-halves,\n.wy-control-group .wy-form-thirds {\n padding-bottom: 12px;\n}\n.wy-control-group .wy-form-full select,\n.wy-control-group .wy-form-halves select,\n.wy-control-group .wy-form-thirds select {\n width: 100%;\n}\n.wy-control-group .wy-form-full input[type=\"text\"],\n.wy-control-group .wy-form-full input[type=\"password\"],\n.wy-control-group .wy-form-full input[type=\"email\"],\n.wy-control-group .wy-form-full input[type=\"url\"],\n.wy-control-group .wy-form-full input[type=\"date\"],\n.wy-control-group .wy-form-full input[type=\"month\"],\n.wy-control-group .wy-form-full input[type=\"time\"],\n.wy-control-group .wy-form-full input[type=\"datetime\"],\n.wy-control-group .wy-form-full input[type=\"datetime-local\"],\n.wy-control-group .wy-form-full input[type=\"week\"],\n.wy-control-group .wy-form-full input[type=\"number\"],\n.wy-control-group .wy-form-full input[type=\"search\"],\n.wy-control-group .wy-form-full input[type=\"tel\"],\n.wy-control-group .wy-form-full input[type=\"color\"],\n.wy-control-group .wy-form-halves input[type=\"text\"],\n.wy-control-group .wy-form-halves input[type=\"password\"],\n.wy-control-group .wy-form-halves input[type=\"email\"],\n.wy-control-group .wy-form-halves input[type=\"url\"],\n.wy-control-group .wy-form-halves input[type=\"date\"],\n.wy-control-group .wy-form-halves input[type=\"month\"],\n.wy-control-group .wy-form-halves input[type=\"time\"],\n.wy-control-group .wy-form-halves input[type=\"datetime\"],\n.wy-control-group .wy-form-halves input[type=\"datetime-local\"],\n.wy-control-group .wy-form-halves input[type=\"week\"],\n.wy-control-group .wy-form-halves input[type=\"number\"],\n.wy-control-group .wy-form-halves input[type=\"search\"],\n.wy-control-group .wy-form-halves input[type=\"tel\"],\n.wy-control-group .wy-form-halves input[type=\"color\"],\n.wy-control-group .wy-form-thirds input[type=\"text\"],\n.wy-control-group .wy-form-thirds input[type=\"password\"],\n.wy-control-group .wy-form-thirds input[type=\"email\"],\n.wy-control-group .wy-form-thirds input[type=\"url\"],\n.wy-control-group .wy-form-thirds input[type=\"date\"],\n.wy-control-group .wy-form-thirds input[type=\"month\"],\n.wy-control-group .wy-form-thirds input[type=\"time\"],\n.wy-control-group .wy-form-thirds input[type=\"datetime\"],\n.wy-control-group .wy-form-thirds input[type=\"datetime-local\"],\n.wy-control-group .wy-form-thirds input[type=\"week\"],\n.wy-control-group .wy-form-thirds input[type=\"number\"],\n.wy-control-group .wy-form-thirds input[type=\"search\"],\n.wy-control-group .wy-form-thirds input[type=\"tel\"],\n.wy-control-group .wy-form-thirds input[type=\"color\"] {\n width: 100%;\n}\n.wy-control-group .wy-form-full {\n float: left;\n display: block;\n margin-right: 2.3576515979%;\n width: 100%;\n margin-right: 0;\n}\n.wy-control-group .wy-form-full:last-child {\n margin-right: 0;\n}\n.wy-control-group .wy-form-halves {\n float: left;\n display: block;\n margin-right: 2.3576515979%;\n width: 48.821174201%;\n}\n.wy-control-group .wy-form-halves:last-child {\n margin-right: 0;\n}\n.wy-control-group .wy-form-halves:nth-of-type(2n) {\n margin-right: 0;\n}\n.wy-control-group .wy-form-halves:nth-of-type(2n + 1) {\n clear: left;\n}\n.wy-control-group .wy-form-thirds {\n float: left;\n display: block;\n margin-right: 2.3576515979%;\n width: 31.7615656014%;\n}\n.wy-control-group .wy-form-thirds:last-child {\n margin-right: 0;\n}\n.wy-control-group .wy-form-thirds:nth-of-type(3n) {\n margin-right: 0;\n}\n.wy-control-group .wy-form-thirds:nth-of-type(3n + 1) {\n clear: left;\n}\n.wy-control-group.wy-control-group-no-input .wy-control {\n margin: 6px 0 0 0;\n font-size: 90%;\n}\n.wy-control-no-input {\n display: inline-block;\n margin: 6px 0 0 0;\n font-size: 90%;\n}\n.wy-control-group.fluid-input input[type=\"text\"],\n.wy-control-group.fluid-input input[type=\"password\"],\n.wy-control-group.fluid-input input[type=\"email\"],\n.wy-control-group.fluid-input input[type=\"url\"],\n.wy-control-group.fluid-input input[type=\"date\"],\n.wy-control-group.fluid-input input[type=\"month\"],\n.wy-control-group.fluid-input input[type=\"time\"],\n.wy-control-group.fluid-input input[type=\"datetime\"],\n.wy-control-group.fluid-input input[type=\"datetime-local\"],\n.wy-control-group.fluid-input input[type=\"week\"],\n.wy-control-group.fluid-input input[type=\"number\"],\n.wy-control-group.fluid-input input[type=\"search\"],\n.wy-control-group.fluid-input input[type=\"tel\"],\n.wy-control-group.fluid-input input[type=\"color\"] {\n width: 100%;\n}\n.wy-form-message-inline {\n display: inline-block;\n padding-left: 0.3em;\n color: #666;\n vertical-align: middle;\n font-size: 90%;\n}\n.wy-form-message {\n display: block;\n color: #999;\n font-size: 70%;\n margin-top: 0.3125em;\n font-style: italic;\n}\n.wy-form-message p {\n font-size: inherit;\n font-style: italic;\n margin-bottom: 6px;\n}\n.wy-form-message p:last-child {\n margin-bottom: 0;\n}\ninput {\n line-height: normal;\n}\ninput[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button;\n cursor: pointer;\n font-family: \"Roboto-Regular\", sans-serif;\n *overflow: visible;\n}\ninput[type=\"text\"],\ninput[type=\"password\"],\ninput[type=\"email\"],\ninput[type=\"url\"],\ninput[type=\"date\"],\ninput[type=\"month\"],\ninput[type=\"time\"],\ninput[type=\"datetime\"],\ninput[type=\"datetime-local\"],\ninput[type=\"week\"],\ninput[type=\"number\"],\ninput[type=\"search\"],\ninput[type=\"tel\"],\ninput[type=\"color\"] {\n -webkit-appearance: none;\n padding: 6px;\n display: inline-block;\n border: 1px solid #ccc;\n font-size: 80%;\n font-family: \"Roboto-Regular\", sans-serif;\n box-shadow: inset 0 1px 3px #ddd;\n border-radius: 0;\n -webkit-transition: border 0.3s linear;\n -moz-transition: border 0.3s linear;\n transition: border 0.3s linear;\n}\ninput[type=\"datetime-local\"] {\n padding: 0.34375em 0.625em;\n}\ninput[disabled] {\n cursor: default;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n padding: 0;\n margin-right: 0.3125em;\n *height: 13px;\n *width: 13px;\n}\ninput[type=\"search\"] {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\ninput[type=\"text\"]:focus,\ninput[type=\"password\"]:focus,\ninput[type=\"email\"]:focus,\ninput[type=\"url\"]:focus,\ninput[type=\"date\"]:focus,\ninput[type=\"month\"]:focus,\ninput[type=\"time\"]:focus,\ninput[type=\"datetime\"]:focus,\ninput[type=\"datetime-local\"]:focus,\ninput[type=\"week\"]:focus,\ninput[type=\"number\"]:focus,\ninput[type=\"search\"]:focus,\ninput[type=\"tel\"]:focus,\ninput[type=\"color\"]:focus {\n outline: 0;\n outline: thin dotted \\9;\n border-color: #333;\n}\ninput.no-focus:focus {\n border-color: #ccc !important;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n outline: thin dotted #333;\n outline: 1px auto #129fea;\n}\ninput[type=\"text\"][disabled],\ninput[type=\"password\"][disabled],\ninput[type=\"email\"][disabled],\ninput[type=\"url\"][disabled],\ninput[type=\"date\"][disabled],\ninput[type=\"month\"][disabled],\ninput[type=\"time\"][disabled],\ninput[type=\"datetime\"][disabled],\ninput[type=\"datetime-local\"][disabled],\ninput[type=\"week\"][disabled],\ninput[type=\"number\"][disabled],\ninput[type=\"search\"][disabled],\ninput[type=\"tel\"][disabled],\ninput[type=\"color\"][disabled] {\n cursor: not-allowed;\n background-color: #fafafa;\n}\ninput:focus:invalid,\ntextarea:focus:invalid,\nselect:focus:invalid {\n color: #e74c3c;\n border: 1px solid #e74c3c;\n}\ninput:focus:invalid:focus,\ntextarea:focus:invalid:focus,\nselect:focus:invalid:focus {\n border-color: #e74c3c;\n}\ninput[type=\"file\"]:focus:invalid:focus,\ninput[type=\"radio\"]:focus:invalid:focus,\ninput[type=\"checkbox\"]:focus:invalid:focus {\n outline-color: #e74c3c;\n}\ninput.wy-input-large {\n padding: 12px;\n font-size: 100%;\n}\ntextarea {\n overflow: auto;\n vertical-align: top;\n width: 100%;\n font-family: \"Roboto-Regular\", sans-serif;\n}\nselect,\ntextarea {\n padding: 0.5em 0.625em;\n display: inline-block;\n border: 1px solid #ccc;\n font-size: 80%;\n box-shadow: inset 0 1px 3px #ddd;\n -webkit-transition: border 0.3s linear;\n -moz-transition: border 0.3s linear;\n transition: border 0.3s linear;\n}\nselect {\n border: 1px solid #ccc;\n background-color: #fff;\n}\nselect[multiple] {\n height: auto;\n}\nselect:focus,\ntextarea:focus {\n outline: 0;\n}\nselect[disabled],\ntextarea[disabled],\ninput[readonly],\nselect[readonly],\ntextarea[readonly] {\n cursor: not-allowed;\n background-color: #fafafa;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled] {\n cursor: not-allowed;\n}\n.wy-checkbox,\n.wy-radio {\n margin: 6px 0;\n color: #404040;\n display: block;\n}\n.wy-checkbox input,\n.wy-radio input {\n vertical-align: baseline;\n}\n.wy-form-message-inline {\n display: inline-block;\n *display: inline;\n *zoom: 1;\n vertical-align: middle;\n}\n.wy-input-prefix,\n.wy-input-suffix {\n white-space: nowrap;\n padding: 6px;\n}\n.wy-input-prefix .wy-input-context,\n.wy-input-suffix .wy-input-context {\n line-height: 27px;\n padding: 0 8px;\n display: inline-block;\n font-size: 80%;\n\n background: #fafafa;\n border: solid 1px #ccc;\n color: #999;\n}\n.wy-input-suffix .wy-input-context {\n border-left: 0;\n}\n.wy-input-prefix .wy-input-context {\n border-right: 0;\n}\n.wy-switch {\n position: relative;\n display: block;\n height: 24px;\n margin-top: 12px;\n cursor: pointer;\n}\n.wy-switch:before {\n position: absolute;\n content: \"\";\n display: block;\n left: 0;\n top: 0;\n width: 36px;\n height: 12px;\n border-radius: 4px;\n background: #ccc;\n -webkit-transition: all 0.2s ease-in-out;\n -moz-transition: all 0.2s ease-in-out;\n transition: all 0.2s ease-in-out;\n}\n.wy-switch:after {\n position: absolute;\n content: \"\";\n display: block;\n width: 18px;\n height: 18px;\n border-radius: 4px;\n background: #999;\n left: -3px;\n top: -3px;\n -webkit-transition: all 0.2s ease-in-out;\n -moz-transition: all 0.2s ease-in-out;\n transition: all 0.2s ease-in-out;\n}\n.wy-switch span {\n position: absolute;\n left: 48px;\n display: block;\n font-size: 12px;\n color: #ccc;\n line-height: 1;\n}\n.wy-switch.active:before {\n background: #1e8449;\n}\n.wy-switch.active:after {\n left: 24px;\n background: #27ae60;\n}\n.wy-switch.disabled {\n cursor: not-allowed;\n opacity: 0.8;\n}\n.wy-control-group.wy-control-group-error .wy-form-message,\n.wy-control-group.wy-control-group-error > label {\n color: #e74c3c;\n}\n.wy-control-group.wy-control-group-error input[type=\"text\"],\n.wy-control-group.wy-control-group-error input[type=\"password\"],\n.wy-control-group.wy-control-group-error input[type=\"email\"],\n.wy-control-group.wy-control-group-error input[type=\"url\"],\n.wy-control-group.wy-control-group-error input[type=\"date\"],\n.wy-control-group.wy-control-group-error input[type=\"month\"],\n.wy-control-group.wy-control-group-error input[type=\"time\"],\n.wy-control-group.wy-control-group-error input[type=\"datetime\"],\n.wy-control-group.wy-control-group-error input[type=\"datetime-local\"],\n.wy-control-group.wy-control-group-error input[type=\"week\"],\n.wy-control-group.wy-control-group-error input[type=\"number\"],\n.wy-control-group.wy-control-group-error input[type=\"search\"],\n.wy-control-group.wy-control-group-error input[type=\"tel\"],\n.wy-control-group.wy-control-group-error input[type=\"color\"] {\n border: solid 1px #e74c3c;\n}\n.wy-control-group.wy-control-group-error textarea {\n border: solid 1px #e74c3c;\n}\n.wy-inline-validate {\n white-space: nowrap;\n}\n.wy-inline-validate .wy-input-context {\n padding: 0.5em 0.625em;\n display: inline-block;\n font-size: 80%;\n}\n.wy-inline-validate.wy-inline-validate-success .wy-input-context {\n color: #27ae60;\n}\n.wy-inline-validate.wy-inline-validate-danger .wy-input-context {\n color: #e74c3c;\n}\n.wy-inline-validate.wy-inline-validate-warning .wy-input-context {\n color: #e67e22;\n}\n.wy-inline-validate.wy-inline-validate-info .wy-input-context {\n color: #2980b9;\n}\n.rotate-90 {\n -webkit-transform: rotate(90deg);\n -moz-transform: rotate(90deg);\n -ms-transform: rotate(90deg);\n -o-transform: rotate(90deg);\n transform: rotate(90deg);\n}\n.rotate-180 {\n -webkit-transform: rotate(180deg);\n -moz-transform: rotate(180deg);\n -ms-transform: rotate(180deg);\n -o-transform: rotate(180deg);\n transform: rotate(180deg);\n}\n.rotate-270 {\n -webkit-transform: rotate(270deg);\n -moz-transform: rotate(270deg);\n -ms-transform: rotate(270deg);\n -o-transform: rotate(270deg);\n transform: rotate(270deg);\n}\n.mirror {\n -webkit-transform: scaleX(-1);\n -moz-transform: scaleX(-1);\n -ms-transform: scaleX(-1);\n -o-transform: scaleX(-1);\n transform: scaleX(-1);\n}\n.mirror.rotate-90 {\n -webkit-transform: scaleX(-1) rotate(90deg);\n -moz-transform: scaleX(-1) rotate(90deg);\n -ms-transform: scaleX(-1) rotate(90deg);\n -o-transform: scaleX(-1) rotate(90deg);\n transform: scaleX(-1) rotate(90deg);\n}\n.mirror.rotate-180 {\n -webkit-transform: scaleX(-1) rotate(180deg);\n -moz-transform: scaleX(-1) rotate(180deg);\n -ms-transform: scaleX(-1) rotate(180deg);\n -o-transform: scaleX(-1) rotate(180deg);\n transform: scaleX(-1) rotate(180deg);\n}\n.mirror.rotate-270 {\n -webkit-transform: scaleX(-1) rotate(270deg);\n -moz-transform: scaleX(-1) rotate(270deg);\n -ms-transform: scaleX(-1) rotate(270deg);\n -o-transform: scaleX(-1) rotate(270deg);\n transform: scaleX(-1) rotate(270deg);\n}\n@media only screen and (max-width: 480px) {\n .wy-form button[type=\"submit\"] {\n margin: 0.7em 0 0;\n }\n .wy-form input[type=\"text\"],\n .wy-form input[type=\"password\"],\n .wy-form input[type=\"email\"],\n .wy-form input[type=\"url\"],\n .wy-form input[type=\"date\"],\n .wy-form input[type=\"month\"],\n .wy-form input[type=\"time\"],\n .wy-form input[type=\"datetime\"],\n .wy-form input[type=\"datetime-local\"],\n .wy-form input[type=\"week\"],\n .wy-form input[type=\"number\"],\n .wy-form input[type=\"search\"],\n .wy-form input[type=\"tel\"],\n .wy-form input[type=\"color\"] {\n margin-bottom: 0.3em;\n display: block;\n }\n .wy-form label {\n margin-bottom: 0.3em;\n display: block;\n }\n .wy-form input[type=\"password\"],\n .wy-form input[type=\"email\"],\n .wy-form input[type=\"url\"],\n .wy-form input[type=\"date\"],\n .wy-form input[type=\"month\"],\n .wy-form input[type=\"time\"],\n .wy-form input[type=\"datetime\"],\n .wy-form input[type=\"datetime-local\"],\n .wy-form input[type=\"week\"],\n .wy-form input[type=\"number\"],\n .wy-form input[type=\"search\"],\n .wy-form input[type=\"tel\"],\n .wy-form input[type=\"color\"] {\n margin-bottom: 0;\n }\n .wy-form-aligned .wy-control-group label {\n margin-bottom: 0.3em;\n text-align: left;\n display: block;\n width: 100%;\n }\n .wy-form-aligned .wy-control {\n margin: 1.5em 0 0 0;\n }\n .wy-form .wy-help-inline,\n .wy-form-message-inline,\n .wy-form-message {\n display: block;\n font-size: 80%;\n padding: 6px 0;\n }\n}\n@media screen and (max-width: 768px) {\n .tablet-hide {\n display: none;\n }\n}\n@media screen and (max-width: 480px) {\n .mobile-hide {\n display: none;\n }\n}\n.float-left {\n float: left;\n}\n.float-right {\n float: right;\n}\n.full-width {\n width: 100%;\n}\n.wy-table,\n.rst-content table.docutils,\n.rst-content table.field-list {\n border-collapse: collapse;\n border-spacing: 0;\n empty-cells: show;\n margin-bottom: 24px;\n}\n.wy-table caption,\n.rst-content table.docutils caption,\n.rst-content table.field-list caption {\n color: #000;\n font: italic 85%/1 arial, sans-serif;\n padding: 1em 0;\n text-align: center;\n}\n.wy-table td,\n.rst-content table.docutils td,\n.rst-content table.field-list td,\n.wy-table th,\n.rst-content table.docutils th,\n.rst-content table.field-list th {\n font-size: 14px;\n margin: 0;\n overflow: visible;\n /* padding: 8px 16px; */\n}\n\n.wy-table-responsive .wy-table td:first-child,\n.rst-content table.docutils td:first-child,\n.rst-content table.field-list td:first-child,\n.wy-table th:first-child,\n.rst-content table.docutils th:first-child,\n.rst-content table.field-list th:first-child {\n border: none;\n}\n.wy-table thead,\n.rst-content table.docutils thead,\n.rst-content table.field-list thead {\n color: #000;\n text-align: left;\n vertical-align: bottom;\n white-space: nowrap;\n}\n.wy-table thead th,\n.rst-content table.docutils thead th,\n.rst-content table.field-list thead th {\n font-weight: bold;\n border-bottom: solid 2px #e1e4e5;\n}\n.wy-table td,\n.rst-content table.docutils td,\n.rst-content table.field-list td {\n background-color: transparent;\n vertical-align: middle;\n}\n.wy-table td p,\n.rst-content table.docutils td p,\n.rst-content table.field-list td p {\n line-height: 18px;\n}\n.wy-table td p:last-child,\n.rst-content table.docutils td p:last-child,\n.rst-content table.field-list td p:last-child {\n margin-bottom: 0;\n}\n.wy-table .wy-table-cell-min,\n.rst-content table.docutils .wy-table-cell-min,\n.rst-content table.field-list .wy-table-cell-min {\n width: 1%;\n padding-right: 0;\n}\n.wy-table .wy-table-cell-min input[type=\"checkbox\"],\n.rst-content table.docutils .wy-table-cell-min input[type=\"checkbox\"],\n.rst-content table.field-list .wy-table-cell-min input[type=\"checkbox\"],\n.wy-table .wy-table-cell-min input[type=\"checkbox\"],\n.rst-content table.docutils .wy-table-cell-min input[type=\"checkbox\"],\n.rst-content table.field-list .wy-table-cell-min input[type=\"checkbox\"] {\n margin: 0;\n}\n.wy-table-secondary {\n color: gray;\n font-size: 90%;\n}\n.wy-table-tertiary {\n color: gray;\n font-size: 80%;\n}\n.wy-table-odd td,\n.wy-table-striped tr:nth-child(2n-1) td,\n.rst-content table.docutils:not(.field-list) tr:nth-child(2n-1) td {\n border: none;\n}\n.wy-table-backed {\n background-color: #f3f6f6;\n}\n\ntr.row-odd {\n background: #fafafa;\n /* border: 1px solid #e6e6e6; */\n border-radius: 8px;\n}\n\n.wy-table-bordered-all,\n.rst-content table.docutils {\n border: none;\n}\n.wy-table-bordered-all td,\n.rst-content table.docutils td {\n padding: 24px 16px;\n border: none;\n}\n.wy-table-bordered-all tbody > tr:last-child td,\n.rst-content table.docutils tbody > tr:last-child td {\n border-bottom-width: 0;\n}\n.wy-table-bordered {\n border: 1px solid #e1e4e5;\n}\n.wy-table-bordered-rows td {\n border-bottom: 1px solid #e1e4e5;\n}\n.wy-table-bordered-rows tbody > tr:last-child td {\n border-bottom-width: 0;\n}\n.wy-table-horizontal tbody > tr:last-child td {\n border-bottom-width: 0;\n}\n.wy-table-horizontal td,\n.wy-table-horizontal th {\n border-width: 0 0 1px 0;\n border-bottom: 1px solid #e1e4e5;\n}\n.wy-table-horizontal tbody > tr:last-child td {\n border-bottom-width: 0;\n}\n.wy-table-responsive {\n margin-bottom: 24px;\n max-width: 100%;\n overflow: auto;\n}\n.wy-table-responsive table {\n margin-bottom: 0 !important;\n}\n.wy-table-responsive table td,\n.wy-table-responsive table th {\n white-space: nowrap;\n}\na {\n color: #0f6dee;\n text-decoration: none;\n cursor: pointer;\n}\na:hover {\n color: #0f6dee;\n}\nhtml {\n height: 100%;\n overflow-x: hidden;\n}\nbody {\n font-family: \"Roboto-Regular\", sans-serif;\n font-weight: normal;\n color: #404040;\n min-height: 100%;\n background: #edf0f2;\n}\n.wy-text-left {\n text-align: left;\n}\n.wy-text-center {\n text-align: center;\n}\n.wy-text-right {\n text-align: right;\n}\n.wy-text-large {\n font-size: 120%;\n}\n.wy-text-normal {\n font-size: 100%;\n}\n.wy-text-small,\nsmall {\n font-size: 80%;\n}\n.wy-text-strike {\n text-decoration: line-through;\n}\n.wy-text-warning {\n color: #e67e22 !important;\n}\na.wy-text-warning:hover {\n color: #eb9950 !important;\n}\n.wy-text-info {\n color: #2980b9 !important;\n}\na.wy-text-info:hover {\n color: #409ad5 !important;\n}\n.wy-text-success {\n color: #27ae60 !important;\n}\na.wy-text-success:hover {\n color: #36d278 !important;\n}\n.wy-text-danger {\n color: #e74c3c !important;\n}\na.wy-text-danger:hover {\n color: #ed7669 !important;\n}\n.wy-text-neutral {\n color: #404040 !important;\n}\na.wy-text-neutral:hover {\n color: #595959 !important;\n}\nh1,\nh2,\n.rst-content .toctree-wrapper p.caption,\nh3,\nh4,\nh5,\nh6,\nlegend {\n margin-top: 0;\n font-weight: normal;\n font-family: \"Roboto-Regular\", sans-serif;\n}\np {\n line-height: 20px;\n margin: 0;\n font-size: 14px;\n margin-bottom: 24px;\n}\nh1 {\n font-family: \"Roboto-Medium\", sans-serif;\n font-size: 34px;\n line-height: 54px;\n margin-bottom: 16px;\n}\n\nh2 {\n font-family: \"Roboto-Medium\", sans-serif;\n font-size: 24px;\n line-height: 36px;\n}\n\n.rst-content .toctree-wrapper p.caption {\n font-family: Roboto-Medium;\n font-weight: 400;\n font-size: 20px;\n color: rgba(0, 0, 0, 0.87);\n line-height: 32px;\n margin-bottom: 8px;\n}\nh3 {\n font-size: 125%;\n}\nh4 {\n font-family: \"Roboto Mono\", monospace;\n font-size: 16px;\n line-height: 24px;\n color: rgba(0, 0, 0, 0.8);\n font-weight: 700;\n}\nh5 {\n font-size: 110%;\n}\nh6 {\n font-size: 100%;\n}\nhr {\n margin-left: 0;\n margin-right: 0;\n margin-top: 3rem;\n margin-bottom: 3rem;\n padding-bottom: 0;\n padding-left: 0;\n padding-right: 0;\n padding-top: 0;\n background: hsla(0, 0%, 0%, 0.2);\n border: none;\n height: 1px;\n}\ncode,\n.rst-content tt,\n.rst-content code {\n white-space: nowrap;\n max-width: 100%;\n background: #fafafa;\n border: 1px solid #e6e6e6;\n border-radius: 8px;\n /* font-size: 75%; */\n padding: 0 5px;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n color: #e74c3c;\n overflow-x: auto;\n}\ncode.code-large,\n.rst-content tt.code-large {\n font-size: 90%;\n}\n.wy-plain-list-disc,\n.rst-content .section ul,\n.rst-content .toctree-wrapper ul,\narticle ul {\n list-style: disc;\n font-size: 14px;\n line-height: 24px;\n margin-bottom: 24px;\n}\n.wy-plain-list-disc li,\n.rst-content .section ul li,\n.rst-content .toctree-wrapper ul li,\narticle ul li {\n /* list-style: disc;\n margin-left: 24px; */\n}\n.wy-plain-list-disc li p:last-child,\n.rst-content .section ul li p:last-child,\n.rst-content .toctree-wrapper ul li p:last-child,\narticle ul li p:last-child {\n margin-bottom: 0;\n}\n.wy-plain-list-disc li ul,\n.rst-content .section ul li ul,\n.rst-content .toctree-wrapper ul li ul,\narticle ul li ul {\n margin-bottom: 0;\n margin-left: 24px;\n}\n.wy-plain-list-disc li li,\n.rst-content .section ul li li,\n.rst-content .toctree-wrapper ul li li,\narticle ul li li {\n list-style: none;\n}\n.wy-plain-list-disc li li li,\n.rst-content .section ul li li li,\n.rst-content .toctree-wrapper ul li li li,\narticle ul li li li {\n list-style: none;\n}\n.wy-plain-list-disc li ol li,\n.rst-content .section ul li ol li,\n.rst-content .toctree-wrapper ul li ol li,\narticle ul li ol li {\n list-style: decimal;\n}\n.wy-plain-list-decimal,\n.rst-content .section ol,\n.rst-content ol.arabic,\narticle ol {\n list-style: decimal;\n line-height: 24px;\n margin-bottom: 24px;\n}\n.wy-plain-list-decimal li,\n.rst-content .section ol li,\n.rst-content ol.arabic li,\narticle ol li {\n list-style: decimal;\n margin-left: 24px;\n}\n.wy-plain-list-decimal li p:last-child,\n.rst-content .section ol li p:last-child,\n.rst-content ol.arabic li p:last-child,\narticle ol li p:last-child {\n margin-bottom: 0;\n}\n.wy-plain-list-decimal li ul,\n.rst-content .section ol li ul,\n.rst-content ol.arabic li ul,\narticle ol li ul {\n margin-bottom: 0;\n}\n.wy-plain-list-decimal li ul li,\n.rst-content .section ol li ul li,\n.rst-content ol.arabic li ul li,\narticle ol li ul li {\n list-style: disc;\n}\n.wy-breadcrumbs {\n *zoom: 1;\n font-size: 12px;\n text-transform: uppercase;\n color: rgba(0, 0, 0, 0.54);\n line-height: 16px;\n margin-bottom: 4px;\n font-weight: 700;\n}\n.wy-breadcrumbs:before,\n.wy-breadcrumbs:after {\n display: table;\n content: \"\";\n}\n.wy-breadcrumbs:after {\n clear: both;\n}\n.wy-breadcrumbs li {\n display: inline-block;\n}\n.wy-breadcrumbs li.wy-breadcrumbs-aside {\n float: right;\n}\n.wy-breadcrumbs li a {\n display: inline-block;\n color: rgba(0, 0, 0, 0.54);\n}\n.wy-breadcrumbs li a:hover {\n display: inline-block;\n color: rgba(15, 109, 238, 0.87);\n text-decoration: underline;\n}\n.wy-breadcrumbs li a:first-child {\n padding-left: 0;\n}\n.wy-breadcrumbs li code,\n.wy-breadcrumbs li .rst-content tt,\n.rst-content .wy-breadcrumbs li tt {\n /* padding: 5px; */\n border: none;\n background: none;\n}\n.wy-breadcrumbs li code.literal,\n.wy-breadcrumbs li .rst-content tt.literal,\n.rst-content .wy-breadcrumbs li tt.literal {\n color: #404040;\n}\n.wy-breadcrumbs-extra {\n margin-bottom: 0;\n color: #b3b3b3;\n font-size: 80%;\n display: inline-block;\n}\n@media screen and (max-width: 480px) {\n .wy-breadcrumbs-extra {\n display: none;\n }\n .wy-breadcrumbs li.wy-breadcrumbs-aside {\n display: none;\n }\n}\n@media print {\n .wy-breadcrumbs li.wy-breadcrumbs-aside {\n display: none;\n }\n}\n.wy-affix {\n position: fixed;\n top: 1.618em;\n}\n.wy-menu a:hover {\n text-decoration: none;\n}\n.wy-menu-horiz {\n *zoom: 1;\n}\n.wy-menu-horiz:before,\n.wy-menu-horiz:after {\n display: table;\n content: \"\";\n}\n.wy-menu-horiz:after {\n clear: both;\n}\n.wy-menu-horiz ul,\n.wy-menu-horiz li {\n display: inline-block;\n}\n.wy-menu-horiz li:hover {\n background: rgba(255, 255, 255, 0.1);\n}\n.wy-menu-horiz li.divide-left {\n border-left: solid 1px #404040;\n}\n.wy-menu-horiz li.divide-right {\n border-right: solid 1px #404040;\n}\n.wy-menu-horiz a {\n height: 32px;\n display: inline-block;\n line-height: 32px;\n padding: 0 16px;\n}\n.wy-menu-vertical {\n width: 100%;\n}\n.wy-menu-vertical header,\n.wy-menu-vertical p.caption {\n font-family: \"Roboto-Regular\", sans-serif;\n font-size: 12px;\n line-height: 16px;\n color: rgba(0, 0, 0, 0.54);\n text-transform: uppercase;\n margin-bottom: 4px;\n color: rgba(0, 0, 0, 0.54);\n height: 32px;\n display: inline-block;\n line-height: 32px;\n padding: 0 32px;\n margin: 48px 0 0 0;\n white-space: nowrap;\n font-weight: bold;\n}\n.wy-menu-vertical ul {\n margin-bottom: 48px;\n}\n.wy-menu-vertical li.divide-top {\n border-top: solid 1px #404040;\n}\n.wy-menu-vertical li.divide-bottom {\n border-bottom: solid 1px #404040;\n}\n.wy-menu-vertical li.current {\n /* background: #e3e3e3; */\n}\n.wy-menu-vertical li.current a {\n /* color: gray; */\n /* border-right: solid 1px #c9c9c9; */\n padding: 0.4045em 2.427em;\n padding-left: 48px;\n}\n.wy-menu-vertical li.current a:hover {\n /* background: #d6d6d6; */\n}\n.wy-menu-vertical li code,\n.wy-menu-vertical li .rst-content tt,\n.rst-content .wy-menu-vertical li tt {\n border: none;\n background: inherit;\n color: inherit;\n padding-left: 0;\n padding-right: 0;\n}\n.wy-menu-vertical li span.toctree-expand {\n display: block;\n float: left;\n margin-left: -1.2em;\n font-size: 0.8em;\n line-height: 1.6em;\n color: #4d4d4d;\n}\n.wy-menu-vertical li.on a,\n.wy-menu-vertical li.current > a {\n padding: 0.4045em 32px;\n font-weight: bold;\n position: relative;\n /* background: #fcfcfc; */\n border: none;\n padding-left: 1.618em -4px;\n}\n.wy-menu-vertical li.on a:hover,\n.wy-menu-vertical li.current > a:hover {\n /* background: #fcfcfc; */\n}\n.wy-menu-vertical li.on a:hover span.toctree-expand,\n.wy-menu-vertical li.current > a:hover span.toctree-expand {\n /* color: gray; */\n}\n.wy-menu-vertical li.on a span.toctree-expand,\n.wy-menu-vertical li.current > a span.toctree-expand {\n display: block;\n font-size: 0.8em;\n line-height: 1.6em;\n /* color: #333; */\n}\n.wy-menu-vertical li.toctree-l1.current > a {\n /* border-bottom: solid 1px #c9c9c9; */\n /* border-top: solid 1px #c9c9c9; */\n}\n.wy-menu-vertical li.toctree-l2 a,\n.wy-menu-vertical li.toctree-l3 a,\n.wy-menu-vertical li.toctree-l4 a {\n color: rgba(15, 109, 238, 0.87);\n}\n.wy-menu-vertical li.toctree-l1.current li.toctree-l2 > ul,\n.wy-menu-vertical li.toctree-l2.current li.toctree-l3 > ul {\n display: none;\n}\n.wy-menu-vertical li.toctree-l1.current li.toctree-l2.current > ul,\n.wy-menu-vertical li.toctree-l2.current li.toctree-l3.current > ul {\n display: block;\n}\n.wy-menu-vertical li.toctree-l2.current > a {\n /* background: #c9c9c9; */\n padding: 0.4045em 2.427em;\n padding-left: 48px;\n}\n.wy-menu-vertical li.toctree-l2.current li.toctree-l3 > a {\n display: block;\n /* background: #c9c9c9; */\n padding: 0.4045em 4.045em;\n}\n.wy-menu-vertical li.toctree-l2 a:hover span.toctree-expand {\n /* color: gray; */\n}\n.wy-menu-vertical li.toctree-l2 span.toctree-expand {\n /* color: #a3a3a3; */\n}\n.wy-menu-vertical li.toctree-l3 {\n font-size: 0.9em;\n}\n.wy-menu-vertical li.toctree-l3.current > a {\n /* background: #bdbdbd; */\n padding: 0.4045em 32px;\n}\n.wy-menu-vertical li.toctree-l3.current li.toctree-l4 > a {\n display: block;\n background: #bdbdbd;\n padding: 0.4045em 5.663em;\n}\n.wy-menu-vertical li.toctree-l3 a:hover span.toctree-expand {\n color: gray;\n}\n.wy-menu-vertical li.toctree-l3 span.toctree-expand {\n color: #969696;\n}\n.wy-menu-vertical li.toctree-l4 {\n font-size: 0.9em;\n}\n.wy-menu-vertical li.current ul {\n display: block;\n}\n.wy-menu-vertical li ul {\n margin-bottom: 0;\n display: none;\n}\n.wy-menu-vertical li ul li a {\n margin-bottom: 0;\n /* color: #d9d9d9; */\n font-weight: normal;\n}\n.wy-menu-vertical a {\n display: inline-block;\n line-height: 18px;\n padding: 0.4045em 32px;\n display: block;\n position: relative;\n font-size: 14px;\n color: rgba(15, 109, 238, 0.87);\n}\n.wy-menu-vertical a:hover {\n /* background-color: #4e4a4a; */\n cursor: pointer;\n}\n.wy-menu-vertical a:hover span.toctree-expand {\n /* color: #d9d9d9; */\n}\n.wy-menu-vertical a:active {\n /* background-color: #2980b9; */\n cursor: pointer;\n /* color: #fff; */\n}\n.wy-menu-vertical a:active span.toctree-expand {\n /* color: #fff; */\n}\n.wy-side-nav-search {\n display: none;\n width: 100%;\n padding: 0.809em;\n margin-bottom: 0.809em;\n /* z-index: 200; */\n background-color: #2980b9;\n text-align: center;\n padding: 0.809em;\n color: #fcfcfc;\n margin-bottom: 0.809em;\n}\n.wy-side-nav-search input[type=\"text\"] {\n width: 100%;\n border-radius: 50px;\n padding: 6px 12px;\n border-color: #2472a4;\n}\n.wy-side-nav-search img {\n display: block;\n margin: auto auto 0.809em auto;\n height: 45px;\n width: 45px;\n background-color: #2980b9;\n padding: 5px;\n border-radius: 100%;\n}\n.wy-side-nav-search > a,\n.wy-side-nav-search .wy-dropdown > a {\n color: #fcfcfc;\n font-size: 100%;\n font-weight: bold;\n display: inline-block;\n padding: 4px 6px;\n margin-bottom: 0.809em;\n}\n.wy-side-nav-search > a:hover,\n.wy-side-nav-search .wy-dropdown > a:hover {\n background: rgba(255, 255, 255, 0.1);\n}\n.wy-side-nav-search > a img.logo,\n.wy-side-nav-search .wy-dropdown > a img.logo {\n display: block;\n margin: 0 auto;\n height: auto;\n width: auto;\n border-radius: 0;\n max-width: 100%;\n background: transparent;\n}\n.wy-side-nav-search > a.icon img.logo,\n.wy-side-nav-search .wy-dropdown > a.icon img.logo {\n margin-top: 0.85em;\n}\n.wy-side-nav-search > div.version {\n margin-top: -0.4045em;\n margin-bottom: 0.809em;\n font-weight: normal;\n color: rgba(255, 255, 255, 0.3);\n}\n.wy-nav .wy-menu-vertical header {\n color: #2980b9;\n}\n.wy-nav .wy-menu-vertical a {\n color: #b3b3b3;\n}\n.wy-nav .wy-menu-vertical a:hover {\n background-color: #2980b9;\n color: #fff;\n}\n[data-menu-wrap] {\n -webkit-transition: all 0.2s ease-in;\n -moz-transition: all 0.2s ease-in;\n transition: all 0.2s ease-in;\n position: absolute;\n opacity: 1;\n width: 100%;\n opacity: 0;\n}\n[data-menu-wrap].move-center {\n left: 0;\n right: auto;\n opacity: 1;\n}\n[data-menu-wrap].move-left {\n right: auto;\n left: -100%;\n opacity: 0;\n}\n[data-menu-wrap].move-right {\n right: -100%;\n left: auto;\n opacity: 0;\n}\n.wy-body-for-nav {\n background: #fff;\n}\n.wy-grid-for-nav {\n display: flex;\n width: 100%;\n height: 100%;\n min-height: 100vh;\n}\n.wy-nav-side {\n /* padding-bottom: 2em; */\n width: 320px;\n /* overflow-x: hidden;\n overflow-y: hidden; */\n min-height: 100%;\n height: 100vh;\n color: #9b9b9b;\n border-right: 0.5px solid lightgrey;\n background-color: #fafafa;\n z-index: 200;\n visibility: visible;\n /* margin-top: 111px; */\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n\n position: sticky;\n top: 0;\n}\n.wy-side-scroll {\n width: 320px;\n color: #9b9b9b;\n border-right: 0.5px solid lightgrey;\n background-color: #fafafa;\n position: relative;\n overflow-x: hidden;\n overflow-y: scroll;\n height: 100%;\n}\n.wy-nav-top {\n display: none;\n background: #2980b9;\n color: #fff;\n padding: 0.4045em 0.809em;\n position: relative;\n line-height: 50px;\n text-align: center;\n font-size: 100%;\n *zoom: 1;\n}\n.wy-nav-top:before,\n.wy-nav-top:after {\n display: table;\n content: \"\";\n}\n.wy-nav-top:after {\n clear: both;\n}\n.wy-nav-top a {\n color: #fff;\n font-weight: bold;\n}\n.wy-nav-top img {\n margin-right: 12px;\n height: 45px;\n width: 45px;\n background-color: #2980b9;\n padding: 5px;\n border-radius: 100%;\n}\n.wy-nav-top i {\n font-size: 30px;\n float: left;\n cursor: pointer;\n padding-top: inherit;\n}\n.wy-nav-content-wrap {\n width: 100%;\n min-height: 100%;\n margin: 0px 32px;\n}\n.wy-nav-content {\n padding: 64px 0px;\n height: 100%;\n max-width: 1140px;\n margin: auto;\n}\n.wy-body-mask {\n position: fixed;\n width: 100%;\n height: 100%;\n background: rgba(0, 0, 0, 0.2);\n display: none;\n z-index: 499;\n}\n.wy-body-mask.on {\n display: block;\n}\nfooter {\n color: gray;\n}\nfooter p {\n margin-bottom: 12px;\n}\nfooter span.commit code,\nfooter span.commit .rst-content tt,\n.rst-content footer span.commit tt {\n padding: 0px;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n font-size: 1em;\n background: none;\n border: none;\n color: gray;\n}\n.rst-footer-buttons {\n display: flex;\n}\n.rst-footer-buttons:before,\n.rst-footer-buttons:after {\n width: 100%;\n}\n.rst-footer-buttons:before,\n.rst-footer-buttons:after {\n display: none;\n content: \"\";\n}\n.rst-footer-buttons:after {\n clear: both;\n}\n.rst-breadcrumbs-buttons {\n margin-top: 12px;\n *zoom: 1;\n}\n.rst-breadcrumbs-buttons:before,\n.rst-breadcrumbs-buttons:after {\n display: none;\n content: \"\";\n}\n.rst-breadcrumbs-buttons:after {\n clear: both;\n}\n#search-results .search li {\n margin-bottom: 24px;\n border-bottom: solid 1px #e1e4e5;\n padding-bottom: 24px;\n}\n#search-results .search li:first-child {\n border-top: solid 1px #e1e4e5;\n padding-top: 24px;\n}\n#search-results .search li a {\n font-size: 120%;\n margin-bottom: 12px;\n display: inline-block;\n}\n#search-results .context {\n color: gray;\n font-size: 90%;\n}\n.genindextable li > ul {\n margin-left: 24px;\n}\n\n/* GS Developer Action Button */\n\n#gs-developer-action-button-container {\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-tap-highlight-color: transparent;\n user-select: none;\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n z-index: 999;\n justify-content: center;\n align-items: center;\n position: fixed;\n display: flex;\n right: 32px;\n bottom: 32px;\n background: #0f6dee;\n color: white;\n width: 64px;\n height: 64px;\n border-radius: 50%;\n visibility: hidden;\n opacity: 0;\n box-shadow: 0 10px 20px 0 rgba(48, 48, 50, 0.19),\n 0 6px 6px 0 rgba(48, 48, 50, 0.23);\n}\n\n#gs-developer-action-button-container:active {\n transform: scale(0.95);\n background: #0a5cce;\n}\n\n#gs-nav-action {\n margin-top: 4px;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-tap-highlight-color: transparent;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n\n#gs-nav-action:before {\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-tap-highlight-color: transparent;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n content: url(../images/menu-open.svg);\n}\n\n#gs-nav-action.active:before {\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-tap-highlight-color: transparent;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n content: url(../images/menu-close.svg);\n}\n\n@media screen and (max-width: 80rem) {\n .wy-body-for-nav {\n background: #fff;\n }\n\n #gs-developer-action-button-container {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n opacity: 1;\n visibility: visible;\n }\n\n .wy-nav-top {\n display: block;\n }\n .wy-nav-side {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n visibility: hidden;\n max-width: 0px;\n height: 100vh;\n }\n .wy-nav-side.shift {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n visibility: visible;\n max-width: 320px;\n height: 100vh;\n }\n .wy-side-scroll {\n width: 320px;\n }\n .wy-side-nav-search {\n width: auto;\n }\n .wy-menu.wy-menu-vertical {\n width: auto;\n }\n .wy-nav-content-wrap {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n /* margin-left: 0; */\n }\n .wy-nav-content-wrap .wy-nav-content {\n /* padding: 1.618em; */\n }\n .wy-nav-content-wrap.shift {\n /* transform: translate3D(0, 0, 0);\n transition: ease all 0.5s; */\n /* position: fixed; */\n min-width: 100%;\n /* left: 85%; */\n top: 0;\n height: 100%;\n overflow: hidden;\n }\n}\n@media screen and (min-width: 1100px) {\n .wy-nav-content-wrap {\n background: #fff;\n }\n .wy-nav-content {\n margin: 0 auto;\n /* background: #fcfcfc; */\n }\n}\n@media print {\n .rst-versions,\n footer,\n .wy-nav-side {\n display: none;\n }\n .wy-nav-content-wrap {\n margin-left: 0;\n }\n}\n.rst-versions {\n position: fixed;\n bottom: 0;\n left: 0;\n width: 300px;\n color: #fcfcfc;\n background: #1f1d1d;\n font-family: \"Roboto-Regular\", sans-serif;\n z-index: 400;\n}\n.rst-versions a {\n color: #2980b9;\n text-decoration: none;\n}\n.rst-versions .rst-badge-small {\n display: none;\n}\n.rst-versions .rst-current-version {\n padding: 12px;\n background-color: #272525;\n display: block;\n text-align: right;\n font-size: 90%;\n cursor: pointer;\n color: #27ae60;\n *zoom: 1;\n}\n.rst-versions .rst-current-version:before,\n.rst-versions .rst-current-version:after {\n display: table;\n content: \"\";\n}\n.rst-versions .rst-current-version:after {\n clear: both;\n}\n.rst-versions .rst-current-version .fa,\n.rst-versions .rst-current-version .wy-menu-vertical li span.toctree-expand,\n.wy-menu-vertical li .rst-versions .rst-current-version span.toctree-expand,\n.rst-versions .rst-current-version .rst-content .admonition-title,\n.rst-content .rst-versions .rst-current-version .admonition-title,\n.rst-versions .rst-current-version .rst-content h1 .headerlink,\n.rst-content h1 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h2 .headerlink,\n.rst-content h2 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h3 .headerlink,\n.rst-content h3 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h4 .headerlink,\n.rst-content h4 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h5 .headerlink,\n.rst-content h5 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content h6 .headerlink,\n.rst-content h6 .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content dl dt .headerlink,\n.rst-content dl dt .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content p.caption .headerlink,\n.rst-content p.caption .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content table > caption .headerlink,\n.rst-content table > caption .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content .code-block-caption .headerlink,\n.rst-content .code-block-caption .rst-versions .rst-current-version .headerlink,\n.rst-versions .rst-current-version .rst-content tt.download span:first-child,\n.rst-content tt.download .rst-versions .rst-current-version span:first-child,\n.rst-versions .rst-current-version .rst-content code.download span:first-child,\n.rst-content code.download .rst-versions .rst-current-version span:first-child,\n.rst-versions .rst-current-version .icon {\n color: #fcfcfc;\n}\n.rst-versions .rst-current-version .fa-book,\n.rst-versions .rst-current-version .icon-book {\n float: left;\n}\n.rst-versions .rst-current-version .icon-book {\n float: left;\n}\n.rst-versions .rst-current-version.rst-out-of-date {\n background-color: #e74c3c;\n color: #fff;\n}\n.rst-versions .rst-current-version.rst-active-old-version {\n background-color: #f1c40f;\n color: #000;\n}\n.rst-versions.shift-up {\n height: auto;\n max-height: 100%;\n overflow-y: scroll;\n}\n.rst-versions.shift-up .rst-other-versions {\n display: block;\n}\n.rst-versions .rst-other-versions {\n font-size: 90%;\n padding: 12px;\n color: gray;\n display: none;\n}\n.rst-versions .rst-other-versions hr {\n display: block;\n height: 1px;\n border: 0;\n margin: 20px 0;\n padding: 0;\n border-top: solid 1px #413d3d;\n}\n.rst-versions .rst-other-versions dd {\n display: inline-block;\n margin: 0;\n}\n.rst-versions .rst-other-versions dd a {\n display: inline-block;\n padding: 6px;\n color: #fcfcfc;\n}\n.rst-versions.rst-badge {\n width: auto;\n bottom: 20px;\n right: 20px;\n left: auto;\n border: none;\n max-width: 300px;\n max-height: 90%;\n}\n.rst-versions.rst-badge .icon-book {\n float: none;\n}\n.rst-versions.rst-badge .fa-book,\n.rst-versions.rst-badge .icon-book {\n float: none;\n}\n.rst-versions.rst-badge.shift-up .rst-current-version {\n text-align: right;\n}\n.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,\n.rst-versions.rst-badge.shift-up .rst-current-version .icon-book {\n float: left;\n}\n.rst-versions.rst-badge.shift-up .rst-current-version .icon-book {\n float: left;\n}\n.rst-versions.rst-badge .rst-current-version {\n width: auto;\n height: 30px;\n line-height: 30px;\n padding: 0 6px;\n display: block;\n text-align: center;\n}\n@media screen and (max-width: 768px) {\n .rst-versions {\n width: 85%;\n display: none;\n }\n .rst-versions.shift {\n display: block;\n }\n}\n.rst-content img {\n max-width: 100%;\n height: auto;\n}\n.rst-content div.figure {\n margin-bottom: 24px;\n}\n.rst-content div.figure p.caption {\n font-style: italic;\n}\n.rst-content div.figure p:last-child.caption {\n margin-bottom: 0px;\n}\n.rst-content div.figure.align-center {\n text-align: center;\n}\n.rst-content .section > img,\n.rst-content .section > a > img {\n margin-bottom: 24px;\n}\n.rst-content abbr[title] {\n text-decoration: none;\n}\n.rst-content.style-external-links a.reference.external:after {\n content: \"\";\n color: #b3b3b3;\n vertical-align: super;\n font-size: 60%;\n margin: 0 0.2em;\n}\n.rst-content blockquote {\n margin-left: 24px;\n line-height: 24px;\n margin-bottom: 24px;\n}\n.rst-content pre.literal-block {\n white-space: pre;\n margin: 0;\n padding: 12px 12px;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n display: block;\n overflow: auto;\n}\n.rst-content pre.literal-block,\n.rst-content div[class^=\"highlight\"] {\n font-family: \"Roboto Mono\";\n display: flex;\n flex-wrap: wrap;\n margin: 16px 0;\n font-size: 90%;\n line-height: normal;\n background: #e7f2fa;\n color: #2980b9;\n padding: 16px 12px;\n position: relative;\n background: #fafafa;\n border: 1px solid #e6e6e6;\n border-radius: 8px;\n}\n.rst-content pre.literal-block div[class^=\"highlight\"],\n.rst-content div[class^=\"highlight\"] div[class^=\"highlight\"] {\n padding: 0px;\n border: none;\n margin: 0;\n}\n.rst-content div[class^=\"highlight\"] td.code {\n width: 100%;\n}\n.rst-content .linenodiv pre {\n border-right: solid 1px #e6e9ea;\n margin: 0;\n padding: 12px 12px;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n user-select: none;\n pointer-events: none;\n}\n.rst-content div[class^=\"highlight\"] pre {\n white-space: pre;\n margin: 0;\n /* padding: 12px 12px; */\n display: block;\n overflow: auto;\n}\n.rst-content div[class^=\"highlight\"] pre .hll {\n display: block;\n margin: 0 -12px;\n padding: 0 12px;\n}\n.rst-content pre.literal-block,\n.rst-content div[class^=\"highlight\"] pre,\n.rst-content .linenodiv pre {\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n font-size: 14px;\n line-height: 1.5;\n}\n.rst-content .code-block-caption {\n font-style: italic;\n line-height: 1;\n padding: 1em 0;\n text-align: center;\n}\n@media print {\n .rst-content .codeblock,\n .rst-content div[class^=\"highlight\"],\n .rst-content div[class^=\"highlight\"] pre {\n white-space: pre-wrap;\n }\n}\n.rst-content .note .last,\n.rst-content .attention .last,\n.rst-content .caution .last,\n.rst-content .danger .last,\n.rst-content .error .last,\n.rst-content .hint .last,\n.rst-content .important .last,\n.rst-content .tip .last,\n.rst-content .warning .last,\n.rst-content .seealso .last,\n.rst-content .admonition-todo .last,\n.rst-content .admonition .last {\n margin-bottom: 0;\n}\n.rst-content .admonition-title:before {\n margin-right: 4px;\n}\n.rst-content .admonition table {\n border-color: rgba(0, 0, 0, 0.1);\n}\n.rst-content .admonition table td,\n.rst-content .admonition table th {\n background: transparent !important;\n border-color: rgba(0, 0, 0, 0.1) !important;\n}\n.rst-content .section ol.loweralpha,\n.rst-content .section ol.loweralpha li {\n list-style: lower-alpha;\n}\n.rst-content .section ol.upperalpha,\n.rst-content .section ol.upperalpha li {\n list-style: upper-alpha;\n}\n.rst-content .section ol p,\n.rst-content .section ul p {\n margin-bottom: 12px;\n}\n.rst-content .section ol p:last-child,\n.rst-content .section ul p:last-child {\n margin-bottom: 24px;\n}\n.rst-content .line-block {\n margin-left: 0px;\n margin-bottom: 24px;\n line-height: 24px;\n}\n.rst-content .line-block .line-block {\n margin-left: 24px;\n margin-bottom: 0px;\n}\n.rst-content .topic-title {\n font-weight: bold;\n margin-bottom: 12px;\n}\n.rst-content .toc-backref {\n color: #404040;\n}\n.rst-content .align-right {\n float: right;\n margin: 0px 0px 24px 24px;\n}\n.rst-content .align-left {\n float: left;\n margin: 0px 24px 24px 0px;\n}\n.rst-content .align-center {\n margin: auto;\n}\n.rst-content .align-center:not(table) {\n display: block;\n}\n.rst-content h1 .headerlink,\n.rst-content h2 .headerlink,\n.rst-content .toctree-wrapper p.caption .headerlink,\n.rst-content h3 .headerlink,\n.rst-content h4 .headerlink,\n.rst-content h5 .headerlink,\n.rst-content h6 .headerlink,\n.rst-content dl dt .headerlink,\n.rst-content p.caption .headerlink,\n.rst-content table > caption .headerlink,\n.rst-content .code-block-caption .headerlink {\n visibility: hidden;\n font-size: 14px;\n}\n.rst-content h1 .headerlink:after,\n.rst-content h2 .headerlink:after,\n.rst-content .toctree-wrapper p.caption .headerlink:after,\n.rst-content h3 .headerlink:after,\n.rst-content h4 .headerlink:after,\n.rst-content h5 .headerlink:after,\n.rst-content h6 .headerlink:after,\n.rst-content dl dt .headerlink:after,\n.rst-content p.caption .headerlink:after,\n.rst-content table > caption .headerlink:after,\n.rst-content .code-block-caption .headerlink:after {\n content: url(../images/gs-link.svg);\n position: relative;\n top: 2px;\n}\n.rst-content h1:hover .headerlink:after,\n.rst-content h2:hover .headerlink:after,\n.rst-content .toctree-wrapper p.caption:hover .headerlink:after,\n.rst-content h3:hover .headerlink:after,\n.rst-content h4:hover .headerlink:after,\n.rst-content h5:hover .headerlink:after,\n.rst-content h6:hover .headerlink:after,\n.rst-content dl dt:hover .headerlink:after,\n.rst-content p.caption:hover .headerlink:after,\n.rst-content table > caption:hover .headerlink:after,\n.rst-content .code-block-caption:hover .headerlink:after {\n visibility: visible;\n}\n.rst-content table > caption .headerlink:after {\n font-size: 12px;\n}\n.rst-content .centered {\n text-align: center;\n}\n.rst-content .sidebar {\n float: right;\n width: 40%;\n display: block;\n margin: 0 0 24px 24px;\n padding: 24px;\n background: #f3f6f6;\n border: solid 1px #e1e4e5;\n}\n.rst-content .sidebar p,\n.rst-content .sidebar ul,\n.rst-content .sidebar dl {\n font-size: 90%;\n}\n.rst-content .sidebar .last {\n margin-bottom: 0;\n}\n.rst-content .sidebar .sidebar-title {\n display: block;\n font-family: \"Roboto-Regular\", sans-serif;\n font-weight: bold;\n background: #e1e4e5;\n padding: 6px 12px;\n margin: -24px;\n margin-bottom: 24px;\n font-size: 100%;\n}\n.rst-content .highlighted {\n background: #f1c40f;\n display: inline-block;\n font-weight: bold;\n padding: 0 6px;\n}\n.rst-content .footnote-reference,\n.rst-content .citation-reference {\n vertical-align: baseline;\n position: relative;\n top: -0.4em;\n line-height: 0;\n font-size: 90%;\n}\n.rst-content table.docutils.citation,\n.rst-content table.docutils.footnote {\n background: none;\n border: none;\n color: gray;\n}\n.rst-content table.docutils.citation td,\n.rst-content table.docutils.citation tr,\n.rst-content table.docutils.footnote td,\n.rst-content table.docutils.footnote tr {\n border: none;\n background-color: transparent !important;\n white-space: normal;\n}\n.rst-content table.docutils.citation td.label,\n.rst-content table.docutils.footnote td.label {\n padding-left: 0;\n padding-right: 0;\n vertical-align: top;\n}\n.rst-content table.docutils.citation tt,\n.rst-content table.docutils.citation code,\n.rst-content table.docutils.footnote tt,\n.rst-content table.docutils.footnote code {\n color: #555;\n}\n.rst-content .wy-table-responsive.citation,\n.rst-content .wy-table-responsive.footnote {\n margin-bottom: 0;\n}\n.rst-content .wy-table-responsive.citation + :not(.citation),\n.rst-content .wy-table-responsive.footnote + :not(.footnote) {\n margin-top: 24px;\n}\n.rst-content .wy-table-responsive.citation:last-child,\n.rst-content .wy-table-responsive.footnote:last-child {\n margin-bottom: 24px;\n}\n.rst-content table.docutils th {\n border-color: #e1e4e5;\n}\n.rst-content table.docutils td .last,\n.rst-content table.docutils td .last :last-child {\n margin-bottom: 0;\n}\n.rst-content table.field-list {\n border: none;\n}\n.rst-content table.field-list td {\n border: none;\n}\n.rst-content table.field-list td p {\n font-size: inherit;\n line-height: inherit;\n}\n.rst-content table.field-list td > strong {\n display: inline-block;\n}\n.rst-content table.field-list .field-name {\n padding-right: 10px;\n text-align: left;\n white-space: nowrap;\n}\n.rst-content table.field-list .field-body {\n text-align: left;\n}\n.rst-content tt,\n.rst-content tt,\n.rst-content code {\n color: #000;\n font-family: \"Roboto Mono\", SFMono-Regular, Menlo, Monaco, Consolas,\n \"Liberation Mono\", \"Courier New\", Courier, monospace;\n padding: 2px 5px;\n}\n.rst-content tt big,\n.rst-content tt em,\n.rst-content tt big,\n.rst-content code big,\n.rst-content tt em,\n.rst-content code em {\n font-size: 100% !important;\n line-height: normal;\n}\n.rst-content tt.literal,\n.rst-content tt.literal,\n.rst-content code.literal {\n color: 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\n\n{% endif %}\n" }, { "path": "docs/analytics.rst", "content": "Analytics Package\n============\n\nDataGrid\n-----------\n\n.. currentmodule:: gs_quant.analytics.datagrid.datagrid\n\n.. autosummary::\n :toctree: classes\n :template: processors.rst\n\n DataGrid\n\nProcessors\n-------\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autosummary::\n :toctree: classes\n :template: processors.rst\n\n AppendProcessor\n AdditionProcessor\n SubtractionProcessor\n MultiplicationProcessor\n DivisionProcessor\n ChangeProcessor\n CoordinateProcessor\n CorrelationProcessor\n EntityProcessor\n LastProcessor\n PercentilesProcessor\n SharpeRatioProcessor\n VolatilityProcessor\n\nWorkspaces\n-----------\n\n.. currentmodule:: gs_quant.analytics.workspaces\n\n.. autosummary::\n :toctree: classes\n :template: processors.rst\n\n Components\n Workspaces" }, { "path": "docs/classes/gs_quant.analytics.datagrid.datagrid.DataGrid.rst", "content": "DataGrid\n========\n\n.. currentmodule:: gs_quant.analytics.datagrid.datagrid\n\n.. autoclass:: DataGrid\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: add_filter\n \n \n .. automethod:: add_sort\n \n \n .. automethod:: aggregate_queries\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: create\n \n \n .. automethod:: delete\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_id\n \n \n .. automethod:: initialize\n \n \n .. automethod:: open\n \n \n .. automethod:: poll\n \n \n .. automethod:: save\n \n \n .. automethod:: set_filters\n \n \n .. automethod:: set_primary_column_index\n \n \n .. automethod:: set_sorts\n \n \n .. automethod:: to_frame\n \n \n \n\n\n\n \n \n .. rubric:: Properties\n \n \n .. autoattribute:: polling_time\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.AdditionProcessor.rst", "content": "AdditionProcessor\n=================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: AdditionProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.AppendProcessor.rst", "content": "AppendProcessor\n===============\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: AppendProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.ChangeProcessor.rst", "content": "ChangeProcessor\n===============\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: ChangeProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.CoordinateProcessor.rst", "content": "CoordinateProcessor\n===================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: CoordinateProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.CorrelationProcessor.rst", "content": "CorrelationProcessor\n====================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: CorrelationProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_benchmark_coordinate\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.DivisionProcessor.rst", "content": "DivisionProcessor\n=================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: DivisionProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.EntityProcessor.rst", "content": "EntityProcessor\n===============\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: EntityProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.LastProcessor.rst", "content": "LastProcessor\n=============\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: LastProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.MultiplicationProcessor.rst", "content": "MultiplicationProcessor\n=======================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: MultiplicationProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.PercentilesProcessor.rst", "content": "PercentilesProcessor\n====================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: PercentilesProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.ReturnsProcessor.rst", "content": "ReturnsProcessor\n===============\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: ReturnsProcessor\n\n\n\n .. rubric:: Methods\n\n\n .. automethod:: __init__\n\n\n .. automethod:: as_dict\n\n\n .. automethod:: build_graph\n\n\n .. automethod:: calculate\n\n\n .. automethod:: from_dict\n\n\n .. automethod:: get_plot_expression\n\n\n .. automethod:: process\n\n\n .. automethod:: update\n\n\n\n\n\n\n\n\n" }, { "path": "docs/classes/gs_quant.analytics.processors.SharpeRatioProcessor.rst", "content": "SharpeRatioProcessor\n====================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: SharpeRatioProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_excess_returns_query\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.SubtractionProcessor.rst", "content": "SubtractionProcessor\n====================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: SubtractionProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.processors.VolatilityProcessor.rst", "content": "VolatilityProcessor\n===================\n\n.. currentmodule:: gs_quant.analytics.processors\n\n.. autoclass:: VolatilityProcessor\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: build_graph\n \n \n .. automethod:: calculate\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_default_params\n \n \n .. automethod:: get_plot_expression\n \n \n .. automethod:: post_process\n \n \n .. automethod:: process\n \n \n .. automethod:: update\n \n \n \n\n\n\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.workspaces.Component.rst", "content": "Component\n=========\n\n.. currentmodule:: gs_quant.analytics.workspaces\n\n.. autoclass:: Component\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: from_dict\n \n \n \n\n\n\n \n \n .. rubric:: Properties\n \n \n .. autoattribute:: container_ids\n \n \n .. autoattribute:: height\n \n \n .. autoattribute:: id_\n \n \n .. autoattribute:: selections\n \n \n .. autoattribute:: width\n \n \n " }, { "path": "docs/classes/gs_quant.analytics.workspaces.Workspace.rst", "content": "Workspace\n=========\n\n.. currentmodule:: gs_quant.analytics.workspaces\n\n.. autoclass:: Workspace\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: as_dict\n \n \n .. automethod:: create\n \n \n .. automethod:: delete\n \n \n .. automethod:: delete_all\n \n \n .. automethod:: from_dict\n \n \n .. automethod:: get_by_alias\n \n \n .. automethod:: get_by_id\n \n \n .. automethod:: open\n \n \n .. automethod:: save\n \n \n \n\n\n\n \n \n .. rubric:: Properties\n \n \n .. autoattribute:: PERSISTED_COMPONENTS\n \n \n .. autoattribute:: alias\n \n \n .. autoattribute:: call_to_action\n \n \n .. autoattribute:: description\n \n \n .. autoattribute:: disclaimer\n \n \n .. autoattribute:: entitlements\n \n \n .. autoattribute:: id\n \n \n .. autoattribute:: maintainers\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: rows\n \n \n .. autoattribute:: selector_components\n \n \n .. autoattribute:: tabs\n \n \n .. autoattribute:: tags\n \n \n " }, { "path": "docs/classes/gs_quant.base.Priceable.rst", "content": "gs\\_quant.base.Priceable\n========================\n\n.. currentmodule:: gs_quant.base\n\n.. autoclass:: Priceable\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Priceable.__init__\n ~Priceable.as_dict\n ~Priceable.calc\n ~Priceable.clone\n ~Priceable.default_instance\n ~Priceable.dollar_price\n ~Priceable.from_dict\n ~Priceable.from_instance\n ~Priceable.from_json\n ~Priceable.price\n ~Priceable.properties\n ~Priceable.properties_init\n ~Priceable.resolve\n ~Priceable.schema\n ~Priceable.to_dict\n ~Priceable.to_json\n \n \n\n \n \n " }, { "path": "docs/classes/gs_quant.data.DataContext.rst", "content": "DataContext\n==========================\n\n.. currentmodule:: gs_quant.data\n\n.. autoclass:: DataContext\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~DataContext.__init__\n ~DataContext.default_value\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~DataContext.end_date\n ~DataContext.end_time\n ~DataContext.is_entered\n ~DataContext.start_date\n ~DataContext.start_time\n \n " }, { "path": "docs/classes/gs_quant.data.Dataset.rst", "content": "Dataset\n======================\n\n.. currentmodule:: gs_quant.data\n\n.. autoclass:: Dataset\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Dataset.__init__\n ~Dataset.get_coverage\n ~Dataset.get_data\n ~Dataset.get_data_last\n ~Dataset.get_data_series\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Dataset.id\n ~Dataset.name\n ~Dataset.provider\n \n " }, { "path": "docs/classes/gs_quant.data.Fields.rst", "content": "Fields\n=====================\n\n.. currentmodule:: gs_quant.data\n\n.. autoclass:: Fields\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Fields.ADJUSTED_ASK_PRICE\n ~Fields.ADJUSTED_BID_PRICE\n ~Fields.ADJUSTED_HIGH_PRICE\n ~Fields.ADJUSTED_LOW_PRICE\n ~Fields.ADJUSTED_TRADE_PRICE\n ~Fields.ADJUSTED_VOLUME\n ~Fields.ASK_PRICE\n ~Fields.ASSET_ID\n ~Fields.BID_PRICE\n ~Fields.CLOSE_PRICE\n ~Fields.EXPIRATION_DATE\n ~Fields.HIGH_PRICE\n ~Fields.IMPLIED_VOLATILITY\n ~Fields.LOW_PRICE\n ~Fields.MID_PRICE\n ~Fields.NAME\n ~Fields.OPEN_PRICE\n ~Fields.RELATIVE_STRIKE\n ~Fields.RIC\n ~Fields.SPOT_PRICE\n ~Fields.STRIKE_REFERENCE\n ~Fields.TENOR\n ~Fields.TRADE_PRICE\n ~Fields.UPDATE_TIME\n ~Fields.VAR_SWAP\n ~Fields.VOLUME\n \n " }, { "path": "docs/classes/gs_quant.data.core.DataContext.rst", "content": "gs\\_quant.data.core.DataContext\n===============================\n\n.. currentmodule:: gs_quant.data.core\n\n.. autoclass:: DataContext\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~DataContext.__init__\n ~DataContext.default_value\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~DataContext.end_date\n ~DataContext.end_time\n ~DataContext.interval\n ~DataContext.is_entered\n ~DataContext.start_date\n ~DataContext.start_time\n \n " }, { "path": "docs/classes/gs_quant.data.dataset.Dataset.rst", "content": "gs\\_quant.data.dataset.Dataset\n==============================\n\n.. currentmodule:: gs_quant.data.dataset\n\n.. autoclass:: Dataset\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Dataset.__init__\n ~Dataset.delete\n ~Dataset.delete_data\n ~Dataset.get_coverage\n ~Dataset.get_coverage_async\n ~Dataset.get_data\n ~Dataset.get_data_async\n ~Dataset.get_data_bulk\n ~Dataset.get_data_last\n ~Dataset.get_data_series\n ~Dataset.get_data_series_async\n ~Dataset.undelete\n ~Dataset.upload_data\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Dataset.id\n ~Dataset.name\n ~Dataset.provider\n \n " }, { "path": "docs/classes/gs_quant.data.fields.Fields.rst", "content": "gs\\_quant.data.fields.Fields\n============================\n\n.. currentmodule:: gs_quant.data.fields\n\n.. autoclass:: Fields\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Fields.__init__\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Fields.unit\n ~Fields.ASK_PRICE\n ~Fields.BID_PRICE\n ~Fields.HIGH_PRICE\n ~Fields.MID_PRICE\n ~Fields.LOW_PRICE\n ~Fields.OPEN_PRICE\n ~Fields.CLOSE_PRICE\n ~Fields.TRADE_PRICE\n ~Fields.SPOT_PRICE\n ~Fields.VOLUME\n ~Fields.ADJUSTED_ASK_PRICE\n ~Fields.ADJUSTED_BID_PRICE\n ~Fields.ADJUSTED_HIGH_PRICE\n ~Fields.ADJUSTED_LOW_PRICE\n ~Fields.ADJUSTED_OPEN_PRICE\n ~Fields.ADJUSTED_CLOSE_PRICE\n ~Fields.ADJUSTED_TRADE_PRICE\n ~Fields.ADJUSTED_VOLUME\n ~Fields.IMPLIED_VOLATILITY\n ~Fields.VAR_SWAP\n ~Fields.PRICE\n ~Fields.NAV_PRICE\n ~Fields.SPREAD\n ~Fields.NAV_SPREAD\n ~Fields.IMPLIED_VOLATILITY_BY_DELTA_STRIKE\n ~Fields.FORWARD_POINT\n ~Fields.DIVIDEND_YIELD\n ~Fields.EARNINGS_PER_SHARE\n ~Fields.EARNINGS_PER_SHARE_POSITIVE\n ~Fields.NET_DEBT_TO_EBITDA\n ~Fields.PRICE_TO_BOOK\n ~Fields.PRICE_TO_CASH\n ~Fields.PRICE_TO_EARNINGS\n ~Fields.PRICE_TO_EARNINGS_POSITIVE\n ~Fields.PRICE_TO_EARNINGS_POSITIVE_EXCLUSIVE\n ~Fields.PRICE_TO_SALES\n ~Fields.RETURN_ON_EQUITY\n ~Fields.SALES_PER_SHARE\n ~Fields.CURRENT_CONSTITUENTS_DIVIDEND_YIELD\n ~Fields.CURRENT_CONSTITUENTS_EARNINGS_PER_SHARE\n ~Fields.CURRENT_CONSTITUENTS_EARNINGS_PER_SHARE_POSITIVE\n ~Fields.CURRENT_CONSTITUENTS_NET_DEBT_TO_EBITDA\n ~Fields.CURRENT_CONSTITUENTS_PRICE_TO_BOOK\n ~Fields.CURRENT_CONSTITUENTS_PRICE_TO_CASH\n ~Fields.CURRENT_CONSTITUENTS_PRICE_TO_EARNINGS\n ~Fields.CURRENT_CONSTITUENTS_PRICE_TO_EARNINGS_POSITIVE\n ~Fields.CURRENT_CONSTITUENTS_PRICE_TO_SALES\n ~Fields.CURRENT_CONSTITUENTS_RETURN_ON_EQUITY\n ~Fields.CURRENT_CONSTITUENTS_SALES_PER_SHARE\n ~Fields.ONE_YEAR\n ~Fields.TWO_YEARS\n ~Fields.THREE_YEARS\n ~Fields.FORWARD\n ~Fields.TRAILING\n ~Fields.ASSET_ID\n ~Fields.NAME\n ~Fields.RIC\n ~Fields.TENOR\n ~Fields.STRIKE_REFERENCE\n ~Fields.RELATIVE_STRIKE\n ~Fields.EXPIRATION_DATE\n ~Fields.UPDATE_TIME\n \n " }, { "path": "docs/classes/gs_quant.datetime.relative_date.RelativeDate.rst", "content": "gs\\_quant.datetime.relative\\_date.RelativeDate\n==============================================\n\n.. currentmodule:: gs_quant.datetime.relative_date\n\n.. autoclass:: RelativeDate\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~RelativeDate.apply_rule\n \n ~RelativeDate.as_dict\n \n " }, { "path": "docs/classes/gs_quant.instrument.CommodOTCSwap.rst", "content": "CommodOTCSwap\n=============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: CommodOTCSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: end\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: number_of_periods\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: quantity_period\n \n \n .. autoattribute:: quantity_unit\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement\n \n \n .. autoattribute:: start\n \n \n .. autoattribute:: strategy\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.EqCliquet.rst", "content": "EqCliquet\n=========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqCliquet\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: currency\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: first_valuation_date\n \n \n .. autoattribute:: global_cap\n \n \n .. autoattribute:: global_floor\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: last_valuation_date\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: payment_frequency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: return_style\n \n \n .. autoattribute:: return_type\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: valuation_period\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.EqForward.rst", "content": "EqForward\n=========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqForward\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: forward_price\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: number_of_shares\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.EqOption.rst", "content": "EqOption\n========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqOption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: exchange\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: future_contract\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: multiplier\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: number_of_options\n \n \n .. autoattribute:: option_style\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: trade_as\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: valuation_time\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.EqSynthetic.rst", "content": "EqSynthetic\n===========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqSynthetic\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: commission\n \n \n .. autoattribute:: commission_type\n \n \n .. autoattribute:: commission_units\n \n \n .. autoattribute:: currency\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: designated_maturity\n \n \n .. autoattribute:: dividend_pay_ratio\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: eq_leg\n \n \n .. autoattribute:: expiry\n \n \n .. autoattribute:: funding_leg\n \n \n .. autoattribute:: fx_data_source\n \n \n .. autoattribute:: initial_valuation_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: num_of_underlyers\n \n \n .. autoattribute:: oet_terms\n \n \n .. autoattribute:: payment_bdc\n \n \n .. autoattribute:: payment_delay\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: rate_option\n \n \n .. autoattribute:: rate_tenor\n \n \n .. autoattribute:: reset_delay\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: schedule_type\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_delay\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: swap_type\n \n \n .. autoattribute:: trade_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: valuation_bdc\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.EqVarianceSwap.rst", "content": "EqVarianceSwap\n==============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: EqVarianceSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: days_in_contract\n \n \n .. autoattribute:: denominated\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiry_settlement_days\n \n \n .. autoattribute:: fixing_schedule_dates\n \n \n .. autoattribute:: force_forward_tradable\n \n \n .. autoattribute:: holiday_calendar\n \n \n .. autoattribute:: initial_date\n \n \n .. autoattribute:: initial_spot\n \n \n .. autoattribute:: initial_spot_source_flag\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: underlier\n \n \n .. autoattribute:: underlier_type\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: valuation_time\n \n \n .. autoattribute:: variance_cap\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXAccumulator.rst", "content": "FXAccumulator\n=============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXAccumulator\n\n \n .. rubric:: Properties\n \n \n \n .. autoattribute:: accum_or_decum\n \n \n .. autoattribute:: accumulator_type\n \n \n \n .. autoattribute:: coupon_frequency\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: european_knock_in\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: first_fixing_date\n \n \n .. autoattribute:: fixing_rate_option\n \n \n .. autoattribute:: guaranteed_coupons\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knock_out_level\n \n \n .. autoattribute:: leverage_ratio\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: new_or_unwind\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: number_of_expiry\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: schedules\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXAccumulatorScheduleLeg.rst", "content": "FXAccumulatorScheduleLeg\n========================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXAccumulatorScheduleLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: european_knock_in\n \n \n .. autoattribute:: fixing_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knock_out_level\n \n \n .. autoattribute:: leverage_ratio\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXBinary.rst", "content": "FXBinary\n========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXBinary\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXCorrelationSwap.rst", "content": "FXCorrelationSwap\n=================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXCorrelationSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: calculate_mean_return\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: first_fixing_date\n \n \n .. autoattribute:: fixing_frequency\n \n \n .. autoattribute:: fixing_source\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: last_fixing_date\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_corr\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXCorrelationSwapLeg.rst", "content": "FXCorrelationSwapLeg\n====================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXCorrelationSwapLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXDoubleKnockout.rst", "content": "FXDoubleKnockout\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXDoubleKnockout\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knock_in_or_out\n \n \n .. autoattribute:: knockout_convention\n \n \n .. autoattribute:: lower_barrier_level\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: upper_barrier_level\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXDoubleOneTouch.rst", "content": "FXDoubleOneTouch\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXDoubleOneTouch\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knockout_convention\n \n \n .. autoattribute:: lower_barrier_level\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: payout_type\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: touch_or_no_touch\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: upper_barrier_level\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXDualDoubleKnockout.rst", "content": "FXDualDoubleKnockout\n====================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXDualDoubleKnockout\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXDualDoubleKnockoutLeg.rst", "content": "FXDualDoubleKnockoutLeg\n=======================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXDualDoubleKnockoutLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knockout_convention\n \n \n .. autoattribute:: lower_barrier_level\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: upper_barrier_level\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXEuropeanKnockout.rst", "content": "FXEuropeanKnockout\n==================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXEuropeanKnockout\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: barrier_level\n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knock_in_or_out\n \n \n .. autoattribute:: knock_up_or_down\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXForward.rst", "content": "FXForward\n=========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXForward\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: forward_rate\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_amount_in_other_currency\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXKnockout.rst", "content": "FXKnockout\n==========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXKnockout\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: barrier_level\n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knock_in_or_out\n \n \n .. autoattribute:: knock_up_or_down\n \n \n .. autoattribute:: knockout_convention\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXMultiCrossBinary.rst", "content": "FXMultiCrossBinary\n==================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXMultiCrossBinary\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXMultiCrossBinaryLeg.rst", "content": "FXMultiCrossBinaryLeg\n=====================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXMultiCrossBinaryLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: fixing_source\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXMultiCrossDoubleBinary.rst", "content": "FXMultiCrossDoubleBinary\n========================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXMultiCrossDoubleBinary\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXMultiCrossDoubleBinaryLeg.rst", "content": "FXMultiCrossDoubleBinaryLeg\n===========================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXMultiCrossDoubleBinaryLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: fixing_source\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: lower_barrier_level\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: upper_barrier_level\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXMultiCrossDoubleOneTouch.rst", "content": "FXMultiCrossDoubleOneTouch\n==========================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXMultiCrossDoubleOneTouch\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: payout_type\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: touch_or_no_touch\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXMultiCrossDoubleOneTouchLeg.rst", "content": "FXMultiCrossDoubleOneTouchLeg\n=============================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXMultiCrossDoubleOneTouchLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knockout_convention\n \n \n .. autoattribute:: lower_barrier_level\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: upper_barrier_level\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXOneTouch.rst", "content": "FXOneTouch\n==========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXOneTouch\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knock_up_or_down\n \n \n .. autoattribute:: knockout_convention\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: payout_type\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: touch_or_no_touch\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXOption.rst", "content": "FXOption\n========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXOption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: exercise_style\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_amount_in_other_currency\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXOptionLeg.rst", "content": "FXOptionLeg\n===========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXOptionLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: exercise_style\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_amount_in_other_currency\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXOptionStrategy.rst", "content": "FXOptionStrategy\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXOptionStrategy\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: exercise_style\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_amount_in_other_currency\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strategy_name\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXPivot.rst", "content": "FXPivot\n=======\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXPivot\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: coupon_frequency\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: first_fixing_date\n \n \n .. autoattribute:: fixing_rate_option\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: lower_knock_in\n \n \n .. autoattribute:: lower_leverage_ratio\n \n \n .. autoattribute:: lower_strike\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: new_or_unwind\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: number_of_expiry\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: payment_on_hitting_target\n \n \n .. autoattribute:: pivot\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: schedules\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: target\n \n \n .. autoattribute:: target_adj_notional_or_strike\n \n \n .. autoattribute:: target_type\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: upper_knock_in\n \n \n .. autoattribute:: upper_leverage_ratio\n \n \n .. autoattribute:: upper_strike\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXPivotScheduleLeg.rst", "content": "FXPivotScheduleLeg\n==================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXPivotScheduleLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: fixing_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: lower_knock_in\n \n \n .. autoattribute:: lower_leverage_ratio\n \n \n .. autoattribute:: lower_strike\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: payment_date\n \n \n .. autoattribute:: pivot\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: upper_knock_in\n \n \n .. autoattribute:: upper_leverage_ratio\n \n \n .. autoattribute:: upper_strike\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXShiftingBermForward.rst", "content": "FXShiftingBermForward\n=====================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXShiftingBermForward\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: exercise_decision_freq\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_amount_in_other_currency\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: window_start_date\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXTarf.rst", "content": "FXTarf\n======\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXTarf\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: coupon_frequency\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: european_knock_in\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: first_fixing_date\n \n \n .. autoattribute:: fixing_rate_option\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: leverage_ratio\n \n \n .. autoattribute:: long_or_short\n \n \n .. autoattribute:: loss_strike\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: new_or_unwind\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: number_of_expiry\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: payment_on_hitting_target\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: profit_strike\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: schedules\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: target\n \n \n .. autoattribute:: target_adj_notional_or_strike\n \n \n .. autoattribute:: target_type\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXTarfScheduleLeg.rst", "content": "FXTarfScheduleLeg\n=================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXTarfScheduleLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: european_knock_in\n \n \n .. autoattribute:: fixing_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: leverage_ratio\n \n \n .. autoattribute:: loss_strike\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: payment_date\n \n \n .. autoattribute:: profit_strike\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXVarianceSwap.rst", "content": "FXVarianceSwap\n==============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXVarianceSwap\n\n \n .. rubric:: Properties\n \n \n \n .. autoattribute:: annualization_factor\n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: calculate_mean_return\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: first_fixing_date\n \n \n .. autoattribute:: fixing_frequency\n \n \n .. autoattribute:: fixing_source\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: last_fixing_date\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_vol\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXVolatilitySwap.rst", "content": "FXVolatilitySwap\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXVolatilitySwap\n\n \n .. rubric:: Properties\n \n \n \n .. autoattribute:: annualization_factor\n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: calculate_mean_return\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: first_fixing_date\n \n \n .. autoattribute:: fixing_frequency\n \n \n .. autoattribute:: fixing_source\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: last_fixing_date\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: strike_vol\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXWorstOf.rst", "content": "FXWorstOf\n=========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXWorstOf\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: best_or_worst\n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXWorstOfKO.rst", "content": "FXWorstOfKO\n===========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXWorstOfKO\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: best_or_worst\n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: legs\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: method_of_settlement\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_currency\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_currency\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.FXWorstOfKOLeg.rst", "content": "FXWorstOfKOLeg\n==============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: FXWorstOfKOLeg\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: barrier_level\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: expiration_time\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: knock_in_or_out\n \n \n .. autoattribute:: knock_up_or_down\n \n \n .. autoattribute:: knockout_convention\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: option_type\n \n \n .. autoattribute:: pair\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement_date\n \n \n .. autoattribute:: settlement_rate_option\n \n \n .. autoattribute:: strike_price\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.Forward.rst", "content": "Forward\n=======\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: Forward\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: currency\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRBasisSwap.rst", "content": "IRBasisSwap\n===========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRBasisSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: clearing_house\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: payer_business_day_convention\n \n \n .. autoattribute:: payer_day_count_fraction\n \n \n .. autoattribute:: payer_designated_maturity\n \n \n .. autoattribute:: payer_frequency\n \n \n .. autoattribute:: payer_rate_option\n \n \n .. autoattribute:: payer_spread\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: receiver_business_day_convention\n \n \n .. autoattribute:: receiver_day_count_fraction\n \n \n .. autoattribute:: receiver_designated_maturity\n \n \n .. autoattribute:: receiver_frequency\n \n \n .. autoattribute:: receiver_rate_option\n \n \n .. autoattribute:: receiver_spread\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRCMSOption.rst", "content": "IRCMSOption\n===========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCMSOption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: cap_floor\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: index\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: multiplier\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: rate_option\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: start_date\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRCMSOptionStrip.rst", "content": "IRCMSOptionStrip\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCMSOptionStrip\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: cap_floor\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: index\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: multiplier\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: reset_delay\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRCMSSpreadOption.rst", "content": "IRCMSSpreadOption\n=================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCMSSpreadOption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: cap_floor\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: index1_tenor\n \n \n .. autoattribute:: index2_tenor\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRCMSSpreadOptionStrip.rst", "content": "IRCMSSpreadOptionStrip\n======================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCMSSpreadOptionStrip\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: cap_floor\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: index1\n \n \n .. autoattribute:: index2\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: reset_delay\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRCap.rst", "content": "IRCap\n=====\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRCap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: cap_rate\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRFloor.rst", "content": "IRFloor\n=======\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRFloor\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: floor_rate\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRSwap.rst", "content": "IRSwap\n======\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: clearing_house\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: fixed_first_stub\n \n \n .. autoattribute:: fixed_holidays\n \n \n .. autoattribute:: fixed_last_stub\n \n \n .. autoattribute:: fixed_rate\n \n \n .. autoattribute:: fixed_rate_accrual_convention\n \n \n .. autoattribute:: fixed_rate_business_day_convention\n \n \n .. autoattribute:: fixed_rate_day_count_fraction\n \n \n .. autoattribute:: fixed_rate_frequency\n \n \n .. autoattribute:: floating_first_stub\n \n \n .. autoattribute:: floating_holidays\n \n \n .. autoattribute:: floating_last_stub\n \n \n .. autoattribute:: floating_rate_accrual_convention\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_for_the_initial_calculation_period\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: floating_rate_spread\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pay_or_receive\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: roll_convention\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRSwaption.rst", "content": "IRSwaption\n==========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRSwaption\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: buy_sell\n \n \n .. autoattribute:: clearing_house\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: expiration_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: fixed_rate_business_day_convention\n \n \n .. autoattribute:: fixed_rate_day_count_fraction\n \n \n .. autoattribute:: fixed_rate_frequency\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: floating_rate_spread\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pay_or_receive\n \n \n .. autoattribute:: premium\n \n \n .. autoattribute:: premium_payment_date\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: settlement\n \n \n .. autoattribute:: strike\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRXccySwap.rst", "content": "IRXccySwap\n==========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRXccySwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: initial_fx_rate\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_reset_side\n \n \n .. autoattribute:: payer_business_day_convention\n \n \n .. autoattribute:: payer_currency\n \n \n .. autoattribute:: payer_day_count_fraction\n \n \n .. autoattribute:: payer_designated_maturity\n \n \n .. autoattribute:: payer_first_stub\n \n \n .. autoattribute:: payer_frequency\n \n \n .. autoattribute:: payer_holidays\n \n \n .. autoattribute:: payer_last_stub\n \n \n .. autoattribute:: payer_rate_option\n \n \n .. autoattribute:: payer_spread\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: receiver_business_day_convention\n \n \n .. autoattribute:: receiver_currency\n \n \n .. autoattribute:: receiver_day_count_fraction\n \n \n .. autoattribute:: receiver_designated_maturity\n \n \n .. autoattribute:: receiver_first_stub\n \n \n .. autoattribute:: receiver_frequency\n \n \n .. autoattribute:: receiver_holidays\n \n \n .. autoattribute:: receiver_last_stub\n \n \n .. autoattribute:: receiver_rate_option\n \n \n .. autoattribute:: receiver_spread\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRXccySwapFixFix.rst", "content": "IRXccySwapFixFix\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRXccySwapFixFix\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: payer_business_day_convention\n \n \n .. autoattribute:: payer_currency\n \n \n .. autoattribute:: payer_day_count_fraction\n \n \n .. autoattribute:: payer_frequency\n \n \n .. autoattribute:: payer_rate\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: receiver_business_day_convention\n \n \n .. autoattribute:: receiver_currency\n \n \n .. autoattribute:: receiver_day_count_fraction\n \n \n .. autoattribute:: receiver_frequency\n \n \n .. autoattribute:: receiver_notional_amount\n \n \n .. autoattribute:: receiver_rate\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.IRXccySwapFixFlt.rst", "content": "IRXccySwapFixFlt\n================\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: IRXccySwapFixFlt\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fee_currency\n \n \n .. autoattribute:: fee_payment_date\n \n \n .. autoattribute:: fixed_first_stub\n \n \n .. autoattribute:: fixed_holidays\n \n \n .. autoattribute:: fixed_last_stub\n \n \n .. autoattribute:: fixed_rate\n \n \n .. autoattribute:: fixed_rate_business_day_convention\n \n \n .. autoattribute:: fixed_rate_currency\n \n \n .. autoattribute:: fixed_rate_day_count_fraction\n \n \n .. autoattribute:: fixed_rate_frequency\n \n \n .. autoattribute:: floating_first_stub\n \n \n .. autoattribute:: floating_holidays\n \n \n .. autoattribute:: floating_last_stub\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_currency\n \n \n .. autoattribute:: floating_rate_day_count_fraction\n \n \n .. autoattribute:: floating_rate_designated_maturity\n \n \n .. autoattribute:: floating_rate_for_the_initial_calculation_period\n \n \n .. autoattribute:: floating_rate_frequency\n \n \n .. autoattribute:: floating_rate_option\n \n \n .. autoattribute:: floating_rate_spread\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: pay_or_receive\n \n \n .. autoattribute:: principal_exchange\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.InflationSwap.rst", "content": "InflationSwap\n=============\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: InflationSwap\n\n \n .. rubric:: Properties\n \n \n \n \n .. autoattribute:: base_cpi\n \n \n .. autoattribute:: clearing_house\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: effective_date\n \n \n .. autoattribute:: fee\n \n \n .. autoattribute:: fixed_rate\n \n \n .. autoattribute:: fixed_rate_business_day_convention\n \n \n .. autoattribute:: floating_rate_business_day_convention\n \n \n .. autoattribute:: index\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: notional_amount\n \n \n .. autoattribute:: notional_currency\n \n \n .. autoattribute:: pay_or_receive\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: termination_date\n \n \n .. autoattribute:: type_\n \n \n .. autoattribute:: unresolved\n \n \n " }, { "path": "docs/classes/gs_quant.instrument.Security.rst", "content": "Security\n========\n\nFor methods of this class, see :doc:`gs_quant.base.Priceable`\n\n.. currentmodule:: gs_quant.instrument\n\n.. autoclass:: Security\n\n \n .. rubric:: Properties\n \n \n \n .. autoattribute:: bbgid\n \n \n .. autoattribute:: bbid\n \n \n .. autoattribute:: bbid_equivalent\n \n \n .. autoattribute:: bcid\n \n \n .. autoattribute:: cid\n \n \n .. autoattribute:: cins\n \n \n .. autoattribute:: cm_id\n \n \n .. autoattribute:: coin_metrics_id\n \n \n .. autoattribute:: cross\n \n \n .. autoattribute:: cusip\n \n \n .. autoattribute:: dataclass_json_config\n \n \n .. autoattribute:: delisted\n \n \n .. autoattribute:: display_id\n \n \n .. autoattribute:: dollar_cross\n \n \n .. autoattribute:: eid\n \n \n .. autoattribute:: em_id\n \n \n .. autoattribute:: exchange_code\n \n \n .. autoattribute:: gsid\n \n \n .. autoattribute:: gsid_equivalent\n \n \n .. autoattribute:: gsideid\n \n \n .. autoattribute:: gsn\n \n \n .. autoattribute:: gss\n \n \n .. autoattribute:: instrument_quantity\n \n \n .. autoattribute:: isin\n \n \n .. autoattribute:: jsn\n \n \n .. autoattribute:: lms_id\n \n \n .. autoattribute:: mdapi\n \n \n .. autoattribute:: mdapi_class\n \n \n .. autoattribute:: metadata\n \n \n .. autoattribute:: mic\n \n \n .. autoattribute:: mq_symbol\n \n \n .. autoattribute:: name\n \n \n .. autoattribute:: pl_id\n \n \n .. autoattribute:: plot_id\n \n \n .. autoattribute:: pnode_id\n \n \n .. autoattribute:: primary_country_ric\n \n \n .. autoattribute:: prime_id\n \n \n .. autoattribute:: provider\n \n \n .. autoattribute:: ps_id\n \n \n .. autoattribute:: quantity_\n \n \n .. autoattribute:: rcic\n \n \n .. autoattribute:: resolution_key\n \n \n .. autoattribute:: ric\n \n \n .. autoattribute:: sec_master_id\n \n \n .. autoattribute:: sec_name\n \n \n .. autoattribute:: sedol\n \n \n .. autoattribute:: sf_id\n \n \n .. autoattribute:: simon_id\n \n \n .. autoattribute:: tdapi\n \n \n .. autoattribute:: ticker\n \n \n .. autoattribute:: tsdb_shortname\n \n \n .. autoattribute:: unresolved\n \n \n .. autoattribute:: valoren\n \n \n .. autoattribute:: wi_id\n \n \n .. autoattribute:: wpk\n \n \n " }, { "path": "docs/classes/gs_quant.markets.baskets.Basket.rst", "content": "gs\\_quant.markets.baskets.Basket\n================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. autoclass:: Basket\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Basket.__init__\n ~Basket.add_factor_risk_report\n ~Basket.cancel_rebalance\n ~Basket.clone\n ~Basket.create\n ~Basket.delete_factor_risk_report\n ~Basket.entity_type\n ~Basket.get\n ~Basket.get_all_esg_data\n ~Basket.get_all_thematic_exposures\n ~Basket.get_asset_measures\n ~Basket.get_bottom_five_thematic_exposures\n ~Basket.get_carbon_analytics\n ~Basket.get_carbon_attribution_table\n ~Basket.get_carbon_coverage\n ~Basket.get_carbon_emissions\n ~Basket.get_carbon_emissions_allocation\n ~Basket.get_carbon_sbti_netzero_coverage\n ~Basket.get_close_price_for_date\n ~Basket.get_close_prices\n ~Basket.get_corporate_actions\n ~Basket.get_data_coordinate\n ~Basket.get_data_series\n ~Basket.get_details\n ~Basket.get_entitlements\n ~Basket.get_entity\n ~Basket.get_esg_bottom_ten\n ~Basket.get_esg_by_region\n ~Basket.get_esg_by_sector\n ~Basket.get_esg_quintiles\n ~Basket.get_esg_summary\n ~Basket.get_esg_top_ten\n ~Basket.get_factor_risk_report\n ~Basket.get_factor_risk_reports\n ~Basket.get_factor_scenario_analytics\n ~Basket.get_fundamentals\n ~Basket.get_hloc_prices\n ~Basket.get_identifier\n ~Basket.get_identifiers\n ~Basket.get_last_positions_data\n ~Basket.get_latest_close_price\n ~Basket.get_latest_position_set\n ~Basket.get_latest_rebalance_data\n ~Basket.get_latest_rebalance_date\n ~Basket.get_live_date\n ~Basket.get_marquee_id\n ~Basket.get_position_dates\n ~Basket.get_position_set_for_date\n ~Basket.get_position_sets\n ~Basket.get_positions_data\n ~Basket.get_rebalance_approval_status\n ~Basket.get_reports\n ~Basket.get_status_of_reports\n ~Basket.get_thematic_beta\n ~Basket.get_thematic_breakdown\n ~Basket.get_thematic_exposure\n ~Basket.get_thematic_report\n ~Basket.get_top_five_thematic_exposures\n ~Basket.get_type\n ~Basket.get_unique_entity_key\n ~Basket.get_url\n ~Basket.poll_report\n ~Basket.poll_status\n ~Basket.update\n ~Basket.update_positions\n ~Basket.upload_position_history\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Basket.allow_ca_restricted_assets\n ~Basket.allow_limited_access_assets\n ~Basket.asset_class\n ~Basket.backtest_parameters\n ~Basket.benchmark\n ~Basket.bloomberg_publish_parameters\n ~Basket.cash_reinvestment_treatment\n ~Basket.clone_parent_id\n ~Basket.currency\n ~Basket.data_dimension\n ~Basket.default_backcast\n ~Basket.description\n ~Basket.divisor\n ~Basket.entitlements\n ~Basket.flagship\n ~Basket.hedge_id\n ~Basket.historical_methodology\n ~Basket.id\n ~Basket.include_price_history\n ~Basket.initial_price\n ~Basket.name\n ~Basket.parent_basket\n ~Basket.position_set\n ~Basket.positioned_entity_type\n ~Basket.preferred_risk_model\n ~Basket.publish_to_bloomberg\n ~Basket.publish_to_factset\n ~Basket.publish_to_reuters\n ~Basket.rebalance_calendar\n ~Basket.return_type\n ~Basket.reweight\n ~Basket.target_notional\n ~Basket.ticker\n ~Basket.weighting_strategy\n \n " }, { "path": "docs/classes/gs_quant.markets.core.PricingContext.rst", "content": "gs\\_quant.markets.core.PricingContext\n=====================================\n\n.. currentmodule:: gs_quant.markets.core\n\n.. autoclass:: PricingContext\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~PricingContext.__init__\n ~PricingContext.calc\n ~PricingContext.clone\n ~PricingContext.default_value\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~PricingContext.active_context\n ~PricingContext.csa_term\n ~PricingContext.is_async\n ~PricingContext.is_batch\n ~PricingContext.is_current\n ~PricingContext.is_entered\n ~PricingContext.market\n ~PricingContext.market_behaviour\n ~PricingContext.market_data_location\n ~PricingContext.pricing_date\n ~PricingContext.provider\n ~PricingContext.request_priority\n ~PricingContext.set_parameters_only\n ~PricingContext.show_progress\n ~PricingContext.timeout\n ~PricingContext.use_cache\n ~PricingContext.use_historical_diddles_only\n ~PricingContext.use_server_cache\n ~PricingContext.visible_to_gs\n \n " }, { "path": "docs/classes/gs_quant.markets.historical.HistoricalPricingContext.rst", "content": "gs\\_quant.markets.historical.HistoricalPricingContext\n=====================================================\n\n.. currentmodule:: gs_quant.markets.historical\n\n.. autoclass:: HistoricalPricingContext\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~HistoricalPricingContext.__init__\n ~HistoricalPricingContext.calc\n ~HistoricalPricingContext.clone\n ~HistoricalPricingContext.default_value\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~HistoricalPricingContext.active_context\n ~HistoricalPricingContext.csa_term\n ~HistoricalPricingContext.date_range\n ~HistoricalPricingContext.is_async\n ~HistoricalPricingContext.is_batch\n ~HistoricalPricingContext.is_current\n ~HistoricalPricingContext.is_entered\n ~HistoricalPricingContext.market\n ~HistoricalPricingContext.market_behaviour\n ~HistoricalPricingContext.market_data_location\n ~HistoricalPricingContext.pricing_date\n ~HistoricalPricingContext.provider\n ~HistoricalPricingContext.request_priority\n ~HistoricalPricingContext.set_parameters_only\n ~HistoricalPricingContext.show_progress\n ~HistoricalPricingContext.timeout\n ~HistoricalPricingContext.use_cache\n ~HistoricalPricingContext.use_historical_diddles_only\n ~HistoricalPricingContext.use_server_cache\n ~HistoricalPricingContext.visible_to_gs\n \n " }, { "path": "docs/classes/gs_quant.markets.index.Index.rst", "content": "gs\\_quant.markets.index.Index\n=============================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. autoclass:: Index\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Index.__init__\n ~Index.entity_type\n ~Index.get\n ~Index.get_all_esg_data\n ~Index.get_all_thematic_exposures\n ~Index.get_asset_measures\n ~Index.get_bottom_five_thematic_exposures\n ~Index.get_carbon_analytics\n ~Index.get_carbon_attribution_table\n ~Index.get_carbon_coverage\n ~Index.get_carbon_emissions\n ~Index.get_carbon_emissions_allocation\n ~Index.get_carbon_sbti_netzero_coverage\n ~Index.get_close_price_for_date\n ~Index.get_close_prices\n ~Index.get_constituent_instruments\n ~Index.get_constituent_instruments_for_date\n ~Index.get_constituents\n ~Index.get_constituents_for_date\n ~Index.get_currency\n ~Index.get_data_coordinate\n ~Index.get_data_series\n ~Index.get_entitlements\n ~Index.get_entity\n ~Index.get_esg_bottom_ten\n ~Index.get_esg_by_region\n ~Index.get_esg_by_sector\n ~Index.get_esg_quintiles\n ~Index.get_esg_summary\n ~Index.get_esg_top_ten\n ~Index.get_factor_risk_report\n ~Index.get_factor_risk_reports\n ~Index.get_factor_scenario_analytics\n ~Index.get_fundamentals\n ~Index.get_hloc_prices\n ~Index.get_identifier\n ~Index.get_identifiers\n ~Index.get_last_positions_data\n ~Index.get_latest_close_price\n ~Index.get_latest_constituent_instruments\n ~Index.get_latest_constituents\n ~Index.get_latest_position_set\n ~Index.get_marquee_id\n ~Index.get_position_dates\n ~Index.get_position_set_for_date\n ~Index.get_position_sets\n ~Index.get_positions_data\n ~Index.get_reports\n ~Index.get_return_type\n ~Index.get_status_of_reports\n ~Index.get_thematic_beta\n ~Index.get_thematic_breakdown\n ~Index.get_thematic_exposure\n ~Index.get_thematic_report\n ~Index.get_top_five_thematic_exposures\n ~Index.get_type\n ~Index.get_underlier_attribution\n ~Index.get_underlier_tree\n ~Index.get_underlier_weights\n ~Index.get_unique_entity_key\n ~Index.get_url\n ~Index.poll_report\n ~Index.update_positions\n ~Index.visualise_tree\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Index.data_dimension\n ~Index.id\n ~Index.positioned_entity_type\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.BasketType.rst", "content": "gs\\_quant.markets.indices\\_utils.BasketType\n===========================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: BasketType\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~BasketType.CUSTOM_BASKET\n ~BasketType.RESEARCH_BASKET\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.CorporateActionType.rst", "content": "gs\\_quant.markets.indices\\_utils.CorporateActionType\n====================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: CorporateActionType\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~CorporateActionType.ACQUISITION\n ~CorporateActionType.CASH_DIVIDEND\n ~CorporateActionType.IDENTIFIER_CHANGE\n ~CorporateActionType.RIGHTS_ISSUE\n ~CorporateActionType.SHARE_CHANGE\n ~CorporateActionType.SPECIAL_DIVIDEND\n ~CorporateActionType.SPIN_OFF\n ~CorporateActionType.STOCK_DIVIDEND\n ~CorporateActionType.STOCK_SPLIT\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.CustomBasketStyles.rst", "content": "gs\\_quant.markets.indices\\_utils.CustomBasketStyles\n===================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: CustomBasketStyles\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~CustomBasketStyles.AD_HOC_DESK_WORK\n ~CustomBasketStyles.CLIENT_CONSTRUCTED_WRAPPER\n ~CustomBasketStyles.CONSUMER\n ~CustomBasketStyles.ENERGY\n ~CustomBasketStyles.ENHANCED_INDEX_SOLUTIONS\n ~CustomBasketStyles.ESG\n ~CustomBasketStyles.FACTORS\n ~CustomBasketStyles.FINANCIALS\n ~CustomBasketStyles.FLAGSHIP\n ~CustomBasketStyles.GEOGRAPHIC\n ~CustomBasketStyles.GROWTH\n ~CustomBasketStyles.HEALTHCARE\n ~CustomBasketStyles.HEDGING\n ~CustomBasketStyles.INDUSTRIALS\n ~CustomBasketStyles.MATERIALS\n ~CustomBasketStyles.MOMENTUM\n ~CustomBasketStyles.PIPG\n ~CustomBasketStyles.SECTORS_INDUSTRIES\n ~CustomBasketStyles.SIZE\n ~CustomBasketStyles.STRUCTURED_ONE_DELTA\n ~CustomBasketStyles.THEMATIC\n ~CustomBasketStyles.TMT\n ~CustomBasketStyles.UTILITIES\n ~CustomBasketStyles.VALUE\n ~CustomBasketStyles.VOLATILITY\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.FundamentalMetricPeriod.rst", "content": "gs\\_quant.markets.indices\\_utils.FundamentalMetricPeriod\n========================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: FundamentalMetricPeriod\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~FundamentalMetricPeriod.ONE_YEAR\n ~FundamentalMetricPeriod.THREE_YEARS\n ~FundamentalMetricPeriod.TWO_YEARS\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.FundamentalMetricPeriodDirection.rst", "content": "gs\\_quant.markets.indices\\_utils.FundamentalMetricPeriodDirection\n=================================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: FundamentalMetricPeriodDirection\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~FundamentalMetricPeriodDirection.FORWARD\n ~FundamentalMetricPeriodDirection.TRAILING\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.FundamentalsMetrics.rst", "content": "gs\\_quant.markets.indices\\_utils.FundamentalsMetrics\n====================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: FundamentalsMetrics\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~FundamentalsMetrics.DIVIDEND_YIELD\n ~FundamentalsMetrics.EARNINGS_PER_SHARE\n ~FundamentalsMetrics.EARNINGS_PER_SHARE_POSITIVE\n ~FundamentalsMetrics.NET_DEBT_TO_EBITDA\n ~FundamentalsMetrics.PRICE_TO_BOOK\n ~FundamentalsMetrics.PRICE_TO_CASH\n ~FundamentalsMetrics.PRICE_TO_EARNINGS\n ~FundamentalsMetrics.PRICE_TO_EARNINGS_POSITIVE\n ~FundamentalsMetrics.PRICE_TO_SALES\n ~FundamentalsMetrics.RETURN_ON_EQUITY\n ~FundamentalsMetrics.SALES_PER_SHARE\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.IndicesDatasets.rst", "content": "gs\\_quant.markets.indices\\_utils.IndicesDatasets\n================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: IndicesDatasets\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~IndicesDatasets.BASKET_FUNDAMENTALS\n ~IndicesDatasets.CORPORATE_ACTIONS\n ~IndicesDatasets.GIRBASKETCONSTITUENTS\n ~IndicesDatasets.GSBASKETCONSTITUENTS\n ~IndicesDatasets.GSCB_FLAGSHIP\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.Region.rst", "content": "gs\\_quant.markets.indices\\_utils.Region\n=======================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: Region\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Region.AMERICAS\n ~Region.ASIA\n ~Region.EM\n ~Region.EUROPE\n ~Region.GLOBAL\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.ResearchBasketStyles.rst", "content": "gs\\_quant.markets.indices\\_utils.ResearchBasketStyles\n=====================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: ResearchBasketStyles\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~ResearchBasketStyles.ASIA_EX_JAPAN\n ~ResearchBasketStyles.EQUITY_THEMATIC\n ~ResearchBasketStyles.EUROPE\n ~ResearchBasketStyles.FUNDAMENTALS\n ~ResearchBasketStyles.FUND_OWNERSHIP\n ~ResearchBasketStyles.FX_OIL\n ~ResearchBasketStyles.GEOGRAPHICAL_EXPOSURE\n ~ResearchBasketStyles.HEDGE_FUND\n ~ResearchBasketStyles.IP_FACTORS\n ~ResearchBasketStyles.JAPAN\n ~ResearchBasketStyles.MACRO\n ~ResearchBasketStyles.MACRO_SLICE_STYLES\n ~ResearchBasketStyles.MUTUAL_FUND\n ~ResearchBasketStyles.PORTFOLIO_STRATEGY\n ~ResearchBasketStyles.POSITIONING\n ~ResearchBasketStyles.RISK_AND_LIQUIDITY\n ~ResearchBasketStyles.SECTOR\n ~ResearchBasketStyles.SHAREHOLDER_RETURN\n ~ResearchBasketStyles.STYLES_THEMES\n ~ResearchBasketStyles.STYLE_FACTOR_AND_FUNDAMENTAL\n ~ResearchBasketStyles.TACTICAL_RESEARCH\n ~ResearchBasketStyles.THEMATIC\n ~ResearchBasketStyles.US\n ~ResearchBasketStyles.WAVEFRONTS\n ~ResearchBasketStyles.WAVEFRONT_COMPONENTS\n ~ResearchBasketStyles.WAVEFRONT_PAIRS\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.ReturnType.rst", "content": "gs\\_quant.markets.indices\\_utils.ReturnType\n===========================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: ReturnType\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~ReturnType.GROSS_RETURN\n ~ReturnType.PRICE_RETURN\n ~ReturnType.TOTAL_RETURN\n \n " }, { "path": "docs/classes/gs_quant.markets.indices_utils.WeightingStrategy.rst", "content": "gs\\_quant.markets.indices\\_utils.WeightingStrategy\n==================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autoclass:: WeightingStrategy\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~WeightingStrategy.EQUAL\n ~WeightingStrategy.MARKET_CAPITALIZATION\n ~WeightingStrategy.QUANTITY\n ~WeightingStrategy.WEIGHT\n \n " }, { "path": "docs/classes/gs_quant.markets.portfolio.Grid.rst", "content": "gs\\_quant.markets.portfolio.Grid\n================================\n\n.. currentmodule:: gs_quant.markets.portfolio\n\n.. autoclass:: Grid\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Grid.__init__\n ~Grid.append\n ~Grid.as_dict\n ~Grid.calc\n ~Grid.clone\n ~Grid.default_instance\n ~Grid.dollar_price\n ~Grid.extend\n ~Grid.from_asset_id\n ~Grid.from_asset_name\n ~Grid.from_book\n ~Grid.from_csv\n ~Grid.from_dict\n ~Grid.from_eti\n ~Grid.from_frame\n ~Grid.from_instance\n ~Grid.from_json\n ~Grid.from_portfolio_id\n ~Grid.from_portfolio_name\n ~Grid.from_quote\n ~Grid.get\n ~Grid.market\n ~Grid.paths\n ~Grid.pop\n ~Grid.price\n ~Grid.properties\n ~Grid.properties_init\n ~Grid.resolve\n ~Grid.save\n ~Grid.save_as_quote\n ~Grid.save_to_shadowbook\n ~Grid.scale\n ~Grid.schema\n ~Grid.subset\n ~Grid.to_csv\n ~Grid.to_dict\n ~Grid.to_frame\n ~Grid.to_json\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Grid.all_instruments\n ~Grid.all_paths\n ~Grid.all_portfolios\n ~Grid.id\n ~Grid.instruments\n ~Grid.portfolios\n ~Grid.priceables\n ~Grid.quote_id\n \n " }, { "path": "docs/classes/gs_quant.markets.portfolio.Portfolio.rst", "content": "gs\\_quant.markets.portfolio.Portfolio\n=====================================\n\n.. currentmodule:: gs_quant.markets.portfolio\n\n.. autoclass:: Portfolio\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Portfolio.__init__\n ~Portfolio.append\n ~Portfolio.as_dict\n ~Portfolio.calc\n ~Portfolio.clone\n ~Portfolio.default_instance\n ~Portfolio.dollar_price\n ~Portfolio.extend\n ~Portfolio.from_asset_id\n ~Portfolio.from_asset_name\n ~Portfolio.from_book\n ~Portfolio.from_csv\n ~Portfolio.from_dict\n ~Portfolio.from_eti\n ~Portfolio.from_frame\n ~Portfolio.from_instance\n ~Portfolio.from_json\n ~Portfolio.from_portfolio_id\n ~Portfolio.from_portfolio_name\n ~Portfolio.from_quote\n ~Portfolio.get\n ~Portfolio.market\n ~Portfolio.paths\n ~Portfolio.pop\n ~Portfolio.price\n ~Portfolio.properties\n ~Portfolio.properties_init\n ~Portfolio.resolve\n ~Portfolio.save\n ~Portfolio.save_as_quote\n ~Portfolio.save_to_shadowbook\n ~Portfolio.scale\n ~Portfolio.schema\n ~Portfolio.subset\n ~Portfolio.to_csv\n ~Portfolio.to_dict\n ~Portfolio.to_frame\n ~Portfolio.to_json\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Portfolio.all_instruments\n ~Portfolio.all_paths\n ~Portfolio.all_portfolios\n ~Portfolio.id\n ~Portfolio.instruments\n ~Portfolio.portfolios\n ~Portfolio.priceables\n ~Portfolio.quote_id\n \n " }, { "path": "docs/classes/gs_quant.markets.portfolio_manager.PortfolioManager.rst", "content": "PortfolioManager\n================\n\n.. currentmodule:: gs_quant.markets.portfolio_manager\n\n.. autoclass:: PortfolioManager\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: get_all_esg_data\n \n \n .. automethod:: get_all_fund_of_fund_tags\n \n \n .. automethod:: get_all_thematic_exposures\n \n \n .. automethod:: get_aum\n \n \n .. automethod:: get_aum_source\n \n \n .. automethod:: get_bottom_five_thematic_exposures\n \n \n .. automethod:: get_carbon_analytics\n \n \n .. automethod:: get_carbon_attribution_table\n \n \n .. automethod:: get_carbon_coverage\n \n \n .. automethod:: get_carbon_emissions\n \n \n .. automethod:: get_carbon_emissions_allocation\n \n \n .. automethod:: get_carbon_sbti_netzero_coverage\n \n \n .. automethod:: get_custom_aum\n \n \n .. automethod:: get_entitlements\n \n \n .. automethod:: get_esg_bottom_ten\n \n \n .. automethod:: get_esg_by_region\n \n \n .. automethod:: get_esg_by_sector\n \n \n .. automethod:: get_esg_quintiles\n \n \n .. automethod:: get_esg_summary\n \n \n .. automethod:: get_esg_top_ten\n \n \n .. automethod:: get_factor_risk_report\n \n \n .. automethod:: get_factor_risk_reports\n \n \n .. automethod:: get_factor_scenario_analytics\n \n \n .. automethod:: get_last_positions_data\n \n \n .. automethod:: get_latest_position_set\n \n \n .. automethod:: get_macro_exposure\n \n \n .. automethod:: get_performance_report\n \n \n .. automethod:: get_pnl_contribution\n \n \n .. automethod:: get_position_dates\n \n \n .. automethod:: get_position_set_for_date\n \n \n .. automethod:: get_position_sets\n \n \n .. automethod:: get_positions_data\n \n \n .. automethod:: get_reports\n \n \n .. automethod:: get_schedule_dates\n \n \n .. automethod:: get_status_of_reports\n \n \n .. automethod:: get_tag_name_hierarchy\n \n \n .. automethod:: get_thematic_beta\n \n \n .. automethod:: get_thematic_breakdown\n \n \n .. automethod:: get_thematic_exposure\n \n \n .. automethod:: get_thematic_report\n \n \n .. automethod:: get_top_five_thematic_exposures\n \n \n .. automethod:: poll_report\n \n \n .. automethod:: run_reports\n \n \n .. automethod:: schedule_reports\n \n \n .. automethod:: set_aum_source\n \n \n .. automethod:: set_currency\n \n \n .. automethod:: set_entitlements\n \n \n .. automethod:: set_tag_name_hierarchy\n \n \n .. automethod:: share\n \n \n .. automethod:: update_portfolio_tree\n \n \n .. automethod:: update_positions\n \n \n .. automethod:: upload_custom_aum\n \n \n " }, { "path": "docs/classes/gs_quant.markets.position_set.Position.rst", "content": "gs\\_quant.markets.position\\_set.Position\n========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. autoclass:: Position\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Position.__init__\n ~Position.as_dict\n ~Position.to_target\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Position.asset_id\n ~Position.hard_to_borrow\n ~Position.identifier\n ~Position.name\n ~Position.quantity\n ~Position.restricted\n ~Position.tags\n ~Position.weight\n \n " }, { "path": "docs/classes/gs_quant.markets.position_set.PositionSet.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet\n===========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. autoclass:: PositionSet\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~PositionSet.__init__\n ~PositionSet.equalize_position_weights\n ~PositionSet.from_dicts\n ~PositionSet.from_frame\n ~PositionSet.from_list\n ~PositionSet.from_target\n ~PositionSet.get_hard_to_borrow_positions\n ~PositionSet.get_positions\n ~PositionSet.get_restricted_positions\n ~PositionSet.get_unpriced_positions\n ~PositionSet.get_unresolved_positions\n ~PositionSet.price\n ~PositionSet.price_many\n ~PositionSet.redistribute_weights\n ~PositionSet.remove_hard_to_borrow_positions\n ~PositionSet.remove_restricted_positions\n ~PositionSet.remove_unpriced_positions\n ~PositionSet.remove_unresolved_positions\n ~PositionSet.resolve\n ~PositionSet.resolve_many\n ~PositionSet.to_frame\n ~PositionSet.to_frame_many\n ~PositionSet.to_target\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~PositionSet.date\n ~PositionSet.divisor\n ~PositionSet.positions\n ~PositionSet.reference_notional\n ~PositionSet.unpriced_positions\n ~PositionSet.unresolved_positions\n \n " }, { "path": "docs/classes/gs_quant.markets.report.FactorRiskReport.rst", "content": "FactorRiskReport\n================\n\n.. currentmodule:: gs_quant.markets.report\n\n.. autoclass:: FactorRiskReport\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: delete\n \n \n .. automethod:: from_target\n \n \n .. automethod:: get\n \n \n .. automethod:: get_annual_risk\n \n \n .. automethod:: get_benchmark_id\n \n \n .. automethod:: get_daily_risk\n \n \n .. automethod:: get_ex_ante_var\n \n \n .. automethod:: get_factor_exposure\n \n \n .. automethod:: get_factor_pnl\n \n \n .. automethod:: get_factor_proportion_of_risk\n \n \n .. automethod:: get_most_recent_job\n \n \n .. automethod:: get_results\n \n \n .. automethod:: get_risk_model_id\n \n \n .. automethod:: get_table\n \n \n .. automethod:: get_view\n \n \n .. automethod:: run\n \n \n .. automethod:: save\n \n \n .. automethod:: schedule\n \n \n .. automethod:: set_position_source\n \n \n " }, { "path": "docs/classes/gs_quant.markets.report.PerformanceReport.rst", "content": "PerformanceReport\n=================\n\n.. currentmodule:: gs_quant.markets.report\n\n.. autoclass:: PerformanceReport\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: delete\n \n \n .. automethod:: from_target\n \n \n .. automethod:: get\n \n \n .. automethod:: get_asset_count\n \n \n .. automethod:: get_asset_count_long\n \n \n .. automethod:: get_asset_count_priced\n \n \n .. automethod:: get_asset_count_short\n \n \n .. automethod:: get_aum\n \n \n .. automethod:: get_aum_source\n \n \n .. automethod:: get_brinson_attribution\n \n \n .. automethod:: get_custom_aum\n \n \n .. automethod:: get_gross_exposure\n \n \n .. automethod:: get_long_exposure\n \n \n .. automethod:: get_many_measures\n \n \n .. automethod:: get_measure\n \n \n .. automethod:: get_most_recent_job\n \n \n .. automethod:: get_net_exposure\n \n \n .. automethod:: get_pnl\n \n \n .. automethod:: get_pnl_contribution\n \n \n .. automethod:: get_pnl_measure\n \n \n .. automethod:: get_portfolio_constituents\n \n \n .. automethod:: get_positions_data\n \n \n .. automethod:: get_servicing_cost_long_pnl\n \n \n .. automethod:: get_servicing_cost_short_pnl\n \n \n .. automethod:: get_short_exposure\n \n \n .. automethod:: get_trading_cost_pnl\n \n \n .. automethod:: get_trading_pnl\n \n \n .. automethod:: get_turnover\n \n \n .. automethod:: run\n \n \n .. automethod:: save\n \n \n .. automethod:: schedule\n \n \n .. automethod:: set_aum_source\n \n \n .. automethod:: set_position_source\n \n \n .. automethod:: upload_custom_aum\n \n \n " }, { "path": "docs/classes/gs_quant.markets.report.Report.rst", "content": "Report\n======\n\n.. currentmodule:: gs_quant.markets.report\n\n.. autoclass:: Report\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: delete\n \n \n .. automethod:: from_target\n \n \n .. automethod:: get\n \n \n .. automethod:: get_most_recent_job\n \n \n .. automethod:: run\n \n \n .. automethod:: save\n \n \n .. automethod:: schedule\n \n \n .. automethod:: set_position_source\n \n \n " }, { "path": "docs/classes/gs_quant.markets.report.ReportJobFuture.rst", "content": "ReportJobFuture\n===============\n\n.. currentmodule:: gs_quant.markets.report\n\n.. autoclass:: ReportJobFuture\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: done\n \n \n .. automethod:: result\n \n \n .. automethod:: status\n \n \n " }, { "path": "docs/classes/gs_quant.markets.report.ThematicReport.rst", "content": "ThematicReport\n==============\n\n.. currentmodule:: gs_quant.markets.report\n\n.. autoclass:: ThematicReport\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: delete\n \n \n .. automethod:: from_target\n \n \n .. automethod:: get\n \n \n .. automethod:: get_all_thematic_exposures\n \n \n .. automethod:: get_bottom_five_thematic_exposures\n \n \n .. automethod:: get_most_recent_job\n \n \n .. automethod:: get_thematic_betas\n \n \n .. automethod:: get_thematic_breakdown\n \n \n .. automethod:: get_thematic_data\n \n \n .. automethod:: get_thematic_exposure\n \n \n .. automethod:: get_top_five_thematic_exposures\n \n \n .. automethod:: run\n \n \n .. automethod:: save\n \n \n .. automethod:: schedule\n \n \n .. automethod:: set_position_source\n \n \n " }, { "path": "docs/classes/gs_quant.markets.securities.Asset.rst", "content": "gs\\_quant.markets.securities.Asset\n==================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: Asset\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Asset.__init__\n ~Asset.entity_type\n ~Asset.get\n ~Asset.get_asset_measures\n ~Asset.get_close_price_for_date\n ~Asset.get_close_prices\n ~Asset.get_data_coordinate\n ~Asset.get_data_series\n ~Asset.get_entitlements\n ~Asset.get_entity\n ~Asset.get_hloc_prices\n ~Asset.get_identifier\n ~Asset.get_identifiers\n ~Asset.get_latest_close_price\n ~Asset.get_marquee_id\n ~Asset.get_type\n ~Asset.get_unique_entity_key\n ~Asset.get_url\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Asset.data_dimension\n \n " }, { "path": "docs/classes/gs_quant.markets.securities.AssetClass.rst", "content": "gs\\_quant.markets.securities.AssetClass\n=======================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: AssetClass\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~AssetClass.Cash\n ~AssetClass.Commod\n ~AssetClass.Credit\n ~AssetClass.Cross_Asset\n ~AssetClass.Digital_Asset\n ~AssetClass.Econ\n ~AssetClass.Equity\n ~AssetClass.Fund\n ~AssetClass.FX\n ~AssetClass.Mortgage\n ~AssetClass.Rates\n ~AssetClass.Repo\n ~AssetClass.Loan\n ~AssetClass.Social\n ~AssetClass.Cryptocurrency\n \n " }, { "path": "docs/classes/gs_quant.markets.securities.AssetIdentifier.rst", "content": "gs\\_quant.markets.securities.AssetIdentifier\n============================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: AssetIdentifier\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~AssetIdentifier.MARQUEE_ID\n ~AssetIdentifier.REUTERS_ID\n ~AssetIdentifier.BLOOMBERG_ID\n ~AssetIdentifier.BLOOMBERG_COMPOSITE_ID\n ~AssetIdentifier.CUSIP\n ~AssetIdentifier.ISIN\n ~AssetIdentifier.SEDOL\n ~AssetIdentifier.TICKER\n ~AssetIdentifier.PLOT_ID\n ~AssetIdentifier.GSID\n \n " }, { "path": "docs/classes/gs_quant.markets.securities.AssetType.rst", "content": "gs\\_quant.markets.securities.AssetType\n======================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: AssetType\n\n \n .. automethod:: __init__\n\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~AssetType.INDEX\n ~AssetType.ETF\n ~AssetType.CUSTOM_BASKET\n ~AssetType.RESEARCH_BASKET\n ~AssetType.STOCK\n ~AssetType.FUTURE\n ~AssetType.CROSS\n ~AssetType.CURRENCY\n ~AssetType.RATE\n ~AssetType.CASH\n ~AssetType.WEATHER_INDEX\n ~AssetType.SWAP\n ~AssetType.SWAPTION\n ~AssetType.OPTION\n ~AssetType.BINARY\n ~AssetType.COMMODITY_REFERENCE_PRICE\n ~AssetType.COMMODITY_NATURAL_GAS_HUB\n ~AssetType.COMMODITY_EU_NATURAL_GAS_HUB\n ~AssetType.COMMODITY_POWER_NODE\n ~AssetType.COMMODITY_POWER_AGGREGATED_NODES\n ~AssetType.BOND\n ~AssetType.FUTURE_MARKET\n ~AssetType.FUTURE_CONTRACT\n ~AssetType.COMMODITY\n ~AssetType.CRYPTOCURRENCY\n ~AssetType.FORWARD\n ~AssetType.FUND\n ~AssetType.DEFAULT_SWAP\n ~AssetType.SYSTEMATIC_HEDGING\n ~AssetType.ACCESS\n ~AssetType.RISK_PREMIA\n ~AssetType.MULTI_ASSET_ALLOCATION\n ~AssetType.ADR\n ~AssetType.GDR\n ~AssetType.DUTCH_CERT\n ~AssetType.NYRS\n ~AssetType.RECEIPT\n ~AssetType.UNIT\n ~AssetType.MUTUAL_FUND\n ~AssetType.RIGHT\n ~AssetType.PREFERRED\n ~AssetType.MISC\n ~AssetType.REIT\n ~AssetType.PRIVATE_COMP\n ~AssetType.PREFERENCE\n ~AssetType.LIMITED_PARTNERSHIP\n ~AssetType.TRACKING_STOCK\n ~AssetType.ROYALTY_TRUST\n ~AssetType.CLOSED_END_FUND\n ~AssetType.OPEN_END_FUND\n ~AssetType.FUND_OF_FUNDS\n ~AssetType.MLP\n ~AssetType.STAPLED_SECURITY\n ~AssetType.SAVINGS_SHARE\n ~AssetType.EQUITY_WRT\n ~AssetType.SAVINGS_PLAN\n ~AssetType.EQUITY_INDEX\n ~AssetType.COMMON_STOCK\n \n " }, { "path": "docs/classes/gs_quant.markets.securities.SecurityMaster.rst", "content": "gs\\_quant.markets.securities.SecurityMaster\n===========================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: SecurityMaster\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~SecurityMaster.__init__\n ~SecurityMaster.asset_type_to_str\n ~SecurityMaster.get_all_identifiers\n ~SecurityMaster.get_all_identifiers_gen\n ~SecurityMaster.get_asset\n ~SecurityMaster.get_asset_async\n ~SecurityMaster.get_identifiers\n ~SecurityMaster.get_many_assets\n ~SecurityMaster.get_many_assets_async\n ~SecurityMaster.map_identifiers\n ~SecurityMaster.set_source\n \n \n\n \n \n " }, { "path": "docs/classes/gs_quant.markets.securities.Stock.rst", "content": "gs\\_quant.markets.securities.Stock\n==================================\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autoclass:: Stock\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~Stock.__init__\n ~Stock.entity_type\n ~Stock.get\n ~Stock.get_asset_measures\n ~Stock.get_close_price_for_date\n ~Stock.get_close_prices\n ~Stock.get_currency\n ~Stock.get_data_coordinate\n ~Stock.get_data_series\n ~Stock.get_entitlements\n ~Stock.get_entity\n ~Stock.get_hloc_prices\n ~Stock.get_identifier\n ~Stock.get_identifiers\n ~Stock.get_latest_close_price\n ~Stock.get_marquee_id\n ~Stock.get_thematic_beta\n ~Stock.get_type\n ~Stock.get_unique_entity_key\n ~Stock.get_url\n \n \n\n \n \n .. rubric:: Attributes\n\n .. autosummary::\n \n ~Stock.data_dimension\n \n " }, { "path": "docs/classes/gs_quant.models.epidemiology.EpidemicModel.rst", "content": "gs\\_quant.models.epidemiology.EpidemicModel\n===========================================\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autoclass:: EpidemicModel\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~EpidemicModel.__init__\n ~EpidemicModel.fit\n ~EpidemicModel.residual\n ~EpidemicModel.solve\n \n \n\n \n \n " }, { "path": "docs/classes/gs_quant.models.epidemiology.SEIR.rst", "content": "gs\\_quant.models.epidemiology.SEIR\n==================================\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autoclass:: SEIR\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~SEIR.__init__\n ~SEIR.calibrate\n ~SEIR.get_parameters\n \n \n\n \n \n " }, { "path": "docs/classes/gs_quant.models.epidemiology.SIR.rst", "content": "gs\\_quant.models.epidemiology.SIR\n=================================\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autoclass:: SIR\n\n \n .. automethod:: __init__\n\n \n .. rubric:: Methods\n\n .. autosummary::\n \n ~SIR.__init__\n ~SIR.calibrate\n ~SIR.get_parameters\n \n \n\n \n \n " }, { "path": "docs/classes/gs_quant.models.risk_model.FactorRiskModel.rst", "content": "FactorRiskModel\n===============\n\n.. currentmodule:: gs_quant.models.risk_model\n\n.. autoclass:: FactorRiskModel\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: delete\n \n \n .. automethod:: delete_factor_metadata\n \n \n .. automethod:: from_many_targets\n \n \n .. automethod:: from_target\n \n \n .. automethod:: get\n \n \n .. automethod:: get_asset_capitalization\n \n \n .. automethod:: get_asset_contribution_to_risk\n \n \n .. automethod:: get_asset_factor_attribution\n \n \n .. automethod:: get_asset_price\n \n \n .. automethod:: get_asset_universe\n \n \n .. automethod:: get_bid_ask_spread\n \n \n .. automethod:: get_calendar\n \n \n .. automethod:: get_composite_value\n \n \n .. automethod:: get_composite_volume\n \n \n .. automethod:: get_covariance_matrix\n \n \n .. automethod:: get_currency\n \n \n .. automethod:: get_currency_exchange_rate\n \n \n .. automethod:: get_daily_return\n \n \n .. automethod:: get_data\n \n \n .. automethod:: get_dates\n \n \n .. automethod:: get_dividend_yield\n \n \n .. automethod:: get_estimation_universe_weights\n \n \n .. automethod:: get_factor\n \n \n .. automethod:: get_factor_data\n \n \n .. automethod:: get_factor_portfolios\n \n \n .. automethod:: get_factor_returns_by_id\n \n \n .. automethod:: get_factor_returns_by_name\n \n \n .. automethod:: get_factor_volatility\n \n \n .. automethod:: get_global_predicted_beta\n \n \n .. automethod:: get_historical_beta\n \n \n .. automethod:: get_issuer_market_cap\n \n \n .. automethod:: get_issuer_specific_covariance\n \n \n .. automethod:: get_many\n \n \n .. automethod:: get_many_factors\n \n \n .. automethod:: get_missing_dates\n \n \n .. automethod:: get_most_recent_date_from_calendar\n \n \n .. automethod:: get_pre_vra_covariance_matrix\n \n \n .. automethod:: get_predicted_beta\n \n \n .. automethod:: get_risk_free_rate\n \n \n .. automethod:: get_specific_return\n \n \n .. automethod:: get_specific_risk\n \n \n .. automethod:: get_total_risk\n \n \n .. automethod:: get_traded_value\n \n \n .. automethod:: get_trading_volume\n \n \n .. automethod:: get_unadjusted_covariance_matrix\n \n \n .. automethod:: get_unadjusted_specific_risk\n \n \n .. automethod:: get_universe_exposure\n \n \n .. automethod:: get_universe_factor_exposure\n \n \n .. automethod:: save\n \n \n .. automethod:: save_factor_metadata\n \n \n .. automethod:: upload_asset_coverage_data\n \n \n .. automethod:: upload_calendar\n \n \n .. automethod:: upload_data\n \n \n .. automethod:: upload_partial_data\n \n \n " }, { "path": "docs/classes/gs_quant.models.risk_model.MacroRiskModel.rst", "content": "MacroRiskModel\n==============\n\n.. currentmodule:: gs_quant.models.risk_model\n\n.. autoclass:: MacroRiskModel\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: delete\n \n \n .. automethod:: delete_factor_metadata\n \n \n .. automethod:: from_many_targets\n \n \n .. automethod:: from_target\n \n \n .. automethod:: get\n \n \n .. automethod:: get_asset_universe\n \n \n .. automethod:: get_calendar\n \n \n .. automethod:: get_data\n \n \n .. automethod:: get_dates\n \n \n .. automethod:: get_factor\n \n \n .. automethod:: get_factor_data\n \n \n .. automethod:: get_factor_returns_by_id\n \n \n .. automethod:: get_factor_returns_by_name\n \n \n .. automethod:: get_factor_standard_deviation\n \n \n .. automethod:: get_factor_z_score\n \n \n .. automethod:: get_fair_value_gap\n \n \n .. automethod:: get_many\n \n \n .. automethod:: get_many_factors\n \n \n .. automethod:: get_missing_dates\n \n \n .. automethod:: get_model_price\n \n \n .. automethod:: get_most_recent_date_from_calendar\n \n \n .. automethod:: get_r_squared\n \n \n .. automethod:: get_specific_risk\n \n \n .. automethod:: get_universe_exposure\n \n \n .. automethod:: get_universe_sensitivity\n \n \n .. automethod:: save\n \n \n .. automethod:: save_factor_metadata\n \n \n .. automethod:: upload_asset_coverage_data\n \n \n .. automethod:: upload_calendar\n \n \n .. automethod:: upload_data\n \n \n .. automethod:: upload_partial_data\n \n \n " }, { "path": "docs/classes/gs_quant.models.risk_model.MarqueeRiskModel.rst", "content": "MarqueeRiskModel\n================\n\n.. currentmodule:: gs_quant.models.risk_model\n\n.. autoclass:: MarqueeRiskModel\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: delete\n \n \n .. automethod:: delete_factor_metadata\n \n \n .. automethod:: from_many_targets\n \n \n .. automethod:: from_target\n \n \n .. automethod:: get\n \n \n .. automethod:: get_asset_universe\n \n \n .. automethod:: get_calendar\n \n \n .. automethod:: get_data\n \n \n .. automethod:: get_dates\n \n \n .. automethod:: get_factor\n \n \n .. automethod:: get_factor_data\n \n \n .. automethod:: get_factor_returns_by_id\n \n \n .. automethod:: get_factor_returns_by_name\n \n \n .. automethod:: get_many_factors\n \n \n .. automethod:: get_missing_dates\n \n \n .. automethod:: get_most_recent_date_from_calendar\n \n \n .. automethod:: get_specific_risk\n \n \n .. automethod:: get_universe_exposure\n \n \n .. automethod:: save\n \n \n .. automethod:: save_factor_metadata\n \n \n .. automethod:: upload_asset_coverage_data\n \n \n .. automethod:: upload_calendar\n \n \n .. automethod:: upload_data\n \n \n .. automethod:: upload_partial_data\n \n \n " }, { "path": "docs/classes/gs_quant.models.risk_model.RiskModel.rst", "content": "RiskModel\n=========\n\n.. currentmodule:: gs_quant.models.risk_model\n\n.. autoclass:: RiskModel\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n " }, { "path": "docs/classes/gs_quant.models.risk_model.ThematicRiskModel.rst", "content": "ThematicRiskModel\n=================\n\n.. currentmodule:: gs_quant.models.risk_model\n\n.. autoclass:: ThematicRiskModel\n\n \n \n .. rubric:: Methods\n \n \n .. automethod:: __init__\n \n \n .. automethod:: delete\n \n \n .. automethod:: delete_factor_metadata\n \n \n .. automethod:: from_many_targets\n \n \n .. automethod:: from_target\n \n \n .. automethod:: get\n \n \n .. automethod:: get_asset_universe\n \n \n .. automethod:: get_calendar\n \n \n .. automethod:: get_data\n \n \n .. automethod:: get_dates\n \n \n .. automethod:: get_factor\n \n \n .. automethod:: get_factor_data\n \n \n .. automethod:: get_factor_returns_by_id\n \n \n .. automethod:: get_factor_returns_by_name\n \n \n .. automethod:: get_many\n \n \n .. automethod:: get_many_factors\n \n \n .. automethod:: get_missing_dates\n \n \n .. automethod:: get_most_recent_date_from_calendar\n \n \n .. automethod:: get_specific_risk\n \n \n .. automethod:: get_universe_exposure\n \n \n .. automethod:: get_universe_sensitivity\n \n \n .. automethod:: save\n \n \n .. automethod:: save_factor_metadata\n \n \n .. automethod:: upload_asset_coverage_data\n \n \n .. automethod:: upload_calendar\n \n \n .. automethod:: upload_data\n \n \n .. automethod:: upload_partial_data\n \n \n " }, { "path": "docs/classes/gs_quant.timeseries.backtesting.Basket.rst", "content": "gs\\_quant.timeseries.backtesting.Basket\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.backtesting\n\n.. autoclass:: Basket\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~Basket.average_forward_vol\n \n ~Basket.average_implied_volatility\n \n ~Basket.average_realized_correlation\n \n ~Basket.average_realized_volatility\n \n ~Basket.get_actual_weights\n \n ~Basket.get_marquee_ids\n \n ~Basket.get_returns\n \n ~Basket.get_spot_data\n \n ~Basket.price\n \n " }, { "path": "docs/classes/gs_quant.timeseries.datetime.Window.rst", "content": "gs\\_quant.timeseries.datetime.Window\n====================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autoclass:: Window\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~Window.as_dict\n \n ~Window.from_dict\n \n " }, { "path": "docs/classes/gs_quant.timeseries.statistics.LinearRegression.rst", "content": "gs\\_quant.timeseries.statistics.LinearRegression\n================================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autoclass:: LinearRegression\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~LinearRegression.coefficient\n \n ~LinearRegression.fitted_values\n \n ~LinearRegression.predict\n \n ~LinearRegression.r_squared\n \n ~LinearRegression.standard_deviation_of_errors\n \n " }, { "path": "docs/classes/gs_quant.timeseries.statistics.RollingLinearRegression.rst", "content": "gs\\_quant.timeseries.statistics.RollingLinearRegression\n=======================================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autoclass:: RollingLinearRegression\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~RollingLinearRegression.coefficient\n \n ~RollingLinearRegression.fitted_values\n \n ~RollingLinearRegression.r_squared\n \n ~RollingLinearRegression.standard_deviation_of_errors\n \n " }, { "path": "docs/classes/gs_quant.timeseries.statistics.SEIRModel.rst", "content": "gs\\_quant.timeseries.statistics.SEIRModel\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autoclass:: SEIRModel\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~SEIRModel.beta\n \n ~SEIRModel.e0\n \n ~SEIRModel.gamma\n \n ~SEIRModel.i0\n \n ~SEIRModel.predict_e\n \n ~SEIRModel.predict_i\n \n ~SEIRModel.predict_r\n \n ~SEIRModel.predict_s\n \n ~SEIRModel.r0\n \n ~SEIRModel.s0\n \n ~SEIRModel.sigma\n \n " }, { "path": "docs/classes/gs_quant.timeseries.statistics.SIRModel.rst", "content": "gs\\_quant.timeseries.statistics.SIRModel\n========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autoclass:: SIRModel\n\n \n \n .. rubric:: Methods\n .. autosummary::\n \n ~SIRModel.beta\n \n ~SIRModel.gamma\n \n ~SIRModel.i0\n \n ~SIRModel.predict_i\n \n ~SIRModel.predict_r\n \n ~SIRModel.predict_s\n \n ~SIRModel.r0\n \n ~SIRModel.s0\n \n " }, { "path": "docs/conf.py", "content": "# -*- coding: utf-8 -*-\n#\n# Configuration file for the Sphinx documentation builder.\n#\n# This file does only contain a selection of the most common options. For a\n# full list see the documentation:\n# http://www.sphinx-doc.org/en/master/config\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\n\nsys.path.insert(0, os.path.abspath('.'))\n\n# -- Project information -----------------------------------------------------\n\nproject = 'gs_quant'\ncopyright = '2019, Goldman Sachs'\nauthor = 'Andy Phillips'\n\n# The short X.Y version\nversion = ''\n# The full version, including alpha/beta/rc tags\nrelease = '0.1'\n\n\n# -- General configuration ---------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'searchdataext',\n 'sphinx.ext.autodoc',\n 'sphinx.ext.napoleon',\n # 'sphinx.ext.intersphinx',\n 'sphinx.ext.mathjax',\n 'sphinx.ext.ifconfig',\n 'sphinx.ext.viewcode',\n 'sphinx.ext.autosummary',\n 'sphinx_autodoc_typehints',\n]\n\n# Do not show packages in function names\nadd_module_names = False\n\n# Generate function doc pages automatically\nautosummary_generate = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = 'en'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = None\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# A string with the fully-qualified name of a HTML Translator class, that is,\n# a subclass of Sphinx’s HTMLTranslator, that is used to translate document\n# trees to HTML. Default is None (use the builtin translator).\n#\nhtml_translator_class = './_writers/html5'\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n# html_theme = 'sphinx_rtd_theme'\nhtml_theme = 'gs'\nhtml_theme_path = ['./_themes']\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\nhtml_theme_options = {\n 'nosidebar': 'false',\n 'body_min_width': '0',\n 'body_max_width': '100%',\n}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# The default sidebars (for documents that don't match any pattern) are\n# defined by theme itself. Builtin themes are using these templates by\n# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',\n# 'searchbox.html']``.\n#\n# html_sidebars = {}\n\n\n# -- Options for HTMLHelp output ---------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'gs_quantdoc'\n\n\n# -- Options for LaTeX output ------------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n #\n # 'papersize': 'letterpaper',\n # The font size ('10pt', '11pt' or '12pt').\n #\n # 'pointsize': '10pt',\n # Additional stuff for the LaTeX preamble.\n #\n # 'preamble': '',\n # Latex figure (float) alignment\n #\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, 'gs_quant.tex', 'gs\\\\_quant Documentation Formatting', 'Andy Phillips', 'manual'),\n]\n\n\n# -- Options for manual page output ------------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, 'gs_quant', 'gs_quant Documentation Formatting', [author], 1)]\n\n\n# -- Options for Texinfo output ----------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (\n master_doc,\n 'gs_quant',\n 'gs_quant Documentation Formatting',\n author,\n 'gs_quant',\n 'One line description of project.',\n 'Miscellaneous',\n ),\n]\n\n\n# -- Options for Epub output -------------------------------------------------\n\n# Bibliographic Dublin Core info.\nepub_title = project\n\n# The unique identifier of the text. This can be a ISBN number\n# or the project homepage.\n#\n# epub_identifier = ''\n\n# A unique identification for the text.\n#\n# epub_uid = ''\n\n# A list of files that should not be packed into the epub file.\nepub_exclude_files = ['search.html']\n\n\n# -- Extension configuration -------------------------------------------------\nmathjax_path = '/resources/mathjax/latest.js?config=TeX-AMS-MML_HTMLorMML'\n\n# -- Options for intersphinx extension ---------------------------------------\n\n# Example configuration for intersphinx: refer to the Python standard library.\nintersphinx_mapping = {'https://docs.python.org/': None}\n" }, { "path": "docs/data.rst", "content": "Data Package\n============\n\nDataContext\n-----------\n\n.. currentmodule:: gs_quant.data.core\n\n.. autosummary::\n :toctree: classes\n\n DataContext\n\nDataset\n-------\n\n.. currentmodule:: gs_quant.data.dataset\n\n.. autosummary::\n :toctree: classes\n\n Dataset\n\nFields\n------\n\n.. currentmodule:: gs_quant.data.fields\n\n.. autosummary::\n :toctree: classes\n\n Fields" }, { "path": "docs/datetime.rst", "content": "Datetime Package\n================\n\nDate\n----\n.. currentmodule:: gs_quant.datetime.date\n\n.. autosummary::\n :toctree: functions\n\n business_day_count\n business_day_offset\n date_range\n is_business_day\n prev_business_date\n\nPoint\n-----\n.. currentmodule:: gs_quant.datetime.point\n\n.. autosummary::\n :toctree: functions\n\n point_sort_order\n\n\nRelativeDate\n-----\n.. currentmodule:: gs_quant.datetime.relative_date\n\n.. autosummary::\n :toctree: classes\n :template: timeseries_class.rst\n\n RelativeDate" }, { "path": "docs/entities.rst", "content": "Entities Package\n===============\n\nPositionedEntity\n----------------\n\n.. currentmodule:: gs_quant.entities.entity\n\n.. autosummary::\n :toctree: classes\n :template: report.rst\n\n PositionedEntity\n\n\n" }, { "path": "docs/functions/gs_quant.datetime.date.business_day_count.rst", "content": "gs\\_quant.datetime.date.business\\_day\\_count\n============================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: business_day_count" }, { "path": "docs/functions/gs_quant.datetime.date.business_day_offset.rst", "content": "gs\\_quant.datetime.date.business\\_day\\_offset\n=============================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: business_day_offset" }, { "path": "docs/functions/gs_quant.datetime.date.date_range.rst", "content": "gs\\_quant.datetime.date.date\\_range\n===================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: date_range" }, { "path": "docs/functions/gs_quant.datetime.date.is_business_day.rst", "content": "gs\\_quant.datetime.date.is\\_business\\_day\n=========================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: is_business_day" }, { "path": "docs/functions/gs_quant.datetime.date.prev_business_date.rst", "content": "gs\\_quant.datetime.date.prev\\_business\\_date\n============================================\n\n.. currentmodule:: gs_quant.datetime.date\n\n.. autofunction:: prev_business_date" }, { "path": "docs/functions/gs_quant.datetime.point.point_sort_order.rst", "content": "gs\\_quant.datetime.point.point\\_sort\\_order\n===========================================\n\n.. currentmodule:: gs_quant.datetime.point\n\n.. autofunction:: point_sort_order" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.add_factor_risk_report.rst", "content": "gs\\_quant.markets.baskets.Basket.add\\_factor\\_risk\\_report\n=============================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.add_factor_risk_report" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.cancel_rebalance.rst", "content": "gs\\_quant.markets.baskets.Basket.cancel\\_rebalance\n==================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.cancel_rebalance" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.clone.rst", "content": "gs\\_quant.markets.baskets.Basket.clone\n======================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.clone" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.create.rst", "content": "gs\\_quant.markets.baskets.Basket.create\n=======================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.create" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.delete_factor_risk_report.rst", "content": "gs\\_quant.markets.baskets.Basket.delete\\_factor\\_risk\\_report\n=============================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.delete_factor_risk_report" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.entity_type.rst", "content": "gs\\_quant.markets.baskets.Basket.entity\\_type\n=============================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.entity_type" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get.rst", "content": "gs\\_quant.markets.baskets.Basket.get\n====================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_close_price_for_date.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_close\\_price\\_for\\_date\n=============================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_close_price_for_date" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_close_prices.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_close\\_prices\n===================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_close_prices" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_corporate_actions.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_corporate\\_actions\n========================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_corporate_actions" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_data_coordinate.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_data\\_coordinate\n======================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_data_coordinate" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_data_series.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_data\\_series\n==================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_data_series" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_details.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_details\n=============================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_details" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_entity.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_entity\n============================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_entity" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_fundamentals.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_fundamentals\n==================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_fundamentals" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_identifier.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_identifier\n================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_identifier" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_identifiers.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_identifiers\n=================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_identifiers" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_latest_close_price.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_latest\\_close\\_price\n==========================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_latest_close_price" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_latest_position_set.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_latest\\_position\\_set\n===========================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_latest_position_set" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_latest_rebalance_data.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_latest\\_rebalance\\_data\n=============================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_latest_rebalance_data" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_latest_rebalance_date.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_latest\\_rebalance\\_date\n=============================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_latest_rebalance_date" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_live_date.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_live\\_date\n================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_live_date" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_marquee_id.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_marquee\\_id\n=================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_marquee_id" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_position_set_for_date.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_position\\_set\\_for\\_date\n==============================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_position_set_for_date" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_position_sets.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_position\\_sets\n====================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_position_sets" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_positions_data.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_positions\\_data\n=====================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_positions_data" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_rebalance_approval_status.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_rebalance\\_approval\\_status\n=================================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_rebalance_approval_status" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_type.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_type\n==========================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_type" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_unique_entity_key.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_unique\\_entity\\_key\n=========================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_unique_entity_key" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.get_url.rst", "content": "gs\\_quant.markets.baskets.Basket.get\\_url\n=========================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.get_url" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.poll_report.rst", "content": "gs\\_quant.markets.baskets.Basket.poll\\_report\n=============================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.poll_report" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.poll_status.rst", "content": "gs\\_quant.markets.baskets.Basket.poll\\_status\n=============================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.poll_status" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.update.rst", "content": "gs\\_quant.markets.baskets.Basket.update\n=======================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.update" }, { "path": "docs/functions/gs_quant.markets.baskets.Basket.upload_position_history.rst", "content": "gs\\_quant.markets.baskets.Basket.upload\\_position\\_history\n==========================================================\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. automethod:: Basket.upload_position_history" }, { "path": "docs/functions/gs_quant.markets.index.Index.get.rst", "content": "gs\\_quant.markets.index.Index.get\n=================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_close_price_for_date.rst", "content": "gs\\_quant.markets.index.Index.get_close_price_for_date\n======================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_close_price_for_date" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_close_prices.rst", "content": "gs\\_quant.markets.index.Index.get_close_prices\n==============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_close_prices" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_constituent_instruments.rst", "content": "gs\\_quant.markets.index.Index.get_constituent_instruments\n=========================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_constituent_instruments" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_constituent_instruments_for_date.rst", "content": "gs\\_quant.markets.index.Index.get_constituent_instruments_for_date\n==================================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_constituent_instruments_for_date" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_constituents.rst", "content": "gs\\_quant.markets.index.Index.get_constituents\n==============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_constituents" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_constituents_for_date.rst", "content": "gs\\_quant.markets.index.Index.get_constituents_for_date\n=======================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_constituents_for_date" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_currency.rst", "content": "gs\\_quant.markets.index.Index.get_currency\n==========================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_currency" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_data_coordinate.rst", "content": "gs\\_quant.markets.index.Index.get_data_coordinate\n=================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_data_coordinate" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_data_series.rst", "content": "gs\\_quant.markets.index.Index.get_data_series\n=============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_data_series" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_entitlements.rst", "content": "gs\\_quant.markets.index.Index.get_entitlements\n==============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_entitlements" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_entity.rst", "content": "gs\\_quant.markets.index.Index.get_entity\n========================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_entity" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_factor_risk_report.rst", "content": "gs\\_quant.markets.index.Index.get_factor_risk_report\n====================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_factor_risk_report" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_fundamentals.rst", "content": "gs\\_quant.markets.index.Index.get_fundamentals\n==============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_fundamentals" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_identifier.rst", "content": "gs\\_quant.markets.index.Index.get_identifier\n============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_identifier" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_identifiers.rst", "content": "gs\\_quant.markets.index.Index.get_identifiers\n=============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_identifiers" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_latest_close_price.rst", "content": "gs\\_quant.markets.index.Index.get_latest_close_price\n====================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_latest_close_price" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_latest_constituent_instruments.rst", "content": "gs\\_quant.markets.index.Index.get_latest_constituent_instruments\n================================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_latest_constituent_instruments" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_latest_constituents.rst", "content": "gs\\_quant.markets.index.Index.get_latest_constituents\n=====================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_latest_constituents" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_latest_position_set.rst", "content": "gs\\_quant.markets.index.Index.get_latest_position_set\n=====================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_latest_position_set" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_marquee_id.rst", "content": "gs\\_quant.markets.index.Index.get_marquee_id\n============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_marquee_id" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_position_dates.rst", "content": "gs\\_quant.markets.index.Index.get_position_dates\n================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_position_dates" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_position_set_for_date.rst", "content": "gs\\_quant.markets.index.Index.get_position_set_for_date\n=======================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_position_set_for_date" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_position_sets.rst", "content": "gs\\_quant.markets.index.Index.get_position_sets\n===============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_position_sets" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_positions_data.rst", "content": "gs\\_quant.markets.index.Index.get_positions_data\n================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_positions_data" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_return_type.rst", "content": "gs\\_quant.markets.index.Index.get_return_type\n=============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_return_type" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_status_of_reports.rst", "content": "gs\\_quant.markets.index.Index.get_status_of_reports\n===================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_status_of_reports" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_type.rst", "content": "gs\\_quant.markets.index.Index.get_type\n======================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_type" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_underlier_attribution.rst", "content": "gs\\_quant.markets.index.Index.get_underlier_attribution\n=======================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_underlier_attribution" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_underlier_tree.rst", "content": "gs\\_quant.markets.index.Index.get_underlier_tree\n================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_underlier_tree" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_underlier_weights.rst", "content": "gs\\_quant.markets.index.Index.get_underlier_weights\n===================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_underlier_weights" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_unique_entity_key.rst", "content": "gs\\_quant.markets.index.Index.get_unique_entity_key\n===================================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_unique_entity_key" }, { "path": "docs/functions/gs_quant.markets.index.Index.get_url.rst", "content": "gs\\_quant.markets.index.Index.get_url\n=====================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.get_url" }, { "path": "docs/functions/gs_quant.markets.index.Index.update_positions.rst", "content": "gs\\_quant.markets.index.Index.update_positions\n==============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.update_positions" }, { "path": "docs/functions/gs_quant.markets.index.Index.visualise_tree.rst", "content": "gs\\_quant.markets.index.Index.visualise_tree\n============================================\n\n.. currentmodule:: gs_quant.markets.index\n\n.. automethod:: Index.visualise_tree" }, { "path": "docs/functions/gs_quant.markets.indices_utils.get_flagship_baskets.rst", "content": "gs\\_quant.markets.indices\\_utils.get\\_flagship\\_baskets\n=======================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autofunction:: get_flagship_baskets" }, { "path": "docs/functions/gs_quant.markets.indices_utils.get_flagships_constituents.rst", "content": "gs\\_quant.markets.indices\\_utils.get\\_flagships\\_constituents\n=============================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autofunction:: get_flagships_constituents" }, { "path": "docs/functions/gs_quant.markets.indices_utils.get_flagships_performance.rst", "content": "gs\\_quant.markets.indices\\_utils.get\\_flagships\\_performance\n============================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autofunction:: get_flagships_performance" }, { "path": "docs/functions/gs_quant.markets.indices_utils.get_flagships_with_assets.rst", "content": "gs\\_quant.markets.indices\\_utils.get\\_flagships\\_with\\_assets\n=============================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autofunction:: get_flagships_with_assets" }, { "path": "docs/functions/gs_quant.markets.indices_utils.get_my_baskets.rst", "content": "gs\\_quant.markets.indices\\_utils.get\\_my\\_baskets\n=================================================\n\n.. currentmodule:: gs_quant.markets.indices_utils\n\n.. autofunction:: get_my_baskets" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.equalize_position_weights.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.equalize\\_position\\_weights\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.equalize_position_weights" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.from_dicts.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.from\\_dicts\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.from_dicts" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.from_frame.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.from\\_frame\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.from_frame" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.from_list.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.from\\_list\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.from_list" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.get_positions.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.get\\_positions\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.get_positions" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.get_unpriced_positions.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.get\\_unpriced\\_positions\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.get_unpriced_positions" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.get_unresolved_positions.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.get\\_unresolved\\_positions\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.get_unresolved_positions" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.price.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.price\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.price" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.price_many.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.price\\_many\n======================================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.price_many" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.remove_unpriced_positions.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.remove\\_unpriced\\_positions\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.remove_unpriced_positions" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.remove_unresolved_positions.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.remove\\_unresolved\\_positions\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.remove_unresolved_positions" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.resolve.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.resolve\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.resolve" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.resolve_many.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.resolve\\_many\n======================================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.resolve_many" }, { "path": "docs/functions/gs_quant.markets.position_set.PositionSet.to_frame.rst", "content": "gs\\_quant.markets.position\\_set.PositionSet.to\\_frame\n==========================================\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. automethod:: PositionSet.to_frame" }, { "path": "docs/functions/gs_quant.timeseries.algebra.abs_.rst", "content": "gs\\_quant.timeseries.algebra.abs\\_\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: abs_" }, { "path": "docs/functions/gs_quant.timeseries.algebra.add.rst", "content": "gs\\_quant.timeseries.algebra.add\n================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: add" }, { "path": "docs/functions/gs_quant.timeseries.algebra.and_.rst", "content": "gs\\_quant.timeseries.algebra.and\\_\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: and_" }, { "path": "docs/functions/gs_quant.timeseries.algebra.ceil.rst", "content": "gs\\_quant.timeseries.algebra.ceil\n=================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: ceil" }, { "path": "docs/functions/gs_quant.timeseries.algebra.divide.rst", "content": "gs\\_quant.timeseries.algebra.divide\n===================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: divide" }, { "path": "docs/functions/gs_quant.timeseries.algebra.exp.rst", "content": "gs\\_quant.timeseries.algebra.exp\n================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: exp" }, { "path": "docs/functions/gs_quant.timeseries.algebra.filter_.rst", "content": "gs\\_quant.timeseries.algebra.filter\\_\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: filter_" }, { "path": "docs/functions/gs_quant.timeseries.algebra.filter_dates.rst", "content": "gs\\_quant.timeseries.algebra.filter\\_dates\n==========================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: filter_dates" }, { "path": "docs/functions/gs_quant.timeseries.algebra.floor.rst", "content": "gs\\_quant.timeseries.algebra.floor\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: floor" }, { "path": "docs/functions/gs_quant.timeseries.algebra.floordiv.rst", "content": "gs\\_quant.timeseries.algebra.floordiv\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: floordiv" }, { "path": "docs/functions/gs_quant.timeseries.algebra.if_.rst", "content": "gs\\_quant.timeseries.algebra.if\\_\n=================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: if_" }, { "path": "docs/functions/gs_quant.timeseries.algebra.log.rst", "content": "gs\\_quant.timeseries.algebra.log\n================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: log" }, { "path": "docs/functions/gs_quant.timeseries.algebra.multiply.rst", "content": "gs\\_quant.timeseries.algebra.multiply\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: multiply" }, { "path": "docs/functions/gs_quant.timeseries.algebra.not_.rst", "content": "gs\\_quant.timeseries.algebra.not\\_\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: not_" }, { "path": "docs/functions/gs_quant.timeseries.algebra.or_.rst", "content": "gs\\_quant.timeseries.algebra.or\\_\n=================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: or_" }, { "path": "docs/functions/gs_quant.timeseries.algebra.power.rst", "content": "gs\\_quant.timeseries.algebra.power\n==================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: power" }, { "path": "docs/functions/gs_quant.timeseries.algebra.sqrt.rst", "content": "gs\\_quant.timeseries.algebra.sqrt\n=================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: sqrt" }, { "path": "docs/functions/gs_quant.timeseries.algebra.subtract.rst", "content": "gs\\_quant.timeseries.algebra.subtract\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: subtract" }, { "path": "docs/functions/gs_quant.timeseries.algebra.weighted_sum.rst", "content": "gs\\_quant.timeseries.algebra.weighted\\_sum\n==========================================\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autofunction:: weighted_sum" }, { "path": "docs/functions/gs_quant.timeseries.analysis.compare.rst", "content": "gs\\_quant.timeseries.analysis.compare\n==================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: compare" }, { "path": "docs/functions/gs_quant.timeseries.analysis.count.rst", "content": "gs\\_quant.timeseries.analysis.count\n===================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: count" }, { "path": "docs/functions/gs_quant.timeseries.analysis.diff.rst", "content": "gs\\_quant.timeseries.analysis.diff\n==================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: diff" }, { "path": "docs/functions/gs_quant.timeseries.analysis.first.rst", "content": "gs\\_quant.timeseries.analysis.first\n===================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: first" }, { "path": "docs/functions/gs_quant.timeseries.analysis.lag.rst", "content": "gs\\_quant.timeseries.analysis.lag\n=================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: lag" }, { "path": "docs/functions/gs_quant.timeseries.analysis.last.rst", "content": "gs\\_quant.timeseries.analysis.last\n==================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: last" }, { "path": "docs/functions/gs_quant.timeseries.analysis.last_value.rst", "content": "gs\\_quant.timeseries.analysis.last\\_value\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autofunction:: last_value" }, { "path": "docs/functions/gs_quant.timeseries.backtesting.basket_series.rst", "content": "gs\\_quant.timeseries.backtesting.basket\\_series\n===============================================\n\n.. currentmodule:: gs_quant.timeseries.backtesting\n\n.. autofunction:: basket_series" }, { "path": "docs/functions/gs_quant.timeseries.datetime.align.rst", "content": "gs\\_quant.timeseries.datetime.align\n===================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: align" }, { "path": "docs/functions/gs_quant.timeseries.datetime.bucketize.rst", "content": "gs\\_quant.timeseries.datetime.bucketize\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: bucketize" }, { "path": "docs/functions/gs_quant.timeseries.datetime.date_range.rst", "content": "gs\\_quant.timeseries.datetime.date\\_range\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: date_range" }, { "path": "docs/functions/gs_quant.timeseries.datetime.day.rst", "content": "gs\\_quant.timeseries.datetime.day\n=================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: day" }, { "path": "docs/functions/gs_quant.timeseries.datetime.interpolate.rst", "content": "gs\\_quant.timeseries.datetime.interpolate\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: interpolate" }, { "path": "docs/functions/gs_quant.timeseries.datetime.month.rst", "content": "gs\\_quant.timeseries.datetime.month\n===================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: month" }, { "path": "docs/functions/gs_quant.timeseries.datetime.prepend.rst", "content": "gs\\_quant.timeseries.datetime.prepend\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: prepend" }, { "path": "docs/functions/gs_quant.timeseries.datetime.quarter.rst", "content": "gs\\_quant.timeseries.datetime.quarter\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: quarter" }, { "path": "docs/functions/gs_quant.timeseries.datetime.union.rst", "content": "gs\\_quant.timeseries.datetime.union\n===================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: union" }, { "path": "docs/functions/gs_quant.timeseries.datetime.value.rst", "content": "gs\\_quant.timeseries.datetime.value\n===================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: value" }, { "path": "docs/functions/gs_quant.timeseries.datetime.weekday.rst", "content": "gs\\_quant.timeseries.datetime.weekday\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: weekday" }, { "path": "docs/functions/gs_quant.timeseries.datetime.year.rst", "content": "gs\\_quant.timeseries.datetime.year\n==================================\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autofunction:: year" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.annualize.rst", "content": "gs\\_quant.timeseries.econometrics.annualize\n===========================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: annualize" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.beta.rst", "content": "gs\\_quant.timeseries.econometrics.beta\n======================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: beta" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.change.rst", "content": "gs\\_quant.timeseries.econometrics.change\n========================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: change" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.correlation.rst", "content": "gs\\_quant.timeseries.econometrics.correlation\n=============================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: correlation" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.excess_returns_.rst", "content": "gs\\_quant.timeseries.econometrics.excess\\_returns\\_\n===================================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: excess_returns_" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.index.rst", "content": "gs\\_quant.timeseries.econometrics.index\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: index" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.max_drawdown.rst", "content": "gs\\_quant.timeseries.econometrics.max\\_drawdown\n===============================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: max_drawdown" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.prices.rst", "content": "gs\\_quant.timeseries.econometrics.prices\n========================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: prices" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.returns.rst", "content": "gs\\_quant.timeseries.econometrics.returns\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: returns" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.sharpe_ratio.rst", "content": "gs\\_quant.timeseries.econometrics.sharpe\\_ratio\n===============================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: sharpe_ratio" }, { "path": "docs/functions/gs_quant.timeseries.econometrics.volatility.rst", "content": "gs\\_quant.timeseries.econometrics.volatility\n============================================\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autofunction:: volatility" }, { "path": "docs/functions/gs_quant.timeseries.statistics.cov.rst", "content": "gs\\_quant.timeseries.statistics.cov\n===================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: cov" }, { "path": "docs/functions/gs_quant.timeseries.statistics.exponential_std.rst", "content": "gs\\_quant.timeseries.statistics.exponential\\_std\n================================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: exponential_std" }, { "path": "docs/functions/gs_quant.timeseries.statistics.generate_series.rst", "content": "gs\\_quant.timeseries.statistics.generate\\_series\n================================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: generate_series" }, { "path": "docs/functions/gs_quant.timeseries.statistics.max_.rst", "content": "gs\\_quant.timeseries.statistics.max\\_\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: max_" }, { "path": "docs/functions/gs_quant.timeseries.statistics.mean.rst", "content": "gs\\_quant.timeseries.statistics.mean\n====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: mean" }, { "path": "docs/functions/gs_quant.timeseries.statistics.median.rst", "content": "gs\\_quant.timeseries.statistics.median\n======================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: median" }, { "path": "docs/functions/gs_quant.timeseries.statistics.min_.rst", "content": "gs\\_quant.timeseries.statistics.min\\_\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: min_" }, { "path": "docs/functions/gs_quant.timeseries.statistics.mode.rst", "content": "gs\\_quant.timeseries.statistics.mode\n====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: mode" }, { "path": "docs/functions/gs_quant.timeseries.statistics.percentile.rst", "content": "gs\\_quant.timeseries.statistics.percentile\n==========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: percentile" }, { "path": "docs/functions/gs_quant.timeseries.statistics.percentiles.rst", "content": "gs\\_quant.timeseries.statistics.percentiles\n===========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: percentiles" }, { "path": "docs/functions/gs_quant.timeseries.statistics.product.rst", "content": "gs\\_quant.timeseries.statistics.product\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: product" }, { "path": "docs/functions/gs_quant.timeseries.statistics.range_.rst", "content": "gs\\_quant.timeseries.statistics.range\\_\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: range_" }, { "path": "docs/functions/gs_quant.timeseries.statistics.std.rst", "content": "gs\\_quant.timeseries.statistics.std\n===================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: std" }, { "path": "docs/functions/gs_quant.timeseries.statistics.sum_.rst", "content": "gs\\_quant.timeseries.statistics.sum\\_\n=====================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: sum_" }, { "path": "docs/functions/gs_quant.timeseries.statistics.var.rst", "content": "gs\\_quant.timeseries.statistics.var\n===================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: var" }, { "path": "docs/functions/gs_quant.timeseries.statistics.winsorize.rst", "content": "gs\\_quant.timeseries.statistics.winsorize\n=========================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: winsorize" }, { "path": "docs/functions/gs_quant.timeseries.statistics.zscores.rst", "content": "gs\\_quant.timeseries.statistics.zscores\n=======================================\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autofunction:: zscores" }, { "path": "docs/functions/gs_quant.timeseries.technicals.bollinger_bands.rst", "content": "gs\\_quant.timeseries.technicals.bollinger\\_bands\n================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: bollinger_bands" }, { "path": "docs/functions/gs_quant.timeseries.technicals.exponential_moving_average.rst", "content": "gs\\_quant.timeseries.technicals.exponential\\_moving\\_average\n============================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: exponential_moving_average" }, { "path": "docs/functions/gs_quant.timeseries.technicals.exponential_spread_volatility.rst", "content": "gs\\_quant.timeseries.technicals.exponential\\_spread\\_volatility\n===============================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: exponential_spread_volatility" }, { "path": "docs/functions/gs_quant.timeseries.technicals.exponential_volatility.rst", "content": "gs\\_quant.timeseries.technicals.exponential\\_volatility\n=======================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: exponential_volatility" }, { "path": "docs/functions/gs_quant.timeseries.technicals.macd.rst", "content": "gs\\_quant.timeseries.technicals.macd\n====================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: macd" }, { "path": "docs/functions/gs_quant.timeseries.technicals.moving_average.rst", "content": "gs\\_quant.timeseries.technicals.moving\\_average\n===============================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: moving_average" }, { "path": "docs/functions/gs_quant.timeseries.technicals.relative_strength_index.rst", "content": "gs\\_quant.timeseries.technicals.relative\\_strength\\_index\n=========================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: relative_strength_index" }, { "path": "docs/functions/gs_quant.timeseries.technicals.seasonally_adjusted.rst", "content": "gs\\_quant.timeseries.technicals.seasonally\\_adjusted\n=========================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: seasonally_adjusted" }, { "path": "docs/functions/gs_quant.timeseries.technicals.smoothed_moving_average.rst", "content": "gs\\_quant.timeseries.technicals.smoothed\\_moving\\_average\n=========================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: smoothed_moving_average" }, { "path": "docs/functions/gs_quant.timeseries.technicals.trend.rst", "content": "gs\\_quant.timeseries.technicals.trend\n=========================================================\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autofunction:: trend" }, { "path": "docs/html/.doctrees/environment.pickle", "content": "" }, { "path": "docs/html/.doctrees/index.doctree", "content": "" }, { "path": "docs/html/.doctrees/timeseries.doctree", "content": "" }, { "path": "docs/html/_sources/timeseries.rst.txt", "content": "\nTimeseries Package\n==================\n\nStatistics functions\n--------------------\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autosummary::\n :toctree: functions\n\n generate_series\n\n\nEconometrics functions\n----------------------\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autosummary::\n :toctree: functions\n\n annualize\n lag\n returns\n prices\n diff\n index\n volatility\n correlation\n beta\n" }, { "path": "docs/html/objects.inv", "content": "" }, { "path": "docs/html/searchindex.js", "content": "Search.setIndex({docnames:[\"index\",\"timeseries\"],envversion:{\"sphinx.domains.c\":1,\"sphinx.domains.changeset\":1,\"sphinx.domains.cpp\":1,\"sphinx.domains.javascript\":1,\"sphinx.domains.math\":2,\"sphinx.domains.python\":1,\"sphinx.domains.rst\":1,\"sphinx.domains.std\":1,\"sphinx.ext.intersphinx\":1,\"sphinx.ext.viewcode\":1,sphinx:55},filenames:[\"index.rst\",\"timeseries.rst\"],objects:{},objnames:{},objtypes:{},terms:{\"function\":0,content:0,econometr:0,index:0,modul:0,packag:0,page:0,search:0,statist:0,timeseri:0},titles:[\"Welcome to GS Quant\",\"Timeseries Package\"],titleterms:{\"function\":1,econometr:1,indic:0,packag:1,quant:0,statist:1,tabl:0,timeseri:1,welcom:0}})" }, { "path": "docs/index.rst", "content": ".. gs_quant documentation master file, created by\n sphinx-quickstart on Sat Jan 5 20:53:00 2019.\n You can adapt this file completely to your liking, but it should at least\n contain the root `toctree` directive.\n\nGS Quant API\n============\n\n.. toctree::\n :maxdepth: 2\n :caption: Packages\n\n analytics\n data\n datetime\n instrument \n markets\n models\n risk\n timeseries\n\n:ref:`genindex`\n\n" }, { "path": "docs/instrument.rst", "content": "\nInstrument Package\n==================\n\n\nPriceable\n---------\n\n.. currentmodule:: gs_quant.base\n\n.. autosummary::\n :toctree: classes\n\n Priceable\n\n\nInstruments\n-----------\n\n.. currentmodule:: gs_quant.instrument\n\n.. autosummary::\n :toctree: classes\n :template: instruments.rst\n\n CommodOTCSwap\n EqCliquet\n EqForward\n EqOption\n EqSynthetic\n EqVarianceSwap\n Forward\n FXAccumulator\n FXAccumulatorScheduleLeg\n FXBinary\n FXCorrelationSwap\n FXCorrelationSwapLeg\n FXDoubleKnockout\n FXDoubleOneTouch\n FXDualDoubleKnockout\n FXDualDoubleKnockoutLeg\n FXEuropeanKnockout\n FXForward\n FXKnockout\n FXMultiCrossBinary\n FXMultiCrossBinaryLeg\n FXMultiCrossDoubleBinary\n FXMultiCrossDoubleBinaryLeg\n FXMultiCrossDoubleOneTouch\n FXMultiCrossDoubleOneTouchLeg\n FXOneTouch\n FXOption\n FXOptionLeg\n FXOptionStrategy\n FXPivot\n FXPivotScheduleLeg\n FXShiftingBermForward\n FXTarf\n FXTarfScheduleLeg\n FXVarianceSwap\n FXVolatilitySwap\n FXWorstOf\n FXWorstOfKO\n FXWorstOfKOLeg\n InflationSwap\n IRBasisSwap\n IRCap\n IRCMSOption\n IRCMSOptionStrip\n IRCMSSpreadOption\n IRCMSSpreadOptionStrip\n IRFloor\n IRSwap\n IRSwaption\n IRXccySwap\n IRXccySwapFixFix\n IRXccySwapFixFlt\n Security\n" }, { "path": "docs/make.bat", "content": "@ECHO OFF\n\npushd %~dp0\n\nREM Command file for Sphinx documentation\n\nif \"%SPHINXBUILD%\" == \"\" (\n\tset SPHINXBUILD=sphinx-build\n)\nset SOURCEDIR=.\nset BUILDDIR=_build\n\nif \"%1\" == \"\" goto help\n\n%SPHINXBUILD% >NUL 2>NUL\nif errorlevel 9009 (\n\techo.\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\n\techo.installed, then set the SPHINXBUILD environment variable to point\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\n\techo.may add the Sphinx directory to PATH.\n\techo.\n\techo.If you don't have Sphinx installed, grab it from\n\techo.http://sphinx-doc.org/\n\texit /b 1\n)\n\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\ngoto end\n\n:help\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\n\n:end\npopd\n" }, { "path": "docs/markets.rst", "content": "\nMarkets Package\n===============\n\nPricingContext\n--------------\n\n.. currentmodule:: gs_quant.markets.core\n\n.. autosummary::\n :toctree: classes\n\n PricingContext\n\n\nHistoricalPricingContext\n------------------------\n\n.. currentmodule:: gs_quant.markets.historical\n\n.. autosummary::\n :toctree: classes\n\n HistoricalPricingContext\n\n\nBasket\n----------\n\n.. currentmodule:: gs_quant.markets.baskets\n\n.. autosummary::\n :toctree: classes\n \n Basket\n\n.. currentmodule:: gs_quant.markets\n\n.. autosummary::\n :toctree: modules\n\n indices_utils\n\n\nIndex\n----------\n\n.. currentmodule:: gs_quant.markets.index\n\n.. autosummary::\n :toctree: classes\n\n Index\n\n.. currentmodule:: gs_quant.markets\n\n.. autosummary::\n :toctree: modules\n\n indices_utils\n\n\nPortfolio\n---------\n\n.. currentmodule:: gs_quant.markets.portfolio\n\n.. autosummary::\n :toctree: classes\n\n Portfolio\n Grid\n\n\nPortfolioManager\n----------------\n\n.. currentmodule:: gs_quant.markets.portfolio_manager\n\n.. autosummary::\n :toctree: classes\n :template: portfolio_manager.rst\n\n PortfolioManager\n\n\nPositionSet\n-----------\n\n.. currentmodule:: gs_quant.markets.position_set\n\n.. autosummary::\n :toctree: classes\n\n Position\n PositionSet\n\n\nSecurities\n----------\n\n.. currentmodule:: gs_quant.markets.securities\n\n.. autosummary::\n :toctree: classes\n\n Asset\n AssetClass\n AssetIdentifier\n AssetType\n Stock\n SecurityMaster\n\n\nReport\n------------\n\n.. currentmodule:: gs_quant.markets.report\n\n.. autosummary::\n :toctree: classes\n :template: report.rst\n\n ReportJobFuture\n Report\n PerformanceReport\n FactorRiskReport\n ThematicReport\n\n\nOptimizer\n------------\n\n.. currentmodule:: gs_quant.markets.optimizer\n\n.. autosummary::\n :toctree: classes\n :template: report.rst\n\n AssetUniverse\n AssetConstraint\n FactorConstraint\n MaxProportionOfRiskByGroupConstraint\n CountryConstraint\n IndustryConstraint\n SectorConstraint\n TurnoverConstraint\n ConstraintPriorities\n OptimizerSettings\n OptimizerUniverse\n OptimizerStrategy\n" }, { "path": "docs/models.rst", "content": "\nModels Package\n==============\n\nEpidemiology\n------------\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autosummary::\n :toctree: classes\n\n SIR\n SEIR\n EpidemicModel\n\n\nRiskModel\n------------\n\n.. currentmodule:: gs_quant.models.risk_model\n\n.. autosummary::\n :toctree: classes\n :template: report.rst\n\n RiskModel\n MarqueeRiskModel\n FactorRiskModel\n MacroRiskModel\n ThematicRiskModel\n\n\n\n" }, { "path": "docs/module/models.rst", "content": "\nModels Package\n==============\n\nEpidemiology\n------------\n\n.. currentmodule:: gs_quant.models.epidemiology\n\n.. autosummary::\n :toctree: classes\n\n SIR\n SEIR\n EpidemicModel\n\n\nRiskModel\n------------\n\n.. currentmodule:: gs_quant.models.risk_model\n\n.. autosummary::\n :toctree: classes\n :template: processors.rst\n\n RiskModel\n FactorRiskModel\n MacroRiskModel\n ThematicRiskModel\n\n\n\n\n" }, { "path": "docs/modules/gs_quant.markets.indices_utils.rst", "content": "gs\\_quant.markets.indices\\_utils\n================================\n\n.. automodule:: gs_quant.markets.indices_utils\n\n \n .. rubric:: Functions\n\n .. autosummary::\n \n get_constituents_dataset_coverage\n get_flagship_baskets\n get_flagships_constituents\n get_flagships_performance\n get_flagships_with_assets\n get_my_baskets\n \n .. rubric:: Classes\n\n .. autosummary::\n \n BasketType\n CorporateActionType\n CustomBasketStyles\n IndicesDatasets\n PriceType\n Region\n ResearchBasketStyles\n ReturnType\n STSIndexType\n WeightingStrategy\n " }, { "path": "docs/risk.rst", "content": "\nRisk Package\n============\n\nFunctions\n---------\n.. currentmodule:: gs_quant.risk\n\n.. autofunction:: aggregate_risk\n.. autofunction:: subtract_risk\n.. autofunction:: sort_risk\n\nMeasures\n--------\n.. autodata:: DollarPrice\n.. autodata:: Price\n.. autodata:: ForwardPrice\n.. autodata:: Theta\n.. autodata:: BaseCPI\n.. autodata:: CommodDelta\n.. autodata:: CommodTheta\n.. autodata:: CommodVega\n.. autodata:: EqDelta\n.. autodata:: EqGamma\n.. autodata:: EqVega\n.. autodata:: EqSpot\n.. autodata:: EqAnnualImpliedVol\n.. autodata:: FairVarStrike\n.. autodata:: FairVolStrike\n.. autodata:: FXDelta\n.. autodata:: FXGamma\n.. autodata:: FXVega\n.. autodata:: FXSpot\n.. autodata:: FXAnnualImpliedVol\n.. autodata:: FXAnnualATMImpliedVol\n.. autodata:: InflationDelta\n.. autodata:: IRBasis\n.. autodata:: IRDelta\n.. autodata:: IRXccyDelta\n.. autodata:: IRGammaParallel\n.. autodata:: IRGammaParallelLocalCcy\n.. autodata:: IRVega\n.. autodata:: IRAnnualImpliedVol\n.. autodata:: IRAnnualATMImpliedVol\n.. autodata:: IRDailyImpliedVol\n.. autodata:: IRSpotRate\n.. autodata:: IRFwdRate\n" }, { "path": "docs/searchdataext/__init__.py", "content": "import json\nimport os\nimport sphinx.search\nfrom pathlib import Path\n\nfrom docutils import nodes\n\ndef setup(app):\n print(\"Adding searchdataext output\")\n sphinx.search.IndexBuilder = IndexBuilder\n\n\nclass IndexBuilder(sphinx.search.IndexBuilder):\n def __init__(self, env, lang, options, scoring):\n super(IndexBuilder, self).__init__(env, lang, options, scoring)\n self._doc_collector=[]\n self._outputfile = Path(os.path.dirname(__file__), '..', '_build', 'search', 'search-index.json').resolve().absolute()\n\n def feed(self, docname, filename, title, doctree):\n super(IndexBuilder, self).feed(docname, filename, title, doctree)\n\n visitor = sphinx.search.WordCollector(doctree, self.lang)\n doctree.walk(visitor)\n\n # index page should be marked as 'Home' for developer site search results\n if ' '.join(visitor.found_title_words) == '':\n visitor.found_title_words = ['Home']\n\n newdoc = {\n 'title': ' '.join(visitor.found_title_words),\n 'body': ' '.join(visitor.found_words),\n 'key': docname\n }\n\n self._doc_collector.append(newdoc)\n\n def freeze(self):\n data = super(IndexBuilder, self).freeze()\n\n def ensure_dir(file_path):\n if not os.path.exists(file_path):\n os.makedirs(file_path)\n\n output_file_path = str(self._outputfile)\n print('Writing search data to ' + output_file_path)\n ensure_dir(os.path.dirname(output_file_path))\n with open(str(self._outputfile), 'w+') as outfile:\n json.dump(self._doc_collector, outfile, sort_keys=True, indent=4)\n return data\n\n" }, { "path": "docs/timeseries.rst", "content": "\nTimeseries Package\n==================\n\nAlgebra\n-------\n\n.. currentmodule:: gs_quant.timeseries.algebra\n\n.. autosummary::\n :toctree: functions\n\n abs_\n add\n and_\n ceil\n divide\n exp\n filter_\n filter_dates\n floor\n floordiv\n if_\n log\n multiply\n not_\n or_\n power\n sqrt\n subtract\n weighted_sum\n\n\nAnalysis\n--------\n\n.. currentmodule:: gs_quant.timeseries.analysis\n\n.. autosummary::\n :toctree: functions\n\n diff\n first\n last\n last_value\n count\n lag\n\n\nBacktesting\n-----------\n\n.. currentmodule:: gs_quant.timeseries.backtesting\n\n.. autosummary::\n :toctree: functions\n\n basket_series\n\n.. autosummary::\n :toctree: classes\n :template: timeseries_class.rst\n\n Basket\n\nDate / Time\n-----------\n\n\n.. currentmodule:: gs_quant.timeseries.datetime\n\n.. autosummary::\n :toctree: functions\n\n align\n interpolate\n value\n day\n weekday\n month\n year\n quarter\n date_range\n prepend\n union\n bucketize\n\n.. autosummary::\n :toctree: classes\n :template: timeseries_class.rst\n\n Window\n\n\nEconometrics\n------------\n\n\n.. currentmodule:: gs_quant.timeseries.econometrics\n\n.. autosummary::\n :toctree: functions\n\n annualize\n beta\n change\n correlation\n excess_returns_\n index\n max_drawdown\n prices\n returns\n sharpe_ratio\n volatility\n\n\nStatistics\n----------\n\n.. currentmodule:: gs_quant.timeseries.statistics\n\n.. autosummary::\n :toctree: functions\n\n cov\n exponential_std\n generate_series\n max_ \n mean\n median \n min_\n mode\n percentile\n percentiles\n product\n range_\n std\n sum_\n var\n winsorize\n zscores\n\n.. autosummary::\n :toctree: classes\n :template: timeseries_class.rst\n\n LinearRegression\n RollingLinearRegression\n SIRModel\n SEIRModel\n\nTechnical Analysis\n------------------\n\n\n.. currentmodule:: gs_quant.timeseries.technicals\n\n.. autosummary::\n :toctree: functions\n\n bollinger_bands\n moving_average\n exponential_moving_average\n exponential_volatility\n exponential_spread_volatility\n smoothed_moving_average\n relative_strength_index\n macd\n " }, { "path": "gs_quant/__init__.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\nimport sys\nfrom importlib.metadata import version as get_lib_version, PackageNotFoundError\n\nfrom ._version import get_versions\n\nname = \"gs_quant\"\n__version__ = get_versions()['version']\n\n\ndef get_environment_summary():\n \"\"\"\n Returns a summary of the Python version and the versions of specific libraries.\n \"\"\"\n libraries = ['gs_quant', 'gs_quant_internal', 'numpy', 'pandas']\n summary = {\n 'Python Version': sys.version,\n }\n for lib in libraries:\n try:\n lib_version = get_lib_version(lib)\n except PackageNotFoundError:\n lib_version = 'Not Installed'\n summary[lib] = lib_version\n return summary\n\n\n__summary__ = get_environment_summary()\ndel get_versions\n\nversion = __version__\nsummary = __summary__\n\n# Set up PyXll, if available\ntry:\n from .xl_interface.instrument_generation import install_hook\n\n install_hook()\nexcept ModuleNotFoundError:\n pass\n\n# Jupyter needs nest_asyncio to avoid event loop issues\ntry:\n from IPython import get_ipython\n\n ipython = get_ipython()\n if ipython and 'IPKernelApp' in get_ipython().config:\n import nest_asyncio\n\n nest_asyncio.apply()\nexcept ImportError:\n pass\n\n# Setup tracing for Jupyter\ntry:\n from gs_quant.tracing import tracing # pylint: disable=unused-import\nexcept ImportError:\n pass\n" }, { "path": "gs_quant/_version.py", "content": "\n# This file helps to compute a version number in source trees obtained from\n# git-archive tarball (such as those provided by githubs download-from-tag\n# feature). Distribution tarballs (built by setup.py sdist) and build\n# directories (produced by setup.py build) will contain a much shorter file\n# that just contains the computed version number.\n\n# This file is released into the public domain.\n# Generated by versioneer-0.28\n# https://github.com/python-versioneer/python-versioneer\n\n\"\"\"Git implementation of _version.py.\"\"\"\n\nimport errno\nimport os\nimport re\nimport subprocess\nimport sys\nfrom typing import Callable, Dict\nimport functools\n\n\ndef get_keywords():\n \"\"\"Get the keywords needed to look up the version information.\"\"\"\n # these strings will be replaced by git during git-archive.\n # setup.py/versioneer.py will grep for the variable names, so they must\n # each be defined on a line of their own. _version.py will just call\n # get_keywords().\n git_refnames = \"$Format:%d$\"\n git_full = \"$Format:%H$\"\n git_date = \"$Format:%ci$\"\n keywords = {\"refnames\": git_refnames, \"full\": git_full, \"date\": git_date}\n return keywords\n\n\nclass VersioneerConfig:\n \"\"\"Container for Versioneer configuration parameters.\"\"\"\n\n\ndef get_config():\n \"\"\"Create, populate and return the VersioneerConfig() object.\"\"\"\n # these strings are filled in when 'setup.py versioneer' creates\n # _version.py\n cfg = VersioneerConfig()\n cfg.VCS = \"git\"\n cfg.style = \"pep440\"\n cfg.tag_prefix = \"release-\"\n cfg.parentdir_prefix = \"gs_quant-\"\n cfg.versionfile_source = \"gs_quant/_version.py\"\n cfg.verbose = False\n return cfg\n\n\nclass NotThisMethod(Exception):\n \"\"\"Exception raised if a method is not valid for the current scenario.\"\"\"\n\n\nLONG_VERSION_PY: Dict[str, str] = {}\nHANDLERS: Dict[str, Dict[str, Callable]] = {}\n\n\ndef register_vcs_handler(vcs, method): # decorator\n \"\"\"Create decorator to mark a method as the handler of a VCS.\"\"\"\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n return decorate\n\n\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,\n env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n process = None\n\n popen_kwargs = {}\n if sys.platform == \"win32\":\n # This hides the console window if pythonw.exe is used\n startupinfo = subprocess.STARTUPINFO()\n startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n popen_kwargs[\"startupinfo\"] = startupinfo\n\n for command in commands:\n try:\n dispcmd = str([command] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n process = subprocess.Popen([command] + args, cwd=cwd, env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr\n else None), **popen_kwargs)\n break\n except OSError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None, None\n stdout = process.communicate()[0].strip().decode()\n if process.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, process.returncode\n return stdout, process.returncode\n\n\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for _ in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\"version\": dirname[len(parentdir_prefix):],\n \"full-revisionid\": None,\n \"dirty\": False, \"error\": None, \"date\": None}\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\"Tried directories %s but none started with prefix %s\" %\n (str(rootdirs), parentdir_prefix))\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")\n\n\n@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n with open(versionfile_abs, \"r\") as fobj:\n for line in fobj:\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n except OSError:\n pass\n return keywords\n\n\n@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n \"\"\"Get version information from git keywords.\"\"\"\n if \"refnames\" not in keywords:\n raise NotThisMethod(\"Short version file found\")\n date = keywords.get(\"date\")\n if date is not None:\n # Use only the last line. Previous lines may contain GPG signature\n # information.\n date = date.splitlines()[-1]\n\n # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = {r.strip() for r in refnames.strip(\"()\").split(\",\")}\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = {r for r in refs if re.search(r'\\d', r)}\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n if verbose:\n print(\"likely tags: %s\" % \",\".join(sorted(tags)))\n for ref in sorted(tags):\n # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n if ref.startswith(tag_prefix):\n r = ref[len(tag_prefix):]\n # Filter out refs that exactly match prefix or that don't start\n # with a number once the prefix is stripped (mostly a concern\n # when prefix is '')\n if not re.match(r'\\d', r):\n continue\n if verbose:\n print(\"picking %s\" % r)\n return {\"version\": r,\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": None,\n \"date\": date}\n # no suitable tags, so version is \"0+unknown\", but full hex is still there\n if verbose:\n print(\"no suitable tags, using unknown + full revision id\")\n return {\"version\": \"0+unknown\",\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": \"no suitable tags\", \"date\": None}\n\n\n@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n # GIT_DIR can interfere with correct operation of Versioneer.\n # It may be intended to be passed to the Versioneer-versioned project,\n # but that should not change where we get our version from.\n env = os.environ.copy()\n env.pop(\"GIT_DIR\", None)\n runner = functools.partial(runner, env=env)\n\n _, rc = runner(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root,\n hide_stderr=not verbose)\n if rc != 0:\n if verbose:\n print(\"Directory %s not under git control\" % root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = runner(GITS, [\n \"describe\", \"--tags\", \"--dirty\", \"--always\", \"--long\",\n \"--match\", f\"{tag_prefix}[[:digit:]]*\"\n ], cwd=root)\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = runner(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n branch_name, rc = runner(GITS, [\"rev-parse\", \"--abbrev-ref\", \"HEAD\"],\n cwd=root)\n # --abbrev-ref was added in git-1.6.3\n if rc != 0 or branch_name is None:\n raise NotThisMethod(\"'git rev-parse --abbrev-ref' returned error\")\n branch_name = branch_name.strip()\n\n if branch_name == \"HEAD\":\n # If we aren't exactly on a branch, pick a branch which represents\n # the current commit. If all else fails, we are on a branchless\n # commit.\n branches, rc = runner(GITS, [\"branch\", \"--contains\"], cwd=root)\n # --contains was added in git-1.5.4\n if rc != 0 or branches is None:\n raise NotThisMethod(\"'git branch --contains' returned error\")\n branches = branches.split(\"\\n\")\n\n # Remove the first line if we're running detached\n if \"(\" in branches[0]:\n branches.pop(0)\n\n # Strip off the leading \"* \" from the list of branches.\n branches = [branch[2:] for branch in branches]\n if \"master\" in branches:\n branch_name = \"master\"\n elif not branches:\n branch_name = None\n else:\n # Pick the first branch that is returned. Good or bad.\n branch_name = branches[0]\n\n pieces[\"branch\"] = branch_name\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[:git_describe.rindex(\"-dirty\")]\n\n # now we have TAG-NUM-gHEX or HEX\n\n if \"-\" in git_describe:\n # TAG-NUM-gHEX\n mo = re.search(r'^(.+)-(\\d+)-g([0-9a-f]+)$', git_describe)\n if not mo:\n # unparsable. Maybe git-describe is misbehaving?\n pieces[\"error\"] = (\"unable to parse git-describe output: '%s'\"\n % describe_out)\n return pieces\n\n # tag\n full_tag = mo.group(1)\n if not full_tag.startswith(tag_prefix):\n if verbose:\n fmt = \"tag '%s' doesn't start with prefix '%s'\"\n print(fmt % (full_tag, tag_prefix))\n pieces[\"error\"] = (\"tag '%s' doesn't start with prefix '%s'\"\n % (full_tag, tag_prefix))\n return pieces\n pieces[\"closest-tag\"] = full_tag[len(tag_prefix):]\n\n # distance: number of commits since tag\n pieces[\"distance\"] = int(mo.group(2))\n\n # commit: short hex revision ID\n pieces[\"short\"] = mo.group(3)\n\n else:\n # HEX: no tags\n pieces[\"closest-tag\"] = None\n out, rc = runner(GITS, [\"rev-list\", \"HEAD\", \"--left-right\"], cwd=root)\n pieces[\"distance\"] = len(out.split()) # total number of commits\n\n # commit date: see ISO-8601 comment in git_versions_from_keywords()\n date = runner(GITS, [\"show\", \"-s\", \"--format=%ci\", \"HEAD\"], cwd=root)[0].strip()\n # Use only the last line. Previous lines may contain GPG signature\n # information.\n date = date.splitlines()[-1]\n pieces[\"date\"] = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n\n return pieces\n\n\ndef plus_or_dot(pieces):\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"\n\n\ndef render_pep440(pieces):\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_branch(pieces):\n \"\"\"TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .\n\n The \".dev0\" means not master branch. Note that .dev0 sorts backwards\n (a feature branch will appear \"older\" than the master branch).\n\n Exceptions:\n 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0\"\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += \"+untagged.%d.g%s\" % (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef pep440_split_post(ver):\n \"\"\"Split pep440 version string at the post-release segment.\n\n Returns the release segments before the post-release and the\n post-release version number (or -1 if no post-release segment is present).\n \"\"\"\n vc = str.split(ver, \".post\")\n return vc[0], int(vc[1] or 0) if len(vc) == 2 else None\n\n\ndef render_pep440_pre(pieces):\n \"\"\"TAG[.postN.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post0.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n if pieces[\"distance\"]:\n # update the post release segment\n tag_version, post_version = pep440_split_post(pieces[\"closest-tag\"])\n rendered = tag_version\n if post_version is not None:\n rendered += \".post%d.dev%d\" % (post_version + 1, pieces[\"distance\"])\n else:\n rendered += \".post0.dev%d\" % (pieces[\"distance\"])\n else:\n # no commits, use the tag as the version\n rendered = pieces[\"closest-tag\"]\n else:\n # exception #1\n rendered = \"0.post0.dev%d\" % pieces[\"distance\"]\n return rendered\n\n\ndef render_pep440_post(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered\n\n\ndef render_pep440_post_branch(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .\n\n The \".dev0\" means not master branch.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_old(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered\n\n\ndef render_git_describe(pieces):\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered\n\n\ndef render_git_describe_long(pieces):\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered\n\n\ndef render(pieces, style):\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None}\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-branch\":\n rendered = render_pep440_branch(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-post-branch\":\n rendered = render_pep440_post_branch(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\"version\": rendered, \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"], \"error\": None,\n \"date\": pieces.get(\"date\")}\n\n\ndef get_versions():\n \"\"\"Get version information or return default if unable to do so.\"\"\"\n # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have\n # __file__, we can work backwards from there to the root. Some\n # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which\n # case we can only use expanded keywords.\n\n cfg = get_config()\n verbose = cfg.verbose\n\n try:\n return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,\n verbose)\n except NotThisMethod:\n pass\n\n try:\n root = os.path.realpath(__file__)\n # versionfile_source is the relative path from the top of the source\n # tree (where the .git directory might live) to this file. Invert\n # this to find the root from __file__.\n for _ in cfg.versionfile_source.split('/'):\n root = os.path.dirname(root)\n except NameError:\n return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to find root of source tree\",\n \"date\": None}\n\n try:\n pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)\n return render(pieces, cfg.style)\n except NotThisMethod:\n pass\n\n try:\n if cfg.parentdir_prefix:\n return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)\n except NotThisMethod:\n pass\n\n return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to compute version\", \"date\": None}\n" }, { "path": "gs_quant/analytics/__init__.py", "content": "" }, { "path": "gs_quant/analytics/common/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .constants import *\nfrom .enumerators import *\n" }, { "path": "gs_quant/analytics/common/constants.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nDATAGRID_HELP_MSG: str = (\n '\\nDatagrid is currently experimental. The API is likely to change. For'\n ' questions contact gs-marquee-markets@gs.com\\n'\n)\n\nDATA_CELL_NOT_CALCULATED: str = 'Cell has not been calculated'\n\nCELL_GRAPH: str = 'cell_graph'\nQUERIES_TO_PROCESSORS: str = 'queries_to_processors'\nDATA_COORDINATE: str = 'dataCoordinate'\nPROCESSOR: str = 'processor'\nPROCESSOR_NAME: str = 'processorName'\nENTITY: str = 'entity'\nENTITY_ID: str = 'entityId'\nENTITY_TYPE: str = 'entityType'\nDATE: str = 'date'\nDATETIME: str = 'datetime'\nTYPE: str = 'type'\nLIST: str = 'list'\nVALUE: str = 'value'\nPARAMETER: str = 'parameter'\nPARAMETERS: str = 'parameters'\nDATA_ROW: str = 'dataRow'\nREFERENCE: str = 'reference'\nRELATIVE_DATE: str = 'relativeDate'\n" }, { "path": "gs_quant/analytics/common/enumerators.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom enum import Enum\n\n\nclass ScaleShape(Enum):\n DIAMOND = 'diamond'\n PIPE = 'pipe'\n BAR = 'bar'\n" }, { "path": "gs_quant/analytics/common/helpers.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom typing import List, Dict\n\nfrom gs_quant.analytics.common import TYPE, DATA_ROW, PROCESSOR, REFERENCE, PARAMETER, ENTITY_ID, ENTITY_TYPE\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom gs_quant.entities.entity import Entity\nfrom gs_quant.errors import MqValueError, MqRequestError\n\n_logger = logging.getLogger(__name__)\n\n\ndef is_of_builtin_type(obj):\n return type(obj).__module__ in ('builtins', '__builtin__')\n\n\ndef resolve_entities(reference_list: List[Dict], entity_cache: Dict = None):\n \"\"\"\n Utility function to fetch entities (assets, countries, etc.). Allows us to split functionality that requires data\n fetching.\n :param reference_list: A list of entity references (entityId and entityType dictionaries)\n :param entity_cache: Map of entity id to the entity for external cache management\n :return: None\n \"\"\"\n entity_cache = entity_cache or {}\n for reference in reference_list:\n # Check if the entity is in the cache\n entity_id = reference.get(ENTITY_ID)\n if entity_id in entity_cache:\n entity = entity_cache[entity_id]\n else:\n try:\n entity = Entity.get(entity_id, 'MQID', reference.get(ENTITY_TYPE))\n except MqRequestError as e:\n _logger.warning(e)\n entity = entity_id\n\n if reference[TYPE] == DATA_ROW:\n # If the reference is for a data row, simply set the entity of the row.\n reference[REFERENCE].entity = entity\n elif reference[TYPE] == PROCESSOR:\n # If the reference is for a processor, set the given parameter as the entity.\n setattr(reference[REFERENCE], reference[PARAMETER], entity)\n data_query_info = reference[REFERENCE].children.get(reference[PARAMETER])\n if not data_query_info:\n raise MqValueError(\n f'{reference[PARAMETER]} does not exist in children of {reference[REFERENCE].__class__.__name__}'\n )\n data_query_info.entity = entity\n\n\ndef get_rdate_cache_key(rule: str, base_date: str, currencies: List[str], exchanges: List[str]) -> str:\n return f'{rule}::{base_date}::{currencies}::{exchanges}'\n\n\ndef get_entity_rdate_key(entity_id: str, rule: str, base_date):\n return f'{entity_id}::{rule}::{base_date}'\n\n\ndef get_entity_rdate_key_from_rdate(entity_id: str, rdate: RelativeDate):\n base_date = str(rdate.base_date) if rdate.base_date_passed_in else None\n return f'{entity_id}::{rdate.rule}::{base_date}'\n" }, { "path": "gs_quant/analytics/core/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .processor import BaseProcessor\n" }, { "path": "gs_quant/analytics/core/processor.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport asyncio\nimport datetime as dt\nimport functools\nimport logging\nimport uuid\nfrom abc import ABCMeta, abstractmethod\nfrom concurrent.futures.process import ProcessPoolExecutor\nfrom dataclasses import dataclass\nfrom enum import Enum, EnumMeta\nfrom typing import List, Optional, Union, Dict, get_type_hints, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom pydash import decapitalize, get\n\nfrom gs_quant.analytics.common import (\n TYPE,\n PROCESSOR,\n PARAMETERS,\n DATA_COORDINATE,\n ENTITY,\n VALUE,\n DATE,\n DATETIME,\n PROCESSOR_NAME,\n ENTITY_ID,\n ENTITY_TYPE,\n PARAMETER,\n REFERENCE,\n RELATIVE_DATE,\n LIST,\n)\nfrom gs_quant.analytics.common.enumerators import ScaleShape\nfrom gs_quant.analytics.common.helpers import is_of_builtin_type, get_entity_rdate_key_from_rdate\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.common import Currency\nfrom gs_quant.data import DataCoordinate, DataFrequency\nfrom gs_quant.data.coordinate import DateOrDatetime\nfrom gs_quant.data.query import DataQuery, DataQueryType\nfrom gs_quant.entities.entity import Entity\nfrom gs_quant.timeseries import Window, Returns, RelativeDate\n\nPARSABLE_OBJECT_MAP = {'window': Window, 'returns': Returns, 'currency': Currency, 'scaleShape': ScaleShape}\n\n_logger = logging.getLogger(__name__)\n\n\n@dataclass\nclass DataQueryInfo:\n attr: str\n processor: 'BaseProcessor'\n query: DataQuery\n entity: Entity\n data: pd.Series = None\n\n\n@dataclass\nclass MeasureQueryInfo:\n attr: str\n processor: 'BaseProcessor'\n entity: Entity\n\n\nDateOrDatetimeOrRDate = Union[DateOrDatetime, RelativeDate]\n\n\nclass BaseProcessor(metaclass=ABCMeta):\n def __init__(self, **kwargs):\n self.id = f'{self.__class__.__name__}-{str(uuid.uuid4())}'\n self.value: ProcessorResult = ProcessorResult(False, 'Value not set')\n self.parent: Optional[Union[BaseProcessor]] = None\n self.parent_attr: Optional[str] = None\n self.children: Dict[str, Union[DataCoordinateOrProcessor, DataQueryInfo]] = {}\n self.children_data: Dict[str, ProcessorResult] = {}\n self.data_cell = None\n self.last_value = kwargs.get('last_value', False)\n self.measure_processor = kwargs.get('measure_processor', False)\n\n @abstractmethod\n def process(self, *args):\n \"\"\"Handle the calculation of the data with given coordinate data series\"\"\"\n pass\n\n def post_process(self):\n if self.last_value:\n if (\n isinstance(self.value, ProcessorResult)\n and self.value.success\n and isinstance(self.value.data, pd.Series)\n and not self.value.data.empty\n ):\n self.value.data = self.value.data.iloc[-1:]\n\n def __handle_date_range(self, result, rdate_entity_map: Dict[str, dt.date]):\n \"\"\"\n Applies a date/datetime mask on the result using the start/end parameters on a processor\n :param result:\n :param rdate_entity_map: map of entity, rule, base_date to date value\n :return: None\n \"\"\"\n if not isinstance(result, ProcessorResult) or not result.success:\n return\n start, end = get(self, 'start'), get(self, 'end')\n if not (start or end):\n return\n\n entity = self.data_cell.entity\n # If the entity wasn't found default to base rule (no entity)\n entity_id = '' if isinstance(entity, str) else entity.get_marquee_id()\n if start and end:\n if isinstance(start, RelativeDate):\n key = get_entity_rdate_key_from_rdate(entity_id, start)\n start = rdate_entity_map[key]\n if isinstance(end, RelativeDate):\n key = get_entity_rdate_key_from_rdate(entity_id, end)\n end = rdate_entity_map[key]\n mask = (result.data.index >= np.datetime64(start)) & (result.data.index <= np.datetime64(end))\n elif start:\n if isinstance(start, RelativeDate):\n key = get_entity_rdate_key_from_rdate(entity_id, start)\n start = rdate_entity_map[key]\n mask = result.data.index >= np.datetime64(start)\n else:\n if isinstance(end, RelativeDate):\n key = get_entity_rdate_key_from_rdate(entity_id, end)\n end = rdate_entity_map[key]\n mask = result.data.index >= np.datetime64(end)\n\n result.data = result.data[mask]\n\n async def update(\n self,\n attribute: str,\n result: ProcessorResult,\n rdate_entity_map: Dict[str, dt.date],\n pool: ProcessPoolExecutor = None,\n query_info: Union[DataQueryInfo, MeasureQueryInfo] = None,\n ):\n \"\"\"Handle the update of a single coordinate and recalculate the value\n\n :param attribute: Attribute alinging to data coordinate in the processor\n :param result: Processor result including success and series from data query\n \"\"\"\n if not self.measure_processor:\n self.__handle_date_range(result, rdate_entity_map)\n self.children_data[attribute] = result\n\n if isinstance(result, ProcessorResult):\n if result.success:\n try:\n if pool:\n if self.measure_processor:\n value = await asyncio.get_running_loop().run_in_executor(\n pool, functools.partial(self.process, query_info.entity)\n )\n else:\n value = await asyncio.get_running_loop().run_in_executor(pool, self.process)\n self.value = value\n else:\n if self.measure_processor:\n self.process(query_info.entity)\n else:\n self.process()\n self.post_process()\n except Exception as e:\n self.value = ProcessorResult(\n False, f'Error Calculating processor {self.__class__.__name__} due to {e}'\n )\n else:\n self.value = result\n\n @abstractmethod\n def get_plot_expression(self):\n \"\"\"Returns a plot expression used to go from grid to plottool\"\"\"\n pass\n\n def __add_required_rdates(self, entity: Entity, rdate_entity_map: Dict[str, Set[Tuple]]):\n start, end = get(self, 'start'), get(self, 'end')\n entity_id = entity.get_marquee_id() if isinstance(entity, Entity) else ''\n\n if isinstance(start, RelativeDate):\n base_date = str(start.base_date) if start.base_date_passed_in else None\n rdate_entity_map[entity_id].add((start.rule, base_date))\n if isinstance(end, RelativeDate):\n base_date = str(end.base_date) if end.base_date_passed_in else None\n rdate_entity_map[entity_id].add((end.rule, base_date))\n\n def build_graph(\n self,\n entity: Entity,\n cell,\n queries: List[Union[DataQueryInfo, MeasureQueryInfo]],\n rdate_entity_map: Dict[str, Set[Tuple]],\n overrides: Optional[List],\n ):\n \"\"\"Generates the nested cell graph and keeps a map of leaf data queries to processors\"\"\"\n self.data_cell = cell\n self.__add_required_rdates(entity, rdate_entity_map)\n\n attributes = self.__dict__\n\n if self.measure_processor:\n queries.append(MeasureQueryInfo(attr='a', processor=self, entity=entity))\n\n for attr_name, child in self.children.items():\n if isinstance(child, DataCoordinate):\n # Override coordinate dimensions\n if overrides:\n override_dimensions = list(filter(lambda x: x.coordinate == child, overrides))\n if len(override_dimensions):\n use_default = True\n for override in overrides:\n if override.coordinate_id == child.id:\n child.set_dimensions(override.dimensions)\n use_default = False\n break\n if use_default and overrides[0].coordinate_id is None:\n child.set_dimensions(overrides[0].dimensions)\n\n if child.frequency == DataFrequency.DAILY:\n query = DataQuery(coordinate=child, start=attributes.get('start'), end=attributes.get('end'))\n else:\n query = DataQuery(coordinate=child, query_type=DataQueryType.LAST)\n\n # track the leaf data query\n queries.append(DataQueryInfo(attr=attr_name, processor=self, query=query, entity=entity))\n\n elif isinstance(child, BaseProcessor):\n # Set the children's parent fields\n child.parent = self\n child.parent_attr = attr_name\n child.build_graph(entity, cell, queries, rdate_entity_map, overrides)\n\n elif isinstance(child, DataQueryInfo):\n child.parent = self\n child.parent_attr = attr_name\n child.processor = self\n queries.append(child)\n\n async def calculate(\n self,\n attribute: str,\n result: ProcessorResult,\n rdate_entity_map: Dict[str, dt.date],\n pool: ProcessPoolExecutor = None,\n query_info: Union[DataQueryInfo, MeasureQueryInfo] = None,\n ):\n \"\"\"Sets the result on the processor and recursively calls parent to set and calculate value\n\n :param attribute: Attribute alinging to data coordinate in the processor\n :param result: Processor result including success and series from data query\n \"\"\"\n # update the result\n await self.update(attribute, result, rdate_entity_map, pool, query_info)\n\n # if there is a parent, traverse up and recompute\n if self.parent:\n value: ProcessorResult = self.value\n if isinstance(value, ProcessorResult):\n # only traverse if processor successful calculates\n if value.success:\n if isinstance(self.parent, BaseProcessor):\n await self.parent.calculate(self.parent_attr, value, rdate_entity_map, pool)\n else:\n # Must be the data cell\n self.parent.update(value)\n else:\n self.data_cell.value = value # Put the error on the data cell\n self.data_cell.updated_time = f'{dt.datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%S.%f\")[:-3]}Z'\n\n def as_dict(self) -> Dict:\n \"\"\"\n Create a dictionary representation of the processor. Used for eventually turning the processor into json for\n API usage. Allows for nested processors.\n :return: Dictionary representation of the processor\n \"\"\"\n processor = {TYPE: PROCESSOR, PARAMETERS: self.get_default_params()}\n\n if isinstance(self, BaseProcessor):\n processor[PROCESSOR_NAME] = self.__class__.__name__\n\n parameters = processor[PARAMETERS]\n\n for parameter, alias in get_type_hints(self.__init__).items():\n if alias in (\n DataCoordinateOrProcessor,\n DataCoordinate,\n Union[DataCoordinateOrProcessor, None],\n BaseProcessor,\n ):\n # If the parameter is a DataCoordinate or processor, recursively call as_dict()\n attribute = self.children[parameter]\n if attribute is None:\n continue\n parameters[parameter] = {}\n this_parameter = parameters[parameter]\n\n if isinstance(attribute, BaseProcessor):\n this_parameter[TYPE] = PROCESSOR\n this_parameter[PROCESSOR_NAME] = attribute.__class__.__name__\n elif isinstance(attribute, DataCoordinate):\n this_parameter[TYPE] = DATA_COORDINATE\n else:\n # Continue if not an expected type or None\n continue\n\n this_parameter.update(**attribute.as_dict())\n else:\n # Handle the less complex types such as python built in types, enums, entities, datetimes\n attribute = getattr(self, parameter)\n if attribute is not None:\n parameters[parameter] = {}\n if isinstance(attribute, list):\n value = [item.as_dict() for item in attribute]\n elif is_of_builtin_type(attribute):\n value = attribute\n elif isinstance(attribute, Enum):\n value = attribute.value\n elif isinstance(attribute, Entity):\n parameters[parameter].update(\n {\n TYPE: ENTITY,\n ENTITY_ID: attribute.get_marquee_id(),\n ENTITY_TYPE: attribute.entity_type().value,\n }\n )\n continue\n elif isinstance(attribute, (dt.date, dt.datetime)):\n if isinstance(attribute, dt.date):\n value = str(attribute)\n else:\n value = f\"{attribute.strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3]}Z\"\n else:\n value = attribute.as_dict()\n parameters[parameter].update({TYPE: decapitalize(type(attribute).__name__), VALUE: value})\n\n return processor\n\n def get_default_params(self):\n # Handle all the kwarg based parameters that all processors take\n default_params = {}\n if self.last_value:\n default_params['last_value'] = dict(type='bool', value=True)\n return default_params\n\n @classmethod\n def from_dict(cls, obj: Dict, reference_list: List):\n processor_name: str = obj.get(PROCESSOR_NAME, 'None')\n # Dynamically import the processor to for instantiation.\n processor = getattr(__import__('gs_quant.analytics.processors', fromlist=['']), processor_name, None)\n\n parameters = obj.get(PARAMETERS, {})\n\n local_reference_list = []\n arguments = {}\n\n for parameter, parameters_dict in parameters.items():\n # Loop through all the parameters and turned them into objects based off their dictionary values.\n # Will recursively handle the more complex objects such as DataCoordinate and Processors.\n parameter_type: str = parameters_dict.get(TYPE)\n\n if parameter_type == DATA_COORDINATE:\n # Handle the DataCoordinate parameters\n arguments[parameter] = DataCoordinate.from_dict(parameters_dict)\n elif parameter_type == PROCESSOR:\n # Handle the BaseProcessor parameters\n arguments[parameter] = BaseProcessor.from_dict(parameters_dict, reference_list)\n elif parameter_type == ENTITY:\n # Handle the entity parameter list and put into the reference mapped to be resolved later\n local_reference_list.append(\n {\n TYPE: PROCESSOR,\n ENTITY_ID: parameters_dict.get(ENTITY_ID),\n ENTITY_TYPE: parameters_dict.get(ENTITY_TYPE),\n PARAMETER: parameter,\n }\n )\n\n arguments[parameter] = None\n elif parameter_type in (DATE, DATETIME, RELATIVE_DATE):\n # Handle date/datetime parameters\n if parameter_type == DATE:\n arguments[parameter] = dt.datetime.strptime(parameters_dict.get(VALUE), '%Y-%m-%d').date()\n elif parameter_type == RELATIVE_DATE:\n val = parameters_dict.get(VALUE)\n base_date = val.get('baseDate')\n base_date = dt.datetime.strptime(base_date, '%Y-%m-%d').date() if base_date else None\n arguments[parameter] = RelativeDate(rule=val['rule'], base_date=base_date)\n else:\n arguments[parameter] = dt.datetime.strptime(\n parameters_dict.get(VALUE)[0:-1], '%Y-%m-%dT%H:%M:%S.%f'\n )\n else:\n # Handle all other object which should be mapped in the PARSABLE_OBJECT_MAP\n if parameter_type in PARSABLE_OBJECT_MAP:\n parameter_obj = PARSABLE_OBJECT_MAP[parameter_type]\n if isinstance(parameter_obj, (Enum, EnumMeta)):\n arguments[parameter] = parameter_obj(parameters_dict.get(VALUE, {}))\n else:\n arguments[parameter] = parameter_obj.from_dict(parameters_dict.get(VALUE, {}))\n elif parameter_type == LIST:\n arguments[parameter] = [\n BaseProcessor.from_dict(item, reference_list) if isinstance(item, dict) else item\n for item in parameters_dict['value']\n ]\n else:\n # Handles built in types that are stored natively\n arguments[parameter] = parameters_dict.get(VALUE)\n\n processor = processor(**arguments) if processor else None # Instantiate the processor with all arguments\n\n # Add all the references to entities to the list which will be resolved later\n for reference in local_reference_list:\n reference[REFERENCE] = processor\n\n reference_list.extend(local_reference_list)\n return processor\n\n\nDataCoordinateOrProcessor = Union[DataCoordinate, BaseProcessor]\n" }, { "path": "gs_quant/analytics/core/processor_result.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Union, Dict\n\nimport pandas as pd\n\n\n@dataclass\nclass ProcessorResult:\n success: bool\n data: Union[str, pd.Series, Dict]\n" }, { "path": "gs_quant/analytics/core/query_helpers.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport asyncio\nimport logging\nfrom collections import defaultdict\nfrom typing import Dict, Tuple, Union\n\nimport pandas as pd\n\nfrom gs_quant.analytics.core.processor import MeasureQueryInfo\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.data import DataFrequency\nfrom gs_quant.session import GsSession\n\n_logger = logging.getLogger(__name__)\n\n\ndef aggregate_queries(query_infos):\n mappings = defaultdict(dict) # DataSet -> start/end\n for query_info in query_infos:\n if isinstance(query_info, MeasureQueryInfo):\n continue\n query = query_info.query\n coordinate = query.coordinate\n dataset_id = coordinate.dataset_id\n dataset_mappings = mappings[dataset_id]\n query_key = query.get_range_string()\n if dataset_id is None:\n series: ProcessorResult = ProcessorResult(\n False, f'No dataset resolved for measure={coordinate.measure} with dimensions={coordinate.dimensions}'\n )\n asyncio.get_event_loop().run_until_complete(query_info.processor.calculate(query_info.attr, series, None))\n continue\n dataset_mappings.setdefault(\n query_key,\n {\n 'datasetId': dataset_id,\n 'parameters': {},\n 'queries': defaultdict(list),\n 'range': {},\n 'realTime': True if coordinate.frequency == DataFrequency.REAL_TIME else False,\n 'measures': set(),\n },\n )\n query_map = dataset_mappings[query_key]\n if not query_map['range']:\n if isinstance(query.start, dt.date):\n query_map['range']['startDate'] = query.start\n elif isinstance(query.start, dt.datetime):\n query_map['range']['startTime'] = query.start\n\n if isinstance(query.end, dt.date):\n query_map['range']['endDate'] = query.end\n elif isinstance(query.end, dt.datetime):\n query_map['range']['endTime'] = query.end\n\n queries = query_map['queries']\n queries[coordinate.get_dimensions()].append(query_info)\n parameters = query_map['parameters']\n query_map['measures'].add(coordinate.measure)\n for dimension, value in coordinate.dimensions.items():\n parameters.setdefault(dimension, set())\n parameters[dimension].add(value)\n\n return mappings\n\n\ndef fetch_query(query_info: Dict):\n where = {}\n for key, value in query_info['parameters'].items():\n value_list = list(value)\n if isinstance(value_list[0], bool):\n if len(value_list) == 1:\n # If only 1 bool value is given (True/False) set the where to the value. Else skip.\n where[key] = value_list[0]\n continue # Skip adding as both True/False must be there\n where[key] = list(value)\n query = {'where': where, **query_info['range'], 'useFieldAlias': True, 'remapSchemaToAlias': True}\n try:\n if query_info['realTime'] and not query_info['range']:\n response = GsSession.current.sync.post(f'/data/{query_info[\"datasetId\"]}/last/query', payload=query)\n else:\n response = GsSession.current.sync.post(f'/data/{query_info[\"datasetId\"]}/query', payload=query)\n except Exception as e:\n _logger.error(f'Error fetching query due to {e}')\n return pd.DataFrame()\n\n df = pd.DataFrame(response.get('data', {}))\n if df.empty:\n return df\n df = df.set_index('date' if 'date' in df.columns else 'time')\n df.index = pd.to_datetime(df.index).tz_localize(None)\n return df\n\n\ndef build_query_string(dimensions):\n output = ''\n for count, dimension in enumerate(dimensions):\n value = dimension[1]\n if isinstance(value, str):\n value = f'\"{value}\"'\n if count == 0:\n output += f'{dimension[0]} == {value}'\n else:\n output += f' & {dimension[0]} == {value}'\n return output\n\n\ndef valid_dimensions(query_dimensions: Tuple[str, Union[str, float, bool]], df: pd.DataFrame) -> bool:\n columns = df.columns\n for query_dimension in query_dimensions:\n dimension = query_dimension[0]\n if dimension not in columns:\n return False\n return True\n" }, { "path": "gs_quant/analytics/datagrid/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .data_column import DataColumn, ColumnFormat\nfrom .data_row import DataRow, Override, DimensionsOverride, ProcessorOverride\nfrom .datagrid import DataGrid\n" }, { "path": "gs_quant/analytics/datagrid/data_cell.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport uuid\nfrom typing import List, Optional, Dict, Set, Tuple, Union\n\nimport pandas as pd\n\nfrom gs_quant.analytics.common import DATA_CELL_NOT_CALCULATED\nfrom gs_quant.analytics.core import BaseProcessor\nfrom gs_quant.analytics.core.processor import DataQueryInfo, MeasureQueryInfo\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.analytics.datagrid import Override\nfrom gs_quant.analytics.datagrid.utils import get_utc_now\nfrom gs_quant.entities.entity import Entity\n\n\nclass DataCell:\n \"\"\"Entity data cell\n Computes value and manages formatting of a data cell\n \"\"\"\n\n def __init__(\n self,\n name: str,\n processor: BaseProcessor,\n entity: Entity,\n dimension_overrides: List[Override],\n column_index: int,\n row_index: int,\n row_group: str = None,\n ):\n # Cell starts with root processor\n # Deep copies so the processor and children are unique objects\n self.cell_id = str(uuid.uuid4())\n self.processor: BaseProcessor = copy.deepcopy(processor)\n self.entity: Entity = entity\n self.name: str = name\n self.dimension_overrides = dimension_overrides\n self.column_index = column_index\n self.row_index = row_index\n self.row_group = row_group\n\n self.updated_time: Optional[str] = None\n\n # Default the value for a cell processor\n self.value: ProcessorResult = ProcessorResult(False, DATA_CELL_NOT_CALCULATED)\n\n # Store the cell data queries\n self.data_queries: List[DataQueryInfo] = []\n\n def build_cell_graph(\n self, all_queries: List[Union[DataQueryInfo, MeasureQueryInfo]], rdate_entity_map: Dict[str, Set[Tuple]]\n ) -> None:\n \"\"\"Generate and store the cell graph and data queries\n\n This can be modified to return the data queries rather than store on the cell\n \"\"\"\n # Set the root processor node parent to data cell\n if self.processor:\n self.processor.parent = self\n cell_queries: List[DataQueryInfo] = []\n self.processor.build_graph(self.entity, self, cell_queries, rdate_entity_map, self.dimension_overrides)\n\n self.data_queries = cell_queries\n all_queries.extend(cell_queries) # Add this cell's queries to the entire datagrid's list of queries\n\n def update(self, result: ProcessorResult) -> None:\n \"\"\"Sets the value of the cell\"\"\"\n if isinstance(result.data, pd.Series):\n if result.data.empty:\n self.value = ProcessorResult(False, 'Empty series as a result of processing.')\n else:\n self.value = ProcessorResult(True, result.data.iloc[-1])\n else:\n self.value = ProcessorResult(True, result.data)\n self.updated_time = get_utc_now()\n" }, { "path": "gs_quant/analytics/datagrid/data_column.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom dataclasses import dataclass, asdict, fields, field\nfrom typing import Dict, List, Union\nfrom gs_quant.analytics.core.processor import BaseProcessor\n\nDEFAULT_WIDTH = 100\n\n\nclass RenderType:\n DEFAULT = 'default'\n HEATMAP = 'heatmap'\n BOXPLOT = 'boxplot'\n SCALE = 'scale'\n DATE_MMM_YY = 'dateMmmYy'\n TIME_HH_MM = 'timeHhMm'\n\n\n@dataclass\nclass HeatMapColorRange:\n \"\"\"\n Dataclass for HeatMap color ranges. All values must be in hex format. Example: '#ffffff' for white.\n \"\"\"\n\n low: str\n mid: str\n high: str\n\n @classmethod\n def from_dict(cls, dict_: dict):\n class_fields = {f.name for f in fields(cls)}\n return HeatMapColorRange(**{k: v for k, v in dict_.items() if k in class_fields})\n\n\n@dataclass\nclass MultiColumnGroup:\n \"\"\"\n MultiColumnGroup allows you to group DataGrid columns by index. Useful when working with\n heatmap renderers.\n\n :param id (Union[int, str]): Integer or string to identify the group. Defaults to 0.\n :param columnIndices (List[int]): Optional List of integers specifying the column indices to group\n :param groupAll (List[int]): Optional flag that allows you to easily group all columns so you don't have to\n pass every column index to the columnIndices list.\n :param heatMapColorRange (HeatMapColorRange): Optional HeatMapColorRange which allows you to specify a color theme\n for your grouped heatmap columns\n \"\"\"\n\n id: Union[int, str] = 0\n columnIndices: List[int] = field(default_factory=list)\n groupAll: bool = False\n heatMapColorRange: HeatMapColorRange = None\n\n def asdict(self):\n obj = {'id': self.id, 'columnIndices': self.columnIndices}\n if self.groupAll:\n obj['groupAll'] = True\n if self.heatMapColorRange:\n obj['heatMapColorRange'] = asdict(self.heatMapColorRange)\n\n return obj\n\n @classmethod\n def from_dict(cls, dict_: dict):\n heat_map_color_range = dict_.get('heatMapColorRange')\n return MultiColumnGroup(\n id=dict_.get('id'),\n groupAll=dict_.get('groupAll', False),\n columnIndices=dict_.get('columnIndices'),\n heatMapColorRange=HeatMapColorRange.from_dict(heat_map_color_range) if heat_map_color_range else None,\n )\n\n\nclass ColumnFormat:\n def __init__(\n self,\n *,\n renderType: RenderType = RenderType.DEFAULT,\n precision: int = 2,\n humanReadable: bool = True,\n tooltip: str = None,\n displayValues: bool = True,\n ):\n \"\"\"\n Use this class to specify the format options for your column.\n :param renderType: Type to use when rendering the column.\n :param precision: Number of precision points to use.\n :param humanReadable: Formats number to have commas.\n :param tooltip: Helper text to use as tooltip for the column.\n :param displayValues: For graphical render types only. True will show numerical values and title in the column.\n else only the graphics will be displayed.\n \"\"\"\n self.renderType = renderType\n self.precision = precision\n self.humanReadable = humanReadable\n self.tooltip = tooltip\n self.displayValues = displayValues\n\n def as_dict(self):\n format_ = {\n 'renderType': self.renderType,\n 'precision': self.precision,\n 'humanReadable': self.humanReadable,\n }\n\n if self.tooltip:\n format_['tooltip'] = self.tooltip\n\n if self.renderType != RenderType.DEFAULT:\n format_['displayValues'] = self.displayValues\n\n return format_\n\n @classmethod\n def from_dict(cls, obj: dict):\n return ColumnFormat(\n renderType=obj.get('renderType'),\n precision=obj.get('precision'),\n humanReadable=obj.get('humanReadable'),\n tooltip=obj.get('tooltip'),\n displayValues=obj.get('displayValues'),\n )\n\n\nclass DataColumn:\n \"\"\"Base class for grid column\"\"\"\n\n def __init__(\n self,\n name: str,\n processor: BaseProcessor = None,\n *,\n format_: ColumnFormat = ColumnFormat(),\n width: int = DEFAULT_WIDTH,\n ):\n \"\"\"DataColumn\n\n :param name: Name of the column\n :param processor: Processor to apply to the column for calculation\n :param format_: Formatting information for the column result\n :param width: Size of the column in pixels when presented on the UI\n \"\"\"\n self.name = name\n self.processor = processor\n self.format_ = format_\n self.width = width\n\n def as_dict(self):\n format_ = self.format_.as_dict()\n\n column = {'name': self.name, 'format': format_, 'width': self.width}\n\n processor = self.processor\n if processor:\n column['processorName'] = processor.__class__.__name__\n column.update(**processor.as_dict())\n\n return column\n\n @classmethod\n def from_dict(cls, obj: Dict, reference_list: List):\n processor = BaseProcessor.from_dict(obj, reference_list)\n\n return DataColumn(\n name=obj['name'],\n processor=processor,\n format_=ColumnFormat.from_dict(obj.get('format', {})),\n width=obj.get('width', DEFAULT_WIDTH),\n )\n" }, { "path": "gs_quant/analytics/datagrid/data_row.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom abc import ABC\nfrom enum import Enum\nfrom typing import Dict, List, Optional, Union\n\nfrom gs_quant.analytics.core import BaseProcessor\nfrom gs_quant.data import DataCoordinate\nfrom gs_quant.data.fields import DataDimension\nfrom gs_quant.entities.entity import Entity\n\nDataDimensions = Dict[Union[DataDimension, str], Union[str, float]]\n\n# Override Types\nDIMENSIONS_OVERRIDE = 'dimensionsOverride'\nPROCESSOR_OVERRIDE = 'processorOverride'\nVALUE_OVERRIDE = 'valueOverride'\n\n# Row Types\nDATA_ROW = 'dataRow'\nROW_SEPARATOR = 'rowSeparator'\n\n\nclass Override(ABC):\n \"\"\"Base class for a DataGrid row override\"\"\"\n\n def __init__(self, column_names: List[str]):\n \"\"\"Abstract Row Override\n\n :param column_names: column names to override with the specified dimensions\n \"\"\"\n self.column_names = column_names\n super().__init__()\n\n def as_dict(self) -> Dict:\n return {'columnNames': self.column_names}\n\n @classmethod\n def from_dict(cls, obj, reference_list):\n pass\n\n\nclass ValueOverride(Override):\n def __init__(self, column_names: List[str], value: Union[float, str, bool]):\n \"\"\"\n Allows the ability to set a cell to a specific value.\n\n :param column_names: Name of columns to apply the value override.\n :param value: Value to set to the row and column intersections.\n \"\"\"\n super().__init__(column_names)\n self.value = value\n\n def as_dict(self):\n override = super().as_dict()\n override['type'] = VALUE_OVERRIDE\n override['value'] = self.value\n return override\n\n @classmethod\n def from_dict(cls, obj, ref):\n return ValueOverride(column_names=obj.get('columnNames', []), value=obj['value'])\n\n\nclass DimensionsOverride(Override):\n def __init__(\n self, column_names: List[str], dimensions: DataDimensions, coordinate: DataCoordinate, coordinate_id: str = None\n ):\n \"\"\"Override dimensions for the given coordinate\n\n :param column_names: column names to override with the specified dimensions\n :param dimensions: dict of dimensions to override columns when fetching data\n :param coordinate: coordinate to apply the override\n :param coordinate_id: id of the coordinate to apply override. Gives additional control vs just passing\n coordinate\n \"\"\"\n super().__init__(column_names)\n # Following coordinate model, convert override dimensions to match coordinate dimension\n self.dimensions = {k.value if isinstance(k, Enum) else k: v for k, v in dimensions.items()}\n self.coordinate = coordinate\n self.coordinate_id = coordinate_id\n\n def as_dict(self):\n override = super().as_dict()\n override['type'] = DIMENSIONS_OVERRIDE\n override['dimensions'] = self.dimensions\n override['coordinate'] = self.coordinate.as_dict()\n if self.coordinate_id:\n override['coordinateId'] = self.coordinate_id\n return override\n\n @classmethod\n def from_dict(cls, obj, reference_list):\n parsed_dimensions = {}\n data_dimension_map = DataDimension._value2member_map_\n for key, value in obj.get('dimensions', {}).items():\n if key in data_dimension_map:\n parsed_dimensions[DataDimension(key)] = value\n else:\n parsed_dimensions[key] = value\n return DimensionsOverride(\n column_names=obj.get('columnNames', []),\n dimensions=parsed_dimensions,\n coordinate=DataCoordinate.from_dict(obj.get('coordinate', {})),\n coordinate_id=obj.get('coordinateId'),\n )\n\n\nclass ProcessorOverride(Override):\n def __init__(self, column_names: List[str], processor: BaseProcessor):\n \"\"\"Abstract Row Override\n\n :param column_names: column names to override with the specified dimensions\n :param processor: processor to override\n \"\"\"\n super().__init__(column_names=column_names)\n self.processor = processor\n\n def as_dict(self):\n override = super().as_dict()\n override['type'] = PROCESSOR_OVERRIDE\n if self.processor:\n override['processor'] = self.processor.as_dict()\n override['processor']['processorName'] = self.processor.__class__.__name__\n else:\n override['processor'] = None\n override['processor']['processorName'] = None\n return override\n\n @classmethod\n def from_dict(cls, obj, reference_list):\n return ProcessorOverride(\n column_names=obj.get('columnNames', []),\n processor=BaseProcessor.from_dict(obj.get('processor', {}), reference_list),\n )\n\n\nclass RowSeparator:\n def __init__(self, name: str):\n \"\"\"Row Separator\n\n :param name: name of the row separator\n \"\"\"\n self.name = name\n\n def as_dict(self):\n return {'type': ROW_SEPARATOR, 'name': self.name}\n\n @classmethod\n def from_dict(cls, obj):\n return RowSeparator(obj['name'])\n\n\nclass DataRow:\n \"\"\"Row object for DataGrid\"\"\"\n\n def __init__(self, entity: Entity, overrides: Optional[List[Override]] = None):\n \"\"\"Data row\n\n :param entity: Specified entity for the DataRow\n :param overrides: Optional List of DataRowOverride's for retrieving data\n \"\"\"\n self.entity = entity\n self.overrides: List[Override] = overrides or []\n\n def as_dict(self):\n data_row = {\n 'type': DATA_ROW,\n 'entityId': self.entity.get_marquee_id() if isinstance(self.entity, Entity) else self.entity,\n 'entityType': self.entity.entity_type().value if isinstance(self.entity, Entity) else '',\n }\n if len(self.overrides):\n data_row['overrides'] = [override.as_dict() for override in self.overrides]\n return data_row\n\n @classmethod\n def from_dict(cls, obj, reference_list):\n overrides = []\n for override_dict in obj.get('overrides', []):\n override_type = override_dict.get('type')\n if override_type == PROCESSOR_OVERRIDE:\n override = ProcessorOverride.from_dict(override_dict, reference_list)\n elif override_type == DIMENSIONS_OVERRIDE:\n override = DimensionsOverride.from_dict(override_dict, reference_list)\n else:\n override = ValueOverride.from_dict(override_dict, reference_list)\n overrides.append(override)\n\n data_row = DataRow(entity=None, overrides=overrides) # Entity gets resolved later\n\n reference_list.append(\n {\n 'type': DATA_ROW,\n 'entityId': obj.get('entityId', ''),\n 'entityType': obj.get('entityType', ''),\n 'reference': data_row,\n }\n )\n\n return data_row\n" }, { "path": "gs_quant/analytics/datagrid/datagrid.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport asyncio\nimport datetime as dt\nimport json\nimport logging\nimport webbrowser\nfrom collections import defaultdict\nfrom dataclasses import asdict\nfrom numbers import Number\nfrom typing import List, Dict, Optional, Tuple, Union, Set\n\nimport numpy as np\nimport pandas as pd\n\nfrom gs_quant.analytics.common import DATAGRID_HELP_MSG\nfrom gs_quant.analytics.common.helpers import (\n resolve_entities,\n get_entity_rdate_key,\n get_entity_rdate_key_from_rdate,\n get_rdate_cache_key,\n)\nfrom gs_quant.analytics.core.processor import DataQueryInfo, MeasureQueryInfo\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.analytics.core.query_helpers import aggregate_queries, fetch_query, build_query_string, valid_dimensions\nfrom gs_quant.analytics.datagrid.data_cell import DataCell\nfrom gs_quant.analytics.datagrid.data_column import DataColumn, ColumnFormat, MultiColumnGroup\nfrom gs_quant.analytics.datagrid.data_row import (\n DataRow,\n DimensionsOverride,\n ProcessorOverride,\n Override,\n ValueOverride,\n RowSeparator,\n)\nfrom gs_quant.analytics.datagrid.serializers import row_from_dict\nfrom gs_quant.analytics.datagrid.utils import (\n DataGridSort,\n SortOrder,\n SortType,\n DataGridFilter,\n FilterOperation,\n FilterCondition,\n get_utc_now,\n)\nfrom gs_quant.analytics.processors import CoordinateProcessor, EntityProcessor\nfrom gs_quant.common import Entitlements as Entitlements_\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom gs_quant.entities.entitlements import Entitlements\nfrom gs_quant.entities.entity import Entity\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.session import GsSession, OAuth2Session\n\n_logger = logging.getLogger(__name__)\n\nAPI = '/data/grids'\nDATAGRID_HEADERS: Dict[str, str] = {'Content-Type': 'application/json;charset=utf-8'}\n\n\nclass DataGrid:\n \"\"\"\n DataGrid is an object for fetching Marquee data and applying processors (functions). DataGrids can be\n persisted via the DataGrid API and utilized on the Marquee Markets platform.\n\n :param name: Name of the DataGrid\n :param rows: List of DataGrid rows for the grid\n :param columns: List of DataGrid columns for the grid\n :param id_: Unique identifier of the grid\n :param entitlements: Marquee entitlements of the grid for the Marquee Market's platform\n :param sorts: Optional list of DataGridSort. Use this if you want to sort your columns.\n :param filters: Optional list of DataGridFilter. Use this to filter column's data.\n :param multiColumnGroups: Optional list of MultiColumnGroup. Useful to group columns for heatmaps.\n\n **Usage**\n\n To create a DataGrid, we define two components, rows and columns:\n\n >>> from gs_quant.markets.securities import Asset, AssetIdentifier\n >>> from gs_quant.data.coordinate import DataMeasure, DataFrequency\n >>> from gs_quant.analytics.processors import LastProcessor\n >>>\n >>> GS = Asset.get(\"GS UN\", AssetIdentifier.BLOOMBERG_ID)\n >>> AAPL = Asset.get(\"AAPL UW\", AssetIdentifier.BLOOMBERG_ID)\n >>> rows = [\n >>> DataRow(GS),\n >>> DataRow(AAPL)\n >>> ]\n >>> trade_price = DataCoordinate(\n >>> measure=DataMeasure.TRADE_PRICE,\n >>> frequency=DataFrequency.REAL_TIME,\n >>> )\n >>>\n >>> col_0 = DataColumn(name=\"Name\", processor=EntityProcessor(field=\"short_name\"))\n >>> col_1 = DataColumn(name=\"Last\", processor=LastProcessor(trade_price))\n >>> columns = [ col_0, col_1 ]\n >>>\n >>> datagrid = DataGrid(name=\"Example DataGrid\", rows=rows, columns=columns)\n >>> datagrid.initialize()\n >>> datagrid.poll()\n >>> print(datagrid.to_frame())\n\n **Documentation**\n\n Full Documentation and examples can be found here:\n https://developer.gs.com/docs/gsquant/tutorials/Data/DataGrid/\n \"\"\"\n\n def __init__(\n self,\n name: str,\n rows: List[Union[DataRow, RowSeparator]],\n columns: List[DataColumn],\n *,\n id_: str = None,\n entitlements: Union[Entitlements, Entitlements_] = None,\n polling_time: int = None,\n sorts: Optional[List[DataGridSort]] = None,\n filters: Optional[List[DataGridFilter]] = None,\n multiColumnGroups: Optional[List[MultiColumnGroup]] = None,\n **kwargs,\n ):\n self.id_ = id_\n self.entitlements = entitlements\n self.name = name\n self.rows = rows\n self.columns = columns\n self.sorts = sorts or []\n self.filters = filters or []\n self.multiColumnGroups = multiColumnGroups\n self.polling_time = polling_time or 0\n\n # store the graph, data queries to leaf processors and results\n self._primary_column_index: int = kwargs.get('primary_column_index', 0)\n self._cells: List[DataCell] = []\n self._data_queries: List[Union[DataQueryInfo, MeasureQueryInfo]] = []\n self._entity_cells: List[DataCell] = []\n self._coord_processor_cells: List[DataCell] = []\n self._value_cells: List[DataCell] = []\n self.entity_map: Dict[str, Entity] = {}\n\n # RDate Mappings\n self.rdate_entity_map: Dict[str, Set[Tuple]] = defaultdict(set)\n self.rule_cache: Dict[str, dt.date] = {}\n\n self.results: List[List[DataCell]] = []\n self.is_initialized: bool = False\n print(DATAGRID_HELP_MSG)\n\n def get_id(self) -> Optional[str]:\n \"\"\"Get the unique DataGrid identifier. Will only exists if the DataGrid has been persisted.\"\"\"\n return self.id_\n\n def initialize(self) -> None:\n \"\"\"\n Initializes the DataGrid.\n\n Iterates over all rows and columns, preparing cell structures.\n Cells then contain a graph and data queries to leaf processors.\n\n Upon providing data to a leaf, the leaf processor is calculated and propagated up the graph to the cell level.\n \"\"\"\n all_queries: List[Union[DataQueryInfo, MeasureQueryInfo]] = []\n entity_cells: List[DataCell] = []\n current_row_group = None\n\n # Loop over rows, columns\n for row_index, row in enumerate(self.rows):\n if isinstance(row, RowSeparator):\n current_row_group = row.name\n continue\n entity: Entity = row.entity\n if isinstance(entity, Entity):\n self.entity_map[entity.get_marquee_id()] = entity\n else:\n self.entity_map[''] = entity\n cells: List[DataCell] = []\n row_overrides = row.overrides\n\n for column_index, column in enumerate(self.columns):\n column_name = column.name\n column_processor = column.processor\n\n # Get all the data coordinate overrides and apply the processor override if it exists\n data_overrides, value_override, processor_override = _get_overrides(row_overrides, column_name)\n\n # Create the cell\n cell: DataCell = DataCell(\n column_name, column_processor, entity, data_overrides, column_index, row_index, current_row_group\n )\n\n if processor_override:\n # Check if there is a processor override and apply if so\n cell.processor = processor_override\n if value_override:\n cell.value = ProcessorResult(True, value_override.value)\n cell.updated_time = get_utc_now()\n elif isinstance(column_processor, EntityProcessor):\n # store these cells to fetch entity data during poll\n entity_cells.append(cell)\n elif isinstance(column_processor, CoordinateProcessor):\n # store these cells to fetch entity data during poll\n if len(data_overrides):\n # Get the last in the list if more than 1 override is given\n cell.processor.children['a'].set_dimensions(data_overrides[-1].dimensions)\n\n self._coord_processor_cells.append(cell)\n elif column_processor.measure_processor:\n all_queries.append(MeasureQueryInfo(attr='', entity=entity, processor=column_processor))\n else:\n # append the required queries to the map\n cell.build_cell_graph(all_queries, self.rdate_entity_map)\n\n cells.append(cell)\n\n self._cells.extend(cells)\n self.results.append(cells)\n\n self._data_queries = all_queries\n self._entity_cells = entity_cells\n self.is_initialized = True\n\n def poll(self) -> None:\n \"\"\"Poll the data queries required to process this grid.\n Set the results at the leaf processors\n \"\"\"\n self._resolve_rdates()\n self._resolve_queries()\n self._process_special_cells()\n self._fetch_queries()\n\n def save(self) -> str:\n \"\"\"\n Saves the DataGrid. If the DataGrid has already been created, the DataGrid will be updated.\n If the DataGrid has not been created it will be added to the DataGrid service.\n :return: Unique identifier of the DataGrid\n \"\"\"\n datagrid_json = self.__as_json()\n if self.id_:\n response = GsSession.current.sync.put(f'{API}/{self.id_}', datagrid_json, request_headers=DATAGRID_HEADERS)\n else:\n response = GsSession.current.sync.post(f'{API}', datagrid_json, request_headers=DATAGRID_HEADERS)\n self.id_ = response['id']\n return DataGrid.from_dict(response).id_\n\n def create(self):\n \"\"\"\n Creates a new DataGrid even if the DataGrid already exists.\n If the DataGrid has already been persisted, the DataGrid id will be replaced with the newly persisted DataGrid.\n :return: New DataGrid unique identifier\n \"\"\"\n datagrid_json = self.__as_json()\n response = GsSession.current.sync.post(f'{API}', datagrid_json, request_headers=DATAGRID_HEADERS)\n self.id_ = response['id']\n return response['id']\n\n def delete(self):\n \"\"\"\n Deletes the DataGrid if it has been persisted.\n :return: None\n \"\"\"\n if self.id_:\n GsSession.current.sync.delete(f'{API}/{self.id_}', request_headers=DATAGRID_HEADERS)\n else:\n raise MqValueError('DataGrid has not been persisted.')\n\n def open(self):\n \"\"\"\n Opens the DataGrid in the default browser.\n :return: None\n \"\"\"\n if self.id_ is None:\n raise MqValueError('DataGrid must be created or saved before opening.')\n domain = GsSession.current.domain.replace(\".web\", \"\")\n if domain == 'https://api.gs.com':\n domain = 'https://marquee.gs.com'\n url = f'{domain}/s/markets/grids/{self.id_}'\n webbrowser.open(url)\n\n @property\n def polling_time(self):\n return self.__polling_time\n\n @polling_time.setter\n def polling_time(self, value):\n if value is None:\n self.__polling_time = 0\n elif value != 0 and value < 5000:\n raise MqValueError('polling_time must be >= than 10000ms.')\n self.__polling_time = value\n\n def _process_special_cells(self) -> None:\n \"\"\"\n Processes Coordinate and Entity cells\n\n :return: None\n \"\"\"\n # fetch entity cells\n for cell in self._entity_cells:\n try:\n cell.value = cell.processor.process(cell.entity)\n except Exception as e:\n cell.value = (\n f'Error Calculating processor {cell.processor.__class__.__name__} '\n f'for entity: {cell.entity.get_marquee_id()} due to {e}'\n )\n\n cell.updated_time = get_utc_now()\n\n for cell in self._coord_processor_cells:\n try:\n cell.value = cell.processor.process()\n except Exception as e:\n cell.value = (\n f'Error Calculating processor {cell.processor.__class__.__name__} '\n f'for entity: {cell.entity.get_marquee_id()} due to {e}'\n )\n\n cell.updated_time = get_utc_now()\n\n def _resolve_rdates(self, rule_cache: Dict = None):\n # TODO: Thread this...\n rule_cache = rule_cache or {}\n # Default to no calendar for rdate for external and oauth\n calendar = [] if not GsSession.current.is_internal() and isinstance(GsSession.current, OAuth2Session) else None\n\n for entity_id, rules in self.rdate_entity_map.items():\n entity = self.entity_map.get(entity_id)\n currencies = None\n exchanges = None\n if isinstance(entity, Entity):\n entity_dict = entity.get_entity()\n currency = entity_dict.get(\"currency\")\n exchange = entity_dict.get(\"exchange\")\n currencies = [currency] if currency else None\n exchanges = [exchange] if exchange else None\n for rule_base_date_tuple in rules:\n rule, base_date = rule_base_date_tuple[0], rule_base_date_tuple[1]\n cache_key = get_rdate_cache_key(rule_base_date_tuple[0], rule_base_date_tuple[1], currencies, exchanges)\n date_value = rule_cache.get(cache_key)\n if date_value is None:\n if base_date:\n base_date = dt.datetime.strptime(base_date, \"%Y-%m-%d\").date()\n date_value = RelativeDate(rule, base_date).apply_rule(\n currencies=currencies, exchanges=exchanges, holiday_calendar=calendar\n )\n rule_cache[cache_key] = date_value\n self.rule_cache[get_entity_rdate_key(entity_id, rule, base_date)] = date_value\n\n def _resolve_queries(self, availability_cache: Dict = None) -> None:\n \"\"\"Resolves the dataset_id for each data query\n This is used to query data thereafter\n \"\"\"\n availability_cache = availability_cache or {}\n\n for query in self._data_queries:\n entity = query.entity\n if isinstance(entity, str) or isinstance(query, MeasureQueryInfo):\n # If we were unable to fetch entity (404/403) or if we're processing a measure processor\n continue\n query = query.query\n coord = query.coordinate\n entity_dimension = entity.data_dimension\n entity_id = entity.get_marquee_id()\n\n query_start = query.start\n query_end = query.end\n if isinstance(query_start, RelativeDate):\n key = get_entity_rdate_key_from_rdate(entity_id, query_start)\n query.start = self.rule_cache[key]\n\n if isinstance(query_end, RelativeDate):\n key = get_entity_rdate_key_from_rdate(entity_id, query_end)\n query.end = self.rule_cache[key]\n\n if entity_dimension not in coord.dimensions:\n if coord.dataset_id:\n # don't need to fetch the data set if user supplied it\n coord.set_dimensions({entity_dimension: entity.get_marquee_id()})\n query.coordinate = coord\n else:\n # Need to resolve the dataset from availability\n entity_id = entity.get_marquee_id()\n try:\n raw_availability = availability_cache.get(entity_id)\n if raw_availability is None:\n raw_availability: Dict = GsSession.current.sync.get(\n f'/data/measures/{entity_id}/availability'\n )\n availability_cache[entity.get_marquee_id()] = raw_availability\n query.coordinate = entity.get_data_coordinate(\n measure=coord.measure,\n dimensions=coord.dimensions,\n frequency=coord.frequency,\n availability=raw_availability,\n )\n except Exception as e:\n _logger.info(f'Could not get DataCoordinate with {coord} for entity {entity_id} due to {e}')\n\n def _fetch_queries(self):\n query_aggregations = aggregate_queries(self._data_queries)\n\n for dataset_id, query_map in query_aggregations.items():\n for query in query_map.values():\n df = fetch_query(query)\n for query_dimensions, query_infos in query['queries'].items():\n if valid_dimensions(query_dimensions, df):\n queried_df = df.query(build_query_string(query_dimensions))\n for query_info in query_infos:\n measure = query_info.query.coordinate.measure\n query_info.data = queried_df[measure if isinstance(measure, str) else measure.value]\n else:\n for query_info in query_infos:\n query_info.data = pd.Series(dtype=float)\n\n for query_info in self._data_queries:\n if isinstance(query_info, MeasureQueryInfo):\n asyncio.get_event_loop().run_until_complete(\n query_info.processor.calculate(\n query_info.attr, ProcessorResult(True, None), self.rule_cache, query_info=query_info\n )\n )\n elif query_info.data is None or len(query_info.data) == 0:\n asyncio.get_event_loop().run_until_complete(\n query_info.processor.calculate(\n query_info.attr,\n ProcessorResult(False, f'No data found for Coordinate {query_info.query.coordinate}'),\n self.rule_cache,\n )\n )\n else:\n asyncio.get_event_loop().run_until_complete(\n query_info.processor.calculate(\n query_info.attr, ProcessorResult(True, query_info.data), self.rule_cache\n )\n )\n\n @staticmethod\n def aggregate_queries(query_infos):\n mappings = defaultdict(dict)\n for query_info in query_infos:\n query = query_info.query\n coordinate = query.coordinate\n dataset_mappings = mappings[coordinate.dataset_id]\n query_key = query.get_range_string()\n dataset_mappings.setdefault(query_key, {'parameters': {}, 'queries': {}})\n queries = dataset_mappings[query_key]['queries']\n queries[coordinate.get_dimensions()] = query_info\n parameters = dataset_mappings[query_key]['parameters']\n for dimension, value in coordinate.dimensions.items():\n parameters.setdefault(dimension, set())\n parameters[dimension].add(value)\n\n def _post_process(self) -> pd.DataFrame:\n columns = self.columns\n results = defaultdict(list)\n for row in self.results:\n if len(row):\n results['rowGroup'].append(row[0].row_group or '')\n for column in row:\n column_value = column.value\n if column_value.success is True:\n column_data = column_value.data\n if isinstance(column_data, Number):\n format_: ColumnFormat = columns[column.column_index].format_\n results[column.name].append(round(column_data, format_.precision))\n else:\n results[column.name].append(column_data)\n else:\n results[column.name].append(np.nan)\n\n df = pd.DataFrame.from_dict(results)\n row_groups = list(df['rowGroup'].unique())\n sub_dfs = []\n for row_group in row_groups:\n sub_df = self.__handle_filters(df[df['rowGroup'] == row_group])\n sub_df = self.__handle_sorts(sub_df)\n sub_dfs.append(sub_df)\n df = pd.concat(sub_dfs)\n df = df.set_index(['rowGroup', df.index]).rename_axis(index=['', ''])\n return df\n\n def __handle_sorts(self, df):\n \"\"\"\n Handles sorting of the dataframe\n :param df: incoming dataframe to be sorted\n :return: dataframe with sorting applied if any\n \"\"\"\n for sort in self.sorts:\n ascending = True if sort.order == SortOrder.ASCENDING else False\n if sort.sortType == SortType.ABSOLUTE_VALUE:\n df = df.reindex(df[sort.columnName].abs().sort_values(ascending=ascending, na_position='last').index)\n else:\n df = df.sort_values(by=sort.columnName, ascending=ascending, na_position='last')\n return df\n\n def __handle_filters(self, df) -> pd.DataFrame:\n \"\"\"\n Handles filtering the dataframe\n :param df: incoming dataframe to be filtered\n :return: dataframe with filters applied if any\n \"\"\"\n if not len(df):\n return df\n starting_df = df.copy()\n running_df = df\n for filter_ in self.filters:\n filter_value = filter_.value\n if filter_value is None:\n continue\n filter_condition = filter_.condition\n if filter_condition == FilterCondition.OR:\n df = starting_df\n else:\n df = running_df\n\n column_name = filter_.columnName\n operation = filter_.operation\n if operation == FilterOperation.TOP:\n df = df.sort_values(by=column_name, ascending=False, na_position='last').head(filter_value)\n elif operation == FilterOperation.BOTTOM:\n df = df.sort_values(by=column_name, ascending=True, na_position='last').head(filter_value)\n elif operation == FilterOperation.ABSOLUTE_TOP:\n df = df.reindex(df[column_name].abs().sort_values(ascending=False, na_position='last').index).head(\n filter_value\n )\n elif operation == FilterOperation.ABSOLUTE_BOTTOM:\n df = df.reindex(df[column_name].abs().sort_values(ascending=True, na_position='last').index).head(\n filter_value\n )\n elif operation == FilterOperation.EQUALS:\n if not isinstance(filter_value, list):\n filter_value = [filter_value]\n # Special case to handle different types of floats\n if isinstance(filter_value[0], str):\n df = df.loc[df[column_name].isin(filter_value)]\n else:\n # Add a tolerance for the special case to handle different types of floats\n df = df[np.isclose(df[column_name].values[:, None], filter_value, atol=1e-10).any(axis=1)]\n elif operation == FilterOperation.NOT_EQUALS:\n if not isinstance(filter_value, list):\n filter_value = [filter_value]\n if isinstance(filter_value[0], str):\n df = df.loc[~df[column_name].isin(filter_value)]\n else:\n # Add a tolerance for the special case to handle different types of float\n df = df[~np.isclose(df[column_name].values[:, None], filter_value, atol=1e-10).any(axis=1)]\n elif operation == FilterOperation.GREATER_THAN:\n df = df[df[column_name] > filter_value]\n elif operation == FilterOperation.LESS_THAN:\n df = df[df[column_name] < filter_value]\n elif operation == FilterOperation.LESS_THAN_EQUALS:\n df = df[df[column_name] <= filter_value]\n elif operation == FilterOperation.GREATER_THAN_EQUALS:\n df = df[df[column_name] >= filter_value]\n else:\n raise MqValueError(f'Invalid Filter operation Type: {operation}')\n\n if filter_.condition == FilterCondition.OR:\n # Need to merge the results\n running_df = running_df.merge(df, how='outer')\n else:\n running_df = df\n\n return running_df\n\n def to_frame(self) -> pd.DataFrame:\n \"\"\"\n Returns the results of the DataGrid data fetching and applied processors.\n :return: pd.DataFrame of results\n \"\"\"\n if not self.is_initialized:\n _logger.info(\"Grid has not been initialized. Ensure to run DataGrid.initialize()\")\n return pd.DataFrame()\n\n return self._post_process()\n\n @classmethod\n def from_dict(cls, obj, reference_list: Optional[List] = None):\n id_ = obj.get('id', None)\n name = obj.get('name', '')\n parameters = obj.get('parameters', {})\n entitlements = Entitlements_.from_dict(obj.get('entitlements', {}))\n\n # If a reference list is given, then the entities will be resolved by the caller\n if reference_list is not None:\n should_resolve_entities = False\n else:\n should_resolve_entities = True\n reference_list = []\n\n rows = [row_from_dict(row, reference_list) for row in parameters.get('rows', [])]\n columns = [DataColumn.from_dict(column, reference_list) for column in parameters.get('columns', [])]\n sorts = [DataGridSort.from_dict(sort) for sort in parameters.get('sorts', [])]\n filters = [DataGridFilter.from_dict(filter_) for filter_ in parameters.get('filters', [])]\n multi_column_groups = [MultiColumnGroup.from_dict(group) for group in parameters.get('multiColumnGroups', [])]\n\n if should_resolve_entities:\n resolve_entities(reference_list)\n\n return DataGrid(\n name=name,\n rows=rows,\n columns=columns,\n id_=id_,\n entitlements=entitlements,\n primary_column_index=parameters.get('primaryColumnIndex', 0),\n polling_time=parameters.get('pollingTime', 0),\n multiColumnGroups=multi_column_groups,\n sorts=sorts,\n filters=filters,\n )\n\n def as_dict(self):\n datagrid = {\n 'name': self.name,\n 'parameters': {\n 'rows': [row.as_dict() for row in self.rows],\n 'columns': [column.as_dict() for column in self.columns],\n 'primaryColumnIndex': self._primary_column_index,\n 'pollingTime': self.polling_time or 0,\n },\n }\n if self.entitlements:\n if isinstance(self.entitlements, Entitlements_):\n datagrid['entitlements'] = self.entitlements.as_dict()\n elif isinstance(self.entitlements, Entitlements):\n datagrid['entitlements'] = self.entitlements.to_dict()\n else:\n datagrid['entitlements'] = self.entitlements\n if len(self.sorts):\n datagrid['parameters']['sorts'] = [asdict(sort) for sort in self.sorts]\n if len(self.filters):\n datagrid['parameters']['filters'] = [asdict(filter_) for filter_ in self.filters]\n if self.multiColumnGroups:\n datagrid['parameters']['multiColumnGroups'] = [group.asdict() for group in self.multiColumnGroups]\n return datagrid\n\n def set_primary_column_index(self, index: int):\n \"\"\"\n Sets the primary column index which affects which row will expand to fill any additional horizontal space.\n :param index: index of the column to make primary\n :return: None\n \"\"\"\n self._primary_column_index = index\n\n def set_sorts(self, sorts: List[DataGridSort]):\n \"\"\"\n Set the sorts parameter of the grid response\n :param sorts: value of grid sorts\n :return: None\n \"\"\"\n self.sorts = sorts\n\n def add_sort(self, sort: DataGridSort, index: int = None):\n \"\"\"\n Add a sort to the grid response\n :param sort: DataGridSort\n :param index: index of the sort object to be added, defaults to end of sorts list\n :return: None\n \"\"\"\n if index:\n self.sorts.insert(index, sort)\n else:\n self.sorts.append(sort)\n\n def set_filters(self, filters: List[DataGridFilter]):\n \"\"\"\n Set the filters parameter of the grid response\n :param filters: value of grid sorts\n :return: None\n \"\"\"\n self.filters = filters\n\n def add_filter(self, filter_: DataGridFilter, index: int = None):\n \"\"\"\n Add a filter to the grid response\n :param filter_: DataGridFilter\n :param index: index of the sort object to be added, defaults to end of filters list\n :return: None\n \"\"\"\n if index:\n self.filters.insert(index, filter_)\n else:\n self.filters.append(filter_)\n\n def __as_json(self) -> str:\n return json.dumps(self.as_dict())\n\n\ndef _get_overrides(\n row_overrides: List[Override], column_name: str\n) -> Tuple[List[DimensionsOverride], Optional[ValueOverride], Optional[ProcessorOverride]]:\n if not row_overrides:\n return [], None, None\n\n dimensions_overrides, value_override, processor_override = [], None, None\n for override in row_overrides:\n if column_name in override.column_names:\n if isinstance(override, DimensionsOverride):\n dimensions_overrides.append(override)\n elif isinstance(override, ValueOverride):\n value_override = override\n elif isinstance(override, ProcessorOverride):\n processor_override = override.processor\n\n return dimensions_overrides, value_override, processor_override\n" }, { "path": "gs_quant/analytics/datagrid/serializers.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on ans\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import List, Dict\n\nfrom gs_quant.analytics.datagrid.data_row import RowSeparator, DataRow, ROW_SEPARATOR\n\n\ndef row_from_dict(row: Dict, reference_list: List):\n if row.get('type') == ROW_SEPARATOR:\n return RowSeparator.from_dict(row)\n else:\n return DataRow.from_dict(row, reference_list)\n" }, { "path": "gs_quant/analytics/datagrid/utils.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom dataclasses import dataclass, fields\nimport datetime as dt\nfrom enum import Enum\nfrom typing import Union, List\n\n\ndef get_utc_now() -> str:\n return f'{dt.datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M:%S.%f\")[:-3]}Z'\n\n\nclass SortType(str, Enum):\n VALUE = 'value' # Sort the column based off the value\n ABSOLUTE_VALUE = 'absoluteValue' # Sort the column based off the absolute value\n\n\nclass SortOrder(str, Enum):\n ASCENDING = 'ascending' # Sort the column in ascending order\n DESCENDING = 'descending' # Sort the column in descending order\n\n\nclass FilterOperation(str, Enum):\n TOP = 'top' # Top n rows by value\n BOTTOM = 'bottom' # Bottom n rows by value\n ABSOLUTE_TOP = 'absoluteTop' # Top n rows by absolute value\n ABSOLUTE_BOTTOM = 'absoluteBottom' # Bottom n rows by absolute value\n EQUALS = 'equals' # Rows with column value equal to a value or list of values\n NOT_EQUALS = 'notEquals' # Rows with column value not equal to a value or list of values\n GREATER_THAN = 'greaterThan' # Rows with column value greater than a value\n LESS_THAN = 'lessThan' # Rows with column value less than a value\n LESS_THAN_EQUALS = 'lessThanEquals' # Rows with column value greater than or equal to a value\n GREATER_THAN_EQUALS = 'greaterThanEquals' # Rows with column value less than or equal to a value\n\n\nclass FilterCondition(str, Enum):\n AND = 'and' # Intersect the rows that match the filter\n OR = 'or' # Union the rows that match the filter\n\n\n@dataclass\nclass DataGridSort:\n columnName: str\n sortType: SortType = SortType.VALUE\n order: SortOrder = SortOrder.ASCENDING\n\n def __post_init__(self):\n self.sortType = SortType(self.sortType)\n self.order = SortOrder(self.order)\n\n @classmethod\n def from_dict(cls, dict_):\n class_fields = {f.name for f in fields(cls)}\n return DataGridSort(**{k: v for k, v in dict_.items() if k in class_fields})\n\n\n@dataclass\nclass DataGridFilter:\n columnName: str\n operation: FilterOperation\n value: Union[float, str, List[float], List[str]]\n condition: FilterCondition = FilterCondition.AND\n\n def __post_init__(self):\n self.operation = FilterOperation(self.operation)\n self.condition = FilterCondition(self.condition)\n\n @classmethod\n def from_dict(cls, dict_):\n class_fields = {f.name for f in fields(cls)}\n return DataGridFilter(**{k: v for k, v in dict_.items() if k in class_fields})\n" }, { "path": "gs_quant/analytics/processors/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .analysis_processors import DiffProcessor\nfrom .econometrics_processors import SharpeRatioProcessor, VolatilityProcessor, CorrelationProcessor, ChangeProcessor, \\\n ReturnsProcessor, BetaProcessor, FXImpliedCorrProcessor\nfrom .special_processors import EntityProcessor, CoordinateProcessor\nfrom .statistics_processors import PercentilesProcessor, PercentileProcessor, StdMoveProcessor, \\\n CovarianceProcessor, ZscoresProcessor, MeanProcessor, VarianceProcessor, SumProcessor, StdDevProcessor, \\\n CompoundGrowthRate\nfrom .utility_processors import LastProcessor, AppendProcessor, AdditionProcessor, SubtractionProcessor, \\\n MultiplicationProcessor, DivisionProcessor, MinProcessor, MaxProcessor, NthLastProcessor, OneDayProcessor\nfrom .scale_processors import ScaleProcessor, BarMarkerProcessor, SpotMarkerProcessor, ScaleShape\n" }, { "path": "gs_quant/analytics/processors/analysis_processors.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Optional\n\nfrom gs_quant.analytics.core.processor import BaseProcessor, DataCoordinateOrProcessor, DateOrDatetimeOrRDate\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.timeseries import diff\n\n\nclass DiffProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n obs: int = 1,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n **kwargs,\n ):\n \"\"\"DiffProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param obs: number of observations to lag\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n **Usage**\n\n Compute the difference in series values over a given lag:\n\n :math:`R_t = X_t - X_{t-obs}`\n\n where :math:`obs` is the number of observations to lag series in diff function\n\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n self.obs = obs\n self.start = start\n self.end = end\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n result = diff(a_data.data, self.obs)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"DiffProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"DiffProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n" }, { "path": "gs_quant/analytics/processors/econometrics_processors.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Optional, Union\n\nimport pandas as pd\n\nfrom gs_quant.analytics.core.processor import (\n BaseProcessor,\n DataCoordinateOrProcessor,\n DataQueryInfo,\n DateOrDatetimeOrRDate,\n)\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.analytics.processors.special_processors import MeasureProcessor\nfrom gs_quant.common import Currency\nfrom gs_quant.data import DataFrequency\nfrom gs_quant.data.coordinate import DataCoordinate\nfrom gs_quant.data.query import DataQuery\nfrom gs_quant.entities.entity import Entity\nfrom gs_quant.markets.securities import Stock, Cross\nfrom gs_quant.timeseries import correlation, Window, SeriesType, DataMeasure, DataContext, fx_implied_correlation\nfrom gs_quant.timeseries import excess_returns_pure\nfrom gs_quant.timeseries.econometrics import get_ratio_pure, SharpeAssets, change, returns\nfrom gs_quant.timeseries.econometrics import volatility, Returns, beta\nfrom gs_quant.timeseries.helper import CurveType\n\n\nclass VolatilityProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = Window(None, 0),\n returns_type: Returns = Returns.SIMPLE,\n **kwargs,\n ):\n \"\"\"VolatilityProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series to apply the volatility timeseries function\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param returns_type: returns type: simple, logarithmic or absolute\n \"\"\"\n super().__init__(**kwargs)\n # coordinate\n self.children['a'] = a\n\n self.start = start\n self.end = end\n\n self.w = w\n self.returns_type = returns_type\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n result = volatility(a_data.data, self.w, self.returns_type)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, 'Could not compute volatility')\n else:\n self.value = ProcessorResult(False, 'Processor does not have data')\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass SharpeRatioProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n currency: Currency,\n w: Union[Window, int] = None,\n curve_type: CurveType = CurveType.PRICES,\n **kwargs,\n ):\n \"\"\"SharpeRatioProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series of prices or excess returns\n :param currency: currency for risk-free rate, defaults to USD\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value.\n :param curve_type: whether input series is of prices or excess returns, defaults to prices\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n # datetime\n self.start = start\n self.end = end\n # parameters\n self.currency = currency\n self.w = w\n self.curve_type = curve_type\n # additional queries\n self.children['excess_returns'] = self.get_excess_returns_query()\n\n def get_excess_returns_query(self) -> DataQueryInfo:\n marquee_id = SharpeAssets[self.currency.value].value\n entity = Stock(marquee_id, \"\", \"\")\n\n coordinate: DataCoordinate = DataCoordinate(measure=DataMeasure.CLOSE_PRICE, frequency=DataFrequency.DAILY)\n\n data_query: DataQuery = DataQuery(coordinate=coordinate, start=self.start, end=self.end)\n\n return DataQueryInfo('excess_returns', None, data_query, entity)\n\n def process(self):\n a_data = self.children_data.get('a')\n excess_returns_data = self.children_data.get('excess_returns')\n if isinstance(a_data, ProcessorResult) and isinstance(excess_returns_data, ProcessorResult):\n if a_data.success and excess_returns_data.success:\n if self.curve_type == CurveType.PRICES:\n excess_returns = excess_returns_pure(a_data.data, excess_returns_data.data)\n else:\n excess_returns = a_data.data\n ratio = get_ratio_pure(excess_returns, self.w)\n self.value = ProcessorResult(True, ratio)\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass CorrelationProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n benchmark: Entity,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = Window(None, 0),\n type_: SeriesType = SeriesType.PRICES,\n **kwargs,\n ):\n \"\"\"CorrelationProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param benchmark: benchmark to compare price series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window\n size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param type_: type of both input series: prices or returns\n \"\"\"\n super().__init__(**kwargs)\n # coordinate\n self.children['a'] = a\n\n # Used for additional query\n self.benchmark: Entity = benchmark\n\n # datetime\n self.start = start\n self.end = end\n\n self.children['benchmark'] = self.get_benchmark_coordinate()\n\n # parameters\n self.w = w\n self.type_ = type_\n\n def get_benchmark_coordinate(self) -> DataQueryInfo:\n coordinate = DataCoordinate(measure=DataMeasure.CLOSE_PRICE, frequency=DataFrequency.DAILY)\n data_query = DataQuery(coordinate=coordinate, start=self.start, end=self.end)\n return DataQueryInfo('benchmark', None, data_query, self.benchmark)\n\n def process(self):\n a_data = self.children_data.get('a')\n benchmark_data = self.children_data.get('benchmark')\n if isinstance(a_data, ProcessorResult) and isinstance(benchmark_data, ProcessorResult):\n if a_data.success and benchmark_data.success:\n result = correlation(a_data.data, benchmark_data.data, w=self.w, type_=self.type_)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"Processor does not have A and Benchmark data yet\")\n else:\n self.value = ProcessorResult(False, \"Processor does not have A and Benchmark data yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass ChangeProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n **kwargs,\n ):\n \"\"\"ChangeProcessor computes the change of a series\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n \"\"\"\n super().__init__(**kwargs)\n # coordinate\n self.children['a'] = a\n\n # datetime\n self.start = start\n self.end = end\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n value = change(a_data.data)\n self.value = ProcessorResult(True, value)\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass ReturnsProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n observations: Optional[int] = None,\n type_: Returns = Returns.SIMPLE,\n **kwargs,\n ):\n \"\"\"ReturnsProcessor computes the returns of a series\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param observations: number of observations, defaults to the return of the entire series as a single value\n :param type_: simple, logarithmic or absolute\n \"\"\"\n super().__init__(**kwargs)\n # coordinate\n self.children['a'] = a\n\n # datetime\n self.start = start\n self.end = end\n\n self.observations = observations\n self.type_ = type_\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n data = a_data.data\n if self.observations is None:\n if len(data) > 1:\n self.value = ProcessorResult(True, pd.Series([(data.iloc[-1] - data.iloc[0]) / data.iloc[0]]))\n else:\n self.value = ProcessorResult(True, 'Series has is less than 2.')\n else:\n value = returns(a_data.data, self.observations, self.type_)\n self.value = ProcessorResult(True, value)\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass BetaProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n b: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = Window(None, 0),\n **kwargs,\n ):\n \"\"\"BetaProcessor\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param b: DataCoordinate or BaseProcessor for the second series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n\n **Usage**\n\n Calculate rolling `beta `_\n If window is not provided, computes beta over the full series\n\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n self.children['b'] = b\n\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n b_data = self.children_data.get('b')\n # Need to check if the child node b was set in the first place.\n if self.children.get('b') and isinstance(b_data, ProcessorResult):\n if b_data.success:\n result = beta(a_data.data, b_data.data, w=self.w)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(True, \"BetaProcessor does not have 'b' series values yet.\")\n else:\n self.value = ProcessorResult(True, 'BetaProcessor: b is not a valid series.')\n else:\n self.value = ProcessorResult(False, \"BetaProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"BetaProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass FXImpliedCorrProcessor(MeasureProcessor):\n def __init__(\n self,\n *,\n cross2: Entity = None,\n tenor: str = '3m',\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n **kwargs,\n ):\n \"\"\"CorrelationProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param benchmark: benchmark to compare price series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window\n size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param type_: type of both input series: prices or returns\n \"\"\"\n super().__init__(\n **kwargs,\n )\n self.cross2: Entity = cross2\n self.tenor: str = tenor\n # datetime\n self.start = start\n self.end = end\n\n def process(self, cross1: Entity) -> ProcessorResult:\n if isinstance(cross1, Cross) and isinstance(self.cross2, Cross):\n try:\n with DataContext(self.start, self.end):\n result = fx_implied_correlation(cross1, self.cross2, self.tenor)\n self.value = ProcessorResult(True, result)\n except Exception as e:\n self.value = ProcessorResult(False, str(e))\n else:\n self.value = ProcessorResult(False, \"Processor does not have valid crosses as inputs\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n" }, { "path": "gs_quant/analytics/processors/scale_processors.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport math\nfrom typing import Union, List, Tuple, Dict\n\nfrom gs_quant.analytics.common.enumerators import ScaleShape\nfrom gs_quant.analytics.core import BaseProcessor\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\n\n\nclass SpotMarkerProcessor(BaseProcessor):\n def __init__(self, a: BaseProcessor, *, name: str, shape: ScaleShape):\n \"\"\"SpotMarkerProcessor\n Spot Marker Processors are used to accommodate a single value in your scale and this value\n can take either a PIPE shape or a DIAMOND shape.\n\n :param a: BaseProcessor that should resolve in a single value\n :param name: Name of the Scale Marker\n :param shape: ScaleShape.PIPE or ScaleShape.DIAMOND\n \"\"\"\n if shape not in [ScaleShape.PIPE, ScaleShape.DIAMOND]:\n raise TypeError(\"SpotMarkerProcessor only allows PIPE or DIAMOND ScaleShapes.\")\n super().__init__()\n self.children['a'] = a\n self.name = name\n self.shape = shape\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n self.value = ProcessorResult(\n True, {'name': self.name, 'value': a_data.data.get(-1), 'shape': self.shape.value}\n )\n else:\n self.value = ProcessorResult(False, 'Could not compute pipe marker')\n else:\n self.value = ProcessorResult(False, 'Processor does not have data')\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass BarMarkerProcessor(BaseProcessor):\n def __init__(self, start: BaseProcessor, end: BaseProcessor, *, name: str):\n \"\"\"BarMarkerProcessor\n Bar Marker Processors require 2 values to render correctly in the scale. Both start and end\n processors should resolve in single values.\n\n :param start: BaseProcessor to obtain a starting value\n :param end: BaseProcessor to obtain an ending value\n :param name: Name of the Scale Marker\n \"\"\"\n super().__init__()\n self.children['start'] = start\n self.children['end'] = end\n self.name = name\n self.shape = ScaleShape.BAR\n\n def process(self):\n start = self.children_data.get('start')\n end = self.children_data.get('end')\n if isinstance(start, ProcessorResult) and isinstance(end, ProcessorResult):\n if start.success and end.success:\n self.value = ProcessorResult(\n True,\n {'name': self.name, 'start': start.data.get(0), 'end': end.data.get(0), 'shape': self.shape.value},\n )\n else:\n self.value = ProcessorResult(False, \"Processor does not have start and end values yet\")\n else:\n self.value = ProcessorResult(False, \"Processor does not have start and end data yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nSpotOrBarMarker = Union[SpotMarkerProcessor, BarMarkerProcessor]\n\n\ndef validate_markers_data(result: Dict, marker_data: Dict) -> Tuple[bool, str]:\n min_val = result['min']\n max_val = result['max']\n if not min_val or math.isnan(min_val):\n result['min'] = None\n return False, 'Min Value needs to exist for Scale to render'\n if not max_val or math.isnan(max_val):\n result['max'] = None\n return False, 'Max Value needs to exist for Scale to render'\n\n marker_name = marker_data[\"name\"]\n if ScaleShape(marker_data['shape']) == ScaleShape.BAR:\n starting_val = marker_data['start']\n ending_val = marker_data['end']\n if starting_val > ending_val or starting_val < min_val or starting_val > max_val:\n return (\n False,\n f'Invalid marker={marker_name} with starting value={starting_val}.'\n f'Has to be within range (min={min_val}, max={max_val}) and less '\n f'than or equal to ending value=({ending_val})',\n )\n if ending_val < starting_val or ending_val < min_val or ending_val > max_val:\n return (\n False,\n f'Invalid marker={marker_name} with ending value={ending_val}.'\n f'Has to be within range (min={min_val}, max={max_val}) and greater '\n f'than or equal to starting value=({starting_val})',\n )\n else:\n if marker_data['value'] < min_val or marker_data['value'] > max_val:\n return (\n False,\n f'Invalid marker={marker_data[\"name\"]} with value={marker_data[\"value\"]}. Has to be '\n f'within range (min={min_val}, max={max_val})',\n )\n return True, ''\n\n\nclass ScaleProcessor(BaseProcessor):\n def __init__(self, minimum: BaseProcessor, maximum: BaseProcessor, *, markers: List[SpotOrBarMarker]):\n \"\"\"ScaleProcessor\n A Scale processor can be used to render a scale in a DataGrid Column. It takes in the min value and max value\n processors which should resolve to single values. Additionally it take a list of markers to render within the\n scale. At the moment you can use wither a Spot Marker or a Bar Marker Processor.\n\n :param minimum: BaseProcessor to obtain min value for the scale\n :param maximum: BaseProcessor to obtain max value for the scale\n :param markers: List of either Spot or Bar Markers to display in the scale\n \"\"\"\n super().__init__()\n self.children['minimum'] = minimum\n self.children['maximum'] = maximum\n self.markers = markers\n for marker in markers:\n self.children[marker.name] = marker\n\n def process(self):\n min_data = self.children_data.get('minimum')\n max_data = self.children_data.get('maximum')\n markers_data = [self.children_data.get(marker.name) for marker in self.markers]\n if isinstance(min_data, ProcessorResult) and isinstance(max_data, ProcessorResult):\n if min_data.success and max_data.success:\n result = {'min': min_data.data.get(0), 'max': max_data.data.get(0), 'markers': []}\n for marker_data in markers_data:\n if marker_data and marker_data.success and marker_data.data:\n valid, reason = validate_markers_data(result, marker_data.data)\n if valid:\n result['markers'].append(marker_data.data)\n else:\n result['markers'].append({**marker_data.data, **{'invalidReason': reason}})\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"Processor does not have min, max values yet\")\n else:\n self.value = ProcessorResult(False, \"Processor does not have min, max data yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n" }, { "path": "gs_quant/analytics/processors/special_processors.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom enum import Enum\nfrom typing import Union\n\nfrom pydash import get\n\nfrom gs_quant.analytics.core.processor import BaseProcessor\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.data import DataDimension, DataCoordinate\nfrom gs_quant.entities.entity import Entity\n\n\nclass EntityProcessor(BaseProcessor):\n def __init__(self, field: str):\n \"\"\"EntityProcessor gets a value off an entity object\n\n :param field: The entity property to be returned. If a nested field, separate each level with \".\",\n i.e. 'xref.bbid'\n \"\"\"\n super().__init__()\n self.field = field\n\n def process(self, entity: Entity) -> ProcessorResult:\n \"\"\"Fetch the entity and resolve the field\"\"\"\n if isinstance(entity, str): # If we were unable to fetch entity (404/403)\n return ProcessorResult(False, f\"Unable to resolve Entity {entity}\")\n try:\n # First try to get the value off the entity\n entity_dict = entity.get_entity()\n data = get(entity_dict, self.field)\n if data:\n return ProcessorResult(True, data)\n\n # If not found, try to get the value from the asset identifiers\n identifier = next(iter(filter(lambda x: x['type'] == self.field, entity_dict.get('identifiers', []))), None)\n if identifier:\n return ProcessorResult(True, identifier['value'])\n\n # Return a failed processor result if no field was found on the object or it's identifiers\n return ProcessorResult(\n False, f'Unable to find {self.field} in identifiers for entity {entity.get_marquee_id()}'\n )\n\n except ValueError:\n return ProcessorResult(False, \"Could not get field on entity\")\n\n def update(self, attribute: str, result: ProcessorResult) -> None:\n \"\"\"This method does not apply for entity processor\"\"\"\n pass\n\n def get_plot_expression(self):\n \"\"\"This method does not apply for entity processor\"\"\"\n pass\n\n\nclass CoordinateProcessor(BaseProcessor):\n def __init__(self, a: DataCoordinate, dimension: Union[DataDimension, str]):\n \"\"\"Returns a field from a coordinate\n\n :param a: coordinate to get the field\n :param dimension: dimension to get from the coordinate\n \"\"\"\n super().__init__()\n # coordinate\n self.children['a'] = a\n # parameters\n self.dimension = dimension\n\n def process(self):\n key: str = self.dimension.value if isinstance(self.dimension, Enum) else self.dimension\n coordinate = self.children.get('a')\n dimension_value = coordinate.dimensions.get(key) if coordinate else None\n if dimension_value:\n return ProcessorResult(True, dimension_value)\n else:\n return ProcessorResult(False, f'Dimension {key} not in given coordinate')\n\n def update(self, attribute: str, result: ProcessorResult):\n pass\n\n def get_plot_expression(self):\n pass\n\n\nclass MeasureProcessor(BaseProcessor):\n def __init__(self, **kwargs):\n super().__init__(**kwargs, measure_processor=True)\n\n def process(self, *args):\n pass\n\n def get_plot_expression(self):\n pass\n" }, { "path": "gs_quant/analytics/processors/statistics_processors.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Optional, Union\n\nimport pandas as pd\n\nfrom gs_quant.analytics.core.processor import BaseProcessor, DataCoordinateOrProcessor, DateOrDatetimeOrRDate\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.timeseries import returns\nfrom gs_quant.timeseries.statistics import percentiles, percentile, Window, mean, sum_, std, var, cov, zscores\n\n\nclass PercentilesProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n b: Optional[DataCoordinateOrProcessor] = None,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = Window(None, 0),\n **kwargs,\n ):\n \"\"\"PercentilesProcessor\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param b: DataCoordinate or BaseProcessor for the second series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n self.children['b'] = b\n\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n b_data = self.children_data.get('b')\n # Need to check if the child node b was set in the first place.\n if self.children.get('b') and isinstance(b_data, ProcessorResult):\n if b_data.success:\n result = percentiles(a_data.data, b_data.data, w=self.w)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(True, 'PercentilesProcessor: b is not a valid series.')\n result = percentiles(a_data.data, w=self.w)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"PercentilesProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"PercentilesProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass PercentileProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n n: float,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = None,\n **kwargs,\n ):\n \"\"\"PercentileProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param n: Percentile\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n self.n = n\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n series_length = len(a_data.data)\n window = None\n if self.w:\n window = self.w if self.w <= series_length else series_length\n result = percentile(a_data.data, self.n, w=window)\n if not isinstance(result, pd.Series):\n result = pd.Series(result)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"PercentileProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"PercentileProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass MeanProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = None,\n **kwargs,\n ):\n \"\"\"MeanProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n\n **Usage**\n\n Calculates `arithmetic mean `_ of the series over a rolling\n window\n\n If window is not provided, computes rolling mean over the full series. If the window size is greater than the\n available data, will return mean of available values.\n\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n series_length = len(a_data.data)\n window = None\n if self.w:\n window = self.w if self.w <= series_length else series_length\n result = mean(a_data.data, w=window)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"MeanProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"MeanProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass SumProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = None,\n **kwargs,\n ):\n \"\"\"SumProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n\n **Usage**\n\n Calculate the sum of observations over a given rolling window.\n\n If window is not provided, computes sum over the full series. If the window size is greater than the available\n data, will return sum of available values.\n\n \"\"\"\n\n super().__init__(**kwargs)\n self.children['a'] = a\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n series_length = len(a_data.data)\n window = None\n if self.w:\n window = self.w if self.w <= series_length else series_length\n result = sum_(a_data.data, w=window)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"SumProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"SumProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass StdDevProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = Window(None, 0),\n **kwargs,\n ):\n \"\"\"StdDevProcessor\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n\n **Usage**\n\n Provides `unbiased estimator `_ of\n sample standard deviation `_ over a rolling window\n\n If window is not provided, computes standard deviation over the full series\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n series_length = len(a_data.data)\n window = None\n if self.w:\n window = self.w if self.w <= series_length else series_length\n result = std(a_data.data, w=window)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"StdDevProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"StdDevProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass VarianceProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = Window(None, 0),\n **kwargs,\n ):\n \"\"\"VarianceProcessor\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n\n **Usage**\n\n Provides `unbiased estimator `_ of\n sample variance `_ over a rolling window\n If window is not provided, computes variance over the full series\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n series_length = len(a_data.data)\n window = None\n if self.w:\n window = self.w if self.w <= series_length else series_length\n result = var(a_data.data, w=window)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"VarianceProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"VarianceProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass CovarianceProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n b: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = Window(None, 0),\n **kwargs,\n ):\n \"\"\"CovarianceProcessor\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param b: DataCoordinate or BaseProcessor for the second series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n **Usage**\n\n Provides `unbiased estimator `_ of\n sample co-variance `_ over a rolling window:\n\n If window is not provided, computes variance over the full series\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n self.children['b'] = b\n\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n b_data = self.children_data.get('b')\n # Need to check if the child node b was set in the first place.\n if self.children.get('b') and isinstance(b_data, ProcessorResult):\n if b_data.success:\n result = cov(a_data.data, b_data.data, w=self.w)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(True, \"CovarianceProcessor does not 'b' series values yet.\")\n else:\n self.value = ProcessorResult(True, 'CovarianceProcessor: b is not a valid series.')\n else:\n self.value = ProcessorResult(False, \"CovarianceProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"CovarianceProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass ZscoresProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = None,\n **kwargs,\n ):\n \"\"\"ZscoresProcessor\n\n :param a: DataCoordinate or BaseProcessor for the series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n\n **Usage**\n\n Calculate `standard score `_ of each value in series over given\n window. Standard deviation and sample mean are computed over the specified rolling window, then element is\n normalized to provide a rolling z-score\n\n If window is not provided, computes z-score relative to mean and standard deviation over the full series\n\n\n \"\"\"\n\n super().__init__(**kwargs)\n self.children['a'] = a\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n result = zscores(a_data.data, w=Window(None, 0) if self.w is None else self.w)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"ZscoresProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"ZscoresProcessor does not have 'a' series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass StdMoveProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n w: Union[Window, int] = None,\n **kwargs,\n ):\n \"\"\"StdMoveProcessor: Returns normalized by std deviation of series a\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n\n self.start = start\n self.end = end\n self.w = w\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n data_series = a_data.data\n change_pd = data_series.tail(2)\n change = returns(change_pd).iloc[-1]\n\n # Pass in all values except last value (which is last value)\n returns_series = returns(data_series.head(-1))\n std_result = std(returns_series, w=Window(None, 0) if self.w is None else self.w).iloc[-1]\n\n if change is not None and std_result != 0:\n self.value = ProcessorResult(True, pd.Series([change / std_result]))\n else:\n self.value = ProcessorResult(False, \"StdMoveProcessor returns a NaN\")\n else:\n self.value = ProcessorResult(False, \"StdMoveProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"StdMoveProcessor does not have 'a' series yet\")\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass CompoundGrowthRate(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n n: Optional[float] = None,\n **kwargs,\n ):\n \"\"\"CompoundGrowthRate: indicates the growth rate over given time period n\n\n :param a: DataCoordinate or BaseProcessor of series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param n: Number of time\n \"\"\"\n super().__init__(**kwargs)\n self.children['a'] = a\n\n self.start = start\n self.end = end\n self.n = n\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success:\n data_series = a_data.data\n self.value = ProcessorResult(\n True, pd.Series([(data_series.iloc[-1] / data_series.iloc[0]) ** (1 / self.n) - 1])\n )\n else:\n self.value = ProcessorResult(False, \"CompoundGrowthRate does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"CompoundGrowthRate does not have 'a' series yet\")\n return self.value\n\n def get_plot_expression(self):\n pass\n" }, { "path": "gs_quant/analytics/processors/utility_processors.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Optional\n\nimport pandas as pd\n\nfrom gs_quant.analytics.core.processor import BaseProcessor, DataCoordinateOrProcessor, DateOrDatetimeOrRDate\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\n\n\nclass LastProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n **kwargs,\n ):\n \"\"\"LastProcessor returns the last value of the series\n\n :param a: DataCoordinate or BaseProcessor for the first coordinate\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n\n # datetime\n self.start = start\n self.end = end\n\n def process(self):\n \"\"\"Calculate the result and store it as the processor value\"\"\"\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success and isinstance(a_data.data, pd.Series):\n self.value = ProcessorResult(True, pd.Series(a_data.data[-1:]))\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass MinProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n **kwargs,\n ):\n \"\"\"MinProcessor returns the minimum value of the series\n\n :param a: DataCoordinate or BaseProcessor for the coordinate\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n\n # datetime\n self.start = start\n self.end = end\n\n def process(self):\n \"\"\"Calculate the result and store it as the processor value\"\"\"\n a = self.children_data.get('a')\n if isinstance(a, ProcessorResult):\n if a.success and isinstance(a.data, pd.Series):\n self.value = ProcessorResult(True, pd.Series(min(a.data)))\n else:\n self.value = ProcessorResult(False, \"Processor does not data series yet\")\n else:\n self.value = ProcessorResult(False, \"Processor does not have series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass MaxProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n **kwargs,\n ):\n \"\"\"MaxProcessor returns the maximum value of the series\n\n :param a: DataCoordinate or BaseProcessor for the coordinate\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n\n # datetime\n self.start = start\n self.end = end\n\n def process(self):\n \"\"\"Calculate the result and store it as the processor value\"\"\"\n a = self.children_data.get('a')\n if isinstance(a, ProcessorResult):\n if a.success and isinstance(a.data, pd.Series):\n self.value = ProcessorResult(True, pd.Series(max(a.data)))\n else:\n self.value = ProcessorResult(False, \"Processor does not have data series yet\")\n else:\n self.value = ProcessorResult(False, \"Processor does not have series yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass AppendProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n b: DataCoordinateOrProcessor,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n **kwargs,\n ):\n \"\"\"AppendProcessor appends both a and b data series into one series\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param b: DataCoordinate or BaseProcessor for the second series\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n self.children['b'] = b\n\n # datetime\n self.start = start\n self.end = end\n\n def process(self):\n a_data = self.children_data.get('a')\n b_data = self.children_data.get('b')\n if isinstance(a_data, ProcessorResult) and isinstance(b_data, ProcessorResult):\n if a_data.success and b_data.success:\n result = a_data.data.append(b_data.data)\n self.value = ProcessorResult(True, result)\n else:\n self.value = ProcessorResult(False, \"Processor does not have A and B data yet\")\n else:\n self.value = ProcessorResult(False, \"Processor does not have A and B data yet\")\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass AdditionProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n b: Optional[DataCoordinateOrProcessor] = None,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n addend: Optional[float] = None,\n **kwargs,\n ):\n \"\"\"AdditionProcessor adds two series or an addend to a series\n\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param b: DataCoordinate or BaseProcessor for the second series to add to the first\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param addend: number to add to all values in the series\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n self.children['b'] = b\n # datetime\n self.start = start\n self.end = end\n\n self.addend = addend\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if not a_data.success:\n self.value = a_data\n return self.value\n if self.addend:\n value = a_data.data.add(self.addend)\n self.value = ProcessorResult(True, value)\n return self.value\n b_data = self.children_data.get('b')\n if isinstance(b_data, ProcessorResult):\n if b_data.success:\n value = a_data.data.add(b_data.data)\n self.value = ProcessorResult(True, value)\n else:\n self.value = ProcessorResult(True, b_data.data)\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass SubtractionProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n b: Optional[DataCoordinateOrProcessor] = None,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n subtrahend: Optional[float] = None,\n **kwargs,\n ):\n \"\"\"SubtractionProcessor subtract two series or a subtrahend to a series\n\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param b: DataCoordinate or BaseProcessor for the second series to subtract to the first\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param subtrahend: number to subtract from all values in the series\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n self.children['b'] = b\n # datetime\n self.start = start\n self.end = end\n\n self.subtrahend = subtrahend\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if not a_data.success:\n self.value = a_data\n return self.value\n if self.subtrahend:\n value = a_data.data.sub(self.subtrahend)\n self.value = ProcessorResult(True, value)\n return self.value\n b_data = self.children_data.get('b')\n if isinstance(b_data, ProcessorResult):\n if b_data.success:\n value = a_data.data.sub(b_data.data)\n self.value = ProcessorResult(True, value)\n else:\n self.value = b_data\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass MultiplicationProcessor(BaseProcessor):\n \"\"\"Multiply scalar or series together\"\"\"\n\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n b: Optional[DataCoordinateOrProcessor] = None,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n factor: Optional[float] = None,\n **kwargs,\n ):\n \"\"\"MultiplicationProcessor multiply two series or a factor to a series\n\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param b: DataCoordinate or BaseProcessor for the second series to multiply to the first\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param factor: number to multiply all values in the series\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n self.children['b'] = b\n # datetime\n self.start = start\n self.end = end\n\n self.factor = factor\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if not a_data.success:\n self.value = a_data\n return a_data\n if self.factor:\n value = a_data.data.mul(self.factor)\n self.value = ProcessorResult(True, value)\n return self.value\n b_data = self.children_data.get('b')\n if isinstance(b_data, ProcessorResult):\n if b_data.success:\n value = a_data.data.mul(b_data.data)\n self.value = ProcessorResult(True, value)\n else:\n self.value = b_data\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass DivisionProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n b: Optional[DataCoordinateOrProcessor] = None,\n *,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n dividend: Optional[float] = None,\n **kwargs,\n ):\n \"\"\"DivisionProcessor divides two series or divides a dividend to a series\n\n\n :param a: DataCoordinate or BaseProcessor for the first series\n :param b: DataCoordinate or BaseProcessor for the second series to multiply to the first\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n :param dividend: number to divide all values in the series\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n self.children['b'] = b\n # datetime\n self.start = start\n self.end = end\n\n self.dividend = dividend\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if not a_data.success:\n self.value = a_data\n return self.value\n if self.dividend:\n value = a_data.data.div(self.dividend)\n self.value = ProcessorResult(True, value)\n return self.value\n b_data = self.children_data.get('b')\n if isinstance(b_data, ProcessorResult):\n if b_data.success:\n value = a_data.data.div(b_data.data)\n self.value = ProcessorResult(True, value)\n else:\n self.value = b_data\n\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass OneDayProcessor(BaseProcessor):\n def __init__(self, a: DataCoordinateOrProcessor, **kwargs):\n super().__init__(**kwargs)\n self.children['a'] = a\n\n def process(self):\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if not a_data.success:\n self.value = a_data\n return self.value\n\n data = a_data.data\n if len(data) >= 2:\n value = data.drop(data.index[-1].date(), errors='ignore')\n if len(value) >= 2:\n self.value = ProcessorResult(True, value[-2:])\n return self.value\n\n self.value = ProcessorResult(False, 'Not enough values given to OneDayProcessor.')\n return self.value\n\n def get_plot_expression(self):\n pass\n\n\nclass NthLastProcessor(BaseProcessor):\n def __init__(\n self,\n a: DataCoordinateOrProcessor,\n *,\n n: int = 1,\n start: Optional[DateOrDatetimeOrRDate] = None,\n end: Optional[DateOrDatetimeOrRDate] = None,\n **kwargs,\n ):\n \"\"\"LastProcessor returns the last value of the series\n\n :param a: DataCoordinate or BaseProcessor for the first coordinate\n :param start: start date or time used in the underlying data query\n :param end: end date or time used in the underlying data query\n \"\"\"\n super().__init__(**kwargs)\n # coordinates\n self.children['a'] = a\n\n # datetime\n self.start = start\n self.end = end\n self.n = n\n\n def process(self):\n \"\"\"Calculate the result and store it as the processor value\"\"\"\n a_data = self.children_data.get('a')\n if isinstance(a_data, ProcessorResult):\n if a_data.success and isinstance(a_data.data, pd.Series):\n index = -1 * self.n\n self.value = ProcessorResult(True, pd.Series(a_data.data[index]))\n else:\n self.value = ProcessorResult(False, \"NthLastProcessor does not have 'a' series values yet\")\n else:\n self.value = ProcessorResult(False, \"NthLastProcessor does not have 'a' series values yet\")\n return self.value\n\n def get_plot_expression(self):\n pass\n" }, { "path": "gs_quant/analytics/workspaces/__init__.py", "content": "from .components import (ArticleComponent,\n CommentaryComponent,\n Component,\n ContainerComponent,\n DataGridComponent,\n LegendComponent,\n MonitorComponent,\n PlotComponent,\n PromoComponent,\n RelatedLinksComponent,\n SelectorComponent,\n SeparatorComponent,\n Selection,\n LegendItem,\n RelatedLink,\n RelatedLinkType)\nfrom .workspace import (Workspace,\n WorkspaceColumn,\n WorkspaceRow,\n WorkspaceCallToAction,\n WorkspaceTab)\n" }, { "path": "gs_quant/analytics/workspaces/components.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport uuid\nfrom abc import ABC, abstractmethod\nfrom enum import Enum\nfrom typing import Dict, List, Optional\n\nfrom pydash import unset, snake_case\n\n\nclass Selection:\n def __init__(self, selector_id: str, tag: str):\n \"\"\"\n Selection option.\n :param selector_id: identifier of the selector which applies to this selection\n :param tag: tag to match in the selector. Will show up as the option in the associated selector.\n \"\"\"\n self.__selector_id = selector_id\n self.__tag = tag\n\n @property\n def selector_id(self):\n return self.__selector_id\n\n @selector_id.setter\n def selector_id(self, value):\n self.__selector_id = value\n\n @property\n def tag(self):\n return self.__tag\n\n @tag.setter\n def tag(self, value):\n self.__tag = value\n\n def as_dict(self):\n return {'selectorId': self.__selector_id, 'tag': self.__tag}\n\n @classmethod\n def from_dict(cls, obj):\n return Selection(obj['selectorId'], obj['tag'])\n\n\nclass LegendItem:\n def __init__(self, color: str, icon: str, name: str, tooltip: str = None):\n \"\"\"\n Item in the legend component\n :param color: color of the legend item\n :param icon: icon of the legend\n :param name: name of the legend item\n :param tooltip: tooltip to display on the the name\n \"\"\"\n self.color = color\n self.icon = icon\n self.name = name\n self.tooltip = tooltip\n\n def as_dict(self):\n dict_ = {'color': self.color, 'icon': self.icon, 'name': self.name}\n if self.tooltip:\n dict_['tooltip'] = self.tooltip\n return dict_\n\n @classmethod\n def from_dict(cls, obj):\n return LegendItem(color=obj['color'], icon=obj['icon'], name=obj['name'], tooltip=obj.get('tooltip'))\n\n\nclass RelatedLinkType(Enum):\n anchor = 'anchor'\n internal = 'internal'\n external = 'external'\n mail = 'mail'\n notification = 'notification'\n\n\nclass RelatedLink:\n def __init__(self, type_: RelatedLinkType, name: str, link: str, description: str = None):\n \"\"\"\n Related Link Item\n :param type_: Type of the Related Link\n :param name: Name that appears on the related links\n :param link: link to navigate when the item is clicked\n :param description: description of the link\n \"\"\"\n self.type_ = type_\n self.name = name\n self.link = link\n self.description = description\n # TODO: self.notification_properties\n\n def as_dict(self):\n dict_ = {'type': self.type_.value, 'name': self.name, 'link': self.link}\n if self.description:\n dict_['description'] = self.description\n return dict_\n\n @classmethod\n def from_dict(cls, obj):\n return RelatedLink(\n type_=RelatedLinkType(obj['type']), name=obj['name'], link=obj['link'], description=obj.get('description')\n )\n\n\nclass PromoSize(Enum):\n DEFAULT = 'default'\n LARGE = 'large'\n\n\nclass Component(ABC):\n def __init__(\n self,\n height: Optional[int] = None,\n id_: Optional[str] = None,\n *,\n width: int = None,\n selections: List[Selection] = None,\n container_ids: List[str] = None,\n ):\n self.__id = id_ or f'{self.__class__.__name__}-{str(uuid.uuid4())[0:5]}'\n self._height = height\n self.__width = width\n self.__selections = selections\n self.__container_ids = container_ids\n self._type = None\n\n @property\n def id_(self):\n return self.__id\n\n @id_.setter\n def id_(self, value):\n self.__id = value or f'{self.__class__.__name__}-{str(uuid.uuid4())[0:5]}'\n\n @property\n def width(self):\n return self.__width\n\n @width.setter\n def width(self, value):\n self.__width = value\n\n @property\n def height(self):\n return self._height\n\n @height.setter\n def height(self, value):\n self._height = value\n\n @property\n def selections(self):\n return self.__selections\n\n @selections.setter\n def selections(self, value):\n self.__selections = value\n\n @property\n def container_ids(self):\n return self.__container_ids\n\n @container_ids.setter\n def container_ids(self, value):\n self.__container_ids = value\n\n @abstractmethod\n def as_dict(self) -> Dict:\n dict_ = {'id': self.__id, 'type': self._type, 'parameters': {'height': self._height or 200}}\n if self.__selections:\n dict_['selections'] = [selection.as_dict() for selection in self.__selections]\n if self.__container_ids:\n dict_['containerIds'] = [containerId for containerId in self.__container_ids]\n\n return dict_\n\n @classmethod\n def from_dict(cls, obj, scale: int = None):\n parameters = obj.get('parameters', {})\n height = parameters.get('height', 200)\n unset(parameters, 'height')\n unset(parameters, 'width')\n component = TYPE_TO_COMPONENT[obj['type']](\n id_=obj['id'], height=height, width=scale, **{snake_case(k): v for k, v in parameters.items()}\n )\n selections, container_ids, tags = obj.get('selections'), obj.get('containerIds'), obj.get('tags')\n if selections:\n component.selections = [Selection.from_dict(selection) for selection in selections]\n if container_ids:\n component.__container_ids = [containerId for containerId in container_ids]\n if tags:\n component.tags = tags\n return component\n\n\nclass PlotComponent(Component):\n def __init__(\n self,\n height: int,\n id_: str,\n *,\n width: int = None,\n selections: List[Selection] = None,\n tooltip: str = None,\n hide_legend: bool = False,\n ):\n \"\"\"\n Plot Component\n :param id_: identifier of the plot\n :param height: height of the component\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param tooltip: text to show in a tooltip on the chart name\n :param hide_legend: whether to hide the series legend under the plot\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'plot'\n self.tooltip = tooltip\n self.hide_legend = hide_legend\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n dict_['parameters']['hideLegend'] = self.hide_legend\n if self.tooltip:\n dict_['parameters']['tooltip'] = self.tooltip\n return dict_\n\n\nclass DataVizComponent(Component):\n def __init__(self, height: int, id_: str, *, width: int = None):\n \"\"\"\n Data Visualization Component\n :param id_: identifier of the Visualization\n :param height: height of the componentTYPE_TO_COMPONENT\n :param width: width of the component integers 1-12\n \"\"\"\n super().__init__(id_=id_, height=height, width=width)\n self._type = 'dataviz'\n\n def as_dict(self) -> Dict:\n return super().as_dict()\n\n\nclass DataGridComponent(Component):\n def __init__(\n self, height: int, id_: str, *, width: int = None, selections: List[Selection] = None, tooltip: str = None\n ):\n \"\"\"\n DataGrid Component\n :param height: height of the component\n :param id_: unique identifier of the DataGrid\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param tooltip: text to show in a tooltip on the DataGrid name\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'datagrid'\n self.tooltip = tooltip\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n if self.tooltip:\n dict_['parameters']['tooltip'] = self.tooltip\n\n return dict_\n\n\nclass DataScreenerComponent(Component):\n def __init__(self, height: int, id_: str, *, width: int = None, tooltip: str = None):\n \"\"\"\n Data Screener Component\n :param height: height of the component\n :param id_: unique identifier of the Data Screener\n :param width: width of the component integers 1-12\n :param tooltip: text to show in a tooltip on the Data Screener name\n \"\"\"\n super().__init__(id_=id_, height=height, width=width)\n self._type = 'screener'\n self.tooltip = tooltip\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n if self.tooltip:\n dict_['parameters']['tooltip'] = self.tooltip\n\n return dict_\n\n\nclass ArticleComponent(Component):\n def __init__(\n self,\n height: int,\n id_: Optional[str] = None,\n *,\n width: int = None,\n selections: List[Selection] = None,\n tooltip: str = None,\n commentary_channels: List[str] = None,\n commentary_to_desktop_link: bool = None,\n ):\n \"\"\"\n Article Component\n :param height: height of the component\n :param id_: unique identifier of the component\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param tooltip: text to show in a tooltip on the article component name\n :param commentary_channels: List of commentary channels that provides data\n :param commentary_to_desktop_link: Whether or not to display a link from commentary to desktop in the header\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'article'\n self.tooltip = tooltip\n self.commentary_channels = commentary_channels\n self.commentary_to_desktop_link = commentary_to_desktop_link\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n if self.tooltip:\n dict_['parameters']['tooltip'] = self.tooltip\n if self.commentary_channels:\n dict_['parameters']['commentaryChannels'] = self.commentary_channels\n if self.commentary_to_desktop_link:\n dict_['parameters']['commentaryToDesktopLink'] = self.commentary_to_desktop_link\n\n return dict_\n\n\nclass CommentaryComponent(Component):\n def __init__(\n self,\n height: int,\n id_: Optional[str] = None,\n *,\n width: int = None,\n selections: List[Selection] = None,\n tooltip: str = None,\n commentary_channels: List[str] = None,\n commentary_to_desktop_link: bool = None,\n ):\n \"\"\"\n Commentary Component\n :param height: height of the component\n :param id_: unique identifier of the component\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param tooltip: text to show in a tooltip on the article component name\n :param commentary_channels: List of commentary channels that provides data\n :param commentary_to_desktop_link: Whether or not to display a link from commentary to desktop in the header\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'plot'\n self.tooltip = tooltip\n self.commentary_channels = commentary_channels\n self.commentary_to_desktop_link = commentary_to_desktop_link\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n if self.tooltip:\n dict_['parameters']['tooltip'] = self.tooltip\n if self.commentary_channels:\n dict_['parameters']['commentaryChannels'] = self.commentary_channels\n if self.commentary_to_desktop_link:\n dict_['parameters']['commentaryToDesktopLink'] = self.commentary_to_desktop_link\n\n return dict_\n\n\nclass ContainerComponent(Component):\n def __init__(self, id_: Optional[str] = None, *, width: int = None, component_id: str = None):\n \"\"\"\n Container Component which acts as a placeholder for components used with selectors\n :param id_: unique identifier of the component\n :param width: width of the component integers 1-12\n :param component_id: default component id to use in the container\n \"\"\"\n super().__init__(id_=id_, width=width, selections=None)\n self._type = 'container'\n self.component_id = component_id\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n if self.component_id:\n dict_['parameters']['componentId'] = self.component_id\n del dict_['parameters']['height']\n return dict_\n\n\nclass SelectorComponent(Component):\n def __init__(\n self,\n height: int,\n id_: Optional[str] = None,\n *,\n container_ids: List[str],\n width: int = None,\n title: str = None,\n default_option_index: int = None,\n tooltip: str = None,\n parent_selector_id: str = None,\n ):\n \"\"\"\n Selector Component to conditionally pick components based on their selection tags.\n :param height: height of the component\n :param id_: unique identifier of the component\n :param container_ids: Name of containers affected by the selector\n :param width: width of the component integers 1-12\n :param title: Text to show next to selector dropdown\n :param default_option_index: default index of the dropdown\n :param tooltip: text to show in tooltip on the title\n :param parent_selector_id: unique identifier of the parent selector component for nested selections\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=None)\n self._type = 'selector'\n self.container_ids = container_ids\n self.title = title\n self.default_option_index = default_option_index\n self.tooltip = tooltip\n self.parent_selector_id = parent_selector_id\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n dict_['parameters']['containerIds'] = self.container_ids\n\n if self.default_option_index:\n dict_['parameters']['defaultOptionIndex'] = self.default_option_index\n\n if self.title:\n dict_['parameters']['title'] = self.title\n\n if self.tooltip:\n dict_['parameters']['tooltip'] = self.tooltip\n\n if self.parent_selector_id:\n dict_['parameters']['parentSelectorId'] = self.parent_selector_id\n\n return dict_\n\n @classmethod\n def from_dict(cls, obj, scale: int = None):\n parameters = obj.get('parameters', {})\n\n return SelectorComponent(\n id_=obj['id'],\n height=parameters.get('height', 200),\n width=scale,\n title=parameters.get('title'),\n container_ids=parameters['containerIds'],\n tooltip=parameters.get('tooltip'),\n default_option_index=parameters.get('defaultOptionIndex'),\n parent_selector_id=parameters.get('parentSelectorId'),\n )\n\n\nclass PromoComponent(Component):\n def __init__(\n self,\n height: int,\n id_: Optional[str] = None,\n *,\n width: int = None,\n selections: List[Selection] = None,\n tooltip: str = None,\n transparent: bool = None,\n body: str = None,\n size: PromoSize = None,\n hide_border: bool = None,\n ):\n \"\"\"\n Promo Component for arbitrary text\n :param height: height of the component\n :param id_: unique identifier of the component\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param tooltip: text to show in tooltip on the title\n :param transparent: whether or not the background of the component is transparent\n :param body: text to show in the component that can have html tags\n :param size: size of the component text\n :param hide_border: whether to hide the border of the component\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'promo'\n self.tooltip = tooltip\n self.transparent = transparent\n self.body = body\n self.size = size\n self.hide_border = hide_border\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n if self.tooltip:\n dict_['parameters']['tooltip'] = self.tooltip\n if self.body:\n dict_['parameters']['body'] = self.body\n if self.size:\n dict_['parameters']['size'] = self.size.value\n if self.hide_border is not None:\n dict_['parameters']['hideBorder'] = self.size\n if self.transparent is not None:\n dict_['parameters']['transparent'] = self.transparent\n\n return dict_\n\n @classmethod\n def from_dict(cls, obj: Dict, scale: int = None):\n parameters = obj.get('parameters', {})\n size = parameters.get('size')\n size = PromoSize(size) if size else None\n return PromoComponent(\n id_=obj['id'],\n height=parameters.get('height', 200),\n width=scale,\n tooltip=parameters.get('tooltip'),\n body=parameters.get('body'),\n size=size,\n hide_border=parameters.get('hideBorder'),\n )\n\n\nclass SeparatorComponent(Component):\n def __init__(\n self,\n height: int,\n id_: Optional[str] = None,\n *,\n width: int = None,\n selections: List[Selection] = None,\n name: str = None,\n size: str = None,\n show_more_url: str = None,\n ):\n \"\"\"\n Separator Component\n :param height: height of the component\n :param id_: unique identifier of the component\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param name: Text of the separator\n :param size: size of the component\n :param show_more_url: Url link to redirect\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'separator'\n self.name = name\n self.size = size\n self.show_more_url = show_more_url\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n if self.name:\n dict_['parameters']['name'] = self.name\n if self.size:\n dict_['parameters']['size'] = self.size\n if self.show_more_url:\n dict_['parameters']['showMoreUrl'] = self.show_more_url\n\n return dict_\n\n\nclass LegendComponent(Component):\n def __init__(\n self,\n height: int,\n id_: Optional[str] = None,\n *,\n width: int = None,\n selections: List[Selection] = None,\n items: List[LegendItem] = None,\n position: str = None,\n transparent: bool = None,\n ):\n \"\"\"\n Legend Component\n :param height: height of the component\n :param id_: unique identifier of the component\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param items: Legend items to appear in the legend\n :param position: position of the legend\n :param transparent: Whether the background of the legend is transparent\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'legend'\n self.items = items\n self.position = position\n self.transparent = transparent\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n dict_['parameters']['items'] = [item.as_dict() for item in self.items]\n if self.position:\n dict_['parameters']['position'] = self.position\n if self.transparent:\n dict_['parameters']['transparent'] = self.transparent\n\n return dict_\n\n @classmethod\n def from_dict(cls, obj: Dict, scale: int = None):\n parameters = obj.get('parameters', {})\n items = [LegendItem.from_dict(item) for item in parameters.get('items', [])]\n\n return LegendComponent(\n id_=obj['id'],\n height=parameters.get('height', 200),\n width=scale,\n selections=obj.get('selections'),\n position=parameters.get('position'),\n transparent=parameters.get('transparent'),\n items=items,\n )\n\n\nclass MonitorComponent(Component):\n def __init__(\n self, height: int, id_: str, *, width: int = None, selections: List[Selection] = None, tooltip: str = None\n ):\n \"\"\"\n Monitor Component\n :param height: height of the component\n :param id_: unique identifier of the component\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param tooltip: text to show in a tooltip when hovering over monitor name\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'monitor'\n self.tooltip = tooltip\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n if self.tooltip:\n dict_['parameters']['tooltip'] = self.tooltip\n\n return dict_\n\n\nclass RelatedLinksComponent(Component):\n def __init__(\n self,\n height: int,\n id_: Optional[str] = None,\n *,\n width: int = None,\n selections: List[Selection] = None,\n links: List[RelatedLink],\n title: str,\n ):\n \"\"\"\n Related Links Component\n :param height: height of the component\n :param id_: unique identifier of the component\n :param width: width of the component integers 1-12\n :param selections: List of selections used for selectors\n :param links: links to add to the component\n :param title: title of the component\n \"\"\"\n super().__init__(id_=id_, height=height, width=width, selections=selections)\n self._type = 'relatedLinks'\n self.links = links\n self.title = title\n\n def as_dict(self) -> Dict:\n dict_ = super().as_dict()\n dict_['parameters']['title'] = self.title\n dict_['parameters']['links'] = [link.as_dict() for link in self.links]\n return dict_\n\n @classmethod\n def from_dict(cls, obj, scale: int = None):\n parameters = obj.get('parameters', {})\n return RelatedLinksComponent(\n id_=obj['id'],\n height=parameters.get('height', 200),\n width=scale,\n selections=obj.get('selections'),\n title=parameters['title'],\n links=[RelatedLink.from_dict(link) for link in parameters['links']],\n )\n\n\nTYPE_TO_COMPONENT = {\n 'article': ArticleComponent,\n 'container': ContainerComponent,\n 'datagrid': DataGridComponent,\n 'dataviz': DataVizComponent,\n 'legend': LegendComponent,\n 'monitor': MonitorComponent,\n 'plot': PlotComponent,\n 'promo': PromoComponent,\n 'relatedLinks': RelatedLinksComponent,\n 'selector': SelectorComponent,\n 'separator': SeparatorComponent,\n 'screener': DataScreenerComponent,\n}\n" }, { "path": "gs_quant/analytics/workspaces/workspace.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nimport webbrowser\nfrom collections import deque\nfrom typing import List, Tuple, Union, Dict\n\nfrom pydash import get\n\nfrom gs_quant.analytics.workspaces.components import (\n Component,\n TYPE_TO_COMPONENT,\n RelatedLink,\n DataGridComponent,\n MonitorComponent,\n PlotComponent,\n DataScreenerComponent,\n)\nfrom gs_quant.common import Entitlements as Entitlements_\nfrom gs_quant.entities.entitlements import Entitlements\nfrom gs_quant.errors import MqValueError, MqRequestError\nfrom gs_quant.session import GsSession\n\n_logger = logging.getLogger(__name__)\n\nAPI = '/workspaces/markets'\nHEADERS = {'Content-Type': 'application/json;charset=utf-8'}\n\n\nclass WorkspaceCallToAction:\n def __init__(self, actions: List[RelatedLink], text: str, name: str = None):\n \"\"\"\n Call to action displayed on the top right of the page.\n :param actions: link to external/internal pages, embed a mail to link, anchor links within page or notifications\n :param text: description below the link\n :param name: name of the link/button\n \"\"\"\n self.actions = actions\n self.text = text\n self.name = name\n\n def as_dict(self):\n actions = []\n for action in self.actions:\n if isinstance(action, RelatedLink):\n actions.append(action.as_dict())\n else:\n actions.append(action)\n cta_dict = {'actions': actions, 'text': self.text}\n if self.name:\n cta_dict['name'] = self.name\n return cta_dict\n\n @classmethod\n def from_dict(cls, obj):\n actions = []\n for action in obj['actions']:\n if isinstance(action, Dict):\n actions.append(RelatedLink.from_dict(action))\n else:\n actions.append(action)\n return WorkspaceCallToAction(actions=actions, text=obj['text'], name=obj['name'])\n\n\nclass WorkspaceTab:\n def __init__(self, id_: str, name: str):\n \"\"\"\n Workspace Tab to connect other workspaces.\n :param id_: alias of the workspace to create a tab\n :param name: Name of the tab\n \"\"\"\n self.id_ = id_\n self.name = name\n\n def as_dict(self):\n return {'id': self.id_, 'name': self.name}\n\n @classmethod\n def from_dict(cls, obj):\n return WorkspaceTab(id_=obj['id'], name=obj['name'])\n\n\nclass WorkspaceColumn:\n def __init__(self, components: List[Union[Component, 'WorkspaceRow']], width: int = None):\n \"\"\"\n :param components: List of components in the same row\n \"\"\"\n self.__components = []\n self.components = components\n self.__width = width\n\n @property\n def components(self):\n return self.__components\n\n @components.setter\n def components(self, value):\n if len(value) > 12:\n raise MqValueError(f'{value} exceeds the max number of columns of 12.')\n width_sum = 0\n for component in self.__components:\n if not isinstance(component, WorkspaceRow):\n width_sum += component.width\n if width_sum > 12:\n raise MqValueError(f'{width_sum} exceeds the max sum of widths of 12.')\n\n without_width_count = 0\n for component in value:\n if not isinstance(component, WorkspaceRow):\n without_width_count += component.width or 1\n if width_sum + without_width_count > 12:\n raise MqValueError(\n f'Cannot fit all components in column due to given total width of {width_sum} '\n f'and {without_width_count} components without a width.'\n )\n self.__components = value\n\n @property\n def width(self):\n return self.__width\n\n @width.setter\n def width(self, value):\n self.__width = value\n\n def get_layout(self, count):\n layout = ''\n width_sum = 0\n for component in self.__components:\n if not isinstance(component, WorkspaceRow):\n width_sum += component.width or 0\n components_length = len(self.__components)\n if width_sum == 0:\n # Equally spread out\n size = int(12 / components_length)\n last_size = 12 % components_length\n for i, component in enumerate(self.__components):\n if isinstance(component, WorkspaceRow):\n sub_layout, count = component.get_layout(count)\n layout += sub_layout\n elif isinstance(component, WorkspaceColumn):\n sub_layout, count = component.get_layout(count)\n layout += (\n f'c{size + last_size if i == components_length - 1 and last_size != 0 else size}({sub_layout})'\n )\n else:\n # Case: Component\n layout += (\n f'c{size + last_size if i == components_length - 1 and last_size != 0 else size}(${count})'\n )\n count += 1\n else:\n used_sum = 0\n if width_sum == 12:\n default_width = 0\n else:\n default_width = int(12 - width_sum / sum(1 for component in self.components if component.width is None))\n for i, component in enumerate(self.components):\n if i == components_length - 1 and not component.width:\n layout += f'c{12 - used_sum}(${count})'\n elif component.width is None:\n layout += f'c{default_width}(${count})'\n used_sum += default_width\n else:\n width = component.width or 1\n layout += f'c{width}(${count})'\n used_sum += width\n count += 1\n\n return layout, count\n\n def _add_components(self, components):\n for component in self.__components:\n if isinstance(component, (WorkspaceRow, WorkspaceColumn)):\n component._add_components(components)\n else:\n components.append(component.as_dict())\n\n\nclass WorkspaceRow:\n \"\"\"\n Wrapper on a list of components in the same row.\n \"\"\"\n\n def __init__(self, components: List[Union[Component, WorkspaceColumn]]):\n \"\"\"\n :param components: List of components in the same row\n \"\"\"\n self.__components = []\n self.components = components\n\n @property\n def components(self):\n return self.__components\n\n @components.setter\n def components(self, value):\n if len(value) > 12:\n raise MqValueError(f'{value} exceeds the max number of columns of 12.')\n width_sum = 0\n for component in self.__components:\n if not isinstance(component, WorkspaceRow):\n width_sum += component.width\n if width_sum > 12:\n raise MqValueError(f'{width_sum} exceeds the max sum of widths of 12.')\n\n without_width_count = 0\n for component in value:\n if not isinstance(component, WorkspaceRow):\n without_width_count += component.width or 1\n if width_sum + without_width_count > 12:\n raise MqValueError(\n f'Cannot fit all components in row due to given total width of {width_sum} '\n f'and {without_width_count} components without a width.'\n )\n self.__components = value\n\n def get_layout(self, count: int) -> Tuple[str, int]:\n layout = 'r('\n width_sum = 0\n for component in self.__components:\n if not isinstance(component, WorkspaceRow):\n width_sum += component.width or 0\n components_length = len(self.__components)\n if width_sum == 0:\n # Equally spread out\n size = int(12 / components_length)\n last_size = 12 % components_length\n for i, component in enumerate(self.__components):\n if isinstance(component, WorkspaceColumn):\n sub_layout, count = component.get_layout(count)\n layout += (\n f'c{size + last_size if i == components_length - 1 and last_size != 0 else size}({sub_layout})'\n )\n else:\n # Case: Component\n layout += (\n f'c{size + last_size if i == components_length - 1 and last_size != 0 else size}(${count})'\n )\n count += 1\n else:\n used_sum = 0\n if width_sum == 12:\n default_width = 0\n else:\n default_width = (\n self.components[0].width\n if len(self.components) == 1\n else int(12 - width_sum / sum(1 for component in self.components if component.width is None))\n )\n for i, component in enumerate(self.components):\n if i == components_length - 1 and not component.width:\n if isinstance(component, WorkspaceColumn):\n sub_layout, count = component.get_layout(count)\n layout += f'c{12 - used_sum}({sub_layout})'\n else:\n layout += f'c{12 - used_sum}(${count})'\n count += 1\n elif component.width is None:\n if isinstance(component, WorkspaceColumn):\n sub_layout, count = component.get_layout(count)\n layout += f'c{default_width}({sub_layout})'\n else:\n layout += f'c{default_width}(${count})'\n count += 1\n used_sum += default_width\n else:\n width = component.width or 1\n if isinstance(component, WorkspaceColumn):\n sub_layout, count = component.get_layout(count)\n layout += f'c{width}({sub_layout})'\n else:\n layout += f'c{width}(${count})'\n count += 1\n used_sum += width\n\n layout += ')'\n return layout, count\n\n def _add_components(self, components):\n for component in self.__components:\n if isinstance(component, (WorkspaceRow, WorkspaceColumn)):\n component._add_components(components)\n else:\n components.append(component.as_dict())\n\n\nclass Workspace:\n PERSISTED_COMPONENTS = {\n DataGridComponent: '/data/grids',\n MonitorComponent: '/monitors',\n PlotComponent: '/charts',\n DataScreenerComponent: '/data/screens',\n }\n\n def __init__(\n self,\n name: str,\n rows: List[WorkspaceRow] = None,\n alias: str = None,\n description: str = None,\n entitlements: Union[Entitlements, Entitlements_] = None,\n tabs: List[WorkspaceTab] = None,\n selector_components: List[Component] = None,\n disclaimer: str = None,\n maintainers: List[str] = None,\n call_to_action: Union[WorkspaceCallToAction, Dict] = None,\n tags: List[str] = None,\n ):\n self.__id = None\n self.__name = name\n self.__rows = rows or []\n self.__selector_components = selector_components or []\n self.__alias = alias\n self.__entitlements = entitlements\n self.__description = description\n self.__disclaimer = disclaimer\n self.__maintainers = maintainers or []\n self.__tabs = tabs or []\n self.__call_to_action = call_to_action\n self.__tags = tags or []\n\n @classmethod\n def get_by_id(cls, workspace_id: str) -> 'Workspace':\n resp = GsSession.current.sync.get(f'{API}/{workspace_id}')\n return Workspace.from_dict(resp)\n\n @classmethod\n def get_by_alias(cls, alias: str) -> 'Workspace':\n resp = get(GsSession.current.sync.get(f'{API}?alias={alias}'), 'results.0')\n if not resp:\n raise MqValueError(f'Workspace not found with alias {alias}')\n return Workspace.from_dict(resp)\n\n def save(self):\n if self.__id:\n GsSession.current.sync.put(f'{API}/{self.__id}', self.as_dict(), request_headers=HEADERS)\n elif self.__alias:\n id_ = get(GsSession.current.sync.get(f'{API}?alias={self.__alias}'), 'results.0.id')\n if id_:\n self.__id = GsSession.current.sync.put(f'{API}/{id_}', self.as_dict(), request_headers=HEADERS)['id']\n else:\n self.__id = GsSession.current.sync.post(API, self.as_dict(), request_headers=HEADERS)['id']\n\n def open(self):\n if self.__id is None:\n raise MqValueError('Workspace must be created or saved before opening.')\n\n domain = GsSession.current.domain.replace(\".web\", \"\")\n if domain == 'https://api.gs.com':\n domain = 'https://marquee.gs.com'\n url = f'{domain}/s/markets/{self.__alias or self.__id}'\n webbrowser.open(url)\n\n def create(self):\n resp = GsSession.current.sync.post(f'{API}', self.as_dict(), request_headers=HEADERS)\n self.__id = resp['id']\n\n def delete(self):\n if self.__id is None:\n raise MqValueError('Workspace must have an id to be deleted.')\n resp = GsSession.current.sync.delete(f'{API}/{self.__id}')\n self.__id = resp['id']\n\n def delete_all(self, include_tabs: bool = False):\n \"\"\"\n Deletes the workspace and all persisted components.\n :param include_tabs: whether to delete all tabs and their persisted components also\n :return: None\n \"\"\"\n for row in self.__rows:\n self.__delete_components(row.components)\n self.__delete_components(self.__selector_components)\n if include_tabs:\n for tab in self.__tabs:\n tab_workspace = self.get_by_alias(tab.id_)\n tab_workspace.delete_all()\n\n @property\n def name(self):\n return self.__name\n\n @name.setter\n def name(self, value):\n self.__name = value\n\n @property\n def alias(self):\n return self.__alias\n\n @alias.setter\n def alias(self, value):\n self.__alias = value\n\n @property\n def rows(self) -> List[WorkspaceRow]:\n return self.__rows\n\n @rows.setter\n def rows(self, value: List[WorkspaceRow]):\n self.__rows = value\n\n @property\n def entitlements(self):\n return self.__entitlements\n\n @entitlements.setter\n def entitlements(self, value):\n self.__entitlements = value\n\n @property\n def description(self):\n return self.__description\n\n @description.setter\n def description(self, value):\n self.__description = value\n\n @property\n def disclaimer(self):\n return self.__disclaimer\n\n @disclaimer.setter\n def disclaimer(self, value):\n self.__disclaimer = value\n\n @property\n def maintainers(self):\n return self.__maintainers\n\n @maintainers.setter\n def maintainers(self, value):\n self.__maintainers = value\n\n @property\n def tabs(self):\n return self.__tabs\n\n @tabs.setter\n def tabs(self, value):\n self.__tabs = value\n\n @property\n def selector_components(self):\n return self.__selector_components\n\n @selector_components.setter\n def selector_components(self, value):\n self.__selector_components = value\n\n @property\n def call_to_action(self):\n return self.__call_to_action\n\n @call_to_action.setter\n def call_to_action(self, value):\n self.__call_to_action = value\n\n @property\n def tags(self):\n return self.__tags\n\n @tags.setter\n def tags(self, value):\n self.__tags = value\n\n @property\n def id(self):\n return self.__id\n\n @classmethod\n def _parse(cls, layout: str, workspace_components: List[Dict]):\n current_str = ''\n outside_components = []\n stack = deque()\n for c in layout:\n current_str += c\n if c == '(':\n stack.append('(')\n elif c == ')':\n stack.pop()\n if len(stack) == 0:\n if current_str.startswith('c'):\n is_component = current_str[current_str.index('(') + 1 :].startswith('$')\n if is_component:\n # Component Case\n scale, id_ = current_str.split('($')\n scale = int(scale[1:])\n id_ = int(id_[0:-1])\n component = workspace_components[id_]\n component_type = component['type']\n component = TYPE_TO_COMPONENT[component_type].from_dict(component, scale)\n outside_components.append(component)\n else:\n # Column Case\n column_layout = current_str[current_str.index('(') + 1 : -1]\n components = Workspace._parse(column_layout, workspace_components)\n width = int(current_str[1 : current_str.index('(')])\n outside_components.append(WorkspaceColumn(components, width))\n elif current_str.startswith('r'):\n # Row Case\n row_layout = current_str[current_str.index('(') + 1 : -1]\n components = Workspace._parse(row_layout, workspace_components)\n outside_components.append(WorkspaceRow(components))\n current_str = ''\n\n return outside_components\n\n @classmethod\n def from_dict(cls, obj):\n workspace_components = obj['parameters']['components']\n layout = obj['parameters']['layout']\n stack = deque()\n row_layout = ''\n row_layouts = []\n for c in layout[1:]:\n if c == '(':\n stack.append('(')\n elif c == ')':\n stack.pop()\n if len(stack) == 0:\n row_layouts.append(row_layout[row_layout.index('c') :])\n row_layout = ''\n row_layout += c\n\n workspace_rows = [\n WorkspaceRow(components=Workspace._parse(row_layout, workspace_components)) for row_layout in row_layouts\n ]\n\n component_count = 0\n # The rest of the components not in the layout should be selector components\n selector_components = []\n if component_count < len(workspace_components):\n for i in range(component_count, len(workspace_components)):\n component = workspace_components[i]\n selector_components.append(TYPE_TO_COMPONENT[component['type']].from_dict(component))\n\n params = obj['parameters']\n tabs = [WorkspaceTab.from_dict(tab) for tab in params.get('tabs', [])]\n return Workspace(\n name=obj['name'],\n rows=workspace_rows,\n selector_components=selector_components,\n alias=obj.get('alias'),\n tabs=tabs,\n entitlements=Entitlements.from_dict(obj.get('entitlements', {})),\n description=obj.get('description'),\n disclaimer=params.get('disclaimer'),\n maintainers=params.get('maintainers'),\n )\n\n def as_dict(self):\n components, count, layout = [], 0, ''\n for row in self.__rows:\n row_layout, count = row.get_layout(count)\n layout += row_layout\n for component in row.components:\n if isinstance(component, (WorkspaceRow, WorkspaceColumn)):\n component._add_components(components)\n else:\n components.append(component.as_dict())\n\n # Add the hidden components at the end\n components.extend([component.as_dict() for component in self.__selector_components])\n\n parameters = {'layout': layout, 'components': components}\n\n if len(self.__maintainers):\n parameters['maintainers'] = self.__maintainers\n if self.__call_to_action:\n if isinstance(self.__call_to_action, WorkspaceCallToAction):\n parameters['callToAction'] = self.__call_to_action.as_dict()\n else:\n parameters['callToAction'] = self.__call_to_action\n if len(self.__tabs):\n parameters['tabs'] = [tab.as_dict() for tab in self.__tabs]\n if self.__disclaimer:\n parameters['disclaimer'] = self.__disclaimer\n\n dict_ = {'name': self.__name, 'parameters': parameters}\n\n if self.__alias:\n dict_['alias'] = self.__alias\n if self.__entitlements:\n if isinstance(self.__entitlements, Entitlements_):\n dict_['entitlements'] = self.__entitlements.as_dict()\n elif isinstance(self.__entitlements, Entitlements):\n dict_['entitlements'] = self.__entitlements.to_dict()\n else:\n dict_['entitlements'] = self.__entitlements\n if len(self.__tags):\n dict_['tags'] = self.__tags\n if self.__description:\n dict_['description'] = self.__description\n\n return dict_\n\n @classmethod\n def __delete_components(cls, components: List[Component]):\n for component in components:\n if isinstance(component, (WorkspaceRow, WorkspaceColumn)):\n cls.__delete_components(component.components)\n else:\n type_ = type(component)\n if type_ in cls.PERSISTED_COMPONENTS:\n try:\n GsSession.current.sync.delete(f'{cls.PERSISTED_COMPONENTS[type_]}/{component.id_}')\n except MqRequestError as ex:\n _logger.warning(\n f'Failed to delete {type_.__name__} with id {component.id_} due to {ex.message}'\n )\n\n\ndef __get_layout(components, count):\n layout = 'r('\n width_sum = 0\n for component in components:\n if not isinstance(component, WorkspaceRow):\n width_sum += component.width or 0\n components_length = len(components)\n if width_sum == 0:\n # Equally spread out\n size = int(12 / components_length)\n last_size = 12 % components_length\n for i, component in enumerate(components):\n if isinstance(component, WorkspaceRow):\n sub_layout, count = __get_layout(component.components, count)\n layout += (\n f'c{size + last_size if i == components_length - 1 and last_size != 0 else size}({sub_layout})'\n )\n elif isinstance(component, WorkspaceColumn):\n sub_layout, count = __get_layout(component.components, count)\n layout += (\n f'c{size + last_size if i == components_length - 1 and last_size != 0 else size}({sub_layout})'\n )\n else:\n # Case: Component\n layout += f'c{size + last_size if i == components_length - 1 and last_size != 0 else size}(${count})'\n count += 1\n else:\n used_sum = 0\n if width_sum == 12:\n default_width = 0\n else:\n default_width = int(12 - width_sum / sum(1 for component in components if component.width is None))\n for i, component in enumerate(components):\n if i == components_length - 1 and not component.width:\n layout += f'c{12 - used_sum}(${count})'\n elif component.width is None:\n layout += f'c{default_width}(${count})'\n used_sum += default_width\n else:\n width = component.width or 1\n layout += f'c{width}(${count})'\n used_sum += width\n count += 1\n\n layout += ')'\n return layout, count\n" }, { "path": "gs_quant/api/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\n__name__ = 'api'\n" }, { "path": "gs_quant/api/api_cache.py", "content": "\"\"\"\nCopyright 2023 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom enum import Enum\nfrom typing import Any, Tuple\n\nimport cachetools\nimport pandas as pd\n\nfrom gs_quant.base import Base\nfrom gs_quant.session import GsSession\n\n\nclass CacheEvent(Enum):\n PUT = 'Put'\n GET = 'Get'\n\n\nclass ApiRequestCache(ABC):\n def get(self, session: GsSession, key: Any, **kwargs):\n cache_lookup = self._get(session, key, **kwargs)\n if cache_lookup is not None:\n self.record(session, key, CacheEvent.GET, **kwargs)\n return cache_lookup\n\n @abstractmethod\n def _get(self, session: GsSession, key: Any, **kwargs):\n pass\n\n def record(self, session: GsSession, key: Any, method: CacheEvent, **kwargs):\n pass\n\n def put(self, session: GsSession, key: Any, value, **kwargs):\n self._put(session, key, value, **kwargs)\n self.record(session, key, CacheEvent.PUT, **kwargs)\n\n @abstractmethod\n def _put(self, session: GsSession, key: Any, value, **kwargs):\n pass\n\n\nclass InMemoryApiRequestCache(ApiRequestCache):\n def __init__(self, max_size=1000, ttl_in_seconds=3600):\n self._cache = cachetools.TTLCache(max_size, ttl_in_seconds)\n self._records = []\n\n def get_events(self) -> Tuple[Tuple[CacheEvent, Any], ...]:\n return tuple(self._records)\n\n def clear_events(self):\n self._records.clear()\n\n def _make_str_key(self, key: Any):\n if isinstance(key, (list, tuple)):\n return \"_\".join(self._make_str_key(k) for k in key)\n elif isinstance(key, (Base, pd.DataFrame)):\n return key.to_json()\n elif isinstance(key, dict):\n return self._make_str_key(list(key.items()))\n return str(key)\n\n def _get(self, session: GsSession, key: Any, **kwargs):\n return self._cache.get(self._make_str_key(key))\n\n def record(self, session: GsSession, key: Any, method: CacheEvent, **kwargs):\n self._records.append((method, key))\n\n def _put(self, session: GsSession, key, value, **kwargs):\n self._cache[self._make_str_key(key)] = value\n" }, { "path": "gs_quant/api/api_session.py", "content": "\"\"\"\nCopyright 2023 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Callable, Optional\n\nfrom gs_quant.session import GsSession\n\n\nclass ApiWithCustomSession:\n __SESSION_SUPPLIER: Optional[Callable[[], GsSession]] = None\n\n @classmethod\n def set_session_provider(cls, session_supplier: Callable[[], GsSession]):\n \"\"\"\n To allow session context override specific to this API, set a factory/supplier.\n Default is GsSession.current.\n :param session_supplier: callable which returns a GsSession\n \"\"\"\n cls.__SESSION_SUPPLIER = session_supplier\n\n @classmethod\n def set_session(cls, session: GsSession):\n \"\"\"\n To allow session context override specific to this API, set a session directly.\n Default is GsSession.current.\n :param session: a GsSession\n \"\"\"\n cls.__SESSION_SUPPLIER = None if session is None else lambda: session\n\n @classmethod\n def get_session(cls) -> GsSession:\n if cls.__SESSION_SUPPLIER:\n return cls.__SESSION_SUPPLIER()\n else:\n return GsSession.current\n" }, { "path": "gs_quant/api/data.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom abc import ABCMeta\nfrom typing import Optional, Union, List\n\nimport inflection\nimport pandas as pd\n\nfrom gs_quant.api.api_session import ApiWithCustomSession\nfrom gs_quant.api.fred.fred_query import FredQuery\nfrom gs_quant.base import Base\nfrom gs_quant.target.coordinates import MDAPIDataQuery\nfrom gs_quant.target.data import DataQuery\n\n_logger = logging.getLogger(__name__)\n\n\nclass DataApi(ApiWithCustomSession, metaclass=ABCMeta):\n @classmethod\n def query_data(cls, query: Union[DataQuery, FredQuery], dataset_id: str = None) -> Union[list, tuple]:\n raise NotImplementedError('Must implement get_data')\n\n @classmethod\n def last_data(cls, query: DataQuery, dataset_id: str = None) -> Union[list, tuple]:\n raise NotImplementedError('Must implement last_data')\n\n @classmethod\n def symbol_dimensions(cls, dataset_id: str) -> tuple:\n raise NotImplementedError('Must implement symbol_dimensions')\n\n @classmethod\n def time_field(cls, dataset_id: str) -> str:\n raise NotImplementedError('Must implement time_field')\n\n @classmethod\n def construct_dataframe_with_types(\n cls, dataset_id: str, data: Union[Base, list, tuple, pd.Series], schema_varies=False, standard_fields=False\n ) -> pd.DataFrame:\n raise NotImplementedError('Must implement time_field')\n\n @staticmethod\n def build_query(\n start: Optional[Union[dt.date, dt.datetime]] = None,\n end: Optional[Union[dt.date, dt.datetime]] = None,\n as_of: Optional[dt.datetime] = None,\n since: Optional[dt.datetime] = None,\n restrict_fields: bool = False,\n format: str = 'MessagePack',\n dates: List[dt.date] = None,\n empty_intervals: Optional[bool] = None,\n **kwargs,\n ):\n end_is_time = isinstance(end, dt.datetime)\n start_is_time = isinstance(start, dt.datetime)\n\n if kwargs.get('market_data_coordinates'):\n real_time = (start is None or start_is_time) and (end is None or end_is_time)\n query = MDAPIDataQuery(\n start_time=start if real_time else None,\n end_time=end if real_time else None,\n start_date=start if not real_time else None,\n end_date=end if not real_time else None,\n format=format,\n real_time=real_time,\n **kwargs,\n )\n else:\n if start_is_time and end is not None and not end_is_time:\n raise ValueError('If start is of type datetime, so must end be!')\n\n if isinstance(start, dt.date) and end is not None and not isinstance(end, dt.date):\n raise ValueError('If start is of type date, so must end be!')\n\n query = DataQuery(\n start_date=start if not start_is_time else None,\n start_time=start if start_is_time else None,\n end_date=end if not end_is_time else None,\n end_time=end if end_is_time else None,\n as_of_time=as_of,\n since=since,\n format=format,\n dates=dates,\n empty_intervals=empty_intervals,\n )\n\n query_properties = query.properties()\n query.where = dict()\n for field, value in kwargs.items():\n snake_case_field = inflection.underscore(field)\n if snake_case_field in query_properties and snake_case_field not in ('name',):\n setattr(query, snake_case_field, value)\n else:\n query.where[field] = value\n\n if getattr(query, 'fields', None) is not None:\n try:\n query.restrict_fields = restrict_fields\n except AttributeError as e:\n _logger.debug('unable to set restrict_fields', exc_info=e)\n\n return query\n" }, { "path": "gs_quant/api/fred/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\nThis product uses the FRED® API but is not endorsed or certified\nby the Federal Reserve Bank of St. Louis. FRED terms of use\navailable at https://research.stlouisfed.org/docs/api/terms_of_use.html\n\nPortions copyright Maverick Lin. Licensed under Apache 2.0 license\n\n\"\"\"\n\n__name__ = 'fred'\n" }, { "path": "gs_quant/api/fred/data.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\nThis product uses the FRED® API but is not endorsed or certified\nby the Federal Reserve Bank of St. Louis. FRED terms of use\navailable at https://research.stlouisfed.org/docs/api/terms_of_use.html\n\"\"\"\n\nfrom typing import Iterable, Optional, Union\n\nimport pandas as pd\nimport datetime as dt\nimport textwrap\nfrom gs_quant.api.utils import handle_proxy\n\nfrom requests.exceptions import HTTPError\n\nfrom dataclasses import asdict, replace\n\nfrom gs_quant.api.data import DataApi\nfrom gs_quant.api.fred.fred_query import FredQuery\n\n\"\"\"\nFred Data API that provides functions to query the Fred dataset.\nYou need to specify a valid API key by passing in the string via api_key.\nYou can sign up for a free API key on the Fred website at:\nhttp://research.stlouisfed.org/fred2/\n\"\"\"\n\n\nclass FredDataApi(DataApi):\n earliest_realtime_start = '1776-07-04'\n latest_realtime_end = '9999-12-31'\n root_url = 'https://api.stlouisfed.org/fred/series/observations'\n\n def __init__(self, api_key=None):\n if api_key is not None:\n self.api_key = api_key\n else:\n raise ValueError(\n textwrap.dedent(\"\"\"\n Please pass a string with your API key. You can sign up for a free api key on the Fred website at\n http://research.stlouisfed.org/fred2/\"\"\")\n )\n\n def build_query(\n self,\n start: Optional[Union[dt.date, dt.datetime]] = None,\n end: Optional[Union[dt.date, dt.datetime]] = None,\n as_of: Optional[dt.datetime] = None,\n since: Optional[dt.datetime] = None,\n fields: Optional[Iterable[str]] = None,\n **kwargs,\n ) -> FredQuery:\n \"\"\"\n Builds a FRED URL to query.\n\n :param start: Requested start date/datetime for data\n :param end: Requested end date/datetime for data\n :param as_of: Request data as_of\n :param since: Request data since\n :param fields: DataSet fields to include\n :param kwargs: Extra query arguments\n :return: a url string of the requested data\n \"\"\"\n\n if start is not None and end is not None:\n if type(start) is not type(end):\n raise ValueError('Start and end types must match!')\n\n request = FredQuery(observation_start=start, observation_end=end, realtime_end=as_of, realtime_start=since)\n return request\n\n def query_data(self, query: FredQuery, dataset_id: str, asset_id_type: str = None) -> pd.Series:\n \"\"\"\n Query data given a valid FRED series id and url. Will raise an HTTPError if the response was an HTTP error.\n\n :param query: A url string of the requested data\n :param id: A FRED series id\n :return: with id as key and requested DataFrame as value.\n \"\"\"\n request = replace(query, api_key=self.api_key, series_id=dataset_id)\n response = handle_proxy(self.root_url, asdict(request))\n handled = self.__handle_response(response)\n handled.name = dataset_id\n return handled\n\n def last_data(self, query: FredQuery, dataset_id: str) -> pd.Series:\n \"\"\"\n Get the last point for a data series, at or before as_of\n\n :param query: A url string of the requested data\n :param id: A FRED series id\n :param kwargs: Extra query arguments\n :return: with id as key and last point for this DataSet as value.\n \"\"\"\n\n data = self.query_data(query, dataset_id)\n return data.last('1D')\n\n @staticmethod\n def __handle_response(response: str) -> pd.Series:\n \"\"\"\n Helper function for handling the response given a request URL.\n Will raise an HTTPError if the response was an HTTP error.\n \"\"\"\n try:\n response.raise_for_status()\n json_data = response.json()\n except HTTPError:\n raise ValueError(response.json()['error_message'])\n if not len(json_data['observations']):\n raise ValueError('No data exists for {} for the provided parameters... '.format(id))\n\n data = pd.DataFrame(json_data['observations'])[['date', 'value']]\n data = data[data.value != '.']\n data['date'] = pd.to_datetime(data['date'])\n data['value'] = data['value'].astype(float)\n data = data.set_index('date')['value']\n data = data.sort_index()\n return data\n\n def construct_dataframe_with_types(\n self, dataset_id: str, data: pd.Series, schema_varies=False, standard_fields=False\n ) -> pd.DataFrame:\n \"\"\"\n Constructs a dataframe with correct date types.\n\n :param data: Data to convert with correct types\n :return: Dataframe with correct types\n \"\"\"\n if len(data) and isinstance(data, pd.Series):\n return data.to_frame()\n else:\n return pd.DataFrame({})\n\n def symbol_dimensions(self, dataset_id: str) -> tuple:\n query = FredQuery()\n data = self.query_data(query, dataset_id)\n return data.shape\n\n def time_field(self, dataset_id: str) -> str:\n pass\n" }, { "path": "gs_quant/api/fred/fred_query.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\nThis product uses the FRED® API but is not endorsed or certified\nby the Federal Reserve Bank of St. Louis. FRED terms of use\navailable at https://research.stlouisfed.org/docs/api/terms_of_use.html\n\"\"\"\n\nfrom typing import Union\nimport datetime as dt\n\nfrom dataclasses import dataclass\n\n\n@dataclass\nclass FredQuery:\n api_key: str = ''\n series_id: str = ''\n file_type: str = 'json'\n observation_start: Union[dt.date, dt.datetime] = None\n observation_end: Union[dt.date, dt.datetime] = None\n realtime_end: dt.datetime = None\n realtime_start: dt.datetime = None\n" }, { "path": "gs_quant/api/gs/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\n__name__ = 'gs'\n" }, { "path": "gs_quant/api/gs/assets.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport os\nimport threading\nfrom enum import auto, Enum\nfrom functools import wraps\nfrom typing import Iterable, List, Optional, Tuple, Union, Callable\n\nimport backoff\nimport cachetools\nimport cachetools.keys\nimport pandas as pd\nfrom pydash import get, has\nfrom requests.exceptions import HTTPError\n\nfrom gs_quant.api.api_cache import ApiRequestCache, InMemoryApiRequestCache\nfrom gs_quant.common import Entitlements, PositionType\nfrom gs_quant.context_base import nullcontext\nfrom gs_quant.errors import MqValueError, MqRateLimitedError, MqTimeoutError, MqInternalServerError\nfrom gs_quant.instrument import Instrument, Security\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.assets import (\n Asset as __Asset,\n AssetToInstrumentResponse,\n TemporalXRef,\n Position,\n EntityQuery,\n PositionSet,\n)\nfrom gs_quant.target.assets import FieldFilterMap\nfrom gs_quant.target.reports import Report\nfrom gs_quant.tracing import Tracer\n\n_logger = logging.getLogger(__name__)\nIdList = Union[Tuple[str, ...], List]\nENABLE_ASSET_CACHING = 'GSQ_SEC_MASTER_CACHE'\n\n\nclass AssetCache:\n def __init__(self, cache: ApiRequestCache, ttl: int, construct_key_fn: Callable):\n self.__cache = cache\n self.__ttl = ttl\n self.__construct_key_fn = construct_key_fn\n\n @property\n def ttl(self):\n return self.__ttl\n\n @property\n def cache(self):\n return self.__cache\n\n @property\n def construct_key_fn(self):\n return self.__construct_key_fn\n\n def construct_key(self, session: GsSession, *args, **kwargs):\n return self.construct_key_fn(session, *args, **kwargs)\n\n\ndef get_default_cache() -> AssetCache:\n ttl = 30 # seconds\n\n def in_memory_key_fn(session, *args, **kwargs):\n args = [tuple(x) if isinstance(x, list) else x for x in args] # tuples are hashable\n for k, v in kwargs.items():\n if isinstance(v, list):\n kwargs[k] = tuple(v)\n\n k = cachetools.keys.hashkey(session, *args, **kwargs)\n return k\n\n return AssetCache(cache=InMemoryApiRequestCache(1024, ttl), ttl=ttl, construct_key_fn=in_memory_key_fn)\n\n\ndef _cached(fn):\n _fn_cache_lock = threading.Lock()\n # short-term cache to avoid retrieving the same data several times in succession\n fallback_cache: AssetCache = get_default_cache()\n\n @wraps(fn)\n def wrapper(cls, *args, **kwargs):\n if os.environ.get(ENABLE_ASSET_CACHING):\n _logger.debug(\"Asset caching is enabled\")\n asset_cache = cls.get_cache() or fallback_cache\n k = asset_cache.construct_key(GsSession.current, fn.__name__, *args, **kwargs)\n with Tracer(\"acquiring cache lock\"):\n _logger.debug('cache get: %s', k)\n with _fn_cache_lock:\n result = asset_cache.cache.get(GsSession.current, k)\n if result:\n _logger.debug('cache hit: %s', k)\n return result\n with Tracer(\"Executing function\"):\n result = fn(cls, *args, **kwargs)\n with Tracer(\"acquiring cache lock\"):\n _logger.debug('cache set: %s', k)\n with _fn_cache_lock:\n asset_cache.cache.put(GsSession.current, k, result, ttl=asset_cache.ttl)\n else:\n _logger.debug(\"Asset caching is disabled, calling function\")\n result = fn(cls, *args, **kwargs)\n return result\n\n return wrapper\n\n\ndef _cached_async(fn):\n _fn_cache_lock = threading.Lock()\n # short-term cache to avoid retrieving the same data several times in succession\n fallback_cache: AssetCache = get_default_cache()\n\n @wraps(fn)\n async def wrapper(cls, *args, **kwargs):\n if os.environ.get(ENABLE_ASSET_CACHING):\n _logger.debug(\"Asset caching is enabled\")\n asset_cache = cls.get_cache() or fallback_cache\n k = asset_cache.construct_key(GsSession.current, fn.__name__, *args, **kwargs)\n with Tracer(\"acquiring cache lock\"):\n _logger.debug('cache get: %s', k)\n with _fn_cache_lock:\n result = asset_cache.cache.get(GsSession.current, k)\n if result:\n _logger.debug('cache hit: %s', k)\n return result\n with Tracer(\"Executing function\"):\n result = await fn(cls, *args, **kwargs)\n with Tracer(\"acquiring cache lock\"):\n _logger.debug('cache set: %s', k)\n with _fn_cache_lock:\n asset_cache.cache.put(GsSession.current, k, result, ttl=asset_cache.ttl)\n else:\n _logger.debug(\"Asset caching is disabled, calling function\")\n result = await fn(cls, *args, **kwargs)\n return result\n\n return wrapper\n\n\nclass GsIdType(Enum):\n \"\"\"GS Asset API identifier type enumeration\"\"\"\n\n ric = auto()\n bbid = auto()\n bcid = auto()\n cusip = auto()\n isin = auto()\n sedol = auto()\n mdapi = auto()\n primeId = auto()\n id = auto()\n gsid = auto()\n rcic = auto()\n ticker = auto()\n\n\nclass GsAsset(__Asset):\n \"\"\"GS Asset API object model for an asset object\"\"\"\n\n pass\n\n\nclass GsTemporalXRef(TemporalXRef):\n pass\n\n\nclass GsAssetApi:\n \"\"\"GS Asset API client implementation\"\"\"\n\n _cache: Optional[AssetCache] = None\n\n @classmethod\n def set_cache(cls, cache: AssetCache):\n cls._cache = cache\n\n @classmethod\n def get_cache(cls) -> Optional[AssetCache]:\n return cls._cache\n\n @classmethod\n def __create_query(\n cls,\n fields: Union[List, Tuple] = None,\n as_of: dt.datetime = None,\n limit: int = None,\n scroll: str = None,\n scroll_id: str = None,\n order_by: List[str] = None,\n **kwargs,\n ) -> EntityQuery:\n keys = set(kwargs.keys())\n valid = keys.intersection(FieldFilterMap.properties())\n invalid = keys.difference(valid)\n\n if invalid:\n bad_args = ['{}={}'.format(k, kwargs[k]) for k in invalid]\n raise KeyError('Invalid asset query argument(s): {}'.format(', '.join(bad_args)))\n\n return EntityQuery(\n where=FieldFilterMap(**kwargs),\n fields=fields,\n asOfTime=as_of or dt.datetime.utcnow(),\n limit=limit,\n scroll=scroll,\n scroll_id=scroll_id,\n order_by=order_by,\n )\n\n @staticmethod\n def _set_tags(scope, kwargs):\n if kwargs and scope and scope.span:\n for k, v in kwargs.items():\n if isinstance(v, (list, tuple)):\n if len(v) > 5:\n scope.span.set_tag(f'request.payload.{k}', len(v))\n else:\n scope.span.set_tag(f'request.payload.{k}', \", \".join((str(x) for x in v)))\n elif isinstance(v, (int, float, bool, str)):\n scope.span.set_tag(f'request.payload.{k}', v)\n\n @classmethod\n @_cached\n def get_many_assets(\n cls,\n fields: IdList = None,\n as_of: dt.datetime = None,\n limit: int = 100,\n return_type: Optional[type] = GsAsset,\n order_by: List[str] = None,\n **kwargs,\n ) -> Union[Tuple[GsAsset, ...], Tuple[dict, ...]]:\n span = Tracer.active_span()\n tracer = Tracer('GsAsset.get_many_assets') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n cls._set_tags(scope, kwargs)\n query = cls.__create_query(fields, as_of, limit, order_by=order_by, **kwargs)\n response = GsSession.current.sync.post('/assets/query', payload=query, cls=return_type)\n return response['results']\n\n @classmethod\n @_cached_async\n async def get_many_assets_async(\n cls,\n fields: IdList = None,\n as_of: dt.datetime = None,\n limit: int = 100,\n return_type: Optional[type] = GsAsset,\n order_by: List[str] = None,\n **kwargs,\n ) -> Union[Tuple[GsAsset, ...], Tuple[dict, ...]]:\n span = Tracer.active_span()\n tracer = Tracer('GsAsset.get_many_assets_async') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n cls._set_tags(scope, kwargs)\n query = cls.__create_query(fields, as_of, limit, order_by=order_by, **kwargs)\n response = await GsSession.current.async_.post('/assets/query', payload=query, cls=return_type)\n return response['results']\n\n @classmethod\n @_cached\n def get_many_assets_scroll(\n cls,\n scroll: str = '1m',\n fields: IdList = None,\n as_of: dt.datetime = None,\n limit: int = 1000,\n return_type: Optional[type] = GsAsset,\n order_by: List[str] = None,\n **kwargs,\n ) -> Union[Tuple[GsAsset, ...], Tuple[dict, ...]]:\n span = Tracer.active_span()\n tracer = Tracer('GsAsset.get_many_assets_scroll') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n cls._set_tags(scope, kwargs)\n query = cls.__create_query(fields, as_of, limit, scroll, order_by=order_by, **kwargs)\n response = GsSession.current.sync.post('/assets/query', payload=query, cls=return_type)\n results = get(response, 'results')\n while has(response, 'scrollId') and len(get(response, 'results')):\n query = cls.__create_query(fields, as_of, limit, scroll, get(response, 'scrollId'), **kwargs)\n response = GsSession.current.sync.post('/assets/query', payload=query, cls=return_type)\n results += get(response, 'results')\n return results\n\n @classmethod\n @_cached\n def get_many_assets_data(\n cls, fields: IdList = None, as_of: dt.datetime = None, limit: int = None, source: Optional[str] = None, **kwargs\n ) -> dict:\n span = Tracer.active_span()\n tracer = Tracer('GsAsset.get_many_assets_data') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n cls._set_tags(scope, kwargs)\n query = cls.__create_query(fields, as_of, limit, **kwargs)\n request_headers = {'X-Application': 'Studio'} if source == \"Basket\" else None\n response = GsSession.current.sync.post('/assets/data/query', payload=query, request_headers=request_headers)\n return response['results']\n\n @classmethod\n @_cached\n async def get_many_assets_data_async(\n cls, fields: IdList = None, as_of: dt.datetime = None, limit: int = None, **kwargs\n ) -> dict:\n span = Tracer.active_span()\n tracer = Tracer('GsAsset.get_many_assets_data_async') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n cls._set_tags(scope, kwargs)\n query = cls.__create_query(fields, as_of, limit, **kwargs)\n response = await GsSession.current.async_.post('/assets/data/query', payload=query)\n return response['results']\n\n @classmethod\n @_cached\n def get_many_assets_data_scroll(\n cls,\n scroll: str = '1m',\n fields: IdList = None,\n as_of: dt.datetime = None,\n limit: int = None,\n source: Optional[str] = None,\n **kwargs,\n ) -> dict:\n span = Tracer.active_span()\n tracer = Tracer('GsAsset.get_many_assets_data_scroll') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n cls._set_tags(scope, kwargs)\n query = cls.__create_query(fields, as_of, limit, scroll, **kwargs)\n request_headers = {'X-Application': 'Studio'} if source == \"Basket\" else None\n response = GsSession.current.sync.post('/assets/data/query', payload=query, request_headers=request_headers)\n results = get(response, 'results')\n while has(response, 'scrollId') and len(get(response, 'results')):\n query = cls.__create_query(fields, as_of, limit, scroll, get(response, 'scrollId'), **kwargs)\n response = GsSession.current.sync.post(\n '/assets/data/query', payload=query, request_headers=request_headers\n )\n results += get(response, 'results')\n return results\n\n @classmethod\n @_cached\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def resolve_assets(\n cls,\n identifier: [str],\n fields: IdList = [],\n limit: int = 100,\n as_of: dt.datetime = dt.datetime.today(),\n **kwargs,\n ) -> Tuple[dict, ...]:\n where = dict(identifier=identifier, **kwargs)\n query = dict(where=where, limit=limit, fields=fields, asOfTime=as_of.strftime(\"%Y-%m-%dT%H:%M:%SZ\"))\n return GsSession.current.sync.post('/positions/resolver', payload=query)\n\n @classmethod\n def get_many_asset_xrefs(\n cls,\n identifier: [str],\n fields: IdList = [],\n limit: int = 100,\n as_of: dt.datetime = dt.datetime.today(),\n **kwargs,\n ) -> Tuple[dict, ...]:\n where = dict(identifier=identifier, **kwargs)\n query = dict(where=where, limit=limit, fields=fields, asOfTime=as_of.strftime(\"%Y-%m-%dT%H:%M:%SZ\"))\n\n return GsSession.current.sync.post('/assets/xrefs/query', payload=query).get('results')\n\n @classmethod\n @_cached\n def get_asset_xrefs(cls, asset_id: str) -> Tuple[GsTemporalXRef, ...]:\n response = GsSession.current.sync.get('/assets/{id}/xrefs'.format(id=asset_id))\n return tuple(GsTemporalXRef.from_dict(x) for x in response.get('xrefs', ()))\n\n @classmethod\n def put_asset_xrefs(cls, asset_id: str, xrefs: List[TemporalXRef]):\n return GsSession.current.sync.put(f'/assets/{asset_id}/xrefs', payload=xrefs)\n\n @classmethod\n @_cached\n def get_asset(\n cls,\n asset_id: str,\n ) -> GsAsset:\n return GsSession.current.sync.get('/assets/{id}'.format(id=asset_id), cls=GsAsset)\n\n @classmethod\n @_cached_async\n async def get_asset_async(\n cls,\n asset_id: str,\n ) -> GsAsset:\n return await GsSession.current.async_.get('/assets/{id}'.format(id=asset_id), cls=GsAsset)\n\n @classmethod\n def get_asset_by_name(cls, name: str) -> GsAsset:\n ret = GsSession.current.sync.get('/assets?name={}'.format(name))\n num_found = ret.get('totalResults', 0)\n\n if num_found == 0:\n raise ValueError('Asset {} not found'.format(name))\n elif num_found > 1:\n raise ValueError('More than one asset named {} found'.format(name))\n else:\n return GsAsset.from_dict(ret['results'][0])\n\n @classmethod\n def create_asset(cls, asset: GsAsset) -> GsAsset:\n return GsSession.current.sync.post('/assets', payload=asset, cls=GsAsset)\n\n @classmethod\n def delete_asset(cls, asset_id: str):\n return GsSession.current.sync.delete(f'/assets/{asset_id}')\n\n @staticmethod\n def get_position_dates(asset_id: str) -> Tuple[dt.date, ...]:\n position_dates = GsSession.current.sync.get(f'/assets/{asset_id}/positions/dates')['results']\n return tuple(dt.datetime.strptime(d, '%Y-%m-%d').date() for d in position_dates)\n\n @staticmethod\n def get_asset_positions_for_date(\n asset_id: str,\n position_date: dt.date,\n position_type: PositionType = None,\n ) -> Tuple[PositionSet, ...]:\n position_date_str = position_date.isoformat()\n url = f'/assets/{asset_id}/positions/{position_date_str}'\n\n if position_type is not None:\n url += f'?type={position_type}' if isinstance(position_type, str) else f'?type={position_type.value}'\n\n results = GsSession.current.sync.get(url)['results']\n return tuple(PositionSet.from_dict(r) for r in results)\n\n @staticmethod\n def get_asset_positions_for_dates(\n asset_id: str,\n start_date: dt.date,\n end_date: dt.date,\n position_type: PositionType = PositionType.CLOSE,\n ) -> Tuple[PositionSet, ...]:\n position_type = position_type if isinstance(position_type, str) else position_type.value\n position_sets = []\n periods = (end_date - start_date).days // 30\n start_date_str = start_date.isoformat()\n\n if periods > 1:\n end_dates = pd.date_range(start=start_date, end=end_date, periods=periods, inclusive='right')\n for date in end_dates:\n end_date_str = date.date().isoformat()\n url = f'/assets/{asset_id}/positions?startDate={start_date_str}&endDate={end_date_str}&type={position_type}'\n try:\n position_sets += GsSession.current.sync.get(url)['positionSets']\n start_date_str = (date.date() + dt.timedelta(days=1)).isoformat()\n except HTTPError as err:\n raise ValueError(f'Unable to fetch position data at {url} with {err}')\n else:\n end_date_str = end_date.isoformat()\n url = f'/assets/{asset_id}/positions?startDate={start_date_str}&endDate={end_date_str}&type={position_type}'\n try:\n position_sets += GsSession.current.sync.get(url)['positionSets']\n except HTTPError as err:\n raise ValueError(f'Unable to fetch position data at {url} with {err}')\n return tuple(PositionSet.from_dict(r) for r in position_sets)\n\n @staticmethod\n def get_latest_positions(asset_id: str, position_type: PositionType = None) -> PositionSet:\n url = '/assets/{id}/positions/last'.format(id=asset_id)\n if position_type is not None and position_type is not PositionType.ANY:\n url += '?type={ptype}'.format(\n ptype=position_type if isinstance(position_type, str) else position_type.value\n )\n\n results = GsSession.current.sync.get(url)['results']\n\n return PositionSet.from_dict(results)\n\n @staticmethod\n def get_or_create_asset_from_instrument(instrument: Instrument) -> str:\n asset = GsAsset(\n asset_class=instrument.asset_class,\n type_=instrument.type,\n name=instrument.name or '',\n parameters=instrument.as_dict(as_camel_case=True),\n )\n\n results = GsSession.current.sync.post('/assets', asset)\n return results['id']\n\n @staticmethod\n def get_instruments_for_asset_ids(asset_ids: Tuple[str, ...]) -> Tuple[Optional[Union[Instrument, Security]]]:\n instrument_infos = GsSession.current.sync.post('/assets/instruments', asset_ids, cls=AssetToInstrumentResponse)\n instrument_lookup = {i.assetId: i.instrument for i in instrument_infos if i}\n ret: Tuple[Optional[Union[Instrument, Security]], ...] = tuple(instrument_lookup.get(a) for a in asset_ids)\n\n return ret\n\n @staticmethod\n def get_instruments_for_positions(positions: Iterable[Position]) -> Tuple[Optional[Union[Instrument, Security]]]:\n asset_ids = tuple(filter(None, (p.asset_id for p in positions)))\n instrument_infos = (\n GsSession.current.sync.post('/assets/instruments', asset_ids, cls=AssetToInstrumentResponse)\n if asset_ids\n else {}\n )\n\n instrument_lookup = {i.assetId: (i.instrument, i.sizeField) for i in instrument_infos if i}\n ret = ()\n\n for position in positions:\n instrument = None\n\n if position.instrument:\n instrument = position.instrument\n else:\n instrument_info = instrument_lookup.get(position.assetId)\n if instrument_info:\n instrument, size_field = instrument_info\n if (\n instrument is not None\n and size_field is not None\n and getattr(instrument, size_field, None) is None\n ):\n setattr(instrument, size_field, position.quantity)\n\n ret += (instrument,)\n\n return ret\n\n @staticmethod\n def get_asset_positions_data(\n asset_id: str,\n start_date: dt.date,\n end_date: dt.date,\n fields: IdList = None,\n position_type: PositionType = None,\n ) -> List[dict]:\n start_date_str = start_date.isoformat()\n end_date_str = end_date.isoformat()\n url = '/assets/{id}/positions/data?startDate={start_date}&endDate={end_date}'.format(\n id=asset_id, start_date=start_date_str, end_date=end_date_str\n )\n if fields is not None:\n url += '&fields='.join([''] + fields)\n\n if position_type is not None:\n url += '&type=' + position_type.value\n\n results = GsSession.current.sync.get(url)['results']\n return results\n\n @staticmethod\n def update_asset_entitlements(asset_id: str, entitlements: Entitlements) -> dict:\n url = f'/assets/{asset_id}/entitlements'\n try:\n results = GsSession.current.sync.put(url, payload=entitlements)\n except HTTPError as err:\n raise ValueError(f'Unable to update asset entitlements with {err}')\n return results\n\n @classmethod\n def get_reports(cls, asset_id: str) -> Tuple[Report, ...]:\n return GsSession.current.sync.get(f'/assets/{asset_id}/reports', cls=Report)['results']\n\n @classmethod\n @_cached\n def map_identifiers(\n cls,\n input_type: Union[GsIdType, str],\n output_type: Union[GsIdType, str],\n ids: IdList,\n as_of: dt.datetime = None,\n multimap: bool = False,\n limit: int = None,\n **kwargs,\n ) -> dict:\n if isinstance(input_type, GsIdType):\n input_type = input_type.name\n elif not isinstance(input_type, str):\n raise ValueError('input_type must be of type str or IdType')\n\n if isinstance(output_type, GsIdType):\n output_type = output_type.name\n elif not isinstance(output_type, str):\n raise ValueError('output_type must be of type str or IdType')\n\n the_args = kwargs\n the_args[input_type] = ids\n\n limit = limit or 4 * len(ids)\n query = cls.__create_query((input_type, output_type), as_of, limit, **the_args)\n results = GsSession.current.sync.post('/assets/data/query', payload=query)\n if len(results) >= query.limit:\n raise MqValueError('number of results may have exceeded capacity')\n\n if 'results' in results:\n results = results['results']\n\n out = {}\n for entry in results:\n key = entry.get(input_type)\n value = entry.get(output_type)\n if multimap:\n bunch = out.setdefault(key, [])\n bunch.append(value)\n else:\n if key in out:\n _logger.warning('%s: more than one mapping for %s', GsAssetApi.map_identifiers.__name__, key)\n out[key] = value\n return out\n" }, { "path": "gs_quant/api/gs/backtests.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom typing import Tuple, Optional\nfrom urllib.parse import urlencode\n\nfrom gs_quant.common import FieldValueMap\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.session import GsSession, DEFAULT_TIMEOUT\nfrom gs_quant.target.backtests import (\n Backtest,\n BacktestResult,\n BacktestRisk,\n ComparisonBacktestResult,\n BacktestRiskRequest,\n BacktestRefData,\n)\n\n_logger = logging.getLogger(__name__)\n\n\nclass GsBacktestApi:\n \"\"\"GS Backtest API client implementation\"\"\"\n\n @classmethod\n def get_many_backtests(\n cls, limit: int = 100, backtest_id: str = None, owner_id: str = None, name: str = None, mq_symbol: str = None\n ) -> Tuple[Backtest, ...]:\n query_string = urlencode(\n dict(\n filter(\n lambda item: item[1] is not None,\n dict(id=backtest_id, ownerId=owner_id, name=name, mqSymbol=mq_symbol, limit=limit).items(),\n )\n )\n )\n return GsSession.current.sync.get('/backtests?{query}'.format(query=query_string), cls=Backtest)['results']\n\n @classmethod\n def get_backtest(cls, backtest_id: str) -> Backtest:\n return GsSession.current.sync.get('/backtests/{id}'.format(id=backtest_id), cls=Backtest)\n\n @classmethod\n def create_backtest(cls, backtest: Backtest) -> Backtest:\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n return GsSession.current.sync.post('/backtests', backtest, request_headers=request_headers, cls=Backtest)\n\n @classmethod\n def update_backtest(cls, backtest: Backtest):\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n return GsSession.current.sync.put(\n '/backtests/{id}'.format(id=backtest.id), backtest, request_headers=request_headers, cls=Backtest\n )\n\n @classmethod\n def delete_backtest(cls, backtest_id: str) -> dict:\n return GsSession.current.sync.delete('/backtests/{id}'.format(id=backtest_id))\n\n @classmethod\n def get_results(cls, backtest_id: str) -> Tuple[BacktestResult, ...]:\n return GsSession.current.sync.get('/backtests/results?id={id}'.format(id=backtest_id))['backtestResults']\n\n @classmethod\n def get_comparison_results(\n cls,\n limit: int = 100,\n start_date: dt.date = None,\n end_date: dt.date = None,\n backtest_id: str = None,\n comparison_id: str = None,\n owner_id: str = None,\n name: str = None,\n mq_symbol: str = None,\n ) -> Tuple[Tuple[BacktestResult, ...], Tuple[ComparisonBacktestResult, ...]]:\n query_string = urlencode(\n dict(\n filter(\n lambda item: item[1] is not None,\n dict(\n id=backtest_id,\n comparisonIds=comparison_id,\n ownerId=owner_id,\n name=name,\n mqSymbol=mq_symbol,\n limit=limit,\n startDate=start_date.isoformat(),\n endDate=end_date.isoformat(),\n ).items(),\n )\n )\n )\n result = GsSession.current.sync.get('/backtests/results?{query}'.format(query=query_string))\n return result['backtestResults'], result['comparisonResults']\n\n @classmethod\n def schedule_backtest(cls, backtest_id: str) -> dict:\n return GsSession.current.sync.post('/backtests/{id}/schedule'.format(id=backtest_id))\n\n @classmethod\n def run_backtest(\n cls, backtest: Backtest, correlation_id: str = None, timeout: Optional[int] = DEFAULT_TIMEOUT\n ) -> BacktestResult:\n \"\"\"\n :param backtest: definition of a backtest which should be run on Marquee API\n :param correlation_id: used for logging purposes; helps in tracking all the requests which ultimately serve\n the same purpose (e.g. calculating a backtest)\n :return: result of running the backtest\n \"\"\"\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n if correlation_id is not None:\n request_headers[\"X-CorrelationId\"] = correlation_id\n\n response = GsSession.current.sync.post(\n '/backtests/calculate', backtest, request_headers=request_headers, timeout=timeout\n )\n return cls.backtest_result_from_response(response)\n\n @classmethod\n def backtest_result_from_response(cls, response: dict) -> BacktestResult:\n if 'RiskData' not in response:\n raise MqValueError('No risk data received')\n\n portfolio = response['Portfolio'] if 'Portfolio' in response else None\n risks = tuple(\n BacktestRisk(name=k, timeseries=tuple(FieldValueMap(date=r['date'], value=r['value']) for r in v))\n for k, v in response['RiskData'].items()\n )\n\n return BacktestResult(portfolio=portfolio, risks=risks)\n\n @classmethod\n def calculate_position_risk(\n cls, backtestRiskRequest: BacktestRiskRequest, timeout: Optional[int] = DEFAULT_TIMEOUT\n ) -> dict:\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n return GsSession.current.sync.post(\n '/backtests/calculate-position-risk', backtestRiskRequest, request_headers=request_headers, timeout=timeout\n )\n\n @classmethod\n def get_ref_data(cls) -> BacktestRefData:\n return GsSession.current.sync.get('/backtests/refData', cls=BacktestRefData)\n\n @classmethod\n def update_ref_data(cls, backtest_ref_data: BacktestRefData):\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n return GsSession.current.sync.put(\n '/backtests/refData', backtest_ref_data, request_headers=request_headers, cls=backtest_ref_data\n )\n\n\nclass GsBacktestApiAsync(GsBacktestApi):\n @classmethod\n async def calculate_position_risk(\n cls, backtestRiskRequest: BacktestRiskRequest, timeout: Optional[int] = DEFAULT_TIMEOUT\n ) -> dict:\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n response = await GsSession.current.async_.post(\n '/backtests/calculate-position-risk', backtestRiskRequest, request_headers=request_headers, timeout=timeout\n )\n return response\n\n @classmethod\n async def run_backtest(\n cls, backtest: Backtest, correlation_id: str = None, timeout: Optional[int] = DEFAULT_TIMEOUT\n ) -> BacktestResult:\n \"\"\"\n :param backtest: definition of a backtest which should be run on Marquee API\n :param correlation_id: used for logging purposes; helps in tracking all the requests which ultimately serve\n the same purpose (e.g. calculating a backtest)\n :return: result of running the backtest\n \"\"\"\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n if correlation_id is not None:\n request_headers[\"X-CorrelationId\"] = correlation_id\n\n response = await GsSession.current.async_.post(\n '/backtests/calculate', backtest, request_headers=request_headers, timeout=timeout\n )\n return cls.backtest_result_from_response(response)\n" }, { "path": "gs_quant/api/gs/backtests_xasset/__init__.py", "content": "" }, { "path": "gs_quant/api/gs/backtests_xasset/apis.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.api.gs.backtests_xasset.request import RiskRequest, BasicBacktestRequest\nfrom gs_quant.api.gs.backtests_xasset.response import RiskResponse, BasicBacktestResponse\nfrom gs_quant.session import GsSession\n\n\nclass GsBacktestXassetApi:\n HEADERS = {'Accept': 'application/json'}\n TIMEOUT = 90\n\n @classmethod\n def calculate_risk(cls, risk_request: RiskRequest) -> RiskResponse:\n response = GsSession.current.sync.post(\n '/backtests/xasset/risk', risk_request.to_json(), request_headers=cls.HEADERS, timeout=cls.TIMEOUT\n )\n result = RiskResponse.from_dict(response)\n return result\n\n @classmethod\n def calculate_basic_backtest(\n cls, backtest_request: BasicBacktestRequest, decode_instruments: bool = True\n ) -> BasicBacktestResponse:\n response = GsSession.current.sync.post(\n '/backtests/xasset/strategy/basic',\n backtest_request.to_json(),\n request_headers=cls.HEADERS,\n timeout=cls.TIMEOUT,\n )\n result = BasicBacktestResponse.from_dict_custom(response, decode_instruments)\n return result\n\n\nclass GsBacktestXassetApiAsync(GsBacktestXassetApi):\n @classmethod\n async def calculate_risk(cls, risk_request: RiskRequest) -> RiskResponse:\n response = await GsSession.current.async_.post(\n '/backtests/xasset/risk', risk_request.to_json(), request_headers=cls.HEADERS, timeout=cls.TIMEOUT\n )\n result = RiskResponse.from_dict(response)\n return result\n\n @classmethod\n async def calculate_basic_backtest(\n cls, backtest_request: BasicBacktestRequest, decode_instruments: bool = True\n ) -> BasicBacktestResponse:\n response = await GsSession.current.async_.post(\n '/backtests/xasset/strategy/basic',\n backtest_request.to_json(),\n request_headers=cls.HEADERS,\n timeout=cls.TIMEOUT,\n )\n result = BasicBacktestResponse.from_dict_custom(response, decode_instruments)\n return result\n" }, { "path": "gs_quant/api/gs/backtests_xasset/json_encoders/__init__.py", "content": "" }, { "path": "gs_quant/api/gs/backtests_xasset/json_encoders/request_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Any, Iterable\n\nfrom gs_quant.base import EnumBase\nfrom gs_quant.common import RiskMeasure\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.json_convertors_common import encode_risk_measure\n\n\ndef encode_request_object(data: Any):\n if isinstance(data, RiskMeasure):\n return encode_risk_measure(data)\n if isinstance(data, Instrument):\n return data.to_dict()\n if isinstance(data, tuple):\n return tuple(encode_request_object(d) for d in data)\n\n\ndef legs_decoder(data: Any):\n if data is None:\n return None\n result = [Instrument.from_dict(d) for d in data]\n names = set(getattr(i, 'name', None) for i in result)\n name_idx = 0\n for i in result:\n if i.name is not None:\n continue\n cur_name = 'leg_' + str(name_idx)\n while cur_name in names:\n name_idx += 1\n cur_name = 'leg_' + str(name_idx)\n i.name = cur_name\n name_idx += 1\n return result\n\n\ndef legs_encoder(data: Iterable[Instrument]):\n return [i.to_dict() for i in data]\n\n\ndef enum_decode(enum_class):\n def decode_value(value):\n if value is None or 'null' == value:\n return None\n if isinstance(value, EnumBase):\n return value\n if isinstance(value, str):\n try:\n return enum_class(value)\n except ValueError:\n pass\n raise ValueError(f'Unable to decode {value} into any enum class')\n\n return decode_value\n" }, { "path": "gs_quant/api/gs/backtests_xasset/json_encoders/response_datatypes/__init__.py", "content": "" }, { "path": "gs_quant/api/gs/backtests_xasset/json_encoders/response_datatypes/generic_datatype_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom typing import Dict, Tuple\n\nfrom gs_quant.instrument import Instrument\n\n\ndef decode_inst(i: dict) -> Instrument:\n return Instrument.from_dict(i)\n\n\ndef decode_inst_tuple(t: tuple) -> Tuple[Instrument, ...]:\n return tuple(decode_inst(i) for i in t)\n\n\ndef decode_daily_portfolio(results: dict, decode_instruments: bool = True) -> Dict[dt.date, Tuple[Instrument, ...]]:\n return {dt.date.fromisoformat(k): decode_inst_tuple(v) if decode_instruments else v for k, v in results.items()}\n" }, { "path": "gs_quant/api/gs/backtests_xasset/json_encoders/response_datatypes/risk_result_datatype_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport pandas as pd\nfrom typing import Dict\n\n\ndef encode_series_result(s: pd.Series) -> Dict:\n return {'index': tuple(s.index), 'name': s.name, 'values': tuple(s.values)}\n\n\ndef encode_dataframe_result(df: pd.DataFrame) -> Dict:\n return {'index': tuple(df.index), 'columns': tuple(df.columns), 'values': tuple(tuple(v) for v in df.values)}\n\n\ndef _convert_list_to_dates(lst: list):\n if not (lst and isinstance(lst[0], str)):\n return lst\n try:\n lst = tuple(dt.date.fromisoformat(v) for v in lst)\n except ValueError:\n pass\n return lst\n\n\ndef decode_series_result(s: dict) -> pd.Series:\n return pd.Series(s['values'], index=_convert_list_to_dates(s['index']), name=s['name'])\n\n\ndef decode_dataframe_result(s: dict) -> pd.DataFrame:\n return pd.DataFrame(s['values'], index=_convert_list_to_dates(s['index']), columns=s['columns'])\n" }, { "path": "gs_quant/api/gs/backtests_xasset/json_encoders/response_datatypes/risk_result_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom typing import Any, Type\n\nimport pandas as pd\n\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result import (\n RiskResultsByDate,\n RefType,\n RiskResultsError,\n RiskResults,\n)\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result_datatypes import (\n FloatWithData,\n StringWithData,\n VectorWithData,\n MatrixWithData,\n RiskResultWithData,\n DefnValuesWithData,\n DictsWithData,\n)\nfrom gs_quant.priceable import PriceableImpl\n\n_type_to_datatype_map = {\n 'float': FloatWithData,\n 'string': StringWithData,\n 'vector': VectorWithData,\n 'matrix': MatrixWithData,\n 'defn': DefnValuesWithData,\n 'dict': DictsWithData,\n}\n\n\ndef map_result_to_datatype(data: Any) -> Type[RiskResultWithData]:\n if isinstance(data, (float, int)):\n return FloatWithData\n if isinstance(data, str):\n return StringWithData\n if isinstance(data, pd.Series):\n return VectorWithData\n if isinstance(data, pd.DataFrame):\n return MatrixWithData\n if isinstance(data, PriceableImpl):\n return DefnValuesWithData\n if isinstance(data, dict):\n return DictsWithData\n raise ValueError('Cannot assign result type to data')\n\n\ndef decode_risk_result_with_data(r: dict) -> RiskResultWithData:\n return _type_to_datatype_map[r['type']].from_dict(r)\n\n\ndef decode_risk_result(d: dict) -> RiskResults:\n refs = {RefType(k): v for k, v in d['refs'].items()}\n if 'result' in d:\n result = {dt.date.fromisoformat(k): decode_risk_result_with_data(v) for k, v in d['result'].items()}\n return RiskResultsByDate(refs, result)\n else:\n return RiskResultsError(refs, d['error'], d['trace_id'])\n" }, { "path": "gs_quant/api/gs/backtests_xasset/json_encoders/response_datatypes/test_backtest_datatypes_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport json\n\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import (\n TransactionCostConfig,\n TradingCosts,\n FixedCostModel,\n ScaledCostModel,\n TransactionCostScalingType,\n AggregateCostModel,\n CostAggregationType,\n)\n\n\ndef test_transaction_cost_config_encoding():\n tc = TransactionCostConfig.from_dict(\n {\"tradeCostModel\": {\"entry\": {\"cost\": 5.0, \"type\": \"fixed_cost_model\"}}, \"hedgeCostModel\": None}\n )\n assert tc == TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(cost=5.0), exit=None), hedge_cost_model=None\n )\n\n tc = TransactionCostConfig.from_dict(\n {\n \"tradeCostModel\": {\n \"entry\": {\"scalingLevel\": 5.0, \"scalingQuantityType\": \"Vega\", \"type\": \"scaled_cost_model\"},\n \"exit\": {\n \"models\": [\n {\"scalingLevel\": 7.0, \"scalingQuantityType\": \"Notional\", \"type\": \"scaled_cost_model\"},\n {\"cost\": 9.0, \"type\": \"fixed_cost_model\"},\n ],\n \"aggregationType\": \"Sum\",\n \"type\": \"aggregate_cost_model\",\n },\n },\n \"hedgeCostModel\": {\"entry\": {\"cost\": 10, \"type\": \"fixed_cost_model\"}},\n }\n )\n assert tc == TransactionCostConfig(\n TradingCosts(\n ScaledCostModel(5.0, TransactionCostScalingType.Vega),\n AggregateCostModel(\n models=(ScaledCostModel(7.0, TransactionCostScalingType.Notional), FixedCostModel(9.0)),\n aggregation_type=CostAggregationType.Sum,\n ),\n ),\n TradingCosts(FixedCostModel(10)),\n )\n assert tc == TransactionCostConfig.from_dict(json.loads(tc.to_json()))\n\n tc = TransactionCostConfig.from_dict(\n {\n \"tradeCostModel\": {\n \"entry\": {\"scalingLevel\": 5.0, \"scalingQuantityType\": \"Quantity\", \"type\": \"ScaledCostModel\"},\n \"exit\": {\n \"models\": [\n {\"scalingLevel\": 7.0, \"scalingQuantityType\": \"Notional\", \"type\": \"ScaledCostModel\"},\n {\"cost\": 9.0, \"type\": \"FixedCostModel\"},\n ],\n \"aggregationType\": \"Sum\",\n \"type\": \"AggregateCostModel\",\n },\n },\n \"hedgeCostModel\": {\"entry\": {\"cost\": 10, \"type\": \"FixedCostModel\"}},\n }\n )\n assert tc == TransactionCostConfig(\n TradingCosts(\n ScaledCostModel(5.0, TransactionCostScalingType.Quantity),\n AggregateCostModel(\n models=(ScaledCostModel(7.0, TransactionCostScalingType.Notional), FixedCostModel(9.0)),\n aggregation_type=CostAggregationType.Sum,\n ),\n ),\n TradingCosts(FixedCostModel(10)),\n )\n assert tc == TransactionCostConfig.from_dict(json.loads(tc.to_json()))\n" }, { "path": "gs_quant/api/gs/backtests_xasset/json_encoders/response_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport pandas as pd\n\nfrom typing import Dict, Any, Tuple, Union\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.generic_datatype_encoders import (\n decode_inst_tuple,\n decode_inst,\n)\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.risk_result_datatype_encoders import (\n encode_series_result,\n encode_dataframe_result,\n)\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.risk_result_encoders import (\n decode_risk_result,\n decode_risk_result_with_data,\n)\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import Transaction, TransactionDirection\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result_datatypes import RiskResultWithData\nfrom gs_quant.common import Currency, CurrencyName, RiskMeasure\nfrom gs_quant.json_convertors_common import encode_risk_measure, decode_risk_measure\nfrom gs_quant.priceable import PriceableImpl\nfrom gs_quant.target.backtests import FlowVolBacktestMeasure\n\n\ndef encode_response_obj(data: Any) -> Dict:\n if isinstance(data, RiskMeasure):\n return encode_risk_measure(data)\n if isinstance(data, pd.Series):\n return encode_series_result(data)\n if isinstance(data, pd.DataFrame):\n return encode_dataframe_result(data)\n return data.to_dict()\n\n\ndef decode_leg_refs(d: dict) -> Dict[str, PriceableImpl]:\n return {k: decode_inst(v) for k, v in d.items()}\n\n\ndef decode_risk_measure_refs(d: dict) -> Dict[str, RiskMeasure]:\n return {k: decode_risk_measure(v) for k, v in d.items()}\n\n\ndef decode_result_tuple(results: tuple):\n return tuple(decode_risk_result(r) for r in results)\n\n\ndef decode_basic_bt_measure_dict(results: dict) -> Dict[FlowVolBacktestMeasure, Dict[dt.date, RiskResultWithData]]:\n return {\n FlowVolBacktestMeasure(k): {dt.date.fromisoformat(d): decode_risk_result_with_data(r) for d, r in v.items()}\n for k, v in results.items()\n }\n\n\ndef decode_basic_bt_transactions(\n results: dict, decode_instruments: bool = True\n) -> Dict[dt.date, Tuple[Transaction, ...]]:\n def to_ccy(s: str) -> Union[Currency, CurrencyName, str]:\n if s in [x.value for x in Currency]:\n return Currency(s)\n elif s in [x.value for x in CurrencyName]:\n return CurrencyName(s)\n else:\n return s\n\n return {\n dt.date.fromisoformat(k): tuple(\n Transaction(\n decode_inst_tuple(t['portfolio']) if decode_instruments else t['portfolio'],\n t.get('portfolio_price'),\n t.get('cost'),\n to_ccy(t['currency']) if t.get('currency') else None,\n TransactionDirection(t['direction']) if t.get('direction') else None,\n t.get('quantity'),\n )\n for t in v\n )\n for k, v in results.items()\n }\n" }, { "path": "gs_quant/api/gs/backtests_xasset/request.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import Optional, Union, Tuple\n\nfrom dataclasses_json import dataclass_json, LetterCase, config\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.request_encoders import legs_encoder, legs_decoder, enum_decode\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import (\n DateConfig,\n Trade,\n Configuration,\n TransactionCostConfig,\n StrategyHedge,\n)\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.generic_backtest_datatypes import Strategy\nfrom gs_quant.base import EnumBase\nfrom gs_quant.common import RiskMeasure\nfrom gs_quant.json_convertors import decode_optional_date, decode_date_tuple, encode_date_tuple\nfrom gs_quant.json_convertors_common import encode_risk_measure_tuple, decode_risk_measure_tuple\nfrom gs_quant.priceable import PriceableImpl\nfrom gs_quant.target.backtests import FlowVolBacktestMeasure\n\n\nclass RiskProviderEnum(EnumBase, Enum):\n Default = \"Default\"\n DataSetProvider = \"DataSetProvider\"\n EqVolRiskProvider = \"EqVolRiskProvider\"\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskRequest:\n start_date: Optional[dt.date] = field(default=None, metadata=config(decoder=decode_optional_date))\n end_date: Optional[dt.date] = field(default=None, metadata=config(decoder=decode_optional_date))\n additional_dates: Optional[Tuple[dt.date, ...]] = field(\n default=None, metadata=config(encoder=encode_date_tuple, decoder=decode_date_tuple)\n )\n legs: Optional[Tuple[PriceableImpl, ...]] = field(\n default=None, metadata=config(encoder=legs_encoder, decoder=legs_decoder)\n )\n measures: Optional[Tuple[RiskMeasure, ...]] = field(\n default=None, metadata=config(encoder=encode_risk_measure_tuple, decoder=decode_risk_measure_tuple)\n )\n risk_provider: Optional[RiskProviderEnum] = field(\n default=None, metadata=config(decoder=enum_decode(RiskProviderEnum))\n )\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BasicBacktestRequest:\n dates: DateConfig\n trades: Tuple[Trade, ...]\n measures: Tuple[FlowVolBacktestMeasure, ...]\n delta_hedge_frequency: Optional[str] = None\n transaction_costs: Optional[TransactionCostConfig] = None\n configuration: Optional[Configuration] = None\n hedge: Optional[StrategyHedge] = None\n risk_provider: Optional[RiskProviderEnum] = field(\n default=None, metadata=config(decoder=enum_decode(RiskProviderEnum))\n )\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass GenericBacktestRequest:\n strategy: Strategy\n dates: Union[DateConfig, Tuple[dt.date, ...]]\n configuration: Optional[Configuration] = None\n" }, { "path": "gs_quant/api/gs/backtests_xasset/response.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nfrom dataclasses import dataclass, field\nfrom typing import Dict, Optional, Tuple, Any\n\nfrom dataclasses_json import dataclass_json, LetterCase, config\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.generic_datatype_encoders import (\n decode_daily_portfolio,\n)\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_encoders import (\n decode_leg_refs,\n decode_risk_measure_refs,\n decode_result_tuple,\n decode_basic_bt_measure_dict,\n decode_basic_bt_transactions,\n)\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import Transaction, AdditionalResults\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result import RiskResults\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result_datatypes import RiskResultWithData\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.priceable import PriceableImpl\nfrom gs_quant.common import RiskMeasure\nfrom gs_quant.target.backtests import FlowVolBacktestMeasure\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass RiskResponse:\n legRefs: Dict[str, PriceableImpl] = field(default=None, metadata=config(decoder=decode_leg_refs))\n riskMeasureRefs: Dict[str, RiskMeasure] = field(default=None, metadata=config(decoder=decode_risk_measure_refs))\n results: Tuple[RiskResults, ...] = field(default=None, metadata=config(decoder=decode_result_tuple))\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass BasicBacktestResponse:\n measures: Dict[FlowVolBacktestMeasure, Dict[dt.date, RiskResultWithData]] = field(\n default=None, metadata=config(decoder=decode_basic_bt_measure_dict)\n )\n portfolio: Dict[dt.date, Tuple[Instrument, ...]] = field(\n default=None, metadata=config(decoder=decode_daily_portfolio)\n )\n transactions: Dict[dt.date, Tuple[Transaction, ...]] = field(\n default=None, metadata=config(decoder=decode_basic_bt_transactions)\n )\n additional_results: Optional[AdditionalResults] = field(default=None)\n\n @classmethod\n def from_dict_custom(cls, data: Any, decode_instruments: bool = True):\n if decode_instruments:\n return cls.from_dict(data)\n return BasicBacktestResponse(\n measures=decode_basic_bt_measure_dict(data['measures']),\n portfolio=decode_daily_portfolio(data['portfolio'], decode_instruments),\n transactions=decode_basic_bt_transactions(data['transactions'], decode_instruments),\n additional_results=AdditionalResults.from_dict_custom(data['additional_results'], decode_instruments)\n if data['additional_results'] is not None\n else None,\n )\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass GenericBacktestResponse:\n pass\n" }, { "path": "gs_quant/api/gs/backtests_xasset/response_datatypes/__init__.py", "content": "" }, { "path": "gs_quant/api/gs/backtests_xasset/response_datatypes/backtest_datatypes.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport dataclasses\nimport datetime as dt\nfrom abc import abstractmethod\n\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import Optional, Tuple, Dict, Union, Any\n\nfrom dataclasses_json import dataclass_json, LetterCase, config\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.request_encoders import legs_decoder\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.generic_datatype_encoders import (\n decode_daily_portfolio,\n)\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.interfaces.algebra import AlgebraicType\nfrom gs_quant.json_convertors import (\n decode_optional_date,\n encode_date_tuple,\n decode_date_tuple,\n decode_dict_date_key_or_float,\n)\nfrom gs_quant.target.backtests import BacktestTradingQuantityType, EquityMarketModel\nfrom gs_quant.common import Currency, CurrencyName, PricingLocation\n\n\nclass TransactionCostModel(Enum):\n Fixed = 'Fixed'\n\n\nclass TransactionDirection(Enum):\n Entry = 'Entry'\n Exit = 'Exit'\n\n\nclass RollDateMode(Enum):\n OTC = 'OTC'\n Listed = 'Listed'\n\n @classmethod\n def _missing_(cls, value):\n if value is None:\n return None\n for member in cls:\n if member.value.lower() == value.lower():\n return member\n return None\n\n\nclass TransactionCostScalingType(Enum):\n Quantity = 'Quantity'\n Notional = 'Notional'\n Delta = 'Delta'\n Vega = 'Vega'\n\n\nclass CostAggregationType(Enum):\n Sum = 'Sum'\n Max = 'Max'\n Min = 'Min'\n\n\nclass HedgeRiskMeasure(Enum):\n Delta = 'Delta'\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass Transaction:\n portfolio: Tuple[Instrument, ...]\n portfolio_price: Optional[float] = None\n cost: Optional[float] = None\n currency: Optional[Union[Currency, CurrencyName, str]] = None\n direction: Optional[TransactionDirection] = None\n quantity: Optional[float] = None\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass TradeEvent:\n direction: TransactionDirection\n price: float\n trade_id: Optional[str] = None\n\n\ndef decode_trade_event_tuple_dict(results: dict) -> Dict[dt.date, Tuple[TradeEvent, ...]]:\n return {dt.date.fromisoformat(k): tuple(TradeEvent.from_dict(e) for e in v) for k, v in results.items()}\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass AdditionalResults:\n hedges: Optional[Dict[dt.date, Tuple[Instrument, ...]]] = field(\n default=None, metadata=config(decoder=decode_daily_portfolio)\n )\n hedge_pnl: Optional[Dict[dt.date, float]] = None\n no_of_calculations: Optional[int] = None\n trade_events: Optional[Dict[dt.date, Tuple[TradeEvent, ...]]] = field(\n default=None, metadata=config(decoder=decode_trade_event_tuple_dict)\n )\n hedge_events: Optional[Dict[dt.date, Tuple[TradeEvent, ...]]] = field(\n default=None, metadata=config(decoder=decode_trade_event_tuple_dict)\n )\n\n @classmethod\n def from_dict_custom(cls, data: Any, decode_instruments: bool = True):\n if decode_instruments:\n return cls.from_dict(data)\n return AdditionalResults(\n hedges=decode_daily_portfolio(data['hedges'], decode_instruments),\n hedge_pnl=data['hedge_pnl'],\n no_of_calculations=data['no_of_calculations'],\n trade_events=decode_trade_event_tuple_dict(data['trade_events']),\n hedge_events=decode_trade_event_tuple_dict(data['hedge_events']),\n )\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DateConfig:\n start_date: dt.date = field(default=None, metadata=config(decoder=decode_optional_date))\n end_date: dt.date = field(default=None, metadata=config(decoder=decode_optional_date))\n frequency: str = '1b'\n holiday_calendar: Optional[str] = None\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Trade:\n legs: Optional[Tuple[Instrument, ...]] = field(default=None, metadata=config(decoder=legs_decoder))\n buy_frequency: str = None\n buy_dates: Optional[Tuple[dt.date, ...]] = field(\n default=None, metadata=config(encoder=encode_date_tuple, decoder=decode_date_tuple)\n )\n holding_period: str = None\n exit_dates: Optional[Tuple[dt.date, ...]] = field(\n default=None, metadata=config(encoder=encode_date_tuple, decoder=decode_date_tuple)\n )\n quantity: Optional[Union[float, dict[dt.date, float]]] = field(\n default=None, metadata=config(decoder=decode_dict_date_key_or_float)\n )\n quantity_type: BacktestTradingQuantityType = BacktestTradingQuantityType.quantity\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Model(AlgebraicType):\n pass\n\n @property\n @abstractmethod\n def scaling_property(self): ...\n\n def set_scaling_property(self, value: float):\n setattr(self, self.scaling_property, value)\n\n def __add__(self, other):\n if not isinstance(other, Model):\n raise TypeError('Can only add to another cost model')\n if isinstance(other, AggregateCostModel):\n return other + self\n scaling_prop_name = self.scaling_property\n can_add_scaling = type(self) is type(other) and other == dataclasses.replace(\n self, **{scaling_prop_name: getattr(other, scaling_prop_name)}\n )\n if can_add_scaling:\n result = dataclasses.replace(self)\n result.set_scaling_property(getattr(self, scaling_prop_name) + getattr(other, scaling_prop_name))\n return result\n return AggregateCostModel((self, other), CostAggregationType.Sum)\n\n def __sub__(self, other):\n raise NotImplementedError('Multiplication not implemented for cost models')\n\n def __mul__(self, other):\n raise NotImplementedError('Multiplication not implemented for cost models')\n\n def __div__(self, other):\n raise NotImplementedError('Division not implemented for cost models')\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FixedCostModel(Model):\n cost: float = 0.0\n type: str = 'FixedCostModel'\n\n @property\n def scaling_property(self):\n return 'cost'\n\n def __eq__(self, other):\n if not isinstance(other, FixedCostModel):\n return False\n return (self.cost,) == (other.cost,)\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScaledCostModel(Model):\n scaling_level: float = 0.0\n scaling_quantity_type: TransactionCostScalingType = TransactionCostScalingType.Quantity\n type: str = 'ScaledCostModel'\n\n @property\n def scaling_property(self):\n return 'scaling_level'\n\n def __eq__(self, other):\n if not isinstance(other, ScaledCostModel):\n return False\n return (self.scaling_level, self.scaling_quantity_type) == (other.scaling_level, other.scaling_quantity_type)\n\n\n_type_to_basic_model_map = {\n 'fixed_cost_model': FixedCostModel,\n 'scaled_cost_model': ScaledCostModel,\n 'FixedCostModel': FixedCostModel,\n 'ScaledCostModel': ScaledCostModel,\n}\n\n\ndef basic_tc_tuple_decoder(data: Optional[Tuple[dict, ...]]) -> Optional[Union[FixedCostModel, ScaledCostModel]]:\n if data is None:\n return None\n return tuple(_type_to_basic_model_map[m['type']].from_dict(m) for m in data)\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AggregateCostModel(Model):\n models: Tuple[Union[FixedCostModel, ScaledCostModel], ...] = field(metadata=config(decoder=basic_tc_tuple_decoder))\n aggregation_type: CostAggregationType\n type: str = 'AggregateCostModel'\n\n @property\n def scaling_property(self):\n return None\n\n def set_scaling_property(self, value: float):\n pass\n\n def __add__(self, other):\n if isinstance(other, AggregateCostModel) and self.aggregation_type is other.aggregation_type:\n return AggregateCostModel(self.models + other.models, self.aggregation_type)\n raise TypeError('Can only add with AggregateCostModels with the same aggregation type')\n\n def __eq__(self, other):\n if not isinstance(other, AggregateCostModel):\n return False\n return (self.models, self.aggregation_type) == (other.models, other.aggregation_type)\n\n\ndef tcm_decoder(data: Optional[dict]) -> Optional[Union[FixedCostModel, ScaledCostModel, AggregateCostModel]]:\n full_type_map = {\n **_type_to_basic_model_map,\n **{'aggregate_cost_model': AggregateCostModel, 'AggregateCostModel': AggregateCostModel},\n }\n return full_type_map[data['type']].from_dict(data) if data is not None else None\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TradingCosts:\n entry: Union[FixedCostModel, ScaledCostModel, AggregateCostModel] = field(\n default=FixedCostModel(0), metadata=config(decoder=tcm_decoder)\n )\n exit: Optional[Union[FixedCostModel, ScaledCostModel, AggregateCostModel]] = field(\n default=None, metadata=config(decoder=tcm_decoder)\n )\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TransactionCostConfig:\n trade_cost_model: TradingCosts\n hedge_cost_model: Optional[TradingCosts] = None\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Configuration:\n market_data_location: Optional[PricingLocation] = None\n market_model: Optional[EquityMarketModel] = None\n cash_accrual: bool = False\n roll_date_mode: Optional[RollDateMode] = None\n combine_roll_signal_entries: bool = False\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass StrategyHedge:\n risk: HedgeRiskMeasure = HedgeRiskMeasure.Delta\n frequency: str = '1b'\n risk_percentage: float = 100\n" }, { "path": "gs_quant/api/gs/backtests_xasset/response_datatypes/generic_backtest_datatypes.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom dataclasses_json import dataclass_json\n\n\n@dataclass_json\n@dataclass\nclass Strategy(object):\n \"\"\"\n A strategy object on which one may run a backtest\n \"\"\"\n\n pass\n" }, { "path": "gs_quant/api/gs/backtests_xasset/response_datatypes/risk_result.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Dict, Optional\n\nfrom dataclasses_json import dataclass_json, LetterCase\n\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result_datatypes import RiskResultWithData\n\n\nclass RefType(Enum):\n LEG_ID = 'legId'\n RISK_MEASURE = 'riskMeasure'\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass RiskResults:\n refs: Dict[RefType, str]\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass RiskResultsByDate(RiskResults):\n result: Dict[dt.date, RiskResultWithData]\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass RiskResultsError(RiskResults):\n error: Optional[str] = None\n trace_id: Optional[str] = None\n" }, { "path": "gs_quant/api/gs/backtests_xasset/response_datatypes/risk_result_datatypes.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Optional\n\nimport pandas as pd\nfrom dataclasses_json import dataclass_json, LetterCase, config\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.generic_datatype_encoders import decode_inst\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.risk_result_datatype_encoders import (\n encode_series_result,\n decode_series_result,\n encode_dataframe_result,\n decode_dataframe_result,\n)\nfrom gs_quant.priceable import PriceableImpl\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass RiskResultWithData:\n unit: Optional[str] = None\n\n def check_can_aggregate(self, other):\n if isinstance(other, RiskResultWithData):\n if self.unit != other.unit:\n raise ValueError(f'Cannot aggregate risk results with different units: {self.unit}, {other.unit}')\n else:\n if not isinstance(other, type(self.result)):\n raise TypeError(f'Incorrect type for other operand {type(other)}')\n\n def __add__(self, other):\n self.check_can_aggregate(other)\n other_operand = other.result if isinstance(other, RiskResultWithData) else other\n return type(self)(unit=self.unit, result=self.result + other_operand)\n\n def __radd__(self, other):\n self.check_can_aggregate(other)\n other_operand = other.result if isinstance(other, RiskResultWithData) else other\n return type(self)(unit=self.unit, result=other_operand + self.result)\n\n def __sub__(self, other):\n self.check_can_aggregate(other)\n other_operand = other.result if isinstance(other, RiskResultWithData) else other\n return type(self)(unit=self.unit, result=self.result - other_operand)\n\n def __mul__(self, other):\n self.check_can_aggregate(other)\n other_operand = other.result if isinstance(other, RiskResultWithData) else other\n return type(self)(unit=self.unit, result=self.result * other_operand)\n\n def __rmul__(self, other):\n self.check_can_aggregate(other)\n other_operand = other.result if isinstance(other, RiskResultWithData) else other\n return type(self)(unit=self.unit, result=other_operand * self.result)\n\n def __truediv__(self, other):\n self.check_can_aggregate(other)\n other_operand = other.result if isinstance(other, RiskResultWithData) else other\n return type(self)(unit=self.unit, result=self.result / other_operand)\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass FloatWithData(RiskResultWithData):\n result: Optional[float] = None\n type: str = 'float'\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass StringWithData(RiskResultWithData):\n result: Optional[str] = None\n type: str = 'string'\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass VectorWithData(RiskResultWithData):\n result: Optional[pd.Series] = field(\n default=None, metadata=config(encoder=encode_series_result, decoder=decode_series_result)\n )\n type: str = 'vector'\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass MatrixWithData(RiskResultWithData):\n result: pd.DataFrame = field(\n default=None, metadata=config(encoder=encode_dataframe_result, decoder=decode_dataframe_result)\n )\n type: str = 'matrix'\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass DefnValuesWithData(RiskResultWithData):\n result: Optional[PriceableImpl] = field(default=None, metadata=config(decoder=decode_inst))\n type: str = 'defn'\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass\nclass DictsWithData(RiskResultWithData):\n result: Optional[dict] = None\n type: str = 'dict'\n" }, { "path": "gs_quant/api/gs/base_screener.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom typing import Tuple, Dict, Any, List\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.base_screener import Screener\n\n_logger = logging.getLogger(__name__)\n\n\nclass GsBaseScreenerApi:\n @classmethod\n def get_screeners(cls) -> Tuple[Screener, ...]:\n \"\"\"\n Retrieves screener information about all screeners accessible to the current user.\n\n :return: Screener tuple, a tuple containing each screener available to the user.\n \"\"\"\n return GsSession.current.sync.get('/data/screeners', cls=Screener)['results']\n\n @classmethod\n def get_screener(cls, screener_id: str) -> Screener:\n \"\"\"\n Retrieves information about the screener specified in parameter screener_id.\n\n :param screener_id: str, the id of the screener whose information is being retrieved.\n\n :return: Screener, an object containing information about the screener associated\n with screener_id.\n \"\"\"\n return GsSession.current.sync.get('/data/screeners/{id}'.format(id=screener_id), cls=Screener)\n\n @classmethod\n def create_screener(cls, screener: Screener) -> Screener:\n \"\"\"\n Creates a new screener with a new id from a Screener object. The new screener copies\n the specifications of the Screener object passed into this function. To make changes to\n the new screener's schema upon creation, change the attributes of the Screener object\n before passing it into this function.\n\n User must be a part of the PlotScreenerAdmins group to perform this action.\n\n :param screener: Screener, the screener object that is copied to make the new screener.\n\n :return: Screener, the new screener object containing a new id\n \"\"\"\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.post('/data/screeners', screener, request_headers=request_headers, cls=Screener)\n\n @classmethod\n def edit_screener(cls, screener_id: str, screener: Screener) -> Screener:\n \"\"\"\n Edits the existing screener with ID screener_id to follow the schema specified in\n the passed in Screener object. The existing screener's original schema will be\n entirely overwritten by this object's schema. The edited screener will retain its\n screener ID after being altered by this function.\n\n User must be a part of the PlotScreenerAdmins group to perform this action.\n\n Throws an error if the screener_id provided does not match the ID of the\n screener object provided.\n\n :param screener_id: str, the ID of the screener being edited. Must be the same\n ID as contained in the\n screener parameter.\n :param screener: Screener, the object representing the screener to be edited.\n This object should contain any desired changes to the screener.\n\n :return: Screener, an object containing information about the updated screener.\n \"\"\"\n assert screener_id == screener.id\n\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.put(\n '/data/screeners/{id}'.format(id=screener_id), screener, request_headers=request_headers, cls=Screener\n )\n\n @classmethod\n def publish_to_screener(cls, screener_id: str, data: Dict[str, List[Dict[str, Any]]]) -> List[Dict[str, Any]]:\n \"\"\"\n Permanently publishes additional data specified in screener_rows to the existing screener\n with ID screener_id. Although this function returns a dictionary of all published data,\n only data formats consistent with the screener schema will be persisted.\n\n User must be a part of the PlotScreenerAdmins group to perform this action.\n\n :param screener_id: str, the ID of the screener to which data will be published.\n :param data:, dict list, a list of dictionaries where each dictionary\n represents a row of data to publish to the screener.\n\n :return: dict list, a list of dictionaries, where each dictionary represents a row\n of data to be published to the screener.\n \"\"\"\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.post(\n '/data/screeners/{id}/publish'.format(id=screener_id), data, request_headers=request_headers\n )['data']\n\n @classmethod\n def clear_screener(cls, screener_id: str) -> Dict[str, Any]:\n \"\"\"\n Permanently clears all data from a screener with the corresponding screener_id,\n but does not delete the screener. This function leaves the schema of the screener\n unchanged.\n\n User must be a part of the PlotScreenerAdmins group to perform this action.\n\n :param screener_id: str, the ID of the screener whose data is being cleared.\n\n :return: dict, a dictionary with information about if the screener was successfully cleared.\n \"\"\"\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.post(\n '/data/screeners/{id}/clear'.format(id=screener_id), {}, request_headers=request_headers\n )\n\n @classmethod\n def delete_screener(cls, screener_id: str) -> None:\n \"\"\"\n Permanently deletes the screener associated with ID screener_id, as well as all of its data.\n\n User must be a part of the PlotScreenerAdmins group to perform this action.\n\n :param screener_id: str, the ID of the screener being deleted.\n\n :return: None\n \"\"\"\n return GsSession.current.sync.delete('/data/screeners/{id}'.format(id=screener_id))\n" }, { "path": "gs_quant/api/gs/carbon.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom enum import Enum\nfrom typing import Dict, List\nfrom urllib.parse import urlencode\n\nfrom gs_quant.common import Currency\nfrom gs_quant.session import GsSession\n\n_logger = logging.getLogger(__name__)\n\n\nclass CarbonCard(Enum):\n \"\"\"\n Carbon Cards\n \"\"\"\n\n COVERAGE = 'coverage'\n SBTI_AND_NET_ZERO_TARGETS = 'sbtiAndNetZeroTargets'\n EMISSIONS = 'emissions'\n ALLOCATIONS = 'allocations'\n ATTRIBUTION = 'attribution'\n\n # highestEmitters should not be accessible through API\n\n def __str__(self):\n return self.value\n\n\nclass CarbonCoverageCategory(Enum):\n \"\"\"\n Carbon Coverage Category\n \"\"\"\n\n WEIGHTS = 'weights'\n NUMBER_OF_COMPANIES = 'numberOfCompanies'\n\n def __str__(self):\n return self.value\n\n\nclass CarbonTargetCoverageCategory(Enum):\n \"\"\"\n Carbon Targets Coverage Category\n \"\"\"\n\n CAPITAL_ALLOCATED = 'capitalAllocated'\n PORTFOLIO_EMISSIONS = 'portfolioEmissions'\n\n def __str__(self):\n return self.value\n\n\nclass CarbonScope(Enum):\n \"\"\"\n Carbon Scopes\n \"\"\"\n\n TOTAL_GHG = 'totalGHG'\n SCOPE1 = 'scope1'\n SCOPE2 = 'scope2'\n\n def __str__(self):\n return self.value\n\n\nclass CarbonEmissionsAllocationCategory(Enum):\n \"\"\"\n Carbon Emissions Allocation Category\n \"\"\"\n\n GICS_SECTOR = 'gicsSector'\n GICS_INDUSTRY = 'gicsIndustry'\n REGION = 'region'\n\n def __str__(self):\n return self.value\n\n\nclass CarbonEmissionsIntensityType(Enum):\n \"\"\"\n Carbon Emissions Intensity Type\n \"\"\"\n\n EI_ENTERPRISE_VALUE = 'emissionsIntensityEnterpriseValue'\n EI_REVENUE = 'emissionsIntensityRevenue'\n EI_MARKETCAP = 'emissionsIntensityMarketCap'\n\n def __str__(self):\n return self.value\n\n\nclass CarbonEntityType(Enum):\n \"\"\"\n Carbon analytics at portfolio or benchmark\n \"\"\"\n\n PORTFOLIO = 'portfolio'\n BENCHMARK = 'benchmark'\n\n def __str__(self):\n return self.value\n\n\nclass CarbonAnalyticsView(Enum):\n \"\"\"\n Carbon analytics at Long or Short component of\n \"\"\"\n\n LONG = 'Long'\n SHORT = 'Short'\n\n def __str__(self):\n return self.value\n\n\nclass GsCarbonApi:\n \"\"\"GS Carbon API client implementation\"\"\"\n\n @classmethod\n def get_carbon_analytics(\n cls,\n entity_id: str,\n benchmark_id: str = None,\n reporting_year: str = 'Latest',\n currency: Currency = None,\n include_estimates: bool = False,\n use_historical_data: bool = False,\n normalize_emissions: bool = False,\n cards: List[CarbonCard] = [],\n analytics_view: CarbonAnalyticsView = CarbonAnalyticsView.LONG,\n ) -> Dict:\n url = f'/carbon/{entity_id}?'\n url += urlencode(\n dict(\n filter(\n lambda item: item[1] is not None,\n dict(\n benchmark=benchmark_id,\n reportingYear=reporting_year,\n currency=currency.value if currency is not None else None,\n includeEstimates=str(include_estimates).lower(),\n useHistoricalData=str(use_historical_data).lower(),\n normalizeEmissions=str(normalize_emissions).lower(),\n card=[c for c in CarbonCard] if len(cards) == 0 else cards,\n analyticsView=analytics_view.value,\n ).items(),\n )\n ),\n True,\n )\n\n # TODO: Add scope as API parameter\n return GsSession.current.sync.get(url)\n" }, { "path": "gs_quant/api/gs/content.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom base64 import b64decode\nfrom collections import OrderedDict\nfrom enum import Enum\nfrom typing import List, Tuple\nfrom urllib.parse import quote\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.content import ContentResponse, GetManyContentsResponse\n\n\nclass OrderBy(Enum):\n \"\"\"\n Content ordering\n \"\"\"\n\n ASC = 'asc'\n DESC = 'desc'\n\n def __str__(self):\n return str(self.value)\n\n\nclass GsContentApi:\n \"\"\"GS Content API client implementation\"\"\"\n\n @classmethod\n def get_contents(\n cls,\n channels: set = None,\n asset_ids: set = None,\n author_ids: set = None,\n tags: set = None,\n offset: int = 0,\n limit: int = 10,\n order_by: dict = {'direction': OrderBy.DESC, 'field': 'createdTime'},\n ) -> List[ContentResponse]:\n \"\"\"\n Get contents for given parameters\n\n :param channels: Channels\n :param asset_ids: Marquee Asset Ids\n :param author_ids: Marquee Author Guids\n :param tags: Tags\n :param limit: Limit (number of contents to return)\n :param offset: Offset (offset of contents)\n :param order_by: OrderBy (dict for specifying how to sort contents)\n :return: A list of ContentResponse objects\n\n **Examples**\n\n >>> from gs_quant.api.gs.content import GsContentApi\n >>> from gs_quant.session import GsSession\n >>>\n >>> GsSession.use()\n >>> contents = GsContentApi.get_contents(channels=['G10'])\n \"\"\"\n\n if limit and limit > 1000:\n raise ValueError('Limit is too large. Limit must be <= 1000.')\n\n if offset and (offset < 0 or offset >= limit):\n raise ValueError('Invalid offset. Offset must be >= 0 and < limit')\n\n parameters_dict = cls._build_parameters_dict(\n channel=channels,\n asset_id=asset_ids,\n author_id=author_ids,\n tag=tags,\n offset=[offset] if offset else None,\n limit=[limit] if limit else None,\n order_by=[order_by] if order_by else None,\n )\n\n query_string = '' if not parameters_dict else cls._build_query_string(parameters_dict)\n contents = GsSession.current.sync.get(f'/content{query_string}', cls=GetManyContentsResponse)\n return contents.data\n\n @staticmethod\n def get_text(contents: List[ContentResponse]) -> List[Tuple[str, str]]:\n \"\"\"\n Get text for contents\n\n :param contents: List of ContentResponse objects\n :return: A list of tuples representing (, )\n\n **Examples**\n\n >>> from gs_quant.api.gs.content import GsContentApi\n >>> from gs_quant.session import GsSession\n >>>\n >>> GsSession.use()\n >>> contents = GsContentApi.get_contents(channels=['G10'])\n >>> text = gs_content_api.get_text(contents)\n \"\"\"\n return [(content.id, b64decode(content.content.body)) for content in contents]\n\n @classmethod\n def _build_parameters_dict(cls, **kwargs) -> dict:\n \"\"\"\n Builds dict of valid parameters to their value,\n filtering out any parameters for which \"None\" is\n the value.\n \"\"\"\n parameters = {}\n for key, value in kwargs.items():\n if value:\n parameters.setdefault(key, []).extend(sorted(value))\n return OrderedDict(parameters)\n\n @classmethod\n def _build_query_string(cls, parameters: dict) -> str:\n \"\"\"\n Builds a query string accepted by the Content API\n\n Example:\n\n In: { 'channel': ['G10', 'EM'], 'limit': 10 }\n Out: ?channel=G10&channel=EM&limit=10\n \"\"\"\n query_string = '?'\n\n # Builds a list of tuples for easy iteration like:\n # [('channel', 'channel-1'), ('channel', 'channel-2'), ('assetId', 'asset-1'), ...]\n parameter_tuples = [\n (parameter_name, parameter_value)\n for parameter_name, parameter_values in parameters.items()\n for parameter_value in parameter_values\n ]\n\n for index, parameter_tuple in enumerate(parameter_tuples):\n name, value = parameter_tuple\n value = quote(value.encode()) if isinstance(value, str) else value\n\n if name == 'order_by':\n value = cls._convert_order_by(value)\n\n query_string += f'{name}={value}' if index == 0 else f'&{name}={value}'\n\n return query_string\n\n @classmethod\n def _convert_order_by(cls, order_by: dict) -> str:\n \"\"\"\n Converts an orderByDirection and orderByField to an acceptable query parameter format\n expected by the Content API\n \"\"\"\n direction = order_by['direction']\n\n if direction == OrderBy.DESC:\n order_by_parameter = '>'\n else:\n order_by_parameter = '<'\n\n return order_by_parameter + order_by['field']\n" }, { "path": "gs_quant/api/gs/countries.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Tuple\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.countries import Country, Subdivision\n\n\nclass GsCountryApi:\n \"\"\"GS Country API client implementation\"\"\"\n\n @classmethod\n def get_many_countries(cls, limit: int = 100) -> Tuple[Country, ...]:\n return GsSession.current.sync.get('/countries?limit={limit}'.format(limit=limit), cls=Country)['results']\n\n @classmethod\n async def get_many_countries_async(cls, limit: int = 100) -> Tuple[Country, ...]:\n response = await GsSession.current.async_.get('/countries', payload={\"limit\": limit}, cls=Country)\n return response.get(\"results\")\n\n @classmethod\n def get_country(cls, country_id: str) -> Country:\n return GsSession.current.sync.get('/countries/{id}'.format(id=country_id), cls=Country)\n\n @classmethod\n async def get_country_async(cls, country_id: str) -> Country:\n response = await GsSession.current.async_.get(f'/countries/{country_id}', cls=Country)\n return response\n\n @classmethod\n def create_country(cls, country: Country) -> Country:\n return GsSession.current.sync.post('/countries', country, cls=Country)\n\n @classmethod\n def update_country(cls, country: Country):\n return GsSession.current.sync.put('/countries/{id}'.format(id=country.id), country, cls=Country)\n\n @classmethod\n def delete_country(cls, country_id: str) -> dict:\n return GsSession.current.sync.delete('/countries/{id}'.format(id=country_id))\n\n @classmethod\n def get_many_subdivisions(cls, limit: int = 100) -> Tuple[Subdivision, ...]:\n return GsSession.current.sync.get('/countries/subdivisions?limit={limit}'.format(limit=limit), cls=Subdivision)[\n 'results'\n ]\n\n @classmethod\n def get_subdivision(cls, subdivision_id: str) -> Subdivision:\n return GsSession.current.sync.get('/countries/subdivisions/{id}'.format(id=subdivision_id), cls=Subdivision)\n\n @classmethod\n def create_subdivision(cls, subdivision: Subdivision) -> Subdivision:\n return GsSession.current.sync.post('/countries/subdivisions', subdivision, cls=Subdivision)\n\n @classmethod\n def update_subdivision(cls, subdivision: Subdivision):\n return GsSession.current.sync.put(\n '/countries/subdivisions/{id}'.format(id=subdivision.id), subdivision, cls=Subdivision\n )\n\n @classmethod\n def delete_subdivision(cls, subdivision_id: str) -> dict:\n return GsSession.current.sync.delete('/countries/subdivisions/{id}'.format(id=subdivision_id))\n" }, { "path": "gs_quant/api/gs/data.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport asyncio\nimport datetime as dt\nimport json\nimport logging\nimport time\nfrom copy import copy, deepcopy\nfrom enum import Enum\nfrom itertools import chain\nfrom typing import Iterable, List, Optional, Tuple, Union, Dict\n\nimport cachetools\nimport pandas as pd\nfrom cachetools import TTLCache\nfrom dateutil import parser\nfrom pydash import get\n\nfrom gs_quant.api.data import DataApi\nfrom gs_quant.base import Base\nfrom gs_quant.common import MarketDataVendor, PricingLocation, Format\nfrom gs_quant.data.core import DataContext, DataFrequency\nfrom gs_quant.data.log import log_debug, log_warning\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.markets import MarketDataCoordinate\nfrom gs_quant.target.coordinates import MDAPIDataBatchResponse, MDAPIDataQuery, MDAPIDataQueryResponse, MDAPIQueryField\nfrom gs_quant.target.data import DataQuery, DataQueryResponse, DataSetCatalogEntry, DataSetEntity, DataSetFieldEntity\nfrom .assets import GsIdType\nfrom ..api_cache import ApiRequestCache\nfrom ...target.assets import EntityQuery, FieldFilterMap\n\n_logger = logging.getLogger(__name__)\n_REQUEST_HEADERS = \"request_headers\"\n\n\nclass QueryType(Enum):\n IMPLIED_VOLATILITY = \"Implied Volatility\"\n IMPLIED_VOLATILITY_BY_EXPIRATION = \"Implied Volatility By Expiration\"\n IMPLIED_CORRELATION = \"Implied Correlation\"\n REALIZED_CORRELATION = \"Realized Correlation\"\n AVERAGE_IMPLIED_VOLATILITY = \"Average Implied Volatility\"\n AVERAGE_IMPLIED_VARIANCE = \"Average Implied Variance\"\n AVERAGE_REALIZED_VOLATILITY = \"Average Realized Volatility\"\n SWAP_RATE = \"Swap Rate\"\n SWAP_ANNUITY = \"Swap Annuity\"\n SWAPTION_PREMIUM = \"Swaption Premium\"\n SWAPTION_ANNUITY = \"Swaption Annuity\"\n BASIS_SWAP_RATE = \"Basis Swap Rate\"\n XCCY_SWAP_SPREAD = \"Xccy Swap Spread\"\n SWAPTION_VOL = \"Swaption Vol\"\n MIDCURVE_VOL = \"Midcurve Vol\"\n CAP_FLOOR_VOL = \"Cap Floor Vol\"\n SPREAD_OPTION_VOL = \"Spread Option Vol\"\n INFLATION_SWAP_RATE = \"Inflation Swap Rate\"\n FORWARD = \"Forward\"\n PRICE = \"Price\"\n ATM_FWD_RATE = \"Atm Fwd Rate\"\n BASIS = \"Basis\"\n VAR_SWAP = \"Var Swap\"\n MIDCURVE_PREMIUM = \"Midcurve Premium\"\n MIDCURVE_ANNUITY = \"Midcurve Annuity\"\n MIDCURVE_ATM_FWD_RATE = \"Midcurve Atm Fwd Rate\"\n CAP_FLOOR_ATM_FWD_RATE = \"Cap Floor Atm Fwd Rate\"\n SPREAD_OPTION_ATM_FWD_RATE = \"Spread Option Atm Fwd Rate\"\n FORECAST = \"Forecast\"\n IMPLIED_VOLATILITY_BY_DELTA_STRIKE = \"Implied Volatility By Delta Strike\"\n FUNDAMENTAL_METRIC = \"Fundamental Metric\"\n POLICY_RATE_EXPECTATION = \"Policy Rate Expectation\"\n CENTRAL_BANK_SWAP_RATE = \"Central Bank Swap Rate\"\n FORWARD_PRICE = \"Forward Price\"\n FAIR_PRICE = \"Fair Price\"\n PNL = \"Pnl\"\n SPOT = \"Spot\"\n AUM = \"Aum\"\n RATE = \"Rate\"\n ES_NUMERIC_SCORE = \"Es Numeric Score\"\n ES_NUMERIC_PERCENTILE = \"Es Numeric Percentile\"\n ES_POLICY_SCORE = \"Es Policy Score\"\n ES_POLICY_PERCENTILE = \"Es Policy Percentile\"\n ES_SCORE = \"Es Score\"\n ES_PERCENTILE = \"Es Percentile\"\n ES_PRODUCT_IMPACT_SCORE = \"Es Product Impact Score\"\n ES_PRODUCT_IMPACT_PERCENTILE = \"Es Product Impact Percentile\"\n G_SCORE = \"G Score\"\n G_PERCENTILE = \"G Percentile\"\n ES_MOMENTUM_SCORE = \"Es Momentum Score\"\n ES_MOMENTUM_PERCENTILE = \"Es Momentum Percentile\"\n G_REGIONAL_SCORE = \"G Regional Score\"\n G_REGIONAL_PERCENTILE = \"G Regional Percentile\"\n ES_DISCLOSURE_PERCENTAGE = \"Es Disclosure Percentage\"\n CONTROVERSY_SCORE = \"Controversy Score\"\n CONTROVERSY_PERCENTILE = \"Controversy Percentile\"\n RATING = \"Rating\"\n CONVICTION_LIST = \"Conviction List\"\n FAIR_VALUE = \"Fair Value\"\n FX_FORECAST = \"Fx Forecast\"\n GROWTH_SCORE = \"Growth Score\"\n FINANCIAL_RETURNS_SCORE = \"Financial Returns Score\"\n MULTIPLE_SCORE = \"Multiple Score\"\n INTEGRATED_SCORE = \"Integrated Score\"\n COMMODITY_FORECAST = \"Commodity Forecast\"\n FORECAST_VALUE = \"Forecast Value\"\n FORWARD_POINT = \"Forward Point\"\n FCI = \"Fci\"\n LONG_RATES_CONTRIBUTION = \"Long Rates Contribution\"\n SHORT_RATES_CONTRIBUTION = \"Short Rates Contribution\"\n CORPORATE_SPREAD_CONTRIBUTION = \"Corporate Spread Contribution\"\n SOVEREIGN_SPREAD_CONTRIBUTION = \"Sovereign Spread Contribution\"\n EQUITIES_CONTRIBUTION = \"Equities Contribution\"\n REAL_LONG_RATES_CONTRIBUTION = \"Real Long Rates Contribution\"\n REAL_SHORT_RATES_CONTRIBUTION = \"Real Short Rates Contribution\"\n REAL_FCI = \"Real Fci\"\n DWI_CONTRIBUTION = \"Dwi Contribution\"\n REAL_TWI_CONTRIBUTION = \"Real Twi Contribution\"\n TWI_CONTRIBUTION = \"Twi Contribution\"\n COVARIANCE = \"Covariance\"\n FACTOR_EXPOSURE = \"Factor Exposure\"\n FACTOR_RETURN = \"Factor Return\"\n HISTORICAL_BETA = \"Historical Beta\"\n FACTOR_PNL = \"Factor Pnl\"\n FACTOR_PROPORTION_OF_RISK = \"Factor Proportion Of Risk\"\n DAILY_RISK = \"Daily Risk\"\n ANNUAL_RISK = \"Annual Risk\"\n VOLATILITY = \"Volatility\"\n CORRELATION = \"Correlation\"\n OIS_XCCY = \"Ois Xccy\"\n OIS_XCCY_EX_SPIKE = \"Ois Xccy Ex Spike\"\n USD_OIS = \"Usd Ois\"\n NON_USD_OIS = \"Non Usd Ois\"\n SETTLEMENT_PRICE = \"Settlement Price\"\n THEMATIC_EXPOSURE = \"Thematic Exposure\"\n THEMATIC_BETA = \"Thematic Beta\"\n THEMATIC_MODEL_BETA = \"Thematic Model Beta\"\n CDS_SPREAD_100 = \"Spread At100\"\n CDS_SPREAD_250 = \"Spread At250\"\n CDS_SPREAD_500 = \"Spread At500\"\n STRIKE_VOL = \"Strike Vol\"\n OPTION_PREMIUM = \"Option Premium\"\n ABSOLUTE_STRIKE = \"Absolute Strike\"\n RETAIL_PCT_SHARES = 'impliedRetailPctShares'\n RETAIL_PCT_NOTIONAL = 'impliedRetailPctNotional'\n RETAIL_SHARES = 'impliedRetailShares'\n RETAIL_NOTIONAL = 'impliedRetailNotional'\n SHARES = 'shares'\n NOTIONAL = 'notional'\n RETAIL_BUY_NOTIONAL = 'impliedRetailBuyNotional'\n RETAIL_BUY_PCT_NOTIONAL = 'impliedRetailBuyPctNotional'\n RETAIL_BUY_PCT_SHARES = 'impliedRetailBuyPctShares'\n RETAIL_BUY_SHARES = 'impliedRetailBuyShares'\n RETAIL_SELL_NOTIONAL = 'impliedRetailSellNotional'\n RETAIL_SELL_PCT_NOTIONAL = 'impliedRetailSellPctNotional'\n RETAIL_SELL_PCT_SHARES = 'impliedRetailSellPctShares'\n RETAIL_SELL_SHARES = 'impliedRetailSellShares'\n FWD_POINTS = 'Fwd Points'\n S3_AGGREGATE_DATA = 'value'\n HIT_RATE = 'Hit Rate'\n MAX_DRAWDOWN = \"Max Drawdown\"\n STANDARD_DEVIATION = \"Standard Deviation\"\n DOWNSIDE_RISK = \"Downside Risk\"\n KURTOSIS = 'Kurtosis'\n SKEWNESS = 'Skewness'\n REALIZED_VAR = 'Realized VaR'\n TRACKING_ERROR = 'Tracking Error'\n TRACKING_ERROR_BEAR = 'Tracking Error Bear'\n TRACKING_ERROR_BULL = 'Tracking Error Bull'\n SHARPE_RATIO = 'Sharpe Ratio'\n CALMAR_RATIO = 'Calmar Ratio'\n SORTINO_RATIO = 'Sortino Ratio'\n INFORMATION_RATIO = 'Information Ratio'\n MODIGLIANI_RATIO = 'Modigliani Ratio'\n TREYNOR_RATIO = 'Treynor Ratio'\n ALPHA = 'Alpha'\n BETA = 'Beta'\n R_SQUARED = 'R Squared'\n CAPTURE_RATIO = 'Capture Ratio'\n\n\nclass GsDataApi(DataApi):\n __definitions = {}\n __asset_coordinates_cache = TTLCache(10000, 86400)\n _api_request_cache: ApiRequestCache = None\n DEFAULT_SCROLL = '30s'\n\n # DataApi interface\n\n @classmethod\n def set_api_request_cache(cls, cache: ApiRequestCache):\n cls._api_request_cache = cache\n\n @classmethod\n def _construct_cache_key(cls, url, **kwargs) -> tuple:\n def fallback_encoder(v) -> str:\n if isinstance(v, dt.date):\n return v.isoformat()\n\n def serialize_value(v):\n if any(isinstance(v, class_) for class_ in {MDAPIDataQuery, DataQuery}):\n return v.to_json(sort_keys=True, default=fallback_encoder)\n encoded_v = fallback_encoder(v)\n return encoded_v or v\n\n json_kwargs = {k: serialize_value(v) for k, v in kwargs.items() if k not in {_REQUEST_HEADERS}}\n cache_key = (url, 'POST', json_kwargs)\n return cache_key\n\n @classmethod\n def _check_cache(cls, url, **kwargs):\n session = cls.get_session()\n cached_val, cache_key = None, None\n if cls._api_request_cache:\n cache_key = cls._construct_cache_key(url, **kwargs)\n cached_val = cls._api_request_cache.get(session, cache_key)\n return cached_val, cache_key, session\n\n @classmethod\n def _post_with_cache_check(cls, url, validator=lambda x: x, domain=None, **kwargs):\n result, cache_key, session = cls._check_cache(url=url, **kwargs)\n if result is None:\n result = validator(session.sync.post(url, domain=domain, **kwargs))\n if cls._api_request_cache:\n cls._api_request_cache.put(session, cache_key, result)\n return result\n\n @classmethod\n def _get_with_cache_check(cls, url, validator=lambda x: x, domain=None, **kwargs):\n result, cache_key, session = cls._check_cache(url, **kwargs)\n if result is None:\n result = validator(session.sync.get(url, domain=domain, **kwargs))\n if cls._api_request_cache:\n cls._api_request_cache.put(session, cache_key, result)\n return result\n\n @classmethod\n async def _get_with_cache_check_async(cls, url, validator=lambda x: x, domain=None, **kwargs):\n result, cache_key, session = cls._check_cache(url, **kwargs)\n if result is None:\n result = await session.async_.get(url, domain=domain, **kwargs)\n result = validator(result)\n if cls._api_request_cache:\n cls._api_request_cache.put(session, cache_key, result)\n return result\n\n @classmethod\n async def _post_with_cache_check_async(cls, url, validator=lambda x: x, domain=None, **kwargs):\n result, cache_key, session = cls._check_cache(url, **kwargs)\n if result is None:\n result = await session.async_.post(url, domain=domain, **kwargs)\n result = validator(result)\n if cls._api_request_cache:\n cls._api_request_cache.put(session, cache_key, result)\n return result\n\n @classmethod\n def query_data(\n cls, query: Union[DataQuery, MDAPIDataQuery], dataset_id: str = None, asset_id_type: Union[GsIdType, str] = None\n ) -> Union[MDAPIDataBatchResponse, DataQueryResponse, tuple, list]:\n if isinstance(query, MDAPIDataQuery) and query.market_data_coordinates:\n # Don't use MDAPIDataBatchResponse for now - it doesn't handle quoting style correctly\n results: Union[MDAPIDataBatchResponse, dict] = cls.execute_query('coordinates', query)\n if isinstance(results, dict):\n return results.get('responses', ())\n else:\n return results.responses if results.responses is not None else ()\n response: Union[DataQueryResponse, dict] = cls.execute_query(dataset_id, query)\n return cls.get_results(dataset_id, response, query)\n\n @classmethod\n async def query_data_async(\n cls, query: Union[DataQuery, MDAPIDataQuery], dataset_id: str = None\n ) -> Union[MDAPIDataBatchResponse, DataQueryResponse, tuple, list]:\n if isinstance(query, MDAPIDataQuery) and query.market_data_coordinates:\n # Don't use MDAPIDataBatchResponse for now - it doesn't handle quoting style correctly\n results: Union[MDAPIDataBatchResponse, dict] = await cls.execute_query_async('coordinates', query)\n if isinstance(results, dict):\n return results.get('responses', ())\n else:\n return results.responses if results.responses is not None else ()\n response: Union[DataQueryResponse, dict] = await cls.execute_query_async(dataset_id, query)\n results = await cls.get_results_async(dataset_id, response, query)\n return results\n\n @classmethod\n def execute_query(cls, dataset_id: str, query: Union[DataQuery, MDAPIDataQuery]):\n kwargs = {'payload': query}\n if getattr(query, 'format', None) in (Format.MessagePack, 'MessagePack'):\n kwargs[_REQUEST_HEADERS] = {'Accept': 'application/msgpack'}\n\n domain = cls._check_data_on_cloud(dataset_id)\n return cls._post_with_cache_check('/data/{}/query'.format(dataset_id), domain=domain, **kwargs)\n\n @classmethod\n async def execute_query_async(cls, dataset_id: str, query: Union[DataQuery, MDAPIDataQuery]):\n kwargs = {'payload': query}\n if getattr(query, 'format', None) in (Format.MessagePack, 'MessagePack'):\n kwargs[_REQUEST_HEADERS] = {'Accept': 'application/msgpack'}\n\n domain = await cls._check_data_on_cloud_async(dataset_id)\n result = await cls._post_with_cache_check_async('/data/{}/query'.format(dataset_id), domain=domain, **kwargs)\n return result\n\n @classmethod\n def _check_data_on_cloud(cls, dataset_id: str):\n session = cls.get_session()\n if session.redirect_to_mds and dataset_id != 'coordinates':\n dataset_data = cls._get_with_cache_check('/data/datasets/{}'.format(dataset_id))\n database_id_exists = get(dataset_data, 'parameters.databaseId')\n\n if database_id_exists:\n return cls.get_session()._get_mds_domain()\n return None\n\n @classmethod\n async def _check_data_on_cloud_async(cls, dataset_id: str):\n session = cls.get_session()\n if session.redirect_to_mds and dataset_id != 'coordinates':\n dataset_data = await cls._get_with_cache_check(f'/data/datasets/{dataset_id}')\n database_id_exists = get(dataset_data, 'parameters.databaseId')\n\n if database_id_exists:\n return cls.get_session()._get_mds_domain()\n return None\n\n @staticmethod\n def _get_results(response: Union[DataQueryResponse, dict]):\n if isinstance(response, dict):\n total_pages = response.get('totalPages')\n results = response.get('data', [])\n if 'groups' in response:\n group_by = set()\n for group in response['groups']:\n group_by.update(group['context'].keys())\n for row in group['data']:\n row.update(group['context'])\n results += group['data']\n results = (results, list(group_by))\n else:\n total_pages = response.total_pages if response.total_pages is not None else 0\n results = response.data if response.data is not None else ()\n return results, total_pages\n\n @staticmethod\n def get_results(\n dataset_id: str, response: Union[DataQueryResponse, dict], query: DataQuery\n ) -> Union[list, Tuple[list, list]]:\n results, total_pages = GsDataApi._get_results(response)\n if total_pages:\n if query.page is None:\n query.page = total_pages - 1\n results = results + GsDataApi.get_results(dataset_id, GsDataApi.execute_query(dataset_id, query), query)\n elif query.page - 1 > 0:\n query.page -= 1\n results = results + GsDataApi.get_results(dataset_id, GsDataApi.execute_query(dataset_id, query), query)\n else:\n return results\n return results\n\n @staticmethod\n async def get_results_async(\n dataset_id: str, response: Union[DataQueryResponse, dict], query: DataQuery\n ) -> Union[list, Tuple[list, list]]:\n results, total_pages = GsDataApi._get_results(response)\n if total_pages and total_pages > 1:\n futures = []\n for page in range(1, total_pages):\n query = deepcopy(query)\n query.page = page\n futures.append(GsDataApi.execute_query_async(dataset_id, query))\n all_responses = await asyncio.gather(*futures, return_exceptions=True)\n for response_crt in all_responses:\n results += GsDataApi._get_results(response_crt)[0]\n return results\n\n @classmethod\n def last_data(\n cls, query: Union[DataQuery, MDAPIDataQuery], dataset_id: str = None, timeout: int = None\n ) -> Union[list, tuple]:\n kwargs = {}\n if timeout is not None:\n kwargs['timeout'] = timeout\n if getattr(query, 'marketDataCoordinates', None):\n result = cls._post_with_cache_check('/data/coordinates/query/last', payload=query, **kwargs)\n return result.get('responses', ())\n else:\n domain = cls._check_data_on_cloud(dataset_id)\n result = cls._post_with_cache_check(\n '/data/{}/last/query'.format(dataset_id), payload=query, domain=domain, **kwargs\n )\n return result.get('data', ())\n\n @classmethod\n def symbol_dimensions(cls, dataset_id: str) -> tuple:\n definition = cls.get_definition(dataset_id)\n return definition.dimensions.symbolDimensions\n\n @classmethod\n def time_field(cls, dataset_id: str) -> str:\n definition = cls.get_definition(dataset_id)\n return definition.dimensions.timeField\n\n # GS-specific functionality\n @classmethod\n def _build_params(\n cls,\n scroll: str,\n scroll_id: Optional[str],\n limit: int,\n offset: int,\n fields: List[str],\n include_history: bool,\n **kwargs,\n ) -> dict:\n params = {'limit': limit or 4000, 'scroll': scroll}\n if scroll_id:\n params['scrollId'] = scroll_id\n if offset:\n params['offset'] = offset\n if fields:\n params['fields'] = fields\n if include_history:\n params['includeHistory'] = 'true'\n params = {**params, **kwargs}\n return params\n\n @classmethod\n def get_coverage(\n cls,\n dataset_id: str,\n scroll: str = DEFAULT_SCROLL,\n scroll_id: Optional[str] = None,\n limit: int = None,\n offset: int = None,\n fields: List[str] = None,\n include_history: bool = False,\n **kwargs,\n ) -> List[dict]:\n session = cls.get_session()\n params = cls._build_params(scroll, scroll_id, limit, offset, fields, include_history, **kwargs)\n body = session.sync.get(f'/data/{dataset_id}/coverage', payload=params)\n results = scroll_results = body['results']\n total_results = body['totalResults']\n while len(scroll_results) and len(results) < total_results:\n scroll_id = body.get('scrollId')\n if scroll_id is None:\n break\n params['scrollId'] = scroll_id\n body = session.sync.get(f'/data/{dataset_id}/coverage', payload=params)\n scroll_results = body['results']\n results += scroll_results\n\n return results\n\n @classmethod\n async def get_coverage_async(\n cls,\n dataset_id: str,\n scroll: str = DEFAULT_SCROLL,\n scroll_id: Optional[str] = None,\n limit: int = None,\n offset: int = None,\n fields: List[str] = None,\n include_history: bool = False,\n **kwargs,\n ) -> List[dict]:\n session = cls.get_session()\n params = cls._build_params(scroll, scroll_id, limit, offset, fields, include_history, **kwargs)\n body = await session.async_.get(f'/data/{dataset_id}/coverage', payload=params)\n results = scroll_results = body['results']\n total_results = body['totalResults']\n while len(scroll_results) and len(results) < total_results:\n params['scrollId'] = body['scrollId']\n body = await session.async_.get(f'/data/{dataset_id}/coverage', payload=params)\n scroll_results = body['results']\n if scroll_results:\n results += scroll_results\n return results\n\n @classmethod\n def create(cls, definition: Union[DataSetEntity, dict]) -> DataSetEntity:\n result = cls.get_session().sync.post('/data/datasets', payload=definition)\n return result\n\n @classmethod\n def get_catalog_url(cls, dataset_id: str) -> DataSetEntity:\n url = cls.get_session()._build_url(\n domain=cls.get_session()._get_web_domain(),\n path=f'/s/data-services/datasets/{dataset_id}',\n include_version=False,\n )\n return url\n\n @classmethod\n def delete_dataset(cls, dataset_id: str) -> dict:\n result = cls.get_session().sync.delete(f'/data/datasets/{dataset_id}')\n return result\n\n @classmethod\n def undelete_dataset(cls, dataset_id: str) -> dict:\n result = cls.get_session().sync.put(f'/data/datasets/{dataset_id}/undelete')\n return result\n\n @classmethod\n def update_definition(cls, dataset_id: str, definition: Union[DataSetEntity, dict]) -> DataSetEntity:\n result = cls.get_session().sync.put(\n '/data/datasets/{}'.format(dataset_id), payload=definition, cls=DataSetEntity\n )\n return result\n\n @classmethod\n def upload_data(cls, dataset_id: str, data: Union[pd.DataFrame, list, tuple]) -> dict:\n if isinstance(data, pd.DataFrame):\n # We require the Dataframe to return a list in the 'records' format:\n # https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html\n data = data.to_json(orient='records')\n # Don't use msgpack for MDS\n session = cls.get_session()\n headers = None if 'us-east' in session.domain else {'Content-Type': 'application/x-msgpack'}\n result = session.sync.post('/data/{}'.format(dataset_id), payload=data, request_headers=headers)\n return result\n\n @classmethod\n def delete_data(cls, dataset_id: str, delete_query: Dict) -> Dict:\n \"\"\"\n Delete data from dataset. You must have admin access to the dataset to delete data.\n All data deleted is not recoverable.\n \"\"\"\n return cls.get_session().sync.delete(f'/data/{dataset_id}', payload=delete_query, use_body=True)\n\n @classmethod\n def get_definition(cls, dataset_id: str) -> DataSetEntity:\n definition = cls.__definitions.get(dataset_id)\n if not definition:\n definition = cls.get_session().sync.get('/data/datasets/{}'.format(dataset_id), cls=DataSetEntity)\n if not definition:\n raise MqValueError('Unknown dataset {}'.format(dataset_id))\n\n cls.__definitions[dataset_id] = definition\n\n return definition\n\n @classmethod\n def get_many_definitions(\n cls,\n limit: int = 100,\n offset: int = None,\n scroll: str = DEFAULT_SCROLL,\n scroll_id: Optional[str] = None,\n ) -> Tuple[DataSetEntity, ...]:\n params = dict(\n filter(\n lambda item: item[1] is not None,\n dict(limit=limit, offset=offset, scroll=scroll, scrollId=scroll_id, enablePagination='true').items(),\n )\n )\n\n body = cls.get_session().sync.get('/data/datasets', payload=params, cls=DataSetEntity)\n results = scroll_results = body['results']\n total_results = body['totalResults']\n\n while len(scroll_results) and len(results) < total_results:\n params['scrollId'] = body['scrollId']\n body = cls.get_session().sync.get('/data/datasets', payload=params, cls=DataSetEntity)\n scroll_results = body['results']\n results = results + scroll_results\n\n return results\n\n @classmethod\n def get_catalog(\n cls,\n dataset_ids: List[str] = None,\n limit: int = 100,\n offset: int = None,\n scroll: str = DEFAULT_SCROLL,\n scroll_id: Optional[str] = None,\n ) -> Tuple[DataSetCatalogEntry, ...]:\n query = f'dataSetId={\"&dataSetId=\".join(dataset_ids)}' if dataset_ids else ''\n gs_session = cls.get_session()\n if len(query):\n return gs_session.sync.get(f'/data/catalog?{query}', cls=DataSetCatalogEntry)['results']\n else:\n params = dict(\n filter(\n lambda item: item[1] is not None,\n dict(\n limit=limit, offset=offset, scroll=scroll, scrollId=scroll_id, enablePagination='true'\n ).items(),\n )\n )\n\n body = gs_session.sync.get('/data/catalog', payload=params, cls=DataSetEntity)\n results = scroll_results = body['results']\n total_results = body['totalResults']\n\n while len(scroll_results) and len(results) < total_results:\n params['scrollId'] = body['scrollId']\n body = gs_session.sync.get('/data/catalog', payload=params, cls=DataSetEntity)\n scroll_results = body['results']\n results = results + scroll_results\n\n return results\n\n @classmethod\n @cachetools.cached(__asset_coordinates_cache)\n def get_many_coordinates(\n cls,\n mkt_type: str = None,\n mkt_asset: str = None,\n mkt_class: str = None,\n mkt_point: Tuple[str, ...] = (),\n *,\n limit: int = 100,\n return_type: type = str,\n ) -> Union[Tuple[str, ...], Tuple[MarketDataCoordinate, ...]]:\n where = FieldFilterMap(\n mkt_type=mkt_type.upper() if mkt_type is not None else None,\n mkt_asset=mkt_asset.upper() if mkt_asset is not None else None,\n mkt_class=mkt_class.upper() if mkt_class is not None else None,\n )\n for index, point in enumerate(mkt_point):\n setattr(where, 'mkt_point' + str(index + 1), point.upper())\n\n query = EntityQuery(where=where, limit=limit)\n results = cls._post_with_cache_check('/data/mdapi/query', payload=query)['results']\n\n if return_type is str:\n return tuple(coordinate['name'] for coordinate in results)\n elif return_type is MarketDataCoordinate:\n return tuple(\n MarketDataCoordinate(\n mkt_type=coordinate['dimensions']['mktType'],\n mkt_asset=coordinate['dimensions']['mktAsset'],\n mkt_class=coordinate['dimensions']['mktClass'],\n mkt_point=tuple(coordinate['dimensions']['mktPoint'].values()),\n mkt_quoting_style=coordinate['dimensions']['mktQuotingStyle'],\n )\n for coordinate in results\n )\n else:\n raise NotImplementedError('Unsupported return type')\n\n @classmethod\n def _to_zulu(cls, d):\n return d.strftime('%Y-%m-%dT%H:%M:%SZ')\n\n @classmethod\n def get_mxapi_curve_measure(\n cls,\n curve_type=None,\n curve_asset=None,\n curve_point=None,\n curve_tags=None,\n measure=None,\n start_time=None,\n end_time=None,\n request_id=None,\n close_location=None,\n real_time=None,\n ) -> pd.DataFrame:\n real_time = real_time or isinstance(start_time, dt.datetime)\n\n if not start_time:\n if real_time:\n start_time = DataContext.current.start_time\n else:\n start_time = DataContext.current.start_date\n\n if not end_time:\n if real_time:\n end_time = DataContext.current.end_time\n else:\n end_time = DataContext.current.end_date\n\n if not real_time and not close_location:\n close_location = 'NYC'\n\n if real_time and not isinstance(end_time, dt.date):\n raise ValueError(\"Start and end need to be either both date or both time\")\n\n if real_time:\n request_dict = {\n 'type': 'MxAPI Measure Request',\n 'modelType': curve_type,\n 'modelAsset': curve_asset,\n 'point': curve_point,\n 'tags': curve_tags,\n 'startTime': cls._to_zulu(start_time),\n 'endTime': cls._to_zulu(end_time),\n 'measureName': measure,\n }\n else:\n request_dict = {\n 'type': 'MxAPI Measure Request EOD',\n 'modelType': curve_type,\n 'modelAsset': curve_asset,\n 'point': curve_point,\n 'tags': curve_tags,\n 'startDate': start_time.isoformat(),\n 'endDate': end_time.isoformat(),\n 'close': close_location,\n 'measureName': measure,\n }\n\n url = '/mxapi/mq/measure' if real_time else '/mxapi/mq/measure/eod'\n\n start = time.perf_counter()\n try:\n body = cls._post_with_cache_check(url, payload=request_dict)\n except Exception as e:\n log_warning(request_id, _logger, f'Mxapi measure query {request_dict} failed due to {e}')\n raise e\n log_debug(\n request_id,\n _logger,\n 'MxAPI measure query (%s) with payload (%s) ran in %.3f ms',\n body.get('requestId'),\n request_dict,\n (time.perf_counter() - start) * 1000,\n )\n\n if real_time:\n values = body['measures']\n valuation_times = body['measureTimes']\n timestamps = [parser.parse(s) for s in valuation_times]\n column_name = body['measureName']\n\n d = {column_name: values, 'timeStamp': timestamps}\n df = MarketDataResponseFrame(pd.DataFrame(data=d))\n df = df.set_index('timeStamp')\n return df\n else:\n values = body['measures']\n valuation_date_strings = body['measureDates']\n valuation_dates = [dt.date.fromisoformat(s) for s in valuation_date_strings]\n column_name = body['measureName']\n\n d = {column_name: values, 'date': valuation_dates}\n df = MarketDataResponseFrame(pd.DataFrame(data=d))\n df = df.set_index('date')\n return df\n\n @classmethod\n def get_mxapi_vector_measure(\n cls,\n curve_type=None,\n curve_asset=None,\n curve_point=None,\n curve_tags=None,\n vector_measure=None,\n as_of_time=None,\n request_id=None,\n close_location=None,\n ) -> pd.DataFrame:\n if not vector_measure:\n raise ValueError(\"Vector measure must be specified.\")\n\n if not as_of_time:\n raise ValueError(\"As-of date or time must be specified.\")\n\n real_time = isinstance(as_of_time, dt.datetime)\n\n if not real_time and not isinstance(as_of_time, dt.date):\n raise ValueError(\"As-of date or time must be specified.\")\n\n if not real_time and not close_location:\n close_location = 'NYC'\n\n if real_time:\n request_dict = {\n 'type': 'MxAPI Curve Request',\n 'modelType': curve_type,\n 'modelAsset': curve_asset,\n 'point': curve_point,\n 'tags': curve_tags,\n 'asOfTime': cls._to_zulu(as_of_time),\n 'curveName': vector_measure,\n }\n else:\n request_dict = {\n 'type': 'MxAPI Curve Request EOD',\n 'modelType': curve_type,\n 'modelAsset': curve_asset,\n 'point': curve_point,\n 'tags': curve_tags,\n 'asOfDate': as_of_time.isoformat(),\n 'close': close_location,\n 'curveName': vector_measure,\n }\n\n url = '/mxapi/mq/curve' if real_time else '/mxapi/mq/curve/eod'\n\n start = time.perf_counter()\n try:\n body = cls._post_with_cache_check(url, payload=request_dict)\n except Exception as e:\n log_warning(request_id, _logger, f'Mxapi curve query {request_dict} failed due to {e}')\n raise e\n log_debug(\n request_id,\n _logger,\n 'MxAPI curve query (%s) with payload (%s) ran in %.3f ms',\n body.get('requestId'),\n request_dict,\n (time.perf_counter() - start) * 1000,\n )\n\n values = body['curve']\n value_col_name = body['curveName']\n knots = body['knots']\n column_name = body['knotType']\n\n if len(values) == 0 and len(body['errMsg']) > 0:\n raise RuntimeError(body['errMsg'])\n\n d = {value_col_name: values, column_name: knots}\n df = MarketDataResponseFrame(pd.DataFrame(data=d))\n df = df.set_index(column_name)\n return df\n\n @classmethod\n def get_mxapi_backtest_data(\n cls,\n builder,\n start_time=None,\n end_time=None,\n num_samples=120,\n csa=None,\n request_id=None,\n close_location=None,\n real_time=None,\n ) -> pd.DataFrame:\n real_time = real_time or isinstance(start_time, dt.datetime)\n\n if not start_time:\n if real_time:\n start_time = DataContext.current.start_time\n else:\n start_time = DataContext.current.start_date\n\n if not end_time:\n if real_time:\n end_time = DataContext.current.end_time\n else:\n end_time = DataContext.current.end_date\n\n if not csa:\n csa = 'Default'\n\n if not real_time and not close_location:\n close_location = 'NYC'\n\n if real_time and not isinstance(end_time, dt.date):\n raise ValueError(\"Start and end need to be either both date or both time\")\n\n leg = builder.resolve(in_place=False)\n leg_dict_string = json.dumps(leg, cls=JSONEncoder)\n leg_dict = json.loads(leg_dict_string)\n\n if real_time:\n request_dict = {\n 'type': 'MxAPI Backtest Request MQ',\n 'builder': leg_dict,\n 'startTime': cls._to_zulu(start_time),\n 'endTime': cls._to_zulu(end_time),\n 'sampleSize': num_samples,\n 'csa': csa,\n }\n else:\n request_dict = {\n 'type': 'MxAPI Backtest Request MQEOD',\n 'builder': leg_dict,\n 'startDate': start_time.isoformat(),\n 'endDate': end_time.isoformat(),\n 'sampleSize': num_samples,\n 'csa': csa,\n 'close': close_location,\n }\n\n url = '/mxapi/mq/backtest' if real_time else '/mxapi/mq/backtest/eod'\n\n start = time.perf_counter()\n try:\n body = cls._post_with_cache_check(url, payload=request_dict)\n except Exception as e:\n log_warning(request_id, _logger, f'Mxapi backtest query {request_dict} failed due to {e}')\n raise e\n log_debug(\n request_id,\n _logger,\n 'MxAPI backtest query (%s) with payload (%s) ran in %.3f ms',\n body.get('requestId'),\n request_dict,\n (time.perf_counter() - start) * 1000,\n )\n\n if real_time:\n values = body['valuations']\n valuation_times = body['valuationTimes']\n timestamps = [parser.parse(s) for s in valuation_times]\n column_name = body['valuationName']\n\n d = {column_name: values, 'timeStamp': timestamps}\n df = MarketDataResponseFrame(pd.DataFrame(data=d))\n df = df.set_index('timeStamp')\n return df\n else:\n values = body['valuations']\n valuation_date_strings = body['valuationDates']\n valuation_dates = [dt.date.fromisoformat(s) for s in valuation_date_strings]\n column_name = body['valuationName']\n\n d = {column_name: values, 'date': valuation_dates}\n df = MarketDataResponseFrame(pd.DataFrame(data=d))\n df = df.set_index('date')\n return df\n\n @staticmethod\n def _get_market_data_filters(\n asset_ids: List[str],\n query_type: Union[QueryType, str],\n where: Union[FieldFilterMap, Dict] = None,\n source: Union[str] = None,\n real_time: bool = False,\n measure='Curve',\n vendor: str = '',\n ):\n inner = {\n 'entityIds': asset_ids,\n 'queryType': query_type.value if isinstance(query_type, QueryType) else query_type,\n 'where': where or {},\n 'source': source or 'any',\n 'frequency': 'Real Time' if real_time else 'End Of Day',\n 'measures': [measure],\n }\n if vendor != '':\n inner['vendor'] = vendor\n return inner\n\n @staticmethod\n def build_interval_chunked_market_data_queries(\n asset_ids: List[str],\n query_type: Union[QueryType, str],\n where: Union[FieldFilterMap, Dict] = None,\n source: Union[str] = None,\n real_time: bool = False,\n measure='Curve',\n vendor: str = '',\n ) -> List[dict]:\n parallel_interval = 365 # chunk over a year\n\n def chunk_time(start, end) -> tuple:\n # chunk the time interval into 1 year chunks\n s = start\n while s < end:\n e = min(s + dt.timedelta(days=parallel_interval), end)\n yield s, e\n s = e\n\n queries = []\n if real_time:\n start, end = DataContext.current.start_time, DataContext.current.end_time\n start_key, end_key = 'startTime', 'endTime'\n else:\n start, end = DataContext.current.start_date, DataContext.current.end_date\n start_key, end_key = 'startDate', 'endDate'\n\n for s, e in chunk_time(start, end):\n inner = copy(\n GsDataApi._get_market_data_filters(asset_ids, query_type, where, source, real_time, measure, vendor)\n )\n inner[start_key], inner[end_key] = s, e\n queries.append({'queries': [inner]})\n\n log_debug(\"\", _logger, f\"Created {len(queries)} market data queries\")\n\n return queries\n\n @staticmethod\n def build_market_data_query(\n asset_ids: List[str],\n query_type: Union[QueryType, str],\n where: Union[FieldFilterMap, Dict] = None,\n source: Union[str] = None,\n real_time: bool = False,\n measure='Curve',\n parallelize_queries: bool = False,\n vendor: str = '',\n ) -> Union[dict, List[dict]]:\n if parallelize_queries:\n return GsDataApi.build_interval_chunked_market_data_queries(\n asset_ids, query_type, where, source, real_time, measure, vendor\n )\n\n inner = GsDataApi._get_market_data_filters(asset_ids, query_type, where, source, real_time, measure, vendor)\n if DataContext.current.interval is not None:\n inner['interval'] = DataContext.current.interval\n if real_time:\n inner['startTime'] = DataContext.current.start_time\n inner['endTime'] = DataContext.current.end_time\n else:\n inner['startDate'] = DataContext.current.start_date\n inner['endDate'] = DataContext.current.end_date\n return {'queries': [inner]}\n\n @classmethod\n def get_data_providers(cls, entity_id: str, availability: Optional[Dict] = None) -> Dict:\n \"\"\"Return daily and real-time data providers\n\n :param entity_id: identifier of entity i.e. asset, country, subdivision\n :param availability: Optional Measures Availability response for the entity\n :return: dictionary of available data providers\n\n ** Usage **\n\n Return a dictionary containing a set of dataset providers for each available data field.\n For each field will return a dict of daily and real-time dataset providers where available.\n \"\"\"\n response = (\n availability if availability else cls.get_session().sync.get(f'/data/measures/{entity_id}/availability')\n )\n if 'errorMessages' in response:\n raise MqValueError(\n f\"Data availability request {response['requestId']} failed: {response.get('errorMessages', '')}\"\n )\n\n if 'data' not in response:\n return {}\n\n providers = {}\n all_data_mappings = sorted(response['data'], key=lambda x: x['rank'], reverse=True)\n\n for source in all_data_mappings:\n freq = source.get('frequency', 'End Of Day')\n dataset_field = source.get('datasetField', '')\n rank = source.get('rank')\n\n providers.setdefault(dataset_field, {})\n\n if rank:\n if freq == 'End Of Day':\n providers[dataset_field][DataFrequency.DAILY] = source['datasetId']\n elif freq == 'Real Time':\n providers[dataset_field][DataFrequency.REAL_TIME] = source['datasetId']\n\n return providers\n\n @classmethod\n def get_market_data(cls, query, request_id=None, ignore_errors: bool = False) -> pd.DataFrame:\n def validate(body):\n for e in body['responses']:\n container = e['queryResponse'][0]\n if 'errorMessages' in container:\n msg = f'measure service request {body[\"requestId\"]} failed: {container[\"errorMessages\"]}'\n raise MqValueError(msg)\n return body\n\n start = time.perf_counter()\n try:\n body = cls._post_with_cache_check(url='/data/measures', validator=validate, payload=query)\n except Exception as e:\n log_warning(request_id, _logger, f'Market data query {query} failed due to {e}')\n raise e\n log_debug(\n request_id,\n _logger,\n 'market data query (%s) with payload (%s) ran in %.3f ms',\n body.get('requestId'),\n query,\n (time.perf_counter() - start) * 1000,\n )\n\n ids = []\n parts = []\n for e in body['responses']:\n container = e['queryResponse'][0]\n ids.extend(container.get('dataSetIds', ()))\n if 'errorMessages' in container:\n msg = f'measure service request {body[\"requestId\"]} failed: {container[\"errorMessages\"]}'\n if ignore_errors:\n log_warning(request_id, _logger, msg)\n else:\n raise MqValueError(msg)\n if 'response' in container:\n df = MarketDataResponseFrame(container['response']['data'])\n df = df.set_index('date' if 'date' in df.columns else 'time')\n df.index = pd.to_datetime(df.index)\n parts.append(df)\n\n log_debug(request_id, _logger, f'fetched data from {ids}')\n df = pd.concat(parts) if len(parts) > 0 else MarketDataResponseFrame()\n df.dataset_ids = tuple(ids)\n return df\n\n @classmethod\n def __normalise_coordinate_data(\n cls, data: Iterable[Union[MDAPIDataQueryResponse, Dict]], fields: Optional[Tuple[MDAPIQueryField, ...]] = None\n ) -> Iterable[Iterable[Dict]]:\n ret = []\n for response in data:\n coord_data = []\n rows = (\n (r.as_dict() for r in response.data)\n if isinstance(response, MDAPIDataQueryResponse)\n else response.get('data', ())\n )\n\n for pt in rows:\n if not pt:\n continue\n\n if not fields and 'value' not in pt:\n value_field = pt['mktQuotingStyle']\n if value_field not in pt:\n continue\n pt['value'] = pt.pop(value_field)\n\n coord_data.append(pt)\n ret.append(coord_data)\n\n return ret\n\n @classmethod\n def __df_from_coordinate_data(\n cls, data: Iterable[Dict], *, use_datetime_index: Optional[bool] = True\n ) -> pd.DataFrame:\n df = cls._sort_coordinate_data(pd.DataFrame.from_records(data))\n index_field = next((f for f in ('time', 'date') if f in df.columns), None)\n if index_field and use_datetime_index:\n df = df.set_index(pd.DatetimeIndex(df.loc[:, index_field].values))\n\n return df\n\n @classmethod\n def _sort_coordinate_data(\n cls,\n df: pd.DataFrame,\n by: Tuple[str, ...] = (\n 'date',\n 'time',\n 'mktType',\n 'mktAsset',\n 'mktClass',\n 'mktPoint',\n 'mktQuotingStyle',\n 'value',\n ),\n ) -> pd.DataFrame:\n columns = df.columns\n field_order = [f for f in by if f in columns]\n field_order.extend(f for f in columns if f not in field_order)\n return df[field_order]\n\n @classmethod\n def _coordinate_from_str(cls, coordinate_str: str) -> MarketDataCoordinate:\n tmp = coordinate_str.rsplit(\".\", 1)\n dimensions = tmp[0].split(\"_\")\n if len(dimensions) < 2:\n raise MqValueError('invalid coordinate ' + coordinate_str)\n\n kwargs = {\n 'mkt_type': dimensions[0],\n 'mkt_asset': dimensions[1] or None,\n 'mkt_quoting_style': tmp[-1] if len(tmp) > 1 else None,\n }\n\n if len(dimensions) > 2:\n kwargs['mkt_class'] = dimensions[2] or None\n\n if len(dimensions) > 3:\n kwargs['mkt_point'] = tuple(dimensions[3:]) or None\n\n return MarketDataCoordinate(**kwargs)\n\n @classmethod\n def coordinates_last(\n cls,\n coordinates: Union[Iterable[str], Iterable[MarketDataCoordinate]],\n as_of: Union[dt.datetime, dt.date] = None,\n vendor: MarketDataVendor = MarketDataVendor.Goldman_Sachs,\n as_dataframe: bool = False,\n pricing_location: Optional[PricingLocation] = None,\n timeout: int = None,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"\n Get last value of coordinates data\n\n :param coordinates: market data coordinate(s)\n :param as_of: snapshot date or time\n :param vendor: data vendor\n :param as_dataframe: whether to return the result as Dataframe\n :param pricing_location: the location where close data has been recorded (not used for real-time query)\n :param timeout: data query timeout; if timeout is not set then the default timeout is used\n :return: Dataframe or dictionary of the returned data\n\n **Examples**\n\n >>> coordinate = (\"FX Fwd_USD/EUR_Fwd Pt_2y\",)\n >>> data = GsDataApi.coordinates_last(coordinate, dt.datetime(2019, 11, 19))\n \"\"\"\n market_data_coordinates = tuple(\n cls._coordinate_from_str(coord) if isinstance(coord, str) else coord for coord in coordinates\n )\n query = cls.build_query(\n end=as_of, market_data_coordinates=market_data_coordinates, vendor=vendor, pricing_location=pricing_location\n )\n\n kwargs = {}\n if timeout is not None:\n kwargs['timeout'] = timeout\n\n data = cls.last_data(query, **kwargs)\n\n if not as_dataframe:\n ret = {coordinate: None for coordinate in market_data_coordinates}\n for idx, row in enumerate(cls.__normalise_coordinate_data(data)):\n try:\n ret[market_data_coordinates[idx]] = row[0]['value']\n except IndexError:\n ret[market_data_coordinates[idx]] = None\n return ret\n\n ret = []\n datetime_field = 'time' if isinstance(as_of, dt.datetime) else 'date'\n for idx, row in enumerate(cls.__normalise_coordinate_data(data)):\n coordinate_as_dict = market_data_coordinates[idx].as_dict(as_camel_case=True)\n try:\n ret.append(\n dict(\n chain(\n coordinate_as_dict.items(),\n (('value', row[0]['value']), (datetime_field, row[0][datetime_field])),\n )\n )\n )\n except IndexError:\n ret.append(dict(chain(coordinate_as_dict.items(), (('value', None), (datetime_field, None)))))\n return cls.__df_from_coordinate_data(ret, use_datetime_index=False)\n\n @classmethod\n def coordinates_data(\n cls,\n coordinates: Union[str, MarketDataCoordinate, Iterable[str], Iterable[MarketDataCoordinate]],\n start: Union[dt.datetime, dt.date] = None,\n end: Union[dt.datetime, dt.date] = None,\n vendor: MarketDataVendor = MarketDataVendor.Goldman_Sachs,\n as_multiple_dataframes: bool = False,\n pricing_location: Optional[PricingLocation] = None,\n fields: Optional[Tuple[MDAPIQueryField, ...]] = None,\n **kwargs,\n ) -> Union[pd.DataFrame, Tuple[pd.DataFrame]]:\n \"\"\"\n Get coordinates data\n\n :param coordinates: market data coordinate(s)\n :param start: start date or time\n :param end: end date or time\n :param vendor: data vendor\n :param as_multiple_dataframes: whether to return the result as one or multiple Dataframe(s)\n :param pricing_location: the location where close data has been recorded (not used for real-time query)\n :param fields: value fields to return\n :param kwargs: Extra query arguments\n :return: Dataframe(s) of the returned data\n\n **Examples**\n\n >>> coordinate = (\"FX Fwd_USD/EUR_Fwd Pt_2y\",)\n >>> data = GsDataApi.coordinates_data(coordinate, dt.datetime(2019, 11, 18), dt.datetime(2019, 11, 19))\n \"\"\"\n coordinates_iterable = (coordinates,) if isinstance(coordinates, (MarketDataCoordinate, str)) else coordinates\n query = cls.build_query(\n market_data_coordinates=tuple(\n cls._coordinate_from_str(coord) if isinstance(coord, str) else coord for coord in coordinates_iterable\n ),\n vendor=vendor,\n start=start,\n end=end,\n pricing_location=pricing_location,\n fields=fields,\n **kwargs,\n )\n\n results = cls.__normalise_coordinate_data(cls.query_data(query), fields=fields)\n\n if as_multiple_dataframes:\n return tuple(GsDataApi.__df_from_coordinate_data(r) for r in results)\n else:\n return cls.__df_from_coordinate_data(chain.from_iterable(results))\n\n @classmethod\n def coordinates_data_series(\n cls,\n coordinates: Union[str, MarketDataCoordinate, Iterable[str], Iterable[MarketDataCoordinate]],\n start: Union[dt.datetime, dt.date] = None,\n end: Union[dt.datetime, dt.date] = None,\n vendor: MarketDataVendor = MarketDataVendor.Goldman_Sachs,\n pricing_location: Optional[PricingLocation] = None,\n **kwargs,\n ) -> Union[pd.Series, Tuple[pd.Series]]:\n \"\"\"\n Get coordinates data series\n\n :param coordinates: market data coordinate(s)\n :param start: start date or time\n :param end: end date or time\n :param vendor: data vendor\n :param pricing_location: the location where close data has been recorded (not used for real-time query)\n :param kwargs: Extra query arguments\n :return: Series of the returned data\n\n **Examples**\n\n >>> coordinate = (\"FX Fwd_USD/EUR_Fwd Pt_2y\",)\n >>> data = GsDataApi.coordinates_data_series(coordinate, dt.datetime(2019, 11, 18), dt.datetime(2019, 11, 19))\n \"\"\"\n dfs = cls.coordinates_data(\n coordinates,\n start=start,\n end=end,\n pricing_location=pricing_location,\n vendor=vendor,\n as_multiple_dataframes=True,\n **kwargs,\n )\n\n ret = tuple(\n pd.Series(dtype=float) if df.empty else pd.Series(index=df.index, data=df.value.values) for df in dfs\n )\n if isinstance(coordinates, (MarketDataCoordinate, str)):\n return ret[0]\n else:\n return ret\n\n @classmethod\n @cachetools.cached(TTLCache(ttl=3600, maxsize=128))\n def get_types(cls, dataset_id: str):\n results = cls.get_session().sync.get(f'/data/catalog/{dataset_id}')\n fields = results.get(\"fields\")\n if fields:\n field_types = {}\n for key, value in fields.items():\n field_type = value.get('type')\n field_format = value.get('format')\n field_types[key] = field_format or field_type\n return field_types\n raise RuntimeError(f\"Unable to get Dataset schema for {dataset_id}\")\n\n @classmethod\n def get_field_types(cls, field_names: Union[str, List[str]]):\n try:\n fields = cls.get_dataset_fields(names=field_names, limit=len(field_names))\n except Exception:\n return {}\n if fields:\n field_types = {}\n field: DataSetFieldEntity\n for field in fields:\n field_name = field.name\n field_type = field.type_\n field_format = field.parameters.get('format') if field.parameters else None\n field_types[field_name] = field_format or field_type\n return field_types\n return {}\n\n @classmethod\n def construct_dataframe_with_types(\n cls, dataset_id: str, data: Union[Base, List, Tuple], schema_varies=False, standard_fields=False\n ) -> pd.DataFrame:\n \"\"\"\n Constructs a dataframe with correct date types.\n :param dataset_id: id of the dataset\n :param data: data to convert with correct types\n :param schema_varies: if set, method will not assume that all rows have the same columns\n :param standard_fields: if set, will use fields api instead of catalog api to get fieldTypes\n :return: dataframe with correct types\n \"\"\"\n if len(data):\n # Use first row to infer fields from data\n sample = data if schema_varies else [data[0]]\n incoming_data_data_types = pd.DataFrame(sample).dtypes.to_dict()\n dataset_types = (\n cls.get_types(dataset_id)\n if not standard_fields\n else cls.get_field_types(field_names=list(incoming_data_data_types.keys()))\n )\n\n # fallback approach in case fields api doesn't return results\n if dataset_types == {} and standard_fields:\n dataset_types = cls.get_types(dataset_id)\n\n df = pd.DataFrame(data, columns={**dataset_types, **incoming_data_data_types})\n\n for field_name, type_name in dataset_types.items():\n if (\n df.get(field_name) is not None\n and type_name in ('date', 'date-time')\n and len(df.get(field_name).value_counts()) > 0\n ):\n if int(pd.__version__.split('.')[0]) > 1:\n df[field_name] = pd.to_datetime(df[field_name], format='ISO8601')\n else:\n df[field_name] = pd.to_datetime(df[field_name])\n\n field_names = dataset_types.keys()\n\n if 'date' in field_names:\n df = df.set_index('date')\n elif 'time' in field_names:\n df = df.set_index('time')\n\n return df\n else:\n return pd.DataFrame({})\n\n @classmethod\n def get_dataset_fields(\n cls,\n ids: Union[str, List[str]] = None,\n names: Union[str, List[str]] = None,\n limit: int = 10,\n ) -> Union[Tuple[DataSetFieldEntity, ...], Tuple[dict, ...]]:\n \"\"\"\n Get many dataset fields\n\n :param ids: ID(s) of the field(s)\n :param names: Name(s) of the field(s)\n :param limit: Limit on the number of results returned. Default: 10\n :return: Tuple of DataSetFieldEntity\n\n **Examples**\n\n >>> from gs_quant.api.gs.data import GsDataApi\n >>> fields = GsDataApi.get_dataset_fields(names = ['adjustedClosePrice', 'adjustedOpenPrice'])\n \"\"\"\n\n where = dict(filter(lambda item: item[1] is not None, dict(id=ids, name=names).items()))\n response = cls.get_session().sync.post(\n '/data/fields/query', payload={'where': where, 'limit': limit}, cls=DataSetFieldEntity\n )\n return response['results']\n\n @classmethod\n def create_dataset_fields(\n cls, fields: List[DataSetFieldEntity]\n ) -> Union[Tuple[DataSetFieldEntity, ...], Tuple[dict, ...]]:\n \"\"\"\n Create many dataset fields\n\n :param fields: Fields to be created\n :return: Tuple of DataSetFieldEntity\n\n **Examples**\n\n >>> from gs_quant.api.gs.data import GsDataApi\n >>> from gs_quant.target.data import DataSetFieldEntity\n >>> fields = [\n >>> DataSetFieldEntity(name='price', type_='number', description='Price of the instrument.'),\n >>> DataSetFieldEntity(name='strikeReference', type_='string', description='Reference for strike level.',\n >>> parameters={'enum': ['delta', 'spot', 'forward', 'normalized']})\n >>> ]\n >>> GsDataApi.create_dataset_fields(fields)\n \"\"\"\n params = {'fields': fields}\n response = cls.get_session().sync.post('/data/fields/bulk', payload=params, cls=DataSetFieldEntity)\n return response['results']\n\n @classmethod\n def update_dataset_fields(\n cls, fields: List[DataSetFieldEntity]\n ) -> Union[Tuple[DataSetFieldEntity, ...], Tuple[dict, ...]]:\n \"\"\"\n Update many dataset fields\n\n :param fields: Fields to be created\n :return: Tuple of DataSetFieldEntity\n\n **Examples**\n\n >>> from gs_quant.api.gs.data import GsDataApi\n >>> from gs_quant.target.data import DataSetFieldEntity\n >>> fields = [\n >>> DataSetFieldEntity(id='FIMFMQ0P19AZ2XK9', name='price', type_='number',\n >>> description='Price of the instrument.'),\n >>> DataSetFieldEntity(id='FI7EFDC3SQQBMDX8', name='strikeReference', type_='string',\n >>> description='Reference for strike level.',\n >>> parameters={'enum': ['delta', 'spot', 'forward', 'normalized']})\n >>> ]\n >>> GsDataApi.update_dataset_fields(fields)\n \"\"\"\n params = {'fields': fields}\n response = cls.get_session().sync.put('/data/fields/bulk', payload=params, cls=DataSetFieldEntity)\n return response['results']\n\n\nclass MarketDataResponseFrame(pd.DataFrame):\n _internal_names = pd.DataFrame._internal_names + ['dataset_ids']\n _internal_names_set = set(_internal_names)\n\n @property\n def _constructor(self):\n return MarketDataResponseFrame\n\n def __finalize__(self, other, method=None, **kwargs):\n # Call the parent class's __finalize__ method\n super().__finalize__(other, method, **kwargs)\n # Copy custom attributes from the other DataFrame\n if isinstance(other, MarketDataResponseFrame) and hasattr(other, 'dataset_ids'):\n self.dataset_ids = getattr(other, 'dataset_ids', None)\n return self\n" }, { "path": "gs_quant/api/gs/data_screen.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom typing import Tuple, Dict\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.data_screen import AnalyticsScreen, FilterRequest, DataRow\n\n_logger = logging.getLogger(__name__)\n\n\nclass GsDataScreenApi:\n @classmethod\n def get_screens(cls) -> Tuple[AnalyticsScreen, ...]:\n \"\"\"\n Retrieves screen information about all screens accessible to the current user.\n\n :return: AnalyticsScreen tuple, a tuple containing each screen available to the user.\n \"\"\"\n return GsSession.current.sync.get('/data/screens', cls=AnalyticsScreen)['results']\n\n @classmethod\n def get_screen(cls, screen_id: str) -> AnalyticsScreen:\n \"\"\"\n Retrieves information about the screen specified in parameter screen_id.\n\n :param screen_id: str, the id of the screen whose information is being retrieved.\n\n :return: AnalyticsScreen, an object containing information about the screen associated with screen_id.\n \"\"\"\n return GsSession.current.sync.get('/data/screens/{id}'.format(id=screen_id), cls=AnalyticsScreen)\n\n @classmethod\n def get_column_info(cls, screen_id: str) -> Dict[str, Dict]:\n \"\"\"\n Retrieves information about each column in the screen with id corresponding to screen_id. This column\n information can be used to create filters on the screen.\n\n :param screen_id: str, the id of the screen whose column information is being retrieved.\n\n :return: dict, a dictionary where each key identifies a column and each value identifies properties of that\n column.\n \"\"\"\n return GsSession.current.sync.get('/data/screens/{id}/filters'.format(id=screen_id))['aggregations']\n\n @classmethod\n def delete_screen(cls, screen_id: str) -> None:\n \"\"\"\n Deletes the screen identified by screen_id. A deleted screen's data and information cannot be retrieved again.\n\n :param screen_id: str, the id of the screen that is being deleted\n\n :return: None\n \"\"\"\n return GsSession.current.sync.delete('/data/screens/{id}'.format(id=screen_id))\n\n @classmethod\n def create_screen(cls, screen: AnalyticsScreen) -> AnalyticsScreen:\n \"\"\"\n Creates a new screen with a new id from an existing screen object. The new screen is a copy of the screen\n object passed into this function. To make changes to the new screen upon creation, change the attributes of\n the AnalyticsScreen object before passing it into this function.\n\n :param screen: AnalyticsScreen, the screen object that is copied to make the new screen.\n\n :return: AnalyticsScreen, the new screen object containing a new id\n \"\"\"\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.post(\n '/data/screens', screen, request_headers=request_headers, cls=AnalyticsScreen\n )\n\n @classmethod\n def filter_screen(cls, screen_id: str, filter_request: FilterRequest) -> Tuple[DataRow, ...]:\n \"\"\"\n Returns filtered data from the screen associated with this screen_id. The filters applied to the data are\n contained in the filters parameter. Any filters previously applied to the relevant screen will be temporarily\n ignored in favor of the filters passed into this function. To see the data from this screen with the\n screen's current filters, simply pass in the screen's id and filter_parameters from the screen's\n AnalyticsScreen object.\n\n :param screen_id: str, the id of the screen whose data is being retrieved.\n :param filter_request: FilterRequest, an object populated with information about how the retrieved data should\n be filtered.\n\n :return: Tuple of DataRow objects, each DataRow in the tuple is a row of filtered data from this screen.\n \"\"\"\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.post(\n '/data/screens/{id}/filter'.format(id=screen_id),\n filter_request,\n request_headers=request_headers,\n cls=DataRow,\n )['results']\n\n @classmethod\n def update_screen(cls, screen_id: str, screen: AnalyticsScreen) -> AnalyticsScreen:\n \"\"\"\n Permanently overwrites the screen at screen_id with the screen object provided in the screen parameter.\n This includes overwriting any filters applied to the screen at screen_id.\n\n Throws an error if the screen_id provided does not match the ID of the screen object provided.\n\n :param screen_id: str, the id of the screen being updated. Must be the same id as is contained in the screen\n parameter.\n :param screen: AnalyticsScreen, the object representing the screen to be updated. This object should contain\n any desired changes to the screen\n\n :return: AnalyticsScreen, an object containing information about the updated screen.\n\n \"\"\"\n assert screen_id == screen.id_\n\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.put(\n '/data/screens/{id}'.format(id=screen_id), screen, request_headers=request_headers, cls=AnalyticsScreen\n )\n" }, { "path": "gs_quant/api/gs/datagrid.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport json\nimport urllib.parse\nfrom typing import List\n\nfrom pydash import get\n\nfrom gs_quant.analytics.datagrid import DataGrid\nfrom gs_quant.analytics.datagrid.datagrid import API, DATAGRID_HEADERS\nfrom gs_quant.session import GsSession\n\n\nclass GsDataGridApi:\n \"\"\"GS DataGrids API implementation\"\"\"\n\n @classmethod\n def get_datagrids(cls, limit: int = 10, **kwargs) -> List[DataGrid]:\n raw_datagrids = get(\n GsSession.current.sync.get(\n f'{API}?limit={limit}&orderBy=>lastUpdatedTime&{urllib.parse.urlencode(kwargs)}'\n ),\n 'results',\n [],\n )\n return [DataGrid.from_dict(raw_datagrid) for raw_datagrid in raw_datagrids]\n\n @classmethod\n def get_my_datagrids(cls, limit: int = 10, **kwargs) -> List[DataGrid]:\n user_id = GsSession.current.sync.get('/users/self')['id']\n raw_datagrids = get(\n GsSession.current.sync.get(\n f'{API}?limit={limit}&ownerId={user_id}&orderBy=>lastUpdatedTime&{urllib.parse.urlencode(kwargs)}'\n ),\n 'results',\n [],\n )\n return [DataGrid.from_dict(raw_datagrid) for raw_datagrid in raw_datagrids]\n\n @classmethod\n def get_datagrid(cls, datagrid_id: str) -> DataGrid:\n raw_datagrid = GsSession.current.sync.get(f'{API}/{datagrid_id}')\n return DataGrid.from_dict(raw_datagrid)\n\n @classmethod\n def create_datagrid(cls, datagrid: DataGrid) -> DataGrid:\n datagrid_json = json.dumps(datagrid.as_dict())\n response = GsSession.current.sync.post(f'{API}', datagrid_json, request_headers=DATAGRID_HEADERS)\n return DataGrid.from_dict(response)\n\n @classmethod\n def update_datagrid(cls, datagrid: DataGrid):\n datagrid_json = json.dumps(datagrid.as_dict())\n datagrid = GsSession.current.sync.put(f'{API}/{datagrid.id_}', datagrid_json, request_headers=DATAGRID_HEADERS)\n return DataGrid.from_dict(datagrid)\n\n @classmethod\n def delete_datagrid(cls, datagrid: DataGrid):\n return GsSession.current.sync.delete(f'{API}/{datagrid.id_}')\n" }, { "path": "gs_quant/api/gs/esg.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom enum import Enum\nfrom typing import List, Dict\n\nfrom gs_quant.session import GsSession\n\n_logger = logging.getLogger(__name__)\n\n\nclass ESGCard(Enum):\n \"\"\"\n ESG Cards\n \"\"\"\n\n SUMMARY = 'summary'\n QUINTILES = 'quintiles'\n WEIGHTS_BY_SECTOR = 'weightsBySector'\n MEASURES_BY_SECTOR = 'measuresBySector'\n WEIGHTS_BY_REGION = 'weightsByRegion'\n MEASURES_BY_REGION = 'measuresByRegion'\n TOP_TEN_RANKED = 'topTenRanked'\n BOTTOM_TEN_RANKED = 'bottomTenRanked'\n NO_ESG_DATA = 'noEsgData'\n NO_PRICING_DATA = 'noPricingData'\n\n def __str__(self):\n return self.value\n\n\nclass ESGMeasure(Enum):\n \"\"\"\n ESG Measures\n \"\"\"\n\n G_PERCENTILE = 'gPercentile'\n G_REGIONAL_PERCENTILE = 'gRegionalPercentile'\n ES_PERCENTILE = 'esPercentile'\n ES_DISCLOSURE_PERCENTAGE = 'esDisclosurePercentage'\n ES_MOMENTUM_PERCENTILE = 'esMomentumPercentile'\n\n def __str__(self):\n return self.value\n\n\nclass GsEsgApi:\n \"\"\"GS ESG API client implementation\"\"\"\n\n @classmethod\n def get_esg(\n cls,\n entity_id: str,\n benchmark_id: str = None,\n pricing_date: dt.date = None,\n measures: List[ESGMeasure] = [],\n cards: List[ESGCard] = [],\n ) -> Dict:\n url = f'/esg/{entity_id}?'\n if pricing_date:\n url += f'&date={pricing_date.strftime(\"%Y-%m-%d\")}'\n if benchmark_id:\n url += f'&benchmark={benchmark_id}'\n for measure in measures:\n url += f'&measure={measure}'\n for card in cards:\n url += f'&card={card}'\n\n return GsSession.current.sync.get(url)\n" }, { "path": "gs_quant/api/gs/federated_secmaster.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom enum import Enum\nfrom gs_quant.target.secmaster import SecMasterAssetType\nfrom gs_quant.common import AssetType, AssetClass\nfrom typing import Union, Iterable, Dict, Optional\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.session import GsSession\nimport json\nimport datetime as dt\n\nSECURITIES_FEDERATED = '/markets/securities/federated'\n\n\nclass FederatedIdentifiers(Enum):\n IDENTIFIER = 'identifier'\n ID = 'id'\n ASSET_ID = 'assetId'\n GSID = 'gsid'\n TICKER = 'ticker'\n BBID = 'bbid'\n BCID = 'bcid'\n RIC = 'ric'\n RCIC = 'rcic'\n CUSIP = 'cusip'\n CINS = 'cins'\n SEDOL = 'sedol'\n ISIN = 'isin'\n PRIMEID = 'primeId'\n\n\nclass GsSecurityMasterFederatedApi:\n @classmethod\n def get_a_security(cls, id: str, effective_date: dt.date = None) -> Optional[dict]:\n \"\"\"\n Get security or asset for given security id or asset id.\n @param id: secmaster id(e.g. 'GSPD111E123') or asset id(e.g. 'MANYS1FCCWWV45P7')\n @param effective_date: As of date for query\n @return: dict\n \"\"\"\n\n if not id.startswith(\"GS\") and not id.startswith(\"MA\"):\n raise ValueError(f\"Invalid id: {id}. Security id starts with 'GS' and Asset id starts with 'MA'\")\n\n params = {}\n if effective_date is not None:\n params[\"effectiveDate\"] = effective_date\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n\n return GsSession.current.sync.get(f'{SECURITIES_FEDERATED}/{id}', payload=payload)\n\n @classmethod\n def get_security_identifiers(cls, id: str) -> Optional[dict]:\n \"\"\"\n Get identifiers history for given security id or asset id.\n @param id: secmaster id(e.g. 'GSPD111E123') or asset id(e.g. 'MANYS1FCCWWV45P7')\n @return: dict\n \"\"\"\n\n if not id.startswith(\"GS\") and not id.startswith(\"MA\"):\n raise ValueError(f\"Invalid id: {id}. Security id starts with 'GS' and Asset id starts with 'MA'\")\n\n return GsSession.current.sync.get(f'{SECURITIES_FEDERATED}/{id}/identifiers')\n\n @classmethod\n def get_many_securities(\n cls,\n type_: Union[SecMasterAssetType, AssetType] = None,\n effective_date: dt.date = None,\n limit: int = 50,\n is_primary=None,\n offset_key: str = None,\n **query_params: Dict[FederatedIdentifiers, Union[str, Iterable[str]]],\n ) -> Optional[dict]:\n \"\"\"\n Get reference data for a single page. Use returned offsetKey to fetch next page.\n @param is_primary:\n @param type_: security or asset type\n @param effective_date: As of date for query\n @param limit: integer of individual page\n @param offset_key: string, an offset indicating where the page ends\n @return: dict\n \"\"\"\n\n return cls.__query_securities(\n type_=type_,\n effective_date=effective_date,\n limit=limit,\n flatten=False,\n is_primary=is_primary,\n offset_key=offset_key,\n **query_params,\n )\n\n @classmethod\n def get_securities_data(\n cls,\n type_: Union[SecMasterAssetType, AssetType] = None,\n effective_date: dt.date = None,\n limit: int = 50,\n is_primary=None,\n offset_key: str = None,\n **query_params: Dict[FederatedIdentifiers, Union[str, Iterable[str]]],\n ) -> Optional[dict]:\n \"\"\"\n Get flattened reference data for a single page. Use returned offsetKey to fetch next page.\n @param is_primary:\n @param type_: security or asset type\n @param effective_date: As of date for query\n @param limit: integer of individual page\n @param offset_key: string, an offset indicating where the page ends\n @return: dict\n \"\"\"\n\n return cls.__query_securities(\n type_=type_,\n effective_date=effective_date,\n limit=limit,\n flatten=True,\n is_primary=is_primary,\n offset_key=offset_key,\n **query_params,\n )\n\n @classmethod\n def search_many_securities(\n cls,\n q: str = None,\n limit: int = 10,\n offset_key: str = None,\n asset_class: AssetClass = None,\n type_: Union[SecMasterAssetType, AssetType] = None,\n is_primary: bool = None,\n ) -> Optional[dict]:\n \"\"\"\n Search reference data by a query string. It does a full text search among names, identifiers, company.\n @param q: query string\n @param limit: number of returned matches\n @param offset_key: string, an offset indicating where the page ends\n @param asset_class: filter restricting the class of results\n @param type_: filter restricting the type of results\n @param is_primary: filter restricting the matches to primary listings\n @return: dict\n \"\"\"\n\n return cls.__search_securities(\n q=q,\n limit=limit,\n offset_key=offset_key,\n flatten=False,\n asset_class=asset_class,\n type_=type_,\n is_primary=is_primary,\n )\n\n @classmethod\n def search_securities_data(\n cls,\n q: str = None,\n limit: int = 10,\n offset_key: str = None,\n asset_class: AssetClass = None,\n type_: Union[SecMasterAssetType, AssetType] = None,\n is_primary: bool = None,\n ) -> Optional[dict]:\n \"\"\"\n Search flattened reference data by a query string. It does a full text search among names, identifiers, company.\n @param q: query string\n @param limit: number of returned matches\n @param offset_key: string, an offset indicating where the page ends\n @param asset_class: filter restricting the class of results\n @param type_: filter restricting the type of results\n @param is_primary: filter restricting the matches to primary listings\n @return: dict\n \"\"\"\n\n return cls.__search_securities(\n q=q,\n limit=limit,\n offset_key=offset_key,\n flatten=True,\n asset_class=asset_class,\n type_=type_,\n is_primary=is_primary,\n )\n\n @classmethod\n def __query_securities(\n cls,\n type_: Union[SecMasterAssetType, AssetType] = None,\n effective_date: dt.date = None,\n limit: int = 50,\n flatten=False,\n is_primary=None,\n offset_key: str = None,\n **query_params: Dict[FederatedIdentifiers, Union[str, Iterable[str]]],\n ) -> Optional[dict]:\n if (query_params is None or len(query_params) == 0) and type_ is None:\n raise ValueError(\"Neither '_type' nor 'query_params' are provided\")\n\n params = {\"limit\": limit}\n cls.__prepare_params(params, effective_date, offset_key, is_primary, type_, None)\n params = {**params, **query_params}\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n if flatten:\n return GsSession.current.sync.get(f'{SECURITIES_FEDERATED}/data', payload=payload)\n return GsSession.current.sync.get(f'{SECURITIES_FEDERATED}', payload=payload)\n\n @classmethod\n def __search_securities(\n cls,\n q: str = None,\n limit: int = 10,\n offset_key: str = None,\n flatten=False,\n asset_class: AssetClass = None,\n type_: Union[SecMasterAssetType, AssetType] = None,\n is_primary: bool = None,\n ) -> Optional[dict]:\n if q is None:\n raise ValueError(\"No search query provided\")\n\n params = {\"q\": q, \"limit\": limit}\n cls.__prepare_params(params, None, offset_key, is_primary, type_, asset_class)\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n if flatten:\n return GsSession.current.sync.get(f'{SECURITIES_FEDERATED}/search/data', payload=payload)\n return GsSession.current.sync.get(f'{SECURITIES_FEDERATED}/search', payload=payload)\n\n @classmethod\n def __prepare_params(cls, params, effective_date, offset_key, is_primary, type_, asset_class):\n if effective_date is not None:\n params[\"effectiveDate\"] = effective_date\n if offset_key is not None:\n params[\"offsetKey\"] = offset_key\n if is_primary is not None:\n params[\"isPrimary\"] = is_primary\n if type_ is not None:\n params[\"type\"] = type_.value\n if asset_class is not None:\n params[\"assetClass\"] = asset_class.value\n" }, { "path": "gs_quant/api/gs/groups.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import List, Dict\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.groups import Group\n\n\nclass GsGroupsApi:\n @classmethod\n def get_groups(\n cls,\n ids: List[str] = None,\n names: List[str] = None,\n oe_ids: List[str] = None,\n owner_ids: List[str] = None,\n tags: List[str] = None,\n user_ids: List[str] = None,\n scroll_id: str = None,\n scroll_time: str = None,\n limit: int = 100,\n offset: int = 0,\n order_by: str = None,\n ) -> List:\n url = f'/groups?limit={limit}&offset={offset}'\n if ids:\n url += f'&id={\"&id=\".join(ids)}'\n if names:\n url += f'&name={\"&name=\".join(names)}'\n if oe_ids:\n url += f'&oeId={\"&oeId=\".join(oe_ids)}'\n if owner_ids:\n url += f'&ownerId={\"&ownerId=\".join(owner_ids)}'\n if tags:\n url += f'&tags={\"&tags=\".join(tags)}'\n if user_ids:\n url += f'&userIds={\"&userIds=\".join(user_ids)}'\n if scroll_id:\n url += f'&scrollId={scroll_id}'\n if scroll_time:\n url += f'&scrollTime={scroll_time}'\n if order_by:\n url += f'&orderBy={order_by}'\n return GsSession.current.sync.get(url, cls=Group)['results']\n\n @classmethod\n def create_group(cls, group: Group) -> Dict:\n return GsSession.current.sync.post('/groups', group, cls=Group)\n\n @classmethod\n def get_group(cls, group_id: str) -> Group:\n return GsSession.current.sync.get(f'/groups/{group_id}', cls=Group)\n\n @classmethod\n def update_group(cls, group_id: str, group: Group) -> Group:\n # PUT request for updating a group can't have the group ID in it\n group_dict = group.to_json()\n if group_dict.get('entitlements'):\n group_dict['entitlements'] = group_dict['entitlements'].to_json()\n group_dict.pop('id')\n return GsSession.current.sync.put(f'/groups/{group_id}', group_dict, cls=Group)\n\n @classmethod\n def delete_group(cls, group_id: str):\n GsSession.current.sync.delete(f'/groups/{group_id}')\n\n @classmethod\n def get_users_in_group(cls, group_id: str) -> List:\n return GsSession.current.sync.get(f'/groups/{group_id}/users').get('users', [])\n\n @classmethod\n def add_users_to_group(cls, group_id: str, user_ids: List[str]):\n GsSession.current.sync.post(f'/groups/{group_id}/users', {'userIds': user_ids})\n\n @classmethod\n def delete_users_from_group(cls, group_id: str, user_ids: List[str]):\n GsSession.current.sync.delete(f'/groups/{group_id}/users', {'userIds': user_ids}, use_body=True)\n" }, { "path": "gs_quant/api/gs/hedges.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom typing import Tuple, List, Dict\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.hedge import Hedge\nfrom gs_quant.target.hedge import PerformanceHedgeParameters, ClassificationConstraint, AssetConstraint, Target\n\n_logger = logging.getLogger(__name__)\nCALCULATION_TIMEOUT = 180\n\n\nclass GsHedgeApi:\n \"\"\"GS Hedge API client implementation\"\"\"\n\n @classmethod\n def get_many_hedges(cls, ids: List[str] = None, names: List[str] = None, limit: int = 100):\n url = f'/hedges?limit={limit}'\n if ids:\n url += f'&id={\"&id=\".join(ids)}'\n if names:\n url += f'&name={\"&name=\".join(names)}'\n return GsSession.current.sync.get(url, cls=Hedge)\n\n @classmethod\n def create_hedge(cls, hedge: Dict) -> Hedge:\n return GsSession.current.sync.post('/hedges', hedge, cls=Hedge)\n\n @classmethod\n def get_hedge(cls, hedge_id: str) -> Hedge:\n return GsSession.current.sync.get(f'/hedges/{hedge_id}', cls=Hedge)\n\n @classmethod\n def get_hedge_data(cls, ids: List[str] = None, names: List[str] = None, limit: int = 100) -> List[Dict]:\n url = f'/hedges/data?limit={limit}'\n if ids:\n url += f'&id={\"&id=\".join(ids)}'\n if names:\n url += f'&name={\"&name=\".join(names)}'\n return GsSession.current.sync.get(url, cls=Hedge)['results']\n\n @classmethod\n def get_hedge_results(cls, hedge_id: str, start_date: dt.date = None, end_date: dt.date = None) -> Dict:\n url = f'/hedges/results?id={hedge_id}'\n if start_date is not None:\n url += f'&startDate={start_date.strftime(\"%Y-%m-%d\")}'\n if end_date is not None:\n url += f'&endDate={end_date.strftime(\"%Y-%m-%d\")}'\n return GsSession.current.sync.get(url)['results'][0]\n\n @classmethod\n def update_hedge(cls, hedge_id: str, hedge: Hedge) -> Hedge:\n return GsSession.current.sync.put(f'/hedges/{hedge_id}', hedge, cls=Hedge)\n\n @classmethod\n def delete_hedge(cls, hedge_id: str):\n return GsSession.current.sync.delete(f'/hedges/{hedge_id}', cls=Hedge)\n\n @classmethod\n def construct_performance_hedge_query(\n cls,\n hedge_target: str,\n universe: Tuple[str, ...],\n notional: float,\n observation_start_date: dt.date,\n observation_end_date: dt.date,\n backtest_start_date: dt.date,\n backtest_end_date: dt.date,\n use_machine_learning: bool = False,\n lasso_weight: float = None,\n ridge_weight: float = None,\n max_return_deviation: float = 5,\n max_adv_percentage: float = 15,\n max_leverage: float = 100,\n max_weight: float = 100,\n min_market_cap: float = None,\n max_market_cap: float = None,\n asset_constraints: Tuple[AssetConstraint, ...] = None,\n benchmarks: Tuple[str, ...] = None,\n classification_constraints: Tuple[ClassificationConstraint, ...] = None,\n exclude_corporate_actions: bool = False,\n exclude_corporate_actions_types: Tuple = None,\n exclude_hard_to_borrow_assets: bool = False,\n exclude_restricted_assets: bool = False,\n exclude_target_assets: bool = True,\n explode_universe: bool = True,\n market_participation_rate: float = 10,\n sampling_period: str = 'Daily',\n ) -> dict:\n \"\"\"\n Function to construct a performance hedge query (for both the New Performance Hedger and Standard Hedger)\n by passing in required/optional arguments similar to the performance hedger on the Marquee UI.\n\n :param hedge_target: str, the target asset we hedge - in Marquee ID (MQID) form\n :param universe: Tuple[str, ...], the universe(s) used to create the hedge - in Marquee ID (MQID) form\n :param notional: float, the total notional dollar amount of the single asset to hedge\n :param observation_start_date: datetime.date, the observation start date of the hedge specified in datetime\n form\n :param observation_end_date: datetime.date, the observation end date of the hedge specified in datetime form\n :param backtest_start_date: datetime.date, the backtest start date of the hedge specified in datetime\n form\n :param backtest_end_date: datetime.date, the backtest end date of the hedge specified in datetime form\n :param use_machine_learning: bool, whether to run the New Performance Hedger or Standard Hedger\n :param lasso_weight: float, the value of Concentration (Lasso) include in the hedge if using the New Performance\n Hedger\n :param ridge_weight: float, the value of Diversity (Ridge) include in the hedge if using the New Performance\n Hedger\n :param max_return_deviation: float, the maximum amount that a hedge portfolio's returns can deviate from the\n target asset returns, as a percentage\n :param max_adv_percentage: float, the maximum trading liquidity of each asset in the hedge portfolio, as a\n percentage\n :param max_leverage: float, the maximum amount of equity used to construct the hedge, as a percentage (i.e.\n if equal to 60, then 60% equity and 40% cash are used)\n :param max_weight: float, the maximum weight that any individual asset can hold in the hedge portfolio\n :param min_market_cap: float, the minimum market cap to filter assets chosen in the hedge portfolio by\n :param max_market_cap: float, the maximum market cap to filter assets chosen in the hedge portfolio by\n :param asset_constraints: Tuple[AssetConstraint, ...], constraints on individual assets to limit how much weight\n they can contribute in the hedge portfolio (i.e.\n [{assetId: \"MA4B66MW5E27UAL9SUX\", min: 0.01, max: 100}])\n :param benchmarks: Tuple[str, ...], benchmarks to compare the hedge against\n :param classification_constraints: Tuple[ClassificationConstraint, ...], constraints on classifications such as\n Sector/Industry/etc. (i.e [{type: \"Sector\", min: 0, max: 38, name: \"Energy\"},\n …])\n :param exclude_corporate_actions: bool, whether to exclude assets in the hedge portfolio that have pending\n corporate actions (such as a merger)\n :param exclude_corporate_actions_types: Tuple[Union[CorporateActionsTypes, str], ...], if excluding assets with\n corporate actions, this includes all of the types of corporate actions\n (i.e. [\"Mergers\", \"Spinoffs\", \"Reorganization\"])\n :param exclude_hard_to_borrow_assets: bool, whether to exclude assets in the hedge portfolio that are harder\n to borrow\n :param exclude_restricted_assets: bool, whether to exclude assets in the hedge portfolio that are considered\n restricted companies\n :param exclude_target_assets: bool, whether to exclude to the target asset in the hedge portfolio (important\n to leave this as true for a valid hedge)\n :param explode_universe: bool, whether to explode the underlying universe into its constituents in the hedge\n :param market_participation_rate: float, the percentage of market to use to incur transaction costs, used by\n Marquee\n :param sampling_period: str, the sampling period to use (i.e. 'Daily' or 'Weekly')\n :return: dict, the hedge query represented by a dictionary of inputs\n \"\"\"\n hedge_dict = {\n 'objective': 'Replicate Performance',\n 'parameters': PerformanceHedgeParameters(\n Target(id=hedge_target),\n universe,\n notional,\n observation_start_date,\n observation_end_date,\n max_leverage,\n backtest_start_date,\n backtest_end_date,\n sampling_period,\n exclude_target_assets,\n exclude_corporate_actions,\n exclude_corporate_actions_types,\n exclude_hard_to_borrow_assets,\n exclude_restricted_assets,\n max_adv_percentage,\n explode_universe,\n max_return_deviation,\n max_weight,\n min_market_cap,\n max_market_cap,\n market_participation_rate,\n asset_constraints,\n classification_constraints,\n benchmarks,\n use_machine_learning,\n lasso_weight,\n ridge_weight,\n ),\n }\n return hedge_dict\n\n @classmethod\n def calculate_hedge(cls, hedge_query: dict) -> dict:\n \"\"\"\n This function is designed to take in a performance hedge query and then return the hedge results (in the\n form of a dictionary) from calling the performance hedger API.\n\n :param hedge_query: dict, hedge data that is sent to the Marquee API as input to the performance hedger\n :return: dict, the results of calling the Marquee performance hedger\n \"\"\"\n return GsSession.current.sync.post('/hedges/calculations', payload=hedge_query, timeout=CALCULATION_TIMEOUT)\n\n @classmethod\n def share_hedge_group(\n cls,\n hedge_group_id: str,\n strategy_request: Dict,\n optimization_response: Dict,\n hedge_name: str = \"Custom Hedge\",\n group_name: str = \"New Hedge Group\",\n view_emails: List[str] = None,\n admin_emails: List[str] = None,\n ) -> Dict:\n \"\"\"\n Share a saved hedge group with other users by updating entitlements using email addresses.\n\n :param hedge_group_id: The ID of the hedge group to share (from save response)\n :param strategy_request: The strategy_as_dict from run() - the request payload sent to optimizer\n :param optimization_response: The optimization_results from run() - the response from optimizer\n :param hedge_name: Name for the individual hedge\n :param group_name: Name for the hedge group\n :param view_emails: List of user email addresses to grant view access\n :param admin_emails: List of user email addresses to grant admin access\n :return: API response with updated entitlements\n\n Example:\n >>> from gs_quant.api.gs.hedges import GsHedgeApi\n >>> response = GsHedgeApi.share_hedge_group(\n ... hedge_group_id=\"HEDGE123\",\n ... strategy_request=strategy_request,\n ... optimization_response=optimization_response,\n ... hedge_name=\"Factor Hedge\",\n ... group_name=\"My Hedge Group\",\n ... view_emails=[\"user1@example.com\", \"user2@example.com\"]\n ... )\n \"\"\"\n from gs_quant.entities.entitlements import User\n\n url = f\"/hedges/groups/{hedge_group_id}\"\n\n try:\n # First, GET the current hedge group to retrieve existing metadata\n hedge_group_data = GsSession.current.sync.get(url)\n\n # Get current user's GUID from existing entitlements\n current_user_guid = hedge_group_data.get('ownerId', '')\n if current_user_guid:\n current_user_guid = f\"guid:{current_user_guid}\"\n\n # Build updated entitlements, preserving owner's access\n entitlements = hedge_group_data.get('entitlements', {})\n\n # Ensure owner always has access\n if current_user_guid:\n # Admin access\n if 'admin' not in entitlements:\n entitlements['admin'] = []\n if current_user_guid not in entitlements['admin']:\n entitlements['admin'].append(current_user_guid)\n\n # Edit access\n if 'edit' not in entitlements:\n entitlements['edit'] = []\n if current_user_guid not in entitlements['edit']:\n entitlements['edit'].append(current_user_guid)\n\n # View access\n if 'view' not in entitlements:\n entitlements['view'] = []\n if current_user_guid not in entitlements['view']:\n entitlements['view'].append(current_user_guid)\n\n # Convert emails to user GUIDs and add view access\n if view_emails:\n view_users = User.get_many(emails=view_emails)\n for user in view_users:\n user_guid = f\"guid:{user.id}\"\n if user_guid not in entitlements['view']:\n entitlements['view'].append(user_guid)\n\n # Convert emails to user GUIDs and add admin access (admin users also need edit and view access)\n if admin_emails:\n admin_users = User.get_many(emails=admin_emails)\n for user in admin_users:\n user_guid = f\"guid:{user.id}\"\n if user_guid not in entitlements['admin']:\n entitlements['admin'].append(user_guid)\n if user_guid not in entitlements['edit']:\n entitlements['edit'].append(user_guid)\n if user_guid not in entitlements['view']:\n entitlements['view'].append(user_guid)\n\n # Build the complete payload with updated entitlements\n payload = {\n \"active\": True,\n \"entitlements\": entitlements,\n \"hedges\": [\n {\n \"entitlements\": entitlements,\n \"id\": hedge_group_data.get('hedgeIds', [])[0] if hedge_group_data.get('hedgeIds') else None,\n \"name\": hedge_name,\n \"objective\": strategy_request.get(\"objective\", \"Minimize Factor Risk\"),\n \"parameters\": strategy_request.get(\"parameters\", {}),\n \"result\": optimization_response.get(\"result\", {}),\n }\n ],\n \"id\": hedge_group_id,\n \"name\": group_name,\n \"objective\": strategy_request.get(\"objective\", \"Minimize Factor Risk\"),\n \"ownerId\": hedge_group_data.get('ownerId', ''),\n \"createdById\": hedge_group_data.get('createdById', ''),\n \"createdTime\": hedge_group_data.get('createdTime', ''),\n \"lastUpdatedById\": hedge_group_data.get('lastUpdatedById', ''),\n \"lastUpdatedTime\": hedge_group_data.get('lastUpdatedTime', ''),\n \"hedgeIds\": hedge_group_data.get('hedgeIds', []),\n }\n\n # PUT the updated hedge group back\n result = GsSession.current.sync.put(url, payload)\n\n print(\"Hedge group shared successfully!\")\n print(f\" Hedge Group ID: {hedge_group_id}\")\n print(\"\\n Updated Entitlements:\")\n print(f\" View Access: {len(result.get('entitlements', {}).get('view', []))} users/groups\")\n print(f\" Admin Access: {len(result.get('entitlements', {}).get('admin', []))} users/groups\")\n\n return result\n\n except Exception as e:\n print(f\"Failed to share hedge: {e}\")\n raise\n\n print(\"Hedge group shared successfully!\")\n print(f\" Hedge Group ID: {hedge_group_id}\")\n print(\"\\n Updated Entitlements:\")\n print(f\" View Access: {len(result.get('entitlements', {}).get('view', []))} users/groups\")\n print(f\" Admin Access: {len(result.get('entitlements', {}).get('admin', []))} users/groups\")\n\n return result\n\n except Exception as e:\n print(f\"✗ Failed to share hedge: {e}\")\n raise\n" }, { "path": "gs_quant/api/gs/indices.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom typing import Dict, List, Union\n\nimport backoff\n\nfrom gs_quant.api.gs.assets import IdList\nfrom gs_quant.common import PositionType\nfrom gs_quant.errors import MqTimeoutError, MqInternalServerError, MqRateLimitedError\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.indices import (\n CustomBasketsCreateInputs,\n CustomBasketsRebalanceInputs,\n CustomBasketsRebalanceAction,\n CustomBasketsResponse,\n CustomBasketsEditInputs,\n CustomBasketsBackcastInputs,\n CustomBasketsRiskScheduleInputs,\n CustomBasketRiskParams,\n ISelectResponse,\n ISelectRequest,\n ISelectRebalance,\n ISelectActionRequest,\n IndicesDynamicConstructInputs,\n IndicesRebalanceInputs,\n IndicesEditInputs,\n DynamicConstructionResponse,\n ApprovalCustomBasketResponse,\n IndicesBackcastInputs,\n)\n\n# type aliases -- can add for edit/backcast/etc. if STS implements them in future\nCreateRequest = Union[CustomBasketsCreateInputs, IndicesDynamicConstructInputs]\nCreateRepsonse = Union[CustomBasketsResponse, DynamicConstructionResponse]\nRebalanceRequest = Union[CustomBasketsRebalanceInputs, ISelectRebalance, ISelectRequest]\nRebalanceResponse = Union[CustomBasketsResponse, ISelectResponse]\nRebalanceCancelRequest = Union[CustomBasketsRebalanceAction, ISelectActionRequest]\nRebalanceCancelResponse = Union[Dict, ISelectResponse]\nValidatedRequest = Union[CreateRequest, RebalanceRequest]\n\n\nclass GsIndexApi:\n \"\"\"GS Index API client implementation\"\"\"\n\n _response_cls = {\n CustomBasketsCreateInputs: CustomBasketsResponse,\n IndicesDynamicConstructInputs: DynamicConstructionResponse,\n CustomBasketsRebalanceInputs: CustomBasketsResponse,\n ISelectRebalance: ISelectResponse,\n ISelectRequest: ISelectResponse,\n CustomBasketsRebalanceAction: Dict,\n ISelectActionRequest: ISelectResponse,\n }\n\n @classmethod\n def create(cls, inputs: CreateRequest) -> CreateRepsonse:\n \"\"\"Create new basket or iselect strategy\"\"\"\n response_cls = cls._response_cls[type(inputs)]\n return GsSession.current.sync.post('/indices', payload=inputs, cls=response_cls)\n\n @classmethod\n def edit(cls, id_: str, inputs: CustomBasketsEditInputs) -> CustomBasketsResponse:\n \"\"\"Update basket metadata\"\"\"\n url = f'/indices/{id_}/edit'\n inputs = IndicesEditInputs(parameters=inputs)\n return GsSession.current.sync.post(url, payload=inputs, cls=CustomBasketsResponse)\n\n @classmethod\n def rebalance(cls, id_: str, inputs: RebalanceRequest) -> RebalanceResponse:\n \"\"\"Rebalance existing index with new composition\"\"\"\n url = f'/indices/{id_}/rebalance'\n response_cls = cls._response_cls[type(inputs)]\n inputs = IndicesRebalanceInputs(parameters=inputs) if not isinstance(inputs, ISelectRequest) else inputs\n return GsSession.current.sync.post(url, payload=inputs, cls=response_cls)\n\n @classmethod\n def cancel_rebalance(cls, id_: str, inputs: RebalanceCancelRequest) -> RebalanceCancelResponse:\n \"\"\"Cancel most recent rebalance submission if not yet approved\"\"\"\n url = f'/indices/{id_}/rebalance/cancel'\n response_cls = cls._response_cls[type(inputs)]\n return GsSession.current.sync.post(url, payload=inputs, cls=response_cls)\n\n @classmethod\n def last_rebalance_data(cls, id_: str) -> Dict:\n \"\"\"Get latest basket rebalance data\"\"\"\n url = f'/indices/{id_}/rebalance/data/last'\n return GsSession.current.sync.get(url)\n\n @classmethod\n def last_rebalance_approval(cls, id_: str) -> ApprovalCustomBasketResponse:\n \"\"\"Get latest basket rebalance approval info\"\"\"\n url = f'/indices/{id_}/rebalance/approvals/last'\n return GsSession.current.sync.get(url, cls=ApprovalCustomBasketResponse)\n\n @classmethod\n def initial_price(cls, id_: str, date: dt.date) -> Dict:\n \"\"\"Get initial basket price\"\"\"\n url = f'/indices/{id_}/rebalance/initialprice/{date.isoformat()}'\n return GsSession.current.sync.get(url)\n\n @classmethod\n def validate_ticker(cls, ticker: str):\n \"\"\"Validate basket ticker\"\"\"\n url = '/indices/validate'\n GsSession.current.sync.post(url, payload={'ticker': ticker})\n\n @classmethod\n def backcast(cls, _id: str, inputs: CustomBasketsBackcastInputs) -> CustomBasketsResponse:\n \"\"\"Backcast basket composition history before live date\"\"\"\n url = f'/indices/{_id}/backcast'\n inputs = IndicesBackcastInputs(parameters=inputs)\n return GsSession.current.sync.post(url, payload=inputs, cls=CustomBasketsResponse, timeout=240)\n\n @classmethod\n def update_risk_reports(cls, _id: str, inputs: CustomBasketRiskParams):\n \"\"\"Create, modify, or delete a custom basket factor risk report\"\"\"\n url = f'/indices/{_id}/risk/reports'\n inputs = CustomBasketsRiskScheduleInputs(risk_models=inputs)\n return GsSession.current.sync.post(url, payload=inputs)\n\n @staticmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_positions_data(\n asset_id: str,\n start_date: dt.date,\n end_date: dt.date,\n fields: IdList = None,\n position_type: PositionType = None,\n ) -> List[dict]:\n start_date_str = start_date.isoformat()\n end_date_str = end_date.isoformat()\n url = '/indices/{id}/positions/data?startDate={start_date}&endDate={end_date}'.format(\n id=asset_id, start_date=start_date_str, end_date=end_date_str\n )\n if fields is not None:\n url += '&fields='.join([''] + fields)\n\n if position_type is not None:\n url += '&type=' + position_type.value\n\n results = GsSession.current.sync.get(url)['results']\n return results\n\n @staticmethod\n def get_last_positions_data(\n asset_id: str,\n fields: IdList = None,\n position_type: PositionType = None,\n ) -> List[dict]:\n url = f'/indices/{asset_id}/positions/last/data'\n params = ''\n if fields is not None:\n params += '&fields='.join([''] + fields)\n\n if position_type is not None:\n params += '&type=' + position_type.value\n\n if len(params):\n url = f'{url}?{params}'\n\n results = GsSession.current.sync.get(url)['results']\n return results\n" }, { "path": "gs_quant/api/gs/monitors.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom typing import Tuple\nfrom urllib.parse import urlencode\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.monitor import Monitor, MonitorResponseData\n\n_logger = logging.getLogger(__name__)\n\n\nclass GsMonitorsApi:\n \"\"\"GS Monitor API client implementation\"\"\"\n\n @classmethod\n def get_monitors(\n cls,\n limit: int = 100,\n monitor_id: str = None,\n owner_id: str = None,\n name: str = None,\n folder_name: str = None,\n monitor_type: str = None,\n tags: str = None,\n ) -> Tuple[Monitor, ...]:\n query_string = urlencode(\n dict(\n filter(\n lambda item: item[1] is not None,\n dict(\n id=monitor_id,\n ownerId=owner_id,\n name=name,\n folderName=folder_name,\n type=monitor_type,\n tags=tags,\n limit=limit,\n ).items(),\n )\n )\n )\n return GsSession.current.sync.get('/monitors?{query}'.format(query=query_string), cls=Monitor)['results']\n\n @classmethod\n def get_monitor(cls, monitor_id: str) -> Monitor:\n return GsSession.current.sync.get('/monitors/{id}'.format(id=monitor_id), cls=Monitor)\n\n @classmethod\n def create_monitor(cls, monitor: Monitor) -> Monitor:\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.post('/monitors', monitor, request_headers=request_headers, cls=Monitor)\n\n @classmethod\n def update_monitor(cls, monitor: Monitor):\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n return GsSession.current.sync.put(\n '/monitors/{id}'.format(id=monitor.id), monitor, request_headers=request_headers, cls=Monitor\n )\n\n @classmethod\n def delete_monitor(cls, monitor_id: str) -> dict:\n return GsSession.current.sync.delete('/monitors/{id}'.format(id=monitor_id))\n\n @classmethod\n def calculate_monitor(cls, monitor_id) -> MonitorResponseData:\n return GsSession.current.sync.get('/monitors/{id}/data'.format(id=monitor_id), cls=MonitorResponseData)\n" }, { "path": "gs_quant/api/gs/parser.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\n\nfrom gs_quant.session import GsSession\n\n_logger = logging.getLogger(__name__)\n\n\nclass GsParserApi:\n \"\"\"GS instrument parser API client implementation\"\"\"\n\n @classmethod\n def get_instrument_from_text_asset_class(cls, text: str, asset_class: str) -> dict:\n res = GsSession.current.sync.post('/parser/quoteTicket', payload={'message': text, 'assetClass': asset_class})\n return res['ticket']['quote']['instrument']\n\n @classmethod\n def get_instrument_from_text(cls, text: str) -> dict:\n res = GsSession.current.sync.post('/parser/portfolio', payload={'message': text})\n return res['instruments']\n" }, { "path": "gs_quant/api/gs/plots.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom collections.abc import Iterable\nfrom typing import Tuple, Union, Sequence\n\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.charts import Chart, ChartShare\nimport datetime as dt\n\nfrom gs_quant.common import TimeFilter\n\n\nclass GsPlotApi:\n \"\"\"GS Chart API client implementation\"\"\"\n\n # CRUD\n\n @classmethod\n def get_many_charts(cls, limit: int = 100) -> Tuple[Chart, ...]:\n return GsSession.current.sync.get(f'/charts?limit={limit}', cls=Chart)['results']\n\n @classmethod\n async def get_many_charts_async(cls, limit: int = 100) -> Tuple[Chart, ...]:\n return (await GsSession.current.async_.get(f'/charts?limit={limit}', cls=Chart))['results']\n\n @classmethod\n def get_chart(cls, chart_id: str) -> Chart:\n return GsSession.current.sync.get(f'/charts/{chart_id}', cls=Chart)\n\n @classmethod\n async def get_chart_async(cls, chart_id: str) -> Chart:\n return await GsSession.current.async_.get(f'/charts/{chart_id}', cls=Chart)\n\n @classmethod\n def create_chart(cls, chart: Chart) -> Chart:\n return GsSession.current.sync.post('/charts', chart, cls=Chart)\n\n @classmethod\n async def create_chart_async(cls, chart: Chart) -> Chart:\n return await GsSession.current.async_.post('/charts', chart, cls=Chart)\n\n @classmethod\n def update_chart(cls, chart: Chart):\n return GsSession.current.sync.put(f'/charts/{chart.id}', chart, cls=Chart)\n\n @classmethod\n async def update_chart_async(cls, chart: Chart):\n return await GsSession.current.async_.put(f'/charts/{chart.id}', chart, cls=Chart)\n\n @classmethod\n def delete_chart(cls, chart_id: str) -> dict:\n return GsSession.current.sync.delete(f'/charts/{chart_id}')\n\n @classmethod\n async def delete_chart_async(cls, chart_id: str) -> dict:\n return await GsSession.current.async_.delete(f'/charts/{chart_id}')\n\n # Additional methods\n\n @staticmethod\n def _build_plot_payload(\n expressions: Sequence[str],\n start: Union[dt.date, dt.datetime, RelativeDate, str] = RelativeDate(\"-1y\"),\n end: Union[dt.date, dt.datetime, RelativeDate, str] = RelativeDate(\"-1b\"),\n real_time: bool = False,\n *,\n statistics: bool = False,\n interval: str = None,\n time_filter: TimeFilter = None,\n ) -> dict:\n start = start.apply_rule() if isinstance(start, RelativeDate) else start\n end = end.apply_rule() if isinstance(end, RelativeDate) else end\n expressions = [expressions] if isinstance(expressions, str) else expressions\n if real_time and (\n (isinstance(start, dt.date) and not isinstance(start, dt.datetime))\n or (isinstance(end, dt.date) and not isinstance(end, dt.datetime))\n ):\n raise ValueError(\"Real-time plots require start and end to be datetimes, not dates.\")\n return {\n \"expressions\": expressions,\n \"statistics\": statistics,\n \"realTime\": real_time,\n \"interval\": interval or (\"1m\" if real_time else \"1D\"),\n **({\"startTime\": start, \"endTime\": end} if real_time else {\"startDate\": start, \"endDate\": end}),\n **({\"timeFilter\": time_filter} if time_filter else {}),\n }\n\n @classmethod\n async def plot_runner_async(\n cls,\n expressions: Sequence[str],\n start: Union[dt.date, dt.datetime, RelativeDate, str] = RelativeDate(\"-1y\"),\n end: Union[dt.date, dt.datetime, RelativeDate, str] = RelativeDate(\"-1b\"),\n real_time: bool = False,\n *,\n statistics: bool = False,\n interval: str = None,\n time_filter: TimeFilter = None,\n ) -> dict:\n payload = cls._build_plot_payload(\n expressions, start, end, real_time, statistics=statistics, interval=interval, time_filter=time_filter\n )\n return await GsSession.current.async_.post('/plots/runner', payload)\n\n @classmethod\n def plot_runner(\n cls,\n expressions: Sequence[str],\n start: Union[dt.date, dt.datetime, RelativeDate, str] = RelativeDate(\"-1y\"),\n end: Union[dt.date, dt.datetime, RelativeDate, str] = RelativeDate(\"-1b\"),\n real_time: bool = False,\n *,\n statistics: bool = False,\n interval: str = None,\n time_filter: TimeFilter = None,\n ) -> dict:\n payload = cls._build_plot_payload(\n expressions, start, end, real_time, statistics=statistics, interval=interval, time_filter=time_filter\n )\n return GsSession.current.sync.post('/plots/runner', payload)\n\n @classmethod\n def share_chart(cls, chart_id: str, users: Iterable):\n # endpoint silently discards tokens not prefixed with 'guid:' => can't be used for roles, groups, etc.\n if any(map(lambda x: not x.startswith('guid:'), users)):\n raise ValueError('Chart can only be shared with individual users via this method.')\n chart = cls.get_chart(chart_id)\n share = ChartShare(tuple(users), chart.version)\n return GsSession.current.sync.post(f'/charts/{chart_id}/share', share, cls=Chart)\n\n @classmethod\n async def share_chart_async(cls, chart_id: str, users: Iterable):\n # endpoint silently discards tokens not prefixed with 'guid:' => can't be used for roles, groups, etc.\n if any(map(lambda x: not x.startswith('guid:'), users)):\n raise ValueError('Chart can only be shared with individual users via this method.')\n chart = await cls.get_chart_async(chart_id)\n share = ChartShare(tuple(users), chart.version)\n return await GsSession.current.async_.post(f'/charts/{chart_id}/share', share, cls=Chart)\n" }, { "path": "gs_quant/api/gs/portfolios.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport backoff\nimport deprecation\nimport logging\nfrom time import sleep\nfrom typing import Tuple, Union, List, Dict\n\nfrom gs_quant.api.api_session import ApiWithCustomSession\n\nfrom gs_quant.common import PositionType, PositionTag\nfrom gs_quant.common import RiskRequest, Currency\nfrom gs_quant.errors import MqInternalServerError, MqTimeoutError, MqRateLimitedError\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.portfolios import Portfolio, Position, PositionSet, PortfolioTree\nfrom gs_quant.target.reports import Report\nfrom gs_quant.target.risk_models import RiskModelTerm as Term\nfrom gs_quant.workflow import WorkflowPosition, WorkflowPositionsResponse, SaveQuoteRequest\n\n_logger = logging.getLogger(__name__)\n\n\nclass GsPortfolioApi(ApiWithCustomSession):\n \"\"\"GS Asset API client implementation.\n\n To pass additional query parameters, use kwargs.\"\"\"\n\n @classmethod\n def get_portfolios(\n cls, portfolio_ids: List[str] = None, portfolio_names: List[str] = None, limit: int = 100, **kwargs\n ) -> Tuple[Portfolio, ...]:\n url = '/portfolios?'\n if portfolio_ids:\n url += f'&id={\"&id=\".join(portfolio_ids)}'\n if portfolio_names:\n url += f'&name={\"&name=\".join(portfolio_names)}'\n for k, v in kwargs.items():\n if isinstance(v, list):\n for i in v:\n # in case v is not a list of strings\n url += f'&{k}={i}'\n else:\n url += f'&{k}={v}'\n return GsSession.current.sync.get(f'{url}&limit={limit}', cls=Portfolio)['results']\n\n @classmethod\n def get_portfolio(cls, portfolio_id: str) -> Portfolio:\n return GsSession.current.sync.get('/portfolios/{id}'.format(id=portfolio_id), cls=Portfolio)\n\n @classmethod\n def get_portfolio_by_name(cls, name: str) -> Portfolio:\n ret = cls.get_session().sync.get('/portfolios?name={}'.format(name))\n num_found = ret.get('totalResults', 0)\n\n if num_found == 0:\n raise ValueError('Portfolio {} not found'.format(name))\n elif num_found > 1:\n raise ValueError('More than one portfolio named {} found'.format(name))\n else:\n return Portfolio.from_dict(ret['results'][0])\n\n @classmethod\n def create_portfolio(cls, portfolio: Portfolio) -> Portfolio:\n return GsSession.current.sync.post('/portfolios', portfolio, cls=Portfolio)\n\n @classmethod\n def update_portfolio(cls, portfolio: Portfolio):\n return GsSession.current.sync.put('/portfolios/{id}'.format(id=portfolio.id), portfolio, cls=Portfolio)\n\n @classmethod\n def delete_portfolio(cls, portfolio_id: str) -> dict:\n return GsSession.current.sync.delete('/portfolios/{id}'.format(id=portfolio_id))\n\n # manage portfolio positions\n @classmethod\n def get_portfolio_analyze(cls, portfolio_id: str) -> dict:\n url = '/portfolios/{id}/analyze'.format(id=portfolio_id)\n res = GsSession.current.sync.get(url)\n return res\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_positions(\n cls, portfolio_id: str, start_date: dt.date = None, end_date: dt.date = None, position_type: str = 'close'\n ) -> Tuple[PositionSet, ...]:\n url = '/portfolios/{id}/positions?type={positionType}'.format(id=portfolio_id, positionType=position_type)\n if start_date is not None:\n url += '&startDate={sd}'.format(sd=start_date.isoformat())\n if end_date is not None:\n url += '&endDate={sd}'.format(sd=end_date.isoformat())\n\n res = GsSession.current.sync.get(url)\n return tuple(PositionSet.from_dict(v) for v in res.get('positionSets', ()))\n\n @classmethod\n def get_positions_for_date(\n cls, portfolio_id: str, position_date: dt.date, position_type: str = 'close'\n ) -> PositionSet:\n url = '/portfolios/{id}/positions/{date}?type={ptype}'.format(\n id=portfolio_id, date=position_date.isoformat(), ptype=position_type\n )\n position_sets = GsSession.current.sync.get(url, cls=PositionSet)['results']\n return position_sets[0] if len(position_sets) > 0 else None\n\n @classmethod\n def get_position_set_by_position_type(\n cls, positions_type: str, positions_id: str, activity_type: str = 'position'\n ) -> Tuple[PositionSet, ...]:\n root = 'deals' if positions_type == 'ETI' else 'books/' + positions_type\n if activity_type != 'position':\n url = '/risk-internal/{}/{}/positions?activityType={}'.format(root, positions_id, activity_type)\n else:\n url = '/risk-internal/{}/{}/positions'.format(root, positions_id)\n\n results = cls.get_session().sync.get(url, timeout=181)\n return tuple(cls._unpack_position_set(res) for res in results['positionSets'])\n\n @classmethod\n def _unpack_position_set(cls, kvs: dict) -> PositionSet:\n position_set = PositionSet.from_dict(kvs)\n # NY - this is rather unfortunate: we end up with instruments all with the name of the ETI\n for position in position_set.positions:\n position.instrument.name = None\n\n return position_set\n\n @classmethod\n def get_instruments_by_position_type(\n cls, positions_type: str, positions_id: str, activity_type: str\n ) -> Tuple[Instrument, ...]:\n position_sets = cls.get_position_set_by_position_type(\n positions_type=positions_type, positions_id=positions_id, activity_type=activity_type\n )\n\n instruments = []\n for position_set in position_sets:\n for positions in position_set.positions:\n instrument = positions.instrument\n instrument.metadata = {\n 'trade_date': position_set.position_date,\n 'tags': positions.tags,\n 'external_ids': {id_['idType']: id_['idValue'] for id_ in positions.external_ids},\n 'party_from': positions.party_from,\n 'party_to': positions.party_to,\n }\n instruments.append(instrument)\n\n return tuple(instruments)\n\n @classmethod\n def get_latest_positions(cls, portfolio_id: str, position_type: str = 'close') -> Union[PositionSet, dict]:\n url = '/portfolios/{id}/positions/last?type={ptype}'.format(id=portfolio_id, ptype=position_type)\n results = GsSession.current.sync.get(url)['results']\n\n # Annoyingly, different types are returned depending on position_type\n\n if isinstance(results, dict) and 'positions' in results:\n results['positions'] = tuple(Position.from_dict(p) for p in results['positions'])\n\n return PositionSet.from_dict(results)\n\n @classmethod\n def get_instruments_by_workflow_id(\n cls, workflow_id: str, prefer_instruments: bool = False\n ) -> Tuple[Instrument, ...]:\n root = 'quote'\n url = '/risk{}/{}/{}'.format('-internal' if not prefer_instruments else '', root, workflow_id)\n results = cls.get_session().sync.get(url, timeout=181)\n\n instruments = []\n for position in results.get('workflowPositions').get(workflow_id):\n for insts in position['positions']:\n instrument_values = insts['instrument']\n instrument = Instrument.from_dict(instrument_values)\n name = instrument_values.get('name')\n if name:\n instrument.name = name\n\n instruments.append(instrument)\n\n return tuple(instruments)\n\n @classmethod\n def get_position_dates(cls, portfolio_id: str) -> Tuple[dt.date, ...]:\n position_dates = cls.get_session().sync.get('/portfolios/{id}/positions/dates'.format(id=portfolio_id))[\n 'results'\n ]\n return tuple(dt.datetime.strptime(d, '%Y-%m-%d').date() for d in position_dates)\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def update_positions(cls, portfolio_id: str, position_sets: List[PositionSet], net_positions: bool = True) -> float:\n url = f'/portfolios/{portfolio_id}/positions?netPositions={str(net_positions).lower()}'\n return GsSession.current.sync.put(url, position_sets)\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_positions_data(\n cls,\n portfolio_id: str,\n start_date: dt.date,\n end_date: dt.date,\n fields: List[str] = None,\n performance_report_id: str = None,\n position_type: PositionType = None,\n include_all_business_days: bool = False,\n ) -> List[dict]:\n start_date_str = start_date.isoformat()\n end_date_str = end_date.isoformat()\n url = f'/portfolios/{portfolio_id}/positions/data?startDate={start_date_str}&endDate={end_date_str}'\n if fields is not None:\n url += '&fields='.join([''] + fields)\n if performance_report_id is not None:\n url += f'&reportId={performance_report_id}'\n if position_type is not None:\n url += '&type=' + position_type.value\n if include_all_business_days:\n url += '&includeAllBusinessDays=true'\n\n return GsSession.current.sync.get(url)['results']\n\n @classmethod\n def update_quote(cls, quote_id: str, request: RiskRequest):\n return cls.get_session().sync.put('/risk-internal/quote/save/{id}'.format(id=quote_id), request)\n\n @classmethod\n def save_quote(cls, request: RiskRequest) -> str:\n return cls.get_session().sync.post('/risk-internal/quote/save', request)['results']\n\n @classmethod\n def update_workflow_quote(cls, quote_id: str, request: SaveQuoteRequest):\n headers = {'Content-Type': 'application/x-msgpack'}\n return cls.get_session().sync.put(\n '/risk-internal/quote/workflow/save/{id}'.format(id=quote_id), tuple([request]), request_headers=headers\n )['results']\n\n @classmethod\n def save_workflow_quote(cls, request: SaveQuoteRequest) -> str:\n headers = {'Content-Type': 'application/x-msgpack'}\n return cls.get_session().sync.post(\n '/risk-internal/quote/workflow/save', tuple([request]), request_headers=headers\n )['results']\n\n @classmethod\n def share_workflow_quote(cls, request: SaveQuoteRequest) -> str:\n headers = {'Content-Type': 'application/x-msgpack'}\n return cls.get_session().sync.post(\n '/risk-internal/quote/workflow/share', tuple([request]), request_headers=headers\n )['results']\n\n @classmethod\n def get_workflow_quote(cls, workflow_id: str) -> Tuple[WorkflowPosition, ...]:\n url = f'/risk-internal/quote/workflow/{workflow_id}'\n results = cls.get_session().sync.get(url, timeout=181)\n wf_pos_res = WorkflowPositionsResponse.from_dict(results)\n if wf_pos_res:\n return wf_pos_res.results\n else:\n return ()\n\n @classmethod\n def get_shared_workflow_quote(cls, workflow_id: str) -> Tuple[WorkflowPosition, ...]:\n url = f'/risk-internal/quote/workflow/shared/{workflow_id}'\n results = cls.get_session().sync.get(url, timeout=181)\n wf_pos_res = WorkflowPositionsResponse.from_dict(results)\n if wf_pos_res:\n return wf_pos_res.results\n else:\n return ()\n\n @classmethod\n def save_to_shadowbook(cls, request: RiskRequest, name: str) -> str:\n return cls.get_session().sync.put(f'/risk-internal/shadowbook/save/{name}', request)['results']\n\n @classmethod\n def get_risk_models_by_coverage(cls, portfolio_id: str, term: Term = Term.Medium):\n return cls.get_session().sync.get(f'/portfolios/{portfolio_id}/models?sortByTerm={term.value}')['results']\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_reports(cls, portfolio_id: str, tags: Dict) -> Tuple[Report, ...]:\n results = cls.get_session().sync.get('/portfolios/{id}/reports'.format(id=portfolio_id), cls=Report)['results']\n if tags is not None:\n tags_as_list = tuple(PositionTag(name=key, value=tags[key]) for key in tags)\n results = [r for r in results if r.parameters.tags == tags_as_list]\n return results\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def schedule_reports(\n cls, portfolio_id: str, start_date: dt.date = None, end_date: dt.date = None, backcast: bool = False\n ) -> dict:\n payload = {'parameters': {'backcast': backcast}}\n if start_date is not None:\n payload['startDate'] = start_date.isoformat()\n if end_date is not None:\n payload['endDate'] = end_date.isoformat()\n portfolio = cls.get_portfolio(portfolio_id)\n if portfolio.tag_name_hierarchy is None or len(portfolio.tag_name_hierarchy) == 0:\n cls.get_session().sync.post(f'/portfolios/{portfolio_id}/schedule', payload)\n else:\n count = 10\n for report_id in portfolio.report_ids:\n if count == 0:\n sleep(2)\n count = 10\n cls.get_session().sync.post(f'/reports/{report_id}/schedule', payload)\n else:\n cls.get_session().sync.post(f'/reports/{report_id}/schedule', payload)\n count -= 1\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_schedule_dates(cls, portfolio_id: str, backcast: bool = False) -> List[dt.date]:\n results = cls.get_session().sync.get(\n f'/portfolios/{portfolio_id}/schedule/dates?backcast={str(backcast).lower()}'\n )\n return [\n dt.datetime.strptime(results['startDate'], '%Y-%m-%d').date(),\n dt.datetime.strptime(results['endDate'], '%Y-%m-%d').date(),\n ]\n\n @classmethod\n @deprecation.deprecated(\n deprecated_in='1.0.10',\n details='GsPortfolioApi.get_custom_aum is now deprecated, please use GsReportApi.get_custom_aum instead.',\n )\n def get_custom_aum(cls, portfolio_id: str, start_date: dt.date = None, end_date: dt.date = None) -> dict:\n url = f'/portfolios/{portfolio_id}/aum?'\n if start_date:\n url += f\"&startDate={start_date.strftime('%Y-%m-%d')}\"\n if end_date:\n url += f\"&endDate={end_date.strftime('%Y-%m-%d')}\"\n return GsSession.current.sync.get(url)['data']\n\n @classmethod\n @deprecation.deprecated(\n deprecated_in='1.0.10',\n details='GsPortfolioApi.upload_custom_aum is now deprecated, please use GsReportApi.upload_custom_aum instead.',\n )\n def upload_custom_aum(cls, portfolio_id: str, aum_data: List[Dict], clear_existing_data: bool = None) -> dict:\n url = f'/portfolios/{portfolio_id}/aum'\n payload = {'data': aum_data}\n if clear_existing_data:\n url += '?clearExistingData=true'\n return GsSession.current.sync.post(url, payload)\n\n @classmethod\n def update_portfolio_tree(cls, portfolio_id: str):\n return GsSession.current.sync.post(f'/portfolios/{portfolio_id}/tree', {})\n\n @classmethod\n def get_portfolio_tree(cls, portfolio_id: str):\n return GsSession.current.sync.get(f'/portfolios/{portfolio_id}/tree', cls=PortfolioTree)\n\n @classmethod\n def get_attribution(\n cls,\n portfolio_id: str,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n performance_report_id: str = None,\n ) -> Dict:\n url = f'/attribution/{portfolio_id}?'\n if start_date:\n url += f\"&startDate={start_date.strftime('%Y-%m-%d')}\"\n if end_date:\n url += f\"&endDate={end_date.strftime('%Y-%m-%d')}\"\n if currency:\n url += f\"¤cy={currency.value}\"\n if performance_report_id:\n url += f'&reportId={performance_report_id}'\n return GsSession.current.sync.get(url)['results']\n" }, { "path": "gs_quant/api/gs/price.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport backoff\n\nfrom gs_quant.errors import MqRateLimitedError, MqTimeoutError, MqInternalServerError\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.positions_v2_pricing import PositionsPricingRequest\nfrom gs_quant.target.price import PositionSetPriceInput, PositionSetPriceResponse\n\n\nclass GsPriceApi:\n \"\"\"GS Price API client implementation\"\"\"\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(60), MqRateLimitedError, max_tries=5)\n def price_positions(cls, inputs: PositionSetPriceInput) -> PositionSetPriceResponse:\n url = '/price/positions'\n return GsSession.current.sync.post(url, payload=inputs, cls=PositionSetPriceResponse)\n\n @classmethod\n def price_many_positions(cls, pricing_request: PositionsPricingRequest) -> dict:\n url = '/positions/price/bulk'\n GsSession.current.api_version = \"v2\"\n pricing_response = GsSession.current.sync.post(url, payload=pricing_request)\n GsSession.current.api_version = \"v1\"\n\n positions = pricing_response.get(\"positions\")\n\n return positions\n" }, { "path": "gs_quant/api/gs/reports.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport urllib.parse\nfrom enum import Enum\nfrom typing import Tuple, List, Dict\n\nimport backoff\n\nfrom gs_quant.base import EnumBase\nfrom gs_quant.common import Currency, PositionTag\nfrom gs_quant.errors import MqTimeoutError, MqInternalServerError, MqRateLimitedError\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.reports import Report\n\n_logger = logging.getLogger(__name__)\n\n\nclass OrderType(EnumBase, Enum):\n \"\"\"Source object for position data\"\"\"\n\n Ascending = 'Ascending'\n Descending = 'Descending'\n\n\nclass FactorRiskTableMode(EnumBase, Enum):\n \"\"\"Source object for position data\"\"\"\n\n Pnl = 'Pnl'\n Exposure = 'Exposure'\n ZScore = 'ZScore'\n Mctr = 'Mctr'\n\n\nclass GsReportApi:\n \"\"\"GS Reports API client implementation\"\"\"\n\n @classmethod\n def create_report(cls, report: Report) -> Report:\n return GsSession.current.sync.post('/reports', report, cls=Report)\n\n @classmethod\n def get_report(cls, report_id: str) -> Report:\n return GsSession.current.sync.get('/reports/{id}'.format(id=report_id), cls=Report)\n\n @classmethod\n def get_reports(\n cls,\n limit: int = 100,\n offset: int = None,\n position_source_type: str = None,\n position_source_id: str = None,\n status: str = None,\n report_type: str = None,\n order_by: str = None,\n tags: Dict = None,\n scroll: str = None,\n ) -> Tuple[Report, ...]:\n def build_url(scroll_id=None):\n url = f'/reports?limit={limit}'\n if scroll:\n url += '&scroll={scroll}'.format(scroll=scroll)\n if scroll_id:\n url += f'&scrollId={scroll_id}'\n if offset:\n url += '&offset={offset}'.format(offset=offset)\n if position_source_type:\n url += f'&positionSourceType={position_source_type}'\n if position_source_id:\n url += f'&positionSourceId={position_source_id}'\n if status:\n url += f'&status={status}'\n if report_type:\n url += f'&reportType={urllib.parse.quote(report_type)}'\n if order_by:\n url += f'&orderBy={order_by}'\n return url\n\n response = GsSession.current.sync.get(build_url(), cls=Report)\n results = response.get('results', [])\n\n while response.get('scrollId') and response.get('results'):\n response = GsSession.current.sync.get(build_url(scroll_id=response.get('scrollId')), cls=Report)\n results += response.get('results', [])\n\n if tags is not None:\n tags_as_list = tuple(PositionTag(name=key, value=tags[key]) for key in tags)\n results = [r for r in results if r.parameters.tags == tags_as_list]\n else:\n results = [r for r in results if r.parameters.tags is None]\n return tuple(results)\n\n @classmethod\n def update_report(cls, report: Report) -> dict:\n return GsSession.current.sync.put('/reports/{id}'.format(id=report.id), report, cls=Report)\n\n @classmethod\n def delete_report(cls, report_id: str) -> dict:\n return GsSession.current.sync.delete('/reports/{id}'.format(id=report_id))\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def schedule_report(cls, report_id: str, start_date: dt.date, end_date: dt.date, backcast: bool = False) -> dict:\n report_schedule_request = {\n 'startDate': start_date.strftime('%Y-%m-%d'),\n 'endDate': end_date.strftime('%Y-%m-%d'),\n }\n if backcast:\n report_schedule_request['parameters'] = {'backcast': backcast}\n return GsSession.current.sync.post('/reports/{id}/schedule'.format(id=report_id), report_schedule_request)\n\n @classmethod\n def get_report_status(cls, report_id: str) -> Tuple[dict, ...]:\n return GsSession.current.sync.get('/reports/{id}/status'.format(id=report_id))\n\n @classmethod\n def get_report_jobs(cls, report_id: str) -> Tuple[dict, ...]:\n return GsSession.current.sync.get('/reports/{id}/jobs'.format(id=report_id))['results']\n\n @classmethod\n def get_report_job(cls, report_job_id: str) -> dict:\n return GsSession.current.sync.get('/reports/jobs/{report_job_id}'.format(report_job_id=report_job_id))\n\n @classmethod\n def reschedule_report_job(cls, report_job_id: str):\n return GsSession.current.sync.post(f'/reports/jobs/{report_job_id}/reschedule', {})\n\n @classmethod\n def cancel_report_job(cls, report_job_id: str) -> dict:\n return GsSession.current.sync.post('/reports/jobs/{report_job_id}/cancel'.format(report_job_id=report_job_id))\n\n @classmethod\n def update_report_job(cls, report_job_id: str, status: str) -> dict:\n status_body = {\"status\": '{status}'.format(status=status)}\n return GsSession.current.sync.post(\n '/reports/jobs/{report_job_id}/update'.format(report_job_id=report_job_id), status_body\n )\n\n @classmethod\n def get_custom_aum(cls, report_id: str, start_date: dt.date = None, end_date: dt.date = None) -> dict:\n url = f'/reports/{report_id}/aum?'\n if start_date:\n url += f\"&startDate={start_date.strftime('%Y-%m-%d')}\"\n if end_date:\n url += f\"&endDate={end_date.strftime('%Y-%m-%d')}\"\n return GsSession.current.sync.get(url)['data']\n\n @classmethod\n def upload_custom_aum(cls, report_id: str, aum_data: List[dict], clear_existing_data: bool = None) -> dict:\n url = f'/reports/{report_id}/aum'\n payload = {'data': aum_data}\n if clear_existing_data:\n url += '?clearExistingData=true'\n return GsSession.current.sync.post(url, payload)\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_factor_risk_report_results(\n cls,\n risk_report_id: str,\n view: str = None,\n factors: List[str] = None,\n factor_categories: List[str] = None,\n currency: Currency = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n unit: str = None,\n ) -> dict:\n url = f'/risk/factors/reports/{risk_report_id}/results?'\n if view is not None:\n url += f'&view={view}'\n if factors is not None:\n factors = map(urllib.parse.quote, factors) # to support factors like \"Automobiles & Components\"\n url += f'&factors={\"&factors=\".join(factors)}'\n if factor_categories is not None:\n url += f'&factorCategories={\"&factorCategories=\".join(factor_categories)}'\n if currency is not None:\n url += f'¤cy={currency.value}'\n if start_date is not None:\n url += f'&startDate={start_date.strftime(\"%Y-%m-%d\")}'\n if end_date is not None:\n url += f'&endDate={end_date.strftime(\"%Y-%m-%d\")}'\n if unit is not None:\n url += f'&unit={unit}'\n\n return GsSession.current.sync.get(url)\n\n @classmethod\n def get_factor_risk_report_view(\n cls,\n risk_report_id: str,\n factor: str = None,\n factor_category: str = None,\n currency: Currency = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n unit: str = None,\n ) -> dict:\n query_string = urllib.parse.urlencode(\n dict(\n filter(\n lambda item: item[1] is not None,\n dict(\n factor=factor,\n factorCategory=factor_category,\n currency=currency,\n startDate=start_date,\n endDate=end_date,\n unit=unit,\n ).items(),\n )\n )\n )\n\n GsSession.current.api_version = \"v2\"\n url = f'/factor/risk/{risk_report_id}/views?{query_string}'\n response = GsSession.current.sync.get(url)\n GsSession.current.api_version = \"v1\"\n return response\n\n @classmethod\n def get_factor_risk_report_table(\n cls,\n risk_report_id: str,\n mode: FactorRiskTableMode = None,\n unit: str = None,\n currency: Currency = None,\n date: dt.date = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n ) -> dict:\n GsSession.current.api_version = \"v2\"\n url = f'/factor/risk/{risk_report_id}/tables?'\n if mode is not None:\n url += f'&mode={mode.value}'\n if unit is not None:\n url += f'&unit={unit}'\n if currency is not None:\n url += f'¤cy={currency.value}'\n if date is not None:\n url += f'&date={date.strftime(\"%Y-%m-%d\")}'\n if start_date is not None:\n url += f'&startDate={start_date.strftime(\"%Y-%m-%d\")}'\n if end_date is not None:\n url += f'&endDate={end_date.strftime(\"%Y-%m-%d\")}'\n\n response = GsSession.current.sync.get(url)\n GsSession.current.api_version = \"v1\"\n return response\n\n @classmethod\n def get_brinson_attribution_results(\n cls,\n portfolio_id: str,\n benchmark: str = None,\n currency: Currency = None,\n include_interaction: bool = None,\n aggregation_type: str = None,\n aggregation_category: str = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n ):\n url = f'/attribution/{portfolio_id}/brinson?'\n if benchmark is not None:\n url += f'&benchmark={benchmark}'\n if currency is not None:\n url += f'¤cy={currency.value}'\n if include_interaction is not None:\n url += f'&includeInteraction={str(include_interaction).lower()}'\n if aggregation_type is not None:\n url += f'&aggregationType={aggregation_type}'\n if aggregation_category is not None:\n url += f'&aggregationCategory={aggregation_category}'\n if start_date is not None:\n url += f'&startDate={start_date.strftime(\"%Y-%m-%d\")}'\n if end_date is not None:\n url += f'&endDate={end_date.strftime(\"%Y-%m-%d\")}'\n\n return GsSession.current.sync.get(url)\n" }, { "path": "gs_quant/api/gs/risk.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport asyncio\nimport base64\nimport datetime as dt\nimport json\nimport logging\nimport math\nimport os\nimport sys\nimport time\nfrom socket import gaierror\nfrom typing import Iterable, Optional, Union\nimport re\n\nimport msgpack\nfrom websockets import ConnectionClosed\n\nfrom gs_quant.api.risk import RiskApi\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.risk import RiskRequest\nfrom gs_quant.target.risk import OptimizationRequest\nfrom gs_quant.tracing import Tracer, TracingSpan\n\n_logger = logging.getLogger(__name__)\n\n\nclass WebsocketUnavailable(Exception):\n pass\n\n\nclass GsRiskApi(RiskApi):\n USE_MSGPACK = True\n POLL_FOR_BATCH_RESULTS = False\n WEBSOCKET_RETRY_ON_CLOSE_CODES = (1000, 1001, 1006)\n PRICING_API_VERSION = None\n\n @classmethod\n def calc_multi(cls, requests: Iterable[RiskRequest]) -> dict:\n requests = tuple(requests)\n results = cls._exec(requests)\n\n if len(results) < len(requests):\n results = [RuntimeError('Missing results')] * len(requests)\n\n return dict(zip(requests, results))\n\n @classmethod\n def calc(cls, request: RiskRequest) -> Iterable:\n return cls._exec(request)\n\n @classmethod\n def _exec(cls, request: Union[RiskRequest, Iterable[RiskRequest]]) -> Union[Iterable, dict]:\n use_msgpack = cls.USE_MSGPACK and not isinstance(request, RiskRequest)\n headers = {'Content-Type': 'application/x-msgpack'} if use_msgpack else {}\n risk_session = cls.get_session()\n version = GsRiskApi.PRICING_API_VERSION or risk_session.api_version\n result, request_id = risk_session.sync.post(\n f'/{version}' + cls.__url(request),\n request,\n include_version=False,\n request_headers=headers,\n timeout=181,\n return_request_id=True,\n )\n\n for sub_request in request:\n sub_request._id = request_id\n\n return result\n\n @classmethod\n def __url(cls, request: Union[RiskRequest, Iterable[RiskRequest]]):\n is_bulk = not isinstance(request, RiskRequest)\n return '/risk/calculate{}'.format('/bulk' if is_bulk else '')\n\n @classmethod\n async def get_results(\n cls,\n responses: asyncio.Queue,\n results: asyncio.Queue,\n timeout: Optional[int] = None,\n span: Optional[TracingSpan] = None,\n ) -> Optional[str]:\n if cls.POLL_FOR_BATCH_RESULTS:\n return await cls.__get_results_poll(responses, results, timeout=timeout)\n else:\n try:\n return await cls.__get_results_ws(responses, results, timeout=timeout)\n except WebsocketUnavailable:\n return await cls.__get_results_poll(responses, results, timeout=timeout)\n\n @classmethod\n async def __get_results_poll(cls, responses: asyncio.Queue, results: asyncio.Queue, timeout: Optional[int] = None):\n run = True\n pending_requests = {}\n end_time = dt.datetime.now() + dt.timedelta(seconds=timeout) if timeout else None\n\n while pending_requests or run:\n # Check for timeout\n if end_time is not None and dt.datetime.now() > end_time:\n _logger.error('Fatal error: timeout while waiting for results')\n cls.shutdown_queue_listener(results)\n return\n\n shutdown, items = await cls.drain_queue_async(responses, timeout=2)\n\n if shutdown:\n run = False\n\n if items:\n # ... update the pending requests ...\n pending_requests.update(((i[1]['reportId'], i[0]) for i in items))\n\n if not pending_requests:\n continue\n\n # ... poll for completed requests ...\n\n try:\n risk_session = cls.get_session()\n version = GsRiskApi.PRICING_API_VERSION or risk_session.api_version\n calc_results = risk_session.sync.post(\n f'/{version}/risk/calculate/results/bulk', list(pending_requests.keys()), include_version=False\n )\n\n # ... enqueue the request and result for the listener to handle ...\n for result in calc_results:\n if 'error' in result:\n results.put_nowait((pending_requests.pop(result['requestId']), RuntimeError(result['error'])))\n elif 'result' in result:\n results.put_nowait((pending_requests.pop(result['requestId']), result['result']))\n except Exception as e:\n error_str = f'Fatal error polling for results: {e}'\n _logger.error(error_str)\n cls.shutdown_queue_listener(results)\n return error_str\n\n @classmethod\n async def __get_results_ws(cls, responses: asyncio.Queue, results: asyncio.Queue, timeout: Optional[int] = None):\n async def handle_websocket():\n nonlocal all_requests_dispatched\n ret = ''\n\n try:\n # If we're re-connecting then re-send any in-flight request ids\n if pending_requests:\n _logger.info(f'Re-subscribing {len(pending_requests)} requests')\n await asyncio.wait_for(ws.send(json.dumps(list(pending_requests.keys()))), timeout=send_timeout)\n\n while pending_requests or not all_requests_dispatched:\n # Continue while we have pending or un-dispatched requests\n _logger.debug(f'waiting for {\", \".join(pending_requests.keys())}')\n request_listener = (\n asyncio.ensure_future(cls.drain_queue_async(responses)) if not all_requests_dispatched else None\n )\n result_listener = asyncio.ensure_future(ws.recv())\n listeners = tuple(filter(None, (request_listener, result_listener)))\n\n # Wait for either a request or result\n complete, pending = await asyncio.wait(listeners, return_when=asyncio.FIRST_COMPLETED)\n\n # Check results before sending more requests. Results can be lost otherwise\n if result_listener in complete:\n # New results have been received\n request_id = None\n try:\n raw_res = result_listener.result()\n # Message of the form ;\n # The status char tells us the format of the data:\n # 'E' - an error string - encoded as utf-8 bytes\n # 'R' - json data encoded as utf-8 string\n # 'M' - msgpack data encoded as base64 string\n # 'B' - msgpack data (raw binary)\n is_bytes_response = isinstance(raw_res, bytes)\n separator = b';' if is_bytes_response else ';'\n # Use partition to split the byte or char sequence at the first semicolon\n request_id_raw, _, result_data_raw = raw_res.partition(separator)\n if is_bytes_response:\n request_id = request_id_raw.decode()\n status, risk_data = chr(result_data_raw[0]), result_data_raw[1:]\n else:\n request_id = request_id_raw\n status, risk_data = result_data_raw[0], result_data_raw[1:]\n except ConnectionClosed as conn_closed:\n if conn_closed.rcvd and conn_closed.rcvd.code in cls.WEBSOCKET_RETRY_ON_CLOSE_CODES:\n # websocket closed, but we can retry\n if request_listener:\n if request_listener in complete:\n # WebSocket Closed on us, but had we had results just dispatched\n # They need subscribing so re-queue them to pick up later\n _, res = request_listener.result()\n cls.enqueue(responses, res, wait=False)\n else:\n # Conn Closed, cancelling request listener as no-one will hear the response\n # And we don't want to take anything from the queue\n request_listener.cancel()\n # Now re-raise connection closed to be handled and potentially we'll try again\n raise\n status = 'E'\n risk_data = str(conn_closed)\n except Exception as ee:\n status = 'E'\n risk_data = str(ee)\n\n if status == 'E':\n # An error\n result = RuntimeError(risk_data.decode() if isinstance(risk_data, bytes) else risk_data)\n else:\n # Unpack the result\n try:\n result = (\n msgpack.unpackb(risk_data)\n if status == 'B'\n else msgpack.unpackb(base64.b64decode(risk_data), raw=False)\n if status == 'M'\n else json.loads(risk_data)\n )\n except Exception as ee:\n result = ee\n if request_id is None:\n # Certain fatal websocket errors (e.g. ConnectionClosed) that are caught above will mean\n # we have no request_id - In this case we abort and set the error on all results\n result_listener.cancel()\n for req in pending_requests.values():\n results.put_nowait((req, result))\n # Give up\n pending_requests.clear()\n all_requests_dispatched = True\n else:\n # Enqueue the request and result for the listener to handle\n results.put_nowait((pending_requests.pop(request_id), result))\n else:\n result_listener.cancel()\n\n if request_listener:\n if request_listener in complete:\n # New requests have been posted ...\n\n all_requests_dispatched, items = request_listener.result()\n if items:\n if not all([isinstance(i[1], dict) for i in items]):\n error_item = next(i[1] for i in items if not isinstance(i[1], dict))\n raise RuntimeError(error_item[0][0][0]['errorString'])\n\n # ... extract the request IDs ...\n request_ids = [i[1]['reportId'] for i in items]\n\n # ... update the pending requests ...\n pending_requests.update(zip(request_ids, (i[0] for i in items)))\n\n # ... add to our result subscription ...\n await asyncio.wait_for(ws.send(json.dumps(request_ids)), timeout=send_timeout)\n\n # ... note dispatched\n dispatched.update(request_ids)\n else:\n request_listener.cancel()\n except ConnectionClosed:\n raise\n except Exception as ee:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n ret = f'{exc_type} {fname} ln:{exc_tb.tb_lineno}' + str(ee)\n\n return ret\n\n all_requests_dispatched = False\n pending_requests = {}\n dispatched = set()\n error = ''\n exc_info = None\n attempts = 0\n max_attempts = 5\n send_timeout = 30\n\n while attempts < max_attempts:\n if attempts > 0:\n await asyncio.sleep(math.pow(2, attempts - 1))\n _logger.error(f'{error} error, retrying (attempt {attempts + 1} of {max_attempts})')\n\n try:\n risk_session = cls.get_session()\n api_version = GsRiskApi.PRICING_API_VERSION or risk_session.api_version\n ws_url = f'/{api_version}/risk/calculate/results/subscribe'\n subprotocols = [\"msgpack-binary\"] if cls.USE_MSGPACK else None\n # we set a 50ms timeout for the websocket close to avoid manually waiting for marquee\n # to acknowledge the close and close the underlying TCP stream. We have seen delays of up to 1000ms here\n ws_close_timeout = 0.05\n async with risk_session.async_.connect_websocket(\n ws_url, include_version=False, subprotocols=subprotocols, close_timeout=ws_close_timeout\n ) as ws:\n error = await handle_websocket()\n\n attempts = max_attempts\n except ConnectionClosed as cce:\n error = (\n f'Unexpected Connection Closed ({len(pending_requests)}/{len(dispatched)} '\n f'request(s) still pending): {cce}'\n )\n attempts += 1\n except asyncio.TimeoutError:\n error = 'Timed out'\n attempts = max_attempts\n except gaierror:\n raise WebsocketUnavailable()\n except Exception as e:\n error = str(e)\n exc_info = e\n attempts = max_attempts\n\n if error != '':\n _logger.error(f'Fatal error with websocket: {error}', exc_info=exc_info)\n span = Tracer.active_span()\n if span and span.is_recording():\n span.set_tag('error', True)\n span.log_kv({'event': 'error', 'message': error})\n cls.shutdown_queue_listener(results)\n return error\n\n @classmethod\n def create_pretrade_execution_optimization(cls, request: OptimizationRequest) -> str:\n try:\n response = cls.get_session().sync.post(r'/risk/execution/pretrade', request)\n _logger.info('New optimization is created with id: {}'.format(response.get(\"optimizationId\")))\n return response\n except Exception as e:\n error = str(e)\n _logger.error(error)\n return error\n\n @classmethod\n def get_pretrade_execution_optimization(cls, optimization_id: str, max_attempts: int = 15):\n url = '/risk/execution/pretrade/{}/results'.format(optimization_id)\n attempts = 0\n start = time.perf_counter()\n results = {}\n\n while attempts < max_attempts:\n if attempts > 0:\n time.sleep(math.pow(2, attempts))\n _logger.error('Retrying (attempt {} of {})'.format(attempts, max_attempts))\n try:\n results = cls.get_session().sync.get(url)\n if results.get('status') == 'Running':\n attempts += 1\n else:\n break\n except Exception as e:\n error = str(e)\n _logger.error(error)\n return error\n\n if results.get('status') == 'Running':\n _logger.info('Optimization is still running. Please retry fetching the results.')\n return results\n else:\n _logger.info('Optimization is fetched in {:.3f}s.'.format(time.perf_counter() - start))\n return results\n\n @classmethod\n def get_liquidity_and_factor_analysis(\n cls,\n positions: list,\n risk_model: str,\n date: dt.date,\n currency: str = 'USD',\n participation_rate: float = 0.1,\n measures: Optional[list] = None,\n notional: Optional[float] = None,\n time_series_benchmark_ids: Optional[list] = None,\n ):\n \"\"\"\n Get liquidity and factor analysis for a portfolio using the /risk/liquidity endpoint.\n\n :param positions: List of positions with assetId and quantity/weight\n :param risk_model: Risk model identifier (e.g., 'BARRA_EFM_USALTL', 'AXIOMA_AXUS4S')\n :param date: Analysis date\n :param currency: Currency for analysis (default: USD)\n :param participation_rate: Market participation rate (default: 0.1 = 10%)\n :param measures: List of measures to include (default: all available measures)\n :param notional: Optional reference notional\n :param time_series_benchmark_ids: Optional benchmark IDs for time series comparison\n :return: Dictionary with liquidity and factor analysis results\n \"\"\"\n if measures is None:\n measures = [\n \"Time Series Data\",\n \"Risk Buckets\",\n \"Factor Risk Buckets\",\n \"Factor Exposure Buckets\",\n \"Exposure Buckets\",\n ]\n\n payload = {\n \"currency\": currency,\n \"date\": date.isoformat() if isinstance(date, dt.date) else date,\n \"positions\": positions,\n \"participationRate\": participation_rate,\n \"riskModel\": risk_model,\n \"timeSeriesBenchmarkIds\": time_series_benchmark_ids or [],\n \"measures\": measures,\n }\n\n if notional is not None:\n payload[\"notional\"] = notional\n\n try:\n response = cls.get_session().sync.post('/risk/liquidity', payload)\n\n if isinstance(response, dict) and 'errorMessage' in response:\n error_msg = response['errorMessage']\n\n asset_ids_pattern = (\n r'Assets with the following ids are missing in marquee:'\n r'\\s*\\[\\s*([^\\]]+)\\s*\\]'\n )\n asset_ids_match = re.search(asset_ids_pattern, error_msg, re.IGNORECASE)\n if asset_ids_match:\n clean_error_pattern = (\n r'(Assets with the following ids are missing in '\n r'marquee:\\s*\\[[^\\]]+\\])'\n )\n clean_error_line = re.search(clean_error_pattern, error_msg, re.IGNORECASE)\n if clean_error_line:\n clean_message = f\"ERROR: liquidity analysis failed\\n{clean_error_line.group(1)}\"\n _logger.error(clean_message)\n raise MqValueError(clean_message)\n else:\n missing_assets = asset_ids_match.group(1).strip()\n clean_message = (\n f\"ERROR: liquidity analysis failed\\n\"\n f\"Assets with the following ids are missing in marquee: \"\n f\"[ {missing_assets} ]\"\n )\n _logger.error(clean_message)\n raise MqValueError(clean_message)\n else:\n _logger.error(f'Liquidity analysis failed: {error_msg}')\n raise MqValueError(\"ERROR: liquidity analysis failed\")\n\n _logger.info('Liquidity analysis completed successfully')\n return response\n except Exception:\n raise\n" }, { "path": "gs_quant/api/gs/risk_models.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom enum import Enum\nfrom typing import Tuple, Dict, List, Union\n\nimport backoff\n\nfrom gs_quant.errors import MqRateLimitedError, MqTimeoutError, MqInternalServerError\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.risk_models import (\n RiskModel,\n RiskModelCalendar,\n Factor,\n RiskModelData,\n RiskModelDataAssetsRequest,\n RiskModelDataMeasure,\n RiskModelEventType,\n RiskModelTerm,\n)\n\n_logger = logging.getLogger(__name__)\n\n\nclass IntradayFactorDataSource(Enum):\n \"\"\"Data source for intraday factor data\"\"\"\n\n GS_FMP = \"GS_FMP\"\n GS_REGRESSION = \"GS_Regression\"\n BARRA = \"BARRA\"\n AXIOMA = \"AXIOMA\"\n WOLFE = \"WOLFE\"\n QI = \"QI\"\n\n\nclass GsRiskModelApi:\n \"\"\"GS Risk Models API client implementation\"\"\"\n\n @classmethod\n def create_risk_model(cls, model: RiskModel) -> RiskModel:\n return GsSession.current.sync.post('/risk/models', model, cls=RiskModel)\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_risk_model(cls, model_id: str) -> RiskModel:\n return GsSession.current.sync.get(f'/risk/models/{model_id}', cls=RiskModel)\n\n @classmethod\n def get_risk_models(\n cls,\n ids: List[str] = None,\n limit: int = None,\n offset: int = None,\n terms: List[str] = None,\n versions: List[str] = None,\n vendors: List[str] = None,\n names: List[str] = None,\n types: List[str] = None,\n coverages: List[str] = None,\n ) -> Tuple[RiskModel, ...]:\n url = '/risk/models?'\n if limit:\n url += f'&limit={limit}'\n if ids:\n url += '&id={ids}'.format(ids='&id='.join(ids))\n if offset:\n url += f'&offset={offset}'\n if terms:\n url += f'&term={terms}'\n if versions:\n url += '&version={versions}'.format(versions='&version='.join(versions))\n if vendors:\n url += '&vendor={vendors}'.format(vendors='&vendor='.join(vendors))\n if names is not None:\n url += '&name={names}'.format(names='&name='.join(names))\n if coverages is not None:\n url += '&coverage={cov}'.format(cov='&coverage='.join(coverages))\n if types is not None:\n url += '&type={type}'.format(type='&type='.join(types))\n return GsSession.current.sync.get(url, cls=RiskModel)['results']\n\n @classmethod\n def update_risk_model(cls, model: RiskModel) -> RiskModel:\n return GsSession.current.sync.put(f'/risk/models/{model.id}', model, cls=RiskModel)\n\n @classmethod\n def delete_risk_model(cls, model_id: str) -> Dict:\n return GsSession.current.sync.delete(f'/risk/models/{model_id}')\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_risk_model_calendar(cls, model_id: str) -> RiskModelCalendar:\n return GsSession.current.sync.get(f'/risk/models/{model_id}/calendar', cls=RiskModelCalendar)\n\n @classmethod\n def upload_risk_model_calendar(cls, model_id: str, model_calendar: RiskModelCalendar) -> RiskModelCalendar:\n return GsSession.current.sync.put(f'/risk/models/{model_id}/calendar', model_calendar, cls=RiskModelCalendar)\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_risk_model_dates(\n cls, model_id: str, start_date: dt.date = None, end_date: dt.date = None, event_type: RiskModelEventType = None\n ) -> List:\n url = f'/risk/models/{model_id}/dates?'\n if start_date is not None:\n url += f'&startDate={start_date.strftime(\"%Y-%m-%d\")}'\n if end_date is not None:\n url += f'&endDate={end_date.strftime(\"%Y-%m-%d\")}'\n if event_type is not None:\n url += f'&eventType={event_type.value}'\n return GsSession.current.sync.get(url)['results']\n\n\nclass GsFactorRiskModelApi(GsRiskModelApi):\n def __init__(self):\n super().__init__()\n\n @classmethod\n def get_risk_model_factors(cls, model_id: str) -> Tuple[Factor, ...]:\n return GsSession.current.sync.get(f'/risk/models/{model_id}/factors', cls=Factor)['results']\n\n @classmethod\n def create_risk_model_factor(cls, model_id: str, factor: Factor) -> Factor:\n return GsSession.current.sync.post(f'/risk/models/{model_id}/factors', factor, cls=Factor)\n\n @classmethod\n def get_risk_model_factor(cls, model_id: str, factor_id: str) -> Factor:\n return GsSession.current.sync.get(f'/risk/models/{model_id}/factors/{factor_id}')\n\n @classmethod\n def update_risk_model_factor(cls, model_id: str, factor: Factor) -> Factor:\n url = f'/risk/models/{model_id}/factors/{factor.identifier}'\n return GsSession.current.sync.put(url, factor, cls=Factor)\n\n @classmethod\n def delete_risk_model_factor(cls, model_id: str, factor_id: str) -> Dict:\n return GsSession.current.sync.delete(f'/risk/models/{model_id}/factors/{factor_id}')\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_risk_model_factor_data(\n cls,\n model_id: str,\n start_date: dt.date = None,\n end_date: dt.date = None,\n identifiers: List[str] = None,\n include_performance_curve: bool = False,\n factor_categories: List[str] = None,\n names: List[str] = None,\n ) -> List[Dict]:\n url = f'/risk/models/{model_id}/factors/data?'\n if start_date is not None:\n url += f'&startDate={start_date.strftime(\"%Y-%m-%d\")}'\n if end_date is not None:\n url += f'&endDate={end_date.strftime(\"%Y-%m-%d\")}'\n if identifiers is not None:\n url += '&identifiers={ids}'.format(ids='&identifiers='.join(identifiers))\n if include_performance_curve:\n url += '&includePerformanceCurve=true'\n if names:\n url += '&name={names}'.format(names='&name='.join(names))\n if factor_categories:\n url += '&factorCategory={factor_categories}'.format(\n factor_categories='&factorCategory='.join(factor_categories)\n )\n return GsSession.current.sync.get(url)['results']\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_risk_model_factor_data_intraday(\n cls,\n model_id: str,\n start_time: dt.datetime = None,\n end_time: dt.datetime = None,\n factor_ids: List[str] = None,\n factor_categories: List[str] = None,\n factors: List[str] = None,\n data_source: Union[IntradayFactorDataSource, str] = None,\n ) -> List[Dict]:\n url = f'/risk/models/{model_id}/factors/data/intraday?'\n if start_time is not None:\n url += f'&startTime={start_time.strftime(\"%Y-%m-%dT%H:%M:%SZ\")}'\n if end_time is not None:\n url += f'&endTime={end_time.strftime(\"%Y-%m-%dT%H:%M:%SZ\")}'\n if factor_ids is not None:\n url += '&factorId={ids}'.format(ids='&factorId='.join(factor_ids))\n if data_source:\n url += f'&dataSource={data_source if isinstance(data_source, str) else data_source.value}'\n if factors:\n url += '&factor={names}'.format(names='&factor='.join(factors))\n if factor_categories:\n url += '&factorCategory={factor_categories}'.format(\n factor_categories='&factorCategory='.join(factor_categories)\n )\n return GsSession.current.sync.get(url)['results']\n\n @classmethod\n def get_risk_model_coverage(\n cls, asset_ids: List[str] = None, as_of_date: dt.datetime = None, sort_by_term: RiskModelTerm = None\n ) -> List[Dict]:\n query = {}\n if asset_ids is not None:\n query['assetIds'] = asset_ids\n if as_of_date is not None:\n query['asOfDate'] = as_of_date.strftime('%Y-%m-%d')\n if sort_by_term is not None:\n query['sortByTerm'] = sort_by_term\n return GsSession.current.sync.post('/risk/models/coverage', query, timeout=200)['results']\n\n @classmethod\n def upload_risk_model_data(\n cls,\n model_id: str,\n model_data: Union[Dict, RiskModelData],\n partial_upload: bool = False,\n target_universe_size: float = None,\n final_upload: bool = None,\n aws_upload: bool = False,\n ) -> str:\n url = f'/risk/models/data/{model_id}'\n if partial_upload:\n url += '?partialUpload=true'\n if target_universe_size:\n url += f'&targetUniverseSize={target_universe_size}'\n if final_upload is not None:\n final_upload_flag = 'true' if final_upload else 'false'\n url += f'&finalUpload={final_upload_flag}'\n if aws_upload:\n url += '&awsUpload=true'\n else:\n if aws_upload:\n url += '?awsUpload=true'\n return GsSession.current.sync.post(url, model_data, timeout=200)\n\n @classmethod\n @backoff.on_exception(lambda: backoff.expo(base=2, factor=2), (MqTimeoutError, MqInternalServerError), max_tries=5)\n @backoff.on_exception(lambda: backoff.constant(90), MqRateLimitedError, max_tries=5)\n def get_risk_model_data(\n cls,\n model_id: str,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: RiskModelDataAssetsRequest = None,\n measures: List[RiskModelDataMeasure] = None,\n factors: list = None,\n limit_factors: bool = None,\n ) -> Dict:\n end_date = cls.get_risk_model_dates(model_id)[-1] if not end_date else end_date.strftime('%Y-%m-%d')\n query = {'startDate': start_date.strftime('%Y-%m-%d'), 'endDate': end_date}\n if assets is not None:\n query['assets'] = assets\n if measures is not None:\n query['measures'] = measures\n if factors is not None:\n query['factors'] = factors\n if limit_factors is not None:\n query['limitFactors'] = limit_factors\n return GsSession.current.sync.post(f'/risk/models/data/{model_id}/query', query, timeout=200)\n" }, { "path": "gs_quant/api/gs/scenarios.py", "content": "\"\"\"\nCopyright 2024 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom typing import Dict, List, Tuple\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.risk import Scenario\n\n_logger = logging.getLogger(__name__)\n\n\nclass GsScenarioApi:\n \"\"\"GS Scenarios API client implementation\"\"\"\n\n @classmethod\n def create_scenario(cls, scenario: Scenario) -> Scenario:\n return GsSession.current.sync.post('/risk/scenarios', scenario, cls=Scenario)\n\n @classmethod\n def get_scenario(cls, scenario_id: str) -> Scenario:\n return GsSession.current.sync.get(f'/risk/scenarios/{scenario_id}', cls=Scenario)\n\n @classmethod\n def get_many_scenarios(\n cls, ids: List[str] = None, names: List[str] = None, limit: int = 100, **kwargs\n ) -> Tuple[Scenario, ...]:\n url = f'/risk/scenarios?limit={limit}'\n if ids:\n url += f'&id={\"&id=\".join(ids)}'\n if names:\n url += f'&name={\"&name=\".join(names)}'\n if kwargs:\n for k, v in kwargs.items():\n url += f'&{k}={f\"&{k}=\".join(v)}' if isinstance(v, list) else f'&{k}={v}'\n\n return GsSession.current.sync.get(url, cls=Scenario).get('results', [])\n\n @classmethod\n def get_scenario_by_name(cls, name: str) -> Scenario:\n url = f\"/risk/scenarios?name={name}\"\n ret = GsSession.current.sync.get(url, cls=Scenario)\n num_found = ret.get('totalResults', 0)\n\n if num_found == 0:\n raise ValueError(f'Scenario {name}not found')\n elif num_found > 1:\n raise ValueError(f'More than one scemario named {name}')\n else:\n return ret['results'][0]\n\n @classmethod\n def update_scenario(cls, scenario: Scenario) -> Dict:\n return GsSession.current.sync.put(f'/risk/scenarios/{scenario.id_}', scenario, cls=Scenario)\n\n @classmethod\n def delete_scenario(cls, scenario_id: str) -> Dict:\n return GsSession.current.sync.delete(f'/risk/scenarios/{scenario_id}')\n\n @classmethod\n def calculate_scenario(cls, request: Dict) -> Dict:\n return GsSession.current.sync.post('/scenarios/calculate', request)\n\n\nclass GsFactorScenarioApi(GsScenarioApi):\n def __init__(self):\n super().__init__()\n\n @classmethod\n def get_many_scenarios(\n cls,\n ids: List[str] = None,\n names: List[str] = None,\n limit: int = 100,\n type: str = None,\n risk_model: str = None,\n shocked_factors: List[str] = None,\n shocked_factor_categories: List[str] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n tags: List[str] = None,\n ) -> Tuple[Scenario, ...]:\n factor_scenario_args = {}\n if risk_model:\n factor_scenario_args['riskModel'] = risk_model\n if type:\n factor_scenario_args['factorScenarioType'] = type\n if shocked_factors:\n factor_scenario_args['shockedFactor'] = shocked_factors\n if shocked_factor_categories:\n factor_scenario_args['shockedFactorCategory'] = shocked_factor_categories\n if start_date:\n factor_scenario_args['historicalSimulationStartDate'] = start_date\n if end_date:\n factor_scenario_args['historicalSimulationEndDate'] = end_date\n if tags:\n factor_scenario_args['tags'] = tags\n\n many_scenarios = super().get_many_scenarios(ids=ids, names=names, limit=limit, **factor_scenario_args)\n many_scenarios = tuple([scenario for scenario in many_scenarios if scenario.type_])\n\n return many_scenarios\n\n @classmethod\n def calculate_scenario(cls, calculation_request: Dict) -> Dict:\n return super().calculate_scenario(request=calculation_request)\n" }, { "path": "gs_quant/api/gs/screens.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom typing import Tuple, List\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.screens import Screen\nfrom gs_quant.target.assets_screener import AssetScreenerRequest\n\n_logger = logging.getLogger(__name__)\n\n\nclass GsScreenApi:\n @classmethod\n def get_screens(\n cls, screen_ids: List[str] = None, screen_names: List[str] = None, limit: int = 100\n ) -> Tuple[Screen, ...]:\n url = '/screens?'\n if screen_ids:\n url += f'&id={\"&id=\".join(screen_ids)}'\n if screen_names:\n url += f'&name={\"&name=\".join(screen_names)}'\n return GsSession.current.sync.get(f'{url}&limit={limit}', cls=Screen)['results']\n\n @classmethod\n def get_screen(cls, screen_id: str) -> Screen:\n return GsSession.current.sync.get(f'/screens/{screen_id}', cls=Screen)\n\n @classmethod\n def create_screen(cls, screen: Screen) -> Screen:\n return GsSession.current.sync.post('/screens', screen, cls=Screen)\n\n @classmethod\n def update_screen(cls, screen: Screen) -> Screen:\n return GsSession.current.sync.put(f'/screens/{screen.id}', screen, cls=Screen)\n\n @classmethod\n def delete_screen(cls, screen_id: str) -> str:\n return GsSession.current.sync.delete(f'/screens/{screen_id}')\n\n @classmethod\n def get_filter_options(cls) -> dict:\n return GsSession.current.sync.get('/assets/screener/options')\n\n @classmethod\n def calculate(cls, payload: AssetScreenerRequest) -> dict:\n return GsSession.current.sync.post('/assets/screener', payload=payload)\n" }, { "path": "gs_quant/api/gs/secmaster.py", "content": "\"\"\"\nCopyright 2023 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport json\nimport math\nfrom enum import Enum\nfrom functools import partial\nfrom itertools import groupby\nfrom typing import Union, Iterable, Dict, Optional\n\nimport tqdm\n\nfrom gs_quant.data.utilities import SecmasterXrefFormatter\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.secmaster import SecMasterAssetType\n\nDEFAULT_SCROLL_PAGE_SIZE = 500\n\n\nclass SecMasterIdentifiers(Enum):\n CUSIP = 'cusip'\n TICKER = 'ticker'\n ISIN = 'isin'\n GSID = 'gsid'\n BBG = 'bbg'\n BBID = 'bbid'\n BCID = 'bcid'\n RIC = 'ric'\n RCIC = 'rcic'\n ID = 'id'\n ASSET_ID = 'assetId'\n CUSIP8 = 'cusip8'\n SEDOL = 'sedol'\n CINS = 'cins'\n PRIMEID = 'primeId'\n FACTSET_REGIONAL_ID = 'factSetRegionalId'\n TOKEN_ID = 'tokenId'\n COMPOSITE_FIGI = 'compositeFigi'\n BARRA_ID = 'barraId'\n AXIOMA_ID = 'axiomaId'\n FIGI = 'figi'\n\n\ndef __extend_enum(base_enum, new_values):\n members = {item.name: item.value for item in base_enum}\n members.update(new_values)\n return Enum('CapitalStructureIdentifiers', members)\n\n\n# FIXME Create an enum in service def for this and reference once module_generator get fixed.\nCapitalStructureIdentifiers = __extend_enum(SecMasterIdentifiers, {\"ISSUER_ID\": \"issuerId\"})\n\n\nclass ExchangeId(Enum):\n RIC_SUFFIX_CODE = \"ricSuffixCode\"\n RIC_EXCHANGE_CODE = \"ricExchangeCode\"\n DATASCOPE_IPC_CODE = \"datascopeIpcCode\"\n BBG_EXCHANGE_CODE = \"bbgExchangeCode\"\n TAQ_EXCHANGE_CODE = \"taqExchangeCode\"\n IVERSON_EXCHANGE_CODE = \"iversonExchangeCode\"\n INDEX_CHANGE_EXCHANGE_CODE = \"indexChangeExchangeCode\"\n DOW_JONES_EXCHANGE_CODE = \"dowJonesExchangeCode\"\n STOXX_EXCHANGE_CODE = \"stoxxExchangeCode\"\n ML_ETF_EXCHANGE_CODE = \"mlEtfExchangeCode\"\n FTSE_EXCHANGE_CODE = \"ftseExchangeCode\"\n DJGI_EXCHANGE_CODE = \"djgiExchangeCode\"\n SECDB_EXCHANGE_CODE = \"secdbExchangeCode\"\n DADD_EXCHANGE_CODE = \"daddExchangeCode\"\n MIC = \"mic\"\n OPERATING_MIC = \"operatingMic\"\n GS_EXCHANGE_ID = \"gsExchangeId\"\n COUNTRY = \"country\"\n EXCHANGE_NAME = \"name\"\n\n\nclass GsSecurityMasterApi:\n @classmethod\n def get_security(cls, id_value: str, id_type: SecMasterIdentifiers, effective_date: dt.date = None):\n \"\"\"\n Get flatten asset reference data\n\n @param id_value: identifier value\n @param id_type: identifier type\n @param effective_date: As of date for query\n @return: dict or None\n \"\"\"\n args = {id_type.value: id_value}\n results = cls.get_many_securities(effective_date=effective_date, flatten=False, **args)\n if results is not None:\n return results[\"results\"][0]\n return results\n\n @classmethod\n def get_many_securities(\n cls,\n type_: SecMasterAssetType = None,\n effective_date: dt.date = None,\n limit: int = 10,\n flatten=False,\n is_primary=None,\n offset_key: str = None,\n **query_params: Dict[SecMasterIdentifiers, Union[str, Iterable[str]]],\n ) -> Optional[dict]:\n \"\"\"\n Get reference data for a single page of a given asset type. Use returned offsetKey to fetch next page.\n\n @param is_primary:\n @param flatten: flag if data should be flattened\n @param type_: asset type\n @param effective_date: As of date for query\n @param limit: integer of individual page\n @param offset_key: string, an offset indicating where the page ends.\n @return: list of dict\n \"\"\"\n\n if (query_params is None or len(query_params) == 0) and type_ is None:\n raise ValueError(\"Neither '_type' nor 'query_params' are provided\")\n\n params = {\"limit\": limit}\n\n cls.prepare_params(params, is_primary, offset_key, type_, effective_date)\n\n params = {**params, **query_params}\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n\n if flatten:\n r = GsSession.current.sync.get('/markets/securities/data', payload=payload)\n else:\n r = GsSession.current.sync.get('/markets/securities', payload=payload)\n if r['totalResults'] == 0:\n return None\n return r\n\n @classmethod\n def get_all_securities(\n cls,\n type_: SecMasterAssetType = None,\n effective_date: dt.date = None,\n is_primary=None,\n flatten=False,\n **query_params,\n ) -> Optional[dict]:\n \"\"\"\n Get all securities reference data matching the type, with respect of effective_date property.\n Function runs in batches fetching all securities.\n\n @param flatten: Flag, whether data should be flattened\n @param is_primary: Restrict to primary listings\n @param type_: asset type\n @param effective_date: As of date for query\n @return:\" list of dict\n \"\"\"\n if 'limit' in query_params:\n limit = query_params['limit']\n del query_params['limit']\n else:\n limit = DEFAULT_SCROLL_PAGE_SIZE\n\n response = cls.get_many_securities(\n type_, effective_date, limit=limit, offset_key=None, flatten=flatten, is_primary=is_primary, **query_params\n )\n if response is None or \"offsetKey\" not in response:\n return response\n\n if response['totalResults'] == 0:\n return None\n\n results = response[\"results\"]\n offset_key = response[\"offsetKey\"]\n\n fn = partial(\n cls.get_many_securities,\n type_=type_,\n effective_date=effective_date,\n limit=limit,\n flatten=flatten,\n **query_params,\n )\n results.extend(cls.__fetch_all(fn, offset_key))\n response[\"totalResults\"] = len(results)\n response[\"results\"] = results\n\n return response\n\n @classmethod\n def get_security_data(\n cls, id_value: str, id_type: SecMasterIdentifiers, effective_date: dt.date = None\n ) -> Optional[dict]:\n \"\"\"\n Get flatten asset reference data\n\n @param id_value: identifier value\n @param id_type: identifier type\n @param effective_date: As of date for query\n @return: dict or None\n \"\"\"\n args = {id_type.value: id_value}\n results = cls.get_many_securities(effective_date=effective_date, flatten=True, **args)\n if results is not None:\n return results[\"results\"][0]\n return results\n\n @classmethod\n def get_identifiers(cls, secmaster_id: str) -> dict:\n \"\"\"\n Get identifiers history for given secmaster id.\n\n @param secmaster_id: secmaster id e.g. ['GSPD111E123']\n @return: list of dict with date ranges of the identifiers.\n \"\"\"\n if not secmaster_id.startswith(\"GS\"):\n raise ValueError(f\"Invalid id_value {secmaster_id}. Secmaster id starts with 'GS'\")\n r = GsSession.current.sync.get(f'/markets/securities/{secmaster_id}/identifiers')\n return r['results']\n\n @classmethod\n def get_many_identifiers(cls, ids: Iterable[str], limit=100, xref_format=False) -> dict:\n \"\"\"\n Get identifiers for a list of secmaster ids. It runs in batches till all data is fetched.\n\n This method retrieves identifier information for multiple securities.\n The data can be returned in either standard format or transformed using the SecmasterXrefFormatter\n for time-based cross-reference analysis.\n\n Args:\n ids (Iterable[str]): An iterable collection of secmaster identifiers to query.\n Examples: ['GSPD111E123', 'GSPD222F456', 'GSPD333G789']\n\n limit (int, optional): Maximum number of identifier records to return per secmaster id.\n Defaults to 100. Used to control response size and prevent\n memory issues with large datasets.\n\n xref_format (bool, optional): Flag to control output format. Defaults to False.\n - False: Returns raw identifier data in standard format\n - True: Returns data transformed by SecmasterXrefFormatter,\n where identifiers are grouped by overlapping date\n ranges\n\n Returns:\n dict: A dictionary containing identifier information for the requested secmaster ids.\n\n Standard format (xref_format=False):\n {\n 'secmaster_id_1': [\n {\n 'type': 'ISIN',\n 'value': 'US1234567890',\n 'startDate': '2020-01-01',\n 'endDate': '2023-12-31'\n },\n ...\n ],\n 'secmaster_id_2': [...],\n ...\n }\n\n Xref format (xref_format=True):\n {\n 'secmaster_id_1': {\n 'xrefs': [\n {\n 'startDate': '2020-01-01',\n 'endDate': '2023-12-31',\n 'identifiers': [\n {'type': 'ISIN', 'value': 'US1234567890'},\n {'type': 'CUSIP', 'value': '123456789'},\n ...\n ]\n },\n ...\n ]\n },\n 'secmaster_id_2': {...},\n ...\n }\n\n Raises:\n ValueError: If ids parameter is empty or contains invalid secmaster identifiers\n ConnectionError: If unable to connect to secmaster database\n TimeoutError: If database query exceeds timeout limits\n\n Note:\n - The method processes requests in batches to handle large datasets efficiently\n - Infinity dates ('9999-99-99') are normalized to '9999-12-31' in xref format\n\n\n Example:\n >>> # Standard format\n >>> result = GsSecurityMasterApi.get_many_identifiers(['GSPD111E123'], limit=50, xref_format=False)\n >>> print(result['GSPD111E123'][0]['type']) # 'ISIN'\n\n >>> # Xref format for time-based analysis\n >>> xref_result = GsSecurityMasterApi.get_many_identifiers(['GSPD111E123'], xref_format=True)\n >>> print(xref_result['GSPD111E123']['xrefs'][0]['startDate']) # '2020-01-01'\n \"\"\"\n if not isinstance(ids, Iterable):\n raise ValueError(f\"secmaster_id must be an iterable, got {type(ids)}\")\n if len(ids) == 0:\n raise ValueError(\"secmaster_id cannot be an empty iterable\")\n ids = list(ids)\n\n for id_value in ids:\n if not id_value.startswith(\"GS\"):\n raise ValueError(f\"Invalid id_value {id_value}. Secmaster id starts with 'GS'\")\n\n consolidated_results = {}\n current_offset_key = None\n\n while True:\n payload = {'id': ids}\n\n if current_offset_key is not None:\n payload['offsetKey'] = current_offset_key\n if limit is not None:\n payload['limit'] = limit\n\n payload = json.loads(json.dumps(payload, cls=JSONEncoder))\n response = GsSession.current.sync.get('/markets/securities/identifiers', payload=payload)\n\n if 'results' in response:\n for entity_id, data in response['results'].items():\n if entity_id not in consolidated_results:\n consolidated_results[entity_id] = []\n consolidated_results[entity_id].extend(data)\n\n current_offset_key = response.get('offsetKey')\n if current_offset_key is None:\n break\n\n if xref_format:\n return SecmasterXrefFormatter.convert(consolidated_results)\n return consolidated_results\n\n @classmethod\n def map(\n cls,\n input_type: SecMasterIdentifiers,\n ids: Iterable[str],\n output_types: Iterable[SecMasterIdentifiers] = frozenset([SecMasterIdentifiers.GSID]),\n start_date: dt.date = None,\n end_date: dt.date = None,\n effective_date: dt.date = None,\n ) -> Iterable[dict]:\n \"\"\"\n Map to other identifier types, from given IDs.\n\n :param input_type: type of input IDs\n :param ids: security IDs\n :param output_types: types of IDs to map to\n :param start_date: first as-of date (defaults to current date)\n :param end_date: last as-of date (defaults to current date)\n :param effective_date: an exact as-of date for mapping\n :return: dict containing mappings for as-of date(s)\n \"\"\"\n params = {\n input_type.value: list(ids),\n 'toIdentifiers': [identifier.value for identifier in output_types],\n 'compact': True,\n }\n if effective_date is not None:\n if (start_date or end_date) is not None:\n raise ValueError('provide (start date / end date) or effective_date, but not both')\n params['effectiveDate'] = effective_date\n\n if start_date is not None:\n params['startDate'] = start_date\n if end_date is not None:\n params['endDate'] = end_date\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n\n r = GsSession.current.sync.get('/markets/securities/map', payload)\n results = r['results']\n return results\n\n @classmethod\n def search(\n cls,\n q: str,\n limit: int = 10,\n type_: SecMasterAssetType = None,\n is_primary: bool = None,\n active_listing: bool = None,\n ) -> Union[Iterable[dict], None]:\n \"\"\"\n Search securities by a query string. It does a full text search among names, identifiers, company\n @param q: query string\n @param limit: number of returned matches\n @param type_: filter restricting the type of results\n @param is_primary: filter restricting the matches to primary listings\n @param active_listing: filter restricting the matches to active listings\n @return:\n \"\"\"\n params = {\"q\": q, \"limit\": limit}\n if type_ is not None:\n params[\"type\"] = type_.value\n if is_primary is not None:\n params[\"isPrimary\"] = is_primary\n if active_listing is not None:\n params[\"activeListing\"] = active_listing\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n r = GsSession.current.sync.get('/v2/markets/securities/search', payload=payload, include_version=False)\n if r['totalResults'] == 0:\n return None\n return r[\"results\"]\n\n @classmethod\n def __stringify_boolean(cls, bool_value):\n return str(bool_value).lower()\n\n @classmethod\n def __fetch_all(cls, fetch_fn, offset_key, total_batches=None, extract_results=True):\n accumulator = []\n offset = offset_key\n progress_info = (\n tqdm.tqdm(desc=\"Processing\", unit=\" batch\")\n if total_batches is None\n else tqdm.tqdm(range(total_batches), desc=\"Processing\", unit=\" batch\", ascii=True)\n )\n while True:\n progress_info.update(1)\n data = fetch_fn(offset_key=offset)\n if data is not None:\n if extract_results is True:\n accumulator.extend(data['results'])\n else:\n accumulator.append(data)\n if 'offsetKey' not in data:\n progress_info.close()\n break\n offset = data[\"offsetKey\"]\n return accumulator\n\n @classmethod\n def _get_corporate_actions(cls, id_value: str, id_type: SecMasterIdentifiers, effective_date: dt.date, offset_key):\n params = {\n id_type.value: id_value,\n }\n if effective_date is not None:\n params['effectiveDate'] = effective_date\n\n if offset_key is not None:\n params[\"offsetKey\"] = offset_key\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n r = GsSession.current.sync.get(\"/markets/corpactions\", payload=payload)\n return r\n\n @classmethod\n def get_corporate_actions(\n cls, id_value: str, id_type: SecMasterIdentifiers = SecMasterIdentifiers.GSID, effective_date: dt.date = None\n ) -> Iterable[dict]:\n \"\"\"\n Get corporate actions from a given security.\n @param effective_date: parameter to query securities at a given date\n @param id_value: identifier value\n @param id_type: identifier type\n @return:\n \"\"\"\n supported_identifiers = [SecMasterIdentifiers.GSID, SecMasterIdentifiers.ID]\n if id_type not in supported_identifiers:\n raise ValueError(\n f\"Unsupported identifier {id_type} for this endpoint. Use one of this {supported_identifiers}\"\n )\n\n fn = partial(cls._get_corporate_actions, id_value, id_type, effective_date)\n results = cls.__fetch_all(fn, None)\n return results\n\n @classmethod\n def get_capital_structure(\n cls,\n id_value: Union[str, list],\n id_type: CapitalStructureIdentifiers,\n type_: SecMasterAssetType = None,\n is_primary: bool = None,\n effective_date: dt.date = None,\n ) -> dict:\n \"\"\"\n Get a capital structure of the given company by id_value of the security.\n It runs in batches till all data is fetched\n @param is_primary: filter to select primary listings only\n @param type_: filter to restrict data to a given type\n @param effective_date: parameter to query securities at a given date\n @param id_value: identifier value\n @param id_type: identifier type\n @return: dict\n \"\"\"\n response = cls._get_capital_structure(\n id_value=id_value,\n id_type=id_type,\n type_=type_,\n is_primary=is_primary,\n effective_date=effective_date,\n offset_key=None,\n )\n if \"offsetKey\" not in response:\n return response\n\n asset_types_total = response[\"assetTypesTotal\"]\n batch_count = math.floor(sum(asset_types_total.values()) / 100)\n results = response[\"results\"]\n offset_key = response[\"offsetKey\"]\n fn = partial(cls._get_capital_structure, id_value, id_type, type_, is_primary, effective_date)\n\n results.extend(cls.__fetch_all(fn, offset_key, total_batches=batch_count))\n aggregated_results, total_results = cls.__capital_structure_aggregate(asset_types_total, results)\n response[\"results\"] = aggregated_results\n response[\"totalResults\"] = total_results\n del response[\"offsetKey\"]\n\n return response\n\n @classmethod\n def __capital_structure_aggregate(cls, asset_types_total, results):\n group_by_issuer_id = {k: list(g) for k, g in groupby(results, lambda x: x[\"issuerId\"])}\n aggregated_results = []\n aggregated_total_results = 0\n for issuer_id in group_by_issuer_id:\n issuer_id_data = group_by_issuer_id[issuer_id]\n issuer_id_data_instance = issuer_id_data[0]\n consolidated_types_obj = {key: [] for key in asset_types_total}\n for obj in group_by_issuer_id[issuer_id]:\n aggregated_total_results += sum(len(e) for e in obj[\"types\"].values())\n [\n consolidated_types_obj.get(asset_type).extend(obj[\"types\"].get(asset_type))\n for asset_type in obj[\"types\"]\n ]\n issuer_id_data_instance[\"types\"] = consolidated_types_obj\n aggregated_results.append(issuer_id_data_instance)\n return aggregated_results, aggregated_total_results\n\n @classmethod\n def _get_capital_structure(\n cls,\n id_value: Union[str, list],\n id_type: Union[CapitalStructureIdentifiers, SecMasterIdentifiers],\n type_,\n is_primary,\n effective_date,\n offset_key: Union[str, None],\n ):\n params = {id_type.value: id_value}\n cls.prepare_params(params, is_primary, offset_key, type_, effective_date)\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n r = GsSession.current.sync.get(\"/markets/capitalstructure\", payload=payload)\n return r\n\n @classmethod\n def prepare_params(cls, params, is_primary, offset_key, type_, effective_date=None):\n if type_ is not None:\n params[\"type\"] = type_.value\n if is_primary is not None:\n params[\"isPrimary\"] = is_primary\n if offset_key is not None:\n params[\"offsetKey\"] = offset_key\n if effective_date is not None:\n params[\"effectiveDate\"] = effective_date\n\n @classmethod\n def _get_deltas(\n cls,\n start_time: dt.datetime = None,\n end_time: dt.datetime = None,\n raw: bool = None,\n scope: list = None,\n limit: int = None,\n offset_key: str = None,\n ) -> Iterable[dict]:\n params = {}\n if raw is not None:\n params[\"raw\"] = GsSecurityMasterApi.__stringify_boolean(raw)\n if start_time is not None:\n params[\"startTime\"] = start_time\n if end_time is not None:\n params[\"endTime\"] = end_time\n if scope is not None:\n params[\"scope\"] = scope\n if limit is not None:\n params[\"limit\"] = limit\n if offset_key is not None:\n params[\"offsetKey\"] = offset_key\n\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n r = GsSession.current.sync.get(\"/markets/securities/identifiers/updates-feed\", payload=payload)\n return r\n\n @classmethod\n def get_deltas(\n cls,\n start_time: dt.datetime = None,\n end_time: dt.datetime = None,\n raw: bool = None,\n scope: list = None,\n limit: int = None,\n offset_key: str = None,\n scroll_all_pages: bool = True,\n ) -> Union[dict, Iterable[dict]]:\n \"\"\"\n Get all identifier changes between two time stamps\n @param scroll_all_pages:\n @param start_time: start time\n @param end_time: end time\n @param limit: page size of returned matches\n @param scope: narrow down the search to a specific set of events\n @param offset_key: offset key to fetch next page\n @param raw: flag, if true (default) aggregates data to more readable form, if false shows unprocessed results.\n @return: list of dict\n \"\"\"\n if scroll_all_pages:\n fn = partial(cls._get_deltas, start_time, end_time, raw, scope, limit)\n results = cls.__fetch_all(fn, offset_key, extract_results=False)\n latest_update_time = max(result['lastUpdateTime'] for result in results)\n res = [item for result in results for item in result[\"results\"]]\n request_id = results[0][\"requestId\"] if results else None\n return {\"results\": res, \"lastUpdateTime\": latest_update_time, \"requestId\": request_id}\n else:\n results = cls._get_deltas(start_time, end_time, raw, scope, limit, offset_key)\n return results\n\n @classmethod\n def get_exchanges(cls, effective_date: dt.date = None, **query_params: Dict[str, Union[str, Iterable[str]]]):\n \"\"\"\n Returns reference data for exchanges - e.g. MICs, exchange codes, name, listing country.\n\n @param effective_date: As of date for query\n @param query_params: one of allowed params:\n 'ricSuffixCode', 'ricExchangeCode', 'datascopeIpcCode', 'bbgExchangeCode', 'taqExchangeCode',\n 'iversonExchangeCode', 'indexChangeExchangeCode', 'dowJonesExchangeCode', 'stoxxExchangeCode',\n 'mlEtfExchangeCode', 'ftseExchangeCode', 'djgiExchangeCode', 'secdbExchangeCode', 'daddExchangeCode', 'mic',\n 'operatingMic', 'gsExchangeId', 'country', 'name'\n @return:\n \"\"\"\n results = []\n fn = partial(cls._get_exchanges, effective_date, DEFAULT_SCROLL_PAGE_SIZE, query_params)\n results.extend(cls.__fetch_all(fn, offset_key=None))\n response = dict()\n response[\"totalResults\"] = len(results)\n response[\"results\"] = results\n\n return response\n\n @classmethod\n def _get_exchanges(\n cls, effective_date: dt.date = None, limit: int = 10, query_params=None, offset_key: Union[str, None] = None\n ):\n if query_params is None:\n query_params = dict()\n allowed_keys = list(ExchangeId._value2member_map_.keys())\n for qp in query_params.keys():\n if qp not in allowed_keys:\n raise ValueError(f\" Parameter '{qp}' is not supported. Allowed parameters: {allowed_keys}\")\n params = {\"limit\": limit}\n if effective_date is not None:\n params['effectiveDate'] = effective_date\n\n params = {**params, **query_params}\n if offset_key is not None:\n params[\"offsetKey\"] = offset_key\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n r = GsSession.current.sync.get('/markets/exchanges', payload=payload)\n if r['totalResults'] == 0:\n return None\n return r\n\n @classmethod\n def get_exchange_identifiers_history(cls, gs_exchange_id: str) -> Iterable[dict]:\n \"\"\"\n Get identifiers history for given exchange id.\n\n @param gs_exchange_id: exchange_id id\n @return: list of dict with date ranges of the identifiers.\n \"\"\"\n r = GsSession.current.sync.get(f'/markets/exchanges/{gs_exchange_id}/identifiers')\n return r['results']\n\n @classmethod\n def _prepare_string_or_list_param(cls, value: Union[str, list], param_name: str):\n if isinstance(value, str):\n return [value] # Wrap single string in a list\n elif isinstance(value, list) and all(isinstance(item, str) for item in value):\n return value # Use the list directly\n else:\n raise ValueError(f\"{param_name} must be a string or a list of strings\")\n\n @classmethod\n def _prepare_underlyers_params(\n cls,\n params,\n id_value,\n offset_key=None,\n type_=None,\n effective_date=None,\n country_code=None,\n currency=None,\n include_inactive=None,\n ):\n if id_value is not None:\n params[\"id\"] = cls._prepare_string_or_list_param(id_value, \"id_value\")\n else:\n raise ValueError(\"id_value must be defined\")\n\n if type_ is not None:\n if isinstance(type_, list):\n if all(isinstance(t, SecMasterAssetType) for t in type_):\n params[\"type\"] = [t.value for t in type_]\n else:\n raise ValueError(\"All elements in the type_ list must be instances of SecMasterAssetType\")\n elif isinstance(type_, SecMasterAssetType):\n params[\"type\"] = type_.value\n else:\n raise ValueError(\"type_ must be either a SecMasterAssetType or a list of SecMasterAssetType\")\n\n if country_code is not None:\n params[\"countryCode\"] = cls._prepare_string_or_list_param(country_code, \"country_code\")\n if currency is not None:\n params[\"currency\"] = cls._prepare_string_or_list_param(currency, \"currency\")\n if offset_key is not None:\n params[\"offsetKey\"] = offset_key\n if effective_date is not None:\n params[\"effectiveDate\"] = effective_date\n if include_inactive is not None:\n params[\"includeInactive\"] = include_inactive\n\n @classmethod\n def _get_securities_by_underlyers(\n cls,\n id_value: Union[str, list],\n type_: Union[SecMasterAssetType, list] = None,\n effective_date: dt.date = None,\n limit: int = 100,\n country_code: Union[str, list] = None,\n currency: Union[str, list] = None,\n include_inactive: bool = False,\n offset_key: str = None,\n ) -> Optional[dict]:\n params = {\"limit\": limit}\n\n cls._prepare_underlyers_params(\n params=params,\n id_value=id_value,\n offset_key=offset_key,\n type_=type_,\n effective_date=effective_date,\n country_code=country_code,\n currency=currency,\n include_inactive=include_inactive,\n )\n\n payload = json.loads(json.dumps(params, cls=JSONEncoder))\n\n r = GsSession.current.sync.get('/markets/securities/underlyers', payload=payload)\n if r['totalResults'] == 0:\n return None\n return r\n\n @classmethod\n def get_securities_by_underlyers(\n cls,\n id_value: Union[str, list],\n type_: Union[SecMasterAssetType, list] = None,\n effective_date: dt.date = None,\n limit: int = None,\n offset_key: str = None,\n country_code: Union[str, list] = None,\n currency: Union[str, list] = None,\n include_inactive: bool = False,\n scroll_all_pages: bool = False,\n ) -> Optional[dict]:\n \"\"\"\n Retrieve reference data for listed derivatives based on their underlyers.\n\n This method retrieves securities linked to specific underlyers, with optional filters for asset type, effective\n date, country code and currency. The results can be paginated, and the method supports fetching either\n a single page or all pages of results.\n\n Args:\n id_value (Union[str, list]): Identifier(s) of the underlyers. Can be a single string or a list of strings.\n Example: 'GSPD100E0' or ['GSPD100E0'].\n type_ (Union[SecMasterAssetType, list], optional): Asset type(s) to filter the results. Can be a single\n `SecMasterAssetType` or a list of `SecMasterAssetType`.\n Defaults to None.\n effective_date (dt.date, optional): Effective date for the query. Defaults to None.\n limit (int, optional): Maximum number of results to return per page. Defaults to None.\n offset_key (str, optional): String indicating where the page ends, used for pagination. Defaults to None.\n country_code (Union[str, list], optional): Country code(s) to filter the results. Can be a single string\n or a list of strings. Defaults to None.\n currency (Union[str, list], optional): Currency code(s) to filter the results. Can be a single string\n or a list of strings. Defaults to None.\n include_inactive (bool, optional): Whether to include inactive listed derivatives in the results.\n Defaults to False.\n scroll_all_pages (bool, optional): Whether to fetch all pages of results. If True, retrieves all pages.\n Defaults to False.\n\n Returns:\n Optional[dict]: A dictionary containing the results for the requested page(s), or None if no results are\n found.\n Example:\n {\n \"totalResults\": 10,\n \"offsetKey\": \"ABCD=\",\n \"results\": [...],\n \"asOfTime\": \"2025-01-11T11:22:33.740Z\",\n \"requestId\": \"abc123\"\n }\n\n Raises:\n ValueError: If `id_value` is not provided or is invalid.\n ValueError: If `type_` is not a valid `SecMasterAssetType` or a list of valid `SecMasterAssetType`.\n ValueError: If `country_code` or `currency` is not a string or a list of strings.\n ValueError: If `effective_date` is not in the format 'YYYY-MM-DD' when provided as a string.\n TypeError: If `effective_date` is not of type `datetime.date` or `str`.\n\n Note:\n - This method fetches a single page of results by default. To fetch all pages, set `scroll_all_pages=True`.\n - The `effective_date` parameter is used to query securities as of a specific date.\n - The `offset_key` parameter is used for pagination to fetch subsequent pages of results.\n\n Example:\n >>> result = GsSecurityMasterApi.get_securities_by_underlyers(\n ... id_value=\"GSPD100E0\",\n ... type_=SecMasterAssetType.Equity_Option,\n ... effective_date=\"2023-10-01\",\n ... country_code=\"US\",\n ... currency=\"USD\",\n ... include_inactive=False,\n ... scroll_all_pages=True\n ... )\n >>> print(result[\"results\"])\n \"\"\"\n\n supported_types = [\n SecMasterAssetType.Equity_Option,\n SecMasterAssetType.Future,\n SecMasterAssetType.Future_Option,\n ]\n if type_ is not None:\n if isinstance(type_, SecMasterAssetType):\n if type_ not in supported_types:\n raise ValueError(f\"Unsupported type {type_}. Supported types are {supported_types}\")\n elif isinstance(type_, list):\n for t in type_:\n if t not in supported_types:\n raise ValueError(f\"Unsupported type {t}. Supported types are {supported_types}\")\n\n if scroll_all_pages:\n if limit is None:\n limit = 500\n fn = partial(\n cls._get_securities_by_underlyers,\n id_value=id_value,\n type_=type_,\n effective_date=effective_date,\n limit=limit,\n country_code=country_code,\n currency=currency,\n include_inactive=include_inactive,\n )\n results = cls.__fetch_all(fn, offset_key, extract_results=False)\n as_of_time = max(result['asOfTime'] for result in results)\n res = [item for result in results for item in result[\"results\"]]\n request_id = results[0][\"requestId\"] if results else None\n total_results = len(res)\n return {\"totalResults\": total_results, \"results\": res, \"asOfTime\": as_of_time, \"requestId\": request_id}\n\n else:\n return cls._get_securities_by_underlyers(\n id_value=id_value,\n type_=type_,\n effective_date=effective_date,\n limit=limit,\n offset_key=offset_key,\n country_code=country_code,\n currency=currency,\n include_inactive=include_inactive,\n )\n" }, { "path": "gs_quant/api/gs/thematics.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport json\nfrom enum import Enum\nfrom typing import List\n\nfrom gs_quant.session import GsSession\n\n\nclass ThematicMeasure(Enum):\n \"\"\"\n Thematic Measures\n \"\"\"\n\n ALL_THEMATIC_EXPOSURES = 'allThematicExposures'\n TOP_FIVE_THEMATIC_EXPOSURES = 'topFiveThematicExposures'\n BOTTOM_FIVE_THEMATIC_EXPOSURES = 'bottomFiveThematicExposures'\n THEMATIC_BREAKDOWN_BY_ASSET = 'thematicBreakdownByAsset'\n NO_THEMATIC_DATA = 'noThematicData'\n NO_PRICING_DATA = 'noPricingData'\n\n def __str__(self):\n return self.value\n\n\nclass Region(Enum):\n \"\"\"\n Thematic Regions\n \"\"\"\n\n AMERICAS = 'Americas'\n ASIA = 'Asia'\n EUROPE = 'Europe'\n\n\nclass GsThematicApi:\n \"\"\"GS Thematic API client implementation\"\"\"\n\n @classmethod\n def get_thematics(\n cls,\n entity_id: str,\n basket_ids: List[str] = None,\n regions: List[Region] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n measures: List[ThematicMeasure] = None,\n notional: float = None,\n ) -> List:\n payload = {\n 'id': entity_id,\n }\n if basket_ids:\n payload['basketId'] = basket_ids\n if regions:\n payload['region'] = [r.value for r in regions]\n if start_date:\n payload['startDate'] = start_date.strftime(\"%Y-%m-%d\")\n if end_date:\n payload['endDate'] = end_date.strftime(\"%Y-%m-%d\")\n if measures:\n payload['measures'] = [m.value for m in measures]\n if notional:\n payload['notional'] = notional\n return GsSession.current.sync.post('/thematics', payload=json.dumps(payload)).get('results', [])\n" }, { "path": "gs_quant/api/gs/users.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import List, Any, Dict, Optional\n\nfrom pydash import get\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.reports import User\n\nDEFAULT_SEARCH_FIELDS = [\n \"id\",\n \"name\",\n \"firstName\",\n \"lastName\",\n \"kerberos\",\n \"company\",\n \"departmentName\",\n \"divisionName\",\n \"city\",\n \"country\",\n \"region\",\n \"title\",\n \"email\",\n \"internal\",\n]\n\n\nclass GsUsersApi:\n @classmethod\n def get_users(\n cls,\n user_ids: List[str] = None,\n user_emails: List[str] = None,\n user_names: List[str] = None,\n user_companies: List[str] = None,\n limit: int = 100,\n offset: int = 0,\n ) -> List:\n url = '/users?'\n if user_ids:\n url += f'&id={\"&id=\".join(user_ids)}'\n if user_emails:\n url += f'&email={\"&email=\".join(user_emails)}'\n if user_names:\n url += f'&name={\"&name=\".join(user_names)}'\n if user_companies:\n url += f'&company={\"&company=\".join(user_companies)}'\n return GsSession.current.sync.get(f'{url}&limit={limit}&offset={offset}', cls=User)['results']\n\n @classmethod\n def get_my_guid(cls) -> str:\n return f\"guid:{GsSession.current.sync.get('/users/self')['id']}\"\n\n @classmethod\n def get_current_user_info(cls) -> Dict[str, Any]:\n \"\"\"\n Gets user\n :return: user\n \"\"\"\n return GsSession.current.sync.get('/users/self')\n\n @classmethod\n def get_current_app_managers(cls) -> List[str]:\n return [f\"guid:{manager}\" for manager in get(GsSession.current.sync.get('/users/self'), 'appManagers', [])]\n\n @classmethod\n def get_many(cls, key_type: str, keys: List[str], fields: Optional[List[str]] = None) -> dict:\n users_by_key = {}\n chunk_size = 100\n glue = \"&\" + key_type + \"=\"\n if fields is not None and key_type not in fields:\n fields = [*fields, key_type]\n for i in range(0, len(keys), chunk_size):\n chunk = keys[i : i + chunk_size]\n fields_str = f\"fields={','.join(fields)}&\" if fields else ''\n url = f'/users?{fields_str}{key_type}={glue.join(chunk)}&limit=200'\n response = GsSession.current.sync.get(url)\n for user in response.get('results', []):\n users_by_key[user[key_type]] = user\n return users_by_key\n\n @classmethod\n def search(\n cls, query: str, fields: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None\n ) -> Dict[str, Any]:\n payload = {\n \"q\": query,\n \"fields\": fields or DEFAULT_SEARCH_FIELDS,\n **({\"where\": where} if where else {}),\n }\n return GsSession.current.sync.post(\"/search/users/query\", payload=payload)\n" }, { "path": "gs_quant/api/gs/workspaces.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport urllib.parse\nfrom typing import Tuple, Dict\n\nfrom pydash import get\n\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.workspaces_markets import Workspace\n\nimport webbrowser\n\nAPI = '/workspaces/markets'\nWORKSPACES_MARKETS_HEADERS: Dict[str, str] = {'Content-Type': 'application/json;charset=utf-8'}\n\n\nclass GsWorkspacesMarketsApi:\n \"\"\"GS Workspaces Markets API implementation\"\"\"\n\n @classmethod\n def get_workspaces(cls, limit: int = 10, **kwargs) -> Tuple[Workspace, ...]:\n return GsSession.current.sync.get(f'{API}?limit={limit}&{urllib.parse.urlencode(kwargs)}', cls=Workspace)[\n 'results'\n ]\n\n @classmethod\n def get_workspace(cls, workspace_id: str):\n return GsSession.current.sync.get(f'{API}/{workspace_id}', cls=Workspace)\n\n @classmethod\n def get_workspace_by_alias(cls, alias: str) -> Workspace:\n workspace = get(GsSession.current.sync.get(f'{API}?alias={alias}', cls=Workspace), 'results.0')\n if not workspace:\n raise ValueError(f'Workspace with alias {alias} not found')\n return workspace\n\n @classmethod\n def create_workspace(cls, workspace: Workspace) -> Workspace:\n return GsSession.current.sync.post(\n f'{API}', workspace, cls=Workspace, request_headers=WORKSPACES_MARKETS_HEADERS\n )\n\n @classmethod\n def update_workspace(cls, workspace: Workspace):\n return GsSession.current.sync.put(\n f'{API}/{workspace.id}', workspace, cls=Workspace, request_headers=WORKSPACES_MARKETS_HEADERS\n )\n\n @classmethod\n def delete_workspace(cls, workspace_id: str) -> Dict:\n return GsSession.current.sync.delete(f'{API}/{workspace_id}')\n\n @classmethod\n def open_workspace(cls, workspace: Workspace):\n if workspace.alias:\n webbrowser.open(f'{GsSession.current.domain.replace(\".web\", \"\")}/s/markets/{workspace.alias}')\n else:\n webbrowser.open(f'{GsSession.current.domain.replace(\".web\", \"\")}/s/markets/{workspace.id}')\n" }, { "path": "gs_quant/api/risk.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport asyncio\nimport itertools\nimport logging\nimport queue\nimport sys\nfrom abc import ABCMeta, abstractmethod\nfrom concurrent.futures import TimeoutError\nfrom threading import Thread\nfrom typing import Iterable, Optional, Union, Tuple, Dict, Any\n\nfrom tqdm import tqdm\n\nfrom gs_quant.api.api_session import ApiWithCustomSession\nfrom gs_quant.base import RiskKey, Sentinel, Priceable\nfrom gs_quant.risk import ErrorValue, RiskRequest\nfrom gs_quant.risk.result_handlers import result_handlers\nfrom gs_quant.risk.results import PricingFuture\nfrom gs_quant.session import GsSession\nfrom gs_quant.tracing import Tracer, TracingSpan\n\n_logger = logging.getLogger(__name__)\n\n\nclass GenericRiskApi(ApiWithCustomSession, metaclass=ABCMeta):\n batch_dates = True\n\n @classmethod\n @abstractmethod\n def populate_pending_futures(\n cls, requests: list, session: GsSession, pending: Dict[Tuple[RiskKey, Priceable], PricingFuture], **kwargs\n ): ...\n\n @classmethod\n @abstractmethod\n def build_keyed_results(\n cls, request: RiskRequest, results: Union[Iterable, Exception]\n ) -> Dict[Tuple[RiskKey, Priceable], Any]: ...\n\n\nclass RiskApi(GenericRiskApi, metaclass=ABCMeta):\n __SHUTDOWN_SENTINEL = Sentinel('QueueListenerShutdown')\n\n @classmethod\n def populate_pending_futures(\n cls, requests: list, session: GsSession, pending: Dict[Tuple[RiskKey, Priceable], PricingFuture], **kwargs\n ):\n results = queue.Queue()\n done = False\n max_concurrent, progress_bar, timeout, span, cache_impl, is_async = [\n kwargs.get(arg) for arg in ['max_concurrent', 'progress_bar', 'timeout', 'span', 'cache_impl', 'is_async']\n ]\n try:\n with session:\n cls.run(requests, results, max_concurrent, progress_bar, timeout=timeout, span=span)\n except Exception as e:\n cls.enqueue(results, ((k, e) for k in pending.keys()))\n\n while pending and not done:\n done, chunk_results = cls.drain_queue(results)\n for (risk_key_, priceable_), result in chunk_results:\n future = pending.pop((risk_key_, priceable_), None)\n if future is not None:\n future.set_result(result)\n\n if cache_impl is not None:\n cache_impl.put(risk_key_, priceable_, result)\n\n if not is_async:\n # In async mode we can't tell if we've completed, we could be re-used\n while pending:\n (risk_key_, _), future = pending.popitem()\n future.set_result(ErrorValue(risk_key_, 'No result returned'))\n\n @classmethod\n @abstractmethod\n async def get_results(\n cls,\n responses: asyncio.Queue,\n results: asyncio.Queue,\n timeout: Optional[int] = None,\n span: Optional[TracingSpan] = None,\n ) -> Optional[str]: ...\n\n @classmethod\n @abstractmethod\n def calc(cls, request: RiskRequest) -> Iterable: ...\n\n @classmethod\n def calc_multi(cls, requests: Iterable[RiskRequest]) -> dict:\n return {request: cls.calc(request) for request in requests}\n\n @classmethod\n def __handle_queue_update(cls, q: Union[queue.Queue, asyncio.Queue], first: object) -> Tuple[bool, list]:\n if first is cls.__SHUTDOWN_SENTINEL:\n return True, []\n\n ret = [first]\n shutdown = False\n\n while True:\n try:\n elem = q.get_nowait()\n if elem is cls.__SHUTDOWN_SENTINEL:\n shutdown = True\n else:\n ret.append(elem)\n except (asyncio.QueueEmpty, queue.Empty):\n break\n\n return shutdown, ret\n\n @classmethod\n def drain_queue(cls, q: queue.Queue, timeout: Optional[int] = None) -> Tuple[bool, list]:\n try:\n return cls.__handle_queue_update(q, q.get(timeout=timeout))\n except queue.Empty:\n return False, []\n\n @classmethod\n async def drain_queue_async(cls, q: asyncio.Queue, timeout: Optional[int] = None) -> Tuple[bool, list]:\n try:\n elem = await asyncio.wait_for(q.get(), timeout=timeout) if timeout else await q.get()\n return cls.__handle_queue_update(q, elem)\n except (TimeoutError, asyncio.TimeoutError):\n return False, []\n\n @classmethod\n def enqueue(\n cls,\n q: Union[queue.Queue, asyncio.Queue],\n items: Iterable,\n loop: Optional[asyncio.AbstractEventLoop] = None,\n wait: Optional[bool] = False,\n ):\n try:\n iter(items)\n except TypeError:\n items = (items,)\n\n put = q.put if wait else q.put_nowait\n\n for item in items:\n if loop:\n loop.call_soon_threadsafe(put, item)\n else:\n put(item)\n\n @classmethod\n def shutdown_queue_listener(\n cls, q: Union[queue.Queue, asyncio.Queue], loop: Optional[asyncio.AbstractEventLoop] = None\n ):\n if loop and not loop.is_closed():\n loop.call_soon_threadsafe(q.put_nowait, cls.__SHUTDOWN_SENTINEL)\n else:\n q.put_nowait(cls.__SHUTDOWN_SENTINEL)\n\n @classmethod\n def run(\n cls,\n requests: list,\n results: queue.Queue,\n max_concurrent: int,\n progress_bar: Optional[tqdm] = None,\n timeout: Optional[int] = None,\n span: Optional[str] = None,\n ):\n def _process_results(completed: list):\n chunk_results = tuple(\n itertools.chain.from_iterable(\n cls.build_keyed_results(request, result).items() for request, result in completed\n )\n )\n cls.enqueue(results, chunk_results, wait=True)\n\n def process_results(unprocessed_results: queue.Queue):\n shutdown = False\n\n while not shutdown:\n shutdown, completed = cls.drain_queue(unprocessed_results)\n _process_results(completed)\n\n def execute_requests(\n outstanding_requests: queue.Queue,\n responses: asyncio.Queue,\n raw_results: asyncio.Queue,\n session: GsSession,\n loop: asyncio.AbstractEventLoop,\n active_span,\n ):\n with Tracer.activate_span(active_span), session:\n shutdown = False\n while not shutdown:\n shutdown, requests_chunk = cls.drain_queue(outstanding_requests)\n if requests_chunk:\n try:\n # Get the responses for our requests chunk\n responses_chunk = cls.calc_multi(requests_chunk)\n\n # Enqueue the replies for either the result subscriber (if async requests) or directly\n cls.enqueue(responses, responses_chunk.items(), loop=loop)\n except Exception as e:\n # Enqueue the error as a reply\n cls.enqueue(raw_results, ((r, e) for r in requests_chunk), loop=loop)\n\n if responses != raw_results:\n # If we are in async mode, indicate to the result subscriber that there are no more requests\n cls.shutdown_queue_listener(responses, loop=loop)\n\n async def run_async(current_span):\n def num_risk_jobs(request: RiskRequest):\n # size of calculation job\n return len(request.pricing_and_market_data_as_of) * len(request.positions)\n\n def num_risk_keys(request: RiskRequest):\n # total number of risk calculations\n return num_risk_jobs(request) * len(request.measures)\n\n is_async = not requests[0].wait_for_results\n loop = asyncio.get_event_loop()\n raw_results = asyncio.Queue()\n responses = asyncio.Queue() if is_async else raw_results\n outstanding_requests = queue.Queue()\n unprocessed_results = None\n results_handler = None\n\n # determine session to use\n session = cls.get_session()\n\n # The requests library (which we use for dispatching) is not async, so we need a thread for concurrency\n Thread(\n daemon=True,\n target=execute_requests,\n args=(outstanding_requests, responses, raw_results, session, loop, current_span),\n ).start()\n\n if is_async:\n # If async we need a task to handle result subscription\n results_handler = loop.create_task(\n cls.get_results(responses, raw_results, timeout=timeout, span=current_span)\n )\n\n expected = sum(num_risk_jobs(r) for r in requests)\n received = 0\n chunk_size = min(max_concurrent, expected)\n result_thread = None\n\n if expected > chunk_size:\n # Result handling can occur while we're blocked on I/O\n unprocessed_results = queue.Queue()\n result_thread = Thread(daemon=True, target=process_results, args=(unprocessed_results,))\n result_thread.start()\n\n while received < expected:\n if requests:\n # Enqueue requests for dispatch\n dispatch_risk_keys = 0\n dispatch_requests = []\n while requests and dispatch_risk_keys < chunk_size:\n dispatch_request = requests.pop()\n dispatch_requests.append(dispatch_request)\n dispatch_risk_keys += num_risk_jobs(dispatch_request)\n\n cls.enqueue(outstanding_requests, dispatch_requests, loop=loop)\n\n # Wait for results\n shutdown, completed = await cls.drain_queue_async(raw_results)\n if shutdown:\n # Only happens on error\n break\n\n # Enable as many new requests as we've received results, to keep the outstanding number constant\n risk_jobs_received = sum(num_risk_jobs(request) for request, _ in completed)\n chunk_size = min(risk_jobs_received, expected - received)\n\n if progress_bar:\n risk_calcs_received = sum(num_risk_keys(request) for request, _ in completed)\n progress_bar.update(risk_calcs_received)\n progress_bar.refresh()\n\n received += risk_jobs_received\n\n # Handle the results\n if unprocessed_results is not None:\n cls.enqueue(unprocessed_results, completed)\n else:\n _process_results(completed)\n\n cls.shutdown_queue_listener(outstanding_requests)\n\n if results_handler:\n results_error = await results_handler\n if results_error:\n # Raise an exception so that pending results can be filled with an error\n raise RuntimeError(f'Fatal Error subscribing to results: {results_error}')\n\n if progress_bar:\n progress_bar.close()\n\n if result_thread is not None:\n cls.shutdown_queue_listener(unprocessed_results)\n result_thread.join()\n\n cls.shutdown_queue_listener(results)\n\n if sys.version_info >= (3, 7):\n asyncio.run(run_async(span))\n else:\n try:\n existing_event_loop = asyncio.get_event_loop()\n except RuntimeError:\n existing_event_loop = None\n\n use_existing = existing_event_loop and existing_event_loop.is_running()\n main_loop = existing_event_loop if use_existing else asyncio.new_event_loop()\n\n if not use_existing:\n asyncio.set_event_loop(main_loop)\n\n try:\n main_loop.run_until_complete(run_async(span))\n except Exception:\n if not use_existing:\n main_loop.stop()\n raise\n finally:\n if not use_existing:\n main_loop.close()\n asyncio.set_event_loop(None)\n\n @classmethod\n def build_keyed_results(\n cls, request: RiskRequest, results: Union[Iterable, Exception]\n ) -> Dict[Tuple[RiskKey, Priceable], Any]:\n formatted_results = {}\n\n if isinstance(results, Exception):\n date_results = [{'$type': 'Error', 'errorString': str(results)}] * len(\n request.pricing_and_market_data_as_of\n )\n position_results = [date_results] * len(request.positions)\n results = [position_results] * len(request.measures)\n\n for risk_measure, position_results in zip(request.measures, results):\n for position, date_results in zip(request.positions, position_results):\n for as_of, date_result in zip(request.pricing_and_market_data_as_of, date_results):\n handler = result_handlers.get(date_result.get('$type'))\n risk_key = RiskKey(\n cls, as_of.pricing_date, as_of.market, request.parameters, request.scenario, risk_measure\n )\n\n try:\n result = (\n handler(\n date_result, risk_key, position.instrument, request_id=getattr(request, '_id', None)\n )\n if handler\n else date_result\n )\n except Exception as e:\n result = ErrorValue(risk_key, str(e))\n _logger.error(result)\n\n formatted_results[(risk_key, position.instrument)] = result\n\n return formatted_results\n" }, { "path": "gs_quant/api/utils.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport concurrent\nimport socket\nfrom concurrent.futures.thread import ThreadPoolExecutor\nfrom typing import List, Callable, Optional\n\nimport requests\n\nfrom gs_quant.data import DataContext\nfrom gs_quant.errors import MqUninitialisedError\nfrom gs_quant.session import GsSession\nfrom gs_quant.tracing import Tracer, TracingSpan\n\n\ndef handle_proxy(url, params):\n try:\n internal = GsSession.current.is_internal()\n except MqUninitialisedError:\n internal = False\n if internal or socket.getfqdn().split('.')[-2:] == ['gs', 'com']:\n try:\n import gs_quant_auth\n\n proxies = gs_quant_auth.__proxies__\n response = requests.get(url, params=params, proxies=proxies)\n except ModuleNotFoundError:\n raise RuntimeError('You must install gs_quant_auth to be able to use this endpoint')\n else:\n response = requests.get(url, params=params)\n return response\n\n\nclass ThreadPoolManager:\n __executor: ThreadPoolExecutor = None\n\n @classmethod\n def initialize(cls, max_workers: int):\n cls.__executor = ThreadPoolExecutor(max_workers=max_workers)\n\n @classmethod\n def run_async(cls, tasks: List[Callable]) -> List:\n if not cls.__executor:\n cls.__executor = ThreadPoolExecutor()\n\n tasks_to_idx = {}\n for i, task in enumerate(tasks):\n tasks_to_idx[\n cls.__executor.submit(cls.__run, GsSession.current, DataContext.current, Tracer.active_span(), task)\n ] = i\n results = [None] * len(tasks_to_idx)\n for task in concurrent.futures.as_completed(tasks_to_idx):\n idx = tasks_to_idx[task]\n results[idx] = task.result()\n\n return results\n\n @staticmethod\n def __run(session, data_context, span: Optional[TracingSpan], func):\n with Tracer.activate_span(span), session:\n with data_context:\n return func()\n" }, { "path": "gs_quant/backtests/SKILL.md", "content": "yea---\nname: gs-quant-backtesting\ndescription: Guide to the gs_quant backtesting framework — engines, triggers, actions, strategies, and result extraction. Covers GenericEngine (multi-asset OTC), EquityVolEngine, and PredefinedAssetEngine.\n---\n\n# SKILL.md — gs_quant Backtesting Guide\n\nThis document covers the backtesting framework in `gs_quant.backtests`. It explains how to construct strategies from triggers and actions, choose the right engine, run backtests, and extract results.\n\n---\n\n## 1. Architecture Overview\n\nA backtest in gs_quant is built from three core concepts:\n\n| Concept | Description |\n|---|---|\n| **Strategy** | Combines an optional initial portfolio with one or more `Trigger` objects. |\n| **Trigger** | Defines *when* to act — on a schedule, when a risk threshold is breached, when market data crosses a level, etc. Each trigger holds one or more `Action` objects. |\n| **Action** | Defines *what* to do when the trigger fires — add a trade, hedge a risk, exit a position, rebalance, etc. |\n\nA backtest **Engine** runs the strategy over a date range, resolving instruments, computing risks, and building the P&L time series.\n\n### Import Map\n\n```python\n# Strategy\nfrom gs_quant.backtests.strategy import Strategy\n\n# Triggers\nfrom gs_quant.backtests.triggers import (\n PeriodicTrigger, PeriodicTriggerRequirements,\n StrategyRiskTrigger, RiskTriggerRequirements,\n MktTrigger, MktTriggerRequirements,\n AggregateTrigger, AggregateTriggerRequirements,\n DateTrigger, DateTriggerRequirements,\n MeanReversionTrigger, MeanReversionTriggerRequirements,\n PortfolioTrigger, PortfolioTriggerRequirements,\n NotTrigger, NotTriggerRequirements,\n TriggerDirection, AggType,\n)\n\n# Actions\nfrom gs_quant.backtests.actions import (\n AddTradeAction,\n AddScaledTradeAction,\n HedgeAction,\n ExitTradeAction,\n ExitAllPositionsAction,\n EnterPositionQuantityScaledAction,\n RebalanceAction,\n)\n\n# Engines\nfrom gs_quant.backtests.generic_engine import GenericEngine\nfrom gs_quant.backtests.equity_vol_engine import EquityVolEngine\n\n# Data sources (for market triggers)\nfrom gs_quant.backtests.data_sources import GenericDataSource, GsDataSource, MissingDataStrategy\n```\n\n---\n\n## 2. Engines\n\ngs_quant ships three backtest engines. Choose the one that matches your instrument type and use case:\n\n| Engine | Best For | Instruments | Notes |\n|---|---|---|---|\n| **GenericEngine** | Multi-asset OTC strategies | IRSwap, IRSwaption, FXOption, FXForward, FXBinary, EqOption, etc. | Most flexible. Prices via the GS analytics API. Supports all trigger and action types. |\n| **EquityVolEngine** | Equity vol strategies | EqOption, EqVarianceSwap | Server-side execution — faster for simple equity vol roll strategies. Supports delta hedging and signals. |\n| **PredefinedAssetEngine** | Strategies on predefined assets with intraday logic | Custom order-based | For advanced users building execution-style backtests. |\n\n**Best practice:** Use `GenericEngine` unless you specifically need `EquityVolEngine` performance for equity options.\n\n---\n\n## 3. Strategy Construction\n\nA `Strategy` takes an optional initial portfolio and one or more triggers:\n\n```python\nfrom gs_quant.backtests.strategy import Strategy\n\n# Empty starting portfolio, one trigger\nstrategy = Strategy(None, trigger)\n\n# Start with an instrument already in the portfolio\nstrategy = Strategy(initial_instrument, trigger)\n\n# Multiple triggers\nstrategy = Strategy(None, [trigger_add_trade, trigger_hedge])\n```\n\n---\n\n## 4. Triggers\n\nTriggers define *when* actions fire. Each trigger pairs a `TriggerRequirements` (the condition) with one or more `Action` objects (what to do).\n\n### 4.1 PeriodicTrigger — Trade on a Schedule\n\nThe most common trigger. Fires on a regular frequency (e.g. monthly, weekly).\n\n```python\nfrom datetime import date\nfrom gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\nfrom gs_quant.backtests.actions import AddTradeAction\n\nstart_date = date(2023, 1, 3)\nend_date = date(2024, 12, 31)\n\ntrig_req = PeriodicTriggerRequirements(\n start_date=start_date,\n end_date=end_date,\n frequency='1m', # '1b' (daily), '1w', '1m', '3m', '1y', etc.\n)\n\naction = AddTradeAction(instrument, trade_duration='1m')\ntrigger = PeriodicTrigger(trig_req, action)\n```\n\n**Key parameters for `PeriodicTriggerRequirements`:**\n- `start_date` / `end_date` — date range for the schedule\n- `frequency` — tenor string: `'1b'` (daily), `'1w'`, `'1m'`, `'3m'`, `'6m'`, `'1y'`\n- `calendar` — optional holiday calendar (iterable of dates)\n\n### 4.2 StrategyRiskTrigger — Trigger on Risk Breach\n\nFires when a portfolio risk measure breaches a threshold.\n\n```python\nfrom gs_quant.backtests.triggers import StrategyRiskTrigger, RiskTriggerRequirements, TriggerDirection\nfrom gs_quant.backtests.actions import HedgeAction\nfrom gs_quant.risk import FXDelta\nfrom gs_quant.common import AggregationLevel\n\nhedge_risk = FXDelta(aggregation_level=AggregationLevel.Type, currency='USD')\n\ntrig_req = RiskTriggerRequirements(\n risk=hedge_risk,\n trigger_level=50_000,\n direction=TriggerDirection.ABOVE, # ABOVE, BELOW, or EQUAL\n)\n\ntrigger = StrategyRiskTrigger(trig_req, hedge_action)\n```\n\n### 4.3 MktTrigger — Trigger on Market Data\n\nFires when an external data series crosses a level.\n\n```python\nfrom gs_quant.backtests.triggers import MktTrigger, MktTriggerRequirements, TriggerDirection\nfrom gs_quant.backtests.data_sources import GenericDataSource, MissingDataStrategy\n\n# Build a data source from a pandas Series\ndata_source = GenericDataSource(pandas_series, MissingDataStrategy.fill_forward)\n\ntrig_req = MktTriggerRequirements(\n data_source=data_source,\n trigger_level=100.0,\n direction=TriggerDirection.BELOW,\n)\n\ntrigger = MktTrigger(trig_req, action)\n```\n\nYou can also use `GsDataSource` to pull data directly from the GS Marquee Data Catalog.\n\n### 4.4 DateTrigger — Trigger on Specific Dates\n\n```python\nfrom gs_quant.backtests.triggers import DateTrigger, DateTriggerRequirements\n\ntrig_req = DateTriggerRequirements(\n dates=[date(2024, 3, 15), date(2024, 6, 15), date(2024, 9, 15)],\n)\n\ntrigger = DateTrigger(trig_req, action)\n```\n\n### 4.5 AggregateTrigger — Combine Triggers with AND/OR Logic\n\n```python\nfrom gs_quant.backtests.triggers import AggregateTrigger, AggregateTriggerRequirements, AggType\n\nagg_req = AggregateTriggerRequirements(\n triggers=[periodic_trigger, risk_trigger], # can be Trigger or TriggerRequirements\n aggregate_type=AggType.ALL_OF, # ALL_OF (AND) or ANY_OF (OR)\n)\n\ntrigger = AggregateTrigger(agg_req, action)\n```\n\n### 4.6 NotTrigger — Invert a Trigger\n\n```python\nfrom gs_quant.backtests.triggers import NotTrigger, NotTriggerRequirements\n\nnot_req = NotTriggerRequirements(trigger=some_trigger_requirements)\ntrigger = NotTrigger(not_req, action)\n```\n\n---\n\n## 5. Actions\n\nActions define *what* happens when a trigger fires.\n\n### 5.1 AddTradeAction — Add a Trade\n\nThe most common action. Resolves an instrument on the trigger date and adds it to the portfolio.\n\n```python\nfrom gs_quant.backtests.actions import AddTradeAction\n\naction = AddTradeAction(\n priceables=instrument, # single instrument or list of instruments\n trade_duration='1m', # how long to hold: tenor, date, 'expiration_date', or None (forever)\n name='my_trade', # optional name prefix\n)\n```\n\n**`trade_duration` options:**\n- `None` — hold forever (trade stays in portfolio until backtest ends)\n- Tenor string (`'1m'`, `'3m'`, `'1y'`) — hold for that period then unwind\n- `'expiration_date'` — hold until the instrument's expiration date (useful for options)\n- `'next schedule'` — hold until the next periodic trigger date (auto-rolling)\n- Explicit `datetime.date` — hold until that date\n- `datetime.timedelta` — hold for that time delta\n\n### 5.2 HedgeAction — Delta Hedge\n\nComputes a risk measure on the portfolio and scales a hedge instrument to offset it.\n\n```python\nfrom gs_quant.backtests.actions import HedgeAction\nfrom gs_quant.risk import FXDelta, IRDelta\nfrom gs_quant.instrument import FXForward, IRSwap\n\n# FX Delta hedge\nhedge_risk = FXDelta(aggregation_level='Type', currency='USD')\nhedge_instrument = FXForward(pair='EURUSD', settlement_date='1y', name='hedge_fwd')\n\naction = HedgeAction(\n risk=hedge_risk,\n priceables=hedge_instrument,\n trade_duration='1m', # optional — how long to hold the hedge\n)\n```\n\n### 5.3 AddScaledTradeAction — Scale a Trade\n\nAdds a trade scaled by a risk measure, size, or NAV.\n\n```python\nfrom gs_quant.backtests.actions import AddScaledTradeAction, ScalingActionType\n\naction = AddScaledTradeAction(\n priceables=instrument,\n trade_duration='1m',\n scaling_type=ScalingActionType.size,\n scaling_level=1_000_000, # target notional\n)\n```\n\n### 5.4 EnterPositionQuantityScaledAction — Enter with Specific Quantity\n\nUsed primarily with the EquityVolEngine for quantity-based trading.\n\n```python\nfrom gs_quant.backtests.actions import EnterPositionQuantityScaledAction\nfrom gs_quant.target.backtests import BacktestTradingQuantityType\n\naction = EnterPositionQuantityScaledAction(\n priceables=eq_option,\n trade_duration='1m',\n trade_quantity=1000,\n trade_quantity_type=BacktestTradingQuantityType.quantity,\n)\n```\n\n### 5.5 ExitTradeAction / ExitAllPositionsAction — Close Positions\n\n```python\nfrom gs_quant.backtests.actions import ExitTradeAction, ExitAllPositionsAction\n\n# Exit a specific named trade\nexit_named = ExitTradeAction(priceable_names='my_trade')\n\n# Exit everything\nexit_all = ExitAllPositionsAction()\n```\n\n### 5.6 Combining Actions on a Single Trigger\n\nA trigger can have multiple actions. **Order matters** — they execute in sequence:\n\n```python\n# First exit old position, then add new one\ntrigger = StrategyRiskTrigger(trig_req, [exit_action, add_action])\n```\n\n---\n\n## 6. Running a Backtest with GenericEngine\n\n### Basic Example — Monthly FX Option Roll\n\n```python\nfrom datetime import date, datetime\nfrom gs_quant.session import GsSession\nfrom gs_quant.instrument import FXOption\nfrom gs_quant.common import BuySell, OptionType\nfrom gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\nfrom gs_quant.backtests.actions import AddTradeAction\nfrom gs_quant.backtests.generic_engine import GenericEngine\nfrom gs_quant.backtests.strategy import Strategy\nfrom gs_quant.risk import Price\n\nGsSession.use()\n\nstart_date = date(2023, 1, 3)\nend_date = date(2024, 12, 31)\n\n# Define instrument — remember premium=0 for FX options!\ncall = FXOption(\n buy_sell=BuySell.Buy,\n option_type=OptionType.Call,\n pair='USDJPY',\n strike_price='ATMF',\n expiration_date='2y',\n name='2y_call',\n premium=0,\n)\n\n# Periodic trigger: roll monthly, hold for 1 month\ntrig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m')\naction = AddTradeAction(call, '1m')\ntrigger = PeriodicTrigger(trig_req, action)\n\nstrategy = Strategy(None, trigger)\n\n# Run\nGE = GenericEngine()\nbacktest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n```\n\n### GenericEngine.run_backtest Parameters\n\n| Parameter | Description | Default |\n|---|---|---|\n| `strategy` | The Strategy object | *required* |\n| `start` | Backtest start date | None |\n| `end` | Backtest end date | None |\n| `frequency` | How often to evaluate: `'1b'` (daily), `'1w'`, `'1m'` | `'1m'` |\n| `states` | Explicit list of dates (overrides start/end/frequency) | None |\n| `risks` | Additional risk measures to compute | None |\n| `show_progress` | Show progress bar | True |\n| `csa_term` | CSA term for discounting | None |\n| `initial_value` | Starting cash value | 0 |\n| `result_ccy` | Currency for results | None |\n| `market_data_location` | `'LDN'`, `'NYC'`, `'HKG'` | None |\n| `is_batch` | Use websocket batching | True |\n\n---\n\n## 7. Extracting Backtest Results\n\nThe `run_backtest` call returns a `BackTest` object with several useful views.\n\n### 7.1 result_summary — Main P&L DataFrame\n\n```python\nsummary = backtest.result_summary\n```\n\nThis is a `pandas.DataFrame` indexed by date with columns:\n- `Price` — mark-to-market of live instruments\n- `Cumulative Cash` — cumulative cash from unwound trades\n- `Transaction Costs` — cumulative transaction costs\n- `Total` — `Price + Cumulative Cash + Transaction Costs` (the total strategy P&L)\n- Additional risk columns if requested\n\n### 7.2 Plotting Performance\n\n```python\nimport pandas as pd\n\n# Total performance (MTM + Cash)\nbacktest.result_summary['Total'].plot(figsize=(10, 6), title='Strategy Performance')\n\n# Or build manually\nperf = backtest.result_summary[Price] + backtest.result_summary['Cumulative Cash']\nperf.plot(figsize=(10, 6), title='Performance')\n```\n\n### 7.3 trade_ledger — Trade History\n\n```python\nledger = backtest.trade_ledger()\n```\n\nReturns a DataFrame showing each trade: when it was entered, when it was closed, entry/exit values, and P&L.\n\n### 7.4 risk_summary — Risk Time Series\n\n```python\nrisk_df = backtest.risk_summary\n```\n\nLike `result_summary` but fills zero for dates with no instruments held (useful for risk plots).\n\n### 7.5 Accessing Additional Risks\n\nPass extra risk measures via the `risks` parameter:\n\n```python\nfrom gs_quant.risk import Price, FXDelta\nfrom gs_quant.common import AggregationLevel\n\nbacktest = GE.run_backtest(\n strategy, start=start_date, end=end_date, frequency='1b',\n risks=[Price, FXDelta(aggregation_level=AggregationLevel.Type, currency='USD')],\n)\n\n# Access the risk time series\ndelta_series = backtest.result_summary[FXDelta(aggregation_level=AggregationLevel.Type, currency='USD')]\n```\n\n### 7.6 summary_stats — Strategy Performance Statistics\n\nCall `summary_stats()` to get a pandas Series of key metrics for evaluating and comparing backtests:\n\n```python\nstats = backtest.summary_stats()\nprint(stats)\n```\n\nOutput includes:\n\n| Metric | Description |\n|---|---|\n| Start Date / End Date | Backtest date range |\n| Duration (days) | Calendar days in backtest |\n| Total PnL | Final cumulative P&L |\n| Total Transaction Costs | Cumulative transaction costs |\n| Total Trades | Number of trades entered |\n| Peak PnL | Highest P&L reached |\n| Annualised Return | Average daily P&L × 252 |\n| Annualised Volatility | Std dev of daily P&L × √252 |\n| Sharpe Ratio | Annualised return / annualised volatility |\n| Sortino Ratio | Annualised return / annualised downside deviation |\n| Max Drawdown | Largest peak-to-trough decline |\n| Max Drawdown Duration (days) | Longest period spent in drawdown |\n| Calmar Ratio | Annualised return / |max drawdown| |\n| Current Drawdown | Drawdown at end of backtest |\n| Average Daily PnL | Mean of daily P&L changes |\n| Daily PnL Std Dev | Std dev of daily P&L changes |\n| Best Day / Worst Day | Largest gain and loss in a single day |\n| % Positive Days | Proportion of days with positive P&L |\n| Skewness | Skewness of daily P&L distribution |\n| Kurtosis | Excess kurtosis of daily P&L distribution |\n\nYou can customise the annualisation factor (default 252 business days):\n\n```python\n# Use 260 for a different convention\nstats = backtest.summary_stats(annualisation_factor=260)\n```\n\nTo compare two backtests side by side:\n\n```python\ncomparison = pd.DataFrame({\n 'Strategy A': backtest_a.summary_stats(),\n 'Strategy B': backtest_b.summary_stats(),\n})\ncomparison\n```\n\n---\n\n## 8. Common Patterns and Best Practices\n\n### 8.1 FX Options — Always Set premium=0\n\nWhen backtesting FX options, always set `premium=0`. Otherwise resolution sets a premium that makes `DollarPrice` zero, and your backtest P&L will be meaningless.\n\n```python\ncall = FXOption(\n buy_sell=BuySell.Buy,\n option_type=OptionType.Call,\n pair='EURUSD',\n strike_price='ATMF',\n expiration_date='1y',\n name='1y_call',\n premium=0, # <-- Essential for backtests!\n)\n```\n\n### 8.2 trade_duration — Controlling Position Lifetime\n\nThe `trade_duration` on `AddTradeAction` is critical:\n\n- Use a **tenor matching the trigger frequency** for roll strategies (e.g. `'1m'` trade_duration with `'1m'` trigger frequency)\n- Use **`'expiration_date'`** to hold options until expiry\n- Use **`None`** if you want the trade to stay in the portfolio indefinitely\n- Use **`'next schedule'`** to auto-exit when the next periodic trigger fires\n\n### 8.3 Multiple Triggers — Order Matters\n\nWhen passing multiple triggers to a Strategy, they are evaluated in order on each date. Put entry triggers before hedge triggers:\n\n```python\nstrategy = Strategy(None, [entry_trigger, hedge_trigger])\n```\n\nWhen a single trigger has multiple actions, they execute in sequence:\n\n```python\n# Exit old positions first, then add new ones\ntrigger = PeriodicTrigger(trig_req, [exit_action, add_action])\n```\n\n### 8.4 Running Daily vs Monthly\n\n- `frequency='1b'` — evaluate every business day (most common for accurate P&L)\n- `frequency='1m'` — evaluate monthly (faster but misses intra-month dynamics)\n- The trigger frequency and the backtest evaluation frequency are independent — e.g. you can run daily (`'1b'`) evaluation with monthly (`'1m'`) trigger to see daily P&L of a monthly roll strategy.\n\n### 8.5 Naming Instruments\n\nAlways name your instruments. Names appear in the trade ledger and make debugging much easier:\n\n```python\ncall = FXOption(..., name='1y_call')\nhedge = FXForward(..., name='hedge_fwd')\n```\n\n### 8.6 Starting with a Pre-Existing Portfolio\n\nPass an instrument or list of instruments as the first argument to Strategy:\n\n```python\n# Start with a swaption already in the portfolio\nstrategy = Strategy(swaption, trigger)\n\n# Start with multiple instruments\nstrategy = Strategy([swaption, swap], trigger)\n```\n\n### 8.7 Transaction Costs\n\nActions accept `transaction_cost` and `transaction_cost_exit` parameters. There are three transaction cost models, which can be used individually or combined.\n\n#### ConstantTransactionModel — Fixed Cost per Transaction\n\nA flat cash amount charged every time a trade is entered (or exited).\n\n```python\nfrom gs_quant.backtests.backtest_objects import ConstantTransactionModel\nfrom gs_quant.backtests.actions import AddTradeAction\n\n# $500 flat cost on entry; same cost on exit (default)\naction = AddTradeAction(\n instrument,\n trade_duration='1m',\n transaction_cost=ConstantTransactionModel(500),\n)\n\n# Different cost for entry vs exit\naction = AddTradeAction(\n instrument,\n trade_duration='1m',\n transaction_cost=ConstantTransactionModel(500), # entry cost\n transaction_cost_exit=ConstantTransactionModel(250), # exit cost\n)\n```\n\n#### ScaledTransactionModel — Cost Proportional to an Instrument Attribute or Risk\n\nThe cost is computed by reading an instrument attribute (e.g. `notional_amount`) or calculating a risk measure, then multiplying by a `scaling_level`.\n\n**Scaling by instrument attribute** (e.g. notional):\n\n```python\nfrom gs_quant.backtests.backtest_objects import ScaledTransactionModel\n\n# Cost = notional_amount × 0.0001 (i.e. 1bp of notional)\naction = AddTradeAction(\n instrument,\n trade_duration='1m',\n transaction_cost=ScaledTransactionModel(\n scaling_type='notional_amount', # any instrument property name\n scaling_level=0.0001, # multiplier applied to the attribute value\n ),\n)\n```\n\n**Scaling by a risk measure** (e.g. dollar price, vega):\n\n```python\nfrom gs_quant.backtests.backtest_objects import ScaledTransactionModel\nfrom gs_quant.risk import Price, IRVega\n\n# Cost = |Price| × 0.01 (i.e. 1% of premium)\naction = AddTradeAction(\n instrument,\n trade_duration='1m',\n transaction_cost=ScaledTransactionModel(\n scaling_type=Price, # a RiskMeasure — will be calculated on the instrument\n scaling_level=0.01,\n ),\n)\n\n# Cost = |IRVega| × 0.05\naction = AddTradeAction(\n instrument,\n trade_duration='1m',\n transaction_cost=ScaledTransactionModel(\n scaling_type=IRVega,\n scaling_level=0.05,\n ),\n)\n```\n\nThe formula is: **cost = |scaling_type value| × scaling_level**. The absolute value is always taken for risk-based costs.\n\n#### AggregateTransactionModel — Combine Multiple Models\n\nCombines multiple transaction models using SUM, MAX, or MIN aggregation.\n\n```python\nfrom gs_quant.backtests.backtest_objects import (\n AggregateTransactionModel,\n ConstantTransactionModel,\n ScaledTransactionModel,\n)\n\n# Total cost = fixed $100 + 0.5bp of notional\ncombined = AggregateTransactionModel(\n transaction_models=(\n ConstantTransactionModel(100),\n ScaledTransactionModel('notional_amount', 0.00005),\n ),\n # aggregate_type defaults to TransactionAggType.SUM\n)\n\naction = AddTradeAction(\n instrument,\n trade_duration='1m',\n transaction_cost=combined,\n)\n```\n\n```python\nfrom gs_quant.backtests.backtest_objects import AggregateTransactionModel, TransactionAggType\n\n# Pay the MAX of a fixed cost or a scaled cost (e.g. minimum fee with proportional cost)\nfloor_model = AggregateTransactionModel(\n transaction_models=(\n ConstantTransactionModel(1000), # minimum $1000\n ScaledTransactionModel('notional_amount', 0.0001), # or 1bp of notional\n ),\n aggregate_type=TransactionAggType.MAX,\n)\n```\n\n#### Transaction Costs on HedgeAction\n\nTransaction costs work the same way on `HedgeAction`:\n\n```python\naction = HedgeAction(\n risk=hedge_risk,\n priceables=hedge_instrument,\n trade_duration='1m',\n transaction_cost=ScaledTransactionModel('notional_amount', 0.00005),\n transaction_cost_exit=ConstantTransactionModel(0), # no cost on hedge unwind\n)\n```\n\n#### Viewing Transaction Costs in Results\n\nTransaction costs appear in `backtest.result_summary` as the `'Transaction Costs'` column (cumulative). They are also included in the `'Total'` column:\n\n```python\n# Total = Price + Cumulative Cash + Transaction Costs\nbacktest.result_summary[['Total', 'Transaction Costs']].plot(title='Performance & Transaction Costs')\n```\n\n---\n\n## 9. EquityVolEngine — Equity Vol Strategies\n\nThe `EquityVolEngine` runs equity option and variance swap backtests server-side for better performance.\n\n```python\nfrom gs_quant.instrument import EqOption, OptionType, OptionStyle\nfrom gs_quant.backtests.strategy import Strategy\nfrom gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\nfrom gs_quant.backtests.actions import EnterPositionQuantityScaledAction\nfrom gs_quant.backtests.equity_vol_engine import EquityVolEngine\nfrom gs_quant.target.backtests import BacktestTradingQuantityType\n\noption = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n)\n\naction = EnterPositionQuantityScaledAction(\n priceables=option,\n trade_duration='1m',\n trade_quantity=1000,\n trade_quantity_type=BacktestTradingQuantityType.quantity,\n)\n\ntrig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m')\ntrigger = PeriodicTrigger(trig_req, action)\n\nstrategy = Strategy(None, trigger)\n\nengine = EquityVolEngine()\nbacktest = engine.run_backtest(strategy, start=start_date, end=end_date)\n```\n\n---\n\n## 10. Quick Reference — Minimal Backtest Template\n\n```python\nfrom datetime import date\nfrom gs_quant.session import GsSession\nfrom gs_quant.instrument import IRSwaption\nfrom gs_quant.common import Currency\nfrom gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\nfrom gs_quant.backtests.actions import AddTradeAction\nfrom gs_quant.backtests.generic_engine import GenericEngine\nfrom gs_quant.backtests.strategy import Strategy\nfrom gs_quant.risk import Price\n\nGsSession.use()\n\nstart_date = date(2023, 1, 3)\nend_date = date(2024, 12, 31)\n\n# 1. Define the instrument\ninstrument = IRSwaption('Pay', '10y', Currency.USD, expiration_date='6m', name='6m10y')\n\n# 2. Define trigger + action\ntrig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='6m')\naction = AddTradeAction(instrument, trade_duration='6m')\ntrigger = PeriodicTrigger(trig_req, action)\n\n# 3. Build strategy\nstrategy = Strategy(None, trigger)\n\n# 4. Run\nGE = GenericEngine()\nbacktest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n# 5. View results\nbacktest.result_summary['Total'].plot(title='Performance')\nbacktest.trade_ledger()\n```\n\n\n" }, { "path": "gs_quant/backtests/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\nfrom .core import *\nfrom .strategy_systematic import StrategySystematic\nfrom gs_quant.target.backtests import DeltaHedgeParameters" }, { "path": "gs_quant/backtests/action_handler.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom abc import abstractmethod\nfrom typing import Union, Iterable, Any, TypeVar\n\nfrom gs_quant.backtests.actions import TAction\nfrom gs_quant.backtests.backtest_objects import TBaseBacktest\n\n\nclass ActionHandler:\n def __init__(self, action: TAction) -> None:\n self._action = action\n\n @property\n def action(self) -> TAction:\n return self._action\n\n @abstractmethod\n def apply_action(\n self,\n state: Union[dt.date, Iterable[dt.date]],\n backtest: TBaseBacktest,\n trigger_info: Any,\n ) -> Any:\n pass\n\n\nTActionHandler = TypeVar('TActionHandler', bound='ActionHandler')\n\n\nclass ActionHandlerBaseFactory:\n @abstractmethod\n def get_action_handler(self, action: TAction) -> TActionHandler:\n pass\n" }, { "path": "gs_quant/backtests/actions.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport warnings\nfrom collections import namedtuple\nfrom dataclasses import field, dataclass\nfrom enum import Enum\nfrom typing import List, Optional, Iterable, Union, Callable\nfrom typing import TypeVar, ClassVar\n\nfrom dataclasses_json import config, dataclass_json\n\nfrom gs_quant.backtests.backtest_objects import ConstantTransactionModel, TransactionModel\nfrom gs_quant.backtests.backtest_utils import make_list, CalcType, CustomDuration\nfrom gs_quant.base import Priceable, static_field\nfrom gs_quant.common import RiskMeasure\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.json_convertors import (\n decode_named_instrument,\n dc_decode,\n encode_named_instrument,\n decode_date_or_str,\n decode_dict_date_key_or_float,\n)\nfrom gs_quant.json_convertors_common import decode_risk_measure, encode_risk_measure\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk.transform import Transformer\nfrom gs_quant.target.backtests import BacktestTradingQuantityType\n\naction_count = 1\n\n\nDuration = Union[str, dt.date, dt.timedelta, CustomDuration]\n\n\ndef default_transaction_cost():\n return ConstantTransactionModel(0)\n\n\nclass ScalingActionType(Enum):\n risk_measure = 'risk_measure'\n size = 'size'\n NAV = 'NAV'\n\n\n@dataclass_json\n@dataclass\nclass Action(object):\n _needs_scaling = False\n _calc_type = CalcType.simple\n _risk = None\n _transaction_cost = ConstantTransactionModel(0)\n _transaction_cost_exit = None\n name = None\n __sub_classes: ClassVar[List[type]] = []\n\n def __init_subclass__(cls, **kwargs):\n super().__init_subclass__(**kwargs)\n Action.__sub_classes.append(cls)\n\n @staticmethod\n def sub_classes():\n return tuple(Action.__sub_classes)\n\n def __post_init__(self):\n self.set_name(self.name)\n\n @property\n def calc_type(self):\n return self._calc_type\n\n @property\n def risk(self):\n return self._risk\n\n def set_name(self, name: str):\n global action_count\n if self.name is None:\n self.name = 'Action{}'.format(action_count)\n action_count += 1\n\n @property\n def transaction_cost(self):\n return self._transaction_cost\n\n @transaction_cost.setter\n def transaction_cost(self, value):\n self._transaction_cost = value\n\n @property\n def transaction_cost_exit(self):\n return self._transaction_cost_exit\n\n @transaction_cost_exit.setter\n def transaction_cost_exit(self, value):\n self._transaction_cost_exit = value\n\n\nTAction = TypeVar('TAction', bound='Action')\n\n\n@dataclass_json\n@dataclass\nclass AddTradeAction(Action):\n \"\"\"\n create an action which adds a trade when triggered. The trades are resolved on the trigger date (state) and\n last until the trade_duration if specified or for all future dates if not.\n :param priceables: a priceable or a list of pricables.\n :param trade_duration: an instrument attribute eg. 'expiration_date' or a date or a tenor or timedelta\n if left as None the\n trade will be added for all future dates\n can also specify 'next schedule' in order to exit at the next periodic trigger date\n :param name: optional additional name to the priceable name\n :param transaction_cost: optional a cash amount paid for each transaction\n :param transaction_cost_exit: optionally specify a different model for exits; defaults to entry cost if None\n \"\"\"\n\n priceables: Union[Instrument, Iterable[Instrument]] = field(\n default=None, metadata=config(decoder=decode_named_instrument, encoder=encode_named_instrument)\n )\n trade_duration: Duration = field(\n default=None, # de/encoder doesn't handle timedelta\n metadata=config(decoder=decode_date_or_str),\n )\n name: str = None\n transaction_cost: TransactionModel = field(\n default_factory=default_transaction_cost, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n transaction_cost_exit: Optional[TransactionModel] = field(\n default=None, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n holiday_calendar: Iterable[dt.date] = None\n class_type: str = static_field('add_trade_action')\n\n def __post_init__(self):\n super().__post_init__()\n self._dated_priceables = {}\n named_priceables = []\n for i, p in enumerate(make_list(self.priceables)):\n if p.name is None:\n named_priceables.append(p.clone(name=f'{self.name}_Priceable{i}'))\n elif p.name.startswith(self.name):\n named_priceables.append(p)\n else:\n named_priceables.append(p.clone(name=f'{self.name}_{p.name}'))\n self.priceables = named_priceables\n if self.transaction_cost is None:\n self.transaction_cost = ConstantTransactionModel(0)\n if self.transaction_cost_exit is None:\n self.transaction_cost_exit = self.transaction_cost\n\n def set_dated_priceables(self, state, priceables):\n self._dated_priceables[state] = make_list(priceables)\n\n @property\n def dated_priceables(self):\n return self._dated_priceables\n\n\nAddTradeActionInfo = namedtuple('AddTradeActionInfo', ['scaling', 'next_schedule'])\nHedgeActionInfo = namedtuple('HedgeActionInfo', 'next_schedule')\nExitTradeActionInfo = namedtuple('ExitTradeActionInfo', 'not_applicable')\nRebalanceActionInfo = namedtuple('RebalanceActionInfo', 'not_applicable')\nAddScaledTradeActionInfo = namedtuple('AddScaledActionInfo', 'next_schedule')\nAddWeightedTradeActionInfo = namedtuple('AddWeightedActionInfo', 'next_schedule')\n\n\n@dataclass_json\n@dataclass\nclass AddScaledTradeAction(Action):\n \"\"\"\n create an action which adds a trade when triggered. The trade is scaled by a measure or trade property.\n The trades are resolved on the trigger date (state) and last until the trade_duration if specified or for\n all future dates if not.\n :param priceables: a priceable or a list of pricables.\n :param trade_duration: an instrument attribute eg. 'expiration_date' or a date or a tenor or timedelta\n if left as None the\n trade will be added for all future dates\n can also specify 'next schedule' in order to exit at the next periodic trigger date\n :param name: optional additional name to the priceable name\n :param scaling_type: the type of scaling we are doing\n :param scaling_risk: if the scaling type is a measure then this is the definition of the measure\n :param scaling_level: the level of scaling to be done\n :param transaction_cost: optional a cash amount paid for each transaction\n :param transaction_cost_exit: optionally specify a different model for exits; defaults to entry cost if None\n \"\"\"\n\n priceables: Union[Priceable, Iterable[Priceable]] = field(\n default=None, metadata=config(decoder=decode_named_instrument, encoder=encode_named_instrument)\n )\n trade_duration: Duration = field(\n default=None, # de/encoder doesn't handle timedelta\n metadata=config(decoder=decode_date_or_str),\n )\n name: str = None\n scaling_type: ScalingActionType = ScalingActionType.size\n scaling_risk: RiskMeasure = None\n scaling_level: Union[float, dict[dt.date, Union[float, int]]] = field(\n default=1, metadata=config(decoder=decode_dict_date_key_or_float)\n )\n transaction_cost: TransactionModel = field(\n default_factory=default_transaction_cost, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n transaction_cost_exit: Optional[TransactionModel] = field(\n default=None, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n holiday_calendar: Iterable[dt.date] = None\n dated_priceables: dict[dt.date, Priceable] = None\n class_type: str = static_field('add_scaled_trade_action')\n\n def __post_init__(self):\n super().__post_init__()\n named_priceables = []\n for i, p in enumerate(make_list(self.priceables)):\n if p.name is None:\n named_priceables.append(p.clone(name=f'{self.name}_Priceable{i}'))\n elif p.name.startswith(self.name):\n named_priceables.append(p)\n else:\n named_priceables.append(p.clone(name=f'{self.name}_{p.name}'))\n self.priceables = named_priceables\n if self.transaction_cost_exit is None:\n self.transaction_cost_exit = self.transaction_cost\n\n\n@dataclass_json\n@dataclass\nclass AddWeightedTradeAction(Action):\n \"\"\"\n create an action which adds trades when triggered. The trades are weighted by a measure.\n The trades are resolved on the trigger date (state) and last until the trade_duration if specified or for\n all future dates if not.\n :param priceables: a portfolio.\n :param trade_duration: an instrument attribute eg. 'expiration_date' or a date or a tenor or timedelta\n if left as None the\n trades will be added for all future dates\n can also specify 'next schedule' in order to exit at the next periodic trigger date\n :param name: optional additional name to the priceable name\n :param scaling_risk: if the scaling type is a measure then this is the definition of the measure\n :param total_size: the total notional that we are scaling to\n :param transaction_cost: optional a cash amount paid for each transaction\n :param transaction_cost_exit: optionally specify a different model for exits; defaults to entry cost if None\n :param holiday_calendar: optional an iterable list of holiday dates\n \"\"\"\n\n priceables: Portfolio = field(\n default=None, metadata=config(decoder=decode_named_instrument, encoder=encode_named_instrument)\n )\n trade_duration: Duration = field(\n default=None, # de/encoder doesn't handle timedelta\n metadata=config(decoder=decode_date_or_str),\n )\n name: str = None\n scaling_risk: RiskMeasure = None\n total_size: float = 100000.0\n transaction_cost: TransactionModel = field(\n default_factory=default_transaction_cost, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n transaction_cost_exit: Optional[TransactionModel] = field(\n default=None, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n holiday_calendar: Iterable[dt.date] = None\n class_type: str = static_field('add_weighted_trade_action')\n\n def __post_init__(self):\n super().__post_init__()\n self._calc_type = CalcType.semi_path_dependent\n named_priceables = []\n for i, p in enumerate(make_list(self.priceables)):\n if p.name is None:\n named_priceables.append(p.clone(name=f'{self.name}_Priceable{i}'))\n elif p.name.startswith(self.name):\n named_priceables.append(p)\n else:\n named_priceables.append(p.clone(name=f'{self.name}_{p.name}'))\n self.priceables = named_priceables\n if self.transaction_cost_exit is None:\n self.transaction_cost_exit = self.transaction_cost\n\n\n@dataclass_json\n@dataclass\nclass EnterPositionQuantityScaledAction(Action):\n \"\"\"\n create an action which enters trades when triggered. The trades are executed with specified quantity and\n last until the trade_duration if specified, or for all future dates if not.\n :param priceables: a priceable or a list of pricables.\n :param trade_duration: an instrument attribute eg. 'expiration_date' or a date or a tenor if left as None the\n trade will be added for all future dates\n :param name: optional additional name to the priceable name\n :param trade_quantity: the amount, in units of trade_quantity_type to be traded\n :param trade_quantity_type: the quantity type used to scale trade. eg. quantity for units, notional for\n underlier notional\n :param transaction_cost: optional a cash amount paid for each transaction\n :param transaction_cost_exit: optionally specify a different model for exits; defaults to entry cost if None\n \"\"\"\n\n priceables: Union[Priceable, Iterable[Priceable]] = field(\n default=None, metadata=config(decoder=decode_named_instrument, encoder=encode_named_instrument)\n )\n trade_duration: Duration = field(\n default=None, # de/encoder doesn't handle timedelta\n metadata=config(decoder=decode_date_or_str),\n )\n name: str = None\n trade_quantity: Union[float, dict[dt.date, Union[float, int]]] = field(\n default=1, metadata=config(decoder=decode_dict_date_key_or_float)\n )\n trade_quantity_type: BacktestTradingQuantityType = BacktestTradingQuantityType.quantity\n transaction_cost: TransactionModel = field(\n default_factory=default_transaction_cost, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n transaction_cost_exit: Optional[TransactionModel] = field(\n default=None, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n class_type: str = static_field('enter_position_quantity_scaled_action')\n\n def __post_init__(self):\n super().__post_init__()\n named_priceables = []\n for i, p in enumerate(make_list(self.priceables)):\n if p.name is None:\n named_priceables.append(p.clone(name=f'{self.name}_Priceable{i}'))\n elif p.name.startswith(self.name):\n named_priceables.append(p)\n else:\n named_priceables.append(p.clone(name=f'{self.name}_{p.name}'))\n self.priceables = named_priceables\n if self.transaction_cost_exit is None:\n self.transaction_cost_exit = self.transaction_cost\n\n\n@dataclass_json\n@dataclass\nclass ExitPositionAction(Action):\n name: str = None\n class_type: str = 'exit_position_action'\n\n\n@dataclass_json\n@dataclass\nclass ExitTradeAction(Action):\n priceable_names: Union[str, Iterable[str]] = None\n name: str = None\n transaction_cost: TransactionModel = field(\n default_factory=default_transaction_cost, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n class_type: str = static_field('exit_trade_action')\n\n def __post_init__(self):\n super().__post_init__()\n self.priceables_names = make_list(self.priceable_names)\n\n\n@dataclass_json\n@dataclass\nclass ExitAllPositionsAction(ExitTradeAction):\n \"\"\"\n Fully exit all held positions\n \"\"\"\n\n class_type: str = static_field('exit_all_positions_action')\n\n def __post_init__(self):\n super().__post_init__()\n self._calc_type = CalcType.path_dependent\n\n\n@dataclass_json\n@dataclass\nclass HedgeAction(Action):\n \"\"\"\n create an action which adds a hedge trade when triggered. This trade will be scaled to hedge the risk\n specified. The trades are resolved on the trigger date (state) and\n last until the trade_duration if specified or for all future dates if not.\n :param risk: a risk measure which should be hedged\n :param priceables: a priceable or a list of pricables these should have sensitivity to the risk.\n :param trade_duration: an instrument attribute eg. 'expiration_date' or a date or a tenor or timedelta\n if left as None the\n trade will be added for all future dates\n can also specify 'next schedule' in order to exit at the next periodic trigger date\n :param name: optional additional name to the priceable name\n :param transaction_cost: optional a cash amount paid for each transaction\n :param transaction_cost_exit: optionally specify a different model for exits; defaults to entry cost if None\n :param risk_transformation: optional a Transformer which will be applied to the raw risk numbers before hedging\n :param holiday_calendar: optional an iterable list of holiday dates\n :param risk_percentage: proportion of risk to hedge expressed as a percentage. Default is 100%\n \"\"\"\n\n risk: RiskMeasure = field(default=None, metadata=config(decoder=decode_risk_measure, encoder=encode_risk_measure))\n priceables: Optional[Priceable] = field(\n default=None, metadata=config(decoder=decode_named_instrument, encoder=encode_named_instrument)\n )\n trade_duration: Duration = field(\n default=None, # de/encoder doesn't handle timedelta\n metadata=config(decoder=decode_date_or_str),\n )\n name: str = None\n csa_term: str = None\n scaling_parameter: str = 'notional_amount'\n transaction_cost: TransactionModel = field(\n default_factory=default_transaction_cost, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n transaction_cost_exit: Optional[TransactionModel] = field(\n default=None, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n risk_transformation: Transformer = None\n holiday_calendar: Iterable[dt.date] = None\n risk_percentage: float = 100\n class_type: str = static_field('hedge_action')\n\n def __post_init__(self):\n super().__post_init__()\n self._calc_type = CalcType.semi_path_dependent\n portfolio = (\n self.priceables\n if isinstance(self.priceables, Portfolio)\n else Portfolio(self.priceables.clone(name=None), name=self.priceables.name)\n if isinstance(self.priceables, Priceable)\n else None\n )\n\n if not Portfolio:\n raise RuntimeError('hedge action only accepts one trade or one portfolio')\n\n named_priceables = []\n for i, priceable in enumerate(portfolio):\n if priceable.name is None:\n named_priceables.append(priceable.clone(name=f'{self.name}_Priceable{i}'))\n elif priceable.name.startswith(self.name):\n named_priceables.append(priceable)\n else:\n named_priceables.append(priceable.clone(name=f'{self.name}_{priceable.name}'))\n named_priceable = Portfolio(named_priceables, name=portfolio.name)\n\n self.priceables = named_priceable\n if self.transaction_cost_exit is None:\n self.transaction_cost_exit = self.transaction_cost\n\n if self.scaling_parameter != 'notional_amount':\n warnings.warn(\n 'HedgeAction.scaling_parameter is deprecated. It is no longer used and will be removed '\n 'in a future release',\n DeprecationWarning,\n stacklevel=2,\n )\n\n @property\n def priceable(self):\n return self.priceables\n\n\n@dataclass_json\n@dataclass\nclass RebalanceAction(Action):\n priceable: Priceable = field(\n default=None, metadata=config(decoder=decode_named_instrument, encoder=encode_named_instrument)\n )\n size_parameter: Union[str, float] = None\n method: Callable = None\n transaction_cost: TransactionModel = field(\n default_factory=default_transaction_cost, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n transaction_cost_exit: Optional[TransactionModel] = field(\n default=None, metadata=config(decoder=dc_decode(ConstantTransactionModel))\n )\n name: str = None\n\n def __post_init__(self):\n super().__post_init__()\n self._calc_type = CalcType.path_dependent\n if self.priceable.unresolved is None:\n raise ValueError(\"Please specify a resolved priceable to rebalance.\")\n if self.priceable is not None:\n if self.priceable.name is None:\n self.priceable = self.priceable.clone(name=f'{self.name}_Priceable0')\n else:\n self.priceable = self.priceable.clone(name=f'{self.name}_{self.priceable.name}')\n if self.transaction_cost_exit is None:\n self.transaction_cost_exit = self.transaction_cost\n" }, { "path": "gs_quant/backtests/backtest_engine.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom abc import abstractmethod\n\nfrom gs_quant.backtests.action_handler import TActionHandler\nfrom gs_quant.backtests.actions import TAction\n\n\nclass BacktestBaseEngine:\n @abstractmethod\n def get_action_handler(self, action: TAction) -> TActionHandler:\n pass\n" }, { "path": "gs_quant/backtests/backtest_objects.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom abc import ABC\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import dataclass_json, config\nfrom enum import Enum\nfrom queue import Queue as FifoQueue\nfrom typing import Iterable, TypeVar, Optional, Union, Callable, Tuple, ClassVar\n\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\n\nfrom gs_quant.backtests.backtest_utils import make_list\nfrom gs_quant.backtests.core import ValuationMethod\nfrom gs_quant.backtests.data_handler import DataHandler\nfrom gs_quant.backtests.data_sources import DataSource, GenericDataSource, MissingDataStrategy\nfrom gs_quant.backtests.event import FillEvent\nfrom gs_quant.backtests.order import OrderBase, OrderCost\nfrom gs_quant.base import field_metadata, static_field\nfrom gs_quant.common import RiskMeasure\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom gs_quant.instrument import Cash, IRSwap, Instrument\nfrom gs_quant.json_convertors import dc_decode\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk import ErrorValue, Cashflows\nfrom gs_quant.risk.transform import Transformer\nfrom gs_quant.risk.results import PricingFuture, PortfolioRiskResult\n\n\nclass BaseBacktest(ABC):\n pass\n\n\nTBaseBacktest = TypeVar('TBaseBacktest', bound='BaseBacktest')\n\n\nclass TransactionAggType(Enum):\n SUM = 'sum'\n MAX = 'max'\n MIN = 'min'\n\n\n@dataclass_json()\n@dataclass\nclass PnlAttribute:\n attribute_name: str\n attribute_metric: RiskMeasure\n market_data_metric: RiskMeasure\n scaling_factor: float\n second_order: bool = False\n\n def get_risks(self):\n return [self.attribute_metric, self.market_data_metric]\n\n\n@dataclass_json()\n@dataclass\nclass PnlDefinition:\n attributes: Iterable[PnlAttribute]\n\n def get_risks(self):\n return [risk for attribute in self.attributes for risk in attribute.get_risks()]\n\n\n@dataclass_json\n@dataclass\nclass BackTest(BaseBacktest):\n CUMULATIVE_CASH_COLUMN: ClassVar[str] = \"Cumulative Cash\"\n TRANSACTION_COSTS_COLUMN: ClassVar[str] = \"Transaction Costs\"\n TOTAL_COLUMN: ClassVar[str] = \"Total\"\n\n strategy: object\n states: Iterable\n risks: Iterable[RiskMeasure]\n price_measure: RiskMeasure\n holiday_calendar: Iterable[dt.date] = None\n pnl_explain_def: Optional[PnlDefinition] = None\n\n def __post_init__(self):\n self._portfolio_dict = defaultdict(Portfolio) # portfolio by state\n self._cash_dict = {} # cash by state\n self._hedges = defaultdict(list) # list of Hedge by date\n self._weighted_trades = defaultdict(list) # list of WeightedTrade by date\n self._cash_payments = defaultdict(list) # list of cash payments (entry, unwind)\n self._transaction_costs = defaultdict(int) # list of transaction costs by date\n self._transaction_cost_entries = defaultdict(list) # entries tracking transaction costs by state\n self.strategy = deepcopy(self.strategy) # the strategy definition\n self._results = defaultdict(list)\n self._trade_exit_risk_results = defaultdict(list)\n self.risks = make_list(self.risks) # list of risks to calculate\n self._risk_summary_dict = None # Summary dict shared between output views, only initialized once\n self._calc_calls = 0\n self._calculations = 0\n\n @property\n def cash_dict(self):\n return self._cash_dict\n\n @property\n def portfolio_dict(self):\n return self._portfolio_dict\n\n @portfolio_dict.setter\n def portfolio_dict(self, portfolio_dict):\n self._portfolio_dict = portfolio_dict\n\n @property\n def cash_payments(self):\n return self._cash_payments\n\n @cash_payments.setter\n def cash_payments(self, cash_payments):\n self._cash_payments = cash_payments\n\n @property\n def transaction_costs(self):\n return self._transaction_costs\n\n @transaction_costs.setter\n def transaction_costs(self, transaction_costs):\n self._transaction_costs = transaction_costs\n\n @property\n def transaction_cost_entries(self):\n return self._transaction_cost_entries\n\n @property\n def hedges(self):\n return self._hedges\n\n @hedges.setter\n def hedges(self, hedges):\n self._hedges = hedges\n\n @property\n def weighted_trades(self):\n return self._weighted_trades\n\n @weighted_trades.setter\n def weighted_trades(self, weighted_trades):\n self._weighted_trades = weighted_trades\n\n @property\n def results(self):\n return self._results\n\n def set_results(self, date, results):\n self._results[date] = results\n\n def add_results(self, date, results, replace=False):\n if date in self._results and len(self._results[date]) and not replace:\n self._results[date] += results\n else:\n self._results[date] = results\n\n @property\n def trade_exit_risk_results(self):\n return self._trade_exit_risk_results\n\n @property\n def calc_calls(self):\n return self._calc_calls\n\n @calc_calls.setter\n def calc_calls(self, calc_calls):\n self._calc_calls = calc_calls\n\n @property\n def calculations(self):\n return self._calculations\n\n @calculations.setter\n def calculations(self, calculations):\n self._calculations = calculations\n\n def get_risk_summary_df(self, zero_on_empty_dates=False):\n if self._risk_summary_dict is not None:\n summary_dict = self._risk_summary_dict\n else:\n if not self._results:\n return pd.DataFrame(columns=self.risks)\n dates_with_results = list(filter(lambda x: len(x[1]), self._results.items()))\n summary_dict = defaultdict(dict)\n for date, results in dates_with_results:\n for risk in results.risk_measures:\n try:\n value = results[risk].aggregate(True, True)\n except TypeError:\n value = ErrorValue(None, error='Could not aggregate risk results')\n summary_dict[date][risk] = value\n self._risk_summary_dict = summary_dict\n zero_risk_sd_copy = summary_dict.copy()\n if zero_on_empty_dates:\n for cash_only_date in set(self._cash_dict.keys()).difference(zero_risk_sd_copy.keys()):\n for risk in self.risks:\n zero_risk_sd_copy[cash_only_date][risk] = 0\n result = pd.DataFrame(zero_risk_sd_copy).T.sort_index()\n return result\n\n @property\n def result_summary(self):\n \"\"\"\n Get a dataframe showing the PV and other risks and cash on each day in the backtest\n \"\"\"\n summary = self.get_risk_summary_df()\n cash_summary = defaultdict(dict)\n for date, results in self._cash_dict.items():\n for ccy, value in results.items():\n cash_summary[f'Cumulative Cash {ccy}'][date] = value\n if len(cash_summary) > 1:\n raise RuntimeError('Cannot aggregate cash in multiple currencies')\n elif len(cash_summary) == 1:\n cash = pd.concat(\n [pd.Series(cash_dict, name=self.CUMULATIVE_CASH_COLUMN) for name, cash_dict in cash_summary.items()],\n axis=1,\n sort=True,\n )\n else:\n cash = pd.DataFrame(columns=[self.CUMULATIVE_CASH_COLUMN])\n transaction_costs = pd.Series(self.transaction_costs, name=self.TRANSACTION_COSTS_COLUMN)\n transaction_costs = transaction_costs.sort_index().cumsum()\n df = pd.concat([summary, cash, transaction_costs], axis=1, sort=True).ffill().fillna(0)\n df[self.TOTAL_COLUMN] = (\n df[self.price_measure] + df[self.CUMULATIVE_CASH_COLUMN] + df[self.TRANSACTION_COSTS_COLUMN]\n )\n return df[: self.states[-1]]\n\n @property\n def risk_summary(self):\n \"\"\"\n Get a dataframe showing the risks in the backtest with zero values for days with no instruments held\n \"\"\"\n return self.get_risk_summary_df(zero_on_empty_dates=True)\n\n def trade_ledger(self):\n # this is a ledger of each instrument when it was entered and when it was closed out. The cash associated\n # with the entry and exit are used in the open value and close value and PnL calc. If the PnL is None it\n # means the instrument is still live and therefore will show up in the PV\n ledger = {}\n names = []\n for date in sorted(self.cash_payments.keys()):\n cash_list = self.cash_payments[date]\n for cash in cash_list:\n if cash.direction == 0:\n ledger[cash.trade.name] = {\n 'Open': date,\n 'Close': date,\n 'Open Value': 0,\n 'Close Value': 0,\n 'Long Short': cash.direction,\n 'Status': 'closed',\n 'Trade PnL': 0,\n }\n elif cash.trade.name in names:\n if len(cash.cash_paid) > 0:\n ledger[cash.trade.name]['Close'] = date\n ledger[cash.trade.name]['Close Value'] += sum(cash.cash_paid.values())\n open_value = ledger[cash.trade.name]['Open Value']\n ledger[cash.trade.name]['Trade PnL'] = ledger[cash.trade.name]['Close Value'] + open_value\n ledger[cash.trade.name]['Status'] = 'closed'\n else:\n names.append(cash.trade.name)\n ledger[cash.trade.name] = {\n 'Open': date,\n 'Close': None,\n 'Open Value': sum(cash.cash_paid.values()),\n 'Close Value': 0,\n 'Long Short': cash.direction,\n 'Status': 'open',\n 'Trade PnL': None,\n }\n return pd.DataFrame(ledger).T.sort_index()\n\n def strategy_as_time_series(self):\n \"\"\"\n Get a dataframe indexed by strategy dates and instruments present on the respective dates\n For each tradable, displays calculated risk measures, cash payment amount and ccy (if any) and static data.\n \"\"\"\n # Construct a table of cash payments for each date and concat them in a single table of all cash payments\n cp_table = pd.concat(\n [pd.concat([cp.to_frame() for cp in date_payments]) for _, date_payments in self.cash_payments.items()]\n )\n cp_table = cp_table.set_index(['Pricing Date', 'Instrument Name']).sort_index()\n cp_table.columns = pd.MultiIndex.from_product([['Cash Payments'], cp_table.columns])\n\n risk_measure_dict = {\n date: risk_res.to_frame(values='value', index='instrument_name', columns='risk_measure').assign(\n pricing_date=[date] * len(risk_res)\n )\n for date, risk_res in self.results.items()\n }\n risk_measure_table = pd.concat(risk_measure_dict.values())\n risk_measure_table = risk_measure_table.reset_index()\n risk_measure_table = risk_measure_table.rename(\n columns={'pricing_date': 'Pricing Date', 'instrument_name': 'Instrument Name'}\n )\n risk_measure_table = risk_measure_table.set_index(['Pricing Date', 'Instrument Name'])\n risk_measure_table.columns = pd.MultiIndex.from_product(\n [['Risk Measures'], [str(col) for col in risk_measure_table.columns]]\n )\n\n risk_and_cp_joined = risk_measure_table.join(cp_table, how='outer')\n\n static_inst_info = pd.concat([info.portfolio.to_frame() for info in self.results.values()])\n static_inst_info = static_inst_info.rename(columns={'name': 'Instrument Name'})\n static_inst_info = static_inst_info.set_index(['Instrument Name'])\n static_inst_info = static_inst_info[~static_inst_info.index.duplicated(keep='first')]\n static_inst_info.columns = pd.MultiIndex.from_product([['Static Instrument Data'], static_inst_info.columns])\n\n result = static_inst_info.join(risk_and_cp_joined, how='outer')\n\n return result.sort_index()\n\n def pnl_explain(self):\n \"\"\"\n Get a dictionary of risk attributions which explain the pnl\n \"\"\"\n if self.pnl_explain_def is None:\n return None\n\n risk_results = self.results\n exit_risk_results = self.trade_exit_risk_results\n dates = sorted(set(risk_results.keys()).union(exit_risk_results.keys()))\n\n pnl_explain_results = {}\n\n for attribute in self.pnl_explain_def.attributes:\n result = {}\n cum_total = 0.0\n for idx in range(1, len(dates)):\n metric_pnl = 0.0\n cur_date = dates[idx]\n prev_date = dates[idx - 1]\n if prev_date not in risk_results:\n result[cur_date] = cum_total\n continue\n for prev_date_inst in risk_results[prev_date].portfolio.all_instruments:\n prev_date_risk = risk_results[prev_date][prev_date_inst][attribute.attribute_metric]\n if prev_date_risk == 0:\n continue\n prev_date_mkt_data = risk_results[prev_date][prev_date_inst][attribute.market_data_metric]\n if cur_date in risk_results and prev_date_inst in risk_results[cur_date].portfolio:\n cur_date_mkt_data = risk_results[cur_date][prev_date_inst][attribute.market_data_metric]\n else:\n cur_date_mkt_data = exit_risk_results[cur_date][prev_date_inst][attribute.market_data_metric]\n if attribute.second_order:\n metric_pnl += (\n 0.5\n * attribute.scaling_factor\n * prev_date_risk\n * (cur_date_mkt_data - prev_date_mkt_data)\n * (cur_date_mkt_data - prev_date_mkt_data)\n )\n else:\n metric_pnl += (\n attribute.scaling_factor * prev_date_risk * (cur_date_mkt_data - prev_date_mkt_data)\n )\n cum_total += metric_pnl\n result[cur_date] = cum_total\n pnl_explain_results[attribute.attribute_name] = result\n return pnl_explain_results\n\n def summary_stats(self, annualisation_factor: int = 252) -> pd.Series:\n \"\"\"\n Compute summary statistics for the backtest useful for evaluating and comparing strategies.\n\n Returns a pandas Series with the following metrics:\n\n - **Total PnL**: final value of the Total performance series\n - **Total Transaction Costs**: cumulative transaction costs (always negative or zero)\n - **Total Trades**: number of trades entered during the backtest\n - **Start Date / End Date**: backtest date range\n - **Duration (days)**: calendar days in the backtest\n - **Annualised Return**: total return annualised assuming the given annualisation_factor\n - **Annualised Volatility**: standard deviation of daily P&L changes, annualised\n - **Sharpe Ratio**: annualised return / annualised volatility (assumes zero risk-free rate)\n - **Sortino Ratio**: annualised return / annualised downside deviation\n - **Max Drawdown**: largest peak-to-trough decline in the Total series\n - **Max Drawdown Duration (days)**: longest period (calendar days) spent in drawdown\n - **Calmar Ratio**: annualised return / |max drawdown|\n - **Average Daily PnL**: mean of daily PnL changes\n - **Daily PnL Std Dev**: standard deviation of daily PnL changes\n - **Best Day**: largest single-day gain\n - **Worst Day**: largest single-day loss\n - **% Positive Days**: proportion of days with positive PnL change\n - **Skewness**: skewness of daily PnL changes\n - **Kurtosis**: excess kurtosis of daily PnL changes\n - **Peak PnL**: highest Total value reached\n - **Current Drawdown**: drawdown at the end of the backtest\n\n :param annualisation_factor: number of business days per year (default 252)\n :return: pandas Series of summary statistics\n \"\"\"\n summary = self.result_summary\n if summary.empty:\n return pd.Series(dtype=float)\n\n total = summary[self.TOTAL_COLUMN]\n daily_pnl = total.diff().dropna()\n\n # Basic info\n start_date = total.index[0]\n end_date = total.index[-1]\n duration_days = (end_date - start_date).days\n num_periods = len(daily_pnl)\n\n # Total PnL and transaction costs\n total_pnl = total.iloc[-1]\n total_tc = summary[self.TRANSACTION_COSTS_COLUMN].iloc[-1] if self.TRANSACTION_COSTS_COLUMN in summary else 0\n\n # Trade count\n try:\n ledger = self.trade_ledger()\n num_trades = len(ledger)\n except Exception:\n num_trades = np.nan\n\n # Annualised return and volatility\n avg_daily = daily_pnl.mean()\n std_daily = daily_pnl.std()\n ann_return = avg_daily * annualisation_factor\n ann_vol = std_daily * np.sqrt(annualisation_factor)\n\n # Sharpe ratio (excess return over zero risk-free rate)\n sharpe = ann_return / ann_vol if ann_vol != 0 else np.nan\n\n # Sortino ratio (downside deviation)\n downside = daily_pnl[daily_pnl < 0]\n downside_std = np.sqrt((downside**2).mean()) if len(downside) > 0 else 0.0\n ann_downside = downside_std * np.sqrt(annualisation_factor)\n sortino = ann_return / ann_downside if ann_downside != 0 else np.nan\n\n # Drawdown analysis\n running_max = total.cummax()\n drawdown = total - running_max\n max_drawdown = drawdown.min()\n\n # Calmar ratio\n calmar = ann_return / abs(max_drawdown) if max_drawdown != 0 else np.nan\n\n # Current drawdown\n current_drawdown = drawdown.iloc[-1]\n\n # Peak PnL\n peak_pnl = running_max.iloc[-1]\n\n # Max drawdown duration (longest streak below the running max)\n in_drawdown = drawdown < 0\n if in_drawdown.any():\n dd_groups = (~in_drawdown).cumsum()\n dd_durations = in_drawdown.groupby(dd_groups).apply(\n lambda g: (g.index[-1] - g.index[0]).days if g.any() else 0\n )\n max_dd_duration = dd_durations.max()\n else:\n max_dd_duration = 0\n\n # Best / worst day\n best_day = daily_pnl.max()\n worst_day = daily_pnl.min()\n\n # Percentage of positive days\n pct_positive = (daily_pnl > 0).sum() / num_periods * 100 if num_periods > 0 else np.nan\n\n # Higher moments\n skewness = daily_pnl.skew()\n kurtosis = daily_pnl.kurtosis() # excess kurtosis\n\n stats = pd.Series(\n {\n 'Start Date': start_date,\n 'End Date': end_date,\n 'Duration (days)': duration_days,\n 'Total PnL': total_pnl,\n 'Total Transaction Costs': total_tc,\n 'Total Trades': num_trades,\n 'Peak PnL': peak_pnl,\n 'Annualised Return': ann_return,\n 'Annualised Volatility': ann_vol,\n 'Sharpe Ratio': sharpe,\n 'Sortino Ratio': sortino,\n 'Max Drawdown': max_drawdown,\n 'Max Drawdown Duration (days)': max_dd_duration,\n 'Calmar Ratio': calmar,\n 'Current Drawdown': current_drawdown,\n 'Average Daily PnL': avg_daily,\n 'Daily PnL Std Dev': std_daily,\n 'Best Day': best_day,\n 'Worst Day': worst_day,\n '% Positive Days': pct_positive,\n 'Skewness': skewness,\n 'Kurtosis': kurtosis,\n }\n )\n\n return stats\n\n\nclass ScalingPortfolio:\n def __init__(\n self, trade, dates, risk, csa_term=None, risk_transformation: Transformer = None, risk_percentage: float = 100\n ):\n self.trade = trade\n self.dates = dates\n self.risk = risk\n self.csa_term = csa_term\n self.risk_transformation = risk_transformation\n self.risk_percentage = risk_percentage\n self.results = None\n\n\n@dataclass_json()\n@dataclass(frozen=True)\nclass TransactionModel:\n def get_unit_cost(self, state, info, instrument) -> float:\n pass\n\n\n@dataclass_json()\n@dataclass(frozen=True)\nclass ConstantTransactionModel(TransactionModel):\n cost: Union[float, int] = 0\n class_type: str = static_field('constant_transaction_model')\n\n def get_unit_cost(self, state, info, instrument) -> float:\n return self.cost\n\n\n@dataclass_json\n@dataclass(frozen=True)\nclass ScaledTransactionModel(TransactionModel):\n scaling_type: Union[str, RiskMeasure] = 'notional_amount'\n scaling_level: Union[float, int] = 0.0001\n class_type: str = static_field('scaled_transaction_model')\n\n def get_unit_cost(self, state, info, instrument) -> Union[float, PricingFuture]:\n if isinstance(self.scaling_type, str):\n try:\n return getattr(instrument, self.scaling_type)\n except AttributeError:\n raise RuntimeError(f'{self.scaling_type} not recognised for instrument {instrument.type}')\n if state > dt.date.today():\n return np.nan\n with PricingContext(state):\n risk = instrument.calc(self.scaling_type)\n return risk\n\n\n@dataclass_json()\n@dataclass(frozen=True)\nclass AggregateTransactionModel(TransactionModel):\n transaction_models: tuple = tuple()\n aggregate_type: TransactionAggType = field(default=TransactionAggType.SUM, metadata=field_metadata)\n\n def get_unit_cost(self, state, info, instrument) -> float:\n if not self.transaction_models:\n return 0\n if self.aggregate_type == TransactionAggType.SUM:\n return sum(model.get_unit_cost(state, info, instrument) for model in self.transaction_models)\n elif self.aggregate_type == TransactionAggType.MAX:\n return max(model.get_unit_cost(state, info, instrument) for model in self.transaction_models)\n elif self.aggregate_type == TransactionAggType.MIN:\n return min(model.get_unit_cost(state, info, instrument) for model in self.transaction_models)\n else:\n raise RuntimeError(f'unrecognised aggregation type:{str(self.aggregation_type)}')\n\n\nclass TransactionCostEntry:\n \"\"\"\n Stateful wrapper around TransactionModel used in the Generic Engine.\n Used to link costs to CashPayments, which can be scaled throughout the backtest (e.g. hedges), and\n to resolve risk-based costs under the same PricingContext for efficiency.\n \"\"\"\n\n def __init__(self, date: dt.date, instrument: Instrument, transaction_model: TransactionModel):\n self._date = date\n self._instrument = instrument\n self._transaction_model = transaction_model\n self._unit_cost_by_model_by_inst = {}\n self._additional_scaling = 1\n\n @property\n def all_instruments(self) -> Tuple[Instrument, ...]:\n return self._instrument.all_instruments if isinstance(self._instrument, Portfolio) else (self._instrument,)\n\n @property\n def all_transaction_models(self):\n return (\n self._transaction_model.transaction_models\n if isinstance(self._transaction_model, AggregateTransactionModel)\n else (self._transaction_model,)\n )\n\n @property\n def cost_aggregation_func(self) -> Callable:\n if isinstance(self._transaction_model, AggregateTransactionModel):\n if self._transaction_model.aggregate_type is TransactionAggType.SUM:\n return sum\n if self._transaction_model.aggregate_type is TransactionAggType.MAX:\n return max\n if self._transaction_model.aggregate_type is TransactionAggType.MIN:\n return min\n return sum\n\n @property\n def additional_scaling(self):\n return self._additional_scaling\n\n @additional_scaling.setter\n def additional_scaling(self, value: float):\n self._additional_scaling = value\n\n @property\n def date(self):\n return self._date\n\n @date.setter\n def date(self, value: dt.date):\n self._date = value\n\n @property\n def no_of_risk_calcs(self) -> int:\n return len(\n [\n m\n for m in self.all_transaction_models\n if isinstance(m, ScaledTransactionModel) and isinstance(m.scaling_type, RiskMeasure)\n ]\n )\n\n def calculate_unit_cost(self):\n for m in self.all_transaction_models:\n self._unit_cost_by_model_by_inst[m] = {}\n for i in self.all_instruments:\n self._unit_cost_by_model_by_inst[m][i] = m.get_unit_cost(self._date, None, i)\n\n @staticmethod\n def __resolved_cost(cost: Union[float, PricingFuture]) -> float:\n if isinstance(cost, PortfolioRiskResult):\n return cost.aggregate()\n elif isinstance(cost, PricingFuture):\n return cost.result()\n return cost\n\n def get_final_cost(self):\n final_costs = []\n for m in self.all_transaction_models:\n # charges may net out for portfolios\n cost = sum(self.__resolved_cost(self._unit_cost_by_model_by_inst[m][i]) for i in self.all_instruments)\n if isinstance(m, ScaledTransactionModel):\n cost = m.scaling_level * abs(cost * self._additional_scaling)\n final_costs.append(cost)\n return self.cost_aggregation_func(final_costs) if final_costs else 0\n\n def get_cost_by_component(self) -> Tuple[float, float]:\n fixed_costs = []\n scaled_costs = []\n for m in self.all_transaction_models:\n # charges may net out for portfolios\n cost = sum(self.__resolved_cost(self._unit_cost_by_model_by_inst[m][i]) for i in self.all_instruments)\n if isinstance(m, ScaledTransactionModel):\n cost = abs(cost * m.scaling_level * self._additional_scaling)\n scaled_costs.append(cost)\n else:\n fixed_costs.append(cost)\n fixed_cost = self.cost_aggregation_func(fixed_costs) if fixed_costs else None\n scaled_cost = self.cost_aggregation_func(scaled_costs) if scaled_costs else None\n if scaled_cost is None:\n return fixed_cost, 0\n elif fixed_cost is None:\n return 0, scaled_cost\n\n if self.cost_aggregation_func is sum:\n return fixed_cost, scaled_cost\n else:\n # min or max\n if self.cost_aggregation_func([fixed_cost, scaled_cost]) == fixed_cost:\n return fixed_cost, 0\n elif self.cost_aggregation_func([fixed_cost, scaled_cost]) == scaled_cost:\n return 0, scaled_cost\n else:\n raise ValueError(f\"Unable to split cost for aggregation function {self.cost_aggregation_func}\")\n\n\nclass CashPayment:\n def __init__(\n self, trade, effective_date=None, direction=1, transaction_cost_entry: Optional[TransactionCostEntry] = None\n ):\n self.trade = trade\n self.effective_date = effective_date\n self.direction = direction\n self.cash_paid = defaultdict(float)\n self.transaction_cost_entry = transaction_cost_entry\n\n def to_frame(self):\n df = pd.DataFrame(self.cash_paid.items(), columns=['Cash Ccy', 'Cash Amount'])\n df['Instrument Name'] = self.trade.name\n df['Pricing Date'] = self.effective_date\n return df\n\n\nclass Hedge:\n def __init__(\n self, scaling_portfolio: ScalingPortfolio, entry_payment: CashPayment, exit_payment: Optional[CashPayment]\n ):\n self.scaling_portfolio = scaling_portfolio\n self.entry_payment = entry_payment\n self.exit_payment = exit_payment\n\n\nclass WeightedScalingPortfolio:\n \"\"\"\n Similar to ScalingPortfolio but for weighted trade actions where each instrument\n in the portfolio is scaled to have equal risk contribution.\n \"\"\"\n\n def __init__(\n self,\n trades: Portfolio,\n dates: list,\n risk: RiskMeasure,\n total_size: float,\n csa_term=None,\n ):\n self.trades = trades # Portfolio of instruments to be weighted\n self.dates = dates\n self.risk = risk # The risk measure used for weighting\n self.total_size = total_size # Total notional to distribute equally by risk\n self.csa_term = csa_term\n self.results = None # Will store risk calculation results\n\n\nclass WeightedTrade:\n \"\"\"\n Represents a weighted trade entry containing the scaling portfolio and cash payments.\n Similar to Hedge but for weighted trade actions.\n \"\"\"\n\n def __init__(\n self,\n scaling_portfolio: WeightedScalingPortfolio,\n entry_payments: list, # List of CashPayment for each instrument\n exit_payments: list, # List of CashPayment for each instrument (or None)\n ):\n self.scaling_portfolio = scaling_portfolio\n self.entry_payments = entry_payments\n self.exit_payments = exit_payments\n\n\n@dataclass_json\n@dataclass\nclass PredefinedAssetBacktest(BaseBacktest):\n data_handler: DataHandler\n initial_value: float\n\n def __post_init__(self):\n self.performance = pd.Series(dtype=float)\n self.cash_asset = Cash('USD')\n self.holdings = defaultdict(float)\n self.historical_holdings = pd.Series(dtype=float)\n self.historical_weights = pd.Series(dtype=float)\n self.orders = []\n self.results = {}\n\n def set_start_date(self, start: dt.date):\n self.performance[start] = self.initial_value\n self.holdings[self.cash_asset] = self.initial_value\n\n def record_orders(self, orders: Iterable[OrderBase]):\n self.orders.extend(orders)\n\n def update_fill(self, fill: FillEvent):\n inst = fill.order.instrument\n self.holdings[self.cash_asset] -= fill.filled_price * fill.filled_units\n self.holdings[inst] += fill.filled_units\n\n def trade_ledger(self):\n instrument_queues = {}\n order_pairs = {}\n for o in self.orders:\n if o.instrument not in instrument_queues:\n instrument_queues[o.instrument] = (FifoQueue(), FifoQueue()) # (longs, shorts)\n longs, shorts = instrument_queues[o.instrument]\n if o.quantity < 0:\n shorts.put(o)\n else:\n longs.put(o)\n\n # match up the longs and shorts\n for inst in instrument_queues.keys():\n longs, shorts = instrument_queues[inst]\n open_close_order_pairs = []\n while not longs.empty() and not shorts.empty():\n long, short = longs.get(), shorts.get()\n open_order, close_order = (\n (long, short) if long.execution_end_time() < short.execution_end_time() else (short, long)\n )\n open_close_order_pairs.append((open_order, close_order))\n\n # handle unmatched longs or shorts i.e. positions currently open\n while not longs.empty() or not shorts.empty():\n unclosed_open_order = longs.get() if not longs.empty() else shorts.get()\n open_close_order_pairs.append((unclosed_open_order, None))\n order_pairs[inst] = open_close_order_pairs\n\n trade_df = []\n for inst in order_pairs.keys():\n for open_order, close_order in [(o, c) for o, c in order_pairs[inst]]:\n if close_order:\n end_dt = close_order.execution_end_time()\n end_value = close_order.executed_price\n status = 'closed'\n else:\n end_dt = None\n end_value = None\n status = 'open'\n start_dt, open_value = open_order.execution_end_time(), open_order.executed_price\n long_or_short = np.sign(open_order.quantity)\n trade_df.append(\n (\n inst,\n start_dt,\n end_dt,\n open_value,\n end_value,\n status,\n (end_value - open_value) * long_or_short if status == 'closed' else None,\n )\n )\n return pd.DataFrame(\n trade_df, columns=['Instrument', 'Open', 'Close', 'Open Value', 'Close Value', 'Status', 'Trade PnL']\n )\n\n def mark_to_market(self, state: dt.datetime, valuation_method: ValuationMethod):\n epsilon = 1e-12\n date = state.date()\n mtm = 0\n self.historical_holdings[date] = {}\n self.historical_weights[date] = {}\n for instrument, units in self.holdings.items():\n if abs(units) > epsilon:\n self.historical_holdings[date][instrument] = units\n\n if isinstance(instrument, Cash):\n fixing = 1\n else:\n tag, window = valuation_method.data_tag, valuation_method.window\n if window:\n start = dt.datetime.combine(state.date(), window.start)\n end = dt.datetime.combine(state.date(), window.end)\n fixings = self.data_handler.get_data_range(start, end, instrument, tag)\n fixing = np.mean(fixings) if len(fixings) else np.nan\n else: # no time window specified, use daily fixing\n fixing = self.data_handler.get_data(state.date(), instrument, tag)\n\n notional = fixing * units\n self.historical_weights[date][instrument] = notional\n mtm += notional\n\n self.performance[date] = mtm\n\n for instrument, notional in self.historical_weights[date].items():\n self.historical_weights[date][instrument] = notional / mtm\n\n def get_level(self, date: dt.date) -> float:\n return self.performance[date]\n\n def get_costs(self) -> pd.Series(dtype=float):\n costs = defaultdict(float)\n for order in self.orders:\n if isinstance(order, OrderCost):\n costs[order.execution_end_time().date()] += order.execution_quantity(self.data_handler)\n\n return pd.Series(costs)\n\n def get_orders_for_date(self, date: dt.date) -> pd.DataFrame():\n return pd.DataFrame(\n [order.to_dict(self.data_handler) for order in self.orders if order.execution_end_time().date() == date]\n )\n\n\n@dataclass_json\n@dataclass\nclass CashAccrualModel:\n class_type: str = static_field('cash_accrual_model')\n\n def get_accrued_value(self, current_value, to_state) -> dict:\n pass\n\n\n@dataclass_json\n@dataclass\nclass ConstantCashAccrualModel(CashAccrualModel):\n rate: float = 0\n annual: bool = True\n class_type: str = static_field('cash_accrual_model')\n\n def get_accrued_value(self, current_value, to_state) -> dict:\n new_value = {}\n from_state = current_value[1]\n days = (to_state - from_state).days\n for currency, value in current_value[0].items():\n new_value[currency] = value * (1 + (self.rate / (365 if self.annual else 1))) ** days\n\n return new_value\n\n\n@dataclass_json\n@dataclass\nclass DataCashAccrualModel(CashAccrualModel):\n data_source: DataSource = field(\n default=None, metadata=config(decoder=dc_decode(*DataSource.sub_classes(), allow_missing=True))\n )\n annual: bool = True\n class_type: str = static_field('cash_accrual_model')\n\n def get_accrued_value(self, current_value, to_state) -> dict:\n new_value = {}\n from_state = current_value[1]\n days = (to_state - from_state).days\n rate = self.data_source.get_data(from_state)\n for currency, value in current_value[0].items():\n new_value[currency] = value * (1 + (rate / (365 if self.annual else 1))) ** days\n return new_value\n\n\nois_fixings = {}\n\n\n@dataclass_json\n@dataclass\nclass OisFixingCashAccrualModel(CashAccrualModel):\n start_date: Union[dt.date, str] = '-1y'\n end_date: Union[dt.date, str] = dt.date.today()\n class_type: str = static_field('ois_fixing_cash_accrual_model')\n\n def get_accrued_value(self, current_value, to_state) -> dict:\n for currency in current_value[0].keys():\n if currency not in ois_fixings:\n start_date = (\n self.start_date\n if isinstance(self.start_date, dt.date)\n else RelativeDate(self.start_date).apply_rule()\n )\n start_date = start_date - dt.timedelta(days=7)\n swap = IRSwap(\n notional_currency=currency,\n floating_rate_frequency='1b',\n effective_date=start_date,\n termination_date=self.end_date\n if isinstance(self.end_date, dt.date)\n else RelativeDate(self.end_date).apply_rule(),\n floating_rate_option='OIS',\n )\n with PricingContext():\n result = swap.calc(Cashflows)\n\n ois_fixings[currency] = GenericDataSource(\n result.result()[result.result()['payment_type'] == 'Flt'].set_index(['accrual_start_date'])['rate'],\n MissingDataStrategy.fill_forward,\n )\n ds_accrual_model = DataCashAccrualModel(ois_fixings[currency], True)\n return ds_accrual_model.get_accrued_value(current_value, to_state)\n" }, { "path": "gs_quant/backtests/backtest_utils.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport pandas as pd\nfrom enum import Enum\n\nfrom dataclasses import dataclass\nfrom dataclasses_json import dataclass_json\n\nfrom typing import Callable, Tuple, Union\n\nfrom gs_quant.common import CurrencyName\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.timeseries import interpolate, Interpolate\n\n\nclass CalcType(Enum):\n simple = 'simple'\n semi_path_dependent = 'semi_path_dependent'\n path_dependent = 'path_dependent'\n\n\n@dataclass_json\n@dataclass\nclass CustomDuration:\n durations: Tuple[Union[str, dt.date, dt.timedelta], ...]\n function: Callable[[Tuple[Union[str, dt.date, dt.timedelta], ...]], Union[str, dt.date, dt.timedelta]]\n\n def __hash__(self):\n return hash((self.durations, self.function))\n\n\ndef make_list(thing):\n if thing is None:\n return []\n if isinstance(thing, str):\n return [thing]\n else:\n try:\n iter(thing)\n except TypeError:\n return [thing]\n else:\n return list(thing)\n\n\nfinal_date_cache = {}\n\n\ndef get_final_date(inst, create_date, duration, holiday_calendar=None, trigger_info=None):\n cache_key = (inst, create_date, duration, holiday_calendar)\n if cache_key in final_date_cache:\n return final_date_cache[cache_key]\n\n if duration is None:\n final_date_cache[cache_key] = dt.date.max\n return dt.date.max\n if isinstance(duration, (dt.datetime, dt.date)):\n final_date_cache[cache_key] = duration\n return duration\n if hasattr(inst, str(duration)):\n final_date_cache[cache_key] = getattr(inst, str(duration))\n return getattr(inst, str(duration))\n if str(duration).lower() == 'next schedule':\n if hasattr(trigger_info, 'next_schedule'):\n return trigger_info.next_schedule or dt.date.max\n raise RuntimeError('Next schedule not supported by action')\n if isinstance(duration, CustomDuration):\n return duration.function(\n *(get_final_date(inst, create_date, d, holiday_calendar, trigger_info) for d in duration.durations)\n )\n\n final_date_cache[cache_key] = RelativeDate(duration, create_date).apply_rule(holiday_calendar=holiday_calendar)\n return final_date_cache[cache_key]\n\n\ndef scale_trade(inst: Instrument, ratio: float):\n new_inst = inst.scale(ratio)\n return new_inst\n\n\ndef map_ccy_name_to_ccy(currency_name: Union[str, CurrencyName]):\n map = {\n 'United States Dollar': 'USD',\n 'Australian Dollar': 'AUD',\n 'Canadian Dollar': 'CAD',\n 'Swiss Franc': 'CHF',\n 'Yuan Renminbi (Hong Kong)': 'CNH',\n 'Czech Republic Koruna': 'CZK',\n 'Euro': 'EUR',\n 'Pound Sterling': 'GBP',\n 'Japanese Yen': 'JPY',\n 'South Korean Won': 'KRW',\n 'Malasyan Ringgit': 'MYR',\n 'Norwegian Krone': 'NOK',\n 'New Zealand Dollar': 'NZD',\n 'Polish Zloty': 'PLN',\n 'Russian Rouble': 'RUB',\n 'Swedish Krona': 'SEK',\n 'South African Rand': 'ZAR',\n 'Yuan Renminbi (Onshore)': 'CHY',\n }\n\n return map.get(currency_name.value if isinstance(currency_name, CurrencyName) else currency_name)\n\n\ndef interpolate_signal(signal: dict[dt.date, float], method=Interpolate.STEP) -> pd.Series:\n min_date = min(signal.keys())\n max_date = max(signal.keys())\n all_dates = [min_date + dt.timedelta(days=day) for day in range((max_date - min_date).days + 1)]\n signal_curve = interpolate(pd.Series(signal).sort_index(), all_dates, method=method)\n return signal_curve\n" }, { "path": "gs_quant/backtests/core.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom enum import Enum\nfrom typing import Tuple, NamedTuple, Union\nimport datetime as dt\nfrom gs_quant.base import EnumBase\nfrom gs_quant.target.backtests import Backtest as __Backtest, BacktestResult\nfrom typing import Optional\n\n\n# TODO add these in Studio as a standalone JSON, so they will be generated\n\n\nclass TradeInMethod(EnumBase, Enum):\n FixedRoll = 'fixedRoll'\n\n\nclass Backtest(__Backtest):\n def get_results(self) -> Tuple[BacktestResult, ...]:\n from gs_quant.api.gs.backtests import GsBacktestApi\n\n return GsBacktestApi.get_results(backtest_id=self.id)\n\n\nclass MarketModel(EnumBase, Enum):\n STICKY_FIXED_STRIKE = \"SFK\"\n STICKY_DELTA = \"SD\"\n\n\nclass TimeWindow(NamedTuple):\n start: Union[dt.time, dt.datetime] = None\n end: Union[dt.time, dt.datetime] = None\n\n\nclass ValuationFixingType(EnumBase, Enum):\n PRICE = 'price'\n\n\nclass ValuationMethod(NamedTuple):\n data_tag: ValuationFixingType = ValuationFixingType.PRICE\n window: Optional[TimeWindow] = None\n" }, { "path": "gs_quant/backtests/data_handler.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom typing import Union\nfrom gs_quant.backtests.data_sources import DataManager\n\n\nclass Clock(object):\n def __init__(self):\n self._time = None\n self.reset()\n\n def update(self, time: dt.datetime):\n compare_time = (\n self._time.replace(tzinfo=None)\n if time.tzinfo is None or time.tzinfo.utcoffset(time) is None\n else self._time\n )\n\n if time < compare_time:\n raise RuntimeError(f'current time is {compare_time}, cannot run backwards to {time}')\n self._time = time\n\n def reset(self):\n self._time = dt.datetime(1900, 1, 1).replace(tzinfo=dt.timezone.utc)\n\n def time_check(self, state: Union[dt.date, dt.datetime]):\n if isinstance(state, dt.datetime):\n if state.tzinfo is None or state.tzinfo.utcoffset(state) is None: # timezone naive\n lookahead = state > self._time.replace(tzinfo=None)\n else:\n lookahead = state > self._time\n else:\n lookahead = state > self._time.date()\n\n if lookahead:\n raise RuntimeError(f'accessing data at {state} not allowed, current time is {self._time}')\n\n\nclass DataHandler(object):\n def __init__(self, data_mgr: DataManager, tz: dt.timezone):\n self._data_mgr = data_mgr\n self._clock = Clock()\n self._tz = tz\n\n def reset_clock(self):\n self._clock.reset()\n\n def update(self, state: dt.datetime):\n self._clock.update(state)\n\n def _utc_time(self, state: Union[dt.date, dt.datetime]):\n # only switch to utc time if the datetime you've been sent is timezone naive\n if isinstance(state, dt.datetime) and (state.tzinfo is None or state.tzinfo.utcoffset(state) is None):\n return state.replace(tzinfo=self._tz).astimezone(dt.timezone.utc).replace(tzinfo=None)\n else:\n return state\n\n def get_data(self, state: Union[dt.date, dt.datetime], *key):\n self._clock.time_check(state)\n return self._data_mgr.get_data(self._utc_time(state), *key)\n\n def get_data_range(self, start: Union[dt.date, dt.datetime], end: Union[dt.date, dt.datetime], *key):\n self._clock.time_check(end)\n if type(start) is not type(end):\n raise RuntimeError('expect same type for start and end when asking for data range')\n return self._data_mgr.get_data_range(self._utc_time(start), self._utc_time(end), *key)\n" }, { "path": "gs_quant/backtests/data_sources.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import dataclass_json, config\nimport datetime as dt\nfrom enum import Enum\nimport numpy as np\nimport pandas as pd\nfrom typing import Union, Iterable, ClassVar, List\n\nfrom gs_quant.backtests.core import ValuationFixingType\nfrom gs_quant.base import field_metadata, static_field\nfrom gs_quant.data import DataFrequency, Dataset\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.json_convertors import decode_pandas_series, encode_pandas_series\n\n\nclass MissingDataStrategy(Enum):\n fill_forward = 'fill_forward'\n interpolate = 'interpolate'\n fail = 'fail'\n\n\n@dataclass_json\n@dataclass\nclass DataSource:\n __sub_classes: ClassVar[List[type]] = []\n\n def __init_subclass__(cls, **kwargs):\n super().__init_subclass__(**kwargs)\n DataSource.__sub_classes.append(cls)\n\n @staticmethod\n def sub_classes():\n return tuple(DataSource.__sub_classes)\n\n def get_data(self, state, **kwargs):\n raise RuntimeError(\"Implemented by subclass\")\n\n def get_data_range(self, start: Union[dt.date, dt.datetime], end: Union[dt.date, dt.datetime, int], **kwargs):\n raise RuntimeError(\"Implemented by subclass\")\n\n\n@dataclass_json\n@dataclass\nclass GsDataSource(DataSource):\n data_set: str\n asset_id: str\n min_date: dt.date = None\n max_date: dt.date = None\n value_header: str = 'rate'\n class_type: str = static_field('gs_data_source')\n\n def __post_init__(self):\n self.loaded_data = None\n\n def get_data(self, state: Union[dt.date, dt.datetime] = None, **kwargs):\n if self.loaded_data is None:\n ds = Dataset(self.data_set)\n if self.min_date:\n self.loaded_data = ds.get_data(self.min_date, self.max_date, assetId=(self.asset_id,))\n elif state is not None:\n return ds.get_data(state, state, assetId=(self.asset_id,))[self.value_header]\n else:\n return ds.get_data(dt.datetime(2000, 1, 1), **kwargs)[self.value_header]\n return self.loaded_data[self.value_header].at[pd.to_datetime(state)]\n\n def get_data_range(self, start: Union[dt.date, dt.datetime], end: Union[dt.date, dt.datetime, int], **kwargs):\n if self.loaded_data is None:\n ds = Dataset(self.data_set)\n if self.asset_id is not None:\n kwargs['assetId'] = (self.asset_id,)\n if self.min_date:\n self.loaded_data = ds.get_data(self.min_date, self.max_date, **kwargs)\n else:\n self.loaded_data = ds.get_data(start, self.max_date, **kwargs)\n if isinstance(end, int):\n return self.loaded_data.loc[self.loaded_data.index < start].tail(end)\n return self.loaded_data.loc[(start < self.loaded_data.index) & (self.loaded_data.index <= end)]\n\n\n@dataclass_json\n@dataclass\nclass GenericDataSource(DataSource):\n \"\"\"\n A data source which holds a pandas series indexed by date or datetime\n :param data_set: a pandas dataframe indexed by date or datetime\n :param missing_data_strategy: MissingDataStrategy which defines behaviour if data is missing, will only take\n effect if using get_data, get_data_range has no expectations of the number of\n expected data points.\n \"\"\"\n\n data_set: pd.Series = field(\n default=None, metadata=config(decoder=decode_pandas_series, encoder=encode_pandas_series)\n )\n\n missing_data_strategy: MissingDataStrategy = field(default=MissingDataStrategy.fail, metadata=field_metadata)\n class_type: str = static_field('generic_data_source')\n\n def __eq__(self, other):\n if not isinstance(other, GenericDataSource):\n return False\n return self.missing_data_strategy == other.missing_data_strategy and self.data_set.equals(other.data_set)\n\n def __post_init__(self):\n self._tz_aware = isinstance(self.data_set.index[0], dt.datetime) and self.data_set.index[0].tzinfo is not None\n\n def get_data(self, state: Union[dt.date, dt.datetime, Iterable]):\n \"\"\"\n Get the value of the dataset at a time or date. If a list of dates or times is provided return list of values\n :param state: a date, datetime or a list of dates or datetimes\n :return: float value\n \"\"\"\n\n if state is None:\n return self.data_set\n\n if isinstance(state, Iterable):\n return [self.get_data(i) for i in state]\n\n if self._tz_aware and (state.tzinfo is None or state.tzinfo.utcoffset(state) is None):\n state = state.replace(tzinfo=dt.timezone.utc)\n if pd.Timestamp(state) in self.data_set:\n return self.data_set[pd.Timestamp(state)]\n elif state in self.data_set or self.missing_data_strategy == MissingDataStrategy.fail:\n return self.data_set[state]\n else:\n if isinstance(self.data_set.index, pd.DatetimeIndex):\n self.data_set.at[pd.to_datetime(state)] = np.nan\n self.data_set = self.data_set.sort_index()\n else:\n self.data_set.at[state] = np.nan\n self.data_set.sort_index()\n if self.missing_data_strategy == MissingDataStrategy.interpolate:\n self.data_set = self.data_set.interpolate()\n elif self.missing_data_strategy == MissingDataStrategy.fill_forward:\n self.data_set = self.data_set.ffill()\n else:\n raise RuntimeError(f'unrecognised missing data strategy: {str(self.missing_data_strategy)}')\n return (\n self.data_set[pd.to_datetime(state)]\n if isinstance(self.data_set.index, pd.DatetimeIndex)\n else self.data_set[state]\n )\n\n def get_data_range(self, start: Union[dt.date, dt.datetime], end: Union[dt.date, dt.datetime, int]):\n \"\"\"\n get a range of values from the dataset.\n :param start: a date or datetime\n :param end: a date, datetime or an int. If an int is provided we return that many data points back from the\n start date\n :return: pd.Series\n \"\"\"\n\n if isinstance(end, int):\n return self.data_set.loc[self.data_set.index < start].tail(end)\n return self.data_set.loc[(start < self.data_set.index) & (self.data_set.index <= end)]\n\n\n@dataclass_json\n@dataclass\nclass DataManager:\n def __post_init__(self):\n self._data_sources = {}\n\n def add_data_source(\n self,\n series: Union[pd.Series, DataSource],\n data_freq: DataFrequency,\n instrument: Instrument,\n valuation_type: ValuationFixingType,\n ):\n if not isinstance(series, DataSource) and not len(series):\n return\n if instrument.name is None:\n raise RuntimeError('Please add a name identify your instrument')\n key = (data_freq, instrument.name, valuation_type)\n if key in self._data_sources:\n raise RuntimeError(\n 'A dataset with this frequency instrument name and valuation type already added to Data Manager'\n )\n self._data_sources[key] = GenericDataSource(series) if isinstance(series, pd.Series) else series\n\n def get_data(self, state: Union[dt.date, dt.datetime], instrument: Instrument, valuation_type: ValuationFixingType):\n key = (\n DataFrequency.REAL_TIME if isinstance(state, dt.datetime) else DataFrequency.DAILY,\n instrument.name.split('_')[-1],\n valuation_type,\n )\n return self._data_sources[key].get_data(state)\n\n def get_data_range(\n self,\n start: Union[dt.date, dt.datetime],\n end: Union[dt.date, dt.datetime],\n instrument: Instrument,\n valuation_type: ValuationFixingType,\n ):\n key = (\n DataFrequency.REAL_TIME if isinstance(start, dt.datetime) else DataFrequency.DAILY,\n instrument.name.split('_')[-1],\n valuation_type,\n )\n return self._data_sources[key].get_data_range(start, end)\n" }, { "path": "gs_quant/backtests/decorator.py", "content": "\"\"\"\r\nCopyright 2019 Goldman Sachs.\r\nLicensed under the Apache License, Version 2.0 (the \"License\");\r\nyou may not use this file except in compliance with the License.\r\nYou may obtain a copy of the License at\r\n\r\n http://www.apache.org/licenses/LICENSE-2.0\r\n\r\nUnless required by applicable law or agreed to in writing,\r\nsoftware distributed under the License is distributed on an\r\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\r\nKIND, either express or implied. See the License for the\r\nspecific language governing permissions and limitations\r\nunder the License.\r\n\"\"\"\r\n\r\n\r\ndef plot_backtest():\r\n \"\"\"\r\n Indicates that class should be exported to plottool as a member function / pseudo-measure (of the backtest).\r\n \"\"\"\r\n\r\n def decorator(obj):\r\n obj._plot_backtest = True\r\n return obj\r\n\r\n return decorator\r\n" }, { "path": "gs_quant/backtests/equity_vol_engine.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport re\nimport warnings\nfrom functools import reduce\nfrom typing import Union\n\nimport datetime as dt\nimport pandas as pd\n\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import (\n TransactionCostConfig,\n TradingCosts,\n FixedCostModel,\n ScaledCostModel,\n TransactionCostScalingType,\n AggregateCostModel,\n CostAggregationType,\n)\nfrom gs_quant.backtests import actions as a\nfrom gs_quant.backtests import triggers as t\nfrom gs_quant.backtests.backtest_objects import (\n TransactionModel,\n ConstantTransactionModel,\n ScaledTransactionModel,\n AggregateTransactionModel,\n)\n\nfrom gs_quant.backtests.strategy_systematic import StrategySystematic, DeltaHedgeParameters, TradeInMethod\nfrom gs_quant.base import get_enum_value\nfrom gs_quant.common import OptionType, BuySell\nfrom gs_quant.instrument import EqOption, EqVarianceSwap\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk import EqDelta, EqSpot, EqGamma, EqVega\nfrom gs_quant.target.backtests import (\n BacktestSignalSeriesItem,\n BacktestTradingQuantityType,\n EquityMarketModel,\n FlowVolBacktestMeasure,\n)\nfrom gs_quant.common import TradeAs\n\n\ndef get_backtest_trading_quantity_type(scaling_type, risk):\n if scaling_type == a.ScalingActionType.size:\n return a.BacktestTradingQuantityType.quantity\n if scaling_type == a.ScalingActionType.NAV:\n return a.BacktestTradingQuantityType.NAV\n if risk == EqSpot:\n return BacktestTradingQuantityType.notional\n if risk == EqGamma:\n return BacktestTradingQuantityType.gamma\n if risk == EqVega:\n return BacktestTradingQuantityType.vega\n raise ValueError(f\"unable to translate {scaling_type} and {risk}\")\n\n\ndef is_synthetic_forward(priceable):\n is_portfolio = isinstance(priceable, Portfolio)\n is_syn_fwd = is_portfolio\n if is_portfolio:\n is_size_two = len(priceable) == 2\n is_syn_fwd = is_size_two\n if is_size_two:\n has_two_eq_options = isinstance(priceable[0], EqOption) and isinstance(priceable[1], EqOption)\n is_syn_fwd &= has_two_eq_options\n if has_two_eq_options:\n is_syn_fwd &= (\n (priceable[0].underlier == priceable[1].underlier)\n and (priceable[0].expiration_date == priceable[1].expiration_date)\n and (priceable[0].strike_price == priceable[1].strike_price)\n )\n is_syn_fwd &= (OptionType.Call, BuySell.Buy) and (OptionType.Put, BuySell.Sell) in {\n (priceable[0].option_type, priceable[0].buy_sell),\n (priceable[1].option_type, priceable[1].buy_sell),\n }\n\n return is_syn_fwd\n\n\nclass BacktestResult:\n def __init__(self, results):\n self._results = results\n\n def get_measure_series(self, measure: FlowVolBacktestMeasure):\n data = next(iter(r.timeseries for r in self._results.risks if r.name == measure.value), ())\n df = pd.DataFrame.from_records(data)\n if len(df) == 0:\n return df\n\n df['date'] = pd.to_datetime(df['date'])\n return df.set_index('date').value\n\n def get_portfolio_history(self):\n data = []\n for item in self._results.portfolio:\n positions = list(\n map(\n lambda x: dict({'date': item['date'], 'quantity': x['quantity']}, **x['instrument']),\n item['positions'],\n )\n )\n data = data + positions\n\n return pd.DataFrame(data)\n\n def get_trade_history(self):\n data = []\n for item in self._results.portfolio:\n for transaction in item['transactions']:\n trades = list(\n map(\n lambda x: dict(\n {\n 'date': item['date'],\n 'quantity': x['quantity'],\n 'transactionType': transaction['type'],\n 'price': x['price'],\n 'cost': transaction.get('cost') if len(transaction['trades']) == 1 else None,\n },\n **x['instrument'],\n ),\n transaction['trades'],\n )\n )\n data = data + trades\n\n return pd.DataFrame(data)\n\n\nclass EquityVolEngine(object):\n @classmethod\n def check_strategy(cls, strategy):\n check_results = []\n if len(strategy.initial_portfolio) > 0:\n check_results.append('Error: initial_portfolio must be empty or None')\n\n # Validate Triggers\n\n if len(strategy.triggers) > 3:\n check_results.append('Error: Maximum of 3 triggers')\n\n if not all(isinstance(x, (t.AggregateTrigger, t.PeriodicTrigger)) for x in strategy.triggers):\n check_results.append('Error: Only AggregateTrigger and PeriodTrigger supported')\n\n # aggregate triggers composed of a dated and portfolio trigger define a signal\n aggregate_triggers = [x for x in strategy.triggers if isinstance(x, t.AggregateTrigger)]\n for at in aggregate_triggers:\n if not len(at.trigger_requirements.triggers) == 2:\n check_results.append('Error: AggregateTrigger must be composed of 2 triggers')\n if not len([x for x in at.trigger_requirements.triggers if isinstance(x, t.DateTriggerRequirements)]) == 1:\n check_results.append('Error: AggregateTrigger must be contain 1 DateTrigger')\n portfolio_triggers = [\n x for x in at.trigger_requirements.triggers if isinstance(x, t.PortfolioTriggerRequirements)\n ]\n if not len(portfolio_triggers) == 1:\n check_results.append('Error: AggregateTrigger must be contain 1 PortfolioTrigger')\n if not (portfolio_triggers[0].data_source == 'len' and portfolio_triggers[0].trigger_level == 0):\n check_results.append(\n 'Error: PortfolioTrigger.trigger_requirements must have data_source = \\'len\\' and trigger_level = 0'\n )\n\n # Validate Actions\n\n all_actions = reduce(lambda acc, x: acc + x, (map(lambda x: x.actions, strategy.triggers)), [])\n\n if any(isinstance(x, a.ExitPositionAction) for x in all_actions):\n warnings.warn('ExitPositionAction will be deprecated soon, use ExitTradeAction.', DeprecationWarning, 2)\n if any(isinstance(x, a.EnterPositionQuantityScaledAction) for x in all_actions):\n warnings.warn(\n 'EnterPositionQuantityScaledAction will be deprecated soon, use AddScaledTradeAction.',\n DeprecationWarning,\n 2,\n )\n\n if not all(\n isinstance(\n x,\n (\n a.EnterPositionQuantityScaledAction,\n a.HedgeAction,\n a.ExitPositionAction,\n a.ExitTradeAction,\n a.AddTradeAction,\n a.AddScaledTradeAction,\n ),\n )\n for x in all_actions\n ):\n check_results.append(\n 'Error: actions must be one of EnterPositionQuantityScaledAction, HedgeAction, ExitPositionAction, '\n 'ExitTradeAction, AddTradeAction, AddScaledTradeAction'\n )\n\n # no duplicate actions\n if not len(set(map(lambda x: type(x), all_actions))) == len(all_actions):\n check_results.append('Error: There are multiple actions of the same type')\n\n all_child_triggers = reduce(\n lambda acc, x: acc + x,\n map(\n lambda x: x.trigger_requirements.triggers if isinstance(x, t.AggregateTriggerRequirements) else [x],\n strategy.triggers,\n ),\n [],\n )\n\n for trigger in all_child_triggers:\n if isinstance(trigger, t.PortfolioTrigger):\n continue\n\n # action one of enter position, exit position, hedge\n if len(trigger.actions) != 1:\n check_results.append('Error: All triggers must contain only 1 action')\n\n for action in trigger.actions:\n if isinstance(action, (a.EnterPositionQuantityScaledAction, a.AddTradeAction, a.AddScaledTradeAction)):\n if (\n isinstance(trigger, t.PeriodicTrigger)\n and not trigger.trigger_requirements.frequency == action.trade_duration\n ):\n check_results.append(\n f'Error: {type(action).__name__}: PeriodicTrigger frequency must be the same '\n 'as trade_duration'\n )\n if not all((isinstance(p, (EqOption, EqVarianceSwap))) for p in action.priceables):\n check_results.append(\n f'Error: {type(action).__name__}: Only EqOption or EqVarianceSwap supported'\n )\n if isinstance(action, a.EnterPositionQuantityScaledAction):\n if action.trade_quantity is None or action.trade_quantity_type is None:\n check_results.append(\n 'Error: EnterPositionQuantityScaledAction trade_quantity or trade_quantity_type is None'\n )\n if isinstance(action, a.AddScaledTradeAction):\n if action.scaling_level is None or action.scaling_type is None:\n check_results.append('Error: AddScaledTradeAction scaling_level or scaling_type is None')\n expiry_date_modes = map(lambda x: TenorParser(x.expirationDate).get_mode(), action.priceables)\n expiry_date_modes = list(set(expiry_date_modes))\n if len(expiry_date_modes) > 1:\n check_results.append(\n f'Error: {type(action).__name__} all priceable expiration_date modifiers must '\n 'be the same. Found [' + ', '.join([str(edm) for edm in expiry_date_modes]) + ']'\n )\n if expiry_date_modes[0] is not None and expiry_date_modes[0] not in ['otc', 'listed']:\n check_results.append(\n f'Error: {type(action).__name__} invalid expiration_date modifier ' + expiry_date_modes[0]\n )\n\n size_fields = ('quantity', 'number_of_options', 'multiplier')\n priceable_size_values = [[getattr(p, sf, 1) or 1 for sf in size_fields] for p in action.priceables]\n priceable_sizes = [reduce(lambda x, y: x * y, size_vals, 1) for size_vals in priceable_size_values]\n\n if not all(priceable_size == 1 for priceable_size in priceable_sizes):\n check_results.append(\n f'Error: {type(action).__name__} every priceable should have a unit size of 1. '\n 'Found [' + ', '.join([str(s) for s in priceable_sizes]) + ']'\n )\n elif isinstance(action, a.HedgeAction):\n if not is_synthetic_forward(action.priceable):\n check_results.append(\n 'Error: HedgeAction: Hedge instrument must be a synthetic forward - a portfolio of two '\n 'equity options (long call and short put) with the same underlier, strike price and '\n 'expiration date'\n )\n if not trigger.trigger_requirements.frequency == action.trade_duration:\n check_results.append(\n 'Error: HedgeAction: PeriodicTrigger frequency must be the same as trade_duration'\n )\n if not action.risk == EqDelta:\n check_results.append('Error: HedgeAction: risk type must be EqDelta')\n elif isinstance(action, (a.ExitPositionAction, a.ExitTradeAction)):\n continue\n else:\n check_results.append('Error: Unsupported action type \\'{}\\''.format(type(action)))\n\n return check_results\n\n @classmethod\n def supports_strategy(cls, strategy):\n check_result = cls.check_strategy(strategy)\n if len(check_result):\n return False\n\n return True\n\n @classmethod\n def run_backtest(cls, strategy, start, end, market_model=EquityMarketModel.SFK, cash_accrual=True):\n check_result = cls.check_strategy(strategy)\n if len(check_result):\n raise RuntimeError(check_result)\n\n underlier_list = None\n roll_frequency = None\n trade_quantity = None\n trade_quantity_type = None\n trade_in_signals = None\n trade_out_signals = None\n hedge = None\n\n transaction_cost = TransactionCostConfig(None)\n for trigger in strategy.triggers:\n if isinstance(trigger, t.AggregateTrigger):\n child_triggers = trigger.trigger_requirements.triggers\n\n date_trigger = [x for x in child_triggers if isinstance(x, t.DateTriggerRequirements)][0]\n date_signal = list(map(lambda x: BacktestSignalSeriesItem(x, True), date_trigger.dates))\n\n portfolio_trigger = [x for x in child_triggers if isinstance(x, t.PortfolioTriggerRequirements)][0]\n if portfolio_trigger.direction == t.TriggerDirection.EQUAL and portfolio_trigger.trigger_level == 0:\n is_trade_in = True\n else:\n is_trade_in = False\n\n if is_trade_in:\n trade_in_signals = date_signal\n else:\n trade_out_signals = date_signal\n\n action = trigger.actions[0]\n if isinstance(action, a.EnterPositionQuantityScaledAction):\n underlier_list = cls.__get_underlier_list(action.priceables)\n tp = TenorParser(action.trade_duration)\n roll_frequency = tp.get_date()\n roll_date_mode = tp.get_mode()\n trade_quantity = action.trade_quantity\n trade_quantity_type = action.trade_quantity_type\n expiry_date_mode = TenorParser(action.priceables[0].expiration_date).get_mode()\n transaction_cost.trade_cost_model = TradingCosts(\n cls.__map_tc_model(action.transaction_cost), cls.__map_tc_model(action.transaction_cost_exit)\n )\n elif isinstance(action, a.AddTradeAction):\n underlier_list = cls.__get_underlier_list(action.priceables)\n tp = TenorParser(action.trade_duration)\n roll_frequency = tp.get_date()\n roll_date_mode = tp.get_mode()\n trade_quantity = 1\n trade_quantity_type = BacktestTradingQuantityType.quantity\n expiry_date_mode = TenorParser(action.priceables[0].expiration_date).get_mode()\n transaction_cost.trade_cost_model = TradingCosts(\n cls.__map_tc_model(action.transaction_cost), cls.__map_tc_model(action.transaction_cost_exit)\n )\n elif isinstance(action, a.AddScaledTradeAction):\n underlier_list = cls.__get_underlier_list(action.priceables)\n tp = TenorParser(action.trade_duration)\n roll_frequency = tp.get_date()\n roll_date_mode = tp.get_mode()\n trade_quantity = action.scaling_level\n trade_quantity_type = get_backtest_trading_quantity_type(action.scaling_type, action.scaling_risk)\n expiry_date_mode = TenorParser(action.priceables[0].expiration_date).get_mode()\n transaction_cost.trade_cost_model = TradingCosts(\n cls.__map_tc_model(action.transaction_cost), cls.__map_tc_model(action.transaction_cost_exit)\n )\n elif isinstance(action, a.HedgeAction):\n hedge = DeltaHedgeParameters(\n frequency=trigger.trigger_requirements.frequency, notional=action.risk_percentage\n )\n transaction_cost.hedge_cost_model = TradingCosts(\n cls.__map_tc_model(action.transaction_cost), cls.__map_tc_model(action.transaction_cost_exit)\n )\n\n transaction_cost_config = (\n transaction_cost if (transaction_cost.trade_cost_model or transaction_cost.hedge_cost_model) else None\n )\n\n strategy = StrategySystematic(\n name=\"Flow Vol Backtest\",\n underliers=underlier_list,\n index_initial_value=0,\n delta_hedge=hedge,\n quantity=trade_quantity,\n quantity_type=trade_quantity_type,\n trade_in_method=TradeInMethod.FixedRoll,\n roll_frequency=roll_frequency,\n trade_in_signals=trade_in_signals,\n trade_out_signals=trade_out_signals,\n market_model=market_model,\n expiry_date_mode=expiry_date_mode,\n roll_date_mode=roll_date_mode,\n cash_accrual=cash_accrual,\n transaction_cost_config=transaction_cost_config,\n use_xasset_backtesting_service=True,\n )\n\n result = strategy.backtest(start, end)\n return BacktestResult(result)\n\n @classmethod\n def __get_underlier_list(cls, priceables):\n priceables_copy = copy.deepcopy(priceables)\n for priceable in priceables_copy:\n edp = TenorParser(priceable.expiration_date)\n priceable.expiration_date = edp.get_date()\n if hasattr(priceable, 'trade_as'):\n expiry_date_mode = get_enum_value(TradeAs, edp.get_mode())\n priceable.trade_as = (\n priceable.trade_as or expiry_date_mode if isinstance(expiry_date_mode, TradeAs) else None\n )\n print(priceable.trade_as)\n return priceables_copy\n\n @classmethod\n def __map_tc_model(cls, model: TransactionModel):\n if isinstance(model, ConstantTransactionModel):\n return FixedCostModel(cost=model.cost)\n elif isinstance(model, ScaledTransactionModel):\n if model.scaling_type == EqVega:\n scaling_quantity_type = TransactionCostScalingType.Vega\n else:\n scaling_quantity_type = get_enum_value(TransactionCostScalingType, model.scaling_type)\n if not isinstance(scaling_quantity_type, TransactionCostScalingType):\n raise RuntimeError(f'unsupported scaled transaction quantity type \"{model.scaling_type}\"')\n return ScaledCostModel(scaling_quantity_type=scaling_quantity_type, scaling_level=model.scaling_level)\n elif isinstance(model, AggregateTransactionModel):\n return AggregateCostModel(\n models=[cls.__map_tc_model(m) for m in model.transaction_models],\n aggregation_type=CostAggregationType(model.aggregate_type.value),\n )\n return None\n\n\nclass TenorParser(object):\n # match expiration dates expressed as 3m@listed\n expiry_regex = '(.*)@(.*)'\n\n def __init__(self, expiry: Union[str, dt.date]):\n self.expiry = expiry\n\n def get_date(self):\n if isinstance(self.expiry, dt.date):\n return self.expiry\n\n parts = re.search(self.expiry_regex, self.expiry)\n if parts:\n return parts.group(1)\n else:\n return self.expiry\n\n def get_mode(self):\n if isinstance(self.expiry, dt.date):\n return None\n\n parts = re.search(self.expiry_regex, self.expiry)\n if parts:\n return parts.group(2)\n else:\n return None\n" }, { "path": "gs_quant/backtests/event.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.backtests.order import OrderBase\n\n\nclass Event(object):\n pass\n\n\nclass MarketEvent(Event):\n def __init__(self):\n self.type = 'Market'\n\n\nclass ValuationEvent(Event):\n def __init__(self):\n self.type = 'Valuation'\n\n\nclass OrderEvent(Event):\n def __init__(self, order: OrderBase):\n self.type = 'Order'\n self.order = order\n\n\nclass FillEvent(Event):\n def __init__(self, order: OrderBase, filled_units: float, filled_price: float):\n self.type = 'Fill'\n self.order = order\n self.filled_units = filled_units\n self.filled_price = filled_price\n" }, { "path": "gs_quant/backtests/execution_engine.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nfrom gs_quant.backtests.data_handler import DataHandler\nfrom gs_quant.backtests.event import OrderEvent, FillEvent\nfrom gs_quant.backtests.order import OrderBase\n\n\nclass ExecutionEngine(object):\n pass\n\n\nclass SimulatedExecutionEngine(ExecutionEngine):\n def __init__(self, data_handler: DataHandler):\n self.data_handler = data_handler\n self.orders = []\n\n def submit_order(self, order: OrderEvent):\n self.orders.append(order)\n self.orders.sort(key=lambda e: e.order.execution_end_time())\n\n def ping(self, state: dt.datetime):\n fill_events = []\n while self.orders:\n order: OrderBase = self.orders[0].order\n end_time = order.execution_end_time()\n if end_time > state:\n break\n else:\n fill = FillEvent(\n order=order,\n filled_price=order.execution_price(self.data_handler),\n filled_units=order.execution_quantity(),\n )\n fill_events.append(fill)\n self.orders.pop(0)\n return fill_events\n" }, { "path": "gs_quant/backtests/generic_engine.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport datetime as dt\nimport logging\n\nfrom collections import defaultdict\nfrom functools import reduce\nfrom typing import Union, Iterable, Optional\n\nfrom gs_quant.backtests.action_handler import ActionHandlerBaseFactory, ActionHandler\nfrom gs_quant.backtests.actions import (\n Action,\n AddTradeAction,\n HedgeAction,\n ExitTradeAction,\n RebalanceAction,\n ExitAllPositionsAction,\n AddScaledTradeAction,\n AddWeightedTradeAction,\n)\nfrom gs_quant.backtests.backtest_engine import BacktestBaseEngine\nfrom gs_quant.backtests.backtest_objects import BackTest, CashPayment, PnlDefinition\nfrom gs_quant.backtests.backtest_utils import make_list, CalcType, get_final_date, map_ccy_name_to_ccy\nfrom gs_quant.backtests.generic_engine_action_impls import (\n AddTradeActionImpl,\n AddScaledTradeActionImpl,\n HedgeActionImpl,\n ExitTradeActionImpl,\n RebalanceActionImpl,\n AddWeightedTradeActionImpl,\n)\nfrom gs_quant.backtests.strategy import Strategy\nfrom gs_quant.common import Currency, ParameterisedRiskMeasure, RiskMeasure\nfrom gs_quant.context_base import nullcontext\nfrom gs_quant.datetime.relative_date import RelativeDateSchedule\nfrom gs_quant.markets import PricingContext, HistoricalPricingContext\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk import Price\nfrom gs_quant.risk.results import PortfolioRiskResult\nfrom gs_quant.tracing import Tracer\n\n# priority set to contexts making requests to the pricing API (min. 1 - max. 10)\nDEFAULT_REQUEST_PRIORITY = 5\n\n\ndef raiser(ex):\n raise RuntimeError(ex)\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass GenericEngineActionFactory(ActionHandlerBaseFactory):\n def __init__(self, action_impl_map=None):\n self.action_impl_map = {\n AddTradeAction: AddTradeActionImpl,\n HedgeAction: HedgeActionImpl,\n ExitTradeAction: ExitTradeActionImpl,\n ExitAllPositionsAction: ExitTradeActionImpl,\n RebalanceAction: RebalanceActionImpl,\n AddScaledTradeAction: AddScaledTradeActionImpl,\n AddWeightedTradeAction: AddWeightedTradeActionImpl,\n } | (action_impl_map or {})\n\n def get_action_handler(self, action: Action) -> ActionHandler:\n if type(action) in self.action_impl_map:\n return self.action_impl_map[type(action)](action)\n raise RuntimeError(f'Action {type(action)} not supported by engine')\n\n\nclass GenericEngine(BacktestBaseEngine):\n def __init__(self, action_impl_map=None, price_measure=Price):\n self.action_impl_map = {} if action_impl_map is None else action_impl_map\n self.price_measure = price_measure\n self._pricing_context_params = None\n self._initial_pricing_context = None\n self._tracing_enabled = False\n\n def get_action_handler(self, action: Action) -> ActionHandler:\n handler_factory = GenericEngineActionFactory(self.action_impl_map)\n return handler_factory.get_action_handler(action)\n\n def supports_strategy(self, strategy):\n all_actions = reduce(lambda x, y: x + y, (map(lambda x: x.actions, strategy.triggers)))\n try:\n for x in all_actions:\n self.get_action_handler(x)\n except RuntimeError:\n return False\n return True\n\n def new_pricing_context(self):\n \"\"\"\n generate context with the same params to avoid duplication\n \"\"\"\n context_params = self._pricing_context_params\n\n show_progress = context_params.get('show_progress', False)\n csa_term = context_params.get('csa_term')\n market_data_location = context_params.get('market_data_location')\n request_priority = context_params.get('request_priority', DEFAULT_REQUEST_PRIORITY)\n is_batch = context_params.get('is_batch', True)\n\n context = PricingContext(\n set_parameters_only=True,\n show_progress=show_progress,\n csa_term=csa_term,\n market_data_location=market_data_location,\n request_priority=request_priority,\n is_batch=is_batch,\n use_historical_diddles_only=True,\n )\n\n context._max_concurrent = 1500\n context._dates_per_batch = 200\n\n return context\n\n def run_backtest(\n self,\n strategy: Strategy,\n start: Optional[dt.date] = None,\n end: Optional[dt.date] = None,\n frequency: Optional[str] = '1m',\n states: Optional[Iterable[dt.date]] = None,\n risks: Optional[Iterable[RiskMeasure]] = None,\n show_progress: bool = True,\n csa_term: Optional[str] = None,\n visible_to_gs: bool = False,\n initial_value: float = 0,\n result_ccy: Optional[Union[str, Currency]] = None,\n holiday_calendar: Optional[str] = None,\n market_data_location: Optional[str] = None,\n is_batch: bool = True,\n calc_risk_at_trade_exits: bool = False,\n pnl_explain: Optional[PnlDefinition] = None,\n ):\n \"\"\"\n run the backtest following the triggers and actions defined in the strategy. If states are entered run on\n those dates otherwise build a schedule from the start, end, frequency\n using gs_quant.datetime.relative_date.RelativeDateSchedule\n :param strategy: the strategy object\n :param start: a datetime\n :param end: a datetime\n :param frequency: str, default '1m'\n :param states: a list of dates will override the start, end, freq if provided\n :param risks: risks to run\n :param show_progress: boolean default true\n :param csa_term: the csa term to use\n :param visible_to_gs: are the contents of risk requests visible to GS (defaults to False)\n :param initial_value: initial cash value of strategy defaults to 0\n :param result_ccy: ccy of all risks, pvs and cash\n :param holiday_calendar for date maths - list of dates\n :param market_data_location: location for the market data\n :param is_batch: use websockets to reduce timeout issues\n :param calc_risk_at_trade_exits: separate results for requested risk measures on tradable exit dates;\n not to be included in main results but useful for PnL decomposition\n :param pnl_explain: a Pnl Definition object which defines the risk attribution and mkt data for a pnl explain\n :return: a backtest object containing the portfolios on each day and results which show all risks on all days\n\n \"\"\"\n\n logger.info(f'Starting Backtest: Building Date Schedule - {dt.datetime.now()}')\n self._tracing_enabled = Tracer.active_span() is not None and Tracer.active_span().is_recording()\n self._pricing_context_params = {\n 'show_progress': show_progress,\n 'csa_term': csa_term,\n 'visible_to_gs': visible_to_gs,\n 'market_data_location': market_data_location,\n 'is_batch': is_batch,\n }\n\n with self.new_pricing_context():\n return self.__run(\n strategy,\n start,\n end,\n frequency,\n states,\n risks,\n initial_value,\n result_ccy,\n holiday_calendar,\n calc_risk_at_trade_exits,\n pnl_explain,\n )\n\n def _trace(self, label: str):\n if self._tracing_enabled:\n return Tracer(label)\n else:\n return nullcontext()\n\n def __run(\n self,\n strategy,\n start,\n end,\n frequency,\n states,\n risks,\n initial_value,\n result_ccy,\n holiday_calendar,\n calc_risk_at_trade_exits,\n pnl_explain,\n ):\n \"\"\"\n Run the backtest strategy using the ambient pricing context\n \"\"\"\n with self._trace('Relative Schedule'):\n strategy_pricing_dates = (\n RelativeDateSchedule(frequency, start, end).apply_rule(holiday_calendar=holiday_calendar)\n if states is None\n else states\n )\n\n strategy_pricing_dates.sort()\n\n strategy_start_date = strategy_pricing_dates[0]\n strategy_end_date = strategy_pricing_dates[-1]\n\n for trigger in strategy.triggers:\n strategy_pricing_dates += [\n t for t in trigger.get_trigger_times() if strategy_start_date <= t <= strategy_end_date\n ]\n\n strategy_pricing_dates = list(set(strategy_pricing_dates))\n strategy_pricing_dates.sort()\n if pnl_explain is not None:\n calc_risk_at_trade_exits = True\n pnl_risks = pnl_explain.get_risks()\n else:\n pnl_risks = []\n risks = list(set(make_list(risks) + strategy.risks + pnl_risks + [self.price_measure]))\n if result_ccy is not None:\n risks = [\n (\n r(currency=result_ccy)\n if isinstance(r, ParameterisedRiskMeasure)\n else raiser(f'Unparameterised risk: {r}')\n )\n for r in risks\n ]\n\n if result_ccy is not None:\n if isinstance(self.price_measure, ParameterisedRiskMeasure):\n price_risk = self.price_measure(currency=result_ccy)\n else:\n raiser(f'Unparameterised price measure: {self.price_measure}')\n else:\n price_risk = self.price_measure\n\n backtest = BackTest(strategy, strategy_pricing_dates, risks, price_risk, holiday_calendar, pnl_explain)\n\n logger.info('Resolving initial portfolio')\n with self._trace('Resolve initial portfolio'):\n self._resolve_initial_portfolio(\n strategy.initial_portfolio, backtest, strategy_start_date, strategy_pricing_dates, holiday_calendar\n )\n\n logger.info('Building simple and semi-deterministic triggers and actions')\n self._build_simple_and_semi_triggers_and_actions(strategy, backtest, strategy_pricing_dates)\n\n logger.info(f'Filtering strategy calculations to run from {strategy_start_date} to {strategy_end_date}')\n backtest.portfolio_dict = defaultdict(\n Portfolio,\n {\n k: backtest.portfolio_dict[k]\n for k in backtest.portfolio_dict\n if strategy_start_date <= k <= strategy_end_date\n },\n )\n backtest.hedges = defaultdict(\n list, {k: backtest.hedges[k] for k in backtest.hedges if strategy_start_date <= k <= strategy_end_date}\n )\n backtest.weighted_trades = defaultdict(\n list,\n {\n k: backtest.weighted_trades[k]\n for k in backtest.weighted_trades\n if strategy_start_date <= k <= strategy_end_date\n },\n )\n\n logger.info('Pricing simple and semi-deterministic triggers and actions')\n with self._trace('Pricing semi-det Triggers'):\n self._price_semi_det_triggers(backtest, risks)\n\n logger.info('Scaling semi-determ triggers and actions and calculating path dependent triggers and actions')\n with self._trace('Process dates') as scope:\n if scope:\n scope.span.set_tag('dates.length', len(strategy_pricing_dates))\n for d in strategy_pricing_dates:\n if scope:\n scope.span.log_kv({'date': str(d)})\n self._process_triggers_and_actions_for_date(d, strategy, backtest, risks)\n\n with self._trace('Calc New Trades'):\n self._calc_new_trades(backtest, risks)\n\n with self._trace('Handle Cash'):\n self._handle_cash(\n backtest,\n risks,\n price_risk,\n strategy_pricing_dates,\n strategy_end_date,\n initial_value,\n calc_risk_at_trade_exits,\n strategy.cash_accrual,\n )\n\n with self._trace('Populate Transaction Costs'):\n backtest.transaction_costs = {\n d: -sum(tce.get_final_cost() for tce in tce_list)\n for d, tce_list in backtest.transaction_cost_entries.items()\n }\n\n logger.info(f'Finished Backtest:- {dt.datetime.now()}')\n return backtest\n\n def _resolve_initial_portfolio(\n self, initial_portfolio, backtest, strategy_start_date, strategy_pricing_dates, holiday_calendar, duration=None\n ):\n if isinstance(initial_portfolio, dict):\n sorted_dates = sorted(list(initial_portfolio.keys()))\n for i, d in enumerate(sorted_dates):\n portfolio = make_list(initial_portfolio[d])\n end_date = sorted_dates[i + 1] if i + 1 < len(sorted_dates) else strategy_pricing_dates[-1]\n self._resolve_initial_portfolio(\n portfolio, backtest, d, strategy_pricing_dates, holiday_calendar, end_date\n )\n else:\n if len(initial_portfolio):\n renamed_port = []\n for index in range(len(initial_portfolio)):\n old_name = initial_portfolio[index].name\n renamed_inst = initial_portfolio[index].clone(\n name=f'{old_name}_{strategy_start_date.strftime(\"%Y-%m-%d\")}'\n )\n renamed_port.append(renamed_inst)\n entry_payment = CashPayment(renamed_inst, effective_date=strategy_start_date, direction=-1)\n backtest.cash_payments[strategy_start_date].append(entry_payment)\n final_date = get_final_date(renamed_inst, strategy_start_date, duration, holiday_calendar)\n exit_payment = CashPayment(initial_portfolio[index], effective_date=final_date)\n backtest.cash_payments[final_date].append(exit_payment)\n init_port = Portfolio(renamed_port)\n with PricingContext(strategy_start_date):\n init_port.resolve()\n for d in strategy_pricing_dates:\n if duration is None or (\n d >= strategy_start_date and (d < duration or duration == strategy_pricing_dates[-1])\n ):\n backtest.portfolio_dict[d].append(init_port.instruments)\n\n def _build_simple_and_semi_triggers_and_actions(self, strategy, backtest, strategy_pricing_dates):\n for trigger in strategy.triggers:\n if trigger.calc_type != CalcType.path_dependent:\n triggered_dates = []\n trigger_infos = defaultdict(list)\n with self._trace('Build semi-det trigger') as scope:\n for d in strategy_pricing_dates:\n t_info = trigger.has_triggered(d, backtest)\n if t_info:\n triggered_dates.append(d)\n if t_info.info_dict:\n for k, v in t_info.info_dict.items():\n trigger_infos[k].append(v)\n if scope:\n scope.span.set_tag('trigger.type', type(trigger).__name__)\n scope.span.set_tag('dates.triggered', len(triggered_dates))\n scope.span.set_tag('action.count', len(trigger.actions))\n\n for action in trigger.actions:\n if action.calc_type != CalcType.path_dependent:\n with self._trace('Build semi-det action') as scope:\n if scope:\n scope.span.set_tag('action.type', type(action).__name__)\n trigger_info = None\n if type(action) in trigger_infos:\n trigger_info = trigger_infos[type(action)]\n else:\n for mapped_action_type, action_trigger_info in trigger_infos.items():\n if isinstance(action, mapped_action_type):\n trigger_info = action_trigger_info\n break\n self.get_action_handler(action).apply_action(triggered_dates, backtest, trigger_info)\n\n @staticmethod\n def _price_semi_det_triggers(backtest, risks):\n with PricingContext():\n backtest.calc_calls += 1\n for day, portfolio in backtest.portfolio_dict.items():\n if isinstance(day, dt.date):\n with PricingContext(day):\n backtest.calculations += len(portfolio) * len(risks)\n backtest.add_results(day, portfolio.calc(tuple(risks)))\n\n # semi path dependent initial calc for hedges\n for _, hedge_list in backtest.hedges.items():\n scaling_list = [h.scaling_portfolio for h in hedge_list]\n for p in scaling_list:\n with HistoricalPricingContext(dates=p.dates):\n backtest.calculations += len(risks) * len(p.dates)\n port = p.trade if isinstance(p.trade, Portfolio) else Portfolio([p.trade])\n p.results = port.calc(tuple(risks))\n\n # semi path dependent initial calc for weighted trades\n for _, weighted_trade_list in backtest.weighted_trades.items():\n for wt in weighted_trade_list:\n sp = wt.scaling_portfolio\n with HistoricalPricingContext(dates=sp.dates):\n backtest.calculations += len(risks) * len(sp.dates) * len(sp.trades)\n sp.results = sp.trades.calc(tuple(risks))\n\n @staticmethod\n def __ensure_risk_results(dates, backtest: BackTest, risks):\n port_by_date = {}\n for d in dates:\n port = []\n for t in backtest.portfolio_dict[d]:\n if not backtest.results[d] or t.name not in backtest.results[d].portfolio:\n port.append(t)\n if len(port):\n port_by_date[d] = port\n\n if len(port_by_date):\n results_by_date = {}\n with PricingContext():\n for d, port in port_by_date.items():\n with PricingContext(pricing_date=d):\n results_by_date[d] = Portfolio(port).calc(tuple(risks))\n\n for d, results in results_by_date.items():\n backtest.add_results(d, results)\n\n def _process_triggers_and_actions_for_date(self, d, strategy, backtest: BackTest, risks):\n logger.debug(f'{d}: Processing triggers and actions')\n\n # need to ensure risk results for the day are available prior to the path-dependent action/trigger being applied\n # note that __ensure_risk_results sends a risk calculation for the day, so it should only happen when required\n trigger_infos = defaultdict(list)\n for trigger in strategy.triggers:\n if trigger.calc_type == CalcType.path_dependent:\n t_info = trigger.has_triggered(d, backtest)\n if t_info:\n if t_info.info_dict:\n for k, v in t_info.info_dict.items():\n trigger_infos[k].append(v)\n for action in trigger.actions:\n self.__ensure_risk_results([d], backtest, risks)\n trigger_info = trigger_infos.get(type(action), None)\n self.get_action_handler(action).apply_action(d, backtest, trigger_info)\n else:\n for action in trigger.actions:\n if action.calc_type == CalcType.path_dependent:\n t_info = trigger.has_triggered(d, backtest)\n if t_info:\n trigger_info = t_info.info_dict.get(type(action), None) if t_info.info_dict else None\n self.__ensure_risk_results([d], backtest, risks)\n self.get_action_handler(action).apply_action(d, backtest, trigger_info)\n # explicit check needed because backtest.hedges is a defaultdict that gets populated on access below\n if d not in backtest.hedges and d not in backtest.weighted_trades:\n return\n for hedge in backtest.hedges[d]:\n sp = hedge.scaling_portfolio\n if sp.results is None:\n with HistoricalPricingContext(dates=sp.dates):\n backtest.calculations += len(risks) * len(sp.dates)\n port_sp = sp.trade if isinstance(sp.trade, Portfolio) else Portfolio([sp.trade])\n sp.results = port_sp.calc(tuple(risks))\n\n # semi path dependent scaling for hedges; only apply if there are any hedges to scale\n if backtest.hedges[d]:\n # ensure all risk results are available (including the hedge risk required to scale below)\n # this is useful when there is overlap between hedges (e.g. an instrument hedging risk from another hedge)\n # note it does not get applied when there is no overlap between hedges, as the ensure fn will skip the calc\n self.__ensure_risk_results([d], backtest, risks)\n # this needs to be applied after the \"ensure\" line above, which may add results\n # if there is no risk to hedge, skip to the next day; hedges for this day can be ignored\n if d not in backtest.results:\n return\n for hedge in backtest.hedges[d]:\n p = hedge.scaling_portfolio\n current_risk = (\n backtest.results[d][p.risk]\n .transform(risk_transformation=p.risk_transformation)\n .aggregate(allow_mismatch_risk_keys=True)\n )\n hedge_risk = p.results[d][p.risk].transform(risk_transformation=p.risk_transformation).aggregate()\n if hedge_risk == 0:\n continue\n if current_risk.unit != hedge_risk.unit:\n raise RuntimeError('cannot hedge in a different currency')\n scaling_factor = current_risk / hedge_risk * hedge.scaling_portfolio.risk_percentage / 100\n hedge.entry_payment.transaction_cost_entry.additional_scaling = scaling_factor\n if hedge.exit_payment is not None:\n hedge.exit_payment.transaction_cost_entry.additional_scaling = scaling_factor\n if isinstance(p.trade, Portfolio):\n # Scale the portfolio by risk target\n scaled_portfolio_position = copy.deepcopy(p.trade)\n scaled_portfolio_position.name = f'Scaled_{scaled_portfolio_position.name}'\n for instrument in scaled_portfolio_position.all_instruments:\n instrument.name = f'Scaled_{instrument.name}'\n\n # trade hedge in opposite direction\n scale_direction = -1\n scaled_portfolio_position.scale(scaling_factor * scale_direction)\n\n for day in p.dates:\n # add scaled hedge position to portfolio for day.\n # NOTE this adds leaves, not the portfolio\n backtest.portfolio_dict[day] += copy.deepcopy(scaled_portfolio_position)\n\n # scale trade in hedge cash payments\n hedge.entry_payment.trade = copy.deepcopy(scaled_portfolio_position)\n if hedge.exit_payment is not None:\n hedge.exit_payment.trade = copy.deepcopy(scaled_portfolio_position)\n else:\n raise RuntimeError('Hedge trade instrument must be a Portfolio')\n\n # Add cash payments for hedge entry and exit\n backtest.cash_payments[hedge.entry_payment.effective_date].append(hedge.entry_payment)\n if hedge.exit_payment is not None:\n backtest.cash_payments[hedge.exit_payment.effective_date].append(hedge.exit_payment)\n\n # semi path dependent scaling for weighted trades\n if d in backtest.weighted_trades and backtest.weighted_trades[d]:\n for weighted_trade in backtest.weighted_trades[d]:\n sp = weighted_trade.scaling_portfolio\n if sp.results is None:\n with HistoricalPricingContext(dates=sp.dates):\n backtest.calculations += len(risks) * len(sp.dates) * len(sp.trades)\n sp.results = sp.trades.calc(tuple(risks))\n\n # Get risk for each instrument on the entry date\n instrument_risks = {}\n for inst in sp.trades.all_instruments:\n try:\n inst_risk = sp.results[d][sp.risk][inst.name]\n if hasattr(inst_risk, 'aggregate'):\n inst_risk = inst_risk.aggregate()\n instrument_risks[inst.name] = abs(inst_risk) if inst_risk != 0 else 0\n except (KeyError, ValueError):\n instrument_risks[inst.name] = 0\n\n # Calculate scaling factors so each instrument is scaled by its proportion of total portfolio risk\n # and the sum of all notionals equals total_size\n num_instruments = len(sp.trades.all_instruments)\n if num_instruments == 0:\n continue\n\n total_portfolio_risk = sum(instrument_risks.values())\n if total_portfolio_risk == 0:\n continue\n\n scaling_factors = {}\n for inst_name, inst_risk in instrument_risks.items():\n if inst_risk != 0:\n # Weight is the proportion of this instrument's risk to total portfolio risk\n weight = inst_risk / total_portfolio_risk\n # Notional for this instrument = weight * total_size\n # Scaling factor = notional (since unit notional instruments)\n scaling_factors[inst_name] = weight * sp.total_size\n else:\n scaling_factors[inst_name] = 0\n\n # Scale each instrument and add to portfolio\n for idx, inst in enumerate(sp.trades.all_instruments):\n scaling_factor = scaling_factors.get(inst.name, 0)\n if scaling_factor == 0:\n continue\n\n scaled_inst = copy.deepcopy(inst)\n scaled_inst.name = f'Weighted_{inst.name}'\n scaled_inst.scale(scaling_factor)\n\n # Update transaction cost entries with scaling\n entry_payment = weighted_trade.entry_payments[idx]\n entry_payment.transaction_cost_entry.additional_scaling = scaling_factor\n if weighted_trade.exit_payments[idx] is not None:\n weighted_trade.exit_payments[idx].transaction_cost_entry.additional_scaling = scaling_factor\n\n # Add scaled instrument to portfolio for each active date\n for day in sp.dates:\n backtest.portfolio_dict[day].append(copy.deepcopy(scaled_inst))\n\n # Update cash payment trades with scaled version\n entry_payment.trade = copy.deepcopy(scaled_inst)\n if weighted_trade.exit_payments[idx] is not None:\n weighted_trade.exit_payments[idx].trade = copy.deepcopy(scaled_inst)\n\n # Add cash payments\n backtest.cash_payments[entry_payment.effective_date].append(entry_payment)\n if weighted_trade.exit_payments[idx] is not None:\n backtest.cash_payments[weighted_trade.exit_payments[idx].effective_date].append(\n weighted_trade.exit_payments[idx]\n )\n\n def _calc_new_trades(self, backtest, risks):\n logger.info('Calculating and scaling newly added portfolio positions')\n # test to see if new trades have been added and calc\n with PricingContext():\n backtest.calc_calls += 1\n leaves_by_date = {}\n for day, portfolio in backtest.portfolio_dict.items():\n # Nothing to schedule for calculation, continue\n if not portfolio:\n continue\n results_for_date = backtest.results[day]\n\n trades_for_date = (\n results_for_date.portfolio if isinstance(results_for_date, PortfolioRiskResult) else []\n )\n leaves = []\n for leaf in portfolio:\n if leaf.name not in trades_for_date:\n logger.debug(f'{day}: new portfolio position {leaf.name} scheduled for calculation')\n leaves.append(leaf)\n\n if len(leaves):\n with PricingContext(pricing_date=day):\n leaves_by_date[day] = Portfolio(leaves).calc(tuple(risks))\n backtest.calculations += len(leaves) * len(risks)\n\n logger.info('Processing results for newly added portfolio positions')\n for day, leaves in leaves_by_date.items():\n backtest.add_results(day, leaves)\n\n @staticmethod\n def _handle_cash(\n backtest,\n risks,\n price_risk,\n strategy_pricing_dates,\n strategy_end_date,\n initial_value,\n calc_risk_at_trade_exits,\n cash_accrual,\n ):\n logger.info('Calculating prices for cash payments')\n # run any additional calcs to handle cash scaling (e.g. unwinds)\n cash_results = {}\n cash_trades_by_date = defaultdict(list)\n exited_cash_trades_by_date = defaultdict(list)\n for _, cash_payments in backtest.cash_payments.items():\n for cp in cash_payments:\n # only calc if additional point is required\n trades = cp.trade.all_instruments if isinstance(cp.trade, Portfolio) else [cp.trade]\n for trade in trades:\n if cp.effective_date and cp.effective_date <= strategy_end_date:\n if (\n cp.effective_date not in backtest.results\n or trade not in backtest.results[cp.effective_date]\n ):\n cash_trades_by_date[cp.effective_date].append(trade)\n if calc_risk_at_trade_exits and cp.direction == 1:\n exited_cash_trades_by_date[cp.effective_date].append(trade)\n\n with PricingContext():\n backtest.calc_calls += 1\n for cash_date, trades in cash_trades_by_date.items():\n with PricingContext(cash_date):\n backtest.calculations += len(risks)\n cash_results[cash_date] = Portfolio(trades).calc(price_risk)\n if calc_risk_at_trade_exits and cash_date in exited_cash_trades_by_date:\n expiring_trades = exited_cash_trades_by_date[cash_date]\n backtest.trade_exit_risk_results[cash_date] = Portfolio(expiring_trades).calc(risks)\n\n # handle cash\n current_value = None\n for d in sorted(set(strategy_pricing_dates + list(backtest.cash_payments.keys()))):\n if d <= strategy_end_date:\n if current_value is not None:\n backtest.cash_dict[d] = (\n current_value[0] if cash_accrual is None else cash_accrual.get_accrued_value(current_value, d)\n )\n if d in backtest.cash_payments:\n for cp in backtest.cash_payments[d]:\n trades = cp.trade.all_instruments if isinstance(cp.trade, Portfolio) else [cp.trade]\n for trade in trades:\n value = cash_results.get(cp.effective_date, {}).get(price_risk, {}).get(trade.name, {})\n try:\n value = (\n backtest.results[cp.effective_date][price_risk][trade.name]\n if value == {}\n else value\n )\n except (KeyError, ValueError):\n raise RuntimeError(\n f'failed to get cash value for {trade.name} on '\n f'{cp.effective_date} received value of {value}'\n )\n if not isinstance(value, float):\n raise RuntimeError(\n f'failed to get cash value for {trade.name} on '\n f'{cp.effective_date} received value of {value}'\n )\n ccy = map_ccy_name_to_ccy(next(iter(value.unit)))\n if d not in backtest.cash_dict:\n backtest.cash_dict[d] = {ccy: initial_value}\n if ccy not in backtest.cash_dict[d]:\n backtest.cash_dict[d][ccy] = 0\n\n cp.cash_paid[ccy] += value * cp.direction\n\n for ccy, cash_paid in cp.cash_paid.items():\n backtest.cash_dict[d][ccy] += cash_paid\n\n current_value = backtest.cash_dict[d], d\n\n current_value = copy.deepcopy(current_value)\n" }, { "path": "gs_quant/backtests/generic_engine_action_impls.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom abc import ABCMeta\nimport datetime as dt\nfrom collections import defaultdict, namedtuple\nfrom itertools import zip_longest\nfrom typing import Union, Iterable, Optional, Dict, Collection\n\nfrom gs_quant.backtests.actions import (\n Action,\n AddTradeAction,\n HedgeAction,\n AddTradeActionInfo,\n HedgeActionInfo,\n ExitTradeAction,\n ExitTradeActionInfo,\n RebalanceAction,\n RebalanceActionInfo,\n AddScaledTradeAction,\n ScalingActionType,\n AddScaledTradeActionInfo,\n AddWeightedTradeAction,\n AddWeightedTradeActionInfo,\n)\nfrom gs_quant.backtests.action_handler import ActionHandler\nfrom gs_quant.backtests.backtest_objects import (\n BackTest,\n ScalingPortfolio,\n CashPayment,\n Hedge,\n TransactionCostEntry,\n WeightedScalingPortfolio,\n WeightedTrade,\n)\nfrom gs_quant.backtests.backtest_utils import make_list, get_final_date, interpolate_signal\nfrom gs_quant.common import RiskMeasure\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.markets import PricingContext, HistoricalPricingContext\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk.results import PortfolioRiskResult\nfrom gs_quant.target.measures import ResolvedInstrumentValues\n\n\n# Action Implementations\nclass OrderBasedActionImpl(ActionHandler, metaclass=ABCMeta):\n def __init__(self, action: Action):\n self._order_valuations = [ResolvedInstrumentValues]\n super().__init__(action)\n\n def get_base_orders_for_states(self, states: Collection[dt.date], **kwargs):\n orders = {}\n dated_priceables = getattr(self.action, 'dated_priceables', {}) or {}\n with PricingContext():\n for s in states:\n active_portfolio = dated_priceables.get(s) or self.action.priceables\n with PricingContext(pricing_date=s):\n orders[s] = Portfolio(active_portfolio).calc(tuple(self._order_valuations))\n return orders\n\n def get_instrument_final_date(self, inst: Instrument, order_date: dt.date, info: namedtuple):\n return get_final_date(inst, order_date, self.action.trade_duration, self.action.holiday_calendar, info)\n\n\nclass AddTradeActionImpl(OrderBasedActionImpl):\n def __init__(self, action: AddTradeAction):\n super().__init__(action)\n\n def _raise_order(\n self,\n state: Union[dt.date, Iterable[dt.date]],\n trigger_info: Optional[Union[AddTradeActionInfo, Iterable[AddTradeActionInfo]]] = None,\n ):\n state_list = make_list(state)\n if trigger_info is None or isinstance(trigger_info, AddTradeActionInfo):\n trigger_info = [trigger_info for _ in range(len(state_list))]\n ti_by_state = {}\n for s, ti in zip_longest(state_list, trigger_info):\n ti_by_state[s] = ti\n orders = self.get_base_orders_for_states(state_list, trigger_infos=ti_by_state)\n final_orders = {}\n for d, p in orders.items():\n new_port = Portfolio([t.clone(name=f'{t.name}_{d}') for t in p.result()])\n ti = ti_by_state[d]\n final_orders[d] = (new_port.scale(None if ti is None else ti.scaling, in_place=False), ti)\n\n return final_orders\n\n def apply_action(\n self,\n state: Union[dt.date, Iterable[dt.date]],\n backtest: BackTest,\n trigger_info: Optional[Union[AddTradeActionInfo, Iterable[AddTradeActionInfo]]] = None,\n ):\n orders = self._raise_order(state, trigger_info)\n\n current_tc_entries = []\n # record entry and unwind cashflows\n for create_date, (portfolio, info) in orders.items():\n for inst in portfolio.all_instruments:\n tc_enter = TransactionCostEntry(create_date, inst, self.action.transaction_cost)\n current_tc_entries.append(tc_enter)\n backtest.cash_payments[create_date].append(\n CashPayment(inst, effective_date=create_date, direction=-1, transaction_cost_entry=tc_enter)\n )\n backtest.transaction_cost_entries[create_date].append(tc_enter)\n final_date = self.get_instrument_final_date(inst, create_date, info)\n tc_exit = TransactionCostEntry(final_date, inst, self.action.transaction_cost_exit)\n current_tc_entries.append(tc_exit)\n backtest.cash_payments[final_date].append(\n CashPayment(inst, effective_date=final_date, transaction_cost_entry=tc_exit)\n )\n backtest.transaction_cost_entries[final_date].append(tc_exit)\n backtest_states = (s for s in backtest.states if final_date > s >= create_date)\n for s in backtest_states:\n backtest.portfolio_dict[s].append(inst)\n\n with PricingContext(is_async=True):\n if any(tce.no_of_risk_calcs > 0 for tce in current_tc_entries):\n backtest.calc_calls += 1\n for tce in current_tc_entries:\n backtest.calculations += tce.no_of_risk_calcs\n tce.calculate_unit_cost()\n\n return backtest\n\n\nclass AddScaledTradeActionImpl(OrderBasedActionImpl):\n def __init__(self, action: AddScaledTradeAction):\n super().__init__(action)\n self._scaling_level_signal = (\n interpolate_signal(self.action.scaling_level) if isinstance(self.action.scaling_level, dict) else None\n )\n\n @staticmethod\n def __portfolio_scaling_for_available_cash(\n portfolio, available_cash, cur_day, unscaled_prices_by_day, unscaled_entry_tces_by_day\n ) -> float:\n fixed_tcs = 0\n scaling_based_tcs = 0\n for inst in portfolio:\n insed_fixed_tc, inst_scaling_tc = unscaled_entry_tces_by_day[cur_day][inst].get_cost_by_component()\n fixed_tcs += insed_fixed_tc\n scaling_based_tcs += inst_scaling_tc\n # solve such that fixed TCs, instrument prices and scaled transaction costs (under the same scaling factor)\n # add up to available_cash\n # do not floor to zero on the first iteration - first scale factor can be negative,\n # e.g. if the aggregation operator is \"min\" and the fixed cost is the minimum but it exceeds the available cash,\n # it would be too early to floor to zero, must solve again in case there still is an acceptable scaling level\n first_scale_factor = (available_cash - fixed_tcs) / (\n unscaled_prices_by_day[cur_day].aggregate() + scaling_based_tcs\n )\n if first_scale_factor == 0:\n return 0\n # set additional scaling on TCE and solve again in case aggregation (min/max) has been affected by scaling\n fixed_tcs = 0\n scaling_based_tcs = 0\n for inst in portfolio:\n unscaled_entry_tces_by_day[cur_day][inst].additional_scaling = first_scale_factor\n insed_fixed_tc, inst_scaling_tc = unscaled_entry_tces_by_day[cur_day][inst].get_cost_by_component()\n fixed_tcs += insed_fixed_tc\n scaling_based_tcs += inst_scaling_tc\n # this is 1 if aggregation is unaffected (e.g. switch from Scaled to Fixed), otherwise additional scaling needed\n second_scale_factor = max(available_cash - fixed_tcs, 0) / (\n unscaled_prices_by_day[cur_day].aggregate() * first_scale_factor + scaling_based_tcs\n )\n return first_scale_factor * second_scale_factor\n\n def _nav_scale_orders(self, orders, price_measure, trigger_infos):\n sorted_order_days = sorted(make_list(orders.keys()))\n final_days_orders = {}\n unscaled_entry_tces_by_day = defaultdict(dict)\n unscaled_unwind_tces_by_day = defaultdict(dict)\n # Populate dict of dates and instruments sold on those dates\n for create_date, portfolio in orders.items():\n info = trigger_infos[create_date]\n for inst in portfolio.all_instruments:\n tc_enter = TransactionCostEntry(create_date, inst, self.action.transaction_cost)\n unscaled_entry_tces_by_day[create_date][inst] = tc_enter\n d = self.get_instrument_final_date(inst, create_date, info)\n tc_exit = TransactionCostEntry(d, inst, self.action.transaction_cost_exit)\n unscaled_unwind_tces_by_day[d][inst] = tc_exit\n if d not in final_days_orders.keys():\n final_days_orders[d] = []\n final_days_orders[d].append(inst)\n\n unscaled_prices_by_day = {}\n unscaled_unwind_prices_by_day = {}\n # Send all unscaled prices and transaction costs to calculate together\n with PricingContext(is_async=True):\n for day, portfolio in orders.items():\n with PricingContext(pricing_date=day):\n unscaled_prices_by_day[day] = portfolio.calc(price_measure)\n for unwind_day, unwind_instruments in final_days_orders.items():\n if unwind_day <= dt.date.today():\n with PricingContext(pricing_date=unwind_day):\n unscaled_unwind_prices_by_day[unwind_day] = Portfolio(unwind_instruments).calc(price_measure)\n for day, inst_tce_map in unscaled_entry_tces_by_day.items():\n for inst, tce in inst_tce_map.items():\n tce.calculate_unit_cost()\n for day, inst_tce_map in unscaled_unwind_tces_by_day.items():\n for inst, tce in inst_tce_map.items():\n tce.calculate_unit_cost()\n\n # Start with scaling_level, then only use proceeds from selling instruments\n available_cash = self.action.scaling_level\n scaling_factors_by_inst = {}\n scaling_factors_by_day = {}\n # Go through each order day of the strategy in sorted order\n for idx, cur_day in enumerate(sorted_order_days):\n portfolio = orders[cur_day]\n scale_factor = self.__portfolio_scaling_for_available_cash(\n portfolio, available_cash, cur_day, unscaled_prices_by_day, unscaled_entry_tces_by_day\n )\n scaling_factors_by_day[cur_day] = scale_factor\n for inst in portfolio:\n scaling_factors_by_inst[inst] = scale_factor\n\n available_cash = 0\n\n if idx + 1 < len(sorted_order_days):\n next_day = sorted_order_days[idx + 1]\n else:\n break\n\n # Cash received from unwinds is the cash available for the next order\n for d, p in final_days_orders.items():\n # Only consider final days between current order date and the next in an iteration\n if cur_day < d <= next_day:\n for inst in p:\n available_cash += unscaled_unwind_prices_by_day[d][inst] * scaling_factors_by_inst[inst]\n tce = unscaled_unwind_tces_by_day[d][inst]\n # additional_scaling only scales the scaling part of the TC\n tce.additional_scaling = scaling_factors_by_inst[inst]\n available_cash -= unscaled_unwind_tces_by_day[d][inst].get_final_cost()\n available_cash = max(available_cash, 0)\n\n # portfolio.scale() applies a deepcopy so interferes with inst dict lookup; apply at the end\n for day in sorted_order_days:\n if scaling_factors_by_day[day] == 0:\n del orders[day]\n else:\n orders[day].scale(scaling_factors_by_day[day])\n\n def _scaling_level_for_date(self, d: dt.date) -> float:\n if self._scaling_level_signal is not None:\n if d in self._scaling_level_signal:\n return self._scaling_level_signal[d]\n return 0\n else:\n return self.action.scaling_level\n\n def _scale_order(self, orders, daily_risk, price_measure, trigger_infos):\n if self.action.scaling_type == ScalingActionType.size:\n for day, portfolio in orders.items():\n portfolio.scale(self._scaling_level_for_date(day))\n elif self.action.scaling_type == ScalingActionType.NAV:\n self._nav_scale_orders(orders, price_measure, trigger_infos)\n elif self.action.scaling_type == ScalingActionType.risk_measure:\n for day, portfolio in orders.items():\n scaling_factor = self._scaling_level_for_date(day) / daily_risk[day]\n portfolio.scale(scaling_factor)\n else:\n raise RuntimeError(f'Scaling Type {self.action.scaling_type} not supported by engine')\n\n def _raise_order(\n self,\n state_list: Collection[dt.date],\n price_measure: RiskMeasure,\n trigger_infos: Dict[dt.date, Optional[Union[AddScaledTradeActionInfo, Iterable[AddScaledTradeActionInfo]]]],\n ):\n if self.action.scaling_type == ScalingActionType.risk_measure:\n self._order_valuations.append(self.action.scaling_risk)\n orders = self.get_base_orders_for_states(state_list, trigger_infos=trigger_infos)\n\n final_orders = {}\n for d, res in orders.items():\n new_port = []\n dated_priceables = getattr(self.action, 'dated_priceables', {}) or {}\n instruments = dated_priceables.get(d) or self.action.priceables\n for inst in instruments:\n new_inst = res[inst]\n if len(self._order_valuations) > 1:\n new_inst = new_inst[ResolvedInstrumentValues]\n new_inst.name = f'{new_inst.name}_{d}'\n new_port.append(new_inst)\n final_orders[d] = Portfolio(new_port)\n daily_risk = (\n {d: res[self.action.scaling_risk].aggregate() for d, res in orders.items()}\n if self.action.scaling_type == ScalingActionType.risk_measure\n else None\n )\n\n self._scale_order(final_orders, daily_risk, price_measure, trigger_infos)\n\n return final_orders\n\n def apply_action(\n self,\n state: Union[dt.date, Iterable[dt.date]],\n backtest: BackTest,\n trigger_info: Optional[Union[AddScaledTradeActionInfo, Iterable[AddScaledTradeActionInfo]]] = None,\n ):\n state_list = make_list(state)\n if trigger_info is None or isinstance(trigger_info, AddScaledTradeActionInfo):\n trigger_info = [trigger_info for _ in range(len(state_list))]\n trigger_infos = dict(zip_longest(state_list, trigger_info))\n orders = self._raise_order(state_list, backtest.price_measure, trigger_infos)\n\n current_tc_entries = []\n # record entry and unwind cashflows\n for create_date, portfolio in orders.items():\n info = trigger_infos[create_date]\n for inst in portfolio.all_instruments:\n tc_enter = TransactionCostEntry(create_date, inst, self.action.transaction_cost)\n current_tc_entries.append(tc_enter)\n backtest.cash_payments[create_date].append(\n CashPayment(inst, effective_date=create_date, direction=-1, transaction_cost_entry=tc_enter)\n )\n backtest.transaction_cost_entries[create_date].append(tc_enter)\n final_date = self.get_instrument_final_date(inst, create_date, info)\n tc_exit = TransactionCostEntry(final_date, inst, self.action.transaction_cost_exit)\n current_tc_entries.append(tc_exit)\n backtest.cash_payments[final_date].append(\n CashPayment(inst, effective_date=final_date, transaction_cost_entry=tc_exit)\n )\n backtest.transaction_cost_entries[final_date].append(tc_exit)\n backtest_states = (s for s in backtest.states if final_date > s >= create_date)\n for s in backtest_states:\n backtest.portfolio_dict[s].append(inst)\n\n with PricingContext(is_async=True):\n if any(tce.no_of_risk_calcs > 0 for tce in current_tc_entries):\n backtest.calc_calls += 1\n for tce in current_tc_entries:\n backtest.calculations += tce.no_of_risk_calcs\n tce.calculate_unit_cost()\n\n return backtest\n\n\nclass HedgeActionImpl(OrderBasedActionImpl):\n def __init__(self, action: HedgeAction):\n super().__init__(action)\n\n def get_base_orders_for_states(self, states: Collection[dt.date], **kwargs):\n with HistoricalPricingContext(dates=states, csa_term=self.action.csa_term):\n f = Portfolio(self.action.priceable).resolve(in_place=False)\n return f.result()\n\n def apply_action(\n self,\n state: Union[dt.date, Iterable[dt.date]],\n backtest: BackTest,\n trigger_info: Optional[Union[HedgeActionInfo, Iterable[HedgeActionInfo]]] = None,\n ):\n state_list = make_list(state)\n if trigger_info is None or isinstance(trigger_info, HedgeActionInfo):\n trigger_info = [trigger_info for _ in range(len(state_list))]\n trigger_infos = dict(zip_longest(state_list, trigger_info))\n backtest.calc_calls += 1\n backtest.calculations += len(state_list)\n orders = self.get_base_orders_for_states(state_list, trigger_infos=trigger_infos)\n\n current_tc_entries = []\n for create_date, portfolio in orders.items():\n info = trigger_infos[create_date]\n hedge_trade = portfolio.priceables[0]\n hedge_trade.name = f'{hedge_trade.name}_{create_date.strftime(\"%Y-%m-%d\")}'\n if isinstance(hedge_trade, Portfolio):\n for instrument in hedge_trade.all_instruments:\n instrument.name = f'{hedge_trade.name}_{instrument.name}'\n final_date = self.get_instrument_final_date(hedge_trade, create_date, info)\n active_dates = [s for s in backtest.states if create_date <= s < final_date]\n\n if len(active_dates):\n scaling_portfolio = ScalingPortfolio(\n trade=hedge_trade,\n dates=active_dates,\n risk=self.action.risk,\n csa_term=self.action.csa_term,\n risk_transformation=self.action.risk_transformation,\n risk_percentage=self.action.risk_percentage,\n )\n tc_enter = TransactionCostEntry(create_date, hedge_trade, self.action.transaction_cost)\n current_tc_entries.append(tc_enter)\n entry_payment = CashPayment(\n trade=hedge_trade, effective_date=create_date, direction=-1, transaction_cost_entry=tc_enter\n )\n backtest.transaction_cost_entries[create_date].append(tc_enter)\n tc_exit = TransactionCostEntry(final_date, hedge_trade, self.action.transaction_cost_exit)\n current_tc_entries.append(tc_exit)\n exit_payment = (\n CashPayment(trade=hedge_trade, effective_date=final_date, transaction_cost_entry=tc_exit)\n if final_date <= dt.date.today()\n else None\n )\n backtest.transaction_cost_entries[final_date].append(tc_exit)\n hedge = Hedge(\n scaling_portfolio=scaling_portfolio, entry_payment=entry_payment, exit_payment=exit_payment\n )\n backtest.hedges[create_date].append(hedge)\n\n with PricingContext(is_async=True):\n if any(tce.no_of_risk_calcs > 0 for tce in current_tc_entries):\n backtest.calc_calls += 1\n for tce in current_tc_entries:\n backtest.calculations += tce.no_of_risk_calcs\n tce.calculate_unit_cost()\n\n return backtest\n\n\nclass ExitTradeActionImpl(ActionHandler):\n def __init__(self, action: ExitTradeAction):\n super().__init__(action)\n\n def apply_action(\n self,\n state: Union[dt.date, Iterable[dt.date]],\n backtest: BackTest,\n trigger_info: Optional[Union[ExitTradeActionInfo, Iterable[ExitTradeActionInfo]]] = None,\n ):\n for s in make_list(state):\n trades_to_remove = []\n if self.action.priceable_names is None:\n current_trade_names = [i.name for i in list(backtest.portfolio_dict[s].all_instruments)]\n\n fut_dates = list(filter(lambda d: d >= s and type(d) is dt.date, backtest.states))\n for port_date in fut_dates:\n res_fut = []\n res_futures = []\n pos_fut = list(backtest.portfolio_dict[port_date].all_instruments)\n if backtest.results[port_date]: # there are results in future dates which need removing\n res_fut = list(backtest.results[port_date].portfolio.all_instruments)\n res_futures = list(backtest.results[port_date].futures)\n\n # We expect tradable names to be defined as __\n if self.action.priceable_names:\n # List of trade names provided -> TradeDate <= exit trigger date and TradeName is present in list\n port_indexes_to_remove = [\n i\n for i, x in enumerate(pos_fut)\n if dt.datetime.strptime(x.name.split('_')[-1], '%Y-%m-%d').date() <= s\n and x.name.split('_')[-2] in self.action.priceable_names\n ]\n result_indexes_to_remove = [\n i\n for i, x in enumerate(res_fut)\n if dt.datetime.strptime(x.name.split('_')[-1], '%Y-%m-%d').date() <= s\n and x.name.split('_')[-2] in self.action.priceable_names\n ]\n else:\n # List of trade names not provided -> TradeDate <= exit trigger date and trade present on trigger\n # date\n port_indexes_to_remove = [i for i, x in enumerate(pos_fut) if x.name in current_trade_names]\n result_indexes_to_remove = [i for i, x in enumerate(res_fut) if x.name in current_trade_names]\n\n for index in sorted(port_indexes_to_remove, reverse=True):\n # Get list of trades that have been removed to check for their future cash flow date\n if pos_fut[index].name not in trades_to_remove:\n trades_to_remove.append(pos_fut[index])\n del pos_fut[index]\n for index in sorted(result_indexes_to_remove, reverse=True):\n del res_fut[index]\n del res_futures[index]\n backtest.portfolio_dict[port_date] = Portfolio(tuple(pos_fut))\n if result_indexes_to_remove:\n backtest.results[port_date] = PortfolioRiskResult(\n Portfolio(res_fut), backtest.results[port_date].risk_measures, res_futures\n )\n\n for cp_date, cp_list in list(backtest.cash_payments.items()):\n if cp_date > s:\n indexes_to_remove = [\n i for i, cp in enumerate(cp_list) if cp.trade.name in [x.name for x in trades_to_remove]\n ]\n for index in sorted(indexes_to_remove, reverse=True):\n cp = cp_list[index]\n prev_pos = [i for i, x in enumerate(backtest.cash_payments[s]) if cp.trade.name == x.trade.name]\n # If trade already exists in exit trigger date cash payments, net out the position\n if prev_pos:\n backtest.cash_payments[s][prev_pos[0]].direction += cp.direction\n else:\n cp.effective_date = s\n backtest.cash_payments[s].append(cp)\n backtest.transaction_cost_entries[s].append(cp.transaction_cost_entry)\n backtest.transaction_cost_entries[cp_date].remove(cp.transaction_cost_entry)\n cp.transaction_cost_entry.date = s\n del backtest.cash_payments[cp_date][index]\n\n if not backtest.cash_payments[cp_date]:\n del backtest.cash_payments[cp_date]\n\n for trade in trades_to_remove:\n if trade.name not in [x.trade.name for x in backtest.cash_payments[s]]:\n # to_dict omits name\n trade_instruments = (\n set(t.to_dict() for t in trade.all_instruments)\n if isinstance(trade, Portfolio)\n else {trade.to_dict()}\n )\n # find TCE corresponding to trade\n trade_tce = [\n tce\n for tce in backtest.transaction_cost_entries[s]\n if set(i.to_dict() for i in tce.all_instruments) == trade_instruments\n ]\n tce = trade_tce[0] if trade_tce else None\n backtest.cash_payments[s].append(CashPayment(trade, effective_date=s, transaction_cost_entry=tce))\n\n return backtest\n\n\nclass RebalanceActionImpl(ActionHandler):\n def __init__(self, action: RebalanceAction):\n super().__init__(action)\n\n def apply_action(\n self,\n state: Union[dt.date, Iterable[dt.date]],\n backtest: BackTest,\n trigger_info: Optional[Union[RebalanceActionInfo, Iterable[RebalanceActionInfo]]] = None,\n ):\n new_size = self.action.method(state, backtest, trigger_info)\n current_size = 0\n for trade in backtest.portfolio_dict[state]:\n if self.action.priceable.name.split('_')[-1] in trade.name:\n current_size += getattr(trade, self.action.size_parameter)\n # if we are already at the required size then do nothing.\n if new_size - current_size == 0:\n return backtest\n pos = self.action.priceable.clone(\n **{self.action.size_parameter: new_size - current_size, 'name': f'{self.action.priceable.name}_{state}'}\n )\n\n current_tc_entries = []\n tc_enter = TransactionCostEntry(state, pos, self.action.transaction_cost)\n current_tc_entries.append(tc_enter)\n backtest.cash_payments[state].append(\n CashPayment(pos, effective_date=state, direction=-1, transaction_cost_entry=tc_enter)\n )\n backtest.transaction_cost_entries[state].append(tc_enter)\n unwind_payment = None\n cash_payment_dates = backtest.cash_payments.keys()\n for d in reversed(sorted(cash_payment_dates)):\n for cp in backtest.cash_payments[d]:\n if self.action.priceable.name.split('_')[-1] in cp.trade.name and cp.direction == 1:\n tc_exit = TransactionCostEntry(d, pos, self.action.transaction_cost_exit)\n current_tc_entries.append(tc_exit)\n unwind_payment = CashPayment(pos, effective_date=d, transaction_cost_entry=tc_exit)\n backtest.cash_payments[d].append(unwind_payment)\n backtest.transaction_cost_entries[d].append(exit)\n break\n if unwind_payment:\n break\n\n if unwind_payment is None:\n raise ValueError(\"Found no final cash payment to rebalance for trade.\")\n\n for s in backtest.states:\n if unwind_payment.effective_date > s >= state:\n backtest.portfolio_dict[s].append(pos)\n\n with PricingContext(is_async=True):\n if any(tce.no_of_risk_calcs > 0 for tce in current_tc_entries):\n backtest.calc_calls += 1\n for tce in current_tc_entries:\n backtest.calculations += tce.no_of_risk_calcs\n tce.calculate_unit_cost()\n\n return backtest\n\n\nclass AddWeightedTradeActionImpl(OrderBasedActionImpl):\n def __init__(self, action: AddWeightedTradeAction):\n super().__init__(action)\n\n def get_base_orders_for_states(self, states: Collection[dt.date], **kwargs):\n with HistoricalPricingContext(dates=states):\n f = Portfolio(self.action.priceables).resolve(in_place=False)\n return f.result()\n\n def apply_action(\n self,\n state: Union[dt.date, Iterable[dt.date]],\n backtest: BackTest,\n trigger_info: Optional[Union[AddWeightedTradeActionInfo, Iterable[AddWeightedTradeActionInfo]]] = None,\n ):\n state_list = make_list(state)\n if trigger_info is None or isinstance(trigger_info, AddWeightedTradeActionInfo):\n trigger_info = [trigger_info for _ in range(len(state_list))]\n trigger_infos = dict(zip_longest(state_list, trigger_info))\n backtest.calc_calls += 1\n backtest.calculations += len(state_list)\n orders = self.get_base_orders_for_states(state_list, trigger_infos=trigger_infos)\n\n current_tc_entries = []\n for create_date, portfolio in orders.items():\n info = trigger_infos[create_date]\n # Get all instruments from the resolved portfolio\n instruments = portfolio.priceables\n if not instruments:\n continue\n\n # Rename instruments with the create date\n renamed_instruments = []\n for inst in instruments:\n renamed_inst = inst.clone(name=f'{inst.name}_{create_date.strftime(\"%Y-%m-%d\")}')\n renamed_instruments.append(renamed_inst)\n\n weighted_portfolio = Portfolio(renamed_instruments)\n final_date = self.get_instrument_final_date(renamed_instruments[0], create_date, info)\n active_dates = [s for s in backtest.states if create_date <= s < final_date]\n\n if len(active_dates):\n scaling_portfolio = WeightedScalingPortfolio(\n trades=weighted_portfolio,\n dates=active_dates,\n risk=self.action.scaling_risk,\n total_size=self.action.total_size,\n )\n\n # Create entry and exit payments for each instrument\n entry_payments = []\n exit_payments = []\n for inst in renamed_instruments:\n tc_enter = TransactionCostEntry(create_date, inst, self.action.transaction_cost)\n current_tc_entries.append(tc_enter)\n entry_payment = CashPayment(\n trade=inst, effective_date=create_date, direction=-1, transaction_cost_entry=tc_enter\n )\n entry_payments.append(entry_payment)\n backtest.transaction_cost_entries[create_date].append(tc_enter)\n\n tc_exit = TransactionCostEntry(final_date, inst, self.action.transaction_cost_exit)\n current_tc_entries.append(tc_exit)\n exit_payment = (\n CashPayment(trade=inst, effective_date=final_date, transaction_cost_entry=tc_exit)\n if final_date <= dt.date.today()\n else None\n )\n exit_payments.append(exit_payment)\n backtest.transaction_cost_entries[final_date].append(tc_exit)\n\n weighted_trade = WeightedTrade(\n scaling_portfolio=scaling_portfolio,\n entry_payments=entry_payments,\n exit_payments=exit_payments,\n )\n backtest.weighted_trades[create_date].append(weighted_trade)\n\n with PricingContext(is_async=True):\n if any(tce.no_of_risk_calcs > 0 for tce in current_tc_entries):\n backtest.calc_calls += 1\n for tce in current_tc_entries:\n backtest.calculations += tce.no_of_risk_calcs\n tce.calculate_unit_cost()\n\n return backtest\n" }, { "path": "gs_quant/backtests/order.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom abc import ABCMeta\nfrom gs_quant.instrument import Instrument, Cash\nfrom gs_quant.backtests.core import TimeWindow, ValuationFixingType\nfrom gs_quant.backtests.data_handler import DataHandler\nimport numpy as np\nimport datetime as dt\n\n\nclass OrderBase(metaclass=ABCMeta):\n def __init__(self, instrument: Instrument, quantity: float, generation_time: dt.datetime, source: str):\n \"\"\"\n Create an order\n :param instrument: an Instrument or Security to be traded\n :param quantity: quantity to be traded in instrument\n :param generation_time: the time when this order was generated\n :param source: the name of the entity that generated this order\n \"\"\"\n self.instrument = instrument\n self.quantity = quantity\n self.generation_time = generation_time\n self.source = source\n self.executed_price = None\n\n def execution_end_time(self) -> dt.datetime:\n raise RuntimeError('The method execution_end_time is not implemented on OrderBase')\n\n def _execution_price(self, data_handler: DataHandler) -> float:\n raise RuntimeError('The method execution_price is not implemented on OrderBase')\n\n def execution_price(self, data_handler: DataHandler) -> float:\n price = self._execution_price(data_handler)\n if np.isnan(price):\n raise RuntimeError('can not compute the execution price')\n else:\n return price\n\n def execution_quantity(self) -> float:\n raise RuntimeError('The method execution_price is not implemented on OrderBase')\n\n def execution_notional(self, data_hander: DataHandler) -> float:\n return self.execution_price(data_hander) * self.execution_quantity()\n\n def _short_name(self) -> str:\n raise RuntimeError('The method _short_name is not implemented on OrderBase')\n\n def to_dict(self, data_hander: DataHandler) -> dict:\n return {\n 'Instrument': self.instrument.ric,\n 'Type': self._short_name(),\n 'Price': self.execution_price(data_hander),\n 'Quantity': self.execution_quantity(),\n }\n\n\nclass OrderTWAP(OrderBase):\n def __init__(\n self, instrument: Instrument, quantity: float, generation_time: dt.datetime, source: str, window: TimeWindow\n ):\n super().__init__(instrument, quantity, generation_time, source)\n \"\"\"\n Create a TWAP order\n :param window: TWAP window\n \"\"\"\n self.window = window\n\n def execution_end_time(self) -> dt.datetime:\n return self.window.end\n\n def _execution_price(self, data_handler: DataHandler) -> float:\n if self.executed_price is None:\n fixings = data_handler.get_data_range(\n self.window.start, self.window.end, self.instrument, ValuationFixingType.PRICE\n )\n self.executed_price = np.mean(fixings)\n return self.executed_price\n\n def execution_quantity(self) -> float:\n return self.quantity\n\n def _short_name(self) -> str:\n return 'TWAP {0}:{1}'.format(self.window.start, self.window.end)\n\n\nclass OrderMarketOnClose(OrderBase):\n def __init__(\n self,\n instrument: Instrument,\n quantity: float,\n generation_time: dt.datetime,\n execution_date: dt.date,\n source: str,\n ):\n super().__init__(instrument, quantity, generation_time, source)\n self.execution_date = execution_date\n\n def execution_end_time(self) -> dt.datetime:\n return dt.datetime.combine(self.execution_date, dt.time(23, 0, 0))\n\n def _execution_price(self, data_handler: DataHandler) -> float:\n if self.executed_price is None:\n self.executed_price = data_handler.get_data(self.execution_date, self.instrument, ValuationFixingType.PRICE)\n return self.executed_price\n\n def execution_quantity(self) -> float:\n return self.quantity\n\n def _short_name(self) -> str:\n return 'MOC'\n\n\nclass OrderCost(OrderBase):\n def __init__(self, currency: str, quantity: float, source: str, execution_time: dt.datetime):\n super().__init__(Cash(currency), quantity, generation_time=execution_time, source=source)\n \"\"\"\n Create a cost order e.g. transaction or servicing cost\n :param execution_time: the time when the order is executed\n \"\"\"\n self.execution_time = execution_time\n\n def execution_end_time(self) -> dt.datetime:\n return self.execution_time\n\n def _execution_price(self, data_handler: DataHandler) -> float:\n if self.executed_price is None:\n self.executed_price = 0\n return self.executed_price\n\n def execution_quantity(self) -> float:\n return self.quantity\n\n def _short_name(self) -> str:\n return 'Cost'\n\n def to_dict(self, data_hander: DataHandler) -> dict:\n return {\n 'Instrument': self.instrument.currency,\n 'Type': self._short_name(),\n 'Price': self.execution_price(data_hander),\n 'Quantity': self.execution_quantity(),\n }\n\n\nclass OrderAtMarket(OrderBase):\n def __init__(\n self,\n instrument: Instrument,\n quantity: float,\n generation_time: dt.datetime,\n execution_datetime: dt.datetime,\n source: str,\n ):\n super().__init__(instrument, quantity, generation_time, source)\n self.execution_datetime = execution_datetime\n\n def execution_end_time(self) -> dt.datetime:\n return self.execution_datetime\n\n def _execution_price(self, data_handler: DataHandler) -> float:\n if self.executed_price is None:\n self.executed_price = data_handler.get_data(\n self.execution_datetime, self.instrument, ValuationFixingType.PRICE\n )\n return self.executed_price\n\n def execution_quantity(self) -> float:\n return self.quantity\n\n def _short_name(self) -> str:\n return 'Market'\n\n\nclass OrderTwapBTIC(OrderTWAP):\n def __init__(\n self,\n instrument: Instrument,\n quantity: float,\n generation_time: dt.datetime,\n source: str,\n window: TimeWindow,\n btic_instrument: Instrument,\n future_underlying,\n ):\n super().__init__(instrument, quantity, generation_time, source, window)\n \"\"\"\n Create a BTIC TWAP order: an order for a future executed at underlying spot + BTIC TWAP\n \"\"\"\n self.btic_instrument = btic_instrument\n self.future_underlying = future_underlying\n\n def _execution_price(self, data_handler: DataHandler) -> float:\n if self.executed_price is None:\n btic_fixings = data_handler.get_data_range(\n self.window.start, self.window.end, self.btic_instrument, ValuationFixingType.PRICE\n )\n btic_twap = np.mean(btic_fixings)\n close = data_handler.get_data(self.window.end.date(), self.future_underlying)\n self.executed_price = close + btic_twap\n return self.executed_price\n\n def _short_name(self) -> str:\n return 'TwapBTIC'\n" }, { "path": "gs_quant/backtests/predefined_asset_engine.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom collections import deque\nfrom functools import reduce\nfrom itertools import compress\nfrom typing import Union, Tuple\n\nimport pandas as pd\nfrom pandas.tseries.offsets import BDay # noqa\nfrom tqdm import tqdm\n\nfrom gs_quant.backtests import ValuationFixingType\nfrom gs_quant.backtests.action_handler import ActionHandlerBaseFactory, ActionHandler\nfrom gs_quant.backtests.actions import Action, AddTradeAction, AddTradeActionInfo\nfrom gs_quant.backtests.backtest_engine import BacktestBaseEngine\nfrom gs_quant.backtests.backtest_objects import PredefinedAssetBacktest\nfrom gs_quant.backtests.core import ValuationMethod\nfrom gs_quant.backtests.data_handler import DataHandler\nfrom gs_quant.backtests.data_sources import DataManager\nfrom gs_quant.backtests.event import ValuationEvent, OrderEvent, MarketEvent\nfrom gs_quant.backtests.execution_engine import SimulatedExecutionEngine\nfrom gs_quant.backtests.order import OrderAtMarket\nfrom gs_quant.datetime import is_business_day, prev_business_date, business_day_offset\n\n\n# Action Implementations\n\n\nclass AddTradeActionImpl(ActionHandler):\n def __init__(self, action: AddTradeAction):\n super().__init__(action)\n\n def generate_orders(self, state: dt.datetime, backtest: PredefinedAssetBacktest, info: AddTradeActionInfo):\n orders = []\n for pricable in self.action.priceables:\n quantity = pricable.instrument_quantity * 1 if info is None or info.scaling is None else info.scaling\n orders.append(\n OrderAtMarket(\n instrument=pricable,\n quantity=quantity,\n generation_time=state,\n execution_datetime=state,\n source=self.action.name,\n )\n )\n if isinstance(self.action.trade_duration, dt.timedelta):\n # create close order\n orders.append(\n OrderAtMarket(\n instrument=pricable,\n quantity=quantity * -1,\n generation_time=state,\n execution_datetime=state + self.action.trade_duration,\n source=self.action.name,\n )\n )\n return orders\n\n def apply_action(self, state: dt.datetime, backtest: PredefinedAssetBacktest, info=None):\n orders = self.generate_orders(state, backtest, info)\n return orders\n\n\nclass SubmitOrderActionImpl(ActionHandler):\n \"\"\"\n The apply_action method simply returns the orders generated by the trigger.\n \"\"\"\n\n def __init__(self, action: Action):\n super().__init__(action)\n\n def apply_action(self, state: dt.datetime, backtest: PredefinedAssetBacktest, info=None):\n return info\n\n\nclass PredefinedAssetEngineActionFactory(ActionHandlerBaseFactory):\n def __init__(self, action_impl_map=None):\n action_impl_map = action_impl_map or {}\n self.action_impl_map = action_impl_map\n self.action_impl_map[AddTradeAction] = AddTradeActionImpl\n\n def get_action_handler(self, action: Action) -> Action:\n if type(action) in self.action_impl_map:\n return self.action_impl_map[type(action)](action)\n raise RuntimeError(f'Action {type(action)} not supported by engine')\n\n\nclass PredefinedAssetEngine(BacktestBaseEngine):\n def get_action_handler(self, action: Action) -> Action:\n handler_factory = PredefinedAssetEngineActionFactory(self.action_impl_map)\n return handler_factory.get_action_handler(action)\n\n def supports_strategy(self, strategy):\n all_actions = reduce(lambda x, y: x + y, (map(lambda x: x.actions, strategy.triggers)))\n try:\n for x in all_actions:\n self.get_action_handler(x)\n except RuntimeError:\n return False\n return True\n\n def __init__(\n self,\n data_mgr: DataManager = DataManager(),\n calendars: Union[str, Tuple[str, ...]] = None,\n tz: dt.timezone = dt.timezone.utc,\n valuation_method: ValuationMethod = ValuationMethod(ValuationFixingType.PRICE),\n action_impl_map=None,\n ):\n if action_impl_map is None:\n action_impl_map = {Action: SubmitOrderActionImpl}\n self.action_impl_map = action_impl_map\n self.calendars = calendars\n self.tz = tz\n self.data_handler = DataHandler(data_mgr, tz=tz)\n self.valuation_method = valuation_method\n self.execution_engine = None\n\n def _eod_valuation_time(self):\n if self.valuation_method.window:\n return self.valuation_method.window.end\n else:\n return dt.time(23)\n\n def _timer(self, strategy, start, end, frequency, states=None):\n dates = (\n list(map(lambda x: x.date(), pd.to_datetime(pd.bdate_range(start=start, end=end, freq=frequency))))\n if states is None\n else states\n )\n\n all_times = []\n times = list()\n for trigger in strategy.triggers:\n if hasattr(trigger, 'get_trigger_times'):\n for t in trigger.get_trigger_times():\n # allow user to define their trigger times as a time, in which case add that time to every date\n # or as a datetime itself in which case just add it to the timer\n if isinstance(t, dt.datetime):\n all_times.append(t)\n else:\n times.append(t)\n times.append(self._eod_valuation_time())\n times = list(dict.fromkeys(times))\n\n if self.calendars is not None:\n dates = list(\n compress(\n dates, is_business_day(dates, (None if self.calendars.lower() == 'weekend' else self.calendars))\n )\n )\n\n for d in dates:\n if isinstance(d, dt.datetime):\n all_times.append(d)\n for t in times:\n if d.tzinfo is not None and d.tzinfo.utcoffset(d) is not None:\n all_times.append(dt.datetime.combine(d.date(), t, d.tzinfo))\n else:\n for t in times:\n all_times.append(dt.datetime.combine(d, t))\n all_times = list(set(all_times))\n all_times.sort()\n return all_times\n\n def _adjust_date(self, date):\n date = (date + BDay(1) - BDay(1)).date() # 1st move to latest weekday.\n if self.calendars is None or self.calendars.lower() == 'weekend' or is_business_day(date, self.calendars):\n return date\n else:\n return prev_business_date(date, None if self.calendars.lower() == 'weekend' else self.calendars)\n\n def run_backtest(self, strategy, start, end, frequency=\"B\", states=None, initial_value=100):\n # initialize backtest object\n self.data_handler.reset_clock()\n backtest = PredefinedAssetBacktest(self.data_handler, initial_value)\n\n # initialize execution engine\n self.execution_engine = SimulatedExecutionEngine(self.data_handler)\n\n if states is not None:\n timer = self._timer(strategy, start, end, frequency, states)\n else:\n # if start is a holiday, go back to the previous day on the backtest calendar\n adjusted_start = self._adjust_date(start)\n backtest.set_start_date(adjusted_start)\n\n # create timer\n timer_start = (\n (adjusted_start + BDay(1)).date()\n if self.calendars is None\n else business_day_offset(\n adjusted_start,\n 1,\n roll='forward',\n calendars=None if self.calendars.lower() == 'weekend' else self.calendars,\n )\n )\n timer_end = self._adjust_date(end)\n timer = self._timer(strategy, timer_start, timer_end, frequency)\n self._run(strategy, timer, backtest)\n return backtest\n\n def _run(self, strategy, timer, backtest: PredefinedAssetBacktest):\n events = deque()\n\n for state in tqdm(timer):\n # update to latest data\n self.data_handler.update(state)\n\n # see if any submitted orders have been executed\n fills = self.execution_engine.ping(state)\n events.extend(fills)\n\n # generate a market event\n events.append(MarketEvent())\n\n # create valuation event when it's due for daily valuation\n if state.time() == self._eod_valuation_time():\n events.append(ValuationEvent())\n\n while events:\n event = events.popleft()\n\n if event.type == 'Market': # market event (new mkt data coming in)\n for trigger in strategy.triggers:\n trigger_info = trigger.has_triggered(state, backtest)\n if trigger_info.triggered:\n for action in trigger.actions:\n info_dict = trigger_info.info_dict\n info = info_dict[type(action)] if info_dict and type(action) in info_dict else None\n orders = self.get_action_handler(action).apply_action(state, backtest, info)\n backtest.record_orders(orders)\n events.extend([OrderEvent(o) for o in orders])\n elif event.type == 'Order': # order event (submit the order to execution engine)\n self.execution_engine.submit_order(event)\n elif event.type == 'Fill': # fill event (update backtest with the fill results)\n backtest.update_fill(event)\n elif event.type == 'Valuation': # valuation event (calculate daily level)\n backtest.mark_to_market(state, self.valuation_method)\n\n return backtest\n" }, { "path": "gs_quant/backtests/strategy.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom typing import Tuple, Optional, Union, Iterable\n\nfrom dataclasses_json import dataclass_json, config\n\nfrom gs_quant.backtests.backtest_objects import CashAccrualModel\nfrom gs_quant.backtests.backtest_utils import make_list\nfrom gs_quant.backtests.triggers import Trigger\nfrom gs_quant.base import Priceable\nfrom gs_quant.json_convertors import decode_named_instrument, encode_named_instrument, dc_decode\n\n\ndef _backtest_engines():\n from gs_quant.backtests.equity_vol_engine import EquityVolEngine\n from gs_quant.backtests.generic_engine import GenericEngine\n from gs_quant.backtests.predefined_asset_engine import PredefinedAssetEngine\n\n return [GenericEngine(), PredefinedAssetEngine(), EquityVolEngine()]\n\n\n@dataclass_json\n@dataclass\nclass Strategy:\n \"\"\"\n A strategy object on which one may run a backtest\n \"\"\"\n\n initial_portfolio: Optional[Union[Tuple[Priceable, ...], dict]] = field(\n default=None, metadata=config(decoder=decode_named_instrument, encoder=encode_named_instrument)\n )\n triggers: Union[Trigger, Iterable[Trigger]] = field(\n default=None, metadata=config(decoder=dc_decode(*Trigger.sub_classes(), allow_missing=True))\n )\n cash_accrual: CashAccrualModel = None\n risks = None\n\n def __post_init__(self):\n if not isinstance(self.initial_portfolio, dict):\n self.initial_portfolio = make_list(self.initial_portfolio)\n self.triggers = make_list(self.triggers)\n self.risks = self.get_risks()\n\n def get_risks(self):\n risk_list = []\n for t in self.triggers:\n risk_list += t.risks if t.risks is not None else []\n return risk_list\n\n def get_available_engines(self):\n return [engine for engine in _backtest_engines() if engine.supports_strategy(self)]\n" }, { "path": "gs_quant/backtests/strategy_systematic.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom typing import Iterable, Union, Tuple\n\nimport gs_quant.target.backtests as backtests\nfrom gs_quant.api.gs.backtests import GsBacktestApi\nfrom gs_quant.api.gs.backtests_xasset.apis import GsBacktestXassetApi\nfrom gs_quant.api.gs.backtests_xasset.request import BasicBacktestRequest\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import (\n DateConfig,\n Trade,\n Configuration,\n RollDateMode,\n TransactionCostConfig,\n StrategyHedge,\n)\nfrom gs_quant.backtests.core import Backtest, TradeInMethod\nfrom gs_quant.base import get_enum_value, Base\nfrom gs_quant.common import Currency\nfrom gs_quant.common import FieldValueMap, AssetClass\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.instrument import (\n EqOption,\n EqVarianceSwap,\n Instrument,\n)\nfrom gs_quant.target.backtests import (\n BacktestResult,\n BacktestRisk,\n BacktestTradingQuantityType,\n DeltaHedgeParameters,\n BacktestSignalSeriesItem,\n BacktestStrategyUnderlier,\n BacktestStrategyUnderlierHedge,\n EquityMarketModel,\n BacktestTradingParameters,\n FlowVolBacktestMeasure,\n)\n\n_logger = logging.getLogger(__name__)\n\nBACKTEST_TYPE_NAME = 'VolatilityFlow'\nBACKTEST_TYPE_VALUE = 'Volatility Flow'\nISO_FORMAT = r\"^([0-9]{4})-([0-9]{2})-([0-9]{2})$\"\n\n\nclass StrategySystematic:\n \"\"\"Equity back testing systematic strategy\"\"\"\n\n _supported_eq_instruments = (EqOption, EqVarianceSwap)\n\n def __init__(\n self,\n underliers: Union[Instrument, Iterable[Instrument]],\n quantity: float = 1,\n quantity_type: Union[BacktestTradingQuantityType, str] = BacktestTradingQuantityType.notional,\n trade_in_method: Union[TradeInMethod, str] = TradeInMethod.FixedRoll,\n roll_frequency: str = None,\n scaling_method: str = None,\n index_initial_value: float = 0.0,\n delta_hedge: DeltaHedgeParameters = None,\n name: str = None,\n cost_netting: bool = False,\n currency: Union[Currency, str] = Currency.USD,\n trade_in_signals: Tuple[BacktestSignalSeriesItem, ...] = None,\n trade_out_signals: Tuple[BacktestSignalSeriesItem, ...] = None,\n market_model: Union[EquityMarketModel, str] = EquityMarketModel.SFK,\n roll_date_mode: str = None,\n expiry_date_mode: str = None,\n cash_accrual: bool = True,\n combine_roll_signal_entries: bool = False,\n transaction_cost_config: TransactionCostConfig = None,\n use_xasset_backtesting_service: bool = True,\n ):\n self.__cost_netting = cost_netting\n self.__currency = get_enum_value(Currency, currency)\n self.__name = name\n self.__backtest_type = BACKTEST_TYPE_NAME\n self.__cash_accrual = cash_accrual\n\n trade_in_method = get_enum_value(TradeInMethod, trade_in_method).value\n market_model = get_enum_value(EquityMarketModel, market_model).value\n\n self.__trading_parameters = BacktestTradingParameters(\n quantity=quantity,\n quantity_type=get_enum_value(BacktestTradingQuantityType, quantity_type).value,\n trade_in_method=trade_in_method,\n roll_frequency=roll_frequency,\n trade_in_signals=trade_in_signals,\n trade_out_signals=trade_out_signals,\n roll_date_mode=roll_date_mode,\n )\n\n self.__underliers = []\n trade_instruments = []\n\n def is_unsupported_eq_instrument(inst):\n return inst.__class__.__name__.startswith('Eq') and not isinstance(inst, self._supported_eq_instruments)\n\n if isinstance(underliers, Iterable):\n for underlier in underliers:\n if isinstance(underlier, tuple):\n instrument = underlier[0]\n notional_percentage = underlier[1]\n else:\n instrument = underlier\n notional_percentage = 100\n\n if is_unsupported_eq_instrument(instrument):\n raise MqValueError('The format of the backtest asset is incorrect.')\n else:\n instrument = instrument.scale(notional_percentage / 100, in_place=False, check_resolved=False)\n trade_instruments.append(instrument)\n self.__underliers.append(\n BacktestStrategyUnderlier(\n instrument=instrument,\n notional_percentage=notional_percentage,\n hedge=BacktestStrategyUnderlierHedge(risk_details=delta_hedge),\n market_model=market_model,\n expiry_date_mode=expiry_date_mode,\n )\n )\n else:\n instrument = underliers\n if is_unsupported_eq_instrument(instrument):\n raise MqValueError('The format of the backtest asset is incorrect.')\n notional_percentage = 100\n trade_instruments.append(instrument)\n self.__underliers.append(\n BacktestStrategyUnderlier(\n instrument=instrument,\n notional_percentage=notional_percentage,\n hedge=BacktestStrategyUnderlierHedge(risk_details=delta_hedge),\n market_model=market_model,\n expiry_date_mode=expiry_date_mode,\n )\n )\n # xasset backtesting service fields\n trade_buy_dates = tuple(s.date for s in trade_in_signals if s.value) if trade_in_signals is not None else None\n trade_exit_dates = (\n tuple(s.date for s in trade_out_signals if s.value) if trade_out_signals is not None else None\n )\n self.__trades = (\n Trade(\n tuple(trade_instruments),\n roll_frequency,\n trade_buy_dates,\n roll_frequency,\n trade_exit_dates,\n quantity,\n quantity_type,\n ),\n )\n\n if delta_hedge:\n self.__hedge_params = StrategyHedge()\n if delta_hedge.frequency:\n self.__hedge_params.frequency = '1b' if delta_hedge.frequency == 'Daily' else delta_hedge.frequency\n if delta_hedge.notional:\n self.__hedge_params.risk_percentage = delta_hedge.notional\n else:\n self.__hedge_params = None\n\n self.__transaction_cost_config = transaction_cost_config\n self.__xasset_bt_service_config = Configuration(\n roll_date_mode=RollDateMode(roll_date_mode) if roll_date_mode is not None else None,\n market_model=EquityMarketModel(market_model) if market_model else None,\n cash_accrual=cash_accrual,\n combine_roll_signal_entries=combine_roll_signal_entries,\n )\n\n backtest_parameters_class: Base = getattr(backtests, self.__backtest_type + 'BacktestParameters')\n backtest_parameter_args = {\n 'trading_parameters': self.__trading_parameters,\n 'underliers': self.__underliers,\n 'trade_in_method': trade_in_method,\n 'scaling_method': scaling_method,\n 'index_initial_value': index_initial_value,\n }\n self.__backtest_parameters = backtest_parameters_class.from_dict(backtest_parameter_args)\n self.__use_xasset_backtesting_service = use_xasset_backtesting_service\n\n def __run_service_based_backtest(\n self, start: dt.date, end: dt.date, measures: Iterable[FlowVolBacktestMeasure]\n ) -> BacktestResult:\n date_cfg = DateConfig(start, end)\n if not measures:\n measures = (FlowVolBacktestMeasure.PNL,)\n basic_bt_request = BasicBacktestRequest(\n dates=date_cfg,\n trades=self.__trades,\n measures=measures,\n transaction_costs=self.__transaction_cost_config,\n configuration=self.__xasset_bt_service_config,\n hedge=self.__hedge_params,\n )\n basic_bt_response = GsBacktestXassetApi.calculate_basic_backtest(basic_bt_request, decode_instruments=False)\n risks = tuple(\n BacktestRisk(name=k.value, timeseries=tuple(FieldValueMap(date=d, value=r.result) for d, r in v.items()))\n for k, v in basic_bt_response.measures.items()\n )\n events = []\n if basic_bt_response.additional_results is not None:\n if basic_bt_response.additional_results.trade_events is not None:\n events.append(\n BacktestRisk(\n name=\"trade_events\",\n timeseries=tuple(\n FieldValueMap(date=d, value=e)\n for d, e in basic_bt_response.additional_results.trade_events.items()\n ),\n )\n )\n if basic_bt_response.additional_results.hedge_events is not None:\n events.append(\n BacktestRisk(\n name=\"hedge_events\",\n timeseries=tuple(\n FieldValueMap(date=d, value=e)\n for d, e in basic_bt_response.additional_results.hedge_events.items()\n ),\n )\n )\n portfolio = []\n for d in sorted(set().union(basic_bt_response.portfolio.keys(), basic_bt_response.transactions.keys())):\n if d in basic_bt_response.portfolio:\n positions = [\n {'instrument': i if i is not None else {}, 'quantity': self.__position_quantity(i)}\n for i in basic_bt_response.portfolio[d]\n ]\n else:\n positions = []\n transactions = []\n if d in basic_bt_response.transactions:\n for t in basic_bt_response.transactions[d]:\n trades = (\n [\n {\n 'instrument': i if i is not None else {},\n 'price': t.portfolio_price,\n 'quantity': t.quantity if t.quantity is not None else None,\n }\n for i in t.portfolio\n ]\n if t.portfolio is not None\n else []\n )\n transactions.append({'type': t.direction.value, 'trades': trades, 'cost': t.cost})\n portfolio.append({'date': d, 'positions': positions, 'transactions': transactions})\n return BacktestResult(risks=risks, events=tuple(events), portfolio=portfolio)\n\n def __position_quantity(self, instrument: dict) -> float:\n if instrument.get('assetClass') == AssetClass.Equity.value:\n direction = 1 if instrument['buySell'] == 'Buy' else -1\n quantity = instrument['numberOfOptions'] if instrument['type'] == 'Option' else instrument['quantity']\n return direction * quantity\n return None\n\n def backtest(\n self,\n start: dt.date = None,\n end: dt.date = dt.date.today() - dt.timedelta(days=1),\n is_async: bool = False,\n measures: Iterable[FlowVolBacktestMeasure] = (FlowVolBacktestMeasure.ALL_MEASURES,),\n correlation_id: str = None,\n ) -> Union[Backtest, BacktestResult]:\n if self.__use_xasset_backtesting_service:\n return self.__run_service_based_backtest(start, end, measures)\n params_dict = self.__backtest_parameters.as_dict()\n params_dict['measures'] = [m.value for m in measures]\n backtest_parameters_class: Base = getattr(backtests, self.__backtest_type + 'BacktestParameters')\n params = backtest_parameters_class.from_dict(params_dict)\n\n backtest = Backtest(\n name=self.__name,\n mq_symbol=self.__name,\n parameters=params,\n start_date=start,\n end_date=end,\n type=BACKTEST_TYPE_VALUE,\n asset_class=AssetClass.Equity,\n currency=self.__currency,\n cost_netting=self.__cost_netting,\n cash_accrual=self.__cash_accrual,\n )\n\n if is_async:\n # Create back test ...\n response = GsBacktestApi.create_backtest(backtest)\n\n # ... and schedule it\n GsBacktestApi.schedule_backtest(backtest_id=response.id)\n else:\n # Run on-the-fly back test\n response = GsBacktestApi.run_backtest(backtest, correlation_id)\n\n return response\n" }, { "path": "gs_quant/backtests/triggers.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom typing import ClassVar, List, Optional, Iterable, Union\n\nfrom dataclasses_json import dataclass_json, config\n\nfrom gs_quant.backtests.actions import (\n AddTradeAction,\n AddTradeActionInfo,\n AddScaledTradeAction,\n AddScaledTradeActionInfo,\n HedgeAction,\n HedgeActionInfo,\n Action,\n)\nfrom gs_quant.backtests.backtest_objects import BackTest, PredefinedAssetBacktest\nfrom gs_quant.backtests.backtest_utils import make_list, CalcType\nfrom gs_quant.backtests.data_sources import DataSource, GsDataSource\nfrom gs_quant.base import field_metadata, exclude_none, static_field\nfrom gs_quant.data import Dataset\nfrom gs_quant.datetime.relative_date import RelativeDateSchedule\nfrom gs_quant.json_convertors import decode_iso_date_or_datetime, decode_date_tuple, dc_decode\nfrom gs_quant.json_convertors_common import encode_risk_measure, decode_risk_measure\nfrom gs_quant.risk import RiskMeasure\nfrom gs_quant.risk.transform import Transformer\n\n\nclass TriggerDirection(Enum):\n ABOVE = 1\n BELOW = 2\n EQUAL = 3\n\n\nclass AggType(Enum):\n ALL_OF = 1\n ANY_OF = 2\n\n\n@dataclass_json\n@dataclass\nclass TriggerRequirements:\n __sub_classes: ClassVar[List[type]] = []\n\n def __init_subclass__(cls, **kwargs):\n super().__init_subclass__(**kwargs)\n TriggerRequirements.__sub_classes.append(cls)\n\n @staticmethod\n def sub_classes():\n return tuple(TriggerRequirements.__sub_classes)\n\n def get_trigger_times(self):\n return []\n\n @property\n def calc_type(self):\n return CalcType.simple\n\n\n@dataclass_json\n@dataclass\nclass TriggerInfo(object):\n triggered: bool\n info_dict: Optional[dict] = None\n\n def __eq__(self, other):\n return self.triggered is other\n\n def __bool__(self):\n return self.triggered\n\n\ndef check_barrier(direction, test_value, trigger_level) -> TriggerInfo:\n if direction == TriggerDirection.ABOVE:\n if test_value > trigger_level:\n return TriggerInfo(True)\n elif direction == TriggerDirection.BELOW:\n if test_value < trigger_level:\n return TriggerInfo(True)\n else:\n if test_value == trigger_level:\n return TriggerInfo(True)\n return TriggerInfo(False)\n\n\n@dataclass_json\n@dataclass\nclass PeriodicTriggerRequirements(TriggerRequirements):\n start_date: Optional[dt.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[dt.date] = field(default=None, metadata=field_metadata)\n frequency: Optional[str] = field(default=None, metadata=field_metadata)\n calendar: Optional[Iterable[dt.date]] = field(\n default=None, metadata=config(exclude=exclude_none, decoder=decode_date_tuple)\n )\n trigger_dates = []\n class_type: str = static_field('periodic_trigger_requirements')\n\n def get_trigger_times(self) -> [dt.date]:\n if not self.trigger_dates:\n self.trigger_dates = RelativeDateSchedule(self.frequency, self.start_date, self.end_date).apply_rule(\n holiday_calendar=self.calendar\n )\n return self.trigger_dates\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n if not self.trigger_dates:\n self.get_trigger_times()\n if state in self.trigger_dates:\n next_state = None\n if self.trigger_dates.index(state) != len(self.trigger_dates) - 1:\n next_state = self.trigger_dates[self.trigger_dates.index(state) + 1]\n return TriggerInfo(\n True,\n {\n AddTradeAction: AddTradeActionInfo(scaling=None, next_schedule=next_state),\n AddScaledTradeAction: AddScaledTradeActionInfo(next_schedule=next_state),\n HedgeAction: HedgeActionInfo(next_schedule=next_state),\n },\n )\n return TriggerInfo(False)\n\n\n@dataclass_json\n@dataclass\nclass IntradayTriggerRequirements(TriggerRequirements):\n start_time: Optional[dt.time] = field(default=None, metadata=field_metadata)\n end_time: Optional[dt.time] = field(default=None, metadata=field_metadata)\n frequency: Optional[float] = field(default=None, metadata=field_metadata)\n class_type: str = static_field('intraday_trigger_requirements')\n\n def __post_init__(self):\n # generate all the trigger times\n self._trigger_times = []\n time = self.start_time\n while time <= self.end_time:\n self._trigger_times.append(time)\n time = (dt.datetime.combine(dt.date.today(), time) + dt.timedelta(minutes=self.frequency)).time()\n\n def get_trigger_times(self):\n return self._trigger_times\n\n def has_triggered(self, state: Union[dt.date, dt.datetime], backtest: BackTest = None) -> TriggerInfo:\n return TriggerInfo(state.time() in self._trigger_times)\n\n\n@dataclass_json\n@dataclass\nclass MktTriggerRequirements(TriggerRequirements):\n data_source: DataSource = field(\n default=None, metadata=config(decoder=dc_decode(*DataSource.sub_classes(), allow_missing=True))\n )\n trigger_level: float = field(default=None, metadata=field_metadata)\n direction: TriggerDirection = field(default=None, metadata=field_metadata)\n class_type: str = static_field('mkt_trigger_requirements')\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n data_value = self.data_source.get_data(state)\n try:\n triggered = check_barrier(self.direction, data_value, self.trigger_level)\n except TypeError:\n raise RuntimeError(f'unable to determine trigger state on {str(state)}, data value was {data_value}')\n return triggered\n\n\n@dataclass_json\n@dataclass\nclass RiskTriggerRequirements(TriggerRequirements):\n risk: RiskMeasure = field(default=None, metadata=config(decoder=decode_risk_measure, encoder=encode_risk_measure))\n trigger_level: float = field(default=None, metadata=field_metadata)\n direction: TriggerDirection = field(default=None, metadata=field_metadata)\n risk_transformation: Optional[Transformer] = field(default=None, metadata=field_metadata)\n class_type: str = static_field('risk_trigger_requirements')\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n if state not in backtest.results:\n return TriggerInfo(False)\n if self.risk_transformation is None:\n risk_value = backtest.results[state][self.risk].aggregate()\n else:\n risk_value = (\n backtest.results[state][self.risk]\n .transform(risk_transformation=self.risk_transformation)\n .aggregate(allow_mismatch_risk_keys=True)\n )\n return check_barrier(self.direction, risk_value, self.trigger_level)\n\n @property\n def calc_type(self):\n return CalcType.path_dependent\n\n\n@dataclass_json\n@dataclass\nclass AggregateTriggerRequirements(TriggerRequirements):\n triggers: Iterable[TriggerRequirements] = field(default=None, metadata=field_metadata)\n aggregate_type: AggType = field(default=AggType.ALL_OF, metadata=field_metadata)\n class_type: str = static_field('aggregate_trigger_requirements')\n\n def __setattr__(self, key, value):\n if key == 'triggers':\n if all([isinstance(v, Trigger) for v in value]):\n value = tuple(v.trigger_requirements for v in value)\n super().__setattr__(key, value)\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n info_dict = {}\n if self.aggregate_type == AggType.ALL_OF:\n for trigger in self.triggers:\n t_info = trigger.has_triggered(state, backtest)\n if not t_info:\n return TriggerInfo(False)\n else:\n if t_info.info_dict:\n info_dict.update(t_info.info_dict)\n return TriggerInfo(True, info_dict)\n elif self.aggregate_type == AggType.ANY_OF:\n triggered = False\n for trigger in self.triggers:\n t_info = trigger.has_triggered(state, backtest)\n if t_info:\n triggered = True\n if t_info.info_dict:\n info_dict.update(t_info.info_dict)\n return TriggerInfo(True, info_dict) if triggered else TriggerInfo(False)\n else:\n raise RuntimeError(f'Unrecognised aggregation type: {self.aggregate_type}')\n\n @property\n def calc_type(self):\n seen_types = set()\n for trigger in self.triggers:\n seen_types.add(trigger.calc_type)\n\n if CalcType.path_dependent in seen_types:\n return CalcType.path_dependent\n elif CalcType.semi_path_dependent in seen_types:\n return CalcType.semi_path_dependent\n else:\n return CalcType.simple\n\n\n@dataclass_json\n@dataclass\nclass NotTriggerRequirements(TriggerRequirements):\n trigger: TriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('not_trigger_requirements')\n\n def __setattr__(self, key, value):\n if key == 'trigger':\n if isinstance(value, Trigger):\n value = value.trigger_requirements\n super().__setattr__(key, value)\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n t_info = self.trigger.has_triggered(state, backtest)\n if t_info:\n return TriggerInfo(False)\n else:\n return TriggerInfo(True)\n\n\n@dataclass_json\n@dataclass\nclass DateTriggerRequirements(TriggerRequirements):\n dates: Iterable[Union[dt.datetime, dt.date]] = field(\n default=None, metadata=config(exclude=exclude_none, decoder=decode_iso_date_or_datetime)\n )\n entire_day: bool = field(default=False, metadata=field_metadata)\n class_type: str = static_field('date_trigger_requirements')\n dates_from_datetimes = []\n\n def __post_init__(self):\n self.dates_from_datetimes = (\n [d.date() if isinstance(d, dt.datetime) else d for d in self.dates] if self.entire_day else None\n )\n\n def has_triggered(self, state: Union[dt.date, dt.datetime], backtest: BackTest = None) -> TriggerInfo:\n if self.entire_day:\n dates = sorted(self.dates_from_datetimes)\n if isinstance(state, dt.datetime):\n state = state.date()\n else:\n dates = sorted(self.dates)\n if state in dates:\n next_state = None\n if dates.index(state) < len(dates) - 1:\n next_state = dates[dates.index(state) + 1]\n return TriggerInfo(\n True,\n {\n AddTradeAction: AddTradeActionInfo(scaling=None, next_schedule=next_state),\n AddScaledTradeAction: AddScaledTradeActionInfo(next_schedule=next_state),\n HedgeAction: HedgeActionInfo(next_schedule=next_state),\n },\n )\n return TriggerInfo(False)\n\n def get_trigger_times(self):\n return self.dates_from_datetimes or self.dates\n\n\n@dataclass_json\n@dataclass\nclass PortfolioTriggerRequirements(TriggerRequirements):\n data_source: str = field(default=None, metadata=field_metadata)\n trigger_level: float = field(default=None, metadata=field_metadata)\n direction: TriggerDirection = field(default=None, metadata=field_metadata)\n class_type: str = static_field('portfolio_trigger_requirements')\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n if self.data_source == 'len':\n value = len(backtest.portfolio_dict)\n if self.direction == TriggerDirection.ABOVE:\n if value > self.trigger_level:\n return TriggerInfo(True)\n elif self.direction == TriggerDirection.BELOW:\n if value < self.trigger_level:\n return TriggerInfo(True)\n else:\n if value == self.trigger_level:\n return TriggerInfo(True)\n return TriggerInfo(False)\n\n\n@dataclass_json\n@dataclass\nclass MeanReversionTriggerRequirements(TriggerRequirements):\n data_source: DataSource = field(\n default=None, metadata=config(decoder=dc_decode(*DataSource.sub_classes(), allow_missing=True))\n )\n z_score_bound: float = field(default=None, metadata=field_metadata)\n rolling_mean_window: int = field(default=None, metadata=field_metadata)\n rolling_std_window: int = field(default=None, metadata=field_metadata)\n current_position = 0\n class_type: str = static_field('mean_reversion_trigger_requirements')\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n rolling_mean = self.data_source.get_data_range(state, self.rolling_mean_window).mean()\n rolling_std = self.data_source.get_data_range(state, self.rolling_std_window).std()\n current_price = self.data_source.get_data(state)\n if self.current_position == 0:\n if abs((current_price - rolling_mean) / rolling_std) > self.z_score_bound:\n if current_price > rolling_mean:\n self.current_position = -1\n return TriggerInfo(True, {AddTradeAction: AddTradeActionInfo(scaling=-1)})\n else:\n self.current_position = 1\n return TriggerInfo(True, {AddTradeAction: AddTradeActionInfo(scaling=1)})\n elif self.current_position == 1:\n if current_price > rolling_mean:\n self._current_position = 0\n return TriggerInfo(True, {AddTradeAction: AddTradeActionInfo(scaling=-1)})\n elif self.current_position == -1:\n if current_price > rolling_mean:\n self.current_position = 0\n return TriggerInfo(True, {AddTradeAction: AddTradeActionInfo(scaling=1)})\n else:\n raise RuntimeWarning(f'unexpected current position: {self.current_position}')\n return TriggerInfo(False)\n\n\n@dataclass_json\n@dataclass\nclass TradeCountTriggerRequirements(TriggerRequirements):\n trade_count: float = field(default=None, metadata=field_metadata)\n direction: TriggerDirection = field(default=None, metadata=field_metadata)\n class_type: str = static_field('trade_count_requirements')\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n value = len(backtest.portfolio_dict.get(state, []))\n if self.direction == TriggerDirection.ABOVE:\n if value > self.trade_count:\n return TriggerInfo(True)\n elif self.direction == TriggerDirection.BELOW:\n if value < self.trade_count:\n return TriggerInfo(True)\n else:\n if value == self.trade_count:\n return TriggerInfo(True)\n return TriggerInfo(False)\n\n @property\n def calc_type(self):\n return CalcType.path_dependent\n\n\n@dataclass_json\n@dataclass\nclass EventTriggerRequirements(TriggerRequirements):\n event_name: str = field(default=None, metadata=field_metadata)\n offset_days: int = 0\n data_source: DataSource = field(\n default=None, metadata=config(decoder=dc_decode(*DataSource.sub_classes(), allow_missing=True))\n )\n class_type: str = static_field('event_requirements')\n trigger_dates = []\n\n def __post_init__(self):\n if self.data_source is None:\n self.data_source = GsDataSource(data_set='MACRO_EVENTS_CALENDAR', asset_id=None, value_header='eventName')\n\n def get_trigger_times(self) -> [dt.date]:\n if not self.trigger_dates:\n kwargs = {'eventName': self.event_name}\n self.trigger_dates = [\n d.date() + dt.timedelta(days=self.offset_days) for d in self.data_source.get_data(None, **kwargs).index\n ]\n return self.trigger_dates\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n dates = sorted(self.trigger_dates)\n if state in dates:\n next_state = None\n if dates.index(state) < len(dates) - 1:\n next_state = dates[dates.index(state) + 1]\n return TriggerInfo(\n True,\n {\n AddTradeAction: AddTradeActionInfo(scaling=None, next_schedule=next_state),\n AddScaledTradeAction: AddScaledTradeActionInfo(next_schedule=next_state),\n HedgeAction: HedgeActionInfo(next_schedule=next_state),\n },\n )\n return TriggerInfo(False)\n\n @staticmethod\n def list_events(currency: str, start=Optional[dt.datetime], end=Optional[dt.datetime], **kwargs):\n kwargs['currency'] = currency\n dataset = Dataset('MACRO_EVENTS_CALENDAR')\n return dataset.get_data(start, end, **kwargs)['eventName'].unique()\n\n\n@dataclass_json\n@dataclass\nclass Trigger:\n trigger_requirements: Optional[TriggerRequirements] = field(default=None, metadata=field_metadata)\n actions: Union[Action, Iterable[Action]] = field(\n default=None, metadata=config(decoder=dc_decode(*Action.sub_classes(), allow_missing=True))\n )\n __sub_classes: ClassVar[List[type]] = []\n\n def __init_subclass__(cls, **kwargs):\n super().__init_subclass__(**kwargs)\n Trigger.__sub_classes.append(cls)\n\n @staticmethod\n def sub_classes():\n return tuple(Trigger.__sub_classes)\n\n def __post_init__(self):\n self.actions = make_list(self.actions)\n\n def has_triggered(self, state: dt.date, backtest: BackTest = None) -> TriggerInfo:\n \"\"\"\n implemented by sub classes\n :param state:\n :param backtest:\n :return:\n TriggerInfo containing a bool indication of whether the trigger has triggered and optionally info\n in the form of a dictionary of action type to object info understood by that action.\n \"\"\"\n return self.trigger_requirements.has_triggered(state, backtest)\n\n def get_trigger_times(self):\n return self.trigger_requirements.get_trigger_times()\n\n @property\n def calc_type(self):\n return self.trigger_requirements.calc_type\n\n @property\n def risks(self):\n return [x.risk for x in make_list(self.actions) if x.risk is not None]\n\n\n@dataclass_json\n@dataclass\nclass PeriodicTrigger(Trigger):\n trigger_requirements: PeriodicTriggerRequirements = field(default=None, metadata=field_metadata)\n _trigger_dates = None\n class_type: str = static_field('periodic_trigger')\n\n\n@dataclass_json\n@dataclass\nclass IntradayPeriodicTrigger(Trigger):\n trigger_requirements: IntradayTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('intraday_periodic_trigger')\n\n\n@dataclass_json\n@dataclass\nclass MktTrigger(Trigger):\n trigger_requirements: MktTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('mkt_trigger')\n\n\n@dataclass_json\n@dataclass\nclass StrategyRiskTrigger(Trigger):\n trigger_requirements: RiskTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('strategy_risk_trigger')\n\n @property\n def risks(self):\n return [x.risk for x in make_list(self.actions) if x.risk is not None] + [self.trigger_requirements.risk]\n\n\n@dataclass_json\n@dataclass\nclass AggregateTrigger(Trigger):\n trigger_requirements: AggregateTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('aggregate_trigger')\n\n\n@dataclass_json\n@dataclass\nclass NotTrigger(Trigger):\n trigger_requirements: NotTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('not_trigger')\n\n\n@dataclass_json\n@dataclass\nclass DateTrigger(Trigger):\n trigger_requirements: DateTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('date_trigger')\n\n\n@dataclass_json\n@dataclass\nclass PortfolioTrigger(Trigger):\n trigger_requirements: PortfolioTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('portfolio_trigger')\n\n\n@dataclass_json\n@dataclass\nclass MeanReversionTrigger(Trigger):\n trigger_requirements: MeanReversionTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('mean_reversion_trigger')\n\n\n@dataclass_json\n@dataclass\nclass TradeCountTrigger(Trigger):\n trigger_requirements: TradeCountTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('trade_count_trigger')\n\n\n@dataclass_json\n@dataclass\nclass EventTrigger(Trigger):\n trigger_requirements: EventTriggerRequirements = field(default=None, metadata=field_metadata)\n class_type: str = static_field('event_trigger')\n\n\n@dataclass_json\n@dataclass\nclass OrdersGeneratorTrigger(Trigger):\n \"\"\"Base class for triggers used with the PredefinedAssetEngine.\"\"\"\n\n def __post_init__(self):\n if not self.actions:\n self.actions = [Action()]\n super().__post_init__()\n\n def get_trigger_times(self) -> list:\n \"\"\"\n Returns the set of times when orders can be generated e.g. every 30 min\n :return: list\n \"\"\"\n raise RuntimeError('get_trigger_times must be implemented by subclass')\n\n def generate_orders(self, state: dt.datetime, backtest: PredefinedAssetBacktest = None) -> list:\n \"\"\"\n Returns the orders generated at state\n :param state: the time when orders are generated\n :param backtest: the backtest, used to access the holdings and orders generated so far\n :return: list\n \"\"\"\n raise RuntimeError('generate_orders must be implemented by subclass')\n\n def has_triggered(self, state: dt.datetime, backtest: PredefinedAssetBacktest = None) -> TriggerInfo:\n \"\"\"\n Calls generate_orders if state is among the trigger times\n :param state: the time of the trigger\n :param backtest: the backtest, used to access the holdings and orders generated so far\n :return: list\n \"\"\"\n if state.time() not in self.get_trigger_times():\n return TriggerInfo(False)\n else:\n orders = self.generate_orders(state, backtest)\n return TriggerInfo(True, {type(a): orders for a in self.actions}) if len(orders) else TriggerInfo(False)\n\n\n# These special trigger requirements have a special dependency on other trigger types\ntriggers_field = AggregateTriggerRequirements.__dataclass_fields__['triggers']\ntriggers_field.metadata = config(decoder=dc_decode(*TriggerRequirements.sub_classes(), allow_missing=True))\n\ntrigger_field = NotTriggerRequirements.__dataclass_fields__['trigger']\ntrigger_field.metadata = config(decoder=dc_decode(*TriggerRequirements.sub_classes(), allow_missing=True))\n" }, { "path": "gs_quant/base.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport builtins\nimport copy\nimport datetime as dt\nimport logging\nimport sys\nimport typing\nfrom abc import ABC, ABCMeta, abstractmethod\nfrom collections import namedtuple\nfrom dataclasses import Field, InitVar, MISSING, dataclass, field, fields, replace\nfrom enum import EnumMeta, Enum\nfrom functools import update_wrapper\nfrom typing import Iterable, Mapping, Optional, Union, Tuple\n\nimport numpy as np\nfrom dataclasses_json import config, global_config, LetterCase, dataclass_json\nfrom dataclasses_json.core import _decode_generic, _is_supported_generic\nfrom inflection import camelize, underscore\n\nfrom gs_quant.context_base import ContextBase, ContextMeta\nfrom gs_quant.json_convertors import (\n encode_date_or_str,\n decode_date_or_str,\n decode_optional_date,\n encode_datetime,\n decode_datetime,\n decode_float_or_str,\n decode_instrument,\n encode_dictable,\n decode_quote_report,\n decode_quote_reports,\n decode_custom_comment,\n decode_custom_comments,\n decode_optional_time,\n encode_optional_time,\n)\n\n_logger = logging.getLogger(__name__)\n\n__builtins = set(dir(builtins))\n__getattribute__ = object.__getattribute__\n__setattr__ = object.__setattr__\n\n_rename_cache = {}\n_is_supported_generic_cache = {}\n\n\ndef exclude_none(o):\n return o is None\n\n\ndef exclude_always(_o):\n return True\n\n\ndef is_iterable(o, t):\n return isinstance(o, Iterable) and all(isinstance(it, t) for it in o)\n\n\ndef is_instance_or_iterable(o, t):\n return isinstance(o, t) or is_iterable(o, t)\n\n\ndef _get_underscore(arg):\n if arg not in _rename_cache:\n _rename_cache[arg] = underscore(arg)\n\n return _rename_cache[arg]\n\n\ndef _get_is_supported_generic(arg):\n if arg in _is_supported_generic_cache:\n is_supported_generic = _is_supported_generic_cache[arg]\n else:\n is_supported_generic = _is_supported_generic(arg)\n _is_supported_generic_cache[arg] = is_supported_generic\n return is_supported_generic\n\n\ndef handle_camel_case_args(cls):\n init = cls.__init__\n\n def wrapper(self, *args, **kwargs):\n normalised_kwargs = {}\n\n for arg, value in kwargs.items():\n if not arg.isupper():\n snake_case_arg = _get_underscore(arg)\n if snake_case_arg != arg and snake_case_arg in kwargs:\n raise ValueError('{} and {} both specified'.format(arg, snake_case_arg))\n\n arg = snake_case_arg\n\n arg = cls._field_mappings().get(arg, arg)\n normalised_kwargs[arg] = value\n\n return init(self, *args, **normalised_kwargs)\n\n cls.__init__ = update_wrapper(wrapper=wrapper, wrapped=init)\n\n return cls\n\n\ndef static_field(val):\n return field(init=False, default=val)\n\n\nfield_metadata = config(exclude=exclude_none)\nname_metadata = config(exclude=exclude_always)\n\n\nclass RiskKey(namedtuple('RiskKey', ('provider', 'date', 'market', 'params', 'scenario', 'risk_measure'))):\n @property\n def ex_measure(self):\n from gs_quant.target.common import RiskRequestParameters # noqa\n\n return RiskKey(\n self.provider,\n self.date,\n self.market,\n RiskRequestParameters(self.params.csa_term, self.params.raw_results, False, self.params.market_behaviour),\n self.scenario,\n None,\n )\n\n @property\n def ex_historical_diddle(self):\n from gs_quant.target.common import RiskRequestParameters # noqa\n\n return RiskKey(\n self.provider,\n self.date,\n self.market,\n RiskRequestParameters(self.params.csa_term, self.params.raw_results, False, self.params.market_behaviour),\n self.scenario,\n self.risk_measure,\n )\n\n @property\n def fields(self):\n return self._fields\n\n\nclass EnumBase:\n @classmethod\n def _missing_(cls: EnumMeta, key):\n if not isinstance(key, str):\n key = str(key)\n return next((m for m in cls.__members__.values() if m.value.lower() == key.lower()), None)\n\n def __reduce_ex__(self, protocol):\n return self.__class__, (self.value,)\n\n def __lt__(self: EnumMeta, other):\n return self.value < other.value\n\n def __repr__(self):\n return str(self)\n\n def __str__(self):\n return self.value\n\n\nclass HashableDict(dict):\n @staticmethod\n def hashables(in_dict) -> Tuple:\n hashables = []\n for it in in_dict.items():\n if isinstance(it[1], dict):\n hashables.append((it[0], HashableDict.hashables(it[1])))\n else:\n hashables.append(it)\n return tuple(hashables)\n\n def __hash__(self):\n return hash(HashableDict.hashables(self))\n\n\nclass DictBase(HashableDict):\n _PROPERTIES = set()\n\n def __init__(self, *args, **kwargs):\n if self._PROPERTIES:\n invalid_arg = next((k for k in kwargs.keys() if k not in self._PROPERTIES), None)\n if invalid_arg is not None:\n raise AttributeError(f\"'{self.__class__.__name__}' has no attribute '{invalid_arg}'\")\n\n super().__init__(\n *args, **{camelize(k, uppercase_first_letter=False): v for k, v in kwargs.items() if v is not None}\n )\n\n def __getitem__(self, item):\n return super().__getitem__(camelize(item, uppercase_first_letter=False))\n\n def __setitem__(self, key, value):\n if value is not None:\n return super().__setitem__(camelize(key, uppercase_first_letter=False), value)\n\n def __getattr__(self, item):\n if self._PROPERTIES:\n if _get_underscore(item) in self._PROPERTIES:\n return self.get(item)\n elif item in self:\n return self[item]\n\n raise AttributeError(f\"'{self.__class__.__name__}' has no attribute '{item}'\")\n\n def __setattr__(self, key, value):\n if key in dir(self):\n return super().__setattr__(key, value)\n elif self._PROPERTIES and _get_underscore(key) not in self._PROPERTIES:\n raise AttributeError(f\"'{self.__class__.__name__}' has no attribute '{key}'\")\n\n self[key] = value\n\n @classmethod\n def properties(cls) -> set:\n return cls._PROPERTIES\n\n\nclass Base(ABC):\n \"\"\"The base class for all generated classes\"\"\"\n\n __fields_by_name = None\n __field_mappings = None\n\n def __getattr__(self, item):\n fields_by_name = __getattribute__(self, '_fields_by_name')()\n\n if item.startswith('_') or item in fields_by_name:\n return __getattribute__(self, item)\n\n # Handle setting via camelCase names (legacy behaviour) and field mappings from disallowed names\n snake_case_item = _get_underscore(item)\n field_mappings = __getattribute__(self, '_field_mappings')()\n snake_case_item = field_mappings.get(snake_case_item, snake_case_item)\n\n try:\n return __getattribute__(self, snake_case_item)\n except AttributeError:\n return __getattribute__(self, item)\n\n def __setattr__(self, key, value):\n # Handle setting via camelCase names (legacy behaviour)\n snake_case_key = _get_underscore(key)\n snake_case_key = self._field_mappings().get(snake_case_key, snake_case_key)\n fld = self._fields_by_name().get(snake_case_key)\n\n if fld:\n if not fld.init:\n raise ValueError(f'{key} cannot be set')\n\n key = snake_case_key\n value = self.__coerce_value(fld.type, value)\n\n __setattr__(self, key, value)\n\n def __repr__(self):\n if self.name is not None:\n return f'{self.name} ({self.__class__.__name__})'\n\n return super().__repr__()\n\n @classmethod\n def __is_type_match(cls, tp, val):\n if sys.version_info >= (3, 9):\n from types import GenericAlias\n\n is_generic_alias = isinstance(tp, (typing._GenericAlias, GenericAlias))\n if sys.version_info >= (3, 10) and not is_generic_alias:\n from types import UnionType\n\n if isinstance(tp, UnionType):\n return any(cls.__is_type_match(arg, val) for arg in tp.__args__)\n else:\n is_generic_alias = isinstance(tp, typing._GenericAlias)\n if not is_generic_alias:\n # Do not convert Enums to strings\n is_enum_to_str = isinstance(val, Enum) and tp is str\n return isinstance(tp, type) and (isinstance(val, tp) or is_enum_to_str)\n if getattr(tp, '_special', False):\n return False\n origin = tp.__origin__\n args = tp.__args__\n if float in args:\n args += (int,)\n if origin == Union:\n return any(cls.__is_type_match(arg, val) for arg in args)\n if origin is tuple:\n if not isinstance(val, tuple) or not args:\n return False\n if len(args) == 1 or args[1] == Ellipsis:\n return all(cls.__is_type_match(args[0], x) for x in val)\n else:\n return len(args) == len(val) and all(cls.__is_type_match(arg, x) for arg, x in zip(args, val))\n return False\n\n @classmethod\n def __coerce_value(cls, typ: type, value):\n if cls.__is_type_match(typ, value):\n return value\n if isinstance(value, np.generic):\n # Handle numpy types\n return value.item()\n elif hasattr(value, 'tolist'):\n # tolist converts scalar or array to native python type if not already native.\n return value.tolist()\n elif typ in (DictBase, Optional[DictBase]) and isinstance(value, Base):\n return value.to_dict()\n is_supported_generic = _get_is_supported_generic(typ)\n if is_supported_generic:\n return _decode_generic(typ, value, False)\n else:\n return value\n\n @classmethod\n def _fields_by_name(cls) -> Mapping[str, Field]:\n if cls is Base:\n return {}\n\n if cls.__fields_by_name is None:\n cls.__fields_by_name = {f.name: f for f in fields(cls)}\n\n return cls.__fields_by_name\n\n @classmethod\n def _field_mappings(cls) -> Mapping[str, str]:\n if cls is Base:\n return {}\n\n if cls.__field_mappings is None:\n field_mappings = {}\n for fld in fields(cls):\n config_fn = fld.metadata.get('dataclasses_json', {}).get('letter_case')\n if config_fn:\n mapped_name = config_fn('field_name')\n if mapped_name:\n field_mappings[mapped_name] = fld.name\n\n cls.__field_mappings = field_mappings\n return cls.__field_mappings\n\n def clone(self, **kwargs):\n \"\"\"\n Clone this object, overriding specified values\n\n :param kwargs: property names and values, e.g. swap.clone(fixed_rate=0.01)\n\n **Examples**\n\n To change the market data location of the default context:\n\n >>> from gs_quant.instrument import IRCap\n >>> cap = IRCap('5y', 'GBP')\n >>>\n >>> new_cap = cap.clone(cap_rate=0.01)\n \"\"\"\n return replace(self, **kwargs)\n\n @classmethod\n def properties(cls) -> set:\n \"\"\"The public property names of this class\"\"\"\n return set(f[:-1] if f[-1] == '_' else f for f in cls._fields_by_name().keys())\n\n @classmethod\n def properties_init(cls) -> set:\n \"\"\"The public property names of this class\"\"\"\n return set(f[:-1] if f[-1] == '_' else f for f, v in cls._fields_by_name().items() if v.init)\n\n def as_dict(self, as_camel_case: bool = False) -> dict:\n \"\"\"Dictionary of the public, non-null properties and values\"\"\"\n\n # to_dict() converts all the values to JSON type, does camel case and name mappings\n # asdict() does not convert values or case of the keys or do name mappings\n\n ret = {}\n field_mappings = {v: k for k, v in self._field_mappings().items()}\n\n for key in self.__fields_by_name.keys():\n value = __getattribute__(self, key)\n key = field_mappings.get(key, key)\n\n if value is not None:\n if as_camel_case:\n key = camelize(key, uppercase_first_letter=False)\n\n ret[key] = value\n\n return ret\n\n @classmethod\n def default_instance(cls):\n \"\"\"\n Construct a default instance of this type\n \"\"\"\n required = {f.name: None if f.default == MISSING else f.default for f in fields(cls) if f.init}\n return cls(**required)\n\n def from_instance(self, instance):\n \"\"\"\n Copy the values from an existing instance of the same type to our self\n :param instance: from which to copy:\n :return:\n \"\"\"\n if not isinstance(instance, type(self)):\n raise ValueError('Can only use from_instance with an object of the same type')\n\n for fld in fields(self.__class__):\n if fld.init:\n __setattr__(self, fld.name, __getattribute__(instance, fld.name))\n\n\n@dataclass_json\n@dataclass\nclass Priceable(Base):\n def resolve(self, in_place: bool = True):\n \"\"\"\n Resolve non-supplied properties of an instrument\n\n **Examples**\n\n >>> from gs_quant.instrument import IRSwap\n >>>\n >>> swap = IRSwap('Pay', '10y', 'USD')\n >>> rate = swap.fixedRate\n\n rate is None\n\n >>> swap.resolve()\n >>> rate = swap.fixedRate\n\n rates is now the solved fixed rate\n \"\"\"\n raise NotImplementedError\n\n def dollar_price(self):\n \"\"\"\n Present value in USD\n\n :return: a float or a future, depending on whether the current PricingContext is async, or has been entered\n\n **Examples**\n\n >>> from gs_quant.instrument import IRCap\n >>>\n >>> cap = IRCap('1y', 'EUR')\n >>> price = cap.dollar_price()\n\n price is the present value in USD (a float)\n\n >>> cap_usd = IRCap('1y', 'USD')\n >>> cap_eur = IRCap('1y', 'EUR')\n >>>\n >>> from gs_quant.markets import PricingContext\n >>>\n >>> with PricingContext():\n >>> price_usd_f = cap_usd.dollar_price()\n >>> price_eur_f = cap_eur.dollar_price()\n >>>\n >>> price_usd = price_usd_f.result()\n >>> price_eur = price_eur_f.result()\n\n price_usd_f and price_eur_f are futures, price_usd and price_eur are floats\n \"\"\"\n raise NotImplementedError\n\n def price(self):\n \"\"\"\n Present value in local currency. Note that this is not yet supported on all instruments\n\n ***Examples**\n\n >>> from gs_quant.instrument import IRSwap\n >>>\n >>> swap = IRSwap('Pay', '10y', 'EUR')\n >>> price = swap.price()\n\n price is the present value in EUR (a float)\n \"\"\"\n raise NotImplementedError\n\n def calc(self, risk_measure, fn=None):\n \"\"\"\n Calculate the value of the risk_measure\n\n :param risk_measure: the risk measure to compute, e.g. IRDelta (from gs_quant.risk)\n :param fn: a function for post-processing results\n :return: a float or dataframe, depending on whether the value is scalar or structured, or a future thereof\n (depending on how PricingContext is being used)\n\n **Examples**\n\n >>> from gs_quant.instrument import IRCap\n >>> from gs_quant.risk import IRDelta\n >>>\n >>> cap = IRCap('1y', 'USD')\n >>> delta = cap.calc(IRDelta)\n\n delta is a dataframe\n\n >>> from gs_quant.instrument import EqOption\n >>> from gs_quant.risk import EqDelta\n >>>\n >>> option = EqOption('.SPX', '3m', 'ATMF', 'Call', 'European')\n >>> delta = option.calc(EqDelta)\n\n delta is a float\n\n >>> from gs_quant.markets import PricingContext\n >>>\n >>> cap_usd = IRCap('1y', 'USD')\n >>> cap_eur = IRCap('1y', 'EUR')\n\n >>> with PricingContext():\n >>> usd_delta_f = cap_usd.calc(IRDelta)\n >>> eur_delta_f = cap_eur.calc(IRDelta)\n >>>\n >>> usd_delta = usd_delta_f.result()\n >>> eur_delta = eur_delta_f.result()\n\n usd_delta_f and eur_delta_f are futures, usd_delta and eur_delta are dataframes\n \"\"\"\n raise NotImplementedError\n\n\nclass __ScenarioMeta(ABCMeta, ContextMeta):\n pass\n\n\n@dataclass\nclass Scenario(Base, ContextBase, ABC, metaclass=__ScenarioMeta):\n def __lt__(self, other):\n if self.__repr__ != other.__repr__:\n return self.name < other.name\n return False\n\n def __repr__(self):\n if self.name:\n return self.name\n else:\n params = self.as_dict()\n sorted_keys = sorted(params.keys(), key=lambda x: x.lower())\n params = ', '.join(\n [f'{k}:{params[k].__repr__ if isinstance(params[k], Base) else params[k]}' for k in sorted_keys]\n )\n return self.scenario_type + '(' + params + ')'\n\n\n@dataclass\nclass RiskMeasureParameter(Base, ABC):\n def __repr__(self):\n params = self.as_dict()\n params.pop(\"parameter_type\", None)\n sorted_keys = sorted(params.keys(), key=lambda x: x.lower())\n params = \", \".join(\n [f\"{k}:{params[k].value if isinstance(params[k], EnumBase) else params[k]}\" for k in sorted_keys]\n )\n return f\"{self.parameter_type}({params})\"\n\n\n@dataclass\nclass InstrumentBase(Base, ABC):\n quantity_: InitVar[float] = field(default=1, init=False)\n\n @property\n @abstractmethod\n def provider(self): ...\n\n @property\n def instrument_quantity(self) -> float:\n return self.quantity_\n\n @property\n def resolution_key(self) -> Optional[RiskKey]:\n try:\n return self.__resolution_key\n except AttributeError:\n return None\n\n @property\n def unresolved(self):\n try:\n return self.__unresolved\n except AttributeError:\n return None\n\n @property\n def metadata(self):\n try:\n return self.__metadata\n except AttributeError:\n return None\n\n @metadata.setter\n def metadata(self, value):\n self.__metadata = value\n\n def from_instance(self, instance):\n self.__resolution_key = None\n super().from_instance(instance)\n self.__unresolved = instance.__unresolved\n self.__resolution_key = instance.__resolution_key\n\n def resolved(self, values: dict, resolution_key: RiskKey):\n all_values = self.as_dict(True)\n all_values.update(values)\n new_instrument = self.from_dict(all_values)\n new_instrument.name = self.name\n new_instrument.__unresolved = copy.copy(self)\n new_instrument.__resolution_key = resolution_key\n return new_instrument\n\n def clone(self, **kwargs):\n new_instrument = super().clone(**kwargs)\n new_instrument.__unresolved = self.unresolved\n new_instrument.metadata = self.metadata\n new_instrument.__resolution_key = self.resolution_key\n return new_instrument\n\n\n@dataclass\nclass Market(ABC):\n def __hash__(self):\n return hash(self.market or self.location)\n\n def __eq__(self, other):\n return (self.market or self.location) == (other.market or other.location)\n\n def __lt__(self, other):\n return repr(self) < repr(other)\n\n @property\n @abstractmethod\n def market(self): ...\n\n @property\n @abstractmethod\n def location(self): ...\n\n def to_dict(self):\n return self.market.to_dict()\n\n\nclass Sentinel:\n def __init__(self, name: str):\n self.__name = name\n\n def __eq__(self, other):\n return self.__name == other.__name\n\n\n@dataclass\nclass QuoteReport(Base, ABC):\n pass\n\n\n@dataclass\nclass CustomComments(Base, ABC):\n pass\n\n\ndef get_enum_value(enum_type: EnumMeta, value: Union[EnumBase, str]):\n if value in (None,):\n return None\n\n if isinstance(value, enum_type):\n return value\n\n try:\n enum_value = enum_type(value)\n except ValueError:\n _logger.warning('Setting value to {}, which is not a valid entry in {}'.format(value, enum_type))\n enum_value = value\n\n return enum_value\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MarketDataScenario(Base):\n scenario: Scenario = field(default=None, metadata=field_metadata)\n subtract_base: Optional[bool] = field(default=False, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n# Yes, I know this is a little evil ...\nglobal_config.encoders[dt.date] = dt.date.isoformat\nglobal_config.encoders[Optional[dt.date]] = encode_date_or_str\nglobal_config.decoders[dt.date] = decode_optional_date\nglobal_config.decoders[Optional[dt.date]] = decode_optional_date\nglobal_config.encoders[Union[dt.date, str]] = encode_date_or_str\nglobal_config.encoders[Optional[Union[dt.date, str]]] = encode_date_or_str\nglobal_config.decoders[Union[dt.date, str]] = decode_date_or_str\nglobal_config.decoders[Optional[Union[dt.date, str]]] = decode_date_or_str\nglobal_config.encoders[dt.time] = dt.time.isoformat\nglobal_config.decoders[dt.time] = dt.time.fromisoformat\nglobal_config.encoders[Optional[dt.time]] = encode_optional_time\nglobal_config.decoders[Optional[dt.time]] = decode_optional_time\nglobal_config.encoders[dt.datetime] = encode_datetime\nglobal_config.encoders[Optional[dt.datetime]] = encode_datetime\nglobal_config.decoders[dt.datetime] = decode_datetime\nglobal_config.decoders[Optional[dt.datetime]] = decode_datetime\nglobal_config.decoders[Union[float, str]] = decode_float_or_str\nglobal_config.decoders[Optional[Union[float, str]]] = decode_float_or_str\n\nglobal_config.decoders[InstrumentBase] = decode_instrument\nglobal_config.decoders[Optional[InstrumentBase]] = decode_instrument\nglobal_config.decoders[QuoteReport] = decode_quote_report\nglobal_config.decoders[Optional[Tuple[QuoteReport, ...]]] = decode_quote_reports\nglobal_config.decoders[CustomComments] = decode_custom_comment\nglobal_config.decoders[Optional[Tuple[CustomComments, ...]]] = decode_custom_comments\nglobal_config.encoders[Market] = encode_dictable\nglobal_config.encoders[Optional[Market]] = encode_dictable\n" }, { "path": "gs_quant/common.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.target.common import * # noqa\nfrom gs_quant.target.common import PayReceive as _PayReceive # noqa\nfrom gs_quant.target.common import RiskMeasure as __RiskMeasure # noqa\nfrom gs_quant.target.common import RiskMeasureType, AssetClass, RiskMeasureUnit # noqa\nfrom gs_quant.base import EnumBase, RiskMeasureParameter # noqa\n\nfrom enum import Enum\nfrom typing import Union\nimport datetime as dt\n\n\nclass PositionType(Enum):\n \"\"\"Position type enumeration\n\n Enumeration of different position types for a portfolio or index\n\n \"\"\"\n\n OPEN = \"open\" #: Open positions (corporate action adjusted)\n CLOSE = \"close\" #: Close positions (reflect trading activity on the close)\n ANY = 'any'\n\n\nclass DateLimit(Enum):\n \"\"\"Datetime date constants\"\"\"\n\n LOW_LIMIT = dt.date(1952, 1, 1)\n\n\nclass PayReceive(EnumBase, Enum):\n \"\"\"Pay or receive fixed\"\"\"\n\n Pay = 'Pay'\n Receive = 'Rec'\n Straddle = 'Straddle'\n\n @classmethod\n def _missing_(cls, key):\n if isinstance(key, _PayReceive):\n key = key.value\n\n return cls.Receive if key.lower() in ('receive', 'receiver') else super()._missing_(key)\n\n\nclass RiskMeasure(__RiskMeasure):\n def __lt__(self, other):\n if self.name != other.name:\n return self.name < other.name\n elif self.parameters is not None:\n if other.parameters is None:\n return False\n if not isinstance(other.parameters, type(self.parameters)):\n return self.parameters.parameter_type < other.parameters.parameter_type\n else:\n return repr(self.parameters) < repr(other.parameters)\n elif other.parameters is not None:\n return True\n return False\n\n def __repr__(self):\n return self.name or self.measure_type.name\n\n @property\n def pricing_context(self):\n from gs_quant.markets import PricingContext\n\n return PricingContext.current\n\n\nclass ParameterisedRiskMeasure(RiskMeasure):\n def __init__(\n self,\n asset_class: Union[AssetClass, str] = None,\n measure_type: Union[RiskMeasureType, str] = None,\n unit: Union[RiskMeasureUnit, str] = None,\n value: Union[float, str] = None,\n parameters: RiskMeasureParameter = None,\n name: str = None,\n ):\n super().__init__(asset_class=asset_class, measure_type=measure_type, unit=unit, value=value, name=name)\n if parameters:\n if isinstance(parameters, RiskMeasureParameter):\n self.parameters = parameters\n else:\n raise TypeError(f\"Unsupported parameter {parameters}\")\n\n def __repr__(self):\n name = self.name or self.measure_type.name\n params = None\n if self.parameters:\n params = self.parameters.as_dict()\n params.pop('parameter_type', None)\n sorted_keys = sorted(params.keys(), key=lambda x: x.lower())\n params = ', '.join(\n [f'{k}:{params[k].value if isinstance(params[k], EnumBase) else params[k]}' for k in sorted_keys]\n )\n return name + '(' + params + ')' if params else name\n\n def parameter_is_empty(self):\n return self.parameters is None\n" }, { "path": "gs_quant/config/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .options import *\n" }, { "path": "gs_quant/config/options.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\n\nclass DisplayOptions:\n \"\"\"\n config options\n \"\"\"\n\n def __init__(self, show_na: bool = False):\n self.__show_na = show_na\n\n @property\n def show_na(self):\n return self.__show_na\n\n @show_na.setter\n def show_na(self, show_na):\n self.__show_na = show_na\n\n\ndisplay_options = DisplayOptions()\n" }, { "path": "gs_quant/config.ini", "content": "[DEFAULT]\nAppDomain = https://api.gs.com\nMdsDomainEast = https://us-east.data.gsapis.com\nMdsWebDomain = https://data.gs.com\nAuthURL = https://idfs.gs.com/as/token.oauth2\nMarqueeWebDomain = https://marquee.gs.com\n\n[QA]\nAppDomain = https://api.marquee-qa.gs.com\nMdsDomainEast = https://qa.us-east.data.gsapis.com\nMdsWebDomain = https://data-qa.gs.com\nAuthURL = https://idfs-qa.gs.com/as/token.oauth2\nMarqueeWebDomain = https://marquee-qa.gs.com\n\n[DEV]\nAppDomain = https://api.marquee-dev-ext.web.gs.com\nMdsDomainEast = https://dev.us-east.data.gsapis.com\nMdsWebDomain = https://data-dev.gs.com\nAuthURL = https://idfs-dev.nimbus.gs.com:9031/as/token.oauth2\nMarqueeWebDomain = https://marquee-dev.web.gs.com\n\n[PROD]\nAppDomain = https://api.gs.com\nMdsDomainEast = https://us-east.data.gsapis.com\nMdsWebDomain = https://data.gs.com\nAuthURL = https://idfs.gs.com/as/token.oauth2\nMarqueeWebDomain = https://marquee.gs.com" }, { "path": "gs_quant/content/Contents.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67001\",\n \"metadata\": {},\n \"source\": [\n \"# Contents\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67002\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 events\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67003\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **00_gsquant_meets_markets**\\n\",\n \" - 📁 00_us_election_analysis\\n\",\n \" - 📄 [0000_macro_big_picture.ipynb](events/00_gsquant_meets_markets/00_us_election_analysis/0000_macro_big_picture.ipynb)\\n\",\n \" - 📄 [0001_portfolios_and_var.ipynb](events/00_gsquant_meets_markets/00_us_election_analysis/0001_portfolios_and_var.ipynb)\\n\",\n \" - 📄 [0002_past_elections.ipynb](events/00_gsquant_meets_markets/00_us_election_analysis/0002_past_elections.ipynb)\\n\",\n \" - 📄 [0003_trades.ipynb](events/00_gsquant_meets_markets/00_us_election_analysis/0003_trades.ipynb)\\n\",\n \" - 📁 01_ideas_for_risk_re_rating\\n\",\n \" - 📄 [eq_sx5e_spx_vol_spread_trade.ipynb](events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/eq_sx5e_spx_vol_spread_trade.ipynb)\\n\",\n \" - 📄 [fx_covid19_recovery_trade.ipynb](events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/fx_covid19_recovery_trade.ipynb)\\n\",\n \" - 📄 [inflation_covid19_recovery_trade.ipynb](events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/inflation_covid19_recovery_trade.ipynb)\\n\",\n \" - 📁 02_optimizing_equity_trading\\n\",\n \" - 📄 [quants_meet_markets_qes.ipynb](events/00_gsquant_meets_markets/02_optimizing_equity_trading/quants_meet_markets_qes.ipynb)\\n\",\n \" - 📄 [section_a_liquidity_and_clusters.ipynb](events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_a_liquidity_and_clusters.ipynb)\\n\",\n \" - 📄 [section_b_ownership.ipynb](events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_b_ownership.ipynb)\\n\",\n \" - 📄 [section_c_apex.ipynb](events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_c_apex.ipynb)\\n\",\n \" - 📁 03_esg_basket_portfolio_optimisation\\n\",\n \" - 📄 [quants_meet_markets_msci.ipynb](events/00_gsquant_meets_markets/03_esg_basket_portfolio_optimisation/quants_meet_markets_msci.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67004\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 made_with_gs_quant\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67005\",\n \"metadata\": {},\n \"source\": [\n \"- 📄 [1-Navigating Rates.ipynb](made_with_gs_quant/1-Navigating%20Rates.ipynb)\\n\",\n \"- 📄 [10-Explaining Performance Drivers.ipynb](made_with_gs_quant/10-Explaining%20Performance%20Drivers.ipynb)\\n\",\n \"- 📄 [11-FX Election Hedge.ipynb](made_with_gs_quant/11-FX%20Election%20Hedge.ipynb)\\n\",\n \"- 📄 [12-Structuring for Uncertaintly.ipynb](made_with_gs_quant/12-Structuring%20for%20Uncertaintly.ipynb)\\n\",\n \"- 📄 [13-Cyclicals.ipynb](made_with_gs_quant/13-Cyclicals.ipynb)\\n\",\n \"- 📄 [14-Curve Inversions.ipynb](made_with_gs_quant/14-Curve%20Inversions.ipynb)\\n\",\n \"- 📄 [15-Hypothetical Lifetime Risk Calculator.ipynb](made_with_gs_quant/15-Hypothetical%20Lifetime%20Risk%20Calculator.ipynb)\\n\",\n \"- 📄 [2-Levels and Tails.ipynb](made_with_gs_quant/2-Levels%20and%20Tails.ipynb)\\n\",\n \"- 📄 [3-Systematic Selling.ipynb](made_with_gs_quant/3-Systematic%20Selling.ipynb)\\n\",\n \"- 📄 [4-Delta Hedging.ipynb](made_with_gs_quant/4-Delta%20Hedging.ipynb)\\n\",\n \"- 📄 [5-What's New.ipynb](made_with_gs_quant/5-What's%20New.ipynb)\\n\",\n \"- 📄 [6-Evolving Correlations.ipynb](made_with_gs_quant/6-Evolving%20Correlations.ipynb)\\n\",\n \"- 📄 [7-Predicting Performance and Live Risk.ipynb](made_with_gs_quant/7-Predicting%20Performance%20and%20Live%20Risk.ipynb)\\n\",\n \"- 📄 [8-What's New Internal.ipynb](made_with_gs_quant/8-What's%20New%20Internal.ipynb)\\n\",\n \"- 📄 [9-Hedging using Machine Learning Techniques.ipynb](made_with_gs_quant/9-Hedging%20using%20Machine%20Learning%20Techniques.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67006\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 reports_and_screens\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67007\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **00_fx**\\n\",\n \" - 📄 [0000_vol_screen.ipynb](reports_and_screens/00_fx/0000_vol_screen.ipynb)\\n\",\n \" - 📄 [0001_vol_calendar_screen.ipynb](reports_and_screens/00_fx/0001_vol_calendar_screen.ipynb)\\n\",\n \" - 📄 [0002_forward_vol_screen.ipynb](reports_and_screens/00_fx/0002_forward_vol_screen.ipynb)\\n\",\n \" - 📄 [0003_10x_binary_screen.ipynb](reports_and_screens/00_fx/0003_10x_binary_screen.ipynb)\\n\",\n \" - 📄 [0004_bear_put_spread_screen.ipynb](reports_and_screens/00_fx/0004_bear_put_spread_screen.ipynb)\\n\",\n \" - 📄 [0005_vol_skew.ipynb](reports_and_screens/00_fx/0005_vol_skew.ipynb)\\n\",\n \" - 📄 [0006_vol_swap_spread.ipynb](reports_and_screens/00_fx/0006_vol_swap_spread.ipynb)\\n\",\n \" - 📄 [0007_eq_fx_hedges.ipynb](reports_and_screens/00_fx/0007_eq_fx_hedges.ipynb)\\n\",\n \"- 📁 **02_rates**\\n\",\n \" - 📄 [0000_vol_fixed_strike_grid.ipynb](reports_and_screens/02_rates/0000_vol_fixed_strike_grid.ipynb)\\n\",\n \" - 📄 [0001_vol_moneyness_screen.ipynb](reports_and_screens/02_rates/0001_vol_moneyness_screen.ipynb)\\n\",\n \" - 📄 [0002_swaption_carry_grid.ipynb](reports_and_screens/02_rates/0002_swaption_carry_grid.ipynb)\\n\",\n \" - 📄 [0003_spot_vol_shock.ipynb](reports_and_screens/02_rates/0003_spot_vol_shock.ipynb)\\n\",\n \"- 📁 **03_prime**\\n\",\n \" - 📄 [0001_gs_pb_data_case_study_sector_positioning.ipynb](reports_and_screens/03_prime/0001_gs_pb_data_case_study_sector_positioning.ipynb)\\n\",\n \" - 📄 [0002_gs_pb_data_case_study_flows.ipynb](reports_and_screens/03_prime/0002_gs_pb_data_case_study_flows.ipynb)\\n\",\n \" - 📄 [0003_gs_pb_data_case_study_factors.ipynb](reports_and_screens/03_prime/0003_gs_pb_data_case_study_factors.ipynb)\\n\",\n \" - 📄 [0004_gs_pb_data_case_study_country_positioning.ipynb](reports_and_screens/03_prime/0004_gs_pb_data_case_study_country_positioning.ipynb)\\n\",\n \" - 📄 [0005_gs_pb_data_case_study_leverage.ipynb](reports_and_screens/03_prime/0005_gs_pb_data_case_study_leverage.ipynb)\\n\",\n \"- 📄 [README.md](reports_and_screens/README.md)\\n\"\n ]\n }\n ],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/Index.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"06d212c0\",\n \"metadata\": {},\n \"source\": [\n \"# Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8bdc7267\",\n \"metadata\": {},\n \"source\": []\n }\n ],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/README.md", "content": "# Content Overview\n\nContent and research produced using gs_quant - featuring tactical case studies looking at screens,\nbacktesting strategies, attribution analytics and more.\n\n- 📄 [Contents.ipynb](Contents.ipynb)\n- 📄 [Index.ipynb](Index.ipynb)\n- 📄 [README.md](README.md)\n- 📁 **events**\n - 📁 00_gsquant_meets_markets\n - 📁 00_us_election_analysis\n - 📄 [0000_macro_big_picture.ipynb](events/00_gsquant_meets_markets/00_us_election_analysis/0000_macro_big_picture.ipynb)\n - 📄 [0001_portfolios_and_var.ipynb](events/00_gsquant_meets_markets/00_us_election_analysis/0001_portfolios_and_var.ipynb)\n - 📄 [0002_past_elections.ipynb](events/00_gsquant_meets_markets/00_us_election_analysis/0002_past_elections.ipynb)\n - 📄 [0003_trades.ipynb](events/00_gsquant_meets_markets/00_us_election_analysis/0003_trades.ipynb)\n - 📁 01_ideas_for_risk_re_rating\n - 📄 [eq_sx5e_spx_vol_spread_trade.ipynb](events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/eq_sx5e_spx_vol_spread_trade.ipynb)\n - 📄 [fx_covid19_recovery_trade.ipynb](events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/fx_covid19_recovery_trade.ipynb)\n - 📄 [inflation_covid19_recovery_trade.ipynb](events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/inflation_covid19_recovery_trade.ipynb)\n - 📁 02_optimizing_equity_trading\n - 📄 [quants_meet_markets_qes.ipynb](events/00_gsquant_meets_markets/02_optimizing_equity_trading/quants_meet_markets_qes.ipynb)\n - 📄 [section_a_liquidity_and_clusters.ipynb](events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_a_liquidity_and_clusters.ipynb)\n - 📄 [section_b_ownership.ipynb](events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_b_ownership.ipynb)\n - 📄 [section_c_apex.ipynb](events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_c_apex.ipynb)\n - 📁 03_esg_basket_portfolio_optimisation\n - 📄 [quants_meet_markets_msci.ipynb](events/00_gsquant_meets_markets/03_esg_basket_portfolio_optimisation/quants_meet_markets_msci.ipynb)\n- 📁 **made_with_gs_quant**\n - 📄 [1-Navigating Rates.ipynb](made_with_gs_quant/1-Navigating%20Rates.ipynb)\n - 📄 [10-Explaining Performance Drivers.ipynb](made_with_gs_quant/10-Explaining%20Performance%20Drivers.ipynb)\n - 📄 [11-FX Election Hedge.ipynb](made_with_gs_quant/11-FX%20Election%20Hedge.ipynb)\n - 📄 [12-Structuring for Uncertaintly.ipynb](made_with_gs_quant/12-Structuring%20for%20Uncertaintly.ipynb)\n - 📄 [13-Cyclicals.ipynb](made_with_gs_quant/13-Cyclicals.ipynb)\n - 📄 [14-Curve Inversions.ipynb](made_with_gs_quant/14-Curve%20Inversions.ipynb)\n - 📄 [15-Hypothetical Lifetime Risk Calculator.ipynb](made_with_gs_quant/15-Hypothetical%20Lifetime%20Risk%20Calculator.ipynb)\n - 📄 [2-Levels and Tails.ipynb](made_with_gs_quant/2-Levels%20and%20Tails.ipynb)\n - 📄 [3-Systematic Selling.ipynb](made_with_gs_quant/3-Systematic%20Selling.ipynb)\n - 📄 [4-Delta Hedging.ipynb](made_with_gs_quant/4-Delta%20Hedging.ipynb)\n - 📄 [5-What's New.ipynb](made_with_gs_quant/5-What's%20New.ipynb)\n - 📄 [6-Evolving Correlations.ipynb](made_with_gs_quant/6-Evolving%20Correlations.ipynb)\n - 📄 [7-Predicting Performance and Live Risk.ipynb](made_with_gs_quant/7-Predicting%20Performance%20and%20Live%20Risk.ipynb)\n - 📄 [8-What's New Internal.ipynb](made_with_gs_quant/8-What's%20New%20Internal.ipynb)\n - 📄 [9-Hedging using Machine Learning Techniques.ipynb](made_with_gs_quant/9-Hedging%20using%20Machine%20Learning%20Techniques.ipynb)\n- 📁 **reports_and_screens**\n - 📁 00_fx\n - 📄 [0000_vol_screen.ipynb](reports_and_screens/00_fx/0000_vol_screen.ipynb)\n - 📄 [0001_vol_calendar_screen.ipynb](reports_and_screens/00_fx/0001_vol_calendar_screen.ipynb)\n - 📄 [0002_forward_vol_screen.ipynb](reports_and_screens/00_fx/0002_forward_vol_screen.ipynb)\n - 📄 [0003_10x_binary_screen.ipynb](reports_and_screens/00_fx/0003_10x_binary_screen.ipynb)\n - 📄 [0004_bear_put_spread_screen.ipynb](reports_and_screens/00_fx/0004_bear_put_spread_screen.ipynb)\n - 📄 [0005_vol_skew.ipynb](reports_and_screens/00_fx/0005_vol_skew.ipynb)\n - 📄 [0006_vol_swap_spread.ipynb](reports_and_screens/00_fx/0006_vol_swap_spread.ipynb)\n - 📄 [0007_eq_fx_hedges.ipynb](reports_and_screens/00_fx/0007_eq_fx_hedges.ipynb)\n - 📁 02_rates\n - 📄 [0000_vol_fixed_strike_grid.ipynb](reports_and_screens/02_rates/0000_vol_fixed_strike_grid.ipynb)\n - 📄 [0001_vol_moneyness_screen.ipynb](reports_and_screens/02_rates/0001_vol_moneyness_screen.ipynb)\n - 📄 [0002_swaption_carry_grid.ipynb](reports_and_screens/02_rates/0002_swaption_carry_grid.ipynb)\n - 📄 [0003_spot_vol_shock.ipynb](reports_and_screens/02_rates/0003_spot_vol_shock.ipynb)\n - 📁 03_prime\n - 📄 [0001_gs_pb_data_case_study_sector_positioning.ipynb](reports_and_screens/03_prime/0001_gs_pb_data_case_study_sector_positioning.ipynb)\n - 📄 [0002_gs_pb_data_case_study_flows.ipynb](reports_and_screens/03_prime/0002_gs_pb_data_case_study_flows.ipynb)\n - 📄 [0003_gs_pb_data_case_study_factors.ipynb](reports_and_screens/03_prime/0003_gs_pb_data_case_study_factors.ipynb)\n - 📄 [0004_gs_pb_data_case_study_country_positioning.ipynb](reports_and_screens/03_prime/0004_gs_pb_data_case_study_country_positioning.ipynb)\n - 📄 [0005_gs_pb_data_case_study_leverage.ipynb](reports_and_screens/03_prime/0005_gs_pb_data_case_study_leverage.ipynb)\n - 📄 [README.md](reports_and_screens/README.md)\n" }, { "path": "gs_quant/content/__init__.py", "content": "" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/00_us_election_analysis/0000_macro_big_picture.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.markets.securities import Asset, AssetIdentifier, SecurityMaster\\n\",\n \"from gs_quant.datetime import *\\n\",\n \"from gs_quant.api.fred.data import FredDataApi\\n\",\n \"from gs_quant.timeseries import returns, diff\\n\",\n \"from sklearn.decomposition import PCA\\n\",\n \"from collections import defaultdict\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import datetime as dt\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we'll take a look at the current macro landscape and understand how risk has evolved in the last 6mo through the lens of principal component analysis (PCA).\\n\",\n \"\\n\",\n \"The content of this notebook is split into the following parts:\\n\",\n \"* [1: First, the data](#1:-First,-the-data)\\n\",\n \"* [2: PCA](#2:-PCA)\\n\",\n \"* [3: Interpreting top 2 risk drivers](#3:-Interpreting-top-2-risk-drivers)\\n\",\n \"* [4: Realized vs predicted](#4:-Realized-vs-predicted)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1: First, the data\\n\",\n \"\\n\",\n \"Let’s start by pulling volatility data from [gs data catalog](https://marquee.gs.com/s/discover/data-services/catalog) to get a sense for the macro landscape.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"start_date = dt.date(2012, 1, 3)\\n\",\n \"end_date = business_day_offset(dt.date.today(), -1, roll='preceding')\\n\",\n \"\\n\",\n \"# gs data sets\\n\",\n \"fxspot, irspot, fxvol, irvol = [Dataset(x) for x in ['FXSPOT_PREMIUM', 'IR_SWAP_RATES', 'FXIMPLIEDVOL_PREMIUM', 'IR_SWAPTION_VOLS']]\\n\",\n \"\\n\",\n \"fxv = fxvol.get_data(start_date, end_date, bbid=['EURUSD', 'USDJPY', 'AUDJPY', 'CADUSD', 'AUDUSD'], tenor='3m', deltaStrike='DN', location='NYC')\\n\",\n \"pd.pivot_table(fxv, values='impliedVolatility', index=['date'], columns=['bbid'], aggfunc=np.sum).plot(figsize=(10, 6), title='3m implied fx vol')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"irv = irvol.get_data(start_date, end_date, payOrReceive='Pay', currency=['USD', 'EUR', 'JPY', 'AUD'], terminationTenor='10y', expirationTenor='3m', strikeRelative='ATM')\\n\",\n \"pd.pivot_table(irv, values='impliedNormalVolatility', index=['date'], columns=['currency'], aggfunc=np.sum).plot(figsize=(10, 6), title='3m10y implied rate vol')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"FRED_API_KEY = 'insert key'\\n\",\n \"fred_API = FredDataApi(api_key=FRED_API_KEY)\\n\",\n \"fred_pull = fred_API.build_query(start=start_date, end=end_date)\\n\",\n \"fred_API.query_data(fred_pull, dataset_id='VIXCLS').plot(figsize=(10, 6), title='VIX')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2: PCA\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's now look across a number of macro series and run PCA to understand what's driving risk - to start we'll grab more data from [gs data catalog](https://marquee.gs.com/s/discover/data-services/catalog) and FRED and normalize it.\\n\",\n \"\\n\",\n \"* GS FX spot [here](https://marquee.gs.com/s/developer/datasets/FXSPOT_PREMIUM) and vol [here](https://marquee.gs.com/s/developer/datasets/FXIMPLIEDVOL_PREMIUM)\\n\",\n \"* GS Cash and Swap Rates [here](https://marquee.gs.com/s/developer/datasets/IR_SWAP_RATES) and vol [here]()\\n\",\n \"* Fred API [here](https://research.stlouisfed.org/useraccount/login/secure/) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"instruments = {\\n\",\n \" 'Equities' : ['SPX', 'NDX', 'N225'],\\n\",\n \" 'Commodities': ['Gold', 'WTI'],\\n\",\n \" 'Rates': ['5y', '30y'],\\n\",\n \" 'FX': ['USDJPY', 'EURUSD', 'USDCAD', 'AUDJPY'],\\n\",\n \" 'Credit': ['HY'],\\n\",\n \" 'Fundamental': ['Breakevens', 'VIX']\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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SPXNDXN225GoldWTI5y1y5y_curve5y_xmkt3m5y_impvol3m5y_impreal...EURUSD_3m_imprealUSDCADUSDCAD_3m_impvolUSDCAD_3m_imprealAUDJPYAUDJPY_3m_impvolAUDJPY_3m_imprealHYBreakevensVIX
date
2020-09-013526.6512292.86323138.071987.9542.6132.0547038.77615169.4507282.6415591.220437...1.1919311.307480.07040.98809178.0690.10870.9562714.971.7626.12
2020-09-023580.8412420.54323247.151969.0042.6132.5516078.56411673.3431462.6274511.234413...1.1617211.307050.07080.98792577.7760.11050.9706064.931.7326.57
2020-09-033455.0611771.36723465.531934.1042.6130.9082237.56001072.9130062.7010931.268510...1.1473921.313400.07341.11124477.1600.11631.0682325.041.6533.60
2020-09-043426.9611622.13323205.431937.6042.6136.66436312.72093177.8652802.7022461.266355...1.1262251.304480.07251.09161277.5070.11731.1690465.131.7030.75
2020-09-083331.8411068.25823274.131920.6042.6136.66436312.72093177.8652802.7184081.252479...1.1673261.322240.07511.11292676.5380.12241.2310855.251.6831.46
\\n\",\n \"

5 rows × 34 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" SPX NDX N225 Gold WTI 5y \\\\\\n\",\n \"date \\n\",\n \"2020-09-01 3526.65 12292.863 23138.07 1987.95 42.61 32.054703 \\n\",\n \"2020-09-02 3580.84 12420.543 23247.15 1969.00 42.61 32.551607 \\n\",\n \"2020-09-03 3455.06 11771.367 23465.53 1934.10 42.61 30.908223 \\n\",\n \"2020-09-04 3426.96 11622.133 23205.43 1937.60 42.61 36.664363 \\n\",\n \"2020-09-08 3331.84 11068.258 23274.13 1920.60 42.61 36.664363 \\n\",\n \"\\n\",\n \" 1y5y_curve 5y_xmkt 3m5y_impvol 3m5y_impreal ... \\\\\\n\",\n \"date ... \\n\",\n \"2020-09-01 8.776151 69.450728 2.641559 1.220437 ... \\n\",\n \"2020-09-02 8.564116 73.343146 2.627451 1.234413 ... \\n\",\n \"2020-09-03 7.560010 72.913006 2.701093 1.268510 ... \\n\",\n \"2020-09-04 12.720931 77.865280 2.702246 1.266355 ... \\n\",\n \"2020-09-08 12.720931 77.865280 2.718408 1.252479 ... \\n\",\n \"\\n\",\n \" EURUSD_3m_impreal USDCAD USDCAD_3m_impvol USDCAD_3m_impreal \\\\\\n\",\n \"date \\n\",\n \"2020-09-01 1.191931 1.30748 0.0704 0.988091 \\n\",\n \"2020-09-02 1.161721 1.30705 0.0708 0.987925 \\n\",\n \"2020-09-03 1.147392 1.31340 0.0734 1.111244 \\n\",\n \"2020-09-04 1.126225 1.30448 0.0725 1.091612 \\n\",\n \"2020-09-08 1.167326 1.32224 0.0751 1.112926 \\n\",\n \"\\n\",\n \" AUDJPY AUDJPY_3m_impvol AUDJPY_3m_impreal HY Breakevens \\\\\\n\",\n \"date \\n\",\n \"2020-09-01 78.069 0.1087 0.956271 4.97 1.76 \\n\",\n \"2020-09-02 77.776 0.1105 0.970606 4.93 1.73 \\n\",\n \"2020-09-03 77.160 0.1163 1.068232 5.04 1.65 \\n\",\n \"2020-09-04 77.507 0.1173 1.169046 5.13 1.70 \\n\",\n \"2020-09-08 76.538 0.1224 1.231085 5.25 1.68 \\n\",\n \"\\n\",\n \" VIX \\n\",\n \"date \\n\",\n \"2020-09-01 26.12 \\n\",\n \"2020-09-02 26.57 \\n\",\n \"2020-09-03 33.60 \\n\",\n \"2020-09-04 30.75 \\n\",\n \"2020-09-08 31.46 \\n\",\n \"\\n\",\n \"[5 rows x 34 columns]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fred_symbols = {\\n\",\n \" 'SPX': 'SP500',\\n\",\n \" 'NDX': 'NASDAQ100',\\n\",\n \" 'N225': 'NIKKEI225',\\n\",\n \" 'Gold': 'GOLDAMGBD228NLBM',\\n\",\n \" 'WTI': 'DCOILWTICO', \\n\",\n \" 'HY': 'BAMLH0A0HYM2', \\n\",\n \" 'VIX': 'VIXCLS',\\n\",\n \" 'Breakevens': 'T10YIE'\\n\",\n \"}\\n\",\n \"\\n\",\n \"color_map = {\\n\",\n \" 'Equities': '#20396D',\\n\",\n \" 'FX': '#68A2BF',\\n\",\n \" 'Rates': '#CD252B',\\n\",\n \" 'Commodities': '#E3E311',\\n\",\n \" 'Credit': '#E3E000',\\n\",\n \" 'Vol': '#67E311',\\n\",\n \" 'Fundamental': '#25cdae',\\n\",\n \"}\\n\",\n \"\\n\",\n \"realVolWindow = 66 \\n\",\n \"asset_map = {}\\n\",\n \"df = pd.DataFrame()\\n\",\n \"\\n\",\n \"for asset_type, asset in instruments.items():\\n\",\n \" for x in asset:\\n\",\n \" asset_map[x] = asset_type \\n\",\n \" if asset_type=='FX':\\n\",\n \" asset_map['{}_3m_impvol'.format(x)]= asset_type\\n\",\n \" asset_map['{}_3m_impreal'.format(x)]= asset_type\\n\",\n \" \\n\",\n \" df[x] = fxspot.get_data(start_date, end_date, bbid=x)[['spot']]\\n\",\n \" realVol_f = df[x].copy()\\n\",\n \" realVol_f = [returns(realVol_f)[r-realVolWindow:r].std()*15.875 for r in range(len(realVol_f))]\\n\",\n \" \\n\",\n \" df['{}_3m_impvol'.format(x)] = fxvol.get_data(start_date, end_date, bbid=x, tenor='3m', deltaStrike='DN', location='NYC')[['impliedVolatility']]\\n\",\n \" df['{}_3m_impreal'.format(x)] = fxvol.get_data(start_date, end_date, bbid=x, tenor='3m', deltaStrike='DN', location='NYC')[['impliedVolatility']]\\n\",\n \" df['{}_3m_impreal'.format(x)] /= realVol_f\\n\",\n \" elif asset_type=='Rates':\\n\",\n \" asset_map['1y{}_curve'.format(x)] = asset_type\\n\",\n \" asset_map['{}_xmkt'.format(x)] = asset_type\\n\",\n \" asset_map['3m{}_impvol'.format(x)]= asset_type\\n\",\n \" asset_map['3m{}_impreal'.format(x)]= asset_type\\n\",\n \" asset_map['3y_vs_3m_{}_calendar'.format(x)]= asset_type\\n\",\n \" asset_map['3y{}_skew'.format(x)]= asset_type\\n\",\n \" \\n\",\n \" irspot_d = irspot.get_data(start_date, end_date, currency='USD',effectiveTenor = '0b', terminationTenor=x, floatingRateOption='USD-LIBOR-BBA', floatingRateDesignatedMaturity='3m', clearingHouse='LCH', pricingLocation='NYC')\\n\",\n \" irfront_d = irspot.get_data(start_date, end_date, currency='USD',effectiveTenor = '0b', terminationTenor='1y', floatingRateOption='USD-LIBOR-BBA', floatingRateDesignatedMaturity='3m', clearingHouse='LCH', pricingLocation='NYC')\\n\",\n \" irspot_eur_d = irspot.get_data(start_date, end_date, currency='EUR',effectiveTenor = '0b', terminationTenor=x, floatingRateOption='EUR-EURIBOR-TELERATE', floatingRateDesignatedMaturity='6m', clearingHouse='LCH', pricingLocation='LDN')\\n\",\n \" gammaVolA = irvol.get_data(start_date, end_date, payOrReceive='Pay', currency='USD', terminationTenor=x, floatingRateOption='USD-LIBOR-BBA', expirationTenor='3m', clearingHouse='LCH', strikeRelative='ATM', pricingLocation='NYC')[['impliedNormalVolatility']]\\n\",\n \" vegaVolA = irvol.get_data(start_date, end_date, payOrReceive='Pay', currency='USD', terminationTenor=x, floatingRateOption='USD-LIBOR-BBA', expirationTenor='3y', clearingHouse='LCH', strikeRelative='ATM', pricingLocation='NYC')[['impliedNormalVolatility']]\\n\",\n \" vegaVolhigh = irvol.get_data(start_date, end_date, payOrReceive='Pay', currency='USD', terminationTenor=x, floatingRateOption='USD-LIBOR-BBA', expirationTenor='3y', clearingHouse='LCH', strikeRelative='ATM+50', pricingLocation='NYC')[['impliedNormalVolatility']] \\n\",\n \" df[x] = irspot_d.rate.copy()*1e4\\n\",\n \" realVol_r = [df[x].diff()[r-realVolWindow:r].std() for r in range(len(df[x]))]\\n\",\n \" \\n\",\n \" df['1y{}_curve'.format(x)] = (irspot_d.rate - irfront_d.rate) * 1e4\\n\",\n \" df['{}_xmkt'.format(x)] = (irspot_d.rate - irspot_eur_d.rate)*1e4 \\n\",\n \" df['3m{}_impvol'.format(x)] = gammaVolA\\n\",\n \" df['3m{}_impreal'.format(x)] = gammaVolA\\n\",\n \" df['3m{}_impreal'.format(x)] /= realVol_r\\n\",\n \" df['3y_vs_3m_{}_calendar'.format(x)] = vegaVolA/gammaVolA\\n\",\n \" df['3y{}_skew'.format(x)] = vegaVolhigh/vegaVolA\\n\",\n \" else:\\n\",\n \" df[x] = fred_API.query_data(fred_pull, dataset_id=fred_symbols[x])\\n\",\n \" \\n\",\n \"data = df\\n\",\n \"data = data.fillna(method='ffill').dropna()\\n\",\n \"data.tail().head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"frame_t = data.copy().apply(lambda x: x.diff() if x.name in instruments['Rates'] else returns(x))\\n\",\n \"frame_t.dropna(inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's run a 3-factor PCA for a 126 day (6mo) rolling period and record how much variance is explained by each component over our time frame.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"period = 126\\n\",\n \"components = 3\\n\",\n \"f_loadings = defaultdict(list)\\n\",\n \"\\n\",\n \"for start in range(len(frame_t) - period):\\n\",\n \" d = frame_t.iloc[start:start + period]\\n\",\n \" d = (d - d.mean()) / d.std()\\n\",\n \" model = PCA(n_components=components)\\n\",\n \" model.fit(d)\\n\",\n \" for i, c in enumerate(model.explained_variance_ratio_):\\n\",\n \" f_loadings[i].append(c)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Contribution to variance from each component')\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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ghhBCTRGRPckFHAevq1gXvH2w7ON5NSlkSJAshhBBCTGI1PTXB/xd0FNDn7JvA1qQOCZKFEEIIISaJyHQL8E0qYnfb8WovBquB3+z8zQS0LPVkTHQDhBBCCCFEfESmWwBsb95O1sEsALrsXZgdZjptncyZOme8m5dSJEgWQgghhJgkovUk9zn7WFm1MmzZjzb+iOc+9xzzcuaNV9NSjqRbCCGEEEJMEtGC5GhqzDW8Xfl2gluT2iRIFkIIIYSYJKKlWwymobchgS1JfRIkCyGEEEKcgHrsPRPdhKQmQbIQQiQBKckkhIiHkfQkG21GKk2V2N32BLYodUmQLIQQ48zsMIfdL+8qZ2fzzglqjRBiMok1JxmgvreeH2z8AXta91DbUzvgu+lEJ9UthBAiwZweJ+Vd5eRk5nD2rLO5c9edPPPZZwAo7SxlV/Mu5uRIKSYhxNiNJEh2e92YHWZ+uf2XAMzPmc9pM07jexd+jysXXJmoJqYM6UkWQogEa7e085/r/5M/7fsTWmsquiqCPTYH2w9ysP0gNpdtglsphJgMtNbkZuSQkRZ7P6hXe/FqL62WVva07uHePffS3NecwFamBgmShRAiwSwuCwBFxiL+WvBXuu3d/KPsH1hdVva07KGsqwybW4JkIcTYaTQX5i5mVtb0Ue+j3dLOHTvuwOV1xbFlqUfSLYQQIsH6Xf3B//+j/B8ArKxaiUZzsP0gAAUdBXE9ptYapVRc9ymESA3XejMxZUzDSPeo91HeVc6Nq28kNyOXk3NP5sFrH2RqxtQ4tjL5SU+yEEIkWI/jeJklr/YC0G3vZnnJ8uDyfW37sLqscTumyWGK276EEKlDa835vZ2cmpk35n3Vmeso7SplS+MWfrn9l/Q7+4ffaBKRIFkIIRKssKMwpvW67aPv9YlktBrjti8hROrwai8zbb181BX7AL5Y7G7ZzbtV78Z1n8lO0i2EECLBAjnJw2npb2Fx3uK4HNNgNXDOSefEZV9CiNSh0cyy9vCF+sM8NCc7rvt+4+gbzMuZh81to6yrjMvnX87nln5u2O1cHhdpKo30tPS4tifRJEgWQogEC81JHkqtuZYrFlwx5uM5PU421G+gx9HD9WdcP+b9CSFSS66jD6W9wLy47re5v5lf7/x18P6q6lVsbdrK9y/4/pAn5Ue7j5KVnpVyJ+6SbiGEEAkWa09ynbkuLseze+ysrV3L/rb9cdmfECJ1aK1RWpPucSf8WA6Pg3W163g0/9Ehv+fW1q5NyZJyEiQLIUSCxdqTnN+Rj9vr+2Hb17pv1MezuWx4tTeuAwGFEKlB4wuSM7yecTvmvrZ9rK9bH/Uxo9XI60dfp9sRvzEX40XSLYQQIsFiDVarTFX0OHpwe908UfgEn1j4iVEd75jpGBB7D7YQYvIIBMkKTZpKC1bUSbTXKl6jx9HDlQuuJDczl9NmnMaG+g2UGEsAUnLKawmShRBJY0vjFvLb8/nhRT9kztTJM01zrD3JAIfaD7GxfiNm5/EflH2t+7hwzoXkZcVW0qnL1gVAu7Wdva17uWrhVSNrsBAiZWl/gAyQrtLHLUiu7qlmWeEylrGMeVPnsXTGUgo7CpmX48uL7nX2jks74knSLYQQE6aprwlPyCXB8q5yXq14ddLl0lqcsffo/mbnb9jSuCVYj9TlcfHXgr9y3977aOprimkfgaC8zlzHb3b+hp3NO+m0dY684UKIlKPRoH1BcoaamGoSBpuBg+0HcWs3rZZWAOxu+4S0ZSxiCpKVUtcppY4ppaqVUncNss6nlFJFSqkypdSO+DZTCJGqhurFuHXzrXxtzdewuqy4ve7gwLXf7votFV0V49XEhLO4R5720OPoochQRLulnWOmY2xq2MT2pu2Y7CYqTZVDbhta8N/sMPPTLT9lWeEy7th+x4jbIYRIMZrjPclJVHItFYPkYdMtlFLpwFPAvwHNwCGl1GqtdXnIOjOBvwPXaa0blVLxrTkihEhZdrednMycsGUuj4v63nrqe+sBeLnsZeweOx82fBhcZ3/bfs6bfd54NjUhrC7rqC53ajS3brkVh9sR3P7hQw/zYumL/PJjv+TsWWdH3a6pt4nVNasHLN9QtwG7x06/s59pWdNG3B4hRGoI5CSDL90iWUzKIBm4HKjWWtcCKKXeBG4AykPW+Rbwrta6EUBrbYh3Q4UQqcnqtg4Ikiu6K/ivD/8reH9l5coBs80FAuhUN5J85Eh9zr6w+xpNp60Tg3Xwr9hXK16luX9gqSW7x/cD1ePokSBZiElME5qTnDxZtTaPbaKbMGKxvHqLgNBEuGb/slBnA7OUUtuVUgVKqe/Eq4Hx1m5pH/Sxo91Hx7ElQpwYbK7jX4yB8mbtlvaw4NFgM+DW4TU9DxsOs79tf8qXMUtEhYmXyl4aEEAHjrW3de+Q275+9PW4Tn8thEgeHq8nWCcZJi4nOZpU7EmOJUhWUZZFTgieAXwM+CLweeBepdSAa4FKqZ8opfKVUvlGo3HEjY2Hgo6CsB9drTXPHHmGPmcfL5a+OG6jQIU4UVjdxz9va2vXArGNcq4z1/HjTT+mw9qRsLaNh9D84HgxO8xRT/jv23vfsD3wK8pX8J31SduPIYQYg1ZLa9L2JE/WILkZWBJyfzHQGmWdDVpri9a6E9gJfCRyR1rr57TWH9daf3zu3LmjbfOYeLWX/9783zxz5BlcXhfPFj/LU0VP8feiv7OhbgMFHQUT0i4hJiunxxnsQX6x9EXcXnfUXtDBHGo/lKimjYuRPNeROGI8gsfrCZ70e7WX7U3bY9q2obeBvS1D9zgLIVJPQ29DWGdfUuUke1IvSI4lJ/kQcJZS6jSgBbgJXw5yqPeBvymlMoAs4Argr/FsaDwVGgopNBSyo2kHR02+FItV1avQaEx20wS3TojJxaM9OD1OSjpLqDPX0WnrpM3SFvP2zxY/y5kzz+TSky9NYCvjy+qy0tTXxDknnUOfKzFB8kOHHmJj/UZMdhN/uvpPlHeVj+hH6PHCx7lqkdRPFmIyaehtwOv1cob/vqRbjM2wPclaazfwM2AjUAH8U2tdppS6RSl1i3+dCmADUAwcBJZrrUsT1+z4KO0qDfZwBfIjQwv4D0ZmsRIidl7txeq2BqdZ3tu6l5WVK2Pe3mA1cPfuuzliPJKoJsZdU18Td+y4g05bJ819AwfRxYPNbWN/236OmY7xzJFnuG/ffSPavsfRk5B2CSEmhtaaDmsHjb0NwWVzM3KG2GJ82dyTc+AeWusPtNZna63P0Fo/4F/2jNb6mZB1HtZan6+1vlBr/XiiGpxo3bbhB7QUG4vHoSXJI789f9DHbG4bhR2F0gMvBuXRHnodvcEpSR/Y/wBOr3NE+2jpbwmmXQw1+DZZdNm7aLe083TR06yrW5fw421r2jbibRKVBiKEmBgWl4VuWzcNfceD5DPVlAlsUTiHxzHRTRix5MnoThJG29ADCq0uK8e6j6XkGdFoHOs+xmMFjw06W9eBtgM8mv8om+o3jXPLRKrwaA9HjEcwOXwnUiMNkAOeK36Ohw89zL177o1n8xLiiPEIDo+Dd6vfpbanNuHH0wPGUg/Poz3DryROaFoff195vJ6w2TFF8jFYDTg8Dpp6jxckO9M5uu/bRJiU6RYnmuF6qY6ZjvFs8bM8X/z8gMcmYxrGEeMRyrrKqDJVDXjMaDWyvGQ5xZ3FwWknhYjk8Xq4b999bGsceW9nKJvbxivlr1Brjk/QuadlT9iPSTwd7jgM+EreJWswKpV8xFCKjcW8fvR1bG4b25u283+H/o+ffPgTGkIu5Yv467B08LfDfxtyVs12S3vYCUxwubUdl9cVlkp1Vm/yTEfv8DiitjuZSZAcobqnmsbeRgo6CtjZvHPA45sbNtPv6h+QclFiLKG8q3zA+qnO5rbh1d6okxNsatgUzBNNVN6lSH0e7cGrvXEb2WywGsZcVq3f2c8tm2+hqmfgyV88RPu8JBsJkgUM3rtXbCymvKucIkMRdeY63jr2FgfbD8p8AqOU355Pvbl+2PUqTZU8W/wsN39w86D1zP9a8NeoQXSHpQOnxxmW1rDIHPsg6UTT6JSrcCFBcoSW/hb+fuTv/HjTj1lds5oOS0cw39bldfF+zftAeJ1Xt9fNu9XvsqJ8BRvqNkxIuxMlUOM2EAQHJiqoN9ezrHBZcL1UCArExEhEMFZnrhvT9oHprwN50vFksBpo60+eH6bBSJAsAN44+kbUQLnD2sH+tv08dOghNjdsDr5fUmFMwHgZyURHfyv6G28ee3PY9brsXYCvg6qlryXqOntb97KjeceA5SaHCZfXFRYkz+3tICeJBu+lWsqFBMlRbKjbgMvrwmA1sLlxczCVoNpUHfxRDQx6ea/qPQ4bDrOxbiPbmrZRY66ZsHYnQiD3urSzlEpTJb/Y9gvu2X0P39/4/bC8bOlJFoNJRB5jdU/1iI5f2FEYtqysq8z3WAJSIe7edfeA2QOTkQTJAuDtyrfDroIGei+7bF0YrAaqe6op7jx+5fSw4TDHuo+NezuTTUNvA19e9eXgd8lQOm2dFHQU8FrFa8P+VlZ0VQT/39jXOOCYy0uW0+PoiVo/vs/ZN6AnWaH5dN7pw7ZxvEiQPAkEfjgPGw7z9JGnefPom9SZ63i+5HgessVlodPWyd+P/J3/2fo/wVqoRuvEzCSYKIEphQ+2H+T2bbdjdVt5v+b9AQP5ep29dNmOnwHLAA8RkIhAtKSzJPj/Xc27hly30lRJkbEoeL/T1hn8IQqUgIwXl9fFgfYDcd1nomh0yuUHivhq6G2gqa+Jgo4CGnobePPom1z/3vW4PC7yO6JXNdrSuCUYGO5q3kVLf/TezskqEIDmt+fTYe3gRxt/FPV7pLmvmQ11G/jzgT/zrXXHp5YIBLeDffYquo8Hyf8o+wdmhxm3102tuZYvvfel4BXcaL3Yfc4+HB4H9ogqEvN1tImTJ4bNk1pFD2KZTOSEZnaYWVOzBofbEbxEC74ao6+UvTLg0lM8yiq5vW5WVa/i6oVXs2DagjHvbyxC00qa+oYe5FRrrmX21Nmsrl6NR3v41nmRc86IE1Eieiy3NG7hO+d/h5nZM1leshylFNcsumbAelaXlW1N22jtPz6wtKyzLJjPF+8APtV6Sbzam1Qzconx4/A4WFG+AoCXyl7Coz1sa9pGr7OXjQ0bh5zw56nDT7Fw2kIeOPAAD177IIumLRqvZk8Yl9dFZlomzxU/R2ZaJu9UvgP45ljod/Yzc8rMsPU/bPiQxwoeG7CfA+0H6LR1ctqM0/jsqZ8Ne+zl0pc5bDgcvF/RXcGNq29kSd6SAd+jLq8r7L7b68bqsnK0+yhZ6Vlhj2Un0clwqn1HSk9yDDzaw/r69WHLNJqXyl4asG48SsPdvetu/rjvj/xm529we93jVjWj3dJOa38rWmtsbhtPFD4x6MCBaMq7ynm59GUeLXg0YQOiROoJTNQTT932btbUrqG4s5gqUxX377+fXc27gulQgYF9FpeFd6veZUvjluDVjYKOguDgkXj3JKdaaUgvknJxojpsOMxbx94CfJ07h9oPBQeW/WHPH4bc1mAz8LMtP6Olv2VEebmpyGA14NVeantqfScQ9Rt5qugpOqwdwXWizao52BWlD+s/5MnDT0YtNxstXbPD2kF+Rz6FhvCUscgg2aM9WN3WqCf+WV4JkkdLguQ4s7ltlHWWDVpXONRrFa8NWGZ1WYMBeWlnKXtb97K8ZHnc2xmpyFDEr3f8mp9u+SnvVL3Ds0ee5fmS58Mu/Qzn+ZLnebTgUWxuG6Wdpbg8ruE3EpPW3pa99Dn7hixlNBbvVL7DysqV9Ln6aOlv4dYtt9LY68vhu2fPPTg8DiwuCx3WDvpd/cHScaGpGvEOklPtB0Dykic3q8s6aOpbZP3u/I784EDtWGqZB9IOAieGgw0ATHVvV77NivIVrChfwd8O/y1qCbyVlSsHfJYC6YeRnF4nGh1Mu3B5XHTZulhTsyY4MD4WoUGyy+vC4/UEO9QU4ekV0pM8ehIkx5ndbWdt7dphS1Q5PU4eOfTIgJ7a0NH2bu1mX+u+ATPeJWJE/tamrRQZi6juqeZP+/7EC6UvjPhYoese7T7KHTvuoMPSMdfggEoAACAASURBVMQWvryuwHTFIvWF5tn97/7/pbqnOmGDfLrt3Wxt2hq2rN/VT7e9m8KOQt9A25Bp5rc3bcfmtoVd5Yh3ukXK9SRLkDypvXH0jQE9kOD7nG6oD6/ENNr3QlNfE819zTx06KFJNyYHwGQ3cdhwmP1t+3nj6BtR13mh9AUOth8MnqQHthtKW38bG+s3UtJZQnVPNXfvvhuD1RBzu5ye4ycyndZO3NodfP0jM5CzkmiMUKrlJEuQHGcd1g5W16yOWgvQ5XHxesXrdNo6MdqMuLWb7234Hg8eeDC4TuRgiUPthwbkhg2VKxYLq8tKYUdh2JluaFA/mtm7otnWtI2D7QeHXGdV9SqeOfLMkOuI1GBz2/jO+u/Q0t+C1pp2Sztdtq6E9SRH82zxs3xj7TcwOUw09zeH9fq8cfQNfrvrt1icx9OX4tmT/PChh/nP9f8Zt/2NBwmSJy+Ly8LblW+zo2kHWuvg39qrvZR1lYXlvo7FU0VP8esdv8btdePSk+/qoc1tw+KyDJv2uK1xGz/d8lPAV1FnuFTFTnsn71S+Q6etMyx1I1ah7emyd+H0OIPpGkqFh8lTkyhIdrhTa2pqCZLjzGgz0uvsjXpJYU/rHh48+CCFHYV023wfoDpzHf889s/gF1hkz22lqZJOWycer4eyrjKOdh+lobdhTKPSq3uq+dnWn4WlcYx1cobBBEoLDfZjXNZVNqovCJF8tjdtp8hYxNqatZgdZjzawyvlryQkJ3kwZZ1lwcG0Lf0tYZeUjTYjWxq3hJVnc3vdwwbKWuthZ/nb37afV8pfkZ5kkTTu3HknLf0tlHSWcOOaG7lh1Q102jrptndT1jl82bJYOTwOSrtKgfinLyUDr/bSbe8e9ntsZdVK6nvrWV+3nt/t+d2AnOFI7ZZ29rftp9PWyf7W/SNuV6+zl4KOAipNldjd9rCrtpHpFhcZ60e8/0RJ1hlIByPVLRIksifZq728Uv4K4Htzh9YxDFwmOTn35GBOWIBG49EeDFYDL5W+xCl5p5CXlcdt227jkU8+QnZ6dtTj9zp7mZ41nUpTJWfPOhvwnRE73A62Nm6lz9nHqxWvsiB3ATXmGva07onn0w9689ibmB1mbG4bGs3jn348vJ2OXrod3Xi1lzQl52yp7P799wNgcVuC06LGq7cqVqGfuzpz3bDpQm7tZn3deq5ccCVKKeZMnTNgnVXVq3i88HE+veTT/OJjv2BG9oywx8u6yvjTvj/F5wmMMwmSJw+nx0lmWiZKqbATu9B0izU1a7h8weWUdydmdtjhAsNU5NGesDSKwQR+0+/addeIPlclnSWsr1s//IpR/GTTT3jgmgeYnj2dZwoGvyK7tLOWhfM+Tqst9nQO4SNBcoJE9iTvbd1LQUcB4KtNGZkP1mnv5OTckwftiWqztJHfnk+7pZ2rFl7F9qbtvF/9PidNOWlAGRnwTXJy7eJreafyHe6+4m4AqkxVPHn4Sfa3HT9rfTj/4TE9z+G4vW7W1K4BCCsT9HLpyxhtRvpcfbi9brpsXczNmZvQtoj4CpREAl/t4UC5QKvLGgySJ1K1qXrY3h+P18OrFa9S0llCj6OHr5/9dS6bf1nYOu/XvE+3vZuVVSup6alhxb+vCD62tXErjxc+PqDof6qQIHnyONZ9jJVVK7n+jOupMlVFnbSipb8Fo9XI7ubdCWnDZO1JHslUyiP9TK2vWz/q3lWn14nRZmRqxtSwahqR6RYAn5hyMislSB4xCZITZHvTdi6YfUEw8CvrLAt+eF6reG3AGXcgP3iwtIfyrnK67d102buCU96uKF+BR3u4eO7FOD1OFuctDq6/vGQ5j+Q/whXzr8DlcZGZnkmlqXLYHOFECuRoHes+xstlL7Mob1HwpKDd0k5OZg65mbkT1j4xMg3mBpZMX8ILJS/wmVM+E1xeaChMih6lht6GAVdmItX01FDeVR5MCzpiOMLGr20MWyd0iukiYxF37bqL60+/nssXXM7a2rVjniJ7Ik3kpc9eZy9ppOHRngG982Lklh1exoG2A6SpNHY274w6tqTf1c/ult0YEhQsTdYgOZHG+hnstnczP3d+2LLIdAuARZ7j74ezp51CZX9qntiPNwmSE2Rt7Vpa+lv441V/ZOG0hWHpDNECiJb+Fo52H2V1zeqo+1tWuCz4pRf4gqvvrSczLZM6cx2HDYf59JJPU2mq5KqFVwV78urMdaysWslN595EpalyQnuObG4bdred50uep8veFZyjHny1KLc1beOrZ301LNgXyanD0sGB9gPsbtnN00eeJiPt+FdJlamKKtPE18mOVrs0UmQt0w5rBy6vi9XVq+mwdnDrJbcOCLTX1a6jsKOQKxZcETbBUCqayBn32vrbuH3b7czPnc89V97DGTPPmLC2pKK9rXtRKDLSMshOz+ZAm++9/Hbl24Nu02HpoMqZuM/mZAySk332WJPdFFbpAqIHyXme43+b8zNnMH7DqVObBMkJ4vA42N+2n2+u+yabv7Z52JGuTX1NGK3GQXu+Brvc4/K6uHXzrTi9TurMdXxQ9wF/ufYvwccNNgOP5j/Kl8/48oDZASeCyW6KOmikqqeKVdWr+Oi8j0qQnGCvlL3C2SedzZULrhz1Pm54/wZOm34aDX2+6hEH2ybuCsVYRP6oe7SHx/IfY13tOjzaw9WLro46WUKbpY1V1avGq5kJM5E9ye2Wdpr7m2nub6agoyDuQXJrfysLpy2M6z6TRVlnGffvv58OSwdnzzqbz5z6meE3wjd4NZHTSCfDFaR4S/aUpF5nL6WdpWHLok1CPd19/G9zvtPN3imzMdij13IWx8lIqQTrtnfzkw9/MuyUzk19TaOu8hAo/L65YTNAWM4x+ALs8q7yhNRXHqlue3fU57mqehVGm5HizuIJ7d1KdX3OPpr6mgYt2L6udh0P5z88pqA2MAtkaVdpcBr2sq74jZafaK9WvIrJYaLX2cvNH9wc08QK8RDaGx8P0XqTIk3UZ83pcYYN6kzEAM81NWu4c+edk/L75IXSF2jqa8LpdVLaVcq7Ve/GtJ3Rakxob+9k7ElO9lkpzQ4zG+vDU8Si5SSf19VIVppvuupTrGb+bJc+0lhIkDwO8jvyhz0bbe5rHnM92cCPeWSQDPBy2ctJUWqt1lw7aLoJwDNHnpG6yWPwrXXfotpUzTFT+AQe3fZuNtVv4h9l/wAYUdH6yJkTow0uHc8yb5PRzKwZXD39rLjuMzs9a9h1JqonucPawb62fWH3483kMPFB3QfDXsVLRa39rWH3h+uECRguR3+sJtPJckCylyw7bDgcZYrrgUHyacYatrZ18f9mXcRUt5NTetq5bEZ8v3MmIwmSk0R9b33cZiaLllaxo3lHQi+zxSowQGoogSogIxHLNOCTnc1to763ng5rB4c7DlNkKKKxt5Fj3cf4w54/cMeOO4LTjA/8Ug3n1V5cXhe/2PYLblxzI522TpweJxaXhR77xFeuSDXTMnP5v+wzWJRzMgDXzbog+NipuQv5Ys6pTFfpcT3mYOUhQ41XL2vk59PpcYZ9F5jsJp498mxcjxmYbKHdOvFpZvGWrIHblsYtE92EQUWmTcXyWwTg9SZ3T3L0FJfon+sZVhP/W7iOS5qKWGBq4sWiLXxr1sWJbWCKkyA5Sbi97rjNdJfM3qt+b9h1Yu0VCbC6rPzl4F+GX3GSC6RYPHn4STbUb+A7679DVU8VK8pXsL15e9i6RptxyKsbTX1NXP/e9Wxu3EyduY4NdRvYWL+RPS17uGfPPYl8GpPS7KwZ/PvRbfzCnQPAj1qPzwR4VtYsrurtJiuG9IiRyE4bvif51YpXxyVQjixHFvnDXmeu49WKV+M2SOqw4XCwB7m5rzmYFjRZJGtaQ+hslsmmrKss+B3p8rooMhQFH4tWLi8g2XOSoxku1Sot5Dl91DK5PhvxJkGyGFfDTe0J0GppHVGv+tamrXHrhU9lgWL2vc5eyrrK0Gg2N2yOWjHFaDWGzdAUyWA1hF15ONB2gKeKnuKhQw+FTU4gYjPVX0/63K4m/n3WhZxhqAz+kC3S6VzQUUV2nIPkKf5jDuX1o68nPOfa5XWxvm49L5W+dHxZRAqPR3vocfSwpnYNXbYuNtZv5IjxyKiP+XLpy+xs3gnAH/f+kd/v+T3d9u4Bx01VydqTnOh0jtEInHjVmet4vuR5wDeJVSDlzOwwc++eewffPklf66HEMh4hYHH/5EtHiicJkkVSWla4bNh1THYTTxQ+wab6TbRZ2oKTWZyoog3WW1u7NuoVih5HDzXmmqj7Kess4w97/xC2bE/rHlr6W5Iirz0V5fgD1lM7a/m/wg/I8Lq53J8PuMjlYna/kaw49ugqFFkxDgQMnf0zEVweF68ffZ3HCh4LHmuwKgh7W/byaP6jPLD/geDl8GhjLIbTZjle27rP1ceO5h186b0v8VjBY8FjJ2tvbCyStXez39mPy+NKqte2yFjE9qbt1PfWU9NTw5bGLdy7517MTt9A9g11GzhiPDLoYOdkfa2HEmXc3qDOMNYwPSsvcY1JcRIki6Q02A9jQUcBN39wM522Tt469hYrq1ayrWkbDo+DbY3beK74uXFuafIYabBTYiyhz9k34EdgecnyASkvk7G003iaHqVX97ZOI7kZOZxu8fXkZMUx6yEjLYP0GKd5j6yxGm+h753A9L6DvZ82N26msa8Rk8MUHJz2Stkrwx6j2lQddj+yZKbL66LP2cerFa+yonwFZZ1lPFsc3xzo8ZRMQWgot3ZT0lmSVFf2eh29PF/yPMXGYnY07eD2bbezq2UX3bZuzA4z71S9g8vrGjRHOSWD5BH0JE91WnnRrMnLnJbAFqUuCZJFUnJ5XZjspgHLtzZu5YjxCPta97G2dm3YyPU/7f8T2xq3jekybSobaZD8TtU7fObtz3DYcJjCjkL2tu4FfINIRXxd7hr4o3Vx8xF+PmUpl9flA3CWrZ+0GAPb4WSkZZAW4w9looPk0IDugQMP0G5pHzRIdnldwRkMNzdsxua2sa9137CDRX+/9/dhA7MG6xUEX2WIH236UVIFciOVzCkA71a9mxQ1+QP6XH0UG4s5YjyCWx9/L25v3s49u+8JdgiYHAN/byA1g+TolZIHd057OZ+YdmqC2pLaJEgWSSsQtIUK9ERtb9pOQ29D2GMOj4PSrlKePvL0uLQvmWxu2MzKqpUj2sZgNWBz23in8h0MVgNvHn2T1ypeo7qneviNxYjMdEcPRL9dsgHlT4e57tgOTs1ZEJfjZaZlkBFjwJ3wdIuQgLigo4A2S9uQucGBtKnm/ma+te5buLWb3a27h6x+YbKbwgKzoYLkD2o/oN/Vj9E6dIWXZJbMs8C9X/P+uOQme7V3yPragZOm/a3Rr0p6tZddLbuC42SilbY0O8xJfUIymJGkWwRc6UjOqxMTTYJkkbQMVgMVXRXB+17tDc7wtqlh06DbtfcnTy/GeHn6yNMxTygQaXPDZvI78intLOXFkhfj3DIBkOeKrbf2kqyT4nK8DDXxPcn15nq01gN6jW0uW8w14QMnbL/d9VteKnsp2CsdeWnc4rKwpXELDx96mF5n75BBWmC6coMt9lrhySbZA7doAWc8Hes+xo2rb+SNijcGXWdH8w7W1KyJ2tkSEPo6RptZs6K7IiV7kkeSbhHwMWMdX5h1IRdOPy0BLUpdEiSLpNVl6wr7gstvzx+yVE/AZKyLOpwex+hrF9s9drY1bcPsMNNpl3rTiTDNGVvQcKn9eK/u1PQpoz5ebno26TF2J8Wz18/tdbOp3ncCe/+B+3mt4rWok9GMZDKbAIvLwrtV71JnruP3e35Pvbmepr4mdjbvxOw088ThJ3i94nW++O4XY+od77Z1BwOgaAFSsqkyVQX/n+xB8mATuGitx1yvv9fZy9+K/kZ1T/WgKRLgy0W+e/fddMU49XK0wL6iqyLpX+toFKAzRvb9cbqhmnsq9vBK6T7+4ZnD3CnxOWFPdRIki6S1tWlr2Kjjo91HYxpAZnFZJuUsW5Fqe2opMhRR21M7qqAjlMFqwOl1pmSvSSrIc8YWhH26voDMtEwUit9mnRL22H/PuIj5U+fGtJ+zs08iPcbepJqeGnY172JX864xXcb3ai/LS5Zzx447uHfPvZR2lvJo/qP8x9r/CFvP5rGNuqfx/v3384213+CY6Rhfef8r3LH9Du7YfkfwfevW7phPGN3aTaetE7vbzh077uBY97GETI8dLw8deiiYm53M6Rbg+26Kxuwws7Zm7Zj2/XLpy2xv2g4wZAA80mpHZoev2sWOph3Bzpl2SztdttiC7GSiUHhHEeROt5nJ9Di5tLGQx/p9YxtOdBIki6TV1NfE7pbdHGw/CAw/S1yoVdWrEtWspHGw/SBPHH6CF0pfmOimiGHkOWKbZGGG1cTV08/iyplnc35X+FWTLzWX89EpJ8e0n+mkx1zd4oEDD7CtaRs/3/rzsJnxjnUfw6u9wanMh9Juaee9qveC1WVWVa/C4rLg1u4Bvbp9zr4B1SdipdHBANujPVR0V4x6XwBPFD7BN9d9k90tu/nuhu/ySP4jo95Xopkd5uCkQMneuzlYeck+Z9+IJ4uKFDoh1VB55QfaD4xov1sat6C1ZnnJcva2+ILkXmdvzD3RyUQphTN7bD3BlzQV8fvs0+PUotQlQbJIai6vK3j5diQ1eleUr0hUk5LGnpY9HGo/FHWyEJFcptnMMa/7xOGNPFmyi5yQFIA7p53Pgu5mflexl1tnXDT88bSKuSfZ7XWzv20/Hu0JXrnpc/Zx9+67uWP7HTx5+MmoVypCe4NLO0u5b999MV3pWV29OmnSG9bWrg3mPVtclqSegtjmtrG9aTuvV7yetCXgAhp6G7C77QPeD72usQWdfc6+sBO5HkcPTxcNHKh9rPsYB9pGFiTXmmu5bdttHDEeYVvTNnY176Ksq2zUbZ1oroycMe/jX/1Xtk5kEiSLpLeubh1lXWXsadkT8zadts5Bexm82sur5a+GLXuhJPV6YxM9OEbEh0IxzRH71K8KTbbbTo7DF0jmZuRwU9lmMr0uZth6+FHxJk7LXTTkPvK0jjlIhuNTwb9U+hLfWPsN7t9/P5WmSjY3bsbhcfDnA3/mg9oPguu39Ldwy4e3BC//B2a3i0VpVyn5Hfkxr59IkT2yydhD69VePqj9AJvbxpbGLTx48MGkr1vu8Dj41Y5f8T9b/ydseZ+zj+a+5rCp0IeqRBIpUB4w1KaGTexq3hW2bFfLrgHrxWJ703Y0msa+Rm7behtt/W3Db5SEFAq3yh7zfmZYTdyRc+YJnZ8sCSci6bm9bm7betuIc8yMNiNzcwbmcK6pWcNTRU9x07k3ka7S+UfZP3i88HG+fs7XmZ41PV7Njrv9bftZNG0RS/KWABIkp4rczBzSRpHrPdNq4jszryQLTYb3aHB5ptfFl9Nn8aRqGzSH/OrOZprnLebC6adR2jswsBhMaVcp4OtVC7WlcQslnSWYHCaeK36OGdkzqDPXce1b1/LKF14ZcXpTosvOjVayBcmP5T/Ge9Xv0ePoIV2lT3RzRmRH8w6mpE/B4/WQnuZre5+zj/reeu7cdScXz7kYg81Afns+j3zyEVaUryA7PZvbP3b7gH0VdhQyL2de1Bru1T3V/GL7L9j8tc3MnDITYMwpHeDLWXd7krvHfjAKcKWNPUgG+HbJRq6bNpdlZ1/Oe6aSuOwzlUiQLFLCaAamBQZihGrobeCBAw9gc9vI78hnb+tedjTtAOChgw9x/zX3j7mtifLXgr9y41k3suQcf5DskSA5FUwb5WXPdO3h14ejD3L60ZEPWHnB5TRHqeTy6Vnnc3HhBqa4HOyft5TSUR19IIPVwF8O/gUIr17wSP4jUac+T0XJNCDO4/XwasWrwV7jZAvgY2H32GnobeD0mb7c1j6n74rK+rr1rK9bH1zvp1t+SnVPNV86/UsD9mF2mFlWuIwOawdLZyyNehyHx8GO5h3ccOYNAMHaxycqhcIZpyAZYHa/kf+qL2HdrCyc3sROPpRsJN0ixFdnXZRyZ+ticBvrN+LwOHB73exs3sn2pu1UmiqDPbBbGrawoW5DsNdsdc3qQafDnmj72/ZT3lVOu6Udi8tCWVdZSo66PhFNG0Mpt6HkpUf/EfyU3df7dXbHURbbEx8spPLMdZGSKRDd0rgl6dMqYmF2Hu+sCATJkQJ54Q29Dexr3Rf22LLCZRQaCmnpbxky5e73e3/P73b/DoB+V/9Ym53UhquDrJSiX+XF9ZiLuhv59dTT4zYraKo4sZ4t8AnnwC/BwB/9h43lXJC3dJxbJBJlZdVK3jr6Fruad/GrHb9iWeEyWvtbg4+/X/M+bZbjOWcazcG2gxPR1GEVG4sBeL/6fb67/ru8W/nuCVHmbjLITctKyH4XZ0wbsOxjM87iqtbjqRnTXKOv/BCrZE2dGI1kCJKNViPratdNmqo1/c7jAetwKXNlXWX8YvsvMNmP1z+OtaSfV3tZXbOamp4aum2T+7vx9GlDj0lQwFr7xXE/7k2lm/j0zHPjvt9kdsIFyXOcduZNmR0s1L84Zz4/nH4BAPNNrZyUoB80MTFeKH2BF0tfxOa2YbKbwgrZR8vpbbO0YXVZk2r0eODLH3yzhFWaKvmw4cMJbpWIVW6Cao1+1monXaUzNWNqcNkfWuqZ33O8dNwFbUf5+qzhq2EIn2SoE76qehXPFj/L0e6jw6+cAizu41czButJDvBqLxaXhbcr3w4uG2kllBtX30hFd8XwK6awG5nOzKwZQ6yhWGlYiE5Ar+8fjx7g3txzuWXGRdw17XyumeRB8wkXJAP8wTmVB9Lm8/lZF/DX7n6+1FrJ7OxZZHkc5Ei6xaTSbe+myFgE+Hok6s31Q65fa67ly6u+PGDa24lUb66nobcheF+jh5xpSiSXXJWYIPkz1ftYlraQp5x5zMyawcysGSw1hg+4y3X08dPKg5ySs4Czpp0yyJ5EQLxOjqNd5Yll4PHrFa+zvn49dea6pAjY48FoNQZ7hkN7lYfy5OEnebfqXbTWI54RMhmuBiTa5xqPcF7O/EEfT1MKszsd2+z4nyDPsJr4j9JN/LRoHd8u2cDjJTt5YMpZwwTtqeuEDJL/pWYv/1a5k0cK13NuWzmnG6r4k8s3uGZaDGWTsgfJBRTJzaM9w5aeKu8qp8PaMaKJSxJN0ipSU6CHdzaJOfHOdtv5ZPUeLmvI5/aMk1nb1IyKMoBudr+RVRWFfJWB6RkiXLwC00Bt9wCry8r71e8PuY3JbmJF+Yqw6acngx1NO4JXvkaSK/xI/iO8Uv5KWAeBgDnZJzG3t4OPegevXzwtLQuvVhycenXC25PttvPlii38d9bChB9rIpyQQXI019b4BgssdA//JXnOMDVKTzRzxjizz3iKdSDMSGp3Jtpkuex6IslIy+CmaWcCcIk18YPnbizbzAzr4FcXMr0uZrocnJd3asLbksri1QvZ3NdMh6WDbns3dredX+34FXtb97KhfsOg2xQZimjubx708VSV35EfrFs8kl7hPmcfj+Y/Kp0EITLTMrnPnUua9nJh/+Cf97y0LDRQ55o9bm37evm2SXm1SoLkCEttQ5/pKhQXpUmPTMDJU+fw6ZxFLBni0k8qSqYgudBQONFNECO0eOrJ3FxbyJUzz+G87rHXbI2HSztqeKNkr1wJG0K8gmSTw0SXvYvG3kZ6HD3sbd1LbU8th9oORV3f6XEGKzxMNh7tocPimy11pLXdJ0tpwZG4dMaZvGObxidmnhO2fHpWHn/OWMInq30VPnJcgw+YnabS0Sh6vOP3Wc/0OPlo5uRLuZAgOcI8mzk4qC+ak7Jn8p36Ym6aFf+Ro6noS9kLuLN4C1dlDZy0I5XZPYkLkgM/GLEKnYZVpIaZ6VOY3Wvg50YDc3uTI3VnoamRdO1hZlZ8S0NNJl6vl38e++eYc5MdHgcFHQW0W9vZ2rgVj/bQamnF7rEHT8DdXncwDWFd7TqeOPzEmNufrGp6alhTs+aEngBpdvasmErMLk6bwjnt5TxTtIUlOfPJUBkoFF/KPY3rjm0Prpc5SE3vaZm55JGGV2v6POM7pfQXutpZlHPyuB4z0SRIjnB2eyXXTD9j0GkYz5w6l4WmRq7plYFTc7JP4gtttWS77Sz0TI5BJgGJ7EmOZcrUzQ2bWV6yHIg+KcpkdemMM3lGLUypFJ5o5qZPIV17uLj5CHn25Pr7nZE9fpdgU41bu3mu+LngNMfDTS7i8Xqi5jE7PA6WFS7j2SPP8uDBB4PL7W47a2rXAL4plu/edTde7eWw4XAcn0XycXqdrK5ZHfPAvVSRMYLKNV+bspjrZp437Hrnu3zvuTTt5WGTlXUmN0+rBfy2MHxioYwoJ3IKxZlT55OnwYvC7h3fQgQfbyhgVWUps7NnjetxE0mC5AjZbju/qS3mD65cbp55MTOzZpCbkcP0rDx+MPMiPu71lYibZ5Eg+V5PHue0+0rtXNA3uXo7jTYj1ab4X/7UWlPaOfQcaHa3nTePvsmLJS9idVlPqCD5aj2Fq2v3My2krFkqutqRvJNAPFBdTF6mpIxF49Ve+px9PFP8DMtLlvNS2UtDrm92mmnuG5hH7PK4cHgcA1Io7B57cLKMo91HsXvs1PbUJtVA4UQp6CgIq0uf6s6adgor+9I5KcaA8OquVubp4QsDnGs+/lt6QWspC02NXF07cJKrjChTZmelZ3FKeg55Xi9aj3+QDDDFZeP7WQs51z/+4YqZZ5Odns0pOQtS8ntHguQo5ve08MnqPVxs7ef0qXOZnTWDa3JP5f81H+Uyk+9S+SzbiRO4RHPNzHP516rjPaKLzMdTCLLSslJ+Vp7V1at5p+qduO/XaDOyq3kXO5t3DrrO/fvvp7SrlD5XHz/+8McxlY6aDNJUGld0++pYT0sfn3rl2pHURAAAIABJREFUiXqfnjrEoJqJNqevg9+mT64xBPHi8rqwuq2Ud5WzrHAZywqXDTlZSq+jl431G4HwXufBtul39nOg7QAWl4WVVSsB+MPeP0zorIVDBXmzIsp65WbkcGVErmysJsPsgaHOzpzO6YYqPpmzeNh1czNyuKSpiCvMXUMOnv3yrIu4tDG2MSiZUXqSs9IyOUlDnsf3XrRNQJAM8N3i9fy9voqn0hbz59oy7s46lRvTT2LxlDkT0p6xSO1IJsHyXHYWp01lZsZUFnsVSzrrubjZN/PZ7F7DCTkA5tIZZ5KRlsF/dYb3fCwwtQQvPV01/Ux+OP0Cbpx1ESdPTb0PBUCfq48P6+M3YUegiH5rfysGm4F799yLxRW96kF5d3nwsWJj8aT7cRnM9TPP5yNNRwBYkJ6T8LqbF04/LWHVHqY5kjv38gvHdqZkr06iRUudMFgNwMC0J7vbzp7WPbxT+Q5e7eWpoqeCj9WZ66Luv8hYRK+zl8+987ngLJrFncUT2pP8a2ZH/S37yqyL+PaUJVwdMlnEJdNO4VJ9Yk+4dfXMc1k4dR7/1uf7Tv9B09Fhp4m+bcqpKDRX1+7nE+nTSVNpUfOTP+JwRi3jGE20dIustExme7xMc/ses3smLsSb29vOv9TsZZ65jU82HuEHxes5OT1nwtozWhIkD+EMUwt3le/mO3bNfJeTdO0h0x+wZHpdXDBtyQS3cHzNmzKHF4/s4P8yFnOWIfwyYrr2sHCKb/Det3u6WeJ0cFfJVi6ZkrpJ/CMtYj+UHc07AIKXG7vt3Tya/ygvl76M0+MMrlfTU4PROvkvvYbKycghOz2bb7Ufr4f6v+V7uGnqKcybkpj82cy0TF4/spNT0nMTsv/cJM+9zPC6uTnntIluRkoIDLRdW7uWdks7ALuad3H37rt5NP9RWi2t5Lfn8371+xztPkpzXzNd9q6o+woE4b3O3qQ4+b10xplc23iYi/ylu9JUGv82yzcD7cUOJx/p7eSLdjen5i7k4azTubupinMsvSyYenygdkaCJstJVt/r6eFvXX38a9VuAJYaa4bMwT0tdxHfLDleN/sCSx+/yT2Hz0SZqW7eCE6uM6OkW2SnZXKSy0mef0zNRPUkR5rd7/tNW0rqvVdSr8XjaIHJV7rp88d2YM0e2OtysZpK4MJIhsrArZNnKuN4Wpq7iFvd2RyZMo10XcjnjkVPFViSNYM+t5ULG44yb/rJTHHZmK+T40M6GhaXhX8e+yf/cc5/jGk/laZK1tet59pF14ZNFBCYevWCORdw2fzLsLvtPFX0FD2OnjEdL9XcOvU0etPSOL96XXDZNHsvC5wOPjt1Eas9dvoH6XUfrTNyF6KoYUoMkweNxjRH4msjj9W1xkaePrE7BWNyqOMQp888nYcPPcybR9/klo/cwr177g0Lcp88/CQGm4Hvbfget1x8ywS2dmS+afMww2rieqdi/qwLyUXxu8IPuOLMs5jrsHFl3UE0ijmnXcYn6rYDcEpnHccu+SJ/txn5Vd4FrFdWynqj95xPJhkqg/PyTuGS8gNMcYUHszdnLeBxR/R6zmdmzQzrHb62Pp9Mt5Peiz7Hpoh1p7liD5KnOvqB8KttMzJymOJwkef0pfskS5AcMMuTerMhSk9yjHIcA3uG/sVkICfDd/ngmhlnDXvJJRXNnXIST3YY+MLR7Xy5Y+gvwtPI4iWTg2n2Xk43+ILBue6hTxyW5i5K2rQVjR5zjWK7286q6lXsbN7JbVtvY0vjlgHrHGo/hMfrYW/r3qiPT2ZnTTuFzzcW8/X6ogGPneS0scjlZnrG2Hp701U6F08/PWzZZem+H5dE/ISck3cqsyzRexKTyUx730Q3ISX8vejv3PLhLXi0h/reev5y8C8DeoGLjL73r8VlYdnhZRPRzFGZb/WdkH+lYisXuTxc19mOQnPN9DO4vMn3nBSaj7SWh233kyMb+K8ZF/FvTWVMPUF6kr868zyeqi4ZECAD/LB4PeflLQ1btjR3IV+ddRHX2sPfK1OdVjK8bi41Gzhr2ilhaU8jSdOabusdMKZibvpUMrxecgI9yROYbhFNlk69utcnxrs7QS6rP8TPLrqOl9zt3GCx0j39dIp7aya6WXH1L1MXsdTo6+E7v7VsyHW/X1vAPHP46OUrOhth8LLT3JA+i6682bzaUzzmtiaCwz34oJ1YlHSW8ObRN4HBJwUp7Cjkw8YP6bB0xG1a3FSgULxUXUqevZe0KM/72hrfiO61F11Fdu4i6iy+QX3pKn1Ekz6cmjOfj6XlEniHzcyawcVW30lvIoLkn1tT42843doD06dPdDNSQkV3RfD/w13pGWuN5fGSlzmNUw2+yhxp2sv5PR0s7WoE4LHC9WHrRnYSpWsPPyvy/S5MWTowbWAy+m5j+ZAnv18kh4qQ+1dkzeaewnWDrj/DbuWG6XP5cGomtuyTmJUxhek9lTG3R6GZlplLr/P4ye5clUGm18NUf0+yJemC5NT4bgyVXK9gCrq5ZCN3eafzyZoDXKlywgY5TAbTR3DiFxkgA5zbVj6gFy/UZd0tfNHQMOjjE22oke2xaOxtHDb3sMBQQFNvU9gP8WQ3PSuPb866iBm2nqgBMvh+iNO1h3u7zTxu6GTh1HksnDqPJ9SCER3rgqyTOM1x/O94fs58LjLUApAR544NheKquuizqiWbaKPjxYnj0twlYUHfxc3FzLSOfAroqTFMkDEZTLcOXdHqGxU7wnqTP24ZOuVqbr+Rf20pZ3Z6Ngsycll+eDMLTY0jatPMjGlhczrM9kKG18NUf2/3RJSAG0qmd5IGyUqp65RSx5RS1Uqpu6I8/imllFkpVeS//T7+TU1OCs3nju0k0+PkPyv38RE9vjPcJFqed+xRxE3ONE6ftpistIEJkCdZe/j/7J13eBzltf+/78xs72qrbluWLcu2XHDDxhTTiQGb4gCBkARCIKEkIZVLSSD5JSG5uclNQvpNCLkpNzfcNMB0AoRu3OQuWb331Wr7zr6/P1ba1WqLVqstM6v38zw87M68M3u8mp0573nP+R6rfWDen5Ep5uskt9tnnwD4A348cewJvNcnD+cqHdRrosXx49HQdRg1A034TU8f/sPmwfLh5B8kCk6BC+3jOKfjUCgdahVVomLyYZTuR4hG0EAxrRBTypBZGmUw8pvLXJHXabzJ6mysEPMvzXA6hSoLluurYXQlXkFQ+1z4ylB4klHhSDzhKJwYRNVwOwrBw5piyso2VTFqVEEFKQICi+iPdJKlFkmW4T1n1m+QEMIDeAzAZQBWAriBELIyxtDXKaXrJv97JM12ygKzcwSl3vk5VVJDn4ZE+50n/omfd7ThN+7oBhF6lx2F9sE5dS7KJh7RM69Wqh3jyTl0Y56xkNTUQqAgxoRpNkrHurCquxHWsW5okmw2ohU0OL/pdRRODKJGXwEFp8CZY+HvOd1XnU5IkFskMXgZRnUY6WGJrgI7T7ySlnN99MhL2GxanreSgmdoy/FpZyCpScTKnqOhe5PZmVwBdolIcf5YajUMdx3/V+heyhMeJr8PKtEPjXcqkiytCUxeOskANgNoppS2UEq9AP4IYFdmzZIvGn/uZX3SiXGeOblAMEJhtfWgYNpylZpXgYBA77aDowEUS7SNpdPvxJ9O/imlY11+F97uje6UxACMSXSeigcBxTWGZaH3BSpz3LGaaUWhF/IWXGyqw6reE6FtfJrTLXS8fJxkMoe8bkb+YFaacIYyfW3flaIHPzj+Dv57zIdyTUnazisVqiiH9d2J63GmIKC4SVcLACicSM7xva75nZgd9ZLB5BqDEQQEBDzHw+DzYGXPUfCTv22RcqASauyVr05yBYDOae+7JrfNZCsh5BAhZC8hZFVarJMhahleBIkw+dIXGTdPy3/7gnoJnvAXhHSnrQojKrRWyTUfaR5txs8O/wwj7uDS2U8P/RROX3L6yb6AL27DkIXOXHLdY3HHiTdCr+/krVGpPDX6YBcszbTtV3Ydw8OHX4bOEy50EdJYba3mVdigzIyucybgZVhEw5g/xUoj6r3pDeboPHbUDDSBl5BDli4K/SIM7uQ77N56/DX8iK+C1pvcvX++SjiGAGBSBhuUlE2MTDYZmRaEkNAqbSxtZ6mTzBUdK+Qz88myH8AiSulaAD8E8NeYJyLkE4SQfYSQfYOD+dkwQSmTfMTZ4AmPreY6lE7MvZAjHlqvAypehQKVGbtOvIZ1nQdC+6y8GlcoSrBaXYLtEip+pKCwe+3408k/weFz4McHfxy3UcBM5FLlnmmmO7BL9ZUoUhVgT/v81Ey0kzrERaoCbO07hfvUkY0xHh5zwaDQwzAtsls91ArVpDTSFOnMSb7CuBxfTVDNLjVSzUHNFOeZY2XxMdJNiaDD1r6m2QemQKwucnLHOEfHTuex49zmN2YfmCYMgQAKlQbwhENd32TxN5nuJEunTkohwyBiMk5yF4DpreUqAfRMH0ApHaeUTky+fgaAghASFRKklP6cUrqRUrqxuLh45u68QC3DmdIUWkGLKm0pClUWXGKux88PvIDa/pNp/QyTwoBblOVRzspl9gmcO9iB+04fxp0D0SoZuebg4EG02dpAQfGHE3/AuHccQPz2swDgE/Mr9SZVtNPydPdQAx4S9aFGPamiEL1BcX9NCSpGOnHt0RfwR68JdYZF4AmP5f2nsFJXjsWztEHlKUWJuggWpQnqeeh1V2lLcd+h9LUxzxYzdVZzyf2tjdhmrgMAfNi8BlsnX6fKMn01Pm5ugEJCToIUsBIFqocy0/yDJ9LKgU0HUl9x0Yt+mHkNFBHFf9OdZOlMXJQS6DI5V5K5Q74HYBkhZAkhRAngegB/nz6AEFJKSPDXQQjZPHle6avpZwCVX76R5I9pa/CT/iF806PCNw88m5HPMAtanNnfFrX93NNvYXV3I6y2HtQONCddmJUtRlwjGHQFVz9+e+y3ePLUkwCAkyPxJxFSaDsrBabygnnC49K2/dgx2c51vpRpirBF5EMR0VXdjdByCizRlUPrdWARUWGJmDidQgBFqdKEXdpF2GlcnrItH+IKZKNqMR1OIiqgWkGL0rFu1EGJbeY6fKRlP+oxv3aAl3BG3H1wL0oz1NpcDpxjro9KYbNm0OeTyvWUTjiJN8BQBwJQEQ7bddUx91MJTRKVMqzZmvWKppT6AdwF4DkAxwH8iVJ6lBByByFkqv/mtQCOEEIOAfgBgOsplfiVlSE0fg92WRpQIrMb802WNbjj0DNYNNSCra3vZmwp1sKrUWrvj9ouTEtNUPtc+Mhk8YNUGPWMYtQ9Gnr/QnswatjjiFhUgU/04fhwcMmLOclBpvKCyzTFKJxIX5rV94YnsLM1skGLkSiwWREsAq3yB1DjTNxVTqCAkVPgcweewvaJcdxjXB01Zpm+Gjss8VMB1hiX4upT6XH8s41UIsnFk8WXV/Q04yM2OwrtA6h1Byv01xmXpnROTSCoSHAbNeIiy8Isk/noyDA2q8twi7kB9+nroeSUuLKjMWOfl4+RZKk7yaqAD0pwuGq4L7xx2t+BSignWSnDlfakvr3JFIpnZmz76bTXPwLwo/SaJk9qBprwyOBpXLFyU65NmRWBCPDT4EVbMkv76HRRwalgco7OOu4Th5/HzxdXSKYDXZ+jD997/3uh96dGT6FjvAOd9si0ge6JbjQONaK+sJ7lJE+i4gQIRMBaVXqLMuv6jkVtu22oD2s7DwX324dRNkuFuZIGcMN4sJvYhadeh0NlwA/KI5VWLuJN+Fjjy9ixZDEmfA7ssKzEYsqjFSLKIOBTJ9+M2bZeDnAScWr0k6sNSweaYDCVQQj4saGvGYJFwK0OH+5O4ZzayfzHq469iNIlmyG/ZJj5sdG0DJsOvoRh9bnY3n4Aevc4bOt2ovJ05vLmuZglTPKGiyrBkhYavx8qwmFj+/SAgVSdZDmutjHSDkcDUErowpwJAUGNvhKfMNaHtunSoIecDOvcyallKAI+FKnSJ1OUDkY9YefeI3qwt3Uv9vXtC8m8DbuGcWDgQChPmUWSg3CEg4pXYnEWLrFlA+G28Cv7m1A11JZw/DldR3HO6TdD73UeO84w1YKAwKg0AADO62+F2ufC4+ME51tW4myPiHsPPI0fHHgOn2t8MaUuZVKBSCSSrJ9cEuZoAKVjwfbj5aMdeHKCwzmn35yzBq+SU0IzLWq1tfVdnGuuT3BE/nGDK3j/ufTkq9C7gzUU62yZLZjPR3ULqeckq0UflgY4kOnO/LS5Ck2xUUkmUMiwTif/rmiJoJrFSVbzKjygW5GTauCl+kr8V9tpfPTYKxAmf0D6LF28lzS/lfTYbZrk2w9fZoleJs80fzv9N3RPdOON7mAl8/GR43jozYfQNt6GTnsnc5In4UGgEzQwZKGyeXpE1+QcDemFxmPKIZvOT469g79PCHgkEJykWVxBB6Ou7xgebXwV64eCDWIIaFQBqtyQilNTSWIXTdYMNIOjAazUlc/pfI8oqlA/0hWx7YHWI6gzLAIQzIG+NE9TMKbUZKptQ1H7Km19UdvSST5GkonE0y0qxvvxkWP/jLtfUpHkNErKZgtp3CHzEFUc5/dewyqsNS6FWWnEdUeex33aZajSlkaMMSj0oZt5JqhU6FFk74fG68RKQzX+x2PA+affydjnTSdZ7UgA0FOCTaZlUdun96oHgAZjDa4ejX4gZJopR/jvp/+OAA2ENJHbbG3Y17dvXp368gkOBGZBB6NMija0XgcWD57G+p7j4AkPzTRdZbXPlXbFl1xCcuzU3GBeg9tMDbj3eOKc7ku8BFvMyRdWVtuHUDMQKXNWOtaNb/f1Yo1xKdboq7DFI4/rMVk0ggaXW1Zju6kWuywNqIqhIFM21hPjyPSRjznJ6W44lG4qRjpiPFfDfwefIJ1OiHJMt5DOFCPPUMaI0Cg4BXaffhdH6zbDEQheLNcdeR6XqwzYWl4ACgqBE/BSWxu4QAt+v3IH/sOeXKefZClQmfHZrvCS9PfaTqLEJj3JNQAwUIrNVIX9hIdIRQhEwMeM9biq8yg+bi3BgHsEn9HXYZl9FBXj/YAxN3aOuEfQZe8KNRnpcfTgzZ434fQn13Qk3+EIwdeGR0MRWblQ4BjCtxSrofV0zT5YpuRS11bDq3Fry35YbbM7blcdfwXLKlbhnSSfWPGq6GsGmrG9vA5GvwhDnv0+l2nL8I39ezFsKAEojWiaM4WQ4ToJPg8jyVLTE0+KaZMVt8IEXQ5NmQ6LJDNCqGM8fOr1VbA4hqEDB/20Bgs6jx1mZdDDMyr00HidUPnduKzjMCq1pWkV2d+tqULNQHPovVQdZAAo9nlR4XVj1WRUfbm+EmeN9KFquA2b1VY8oKnBpqEObGt5G5XDHSjT5E57+/3+99E23gYACNAAnm17NpSGsdDhQLCy5+i8tZFzwaUn/xnqCpmP5LJwr0RtScpBBoLOnd4bf2VGmJF3qUoQsSr1enBp6/sweOX3wE7Eh90UBBRF9n4UTQzkxAapFIKmE6kX7sUm/Hdw8oYc2hGJPGUyGRlhRSDyps0THp+yB/MXTRT45HhkFMOoCM71ylThyvoSWy82q4rxobH0FAZZlCbs6pbPUvHFLe9hkX0Y20nwu9nIG9HQfQQAcH1/B645+iJW9gQj7QQUa9QlOVs+/v2J3+Pxo49HbBNnyYddKORjdClfyJWurZJTokCYW3xL74mfqjW9CBlIrFd/SfNbKJwYhMEr/0iywAkwKPQo0xTj0pOv5tqcvPytcwEZRpKn/R0miHScZAIKhcyuEeYkZ4grO4+iQmsNvT/bvBxntQRVEC7ra8W21sgc4CkJpFV8OH+IowGsdXtQ4hhLi03LNNaIKLLUMbnGsKbrEK5p3Q81r8J5o/1QisHoz6ruaK3P7+x/Ft9WLonang1OjJyQjFyd1MjH6FK+kKu/jVlpQA03t4ZBZsdIKGI8s9nQZd0nQmokAKD0xS+onMrfNCRwuuXANZYG/MJvwXf8RpyrTr7IOZPkY+GePCPJYWyQTk4yAChldo0wJzlDVA234Wf9YY3WJTQcWa7vjdZ3NUymXxhn/B7XDbWjcGJ+RWkaPtgWeBlRzzJSmpTYevE1vgKb2t5LOI6AYmPPcXzS1JAlyxjJkI8PznwhV81EtLwKW5xzc1LVPheuMdejVl+F+4XKiH1Gpw0VqgI0GGtAQKDyz55KYZiWI7/BtAxWTRE+JZN7R5mmGHefehcb29/HtpZ3cI5NGg1upRxJTjX/npe4ukUs6LTJ7yiVlpOsoNK9RmLBnOQMsmioBd9R1gAAto0l1qcsIkGdUKMYGY1cMtAMk3M0Iudurg+2T2mDHauWyjgH75zWxA7yFEX2fmwYi+7ox8gd7CYjXXI1geEJh20dh+Z83AP7n8YTTUewpfcEVLwKVk0RzjKvgMk5ii/YXPj3jhZ8Sb8C+hhFazMxTmoHa3g1fnByH37X3YezhuRRpPlxrjDUvZKA4uzTyUtrZhIprxrVG6qxTB+7dXMiiMzTLYZEbQ7tiEYp3UskJuz5lWEuaH4TBAQWZ+LK/lo/xQWWlbhiRptdgmAxhkVlxGJdOZbrq1Gmmb1zmWoyfWOnZTXO7z4ebAmcoPBF6sxFOq7cnlnBfMbcyEdZqHwhW5HkmbUCHEhSnTdjYXDbUDrWjT+NA9crSvGxsaAe9qb2fSgf7cCNjc8ldR6F6IWGV+M8Yy2MLhusth7wWdDyTgerRjIr5ZYqudNKiY9BoUeF1ooaXoebxNh63NNTdWYi9WYiMZl2zx2UmpMss8A8c5IzjEL04gv6ehQ5Ei+HLXPYsNEXrEyORaHCgO2KQtQoDFisnL0T3fnGZSjVFOMrR/4Jq60PDeoSGGTsJM+FstHuqGp3Ru5g6RbS5UpFcVYc5RJNYcR7IQ2fWTPQhHP6WrAlyVWmWHxUvwzf3v9M6L1cHCJeotFNjQTd5GXaUnzZo0KNn8Li82CruQ46QYsGY01ozJc5a0QN0XQIpPldJ0uPVyoCcEGYk8yI4sONz4aWxuKxfLgD5e741daFvAZbx0dRHeCwAgqcZV4Bo9KAKy3hHLqp6DEAbPL48ElYQnJyX2hphNUh3/a5c0EI+LHGuDjXZjAmkXKe4kLn9kN7oZhsCZ1J1qmtsChNoffp6vS3vP/EvI7/5MFnIt7LRclgtk6SuaLeJz27tERAgXscdY4xnNXyHn568CWUqCxYzgedRw2vxs4T/0RtnOCTHHOSp6dbtLh0oDnUQ5+JQmaFkCzcJhFKx7oSVmQXEAHLh9tRO9KFCaUWn+k/gb/VX4CzT76DIzXLYebVICDY5SX4tr8bF7fui1jOLB2TR65duniopwu7pTWBXrCwmbh04WgASk4Bj5i5egWe8Pj3/Xth05ixvTSoB89L5KogMx7Y8okkZ7YpSKpsGuoAJFYfriU86vpOQen3hP7edUozSib/1Iu1VnD0FFQxJm4aQQPOI49rIpKwk3xyQguxoBSCvTuH9oRhkWRGyhQ44qtYlAcICu2DKB/tCEVPdh1/CQWOIeyBEb9s/BeudVNcdexFrNCWp5zvly8sHTiFApU512YwwNItpI4yw5FkgQvGYkyuMWiFYH6kVPPUOYlGaGciSDR3WpMg0JMrDOCg8rsjJkT/79DLMIjB7/AuR/D/6hj3qTptuUw77oVfOkQOvab1ubNlBkqZfZ/MSZYJtxx/NW63mpsan4VC9GLHZG7eZqlN5XNEoSJ2MQZHOJxhqg29r9Ba8bBmec4akeQ7hew2I2mUXGYXFBXTzr9HXwurpkiyKTjyiSRL006NL7t1L8nk0180Fl0PpBQ9MPh92GJajlV9pwAAmhjSZCt4HThZpltE0knKc21CCKVEr914sKeXTNB6JmYdo5uUPdoyIt1W09nEMC1H+yLLKtQZFkEnaPFpfT3+vSWoVU1AcLVQjKuPvYirLKtzZWpeU+uWXnSJESbjkeRpRbSfP/AUShUm6UaSJRqhnYlU0y00nux1MdQKWtxlWBl3/xmmWvyEq8Dmtvdj7i9xT2ADVLA4g7U6sUJLZX4RBbJclY38fXmQ+bqDZFHIZCI6BXOS85CGruhudAuRTdCAJzw0vBoPH3sTd7ooGvSVuOXwXhSP96FGX4lPmlbjio7g93Xn6f0wKY05tjp/ONdcjwKVGbXjTJJPyigyXNQjzDi/mvCSyUmeiVyKtARRmk6y3j0OgyI7zSs26RdhpT1+MbqVU2P76begCPhi7t/a+i52dh0LpVPoA9F/ewWlKB7vS4/BWYTOmIR6qXTKz5SiPCaiU0jzTsWYF/FuCguN2xtfwC8CRbhDuxQGtw1V4wOoIOHo8rmCBR/oOo6y0U4Awc5+F+gW5crcvOMbJ97GWm05agdbcm0KIwGqDKdbCFykk6whPASJplvIJZIsSPQeT0CxWV8doWSSCZScEldNOFHiiB/l1c1yjRFQVA+1ht5bfdHpjHKLeoaZGUmWkJMss++UOcmMvEUherGp7T3ccngvAMDotqNcDEcLruxpRsVIR8QxxfL6/UoWAgK9245P9naE0oAY0sTEKTNy3gZjDVS8KkYkmZNsuoWUm4mUaYpDr6Vs5+faT2Cltiyjn1GrK8cFTa+jfKwnbrtp3RzbH1c6xoLn1leFtsktfzZM5L/dFZBOuoVc8v6nYE4yY8FgdI6hwhMuLKntPwlhRm5foV+ay5hyQ6fQgqMB1Pcey7UpjFkoIkos1lVEbf+QZQ0UnAI3mteEVCnioVfoYNUUYaNpGR7UrQABwRdHxvBBQ11U4xA1OJRLaPl3OlKOJN8rGkJa+FLNSQaAquE26FNI4SlUWVCdhHOt5lWw8MEMYp3HjivMsfOSjXN0cOsGmvBR8xp8wSFisS5Y6CbfSHIk7ztLcm2CbGFOMmPBoPa5sGGykjkeJr8XFqUpKw0W5I4ugeNkylJeImNZe4KpAAAgAElEQVT+3H/kn9jFWyKuebPShPv2P4V7dMvx5QNP4T5FJeoNkalI0xsZrdJWoFZVhPt7u/DBI8/jtX476ntP4LrOY/hZd2QL5QqRotonzXQBKTufVqcNlxiX49909VD6YysdSQV9Cq7FLk0lPkKi0zQ4wqHBWAOBCOAIhw2GJagi4dWP8+y2qGMETsDlHUfm9PlGlw2fO/AUtrW8jRthxJnmOqzvb57zv0MSzFipebK/BAFtUY6MkTfMSWYsKErHEguqmz1O/L2zG6v01VmySJ5oBA3qdOUoUUfeeC+zrMZ55pUoFpiTLBe0XgcKfV48PzABraBFgcqMbbrgkvPVTW8CAHYfexEPDY+FjtlgWoYHG1/GdvOK4Huo8OODL6K2/yQAwOwcgcrvxqKhFpSPRqY0bRzrh06ihWc8DUi2eFcZ8ONDfe244chzUStgUsOYoP7xrMlrJjRWacBG0zLcfeg56GI4/5ebV+GrfX24T7MUpeoiXONw4/qucLBjiS26sG6VflHUdTcXrj/yPH5x4AVUjqR+jtwS6SSLlENXwdYc2SJvmJPMYExj6UgHzM4RFE6Tj8tHClSWeR3/fPcgbnT5cZ+ow4fNawAE85A/29qIs70irvNIM+eUEZstvadQZO/HpYaleEg04dH9wXbNRlc4Slc+1os9lgb8ya3Hl/t7ofa58JMDz2OruQ6fPPh00k0XVvUeh0mCTSeAYLrFZ4QyaARNrk2JQuH3YlXP3KKjucLqi+3Eq3kVdrnCjvDZ5nr8rWcIX+9oghDwo2piFPcaVkUcs3N0CMv7T+Cq4//E2Worzuw4hKUDYSe5crgT10xb1QCAFXzi9KB8Z6a6BQA0k3DgZ7js3GyaI2ukmRjGYOQIqy2oMa2S6fxRr9BBL2jR54ovu3ahZRVM4PCkJzX9T57wMDtHUOqwoa7vJJaby/BbA3CNZTXKWp/G5c7RpHS9GdJhKur20IFnwcfpOlfgGMJD+5+O2n7LWPRydyI0XifW9hyfu5FZgA+IuLjlXbSsOBvPuXug4Hh0O/vjjicgoMiObJxKlGaKSiys3th6yUaFHqsG28EZOQRoAB8bHUGRPfz9ruo+goauw3h8xRqMeEZRoLLAOhK8TykCPtze/D4M7sjrTSl68OXGl3FgxXq0THQBAGq98vmuskW7vwBU0IAqdNjHr8P5pk4obEx5aDbk6QkwGBlGI1GJqkRwhMNvbBQ1qoK4Yyq0Vnzn4AuwzqMeRT0ZZV/TdQgqvxvVQ61Yb6rFgweCKiLMQZYv8RzkRDSk4PCanfH1bXMJH/BD77bjiweewkvH9+NPLaegTKD+YVGZUaIujNqebq3gSm0plDJykkucsSdOBkGD6qFWPKBZhm+oa7F0sDViP09FEFB8lisGRzjcqiiLiBrH0yxW+1y4lA+vjqllpsWbDX7WsxT7ym7AJd5H8f2+BjzI3Qmq1OXaLMnDnGQGIwZqefQUiEDDq7G8/wQKSPyiw7tEA4SAH4X+1B64al6FIpU5avsZRJP0cjsjv8gniT8h4I+4jo0uG0pjOMFTFCr0uEgdVAaZnmtrTpOTfJWlAbeZGvCXU43QuuXzPVvtg+AJj5snU7Gm0HPBCfaeoy/giuMvo8AxFPP43cdexF8mBNx45IWkP7PGFZ6cqyWa8549ooM8fR4l9jRdiCaHBscntNg7VIxt3sfwfOU9ObBPPjAnmcGIQawWpVJHxQcjXjUisGKGEkG1tgy3mxpQMSm+b/G6oo6fDQ2vxhmGxfg3R/QMYql77udjMOSAddLhjdUgY4XCjI+2HoRABNw0bocw2ZjFMotkXjIInICvHHgW9xx8GmqfCybX2OwHSYSi8QEs0pXhCweeioi0G+agGlQz0DynlY1NXUdRrglKnWlkFHXPCEnokNt8AnrdSnyi+Uw0Vt8EmuHOm3KFOckMRgzKfD5wRF4/D+XkA+im4//Ejf5w4SFPeHzRI+Cug09jXedBAECBO3bO4EwETkDBZOT4CuMy3DI2hm2t70SNaxhqn6/5CwKb9cxcm8CYIyWcCgQEW3VVUfu2OV0oHevCBeY6NPSewL3a5QDS06Blh2l5SukvUoCnIi6YTH8oU4ZXngwkc2VQBY4hPOIKOodqUdoSeVLjilMfgLOoYfaBCxB5eQEMRpa4uPV9XGFeNftACTHVXljjdWLnyddQoi6CWWnCbvNKnNv8BoBgK1YAsCQRldLwamw3LsevRtzYbFqONS43trS+F3Ps4sHTafpX5CeUV6G1cjee5C7KtSmMOfKZlsM4w1SLa4cHQtu2m1fgIW0ddp54BQBwV+cpmJyjuObk6+AIh+IZKU8khRqHa8fkEzmOxe1HXgIQGT02zrEL3lzZ0voe1LwKZrcjo58jfeb+Pbeq5fW8yxbMSWYwYmBxDOOuloO5NmNOKKdFaRSiF3/o6cUKrRUrPdFRlULHCIxKQ9xzVWiteKF7AI8efR1LB07hPFFA/UhijWlGfLymxbjw9LV4pLUeoq401+Yw5kDpWBe+23wYm9r3YbVxCXSCFj86+BL2HH0hNOmcmiRqvQ6sNizGKo8XFVoreMJDI2iwRB/d0TARy/TVqB7rmX2ghFH5gzJ/BhJejTJkodbjbs1SLJvU616o0BSc5Ne9y+E118JmPRM+05IMWCVPmJPMYMTBMjEcUnKQAomq7AFAPSPfr8TWi0qiws7mt6PGGp1jqFAlKEgSdDC5xqD1BiMy60e6UTMo0+5TEmBQWwuRBm+37RYm6i83CieCkorX+RSo1pQkTIM4k+hwcev72KoqQYWmBFZVAeoU5qSblJxtrsd9dg+sY71psT3X7B4dxvXqYKqKYY6tolPh5sZns1ZELNkJbxI5yTP5w0AV7hE/jbXt92DjyMOYKF6fAcPkB3OSGYw4qPxufFqzNNdmAADqDYtwlakOekVQsmeNsQYGhR4VWisA4CbzGuwMRDv0H+04HlN9gICiXoiMJAvTItHn08hmCqu7GyXf5UvKdJOy0Ot3AisQ0Bbn0BpGquw+9iJ+2XQo4Zi7Dz4Ns3MEa11ubFAV4T4nxXIRuFS3KOFxUyyHgOUDzVAE8qP4bFvrO1hlD0r+GfJIdSKgLcKZ9kfhLlyZa1NiMHcnucOlxnNDQflQm0/AE9zulCLS+QZzkhmMBJh90igAWSoY8MD+p3GhvgYl6kJcSNV4tbkJX/KqsM64FJ8/uBc3Nj4Xddyiofhi8YUzgi3nmoNFR+dbVuLWw3vTav9Cp1ksCb3+964VuId/MIfWMObD9C6EiVg71IZH9j+NbS1v49LOI9hqH8cqY+Jl7F2WBmwbHYDJmVqjH6limmwuYojRdlpu+IyL4CxaiwOFV2DQq8CrmuTqDH5d9iAGy8/PsHXzg07LGf92+zI8U/mZHFojDVjHPQYjAUqJRE+LJpfqL7aN4AaXF1ygA4qADzua/oWNalNKVfCaGUuSZ3lEvATgjt6OdJjMmMYpd7jRwbBXgVdGLezum+csHgxPUCtHOkAAXKlagaNxxmsEDT7U14aVPfFGyBerfRh11Uugccg/Ov4Pwwfx1NhivNMXTJ+5t20jjqg1IP74MpiirgTfaF+BbUtPIjtrSOmJAH+xdQMuMxaCcw2n5XxyhEWSGYwEqCSyPLhpPLhcefbpt7Cy5yjq+sJdzma2aU2WxS4HPmBZDSDYJGT1SDcsShNqBlmr0nQz7IvMJ3f4eaZLmueQGe2qK0Y6oJqRk7vBtCz0+gz9orx0kIFg8ePtbpIX0mzP2arx8rAFDn/w9+vw8+i1nht3POUUuIt7AL4AwTjS24kx7memKUvCIXL4nuFenKy6Du6C+vScVGYwJ5nBSIAUIskFKjPOaonUJp75AE6Fi069hq8ceRUAsNGwBHqvA//uUYeq0hnpY0yMLrpkeckLD1UgEGquYdUU4XtNYQWdsgSdMvOBVYNtUKfY6VMKUE4Bv6ES/xyxRO27ovMG+A2VMY8bsW7F3sEiAMAQTa54c/6kL5f4hx1LcEnTLlxh+zyoYuG1sWZOMoORAKUEIsmL1UUZayqg9TpQrS3DnQO9UPs8qBlqy8jnLHRGvNG5FS9YrgPlWM7FQkIV8OFCTVAOrkDQweIYhl6hwyJdOW7ozm+t8RJbD3Q++U7AO8svxTOm6+AJRLtNw14FfqS5A+Mlm0LbJorX49dlD+LX/ktD29p80Q62XGhyaPDH4ntA+fk3ypETzElmMBKgEn0oVOX2xnZRjChkOtmhLMbq7kaovS6YnPJuYCBVRnzRUcI7mrdgf8WHc2ANI1eoRBFrnRMAAOtk6+qlmlJsVhZief+JXJqWcYSAH6U2+cra3dq7G/c0b4i7//sdNXjE+6HQ+5PqdXi4tR4/6lwc2vav8ZIYR6YfmqHOhve1NOBo+Z6MnFuqsDAGg5GAMlsfPlK4Ef/hyV21ebl7IqPn3z7WDyD13GbG7AzHcJIB4NrmC/Hq0iFUd/0DFCQtaTQM6aIK+LB0oAswAVsnMw82ES0qPZ7cGpYl9O7xXJuQMj3u2YMVf+kvxnfUPAgV8Yp7WdT+A+N6IAtZNWIGo73XtVyGj1fUgwOg4fwo5J3YSE6geuAlEG/+dTpkTjKDkYDCiUGsH+0L/VJ2WxrwvP00nH5n1mywZLjF6oaOxLqvjPlBBTV87tg5gpQSfMt2ER5TvIzxggaY+qMbvzDyhzXdR6HwewFTOZZOBItxz7QNwCWhpkWMaCgnwCHOXmgrUg5j1jNh6XsDfx0si9rvFHlQlQCS4VoXkcvc9eQQOfxnR82Mrevwg9pluLLruxn73FzB0i0YjFlYMtQK1eRD7NNN+2DKcvGCxpfZKJMiDyrOpQxVaBPuf2awCI2lV8OuiN8BkZEfaLxOCAE/bjKvwaLRYOrB+o7DWDXAullKmll+w9P5uutatFdeiS53HEd1DudKFX8GneR4fPr0GXAUr8v652Ya5iQzGLNgco1hl3E5rJoiFNn7YRbm5yQLc8wX0zC1CVkTEGZ/KO5118MPJgm3UPjSgadQOtYFAFCKHhSP9+XYIkY8qKBGYA6BkSf7rbi87dq4+5O5H8wXP8l+cR2lBJ3quqx/bqZhTjKDkQSbHRNYrAq27Pxud0eoHXQqbDHVzmm8xhdfpJ4hfcQkHoqvjhZDzHMJMAZDjowWbcRblivndIzdHz8Qkg0n2ZeDSDIA/N6xCX5DRU4+O1MwJ5nBSILq8UFYJp2YquE2XCUENW7XGpfO+Vyr6ezO0CWWVbjZvAYaQQO1J3v5z4z0IyaRb3psQsciyQyGBLEpivDh5nPSdj6nOvMKF74cRJIB4Imecmwb/waoMjtNU7IBc5IZjCSoGu3CBm9Yq3jbcHCptJZPLiqwRBecXRsUelR4E+cYm5UmnOd04wsHnsIfbBTaPKwYXkj4OXVS43ysjprBkBxjMIGmq4UdgD5lddrOFQ9fDlelBjwKjBbkT24yc5IZjCTQu8dx/ZHnQ++XDragwViDxf5AgqPCnKEsgMAJsKosqHAmllr7asCMC08HVQ6WDpyCIIGuf4zUSbaIxs+cZAZDcoymuZV0Cy2f9zlEXWnC/V7ktuHHWZ2341B1UANe1M//35tLmJPMYKSA1jOB77edxNaBdnAk8c+o3rAIuwZ78AFTPayCDmu7GvFTUh63gK/UMQo1y0POG3xJRpJZugWDIT3GAunNIT7ssYIqtAhoilI+R595fcL9nhw7yS6Rx82tF+E2/Y/wqPqenNoyX5iTzGCkSImtF3V9x/AR06qI7QouvNRVpS3F1waGsL7zAD7RcQxlRAGV342zWt7GFlMttpiWh+TlptB7WQ5yPpFsEY0vG10GGAzGnHDT9K7wPN5TiYsDP0R74faUz/HdiUtAVca4+z0SuJfYfAJeGCrAM0NW+I2ZTzHJFMxJZjDmyc7eltBrg0KPjcaw0Hq10oy6vuMAgNKxXny462Ro3/ePvI4fN76O1frIG4jObc+wxYxs8j6tT2rcXsfyDFvCYDDmSiDNbpJL5NHk0OCtQHL3hVh0eVTwqePrqrtzHEmeTpdbha8Jd+fajJRhTjKDMU+W950AT4JL5ZXqQhRPK5oompZSofK7UTOtaYDa54JS9OAMhJfjt5iXw8Cc5LziydGZ3ali80RPOSjSVyDEYDDmj5jGor3pnHRbUj62z6OCR5XASU5CQSmb/G2wBF7LMlCVKdemzJmknGRCyKWEkJOEkGZCyJcTjNtECBEJIfGVtBmMPIOAYoWhGmuMNdgjqlESCN9Up7+Oxzr7KM4yr0CZphif6+uBijUPyRvchStxwjGH5jNCcvnLDAYjOwQy5CQP+2KnYY1bt+Azpu/DZj0z5n7KKdDrUWFUGd32egqpOcljPgUO6c+GZ5aCQykyq5NMCOEBPAbgMgArAdxACFkZZ9yjAJ5Lt5EMhtS5wSuggtdiz9EXUOz3AQAICIr9sytTbOhqRBFRYIPaivreY5k2lZElAmoLHuNvxIAn+QcWZU4ygyEpAhla3RnyKdFReUVUvu6gshJ/7S/BGR13wW+ojDpusPRs+AIEJ7Ak7rldEnOSAWBP04VwKwtybcacSSaSvBlAM6W0hVLqBfBHALtijLsbwJMABtJoH4MhCy4+/RZW+SkAoMQbVKZQC2oscozOeqzOY4eBAiUZilgwcsOoZQ26PXOrjGdOMoMhLcQMOcmNE3o8PHoJnOpw91aqMuJIIOj8ipTDYfOFGC/ZBCqoQQmPgNqMbzmC3f8avfG7vjrTXGyYLty8/JqMJPNNVgDonPa+C8CW6QMIIRUArgJwPoBNabOOwZAJGq8TNx9+FgBQ5BqHoBCg4pRY1XcqqePXOiegCCSnucyQPlShw1cmrsHxuaRaAAjwGiYEx2BIiEylWzj8PF4aLoDLZMSUTsU7JdeheVqu8tVNFwO4GDeX92CDpg9f72zA4FgwSuwJxL9TuETpRZKB3HUCnA/JOMmxrhA64/33AXyJUioSEv+CIoR8AsAnAKC6Wr6SIAxGLMjkz8I6MYLFldUY9zthco0ldeylJ19FYBa9ZYZ88Oor8VTv3HVQvUqjBMSbGAzGFP4M6xs4OQP8hgoEeA3+bF+JZmf0xPqJnnI8gcimHL449S5UZYBDopFkH0lODlNKJPPX7wJQNe19JYCeGWM2AvgjIaQNwLUAfkwI2T3zRJTSn1NKN1JKNxYXF6doMoMhbYptvVipMEPJzc3d4SiLJOcLLlVquXcTivgV6wwGI/tkKpI8xTgxYNhQj9t8n8WRCQMOjieXkuCjsd03r64cTolGkr0yDAEkM914D8AyQsgSAN0ArgfwoekDKKWhDHJCyOMAnqKU/jWNdjIYskEI+LHa68cRwhbOFyrtytqUjrPxBYifachgMLJNpiTgprBBDwVfjFeH5yYJ56Wxny8TKiscLmlGkj35mG5BKfUTQu5CULWCB/ArSulRQsgdk/t/mmEbGQzZUehzQaWS5o2KkXlOi6lJHQ3BAtZShMGQDplSt5ji0b4zUKr0zPm4eJFkN6eDU5RmgMZD89BJBgBK6TMAnpmxLaZzTCn96PzNYjDkzbmn38XBhgtzbQZjFgKaInCuobSft8+fWhV3X0B+YvsMRj6T6UjyUbsORzG3Al8A8AZiO8kuTguXKM36Fil1AkwWaX6TDIbMUfnduLn1QK7NYCSAEg52Y2ppEYlwFa1Gv29u0m9TdPsMabaGwWDMh0xHklPFF8d5dxENnAFprmL6Zajdw5xkBiNDlI5159oERgLuMXwP9jQXylFBjb8orsBgnG5as9HqMc4+iMFgZI1Mq1ukiidOJNlBNXBKNJLsi5NHLWWk+U0yGAxGhnl2qAi+NFdbU4Uer02UY8CbmpN80M4iyQxGtqEJlIhEiYoO+ePkJE9ALdmc5HjFhlKGOckMBmPBQZV6+AIEYpoVSESFDq8MW9DuSq1zXotTA8rLL2+PwZAz7eUfiLsvIFE3yRtHJ3k8IF0n2cfSLRgMBkP6+DXBRh9iGm/aVFBDVOjgCXAY9KYeoaYqlnLBkA8BTSGoUo/hsnNBeek2i6AKLZqrronertTj3sGdCGhjN/8RZ7ZOkwjeOJFkW0ANh0TTLbwSbXKSCGl+kwwGg5FBHJoyAOl1kttLL8HWgS/P+zx+pTkN1jAYyeEoXjev1Ys/F3wctyoexRfH90DUlaTRsvTiNi7BX90bQJV60GkrSH5NMfbb9LAZ62IeJ0q0cK/XE3tCYhNVknWS48nWSRn5WcxgMBjzZEQRdJLTUW1NOQFU0OB5/3qM+uYfKXlFv5NFkxlZ4znlRRgt3pzy8TZRhZeHLXhpuABUwi7FhMqKP/RXYKhoM6g6LLX4D/21AAAfF9vpjJf7m2sGPAoE1JETasoJaHYZIErUZq8M0y3kF/tmMBiMedKNYMRrPpFkUVcKh64Kv8GVWKkawDebl6XFtq90roe52oECjGFZ55/Tck4GIxYBTSH22hZBaRZxOf6V0jkmAtOi0ESazhkAjPBFGPYq8Jj7UtyvbIVXUwLVRBd+PRD83fridIPLtE7yfPCrLFCIXhCfE0AwVeuEIzWN9mzgCTAnmcFgMCTPaX8xAEBMMfJFBQ3W2L4Ds8OPbrcKKm4JaJoepn0eJW4+fR52WwfwKJiTzMgcewtvxtttRpQry3B5iuew+cPOpUTTd0FVJvQhKPf4eE8l7l5SjSGhFCZNFTo7ghFkfzwnWaLpFgAwrlsErUIP7VAjAEBUGnF6LLWi4WzA1C0YDAZDBhx3WwCknm5xquxKOPw8ut3BB2w8zdJU8QQ4jPnSK0/HYMzkTUcF7H4BzwyVYLjsnJTOMS5KO5LsMy3GY+bPoUu0hLZ9cvQGDMKMg2Rl6HfmI9G/N1FfBjGOioQU+JO4A35eE3rvUxjSNlnPBB6JpoEkQn4WMxgMxjw5MB7MSUwl3ULUWfF399p0mxTFCHOSGRnEb6zGP0eD0dVBrwJtQs2czzFcdi5eGSkIvacSdJLdqiK8bivBk8OLQtveHTPCTjV4y1kZ2uaN0TJ5xFgv6Ujyf3YuhY8LO8leQbqpFgDglWG6hfSuaAaDwcggVKHFKUfwwZJMNy2q0OGVqjtBJx+WjQWX4Jfd1Rm1EQCGmZPMyCAHzReGVkIAoCMQWwItFu7ClQCAJ8SLZhSrSs+l8HFqvDNmxH5bZKOeXtGAPw+Uht57YzQWahcWwyVRzWEguOLknVZw6OYl7iSzSDKDwWBIG782/GD0gwfllQklsJpKP4CPNZ2F3opLQHkVTgXK0p5eEYt2lwojpdsz/jmMhQdV6PDFrshrq8lTAHfhSrgL6mc9/lPuO/GH8vvwh/6qyPMS6UVdfVzsHN0n+pbA4Q87wA5oosY4qQrv2KStNOMl4X+fk0jbSXbLMJLMCvcYDMaCYlwbXmL1Uw4ecy040Q1KBPgFLVTuQTi1FTCMnQTx2DCKYGrGRe03ol53FUZ7s3PbFCmH3wYuxe7KAizq+ntWPpOxMHAbl6ClO9J5POSw4Br/Z3FFQTfuwFfjHussasDLXRa8PGyJsVd6TrI3jrRb24yumIOB6JbwfnCSjiQDgHuak+zgdDm0ZHayEVxIN8xJZjAYC4oWoTb0WgQHr6DH/ymvwevjVrzUW4BrrP14va8A/1H9JrZ3/gyvu4O5mg4/j3226AdpJvleRw3WLuvBotmHMhhJ41BFp1a8MRqcDJoFK+5IcOwTfHTXuimkmJPsI8l1Aezxx3CSZaDG4Eb43zdOpe0kuwPycznlZzGDwWDMgz4ajoC5qQIjijJ8tTm8xPxkvxUA8JZ7EbYDOObM7XLrSEDaDz6GvKCCGgN8Wdz9h+06QAjKHLpNNdAMH43Yf9qT6PcgvUhyG8qTGnfAURi1LZmahVzjRjiS3BeQdmqIR8LKG/GQ/hUgQaiCPbQYDLnS6Qs/SEZFDX7lPDvmuEZHsJvVuD+3BXR/HV2S089n5Beithht1Bp3v90voKXqavxnwf1o1a6J2t/kjJ/3KqVIsqgrRWvVbrzpqpp9MIAXhgpwt/E/0V55ZWibj0o/juicLDikIOjymWYZnVvcEk9diYX0rwAJMmGph2FgX67NYDAYKdAyLRLW49PhLwMlMccdGNeDKhUY9+f2xv7GqBFUrwPxOXJqByM/cKuK0eiJfc1PcX5TsFXz5mUdEdsp4XF0In6QiGY5kizqSsE7+mLuGzGtxI6mD4KQ5Fuc/GOgGAPeD+DxwhZoho/AJwM1BiedTLfgeDw/Gn/yIwWCOclSbTkTG+lfARLkf7idGCo/D35D5eyDGQyGpGhzaUOv/z5YCrs/dqzA7hfQUX7ZDImr7CNSDk8W35VTGxj5w4SyEI/3JBddfd8bOc5rroUvQXONbEeSn7PcEHefe1I/eK7NNd4ZM6JRuwVA6s2GsolnKtpNeBy1S3uV2yXKz+WUn8U5hqpM+HpbHTa2fAKP624FJdL/ETEYCxlKOIj6cF6ie9pDftCbOJXitt5dGMtxugUA/KC3DlQp7QcgQ/pQEAxwpUkrNpx2RxazvavfMcsR2YskBzRFeHkivl65h6TenvmXo+vhN1RgIhBfGlIquKecZE76vohLhuoW8rM4x7SUXBh6/fW2OoyWboPPyGrPGQyp4jdUod8Uzq30zEGr85RDkzByli06XGpc6P8B/lbxeUnlfTLkxamqPXh8fEPS420zVlG+N3BGwvHZvDbtxqV4a8wcN1A1vaBtrjw/VIBbxPvx/Fj8AkepEI4k5/4+NRty1Elmd9s58qWBiyPeP+K8BmeOPZIjaxgMxmyM6xajhQtPZOW45AcAp50afLdnJX5a8hXYSzbm2hyGjKCCBs9Ufha/sG/F04PJd9azieFIKuUUODSuTTA6uznJfcpF6Har0Fq5K+Z+F5KTfovHayNmvG+TdnMOYNqkXwar2i5R+o78TOT5tMgRos6Kxhk5P3/tL8GwV8EeWshU8q0AACAASURBVAyGRDkp1OE5R20o4uSRQGQ4VTpcajzavgyvKM/JtSmygCoSO3ULhaNlV+NTzZvwxphlTg0dRr1hx2vEuhXiLIVs2Yoke821+HzfRQCAXzu2xrCDx9OOFfP+nNn+vVLAQwVQTiGL1E8WSc5zxg21cW8wDR33wm+oyLJFDMbCxGtZltQ4Kmhwe+t2/Lm/FD7TYgQ0BXDLMC9uJs/aFufaBMlDVUZ80/RQrs3IOaKuFN8aDrag7nXPLcd2wKuCzXomAKBHSKbYLzsTUJ9CHwpYvTZaCMpHRo2dhavxeM/CKKx3Uz7oe8jASfYFSNYVUOaL/J8WWcTNJ156GTGsiCgQYjAY6YNyApxFa0B5Fbr0DaHtHktd/GOUOtj9AlwijyPGs/FK0Y2ybI06k+eGChIW8sntQZQJvNpS/LyrGgG1Odem5Ayq1OMTeAD/GklNP9chcrh17GPwWJbjto6LZv+8bEWS+fC13+5S45fFX47oX9CuWZkVO6SAU+Rh11SCcvK4r/ll4MxPRx7fqkRwzdIX/ff+HXjZfG2WrGEwFhbjxRtxne1u7BF+gN85t4S2P6e7HL8vvw9tlbsQ0BZHHCMqwhPb7w9txq1NW+HNAydZpBz86vi5pSJb1YJbWQAA8GqlX3yVKXy6Mrw0XDCvc+yzGXD+6H3o88wehc7W5MzDRabR/L+2OrxeenPo/anAwrn+h31qfNezGyOm1bk2JSlEknu1oLkg/6dFFhlF4paP3++oQY8o7baQ6aS/PKj0QbnYOrKUVyGgSb5IhMFIxMPu69Fo12GfzYD/6q4CBUFAU4DfD9Xi31oacF7zdfBoIsX0/ULYSX5tJL8iiqIQP9/WrmOKOy2q4ArDhNoKUZe4eUa+ckR/VlrO0+1OrgguW07yCB/9XDnsDU+GXFRejth8eGqwCL/rLceVPTfPPlgCiDLrYbfgnGQXl3ohRz+d/SHrCsjrAkiVieIzcEPfh+A3VmO05Myo/ZRw+AB5DHbj0hxYx8g3xq2b8X/9MxwdQQ2nfjHesYUnpuLM3ERlYTbMywk+If7KloOXdnvabDBOg/f6H7p34jnLjTm2JvtQhRZf7d+W3c/MQrqFx1KH173RKVb/3VcNqgxOip0yaCedbuaab54rWLqFxGlGfPHxWFBBAyqoMVi+A1/rWjfrePcC+XG+otqBFqcatQPfglOIfCCL+nK0V1yB4xNaeOLkcVMhdQ1LRv4S0BTCUbwuolKb8iq8p9wSNZYKagyqqyM6avm54INiKqI1xuexkzzZUSwWdm7hrGjFwz0ZTfxNTwXec5bm2Jrs80rpLTg8nl0Js2xEkh/BbfifwejneJ9HiaPWoBycS1w4kWS5wSLJEue0rwgBtSXp8Y6ClfAaF+GTw9cnNVNbKJHkfznCOV9OEhmd96iLsafjagDh1qAz6bWeh7erbsucgQxZ8nbRNVjV+UWIhnAB7EjJFtzVsjlqbEChxf7A8ohtfhL8jQ6XnwsAGCL56yQPKOIXCTuhAeXlEVnKFO5pS+6vjBYvuIn5K47sp9zQebgUfRUXo6lqT+LzK7T4XW85Wpyx/5b/6wgGspwL5DksR3wyaPU9nQXnJAcAnOX+HtorrwTlVfh75ecwURK/A9GoshQvaHdin80Qd8x08t1JdhatQU/FpXh2KJwT9kPbdgyWh9uVugVDqN2vk0QvCVPCY5AvxvVNO1irXQbcBfWh14e8wcmXWxW8viivRLNQF7ON7qBxFU57IlOgpgp6/hoIOsk9gfxNOzhB40ty9VMzwJzk0Os2lxpfMX49h9ZkB6oyhVZiuj3Z14hONd3Cb6jEi3QTTtHEsm3iLDUuv+mpAFVo4cjz57Cc8bNIsvTpdStxedu1+FnRfbineQOeFoLSNj7jIlBV5DLlGDHjkc61SZ/bGeBBBc2cotVy4VTVHqzv+SK2nb45ol3pPwaKMcKHVQUcQthxGYIZo6Vn4WuFj+KFynsAAGPWM3FHxwUAAFGdv5E+RnK06dbAb6hEX8VF+FXvYgDAQfVmBLTFOFR+Pf40HlvO6S2sRbM78vf6C9cO/KH8Pvyweyko4dHhy69ivel0emNPAJqrrsFnWzZEaccuNJwzird+21uGlsqr4C6Yf5MJqfJs8cewve9eXKX4CU46cuAkp3KMoMEzpuvwQMsqTAQSX7Nu1ezPC1FdgNPu5IJajOwjNydZXtamEbtfwLfag0u1D7Q1YN3SD+KRoR24q+QAzhz4HyDgh19ThDEYMOBJPr/JIfIYL1wLjXcYSvdopszPCW/4V8TVmB1B2BlpJeEI12FvOb4+vAknJrRoNJtwEYCDirWh1JVO4xlYMt6RUbsZ0qaTWnHKdB3uaQ6v6NzecjauttbihY7iuNJTD7athWNGi+lgA4FgNMpXVIPTbum3lU2Vt+1FuEuhAwI+ENEb2v6itwGeALfgneQJMfK6oZRgT8du/HSRBZtGTuTIqszyykQ1Rn0CRn25ue5TSbd4pewWfLr5DADABI39W6eEAyiFUzF78Ok7qrvwr678XUGSOyzdQob4AgS3dl6GN0ZN+N/xlThVcgke0D+Mvxiux9MTyXX2muK0U4df0iviVp77jXMrHJQKlPD4cXdt3P1f79sUev2iI6xo8e32ZTgxEYxovGczgCp0eNoe/k4fHpldoJ6R3zxlr8GD7ZGrNQ6Rw+97yxJqs850kGdyxHAWTjjyN6L0xqgJa1w/wa9LvgQqqEEJh+Gyc/H0SDBlJbCAneS2yl34bke0ss6wV4Fhmp/XhM9Ug71DuZXcpGRuhXuUV+LBzo2h4ts2jyFm+uOhypvwctVdaObjP4Om+FmXPJ+xCwU/lZeTvGAjyTPpmtSB/L/+EhyZuASnHBr8DnPvnnfKocEpxxLcujj2Utd/6z6Ca1VPQT94YF72ZhubdQsG2+JH1I/adaAmI4hnHMcdcRQtKIG9YBX2doVTM96xGeEvrIIw3pl2mxnyoNejikjfmUKk85vDf7n7bDTFKfDJF+x+AY+01qOg9i4ccRfjl63hVRyRV2Kh1vifxOK4+457i3EJrwQoBQn4smdUhtlvvAD2/tw+0gNJRAkDmiJwriEAQGP5dehuCk/mnhq0YtT8EfwQ+0EmkzfGrZvxgnsFftpVDTUL68keH5OAkz+nHPGllZLlef7smNuH/Bo4FQWgCl3WWnjOB0o4dFXuxD5F/OLGKdyGarxSdSeOTcRf6ntW2BERAXSJPB4RPp0WWxnypDfJRgVz5ZRDEyEPl898pvkM/LIrspAvwC3gSLIv/rL8r3qq8ZfST6O5YncWLco8n2rfnmsT4CWzX3PvFV0Vet0aiGz+M+oT8NRgEURj+Fo+pDwDj3Uuhki5WVePGNLHG2BOMgPAw20NeCeGxFmnVwsvp4ZPWwK/cXH2DZsj/eUX4OfOHfif0Wjx9pm8pr4AH2s6K+GN7Jsdq6K2dbjnPylhyJPeikvQk0S7W8bcEbmF+732+OKr5tj9Au49vR4nAvHVQeQGFdQY9uZ+3cCThJP8m7EGiLpSUJURbXEKa3+lvSX0ul9k+cX5hE9mCQzMSc4QDpHDh5rPxUtVd4e22Us24pnBYvRxVrxg2I024+zR2VwzyFvxu95SvDBUMOvYJ8dmzxcbjbGs3uZiTvJC5c/+7fNOq2DExr+AI8nuJPIex2n+pOIE1LPfn7OBG4mvOUp4PDdUgLu4f8M3Df+Gp4bLYo77RttyHKu6AQAwIGZfpYOROXwyy0lmT6cMIlIOtzZtRWvVbgQ0Rfi2/zr4AgR/tq/Cnc2b8KWBC3GwStr91m1Ul7QT88JwajfqdrcqosMaY+HQ5WU62ZnCxRsQUOevBF4i3HFUeKYzJubPJKLftCbXJgAAXLM5ySojRMph72ARft5VjaYEqY339l4AqtCi38ec5HzCK7OgiLyslSk7mj6IL6rux297glXn/9sXzMPabzNgd9Ol+Efl57LSzjMVxmjyTkyq+Z+UElBN/ulKMxJDQbDPxpZSM8VdQ1eht3Brrs1ICarUw2NZPvvAOHiSyHsc9edPJHmAK8m1CQCAE/7E7b8DyuSVRU5MaLHV+2M8MySNfxsjPbB0C0ZM/twXLlCYGZm9u3kDAjrrzEMkQa8/O3JJPpU0lgsZ2SOgs+K0k6XaZIr9NgMOEXk2zviW8QHs15+X8vHuJAq8hv3yjiRPBVYo4XHYLw3Zs0Zn8D5OVcHnRkBbHLHfp5ibfnOfRxnq3srIDzws3YKRCn5VOKLWVLUndJPJJaKuBO9NFM8+MA14lCySvNBwaecusciYG/e1r8fxqutzbcac+fNAGf4xESwW9hkXgQrByVRL5VUxdXRnkky6xbBP3oWNXZU78U7VbTgXv8BDrbG7UmYbhxiMEvYUnQVRV4qznN8B5cKRQx/P0qsWOqLMFIeYkywRPEKwva7fUImLmq7CSMEZObWHqkxYOfofeD6Jgr104ObztzMaIzYjqopcm5D32HwCvjp4HgJq+UxCA9oiDHsVeG/cDHfBCnyefBbiZETySc8m2BSzT9zdSaRbDHjl7SS/KK7HdU070OGSTtrIhBj83l+hG+DQV6PXrQRVhVvHewR2n2fIC+YkSwS7Itgp6ZQ5qK98n30PAprcpCB4zbV4s+T6uC2oM0GAFe4tCKgQfKCLOiu+ZWPdFrPBO2NGbHF+H2Ol8shP9mqCOahNDg3W99+Pv/WXwK0qBAC0u/UY5QpnPYcriXSLAY+8l/GnUhukxMRkJPnV8TJ8w3sdAEBUhp1kN8eK8BjygjnJEuHPvrMAADYEZ9rPDxXgaNFloEo9qCq7xU1P63bjxqZzs/qZfpkl8zNSo6nsClAQdFm24JnB3LbQXUgMehXoUtTk2oyk8PFhR8o1GZk8pVkLqjLgfbsR/SiA3xipcTxcdg6oMryU70pigj/iU8haVedfY9Jzku3+4PfZ61Hij71BeTefwhTKn3YS5iQz5AVzkiXC9zuXoKfiUrSI01o2+2oxUrgBr5bcmFVbBsXsL4mJhDnJC4HfOTZhqPw8/MC+I9emLDju7Tkf3yj6Vq7NmBUfF13M+an287DO+Rh63Urc37EBn6FfCO2jKgM+bbsBfk1YBSGZdAsA6KjYOX+Dc4Cos0oyEj7lJPdM66LpVFrgKmoAADjAcpIZ8oI5yRKBUoIL2m7E/S2rQ9v+q28JBoQyNPmyUzw3RY83+7N95iTnPwFNAf7YV46LOz+GJ/ulqeaSz5xyaPDrnirJR089fLST3OdRwjbZiKjPo8RTg0WYKF4Pr2UZDlqvRrNDA78QvG95LHUY9ib3b/xw9244itdhvGQTApoiDJbLY/LWaTkz1ybEZMinQEBbhBFf2IFvE2rQqNkMAJgAU7NhyAvmJEuIqaXFKXrdShwLVKE3y2LqrZ7sz/b9kPaDmzF/9hVdBU+Ai9l1kZEdfAECKvEivgnOOPsgALfYb0e3fhXu7jgPfR4lfLwWXssyfMZ/Z9INkDpcauweuRsPem7EuGk5vuu6fB6WZ483A9JQs5gJpQTdBZEO/IdP78DbnsUA8qvLIWNhwJ5WEufF8arZB6WZkxO5cJLZpZjv2FkUSRL41AVQuYZybUZMKKfAH52bkhr77pgRrcUV6Jpc2n9auBAGpQd7m+eW697k0KDJocEBzcexxTQ2Z5tzgU2UrrP5utgQ8d4l8uiZDPTYAuwewJAXzDOROC8OF2Kl3pGVzxouOwejQjH6mrIvjcQiyfkPe0BKA7u6HCqcyrUZMWktvxz/1Zx8YOCp8WWh1/e1NCQYOTsdLjU2GuWh4ToqSle+7tGu+qhtTc5gB0VbHrUCZywMmJMscXwBgk6XCpn2ISkIHpi4Nmu6yDPxs5zkvIaqTHjTwZqHSIEOYRGkqisyQsxzGv9//eltWSxCHk6yKyDd+6UtRjrVfpse/1u4B2My16ZmLDySStwihFxKCDlJCGkmhHw5xv5dhJDDhJCDhJB9hJDt6Td14TLiU4DymZ2Bj5Sdg72DRUnn8qUbH5XuTZ8xP/yGCjxdfGtEa3ZG7jjql24TFztyKxEmBuThJPtl1rUMAN60WzEm81bgjIXHrB4RIYQH8BiAywCsBHADIWRm1cBLANZSStcBuAXAL9Nt6EInoM6sVvIRIbeFII0+6T64GfMjwKvxlY61uTaDMcmbE6Uh3VqpYae5daLkEkkOyLDm/vVRs+xbgTMWHsn80jYDaKaUtlBKvQD+CGDX9AGU0glKKZ18qwNAwUgrfuXcliHnylAgt+1CH+1Yjnerbs164xRG5qGEw7BXepquC5UXhwsxVrot12ZE8a+q23HSk9tEkIBMIrQBGT5h7X4Bb49l9jnGYKSbZJzkCgCd0953TW6LgBByFSHkBICnEYwmR0EI+cRkOsa+wcHBVOxdsHgVYVkkStIfRej1G9J+zrngCxB8sOkCOEy1ObWDkQnkF/XKZ3wBglMK6UmI3Xr6bDzWuTinNogycZLlYudMHEm0C2cwpEQyV2ysX2PUPJZS+hdK6QoAuwF8LdaJKKU/p5RupJRuLC7OboMMueMSwhFWUV+WtvNSEJyuugav2dJbAJMqHl6PgMR1XBlzgxJ5PtDzmUf6Nmdksp0qVKGDJ4lW0plGlEmENiCTtBAGQ+4kc1fqAjBdk6cSQE+8wZTS1wAsJYRItYBalth4Cxqrb4LXXAOnJn0qAceqbsAFTdfg3bHkBPwzzXFhBW7ANzNeqPj/27v3+LjKet/j39/M5N60TXpNm5S2UAqllEtruXhBYVduYlU4L7n78sLNDW6PW/eBLeh2I9sDx+1RNh6Rl6IH7wfRFyAgKgiIgJabQEFKeqFNb2mTNE3SZDKX5/wx03SykjQzyZqZNenn/Xrl1claa9Y86+lk1nee9aznQeEEKYwhZW1XjfbOXFnsYgxIlhf3StZ+pRI+S7G7BVCKsjl7rZG0yMwWmFm5pAslPZC5gZkdYZZqLjKzEyWVS2rzu7CHsrfVoE9sWqXdNUeqtWKeJPkyvewPurIbuL9QPr/5FP1lz2StmXNpsYsC3xCSg+hXoVXFLsKARFlx74nYr1RGjSjWKETAoWbUcbecc3Ezu1bSo0qN1nu3c26tmV2dXn+npPMlXW5mMUm9kj6acSMffHDD2yu0q79Mz+lYdccr1Nt0iWIq04bELF2w7dYx7dNZWL9vm+ZzScdnRzR19/PtbSv0Y32/yKWBH+huEUzr+oJzk2w8Utyh3/Yrlb6+yWIXADhEZDU4rXPuYUkPe5bdmfH4VkljS2rIyq706ACfW3+CJMnsaE2OJNQZi+jMeSs0qeMNWSy3mfn6645Q57Zgjk/8bEetXFWZLBkrdlEwTo6W5EBqiwVnauNYmJCcC1qSgcLgL61EOWcDMxtd0/1Jxaty7wK+rmaF38XyTcKF1Ft/VLGLAT/QkhxIu2PBGZYvGq4pdhEkldA4yVynBQqCkDwBPN0+RYlIVU7PSVZP1+PRo/NUIn+8Xv2OwE56gOzRkhxMrdHgTOywIbyg2EWQVEotycUuAXBoCOa1duQsHs4+JCcmNeis3q+quSM4l1uH84u9S3Vs3RGq6Hir2EXBODC6RTB1xsOBOQP8ujsY4zaXSvhMcnUGKAjOXhNELiG5r2qW3uqpkgt4q8m9O2brx5UXyYUPtHglK6fKlQfjTnhkh6sBwdSdGP/oOH7Y0niufrkjGOO0x0ukr2+pzAwIlLrS+ETAqPpD2YfknrJgjWhxMDdvPEo99ccM/P6Tun/UG7POG7KdCwWkSayADjYEoLOw4pOb1D+1+DMY0pIcTM6ZXKi4/ZK3zz1T17WuDsyNaKXS17dUuoUApS4Yn0wYt7iNfrJz4Qrd03CjLth+SQFK5J9NlalLsS5Sqe9uP0If3Xi2ovWL1dZwmtY2Xay++qN027Sb1T/18CKXtLB+M/ezik2ZL0lKem7c3DPrZJ3e9RW1Vwehrycn9KCK1zYV9Qvmp1ov0Mt7g3NlqHTGSS6NcgKljpA8QcRs9JtwYrWN+tLGJdrcG+y+yF5P9C+WJHVOP1EtfRXqike0vfoo/TV8vD6y/hy9r/16fWfLYeqqaixySQsnWTVd1zUv1y3hT6ul8Vytn/7eQevby2Zrc2+ldoWLfxmbluTgaqldJpUVZ/i1xKQ5WtsVjFEt9iuV8FkqLd5AqePsNUHERmhJdhmjXpRqiPzu1vly5ZO0L3xg6uxWm67fdy1QNBnS9r7UF4SnQ8GaPTCfemrnS5J+uK1R715/se7Y+y69NO9jkqRtc8/Sk/FU6/vj0aO1e857B57nwhVyFYWdApg+ycH11+RRSpYVPqi6UJl6qxsK/rqjiZdI+CyVoeqAUnfodeScoGIaHJLbGt6jfeFaSaamlt9IknZG5hShZOPXFY9ow6wzlcz4Tvfl7adow77BLeLXbzpOhy28XMftvE/Wn9vEKqWmtXzewGPnTPfvnKmtfafqtqYu3dX9Tv18eyqA/O/NC3VH6Aqtq31J8cp6PVR7vlrjk3RZ7JeqanutIGVlCLjgur/jMH24olphSa6iVhbtkiR1zzhRpqRqdr2cl9dNVtVpS8WivOx7PJIuVBK9g7hxDygMQvIE0e8JyVe0X6oXOyfpG4e/pCalQvJmV/xL72P1o56TtDl6oMXrje6hl4h7E2F96K2z9M+HLdJ1O28a0+u4SJUs3jvmchbKRg39wvN8Z61O77xgyPJY0nRl2S36/c56aWdq2aojjtACFSgk090isJ7pmKJ44yQl649ST8UsTdv+pGKTD9M5u6/Vdxt/p6OVp5AcqdHvokvzsu/xiDuVREgulaHqgFLH2WuCyAzJrmKKXuxM3Qzz091H6ImmT0uStiUmD/vcUvDDbY16vK0uq23X942tO0Gyql57py0b03MLKVEzU6/1z87pOb/fXT/o924r3CX2JB8zgbavbKq21hyjTZGFWtt0sc7v+6I291bqqpb3K16bny5azkL63rZ5o29YYKXSJ7lUygmUOs5eE8Q+VQw87k33V5VSrYsfb36nXFmNuhIVwzxz4nm+c4pcKKL45KYRt8kcPi1RM0trmy7Wwo479IfIaYUo4rjcP/VjemD3+LrOdKlwIdkx8UGgPRteob8kjtRT0cP1kfXn6JX0aBObeysVK5+al9d0FlZXPHgXMuPOtLHxQ8UuxqiSpdDcDUwAhOQJ4gdtSwdukGqvHNz645ypf9Jc7U0Ud0zUQmnpq9AXp9yq/vL6Ebf5Y+M1A49frD9X5771AUnSzniN/t70Ubki3Mw0mtY5Z6h/6hH62palQ/pj56rTFW5EA/okB9t1zcv1rxuO1bc2L1Q0Ofj/Kh4e+X22sWnsYTKoXXASznTV9vP04zlflAtF1D/1cLlw8BoXGN0CKIxgflIhZ0+016l3+lK93fhB/dUNneK1q7JBnfFDIyRL0k+3Nyg2wgnelVXrnzetVEvjufphw036xu4Do2K82D1NZ721Wp3TjitUUbN2Q+eH9Q+d/6pd/eP/f2xPFjIk0+pVquKhCsWmzFe0brFaGs8ZWO4spM/sOFdtDWO78uIseK3IktTvQnq7t0I3bjhGP5v9Bf2t9jTFaueOuL2LVB50Up98KZXxnIFSF8xPKozJxXuv08stww/M/1RopZp7gzNofyH0h4cPgttnvkcd6yN634aLFUsOPtk82ZHq9xzNYQbDQuidtlSPbR25ZTxXbfHCHV9QWw0xuu5InZ6tXan/aFmmI/t7dLce1qvzLtX9+47Vqy01Wt51lTbWPCdLRHPab1DfE6nPg9Rnws1vL9Gs8kU6tvY4ndn4tlb1PKSKjjcHbR+vma1QIqpw9/aClpPRLYDCICRPIAebueqGjccNuZQ60bWHZ2pG+rGTyZS6RvlQ4iRJGhKQM5clspjBsJB2VPk7vfSueOEmlKH/ZOm6seNsPZm+YdZJWtt0sc5bd86gbVx5rax3YoTkTL2JsDb1hrWpt1IPts7QwuqleizyaVm8b2Cbpyafpz/1NOnLFbfIop0FKxujWwCFEfxPKvjiUAvIkvRfe96ptobTtG/6Mn1r5s16rekSxaYs1O0to09fHQ/Q5eDEpDl6xfkbknf2F7AlmY+ZkvVkxogyW/sqBvruZ4pWz1LftKFdvDIlq6YNPI5NWVCULgrjtWFfpfprUyNyuIop6p65XNduWKkfbmtUZ93Bj1/y94sBk4kAhcHZCxPWb3ZN1/KNV+mYrf9D39y8UJdsOlNfi1yV1V31iQBdZOmaNF//tP5EX/e5PTr6NOZ+iQesVR7+eqTyHN2YuOKg2zw67TK5SKV2zFmlP00+p2SHBYxHUl24bp/yBV3Qfo16E6mwv7VswajP3TPrlKxew/sFwsn0ctPlg5bR3QIojOAkASBPXPqE0hmL6O6tIw8Ll2mkab6L4dnISt/3uT1aUbCvyO2W3fjWKE2fW3+CasJJ/a/qalls35D1Llyua5pX6v4j2/Tt9pM0JdKv5RUvFKGk49cfrlF1eY1+uH2eOmIHTp/NyTk6RlJ88jxF9m4e8jxnIf05cpLeM7Nfk1vXDLvv/V3C7mv4vKaEerUinqqjR0Lv0U3Nx6i55t6BiY7obgEUBiEZGEY8QH8azbFpo2+Uo939ZXLVEVky7vu+vXa6KXl/DRRXTyKkWE2Dyves1465qzSz4yVZrFcW65HCqasWq9edLUl6Z12nkpWl191CkqKhanXWH6eOTYM/H+7fs0AfjFTqRzUf08f33ixXUSsXKlOot10uXK7uacu0MzFJz5SfqrM0fEjunX6srtx3jf68cXL6i/3Jg9dPPULVu1+VxGQiQKGU5jUvIM9iAQrJW/vzM1xbsrow05S3xAnJh4JoeZ2idUdq1duX6fTe29SV7qfrPEMxvt1bWRI37g3n3JZL9Zm9lw5Z/nhbnZobztND7akx6mNVMxWvqJOrmKJk5VS9VnGitvbXHPRzZWflQj3d4z4YMAAAFNdJREFUPmXgypfX56NXDAynSJ9koDBK85MKyDO/W5ITNWMPpN15Gt9681T/u3EMZ1O0dKdDR/aeKz9VH+r6F3XFI9rUW6mdFYdJSnW3yLQ1Wh640WOy1dZfpqfah5+FcNVbH9bfuibJhSvUXnO4NtSuUG/tYYpV1OtTG96jR9tmKuZGPuX+NnbCQV/74V3TtWf2qZLobgEUSnCay4AA6fR52uYdU5drtl5UuGdnzs/tSuTn0nSPjf8YnYUls4N223irN3izF8J/VzQP7h5wV9epuq3iUSU9Idk5U3doYn5xiiVNfVMX6Q/uHdrUO1kNZYs1yfrVkwipJ1Ghfjf8Kbdv2hLd+vaiUfe/JrJcdU2L1dNcmt1VgFJDSAaGccvWE/TuKQtU1rnRl/31hCapZ9JhmjyGkJyvmRK7Nb5uHK68RrHq2QrHukcM/y5crje6CcmHont3zNYzlbfr6vrmIes6bPjW2Ilga/ViRWNh/Wjb3CFDb47UkryncuRZ/TLduGW5WqOl2QoPlCK6WwDDWL+vSg9OusC3/XWrWm1lc7La9pWmy9Q562TFpqSGldqbxZB1Y3Fn2/Ha0PSRMT8/XjVLG2uXK1Yx8kyA6+ecl9WQe5iYtvZV6KaNxwxZ/uO9xxehNIXxl8Ri9brIsGPT92v4FuC3Q4dltW8CMlBYhGRgBJ0Jf2al2zFnlb7ZdpK22aystn88uljv3fZpXRX/vJyF1BnPz6XVJ9rrdEbz+YMmeshFb+V0PRJdpu3Vi4ddn6yapnt6shsbFoeWX+0szE2jxfDIniZFk8P/zfaPsJwRYIBgIiQDI+hIVviyn6t3/zc91T5V6+PTs9q+pb9GHbGIHm+r0/v0PbXH8tcS65ypY+pSSVKyesaw28Rrh14Kjk9u0saKxXqsY6aaNW/Y5/1Hzb/onm3ZtZ4DE8XT7VO0vq922HX9bnBI7plxvJxMexKFmwETQPYIycAIdsfGf+Lqqz9aL++dJEl6pTe7kNweO3BJdVNv5YhDQvnlYb1LrXPO0D1TrlZL47mSpETNLH1v9pflwuX6dtU1cmWD+xX/z/Jr9dveJfp7T43Wx4bvbvFkR3bHC0w0j+4e/m+iP6NPsquo1fta/7v2zjpJ7T5dtQLgL0IyMILXe4ZvDcpFd+WBLhZPd9Tp+XmfkCufdNDntMcKN2W0JN208Rit3PBJ/bR1vt7VfIkSNbMUL5+qr25arC0NZ+r2LfPVOW3ZoOfct7NBP98xV7Gkaf0IQ7zlswUcCLLECDfoRV1YyapUgI5XzlBrtEyf675UL3czKyUQRIRkYASvdlWPGmhH0xU6cBf/jmi5Llj3D3ps1scP+pxd/cUJl+t6Ui3nG+rfrVhZ6ri/1H6WEi6kLWULB23bnQgPTMv75r7h64iQDAwWTYa0bvoqSVJ/eerL5WNt9XqinZAMBBEhGRhBwoXUPu3Ece1jjw29Iefv/cP3/ZVS4w639vvTF3qsvrDz/fprReqGu/0n79cTTQPrnYUVSx7oAvJmd/XADGrOUn0uXaQq791EgFKzua9aa+KHS5L6IhNzrGhgIiEkAwfxamTpuJ7fpqEnwtf3DR4j1kUO9H3umX7coABaDC/vnaRPvHXqoGVPdzeod3q6LiKDQ3w0GVKyOtX/eHfDaZIkV5afqbSBUvZa9yT9YW/qRtj2yMQd4QOYKAjJwEFsjo9+GdRp5FDbGh/aFeGR3dP0pfqvK1mZCst/brhcyapUyPxDxeljLGl+/WbXdP2u4ixJQ6cZlqRoVarv9S09H5SrmKJodUNByweUgrb+Mm3tTd2kt1WEZCDoCMnAQXRkMTRT16yVI67bGh96859zpnu2zVHzjFXqnb5U9+49Wq11JyhZWacnuxrHVd58cc70zL7UcG7DheTu8pna1Lha97fO0JdrbtIX+q8sdBGBktCdnmZ+Q3xs45MDKBxCMnAQ7fGD9w9OVtXris6Rb8R7u2/kG/9Wb/iglm27QQ+0zlB/qFIddcv0u93BHTbtF9tna0vjB7S3dtGQdX8LHaPLt64e+ALwm13BPQ6gmLrSkwO90cvNekDQcfs5cBAt0SpF6xcraWWqantNLhSRJeMD6zumLtWbLVUabrZZZyH9sWPkE2Fv4sCTeq1SvZEq9SSC/b313c0XqyqcGLL8Ky0nqKWvuDccAqWgJxHWkwuv0dNbRp7OHUAwBPuMDBTZY231OnHnTdpYfaxcuEJn2ncHTayxIXKEOuMjfNcMV6gnyyml+6xS693Qme2CKDPc70dABrJ39YZTtb2vsOOhA8gdIRkYRU8ipKf6F6tr+nFa11OlROWB1uEX+ufJOZMLDxMSQ9n/eT0VPVK/3nO4H8UFEHDDfdEEEDx0twCy8Oq+em2fukCStHHqKZpdtVG1rc9r7f5+hZFKKRGVlBrtwuQky/5E+J9vE5ABAAgSWpKBLLywd7Ku2/Z+SdJZzav17/0Xq3XOGXqwNTUxSNfUoyRJiZpZ6p2emsLZhfgOCgBAqSIkA1nYES0fmLY54UL6W9cUXbjjkoH1a8rfIUnaV9OkfeXp1mXjzwsAgFLFWRwYg3U9Vdqwr3Lg95+0Hy1J2heZqt5Q6sY+l0N3CwAAECxcDwZ88HhbnTrmv1O7wrNUppgkulsAAFDKOIsDPrl498cVTYb09canUgvobgEAQMkiJAM+eaO7WpK016X+pbsFAACli6YuwGd7XeoGP0dLMgAAJYuzOOCzPYn9IZkLNQAAlCpCMuCz9kRq1AtakgEAKF2cxQGftcVTU1TTJxkAgNJFSAZ8tiu2vyWZkAwAQKnKKiSb2Vlm9qaZNZvZ9cOsv8TMXkn/PGNmx/lfVKA0bIvub0nmOygAAKVq1LO4mYUlfVvS2ZKWSLrIzJZ4Ntso6TTn3DJJN0u6y++CAqVifU+1emYcT0syAAAlLJumrpWSmp1zG5xz/ZJ+Lml15gbOuWeccx3pX5+T1OhvMYHS0ZMI6emK0xQNTyp2UQAAwBhlM0bVXElbMn5vkXTSQbb/pKRHxlMooNTdtm2Z3jFlXrGLAQAAxiibkGzDLHPDbmj2PqVC8rtGWH+lpCslad48AgQmrvX7qrR+X1WxiwEAAMYom+4WLZKaMn5vlLTNu5GZLZP0PUmrnXNtw+3IOXeXc26Fc27FjBkzxlJeAAAAIO+yCclrJC0yswVmVi7pQkkPZG5gZvMk/UrSZc65df4XEwAAACicUbtbOOfiZnatpEclhSXd7Zxba2ZXp9ffKelLkqZJ+j9mJklx59yK/BUbAAAAyJ9s+iTLOfewpIc9y+7MePwpSZ/yt2gAAABAcTDbAQAAAOBBSAYAAAA8CMkAAACAByEZAAAA8CAkAwAAAB6EZAAAAMCDkAwAAAB4EJIBAAAAD0IyAAAA4EFIBgAAADwIyQAAAIAHIRkAAADwICQDAAAAHoRkAAAAwIOQDAAAAHgQkgEAAAAPQjIAAADgQUgGAAAAPAjJAAAAgAchGQAAAPAgJAMAAAAehGQAAADAg5AMAAAAeBCSAQAAAA9CMgAAAOBBSAYAAAA8CMkAAACAByEZAAAA8CAkAwAAAB6EZAAAAMCDkAwAAAB4EJIBAAAAD0IyAAAA4EFIBgAAADwIyQAAAIAHIRkAAADwICQDAAAAHoRkAAAAwIOQDAAAAHgQkgEAAAAPQjIAAADgQUgGAAAAPAjJAAAAgAchGQAAAPAgJAMAAAAehGQAAADAg5AMAAAAeBCSAQAAAA9CMgAAAOBBSAYAAAA8CMkAAACAR1Yh2czOMrM3zazZzK4fZv1RZvasmUXN7PP+FxMAAAAonMhoG5hZWNK3Ja2S1CJpjZk94Jx7PWOzdkmfkfShvJQSAAAAKKBsWpJXSmp2zm1wzvVL+rmk1ZkbOOdanXNrJMXyUEYAAACgoLIJyXMlbcn4vSW9LGdmdqWZPW9mz+/atWssuwAAAADyLpuQbMMsc2N5MefcXc65Fc65FTNmzBjLLgAAAIC8yyYkt0hqyvi9UdK2/BQHAAAAKL5sQvIaSYvMbIGZlUu6UNID+S0WAAAAUDyjjm7hnIub2bWSHpUUlnS3c26tmV2dXn+nmc2W9LykyZKSZvZZSUucc3vzWHYAAAAgL0YNyZLknHtY0sOeZXdmPN6hVDcMAAAAoOQx4x4AAADgQUgGAAAAPAjJAAAAgAchGQAAAPAgJAMAAAAehGQAAADAg5AMAAAAeBCSAQAAAA9CMgAAAOBBSAYAAAA8CMkAAACAByEZAAAA8CAkAwAAAB6EZAAAAMCDkAwAAAB4EJIBAAAAD0IyAAAA4EFIBgAAADwIyQAAAIAHIRkAAADwICQDAAAAHoRkAAAAwIOQDAAAAHgQkgEAAAAPQjIAAADgQUgGAAAAPAjJAAAAgAchGQAAAPAgJAMAAAAehGQAAADAg5AMAAAAeBCSAQAAAA9CMgAAAOBBSAYAAAA8CMkAAACAByEZAAAA8CAkAwAAAB6EZAAAAMCDkAwAAAB4EJIBAAAAD0IyAAAA4EFIBgAAADwIyQAAAIAHIRkAAADwICQDAAAAHoRkAAAAwIOQDAAAAHgQkgEAAAAPQjIAAADgQUgGAAAAPLIKyWZ2lpm9aWbNZnb9MOvNzG5Pr3/FzE70v6gAAABAYYwaks0sLOnbks6WtETSRWa2xLPZ2ZIWpX+ulPQdn8sJAAAAFEw2LckrJTU75zY45/ol/VzSas82qyXd41KekzTVzBp8LisAAABQEJEstpkraUvG7y2STspim7mSto+rdHlQV12upXMnF7sYAAAAh5S66vJiFyEn2YRkG2aZG8M2MrMrleqOIUndZvZmFq8fZNMl7S52IUoI9ZU76ix31FnuqLPcUWe5o85yN6Hq7KHCvMxIdXZYrjvKJiS3SGrK+L1R0rYxbCPn3F2S7sqxjIFlZs8751YUuxylgvrKHXWWO+osd9RZ7qiz3FFnuaPOcudnnWXTJ3mNpEVmtsDMyiVdKOkBzzYPSLo8PcrFyZI6nXOB62oBAAAAZGPUlmTnXNzMrpX0qKSwpLudc2vN7Or0+jslPSzpHEnNkvZJ+nj+igwAAADkVzbdLeSce1ipIJy57M6Mx07SP/pbtJIwYbqOFAj1lTvqLHfUWe6os9xRZ7mjznJHneXOtzqzVL4FAAAAsB/TUgMAAAAehOQMZtZkZn80szfMbK2Z/VN6eb2Z/d7M3kr/W5dePi29fbeZ3eHZ12/N7G/p/dyZnrlwQvGzvjL2+YCZvVbI4ygkn99jT6Sni385/TOzGMeUbz7XWbmZ3WVm68zs72Z2fjGOKd/8qjMzq814f71sZrvN7JvFOq588vl9dpGZvWpmr6TPBdOLcUz55nOdfTRdX2vN7LZiHE8hjKHOVpnZC+n30wtmdnrGvpanlzeb2e1mNtxwvCXP5zq7xcy2mFl3Vi/unOMn/SOpQdKJ6ce1ktYpNRX3bZKuTy+/XtKt6cc1kt4l6WpJd3j2NTn9r0m6T9KFxT6+INdXev1HJP1U0mvFPrZSqDNJT0haUexjKrE6+4qkr6YfhyRNL/bxBb3OPPt9QdJ7in18Qa4zpe71ad3/3ko//9+KfXwBr7NpkjZLmpH+/f9KOqPYxxeQOjtB0pz046WStmbs66+STlEqZzwi6exiH18J1NnJ6f11Z/PatCRncM5td869mH7cJekNpWYOXK3UH63S/34ovU2Pc+5pSX3D7Gtv+mFEUrmGmVyl1PlZX2Y2SdLnJH21AEUvGj/r7FDhc519QtLX0tslnXMTZpD+TPl4n5nZIkkzJf0pj0UvGh/rzNI/NemWvckaZt6AicDHOlsoaZ1zblf69z9ImpBXecZQZy855/a/f9ZKqjSzCjNrUKox7lmXSn/37H/ORONXnaXXPedyGKKYkDwCM5uv1LeRv0iatb9S0/9mdVnbzB5VqkWhS9Iv81LQgPChvm6W9J9KDSF4SPDjPSbpB+nL4DdN1EttmcZTZ2Y2Nf3wZjN70czuNbNZeSxuIPj0PpOkiyT9In1CntDGU2fOuZikayS9qlQ4XiLp+3ksbiCM833WLOkoM5tvZhGlwk7TKM8peWOos/MlveSciyoVElsy1rWkl01o46yznBGSh5Fu1bxP0mczWoRz5pw7U6lm/QpJp4+yeckab32Z2fGSjnDO/dr3wgWUT++xS5xzx0p6d/rnMr/KF0Q+1FlEqdlA/+ycO1HSs5K+7mMRA8evz7K0CyX9bPylCjYfPs/KlArJJ0iaI+kVSTf4WsiAGW+dOec6lKqzXyh1pWKTpLifZQyaXOvMzI6RdKukq/YvGmazCf0F1oc6yxkh2SP9AXefpJ84536VXrwzfWlD6X9bs92fc65PqRkJV/td1iDwqb5OkbTczDZJelrSkWb2RH5KXHx+vcecc1vT/3Yp1Zd7ZX5KXHw+1VmbUlcq9n8Zu1fSiXkobiD4+VlmZsdJijjnXshLYQPCpzo7XpKcc+vTre7/T9KpeSpy0fn4efagc+4k59wpkt6U9Fa+ylxsudaZmTUq9bl1uXNufXpxi1Jf+vdr1ATt1iP5Vmc5IyRnSF+u/r6kN5xz38hY9YCkj6Uff0zS/aPsZ1LGf1xEqdkI/+5/iYvLr/pyzn3HOTfHOTdfqZs61jnn3ut/iYvPx/dYxNJ3zKc/PD4gaUKOCuLj+8xJelDSe9OLzpD0uq+FDQi/6izDRZrgrcg+1tlWSUvMbEb691VK9aGccPx8n1l6dJ70CAWflvQ9f0sbDLnWWbqb2EOSbnDO/Xn/xunuBV1mdnJ6n5cr+7/nkuJXnY2JC8Cdi0H5USqgOaUuj72c/jlHqTtvH1Pqm+1jkuoznrNJUrukbqW+2S2RNEvSmvR+1kr6L6VaYYp+jEGsL88+52tij27h13usRqmRBva/x74lKVzs4wtynaWXHybpqfS+HpM0r9jHF/Q6S6/bIOmoYh9XqdSZUqM3vJHe14OSphX7+Eqgzn6m1JfW1zUBR4Maa51JulFST8a2L0uamV63QqnGkfWS7lB6griJ9uNznd2Wft8l0//+28Femxn3AAAAAA+6WwAAAAAehGQAAADAg5AMAAAAeBCSAQAAAA9CMgAAAOBBSAYAAAA8CMkAAACAByEZAAAA8Pj/JGs/ocwhxhAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"factors = pd.DataFrame(f_loadings)\\n\",\n \"factors.index = frame_t.index[period:]\\n\",\n \"plt.figure(figsize=(12, 8))\\n\",\n \"plt.stackplot(factors.index, factors.transpose())\\n\",\n \"plt.title('Contribution to variance from each component')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3: Interpreting top 2 risk drivers\\n\",\n \"Let’s now look at the top 2 components explaining risk in 2020 as well as over the entire period. Note, components can rotate over time, so we look at the absolute ratios of PC1 vs PC2 and vise versa to understand the primary drivers.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# model 1 trained on full dataset\\n\",\n \"full_model = PCA(n_components=components)\\n\",\n \"full_data = (frame_t - frame_t.mean()) / frame_t.std()\\n\",\n \"full_model.fit(full_data)\\n\",\n \"components_full = pd.DataFrame(full_model.components_, columns=frame_t.columns)\\n\",\n \"\\n\",\n \"# model 2 trained on 2020\\n\",\n \"model_2020 = PCA(n_components=components)\\n\",\n \"data2 = frame_t[frame_t.index.year == 2020]\\n\",\n \"model_2020.fit((data2 - data2.mean()) / data2.std())\\n\",\n \"components_2020 = pd.DataFrame(model_2020.components_, columns=data.columns, index=factors.columns)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'PC2')\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(16, 16))\\n\",\n \"comp1, comp2 = 0, 1\\n\",\n \"for i, row in components_full.iteritems():\\n\",\n \" plt.scatter(row[comp1], row[comp2], color=color_map[asset_map[row.name]], label=row.name)\\n\",\n \" plt.text(row[comp1]-0.02, row[comp2]-.01, row.name, fontsize=9)\\n\",\n \"\\n\",\n \"plt.axhline(0, color='grey')\\n\",\n \"plt.axvline(0, color='grey')\\n\",\n \"plt.title('PC1 vs PC2 - Full History')\\n\",\n \"plt.xlabel('PC'+str(comp1+1))\\n\",\n \"plt.ylabel('PC'+str(comp2+1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'PC2')\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(20, 20))\\n\",\n \"\\n\",\n \"for (_, col1), (_, col2) in zip(components_full.iteritems(), components_2020.iteritems()):\\n\",\n \" plt.scatter(col2[comp1], col2[comp2], color='pink')\\n\",\n \" plt.scatter(col1[comp1], col1[comp2], color=color_map[asset_map[col2.name]])\\n\",\n \" plt.plot((col2[comp1], col1[comp1]), (col2[comp2], col1[comp2]), color='grey', alpha=0.3)\\n\",\n \" plt.text(col2[comp1], col2[comp2], col1.name, fontsize=9)\\n\",\n \"\\n\",\n \"plt.axhline(0, color='grey')\\n\",\n \"plt.axvline(0, color='grey')\\n\",\n \"\\n\",\n \"plt.title('PC1 vs PC2 - Full History vs 2020 Data only')\\n\",\n \"plt.xlabel('PC'+str(comp1+1))\\n\",\n \"plt.ylabel('PC'+str(comp2+1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"PC1_f, PC2_f = components_full.iloc[0].abs(), components_full.iloc[1].abs()\\n\",\n \"PC1_2020, PC2_2020 = components_2020.iloc[0].abs(), components_2020.iloc[1].abs()\\n\",\n \"\\n\",\n \"plt.subplot(2, 2, 1)\\n\",\n \"(PC1_f/PC2_f).sort_values(ascending=False).head(15).plot(kind='barh', title='PC1/PC2 - full', figsize=(16, 8))\\n\",\n \"plt.subplot(2, 2, 2)\\n\",\n \"(PC2_f/PC1_f).sort_values(ascending=False).head(15).plot(kind='barh', title='PC2/PC1 - full', figsize=(16, 8))\\n\",\n \"plt.subplot(2, 2, 3)\\n\",\n \"(PC1_2020/PC2_2020).sort_values(ascending=False).head(15).plot(kind='barh', title='PC1/PC2 - 2020', figsize=(16, 8))\\n\",\n \"plt.subplot(2, 2, 4)\\n\",\n \"(PC2_2020/PC1_2020).sort_values(ascending=False).head(15).plot(kind='barh', title='PC2/PC1 - 2020', figsize=(16, 8))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4: Realized vs modeled\\n\",\n \"\\n\",\n \"Let’s now look at where the 3 component PCA model would tell us the returns for several series of interest should be vs where they have realized. The series with the largest model deviations present potential dislocations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pca_returns = pd.DataFrame(full_model.transform(full_data), index=full_data.index)\\n\",\n \"res = pca_returns.dot(components_full) * frame_t.std() + frame_t.mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def transform(tr_df, since):\\n\",\n \" t_d = tr_df.copy()[since:]\\n\",\n \" for key, v in t_d.items():\\n\",\n \" t_d[key] = (1 + t_d[key] / 100).cumprod() if key in instruments['Rates'] else t_d[key].cumsum()\\n\",\n \" return t_d\\n\",\n \"\\n\",\n \"def compare_plot(asset, actual, predicted):\\n\",\n \" plt.figure(figsize=(10, 4))\\n\",\n \" plt.plot(actual[asset], label='actual')\\n\",\n \" plt.plot(predicted[asset], label='predicted')\\n\",\n \" plt.title(asset)\\n\",\n \" plt.legend()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# compare each of the variables since beginning of the year\\n\",\n \"since = '2020-01-01'\\n\",\n \"predicted = transform(res, since)\\n\",\n \"actual = transform(frame_t, since)\\n\",\n \"\\n\",\n \"fx_graphs = ['USDCAD', 'AUDJPY'] + [f+'_3m_impvol' for f in ['USDJPY', 'AUDJPY']]\\n\",\n \"for asset in fx_graphs:\\n\",\n \" compare_plot(asset, actual, predicted)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rates_graphs = ['5y', '30y']\\n\",\n \"rates_vols = ['3m'+r+'_impvol' for r in rates_graphs] + ['3y_vs_3m_'+r+'_calendar' for r in rates_graphs] + ['3y5y_skew']\\n\",\n \"\\n\",\n \"for asset in rates_vols:\\n\",\n \" compare_plot(asset, actual, predicted)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimers\\n\",\n \"\\n\",\n \"Scenarios/predictions: Simulated results are for illustrative purposes only. GS provides no assurance or guarantee that the strategy will operate or would have operated in the past in a manner consistent with the above analysis. Past performance figures are not a reliable indicator of future results.\\n\",\n \"\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"\\n\",\n \"Backtesting, Simulated Results, Sensitivity/Scenario Analysis or Spreadsheet Calculator or Model: There may be data presented herein that is solely for illustrative purposes and which may include among other things back testing, simulated results and scenario analyses. The information is based upon certain factors, assumptions and historical information that Goldman Sachs may in its discretion have considered appropriate, however, Goldman Sachs provides no assurance or guarantee that this product will operate or would have operated in the past in a manner consistent with these assumptions. In the event any of the assumptions used do not prove to be true, results are likely to vary materially from the examples shown herein. Additionally, the results may not reflect material economic and market factors, such as liquidity, transaction costs and other expenses which could reduce potential return.\\n\",\n \"\\n\",\n \"OTC Derivatives Risk Disclosures: \\n\",\n \"Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \\n\",\n \" \\n\",\n \"Liquidity Risk: There is no public market for OTC derivative transactions and, therefore, it may be difficult or impossible to liquidate an existing position on favorable terms. Transfer Restrictions: OTC derivative transactions entered into with one or more affiliates of The Goldman Sachs Group, Inc. (Goldman Sachs) cannot be assigned or otherwise transferred without its prior written consent and, therefore, it may be impossible for you to transfer any OTC derivative transaction to a third party. \\n\",\n \" \\n\",\n \"Conflict of Interests: Goldman Sachs may from time to time be an active participant on both sides of the market for the underlying securities, commodities, futures, options or any other derivative or instrument identical or related to those mentioned herein (together, \\\"the Product\\\"). Goldman Sachs at any time may have long or short positions in, or buy and sell Products (on a principal basis or otherwise) identical or related to those mentioned herein. Goldman Sachs hedging and trading activities may affect the value of the Products. \\n\",\n \" \\n\",\n \"Counterparty Credit Risk: Because Goldman Sachs, may be obligated to make substantial payments to you as a condition of an OTC derivative transaction, you must evaluate the credit risk of doing business with Goldman Sachs or its affiliates. \\n\",\n \" \\n\",\n \"Pricing and Valuation: The price of each OTC derivative transaction is individually negotiated between Goldman Sachs and each counterparty and Goldman Sachs does not represent or warrant that the prices for which it offers OTC derivative transactions are the best prices available, possibly making it difficult for you to establish what is a fair price for a particular OTC derivative transaction; The value or quoted price of the Product at any time, however, will reflect many factors and cannot be predicted. If Goldman Sachs makes a market in the offered Product, the price quoted by Goldman Sachs would reflect any changes in market conditions and other relevant factors, and the quoted price (and the value of the Product that Goldman Sachs will use for account statements or otherwise) could be higher or lower than the original price, and may be higher or lower than the value of the Product as determined by reference to pricing models used by Goldman Sachs. If at any time a third party dealer quotes a price to purchase the Product or otherwise values the Product, that price may be significantly different (higher or lower) than any price quoted by Goldman Sachs. Furthermore, if you sell the Product, you will likely be charged a commission for secondary market transactions, or the price will likely reflect a dealer discount. Goldman Sachs may conduct market making activities in the Product. To the extent Goldman Sachs makes a market, any price quoted for the OTC derivative transactions, Goldman Sachs may differ significantly from (i) their value determined by reference to Goldman Sachs pricing models and (ii) any price quoted by a third party. The market price of the OTC derivative transaction may be influenced by many unpredictable factors, including economic conditions, the creditworthiness of Goldman Sachs, the value of any underlyers, and certain actions taken by Goldman Sachs. \\n\",\n \" \\n\",\n \"Market Making, Investing and Lending: Goldman Sachs engages in market making, investing and lending businesses for its own account and the accounts of its affiliates in the same or similar instruments underlying OTC derivative transactions (including such trading as Goldman Sachs deems appropriate in its sole discretion to hedge its market risk in any OTC derivative transaction whether between Goldman Sachs and you or with third parties) and such trading may affect the value of an OTC derivative transaction. \\n\",\n \" \\n\",\n \"Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\\n\",\n \"Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf, http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. Certain transactions - including those involving futures, options, equity swaps, and other derivatives as well as non-investment-grade securities - give rise to substantial risk and are not available to nor suitable for all investors. If you have any questions about whether you are eligible to enter into these transactions with Goldman Sachs, please contact your sales representative. Foreign-currency-denominated securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\\n\",\n \"Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. Transaction costs may be a significant factor in option strategies calling for multiple purchases and sales of options, such as spreads. When purchasing long options an investor may lose their entire investment and when selling uncovered options the risk is potentially unlimited. Supporting documentation for any comparisons, recommendations, statistics, technical data, or other similar information will be supplied upon request.\\n\",\n \"This material is for the private information of the recipient only. This material is not sponsored, endorsed, sold or promoted by any sponsor or provider of an index referred herein (each, an \\\"Index Provider\\\"). GS does not have any affiliation with or control over the Index Providers or any control over the computation, composition or dissemination of the indices. While GS will obtain information from publicly available sources it believes reliable, it will not independently verify this information. Accordingly, GS shall have no liability, contingent or otherwise, to the user or to third parties, for the quality, accuracy, timeliness, continued availability or completeness of the data nor for any special, indirect, incidental or consequential damages which may be incurred or experienced because of the use of the data made available herein, even if GS has been advised of the possibility of such damages.\\n\",\n \"Standard & Poor's ® and S&P ® are registered trademarks of The McGraw-Hill Companies, Inc. and S&P GSCI™ is a trademark of The McGraw-Hill Companies, Inc. and have been licensed for use by the Issuer. This Product (the \\\"Product\\\") is not sponsored, endorsed, sold or promoted by S&P and S&P makes no representation, warranty or condition regarding the advisability of investing in the Product.\\n\",\n \"Notice to Brazilian Investors\\n\",\n \"Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/00_us_election_analysis/0001_portfolios_and_var.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import datetime\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType, PnlExplain\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"plt.rcParams['figure.figsize'] = (16, 8)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we'll load a portfolio and run analytics to understand it's risk and pnl. We'll also run a number of vol and spot scenarios which we'll use to compute VaR.\\n\",\n \"\\n\",\n \"The content of this notebook is split into the following parts:\\n\",\n \"* [1: First, the portfolio](#1:-First,-the-portfolio)\\n\",\n \"* [2: Run risk and PnL explain](#2:-Run-risk-and-PnL-explain)\\n\",\n \"* [3: Scenarios grid and VaR](#3:-Scenarios-grid-and-VaR)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1: First, the portfolio\\n\",\n \"\\n\",\n \"Let’s first load a portfolio from csv, mapping each column and look at it as a dataframe. Internal users can load this books directly using `from book`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
instrument<gs_quant.target.instrument.FXOption object at 0x0000000019252518>
asset_classAssetClass.FX
typeAssetType.Option
buy_sellBuySell.Buy
call_amount25000000
call_currencyCurrency.AUD
expiration_date2020-10-21
expiration_timeNYC
method_of_settlementOptionSettlementMethod.Physical
notional_amount2.5e+07
option_typeOptionType.Call
pairAUD USD
premium0
premium_currencyCurrency.USD
premium_payment_date2020-10-23
put_amount18750000
put_currencyCurrency.USD
settlement_date2020-10-23
strike_price0.75
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \"instrument \\n\",\n \"asset_class AssetClass.FX \\n\",\n \"type AssetType.Option \\n\",\n \"buy_sell BuySell.Buy \\n\",\n \"call_amount 25000000 \\n\",\n \"call_currency Currency.AUD \\n\",\n \"expiration_date 2020-10-21 \\n\",\n \"expiration_time NYC \\n\",\n \"method_of_settlement OptionSettlementMethod.Physical \\n\",\n \"notional_amount 2.5e+07 \\n\",\n \"option_type OptionType.Call \\n\",\n \"pair AUD USD \\n\",\n \"premium 0 \\n\",\n \"premium_currency Currency.USD \\n\",\n \"premium_payment_date 2020-10-23 \\n\",\n \"put_amount 18750000 \\n\",\n \"put_currency Currency.USD \\n\",\n \"settlement_date 2020-10-23 \\n\",\n \"strike_price 0.75 \"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mappers = {\\n\",\n \" 'pair': lambda row: row['foreign ccy'] + 'USD',\\n\",\n \" 'notional_amount': 'notional',\\n\",\n \" 'expiration_date': 'expiry',\\n\",\n \" 'option_type': lambda row: 'Call' if row['C/P'] == 'C' else 'Put',\\n\",\n \" 'strike_price': 'strike',\\n\",\n \" 'premium': lambda row: 0\\n\",\n \"}\\n\",\n \"portfolio = Portfolio.from_csv(r'FXBook.csv', mappings=mappers)\\n\",\n \"portfolio.resolve()\\n\",\n \"frame = portfolio.to_frame()\\n\",\n \"frame.index = frame.index.droplevel(0)\\n\",\n \"frame.head(1).transpose()\\n\",\n \"\\n\",\n \"# for internal users:\\n\",\n \"# portfolio = Portfolio.from_book('my_book_id')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2: Run risk and PnL explain\\n\",\n \"\\n\",\n \"With the portfolio in hand, let’s use gs-quant to understand risk and pnl over the last business day.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.datetime.date import business_day_offset\\n\",\n \"from gs_quant.markets import CloseMarket, PricingContext, close_market_date\\n\",\n \"from gs_quant.risk import Price, DollarPrice, PnlExplain, Theta, FXDelta, FXGamma, FXVega\\n\",\n \"\\n\",\n \"to_date = business_day_offset(close_market_date(), -1)\\n\",\n \"\\n\",\n \"# Previous business day\\n\",\n \"from_date = business_day_offset(to_date, -1)\\n\",\n \"\\n\",\n \"# A risk measure for calculating PnlExplain from that date\\n\",\n \"explain = PnlExplain(CloseMarket(date=to_date))\\n\",\n \"\\n\",\n \"# Calculate PnlExplain and dollar price from 1 day ago\\n\",\n \"with PricingContext(pricing_date=from_date):\\n\",\n \" result = portfolio.calc((FXDelta, FXGamma, FXVega, DollarPrice, Theta, explain))\\n\",\n \" \\n\",\n \"# Calculate dollar price with today's market\\n\",\n \"with PricingContext(pricing_date=to_date):\\n\",\n \" target_price = portfolio.calc((DollarPrice))\\n\",\n \"\\n\",\n \"with PricingContext(pricing_date=from_date, market=CloseMarket(date=to_date)):\\n\",\n \" target_price_ex_theta = portfolio.calc((DollarPrice))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Risk and 1day Pnl as of 2020-09-04\\n\",\n \"Book PV (in mUSD): 15.8\\n\",\n \"Book Delta (in mUSD): 186\\n\",\n \"Book Vega (in kUSD): 1390\\n\",\n \"Dollar price day on day change (in kUSD): 770\\n\",\n \"Dollar price day on day change(ex theta) (in kUSD): 799\\n\",\n \"Pnl explain total (in kUSD): 799\\n\",\n \"Theta total (in kUSD): -61\\n\",\n \"Theta + Pnl explain total (in kUSD): 738\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
mkt_typemkt_assetvalue
0FXUSD/AUD775947.0
3FX EVENTSUSD-12.0
4FX FWDUSD/AUD1419.0
5FX VOLUSD/AUD58135.0
6IRAUD-50239.0
7IR BASISAUD OIS/AUD-3M35.0
8IR BASISAUD-3M/AUD-6M2843.0
11IR XCAUD11020.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" mkt_type mkt_asset value\\n\",\n \"0 FX USD/AUD 775947.0\\n\",\n \"3 FX EVENTS USD -12.0\\n\",\n \"4 FX FWD USD/AUD 1419.0\\n\",\n \"5 FX VOL USD/AUD 58135.0\\n\",\n \"6 IR AUD -50239.0\\n\",\n \"7 IR BASIS AUD OIS/AUD-3M 35.0\\n\",\n \"8 IR BASIS AUD-3M/AUD-6M 2843.0\\n\",\n \"11 IR XC AUD 11020.0\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"print('Risk and 1day Pnl as of '+str(from_date))\\n\",\n \"print(f'Book PV (in mUSD): {target_price[DollarPrice].aggregate()/1e6:.1f}')\\n\",\n \"print(f'Book Delta (in mUSD): {result[FXDelta].aggregate().value.sum()/1e6:.0f}')\\n\",\n \"print(f'Book Vega (in kUSD): {result[FXVega].aggregate().value.sum():.0f}')\\n\",\n \"print(f'Dollar price day on day change (in kUSD): {target_price[DollarPrice].aggregate()/1e3 - result[DollarPrice].aggregate()/1e3:.0f}')\\n\",\n \"print(f'Dollar price day on day change(ex theta) (in kUSD): {target_price_ex_theta[DollarPrice].aggregate()/1e3 - result[DollarPrice].aggregate()/1e3:.0f}')\\n\",\n \"print(f'Pnl explain total (in kUSD): {result[explain].aggregate().value.sum()/1e3:.0f}')\\n\",\n \"print(f'Theta total (in kUSD): {result[Theta].aggregate().value.sum()/1e3:.0f}')\\n\",\n \"print(f'Theta + Pnl explain total (in kUSD): {result[Theta].aggregate().value.sum()/1e3 + result[explain].aggregate().value.sum()/1e3:.0f}')\\n\",\n \"\\n\",\n \"# Show the PnlExplain breakdown\\n\",\n \"explain_all = result[explain].aggregate()\\n\",\n \"explain_all[explain_all.value.abs() > 1.0].round(0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3: Scenarios grid and VaR\\n\",\n \"\\n\",\n \"We can also better understand risk by applying a number of market shocks – in this case we’ll look at a grid of vol and spot shocks, we are can also use to calculate VaR by looking at the 95% percentile price change.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"shocks_fx = [-10, -7.5, -5, -2,-1, 0, 1, 2, 5, 7.5, 10]\\n\",\n \"shocks_vols = [-5, -2, -1, -0.5, 0, 0.5, 1, 2, 5, 7.5, 10]\\n\",\n \"shocked_prices = pd.DataFrame(index=shocks_vols, columns=shocks_fx)\\n\",\n \"\\n\",\n \"cross = explain_all[explain_all['mkt_type'] == 'FX']['mkt_asset'][0]\\n\",\n \"\\n\",\n \"with PricingContext(is_async=True):\\n\",\n \" for fx in shocks_fx:\\n\",\n \" for vol in shocks_vols:\\n\",\n \" with MarketDataShockBasedScenario({\\n\",\n \" MarketDataPattern('FX', cross): MarketDataShock(MarketDataShockType.Proportional, fx / 1e2),\\n\",\n \" MarketDataPattern('FX Vol', cross, 'ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, vol / 1e2),\\n\",\n \" }):\\n\",\n \" shocked_prices[fx][vol] = portfolio.calc((DollarPrice))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Aggregate and compute pnl by substracting 0 shock PV\\n\",\n \"shocked_prices_res = pd.DataFrame(index=shocks_vols, columns=shocks_fx, dtype='float')\\n\",\n \"\\n\",\n \"for fx in shocks_fx:\\n\",\n \" for vol in shocks_vols:\\n\",\n \" shocked_prices_res[fx][vol] = shocked_prices[fx][vol].aggregate()\\n\",\n \" \\n\",\n \"shocked_prices_res -= shocked_prices[0][0].aggregate()\\n\",\n \"shocked_prices_res /= 1e3\\n\",\n \"\\n\",\n \"ax = sns.heatmap(shocked_prices_res, cmap='coolwarm', annot=True, fmt='.1f')\\n\",\n \"ax.set(ylabel='absolute vol point moves', xlabel='% spot change', title='PV changes ($k)')\\n\",\n \"ax.xaxis.tick_top()\\n\",\n \"ax.xaxis.set_label_position('top')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Compute VaR\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Portfolio base price ($m): 15.2\\n\",\n \"Scenario Based VaR with spot/vol grid ($m): -10.7\\n\"\n ]\n }\n ],\n \"source\": [\n \"p = np.percentile(shocked_prices_res, 5) # return 95th percentile\\n\",\n \"print('Portfolio base price ($m): {:,.1f}'.format(portfolio.price().aggregate()/1e6))\\n\",\n \"print('Scenario Based VaR with spot/vol grid ($m): {:,.1f}'.format(p/1e3))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimers\\n\",\n \"\\n\",\n \"Scenarios/predictions: Simulated results are for illustrative purposes only. GS provides no assurance or guarantee that the strategy will operate or would have operated in the past in a manner consistent with the above analysis. Past performance figures are not a reliable indicator of future results.\\n\",\n \"\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"\\n\",\n \"Backtesting, Simulated Results, Sensitivity/Scenario Analysis or Spreadsheet Calculator or Model: There may be data presented herein that is solely for illustrative purposes and which may include among other things back testing, simulated results and scenario analyses. The information is based upon certain factors, assumptions and historical information that Goldman Sachs may in its discretion have considered appropriate, however, Goldman Sachs provides no assurance or guarantee that this product will operate or would have operated in the past in a manner consistent with these assumptions. In the event any of the assumptions used do not prove to be true, results are likely to vary materially from the examples shown herein. Additionally, the results may not reflect material economic and market factors, such as liquidity, transaction costs and other expenses which could reduce potential return.\\n\",\n \"\\n\",\n \"OTC Derivatives Risk Disclosures: \\n\",\n \"Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \\n\",\n \" \\n\",\n \"Liquidity Risk: There is no public market for OTC derivative transactions and, therefore, it may be difficult or impossible to liquidate an existing position on favorable terms. Transfer Restrictions: OTC derivative transactions entered into with one or more affiliates of The Goldman Sachs Group, Inc. (Goldman Sachs) cannot be assigned or otherwise transferred without its prior written consent and, therefore, it may be impossible for you to transfer any OTC derivative transaction to a third party. \\n\",\n \" \\n\",\n \"Conflict of Interests: Goldman Sachs may from time to time be an active participant on both sides of the market for the underlying securities, commodities, futures, options or any other derivative or instrument identical or related to those mentioned herein (together, \\\"the Product\\\"). Goldman Sachs at any time may have long or short positions in, or buy and sell Products (on a principal basis or otherwise) identical or related to those mentioned herein. Goldman Sachs hedging and trading activities may affect the value of the Products. \\n\",\n \" \\n\",\n \"Counterparty Credit Risk: Because Goldman Sachs, may be obligated to make substantial payments to you as a condition of an OTC derivative transaction, you must evaluate the credit risk of doing business with Goldman Sachs or its affiliates. \\n\",\n \" \\n\",\n \"Pricing and Valuation: The price of each OTC derivative transaction is individually negotiated between Goldman Sachs and each counterparty and Goldman Sachs does not represent or warrant that the prices for which it offers OTC derivative transactions are the best prices available, possibly making it difficult for you to establish what is a fair price for a particular OTC derivative transaction; The value or quoted price of the Product at any time, however, will reflect many factors and cannot be predicted. If Goldman Sachs makes a market in the offered Product, the price quoted by Goldman Sachs would reflect any changes in market conditions and other relevant factors, and the quoted price (and the value of the Product that Goldman Sachs will use for account statements or otherwise) could be higher or lower than the original price, and may be higher or lower than the value of the Product as determined by reference to pricing models used by Goldman Sachs. If at any time a third party dealer quotes a price to purchase the Product or otherwise values the Product, that price may be significantly different (higher or lower) than any price quoted by Goldman Sachs. Furthermore, if you sell the Product, you will likely be charged a commission for secondary market transactions, or the price will likely reflect a dealer discount. Goldman Sachs may conduct market making activities in the Product. To the extent Goldman Sachs makes a market, any price quoted for the OTC derivative transactions, Goldman Sachs may differ significantly from (i) their value determined by reference to Goldman Sachs pricing models and (ii) any price quoted by a third party. The market price of the OTC derivative transaction may be influenced by many unpredictable factors, including economic conditions, the creditworthiness of Goldman Sachs, the value of any underlyers, and certain actions taken by Goldman Sachs. \\n\",\n \" \\n\",\n \"Market Making, Investing and Lending: Goldman Sachs engages in market making, investing and lending businesses for its own account and the accounts of its affiliates in the same or similar instruments underlying OTC derivative transactions (including such trading as Goldman Sachs deems appropriate in its sole discretion to hedge its market risk in any OTC derivative transaction whether between Goldman Sachs and you or with third parties) and such trading may affect the value of an OTC derivative transaction. \\n\",\n \" \\n\",\n \"Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\\n\",\n \"Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf, http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. Certain transactions - including those involving futures, options, equity swaps, and other derivatives as well as non-investment-grade securities - give rise to substantial risk and are not available to nor suitable for all investors. If you have any questions about whether you are eligible to enter into these transactions with Goldman Sachs, please contact your sales representative. Foreign-currency-denominated securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\\n\",\n \"Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. Transaction costs may be a significant factor in option strategies calling for multiple purchases and sales of options, such as spreads. When purchasing long options an investor may lose their entire investment and when selling uncovered options the risk is potentially unlimited. Supporting documentation for any comparisons, recommendations, statistics, technical data, or other similar information will be supplied upon request.\\n\",\n \"This material is for the private information of the recipient only. This material is not sponsored, endorsed, sold or promoted by any sponsor or provider of an index referred herein (each, an \\\"Index Provider\\\"). GS does not have any affiliation with or control over the Index Providers or any control over the computation, composition or dissemination of the indices. While GS will obtain information from publicly available sources it believes reliable, it will not independently verify this information. Accordingly, GS shall have no liability, contingent or otherwise, to the user or to third parties, for the quality, accuracy, timeliness, continued availability or completeness of the data nor for any special, indirect, incidental or consequential damages which may be incurred or experienced because of the use of the data made available herein, even if GS has been advised of the possibility of such damages.\\n\",\n \"Standard & Poor's ® and S&P ® are registered trademarks of The McGraw-Hill Companies, Inc. and S&P GSCI™ is a trademark of The McGraw-Hill Companies, Inc. and have been licensed for use by the Issuer. This Product (the \\\"Product\\\") is not sponsored, endorsed, sold or promoted by S&P and S&P makes no representation, warranty or condition regarding the advisability of investing in the Product.\\n\",\n \"Notice to Brazilian Investors\\n\",\n \"Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/00_us_election_analysis/0002_past_elections.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.timeseries import returns\\n\",\n \"from gs_quant.risk import MarketDataPattern, MarketDataShock, MarketDataShockBasedScenario, MarketDataShockType\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.common import PayReceive\\n\",\n \"from gs_quant import risk\\n\",\n \"import datetime\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from gs_quant.datetime.date import business_day_offset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we'll look at how several rate and fx instruments moved in the 2012 and 2016 elections and use these moves as shocks for our portfolio.\\n\",\n \"\\n\",\n \"The content of this notebook is split into the following parts:\\n\",\n \"* [1: Visualize last 2 elections](#1:-Visualize-last-2-elections)\\n\",\n \"* [2: Grab portfolio](#2:-Grab-portfolio)\\n\",\n \"* [3: Use prior elections as shocks](#3:-Use-prior-elections-as-shocks)\\n\",\n \"* [4: Custom shocks](#4:-Custom-shocks)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1: Visualize last 2 elections\\n\",\n \"\\n\",\n \"Let's start by retrieving the data from the [Marquee data catalog](https://marquee.gs.com/s/discover/data-services/catalog) and visualizing it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"elec_12 = datetime.date(2012, 11, 6)\\n\",\n \"elec_16 = datetime.date(2016, 11, 8)\\n\",\n \"elec_20 = datetime.date(2020, 11, 3)\\n\",\n \"\\n\",\n \"t0 = datetime.date(2012, 1, 1)\\n\",\n \"tn = datetime.date.today()\\n\",\n \"\\n\",\n \"fx = Dataset('FXSPOT_PREMIUM').get_data(t0, tn, bbid='AUDJPY', tenor='3m', fields=('spot',))[['spot']].spot\\n\",\n \"fx_vol = Dataset('FXIMPLIEDVOL_PREMIUM').get_data(t0, tn, bbid='AUDJPY', deltaStrike='25DC', tenor='3m')[['impliedVolatility']].impliedVolatility\\n\",\n \"rates = Dataset('IR_SWAP2').get_data(t0, assetId=['MAAXGV0GZTW4GFNC'], tenor='30y', fields=('rate',))[['rate']].rate\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def slice_data(df, y_0):\\n\",\n \" df_period = pd.concat((df.loc[x:y].reset_index() for x, y in\\n\",\n \" [(datetime.date(t, 8, 1), datetime.date(t + 1, 1, 31)) for t in [2012, 2016]]), axis=1)\\n\",\n \" df_period['month'] = pd.DatetimeIndex(df_period.date.iloc[:, 0]).month_name()\\n\",\n \" df_period.set_index('month', drop=True, inplace=True)\\n\",\n \" df_period.drop('date', axis=1, inplace=True)\\n\",\n \" df_period.columns = ['Democrat win 2012', 'Republican win 2016']\\n\",\n \" return df_period\\n\",\n \"\\n\",\n \"def plot_data(title, data):\\n\",\n \" ax = data.plot(title=title, figsize=(10, 4))\\n\",\n \" plt.axvline(x=len(fx[datetime.date(2012, 8, 1):datetime.date(2012, 11, 5)]), linestyle='--', color='r')\\n\",\n \" ax.set_xticks([21*k for k in range(6)])\\n\",\n \" _ = ax.set_xticklabels(['August', 'September', 'October', 'November', 'December', 'January'], horizontalalignment='left')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fx_sliced, fx_vol_sliced, rates_sliced = [slice_data(x, 2012) for x in [fx, fx_vol, rates]]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_data('AUDJPY', fx_sliced)\\n\",\n \"plot_data('AUDJPY 3m vol', fx_vol_sliced)\\n\",\n \"plot_data('30y rates', rates_sliced)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2: Grab portfolio\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's use our FX book from the previous notebook and add a few rates trades. We'll shock this book in the next step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
asset_classtypebuy_sellcall_amountcall_currencyclearing_houseeffective_dateexpiration_dateexpiration_timefee...pay_or_receivepremiumpremium_currencypremium_payment_dateprincipal_exchangeput_amountput_currencysettlement_datestrike_pricetermination_date
instrument
<gs_quant.target.instrument.FXOption object at 0x000000001CE38860>AssetClass.FXAssetType.OptionBuySell.Sell-27300000Currency.USD2027-03-24NYC...0Currency.USD2027-03-30-42000000Currency.AUD2027-03-300.65
<gs_quant.target.instrument.IRSwap object at 0x00000000050652E8>AssetClass.RatesAssetType.SwapSwapClearingHouse.NONE2020-09-110...PayReceive.ReceivePrincipalExchange._None2030-09-11
<gs_quant.target.instrument.IRSwap object at 0x000000001CE32D68>AssetClass.RatesAssetType.SwapSwapClearingHouse.NONE2020-09-110...PayReceive.PayPrincipalExchange._None2025-09-11
\\n\",\n \"

3 rows × 37 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" asset_class \\\\\\n\",\n \"instrument \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
spotimpliedVolatilityrate
2012 Election0.1-1.1-6.2
2016 Election2.36.025.9
\\n\",\n \"\"\n ],\n \"text/plain\": [\n \" spot impliedVolatility rate\\n\",\n \"2012 Election 0.1 -1.1 -6.2\\n\",\n \"2016 Election 2.3 6.0 25.9\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"time_window = 2 \\n\",\n \"effective12 = business_day_offset(elec_12, time_window) \\n\",\n \"effective16 = business_day_offset(elec_16, time_window) \\n\",\n \"\\n\",\n \"fx_shocks = fx.pct_change(time_window+5).loc[[effective12, effective16]] \\n\",\n \"fx_vol_shocks = fx_vol.pct_change(time_window+5).loc[[effective12, effective16]] \\n\",\n \"ir_shocks = rates.diff(time_window+5).loc[[effective12, effective16]] \\n\",\n \"\\n\",\n \"shock_df = pd.concat([fx_shocks*1e2, fx_vol_shocks*1e2, ir_shocks*1e4], axis=1).round(1) \\n\",\n \"shock_df.index = ['2012 Election', '2016 Election'] \\n\",\n \"\\n\",\n \"def get_market_scenario(s_date): \\n\",\n \" return MarketDataShockBasedScenario(shocks={ \\n\",\n \" MarketDataPattern('IR', 'USD'): MarketDataShock(MarketDataShockType.Absolute, ir_shocks.loc[s_date]), \\n\",\n \" MarketDataPattern('FX', 'USD/AUD'): MarketDataShock(MarketDataShockType.Proportional, fx_shocks.loc[s_date]), \\n\",\n \" MarketDataPattern('FX', 'JPY/USD'): MarketDataShock(MarketDataShockType.Proportional, 0), \\n\",\n \" MarketDataPattern('FX Vol', 'JPY/AUD', 'ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, fx_vol_shocks.loc[s_date]),\\n\",\n \" MarketDataPattern('FX Vol', 'USD/AUD', 'ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, fx_vol_shocks.loc[s_date]),\\n\",\n \" MarketDataPattern('FX Vol', 'JPY/USD', 'ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, fx_vol_shocks.loc[s_date]),\\n\",\n \"\\n\",\n \" }) \\n\",\n \"\\n\",\n \"win_12 = get_market_scenario(effective12) \\n\",\n \"win_16 = get_market_scenario(effective16) \\n\",\n \"shock_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# scenario carrying swap to election day and then applying spot shock\\n\",\n \"results, results_agg = {}, {}\\n\",\n \"\\n\",\n \"with win_12:\\n\",\n \" results['2012'] = portfolio.dollar_price()\\n\",\n \"\\n\",\n \"with win_16:\\n\",\n \" results['2016'] = portfolio.dollar_price()\\n\",\n \"\\n\",\n \"results['eod'] = portfolio.dollar_price()\\n\",\n \"\\n\",\n \"for scenario in ['2012', '2016']:\\n\",\n \" results_agg[scenario] = results[scenario].aggregate() - results['eod'].aggregate()\\n\",\n \"\\n\",\n \"plt.bar(results_agg.keys(), results_agg.values())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4: Custom shocks\\n\",\n \"\\n\",\n \"Finally let's look at how to run a completely custom shock.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
mkt_typemkt_assetmkt_classmkt_pointvalue
25IRAUDSWAP5Y-31481.0
54IRUSDSWAP5Y275314.0
59IRUSDSWAP10Y-249668.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" mkt_type mkt_asset mkt_class mkt_point value\\n\",\n \"25 IR AUD SWAP 5Y -31481.0\\n\",\n \"54 IR USD SWAP 5Y 275314.0\\n\",\n \"59 IR USD SWAP 10Y -249668.0\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# get points available to shock by looking at irdelta \\n\",\n \"threshold = 25000\\n\",\n \"delta = portfolio.calc(risk.IRDelta)\\n\",\n \"delta.aggregate()[(delta.aggregate().value>threshold) | (delta.aggregate().value<-threshold)].round()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
mkt_typemkt_assetmkt_classmkt_pointshock
0IRAUDSWAP5YNaN
1IRUSDSWAP10YNaN
2IRUSDSWAP5YNaN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" mkt_type mkt_asset mkt_class mkt_point shock\\n\",\n \"0 IR AUD SWAP 5Y NaN\\n\",\n \"1 IR USD SWAP 10Y NaN\\n\",\n \"2 IR USD SWAP 5Y NaN\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"custom_shocks = pd.read_csv(r'curve_shocks.csv', na_values='')\\n\",\n \"custom_shocks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
mkt_typemkt_assetmkt_classmkt_pointshock
0IRAUDSWAP5Y50
1IRUSDSWAP10Y40
2IRUSDSWAP5Y30
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" mkt_type mkt_asset mkt_class mkt_point shock\\n\",\n \"0 IR AUD SWAP 5Y 50\\n\",\n \"1 IR USD SWAP 10Y 40\\n\",\n \"2 IR USD SWAP 5Y 30\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Fill in your shocks programatically or in excel\\n\",\n \"custom_shocks['shock'] = [50, 40, 30]\\n\",\n \"custom_shocks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"shocks = {\\n\",\n \" MarketDataPattern(custom_shocks.at[i, 'mkt_type'], custom_shocks.at[i, 'mkt_asset'], custom_shocks.at[i, 'mkt_class']):\\n\",\n \" MarketDataShock(MarketDataShockType.Absolute, custom_shocks.at[i, 'shock'] / 1e4) for i in range(len(custom_shocks)-2)\\n\",\n \"}\\n\",\n \"\\n\",\n \"shocks.update({\\n\",\n \" MarketDataPattern(custom_shocks.at[i, 'mkt_type'], custom_shocks.at[i, 'mkt_asset'], custom_shocks.at[i, 'mkt_class'],\\n\",\n \" (custom_shocks.at[i, 'mkt_point'],)): \\n\",\n \" MarketDataShock(MarketDataShockType.Absolute, custom_shocks.at[i, 'shock'] / 1e4) for i in [len(custom_shocks)-2, len(custom_shocks)-1]\\n\",\n \"})\\n\",\n \"\\n\",\n \"with MarketDataShockBasedScenario(shocks):\\n\",\n \" results_agg['custom shocks'] = portfolio.dollar_price().aggregate() - results['eod'].aggregate()\\n\",\n \"\\n\",\n \"plt.bar(results_agg.keys(), results_agg.values())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimers\\n\",\n \"\\n\",\n \"Scenarios/predictions: Simulated results are for illustrative purposes only. GS provides no assurance or guarantee that the strategy will operate or would have operated in the past in a manner consistent with the above analysis. Past performance figures are not a reliable indicator of future results.\\n\",\n \"\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"\\n\",\n \"Backtesting, Simulated Results, Sensitivity/Scenario Analysis or Spreadsheet Calculator or Model: There may be data presented herein that is solely for illustrative purposes and which may include among other things back testing, simulated results and scenario analyses. The information is based upon certain factors, assumptions and historical information that Goldman Sachs may in its discretion have considered appropriate, however, Goldman Sachs provides no assurance or guarantee that this product will operate or would have operated in the past in a manner consistent with these assumptions. In the event any of the assumptions used do not prove to be true, results are likely to vary materially from the examples shown herein. Additionally, the results may not reflect material economic and market factors, such as liquidity, transaction costs and other expenses which could reduce potential return.\\n\",\n \"\\n\",\n \"OTC Derivatives Risk Disclosures: \\n\",\n \"Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \\n\",\n \" \\n\",\n \"Liquidity Risk: There is no public market for OTC derivative transactions and, therefore, it may be difficult or impossible to liquidate an existing position on favorable terms. Transfer Restrictions: OTC derivative transactions entered into with one or more affiliates of The Goldman Sachs Group, Inc. (Goldman Sachs) cannot be assigned or otherwise transferred without its prior written consent and, therefore, it may be impossible for you to transfer any OTC derivative transaction to a third party. \\n\",\n \" \\n\",\n \"Conflict of Interests: Goldman Sachs may from time to time be an active participant on both sides of the market for the underlying securities, commodities, futures, options or any other derivative or instrument identical or related to those mentioned herein (together, \\\"the Product\\\"). Goldman Sachs at any time may have long or short positions in, or buy and sell Products (on a principal basis or otherwise) identical or related to those mentioned herein. Goldman Sachs hedging and trading activities may affect the value of the Products. \\n\",\n \" \\n\",\n \"Counterparty Credit Risk: Because Goldman Sachs, may be obligated to make substantial payments to you as a condition of an OTC derivative transaction, you must evaluate the credit risk of doing business with Goldman Sachs or its affiliates. \\n\",\n \" \\n\",\n \"Pricing and Valuation: The price of each OTC derivative transaction is individually negotiated between Goldman Sachs and each counterparty and Goldman Sachs does not represent or warrant that the prices for which it offers OTC derivative transactions are the best prices available, possibly making it difficult for you to establish what is a fair price for a particular OTC derivative transaction; The value or quoted price of the Product at any time, however, will reflect many factors and cannot be predicted. If Goldman Sachs makes a market in the offered Product, the price quoted by Goldman Sachs would reflect any changes in market conditions and other relevant factors, and the quoted price (and the value of the Product that Goldman Sachs will use for account statements or otherwise) could be higher or lower than the original price, and may be higher or lower than the value of the Product as determined by reference to pricing models used by Goldman Sachs. If at any time a third party dealer quotes a price to purchase the Product or otherwise values the Product, that price may be significantly different (higher or lower) than any price quoted by Goldman Sachs. Furthermore, if you sell the Product, you will likely be charged a commission for secondary market transactions, or the price will likely reflect a dealer discount. Goldman Sachs may conduct market making activities in the Product. To the extent Goldman Sachs makes a market, any price quoted for the OTC derivative transactions, Goldman Sachs may differ significantly from (i) their value determined by reference to Goldman Sachs pricing models and (ii) any price quoted by a third party. The market price of the OTC derivative transaction may be influenced by many unpredictable factors, including economic conditions, the creditworthiness of Goldman Sachs, the value of any underlyers, and certain actions taken by Goldman Sachs. \\n\",\n \" \\n\",\n \"Market Making, Investing and Lending: Goldman Sachs engages in market making, investing and lending businesses for its own account and the accounts of its affiliates in the same or similar instruments underlying OTC derivative transactions (including such trading as Goldman Sachs deems appropriate in its sole discretion to hedge its market risk in any OTC derivative transaction whether between Goldman Sachs and you or with third parties) and such trading may affect the value of an OTC derivative transaction. \\n\",\n \" \\n\",\n \"Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\\n\",\n \"Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf, http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. Certain transactions - including those involving futures, options, equity swaps, and other derivatives as well as non-investment-grade securities - give rise to substantial risk and are not available to nor suitable for all investors. If you have any questions about whether you are eligible to enter into these transactions with Goldman Sachs, please contact your sales representative. Foreign-currency-denominated securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\\n\",\n \"Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. Transaction costs may be a significant factor in option strategies calling for multiple purchases and sales of options, such as spreads. When purchasing long options an investor may lose their entire investment and when selling uncovered options the risk is potentially unlimited. Supporting documentation for any comparisons, recommendations, statistics, technical data, or other similar information will be supplied upon request.\\n\",\n \"This material is for the private information of the recipient only. This material is not sponsored, endorsed, sold or promoted by any sponsor or provider of an index referred herein (each, an \\\"Index Provider\\\"). GS does not have any affiliation with or control over the Index Providers or any control over the computation, composition or dissemination of the indices. While GS will obtain information from publicly available sources it believes reliable, it will not independently verify this information. Accordingly, GS shall have no liability, contingent or otherwise, to the user or to third parties, for the quality, accuracy, timeliness, continued availability or completeness of the data nor for any special, indirect, incidental or consequential damages which may be incurred or experienced because of the use of the data made available herein, even if GS has been advised of the possibility of such damages.\\n\",\n \"Standard & Poor's ® and S&P ® are registered trademarks of The McGraw-Hill Companies, Inc. and S&P GSCI™ is a trademark of The McGraw-Hill Companies, Inc. and have been licensed for use by the Issuer. This Product (the \\\"Product\\\") is not sponsored, endorsed, sold or promoted by S&P and S&P makes no representation, warranty or condition regarding the advisability of investing in the Product.\\n\",\n \"Notice to Brazilian Investors\\n\",\n \"Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/00_us_election_analysis/0003_trades.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"from datetime import date\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from gs_quant.markets import PricingContext, BackToTheFuturePricingContext\\n\",\n \"from gs_quant.risk import RollFwd, MarketDataPattern, MarketDataShock, MarketDataShockBasedScenario, MarketDataShockType\\n\",\n \"from gs_quant.instrument import FXOption, IRSwaption\\n\",\n \"from gs_quant.timeseries import *\\n\",\n \"from gs_quant.timeseries import percentiles\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"sns.set(style=\\\"darkgrid\\\", color_codes=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we'll look at entry points for G10 vol, look for crosses with the largest downside sensivity to SPX, indicatively price several structures and analyze their carry profile.\\n\",\n \"\\n\",\n \"* [1: FX entry point vs richness](#1:-FX-entry-point-vs-richness)\\n\",\n \"* [2: Downside sensitivity to SPX](#2:-Downside-sensitivity-to-SPX)\\n\",\n \"* [3: AUDJPY conditional relationship with SPX](#3:-AUDJPY-conditional-relationship-with-SPX)\\n\",\n \"* [4: Price structures](#4:-Price-structures)\\n\",\n \"* [5: Analyse rates package](#5:-Analyse-rates-package)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1: FX entry point vs richness\\n\",\n \"Let's pull [GS FX Spot](https://marquee.gs.com/s/developer/datasets/FXSPOT_PREMIUM) and [GS FX Implied Volatility](https://marquee.gs.com/s/developer/datasets/FXIMPLIEDVOL_PREMIUM) and look at implied vs realized vol as well as current implied level as percentile relative to the last 2 years.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"hide_input\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def format_df(data_dict):\\n\",\n \" df = pd.concat(data_dict, axis=1)\\n\",\n \" df.columns = data_dict.keys()\\n\",\n \" return df.fillna(method='ffill').dropna()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF', 'AUDJPY']\\n\",\n \"start_date = date(2005, 8, 26)\\n\",\n \"end_date = business_day_offset(date.today(), -1, roll='preceding')\\n\",\n \"fxspot_dataset, fxvol_dataset = Dataset('FXSPOT_PREMIUM'), Dataset('FXIMPLIEDVOL_PREMIUM')\\n\",\n \"\\n\",\n \"spot_data, impvol_data, spot_fx = {}, {}, {}\\n\",\n \"for cross in g10:\\n\",\n \" spot = fxspot_dataset.get_data(start_date, end_date, bbid=cross)[['spot']].drop_duplicates(keep='last')\\n\",\n \" spot_fx[cross] = spot['spot']\\n\",\n \" spot_data[cross] = volatility(spot['spot'], 63) # realized vol \\n\",\n \" vol = fxvol_dataset.get_data(start_date, end_date, bbid=cross, tenor='3m', deltaStrike='DN', location='NYC')[['impliedVolatility']]\\n\",\n \" impvol_data[cross] = vol.drop_duplicates(keep='last') * 100\\n\",\n \"\\n\",\n \"spdata, ivdata = format_df(spot_data), format_df(impvol_data)\\n\",\n \"diff = ivdata.subtract(spdata).dropna()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"_slice = ivdata['2018-09-01': '2020-09-08']\\n\",\n \"pct_rank = {}\\n\",\n \"for x in _slice.columns:\\n\",\n \" pct = percentiles(_slice[x])\\n\",\n \" pct_rank[x] = pct.iloc[-1]\\n\",\n \"\\n\",\n \"for fx in pct_rank:\\n\",\n \" plt.scatter(pct_rank[fx], diff[fx]['2020-09-08'])\\n\",\n \" plt.legend(pct_rank.keys(),loc='best', bbox_to_anchor=(0.9, -0.13), ncol=3)\\n\",\n \" \\n\",\n \"plt.xlabel('Percentile of Current Implied Vol')\\n\",\n \"plt.ylabel('Implied vs Realized Vol')\\n\",\n \"plt.title('Entry Point vs Richness')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2: Downside sensitivity to SPX\\n\",\n \"\\n\",\n \"Let's now look at beta and correlation with SPX across G10.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spx_spot = Dataset('TREOD').get_data(start_date, end_date, bbid='SPX')[['closePrice']]\\n\",\n \"spx_spot = spx_spot.fillna(method='ffill').dropna()\\n\",\n \"df = pd.DataFrame(spx_spot)\\n\",\n \"\\n\",\n \"#FX Spot data\\n\",\n \"fx_spots = format_df(spot_fx)\\n\",\n \"data = pd.concat([spx_spot, fx_spots], axis=1).dropna()\\n\",\n \"data.columns = ['SPX'] + g10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"beta_spx, corr_spx = {}, {}\\n\",\n \"\\n\",\n \"#calculate rolling 84d or 4m beta to S&P\\n\",\n \"for cross in g10:\\n\",\n \" beta_spx[cross] = beta(data[cross],data['SPX'], 84)\\n\",\n \" corr_spx[cross] = correlation(data['SPX'], data[cross], 84)\\n\",\n \"\\n\",\n \"fig, axs = plt.subplots(5, 2, figsize=(18, 20))\\n\",\n \"for j in range(2):\\n\",\n \" for i in range(5):\\n\",\n \" color='tab:blue'\\n\",\n \" axs[i,j].plot(beta_spx[g10[i + j*5]], color=color)\\n\",\n \" axs[i,j].set_title(g10[i + j*5])\\n\",\n \" color='tab:blue'\\n\",\n \" axs[i,j].set_ylabel('Beta', color=color)\\n\",\n \" axs[i,j].plot(beta_spx[g10[i + j*5]], color=color)\\n\",\n \" ax2 = axs[i,j].twinx()\\n\",\n \" color = 'tab:orange' \\n\",\n \" ax2.plot(corr_spx[g10[i + j*5]], color=color)\\n\",\n \" ax2.set_ylabel('Correlation', color=color)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Part 3: AUDJPY conditional relationship with SPX\\n\",\n \"\\n\",\n \"Let's focus on AUDJPY and look at its relationship with SPX when SPX is significantly up and down.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# resample data to weekly from daily & get weekly returns\\n\",\n \"wk_data = data.resample('W-FRI').last()\\n\",\n \"rets = returns(wk_data, 1)\\n\",\n \"sns.set(style='white', color_codes=True)\\n\",\n \"spx_returns = [-.1, -.05, .05, .1]\\n\",\n \"\\n\",\n \"betas = pd.DataFrame(index=spx_returns, columns=g10)\\n\",\n \"for ret in spx_returns:\\n\",\n \" dns = rets[rets.SPX <= ret].dropna() if ret < 0 else rets[rets.SPX >= ret].dropna() \\n\",\n \" j = sns.jointplot(x='SPX', y='AUDJPY', data=dns, kind='reg')\\n\",\n \" j.set_axis_labels('SPX with {}% Returns'.format(ret*100), 'AUDJPY')\\n\",\n \" j.fig.subplots_adjust(wspace=.02)\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's use the beta for all S&P returns to price a structure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sns.jointplot(x='SPX', y='AUDJPY', data=rets, kind='reg')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4: Price structures \\n\",\n \"\\n\",\n \"##### Let's now look at a few AUDJPY structures as potential hedges\\n\",\n \"\\n\",\n \"* Buy 4m AUDJPY put using spx beta to size. Max loss limited to premium paid.\\n\",\n \"* Buy 4m AUDJPY put spread (4.2%/10.6% OTMS). Max loss limited to premium paid.\\n\",\n \"\\n\",\n \"For more info on this trade, check out our market strats piece [here](https://marquee.gs.com/content/#/article/2020/08/28/gs-marketstrats-audjpy-as-us-election-hedge)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#buy 4m AUDJPY put\\n\",\n \"audjpy_put = FXOption(option_type='Put', pair='AUDJPY', strike_price= 's-4.2%', expiration_date='4m', buy_sell='Buy') \\n\",\n \"print('cost in bps: {:,.2f}'.format(audjpy_put.premium / audjpy_put.notional_amount * 1e4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#buy 4m AUDJPY put spread (5.3%/10.6% OTMS)\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"put1 = FXOption(option_type='Put', pair='AUDJPY', strike_price= 's-4.2%', expiration_date='4m', buy_sell='Buy')\\n\",\n \"put2 = FXOption(option_type='Put', pair='AUDJPY', strike_price= 's-10.6%', expiration_date='4m', buy_sell='Sell')\\n\",\n \"\\n\",\n \"fx_package = Portfolio((put1, put2))\\n\",\n \"cost = put2.premium/put2.notional_amount - put1.premium/put1.notional_amount \\n\",\n \"print('cost in bps: {:,.2f}'.format(cost * 1e4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### ...And some rates ideas\\n\",\n \"\\n\",\n \"* Sell straddle. Max loss unlimited.\\n\",\n \"* Sell 3m30y straddle, buy 2y30y straddle in a 0 pv package. Max loss unlimited.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"leg = IRSwaption('Straddle', '30y', notional_currency='USD', expiration_date='3m', buy_sell='Sell')\\n\",\n \"print('PV in USD: {:,.2f}'.format(leg.dollar_price()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rates_package = Portfolio((IRSwaption('Straddle', '30y', notional_currency='USD', expiration_date='3m', buy_sell='Sell',\\n\",\n \" name='3m30y ATM Straddle'),\\n\",\n \" IRSwaption('Straddle', '30y', notional_currency='USD', expiration_date='2y', buy_sell='Buy', \\n\",\n \" notional_amount='=solvefor([3m30y ATM Straddle].risk.Price,pv)', \\n\",\n \" name = '2y30y ATM Straddle')))\\n\",\n \"rates_package.resolve()\\n\",\n \"\\n\",\n \"print('Package cost in USD: {:,.2f}'.format(rates_package.price().aggregate()))\\n\",\n \"print('PV Flat notionals ($$m):', round(leg1.notional_amount/1e6, 1),' by ',round(leg2.notional_amount/1e6, 1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5: Analyse rates package\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dates = pd.bdate_range(date(2020, 6, 8), leg1.expiration_date, freq='5B').date.tolist()\\n\",\n \"\\n\",\n \"with BackToTheFuturePricingContext(dates=dates, roll_to_fwds=True):\\n\",\n \" future = rates_package.price()\\n\",\n \"rates_future = future.result().aggregate()\\n\",\n \"\\n\",\n \"rates_future.plot(figsize=(10, 6), title='Historical PV and carry for rates package')\\n\",\n \"\\n\",\n \"print('PV breakdown between legs:')\\n\",\n \"results = future.result().to_frame()\\n\",\n \"results /= 1e6\\n\",\n \"results.loc['Total'] = results.sum()\\n\",\n \"results.round(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's focus on the next 3m and how the calendar carries in different rates shocks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dates = pd.bdate_range(dt.date.today(), leg1.expiration_date, freq='5B').date.tolist()\\n\",\n \"shocked_pv = pd.DataFrame(columns=['Base', '5bp per week', '50bp instantaneous'], index=dates)\\n\",\n \"\\n\",\n \"p1, p2, p3 = [], [], []\\n\",\n \"with PricingContext(is_batch=True):\\n\",\n \" for t, d in enumerate(dates):\\n\",\n \" with RollFwd(date=d, realise_fwd=True):\\n\",\n \" p1.append(rates_package.price())\\n\",\n \" with MarketDataShockBasedScenario({MarketDataPattern('IR', 'USD'): MarketDataShock(MarketDataShockType.Absolute, t*0.0005)}):\\n\",\n \" p2.append(rates_package.price())\\n\",\n \" with MarketDataShockBasedScenario({MarketDataPattern('IR', 'USD'): MarketDataShock(MarketDataShockType.Absolute, 0.005)}):\\n\",\n \" p3.append(rates_package.price())\\n\",\n \"\\n\",\n \"shocked_pv.Base = [p.result().aggregate() for p in p1]\\n\",\n \"shocked_pv['5bp per week'] = [p.result().aggregate() for p in p2]\\n\",\n \"shocked_pv['50bp instantaneous'] = [p.result().aggregate() for p in p3]\\n\",\n \"\\n\",\n \"shocked_pv/=1e6\\n\",\n \"shocked_pv.round(1)\\n\",\n \"shocked_pv.plot(figsize=(10, 6), title='Carry + scenario analysis')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimers\\n\",\n \"\\n\",\n \"Scenarios/predictions: Simulated results are for illustrative purposes only. GS provides no assurance or guarantee that the strategy will operate or would have operated in the past in a manner consistent with the above analysis. Past performance figures are not a reliable indicator of future results.\\n\",\n \"\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"\\n\",\n \"Backtesting, Simulated Results, Sensitivity/Scenario Analysis or Spreadsheet Calculator or Model: There may be data presented herein that is solely for illustrative purposes and which may include among other things back testing, simulated results and scenario analyses. The information is based upon certain factors, assumptions and historical information that Goldman Sachs may in its discretion have considered appropriate, however, Goldman Sachs provides no assurance or guarantee that this product will operate or would have operated in the past in a manner consistent with these assumptions. In the event any of the assumptions used do not prove to be true, results are likely to vary materially from the examples shown herein. Additionally, the results may not reflect material economic and market factors, such as liquidity, transaction costs and other expenses which could reduce potential return.\\n\",\n \"\\n\",\n \"OTC Derivatives Risk Disclosures: \\n\",\n \"Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \\n\",\n \" \\n\",\n \"Liquidity Risk: There is no public market for OTC derivative transactions and, therefore, it may be difficult or impossible to liquidate an existing position on favorable terms. Transfer Restrictions: OTC derivative transactions entered into with one or more affiliates of The Goldman Sachs Group, Inc. (Goldman Sachs) cannot be assigned or otherwise transferred without its prior written consent and, therefore, it may be impossible for you to transfer any OTC derivative transaction to a third party. \\n\",\n \" \\n\",\n \"Conflict of Interests: Goldman Sachs may from time to time be an active participant on both sides of the market for the underlying securities, commodities, futures, options or any other derivative or instrument identical or related to those mentioned herein (together, \\\"the Product\\\"). Goldman Sachs at any time may have long or short positions in, or buy and sell Products (on a principal basis or otherwise) identical or related to those mentioned herein. Goldman Sachs hedging and trading activities may affect the value of the Products. \\n\",\n \" \\n\",\n \"Counterparty Credit Risk: Because Goldman Sachs, may be obligated to make substantial payments to you as a condition of an OTC derivative transaction, you must evaluate the credit risk of doing business with Goldman Sachs or its affiliates. \\n\",\n \" \\n\",\n \"Pricing and Valuation: The price of each OTC derivative transaction is individually negotiated between Goldman Sachs and each counterparty and Goldman Sachs does not represent or warrant that the prices for which it offers OTC derivative transactions are the best prices available, possibly making it difficult for you to establish what is a fair price for a particular OTC derivative transaction; The value or quoted price of the Product at any time, however, will reflect many factors and cannot be predicted. 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To the extent Goldman Sachs makes a market, any price quoted for the OTC derivative transactions, Goldman Sachs may differ significantly from (i) their value determined by reference to Goldman Sachs pricing models and (ii) any price quoted by a third party. The market price of the OTC derivative transaction may be influenced by many unpredictable factors, including economic conditions, the creditworthiness of Goldman Sachs, the value of any underlyers, and certain actions taken by Goldman Sachs. \\n\",\n \" \\n\",\n \"Market Making, Investing and Lending: Goldman Sachs engages in market making, investing and lending businesses for its own account and the accounts of its affiliates in the same or similar instruments underlying OTC derivative transactions (including such trading as Goldman Sachs deems appropriate in its sole discretion to hedge its market risk in any OTC derivative transaction whether between Goldman Sachs and you or with third parties) and such trading may affect the value of an OTC derivative transaction. \\n\",\n \" \\n\",\n \"Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\\n\",\n \"Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf, http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. 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In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\\n\",\n \"Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. 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Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Edit Metadata\",\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/eq_sx5e_spx_vol_spread_trade.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.securities import AssetIdentifier, SecurityMaster\\n\",\n \"from gs_quant.timeseries.measures import forward_vol, VolReference, implied_volatility\\n\",\n \"from gs_quant.timeseries.algebra import *\\n\",\n \"from gs_quant.timeseries.analysis import *\\n\",\n \"from gs_quant.data import DataContext\\n\",\n \"from gs_quant.instrument import EqOption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import EqDelta, EqGamma, EqVega, DollarPrice, Price\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests import FlowVolBacktestMeasure\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.equity_vol_engine import EquityVolEngine\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"from datetime import date, datetime\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"pd.options.display.float_format = '{:,.2f}'.format\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we will take a look at historical and future implied trends in equity implied volatility, formulate a trade idea, analyze a trading strategy backtest and calculate spot price and greeks. This is an example of the GS Quant functionalities and it is not a recommendation of a trade in the instruments discussed, please follow up with your salesperson to discuss specific implementations.\\n\",\n \"\\n\",\n \"* [1 - Analyze Vol Spread](#2---Analyze-Vol-Spread)\\n\",\n \"* [2 - Trade Idea & Backtest](#3---Trade-Idea-&-Backtest)\\n\",\n \"* [3 - Compare Performance](#4---Compare-Performance)\\n\",\n \"* [4 - Price & Greeks](#5---Price-&-Greeks)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 - Analyze Vol Spread\\n\",\n \"\\n\",\n \"Let's start by looking at historical forward vol for SPX and SX5E.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def forward_vol_spread(asset1, asset2, tenor, forward_date):\\n\",\n \" '''\\n\",\n \" Calculate the forward starting vol spread between two assets\\n\",\n \" '''\\n\",\n \" asset1_sec = SecurityMaster.get_asset(asset1, AssetIdentifier.BLOOMBERG_ID)\\n\",\n \" asset2_sec = SecurityMaster.get_asset(asset2, AssetIdentifier.BLOOMBERG_ID)\\n\",\n \" \\n\",\n \" implied = lambda asset: implied_volatility(asset, tenor, VolReference.FORWARD, 100)\\n\",\n \" forward = lambda asset: forward_vol(asset, tenor, forward_date, VolReference.FORWARD, 100)\\n\",\n \" \\n\",\n \" asset1_fvol = implied(asset1_sec) if forward_date=='0m' else forward(asset1_sec)\\n\",\n \" asset2_fvol = implied(asset2_sec) if forward_date=='0m' else forward(asset2_sec)\\n\",\n \" \\n\",\n \" return subtract(asset1_fvol, asset2_fvol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"start_date, end_date = date(2014, 1, 1), date(2020, 11, 2)\\n\",\n \"DataContext.current = DataContext(start=start_date, end=end_date)\\n\",\n \"\\n\",\n \"sx5e_spx_fvs = forward_vol_spread('SX5E', 'SPX', '2y', '1y')\\n\",\n \"\\n\",\n \"current_roll_data = {}\\n\",\n \"for rMonth in [0, 6, 9, 12]:\\n\",\n \" d = end_date + relativedelta(months=rMonth)\\n\",\n \" months_to_start = 12 - rMonth\\n\",\n \" curve = forward_vol_spread('SX5E', 'SPX', '2y', f'{months_to_start}m')\\n\",\n \" if curve.size:\\n\",\n \" current_roll_data[d] = last_value(curve)\\n\",\n \"current_roll = pd.Series(current_roll_data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sx5e_spx_fvs.plot(figsize=(12, 6), legend=True, label='SX5E vs SPX', title='Vol Spread')\\n\",\n \"current_roll.plot(figsize=(12, 6), legend=True, label='Current Spread Roll-Up')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 - Trade Idea & Backtest\\n\",\n \"\\n\",\n \"The analysis shows a projected narrowing of vol spread between SX5E and SPX. Let's create an option trading strategy that takes advantage of this and backtest it to see how it could have performed.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create instruments\\n\",\n \"sx5e_straddle = (EqOption('.STOXX50E', '3y', 'ATMF', 'Call'), EqOption('.STOXX50E', '3y', 'ATMF','Put'))\\n\",\n \"spx_straddle = (EqOption('.SPX', '3y', 'ATMF', 'Call', multiplier=1), EqOption('.SPX', '3y', 'ATMF', 'Put', multiplier=1))\\n\",\n \"\\n\",\n \"# Define triggers for trade and hedge actions.\\n\",\n \"\\n\",\n \"# Trade and roll 1 sx5e_straddle every 1m\\n\",\n \"trade_sx5e_action = EnterPositionQuantityScaledAction(priceables=Portfolio(sx5e_straddle), trade_duration='1m', trade_quantity=1, trade_quantity_type=BacktestTradingQuantityType.quantity)\\n\",\n \"trade_sx5e_trigger = PeriodicTrigger(trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_sx5e_action)\\n\",\n \"\\n\",\n \"# Hedge sx5e_straddle delta every business day\\n\",\n \"hedge_sx5e_action = HedgeAction(EqDelta, priceables=Portfolio(sx5e_straddle), trade_duration='B')\\n\",\n \"hedge_sx5e_trigger = PeriodicTrigger(trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='B'),\\n\",\n \" actions=hedge_sx5e_action)\\n\",\n \"\\n\",\n \"# Trade and roll 1 spx_straddle every 1m\\n\",\n \"trade_spx_action = EnterPositionQuantityScaledAction(priceables=Portfolio(spx_straddle), trade_duration='1m', trade_quantity=1, trade_quantity_type=BacktestTradingQuantityType.quantity)\\n\",\n \"trade_spx_trigger = PeriodicTrigger(trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_spx_action)\\n\",\n \"\\n\",\n \"# Hedge spx_straddle delta every business day\\n\",\n \"hedge_spx_action = HedgeAction(EqDelta, priceables=Portfolio(spx_straddle), trade_duration='B')\\n\",\n \"hedge_spx_trigger = PeriodicTrigger(trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='B'),\\n\",\n \" actions=hedge_spx_action)\\n\",\n \"\\n\",\n \"EqVolE = EquityVolEngine()\\n\",\n \"\\n\",\n \"# Define SX5E backtest\\n\",\n \"triggers_sx5e = [trade_sx5e_trigger, hedge_sx5e_trigger]\\n\",\n \"\\n\",\n \"strategy_sx5e_result = EqVolE.run_backtest(Strategy(None, triggers_sx5e), start=start_date, end=end_date)\\n\",\n \"perf_sx5e = strategy_sx5e_result.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"\\n\",\n \"# Define SPX backtest\\n\",\n \"triggers_spx = [trade_spx_trigger, hedge_spx_trigger]\\n\",\n \"\\n\",\n \"strategy_spx_result = EqVolE.run_backtest(Strategy(None, triggers_spx), start=start_date, end=end_date)\\n\",\n \"perf_spx = strategy_spx_result.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"perf_sx5e_spx_spread = subtract(perf_sx5e, perf_spx)\\n\",\n \"perf_sx5e_spx_spread.index = pd.DatetimeIndex(perf_sx5e_spx_spread.index)\\n\",\n \"perf_sx5e_spx_spread.plot(figsize=(12, 6), title='Strategy SX5E vs SPX 3y Vol Spread')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 - Compare Performance\\n\",\n \"\\n\",\n \"Now let's plot the trading strategy with the actual SX5E vs SPX vol spread to see how the two compare.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax1 = plt.subplots()\\n\",\n \"\\n\",\n \"# left axis\\n\",\n \"ax1.set_xlabel('date')\\n\",\n \"ax1.set_ylabel('Vol Spread',color='tab:red')\\n\",\n \"\\n\",\n \"#right axis\\n\",\n \"ax2 = ax1.twinx() \\n\",\n \"ax2.set_ylabel('Strategy PNL', color='tab:blue') \\n\",\n \"\\n\",\n \"sx5e_spx_fvs.plot(figsize=(12, 6), ax=ax1, color='tab:red')\\n\",\n \"current_roll.plot(figsize=(12, 6), ax=ax1, color='tab:green', legend=True, label='Current Spread Roll-Up')\\n\",\n \"perf_sx5e_spx_spread.plot(figsize=(12, 6), ax=ax2, color='tab:blue')\\n\",\n \"\\n\",\n \"fig.tight_layout() \\n\",\n \"plt.xlim(start_date, end_date + relativedelta(years=1))\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 - Price & Greeks\\n\",\n \"\\n\",\n \"Finallly, let's calculate current strategy price and greeks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Strategy Price$: -4.5888085670912915\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SPXSX5E
valuevalue
risk_measure
EqDelta613.43619.86
EqGamma86.84129.77
EqVega50.9057.17
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" SPX SX5E\\n\",\n \" value value\\n\",\n \"risk_measure \\n\",\n \"EqDelta 613.43 619.86\\n\",\n \"EqGamma 86.84 129.77\\n\",\n \"EqVega 50.90 57.17\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sx5e_straddle_port, spx_straddle_port = Portfolio(sx5e_straddle), Portfolio(spx_straddle)\\n\",\n \"\\n\",\n \"# Price and greeks in USD\\n\",\n \"sx5e_straddle_price = sx5e_straddle_port.calc(DollarPrice).aggregate()\\n\",\n \"spx_straddle_price = spx_straddle_port.calc(DollarPrice).aggregate()\\n\",\n \"print(f'Strategy Price$: {sx5e_straddle_price - spx_straddle_price}')\\n\",\n \"\\n\",\n \"greeks = (EqDelta, EqGamma, EqVega)\\n\",\n \"sx5e_straddle_greeks = sx5e_straddle_port.calc(greeks).aggregate().to_frame()\\n\",\n \"spx_straddle_greeks = spx_straddle_port.calc(greeks).aggregate().to_frame()\\n\",\n \"pd.concat([spx_straddle_greeks,sx5e_straddle_greeks],keys=['SPX', 'SX5E']).transpose()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimers\\n\",\n \"\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"Backtesting, Simulated Results, Sensitivity/Scenario Analysis or Spreadsheet Calculator or Model: There may be data presented herein that is solely for illustrative purposes and which may include among other things back testing, simulated results and scenario analyses. The information is based upon certain factors, assumptions and historical information that Goldman Sachs may in its discretion have considered appropriate, however, Goldman Sachs provides no assurance or guarantee that this product will operate or would have operated in the past in a manner consistent with these assumptions. In the event any of the assumptions used do not prove to be true, results are likely to vary materially from the examples shown herein. Additionally, the results may not reflect material economic and market factors, such as liquidity, transaction costs and other expenses which could reduce potential return.\\n\",\n \"OTC Derivatives Risk Disclosures: \\n\",\n \"Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \\n\",\n \"\\n\",\n \"Liquidity Risk: There is no public market for OTC derivative transactions and, therefore, it may be difficult or impossible to liquidate an existing position on favorable terms. Transfer Restrictions: OTC derivative transactions entered into with one or more affiliates of The Goldman Sachs Group, Inc. (Goldman Sachs) cannot be assigned or otherwise transferred without its prior written consent and, therefore, it may be impossible for you to transfer any OTC derivative transaction to a third party. \\n\",\n \"\\n\",\n \"Conflict of Interests: Goldman Sachs may from time to time be an active participant on both sides of the market for the underlying securities, commodities, futures, options or any other derivative or instrument identical or related to those mentioned herein (together, \\\"the Product\\\"). Goldman Sachs at any time may have long or short positions in, or buy and sell Products (on a principal basis or otherwise) identical or related to those mentioned herein. Goldman Sachs hedging and trading activities may affect the value of the Products. \\n\",\n \"\\n\",\n \"Counterparty Credit Risk: Because Goldman Sachs, may be obligated to make substantial payments to you as a condition of an OTC derivative transaction, you must evaluate the credit risk of doing business with Goldman Sachs or its affiliates. \\n\",\n \"\\n\",\n \"Pricing and Valuation: The price of each OTC derivative transaction is individually negotiated between Goldman Sachs and each counterparty and Goldman Sachs does not represent or warrant that the prices for which it offers OTC derivative transactions are the best prices available, possibly making it difficult for you to establish what is a fair price for a particular OTC derivative transaction; The value or quoted price of the Product at any time, however, will reflect many factors and cannot be predicted. If Goldman Sachs makes a market in the offered Product, the price quoted by Goldman Sachs would reflect any changes in market conditions and other relevant factors, and the quoted price (and the value of the Product that Goldman Sachs will use for account statements or otherwise) could be higher or lower than the original price, and may be higher or lower than the value of the Product as determined by reference to pricing models used by Goldman Sachs. If at any time a third party dealer quotes a price to purchase the Product or otherwise values the Product, that price may be significantly different (higher or lower) than any price quoted by Goldman Sachs. Furthermore, if you sell the Product, you will likely be charged a commission for secondary market transactions, or the price will likely reflect a dealer discount. Goldman Sachs may conduct market making activities in the Product. To the extent Goldman Sachs makes a market, any price quoted for the OTC derivative transactions, Goldman Sachs may differ significantly from (i) their value determined by reference to Goldman Sachs pricing models and (ii) any price quoted by a third party. The market price of the OTC derivative transaction may be influenced by many unpredictable factors, including economic conditions, the creditworthiness of Goldman Sachs, the value of any underlyers, and certain actions taken by Goldman Sachs. \\n\",\n \"\\n\",\n \"Market Making, Investing and Lending: Goldman Sachs engages in market making, investing and lending businesses for its own account and the accounts of its affiliates in the same or similar instruments underlying OTC derivative transactions (including such trading as Goldman Sachs deems appropriate in its sole discretion to hedge its market risk in any OTC derivative transaction whether between Goldman Sachs and you or with third parties) and such trading may affect the value of an OTC derivative transaction. \\n\",\n \"\\n\",\n \"Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\\n\",\n \"Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf, http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. Certain transactions - including those involving futures, options, equity swaps, and other derivatives as well as non-investment-grade securities - give rise to substantial risk and are not available to nor suitable for all investors. If you have any questions about whether you are eligible to enter into these transactions with Goldman Sachs, please contact your sales representative. Foreign-currency-denominated securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\\n\",\n \"Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. Transaction costs may be a significant factor in option strategies calling for multiple purchases and sales of options, such as spreads. When purchasing long options an investor may lose their entire investment and when selling uncovered options the risk is potentially unlimited. Supporting documentation for any comparisons, recommendations, statistics, technical data, or other similar information will be supplied upon request.\\n\",\n \"This material is for the private information of the recipient only. This material is not sponsored, endorsed, sold or promoted by any sponsor or provider of an index referred herein (each, an \\\"Index Provider\\\"). GS does not have any affiliation with or control over the Index Providers or any control over the computation, composition or dissemination of the indices. While GS will obtain information from publicly available sources it believes reliable, it will not independently verify this information. Accordingly, GS shall have no liability, contingent or otherwise, to the user or to third parties, for the quality, accuracy, timeliness, continued availability or completeness of the data nor for any special, indirect, incidental or consequential damages which may be incurred or experienced because of the use of the data made available herein, even if GS has been advised of the possibility of such damages.\\n\",\n \"iTraxx® is a registered trade mark of International Index Company Limited.\\n\",\n \"iTraxx® is a trade mark of International Index Company Limited and has been licensed for the use by Goldman Sachs Japan Co., Ltd. International Index Company Limited does not approve, endorse or recommend Goldman Sachs Japan Co., Ltd. or iTraxx® derivatives products.\\n\",\n \"iTraxx® derivatives products are derived from a source considered reliable, but neither International Index Company Limited nor any of its employees, suppliers, subcontractors and agents (together iTraxx Associates) guarantees the veracity, completeness or accuracy of iTraxx® derivatives products or other information furnished in connection with iTraxx® derivatives products. No representation, warranty or condition, express or implied, statutory or otherwise, as to condition, satisfactory quality, performance, or fitness for purpose are given or assumed by International Index Company Limited or any of the iTraxx Associates in respect of iTraxx® derivatives products or any data included in such iTraxx® derivatives products or the use by any person or entity of iTraxx® derivatives products or that data and all those representations, warranties and conditions are excluded save to the extent that such exclusion is prohibited by law.\\n\",\n \"None of International Index Company Limited nor any of the iTraxx Associates shall have any liability or responsibility to any person or entity for any loss, damages, costs, charges, expenses or other liabilities whether caused by the negligence of International Index Company Limited or any of the iTraxx Associates or otherwise, arising in connection with the use of iTraxx® derivatives products or the iTraxx® indices.\\n\",\n \"Standard & Poor's ® and S&P ® are registered trademarks of The McGraw-Hill Companies, Inc. and S&P GSCI™ is a trademark of The McGraw-Hill Companies, Inc. and have been licensed for use by the Issuer. This Product (the \\\"Product\\\") is not sponsored, endorsed, sold or promoted by S&P and S&P makes no representation, warranty or condition regarding the advisability of investing in the Product.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/fx_covid19_recovery_trade.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import itertools\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"from gs_quant.instrument import FXBinary, FXMultiCrossBinary, FXMultiCrossBinaryLeg\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.risk import FXSpot\\n\",\n \"from gs_quant.session import GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we will screen for attractive dual binaries in FX to express a leveraged view on a potential vaccine led COVID recovery. This is an example of the GS Quant functionalities and it is not a recommendation of a trade in the instruments discussed, please follow up with your salesperson to discuss specific implementations\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 - Define a few functions and inputs\\n\",\n \"\\n\",\n \"Let's start by defining a few functions and inputs to improve the readability of the notebook.\\n\",\n \"\\n\",\n \"First, let's define a tenor and our targets for various FX crosses. In this example, we are leveraging GS GIR's work for analyzing FX price targets in the event of an approved EUA of a COVID vaccine by the end of the year ([source](https://marquee.gs.com/content/research/en/reports/2020/11/08/e8589bbb-cc23-4480-859a-000a8014bd1d.html)). However, the framework we build in this notebook could be deployed to your own targets on vaccine developments (or any other relevant themes).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# note that we are representing FX crosses as strings in mathematical notation (e.g. USD per GBP)\\n\",\n \"targets = {\\n\",\n \" 'USD/GBP': (1.34, 'call'),\\n\",\n \" 'USD/AUD': (0.75, 'call'),\\n\",\n \" 'USD/EUR': (1.20, 'call'),\\n\",\n \" 'CAD/USD': (1.28, 'put'),\\n\",\n \" 'JPY/USD': (104.1, 'call'),\\n\",\n \" 'KRW/USD': (1099, 'put'),\\n\",\n \" 'BRL/USD': (5.26, 'put'),\\n\",\n \" 'MXN/USD': (19.87, 'put')\\n\",\n \"}\\n\",\n \"\\n\",\n \"tenor = '2m'\\n\",\n \"crosses = targets.keys()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, we'll define a few utility functions we'll use throughout the notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def build_binary(tenor, cross, strike, opt_type, size=10e6, denominated='USD', buy_sell='Buy'):\\n\",\n \" '''\\n\",\n \" utility method to construct a FXBinaryBuilder\\n\",\n \" '''\\n\",\n \" return FXBinary(\\n\",\n \" buy_sell=buy_sell,\\n\",\n \" expiration_date=tenor,\\n\",\n \" option_type=opt_type,\\n\",\n \" notional_currency=denominated,\\n\",\n \" pair=cross,\\n\",\n \" strike_price=strike,\\n\",\n \" premium=0, \\n\",\n \" notional_amount=size,\\n\",\n \" payment_date=tenor\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"def build_dual_binary(tenor, legs, size=10e6, denominated='USD', buy_sell='Buy'):\\n\",\n \" '''\\n\",\n \" utility method to construct an MCB builder\\n\",\n \" '''\\n\",\n \" built_legs = []\\n\",\n \" for leg in legs:\\n\",\n \" built_legs.append(FXMultiCrossBinaryLeg(\\n\",\n \" strike_price=leg['strike'], pair=leg['cross'], option_type='Binary %s' % leg['opt_type']\\n\",\n \" ))\\n\",\n \" return FXMultiCrossBinary(buy_sell, tenor, legs=tuple(built_legs), premium=0, notional_amount=size,\\n\",\n \" notional_currency=denominated)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"def plot_heatmap(data, cmap, ylabel, xlabel, title):\\n\",\n \" '''\\n\",\n \" Utility function to build a heatmap\\n\",\n \" '''\\n\",\n \" plt.subplots(figsize=(16, 10))\\n\",\n \"\\n\",\n \" vmax = data.max().max()\\n\",\n \" vmin = data.min().min()\\n\",\n \"\\n\",\n \" ax = sns.heatmap(data, annot=True, vmin=vmin, vmax=vmax, fmt='.2f', cmap=cmap)\\n\",\n \" ax.set(ylabel=ylabel, xlabel=xlabel, title=title)\\n\",\n \" ax.xaxis.tick_top()\\n\",\n \" ax.xaxis.set_label_position('top')\\n\",\n \" \\n\",\n \" \\n\",\n \"def corr(mcb_price, p1, p2):\\n\",\n \" '''\\n\",\n \" compute the correlation implied by the dual binary price and individuals\\n\",\n \" the sign is just the correlation between the payouts (so negative always indicates higher savings regardless of call/put combo)\\n\",\n \" '''\\n\",\n \" return (mcb_price - p1 * p2) / np.sqrt(p1 * (1 - p1) * p2 * (1 - p2))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 - Vanilla Binaries\\n\",\n \"\\n\",\n \"First, a quick example that shows how to compute the percent price of a binary:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.5046423444297603\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"binary = build_binary('2m', 'JPY/USD', 104, 'Put')\\n\",\n \"binary.price() / 10e6\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's calculate the vanilla binary prices for each of our individual targets to get a sense of the probability the market is assigning to these targets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"vanilla_binaries = pd.DataFrame(index=crosses, columns=['spot_ref', 'target', 'price', 'price_future', 'risk_future'])\\n\",\n \"\\n\",\n \"with PricingContext(is_batch=True):\\n\",\n \" for cross, (strike, opt_type) in targets.items():\\n\",\n \" binary = build_binary(tenor, cross, strike, opt_type)\\n\",\n \" \\n\",\n \" vanilla_binaries.loc[cross, 'price_future'] = binary.price()\\n\",\n \" vanilla_binaries.loc[cross, 'risk_future'] = binary.calc(FXSpot) \\n\",\n \" vanilla_binaries.loc[cross, 'target'] = strike\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
spot_reftargetprice
CAD/USD1.306651.280.215593
USD/AUD0.732080.750.255526
USD/EUR1.18671.20.318962
MXN/USD20.244619.870.36556
KRW/USD1105.2510990.426555
USD/GBP1.33011.340.456705
BRL/USD5.30625.260.466285
JPY/USD103.745104.10.478362
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" spot_ref target price\\n\",\n \"CAD/USD 1.30665 1.28 0.215593\\n\",\n \"USD/AUD 0.73208 0.75 0.255526\\n\",\n \"USD/EUR 1.1867 1.2 0.318962\\n\",\n \"MXN/USD 20.2446 19.87 0.36556\\n\",\n \"KRW/USD 1105.25 1099 0.426555\\n\",\n \"USD/GBP 1.3301 1.34 0.456705\\n\",\n \"BRL/USD 5.3062 5.26 0.466285\\n\",\n \"JPY/USD 103.745 104.1 0.478362\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# once all results are computed summarize relevant details\\n\",\n \"for cross in crosses:\\n\",\n \" vanilla_binaries.loc[cross, 'price'] = vanilla_binaries.loc[cross, 'price_future'].result() / 10e6\\n\",\n \" vanilla_binaries.loc[cross, 'spot_ref'] = vanilla_binaries.loc[cross, 'risk_future'].result()\\n\",\n \" \\n\",\n \"vanilla_binaries[['spot_ref', 'target', 'price']].sort_values('price')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## 3 - Dual Binaries\\n\",\n \"\\n\",\n \"Now calculate the price for ever combination of dual binaries struck at the targets defined above. The top row displays the cheapest dual binary by price, with the right column indicating the savings to the cheapest digi. Darker colors indicate potentially higher savings and therefore potentially more attractive dual digis.\\n\",\n \"\\n\",\n \"Please note that the payout of dual binaries is not guaranteed and is dependent on the level of FX spot in both crosses at the time of expiry.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dual_binary_dict = []\\n\",\n \"with PricingContext(is_batch=True):\\n\",\n \" # iterate over all 2-cross combinations\\n\",\n \" for idx, (cross1, cross2) in enumerate(itertools.combinations(crosses, 2)):\\n\",\n \" # build the dual binary\\n\",\n \" legs = [\\n\",\n \" {'cross': cross1, 'strike': targets[cross1][0], 'opt_type': targets[cross1][1]},\\n\",\n \" {'cross': cross2, 'strike': targets[cross2][0], 'opt_type': targets[cross2][1]}\\n\",\n \" ]\\n\",\n \" mcb = build_dual_binary(tenor, legs)\\n\",\n \"\\n\",\n \" dual_binary_dict.append({\\n\",\n \" 'cross1': cross1,\\n\",\n \" 'cross2': cross2, \\n\",\n \" 'price_future': mcb.price(),\\n\",\n \" }) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for row in dual_binary_dict:\\n\",\n \" row['price'] = row['price_future'].result() / 10e6\\n\",\n \" \\n\",\n \" # individual prices\\n\",\n \" row['individual_1'] = vanilla_binaries.loc[row['cross1'], 'price']\\n\",\n \" row['individual_2'] = vanilla_binaries.loc[row['cross2'], 'price']\\n\",\n \" \\n\",\n \" row['corr'] = corr(row['price'], row['individual_1'], row['individual_2'])\\n\",\n \" # define savings as the % savings from the cheapest individual binary\\n\",\n \" row['savings'] = 1 - row['price'] / min(row['individual_1'], row['individual_2'])\\n\",\n \" \\n\",\n \" \\n\",\n \"dual_binaries = pd.DataFrame(dual_binary_dict)[['cross1', 'cross2', 'price', 'savings', 'corr', 'individual_1', 'individual_2']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Format the results, showing only the 10 dual binaries with the absolute cheapest mid prices\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
cross1 cross2 price savings corr individual_1 individual_2
18CAD/USDJPY/USD7.3%65-14.5%21.6%47.8%
9USD/AUDJPY/USD7.4%70-21.9%25.6%47.8%
14USD/EURJPY/USD8.7%72-28.1%31.9%47.8%
21CAD/USDMXN/USD12.4%4222.9%21.6%36.6%
13USD/EURCAD/USD14.1%3437.7%31.9%21.6%
8USD/AUDCAD/USD14.5%3250.4%25.6%21.6%
24JPY/USDMXN/USD15.1%58-10.1%47.8%36.6%
2USD/GBPCAD/USD15.8%2629.2%45.7%21.6%
3USD/GBPJPY/USD15.9%65-23.8%45.7%47.8%
19CAD/USDKRW/USD16.4%2335.5%21.6%42.7%
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display_table = dual_binaries.sort_values('price').head(10)\\n\",\n \"\\n\",\n \"display_table['price'] = pd.Series(['{0:.1f}%'.format(val * 100) for val in display_table['price']], index=display_table.index)\\n\",\n \"display_table['savings'] = pd.Series([int(val * 100) for val in display_table['savings']], index=display_table.index)\\n\",\n \"\\n\",\n \"display_table['corr'] = pd.Series(['{0:.1f}%'.format(val * 100) for val in display_table['corr']], index=display_table.index)\\n\",\n \"\\n\",\n \"display_table['individual_1'] = pd.Series(['{0:.1f}%'.format(val * 100) for val in display_table['individual_1']], index=display_table.index)\\n\",\n \"display_table['individual_2'] = pd.Series(['{0:.1f}%'.format(val * 100) for val in display_table['individual_2']], index=display_table.index)\\n\",\n \"\\n\",\n \"display_table.style.background_gradient(subset=['savings'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 - Dual Binary Grids\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"We will now consider a wider range of strikes for the cheapest dual binary in\\n\",\n \"the table above. Let us consider strikes within a 3% range from the targets\\n\",\n \"defined above. The tables below show both the mid price and the implied correlation. Optimizing both of these outputs allows us to determine\\n\",\n \"our preferable strikes for the trade.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"min_price_index = dual_binaries['price'].idxmin()\\n\",\n \"cross1 = dual_binaries.iloc[min_price_index]['cross1']\\n\",\n \"cross2 = dual_binaries.iloc[min_price_index]['cross2']\\n\",\n \"\\n\",\n \"# % from target strikes to compute for the grid\\n\",\n \"strikes = [-3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3]\\n\",\n \"\\n\",\n \"mcb_ppct_future = pd.DataFrame(index=strikes, columns=strikes)\\n\",\n \"indiv_ppct_future = pd.DataFrame(index=strikes, columns=[cross1, cross2])\\n\",\n \"\\n\",\n \"with PricingContext(is_batch=True):\\n\",\n \" # loop over all dual binary combinations\\n\",\n \" for (i, j) in itertools.product(strikes, strikes):\\n\",\n \" strike1 = targets[cross1][0] * (1 + i / 100)\\n\",\n \" strike2 = targets[cross2][0] * (1 + j / 100)\\n\",\n \"\\n\",\n \" # build the dual binary\\n\",\n \" legs = [\\n\",\n \" {'cross': cross1, 'strike': strike1, 'opt_type': targets[cross1][1]},\\n\",\n \" {'cross': cross2, 'strike': strike2, 'opt_type': targets[cross2][1]}\\n\",\n \" ]\\n\",\n \" mcb = build_dual_binary(tenor, legs)\\n\",\n \" mcb_ppct_future.loc[i, j] = mcb.price()\\n\",\n \"\\n\",\n \" # loop over all indvidual binary combinations for both crosses\\n\",\n \" # we'll use these to compute savings later as well\\n\",\n \" for cross in [cross1, cross2]:\\n\",\n \" for i in strikes:\\n\",\n \" strike = targets[cross][0] * (1 + i / 100)\\n\",\n \" binary = build_binary(tenor, cross, strike, targets[cross][1])\\n\",\n \" indiv_ppct_future.loc[i, cross] = binary.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"price_matrix = pd.DataFrame(index=strikes, columns=strikes, dtype='float64')\\n\",\n \"savings_matrix = pd.DataFrame(index=strikes, columns=strikes, dtype='float64')\\n\",\n \"corr_matrix = pd.DataFrame(index=strikes, columns=strikes, dtype='float64')\\n\",\n \"\\n\",\n \"for (i, j) in itertools.product(strikes, strikes):\\n\",\n \" # price pct of the mcb\\n\",\n \" price_pct = mcb_ppct_future.loc[i, j].result() / 10e6\\n\",\n \" price_matrix.loc[i, j] = price_pct * 100\\n\",\n \"\\n\",\n \" # calc savings \\n\",\n \" p_1 = indiv_ppct_future.loc[i, cross1].result() / 10e6\\n\",\n \" p_2 = indiv_ppct_future.loc[j, cross2].result() / 10e6\\n\",\n \" savings_matrix.loc[i, j] = 1 - (price_pct / min(p_1, p_2))\\n\",\n \" \\n\",\n \" # calc correlation\\n\",\n \" corr_matrix.loc[i, j] = 100 * corr(price_pct, p_1, p_2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Quickly sanity check the percent prices of the individual binaries at the different strike combinations:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
CAD/USDJPY/USD
-3.03.485.0
-2.54.881.4
-2.06.677.0
-1.59.171.3
-1.012.364.5
-0.516.456.5
0.021.647.8
0.527.739.1
1.034.830.9
1.542.623.6
2.050.817.6
2.558.712.8
3.065.99.2
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" CAD/USD JPY/USD\\n\",\n \"-3.0 3.4 85.0\\n\",\n \"-2.5 4.8 81.4\\n\",\n \"-2.0 6.6 77.0\\n\",\n \"-1.5 9.1 71.3\\n\",\n \"-1.0 12.3 64.5\\n\",\n \"-0.5 16.4 56.5\\n\",\n \" 0.0 21.6 47.8\\n\",\n \" 0.5 27.7 39.1\\n\",\n \" 1.0 34.8 30.9\\n\",\n \" 1.5 42.6 23.6\\n\",\n \" 2.0 50.8 17.6\\n\",\n \" 2.5 58.7 12.8\\n\",\n \" 3.0 65.9 9.2\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"round(indiv_ppct_future.applymap(lambda x: x.result() / 10e6) * 100, 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, let's plot the mid price of the dual binary for all of the strike combinations:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_heatmap(price_matrix, \\\"coolwarm\\\", cross2 + \\\" \\\" + targets[cross2][1],\\n\",\n \" cross1 + \\\" \\\" + targets[cross1][1], \\\"MCB Price (%) By Strikes\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, let's plot the savings vs. the cheapest inividual binary for all of the combinations on the grid. Negative numbers indicate correlations that provide more savings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_heatmap(savings_matrix, \\\"Blues\\\", cross2 + \\\" \\\" + targets[cross2][1],\\n\",\n \" cross1 + \\\" \\\" + targets[cross1][1], \\\"Savings (%) By Strikes\\\" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Appendix - Dual Binary Carry\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with PricingContext(is_batch=True):\\n\",\n \" for row in dual_binary_dict:\\n\",\n \" # build the dual binary\\n\",\n \" legs = [\\n\",\n \" {'cross': row['cross1'], 'strike': targets[row['cross1']][0], 'opt_type': targets[row['cross1']][1]},\\n\",\n \" {'cross': row['cross2'], 'strike': targets[row['cross2']][0], 'opt_type': targets[row['cross2']][1]}\\n\",\n \" ]\\n\",\n \" mcb = build_dual_binary('1m', legs) # simple static carry, just reduce tenor to 1m\\n\",\n \"\\n\",\n \" row['price_fwd_future'] = mcb.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
cross1cross2pricesavingsstatic_carry_1m
23JPY/USDBRL/USD0.2017740.567274-0.015183
25KRW/USDBRL/USD0.2298530.461141-0.017895
24JPY/USDMXN/USD0.1506490.587894-0.021028
5USD/GBPBRL/USD0.2605220.429560-0.022684
4USD/GBPKRW/USD0.2502000.413440-0.025069
27BRL/USDMXN/USD0.2520880.310407-0.025489
14USD/EURJPY/USD0.0872490.726459-0.027515
3USD/GBPJPY/USD0.1591570.651511-0.027681
22JPY/USDKRW/USD0.1727470.595019-0.028986
9USD/AUDJPY/USD0.0744240.708740-0.030358
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" cross1 cross2 price savings static_carry_1m\\n\",\n \"23 JPY/USD BRL/USD 0.201774 0.567274 -0.015183\\n\",\n \"25 KRW/USD BRL/USD 0.229853 0.461141 -0.017895\\n\",\n \"24 JPY/USD MXN/USD 0.150649 0.587894 -0.021028\\n\",\n \"5 USD/GBP BRL/USD 0.260522 0.429560 -0.022684\\n\",\n \"4 USD/GBP KRW/USD 0.250200 0.413440 -0.025069\\n\",\n \"27 BRL/USD MXN/USD 0.252088 0.310407 -0.025489\\n\",\n \"14 USD/EUR JPY/USD 0.087249 0.726459 -0.027515\\n\",\n \"3 USD/GBP JPY/USD 0.159157 0.651511 -0.027681\\n\",\n \"22 JPY/USD KRW/USD 0.172747 0.595019 -0.028986\\n\",\n \"9 USD/AUD JPY/USD 0.074424 0.708740 -0.030358\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"for row in dual_binary_dict:\\n\",\n \" row['price_fwd'] = row['price_fwd_future'].result() / 10e6\\n\",\n \" row['static_carry_1m'] = row['price_fwd'] - row['price']\\n\",\n \"\\n\",\n \"carry_table = pd.DataFrame(dual_binary_dict).sort_values('static_carry_1m', ascending=False)\\n\",\n \"carry_table.head(10)[['cross1', 'cross2', 'price', 'savings', 'static_carry_1m']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimers\\n\",\n \"\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"Backtesting, Simulated Results, Sensitivity/Scenario Analysis or Spreadsheet Calculator or Model: There may be data presented herein that is solely for illustrative purposes and which may include among other things back testing, simulated results and scenario analyses. The information is based upon certain factors, assumptions and historical information that Goldman Sachs may in its discretion have considered appropriate, however, Goldman Sachs provides no assurance or guarantee that this product will operate or would have operated in the past in a manner consistent with these assumptions. In the event any of the assumptions used do not prove to be true, results are likely to vary materially from the examples shown herein. Additionally, the results may not reflect material economic and market factors, such as liquidity, transaction costs and other expenses which could reduce potential return.\\n\",\n \"OTC Derivatives Risk Disclosures: \\n\",\n \"Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \\n\",\n \"\\n\",\n \"Liquidity Risk: There is no public market for OTC derivative transactions and, therefore, it may be difficult or impossible to liquidate an existing position on favorable terms. Transfer Restrictions: OTC derivative transactions entered into with one or more affiliates of The Goldman Sachs Group, Inc. (Goldman Sachs) cannot be assigned or otherwise transferred without its prior written consent and, therefore, it may be impossible for you to transfer any OTC derivative transaction to a third party. \\n\",\n \"\\n\",\n \"Conflict of Interests: Goldman Sachs may from time to time be an active participant on both sides of the market for the underlying securities, commodities, futures, options or any other derivative or instrument identical or related to those mentioned herein (together, \\\"the Product\\\"). Goldman Sachs at any time may have long or short positions in, or buy and sell Products (on a principal basis or otherwise) identical or related to those mentioned herein. Goldman Sachs hedging and trading activities may affect the value of the Products. \\n\",\n \"\\n\",\n \"Counterparty Credit Risk: Because Goldman Sachs, may be obligated to make substantial payments to you as a condition of an OTC derivative transaction, you must evaluate the credit risk of doing business with Goldman Sachs or its affiliates. \\n\",\n \"\\n\",\n \"Pricing and Valuation: The price of each OTC derivative transaction is individually negotiated between Goldman Sachs and each counterparty and Goldman Sachs does not represent or warrant that the prices for which it offers OTC derivative transactions are the best prices available, possibly making it difficult for you to establish what is a fair price for a particular OTC derivative transaction; The value or quoted price of the Product at any time, however, will reflect many factors and cannot be predicted. If Goldman Sachs makes a market in the offered Product, the price quoted by Goldman Sachs would reflect any changes in market conditions and other relevant factors, and the quoted price (and the value of the Product that Goldman Sachs will use for account statements or otherwise) could be higher or lower than the original price, and may be higher or lower than the value of the Product as determined by reference to pricing models used by Goldman Sachs. If at any time a third party dealer quotes a price to purchase the Product or otherwise values the Product, that price may be significantly different (higher or lower) than any price quoted by Goldman Sachs. Furthermore, if you sell the Product, you will likely be charged a commission for secondary market transactions, or the price will likely reflect a dealer discount. Goldman Sachs may conduct market making activities in the Product. To the extent Goldman Sachs makes a market, any price quoted for the OTC derivative transactions, Goldman Sachs may differ significantly from (i) their value determined by reference to Goldman Sachs pricing models and (ii) any price quoted by a third party. The market price of the OTC derivative transaction may be influenced by many unpredictable factors, including economic conditions, the creditworthiness of Goldman Sachs, the value of any underlyers, and certain actions taken by Goldman Sachs. \\n\",\n \"\\n\",\n \"Market Making, Investing and Lending: Goldman Sachs engages in market making, investing and lending businesses for its own account and the accounts of its affiliates in the same or similar instruments underlying OTC derivative transactions (including such trading as Goldman Sachs deems appropriate in its sole discretion to hedge its market risk in any OTC derivative transaction whether between Goldman Sachs and you or with third parties) and such trading may affect the value of an OTC derivative transaction. \\n\",\n \"\\n\",\n \"Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\\n\",\n \"Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf, http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. Certain transactions - including those involving futures, options, equity swaps, and other derivatives as well as non-investment-grade securities - give rise to substantial risk and are not available to nor suitable for all investors. If you have any questions about whether you are eligible to enter into these transactions with Goldman Sachs, please contact your sales representative. Foreign-currency-denominated securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\\n\",\n \"Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. Transaction costs may be a significant factor in option strategies calling for multiple purchases and sales of options, such as spreads. When purchasing long options an investor may lose their entire investment and when selling uncovered options the risk is potentially unlimited. Supporting documentation for any comparisons, recommendations, statistics, technical data, or other similar information will be supplied upon request.\\n\",\n \"This material is for the private information of the recipient only. This material is not sponsored, endorsed, sold or promoted by any sponsor or provider of an index referred herein (each, an \\\"Index Provider\\\"). GS does not have any affiliation with or control over the Index Providers or any control over the computation, composition or dissemination of the indices. While GS will obtain information from publicly available sources it believes reliable, it will not independently verify this information. Accordingly, GS shall have no liability, contingent or otherwise, to the user or to third parties, for the quality, accuracy, timeliness, continued availability or completeness of the data nor for any special, indirect, incidental or consequential damages which may be incurred or experienced because of the use of the data made available herein, even if GS has been advised of the possibility of such damages.\\n\",\n \"iTraxx® is a registered trade mark of International Index Company Limited.\\n\",\n \"iTraxx® is a trade mark of International Index Company Limited and has been licensed for the use by Goldman Sachs Japan Co., Ltd. International Index Company Limited does not approve, endorse or recommend Goldman Sachs Japan Co., Ltd. or iTraxx® derivatives products.\\n\",\n \"iTraxx® derivatives products are derived from a source considered reliable, but neither International Index Company Limited nor any of its employees, suppliers, subcontractors and agents (together iTraxx Associates) guarantees the veracity, completeness or accuracy of iTraxx® derivatives products or other information furnished in connection with iTraxx® derivatives products. No representation, warranty or condition, express or implied, statutory or otherwise, as to condition, satisfactory quality, performance, or fitness for purpose are given or assumed by International Index Company Limited or any of the iTraxx Associates in respect of iTraxx® derivatives products or any data included in such iTraxx® derivatives products or the use by any person or entity of iTraxx® derivatives products or that data and all those representations, warranties and conditions are excluded save to the extent that such exclusion is prohibited by law.\\n\",\n \"None of International Index Company Limited nor any of the iTraxx Associates shall have any liability or responsibility to any person or entity for any loss, damages, costs, charges, expenses or other liabilities whether caused by the negligence of International Index Company Limited or any of the iTraxx Associates or otherwise, arising in connection with the use of iTraxx® derivatives products or the iTraxx® indices.\\n\",\n \"Standard & Poor's ® and S&P ® are registered trademarks of The McGraw-Hill Companies, Inc. and S&P GSCI™ is a trademark of The McGraw-Hill Companies, Inc. and have been licensed for use by the Issuer. This Product (the \\\"Product\\\") is not sponsored, endorsed, sold or promoted by S&P and S&P makes no representation, warranty or condition regarding the advisability of investing in the Product.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/inflation_covid19_recovery_trade.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import warnings\\n\",\n \"from datetime import date\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"import statsmodels.api as sm\\n\",\n \"\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"\\n\",\n \"from gs_quant.instrument import InflationSwap\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import IRFwdRate\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.datetime import business_day_offset, business_day_count\\n\",\n \"from gs_quant.api.fred.data import FredDataApi\\n\",\n \"from gs_quant.timeseries import correlation, std, diff\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"\\n\",\n \"pd.options.display.float_format = '{:,.2f}'.format\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we will focus on inflation curves. Using macro economic drivers such as rates, equities, credit and oil, we will analyse trends and compare pre and post Covid-19 pandemic relationships. This is an example of the GS Quant functionalities and it is not a recommendation of a trade in the instruments discussed, please follow up with your salesperson to discuss specific implementations.\\n\",\n \"\\n\",\n \"* [1 - Define a few function and inputs](#1---Define-a-few-functions-and-inputs)\\n\",\n \"* [2 - Build the inflation portfolio](#2---Build-the-inflation-portfolio)\\n\",\n \"* [3 - Retrieve macro drivers data](#3---Retrieve-macro-drivers-data)\\n\",\n \"* [4 - Carry](#4---Carry)\\n\",\n \"* [5 - Correlation to macro drivers](#5---Correlation-to-macro-drivers)\\n\",\n \"* [6 - Actual vs Predicted](#6---Actual-vs-Predicted)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 - Define a few functions and inputs\\n\",\n \"\\n\",\n \"Let's start by defining a few handy functions and inputs to improve readability of the notebook. The functions created here help with handling date offsets and rolling of trades, aggregating the portfolio at the instrument level rather than leg level (which proves useful for the curves), and finally computing correlations and multi-factor linear regressions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def roll_forward_1y(trade):\\n\",\n \" '''\\n\",\n \" Roll the trade 1y\\n\",\n \" For forward starting trade decrease the fwd start\\n\",\n \" For spot starting roll the tenor\\n\",\n \" '''\\n\",\n \" fwd, tenor = trade[-2:]\\n\",\n \" tenor = int(tenor[:-1])\\n\",\n \" if fwd is None:\\n\",\n \" trade[-1] = '{}y'.format(tenor - 1)\\n\",\n \" else:\\n\",\n \" fwd = int(fwd[:-1])\\n\",\n \" trade[-2] = '{}y'.format(fwd - 1)\\n\",\n \" return trade\\n\",\n \"\\n\",\n \"\\n\",\n \"def date_subtract_period_str(ref_date, period): \\n\",\n \" '''\\n\",\n \" Substract 1y/m/d to a fixed date\\n\",\n \" '''\\n\",\n \" time_letter = period[-1]\\n\",\n \" num = int(period[:-1])\\n\",\n \"\\n\",\n \" if time_letter == 'd':\\n\",\n \" past_date = ref_date - dt.timedelta(days=num)\\n\",\n \" elif time_letter == 'm':\\n\",\n \" past_date = date(ref_date.year, ref_date.month - num, ref_date.day)\\n\",\n \" else: \\n\",\n \" past_date = date(ref_date.year - num, ref_date.month, ref_date.day)\\n\",\n \"\\n\",\n \" return past_date\\n\",\n \"\\n\",\n \"\\n\",\n \"def create_inflation_swap(trade):\\n\",\n \" '''\\n\",\n \" Create an inflation swap from\\n\",\n \" name, CCY, pay/receive, fwd start\\n\",\n \" and tenor input\\n\",\n \" '''\\n\",\n \" trade_name, ccy, payrec, fwd, tenor = trade\\n\",\n \" return InflationSwap(pay_or_receive=payrec, termination_date=tenor, notional_currency=ccy, effective_date=fwd,\\n\",\n \" name=trade_name)\\n\",\n \"\\n\",\n \"\\n\",\n \"def negate_leg(col):\\n\",\n \" '''\\n\",\n \" In order to aggregate price for the flatteners we need to negate\\n\",\n \" the receive leg\\n\",\n \" '''\\n\",\n \" instr = col.name\\n\",\n \" if 'flattener' in instr.name and instr.pay_or_receive == PayReceive.Receive:\\n\",\n \" return -col\\n\",\n \" return col\\n\",\n \" \\n\",\n \"\\n\",\n \"def negate_carry(port, series):\\n\",\n \" '''\\n\",\n \" In order to aggregate carry for the flatteners we need to negate\\n\",\n \" carry numbers for the receive leg\\n\",\n \" '''\\n\",\n \" mask = port.to_frame()['pay_or_receive'] == PayReceive.Receive \\n\",\n \" return series.where(mask.array, -series)\\n\",\n \"\\n\",\n \"\\n\",\n \"def flip_flatteners(prices):\\n\",\n \" '''\\n\",\n \" When computing correlation we flip prices for the flatteners to visualise \\n\",\n \" numbers as if we were putting on the trade\\n\",\n \" '''\\n\",\n \" flipped_p = prices\\n\",\n \" if 'flattener' in prices.name:\\n\",\n \" flipped_p = prices * (-1)\\n\",\n \" return flipped_p\\n\",\n \"\\n\",\n \"\\n\",\n \"def correl_map(drivers_df, prices_df, start_date):\\n\",\n \" '''\\n\",\n \" Compute correlation of returns between two timeseries from a specific date \\n\",\n \" '''\\n\",\n \" start_date = business_day_offset(start_date, 0, roll='preceding')\\n\",\n \" drivers_diff = diff(drivers_df[drivers_df.index > start_date])\\n\",\n \" prices_diff = diff(prices_df[prices_df.index > start_date])\\n\",\n \"\\n\",\n \" correl_df = pd.DataFrame(index=prices_df.columns, columns=drivers_df.columns)\\n\",\n \" for x in range(len(drivers_df.columns)):\\n\",\n \" for y in range(len(prices_df.columns)):\\n\",\n \" correl = correlation(drivers_diff.iloc[:, x], prices_diff.iloc[:, y], type_='returns')\\n\",\n \" correl_df.iloc[y, x] = correl.iloc[-1]\\n\",\n \"\\n\",\n \" return correl_df\\n\",\n \"\\n\",\n \"\\n\",\n \"def regression(y, x, regr_start, regr_end, select_x=False):\\n\",\n \" '''\\n\",\n \" Compute a multi-factors linear regression of y on a subset of x\\n\",\n \" '''\\n\",\n \" y = y.loc[regr_start:regr_end]\\n\",\n \" x = x.loc[regr_start:regr_end]\\n\",\n \" if select_x:\\n\",\n \" x = x[select_x]\\n\",\n \"\\n\",\n \" x = sm.add_constant(x) # adding a constant\\n\",\n \" model = sm.OLS(y, x).fit()\\n\",\n \"\\n\",\n \" return model\\n\",\n \"\\n\",\n \"\\n\",\n \"def regr_prediction(y, x, regr_start, regr_end, predict_start, predict_end=None, select_x=False):\\n\",\n \" '''\\n\",\n \" Compute a multi-factors linear regression of y on a subset of x between regr_start and regr_end\\n\",\n \" and return predictions vs actuals for y from predict_start to predict_end\\n\",\n \" '''\\n\",\n \" model = regression(y, x, regr_start, regr_end, select_x=select_x)\\n\",\n \" y_actual = y.loc[slice(predict_start, predict_end)]\\n\",\n \" x_actual = x.loc[slice(predict_start, predict_end)]\\n\",\n \" \\n\",\n \" prediction = model.predict(sm.add_constant(x_actual))\\n\",\n \" prediction.name = 'Predicted'\\n\",\n \"\\n\",\n \" return pd.DataFrame([y_actual, prediction]).T\\n\",\n \"\\n\",\n \"\\n\",\n \"today = date.today()\\n\",\n \"\\n\",\n \"start_date = business_day_offset(date(2015, 11, 1), 0, roll='following')\\n\",\n \"end_date = business_day_offset(today, -1, roll='preceding')\\n\",\n \"\\n\",\n \"regr_start = date(2018, 10, 1)\\n\",\n \"regr_end = date(2019, 10, 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 - Build the inflation portfolio\\n\",\n \"\\n\",\n \"let's build a portfolio of inflation swaps and curves, then retrieve historical rates.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
asset_classtypeeffective_datefeenotional_currencypay_or_receivetermination_date
instrument
2y2y HICP (InflationSwap)AssetClass.RatesAssetType.InflationSwap2y0Currency.EURPayReceive.Receive2y
5y5y HICP (InflationSwap)AssetClass.RatesAssetType.InflationSwap5y0Currency.EURPayReceive.Receive5y
5s20s HICP flattener (InflationSwap)AssetClass.RatesAssetType.InflationSwapdefault0Currency.EURPayReceive.Receive5y
5s20s HICP flattener (InflationSwap)AssetClass.RatesAssetType.InflationSwapdefault0Currency.EURPayReceive.Pay20y
10s30s HICP flattener (InflationSwap)AssetClass.RatesAssetType.InflationSwapdefault0Currency.EURPayReceive.Receive10y
10s30s HICP flattener (InflationSwap)AssetClass.RatesAssetType.InflationSwapdefault0Currency.EURPayReceive.Pay30y
5y5y/10y10y HICP flattener (InflationSwap)AssetClass.RatesAssetType.InflationSwap5y0Currency.EURPayReceive.Receive5y
5y5y/10y10y HICP flattener (InflationSwap)AssetClass.RatesAssetType.InflationSwap10y0Currency.EURPayReceive.Pay10y
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" asset_class \\\\\\n\",\n \"instrument \\n\",\n \"2y2y HICP (InflationSwap) AssetClass.Rates \\n\",\n \"5y5y HICP (InflationSwap) AssetClass.Rates \\n\",\n \"5s20s HICP flattener (InflationSwap) AssetClass.Rates \\n\",\n \"5s20s HICP flattener (InflationSwap) AssetClass.Rates \\n\",\n \"10s30s HICP flattener (InflationSwap) AssetClass.Rates \\n\",\n \"10s30s HICP flattener (InflationSwap) AssetClass.Rates \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) AssetClass.Rates \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) AssetClass.Rates \\n\",\n \"\\n\",\n \" type \\\\\\n\",\n \"instrument \\n\",\n \"2y2y HICP (InflationSwap) AssetType.InflationSwap \\n\",\n \"5y5y HICP (InflationSwap) AssetType.InflationSwap \\n\",\n \"5s20s HICP flattener (InflationSwap) AssetType.InflationSwap \\n\",\n \"5s20s HICP flattener (InflationSwap) AssetType.InflationSwap \\n\",\n \"10s30s HICP flattener (InflationSwap) AssetType.InflationSwap \\n\",\n \"10s30s HICP flattener (InflationSwap) AssetType.InflationSwap \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) AssetType.InflationSwap \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) AssetType.InflationSwap \\n\",\n \"\\n\",\n \" effective_date fee \\\\\\n\",\n \"instrument \\n\",\n \"2y2y HICP (InflationSwap) 2y 0 \\n\",\n \"5y5y HICP (InflationSwap) 5y 0 \\n\",\n \"5s20s HICP flattener (InflationSwap) default 0 \\n\",\n \"5s20s HICP flattener (InflationSwap) default 0 \\n\",\n \"10s30s HICP flattener (InflationSwap) default 0 \\n\",\n \"10s30s HICP flattener (InflationSwap) default 0 \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) 5y 0 \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) 10y 0 \\n\",\n \"\\n\",\n \" notional_currency \\\\\\n\",\n \"instrument \\n\",\n \"2y2y HICP (InflationSwap) Currency.EUR \\n\",\n \"5y5y HICP (InflationSwap) Currency.EUR \\n\",\n \"5s20s HICP flattener (InflationSwap) Currency.EUR \\n\",\n \"5s20s HICP flattener (InflationSwap) Currency.EUR \\n\",\n \"10s30s HICP flattener (InflationSwap) Currency.EUR \\n\",\n \"10s30s HICP flattener (InflationSwap) Currency.EUR \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) Currency.EUR \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) Currency.EUR \\n\",\n \"\\n\",\n \" pay_or_receive \\\\\\n\",\n \"instrument \\n\",\n \"2y2y HICP (InflationSwap) PayReceive.Receive \\n\",\n \"5y5y HICP (InflationSwap) PayReceive.Receive \\n\",\n \"5s20s HICP flattener (InflationSwap) PayReceive.Receive \\n\",\n \"5s20s HICP flattener (InflationSwap) PayReceive.Pay \\n\",\n \"10s30s HICP flattener (InflationSwap) PayReceive.Receive \\n\",\n \"10s30s HICP flattener (InflationSwap) PayReceive.Pay \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) PayReceive.Receive \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) PayReceive.Pay \\n\",\n \"\\n\",\n \" termination_date \\n\",\n \"instrument \\n\",\n \"2y2y HICP (InflationSwap) 2y \\n\",\n \"5y5y HICP (InflationSwap) 5y \\n\",\n \"5s20s HICP flattener (InflationSwap) 5y \\n\",\n \"5s20s HICP flattener (InflationSwap) 20y \\n\",\n \"10s30s HICP flattener (InflationSwap) 10y \\n\",\n \"10s30s HICP flattener (InflationSwap) 30y \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) 5y \\n\",\n \"5y5y/10y10y HICP flattener (InflationSwap) 10y \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trades_info = (\\n\",\n \" ['2y2y HICP', Currency.EUR, PayReceive.Receive, '2y', '2y'],\\n\",\n \" ['5y5y HICP', Currency.EUR, PayReceive.Receive, '5y', '5y'],\\n\",\n \" ['5s20s HICP flattener', Currency.EUR, PayReceive.Receive, None, '5y'],\\n\",\n \" ['5s20s HICP flattener', Currency.EUR, PayReceive.Pay, None, '20y'],\\n\",\n \" ['10s30s HICP flattener', Currency.EUR, PayReceive.Receive, None, '10y'],\\n\",\n \" ['10s30s HICP flattener', Currency.EUR, PayReceive.Pay, None, '30y'],\\n\",\n \" ['5y5y/10y10y HICP flattener', Currency.EUR, PayReceive.Receive, '5y', '5y'],\\n\",\n \" ['5y5y/10y10y HICP flattener', Currency.EUR, PayReceive.Pay, '10y', '10y'],\\n\",\n \")\\n\",\n \"\\n\",\n \"infla_port = Portfolio(map(create_inflation_swap, trades_info), name = 'inflation swaps')\\n\",\n \"infla_df = infla_port.to_frame()\\n\",\n \"infla_df.index = infla_df.index.droplevel(0)\\n\",\n \"infla_df.fillna('default')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
2y2y HICP5y5y HICP5s20s HICP flattener10s30s HICP flattener5y5y/10y10y HICP flattener
2020-11-1185.58118.1243.5041.7539.26
2020-11-1283.83116.2942.8241.1338.61
2020-11-1381.97114.6142.1441.0638.91
2020-11-1684.71116.0339.7539.3237.92
2020-11-1781.40114.1640.5039.6338.10
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 2y2y HICP 5y5y HICP 5s20s HICP flattener 10s30s HICP flattener \\\\\\n\",\n \"2020-11-11 85.58 118.12 43.50 41.75 \\n\",\n \"2020-11-12 83.83 116.29 42.82 41.13 \\n\",\n \"2020-11-13 81.97 114.61 42.14 41.06 \\n\",\n \"2020-11-16 84.71 116.03 39.75 39.32 \\n\",\n \"2020-11-17 81.40 114.16 40.50 39.63 \\n\",\n \"\\n\",\n \" 5y5y/10y10y HICP flattener \\n\",\n \"2020-11-11 39.26 \\n\",\n \"2020-11-12 38.61 \\n\",\n \"2020-11-13 38.91 \\n\",\n \"2020-11-16 37.92 \\n\",\n \"2020-11-17 38.10 \"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"with HistoricalPricingContext(start_date, end_date, is_batch=True, csa_term='EUR-OIS'):\\n\",\n \" port_hist_rate = infla_port.calc(IRFwdRate)\\n\",\n \"\\n\",\n \"prices = pd.DataFrame(port_hist_rate)\\n\",\n \"prices.index = infla_port.instruments\\n\",\n \"prices_T = prices.T.apply(negate_leg)\\n\",\n \"prices_T.columns = [instr.name for instr in infla_port.instruments]\\n\",\n \"prices_T = prices_T.groupby(level=0, axis=1, sort=False).sum()\\n\",\n \"prices_T *= 1e4\\n\",\n \"prices_T.tail()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 - Retrieve macro drivers data\\n\",\n \"\\n\",\n \"\\n\",\n \"For this analysis we use rates, credit and equities data from [Marquee data catalog](https://marquee.gs.com/s/discover/data-services/catalog) and oil data from [FRED](https://fred.stlouisfed.org/). You will need to use your own FRED API key. See [here](https://research.stlouisfed.org/useraccount/login/secure/) to create one (takes under a minute). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"FRED_API_KEY = 'YOUR_KEY_HERE'\\n\",\n \"fred_API = FredDataApi(api_key=FRED_API_KEY)\\n\",\n \"fred_pull = fred_API.build_query(start=start_date, end=end_date)\\n\",\n \"\\n\",\n \"drivers = {\\n\",\n \" 'Rates': {'instr': ['EUR', 'GBP', 'USD'], 'tenor': ['10y', '30y']},\\n\",\n \" 'Credit': {'instr': ['Xover', 'CDX_IG'], 'tenor': ['5y']},\\n\",\n \" 'Equities': ['SPX', 'SX5E'],\\n\",\n \" 'Commodities': ['WTI']\\n\",\n \"}\\n\",\n \"\\n\",\n \"dataset_map = {\\n\",\n \" 'Rates': {'id': 'IR_SWAP2', 'value': 'rate'},\\n\",\n \" 'Credit': {'id': 'CDSLEVELS', 'value': 'spread'},\\n\",\n \" 'Equities': {'id': 'TREOD', 'value': 'closePrice'}, \\n\",\n \" 'Commodities': 'fred'\\n\",\n \"}\\n\",\n \"\\n\",\n \"id_map = {\\n\",\n \" 'EUR': 'MA5WM2QWRVMYKDK0',\\n\",\n \" 'GBP': 'MABXTJXXN8WJR7R8',\\n\",\n \" 'USD': 'MAAXGV0GZTW4GFNC',\\n\",\n \" 'Xover': 'MA67A5J6SPG8F7R6',\\n\",\n \" 'CDX_IG': 'MANJG63BKCNEHYW3',\\n\",\n \" 'SX5E': 'MA4B66MW5E27U8P32SY',\\n\",\n \" 'SPX': 'MA4B66MW5E27U8P32SB',\\n\",\n \" 'WTI': 'DCOILWTICO'\\n\",\n \"}\\n\",\n \"id_map_inv = {v: k for k, v in id_map.items()}\\n\",\n \"\\n\",\n \"df_list = []\\n\",\n \"\\n\",\n \"for asset_type, assets in drivers.items():\\n\",\n \" ds_info = dataset_map[asset_type]\\n\",\n \" \\n\",\n \" if ds_info == 'fred': \\n\",\n \" sub_df = pd.DataFrame(fred_API.query_data(fred_pull, dataset_id=id_map['WTI']))\\n\",\n \" sub_df.columns = ['WTI']\\n\",\n \"\\n\",\n \" else: \\n\",\n \" col = ['instr name']\\n\",\n \"\\n\",\n \" if asset_type in ['Rates', 'Credit']:\\n\",\n \" id_keys = assets['instr']\\n\",\n \" kwargs = {'tenor': assets['tenor']}\\n\",\n \" col.append('tenor')\\n\",\n \" separator = '_'\\n\",\n \" else:\\n\",\n \" id_keys = assets\\n\",\n \" kwargs = {}\\n\",\n \" separator = ''\\n\",\n \"\\n\",\n \" asset_ids = [id_map[x] for x in id_keys]\\n\",\n \" raw_data = Dataset(ds_info['id']).get_data(start_date, end_date, assetId=asset_ids, **kwargs)\\n\",\n \" raw_data['instr name'] = raw_data.apply(lambda x: id_map_inv[x['assetId']], axis=1)\\n\",\n \" sub_df = pd.pivot_table(raw_data, values=ds_info['value'], index=['date'], columns=col)\\n\",\n \" sub_df.columns = sub_df.columns.map(separator.join)\\n\",\n \"\\n\",\n \" df_list.append(sub_df)\\n\",\n \"\\n\",\n \"drivers_df = pd.concat(df_list, axis=1)\\n\",\n \"drivers_df = drivers_df.fillna(method='ffill').dropna()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(18, 25))\\n\",\n \"label = 'Covid-19 western outbreak'\\n\",\n \"prices_T.plot(title='Inflation rates in bps', ax=ax1) \\n\",\n \"ax1.axvspan(date(2020, 2, 27), date(2020, 3, 26), color='red', alpha=0.1, label=label)\\n\",\n \"ax1.legend(loc='upper left')\\n\",\n \"\\n\",\n \"drivers_df[['SPX', 'SX5E']].plot(title='Equity prices in bps', ax=ax2)\\n\",\n \"ax2.axvspan(date(2020, 2, 27), date(2020, 3, 26), color='red', alpha=0.1, label=label)\\n\",\n \"ax2.xaxis.label.set_visible(False)\\n\",\n \"ax2.legend(loc='upper left')\\n\",\n \"\\n\",\n \"rates = drivers_df[['EUR_10y', 'EUR_30y', 'GBP_10y', 'GBP_30y', 'USD_10y', 'USD_30y']] * 1e4\\n\",\n \"rates.plot(title='Rates prices in bps', ax=ax3)\\n\",\n \"ax3.axvspan(date(2020, 2, 27), date(2020, 3, 26), color='red', alpha=0.1, label=label)\\n\",\n \"ax3.xaxis.label.set_visible(False)\\n\",\n \"ax3.legend()\\n\",\n \"\\n\",\n \"drivers_df[['CDX_IG_5y', 'Xover_5y', 'WTI']].plot(title='Credit and Oil prices in bps', ax=ax4)\\n\",\n \"ax4.axvspan(date(2020, 2, 27), date(2020, 3, 26), color='red', alpha=0.1, label=label)\\n\",\n \"ax4.xaxis.label.set_visible(False)\\n\",\n \"ax4.legend()\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 - Carry\\n\",\n \"\\n\",\n \"Let's compute 3 months carry and vol adjusted carry using 3m realised volatility. Here we assume linearity of the roll over the year thus compute 3m roll based on 1y roll.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"infla_port.resolve()\\n\",\n \"base_rate = infla_port.to_frame()['fixed_rate']\\n\",\n \"\\n\",\n \"roll_port = Portfolio(map(lambda x: create_inflation_swap(roll_forward_1y(x)), trades_info), name='inflation swaps roll fwd')\\n\",\n \"roll_port.resolve()\\n\",\n \"fwd_rate = roll_port.to_frame()['fixed_rate']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"carry_df = pd.concat([pd.DataFrame(base_rate.values,columns=['Base']), pd.DataFrame(fwd_rate.values,columns=['Roll Fwd'])], axis=1)\\n\",\n \"carry_df['1y Carry'] = negate_carry(infla_port, carry_df['Roll Fwd'] - carry_df['Base'])\\n\",\n \"carry_df['3m Carry (bps)'] = carry_df['1y Carry'] * 1e4 / 4\\n\",\n \"carry_df.index = [instr.name for instr in infla_port.instruments]\\n\",\n \"carry_df = carry_df.groupby(level=0, sort=False).sum()\\n\",\n \"\\n\",\n \"back_3m = date(today.year, today.month - 3, today.day)\\n\",\n \"infla_diff = diff(prices_T[prices_T.index > back_3m])\\n\",\n \"infla_vol = infla_diff.apply(lambda x: std(x))\\n\",\n \"vol_3m = infla_vol.iloc[-1] * np.sqrt(business_day_count(back_3m, today))\\n\",\n \"\\n\",\n \"carry_df['3m Vol-adjusted Carry ratio in %'] = carry_df['3m Carry (bps)'] / vol_3m * 100\\n\",\n \"carry_df.reset_index(inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"clr_palette = sns.color_palette()\\n\",\n \"\\n\",\n \"f,(ax_b1, ax_b2) = plt.subplots(1, 2, figsize=(19, 6))\\n\",\n \"\\n\",\n \"ax_b1.tick_params('x', labelrotation=45)\\n\",\n \"ax_b2.tick_params('x', labelrotation=45)\\n\",\n \"ax_b1.set_title('Carry')\\n\",\n \"ax_b2.set_title('Vol-adjusted carry')\\n\",\n \"\\n\",\n \"b1 = sns.barplot(x='index', y='3m Carry (bps)', data=carry_df, ax=ax_b1, palette=[clr_palette[0]])\\n\",\n \"for index, row in carry_df.iterrows():\\n\",\n \" b1.text(index, row['3m Carry (bps)'], round(row['3m Carry (bps)'],2), color='black', ha=\\\"center\\\")\\n\",\n \" \\n\",\n \"b1.xaxis.set_label_text('')\\n\",\n \"b2 = sns.barplot(x='index', y='3m Vol-adjusted Carry ratio in %', data=carry_df, ax=ax_b2, palette=[clr_palette[1]])\\n\",\n \"for index, row in carry_df.iterrows():\\n\",\n \" b2.text(index, row['3m Vol-adjusted Carry ratio in %'], round(row['3m Vol-adjusted Carry ratio in %'], 2), color='black', ha=\\\"center\\\")\\n\",\n \"b2.xaxis.set_label_text('')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5 - Correlation to macro drivers\\n\",\n \"\\n\",\n \"Now we look at the correlations between inflation rates and our macro series over various timeframes. From a trade's perspective, a flattening move should be positively correlated to an equity rally and bond sell-off. To correct for the directional bias of flattening, we to flip the curves' direction in the correlation calculations in order to keep the correlation sign accurante.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"correl_str = ['3m','1y','3y','5y']\\n\",\n \"correl_start_dates = [date_subtract_period_str(today, t) for t in correl_str]\\n\",\n \"\\n\",\n \"flipped_prices = prices_T.apply(flip_flatteners)\\n\",\n \"drivers_df.index = drivers_df.index.date\\n\",\n \"correl_matrices = [correl_map(drivers_df, flipped_prices, x) for x in correl_start_dates] \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axn = plt.subplots(2, 2, figsize=(25, 12))\\n\",\n \"cbar_ax = fig.add_axes([0.91, 0.3, 0.03, 0.4])\\n\",\n \"lim = max([np.nanmax(abs(mat)) for mat in correl_matrices])\\n\",\n \"\\n\",\n \"for i, ax in enumerate(axn.flat):\\n\",\n \" g = sns.heatmap(correl_matrices[i].apply(pd.to_numeric), ax=ax,\\n\",\n \" cbar=i == 0,\\n\",\n \" vmin=-lim, vmax=lim, center=0,\\n\",\n \" annot=True, cmap='RdBu', fmt='.1%',\\n\",\n \" cbar_ax=None if i else cbar_ax)\\n\",\n \" ax.set_title('{} Correlation'.format(correl_str[i]), fontsize=14)\\n\",\n \" if i % 2 == 0:\\n\",\n \" ax.set_ylabel('Inflation', fontsize=14)\\n\",\n \" else:\\n\",\n \" ax.axes.get_yaxis().set_visible(False)\\n\",\n \" if i // 2 == 0:\\n\",\n \" ax.xaxis.tick_top()\\n\",\n \"\\n\",\n \"fig.tight_layout(rect=[0, 0, 0.9, 1], pad=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6 - Actual vs Predicted\\n\",\n \"\\n\",\n \"Finally, we run a multifactors linear regression on our macro drivers using pre-covid data. \\n\",\n \"We then visualise where we would have expected inflation rates to be, given previously determined pre-covid relationship, versus where they are now.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regr_data = pd.concat([drivers_df, prices_T],axis=1).fillna(method='ffill').dropna() # to ensure same data for both drivers and inflation\\n\",\n \"regr_drivers = regr_data[['EUR_10y', 'EUR_30y', 'Xover_5y', 'SPX', 'SX5E', 'WTI']]\\n\",\n \"regr_infla = regr_data[list(prices_T.columns)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pred = {}\\n\",\n \"for x in regr_infla.columns:\\n\",\n \" pred[x] = regr_prediction(regr_infla[x], regr_drivers, regr_start=regr_start, regr_end=regr_end, predict_start=regr_start)['Predicted']\\n\",\n \"pred_df = pd.DataFrame.from_dict(pred, orient='columns')\\n\",\n \"pred_df.columns = pred_df.columns + ' pred'\\n\",\n \"\\n\",\n \"df = regr_infla[regr_infla.index > dt.date(2019, 1, 1)].join(pred_df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for x in regr_infla.columns:\\n\",\n \" fig,ax = plt.subplots(figsize=(14, 4))\\n\",\n \" ax.plot(df[[x]], label='actual')\\n\",\n \" ax.plot(df[[x + ' pred']], color=\\\"orange\\\", linestyle='--', label='predicted')\\n\",\n \" plt.axvline(x=regr_end, linestyle=':', color='red', label='end of regression timeframe')\\n\",\n \" ax.set_title(x)\\n\",\n \" ax.legend()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimers\\n\",\n \"\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"Backtesting, Simulated Results, Sensitivity/Scenario Analysis or Spreadsheet Calculator or Model: There may be data presented herein that is solely for illustrative purposes and which may include among other things back testing, simulated results and scenario analyses. The information is based upon certain factors, assumptions and historical information that Goldman Sachs may in its discretion have considered appropriate, however, Goldman Sachs provides no assurance or guarantee that this product will operate or would have operated in the past in a manner consistent with these assumptions. In the event any of the assumptions used do not prove to be true, results are likely to vary materially from the examples shown herein. Additionally, the results may not reflect material economic and market factors, such as liquidity, transaction costs and other expenses which could reduce potential return.\\n\",\n \"OTC Derivatives Risk Disclosures: \\n\",\n \"Terms of the Transaction: To understand clearly the terms and conditions of any OTC derivative transaction you may enter into, you should carefully review the Master Agreement, including any related schedules, credit support documents, addenda and exhibits. You should not enter into OTC derivative transactions unless you understand the terms of the transaction you are entering into as well as the nature and extent of your risk exposure. You should also be satisfied that the OTC derivative transaction is appropriate for you in light of your circumstances and financial condition. You may be requested to post margin or collateral to support written OTC derivatives at levels consistent with the internal policies of Goldman Sachs. \\n\",\n \"\\n\",\n \"Liquidity Risk: There is no public market for OTC derivative transactions and, therefore, it may be difficult or impossible to liquidate an existing position on favorable terms. Transfer Restrictions: OTC derivative transactions entered into with one or more affiliates of The Goldman Sachs Group, Inc. (Goldman Sachs) cannot be assigned or otherwise transferred without its prior written consent and, therefore, it may be impossible for you to transfer any OTC derivative transaction to a third party. \\n\",\n \"\\n\",\n \"Conflict of Interests: Goldman Sachs may from time to time be an active participant on both sides of the market for the underlying securities, commodities, futures, options or any other derivative or instrument identical or related to those mentioned herein (together, \\\"the Product\\\"). Goldman Sachs at any time may have long or short positions in, or buy and sell Products (on a principal basis or otherwise) identical or related to those mentioned herein. Goldman Sachs hedging and trading activities may affect the value of the Products. \\n\",\n \"\\n\",\n \"Counterparty Credit Risk: Because Goldman Sachs, may be obligated to make substantial payments to you as a condition of an OTC derivative transaction, you must evaluate the credit risk of doing business with Goldman Sachs or its affiliates. \\n\",\n \"\\n\",\n \"Pricing and Valuation: The price of each OTC derivative transaction is individually negotiated between Goldman Sachs and each counterparty and Goldman Sachs does not represent or warrant that the prices for which it offers OTC derivative transactions are the best prices available, possibly making it difficult for you to establish what is a fair price for a particular OTC derivative transaction; The value or quoted price of the Product at any time, however, will reflect many factors and cannot be predicted. If Goldman Sachs makes a market in the offered Product, the price quoted by Goldman Sachs would reflect any changes in market conditions and other relevant factors, and the quoted price (and the value of the Product that Goldman Sachs will use for account statements or otherwise) could be higher or lower than the original price, and may be higher or lower than the value of the Product as determined by reference to pricing models used by Goldman Sachs. If at any time a third party dealer quotes a price to purchase the Product or otherwise values the Product, that price may be significantly different (higher or lower) than any price quoted by Goldman Sachs. Furthermore, if you sell the Product, you will likely be charged a commission for secondary market transactions, or the price will likely reflect a dealer discount. Goldman Sachs may conduct market making activities in the Product. To the extent Goldman Sachs makes a market, any price quoted for the OTC derivative transactions, Goldman Sachs may differ significantly from (i) their value determined by reference to Goldman Sachs pricing models and (ii) any price quoted by a third party. The market price of the OTC derivative transaction may be influenced by many unpredictable factors, including economic conditions, the creditworthiness of Goldman Sachs, the value of any underlyers, and certain actions taken by Goldman Sachs. \\n\",\n \"\\n\",\n \"Market Making, Investing and Lending: Goldman Sachs engages in market making, investing and lending businesses for its own account and the accounts of its affiliates in the same or similar instruments underlying OTC derivative transactions (including such trading as Goldman Sachs deems appropriate in its sole discretion to hedge its market risk in any OTC derivative transaction whether between Goldman Sachs and you or with third parties) and such trading may affect the value of an OTC derivative transaction. \\n\",\n \"\\n\",\n \"Early Termination Payments: The provisions of an OTC Derivative Transaction may allow for early termination and, in such cases, either you or Goldman Sachs may be required to make a potentially significant termination payment depending upon whether the OTC Derivative Transaction is in-the-money to Goldman Sachs or you at the time of termination. Indexes: Goldman Sachs does not warrant, and takes no responsibility for, the structure, method of computation or publication of any currency exchange rates, interest rates, indexes of such rates, or credit, equity or other indexes, unless Goldman Sachs specifically advises you otherwise.\\n\",\n \"Risk Disclosure Regarding futures, options, equity swaps, and other derivatives as well as non-investment-grade securities and ADRs: Please ensure that you have read and understood the current options, futures and security futures disclosure document before entering into any such transactions. Current United States listed options, futures and security futures disclosure documents are available from our sales representatives or at http://www.theocc.com/components/docs/riskstoc.pdf, http://www.goldmansachs.com/disclosures/risk-disclosure-for-futures.pdf and https://www.nfa.futures.org/investors/investor-resources/files/security-futures-disclosure.pdf, respectively. Certain transactions - including those involving futures, options, equity swaps, and other derivatives as well as non-investment-grade securities - give rise to substantial risk and are not available to nor suitable for all investors. If you have any questions about whether you are eligible to enter into these transactions with Goldman Sachs, please contact your sales representative. Foreign-currency-denominated securities are subject to fluctuations in exchange rates that could have an adverse effect on the value or price of, or income derived from, the investment. In addition, investors in securities such as ADRs, the values of which are influenced by foreign currencies, effectively assume currency risk.\\n\",\n \"Options Risk Disclosures: Options may trade at a value other than that which may be inferred from the current levels of interest rates, dividends (if applicable) and the underlier due to other factors including, but not limited to, expectations of future levels of interest rates, future levels of dividends and the volatility of the underlier at any time prior to maturity. Note: Options involve risk and are not suitable for all investors. Please ensure that you have read and understood the current options disclosure document before entering into any standardized options transactions. United States listed options disclosure documents are available from our sales representatives or at http://theocc.com/publications/risks/riskstoc.pdf. A secondary market may not be available for all options. Transaction costs may be a significant factor in option strategies calling for multiple purchases and sales of options, such as spreads. When purchasing long options an investor may lose their entire investment and when selling uncovered options the risk is potentially unlimited. Supporting documentation for any comparisons, recommendations, statistics, technical data, or other similar information will be supplied upon request.\\n\",\n \"This material is for the private information of the recipient only. This material is not sponsored, endorsed, sold or promoted by any sponsor or provider of an index referred herein (each, an \\\"Index Provider\\\"). GS does not have any affiliation with or control over the Index Providers or any control over the computation, composition or dissemination of the indices. While GS will obtain information from publicly available sources it believes reliable, it will not independently verify this information. Accordingly, GS shall have no liability, contingent or otherwise, to the user or to third parties, for the quality, accuracy, timeliness, continued availability or completeness of the data nor for any special, indirect, incidental or consequential damages which may be incurred or experienced because of the use of the data made available herein, even if GS has been advised of the possibility of such damages.\\n\",\n \"iTraxx® is a registered trade mark of International Index Company Limited.\\n\",\n \"iTraxx® is a trade mark of International Index Company Limited and has been licensed for the use by Goldman Sachs Japan Co., Ltd. International Index Company Limited does not approve, endorse or recommend Goldman Sachs Japan Co., Ltd. or iTraxx® derivatives products.\\n\",\n \"iTraxx® derivatives products are derived from a source considered reliable, but neither International Index Company Limited nor any of its employees, suppliers, subcontractors and agents (together iTraxx Associates) guarantees the veracity, completeness or accuracy of iTraxx® derivatives products or other information furnished in connection with iTraxx® derivatives products. No representation, warranty or condition, express or implied, statutory or otherwise, as to condition, satisfactory quality, performance, or fitness for purpose are given or assumed by International Index Company Limited or any of the iTraxx Associates in respect of iTraxx® derivatives products or any data included in such iTraxx® derivatives products or the use by any person or entity of iTraxx® derivatives products or that data and all those representations, warranties and conditions are excluded save to the extent that such exclusion is prohibited by law.\\n\",\n \"None of International Index Company Limited nor any of the iTraxx Associates shall have any liability or responsibility to any person or entity for any loss, damages, costs, charges, expenses or other liabilities whether caused by the negligence of International Index Company Limited or any of the iTraxx Associates or otherwise, arising in connection with the use of iTraxx® derivatives products or the iTraxx® indices.\\n\",\n \"Standard & Poor's ® and S&P ® are registered trademarks of The McGraw-Hill Companies, Inc. and S&P GSCI™ is a trademark of The McGraw-Hill Companies, Inc. and have been licensed for use by the Issuer. This Product (the \\\"Product\\\") is not sponsored, endorsed, sold or promoted by S&P and S&P makes no representation, warranty or condition regarding the advisability of investing in the Product.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/02_optimizing_equity_trading/qes_utils.py", "content": "import os\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings\nimport pandas as pd\nwarnings.filterwarnings('ignore')\n\n\ndef persistXls(xls_report, path, filename, merge_cells=False, indentifier_marqueeid_map=pd.DataFrame()):\n xls_path = os.path.join(path, filename + '.xlsx')\n writer = pd.ExcelWriter(xls_path, options={'remove_timezone': True})\n for sheet, data in xls_report.items():\n data = pd.DataFrame(data)\n if 'assetId' in data.columns and len(indentifier_marqueeid_map) > 0:\n data = data.rename(columns={'assetId': 'marqueeid'}).merge(indentifier_marqueeid_map,\n how='left', on='marqueeid')\n pd.DataFrame(data).to_excel(writer, sheet, merge_cells=merge_cells)\n writer.save()\n writer.close()\n return xls_path\n\n\ndef plotGross(intraday, color_pal='Blues_r'):\n fig, ax = plt.subplots(figsize=(10, 4))\n pal = sns.color_palette(color_pal)\n intraday['gross'].plot.area(ax=ax, color=pal, alpha=0.4)\n ax.set_xlabel('Time/GMT')\n ax.set_title('Gross Remaining')\n ax.set_ylabel('Dollar')\n fig.tight_layout()\n\n\n# Plot cost breakdown\ndef plotCost(intraday):\n intraday_cost = intraday.copy()\n initial_gross = intraday_cost.iloc[0].at['gross']\n intraday_cost = intraday_cost[1:]\n # convert to bps\n intraday_cost[['totalCostPermanent', 'totalCostSpread', 'totalCostVolatility']] /= initial_gross / 10000\n intraday_cost[['totalCostPermanent', 'totalCostSpread',\n 'totalCostVolatility']] = intraday_cost[['totalCostPermanent', 'totalCostSpread',\n 'totalCostVolatility']].cumsum()\n fig, ax = plt.subplots(figsize=(10, 4))\n pal = sns.color_palette('Blues_r')\n intraday_cost[['totalCostPermanent', 'totalCostSpread',\n 'totalCostVolatility']].plot.area(ax=ax, color=pal, alpha=0.4)\n ax.set_xlabel('Time/GMT')\n ax.set_title('Cost Contribution')\n ax.set_ylabel('Cost (bps)')\n fig.tight_layout()\n\n\n# Plot Variance breakdown\ndef plotVar(intraday):\n initial_gross = intraday.iloc[0]['gross']\n risk_list = ['Factor', 'Specific', 'Diagonal']\n intraday = intraday.iloc[1:]\n for r in risk_list:\n intraday[r + 'Risk_bps^2'] = pow(10000 * intraday[r.lower() + 'Risk'] / initial_gross, 1)\n fig, ax = plt.subplots(figsize=(10, 4))\n pal = sns.color_palette('cubehelix', 8)\n intraday[['FactorRisk_bps^2', 'SpecificRisk_bps^2', 'DiagonalRisk_bps^2']].plot.area(ax=ax, color=pal, alpha=0.4)\n ax.legend(['Factor', 'Specific', 'Diagonal'], loc='upper right')\n ax.set_xlabel('Time/GMT')\n ax.set_title('Variance Contribution')\n ax.set_ylabel('Variance (bps)')\n fig.tight_layout()\n\n\n# Plot Buy/Sell/Net\ndef plotBuySellNet(intraday):\n fig, ax = plt.subplots(figsize=(10, 4))\n (100 * intraday['buy'] / intraday['gross'].iloc[0]).plot(ax=ax, color=(0 / 235, 121 / 235, 114 / 235))\n (100 * intraday['sell'] / intraday['gross'].iloc[0]).plot(ax=ax, color=(142 / 235, 215 / 235, 205 / 235))\n (100 * intraday['net'] / intraday['gross'].iloc[0]).plot(ax=ax, color=(32 / 235, 189 / 235, 168 / 235))\n ax.axhline(linestyle='--')\n ax.set_xlabel('Time/GMT')\n ax.set_title('Buy/Sell/Net(%)')\n ax.set_ylabel('Dollar')\n fig.tight_layout()\n\n\ndef plotGrossRemaining(intraday):\n fig, ax = plt.subplots(figsize=(10, 4))\n (100 * intraday['gross'] / intraday['gross'].iloc[0]).plot(ax=ax, color=\"DarkBlue\")\n ax.set_xlabel('Time/GMT')\n ax.set_title('Gross(%,LHS) and Participation Rate (%,RHS)')\n ax.set_ylabel('%')\n ax2 = ax.twinx()\n intraday['advAveragePercentage'].rolling('220min').mean().plot(ax=ax2, color='grey', style='--')\n fig.tight_layout()\n\n\n# function defined to plot three metrics every run\ndef plotMultiStrategyPortfolioLevelAnalytics(results_dict_multi, metrics_list, title, ylabel, color_pal='Blues_r',\n urgency_list=['VERY_LOW', 'LOW', 'MEDIUM', 'HIGH', 'VERY_HIGH']):\n agg_result = [pd.DataFrame(results_dict_multi[u]\n ['analytics']['portfolioAnalyticsIntraday'])[metrics_list + ['time',\n 'gross', 'tradeDayNumber']]\n .assign(urgency=u).assign(time=lambda x: pd.to_datetime(x['time']))\n .set_index('time').pipe(lambda df: df[df['tradeDayNumber'] == 1]) for u in urgency_list]\n initial_gross = agg_result[0].iloc[0]['gross']\n fig, [ax1, ax2, ax3, ax4] = plt.subplots(4, 1, sharex=True, figsize=(10, 12))\n for m, ax, t, y in zip(metrics_list, [ax1, ax2, ax3, ax4], title, ylabel):\n df_plot = pd.concat(agg_result).reset_index().set_index(['time',\n 'urgency'])[m].unstack().reindex(columns=urgency_list)\n df_plot = df_plot[1:]\n if m == 'totalCost':\n df_plot /= initial_gross / 10000\n df_plot = df_plot.cumsum()\n elif m == 'advAveragePercentage':\n df_plot = df_plot.rolling('120min').mean()\n df_plot.plot(ax=ax, alpha=0.5)\n ax.legend(urgency_list, loc='right')\n ax.set_xlabel('Time/GMT')\n ax.set_title(t)\n ax.set_ylabel(y)\n fig.tight_layout()\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/02_optimizing_equity_trading/quants_meet_markets_qes.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# GS Quant Meets Markets: Behind the Scenes of Goldman Sachs' Equity Trading Algos, By QES\\n\",\n \"- For any questions, feedback and assistance, please do not hesitate to email us at [gs-marquee-qes](mailto:gs-marquee-qes)\\n\",\n \"- If you want to explore further, please visit our Quantitative Execution Services hub of research, analytics, datasets and more at the [quantitative-execution-services](https://marquee.gs.com/content/markets/products/quantitative-execution-services.html) site\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# The Quantitative Execution Services group....Who Are We ?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Structure of the guide:\\n\",\n \"- The guide aims to showcase a suite of datasets, computations and optimizations that GS quants typically harness when analyzing equities baskets.\\n\",\n \"- The range of suite of analysis shown in this notebook are being used by GS internal quants, GS trading algos and range of clients globally.\\n\",\n \"\\n\",\n \"In an attempt to provide a realistic set of use-cases which utilize the offerings, we consider a Long\\\\Short Momentum basket rebalance, which a client intends to trade in the market.\\n\",\n \"\\n\",\n \"Throughout the guide, we'll attempt to analyze the basket and the market, via the following prisms:\\n\",\n \"\\n\",\n \"**[Section A](#Section_A): Liquidity through the QES-Marquee prism** \\n\",\n \"
We'll utilize the GS Quant API to explore the liquidity landscape across indices, and provide further analysis across expected impact, through the lens of trade difficulty clusters. Find below API docs, corresponding to the section:\\n\",\n \"- [API Docs: QES Single Stock Volume Forecast](https://marquee.web.gs.com/s/developer/datasets/SINGLE_STOCK_VOLUME_FORECAST_BASE)\\n\",\n \"- [API Docs: QES Single Stock Volume Profiles](https://marquee.web.gs.com/s/developer/datasets/SINGLE_STOCK_VOLUME_PROFILE_BASE)\\n\",\n \"- [API Docs: GS Single Stock Market Impact Estimates](https://marquee.web.gs.com/s/developer/datasets/SINGLE_STOCK_MARKET_IMPACT) and [Global Trading Cost Analysis - March 2021 example](https://marquee.gs.com/content/markets/en/2021/03/18/e750df5a-14bf-4c8a-8d57-298757450512.html)\\n\",\n \"- [GS Equity Trade Clusters](https://marquee.web.gs.com/s/developer/datasets/EQTRADECLUSTERS) and [QES EDGE Vol. 1 | Microstructure Clustering & Trading Implications Paper](https://marquee.gs.com/content/markets/en/2020/10/28/dc0f4cf7-e6b7-4de0-97c6-76d19f7b8a25.html)\\n\",\n \"\\n\",\n \"More about clustering in section A. Here's a high level visual that demonstrates the concept:\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"**[Section B](#Section_B): ETF Passive Ownership Analysis** \\n\",\n \"
Evaluation of the Passive ownership by ETF holders for our example basket\\n\",\n \"- [API Docs: Ownership Scores (Weekly Flow)](https://marquee.gs.com/s/developer/datasets/OWNERSHIP_SCORES_WEEKLY_FLOW)\\n\",\n \"- [API Docs: Ownership Scores (Monthly)](https://marquee.gs.com/s/developer/datasets/OWNERSHIP_SCORES_MONTHLY)\\n\",\n \"- [QES EDGE Vol. 2 | Effect of Shift from Active to Passive](https://marquee.gs.com/content/markets/en/2020/10/28/734dc59c-b03b-4793-9f56-98ec5ebca4d8.html)\\n\",\n \"\\n\",\n \"**[Section C](#Section_C): Algorithmic Portfolio EXecution - APEX**\\n\",\n \"
We'll attempt to explore the basket unwind cost, using our APEX muti-period optimizer across a range of urgency\\\\risk aversion parameters\\n\",\n \"- [APEX - The New Frontier in Portfolio Execution - Overview](https://marquee.gs.com/content/markets/en/2021/02/16/41e547e5-24ea-4ecb-9536-6de19884a8c4.html)\\n\",\n \"- [APEX-in-Marquee UI](https://marquee.web.gs.com/s/portfolios/create/MPGRZ9CYS372EZD1)\\n\",\n \"- [API Docs: APEX-in-Marquee API Docs]()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Disclaimers:\\n\",\n \"###### Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"###### www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"###### Please refer to https://marquee.gs.com/studio/ for price information of corporate equity securities.\\n\",\n \"###### Notice to Australian Investors: When this document is disseminated in Australia by Goldman Sachs & Co. LLC (\\\"GSCO\\\"), Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\"), or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied the GS entities that: \\n\",\n \"###### • the person is a Sophisticated or Professional Investor for the purposes of section 708 of the Corporations Act of Australia; and \\n\",\n \"###### • the person is a wholesale client for the purpose of section 761G of the Corporations Act of Australia. \\n\",\n \"###### To the extent that the GS entities are providing a financial service in Australia, the GS entities are each exempt from the requirement to hold an Australian financial services licence for the financial services they provide in Australia. Each of the GS entities are regulated by a foreign regulator under foreign laws which differ from Australian laws, specifically: \\n\",\n \"###### • GSCO is regulated by the US Securities and Exchange Commission under US laws;\\n\",\n \"###### • GSI is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, under UK laws;\\n\",\n \"###### • GSBE is subject to direct prudential supervision by the European Central Bank and in other respects is supervised by the German Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufischt, BaFin) and Deutsche Bundesbank;\\n\",\n \"###### • GSALLC is regulated by the Hong Kong Securities and Futures Commission under Hong Kong laws; and\\n\",\n \"###### • GSSP is regulated by the Monetary Authority of Singapore under Singapore laws.\\n\",\n \"###### Notice to Brazilian Investors\\n\",\n \"###### Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"###### The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"###### Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"###### Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"###### Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"###### Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \" \\n\",\n \"###### Note to Investors in Israel: GS is not licensed to provide investment advice or investment management services under Israeli law.\\n\",\n \"###### Notice to Investors in Japan\\n\",\n \"###### Marquee is made available in Japan by Goldman Sachs Japan Co., Ltd.\\n\",\n \"\\n\",\n \"###### 本書は情報の提供を目的としております。また、売却・購入が違法となるような法域での有価証券その他の売却若しくは購入を勧めるものでもありません。ゴールドマン・サックスは本書内の取引又はストラクチャーの勧誘を行うものではございません。これらの取引又はストラクチャーは、社内及び法規制等の承認等次第で実際にはご提供できない場合がございます。\\n\",\n \"\\n\",\n \"###### <適格機関投資家限定　転売制限>\\n\",\n \"###### ゴールドマン・サックス証券株式会社が適格機関投資家のみを相手方として取得申込みの勧誘（取得勧誘）又は売付けの申込み若しくは買付けの申込みの勧誘(売付け勧誘等)を行う本有価証券には、適格機関投資家に譲渡する場合以外の譲渡が禁止される旨の制限が付されています。本有価証券は金融商品取引法第４条に基づく財務局に対する届出が行われておりません。なお、本告知はお客様によるご同意のもとに、電磁的に交付させていただいております。\\n\",\n \"###### ＜適格機関投資家用資料＞ \\n\",\n \"###### 本資料は、適格機関投資家のお客さまのみを対象に作成されたものです。本資料における金融商品は適格機関投資家のお客さまのみがお取引可能であり、適格機関投資家以外のお客さまからのご注文等はお受けできませんので、ご注意ください。 商号等/ゴールドマン・サックス証券株式会社 金融商品取引業者　関東財務局長（金商）第６９号 \\n\",\n \"###### 加入協会/　日本証券業協会、一般社団法人金融先物取引業協会、一般社団法人第二種金融商品取引業協会 \\n\",\n \"###### 本書又はその添付資料に信用格付が記載されている場合、日本格付研究所（JCR）及び格付投資情報センター（R&I）による格付は、登録信用格付業者による格付（登録格付）です。その他の格付は登録格付である旨の記載がない場合は、無登録格付です。無登録格付を投資判断に利用する前に、「無登録格付に関する説明書」（http://www.goldmansachs.com/disclaimer/ratings.html）を十分にお読みください。 \\n\",\n \"###### If any credit ratings are contained in this material or any attachments, those that have been issued by Japan Credit Rating Agency, Ltd. (JCR) or Rating and Investment Information, Inc. (R&I) are credit ratings that have been issued by a credit rating agency registered in Japan (registered credit ratings). Other credit ratings are unregistered unless denoted as being registered. Before using unregistered credit ratings to make investment decisions, please carefully read \\\"Explanation Regarding Unregistered Credit Ratings\\\" (http://www.goldmansachs.com/disclaimer/ratings.html).\\n\",\n \"###### Notice to Mexican Investors: Information contained herein is not meant for the general public in Mexico. The services or products provided by or through Goldman Sachs Mexico, Casa de Bolsa, S.A. de C.V. (GS Mexico) may not be offered or sold to the general public in Mexico. You have received information herein exclusively due to your existing relationship with a GS Mexico or any other Goldman Sachs business. The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"###### Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone: \\n\",\n \"###### (i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"###### (ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"###### (iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"###### (iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act. \\n\",\n \"###### No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"###### Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"###### THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_a_liquidity_and_clusters.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Liquidity through the QES-Marquee prism\\n\",\n \"- Liquidity refers to the ease of trading a particular asset, usually defined by the avoidance of market impact while trading\\n\",\n \"- There are several ways of understanding liquidity, the most natural looking at the amount of volume that can be accessed while trading\\n\",\n \"- Better prediction of the amount of volume available for trading, helps avoid opportunity cost as well as excessive market impact\\n\",\n \"- One can analyse stocks by also exploring our market impact estimates as well as our trade clusters, that try to characterise stocks with similar microstructure dynamics\\n\",\n \"\\n\",\n \"\\n\",\n \"Source: GS Global Markets Division 2021 \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Import Libraries and Utilities\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier, AssetType\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"\\n\",\n \"import datetime\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import copy\\n\",\n \"import seaborn as sns\\n\",\n \"plt.rcParams['figure.figsize'] = (16,16 / 1.62)\\n\",\n \"plt.rcParams.update({'font.size': 20})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Supply Credentials\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=client_id, client_secret=client_secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"date = datetime.date(2021, 6, 9)\\n\",\n \"palette = ['#939393', '#7399c6', '#314c74','#434343']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Understanding the liquidity of the market\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### How can I get the market-wide Volume Profiles for an index (e.g. FTSE 100, CAC 40, DAX 30, ATX 20) built from its constituents?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def grabIndexData(index, dateDT, datasetId):\\n\",\n \" ind = SecurityMaster.get_asset(index, AssetIdentifier.BLOOMBERG_ID, asset_type=AssetType.INDEX)\\n\",\n \" tradedStocks = list(filter(None, ind.get_entity()['underlying_asset_ids'])) \\n\",\n \" dt = Dataset(datasetId).get_data(dateDT, dateDT, assetId=tradedStocks, limit=50000)\\n\",\n \" dt['index'] = index\\n\",\n \" return dt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"indicesEuro = ['UKX', 'DAX', 'CAC', 'ATX']\\n\",\n \"dataId = 'SINGLE_STOCK_VOLUME_PROFILE_BASE'\\n\",\n \"fields = ['cumulativeVolumeInPercentage', 'bucketStartTime', 'assetId']\\n\",\n \"volumeProfileIndexEuro = pd.concat([grabIndexData(index, date, dataId)[fields] for index in indicesEuro], axis=0)\\n\",\n \"volumeProfileIndexEuro.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"volumeProfileIndexEuroMean = volumeProfileIndexEuro.loc[str(date)].groupby(by=['index', 'bucketStartTime']).mean()\\n\",\n \"volumeProfileIndexEuroMean['index'] = volumeProfileIndexEuroMean.index.get_level_values(0)\\n\",\n \"volumeProfileIndexEuroMean['bucketStartTime'] = volumeProfileIndexEuroMean.index.get_level_values(1).time.astype('str')\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15, 10))\\n\",\n \"figs = sns.lineplot(x='bucketStartTime', y='cumulativeVolumeInPercentage', hue='index', palette=palette,\\n\",\n \" data=volumeProfileIndexEuroMean)\\n\",\n \"for ind, label in enumerate(figs.get_xticklabels()):\\n\",\n \" label.set_visible(True if ind % 10 == 0 else False)\\n\",\n \"\\n\",\n \"xlabel = plt.xlabel('bucketStartTime (GMT Time)', fontsize = 18)\\n\",\n \"x = plt.xticks(size=18, rotation=90)\\n\",\n \"y = plt.yticks(size=18)\\n\",\n \"figs.xaxis.set_label_coords(0.5, -0.2)\\n\",\n \"title = plt.title('Average Start of Day Volume Profiles for Select Indices (09/06/2021)', fontsize=20)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intraday, do I get a real-time updated forecast? Does it make any difference?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"Source: GS Global Markets Division, Reuters, 01Jan19-31Dec19\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### How is it constructed?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"Source: GS Global Markets Division 2021\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"indicesEuro = ['UKX', 'DAX', 'CAC', 'ATX']\\n\",\n \"dataId = 'SINGLE_STOCK_VOLUME_FORECAST_BASE'\\n\",\n \"dateTime=datetime.datetime(2021, 6, 9, 11, 0, 0)\\n\",\n \"volumeUpdateIndexEuro = pd.concat([grabIndexData(index, dateTime, dataId) for index in indicesEuro], axis=0)\\n\",\n \"volumeUpdateIndexEuro.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"volumeUpdateIndexEuro.groupby('index')['volumeForecastAdjustment'].describe()[['mean', 'std', 'min', '25%', '50%', '75%']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What if I am interested in a single stock?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = Dataset('SINGLE_STOCK_VOLUME_FORECAST_BASE')\\n\",\n \"volumeUpdateAsset = ds.get_data(dateTime, dateTime, bbid='VOD LN', limit=5000)\\n\",\n \"volumeUpdateAsset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dsp = Dataset('SINGLE_STOCK_VOLUME_PROFILE_BASE')\\n\",\n \"volumeProfileAsset= dsp.get_data(date, date, bbid='VOD LN', limit=5000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"volumeProfileStatic = volumeProfileAsset[['cumulativeVolumeInShares', 'bucketVolumeInShares', 'bucketStartTime', 'bbid']].loc[str(date_].groupby(by=['bbid', 'bucketStartTime']).mean()\\n\",\n \"volumeProfileStatic['VolumeInSharesUpdated'] = volumeProfileStatic['bucketVolumeInShares']*volumeUpdateAsset['volumeForecastAdjustment'][0]\\n\",\n \"volumeProfileForecast = volumeProfileStatic[ volumeProfileStatic.index.get_level_values(1).time>=datetime.time(11, 0, 0)]\\n\",\n \"volumeProfileForecast = volumeProfileForecast[['bucketVolumeInShares', 'VolumeInSharesUpdated']].cumsum().reset_index()\\n\",\n \"volumeProfileForecast['bucketStartTime'] = volumeProfileForecast['bucketStartTime'].dt.time.astype('str')\\n\",\n \"plt.figure(figsize=(15, 10))\\n\",\n \"\\n\",\n \"figs = sns.lineplot(x='bucketStartTime', y='bucketVolumeInShares', data=volumeProfileForecast,label='Static Prediction', color=palette[1])\\n\",\n \"figs = sns.lineplot(x='bucketStartTime', y='VolumeInSharesUpdated', data=volumeProfileForecast,label='Dynamic Prediction', color=palette[2])\\n\",\n \"figs.lines[1].set_linestyle('--')\\n\",\n \"\\n\",\n \"for ind, label in enumerate(figs.get_xticklabels()):\\n\",\n \" label.set_visible(True if ind % 10 == 0 else False)\\n\",\n \"xlabel = plt.xlabel('bucketStartTime (GMT Time)')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"x = plt.xticks(size=18, rotation=90)\\n\",\n \"y = plt.yticks(size=18)\\n\",\n \"coords = figs.xaxis.set_label_coords(0.5, -0.2)\\n\",\n \"title = plt.title('Static and Dynamic Volume Profile Prediction (VOD LN 09/06/2021)', fontsize = 20)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Market Impact and Microstructure Stock clusters\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"Source: GS Global Markets Division 2021\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Query Market Impact Estimates for STOXX Total Market Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def fn_marketImpactEstimates(index, date):\\n\",\n \" #List of stocks traded in a particular index\\n\",\n \" ind = SecurityMaster.get_asset(index, AssetIdentifier.BLOOMBERG_ID, asset_type=AssetType.INDEX)\\n\",\n \" tradedStocks = list(filter(None, ind.get_entity()['underlying_asset_ids']))\\n\",\n \" ds = Dataset('SINGLE_STOCK_MARKET_IMPACT')\\n\",\n \" return ds.get_data(date, date, assetId=tradedStocks, percentADV=[1,5,10], limit=50000)\\n\",\n \"\\n\",\n \"marketImpactEstimates = fn_marketImpactEstimates('BKXP', date)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Query Trade Clusters for STOXX Total Market Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_clusters = grabIndexData('BKXP', date, 'EQTRADECLUSTERS')\\n\",\n \"df_clusters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data_01pov = marketImpactEstimates[(marketImpactEstimates['percentADV']==1) & (marketImpactEstimates['participationRate']==1)]\\n\",\n \"data_05pov = marketImpactEstimates[(marketImpactEstimates['percentADV']==5) & (marketImpactEstimates['participationRate']==5)]\\n\",\n \"data_10pov = marketImpactEstimates[(marketImpactEstimates['percentADV']==10) & (marketImpactEstimates['participationRate']==10)]\\n\",\n \"data_marketImpact = pd.concat([data_01pov, data_05pov, data_10pov])\\n\",\n \"data_marketImpact_clusters = pd.merge(data_marketImpact, df_clusters[['assetId', 'clusterClass']], how='left', on=['assetId'])\\n\",\n \"data_marketImpact_clusters['clusterClass'] = data_marketImpact_clusters['clusterClass'].astype('int')\\n\",\n \"data_marketImpact_clusters.sort_values('clusterClass', inplace=True)\\n\",\n \"data_marketImpact_clusters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bp = sns.boxplot(y='estimatedImpact', x='clusterClass', data=data_marketImpact_clusters,\\n\",\n \" palette=palette, hue='percentADV')\\n\",\n \"\\n\",\n \"handles, labels = bp.get_legend_handles_labels()\\n\",\n \"plt.xticks(fontsize=15)\\n\",\n \"plt.yticks(fontsize=15)\\n\",\n \"figs = plt.legend(handles[0:3], ['1%', '5%' , '10%'], title='POV', fontsize=15, title_fontsize=18)\\n\",\n \"plt.title('Estimated Impact by Cluster for different POV rates', fontsize=20)\\n\",\n \"l = plt.ylim([0, 40])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Disclaimers:\\n\",\n \"###### Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"###### www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"###### Please refer to https://marquee.gs.com/studio/ for price information of corporate equity securities.\\n\",\n \"###### Notice to Australian Investors: When this document is disseminated in Australia by Goldman Sachs & Co. LLC (\\\"GSCO\\\"), Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\"), or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied the GS entities that: \\n\",\n \"###### • the person is a Sophisticated or Professional Investor for the purposes of section 708 of the Corporations Act of Australia; and \\n\",\n \"###### • the person is a wholesale client for the purpose of section 761G of the Corporations Act of Australia. \\n\",\n \"###### To the extent that the GS entities are providing a financial service in Australia, the GS entities are each exempt from the requirement to hold an Australian financial services licence for the financial services they provide in Australia. Each of the GS entities are regulated by a foreign regulator under foreign laws which differ from Australian laws, specifically: \\n\",\n \"###### • GSCO is regulated by the US Securities and Exchange Commission under US laws;\\n\",\n \"###### • GSI is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, under UK laws;\\n\",\n \"###### • GSBE is subject to direct prudential supervision by the European Central Bank and in other respects is supervised by the German Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufischt, BaFin) and Deutsche Bundesbank;\\n\",\n \"###### • GSALLC is regulated by the Hong Kong Securities and Futures Commission under Hong Kong laws; and\\n\",\n \"###### • GSSP is regulated by the Monetary Authority of Singapore under Singapore laws.\\n\",\n \"###### Notice to Brazilian Investors\\n\",\n \"###### Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"###### The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"###### Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"###### Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"###### Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"###### Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \" \\n\",\n \"###### Note to Investors in Israel: GS is not licensed to provide investment advice or investment management services under Israeli law.\\n\",\n \"###### Notice to Investors in Japan\\n\",\n \"###### Marquee is made available in Japan by Goldman Sachs Japan Co., Ltd.\\n\",\n \"\\n\",\n \"###### 本書は情報の提供を目的としております。また、売却・購入が違法となるような法域での有価証券その他の売却若しくは購入を勧めるものでもありません。ゴールドマン・サックスは本書内の取引又はストラクチャーの勧誘を行うものではございません。これらの取引又はストラクチャーは、社内及び法規制等の承認等次第で実際にはご提供できない場合がございます。\\n\",\n \"\\n\",\n \"###### <適格機関投資家限定　転売制限>\\n\",\n \"###### ゴールドマン・サックス証券株式会社が適格機関投資家のみを相手方として取得申込みの勧誘（取得勧誘）又は売付けの申込み若しくは買付けの申込みの勧誘(売付け勧誘等)を行う本有価証券には、適格機関投資家に譲渡する場合以外の譲渡が禁止される旨の制限が付されています。本有価証券は金融商品取引法第４条に基づく財務局に対する届出が行われておりません。なお、本告知はお客様によるご同意のもとに、電磁的に交付させていただいております。\\n\",\n \"###### ＜適格機関投資家用資料＞ \\n\",\n \"###### 本資料は、適格機関投資家のお客さまのみを対象に作成されたものです。本資料における金融商品は適格機関投資家のお客さまのみがお取引可能であり、適格機関投資家以外のお客さまからのご注文等はお受けできませんので、ご注意ください。 商号等/ゴールドマン・サックス証券株式会社 金融商品取引業者　関東財務局長（金商）第６９号 \\n\",\n \"###### 加入協会/　日本証券業協会、一般社団法人金融先物取引業協会、一般社団法人第二種金融商品取引業協会 \\n\",\n \"###### 本書又はその添付資料に信用格付が記載されている場合、日本格付研究所（JCR）及び格付投資情報センター（R&I）による格付は、登録信用格付業者による格付（登録格付）です。その他の格付は登録格付である旨の記載がない場合は、無登録格付です。無登録格付を投資判断に利用する前に、「無登録格付に関する説明書」（http://www.goldmansachs.com/disclaimer/ratings.html）を十分にお読みください。 \\n\",\n \"###### If any credit ratings are contained in this material or any attachments, those that have been issued by Japan Credit Rating Agency, Ltd. (JCR) or Rating and Investment Information, Inc. (R&I) are credit ratings that have been issued by a credit rating agency registered in Japan (registered credit ratings). Other credit ratings are unregistered unless denoted as being registered. Before using unregistered credit ratings to make investment decisions, please carefully read \\\"Explanation Regarding Unregistered Credit Ratings\\\" (http://www.goldmansachs.com/disclaimer/ratings.html).\\n\",\n \"###### Notice to Mexican Investors: Information contained herein is not meant for the general public in Mexico. The services or products provided by or through Goldman Sachs Mexico, Casa de Bolsa, S.A. de C.V. (GS Mexico) may not be offered or sold to the general public in Mexico. You have received information herein exclusively due to your existing relationship with a GS Mexico or any other Goldman Sachs business. The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"###### Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone: \\n\",\n \"###### (i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"###### (ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"###### (iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"###### (iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act. \\n\",\n \"###### No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"###### Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"###### THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_b_ownership.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Passive ownership and flows\\n\",\n \"## Why it matters\\n\",\n \"\\n\",\n \"- Passive management has seen unprecedented growth in the past 10-15 years

Source: Sushko, V., and G. Turner (2018) The implications of passive investing for securities markets, BIS Quarterly Review, March 2018, 113–131.\\n\",\n \"- Academics have found that institutional ownership in general tends to be positively associated with security return volatility (Sias, 1996)\\n\",\n \"- Ben-David et al. (2018) find that this is particularly true for ETFs\\n\",\n \"\\n\",\n \"**The main mechanism:**\\n\",\n \"- Liquidity shocks (i.e. inflows and outflows) that affect the fund propagate to the underlying securities, thereby increasing volatility\\n\",\n \"- This mechanism is stronger in the case of passive funds because of the limited discretionality in the timing and direction of trades\\n\",\n \"\\n\",\n \"**Key results of our work:**\\n\",\n \"- In our paper, \\\"The Shift from Active to Passive\\\" (July 2019), we find that passively held stocks tend to be more volatile after correcting for other stock characteristics\\n\",\n \"- The effect of passive ownership on volatility is concentrated at the end of the continuous trading session and at the Close

Source: GS Global Markets Division.\\n\",\n \"- The effect of passive inflows and outflows is similarly concentrated around the Close

Source: GS Global Markets Division.\\n\",\n \"- This is arguably due to the concentration of portfolio trades in the order flow during that phase\\n\",\n \"- We also detect differences in the impact of passive investing between US and European markets due to the different design of the closing auction\\n\",\n \"\\n\",\n \"**Passive ownership and the March 2020 selloff**\\n\",\n \"- In our paper, \\\"Dissecting the Viral Selloff\\\" (April 2020), we group stocks by their level of passive ETF ownership and compare the percentage increase in volatility between January and March 2020

Source: GS Global Markets Division.\\n\",\n \"\\n\",\n \"- Stocks widely held by ETFs have experienced larger volatility increases, both in the US and in Europe\\n\",\n \"- The result holds true after correcting for other effects, including sector, country, size and liquidity\\n\",\n \"\\n\",\n \"## Measuring passive ownership and flows\\n\",\n \"- Starting from fund-level data, we produce stock-level indicators by using the latest information on holdings and flows\\n\",\n \"- Raw ownership ratios:\\n\",\n \"\\\\begin{equation}\\n\",\n \"PassETFRatio_{i}=\\\\frac{\\\\sum_{j\\\\in F_{PassETF}}w_{ij}AuM_{j}}{MktCap_{i}}\\n\",\n \"\\\\label{passETFratio}\\n\",\n \"\\\\end{equation}\\n\",\n \"where $w_{ij}$ is the weight of stock $i$ in fund $j$, $AuM_{j}$ is the total value of fund $j$'s holdings, $F_{PassETF}$ the set of passive ETFs and $MktCap_{i}$ is \\n\",\n \"the market capitalisation in US dollars of stock $i$\\n\",\n \"- Building on our research, we carry out a series of cross-sectional normalisations and adjustments to make the scores comparable across regions and over time\\n\",\n \"- Scores are normalised between -3 and 3. Higher scores indicate a higher level of passive ownership\\n\",\n \"- Similarly, for flow scores a higher score indicates stronger inflows to passive funds holding the stock\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Import libraries\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"import datetime as dt\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Supply credentials\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=client_id, client_secret=client_secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Load basket of stocks from text file\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# load data from csv\\n\",\n \"ptf_df = pd.read_csv('trade_list_world.csv')\\n\",\n \"ptf_df = ptf_df.rename(columns={'Symbol': 'SEDOL', 'Trade Value ($)': 'Value'})\\n\",\n \"ptf_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Map identifiers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# load Marquee identifiers and Bloomberg Ids\\n\",\n \"identifiers_df = {}\\n\",\n \"for sedol_id in ptf_df.SEDOL:\\n\",\n \" identifiers_df[sedol_id] = {}\\n\",\n \" asset = SecurityMaster.get_asset(sedol_id, AssetIdentifier.SEDOL, sort_by_rank=True)\\n\",\n \" identifiers_df[sedol_id]['assetId'] = asset.get_marquee_id()\\n\",\n \" identifiers_df[sedol_id]['BCID'] = asset.get_identifiers(as_of=dt.date(2021, 6, 10))['BCID']\\n\",\n \"\\n\",\n \"identifiers_df = pd.DataFrame.from_dict(identifiers_df, orient='index')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Query Marquee datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## Get monthly ownership data \\n\",\n \"ds = Dataset('OWNERSHIP_SCORES_MONTHLY')\\n\",\n \"data_df = ds.get_data(start=dt.date(2021, 5, 9), end=dt.date(2021, 6, 10),\\n\",\n \" assetId=identifiers_df.assetId.to_list())\\n\",\n \"\\n\",\n \"## Get weekly flow data \\n\",\n \"ds2 = Dataset('OWNERSHIP_SCORES_WEEKLY_FLOW')\\n\",\n \"data2_df = ds2.get_data(start=dt.date(2021, 6, 3), end=dt.date(2021, 6, 10),\\n\",\n \" assetId=identifiers_df.assetId.to_list())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## merge results in a summary table\\n\",\n \"ptf_df = ptf_df.merge(identifiers_df, right_index=True, left_on='SEDOL').merge(data_df, on='assetId')\\n\",\n \"ptf_df = ptf_df.merge(data2_df, on='assetId')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Explore the data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## display most passively held names\\n\",\n \"ratios_names = [x for x in ptf_df.columns if x.endswith('Ratio')]\\n\",\n \"ptf_df[['BCID']+ratios_names].sort_values(by='passiveRatio', ascending=False).head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## display least passively held names\\n\",\n \"ptf_df[['BCID']+ratios_names].sort_values(by='passiveRatio', ascending=True).head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## display most affected by passive inflows\\n\",\n \"ptf_df[['BCID']+ratios_names].sort_values(by='passiveFlowRatio', ascending=False).head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## display most affected by passive outflows\\n\",\n \"ptf_df[['BCID']+ratios_names].sort_values(by='passiveFlowRatio', ascending=True).head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Compute impact of trade on portfolio-level ownership \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# plot breakdown by passive ETF ratio\\n\",\n \"own_slices = [(-3, -2), (-2, -1), (-1, 0), (0, 1), (1, 2), (2, 3.1)]\\n\",\n \"slice_labels = ['[-3,-2)', '[-2,-1)', '[-1,0)', '[0,1)', '[1,2)', '[2,3]']\\n\",\n \"# scores are bounded between -3 and 3\\n\",\n \"\\n\",\n \"# compute total value sold and bought\\n\",\n \"tot_traded_long = ptf_df.loc[ptf_df['Value']>0].sum()['Value']\\n\",\n \"tot_traded_short = ptf_df.loc[ptf_df['Value']<0].sum()['Value']\\n\",\n \"\\n\",\n \"# compute values traded and plot bars\\n\",\n \"text_kwargs = dict(ha='center', va='center', fontsize=12, color='black')\\n\",\n \"fig, ax = plt.subplots(2, 1, figsize=(8, 12))\\n\",\n \"cnc = 0\\n\",\n \"for etf_var, var_label, chart_title in [('passiveEtfRatio', 'Passive ETF ratio', 'Breakdown by ETF ownership'),\\n\",\n \" ('etfFlowRatio', 'Passive ETF flow ratio', 'Breakdown by ETF flows')]:\\n\",\n \" slc = 0\\n\",\n \" for lb, ub in own_slices:\\n\",\n \" ax[cnc].barh(slc, ptf_df.loc[(ptf_df[etf_var]>=lb) & (ptf_df[etf_var]0)].sum()['Value']/tot_traded_long, color='#939393')\\n\",\n \" ax[cnc].barh(slc, -ptf_df.loc[(ptf_df[etf_var]>=lb) & (ptf_df[etf_var]\\n\",\n \"###### ゴールドマン・サックス証券株式会社が適格機関投資家のみを相手方として取得申込みの勧誘（取得勧誘）又は売付けの申込み若しくは買付けの申込みの勧誘(売付け勧誘等)を行う本有価証券には、適格機関投資家に譲渡する場合以外の譲渡が禁止される旨の制限が付されています。本有価証券は金融商品取引法第４条に基づく財務局に対する届出が行われておりません。なお、本告知はお客様によるご同意のもとに、電磁的に交付させていただいております。\\n\",\n \"###### ＜適格機関投資家用資料＞ \\n\",\n \"###### 本資料は、適格機関投資家のお客さまのみを対象に作成されたものです。本資料における金融商品は適格機関投資家のお客さまのみがお取引可能であり、適格機関投資家以外のお客さまからのご注文等はお受けできませんので、ご注意ください。 商号等/ゴールドマン・サックス証券株式会社 金融商品取引業者　関東財務局長（金商）第６９号 \\n\",\n \"###### 加入協会/　日本証券業協会、一般社団法人金融先物取引業協会、一般社団法人第二種金融商品取引業協会 \\n\",\n \"###### 本書又はその添付資料に信用格付が記載されている場合、日本格付研究所（JCR）及び格付投資情報センター（R&I）による格付は、登録信用格付業者による格付（登録格付）です。その他の格付は登録格付である旨の記載がない場合は、無登録格付です。無登録格付を投資判断に利用する前に、「無登録格付に関する説明書」（http://www.goldmansachs.com/disclaimer/ratings.html）を十分にお読みください。 \\n\",\n \"###### If any credit ratings are contained in this material or any attachments, those that have been issued by Japan Credit Rating Agency, Ltd. (JCR) or Rating and Investment Information, Inc. (R&I) are credit ratings that have been issued by a credit rating agency registered in Japan (registered credit ratings). Other credit ratings are unregistered unless denoted as being registered. Before using unregistered credit ratings to make investment decisions, please carefully read \\\"Explanation Regarding Unregistered Credit Ratings\\\" (http://www.goldmansachs.com/disclaimer/ratings.html).\\n\",\n \"###### Notice to Mexican Investors: Information contained herein is not meant for the general public in Mexico. The services or products provided by or through Goldman Sachs Mexico, Casa de Bolsa, S.A. de C.V. (GS Mexico) may not be offered or sold to the general public in Mexico. You have received information herein exclusively due to your existing relationship with a GS Mexico or any other Goldman Sachs business. The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"###### Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone: \\n\",\n \"###### (i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"###### (ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"###### (iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"###### (iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act. \\n\",\n \"###### No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"###### Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"###### THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/02_optimizing_equity_trading/section_c_apex.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" \\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Introduction: What's APEX?\\n\",\n \"APEX is a portfolio trade scheduler that optimizes execution with the latest intraday risk and market impact models from Goldman Sachs’ Quantitative Execution Services (QES) team.\\n\",\n \"\\n\",\n \"## Modeling Pillars\\n\",\n \"\\n\",\n \"\\n\",\n \"## Constraints and Features\\n\",\n \"\\n\",\n \"\\n\",\n \"## The APEX Trade Lifecycle\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### First, let's load a sample portfolio:\\n\",\n \"#### Import Libs and Utils:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from qes_utils import persistXls, plotCost, plotVar, plotBuySellNet, plotGrossRemaining, plotMultiStrategyPortfolioLevelAnalytics\\n\",\n \"\\n\",\n \"from gs_quant.api.gs.assets import GsAssetApi\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.common import Position\\n\",\n \"from gs_quant.target.risk import OptimizationRequest, OptimizationType\\n\",\n \"from gs_quant.api.gs.risk import GsRiskApi\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import datetime\\n\",\n \"import numpy as np\\n\",\n \"import copy\\n\",\n \"import datetime\\n\",\n \"from matplotlib import cm\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Establish GS_Quant Connection: \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- Fill in client_id and client_secret\\\\\\n\",\n \"- Set up Marquee API: https://marquee.gs.com/s/developer/docs/getting-started\\n\",\n \"- Once you create the application, click on the Application page and scroll down to the ‘Scope’ section. Request the “read_product_date”& “run_analytics” scope for your application. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print('INFO: Setting up Marquee Connection')\\n\",\n \"client_id =\\n\",\n \"client_secret =\\n\",\n \"GsSession.use(Environment.PROD, client_id=client_id, client_secret=client_secret, scopes=['read_product_data', 'run_analytics'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Set up the portfolio: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print('INFO: Setting up portfolio to schedule using APEX...')\\n\",\n \"portfolio_input = pd.read_csv('trade_list_world.csv').rename(columns={'Symbol': 'sedol', 'Shares': 'qty'})\\n\",\n \"portfolio_input.dtypes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Convert Identifier (SEDOL) to marqueeids:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"SEDOL access needs to be requested on Marquee with the following steps:\\n\",\n \"- Go to https://marquee.gs.com/s/developer/datasets/SEDOL \\n\",\n \"- Select an application to request access for \\n\",\n \"- Request will be auto approved\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"assets = GsAssetApi.get_many_assets(sedol=list(portfolio_input['sedol']), fields=['sedol', 'rank'], listed=[True], type='Single Stock')\\n\",\n \"identifier_to_marqueeid_map = pd.DataFrame([{'sedol': list(filter(lambda x: x.type=='SED', i.identifiers))[0].value, 'ID': i.id, 'rank': i.rank} for i in assets])\\\\\\n\",\n \" .sort_values(['sedol', 'rank'], ascending=False).groupby('sedol').head(1)[['sedol','ID']].rename(columns={'ID': 'marqueeid'})\\n\",\n \"print(f'found {len(identifier_to_marqueeid_map)} sedols to mrquee ids map...')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Identify assets with missing marquee ids and drop them from the portfolio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio_input = portfolio_input.merge(identifier_to_marqueeid_map, how='left', on='sedol')\\n\",\n \"\\n\",\n \"missing_marqueeids = portfolio_input[portfolio_input['marqueeid'].isnull()]\\n\",\n \"if len(missing_marqueeids):\\n\",\n \" print(f'WARNING: the following bbids are missing marqueeids:\\\\n{missing_marqueeids}\\\\ndropping from the optimization...')\\n\",\n \"else: \\n\",\n \" print('INFO: all the assets has been succesfuly converted to marquee id')\\n\",\n \"portfolio_input = portfolio_input.dropna()\\n\",\n \"portfolio_input.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### At this point, we have a portfolio we can optimize using APEX.\\n\",\n \"Our portfolio is now ready for optimization with APEX.\\n\",\n \"### We'll run two variations:\\n\",\n \"##### 1. single optimization analysis - optimize the basket using defined parameters and investigate the cost-risk trade-off.\\n\",\n \"##### 2. trade scenario analysis - run multiple optimizations upon different risk aversion (urgency) parameters and compare the cost-risk trade-off among optimized execution strategies\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1. APEX Optimization: run my trade list in the APEX optimizer and explore the various analytics:\\n\",\n \"#### in this section, we'll explore how to set optimization parameters and how to display multiple optimal trajectory analytics to develop further intuition for the decisions made by APEX\\n\",\n \"\\n\",\n \"we'll run an APEX-IS (Arrival) risk-cost minimization optimal trade allocation, in the following form:\\n\",\n \"\\\\begin{equation*}\\n\",\n \"Min \\\\displaystyle \\\\Bigg( \\\\lambda \\\\sum_{t=1}^T (\\\\mbox{Risk of Residual Holdings}) + (1-\\\\lambda) \\\\sum_{t=1}^T (\\\\mbox{Market Impact of Trades}) \\\\Bigg) \\n\",\n \"\\\\end{equation*}\\n\",\n \"\\\\begin{equation*}s.t.\\\\end{equation*}\\n\",\n \"\\\\begin{equation*}Ax <= b\\\\end{equation*}\\n\",\n \"\\n\",\n \"where:\\n\",\n \"\\\\begin{equation*}(\\\\mbox{Risk of Residual Holdings})\\\\end{equation*} \\n\",\n \"- Incorporates the intraday and overnight expected risk, utilizing our high frequency intraday QES covariances. in other words, \\\"every $ I decided to trade later, is running at the Risk of missing the arrival price\\\"\\n\",\n \"\\n\",\n \"\\\\begin{equation*}(\\\\mbox{Market Impact of Trades})\\\\end{equation*}\\n\",\n \"- Denote the expected market impact per asset, as a function of the physical interaction with the order book. in other words, \\\"every $ that I will trade now, will incur some expected market impact, based on the intraday predicted evolution of spread\\\\volume\\\\volatility\\\\participation rate, and other intraday calibrated parameters\\\"\\n\",\n \"\\n\",\n \"\\\\begin{equation*}\\\\lambda\\\\end{equation*}\\n\",\n \"- Risk Aversion parameter\\n\",\n \"\\n\",\n \"\\\\begin{equation*}Ax <= b\\\\end{equation*}\\n\",\n \"- set of linear constraints (see features available at the top of the notebook)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Set up the optimization constraints\\n\",\n \"\\n\",\n \"| Optimisation Parameters | Description | Value Chosen |\\n\",\n \"| :- | :- | -: |\\n\",\n \"| Start Time \\\\ End Time | APEX allowed \\\"Day1\\\" trade horizon, in GMT* | 11pm previous day to 11pm |\\n\",\n \"| Urgency | APEX Urgency, from VERY_LOW to VERY_HIGH | Medium |\\n\",\n \"| Target Benchmark | Currently supports 'IS', 'CLOSE' | IS |\\n\",\n \"| Imbalance | (Optional) setting dollar imbalance for the trade duration; \\\"the net residual must be within +-5% of the residual gross to trade, throughout the entire trade duration\\\" | 0.05 (5%) |\\n\",\n \"| Participation rate | Setting volume cap for trading | 0.075 (7.5%) |\\n\",\n \"\\n\",\n \"- Note that APEX allowed start end times range from 23:00 previous day to 23:00 of the query day.\\n\",\n \" For example, if today is the 9th of October, APEX global optimization can run from start time of 23:00 on T-1 to 23:00 on T.\\n\",\n \"- Please also note that APEX will automatically optimize up to 5 business days, providing an optimized intraday solution with granularity of 30\\\\60 minutes.\\n\",\n \"- For a full set of parameters, please refer to the constraints & features image at the top, review the APEX api guide or contact [gs-qes-quant@gs.com](mailto:gs-qes-quant@gs.com)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## set optimization configuration\\n\",\n \"print('INFO: Constructing Optimization Request...')\\n\",\n \"date_today = datetime.datetime.now().strftime('%Y-%m-%d')\\n\",\n \"date_yesterday = (datetime.datetime.now() - datetime.timedelta(days=1)).strftime('%Y-%m-%d')\\n\",\n \"\\n\",\n \"apex_optimization_config = {\\n\",\n \" 'executionStartTime': date_yesterday + 'T23:00:00.000Z', #execution start time\\n\",\n \" 'executionEndTime': date_today +'T21:15:00.000Z', # execution end time (for day 1, can run multiday if not complete on day 1)\\n\",\n \" 'waitForResults': False,\\n\",\n \" 'parameters': {'urgency': 'MEDIUM', #VERY_LOW, LOW, HIGH, VERY_HIGH...\\n\",\n \" 'targetBenchmark': 'IS', #CLOSE\\n\",\n \" 'imbalance': 0.05, #Optional --> setting $ imbalance for the trade duration to never exceed +-20% of residual gross to trade\\n\",\n \" 'participationRate': 0.075 #setting volume cap of 10%\\n\",\n \" },\\n\",\n \" }\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Send Optimization + Analytics request to Marquee\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def sendApexRequestAndGetAnalytics(portfolio_input, apex_optimization_config):\\n\",\n \" positions = [Position(asset_id=row.marqueeid, quantity=row.qty) for _, row in portfolio_input.iterrows()]\\n\",\n \" print('setting up the optimization request....')\\n\",\n \" request = OptimizationRequest(positions=positions,\\n\",\n \" execution_start_time=apex_optimization_config['executionStartTime'],\\n\",\n \" execution_end_time=apex_optimization_config['executionEndTime'],\\n\",\n \" parameters=apex_optimization_config['parameters'],\\n\",\n \" **{'type': OptimizationType.APEX})\\n\",\n \" print('Sending the request to the marquee service...')\\n\",\n \" opt = GsRiskApi.create_pretrade_execution_optimization(request)\\n\",\n \" analytics_results = GsRiskApi.get_pretrade_execution_optimization(opt.get('optimizationId'))\\n\",\n \" print ('COMPLETE!')\\n\",\n \" return analytics_results\\n\",\n \"\\n\",\n \"results_dict = sendApexRequestAndGetAnalytics(portfolio_input, apex_optimization_config)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print('INFO: High Level Cost estimation and % expected Completion:')\\n\",\n \"pd.DataFrame(results_dict['analytics']['portfolioAnalyticsDaily']).set_index('tradeDayNumber')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print('missing assets:')\\n\",\n \"pd.DataFrame(results_dict['analytics']['assetsExcluded'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Actual Optimization Parameters Used in APEX\\n\",\n \"- Although a set of optimization parameters was specified above, APEX might conclude that the parameters joined feasible space does not exist (infeasible set).\\n\",\n \"- APEX can then choose to soften/drop/relax the constraints in a hierarchical fashion. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"constraints_hierarchy = pd.DataFrame(results_dict['analytics']['constraintsConsultations'])['constraints']\\n\",\n \"pd.concat([pd.DataFrame(constraints_hierarchy.values[i]).assign(iteration=i) for i in constraints_hierarchy.index]).set_index(['iteration', 'name'])['status'].unstack().T\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### What kind of Analytics provided by APEX ?\\n\",\n \"##### APEX provide a vast set of numbers that helps understanding unravel the decision made by the optimizer:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results_dict['analytics'].keys()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Visualise Your Optimisation Results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"analytics_result_analytics = results_dict['analytics'] \\n\",\n \"intraday = pd.DataFrame(analytics_result_analytics['portfolioAnalyticsIntraday'])\\n\",\n \"intraday_to_plot = intraday.assign(time = lambda x: pd.to_datetime(x['time'])).set_index('time')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Four examples of visualizing your intraday analysis throughout trade date\\n\",\n \"- Gross Remaining\\n\",\n \"- Buy/Sell/Net\\n\",\n \"- Cost Contribution\\n\",\n \"- Risk Contribution \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"intraday_to_plot.head(5).append(intraday_to_plot.tail(5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plotGrossRemaining(intraday_to_plot)\\n\",\n \"plotBuySellNet(intraday_to_plot)\\n\",\n \"plotCost(intraday_to_plot)\\n\",\n \"plotVar(intraday_to_plot)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"###### Sources: Goldman Sachs, Bloomberg, Reuters, Axioma\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### The creativity around various analytics are endless, here are couple of examples, derived from the various analytics dataframes we use for our APEX clients:\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"###### Sources: Goldman Sachs, Bloomberg, Reuters, Axioma\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### save all results to excel for further exploration:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xls_path = persistXls(xls_report=results_dict['analytics'],\\n\",\n \" path='',\\n\",\n \" filename='apex_optimization_detailed_analytics',\\n\",\n \" indentifier_marqueeid_map=portfolio_input[\\n\",\n \" [identifier_type, 'marqueeid']])\\n\",\n \"print('saving all analytics frames to {0}...'.format(xls_path))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2. APEX Optimization - Trade Scenario Analysis: run my trade list in the APEX optimizer across multiple risk aversions\\\\urgency parameters to assess ideal parameters set.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Define a function for running multiple optimizations, keeping all constrains intact and change urgency only:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def optimisationMulti(portfolio_input, apex_optimization_config, urgency_list = ['VERY_LOW', 'LOW', 'MEDIUM', 'HIGH', 'VERY_HIGH']):\\n\",\n \" results_dict_multi = {}\\n\",\n \" apex_optimization_config_temp = copy.deepcopy(apex_optimization_config)\\n\",\n \" \\n\",\n \" for u in urgency_list:\\n\",\n \" apex_optimization_config_temp['parameters']['urgency'] = u\\n\",\n \" apex_optimization_config_temp['parameters']['imbalance'] = .3\\n\",\n \" apex_optimization_config_temp['parameters']['participationRate'] = .5\\n\",\n \" \\n\",\n \" print('INFO Running urgency={0} optimization....'.format(u))\\n\",\n \" results_dict_multi[u] = sendApexRequestAndGetAnalytics(portfolio_input, apex_optimization_config_temp)\\n\",\n \" \\n\",\n \" print('INFO: High Level Cost estimation and % expected Completion:\\\\n{0}'\\\\\\n\",\n \" .format(pd.DataFrame(results_dict_multi[u]['analytics']['portfolioAnalyticsDaily'])))\\n\",\n \" \\n\",\n \" return results_dict_multi\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Run Optimization Across Urgencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"urgency_list = ['VERY_LOW', 'LOW', 'MEDIUM', 'HIGH', 'VERY_HIGH']\\n\",\n \"results_dict_multi = optimisationMulti(portfolio_input = portfolio_input,\\\\\\n\",\n \" apex_optimization_config = apex_optimization_config,\\\\\\n\",\n \" urgency_list=urgency_list)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Compare Results from Different Urgencies on Day 1:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ordering = ['grey', 'sky_blue', 'black', 'cyan', 'light_blue', 'dark_green']\\n\",\n \"urgency_list = ['VERY_LOW', 'LOW', 'MEDIUM', 'HIGH', 'VERY_HIGH']\\n\",\n \"ptAnalyticsDaily_list = []\\n\",\n \"for u in urgency_list:\\n\",\n \" ptAnalyticsDaily_list.append(pd.DataFrame(results_dict_multi[u]['analytics']['portfolioAnalyticsDaily']).iloc[[0]].assign(urgency=u) )\\n\",\n \"pd.concat(ptAnalyticsDaily_list).set_index('urgency')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Visualise Optimization Results\\n\",\n \"- Plotting 'Trade_cum_sum, Total Cost, Total Risk' against time for the chosen urgencies\\n\",\n \"- Trade_cum_sum: Cumulative sum of the intraday trades\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"metrics_list = ['tradePercentageCumulativeSum', 'totalRiskBps', 'totalCost', 'advAveragePercentage']\\n\",\n \"title = ['Intraday Trade', 'Risk', 'Cost', 'Participation Rate']\\n\",\n \"ylabel = ['Trade Cum Sum %', 'Risk(bps) ', 'Cost(bps)', 'Prate(%)']\\n\",\n \"\\n\",\n \"plotMultiStrategyPortfolioLevelAnalytics(results_dict_multi, metrics_list, title, ylabel)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"###### Sources: Goldman Sachs, Bloomberg, Reuters, Axioma\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Plot the optimal Efficient Frontier - the expected Market Impact vs. Residual Risk Trade-off:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"initial_gross = pd.DataFrame(results_dict_multi['VERY_LOW']['analytics']['portfolioAnalyticsIntraday'])['gross'].iloc[0]\\n\",\n \"risk_cost_tradeoff = pd.concat( [\\\\\\n\",\n \" pd.DataFrame(results_dict_multi[urgency]['analytics']['portfolioAnalyticsDaily'])\\\\\\n\",\n \" [['estimatedCostBps', 'meanExpectedCostVersusBenchmark']]\\\\\\n\",\n \".assign(totalRiskBps = lambda x: x['estimatedCostBps'] - x['meanExpectedCostVersusBenchmark'])\\\\\\n\",\n \".iloc[0].rename(urgency).to_frame()\\n\",\n \"for urgency in ['VERY_LOW', 'LOW', 'MEDIUM']], axis=1).T\\n\",\n \"\\n\",\n \"cmap = cm.get_cmap('Set1')\\n\",\n \"ax = risk_cost_tradeoff.plot.scatter(x='totalRiskBps', y='meanExpectedCostVersusBenchmark',\\\\\\n\",\n \" title='The Example Basket Efficient Frontier',\\\\\\n\",\n \" colormap=cmap, c=range(len(risk_cost_tradeoff)), s=100)\\n\",\n \"\\n\",\n \"for k, v in risk_cost_tradeoff[['totalRiskBps', 'meanExpectedCostVersusBenchmark']].iterrows():\\n\",\n \" ax.annotate(k, v,\\n\",\n \" xytext=(10,-5), textcoords='offset points',\\n\",\n \" family='sans-serif', fontsize=10, color='darkslategrey')\\n\",\n \"\\n\",\n \"ax.plot(risk_cost_tradeoff['totalRiskBps'].values, risk_cost_tradeoff['meanExpectedCostVersusBenchmark'].values,\\n\",\n \" color='grey', alpha=.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"###### Sources: Goldman Sachs, Bloomberg, Reuters, Axioma\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# And That's IT! Find below an holistic view of our APEX platform in visual from:\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Disclaimers:\\n\",\n \"###### Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"###### www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"###### Please refer to https://marquee.gs.com/studio/ for price information of corporate equity securities.\\n\",\n \"###### Notice to Australian Investors: When this document is disseminated in Australia by Goldman Sachs & Co. LLC (\\\"GSCO\\\"), Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\"), or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied the GS entities that: \\n\",\n \"###### • the person is a Sophisticated or Professional Investor for the purposes of section 708 of the Corporations Act of Australia; and \\n\",\n \"###### • the person is a wholesale client for the purpose of section 761G of the Corporations Act of Australia. \\n\",\n \"###### To the extent that the GS entities are providing a financial service in Australia, the GS entities are each exempt from the requirement to hold an Australian financial services licence for the financial services they provide in Australia. Each of the GS entities are regulated by a foreign regulator under foreign laws which differ from Australian laws, specifically: \\n\",\n \"###### • GSCO is regulated by the US Securities and Exchange Commission under US laws;\\n\",\n \"###### • GSI is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, under UK laws;\\n\",\n \"###### • GSBE is subject to direct prudential supervision by the European Central Bank and in other respects is supervised by the German Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufischt, BaFin) and Deutsche Bundesbank;\\n\",\n \"###### • GSALLC is regulated by the Hong Kong Securities and Futures Commission under Hong Kong laws; and\\n\",\n \"###### • GSSP is regulated by the Monetary Authority of Singapore under Singapore laws.\\n\",\n \"###### Notice to Brazilian Investors\\n\",\n \"###### Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"###### The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. 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(JCR) or Rating and Investment Information, Inc. (R&I) are credit ratings that have been issued by a credit rating agency registered in Japan (registered credit ratings). Other credit ratings are unregistered unless denoted as being registered. Before using unregistered credit ratings to make investment decisions, please carefully read \\\"Explanation Regarding Unregistered Credit Ratings\\\" (http://www.goldmansachs.com/disclaimer/ratings.html).\\n\",\n \"###### Notice to Mexican Investors: Information contained herein is not meant for the general public in Mexico. The services or products provided by or through Goldman Sachs Mexico, Casa de Bolsa, S.A. de C.V. (GS Mexico) may not be offered or sold to the general public in Mexico. You have received information herein exclusively due to your existing relationship with a GS Mexico or any other Goldman Sachs business. The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"###### Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone: \\n\",\n \"###### (i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"###### (ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"###### (iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"###### (iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act. \\n\",\n \"###### No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"###### Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"###### THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/02_optimizing_equity_trading/trade_list_world.csv", "content": "Symbol,Description,Shares,Price ($),Trade Value ($),Trade Value (%),Trade Type,Medium-Term Momentum,Country,Industries,20-Day ADV,Trade_Value_to_ADV\nB9M2WX3,L BRANDS INC,283808.6547,65.46,1.86E+07,0.018946035,Buy New,3.4948962,United States,Specialty Retail,3.36E+08,5.5%\nBNDYF48,IAC INTERACTIVECORP NEW,79702.53471,153.83,1.23E+07,0.01250345,Buy New,1.6383283,United States,Internet Software and Services,1.75E+08,7.0%\n6356525,FUJIFILM HOLDINGS,100350.024,69.86313869,7010767.643,0.007149609,Buy New,0.14659888,Japan,Technology Hardware Storage and Peripherals,1.29E+08,5.4%\nBKVD2N4,SEAGATE TECHNOLOGY HLDGS,69534.16827,99.33,6906828.935,0.007043612,Buy New,0.8976723,United States,Technology Hardware Storage and Peripherals,3.46E+08,2.0%\n2630643,NETAPP INC,70741.24269,82.01,5801489.313,0.005916382,Buy New,0.63259625,United States,Technology Hardware Storage and Peripherals,1.40E+08,4.1%\nB44Z3T8,EPAM SYS INC,9377.052449,492.98001,4622699.41,0.004714247,Buy New,0.64648473,United States,IT Services,1.68E+08,2.7%\nBGK4T39,FUTU HLDGS LTD ADR,30263.89615,145.69,4409147.03,0.004496466,Buy New,3.4948962,Hong Kong,Capital Markets,8.12E+08,0.5%\n2352118,FREEPORT-MCMORAN INC,94987.48132,40.7,3865990.49,0.003942552,Buy More,3.4948962,United States,Metals and Mining,9.38E+08,0.4%\n2372763,GARTNER INC,15665.67024,230.28999,3607647.042,0.003679093,Buy New,0.70093536,United States,IT Services,1.45E+08,2.5%\n6643960,NIPPON YUSEN KK,63544.46152,44.98175182,2858341.198,0.002914948,Buy More,2.3644235,Japan,Marine,1.37E+08,2.1%\n6455530,ICL GROUP LTD,133365.5454,7.352578337,980580.62,0.001,Buy New,0.94320285,Israel,Chemicals,1.56E+07,6.3%\nBYSS4X4,NOVOCURE LTD,4181.89719,211.89,886102.1955,9.04E-04,Buy More,2.2161908,United States,Health Care Equipment and Supplies,1.16E+08,0.8%\n2989356,ZEBRA TECHNOLOGIES CORPORATI,1613.966263,505.35001,815617.8671,8.32E-04,Buy More,0.67949533,United States,Electronic Equipment Instruments and Components,1.31E+08,0.6%\nBYVYWS0,HEWLETT PACKARD ENTERPRISE C,49982.04051,15.65,782218.934,7.98E-04,Buy More,0.42147762,United States,Technology Hardware Storage and Peripherals,1.52E+08,0.5%\n6821807,WASHINGTON H SOUL PATTINSON AND COMPANY LTD,6611.168676,23.80220177,157360.3708,1.60E-04,Buy More,0.17212428,Australia,Oil Gas and Consumable Fuels,5291398.151,3.0%\n6883807,SYSMEX CORP,-98.25190551,101.7791971,-10000.00005,-1.02E-05,Sell Some,-0.15122022,Japan,Health Care Equipment and Supplies,4.55E+07,0.0%\n0216238,AVIVA,-1709.998947,5.847957,-10000.00031,-1.02E-05,Sell Some,0.20779254,United Kingdom,Insurance,4.25E+07,0.0%\n0677608,PEARSON,-58305.73693,12.0377511,-701869.9488,-7.16E-04,Sell Some,0.14770593,United Kingdom,Media,1.58E+07,4.4%\n4741714,REMY COINTREAU,-4694.121841,203.132285,-953527.6957,-9.72E-04,Sell Some,-0.067527473,France,Beverages,1.27E+07,7.5%\nBZ1LFG7,ROKU INC,-3596.976023,339.64001,-1221676.973,-0.001245871,Sell Some,1.8364291,United States,Media,1.11E+09,0.1%\n6948836,WESFARMERS LTD,-34812.63048,41.9998065,-1462123.744,-0.00149108,Sell Some,-0.20156763,Australia,Multiline Retail,5.65E+07,2.6%\n6474535,JAPAN TOBACCO INC,-77943.17531,20.09580292,-1566330.69,-0.00159735,Sell Some,-0.49438974,Japan,Tobacco,8.63E+07,1.8%\n2692632,ALTRIA GROUP INC,-36135.80062,49.9,-1803176.451,-0.001838886,Sell Some,-0.16012596,United States,Tobacco,3.14E+08,0.6%\n2380498,GENERAL ELECTRIC CO,-140341.439,13.73,-1926887.958,-0.001965048,Sell Some,0.67412817,United States,Industrial Conglomerates,8.29E+08,0.2%\n2951098,WEST FRASER TIMBER CO LTD,-28270.87841,73.70249369,-2083634.238,-0.002124898,Sell All,0.95223433,Canada,Paper and Forest Products,6.02E+07,3.5%\nBJ2Z0H2,PINTEREST INC,-36048.92935,66.12,-2383555.209,-0.002430759,Sell Some,1.711701,United States,Internet Software and Services,5.57E+08,0.4%\n2002479,AES CORP,-106718.92,25.2,-2689316.783,-0.002742576,Sell Some,0.66341442,United States,Independent Power and Renewable Electricity Producers,1.34E+08,2.0%\nBK73B42,CORTEVA INC,-60406.73097,45.49,-2747902.192,-0.002802322,Sell Some,0.38967612,United States,Chemicals,1.56E+08,1.8%\n6420538,HENDERSON LAND DEV,-753582.2952,4.948484204,-3729090.084,-0.003802941,Sell Some,-0.36262581,Hong Kong,Real Estate Management and Development,1.86E+07,20.0%\nB02K2M3,M3 INC,-61921.90398,64.91788321,-4019838.931,-0.004099448,Sell All,0.19367497,Japan,Health Care Technology,1.55E+08,2.6%\nB616C79,TESLA INC,-7472.422855,598.78003,-4474337.581,-0.004562947,Sell Some,1.9619533,United States,Automobiles,1.71E+10,0.0%\n6770620,SOFTBANK GROUP CO,-61434.82368,73.65875912,-4525212.879,-0.00461483,Sell All,0.20267113,Japan,Wireless Telecommunication Services,1.16E+09,0.4%\nBYNZGK1,SQUARE INC,-21978.55419,210.21001,-4620112.096,-0.004711609,Sell Some,0.81130087,United States,IT Services,2.15E+09,0.2%\n6859927,SUN HUNG KAI PROP,-395711.7491,15.74752415,-6231480.324,-0.006354888,Sell All,-0.32389265,Hong Kong,Real Estate Management and Development,5.44E+07,11.5%\nB28XP76,INVESCO LTD,-245304.3244,28.7,-7040234.111,-0.007179659,Sell Some,1.7908559,United States,Capital Markets,1.34E+08,5.3%\n2831543,SOUTHWEST AIRLS CO,-125580.163,58.42,-7336393.121,-0.007481683,Sell All,0.48997331,United States,Airlines,3.47E+08,2.1%\nBYY88Y7,ALPHABET INC,-4819.316838,2491.3999,-1.20E+07,-0.012244629,Sell All,0.24704795,United States,Internet Software and Services,2.77E+09,0.4%\n" }, { "path": "gs_quant/content/events/00_gsquant_meets_markets/03_esg_basket_portfolio_optimisation/quants_meet_markets_msci.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# GS Quant Meets Markets x MSCI\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"#### Step 1: Import Modules\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Import modules\\n\",\n \"from typing import List\\n\",\n \"\\n\",\n \"from gs_quant.api.utils import ThreadPoolManager\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.api.gs.assets import GsAssetApi\\n\",\n \"from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest\\n\",\n \"from functools import partial\\n\",\n \"\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.markets.indices_utils import ReturnType\\n\",\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import datetime as dt\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"import pandas as pd\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"#### Step 2: Authenticate\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Initialize session -- for external users, input client id and secret below\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes='')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"#### Step 3: Implement basic functions to fetch coverage universe, ratings, factor & liquidity data\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Initialize functions\\n\",\n \"\\n\",\n \"def batch_liquidity(dataset_id: str, asset_ids: list, day: dt.date, size: int=200) -> pd.DataFrame:\\n\",\n \" data = Dataset(dataset_id)\\n\",\n \" tasks = [partial(data.get_data, day, day, assetId=asset_ids[i:i+size]) for i in range(0, len(asset_ids), size)]\\n\",\n \" results = ThreadPoolManager.run_async(tasks)\\n\",\n \" return pd.concat(results)\\n\",\n \"\\n\",\n \"\\n\",\n \"def batch_ratings(dataset_id: str, gsid_ids: list, day: dt.date, fallback_month, filter_value: str= None, size: int=5000) -> pd.DataFrame:\\n\",\n \" data = Dataset(dataset_id)\\n\",\n \" start_date = day - relativedelta(month=fallback_month)\\n\",\n \" tasks = [partial(data.get_data, start_date=start_date, gsid=gsid_ids[i:i+size], rating=filter_value) for i in range(0, len(gsid_ids), size)] if filter_value else \\\\\\n\",\n \" [partial(data.get_data, start_date=start_date, gsid=gsid_ids[i:i + size]) for i in range(0, len(gsid_ids), size)]\\n\",\n \" results = ThreadPoolManager.run_async(tasks)\\n\",\n \" return pd.concat(results)\\n\",\n \"\\n\",\n \"\\n\",\n \"def batch_asset_request(day: dt.date, gsids_list: list, limit: int=1000) -> list:\\n\",\n \" date_time = dt.datetime.combine(day, dt.datetime.min.time())\\n\",\n \" fields = ['gsid', 'bbid', 'id', 'delisted', 'assetClassificationsIsPrimary']\\n\",\n \" tasks = [partial(GsAssetApi.get_many_assets_data, gsid=gsids_list[i:i + limit], as_of=date_time, limit=limit*10, fields=fields) for i in range(0, len(gsids_list), limit)]\\n\",\n \" results = ThreadPoolManager.run_async(tasks)\\n\",\n \" return [item for sublist in results for item in sublist]\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_universe_with_xrefs(day: dt.date, model: FactorRiskModel) -> pd.DataFrame:\\n\",\n \" print(f'---------Getting risk {model.id} coverage universe on {day}------------')\\n\",\n \" # get coverage universe on date\\n\",\n \" universe = model.get_asset_universe(day, day).iloc[:, 0].tolist()\\n\",\n \" print(f'{len(universe)} assets in {model.id} on {day} that map to gsids')\\n\",\n \" # need to map from id -> asset_identifier on date\\n\",\n \" asset_identifiers = pd.DataFrame(batch_asset_request(day, universe))\\n\",\n \" print(f'{len(asset_identifiers)} assets found')\\n\",\n \"\\n\",\n \" asset_identifiers = asset_identifiers[asset_identifiers['assetClassificationsIsPrimary'] != 'false']\\n\",\n \" print(f'{len(asset_identifiers)} asset xrefs after is not primary dropped')\\n\",\n \" asset_identifiers = asset_identifiers[asset_identifiers['delisted'] != 'yes']\\n\",\n \" print(f'{len(asset_identifiers)} asset xrefs after delisted assets are dropped')\\n\",\n \"\\n\",\n \" asset_identifiers = asset_identifiers[['gsid', 'bbid', 'id']].set_index('gsid')\\n\",\n \" asset_identifiers = asset_identifiers[~asset_identifiers.index.duplicated(keep='first')] # remove duplicate gsids\\n\",\n \" asset_identifiers.reset_index(inplace=True)\\n\",\n \" print(f'{len(asset_identifiers)} positions after duplicate gsids removed')\\n\",\n \"\\n\",\n \" return pd.DataFrame(asset_identifiers).set_index('id')\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_and_filter_ratings(day: dt.date, gsid_list: List[str], filter_value: str = None) -> list:\\n\",\n \" # get ratings of assets from the ratings dataset and only keep 'Buy' ratings\\n\",\n \" print(f'---------Filtering coverage universe by rating: {filter_value}------------')\\n\",\n \" fallback_month = 3\\n\",\n \" ratings_df = batch_ratings('RATINGS_CL', gsid_list, day, fallback_month, filter_value)\\n\",\n \" df_by_asset = [ratings_df[ratings_df['gsid'] == asset] for asset in set(ratings_df['gsid'].tolist())]\\n\",\n \" most_recent_rating = pd.concat([df.iloc[-1:] for df in df_by_asset])\\n\",\n \" print(f'{len(most_recent_rating)} unique assets with ratings after filtering applied')\\n\",\n \" return list(most_recent_rating['gsid'].unique())\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_and_filter_factor_exposures(day: dt.date, identifier_list: List[str], factor_model: FactorRiskModel, factors: List[str]= [] , filter_floor: int = 0.5) -> pd.DataFrame:\\n\",\n \" # get factor info and filter by factors\\n\",\n \" print(f'---------Filtering coverage universe by factors: {factors}------------')\\n\",\n \" available_factors = factor_model.get_factor_data(day).set_index('identifier')\\n\",\n \" req = DataAssetsRequest('gsid', identifier_list)\\n\",\n \" factor_exposures = factor_model.get_universe_factor_exposure(day, day, assets=req).fillna(0)\\n\",\n \"\\n\",\n \" factor_exposures.columns = [available_factors.loc[x]['name'] for x in factor_exposures.columns]\\n\",\n \" factor_exposures = factor_exposures.droplevel(1)\\n\",\n \" print(f'{len(factor_exposures)} factor exposures available')\\n\",\n \" for factor in factors:\\n\",\n \" factor_exposures = factor_exposures[factor_exposures[factor] >= filter_floor]\\n\",\n \" print(f'{len(factor_exposures)} factor exposures returned after filtering by {factor} with floor exposure {filter_floor}')\\n\",\n \" return factor_exposures\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_and_filter_liquidity(day: dt.date, asset_ids: List[str], filter_floor: int = 0) -> pd.DataFrame:\\n\",\n \" # get mdv22Day liquidity info and take assets above average adv\\n\",\n \" print(f'---------Filtering coverage universe by liquidity value: {filter_floor}------------')\\n\",\n \" liquidity = batch_liquidity('GSEOD', asset_ids, day).set_index(\\\"assetId\\\")\\n\",\n \" print(f'{len(liquidity)} liquidity data available for requested universe')\\n\",\n \" if filter_floor:\\n\",\n \" liquidity = liquidity[liquidity['mdv22Day'] >= filter_floor]\\n\",\n \" print(f'{len(liquidity)} unique assets with liquidity data returned after filtering')\\n\",\n \" return liquidity\\n\",\n \"\\n\",\n \"\\n\",\n \"def backtest_strategy(day: dt.date, position_set: List[dict], risk_model_id: str):\\n\",\n \" # make a request to pretrade liquidity to get backtest timeseries\\n\",\n \" print(f'---------Backtesting strategy------------')\\n\",\n \" query = {\\\"currency\\\":\\\"USD\\\",\\n\",\n \" \\\"notional\\\": 1000000,\\n\",\n \" \\\"date\\\": day.strftime(\\\"%Y-%m-%d\\\"),\\n\",\n \" \\\"positions\\\":position_set,\\n\",\n \" \\\"participationRate\\\":0.1,\\n\",\n \" \\\"riskModel\\\":risk_model_id,\\n\",\n \" \\\"timeSeriesBenchmarkIds\\\":[],\\n\",\n \" \\\"measures\\\":[\\\"Time Series Data\\\"]}\\n\",\n \" result = GsSession.current._post('/risk/liquidity', query)\\n\",\n \" result = result.get(\\\"timeseriesData\\\")\\n\",\n \" return result\\n\",\n \"\\n\",\n \"\\n\",\n \"def graph_df_list(df_list, title):\\n\",\n \" for df in df_list:\\n\",\n \" plt.plot(df[0], label=df[1])\\n\",\n \" plt.legend(title='Measures')\\n\",\n \" plt.xlabel('Date')\\n\",\n \" plt.title(title)\\n\",\n \" plt.show()\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"#### Step 4: Strategy Implementation\\n\",\n \"\\n\",\n \"Proposed Methodology\\n\",\n \"- Starting universe: Chosen risk model coverage universe\\n\",\n \"- High Conviction names: Retain GS \\\"Buy\\\" ratings only\\n\",\n \"- High ESG names: Retain high ESG scores only, using BARRA GEMLTL ESG model\\n\",\n \"- High Profitability names: Retain high Profitability scores only, using BARRA GEMLTL ESG model\\n\",\n \"- Liquidity adjustment: Removing the tail of illiquid names\\n\",\n \"- Weighting: MDV-based weighting\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Get risk model and available style factors\\n\",\n \"start = dt.datetime.now()\\n\",\n \"\\n\",\n \"# Get risk model\\n\",\n \"model_id = \\\"BARRA_GEMLTL_ESG\\\"\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Get last date of risk model data\\n\",\n \"date = factor_model.get_most_recent_date_from_calendar() - dt.timedelta(1)\\n\",\n \"print(f\\\"-----Available style factors for model {model_id}-----\\\")\\n\",\n \"factor_data = factor_model.get_factor_data(date, date)\\n\",\n \"factor_data = factor_data[factor_data['factorCategoryId'] == 'RI']\\n\",\n \"print(factor_data['name'])\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Get universe\\n\",\n \"mqid_to_id = get_universe_with_xrefs(date, factor_model)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Get available ratings for past 3 months and return most recent ratings data per asset\\n\",\n \"ratings_filter = 'Buy'\\n\",\n \"ratings_universe = get_and_filter_ratings(date, mqid_to_id['gsid'].tolist(), filter_value=ratings_filter)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Pass in factors to filter by\\n\",\n \"factors = ['ESG', 'Profitability']\\n\",\n \"filter_floor = 0.5\\n\",\n \"\\n\",\n \"exposures = get_and_filter_factor_exposures(date, ratings_universe, factor_model, factors=factors, filter_floor=filter_floor)\\n\",\n \"ids = mqid_to_id.reset_index().set_index(\\\"gsid\\\")\\n\",\n \"exposures = exposures.join(ids, how='inner')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Filter by liquidity, which takes in the MQ Id\\n\",\n \"asset_ids = exposures['id'].tolist()\\n\",\n \"liquidity_floor = 1000000\\n\",\n \"liquidity = get_and_filter_liquidity(date, asset_ids, filter_floor=liquidity_floor)\\n\",\n \"liquidity = liquidity.join(mqid_to_id, how='inner')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Get weights as ADV / total ADV\\n\",\n \"total_adv = sum(list(liquidity['mdv22Day']))\\n\",\n \"liquidity['weights'] = liquidity['mdv22Day'] / total_adv\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"#### Step 5: Backtest Strategy\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Backtest composition\\n\",\n \"backtest_set = [{'assetId': index, \\\"weight\\\": row['weights']} for index, row in liquidity.iterrows()]\\n\",\n \"position_set = [{'bbid': row['bbid'], \\\"weight\\\": row['weights']} for index, row in liquidity.iterrows()]\\n\",\n \"print(\\\"Position set for basket create: \\\")\\n\",\n \"print(pd.DataFrame(position_set))\\n\",\n \"print(f'Total time to build position set with requested parameters {dt.datetime.now() - start}')\\n\",\n \"\\n\",\n \"\\n\",\n \"backtest = backtest_strategy(date, backtest_set, model_id)\\n\",\n \"print(\\\"Available measures to plot for backtested strategy: \\\")\\n\",\n \"measures = list(backtest[0].keys())\\n\",\n \"measures.remove(\\\"name\\\")\\n\",\n \"print(measures)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Graph Normalized Performance\\n\",\n \"np = ['normalizedPerformance']\\n\",\n \"series_to_plot = []\\n\",\n \"for measure in np:\\n\",\n \" timeseries = backtest[0].get(measure)\\n\",\n \" timeseries = {dt.datetime.strptime(data[0], \\\"%Y-%m-%d\\\"): data[1] for data in timeseries}\\n\",\n \" timeseries = (pd.Series(timeseries), measure)\\n\",\n \" series_to_plot.append(timeseries)\\n\",\n \"\\n\",\n \"graph_df_list(series_to_plot, \\\"Normalized Performance\\\")\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Plot many measures\\n\",\n \"measures.remove(\\\"netExposure\\\")\\n\",\n \"measures.remove(\\\"cumulativePnl\\\")\\n\",\n \"measures.remove(\\\"maxDrawdown\\\")\\n\",\n \"\\n\",\n \"series_to_plot = []\\n\",\n \"for measure in measures:\\n\",\n \" timeseries = backtest[0].get(measure)\\n\",\n \" timeseries = {dt.datetime.strptime(data[0], \\\"%Y-%m-%d\\\"): data[1] for data in timeseries}\\n\",\n \" timeseries = (pd.Series(timeseries), measure)\\n\",\n \" series_to_plot.append(timeseries)\\n\",\n \"\\n\",\n \"graph_df_list(series_to_plot, \\\"Backtested Strategy Measures\\\")\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"#### Step 6: Basket Creation\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Create basket with positions\\n\",\n \"my_basket = Basket()\\n\",\n \"my_basket.name = 'Basket Name'\\n\",\n \"my_basket.ticker = 'Basket Ticker'\\n\",\n \"my_basket.currency = 'USD'\\n\",\n \"my_basket.return_type = ReturnType.PRICE_RETURN\\n\",\n \"my_basket.publish_to_bloomberg = True\\n\",\n \"my_basket.publish_to_reuters = True\\n\",\n \"my_basket.publish_to_factset = False\\n\",\n \"data=[]\\n\",\n \"for row in position_set:\\n\",\n \" data.append([row['bbid'], row['weight']])\\n\",\n \"\\n\",\n \"positions_df = pd.DataFrame(data, columns=['identifier', 'weight'])\\n\",\n \"position_set = PositionSet.from_frame(positions_df)\\n\",\n \"my_basket.position_set = position_set\\n\",\n \"\\n\",\n \"my_basket.get_details() # we highly recommend verifying the basket state looks correct before calling create!\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Publish basket\\n\",\n \"my_basket.create()\\n\",\n \"my_basket.poll_status(timeout=10000, step=20) # optional: constantly checks create status until report succeeds, fails, or the poll times out (this example checks every 20 seconds for 2 minutes)\\n\",\n \"my_basket.get_url() # will return a url to your Marquee basket page ex. https://marquee.gs.com/s/products/MA9B9TEMQ2RW16K9/summary\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/content/made_with_gs_quant/1-Navigating Rates.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Navigating Rates\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Summary\\n\",\n \"\\n\",\n \"Following the spike in USD swaption implied vol last summer, vols have come down materially. When we dissect the moves of the vol surface over the past 2 years, we find that:\\n\",\n \"\\n\",\n \"* Vols on 1y tails are back at lows while vols on 10y tails haven’t fully retraced from last summer's spike. \\n\",\n \"* Skew on 1y tails remains bid for receivers (and payers trade close to flat vs. ATM) while payer skew on 10y tails has richened translating into a flat skew but very high smile (both receiver and payer skews are high). \\n\",\n \"\\n\",\n \"Given these findings, we look at a hypothetical scenario analysis of a short 3m10y strangle over time. \\n\",\n \"\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs_quant)\\n\",\n \"* [2 - Implied volatility in historical context](#2---Implied-volatility-in-historical-context)\\n\",\n \"* [3 - Skew](#3---Skew)\\n\",\n \"* [4 - Potential structures](#4---Potential-structures)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs_quant\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.95.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Implied volatility in historical context\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's start by looking at the implied volatility of 1y and 10y payer and receiver swaptions expiring at 3m.\\n\",\n \"\\n\",\n \"To do that, let's first create the 8 swaptions (2 types x 2 expiries x 2 strikes) and put them in a portfolio. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.common import Currency, PayReceive\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"\\n\",\n \"currency = Currency.USD\\n\",\n \"expiry = '3m'\\n\",\n \"notional = 1e8\\n\",\n \"\\n\",\n \"tenors = ('1y', '10y')\\n\",\n \"r_strikes = ('ATMF', 'ATMF-50')\\n\",\n \"p_strikes = ('ATMF', 'ATMF+50')\\n\",\n \"\\n\",\n \"receivers = [IRSwaption(PayReceive.Receive, tenor, currency, expiration_date=expiry, strike=strike, notional_amount=notional, name='{},{},{}'.format('R',tenor,strike)) for strike in r_strikes for tenor in tenors]\\n\",\n \"payers = [IRSwaption(PayReceive.Pay, tenor, currency, expiration_date=expiry, strike=strike, notional_amount=notional, name='{},{},{}'.format('P',tenor,strike)) for strike in p_strikes for tenor in tenors]\\n\",\n \"\\n\",\n \"swaptions = payers + receivers\\n\",\n \"port = Portfolio(swaptions)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's now calculate annualized implied volatility for the last two years looking at month ends as well as the most recent date.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"from gs_quant.risk import IRAnnualImpliedVol\\n\",\n \"import pandas as pd\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"dates = pd.date_range('2018-01-01', '2020-01-01', freq='BM').date.tolist()\\n\",\n \"dates.append(dt.date(2020,1,24))\\n\",\n \"\\n\",\n \"with HistoricalPricingContext(dates=dates): \\n\",\n \" vols = port.calc(IRAnnualImpliedVol)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now construct a dataframe with the results and plot it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"d = pd.DataFrame({s.name: vols[s.name] for s in swaptions})\\n\",\n \"d.plot(figsize=(10,6))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see in the charts above, implied vol for both 1y and 10y tenors has come in since 2019 highs but significantly more so for 1y tenor than 10y.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Skew \\n\",\n \"\\n\",\n \"Let's now use the same data to take a look at skew.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"receiverSkew3m10y = d['R,10y,ATMF-50'] - d['R,10y,ATMF']\\n\",\n \"payerSkew3m10y = d['P,10y,ATMF+50'] - d['P,10y,ATMF']\\n\",\n \"\\n\",\n \"receiverSkew3m1y = d['R,1y,ATMF-50'] - d['R,1y,ATMF']\\n\",\n \"payerSkew3m1y = d['P,1y,ATMF+50'] - d['P,1y,ATMF']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,6))\\n\",\n \"plt.plot(receiverSkew3m10y*10000,label='3m10y Receiver Skew')\\n\",\n \"plt.plot(payerSkew3m10y*10000,label='3m10y Payer Skew')\\n\",\n \"plt.legend(loc=\\\"upper left\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10,6))\\n\",\n \"plt.plot(receiverSkew3m1y*10000, label='3m1y Receiver Skew')\\n\",\n \"plt.plot(payerSkew3m1y*10000, label='3m1y Payer Skew')\\n\",\n \"plt.legend(loc=\\\"upper left\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the charts above we can see that 3m10y payer and receiver skew remains high but the spread between them has tightened. For the 1y tenor, however, the payer skew has flattened while receiver skew remains rich in the context of the last 2 years. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Potential structures\\n\",\n \"\\n\",\n \"Let's now look at a 3m10y F+/-25 strangle to fade the steep vol smile. \\n\",\n \"\\n\",\n \"For this exercise, we will age it in a roll to spot scenario. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"strangles = {}\\n\",\n \"labels = ('Inception', 'After 1m', 'After 2m', 'After 3m')\\n\",\n \"expiries = ('3m', '2m', '1m', '1d')\\n\",\n \"\\n\",\n \"for label, expiry in zip(labels, expiries):\\n\",\n \" R = IRSwaption('Receive', '10y', 'USD', expiration_date=expiry, strike='ATMF-25', notional_amount=1e8)\\n\",\n \" P = IRSwaption('Pay', '10y', 'USD', expiration_date=expiry, strike='ATMF+25', notional_amount=1e8)\\n\",\n \" strangle = Portfolio((R, P))\\n\",\n \" strangle.resolve()\\n\",\n \" strangles[label] = strangle\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's now construct a set of spot shocks and examine the impact on price accross the different expiries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.risk import IRVegaParallel, Price\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"shocks = [x for x in range(-50, 55, 5)]\\n\",\n \"results = defaultdict(dict)\\n\",\n \"\\n\",\n \"with PricingContext():\\n\",\n \" for expiry, strangle in strangles.items():\\n\",\n \" for shock in shocks:\\n\",\n \" ir_spot_scenario = MarketDataShockBasedScenario(shocks={\\n\",\n \" MarketDataPattern('IR', 'USD'): MarketDataShock(MarketDataShockType.Absolute, -shock / 10000),\\n\",\n \" MarketDataPattern('IR Reset', 'USD'): MarketDataShock(MarketDataShockType.Absolute, shock / 10000)})\\n\",\n \"\\n\",\n \" with ir_spot_scenario:\\n\",\n \" results[expiry].update({shock:strangle.calc((Price, IRVegaParallel))})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now roll up the visualize the results:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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J46m1Yj3uXLqTPm6dm3GooNzeXHTt28MUXX1wW8Pr06UNeXh5t27blrbfeYs2aNYwZM4aIiAji4uKIi4ujW7dutGzZknvvvbes9W/YsGGMGjWK+++/n3Hjxl32Wq6urtxzzz04XWWynJubG+PGjaNly5Z06dKFP//8k06dOlG3bl3WrFlTqe9ZNSEoSiXL/e03UqdNo+jESZxbtiTgs09xDg/XuixFUTTgEBRE4IzpeA8bRuqMGaRMmULGokX4j3oZ927dqt9daDaMh/OHKveaNcKh+/vXPWTVqlV069aNBg0a4O3tTWxsLJGRkaxZswY3Nzf2798PQHx8PL169WLAgAEAdO7cmc8++4ywsDB27drF888/z9atWwE4fvw4W7ZswdbWtsKl5uXl0alTJ6ZOnUpUVBQTJ05k8+bNHD16lKFDh9KnT59b/CH8mwp4ilJJCv8+Tuq0aeTt2IF97doEzpmN+4MPVr9/wBVF+ZdLM27ztm8ndfoMzr48CqcvvzLe47ZtG63Ls3pLlixh5MiRAAwaNIglS5YQGRl53XNyc3PZuXMnAwcOLNtWVFRU9nzgwIE3Fe7AeM/bbt26ARAeHo6joyP29vaEh4eTkJBwU9e6ERXwFOU2laalkTbnI7JWrMDGzQ3/8ePwHjwY4eCgdWmKopiRy2bcrl5D2pw5ZTNu/Ue/gmNYmNYlVr0btLRVhfT0dLZu3crhw4cRQqDX6xFCMG3atOv+Am4wGPDy8ipr3buSq6vrTddib29f9po2NjY4OjqWPb9yLN/tUmPwFOUWGQoLufDZZ8Q91I2slSvRPf4Y9Tb+hM+wYSrcKYpyTcLWFq/oqH9m3O7dy6m+/UieOFHNuK0Cy5cvZ8iQISQmJpKQkEBSUhKhoaFs3779X8e6u7uTY5oM4+HhQWhoKD/88ANgXMP0wIEDd7T226ECnqLcgovr1xPXvQdps2bj0v5u6q5dQ41XX8VOp9O6NEVRLETZjNvNm/B+4nGyV68h7qFuXNy0SevSrMqSJUuIioq6bFv//v357rvv/nXsoEGDmD59Oi1atCAuLo7FixfzxRdf0Lx5c5o2bcrq1asr9Jp16tRh1KhRfP311wQFBd320iu3QqjbqvyjVatWcs+ePVqXoZi5i5s2cfbFl3Bq0gT/8eNwbaPGzyiKcvuKk5I4O+oViuPijLdBqxuqdUmV4tixYzRu3FjrMize1X6OQoi9UspWVzteteApyk0oPnOWcxNfx6lZM+osXaLCnaIolcYhOJigObMRjo6cfelFDPn5WpekWDAV8BSlgmRJCcmvvAIGA4EffqDG2SmKUunsa9ak1vTpFJ2M4/xbb6N62ZRbpQKeolRQ2pw5FBw4QM3Jb+MQHKx1OYqiWCm3ezrg+/zzZK9eTdby5VqXo1goFfAUpQJyf/uN9AWf4/Xww3h07651OYqiWDnf55/Dtf3dpEx+h0INBugrlk+tg6coN1CSkkryuPE4hoUR8OoErctRLEBBaQGZhZlkFmaSUZhBsb4YD0cPPB098XL0wtPRE0dbR63LVMyYsLWl1owZxEdFc2bky4Qu/wFbDw+ty1IsiAp4inIdUq8neexYDPn5BC78Bpur3FtQsW4GaSCnOIeMwgyyirLIKMwgszDzsueZhZlkFmWWPS/UF97wuk62Tng4epQFPk8HT+Pjpa9rfO9kp/4MVhd23t4EfvgBiU8MIfnVVwn66CN1ZxylwlTAU5TruDBvHvm7dlHz3XdxrF9f63KUSiClJL0wnfSCdDKLMskqNAW1cgGt/POsoiz0Un/VaznbOePt5I2XoxfeTt7U96qPzlGHl5Pxe52jDp2TDkdbRy4WXySrKIvsomwuFl8kuyi77PvsomwSLiaUfV9iKLlm/ZeC4aXQdykgejh6lIXAWq61CPcLx93Bvap+jMod4hIZif/o0aROnUrG19/g859hWpdksVauXEl0dDTHjh2jUaNGZdvHjBnD+vXr6dGjBx06dKBBgwY0adLkll9n8+bNjB8/nuLiYhwcHJg+fToPPPBAZbyFm6ICnqJcQ/7u3Vz4+BM8evfGMzrqxicoZqnUUMrfmX8TmxLLvtR9xKbEkl6YftVjPR090Tnq8HbyprZ7bZr7NTcGNSddWYjTOenKQl1VtKZJKSkoLbgsEGYXZZNdnP3Pc9NXVlEWCRcTyp6XD4YCQX1dfSL8IojwjyDCL4Jg92DVAmSBvIcNpSB2L6kzZuDc/C5cbnAPVeXqlixZwj333MPSpUuZNGlS2fZ58+aRlpaGo6Mjw4YNo1evXjcV8EpLS7Gz+ydO+fr6snbtWmrVqsXhw4d56KGHOHv2bGW+lQpRCx2XoxY6Vi4pzcwkvl8UNk5O1FmxAlu3m7/noKKNwtJCDl04xN6UvexL3cf+1P3klxrXEwt0CyTSP5Kmvk3xc/ZD56Qra2XzdPTEzsZyf+eVUlKoLyxrDdyXuo8DqQc4kHaA3JJcALydvGnu15wI/wha+LegiU8TNRbQQugvXiR+wEBkURGhMSuw8/HRuqQKM4eFjnNzc2nYsCE///wzffr04a+//gKgT58+/Pjjj4SHhxMVFcXs2bPx9PTE09OTFStWADBixAjS0tJwcXFhwYIFNGrUiGHDhuHt7c2+ffuIjIxk5syZV31dKSW+vr4kJyfj6OiIm5sbI0aMYMuWLeh0OqZMmcLYsWM5ffo0s2bNok+fPtd8Dze70LHl/mumKFVESsm58RPQZ2QQvGypCndmLrsou6xlbm/qXo6mH6XUUFrWgtW7Xm8i/SOJDIikhmsNrcutMkIInO2ccbZzpoZrDdrVbAcYxxDGZcWxP20/+1ONXz8n/QyAnY0dTXyaXNbK5+fip+XbUK7B1sODoNmzSHhkEMljxhC8YAHC1lbrsm7a1D+n8lfGX5V6zUbejRjXZtx1j1m1ahXdunWjQYMGeHt7ExsbS2RkJGvWrMHNzY39+/cDEB8fT69evRgwYAAAnTt35rPPPiMsLIxdu3bx/PPPs3XrVgCOHz/Oli1bsL3O/4cVK1bQokULHB2Nv0jl5eXRqVMnpk6dSlRUFBMnTmTz5s0cPXqUoUOHXjfg3SwV8BTlChlff0PuL78Q8NprON3GOAylapzLPUdsaiyxKbHEpsZyMuskYAwrzXyaMaTJEFoGtKS5X3M8HT01rlZ7NsKGMF0YYbowBjYYCEB6QToH0g6wP20/B1IPsPSvpSw8uhAwtnJeCnsR/hGEeYVha2N5QcIaOTVuTMDrEzn/+htcmPspfi/8T+uSLMaSJUsYOXIkYLzf7JIlS4i8QVd3bm4uO3fuZODAgWXbioqKyp4PHDjwuuHuyJEjjBs3jk3l7i3s4OBAt27dAAgPD8fR0RF7e3vCw8NJSEi4lbd2TSrgKUo5BYcOkfrBB7h16Yzu8ce0LqfaM0gDp7JOGQOdKdSdyzsHgKu9KxF+EXQP7U6kfyTNfJupGaYV5OPswwO1H+CB2saB3yX6Eo5lHDN266YdYNe5Xfx46kcAXOxcCPcLp4V/CyL8Igj3C8fDQS3XoRWvAQMo2LOXC3Pn4tyiBW73dNC6pJtyo5a2qpCens7WrVs5fPgwQgj0ej1CCKZNm3bdMakGgwEvL6+y1r0rubpeu3fnzJkzREVFsXDhQurVq1e23d7evuw1bWxsylr2bGxsKC0tvZW3d00q4CmKiT4nh7Mvj8LOz5da776rBqNroERfwtGMo+xL2cfeVOMYuuyibAB8nHyIDIhkaNOhRPpH0kDXQLUsVRJ7W3vu8ruLu/zuAozDFJLzksu6dPen7Wf+wfkYpAGBoJ5XvbJxfGryxp0lhKDGm29QePQoyWPGELoyBvsa1jv0oDIsX76cIUOGMG/evLJt9913H9u3b+fee++97Fh3d3dycnIA8PDwIDQ0lB9++IGBAwcipeTgwYM0b978uq+XlZVFz549ee+99+jQQbsArgKeomAad/fGG5ScO0fIokXYeqquvTvlVPYpfor/ib0pezmYdrBsDbkQjxAeCH6AFv4taBnQUoWIO0gIQaBbIIFugfSs2xOAvJI8Dl04VBb4NsZvZPlx4220gt2DiQ6Lpm+9vmoM3x1g4+JC4OzZJAwYwNmRLxOyaCHC3l7rsszWkiVLGD9+/GXb+vfvz3ffffevgDdo0CCefvpp5syZw/Lly1m8eDHPPfcc77zzDiUlJQwaNOiGAe/jjz/m5MmTTJ48mcmTJwOwadMm/P39K/eN3YCaRVuOmkVbfWUuXcb5SZPwGzUK3+FPa12O1SvWF7MlcQvfH/+evSl7sRE2NNQ1pGVASyIDImnh3wJfZ1+ty1Suo3z3+U8JP7H7/G5shS0dgzrSP6w/HQI7WPSsZEtwcf16zo56Be+hQwmYMP7GJ2jEHGbRWgM1i1ZRblLh38dJee89XDt0wOepJ7Uux6olXkxk+fHlrD65msyiTILcghgZOZJ+9fvh42w5yz4oxskb9XX1qa+rz8MNHybxYiIxJ2JYfXI1Pyf9jL+LP/3q9yM6LJpAt0Cty7VKHj16kL83loxvvsG5ZSQeXbtqXZJiRlQLXjmqBa/6MeTnEz9gIPqci9RduRI7X9VqVNlKDCX8fPpnvj/+PbvO7cJW2HJ/8P0MbDiQdjXbYSNstC5RqUQlhhJ+TfqVFSdWsCN5B1JK2tVsR/8G/bk/+H4cbB20LtGqGIqLSXz8CYpPnSJ0xXIcQkK0LulfVAte5VAteIpyE86/8y7F8fHU/vILFe4q2dncs6w4voKYEzGkF6ZT07UmL7R4gaj6UWqclhWzt7Gnc0hnOod05nzeeVaeXMnKEysZ/ctodI46etfrTf+w/tT1qqt1qVbBxsGBoA8/4FR0f868NJI6S5eoe2YrgAp4SjWWvXYt2TEx+Dz3LK533611OVah1FDKr2d+5fvj37Pz7E6EEHQM7MjAhgPpUKuDmvVazdRwrcFzzZ9jePhw/jj3BytOrOC7Y9+x8OhCWvi3oH9Yf7rW6YqznbPWpVo0+8BAAqdNJemZZzn/zjvUeucdrUtSzIAKeEq1VBQfz/k3J+HcqiV+I0ZoXY7FO593npgTMaw4sYLU/FT8nf15pvkz9A/rb9V3j1AqxtbGlg6BHegQ2IH0gnTWxK0h5kQME3dM5P0/36dn3Z70D+tPYx/VjXer3O67D59nniF93jxcIlvipe6fXe2pMXjlqDF41YOhqIiEQY9SmpxM6OpVag2pW6Q36NmRvIMf/v6BX8/+ipSS9oHtebjBw3QM6qhmUCrXJaVkb8peYk7EsClxE0X6Ihp7N2ZAgwF0D+2Ou4O71iVaHFlayuknn6LgwAHqLFuGU8MGWpcEqDF4leVmx+CpgFeOCnjVw/nJ75C5eDFBc+fi/sD9WpdjcdLy01h5ciXLjy/nXN45fJx8iAqLon9Yf4Lcg7QuT7FA2UXZrI9fz4rjK/g782+c7ZzpGtKV/g36E+EXodY/vAmlaWmcio7G1sWVOiuWY+vmpnVJZhPwVq5cSXR0NMeOHaNRo0Zl28eMGcP69evp0aMHHTp0oEGDBjS5jdtU/vnnnwwfPhww/iIzadIkoqJuv0VVBbzboAKe9bu4eTNnX3jR7NeNMjcGaeCPc3+w/Phyfj79M6WylLY12zKwwUAeCH4Ae1u1yKpy+6SUHE0/yvITy1l/aj35pfnU9axLdFg0fer1Qeek07pEi5C/ezeJw/6D+4MPEvjhB5oHZHMJeA8//DDnzp2jc+fOTJo0qWy7h4cHaWlpODo6MmzYMHr16sWAAQMqfN3S0lLs7P7pscjPz8fBwQE7OzvOnTtH8+bNSU5OvuyYW6EC3m1QAc+6lZw9y6moaBxq16bOd4sRDmq5hhtJL0hnddxqlh9fTlJOEl6OXvSr348BDQYQ4mF+yzEo1iO/JJ+NCRtZcWIFB9IOYGdjR+fanYkOi1bL61TAhQULSJv5AQGvvYb3E49rWos5BLzc3FwaNmzIzz//TJ8+ffjrr78A6NOnDz/++CPh4eFERUUxe/ZsPD098fT0ZMWKFQCMGDGCtLQ0XFxcWLBgAY0aNWLYsGF4e3uzb98+IiMjmTlz5lVfNz4+nnbt2nH27Fns7Oxwc3NjxIgRbNmyBZ1Ox5QpU+R5DvsAACAASURBVBg7diynT59m1qxZ9OnT55rvQS2ToihXIUtKOPvKaNDrCfxgpgp31yGlZE/KHn74+wc2n95MqaGUlgEtGRExgi4hXXC0ddS6RKUacLF3ISosiqiwKE5kniDmRAxrT61lY8JGGuoaMqb1GNrWbKt1mWbL58knKYjdR8q0aTjfFY7zDW6vdaecnzKFomN/Veo1HRs3osarr173mFWrVtGtWzcaNGiAt7c3sbGxREZGsmbNGtzc3Ni/fz9gDGTlW/A6d+7MZ599RlhYGLt27eL5559n69atABw/fpwtW7Zga/vv1QF27drFf//7XxITE1m0aFFZ611eXh6dOnVi6tSpREVFMXHiRDZv3szRo0cZOnTodQPezVIBT6kW0ubMoWD/fgI/mIlD7dpal2O2TmWdYuruqexM3om7gzuDGg5iQIMB1POqp3VpSjUWpgtjXJtxjGw5kk0Jm/h438c8tekp7g++n1davaJak69C2NhQ6/33iI/uz5mRLxMaswI7XfXt4l6yZAkjR44EjPebXbJkCZGRkdc9Jzc3l507dzJw4MCybUVFRWXPBw4ceNVwB9C2bVuOHDnCsWPHGDp0KN27d8fJyQkHBwe6desGQHh4OI6Ojtjb2xMeHk5CQsJtvsvLqYCnWL3c37aTvuBzvAYOxKNHD63LMUs5xTl8euBTlhxbgrOdM2Nbj2Vgg4E42akFUxXz4WjrSO96velapyuLji5iwcEF9Fvdj8GNBvNM82fwcPDQukSzYuvpSeCsWSQOHkzyuHEEf/YZwkbbru0btbRVhfT0dLZu3crhw4cRQqDX6xFCMG3atOuOTzQYDHh5eZW17l3J1dX1hq/duHFjXF1dOXz4MK1atcLe3r7sNW1sbHB0dCx7Xlpaegvv7trUIAbFqpWkppI8bhyOYfUJeHWC1uWYHYM0EHMihl4re/Ht0W/pF9aPddHreKLJEyrcKWbL0daRp8Kf4sfoH+lbry+Lji6iZ0xPlv61lFJD5X5IWjrn8GYEvDqBvF9/I33+fK3L0cTy5csZMmQIiYmJJCQkkJSURGhoKNu3b//Xse7u7uTk5ADGyRehoaH88MMPgHH4yoEDB274evHx8WVhLTExkb///ps6depU3huqoCoLeEKISUKIs0KI/aavHuX2TRBCnBRC/C2EeKjc9pZCiEOmfXOEKeYKIRyFEMtM23cJIeqUO2eoEOKE6Wtoue2hpmNPmM5Vg66qGanXkzx2HIb8fAI//BAbZ7Vafnn7U/fz6I+P8ubONwnxCGFpr6W8efebeDt5a12aolSIr7Mvk9pP4vve3xOmC+PdXe8yYM0AdpzdoXVpZsVr0CA8evUibc5H5P3xh9bl3HFLliz51zIl/fv357vvvvvXsYMGDWL69Om0aNGCuLg4Fi9ezBdffEHz5s1p2rQpq1evvuHrbd++nebNmxMREUFUVBRz587FV4NbYVbZLFohxCQgV0o544rtTYAlQBugFrAFaCCl1Ash/gReAv4A1gNzpJQbhBDPA3dJKZ8VQgwCoqSUjwghvIE9QCtAAnuBllLKTCHE90CMlHKpEOIz4ICU8tPr1axm0VqXC59+StrsOdR89x28+vfXuhyzkZqfyod7P2TdqXX4u/gzquUoeoT20HwpBUW5HVJKtiZtZeaemSTlJHFP4D2MaTVG3fPWxJCXR/zDj6DPyiI0Jgb7AP879trmMIvWGtzsLFotumj7AkullEVSynjgJNBGCFET8JBS/i6NqXMh0K/cOd+Yni8HOpta9x4CNkspM6SUmcBmoJtp3wOmYzGde+laSjWQv2cPaR99jEevXnhGR2tdjlko1hfz+aHP6bWyFxsTNvJ0+NOs7beWnnV7qnCnWDwhBJ1rd2ZV31WMbjWaA6kHiF4TzZRdU8gqzNK6PM3ZuLoSNHsWhvx8zr4yClnJ470U81PVAe9/QoiDQogvhRCXpu8EAknljjlj2hZoen7l9svOkVKWAtmAz3Wu5QNkmY698lqXEUIMF0LsEULsSUtLu7V3qZiV0sxMzr4yGvvgIGpMmlTtw4uUkm1J2+i3uh+zY2fTrmY7VvddzYuRL+Ji76J1eYpSqRxsHRjadCjrotcxoMEAlv29jB4re7Do6CJK9CVal6cpx/r1qfn2WxTs2UvarFlal6NUsdsKeEKILUKIw1f56gt8CtQDIoBzwKVVAK/2aSuvs/1WzrnetS7fKOV8KWUrKWUrPz+/qx2iWBApJecmvIo+I4PADz7A1u3Gs5ysWXx2PM/933O8sPUF7G3smddlHnMemEOwR7DWpSlKlfJ28mZiu4ks772ccN9wpu2eRvSaaLYlbaM6L/Dv2bs3XoMeIf3zL8gxreemWKfbWiZFStmlIscJIRYA60zfngHKf7oEAcmm7UFX2V7+nDNCCDvAE8gwbe90xTnbgAuAlxDCztSKV/5aihXL+OYbcrdtI+C113Bu2lTrcjSTU5zDvAPzWHxsMU52ToxtPZZBjQZhb6NuKaZUL2G6MD7r8hm/nf2N6bun88LWF2hXsx1jWo+hga6B1uVpImDCBAoPHiJ5/ARCY1bgEFT195CWUlb73pTbcSu/lFTlLNqa5b6NAg6bnq8BBplmxoYCYcCfUspzQI4Qop1pDN0QYHW5cy7NkB0AbDWN09sIdBVC6ExdwF2BjaZ9P5uOxXTujae+KBat4NBhUmd+gFuXzugef0zrcjRhkAZWnlhJr5W9WHh0IX3r92VdlHHZExXulOpKCEHHoI7E9I1hfJvxHE0/ysC1A3n797dJL0jXurw7zsbRkcA5swE4++JLGMot3lsVnJycSE9Pr9Ytp7dDSkl6ejpOTje3dFVVzqJdhLF7VgIJwDOmEIcQ4jXgv0ApMFJKucG0vRXwNeAMbABekFJKIYQTsAhogbHlbpCU8pTpnP8Cl1ZOfFdK+ZVpe11gKeAN7AMel1Je90+xmkVrufQ5OcRH90fqS6kbE4Otl5fWJd1xB9MO8t6u9zicfpjmfs2Z0HYCTX2qbyumolxLdlE2nx74lGV/LcPJzonhdw3nscaP4WBbvVbTytm6lTPPj8Dn2WfwN93loSqUlJRw5swZCgsLq+w1rJ2TkxNBQUHY21/+i/r1ZtFWWcCzRCrgWa6zo17h4saNhCxahEtkC63LuaPS8tOYFTuLNXFr8HP2Y1SrUfQMVTNjFeVG4rPjmblnJr+c+YUgtyBGtRpFl9pdqtXfneRx48hev4G6q1fhWFctKWNpVMCrIBXwLFPub7+R9PRwfF98Ab/nn9e6nDumWF/Mt8e+Zd6BeZQYShjSZAhP3/U0rvbVe2KJotysnck7mb57OiezTtIyoCVjW4+liU8Trcu6I0rT04nr3gOnxo2p/fVX1SrcWgMV8CpIBTzLI4uLOdWnL0hJ6No12DhUjy6WX8/8yrTd00i8mEinoE6MaT2G2h61tS5LUSxWqaGUmBMxfLzvY7KKsuhbvy8vtngRPxfrX10hc+lSzk96i1rTp+PZu5fW5Sg3wdwWOlaUSpOx6FuKExIIeO3VahHuErITeH7L84z4vxEIBJ92+ZSPOn+kwp2i3CY7Gzsebvgw66LXGdfRO7WOnit7Mv/gfApLrXvsmNfAgTiFh5MydSr6ixe1LkepJCrgKRarJDWVC598glunTrh17Kh1OVUqtziXD/Z8QNSaKPal7mN0q9HE9InhnsB7tC5NUayKh4MHr7R6hdV9V9O+Vns+2vcRg9cP5lT2Ka1LqzLC1pYak95En5FB2qzZWpejVBIV8BSLlTbzA2RJCQETxmtdSpXadW4XfVb14asjX9G7bm/WRq1laNOh2NuqZU8UparU9qjNrPtnMbfzXC7kX2DQukGsO7XuxidaKOemTdENHkzmkiUUHDp84xMUs6cCnmKR8vftI3v1arz/8x8cQkK0LqdKGKSBzw99zvDNw3F3cGdJzyW83eFtfJ19tS5NUaqNe4Pu5YfeP9DYuzETfpvApJ2TrLbL1u+lF7H19eH8pElIvV7rcpTbpCZZlKMmWVgGqdeT8PAjlKalUW/DemxcrW/W6MXii7y2/TW2JW2jW51uvNX+LXXfWHNnMEBhFuRdgPwLkJdmep4OpYXgrANnb3DxvvzR2QtsbLWuXrmBUkMpc/fPZcGhBYTpwphx3wzqelrfsiLZP/5I8iujCXh9It6PVc8F4y3J9SZZ3NatyhRFC1kxMRQeOUKt6dOtMtz9lfEXo7aN4lzuOca3Gc/gRoPV0gVauF5gK3t+wfh4abu8RquHsL32PgQ4ef47+JU9XiMYOqjAfyfZ2djxYuSLtAxoyYTfJjBo3SDeuPsNetW1rlmnHj16kL1iBWmzZuPRtSt26h7tFku14JWjWvDMnz47m7hu3XGoW5eQbxdZXfBZdXIV7/zxDp6Onsy8byYR/hFal2R9CrIg9ejtBTYnT3DxBVdfcPUDF59yz33B1eef5y4+YGsPRRchPwMKMiA/0/SYcY1H0/7i3Gu/DzuncoFPd3kA9AyG4Lbg1whs1EicypaSl8LYX8cSmxpL/7D+jG8zHie7m7uNlDkrio8nvk9f3Lt1I3D6NK3LUa5DteApViPt40/QZ2dTY+JrVhXuivRFvLfrPVacWEHbGm2Z2nEqPs4+WpdlHXLT4PROSNwJiTvg/GGMd1Aspyyw+YF3XQhqbXzu6lsuyPn+E9jsbmFJHidP4xehFT+ntAgKMm8cBPMzIPWYaXvmP8HUydMY9ILbQu12UCtStfxVggDXAL546IuyLtuDFw5aVZetY2goPk8/xYW5n+LVPxrXdu20Lkm5BaoFrxzVgmfeCo8fJz4qGq+HB1LzzTe1LqfSnMk5w6htoziWcYynw59mRMQIbNWYrFuXlfRPmDv9O1w4btxu5wzBbSCkPQS2AveA2wts5spggMx4SNplfP+nd8GFv437bOygZoQx7F0KfW7+2tZr4Xac3cGE3yZQqC+0qi5bQ2Ehp3r3QdjZEbp6VbVYZ9QSqTtZVJAKeOZLSsnpYf+h8K+/qPfTBux0Oq1LqhS/nvmVCb9NQErJlHun0Cm4k9YlWRYpIT3OGOYSTa102aeN+xw9jQEmpD2EdICaza0ryN2M/AxI+tMY+JJ2wdlY0BcZ93nXheB2ULut8dG3gerWvUnW2mV76TaQfiNfwvfZZ7UuR7kKFfAqSAU883Xxp42cHTmSgDdex3vwYK3LuW16g55PD3zKvIPzaKhryIedPiTYI1jrssyfwQCpR/4Jc4k7IS/VuM/Vzxjmarc3PgY0VbNTr6W0CM4d+KeFL+kP45hDMI7nu7Jb197yw0pVs9ZZtmdeGknutm3UXbcWh2D1b5S5UQGvglTAM0+GggLievbE1sOT0BXLEbaW/aGdWZjJ+N/GszN5J33r9WViu4lW8dt+ldCXGIPIpRa6079DYbZxn0cQ1OnwTwudT32wonGZd9SlltDTvxvD3uldkH7CuM/WwdSta2rhq93OOB5RuSpr67ItSUnhVPceOLduRfBnn1nV2GdroAJeBamAZ57S5nzEhblzCVm0EJfWrbUu57YcSjvEqF9GkVGQwattXyU6LFr9g1leSQGc3fvPGLqkP6Ek37jPJwxC7jaGuZD24KXuv1ul8i6YxvH9YXxM3gf6YuM+n/r/dOvWvluF6yuk5KUw7rdx7E3ZS3RYNOPbjMfZzlnrsm5Z+ldfkzp1KoEfzcHjwQe1LkcpRwW8ClIBz/wUnznDqR49ce/ShcAPZmpdzi2TUvL939/z/u73CXAJYGanmTT1aap1WeYh5SgcXg4JOyA51hQiBAQ0M7XOmb7UZABtlRTCuf3lunV3GWfwgjFsRzwOLR4DzyBt6zQT5bts63vVZ2anmRbbZStLS4nvPwB9djb1flxnleuPWioV8CpIBTzzc+aFF8jdvoN6G9ZjX6OG1uXckvySfCb/MZl1p9Zxb+C9vHfve3g6empdlrZKCuDIKtj7lTEoXJrdeam7tXZb41gwxXxJCRdOGJegORwD8b8AAup3gcgnoEH36juppRxr6bLNj91H4uDBeP/3vwSMHaN1OYqJCngVpAKeecndsYOkJ5/Cb+RIfJ99RutybklCdgIvb3uZuKw4RkSM4Om7nsZGVOMZiml/w56v4MAS410ifOpDy/9AxGDjAr2K5cqIh/2LYd9iyEk2LkHTfBBEDgG/hlpXpylr6bI99/rrZMWsJDQmBqeGDbQuR0EFvApTAc98yJISTvWLQpaUUHftGmwcHbUu6aZtSdzCxB0TsbexZ2rHqbSv1V7rkrRRUgjH1hiD3emdYGMPTfoYg12de9TYLWtj0MPJ/4PYb+D4T2AoNc7IjRwCTfqBo5vWFWriUpft54c+p55XPYvssi3NzORUj544hIYa7ySkltPRnAp4FaQCnvlI//prUt+fStDcubg/cL/W5dyUUkMps2Nn8/WRrwn3DWfmfTOp6VZT67LuvAsnYO/Xxladgkzjemsth0HEY2oWZnWRm2psrY1dZJyV6+AGzfpD5FAIjKyW4d7Su2yzVsRw7rXXqPnuu3j1j9a6nGpPBbwKUgHPPJReuEBct+44t2hB8Px5FjXL9ELBBUb/Mpq9KXt5pOEjjG09FgfbajQOqbQIjq01BruE34xj6xr1glb/gTod1QK61ZWUxtm4+xbBkZXGmdH+TYytenc9Uu265y25y1YaDCQ+/gTFp05Rd8N6q1l03lKpgFdBKuCZh+TXXiN7zVrqrl6NY92buG+nxvam7GX0L6PJLc7ljbvfoHe93lqXdOekxxknTOz/zrhgrq6OsZWmxeNq9qtyucKLcHgFxC40zpq2dTD+EhD5BIR2qja/BFhyl23h38eJj47GKzqKmpMna11OtaYCXgWpgKe9goMHSXj4Ebyf/C8BYyxjppaUkoVHF/Lh3g8Jcg/ig04f0EBXDQYglxbDX+uMwS7+VxC20KiHcWxd3furzQe1chvOHza26h1Yapx0Uw2XW7HULtuUadPJ+PJLQr77DpfIFlqXU22pgFdBKuBpSxoMJAx6lJJzydTbsAFbN/MfjJ1bnMsbO99gc+JmutTuwuQOk3FzMP+6b0tG/D9j6/LSwLM2tBwCLZ4Ad8tcykbRWEmh8ZeF2IXlllvpbOzCrQbLrVhil60hL4+4Xr2x9fAw3mHIzk7rkqolFfAqSAU8bV0avFtr6vt49u2rdTk3dCLzBKO2jSIpJ4mXW77MkCZDLGq84E3Rl8Df640zYU/9bGyta9jd2FpX7351z1el8lTT5VbKd9k292vOx50/Nvv1Mi9u3szZF17Ef/w4fIYN07qcakkFvApSAU87+pwc4rp1xyE4mJDvFpv99PsfT/3IW7+/hau9K9M7TqdVjav+/bJ8mYnG5S72fQu5Kcb7v7Y0ja3zqKV1dYo1u9ZyKy2egGbR4GCdd1PYmLCR8b+Np65nXeY9OA9fZ/OdcS6l5Myzz5G/ezd11/9osYvRWzIV8CpIBTztpLw/lYxvvqHODz/g3My8b+G18MhCpu+ZTqR/JDPum4Gfi5/WJVUugwGOb4A9Xxo/YIWAsIeMM2Hrd1Gtdcqdd+VyK24B0GmCMezZWl/X4M6zOxm5bSS+zr7Mf3A+Qe7mOx6xOCmJU71649apE0GzZ2ldTrVzvYBn3s0kSrVQFBdHxrff4jVggNmHu/kH5zN9z3S6hnTl866fW1+4S9gBC+6HpYON94i9bxyMPASDl0KDh1S4U7Th5g8dXoL/7Yah60AXCutGwqd3w18/GpdhsSLtA9sz/8H5ZBdlM2TDEE5kntC6pGtyCA7G97lnydm4kdzfftO6HKUc1YJXjmrBu/OklCQ9+SQFh49Q76cN2Hmb53pYUko+3v8x8w/Op3fd3rzd4W3sbKyo5SDjFGx+w7iGnUcgdH4Dmg2wytYRxQpIaQx2WyYZW/Rq3w0PTobg1lpXVqlOZJ7gmc3PUKQvYm6XuTT3a651SVdlKC4mvm8/ZGmp8c5DTk5al1RtqBY8xWzlbNlC3s7f8XvhBbMOdzP3zGT+wfn0D+vPO/e8Yz3hriALNk2ET9rCya1w/0T43x7joHYV7hRzJQQ07gXP/wG9PjSuw/hFF1j2BFw4qXV1lSZMF8Y33b/Bw8GDpzc9ze/Jv2td0lXZODhQ4803KElKIn3+fK3LUUxUC145qgXvzjIUFnKqZy9sXJwJXbnSLKfZG6SBKbumsOzvZTza6FHGtxmPjbCC34v0pcb1636eYryNWMRj8MBE8KiGt1RTLF9RLvz+CeyYDfoi4y3x7htnNYtsp+Wn8cyWZ0jITmBqx6k8GPKg1iVd1dkxY8n56SdC16zGMdRyFqm3ZKoFTzFL6V9+ScnZswS8NtEsw53eoGfSzkks+3sZ/2n6Hya0mWD54U5KOLEZPm0P60dDQFN45hfo94kKd4rlcnSDTuPgpf3GO6js+QrmtIBfpkFxntbV3TY/Fz++eugrmvg0YfQvo4k5EaN1SVcVMHYMwsmJlMmTUY1H2rPwTyvFUpWcPUv6/AW4d+uGa7u2WpfzL6WGUl7d/iorT67kuebP8XLLly1/jbuUo/BtNCweAIYSGPQdDF0LNc1zXI+i3DQ3f+j1AYzYZVyf8ed3jUFvz1fGVmsL5unoyfwH53N3zbt5c+ebfHX4K61L+hc7Pz/8Rr5E3s7fubh+vdblVHuqi7Yc1UV755wZ+TK527ZR78d12AcGal3OZUr0JYz7bRybEzfzUuRLPBX+lNYl3Z7cNOMHXew34OgO942H1k9Z/d0B7oTCEj2Z+cWk5xaTmV9MRp7xKzOvmPS84rJ9RaUGvFzs0bk44OlsfPRysTd9OeBl2ubpYo+Hk53l/zJhLk7vMk4eSvoDfBtAl0nQsIdxDJ+FKtGX8Or2V/kp4SeebPYkL0W+ZFZ/XqReT8IjgyhJOU+99euxdXfXuiSrdr0uWvPrF1OsXt4fu8j56Sd8X/if2YW7In0Ro7aN4tczvzKu9Tgeb/K41iXdupJC2PUp/DoTSgugzXDjuCQX85zMojWDQZJdUEJGfrmAVu4xI6+YjCtCXF6x/qrXEgJ0Lg54uzrg7eKAu5Md6bnFxKXlkpVXQk7RtVuTbG0Ens72eDmXC4Au9ng5O5hCoj2eLg7oym3zcrHHzVEFw3+p3Rb++9M/M26XDrb4Gbf2tva8f+/7uDu488XhL8guzmZi24nYmskSRsLWlhpvvknCww+TNnsONSa+pnVJ1ZZqwStHteBVPVlaSnxUNIb8fOr+uM6sptMXlBbw0taX+P3c77ze7nUebviw1iXdGinhyErY8iZknTbey7PrZPAN07oyTekNkuMpOexJzOSvcxfJuCK8ZeYXY7jGP4cuDrboXBzwcXMwPro6oHM1BThXh8v2ebsaW+lsba4dtkr0Bi4WlJCZX0J2QTGZeSVkFZSQlV9MVn4JWQXFxn35JWSatmUXlJB7nWBoZyPwcrHH09meQJ0LLYK9iAzRERHshaez/e3++CyfvhT2LYSf34O8VGjcBzq/Cb71ta7slkgp+WjfRyw4tICuIV15/973sbc1n//P59+eTObSpdT54Xucm5r3+qaWTN3JooJUwKt6GYu+JeXddwn8aA4eD5rPTLC8kjxG/N8I9qXu4+32b9O3vvnfC/eqzuyFjRMgaRcENIOH3oW6nbSuShMFxXr2J2WxJyGDPYmZxCZmlrWcebnY4+fmWBbQrhXULn052ZtH60hxqYHsS0GwoITMPONjWRA07TuVlsfxlBwM0tiaWN/PjcjaOiJDvIisraOenxs21wmgVs3KZtx+c+QbZuyZQfta7fmw04e42LtoXRIA+osXievRE/uaNamzdAnC1jz+DlkbFfAqSAW8qlWakUFct+44N2tG8Befm0130sXiizy35TmOXDjC+/e+T7fQblqXdPOykuD/3oJDP4CrP3R+3bj0iZl029wJqTmF7E3IZE9iJnsSMjiSfJFSU5NcwwB3WtbR0bqOjlYh3gTpnM3mz19VyS0q5UBSFrGJmcSezmRfUhZZ+SUAeDjZEVFbR2RtY+CLqO2Fh5P5tP7cEbmpsO192Ps12DtD+xfh7hHGGbkWZuWJlUz6fRLhvuF80vkTPB09tS4JgOy1a0keM5Yak95EN2iQ1uVYJRXwKkgFvKp17vU3yFq5krqrV+FYr57W5QCQVZjF8M3DOZF1ghn3zaBz7c5al3RzinJhxyzY+ZGxa7b9/+Cel42TKayYwSA5dSGX3QmZ7EnIZE9iBonp+QA42tnQPNiLViE6WtfxJrK2Dk+XahZerkJKyakLeabAl8W+05n8nZKDNLXyhfmbWvlMLX11fatJK9+FE8Zfjo6tteh73G5J3MLYX8cS4hHC/Afnm8VtFKWUnP7Pfyk8epR663/EztdX65Ksjgp4FaQCXtUpOHyEhIED8R4yhIAJ47UuB4ALBRd4etPTJOUk8WGnD7k36F6tS6o4gx72L4at70BuCoQPNN5ezKu21pVVicISPYfOZhvDXEIGe09nlrVG+bg60NIU5lrW0dGslicOdmoFqIrIKSzhQFI2sadNrXyns8guMP5cPZ3tiQj2Kgt8EcFeuFtzK58VzLj9Pfl3Xvr5JbydvFnw4AKCPYK1LomiU6c41bcfnj26U2vqVK3LsTpVFvCEEAOBSUBjoI2Uck+5fROAJwE98KKUcqNpe0vga8AZWA+8JKWUQghHYCHQEkgHHpFSJpjOGQpMNF36HSnlN6btocBSwBuIBZ6QUhYLY9/LbKAHkA8Mk1LG3uj9qIBXNaTBQOLgxyhOSqLeTxvMYtp8Sl4KT216ipT8FD564CPa1jS/tfiu6dQvsPE1SDkEQW3goSkWOyPwWjLyitmbaGyZ25OQyaEz2RTrDQDU9XOlVYiOVnW8aRWiI9TX1eq7W++USy2jsYlZZaHvRGpuWStfA393IkO8aGFq6avnZ2U/eynh7/WwZu6IPAAAIABJREFU+U2LvcftwbSDPP9/z2NvY8+8B+fRQNdA65JInTWL9M/mUfubb3Bt20brcqxKVQa8xoABmAeMvhTwhBBNgCVAG6AWsAVoIKXUCyH+BF4C/sAY8OZIKTcIIZ4H7pJSPiuEGARESSkfEUJ4A3uAVoAE9gItpZSZQojvgRgp5VIhxGfAASnlp0KIHsALGANeW2C2lPKGn+Aq4FWN7NWrSR43nprvvotX/2ityyE5N5knNz5JZlEmczvPJTIgUuuSKubCSdj8uvEDyLM2PDgJmkZbVAvDtSRcyGN3QkZZd2tcmvHuA/a2gvBAT2PrXIiOliE6fNwcNa62erlYWML+05cCn7FrN6fQOFnF09meFrW9uKe+L30jAvFzt5L/N1fOuG0+GLpPBScPrSurkJOZJ3lm8zMU6AuY23kuEf4RmtZjKCzkVK/eCEdH6q6MQTioNTgrS5V30QohtnF5wJsAIKV8z/T9RowtfQnAz1LK/2fvvqOqOrYHjn8PvQlIV8Heu4KKvceW2HuPRmLvRo0tRaOxa+wttmiM3ViiGHsXrLFjB5Xe64V7fn/cm/fj+YwinFsg81mLtVj3Mns2Lw/ZzJnZU1b7eg+gkSzLX/79NbIsX5QkyQx4A7gC3f/+Gu2Y1cApNCt34YCHLMvpkiTV1o5v8ffXyLK8XTvmgTbG6/d9H6LAU15GQiKPW7XEvEBBzUkqE8M+OnsR94KBxwaSqEpkdbPVVHKtZNB8siQtUfMo9soaMLOG+mPBd4hmY3guFpOUxv4br9hx9SV3X8cBmoLBu0h+fLSHISp7OhjNCVZBQ62WeRyeoCn4nscQ+CKaoLAETE0kGpV2pZO3J03LuWFplgf+u6UmwLmFcG4ROHhCh9VQpI6hs8qSkIQQ/I75EZ4czqJGi6hbqK5B80k4fZqXXw7GdexYXPwGGTSXvMQQjY4LoVmh+1uw9jWV9vO3X/97zEsAbcEWCzhnfv2tMc5AjCzL6e+L9dZ77y3wBOVFrFxBRngEXsuXG7y4exLzhC+OfUG6Op0NLTZQ1qmsQfPJkte3YNcAiAyC6n2hydRc284BNMXB+ccR/BYQzNE7b0hLV1OxkD0zPitP3ZIulPw3t+/IJUxMJEq556OUez661dDs+XwUGs/uayHsvR7Mn/fDcLA2p22VgnTy9qSKp0PufYxraafZ21q6Jezxg59bQ73R0Ohro78JppBdITa12sRg/8EMPzGcOfXn0KJoC4PlY9ewIfmaNyNixQrsW7fGwtO4mtznRR8s8CRJOg54vOOtKbIs7/+nYe94TX7P69kZk51Y/0OSJD/AD6Bw4by5Qd1QUp88JWrzFhw6dsS6cmWD5vIg6gF+/n6YSCZsaLGBkvmNvLmpLMPl1ZpHstZO0Hc/FG9o6KyyLTg6iV2BwewMCCYkJhkHa3N61ixMFx9PKhQ0jpYOQvaVcs/HpFZlmdCiDOeCItgdGMxvAS/Zcuk5JVxt6eTtScdqnng4GE9j84/iVRMGn9P0mDy3CIKOQ8e14FbO0Jm9l4u1CxtabmD4n8OZcHoC8WnxdC7d2WD5uH/9NQmt2xA6ZzZey5YZLI9/iw8WeLIsN8tG3GAg8/EdT+CV9nXPd7yeeUyw9hGtAxClfb3RW2NOARGAoyRJZtpVvHfFetc8/0WW5TXAGtA8ov2I71F4D1mWCZ09GxNLS9zGjjFoLnci7uDn74e1mTXrPllHUYeiBs3ngxIjYN9QeHRUs3LQbgXYOhs6q4+WosrA/24ovwW85FxQBAD1SrowsVVZPinvLh695kGmJhINS7vSsLQrcSkqDt16ze7AYOb+8YD5Rx9Qt6QLnb09aVHBI/f997e0g7Y/aW6GOTACVjeE5t9CzS/BwE8n3sfewp7VzVcz9tRYvr34LbGpsQysNNAguZgXKIDL0CGEL1hIwunT2DXMvX+05ga62oNXAdjG/x+y+BMopT1kcRXNAYjLaA5Z/CTL8mFJkoYBlTIdsugoy3JX7SGLQODvnfDX0ByyiJIkaSewO9Mhi1uyLK+QJKkNMJz/P2SxVJblDx7dEXvwlBN/4iTBQ4fiNmkizv37GyyPG2E3GHJ8CA6WDqz7ZB2e+Tw/PMiQnpzWPApKjoJPZmruj81lj7fuvIplZ0Awe6+HEJusopCjNV18POns7YlnfuPosi/o17OIRPZcC2b3tRBCYpLJZ2lGm8oF6OTtiU+R/LnvEW5CGOwfrvkjrHhjaL8C7AsaOqv3UmWomHJ+CkeeHuHzCp8zxnuMQf53l9PSeNK+A3J6OsV/P4CJZR45mGMgujxF2wH4Cc1hiBjghizLLbTvTQEGAOnAaFmWj2hf9+H/26QcAUZo26RYAVuAamhW7rrLsvxEO2YA8LV22lmyLP+sfb04/98m5TrQW5blVG2blGVASzRtUj7P3MLln4gCTxnqtDTNiSlzc4rv24tkbpjeWVffXGXYn8Nws3Fj3Sfr8LB9104DI5GhgpM/aB7/uJSCzhvAIxccANGKTVJx4GYIOwJe8ldIHBamJrSo6EFXH0/qlnAR++oEQLMH89LTSHYHhnDkr9ckpWVQ1NmGjtU96Vi9UO76A0CWNbdgHP0aTC3g00VQ0fBdAt5HLav54fIP7Hiwg46lOjLddzqmBrjtJvHiRV58PgCXEcNxHTZM7/PnJaLRcRaJAk8ZkevXEzZvPl5r12JXv55Bcjgfcp5RJ0fhaefJ2k/WGkVX938U/Qx2DYSQAM1BipZzwMLW0Fl9kFotc/FJJL8FvOSPv96Qmq6mfAF7utXwol3VgjjaGPcmdMGwElPTOfLXG3YHBnPxSSQAtYs708nbk1YVPbC1zCU3SUQ+1qy6hwRA5W7Qeh5YGe++UlmWWXZjGWturaF5kebMqT8HC1P9/6yGjB1L/J8nKH7wdyy8DN+QObcSBV4WiQIv59IjInjcoiU2Pj54rV5lkBxOvjjJuNPjKOFYgtXNV+Nk5WSQPLLk9i44OAaQ4LPFRr8CABASk8yugGB2Br4kODoZeysz2lcrRFcfLyoWMt5fbILxehmVxN7rIey+FszzyCRsLExpVbEAnbwL4VvM2fhXgDPS4ex8OD1X86i2wyooapg/brNqy90tzL06F98CvixpvAQbc/2unqpCQ3ncqjW2tWrhtXKFXufOS0SBl0WiwMu5/9w3e+AAlsWL6X3+o8+OMunMJMo5l2Nls5VGc+n2/0hNgCMT4cZWzW0UndZB/iKGzuofpab/fWAimLOPwpFlqFvSma4+Xrlzw7xglGRZJvB5NLuvBXPw5mviU9Mp5GhNp+qF6Fjdk6IuRr6yHRygWc2LeqK5F7rJNDAz3j1m+4P2M/3CdHwL+LKsyTLMTfW7nSZy/QbC5s3Dc8UK8jVprNe58wpR4GWRKPByJuXePZ527IRT3z64T56s9/l/f/w7U89PpaprVZY3XY6dhZ3ec8iS1ze1ve0eQ4Px0HCS0V5sfu91HL8FvGTf9RCik1QUdLCis48XXbw98XLKRfulhFwnRZXB0Ttv2H0thHOPwlHL4FMkP528PfmsSkHsjPURblqi5irBwJ/BvSJ0XAPuFQyd1T/a+2gv0y9Mp1XRVsxpMAcTSX8ngmWViicdOiCnpFL84O+YWOXSNjoGJAq8LBIFXvbJssyLfv1JffiQEkf/wNRBvytnux/u5tuL31LToyZLmyzV++OGLJFluLQSjs8AG2dNH61i9Q2d1f9IUWWw+1owO66+5FZwLBamJjSv4E43Hy/qlnTB1Ngflwl5zpvYlP88wg0KS8DRxpzBDUvQr3ZRrC2MdPX4wR9wYDikxELTGeA71GjbqWz4awOLAhfRo2wPJtecrNfTtYmXr/CiXz9chg7FdeQIvc2bV4gCL4tEgZd9cf7+hIwYifv0aTj17KnXuf2f+zPu1DjqFKrD4kaLsTIzwr8CE8Jh/1B4dAzKtIa2y4yut12GWmbPtWAW+T/kVWwKZT3y0a2GF+2rFiK/rTgwIRieLMtcexHD0j8fcfphOK75LBneuCTda3oZ59VoCeHw+0jN/dHFGkD7lZorz4yMLMssCFjAprubGFp1KEOqDNHr/CHjJxB/7BjFfz+ARRHj3apijESBl0WiwMsedVoaT9p8imRpQfF9+5DM9Pfo5EbYDb449gVlncqy7pN1xlncPT4Je7+E5BhoMQtqfGFUve1kWebE/TB+/OM+D0MTqOLlyMSWZahd3Dn39ScT/jWuPoti3tEHXHkaRSFHa0Y1LUXH6oUwMzWyVTJZhutb4Ih2K0abhVDJcLdJ/BO1rGba+WkceHyAqbWm0q1sN73NrQoN40nr1lh7V8dr9Wrx785HeF+BZ2Q/CUJuFL15M6qXL3GfNFmvxd2LuBeMPDESNxs3ljZZanzFXYYK/GfAlg5g5QiDTkDNQUZV3AU+j6Lr6osM3BRAeobMyl7V2Te0DnVKuIh/ZAWjVqOoEzv8fNk8oCYudhZ8tfsWnyw6w4Gbr1CrjWjhQpI07Y+GnAOXMrB7oKYtUnK0oTP7LyaSCd/U+YaGng2ZdXkWfzz7Q29zm7u74TJiOIlnzpJw4oTe5s3rxApeJmIF7+P9py1KjRp4rVqpt3mjU6Lpc6QPsamxbG29lSL2RrasH/UEdn8BIYHg3R9azAYL49kXGBQWz9w/HnDsbiiu+SwZ3awUXX28MDe21Q9ByAJZlvG/G8qCYw95EBpPWY98jPukDM3KuRnXHyoZ6Zpm5qfngJ275pGtkd0xnZyezGD/wdyKuMWKpiuoXbC2XuaVVSqeduyEOjGR4ocOYmJtrZd5czvxiDaLRIH38QzRFiUlPYVBxwZxN/Iu61usp6pbVb3Mm2W3dmp625mYwGdLoUJ7Q2f0H29iU1h8/CG/BbzExsKMwQ2LM6BeMWwsjPREoiB8BLVa5vdbr1h8/BFPIxKp4uXIhE/KULekkW03CLmmaacS+Qh8h0HT6WBuPE8g4tLi6P9Hf4Ljg9nQYgMVXSrqZd6kq1d53qcvzoO/xG30aL3MmduJAi+LRIH3cQzRFkUtq5lwegL+z/2Z33A+nxT9RC/zZklqAhyeADe3gZcvdFoLjoUNnRUAsckqVp1+zIZzT5Fl6FO7CMMal8RJHJ4Q8qD0DDW7rwWz5PgjXsWmUKuYExNalMGnqBE1PU9LAv9pcHUduJXXtFMxousJw5PC6XOkD4mqRDa12kRxh+J6mffVxInEHT5CsQP7sSym/16quY0o8LJIFHhZJ8syL/r2I/XRI722RVkQsICNdzYy3mc8/Sr008ucWfLqumZfTfRTaDABGnxlFL3tUlQZbL74jOUnHxOXoqJD1UKMaV5a9LAT/hVS0zPYfvkFy04+JiIhlUZlXBn/SRnjunHlkT/sH6bZk9dkKtQeDga4H/ZdXsS9oM+RPliaWrK51Wa93OedHh7O41atsa5SBa91a41r5dUIiUMWguLi/f1JunoVl5Ej9Fbcbb+/nY13NtKjbA/6lu+rlzk/SK2GC8tgXXNQJUO/36Hx1wYv7jLUMjsDXtJk/il+OHyfaoUdOTSiPgu7VRXFnfCvYWlmSv+6xTjzVSMmtizL9RcxfPrTOYZsDeRRaLyh09Mo1RyGXITSLcB/OmxqC7Ehhs4KgML2hVnVbBVxaXEM9h9MTEqMzuc0c3XFdeRIEs+fJ97fX+fz5WViBS8TsYKXNeq0NJ60boOJtRXF9u7Vy8nZUy9PMerkKBp4NmBxo8WYGsNfuAlhsG8IBB2Hsp9C25/AxrCPgP6n5YmnAxNblaVOCReD5iUIxiAuRcW6s09Zf/YJyaoM2lctxOhmpSnsbAR/9Mgy3NgGR74CCzvosR0KVTd0VgBcfXOVwf6DKetclrXN1+q8kbycns7TTp3JiIujxKGDmNgYwX8fIyUe0WaRKPCyJnLdOsLmL8Br/Trs6tbV+Xx3Iu7w+dHPKe5QnA0tNhjHLRVBf8LewZou9S1/AJ+BBm9/Evg8ijlH7nP1WTTFXGyZ0KIMrSp6iEccgvCWqMQ0Vp1+zKYLz8hQy3St4cWIJiUp4GAEJzdD78K2bpAYDh1XQ/l2hs4IgD+f/8nY02OpXbA2PzX+Sef31iYFBvK8V2+c/fxwGztGp3PlZqLAyyJR4H2YvtuihCSE0OtQL6zMrNjaeisu1kawEnV5NRyZCK5loPMGg98zmbnliYudpuVJtxqi5YkgfEhoXArLTwax/coLJEmij28RhjQqgYudpWETSwiDX3tC8FXNCdt6Yw3+ByRoroT85uI3tC7Wmtn1Z+v83tpXkyYTe+gQxffvw7K4fg555DaiwMsiUeB92Otp04jZu4/ivx/Q+Qmn2NRY+hzpQ2RyJFtabaG4o4F/wNVqzT2yF5ZCmTbQaZ1Be9uJlieCoIyXUUks/fMRu68FY2VuyoC6xRjUoDgO1rpdpXovVYrm8MVfu6BKT/hsMZgZuPAE1t1ex5JrS+hVrhcTa0zU6ROC9MhIHrdshXWlinitXy+eRrzD+wo88ZtAyLKUu3eJ2bUbp759dV7cpWWkMfrkaILjg1nTfI3hi7v0VNg3VPOPrc9AaD3PYCfdMrc8Ucsy/esUY3gT0fJEELLLy8mGeV2qMLhRCRb5P2TZySA2X3zGlw1L0L9OUWwtDfCr0txK80ekS2k49QNEP4NuWw1+h/XAigOJSoliy90tOFk54VfZT2dzmTk74zp6FKHfzyT+6FHsW7bU2Vx5kVjBy0Ss4P2z/2qLcuwopvb2OptLLauZfHYyh58e5sf6P9K6eGudzZUlyTGwozc8OwtNZ0C9MQZ5XPJ2y5P2VQsxVrQ8EQTF3X0Vx0L/Bxy/F4azrQXjW5Shew0vw60g3d6l+QPTviD0/A1cSxsmDy21rGbquan8/uR3pvlOo2uZrjqbS87I4GmXLmRERlHi8CFMbG11NlduJNqkCDn2d1sU11EjdVrcASy7vozDTw8zqvoowxd3scHwcyt4cQk6rIH6+t8Lo1bL7AoM/k/Lk6pejhwcUY9FouWJIOhE+YL2rOtXgz1D61DCzY7Je27TZ/0VgqOTDJNQpc7Q/xCkJcC6ZvD4pGHy0DKRTPi27rc08GzAzEszOfbsmM7mkkxN8Zg2jfTQUCJW6u86zLxArOBlIlbw3k2dmsqTNp9iYm1Nsb17dNoWZdfDXXx78Vs6l+7MdN/pht1zEXoHtnaG1HjovhWKN9J7Cm9iU5iw6yZnH0WIlieCYABqtcy2Ky+YffgeAJNal6NXzcKYmBjg36aYF5oTtuEPoM188Bmg/xwySU5Pxu+YH3ci77Cy2UpqFails7leTZlC7P4DmgMXJUrobJ7cRqzgCTkStXkzquBg3CZN1Glxdzb4LDMvzaReoXpMqTXFsMXd0zOwoSUgw4AjBinu9t8I4ZNFpwl4Fs3M9hXZN6yuKO4EQc9MTCR6+xbh6JgGVCucn2n7/qLXusu8iDTAap5jYRhwFEo00dx3/cdkUGfoPw8tazNrljVdRhH7Iow8MZI7kXd0NpfbuHGY2Njw5rvvEQtTWSMKPOG90sPDiVy1GrvGjXXa8+5+1H3Gnx5P6fylmd9wPmYmBjz/c3sXbOmo2e8y0F/v90PGJKUxfNs1Rv16g5JudhwZVZ/evkXECTJBMCDP/DZsGViTOR0rcTsklhaLz7Dx/FPUaj0XG1b20ONXqDUELq2A7T00TxkMxMHSgdXNV+No6cjQ40N5FvtMJ/OYOTnhNmY0SZcvE3f4sE7myGvEI9pMxCPa//Vq6lRi9x+gxO8HsChaVCdzvEl8Q69DvTAxMeGX1r/gZuOmk3k+SJY1LVD8p0ORutD9F7DOr9cUTj0I46tdt4hOSmN0s9J82aA4ZqKfnSAYlVcxyUzec5vTD8OpWcyJuZ0qU9TFAJv/r66Dw1+Ba1nouQMcvfSfg9bzuOf0PdIXK1MrNrfajLutu+JzyBkZPOvajfSwMIofOYKpnThwIR7RCtmScvcusbv34NSrl86Ku/i0eIYcH0JSehIrmq4wXHGnztA0L/afDhU6QO89ei3uktLSmbL3Nv1/voqjjTn7htVlWOOSorgTBCNU0NGajZ/XYG7nytx7HUfLJWdYd/YJGfpezavxBfTaCbEvYW0TCDbcAkUR+yKsbLaS2LRYBh8fTGxqrOJzSKameMyYTnpEBBHLlyseP68Rvz2Ed5JlmdAfZmPq6IjL0CE6mUOVoWLMqTE8i33GosaLKJW/lE7m+XAiybCzH1xZDbWHQ6cNmh5UehL4PJrWS86y7coL/BoU58DwelQo6KC3+QVB+HiSJNHVxwv/MQ2pU8KFmYfu0XX1RR6HJ+g3kZJNNVtJzK1hYxv4a7d+58+kvHN5ljZeyvO45wz/czjJ6cmKz2FduTKOnTsTtXkzKQ8fKh4/LxEFnvBO8cf8SQoI0FlbFFmW+ebiN1x+fZlv6nyDbwFfxefIkqQo2Nwe7h2EFrOhxSww0c+PRVq6mnlH79Nl1QVUGTLbB/nydetyWJkbpoGyIAgfz8PBivX9fFjYtQpBYQm0XnKWNWce63c1z60sDDoBBavBrgFw6kfNlhMDqFmgJnMbzOVWxC3GnRqHSq1SfA7XsWMwtbMj9PuZ4sDFe4gCT/gf6tRUwubNw7JUKRw7d9bJHKturuLA4wMMrTqUdiUNdJl29DNY/wm8ug5dfobaQ/U29cPQeDqsOM/yk4/pVN2TP0bXx7e4YTvUC4KQPZIk0bG6J/5jGtCgtCs/HL5Pp5UXCArT4+EHWxfoux8qd9fcfLFnkOa6MwNoVqQZ03yncTbkLNPPT0ctqxWNb5Y/P65jx5J09SpxBw8qGjsvEQWe8D+iNmnaorhPnqSTtij7g/az4uYK2pVox+DKgxWPnyWvbsC65pAYBn33afbd6YFaLbP2zBM+/ekcb2JTWNPHm3ldqpDPyoB3XgqCoAg3eyvW9PFmSfeqPI9MpPXSc6w4FUR6hrIFzj8ys4QOq6DJNLi9EzZ9Bgnh+pn7LZ1Ld2ZktZEcfHKQeVfnKb7S5ti5E1aVKhE6dy4Z8YY7RWzMRIEn/BdNW5RV2DVpgm2dOorHv/jqIt9c0DySnVFnhmFafwQd1+xVMbOEAcegiPLf57u8jEqix9pLzDp8j4alXTk6pgGfVPDQy9yCIOiHJEm0q1qIY2Ma0rSsG3P/eEDHlRd48EZPRYgkQYPx0GUTvLkF65pA6F39zP2WLyp9Qe9yvdl6byvr/1qvaGzJ1BSP6dPJiIgkYtkyRWPnFaLAE/5L2OLFqFUq3L+aoHjsh9EPGXtqLMUci7Gw0ULMTQywanX9F/ilK+QvptmY7FZW51PKssxvAS9pteQsd17FMa9zZdb08cbFzlLncwuCYBiu+SxZ2dub5T2rExydzKc/neWnPx+h0tdqXoX28PlhSE/VbEV5dFw/82YiSRITakygTfE2LLm2hN0PlT0AYl2pIo7duhK19RdSHjxQNHZeIAo84T+S79whds9enHr3VrwtSmhiKEOPD8XGzIYVTVeQzyKfovE/SJbh9FzYPxSKNdD8w2dfQOfTRiSk4rclkK923aJCQXuOjKpPFx8DXlouCIJetalcAP8xDWhRwYMF/g9pv/w8d1/F6WfyQt6awxf5i8K2LnB5jX7mzcREMuH7ut9Tr1A9vrv0HcefK1touo0ejWm+fOKGi3cQBZ4AaFaZwmbP0bRFGaLsvrhEVSLD/hxGfFo8y5stx8NWz48lM9Lh91FwchZU6QE9f9N0g9exY3fe0GLRGU4/DGdqm3JsH+SLl5ONzucVBMG4ONtZsqxndVb1rk5oXAptl51j8fGHpKXrYTXPwRMG/AGlWsCRCXBovObfRD0yNzFnQcMFVHKpxFdnvuJ62HXFYps6OuI2fhzJgYHEHTigWNy8QBR4AgDxR4/ppC2KSq1i3OlxBMUEsbDRQso66f6R6H9JS4Rfe8K1TVB/HLRfCWYWOp0yPkXFhJ038dsSiLu9Fb8Pr8cX9Ysb5nJyQRCMRsuKBfAf05A2lQuw+Pgj2i47x18hyjcE/h+WdpqbeWoPh6trYVtXSNHDvJnYmNuwvOlyCtgWYMzJMbxJfKNYbIeOHbGqUpnQufPIiNPT6mguIAo84f/bopQurWhbFFmWmXVpFudDzjPNdxp1C+nuLtt3SgiHjZ9CkD+0WQhNp2s2IOvQpSeRtFx8lt3XghneuCT7htWljIeeH0cLgmC08ttasKR7Ndb29SEyMY12y8+z4NgDUtMzdDuxiammz+dnS+Dpac2+vOhnup3zLQ6WDixtspSUjBRGnxxNSroybVwkExPNgYvoaMKX/qRIzLxAFHgCURs3oQoJUbwtyvq/1rP70W4GVRpEp9KdFIubJZGPYX1zCLsH3X6BGgN1Ol2KKoNZh+7SY+0lzE0ldg6uw/gWZbAwEz9igiD8r+bl3fEf04B2VQvy04kgPvvpHLeCY3Q/sXd/zVWM8a9hbVN4cVn3c2ZSwrEEc+rP4W7kXb69+K1i++asK1Qgf/fuRG/bRsq9e4rEzO3Eb59/OVVYGJGrV2PXtCm2tWsrFvfQk0MsubaENsXbMKLaCMXiZklwgKa4S42D/gehbGudTvdXSCxtl51j7dmn9KpVmMOj6uNdRH/32AqCkDs52liwsGtVNvT3IS45nQ4rLvDjH/d1v5pXvCF88admL/KmT+He77qd7y2NvBoxrOowDj45yOa7mxWL6zpqJKaOjpoDF2o9nVY2YqLA+5cLX7JE0xZlwnjFYt6Pus/089Pxcffhuzrf6ffE6P3DmseylvaaNiiePjqbKj1DzfKTQXRYcZ6YJBUbP6/BzPaVsLFQvjm0IAh5V5Oy7hwd04BO1Qux8tRjuq66yOtY5e9x/S8upTRFXoEq8Fs/uLNXt/O9xa+yH82LNGdh4EIuhFxQJKapgwNu48eTfP06sfv2KxIzNxMF3r+YLtqixKfFM/bUWBytHFnQaAFmJbjcAAAgAElEQVQWpro90PBfrq6HHb3ArZymuHMuobOpnkUk0nX1ReYdfcAnFTw4OroBjcq46Ww+QRDyNgdrc+Z2rsKq3t4EhSXw2U/nuPwkUreT2jhBn73gVVNzh+2tnbqdLxNJkphZdyYlHEsw/sx4XsS9UCSuQ/t2WFerRtj8+WTE6vcgibERBd6/lCzLhM6ejWn+/LgMHaJYzGnnp/E64TXzG87HycpJkbhZmBhOzIRDY6Fkc81jWTtXnU3nfzeU1kvPEhSWwJLuVVneszr5bfVYyAqCkGe1rOjB/uF1sbc2p9e6y2w8/1S3/d0s80GvXVCkLuz1gxvbdTfXW2zMbVjaeCkmkgkjT4wkUZWY45iaAxfTyIiJIXzJEgWyzL1EgfcvFX/0KMkBgbiOHIlpPmVOeW6+u5k/X/zJGO8xVHOrpkjMLDkxE87Mg+p9ofs2sLDVyTSyLLPq9GP8tgRQ0s2Oo2Ma0K5qIZ3MJQjCv1dJt3zsG1aXRmXc+Ob3u4zbeZMUlQ735VnaafqDFq0P+4bAtS26m+stnvk8WdBwAc/injH57GTUcs73zlmVK0f+nj2J3v4ryX/dUSDL3EkUeP9C6tRUwub+3RZFmdOt18OuszhwMc0KN6NP+T6KxMySM/Pg7HxNcffpEjDVzf631PQMJuy6xZwj92ldqQA7/GpTwMFaJ3MJgiDYW5mzpo83Y5qVZs+1EDqvukBwdJLuJrSwgZ47oEQTODAcAjbobq631CpQiwk1JnDy5UlW3lypSEzXkSMwdXbmzfff/WsPXOSowJMkqYskSXckSVJLkuST6fWikiQlS5J0Q/uxKtN73pIk3ZYkKUiSpKWSdge+JEmWkiTt0L5+WZKkopnG9JMk6ZH2o1+m14tpv/aRdqyF9nVJGztIkqRbkiRVz8n3mddEbdyE6tUr3L+erEhblMjkSMafHk8BuwJ8V1ePhyouLNOs3lXuBp8uBhPd/L0SlZhGn3VX2BUYzKimpVjWoxrWFqY6mUsQBOFvJiYSo5qVYl1fH55HJNF22XkuPI7Q3YTm1pqnIKVawMExcGWt7uZ6S8+yPWlfsj2rbq5S5DozU3t73CeMJ+XmLWJ2K3sHbm6R09+IfwEdgTPveO+xLMtVtR+Z775aCfgBpbQfLbWvDwSiZVkuCSwCfgSQJMkJmAHUAmoCMyRJ+rsHxY/AIlmWSwHR2hgArTLF99POKaBpixLxd1sUX98cx8tQZzDp7CRiU2NZ2Gih/u6YvbIWjk2B8u2h3QpNE08deBQaT7vl57gRHMPSHtUY07y0uEdWEAS9albenf3D6+Jka0Gf9VdYd/aJ7vblmVtBt61Qpg0cHg8Xl+tmnrdIksQ032lUdq3M1+e+5mH0wxzHtG/bFmsfb8IXLCQ9OlqBLHOXHBV4sizfk2X5QVa/XpKkAoC9LMsXZc3/OzcD7bVvtwM2aT/fBTTVru61APxlWY6SZTka8Adaat9rov1atGMzx9osa1wCHLVz/+uFL16CrFLh/tUEReKtvrWaS68v8XWtr/V3Ddm1LZp/eEq3gk7rdPZY9tSDMDquuEBympodfr60rVJQJ/MIgiB8SHFXO/YNq0uzcm7MPHSP0TtukJymo315ZhbQdROUawtHv4Zzi3Uzz1ssTC1Y1GgRduZ2jDwxkpiUnDV+liQJj2nTyYiPJ3zhIoWyzD10uQevmCRJ1yVJOi1JUn3ta4WA4ExfE6x97e/3XgLIspwOxALOmV9/a4wzEKP92n+M9Y73/oskSX6SJAVIkhQQHh7+8d9lLpL81x1i9+7FqU8fLIoUyXG88yHnWXVzFe1KtKNDyQ4KZJgFt3bCgRGafSJdNoKpueJTyLLMz+efMmDjVTydbDgwvC7VCovGxYIgGJadpRkre3kzoUUZDtx8RaeVF3gZpaN9eabm0HkDVOgIx2do9jvrgZuNG4sbLyYsKYzxZ8aTrk7/8KD3sCpTGqc+fYjZtYvkmzcVyjJ3+GCBJ0nScUmS/nrHR7v3DHsNFJZluRowFtgmSZI98K5nW3+vM//Tex/7+vti/e+LsrxGlmUfWZZ9XF1111rD0P6rLcqQwR8e8AFvEt8w6ewkSuYvyRTfKfp5bHl3P+z9EorW01w/Zm6l+BSqDDVT9/3Ft7/fpWk5d3YNrk1BR3GYQhAE42BiIjGscUk29K9BcHQSny07x9lHOlqcMDWHjms1+5xPzIRTczRtqXSssmtlZtSeweXXl1kQsCDH8VyGD8fM1ZU3336HnKHjW0KMyAcLPFmWm8myXPEdH//YJlqW5VRZliO1nwcCj4HSaFbSPDN9qSfwSvt5MOAFIEmSGeAARGV+/a0xEWgevZq9L9Y73vtXij96lOTAQFxHjcpxWxRVhorxp8ejUqtY2HAh1mZ6KIAeHoVdA6GQN/T4VXPiS2GxSSr6/3yFXy6/YHDDEqzu7Y2tpbiVQhAE49O4jBsHhtfDPZ8V/TZcYfXpx7rZl2dqBu1XQtVecGq2ptDTQ5HXrmQ7epfrzdZ7W9kflLNbKUztbHGfNJGUu3eJ3rFDoQyNn04e0UqS5CpJkqn28+JoDjs8kWX5NRAvSZKvdg9dX+Dv/3IHgL9PyHYGTmj36R0FPpEkKb/2cMUnwFHteye1X4t2bOZYfbWnaX2BWO3c/0r/aYtSpowibVEWBi7kZvhNvq3zLUUdiuY8wQ95fBJ29AH3CtB7l6Znk8KehCfQYcV5rjyNYn6XKkxqVRYTE3GYQhAE41XUxZY9Q+vQqlIBZh+5z/Dt10lKy9kjzXcyMYW2y6B6P01bKv/peinyxvmMo5ZHLb67+B23wm/lKFa+Vq2w8fUlfPES0iN1fEOIkchpm5QOkiQFA7WBQ5IkHdW+1QC4JUnSTTSHIAbLshylfW8IsA4IQrOyd0T7+nrAWZKkIDSPdScBaMd9D1zVfnyXKdZEYKx2jLM2BsBh4Il2jrXA0Jx8n7ndf9qiTJ6EZJqz06bHnh1j672t9CrXixZFWyiU4Xs8Ow/be4BzSc2VOlYOik9xISiCDisuEJOsYtsgXzp7e354kCAIghGwtTRjWY9qTGpVliO3X9NxxQWeR+b8Roj/YWKiaUflMxAuLNUcvtBxkWdmYsb8hvNxtXFl9MnRhCWFZTuWJEl4TJ+GOjmZsHnzFczSeEk6vQIll/Hx8ZEDAgIMnYai0sPDedyiJTa+vnityNlx9+dxz+l2sBslHEqwseVGzHVwwOG/vLwKW9qDfUHof1gn14/9cvk5M/bfobirLev71cDLSflHv4IgCPpw9lE4I7ZfR62WWdqjmm7ux5Zl+GMyXF4JNf2g1VzQ8R7sh9EP6X24N6Xyl+LnFj/n6I7zsAULiVy7liK/bMXG21vBLA1DkqRAWZZ93vWeuMkijwtbsgS1Am1RUtJTGHtqLOYm5sxvOF/3xd2rG7C1E9i6Qt8Dihd36Rlqvv39DlP2/kW9Ui7sHlJHFHeCIORq9Uu58vvwehTKb8PnG6+y/GSQ8vvyJAlazoY6I+DKGk1DZB3fFFE6f2lm1ZvFrfBbfH/p+xx9Ty5DBmNWsIDmwEW6Dh5nGxFR4OVhKXfvErt7D069emFRtGiOYv1w+QceRT9idv3ZFLDTcUvB0LuwpQNY2UO/A2Cv7HxxKSq+2BzAz+efMaBuMdb19SGflY4LVkEQBD3wcrJhz5A6fFa5IPOOPmDoL9dISFW4kJEkaP491BsLgT/D7yNArdvTqc2LNOfLyl+yL2gf2+5vy3YcExsb3CdPJvXhQ6K2blUwQ+MjCrw8StMWZQ6mDg64DB2So1h7H+1lb9Be/Cr7Ua9QPYUy/AcRj2BzWzCz1BR3joUVDf8iMolOKy5w7lEEszpUZPpn5TEzFT8GgiDkHdYWpizpXpWpbcpx7G4oHZaf52mEwvvyJAmaToeGE+H6Vtg3VOdF3tCqQ2nk1Yh5V+dx5fWVbMfJ16wZtg3qE/HTMlSh2d/XZ+zEb7Y8Kv74cZKuXsVl5AhM7e2zHedB1ANmXZ5FrQK1GFIlZ4XiB0U9hU1tNZ/3PQBOxRUNf+VpFO1XnCcsPpXNA2rSq1bOmz0LgiAYI0mS+KJ+cbYMqElkYhptl53jxP1QpSeBxl9D4ylw61fY4wcZunvsaSKZMLvebIraF2Xc6XEExwd/eNA7SJKEx9SpyCoVYXPnKpyl8RAFXh6kTksjbN58LEqWIH/XrtmOE58Wz9hTY3GwcODH+j9iqqP7XgGIeakp7tKToe9+cC2taPidAS/pte4Sjtbm7B1ahzolXRSNLwiCYIzqlHThwPC6FHG2YeCmAJYcf4RarfC+vIZfQbNv4K9dsHsgZKiUjZ+JnYUdS5ssJUPOYNTJUSSpsneTh0XhwjgPGkTcoUMkXrqkcJbGQRR4eVD0lq2oXrzAfeIkJLPsNeqVZZkZF2YQkhDCvIbzcLZ2VjjLTOJeax7LpsRqWqG4V1AstFotM/vIPSbsukXNYk7sHVqX4q7K99ETBEEwVp75bdg1uA4dqhVi0fGHfLk1kPgUhYuwemPgk1lwdx/s7A/pacrGz6SwfWHmNZhHUEwQU89PzfahC+dBX2Du6cmb775HTtNdvoYiCrw8Jj0qioiVK7FtUB+7+tnfL7f13lb8n/szxnsM1d2rK5jhWxLCYXM7iA/VNDEuWE2x0Imp6Xy5NZDVp5/Qq1ZhNn5eEwcbcZhCEIR/HytzUxZ0qcI3n5XnxP0w2i0/T1BYgrKT1BkOLX+E+wfht76Qnqps/EzqFqrLWO+x+D/3Z+3ttdmKYWJlhfvUKaQ9eULkpk0KZ2h4osDLY8KXLkWdnIz7xInZjnEj7AYLAxbSxKsJfcv3VTC7tyRFafrcxbyAXr+BV03FQofEJNN51UX+vBfKN5+VZ2b7ipiLwxSCIPyLSZJE/7rF+OWLWsQmqWi//DzH7rxRdhLfwdBmATw8Ar/2AlWKsvEz6Vu+L58W/5Sfrv/EqZenshUjX6NG2DVtSsSKlahe5a0bTcVvvDwk5eFDYn7bSf7u3bEsUSJbMaJSohh/ejweth58X+97JF01sEyJha0dIeIhdP8Fiip3Ovfai2jaLTtPcFQSG/rXoH/dYrr7PgRBEHIZ3+LO/D6iHiVcbfHbEsiaM4+VnaDGF/DZUgg6Dtu7Q1r29sl9iCRJzKg9g/LO5Zl0dhJPYp5kK47H15NB23kiLxEFXh4hyzJhc37ExM4Ol+HDshUjQ53B5LOTiU6JZmGjhdhbZP/07XulJsAvXeDNbei6GUo2VSz0/hshdF9zCRsLU/YMraObTu6CIAi5XEFHa3Z8WZtPKxfgh8P3mXPkvrJNkb37QfsV8OQUbOsKaTq4Pg2wMrNiSeMlWJlaMfLkSGJTYz86hnmhQrgMHky8vz8JZ8/qIEvDEAVeHpFw+jSJFy7gOmwoZvnzZyvGmttruPDqApNrTaacczmFM9RSJWv+ogu+Cp3WQ5lWioRVq2UW+j9k1K83qOrpyL5hdSnlnk+R2IIgCHmRlbkpS7pXo7dvYVadfsyk3bdJz1DwVoqqPaHjGnh+HrZ2htR45WJn4mHrwaLGiwhJCGHi2YlkZKMfn9OAz7EoWpQ3M2eiTtXd3kF9EgVeHiCrVIT9OBeLokXJ36NHtmJceHWBlTdW0rZEWzqV6qRwhlrpqZo9Gc/OQYfVUKG9ImHT0tWM/PU6S/98RBdvT7Z8URMn2+zfVSgIgvBvYWoi8X27ioxsWoodAS8Zvu06KSoFGxZX7gqd1sHLy5rrJ1PilIudSTW3akypNYXzIedZcn3JR483sbDAY/o0VM9fELl+vQ4y1D9R4OUB0dt/Je3pU9wmfoVk8fGFzZvEN0w6M4kSjiWYUmuKbvarZahg5+fw+E9ou1TzQ6+A1PQMhv4SyMFbr5nYsixzO1fG0kyH/foEQRDyGEmSGNu8NDM+K88fd94wYONVZa83q9gJuvwMIYGag3UpH/8YNSs6l+5MtzLd+Pmvnzn05NBHj7etU4d8rVoSuXoNacHZa6JsTESBl8tlxMQQvnw5tnVqY9eo0UePV6lVTDg9gdSMVBY2WoiNuY0OkkyHPYPgwSFoPR+qK3MyN0WVwZdbAjl+L4zv21dkSKMS4jCFIAhCNn1etxiLulXh8tMoeq69RGSCgo8qy7fT7Ll+fVOnp2sn1piIt7s3My7M4E7knY8e7z5xIpiaEjpzlg6y0y9R4OVy4ctXoI6Px23ipGwVN4sDF3Mj/Abf1vmWYg7FlE9QrYb9w+DOXs3l1DUHKRI2OS2DQZsDOP0wnNkdK9HHV1w7JgiCkFMdqnmytq83D97E02X1RUJikpULXrYNtF8Jz85q/ujXwd215qbmLGi4ACcrJ0adGEVEcsTHjffwwHXYMBJOnSL+xAnF89MnUeDlYqmPHxO9bRuOXbpgVebjr/Y6/vw4m+9upmfZnrQs1lL5BGUZDo3R3FHYeArUHalI2KS0dAZsvMq5oAjmdqpMj5qFFYkrCIIgQJOy7mz9ohbh8al0XnmBoDAFD0dU7gotZsO9A3B4vOb3hMKcrZ1Z0ngJsamxjDs1DtVHXp3m1LcPlqVKEjpzFupkBQtcPRMFXi4WOncuJtbWuI4c8dFjX8S9YNr5aVRyqcR4n/HKJyfL8MckCNwI9cZCgwmKhE1ITaf/hqtcfhrJoq5V6eLjpUhcQRAE4f/VKOrEDr/aqDJkuqy6yM2XMcoFrz0U6o6GgA1wSje958o5l+O7ut9xLewaS68v/aixkrk57tOmoXr1iojVq3WSnz6IAi+XSjh7jsTTZ3AZMhgz54+7JzYlPYWxp8ZiamLK/IbzMTfVwfVdf34Hl1eB71BoOh0U2BsXl6Ki7/rLBL6IZkn3arSvVkiBRAVBEIR3KV/Qnt1DamNnZUbPtZc4H/Rxjzvfq9k3ULU3nJ4DV9cpFzeTVsVa0a1MNzbe2ciZ4DMfNda2Zk3s235G1PoNpD59qpP8dE0UeLmQnJ5O6I9zMPfyIn+fPh89fvaV2TyIfsDserMpaFdQ+QQDfoZzC8G7P7T4QZHiLjZZRZ/1V7gVHMuyHtX4rIoO8hYEQRD+SxFnW3YProOXkw2f/3yVI7dfKxNYkuCzJVC6JRwaD3f2KRP3LRNqTKBM/jJMOTeFN4kfdy2b+4QJSJaWhM6cpWwTaD0RBV4uFLNzJ2lBj3GbMB6Tj2yLsi9oH3se7WFQpUHU96yvfHKPT8KhcVCyObReoEhxF5OURq91l7j7KpaVvb1pVamAAokKgiAIWeFmb8UOv9pU8nRg2LZrbL/yQpnApmbQ+WfwqqU5dPHktDJxM7E0tWR+w/mkZqQy8cxE0tVZb/9i5uqK66hRJJ4/T/zRY4rnpmuiwMtlMuLiCF+yFJsaNcjXvPlHjX0Q9YBZl2ZRy6MWw6pm7zqz9wq7D7/1A9cy0HmD5oc3hyITUumx9jIPQxNY08eH5uXdFUhUEARB+BgONuZsHViLBqVdmbznNitPPVZmVcvCBnpsB6cSmvYpr2/mPOZbijoUZXrt6VwLu8bKmys/amz+Ht2xLFeO0NmzUSfq5ro1XREFXi4TsXIVGbGxuE/+uLYoCWkJjDs9jnwW+ZjTYA6mJgo3A06M0Nw3aGYJPXeAVc7vsQ2PT6Xn2ss8CU9gXV8fGpcV98oKgiAYirWFKWv7+tCuakF+/OM+Pxy+p0yRZ+MEvXeDlYPmSrOoJzmP+ZZPi39Kh5IdWHtrLRdfXczyOMnMDI/p00gPDSV8xQrF89IlUeDlImnPnhG1dSsOHTtgVb58lsfJssx3F78jOD6YuQ3m4mLtomxiqhT4tSckhEKPX8Ex521LwuJS6L7mIi+ikvi5fw0alHZVIFFBEAQhJ8xNTVjUtSr9ahdh7dmnTNh1S5n7ax0KQZ+9oE6HLR0gISznMd8yqeYkijsUZ/LZyR/VH8+mWjUcOnUkatNmUh89UjwvXREFXi4SOn8+JubmuI4a9VHjDj89zJFnRxhWdRg+Hj7KJiXLmkbGLy9r7pf19M5xyNexyXRbc4nXsSls/LwGdUoqXJAKgiAI2WZiIvFN2wqMblaKXYHBDPnlmjL317qWhl47NcWdDu6ttTG3YX7D+SSqEpl0ZhIZH9Fo2W3cOExsbXnz3fe55sCFKPByicRLl0k4/ifOfn6Yu2X9UeWbxDfMujSLqq5VGVBxgPKJnZoDf+3StEKp0D7H4UJikum2+hLh8alsGViTWsU/rgWMIAiCoHuSJDG6WWm+a1eB4/dC6bfhCvEpH9dQ+J08faDrFgi7q3kypPCVZiXzl2RyrclcfnOZdbez3p7FzMkJtzFjSLp6lbiDH3/PrSGIAi8XkDMyCJ0zB/OCBXHq3y/L49Symqnnp5Iup/NDvR+U33d36zdND6OqvTTNjHPoZVQS3VZfJDopjS0Da+JdxEmBJAVBEARd6Vu7KIu7VSXweTQ91l4iQon7a0s1g3YrNFea7fVT/EqzDiU70KZ4G1bcXEHAm4Asj3Ps0hmrSpUInfsjGfEK3u6hI6LAywVi9uwh9f593MaPw8TKKsvjtt/fzuXXl/mqxld42St848OLS5pHs0XqwaeLc9wO5XlkIt1WXyQ+JZ1tX/hSrXB+hRIVBEEQdKld1UKs7edDUFgCXVZdJDg6KedBq3SDT2bB3f1weIKiV5pJksQ032l45fNi4pmJRKVEZW2cqSke06eTERFJ+E8/KZaProgCz8hlJCQQvmQp1tWqka9VqyyPexLzhEWBi2jo2ZBOpTopm1TUE83SuYMXdNsCZh/Xi+9tT8IT6Lr6IsmqDLYNqkUlTweFEhUEQRD0oXEZN375ohaRCal0XnmRR6EKrHDVGQ51R0HAejg9N+fxMrE1t2V+w/nEpMYw5dwU1HLWDopYV6qIY/duRG/9hZT79xXNSWmiwDNykavXkBERgfvXk7PcFkWlVjH53GSszaz5ps43H9VO5YOSY2BbN5DVms2wNjl7jBoUFk+3NZdIz5DZ7udLhYKiuBMEQciNvIs48dvg2qhlmS6rL3L9RXTOgzb7Fqr0hFM/wNX1OY+XSVmnskyoMYFzIefYdGdTlse5jR6NqYMDb779DlmtwAliHREFnhFLCw4mauNGHNq1xbpSpSyPW31zNXcj7zKj9gxlW6JkqOC3vhD1FLptBecSOQr34E083VZfQpbhVz9fynrkvHeeIAiCYDhlPezZNbgODtbm9Fp3mTMPw3MWUJKg7VIo1UJzS9Ld/cokqtWtTDeaF2nO0mtLuRF2I0tjTB0ccBs/nuTr14ndq5sr1pQgCjwjFjZ/AZia4jpmTJbH3Ay/ybrb62hboi3NijRTLhlZhkNj4elpzQ9b0Xo5CnfnVSzd11zEzFRix5e+lHLPp1CigiAIgiEVdrZh5+DaFHG2ZeCmqxy89SpnAU3NoctG8KwBu7+Ap2cVyRM0+/G+qfMN7rbufHXmK2JTY7M0zqFDe6yrVSNs/nwyYmIUy0dJosAzUkkBAcT/8QfOX3yBuYdH1saokvj67Ne42bgxqeYkZRO68BNc2wz1x0HVnjkKdTs4lp5rL2NtbsoOv9qUcLVTKElBEATBGLjls+JXP1+qejkyYvt1tl56nrOAFjaaW5Kcimv2gL++pUyigL2FPfMazCM8OZzp56dnqc+dZGKCx4zpZMTGErZ4sWK5KEkUeEZIVqsJnT0HM3d3nAd8nuVxCwMX8jL+JbPqzSKfhYIrYvcOgv90KN8eGk/NUajrL6Lpue4SdpZm7PiyNkVdbBVKUhAEQTAmDtbmbB5Qi8Zl3Ji67y+WnXiUsybBNk7Qew9Y2msaIUc9VSzXSq6VGFN9DCdenmDb/W1ZGmNVtiz5e/ciZsdvJN/+S7FclCIKPCMUu/8AKXfu4DZuLCY2Nlkacy7kHDse7KBv+b7U8KihXDKvrsOeQVDIGzqsApPs/18m8HkUfdZfIb+NBb8Nro2XU9a+N0EQBCF3srYwZXUfbzpUK8T8Yw/5/uA91OocFHkOhaDPHlCrFL/SrE/5PjT0bMiCgAXcibyTpTGuI0Zg6uLMm+++Q85Qtl9fTokCz8ioExMJX7QIq8qVsf/00yyNiUmJYfr56ZR0LMmI6iOUSyY2BLZ1BxsX6LEdzK2zHerK0yj6rr+Caz5LdnzpSyHH7McSBEEQcg9zUxMWdKnC53WLsuH8U775/U7OVvJcy0DPnZr7zxW80kySJGbWnYmTlRMTTk8gIS3hg2NM8+XD/auJpNy+TczOXYrkoRRR4BmZyPXrSQ8Lw33SJKQsrJbJssz3l74nOjWa2fVnY2lqqUwiqQmwvRukJWr2Pdhl/Xq0t114HEG/DVfwcLBih58vBRxEcScIgvBvYmIiMf3T8vg1KM7mi8/54fC9nBV5XjWg62YIvQM7ekG6AjdoAI5WjsxtMJdXCa/49uK3WcrR/tM22NSqRdiiRaRHZa1psj6IAs+IqF69InL9Buxbt8amerUsjTn09BDHnh9jWNVhlHUqq0wi6gzNSaXQO5qTS+7lsx3q7KNwPv/5Kl5O1vzqVxs3+6zfxCEIgiDkHZIkMblVWfrVLsLas09Z6P8wZwFLNYf2K+DpGdij3JVm1d2rM7zacP549ge7Hn14VU6SJDymTUWdmEjYggWK5KAEUeAZkbCFiwBwG5e1e13fJL7hh0s/UM2tGp9XyPphjA86Ng0eHoFWczV3AmbTyQdhDNwUQDEXW7YP8sU1n0Kri4IgCEKuJEkSMz6rQPcaXvx0IojlJ4NyFrBKd/hkJtzdB0cmKnal2YCKA6hTsA4/XvmRB1EPPvj1liVL4tSvL7G795B07boiOeSUKPCMRPKNG8QdPIjT5/0xL1Tog1+vltVMPTeVdDmdWXVnYWpiqkwiV9fBpeVQazDUHJTtMMfvhvLl5kBKu9uxfZAvznaiuBMEQRA0j2tndahEh2qFmHf0AevOPslZwDojoM5IuLoWzsxTJkfJhJD1E7IAACAASURBVB/q/UA+i3xMODOBJNWH79d1HToUMw8PzYGL9HRF8sgJUeAZAVmWCZ09B1NXF1wGZa2o2nZvG5ffXOarGl/hZe+lTCJBx+HwV5qO4S1+yHaYi48jGfrLNcoVyMcvA33Jb5uzu2oFQRCEvMXURGJe58q0qVSAmYfuseXis5wFbPYtVOkBJ2dBwAYlUsTZ2pkf6//Is9hnzLo864Nfb2Jri/vXk0m9f5/obVlrtaJLOSrwJEmaJ0nSfUmSbkmStFeSJMdM702WJClIkqQHkiS1yPS6tyRJt7XvLZW0F6VKkmQpSdIO7euXJUkqmmlMP0mSHmk/+mV6vZj2ax9px1poX5e0sYO0uVXPyfepa3GHDpN88yZuo8dgYvvhvnCPYx6z+NpiGno2pFOpTsokEXYPdn4ObuWg83rI5orgvddx+G0OoLCzDZsG1MTBxlyZ/ARBEIQ8xczUhMXdq9KsnDvT9t/ht6svsx/MxATa/gSlPtFeaXZAkRxrFqjJ4CqDOfD4APuDPnxNWr7mzbGtX5/wJUtRhSrXwiU7crqC5w9UlGW5MvAQmAwgSVJ5oDtQAWgJrJAk6e+KYSXgB5TSfrTUvj4QiJZluSSwCPhRG8sJmAHUAmoCMyRJyq8d8yOwSJblUkC0NgZAq0zx/bRzGiV1cjJhCxZgWb4cDh3af/DrVRkqJp+djI2ZDd/U+QZtfZwzCWHwS1dNG5SeO8Aye02SX0Yl0W/DFWwtzdg8oCaONmLlThAEQfhn5qYmLO9VjQalXZm45xb7rodkP9jfV5oV8tYcFHx2TpEcv6z8JTU8ajDr8iyexL7/cbIkSXhMnYKsUhE2d64i82dXjgo8WZaPybL894PmS4Cn9vN2wK+yLKfKsvwUCAJqSpJUALCXZfmirDl7vBlon2nMJu3nu4Cm2tW9FoC/LMtRsixHoykqW2rfa6L9WrRjM8faLGtcAhy1cxudqI0bSX/9OsttUVbdWsW9qHvMqD0DF2uXnCegStZc+5IYDj1+BQfPD495h6jENPr9fIUUVQabB9akoOhzJwiCIGSBpZkpq3t7U6uYE+N23uTI7dfZD2ZhCz1/g/xFYXsPeHM7x/mZmpgyp/4crEytGH96PCnpKe9PoUgRnP38iDt0iMSLF3M8f3YpuQdvwP+1d9/RVRVrA4d/k54QCKmE3jtSQ6iiXhDxooBiAUSaiFIEBakqICCCqHBFRbpUAUEBUVS4oIJISSih9xYCCSGkQHoy3x9nc78AJyen0BLeZ62zPNkz78zEzZLXvacA643vJYGcz1ojjWslje+3Xr8pxkgaEwB/C235A/E5EkyzbZkpe2BkRMcQO2u26ZFuaGie9fdd3sec/XNoX7E9Lcu2dHwA2dmwuh9E7oLnZ0FJ+95kJ6dn0uvbXUReTWFO94ZUKXYHj0kTQghR4Hm6OTO3e8P/nV278VC0/Y15+ZlOu3AvbNoIOf6cw+ML8gpi4qMTOX71OJN3Tc6zvv/rvXEtU4ZL48aTnZ7ucP/2yDPBU0ptVEodMPNpn6POe0AmsOTGJTNNaQvX7Ymxp63bKKX6KKXClFJhly9fNlflrrk8dSpkZhI09N086yZnJDNqyyiCvYIZETrizgzgj4/h4A+myak12tnVREZWNv2X7CYiMp7pnesRWt7vzoxNCCHEQ6WQuwvzezakRoki9Fuym7+OOfB3sk8p07m1GanwXRfT5v0Oal6yOb1q9WLlsZWsP73eYl0nd3eC33+P9NOniZv/rcN92yPPBE9r3UprXcvMZw2YFkAAzwCv6P/f8jkSyLm0sxQQZVwvZeb6TTFKKRfAB4iz0FYsplevLpbaMlN26+83S2sdorUOCQwMzOtfxx2TcuAgCatX49e9G25lyuRZ/7OwzzifdJ4JzSfg7ebt+AD2fgd/fQL1XoVmg+xqQmvNqB/2s/noZcZ3qMVTNYMdH5cQQoiHVhEPVxb2CqVikDd9FoWx/dQV+xsLqgYvzIOYg/DjG6a3Vg4aUG8AdQPr8uE/H3Iu0fKTQe8WLSjcujWxM2aQHunA3EI7ObqKtg0wHGintc65ScxaoJOxMrY8psUOO7XWF4EkpVRjYw5dN2BNjpgbK2RfADYZCeNvQGullK+xuKI18JtRttmoixGbs61uxmraxkCC0fcDQWtN9KSPcfbzw/+NN/KsvyVyCyuOraBbjW40DG7o+ADOboO1b0G5R6Ht52DnQo1Pfz/K9+GRDGpZmVcalXV8XEIIIR56Rb3cWPxaKKV9vej17S7Czzpw/FflVqZtv46sM22h4iBXJ1c+afEJzsqZd/98l/Qsy69fi40cAU5ORE+0f+sxezk6B+9LoDCwQSm1Vyn1DYDW+iCwAjgE/Ar011rfOEOkLzAH08KLk/z/vL25gL9S6gQwGBhhtBUHjAd2GZ9xxjUwJZeDjRh/ow2AX4BTRh+zgX4O/p53VNLvG0gJCydw4ECcC1uerxafGs/obaOpVLQSb9V/y/HOr5yEZa+YJqC+vAhc7Fvp+u3fp/lq80k6h5bh7VaVHR+XEEIIYfD3dmdJ70YEFXanx7xdRETG299YozehfjfY8ilEfO/w2Ip7F2dCswkcjjvM5+GfW6zrWrw4gf37cW3TJpI2bXa4b1sohw77LWBCQkJ0WFjYXe0jOz2dU22fwcnDg/I//oByccm1rtaaIX8OYfP5zSxru4yqflUd6zzlKsxpBclx0Hsj+Fe0q5mfIy4y4LvdtKpejBmv1MfFWfbLFkIIcedFxafw0sx/SErN5LvXG1OjRBH7GspMh0UdIDIMev4CpUIcHtvknZNZfHgx0x6fZnHho87I4NRzz6FTUqmw7iecPO/cLhNKqXCttdlfRv5mvseuLlpExvnzBI0YbjG5A1h3ah0bzm6gf93+jid3memw/FXTaqJOS+xO7radjOWd5XtpUMaX6Z3rSXInhBDirilR1JPvXm+Ml5szr87dwfHoJPsacnGDlxZB4WDT1mAJjs+JG9xgMDX9a/LBtg+4cC339pSrK8GjR5Nx4QKxM2c63K+15G/neyjzyhViZ3xDocda4N2smcW6l65f4uMdH1MvqB49a/Z0vPP1Q+HMFmj3JZRtalcTh6ISeWNhOGX9vZjTPQQP1zt0/q0QQgiRi9J+Xizp3QgnJ8Urc3ZwJva6fQ0V8jdt5p+eDMs6Q7qd7RhcnV2Z0mIKWmuG/TmMjOyM3LsODcWnfTvi5s4j7dRph/q1liR499DlL78kOyWFYsOGWayXrbN5f+v7ZOksPmr+Ec52Hhv2P3sWQ/i30Hww1HnZribOxyXTff5OvD1cWCCnVAghhLiHKgR6s6R3IzKzNV1mb+d8XHLeQebcOI7zYgSs7uvwytrSRUoztulYImIjmL57uuWuhw5FeXgQPWE892J6nCR490ja8ePEL1+Bb6dOuFe0/Hp0yeEl7Li0g2ENh1G6cGmLdfN0McJ0Ll/5x+Bf79vVRNz1dLrP20laRhYLeskpFUIIIe69KsUKs+i1UK6lZdJlznYuJqTY2dBT8OQ4OLQG/sx70+K8PFXuKV6u+jLzD87nr8i/cq3nEhBA4NuDuL7tH5LWW95H706QBO8eiZ78CU7e3gQM6G+x3sn4k0wLn8bjpR7n+crPO9ZpSjys6AaeftBxLtjxJDA5PZOe3+7iQnwKc3vIKRVCCCHun5olfFj0WiOuXs/gldk7iEmyfGxYrpq+BXVfgT8nwYEfHB7X0IZDqeJbhfe2vsfl5Nw3aPbt1AmPGjWI/ngSWdcc33zZEknw7oFrf/3F9a1bCejXFxdf31zrZWRlMHLLSAq5FmJM0zEoO/enA0BrWNMfEs6bDl/2tn0T5xunVOyPjOeLzvVoWE5OqRBCCHF/1SldlPk9G3IxIZWuc3YQd92Oo8CUgmemQunGple1F3Y7NCZ3Z3emPDaF1MxUPtj2Qa6vYJWzM8Fjx5AZG0vs9C8d6jMvkuDdZTojg+jJn+Batgx+XbpYrDtj3wwOxx1mTNMxBHgGONbxti9MGzs+OR7KNLI5XGvNiFWmUyomdHhETqkQQgjxwGhYzo+53UM4eyWZrnN2kJCc+wKHXLm4w8uLoVCQaWVtomPnIVTwqcDgkMH8feFvlh9dnms9z9q1KfrSS8QtXkzq0aMO9WmJJHh32dXvvyf95EmKDR2Kcst9YcLemL3MPTCX9hXb07JM7vvpWOXM37DxQ6jRHhr3tauJKb8dZdXuSN5uVZkujfI+Sk0IIYS4l5pWCmDmqw04EXONbvN3kpRqR5LnHQidv4PURFOSl2HnvD5Dp6qdaFayGZ+FfcbphNxXywa98zbORYpwaeyH6DtwhJo5kuDdRVmJicR+MR2v0FC8W+aetCVnJPPe1vcI9gpmROgIxzpNioaVPcGvvGlLFDte837792m+/sN0SsWglnJKhRBCiAfT41WD+LJLPQ5eSKDXt7tITs+0vZHgWtBxNkTtMU1tcmCFq1KK8U3H4+7izsgtI3PdOsW5aFGC3n2XlD17SPhxtd39WSIJ3l0UO+MbshISKDZyhMX5dJ+Ffcb5pPNMaD4Bbzdv+zvMyoSVvUz/J/LSQvCwfcfvdRFRfLjuEK1rFGNCh1qOzQMUQggh7rLWNYOZ1qku4Wev0ntBGKkZWXkH3apaW2g5Gg6sgr8+dWg8gV6BjGkyhoNXDvLNvm9yrefzXAc869cn5tNPyYp34Ci2XEiCd5eknz1L3OLF+Dz/HB7Vq+da76/Iv1hxbAXda3anYXBDxzrdNB7OboVnp0GxmjaHbzsZy+Dl+wgp68sXnevh7CTJnRBCiAffM7VL8OmLdfjn1BXeXBxOWqYdSV7zd6D2y7B5Ahxa69B4niz7JO0qtmPO/jnsjdlrto5yciJ4zGiyEhOJmTrNof7MkQTvLon59FOUqyuBgwblWudq6lXGbBtDpaKVGFBvgGMdHvkF/p4GDXpCnU42hx+MSqDPwnDKBXgxp1tDOaVCCCFEvvJ8/VJMfO4R/jh6mQFL95CRZePcNqXg2S+gVEP48Q24uM+h8YwMHUnxQsUZtXUUyRnmN2b2qFoVv65diV+xgpR9jvV3K0nw7oLrO3eStGEjAa/3xjUoyGwdrTXjt48nPi2eSY9Owt3Z3f4O407Bj29C8brQZpLN4efjkukxfxeFPVz4tmcoPl6u9o9FCCGEuE86h5Zh7LM12HAomreX7yXT1iTP1QNeXgKevvBdF9O8djt5u3nzUfOPiEyK5JNdn+RaL+CtAbgEBnLpw3HoLDuePOZCErw7TGdnEzNpMi7Fi+PXM/czZNedWseGsxsYUHcAVf2q2t9hRoppM2OlTPPuXD1sCr9yLY1u83aSnpnNQjmlQgghRD7Xo1l5Rj5djZ8jLjJsVQTZ2TYumihczLSyNiUOlr8CGXZupgw0KNaAXrV6ser4Kjaf22y2jrO3N8VGjiD10CGuLltmd1+3kgTvDktYvYbUQ4cIGjwYJw/zyVZsSiyTdk6ibmBdetTs4ViHvwyFS/vh+VngW9am0OT0THotCCMqPoW53UOoLKdUCCGEKADeeKwi77Sqwg+7LzDp1yO2N1C8Djz3DUTugp8GOrSytn/d/lTzq8bYf8YSmxJrtk7hNm0o1LQJl6f9h8zLuZ+EYQtJ8O6g7OvXuTx1Kh51alPkmba51vt4x8ekZKbwYbMPcbbj+LD/2bMY9iyCR4eYztazQUZWNv2MUyqmd65HiJxSIYQQogAZ2LIS3ZqUZdZfp5j/d+570uWqRnt44j2IWG6a424nV2dXPm7+MdfSrzF221izp1wopSj2wQfo1FSip0yxu6+cJMG7g67MnUvm5csUG577tiibzm3i97O/82adN6ngU8H+zi7th5+HQPkWpj+ANrhxSsUfRy/z0XOP0FpOqRBCCFHAKKUY82xNnqxRjHHrDvHrATtOqmgxFGp1NB0ecOQXu8dSybcS7zR4hz8j/2Tl8ZVm67iXL49f79dIXPsT13futLuvGyTBu0MyLl7kyrz5FPn303jVr2e2TlJ6Eh9t/4gqvlXoWSv3+Xl5Sk0wzbvz9IWO88DGp4CfGKdUvNOqCp1D5ZQKIYQQBZOzk+KLTvWoU6oog5btJfxsnG0NKAXtv4ISdWFVb7h0wO6xdKnehcbFGzNl1xTOJp41WyegTx9cS5bk0rhx6HQ7ztjNQRK8OyRm6lTIziZoyJBc63we/jmxqbGMazoOVyc7V6pqDav7Qfw5ePFb0zErNpj/92lm/HGSLo3KMLBlJfvGIIQQQuQTnm7OzO0eQnEfD15bEMbJy9dsa8DVEzp9Zzo84LvOcM2+OXJOyonxzcbj4uTCqC2jyMy+/dQNJ09Pir3/HuknThK3cKFd/fyvLYeiBQApEREkrv0Jvx49cC1Z0mydXZd2sfLYSl6t/io1A2zfhPh/tk2HI+vgyXFQprFNoT/ti2KccUrF+PZySoUQQoiHg7+3Owt6heKsFD3m7+RyUpptDRQpDp2WwvUYWN4VMm2MNwQXCmZ049FExEYwe/9ss3UKP/EE3i1bcvmrr8m4aMdrZYMkeA7SWhM9aTLO/v749+ljtk5qZipjt42llHcp+tfrb39nZ/6GjWNNEz8b97MpdNuJWIaskFMqhBBCPJzK+hdibo+GXE5K47UFdpxbW7I+dPgazm+HdYPtXlnbpnwb2lZoy8x9M9l/eb/ZOsGjRoLWRE/82K4+QBI8hyX9+ispu3cTOGggzt6FzNaZsW8G55LOMabpGDxd7NxnLikaVvYEv/LQ7kvTvAArnY69zpuL5ZQKIYQQD7e6pYsyvXN9DlxIYMDSPbZvhFyrIzw2HPYuhn++tHscoxqNItArkJFbR5o95cK1ZEkC+vYlacMGrv31l119SILngOy0NGI+/Qz3qlUp2rGj2TqHrhxiwcEFPFfpORoXt+2V6v9kZcLKXpCaaNrM2KOI1aFJqRm8vjAMZyfF3O4N5ZQKIYQQD7UnaxRjXPtabDoSwwdrDpjdtsSix0aY3qT9/gEc+82uMRRxK8JHzT7iXOI5Pgv7zGwd/549cKtQgUvjJ5Cdavtmy5LgOSBu4UIyLlyg2IjhKOfbn4plZmcydttYfD18GRKS++KLPG0aD2e3wrPToJj18/eyszXvLN/L6djrfPVKfUr7edk/BiGEEKKA6Nq4LP0er8h3O8/z1eYTtgU7OUGHGRD8CKx8DWIO2zWG0OKhdKvRjRXHVvBX5O1P6ZSbG8GjPyDj/HmuzJ5jc/uS4NkpMzaWK9/MxPuJJyjUpInZOgsOLuBw3GFGNRqFj7uPfR0d+cW0wWKDHlCnk02hUzceY+PhGEY/U4OmFQPs618IIYQogIY+VZXn6pXk09+PsSo80rZgt0LQeRm4ecHSl+H6FbvGMLD+QCr7Vmb036OJS719C5dCjRtTpG1brsyeTfpZ81ur5EYSPDtd/mI62WlpBA0darb8bOJZZuybQcsyLXmy7JP2dRJ3Gn5803RkSpvJNoX+HHGR6ZtO8HJIabo1se0IMyGEEKKgU0oxuWNtmlb0Z/iqCLYct3H7E5+SppW1SZdMe9Nm2r5vnZuzGx83/5jE9EQ+3Pah2dfFQcOHoVxduTR+gk2vkyXBs0Pq0WPEr1yJb+fOuFcof1t5ts5m7LaxuDm5MarRKPs6yUg1/YFRyjTvztX8ubbmHIxK4N3v91G/TFHGdagp26EIIYQQZri5OPHNqw2oFORN38W7ORSVaFsDpUJMGyGf3Qq/vGvXytqqflUZWG8gm85vYvWJ1beVuwYFEThoINe3biXp9w1WtysJno201sRMnoRT4cIE9je/Vcmq46sIiw5jSMgQgryC7Oto/VC4FAHPzwLfclaHXbmWRp+F4fh4uvJN1wa4u8iKWSGEECI3RTxcmd+zId7uLvT8didR8Sm2NVD7RdOZ8LsXwI5v7BpDt5rdaBjckEk7J3E+6fxt5b5duuBerRrREyeSff26VW1Kgmeja3/+yfVt/xDYvx/ORYveVh59PZrPwz4nNDiU5ys/b18ne5bA7oWmPzBVnrI6LCMrm35LdnP5WhozX21AUBHrn/oJIYQQD6viPp5826shyWlZ9Ji/k4SUDNsaeOJ9qPYM/DYKTtu+rYmTcuKjZh/hrJwZtWUUWdlZN5UrFxeCx4wmMzqay19/bV2bNo/iIaYzMoiZ/Alu5crh27nz7eVa89GOj8jIzmBMkzH2vRq9tB9+HgzlW8AT79kUOn7dIXacjmNyx0eoU/r25FMIIYQQ5lULLsLMVxtwOvY6bywKIy0zK++gG5yc4LlvwL+SaVuzxCib+y/uXZxRjUex9/Je5h2Yd1u5V716+LzQkbgFC0k9dizvIdk8gofY1WXLST99mqBhpgmPt/r97O9sPr+Z/nX7U6ZIGds7SE0wzbvz9IWO88DJ+ter3+08x8J/ztKnRQWeq1fK9r6FEEKIh1zTSgFMeaEO20/FMfT7CLKzbZhT514YXloE6cnwfQ/IsvEpINC2fFvalGvD13u/5uCVg7eVBw0ZgnOhQkSPG5/nggtJ8KyUlZBA7Jdf4tW4Md5PPH5beUJaAhN3TKSGfw1erfGq7R1oDav7wdWz8OK34B1odWjYmThGrznAo5UDGN6mmu19CyGEEAKADvVKMqxNVdbui+KT347aFhxUDdpPh/M7TBsh20gpxfuN38fP04+RW0aSknnzfEAXX18ChwwmOSyMxLVrLbYlCZ6VYr+eQVZiomlTYzOvXj8N+5SEtAQ+bPohLk4utnewbTocWQdPjoMy1p94ERWfwpuLd1OyqCdfdq4vZ8wKIYQQDur7WEW6Ni7DN3+eZOE/Z2wLrtURGvWFHTPgwCqb+/Zx92FCswmcTjjNtPBpt5UXfeEFPOrUJvqTKRbbkQTPCulnzhC3dClFX+iIR7Xbn5D9E/UPq0+spmetnlTzs+MJ2pm/YeNYqN4OmvS3Oiw1I4s3FoWTmpHF7G4hcgyZEEIIcQcopRj7bE1aVQ9izNqD/Hbwkm0NPDkOSjeCNW/BZRufAgJNSjSha/WuLD2ylG0Xtt08Nicnio8ZQ9bVqxbbkATPCtFTPsXJ1ZXAgQNvK0vOSObDfz6kXJFyvFnnTdsbT4qGlT1NW6G0/8q0750VtNaMWBXBgagEpr1cl8rFCtvetxBCCCHMcnF24ovO9ahdqigDv9vD7nOWE6qbg91M063cvGB5V0hLsrn/QfUHUdGnIu///T7xqfE3lXnUqIFvly4W4yXBy8P17Tu49t//4v/GG7gE3j4v7qu9X3Hh2gXGNBmDu7O7bY1nZcKq1yA1EV5eBB5FrA6dveUUq/dGMeTJKrSqUcy2foUQQgiRJy83F+Z2DyHYx4PeC8I4HWvdHnQAFCkBL8yDKydg7Vs2b4Ls4eLBx49+zNW0q4zbPu62RRWBbw+yGC8JngU6K4voyZNxKVEcv+7dbis/EHuAxYcX82KVFwkJDrG9g80T4MwWeGYqFKtpddgfR2OYtP4IbR8pTv8nKtnerxBCCCGsEuDtzoKeoQD0mL+T2Gtp1geXbwEtR8PBH+3aBLm6f3X61+3PhrMbWHdq3U1lzt7eFmMlwbMgYfVq0g4fJmjIEJw8bt40OCMrg9HbRhPgEcA7Dd6xvfGj62HrVGjQA+revqdebk5dvsZb3+2hSrHCTHmxthxDJoQQQtxl5QIKMad7CNGJqby2IIzk9Ezrg5u9DVXbwu/vw7ntNvfds2ZP6gfVZ+KOiURds35/PUnwcpF17Tox06bhWbcuRf7979vK5x2Yx/Grx3m/8fsUdrNx/lviRVjdF4JrQ5vJ1oelZvD6wjBcnBSzu4Xg5WbHal0hhBBC2Kx+GV++6FSP/ZHxDPxuD5lZ2dYFKgXPzYCiZWBFd7gWY1O/zk7OTHx0IhrNqK23n3KRG0nwcnFlzmyyLsea3RblVPwpZkbM5KlyT/FEmSdsazg7G1a/CZlppnfzrtYdJ5aVrXl72V7OXEnm61caUNrPy7Z+hRBCCOGQ1jWDGduuJhsPxzD2p4N5bjb8Px4+pk2QUxNMJ11k2fAEECjpXZIRoSMIjw5nwaEFVsVIgmdGRlQUcfO/pUjbtnjWrXtTWbbOZuw/Y/F08WRE6AjbG98xA079AU9NhIDKVod9vuEom47EMObZGjSp6G97v0IIIYRwWLcm5XjzsYos3n6OGX+etD4wuBY8O800937TOJv7bV+xPa3KtGL6nukciTuSZ32HEjyl1BSl1BGlVIRS6kelVFHjejmlVIpSaq/x+SZHTAOl1H6l1Aml1BfKeDymlHJXSi03ru9QSpXLEdNdKXXc+HTPcb28Ufe4EetmXFdG2yeMsdW35feK+XwqAEFDBt9WtvzocvbE7GFYw2EEeAbY0qzpnNmNY03v4hv0sDrsp31RfLX5JJ1DS/Nq47K29SmEEEKIO2rYU1VpX7cEn/x6lNV7LlgfWKcThPSCv/8Dh3+yqU+lFKObjKaoe1FGbhlJWpblxR6OPsHbANTSWtcGjgEjc5Sd1FrXNT45N4ibAfQBKhufNsb114CrWutKwFRgsvEL+QFjgEZAKDBGKeVrxEwGpmqtKwNXjTYAns7Rfh+jT6uk7NtH4rp1+PXsgWuJEjeVXbx2kWnh02haointKraztkmTjBRY1dt0zmy76Vbvd3cwKoGhK/cRUtaXD9vVkkUVQgghxH3m5KT45IXaNK7gx9CV+9h2Itb64DaToER90/GkV2x4Agj4evgyvtl4TsSf4D+7/2N5jDa1fAut9e9a6xsvkrcDFk+5V0oVB4porf/RphfXC4EORnF74MaL5ZVAS+Pp3lPABq11nNb6Kqakso1R9i+jLkZszrYWapPtQFGj7zxFfzwJ58AAAl5//dbflfHbx6PRjG4y2vZEa8NouHwEOsyAQta9Yr1yLY0+C8Px9XJjRtcGuLnIG3UhhBDiQeDu4szMV0MoH1CINxaFPqlOxgAAE/lJREFUc+RSonWBLu7w0kJwcjFtgpxuw956QPOSzXm56sssOrTIYr07mTH0Atbn+Lm8UmqPUupPpdSjxrWSQGSOOpHGtRtl5wGMpDEB8M95/ZYYfyA+R4Jpti0zZTdRSvVRSoUppcLiz0eSsncvQW+/jVOhQjfV++X0L2y5sIW36r1FSW+zTeXu2O+wcxY07geVWloVkpGVTd8lu4m9lsbMVxsQWNjGTZSFEEIIcVf5eLrybc9QvNyd6TFvFxcTUqwLLFoaOs6GmMOw7h2bN0EeEjKEckXKWayTZ4KnlNqolDpg5tM+R533gExgiXHpIlBGa10PGAwsVUoVAcw99rrxW+VWZut1S23dflHrWVrrEK11iGdKMu7Vq+PTocNNda6mXmXyzsnUDqhNl2qWjwa5zbXLsKYfBNWElmOsDvvwp4PsPB3H5I61qV2qqG19CiGEEOKeKFHUk297hnItLZOe83dxLc3KFbKVWsHjIyFiOYTNtalPTxdPJj06yWKdPBM8rXUrrXUtM581YFoAATwDvGK8dkVrnaa1vmJ8DwdOAlUwPUnL+Rq3FHBj175IoLTRpgvgA8TlvH5LTCymV68ultoyU5b775qeQbHhw1HOzjddn7xrMkkZSYxtOhZnJ+dcos01qGFNf9NRZB3nWL0lypIdZ1m8/RxvtKhAh3o2Pi0UQgghxD1VvXgRvunagOMx13h72V6ys618ItdiKFR6EtaPgMhwm/qsGWD5BCxHV9G2AYYD7bTWyTmuByqlnI3vFTAtdjiltb4IJCmlGhtz6LoBa4ywtcCNFbIvAJuMhPE3oLVSytdYXNEa+M0o22zUxYjN2VY3YzVtYyDB6Nsi5wB/CjVudNO1LZFb+PnUz/R+pDeVfa3f1gSAXXPg+G/QejwUq2FVyM7TcYxZc5DHqgQyrE012/oTQgghxH3RvHIAo5+pwcbD0Xy+4Zh1QU5O8PwsKFwcVnSD61fu2HgcnYP3JVAY2HDLdigtgAil1D5MiyDe1FrHGWV9gTnACUxP9m7M25sL+CulTmB6rTsCwIgbD+wyPuNytDUcGGzE+BttAPwCnDL6mA30s+aXcQ0Ovunn6xnXGbd9HBV8KvD6I6/nEpWLmCOmY0kqtYLQPlaFXIhPoe/icEr7efFFp3o4O8mKWSGEECK/6NakLJ1DS/Pl5hP8tM/KY8W8/ODlhXD9Mqx6Daw8qSIvyupdmB8CISEhOiws7H8/T9wxkWVHlrHw6YXUDaprIfIWmWkwuyUkRUHff6BwsTxDUtKzeHHmNs7EJrO6f1MqBdl4/JkQQggh7rv0zGxembOdiMgEVr7ZlEdK+VgXGL4AfhoILYbBv96zKkQpFa61DjFXJvtu5GJvzF6WHVlG52qdbUvuADaNh+j90P4rq5I7rTXDV0VwMCqR/3SqK8mdEEIIkU+5uTgxo2sDArzdeX1hGDFJqdYF1u8GdbvCX5/Asd8cHockeGakZ6UzZtsYggsFM6j+INuCT26GbdMh5DWo+rRVITP/OsXafVG827oqLavnnRAKIYQQ4sEV4O3OrG4NSEjJ4I1F4aRlWvHaVSlo+ykEPwI/9IGrZxwagyR4ZsyKmMWphFOMbjIaL1cv6wOT42B1XwioAq0nWBWy+UgMk389wjO1i9Pv8Yp2jlgIIYQQD5KaJXz4/KU67DkXz3s/HsCqKXGunvDSIkDD8lchw8qnf2ZIgneLY1ePMXf/XJ6p8AzNSza3PlBrWPsWXI81bYnilndiePLyNQYu20P14CJ88kJtOYZMCCGEKECefqQ4g1pWZmV4JHO3nrYuyK88PDcLLkXAL+/a3bckeLcYu20shd0KM6zhMNsC9yyCI+ug5QdQvE6e1RNTM3h9YRiuzk7M6tYALzeXPGOEEEIIkb8MalmZp2sFM/GXw/x57LJ1QVXbwKPvmnKL3Qvt6lcSvByupFxhf+x+RoSOwNfD14bAk7B+OJRvAU3eyrO61prBy/dx7koyX79Sn1K+NrwGFkIIIUS+4eSk+OylOlQNLsKApbs5dfmadYFPjILyj8HP78LFfbb3a3NEARaTEkOLUi14urx1iyMAyMqAVb3B2Q06fGPatDAPc7eeZuPhaEb+uzqNK/g7MGIhhBBCPOi83FyY3a0Bbs5O9F4YRkJKRt5BTs7wwjwoFGCaj5dy1aY+JcHLQaH4oPEHts2F+2MSRO2Gdl+AT97Hiu0+d5VJ64/QukYxejUrZ/9ghRBCCJFvlPL1YkbXBpy7kszA7/aQZc1xZoUC4MUFkBgFP7wB2dlW9ycJXg5lipQhuFBw3hVvOPM3bPkM6nWFGu3zrB6fnM5bS/cQ7OPBlBfqyKIKIYQQ4iESWt6P8R1q8eexy0z+9Yh1QaUbQpuPTUefbv3M6r4kwcvBy8WGuXAp8fDjG6bVLm0m51lda82730cQk5TKl13q4+Pl6sBIhRBCCJEfdQ4tQ/cmZZn11ylWhUdaF9SwNzzyImz6CE5usipEEjx7/TzE9Mj0+dng7p1n9Xl/n2Hj4WhGPF2duqWL3oMBCiGEEOJB9P4zNWha0Z+RP+xn9zkr5tYpBc/+BwKrwcrXIP58niGS4NkjYgUcWAmPj4RSZo+Au8ne8/FMWn+YJ2XenRBCCPHQc3V24qsu9Qn28eCNReFcSrBiQ2O3QvDyYtPizu+7m869t0ASPFtdPWN6elemCTw6OM/qCckZDFi6m6DCHkyRzYyFEEIIAfgWcmNO9xCS0zLpsyiM1AwrjjMLqAQdvoYL4fDbKItVJcGzRVamaRULwHMzTUuYLdBaM3TlPi4lpPJll3oU9XK7B4MUQgghRH5QpVhh/tOpHvsvJDBsZYR1x5nVaAdN34JdcyxWkwTPFls/h/Pboe3n4Fs2z+rz/z7D74eiGfF0NeqVsWHjZCGEEEI8FFrVKMa7rauydl8UM/48aV1Qy7FQtpnFKpLgWev8LtOed4+8CLVfzLP6vvPxfLz+MK2qB/Fa8/L3YIBCCCGEyI/6PV6RZ+uUYMpvR9l4KDrvAGcXeGG+xSqS4FkjLQl+6A1FSsK/P82zekJKBv2NeXefvij73QkhhBAid0opPulYm1olfBi0bA/HopPyDipczGKxJHjWWD8C4s/B8zPB0/IWJ1prhq+M4FJCKl90lnl3QgghhMibp5szs7o1wMvdhd4Lwrh6Pd2h9iTBy8vBH2HvYnh0CJRtmmf1BdvO8OvBSwxrU5UGZWXenRBCCCGsU9zHk5mvNuBSQir9l+4mI8v6o8luJQmeJQmR8NMgKNkAHhueZ/WIyHgm/nKEltWC6N28wj0YoBBCCCEKkvplfJn4/CNsO3mFj34+bHc7LndwTAVLdhb8+KZpa5TnZ4Oz5aPFbsy7C/B249MX6+DkJPPuhBBCCGG7FxqU4uilRGZvOU3V4MJ0Di1jcxvyBC8326bDmS3w9GTwr2ixqtaaEasiiIpPZXqXevgWknl3QgghhLDfiKer81iVQEavOcDO03E2x0uCZ07UXtg0Aaq3g3pd86y+aPtZ1h+4xLCnqtKgrN89GKAQQgghCjJnJ8UXnetR2teLvovDibyabFO8JHi3Sk+GVb2hUKDpYN88tjg5cCGBCesO80TVQF5/VObdCSGEEOLO8PF0ZXb3ENKzsnl9YTjJ6ZlWx0qCd6vf34MrJ+C5b8DL8tO4xFTTvDt/bzc+e6muzLsTQgghxB1VMdCbL7vU5+ilRIas2Ed2thXHmSEJ3s1SEyBsHjQdABUes1hVa83IVfuJvJrC9M718JN5d0IIIYS4Cx6rEsiof1dn/YFLfLHpuFUxkuDlFH8Ogh+Bf32QZ9XFO87x8/6LvNu6KiHlZN6dEEIIIe6e15qXp2P9UkzbeJz1+y/mWV+2SclJZ0PHueDibrHagQsJjP/pEI9XDeSNFjLvTgghhBB3l1KKj56rxanYawxesY+y/oUs1pcneDn5VYDAqharJBnz7vwKufG5zLsTQgghxD3i4erMzK4N8PF05fWFYRbrSoKXk3thi8Vaa0b+YMy76yLz7oQQQghxbwUV8WBWtwbEXkuzWE8SPBss2XGOdREXGfxkFRrKvDshhBBC3Ae1SxXlkxdqW6wjCZ6VDkYlMG7dIVpUCaTvY5ZPthBCCCGEuJva1y1psVwSPCskpWYwYOkefL1cmfqSnDMrhBBCiAebrKLNg9aaUT8e4OyV63z3emP8vS2vsBVCCCGEuN/kCV4evtt5np/2RTGkdVUaVfC/38MRQgghhMiTJHgWHIpKZOxPB3m0coDMuxNCCCFEviEJXi6upWUyYOluinq6MvVl2e9OCCGEEPmHzMEzQ2vNez/u58yV6yx9vTEBMu9OCCGEEPmIPMEzY/mu86zZG8U7rarQWObdCSGEECKfkQTvFocvJjJmrWneXb8nKt3v4QghhBBC2MyhBE8pNV4pFaGU2quU+l0pVSJH2Uil1Aml1FGl1FM5rjdQSu03yr5QSinjurtSarlxfYdSqlyOmO5KqePGp3uO6+WNuseNWDfjujLaPmGMr741v0+21vRfupsinq58/lJdnGXenRBCCCHyIUef4E3RWtfWWtcF1gGjAZRSNYBOQE2gDfC1UsrZiJkB9AEqG582xvXXgKta60rAVGCy0ZYfMAZoBIQCY5RSvkbMZGCq1roycNVoA+DpHO33MfrM04X4FM7EXuc/neoSWFjm3QkhhBAif3IowdNaJ+b4sRCgje/tgWVa6zSt9WngBBCqlCoOFNFa/6O11sBCoEOOmAXG95VAS+Pp3lPABq11nNb6KrABaGOU/cuoixGbs62F2mQ7UNTo26L45AwGtaxC04oBtv6rEEIIIYR4YDi8ilYp9RHQDUgAnjAulwS256gWaVzLML7fev1GzHkArXWmUioB8M95/ZYYfyBea51pqa1byi5a+l0Ke7gw4F8y704IIYQQ+VueT/CUUhuVUgfMfNoDaK3f01qXBpYAA26EmWlKW7huT4w9bd1GKdVHKRWmlAorlJ0s8+6EEEIIke/l+QRPa93KyraWAj9jmi8XCZTOUVYKiDKulzJznRwxkUopF8AHiDOuP35LzB9ALKZXry7GUzxzbZnr59bfbxYwCyAkJMRsEiiEEEIIkZ84uoq2co4f2wFHjO9rgU7GytjymBY77NRaXwSSlFKNjTl03YA1OWJurJB9AdhkzNP7DWitlPI1Fle0Bn4zyjYbdTFic7bVzVhN2xhIMPoWQgghhCjwHJ2DN0kpVRXIBs4CbwJorQ8qpVYAh4BMoL/WOsuI6Qt8C3gC640PwFxgkVLqBKYnd52MtuKUUuOBXUa9cVrrOOP7cGCZUmoCsMdoA+AX4N+YFnckAz0d/D2FEEIIIfINZXoQJsD0ijYsLOx+D0MIIYQQIk9KqXCtdYi5MjnJQgghhBCigJEETwghhBCigJEETwghhBCigJEETwghhBCigJEETwghhBCigJEETwghhBCigJEETwghhBCigJEETwghhBCigJEETwghhBCigJGTLHJQSl3GdORaQRUAxN7vQQi7yf3Lv+Te5W9y//Kvgn7vymqtA80VSIL3EFFKheV2pIl48Mn9y7/k3uVvcv/yr4f53skrWiGEEEKIAkYSPCGEEEKIAkYSvIfLrPs9AOEQuX/5l9y7/E3uX/710N47mYMnhBBCCFHAyBM8IYQQQogCRhK8h4hS6l2llFZKBeS4NlIpdUIpdVQp9dT9HJ+4nVJqilLqiFIqQin1o1KqaI4yuXf5gFKqjXGPTiilRtzv8YjcKaVKK6U2K6UOK6UOKqUGGdf9lFIblFLHjX/63u+xCvOUUs5KqT1KqXXGzw/tvZME7yGhlCoNPAmcy3GtBtAJqAm0Ab5WSjnfnxGKXGwAammtawPHgJEg9y6/MO7JV8DTQA2gs3HvxIMpExiita4ONAb6G/drBPBfrXVl4L/Gz+LBNAg4nOPnh/beSYL38JgKDANyTrpsDyzTWqdprU8DJ4DQ+zE4YZ7W+netdabx43aglPFd7l3+EAqc0Fqf0lqnA8sw3TvxANJaX9Ra7za+J2FKFEpiumcLjGoLgA73Z4TCEqVUKaAtMCfH5Yf23kmC9xBQSrUDLmit991SVBI4n+PnSOOaeDD1AtYb3+Xe5Q9yn/IppVQ5oB6wAyimtb4IpiQQCLp/IxMWTMP0ICM7x7WH9t653O8BiDtDKbURCDZT9B4wCmhtLszMNVlWfY9Zunda6zVGnfcwvT5aciPMTH25dw8euU/5kFLKG1gFvK21TlTK3G0UDxKl1DNAjNY6XCn1+P0ez4NAErwCQmvdytx1pdQjQHlgn/EfqVLAbqVUKKanCaVzVC8FRN3loYpb5HbvblBKdQeeAVrq/9/XSO5d/iD3KZ9RSrliSu6WaK1/MC5HK6WKa60vKqWKAzH3b4QiF82AdkqpfwMeQBGl1GIe4nsnr2gLOK31fq11kNa6nNa6HKa/cOprrS8Ba4FOSil3pVR5oDKw8z4OV9xCKdUGGA6001on5yiSe5c/7AIqK6XKK6XcMC2MWXufxyRyoUz/FzwXOKy1/jxH0Vqgu/G9O7DmXo9NWKa1Hqm1LmX8PdcJ2KS17spDfO/kCd5DTGt9UCm1AjiE6fVff6111n0elrjZl4A7sMF4Artda/2m3Lv8QWudqZQaAPwGOAPztNYH7/OwRO6aAa8C+5VSe41ro4BJwAql1GuYdiJ48T6NT9juob13cpKFEEIIIUQBI69ohRBCCCEKGEnwhBBCCCEKGEnwhBBCCCEKGEnwhBBCCCEKGEnwhBBCCCEKGEnwhBBCCCEKGEnwhBBCCCEKGEnwhBBCCCEKmP8DQKq8tNCnEGUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"p_res = {k:{shock:-x[Price].aggregate() for shock, x in v.items()} for k, v in results.items()}\\n\",\n \"pd.DataFrame(p_res).plot(figsize=(10,6), title='Price by Shock')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"v_res = {k:{shock:-x[IRVegaParallel].aggregate() for shock, x in v.items()} for k, v in results.items()}\\n\",\n \"pd.DataFrame(v_res).plot(figsize=(10,6), title='Vega by Shock')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally let's look at our breakevens as we move from inception to expiry.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.DataFrame(p_res).reindex(range(-50,50)).interpolate()\\n\",\n \"breakeven = df.loc[0]['Inception']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import IRSpotRate\\n\",\n \"spot = strangles['Inception'][0].calc(IRSpotRate) * 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"top = pd.Series(dtype=float)\\n\",\n \"bottom = pd.Series(dtype=float)\\n\",\n \"\\n\",\n \"for column in df.columns:\\n\",\n \" sl = df.iloc[(df[column]-breakeven).abs().argsort()[:2]]\\n\",\n \" a, b = sl[column].index\\n\",\n \" top.at[column] = a if a >= 0 else b\\n\",\n \" bottom.at[column] = a if a < 0 else b\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"(top/100 + spot).reindex(labels).plot(figsize=(10,6))\\n\",\n \"(bottom/100 + spot).reindex(labels).plot(figsize=(10,6))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Please reach out to `gs-quant@gs.com` with any questions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimer\\n\",\n \"This website may contain links to websites and the content of third parties (\\\"Third Party Content\\\"). We do not monitor, review or update, and do not have any control over, any Third Party Content or third party websites. We make no representation, warranty or guarantee as to the accuracy, completeness, timeliness or reliability of any Third Party Content and are not responsible for any loss or damage of any sort resulting from the use of, or for any failure of, products or services provided at or from a third party resource. If you use these links and the Third Party Content, you acknowledge that you are doing so entirely at your own risk.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/10-Explaining Performance Drivers.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Explaining Performance Drivers\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"In this note I team up Maxim Fedotov from Rates Structuring Strats to take a closer look at hedging a popular bond trade and present a new framework for explaining pnl that can be applied across any trade or portfolio in `gs_quant`. Traders, PMs, risk managers or operationally oriented users can deploy this framework to improve their understanding of historical performance drivers and in turn drive better hedging, risk management and cash management decisions.\\n\",\n \"\\n\",\n \"While foreign fixed income assets can present attractive investment opportunities, domestic investors may need to use overlays if they want to receive domestic currency. One popular example of this has been buying JGBs and using a fixfix swap to receive in local. In this bond + swap package, FX of the swap will fully match the bond FX component but clients can still face pnl volatility due to basis and IR differences not matched by the bond accounted at cost.\\n\",\n \"\\n\",\n \"In this notebook I will take a closer look at the fixfix swap tailored for a specific JGB bond and decompose its historical pnl drivers into rates, cross currency, fx and cashflow components to better understand the drivers of this volatility.\\n\",\n \"\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Model bond as FixFix swap](#2---Model-bond-as-fixfix-swap)\\n\",\n \"* [3 - Attribute pnl and calculate cashflows](#3---Attribute-pnl-and-calculate-cashflows)\\n\",\n \"* [4 - Putting it all together](#4---Putting-it-all-together)\\n\",\n \"* [What's New](#What's-New)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the risk functionality and `read_product_data` is required for pulling data covered in this example. Below produced using gs-quant version 0.8.155.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 118,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data')) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 - Model bond as fixfix swap\\n\",\n \"\\n\",\n \"Let's pick a JGB bond to analyze - in this example we will look at `JGB #53 JP1300531GC0` (ISIN) and fill in the relative details. Note here we are manually inputting the details but we'll remove this step once you're able to model bonds in gs quant directly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 119,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"\\n\",\n \"bond_notional = 1e8\\n\",\n \"bond_coupon = 0.006\\n\",\n \"coupon_freq = '6m'\\n\",\n \"bond_maturity = date(2046, 12, 20)\\n\",\n \"last_cpn_date = date(2018, 12, 20) # Last paid coupon date as seen from backtest start date\\n\",\n \"maturity = bond_maturity\\n\",\n \"bond_dirty_price = 97.66\\n\",\n \"\\n\",\n \"# historical window we'll examine\\n\",\n \"start_date = date(2019, 1, 2)\\n\",\n \"end_date = date(2019, 11, 1)\\n\",\n \"CSA = 'EUR-OIS'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, in order to receive payments in local - let's say that is EUR rather than JPY - we can structure a `IRXccySwapFixFix` swap that matches our bond's characteristics outlined above.\\n\",\n \"\\n\",\n \"To do this, we need to size it to the bond notional using the EUR/JPY FX rate at the start of our window. Let's pull it from [Marquee data catalogue](https://marquee.gs.com/s/discover/data-services/catalog) as a first step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 120,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"\\n\",\n \"ds = Dataset('FXSPOT_STANDARD')\\n\",\n \"eurjpy_data = ds.get_data(start_date, end_date, bbid='JPYEUR')\\n\",\n \"fx_rate = eurjpy_data.loc[start_date].spot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With the FX spot as of `start_date` in hand, let's define our swap and `resolve()` to fix any relative parameters as of the same `start date`. We can use `as_dict()` to view what these are. I'll also calculate swap cashflows here that we will use later to add to the total pv we're attributing. Note below we can choose between par or proceeds asset swap format that impact the fixed notional and fee used.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 121,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'termination_date': datetime.date(2046, 12, 20),\\n\",\n \" 'payer_day_count_fraction': ACT/ACT ISDA,\\n\",\n \" 'receiver_rate': 0.01795441569651901,\\n\",\n \" 'fee': 0.0,\\n\",\n \" 'fee_currency': JPY,\\n\",\n \" 'payer_rate': 0.006,\\n\",\n \" 'type': XccySwapFixFix,\\n\",\n \" 'receiver_business_day_convention': Modified Following,\\n\",\n \" 'payer_frequency': '6m',\\n\",\n \" 'notional_amount': 100000000.0,\\n\",\n \" 'receiver_frequency': '1y',\\n\",\n \" 'principal_exchange': Last,\\n\",\n \" 'asset_class': Rates,\\n\",\n \" 'receiver_day_count_fraction': 30/360,\\n\",\n \" 'receiver_currency': EUR,\\n\",\n \" 'effective_date': datetime.date(2018, 12, 20),\\n\",\n \" 'fee_payment_date': datetime.date(2019, 1, 7),\\n\",\n \" 'receiver_notional_amount': 788340.8179999999,\\n\",\n \" 'payer_currency': JPY,\\n\",\n \" 'payer_business_day_convention': Modified Following}\"\n ]\n },\n \"execution_count\": 121,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.instrument import IRXccySwapFixFix\\n\",\n \"from gs_quant import risk\\n\",\n \"\\n\",\n \"ASWType = 'Proceeds' # Can either be par or proceeds\\n\",\n \"fixed_notional = bond_notional * fx_rate * bond_dirty_price / 100 if ASWType=='Proceeds' else bond_notional * fx_rate\\n\",\n \"fee = 0 if ASWType=='Proceeds' else -(bond_dirty_price - 100) / 100 * bond_notional\\n\",\n \"\\n\",\n \"swap = IRXccySwapFixFix(effective_date=last_cpn_date, termination_date=bond_maturity, notional_amount=bond_notional, \\n\",\n \" payer_currency='JPY', receiver_notional_amount=fixed_notional, payer_rate=bond_coupon,\\n\",\n \" receiver_currency='EUR', payer_frequency=coupon_freq, receiver_frequency='1y',\\n\",\n \" payer_day_count_fraction='act/act ISDA', fee=fee, receiver_rate='ATM')\\n\",\n \" \\n\",\n \"with PricingContext(pricing_date=start_date, market_data_location='LDN', csa_term=CSA):\\n\",\n \" swap.resolve()\\n\",\n \" cf = swap.calc(risk.Cashflows)\\n\",\n \"swap.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 - Attribute pnl and calculate cashflows\\n\",\n \"\\n\",\n \"Now, let's break out our swap pv into contributions from various drivers - to do this, we'll use a newly minted gs-quant analytic called `PnlExplain` which attributes the change in value of an individual trade (or portfolio) to market moves. Note the values returned are in USD. In the below, I look at `PnLExplain` every day to get a daily attribution but you can use this for any time period. I also group by`mkt_type` for clarity but you can use the measure to get a more granular view by removing the grouping. You'll notice a `CROSSES` PnL, which represents the cross-effects among the other types.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 122,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import CloseMarket\\n\",\n \"\\n\",\n \"result_dict = {}\\n\",\n \"for d in eurjpy_data.index:\\n\",\n \" exp_measure = risk.PnlExplain(CloseMarket(date=d.date()))\\n\",\n \" with PricingContext(pricing_date=start_date, market_data_location='LDN', is_async=True, csa_term=CSA):\\n\",\n \" exp_res = swap.calc(exp_measure)\\n\",\n \" result_dict[d.date()] = exp_res\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 144,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
mkt_typeCROSSESFXFX FWDIRIR BASISIR CCIR XC
date
2019-01-0336.621530-11980.63919125.769065-1120.076874-20.614371-0.1034772672.806140
2019-01-04-108.035684-5073.44524810.048568-30625.813539350.301729-0.0984693844.754112
2019-01-07-115.6540681844.80506812.789388-20658.964027951.194210-0.0053723189.912983
2019-01-08-89.085150953.0320558.299623-19361.553933-592.2762100.0027934873.192840
2019-01-09-230.0154294820.73888317.457522-16422.007642-607.153929-0.0204741183.107531
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \"mkt_type CROSSES FX FX FWD IR IR BASIS \\\\\\n\",\n \"date \\n\",\n \"2019-01-03 36.621530 -11980.639191 25.769065 -1120.076874 -20.614371 \\n\",\n \"2019-01-04 -108.035684 -5073.445248 10.048568 -30625.813539 350.301729 \\n\",\n \"2019-01-07 -115.654068 1844.805068 12.789388 -20658.964027 951.194210 \\n\",\n \"2019-01-08 -89.085150 953.032055 8.299623 -19361.553933 -592.276210 \\n\",\n \"2019-01-09 -230.015429 4820.738883 17.457522 -16422.007642 -607.153929 \\n\",\n \"\\n\",\n \"mkt_type IR CC IR XC \\n\",\n \"date \\n\",\n \"2019-01-03 -0.103477 2672.806140 \\n\",\n \"2019-01-04 -0.098469 3844.754112 \\n\",\n \"2019-01-07 -0.005372 3189.912983 \\n\",\n \"2019-01-08 0.002793 4873.192840 \\n\",\n \"2019-01-09 -0.020474 1183.107531 \"\n ]\n },\n \"execution_count\": 144,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def format_res(r, d):\\n\",\n \" # here we group and sum by market type - you can get a more granular view when skipping this step\\n\",\n \" df = r.groupby('mkt_type').sum().reset_index()\\n\",\n \" df['date'] = d\\n\",\n \" return df.set_index('date')\\n\",\n \"\\n\",\n \"result_clean = pd.concat([format_res(r.result(), d) for d, r in result_dict.items() if len(r.result())])\\n\",\n \"result = result_clean.groupby(['date', 'mkt_type']).sum().unstack()\\n\",\n \"result.columns = result.columns.droplevel(0)\\n\",\n \"result.loc[start_date] = 0 # first day is 0 pnl\\n\",\n \"result.head() # let's take a peak!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now for the final bit - cashflows. We have already calculated our cashflows in [step 2](#2---Model-bond-as-fixfix-swap). Let's remove anything that's later than our `end_date` and convert the cashflow amount into USD.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 145,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd \\n\",\n \"\\n\",\n \"cfs = cf.result()\\n\",\n \"cfs = cfs[cfs['payment_date'] < end_date]\\n\",\n \"cash = pd.Series(cfs.payment_amount.values, index=cfs.payment_date)\\n\",\n \"cash = (cash * eurjpy_data.spot).fillna(0)\\n\",\n \"result['CASH'] = cash\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 - Putting it all together\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, with all the results in hand, let's take a look!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 146,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 146,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"result['Total'] = result.sum(axis=1)\\n\",\n \"result.plot(figsize=(12, 8), title='PnL Drivers')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see in the chart above, over the backtest period rates drive most of the positive swap performance while cross currency effects are largely a negative drag over the period. FX, although a small positive contributor through mid 2019, ultimately drives much of the negative contribution through the remainder of the year. Remember we are looking at the swap hedge only here so FX will be entirely offset by the bond in the bond+swap package.\\n\",\n \"\\n\",\n \"In this note we decomposed the fixfix swap but you can use this framework to analyse any trade or portfolio - looking forward to hearing your feedback!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What's New\\n\",\n \"* `PnlExplain` which we covered in this note!\\n\",\n \"* `Portfolio.from_frame` and `Portfolio.from_csv` to help map and represent your portfolio object from a dataframe or csv file.\\n\",\n \"* `to_frame` to view complex results - see example [here](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/blob/master/gs_quant/examples/01_pricing_and_risk/00_rates/010014_spread_option_grid_pricing.ipynb)\\n\",\n \"* Solvers for different fields to allow to solve for a strike or fixed rate such that PV=x. Examples to come - please reach out in the meantime.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/11-FX Election Hedge.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## FX Election Hedge\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"With the US election less than 40 days away, election market dynamics are a major risk driver across asset classes. In this note, we look for interesting FX proxies for different election outcomes and find that AUDJPY has a strong relationship to and a higher beta to SPX in downside scenarios than in upside ones, making it a potentially attractive downside hedge. We use `gs-quant` to examine the relationship, price several structures and analyze impact of different election outcomes on these trades.\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - State of FX market](#2---State-of-FX-market)\\n\",\n \"* [3 - Downside sensitivity to SPX](#3---Downside-sensitivity-to-SPX)\\n\",\n \"* [4 - Structures](#4---Structures)\\n\",\n \"* [5 - Pricing election outcomes](#5---Pricing-election-outcomes)\\n\",\n \"* [What's New](#What's-New)\\n\",\n \"\\n\",\n \"P.S. In case you missed it, [check out the replay of our last webinar](https://developer.gs.com/docs/gsquant/videos/gs-quant-meets-markets/) where our traders and strats use code as content to comment on macro trends, in particular around US elections.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.199.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data')) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - State of FX market\\n\",\n \"\\n\",\n \"Let's focus on G10 and grab spot and vol data from [gs data catalog](https://marquee.gs.com/s/discover/data-services/catalog). To start, let's see how things have evolved over the last few months - we'll use the longer history in latter parts of the analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"import pandas as pd\\n\",\n \"import datetime\\n\",\n \"\\n\",\n \"start_date = datetime.date(2005, 8, 26)\\n\",\n \"end_date = datetime.date.today()\\n\",\n \"\\n\",\n \"fx_pairs = ['USDJPY', 'EURUSD', 'AUDUSD', 'AUDJPY', 'USDNOK', 'GBPUSD','USDCAD', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"\\n\",\n \"fx_spot = Dataset('FXSPOT_PREMIUM').get_data(start_date, end_date, bbid=fx_pairs, location='NYC')\\n\",\n \"fx_spot = pd.pivot_table(fx_spot, values='spot', index=['date'], columns=['bbid'])\\n\",\n \"\\n\",\n \"fx_vol = Dataset('FXIMPLIEDVOL_PREMIUM').get_data(start_date, end_date, bbid=fx_pairs, tenor='3m', deltaStrike='DN', location='NYC')\\n\",\n \"fx_vol = pd.pivot_table(fx_vol, values='impliedVolatility', index=['date'], columns=['bbid'])\\n\",\n \"\\n\",\n \"spx_spot = Dataset('TREOD').get_data(start_date, end_date, bbid='SPX')[['closePrice']].rename(columns = {'closePrice':'SPX'})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"last3m = fx_spot.tail(90).apply(lambda x: 1/x if x.name[:3]=='USD' else x)\\n\",\n \"ax = (last3m / last3m.iloc[0]).plot(figsize=(12, 8), title='FX Moves last 3m (SPX on RHS, all crosses vs USD)')\\n\",\n \"spx_spot.tail(90).plot(secondary_y=True, ax=ax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fx_vol.tail(90).plot(figsize=(12, 8), title='FX Vol last 3m')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"USD has weakened relative to the major currencies over the first 2 months of the period but strengthened most recently. Looking to the vol space, 3m vols peaked first half of June, normalizing slightly over next few months but have started to climb again most recently. Let's see their z scores to see how many standard deviations they are away from the mean on a 1, 3, 6, 9 month basis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.timeseries import beta, returns, diff, zscores\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"\\n\",\n \"zs = lambda p, d: zscores(d.tail(p)).tail(1).values[0]\\n\",\n \"fx_vol_zs = {cross: {'1m': zs(21, data), '3m': zs(63, data), '6m': zs(126, data), \\n\",\n \" '9m': zs(189, data)} for cross, data in fx_vol.iteritems()}\\n\",\n \"\\n\",\n \"def plot_crosses(data, xlabel, title):\\n\",\n \" plt.subplots(figsize=(10, 6))\\n\",\n \" center = max(data.max().max(), abs(data.min().min())) \\n\",\n \" ax = sns.heatmap(data, annot=True, vmin=-center, vmax=center, fmt='.2f', cmap='coolwarm_r', )\\n\",\n \" ax.set(ylabel='Crosses', xlabel=xlabel, title=title)\\n\",\n \" ax.xaxis.tick_top()\\n\",\n \" ax.xaxis.set_label_position('top')\\n\",\n \"\\n\",\n \"plot_crosses(pd.DataFrame(fx_vol_zs).T, xlabel='Lookback', title='FX Vol Z Scores',)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Downside sensitivity to SPX\\n\",\n \"\\n\",\n \"Although many parts of the market recovered much of the lost ground since the start of the pandemic, as we can see above, a skittish environment remains. Given an abundance of potential downside catalysts and elections on the horizon, it's no surprise implied volatility in FX and especially in equity markets where investors are looking to downside protection, remains high. \\n\",\n \"\\n\",\n \"Let's look at the relationship of the crosses we examined so far to SPX returns especially in drawdown scenarios to see if we can find FX alternatives with a high SPX beta but potentially lower cost.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"def package_data(data, freq='W-FRI', abs_c=False, w=1):\\n\",\n \" spt_data = pd.concat([data, spx_spot], axis=1)\\n\",\n \" sampled = spt_data.resample(freq).last()\\n\",\n \" return diff(sampled, w).dropna() if abs_c else returns(sampled, w).dropna()\\n\",\n \" \\n\",\n \"def perf_vs_spx(spx_returns, data, show_ret=False):\\n\",\n \" avg_p = defaultdict(dict)\\n\",\n \" for ret in spx_returns:\\n\",\n \" dns = data[data.SPX <= ret].dropna() if ret < 0 else data[data.SPX >= ret].dropna() \\n\",\n \" for cross in fx_pairs:\\n\",\n \" avg_p[ret][cross] = dns[cross].mean() * 100\\n\",\n \" if show_ret:\\n\",\n \" avg_p[ret]['SPX'] = dns.SPX.mean() * 100\\n\",\n \" print(f'# of observations for SPX {ret}: {len(dns)}')\\n\",\n \" return pd.DataFrame(avg_p)\\n\",\n \"\\n\",\n \"spx_returns = [-.1, -.05, .05, .1] \\n\",\n \"spot_perf = perf_vs_spx(spx_returns, package_data(fx_spot), show_ret=True)\\n\",\n \"vol_perf = perf_vs_spx(spx_returns, package_data(fx_vol, abs_c=True)*100)\\n\",\n \"\\n\",\n \"plot_crosses(spot_perf, 'SPX Weekly Move', 'Avg weekly % return during large SPX moves')\\n\",\n \"plot_crosses(vol_perf, 'SPX Weekly Move', 'Avg weekly vol change during large SPX moves')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see above AUDJPY and AUDUSD moved with the largest magnitude in the same direction with as SPX when SPX experienced large moves since the start date (2005 in this case). Instead of looking at returns, let's focus on one of the crosses - AUDJPY - and look at the conditional relationship through a different lens: the R-squared number on each chart will tell us the strength of the relationship with numbers closer to 0 indicating a lack of one.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from scipy import stats\\n\",\n \"\\n\",\n \"r2 = lambda x,y: stats.pearsonr(x,y)[0]**2 \\n\",\n \"\\n\",\n \"def SPX_conditional_downside(thresholds, data, asset):\\n\",\n \" for ret in thresholds:\\n\",\n \" dns = data[data.SPX <= ret].dropna() if ret < 0 else data[data.SPX >= ret].dropna() \\n\",\n \" j = sns.jointplot(x='SPX', y=asset, data=dns, kind='reg')\\n\",\n \" j.set_axis_labels('SPX with {}% Returns'.format(ret*100), asset)\\n\",\n \" j.fig.subplots_adjust(wspace=.02)\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"spot_package = package_data(fx_spot)\\n\",\n \"SPX_conditional_downside(spx_returns, spot_package, 'AUDJPY')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see from the charts above, there's a strong positive relationship between AUDJPY and SPX in large downside scenarios (SPX -5% and -10% on the week) but not on the upside (SPX +5%, +10%). This makes this cross an attractive candidate for an alternative to hedging with SPX directly.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Structures\\n\",\n \"\\n\",\n \"Let's now look at a few AUDJPY structures as potential hedges:\\n\",\n \"\\n\",\n \"* Buy 2m AUDJPY put using spx beta to size. Max loss limited to premium paid.\\n\",\n \"* Buy 2m AUDJPY put spread (5.4%/10% OTMS). Max loss limited to premium paid.\\n\",\n \"\\n\",\n \"For more info on these trades, reach out to receive the piece from our marketstrats team. Note all pricing is based on GS T-1 mid marks and does not include transaction costs.\\n\",\n \"\\n\",\n \"We'll start by looking at the cost of an 10% OTMS SPX put (max loss limited to premium paid).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import EqOption, FXOption\\n\",\n \"\\n\",\n \"spx = EqOption(option_type='Put', underlier='.SPX', exchange='NYSE', strike_price='90%', expiration_date='2m', buy_sell='Buy')\\n\",\n \"spx.resolve()\\n\",\n \"\\n\",\n \"notional = spx.strike_price * spx.multiplier\\n\",\n \"print('cost in bps: {:,.2f}'.format(-spx.price()/notional * 1e4))\\n\",\n \"print('cost in USD: {:,.2f}'.format(-spx.price()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, I will calculate the beta over the last 6m and use that to size the FX hedge.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#buy 4m AUDJPY put\\n\",\n \"b = beta(fx_spot.AUDJPY.resample('W-FRI').last(), spx_spot.SPX.resample('W-FRI').last(), 4 * 6).tail(1).values[0]\\n\",\n \"beta_notional = notional * (1 / b)\\n\",\n \"audjpy_put = FXOption(option_type='Put', pair='AUDJPY', strike_price= f's-{b * 10}%', notional_amount=beta_notional, expiration_date='2m', buy_sell='Buy')\\n\",\n \"audjpy_put.resolve()\\n\",\n \"print('cost in bps: {:,.2f}'.format(audjpy_put.premium / audjpy_put.notional_amount * 1e4))\\n\",\n \"print('cost in USD: {:,.2f}'.format(audjpy_put.premium))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print('Discount to SPX: {:,.2f}%'.format((1 - audjpy_put.premium/-spx.price()) * 100))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can cheapen the structure further by structuring a put spread.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"\\n\",\n \"put1 = FXOption(option_type='Put', pair='AUDJPY', strike_price= f's-{b * 10}%', expiration_date='2m', notional_amount=beta_notional, buy_sell='Buy')\\n\",\n \"put2 = FXOption(option_type='Put', pair='AUDJPY', strike_price='s-10%', expiration_date='2m', notional_amount=beta_notional, buy_sell='Sell')\\n\",\n \"print('cost in USD: {:,.2f}'.format(put1.premium + put2.premium))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print('Discount to put: {:,.2f}%'.format(100 - (put1.premium + put2.premium)/audjpy_put.premium * 100))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5 - Pricing election outcomes\\n\",\n \"\\n\",\n \"Now, let's take this a step further and examine how different potential election outcomes might impact our put spread structure. First, we'll look at FXDelta to see which market assets are driving risk. Then, we'll roll our structure up to the election date and leverage work done by spot trading desk to shock the market assets we have risk to, examining PV impact of these rolling forward + shock scenarios for various election outcomes. Note, here I'm using spot shocks only but you can additionally add vol shocks (example in comment below) for a more representative scenario. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import FXDelta\\n\",\n \"\\n\",\n \"fx_package = Portfolio((put1, put2))\\n\",\n \"fx_package.resolve()\\n\",\n \"fx_package.calc(FXDelta).aggregate()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import RollFwd, MarketDataPattern, MarketDataShock, MarketDataShockBasedScenario, MarketDataShockType\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"\\n\",\n \"scenarios = pd.DataFrame({\\n\",\n \" 'AUDUSD': {'Rep sweep': 2.5, 'Trump + split congress': 2.0, 'Biden + split congress': -2.5, 'Dem sweep': -3.5, 'Still contested': -1.5},\\n\",\n \" 'USDJPY': {'Rep sweep': 2.0, 'Trump + split congress': 1.0, 'Biden + split congress': -2.5, 'Dem sweep': -3.5, 'Still contested': -2.5}\\n\",\n \"})\\n\",\n \"\\n\",\n \"rollfwd_scenario = RollFwd(date = datetime.date(2020, 11, 3))\\n\",\n \"base_price = fx_package.price().aggregate()\\n\",\n \"\\n\",\n \"outcomes = {}\\n\",\n \"with PricingContext(is_batch=True):\\n\",\n \" for scenario, data in scenarios.iterrows():\\n\",\n \" market_shock = MarketDataShockBasedScenario(shocks={ \\n\",\n \" MarketDataPattern('FX', 'USD/AUD'): MarketDataShock(MarketDataShockType.Proportional, data.AUDUSD/100),\\n\",\n \" MarketDataPattern('FX', 'JPY/USD'): MarketDataShock(MarketDataShockType.Proportional, data.USDJPY/100), \\n\",\n \" # note - you can easily add additional shocks here - like vol \\n\",\n \" # MarketDataPattern('FX Vol', 'JPY/AUD', 'ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, x/100)\\n\",\n \" })\\n\",\n \" with rollfwd_scenario, market_shock:\\n\",\n \" s_p = fx_package.price()\\n\",\n \" outcomes[scenario] = s_p\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"final_scen = {s: s_p.aggregate() - base_price for s, s_p in outcomes.items()}\\n\",\n \"pd.Series(final_scen).plot(kind='barh', figsize=(10, 6), title='PV impact of Rolling Forward and Election Market Data Scenarios on Structure')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What's New\\n\",\n \"* `FXOption` used in this notebook\\n\",\n \"* Simplified backtesting - examples to come\\n\",\n \"* `BacktoTheFuturePricingContext` pricing context - combining `HistoricalPricingContext` and `RollFwd`\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/12-Structuring for Uncertaintly.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Structuring for Uncertainty\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"In this notebook, I team up with FX Structurer Lennart Scholte to analyze a strategy for hedging against FX moves when planning for uncertain investments denominated in a foreign currency. As an example, we consider a US company looking to invest into a European subsidiary: the deal may not be certain - as it is pending various approvals - but the company may be looking to lock-in the current market levels especially given the trend of USD depreciating against the EUR. We consider a net zero premium strategy of entering into an option that is restructured into a forward once the deal gets approved and explore the economics under various potential future spot and vol scenarios.\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Spot and vol trends](#2---Spot-and-vol-trends)\\n\",\n \"* [3 - Options](#3---Options)\\n\",\n \"* [4 - Potential future scenarios](#4---Potential-future-scenarios)\\n\",\n \"* [5 - Restructuring into a Forward](#5---Restructuring-into-a-Forward)\\n\",\n \"* [What's New](#What's-New)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.207.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data')) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Spot and vol trends\\n\",\n \"\\n\",\n \"Let's start by looking at spot and vol levels in the context of recent history. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from datetime import date\\n\",\n \"\\n\",\n \"date_end = business_day_offset(date.today(), -1, roll='forward')\\n\",\n \"date_start = business_day_offset(date_end - relativedelta(years=2), -1, roll='forward')\\n\",\n \"\\n\",\n \"pair, term = 'EURUSD', '1y'\\n\",\n \"fx_eurusd = Dataset('FXSPOT_STANDARD').get_data(date_start, date_end, name=pair, fields=('spot',))[['spot']].spot\\n\",\n \"vol_eurusd = Dataset('FXIMPLIEDVOL_STANDARD').get_data(date_start, date_end, name=pair, tenor=term, deltaStrike='ATMS')[['impliedVolatility']].impliedVolatility * 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\\n\",\n \"\\n\",\n \"fx_eurusd.plot(title='EURUSD Spot', ax=axes[0])\\n\",\n \"vol_eurusd.plot(title='EURUSD 1y Implied Volatility', ax=axes[1])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see in the left hand chart, USD has depreciated significantly against EUR year-to-date. Implied 1y volatility, on the other hand, has recently come down (although it's still rich compared to the beginning of the year). This means looking at options may be attractive to hedge against the dollar weakening further.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Options\\n\",\n \"\\n\",\n \"Assuming the company has a budget of 1.2 USD/EUR, let's look at how much a 1.2 call costs and how it compares to the equivalent historically.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"spot vs budget: -1.49%\\n\"\n ]\n }\n ],\n \"source\": [\n \"from gs_quant.instrument import FXOption\\n\",\n \"\\n\",\n \"budget = 1.2\\n\",\n \"call_option = FXOption(pair='EURUSD', expiration_date=term, option_type='Call', strike_price='s', \\n\",\n \" premium_payment_date=term, premium_currency='USD').resolve(in_place=False)\\n\",\n \"print('spot vs budget: {:,.2%}'.format((call_option.strike_price / budget) - 1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see above, at current market levels, the 1.20 strike for a 1Y EURUSD Call option is ~1.5% out-of-the-money spot.\\n\",\n \"\\n\",\n \"Let's look at how the cost of trading a 1.5% OTMS spot call option compares to history. Note we are not calling `resolve()` on our `call_option` here which means it will re-strike to a new 1.5% OTMS option every day of the `HistoricalPricingContext`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import HistoricalPricingContext, PricingContext\\n\",\n \"from gs_quant.risk import DollarPrice, FXSpot\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"call_option = FXOption(pair='EURUSD', expiration_date=term, option_type='Call', strike_price='1.015*S', \\n\",\n \" notional_amount=100e6, premium_payment_date=term, premium_currency='USD')\\n\",\n \"\\n\",\n \"with HistoricalPricingContext(start=date_start, end=date_end, market_data_location='NYC'):\\n\",\n \" inst = call_option.resolve(in_place=False)\\n\",\n \" risks = call_option.calc((DollarPrice, FXSpot))\\n\",\n \"\\n\",\n \"call_info = pd.DataFrame({d: i.as_dict() for d, i in inst.result().items()}).T\\n\",\n \"call_info['spot'] = risks[FXSpot]\\n\",\n \"call_info['premium usd %'] = -call_info.premium/call_info.notional_amount_other_currency * 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\\n\",\n \"call_info['premium usd %'].plot(title='Option Premium of 1.5% OTMS EURUSD 1Y Call (% of USD Notional)', ax=axes[0])\\n\",\n \"call_info[['strike_price', 'spot']].plot(title='EURUSD Spot and Strike Price', ax=axes[1])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see in the left chart, premium as a percentage of notional for a 1.5% OTMS call is relative lows compared to the last 2 years. \\n\",\n \"\\n\",\n \"Let's take this a step further and look at a quick grid across different strikes and tenors to better understand the sensitivity of the premium to different pricing parameters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"tenors = ['6m', '9m', '12m', '15m']\\n\",\n \"strikes = np.arange(1.17, 1.25, 0.01)\\n\",\n \"\\n\",\n \"fxos = Portfolio([Portfolio([FXOption(pair='EURUSD', expiration_date=t, option_type='Call', strike_price=k, \\n\",\n \" notional_amount=100e6, premium_payment_date=t, premium_currency='USD') for t in tenors]) for k in strikes])\\n\",\n \"fxos.resolve()\\n\",\n \"call_grid = pd.DataFrame([[-i.premium / i.notional_amount_other_currency for i in p.priceables] for p in fxos.priceables], index=strikes, columns=tenors)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"fig, ax = plt.subplots(figsize=(15, 4))\\n\",\n \"ax = sns.heatmap(call_grid.apply(pd.to_numeric).T, annot=True, fmt=',.2%', cmap='Blues')\\n\",\n \"ax.set(ylabel='Tenor', xlabel='EURUSD Strike Prices', title='Premium of EURUSD Call Options (% of USD Notional)')\\n\",\n \"ax.xaxis.tick_top()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Potential future scenarios\\n\",\n \"\\n\",\n \"As the company gets certainty about moving forward with the project, it might look to restructure the call option above into a forward to secure a rate.\\n\",\n \"\\n\",\n \"To get a better sense of the economics of a possible restructuring, let's look at how the option carries and how the mark-to-market may vary with various possible levels of FX spot and implied vol.\\n\",\n \"\\n\",\n \"Let's start with the static decay of the option over time: we can use `CarryScenario` to roll forward in time assuming that all market parameters, including the FX spot, remain constant (`roll_to_fwds=False` means roll to spot).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import CarryScenario\\n\",\n \"\\n\",\n \"# we set premium to 0 s.t. day-1 MtM is equal to the premium (as opposed to zero)\\n\",\n \"call_option = FXOption(pair=pair, expiration_date=term, option_type='Call', strike_price=budget, \\n\",\n \" notional_amount=100e6, premium_payment_date=term, premium_currency='USD', premium=0).resolve(in_place=False)\\n\",\n \"\\n\",\n \"future_dates = pd.bdate_range(date_end, call_option.expiration_date, freq='W').date.tolist()\\n\",\n \"roll_to_spot = {}\\n\",\n \"with PricingContext(is_batch=True, market_data_location='NYC'):\\n\",\n \" for d in future_dates:\\n\",\n \" with CarryScenario(date=d, roll_to_fwds=False):\\n\",\n \" roll_to_spot[d] = call_option.dollar_price()\\n\",\n \" \\n\",\n \"static_roll = pd.Series({d: p.result() for d, p in roll_to_spot.items()})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\\n\",\n \"static_roll.plot(title='Call Option Price (in USDm) Forward in Time', ax=axes[0])\\n\",\n \"axes[0].axhline(y=0, linestyle='-', lw=0.75, color='black')\\n\",\n \"(static_roll/static_roll.iloc[0]).plot(title='As % of Initial Premium Paid', ax=axes[1])\\n\",\n \"axes[1].axhline(y=0, linestyle='-', lw=0.75, color='black')\\n\",\n \"axes[1].axhline(y=.5, linestyle='--', lw=0.75, color='red')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see above, in about 7m from now, the time value of the option is still about 50% of the deferred premium payable, which can therefore be recouped when restructuring it into a forward.\\n\",\n \"\\n\",\n \"Let's now see how the option may behave with different levels of spot and implied volatility over time by adding spot and vol shocks on top of the above `CarryScenario`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tenors = ['1b', '3m', '6m', '9m', '1y']\\n\",\n \"vol_bumps = np.arange(-5, 7.5, 2.5) / 100\\n\",\n \"spot_range = np.arange(1.10, 1.27, 0.01)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import MarketDataPattern, MarketDataShock, MarketDataShockBasedScenario,MarketDataShockType\\n\",\n \"from collections import defaultdict\\n\",\n \"call_res = defaultdict(lambda: defaultdict(dict))\\n\",\n \"\\n\",\n \"with PricingContext(market_data_location='NYC', is_batch=True):\\n\",\n \" for t in tenors:\\n\",\n \" for s in spot_range:\\n\",\n \" for v in vol_bumps:\\n\",\n \" scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks = {MarketDataPattern('FX', 'USD/EUR'): MarketDataShock(MarketDataShockType.Override, s),\\n\",\n \" MarketDataPattern('FX Vol', mkt_asset='USD/EUR', mkt_class='ATM Vol'): MarketDataShock(MarketDataShockType.Absolute, v),\\n\",\n \" })\\n\",\n \" with CarryScenario(date=t, roll_to_fwds=False), scenario:\\n\",\n \" price = call_option.dollar_price()\\n\",\n \" call_res[t][s][v] = price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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n1R106Pd8RH1v57rvvGDx4MKtWrcLX1xdfX197hySEEEI0G4dzy7jzi00czC7l8ZHtmDAgvkEMWNRmus0fGAfEAYXAD5qmXa+U+vK0+00AJgBER0dbP1IruOaaa1i+fDm5ublERkby3HPPAfoxI127dsVgMDBr1iw7RymEEEI0H0v3HmfSd9twctD4/NZe9GsdZO+QTqrNONYw4LBSKgdA07SfgAuAU5IkpdRMYCZAUlKSsnKcVvHNN9/847rbbrvNDpEIIYQQzZvZrJi+7ABTlxwgoYUPH1zfnagAD3uHdYraJElHgd6apnmgT7cNBWT3RSGEEEKcl+JKAw99t40le7O5tFsEL4/vhJuzo73D+ofa1CSt1zRtDrAFMAJbsYwYCSGEEEKci/3HS7jzi82k5Zfz3NgEbuwT0yDqj86kVmXjSqlngGdsHIsQQgghmrCFOzOZ/MN2PFyc+PqO3vSM++cGzw2JfdfWCSGEEKLJM5kVr/+ezAd/HaJrtB/vX9edMF83e4f1nyRJEkIIIYTNFJRVc983W1l1MJfrekXz9MUdcHVqePVHZyJJkhBCCCFsYtexIu78YjM5JVW8dllnruwRZe+QzkmzSZIqKysZMGAAVVVVGI1GLr/88pP7JAkhhBDCun7aks7jP+0kwNOFHyb2oUuUn71DOmfNJklydXVl2bJleHl5YTAY6NevHyNHjqR37972Dk0IIYRoMgwmMy8t2Muna1LpHR/AjGu7EeTlau+wzouDvQOoL5qm4eXlBYDBYMBgMKBpGrGxsTz++OMkJiaSlJTEli1bGDFiBC1btuSDDz6wc9RCCCFE45FdUsl1H63n0zWp3NYvji9v69VoEyRoRkkSgMlkIjExkZCQEIYPH06vXr0A/RiVbdu20b9/f26++WbmzJnDunXreOYZ2fVACCGEqI0tRwu4+J1V7DhWyLSrE3lqTAecHOuQZphNkHvQegGeB/tMt/32GGTttG6bYZ1g5P/+9S6Ojo5s27aNwsJCxo8fz65duwAYO3YsAJ06daK0tBRvb2+8vb1xdXWlsLAQPz8/68YqhBBCNCFfrz/KM/N2Eebrxk939aVDC5+6NVhRAHNug4ytcN9m8LDPfkrNpiapJj8/PwYPHsyiRYsAvV4JwMHB4eTXJ743Go12iVEIIYRo6CoNJp6dt5tvN6YxoE0w069OxM/DpW6NZu+Db6+BwjQY/YbdEiSwV5L0HyM+tpCTk4OzszN+fn5UVFSwePFiHn300XqPQwghhGgKMgoruOurLWxPK+Tewa14cHgbHB3qeLzIvgXw0wRw9oCb50O0fRdXNZuapMzMTAYPHkznzp3p0aMHw4cPZ8yYMfYOSwghhGh01qXkcfE7qziUXcoH13dn8oi2dUuQzGZY/ip8ey0EtYYJy+2eIAFoSimrN5qUlKQ2bdp0ynV79+6lffv2Vu/LXpra8xFCCCH+i1KKWatTeXnhXmICPZh5QxKtQrzq1mhVKfwyEfb+Cp2vhoungrO7VeKtLU3TNiulkk6/vlnWJAkhhBDi3FRUm3jspx3M3ZbBhR1CefPKLni7Odet0fwU+PY6yNkHI16B3neBVscpOyuSJEkIIYQQ/+poXjl3frmZfVnFTL6wDXcPaoVDXeuPDv0JP9ysf339T9BycJ3jtDZJkoQQQghxVn/tz+H+b7bqU20392Bw25C6NagUrHsP/pgCwe3g6q8gIN46wVqZJElCCCGE+AelFO8tP8QbfyTTNtSbD2/oTkygZ90aNVTAr5Ngx7fQ/mK45ANwrWNNkw1JkiSEEEKIUxSWVzP5h+0s2ZvN2C4t+N9lnfBwqWPKUHQMvrtO3yBy8JPQfzI4NOxF9pIkCSGEEOKkbWmF3PPVFrJLKnnm4g7cfEEsWl2LqY+uh++uB0M5XP01tBttnWBtrGGncFZWWFjI5ZdfTrt27Wjfvj1r1661d0hCCCFEg6CU4tPVh7nigzUA/DDxAm7pG1f3BGnzp/DpaH1a7fYljSZBgmY2kvTAAw9w0UUXMWfOHKqrqykvL7d3SEIIIYTdlVQaeOzHnSzYmcnQdiG8eWWXuh8vYjLAosdg48fQcghcPgvc/a0TcD1pNiNJRUVFrFixgttuuw0AFxcX/Pz8GDRoEA8++CBJSUm0b9+ejRs3cumll9K6dWumTJli56iFEEII29qTUczF76xi0e4sHh/Zjo9uTKp7glSaA5+P0xOkC+6H6+Y0ugQJmtFI0uHDhwkODuaWW25h+/btdO/enWnTpgF6wrRp0yamTZvGuHHj2Lx5MwEBAbRs2ZIHH3yQwMBAO0cvhBBCWJdSiu82pvHMvN34eTjz7YTe9Ii1wmGymdv1DSLLcuDSj6HzFXVv007+M0nSNK0t8F2Nq+KBp5VSU8+301c3vMq+/H3n+/AzahfQjkd7nv3AWqPRyJYtW3jnnXfo1asXDzzwAP/7n37Q7tixYwHo1KkTCQkJhIeHAxAfH09aWpokSUIIIZqU8mojU37exU9bj9G/dRBvX5VIkJdr3RveOQfm3gsegXDrImjRte5t2tF/TrcppZKVUolKqUSgO1AO/GzrwKwtMjKSyMhIevXqBcDll1/Oli1bAHB11d8YDg4OJ78+8b3RaKz/YIUQQggbOZhdwrgZq/l52zEeHNaGT2/pWfcEyWyCxU/Dj7fpidGE5XVOkPIq8lh8ZHHd4qqjc51uGwocUkodqUun/zbiYythYWFERUWRnJxM27ZtWbp0KR06dGDjxo31HosQQghhD79sPcbjP+3E09WRL2/rRd9WQXVvtKIAfrwdDi6BpNvgov+BU91qmpLzk7lv2X0UVxfTI7QHfm5+dY/zPJxrknQ18I0tAqkP77zzDtdddx3V1dXEx8cze/Zsxo8fb++whBBCCJuqNJh47tc9fLPhKD3jAnjnmq6E+rjVveGcZPjmGig8CmOmQtItdW5yedpyHl3xKF7OXnxy4Sd2S5AANKVU7e6oaS5ABpCglDp+htsnABMAoqOjux85cupg0969e2nfvn2dA24omtrzEUII0TSl5pZx91db2JNZzN2DWvLQ8DY4OVphcXvyb/DjHeDsBld+ATF96tScUorPdn/GW5vfon1ge6YPnk6oZ2jd46wFTdM2K6WSTr/+XEaSRgJbzpQgASilZgIzAZKSkmqXeQkhhBDCZn7bmcn/zdmBg4PGrJuTGNLOCkmHUrDiDfjzJQjvoh9Q6xtZpyarTdW8sO4Ffjn4C8NjhvNSv5dwd3Kve6x1dC5J0jU04qk2IYQQormoNpp55be9zF6dSmKUHzOu7Uqkv0fdG64qhV/ugr3zoPNVcPE0cK5bMpNfmc+Dfz7IluwtTOwykbu63IWD1jC2caxVkqRpmicwHLjTtuEIIYQQoi7SC8q55+utbE8r5Na+cTw2sh0uTlZIOgpS4ZtrIWcvXPgi9LkX6nhkycGCg9y77F5yynN4tf+rjIofVfc4rahWSZJSqgyQzYKEEEKIBmzp3uM89P12zGbF+9d1Y2SncOs0nLIcfrhZn2q7/kf9mJE6Wpm+kkdWPIKboxuzL5pN5+DOdW7T2prNjttCCCFEU2U0mXnjj/188NchElr48N513YgJ9Kx7w0rB+g/g9ychqA1c8zUExNexScWXe7/kjU1v0NqvNTOGziDMM6zusdqAJElCCCFEI3a8uJL7vt7KhtR8ru0VzdNjOuDm7Fj3hg2VsOAh2PYVtBsD4z8AV++6NWky8NL6l/jxwI8MiRrCK/1fwcPZCrVSNtIwKqPqybRp0+jYsSMJCQlMnTrV3uEIIYQQdbLqQC6jpq1kV0YRU69K5OXxnayTIBWkwqwL9QRp0OP6Ev86JkiFlYXcueROfjzwI7d3up23B7/doBMkaEYjSbt27eKjjz5iw4YNuLi4cNFFFzFmzBhatWpl79CEEEKIc2IyK95ZdoBpSw/QOsSL967rRquQuiUxJ+3/HX66Q//6mu+g7UV1bjKlKIX7lt5HZlkmL/d7mYtbXlznNutDsxlJ2rt3L7169cLDwwMnJycGDhzITz/9xKBBg3jwwQdJSkqiffv2bNy4kUsvvZTWrVszZcoUe4cthBBCnCK3tIqbZm1g6pIDjO8awS/39LVOgmQ2wbIX4esrwS8GJvxllQRpTcYarl9wPaWGUmaNmNVoEiRoRiNJHTt25MknnyQvLw93d3cWLlxIUpK+uaaLiwubNm1i2rRpjBs3js2bNxMQEEDLli158MEHCQyUhX1CCCHsb8PhfO77ZguF5QZevawTVyZFodVxGT4AZbn64bQpy6HrDTDq9TrvfwTwzb5veHXDq8T7xTNjyAxaeLWoe6z1yC5JUtbLL1O1d59V23Rt346wJ5446+3t27fn0Ucf5cILL8TT05PExEQcHfV527FjxwLQqVMnEhISCA/Xl0zGx8eTlpYmSZIQQgi7MpsVM1em8PrvyUQHeDD75p50aOFjncbTNsIPN+mJ0th3oNuNdW7SaDbyvw3/47vk7xgYOZBXB7yKp7MVVtvVs2Yz3QZw2223sXnzZlasWIG/vz9t2rQBwNXVFQAHB4eTX5/43mg02iVWIYQQAqCwvJo7Pt/E/37bx0UJYcy7t691EiSlYMNHMHskODjBbX9YJUEqqiririV38V3yd9yScAvTBk9rlAkS2Gkk6d9GfGwpOzubkJAQjh49yk8//cS6dev4/vvv7RKLEEII8V+2pRVyz1dbyC6p5LmxCdzYJ8Y602vVZfDrJNj5PbQeAZd+CO7+dW72SPER7l16L+ml6Tx/wfOMbz2+7rHaUbOpSQK47LLLyMvLw9nZmXfffRc/Pz97hySEEEL8w4nptTd+TybUx405Ey+gS5SfdRrPPQDf3QC5yTDkKej3EDjUfWJpfeZ6Hlr+EA6aAx8N/4iksKQ6tWcuL6cyORmPrl3rHNv50pRSVm80KSlJbdq06ZTr9u7dS/v27a3el700tecjhBCiYcgqquSh77ex5lAeozqF8cr4zvh6OFun8T1z4Zd7wMkFLvsEWg62SrPfJ3/PK+tfIcYnhneGvkOUd1Sd2jPm5ZF2191UHzpEyyWLcfKv+yjXv9E0bbNS6h9ZXbMaSRJCCCEasj92Z/HojzuoNJitu3rNZIAlz8LaGRDZA674FHwj69ys0WzkzU1v8uXeL+kb0ZfXB7yOt0vdtiOoTk3l6IQ7MWZnE/HmGzZPkP6NJElCCCGEnVVUm3hxwR6+Wn+UjhE+TLu6Ky2DvazTeEkW/HALHF0DPe+EC1/UR5Lq2mx1CY+seITVx1ZzffvreTjpYZwc6pZWVGzbRtpddwMQ8+ls3BMT6xxnXUiSJIQQQtjRnoxi7v92KwezS7lzQDwPX9gWFycrLT5PXaUnSNWlcOnH0PkKqzSbVpzGvcvu5WjxUZ7u8zRXtKl7uyVLl3Ls4ck4hYQQPfNDXGJj6x5oHUmSJIQQQtiBUorZq1P532/78PVw5ovbetK/dbC1Goc102HJcxAQBzfNgxDr1NFuytrEg8sfxKzMfDj8Q3qG96xzm/lffcXxl17GrWNHot5/D6cGsj+hJElCCCFEPcstrWLyD9tZnpzD0HYhvHZ5ZwK9XP/7gbVRWQS/3A375kOHcTB2BrhZZ+PJnw/8zPPrnifSK5IZQ2cQ4xNTp/aU2UzO22+T99HHeA0eTMSbb+Dg0XAOvZUkSQghhKhHy5OzmfzDdkoqjTw/LoEbeltp7yOA47v15f0FqTDiZeh9N1ihbZPZxNQtU/l096f0Ce/DG4PewMelbomXubqazCeepHj+fPyuuZqwJ59Ec2pYaUnDisbGbr31VubPn09ISAi7du2ydzhCCCGakSqjiVd/S2bW6sO0DfXmq9t70zbMCgfTnrD9O/j1AXDzhZvnQ8wFVmm2zFDGoyse5a/0v7i67dU82vPROhdom4qLSb/vfsrXryf4oYcIvON26yWKVtSskqSbb76Ze++9lxtvrPu260IIIURtHcwu4b5vtrE3s5ibL4jlsZHtcHN2tE7jxipY9Dhs+gRi+sHls8A71CpNHys9xr1L7+Vw0WGe6PUE17S7ps5tGjIzSZtwJ1WpqbR47VV8LeenNkTN6uy2AQMGEBAQcPL7Q4cO0a1bt5PfHzhw4JTvhRBCiLpQSvHV+iOMeWcVx4sr+eSmJJ4dm2C9BKkwDWZdpCdIF9wPN861WoK0KWsT1y64luNlx3lv2HtWSZAqk5NJvfoaDJmZRM/8sEEnSNDMRpJO17JlS3x9fdm2bRuJiYnMnj2bW265xd5hCSGEaAIKyqp57Kcd/L77OP1bB/HmFV0I8XGzXgcHl8CPd+gbRV75BXSwTsKhlOKbfd/w+sbXifCOYPqQ6cT7xte53bK1a0m/734cPD2J+epL3Nq2tUK0tlWrJEnTND/gY6AjoIBblVJrz7fTld/vJzet9HwffkZBUV70v7LNOT/u9ttvZ/bs2bz11lt89913bNiwwapxCSGEaH7WHMzloe+3k1dWxZOj2nNbvzgcHKxUc2M2w4rXYfkr+rL+K7+AoFZWabrKVMULa19g7qG5DIwcyCv9X6nzDtoARXPnkjHlKVxjY4n6aCbOYWFWiNb2ajuSNA1YpJS6XNM0F6DhrM+ro8suu4znnnuOIUOG0L17dwIbyN4MQgghGh+Dycxbi/fzwV+HiAvy5OOb+tIxwtd6HZTnw08T4OBi6HwVjHkbXDyt0nRWWRaT/pzE7rzdTOwykbu63IWDVreqHKUUeTM/Iuftt/Ho1YvId6bj6GOd7Qjqw38mSZqm+QIDgJsBlFLVQHVdOj2fER9bcXNzY8SIEdx111188skn9g5HCCFEI5WaW8YD325le3oR1/SM4qkxHfBwsWJVy7Et8P1NUJoFo9+CpFutsrwfYGPWRib/NZkqUxVTB09laPTQOrepjEayXnyRwm+/w+fii2nx0otoLud2HIpSyq6r3mqTIsYBOcBsTdO2apr2saZp1klb69k111xDnz59SE5OJjIy8mRSdN111+Hg4MCFF15o5wiFEEI0Nkop5mxOZ/T0laTmlfP+dd145dLO1kuQlIJNs2HWCEDBLYugx21WSZCUUny19ysm/DEBHxcfvh71tVUSJHN5Oen33U/ht98ReMcdtHj1f+ecIB3aks2imbswGc11jud81eYn6AR0A+5TSq3XNG0a8BjwVM07aZo2AZgAEB0dbe04reKbb7454/WrVq3illtuwdHRSqsNhBBCNAtFFQam/LKLX7dn0CsugLevSqSFn7v1Oqguh4WTYdtX0HKIfv6ap3XKQqpMVTy/9nnmHZrHoMhBvNz/ZavUHxnz8ki7624qd+0i9OmnCLj22nNuI3l9Fks/3UNonA8mgxlHa51ld45qkySlA+lKqfWW7+egJ0mnUErNBGYCJCUlKatFaGPjx4/n0KFDLFu2zN6hCCGEaEQ2pebzwLfbyCquZPKFbbhrUCscrVWcDZCzH+bcAsd3wcBH9YuDdf4zX7P+6K4udzGxy8Q61x8BVKemcnTCnRizs4l8ZzreQ899VGr3ymMs/zqZiDZ+jLqrMy5u9luI/589K6WyNE1L0zStrVIqGRgK7LF9aPXj559/tncIQgghGhGjycyMPw8yfekBIv09+GFiH7pF+1uvA6Vg65fw2/+BsztcNwdaD7da8zXrj6YPns7g6MFWabdi2zbS7robgJjPPsW9S5dzbmP70jRW/XCA6IRARt7ZEScX+87w1DY9uw/4yrKyLQWQzYSEEEI0O+kF5Uz6dhubjhRwadcInhuXgLebs/U6qCyC+Q/Crh8htj9c+hH4hFulaaUUX+/7mtc3vk6UdxTThkyzyv5HACVLl3Ls4ck4hYQQ/dFMXGLO/eDbTb+lsn5uCvGJwVx4WwKOzvbf77pWSZJSahuQVNfO7F2lbi1KNZrZRCGEEFby6/YMnvh5J0rB1KsSuaRrhHU7SN+sT68VpcOQKdDvIatNr1UaK3lh3Qt6/VHUIF7uZ536I4D8r77i+Esv49axI1EfvI9TjZMtakMpxfp5KWz+7Qite4Qy7Ob2ODjaP0GCetxx283Njby8PAIDAxt1oqSUIi8vDzc3K+6aKoQQosEqrTLy7LzdzNmcTtdoP6Zd1ZXoQCtuF2g2w5rpsOwF8A6HW36D6F5Waz6zNJNJyyexJ28PdyfezZ2d77RK/ZEym8l5+23yPvoYr8GDiXjrTRzcz61oXSnF6h8Osn1ZGh36hjPwunbW23TTCuotSYqMjCQ9PZ2cnJz66tJm3NzciIyMtHcYQgghbGxbWiGTvt3K0fxy7h/SivuGtsbZmqMcJcfhl4lwaBm0Hwtjp4O79eqbTtQfVZuqeWfIOwyKGmSVds3V1WQ+8STF8+fjd83VhD35JJrTuaUUyqz465tkdq/MoNPgSPpf0RqtASVIUI9JkrOzM3FxcfXVnRBCCHHeqowmpi89wAd/pRDq7co3d/SmV7yVT2Q4uBR+vhOqSvSds7vfYrXNIWvWH0X7RDNt8DTifK3zGWwqLib9vvspX7+e4IceIvCO2895hshsMrPs830kr8+i24gYel8S3yBnmZr1AbdCCCHE6XYdK2LyD9vZl1XC5d0jeWpMB3zdrVicbazWp9bWTIfg9nDjPAjtYLXmK42VPL/2eX5N+ZXBUYN5ud/LeLl4WaVtQ2YmaRPupCo1lRavv4bvxRefcxsmo5nFs3ZzaEsOvcbG0X1kbINMkECSJCGEEALQz11798+DzFh2kABPFz65KYmh7UOt20n+YZhzK2Rs0UeORrwMLtarb8oszeSBPx9gb/5eq9YfAVQmJ5M24U7MZWVEfzQTz969z7kNo8HEopm7OLIzj76XtyJxWMPcfPoESZKEEEI0e/uyinn4++3szihmfNcInrm4A34e53aMxn/aOQd+nQQODnDl59BhnFWb35i1kYeXP4zBbGDGkBkMjBpotbbL1q4l/d77cPDyIuarr3Bre+5nsBqqTCx8fwfp+woYeE0bOg5s+LW9kiQJIYRotowmMx+uSGHqkv34ujvzwfXduahjmHU7qS6Dhf8H276EqF5w2cfgZ70RlBPnr72x6Q2r1x8BFM2dS8aTU3CNiyPqo5k4h53761NdYWT+u9vJOlTE0Jva066PdfZ+sjVJkoQQQjRLB46X8PAP29mRXsTozuG8MK4jAZ5WHj3K3KFPr+UdhP6TYdDj4Gi9j96a9UdDoobwUr+XrFZ/pJQib+ZH5Lz9Nh69ehE54x0cvc99b6XKMgO/Tt9Gblopw29LoHWSlacwbUiSJCGEEM2Kyaz4eGUKby7ej6eLIzOu7cqYzi2s24lSsGEm/DEF3APgxrkQb73pL4CM0gwm/TmJffn7uCfxHiZ0nmC1+iNVXU3m889TNOdHfC6+mBYvvYjmcu4JZHlxNfOmbaPgeBkXTexEXOcgq8RXXyRJEkII0Wyk5JQy+YftbDlayIiEUF68pBPB3q7W7aQ8H+beA8kLofUIuOQ98LRucrAhcwOT/5qMwWzgnSHvWLX+yFRURPr9D1C+fj2Bd00k+L770BzOPfkqLahi7tStlOZXMubuLkR1OLeduBsCSZKEEEI0eWazYvaaVF5btA83Z0emXZ3I2C4trL/0PHUV/HgHlOXAiFeg911W2/sI9CmwL/d+yZub3iTGJ4Zpg6cR6xtrtfarjxwh7c6JGI4do8Wr/8N33PkVlxfnVjB36lYqSgxcfH8XWrS24gHA9UiSJCGEEE3akbwyHvlhBxtS8xnaLoRXLu1EiI+Vj5YyGWHFa7DidfCPg9uXQItEq3ZRYazg+bXPMz9lPkOjh/JSv5fwdPa0WvvlGzeSfu99oGlEfzobj+7dz6udwuPlzJ26FUOViXGTuhIa52O1GOubJElCCCGaJLNZ8eX6I7yycB9OjhpvXNGFy7pFWH/0qChdHz06uga6XAOjXgdX6xwee0LN+qP7ut7H7Z1ut1r9EUDhz7+Q+fTTuERFEfXB+7hEn9/qu7xjpcydtg1lVox7sCvBUdZ9HeqbJElCCCGanLT8ch79cQdrDuUxoE0wr17WiXDfczt8tVb2ztfrj8xGGD8Tulxl9S7WZ65n8l+TMZlNzBg6gwGRA6zWtjKbyZk6jbyZM/Ho05vIadNw9Dm/kZ+coyXMm7YNByeNSx7uRkC49Ua57EWSJCGEEE2GUopvNqTx0oI9ALxyaSeu7hFl/dEjQyX88SRs/BjCE+HyWRDY0qpdKKX4fM/nvL35bWJ9Ypk2ZBoxPjFWa99cUUHGY49T8vvv+F15JWFPTUFzPr/jV7JSivj1ne24uDsyblJX/EKst4u4PUmSJIQQoknIKKzg0R93sPJALhe0DOS1yzsT6W+DD+ucZPjhFsjeDX3uhaHPgJN191cqri7m6dVPs/ToUoZFD+PFfi9atf7IkJ1N+j33UrlrFyGPPkrAzTeddyKZnlzAgvd24OnjwrgHu+IdYOV6LzuSJEkIIUSjppRizuZ0nv91D0az4oVxCVzXKwYHByuPHikFWz6H3x7Vz1u79gdoc6F1+wB25e5i8l+TOV52nEeSHuGGDjdYdSSsct8+0u66G1NREZHvzsB7yJDzbuvI7jx++2AnPkHujJuUiKevlbdTsDNJkoQQQjRax4sreeKnnSzdl03PuADeuLwL0YE2GD2qLNLPXdv9E8QN0OuPfKx7tIZSiq/3fc0bm94g2D2YT0d+SpfgLlbto+TPP8l4eDIO3t7EfvUlbu3bn3dbKVtz+P3jXQS08GTs/Ym4e1t5t/IGQJIkIYQQjY5SirnbMnhm3m6qjCaeHtOBmy+Itf7oEUDaRvjxVig6BkOfhr6TwMHRql2UVJfwzJpnWHxkMYMiB/FivxfxdfW1WvtKKQo+/5zjr76GW/v2RL73Hs6hIefd3v4NWSz5dC8hMd6MubcLbp7nV8vU0EmSJIQQolHJKaniyZ938see43SL9uONK7oQH2yd88pOYTLC6qmw/BXwbgG3LoKonlbvZnfebiYvn0xWWRaTkyZzY4cbrTq9pgwGsl56icJvv8N7+DBavPoqDh7nP9q2Z3UGf365jxat/Bh9T2dc3JpuKtF0n5kQQogmZ/6ODJ76ZRdl1SaeGNWO2/rF42iL0aPcA/DzRDi2CRLGw5ip4O5n1S6UUnyb/C2vb3ydQPdAZl80m8SQRKv2YSop4dikBylbvZrAO24n+MEHz+uIkRN2/JnGyu8OENUhgJETO+HsYt0RtYZGkiQhhBANXn5ZNU/9sosFOzPpEunLm1d2oVWIDTYqNJthw4ew5FlwcoPLPoGOl1n1aBHQp9eeXfMsfxz5g/4R/Xm538v4uflZtY/q9HTSJk6kOvUI4S+9iN9ll9WpvS2/H2Htz4eI6xLEiNs74uhsvc0sG6paJUmapqUCJYAJMCqlkmwZlBBCCHHCol1ZTPllJ0UVBh4Z0ZY7B8Tj5GiDD+iCVJh7L6Su1A+mHTsdvMOs3s2evD1M/msyGaUZPNT9IW5KuMmqu2cDlG/ZSvq996JMJqI/+QTPXuc/TaiUYuP8w2xckErrpBCG3tIBR1u8/g3QuYwkDVZK5dosEiGEEKKG48WVPPfrbhbuzCKhhQ9f3t6LdmE2OAdMKdjyGfz+JKDB2BnQ9Xqrjx4ppfg++Xte3fgq/m7+zL5oNl1Dulq1D4CiX+eT+eSTOIWHEfXBB7jGxZ13W0op1vx0iG2Lj9LugnAGX9/ONsXxDZRMtwkhhGhQTGbFl+uO8PrvyRhMZiZf2IY7B7bE2RajF8UZMO9+OLgYYvvDJe+B3/mdW/ZvSqtLeW7tcyxKXUTfiL680u8V/N38rdqHUorcGe+S++67ePToQcT0aTj5n38fyqxY8d1+dv11jE6DIul/ZWu0ZpQgQe2TJAX8oWmaAj5USs08/Q6apk0AJgBEn+fBeEIIIZq3XceKePLnnWxPL6J/6yBevKQjMYE2OANMKdj5AyycDMZqGPk69Lgd6lDUfDb78vcx+a/JpJek80C3B7i1461Wn14zV1WR+cSTFC9YgO/48YQ/9yyay/nvW2Q2K/78Yi/71mbRdXg0fS5taf2jXRqB2iZJ/ZRSxzRNCwEWa5q2Tym1ouYdLInTTICkpCRl5TiFEEI0YWVVRt5avJ/Zqw8T4OnKtKsTGdulhW0+mMtyYf6DsHceRPaE8R9Y/dw10Ed2ftj/A69ueBU/Vz8+GfEJ3UO7W70fY14e6ffcS8W2bQQ/9BCBd9xep9fNZDKzZPYeDm7KpufFcSSNim2WCRLUMklSSh2z/JutadrPQE9gxb8/SgghhPhvf+zO4pl5u8ksquTaXtE8OqIdvh422pxw73z49QGoKoZhz8IF91t9Y0iAMkMZz619jt8O/0bfFn15uf/LBLgFWL2fqgMHSJt4F8a8PCKmTcNnRN2OSTEZzPz+8S4Ob8/lgktb0fXC5j0z9J9JkqZpnoCDUqrE8vWFwPM2j0wIIUSTllFYwTPzdrN4z3HahXkz49pudI+xbp3OSRWF+plrO76FsM4wfh6EJtikq+T8ZCb/NZmjJUe5r+t93N7pdqtPrwGUrlzFsQcfRHN3I+aLL3Dv1LFO7RmqTfz2wU7S9uQz4Oo2dBoUaaVIG6/ajCSFAj9bhtqcgK+VUotsGpUQQogmy2gy8+maVN5avB+zUjw+sh239ouzTWE2wMGl+tL+0uMw8FHoPxmcrH/OmFKKnw78xCsbXsHHxYePL/yYHmE9rN4PQP7XX3P8pZdxbd2aqPffwzm8bufIVVcaWfDuDjIPFjLkxna0v6CFlSJt3P4zSVJKpQDWPWFPCCFEs7Q9rZAnft7J7oxiBrcN5vlxHYkKsMGBtABVpbD4Kdg0C4LawtVfQUQ3m3RVbijn+XXPsyBlAX3C+/BK/1cIdA+0ej/KZOL4/16l4Isv8Bo0iIg338DBs26F7ZVlBubP2E72kRKG35pA6x6hVoq28ZMtAIQQQthcSaWBN35P5vN1Rwj2cuW967oxsmOY7QqCU1fDL3dB4VG44D4YPAWc3WzS1f6C/Ty8/GGOlhzlnsR7uKPTHTjaoM7JVFrGsYcfouyvFQTcdBMh//cImmPd+qkoqWbe9G3kZ5Zx0YSOxCcGWynapkGSJCGEEDajlOK3XVk89+tuskuquLF3DA+PaIuPm40Ksw0VsOxFWPsu+MfALb9BTB+bdKWU4ueDP/Py+pfxdvHmo+Ef0TPc+gfgAhgyMkibeBdVhw4R9uyz+F99VZ3bLCuqYu7UbRTnVjD6rs5EJ1h/5KuxkyRJCCGETaTll/PMvN0s25dNh3AfPrwhicQoP9t1eGyzfiht7n59z6Nhz4Grl026KjeU8+K6F/k15Vd6hffif/3/R5B7kE36qtixg7S770FVVRE180O8+vatc5sl+ZXMfXsr5cXVXHxfFyLa2KhgvpGTJEkIIYRVGUxmZq06zNQlB9A0mDK6PTdfEGub89ZA3wxyxWuw8i39rLUbfoaWQ2zTF3Cg4ACT/5rM4aLD3N3lbiZ0nmCT6TWA4kWLyHj0MZyCg4n6dDaurVrVuc3C7HLmTt2KodLE2AcSCYv3tUKkTZMkSUIIIaxm85ECnvx5J/uyShjeIZRnxyYQ4eduuw6P74af74SsndDlWrjoFXD3s1l3vxz8hZfWvYSnsyczL5xJ7/DeNulHmUzkTH+HvA8/xL1rVyLfnYFTQN33WcrPKGPutK2YTYpxk7oSHO1thWibLkmShBBC1FlRuYFXf9/HNxuOEubjxswbunNhQpjtOjQZYc10+PNlPSm6+mtoN9pm3ZUbynlp/UvMOzSPnmE9eXXAqzabXjMVF3Ns8mTKVqzE74rLCX3qKRzqcMTICTlpJcybtg0HB43xD3UjoIUNjntpYiRJEkIIcd6UUszbnsEL8/eSX1bFrX3jeHB4G7xcbfjxknsQfpkI6RuhwzgY/TZ42q7o+FDhIR5e/jApRSlM7DKRiZ0n2mx6rXL/ftLvuw9DRqbVCrQBsg4XMf+d7Ti7OjJuUlf8Qm207UITI0mSEEKI83Ikr4wpv+xi5YFcOkf68uktPegYYcP6FrMZNsyEJc+Ckytc9gl0vAxstI2AUopfDv7CKxtewd3JnQ+Gf8AFLS6wSV8Axb//Qcbjj+Pg6UHMZ5/h0a2rVdrNOFDA/Bk7cPdxYdykRHwCbTj92cRIkiSEEOKcVBvNfLQyhelLD+Ds6MBzYxO4vncMjg42PAS14AjMvQdSV0LrEXDxNPCp2y7T/ya/Mp/n1jzHsrRl9Ajrwf/6/48QjxCb9KVMJnKmTSdv5kzcu3QhYvp0nEOt09fRPXn89v5OvAPdGDepK55+rlZpt7mQJEkIIUStbTicz5M/7+RAdimjOoXx9JgEwnxts0kjAErB1i9g0RP692NnQNfrbTZ6BPBX2l88veZpSqpLeLj7w9zQ4QabTa+Zioo4NvkRylauxO+KKwh9aopV6o8ADm/PYdFHu/AP82TcA4m4e1v/KJamTpIkIYQQ/6mwvJpXFu7ju01pRPi5M+vmJIa0s/HxFbkHYcFDcPgviO0Pl7wHfrY7lb7cUM7rm15nzv45tPZvzczhM2kb0NZm/VXu30/6vfdhyMwk7Lnn8L/qSqu1fWDTcZbM2kNQtDcX39cFN08bbd7ZxEmSJIQQ4qyUUvy89RgvLdhLYYWBOwfG88DQ1ni42PDjw1gFq6bCyjfByQ1GvwndbwUHG+2zBGzL3sYTq54gvSSdWxJu4d6u9+LiaLuRl+JFi8h44kkcPT2J+fwzPLpap/4IYN/aTJZ9vpewlr6MuacLLu7yUX++5JUTQghxRpuPFPDC/D1sSyuka7QfX47vRPtwH9t2englzH8Q8g7oRdkjXgFv241YGUwG3t/+Pp/s+oQwjzBmjZhFUliSzfpTJhM5U6eR99FHuCcmEjFtmtXqjwB2/ZXOX9/sJ6q9PyPv6oyzi22mCZsLSZKEEEKcIr2gnFcXJfPr9gxCvF15/fLOXNYtEgdbFmaX5cEfU2D71+AXA9f/CK2G2a4/IKUwhcdWPsbe/L2MazmOx3o+hpeLbY4xATAVFur1R6tW4XfVVYQ++YTV6o8Ati4+ypofDxLbOYgRdyTg5CwJUl1JkiSEEAKAsioj7y8/xEcrUwC4f0gr7hzYEk9b7nmkFGz7Cv54CqqKod9DMOARcLHdPj5mZeabfd/w9ua3cXdy5+1BbzMsxrYJWWXyftLvvRdDVhZhzz+H/5XWqz9SSrFpYSobfj1Mq+4hDLu1A462OgKmmZEkSQghmjmzWTFnSzqv/55MTkkV4xJb8H8XtbPtcSIAOfv1qbUjqyCqN1w8FULa27TLrLIsnlr9FOsy19E/oj/P933eZjtnn1D82296/ZGXl9Xrj5RSrPvlEFt+P0q73mEMvrG9bUf8mhlJkoQQohlbl5LHC/P3sDujmK7Rfnx4Q3e6Rdv4RHhDpV6UveptcPGEi6dD1xtsWpgNsDBlIS+ufxGj2chTvZ/iijZXoNlwKwG9/mgqeR99jHvXrkRMm4pziPXqj5RZsfKHA+z8M52OAyIYcHUbNEmQrEqSJCGEaIaO5JXxysJ9LNqdRQtfN6ZdncjYLi1smjQAcOhPfVl/fgp0vgoufAm8gm3aZVFVES+te4nfUn+jc3BnXu73MjE+MTbt01RYyLGHJ1O2ejV+V19F2BNPoFmx/shsViz/ah97V2eSOCyKCy5rZfufXTMkSZIQQjQjxZUGZiw7yKerU3Fy1Hh4eBvuGBCPm62LfEtz4PcnYOf3EBAPN/wCLQfbtk9gTcYanlr9FPkV+dybeC+3dboNJwfbfvRVJieTfu99GLOyCHvhefyvuMKq7ZtMZpZ+upcDG4+TNDqWnmPiJEGyEUmShBCiGTCazHy7MY23F+8nv7yay7tFMnlEW0J9bLhbNujnrW39HBY/DdXlMPBRvTjb2bb9VhormbplKl/t/Yo43zimD55OQlCCTfsEKF64kIwnp+Do7U3MF5/jnpho1fZNBjN/fLKblG059Bnfkm4jbDsi1txJkiSEEE3civ05vLhgD/uPl9IzLoDPxnSw7UG0Jxzfoxdmp62DmH4w5m0IbmPzbnfn7ebxlY9zuOgw17a7lkndJ+HuZNsidGUykf3WW+R/Mgv3bt2InDYVp2DrTiMaq0389uFOju7Op/9Vrek8OMqq7Yt/qnWSpGmaI7AJOKaUGmO7kIQQQljDwexSXl64l2X7sokO8OCD67sxIiHM9lMz1eWw4jVY8w64+sC49yDxWpuetwZgNBv5ZOcnfLD9AwLcAvhw+Idc0OICm/YJlvqjhx6mbM0a/K65mrDHH7dq/RFAdaWRhe/v4Nj+Qgbf0I4OfVtYtX1xZucykvQAsBew8XarQggh6qKgrJppSw/w5bojuDk78vjIdtzcNxZXp3rYXPDAEr0wu/AIJF4Hw18Az0Cbd3u0+CiPr3qcHTk7uCj2Iqb0noKvq+1Hyyr37dPrj44fJ/zFF/C7/HKr91FVbmD+jO0cTy1h+C0daNMzzOp9iDOrVZKkaVokMBp4CXjIphEJIYQ4LwaTmS/WHmHa0gOUVBq4umc0Dw1vQ5CXq+07L8mCRY/D7p8gsDXcNB/i+tu8W6UUcw7M4fWNr+Pk4MSr/V9lVPwom/cLULRgAZlPTsHRx4eYL7/AvUsXq/dRUVrNr9O3k3eslIvu6Eh8V9uuBBSnqu1I0lTg/wBv24UihBDifCilWLYvm5cW7CUlt4x+rYKYMqY97cLqYeDfbIbNs2DJc/rBtIOfhL4PgJPtE7PcilyeWfMMK9JX0Cu8Fy/2fZEwT9uPsiijkey33iZ/lu3qjwDKiqqYN20bRTkVjLqrMzEdbT8iJ071n0mSpmljgGyl1GZN0wb9y/0mABMAoqOjrRWfEEKIf7Evq5gX5+9l1cFc4oM8+eSmJIa0C6mfJeFZO+HXSXBsE8QN1AuzA1vavl9g6ZGlPLv2WSqMFTzW8zGuaXcNDprtj+IwFhSQ8fDDlK1Zi/+11xD62GNWrz8CKMmvZO7UrZQVVTPm3i5EtrXxBp/ijGozktQXGKtp2ijADfDRNO1LpdT1Ne+klJoJzARISkpSVo9UCCHESbmlVby1eD/fbjiKt5szT4/pwA19YnCujzO7qstg+Suw9j1w94fxM6HzlTYvzAYorS7lfxv+x9xDc2kf0J5X+r9CS7/6Scwq9+0j/Z57MWZnE/7Si/hddplN+inKqWDu21upKjcw9v5EwlvWw0pEcUb/mSQppR4HHgewjCRNPj1BEkIIUT+qjCZmr07l3WUHqTCYuLFPLJOGtcbPw/qjGWeUvAgWToaiNOh2Iwx7DjwC6qXrTVmbeHLVk2SVZ3FHpzu4q8tdODs610vfRfMXkDllCo6+vsR89SXunTvbpJ+CrDLmvr0Vo9HMuAe7EhIja6XsSfZJEkKIRkApxaJdWbzy2z6O5pczpF0IT4xqT6sQr/oJoDgDfvs/2PsrBLeDWxZBTJ966braVM2MbTP4dNenRHpH8tlFn5EYklgvfSujkew33yJ/9mzcu3cncurbNqk/AshNL2XetK2gaYx/qBuBEfX0sxVndU5JklJqObDcJpEIIYQ4ox3phby4YC8bDufTNtSbL27rSf/W9bTKyWSAjZ/AshfBbIChT0Of+8Cpfkau9ubtZcrqKewv2M/lbS7nkaRH8HD2qJe+DcePk/HI/1G+YQP+115L6GOP2qT+COB4ajG/Tt+Gs6sj4yZ1xS+0fp6j+HcykiSEEA3Unoxi3l6yn8V7jhPg6cKLl3Tk6h5RONVH3ZFSsP93WPwU5O6HlkNh9Bv6uWv1oNxQzrvb3uXLvV/i7+rPjCEzGBg1sF76Bij5808yH38Cc1UV4a+8gt/4S2zWV8bBQubP2I67lzPjJnXFJ8i2u4OL2pMkSQghGpj9x0uYumQ/C3dm4e3mxIPD2nBLv1h83Oqn/oasnfD7k3D4LwhsBVd/A21H1kthNsBfaX/x0vqXyCzL5PI2lzOp26R62RgSwFxdTc6bb5L/2ee4tmtHxFtv4RofZ7P+0vbms/D9HXj5uzFuUle8/OthTytRa5IkCSFEA3Eop5SpSw4wf0cGni5O3D+kFbf1i8fXo56So+JMfVpt21fg7gcjX4OkW6GeiqOzy7P534b/sfjIYlr6tuSziz6jW2i3eukboDo1lWMPPUzlnj34X389IY9MxsHVdklL6s5cFn24C79Qd8Y+0BUPn3oqvhe1JkmSEELYWWpuGdOXHeCXrcdwdXJk4sCWTOgfj79nPX1oVpfp56ytnqbXIPW5BwZM1pf31wOzMvN98vdM2zKNalM193W9j1sSbqm3lWsARfPmkfXsc2jOzkS+OwPvoUNt2t/Bzdks/mQ3QVFeXHx/Im6e9fdcRe1JkiSEEHaSll/OO8sO8OOWYzg5aNzWL447B7asn2NEQN8te/s3sOwFKMmEDuNg2LP1VncEsL9gP8+tfY4dOTvoFdaLp/o8RYxPTL31by4rI+v5FyiaOxf3pO5EvP46zuHhNu0zeV0mSz/bS1i8L6Pv7YKru3wUN1TykxFCiHqWUVjBjD8P8v3GNBwcNG7oHcPdg1oS4uNWf0Gk/AV/PKnXH0UkwRWfQnTveuu+wljBh9s/5LPdn+Ht4s3L/V5mTPyY+tkp3KJyzx6OPfgQ1WlpBN1zD0F3TURzsu3H4u6Vx1j+dTKRbf0ZdVdnnF3r4dBhcd4kSRJCiHpyvLiS9/48yDcb0lAoru4ZxT2DWxHuW4+rmXIPwB9Pwf7fwDcaLvsEOl5Wb0XZAGuOreGFdS+QXprOuJbjeDjpYfzd6u/YDaUUBV98Qfbrb+AYEED0p7Px7NnT5v1uX5rGqh8OENMpkIsmdMTJWRKkhk6SJCGEsLGckireX36Ir9YfwWRWXJEUyT2DWxHpX4974ZTlwV//g02zwMldn1brdRc419/oVV5FHq9tfI2FhxcS6xPLrBGz6BHWo976B/3stcwnnqT0zz/xGjSI8Fdexsnf9gnapoWprJ+XQstuwQy/NQFHp3rYxkHUmSRJQghhI/ll1Xz41yE+W5tKtdHMpd0iuX9Ia6ID6zE5MlbB+g9hxRtQXQrdb4ZBj4NXPW1GiV6Y/cvBX3hz05uUG8uZ2GUit3e6HVfH+l3uXrZhAxmP/B+m/HxCn3gC/xuut/n0nlKK9XNT2LzoCG17hTHkxnY41Mc+V8IqJEkSQggrKyyv5qOVKXy6OpVyg4lxXVpw/9DWxAfX4zETSsGeX2DxM1B4BFpfCMNfgJB29RcDkFKYwnNrn2NL9ha6hXTjmT7PEO9Xf4XhoB8tkvv+B+S+/z4uUVFEffctbh062L5fpVj1wwF2LEunQ/8WDLqmLZpD/U1rirqTJEkIIaykqMLArFWHmbXqMCVVRkZ3DufBYa1pFeJdv4GkbdSLstPWQ0gC3PAztBxSryFUmar4eOfHfLzzYzycPHjugue4pNUlOGj1O4piyMzk2COPULFpM77jxhH61FM4ennavF9lViz/Jpk9KzPoPCSSfle0rteidGEdkiQJIUQdlVQa+HR1Kh+tTKG40shFCWFMGt6admH1fIJ7wRFY+hzs+hG8QmHsO5B4HTjUb4HwhswNvLDuBVKLUxkdP5pHkh4h0D2wXmMAKFm2jMzHn0AZDLR47VV8x46tl37NJjNLP9/L/vXH6X5RDL3GxUuC1EhJkiSEEOepvNrIZ2uO8OGKQxSWGxjWPoRJw9rQMaJ+jtA4qbIIVr4F694HzQEG/B/0fQBc6/cU+cLKQt7Y9AZzD80l0iuSD4d9yAURF9RrDADmqiqyX3+Dgi+/xK1DByLeehOX2Nh66dtkNLP4k90c2ppDr7HxJI2qn36FbUiSJIQQ56ii2sRX64/w/vJD5JVVM7BNMA8Nb0OXKL/6DcRkhM2zYfkrUJ4PXa6BIVPAN6Jew1BK8WvKr7yx8Q1Kqku4reNt3NnlTtyd6v+g1qqUwxx76CGq9u0j4KYbCX74YRxc6mfncmO1iUUzd3FkVx79rmhNl6FR9dKvsB1JkoQQopYqDSa+2XCU95YfIqekin6tgnhweGu6xwTUbyBKwYE/9P2OcpMhtj9c+CK0SKzfOIAjxUd4Yd0LrM9cT5fgLjzd52na+Lep9ziUUhT9/AtZL76Ig4sLkR+8j/egQfXWf3WlkYXv7+TY/gIGXtuWjgPqN1EVtiFJkhBC/Icqo4nvN6Xz7rKDZBVX0jMugHeu6Urv+PqvsyFrJ/wxBVKWQ2AruPobaDuyXjeDBDCYDMzePZsPt3+Ii6MLU3pN4Yq2V9R7YTaAqbSMrOeeo/jXX/Ho2ZMWr7+Gc2hovfVfVWFk/jvbOX64iGE3tadtb9seayLqjyRJQghxFgVl1Xy1/gifrT1CTkkV3WP8eevKLvRpGVj/hbjFmfDni7D1K3D3g5GvQdKtUI+HwJ6w5fgWnl/7PIeKDnFhzIU81vMxgj3qb9+lmip27uLYww9jSE8n+IH7CZwwAc2x/grVK0sNzJu+jbz0Ui68vSOtuofUW9/C9iRJEkKI06TmljFr9WF+2JROhcFE/9ZBvHVlF/q1Cqr/5KiiQC/IXjMDTNXQ5x4YMBnc6+8YjxOKqoqYumUqc/bPIdwznHeHvsuAyAH1HgeAMpvJ/+xzst96C6egIGK++ByP7t3rNYby4mrmTt1KUXYFIyd2IrZzUL32L2xPkiQhhECvadl8pICPVqbwx57jODlojEuM4Pb+cfW/lB/0Qux17+m7ZVcVQ4dLYNgzEFC/GzGC/tosSl3EqxtepaCqgJs63MTdiXfj4VyPO4fXYMzPJ+Pxxyn7awVew4bS4sUXcfTzq9cYSgsqmTt1G6UFlYy+pzNR7eu5Lk3UC0mShBDNmtFk5vfdx/loZQrb0grxdXfm7kEtualPLCE+9Xeu2UllubB2Bmz4CKrLoMM4GPAIhHWs/1jQd8x+bdNrrD62moTABN4f9j7tA9vbJRaAsnXr9KNFiooIfWoK/tdeW++je8W5FcydupWKUgMX359Ii1Z+9dq/qD+SJAkhmqXSKiPfb0xj1urDpBdUEBPowfPjEri8eyQeLnb401iaA2umw8ZPwFAOHS/Vk6MQ+yQkuRW5vLftPX468BPuTu482uNRrml3DY71vDHlCcpoJGfGDPI+nIlLbCxRH83ErV39HrECUJBVxtyp2zBWmxg3qSuhsXYYZRT15j//Emia5gasAFwt95+jlHrG1oEJIYQtZBZV8OnqVL7ecJSSSiNJMf5MGd2B4R1CcbTHuVolx/9OjkxV0OkK6D8Zgut/GT1AhbGCz3d/zqxds6g2VXNV26uY2GUi/m71XwN1giEjg2OTH6FiyxZ8L7uUsCefxMGj/qf68o6VMnfqVgAueagbQZH1u1mnqH+1+e9SFTBEKVWqaZozsErTtN+UUutsHJsQQljN7owiPl55mF+3Z2BWipEdw7m9fxxdo+304V+cCaunwuZPwWSAzldB/4chqJVdwjGZTcw7NI8ZW2eQXZHNsOhhPNDtAWJ9Y+0SD+i1UMXz55P1wotgMtHijTfwHTPaLrFkHylm3vRtODk5MO7BrviH2f78N2F//5kkKaUUUGr51tlyUbYMSgghrMFsVvy1P4ePVqaw5lAeHi6O3NAnhlv7xhEVYJ+iY4rSYdVU2PI5KBN0uRr6PQSBLe0TD7AmYw1vbnqT/QX76RTUidcHvk630G52iwf0g2kzn32Wsr9W4N6lCy1efw2X6Gi7xJJ5sJD5M7bj6uHMuAcT8Q2203tH1LtaTbxrmuYIbAZaAe8qpdbbNCohhKiDSoOJX7Ye4+NVhzmYXUqYjxuPjWzHNT2j8XWv/32FACg8Cqvehi1f6N8nXgv9HwL/WPvEA+wv2M9bm99i9bHVRHhF8PqA1xkRO8Kuh7Eqs5nC774j+403UWYzoU88gf9119br3kc1pe/LZ8H7O/H0dWHcpK54B9ihmF/YTa2SJKWUCUjUNM0P+FnTtI5KqV0176Np2gRgAkC0nbJ9IUTzlldaxZfrjvLFulRyS6vpEO7D21d1YXSnFrg41f9O0AAUpMLKN2Hb1/rhs91uhH6TwM9+fyezy7N5d9u7/HLwFzydPZmcNJlr2l2Di2P9nHF2NlWHD5P51FNUbNqM5wUXEPb8c7hERtotntSduSz6cBe+Ie6MfSART19Xu8Ui7OOclnAopQo1TfsTuAjYddptM4GZAElJSTIdJ4SoN4dySvlk1WF+3JxOldHM4LbB3NE/3j47Y5+QdwhWvgXbvwEHJ3137L4PgK/9PvTLDeXM3j2bz3Z/hsFs4Pr21zOh8wR8XX3tFhPoK9fyZs8m950ZaG5uhL/8Mr7jL7HriNahLdn88cluAiO8uPj+Lrh72TeBFPZRm9VtwYDBkiC5A8OBV20emRBC/AulFOsP5/PxyhSW7M3GxcmBS7tGcFu/OFqHetsvsNyDsPIN2PG9fmRIzwl6cuRjv/O8jGYjvxz8hXe3vUtuRS4jYkfwQNcHiPKx/yn1lXv3kvnkFCr37MF7+HBCn5qCc4h9j/ZIXp/F0s/2EhrrzZh7u+DqYacpWmF3tRlJCgc+s9QlOQDfK6Xm2zYsIYQ4M4PJzMKdmXy88jA7jxUR4OnC/UNbc0PvGIK97TgdkpMMK96AXXPA0RV63wUX3A/e9XfQ6umUUqw8tpK3Nr3FoaJDJAYn8vagt0kMSbRbTCeYq6rIfe998j7+GEd/fyKmTcNnxIX2DovdK4+x/OtkIlr7Meruzri4yXaCzVltVrftALrWQyxCCHFWxZUGvtuQxuzVh8koqiQ+yJOXxnfksm6RuDnbp6gXgON7YMXrsPtncHaHPvfqyZGXfQ58PWFv3l7e3Pwm6zPXE+0dzduD3mZo9FC7TmGdUL5lC5lPTqH68GF8L72U0P97pN6PFTmT7UvTWPXDAaITAhh5ZyecXOz4vhINgqTIQogGSynFzmNFfLsxjXnbMiitMtIrLoDnx3VkSLsQHOyx+eMJWbtgxWuwZy64eEG/B/XDZz3te8hpVlkW72x9h18P/Yqvqy+P9XyMK9tcibOj/aeMTKVl5Lz9NgVff41zixZEffwxXv362jssADYvSmXdLynEJwZz4W0JODrbqdBfNCiSJAkhGpyicgO/bDvGtxvT2JtZjJuzA6M6hXPzBbF0jvSzb3CZ2+Gv12DffHD10Y8O6X03eNj3gNPS6lJm7ZrF53s+RynFzR1v5vZOt+Pj0jCOzShduYrMZ57GmJmF//XXEzLpARw87b8ho1KKDb8eZtPCVFr3CGXoze1xdJQESegkSRJCNAhKKdal5PPdxqMs3JVFtdFMpwhfXrykI2MTW+DjZueRkGNb9ORo/2/g6gsDH4PeE8Hdfsd1ABjMBn7c/yPvb3+f/Mp8RsWN4v5u9xPhFWHXuE4wFhSQ/b9XKZo7F5f4eGK++gqPbg2jgkMpxeo5B9m+NI32fcMZdF07+45OigZHkiQhhF1ll1Ty4+ZjfLfxKKl55Xi7OXF1jyiuTIqiY4R9l6ZjNkHyb7DhQzi8Atz8YPAU6DUB3Oy8bF4plqct563Nb5FanEpSaBLvDX2PhKAEu8Z1glKKkt9/J+uFFzEVFRF0910ETpyIg0vDWEqvzIq/vt3P7hXH6DQ4kv5XtEaTBEmcRpIkIUS9M5rMrDiQw7cb0li6LxuTWdEzLoD7h7ZmZMdw3O1dMFuerx8bsvETKDoKPpEw7FlIug3c7D99tSt3F29seoPNxzcT6xPL9MHTGRQ1qEEUZQMYsrPJev55SpcsxS0hgehZn+DWtq29wzrJbDKz7It9JK/LotuIaHpf0rLBvHaiYZEkSQhRb9Lyy/l+Uxo/bEonq7iSIC8Xbu8fx5VJUbQMbgAnqmfthPUfws4fwFgJsf1hxEvQdhQ42v/P5bHSY0zfMp2FhxcS4BbAlF5TuLTNpTg72L8oG/TRo6KffuL4/15FVVcT8shkAm66Cc3J/q/dCSajmcWz9nBoSzY9L44jaVSsJEjirBrOO1cI0SRVGU38sfs4321MY9XBXBw0GNgmmGfHJjC0fQjO9i6SNRn1IuwNM+HIanByh85X6ZtAhnW0b2wWxdXFfLzzY77a8xWapnFHpzu4teOteLk0gMTSojotjcynn6Z87To8evQg/IXncYmNtXdYpzAaTPw+cxepO/O44LJWdB0uR2iJfydJkhDCJvYfL+G7jWn8tCWdgnIDEX7uPDS8DZd3j6SFn7u9w4OyXNj8KWyaBcXH9LPUhr8AXa+3+0q1E/Iq8vhq71d8s+8bygxlXNzyYu7reh9hnmH2Du0kZTKR/8UX5EybjubgQNizz+J35RVoDg1rhZihysTC93eQvq+AAVe3odMg+x0PIxoPSZKEEFZTVmVkwY5Mvt14lC1HC3F21LiwQxhX94yib8ughrFyKGMrrJ8Ju34EUxXEDYRRb0CbEeDQMDYPzCzN5NPdn/LTgZ+oMlUxLGYYEzpPoF1AO3uHdoqqAwfImDKFyu078Bo0iLBnn8E5rOEkcCdUVxiZ/+52sg4VMeTG9rS/wH5HxIjGRZIkIUSdKKXYnl7EdxuPMm9bBmXVJlqFeDFldHvGd40g0KsBnJxuMuibPm6YCWnrwdlTHzHqOQFCGk7ikVKUwqyds1iQsgCAMS3HcEvHW4j3jbdzZKdS1dXkfvQRuR98iKOXFy3efAOfUaMaZG1PZZmBX6dvIzetlOG3JdA6yX7HxIjGR5IkIcR5KSyv5uetx/huYxr7skpwd3ZkTOdwru4ZRbdo/4bxgVmaDZtm61NqpVngHwcjXoHEa8Hdz97RnbQ7bzef7PyEJUeW4OroypVtr+TmhJsJ92p4Ix4VO3aQ+eQUqg4cwGfMGEKffAInf/vuFXU25cXVzJu2jYLjZVx0Z0fiutj3qBjR+EiSJISoNbNZsS4lj283prFot77hY5dIX14e34mLu4Tjbe8NH09I36SvUtv9M5gN0HIojJ0OrYZDA6mVUUqx6fgmPt75MWsy1uDt7M3tnW7nuvbXEegeaO/w/sFcUUHOtOnkf/45TsHBRL7/Ht6DB9s7rLMqLahi3rStlORVMvruzkR3aHivqWj4JEkSQvynw7llzN+ewZwt6RzJK8fHzYlre0ZzZVIUHVrYf98gAIxVsPsXfePHY5vBxRuSboWed0BQa3tHd5JSihXpK/h458dsy9lGgFsAD3R7gKvaXoW3i7e9wzujsnXryHzqaQxpafhdfRUhkyfj6NVwVtadrjivgrlvb6WixMDF93ehReuGOdIlGj5JkoQQZ3Qkr4z5OzJZsCOTPZnFAPSKC+DBYW24qGMYbs4No8iZ4kx9Om3zbCjLgcDWMPJ16HJ1g9j48QSj2cgfqX/wya5P2F+wn3DPcJ7o9QTjW43HzcnN3uGdUdXhw2S/+SalS5biEhND9Oef4dmzp73D+leFx8uZO3UrhioTYyclEhZn513bRaMmSZIQ4qS0/HIW7NQTo53HigDoGu3HU2M6MKpTGOG+DWDpPoBSkLYB1n8Ae+fpx4e0vlA/LiR+SIOZUgOoNlUz79A8Zu2aRVpJGnG+cbzU7yVGxo1sMJtAns6Yn0/ujHcp+P57HFxcCJ70AAE334yDW8NM5k7Iyyhl3tRtmM2KcQ92JTiqYY7MicZDkiQhmrljhRUs3JHJ/J2ZbE8rBKBLlB9PjmrPyE5hRPp72DfAmgyV+tL9DR9C5nb9oNmed0LP2yGgYa0AKzeU88P+H/h89+dkV2STEJjA1EFTGRw9GAet4SRxNZkrKsj/7HPyPvoIc2Ul/lddSdA99+AU2PDreXKOljBv2jYcnDTGP9SNgBae9g5JNAGSJAnRDGUWVbBgRyYLdmay9WghAJ0ifHlsZDtGdwonKqABJUYAuQdh21ew5TMoz4PgdjD6LX1nbNeGVRtTVFXE13u/5qt9X1FUVUTPsJ680O8F+oT3aRgr/s5AmUwUzfuVnGnTMGZl4TV0KCEPP4RrfMNKPM8mK6WIX9/Zjou7I+MmdcUvpIG9f0WjJUmSEM3E8eJKFlqm0jYdKQAgoYUP/3dRW0Z3CicmsIH9z7ssF3b9BDu+1QuxNQdoM1KfUosbCA0s4cgpz+HzPZ/zffL3lBvLGRQ1iNs73U6X4C72Du1fla5eTfbrb1C1bx9unToR8fprePToYe+wau3Y/gIWvLsDdx8Xxk1KxCewgUwJiyZBkiQhmrDskkoW7cpi/vZMNh7JRyloF+bN5AvbMKpTOPEN4VDZmgwVkPwb7PgODi4BsxFCO+rHhXS6HHxa2DvCf0grSWP2rtn8cvAXTMrEyLiR3NbxNlr7N5wVdWdSmbyf7DfeoGzlSpwjIvQNIUeObHDHifybo7vzWPjBTnwC3Rg3qSuefg1g41LRpEiSJEQTk1NSxaLdWSzYkcH6w3pi1DbUmweH6YlRq5AGlhiZzfrBsju+03fFrioG73Dofbc+ndZADpk93f6C/Xyy8xMWpS7CUXNkfKvx3NzxZqK8o+wd2r8yHD9OzvTpFP38Cw7e3oQ8+ij+112Lg4uLvUM7Jynbcvj94134h3ky7oFE3L0bV/yicZAkSYgmIK+0it93H2f+jgzWpeRhVtAy2JP7h7RmdOdw2oQ2wFU+Ocmw/VvY+QMUpelHhXQYqydGcQMazDlqp9ues52Pd3zM8vTleDh5cFOHm7ihww0EezTs3ZxNpWXkffIx+bM/BZOJgBtvJGjinTj6+dk7tHN2YONxFs/eQ3C0Nxff1wU3z4a5SlA0fpIkCdFIFZRV8/vuLBbszGTNoTxMZkV8kCf3Dm7F6M4taBPq1fAKhUuzYeccfdQoc5teZ9RyCAx9BtqNApcGVhdloZRibeZaPt75MRuzNuLr6svdiXdzbbtr8XVt2PvwKKORwh9+IGfGu5jy8vAZNYrghx7EJTLS3qGdl71rMlj2xT7CW/oy5p4uuLjLx5iwnf98d2maFgV8DoQCCpiplJpm68CEEP9UVG7g9z1ZLNiRyeqDuRjNiphADyYOjGd0pxa0D/dueIlRdTkkL9RHjQ4tA2WC8C76GWodLwPvhnvgaFFVEb8e+pU5++dwqOgQIR4hPJL0CJe3uRwP54a9gkopRemff5L9xptUp6TgntSd0Pffw71zZ3uHdt52Lk9nxbf7iWrvz8i7OuPs0jBHG0XTUZsU3Ag8rJTaommaN7BZ07TFSqk9No5NiGZPKUVKbhl/7svmr/05rEvJw2BSRAW4c8eAeEZ3CiehhU/DS4zMJkhdCdu/0zd7rC4Fn0joez90vhpC2tk7wrM6cabajwd+ZHHqYqrN1XQO6szzFzzP6PjRuDg2/NqXip07yX71Nco3bcIlLo7Id2fgNWRIw3ufnIMtfxxh7U+HiO0cxIg7EnBqKDu+iybtP5MkpVQmkGn5ukTTtL1ABCBJkhA2UFFtYl1KHn8mZ7M8OYej+eUAtA7x4ta+cYzuHE6nCN+G+YF3fLelzmgOlGSAqw8kXKInRjF9G9RO2KcrqCxg3qF5zNk/h9TiVLydvbmszWVc1voy2ga0tXd4tVKdfoyct9+meMECHAMCCHvmafwuvxzNufHW7Cil2LgglY3zD9OqewjDbu2Ao2PDfR+JpuWcJnM1TYsFugLrbRKNEM1Uam4Zy5Oz+TNZHy2qMppxd3akb6tAJgyIZ1Db4Ia183VNJVl68fX27+D4TnBwglbDYMRL0HYkODfcfWvMyszGrI3M2T+HpUeXYjAb6BrSlds73c6FsRfi7tRwY6/JVFRE7gcfUvDll+DoSODEOwm8/fYGfQhtbSilWPvTIbYuPkq73mEMvrE9Dg4N8D8HosmqdZKkaZoX8CMwSSlVfIbbJwATAKKjo60WoBBNUaXBxPrD+Sen0Q7nlgEQH+TJdb1iGNwumB6xAQ3nENnTVZXCvvn6qNHhv0CZIaK7frBsx0vBM8jeEf6r3Ipc5h6cy48HfiStJA0fFx+uansVl7W+jFb+rewdXq2Zq6sp+Pprct//AHNxMb7jxxN8/304h4XZO7Q6U2bFyu/2s/OvY3QcEMGAq9ugSYIk6pmmlPrvO2maMzAf+F0p9dZ/3T8pKUlt2rTJCuEJ0XSk5ZefHC1acyiXSoMZVycHLmgZyKC2IQxqG9zwdr2uyWyClD9hx/ew91cwlINftL5kv/NVENSwN080KzPrMtYx58Ac/jz6J0ZlpHtody5vcznDY4bj6th4NiJUSlHy229kv/U2hvR0PPv2JeSRybi1a7i1XufCbFb8+eU+9q3JJHFYFBdc1qphTi+LJkPTtM1KqaTTr6/N6jYN+ATYW5sESQihqzKa2JRawJ/7svkzOZtDOfpoUXSAB1f3iGZg22D6xAc23NEigOoyOPQn7P8N9v8OZTng5gudr9TrjKJ6Neg6I4Ds8mx+OfgLPx34iWOlx/B39ee69tdxaZtLifdtHGeT1VS+aRPHX3udyh07cG3blqiPP8arX197h2U1JpOZpbP3cGBTNkmjY+k5Jk4SJGE3tZlu6wvcAOzUNG2b5bonlFILbRaVEI3UscIKllsKrlcfzKW82oSLowO94gO4rlcMg9oGExfk2bD/6Bdnwv5F+vEgKcvBVAWuvtB6uL7ZY+sR4Oxm7yj/lclsYnXGaubsn8OK9BWYlIleYb2Y1G0SQ6KHNIoVaqerSjlM9ptvUrp0KU4hIYS//DK+48aiOTbgJPscmQxmfv94F4e359JnfEu6jYixd0iimavN6rZVQAP+iy6E/RhMZjalFpxMjJKPlwAQ4efOpd0iGNw2hD4tA/FwacAb3ikFx3dB8iJ9P6OMLfr1fjGQdKtefB1zATg2/BVSWWVZ/HzgZ346+BNZZVkEuAVwU8JNXNb6MqJ9GmetZFVKCvmzP6Xwp59wcHUleNIDBNx0Ew7ujaOovLYM1SYWfbCTo3vy6X9VazoPbtjHu4jmoQH/5RaiYTpeXKnXFu3TR4tKqow4O2r0jAvg8u7tGdwumJbBDXC365qM1XBklT5alPybfiwIGkQmwdCnoe0oCG4HDfk5WBjNRlamr+THAz+y8thKzMrMBS0u4P96/B+DIgfh3AiSu9MppSjfsJH82bMpXb4czcUF/6uuIuieu3EKDLR3eFZXXWlkwbs7yDhYyOAb2tGhb8M7yFg0T5IkCfEfCsqq2ZCaz/qUfNam5LE3U1/cGe7rxpguLRjUNpi+rYLwcm3gv07l+XBwiT5adGAJVJeAkzu0HAwD/0+fRmvAu1+fLqM0g58O/MTPB34muyKbYPdgbut4G5e2vpRI78Z55IYyGChe9Dv5s2dTuWcPjv7+BN1zD/7XXtMkkyOAyjID82dsJ/tICcNv7UCbHo1/ZZ5oOhr4X3Uh6l92SSUbDutJ0YbD+Sen0FydHOgW7c9jI9sxqG0wbUMb4BEgp8s79Pdo0dG1+pEgXqH6Mv22IyFuILg00P2XzsBgNrAibQU/HPiBNcfWANAvoh9PtnmSAZEDcHJonH/STCUlFH7/A/lffokxMxOXuDjCnnsO33FjcXBr2PVfdVFRUs286dvIzyjjojs6Et+1YR8SLJqfxvkXRQgryiisYP3hvJOJUYplzyJPF0e6xwYwNrEFveIC6BTpi6tTAy+SNZsgfZM+WpT8G+Qm69eHJEC/B/VptBZdG/yKtJqUUiQXJLPo8CLmHppLbkUuoR6hTOwykfGtxhPuFW7vEM+b4dgx8j//gsI5czCXleHRsydhTz+F18CBaI3oZ3Q+yoqqmDt1G8W5FYy6uzMxCU1zpEw0bpIkiWZFKcXR/HLWp+Sz/nA+6w/nkV5QAYC3mxM9YwO4umcUPeMC6djCB6fGcPxBVam+f1GyZZl+ea6+63VsP0vh9UXgH2vvKM+JUopdubtYfHQxS44sIa0kDQfNgQGRA7i89eX0jejbaEeNQD9bLX/2bIp//wMAn5EjCbj5Ztw7Jtg5svpRkl/J3Le3UlZczcX3diGirb+9QxLijBrvXxkhakEpxaGcUtZZps42HM4nq7gSgABPF3rGBnBbvzh6xgXQLswHx8ayo29xRo1l+n/py/TdfKH1hfo0Wqth+veNiFmZ2Za9jcVHFrPk6BKyyrJw0pzoFd6L2zrexuDowQS4Bdg7zPOmzGZKly8nf9ZsyjdtwsHLi4AbbyTghutxbtF8CpULs8uZO3Ur1RUmxj2QSFh843qfiuZFkiTRpJjNin1ZJSenzzYczievrBqAEG9XesUH0jMugN5xAbQKaeAr0GoyGSFzOxxaalmmv1W/3j8WetymJ0bRfRrFMv2ajGYjm45vYsmRJSw9upTcilxcHFy4IOIC7ut6HwMjB+Lr2rg/RM0VFRTNnUv+p59RnZqKU4twQh59FL8rLm/0Z6udq/yMMuZO24rZqLjkwa4ER3vbOyQh/pUkSaJRM5rM7M4oPiUpKq40AvpeRQPbBtMrLoBecYHEBHo0nqTIbIKsHZC6Cg6v1Iuuq4rRl+n3gKHPWJbpt20Uy/RrMpgMrMtcx5KjS1h2dBmFVYW4O7nTL6Ifw2OGMyByAJ7ODfh4lloy5uZS8PXXFHz9DabCQtw6dqTFm2/gM2IEmlPz+9Obk1bCvGnb0Bw0LnmoK4ERzStBFI1T8/tNFY1atdHMjvRCSz1RPptT8ymrNgEQF+TJyI7h9IoPoGdcAJH+jWfVFmazvqFj6ipIXQlHVkNlkX5bYGvoeBnE9YfYAeDV+FYAVRorWZOxhsVHFvNX2l+UGErwdPZkYORAhscMp29EX9ydmsbmiFUHD5L36acUz/sVZTDgNXgwgbfcjHtSUuNJ0q0s63AR89/ZjrOrI+MmdcUvtBH9bopmTZIk0WCZzYqU3FK2pxWxI72Q7elF7MksptpoBqBNqBfju0XQK06fQgv1aURLpc1myNmnJ0SHV+hJUUWBfpt/HHQYpydEsf3Ap3Gu3io3lLPi2AqWHFnCivQVVBgr8HHxYUj0EC6MvZDe4b0b5fEgZ6KUonzdOvJmz6ZsxUo0V1d8Lx1PwE034RoXZ+/w7CrjQAHzZ+zA3duZcZO64hPUNJJh0TxIkiQaBKUU6QUV7Eg/kRAVsutYMaVV+tSZh4sjHSN8ualPDN1j/OkRG0CgV+M5tR2lIHe/nhClrtRHjMrz9Nv8oqHtaMtIUT/wbZwbIQKUVJewPG05S44sYXXGaqpMVQS4BTAmfgzDYobRI6wHzg6Nq27q36jqaop/+428Tz+jau9eHAMDCbr/PvyvuQYnf1mxdXRPHr+9vxPvQDfGPtAVL/9G9DsrBJIkCTvJKak6OTq0I72QHelF5FsKrF0cHWgf7s34rhF0jvSlS5QfLYO9Gs/KM9CTorxDkLpCrylKXQVl2fptPpH6KrRYS1Lk37gP8SysLOTPtD/548gfrMtch9FsJMQ9hMtaX8awmGF0C+mGo0MD31/qHJmKiyn47jsKvvgSY3Y2Lq1aEv7iC/hcfDEOrpIIABzensOij3bhH+rJ2AcS8fBpGqOGonmRJEnYXFGFgV3HitieXsgOy9RZRpG+DN9Bg9Yh3gxtF0LnKD+6RPrSNsy74W/aeDqloOCwJSGyJEUlmfpt3uEQP+jvkSL/uEZXbH263Ipclh5ZyuKji9mUtQmTMhHhFcF17a5jWMwwOgd3xkFrBHtMnaPq9HTyP/ucwh9/RJWX49GnN+EvvoBnv35NfvPHc3Fg03GWzNpDUJQXF9+fiJtn0xk9FM2LJEnCqiqqTezJLDpZR7QjvejkDtYAMYEedI8N4NZIXzpH+pHQwgfPhn7m2dkUHPk7ITq8EorT9es9Q/5OiGIHQGDLRp8UgX5W2rKjy1h8ZDFbs7eiUMT6xHJLx1sYHjOc9gHtm2RhsjKZKF+/noLvf6Dkjz/AwQHf0aMIuPlm3Nq3t3d4Dc6+tZks+3wvYS19GXNPF1zcG+nvtxBIkiTqwGAyk5xVUqOOqIj9x0swmRUAoT6udI7049JuEXSO9KNzpC9+Ho10yF0pyE+BtA2WFWgroPCofptHkCUhmgRxAyCoTZNIigoqC9iQtYH1metZn7meoyX6823l14q7utzFsJhhtPJr1TQTI6Wo3LmTovnzKf7tN0w5uTh4exN46y34X389zmFyCOuZ7Pornb++2U9kO39G3dUZZ9dGNiIsxGkkSRK1UlFt4kB2CfuyStiTUcz29EL2ZBRTZVlp5ufhTKcIX4a2a3myjqhRrTY7XUkWHNsMx7bo/2Zs+XtJvrs/xPSFPvfqdUUh7ZtEUlRuKGfz8c16UpS1nn35+wDwdPYkKTSJq9tdTb+IfsT5Nt3VWlWHDlG8YAFF8xdgOHoUzdkZr0GD8BkzBq+BA5r0YbN1tXXxUdb8eJDYToGMmNARJ2dJkETjJ0mSOIXJrDiSV0Zylp4QJWeVkHy8hNS8MpQ+QHRypdkNvWNO1hFFBzSijRpPV1mk72B9IiE6tgVKMvTbNEcITYCE8RDRXb8Et29UB8SejcFkYGfuTtZlrmN95np25O7AaDbi7OBMYkgi9ybeS6/wXiQEJTSpFWmnM2RmUrxwIUXzF1C1dy84OODZuxdBd96J9/BhOPr42DvEBk0pxaaFqWz49TAtu4Uw/NYOODo1/t8PIUCSpGZLKUVOSdUpiVByVgkHskuoNOijQ5oGsYGetA31ZmyXFrQL86ZtmDcxgZ6Na6VZTYZKfdPGkwnRZsg78PftAfEQ21dPhlp0g7BO4NI0Nr4zKzP7C/azPnM96zLXsfn4ZiqMFWhodAjswI0dbqRXeC+6hnRtMhs7no2xoICS3/+geP58yjdtAsCtS2dCn3gc74suwjkkxM4RNg5KKdb9cogtvx+lba8whtzYDofGcCi0ELUkSVIzUFplZL8lCdJHiIpJziqhoNxw8j7B3q60C/Pm+l4xtA3zpl2YD61CvHB3acRD5maTvjdRzYTo+G4wW563V6ieDHW5Sk+IWnQFj8Z7gOrplFKklaSdHCnamLWRgip9w8pYn1jGthxLn/A+JIUlNfrz0WrDXFZGybI/KV6wgNJVq8BoxCU+nuAH7sdn1ChcYhr3Vgz1TZkVK384wM4/00no34KB17RFa6z/eRLiLCRJakIMJjOHc8sso0PFJGeVkny8mLT8ipP38XBxpE2oNyMSwmhrGRlqF+ZDgGcjLag+QSkoSjt1yixzG1SX6re7eENEV+hzz9/TZj4tmkQtUU25Fbknk6L1mevJLNO3IQjxCKF/ZH96hfeiV1gvQj1D7Rxp/VDV1ZSuXk3x/AWULFuGqqjAKSyMgJtuxHfMGFzbtWu808R2ZDYrln+1j72rM+kyNIq+lzfNAn4hJElqhJRSZBZV1qgbKmZfVgkpOWVUm/SpMkcHjfggT7pE+nFVUhRtQvVkKNLfHYem8L+98vxTR4gytkBZjn6bowuEdoQu11gSom76+WdNoI7odCXVJWzK2sT6LD0pOlh4EAAfFx96hvXk1o630iu8F7E+sc3mQ0yZzVRs3kzR/AWULFqEqagIR19ffMeNxXfMGNy7dZM9jerAZDKz9NO9HNh4nKRRsfS8OK7ZvLdE8/OfSZKmabOAMUC2Uqqj7UMSJ5RXGzmcW6ZfcvR/U3LLOJRTSonlpHuAcF832oZ5M7BtsF43FOpDyxDPxrch45mYzfomjdl7LZfdepF1QarlDpq+5L7VcD0ZiuimJ0hOTXPX4ypTFduyt50cKdqVtwuzMuPm6Ea30G5c3PJieoX3op1/uya3y/W/UUpRtXcvRfMXULxwIcasLDR3d7yHDsVnzGi8LrgAzaWRj5Y2ACaDmT8+2U3Kthx6XxJP94ti7R2SEDalqRNLls52B00bAJQCn9c2SUpKSlKbLMWQ4t8ZTGbS8stPJkMpNRKirOLKU+7bwteNuGBP4oL0Yuq2YT60DfXG16MJrDxSSl92n73Hctlr+XcfGP+eLsQvGsITLQlRd/1rt6a5+kgpRXppOrtyd5287M7bTZWpCkfNkY5BHekV3ove4b3pEtylyRwWey6qjxyhaMECiucvoDolBZyc8OrfH5/Ro/EeMhgHj6ZRdN8QGKtN/PbhTo7uzqffFa3pMjTK3iEJYTWapm1WSiWdfv1/jiQppVZomhZrk6iaCbNZcbykksM5liSoxuVofvnJzRcB/D2ciQvypG+rIOItCVFckCexgZ6Nu4i6poqCGknQXjhuSYwqC/++j2eIvv9Q0i36vyEdILgtuHrbLWxby63IZXfubnbm7mRX3i525+6msKoQABcHF9oHtufKtlfSK6wX3UO74+XiZd+A7cSQnU3JokUUzV9A5Y4dAHj06EHATTfhfeFwOVjWBqorjSx8fwfH9hcy6Lq2JPSPsHdIQtQLqUmyosLy6lNGgk6MDKXmllFhMJ28n5uzA3FBXnQI92F0p3A9EQr2JC7QE//GXkBdU3U55CZbEqHdf0+ZndiDCMDVR0+CEi7RE6ETCZFnkN3Crg+l1aXsydvDztyd7M7bza7cXSeLrB00B1r5tWJI9BASAhPoFNSJVv6tmvReRf/FcPw4ZStXUrRgAeXrN4DZjGuH9oQ88gg+o0biHB5u7xCbrKpyA/NnbOd4agnDbu5A216y27hoPqyWJGmaNgGYABAdHW2tZhuckkoDafkVpOZZkqCcMg7nlnI4t+yUJfWODhrRAR7EBXlyQctA4oI8ibckQ6Hebk2jePoEkxHyD9VIhCwjRPkpgGWUzNEVgtvoZ5qFdPg7IfKNbHIrzE5XbaomOT/5lITocNFhlOW1ifKOIjE4kevaX0enoE60C2iHh3PzniYyFRdTvmEDZWvXUbZ2rT6VBjjHRBM0cSI+Y0bjGh9v5yibvorSan6dvp28Y6WMuD2Blt1k/yjRvPxnTRKAZbptfnOoSSqpNJBeUGG5lJ/2bwVFFYZT7h/m43ZyJCg+6O/psagAD5yb2qZqJiMUHYWc/afWDeXuB1O1fh/NQd+QsWYiFJoA/nHg2PQHLk1mE4eLDp+SECUXJGM064X2gW6BdArqREKQPkKUEJiAn5uffYNuAMzV1VRs2UrZ2rWUrVtL5c5dYDajubvj0SMJzz4X4NmnN65t28pKqnpSVlTFvGnbKMqu4KI7OxLbqWmP7orm7bxrkpqa0iqjnvTkn5r8pBfqXxeWn5oEuTs7EunvTqS/O92i/S1fexAb5EFsoGfjPcH+bExGKDwC+Yf10aH8FMiz/Ft4BMx/r6rDJ1JPgloO0ROhkPb6SjPnpr1b8wlKKTLKMk4prN6Tt4dyYzkAXs5eJAQmcGOHG+kU1ImOQR0J9QiVD3n0ZfqVe/dSvnYtZWvWUr5lC6qyEhwdce/cmaCJE/Hs0xv3Ll1kVZodlORXMnfqVsoKqxh9b2ei2jWdTVaFOBe12QLgG2AQEKRpWjrwjFLqE1sHdr7KqoxnHAVKKzhzEuTm7ECkvweR/u50jfo7CTqRGAV4ujS9DzWTQT/BPj/l1CQo/5B+fc1EyNkTAuMhrCN0GAsBLSGwlZ4QufvZ7SnUN6UUWWVZHCg8cMpKs/zKfEAvrG4X0I5xrcadHCmK9YnFQWtio4nnSSmFIS2NsjVrKVu7lvL16zEVFgLg2roVfldegWefPnj06IGjV/MsSG8oinIqmPv2VqrKDYy9P5HwVn72DkkIu6nN6rZr6iOQc3Ewu5Sj+WWknT4aVFB+Sl0QnJoEJUb51UiA9H8Dm2ISBKcmQjWToPyUfyZCLl4QEAdhnaHDJRDYUp8yC2gJXiFNvmaoJrMyk1mWyaHCQycvKUUpHCo8dHKEyEFzIN43ngGRA04mRG382uDs2HwLq8/EmJdH2Tq9pqh8zVoMGXrBvlNYGF6DB+N5QR88evWSc9IakIKsMua+vRWj0cy4B7sSEtM0t9cQorYa5VzRPV9tIfl4CQCuTg4nk57Okb6njAJF+nsQ5NVEkyD4OxE6PQnKs4wIqb9X1OmJULyeCCWM/zsJCohvdokQ6MnQsZJjHCr6OxE6WHiQw0WHqaixL1OwezDxfvGMbz2eeN94Wvq1pH1A+2ZfWH0m5rIyyjdvPjlaVJWcDICDjw+evXoRcPttePbpg0ts89n9uzHJTS9l3rStAIx/qBuBETKiJ0StCrfPla0Lt9en5OFsSY6CvVyb7h9ckwGKM6Ao3XJJ0/89MUL0j0TIW58aq5kAnRgV8gxudokQ6IXU6aXpf48MFR0ipTCFw0WHqTT9vVlniEcIrfxanUyEWvm1Is43rlkc/Hq+lMFAxc6derH12rVUbNsORiOaiwvu3bvh2bsPnhf0wa1DBzTHJrLHVxN1PLWYX6dvw8nFkXGTEvEP87R3SELUqyZVuN0rPtDeIdSdUvqmiicToBpJ0IlLSSYnl9Cf4BGoL5tv0RU6Xnbq1JhnULNMhACMZiNpJWlnTIaqzdUn7xfuGU68Xzw9wnroSZFfPPG+8Xi7NN1NKq1FKUXVgQOUr1unF1tv2IC5vBw0DbeEBAJvuUUvtu7WDQc3N3uHK2op42Ah82dsx83TmUse7IpPUPNYeCFEbTTKJKlRMFadNgp0hiTIUHbqYxxdwTdCT4JaDtb/PXmJAp8IcGne0zwGs4GjxUdPSYQOFh7kSPERDOa/69EivCKI942nT4s+tPRrSUvflsT7xePpLP9Drg1lNmM4epTKPXssl71U7tlzstjaJSYGn3Fj8ezTB8+ePXH087NrvOL8pO3NZ+H7O/Dyd2PcpES8/CW5FaImSZLOh1JQnvfPpKfm96XZ/GMUyDNET3iC20Krof9MgjyCmuRJ9edCKUVeZR7pJemkl6ZzrOQYx0r1S3pJOlnlWZiVGQANjQivCFr6tWRA5ICTyVCcb5zUDJ0DZTBQlZJyMhGq3LuHqr37MJdZknhnZ9xat8Z7+DDcExPx7NMH5xYt7Bu0qLPUnbks+nAXviHujJvUFQ8f2WpBiNNJklSTyQjlufpBq6XH9UvJcSjNOu3rbDCeevgsTu5/JzytL9STnppJkE8EOMv/0kA/kuNE0pNemn5KEpRRmnFKrRBAkHsQEV4RdA3tSoRXBLE+sbT005MhdyeZGjgX5spKqvbvP2V0qGr/flS1PiWpeXjg1rYtvpdcgluH9rh16IBry5ayV1ETc3BzNos/2U1gpBdj70/EzUtWZgpxJs0jSaou/zu5+bcEqDwXLKMUp3DzA+8wfRVYVG/wDtU3UjxlFCig2dYDnc5gMpBRlsGxkmOkl546IpRemk5RVdEp9/dy9jqZ/PSL6EeEVwSR3pFEekXSwqsFbk6SXJ4PU2kpVXv3npoQpaSASS/2d/D1xa19e/yvvx639u1xS+iAS0yMFFk3ccnrs1j66R5C43wZc18XXN2bx8eAEOej8f52KAXl+ZaEJ8uS8JxIfiwJ0Ynrq0v++XjNUU96vEL1UZ4WXcErTE+AvEL//tozREaATmNWZnLKc04mPSeSoROjQdnl2SfPJQNwcnAiwiuCCK8IOgR2INI7Uk+EvCKJ9I7Ex8Wn6a5QrCfG/PxTpssq9+zBcOToydudgoNx7dAer2FDcevQAbf2HXCOaCGvezOze+Uxln+dTEQbf0bd1QkXt8b7ESBEfWicvyGzR0PaejAb/nmbs4ee5HiHQWhHaDnUkviclgB5BDb7+p8zqTBWkFOeQ3Z5Ntnl2eRU6F/nlOeQXaH/m1WWdcqKMdCX0Ed6RdIzrCcR3noCdGJEKNg9GEcHGZ2wBqUUxqysU0aHKvfuxZiVdfI+zpGRuHXogN/48ZaEqD1OwcF2jFo0BNuXprHqhwPEdAzkogkdcXKR30kh/kvjTJJaDobIJEsyZEl6TnztKku5z8RgMvyd8NRMfMqzTyY/OeU5lBj+Oerm5uhGsEcwIR4hJAQmMCR6yMkEKMIrghZeLXB1dLXDs2q6TMXFVB9Nw3D0CNVH06g+ehTD0aNUHTqEqaBAv5ODAy5xcXj06KFPl3XogFv7djj6yt5O4lSbfktl/dwU4rsGc+FtCTg6yX8QhaiNxpkkDZhs7wgaDJPZRH5l/slE50wjQDkVOSfPGKvJycGJYPdggj2CifeNp3d475PJULC75V+PYLydvWVaxsqUUpjy86k+cpTqo0cwWBKh6rSjGI4cPbnU/gSn4GCco6PxGjrk74SobVscPGQVnzg7pRTr56Ww+bcjtOkZytCb2uPgKAmSELXVOJOkJq7cUE5hVSEFlQUUVBVQUFlAfmX+yevyKvPILc8luzyb3Mrck0viT3DQHAh0CyTYI5hwz3A6B3cm2COYUI/QU5IfP1c/OYDVhpTZjPH4cT0RStNHgvSv0zAcOaJvxHiCgwPOYWE4x0TjPWIELtFROEdH4xIdjUtkJA6esr+TODdKKVb/cJDty9Lo0Decgde1w8FB/rMjxLmQJMnGzMpMUVXRyWSnZuJz4uvCykLyK/NPfn36EvgTnDQn/Nz8CHALINgjmNb+rfWRH/eQkyNAIR4hBLgF4OQgP9r6oAwGDBkZ+ijQ0dMSobS0k0vrAXB2xiUiAueYaDy6d9cToJhonKOicY6MwEGW2QsrUWbFX98ks3tlBp0HR9LvytYyGizEeZBP0nOglKLcWE5xVfHfyU1V/skk58RIT82vi6qL/jHSc4Knsyd+rqcmPQFuASev83fzP/m1n5ufTHvZgbmsDGNODsacHAzZ2RiPZ1tGhSx1QhkZJ5fUA2hubrhER+MaH4fXwIF6IhQdhXN0DM7hYbK8Xtic2WRm2ef7SF6fRbeLYug9Ll7+bghxnppdklRprKSkuoSS6hKKq4spri4++f2J6852W0l1CaaaB8rW4KA54Ofqh7+rP/5u/rT0a3nya383f/xd/U+OAp34Woqd7UMppSc/2dkYs/UEyJidfTIZqvn1yV2na3Dw8cElOhr3Th3xGT0Kl+iYk9NjTsHB8oEk7MZkNLN41m4Obcmh19h4kkbF2jskIRq1RpkkFVUVUVhVePZEp8qS6BiKT/m+pLrkH0vXT+fm6Ia3izfeLt74uPgQ6BZIrE/sye99XHzwdvHGz+3vhCjALQBvF2+p77EzpRTm4uIzJjuGE19bkiJVUfGPx2tubjgFB+MUEoJru/9v715j5KrLOI5/n7nszl67QHe19AJNaKi1wBYbRJGLcklVpCaaiLcEAyEGDYgSb2+MISa+MKY1IZCmYhM1EAMkraSiLzTxgpqiRa5VK1jaCi0t3V22u3PZnccX5+wy285sl92Zc3bm/D7JyZzbDs/Dmf3vr+fMnFlL15VXkhnoD9b195MdGCAzMKBPj8miNFGa5Iltz3Hg2eNc8ckLGLxuVdwliTS9pgxJtzxxC/uH9lfdlrEMve1BkOnJ9tDb3suyrmUzgs9U0JlarpxvS+t9IYuJl8uU33yTyeHhYBoaYuLY8ZpnfrxQOO05rLOTbBh0OtavD4NQEIamAlBmYIBUd7fOAklTKhUm2X3/Mxzad4KrP3Mh669aHndJIi2hKUPSHYN3kJ/ITwecyqDTkenQH7pFyCcmmBwZYXJomMnhISaHhylPB5/ht0LQ9DREeWiYyZGR4O7qVaS6u6cDTsfg4PT8dAjq7yfTP0C6W58Mk9ZVHJ/g8fv+wWv/GebaW97F2suXxV2SSMtoypB0/XnXx11CIrk7XijMPLMzFXKGhqbDzXQAqgg/5dHR2k9sRqq3l/SSJdNT28qVwXxfsJyq2JY55xwy/f26R5AkXv5kiV/+6GmOHRzlhtvWc8F7BuIuSaSlNGVIkrkrF4uUT56cfRobm7E8OT0/dtp+lZ/kOk06PSPoZPr7aV9zQUXA6ZsRfKamVE+PPvUl8jaNjRTZtfVpThw5yaYvXsTqi5fGXZJIy1FIitnU2RnP5ykXCvj4ePCYz1Mez+OFPOV8Ac+PB4+FYH3twBOEHD85xuTYGJSqfL9dFZbNkurqmjGle3vJLlv21rrOzmB9Tzfpvr6KMzx9pPuWkOrq0qVOkQiMniiwc8teRt/Ic+Mdl7By3dlxlyTSkhSSQj4xgReLeLFIuViEUolysYgXS8H6UjhfCvfJ5/F8gXJ+PHgs5PHxfPBYGWYKM/fzfD782fx0GJqXTCYML52ku7pIdQZBJjMwMDPshMFm5hSGnYp9TDcyFGkKI8fG2bllL+OjJT525yDnrumLuySRljWnkGRmm4CtQBrY7u7fb2hVZzD06GNMHD0ShpgiXgqDzFSgmbEuDDWlyvUzt3mpBOXqN3x8W7JZUu3tWC5HKpfDcu2k2nNYR450dw+2tP+09an23Mz9cxXL7TlSHeFjrh3LdQSPHR26O7NIAg0dGWPnlr2UCpNsvmsD71jdG3dJIi3tjCHJzNLAfcD1wCFgj5ntcvcXGl1cLW/89KcU9u0DM6ytDctmg8fpKYtlg8dUti04a9J2VpX9pn42WJ+asW5qvsa+1UJMRifmRKQxjh8eZefWp8Gdj391A0tX9MRdkkjLm8tf9cuA/e7+EoCZPQxsBmILSdl7H4CSQyrNmd4CU4fzQ6ebAEbDiTIwFk4iIvVXzE/yx1/8m3TGuOnuSzl7mW5rIRKFuYSk5cDBiuVDwHtP3cnMbgduB1i1qrF3ev3TzgMcPzTLR8pFRFpMz9k5Nt89yJJ+3fpCJCp1uz7k7tuAbQAbN26sfve/Ornh1nczWWrIOSIRkUVpyUAHbTld0heJ0lx+4w4DKyuWV4TrYqNTzSIiItJoc/lG1j3AGjNbbWZtwM3ArsaWJSIiIhKvM55JcvcJM/sy8GuCWwA86O7PN7wyERERkRjN6QK3u+8Gdje4FhEREZFFYy6X20REREQSRyFJREREpAqFJBEREZEqFJJEREREqlBIEhEREalCIUlERESkCoUkERERkSrMvf5fs2ZmrwMH6v7EMy0FjjX4v7FYJbl3SHb/Se4dkt2/ek+uJPcfVe/nuXv/qSsbEpKiYGZPufvGuOuIQ5J7h2T3n+TeIdn9q/dk9g7J7j/u3nW5TURERKQKhSQRERGRKpo5JG2Lu4AYJbl3SHb/Se4dkt2/ek+uJPcfa+9N+54kERERkUZq5jNJIiIiIg2z6EKSmT1oZkfN7Lka29ea2Z/NrGBm95yybZOZ/dPM9pvZN6OpuH7m27uZrTSz35nZC2b2vJndFV3V9bOQYx9uT5vZXjN7vPHV1tcCX/d9ZvaIme0zsxfN7H3RVF0/C+z/7vB1/5yZPWRmuWiqro859P5ZM3vGzJ41syfN7JKKba0+5lXtPUFjXs1jH25v5TFvttd9ZGPeogtJwA5g0yzb3wDuBH5QudLM0sB9wIeBdcCnzWxdg2pslB3Mo3dgAviau68DLge+1IS9w/z7n3IX8GKda4rKDubf+1bgCXdfC1xCc/4/2MH8fu+Xh+s3uvt6IA3c3KAaG2UHs/f+MnC1u18E3Ev4Ho2EjHlVeyc5Y16t/qe08pg3W++RjXmLLiS5++8JBsRa24+6+x6gdMqmy4D97v6SuxeBh4HNjau0/ubbu7u/6u5/D+ffJHjBLG9krY2wgGOPma0APgpsb1yFjTPf3s1sCXAV8ONwv6K7DzWw1IZYyLEHMkCHmWWATuB/jamyMebQ+5PufiJc/AuwIpxPwphXtfcEjXm1jn0SxryqvUc95i26kLQAy4GDFcuHaMJfmoUys/OBDcBfYy4laluArwPlmOuI2mrgdeAn4Wn37WbWFXdRUXH3wwRnl14BXgWG3f038VbVULcCvwrnkzbmVfY+LUFj3qn9byE5Y15l75GOea0UkhLPzLqBR4GvuPtI3PVExcxuBI66+9/iriUGGeBS4H533wCcBJruvSnzZWZnEZw9WQ2cC3SZ2efiraoxzOyDBH8svhF3LVGr1XtSxrxT+0/SmFfl2Ec65rVSSDoMrKxYXhGuSwQzyxIMFj9398firidiVwA3mdl/CS45fMjMfhZvSZE5BBxy96l/RT9CMIAkxXXAy+7+uruXgMeA98dcU92Z2cUEl1U2u/vxcHUixrwavSdmzKvRfyLGvBq9RzrmtVJI2gOsMbPVZtZG8ObNXTHXFAkzM4Lrsy+6+w/jridq7v4td1/h7ucTHPffuntLnk04lbu/Bhw0swvDVdcCL8RYUtReAS43s87w9+BamveNrFWZ2SqC8Pd5d/9XxaaWH/Nq9Z6UMa9W/0kY82bpPdIxb9HdTNLMHgKuIfjm3yPAd4AsgLs/YGbvBJ4CegmuxY4C69x9xMw+QnCdNg086O7fi7yBBZhv78DFwB+AZ3nr+vS33X13lPUv1EKOfcVzXAPc4+43Rln7Qi3wdT9I8K+tNuAl4AsVb3hsCgvs/7vApwg+8bQXuM3dC5E3MU9z6H078AngQPgjE1Nf+JmAMa9q72b2AZIx5tU89hXPcQ2tOebN9rofJKIxb9GFJBEREZHFoJUut4mIiIjUjUKSiIiISBUKSSIiIiJVKCSJiIiIVKGQJCIiIlKFQpKIiIhIFQpJIiIiIlUoJImIiIhU8X/a5BOFNzjD6gAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"unbumped_vols = pd.DataFrame({t: {s: v[0].result() for s, v in i.items()} for t, i in call_res.items()})\\n\",\n \"unbumped_vols.plot(title='MtM Sensitivity to Spot at Different Points in Time', figsize=(10, 5)) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tenor_3m = pd.DataFrame({s: {b: r.result() for b, r in v.items()} for s, v in call_res['3m'].items()}).T\\n\",\n \"tenor_3m.plot(title='MtM Sensitivity to Volatility Changes in 3 Months', figsize=(10, 5))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see above, with vol and/or spot higher, our call option's mark-to-market increases. We can use the prevaling option mtm to secure a rate on the forward - let's look at this next.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5 - Restructuring into a Forward\\n\",\n \"\\n\",\n \"Now that we understand how the mark-to-market of the option may evolve into the future, let's analyze how this option (including the premium payable) can be restructured into a forward once the company get clarity on moving forward with the deal.\\n\",\n \"\\n\",\n \"Specifically, let's assume there is clarity in 3 months and look at possible economics of unwinding the option after 3 months and using the mtm to finance a forward for the remaining 9 months.\\n\",\n \"\\n\",\n \"As a first step, let's define our `FXForward` and calculate the implied forward rate for the 9-month remaining tenor at different spot levels using the `MarketDataShockBasedScenario`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXForward\\n\",\n \"\\n\",\n \"passed_time, remaining_time = '3m', '9m'\\n\",\n \"fxforward = FXForward(pair='EURUSD', settlement_date=remaining_time, notional_amount=100e6)\\n\",\n \"forward_res = {}\\n\",\n \"with PricingContext(market_data_location='NYC', is_batch=True):\\n\",\n \" for s in spot_range:\\n\",\n \" spot_scen = MarketDataShockBasedScenario(shocks={MarketDataPattern('FX', 'USD/EUR'): MarketDataShock(MarketDataShockType.Override, s),})\\n\",\n \" with spot_scen:\\n\",\n \" inst = fxforward.resolve(in_place=False)\\n\",\n \" forward_res[s] = inst\\n\",\n \"forward_rates = pd.DataFrame({s: {'Forward Rate': forward_res[s].result().forward_rate,\\n\",\n \" 'Option MTM ($)': call_res[passed_time][s][0].result()} for s in spot_range}).T\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now calculate the adjustment to the forward rate based on the 3m in option mtm we calculated in the previous section (adjusting for the premium) and the above forward rate and examine how the achieved all-in rate on the forward may look like after the restructuring. \\n\",\n \"\\n\",\n \"Note two things here: (1) the premium payment for the option has been embedded into the new forward so the full structure is net zero premium (2) the below is an approximation that would work less well for currencies that have high carry, we are working on adding a forward rate solver to give a more precise answer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"forward_rates['Forward Rate Adjustment'] = -1 * (forward_rates['Option MTM ($)'] - call_option.dollar_price()) / 100e6\\n\",\n \"forward_rates['All-In Rate'] = forward_rates['Forward Rate'] + forward_rates['Forward Rate Adjustment']\\n\",\n \"forward_rates[['Forward Rate', 'All-In Rate']].plot(title='Forward Rate Post Option Restructuring', figsize=(10, 6)) \\n\",\n \"plt.xlabel('Prevailing EURUSD Spot in 3 Months')\\n\",\n \"plt.ylabel('Estimated Forward Rate')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see above, if the USD weakens vs EUR in 3 months time, the all-in rate will be lower (better) than the equivalent on-market forward - this is due to the EURUSD call option protection. However, if the dollar strenthens, the all-in rate will be higher (worse) than the on-market forward due to the premium payable for the option.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What's New\\n\",\n \"* Ability to overwrite market data used in a risk request - example [here](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/blob/master/gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/0005_market-data.ipynb)\\n\",\n \"* Restructuring of all notebooks into [documentation](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/documentation/) and [content](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/content/)\\n\",\n \"* Ability to use polling instead of websockets for batch mode (allowing python 3.6.0 support and cases where websockets may have firewall issues)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/13-Cyclicals.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Cyclicals\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"With growing focus on cyclical asset recovery and a still very easy stance from the Fed, the short USD theme has gotten renewed attention from the investor community.\\n\",\n \"\\n\",\n \"In the analysis below, we look at G10 currencies' correlation to risk (proxied by SPX) and volatility and find that NOK offers both high exposure to a continued risk-on rally with cheap tails compared to peers.\\n\",\n \"\\n\",\n \"P.S. Missed our webcast? [Click here](https://developer.gs.com/docs/gsquant/videos/gs-quant-meets-markets-nov/) for a replay that touches on this theme. Also checkout [this note](https://marquee.gs.com/content/markets/en/2020/11/25/bbe0fca7-c70b-4c92-a0f3-22880574d957.html) from Brian Friedman for specific implementations of this theme.\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Find cyclical crosses](#2---Find-cyclical-crosses)\\n\",\n \"* [3 - Solve for best expression](#3---Solve-for-best-expresion)\\n\",\n \"* [What's New](#What's-New)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.227.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Find cyclical crosses\\n\",\n \"\\n\",\n \"Let's start by looking for crosses that are most sensitive to risk. To do that, we use SPX returns as the risk proxy and look at the YTD relationship of each cross' weekly returns to SPX's. We pull the FX spot data from FRED ([create token here](https://research.stlouisfed.org/docs/api/api_key.html) if you don't have one), downsample data from daily to weekly and calculate returns at a first step. FX spot also available via [gs data catalog](https://marquee.web.gs.com/s/developer/datasets/FXSPOT_PREMIUM/connect).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SPXEURJPYAUDGBPCADNOKCHFSEKNZD
date
2020-01-100.94-0.48-1.42-0.86-0.24-0.51-0.76-0.27-1.26-0.51
2020-01-171.97-0.23-0.60-0.38-0.24-0.18-0.310.48-0.11-0.44
2020-01-24-1.03-0.600.78-0.770.32-0.55-1.39-0.30-0.53-0.12
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" SPX EUR JPY AUD GBP CAD NOK CHF SEK NZD\\n\",\n \"date \\n\",\n \"2020-01-10 0.94 -0.48 -1.42 -0.86 -0.24 -0.51 -0.76 -0.27 -1.26 -0.51\\n\",\n \"2020-01-17 1.97 -0.23 -0.60 -0.38 -0.24 -0.18 -0.31 0.48 -0.11 -0.44\\n\",\n \"2020-01-24 -1.03 -0.60 0.78 -0.77 0.32 -0.55 -1.39 -0.30 -0.53 -0.12\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.api.fred.data import FredDataApi\\n\",\n \"from gs_quant.timeseries import returns\\n\",\n \"from datetime import date\\n\",\n \"import pandas as pd\\n\",\n \"pd.set_option('display.precision', 2)\\n\",\n \"\\n\",\n \"data = {'SPX': 'SP500', 'EUR': 'DEXUSEU', 'JPY': 'DEXJPUS', 'AUD': 'DEXUSAL', 'GBP': 'DEXUSUK', 'CAD': 'DEXCAUS',\\n\",\n \" 'NOK': 'DEXNOUS', 'CHF': 'DEXSZUS', 'SEK': 'DEXSDUS', 'NZD': 'DEXUSNZ'}\\n\",\n \"pairs = {'EUR': 'EURUSD', 'JPY': 'USDJPY', 'AUD': 'AUDUSD', 'GBP': 'GBPUSD', 'CAD': 'USDCAD', 'NOK': 'USDNOK',\\n\",\n \" 'CHF': 'USDCHF', 'SEK': 'USDSEK', 'NZD': 'NZDUSD'}\\n\",\n \"\\n\",\n \"FRED_API_KEY = 'YOUR_KEY_HERE'\\n\",\n \"fred_API = FredDataApi(api_key=FRED_API_KEY)\\n\",\n \"df = pd.concat([Dataset(symbol, fred_API).get_data(date(2020, 1, 1)) for n, symbol in data.items()], axis=1)\\n\",\n \"df.columns = data.keys()\\n\",\n \"# let's flip these to look at all relative to USD\\n\",\n \"df = df.apply(lambda x: 1/x if x.name in ['JPY', 'CAD', 'SEK', 'NOK', 'CHF'] else x)\\n\",\n \"rets = returns(df.resample('W-FRI').last(), 1).dropna()\\n\",\n \"rets.head(3) * 100\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With the data in hand, let's look at the relationship of SPX to the other pairs in order of the highest r-squared which represent the strongest relationship to SPX. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from scipy.stats import pearsonr\\n\",\n \"\\n\",\n \"crosses = list(pairs.keys())\\n\",\n \"r2 = pd.Series({i: pearsonr(rets.SPX, rets[i])[0] for i in crosses}, name='R2').sort_values(ascending=False)\\n\",\n \"r2.plot(kind='bar', title='YTD R^2 with SPX')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see above, AUD, NOK and CAD have the top 3 highest correlations to SPX YTD.\\n\",\n \"\\n\",\n \"To visually confirm that the relationship is not driven by tail events let's look at a scatter; returns on weeks where SPX has realized greater or less than 10% are denoted in green and red, respectively. The below confirms the relationship still holds especially for the top 3 crosses.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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YllCWVMS1zGnnmvL35qEj7PETEM6eiFdhxazdnqG1vP3vCTp/98qD0aoTRafNgc/vJThq+WEKqSUd9z8iN+BUcGYxKHiUqeQpikCSJqpQqqlKq4t0VwUHArDEzO2c2s3Nmx7srgiOUDGMGZxefzdnFZ8e7K4I4k2ZI4/TC0zm98PR4d0UQB3ITcg9ahK/g6KMksURE/x2j5JpzyTWLa3+kk6RPYm7+XObmz413VwRDxNPqYRlQKklSoSRJGuAy4J29/OzHwKmSJCUNFXU7dahNsBPLG/opz0hAIQ2/eZJm1tLQK4RfgUAgEAgEAoFAIBAIBAKBYCQRN+FXluUAcCdhwXYj8KosyxskSbpfkqRzACRJmiJJUgtwMfAfSZI2DH22D3iAsHi8DLh/qE2wEyub+ilKNe3y/VSTltYB92HskUAgEAgEAoFAIBAIBAKBQCA41MS1fKYsyx8AH+zU9qsd/n8ZYRuH4T77FPDUIe3gCGBlUz9nVGXu8v0Uk5aOQQ+yLCPtIipYIBAIBAKBQCAQCAQCgUAgEBxdxNPqQXCICYZkNrXbKUwx7vIYvUaJRqWgz+k7jD0TCAQCgUAgEAgEAoFAIBAIBIcSIfyOYOp7HCQa1Bi1uw/sTjFpaR/0HKZeCQQCgUAgEAgEAoFAIBAIBIJDjRB+RzAb2mzkW3cd7bsNq1FDhxB+BQKBQCAQCAQCgUAgEAgEghFDXD1+BYeWje02cpP0ezwuyaim3SaEX8G+0ef0Ut1mo8vuJTfJwOgs8x6jywVHPsGQzMZ2G1s67STo1FRmmclM3PN9RCDYGV8gxMZ2G3XdDiwGNVVZFtLMunh3S7AXDLh8rG+10WX3iPv7CESWw/f5mk4HBo2Symwz2YmGeHdLMEJweAJsaBukdcBNullHZZaZRIMm3t0S7CdtA26q2wZxeAOUpCUwOtOMQiHqwhxL7DifSzSoqRTzuaOePqeXDa02uh1e8pLD8zyDRszzRjJidEcwG9vtTMpP2uNxFr2GThHxK9gH7G4/jyyo4YUlTZG235w9mmtmFIjJ4FHOt7U9XPe/ZQRDMgDjcxP555UTyRLir2Af+WJTF7e9sAI5fCoxuzSFRy4ZT2qCNr4dE+wWh8fPnxfU8Ozixkjbr86q4NqZhSjF/X1EsKS+j2ueXIovGAJgVIaJ/14zhbxkIf4KDgx/IMRzixt46KPNkbabji/kR3PLMIjNo6OO1n4Xtz2/knWtgwCoFBJP3zCV40pS4twzweHk802dfO+FlZH53JzSFB4W87mjlkG3nz98uIlXl7dE2h48r4orp+UhSWKeN1IRVg8jmC2ddnL2IuI30aCmfdB9GHokGCls6XJEib4A/+/DTTT0OuPUI8HBYNDl4/53qyOiL8Dq5gHWtQzEr1OCo5Iuu4dfvr0+skgA+HpLD9Vtg/HrlGCv2NLliBJ9AR76aLO4v48QnJ4AD3+8OSL6AmzqcLCysT+OvRKMFBp6nTyyoCaq7YmF9WztdsSpR4IDYU3LYET0BQiEZH7/wUYG3f449kpwOOmyefjl2xui5nNfifncUc2WTnuU6Avw4PvVNPa64tQjweFACL8jFJcvQK/TR3rCntMwkgwaOoTVg2AfGG7C5w2EcHoDceiN4GDh8gVp6Y/dBBpwiQm+YN9weYN0O7wx7QNisXjEY9vF/d3hEff3kYDLHxh2cdczzPUqEOwrNk+AwA6bx5F2t7h/HI30O30xbc29Lty+YBx6I4gHLl9w2OeDEP+PXoab53n8IRxiHT+iEcLvCKWu20lWon6v0u6TDBp67LEPdoFgVxSkGDHtlLJXmmYiey8izAVHLikJWi6cmB3TXpqeEIfeCI5m0s065o3OiGpTKiSKU01x6pFgb8m3xt7fi1ONe5VBJDjySTFpuXhyTkx7VbYlDr0RjDRyk/Qx94pEg5p8q7ARORopz4id/100OUek+B9DpJt1nFqRHtWmVEgUifncUUtBihG9WhnVVpFpFuv4EY4QfkcodT1OMi17Z7pu0atFpIdgnyhMMfK/66YwamhCOKM4mb9dPoFko5gIHs2olQpunl3ERZNyUCok0hK0/OvKiVRmmePdNcFRhl6j5CenlXPmmAwkCXKS9Dx+zaTIPUNw5FKQYuR/10+hInPo/l6UzD+umIjVJO7vIwFJkrh8ah5XT89DpZBIMWn4y6XjGZcjhF/BgZNm1vHvqyYxuSBcY2RMtpn/XTuFXOEffVQyJtvCY1dMIDVBi1IhcemUHK6fWSD83o8h9BolPzl9FGfsMJ974prJYj53FFOUauLp66dQlh4W748vSeHRS8aRJIpwjmiEy/4Ipa7LQfpe7saa9SoG3H6CIVk8yAV7zZTCZF6+ZTo2tx+rUYtRJ24nI4F8q5Hfn1/F908uRatSiKq9gv2mKNXEny8Zz32neTFqlUI4PIqYUpDMSzeH7+/JRg0mnTreXRIcRHKTDfzq7EpumV2MRqUgXdznBQeRqmwL/7tuCv1OHxaDGoteiAlHK1q1kjPHZjGlMBmvP0SGWYdaJeLGjjWKU008esl4firmcyOGaUVWXrl1Bna3H6tJi1EU3xzxiBEeoexLxK9KoSBBq6LX6SVtLzyBBYJtJBo0JIrdwRGHRqUU0TmCg4JWrSRPpPgelYj7+8hGrVSI+7zgkJGgU5MgNoxGDGJ9KBDzuZFHkkEjonyPIcSW3QilocdJxl4KvwAWg5peh/D5FQgEAoFAIBAIBAKBQCAQCEYCQvgdoTT1ufYpdU/4/AoEAoFAIBAIBAKBQCAQCAQjByH8jkAc3gCeQJBE/d6nWAnhVyAQCAQCgUAgEAgEAoFAIBg5xNXjV5Kk04C/AkrgCVmW/7DT+1rgWWAS0AtcKstygyRJBcBGYPPQoYtlWb7tsHX8CKe5z0V6gg5J2vtCbQk6lbB6EBxy6rodrG8dxBcIUZFlZnSmeZ/OU8HhwesPsr5tkC2dDhINasZkW8hOEr5egoOLLMtsaLOxsd2GVq1kTJaZwlRTvLslOEx4/UHWtQ6ypctBkkHN2JxEshL18e6WYBd02T2saxmky+Yl32pgbI5FFP0TDM0XbNR02sPXcXYiWUniOj5a6Rx0s7bVRo/dS1Gqkapsiyj6dIQQDMlsaBtkU7sdg0bJmBwL+VZjvLslGIa2ATdrWwbod/kpSzNRlW1Bq1bGu1uCY5y43cklSVICjwGnAC3AMkmS3pFluXqHw24E+mVZLpEk6TLgIeDSofdqZVkefzj7fLTQ3Ocizbxv1TYTtCLiV3Bo2dJp54rHl9A9dJ5pVQpeunkaE/OT49wzwc58vrmL7z2/MvJ6bLaFf189SYgygoPK8oZ+rnxiCb5gCIB0s5YXbppGSVpCnHsmOBx8srGTO19cFXk9MS+Rf145kQyLuM8caQy4fPz2nQ28v64j0vaLMyu4YVYhCoXYvD2W+WxTF7e/sH2+MC7Hwr+uEvOFo5Feh5efvrmOLzZ3R9p+f34VV0zLj2OvBNtYWt/L1U8uJRCSAchJ0vPcjdMoTBHi75FEx6CHO19cycqmgUjbP66YwFljs+LXKYGA+Fo9TAW2yrJcJ8uyD3gZOHenY84Fnhn6/9eBkyURHrhHWvrdpJj2Tfg169V024XwKzh0fFXTHRF9AbyBEP/5uh5/IBTHXgl2ptvu4bfvVEe1rW0dZEPbYJx6JBiJeP1B/vHF1ojoC9Bp8/JdbW8ceyU4XHTZYu8zK5sG2NBmi1OPBLujptMeJfoC/OnjzTT0OuPUI8GRQLfdw2/f3RDVtqZlkGoxXzgq2dRhjxJ9AX7/wSaa+1xx6pFgG05vgEcW1EREXwiv95c39MWxV4Lh2NA2GCX6Avz2nWo6bZ74dEggGCKewm820LzD65ahtmGPkWU5AAwC1qH3CiVJWiVJ0leSJB2/q18iSdItkiQtlyRpeXd3964OG1G09LtINmr26TNmnYqeEWj1cCyO/5FK24A7pq2pzxkl/BxMxNjvH95AaNjof7snEIfe7D9i/I9sPIEQLf2xi8kO28HZgBTjf2Tj9gfpdR66+4wY/4OLY5hx8QZCuP3BOPRmz4jxPzx4/KFh1w7xni+I8d8/7B5/TJvDG8BzhF7nu2Ikjr/HHxx2HTUS1+4HwpEw9sPd/3qd3qPuOhKMPI7W4m7tQJ4syxOAHwEvSpJkHu5AWZb/K8vyZFmWJ6emph7WTsaL5n7XfkX89jlH3sPjWBz/XdFt9/LV5i7eWtXKupYB/IdIcN0VJ5SnxbRdNS3/kHmHibGPxu0PsLKpnw/XtbO0vpf+XVzv6Qk6LpqUE9WmVEiUph9d6fdi/PeeQbeP72p7mL+yleUNfbi8h37RbtGruWp6bProrGLrMEfvO2L8Y3H7Aqxs7Gf+yla+2drDgCt+z/wMi47zxkfv9asUEqXpB8fjWYx/mE6bhy82dfL2qlY2tA0S3CFabF8oTDWRsNOzemJeIjlHqJerGP8w/U4f32ztYf6qVlY29ePxH9x7e7pZy4UTh7uO4ztfOBbGX5ZlNrbbeHt1K59t7KR9GFFwXylJM6FTR0sDs0tTyDrK7HdG4vhbTVquHGbONLUwiSV1vcxf2cLS+t5hN+mOJY6EsS9NN6HayQLpvPHZZFh0+/2dzX0uvtjcxafVHWztsh9oFwXHKPF0a28Fcnd4nTPUNtwxLZIkqQAL0CvLsgx4AWRZXiFJUi1QBiw/5L0+Cmjt9zC7dN9udgk6Ff1xXAQKDi1dNg8/eWMtX27uRqNU8L0Tilm4pYceh5fZZalMzk865EVaJuYn8eil4/jjR5tx+4PcOruIUyvTD+nvFISRZZkF6zvpsHmo63GiVkhMKkji5PI0zIbo7AC1SsHtJxSjUSl4Y0UL2Ul6fnHmaEZnDru3JjjKcXkD/OPzrTy+sD7S9sszK7h2ZgEqZezecG2Xg4VbumnodTG7NIXJBUmY9fuWYbKNM8Zk4vAGeGJhPSativtOL2d8XuL+/inHPC5fgJWNA3yxuYt0s5Y5ZamUZ4Sv21BI5vWVLfzyre1p2dfOyOfeU8tJ0B/+Al1alZLvzy3FoFHy5qpW8pIN/OLM0VRkiPvM/tLn9LKsoZ9vt/ZQlp7AxPwk7n93A9/VhVOBVQqJp66bwuyyfV8MF6YYeeaGqfz+g42sbxtkbkU6359bimU/r33Bocfm9vGnjzfz4tKmSNv/O38Ml03N3W1RXY8/yKqmfj7f1EWyUcsJ5alU7OL5r1EpuePEErQqBW+sbCU32cAvzqzY5fGCg8eS+j6ueXJpJGtuTLaFf101kZxhCvE6PH6WNfazsKab7CQDc8pSKUmL3WQrSUvguRun8eD71dR0ODh9TAZ3nFiCUSeKux0JXDAhG68/yNPfNmAxqHnw3Cq+qunhb59tiRxz76ll3DK7CI1q14XE6nucLNzSTW23g+NKUplSkESiQdzLDxYVGWaevn4qv/ugmsZeFxdMyObm2UVodzMmu6O+28FXNd3UdDoAKExx4QsEGZ2VeBB7LTgWiOedfBlQKklSIWGB9zLgip2OeQe4FvgOuAj4XJZlWZKkVKBPluWgJElFQClQd/i6fmTTPugm2biPEb86tRB+RzDV7Ta+HPLtuun4Ql5e1kTnUEr1U9808NCFYzl7XCat/W60KgW5yYbdLgz2B5NWxfkTcphdmkogJJNu3v+dT8G+UdftpM/p46GPNrEt4Ou1FS08ce1kjh9mkyjPauRXZ43me3OKMWiUWMSEcMSypcsRJfoCPPTRZmaXpcZEbVW3DXLLcyto6Q9HFj39bQMPnFvJ1TMK9ut3p5t13HliCZdMzkGtUJC8j5kqgmi+2NTFHTsUS/v3V3W8eut0StISaOh18uB7G6OOf+a7Rs4dn83E/KTD3VUA8q1Gfn1OJXecWCLuM7vB7vbTbvNg0CiHFXUgXO392W8b+csOAkBFRlj83Sb8BkIyv313A6/fNpOkfbQDg/Dm7dPXT8HmCWA1afZ7ESs4PNR0OaJEX4D736tmfG4iCoVEaoJm2LXCwi093Pzs9jiaf325lddumxHZRNqZfKuRX59dye0niOt4d3QMuhl0B0hL0O7X9bcjdo+fP3y4McoqbV3rIGuaB4a9R7y/roP73lgbef24RcsrN88gf5iiYFMKknn+hmk4vOHrfHcComD/cPkCtPa70agU5O3DeiszUc8PTynjimn5aFQS7YMe/v75lqhjHv10C3Mr0hm1i82X1n43Nz69jLqesD/7M9828vMzKrjp+MKDvu47VlEoJI4rTeHlm6fj8gVJTdAOG0ixt2zqtPPg+xsj/s5qpcRDprGUpZujvre5z4XHHyQrUX/IsmkFRzdxOytkWQ5IknQn8DGgBJ6SZXmDJEn3A8tlWX4HeBJ4TpKkrUAfYXEYYDZwvyRJfiAE3CbLsnA3B7yBIA5vgETDvkXwGDRKvP4Q3kBQTOZHIAOu7b5dGpUiIvpu448fbcLjC/L2mlbsngBXTsvjokm5mIZ2+d2+IN12Lyadcp83FXbGKsSdw0pjr5PNnXa+qulmxyxfbyDEl5u7KU41oVEqSEmIHheVUkGmqMo94hl0+ylLN3HyqHTc/iDvrW2jx+GL8ihzegO8vbqNHoc3Ivpu408fb2ZuRfp+nyuSJJFuFufZgdLv9PHHjzdHtfU5faxpGaQkLQGnN4B3mEKaHftYbMQfDNFp86BWKg7K5p1a3Gd2y+YOOz+fv47ljf1Y9GruP7eS0yoz0Kqj52lNfS7++WVtVNvGDjvzqjKi2pr73Lh8gYjw5PQF6LX7SNCp9kqMMunUhzw7SHBwGHTF+rW6/UE+29TJwwtqKEkz8sjF4xiXu33jx+7x8+gn0fcRmyfAsoZ+UkxanL4g6QnamPNPzBd2TSgk82VNF/e9vo5uh5dRGQn86aJxjMmx7Pd3unxBGnpjPfK7h/F77bR5+ONHm6LaOga9rGsbHFb4BUjQq+OSCXIs0NDj5PcfbGRBdSd6tZIfzyvj4km5e/3vLUlSxDJgc4cDeSf3nmBIxrYbu4f1bYMR0Xcbj35aw+ljMna5sSjYPywGDZaD8E/60bqOqKJ+/qDMl5u7Ob0qA5VSMTRHb+X3H2zC4Q0wuzSFX59TSXqClj6nH4tBJbJzBEB8I36RZfkD4IOd2n61w/97gIuH+dwbwBuHvINHIR2DHqxGLYp93LWTJAmzXs2Ay0+6WQi/RxuNvU5WNPbTafMyIS+R8gwTTm+QRH14kVaUakQhQUgmZpIA4cVAQYqBqYXJDLr8uP1B1rUOMKM4hS2ddv7fBxv5fHM3hSkGHjhvDLOKrWJnOM74gyG2dNrpsHlo6nMyNieJsdmWqN3fUEjm+cWNmHXqYYvw9Dl9XP3kEpzeIPefW8kJ5WloVEer9fuxQ4/Dy+qmATZ12ChNMzE2x0JQJnK974mWPhefberkk+oubp1dxNicRJ5cVI9Bq+Tq6fnUdjvITd6+iF/fOsjP5q/j+yeXxnyXNxDab99QQSyDLj+rmwdo6HVQkmYi3ayjJG3PfpmBUAi3L/Ya9/nDYm92op7iVCO13dsXfAaNkj6HF5cvgEETOx3stnv4qqaHt1e3MibbwulVGby6vJmXljZj0av5xZkVnFaVgX6Yzwq24/EHWNMcrvKdlqBlcn7SLgWXHXF6AzzwXjXLG/uB8CbN919ezdt3zGJcbmL4GF+APoeX9gE3/lCssL/z8/7c8VmkDm3ybe6w87sPqvm6pofiVCMPnlfF9CLxbD8acHj8DLj9kXt+r8PL6uYBNrXbKE4zMSEviQKrEZ1agce//bwoTTOxtSt8D9ja5eT2F1Yx//aZpA1t4gRDMq5h7iN9Ti+XP76YLV0OTqvM4N555RSnxloF2N1+tGqFiBLdgS1dDm59bgX+YPhi3NRh5/svr+Jvl09gdKYZxU5eoD12L75giLTdRAmmmLRcODGHJxdFZ+uMztj+rNjYPshbq9rItOiGnf+1D7hp6HFSsFf3Ij8qhSJG8BfsmfWtg6xo7EcCphcl8+LSZhZUdwLhtdf9722kND1h2Oy7nelz+nD7A7QNeHhteTNjsixYjRp6d6jZkZag3aX3+sZ2Gw07ib4AvkCIQFDM4w4nG1oHI8/2yflJVGbveiPINcz16w0E6Rj0UJhqGpqjr4+8t6S+j/Wtg9z3XSPLG/upyjJz/3lV5CcZ8ARDZJh1KBXiOX8sImbrI4y2AQ9W0/7t6ph1KvqcPpGCf5TR3Ofi+v+F03a0KgU/Oa2cv3xaw7KGfibmJfLLs0ZTkWnmv1dP5hdvrUepkGIWA1dPz+eFJY102LycXpXBd7U9FKeasLv9/Gz+epY1hAPq63tc3PC/Zbx393GUHWXFvkYSzX0u/vNVLa+taCE1QctV0/L5vzfW8suzKzmuJCVyXL/Lx7tr2rG5/fzqnNEsqY9OjBiVkcD8VWFr9VufX8H822cxfkhQEByZuH0B/vbpFp5d3IhWpeDH88p56tsGltX3MTEviV+eNToiCg2Hw+Pn/verWbChk3Szls82dfH6ihYAfK4Qf/98K/+9ZhKpCdufA9uiQ5QKCaNGiXMHYeCaGfki0usgEQzJPLe4gZpOB0lGDX/6uAaNSsEP55Zy7vis3XoppybouHVOEQ/sYOegUSoiUWXJJi2/PruSv322heWN/RSlGLn75FK2dtnpsXvJs0ZPB4MhmWe+a+Qfn28FoLHXxaDLzwtDqeO9Th8/fHUNmYl6phcdnIJ8I5VPqru466XtFhzb/HLzkncfCtRt97Joa09Me32Pk3G5iVS32/jD+xuZXZ7KgupOTqlIjwgKAMlGDVMKkkhN0NLr8HLSqDTOHPLVVvqD/OSNNaxpHgSgttvJ9U8v4727jturjQZB/FjfOsiD71WzrLGfyflJ/PzMCt5f28Z/vt4uAp4zLovfnVfF/66bwi/eWk9tt5OphUmcOjqDh3aI/mwdcNM26IkIv4kGDbfOKeL/3twuIqgUEhqVMuIv+eH6DoIhmb9eNgG9JiwEtg24eW9tG68sa6Es3cStc4rFXGKIpj5nRPTdRl2PkwUbOnD5AkwtDN8/vf4gn23q4oH3qulz+rhyWh43HFc4bBSmUiFx7Yx8Bt1+5q9qJXFoI25sbvh+X9ft4IrHl9Dv8pNvNXDBhGyeX7Ld9kOvVuL2h/jXV1t58NwxqHex4d/r8LKgupOnv2kg3azl9hNLmFKQLESjvWR1Uz+X/ndxJNvmqul5fLS+I+a46jbbboXfQDDEoq09/PbdDVw2JY+HF2zGH5T5QNvBPaeW8dryZqrb7YzNMfPAuWPIGmZO1uvwcvdLqzitKgOzThUVFXz51DyyxTzusLG6eYDL/vtdZB2uUyt4+ZYZu7xnXjYll092eLYDnDkmk/9908BVM/Kp644W8y+elMMfP9pM61DBx/VtNm54ehnXzijg31/Vcs2MfK6bWUC2iPA+5hDC7wij0+YhaT/9tRJ0avqdwuf3aGN96/a0nYsn5fDUoobIzX5ZQz83Pr2ct+6YxdzR6YzLteDxBZlZbOWpb+qp63Zy3oRstnTaWVDdBcDalkF+evoo+p0+2gY9EdF3G75giPpuhxB+40QwJPP0t/WRSXxLv5uHPt7Ej08t52+fbmFiXmIkes+oVTE6K4HPN3WzaEs3vz+/ileXNaNRKzmlIp03V7VEvleWw9EAYrF2ZFPX4+TZxY0AXDQph6e/bYjYLyxv7OeGp5fx9p2zdpmy19DrZMGG8ARyWqGVLzZ3xRyzrmWQU0dvTxFPHbJncfuC/PT0UXyxuZvWfjfHlaZg0atwegOYRVroAdPY6+SpRQ1cNjV3e9q+F3759gYyLXrmjt59Mcxzx2dj0Kh47rtGshN13DK7mMqs7T5/aqVERqKWO4tLaBtwc98bazlpVBr6YaK4WvpdPP719tIJ04usfLKxM+a4tS0DQvjdDd12Dw++Xx3VVt/jZEPr4B6FX5NWRU6SPsZeJcWkodvu4XvPr8CsU/NtbS9L6/u4YVYB18zIZ3FdL+XpCdx2QjGhkMxplRmY9WqW1PVy3dPL+H/nj2F8bmJE9N2Gxx+ivsclhN8jmPZBNzc+syxi17Wkvo+bn13OaVWZUce9s6aN62YVMKM4hddum4HNHcDlC3DW3xdFWT4ZNEoSd7p3z6vMQK1U8r9v6kk2asIiwk42Mp9s7KTT5qEgxYg/GOKJhXU89U0DALVDhYjevmNWjE/8schw9mZmnQq3P8STC+uZmJeESqlgbcsgt7+wMnLMU980oNcouffU8mGj8POsRn53fhV3nRQurpdh2S7cbWy30T9k9dHY62JKQYh7Tinj/XXtZFh0HF+awmNf1OLwBPj+yWXDCoUA765t5zfvhAuCbu60821tL298b+ZuN5cF23lleXOUxVJDj4vSNBM9juh1VfYuInS3sbHDzo3PLCfJoKa5zxXZSHB4A/z+g43cdFwhf798IikJml2m9LcOuNnS5aDjmwa+P7eUJfV9NPY6mTc6gxNGpe5S/BccfN5Y0RwVfOXxh3hjRfMu118ziq3844oJPLWoHkmCM8dk8eSieioyzTR0O2Ks+pKMmogOsI0Bl5+QLOMNhHh8YT0GjYofzC0VGT7HGEL4HWG0D3pI2kd/320k6FT0iQJvRx07pnAlmzScWJ6K1aRFksLWH68sb6apz0V2kj4SxZdrNTIuJxFfMMQv31rHm6vaor5zSV0f183Mx6BRxuwMA1iEyBM3uu1eXlveEtUmy+D0Bel1ehl0+dnQZsPlDVCUauKHc8tYVt/PmpZBjFoViUYN10zP5/73q2noifaI0xxA8QHB4cGz4/Vu1MSIQr1OH20DbpzeIK0DbtIStJSkmdBFxL3tk7xuu5esRD2NO3kF7rwIHJNj5sTyVNQqiV++vYFxORbyrQbmr2qlz+ljTlkaY3ISD+rfeSziC4aoyjbzbW1vpO30qgxGZSTQNuCmpd+1Ww++FJOWy6fmce74rEiU3o70u3y8tyY62ujjDR3ce2o5qcPXgYnQbfeSnainyx7tD5+WIDKEdocvKDPojvVaHS51c2dSErQ8fPE4vq3tQaVQsKKxnySDmoosM409Lhp7XYzZIT30qaGovPG5iRSkGKnMsvDvr2p5bmijaBv/+bqWx6+ZHBO9D2DRi2XBkUxTryumRkOnzYtZp8KsV3HVtHyUCgmVUkFoSOFNNmpJNmpx+QLceVIJf/ssHMUvSXD/uVXkW6PvKclGLRdNyuGMMRm8saKF+l7XMJsPWgxD0b7tg+6Yc8zlC7Kpwy6EX6AsLYFbji/ivwvDG2mSBDfPLuK57xrJStQRCMmolGHv1ajPpZtI0KlYsKGTfKuBolRTjBWXVqUk3zqMVcNOYs7rK1r4+RmjyEs20G33RjJDilONw278QThC9ImF0XXTAyGZlU39QvjdSzoGo6/Vb2p7eOTicaxrteHwhtdVUwuSmZC76wKrXTYP9T0O7jyxBIfXH7Fv2oY/KLOkvo+755btciwhvJGoVSmwewM8+P5GxmRbKEwxYjao+bqmh0n5yQfwlwr2hY7B2NoK7cO0bcOgUREIhkjQqVFIMq39bk4oT0OpCIu8eVYDJ5SnRoq4qxQSKoUU5Qu8rX0bLy1t4qrp+RHrJ8GxgZjhjTDaB90k7mfEr0kbtnoQHF2UZySgUSpQKiSmFSazpnmQjzd0sKZlkLJ0Ew9fNJZuu4dXlzVTkmaiMsuMVq1ErVKgVg3vxWbUKilKNZKbbODXZ1dyz2trIu+dNTaT8gwxmY8XerWCTIsOm8cR1a5TK7j/3Eq+qunm/XXtLNraQ6JezTM3TOXtO2fROuBm0O1nY7udHoePH55cxg9fXR2J/hmbYyHNLCYARxqhkEynzYNSKZGWoKPAaqQs3URNpwOFJKFUSFEeu2kJWloHPFz5xBL8QRlJgl+cWcGV0/LRqZUUWo3Mq0zn4w2dJBnVnDQqjcn5SejUShZt6aHD5mF6UfQCIN2s5+GLx7FyyI9sTcsgEF6kKiREpMhBIidRH16YDxXouXRKLk19Lh79NFy1+2+fb+GZ66fu1gsOwOUNsqZlgA6bhwKrgbHZiSTo1RjUsVM+jUqBShkb8ZGTZOCW2UX8fcjq4est3fz67NFsaLNFKsmXp5uYmLfrBasAMsw6rplRwH93iJ5WKyXK90IQW9HYzy3PLo9svF44MZsfzC3FatTS6/CRZFBTmWXm5Io0Wvpd1Pc4uWBCDjq1ghSTlg1tg1iNsZu0CTo1mRYtvzxrND99c12k/YIJ2SKT5wgnQafirLGZ5CYZWNXcz+K6PiQJkoxqfji3jEc/qYmcL8292RRYjZFoMINGxS3HFzOnLI1Om4fcJANl6SbaBtysax3E7glQmm6iMsuCWqnAoFFRmWXhxSWNTC1MZumQVVRYMK6M2EOoFAr0aiX+YHSAgFY8FwAw6VTcdVIJUwuTWdU8gE6t4K1VrXTZvfzfGRWRTVnrDoWTL5iQxcmj0/lgbTuPLKghJMMfLxzLeROyd2mz0NAbziTw+EPkJesj84RtZFp09Dq9rGoeAMJ2Eb86q3KXRR3VSgUmbez6YHfioiBMp81DKCRz3Yz8qKwqWQarUcM7d85ia5cDg0ZJeYZ5l+JbbbeDO15YwaaO8DiWpJm459QyXl7eHIn6VSkk7jm1nM83duLxhyjPSBjWO7rAauSnp4/it++GM1DWDWWdfF3TzZyyPfsLCw4el07JRaFQUJxqZF3rIIu29nDplNyY4zZ32NnYPohCkrAatSxr6OOOE0t4aWlTZDNuUl43f7lsAo9cPI5NHXYcHj+l6SbUSkVUpsZFk3L4est266i8ZAN6jbhHH2sI4XeE0T7oZnTm/lWKNWpV9DtjI1MERzYVGWZeuGkqtd1Ofj5/Pa0Dbk6uSGd2WSpddi/PL26KTPQAHrtiAmeOzYq8vmRyLm+sbIlMItRKicun5pGbHI4iOHNsJoUpRhp6nViNWiqzzSQZhUAYLywGDT8/czTXP70sIviNy7FQlW3hjx9tZn2bjSkFSfzsjAr++NEmHl6wmb9eOp7nFjfyv6FUTICbjy/kwXOraLd5wlVhPf49ph4LDi+dNjcvLmnm8YV1GDRK7jttFKdXZfKPKyby+Nd1LG/o47qZBVEFXn56+ih++db6yPUsy/Dg+xuZVmilKtuCUafiV2eN5pxxWby/tp17X1sb+ewP55Zy4aRscpJiI4isJi2TCpI5vjSFhTtMHq+ekU/hXhSHEewZk07NjccXsr7Vxne1PWRadLyyrDnyfo/Dx9+/2MpfLxuPdhfFkwZdPn73wcaIdzfAvaeWc9ucIkZlJlCRYWZjhy3y3vdPLiV3Fx6S1wyN7Tur2xiTbWFmkZW37phJTWd4wVqZZRYecXtAqZC4bmYBJq2Sl5Y2k5ts4EenlDE6c/ch1ja3n9++uz4q2+aNla2cPS4Lly+ESgE/OW0Uf/9sC/NXtXLhpBx+PK+cX7+9gbahyCGNUsGT107Golcx6N7+PT88pQyTTsO547MoTTfR2OsixaSlMsu834EDgsODzeOnrtvBh+s7mFVs5aenjcLm9nN8aSo/emV11Pny+tD5Mqc8LdJm0qmYlL99s6a138Wtz69gfWv4nqCQ4PFrJnNyRdhWZlxuIrIss75tkDPGZKJWSozONFOZtX2dkZWo59555fzq7Q2RtgKrgdFZe0gjOIZI0KuZVWIlEJJ59NPNBEIyf7hgDCfsILiNz02kPN3EBRNz+K62l7tfWk15egI/O6OCv362hZ/NX0dpmpGxw0SH1nU7uOappRExSKNU8MS1k1lc10t1m43zJmQzoziFSQXJVLfZsLn9lKQlUJG5640es17NPaeO4uZnl0faEg3qqPNHEM2g28d7a9p5eMFmAkGZW+cU8eS1k/nd+xuRpPDzdlJ+0lDR7djiiDvzwdr2iOgLsLXLwdL6Pn5/QRWrmwbpc/q4ZkY+972xlqa+8NirlRKPXzOZohQjeTtEgysUEpdMziU3Sc/qlkE0SgXrWgdZ3TTA/51RcfD/MQS7JNGgoWPQzacbO5lSkMQz109lwk5R9GtbBrji8SWRyPBEg5pfnFHBssb+qAyMFU0DLNzSwxXT8phVsn1tfs0MHZPyk2gZcJNsUPPh+g5WDAVvqJUS95xahkkrsnePNYTwO8LoHPQyq/hAIn69ez5QcESxoc3GujYbv3t/Y0QI/Gh9B4GKNKYVWaOEA4DfvFNNZZaZgpTwpGN8biKv3TaDhTU9yMDsshTGZidGjteplUzMT2KimOwdMcwstjL/9plsGRJgMhN1XPvUskhK8eK6PrpsXs6bkM3HGzqobrNFib4ATy6q55kbptLY5yI1QctZYzOHTxkUxI0P13fy18/C0Z4uX5Afv76WdLOW2WVp/P78MQy6/UgKOHlUGq0DblJMGpzeQGSiuA1ZDqfqB4Ih2gc9KBUSGRYdH+xUZOSxL2qjNoV2Jtmo4Q8XjGFJfR8b2mxMyk9iSn7SLkVIwb5TmGIiL9lIUYqRD4cpArOysZ/PN3ZR02lnTnkqyUYtCiAzUY9SIbG2dTBK9AX462c1zC5NYWxuIv++eiLf1faytcvB9CIrkwuSYiKDtpGaoOOCiTlcMDEnqn101v5tLh+rZCXqufvkskjUvVEbPfX2+IOsbh7gu9perCYNM4qsGDTKiBi3IxvbbVz/9DJ+PK+cP360PZpn/spWshP1EdEXwtYhjy+s443bZvJlTTf9Th9zR6ejUylY1zJAntXIpPzk3ab49ti9OH0B0hJ0kUJegkOL2x+InA+pJi3Ti6yUpidQ2+Xguv8ti3hDfr2lB7snwL+vmsjAUDbPznTadz+nX986GHWehWR44P1qKjLN9Dq8aFQKqnIsTCrYfRr4eeOzyUs28M3WHgqsRmYUW3drS3MsoteoOK0qg5nFVkKyHLPJkmc18O+rJ3HHC6uobg+PSXW7jUcW1HDtzAIe+2Irn23qZkuXk7PGZqLdIfJ2aX1flBjkC4b4z9e1PHjuGGyVfnKS9BGv4UzLrv1k/cEQHYNuVAoFmYl6ji9N4eWbp/P1lm6sRg3HlaYI+47dsKy+n5+/tb0w4sMLavjTRWOZf/tMIBy4sS98V9cb01bb5aA4xUhZegIzi62sah6IiL4Qtn7422dbGZdj5vjSVJJNGgqsRhINGoxaFbPLUrEYNHxd081ZYzO488QS0kW6/2GjsdfJDc8swza0Gbu4ro8exwZeuWVG5Bh/IMRzixuj5vIDLj+tA256HLH39NXN/VwxLQ8IZwmuax3k29oeFJLEzBIrJakmLHoNp1WFa3ekJej2uPksGJkI4XeE0Wk/MI/fpj7Xng8UHDH0u3z8+PU1nDI6PSrdG+DzTV3MLE6J+Uyv08tH6zsYl5vI9CIrCoXE+Nwkxucm0WXzMOj2Y/f6d1kgQBB/VEoFY3MSGZuTiDcQ5MUlTTE+knU9Ts4al8mMQivr22IFhG2ny6VTcslO0gvx7gjD6fXz4pLGmPava3qYXZaGWqWIpPDOHNrlf3V5MwlaFakJWrp3WPCrlRJalYI/fLSJZ79tRKdW8PMzYyM8fMEQrp1E453JTjJwQZKBCyYeyF8n2B1KhURltoVOe6zn27TCZP7y6RZyk/XYPAFeXtpEICRz03GFzClPpcsW+xl/UKbD5sZVF2RMloXLpuYdjj9DsBPDFXkC+Lqmm1ueWxF5nWLS8OotM5hTlsoXQ5592wiGwtF8A67o+71Jq6K1P3b+1tTnotfpY2J+EmkJWp7+poGnvqlHIUncPLuIeaPTyUrUR9L2txEIhli4pYdfvBXOIjqlIp37Th9FSdqeo9QEB8ZXm7u57fntRb5SE7S8cst06nqcUQWBAFY1D9Dn8rOxfZCphclR/uAAeXsQX3eu3wDQPuDho/Ud3P9eNQoJbphVyG1ziqMKCHXbPQy4/KQmaEk0aDDr1ZxQnsYJO0QXC4bHrFcTCIbY2G6jtd9FaoKO0nQTBo2K5j53RPTdhsMbQCFtKwgX5J7X1lCSZory2e0c5r7f3OfmvbWt+IIyaqXE6ZUZlGbsWuxpG3DzxMI6nlvciF6j5L55ozh3fBbTi61MLxYFPPeGj9a3x7S9tLSJcydkoVHu2xy7z+HlxPLUmGu6ItPMQx9txu4NUGA1cP5OG7MQPh+KUrO4+bkVBEMy43ItPHLxeLIT9bQPuskw6zhvfBa/fqeaRVt7KEgx8LvzxjCz2CqKfR1iGntdEdF3G1u7nLT0uyNzhKY+V0ztDYDGPhenV2VEZd1BeHP5rVUtjMow4/IF+OErq2kc2gy4dHIOIRleX9mCLMMJZan89tzKXW74C0Y2wtxjBBEKyWHft134Ne2JBJ3w+D3a6LJ52dRhRzVMUa7UBC1ZibooM3eAk0al8cnGLq5/ehl13U4gfO58ubmLc/7xDac8+jXXPLWU6p0KTQiOTHodPpzDiHVqpYRZp2ZcbiKdNg+pO4kOOUl6Pt3Yydw/f8XP56+nWWz6HFGolQoKU2JFlpzkXUfrLK3rY2l9H788c3TEM86sU/GzMypY2zrIEwvr8QVD2DwBGnpcmHcq5FSebsIsCjceMUzITeLm4wsjno4T8xIZlWmmttvBuNxEnlxUj9MXxBsI8diXtXy5uRu9RhkzrqVpJuzeAJf9dzG3Pr+crV2xkYGC+DDo8kX58EHY0mNlcz8/OW0Uo4ai67QqBd+bU8zXW7rxB0ORwlrb6HZ4h/XnPbkinTtfXMVF//qWzzZ28cSielQKBf93xii+2tzNef/8lnMf+4ava7ojxcAg7C1407PLI5XBP9nYye/er8bl2/3GkODAGBjmfOi2e1nTMoBhGD9Gg0aJRqXgqW8aOKE8jbL08DNDo1Rw54klVGXvPqorN1nPzuv/M8ZkYHP7MWiUnDQqjZpOO6uawinCsizzzdYezv/nt5zy6Ndc8fgS1rYM7P8ffIyyoLqTs/6+iJueXcG5j33DkwvrsXv8NPU6h/VG1quV3H1yKa8tD2fwNfY6o96fWhgbkX3u+Cy2dDn4++dbeWJhPQs2dtHn8ODxB4edM769upWnvmnAH5SxuQP8/K31rGwaODh/8DFCwTC2VyVpJtSKfZNbWvvd3PnSKpr63JxcsX0zZd7odJzeAHZvgJwkPRWZ5sg1vyPnjc/isS9qIwFBa5oHWdXUz72vrebkP3/FGX9dyLtr2/EGwsU9G3pc3PD0MrZ2OWK+S3BwSdDFxlyqlVLUM722y8GMotjNlnmVGUwvsnLSqLBFjCTBOeOySE/Q8sG6Dk7/20KufnIpp1VlMqvEilalIN2i47UVYdEX4Muabt5d0xbz3YJjAyH8jiD6XT4MGiXqYUTAvSFBp6bfJYTfowmLXkWqSUvbgDvKH0iS4MfzylFI8PvzqyhJNaFVKThjTAYVmWZWNPbj8YdoGJo8bulycPOzy+kYihpY0zzIPa+tYUCcD0c8Zp2KLpuHCyZkR7XfcUIJHTYPDy/YzMvLmrn9xGIm5iWhVkocV5LC9bMKeHlpc3gneEULb6xsidNfIBgOjUrJrbOL0Km3388zLFqOK4mN4t/GxPwkep0+GnqdnF6VwZ0nlfD7C8agVki8sSJ6fF9e1sTDF41jXI4lck5cOCk3qhCJIL4kGTWcNSaTx66YwK/OGs2E3HCE/xXT8ljfGrsx911tLw09Ln555uiocb13XjkPf1wDwKKtvfzhw024fcHD/ecIhsEXlLF7oqN3VQqJJIOGl5c2clxpCg9fNJZXb52BxaBiaX0fITkcBVi0g8igUkiMzrLw18vGk2nRYdKquHZGPnZPgG6HF6tJG4kkPHtcJi8uaYq8bh/0cPOzy6nt3r7or+txxmQRfbG5e9hq5IKDhy8Qwu6OFeXcviAGjSqmCNONxxVi1iqZnJ/MHz/aRGWWhbtPLuGWOUUUpxox6fa8kfd/Z1RQYDWgUys4b3w2qQk6zAY1t84ppn3Qg8MbxOYJ4PUHqe9xctMzyyO2AtXtNu54cSXdw2QnCIanuc/FT99YG3V9PfJJDW39br6p7eHamQVRx581NoNko5q/f76V/qFI/5SdUvPH5ybyt8vD175RE547JOrVvLMmHIFq8wT408ebWd44wHVPLeXi/3zH/FWtkTn+oNvHq8tj54DDWQ0Ids0po9Ox7hB8ZdQouWp6/j5H0S5v7OPb2l6eW9yI2xfk+yeX8q8rJ6BWKXh+SRO3zSni+NIUajrtrG0e4NFLxpE1NPZXTM0lN9kQWc9BWHz+rq6X99d1IMtg9wb4y6dbOHGHCH1vYPuaUHDoKEk1cfnU6EJu95xaHlUrI8mkYXOnnZuOL8SiV2M1avjRKaWYtEp++MpqTixL4xdnVnD3SaX4AkFWtwyyoLoTWQanL8h/vq5jZnEKOUmGqAKP2/hwfQcev5gDHosIq4cRRKfNu9/RvhBOFdw5fVBwZFLTaWdzhw29RsmD51dx54sruWFWIRdMzKbL7kWnViKH4NfvVNPn9PHAeZU09Lj4eks3H6zb7htpGYrua+x1RopBbWNju522AY8o9nKE02n3Mqc8jYVbuvnDBWNoGXCjVyvJTtTxo1fXEJLD/rAPvFfNCWVp/PPKiTT0OHnw/Y2RHWCA+atauX5mwT57kAkOHRPzk3jr9lls6rChViqoyrKQv8PksNvuYUObjT6nj6IUI1MKk/AFQ3TZPLy6vJl5lRl8sakLjVJBTpKBLTtEcyQbtXy4vp10s47pRVbWtgzy+w82UpVt5oqpeVH+gYL44fYHuf2Fleyowf30tFEohllI5iTpmb+qlfG5Fu4+uRS3P4hRreTPn26hfQfB7tONXXTZPcLT+wggNUHLjccV8vsPNkXaLpuSy+/er6auZ3sWxoTcRB66cCzPf9dIy4CHpxbV84O5pRSkGPEHZcrSw5XclYpkZhWn0Ov0cstzKyLpogMuH+nmIY/PRD1vrIz2gfYGQjT2uiL+nZZhIv9TTdoYf2LBwSXNrOPG4wv5w4fbzweVQqIyy0JLvwuLXs09p5bhDYQwqJV8urGTs8dlcfnUXD7e0BHx9x6daebiK/fsx2PQqPjzghpOH5OB1ahl4ZZuPljn5M+XjuP+d6sjx61s6ic7SY/bF8S9k2DQ3OembcBDaoJu568XDMOAyz+sxUbboJvTqzJ59rtGfjyvHLc/iFalYHxuIjc9sxxvIGzzcdW0vBh/Tr1GxTnjsplZlII/FJ4DXPXE0pjfsbp5gGWN/QRDMj98ZTV/uXQ8503IRqdWUpJqor4nWvjLTdp1hpEglvIMM6/dNoMNbTZCIZlRmWbKM/bdE7l2h7nat7W9fFvbS2WWmXPHZ2H3BFjfamPR1nC6/7++qqPQauT5m6bR6/Dxy7fWkb6Tj/PUgmQ+3hBbM6DX6UOvVkau6eHu+4KDQyAYorrdRm2Xg7PHZjG3Ip0+p4/cZANVWeaozN1R6QnkJRl4Z00b50/IRkKmND2B658OF1oMyZBh0fFJdScXTMxm0U7WDxC2bvH6w3YgOzOz2DpsZoFg5CNmcCOILruH5AMQbRJ0qhifUMGRx5rmAa54fDHOoYitibkWXrllBh5/IBINkGzUYNQoybcaaB/08NdPt3LBxOyoIh6XTs6NLPKG2zAw61TDpqQI4kuPw0t1m41ep5fUBC2PLqihtsfJVdPzSTaqaepz8dryZqYVJnPbnGL++WUtABqVgrmj02jucxMMyVGiL4R9w3SieM8Rx6hMM6OGKcLQY/fyf2+u49ON4QhdSYJ/XD6BU0ensb51kB/NLcPlD/L26nBK1y/PqqCu284Fk3IJhkJYdGpc/iCPLKiJ+t6x2YmolQo8/gDNfW6UConcZMN+Z5II9g+nN0BLv4suu5fcZAOlaSbG5iTiC4bQqCRmFifz4br2SPEms17FmJxE3l3bzpYuB2NzEvEHZRzeAOt2ig7OTdZj2kHAc3j9DLr8JBu1ooBXHDh/QjZalZJnvm0g3aJjdlkqzy9pijpmVfMAXXYPZ43LIi/ZgFaloN/lx6xTMzk/GZNOhcsXoKXfjVqpIDtRT26SISL8+oNhK7DZZSn4AyFMWlVMEchk4/ZF/+hMM6dUpPPJxk4gfH+5/7xK0s1C3DvUnDwqDZvbT5fNS2W2GYnwcz/fauTdtW1Rz+7KLDNpQz67r982ky1ddpQKifL0hBjfZm8gSHOfCwmJ3GQ9GpWS4lQTV03P4/GF9ZHjfnr6KD5YG5sK/Gl1J2eNy4xp16kVmPcwV3T7gvQ5vVj06r2KQh7JZFi0ZCfqIzYqEBb3sxINZJh1eANBehw+jBoVJq2Sxl4nL9w4jX63jySDhvKMBBJ28W9o1Kpo6XehV6soTDGwdqcCkTq1MirS+PGFdZwyOh2jVsXtJxazaGtPRAQsTDEyY5g6IXtLr8OLLxAi3aw7pvxEi1JNFKUemBf65IIkfnhKGcFQCKUk0ev04fIGaO13c+nkXG5/cWXU8fW9Tpr6XMwpS+XvV0zE5vGTbKikodfF26tbcXgDlKSZ6K3vi/qcWaeK2D1cODFnWLugQ0mX3UMoFBYxRzpfb+nmpmeWRzbxpxcl85fLJpAxzDM1Qa/m7pNLOXtcJnaPH51axVebu1BIYdF3Xesg547P4rvaXtoG3ORbDVH3E4BMi46bji9Cr1HyszNG0WX38vQ3DeQmG7hkcu4h9XIedPtweIOkmjRoRP2YIwqh6owguuxeLPtZ2A3CHlLeQAhvICgKPR2hBIIhnlxUFxF9AVY2D9Jp8/Dc4sZIEQClQuKnp4/CatRw43GFPLmoni83d/PjU8tJNmrITTYwOishsrs7KiOBa6bn8+zicDEpSYJb5xTzdU03pw1FggjiT6/Ty6/eXh8VtX3vqeU0flvPPz7fCsBT101GAaxoHqDb6eXfV01kc6edsrQE/t+Hm2jqc3HXSSWUZySwuSPs9ZmgVfG9OcXiuj+K2Nhui4i+ALIMv3p7A+/ddRwFViPNfS4qdhCMH/u8lvvPq+SeV9dEIod+dsYo8pP1kSIQSQY1V07Po23QzSMLNvPW6jZUCombjy/ihuMKSdlFcSrBwaW+28Fv36vmy83d6NQKfnraKFY3D/DnT7aL9L89t5LXvxeOLPIFQuHxf2cDAPMq05lTlka6WUtNp501LQN8NnSuqBQSvztvTKSIyJrmfh54fyNrmwc5rtTKT+aNGnajQXDoSE3Qce3MAs6fkI1GqYgp7rSNDpuHJXV99Lv8vLKsOdJ+32nlnDkmk99/uImP1negVSm486QSfn7mKO55dS3V7Tb0aiWVWWa+f3Lp0ELRyM/mr4t8xyWTc0jaIXAgJUHL7y+o4qq2fPpdPgpTjFH3E8Gho6bTTnXbIDlJBn67Q9TtffPK+e9Vk/jhq2tweAMUpxp56MKxkays7CQ92buI0GwfcPP3L7by8tImJEni2hn53DqnmHSzjjtPLOXE8jQ6bR5ykgyUpRv5rjY2gsysV1OalsD35hTxr6/qIu2/Obtyt9kDmzts/OnjzXxd08OYHAu/OLOCCXlJ+/vPc9STmqDj71dM4O6XVtHS78aiV/PHC8dSkmZCqZBITdDxp49r6NphU+/nZ1Rwzrjs3W7MNfe5+ONHm3hvXTt6lZKHLhrLva9tf95PKUiKsWqx6NWROiAT8pJ4645Z1HTa0aoUjM4yk7OH4oDD4fYF+HRjF7//YCM2t58bjivkiml5ZFpE9PDeolYq+OcXWyNjV5pm4q6TSrj75dWkJmhRKSQCO1nxaJQKJEnCHwzxi7fWs7HdTqJBzS/PGs30wiTaB71c/eTSiLA/KT+R2aWp5FkNpJq0VGSZD1uGp93t54N17Ty8oAZvMMjtJ5Rw0cScGAuTkUKfw8uv39kQlbm1uK6PDa2Dwwq/AEpF2Erngfc24vAGmJCXyE/mjeIPH4WzQf72+RbuP7eS9kEPRSlG1jQPRLSBqmwzM4qsXP3UUlxDbfnJel64aRr5KQYyzIfmWpRlmcV1vdz/XjV13U7OqMrgrpNLD3gjRHDwEMLvCKLb7sVyADvpkiRh1qkYdPlJMwsBKF609rvxBUNkJepihDhvIBSVrr2NXqcvqvJrMCTz7HcNTC+y4g+GSDdrWdc6yKT8RG6aXRjzvQk6NfecWs6MEisb2mzoVEreWtXKli4Heo2SC4apGis4/NR3OylONXFcSUokzeuJRXWcPyGb/33TAMBry5v5w4XjOKPfhU6tJMWkpcvu5bfvVkc8v/7xxVaunZHPdTML0KkUjM1NpFg8mI8qbF4/54zLIs9qoH3AzXtr2+l1+nB4A3z/ldVsbLczo9jKvMoMPt7QwbjcRJ5YWB9ZSAA89NFm/nfdFPzBEIGQTHl6AgUpRv79ZS3zV4UjvvxBmX9+WUtVtoUzxsRGewkOLoFgiFeXN1OcaqIq28J3tb0MegK8tTo6Au//fbCR40tSOK0qPCb+QIjRWWYCIZl8qwGDJjy9G5OTyB8vHMumDjsDLh9FKaZI6mlzn4tr/7csYvH0+aZumvpcvHLLjIgwLDh8bCusWJxm4pTRaXxSHRbrS9NM3DK7CIUkcd6EbH7z7oaozz28oIasRD0frQ9vCHoDIR5ZUENlppkXb55Ga78bk1ZFntWAJEmkmXWoFAp+fGo5Ln8QnVrB8oZ+Hl9Uz2/PqYxE96cm6JhTPvIjsY40ehw+JuQn8+gn0dkYj3xSwwffP54Pvn88drcfs16FPygz4PLtUbD5fFMXL26LIpdlnvqmgcosCxdOysFiUDNzJ+/4W2cXs3BLT0So0KvDhd6MWhW3n1DCSaPS6bB5yEs2UJ6RsMuIzj6nl7tfXh3ZZF7R2M91/1vGu3fOIu8YtpqZmJfE/Ntn0mHzkKhX4w/KdNk9ZFr0LK3vi4i+ADZ3gG9re5k3OmO3wu/8Va28uzbs6evyB7n/3WqeuGYydq8fk1ZNslHDpf/5LnK8JMHtJ5RE2TqVZyTslzXBjqxuHuCul1ZFXv/9860YNEq+d0LJAX3vsYLLG2Bt6yA3zy5iSV0vyxr62dLloK7HiVIh8cG6Dq6bVcATO0TpT8pPpCw9gUGXj5+8vpaN7eHrbcDl58evreHdO49jckEy79w5i61dDgxaJRUZZtLMOibkH/5NmKUNfdz35vaNxz98uIkkg5pLp+Qd9r4cDlz+IG0DsT7ou7PX3NBm4743tv8brWoawKRVMaPIynd1vXj9IQpTjEzMT8LtDfLabTPodXhRK5UkGdU8921jRPQFaOxzs7XLwbRhisYdLDZ32rn2qWX4guF1xvzVbQx6/PzjiomROakgvsR1FCRJOg34K6AEnpBl+Q87va8FngUmAb3ApbIsNwy993/AjUAQuFuW5Y8PY9ePSDoGPQcU8Qtg1qnpd/ljUsQEhx6HJ8A7a1r5w4ebcHgDnDc+mx+cUkZe8vYdd6NWxSWTc6OiQMLslLdPWEBONWn5bGMXd51YitWkYUpB8i6jOi0GNR+sa+fdoWIQ23hlWRPnjs+OVJYXHH68/iCfbOzk129voNfpY2axlXtOLeORBTUMuPxRadtnjsnColdj0VsibWXpCVGFHmQZnv62kTtPKqHQahSi71FIskHDpg4b76xpozDFyE9PH8Wy+j7s3kBk0v9dbS8XTszm+yeXkmnRxdg6BEMymzts3Dy7ONLm9gd5Z5iKv1/XdAvh9zDQafOgUip4YUkDDm+AuRXpUUW8tuHxh6Iqs6tVioh1z85YTVpmlcQKuQ29zpiFx9aucMqoEH7jh0Wv5rfnVHHGmF5c3iD1vU5+Pn89kgRXT8/nrDGZEYEHwtdxS78r5ntWNA1wUkX6sKLgpk4bf1qwOapNo1Rw+wnF+xXlJzh4jM408/WW7pj2QEjG5vZTlp7Asvo+7n55FbXdTioyE/jDBWMYlzu8gCPL8rD39I82tHPhpOE39acUJvPqrTP4qqYbg0bF8aUpVGWH5xQJejVTCpP36m9p7nNHRN9tDLr9NPS6jmnhF8IbKzZPgF+/s4HPN3VjNWr47TmV+IOhmGOb+1yYdmOnYff4Y8a42+Hl801d/Pqcykjbq7fNYGFND05vgNnlqYzLSTxof882ljX0x7S9uLSJy6bkHVAdmmMBpzfA26tbeeyLrdg9AU4alcbtJ4Qt2+yeAFqVAl8wyDXT85mSn8SS+n4qMhOYXmQlJUFLTYed9W3RGSMhGRr7XFRmWyhNT9jlPOFwsqA61m/4xSVN4ayXEZh5mJqg5ZyxmczfYQNfksKbvLtiuEJ732zt4bErJzKrxMrsslTGZFsilg1tA25eXdbC80saUSsVXDU9n7kVaVGZgY19h7Z4X123IyL6buPzTd20DXgo2c3fKjh8xE34lSRJCTwGnAK0AMskSXpHluUdFa0bgX5ZlkskSboMeAi4VJKk0cBlQCWQBXwqSVKZLMvHdInCTruHUekHlopn0qnoH6ryKji8rGke4Gfz10dev7mqlVSzlvvmjYqKpji9KoNuu5cnF9WjVYejdioyzUgSUd5vJ1eks7iul/F5Fi6clEVDr5uGXichWY4I+/5gCJvbj0mnQqtSUjDMRLwoNUGIvnGmut3GnS9uj6D4trYXjUrBlIIkJCS2dNoxaVXcfVIJM0u27+b6AyEG3X5STZqoAg7bUCsVpJo0bOm0M+D2kWnRi0X/UUD7oJvvv7w6EhVU3+Pk0U9rePGmaejUKtRKiUn5ScwoshKU5aGiMRlcNCmHf31VG/VdVTst/LRKBRPyEmPSzSuzRJr34aCux8nfh2xbABZUd5Jm1lKRYWJjx/Zsj9I00y7TukMhmfoeJ71OL1mJ4Wva6w/i8AbCqb1DEZ2mYQp1qRTSsO2Cw0tWop7zJ+Tw2vLmqMiuJxbV88O5pWhVikj0foZZN+yYFadGP89DIZmG3vB5kWnRk2zUMOj2M7s0lRSThrpuBwZR1DHujMmx4AkESTSoozZmshP15CUbaOx1cuMzyyIFwja227nluRW8fccsMoZJp5ckicn5SSzZyd9z/C6EYgjPDSYXJDO5ICzwBkMyvQ4vJq0qEiEqy+HzqdvuJd2sG9buwahVolZKMcWDdydiHit4/UEeWbCZzzeFRf5ep487X1rF49dMivJcBrhgYnZU8aed0auVjM9NZOtOGYFlO0XvVmZZqMyysCf8gRB9Lh9ObyD8HLEYdvm82Zm0YdL1c5MM6MS9ZY+sbR2MWgd+trELs05NSZqJFJOGcbkWfnVWJXlWI3lWI/OqojfjzXoVqSYt3Q5vVLt1HwT3QDBEfY+TAbeP7EQDWYl6eh3houEHq7hn3jDrjKJUIyrFyKwloVUp+f7cMmTgnTVtpJt13H9uVUyRxh0ZzlqtND2BmUVWTq+KDcL4cH07T3/XAEAgFOTxhXX86JQyvtjcHfH1nlEU69kdCsn0u3wYNKoDrvGQoI0NPjTrVejFtX/EEM8n71RgqyzLdQCSJL0MnAvsKPyeC/xm6P9fB/4hhbc2zgVelmXZC9RLkrR16Pu+4ximy+ZleuGBRfwm6FQMCOE3LqzdqQAPwPyVrdx8XFGU71GGRc89p5ZzxbQ8lAqJTIseXyDEv6+cxK/eWU+X3csJZWlUZpl57rtGfn/+GN5e3cZDH22m3+UnL1nPf66ejFop8eSiej7b2MXk/CTuPKmEM8dk8tzixshiw6hRcvnU3MP2byAYnrqe2F3ar2q6efDcSqYWWjFolEiSRFbi9ol5Taed/35dR5fNw+yyFH59zmj+7811kc2Bq6fn4/b5aep3c9OzK/AFQyQZ1Pz7qkmHNBVIcOC09rujUkEhnA466A4wKsPMwxeP4+MNHTz66RYAKjLDi78rpuXhDYSo6bRh8wS4dkYB43KiF4EKhcQV0/JYsKEzsngoTTNFBADBoaW6Ldbf9eP1nfzzygn89M111HY7mVaYzK/PqSR5GO91XyDEu2va+Nn8dXgDIcbnJHL/eZU8820DC7f0MK8ynetnFVKUaqI0zcTlU3N5ael2v9gfzC2lYJgIY0F8mL+qNaZtdfMAp1Vl8PbqNibkJnL/uZVICilKKJyYl8iMYiuhkIxCIREIhvhgXTs/eWMtHn8Is07Fr8+uwOENMX9VK5s6bFw4MQdPIDbaUHB40amVHF+ayn+umsRv3q1mY7stMs5pZh2LtnRHRN9tdNq8tPS7hxV+Ac6dkM1bq1tpHfCgUys4rTKdc8Zm7VV/6rsdPP1tAx+u72B8TiJ3nVxKZZaZj6s7uOfVNbh8QUxaFX+5bDxzK9KjPptvNfLDuWX88ePt0eWXTM6hVER/0WX3RuxZdmTA5ednZ1Tw109rCIRkbp5dxKmjM3b7XSqlgutnFfD5pi76nOE1XFW2mZnFw8/lgsEQyl0IyTUddv7zdS1fb+lhbI6FKQXJ3LFoFY9dMXGvIr2nFiaTk6SnpT9cO0CtlPjB3FJROHQv2DSMv/uXm7t49NLxFFgNXDO9AONuNk0yLHr+3wVjuO35FREP4GtnFDAqMzbKNxiSUUhEFfly+wO8tryFB96rxh+UuW5mPokGDa8uaybDouOeU8uZXmQ94GCguaPTeeqbBnqHzlW9Wsm1MwtHdBHAghQjD100lh+dWo5Brdyjn3Fllpkzx2Tw/lBNF61KwW/PqcQyTAaP2x/kjRWxc4WGHicnlacSQqYqy0LHoBub24dZH/6Opl4nLyxt4q1VrZSnJ/CDU8qYeAD+6xWZZmaXpvD1lu0e8b86q3KvN40Eh57dCr+SJF0oy/Ibw7RrgPtkWX7gAH53NtC8w+sWYNqujpFlOSBJ0iBgHWpfvNNnsw+gLyOCHoeXRP2BCb9GjYr+3XjOCPZMr8PL1i4HwZBMSZppr20zMoepalqabhp2h1WpkKIiMzUqBfOqMpiQl0i/y4fN7cfhDTDn2sm8vKyJtS2DXDezABl4YXETn1Z38OmmLtY0h8XmD9Z3sLyxn/m3z+L122ZQ3WYjJMtUZFoO2O9LEEvbgJu6bgc6tZLSNNOwD/IdSR7m/bwkA3Mr0knfYaHX3Oeipd+NTq3gzVXNjMpIoM/p47NN3cwbnc47d8yi0+YhEJIxaFQYNEou/s93ETG43+Xnh6+s5q07Zgm7l0PMjveJ4jQT6fvw723Rq2OiqCQJEoeiOZUKiQ/Xd3DBxGxykwxIEiyt7+WkinRyk/Wsbu5nSkEyVdmWYX23PP4gF0zMRqdWopDC/vGL63pFcSdgwOVjS6cDbzBIcYqJzMSDO6Ed7jlQkmZkdLaF126bid3tx2rSYNqFn39tl4Mfv74GSZL44dxSBtx+fvPOBsblJnL2uCyeXFSP1xfkptlFDHr8XD+rkLPGZNI84CYvyUBVtiXi8So4+PgCIeq6HXTYPGRYdBSnmnb77z02xxLl3x9uS+SG4wr40dwykoyaiDfwW3fMYmungwSdEm9A5v53qkkyaTijKoPUBC0NvU5unV1Mh83Da8ub6XH4+dPHmyMCwT++2EpIlrn31PIRvQDfG1r6XdT3ODFqVJSkmSL/xoeTaUVWXrp5GoMuf9Q4Jxo0MRleaqUUKda7I4NuP3XdDvzBEM/dOI3OQTcNfW7eWtXC/76t5+LJubu9rzs8fn7x9nq+2Ro+Bxds7GRpYx/P3zCNH7y8OhJ17vAG+MHLq3n/ruPI32HjSK1UcPX0fMbnJdLU5yLDrGNMtoWEA6hHciTh8gXY2uWg3+kjN9lAYYoxSkjbHUatkjyrgYaeaJuWFJOWiyfncubYTOSQTFaifpfXo8Pjp7bbiYxMTpKBt26fyZYuB2qlgvKMhJh5xdYuB2+tauXb2h7mVWZwxphMcnewk+t1eLnzpZXUdIYjhz/b2MXmDjunVmbww1dXM//2maQm7H6uUpRq4vmbprGhdRCPP0R5RsKIyhiSZZm6bict/WFLpJI0Izr1wYmjGy5aujjVxJT85Ijg2zHopqHXhUGjpCjVFJPtceKoNN696zgae51YTVrK000RoQ/CtoLf1vXw7LcNWPQarp2Zz6T8ZJQKiZoOB796O+whn5OkJxCCvwwFELQNerjmqaW8+b2ZjMtNPKC/szzDzGu3zWB9m41gMERFlplRGSPnHNkVWpUyyr7RFwhS2+UM+3sn6ilKMUYi+1MTdDx4XhVXzyjA5vZTmGLcpU2HRqmgMssclaln1quYOzqdTzd20tTrQilJbO1ysrHDzrRCK15/kD9/UhOpH9Fp87K8sZ+375hFaXoCNref2qFnR2GKidS9KLyXkqDlTxeNZV2bjT6Hj6JUI1XZI39cjyb2dKe6RZKkG4E7ZFmuB5Ak6XTgUeCjQ925g4EkSbcAtwDk5Y1M0/Bt9Dr2XOBhTxi1yhFl9XC4x7+p18WPXl3F8sYBIJxq+Z+rJlGyF55Kk/KTGJNtYd1Q5K9ereRHp5Tv0y55mlkXEexqOmxc9eRSuuxeJucnoVEp2Nxh58yxmRSlmqj9OjqVrMvupb7HwXGlqZSkHf1i75F67Ve3D3LdU8siEZunVKRx/3lVu614XJll5sTyVL7YHE4JVCkkHjivKkr0XdnYz43PLKPf5UeS4JGLx/Hj19cSDMloVQpmFFn5ZGMnHYMeThmdzrhcC4tr+6IWjxCe3PU4vEe98Hukjj+EBfofvbo64oVXlGLkP9dMonQvr7uCIU/fB97bGGm7+6RSitPCC+4NrYPcfkIJX27u4s2V4SiAK6bksq7VxmsrWgBY2TTAZxs7+ctl4/lmay9NvU5OqcxgakESyxr6+c/XdVG/MzdZz3njs48aj75DMf6t/W5+MX8dX9SEr8OsRB1PXTfloC5YJuYnMTbHwtqW8HNAp1Zwz6nlGDUqjBpI3sO/f/ugm5AMV03L463VbdQPZQusbBrg+NIU/u/0chINGv762VaSjBpyk/RMyEvkshFWVOVIvP4DwRBvr27lp2+uIxiSUSkk/njRWM4bn71LYee88dm8vqKFHkd4XpZu1nLGmAwseg0WffS5UGA1UmA1sqSulzdXtlCaYWJzh4OvanqYVpjM3z7fSiAY3pD+/twynL5ATGX4Z75t4OoZ+bt9Hh0NHMj4r2sZ4Nr/LYtETp4/IYufnVGxR8HrUJBo0MTM60vSjNx9Uil//WxLpO2np4+icKdI/bYBN796e33E43F6UTLzKjN49JMavndCMS39bh77YivnjMtiVknKsEEGTf2uiOi7jQGXn7oeR1ShUAiLv512T5TwC2FP4JnFKcws5rBxOK5/hyfAfxfW8rfPwtY8erWSx6+ZzHGlsenUw5Fs1HL/OVXc8PSyyHV4QllqRCTN3sOmYqfNw3++riXDrGdzhw2lQuKccdmcUJ42bERml83D955fESkQvbJpgGUNffzl0vGRjcSGXmdE9N1GS7+bZKOaln43PQ7fXl0H2+5F8eJQjv9XNd3c+twKvIEQkgT3nlrOdTMLDooNwvjcRCbmJbKyaQAIR3ned9qoiOhb3Wbj5meX0TrgocBq4O6TS1nbMohGpeC0ygzG5yaiVEhUZJp3uaGzcEs333thZeT1Rxs6eO22GUzMS6J1wB1pP7E8jXfXRvtGB0My1e22AxZ+IbxBUHQI6oscic/+4fAFQry+ooVfvLWekBxe1z166XjOGpsZ2TxKMmqZXrRnwVWpkLh2ZgEfV3dgc4ezQe46sYSfvL4WhzfAOeOyCMphS9AVDf2kmbTIwNs7+YK7fEG2djkwalX89t0NfLyhE4BRGQn84/IJe6VlpFv0UWtTwZHFbu9SsizPkyTpcsIeui8CVUAacJksy6sP8He3AjvmkOcMtQ13TIskSSrAQrjI2958dtvf8F/gvwCTJ0+OrYA1QnB4A8jI6NQHFqlj1Kojk92RwOEe/69quiOiL0Btt5M3VrZy3+mj9vjZ3GQDj18ziep2G25fiLJ00y539xzeAOtaBmjocZFm1jIm2xIj1K1vs9Fl95KgVTGnPJWHPtqeavfmqhZumV3En3eqGj2SPLiOxGvfFwjy76/qotL0P9nYxXkT+jlz7K4flGlmHX+6aCzV7XYGPX5yE/W4fAFeWtJEbrKe4jQjP5u/LhKtn5agZWl9X8TX6abji3juu8ZI6v6ry1t48LwqJg4zeUtL0JJsOjrEvd1xJI7/Nr6u6Y4qgFLX4+S15S383+mj9ipaSK1UcNGEHIpTTTT3uUhL0FKRaY5EnUzKS+LLLT1s2ME2wJqg5bEvtkZ9z1njsrjpmeURUemV5S38/vwq0hNix99q1KJRHT2RoIdi/Jc19EVEX4C2AQ9PLarnd+ePOWhRsjlJBv579WQ2tttw+YKUppso2+E50D7gZl3rIL1OHyWpJsbkmKOijdItOiQp7OlXv5NFzJrmAU6vyoiqFG3Rq7njxGIKU4xxEbYOFUfi9V/f4+Rn89dF7suBkMz/vbmOcTmJuyzyMirTzBu3zWRThx1JCi/Adi6K1dznYl3rIDa3n7E5FpbWh4W6bYIUhBf7d59Uyp8/qWFrl4PGXifjd7J5gbDQqBkBEd/7O/5OX4A/frQ5ah48f1UbZ43N4uSKI+P60KlVnD8hi4rMBLrtXvKsBsbnJsV4wH5T2xNV2CffauTfX9Vy8+wi/vlFLfah4pDvrW3nsSsmcubYWN9IrWoXHr1aFRqlIqqQj16tHNaXMh4cjut/Y4ct6hpz+4P85PU1+5QxdVxJCu/cOYvaHicWnZq8ZAPr22x0buwiz2pgTLZll9HmK5v6yU7U87v3N7Jt/+bNla28dMt0pgxjzbS1yxERfbfx6cYuGnpdkcJ9u1oDKCWJDLNun7xi48mhGv/2ATc/fm1tZNNDluFPH29mZrGVCQeQIr+N7CQD/7pqEtVt4ed/SZoJSYI3VrSQZFTz4pImWgc84UKfM/K597U1kbF/alE9r9w6nUn5u7bjcPkC/HunOg/BkMxXm7uYmJcUlXFk9wRI1KtjCsAah8kSO5I4Ep/9w1HX4+CXb2+IjF8gJPOT19dSlWWmcD8E8apsC2/dPouaTjsqhYIBtw+HN8DoTDNatSKyUfje2nbeWt3KY1dMxKhR4fBG2wYZNUqW1PVGRF+ATR12XlrWzM/PqDjms4GOdvbm6n2VcBG1HwIDwEmyLNfs9hN7xzKgVJKkQsKi7WXAFTsd8w5wLWHv3ouAz2VZliVJegd4UZKkPxMu7lYKLD0IfTpq6bZ7STJo9jrFaFckaFX0OL17PlAwLCub+mLavqntwRcI7lWl0gyLfliPNqc3wIa2QZr63KQnaBl0+7nzpe3Fvk6rSuf/nT+GpB08H7dNTOaOTuf1oSi/bdjcAXRqJSqFFIk0OK0yg5J04bt2KLF7Aiyrjz1Hdp6Me/xB6nscePwh8pINWE1aUhJ0zE7QYff4efC9jbyyfLtTzuPXTGLTDpWzQ6Hw7vE2NEopptjDwws28/5dx/HLsyr4/QebCIZkjBolf75kHBlmsVt7KFnVPBDT9m1tD95AaK83XxbW9vDYF1s5ZXQ6XXYvmzvtXDI5lwyLnnG5Fv650+ReBhSSRGiHEG+1QoqIvtt4ZEENL948jRSTJvKeJMGPTik7aIU9jlY2d8T67y2u68PlDezRrmVX+AIhNrbb2NrlINGgpjLbQoZFR8YOC7DmPhedNg8GrZJ/fL6FD9Ztn5D/+ZJxXDAxJ/K6JNXE/edW0uuI3cA9ZXQ6//4qOpJ70O3H7gnQ5/SPKOH3SKTb4Y0R0LyBED1OL5o+BZ02D1ajhoKd0sW9wRDeQBClUsFOAbo097m46ZllbB6K0itKMXD7CSURf+9tbOqwc+EO58m6lkGunpZHXrKepr7tUV4/P7MC6xEi3sUDuzvA6mHuz9v8SuNFv8tHY68TjVJJgk7J7S+siqT1ShI8cc1kStJMbGiz4Q+GGJNtYcUOm4sQLuJjUKtw+4IR0Xcbj35aw3El1pj7WH6ygbtOKuHPn2w/n44vTWFsTiIPXTSG+15fhy8YQqtS8Mgl42Kijkcy3fbYtVLboIcBt3+vhV+FQmJ0loWsRD1buxwsqe9l/qpWFteF54k/mVfOLbOLhi3s1mXzsrS+L+qeEAjJvLGiZVjhd1eCzY7RwYUpRq6ens9zixsjbaeOTmd96yCPXDLuqM8EO1D6Xf6YuTQMfy7sDd12L019LoxaJYVWI1q1knSzLmLRsbp5gMv++x0ef4irp+eztCF8XlRlhS2Ahhv7XoePzEQ9ozPNMZHfCqRhN6m3nV9l6Qncd1o5f/p4MwuqO/jB3FJ+/8GmyHE5iTrGDrNhKIhla5edje02JElidKY5Jrq52+6NbAJvw+0P0uv0UZi697+n1xE+h3RqJYUpxsjveXcomvekijT+/WX0eqCm00FTn4ufnFYesfaAsLVURaaFv38ePX+AcMDKD+eW7tJmTHB0sCeP3+OAx4BvCUfYzgHelSTpFeB3Q8XV9oshz947gY8BJfCULMsbJEm6H1guy/I7wJPAc0PF2/oIi8MMHfcq4UJwAcJWFMFhf9ExQrfde8A2DxCutLuly77nAwXDclxJCvNXRadOzKvM2CvRd1cEQzKvLGvi/h3Sus8Yk8HxpSksHDJQ/2h9J9fNLIykhDT1ukg2hqMHVEoJ3zAFW/RqBf+9JryzXJRqYlJeIon6o2M3/2jFolcztyI9alIN4UncNnqdXv75RS1PfVOPLIcLc/31sgmRqL+tXY4o0desV+HxhRiVkRARf7sdXnKSDZHK7zuLBRCu2ixJEldPz2dmcQp9Di/ZSQZR2OkwMKvYGrMZc1plxl6Lvl02D++vbWdyfjKPfVFLMCSTl2xgYn4yGRY9qWY9J41Ki6QLAnyxqYtLJufy4tKmSNtw6aC+QAizTs3Lt0xneUM/No+fSXlJjD0IqX1HO8OlN55amX5AfpVf1XRzy3PLI5Yr04uS+eulE0gfEn4Xbe3hzhdXMuDyo1cruePEEja02WnsDftC3v9eNTOKrBGvYa1ayaWT86jtsvP5pk7WtmwXq6uyLTFp2xAWjqwjIMr/SCfTosegUeLybZ+uJmhVIMPZ/1gUGeM/XjSW06syUCkVrGkZ4Ir/LsY59Jl0s5bnb5wWyQZa1zoYEX0B6npcJOjUw97zd4zsmVOWypjsRJ69YRrLG/vptnuYmJd0UFJ4j2aSjGrmlKfy3tr2qPai1Pg9F7d2Obj3tTWsbh5AkuCSSbmUpJkiwq8sw9ZuB798az1tgx4AkgxqfjyvnJeXbZ8rfFzdwY9OKadtIFbE9gaCBIc5Z1RKBdfMKGBsTiLrWwcpTDEyIS+JlAQtZ4/NoirLQpfNQ7pFR1GK6YCDT44mcoaxYihNN+1z1HNdt4Mfv76WFY1hof60qgwunJjNGytbefTTGk4ZnR6T/VfX7cCoVcbYbUBYPBqO0jQTE3ITozaeL5iYHWXJYNCo+P7cUmaXpbKp3Uae1UCmWUuaWS/mhoQz4nKT9TTvsFkmSexX8apN7TbueHEltd1OlAqJO04o5sbjirAYwvOJUEjmmW8b8PjDY7ypw8bE3CS+rOlGrVQMu67rd/l5eMFm6rqd/O/6KRxfGq0g6jRKbj+hmBueWR5p06oUzCkLH2fUqrhhViFzytLod3rJTtIzPjdsPWE1apiUnyTOg71gQ9sglz++OGK7kGzU8OJN0xi1g/1GlkUfWaNtw6JX71O9jy2ddu5+eRUb28MZQTfOKuR7JxZjNWqpzDKTbNQgAcGdPf0Au8fPBROyKUoxsaZlgOxEPZMKkkg1a5mYn8SzO61TTxqVNmxNEEEskiRdByyQZblt6HUDMFmW5Z7dfe5wsKd8rr8AN8uy/D1ZlvtlWX4LmABogTUH+stlWf5AluUyWZaLZVn+3VDbr4ZEX2RZ9siyfLEsyyWyLE+VZbluh8/+buhz5bIsf3igfTnaCQu/B74Lk6AVxd0OhFklqVw8aXtUzUnlqZw9TPrcvtDQ64yyaQD4YF0Hk3fa0d9xUffRhnY+XNfB7ScUk5ag5dIpuVHHalUKJuYncdKodO48qZQzxmQKT57DgEqp4LqZBUzMSwRAIcHNxxcxIT8xcszqpgGeXFQfEYI2ttt5YmEd/h2KqOzI2WOz+MNHG7l0Sm7EfF+pkFAg8+LN07jpuELykg0Yd/KKvnVOMZkWHRqVkopMM7NKU8WE7jAxs9jKpZNz2LZGnlOWytnj9q7COoQXdlXZFp5b3BiJGGjqc/HPL7biHDo/zh6bxbQdKnAnGtRUZCZw10klnFyRxs3HFzK9yIphp/PieycUk2HRUZKWwGVT87hldjGTCpJFwS9gcn4yN8wqYJtePqUgiSum5u136luP3cuv3l4f5bO9uK6PDe1hf9/Wfjd3v7Qqkmrp9gf5y6c1nD9hey3bQbcfz04LfY1KQUWWhb9dNpFfnz2aM8dk8seLxnLGmAxum1MUdaxWpWB6ofWISdEeyRRYDfzjigmY9eHFU6JBzaOXjudn89dFjfGPXl1NbbeTYEjm6W/qI6IvhAuwfLl5u92IzR07X/u0upPTKtOj2rIsusizY1xOIpdPzUOlUlCQYuSiSTl874QSphVZR5Td0/6gVSm5++RSRg8t0FUKiR/MLWVsdmJc+hMMyTy/uDEShSzL8MryZopTjZGNO7NORY/dGxF9ISwAefzBqPlnUYqJ44qtzCq2xth53H5CyS79wxMNGk4oT+POk0o5c2wWWUOCp0qpoDQ9gVlDdSGOtRTg8owEHrpgTMRiL8sStuTakw/7jsiyzGvLWyKiL8BH6zvITTagUSrwB+VhhdzvantZuKWHeaPTY967ZHJuTBuA1aTlL5eN5zfbngkXjuXeU2PriKSYtJwyOp27Ti7l3PHZTC1KEXPDIVIStPzl0gmkm8PPS71aySMXj6N0F1Y9u8LtC/KnjzdT2x22YwqGZP72+VbWtQ5EjgmEQjT0brdrWtbQz4xiKwVWA2taBjiuJNZLemJ+EjWdDgIhmd+9v5HBYWr2zCix8sKNU7l4cg43H1/IK7fOYEz29uATrVrJ6KxtawITUwut3DanmIsn5x4ST96RyGvLWyKiL0Cf08f766I3EwtTjOH5wJB/c7JRw2NXTIgqtrg7/IEQ//mqlo3t4YAfWYYnFtWzZijgoyjVxIs3TSPDouX0qoyoz6YlaEk2hosEH1eawh0nlnDehHAxaIBpRcmcN377mmRcjoVLJucec/f4A+A6wo4Ee82Qpe0hZ0+/ZKosy1FbSrIsu4D7JEl65tB1S7CvdNs9WHQHfs6YdCoGRlBxt8NNhkXHb8+t5PpZBYTk8ELvQNMinN7AsLv6gR281YwaJUVDE7NgSGbBhk6WN/bTPujhpFFp5Cbp+e05o3lrdRvZiXqun1UYWdgIDi/FaSb+d90UGvtcaFQKClOMaHeICN/RsmEbX2zuZsDtJzVBS2GKEatRQ7/LR0gOL8r8IZkFGzo4b3w2WrUCpSRRmGJiUn5yxO+rJM3Ec981sKXLwWVTc5lbkX5MReccSaRb9PzmnEqum1VAMCRTYDXu030iw6JDMczYLanvo9/lw6hVkZ9i5D9XT6K+x0n7oIc3Vrbwy7c3oFUpKE41Udvl4KbZhbx083Se+baB2m4Hl0/N4+SKNHFe7IKUBC33nT6KS6bk4guEyLcasezCf3FvcPmDdNo8Me2DQyJgt8Mb47kfCMlRBblOq8yIRPvuTEGKketTCrl+VmGk7dzxWSTo1Dy/uJHsRB1XzSiI2iAQHDokSeKkUem8f9fx9Di8pJi0DLh8kcX/NvxBmfZBN/lWQ4wNEBDl3TwqIwGFRFSEb6JRw11zSxifl8R7a9uYVmjlwonZhEJw5phMClKM+yROHWuUpSfw/E3TaO5zodeEU7DVcfI3t3v8fL6pK6a9ZcCN1aihy+7FrFcPuwHw6vIWnr9xKjfPLiIQlClMMZJk1FCcZuLFm6fx1KJ6Ou0erp5ewJzyfcgtFgBhgeziyblMKUxm0O0nO0lP2j7a5Ti9AT7d2BnTXt/jJM2sxaxTR8SYHWkdcPP26jZ0agV/umgsb65sQSkpuPH4Qibn79prNt9q5LpZhVy3wzNBsG9Myk/i7Ttn0T7gIdGgpsBq3Oc504DLxze1scF/TX2uyP9rVEqunJbHqh0ytx5esJnnb5yGUiFh1CgpSZvMU4vqkSSJ2WWpvLFDJllLvxuXL4hlp9NHr1YxqzSVWaXimj8UyLI8rC3Yzs9yhULilNEZvHe3mV6Hl7QE3T5Fjg+6/XxVE3sObeqwc1JFeENoVKaZolQjJo2aDLOOZQ39FKeaKM8wYTXuerM/06Lnd+eN4cbjivAHQyN2ziBJkpGwpW0OYfeBB4CHhtpOB9zAFbIsb5Uk6W3gDVmWn5Uk6VZgtizLVw7znRcBk4EXJElyAzOG3rpLkqSzATVwsSzLmyRJ+g1QDBQBTZIk3Q38G9hWlfAHsix/I0nSHOCvQ23y0O/er/T8PSmFxZIkPTzUqXXAvbIstwLIsly9P79QcGjosnsPKN10GyatKsbIXbBvGDQqRmftnweS3e1nRVM/W7rs6FRKshL1pBi1lKaZ2NLlwKJXk5qgpdfuJTtRj06tYHSmmZ+fWRHZiVUqJI4vTWF5Yz9L6vtYMuQp+8C5lbxyy3SUCsWwKd6Cw4fFoGHsLqxZSoZJKZ1WmByJEvP6QzxwXiVtA56hFH89WlUeaqUCq1HDutZBXlzazNt3zIr6jnG5iYzJHkcgFDog6xHBwUGvUVGROfx9wukN4PQGsJq0w16rWpVy2PNkbLYFu2d7lEGiQcOEPA3q1oHI4sEbCFHdbuOm4wpJNepIT9Dz8MXivNhblJJEskGDSaeKSXtb2zLA/FWttPa7uXBiDjOKrZj1agLBEBvabFS32zBpVYzNsZBvNZJu1nL2uCzeXr3dHkghhTdpIFygzaxTkWjQEAiGaBv0oJAg2aBGr1ZyzvhMbptTvE9RmklGLRdOyuHscZniWXAY6BuqmZC8wyIrN9kQieqRCUds2na4bhUStA94+K62l3tOKePW51ZGFdE6aVRa5P8rsy08ce1kfvf+RjptXi6fmsu1M/PJSTJwemUGozPN2N3hTcLKbLPY1NlLko2ag7bQlWWZ1c0DvLGihR6nj4sn5TC9yLpXnulGrYobjyugut3O5xu7Iv6iY7MtVGYm8MTCek4dnc70IiuvLI+2D7p8ah4pCTpSdhIjJUlickEyE/KSCIr7/l5T02Hn3bVtrGsd5JxxWRxfmkJqgu6AoiANGhWzSlIiotC2OX6B1UBIlvn+yaXIQJ/DS/IOWRkziqz888taXlnWwhsrWplckMRFk7MpSzfx2aZOBlx+yjPMjMk2i/E9SDT2OvmkupOva7o5cVQacyvSyE3ev0hoi17NhLxEvquNrvmRtdMm7gllafzyzArWtw2SatIyLjeR0ZlmEoY2nCuyLMwuS2VLp42z/v5N1AbgRRNzcPmCvLGiBRmZqmwLozJEwM+hRpIkLp6cy3d10WN7zrjhs3/zkg3k7RTlGwzJ9Dq8GDUqjDoVvkCQFY0DvLq8OWz3MzmXsdkWphQk8cH6jqjP7lwoVqNSUpaRgCcQpCw9AY1SIjtJH2U7MRxGnYoxI9/P+TSgTZblMwEkSbIQFn4HZVkeI0nSNYTdD84CbgG+kSSpHrgHmD7cF8qy/PqQle29siwvH/pegB5ZlidKknQ7cC9w09BHRgPHybLsliTpReBRWZYXSZKUR9gOt2Lo+DuGRGATEBsxspfsadbxFPAs8DVwDvB34IL9/WWCQ0enzRO1sNhfTDoVdk+AUEgWIf1xYGljH4tr+3hnTStGjYqLJ+fQpffw41PL6bB5aOxzhaN45xRzfKmV2WUnkKBTxYj+Z4/LorXfzeZOO2tbB5mUn8TsslQxATwKGJ+XxLnjt4tBWYk6bj+xGK1KyeYOG19s7mZdyyDJRg2zy1LosXvw+EM8t7gJi17N9TMLePXWaYzKSIj5boVCQqMQ58CRiizLLGvo448fbaa228H5E7K5dmYB+dboxYUvECSEzA9OLuFvn28lJIeFinPGZ3H5fxfz2m0zKE1PYNDlZ2l9Lws2dnLNjHx0agV//3wr540Pf++2e7w4L/aO+h4HTy6q57217YxKT+Anp41i4lB0VXXbIJf9d3HEv3VBdSePXDyOCyfl8F1tL9f+b2lkUZaXrOfZG6ZRkGLkB3NLUUjwzpp2shJ1/PacSiqGJuTpZh2PXDKOb7b2kmHWYtSpMKiVnDgqjdOrMkkxafc7ElE8Cw4OwZA8rHg+6Pbx8YZO/jZUSfv7J5cyrzId804++nnJBh65ZBx3vrgKbyCEUiFx2+wiBtw+anscdNu9/PzMCta0DPDZxi7unVfG1MLtEX1qpYKTRqUzITcJjz9ImlmHUiFR02nnyseXRIRCjVLBCzdPG7bok+DQsr41fG/Ylrn10foO/nXlRE4fs10IGHT7WFrfx6fVnRSkmDh5VBq5yXo+39TNM9824vQFOG9CNm0Dbgbdfta32XhndRvP3DCFSfnJuL0BHr96Eo98spmGXhc3HV/EaVWxNgA7olRIKMV9f69o7HVy1VNL6LKFr6cvN3dz10kl/GBu2QFtnikUEldOy+ObrT3Mq8rA7QtngeRbjVw4MYcVjf38+dMagkGZO08q4fQxmSQZNEzIT+KfV0zkrdWtrGrqZ3xuIqOzzNz0zHI2DmWNSRL8+6pJzKvM2EMvBHui3+Xjx6+vYWl92JLj6y09fLm5i79fPhHzXmb9eP1BVjT28+H6dix6DXedVEq3fT1bu8IZHFdOy4sR2pKNGkZnmXlnTRtf1fQgKSTG5iRGhF8IPwOKUxN47MqJ/PadanocXi6enMuFE7P53fvV1PY4aex1YdAoefnm6aJew2FgdlkqPzmtnMc+34pCCtsFzSiKteaAsJfz6uYBPtrQQTAkc9KoNJbX9/H0dw0Up5m4b94okGSueGJxxBZs/qpWXr55OnedXMrqlgHaBz2My0nkhLIUxu80vh2Dbv7vzXVRRUv/cul4sekfZh3wiCRJDwHvybK8cEikfWno/ZeARwFkWe6UJOlXwBfA+bIsx1Zq3z1vDv13BdFa6juyLG8zDZ8LjN5hg948JPR+A/xZkqQXgDdlWY7e5d0H9iT8Jsiy/PjQ//9JkqSV+/uLBIeWLruXopQD995RKRToNArsnkDEYF5weBh0+VjTNMDjC7dZWXt56KPNPHLxOFY09/PRunYahwoKfLCunR+eUspdJ5bGCPROb4CGHicdNg+ZFh13zy1lXHYiKQnCw/FoIN2s48Hzqrh2ZgEef5DCFCOZQ/7L39b28ocPN6FWStx9cimfbeyiptPBxPxETq/K4NnvGvnF2+t59vqpw1aBFhzZbO60c/WTSyMCwVPfNNDv8vGHC8dG7EC8/iCLtvbw4pJmFBI8csl4BpxeWgc9PLKgBoc3wPq2QUrTE5i/uoXfvLM9OSc9Qctrt4ZFYeHZu284fQEeeK+azzeF/VUX1/dx9ZNLeOfO4yhOM7GmZTCqaBfAXz/bwqwSK3/8eFNUJE5Tn5tVTf0UpBgpTDHxhwvH8qNTyjFolFh3iOpa3tDHqqYBAiGZBdWdVGVbOGdc1kHZ5BUcGP1OHwu3dPPCkibyrQaumJYfteD6rraPn7y+NvL6x6+vxawfXoQ5eVQ6H9x9PCub+mkdcBMMyby+opXa7nAU4Nur27j5+EIW/OD4XXrxJ+0UnfrN1p6o6vO+YIh/f1nL2KssUdZCgkPPd3W9MXZd//hiK7PLUjBqw/Psd9a08cu3tldXf2pRPf+4YgJ3vLh92fX4wjp+dsYo1rfaeGWoaNtry1tIN+v4cH0HCzZ0csroDOZVpjM60yKCNw4imzrsEdF3G//9uo6LJ+WQZz0w/9vS9AT+eeVEbn52OQ1DRTvfW9vOHScU89GGjkghsZ/NX49Jq+ac8Vm09LlY2dRPr9PHHSeWcGplOmtbBiOiL4Q9Px94r5op+UlR0cKCfaeu2xkRfbfxVU0P9T3OvS6I+W1tL9c/vSzy+olFdbxw4zQ8gRAmrYriVBOmnSwbN7bbuOappfiHKi/+56s6XN4Avzq7MmoO9//bu+/wuIqr8ePfoy6terWs5iZ33BvGphMCgZiYHlropAH5vUkgQBJCGgSSN+QFQoDQewklQOiYGooN7r0XuUiyei/z++NeyStpZUnW7t4t5/M8+0g7e3f3SDO3nTt3JjY6kpMm5jKzKJ3G1jbEwItLd1HV2MqMojTOnlHAne9u4NklOzTx6weZibF8/6iRnDYlD4Feh+MC+Gp7Befc91nnEF4PfbKFG04eR0V9C4u3VnD+Pz/nlgUTuswFYQw8t3gHd5w1heevOpzVJdU8s3gnS3dUMaWgmuS4KOLtO9JW767ukvQFa7swZ2Q6Q5LDe24fY8x6EZkGnAz8TkTe7XjJfTG33w8Dyhng+L22jh1IG13zr+5jfUUAc4wx3Xv03ioir9lxfiIiJxpj1h5CDH0mfuNEZCrQcfQQb/+DADDGaCI4QJTVNHktUZscF01FfbMmfv2sqbWNN1f1HOtr2c5KclPiOpO+He55fxOnTcnr0Rvw001lXP7oks7nb63ey7NXHq6J3yCSFBfNtMKu47SV1TTxjw+siwLnzyni6S92sMuenfur7RWcOCGH2cPT+XzLfpbvqmL+aB2/K9hs3FfbI0Hw8tISrjludOfkKku2V3Cp24zM768r5YaTx3WZEBADuysb+Mtb67t81t6aJraV1x/yUDThrKSioTPp26GuuY1NpbWMzE7EeJg12WBobTPs6ZYwALpMohobFdljQo+2dsOynVV8sL6UVSXWeHFfba9kVUk1d393KqW1TcREWpNzaSLP/15dcSBR9/mW/byyrIR/fX9u57r17OIdPd7z3OIdHhO/ERHCyOxEPtpYxl/f2cC1xxd3Jn07PPTJVs6dVUhbu2FbeR01ja3kp8V3uVDgbk9VzzsBd1TU09rWru3FzzxsGjDGdJbvrbYu2rkrrW1irYdxIl/6uqTLOJDprmh+/fIqFq23tk1fba/gnTV7efSS2Z2TvR5MSWUD+2qaSHfF9LjdWPXNQ9Uekh0V9Z1J3w4PfLyFS+cN555FmzrLnvh8G5MLUrjgn190XthZsq2CDftqWTA5l9ioiC7HEKU1TTR4mCNEDZTnmu5v/Te0tHH3+xu7lDW2tPPF1gq+f/TIXt+3fl9tZ9K3w9Nf7uCqo0aS52HsZxFrws8nv9jO459tB6z2kZUUy/fmDmNVSTXGGB3yxw9EpMfQHZ68vHRXl3kb2o11sXBiXjIrd1XT1NpOdUMrInDyxFxGZh+Yzwdgx/4GLn9sSef+ZNH6Uh6+eCZHj7GGhHIf/q1DRX0zTS26XRCRocB+Y8zjIlLJgeEXzgZutX/+1152Fta4v1OBD0TkLWPMll4+ugboedtt394Cfgzcbn/nFGPMUhEZaYxZAawQkZnAWOCQEr99dfnZDfzZ7bEHuMPtoQJEWW0zqYOYZMZdclw0+3WCN79LTYghK6nnmHIZrhiPO+nWdtO54e9QVtPIfR9u7lLWbqzkrwpu7cZ0jvOYHBfdmfTt8NbqvSywZ2HNTAy9QfjDQUJMz4RMUlw0MW638z/1+fYey3yxpZyJdsIpKTaKCXkpXdqLu9Z2Pdg7FDFRER7rp6NsakEa8d3G2r3muGLy0uI5f3Zhl3IRmNTH2GnGGGKjIzqTvh2WbKvgvbX7OPX/PuGkOz/i9jfWUVbTM7GsfKe0pom73ut5Er9iV1Xn83wPk7T0NVv3jKI0YqMiPCYK24yhqbWdJz7fxkl3fsSCuz/h9Hs/ZVVJVc+FgfnFPW8rvWDOsM4epsp/Dh+ZQUy3Oyx+eExx54Sebe2GZo/JuZ7HfemumC4TMM8Ylt6Z9O2wZndNjwsHnny2qZxv3/Uxp939Cd+68yPeXLm7xzGlsowdkkRWt4ssl80fTr6H5NuhaG3r+X9vbe+ZoCtIi2fD3pouvfnBSgYu3lbJ9SeNJTrywHvOnVVIjnb6GLQRmYnMHNa1M8b84kyGZ/avt3d7u6Ghpa1HeVNrzzJ3no45UhNiuhwTdlhdUsUZ937K6yv38NQXXS88ltY0ER8TydkzCzTpG2Aamnu2gebW9i77jCEpcVx7/Gh2VNTzt3c3cvf7m2hqaaeyvpmXl5b0OGZ4+NOtndvy4uzELtsEsCb5HZIysIkoQ9RhwBcishT4NfA7uzxNRJYD1wA/EZFY4H7gEmNMCdYYvw9K7yvTw8C9IrJURAbSrfpqYIaILBeR1cBVdvm1IrLSjqkF+M8APrOLvhK/1wHnGWOOMcYcg/WH1AIrgTMO9UuVd7W3G6uHrpcSv4lxUV0OLJV/xERF8sNjRnXZQKe7YshPS2DuiIweE42cN7uwx2y/6/fWeBy3J0pv+Qt62clxXHXUCMBKHHUnWBNG5CbH6jiOQWp8bgqTuyUEb/zWuM5eA/XNrR57mMRFRzClIJWLjxjGk5fPZnROErkp8Vx5ZNeeJEmxUZ3jx6qBKUxP4OcnjulSNndkBmPsyVLGDU3m6SvmcMGcQo4Zm83fz5/GN8bndE70cc1xxaQmRDMy28X9F8zoM/EbFRlBQS8zPO+zE73txuoV9tX2Co/LKd+IFGtYrO4i3DbMZ0zL73LSnhATyXem5h30cyfmpfDMlXPIT4snu1uy5jtT8mhtbedXL6/q7NG3tayeX7+8iprGnhPyTitK465zp1KQHk+GK4ZfnDS2zzFflW8clpfC01fM4dxZBRw/Lpt/XjSDo9zuyMlNieOqo7puq10xkUzMSyYtwX0sT+HCw4soqWggKymW33x7Apm99PiO6CO5U1LZwA+f/IqyWutYv6aplR8/tZTN/UgYh6OiDBePXzaLHx49knmjMrnjzMlcNHeY18bJHDMkqccx/tkzC1i8pbzzeWxUBOfNKfI4hEekCLVNrTy3eCcXzR1GcnwUl88fwaXzhuuwX16Q5orhjjMmc8PJY5k7MoNfnjKe3592WL/Pu12xUT3W8cgI4cjig9+ZN3FoMuO6zdfxq1PGk9VtwsbaxhZufmU1W8rqrTl6PDTLvNR4jxcElbMWTsvvUTZvVCbLdloXdacVpjK9KJU9lQ0st8va2g0PfLyF1SXVPS4qAkRHRHReNhw7JJlHL5nFpLwUkmKjuOjwIq49frTe+QMYY940xkwyxkwxxszsmIwNuN0un2mM2WiMaTLGTO4Y6cAY84qdG/V4pdQY84IxZoz9uQ3GmGHGmDL7tcXGmKPt3282xtzh9r4yY8zZ9nePN8ZcZZf/2Bgz0S4/1xhzyL09+hrq4V6sgYYRkSOBP2J1QZ4C3IcmfwNCRX0zCbGRXtu5J8ZGsb+u54mE8r3ZwzN44ftzWb6zipioCMblJjE6O4nY6Egev3QWT3y+neU7q1g4LY8TJwzpMbHPovWlHDs2h8/cZhONjpQus4Cr4LVwWj7pCTGUVDUyKjuRjfsOnKSdMmkoe2saeeqKOQzzwnjfyv+GpMRxz3nTWLqjkn01TYwdksSk/NTO18trm5lckMrrK3Z3jhkbIfCtw4Z2mSgIrNvHvzu7kOzkWJ75cgdjhiRxwZwiRmUfyt1HSkQ4Y3o+xTlJrNtTQ25KHFMKUrvcTj25INXjeH9DU+O59vhivju7kNioCFIT+tcjf1JeCnNHZvDppgMn//OLM1m5q2svz6U7KvmGTuDjN+mJsfzkhGJ++tyBMXyT46K6rKuTClJ5/qrDWbazCsFqG/256DKlII0pBWlWsvCLHSzevp8Fk/M46bAhfLm1Z4J/8bYKymube0zwmhATxSmThzJ3VAat7YbsJO3d4xQRYVpRWudEkJ5eP2dWAVmJMTz95Q6KsxO5YO4wJuen8uyVh7NsZyWNLe1MykthYl4K04vSaDeGrKQ46ppaOWVSLq8u3935eTOGpTEq++A9EfdWN1Je17WDR3NbOyWVDRTn6D7CkzFDkvnZN31z4bQow+XxGL+msYWlOyppazdMLkhlwtAUdlU0UJAWz46KA3d9LZyWxztr9rJ+by1/PmsSl80bTnZSnI7z7EVFmS6uOHIkVxzZ+9AMB3PU6CzuOW8qD3+yjZSEaC6dN7zPC8B5aQncd+F0lu6ooqy2iQlDUzy+p7yumS+2Wud9767Zx8Jp+Z3jgAMUpScwd2Rmv489lP9ML0rjsUtmcf/Hm2lrM1w8bzgpcVHcePI4cpJjmVKQSmxUJO+vK+3x3sXb9nPqlFwe/3xb53ARInDxEV0nbz58ZCaPXzab+uY2MhNj9GJQGOsr8RvpNmvd2cB9xpgXgBfsbtEqAOyraSIt3nsbc1es9vh1SoQ9Y6v7CWSH8UNT+O2CiTS3tRMX7flK3bghyfzjw83ccPI4lu2oJDY6gvmjMpmUp2N6hoLMxFjOmFFAXVMLwzISWLazii1ldYwfmsyeqkZykuI06Rvk8tISPI7dBpAcH8V/N5Vxw8njOq/8H5afQrrLc6+TnOQ4zptdxBnT8omOjNCTwEFKjIvmiFGZHDFq4L1mRISc5IEl33JS4rnt9EksWr+P/24q54iRmRgMN7lNAgVWbzHlXyeOH0LaRTH8e1kJBekJfHPikB71MH5oyiGPpz02N5lff3s8Ta0H9vfb99f3WG5kluugvc50IsDgkJ0Ux7mzi1g4LZ+oyIjOnqTFOUk9ErHu4zq7YqP4xUnjmF+cyYfry5g1PJ2jx2T1We/prhhcMZHUud1mHCH0a1xg5Ruej/HjO+8q6ZCXFs9DF8/k9RW7WbaziolDU9hcWsv6vbUUpMeTmRin9RiAkuOjOfmwoRw/LocIkX4n3wrSXRSkH/xCTkp8NGOGWBel1+2tYcyQJK45rpj1e2uYnJ/K8eOzu4wNrgJHbHQk80dnMWdEBkYMMZHWuj9zeEbnMk2tbUwrSuX1FXu6vHdkdhJTCtJ45so5/Hvpbpra2jltylCmFqb2+J7k+GiSvXRneCgzxgzr77IicjdwRLfiO40xD3k1KC/qM/ErIlHGmFbgOOCKAbxX+UlpTROpXpyILTE2ivJaTfwGoogIIS6i99szZg3P4IWvdvKH19cwKjuRtIRoxuUmE9NLolgFJ1dsNKOyk/jbexuJiYzg001lzB2Z2WNCOBVaUuJj+OExo/jeg1+SmRSLAElxUZw+7eC3kMfq+h+0CtITuGDOMC6YMwywZvkekhLLnirrTq+jR2fp0C4OSIqP5rhxORw3znfDJ4hIl4u843OTufiIYTz0yVbAGg7gDwsPI82lvbhCxaFsq/PS4jl7ZiFnzyzse2FbUYaLP50xmWue/toeS9a6hXxkll44dlJfx/gdRmUncfHcOJ5fsoNb31hHU2s78dGR3Hb6JE36BrgYH9xin5oQw+8WTOTih7+ktqmVV5aVcOkRw/jT6ZNI0mRfUOh+B6+72KhIfnTMKL7cWkGpPdTX8eOymV6YRmSEML0onelFehzob8aYHzodw0D1lbx9CmvmujKgAfgIQERGAZ5nlFB+t6+myau3byTFRVFep5PFBKO8tHj+du40NuytobmtnVFZieT2Y1ZRFXzGD03mkYtnsamslvioSCvRrwmAkDe9KJ1/Xz2PbWV1JMVHU5yVqAf2YWRcbjL/+v4RbCqtJTYqguKcJNL09s2wkJoQw/+cMIZvTx5KVUMLRRmufk8upFR3J07I4bWr51NS2UB2UiyjshP1ImEQSYqP5qyZhUwtSqOyroXCjARGaOI+bM0cns6rP57H1vI6UuKjKc5OIjFO++iFivFDU3jxB3PZXFZHfFQkxTmJOnSHGrCDbhGMMb8XkXeBXOAtt0GMI7DG+lUBoLSmiWQvbtyT4qLYVl7ntc9T/pXuimH2iIy+F1RBLy8tXm/fCkPDMlwMy9CET7gamhrfOeGfCi+JcVFM1Ts7lBdERUYwZkiSDhUTxFyxUUwp0O2BsgzLdDFMLwaGrPy0BPJ7GQpOqf7oM1tojPnMQ9l634SjDsXe6gZSvDjGb1JcNPvrdKgHpZRSSimllFJKKaWCld4DEAL2VDcxyou39yTFRlFR3+K1z1MDV1bTyKqSasrrmhmR6WL80GSfjAulAtOWsjpWl1SBCOOGJOnte2FmS2ktq/fUAGj9h4HK+mZW765mT1Uj+WnxjM9N0Vs0w0hJRQOrSqqob2ljdE4SY4ckIaITMYaT7fvrWV1SRUubYcyQJEbnaC/ccFfV0MyqEmu/MDQ1nolDk0mM02Gd1ODVNrayencVOysaGJISx4TcFFK8OFeQCm0llQ2sLqmitqmN0TmJjMtN1mOWARKRHwHXAiOBLGNMmV0uwJ3AyUA98D1jzFciMgZ4EogGrjTG/FdEooA3gG8bY3rO/uuBI2cWIpIOPAMMA7YCZxljKjwsdxFwk/30d8aYR+zyRVjDTzTYr33DGLPPt1EHrn3Vjcws8t6tPklxUVTWa49fp5TVNnHDiyt5a/VeAETg/86dyimThjocmfKHNburOe+Bzzt73aclRPPk5bMZl3tos8Or4LJmdzXfvf+zzotv6a4YnrhsNuNyk/t4pwpG9c2t/H3RJv7x4ebOsutPGsul84YT3c9Zv1Xw2rG/niseXcwa+0JPTGQEj102i9nDdbimcLFpXy0XPfg5OysbAWvSvicvn8PkglRnA1OOaWhu5b4PNnP3ok2dZT87cTRXzB950EmglOpLS1s7T3y+jT/+Z21n2RVHDufa40aTEKsXnNXB7ayo56rHl7ByVzUA0ZHCY5fOZk4wDzF5c8p3gT8AhcB24AZurnrS218jIjFAtDGmDvgEeBVY1G2xk4Bi+zEb+Lv980rgGqy86Z3A6cD3gcf7m/QFa6xeJ1wPvGuMKQbetZ93YSeHf431x84Cfi0i7tnN84wxU+xH2CZ9wRrj17uTu0VT3dhKe7vpe2Hldet213QmfQGMgV+/vIrdVQ0HeZcKFa8uK+ky1EpFfQsvfl3iYETKn15auqvLHRf765p5ZZnWf6jatK+2S9IX4I4317G1TMfZDwdfba/oTPoCNLe1c/ub66hranUwKuVPH28s7Uz6AtQ1t/HPj7fQ2tbuYFTKSZtK67jng01dyv7y9gY2l9U6FJEKFVvK6rj9zXVdyu77cAubSrVtqb4t21HZmfQFaGkz3PqfNdQ0Bumd4lbS936gCBD75/12uVeIyDgR+TOwDhgNYIz52hiz1cPiC4BHjeUzIFVEcoEWIMF+tIhIKnAq8OhAYnEq8bsAeMT+/RHgNA/LnAi8bYzZb/cGfhv4pn/CCx7GGMpqm706q3dkhJAQE0llQ5CuxEGuoqFnb+vyumbqm9ociEb522q3JABAbFQEa3ZX97K0CjUb99bQ/Y6pNSVa/6GqqqFngk8Eqhs18RcO9tU09ijbVlZPfbPWf7jY3O0iT2xUBBv21tDUqonfcFXd0ILp1vemrd3ofkENWk1jK60eOnZ5OhZRqrvS2p45im3l9dQ3B22O4g9YyVR3CXb5IRMRl4hcLCIfYyWWVwOTjDFf9/HWPGCH2/OddtndwA1YedM/AL8E/mCMGdCBglN9+nOMMbvt3/cAOR6W6e0P7/CQiLQBL2ANA+Gxe6qIXAFcAVBYWDjYuANOrd0rJD7Gu+O/psRHs7+uiXSX9xLKTgjG+h+R6SIqQjp3zCJw08njWFVSxUcbyxifm8yk/BTionXM34MJxroHOG3KUN5fu49jxmQzvSiNqoYWxg5JYl9NI9lJcU6HFzSCrf4bW9pYsbOKmcMymJSfSnVjKw9+sgVj4LSpeX1/gOoiWOq/MCOBlPhoqhpayE2J44I5RTQ0t7FxXw0p8VGMytaxPg9FsNT/YXmpPcrOmJ5PVITwwfp9bC6toyAtgckFKWTp9r/fgqX+AY4anc0jn25j9vB0jhqdRUV9CyMyXVQ1tODSW68PiZP1v7uygaU7K9lT1cjonCQm56cOeMz2wowE0l0xXe7+yk2JoyA93tvhhqRAXP937K9n2Y5KyuuaGefgeVxBWjx5qfHsqjxwF2lKfDSFGd1zX8EpEOveH9buqWb5jirajWFSfgrjh/pmeMDxQ3oek54xPZ+sxFiffJ8f9NZIBtt4dgPLgcuMMWv7WrgvxpjtwNEAIjIKyAfWiMhjQAzwS2PM+r4+x2dHFCLyDjDEw0s3uj8xxhgRGeiYAucZY3aJSBJW4vcCeunqbIy5D7gPYMaMGSE3dsHeat8kZ5PjoimvbWZUttc/2q+Csf7HDEnmgYtm8MuXV7JjfwO/PHk8z3+1g9W7D/QE/ds5U/j2FE0GHUww1j3AvOJM/vidiazYVcUdbx24Heu0KUO5ZcFEkuN1Aob+CLb6f3v1Xn781IELwcXZiVx6xHDSXTHML850MLLgFCz1X5iewEPfm8kvX1rJ6dPz+eN/1tDSZoWb7orh6cvnMNrDgbY6uGCp/0n5KfzlrMn87rU1VDe0cNbMAr47u5AHPt7C3e8fuNX7O1PzuGXBBJJ0cqd+CZb6B5hRlModZ05ic2kdf3K7BXvuyAzuPGcqWUlBe0LtGKfqv6ymiZ89v5yPN5Z1lv3m2+O58PBhA5r8KD8tgX9eNIObXlrJqpJqphakcsuCCQxJ1sRvfwTa+r+rop4rHl3Cmj0H7t6669ypnDLZ/3O3ZCfH8Y8LpvPrV1axZFsF43OT+d1pEylMD43Eb6DVvT+s2FnJufd/fqAzYHQkT1/hm3HiJxWkcOc5U/jtq6upqG/hjOl5XHj4MCIignZyt+1Ywzt4Kh+MM4BLgX+JyNPAI8aYbf143y6gwO15vl3m7vdYc6BdDTyANe7vH4Dz+vpwnyV+jTHH9/aaiOwVkVxjzG573ApPY/Tuws5s2/KxB0A2xuyyf9aIyJNYYwAPaIyLULG3upF0l/dPBJLiorpcaVb+ExkhHD0mm5d+cAR1Ta2s3VPTJekL8NtX1zBnRAbZydoDKNRkuGKZVpTGTS+v6lL+0tISLpw7jGmF3pvIUQWG0ppGbnl1dZeyDftq+ckJxZw0MVdnyw1x04rSeOzSWfy/55Z1Jn3BGt/5o41lmvgNYQkxUSycls8RozJpbm1nSHIcm8tq+fuiruN7vvj1Li48vIipuv0POcnxMcwansGvuu3zP91Uzrq91WQlZTkUmRqotXuruyR9AW57Yx3HjMmmMMM1oM+aWpjGk5fPpqq+hbSEGJL0on/QWlVS3SXpC3DLq6uZPSLdkTs5Jual8PDFM6moayYlPpoULw4XqfzvlWW7O5O+AA0tbTy7eIdPEr9x0VEsmJLHnBEZNLW2k5scF+wTTt6ANRSD+5WPerv8kBlj3gLeEpEM4HzgZREpw+oBvPUgb30F+JGdLJ4NVLmNkoCIHAWUGGM2iEgC0G4/+nXlxqmaegW4yP79IuBlD8u8CXxDRNLsSd2+AbwpIlEikgkgItHAKcBKP8QckPZUNXp1fN8OyfFRlGni11EZibEUZrg8jptTUd+s47+FsIaWdto8jMGlE/6EpqbWdio8bG+bWo0mfcNEbHQEuyt7jve6r7pnmQo9OclxFKQnEB0VQUNLG57m1q3TcX9DVlNLm8djvbrGoB03MSx5moujvrntkI/XU+JjKMxwadI3yHk6dt9f5+x5XFJcNIUZLk36hoAdFfU9yrbv71nmTTnJcRTaxyxB7eaqJ4HLgW2AsX9ebpcPmjGm3BhzpzFmClYyuQ1ARK4WkZ1YHVuXi8gD9lteBzYDG7ES0j/o+CyxTghvAn5rF90H3Am8BtzRn3icGjzqVuBZEbkU6x98FoCIzACuMsZcZozZLyK/Bb6033OLXebCSgBHA5HAO1j/mLC0t6aRFB8cECTGRrPfwwDeyv+KcxKJjpQuPcHOnFHAEO3tG7IK0xMYn5vMardJ3TITYxieObAeIyo45CTHcfbMAp74/MCdRdGRQnF2ooNRKX9yxUZz4dwibnyx63Xso8Zob79wU5iewNghSax1m+gzKzGW4Rm6PQhVQ9PiOXZsFu+tLe0sS4iJZGS27vODycisRBJiIrsk8Y8dk8XQNB2iIZwV5yT1OI87Z2YBOXoep7xg4dQ83li5p0vZOTMLella9WAleb2S6D0YY8wXbr//Dfibh2UM8MNe3m+AE9yerwGmDSQGRxK/xphy4DgP5YuBy9yePwg82G2ZOmC6r2MMFrsrG0nz0Ri/pR5mm1b+N25IMo9eMotb31jLtvJ6zpyez4WHDwv+q2yqV+muGO48Zwp3vb+R99ftY3phGv/vG2PITwuNMbhUV9GREVx11EhcsVE8t3gHhekJXH/SWMbnJjsdmvKjE8cPoaG5jX98uJnE2Eh+duJYpvrgVj0V2NJdsfzt3Kn833sb+GB9KTOK0vl/J4wmT5NHIcsVE8UvTxlPbsoWXl2+m7FDkvj5N8fq5I5BZmR2Io9fOpvb3ljL2j01fOuwXC6fPxxXjE7SF87G5ybzyCWzuO0/a9m+v54zZxRwwZwioiP1PE4N3pwRGdxx5iT++s4G2tsNPz6umHmjtNOA6kn3REGupLKBw/K9P3NjSnwUa/fW9L2g8rmICOHwkZk8fulsGprbyEyMDeZB1FU/Feck8aczJlFZ30JyXDTxMf6f/Vf5T0F6Atd/cyyXzRtOfEykTuIUhjKTYrls/ghOm5JHVKSQqrdghq3ROUncceZk3f6HkeGZifzm2xO4+rhiEmOicMXpKVowmlaUxoPfm0ltUyvprhhN7ikiIoS5IzN5/DI9j1PelxwfzRnTCzhmTDYCpCfqhKDKMz2qCHK7qxo52ge3gqbER1OuQz0ElKS4aE0GhZnYqEhykvWEP1xERIhO2KjITNKDdqXb/3AUFRmht3+HAFdsFK5YPcVWXel5nPKlDE34qj7oZcggt6e6kXSX91f0lPgYymqbvP65SimllFJKKaWUUkop39PEbxBram2juqGFVB9M7paSoD1+lVJKKaWUUkoppZQKVpr4DWJ7qhrJTIzxyThBrphImlrbaGxp63thpZRSSimllFJKKaVUQNHEbxDbVdlApo/GcxGxJpbR4R6UUkoppZRSSimllDp0IvIjEdkoIkZEMt3KRUT+Zr+2XESm2eVjRGSJXXa4XRYlIu+ISEJ/v1dHng9iOysafDqQd3pCDPtqmshP63d7UkoppZRSSimllFIqYB32yGHfBf4AFALbgRtWXLTiycF8poikGWMqDrLIJ8CrwKJu5ScBxfZjNvB3++eVwDXAVuBO4HTg+8Djxpj6/salPX6D2K6KetJdMT77/JSEaEprtMevUkoppZRSSimllAp+dtL3fqAIEPvn/Xb5YCwWkSdE5FgR6TEmqzHma2PMVg/vWwA8aiyfAakikgu0AAn2o0VEUoFTgUcHEpT2+A1iW8vryUmO89nnp8ZHs6+60Wef76RdFQ3sqqwnzRXD8AwXUZF6DUQNzo799eyuaiDdFcvwTBeRPhh7Ww1eS2s7m8vqqG5sIT8tntyUeKdDUg4qr21ia3k9cdERDM90kRCjh0Xhavv+evZUWXdSDc9w+WT+BBWYOvbfGfb+W+te9aaptY3NpXXUNbVSmJ5Atg/Pw1TgM8awtayO0tomcpLiKMp0OR2SChA7K+opqWwgzRXDiMxEPS8MTH/ASqa6S7DLB9PrdzRW790fAXeLyGPAw8aYkj7elwfscHu+0y67GyvJG4vV+/eXwB+MMe0DCUrPcILYjv31HJaX4rPPT4mPZm916PX4/XLLfq58fAn765qJiYzgV6eO54zp+cRFRzodmgpSn20q56onllBZ30JsVAS3LJjId6YOJSZK21QgqWtq5cnPt3PbG2tpbTdkJ8Vy34XTmVKQ5nRoygEb9tZw9VNfs2ZPDQDnzynkmuOKyUrSE/lw89GGUn745FdUN7QSGxXBrQsP45TJQ4nWi8Ih7+ONZfzgiSWddf/HhYdxqta98qCyvpn7PtzMvR9sot1AUUYC954/nXG5yU6HphzQ3m54c/Ue/ufZZdQ3t5EYG8Vfz57C8eNznA5NOezzzeVc9fgSKupbiImM4DffnsDCaXnEaq4h0BQOsLxfjDFtWEM5vCoiWcAfge0iMtcY88UhfN524GgAERkF5ANr7IRyDPBLY8z6vj5Hj2qC2M6KBrJ9eIKalhBDSVWDzz7fCaU1Tfzk2aXsr2sGoLmtnZteWsn6vTUOR6aC1d6qRq555msq61sAaGpt5/p/LWfDvlqHI1Pdrd1Tze9fX0NruwFgX00Tv/jXCirrmx2OTPlbS1s7D36ypTPpC/D4Z9v5enulc0EpR+yqaOCap5dS3dAKWNvwnz6/nE2lug0PdSWVDVzz1Ndd6v5nzy9nk+6/lQcrdlVxzyIr6QuwrbyeO95cR0Nzm7OBKUdsLa/j2qeXUm/Xf21TK9c8/TVby+ocjkw5aV91Iz95ZikV9nlhc1s7N7y0Qs8LA9P2AZb3m4ikiMiVwCtYY/ZeAizv4227gAK35/l2mbvfAzcBVwMPAD8Hft2fmDTxG6QaW9qobGghw4dj/Ka5otlTFVpDPZTVNrGzomcye1dlaCW4lf/sq23s0TPeGOuEUgUWT+v+mt01nReCVPiobmhh0brSHuWrd1c7EI1yUmltU49tQFu7YXdlaB3/qJ5Ka5oo91D3odbpQXnHjv0959D5ZFOZXjwOU7urGmlq7XqndV1zG3trdN8Rzkprmyjplj/R88KAdQPQfcNeb5cfMhF5HPgKGA5caIw5yhjzqDGmr43DK8CFYpkDVBljdrt97lFAiTFmA9aQFO32o/twFR5p4jdIbd9fT05SrE/HIUt3xbI3xMb4zXDFkJvSs5d0brKO86kOTWZiLFmJsV3KRGCIjvsWcDyN5zsyy0Vqgu8uoKnAlBwXzdyRGT3Kx+QkORCNclKGK4aU+OguZRGCT+dQUIEhIzGG1ASte9U/Q1N7HkPMLEonuVsbUuEhOymWmG5DwsRHR/Y4J1DhJSMxhpzknm1AzwsDz4qLVjwJXA5sA4z983K7fDCeBcYYY663k7RdiMjVIrITq0fvchF5wH7pdWAzsBFr0rkfuL1HsHr6/tYuug+4E3gNuKM/QWniN0htLq31mMD0pnRXTMiN8ZudHMefz5xMYqw1vHWEwI0nj2P0kESHI1PBKjclnj+fNZmEGGvcpsgI4denjKdYE0gBZ3xuEtceX0zH/Kop8dHcdvok0n1454QKTNFREVx+5AgK0w+cyC+YMpRpRTrec7gpSE/gL2dNJt4eey8qQvj9aYcxKlsn6Ql1+WkJ/O9ZU7rU/W9Pm8ioLD0mVD1Nyk/hwsOLOp9nJ8Vy3UljcemkoGFpRFYifzpjUmfyNzYqgjvOnMRwneAtrA1JjufPZ07B5XZe+KtTxmuuIUCtuGjFkysuWjFsxUUrIuyfg036Yox5xRjTepDX/2aMyTfGRBljhhpjLrPLjTHmh8aYkcaYw4wxi93eY4wxJxhj9tvP1xhjphljJhljPulPXI7sqUQkHXgGGAZsBc4yxlR4WO4NYA7wsTHmFLfy4cDTQAawBLjAGBNW99lsKq3zeY8EV0wkbe3t1DS2kBQXOlez547K5LWr57GzooF0VwwjMl062LoalPnFVpsqqWwkI9FqUzqxW+BJjIvmqqNGcsL4HKrqWyhIT6AgvV93x6gQNHZIMs9dNZctZXXERUUwMjsxpPZ1qv+OHZvNa1fPY3dVI1lJsQzPdOnkXmHi6DFZvH7NfEoqG8hMjGFEVqLWvfIo3WUles+Ynk9dUxvDMhLI9dALWIWHyAjh1MlDmTA0mX01TeQkxzEi04WI7+7GVcFhXnEmr109n12VDWS4YhiRpeeFynlOXaK8HnjXGHOriFxvP7/Ow3K3Y41ZcWW38tuA/zXGPC0i9wKXAn/3ZcCBZv3eGo+3LXuTiJCVFEdJZSNjhoTWyXBRhouiDL0iq7xDRBiemcjwTL2aG+jioiOZMDTF6TBUgMhJjtPbuhUiwoisREZoT8+wY+2/XdpLT/WLKyaKSfmpToehAkRkhFCck6R3+akehmW6GKb7FRVAnLqkvQB4xP79EeA0TwsZY94FatzL7PEtjgWe7+v9oWzD3hry03x/lTkzMUYHI1dKKaWUUkoppZRSKsg4lfjNcZuhbg+QM4D3ZgCVbuNm7ATyeltYRK4QkcUisri0tOcM3sGord2wuayOPD8kfjMSY9hZ0XMW22ARivWv+kfrPrxp/Yc3rf/wpvUf3rT+w5vWf3jT+g9fWvdK9c5niV8ReUdEVnp4LHBfzhhjsGbR8wljzH3GmBnGmBlZWVm++hq/2lpeR2p8NAl+mEwgwxXLtv3Bm/gNxfpX/aN1H960/sOb1n940/oPb1r/4U3rP7xp/YcvrXuleuezzKEx5vjeXhORvSKSa4zZLSK5wL4BfHQ5kCoiUXav33xg1yDDDSqrS6r9NmZMdlIsa/ZU++W7lFJKKaWUUkoppZRS3uHUUA+vABfZv18EvNzfN9o9hN8HzjiU94eCZTsrKfTTTPQ5yXFsKw/eHr9BqbUZqnZBU03fy6rw094O1SVQX+F0JMrbavZAXZnTUShfam2C6l3QVOt0JCpQtDRqm1C+1dxgHVc21zkdiQo0deVQvRuMz26+VYGovc06l2iodDoSFa70nMfvnEr83gqcICIbgOPt54jIDBF5oGMhEfkIeA44TkR2isiJ9kvXAf9PRDZijfn7T79G77CvtlUw0k8zTw9JiWPH/gaMHhD4R9kG+PfVcPcseOw7sP0zpyNSgaRyO7zza7hnDjx4Iqx/E9panI5KDVbNXvjoL/D3w+H+Y2DF83qCHopK18JL34e7ZsKTZ8HOxU5HpJy2bw28eIXVJp46B3Z95XREKtTsWQnPfw/umgHPXAi7lzsdkQoELY2w+hV44Djr2GPRrVYiUIW+iq3w5g3WueZD34JN71uJYKX8oWYvfPy/es7jAEcSv8aYcmPMccaYYmPM8caY/Xb5YmPMZW7LzTfGZBlj4o0x+caYN+3yzcaYWcaYUcaYM40xTU78HU5obm1nze4aRmX7J/GbEBNFXHQEe6ob/fJ9Ya2xBl7/OSx7CpprYeeXVvK3dJ3TkalA0N4OXz4An/4NGqugbJ2VKNi9zOnI1GCt+Te8+xuo328l91+41Fr/VehoqISXfggrX7AOcLd9Ao8vhPLNTkemnFJXDs9fBqtfttrE1o/gidOhYpvTkalQUbMXnjkP1r8BLfWw6R146myr968Kb7u+gmcvgIot0FABH9wKy591Oirla60tVkeDz++17izdtxKeOAP2rHA6MhUu1rwC79zc9Zxnh57z+INTPX7VIVqxq4qhqXF+mditQ35aPJtL9UqMz1XvhM3vdS1rqbd6AStVuweWPNy1zLTD3lWOhKO8pKkGFj/Qs3zTez3LVPCq3A67uvXwbayC/RudiUc5r3KrddLtrn4/7N/kSDgqBFVssXr3uasuscpVeNvxec+yxf+0Lkip0FW7G5Y+0bWsvVU7GSn/aKqxtjPddc9/KJ/QxG+Q+WRjGWOHJPv1O3NT4lm/V8eb9bmoeIjxMGlfbJL/Y1GBJyoeEof0LI/z7/ZAeVlkDKQU9SxPHur/WJTvRMdDVGzP8hj/3L2jAlCMCyI8XMTXNqG8JcYFIp7LVXhzZfYsS86D6Dj/x6L8JyoOXFk9y/VcU/mDnvM4ShO/Qeb9tfs4LC/Fr9+ZlxbP6pJqv35nWEorguNv6Vo26gTIHu9MPCqwJKTBib/rehKXOQaGTnMuJjV4UbEw71rrYKhDYjaMONqpiJQvpI+AY27qWjb+NMga60g4KgCkj4SjrutaNukcyBrjTDwq9GQUw+E/7lo26wrIGO1MPCpwFB4OKYUHnkdEwTE36EWBUJeYDSfd1rUsdyrkTnImHhVeejvnGX60QwGFF/+NF6AGbX9dM+v31nDt8f49YCtKd/Hckh1+/c6wJAKTz4HssVC6HpKGwNCpkOjhyqwKTyOOgUvehn2rrZ6+uVOtCwYquBXOgcvesSbdiY6D3CmQWex0VMqbIiJh+vcgdzKUb4LkXOuiTUK605Epp0RGW0m4/FmwfzOk5Fn7/Dj/XtxXISw6Dub9xDp2qNgKaYXW/iVWk3thL3MUXPgylHwNLXWQc5gm/8JF8Tfhkresc4mEdGu/k5LvdFQqXOg5j2M08RtE3lq1h0kFqcRE+bejdlFGAptKa2lpayc6UjuJ+1RsIgybZz2U6i4yGgpmWg8VOkSshGDuZKcjUb4UlwwjjrIeSgHEp8LIo62HUr6QkA6jjnU6ChWIMkZYDxVeomOhcLb1UMrf9JzHMZrFCyIvfr2L2cP83zsoLjqS3JR41uzW4R6UUkoppZRSSimllAoGmvgNEjsr6lmzu5qphWmOfP+o7ES+2LLfke9WSimllFJKKaWUUkoNjCZ+g8STn2/niFGZfh/mocPYIUl8srHMke9WSimllFJKKaWUUkoNjCZ+g0BjSxtPf7mD48flOBbDxLwUvti6n6bWNsdiUEoppZRSSimllFJK9Y8mfoPAc4t3MCLLxdDUeMdiSI6Lpijdpb1+lVJKKaWUUkoppZQKApr4DXBNrW3c/f4mvj1pqNOhMHt4Os8v3ul0GEoppZRSSimllFJKqT5o4jfAPfrpVvLT4inOSXI6FOaOyuTDDWXsrW7sc9kPdnzAhf+5kFNePIVbP7+V6uZqP0SolFJKKaWUUkoppZQCTfwGtF2VDdz9/ibOmVXodCgAJMZGceToTO55f+NBl3ts9WP85r+/4YihR3DxhIspqSvhvNfOo7Kx0j+BKqWUUkoppZRSSikV5jTxG6AaW9r40ZNf8c2JQ8hzcGzf7r49OY+Xlu5i474aj69/uedLHljxAD+f+XOm5UyjMLmQC8ZfwNj0sVz30XUYY/wcsQ/UlsLeVVC9x+lI1KGq2WvVYe0+pyNRoaKu3N4ulDgdiTqY9nYo3wyla6G53ulolL+1NkPpeijbAK0tTkej/K25Dvathf1bIBSOR9XA1FdY++kqHbYuLOmxf3ir3AF7V0NDpdORKOUIRxK/IpIuIm+LyAb7Z1ovy70hIpUi8mq38odFZIuILLUfU/wSuJ9sLq3l/Ac+xxUTxamTnR/b111KfDQLp+XzP88uo7WtvctrzW3N/OqTX3H+uPPJiM/o8trC4oXsrt3Nq5u7VGXw2fEFPPgN+PtceOAY2LxITx6CzZYP4YFjrTr85wmw/TOnI1LBbtcSeOgkq03dfwxseMdKMKrA0lAJ/70L7p0Ld8+GF6+yEkAqPFSVwJs3wN/nwD1z4N2brUSACg/lm+D5S+Ce2da2+vN/QKMOQxY29qyAR79t1f0/joTVr0CbXvwJG1s/hgeOs8/fToBtnzodkfKX1mZY+SL8Yz78/XB4bCHsXel0VEr5nVM9fq8H3jXGFAPv2s89uR24oJfXfmaMmWI/lvogRr8pr23i3g82cdGDXzDvtvdYeM+njM5J5PtHjSRCxOnwejh+XA4G+L/3ug758MSaJ8hOyGZK9pQe74mKiOLccefylyV/oaG1wT+Belt1CTx7AezffOD509+F/ZucjUv1X/lmq846entUbIVnzrOuAit1KGr2wnMXQ9k6+/keeOa7UL7B2bhUTzu/hLd/CS32PmjNy7DkYU3Sh4sNb8GX90N7G7S3WhcBNr/ndFTKH9pa4bN7YP0b1vOWenjjOij52tm4lH80VMLLP4I9y63n9eXw3EXWnR8q9O3fah/728f6lVv12D+c7FsFL1wMDRXW85Il8Or/6IU/FXacSvwuAB6xf38EOM3TQsaYdwHPYwoEotpS2PZf6yryiudh7WtWT7Cm3v+E15bv5rg/f8AXW8qZWpjKtceN5u7vTuPUyXlERARe0hcgQoQrjxzJY59t47PN5QBUNVXxzxX/5PTi03t936jUUYxIGcHjqx/3V6jeVbXTSuq4a66Dyu3OxKMGrmpHz/Wxrkxv+1OHrnoXVG7rWtbaBBXbPC+vnLNzSc+ylc9Dw37/x6L8yxhY8WzP8lWv+D8W5X91ZbDqXz3LtddXeKgugd1Lu5YZe9gfFfqqtkNjVdey+v16/hYuPA3ts+MzqNntTDxKOSTKoe/NMcZ0rG17gJxD+Izfi8ivsHsMG2OaPC0kIlcAVwAUFnp5kjRjrFv/lz8N69+CpmpILYS4VIiIgrYm66py1Q5IGw5jT4HJ50DGSAAe+XQLd72/iZ9/cyzDM13ejc3H0l0xXD5/BFc/9TWvXT2fx9c9yJTsKeQm5h70faeNOo3bvriNs8acRUpsis/j9Gr9x6dBVBy0Nrp/ASRk9P4e5RiPdZ+QDhJhHfB3iIyx6laFFJ9u+93FpUCMy7oI5E63C47yWP8Zw3sumDMJYhL9GJnyhx71LwIFs2DbJ10XzJ/hQHTK13rUf1wSZE+ArR91XTAl34HolK/1rP8UcGVBXWnXBV1ZDkSnfK1H/cd7OvaPts4JVEjxeOznyuy5YFIu+CEPoVQg8VmPXxF5R0RWengscF/OWLN9DXSQ1F8AY4GZQDpwXW8LGmPuM8bMMMbMyMry0g6+vR1W/ssaJ+iFS61bBo/+BZz9BHzzVjj6ejjyp3DMjfCtv8A5T8G0i6xbfx84Dh4/neff+oC73t/ETSePC7qkb4cpBanMK87k8kf/y7PrX+DUkaf2+Z4hriFMy5nG35f93Q8Rern+00fCSbd3LTvmJsgcM7jPVT7hse4zRsPxN3dd8MQ/QsYov8enfMsn235P0kdY23n3YXmO/BlkjfXdd6o+eaz/wsMhf9aBheJS4KifQXScM0Eqn/FY/5POhuS8AwulDoNxfR+3qODTo/5jXHDcr7te5Bl2JORp4j8U9aj/lDw49a8QEXlgoRmXQc4Ex2JUvtOj/jNHwwm/7brQN36vx/4hyOO+P2ciTL3wwEIRUXDqnZA8xJkglXKIz3r8GmOO7+01EdkrIrnGmN0ikgsMaHpNt97CTSLyEPDTQYQ6kC+Gda/DO7+xDh4mnmH1FpE+8ucRUdbBRc4EmHI+b372Nb9/fw835i4muyUJCN4dz+lT8/jR8ytJ5xLSYvvXa3LByAX86tNfsbB4IaPTRvs4Qi+KiIBJZ0HuYda4UEm5kD1OkwbBJDoWZl4GRUdYt/6l5Ft1GOnUzQ8q6InAhIWQNc4a8iExB3LGQ2xwXtALaamFcPbj1qzerQ3WRbvM4N3/qgHKHgeXvAH71ljrbdY4SC1wOirlLwUz4YpFULoOYhOt+k86lBsOVVAq/iZc8aE1T4cr0+oBHq89/sJCVAzMuAQK53Q79o92OjLlDwnpcMIt1l3XDfshbQRka+cMFX6cyna8AlwE3Gr/fHkgb3ZLGgvW+MC+HaSrvR3W/wcW3Wrdzjvlu1avoUOYeO3VbRH8cuN4fjo3irzadHj7V1bPsMnnWFckg8ynuz/Blf0xDWULeOiDWr53VGKfE9IlxyazsHgh1314HU9+60nio+L9FK0XRMfB0KnWQwWnGJfe3qu8KyoGhk62HiqwJeVosiecpRZaDxWeMouthwo/kVEwZKL1UOEnJkGP/cNZQhoMO8LpKJRylFOJ31uBZ0XkUmAbcBaAiMwArjLGXGY//whrSIdEEdkJXGqMeRN4QkSyAAGWAld5PUJjoHwjrH4ZvnrUGtt14kLrVtG+evh6UN1k+MviRl7b1Mp1s+MoSomA9DmQNw12fAnv3gKJWVB8IhTMDooxR7/Y8wXPrnuOs8acReLYRJ75rJ4/vFTFlcclkZUcedD3zs+bz6bKTfzo3R/xl6P/4pfxfpVSSimllFJKKaWUCheOJH6NMeXAcR7KFwOXuT2f38v7j/VKIO1tsPIFqNgKTTX2RGy7oHy9dSsIWLeCjjnJ+ili3SLUD1XNwuOb4tlcG8nX5dFsro2iyNXKpSMbaKsxbK5xWzhqJIwYDpVbYcnb8MlzVnliNiRkWbciRSdAVKw1Jp2DPYM3V21iRekK1laso6mticNzj2FPRRtQytSR8PXmKH7wYDNJ8e2MyGknPckwZ0wr6Uk9h3E+Mv9Inlv/HPOenseMnBnMyp3FheMvxBWtt0grpZRSSimllFJKKTUYYs2tFh5EpBSrhzEA+UkSteP/JXm8N7e22bTvqm5vOtTv+jJySsRN8b+I7Xie2l5Fqqns1z9bBGIjPQ8k0diGWV8R2TjQeIxpjxKJaB3o+7qLzoiIjYi1JgU0rXhuPiYWTEZn+BFxL7dExS3q9bujM6PjIqIjBGDL7VvW1q2qq3N7ucwY883Bxg09699LMoEyL3+mtwRqbAOJyyv174O6D9T/bYdQiS9Q678vgfb/D6R4Am39D6T/zUAFc+zQd/z+Wv+D+f8YyrH7uv6D+X/Xl2D/2wLx2D/Q/qehHE+g1X+g/a8hMGMC78QVDMf+gfT/D7VYvLb+q67CKvHbHyKy2BgTcoMAherf5bRA/r8GamyBGtdABPrfoPE5K9D+vkCKJ5BigcCLZyCCOXYInPgDJY5DobEH7/f7Uij/bU4JtP+pxuM/gfi3BWJMELhxeVsg/Z0ai+qvgQ9Wq5RSSimllFJKKaWUUiqgaeJXKaWUUkoppZRSSimlQowmfnu6z+kAfCRU/y6nBfL/NVBjC9S4BiLQ/waNz1mB9vcFUjyBFAsEXjwDEcyxQ+DEHyhxHAqNPXi/35dC+W9zSqD9TzUe/wnEvy0QY4LAjcvbAunv1FhUv+gYv0oppZRSSimllFJKKRVitMevUkoppZRSSimllFJKhRhN/CqllFJKKaWUUkoppVSICfvEr4iki8jbIrLB/pnmYZkpIvJfEVklIstF5GwnYu2LiHxTRNaJyEYRud7D67Ei8oz9+uciMsyBMINOf9qIvdxF9jIbROQit/JFdr0stR/Zg4znkOtZRH5hl68TkRMHE4c3YxORYSLS4PY/utfbsQ3GANrAGyJSKSKv+imugF7n+xHfkSLylYi0isgZ/oztUAxmWyAiCSLymoistfclt7ot/z0RKXVr/5cdJAavr/99fWYf/5NDXedPEJElIrLC/nms23u8vc0MqG14P2MO2O18fwTavkDbgH8FQv0HY533RzC3i0A0mHYiXtqv28sHzL59EOuv3/brgxUo9e72voCpf2/EFUxtoUMgtIlAagfhVPdhwxgT1g/gT8D19u/XA7d5WGY0UGz/PhTYDaQ6HXu3GCOBTcAIIAZYBozvtswPgHvt388BnnE67mB49LONpAOb7Z9p9u9p9muLgBlO1zMw3l4+Fhhuf05kILRBYBiw0um6HkwbsF87DjgVeNUPMQX0Ot/P+IYBk4BHgTOcrmdvtIPetgVAAnCMvUwM8BFwkv38e8Bdvqzz3tb//nymj+KZCgy1f58I7HJ7zyK8tM0cbL35Ih4n6tnP60nA7Qu0DfivDQRK/QdbnYd6uwjUx2DaCV7Yr/uqXvvzmT6IxW/79VCo90Cs/3BsC4HSJgKpHYRb3YfLI+x7/AILgEfs3x8BTuu+gDFmvTFmg/17CbAPyPJXgP00C9hojNlsjGkGnsb629y5/63PA8eJiPgxxmDVZxsBTgTeNsbsN8ZUAG8D3/RBLIOp5wXA08aYJmPMFmCj/XmBEFug608bwBjzLlDjp5gC/f/dZ3zGmK3GmOVAu59iGqxD3hYYY+qNMe8D2P+Pr4D8AX6/L9b//nym1+Mxxnxt708BVgHxIhLbz+8dqEDahvdHIG/n+yMQt03aBvwnUOo/2Oq8P4K5XQQqp/frEFj79mDZrw9WINR7h0Cqf6/EFWRtoYPTbSKQ2kG41X1Y0MQv5Bhjdtu/7wFyDrawiMzCuvKxydeBDVAesMPt+U67zOMyxphWoArI8Et0wa0/baSv//9D9i0Nvxzkyc1g6rk/7x2MwbbB4SLytYh8ICLzvRiXNwxoO+Engb7O+7q9OcEb2wJEJBWrZ/i7bsWnizWU0PMiUtDL9/ti/R9MPXmrDZ4OfGWMaXIr89Y2EwJrG94fgbyd749A3BdoG/CfQKn/YKvz/gjmdhGonN6v9+vzGXi9Hmp9B8t+fbACod77/T34r/69FZe7QG8LHZxuE4HUDsKt7sNClNMB+IOIvAMM8fDSje5PjDFGRMxBPicXeAy4yBgTLD3UVD94q4304jxjzC4RSQJeAC7AurVdHbAbKDTGlIvIdOAlEZlgjKn2VwA+bgMqSPi6HYhIFPAU8DdjzGa7+N/AU8aYJhG5EusK+rG9fUYoEZEJwG3AN9yKB7zN1G14yDjkfYG2gZAwoPrXOlf9oft1//LWft0LcWi9OyxQ2oJbPNom/CTQ6l6FSeLXGHN8b6+JyF4RyTXG7LYTu/t6WS4ZeA240RjzmY9CHYxdgPsVpHy7zNMyO+0NUwpQ7p/wApsX2sgu4Gi35/lY49hgjNll/6wRkSexbp841I3cYOq5P+8djEOOzRhjgCYAY8wSEdmENbb2Yi/Gd1De2E74WaCv875ubz7hy22B7T5ggzHmr27f6V4nD2CNM+aJr9b/Q62nQbVBEckHXgQuNMZ03kVzKNvMINqG90cgb+f7w5F9gbaBgGkDfqv/EKvz/gjmduGYAN+vd3x+oOzbA2a/PlhBUO/u3xMo9e+tuAKqLbh9dyC3iUBqByFX9wqd3A24na4Def/JwzIxWN31r3U63oP8HVFYA4wP58Ag3BO6LfNDug7C/azTcQfDo59tJB3YgjXAe5r9e7pdL5n2MtFYY+Bc5UQ9AxPoOvD7Zrw7udtgYsvqiAVrIPldQLrTdT+QNuC27NH4Z3K3gF7n+xOf27IPExyTux3ytsB+7XdYV7gjur0n1+337wCfebvOe1v/B1JPXo4n1V5+oYfP9No2c7D15ot4fPx/9el23g/x+2RfoG3Ar5O7BUT9B1udh3q7CNTHYNqJ/dqg9uu+qtf+fKYPYknFT/v1UKj3QKz/cGwLgdImAqkdhFvdh8vD8QCcfmCNRfIusAF4x23lnQE8YP9+PtACLHV7THE6dg9/y8nAeqzxh2+0y24Bvm3/Hgc8hzXg9xfACKdjDoZHf9qI/fwS+3+7EbjYLnMBS4DlWAOc38kgD7QHU89Yt7JsAtZhzzYaCG0QawygVfa69RVwqtP1foht4COgFGjAGg/pRB/HFdDrfD/im2n/n+qwrhKvcrquvdQOPG0L8gEDrOHAfuQy+7U/2u1/GfA+MNYXdd7b+u/pM33dBoGb7Hpf6vbIxjfbzIDahvvy/3qwevbzuhJQ+wJtA+FX/8FY56HeLgLxMch24pX9uq/q1dNn+rKN4cf9eqjUeyDWf7i1hUBqE4HUDsKp7sPlIXYFKaWUUkoppZRSSimllAoREU4HoJRSSimllFJKKaWUUsq7NPGrlFJKKaWUUkoppZRSIUYTv0oppZRSSimllFJKKRViNPGrlFJKKaWUUkoppZRSIUYTv0oppZRSSimllFJKKRViNPEbJETkRhFZJSLLRWSpiMwWkUUisk5ElonIJyIyRkQiRWSJiBzp9t63RORMJ+NXh05EThMRIyJj7edHi8ir3ZZ5WETOsH/vaBfLRWStiNwlIqkOhK4Gwa7zP7s9/6mI3Oz2/Aq7fteKyBciMs/ttUUiMsP+fbiIbBCRE/36B6hBE5EhIvK0iGyyt+uvi8ho+7VrRaRRRFLclj9aRKpE5Gt7G/ChiJzi3F+gDtXB1n8RedM+Duh4lIjI5/ZrD4vIFvu4YL2IPCoi+Q79GWoQRCRHRJ4Ukc32+v9fEfmO23q+1N7PvyMi2fZ7vicipfZrq0Xkcqf/DnXo+jj271j/n7eXvVlEfmr/Hicib7sfM6jgISJt3bbx19vlW0Uk0225zvOBbuv+WhH5iVPxq8Hr7fhPRFZ2W859ve/Y/3e0m6udiV55i4jUisgwEWlw26/fKyIT7GO8eLdlXxORc52MVwU2TfwGARE5HDgFmGaMmQQcD+ywXz7PGDMZeAS43RjTBvwAuEtEou0NQLsx5jknYldecS7wsf2zv86z28okoAl42ReBKZ9qAha6H+R3sJN5VwLzjDFjgauAJ0VkSLfl8oE3gP8xxrzph5iVl4iIAC8Ci4wxI40x04FfADn2IucCXwILu731I2PMVGPMGOBqrH3Bcf6KW3lNr+u/MeZEY8wUY8wU4AigGrjJbZGf2ccFY4CvgfdEJMYPMSsvsdf/l4APjTEj7PX/HKAjif+R3QYmYW0Hfuj29mfstnE08AcRyUEFnX4c+0+xH2d0e18M8AKwxBhzsz9jVl7T4Fa/U4wxt/bzfc+47RduFJEC34WofKUfx38H8zO3dvM3nwaq/GmTvW5PAsZjHd/9C7gRrE5iQLQx5imnAlSBTxO/wSEXKDPGNAEYY8qMMSXdlvkQGGW//jnwX+Bm4A/Aj/wXqvImEUkE5gGXYp30DYgxphn4OVAoIpO9HJ7yrVbgPsBTr43rsA7uygCMMV9hXfxxP/nPBd4CbjTGvOLjWJX3HQO0GGPu7SgwxiwzxnwkIiOBRKxkX68XhIwxS4Fb0H1AMDrY+u/uTuB1Y8zb3V8wlv8F9gAneT9E5UPHAs3d1v9txpj/c1/IThAkARXdP8AYsw/YBBT5OFblG/059u8uCngG2GCMud7XAarAZIwpBzZitSEVfDwe/3Hgwo8KU8aYVuBTrJzPLcCZIjIFuJWu54BK9aCJ3+DwFlBgd+m/R0SO8rDMqcAKt+e/AK4FnjTGbPRDjMo3FgBvGGPWA+UiMn2gH2D3Al8GjPV2cMrn7gbOE7fb+W0TgCXdyhbb5R0eAe4yxjzvw/iU70ykZx13OAd4GvgIGNNHj76v0HU/WPW2/gMgIguBGVj7+4PRNhB8JmDVW2/mi8hSYDtWT9AHuy8gIiOAEVgJIBV8Dnbs/4Tb7dy3u5X/HOuCwbV+jVR5W3y3oR7OHsibRaQQiAOW+yY85WMHO/4b6d42sO74c3e72+uH+TRK5XcikgAcB6wwxtQDP8Xq/Pe0MWaDo8GpgBfldACqb8aYWjvhNx/rKuAzHeM9YR38NQBbgR+7ve1IoApr56GC17lYPbrASvScC/y7l2XNQT5HvBmU8g9jTLWIPIp1y37DAN/+DnC+iDxsHxyo0HEu8B1jTLuIvACcCdzVy7K67gepg63/IpKHtW84saNH4EFoGwhyInI31t0/zcDPsIZ6OMV+7TrgTxxIAJwt1p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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"rets['spx_thres'] = rets.SPX.apply(lambda x: '>10%' if x >=.1 else '<10%' if x <=-.1 else '')\\n\",\n \"sns.pairplot(rets, y_vars=['SPX'], x_vars=r2.keys().insert(0, 'SPX'), hue='spx_thres', aspect=.75)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Solve for Best Expression\\n\",\n \"\\n\",\n \"Now let's see how far out-of-the-money we need to strike binaries to express a short dollar view and get a 10x payout (note - you can use any ratio below). P.S. You can find more screens like this in our brand new [reports and screens folder](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/content/reports_and_screens).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.instrument import FXBinary\\n\",\n \"from gs_quant.risk import FXSpot\\n\",\n \"\\n\",\n \"payout_ratio = '10%'\\n\",\n \"\\n\",\n \"binaries = Portfolio([FXBinary(pair=cross, expiration_date='3m', option_type='Put' if cross[:3]=='USD' else 'Call',\\n\",\n \" notional_currency='USD', strike_price=f'p={payout_ratio}', premium=0, name=ccy)\\n\",\n \" for ccy, cross in pairs.items()])\\n\",\n \"with PricingContext(): \\n\",\n \" binaries.resolve()\\n\",\n \" spot = binaries.calc(FXSpot)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
option_type strike_price spot R2 realized_vol % otms % otms (vol norm)
pair
USD NOKOptionType.Put8.288.860.8218.186.590.36
AUD USDOptionType.Call0.780.740.8213.025.830.45
NZD USDOptionType.Call0.740.700.7713.035.840.45
USD CADOptionType.Put1.251.300.778.483.930.46
USD SEKOptionType.Put8.118.550.6711.015.190.47
GBP USDOptionType.Call1.411.330.7211.195.370.48
USD JPYOptionType.Put100.04104.360.188.494.130.49
USD CHFOptionType.Put0.870.910.467.434.130.56
EUR USDOptionType.Call1.251.200.537.564.350.58
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from gs_quant.timeseries import volatility\\n\",\n \"\\n\",\n \"bf = binaries.to_frame()\\n\",\n \"bf.index = bf.index.droplevel(0)\\n\",\n \"bf = bf[['pair', 'option_type', 'strike_price']]\\n\",\n \"bf['ccy'] = [x.name for x in bf.index]\\n\",\n \"bf['spot'] = list(spot)\\n\",\n \"\\n\",\n \"vols = pd.Series({ccy: volatility(row, len(row) - 1).iloc[0] for ccy, row in df.iteritems()}, name='realized_vol')\\n\",\n \"bf = pd.concat([bf.set_index('ccy'), r2, vols], axis=1).dropna()\\n\",\n \"bf['% otms'] = abs(bf.strike_price / bf.spot - 1) * 100 \\n\",\n \"bf['% otms (vol norm)'] = bf['% otms'] / bf['realized_vol']\\n\",\n \"\\n\",\n \"bf.set_index(['pair']).sort_values(by=['% otms (vol norm)']).style.background_gradient(subset=['% otms (vol norm)'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, let's plot R-sq vs how far out of the money we need to strike to get a 10x payout. The most attractive trades will be in the top left: crosses that have the highest R^2 and are not as far otm (in green). As we can see below, USDNOK stands out as the best 10x binary implementation of this cyclical theme.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(5, 10))\\n\",\n \"ax.imshow([[.5, 0], [0, 0]], cmap=plt.cm.YlGn, interpolation='bicubic', extent=[.2, .8, .1, .9])\\n\",\n \"ax.scatter(bf['% otms (vol norm)'], bf['R2'])\\n\",\n \"plt.title('% OTMS vs R^2')\\n\",\n \"plt.xlabel('% OTMS for 10x binary (vol normalized)')\\n\",\n \"plt.ylabel('YTD R-sq vs SPX')\\n\",\n \"for i, row in bf.iterrows():\\n\",\n \" ax.annotate(row.pair, (row['% otms (vol norm)'], row['R2']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What's New\\n\",\n \" * Check out [the content from our recent event](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/content/events/00_gsquant_meets_markets/01_ideas_for_risk_re_rating/) focused on ideas for vaccine risk re-rating\\n\",\n \" * *New* [Reports and Screens](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/content/reports_and_screens) content - please share your feedback on screens you'd like to see\\n\",\n \" * *New* index curve shift scenario, [example here](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/blob/master/gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/tutorials/Scenarios.ipynb)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/14-Curve Inversions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Curve Inversions\\n\",\n \"\\n\",\n \"Volatility levels have repriced lower on the back of positive vaccine efficacy news and a known US election outcome. Although tail risks are not negligible heading into 2021, we see flat or inverted term structures in several G10 crosses.\\n\",\n \"\\n\",\n \"In this note, we look at G10 calendar spread ideas to take advantage of term structure dislocations and find that EURUSD stands out as one of the most attractive curve inversions to fade using volatility swaps.\\n\",\n \"\\n\",\n \"The content of the notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Flattest calendar spreads](#2---Flattest-calendar-spreads)\\n\",\n \"* [3 - EURUSD](#3---EURUSD)\\n\",\n \"* [4 - Structure a Vol Steepener](#4---Structure-a-Vol-Steepener)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs_quant\\n\",\n \"\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one here. run_analytics scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.229.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_financial_data')) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Flattest calendar spreads\\n\",\n \"\\n\",\n \"Let's start by looking across G10 to compare how steep or flat the volatility spreads of various tenors screen. First, let's grab the data from the [marquee data catalog](). Note, there's also a [premium version available]() with additional currencies and tenors.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"from datetime import date\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"tenors = ['3m', '6m', '1y']\\n\",\n \"calendars = ['6m-3m', '1y-6m'] \\n\",\n \"end = business_day_offset(date.today(), -1, roll='forward')\\n\",\n \"start = business_day_offset(end - relativedelta(years=2), -1, roll='forward')\\n\",\n \"\\n\",\n \"vol_data = Dataset('FXIVOL_STANDARD').get_data(start, end, bbid=g10, tenor=tenors, strikeReference='delta', location='NYC')\\n\",\n \"vols = pd.pivot_table(vol_data, values='impliedVolatility', index=['date'], columns=['bbid', 'tenor'], aggfunc=np.sum) * 100\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With the data in hand, let's look at the spread and the spread ratio. A ratio of less than 1 means the vol curve is inverted because the implied vol for the shorter tenor is higher than the longer tenor. A ratio that's close to 1 is considered flat. We also look at how the spread compares relative to a 2 year history to see how significant the dislocation is in the context of recent history.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
15 Flattest Calendars
Cross Tenors Vol Far Vol Near Spread Ratio %-ile
6GBPUSD6m-3m8.959.68-0.730.9220.02
7GBPUSD1y-6m8.408.95-0.550.9419.22
11USDNOK1y-6m11.3811.80-0.420.9618.41
10USDNOK6m-3m11.8012.10-0.300.9819.22
15USDSEK1y-6m9.269.37-0.120.9910.16
14USDSEK6m-3m9.379.48-0.110.9916.00
3EURUSD1y-6m6.616.68-0.070.9920.42
2EURUSD6m-3m6.686.75-0.070.9921.03
17USDCHF1y-6m6.586.64-0.060.9912.98
16USDCHF6m-3m6.646.69-0.050.9918.81
13NZDUSD1y-6m9.599.64-0.050.9925.65
5AUDUSD1y-6m9.489.51-0.021.0030.28
9USDCAD1y-6m6.866.840.021.0033.50
4AUDUSD6m-3m9.519.480.031.0033.50
8USDCAD6m-3m6.846.800.041.0132.49
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from gs_quant.timeseries import last_value, percentiles\\n\",\n \"pd.set_option('display.precision', 2)\\n\",\n \"\\n\",\n \"spreads = pd.DataFrame(columns=['Cross', 'Tenors', 'Vol Far', 'Vol Near', 'Spread', 'Ratio', '%-ile'])\\n\",\n \"for cross in g10:\\n\",\n \" for x in calendars:\\n\",\n \" far, near = vols[cross, x[:2]], vols[cross, x[3:]]\\n\",\n \" vols[cross, x] = far - near\\n\",\n \" vols[cross, f'{x}_ratio'] = far / near\\n\",\n \" spreads = spreads.append({'Cross': cross, 'Tenors': x, 'Vol Far': last_value(far), 'Vol Near': last_value(near), \\n\",\n \" 'Spread': last_value(vols[cross, x]), 'Ratio': last_value(vols[cross, f'{x}_ratio']),\\n\",\n \" '%-ile': last_value(percentiles(vols[cross, f'{x}_ratio']))}, ignore_index=True)\\n\",\n \"vols = vols.sort_index(level=0, axis=1)\\n\",\n \"spreads.sort_values(by=['Ratio'], ascending=True).head(15).style.set_caption('15 Flattest Calendars').background_gradient(subset=['Ratio', '%-ile'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"EURUSD calendar looks relatively attractive as the market is pricing elevated uncertainty in the short term as Covid cases remains elevated in the US / EU. On the other hand, longer maturity vol is seeing large supply by European corporate players which keeps it suppressed. Let's take a closer look. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - EURUSD\\n\",\n \"\\n\",\n \"To start, let's visualize 1y-6m EURUSD vol spread vs 6m implied vol. Any value below the green line indicates an inverted curve - including the current point shown in red. Note you can use the function below to examine any cross or spread from the table above.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"def plot_ratio(cross='EURUSD', vol='6m', spread='1y-6m'):\\n\",\n \" k = sns.scatterplot(data=vols, x=vols[cross, vol], y=vols[cross, f'{spread}'])\\n\",\n \" k.scatter(last_value(vols[cross, vol]), last_value(vols[cross, f'{spread}']), color='red')\\n\",\n \" k.axhline(y=0, color='green')\\n\",\n \" plt.title(f'{cross} {spread}')\\n\",\n \" plt.xlabel(f'{cross} {vol} Implied Volatility')\\n\",\n \" plt.ylabel(f'{spread} Implied Vol Spread')\\n\",\n \" plt.legend(['Flat', cross, 'Current'])\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_ratio('EURUSD', '6m', '1y-6m')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see, 6m vol is relatively low which offers attractive opportunity to position for vega neutral vol steepeners. Let's put this together next.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Structure a Vol Steepener\\n\",\n \"\\n\",\n \"Let's put together a structure to take advantage of the 1y-6m inverted spread as well as the relatively low 6m vol. To do that, we'll look at a long a 6m6m EURUSD forward starting vol swap and short a 6m EURUSD spot starting vol swap with same vega notional.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
asset_classtypeannualization_factorbuy_sellcalculate_mean_returnfirst_fixing_datefixing_frequencyfixing_sourcelast_fixing_datenotional_amountnotional_currencypairsettlement_datestrike_vol
instrument
sell6m (FXVolatilitySwap)AssetClass.FXAssetType.VolatilitySwap252.0BuySell.Sell0.02020-12-17Daily/Business DaysWM Company LDN 4pm Mid2021-06-17-250000.0Currency.EUREUR USD2021-01-210.07
buy6m6m (FXVolatilitySwap)AssetClass.FXAssetType.VolatilitySwap252.0BuySell.Buy0.02021-06-17Daily/Business DaysWM Company LDN 4pm Mid2021-12-17250000.0Currency.EUREUR USD2021-01-210.07
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" asset_class type \\\\\\n\",\n \"instrument \\n\",\n \"sell6m (FXVolatilitySwap) AssetClass.FX AssetType.VolatilitySwap \\n\",\n \"buy6m6m (FXVolatilitySwap) AssetClass.FX AssetType.VolatilitySwap \\n\",\n \"\\n\",\n \" annualization_factor buy_sell \\\\\\n\",\n \"instrument \\n\",\n \"sell6m (FXVolatilitySwap) 252.0 BuySell.Sell \\n\",\n \"buy6m6m (FXVolatilitySwap) 252.0 BuySell.Buy \\n\",\n \"\\n\",\n \" calculate_mean_return first_fixing_date \\\\\\n\",\n \"instrument \\n\",\n \"sell6m (FXVolatilitySwap) 0.0 2020-12-17 \\n\",\n \"buy6m6m (FXVolatilitySwap) 0.0 2021-06-17 \\n\",\n \"\\n\",\n \" fixing_frequency fixing_source \\\\\\n\",\n \"instrument \\n\",\n \"sell6m (FXVolatilitySwap) Daily/Business Days WM Company LDN 4pm Mid \\n\",\n \"buy6m6m (FXVolatilitySwap) Daily/Business Days WM Company LDN 4pm Mid \\n\",\n \"\\n\",\n \" last_fixing_date notional_amount \\\\\\n\",\n \"instrument \\n\",\n \"sell6m (FXVolatilitySwap) 2021-06-17 -250000.0 \\n\",\n \"buy6m6m (FXVolatilitySwap) 2021-12-17 250000.0 \\n\",\n \"\\n\",\n \" notional_currency pair settlement_date \\\\\\n\",\n \"instrument \\n\",\n \"sell6m (FXVolatilitySwap) Currency.EUR EUR USD 2021-01-21 \\n\",\n \"buy6m6m (FXVolatilitySwap) Currency.EUR EUR USD 2021-01-21 \\n\",\n \"\\n\",\n \" strike_vol \\n\",\n \"instrument \\n\",\n \"sell6m (FXVolatilitySwap) 0.07 \\n\",\n \"buy6m6m (FXVolatilitySwap) 0.07 \"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from gs_quant.instrument import FXVolatilitySwap\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"\\n\",\n \"vol_swap = FXVolatilitySwap(pair='EURUSD', buy_sell='Sell', last_fixing_date='6m', strike_vol='atm', notional_amount=250000, name='sell6m')\\n\",\n \"fwd_vol_swap = FXVolatilitySwap(pair='EURUSD', buy_sell='Buy', first_fixing_date='6m', last_fixing_date='1y', strike_vol='atm', notional_amount=250000, name='buy6m6m')\\n\",\n \"vol_steepener = Portfolio((vol_swap, fwd_vol_swap)).resolve(in_place=False)\\n\",\n \"\\n\",\n \"vol_trade = vol_steepener.to_frame()\\n\",\n \"vol_trade.index = vol_trade.index.droplevel(0)\\n\",\n \"vol_trade\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sell vol strike: 6.96\\n\",\n \"buy vol strike: 6.57\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f'sell vol strike: {vol_trade.strike_vol.iloc[0] * 100:.2f}')\\n\",\n \"print(f'buy vol strike: {vol_trade.strike_vol.iloc[1] * 100:.2f}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note you can use gs-quant to analyze the risks of this trade using the risk package (`from gs_quant.risk import FXVega, FXGamma, FXDelta`), analyze how it would have performed historically using the `HistoricalPricingContext` and run scenario analysis (check out [the scenario tutorial](https://developer.gs.com/docs/gsquant/tutorials/Pricing-and-Risk/scenarios/) for details).\\n\",\n \"\\n\",\n \"Please reach out with any feedback and thoughts!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/15-Hypothetical Lifetime Risk Calculator.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"f2178096\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import math\\n\",\n \"import gs_quant\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.data import DataMeasure, DataFrequency\\n\",\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier\\n\",\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"7b210150-1de2-4faa-9fa4-918104cca874\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# setup client_id and client_secret for connection\\n\",\n \"GsSession.use(Environment.PROD, client_id, client_secret, 'read_product_data')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"abe77632-5046-418a-9151-797558b7cf12\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def get_vol_and_adv(ticker: str, start_date: dt.date, end_date: dt.date):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Fetch volatility and average daily trading volume for a stock.\\n\",\n \"\\n\",\n \" :param ticker: Stock ticker (e.g., 'AAPL')\\n\",\n \" :param start_date: Start date for the data (e.g., dt.date(2023, 1, 1))\\n\",\n \" :param end_date: End date for the data (e.g., dt.date(2023, 10, 1))\\n\",\n \" :return: Dictionary with volatility and ADV/ADTV\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Get the asset using the ticker\\n\",\n \" asset = SecurityMaster.get_asset(ticker, AssetIdentifier.TICKER)\\n\",\n \"\\n\",\n \" # Fetch historical close prices\\n\",\n \" close_prices = asset.get_data_series(\\n\",\n \" measure=DataMeasure.CLOSE_PRICE,\\n\",\n \" frequency=DataFrequency.DAILY,\\n\",\n \" start=start_date,\\n\",\n \" end=end_date\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Calculate returns and volatility\\n\",\n \" returns = close_prices.pct_change().dropna()\\n\",\n \" volatility = returns.std()\\n\",\n \"\\n\",\n \" # Fetch historical trading volume\\n\",\n \" trading_volume = asset.get_data_series(\\n\",\n \" measure=DataMeasure.VOLUME,\\n\",\n \" frequency=DataFrequency.DAILY,\\n\",\n \" start=start_date,\\n\",\n \" end=end_date\\n\",\n \" )\\n\",\n \"\\n\",\n \" # Calculate average daily trading volume\\n\",\n \" adv = trading_volume.mean()\\n\",\n \"\\n\",\n \" return volatility, adv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"5e5497e7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def lifetime_risk_tcm_bps( notional,\\n\",\n \" ticker,\\n\",\n \" date,\\n\",\n \" daily_vol = \\\"Optional\\\",\\n\",\n \" unwind_speed_as_pct_adv = 0.03):\\n\",\n \" \\n\",\n \" start_date = date.replace(year=date.year - 1)\\n\",\n \" \\n\",\n \" if daily_vol == \\\"Optional\\\":\\n\",\n \" daily_vol, dollar_adv = get_vol_and_adv(ticker, start_date, date)\\n\",\n \" else:\\n\",\n \" dollar_adv = get_vol_and_adv(ticker, start_date, date)[1]\\n\",\n \" days_to_unwind = abs( notional ) / ( unwind_speed_as_pct_adv * dollar_adv )\\n\",\n \" lifetime_risk = math.sqrt( 1 / 3 ) * notional * daily_vol * math.sqrt( days_to_unwind )\\n\",\n \" out = ( lifetime_risk / abs( notional ) ) * 10e3\\n\",\n \" return( out, daily_vol )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"aa9d762d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"notional = 100e6\\n\",\n \"# daily_vol = .05\\n\",\n \"# dollar_adv = 100e6\\n\",\n \"ticker = \\\"AAPL\\\"\\n\",\n \"date = dt.date(2025, 6, 9)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"abf6f445\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1594.1136536329607, 0.020907195709856222)\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lifetime_risk_tcm_bps( notional, ticker, date )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"f25fe349\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"0eff250fd2664b018372f26bb55d482d\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"VBox(children=(HTML(value='

Lifetime Risk Calculator

'), Text(value='1,000,000', description='Notional:…\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import ipywidgets as widgets\\n\",\n \"from IPython.display import display, clear_output\\n\",\n \"import pandas as pd\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"# Custom formatted text input widget\\n\",\n \"class FormattedTextInput:\\n\",\n \" def __init__(self, description, initial_value=0):\\n\",\n \" self.text_widget = widgets.Text(\\n\",\n \" value=f\\\"{initial_value:,}\\\",\\n\",\n \" description=description,\\n\",\n \" style={'description_width': 'initial'},\\n\",\n \" layout=widgets.Layout(width='300px')\\n\",\n \" )\\n\",\n \" self.text_widget.observe(self.on_text_change, names='value')\\n\",\n \" self._updating = False\\n\",\n \"\\n\",\n \" def on_text_change(self, change):\\n\",\n \" if self._updating:\\n\",\n \" return\\n\",\n \"\\n\",\n \" self._updating = True\\n\",\n \" current = change['new']\\n\",\n \"\\n\",\n \" # Remove existing commas for processing\\n\",\n \" clean_value = current.replace(',', '')\\n\",\n \"\\n\",\n \" try:\\n\",\n \" # Only format if it's a valid number\\n\",\n \" num_value = float(clean_value)\\n\",\n \" formatted = f\\\"{num_value:,.2f}\\\"\\n\",\n \" if formatted != current:\\n\",\n \" self.text_widget.value = formatted\\n\",\n \" except ValueError:\\n\",\n \" pass # Keep the current value if invalid\\n\",\n \"\\n\",\n \" self._updating = False\\n\",\n \"\\n\",\n \" def get_value(self):\\n\",\n \" \\\"\\\"\\\"Return the numeric value without commas\\\"\\\"\\\"\\n\",\n \" try:\\n\",\n \" return float(self.text_widget.value.replace(',', ''))\\n\",\n \" except ValueError:\\n\",\n \" return 0.0\\n\",\n \"\\n\",\n \"# Create a class to handle the GUI\\n\",\n \"class LifetimeRiskCalculator:\\n\",\n \" def __init__(self):\\n\",\n \" self.output = widgets.Output()\\n\",\n \"\\n\",\n \" # Create input widgets\\n\",\n \" self.notional_input = FormattedTextInput('Notional:', 1000000)\\n\",\n \" self.ticker = widgets.Text(\\n\",\n \" value=\\\"AAPL\\\",\\n\",\n \" description='Ticker:',\\n\",\n \" style={'description_width': 'initial'},\\n\",\n \" layout=widgets.Layout(width='300px')\\n\",\n \" )\\n\",\n \" self.date = widgets.DatePicker(\\n\",\n \" value=dt.date(2025, 6, 10),\\n\",\n \" description='Date:',\\n\",\n \" style={'description_width': 'initial'},\\n\",\n \" layout=widgets.Layout(width='300px')\\n\",\n \" )\\n\",\n \" self.daily_volatility = FormattedTextInput('Daily Volatility (Optional):', 0.0)\\n\",\n \" self.unwind_speed_as_pct_adv = FormattedTextInput('Unwind Speed as Pct Adv:', 0.03)\\n\",\n \"\\n\",\n \" # Create calculate button\\n\",\n \" self.calculate_button = widgets.Button(\\n\",\n \" description='Calculate Risk',\\n\",\n \" button_style='primary',\\n\",\n \" layout=widgets.Layout(width='300px')\\n\",\n \" )\\n\",\n \" self.calculate_button.on_click(self.on_calculate_clicked)\\n\",\n \"\\n\",\n \" # Create reset button\\n\",\n \" self.reset_button = widgets.Button(\\n\",\n \" description='Reset',\\n\",\n \" button_style='warning',\\n\",\n \" layout=widgets.Layout(width='300px')\\n\",\n \" )\\n\",\n \" self.reset_button.on_click(self.on_reset_clicked)\\n\",\n \"\\n\",\n \" # Display everything\\n\",\n \" self.container = widgets.VBox([\\n\",\n \" widgets.HTML(value=\\\"

Lifetime Risk Calculator

\\\"),\\n\",\n \" self.notional_input.text_widget,\\n\",\n \" self.ticker,\\n\",\n \" self.date,\\n\",\n \" self.daily_volatility.text_widget,\\n\",\n \" self.unwind_speed_as_pct_adv.text_widget,\\n\",\n \" widgets.HBox([self.calculate_button, self.reset_button]),\\n\",\n \" self.output\\n\",\n \" ])\\n\",\n \"\\n\",\n \" def on_calculate_clicked(self, button):\\n\",\n \" with self.output:\\n\",\n \" clear_output()\\n\",\n \"\\n\",\n \" # Get values from inputs\\n\",\n \" notional = self.notional_input.get_value()\\n\",\n \" ticker = self.ticker.value\\n\",\n \" date = self.date.value\\n\",\n \" daily_volatility = self.daily_volatility.get_value()\\n\",\n \" unwind_speed_as_pct_adv = self.unwind_speed_as_pct_adv.get_value()\\n\",\n \"\\n\",\n \" # Handle optional daily volatility\\n\",\n \" daily_volatility = \\\"Optional\\\" if daily_volatility == 0.0 else daily_volatility\\n\",\n \"\\n\",\n \" # Calculate the result\\n\",\n \" result, daily_vol = lifetime_risk_tcm_bps(notional, ticker, date, daily_volatility, unwind_speed_as_pct_adv)\\n\",\n \"\\n\",\n \" # Display the inputs and result\\n\",\n \" data = {\\n\",\n \" 'Parameter': ['Notional', 'Ticker', 'Date', 'Daily Volatility', 'Unwind Speed as Pct Adv', 'Result (TCM BPS)'],\\n\",\n \" 'Value': [f\\\"${notional:,.2f}\\\", ticker, date, f\\\"{daily_vol:.4f}\\\", f\\\"{unwind_speed_as_pct_adv:.2%}\\\", f\\\"{result:.2f}\\\"]\\n\",\n \" }\\n\",\n \"\\n\",\n \" result_df = pd.DataFrame(data)\\n\",\n \" display(result_df)\\n\",\n \"\\n\",\n \" def on_reset_clicked(self, b):\\n\",\n \" self.notional_input.text_widget.value = \\\"1,000,000\\\"\\n\",\n \" self.ticker.value = \\\"AAPL\\\"\\n\",\n \" self.date.value = dt.date(2025, 6, 10)\\n\",\n \" self.daily_volatility.text_widget.value = \\\"0.0\\\"\\n\",\n \" self.unwind_speed_as_pct_adv.text_widget.value = \\\"0.03\\\"\\n\",\n \"\\n\",\n \" with self.output:\\n\",\n \" clear_output()\\n\",\n \" print(\\\"Values reset to defaults.\\\")\\n\",\n \"\\n\",\n \" def display(self):\\n\",\n \" display(self.container)\\n\",\n \"\\n\",\n \"# Create and display the calculator\\n\",\n \"calculator = LifetimeRiskCalculator()\\n\",\n \"calculator.display()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/2-Levels and Tails.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Levels and Tails\\n\",\n \"\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"In this note, I lay down a framework for analyzing swap rates levels both in spot and forward space, and rolldown for G3 currencies. I find that:\\n\",\n \"* In USD swaps, the move over the last 3 months has been an almost parallel rally across the curve in spot space. But when we look at it in forward space, we find some nuances: \\n\",\n \" * 1y2y and 1y3y rallied the most as the market started pricing a deeper cutting cycle in the next 2 years\\n\",\n \" * 9y1y and 30y1y rallied more than points around them as investors use those liquid points for long duration hedges \\n\",\n \"*\\tThe curve is now overall very flat, though the belly of the curve has slowly steepened over the last 6 months\\n\",\n \"*\\tFor investors looking to go long rates, the current shape of the curve would favor going long the backend in forward space (e.g. 5y5y) as that’s where there is a slightly favorable rolldown\\n\",\n \"*\\tOn a comparative basis:\\n\",\n \"\\t* the EUR curve is lower and steeper (as front end rates are at the lower bound) making it slightly more attractive from a roll-down perspective (but with less room to rally); and\\n\",\n \" * the GBP curve lower but flatter making it a slightly more attractive hedge for a broad duration sell-off\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Spot Space](#2---Spot-Space)\\n\",\n \"* [3 - Forward Space](#3---Forward-Space)\\n\",\n \"* [4 - G3 Rolldown](#4---G3-Rolldown)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.99.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Spot Space\\n\",\n \"\\n\",\n \"Let's start by looking at USD spot swap rates accross a range of tails and how current levels compare to 3 and 6 months ago.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.risk import IRFwdRate\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from datetime import timedelta\\n\",\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"def evaluate_with_context(instruments, measure=IRFwdRate, location='NYC', pricing_date=dt.date.today()):\\n\",\n \" \\\"\\\"\\\"evaluates a measure on an iterable of instruments in a pricing context (specific data location and date)\\\"\\\"\\\"\\n\",\n \" with PricingContext(market_data_location=location, pricing_date=pricing_date):\\n\",\n \" calc = [s.calc(measure) for s in instruments]\\n\",\n \" return [s.result() for s in calc]\\n\",\n \"\\n\",\n \"def levels_and_tails(look_back_mo, fwd_points, fixed_on='Tenors', fix_yrs=1, ccy=Currency.USD, p_r=PayReceive.Receive):\\n\",\n \" \\\"\\\"\\\"constructs a swap array w/ varying tenors/effective dates, returns frame evaluated for given lookback periods\\\"\\\"\\\"\\n\",\n \" swaps = [IRSwap(p_r, '{}y'.format(fix_yrs), ccy, effective_date='{}y'.format(f)) for f in fwd_points] \\\\\\n\",\n \" if fixed_on=='Tenors' else [IRSwap(p_r, '{}y'.format(f), ccy, effective_date='{}y'.format(fix_yrs)) \\n\",\n \" for f in fwd_points]\\n\",\n \" return pd.DataFrame({'{}m ago'.format(m) : \\n\",\n \" evaluate_with_context(swaps, pricing_date=dt.date.today() + timedelta(weeks=-4*m)) \\n\",\n \" for m in look_back_mo})*100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spot = levels_and_tails((0, 3, 6), range(1, 31), fixed_on='Forwards', fix_yrs=0)\\n\",\n \"spot.set_index([['{}y'.format(t) for t in spot.index]], inplace=True)\\n\",\n \"ax = spot.plot(figsize=(10, 6), title='Spot Rates Across USD Tails')\\n\",\n \"ax.set(ylabel='Rate (%)', xlabel='Underlying Swap')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see in the graph, swap rates have come in vs 3m ago but are higher than 6mo for tails longer than 2y. Let's visualize the difference for each tenor using a bar chart.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ax = (spot['0m ago']-spot['3m ago']).plot(figsize=(10, 6), kind='bar', title='USD Spot Rates Now vs 3m Ago')\\n\",\n \"ax.set(ylabel='Rate (%)', xlabel='Underlying Swap')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see, compared to 3 months ago, swap rates have come in by ~18bps for all tails > 2y. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ax = (spot['0m ago']-spot['6m ago']).plot(figsize=(10, 6), kind='bar', title='USD Spot Rates Now vs 6m Ago')\\n\",\n \"ax.set(ylabel='Rate (%)', xlabel='Underlying Swap')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But compared to 6 months ago, swap rates are higher by ~5-10bps except at tails <3y where they are lower.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Forward Space\\n\",\n \"\\n\",\n \"Let's look at how spot space compared to forward 1y space (we can use the same function to look at any \\n\",\n \"point).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"forward = levels_and_tails((0, 3, 6), range(0, 31), fixed_on='Tenors', fix_yrs=1)\\n\",\n \"forward.set_index([['{}y1y'.format(t) for t in forward.index]], inplace=True)\\n\",\n \"ax = forward.plot(figsize=(10, 6), title='Forward 1y USD Rates')\\n\",\n \"ax.set(ylabel='Rate (%)', xlabel='Underlying Swap')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see a similiar comparative shift in the forward space - rates are higher vs 6m but lower vs 3m. We can also visualize where the best rolldown might be - more on this later. Let's first look if the changes are consistent across different tails.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ax = (forward['0m ago']-forward['3m ago']).plot(figsize=(10, 6), kind='bar', title='Forward 1y USD Rates Now vs 3m Ago')\\n\",\n \"ax.set(ylabel='Rate (%)', xlabel='Underlying Swap')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Unlike in the spot space, 1y forward changes are less consistent across tenors - biggest changes vs 3 months ago are 2y, 9y and 30y tails.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ax = (forward['0m ago']-forward['6m ago']).plot(figsize=(10, 6), kind='bar', title='Forward 1y USD Rates Now vs 6m Ago')\\n\",\n \"ax.set(ylabel='Rate (%)', xlabel='Underlying Swap')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Compared to 6 months ago, the biggest change is for 4y tails - for longer tails the change falls off almost monotonically.\\n\",\n \"\\n\",\n \"Let's now take a look at where rates are for a number of forward points and tails.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from copy import deepcopy\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"# note, if using pandas version>1.0.0, please delete result_type='broadcast' and replace last line with\\n\",\n \"# return df.apply(lambda x: evaluate_with_context(x, IRFwdRate, location), axis=1)\\n\",\n \"def construct_swap_frame(frame, ccy=Currency.USD, with_rolldown=False, carry_months=3, location='NYC', p_r=PayReceive.Receive):\\n\",\n \" \\\"\\\"\\\"constructs swap frame with option to subtract carry months from effective date and evaluates with pricing context\\\"\\\"\\\"\\n\",\n \" df = frame.apply(lambda x: [ IRSwap(p_r, termination_date='{}m'.format(tenor), notional_currency=ccy, \\n\",\n \" effective_date='{}m'.format(x.name-carry_months if with_rolldown else x.name)) \\n\",\n \" for tenor in x.index], axis=1, result_type='broadcast')\\n\",\n \" return df.apply(lambda x: evaluate_with_context(x, IRFwdRate, location))\\n\",\n \"\\n\",\n \"def styled_heatmap(frame, title=''):\\n\",\n \" \\\"\\\"\\\"styles heatmap by adding labels and centering colors around 0\\\"\\\"\\\"\\n\",\n \" df = deepcopy(frame)*100\\n\",\n \" df.columns = ['{}m'.format(t) if t < 12 else '{}y'.format(int(t/12)) for t in df.columns]\\n\",\n \" df.set_index([['{}m'.format(t) if t < 12 else '{}y'.format(int(t/12)) for t in df.index]], inplace=True)\\n\",\n \" limit = max(abs(min(df.min())), abs(max(df.max())))\\n\",\n \" plt.subplots(figsize=(10, 6))\\n\",\n \" ax = sns.heatmap(df, annot=True, fmt='.2f', cmap='coolwarm_r', vmin=-limit, vmax=limit)\\n\",\n \" ax.set(ylabel='Forward Start', xlabel='Tenor', title=title)\\n\",\n \" ax.xaxis.tick_top()\\n\",\n \" ax.xaxis.set_label_position('top')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap_df = pd.DataFrame(index=(3, 6, 9, 12, 24, 36, 60, 120), columns=(6, 12, 24, 36, 48, 60, 72, 84, 120, 180, 240)) \\n\",\n \"usd_spot = construct_swap_frame(swap_df, Currency.USD)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"styled_heatmap(usd_spot, title='USD Rates Across Tenors and Forwards (%)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Unsurprsingly, rates are lowest at the front of the curve for shorter forward starts and higher at the longer tails. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - G3 Rolldown\\n\",\n \"\\n\",\n \"Let's now look at where the best 3m rolldown opportunities might be by looking a simple roll to spot scenario - more on advanced carry scenarios in later publications.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"usd_rd = construct_swap_frame(swap_df, Currency.USD, with_rolldown=True, carry_months=3)\\n\",\n \"styled_heatmap((usd_spot-usd_rd)*100, title='USD 3m Rolldown (bps)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see, in line with the curves above, there is negative at the front of the USD curve especially for tails and forward points <2y. The best positive rolldown here is at 5y starting point across shorter tenors (<2y). Let's now compare this to EUR and GBP.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eur_spot = construct_swap_frame(swap_df, Currency.EUR)\\n\",\n \"eur_rd = construct_swap_frame(swap_df, Currency.EUR, with_rolldown=True, carry_months=3)\\n\",\n \"styled_heatmap((eur_spot-eur_rd)*100, title='EUR 3m Rolldown (bps)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The big picture for EUR is largely similar but the negative rolldown is smaller at the front of the curve and the best positive rolldown is still 5y starting but for 3y, 4y and 5y tails.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"gbp_spot = construct_swap_frame(swap_df, Currency.GBP)\\n\",\n \"gbp_rd = construct_swap_frame(swap_df, Currency.GBP, with_rolldown=True, carry_months=3)\\n\",\n \"styled_heatmap((gbp_spot-gbp_rd)*100, title='GBP 3m Rolldown (bps)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The negative rolldown for GBP is lower than for EUR but higher than for USD. The best positive rolldown is at 2y forward start for 6m and 1y tenors.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimer\\n\",\n \"This website may contain links to websites and the content of third parties (\\\"Third Party Content\\\"). We do not monitor, review or update, and do not have any control over, any Third Party Content or third party websites. We make no representation, warranty or guarantee as to the accuracy, completeness, timeliness or reliability of any Third Party Content and are not responsible for any loss or damage of any sort resulting from the use of, or for any failure of, products or services provided at or from a third party resource. If you use these links and the Third Party Content, you acknowledge that you are doing so entirely at your own risk.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/3-Systematic Selling.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Systematic Selling\\n\",\n \"\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"In this note I look at running a simple backtest where I sell a 1m10y straddle each day. I examine premium collected at inception, payout on option expiry and mark-to-market over the life of the trade. \\n\",\n \"\\n\",\n \"Look out for future publications where I will build on this strategy with added delta hedging and add analytics for understanding strategy performance!\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Create portfolio](#2---Create-portfolio)\\n\",\n \"* [3 - Evaluate portfolio historically](#3---Evaluate-portfolio-historically)\\n\",\n \"* [4 - Putting it all together](#4---Putting-it-all-together)\\n\",\n \"\\n\",\n \"Note, since this notebook was released, we have built a generic backtesting framework to support the same flexibility with significantly less code. See [how you can achieve the same results using this framework at the end of the notebook](#Generic-backtesting-framework).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.102.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Create portfolio\\n\",\n \"Let's create a portfolio with a rolling strip of straddles. For each date in our date range (start of 2019 through today), we will construct a 1m10y straddle and include it in our portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.common import Currency, PayReceive\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets import HistoricalPricingContext, PricingContext\\n\",\n \"from datetime import datetime, date\\n\",\n \"\\n\",\n \"start_date = date(2020, 12, 1)\\n\",\n \"end_date = datetime.today().date()\\n\",\n \"with HistoricalPricingContext(start=start_date, end=end_date, show_progress=True): \\n\",\n \" f = IRSwaption(PayReceive.Straddle, '10y', Currency.USD, expiration_date='1m', notional_amount=1e8,\\n\",\n \" buy_sell='Sell').resolve(in_place=False)\\n\",\n \"\\n\",\n \"# put resulting swaptions in a portfolio\\n\",\n \"result = f.result().items()\\n\",\n \"portfolio = Portfolio([v[1] for v in sorted(result)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can use `to_frame` to take a look at our portfolio and resolved instrument details as a dataframe. Let's remove any instruments with premium_payment_date larger than today.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"frame = portfolio.to_frame()\\n\",\n \"frame.index = frame.index.droplevel(0)\\n\",\n \"\\n\",\n \"# extend dataframe with trade dates\\n\",\n \"trade_dates = {value:key for key, value in result}\\n\",\n \"frame['trade_date'] = frame.apply(lambda x: trade_dates[x.name], axis=1)\\n\",\n \"\\n\",\n \"frame = frame[frame.premium_payment_date < datetime.today().date()]\\n\",\n \"frame.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Evaluate portfolio historically\\n\",\n \"Let's now evaluate each instrument for the relevant date range (inception to option expiry).\\n\",\n \"\\n\",\n \"Note that I use the async flag in pricing context - this is what makes computing 9000 points so fast (~300 instruments * ~30 days)! It sends off a request for 1 month of data for each instrument to be computed in parallel. I keep track of each future in our portfolio frame.\\n\",\n \"\\n\",\n \"To learn more about async and other compute controls and how to use them, please see our [pricing context guide](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/pricing-context/). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"frame['future'] = len(frame) * [None]\\n\",\n \"\\n\",\n \"with PricingContext(is_batch=True, show_progress=True):\\n\",\n \" for inst, row in frame.iterrows():\\n\",\n \" with HistoricalPricingContext(start=row.trade_date, \\n\",\n \" end=min(row.expiration_date, datetime.today().date()),\\n\",\n \" is_async=True):\\n\",\n \" pv = inst.price()\\n\",\n \" frame.at[inst, 'future'] = pv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now grab all the results and organize them into a dataframe. This call will wait for all the results to come back from the pool so it's as fast as the slowest single request out of the ~300 we sent in the previous step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"timeseries = pd.concat([pd.Series(row.future.result(), name=row.name) for _, row in frame.iterrows()], axis=1, sort=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Putting it all together\\n\",\n \"With the portfolio and historical PV's in hand, let's comb through the data to tease out components we want to track: premium collected, payout on expiry and mark-to-mark of the swaption.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"from collections import defaultdict\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"\\n\",\n \"def get_p(df, first=True):\\n\",\n \" p_p = df.apply(lambda series: series.first_valid_index() if first else series.last_valid_index())\\n\",\n \" g = defaultdict(float)\\n\",\n \" for i, r in p_p.items():\\n\",\n \" g[r]+=df[i][r]\\n\",\n \" return pd.Series(g)\\n\",\n \"\\n\",\n \"premia = get_p(timeseries)\\n\",\n \"payoffs = get_p(timeseries, first=False).reindex(timeseries.index).fillna(0)\\n\",\n \"mtm = timeseries.fillna(0).sum(axis=1)-payoffs\\n\",\n \"\\n\",\n \"overview = pd.concat([premia.cumsum(), payoffs.cumsum(), mtm], axis=1, sort=False)\\n\",\n \"overview.columns = ['Premium Received at Inception', 'Paid at Expiry', 'Mark to Market']\\n\",\n \"overview = overview.sort_index()\\n\",\n \"overview = overview.fillna(method='ffill')[:business_day_offset(datetime.today().date(), -2)]\\n\",\n \"\\n\",\n \"overview.plot(figsize=(12, 8), title='Cumulative Payoff, Premium and Mark-to-Market')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the above, we can see that premium received at inception varies relative to payout at expiry. We can see when this is the case more clearly by looking at the difference between the two.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(overview['Paid at Expiry'] - overview['Premium Received at Inception']).plot(figsize=(12, 8), title='Realized Performance')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But looking at only premium collected and amount paid out doesn't speak to the volatility of this strategy - let's add mark-to-market in to see that. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"p = (overview['Paid at Expiry'] - overview['Premium Received at Inception'] + overview['Mark to Market'])\\n\",\n \"p.plot(figsize=(12, 8), title='Performance Including Mark to Market')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see above, since beginning of 2019 this has mostly been a losing strategy although it has worked well 4Q19.\\n\",\n \"\\n\",\n \"Stay tuned to futher editions of gs quant for ways to modify this strategy (delta hedging, for example) and to analyze performance of strategies like this one.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Generic Backtesting Framework\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction, HedgeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"\\n\",\n \"# dates on which actions will be triggered\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b')\\n\",\n \"\\n\",\n \"# instrument that will be added on AddTradeAction\\n\",\n \"irswaption = IRSwaption(PayReceive.Straddle, '10y', Currency.USD, expiration_date='1m', notional_amount=1e8,\\n\",\n \" buy_sell='Sell', name='1m10y')\\n\",\n \"actions = AddTradeAction(irswaption, 'expiration_date')\\n\",\n \"\\n\",\n \"# starting with empty portfolio (first arg to Strategy), apply actions on trig_req\\n\",\n \"triggers = PeriodicTrigger(trig_req, actions)\\n\",\n \"strategy = Strategy(None, triggers)\\n\",\n \"\\n\",\n \"# run backtest\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# summarized results\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame({'Original series': overview['Paid at Expiry'] - overview['Premium Received at Inception'], \\n\",\n \" 'Generic backtester': backtest.result_summary['Cash'].cumsum()}).plot(figsize=(10, 6), \\n\",\n \" title='Realized performance comparison')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import DollarPrice\\n\",\n \"\\n\",\n \"pd.DataFrame({'Original series': overview['Mark to Market'], \\n\",\n \" 'Generic backtester': backtest.result_summary[DollarPrice]}).plot(figsize=(10, 6),\\n\",\n \" title='Mark to market comparison')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cash'].cumsum() + backtest.result_summary[DollarPrice],\\n\",\n \" 'Original series': p}).plot(figsize=(10, 6), title='Backtest comparison')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimer\\n\",\n \"This website may contain links to websites and the content of third parties (\\\"Third Party Content\\\"). We do not monitor, review or update, and do not have any control over, any Third Party Content or third party websites. We make no representation, warranty or guarantee as to the accuracy, completeness, timeliness or reliability of any Third Party Content and are not responsible for any loss or damage of any sort resulting from the use of, or for any failure of, products or services provided at or from a third party resource. If you use these links and the Third Party Content, you acknowledge that you are doing so entirely at your own risk.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/made_with_gs_quant/4-Delta Hedging.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Delta Hedging\\n\",\n \"\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"Being short volatility hasn't been profitable in this period of extreme implied and realized volatility movements but may be an interesting entry point for some. \\n\",\n \"\\n\",\n \"In this note I take a further look at this strategy and extend it with delta hedging to understand how it can impact performance.\\n\",\n \"\\n\",\n \"Each day I sell a 1m10y straddle (like last time) - but this time I also trade a swap with a matched effective date and termination date to hedge my delta. Each day I unwind the previous day's swap and trade into a new one.\\n\",\n \"\\n\",\n \"I examine premium collected at inception, payout on option expiry and mark-to-market over the life of the trade to compare the two strategies.\\n\",\n \"\\n\",\n \"Look out for future publications where I will build on this analysis further by adding transaction costs and analyzing performance accross strategies.\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Create portfolio](#2---Create-portfolio)\\n\",\n \"* [3 - Grab the data](#3---Grab-the-data)\\n\",\n \"* [4 - Putting it all together](#4---Putting-it-all-together)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.108.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\\n\",\n \"\\n\",\n \"use_batch = True\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Create portfolio\\n\",\n \"Just like in our last analysis, let's start by creating a portfolio with a rolling strip of straddles. For each date in our date range (start of 2019 through today), we will construct a 1m10y straddle and include it in our portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import HistoricalPricingContext, PricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.common import Currency, PayReceive\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"start_date = dt.datetime(2020, 12, 1).date()\\n\",\n \"end_date = dt.datetime.today().date()\\n\",\n \"\\n\",\n \"# create and resolve a new straddle on every day of the pricing context\\n\",\n \"with HistoricalPricingContext(start=start_date, end=end_date, show_progress=True): \\n\",\n \" f = IRSwaption(PayReceive.Straddle, '10y', Currency.USD, expiration_date='1m', \\n\",\n \" notional_amount=1e8, buy_sell='Sell').resolve(in_place=False)\\n\",\n \"\\n\",\n \"# put resulting swaptions in a portfolio\\n\",\n \"result = f.result().items()\\n\",\n \"portfolio = Portfolio([v[1] for v in sorted(result)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"I will now convert the portfolio to a dataframe, extend it with trade dates and remove any instruments with a premium payment date after today.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"frame = portfolio.to_frame()\\n\",\n \"frame.index = frame.index.droplevel(0)\\n\",\n \"\\n\",\n \"# extend dataframe with trade dates\\n\",\n \"trade_dates = {value:key for key, value in result}\\n\",\n \"frame['trade_date'] = frame.apply(lambda x: trade_dates[x.name], axis=1)\\n\",\n \"\\n\",\n \"# filter any swaptions with premium date larger than today\\n\",\n \"frame = frame[frame.premium_payment_date < dt.datetime.today().date()]\\n\",\n \"frame.head(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Grab the Data\\n\",\n \"\\n\",\n \"Now the fun part - we need to calculate a lot of datapoints for this backtest. \\n\",\n \"\\n\",\n \"For each straddle, we need to define a new swap every day and price it the following day when we unwind it. This means about 36,000 points (~300 instruments * 30 days * 4 measures (swaption price, swaption delta, swap price, swap delta)).\\n\",\n \"\\n\",\n \"Like last time I will compute as much as I can asyncrously and keep track of the futures for each measure. \\n\",\n \"\\n\",\n \"Introducing a high-level `PricingContext` to batch requests can improve speed as well. Note just using `PricingContext` will improve speed but `batch=True` can add efficiency.\\n\",\n \"\\n\",\n \"To learn more about async and other compute controls and how to use them, please see our [pricing context guide](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/pricing-context/). \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"I'll start by getting the prices and delta for the swaptions first.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import IRDeltaParallel\\n\",\n \"\\n\",\n \"# insert columns in our frame to track the futures\\n\",\n \"frame['so_price_f'] = len(frame) * [None]\\n\",\n \"frame['so_delta_f'] = len(frame) * [None]\\n\",\n \"\\n\",\n \"with PricingContext(is_batch=use_batch, show_progress=True):\\n\",\n \" for inst, row in frame.iterrows():\\n\",\n \" with HistoricalPricingContext(start=row.trade_date, \\n\",\n \" end=min(row.expiration_date, dt.datetime.today().date()), \\n\",\n \" is_async=True):\\n\",\n \" so_price = inst.price()\\n\",\n \" so_delta = inst.calc(IRDeltaParallel) \\n\",\n \"\\n\",\n \" frame.at[inst, 'so_price_f'] = so_price\\n\",\n \" frame.at[inst, 'so_delta_f'] = so_delta\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Easy enough. I will now do the same for the swaps which I will use to delta hedge. Note instead of pricing the same already resolved swaption each day, here I create and price a new swap each day which will reflect that's day's ATM rate and matches the effective date and termination date of the corresponding swaption.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwap\\n\",\n \"\\n\",\n \"# insert columns in our frame to track the futures\\n\",\n \"frame['s_f'] = len(frame) * [None]\\n\",\n \"frame['s_delta_f'] = len(frame) * [None]\\n\",\n \"\\n\",\n \"with PricingContext(is_batch=use_batch, show_progress=True):\\n\",\n \" for inst, row in frame.iterrows():\\n\",\n \" swap = IRSwap(PayReceive.Pay, row.termination_date, Currency.USD, \\n\",\n \" effective_date=row.effective_date, fixed_rate='ATMF', notional_amount=1e8)\\n\",\n \"\\n\",\n \" with HistoricalPricingContext(start=row.trade_date, \\n\",\n \" end=min(row.expiration_date, dt.datetime.today().date()), \\n\",\n \" is_async=True):\\n\",\n \" # track the resolved swap - we will need to price it when we unwind following day\\n\",\n \" s = swap.resolve(in_place=False)\\n\",\n \" s_delta = swap.calc(IRDeltaParallel)\\n\",\n \"\\n\",\n \" frame.at[inst, 's_f'] = s\\n\",\n \" frame.at[inst, 's_delta_f'] = s_delta\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the above request, we created a new resolved swaption for each day but we still need to price it the following day when we unwind it. In the below, I collect the resolved swaps from the previous request and price lagged 1 day - that is, the following day.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"swaps = pd.concat([pd.Series(row.s_f.result(), name=row.name) for _, row in frame.iterrows()], \\n\",\n \" axis=1, sort=True).shift(periods=1)\\n\",\n \"g = {}\\n\",\n \"\\n\",\n \"with PricingContext(is_batch=use_batch, show_progress=True):\\n\",\n \" for date, row in swaps.iterrows():\\n\",\n \" with PricingContext(date, is_async=True):\\n\",\n \" prices = {k: p if isinstance(p, float) else p.price() for k, p in row.iteritems()}\\n\",\n \" g[date] = prices\\n\",\n \" \\n\",\n \"swap_prices = pd.DataFrame(g).T\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, let's collect all the points and do some arithmetic to create a timeseries for each swaption. I will create two frames - one for the simple vol selling strategy and one taking into account the changing delta hedge.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.timeseries import *\\n\",\n \"\\n\",\n \"not_delta_hedged = []\\n\",\n \"delta_hedged = []\\n\",\n \"\\n\",\n \"for inst, row in frame.iterrows():\\n\",\n \" # collect all the results\\n\",\n \" total_result = pd.concat([row.so_price_f.result(), row.so_delta_f.result(), \\n\",\n \" pd.Series({k: v.result() for k, v in swap_prices[inst].iteritems() \\n\",\n \" if not isinstance(v, float)}), \\n\",\n \" row.s_delta_f.result()], axis=1, sort=True)\\n\",\n \" total_result.columns = ['swaption_prices', 'swaption_delta', 'swap_bought_prices', 'swap_sold_delta']\\n\",\n \" \\n\",\n \" # today's hedge notional will be the ratio of prior day's swaption/swap delta ratio - that's\\n\",\n \" # how much of the swap we bought to hedge so will use it to scale unwind PV of the swap today\\n\",\n \" total_result['hedge_notional'] = -(total_result.swaption_delta/total_result.swap_sold_delta).shift(periods=1)\\n\",\n \" total_result = total_result.fillna(0)\\n\",\n \" \\n\",\n \" # scale the umwind PV of prior day's swap hedge\\n\",\n \" total_result['swap_pos'] = total_result['hedge_notional'] * total_result['swap_bought_prices']\\n\",\n \" \\n\",\n \" # add to swaption price to get total performance cutting off last time due to the lag\\n\",\n \" swaption_pl = diff(total_result['swaption_prices'], 1).fillna(0)\\n\",\n \" total_result['total_pv'] = swaption_pl + total_result['swap_pos']\\n\",\n \" \\n\",\n \" not_delta_hedged.append(pd.Series(swaption_pl[:-1], name=inst))\\n\",\n \" delta_hedged.append(pd.Series(total_result['total_pv'][:-1], name=inst))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Putting it all together\\n\",\n \"Now, let's combine all the results to look at the impact delta hedging makes on the strategy. Unsurprisingly, the delta hedged version provides protection to tail events like March 2020.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"not_dh = pd.concat(not_delta_hedged, axis=1, sort=True).fillna(0).sum(axis=1).cumsum()\\n\",\n \"dh = pd.concat(delta_hedged, axis=1, sort=True).fillna(0).sum(axis=1).cumsum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"comp = pd.DataFrame([dh, not_dh]).T\\n\",\n \"comp.columns = ['Delta Hedged', 'Not Delta Hedged']\\n\",\n \"comp.plot(figsize=(10, 6), title='Hedged vs Not Hedged')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that this backtesting doesn't include transaction costs and the implementation is different from how one might hedge in practice (unwinding and trading a new swap every day) but is economically equivalent to layering the hedges (and is cleaner from a calculation perspective).\\n\",\n \"\\n\",\n \"Look out for future publications for added transaction costs and ways to quantitatively compare these strategies.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Generic Backtesting Framework\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction, HedgeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.common import AggregationLevel\\n\",\n \"from gs_quant.risk import IRDelta, Price\\n\",\n \"\\n\",\n \"# dates on which actions will be triggered\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b')\\n\",\n \"\\n\",\n \"# instrument that will be added on AddTradeAction\\n\",\n \"irswaption = IRSwaption('Straddle', '10y', 'USD', expiration_date='1m', notional_amount=1e8,\\n\",\n \" buy_sell='Sell', name='1m10y')\\n\",\n \"swap_hedge = IRSwap(PayReceive.Pay, '10y', 'USD', fixed_rate='ATMF', notional_amount=1e8, name='10y_swap')\\n\",\n \"action_trade = AddTradeAction(irswaption, 'expiration_date')\\n\",\n \"action_hedge = HedgeAction(IRDelta(aggregation_level=AggregationLevel.Type, currency='local'), swap_hedge, '1b')\\n\",\n \"\\n\",\n \"# starting with empty portfolio (first arg to Strategy), apply actions on trig_req\\n\",\n \"triggers = PeriodicTrigger(trig_req, [action_trade, action_hedge])\\n\",\n \"strategy = Strategy(None, triggers)\\n\",\n \"\\n\",\n \"# run backtest\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note, these strategies won't be exactly the same since the original series. This is because the original series is matching each swaption with a swap and holding both positions while resizing the swap each day. In the generic backtester snippet above, we passed '1b' for hedge holding period. This means that the delta from ALL swaption positions on a given date will be hedged with a single swap which will be replaced each day. Note if instead of '1b', we put '10y' for example (to match swaption tail), the swap hedge will be 'layered' - that is, we will add positive or negative notional each day but not unwind the existing swap hedges.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cash'].cumsum() + backtest.result_summary[Price],\\n\",\n \" 'Original series': dh}).plot(figsize=(10, 6), title='Delta hedged backtest comparison')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/made_with_gs_quant/5-What's New.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What's New\\n\",\n \"\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"Brought to you by popular demand, this edition of `made-with-gs-quant` will showcase some of our newest features and capabilities. Going forward, look out for a `What's New` section in `made_with_gs_quant` that will highlight new additions.\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Cash flows](#2---Cash-flows)\\n\",\n \"* [3 - Portfolios](#3---Portfolios)\\n\",\n \"* [4 - Scenarios](#4---Scenarios)\\n\",\n \"* [5 - Compute](#4---Compute)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.115.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Cash flows\\n\",\n \"\\n\",\n \"Let's start with something small but impactful - ability to model future cashflows. You can simply use `CashFlows` as any of the other risk measures to get a dataframe of future cashflows.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import Cashflows\\n\",\n \"from gs_quant.common import Currency, PayReceive\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"\\n\",\n \"swaption = IRSwaption(PayReceive.Receive, '10y', Currency.USD, expiration_date='6m')\\n\",\n \"cf = swaption.calc(Cashflows)\\n\",\n \"cf.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Portfolios\\n\",\n \"I have used `Portfolio` wrappers in my previous analyses, so this one should be somewhat familiar. `Portfolio` makes working with many positions easier and allows risk to be viewed both at position level and aggregate level (more portfolio features like saving in the pipeline..look for it in `What's New` section in the new editions!)\\n\",\n \"\\n\",\n \"Let's first define a portfolio - I will define a book of swaptions with varying strikes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.common import Currency, PayReceive\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"\\n\",\n \"swaptions = [IRSwaption(PayReceive.Receive, '10y', Currency.USD, expiration_date='6m', strike=strike, \\n\",\n \" name=strike) for strike in ('atm', 'atm-10', 'atm-25', 'atm-50')]\\n\",\n \"\\n\",\n \"# create portfolio from list of instruments\\n\",\n \"portfolio = Portfolio(swaptions, name='swaption_port')\\n\",\n \"\\n\",\n \"# resolve all the instruments in place\\n\",\n \"portfolio.resolve()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can work with this portfolio the same way we do with an instrument - that is, we can price it or calculate any of the available risk measures on it. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import DollarPrice, IRVegaParallel\\n\",\n \"risks = portfolio.calc((DollarPrice, IRVegaParallel))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With `risks` in hand, we can examine these risks on an instrument level or aggregate level:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# all risks for a specific (in this case, first) instrument in the portfolio\\n\",\n \"print(risks[portfolio[0]])\\n\",\n \"\\n\",\n \"# single risk for a specific (in this case, first) instrument in the portfolio\\n\",\n \"print(risks[portfolio[0]][DollarPrice])\\n\",\n \"# or...\\n\",\n \"print(risks[DollarPrice][portfolio[0]])\\n\",\n \"\\n\",\n \"# aggregate risk for the portfolio\\n\",\n \"print(risks[IRVegaParallel].aggregate())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we can convert our portfolio to a dataframe where each row will represent an instrument. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"frame = portfolio.to_frame()\\n\",\n \"frame.head(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can append `RiskResults` as columns to this dataframe as we compute additional measures.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"rks = pd.DataFrame(risks) \\n\",\n \"rks.index = frame.index\\n\",\n \"\\n\",\n \"pd.concat([frame, rks], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note the last two columns in the frame are the risks we appended!\\n\",\n \"\\n\",\n \"Want to see more portfolio examples? Refer to portfolio [examples here](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/examples/02_portfolio/) and [previous editions](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/made_with_gs_quant/) of `made_with_gs_quant`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Scenarios\\n\",\n \"\\n\",\n \"The team has also invested to build our scenario capabilities. In particular we: (1) simplified `MarketDataShock` scenario (2) added aging or `rollfwd` scenario (3) introduced composite scenarios. Let's look at an example of each.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Simple Market Shock\\n\",\n \"\\n\",\n \"`MarketDataShockBasedScenario` is designed to allow users to shock any kind of market data. In this example we will shock the spot by 1bp. Note while before you had to specify each of the underlying curves used to build spot curve, you can now simply indicate `('IR', 'USD')`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType\\n\",\n \"\\n\",\n \"ir_spot_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern('IR', 'USD'): MarketDataShock(MarketDataShockType.Absolute, 1e-4), # 1 bp shock\\n\",\n \" MarketDataPattern('IR Reset', 'USD-3m'): MarketDataShock(shock_type=MarketDataShockType.Absolute, value=1e-4),\\n\",\n \" })\\n\",\n \"\\n\",\n \"with ir_spot_scenario:\\n\",\n \" spot_shock = portfolio.price()\\n\",\n \" \\n\",\n \"print('Base price: {:,.2f}'.format(risks[DollarPrice].aggregate())) # or portfolio.price().aggregate()\\n\",\n \"print('Spot shock price: {:,.2f}'.format(spot_shock.aggregate()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### RollFwd\\n\",\n \"\\n\",\n \"The `RollFwd` Scenario can be used to move the pricing date into the future. In order to do this we do two things: (1) project the market data forward in time (either keeping spot rates constant or fwd rates constant - more on this in a bit) to the new pricing date and (2) lifecycle the trades so that any fixings or expiries that happen between today’s date and the target future date have been handled.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.risk import RollFwd\\n\",\n \"\\n\",\n \"rollfwd_scenario = RollFwd(date='1m', holiday_calendar='NYC')\\n\",\n \"with rollfwd_scenario:\\n\",\n \" rollfwd_shock = portfolio.price()\\n\",\n \" \\n\",\n \"print('Base price: {:,.2f}'.format(risks[DollarPrice].aggregate())) # portfolio.price().aggregate()\\n\",\n \"print('Rolled Forward price: {:,.2f}'.format(rollfwd_shock.aggregate()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Combined Scenarios\\n\",\n \"\\n\",\n \"We can now combine these two scenarios to produce a 'rollfwd then bump' scenario (note all permutations aren't supported yet but are underway):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with rollfwd_scenario, ir_spot_scenario:\\n\",\n \" rollfwd_then_bump_shock = portfolio.price()\\n\",\n \" \\n\",\n \"print('Base price: {:,.2f}'.format(risks[DollarPrice].aggregate())) # portfolio.price().aggregate()\\n\",\n \"print('Rollfwd then bump price: {:,.2f}'.format(rollfwd_then_bump_shock.aggregate()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"More on scenario examples in the [examples folder](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/examples/04_scenario/) in the gs-quant repo.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5 - Compute\\n\",\n \"\\n\",\n \"Finally to optimize both speed and performance we have improved our async and batch framework.\\n\",\n \"\\n\",\n \"In a nutshell, `async` allows the requests to be processed in parallel and `batch` allows them to be grouped together and avoid timeouts for particularly large requests.\\n\",\n \"\\n\",\n \"Additionally, multiple contexts can be controlled by a 'master' `PricingContext` to batch requests together in order to also avoid timeouts and sending too many requests. For example of this please see [`4-Delta Hedging`](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/blob/master/gs_quant/made_with_gs_quant/4-Delta%20Hedging.ipynb) notebook. \\n\",\n \"\\n\",\n \"For now, let's look at a simpler example using the scenarios we discussed in the previous section. Here we will look at how the price of our portfolio evolved as we move it 21 days forward in time both keeping spot rates constant (`roll_to_fwds=False`) and fwd rates constant (`roll_to_fwds=True`). Let's use a `PricingContext` with batch mode to organize these requests.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"roll_to_spot = []\\n\",\n \"roll_to_fwd = []\\n\",\n \"\\n\",\n \"\\n\",\n \"date = business_day_offset(dt.date.today(), -1, roll='preceding')\\n\",\n \"\\n\",\n \"\\n\",\n \"with PricingContext(pricing_date=date, market_data_location='NYC', is_batch=True, visible_to_gs=True):\\n\",\n \" for bus_days in range(21):\\n\",\n \" with PricingContext(is_async=True), RollFwd(date=f'{bus_days}b', holiday_calendar='NYC', realise_fwd=True):\\n\",\n \" roll_to_spot.append(portfolio.price())\\n\",\n \" with PricingContext(is_async=True), RollFwd(date=f'{bus_days}b', holiday_calendar='NYC', realise_fwd=False):\\n\",\n \" roll_to_fwd.append(portfolio.price())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's now visualize the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"pd.Series([r.aggregate() for r in roll_to_spot], name='Roll to Spot', dtype=np.dtype(float)).plot(figsize=(10, 6))\\n\",\n \"pd.Series([r.aggregate() for r in roll_to_fwd], name='Roll to Fwd', dtype=np.dtype(float)).plot(figsize=(10, 6))\\n\",\n \"plt.xlabel('Business Days from Pricing Date')\\n\",\n \"plt.ylabel('PV')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"That's all for this edition - please keep sharing your feedback on what would make `gs-quant` even more useful for you as well as suggestions for `made_with_gs_quant` you would like to see in the future.\\n\",\n \"\\n\",\n \"Happy coding!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/6-Evolving Correlations.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Evolving Correlations\\n\",\n \"\\n\",\n \"### Summary \\n\",\n \"In this note, I team up with Jack DiMassimo from MarketStrats and use `gs_quant` to analyze changes to the correlation structure among assets. We explore where there may be opportunities to trade assets that have strayed from their traditional relationships because of temporary factors. \\n\",\n \"\\n\",\n \"To do this, we perform a PCA and rolling PCA analysis on the correlation matrix of 19 assets sourced from FRED covering global Equities, Rates, FX, Commodities, and Vol. \\n\",\n \"\\n\",\n \"We find that:\\n\",\n \"* Most of the explanatory power is dominated by the first two components where the first component aligns with risk / growth and the second with real rates. Deeper dive in [3 - Interpreting the top 2](#3---Interpreting-the-top-2)\\n\",\n \"* US equities, unsurprisingly, have realized much higher than the model predicts\\n\",\n \"* JPY, WTI and US 2y have realized much lower than predicted suggesting other factors at play and potential opportunity for entry\\n\",\n \"\\n\",\n \"In this analysis we leverage gs-quant FRED connector to pull data as well as a number of [timeseries functions](https://developer.gs.com/docs/gsquant/api/timeseries.html) to transform it.\\n\",\n \"\\n\",\n \"Outside of this analysis, we hope you can keep using this notebook as a way to monitor changing drivers of risk and screen to mispriced trading opportunities. Please reach out with your feedback and suggestions!\\n\",\n \"\\n\",\n \"Finally, check out the [What's New](#What's-New) section for the latest gs-quant additions.\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Grab the data](#1---Grab-the-data)\\n\",\n \"* [2 - Model explanatory factors through time](#2---Model-explanatory-factors-through-time)\\n\",\n \"* [3 - Interpreting the top 2](#3---Interpreting-the-top-2)\\n\",\n \"* [4 - Actual vs Predicted](#4---Actual-vs-Predicted)\\n\",\n \"* [What's New](#What's-New)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Grab the data\\n\",\n \"\\n\",\n \"First - the data. For this analysis I will use public data from [FRED](https://fred.stlouisfed.org/) but of course you can substitute or enhance this piece with your own data. The goal is to get a deep history accross multiple categories or sources we're interested in investigating.\\n\",\n \"\\n\",\n \"I will use gs_quant FRED connector to pull in the data. For this, you will need to use your own FRED API key. Takes under a minute to create one [here](https://research.stlouisfed.org/useraccount/login/secure/). \\n\",\n \"\\n\",\n \"Note in the dictionary, I include a type to indicate whether to difference (b-a) or calculate returns (b/a-1) for each series and an asset class label that I will use later in the analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
WILSNDXN225EURJPYAUDGBPCADUSD 2yUSD 10y2Y10Y10Y3MUSD 5y RYUSD 10y RYBreakevensGoldWTIHYVIX
date
2003-01-0629.491061.488713.331.04690.0083990.57621.60800.6411081.844.092.252.881.792.461.63356.1132.298.4124.91
2003-01-0729.291071.858656.501.04190.0083210.57531.60550.6414371.774.042.272.851.762.421.62348.7131.208.2625.13
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" WILS NDX N225 EUR JPY AUD GBP \\\\\\n\",\n \"date \\n\",\n \"2003-01-06 29.49 1061.48 8713.33 1.0469 0.008399 0.5762 1.6080 \\n\",\n \"2003-01-07 29.29 1071.85 8656.50 1.0419 0.008321 0.5753 1.6055 \\n\",\n \"\\n\",\n \" CAD USD 2y USD 10y 2Y10Y 10Y3M USD 5y RY USD 10y RY \\\\\\n\",\n \"date \\n\",\n \"2003-01-06 0.641108 1.84 4.09 2.25 2.88 1.79 2.46 \\n\",\n \"2003-01-07 0.641437 1.77 4.04 2.27 2.85 1.76 2.42 \\n\",\n \"\\n\",\n \" Breakevens Gold WTI HY VIX \\n\",\n \"date \\n\",\n \"2003-01-06 1.63 356.1 132.29 8.41 24.91 \\n\",\n \"2003-01-07 1.62 348.7 131.20 8.26 25.13 \"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.api.fred.data import FredDataApi\\n\",\n \"\\n\",\n \"FRED_API_KEY = ''\\n\",\n \"fred_API = FredDataApi(api_key=FRED_API_KEY)\\n\",\n \"\\n\",\n \"data = {\\n\",\n \" 'WILS': {'symbol': 'WILL5000INDFC', 'type': 'r', 'asset class': 'Equities'},\\n\",\n \" 'NDX': {'symbol': 'NASDAQ100', 'type': 'r', 'asset class': 'Equities'},\\n\",\n \" 'N225': {'symbol': 'NIKKEI225', 'type': 'r', 'asset class': 'Equities'},\\n\",\n \"\\n\",\n \" 'EUR': {'symbol': 'DEXUSEU', 'type': 'r', 'asset class': 'FX'},\\n\",\n \" 'JPY': {'symbol': 'DEXJPUS', 'type': 'r', 'asset class': 'FX'},\\n\",\n \" 'AUD': {'symbol': 'DEXUSAL', 'type': 'r', 'asset class': 'FX'},\\n\",\n \" 'GBP': {'symbol': 'DEXUSUK', 'type': 'r', 'asset class': 'FX'},\\n\",\n \" 'CAD': {'symbol': 'DEXCAUS', 'type': 'r', 'asset class': 'FX'},\\n\",\n \"\\n\",\n \" 'USD 2y': {'symbol': 'DGS2', 'type': 'd', 'asset class': 'Rates'},\\n\",\n \" 'USD 10y': {'symbol': 'DGS10', 'type': 'd', 'asset class': 'Rates'},\\n\",\n \" '2Y10Y': {'symbol': 'T10Y2Y', 'type': 'd', 'asset class': 'Rates'},\\n\",\n \" '10Y3M': {'symbol': 'T10Y3M', 'type': 'd', 'asset class': 'Rates'},\\n\",\n \" \\n\",\n \" 'USD 5y RY': {'symbol': 'DFII5', 'type': 'd', 'asset class': 'Rates'},\\n\",\n \" 'USD 10y RY': {'symbol': 'DFII10', 'type': 'd', 'asset class': 'Rates'},\\n\",\n \" 'Breakevens': {'symbol': 'T10YIE', 'type': 'd', 'asset class': 'Rates'},\\n\",\n \" \\n\",\n \" 'Gold': {'symbol': 'GOLDAMGBD228NLBM', 'type': 'r', 'asset class': 'Commodities'},\\n\",\n \" 'WTI': {'symbol': 'DCOILWTICO', 'type': 'r', 'asset class': 'Commodities'}, \\n\",\n \" \\n\",\n \" 'HY': {'symbol': 'BAMLH0A0HYM2', 'type': 'd', 'asset class': 'Credit'},\\n\",\n \" 'VIX': {'symbol': 'VIXCLS', 'type': 'd', 'asset class': 'Vol'}\\n\",\n \"}\\n\",\n \"\\n\",\n \"# build dataframe with all series \\n\",\n \"df = pd.concat([Dataset(v['symbol'], fred_API).get_data() for _, v in data.items()], axis=1)\\n\",\n \"df.columns = data.keys()\\n\",\n \"\\n\",\n \"# filter for when data is available accross all and clean\\n\",\n \"frame = df[df.index > '2003-01-01']\\n\",\n \"frame = frame.fillna(method='ffill').dropna()\\n\",\n \"\\n\",\n \"# flip JPY and CAD for more intuitive direction in later analysis\\n\",\n \"frame['JPY'] = 1/frame['JPY']\\n\",\n \"frame['CAD'] = 1/frame['CAD']\\n\",\n \"frame['WTI'] = 100 + frame['WTI'] # negative prices!\\n\",\n \"frame.head(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With all of our data in hand, let's standardize it by differencing levels and calculating returns. Note, I use a 2 day window to smooth the effect of comparing accross different markets. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.timeseries import returns, diff\\n\",\n \"\\n\",\n \"frame_t = frame.copy()\\n\",\n \"for key, v in data.items():\\n\",\n \" frame_t[key] = returns(frame_t[key], 2) if v['type'] == 'r' else diff(frame_t[key], 2)\\n\",\n \"frame_t.dropna(inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Model explanatory factors through time\\n\",\n \"\\n\",\n \"Now the fun part. Let's run a 3-factor PCA for a 42 day rolling period and record how much variance is explained by each component over our time frame. I standardize the values (0 mean, unit variance) to bring them to the same scale. Note, you can play around with number of components and time frame without having to change any code!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.decomposition import PCA\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"period = 42\\n\",\n \"components = 3\\n\",\n \"f_loadings = defaultdict(list)\\n\",\n \"\\n\",\n \"for start in range(len(frame_t) - period):\\n\",\n \" d = frame_t.iloc[start:start + period]\\n\",\n \" d = (d - d.mean()) / d.std()\\n\",\n \" model = PCA(n_components=components)\\n\",\n \" model.fit(d)\\n\",\n \" for i, c in enumerate(model.explained_variance_ratio_):\\n\",\n \" f_loadings[i].append(c)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's now look at variance explained by the top 3 components over time. I smooth the chart by looking at a 21d rolling average. \\n\",\n \"\\n\",\n \"Looking at the graph below, we can see the top 3 components explain ~65-85% of the variance over time. Additionally, variance explained most recently is among the highest over the period. This is not too surprising as with the high volatility we've seen correlations among many assets in question have increased significantly. \\n\",\n \"\\n\",\n \"As Jack pointed our in [his note](https://marquee.gs.com/s/content/articles/9ea25a7d-2560-4658-8122-2a5b1eff7d28), this is on par with the spikes observed during the GFC, Brexit, the August 2015 CNH devaluation, and the Eurozone Debt crisis. Notably, more equity specific crisis like the December 2018 SPX crash and the February 2018 'Volmageddon' lead to correlation spikes, but not to the extent observed during the more global events. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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HPKV6+6tbm8nQWLBJDnyBBm8grr/LDF5wIAKg4ifGZKbBHn7bMwN8J2kNi6BMkaBOFivToB7pSsE1CqZoY+pkCZ92klCZ9Yug7Q2LoEywKbBuaxIGjxVkxBEVF3eRSJjKWibKT9DKdKXAwNmUqQ58UTHWEWHn0CRLMZzhuDS9TqY7veskmfOCVJ87o8dijz6VMpG0z8ehnEMzRpxOPfkpIPPoECx47j+S9z5ffsXPGj1eoSI4+a1vI2AbK1cSjnyl4HL2dGPqpIDH0CRY8vn/Xbu9zr0rDm0kUdY/eMlBKqJsZg8fRK+qmllA3HSEx9AkWPJ46PAnLILz7Jcfh4HhpxlPw8srQd6VNZGwT5Taom8vv2IEf37u79YoJAPgefEpl3TCVk6A9JBx9ggWPcrWG524YxLrBLIQADh4tYd1gbsaOV2TqJmUhY5uxPfq9owV84tpHAQCvec46GEZUu4YEOjgY63H0iUffERKPPsGCR9lxkbYNrO7PAgD2jRVn9HheMNY2kW0jGKtTTCOFqbXF232kgK2HJqa0j4UA9uA5vbKWcPQdITH0CRY8yk4NKdPA8p4MgJlvOceGPpsykbENj7NvBb2idqrneP5//g4v//ffLXrD5xdMJR79VJAY+gQLHmWnhrRtenooM91EulhxYZA0PhnbrJNEaIRRzYt/aO/RKZ0DDzYP7pvafuY7HC/rJqmMbQTHreHaLZGN+zwkhj7Bgkex4iJjGVjRm0Zf1sbjB2eW0ihUXORSFogIWdv0CqhaYSRfwYkrewAAn7vhiSmdQ09ahtd2DudbrLmw4YYLphJDX4cr7t2D//Oj+5uukxj6BAsehYqDrrQ0vMt70hiZnGGPvuo3v8im4hv60UIFq/okvXR4ojwlJUZbURkTpWrH+1gISCQQWmNovNRyncTQJ1jwkB62NLyDXakZp27042VtMzZHP5qvYqArhTefuwEAsOtI5964pTJ2xhe5PnsdR58Y+jrEiVskhj7BgkbFqcGpCXQpKmNJd2rKGS2tUKi4yCrOOGObKDu1WEHR0UIFA7kULjptNQCZBtop+HATi9zQV8MSCEkwtg4j+dazusTQJ1jQ8OUIpOEdyM28R1/UPXr1f7mFVPGh8RIKFRer+7NYo9JA93do6Gs1gaNF+RsXO3Xjenn08jov9iyjTjCSb53BlRj6BAsaFVUFyxzuEkXdPPvSG3H71uEZOWah4iCXkjOIjDpuK55+aFy+jOsHc1jWnYZB8bjVKIyXqp6nu9g9+jBH7ySGvg5xHJvE0CdY0OCCGtuUnPVgVwoAMFqo4g3fuhvnfuYWHJ6Y3rx6naPPqJlEqxRLnnnkUiYMg9CTsTFe7Mwb14O444veo/fvL1Hi0UfhSGLoEyx2sCGwDPkoH7+iJ7B831gRtzx+aFqPqRt6VlVsaeirfpEVIHugHu3Q0OvB30Xv0bv+/TWJEo4+AolHn2DRgwXMLOXRP3t9P9YOZPH2F2301smXp6Yu+dVbtwVooELFRdajbuJx9LriJQD0Zq2OM2a4WMo26WnA0UvDbpoEw6CEugnBcWsYKyTB2ASLHGGPPpeycPs//gk+cuEzsf3fLoRBwOEpyA0cPFrC5294Am/6n3u874oVp23qhvvM5mw5QEzFo88rGmhFb2bRe/RVFYy1DIJlUELdhDCqjPzxy7ubrpcY+gQLGhyUNCOUIA2DsLI34wVCO8H24UkAfhBQCIFCNYq6ae7RH1AZNst70wCA3szUqZuV89TQuzWBD/x0C+7dOTL1fWn31yTCDCtQLzgwbXPBs1Y2XS+WoSei84noCSLaRkQfilj+ASJ6QP17mIhcIhpUy3YS0UNq2eb2f0qCBI2hB+uisKw3MyWP/vEDvpyC49ZQdmoQwufaOe2vlVTxWKGKnrTlzQC60lbsQqswmLpZ0ZvBZNmZd0VE+8eK+Ol9e3HJ9+6b8r6qbg1E0qM3DPLSLRNIHFGplc/ftKTpei316InIBPBVAC8HsBfAvUR0jRDiUV5HCPF5AJ9X618E4L1CCH04f4kQYmZy3RI8rcFT+yiPHgC6UqanH98JhrSMnbFiFQbJ4+S8ginpK7VqJ1isup4wFyALgDptKs4DBM8OJsuOJ+g2H8DibXGyQVqh4grYhgEigmkkwdgw2KNf0pVuul4cj/4sANuEENuFEBUAVwC4uMn6rwPwo3inmWC6cdOjh7DhQ7+a8aIhHduGJvCWy++dk1Q/PSsjCrZpoDKFrkQFbZAYzVe0NEkVjLXjBWPLVRfZlH+Oaau9zlQ6dI4emH9FU/unsR9A1a15szXTSKibMI4oXSdOK26EOIZ+DYA92t971Xd1IKIcgPMBXKl9LQDcSET3EdHbGx2EiN5ORJuJaPPhw4djnFaCKPzg7l0AgDufOjJrx/zCDU/i5seHsHkaONl2wVk3zJWHYZsGqi2McDPoGTsj+YrnTWfbDMYWq66XoQPIAqByh1bLp26kFzffePoDWsWvmKIH7rg1T8DNpCQYGwbXVPRkmpMzcQx91Jy40dW+CMAdIdrmXCHEGQAuAPAuInpR1IZCiG8IIc4UQpy5bNmyGKeVIAqmwRWEs+f6sLYM0ey3xmP6g7VQwkhbxpR6yAY8+kLF7y7lcfTxgrGlqusNDrxdxal1ZAhLVRdEwNLu+Wno9fOpTrHHa8UVsJVEsZmkV9aBn22+Ro0Qx9DvBbBO+3stgEYq969FiLYRQuxX/w8BuBqSCkowQ+BpfGUKXmy7SHuSubNvcJj+SDUw9LZJnkxCJ8hXXCxR0+LJshvoLgV07tHzDKQV5ROFiis7avVkJC8/36gb/XziSjg3QlX9VkAa+lrC0QfguAIGNY5RMeIY+nsBHE9ExxJRCtKYXxNeiYj6ALwYwC+077qIqIc/A3gFgIdj/4oEbYOnclMxbu2CDX2nJf1TARvKtGZEdUyVuimUHSzrkZ5zoeJ4Hn5XSOumlcE+PFHGkm6fR03HLLSKQsVhQy/PYb559JMa3RW3+1YjVN2aVwwnOfrE0OuQMYzWZrxl1o0QwiGidwO4AYAJ4NtCiEeI6BK1/Otq1T8HcKMQQhfZXgHgajWltwD8UAhxfVu/JEFbYEPfaaCvE7BXOxfB2FbUjW1NLRibr7iKIplAoeLWUTeWacAyqKVBG8lXPKpFP195/u1lzFTdGlKWbujnl0evZzl1mkLK0A2ZQYkefRhVjdpqhpaGHgCEENcBuC703ddDf18O4PLQd9sBnBbnGAmmB+xVz+YLwSmHc0LdeB599MOeMg1UOkxjBKQXf9zybhBJ7z4cjOVjt+Loy07NS8UEfKqpkwG54kjj16uom/nWfKSgGfepUzfCa7KSePT1KFbdwHPVCEll7CJCoeJ4JdHVWQzGckBoTqibFhx9yjLqAoL/dds2/OKBfbH2ny+76E5byNqm8uiD6ZWAnNGEC6bGS1V88cYnvOrXslML0Es8MHVCsVVdAdsipC0DBk3da55u6IZ+dIppvo7m0ZuGkeTRh8BtNFshMfSLCEe0XqnOFLMd2gEb+rnw6MN69GHYJgWyboQQ+Nz1T+A9VzwQa//FioOulIlcykK+4noqlDnNo+/N1ksO//qhA/jKLdvwrdt3wHFrcGsiMOvwOPoOPfqUKYuILMOYd5kohYqDjUu7AAC7RgpT2pcr/ECjaSTUTRj5suPFi5ohMfSLCLqhnc2Xn+mT0Rlu4RcFzi5KNeApU6YJpya8/Ov9beR412pK1yZtIZcyUahI6oYoSBUN5Oy6AjWuCh0rVHx6SZti+1k37XvjFc3Ltcz5JwtQqLheMdfkFAd/t1bzDT0l1E0Yk2UH3YlH//SC3pDCmcWsG/aYnzg40WLN6UdFVU42yuG3LfLWA4AnD/nnWGhBeRSrLoSQMgrdaQv5soOJkoPulBU4XlfaqtvXyCQb+qoXqI2ibjrNuuHtTYOmnKs+3ShUXCxVmUr/et1j+MrNWzvel1sTmke/uNMrr7xvLz505YNtbZMvu+hKR2ec6UgM/SKC7lXOZh49G5qRfGXKlZBtH9upNfTmAd/T58Fo17CfFNZqBsLSwl1pC90ZqR9/eLLspVsyuPhJBwup7RsregHJcAAX6MzQ65kotmnMOy+3UAl6mV/8zZMd78utCZjkG/rZpCRnG+//6RZcce+egMPWCvlywtE/7bBDGbHutDWrfDkbOacmsPPI1DjZto+tlchHwfYMvTQQ+vm1atjAL1x32kJvxsJkycHwRDmQJgnI+EA4qDqsDP14sVrXdATQOfr2qRtOrwS4WnR+UTcygG16TdAB4Ef37Mbld+xoul3ZcbFzOB/4zq0JL4/eeJp0mDrURi/hhLp5GmKsWEHKMrCqLzOrOe16sPMlX7htWvcthMAP7t7VMFdcr5yMAhtEHoz0l6iVRz+pe/RpC5NlB8Wqi1xoqixTOIPGdjQvz7dY1app7emjbjyPfp55uW5NyGuUsnDz+1+Mt7zgWADAh696CJ+49tGm237y2kdx3hdu8xwW3p+hefSLVetGn5XFzVQqOy6GJsqxqLvE0C8ijBer6M3YyKbMKVcktoOKW8NZGwa9v3UePIx82cF/3bYt8mGuujW89ht34totvsLGw/vG8dGrH8ZHro4uqC47zSsD7RB1c7RY9SR9R1t49Cxo1pUy0ZOxMVGqIl92Ap45oDx6zWBX3Rq2DslrUKq6Dagb+bkTiq3iCt+jN+eX/gunn3Yr7f13aC0dgeYB8McPjAMAfvekL2roimAe/Xz6rdMJvYG9LgrXDJt3jsptY/RbSAz9IsJ40UFv1pr1F6Li1LB2IIt3vFi+1OHpt47zvnAbPnf9E/j+Xbvqlt3wyEHctX0EX//tU9537HU3UuOsuqJhsRTgNyRhamW8VMUxS3IAZEZMM1S11M3ujPToxwpV9OeCkrBh6qZQdj0vq1Bx/SIre5q0bhzXm8XY8yy9kgdHnvUsCdFczQqoBtR13X/Ulzl2XAHjaRCM3bJ3zPu8o8n7o4OfuXe/5LiW6yaGfhGhpFrcWbM8nWfO+G0vlIa+kR750ULV81w4aaVUdXH1/XshhMDvn5S9aXSDyFx3oxTCiuM29ejZILLnfLRYxfpBaej14PVXb91W1xFJVwbsyViougJH8hX0h5p82KYRCKAVqvLzmv4sChXXO45eZMXn1Ul6pSx7lxfQMmlKWj7Tjbzm0QP1YlvNZlET6hqO5f113Jrv0c/2cz2bOKh58XGfCU5dHexqLaGRGPpFBK6+nO1SceaM2SNr9DLrnhp7sl/6zZN474+34ObHhvDH3XIqqhfZsKFvZMyrGo0RBV7mV+86WNKVwtLuNA6M+S/X5294Atc/chBHtXNnr9wyCT1awKs/F3yxajWBilPDJ655BIBfqbpJNWx+6rDsO6tTPqwP1IlEQMXxg7FRVblzCa8JeoMinmb8MycQHNTiKK7w0ytlcdj8GdSmE1xwl4ohp8HQY0itkBj6RYSy4yJtGbDN2X0hWFjJNKRBbBQIPqAZen6p2dvdvGsUu1RGzEi+4g1UwyofvdGUXQ4yjSVawxx9oeIgl7awtDsV2eruUcUT69voksAA6qib15wpVbwv/8NOAL7xXjsgs054hpOxdUNvgDqUL9DTKzPW7MZjWsE3PtG53c0C4GzsuCE7EMyjt8zF69GXnRoMkjOhuB59PjH0zSGEwLVb9geaSiwGlFUhzaxz9Fq635LuFH5+/75Iz42DTBnb8Dx1zqJ4cO8YKm4NG5bk4NaEZxCGFdVztFiNDOTpx46C7VE3AkIIOQMwDZn7rgy5bij/82Y/55sHS8s0AilsAyFD/6w1ffjbs4/xgrxsvFeq6lCuxtU9eiJCV8oKdLCKC/03p+34HuBsoKB+j369nrmq1/vcjLrhQUK/Jrqht01jVjWcZhNlx0XKMpAyDVSdeO+uN6gmEgjRuGPbEfyfH92P/7ip84q9+YhyVVI3s8llCiGU9oqf6zxaqOJzNzxRty7nrT9jRY/H1Y8pL+4PKth60mppFIbG5XLOKKi6IrKStZUed0oTD9N1cVKWr1Ove5l3bR/BVpU1xC+cbRKOXdblrXO8omR0ZFOW58mzMeOg70FvgAt6uSyr0C4qWpFYxp5fHn0+QvTta68/Ax7+oacAACAASURBVB+58EQAzakbjqPo8Q69YGoxc/QVRbvaVvxGOcWK6zl2rfC0NPRPHPJT3xYTyo6LtG3AMmavWpJnDmxQOcj6m0cP1a07XqoiZRlYO5DzDHhYI4a72X9PZeXogd2jEeqYuhxAFLzKWKcW0MXRM2V0MTgA2DokqQP2Hm3TwKZl3Vg3KKmYDUu7EEbWNlFxpHgZz1Y2LZMDwsHxUuQL2ZWWQmntQAgR0LrJ2GZHmTszhXyER79haRf+97kyn74ZdcP3o+LUPNpML5iyzHol0sWCsoq7pMz64rtGaDWb1fG0NPScqrcslPq10OFRN2bjaslPXPMI7tkxfU28wz0rNyyRRrAY4amOqxx2qfYol+8dLeDsjUu8dd549jEA4LXvG9IqUaOqfVt59Kx1U3VrnpHwXqiQR//l1z0bgD/zCAum/ea9L8Zjl54feZxsSq6jF0itVpWhFadWl3sPsI59e4a+4tYghJ+T38k+ZhJeMDbE0TP9xfc9DLcm4NYEBlSgm/ejF0zZTZ7rhQS3JgLxKsB3WEyDsHc0OmstjFbFgjqelob+8YMy4GbEmPIsJHDWjdWAo999pIDL/7ATf/nfd07bMdkYsrH94l/KPjOrtfJ3xt7RIlb2ZtCbtbzio+HJCl5w/FJvnfWDOazpz2LfWBGlqouJkuNRIFE0h56BEgWPo3c1j14FrPlvnlVw8JQpmKj+sNkIgw34KaHFiusF03Ip08vWydr124ULreKA+XiexdjzzMvNVxrzxhnbaBhoZIdhwOvPqwy90NMrjUVB3fzsvj04+9O3YNuQX1jIHr1BhC17xmJpRrV69nU87Qz90HjJGzHn05R3OsA3vpH40/17Rqf/mCE9+P5cCn915rpImmUkX8GK3jR6MzbKTg1b9sgikRNX9uCEFZLmyNgmlvakcSRf8SgQ39BHcfTNW6npefRh6oaNC2f2sKFnD7lUrZckbgS9SbhujHtVgDZqgIiSTmgF1sbh4803LzdfdpCxo3njtGU2DBzzc8SBbqaAXK1gKtxbYKHiVw8dBAA8tO+o9x07aa86ZRUA4PEYSrBx2wgCT0ND/9Rhv+qsqnGCV963d8HraHAjZbsBR3/HNlmQFEUjdIooPfhc2oxMGyxVXaRtE72q1+lj6mFeP5jDle88B3/855cDAPqzNsYKFS9gy3RQPkLVr9zCq/Hz6IVnTGwVjOWB/pDi0Jd1p5GyDG+QKlZcZG2zoQSyDjbkxaqLclVmUBCRl4kTaegjxNBagQ2lb+jnl5c7qTpyRSFtGw1z/vk5YuqGPXpHL5iaZ3IPnWKdcii2DflppJwa/cpnrQTgx4magSW64+BpY+gPHC1iolTFA3v8UmPOuvjib57A+3+6Bbc+MTRXpzctcGsCttGYo79XaWMUKm5bUqjNoPPejK6UhULVrZt+cgyB89B3HZGDbl/ORk/GxqCati/vSePA0ZLnabNHH1Vc1Iqn1PPo9UEpbZkejTA8KeMARIRVfRkvS6ZYdSMplygEqRs/QMyGPmfXG7+OqBuHPXq/8Ugn7QhnCs1a22Uss6FaJzsGHI/Ja9SNaajfqhyY2ZbCnm7oVdr6dynLwHIlgT0UQ8Gy2kLnSUes5uALHbWawNmfvgUnr+7F845dgq6UCVubuj+wWxr/cAbIQoIQAo7KOY7i6Gs1gZ1H8ti4tAvbh/PYM1II5Dd3inAwFpAevVsTqiG2byh5esqVpU8emoBBfqYNY81AFocnyrh9qxS3YsmCqJzz1hw9eeuxQUxbBnIpf9ZR0Jo3rOzNeIEy2Xg5pqHXPXqtPywb+kyDYGz7HL0y9Gr/tmHMapOZRrh96zAePziuRN+izUpv1oqk9AB/EGdDpwdj+dFiz96pidie7HxESd1zXSa77NTQk7HQl7WRsoyAyFkjtJN187Qw9EPqoj2yfxxrB7JY1Z/FeLHqvfj8ku5roNGyEMCG3TZlH1E3NJ0vqG5Jq/oz2D6cb9ldKS78YKzWcUm96JKv1Qx9VU5PmYd98tAklnSn6/jcjSot8Tt3yhRLNvRRwdhqi+mrnkevD0q5lKk6SAnkK75xWt2fxe+3HvZkDeLw84Dm0VfdwHY8qPVEeLmyD217M6so6qYmgoVFsw0hBN7wrbu9v886djByvRW9Gdy/eyxyGRt2buoyWXYghFC/S15L5urdmkDM8Xdegh2MsEefVnTfsu50LEOfZN2EoL9M2w/nsWFJTmVd+AqDAGKnNc1HMCdvGkYkl+m9SGpqPF0peVHNubnhxPaQCl/ZqSFtG56XO5KveB6cjj9VASkGrx/26Gs1OYtJmY3fetuIoG4sAxnbRE3IcypUfF751LV9GJ6s4Ei+0jJ1Uwc7C6WKG/C0elQ8Yu1gfRZSV9qMjDs0g+fRa9QN/765AlOCjEaD45KuNI40kNRl47dcVRP/8sED4EdY7zAFAAucufEow6BH73rPzLKetOecNkPVSYKxARQ0A7F1aBLrBnOBrAsWsto7OrvdkaYTvrdKkV2HmJNnDrQTjZUoRAVjuYpUL3YSQniUBqfQAYg09IZBAWNhKcmCsEfvB1cbe7KGorICWTeKugHkdZgsOZ6hZtmCwxPlttLX2KMvVNyAjDA7ESt6MnXbdKXbl0AoRWTdALPbDD6Mmx4LFse97+UnRK7Xk5EFYlGJAl7tQV8WPao/L6/nd5iS6y70LlNcDzJW9KniUtWnOQe7Ui2b4gBAuUV3NR1PC0Mfnh6v6M0EUtt4CrU4PHqSXYdCLxM/XDw1bkc18cNXPRSpHw8ENdsZLOMb5iAB6e11KSllAFgeYQABeJLH//rnzwLAVaTB+6iLjjUDD+pljWZiw1xyXOwZLXizEE6HnChVWypjho8B+Pn6/PfzVTHYczfU0xndKSuQ3x8HzO+yR+8Fm+cwVXjroQlsWtaF1521Dr/9wHl49vqByPX42k5GFL7xvc2mTFx4yirsPJL3nmkumOL/51uP3HbBMSD9/Shpgf++rN0wlqGjVHGRSTh6H2HvdUlXCrbl5+TyyLqQg7G+pK4B0zAghKQ2mNfkJhur+oNFQa0wUariR/fsBgC84fnHRBy3PhjLL7T+sOqGnojQm7UlddMbXZ38vpefgL97ySaPO5e6MMFz1j30ZuCioqoWjLXU+ZaqkrrhjB/29PMVp6Uypg5PX77qBtLe/vTUVTjvGcsC6peMnKKLihU39oDCLRW703J//DvmUuzrqcN5nLq2D59+9alN12Maa7xURV9I6lnvq7uiV9ZR8G/SO0wBWPBp0GzgJ8uO944WKq737PVmLE/JsxnGS1XvXWuFWE8XEZ1PRE8Q0TYi+lDE8g8Q0QPq38NE5BLRYJxtZwNhA7G0Oy05ereGo8UqStUasrY0JAu1IIOpGtsgn7fVXn42ukxNxOXod480p7PClbH8uTtt1XGQAJBWXgsPqoNdQSVIhmFQIHujK2UFKDjAH9xa8ZR8r32ayfQMMXPkTBUxVz9ZZq49XtSPf1fFraHq+DMBIoo08kAwgBsXwxPB68ZicnOZSz9aqNQ1TI9Cr7oOUTLWrPnTlbKQS1sQwqdc2Vlhj34+dJm6b9cInvXxG7wU4bioOHJmOdiVghDy2gkhAqm8fVkbE2oQaIaJkuMNnq3Q0tATkQngqwAuAHASgNcR0Un6OkKIzwshThdCnA7gwwB+K4QYibPtbCDM7S7pTnkl8FxE9IqTVwCIFs6aT/jqrdvwtdueqvueX3TTIM/z0ae4YUMfl6PXo//RMsH1efRA/fSzzNkiofVOW9cf6zxyabMhddPK0KdMCqRX2pbMTAJ8SosNPeeA58tOQJWzFfQK3HLMAULXx4mL0UIFPRnLu96WFmyeLejNWYSQqqKN9Od1cKFclN5NUaNu2LPlAYE9ei/rZh4Y+stu2YbJsoNfPnigre04VnbGevncbx2a9Ga7nILbm7UhRPSAqKMSSl9uhjge/VkAtgkhtgshKgCuAHBxk/VfB+BHHW47Iwi/SEu600gr3pabT5yypg/A/Db0tZrA5294Ap+9/vG6ggo/vdII5Bsz2Lte0Rfk6IUQ+MHduxoWaOzXujBFFeZUI4KxABt6nwrzqJvQg3lGAz43jCjqphyTumGOXuf0rZBHzy9MwNC3kaccyNd34qW9+QHc+Jk3hYoTqDz1s25mx/hded9enHbpjbhvl8y0KSvFzjjNL3hmMxFhwEbyVaRVxTLP5Hg9dlw4+2Y+KD7c+oSs8Wg3e41/09oBmTI8WXLqaiNW9Ul6tVW6d7VW8971VojzFK8BsEf7e6/6rg5ElANwPoArO9j27US0mYg2Hz58OGqVjsHeK093V/SkPd7Wv/Dy4s5nQz+mnZsu5QD4PVW5YApAIJf+0HgJAzkbactE2jI8Q79vrIiPXv0wPvCzByOPuWvEP06YOgGi0ysByceOa0E3j7qxWAnyRfjBW5/X7OcGkEtZgVTEt35nM172pd/KY7fwuuW9DmbdsCHmWQKffxdz9GW3rTxlIpKyCm4NFVXO3gpcT9BObKioaEYGn99s6d3coqrHmbLIt9H8guMxv9iyP/D90EQJ375jB45Xeke+Ry/37Rl6dUnnmrrRNfW3HmotVaCDZ5Ar++TMerLs1DksXDeyZ6Sxoa/VBISo78nbCHGe4qg9NbrSFwG4QwjBOrixtxVCfEMIcaYQ4sxly5bFOK34YE9w80dfhp2feRUs04CtpvOTJQeWQV5Zfmma0g5nAsNaDnK4AbfPVxPMiADd/rGipyiZTZne72RP//dbowdXPUMiaiqpp3XqyKWCDTH0YCwAHL+iB+cetxRx0aV59G5NBFL6YnH0Ti3wQrEnzMaEjYtlGsjYhhaMjZ+YljYNlKu12DMBvh+NmqlHoVgJVut6wdiYXYmmCq+aWP3PfQUaxVp0rFC04a9CdMdlt2wDAK+VJN+LiZChp3mSdfPAXr/o6+4dR9ralqmbVZqhDycV9Gb9WWUj6DP4OIiz1l4A67S/1wLY32Dd18KnbdrddsYgS9mNgCwxe3lDE2X051K++uA8arQcxrDGl4endT5Hb8CO4OhHClXvZczapufRs6Fv9O7oaoN6p3qGF4wNGbZsiGpho5+OGdwMI5e2vP2NhXKM41A3FS3rxjYMj9vm7Ias5pF2py1Mlh3p0cekbvzjxKduVvZlYBCwr4203rLjeqmVACID7zMJppl45svtIaNkqaNw3jOW4bhQh67vqgroL7xGSlwzDeRRNxSibubYoz+kfvOrTlmFo8VqWxIUnkffW+/RsxOkp+o2gqPN4OMgzlN8L4DjiehYIkpBGvNrwisRUR+AFwP4RbvbzjQKlXr9DX4pd48UsHFZl/fylGP236w4tVkXVzqsefTh9Cu/v6kfjNUzMSaKfiqWNPTB1NJG0L3ysagOTw1y2bO2FQj4+hx9Z6UbXVrbvbC8dOtgrOF1mLJNgmGQ9zLxoKHTIV2qYKfcrkevtGviFlrZpoEVvRnsG2stYMUoVtyAEqbeQWumsWM4j7u2y8k6Xze+x3GCsYAcEHTqQ/daX3myVG7ke8FGkQczL71yjmOxh1Sby+ccM4CaCL6XrTBZlu/Qsp40DJIz5krY0GuB/UZgj37aOHohhAPg3QBuAPAYgJ8IIR4hokuI6BJt1T8HcKMQIt9q21hnNo0oVOpVCFOmlKnNlx30pC0vEBLHoxdC4IR/+jU+fNVDM3K+jcAZMCnTqMtA0W+8FVEtOal+JyADj/yC6s2ao4Jkparr8dZRmTp+X9Xgo8RaMoxyqGFGu8ilpEdfU2JpOlpSN6pmQve02TCxQqYu3dyl4gFVN77WDeCrUbZTUbu6P4t9Y/ErsvMVF1lNCTMq8D5T0PXTeSbIz0Rclc/BnKz65NTBPaoa/T9fe7q3Dnv07MxwWiWrRc81dXNoooTBrhROVv2N42jHM3jw6s3a3swxHOfyPPomhp7jb9MZjIUQ4johxAlCiE1CiH9V331dCPF1bZ3LhRCvjbPtbKOoFSMwuKkye0h+44jWnhFn6lxx754Wa04vhicrsE3Cqv5MXem84914wxOBcrXpfL7sy8dmNf58TPOuDkVk3hSrridZECWEVnHdQEonIxtqfO0HYzujbtgws2iYjlbG2Muj10rGOXjIg6fuJXelTY87bZu6ceJz9IDUHgr3rG0GmXXjnyv/ntlIr2RHYCBne7O7QjXYhasVBrtSqAl/dnjjIzLWsnGpT+eEOfq6gqk5pm6GxstY3pPGGpXAcSiC0mwETk3tyVjoydhBjt70heqA5tQNU3XmNHL0Cx6HJ8pY0h0MFrFMLUvpMnUTJ13qob1HW64zE2Dd9K6UVZeS5xVMmeRx9Ozl1WoC+YrrGfrutOUFdnU65uDR+iloqep6/Vuj1SNFJB+dtWU3Ifbc2PPLdEjdMPWWrzh17ejiUDfccNr36IOGPuDRq96mNRE/2AVIQ19y3IbXJAr9uXjl7ox82fEqagFdtG3mjR978esGcx7lUuAesTGybgB47+FIvoI9IwWvf/NJq33JbN/QK4/eayU498VhZcfFTY8dAhFheY+UUtmyN1qRMwrDk2X0ZiykLVM6FKX6YGwc6oZnNfZ0evQLHUfyFSwLaarkUiacmkyvTCs1QyCeR89FErOtiT2ar2CwK4XuCDGsqIIp/o4pFPYET1jRjR1KWVLnSw9GevQ1L4gb6dE3kAnw1ByVUeZZUJwKyijoImRh6qZVAyjuPaB3o/KpG2XoNTqkK215olJtefSm4RnAuNut6svi8GQ5UITUDJPlYB49C7rNhib9SL6CXMrEQC7lVbJOlh0Q+WmprcDaRruO5PHCz92KO7cfwQkrugMzQj+PPujR601k5grcu6JWk9XPJ6/pbVk9rmM471cR+9SNvJb8zBgGyazAZsFY7X2Pg6eFoZct4cJZIay74SBtmd4oGsejH8lL4+DUxKw2fcir7j1RVaKBgqkQR8/r8gs00JVC2amhVHUxWqh4WRBR1E25KmcCKctoQN1EV4GyYeZtRvMVZG0zdiVf/f445cz1jOl/vvZ0vP5567FxaVfTbVNmfTYM/z/mZd3459Wd8g19e8FYs67SthU2LM1BxAzouTXhyXUwOHtoNrpMjRYqGMilAvLKEyUH3SkrVrtFwJew/unmvd53YQ/dVOql46GCqahslD0jhdiDZKeoun7iBXvSn7z4ZACyMDBKpK0RihUXOeVkdCvqhuNX+iwwbZlNE0O8mFzSStBHyanvFKRP1dMq9ZKn3q3AL7MQs1tgxcJHXaHiIcD36GTBVJCj99r9qQepPys99LFCFaOFKlb1ZdCXtSPTJ1mDIxfi3BnVBjIBems9AIoi6/xxYw+8UHG863/Sql7865+f4uWSN4JtEqqOCKRLEklj4qpuRYFWiGnLm9m1y9FPtunRs/5LnNaO7ITkorJuZoHOODxRRn/ORn8u5c0E82UH3TH1VgB469746MGm6/VkbC9RwDP0EZTGCz93Ky667HbvXD72i4cbat53gkPjJRz/0V/j2A9fBwCYUPeJZ1VdKauttpxlx/USP7o5FhRRdDjQZXsOZRRcT/At4eg96BKgjODoKT9nLCNWeuWENoKPzpA3cfNjh/AfNz0Z+C5fdtCVstCVrpcDqAY6TAWpGx4EePTnrkdjxQqGxktY1p3Gyt5MJHVTUu30cnbwmEII/M8dO7Dt8GSkJrbeWk/fT6fwVSX9frdxDUwgv107V/4cppN0oah0mxy9Z+hjbse/ISrjKYxiROBzNqmbR/aP48SVvVjZm8GRfAVlx22rry7g38ea8K9zFJ032GV7tFqdR68MPQ98TJ18586d+O6du/CN323v4NdFY49Gy3CBJeCfe5Q0RzPouvPdaQuTpfo8ekA1aWlSMc2OSNIzVkEIOd0Na6zo+dycCZK2zTr+N2p/+8eKOHZpF3YM52M1COgEb/nOZgDAm8891uuwxNlDuQgvwpdAMHyOXhl/XcIY8A39vtEi9h8tYdPybgznKw2zbrIpE7l0MC/+4X3j+OS1jwIAjg8VwAD11E2pDQGmKHBKITcKARDgqpuBm0pXQh2j0paJCTieRj9jw9Kc97lZU5MwUpbhnVtcj55lEOIUTYWbjgCzx1uXHRcj+QqOWZLzsrCOFqptdeECpDEzDYJbEzh9XT/++qz1ODNCq38gl8KTSl6AC6X4mvI7Gu4fwbPrVmJgYRwtVtGbiaaf8tozv3uk4NGgnkefrp9dN0Op6nr9GrrTwawb3dCHK8vD4Pe/d7rUKxc6PGW4EG0Q5dGnLaMuoyOMvaNFTJQdnKUezpnWsNfFxvJM3SiPXi/Yqmp5tWzQ2dDrEsaATxdwQHZlbwZLu1N1aX48SGZURyY9LvDwfj/zKOpFZ8PsFTlV4+m/NIIX3K26mPACgDEfcpKGpeqIyPveF9L01puVN2tTGEbaNLxrHtfQb1rWBYPiNb0pROSs+6l4M0vdbBuSRnfdYBY5TV7ZcUVsnhiQlBk7ASt7M7jglFV1Ay2AgM467z8d4uj1d08IgQf3yGeyne5pTxycwGmfvBE3PTYUubygGfHthye92Xx3yKNvVDz564cO4ML//L03EJeqrqdS2Z2xVBZZPXWTsc2mqqbh82iFRW/ow8pwDP2isncvDX1zz+j3W6Ws8fmnyCq+cDn+dEOnhmQgx0JX2oIbKhxy9WCsJ4Eglzshj55fGPaAcilTZvKEOHhdcTKXMgOiZo/uH/c+Rxk13TAD0qMPz6raARuHf/jxA/jyzVvRnbICkhbNIHvosnywf67dWrqpDt3ItJNZFaCF2hBD60rH43mHVEWmbhhtT72yfY/ebSOZ4PEDsijotLX9gdlaeJYUBxuWyOB5f65x0wy9mbrtBdCV5r96LrnKFABufmwIO5XQ2mTZxbdu34Gf3ecHfBuBqZkf3h3dQU2/L4cmypgsO0iZhscCdKUtOGq2yCg7Lh4/KN+Pd/7gj3j0wLgnAqcXLnanTQjh25CgoTeaZgAWQgkWrfA0MPTs0Tcx9EzdtIh0A8C+sQIsg7zWcCP5meHo2Yb97kkpNlZVBT852/Q8UD0QrAdjmbphL78a4uj98n8/44SnoLpnMqHxkd1pywtEAcAfd/sNoaOMWpi6KZQdzxPsBGGve7INaV/TINRqqOPoVyhhqbDErj4dboduSkfw/3HQHXP6v1+1oFvd5+vKdNpKcHiyjE0fuQ5nf+aWWOsfyfuDTFa7t44r2k4zZuqHhQSjoMdJ+P4wjcaGXo+V3fDIQU93J1928C+/fBT/96dbWp4LxzIbec86/z5RqmKiVA3IPehqp4xv374T5//H7/GwVkm8R83YJkuOVs8in2memejvUcaqp24+f8PjuOlRWWAWRfc0/Z2x1lrAKHq8ZvCn6l6cR93YramboXFZfNWdtpAyjRnLumFN6m/dvgPFius9cLm05RUw6WqWUcqMrkfdcHGF/J12KLUwl5KGvOoGZwnszXAVnx4w1EXVonhsvVk2IMWvWJq1E2RsE59+9Sne3+0UR5okPfqw7HCfx5U29ujjtmoDEBnojYOofrhROKA0cbinAODnmFfblAV4wzfvBhBsLNMMkyWfLmMvsqg6srXr0fO4EB68deiUBHvAtkdJskfvN/H4qea937ndV5RsNWMpaFlhUdA9+omSg21Dk1i/xE/n5eI1faC+b5fUA2I1TkAKEnLhojeTVL9xJF+BQQhkj6XtoKEXQuCrtz6Ft35Xxu7idldjzFtDX6g4OPlj10d2U2oHfLHCmQG60dE5+lZ59OOlqpeeKKdX4cKlWsvBIg7yFQdLu9MoVl184GdbvKlfLmViUHHIOkfpDQQp00u58oOxjTx6X+fF90z0B1uVa6dt9GQsTJQc3L51GB+9+qGAqFpUipfHp3reV7Xpix0HetD3oxc+M/Z2pkGoiXqPnn/rplAwWfcm4wa7gPAssT1DPxmh9R/G/rEilnanAzISRCRF29qkboohI9IKk2UXXYou82drDqo10TK9NQxuQNLMCLO3C2gefagKmAPfLz9ppbfuhaf4nwFfXbMRmI5sRJOUqi6IpHTDeLGKg0dLnl484MeJdM+fB42hiZJXzHckX/EGc36+uIDxyGSlzjHozarqbPUOT4RmfBUnWGTVCvPW0P9x1xjyFRefvf7xWOtfeu2jOPvTN9dF3KMyFQA/IKkvW9qdbunhjBcdTy86Y5t1Rv0V//E7vPFb98Q650YQQmCy5OCZq3oAyErc/3XZHQCkUWaDqbdky1ccpCwjkqOvhgSQ+OEY14qFutX10Keg/LkrbaEnI3nkD/xsC35w927UBLyZRZT2SF0qnFPrWLmSwXrmAPC2F22MvR1TWcWqG/Do+dxW9QZnGrohbcuj1wK37QRxu7UCpEaoODX8ePOeyMClZVLb1I2uHx+n2GqyXPU80YwWjG1UR9EMrz1LKpefsKKn4Tr6YMsDSziVdLLswCDgWK1g7k9OXBHYT6uqVTa+jQY7pyZgG4bn6EyW3cAMkIuf9BkZ07/37x7zZp7DE2XPtnSHqJsj+XId/bl+MIeKW/NSnsN2qVHDn0aYt4b+fsUBn7iy8cOg49t37MCBoyVcqlL+GDxSh42M7nGxx7C6P4v9R0tNPZzxUtUbJHQVSEB6ztsP53HPjpEpSRiXnRqcmoh8qbtSlvcS6FSKLtwW5uj9PProqlAZjJXb6lNVr/WeJRtcuzUR8JBY1KmpoXel3k3FqdUFxNsFdyh653mb2tpObxuo00yvOXMtAGDjssaVtb0NGntHQX/p2knLDHfPisIvH5RtHB47MF63zO7Aox8LBflbIV/2+8Lq8RenVotdtMM4Z9NSPPiJV+CcJo1ndEPPaY/h/rgTJSkH8cLj/f38yYnL8ZxjBvD//uZMAEH6JArsiRsNKntrNQHTIGXoqyhWnEDBWncEdcPO5vUP+0VhR/IV/MOPHwDgO5+8SWMKNwAAIABJREFU7WihWlddzvr+/L7tHA52lPOF0BZ4Hv0uNRLHUarTjSpHuxlc6Rr26PWcWb7gg10pVJyaanYcfWnGS1XPEwlHxndoN2PXkQI2tCjNbwQ2tlGGvjtjeV6mPnupusLj6/h/N0TdcNDMM/TqZc/Zlvd7owy9pTyaMM7aMIgH9x6NNBSBZtleiuvUDH3aMvHwJ1/ZdlCXPfRCxQ142hefvgYXnrKqKc/ZDtee7iDrBkBkxlMY/GJ//KKT6pZJdU6BcaXbFEchdLxYhWUQnJps7t2fa77+RNnxZn26oa+6IrJgrhVaDaBRNRJ+hpGibsoOejI2utIWbnrfiwHId/jKd57jVTy38ug5e6WRnXFqApZB6EnbmCg5KFSDSri5iGAsDx7sdS/rSWN4suzF87huoEfj6NeEGrf0as7c0WIV16r2i2y2Kh5Hv8AlELgcfziGhKtunEZDWTAljbtuBF42GKOH53jR8W5CxjYDkgl60cuRJuXLrcCt5U5YXj+b2bSsG10pEwYFqRtXaxQcLpjiB4/5RMOgQCCZs26AoGei8/4DWobEqWv78Genr8bLTpLT5KgUbss0YBAb+qkpV+roTsdPq2Toxw0b7nYDic2g7zuubC8ApR3T3KvmKfwbnn9M/XFNguPWcOonbsQz/un6lscTQg4KHKeK05x8slT1Zn08kJQdKfOdmUJ9RCNEZeQQyapvdkAmS77A23HLuwOdq0yDMNjlSzUIISJ17PMeRx99/d2agGkSujMWjuQrECIkae1x9L6qavh6nra2D8OTFaQtA684aQWetaZPbqsNZuHnhWs5hicr+MQ1j+DnD/iN+XiGnDKN2BpD89ajZ2M3WqjAcWtNAz47h+WovX4wh4OKeuELcPcOGQFvRhvww8Ie+LahSawbrHdxakrtkj3qcAqUHpBpR2O87veo6SY/EDrYyw9nwTiu8Ay8L4EgX4i8l7HjXwPTIMCVHkLKMrzMhnCWASBnEcu12cWlFz8Lp6/rhxAC733ZCfjbc+qND+BLD3j02RSpm06h3/u4fPIVb39+27rnuqHvaYPyiZNHP5KvoDdjRQ5MtiYABtQrXIZRqtZQdQVW9mawd7QYq4Q/X3a9Z49/Z9UROFqcepA9CpsUnXbucUsC38uaCN+jb1Yw1Juxvevy2m/chVLVxS/e/YLAOjwbjeqeBkBRU4SsbXoaOvqMkt+ph/eN430/2YI3nbMB4yUHG5d2Ybua4S/tTmOLkjZ/zjH+AKanaS4Nyaiv6peD8L07RnD1/fu874WQ72W7vRLmrUd/eKIMyyAIAYy0KEraoTJSzj1uCSpuzaMkdh8p4PI/7ATQ3MPim8UXu1F7vXxFapTzg50OUTf69HtPG31AASmexOmSu9XvWT+Yw0cuPBGf/YtT6rI/erOW19ga8KeYAGCG0iu5uk+vJOWXlm1ZlEfPBSk9GduTlwX8l5CI8J6XHd8wHzplGihXXW/mEEX/zAbSTTz6Rnj+xiU4Z1P85uVAKO7ThkffnbI8vfxGGC1UsaSBxLNlkOfsAH4VayOw8WOPvtVsApCpvJztxbUahaqDYtVtK2AdF/25FH52ydn42hueE/jeNvx4xFix0nRA68v6Wv937xjxjK0OfmfHCtVIr95VHP1gV0prJK954srosyb99+7ahYpTw6tOXQUAePUZa7wkj+HJMpZp9zBtmX5D8JBjwE7Rjzfv8egZzjQ7Wqyi4rqLw9DnK46XxtTKO2ZDxl1qhlSEeuuQ3+KrmdfBF5W9sIkGsqP80OjBWP3h0FMOhybid50RQuB5/3YzLvqKVOHbeaSAFb2yMOXtL9qEv3ruetz+oT/BPR99qbdNb8YOHI8fSMBPQwtTN3qK6ZXvPCdwDlEcPedO52zTC4RmbCO2t5qyTFTcmnctVvR2nkc/FQQ8+hmgGbx9a9523Ck1oAubNfbqR/OVhpWktmkEBOmGW2SOsT4TB/yK1eaziVLVxZF8Bau1lOSU6ev6TEXaohnO3DBYZwC5twAgZ/IbljQOLqxQMxadsgkb86gG9jocV8AyjEA6dqBnr/rtBxQDwcc6ZkkX7vnoS/HpV5/iNVsRAoEcfMCni6Oey5c9cwUMkvt890uO87KLxooVVJ32CtXmpaEXQqrbrYtp6Jma4AvB4lyX3boNgNQtjxMI7GnxwjEnHkyv9L2wo8UqiGQgpR29DVapO3C0hCvv24uf3bcXxwwGH4jekFfdm7ED5+nU/MIV0wh59CpTQOe2w4Fe9kAnA9kDjseJZ2wTj//L+Xjs0vNj/y6WlPDTNOeIuonQhpkJdDqIcKpjs9jQWLESiJOEj6sX7jWiIRgspcCOVCvqhmcIq7SAYcrym6zM5DUNwzIIjiv59smy41XZRmHNQBZDE6WAbHH4GgfbXdbPqNiBWqk5KXq8j52q8ZDN6M1YWN6TQdoyA+qcq/uDzs5albkW9eycurYPNWULsykTfTm/Ir6ddpXAPDX0zI3ySBhVnvzhqx7EP1xxPwCfbjh1reS0uYnx/aobzMWnr4k8zu8/+BL88K3P8/7O2HIqNd7gReEpL3u0mVCBlVTBk1kA7Rh6vcvT+1XZdqvpcE/GCvCyukfvVUtqHH0jTYxVylOxTAMZ28C2oUm843ubcWi8FNDlAOT1acdTTasequUGmU+zBV10ayY9+k4pDA68NdNRz4fyt3WEDW0r/SVOFGBD3+xZfezAOP5UzTT1VGfbNLwir06ybjqFTCUVdSqSUciodpZ6sVFYbTZQ6BRRNMWUqD4b1T167gYVjrHo75tu6JeHOt3xTDoqfqVv1522AtInehOdOJiXwVg29PzDwr0Ty46LH90jG3O//vnHYGiihP6cjeW9GWxa1oXNO/089tc+d13D46wbzNUFXWUApzl1w+cVVpjjwJRlktc0OQ6iPLlXnxE9OHnnmQ32GtU5esMgb8oHKJ2ZCM749n98ScCL6U7bXpvEU9f2y6yGKfDq3Cy7UdHabEE3hO28HO1i3UCLHMUGGOiSz1Oz3gayS1r09bNCWUhjLXokcFyJZxLNVBJ/slm+Z28+d0MgOSBtGZ433G7B1FRgmzLrhp27RmnQgO956wNf+NroMamoina3JmAYFKBuBkOzCDn4BLfNNQi0rg/ZGz7HKPpL73O9rCft0VjjRal42c5Mal569BwgZG8yfAP0lMu3XH4vDh4tedowxy/vwe6RgjdSt5vL3pe1Gnv0dYa+3qPvy9rIhgqpWiEq46K/hXfYn7Vx4GgJV/1RanzoHj0gc9/9VoJupKFfO5DDpmV+Slp3iFrhPOVOwfr+nrDcLHp+OuxZ8ug5M+mZq3pbrBmE/wI3MfSqL0AU+DfZpizsaZRMwOB3wzP0TZ7VoYkyNi7twscvOrnumHNC3ZgGnFo8Q88Do24v6qkbFwOKEomiblidU6du9HcGiP79evKE7sWHnz++p1HPpT5AnLau30sTLlWlxlA7sZF56tHL/9nIhD16vUdkLmVh/1jJoyDWDWZx6xND3hRtoIkUahT6c6mGL0o4GJtVU0NO52RDTxRfLAoINjcAgItOW43nbBhoug1fm/f9ZAtefMIyL2jEMA3y0isLFafpC8GYDFX3TZSqTRUGWyFtSpG4ufbo9esykx69YRCu/4cXYlVvtvXKGph7bdbEptikQxdP+5f3ZJC2jZYePQ8oHKdpxtGPF6voiXA6UqbfTWs2Db1tGqg4wqONws6JDjaEunEPxy8KFRdLulIYLVQjPXpOYwx09Qr93iiDqztIHNPbFFGBzZlwUc+l/u4t70l7M/SS4y6O9EqmXTg4GtbiYEN8ypo+HBwvYdvhSaxTQY21AzmUnRq2qgBSX7Y9Q9WftRu+KGGxf9ZXZ0+ADf3agSwOHI2fXlkMFVh85XXPbplzvm7QNyZ7RotwarWQR0/gy1Zo4NGHoXs+kyVHVUN27gtINdCapiA6V9SNf13iDHhTwYkrez3DHRc9SktIVwTV4aoCmUb3kN+TvqwdSClshPFSFT1pmZOftoym1M14yYnMWJMeveLoZ5m6CXj0TfTY2SDrFeslbVDjqtO1ik6J4ugrTq1lO8mobLKwhMNv3vsiXP7ms+rWY0rIiSjmWqpiN/0527tXgKTeSo7bVl3K/DT0YEOvplTVaI+eg68Vp4bnqLJijmLf+rjsGNOuR7+kO4VH9o9HplqVHCmKxQaVDRef33hRFlP1Zhvz/IAMcH379h3egNZOz0nGRaetxr+oTvTjxark6LUXjsiPdRSU8mAr3PL+F3ufhybKmCw5bak3hpFWPXgPjJWwtDsVGIhmE3qxXZhfnQ8gIqzpz2L/WHRKrtcrtsFAqRv6qMbxYUhhPl/OoFll7IRqsxeG3h93NgvhbNOA4wrv2M0Gbn4/9boCfVBjx2ajoncbUjdKt+hXf/8CXBsquAL8TBq9Ajt8r45f0RNZhPmaM9fivGcsw19ExOT6cja+/obn4CqVCk0kC7cKZUf2j24jiy2WoSei84noCSLaRkQfarDOeUT0ABE9QkS/1b7fSUQPqWWb4xzPp26iPXr2WNjQA8AJKyRvdrySDfjeXbJjTLP0qyhwQ5FbHq9vLVZxgrwY38zxUhVCCIwVpEffm7EDQcgwPnzVQ7j0l496BRxs6O/68EsDufLNYJsGnr1e0jvFqgtXC8YC8PpyArImIRfjodi4rBs7P/MqXHjKSjw1NOmJRnWKtMqjHylUsKxnbnLogWCwckn3/DP0gMywGG6QdcMceqaFR9+btTDYlWraVBpQHr3XCs9CsdK4UGu8gby0nmkyU3n0UbBNwv27R/ErlTTQzNCzk7fzSB5LulKwTQoYeh4QefCPom70HgYnr+7DKWvrq9W5HkEPtMbNTlvek8Hlbz4LxzdQ8jz/WSuxUYsJlB0X37x9h8rCiu/EtrxDRGQC+CqACwCcBOB1RHRSaJ1+AP8F4H8JIU4G8JrQbl4ihDhdCHFmnJMKV2uGR1rm2V5y4nIAUi73xJUyALZeFVDwPloFNcO48BRZ0RYlhhTmxVhbY+vQhFLyE9LQR4iO6Xhgj0z7fGQ/G3oHpkFY0ZuuS79qBp7KF1WnHzPE0bvajCFuf1VAZiPtPJJHsdrewxQG9+AtVJy2KkWnG7ohmo8ePSADb430kRr1VGDwzDdlmVjVl8GBVgqsRV2BtXEPBiGkxEFU2mh6lorQwrBNA/mKi2uUyFczr5advN1HCujJWHWSJWzoBzxDH03dtPp9LEjWropnJ+C7OqFpD8VBnDM7C8A2IcR2IUQFwBUALg6t89cArhJC7AYAIUR0p92Y4Ic0YxtImUZ9MFYp7y3rTmPnZ16F+/755Q331S5fmkuZsE2K5DnL4VZ0qlp0NF8NpF72Nim82qKMPADsOCy5w3zZRa7NHHV5rn7Tg7BHbxB51zFfjufRM9YO5OpmVZ0gpaibfNn1OvHMBfRnYK70dlphaXcawxPRnnhUZbMOnnUZJAOsFadW16hCx3jJ76mQTTVuQs3qlFEevT54zuY1DdN/zWacPKg7NYGejI1MKmToFWXFfRUacfStgs3s0cfR9Z8q3vbCjUhbcrBrJ94Ux9CvAbBH+3uv+k7HCQAGiOg2IrqPiP5GWyYA3Ki+f3uck+LLlbYMaSxCU6q8ChLGMYztPoREhO60ha/d9hTu1lqSAUHNd8CPio8WKkFDn22cLvePVz7ofT6spurFSuPUuWbwe3c6MhhrBg29WxNeE/F2PPoTNBVAlj7oBFxnkC/PtUc/P427jiWqm9jQeAnv+N5mr6MY4PPKjYKxuvPBRTZD440lOHSPPmdbDTn6PaNyVhuW0A0fczY9+j2hmXajwQ8INh/vyVh1ac/M0bPDFkXdlGMUJnFaLUsjvzFCYXS60JuxUHZqcGti2g19lDUNzwstAM8B8CoArwTwz0R0glp2rhDiDEjq511E9KLIgxC9nYg2E9Hm8XGpUZO2TK+6Ukep6jZVo3zgY409/DjgwpV3/fD+wPdSSiDYZs40CGOFqlfdOpCzPbooKl2OX4oNS3JeCmah2t7ozOCHvNSQo9e7xcc3dqeu7fc+X/CsVW2fFyOXki/WTCkcLiZwquOXb9mKGx45hH/6+cPeMo+jb2DUXnricpy6tg9vOmeDl+O9bSjYqOJjv3gYNz16SMWSKr4Ca8pEsUEbPZ5hRGWV6IPnbHL0Tx0O/q5mzl7aMj2Pvydj1fWPeHS/7F1x3DLJjzeSQGjl0TOF+7/P3YAnP3UBLr345KbrTwX6e9TObDvOmnsB6OWlawHsj1hnWAiRB5Anot8BOA3Ak0KI/YCkc4joakgq6HfhgwghvgHgGwCw8ZmnihqkUUxFGPpitdbUA2blu6godxx86S9Pw/t+ssUb6Rn5kOdNROjP2hgtVLzUuDUDWe/BODQe5FyPFqp4eN9RvONFG7F7pOClgBbKTlPPpBFSlmwbyFNsfVprGDLrxteUj/9Q6L9xKpkyXWkLTk1geLI854b+qr87Z0oZRDONY5fKZ/URZXx+v3XYW+Zx9A2e+SXdaVyjskG4E5EuVFaquvjunbvw3Tt34a4PvxT5iuvldGdtA4eORlM3zbJ9OlEEnQ7IyliBt73w2FjxrIEu2yv8y4Yq2cPKtlGG3lGVsc3Qn0vhqX+7EIT2xOw6gR4vaWeWHmfNewEcT0THAtgH4LWQnLyOXwC4jIgsACkAzwPw70TUBcAQQkyoz68AcGmrA3JaIFM3Ye6rVHVbehHcxaUTvPqMtbjpsUN4/MBE4Ptixa3L2ujPybx7zr0f7EohY5vI2iYeD7V9u2fnCGoCOO8Zy3H1/Xu9YJDsaNUZvZBNmZEcvUmEmhBaBWF7+1/SlZpy4JINRE10rgMzXThjffMCtLkGK68+vK9eSrcVR6/D6+eqZdLoBUPcw4Bpx1zKQqGBeqU/wNS/azqdMZse/XV//0IcHC/hhccvi7X+YC6FPSNF5dHXxyOOWZKDbRKIgHJErKImRJ3ERBTmInW4HRag5ZpCCIeI3g3gBgAmgG8LIR4hokvU8q8LIR4jousBPAhJsX9TCPEwEW0EcLUa5SwAPxRCtGyBI4Tki1KW4eVi6yg1KQefLqzpz+Lmx4YCTUwKFQfrU8FZwkAuhdFCBeOlKgySo6xhEJ65qgdPHgrqgu9VnOeJK3uQtnzly0LFQV+HFajMO8o8ev+FY46+E48eAO7+SLw0z2bQB5d2qKOnI/pzdt3slanCEZWNE2dWpNN5DN1TZQlvvjey73E0dcP7iIpxzJVHf/yKnoapiJFQ7+6a/ix2DOcDg95AzsaLjl8GIvKUVsNw3Nqc1X9EgTMNgeaB6DBirSmEuA7AdaHvvh76+/MAPh/6bjskhdMWakLAIvnQNvLop9pouhVW92dRdmo4kq94Aa6ooGlX2sJYoeIVS/E0b91gDvftGg2dt987VQ5g3HrMxaq+zn5PTmVN6K0EAb+wxK8gbG//zTp6xT83S/ucGPpmIJJZZHp17MGjJWxc1o3dIwVkbKOOSowCG2Ddc9WN/uu/eTcAn3fPhnoqAFK+I2MbTSuac7Z/b+dzsJsTIo5d2oXNO0cDv7VUrXlFTrrjpaMm5sZbb4TejO3FGtqpCZmXlbFuTaA7LbNqotIrmwk8TRc400DvAxtuDAxIz6hQcbHzSCEgfLRuIIcDR0uRTQ/SluHJAwBKoqBj6sbycvj1B9K2pMqf59HPQXqjfq3mSv5gIYEznLjw5tVf+wN2Duc9jjlelplRR0NEGTB+vrkyllNxhyfLeO6/3oRTPnGj19eBUzF16N/NpgRCuzjzGEnZbVjaFUglFUKg5Pj6QemI7D4AddIi8wH/f3vnHi1ZWR34367Xfd++/e6mG+i2oYWGbhQ7YGt4KNEAhggTH4hKMBkZXRoxE8fJmDXxETKa0RnJUtQQYzBmkBEjAQ1izCTGFzrSBHzwGkAeTQPdDfTzPuq154/vnKrv1q1bt6r61qlzqvZvrV5ddR51d5063z7723t/e99+1dn8t0u2tlQ8L5aKvqzVwmH10iv9J3GnCKtePuqluU3OzH3ArBgd4KkD0zy05zAv9Op1Lx/NUSrrrHz8MFUrlRIGMmmKZaVYKlcag7SDs+iLc3z02bSbCVUbg0evaH2fcquuo34kTNMLldP+yQJ/ett9Ld3vIsLykRxPHaimV9bzPYduoKFcmrJWc8B/8FA1CPxvj+9nMJuqa7H7Rbs6HYA8Gq6+5FRu+PdnsmnlKMO5ahP2qUJpVqPvgexcFzHMrQobBzasGOGyM49r6ZxYKvrQoodgGX2tRZ+fv5LfYhEugng6GDDFUpl8aW4++qnHLOHwTJEn909VAmpApRuQn2LpB5HD/0Nl3EoE3Wcom/Ys+urPmQ1mQuHq3E4X86orW8589K0QpliG9ZrA+WGn8q25KtcvHZ7VWnA6GD9h2t/S4ersIBxH04Gf/o6Hq2tHHt13ZN64QLf6/7bKQCbNy05wvX/Dgm+qWqkrdExQ3nw+102tAZVUYqnoV40P8OHgpszVCZLMFDuv6EcHMozk0pUUycl5Fq3s2FTtUr9hRTVQG05t/dWxfhA5lP/wjGsi0K4raijIVS+WZvvoB4LemruenySTkoq1GCW+FW+um4X5D2dvYsvacd7gNcsZyKSYLrbmqnTdx2bfd+Ayj779+2dz45U7KvsqZTSCY/YemqnMxHYfmJ7TszXkaGogdYuJ4SylsnIkX6qsYQnHRT3XjapSVpfYkHRiqeiHsmle+gKnQOsFYxt121lMVo0P8kzQ2DpsuFybJujn6vspZ2N1mkn4sYWwvsauIAZwNK6byXxpTtAobLm299AMy0dzixJcbUe2eq+N+hy7bJjbrjqL9UuH+cybTwdcuY8j83QImw/XT9hrVB9Y96vGBjhx9dgsF2M4jsKFdXc/sZ9Nq6p10+ez6JP4e4bfZf9kvvJ9q6385hqUYXzNLPoIqE2vdEGUciSKfulwtrLi9YGnXU79ljoBkH9537nseMHyWdZ9vUbj/gMqtCTCBS7t+rCHc+nKoPaDYmHLtckG/WI7zWDWFH27XLh1La/espqf7z7A85OFeRuD12NsMMNh774Lq1kuH507q6vk3RdKlSJma8YHK0bDfIq+G67Ao2VZpTdvvtLsJ0wzHcik5/jowxrxCy2YSgKJUPS+RV8IOsB3OhgLTvmG095Hn3U58PVaE25cMcKXr3zprI4w4ZTXt6x8i36uom9PEQ5m05WHST0f/XSDzkSdxv9OUTyYe40XrBzlieemeGjP4ZY6fS0Zdqu1w3t3KogN1Qsqhr/RdKHE/knX12DHphUVQ2XtRP3Vp2Ec4TVb2y+RETVheuqeQzNMzoSlQcImQnNdN+HCTbPoI6A2vTJs9NvuAqNWcCvp3N/ec2iakVy6ad/kQhZ9mMccLqo6Gou+WGeKmQuybqbqpIRGha/cj6ZTVb9ywalrKq8nWqjCum3dBIWSVjorNSqaVy2MV6pk6qxdMlhZ6b3jBSvqnjecy/DjD5zHJ17f8jKZrhGWTNhzaLpi0fvNuWtdN+G4ilvWTTvEX9HXrBYMK86tiKCu+FBQ1lTVDZoVLQQ0R3IZRGbXpJ/xGpeMDGQ4YdUoP/qly3Jo30dfVaCzWgmmhWJJmS5E4+aqhz/lNYu+dU47dqJSk6aVTmmhFR5mjDWKaYXbp/Ilnj7o4kVrlgzy6i2rAfj1U1bP+3dWjw92fD3LYhI22/7SHY9V2ndWLPo6WTelUu8o+tibWe4H8FazFRsXeFpMlo/keObgNO/933fznQf21vXPz0cq5cod+xZ9bZmC45cNV9qctavo/QHstxLMpFMUy2WmvC733STOudZxJlxNPdFC7+Nw4V6YYtlogeGQl3Xzoweeq5x/7ZtPp1AqdyWI3ynC77Lv8AyT+RLplFTiWgOZuQ1YSua6iY5cJkVZXR47QCF46i5UI3oxOHbZMJP5Erfc7Yp1PvjMoQXOmM2a8cFKVg0wp0zBOi9femyeNLaFmK/SZDblqvx100dvHD1hSYST1jZf32Xl2AApoeKKadai//L/fRxw9202nerJRW6/dfp6cmlX3sFv9lOvSm6ph4Kxsf8lc97Coky6GpjNRlBIqXZRyF9e3lQnxAprlgxWClIBQbu/6k2zwsuCWNlmnrs/E8ikaiz6kgvGdttt0kyNFqMxm1so5JVNp9yK7eAhMdXgHhjysm7GBzOcfvzSnlBs8zE2mOHQTHFO3KKRou8Fiz7+ij6w3GcKZYZzrlmvv72TnOTlG1/xsg2zKsc1w0AmxXNHqjdPsWaVXaikV4y2XxJ4luvGy7rJpIVCWZnqskV/zx+/mmwm+QOlW1x72enctPOJln/DtRNDs1w38yURhMrukb1HODhd5Owmy/8mlbHBDIdnihyaKc5R9DOleSz6HnA7xl7Rh5Z7oex+hHzRXfyFur4sBlvXVTu+b6yTVrkQtVZCqcZHH06N1yxpviF4Lf7N6vvos6nQopeuBsxa7dlrzOY129bymm2tpzCuHR/kob0u/jOVL7GyTg49VIughRk67dznSWLTylFUXe9m/+E3EGT3+WXJKxZ9jIu2NUvsffTZwAIuBhHw0HWTi8BKFBHe9vINAJWVuq1QG8nPl8qzFjWFbqmNXo2cVvFdN77lkUkLZXXW3GCE9cKNeHDMxBBPPj9FOZjVzfewFxGGsulK8b52XYhJITSqntw/NWvshGOxUKpWmy2aRR8doQUcKvpqMDYaK/UDF57MZWcc11qzg4Da+hkuKOanQ7r/T1jZvqL3P89vAOG7iAYTlAJnLA6nHDPOVKHE/U8fWrBkyHAuXUkaOJrZZRIIVxj7lSthdiwwfF310SffUIq9og8t4IrrphKMjeYpm02n2lLyMHsRhqoymS/Oast2walreeK5KS4949j5PmJBfKvEb+nml5btdjDWiJ6wYfXu/VMNF0zB7FIVyyKnlSsGAAAT2klEQVRYiNhNlo5UXYmzjKTA6soXyxBMako9tGAq9oo+fJqGF72V/pndZiBbrZ+RL5Up69yKju8578Sj+hv1rJLa7ZZe2X/4ZbIbZd1ANd715jOP6+mMG4DlIy71tKyzF6HlAsOoNqYGvaHoYz8nCQMhYbbN/sk8KWHe8qlxInTdqCpTHXpADc2y6Otb8VZQrP+YCCzXvYdnKJa14X0XBmLbiUMljXRKKhlu/kr3iuvGV/S2YCo6MjXB2INTBUYHMomwPHLpYLFXEBCDxbeuh7P1XTe+ck/CQ9FYXMYGMmRSwlNBg41Grpu3vNR1K/qVDcsika3bhAaRP3aqPvpqTK0UuIuToGsWIv6umzAYW/HRa2WaFXfCRs35YrliKQwscgaMn67pV/T0A7BJ6QZkLB4iwsRwlqcOuCBrIwPj6ou38qGLTumpcgeNCGOrA954qfroq1k3YVq9WfQREKZXhmlPhZoUxTgTWg4znqLPdTDV0c9E8qfq7ZZXMJLNxHCu0jJvIZdhvyh5gHRY9sD7zmG6dqHkL3AMLPoeSK+M/a9bm155ZKaYmKYHofU+UyxVsm86udDLt1D8mYNZ9P3J0uEsu5uw6PuNUHEPeNckHJe+oi+HFn1CDMtGJEDRBz764KofnikmRnGFinemUK7cQIvtupn19+ZJr5yvS5DR22xaOVqpK++n9fY7YTvQoTqKPl/HoresmwjIpmZb9Aeni4lxRYSulMhcN76i96z7pMyAjMXF72dsFn2V5UHWjW8Y1c26CdMrzXXTeWot+kPTBcYSorh81021dMPiX/JjgtWMvhUfRdE3I9749W1M0VcJdYo/081VXDd+MLZ38uib0pgicj7w50Aa+LyqfqzOMecC1wBZYJ+qntPsuQ0FrAnGHppOnuvGz7rphI/+r674FZ58fmrWDRlOTxt1CDJ6G79uTRIWGEbFH124hY0rRjnTWzdQz0ffV4peRNLAtcCrgF3AT0TkVlW91ztmAvgMcL6qPi4iq5o9d0EBa9IrD00XkqPovaybTpZXPnntOCfXdL9aMpTlO+87d9b03egv/H4HpuirHLd8mD+84KRZ27LpuVk3/bZg6gzgIVV9RFXzwI3Aa2uOuQz4mqo+DqCqe1o4tyG+RV8olZkulBPjo6+XddNJH30tG1aM9IQ1YrTHirFq3Zok9XbtBrnKWJ1r0ffCgqlmtM464Anv/a5gm89mYKmIfEdEdorI5S2cC4CIXCkid4rInXv37q1sD6dUpbJyOOi/mhiL3su66dSCKcOYj7VLqq0qzUffmLAGVViqBKoJIL1g0TejMet9S615nwFeApwHDAF3iMiPmjzXbVS9DrgOYPv27ZVjKsHYUpmD0y5VbL5uOXGj0h2rWK5W3bQgqdEFxhNiHHWLsGTI4ZliZVvoLu6FxWTN/Pq7AL+O7npgd51j9qnqEeCIiHwXOK3JcxsSplcWSspXd+4CYNOq9uu3R0m4IGOmWKrW0TeL3oiQT1/2Yh585nCla5JRn4FMikxKmMz7ir6/LPqfACeKyEbgSeBSnE/e5xbg0yKSAXLAmcAngfubOLexgF565bfvfQaAbV6Lvzgz4OXmdjK90jDm4ze2HdNtERKBiDCcS3Nkpk9dN6paFJF3A9/CpUh+QVV/ISLvCPZ/TlXvE5HbgZ8CZVwa5c8B6p3bkoDpajB29fgguUwqMVOpAS/AU1kwlRDZDaPfGMqlZ1n0YQZOUvRNI5py3KnqbcBtNds+V/P+48DHmzm3JQG9lbFPH5jmuOXJSRecVdSsFDY1T751YBi9yGA2zXRhbtZNL4zZ2D+q0ilBxLluHn9ukmOXJkfRhzfITKFEvlgml06Zr9QwYspgJs10wXPd9NOCqTiQTaXYd9i1RFu/dGjhE2KCiFT6xhZKav55w4gxg9kU014efei6yVpz8GjIpIUDU3kARhOWJhYq+mK5bIreMGLMYDbNlOejL5WVlPTPgqmuk02neP5IsnLoQway6SC9UnvC12cYvcqSoSwHp/xgrFZihEknEd9iIJPiuSPOok9ao+vBbIqpvKteaRa9YcSXieEs+wPPAbhFmr3QdAQS4roZzKbZc8i1RBtPWBONkVyGyXyJlIgVljKMGLNkKFtp1AIuGNsLOfSQEIt+MJvi+eAHGE9IQbOQoVyaqUKJyUKJoVwinquG0ZeMDmRdPC0IwhbL5Z4pWZKIb+E31BgfSpayHM6lmcyXmMoXGcom4nIbRl8yMuD0zGSQYlksaU+kVkJCFP2gpyCTFowdyjrXzWS+VKmQZxhG/AjHZ1glt1hWs+ijJCyxKpK8BgrDOZeyNVUoWU1ww4gxG4JV9y/72D+zfzJvwdioCevDDGfTiVtZOjLgXDfplDCcsIeUYfQTa4LeywC3//xpCuXecd0kQ9EHaYnDCXPbQLAIo+CybpKWGmoY/YSf0ffskTylkvbEqlhImqJPoKIcyrr6GYJY1o1hxBg/o+/gVIFiuXdcN4l4XFVcNwlUlEPZNIWSki+VExdfMIx+IpdJ8ck3ngbAX3z3ER7Ze8Ty6KMktOhHkmjRezIncUZiGP3EJS9eX3n9yL4jPVGLHhKi6MOsmyRmrfizkCTKbxj9jFn0EbJsJAdAKmEZNwDrvLLKq8YGuiiJYRjNcOHWNZXXlkcfIStHnYKcypcWODJ+HLes2ijlRcdOdFESwzCa4VNvOp2xoBx6r6RXJkLRh2lPZdUuS9I6q8erVvyq8cEGRxqGEQfSKWEsSOXuldLiiUhjCWtQJJHhXIaLTjuGdRPJ6YxlGP1OGBfslXr0iVD0IUnt9PKpN7242yIYhtECA4Giz/ZID4lEfItt6yY4bf0SPnjRlm6LYhhGHxDGYDetHOmuIItEIiz6JcNZbnn3r3ZbDMMw+oQwHBhm/CWdRFj0hmEYURIulFqSsI5282GK3jAMo4awy9Sqsd7IlDNFbxiGUcM5m1cCcMKq0S5Lsjg0pehF5HwReUBEHhKRP6yz/1wROSAidwf//tjb96iI/CzYfudiCm8YhtEJ/uOrNnPHf3klK3tkNfuCwVgRSQPXAq8CdgE/EZFbVfXemkO/p6q/Mc/HvEJV9x2dqIZhGNGQSadYu6R31r40Y9GfATykqo+oah64EXhtZ8UyDMMwFotmFP064Anv/a5gWy07ROQeEfmmiJzibVfgH0Vkp4hcOd8fEZErReROEblz7969TQlvGIZhLEwzefT1lqPWFp25CzheVQ+LyIXA3wMnBvterqq7RWQV8G0RuV9VvzvnA1WvA64D2L59e/KK2hiGYcSUZiz6XcCx3vv1wG7/AFU9qKqHg9e3AVkRWRG83x38vwe4GecKMgzDMCKiGUX/E+BEEdkoIjngUuBW/wARWSPiisWLyBnB5z4rIiMiMhZsHwFeDfx8Mb+AYRiG0ZgFXTeqWhSRdwPfAtLAF1T1FyLyjmD/54DXAe8UkSIwBVyqqioiq4Gbg2dABrhBVW/v0HcxDMMw6iAawxrvIrIXeOwoPmIFENd0TpOtPUy29jDZ2iOJsh2vqivrnRBLRX+0iMidqrq923LUw2RrD5OtPUy29ug12awEgmEYRo9jit4wDKPH6VVFf123BWiAydYeJlt7mGzt0VOy9aSP3jAMw6jSqxa9YRiGEWCK3jAMo8cxRW/EgnBldVyJu3xGb7HY91siFX1YRyeolR8rRGR7UMAtdojIEu913BRX3Jtzxnas2Hhoj34aD7G9eWsRx7CIfBm4BUBVS10Wq4KInCIiPwQ+CEx0Wx4fETlTRG4BPi8ivyMiAxqTKLyI7BCRm4BPiMiWuCkrETlDRP4W+KiIbBWRWIwZGw/t04/jIRY3bTOoYzJ4u0JE3gkQl4EHXAXcrKoXqeqDEA8rQUS24TqEfRW4CXglcEJXhQoILL1PA7fhlnRfBfxOsK+r105EUiLyQeDzwDdxtZreBZzWTblCbDy0R7+Oh7jcFAsiIhkRWQs8A/wurojahKqWu31zB1Nnxf1IiMglIrIeGAred/MGDzuEfQn4NjAIPB7u7LJspwIPqupfA/8D+BrwWhHZHBTF65psqlrG1Vu6QlX/F/CnwPG4wn5dJ7DoYzceRCQtIsuI73h4CfEdD6fRofEQ2zx6EXkTcBJwp6p+3dt+C86y+s/AEeAvVfXhbsomIkPATuA/AW/CFR16Gsir6rxdtTos212qektQQfRx4BPAb+P6C9wH3K+qfxaxbOcA06r64+D9cuBHwK+r6iOBgngPMKaqfxClbPPINwjkgayqzojIV4Av+fdjN2QTkVTwIEJE/h54N90dD/Wu278B76P746FWtlW4jnlxGA8XA1uAe1T1H0RkJfBD4HxVfXhRx4OqxuofrqPVO3A3ytuAB4L/R3AW1TXBcb8JHMR1txrADcZuyPb2YN97cQr1rcH7dcGPdkEXr9uVwb6NwH8HLg/enwN8HdgRkWxjOOvkOeALwFJv39Xeb5oCfhX4LLA2wnuuVr5l4TX1jskGv+fmqORq4tptBv5n8Lob46GRbO8HHu3ieGgk20nAn3VxPKzEdeH7bjBm9wCvC/Z9rBPjIXauG3XfcAfwMXVTmHcBvwacBTwPbBSRrwMfB/4VeExVZ1S10CXZXiEi5+NupgywKjj2SeD7QLnTcjWQ7RwRuUBVf4nzQ+4KDt+Ju7lmopANZxX/M/AWXHey13v7/g44SUTOU2elPotTCgcikq2efK+DyjUNORl4RlUfFJExcQ12uiGbf+12A5tF5Fa6MB4WkO0zOFfNSoh+PDSSTVXvxyn7sBd21ONhE/ADVT1bXT+PPwB+P9j3ZTowHmKh6EXkchE5J5iqgJtKrRORjKr+E/BT3JNtM/Ak8AjwElW9CDhWRF7SZdnOxd1Yvwf8toi8KAiO/RrOqumqbMF09VvABwM/36XAKbibqNOyTajqDC6o+U/Ag8B2EXlhcOg9wI3ANSJyAnAebnaS65RsTcq3OTgubM6zDJgUkStwlunWTvlzm5UNZ7XupjvjoaFs6lqLdms8LHTdAP4R+FDE4+FcERnGPVj+JtieBu4N/gH8DDce/nwxx0PXfPTBBV4D3IB7yj+Mc8+8E7gI2Apcr6r3i8hGXHDiw8B9qpr3PmeJqi6q9deibBuAa4CPqOpdInIZLsh4CvABVf1Fl2X7JPBhVb1bRG7AKawM8B5VvXfuX+iIbFep6r7gmBNxvtEZVf0T79z3Ay8M/r1dVe9bTNnakG9aVa/2zv0ozg9+PW5q/dMuyla5drX3f8TjoZnr9kZckDHq8bDQdRvCFQdbhQuwRz4eRCStqiUReQvwm6r6Bu/c9+MM25NYjPEQhU+qjo8qHfy/Gfjb4HUGN937Is4f+gXgrcCSYP8XccoU3BMuFSPZrgeu9j5DYiTbF4E/CV5ngTURy/Yp4O9qjr0kkPkE3I2fCrbnunDPLSTfcLDtZcAbYybbEDAQbI96PDTzu2aD7VGPh0aynej9ppkujYev1RzzN8AbgtdrvM9YtPGwYM/YxSSYBn8ESIvIbcA4UIJZvWmfwkWibwAuBtYDHw2O+3FwrOLSt+IiWxm4I/ysQL64yFbCZbagzm/7dMSyvQfYLSLnqOq/BttvFpGTgduBUeAV1MzU4iSfiLxCVX8YR9moXrtF9X0vsmxRj4dGsn2T6m96HzEYD8Bh4Jci8hHg34nI+aq6a1HHQyeeZvM84c7B+WM/C7wdF3E+H5epcoZ33LuA24PX24B/wCn4m4ERky2Rsr0T+Bfv/esJUgGBVTG45yKXz2Qz2YLXaWAat2bjGmBlR2TrxIfOcxHOIki1Ct5/JvjCVwA7g20pnE/rJmBDsG0CWGeyJV62rwAbvfPOitk9F6l8JpvJhksX34RT8Kd3UrYos252Al+Rau2GHwDHqer1uGnO76mbfq4Hiqr6KICq7leXmmWyJVu2kro0T1T1e6r6vQ7LFnf5TLb+lq2sqo+p6sOq+l5VvauTgkWm6FV1Ul1+b1h46VXA3uD124CTReQbuDzSjn5pk633ZYu7fCZb38u2E6IruRBpMBYqeaMKrAZuDTYfAj6AS0v8ZQSWqMnWJ7JBvOUz2fpbNg38OZ2mGwumyrg0v33AtuAJ919xU5nvd1MhmGw9KRvEWz6TzWTrPIvh6G/1H/DS4GJ8H/jdbshgsvWPbHGXz2Qz2Tr9rysrY8WVLH0rriBTVPUlmsJka484ywbxls9kaw+TrQV5uqHoDcMwjOiIRVEzwzAMo3OYojcMw+hxTNEbhmH0OKboDcMwehxT9IZRg4h8SETe12D/xSKyJUqZDONoMEVvGK0TNnU2jERg6ZWGAYjIHwGX4/qI7sXVIjkAXIlr4/YQLi/6RcA3gn0HgN8KPuJaXH/USVxHoPujlN8wGmGK3uh7xPVYvR44E1f/6S7gc8Bfq+qzwTFX45qDf0pErge+oapfDfb9H+Adqvr/RORM4KOq+srov4lh1CfyomaGEUPOAm5W1UkAEQkLUZ0aKPgJXMekb9WeKCKjuDaDN3mFCAc6LrFhtIApesNw1JvaXg9crKr3iMgVwLl1jkkB+1X1RZ0TzTCODgvGGoZr+XaJiAyJyBhwUbB9DHhKRLLAm73jDwX7UNWDuH6frwdXX1xETotOdMNYGPPRGwazgrGPAbuAe3E9Rt8fbPsZMKaqV4jIy3G9R2eA1+GqFH4WWIsrT3ujqn4k8i9hGPNgit4wDKPHMdeNYRhGj2OK3jAMo8cxRW8YhtHjmKI3DMPocUzRG4Zh9Dim6A3DMHocU/SGYRg9jil6wzCMHuf/Az+JZYNqGtSqAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"factors = pd.DataFrame(f_loadings)\\n\",\n \"factors.index = frame_t.index[period:]\\n\",\n \"factors.sum(axis=1).rolling(21).mean().plot(title='Variance Explained by Top 3 Components')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Contribution to variance from each component')\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.stackplot(factors.index, factors.to_numpy().T)\\n\",\n \"plt.title('Contribution to variance from each component')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the chart above, we can see the top 2 components dominate the varience explained so let's dig into them in a bit more detail to understand what they intuitively represent.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Interpreting the top 2\\n\",\n \"\\n\",\n \"In order to understand the most important factors that drive the structure of the correlation matrix we plot the loadings for each asset class against the first two PCs in the PCA over the full sample. This can allow us to visually identify how each asset class loads on the 2 principal components. If an asset or asset class has a high absolute value on one and not the other, we can say this is primary driver of this component.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'PC2')\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"full_model = PCA(n_components=components)\\n\",\n \"full_data = (frame_t - frame_t.mean()) / frame_t.std()\\n\",\n \"full_model.fit(full_data)\\n\",\n \"\\n\",\n \"components_full = pd.DataFrame(full_model.components_, columns=frame_t.columns)\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(8, 8))\\n\",\n \"color_map = {\\n\",\n \" 'Equities': '#20396D',\\n\",\n \" 'FX': '#68A2BF',\\n\",\n \" 'Rates': '#CD252B',\\n\",\n \" 'Commodities': '#E3E311',\\n\",\n \" 'Credit': '#E3E000',\\n\",\n \" 'Vol': '#67E311',\\n\",\n \"}\\n\",\n \"\\n\",\n \"for i, row in components_full.iteritems():\\n\",\n \" plt.scatter(row[0], row[1], color=color_map[data[row.name]['asset class']], label=row.name)\\n\",\n \" plt.text(row[0], row[1], row.name, fontsize=9)\\n\",\n \"\\n\",\n \"plt.axhline(0, color='grey')\\n\",\n \"plt.axvline(0, color='grey')\\n\",\n \"\\n\",\n \"plt.xlabel('PC1')\\n\",\n \"plt.ylabel('PC2')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 57,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"PC1, PC2 = components_full.iloc[0].abs(), components_full.iloc[1].abs()\\n\",\n \"(PC1/PC2).sort_values().plot(kind='barh', title='PC1/PC2')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"(PC2/PC1).sort_values().plot(kind='barh', title='PC2/PC1')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Looking at the above, some themes pop out for each of the components:\\n\",\n \" \\n\",\n \"* PC1 = Growth/Risk (PC1): The primary driver of Equities, Credit, Vol\\n\",\n \"* PC2 = Real Rates (PC2): The primary driver of Gold, Real Rates, and FX (though FX is more mixed)\\n\",\n \"\\n\",\n \"Let's now see how the loadings over the whole period compare to the most recent 42 day window to tell us how risk might have evolved.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'PC2')\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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WxwAP7RgHA3r0/GPLhlkv32Y9vhzEGj8fT9M2GaZpb3hysvkGwHtvqZzZzc2D9tx1/B8ePH8eRI0eg6zoAoFQq4ciRIwBAgZ+43qlTp/Drv/7r+N73vmc/tm/fPoTDYTzxxBOIRqN45ZVXcPvtt+Opp57q+6O5FPT7mKIoCAQC2/4jqNVqbavCZzXRWVxchM/nw+DgoN0u90wc2DfaVJA/U4IgQBCEpi4incoirH6s2SzCzMyMHfAtuq5jZmaGgj7pSoIg4Hd/93dx+PBh/Pmf/zk+9KEP4U//9E/7PuADFPT7Vr1eR71ex+jo9sHR2lDXjip8CwsLSKfT8Hq9GB0dxcjISM+uh+9lFsH675lkEVY/ViqVNv3+rR4npBu8+93vxkMPPYSPfvSjSKVSuPbaa50ekitQ0O9TsiyDMbbjDF5VVQQCgT1fey4Wi3j55Zfh8XgwNTW1476CfrPbLEIzSw3rjywGg0FUKpUN7xsOh1EoFDbNLLhlLwIhAPCTn/wEBw8e3PD4Zz/7WbzhDW/AqVOnOj4mt6Kg34esjnrBYHDbmaeu66jX6xgZGdnTny/LMl544QUwxnDuueeecVndfrc6i9DM8UbTNNfcEFx33XX43ve+t2ZJQRRFXHLJJcjn81v+zGZPM9ANAtlravlByMuHYRivYWFuApdemsTjjx+1n7cqbJ533nkYGhrqSCXRbkFBvw/VajXout7Urn0A2zbhaZWqqvjZz34GALjooovoH6MDBEGA1+u1vz7nnHNw1VVX4bnnnoMsy2t2759pFsFSrVbBGMNrr722440C3SSQ7ajlB7FcfC8MowbT9MAwMmjoc1DLDyIUvtPp4bkeBf0+JMsyBEFoKrXv9XrXBIgzUa1W8eyzz8I0Tbz+9a+ngO8ClUoFpVIJl112Gd785jdveP5MswjWnz0eDzjnYIyhXq/bz2+mmSzC6sd6dR8I2Zy8fBicVzA3vw9ebw1AFoAJefkwBf0mUNDvM1ZqPxQKbXsszjRNVCqVPavCZwX8RqOBSy65hAK+C5imiVwuB6/Xi0QisSfvuT6LYLH2JkxOTtqP7ZRFWL9hcassQiunGSiL0P0M4zUAQCBQRrkcQyop4CsPmvbjAHDixIlN/0wo6PedSqUCwzC2PZsPrMzyOed7ktqvVCo4fvw4NE3DxRdfTAHfJQqFAjRNw+TkZEfaJa+3V1mE1Y81k0VotiYCZRHcSRSnYBivIhSUUS7HUamGEQ7JEMUpp4fWFSjo9xlZliGK4o7BXFVViKKIQCBwRj+vXC7jxRdfRL1exwUXXLBnM0pyZmq1GpaWlhCLxdpyHLMdtsoibGazLMJmNwx7lUXweDyO3Dj1o2j8HiwX3wufrwKPR0dFjSISbiAav8fpoXUFR4M+Y+wmAP8VgAjgC5zzP173/J0A/vPpL8sAPsA5/2lnR9k7TNNEuVzesaPeXlXhK5VKOHXqFKrVKvbt27djTQDSGZxzzM/PQxRFDA8POz2ctmg1i7C6cNJeZxG2yixQFmF3rHV7efkwgkEF5fL5CEf/iNbzm+RY0GeMiQDuA/AmAGkAzzDGHuGcP7/qZa8AuJ5zvsQYuxnA5wFc1fnR9oZyuQzTNHdM7ddqNTQajTNK7RcKBWSzWVQqFaRSKSSTyV2/F9lbS0tLqNfrmJiYoDXu01pZ7282i2DdJDSbRdhu2YGyCGuFwnciFL4TiRENr7zyCky+t8eKe5mTM/0DAE5wzk8CAGPsqwBuAWAHfc75U6te/yMAqY6OsMcoigKPx7Njyl5VVTDGdhX0OedYXFxEPp9HrVbDyMgIUqkUzWpcQtM05PN5RCIRRCIRp4fTlc4ki7DVnoR6vW7vt9nuZzbbxKlf/r15vV74/X7Istz21t+9wsmgnwQwu+rrNLafxf8agH/c6knG2HsBvBcApqZoQ8d6hmFAVVUMDAzseEEol8u7qsJnpY2Xl5ehaRpisRhSqdSuuuWRvcc5Ry6XgyAIe15wiWxtN1mE7WoiNBoNuyfGdlmEZm4Oms0iuLkLYyQSweLiIjRN27Pjxb3MyavxZpFn099gxtgNWAn61231Zpzzz2Ml/Y8rr7xy838JfUxRFHDOd5zdWVX4Wl3rNU0T2WzWLuhjtcdtZiZEOqNUKqFSqWBsbIxuxFyq1R4Ne5FFEARh25uDl156CY899hgajQYA93VhjEajWFxchKIotFG4CU7+y08DmFz1dQorVRbWYIy9HsAXANzMOS90aGw9R1EUOxW2HVVVAaClrnqGYSCTyaBWq8Hr9ULXdQwPD+9pJT9yZhqNBhYXFxEMBhGLxZweDtkje5VFWP3f9VmExx9/3A74Fjd1YfR4PAgGg5BlmYJ+E5wM+s8AOI8xdg6ADIA7ALxj9QsYY1MA/h7Ar3LOX+r8EHuDruuoVCpNNbUpl8stVeHTdR3pdBq6riMcDkNRFMTjcVpfc5lcLgfOOZ2g6GO7zSJs1owJcFcXxmg0ivn5edRqtR0nNv3OsS2hnPMGgA8CeAzACwC+xjl/jjH2fsbY+0+/7P8AkADwWcbYs4yxYw4Nt6spigIAO6b2rSp8zc7QNU3D7OwsGo0GEokEyuUyQqEQrRe7jKIoKJfLGBoaojVP0jRRFOH1erfMDLkpYxQOh8EYgyzLTg/F9Rxd2OOcfxvAt9c9dv+qP/86gF/v9Lh6jSzL8Pv9O17wK5UKOOdNpfar1SoymQwYYxgbG0Mul4MkSRgfH++bncPdwDAMLCwswO/3U/aF7Mr09DSOHDkCXdftxyRJwvT0tIOjWssqOKYoCoaHh+katA06/Nnj6vU66vX6jmfzgZXUviAITR3pS6fTEAQBExMTdvvVVCpF575dZnFxEYZhYGxsjC6EZFf279+PQ4cO2TP7WCyGQ4cOuWI9f7VoNIpGo7HlcgRZQUG/xymKAsbYjqn9ZqvwybKMTCYDSZIwOTmJfD4PXdeRTCbtpirEHVRVRalUwsDAAJ2iILt2ww03AAA+/OEP4w/+4A/wnve8B29729uwb98+AMADDzyAD33oQwBWriMHDx7Ev/zLv3R8nFYTMWs5k2yOgn6Pk2UZwWBwx8079XodjUZj29T+0tIS5ubmEAgEMDk5iUKhYB8BO9Ma/WRvtaODHulP73znO/HQQw/ZX//93/89br31Vvt8/1133YUXXngBP/7xj/GFL3wBl19+OS6//PKOj1MQBEQiEft4MtkcBf0eVq1Woet6U5XXrKN6W23iy+fzWFhYQDgcRiqVgizLKJVKSCQSTS0dkM4qFArQdR2jo6NUwpWckV/5lV/BN7/5TTuQPvTQQ7jzzrV17j/72c/iN3/zN3Hffffhk5/8pBPDBLCyWdnqMUI2R1eDHmal9pvZmLdVFT6riluhUEAsFsPExARUVcXCwgIikQjNIl3I6qAXj8e7poMeca9YLIaLL74YTz31FHK5HBYWFnDJJZesec25554LXddx4403tlTjY69ZWU3axb81KsvVozjnUBQF4XB4x811VkGO9ef4OeeYm5uzK10NDQ2hXq9jbm4Ofr+fNoe50OoOes3UZSBkKz9Wv46H5T9E0ciA/4cw7n3gk/ili/8D7rjjjg2vvf/++zE9PY1//ud/xiuvvIJzzjnHgRHD3r+0vLwMwzBoY/EmKOj3qEqlgkaj0VJqf/UdummayGQyqFQqGBkZwcDAABqNBjKZDARBQDKZpLSxCxWLRdTrdSSTSbrgkV37sfp1PLj8YWi8CgAYPCjjG5+awQv/8jK+883/tua12WwWn/vc5/DDH/4Qx44dwwc/+EE8+uijTgwbwMou/qWlJZTLZVfVEnALumr3KFmWIYpi06l9SZLsHd6NRgOzs7OoVqsYHx/HwMCAfRNgGAY10XEpTdNQKBQQiUQcTbGS7vew/IfQeBWCGoKnOATRyzD2CyLK3vkNDc1+53d+B3/0R3+EYDCIX/qlX8LIyAi+8Y1vODRy2DVJKMW/Obpy9yBrI0skEtkx/W5V4bM241lldRuNBpLJJEKhkJ0yrtVq1ETHpay/I+qgR/ZC0cgAAJjhgViOwAgpuP5PgNV90k6cOAEA+PrXv77me7/0pS91aphbikQiKBQKaDQaNEFZh2b6PUhVVZim2dSu+mq1CtM0EQ6HUa/X8dprr9mzeWsnf6FQsCtd0QzSnUqlEqrVKoaHh+kiR87YoJgEABhhGVww4CkNrHnc7axrH53Z34iCfg+SZRkej6eps/NWFT7GGGZnZwEAk5OT9vfKsmzv3B8cHGzruMnuWB30QqEQrWGSPXFL9BPwsgAgcBjRZQi1AHz1OG6JfsLpoTXF6ihKKf6NaErQYwzDgKqqiMfjW6b2T506hcsuuwyXXHIJarUaBEHA2NgY7rjjDtx6662QJAmf+tSnMDIygoMHD+KSSy7B1VdfDU3TcO211+Izn/lMhz8V2U4ulwMA6qBH9sxVobcDWFnbL4aziKvn4heN37Yf7wbRaBQLCwvQNI0aTa1CQb/HlMtlcM53TO1fccUVePTRR/H8888DAO655x6Mjo7apXQNw8DS0hIkSUIqlcLRo0cBADfeeCNeeOEFXHjhhW39HKQ5Vge94eFhKoNM9tRVobfbQX4psoSFhQVUKpWuqf0QiUSwsLAAWZbp+OoqlN7vMbIs26mtnWQyGSwtLWFwcBChUMg+4mUYhp0WSyb/fQ1P13X7poI4zzAM5HI56qBH2i4ej8Pj8djNtbqBx+NBMBikdf11aKbfQ6wOU5tVyXv6ZA4PP3sSRbUOoVzAj59+BrfddhtEUcT5559vzxKtgjyGYSAej8Pr9SKTyeDgwYP46U9/ine96134+Z//+U5/NLKJxcVFmKZJRZJI2zHGkEgkkMvl7MZc3SAajWJ+fh7VapX6g5xGM/0eYt3Rrk/tP30yhwd/9CKKah3gHOXSEgaTZ+Fj9/zf+M53voMHH3wQgUAAtVoNi4uLUFUVkiQhHo8DWJntHz16FM8++yyOHTsGTdM6/tnIWlYHvcHBQTpCSToiFotBkqSumu2Hw2Ewxmi2vwoF/R4iy7JdmGK1h589Cc0wAc4h1RWIhgYTDD9bKNtH8C699FJ85zvfwdLSEmKxGJ5++mlceumla97nrLPOwlve8hZ84Qtf6NhnIhtRBz3iBGu2X6vVuqahjVWgjDrv/TsK+j1C0zTUarVNN/AV1ToAQNSrEAwduhTC4msv44E/OoybbroJBw8exKFDh3Dy5EncdddduO222/C2t70N559//ob3+sAHPoC/+Iu/QKPRaPtnIpvL5/PQdZ3S+qTjotEovF4v8vl81wTRSCRiL30SWtPvGdbGu81q7Q+GfCiqdRhSAKYoIRwcxPvu/RtEwhF86s4320V57r33XkxNTW2oqW9V3gKA4eFhe8c/6bxqtWp30KM1StJp1mx/bm7OrvrpdlbTMUVRumYvQjvRTL9HKIpit5Vc75ZL98ErCgBj4KIEZuqQGMNbLj0PhmFQE50uYbU59ng8GB4edno4pE9FIhF4vV4UCoWumO1b7cUVRYFpmk4Px3F0he8BtVoNmqZteTb/wL5R3Hn1+RgMrWz4GvQCBy9M4ZoLppDJZOw6+3TO292sDnqjo6N0c0Ycwxiz22x3ywa5aDQK0zTtjqL9jNL7PUCWZftudisH9o3iwL6Vim0nT56Ez+fDwsICqtUqJiYmmjrXT5xDHfSIm4TDYfh8Pvt30u17SwKBADweD2RZ7ooliXai6UKX45zba1XN9E+v1+vQdR2aptmVqvr9H4HbUQc94jbW2r6maV0x22eMIRKJQFVVGIbh9HAcRUG/y1WrVTQajaY66gEr57ur1Sqq1Sqi0Sgd+eoCVge9kZER6qBHXCMSicDv93fNTv5oNArOedccN2wXCvpdTpZlCILQ9K7UYrGIcnnlfP7Y2FibR0fOlK7rdge9Zm/sCOmURCIBXde7opudVcOkG8baThT0u5h11xoOh5va2FWr1ZDJZBAKhZBMJl2/Dkeogx5xt3A4DL/f3zU7+aPRKCqVSl/XGaGg38Ws9almZoCmaeLll1+GaZrYt29fU+v/xFmyLENVVQwNDdHJCuJaQ0ND0HUdpVLJ6aHsyNq/1M+zfVog7GKyLNudpLZjNZ6Tl1kAACAASURBVNFRFAUjIyMbbhKOHz+OmZkZlEolxGIxTE9PY//+/e0cOtmBYRhYWFiA3++3eyAQ4kahUAiBQACFQgGxWMzVGUSrA6ksyxgcHHR6OI6gmX6XMgzDroi10z+yfD4PRVHg9/uRSCTWvP748eM4cuSIfZdeKpVw5MgRHD9+vK3jJ9tbWFigDnqkawwNDaHRaGB5ednpoewoGo2iXq+jXq87PRRHUNDvUlZf+52O25VKJRSLRQQCAQQCgQ1nvGdmZqDr+prHdF3HzMzMno+ZNEdVVXsmQh30SDcIBoMIBoMoFouur3pnTZS64ahhO1DQ71KKokCSpG3rr1cqFeRyOYRCIfj9fjDGNiwFbLUO1w3rc72IOuiRbtUts31rSbRf1/Up6Hchq2PUdhv4NE1DNpuF1+vF+Pg4KpUKgsHghl3+sVhs0+/f6nHSXtRBj3SrQCCAUCjUNbN9XddRrVadHkrHUdDvQlZv6K1S+1YTHQBIJpMwDAOapm1avnV6enrDznBJkjA9Pb33Ayfbog56pNslEgkYhoGlpSWnh7Ktfk7xU9DvQoqiwOfzbbreyzlHNpuFrut2Ex2rAtVmBXz279+PQ4cO2TcQ0WgUhw4dot37HWZ10JMkiTroka5l7RtaWlpydblbQRAQDochy3JX1BfYS3Rkr8tomoZqtbplYMjlcqhUKhgfH7dni6qqwufzbXnWe//+/Tj77LORzWZx1llnUfMdB1gd9FKpFHXQI10tkUjg1VdfxfLysqv3pUSjUSiKgkql0nRF015AV5cuY6WjNkvtF4tFlEolJBIJe73fMAxUq9UdO7NZNwT9XKnKKfV6HYVCAdFotK8uPqQ3+f1+hMNhFItFV8/2rSZl/bahj4J+l5FlGcFgcMOsvVwuY3FxEZFIZM3dtaqq4JzvGEys91t/fI+0l5XWpw56pJcMDQ3BNE1Xr+1b7cjL5bLrNx7uJUrvd5FarQZN0zAwMLDh8bm5Ofj9/g27vlVVhSiKO6bsRVGEIAgU9LfQrqqFy8vLqFarGB8fp9LIpGf4fD5EIhEsLS1hYGDAtb/b0WgUpVIJ5XK5bxpa0Uy/iyiKYveFtjQaDWQyGYiiiGQyuWY9mHMOVVURDoebOv7l8Xgovb+JdlUt1HUd+XyeOuiRnjQ0NATOOYrFotND2VIgEIDH4+mrXfwU9LsE5xyyLNvrUMBKIZdMJgPTNJFMJjf0Wq/VajAMo+l1YkmSaKa/iXZVLaQOeqSXeb1eRCIRLC8vu3YywRhDNBq1m5f1Awr6XaJaraLRaNizfM455ufnUavVMD4+vunxvXK5DMZY00GfZvqba0fVQuqgR/pBIpFw/Ww/EomAc943s30K+l1ClmX7bCkAFAoFu2veVjvzy+XyplX4tiJJEhqNRl9tamnGXlcttDroBQIB6qBHeprX60U0GnX1bN/v98Pr9fbNLn4K+l2Ac45yuYxwOAxBECDLst3Gcv2mPoumadA0raUjYHRsb3ObVS0URRE33njjrt7P6qA3OjpKpXZJz7NOExUKBYdHsrVoNIpqtdoXy5uOBn3G2E2MsRcZYycYYx/d5PkLGGM/ZIzVGWO/78QY3cBab7J+Mefn5xEMBrddC1ZVFcDmVfi2Yu0J6Idf/FZYVQutmX0kEsGBAwfs1GUryuUyZFlGIpGgDnqkL0iShFgshlKp5Npri7WRth9S/I4d2WOMiQDuA/AmAGkAzzDGHuGcP7/qZUUAvw3gPzowRNeQZRmiKMLj8WB2dhaSJGFiYmLbWaKqqvB6vfB6vU3/HJrpb23//v1rjugtLy8jl8thfn6+6eY4pmliYWEBPp8Pg4OD7RwuIa4yODiIUqmEQqGAsbExp4ezgdWx1Gpp3cucnOkfAHCCc36Sc64B+CqAW1a/gHO+wDl/BoA7bw87wDRNqKqKYDCIbDYLYKWJznbnXk3TRKVS2bEK33oejweMMdfejbtJPB7H8PAwZFnGwsJCU9+zuLgIXdcprU/6jiRJiMfjkGUZmqY5PZxNRSIR1Ot11Ot1p4fSVk4W50kCmF31dRrAVQ6NZVPtKsjSinK5bJfSNQwDqVRqx9m7VYWv1aDPGIMoijTTb9Lg4CAMw0CxWIQgCNs2yqlWq1heXsbAwAB10CN9aXBwEMvLyygWi66c7UciESwuLtoNzXqVkzP9zaY6u253xBh7L2PsGGPs2OLi4hkMa0W7CrK0yjraZc0Qg8Hgjt/TbBW+zdBZ/dYMDw8jHo+jWCxuuVHJOl4pSRKGhoY6PEJC3MHj8bh6tu/xeBAMBnt+F7+TQT8NYHLV1ykA2d2+Gef885zzKznnV+5Fa9J2FWRpRaPRsHd6JxKJpo6IWTv9Q6HQrlLIFPRbNzIygmg0inw+j+Xl5Q3PFwoFaJqG0dFR6qBH+trg4CAYY67dyR+NRqHrOqrVqtNDaRsnr0DPADiPMXYOY8wL4A4Ajzg4njXaUZClVQsLC1heXsbw8HDTM8RWq/CtZxXo6bce02eCMYaxsTGEw2Hkcrk1M4V6vY5isUgd9AjB2tm+G9fOVx+L7lWOBX3OeQPABwE8BuAFAF/jnD/HGHs/Y+z9AMAYG2OMpQH8JwAfZ4ylGWMdKVK+1wVZWlWv13Hq1CkEg0FMTU01PWtXVbWlKnzrSZIEznnflKTcK4wxTExMIBgMYn5+HuVy2U7rUwc9Qv7d4OAgBEFw5WxfEASEQiEoitKzEx9Hc42c829zzn+Oc/46zvk9px+7n3N+/+k/z3POU5zzKOc8fvrPHbkF26wgiyRJmJ6ebvvPbjQaePXVV6HrOs4555yWUsLlchmBQGDXXa2oxe7uMcaQTCbh9/uRzWaRzWZRq9UwMjLi2i5jhHSaKIoYGBiAoiiunO1Ho1EYhoFKpeL0UNqCFhi3sL4gSygUwqFDh9q+e59zjmw2C0VRMDg42NKZUV3XUa/XzyiNTAV6zowgCHa3w3/7t3+Dx+OhDnqErGO1283n804PZQOrqVmvpvidPLLnelZBFitd+7rXva7tP3N+fh7VahWBQADhcLilZixWFb5Wj+qttl2BHjccYewGoihCkiSIomjfiPXyESBCWmXN9vP5PGq12q5OGrWL1b5clmWYptlzm29769O0STAYhGEYbU9FFQoFyLKMaDQKURRbniGWy+WWq/CtJwiCHaxWc8sRxm4gyzJqtRouvPBCSJKEdDrtyiNKhDjJzbP9SCQC0zRRLpedHsqeo6DfBOtsfDvXeGRZRj6fRzQatSvjWW10m2FV4duLHeKbtdh1wxHGbmAdswwEAkgkEpicnATnHOl0mpZMCFlFEAQMDg5CVVXXHZELBAKQJKkna/FT0G+Cx+OBz+drW9C3mugEAgGMjo5CURQEg8GWNn9VKpVdVeHbzGZn9d1whLEbWHUVrHr8Xq8XqVQKpmkinU5TtUNCVonH4xBF0XU7+a1Jl9XsrJdQ0G9SMBhEtVrd82Mcuq4jm83C4/EgmUyiXq9D1/VdpfYFQdiTEq+bBX2njzB2g3K5DEVRkEgk1iyx+P1+JJNJNBoNpNPpnruIELJbgiAgkUhAVVXX7ZaPRqPgnPfcbJ+CfpOCwSBM09zTNJRpmshkMuCcI5VK2TtGBUFoacbOOYeqqruuwreex+OBaZprgtP09LS9s9/SqSOM3cAwDORyuS076AUCAUxMTEDTNGQyGZim6cAoCXGfWCwGj8fjutm+z+eDz+fruV38FPSbFAgEwBjbs7tRzjnm5uagaRrGx8fh9Xrtu8pQKNTSjtF6vY5Go7EnqX1g8x38+/fvxw033GDvb4jFYh05wtgt8vk8Go3Gtm12Q6EQxsfHUavVkM1me7b4ByGtsNb2K5WKfQLJLSKRCKrVak/tx6Eje00SRXFP1vV/+tOf4qMf/ShKpRLq9TouvvhifPzjH8eBAwdw8cUXQ1EUXH311bj//vsBrNxsXHXVVahUKrj99tvx+7//+xves1wun1EVvvVWn9VffdRs3759GB0d7cjRxW6yuoPeTkePrF3B8/PzyGazmJiYoDa7pO+tblrlpnLVVk8NWZaRSCScHs6eoKDfgmAwiKWlpV2f3SyVSvjVX/1V/M3f/A2CwSAGBgbw0ksvwTAMXHHFFfjyl78MVVXxvve9D8899xwuuugiJJNJHD16FLqu4/LLL8c73vEOTExMrHlfVVXh9/v3rOrbVmf1DcOgynLr7KaDXiwWg2maWFhYQC6Xw+joKAV+0tcYY0gkEsjlcvZSpRtIkoRAIGDv1ekFlN5vQTAYBOd81+v6jz76KG666SaEQiGEQiEMDw/juuuus2eHVgndarW64bieJEm46KKLMDs7u+bxRqOBWq22Z6l9YCWrwRjbkNJqNBob1vX73W476A0MDGBoaAilUgl70QqakG4Xi8UgSZLrzu1Ho1HU63VXlgzeDbqCt2D1un6zd6J/9/1ncPSVJTQECT995Hs4bygEr9cLSZJwww03IJ/P44EHHsCxY8dw5513YmlpCZdddhmmpqbWvI8sy3j22Wc3pNat4hF7eWfMGNt0B79hGGdU+KfXWB30YrHYrv7/TyQSMAwDS0tLEASh6UwBIb3Imu1bFVD3ciJzJiKRCBYWFiDLMvaibbvTaKbfAkEQ4Pf7m17X/7vvP4OZUzIaohcARzQewwuvZvHMyXmMjo7i6NGjuPLKK1Gr1bB//3585StfwUsvvYTx8XF89atfBQBkMhkcPHgQb33rW3H33XdvCAyqqkKSpD0v87pZgR6a6f+71R30zuRCMDIyglgshkKhgGKxuIcjJKT7RKNReL1e5PN512x0FUURwWCwZzrvUdBvUTAYRL1eb+qs9dFXlsAFEZ6aDH95AedcdBle/h//HUeOvWi/ptFowDAMGIaBaDQKxhgGBgbslK+1pn/06FHccccda97fNE2oqtqWO+L1M33DMMA5p6B/2tLSEmq1GkZHR894n8Po6CgikQgWFxep2BHpa9Zsv16vu6oEbjQaha7rqNVqTg/ljNEVvEWhUAiFQgHVanXHYNsQVjbECaYOUa9BiI3hxvf9Fxz9/+/D9Y9+EYFAAFNTU2CM4bnnnsNtt91mV4J66KGHdhyLVYWvHZteJElCo9EA5xyMMfsmhzbyAZqmoVAoIBwOt1QqeSuMMYyPj9u7+gVB2JP3JaQbRSIRFAoF+9+YGza5hsNhCIIAWZb3pACakyjot8jv90MQBFQqlR2DvsfU0RC9aHjDYEYDXPRg6Kxzcct/+hT+/K4326+bnZ3Fs88+i3POOWfDe5w4cWLL91dVFYIg2Gfn99LqY3ter9dO9VPQB3K5HICVGfpeYYxhYmIC6XQac3NzYIy5Zk2TkE5ijGFoaMhuMe6G1tRWwTRFUTAyMuKKG5HdovR+ixhjCAQCTa3rHzxnAMw0YIoSIIgQDA3MNHDwnAH7Nbquo1Kp7OoXu1wu71kVvvXWH9uzZvr9nt4vlUqoVCoYHh7e8/8vBEFAKpWCz+dDNpt1XVlSQjolHA7D5/OhUCi4Zh09EonAMAzXFRBqFQX9XbDW9XdqnnLb9W/A9NlReMwGTMEDSatg+uwobrv+DfZrrLrOraZza7UaGo1G286zrp7pA/8e/Ps56DcaDSwuLiIQCLSt54AV+CVJQiaT6Yk1REJaZc32NU1zTRncUCgEURS7vhZ//17Bz8DqVrs7zdBvu/4NuO36lTKthUIB55577prnrTWiVo/CWXeb7Qr6m830GWO7KkrUK9Z30GsXURQxOTmJ1157Del0GpOTk3t+OoMQtwuHw/D7/SgUCvYmZydZ+61kWd51gTY36M5RO8zn80EUxZbSr9b67OrUkFXwYTebtqxCPu2aeTPG4PF41sz0raI9/WirDnrt4vF4kEqlwBhDOp3uqdrfhDQrkUhA13XXzPaj0ShM03TVyYJWUdDfhVbW9S3WjcLqoK8oin332AqrCl+7S1WuPrbXzyV4d+qg1y5erxepVAqcc8zOzu64nERIrwmHwwgEAq5Z2/f7/ZAkyTU3IbtBQX+XgsEgdF1vegZmNcRRVdX+5ZVlGcFgsOXZunXj0O7d3asL9PRzYZ58Pg/DMNqe1t+Mz+dDKpWCYRhIp9NN1YcgpJdYs3031LCwJmmVSqVrb8Ip6O/S6nX9ZoXDYRiGgVqtZrdr3G1qvx1V+NazZvqc876d6VcqlaY76LWL3+9HMpmEpmlIp9MwTdORcRDihFAoZM/23fC7H41G7Tbo3YiC/i75fD54PJ6Wgn4wGARjDKqq7jq1zzlvqfb/mfB4PHbA78eZvmmayOVykCTJ8Q5bwWAQExMTqNfryGQyrrj4EdIpQ0NDaDQarpjt+3w++Hw+Cvr9KBgMthT0RVGE3+9HuVyGLMt2ladWVCoVmKbZkcIt1g7+er3elyV4d9tBr13C4TDGxsZQqVSQzWZdscZJSCcEg0EEg0EUi0VX3PBGo1E7W9ttnL+SdbFgMIhGowFN05r+nlAohFKpBE3Tdl2Qp11V+NZbHfSB/qrGV6vVsLS0tOsOeu0SjUYxOjoKVVUxNzdHgZ/0DWu2v7y87PRQ7AxtN27oo6B/BqwazK2u61t3iLsJJqqq2ssE7WbN7KvVKoD+Cfqcc+RyOYii6MpWmvF4HMPDw1AUxS4JTEivCwQCCIVCrpjtS5KEQCBAQb/feL1eSJLUUtCXJAmapkEQhJYDd71eh67rHavJLooiBEGwMxn9kt63OuiNjIy49kZncHAQiUQCpVLJ7shISK8bGhqCYRhYWlpyeiiIRqPQNM3OhHYLCvpnyFrXbzbNqqqqXdyl1dSsVRCik+lmSZLsX+p+CPqapiGfz+9ZB712GhoawsDAAIrFIgqFgtPDIaTt/H4/wuEwlpaWHD++GolEwBjrutk+Bf0zFAwGYRhG03d71gY+SZLstHmzVFWF3+/vaPC1gn6/lODN5XJgjO1pB712Gh4eRjQaRT6fd8Xsh5B2SyQSrpjti6KIUCgEWZa7am9N71/F26yV8/pWh6ahoSEIgtBStybDMFCtVjvebtXj8aBer/dFCd52dtBrF8YYxsbGEA6HsbCw4IojTYS0k9tm+41Go+UJnJMo6J8hj8cDr9fbVNAvl8vgnCMejyMQCLQU9NvdYGcrkiSh0Wj0fMC3OugFg8G2ddBrF8YYJiYmEAqFkMvluvb8MCHNGhoagmmajs/2rWPX3ZTip6C/B4LBIKrV6o4pHlmW4fV67TtVa2NeM8rlMjweT8e7rXk8Hsd3ynaC1UFvdHS0K29wrMDv9/sxNzfX9T2/CdmOz+dDJBJxfLYvCALC4bA9oesGFPT3QDAYhGma2/Y+bzQaqFQq9uYwa8bezMWZcw5VVREKhToekCRJcjyF1m6KokBRFAwNDXWkg167CIKAZDIJr9eLbDbbVSlHQlo1NDQEzjmKxaKj44hGo/bSbTegoL8HmlnXt1KuVkEe67hfM78o1WoVL7zwAt71rnfh4MGDuOaaa3DvvfcCAB566CH7TtNy8OBB/MIv/AKuu+46XHvttXjggQd2+9EgSVJPz/QNw8DCwgJ8Ph8GBgacHs4ZE0URqVQKHo8HmUym644TEdIsr9eLSCSC5eVlR5vfBINBiKLYNSl+Cvp7wCqvu13Ql2UZfr9/zUwyFAo1ddxvbm4OH/nIR3Dffffh6NGjePLJJ3HRRRcBAL7yla/g137t1/AP//APa77n61//Op544gk8+uijeOCBB/D444/v+vNxzrsmddWqxcVFxzrotYvH40EqlYIgCJidnW2pYiQh3SSRSDg+27d6qJTL5a6YIFHQ3yPWuv5mf+mapqFWq20ouxsOh2Ga5o6bAI8cOYI3v/nNOO+88wCs/JK95S1vsQPWxz72MXz1q1/d9Hvj8TgOHz6Mhx56aFefyzRNiKLYFb/MrapUKiiVSo520GsXSZKQSqUAAOl0uitrhBOyE6/Xi2g06vhs3+q8tzrj6lYU9PdIMBgE53zTdX0r7bO+2EsgELC77m3Faqd6zjnnbHjub//2b/GOd7wDY2NjEEVxy5Ksk5OTyGQyrXwcm9Vdr9dm+lYHPa/X63gHvXbxer2YnJyEaZpIp9Nd2/+bkO1Y/36dLFAVCAQgSVJXpPgp6O8RK4BvNmtXFAXBYHDD2W+rcc5WQf/pkzn8n3/3A/yPoo6/e+K/4+mTa4P61772NXzpS1/CTTfdhNdee23L2f7s7CySyeSuPpdhGBBFseeCvts66LWLz+dDMplEo9FAOp3u+U2ZpP9IkoRYLIZSqeRoRisajaJSqbj+5rp3r3YdJgjCpuv6tVpt24564XAYmqZtWHd9+mQOD/7oRSjlMs6+5Cr867GncN/D/80O/J/73OcwOjqKmZkZfOc738GTTz6Jb3zjGxvev1Qq4dOf/jTuuOOOXX2uRqNhB/1eCfyrO+h1oluh0wKBACYmJqBpGjKZTE8u1ZD+Njg4CMDZ2X4kEgHn3PV1Mijo76FgMIharbZmNiXLMhhjW1bS2+ro3sPPnoTWaEAwGvCE47jxfR/D41/6f/H2t96Ma665BrlcDm95y1vW/Gy/34+XXnoJAPD2t78d1113HW6++Wa84x3vwPT09K4+k2EYdobC7XewzeCcY35+3rUd9NolFAphYmICtVoNmUymZ27gCAFWZvvxeByyLDu2cdXn88Hn87k+6HdHrdEuEQwGUSgU7HK51l1fKBTaslubJEnwer1QVXXNkbGiunLUqiH5YXq8GJo6F7/8kf8LAPCXv3pw0/f6p3/6JwDA0aNH9+wzNRoNeL1eMMag6zokSdqz93bC0tIS6vU6JiYmXNtBr13C4TBGR0cxPz+PbDaLiYmJnjmxQMjg4CCWl5dRKBQwPj7uyBii0SgWFxehaZpra37QTH8PrV/Xr1araDQaW6b2LdbRvdVp18GQD2ACDG8IXPCsfbyDDMOwf3m7faZvddCLRCKu76DXLrFYDCMjIyiXy5ifn6cZP+kZHo8H8XgciqI4Ntu3ritunu07GvQZYzcxxl5kjJ1gjH10k+cZY+zPTj//M8bY5U6Ms1mMMQQCATvoy7IMQRB2rJdvZQVW7we45dJ98Ipr/3q8ooBbLt239wPfRqPRsEv/dvOxL845crkcBEHAyMiI08Nx1MDAAIaGhiDLMhYWFpweDiF7ZnBwEIwxx9b2JUlCMBh09S5+x4I+Y0wEcB+AmwH8PID/jTH28+tedjOA807/770A/rKjg9yFYDCIer2ORqMBRVEQiUR23B0eCAQ2dN07sG8Ud159vj2zHwz5cOfV5+PAvs62fDUMA5IkwePxdHXQ78YOeu2USCTsdGg+n3d6OITsCWu2L8uyY9UoI5GIXZvFjZy8+h0AcIJzfhIAGGNfBXALgOdXveYWAP8fX8lB/ogxFmeMjXPO5zo/3OZYu8EXFxdhmmZTaWTG2KZH9w7sG+14kF/POqfv8Xi6Nr3fzR302ml4eBiGYaBQKEAQBHsHNCHdbPXa/sTERMd/fiQSwcLCgl2F1W2cDPpJALOrvk4DuKqJ1yQBbBv0C4XCGdWbP1NWZT5BEBAIBJr6nkajAU3T4Pf7XXNunHOOarUKr9cL0zRhmqYrf4l3Uq/X7bHTxrWNNE2zN2y2KwsyPz8PAI7+uyT9Q9d16Lru2PXUuuY0e/3vJCejy2ZX3/W7ipp5zcoLGXsvY+wYY+yY02loQRDsGXKzrJ3kbjxDzRgDY6wrN30ZhmEvUVDA35zX64UoitA0jYr3kJ7g8XjsE0dO/XzOuSuv507O9NMAJld9nQKQ3cVrAACc888D+DwAXHnllfyuu+7as4G26rXXXsOJEyfwhje8oaVd4qdOnYIoipicnNz5xR1QqVQwOzuLyclJ1Ot1LCws4Nxzz+2ao26GYeDUqVPweDyYmpqioL8N0zSRyWRQrVYxMTGxZV2J3bJm+E7+uyT9pVAoIJ/P46yzzup4htI0Tbz88suIRCIYGxvryM9897vf3dTrnJzpPwPgPMbYOYwxL4A7ADyy7jWPAHjX6V38VwMouXk937K6il0rwuEwqtWqa2Zb1jhEUbTP5zudRWlFL3bQaxdBEJBMJuHz+ZDNZndsAkWI2w0MDEAURUc2qgqCYLc8d1uG1LGgzzlvAPgggMcAvADga5zz5xhj72eMvf/0y74N4CSAEwD+CsBvOjLYFjQaDei6jnA43PKFMxQKbTi65yRr4561kW/1Y26nqqrdQc86cki2JwgCUqkUvF4vMpmMa3cfE9IMa3OqqqqoVqsd//nRaBSGYWzbUM0Jju4Y45x/m3P+c5zz13HO7zn92P2c8/tP/5lzzn/r9PP7OefHnBxvMxRFwSc+8Qn81m/9FiqVCk6dOoU3vvGNa15z7rnnAgDuvvtuXHjhhbj++utx4MAB3H333a76JTEMA4wxCILQVTP9fuig1y6iKCKVSkEURaTTaceOPRGyF+LxODwejyOzfavJmtvO7Ltjm3gPyefzeOmllxCPx/Hqq6/uWBnq8OHD+P73v48f/vCH0HUdf/3Xfw1VVV2RErKWKRhjEEURgiB0RdAvFArQdb3nO+i1i8fjweTkJBhjSKfTjlU3I+RMWbP9SqXS8QwqYwyRSATlctk1S7YABf09pWkaHnvsMfzyL/8y7rrrLnzrW99qOkUqiiLuuecefOtb30Kj0XDFDMtqq2vphrP6Vge9eDzeFx302kWSJKRSKXDOkU6nXf/3TshWYrEYPB6PI1X6rM575XK54z97KxT095CiKHj00Ufxnve8B7feeit+8IMftLSWFAgE7FmVG1L8648dSpLk6pn+6g56Q0NDTg+n6/l8PqRSKRiGgdnZWVfNVghp1urZfqevq4FAAF6v11W1+Kke6R54+mQODz97EoXZl/HEj5/BO9/9G4gFvchmszhx4sSa2b6u61t2X6rVavD7/fD7/VBV1fH1aMMw1myCkyTJ1Zu7isUiLF67SAAAIABJREFU6vU6kslk1xwrdDu/349kMol0Oo10Om2v9xPSTeLxOIrFIgqFwo69UPZaJBJBsVhsuXZLu9BM/ww9fTKHB3/0IpYUFSeOPYnLbr4dB95/Nz752b/BZz/7WTz++OPI5/MoFosAVtreXnbZZRvexzRNfOITn8Db3/521xzds9b0LR6PB4ZhuLLghKZpKBQKiEQie37GvN8Fg0Ekk0nU63VkMhlX/v0Tsh3GGBKJBKrVasdn+9Fo1G6z7gbO33Z0uYefPQnNMCE26njxmSfwi7/2v0MzTDz87El87M034sMf/jA+85nP4K1vfSskSYLf78fnPvc5+/vvuece/NVf/RWq1Squv/56HD58GJxz5PN5qKq6Y1vedjEMA5zzDel9AHbJVrew0vrUQa99QqEQxsfHkc1mkc1mkUwmqfYB6SqxWAzFYhH5fL6js32vdyXre+TIEaiqilgshunpaezfv79jY1iNZvpnqKiubLgzpADe+p//H8TGJu3Hw+EwZmZmcPXVV+OJJ57A448/jn/8x3/E1NQUgJUjey+88AK+//3v4+mnn8ZnPvMZ+Hw++Hw+iKLo6Lr+6sI8Frce2yuVSqhWq9RBr82s6mKqqmJubm7TEyYf+MAH8M1vfhMA8Pzzz0MQBDvL9d3vfheiKOLgwYMIBAI4ePAgDh48iCeeeMI+xkpIu1iz/Vqt1tGNdcePH8dTTz1lX89LpRKOHDmC48ePd2wMq1HQP0NW61swAabHu+HxYDCISqXS0hE8xhhCoZCjR/dWF+axuLFAD3XQ66xYLIbh4WEoioJcLrfh+euuuw5PPvkkAODJJ5/EjTfeiKeeegoA8Morr2BmZgZHjx5FMpnE0aNHcfToUVx33XUd/Qykf0WjUXi9XuTz+Y5dW2dmZjZcM3Vdx8zMTEd+/noU9M/QLZfug1dc+3+jVxRwy6X7AKwEfdM0Wz6CFw6HYRiGYxvnNpvpO93EYjNW4OlUfWuy0ro0kUigVCphYWFhzXO/+Iu/iCeeeALAStD/yEc+Yn998uRJXHXV+kaahHSONduv1+sdm+2XSqWWHm83Cvpn6MC+Udx59fn2zH4w5MOdV5+PA/tGAcBurdhqqj4YDIIx5liKf7OZPmMMHo/HNUFfURSUy2UkEgl76YF0xtDQEAYGBrC0tLTm/PPU1BTy+Tyq1Srm5ubwpje9CcePH0exWEQkEnFlq1HSXyKRCLxeLwqFQkdm+1tlIJ3KTNIC6B44sG/UDvLreTwe+Hy+lms/i6JoH91z4sz56hK8q7mlQI9hGMjlcvD7/RgYGHB6OH1peHj4dHe+h5HP/xVCoZcgiklcdtkoHnnkEYyNjUEQBAiCgOeffx7nnXee00MmBIwxDA0NIZvNQlGUtm+Wnp6expEjR9ZMliRJwvT0dFt/7lZopt8Bu1nXB1Z2TNdqNUeC7OoSvKu5pUDP4uIiTNOkDnoOYowhHP5nmOZ/RaGgoVwOwTDSuPTS5/DHf/wxXHPNNQCAK664At/73vfwcz/3cw6PmJAV4XAYPp+vI7P9/fv349ChQ/bMPhaL4dChQ47t3t9xps8YiwIY5py/vO7x13POf9a2kfWQYDCIpaUlVKvVlkrDhsNh++hep1NB60vwWiRJgqIo4Jw7FmytDnqJRII66DlMUT6FRCIN0xxDsTgMr1fDgQMVfPzjr9hB/9prr8UnP/lJmukT17Bm+5lMBrIst/36un//fseC/HrbzvQZY7cD+FcAf8cYe44x9oZVTz/QzoH1kkAgAMZYyw0ffD4fPB6PI+v6W1WP8ng84Jw7VjiIOui5i2FkwBgwNJTDwEAeXq+GCy/0YHZ20L7IvelNb8IXv/jFNUWTTpw4seZ91n9NSLuFw2H4/f6Ore27xU7p/f8C4ArO+aUA3g3gy4yxt51+jnKqTRJFET6fb1ddnpw6umcYxqZB3+mz+vl8HrquU1rfJUQxCQAQBI5IRNnwOCFulkgkoOu669rfttNOQV/knM8BAOf8aQA3ADjMGPttAP1za7QHgsEgarVayyVMw+EwTNNseSPgmVpfgtdi3Qg4EfSr1ardQY92gbtDNPoJMLb274KxAKLRTzg0IkKaFw6HEQgE+mq2v1PQVxhjr7O+OH0DcBDALQAuauO4ek4wGATnvOXg7cTRvc1K8FpWl+LtJM45crkcPB4PhoeHO/qzydZCobcjHv9TiGIKAIMo/k/23j0+rrrO/3+eud8vmczk1qQJlpUVyqVUrVC2LRVRHlwEBHFFl/0CsoqC4u5PRcDqsg+LKwjiheVBXayKrAgiVESXQpfrCq2u28V1FSFJm2Qy9/v9zPn9kZ5j0iZNMplkJpPP8/HoI5mZc/mcyel5vz/vz/v9eq/C47kTu/2SRg9NIJgT6my/UXXzS81siXwf5bAwvqIoaUmS3g1cumijakEmr+vPR/dZp9NhtVrJZrNLZuymE+aZPB69Xr/kM/3JHfQOLyMUNBa7/RJh5AXLFrvdrs32XS5Xyz9fZru6LDBdAfoG4D/rP5zWRTXetazrOxwOisXikhna6YR5JrPUZXuig55AIKgne/bs4aqrrtJeF4tFPvCBD5BMJnn++ec5/fTT2bx5Mxs2bGDPnj0ADAwMsGXLFu2zXbt2TXvsyX0lduzYMafxDA4O4vV62bJlC29/+9u58847Abj55pu5/fbbte3+7u/+jgceeKDGq55gtpn+nUwk8x1O/tBn5y3o7CsMm81GJBKZsRxuJtTIQDabxePxLNbwNI420weWVJVPdNATCASLjdVqRa/XE4vF+PSnP81DDz1EX18fsixrcr16vZ5nnnkGgNHRUc455xyOPfZY3vzmN085ltpXYr6oehayLPOWt7yFq6++mptuuonTTjuNSy65hNHRUYaGhvjrv/7rBV3rbDP9/ulq8RVF2Qv0L+jMKxC1Rn++6/omkwmj0bhk6/rNNNMXHfQEAsFSYDQatbbhTz75JOl0Gr1eP20Nf3d3N9deey0//vGPj/gsGAyyadMmLrroIgYHB4GJRlRqn4pnn32WK6+8csZx5HI5SqUSsixjNpu54447+NjHPsYNN9zAN7/5zQVf52xG33KUz0T69DyxWCzodLqajLfdbq9J1a8WZpLgVTEajVSr1UWv1S+Xy4TDYex2u+igJxAIFhWdTofdbufWW2/llVde4ZRTTuGss85iaGho2u17e3sZGRk54v3BwUH+4z/+g2uuuUYz7n/7t3/Lzp07AfjOd77D1VdffcR++/bt44wzzqCrq4vrrrtOkwfetGkTHo+Hd73rXRxzzDELv85ZPn9FkqQjRidJ0pXAvgWffYUhSdKC1vWr1WpN+86XmSR4VZaqxa7aQa+jY/q+BgKBQDAfoo/tYv/msxj68JUEH/8Z0ccm1uULhQJWq5X29nY6OzvZvn07r732Gpdffjm33HLLtMc6cOAAPT1H6lGovVLOPvtszWG47LLLeOihh0ilUvzv//4vGzZsOGK/U089leeee45nn32WX/7yl1M+W7NmDWvWrFnQtavMFi/9JPATSZI+yJ+N/HrABFxYlxGsMGw2G+FweEbFu5lQs/+z2ey8sv9rYbacg8kCPYslg5tKpchmswQCAdFBTyAQLJjoY7sYvmkbSqHAaqOJ/xsf4bUbJwz6M+NjrFu3DovFQjAYRK/X4/F46OrqmlZbJRgM8q1vfYsf/vCHU97PZDJafsB///d/aw6A3W5n3bp1XHfddbOuya9bt46uri6eeOIJzjnnnDpd/Z85qtVRFGUcOE2SpC3ACYfe/pmiKE/XfSQrBHVdP5fLzau7k06nw2azLcm6/mwOyWIL9MiyTCgUwmKxLEniokAgaH1G77iLaqFA1umgYjZxRS7DR/70e8wf/jC9W7domfY//OEPeemll3A6nZjNZu655x5g4rm0efNmSqUSRqORbdu2cdxxx005x+9+9zuuueYanE4nkiTxL//yL9pn11xzDRs2bOCOO+6Ydayf+tSnuPbaa5fe6EuSZAH+DlgD7Ad2KIrS+L6qyxiz2Yxer5+30YeJEP/4+DilUgmTybRII0RLIJkJg8GAJEmLFt4PhUKig55AIKgruUiUZKCditGIJZ/nbJeH9zjdIEmse/hhbbt77rmH0dFRstksxxxzjBb1fOONN2Y9x9ve9jZ+85vfzPj5xRdfTFtb2xHv9/f389RTT2mv165dy7PPPqu93rZt21wucU7Mtqb/XSbC+fuB9wBfrduZVygLWdefXLq3mMwkwTuZxcrgz2azpFIp2traRAc9gUCwYKrVKuFwmOSb16DodHiiMTyxBLpDSdHGrs4j9vH5fFSrVeLxeF3G8IMf/ICrr76az3/+83U53kKYzei/RVGUyxVF+RfgfcBfLcGYWh6bzUa5XJ630TQajZhMpkU1+keT4D18LPWe6YsOegKBoJ7kcjmGhoaIxWKsvvR9tCfTWApF7XPJYqH7huuP2M9sNuN0OonH43WpUvrgBz/IK6+8wlve8pYFH2uhzGb0Naskwvr1Y/K6/nxRS/fm27hnrswmzKOyGAI9ooOeQCCoB7IsEwwGOXDgADBRXvfm919K/z9+AWN3F0gSxu4u+m7dhu/8c6c9Rnt7O4qiEIvFlnDki89s6eMnSZKk9hyUAOuh1xKgKIoyv0VpATDhRRoMBnK53Lzrzx0OB/F4nFwutyiStLMJ86ioM31FUepioEUHPYFAUA/S6TShUAhZlmlra8Pn82maI77zz53RyB+OyWTC6XSSSCTwer0tIw42W/b+3LViBfPCZrPVNNO3Wq2awM9iGP35zPRhIoN/oUmFagc9o9EoOugJBIKaqFQqhEIh0uk0FouFnp4eLJaj6cvNjs/nI51OE4vFWkYGvLXbCTUxNpuNSqVCsVicfeNJSJK0qKV785npT95+Iagd9AKBQMt3uBIIBPUnmUwyODhIJpPB7/fT19e3YIMPE7N9l8tFIpFY8nbii4V4wjaIhazrOxwOyuXyvB2GuTCbBK/KZIGehVAsFrWWlqKDnkAgmA/lcpkDBw4QDAYxm8309/fT1tZW15wgNak4Go3W7ZiNRBj9BmE0GjEajU1XujebBK9KPQR61LC+6KAnEAjmg5pgNzg4SKFQoKOjg1WrVi2KfonRaMTtdpNMJpe0pfhiIYx+A7HZbOTz+Xk30TEYDJjN5kUx+nNt+ytJEgaDYUEhr0QiQT6fJxAIzKvVsEAgWLkUi0WGh4cJh8PYbDYGBgbweDyLWvGjCuq0wmxfGP0GYrPZkGW5pjC9w+Egn8/XvdPdfHoCLESgp1wuE4lEsNvt81YmFAgEKw9FUYhEIgwNDVGpVOju7qanp2dJsuqNRiMej4dUKkWpVFr08y0mwug3kIXW6yuKUveue7Isz8vo1zrTFx30BALBXMnn8wwODmr5P/39/TidziUdQ6vM9luj8HCZYjAYMJlM5HK5afWYj4bFYkGv15PNZut6889FgldFFeiZb62+6KAnEAjmgiqhm0gkMBqNrFq1atG7jM6EwWDA6/Xym9/8hgcffJBUKoXb7Wbr1q2sXbu2IWOqBWH0G4zNZiOVSs3bcE4u3auXQM5cJXhVjEYjiqLMKzqgdtCzWq2ig55AIJiRTCbD+Pg4lUoFr9dLe3t7w0t6R0ZGePnll7Vl1WQyyeOPPw6wbAy/CO83GLvdTrVapVAozHtfh8NRU63/TMxVmEellgx+tYNeR0eHkNoVCARHIMsyY2NjjIyMoNfr6evraxoNjz179hyRR1Uul9m9e3eDRjR/xEy/waiSs7lcbt7ys2pOQDabrYsQxVyFeVTmK9CTyWRIpVL4fD7RQU8gEBxBKpXSJgbt7e11r7lfKMlkcl7vNyONd51WOHq9HovFUlNCnsFgwGKx1K10b74z/fkI9FSrVUKhEGazWXTQEwgEUyiXyxw8eJCxsTFMJhOrV6/G5/M1lcEHZuyVMt8eKo1EGP0mQK3Xr6VzXj1L9+Y709fpdOj1+jnN9MPhMOVyWYT1BQKBhqIoxONxBgcHNc2O3t7epo0Ebt269YjkY6PRyNatWxs0ovkjjH4TYLPZUBSFfD4/733rqc43VwneycylxW4+n9c6VYkOegKBACZEdg4cOKAl9vb39+P1ept6UrB27VrOO+88bWbvdrs577zzlk0SHzRoTV+SpDbg34B+YBC4VFGU+DTbfQc4FwgpinLCUo5xKbFarUiSRC6Xm3c5itls1kr3FipyM1cJ3snMJtCjKArBYBCj0Uh7e/uCxicQCJY/qoRuNBpFp9PR1dW1rAS61q5du6yM/OE0aqb/WWC3oijHArsPvZ6O+4F3L9WgGoVOp6t5XV+SJOx2u1a6txDmU3qnMpsUbzQapVQq0dHR0RTZtwKBoHHk83mGhoaIRCI4nU76+/uXlcFvBRr1FL4A+O6h378LvHe6jRRFeRaILdWgGonNZqNYLNa0Nu9wOJBluaayv8nMR5hHxWg0IsvytPkIxWKRWCyGy+VqmKCGQCBoPGoi7/DwMLIs09PTQ1dX15JI6Aqm0iij36EoyhjAoZ8LbrEmSdJHJEnaK0nS3nA4vOABLjULWde32WxIkrTgdf356O6rzJTBr4b1RQc9gWBlk81mGRwcJB6P4/F4GBgYEG20G8iiuVmSJD0FdE7z0ecX43yKotwL3Auwfv36hcW5G8Dkdf35/odQy/6y2eyC1s3n2mFvMpMFeiZn3CYSCQqFAl1dXaKDnkCwApFlmXA4TDKZxGQy0dfXJxJ5m4BFM/qKorxzps8kSRqXJKlLUZQxSZK6gNBijWO5oMrq1tpAx+FwEA6Ha5qtw/wleFWmE+gRHfQEgpVNOp0mFAohyzI+n68pa+5XKo0K7z8G/M2h3/8G+GmDxtFUWK1WisViTZ3rFlq6N19hHhU1239yeF900BMIViaVSoWRkRFGR0cxGAysXr2a9vZ2YfCbiEZlUWwHfiRJ0pXAMHAJgCRJ3cB9iqKcc+j1D4HNQLskSQeBLyiKsqMxQ158VFndfD4/7855ZrMZg8FANputSR1qvsI8KpIkTWmxq3bQ6+joWLIOevv372f37t0kk8ll2fVKIFjuKIpCMplEzafy+/1NX3O/UmmI0VcUJQocIWGkKMoocM6k1x9YynE1GovFgk6nI5fL1dQu1263k06na+q6V+tMH/4s0FOpVDShjaWSpdy/fz+PP/64FmlYjl2vBILlTKlUYnx8nFwuh81mo6OjA5PJ1OhhCWZAFE43EfVY169WqzVVANQ604c/C/SojTI6OzuXzMPfvXv3EZUDy63rlUCwHFFFdgYHBykWi3R2dtLb2ysMfpMjiiSbDJvNRiaToVwuzzs8Prl0T10qmCu1SPCqGAwGbcx+v39J/9O3QtcrgWC5USgUGB8fp1Ao4HQ6CQQCouZ+mSBm+k2Gaqxrme3rdDqsVmtNyXy1SPCq6PV6EokEBoOBtra2ee+/EGZaRhBVAwJB/alWq4TDYYaHh6lUKvT09NDd3S0M/jJCGP0mw2QyodfrFxTiLxaLc2p3O5laJHhVkskksiw3pPf1dF2v9Ho9xx9/PGNjYzVVQggEgiPJ5XIMDQ1pKpv9/f1CZGcZItyzJmOh6/qTS/c8Hs+c96tFghcmKg2y2SwOh6Mh3r6arDc5e3/Lli10d3cTj8fJZDL4fD6RSSwQ1Igsy0QiERKJBEajkd7e3nkvHwqaB2H0mxCbzUY6naZUKs17fdxkMmE0Gmsy+vPpYf3b3/6Wz372s8RiMWRZZmBggLvvvnvasPoVV1zBVVddxcaNG6e8v2bNGl577bU5n3MmZup65Xa7CYfDmipYIBAQPQAEgnmQyWQYHx/XInk+n080zlrmCKPfhExe168lKc7hcJBMJudVujcfCd5kMsmHPvQhduzYgcvlYtWqVezatWvBDX/qjclkoqenh0wmQzgc5uDBgzgcjiVPNhQIlhtq+W06ncZsNtPT04PFYmn0sAR1QBj9JkSdredyuXnN1lXsdjvxeJxcLjenme18JXh/9rOfcc455+B2u3E6ndjtdjZs2IBOp+Oaa67h1VdfpVqtcuedd/K2t71tyr7/8A//wPPPP89JJ50077yDWnE4HNp3Eo1GGRwcpK2tjba2NjFrEQgOQxXZqVartLe3NyRXR7B4CKPfpKile7UI7ajNe7LZ7JyNPsxdmGd4eBiXy4VOp0OSJDZv3kwwGOSiiy6iXC7z/PPP8/rrr3PZZZfx8ssva/v95je/Yf/+/bz00ksMDg6yY8fSiStKkkRbWxsul4twOEw0GiWZTOL3+0Wmv0DAhL7F+Pg42WwWq9VKZ2eniIi1IGKa06TYbDZkWaZUKs17X51Oh81mm3Pp3lyFebLZhxgbOxGd7g7++Mf7cDpfprOzkz179nDyySeTyWQ47bTTADjmmGOIx+NT9v/DH/7AW9/6VgD6+/sbos1vMBjo6uqir68Pg8HA2NgYBw4coFgsLvlYBIJmQFEU4vE4g4OD5PN5Ojo6hMhOCyOMfpOitqBcSOleqVSak9Mwl5l+NvsQicQnKRaDnHxyJ88/P8qf/vQpstmHgIn6XZ1Ox/PPPw/A66+/fsTSxLHHHsu+ffuAiWiB2pinEVitVvr6+ujo6KBYLDI0NKQlLAkEK4Viscjw8DChUAibzcbAwAAej0eE81sYEd5vUoxGIyaTiVwuh9frnff+k0v3ZvPY5zLTT6X+EUXJE4t14nTquesuKzfeGKNY/Fvc7h10dXVx2WWXce+997Jx40ZkWebuu++ecox169bxl3/5l7zjHe/ghBNOoLu7e97XVU8kScLj8eB0OolGoyQSCdLpNO3t7bjdbvHgE7QsiqIQjUaJxWLodDq6urrEMtcKQRj9JsZms5FKpWpa11edhmw2O6vTMBcJXlkeoVLRUyqZ8Hhi9PZKfP/7TkBi1aonKRaLDA4Octdddx3RLOj+++/Xfr/99tvndR1LgV6vJxAI4Ha7CYVCjI+Pk0gk6Ojo0CIuAkGrkM/nCQaDlEolXC4XgUCgJo0OwfJEhPebGJvNRrVarbkUzuFwkMvlqFarR91uLhK8en0PBoNMd/dBHI70lPfhz1GC5ayAZzab6e3tpbu7G1mWGR4eFqp+gpahWq0SCoUYHh5GURRWrVpFV1eXMPgrDGH0m5iF6PDDRIhfUZRZ95+LBK/LdTOSZEWnq6L6BpJkxeW6GZiYLet0uiUrw1tMnE4nAwMD+Hw+0uk0b7zxBrFYDEVRGj00gaAmstksg4ODxONxvF4v/f39QqhqhSKMfhOj1+sxm801G32r1YpOp5s1i38uErx2+yV4PHei168CJPT6VXg8d2K3X6Jto7bYbQV0Oh3t7e0MDAxgs9kIh8MMDg6SyWQaPTSBYM7IsszY2BgHDx5Ep9PR19dHIBAQ+hQrGLGm3+TYbDYSiURN6/qqjv9cjP5cJHjt9kumGPnDMRgMLRcKNxqN9PT0kM1mCYVCjIyMYLfbCQQCoqRJ0NSkUilCoRDVahWfz4fP5xPJqQIx0292bDYbiqKQz+dr2t/hcFAul49ahz4fCd6j0Uoz/cOx2+309/fj9/vJ5/MMDg5qqmUCQTNRLpc5ePAgY2NjmEwmVq9eTXt7uzD4AkDM9Jsem82GJEnkcrmaOltNLt2bbjY/Xwneo2EwGJBlWavZbzUmq/pFIhFisRipVEqo+gmaAkVRNAldgEAgIGruBUcgjH6To9PpsFgsNa/rGwwGzGYz2WyWtra2Iz6frwTv0VD72lcqlZYOfRsMBjo7O7USv7GxMRKJBIFAQDQlETSEUqlEMBgkn89jt9vp6OjQ/j8KBJNpvelYC2Kz2SgUCjWHkh0OB/l8flq1ublK8M4F9SHTqiH+w1FV/To7OymVSkLVT7DkqCI7g4ODlEolurq6WLVqlTD4ghkRRn8ZsNB1/aOV7tVzpq86DivF6MNEyN/tdjMwMIDX6yWZTPLGG28Qj8dFiZ9gUSkUCgwNDRGJRHA4HPT394tlJsGsiPD+MsBisWjr+rXU1losFvR6Pdls9gi1vHrO9A0GA5IktVwG/1w4XNUvFAqRTCYJBAI15WIIBDNRrVaJRCIkEgn0ej09PT04HI5GD0uwTBBGfxmg0+mwWq01r+tLkoTdbiebzR5R+jcXCd75nMdgMKyomf7hqKp+6XSacDjMgQMHcDqd+P1+EXIVLJhcLkcwGKRcLuPxeGhvbxeKeoJ5IYz+MsFmsxGJRGour7Pb7aRSKYrF4pRks7lI8M6HVi7bmw9OpxO73U48HicajWqJlG1tbSKbWjBvZFkmHA6TTCYxmUz09vaKCJKgJoTRXyZMluQ9PEQ/n/2z2ewUoz8XCd75YDAYas49aDV0Oh0+nw+Xy0U4HCYSiWghfxGOFcyVdDpNKBRClmUhsiNYMCKRb5lgsVjQ6XQLKt2zWCxHqPPNRYJ3PhiNRiqVikhim4TRaKS7u5ve3l50Oh0jIyMcPHiQUqnU6KEJmphKpcLIyAijo6MYDAYhsrNI7Nmzh6uuukp7ffDgQTZv3gzA888/z+mnn87mzZvZsGEDe/bsAWBgYIAtW7Zon+3atWvaY5999tn4/X5uvfVW7T1FUfjEJz7BGWecwbnnnkssFpvTOO+//37tvBs2bOCZZ55BURS2bNnCK6+8AoAkSQZJkl6WJOlNMx1HzPSXCZIkLWhdHyZK9w5fIpirBO9cMRqNKIpCpVIRa9iHYbPZWL16NYlEQiuz8nq9+Hy+lhQzEtSOKrJTrVbx+/14vV5h7BvApz/9aR566CH6+vqQZVnrvaHX63nmmWcAGB0d5ZxzzuHYY4/lzW9+85T9d+zYwVNPPcXBgwe1937xi1+Qy+V47rnn2LlzJ1/5ylfYvn37nMZz5ZVXctNNNzE8PMwll1zCr371K7797W9zxRVXqJvcAPxEUZQ/zXQM8aRZRthsNkqlUs3OCsv7AAAgAElEQVTZ8ZPV+VTqJcGr0gotdhcTSZK0Lmcul4tYLMYbb7xBMpkU0REBpVKJAwcOEAwGMZvN9Pf3izyQBmK323nyySdJp9Po9XrcbvcR23R3d3Pttdfy4x//+IjPVq1adcR7e/bs4dxzzwXgvPPO49lnnyUej7NhwwZtmy996Ut873vfm3Fck7t+HnfccbznPe8B6AUuAr56tGsSRn8ZsdBWu2azGYPBoBn9ekrwqqw0gZ5aUVX9Vq9ejdFoJBgMcuDAAQqFQqOHJmgAiqIQi8UYHBykWCzS2dlJb29vSytbNproY7vYv/ks/vChvyX2s58TfezIEP2//uu/snfvXk455RTOOusshoaGpj1Wb28vIyMjczpvLBbD6/UC4PF4tNfHHnsse/fuRVEUfvrTn/K+973viH137NjB+vXr2bRpE1/72te09z/3uc8BeIDPKopy1IevMPrLCLPZjF6vr1vpXj2FeVRWokDPQrBYLPT29tLV1UW5XGZoaIhgMCgiJSuIYrHI8PAw4XBYa+w03YxSUD+ij+1i+KZtlEfHMEsShWyW4Zu2EX1sF4VCAavVCsDq1au59957ee2117j88su55ZZbpj3egQMH6OnpmdO529raSCQSwMQyjuoAfOQjH+G+++7jmWee4R3veIc2hslceeWV7N27l3/6p39i9+7d2vuHnMMS8Nps5xdGfxmhtspdyLq+3W5HlmUKhUJdhXlUdDoder1eGK15IEkSLpeLgYEB2traSKVSQtVvBaAoCpFIhKGhISqVCt3d3fT09NT1/6NgekbvuAu5WCTrsNPlcPDHYoFiLsfoHXfxzDPPsG7dOgB+//vfa/t0dXVNK4UeDAb51re+xcUXXzync2/atIknnngCgCeeeIJNmzYBcMYZZ/Db3/6Wu+++e0pi4XR87GMf4+GHHyYSiczpnJMRd9cyw2azkU6nKZfLNSXKqV37Jnfdq7e4h6jVrw2dToff75+i6qc28qlFiVHQvORyOcbHxymVSrjdbvx+vxDZWSKKxSLRfJ58VweKJGHX6/nbNj/XjAxiHB1mtc/Fjh07ALj77rvZu3cvNpsNnU7HPffcA0wsjW7evJlSqYTRaGTbtm0cd9xxR5zr6quv5sUXX6RYLLJ3714effRRzj77bHbt2sUZZ5yBy+Vi586d2vaXXnopDzzwACeffPJRr0Gn03HVVVdx11138Y//+I/zun6pFWcS69evV/bu3dvoYSwKpVKJN954Q+vyVgvDw8MoioLL5SIUCrFmzZq6PnBGRkYol8v09/czODjIKaecwkknnUSxWGT9+vXcfffdNR97cHCQq666iqeeeqpu421WMpkMoVCIcrm87FX97r//foDJWcYrkmq1SjgcJpFIYDQa6ezsFCI7S4CiKGQyGeLxOPl8nqEbb8Z4cARbJoux/OeopLG7i7V7/r1h47zzzjux2+1cffXV895XkqR9iqKsn207MdNfZphMJgwGA7lcrmaj73A4CIfDmM3muknwTsZoNE5Zgjj11FM1I71161ZeffVVjj/+eKD+1QOthMPhwG63E4vFiMViZDIZTdVPlPgtPzKZjNaF0ev10t7eLv6Oi0ylUiGRSJBMJrUyYr/fj/dDlzNyy5dQJhl8yWKh+4brGzbWz3zmM7zyyiv87Gc/W9TziDtuGVKPdX2YeAjVU4JXxWg0Uq1Wj2gxW6lUyOfzOJ1OVq9ezcc+9jEuuOACyuUyV111FVu2bGHjxo28/PLLANxxxx2ceeaZvPWtb+ULX/jCEef5xje+wUc/+lFkWeahhx7ijDPOYOPGjXzpS18C4OKLL+a3v/0tMJFos3XrVoBpt92zZw/vec97+MAHPsDatWt56KGHAPja177G29/+drZs2cJdd91V1+9pLkiShM/no7+/H4fDodX3p9PpJR+LoDYqlQqjo6OMjIyg1+vp6+sjEAgIg7+I5HI5RkdHef3114lGo5jNZnp6erS8mcB7z6fv1m0Yu7tAkjB2d9F36zZ855/bsDHfdtttPP3009Mm8NUTMdNfhthsNk1HvxZhHbV0L51Oa5mj9eTwDP59+/axefNmRkdHOfnkk+nr62NsbIzPfvaz9PX1cc8997BmzRruu+8+xsfHueiii3jhhRe45ppruOGGG6hWq2zcuJErr7xSO8eNN96IyWTi29/+NvF4nNtvv53nnnsOo9HIhRdeyP79+/nwhz/Mzp07uf322/nBD37ABz/4wRm3BQiFQuzatYvx8XHOP/98LrnkEn7wgx/wzDPP4HQ6p03iWSpUVb9cLkcoFGJ0dBSbzUYgEKiruJKgvqRSKUKhENVqlfb2dlFzv4hUq1VSqRSJRIJisYher8fj8eDxeKYtffSdf25DjXyjEEZ/GTK5Xr/WB77D4SAYDOLz+eo5NADSTz3N0PcfIPran0hZLZzYt1qTr7z++ut58MEH6enpoa+vD4D9+/fz4osv8uSTTwITZSwADz/8MPfddx+SJPH6669rZTGvvvoqsViM//zP/wTgtddeY2hoiLPOOguARCLB0NAQ55xzDjfffDOyLPPwww/z9NNP8/vf/37abR0OByeffDJ6vZ7u7m6tpObOO+/kuuuuo1KpcM0117Bx48a6f1/zQVX1SyaTWua3x+PB5/OJZZImolwuMz4+TjabxWq10tnZKWruF4lSqaSF8KvVKmazmc7OTpxOp4imTIMw+ssQo9GorZvXOlO32+1UKpW6l9ZFH9vF2LZbqbR5qOp0VMZDFMJBoo/twnf+uXi9XsLh8BQDdfzxx7NmzRo+9alPAWia9DfffDO///3vMZvNnH766Vr52vHHH89HP/pRLr30Uh588EGOOeYY1qxZw1NPPYXBYKBaraIoCnq9ns2bN/PlL3+Zv/iLv8DpdM647XPPPTftDGzdunVs3LiRgwcPcsEFF7Bv3766fl+1IEkSHo8Hp9NJJBIhHo+TSqXw+/24XC4xk2wgiqKQSCS0UqqOjg7cbrf4m9QZRVHIZrPE43FyuRySJOF0OvF4PIseHl/uCKO/TLHZbGQyGRRFqemBYrPZqFardW/6MnrHXUj5PJLiRj4U5v/ffJZ3f/hD2E46EafTyQMPPDBlffzqq6/mE5/4BFu2bAFg/fr1/PM//zMXXXQRp59+Oscdd9wRXekuvvhijEYjl1xyCT/60Y/45Cc/yZlnnoler8doNLJz5046Ozv5m7/5G97+9rdryTE+n2/abWfiQx/6EJFIhEKhwLXXXlvX72qh6PV6zaiEQiGCwaBW4icefEtPsVhkfHycfD6P3W6no6Nj2VZbNCuVSoVkMkkymaRcLmMwGGhvb8ftdgt9gzkiSvaWKalUirGxMVavXj2lVe5ckWWZX/3qVzidTtauXVu3cf36zWtBUShYzBgqMgY1kiBJrPu//XU7j+BIUqkU4XCYSqWCy+XC7/c3zYOwlUv2FEUhGo0Si8XQ6XQEAgFcLlejh9VS5PN5EokE6XQaRVGw2Wx4PB4cDoeIohxClOy1OJPX9Ws1+mazmUqlUrPQz3QYuzopj45hKRSPeF+wuLhcLi3DPx6Pk8lk8Pl8okPbIpLP5xkfH6dYLOJyuQgEAiK3ok5Uq1XS6TSJRIJCoYBOpztqYp5gbogsh2WKwWDAbDbXXLpXqVSwWCzo9fopXfcWSvcN1yMd5oQ0uv51JaGq+vX392Oz2QiHwwwODtb1byyYMEihUIjh4WGq1SqrVq2iq6tLGPw6UCqVCIfDvP766wSDQRRFoaOjgze96U0EAgFh8BdIQ2b6kiS1Af8G9AODwKWKosQP26YX2Al0AlXgXkVRlr5Quomx2WxaS9b5zuRkWcZgMGhd9zweT13GpJbAjN5xF+WxIMauTrpvuH5FlsY0EpPJRE9PD9lsllAoxMGDB3E4HPj9fvHQXCDZbJbx8XHK5bIQ2akTiqKQy+WIx+Nks1kkScLhcODxeIRiYZ1pVHj/s8BuRVG2S5L02UOvP3PYNhXg04qi/FqSJCewT5Kkf1cU5XdLPdhmxWazEY/Hp3SFmitq1r7L5VpQQuB0rNT612ZE7doWj8c1YR+h6lcbsiwTCoVIpVKYTCb6+vpEwuQCkWWZZDJJIpHQEvN8Ph8ej6dp8lFajUZ9qxcAmw/9/l1gD4cZfUVRxoCxQ7+nJUn6X6AHEEb/EFarVWueM9+HjyzLWplLKpUil8uJpi4tiiRJtLW14XK5CIfDRKNRksmkVuInmJ3JIjs+nw+fzyfyJBZAoVAgkUiQSqVQFAWr1Up7eztOp1N8r4tMo4x+xyGjjqIoY5IkBY62sSRJ/cApwK8Wf2jLB71eX/O6fqVSQa/Xa92jstmsMPotjsFgoKurC4/HQygUYmxsjGQyKVT9jkKlUmF8fJxMJoPFYqGzs1N8VzWiKIqWmJfP59HpdLhcLrxer/hOl5BFM/qSJD3FxHr84Xx+nsdxAA8Dn1QUJXWU7T4CfATQlN5WAmqIv1qtzitcq67p63Q6reY/EDiq7yVoEaxWK319fVNU/dxuN+3t7SIR7RCKopBMJgmHwwAEAgE8Ho+YhdZAuVzWQviyLGMymbSyRnG/LT2LZvQVRXnnTJ9JkjQuSVLXoVl+FxCaYTsjEwb/B4qiPDLL+e4F7oWJOv3aR768sNlsxGIxTRBkrqgzfZhY981kMpRKJZHktUKYrOoXjUa1GmhV6GQlG7dSqUQwGBQiOwskm82SSCTIZrMoijIlMW8l31+NplHh/ceAvwG2H/r508M3kCbuih3A/yqKcsfSDm/5oK7rz3dNvlKpaCE1db9sNiuM/gpDr9cTCAQ0Vb/x8XESiQQdHR0rLklNURRisRjRaBSdTkdnZ2fN7atXKrIsa01vSqUSer0er9eLx+MRjlOT0Cijvx34kSRJVwLDwCUAkiR1A/cpinIOcDrwIWC/JEn/dWi/GxVFeaIRA25WdDodFotl3uv6k/vYG41GTCYT2Wx2UbruCZofs9lMb28v6XRaqz9vNlW/xaRQKBAMBikWizidTgKBwIq47npRLBa1xLxqtYrFYqGrq0sk5jUhDbmrFUWJAluneX8UOOfQ788D4m6ZA2qIf7IhPxqyLKMoypSHmsPhqCk3QNBaOJ1O7HY7sViMWCzW8qp+1WpVUzDU6/X09PQc0edBMD2KopDJZEgkElrTG5fLhcfjqUklVLA0CFe2BbDZbESjUfL5/JweWLIsA0xxENQHfS6XEw+9FY5Op9PW9kOhEOFwWCvxa6V7I5fLMT4+TqlUwuPxiETGOaI2vUkkElQqFYxGI36/H7fbLb6/ZYAw+i2A1WpFp9PN2WCrwjyTZ/rqMbLZbEs92AW1YzQap6j6jYyMYLfbl70UqizLmiNjMpno7e0Vqm9zIJ/Paz0dFEXRkhztdntLRoFaFWH0WwBJkrBarXNe159upi9Jkla619HRsSjjFCxPVFU/tU/84OAgXq8Xn8+37JaC1JwFWZZpa2tbltewlFSrVS0xr1gsotfrRdObZY4w+i2C2lylUqnMmoA03UwfJtb1M5kMxWJRiGUIpiBJEl6vF6fTSSQSIRaLkUqllo2qX6VSIRQKkU6nMZvN9PT0iHXno1AqlUgkEiSTSarVKmazmc7OTpxOp3CSljnC6LcIk1vtzvYQViV4D//PO7l0Txh9wXQYDAatlE1V9UskEgQCgaY1oqrITrVaxe/3t2xS4kJRFGVKbb0q0+3xeFZc+WYrI1y2FsFsNqPX6+cU4q9UKoyNjaHT6fjpT/8skXDcccexa9cutm7dyl/91V9x2WWXUSwWAfjkJz/Jhg0b2LBhA9u3b9f22bx5M+94xzvYvHkzn/jEJ+p/YYKmRFX16+zspFQqMTQ0xPj4uLZ01AyUy2UOHDhAMBjEbDbT399PW1ubMPiHIcsy0WiUN954g5GREYrFIu3t7RxzzDF0dXUJg99iiJl+izCfdX21tO+4447jy1/+Mueff772INy0aRPnnHMOf/EXf8HnPvc5vv/973PllVdy7bXXcuedd1KtVjn99NO55JJLeNOb3gTAQw89xKpVqxb1+gTNhyRJuN1uHA7HFFU/tUtao4yroihaV0GAjo6OFa8yOB35fF77mymKgs1m0yo0xHfVuoiZfgths9kol8uUy+WjbqdK8Pb09LBu3bops/3jjz9eqwQwmUzauv+xxx4LTJRz6fV6LQlQkiQuu+wyzjzzTJ5++ulFujJBM6Oq+q1evRqz2UwoFGJoaKimRlALpVgsMjw8TDgcxmazMTAwIDTzJ1GtVkkmkwwNDTE8PEwmk8HtdtPf309vb68Q01kBiJl+CzF5Xf9o8qGTk/1uvPFG3ve+93HBBRcAYLFY0Ov1/OY3v+GJJ57gxRdfnLLv9773Pd70pjfR398PTMzy29vbOXDgAO985zvZu3cvTqdzEa5O0OxMVvULh8McOHAAp9OJ3+9f9HMrikI0GiUWi6HX6+nu7hb34STK5bKWmKc2veno6MDlconEvBWGMPotxOR1/ZmM/qO/eIX7H9jFH14/wOj/vc7eV8c49dRTefTRR4GJmXsymeS6667jxz/+8ZTkrKeeeorvfve7PP7449p77e3tAPT29nLSSSfx2muvccoppyziVQqaHVXVTw2xZ7NZyuXyommv5/N5gsEgpVIJt9uN3+8XIjFMOEK5XI5EIkEmk0GSpClNbwQrE2H0WwybzTZjWPXRX7zC5297kDYnyDIUS2Vu3P4gN1z5Hm677VYAIpEI11xzDbfccsuUdfpf/epX3Hzzzfz85z/XEnvU/tgul4t0Os3+/ftZvXr14l+koOnR6XT4fD5cLhfhcJhyuYwsy2QymbqJP1WrVcLhMIlEAqPRyKpVq+bVdKpVObzpjcFg0PIsRD8BgaQordeFdv369crevXsbPYyGkEwmCQaDDAwMHCGesfHCLzAeidPZZiQ4Hufg7/+dzrdcSE+Hl5N7ojz55JO8+93v5tFHH6W7uxuj0cj/+3//jyuvvJITTjgB+PPM/vbbb+fEE09kw4YNWK1Wkskk69at45hjjsHtdrN161bWrl275NcvaE6+853vUCqV2LJlS11U/TKZDOPj41QqFbxeL+3t7Ss+TF0oFLTEvGq1itVq1doni3X61keSpH2KoqyfbTvh9rUY6ixcTcSbzGgojkEvUZEVdGYnnW+5UHv/+Z98Q9vuG9/4BsPDwwD09fUB8D//8z/Tnm/fvn3s37+fxx9/XEsgTCaT2hKAMPwC+HM3yEAgQDQarVnVT5ZlQqEQqVQKs9lMd3f3ii4pU6NtiUSCfD6PTqfD6XTi9XqF1oZgWoTRbzFMJhNGo5FcLofH45nyWXfAy8h4nGCsor030GnibX/p4Itf/OKUGbrdbicSicypc9/u3buPqBgol8v88pe/pKenB71ej8FgmPJTzDxWJl6vVwv5q6p+7e3tuFyuWe+JVCpFKBSiWq3S3t6+omvuy+UyyWSSZDJJpVLBZDIRCARwuVwin0FwVITRb0FUDX1FUaY8FP/+787lxu0Pki9OGOiBThOnn+DAoJ/YZvIM/dhjjyUSiZDNZmdV+Esmk9O+n8lkCIfD036mlv0d7gyoPyf/vtLDtq2GXq+ns7MTj8dDKBQiGAySTCZnVPUrl8uMj4+TzWaxWq10dHSs2Fns5MQ8RVGmJOatVAdIMD+E0W9BbDYbyWSSYrE45SH63rPfCsBX79nFaCjO2/7yzwZfpVwus3v3bk444QQMBsOcjL7b7Z7W8Lvdbo499lhkWaZSqUz7U5ZlisUiuVxuRjU3nU43rTMw3U/hICwfLBbLlBK/oaEh3G437e3tGAwGFEXRmvwABAKBFVlzr9bWq4l5er0er9eLx+NZtIoIQesijH4LMrle//CZ03vPfqtm/L/4xS9Ou38ymSSfz2Oz2chms0dEDA5n69atU9b0YaIt69atW9HpdOh0ujk9nBRFOapzUKlUKJfL5PP5ozoIc3EOJgsMCRqHJEm4XC5N1S8ej2sVIYVCgUKhoLVwXWkGrlgskkgkSKVSVKtVLBaLaHojWDDC6LcgBoMBk8lELpejra1txu1mmqHbbDYOHDhAqVTSjnG0JiVqst7u3btJJpM1Z+9LkoTRaJyzgzDZGZjuZ7lcplAoIMsy01WpSJI0J+dAjSCstBnmUqLT6bSOfa+99hq/+93vMBqNrFmzhs7OzkYPb8lQFIVMJkMikSCXy2lNb7xeb9M2NBIsL4TRb1FsNhupVOqos/SZZuhnn3023d3dJBIJotEof/rTn2hra8PpdOJ0OqfNll67du2SZupLkoTBYMBgMMy6vqsoCtVqdUbnQP1ZLBZndRDmGkEQDsL8yefzjI+PYzAY6Ovr08Laane8Vp7pVyoVLYRfqVQwGo34/X7cbreISAnqijD6LYrNZiORSFAoFGYsaZpthu50OpFlmWw2i8ViIZFIEI/HMRqNmgOwHGYfkw32XJgtglCpVCiVSlQqlWkdBGDW5MTJP1e6g1CtVolEIsTjcQwGAz09PTgcDhRFIRaLEYvFyGQytLW10dbW1lKh7cOb3qhLGXa7fcXfF4LFQRj9FmXyuv7R6phnm6G7XC6KxSIdHR1IkkQ2myWVShGPx4nFYphMJs0BaJWMatVAz0U8Zi4RhEKhQKVSoVqtHvV8c0lWbCWDB5DNZhkfH6dcLuPxePD7/do1SpKkqfpFIhGi0SipVAq/37+sdfWr1SrpdJp4PE6xWESv1+PxePB4PAsSLBII5oIw+i2KXq/HbDaTy+Xw+Xw1H8dutxMOh8lms7jdblwuFy6XS5NUTafTxGIxotGo5gC4XK4V8/DS6XRzvtZqtTptcuJ0SwytXskgyzLhcJhkMonJZKKvr29G59RoNNLV1YXb7SYUCjE6OorNZiMQCCwrR7NUKmmJebIsYzabRdMbwZIjjH4Lo4b4q9VqzQ8Vs9msle5NbuKj1+txu9243W4qlYrmAESjUaLRKGazWYsArBQHYDZqrWSYKYowWyWDmvfQbJUM6XSaUCiELMv4fD58Pt+cQtk2m43Vq1eTTCaJRCIMDQ3h8Xjw+XxNu+6tKArZbJZEIkE2m9US8zwez4pWEhQ0DmH0WxibzUY8HqdQKCyoq5bD4ThqUqDBYNDCk5VKhXQ6TTqdJhKJEIlEsFgsmgPQyslY9aTZKhnqkaioKAojIyNkMhksFgurVq2a90xdkiRNT17NA1BD/nNR9VsqZFnWEvPK5TIGg4H29nbcbrdoeiNoKOLua2FUla5cLrcgo2+32zVt79mOYzAY8Hq9eL1eyuWy5gCEw2HC4TBWq1VzAMTDrz40YyXD5N8BLTKRy+Xw+/1HLQGdC3q9no6ODi3kHwwGSSQSBAKBhs6gC4WCpjWgKAo2mw2/34/D4Wgah0SwshFP3RZGbXIyU6vduaI6D9lsdl7Og9Fo1DKuVQdA1U8PhUJYrVZNmEU4AEtDvSsZZFmmVCohy/K0iYqVSoVUKkU2m0Wn02E2m6lUKsRisbpUMlgsFvr6+kilUoTDYYaHh3G5XPj9/iW7pxRF0VrZFgoFdDodbrcbj8ezrHIOBCsD8aRtcWw2G7FYbEHr+jqdTlPn8/v9NR1jsgNQKpW0CMD4+LjmAKgRgGZdn12J1FrJUC6XicVipNNpTSxKp9NpYe/ZKhnmkocw+X4+XNUvk8ng8/kWHFE4GuVymUQiQTKZRJZlremN2+0WiXmCpkUY/RbHZrMRjUbJ5XI4HI6aj2O32wmFQpTL5QWvy5tMJi2Bq1gsHuEA2Gw2nE4nDodDOADLCLWSQQ1xF4tFuru7CQQCvPrqqwAMDAwA9atkmM4Z8Hg8JJNJDh48SCQSobOzc9b+EXNFUZQpTW8kScJut+P1ehe0hCYQLBXC6Lc4VqtVW9dfqNGHibrqw1v2LgSz2YzZbKa9vZ1CoaA5AMFgUHugqg6AmD01N9VqVZtp6/V6uru7Z6ynn28lw2xiSTNVMgSDQQ4ePIjVaqWtrQ2LxTLnPITJyLKshfDVpjc+nw+32y2SUwXLCmH0WxxJkrBarQte1zeZTJhMprob/clYLBYsFgt+v59CoUAqlSKdTmszKofDgdPpxG63CwegycjlcoyPj1MqlXC73fj9/rpFaSYnKs7G4ZUMav6A6ow4HA7sdrsWaZitkkGWZfL5PPl8HkmSsNlsWoe7mRwEgaCZEUZ/BWCz2YhEIsiyvKCHlN1u19ZjF9voHu4AqBGAdDqNTqfDbrfjcrmEXGmDkWWZSCRCIpHAaDTS29vb0DD3dJUMbreb3t5ewuGw1rFOVfWbrpJBTTqNRCLk83mq1SpmsxmbzYZOpyOZTGqNqqarZDhaHkIr36tnn302v/71r7n++uu56aabUBSF6667jv/6r//C7Xazc+dOfv3rX/P1r3+dxx57DIAHH3yQp59+mr//+7/niiuuwGQyUS6X+da3vsVJJ53Etm3buOOOOxgfH9cmL52dndxwww1s27atsRe8TBFGfwUwWZJ3IfKldrudeDxOPp/Xwv2LjRqpsFqt+P1+8vn8EQ7A5AhAKz9Um41MJsP4+DiyLNPW1obP52vaCIzBYKCrqwuPx0MoFGJsbIxkMjlF1a9SqWg6+JVKBa/Xy8DAgNb0ZqGVDHCk5PLRfi63e3nHjh089dRTHDx4EIBf/OIX5HI5nnvuOXbu3MlXvvIVtm/fzve+9z0efvhh3vnOd3Lbbbfx9NNP43Q6eeGFF5Akiaeffppbb72Vhx56CIDjjz+exx57jPe///089thjnHDCCY28zGWPMPp1ZP/+/QtuL7sYWCwWdDrdgo2+1WpFp9ORzWaXzOhPRg2vqhKsuVxOC/+nUin0er3mAKhlhoL6U6lUCIVCpNNpzGYzPT09y6LxEkzcw319fVNU/UwmE3q9nnw+j6IoOBwOPB7PEfdQrZUMMzkKxWKRbDY7o4MwWXJ5PpUMjTNje44AACAASURBVGLVqlVTXu/Zs4dzzz0XgPPOO4977rkHgDvuuEPr8PmZz3wGr9c7Zb9UKsWJJ56ovb700kv50Y9+xPvf/35+9KMfcemll5JIJBb5aloXYfTrxP79+6e0qU0mkzz++OMADTf8qrFc6Lq+WrqXyWQIBAJ1Gl1tqEl+drtdkzpVZ//JZBK9Xj+lFbBwAOpDMpkkHA5TrVZpb2+nra1t2X23kiRp/SOGh4cZGRlBr9fT09NDb29vXWSjG9GTYa5yy0uVhxCLxTSD7vF4iMViAPh8Pj7+8Y/z/e9/n8suu0zbft++fXz84x9neHiYRx55RHvf7/dTqVT44x//qN13wujXjjD6dWL37t1T+tLDRB3vv//7v3P88cc33BNXjXWlUlmQaIndbieTyVAqlZpGU19N8lPbsaoOgJptbTAYcDgcuFwuLBbLsjNSzUC5XGZ8fJxsNovVaqWzs7Np/v7zoVQqadK91WqVtrY2Vq1aRS6Xo1AoMDo6SkdHx5Kq+i1VJYPK0XoyTNe06Wj/X6KP7WL0jrsojwUxdnWSOel48LcD0NbWphnnZDI5ZUa/Zs0a1qxZM+VYp556Ki+99BIvv/wyH//4x3n55Ze1z973vvfxwQ9+kOuuu27W70hwdITRrxNqYs/hpNNpXnvtNSwWixaabsTMc/K6/kJqlieX7jXjQ3+yA1CtVjUHQNVBNxgMUyIAgqOjKAqJRIJIJAKgSd8uJ8dJURQymQyJRIJcLjdj0xtVLroRqn5zZSGVDAvpyTCdg5B+eg9j27+KlMuhkySU0TGif/ojxU1nALBp0yZ+8pOf8N73vpcnnniCTZs2zTjWQqGgLRGpSyuTufDCC/n5z3/Oe9/7Xh599NH5fGWCw2iuO3oZ43a7pzX8TqeTtrY2crmcVjqkJqepTsBSzD7VdcuFGn2j0aiV7h2+Ftds6HQ6zcBXq1WtE2AikSAej2M0GrXPl8ua9FJSLBYJBoMUCgUcDgeBQGBZ1aRXKhXN2atUKhiNxqM2vVGTQWOxGLFYbElU/RaTevZkUH8vlUpUKhUURWHoh/9GxWkH58RE4N7/+jX7c1nKDz/M7zJpHnnkEXbt2sUZZ5yBy+Vi586dM55/9+7d3HbbbdrSw5133jnlc4fDwQMPPLDAb0QAIE3n3S131q9fr+zdu3dJz3n4mj5MGMjzzjtPW9OvVqvkcjntX7FYBP68Vq7+M5lMi/KQGR0dpVAocMwxxyzoOOFwmHg8zpo1axq+bFELsixrDkAul0NRFEwmk+YArHS9dEVRiEajxGIxdDodgUBgwYp2999/PwBXXHHFwgc4C/l8XsvCVxQFu92Ox+OZV3VHuVwmFAqRyWQwmUxa0xzBBLIss+/EU6lKOqp6HVWdDksuj05RQJJY93/7Gz3EFYckSfsURVk/23Zipl8nVMN+tOx9tbxMfXjIsjzFCchkMsBElvDhTkA9sNlspNPpBa/Hq7Ohhar8NQq9Xo/b7cbtdmsOQCqV0iIxJpMJl8uF0+lsyiWMxSSfzxMMBimVSrhcLgKBwLIQoKlWq6TTaU3+V6fTae2ea/kbGo1Genp6yGazhEIhRkZGsNvtBAKBFXdPTIder8fq91MeHTviM2NXZwNGJJgrwujXkbVr184rU39yhjmgJeDkcjltLRomaownOwG1hlgnr+sv5ME1uXRvORr9yUx2ACqVihYBiEQiRCIRzGYzTqcTl8u1rELb86VarWr96Y1GI6tWrWpIWeZ8KZVKJBIJUqkUsixjNpvp6OjA5XLVJQplt9vp7+/X8hoGBwfxer1NrUmwVHTfcD3DN21DKRS09ySLhe4brm/gqASzIYx+E2E0GjEajVootVQqaVGAbDZLKpUCJtbnJycFzjXZyGQyYTAYyOVyC5LSVcvlMpkMHR0dNR+n2TAYDNrssFKpaCWAqgNgsVg0J62VHIBsNsv4+Djlchmv10t7e3tTGzS1QiORSJDNZrXkzekSwOqBJEl4vV6cTieRSIRYLEYqlcLv99etkc9yxHf+RA3+5Oz97huu194XNCfC6Dcxqt69x+NBUZQpToBajgZoEqGqE3C0cKzaIldRFD72sY/xrne9iwsvvJDf/e53nHDCCUQiEdra2vjWt75FNBplaGiI++67jyuuuIKrrrqKjRs3AhMzoD179nD55ZdjNpspFAps376dzZs3L8VXs+gYDAa8Xi9er1eTZVWzu8Ph8JRWwM2W4T1XZFkmFAqRSqUwm8309fU1dUWD2pY3kUhQLpcxGAxHTcyrNwaDgc7OTtxut6bql0gkCAQCKzYR1Hf+ucLILzOW59NqBSJJktaRzuv1oigKhUKBXC6nJS7F43FtO7vdrsnXTp612Ww2UqkUpVKJjRs38sILL3DhhRfywgsvcOaZZ/Liiy9y7rnn8sILL3D11VczNDQ07Xjsdju33XYb3/ve9zjxxBO1tfFWxGg00tbWRltbG6VSSXMAQqHQEa2Al4sDkEqlCIVCVKtVrc1xs2aoFwoFLYSvKAo2m01LrGvEmFVVv1QqRTgcZmhoCI/HQ3t7+7LIfxCsbBryhJIkqQ34N6AfGAQuVRQlftg2FuBZwMzEOH+sKMoXlnakzctkTXqYCHmq+QBqeaCiKEeUB6rb53I5zjjjDO6++24AXnjhBf7hH/6BZ555hnPPPZeXX36Zyy+/fMbzGwwG7HY7TzzxBAMDAzidTtxu9+JfeIMxmUyakVQdgFQqxfj4OKFQCKvVisvlwuFwNKUBOFxkp6OjoymrFRRF0cor8/k8Op0Ot9uNx+NpivFKkoTb7cbhcBCNRrVqAZ/Ph8fjaVoHSiBo1LTks8BuRVG2S5L02UOvP3PYNkXgTEVRMpIkGYHnJUn6uaIo/7nUg10OTNalh4nErMlOgCquonYJq1arDAwMaJ3ExsbGOOuss/j617/OwYMHaW9vnzXU+81vfpPt27dzyimnMDAwwH333cfq1asX/VqbhckOQLFY1CIAwWBQ+3uoEYBGOwCKomgSugCBQKApjVO5XCaRSJBMJpFlGZPJpJUMNvo7nA69Xk8gENBC/qFQSGvk08hugwLBTDTK6F8AbD70+3eBPRxm9JUJAQE1Xmw89K/1RAUWCbX9rJqBPbk88NmX/pv/ePG/GQxmKMgW/r/P30pbWxuVSgWdTsfu3bu1tfujcdxxx/GlL32J7u5uHnnkEW655Ra++93vLvalNSXq0kt7e/uUVsCqA2C32zUHYKmT5EqlEsFgUOuO2NHR0VSJiIqikMvltMQ8YEpt/XLAbDbT29tLJpMhFApx4MABnE4nfr+/qb5rgaBRRr9DUZQxAEVRxiRJmrZ7iyRJemAfsAb4pqIov1rCMbYUanng7hd/zzd/8BJOq0IqKyNZAtz/rzu4+OKLeeONNxgYGOD222/nc5/7HJVK5ajHHBwcxGg0ks1m6erqmrFb2ErDYrFgsViOaAWcyWS0THNV/W0xHQBFUTTtAZ1OR1dXV1Nlm8uyrCWklkol9Ho9bW1tuN3uZWsoHQ4HNpuNeDxONBolm81q+SDNFlURrEwWzehLkvQUMJ1Kw+fnegxFUWTgZEmSPMBPJEk6QVGU/5nhfB8BPgLQ19dXw4hXBl+9ZxfJTAm7xUhVgaqpk0ziGfa/IdPZ2clpp53GN77xDQYGBvjDH/6gzVbL5TKf/OQntVK/T3ziE/zyl7/kpZdewmQyYbfbueeeexgcHGRgYIBHH32UCy64AJhornHTTTfxxS9+kdWrV5PP5znllFO47bbbcLvdbNu2DafTyac//WkKhQKnnXYajz322BGtOpcjat6F3++fEgFIp9OaWJPqANTTKBQKBYLBIMViEafTSSAQaJokw2KxqCXmVatVrFYrXV1dOJ3OljCMOp0On8+Hy+UiHA4TiUS0kP9y17UQLH8aIsMrSdL/AZsPzfK7gD2Korx5ln2+AGQVRfnqbMdvhAzvcuGY069DUaDDa6BUVohnJjpxSRK8/sLXte2KxaK2HDC5Y9fh5YHZbJaxsTFWr16NxWJhcHCQ97znPbjdbl566SUkSdKM/sGDB7npppsA+NrXvsZvf/tb7r//forFIqeddhqPPPII9957Lx0dHS3dTUtNulSNvyzLdXMAqtUq0WiUeDyOXq+no6OjKQzN/fffjyzLnHXWWeTzea29rcfjaflyt1wuRygUolgs/v/t3XtwnFX9+PH32exubnvL/dKmbXoRZCxCR7D+UqC0MNVasQ58xREEuVhwQIvAT6gw0K8/dSrfkZ98K5XB1NpSLj8BsViwzpdKp2CrUEUtSrWmTW+5J7ubZDfJ3s7vj+zzuLm02aab7Cb7ec1kunn2yeacTZrPec5zPucju/qJCZPp2/C+CtwMbIj/u2P4CUqpMiCstfYppfKBq4DvT2orp6Hq8iJOtXpp9UZGHE80PD0wcRDg9/vxegeTLWw2G36/n46ODqqrqwGYMWMGH/rQh9ixYwerV68etR3f+MY3mDdvHrFYjNzcXH7wgx9w/fXXk5OTw1tvvTUBPc8ciYsuy8vLCQaD5vR/d3c3OTk55gCgoKAg6QFAMBg0Z2UyJYUsEomYK/C11kQiEcrKynC73Wlv22QpKChg9uzZ+Hw+Ojs7ZVc/kVbpCvobgJ8rpW4DjgP/AaCUqgbqtdYrgSpga/y+vgX4udZ6Z5raO23cf+cqvrXhBfoG/l0YKD/Xxv13nn6DDaWUeZ+6uLh4yB4BwWCQ994/whObX+N4Sy9WFaGj3cuPf/wNvvSlL5lT/KMpKyujo6OD8vJyLr30Uo4cOcLDDz+cVX8IjUV+hYWF5k5zxgyA3+8fslXz6UoyR6NR2tvb8fv92O12ampq0r5y3FiY19vbi9Yai8WC1WqltrZ2Wkzhny1jVz9jyt/Y1a+0tBSXy5WV74lIj7QEfa11J7B8lONNwMr4478CF09y06a91SsuAQbv7Te1eakuL+L+O1eZx5ORuEfAWweOsPmlP+LI1wT6YsRCvZxq7mTfH//BvHnzqK+vJxaLEQ6HR9Trbm9vp7S0FID169ezdu1a6uvrufnmm7Mi5384Y5Gfw+EwBwDd3d3mYjer1TqkFLBSytwkKBqNUlxcnNarx1gsZrZ1YGCAnJwcioqKzFs9Rh+zWU5ODpWVlXg8Htra2mhpaTHv90/32xwiM2TGyh4xqVavuOSsgvyZGAsDc21WBsIaf3eUvlCUrS+/y+MP/W9uueUWotGouWf5qVOnKCgoYMuWLdTV1WGxWPjLX/7Cvn372Lt3LzU1Naxbt45NmzalpH1TVeIAIBaLmTMAiTsvhkIhYLAO/IwZM9IWNIyiN8b+D3l5eVRWVuJ0OrNq1uZs5OXlUVNTY27tfOzYMdxuN6WlpRmz4FJMT/LbJc5JU5sXrSEWgzy7wh8/3tjk5aKLLqKuro5du3bh8Xj48Y9/zP79++nr6+O8885j3bp1nDx5kjvuuIMf/ehHWCwWbrrpJrZt28Y777zDpZdemta+ZQqLxWJe4cdiMZqamjh58iR9fX3mtL9RkXGyAr8xE+H1egkGgyilcDqdeDyejN6/P5MYixmNXf28Xi89PT2UlpZm5MZJYnpIy+r9iSar9yfPks89yqlWL3abIhLRxOK/TjMqinj7lf8c9WvC4bC5HiAYDJr7AdhstiElhOWKZ6hQKERrayvBYJCCggJKS0vNrYCDwSBaa+x2uzlAmIjtaiORCH6/H7/fTzgcxmazmaWJz/Tz+tnPfgbAl7/85ZS3aboIhUK0tbURCATMnQinyuZEIv0yffW+mCbGszAwMVDA0BLCvb29+P2D8wWJJYQLCgqyZrX3cFprvF4vHR0dWCwWs9IbDO4D4Ha7iUaj5gJAY0Oe3NxccwBwriliRlGnnp4es+iNEZTkijQ17HY7M2fONHf1O3nypOzqJ1JOgr44J6lYGDi8hHBieuDwEsKFhYXmHgHZcL94YGCAlpYW+vv7cTgcVFRUjHpFnZOTg8fjwePxEIlEzPS/jo4OOjo6yM3NxeVy4XQ6kw4gsVjMXEfQ39+PxWIxv4fkmU8ch8NBYWEhXV1ddHV10dvba+7qlw2/82JiSdAX5yyVCwPHSg/0er10dXWZ5yVuFDSdrji11nR2dtLV1UVOTg7V1dU4nc6kvtZqtQ4ZABgzAO3t7bS3t5OXl2fOAIw2AAiFQuYUfjQaJTc3l4qKClwulwSdSaKUoqSkBLfbTXt7O52dnXR3d1NWVpb074EQo5GgLzJaYnpgSUkJsVhsyCDAmMoeXkLYSGmbioLBIK2trYRCIdxuN2VlZeO+tWG1WikqKqKoqIhwODxiAJCfn28WAgqFQni9XgKBgJk94PF40p7zn82sVitVVVVmFb+mpibz1komlBgWU48EfTGlWCyWISWEo9HoaUsIJ64HsNvtGT8IiMVitLe34/P5sNlszJw5M6ULuWw2mzlNbCwA9Pv9NDQ0EAgEsFqtOBwOKisrKSkpkYWUGcTY1c/Y/fLYsWN4PB5KSkqydq2LGB/5Xy2mNGPLWmN/+cQSwsbCQOO84YOATNLb20trayvRaJSioiJKS0sndCrd2DApEomQn59Pbm4uFouFnJwcc2W+MQMgQSUzKKXweDw4nU46Ojrwer3mlL/s6ieSJUFfTCuJ29bCYHqgMRNgbHADg9OmiYOAdK2OjkQitLW10dPTQ25u7oRusqO1Nhfm9fX1YbFYcLvdeDwec6p4YGDAvAXQ0tJi1glwuVwUFhbKACADGIWUjCn/lpYWfD4f5eXlskeCGJOsyhHTms1mw+VyUVlZybx586itraWiosKsENjS0sKRI0c4evQora2tZtW7ydDd3U1jYyO9vb2UlpaalQpTLRwO09HRQUNDA83NzUSjUcrLy5k7dy4VFRVD7g3n5uZSWlpKbW0ts2fPpqioiFAoRHNzMw0NDZw6dcosiSvSKy8vj1mzZlFVVUUkEuH48eM0Nzeb+16M5b333qOuro7LL7+cZcuW8c4773DhhRfS3d0NwNGjR7nkkksIh8PceuutVFVVcfvttw95je9973vU1dWxbNkyGhsb+eCDD1i8eLH5f2j//v2sWLEitR0X50Q25xFZS2s9ZI+AYDBoBrPhJYRTeYUbDodpbW0lEAiQn59PZWXlhNxuCAQC+Hw+AoEAAIWFhebCvLOdCk4sBRyJRLBYLBQWFpqlgMe6FSGb80ysWCxmpvgZK/+LiorO+HNuaWkxf4avv/46zz//PMuWLeNPf/oTGzduZNWqVaxbt466ujpOnTrF4cOH2b59O/X19QAcOnSIu+++mzfeeIO9e/eyadMmXnjhBR599FFcLhdr165lyZIlPPvss8ybN2+y3oqsJZvzCDEGpdSIEsKJmQGJ+9wb6YFGJsF47rdrrfH5fOZiQ2OKNpX3YqPRqLm3QSgUMoveeDyec7qFYfS7rKyM/v5+uru76e3tpaenB4vFYpYCls160sNisZgV+4zMDKOQz+kWg1ZWVpqP7XY7VquVW265heeee44HH3yQmTNnUldXBwyWyz58+PCQr9+zZw+f/vSnAbj88su54447APjWt75FXV0dx48fZ/Xq1RLwM4wEfSHihqcHaq2HZAZ0dXWhtR5XeuDAwACtra309fVRWFhIRUVFStcRDAwM4PP5zKn3vLw8qqqqcDqdKQ3Cie9ReXk5fX195gCgu7vbXFjpdDrHNaMgzo3dbmfGjBkEAgFzVz+Hw0FZWdlpZ5MCgQAPPfQQW7ZsAWDTpk1ccsklNDY2nvF7dXV1UV1dbX5uTOnn5uayYcMGvvrVr/L3v/89NR0TKSNBX4jTMBaxGemBsVjstOmBiYOA3NxcM9glbrJjsVioqqrC5XKlpH1aa3p7e/H5fGbRG5fLhcfjmZTCO4nvj9aaYDBIT0+PuZVy4gBATK7CwkLmzJmD1+uls7OTxsbGwc2ufreflv/734SbW7BVVVL29bu4/af1rFu3jgsuuACABQsWmEV/zqS4uNjcLRMYcgts/vz5zJ49W7YPzkAS9IVIknEf25guHZ4e2N7eDgz+8TPWAfT09BCLxXC5XJSXl6dkbYBR9Mbn8xGJRLDZbJSVleF2u9O2ul4pZb43RgU+Yw2A3++nr6+PnJwcgsHgtNs9MVMppSguLjan/Bt/vYvOrdtxeL3ka83AqSZuuvVWVt54A6tXrz7r17/iiiu45557uOeee9i3bx8f/ehHJ6AXItUk6AsxTsPTAyORiLk3QHNzs3m1W1paCgzm4p9LemBfXx9er5fe3l601uZtgky7j27s5udwOIjFYgQCAXJycohGo5w4cQKr1Wq+b1N558SpwtjVr2nrs1j6+/EXF9E3EOLAsUbe7vbj3b6dX75/kIULF7Jx48ZRX+Phhx/m17/+NS0tLVx11VXs2LGDD3/4wyxZsoS6ujrsdjubN2+e5J6J8ZDV+0KkUCAQoLW1lXA4jMPhoKCgwFwcOJ4SwrFYzFyYNzAwQE5OjjmFn2kbDJ2JsXr/2muvpbu7m0AggNYam802ZAAgJs6fzls4uE6lsID+/HyKOjpRAEqx6B8H0908cY5k9b4QkygajdLW1kZ3dzd2u51Zs2aN2CglsXpgYgnh0dIDQ6EQPp8Pv99PLBYjNzeXyspKnE7nlC56YwT4WCxmrv43iigZAwCXyyX7yk8AW1Ul4aZmCgJBCgLBIcdF9pCgL8Q56unpobW1lVgsRklJCSUlJaNOWQ9PD0wcBPj9frq6uujv7yccDpvnl5SUUFxcPO12WrNYLLhcLlwuF9FodMQAwG63mwMEGQCkRvW9azn+8Hp0f795TOXlUX3v2jS2Skw2CfpCjFMkEqG1tZXe3l7y8vKorKxMOkAllhB2u914vV7a29uJRCJEIhEzbzoQCBCNRqdtCWEYXBvhdrtxu91Eo1FzAWBnZyednZ3k5uaaA4CpdEsj05RcswqApsefMFfvV9+71jwusoMEfSHOktYav99vrtYvLy/H4/GMa5c7n89HT08PWmscDgczZ87E4XCMukdAZ2fnGdMDp4OcnBw8Hg8ej4dIJGIOADo6Oujo6CAvL88cAEg62NkruWaVBPksJ0FfiDEcPHiQ3bt34/f7cblcXHzxxVRVVY1rk51YLGYWvenv7x+16A0MTYGDkSWEh6cHJg4Cpgur1UpRURFFRUVDBgDGjnN5eXm4XC6cTqeUARYiSfI/RYgzOHjwIL/61a/M++zd3d28/fbbXH311Zx33nlJv044HDYX5kWjUXJzc6moqMDlciW1MG94CWEjPXB4CeHE6oH5+fnTZjo8cQAQDofNAUBbWxttbW3k5+ebMwAyABDi9OR/hxBnsHv3bjPgG6LRKPv37+fjH//4Gb/W2KXO5/PR29tr5q8bRW/OhdVqNRfCweCgInEQYFRKO5v0wKnCZrNRXFxMcXExoVBoyACgvb19yABASgELMdTU/wsgxAQy0uqSPQ4ji95YrVZKSkrweDwTFnRtNpu5GA4YUj0wMT3QbrcPGQRM9aBot9vNjImBgQFzANDa2kpbWxsFBQU4nU4cDseU76sQqSBBX4gzcLvdowZ4I7gm6u/vNxfmxWIx8vPzJ6ToTTLsdjt2ux2PxzMiPdAYkABm9UDjdsBU3gPASIksLS01BwDd3d20tLSYaySMAcBU7qcQ50KCvhBnsHz58iH39GHwqnr58uXA4BS+sTCvr68Pi8WC0+mkqKgoYxbVJaYHFhcXjyghbOTGJ5YQnurpgYkDgP7+fnMGwLjNIgMAka0k6AtxBgsXLgQwV++73W6WL1/O+eefT0dHB36/38yrLy8vx+VyZfw08vASwrFYbMggwEgPHE8J4UxkDHhGGwAYRZScTieFhYXjGgDs2bOH7du3U19fD8DJkye58cYb2bNnD2+//TYPPPAANpuN/v5+NmzYwNKlS6mtrWXOnDmEQiFsNhv3338/q1YNTaV77733uPvuu8nJycFqtVJfX8/cuXNT8p6I7CVBX4gxLFy40Az+xsK8o0ePmrn1xsK8qRgQYXB3vMQSwsPTAxNLCCeuB7Db7VOqz4mDnbKyMvr6+swBQE9PDxaLxSwFnKoiRvfddx8vvvgis2bNMncehMFsjDfffBOApqYmVq5cyYIFC4ZkhFRVVbFr1y6cTievv/46jz76KM8888w5t0lkN5nXEmIMsVgMr9fL0aNHOXHiBMFgkKKiIubOncuMGTMyrsrduTLSA8vLy5kzZw7z58+nuroal8tFKBSira2NxsZGGhoaaGpqMhcsTiVKKQoKCqioqGDevHnMnDkTp9NJIBDg1KlTNDQ00NLSYhYGGq/CwkJ27dpFT0+PufPgcNXV1dx111289NJLQ44btRYAc4dGgCVLltDW1gbA3r17ue2228bdPpF95EpfiNMYGBjA5/PR3d1NLBYzt9p1uVzTKsiPZXgJ4XA4TF9fH4FAgGAwSE9PDzB0j4BzKSE82RI3QjLSLI2rf6M8ssPhwOVyDVnn0PnqTpoef4J/NvyLrkiIzld3jtjtbsuWLXz3u9/lscceo7a2lvr6embPnj2iDTU1Nbz33nujti8QCPDQQw+xZcsWAG655Ra2bdvG/fffz09/+lPuvPPOFL8jYjqToC9EAq01vb29+Hw+gsEgSimzlK2Ufh1ks9mw2WzmHgGJ6YGBQMDcIyAxPVBrPSUGSokDgIqKCgKBwJABgNVqxeFwEP7dftrWfwf6+8lViv5AgOMPrweg/4LzzQJJs2fP5umnnwZg69atPPLII2zdunXE9z1x4gQzZswYcTwcDnP99dezbt06LrjgAgC+8IUvsGzZMtasWcMHH3zA4sWLJ+jdENORBH0hGNzhzu/34/P5iEQi2Gw2ysrKcLvdGb8wL92GpwcmDgKM9EAjs8HInTdKCGcyYzMlh8NBLBYzBwDd3d0cffZ5tMdFXp+d6hzF4dYmBoJBmh5/gt/fVPVJSAAADvlJREFU8HkWLVoEwKFDhzj//POBwXv0sVhsxPdpaWlh06ZNPP/880OOx2IxbrzxRlavXs3q1avN44WFhSxatIivf/3rfPGLX5zAd0BMRxL0RVYbXvTGuMKbbvfpJ4tSakQJ4f7+fmw2G7FYDJ/Ph9frHZIeaCyuy+TUOSMV0+l0EovFaGs4wkBeHsHCAlR+Pl/2+bjjVCO2puPMLnGxefNmADZu3MiBAwcoKCjAYrHw1FNPAYOLJZcuXWqu3l+/fr05ODD84he/4LXXXqO1tZXt27ezcOFCNm7cCMAdd9zB4sWLefzxxyf3jRBTngR9kXWMojder5eBgYEhld2my171mcJYMW/c31+wYMGI6oHG1P9USQ+0WCy4PB7CTc3ElCJqtfIpp5tPOd3YqqtY+PLL5rlPPvnkqK9x9OjRMb/Pddddx3XXXXfa56+99lqKi4vPvgMiq0nQF1kjFAqZC/OMojfGCulMvsqcToxV80Z6YCwWO216YCaXEK6+dy3HH16Ppb8fS3zjJpWXR/W9ayf8ez/77LP88Ic/HHVtgBBjkaAvpjWtNYFAAJ/PRyAQQCmF0+nE4/GYi61E+hib4ySWEE4sHJRYQnj4HgHpZKzSb3r8CcLNLdiqKqm+d+2k1Kq/4YYbuOGGGyb8+4jpSYK+mJai0ai5MC8cDmO1WiktLcXtdk+LSnPT1fD0wOElhDMpPbDkmlWTEuSFSCX56yemrIMHD47YHnfBggV4vV5zYV5BQQFlZWU4HI6Mmh4WycnmEsJCTAT5nyGmpIMHDw4phOP3+9mxYweXXnoptbW1uN1uPB5PxhS9EakxvIRwYvXAxBLCubm5QwoHZXp6oBCTRYK+mJJ27949pPIdDE7pv//++1x11VWyMC9LDE8PTBwE+P1+Mz1w+CBAfj9EtpKgL6ak0WrcA2bhFJF9TldC2NgueDqWEBbibEnQF1OS2+0eNfCPVtBEZKfEqnowMj2ws7OTzs7OjE8PFCKV0hL0lVLFwP8D5gCNwOe11t7TnJsDHABOaa1lqawAYPny5UPu6cPg/d7ly5ensVUik42WHpg4CEhMDxw+CBBiukjXlf6DwG6t9Qal1IPxzx84zblrgQ8A12Q1TmQ+o7798NX7xnEhxmJUz3M4HMDI9MDe3l5gaHpgfn5+2vcIEOJcpCvofxZYGn+8FdjDKEFfKTUT+DTwXeDeSWqbmCIWLlwoQV6kjKQHimyQrt/WCq11M4DWulkpVX6a834IfBNwjvWCSqk1wBqAWbNmpaqdQogsNTw9MLF6YGJ6YGIJ4YKCAkkPFBltwoK+UuoNoHKUpx5K8utXAW1a6z8qpZaOdb7W+mngaYCPfexj+iyaKoQQYxpeQjgxPdAoIQyMyAyQbBKRSSYs6Gutrzrdc0qpVqVUVfwqvwpoG+W0OuAapdRKIA9wKaW2a61vnKAmCyFEUk6XHmgMAiQ9UGSqdA1BXwVujj++Gdgx/ASt9Tqt9Uyt9RzgC8BvJeALITKRkR5YUlJCTU0N8+fPp6amxix929XVxYkTJzh8+DAnTpygs7OTvr4+tE79pOSePXu4/fbbzc9PnjzJ0qVLAXj77bepq6tj6dKlLF68mD179gBQW1vLlVdeaT63c+fOUV97xYoVlJWV8Z3vfMc8prXma1/7GpdddhmrVq2iq6sr5X0SqZOue/obgJ8rpW4DjgP/AaCUqgbqtdYr09QuIYQ4ZxaLZUgJ4eHpgYklhIdXD5zImYD77ruPF198kVmzZhGNRs0MhZycHN58800AmpqaWLlyJQsWLOC8884b8vWbN2/mjTfe4OTJk+ax3/zmNwSDQd566y22bdvGY489xoYNGyasD+LcpOVKX2vdqbVerrVeEP+3K368abSAr7XeIzn6QoipykgPLC8vZ86cOcyfP5/q6mpcLhehUIi2tjYaGxtpaGigqakJn89HKBRKeTsKCwvZtWsXPT095OTkjLqZVXV1NXfddRcvvfTSiOdmzpw54tiePXtYtWrwz/NnPvMZ9u7di9frZfHixeY53/72t3nmmWdS2BMxXrLCRAghJplRQriiooLa2lrmzp1LZWUlhYWF9PX10draytGjR2loaKC5uRm/3z+i1sR4bNmyhQMHDnDxxRdz9dVXc+zYsVHPq6mp4dSpU0m9ZldXF0VFRQB4PB7z8wULFnDgwAG01uzYsYPrrrvunNsvzp0kmAohRJqdKT0wEAiYewScKT2w89WdND3+BMeOHKFlIEjnqzspuWYV/f395lbEs2fP5umnnwZg69atPPLII2zdunVEe06cOMGMGTOSantxcbGZueD3+80BwJo1a6ivr6e7u5tPfOITZhtEeknQF0KIDDM8PTBxEJCYHmhUDxx463e0fft7qL4+5tjs/KP1FP/61iMAvNnazKJFiwA4dOgQ559/PgBVVVXEYrER37ulpYVNmzbx/PPPJ9XWK664gldeeYXVq1fz+uuvc8UVVwBw2WWX8c1vfpPW1lYeffTRc35PRGpI0BdCiAxmlAZOLCGcmB7o8/lofO4FosUerKEC7AMhvhwsY03DIXJvuoma5VeyefNmADZu3MiBAwcoKCjAYrHw1FNPAYMLDZcuXUooFMJms7F+/XpzcJDoK1/5Cvv27WNgYIADBw7wy1/+khUrVrBz504uu+wyXC4X27ZtM8///Oc/z3PPPcdFF100OW+WGJMEfSGEmEISqweWlJSgtabrn/9iINdOONdO0FHIJ11uPuV0g1Isevll82uffPLJUV/z6NGjSX3vn/zkJyOOWSyW076uUoo1a9Yk9dpickjQF0KIKUwpRWFJMfamZgA0YCT92apG2xR1cjzwwAO8++67vPbaa2lrgxhJVu8LIcQUV33vWlReHvDvgK/y8qi+d23a2vT973+f3/72t7KAL8PIlb4QQkxxJdcM5sk3Pf4E4eYWbFWVVN+71jwuhEGCvhBCTAMl16ySIC/GJNP7QgghRJaQoC+EEEJkCQn6QgghRJaQoC+EEEJkCQn6QgghRJaQoC+EEEJkCQn6QgghRJaQoC+EEEJkCQn6QgghRJaQoC+EEEJkCQn6QgghRJaQoC+EEEJkCQn6QgghRJaQoC+EEEJkCQn6QgghRJaQoC+EEEJkCQn6QgghRJaQoC+EEEJkCaW1TncbUk4p1Q4cS3c7xqkU6Eh3IyaZ9Dk7SJ+zg/Q5PWZrrcvGOmlaBv2pTCl1QGv9sXS3YzJJn7OD9Dk7SJ8zm0zvCyGEEFlCgr4QQgiRJSToZ56n092ANJA+Zwfpc3aQPmcwuacvhBBCZAm50hdCCCGyhAT9NFNKFSul/kcpdTj+b9Eo5+Qppd5RSv1FKfU3pdR/pqOtqZJkn2uUUm8qpT6I93ltOtqaKsn0OX7eT5VSbUqp9ye7jamilPqkUuofSql/KaUeHOV5pZT67/jzf1VKLUpHO1MpiT6fr5Tar5QaUErdn442ploSfb4h/vP9q1Jqn1Lqo+loZyol0efPxvv7Z6XUAaXUknS084y01vKRxg/gMeDB+OMHge+Pco4CHPHHNuAPwOJ0t32C+1wFLIo/dgL/BC5Id9snss/x5y4HFgHvp7vN4+xnDtAAzAXswF+G/9yAlcCv47/Xi4E/pLvdk9DncuAS4LvA/elu8yT1+X8BRfHHn8qSn7ODf982vxA4lO52D/+QK/30+yywNf54K7B6+Al6UG/8U1v8Yyovxkimz81a6z/FH/cAHwAzJq2FqTdmnwG01nuBrslq1AS4FPiX1vqI1joEvMBg3xN9FtgW/73+PeBRSlVNdkNTaMw+a63btNbvAuF0NHACJNPnfVprb/zT3wMzJ7mNqZZMn3t1POIDhWTg32kJ+ulXobVuhsFAx+AVwQhKqRyl1J+BNuB/tNZ/mMQ2plpSfTYopeYAFzM4wzFVnVWfp7AZwImEz08ycrCWzDlTyXTrTzLOts+3MTi7M5Ul1Wel1OeUUoeA14BbJ6ltSbOmuwHZQCn1BlA5ylMPJfsaWusocJFSygO8opT6iNY6Y+/7pqLP8ddxAC8D92itu1PRtomSqj5PcWqUY8OvdpI5ZyqZbv1JRtJ9VkpdyWDQz7z722cnqT5rrV9h8G/05cD/Aa6a6IadDQn6k0BrfdofulKqVSlVpbVujk9xto3xWj6l1B7gk0DGBv1U9FkpZWMw4D+rtf7FBDU1ZVL5c57CTgI1CZ/PBJrGcc5UMt36k4yk+qyUuhCoBz6lte6cpLZNlLP6OWut9yql5imlSrXW6d6X3yTT++n3KnBz/PHNwI7hJyilyuJX+Cil8hkcOR6atBamXjJ9VsBm4AOt9eOT2LaJMmafp4l3gQVKqVqllB34AoN9T/QqcFN8Ff9iwG/c+piikunzdDNmn5VSs4BfAF/SWv8zDW1MtWT6PD/+t4t4VoodyKzBTrpXEmb7B1AC7AYOx/8tjh+vBl6PP74QeA/4K4NX94+ku92T0OclDE6d/RX4c/xjZbrbPpF9jn/+PNDM4IKvk8Bt6W77OPq6ksFsiwbgofixO4E7448V8GT8+YPAx9Ld5knoc2X859kN+OKPXelu9wT3uR7wJvz/PZDuNk9Cnx8A/hbv735gSbrbPPxDduQTQgghsoRM7wshhBBZQoK+EEIIkSUk6AshhBBZQoK+EEIIkSUk6AshhBBZQoK+ECIpSqlovHrY+0qpF5VSBfHjlUqpF5RSDUqpvyulXldKfSj+3C6llE8ptTO9rRdCgAR9IUTy+rTWF2mtPwKEgDvjG5G8AuzRWs/TWl8AfAuoiH/NfwFfSk9zhRDDSdAXQozHW8B84EogrLV+ynhCa/1nrfVb8ce7gZ70NFEIMZwEfSHEWVFKWRmsj34Q+Ajwx/S2SAiRLAn6Qohk5cfLOx8AjjNYG0EIMYVIlT0hRLL6tNYXJR5QSv0NuC5N7RFCnCW50hdCnIvfArlKqa8YB5RSlyilrkhjm4QQpyFBXwgxbnqwYtfngKvjKXt/A9YTrzOulHoLeBFYrpQ6qZRakbbGCiGkyp4QQgiRLeRKXwghhMgSEvSFEEKILCFBXwghhMgSEvSFEEKILCFBXwghhMgSEvSFEEKILCFBXwghhMgSEvSFEEKILPH/AZYoih3xRvyJAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(8, 8))\\n\",\n \"\\n\",\n \"components_recent = pd.DataFrame(model.components_, columns=df.columns, index=factors.columns)\\n\",\n \"\\n\",\n \"for (_, col1), (_, col2) in zip(components_full.iteritems(), components_recent.iteritems()):\\n\",\n \" plt.scatter(col2[0], col2[1], color=color_map[data[col2.name]['asset class']], label=col2.name)\\n\",\n \" plt.scatter(col1[0], col1[1], color='grey')\\n\",\n \" plt.plot((col2[0], col1[0]), (col2[1], col1[1]), color='grey', alpha=0.3)\\n\",\n \" plt.text(col2[0], col2[1], col1.name, fontsize=9)\\n\",\n \"\\n\",\n \"plt.axhline(0, color='grey')\\n\",\n \"plt.axvline(0, color='grey')\\n\",\n \"\\n\",\n \"plt.xlabel('PC1')\\n\",\n \"plt.ylabel('PC2')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we can see in the above, this has been much rotation though equities, vol and credit are still primary drivers of PC1 and rates of PC2. One of the main changes is FX shifting towards PC1, reflect more about growth in the recent past.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Actual vs Predicted\\n\",\n \"\\n\",\n \"Now let's leverage this PCA model to predict where levels should be as explained by the top three factors (PC1 and PC2 discussed above as the primary two). Let's transform back the levels and plot actual vs predicted to see where there may be interesting disclocations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pca_returns = pd.DataFrame(full_model.transform(full_data), index=full_data.index)\\n\",\n \"res = pca_returns.dot(components_full) * frame_t.std() + frame_t.mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def transform(tr_df, since):\\n\",\n \" t_d = tr_df.copy()[since:]\\n\",\n \" for key, v in t_d.items():\\n\",\n \" t_d[key] = (1 + t_d[key] / 100).cumprod() if data[key]['type'] == 'r' else t_d[key].cumsum()\\n\",\n \" return t_d\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"since = '2020-01-01'\\n\",\n \"predicted = transform(res, since)\\n\",\n \"actual = transform(frame_t, since)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def compare_plot(asset, actual, predicted):\\n\",\n \" plt.figure(figsize=(6, 4))\\n\",\n \" actual[asset].plot(label='actual')\\n\",\n \" predicted[asset].plot(label='predicted')\\n\",\n \" plt.title(asset)\\n\",\n \" plt.legend()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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a396KkzMDnf53kjd9j+WrbqWpnjy/aego7K3P6h5gWHACGt/fiq+2n2RyXxogOXrWf0EBScwtZvicJSzMdzraWONtY4GRjiYvxtYPe4ub5gjx3UAsOobeBZwc4tgY2vqY97DwxtBnMV6nB/H6iJXnY0Nnfmft7BjR1rW9JZWVlmJnVLZNAy5YtrxoYQAsOU6ZMqVNwaEgqOFyF3sKMh/u1ZmJXXz7adJwvt53i54PJPNAzgEf7t8HR5uodsst2nWHu6jiGtPWgW6ALpQZJUlY+G2PTWBeTAkB7bwcGhrgzMMyDcG/H8i+3c9kFvL02Dk8HPRO6+IC5mZb7af0rWr9FymG4+2uwda3x8y3MdDzUtxU7T2Tw+u+x9GzjWj6/A+Bk+kV+PZjMzL6t69S53DXAGRdbS1YfOX/dgkNeUSn3f7GbmHM1J17TCa3fKNzHkSnd/BkQ6m5SU1mzU1oMPz6iDVS4/UMtFX3/5yAvFRLWYTi2lqKDPzLdkMcD1ub87DCR537X0SfIlVZuzWexmJvBqVOnGD58ON26dSM6Oprg4GAWL15M27ZtmTZtGmvXrmX27Nl07dqVWbNmkZaWho2NDZ9++imhoaGcPHmSyZMnU1payvDhwyuVe9ttt3H48GHKysp49tlnWbNmDUIIZsyYgZSS5ORkBgwYgKurKxs3bmTt2rXMmTOHoqIiWrduzaJFi7Czs2P16tU8+eSTuLq6EhnZcJN3VXAwgZONJc+PDOP+ngG8u/YYC/88wdJdZ5g1oA339wyoNv/Q2iPneX7lIfoFu/HRPZFYVOgcllJyLCWP9bEpbIhJ5b8bE/hgQwKudpb0DXYjMTOf3ae0ZU/n3hl+eX6GuZV2x+AdCT8+Cp8OgEnfgkfbKp9/iRCCuXdFMPz9LTyxbD8/zupZXt6CTcexMNPxYO86JCtEm1MytK0Hvx48R1FpWbXzRxpSaZmB2d/s41hKLl8+0JVIf2eyLhaTlV9CVn5x+esL+cWk5xWzITaF6Yv34O1kzdy7wunVpuYA2iz9+bYW/Ccu1QLDJXbuGMIn8VxCO37IH8fcbkXckb2Y25N/5GWz23hq+QGWP9TD5IEIimni4uL4/PPP6dWrF9OmTeOjjz4CQK/X89dffwEwaNAgFixYQFBQEDt37uTRRx9lw4YNPPHEEzzyyCPcd999zJ9f/doxCxcu5OTJk0RHR2Nubl6eq+ndd99l48aNuLq6kp6ezmuvvca6deuwtbXlzTff5N133+WZZ55hxowZbNiwgTZt2pSn8mgIKjjUgbeTNe9MiGB6n0DeXB3L68bmmlfGtK80GW1jbCqPLY0m3MeJj6dUDgygfWGHeNoT4mnPo/3bkHWxmM3H0lgfm8qG2FTc7a14akgwo8K9qv8l2P5OcA6ApZPhkz7QehB0uEvri7CqOufBzd6KN+8MZ/riPTz69T7em9iRvMJSfohOYnKUH272VZunajOsvSfLdify4foEuga64O9iQ4CrbZ3LqY2Ukpd+PsKmuDT+b1yH8uYvB70F/jX00ZaUtWPd0RTeWB3L8ysPsf7v/W6cL8wzO+DPdyD8bggdWWmXlJIXfjrMd3uSeGJQGHcMCYYDBnSn/uTdAVY88PsFFm09xYy+rZqo8o3s9+e01R0bkmcHGPHGVQ/x9fWlV69eAEyZMoUPPvgAoPyLOC8vj23btjF+/Pjyc4qKigDYunUrK1asAODee+/l2WefrVL+unXrePjhhzE3176Oq0sBvmPHDo4ePVpej+LiYnr06EFsbCyBgYEEBQWV1+/SgkTXSgWHegjzcuDLB6LYdjydV3+JYfriPTw+sA1PDg5m+d5Enl95mLZeDnwxtatJHbbOtpaM7eTN2E7VLpddPe/OMHMT7JgPh3+A+DVgbg3Bw7RA0WYIWFzuzxjc1oNXx7Tj5Z+PMva/WwnxtEdKmNmvdd3/AEDP1i1o6ajnvxsTYKO27Y07OjAxyq9e5dVkXUwqX+84w0N9WzG5m2llW5jpGNHBCwszHdMX7+GH6LNM6OJb+4lNLTsJvp0CTv4w4s1Ku6SUvPzzUb7ZeYZH+7fmycHGtUd8uwHQ3+YkXfzD+HH/2Zs3ODSRKwdyXHp/KZGewWDAycmJ/fv3m3T+laSUJh0zZMgQli5dWmn7/v37Gy2djQoO16Bna1dWPtqTF388zAcbElh7NIXY87n0DXbj43sisbVq5D+vgxcMfQ0GvwKJO+Hw93DkRzj6I1g5aJ2Z7e+EVv3BzJx7ewQQ4unAo0v28vvh84zv7FOnzvWKrMzN2PzMAFJyCjmXXcjc1bHMXRPHyHAvHPQNMzlOSsn7647h38KGfwwLqfP5g8Lc6eDtyIcb4hnXybvKHVyzUlIAy+6BkkKY+itYO5fvklLyn19j+HLbKWb0CeQfw0IufyE4B4CtOyJxJ32C+vL++mNk55fU2h92Q6rlF35jOXPmDNu3b6dHjx4sXbqU3r17Ex0dXb7fwcGBwMBAli9fzvjx45FScvDgQSIiIujVqxfLli1jypQpLFmypNryhw4dyoIFC+jfv3+lZiV7e3tyc3NxdXWle/fuzJo1i4SEBNq0aUN+fj5JSUnl/RrHjx+ndevWVYLHtWjG/7fcGPQWZsy9K5xXx7bneFoed0R68/n9XRo/MFSk04F/Dxj1DjwVB1N+0FJ1xP4KS+6ERcO1eRRAVKALvzzWh2m9Anlq6BVfuMnR8OvT2mS909u0NCBXYWGmw8fZhq4BLswZ3Y6s/GLmb0xosMtaH5PKkeQcZg1oU69mISEETw4OIjGzgJX7zjZYvRrFz0/AuQNw56fgdvm/i5SSuWvi+OwvbYb88yPDKv9SFAL8ukHiTrq3ckFK2H0qswku4OYVFhbGV199RXh4OJmZmTzyyCNVjlmyZAmff/45ERERtGvXrnxd6Hnz5jF//ny6du1KdnZ2teVPnz4dPz8/wsPDiYiI4JtvvgFg5syZjBgxggEDBuDm5saXX37JpEmTCA8Pp3v37sTGxqLX61m4cCGjRo2id+/e+Pv7N9h131S5lZraxaLS6xsUalNaBIdXaF/4Vnba6CbfGnIindkJX98JpQVgKL28vUUbbVz9pYdneOVO0gqeXn6AVfuTWf9Uv2tOzCelZMz8rWTlF7Phqf71/tUvpWTs/K1kXmM5jer0Nlg0QkvyOOCflXa998cx5q2P555ufrw2tn31TQjbPoS1L1D4RAwR7xzg3u7+vHBbzYMUbiRNnVup4qiiG11dcys1w/9TblzNKjCANrqp42SYvg4srGHRSPjtH5C0Byr+KDi9Hb6+A+zc4YmD2t3H5O9gwL/ALRQSd8Ef/4bFY2BuIPz8ZOXzjZ4eGoKZTvDG6thrrvqmuDQOJmUze0Cba/pC1+4egknMLOCn/cnXXK9GsXku2LpBr8q5ubYmpDNvfTwTuvjw6pgaAgOU9zvoz+2hk58TO05mNHaNlVtAM/s2UxqFR1uYsRF+fxb2fgW7FoKTH1i7aCkaMhLA0Rvu/0XrxwAtTUfwsMtlXMyA8wfgyErYuwjcw6DbQ5U+xtNRz8y+rZi3Pp4HembSJaBuC69fIqVk3vp4vJ2sGyRFR/8QN1q72fLd7kTu6ly/8rLzS5i7JpakrAL0FjpsLc15uH/rSvNG6iVpj7bA1JBXwLLy3dbvh89hY2nGK2PaX31yn1cEmFkZm5buZ976eJUYsYEEBATcFHcN9aHuHG4VNi5ae/Y/4uH2/2rNQ3bu4OSrdVpP/fVyYKiObQtoPRBumwchI2H1P+HU1iqHPdSvFR4OVrzyy1EMhvo1Wb77xzH2J17g8UFtaswJVRdCCO7q7MuuU5mcSr9Y5/N3n8pk5Ad/8u3uRC7kF3MqPZ9fDp3j3bXHrrlubJ6rBekuD1baLKVkQ0wqfYJca1/H29wKWnaCxF10b9VC63c4qfodlGujgsOtRu8IkffCxCVwz3KYtBTGzq+U0O+qdDoYt0BLO778fu2Xr+FyllkbS3OeHR7KwaRsfoiueyfwkp2n+XBDAnd38W3Q4afjOnmjE7BiX5JJxxeXGvgrPp0XfjzE3Z9sx9xM8P0jPflpdm/W/K0vD/QM4I+YFM4bVxqsl+RobQhyj1lan1AFMedySc4uZFCoiSno/brBuf109NJjaa5j503UtHQz9Is2tfr8DVVwUOpO7wgTv9HSPHw2CN4J0WZspxwBYGxHbyJ8nZi7OpaLRaW1FHbZuqMpvPjjYfqHuPHauKu0sdeDp6OePkFurNibVOMdTWpuId/tTuSh/+2h0ytrmfL5Tr7bk8RdnX345bHedPS9nNJ8cjc/DFKybPeZ+ldqy9va3zJqZpVd643pVUxNiIhvNygrRp92iEg/J3acuDnuHPR6PRkZGSpAXAMpJRkZGej1dVsATfU5KPXjFgKPR0PCHxD/B8T8AgeWQdRMdP2f49+3teXOj7fx8abjPF3LHAWDQbLwzxO8szaOdi0dmT+56qzyhjC+iw+zv4lm2/EMege5IqXk0Nls1sdoM9MPndWGGno56hnTyZuBIe70bNOi2omM/i1s6RvkVp5Gpc71zTkHcb9BrydBXzXb7/rYVCJ8nUyfvW7slCZxJ90CR/Dhhpuj38HHx4ekpCTS0tKauio3NL1ej49P3frbVHBQ6s+2BURM1B75mbDhVW1FvMMr6Hz314zp2JL/bkzgfztO4+FghYeDHnd7fflrDwcrHK0t+WB9PNtPZDC8nSdv3Nmh0UZ9DQ7zwEFvzvd7E/F2tubfPx3mz/h0dAI6+Tnzj2EhDAx1J9TT3qS7lind/ZmxeA/rY1IY3r6OCQgPfact+tTxniq70nKLOJB0gb8Prn7J1mrZukKLIDi5he49pjBvfTx7TmXWe2XE5sLCwoLAwLrl/lIahgoOSsOwcYHb3tNWxPt+GvxvLP93x2LaeoVy9kIBKTmFpOQUcTw1ndTcIkorNO3YWJox985wxnfxabRUAKBNWBzT0Ztvdyfy26HzWJnrePG2ttzRyRtn27qv7T0w1B1vJ2u+3nGmbsFBSti/FHyiwLVNld0b41KREgaGmZ5GHYCQ4bBjAZ3G6LAwE+w9nXXDBwel6ZgUHIQQw4F5gBnwmZTyjSv2OwNfAK2BQmCalPKwcd8TwAxAAJ9KKd83bo8AFgB2wCngHilljhAiAIgB4ozF75BSPlz/S1Suq5ad4IHV8L9x2H4/mYfGfQJ9x1VaI9tgkGTmF5OSU0hqThEhnva0vDKNh5Rwbj/sWwxxqwGpLadqrjc+W2rPFR+Xtrm3hYhJYF/1i3FilC/f7k5kRAdP/jUqDHf7urXDVmSmE0yK8uXttcc4kZZnerrsc/shLUYLptVYH5OCl6Oetl51XFwq7HbY9iH6k+vwcXbjdGZ+3c5XlApqnSEthDADjgFDgCRgNzBJSnm0wjFvAXlSypeFEKHAfCnlICFEe2AZEAUUA6uBR6SU8UKI3cDTUsrNQohpQKCU8kVjcPhFStne1ItoLjOklQoKsmDJeEjara2J7d8TAnqDfy/ty1tXQxu9oUzLDfXX+9qa3OZ6CB6utcuXFkFpobZOd1mx9r6sBMqKLm8rKYTsM6Az14bc9nsWPCv/UyouNTTIEFnQOrF7vL6Bh/q24pnhoaad9NszsPdLePqYtlZ4BUWlZXR65Q/GdfLmP+M61K0yBgO81xZ8unLfxcfIuljMz4+ZsAa6csu62gxpU+4cooAEKeUJY2HLgDHA0QrHtAVeB5BSxgohAoQQHkAY2i//fOO5m4FxwFwgBNhiPP8PYA3wYh2vTWmurJ3hvlVa+o5Tf8HprRCz6vI+v54Q0EsLGlYOcOGMNhlv10JIPwauwVquqPZ3VfkCrVXaMdj3FexfonWWj/mvlqnWqE6BIXm/Vjcre62eHu0qZbt1t9fTJ8iVH6PP8vTQkNpXoisthkPLtXTc1VzXzwfOkV9cxvD2Jg4trkin05It7l9C67DH+SFR3Tko9WdKcPAGEiu8TwK6XXHMAeAO4C8hRBTgD/gAh4H/CCFaAAXASODST/zDwO3AT8B4oOKg9kAhRDSQA7wgpfyzLhelNBOWNtqcish7tfdZp7UgcXqrNoEu7teq57i3g/FfQtiYmu8uauMWrC2K1OsJ+NIywhAAACAASURBVO5+WPGg1pQz6CUwM+GffEmBFtR2f6bNRajINRgmLNZmiBvdEenD40uj2XEyg56ta1lYKH4tFGRCxOQquwwGySebjxPqaU/v+i5QFDYadn9KT3mARQVeN2+GVqXRmRIcqvspdGVb1BvAPCHEfuAQEA2USiljhBBvot0Z5KEFkUsD36cBHwgh/g2sQmt2AjgH+EkpM4QQnYEfhRDtpJSV1ocUQswEZgL4+TXsGgJKI3H21x4djV+MOcla0rmyYnD01WZrO/lX6p+4JnbucN9PsOZ5LTndxXQY+3HN5Wcchz1fQPTXUHhByys18m1tmGhxnrbewpp/wacDYfQ8CJ8AwNC2HthbmfPDvrNXDw7J0dqILjsPbbb5FTbEphKfmsf7d3esf8e8fy+wdqZt9mZgIolZ+TjaONavLOWWZkpwSKLyr3ofoFIGM+MX9wMAQvtXfdL4QEr5OfC5cd//GctDShkLDDVuDwZGGbcXAUXG13uFEMeBYC7fcVz6zIXAQtD6HEy8XqU5cWhZqbmnUZhbwqi3tcR2m/5P+2Ie8vLl/WWl2izl3Z/B8Q1aX0XYaOg6Q2vyuvJLOrCvNhrrhxmw7mVwC0HvFsqLPnasOORA/shAbGyv6EjOz4QNr2mBx9YNxsyv9g7mky3H8XayZlT4NazLbWYOIaPwPLoKC+7idEY+7b1VcFDqzpTgsBsIEkIEAmeBiUCle2IhhBOQL6UsBqYDWy790hdCuEspU4UQfmhNTz2u2K4DXkAbuYQQwg3IlFKWCSFaAUHAiQa4VuVW1u8ZyDsPW9/Xht26hmjBIPZXyEkCB28Y8ILWBHa1VCL2nlpfyt5FWmd7agyc3sqE0kIm6KDsnZeg64PQ5ymtI37/17DuJa2DvtvDWkpufdUv672nM9l9Kos5o9te+wTAtrdjtv9reuqOcCbT5HEdilJJrcFBSlkqhJiN1mFsBnwhpTwihHjYuH8BWsfzYiFEGVpHdcUsYiuMfQ4lwCwpZZZx+yQhxCzj6x+ARcbXfYFXhBClQBnwsJTy5sgFoDQdIbQmorxULf04aMuqBvbVluQMHm5afwRox0XN0B4AhjIMGSd5YeG3DLM8QL9dn2pDcF1aQcph8O2u3b141jz66ONNJ3C2seDurg2QT6pVf7CwYZg4xKHM0ddennJLMun/Binlb8BvV2xbUOH1drRf+NWd26eG7fPQ5k5cuX0FsMKUeilKnejM4M7PtC9ut1Dw665lNG2AcnVubXDpchcPbOrItplz8Nz7rjYUd+wCbQZ5DX0IZQZtCdB1MSk8NSTYpDXHa2XM0hp59gS/qbkOSj2pGdLKrcXCuso6FA1lYpQvn2w5zvv7ynjjrs9rPb6guIwnlkWz9mgK03oF8uiAqrOl6807ktZnFpCcUf3SlIpSG5WVVVEaiI+zDfd08+e7PYkkpOZd9dgL+cVM+nQHf8SkMGd0W/49ui1mtc2RqAvvLljIEhxy4igpM9R+vKJcQQUHRWlAswe2wdrCjHfWxtV4TFpuERMX7uBocg4LpnTmgV6NkFjOuzMA7TnOuQvXsOaEcstSwUFRGpCrnRXT+7Ti98PnOZB4odK+otIyjibnMOGT7ZzOyOeLqV0Z1q4eM6FN4ehDsd6VTroEzqh+B6UeVJ+DojSw6X0C+d+O0zy74iChnvYkZRWQlFVASm4hUoK93pyvp0fR2b9+a2ybRAjKWnYmIuEQu1RwUOpBBQdFaWD2egueHhrCnFWHyS0sxcfZmt5Brvg4W+PjbEO3QBd8XWwavR5W/l1pc2INP6emACqLgFI3KjgoSiOY3M2PSVG+jbo+RW10Plq/g+58NNC1yeqh3JhUn4OiNJKmDAwAtIwEwDnzYNPWQ7khqeCgKDcrayfSrPzwLYhp6pooNyAVHBTlJpblHE57GU/2xeLaD1aUClRwUJSbWKlnJG4im+TE+Kauyq2nrERb3OkGpYKDotzE9AFaR/TFE7uauCa3oBXT4d0wOLKyqWtSLyo4KMpNrEWrjhikgDTV73BdZZ6Eoz9pC1ktnwrf3gt5aU1dqzpRwUFRbmIO9vacxQ39BbUkynW153MQOnhkKwx+CY6tht+ebupa1Yma56AoNzEhBMnmPnjln2zqqtw6Sgq0pWZDR4GTH/T+G6THawFCyoZbBreRqTsHRbnJZVr7416UCAaVnfW6OLxCW/nv0mJQoK1Dnp+hrVN+g1DBQVFucnn2rdBTBDlnm7oqNz8pYden2mJSARXWOfPtpj0n7miaetWDCg6KcpMzOGuLCJWlqeGsje7kZji3H7pOr9x85BoMeidI3Nl0dasj1eegKDc5nUcIHIWLyUdxCBrY1NW5uRRkwcktcHwjnNgIWafA2kVbGrYinQ58o+CMCg6KojQTzq7e5Egbis7HNnVVbnylxZC0WwsExzdC8j6QBrC0h8A+0H0WhI4EK/uq5/p2g/i1kJ8JNo2Yrr2BqOCgKDc5TydrjsuW+KWrZqVrkpMMCwdA3nkQZtpqe33/Aa0GgE8XMLO4+vmX+h2SdkPwsMav7zVSwUFRbnJejno2SS+Cc2peulQxwfb5cDEN7loErQeCtVPdzvfurAWVxJ03RHBQHdKKcpNzsbXkFN7YFqVCUW5TV+fGlJ8JexZB+zuh/R11DwwAljbgFX7D9DuoOwdFuckJIciw9oditMlY3pFNXaUbz+7PoeQi9H7S5FMSUnP542gqqbmFpOUWMaqDFyN8u8PeL7WkfLU1QzUxFRwU5RZQ4NAK0oGMBBUc6qo4H3Z+DEFDwaOdyac9sWw/R5JzsLMyp8wgOZaSy4ghUVpZ5w9qzUzNmAoOinILEM6BlKbrME8/1tRVufFEf63Nbu79N5NPOXw2myPJObw0ui1TewXyyebjvP57LClOEXgAxPzcOMGhrARKi7QRVDU9Sou0IbgFWVctyqTgIIQYDswDzIDPpJRvXLHfGfgCaA0UAtOklIeN+54AZgAC+FRK+b5xewSwALADTgH3SClzjPv+CTwIlAGPSynXmFJPRVGq5+bsQKJ0JyD9GDdGZp9moqwUtn8IPlHg18Pk077fm4SluY6xnbwB6Bvsxuu/x7L5nCUTQkbCX+9pndsj3wYL62uvZ2E2bP0AdnysNX81gFqDgxDCDJgPDAGSgN1CiFVSyqMVDnse2C+lHCeECDUeP0gI0R4tMEShtXiuFkL8KqWMBz4DnpZSbhZCTAP+AbwohGgLTATaAS2BdUKIYCllWYNcsaLcgjwd9Rw3eOGbGq+aC+riyEq4cAaGv2FywrzCkjJWRp9lWDtPnGwsAQj1tMfd3orN8WlMmPg1bHoDtsyF5P0waamWoK++9i2GP+ZAQSa0G2ccFaW74iEuv9ZZaPMsrJ3h5ZoDnin/TqKABCnlCQAhxDJgDFAxOLQFXgeQUsYKIQKEEB5AGLBDSplvPHczMA6YC4QAW4zn/wGsAV40lr1MSlkEnBRCJBjrsN2kP5SiKFV4Oug5LlsyMGsdGMpAZ9bUVWr+pISt87TUF8EjTD5tXUwK2QUlTOjiU75NCEHfYDf+OJpCGTrMBv5Lm/ewYhp8dTtMWw32nnWvY8ZxWPW4dlcz/HVo2bHuZdTAlKGs3kBihfdJxm0VHQDuABBCRAH+gA9wGOgrhGghhLABRgK+xnMOA7cbX4+vsN2Uz1MUpQ48HbXgoCsr0n4JK7U7vgFSDkHPx7X0Fyb6bk8S3k7W9GztWml732A3sgtKOJB0QdsQNBim/KA1Ly0eAxcz6l7HbR+AmSVM+KpBAwOYFhyqu5eSV7x/A3AWQuwHHgOigVIpZQzwJtqdwWq0IFJqPGcaMEsIsRewR2t2MvXzEELMFELsEULsSUu7sVZYUpTrzcvRmoOG1tqb3Z81bWVuFFvngb0XhE8w+ZTkCwX8GZ/GnZ19MNNV/irr08YVIWDLsQrfVz5dYNIyLSfT13dofQemyk2B/Uuh42Swczf9PBOZEhySuPyrHrQ7guSKB0gpc6SUD0gpOwL3AW7ASeO+z6WUkVLKvkAmEG/cHiulHCql7AwsBS4lOq/184znL5RSdpFSdnFzczPhMhTl1uVmb0W88OeAxzhtpu+ZGyd1dJNIjtYyrHZ/BMytaj28uNTArwfPMeubfUgJ4zv7VDnG2daScG/HysEBtJxMExZDymH45m5t6Kwpdn4MhhLo+Zhpx9eRKcFhNxAkhAgUQliidRavqniAEMLJuA9gOrClwsgjd+OzH1rT09IrtuuAF9BGLmEse6IQwkoIEQgEAWp1dEW5BmY6gbu9Fd85PwROvvDjI6Z/Cd2Ktn4AVg7Q+YGrHnYiLY//+y2GHq+vZ9Y3+0jNKeK1se3xdbGp9vi+wW7sT7xAdn5J5R3Bw+COT7XUGt/eow03BbLzS0jLLapaUGEO7P4Cwm6HFq3rdYm1qbVDWkpZKoSYjdZhbAZ8IaU8IoR42Lh/AVrH82IhRBlaR/WDFYpYIYRoAZQAs6SUlwbXThJCzDK+/gFYZCzviBDiO2M5pcZz1EglRblGno56TufpYMx8+Go0rH8ZRrzZ1NVqfnLPw9GfoMejoHeosruwpIzVh8+zdNcZdp7MxFwnGBzmwcQoX/oEuVVpTqqob7AbH25IYM3R80zo4ltpn6HtONIyMvDY+DSHP7iLp+TfiEsrwEFvzp4XhmBpXuG3/N4voSgbej3RUFddhUmj2qSUvwG/XbFtQYXX29F+4Vd3bp8ats9DmztR3b7/AP8xpW6KopjG00HPsZRcCOwPUQ/BzgUQNhoCejd11ZqX6K9BllW5aygtM/DW2jiW7Uoku6AEPxcbnhkewl2dfXC315tUdCdfJ7ydrHnm+4N8s/MMd3b2ITu/mL2ns9h35gLZBS2ZanYfL+UsZo6tJcvCn2fVwfPEns8h3KdCPqfor8G/V6POdldDnhXlFuHpqOfP+HTtzeA52toCPz4Kj2wDK7umrVxzYTDAvq8gsG+V5ppluxP5ZPMJhrfz5N4e/vRo1QLdVe4SqmNupuOXx3rzQ/RZlu06w4s/HgYg2MOOEe09ifR3JtKvH4YYP3pufI1wK29WMZQDiRcuB4f8TEiPg/DxDXLJNda1UUtXFKXZ8HLUk1dUSm5hCfZ6Wxj7MSwaAevmwKh3mrp6zcOJDdpQ38EvVdqcV1TK++uOERXgwsdTIhEmToirjrOtJQ/2DmRarwCOp+XhZqfH0eaKJHxuT0NxLnZb5/Fv61yiE32499J8tbN7tWefqHrXwRQqZbei3CI8HbU0DeezC7UN/j2gxyxtaOuJTU1XseZkzyKwcYXQ0ZU2f7L5OOl5xTw/KuyaAkNFQgjauNtXDQzaThj8MkRMZqpcyanTpy7vS9ylzXRu5MR9Kjgoyi0i2ENrOvr10LnLGwe+AC3awE+ztREwt7Lc8xD3uzZvwNyyfPP57EI+/fMEoyNa0tG3Hus41JcQ0O0hdEj8s3ZcHuGUtEvLDtvITYEqOCjKLSLU04FRHbxYsPk4Zy8UaBstrGHsAsg5C2tfaNoKNrXo/xk7oqdW2vzO2jgMBnhmWMj1r5NnOMV6VwaY7efg2Qta6pOkvY3epAQqOCjKLeWfI0OREt74PfbyRt+uWoqIfV9B/Lqmq1wdzVsXT683NtD51T9oP2cN7669xmVQD32vjQCq0BGdmJnPin1J3NvDv8a5C41Kp4M2Q+irO8iB0+mQGgPFueDb+MFBdUgryi3Ex9mGh/q15oP18dzb3Z+oQBdtR/9/wrHVsOoxeHR7/ZbBbAz5mZAWB2mxkH5Mey7I4iI2hCQWM9GlH+dDxnImM58PNiQQFdiC3kGutZd7pYzjWtnDK61GwFfbTqETgul9AhvogurOMmwYloeXkpuwA5yMmYR8ujb656rgoCi3mEf6tWb5nkRe/vkIq2b31iZtWei10UufDYY/XoTbP7z+FTMYYP/XcO7A5YBwsUKqCXNrcAsGWzeSE8/RwSyV4dm7ISSCwqBRjPzgT575/gCr/9YXB30dl+A8tlp7Dh5evimvqJRvdycyooMXXo4NsOZCfbUaQBlmuKX8iUy0RNi0AJdWjf6xqllJUW4x1pZm/HNkGEeSc1i+p0ICZO9ILclc7K9NU7HEHdqdy8HvoLRQSykx5FWYvByeOAjPJ8NDW9jV61OGZP+LX/usAu8usPIh9GmHeGd8BOdzCnnl56O1f9aV4n4H97bgcvkOYcXeJHKLSnmgV0DDXWN9WDuR7hxB97K9lJ7epfU3NNCIqatRwUFRbkGjw73oGuDMW2viyC6okOfHM1xbEjOvCTIdn92nPT+2D6av09J89HocgoeCsz/odEgpeXN1LB4OVtzbJwQmfgPWLrB0Ep2ci3i0fxu+35vEprhU0z+3IAtOb4OQy2s2GAySL7edoqOvE5F+zg18oXUn2wyhve4UFheOX5f+BlDBQVFuSUII5oxuR2Z+MR+uj7+8wz1Ue06Luf6VSo4GBx+wqznL8rqYVPaezuKJQcFYW5qBvQdMXqalul46kcf7euNmb8XyPUmmf278Om2UUsjI8k2bjqVyMv1i0981GLXodNvlNyo4KIrSmNp7OzKxqy9fbjtFQmqettHtUnC4xpE/9ZEcfdUFa4pLDbz+WwytXG0ZX2GVNTw7wJ2fQfJ+LH+ZzeBQNzYfS6O41GDa58b9Brbu0PJynqIv/jqFh4MVIzt41fdqGpSFVwcydC0oQwctO12Xz1TBQVFuYU8NDcHa0ozXfjW209t7gZWjNmTyeirMhszjVw0OX247yYn0i7w4ui0WZld8dYWO1FJeHFnJjLLvyCsqZedJE1ZWKy2GhHUQMrx8tbfdpzL5KyGdab0Cq35OUxGCOJ+7+KWsO2fzK9epsKSMk+kXOZh0gW3H0zmXXdAgH6lGKynKLczVzoonBgXx2q8xbIxNZUCou9a0lBZb+8kN6dwB7bmGX8WpOYXMWxfPoFB3BoTUsOpZrycgLY5WB/7L3y1T2HSkJX2CalkI7NQWKMopb1KSUvLWmjjc7K24r0dAPS+mcfiOfYnJczfy9L4kZg/UkmAXFJcxfN4WTmdUXpujpaOeSH9nOhsfYV4OdQ50Kjgoyi3uvh4BfLPrDK/+cpRebVyxdAuFmJ9ByusyKgaA5P3as1f1weHN1XGUlElevK1tzWUIAaPfh9JCHj+ynDMH/kKGvouwc4fsRMhOuvy4cEZ7LsgECxsI7AfAn/Hp7DqZycu3t9P6NJoRXxcbugW68P3eJGYNaIMQgmW7z3A6I59/jQwj0NUWa0sz4s7nsu9MFvtOZ/HLQS1Vit5CR4SPE5H+zoR62mNuwprYKjgoyi3O0lzHv29ry9RFu/lq2ylmuIVqs6Uvpl+1c7hBJUeDox/Ytqiya9+ZLFbsS+KR/q0JcLW9ejnmVjB+ERtthuK/8yXEskmV91vaayvhOfpo6zc7+oBfD7C0QUrJO2vj8HayZmKUb/XlN7G7Ovvwj+8Psvd0Fh18HFm45QRRgS7M6Ht53kOvNq5MQxuSm3yhgH1nssrXi/h0ywlKDdKkz1LBQVEU+oe4MzDUnQ/WxzP+rtY4gTZi6XoFh3P7oWVElc0Gg+SlVUfwcLBi9oA2JhfXrs84ev9pzbxO5xjRMVALAo4+oHes8W7oj6MpHEjKZu6d4ViZN6+7hktGdvBizqojrNiXRHxqHueyC3nzzvAaj2/pZE1LJ2tuC28JaP0TiZn5XAoPIVdZCLCZ9LYoitLUXhgVRmFpGR8dNc4uTr1O/Q4FWZB5otr+hu/3JnEwKZt/jgjD1sr037LuDnrCfFxZkB6udTZ7ttdSgtQQGKSUfLghgUBXW+6I9K73pTQ2WytzRrT34ucD5/hoUwIRPo70qUO6EL2FGUEe9gQbH1ejgoOiKAC0crNjRHsvViUYtF/Y12uuw6XOaK/KI5WyC0p4c3Usnf2dGdOxZZ2LHRzmwYHEC6TkFNZ67L4zWRw6m82DvQMxby4jlGpwV2cf8opKScwsKO97aAzN+6+gKMp11crNlpS8IgyuIddvrsOlzugr7hw+WB9PZn4xL9/erl5fgMPbe6ITMOa/W/luTyJlV2lr/2LrKRz05s36ruGSboEu+LnYEOppz+Awj0b7HBUcFEUp5+tsg5SQ5xCkzXWQpnVeXpPkaHDyBxuX8k3xKbl8te0UE7v60d7bsV7FBnnYs2xmDzwc9Tzz/UFGzvuTjbGpyCuu6Vx2AasPn2dilB82ls2/G1anEyyZ3o2vpkXVeQ3rOn1Oo5WsKMoN59KaBSn6AG2Y58XrkGPp3P5Kk9+klLz881FsLM14emjwNRUdFejCj4/2ZP7kSIpKy3jgy91M+nQHBxIvlB/zv+2nkVJyb3f/a/qs68nXxQYPB32jfoYKDoqilPN10VJTn9IZvygbe6Z0wQXIOgVel0cqrT2awl8J6fx9SDAt7Kyu+SOEEIwK92Lt3/rx8u3tiE/JY8z8rcz+Zh/HUnJZuusMQ9p6NM1iPs2YCg6KopTzsNdjaaYjptTYAdzY/Q6XyndvB2hDLV/95SjBHnZMaeBf8pbmOu7vGcCmf/TnsYFtWB+TytD3tpCVX8LUnk23mE9z1fwb2BRFuW50OoGPszWxedbXZ8TSpTQdbtr6zJ9uOUFSVgHfTO/WaKOG7PUWPDU0hHu7+/P++njyi0rp3sql9hNvMSo4KIpSiY+LDYlZhVqG1vT42k+4FunHtBXenPzIyCti/qYERnbwpGebeiz1WUfuDnr+b1yHRv+cG5VqVlIUpRJfZ2sSs/LBpbW2tnJjSosF1yDQmRGfmkdhiYHJUTdOx/DNzKTgIIQYLoSIE0IkCCGeq2a/sxBipRDioBBilxCifYV9TwghDgshjgghnqywvaMQYocQYr8QYo8QIsq4PUAIUWDcvl8IsaAhLlRRFNP4uthwIb+EIscAyE2G4vxaz6m3tLjyNSSyLhYD0MLOsvE+TzFZrcFBCGEGzAdGAG2BSUKIK1MjPg/sl1KGA/cB84zntgdmAFFABHCbECLIeM5c4GUpZUfg38b3lxyXUnY0Ph6u99UpilJnvs7aqJ00C+OEsMwTjfNBRblatlRjf0OGMTi42Krg0ByYcucQBSRIKU9IKYuBZcCYK45pC6wHkFLGAgFCCA8gDNghpcyXUpYCm4FxxnMk4GB87QgkX9OVKIrSIC4NZz0jjKugZTZS01L6Me35ijsHZxsVHJoDU4KDN5BY4X2ScVtFB4A7AIzNQ/6AD3AY6CuEaCGEsAFGApdy4T4JvCWESATeBv5ZobxAIUS0EGKzEKJPHa9JUZRrcOnOIb7EuKhOY/U7XBrGagwOGReLsbcyx9JcdYU2B6b8V6hufvaVc+rfAJyFEPuBx4BooFRKGQO8CfwBrEYLIqXGcx4B/ial9AX+Bnxu3H4O8JNSdgL+DnwjhHDgCkKImca+ij1paddhFqei3CKcbCywszLnRI7Q1lZurDuHtFgwswTnAACy8otxUf0NzYYpwSGJy7/2QbsjqNQEJKXMkVI+YOw/uA9wA04a930upYyUUvYFMoFLY+PuB34wvl6O1nyFlLJISplhfL0XOA5UmUMvpVwopewipezi5nadcs4ryi1ACG2uQ2JWAbRoDRmN1OeQFgctgsBMG1GfebFYNSk1I6YEh91AkBAiUAhhCUwEVlU8QAjhZNwHMB3YIqXMMe5zNz77oTU9LTUelwz0M74eiDFoCCHcjJ3gCCFaAUFAI/3rVBSlOr4uNiRmGoezNuadg9vl332ZF4tpoTqjm41aJ8FJKUuFELOBNYAZ8IWU8ogQ4mHj/gVoHc+LhRBlwFHgwQpFrBBCtABKgFlSyizj9hnAPCGEOVAIzDRu7wu8IoQoBcqAh6WUmdd6oYqimM7X2Ya/4tORLq0QeSnayCKrqy8OUyclBZB1GiIuL+OZdbGYMK8qLchKEzFphrSU8jfgtyu2LajwejvaL/zqzq22Q1lK+RfQuZrtK4AVptRLUZTG4etiTUFJGTm2/jiCNpzVq+oynvWWHg/I8mGsUkoyLharYazNiBoWoChKFX7GDKXJOuNw1oYesXTFSKWCkjKKSg0qODQjKjgoilLFpfTVx8uMw1kbut8hLRaEmdanAWTkGSfAqQ7pZkMFB0VRqvBx1ibCnc4VYO/V8COW0mK1kVDmWjDIylezo5sbFRwURanCxtIcVzvLCiOWGjo4xJX3N8Dl1BnOKjg0Gyo4KIpSLW8na85eKACXwIZtViorgayT4Hp5GGt50j0VHJoNFRwURamWm72etNwirfnnYhoU5jRMwZknwVCqTYC7tEndOTQ7KjgoilItdwcrLTgYO40b7O4hw5gkwbXyBDhzncBBr9Yfay5UcFAUpVpudlZkXCymxMm4vnJDDWe9tLqca5vyTZkXi3G2tUSI6lK5KU1BBQdFUarl7mAFQIalMQlzQwWHjHgtoZ/esXyTSp3R/KjgoChKtdzstOCQWqgDJ39IPdowBacnaEuDVqCS7jU/KjgoilItdwc9AKk5ReAVDucPNkzBGfHQok2lTZn5KnVGc6OCg6Io1XK31+4c0vKKwDNCm+twrSOW8jMhP6PKnUOWyqvU7KjgoChKtVwvNStdunMASDl8bYVmJGjPFYaxlhkkFwpK1DDWZkYFB0VRqmVprsPZxoK0vELwNAaHc9fYtFQ+UulycLiQX4yUagJcc6OCg6IoNXKzt9LuHOw9wdbt2vsdMuJBZ6F1cBupCXDNkwoOiqLUyN1eT2puEQih3T00xJ2DS2D50qBwOTioO4fmRQUHRVFq5G5vnCUNWr9DWgyUFtW/wPT4Sv0NUOHOQQ1lbVZUcFAUpUZuxuAgpdTuHAylkBpTv8LKSrURT65Vh7ECtLBTwaE5UcFBUZQaudlbUVxmIKeg9PIyofXtd7hwGv6/vTuPkqss8zj+/XWn6aSThtBNwhY6CZxgiAGCickgAoIiizosO9vBLAAAFuRJREFUIsMyBGQ7QVBwR3R0QBBxGxkH5OCwyGFHRBnAYRxUVhlICBC2hJCQEAjZIUvToZdn/nhvJTddvVRXd1e9t/N8zqnTVXep/tXb1fXUfe+9721rzt9ySC70M7ymqjdRXR/z4uCc69SmE+HWNcH2Y2GbWlj6fHFPljuMNTXgHoQth2HVg6geVNmbqK6PeXFwznUqN4TGinUboaICdppY/E7pDg5jhbDPwU+Ai48XB+dcp3KD7y3P7ZTeaZ9wIlxba8+fbNVrMKQOauq2mJwbkdXFxYuDc65TI2pzxaEpTNh5X2huLG6E1mUv5201QLh+tB/GGh8vDs65TtVWD2JwVcWWh7MCzPtTz55o+auw5GkY9+m8WavX+4isMfLi4JzrlKTNJ8IBjJwAYw+GP38fHvsFmBX2RE9fB5XVMPn0vFmrGz/ww1gj5MXBOdelEekT4Soq4ZS7YeLx8PAl8MDXux+ptek9eP4OmPh5GLrDFrM2bGyhqbnNtxwiVFBxkHSEpLmS5ku6qIP520u6V9ILkp6WNDE17wJJL0p6SdKFqemTJD0l6TlJMyVNTc37TvK75ko6vLcv0jlXvJG11Zu3HAAGVcNxv4EDLoSZ18PPxsHdp8OrD0DLB/lPMPtWaN4A087Jm/XWu+8DsMvwwf2U3hWr2+IgqRK4GjgSmACcJGlCu8UuBp4zs32A6cBVyboTgbOBqcC+wGcl5fZI/QS4xMwmAd9PHpM894nAh4EjgGuSDM65MhhZW83ytU1bTqyogMMugbMeho9Mh4WPwR0nh0LxXxfAG49DW1u4PX0d7DYNdtkv77kXrWoEYHT90FK8FNcDg7pfhKnAfDNbACDpDuBoIH3NwAnAFQBm9qqkMZJ2BPYCnjKzxmTdR4BjCYXAgG2T9bcD3k7uHw3cYWYbgYWS5icZ/l70q3TOFW1EbTVrm1poam5lcFW772mjpoTb4T+CBX+DOXfDC3fDrJtg211DUVizEA79XofPvXh1KA4NdTX9+yJcjxVSHHYF3kw9XgJMa7fM88BxwONJ99BoYBTwInC5pHrgfeAoYGayzoXAQ5J+RtiC+Vjq9z3V7vftWugLcs71rZG1octnxbqN7NbZh3hlFYw7LNw+2ABz/wQv3AWv3AfbjoIJR3e42uJVG6itHsT2PnRGdAopDupgWvtDFH4MXCXpOWAOMBtoMbNXJF0J/BlYTygiLck65wJfNbN7JJ0AXA98qsDfh6RzgHMAGhoaCngZzrlijEhdLrTT4pC2zVDY+/hwa1wdTpir7PjDf/HqRnarq0Hq6N/elVMhO6SXALulHo9icxcQAGa21sy+mOw/mA6MABYm8643s4+Y2UHAaiA5h57TgN8n9+8mdB0V9PuS573OzKaY2ZQRI0YU8DKcc8XYdCLc2iKG6q6pg2Gd/38uXt3oXUqRKqQ4PAOMkzRW0jaEncX3pReQNDyZB3AW8KiZrU3mjUx+NhC6nm5PlnsbODi5fyibi8Z9wImSqiWNBcYBTxfz4pxzvZcbQmPFuqZuluyZtjbjzTXvM7rei0OMuu1WMrMWSecDDwGVwA1m9pKkGcn8awk7nm+W1ErYUX1m6inuSfY5NAPnmdmaZPrZhK6oQUATSRdR8tx3Jc/TkqxTxEAuzrm+UD+0mgqx5eGsfWDZuiY+aGkrrKvKlVwh+xwwsweBB9tNuzZ1/++Eb/gdrXtgJ9MfByZ3Mu9y4PJCsjnn+ldlhdhx28GbzknoK5sPY/XiECM/Q9o5162GuhoWJx/mfcUPY42bFwfnXLfG1A/ljT4uDm+ubqSyQuwyfEifPq/rG14cnHPdaqivYeX6jWzY2NL9wgVatKqRXYYPpqrSP4Zi5H8V51y3cvsFcl1BfcEPY42bFwfnXLdG14Wxjxb1YdeSF4e4eXFwznWrYdOWw4Y+eb51Tc2s3vABDXU+4F6svDg457q13ZAqhtdU9dmWgx+pFD8vDs65goyuH9pnxeHN1X6OQ+y8ODjnCjK6roZFfdStlNty8LOj4+XFwTlXkNH1Nbz9bhPNrW29fq5FqxrZbkgV2w3xobpj5cXBOVeQhroaWtuMt9b0fhiNxasbvUspcl4cnHMFyV3Kc1EfnOuQu46Di5cXB+dcQTadCLeqd/sdmppbWbLmfXbfwQ9jjZkXB+dcQUbWVjO4qqLXRyy9vmI9rW3GnjvW9lEy1x+8ODjnCiKJ0XW9H4Bv3rJ1AHxoJy8OMfPi4JwrWEN9Ta/Pkp77znqqKsVY71aKmhcH51zBRtfVsHh1I2ZW9HPMW7aOPUYM89FYI+d/HedcwUbX19DU3NarS4bOfWed72/IAC8OzrmCNdT3bnTWdU3NvPXu+76/IQO8ODjnCjYmOZz15/8zlwfnLKWpubVH67+2fD2AbzlkgBcH51zBGupqOO+QPVi4cgNfuvVZpl7+vzy1YFXB6897JzlSyYtD9Lw4OOcKJolvHj6ev3/nk9xy5jR2qK1mxi2zWFTgiXFzl61jSFUlo7b360bHzouDc67HKivEx8ftwA2nfRSAM256hrVNzd2uN2/ZOvbccRgVFerviK6XvDg454o2Zoeh/PqUySxa1cj5t82mpZsRW+e+s973N2SEFwfnXK/sv0c9lx0zkUfnreCyB17pdLlV6zeycv1GP1IpIwaVO4BzLvtOnNrAvGXrueGJhYzbcRinTBudt8y8ZX6kUpYUtOUg6QhJcyXNl3RRB/O3l3SvpBckPS1pYmreBZJelPSSpAtT0++U9Fxye0PSc8n0MZLeT827ti9eqHOuf1181Hg+8aER/OCPL/Hk/JV5831MpWzptjhIqgSuBo4EJgAnSZrQbrGLgefMbB9gOnBVsu5E4GxgKrAv8FlJ4wDM7J/MbJKZTQLuAX6fer7Xc/PMbEavXqFzriQGVVbw7yftx9gdhnLurc+ycOWWRzDNXbaO7YZUMbK2ukwJXU8UsuUwFZhvZgvM7APgDuDodstMAB4GMLNXgTGSdgT2Ap4ys0YzawEeAY5NryhJwAnA7b16Jc65stt2cBXXn/ZRKgRn3vQM7zWGI5gWrFjPX15Zzvidagn/8i52hRSHXYE3U4+XJNPSngeOA5A0FRgNjAJeBA6SVC+pBjgK2K3dugcCy8zstdS0sZJmS3pE0oEFvxrnXNk11Ndw7T9P5s01jZx327M8OX8lx17zJM2tbVx81F7ljucKVMgO6Y7KfPshGX8MXJXsN5gDzAZazOwVSVcCfwbWE4pIS7t1T2LLrYalQIOZrZI0GfiDpA+b2dotQknnAOcANDQ0FPAynHOlMm33ei4/Zm++dc8LPD5/JXuMGMqNp0+lwa8bnRmFFIclbPltfxTwdnqB5IP7i7Cpm2hhcsPMrgeuT+b9KHk+kseDCFsck1PPtRHYmNyfJel1YE9gZrvfeR1wHcCUKVOKHz/YOdcvTvjobixf18TLS9dyxbH7sF1NVbkjuR4opDg8A4yTNBZ4CzgRODm9gKThQGOyT+Is4NHcN31JI81suaQGQiHYP7Xqp4BXzSxdMEYAq82sVdLuwDhgQdGv0DlXNucfOq7cEVyRui0OZtYi6XzgIaASuMHMXpI0I5l/LWHH882SWoGXgTNTT3GPpHqgGTjPzNak5p1I/o7og4BLJbUArcAMM1td3MtzzjlXDPXmik6xmDJlis2cObP7BZ1zzm0iaZaZTelong+f4ZxzLo8XB+ecc3m8ODjnnMvjxcE551weLw7OOefyDIijlSStABaVO0cHdgDyh6fMjtjzx54vLfassecrROyvIcZ8o81sREczBkRxiJWkmZ0dJpYFseePPV9a7Fljz1eI2F9D7Pna824l55xzebw4OOecy+PFoX9dV+4AvRR7/tjzpcWeNfZ8hYj9NcSebwu+z8E551we33JwzjmXx4tDH5Bf97DfeRv3HW/L/jVQ2teLQ5EUfFXSKMto35ykyuRnlG/mLLWxt2X/i7mNB0L7tufFoQiSpgN/BfYD1sb4Zu2KpNMlzQYuKHeWzmSljb0t+1/sbZz19u2M75DuIUkHAI8BU81sZrt5iv1bg6TxwM2EizftDXzNzBZIqjCztvKmC7LSxt6W/S/2Ns56+3bFtxwKkNucBTCzJ4D/I1z9DkkXSfqcpGGxvhEk1ebum9mrwHTg3whX7Ts/mV7Wf7SstLG3Zf+LvY2z3r6F8uLQDUmXAt9Prm2dMwP4raTngOHAl4GfJt9yoiLpImC2pCslnZ5MnptcevVeYA9JByXLluX9kJU29rbsf7G3cdbbt0fMzG8d3IBq4DuEAf3uBT7dbv6XgMnJ/RHAH4DDy527XcZDgUeBscAhwFJgn9T8YcCFwK2paZXext6W3sYDr317evMth841A/cDE4CngEMkjc3NNLNrzGxWcn8FsBqoK0fQLlQBs81soZn9FbgKuCI1fwPwO2C9pB9K+gkwpoT5stTG3pb9L+Y2Hgjt2yNeHDphoU9znpltAO4ERgFTJVXD5sPpJNVJ+jmwD/BMufJ2ogaolzQYwMx+DOws6QvJYwOaCDv6zgVWmNnrpQqXsTb2tux/0bbxAGnfHvHiQOfHTZvZxuTnG8DjwMHA+GSaJd8c7iR84znYzOaXJHA76R146X5YM7sX2AP4bGrxnwBfSz2+AngJaDCzn5YhY1RtLKkudT/WtuwsY1Rt2RlJe3U0PZY27iJfJtq3z5S7X6ucN+Bo4LfApHbTBVQk9yuTn9sCvwJOBk4FPpdMry9j/iMJx1ffDHw3Nb0SqE7un0joxx2TPG4ArgZqk8eDy5gxmjYGjkja6Wbg56npFRG1ZVcZo2nLbl7Dr4CFuTaMsI07y5eJ9u3Ttih3gJK/4M3ndhwCvADMImyibp+en9zfHRieevwVYA0wHziqXPmTD9YZhM3Wo4BphP7QM9otu3uy/KXAfwLnAf8NXBtZxrK0cSrnOYR+5KOTD6O/AUdG1paFZozu/dru8a3As8BZuYJQzjYuIl9U7duvf7tyByjpi93yg38MsDPhCImbCJuBuXkVwEWEoyWOTN6w44EFwMWR5D8KGJd6fCHhBCGSD5OLgBXAgcB2wAGEraRvRpaxLG3cLucEYFByfyRwV/IBnPuW+N0I2rKQjDG/X3M5vwycTdianJia/23CJTRL1sZF5Hsnpvbt79sgthKSzgc+KelR4HYL/YYASyUdDhwsab6ZvQXsBLwHTDCzNcn6bwB7W9ghVXKp/I8BN5vZg5IqJQ0ysxbCSTivJouPJOTfM5cfeELSU2bWGlnGkrdxu/fCHWb2cjJ9P0IXxiDCh8H7wDcob1v2JGOM79dHgLvM7G1J2xC6xk4jfDE7UdLThMND1xK+SJSkjYvMt1cs7VsS5a5OpbgBxxK6Nw4BbgT+A9g3NX9f4Bbg2A7WHRRp/knpfIStn491sG4l7TadI8xYsjbu6r1A6DZoSO4PI3zg7hdJWxaaMdb360eSeZckP08ifOC+QqqfvhRt3Mt8ZW/fUt22lqOVpgG/tnDs9L8SdjhtGsTLzJ4nvFn2lnRocpZmbmyUljLkba+j/F8BMLOW5HC63YBZkkZJOhs25W+15F0dccZStnFHOS9Mci4ws8XJ/fWEI0/qUjnL2ZaFZoz1/fqlZN5nki3LbwN/JOxH2QAlbePe5IuhfUtiQBWH9oekph4vIBxVgJktAh4Ahkr6x9TitxN2Qt0J1Pd/2nxF5D86mT+ekPkC4D768eSbLGQsImdNu/cCkr4HfJhw2CT98YGVhYxd6WH+4ZL2J5zY9qSZTTKzUwlduHsly/Zp/tjzxW5AFQfC8cWbpP6YvwMaUx9USwlHe0xQMIzwpphDOF3/m+3WL5We5t8recPvTngDjwU+Y2ZXtlt/a8tYTM4JAJKOlPQ4sCdwvJm900/5spKxKz3J/xfgIMLQF99OrXasmc3eSvNFbUAUB0n7S7qbMNjVBG2+KEhuh/sawngo5yabhu8R+msHJ2+YJuACM/uMmS3NUP4hSf75wMfN7Nz+yp+FjL3Nmcx/BZhhZtNjbMtSZeyH/EMJ/29tCgcpVACYWdPWli8rMl8cJI0k7FB6EFhF6LY4A0Jfd7LYEMJ48EuB6yTtQrgwR3NuOTNbXuLoQJ/ln2Nm/XaqfhYy9kHOD5Ll3jCzF7fmjF3pZf6WZLlW66cht2PPlynWwz3Ysd2AwwiHpkKo/ocTTrYan0y7jPBG2I/Qz30ZYRP9Gko4amaW82chY1ZyZiFjlvPHni9Lt7IHKOKPfwxwMaHfGsLwuK8BeySP64AfAFcSBvK6LTcv9Rw1nj/bGbOSMwsZs5w/9nxZvmWmW0nSCEl/IAzCtRq4UdLxFobHvYdwZiPAu8DDhDfFYDM72cxe15YDlDWWOH4m8mchY1ZyZiFjV2LPH3u+gSAzxYEwWuMTZnaQmV0LfJ3NozXeDoyX9CkLfYWrgB2BjRBGrrTy9yFmIX8WMmYlZxYydiX2/LHny7yoh8+QNB1YDDxNGCBvYTK9knA92ZeSRecAdwC/lHQM8EnC+CdVUL7rzWYhfxYyZiVnFjJ2Jfb8secbaKIrDpJEOPHkNqANeJ0wENYFZrZMUqWZtSqMub4dbPpj35QcqXAR4YSrs83sXc+fzYxZyZmFjFnOH3u+Aa3cOz3SNzaPjLgncEtyfxBh3PTft1vmZuCE5P5OqefYxvNnO2NWcmYhY5bzx55voN+i2HJITk65FKiU9CDhQhqtsGlcnq8Ab0s62MweSVZbDyyUdClwnKQjzGyJmX3g+bOZMSs5s5Axy/ljz7e1KPsOaUkHE/oPtyecRftDwolTh0iaCptOe7+UMEhWro/xDMJp8NsCh5jZkpKHJxv5s5AxKzmzkLErseePPd9WpdybLoSLe5yaenwN4cpspwOzkmkVhH7Hu4DRhCMVfkkyzK7nz37GrOTMQsYs548939Z0K3+AcGJKNZv7Dk8BrkjuPwd8Obk/hXDRk7Jnzlr+LGTMSs4sZMxy/tjzbU23sncrmVmjmW20zVd8OoxwSUaALxJG9byfcOzyLMgfirecspA/CxmzkjMLGbsSe/7Y821NotghDZv6DY1wssp9yeR1hFPjJwILLVzCE0u+OsQkC/mzkBGykTMLGbsSe/7Y820Nyr7lkNJGOEllJbBP8u3gX4A2M3s890aIWBbyZyEjZCNnFjJ2Jfb8secb+Mrdr5W+Af9AeFM8DpxZ7jwDMX8WMmYlZxYyZjl/7PkG+k3JHyEKkkYBpwK/MLON5c7TU1nIn4WMkI2cWcjYldjzx55voIuqODjnnItDTPscnHPORcKLg3POuTxeHJxzzuXx4uCccy6PFwfnnHN5vDg41wck/aukb3Qx/xhJE0qZybne8OLgXGkcA3hxcJnh5zk4VyRJ3wWmA28SBoebBbwHnANsQ7gewanAJOD+ZN57wOeTp7gaGAE0Ei5j+Wop8zvXFS8OzhVB0mTgJmAaYQDLZ4FrgRvNbFWyzGXAMjP7laSbgPvN7HfJvIeBGWb2mqRphGGpDy39K3GuY9GMyupcxhwI3GtmjQCSciOHTkyKwnBgGPBQ+xUlDQM+BtydGm26ut8TO9cDXhycK15Hm903AceY2fOSTgc+0cEyFcC7Zjap/6I51zu+Q9q54jwKHCtpiKRa4HPJ9FpgqaQqwlXMctYl8zCztcBCSV+AcLEaSfuWLrpz3fN9Ds4VKbVDehGwBHgZ2AB8K5k2B6g1s9MlHQD8BtgIHE8YivrXwM6E6xbcYWaXlvxFONcJLw7OOefyeLeSc865PF4cnHPO5fHi4JxzLo8XB+ecc3m8ODjnnMvjxcE551weLw7OOefyeHFwzjmX5/8B0UbD1H2C87wAAAAASUVORK5CYII=\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n 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X8GKDpJb8HArFlnai1nq61Dtdah/v6+pqlckJYk7ScArQuP61CeYZ1aoyPqyOzNkVddWzJnlgc7Gy4tYM/AL1DvAFjta6wHOYK+EuAiSWzdXoCadJ/L0TtSC3Jo1PRWTqlHO1smdAziDVHEjiZcHELxMKiYn7bd44Bbf0u7I/bIcADNyc7CfgWxlTTMucBW4A2SqkYpdTflFJTlFJTSk5ZBpwEjgNfA4+aolwhROWlXEiNXLkWPsC9PZphb6v4bnP0hce2nEwiMTOPUZ0vDtDa2ih6tvBmswR8i2KqWTrjr3NcA4+ZoiwhRPWkXsiUWbkWPoCvmyO3hQbwy84Y7u7WlA4BHizZE4ubox392/hddm7vEG/+jDQWbDWtxIwgUXNkpa0QViblQi78yrfwAZ4c0Ao3J3tGf7GFRbvPsuLAeW7t2Agne9vLzusd4gNQ7/Lw1GUS8IWwMtVp4QME+7iw5PE+tGvsxt9/3ENGXiEjy5hv39rfFW8XB7ZKt47FkIAvhJVJzS7A1kbh7lT1Hl0/dyfmTe7J+O5BdG7qeWFWzqWUUvQKMfrxJY2CZZAtDoWwMinZ+Xg0sK/2nqmOdrb8585O1zynd4gPv+07R1RiFi18XatVnqg+aeELYWVScwqqNEOnKkpb/pukW8ciSMAXwsqkZudXeg5+VTXzdqaVnyszN0aRX1hsljJF+STgC2FlUrIKrrnTlSkppXh5eDuiErOYWcYKXWFeEvCFsDJpOZXLo1Nd/dv4MbCdP5+sPkZcuuTWqU0S8IWwMinZ+VWeg19Vr41oT0Gx5v+WHzZrueJyEvCFsCJ5hUVk5xfR0MV8LXyAIG9nJt/YgoW7zxIRnWzWssVFEvCFsCKpJatsK7rblSk9enMIjT2ceH3JQYrKyKkvap4EfCGsSOqFtArmbeEDODvY8dKwdhyMTefHHWeu/wRhchLwhbAiKRdSI5u/hQ8wIrQxPZp78d7Kw6SVfPgI85GAL4QVKc2j41FLAV8pxRsjO5CWU8AHfx6plTpYMwn4QliR2uzSKdWusTsTejbj+62nOHw+vdbqYY0k4AthRVIsIOADPDOoNR4N7Hl98UFJrGZGEvCFsCLJWXk42tnQwMH2+ifXIE9nB569tQ3bopL5fb/sdmouEvCFsCI7olNo19i9tqsBwLhuQXQIcOft3w+RnV9Y29WxChLwhbASyVn57I1J5eYrtiKsLbY2in+N7MC5tFy+WHuitqtjFSTgC2El1h9NQGvo38a3tqtyQXiwF7d3DuCr9Sc5m5pT29Wp90wS8JVSQ5RSR5RSx5VS/yzjeH+lVJpSak/J7TVTlCuEqLg1R+LxdnGgUxOP2q7KZZ4f0hatNV9KK7/GVTvgK6Vsgc+AoUB7YLxSqn0Zp27QWncuub1Z3XKFEBVXVKxZfzSBm9r4YmNTvZ2uTC3AswGjuwbyY8QZyaZZw0zRwu8OHNdan9Ra5wPzgVEmuK4QogJWRcax81TyNac37o1JJSW7gP4W0n9/pUf7t6SoWPPVupO1XZV6zRQBvwlwaWKMmJLHrtRLKbVXKbVcKdXBBOUKYfXSsguY9H0Ed32xhaEfb+D7rafIzLt6xsvaw/HYKOjXyqcWanl9Tb2cueOGJszZdoqEjLzark69ZYqAX9b3wyubGruAZlrrMOATYFG5F1NqslIqQikVkZCQYILqCVF/RZ5LR2uY0DMIG6V4ddEBery9ipcW7icy9uIq1jVHEugS1NCsG59U1mM3t6SgqJhvNkgrv6bYmeAaMUDTS34PBGIvPUFrnX7Jz8uUUp8rpXy01olXXkxrPR2YDhAeHi5L8IS4hshzxn+tpwa0xsfVgT1nUpmz7TS/7oxh7rbT3BDkyR03NGH/2TSeHdy6lmt7bc19XBgZFsB3W6IZ2N6fbsFeJr3+hmMJZOUVMqRjY5Nety4xRQt/B9BKKdVcKeUAjAOWXHqCUqqRUkqV/Ny9pFzZxl6IajoYm4afmyO+bo4opbghqCH/GxPGtpcG8MrwdqRlF/Da4oMAFtt/f6mXh7cnwLMBD8zczq7TKSa55rm0HKb+sJP7Zmzn8bm7Scq03i6jagd8rXUh8DiwEjgE/KS1PqiUmqKUmlJy2mjggFJqLzANGKclgYYQ1RYZm077gKtXzno6O/DwjS1Y/Y+bmPtwD965oxMdyjjP0vi6OTJvUk983Ry5f8Z29p5Jrdb1NhxLYMD761hzJJ4HegdTWKxZvCf2+k+sp5Qlx93w8HAdERFR29UQwiLlFRbR4bWVTO7XgueHtK3t6phUbGoOY6dvIS27gLmTetKxCmsHCoqKGfzhepSC7x7sTlMvZ0Z+upGCIs3yp26sgVpbBqXUTq11eFnHZKWtEHXUsbhMCot1mS38ui7AswHzJvXEzcmeCTO2cehc5dMoz99+mqjELF4Z3o6mXs4AjO4ayKFz6RyMTTN1lesECfhC1FGlA7YdAixr5aypBDZ0Zt6knjSwt+Xeb7ax5UQS+YXFFXpuZl4hH606Ro/mXpflDhoZFoCDrQ0/R8TUVLUtmilm6QghakFkbDrODrY0K2m91kdB3s7MndSTsV9tYfzXW3GwtaFtYzda+bnRzNuZZt7OBHk508zbhYbO9pTMDWH6+pMkZeUzY1i7C4+BMbYxqL0/i/ec5aVh7XCwq1ybd39MGl9vOElrf1c6BXrSJcgTN6fa2T2sLKsi4655XAK+EHVUZGw67Rq7W1yqBFNr7uPCyr/3Y/OJJPadTWV/TBqbjify667L0zC4OdrR1Mv4EFh3NIHhoY3p3NTzquuNDg/k9/3n+OtwXKWmaOYXFvPMT3s4lZTNkr3GN40mng1Y+XQ/XB0rFkqz8grZejKJI3EZ3N8rGJcKPu96YlKyeWNJJKsOScAXot4pLtZEnkvnjhvKWtRe/zR0cWB4aGOGh14M0LkFRZxJzuZUUjankrM5nZTF6eRsjsRl4O5kz/O3tinzWje29MHPzZFZm6O5tUOjy74BXMvMTVEci8/k64nh9GjhxbojCTwxbzdfrz/J04PKXuNQVKw5cDaNDccSWH8skd2nUygoMibK7D2Tyhf3dq3yB3ZuQRHrjibw275z/Bl5HoXin0PbMvW/5T9HAr4QdVBMSg6ZeYV1YqplTXGyt6WVvxut/N0q9Tw7Wxsev6Ulry0+yA9bT3Ffr+DrPicmJZuPVx1jYDt/BrX3B+C2sABWHDjP1xtOcm/PIPzcnACIz8jlr0PxbDiWyMbjiaTlGNtKdghw5299W9CvlQ/7z6bxn+WH+Xj1sas+LNJzC5i26hjpuQU8O7gNfu5OF47lFxaz6XgiS/fG8mdkHBl5hXi5OHBXl0Cm9g8hsKEzU6/12iv1TgkhLELkOWOWSX2coWMOE3o0Y9WheN5edojeLX0I8XW95vlvLo0E4I2RlycCfu7WNqw8eJ5pq4/x1u2dWHM4nifn7SYjr5BG7k4Mau/Pja186NPSBx9XxwvP6xXizdG4TD5efYw2jdwY0M4PreHPyDj+/VskCZl52NvYsHz/ef4xuDUhfq78tvccKw6eJy2nAHcnO4Z2asSI0AB6h3hjZ1uxsQgJ+ELUQZGx6djaKFpXsnUrDDY2ivdGh3LrR+t5+sc9/Dq1NynZ+Re7iJKyOZ2czamSbqLEzHxeGNKWwIaXD5AH+7hwb48gfth2Gic7W2ZsiqJdI3f+NyaMdo3dyu0uUkrx9h0dOZGQyaNzdl12rFMTD76eGI57A3teW3yAN0o+bFwd7RjU3p8RoY25sZVvpQecQRZeCVEn/W3WDs6kZPPH0zfVdlXqtOX7zzF1zi4cbG3IL7o45VMpCPBoQJCXMQuofYA79/QIwr6MlnRiZh43vbuGrPwihndqzHtjQnF2qFhbOjkrnwW7YsgrvDgIfFtYALYl/fpaa9YeTSC/sJibWvviZH/9zeevtfBKWvhC1EEHY9Pp2cK0ycWs0dBOjXlleDtiU3MJ8mpAM28XgrydCWzYAEe76wdXAB9XRz4c25m4jDwm9Aiq8CAwgJeLkQKjPEopk+5BLAFfiDrmfFou59NzCStjyqGovGsF3Ioa3KGRCWpS82SlrRB1zJ6ShGJlzTEX4lok4AtRx+w5k4q9raJdY5mhIypHAr4QdczeM6m0b+xeoQG8Clv9JuyZa7rrCYskAV+IOqSoWLMvJtW0/ffJJ2HD+xLwrYAEfCHqkBMJmWTlF5m2/37HDOM+5ZTprikskgR8IeqQPaeNAVuTtfALcmD3D4CC9BgoKjDNdYVFkoAvRB2y+0wq7k52NPd2Mc0FDyyA3FQIGw+6GNLOmOa6wiJJwBeiDtl7xui/N1lK5B1fg29buOFe4/eUaNNcV1gkCfhC1BE5+UUcicswXf/92Z0Quxu6PQwNg43HJODXayYJ+EqpIUqpI0qp40qpf5ZxXCmlppUc36eU6mKKcoWwJgdi0ygq1oQFmijg7/wO7F0gdCy4NQZbBwn49Vy1A75Syhb4DBgKtAfGK6XaX3HaUKBVyW0y8EV1yxXC2ph8wDb+EDTpAk7uYGMLnkES8Os5U7TwuwPHtdYntdb5wHxg1BXnjAJma8NWwFMpdd29xSw4kacQZrfnTCpNPBvg6+Z4/ZMrIvO80bIv1TBYpmbWc6YI+E2AS4f2Y0oeq+w5V0nOyqt25YSoD7TWbItKJjy4oakuCBnnwe2SpF8Ng6WFX8+ZIuCXNV3gyrZ5Rc4xTlRqslIqQikVcT4th4xcmRcsxPH4TBIz8+gd4m2aC+akQFH+1QE/N9U4JuolUwT8GKDpJb8HArFVOAcArfV0rXW41jq8GMVX606aoIpC1G2bTyQB0DvExzQXzDhv3F8a8D2bGffSrVNvmSLg7wBaKaWaK6UcgHHAkivOWQJMLJmt0xNI01qfu96F/RwL+WbjSeLSc6tdycy8wmpfQ4jasvlEIoENG9DUy/n6J1dEZmnAv6IPH6Rbpx6rdsDXWhcCjwMrgUPAT1rrg0qpKUqpKSWnLQNOAseBr4FHK3JtX9ssioo1H/55tEp1yy0oYsGuGO76YjMdX1/JU/N3k1tQVKVrCVFbioo1W08mm647By628F39Lz7WsLSFH226coRFMcmOV1rrZRhB/dLHvrzkZw08Vtnr2uSlcn/3RszceoYgb2ce7tuiwhv3pmUXMPyTDcSk5NDcx4UxXQP5ZVcMJxOymD6xK409GlS2OkLUikPn0knLKTBddw5ARskX7Eu7dJw8oIGXBPx6zLK3OCwu4plm0ZxOC+LdFUdYuOss/769Iz1bXL+l8+7Kw8Sm5vD1xHAGtPXDxkZxa4dGPDV/N8OnbeTmNn6EBnoQGuhBO1PnFhfiCq8vPsDp5GxCAz0Ja+pB7xCfCv/NbT6RCEAvk7bw48DRHRyuyMnTMBhSpQ+/vrLsgG9rj/PhX5k+cQ6rD8Xx+pKDjJu+lTu7NOGlYe3wcS17PvKeM6nM3X6af3SxY5DtLjiQAVozsN0IFj7Wh3dXHGHd0Xh+3RUDgJ2NorW/W8kHgCehgR60aeRW5g71QlRWZl4hs7eewqOBPWuPJqA1BHs78+aojvRr7Xvd528+kUSIrwv+7k6mq1TGuctb96UaBsO5PaYrR1gUyw74DRrC0ZWQncyAdv70DvHh0zXHmL7+JKsPxfP8kDaM7xZ0WSKpomLNK4v2M8TlGI9FvgkHL+mz3z+Q1uN/5Jv7w9Facy4tl30xaew/m8q+mDSWHzjP/B3GcoHmPi58PbErLf3cqlT1gqJizqbkcDo5m1PJ2bT2c6VHBb6ZiPpnX0wqWsOHYzvTPdiLzSeSeGfZISbO3M7w0Ma8NqJ9ucG8oKiY7VHJ3NUl0LSVyowrP+AfWgLFRcbqW1GvWH7ALz4LkYsg/CEaONjy3K1tueOGJryy6AAvLzzAzxExvHV7Rzo28SAjt4BZm6JJjT3BArcPUZ4t4fYvjKXjJ9bA8udg2bMw4kOUUgR4NiDAswFDOhp/+FprziTnEHEqmXeWHeL2zzYzbXxnbmnrX24Vi4s1644lcPR8BqeSszmdlM2p5CxiU3MpKr641MDWRvH9Q93p3dKE/bCiTth7Jg2AzoGeuDjaMai9Pze28mH6+pN8uuY4644k8Myg1kzs1Qy7K75V7u6yGPEAACAASURBVItJJTu/yLQDtmC08Jv2vPrxhs2guBDSzxqpFkS9YtkB394ZfNrAvp8g/KELD7f0c2PepJ4s2nOWt38/xMhPN9LM24WoxCwakMtKt4+xV8Uwbi74tDSe5NPK+CPe9BF4NYfeT4K6fD2YUoogb2eCvJ3p0cKbR76P4G/fRfCvkR2Y2Cu4zCr+748jfL72BAANne0J8nbhhqYNub2zM029nGnm5YyfuxOTZ0fw6NxdLHmsL0HeJppaJ+qEPWdSaObtTEMXhwuPOdnb8uSAVozqHMBriw/y5m+R/LrLaLzcEGSspi0oKuanHUa3Y0XGrSpMa6MPv7wWPhgDtxLw6x3LDvgAYWONDZZ/fhDaDINWA6FBQ5RS3HFDILe08WfaX8c4nZzNmDAfxpx6A5+z0ah7fr4Y7EsNeB1SouDP12Ddu8Yf92W35saHgUdTmng24OdHevPEvN28vuQgTb2cubmN32WXW7I3ls/XnmBct6a8NLwd7k725b6MryeGM+qzTUyaHcGvj/bG1dHy33phGnvPpNGjhVeZx5p5uzDrwW4sP3CeN5dGcucXmxnfPYjB7f35z7LDHInL4O7wwMs+LKotJwWK8q4f8Jv3M12ZwiJYftTpNglST8Ph3+HgAlC20Kw3tB4CbYbi4R3CqyPaQ2Y8zL8HYnbAkP8aHwxXsrGBO6ZD85sg8ajxR510HI6vgsJLFncpGwjqRYPhHzBtfGdGf7GFJ+ftZtFjfQjxdQXgwNk0nv9lL92DvXhzVMeyp4tqDdnJkHySYI8mfHrPDdw/czvP/LiHLyd0Nd0mFsJinU/L5Xx67jVTGiulGNapMf1a+/LRn0f5dnM0c7edpolnA76eGM6g9uV3KVZJWatsS7kHgo298f9D1DtKW3BKyvDwcB0REWH8UlxsbNhwdDkcWQHxB43HfdpAq0FwcBHkJMMdX0L7K5N1XkdxsTGIlRJtfANIOg4R30JeBvR7jpiOjzDqix14NLBncr8W7D+bxsqD53GwtWHJE33xsc2GxOOQfBKST0DSiYs/5xr9tzi4wYPLmHHCjX//FslTA1rx9KDWJnuvhGVaceA8U37YyYJHe9MlqGKJzyJj09kWlcTYbk1xdqiBNtmJv+D7O+CBZRDc5+rjM4dCfiZM2WD6skWNU0rt1FqHl3XM8lv4pWxsoGk34zbgNSPfx9EVcGQZbPsKXHzhweUQ0Llq13ZvbNya9TIe6zEVVrwAa98hMGo9X477mvHf7uGfC/bj5mRHWKAnLw9vh0/kbFj+AujS2UAKPJuCVwvoOBq8Q8C9Cax8GeaM5qGHVnLoXCAfrz5G20ZuDO103SzRog7bcyYVe1tF+8buFX5O+wB32gdU/PxKu1YLH6DlAPjr38a3Zle/ss8RdVLdCfhXatgMejxi3PIywc4RbMvvQ680V18YPRNaDoJFU+nm8BQrnvgSGztHgr1dsFEY4wBr34FWt0L4g0aQbxhs1OVKfu1gxmDUD3fx9v3LOZGQyTM/7aWRh9OFQTpR/+w9k2p5C/sqGvBP/AVh48xXL1Hj6sfKIkdX0wb7S3UeDyM+hGN/0HLjs7SwicPm5F+w9Ckj2IeNN2YDtRkKvm3KDvZgHLvnJ0iPxfGn8Xw1th1eLg7c9cVm3lwaKcnd6qGiYs2+mFTTbUloKhnny15lW6pRGDj7wPHV5q2XqHF1t4VvTuEPQl66Mbvn4IKLj/d8DAa/ZXQJVURQDxg9A+bfi9+fT7Ds8Rm8++cxvt0cxe/7Y/lyQldp7dcjJxIyycovMt2m46aSeb781j0Yf88hNxst/OLiiv99C4sn/5IV1ecpGD/fWMj14HJ45jAMeafy/xnaDoch/4HDv+Gx6d+8fUcnFj7aBwc7GybN3sm5tJyaqb8wO5PvQWsqGecvz5JZlpYDITsRzu8zT52EWUjAr4w2Q6HzPca0UPdqDLb2nArdH4Etn8LqN+kc4MLM+7uRW1DE5Nk7JYVzPbEnJhU3Jzta+JTTdVJbMs5dnge/LCG3GPfHV9V8fYTZSMCvLUP+A50nwIb34ZsBtFIxfDS2Mwdi03jh131Y8nRZUTEHz6bRMcDDstZbXGuV7aVc/aBRJ6NbR9QbEvBri40t3P4ZjP0B0s7CV/0YqHbw7OA2LN4Ty0sLD1BYVFzbtRRVpLXmZGIWLf1ca7sql7vWKtsrhQyAM9sgN73m6yXMQgJ+bWt3Gzy2Dfw7wsIpPNpJ82j/EOZtP80j3+8kO19m79RFSVn5ZOQW0tzSunMy44z7igT8lgOMRGrRsgCrvpCAbwlcfGDs92Brj/ppIs/f0pR/396RNUfiGf/1NhIz82q7hqKSohKzAGjua2EBv3SnK9cKBPymPcHeRaZn1iMS8C2FR6AxZTP+EPz2NPf1COLLCV05cj6du77YTHRJABF1Q1SC8e9leQO2lWjh2zkYCdSOrzL6/kWdJwHfkoTcAv1fhH0/ws8PMDjYnrmTepKRW8idX2xm9+mU2q6hqKCTiVnY2yqaeFrY3sll7WV7LS0HGFseJp+suToJs6lWwFdKeSml/lRKHSu5L3PVkFIqWim1Xym1RykVUZ0y671+zxm5gg7/Dp/3oEv2Fn6daqRTHv/1VlZFxtV2DUUFRCVmEuTlfNWGJrUu9RQ4eZa/yvZKF6ZnSrdOfVDdv8Z/Aqu11q2A1SW/l+dmrXXn8rK4iRI2NnDjP2DyWqMVNn88zTf8gwUPdqC1vxuTv49gzjbZZNrSRSVm0dzHwmboAMTshIAbKn6+d4ixT8QJCfj1QXUD/ijgu5KfvwNur+b1RKlGHeHhv6Df87DvJ3xm38RPA3Po38aPlxceYNrqY7VdQ1GOomJNdFI2LSxtwDYvw0gr3rRH5Z7XcgBEbYBCmTxQ11U34Ptrrc8BlNyXl0tVA38opXYqpSZXs0zrYecAt7wMD/8Jjm44zR/NN14/MC60IR/8eZRd0qdvkWJTc8gvLLa8KZlnd4IuNlKMV0bIACjIgtNba6ZewmyuG/CVUquUUgfKuFVml5E+WusuwFDgMaVUuXunKaUmK6UilFIRCQkJlSiiHmvSFR5ZD72fwGbXd7wTP4VbXU/ysizOskgXpmRaWsA/s8O4b1LJXtXmN4KNnXTr1APXDfha64Fa645l3BYDcUqpxgAl9/HlXCO25D4eWAh0v0Z507XW4VrrcF9f36q8pvrJ3snIzPngcmyUDV8UvUH7uN+YvaXm+/PTcgr419KD7Dwl3ygqojTgW9yUzDPbwLcdNKhkMjdHN2NO/nFJs1DXVbdLZwlwf8nP9wOLrzxBKeWilHIr/RkYDByoZrnWq1kveGQ9KrgP7zt8Seaf/0dcDWbYPJmQyR2fb+LbTdE8/N0OziRn11hZ9UVUYhYuDrb4upWzN0JtKC429nuubHdOqZYDIG7/xXn8ok6qbsD/P2CQUuoYMKjkd5RSAUqpZSXn+AMblVJ7ge3A71rrFdUs17o5uaPu/YXMNnfxpJrPoU/H8PuG7SbPsrn+aAKjPttEanYB748Jo6hYM2l2BFmyWcs1nUzMormvC0pZUNK0pGOQm1r5AdtSLQcY95JMrU6rVsDXWidprQdorVuV3CeXPB6rtR5W8vNJrXVYya2D1vptU1Tc6tk54DpuBsfaP0nvgq0MXDWMuW8/yLII08zeycor5IU5GxnvHMGG1j9z14Zh/NwzmqNxGTzz0x6Ki2XlZXmiEjMtb0rmme3GfWC5vanX5t/J2Dda0iXXaRa2KkRUilK0uvvf2D+1k7QWw3mIxTRYOoX4jNxqX/q3XdHM1S/wUva7uEStBK1ps+dt3hzoz8qDcTwwawfn06pfTn2TV1hETEqOeQdsDy4yFkYVXeOb15lt0KAheLesWhk2NsYirJNrjO4hUSdJwK8HVMNm+N3/HUk9X+RmFcEvP31fretprUna8A3NbeLQd0yH507AhF8gP4t7M2fx5qgO7IhKZvCH61i0+6zk7r/E6aRstDbjgG3qGfj5fvjhTni/Nfz2NERvvDoox+yAwG7V266w5UDIToJze6pXZ1FrJODXI94D/k6qYwC3nPqYzUfPV/k6+6PjuCPrR+I9b0CF3g22dsYm7D2nonZ/z8TABJY9dSOt/N34+497mPrDLpIqkNEzPj2XzccT2Xw8kS0nkupl6ueT5p6SebRkOGzY/6D5TbB3PswaDh+2hxUvQkyEkQM/4TA0rWJ3TqkWNxv3Mj2zzpKAX5/YO+E84h3a2pxh668fkV9Yta/eJ1d+TmOVjOuQV+HSgcebXjC2xlv2D5p7OfHTI714cWhb/jocz+AP17PyYPkfMlGJWQz8YB33fLONe77ZxvivtzLg/XWsOHCuXn1DKJ2SGWyugH9kGXi3gu6TYMy38NxxGD3TWLux4xv4ZgBM62KcW9X++1KuvtA4TPLq1GF2tV0BYVoOHW8ndX037o//ge/WjGXSoErkTQHS0tLpfW42Ua5hNG9zy+UHHd2MtQC//g3m34PtbR/zyE0h9G/jxzM/7eGR73dy5w1NeH1kBzwa2F94WnpuAQ9/twM7Wxu+fbAbzva2pOUU8OGqY0z5YRc3tfalb0sfPJzt8Whgj2cD+0t+dsDJ3sayZrxcw4n4THxcHS57/TUmN91IedBz6sXHHFyg413GLTfNSMJ3YAGkx0KgCdJYhQyATR8b13byqP71hFlJwK9vlMLzzvcp/uomHDf8H2e6fktTL+cKP/3wsmn0UClE9f/i8tZ9qY53QWY8rP4XfNYDhv2PNp1Gs+ixPnz613E+XXOczSeSeHl4O3qHeOPp7MDf5+/hVFI2Pzzcg54tvI3c6vlZ3NKmN99tPc1Hq46y7mj5q6odbG1o29iNj8Z2poWvhc1+ucKu0ymEBlZyYVNVnVgNxQXQZljZx508oPM9xs1UWg6AjR9A1HpjtzZRpyhL/jodHh6uIyIkm3JVZC58Guc93/JWwCe8OnlChVrIWWlJ5H/YmRj7YDq9fJ1t7RKPwaKpxmBg2xEw4kNw9WNfTCrP/LSX4/GZAHi7OJCUlc9bt7Vmwvl3jVkeOSnG1nnN+sKIDyj2bk1mfiFp2QWk5Vy8pWaX3ufz884YCoqK+fSeLtzU2jJXYCdn5dPl33/y/JA2PNq/irNhKmPBI3DsD6Mbx8a25ssDKMyHd1tAp9Fw20fmKVNUilJqZ3lZiaWFX0+5Dv0XWYeWctfZ9/jzwM0M7hR43eccmPcy3XQGsUPeuX4BPq3goZWw5VP4622jtT/8fUI73smyJ29k56kUDpxNY29MKi39XLk35QvY/xN0uhs8m4Kyhe3T4Ys+2PR+AveWA3B39qGphzc09roqgE3o2YxJsyN48Nvt/GNwGyb3a4G9heWaL009Ed7Mq+YLKyqEYyuh9RDzBXu4uAvWidXGN7U60tUmDBLw6ysndxxHvk+HXyby16L/4un6LzoEuOPiWPY/+cnDe+hy7icivIbRveuNFSvDxhb6PAWtbjVa+788CJGLcRj+Pr1CfOgV4m2ct/1r2DgDej8Jg/998fndJ8OfrxpdBBs/uOTCysj34uwDzt7g4kNT/44smPwkzy06wnsrj7B0byxv39GRruYIrhUUEZ2Mg60NoYFm6Ns+s834ptRmaM2XdaWWt8CR341dsLxDzF++qDIJ+PWYXYeRpG4byN9O/8icGYks1f4UeATjFtCGpsGt6BjkTfvG7jjY2pCy8Dn8lAOtx79b+YL82sLf/oTN02Dtf4x54H2fBgdnyE6GNe8YLdGBb1z+PFdfuONLY5ev1NPGHO/sZMhOLPk5CbISjcBy+Decj//Jp3fP5rbQAP619CB3fbGFseFN+efQtjR0cTDFW1YtEadS6BTogZO9GVrcR5aBrcPFHanMqVlf4/70Fgn4dYz04dd3aWcp+HEiNnEHsC26uDK2QNtyRvtymkbkO3gyuHAte9o+Q+dxr1evvLhIWDQFzu29+FjjMHjgd2OWT1VFLjG+Rdg5weiZZDXpw7TVx5ixMQo3JzteHNqO0V0DsbGpnS6G3IIiOr2xkof6NufFoe1qvsBPuxsb39+3oObLupLWRj9+m6Fw++fmL19ck/ThWzOPJthPXm2svMw8D8lR6OQT5J47RoNzR2mXHIV7zlFiHVsQeucL1S/Pvz1MWmuUpWyMvnrnq/vkK639SPBtCz/eC7NH4dJjCi8OfI07uwTyyqL9PP/rPo7FZ/Dy8PbVfw1VsC8mjYIibZ7++9w0SDwCoWNqvqyyKAVBveDU5topX1SZBHxrYWMD7gHgHoAK7oMbcKG9rTUNwHQDcKVlmZpva2Ov31VvwLYv4NgftLntY36c3JcXF+xnxsYobr+hCR0CzD8/POJUMgBdmzWs+cLO7TPuG1dujYVJNetl9ONnnDf2XhZ1gmVNcxC1Q6m6M9vCwQWGvQf3LzXmoH83Apv543ilG3i5OPDKogO1kskzIjqFln6ueJljLKE0l01A55ovqzzNehv30sqvUyTgi7qpeT94bDsMeB1ObcFt1k3MaLuT3adTmb/jjFmrUlysiYhOpluwGVr3ALF7wD0QXHzMU15ZGoWBvYsE/DpGAr6ou+wbwI3PwFN7oNVgQg++y31NzvPfFYeJSzdf6ubjCZmk5xaab4rouT2127oHI6Fe027GTB1RZ0jAF3WfsxfcOR3l2ZTXct/DqSCVwR+uZ97202bp3vnrsLGVs1la+LlpkHS89gM+QFBviDsIOam1XRNRQRLwRf3g5AFjZmGfm8RfIfNp5+/Ciwv2c9eXmzkYm1ZjxW48lsj7fxyhb0sfgiqRs6jKLGHAtlSz3oA2FoEVFcKKl+Dz3vD9nbD4MTi5rrZrKK4gAV/UHwE3wOC3cTm1mnnO7zFzsD2nk7K57ZONvLk0kkwT78V7MDaNKT/sJMTXlc8ndDFPRk9LGLAtFRgONvbGtofz74Gtn4GLt7EC+PDvMPduOLurtmspLiHTMkX90n0SFBei1r/LLSfHsKXNKD5VY/lkcxS/74/ltREdGNapUbWD85nkbB74dgfuTnbMerA77k5mSIcMljFgW8q+gfEhu326seZixIcQ/pBxLCsRpveH+fcaU2nd/GuxoqKUtPBF/aIU9HoUntoL/Z7D4eQqnjlyHztCl9LGOYPH5u7i/m93EF2yUUlVpGTlc/+328krKGLWQ91p5OFkwhdwHZYwYHup1reCXQMYO+disAfjA2ncXMhNhR8nQOH1d0QTNa9aAV8pNUYpdVApVayUKnd3BaXUEKXUEaXUcaXUP6tTphAV4uQBt7xizODp9jA+x37hu8wpLG2zkhOnTjPy042kZudX+rK5BUU8PDuCmJQcvrm/G639q5EuotKFpxsDto0tKOD3fRqePwFty8jJ3zjUSL0Qs93o3smo+rabwjSq28I/ANwJrC/vBKWULfAZMBRoD4xXStXO+ndhfVz9YNi78EQEqsMddDo1m/UOf2dswSJmbjhZ4csUFBVz4Gwaj8/dza7TKXw0tjPdm5s5U2dpfqIACxiwLWVjayyGK0+HO+C2aXB6G3zRGw4vM1/dxFWqFfC11oe01keuc1p34LjW+qTWOh+YD4yqTrlCVFrDYCMz59TN2Ab35mX7uagtH5OeW3DNp6VlF3DfjG10eH0lIz7ZyKpDcbw+oj3DOjU2T70vZUkDtpXR9X54ZJ2RbmP+ePjtacjPru1aWSVz9OE3AS5d+hhT8pgQ5uffHsbPJzVkJE8zl80Lvyr7vFNbKEw7x+PzdrH1ZBL39WzGtPE3sOH5m3mgT3Pz1rmUJQ3YVpZvG3h4NfR6HCJmGgO6pVNMhdlcN+ArpVYppQ6UcatoK72s6RDlroZRSk1WSkUopSISEsrf51SIKrOxwXP8Nxx17MQtR14n5+ByKC4yjqVEw9yx8O0Q0j4fSOSxE7x1e0deHdGekWEBldof2OTiDkKjTrVXfnXZOcKtb8N9i4wFZF/fAps/MTK5CrO47rRMrfXAapYRAzS95PdAIPYa5U0HpoORD7+aZQtRNjtH8sb8wJnZQwj5eZyRF6ZRR6OfXNlyJORBgo7PYanXxwSEWsBm3YX5kHSs7MHRuibkZpi6GZY8AX+8AsdXw+1fgHstdJNZGXN06ewAWimlmiulHIBxwBIzlCvENXVqGcz7QZ/xXPHjnG85xtjYo/0oNg5ZzojDg/nE5xUa5xyDH+8zAm5tSjpmbPzuV0/mO7h4w7g5MOIjOL21ZED399quVb1X3WmZdyilYoBewO9KqZUljwcopZYBaK0LgceBlcAh4Cet9cHqVVsI03hjbB92egxiwMGh7Bz0E2s7vMVDC2Jp39idKZMeRY36FE6ugbUV2Ni9JsUfMu7rS8AHY81E+IPwyHpj967598Dv/4CCnNquWb0lWxwKqxeXnsvYr7aQmJlPflExrfxcmftwTzycS1bPLpwCB36Fx7aBV4vaqeTqN2HTx/DSObCr/f17Ta4wz3iNWz4F/47wwG/QwEzppuuZa21xKCtthdXzd3di7qSeeLs6EOLryg9/63Ex2IORc9/GHla+UnuVjIsE71b1M9jDxQHde36GxKOW0Y1WD0nAFwII8GzAH0/3Y+njfWh45a5V7o2h3z+MLf1O/FU7FYyPBD8zbI5e21oPhpGfQvQGY76+BfdA1EUS8IUo4Whni51tOf8lej5mLN5a8SIkn7w4jdMc8jIh9ZSxhsAahI2Fm16APT/Apo9quzb1imTLFKIi7J3g1neMgcVpN4CdE3i3BJ/WxqKioJ7Qon/NlJ1w2LivTwO219P/RTh/ANa9Bz2mGJk5RbVJwBeiotoOhykbIXY3JBwx+prP7oSDCwFttEr7v2j6DeHjI417a+jSKaUUdH/4Yjda2+G1XaN6QQK+EJXRqNPVq13zs2H5c7Duv5AZB8M/MJKKmUr8IbB3Bs9g012zLgi+EZw84dBSCfgmIgFfiOpycDYGGl39YcP7cGozOLoZ/fzKBmwdjFkoPR6pWuCKjwTftmBjZUNutvbQZpjRyi8qMH4X1WJlf0FC1BClYMBrRipg9yZGy9TVD5y9jWAffwj+eLVqeWPiIq2r//5S7W4z8u5ElZuBXVSCtPCFMKWu9xu3K+37CRZMguj1lRvczUqErHjr6r+/VMjNRp6jQ0uh5YDark2dJy18Icyh3Uho4AU7ZlTueRdSKlhpwLdvAK0GGXl2qjsVtjDP+AC1YhLwhTAHeye44V4jcKWfq/jzSmfo+HeomXrVBe1HGt9yzmyv3nV+vM+YUpt4zDT1qoMk4AthLl0fBF0Eu3+o+HNidxvfDFz9a65elq7VYGPgO3Jx1a9x9A84thLys2D+vZCXYbr61SES8IUwF+8Qo/9+56yKdU8U5sORZUbAM/Xc/rrE0Q1a3wr75hsBu7IK82Hli0Yuont/NjaCXzilagPoeZkQf7jyz7MQEvCFMKfwhyA9BnZ/f/1zo9YbM1Q63F7z9bJ0vZ6AnJTKfTsqteNrI8jf+o4x8Dv433D4NyPldUVz9RQVGlszTrsBPu9prAKugyTgC2FObYZBYDdY+hQs/fu1N/OOXAQObtDiZvPVz1IF9YCmPYz0yUWFFX9eViKs/S+0HGgkZgPo+Sh0ngDr34Mlj5edlTMvA87sgJ3fwfIXjCD/29PGtzQHV1j/buVfQ3ERRG+EZc/DN4OM7jozk2mZQpiTrT08sAz++jdsnganNsFdM6Bx6OXnFRUYrdA2Q40BXwF9njJyGUUugk6jK/ac9e9BfqbRui+lFIz8BNwDjMCdHA1dJkLCIWPNQ/whSDt98fzS7S8HzjEWzv31Fmz4n3Hu9RLaFeYb39QOLTEG7LMTjTxM9s7w/R1w/1Kz7lMsG6AIUVtOrDH6knOSYeAb0GPqxdW0J/4yAsLYOdBuRG3W0nIUF8Nn3Y2VzZPXXX9cI+M8fBwGHUfD7Z+Vfc6+n2DxY1CUDzZ2RjI8v/bGNNjSe89ml69yzk6GjzoZ00XHzLr6mgU5xj69h5bAkRWQl2Z8K2h9qzE9t+VAI/B/OwwKc+GB30067fZaG6BIC1+I2nJhM+/HYeVLFzfzdvM3ZqQ4uMpio0vZ2EDvJ2Dpk0Yrv/3t1w76mz8xvind+Ez554TeDUG9jG8BXiEV22DG2Qu6T4aNH8JNh8Gv7cVjRYXwzUCIO2Ds2NXuNuPWov/l39QcXY3W/bfD4NuhEDrOGKsJ7F6jKTSkhS9EbdMaImbAypeNID/yE+NDoEV/GD2ztmtnWQpy4bNukHra2G6ywx3Q4U5jncKlwT8zwWiFd7gd7vjS9PXISjKu3/pWGPPtxcd3zjLGZ0Z8BDfcB7bXaVMnHoc/X4Pjq6AoDxw9jA13XP2ND4rukypdtWu18CXgC2Ep4g/Brw8brUOAu2dD+1G1WydLlJMCkUvg4AKjf1wXG10xHe6Ejnca+xP88aoxwPvYdvBpVTP1+OstY4zg7u+NxWEFOcYsHo+m8Lc/KjeVNjcdjq6AM9uMrqikE8aYwri5lU64V2MBXyk1BngDaAd011qXGZ2VUtFABlAEFJZXmStJwBdWpyAXVv/LyLj54HKjv1qULzMBDi2Gg4uMGTBo8OsAKVFGoLzrm5oruzAfZt5qBOcp640PoT9fNQblg/tU89p5MGOQ8U1mykbwCKzwU2sy4LcDioGvgGevE/DDtdaVSmQhAV8IUWEZ542xjwMLjJQUD68G39Y1W2ZKNHzZD7yaG9tQNukKE341zbWTTsBX/aBRqNHff73uIYD0cyiPgJoZtNVaHwJQ1rwKUAhhGdwaGXsO9HjEfGU2DIZRn8BPE43fB7xmumt7hxib6SycDN/dZkzdbRhszBpqGAwNm4GDi3FucTHs/BZWvXHNS5prlo4G/lBKaeArrfV0M5UrhBA1q/0oGPgvoxumcZhprx021vjmELnYWGWcn3n5cRdf4wOgKA/O7zd2CSOm3Mtdt0tHKbUKaFTGSeqf7AAADTdJREFUoZe11otLzlnLtbt0ArTWsUopP+BP4AmtdZk7GiilJgOTAYKCgrqeOnXqmvUTQgiroLWxBiAlGlKjjfuUU8Z9Toqx2Xvne1A2NlXv0tFaD6x+PXVsyX28Umoh0B0oM+CXtP6ng9GHX92yhRCiXlAKXLyNW2DXKl2ixnPpKKVclFJupT8Dg4G6mXlICCHqsGoFfKXUHUqpGKAX8LtSamXJ4wFKqWUlp/kDG5VSe4HtwO9a6xXVKVcIIUTlVXeWzkJgYRmPxwLDSn4+CZh4JEMIIURlSXpkIYSwEhLwhRDCSkjAF0IIKyEBXwghrIRFZ8tUSiUAlrbyygeoVE4gC2Pp9bf0+l3K0utq6fWrCEt/DZZYv2Zaa9+yDlh0wLdESqmIimb7tESWXn9Lr9+lLL2ull6/irD012Dp9buSdOkIIYSVkIAvhBBWQgJ+5dX1TJ+WXn9Lr9+lLL2ull6/irD012Dp9buM9OELIYSVkBa+EEJYCQn45VCyjVeNk/fYdOS9rFn15f2VgH8JZXhaKRWo63Bfl1LKtuTe4v5I69p7LO9lzZL317wk4JdQSk0E1gA3AOmW+Ad4PUqpB5RSu4GnarsuZalL77G8lzVL3t/aIYO2gFKqD7AB6H7lNo1KKVUXPt2VUm2B2cBKoBPwjNb6pFLKRmtdXLu1q1vvsbyXNUve39pjtS380q+SAFrrTcA2oF3JsX8qpW5TSrla8j9u6U5iAFrrw8BE4EMgEni85PFa+w9Ul95jeS9rlry/lsEqA75S6k3gNaXUpfkmpgDfKaX2AJ7AE8B7Ja0Ri6OU+iewWyn1X6XUAyUPH9FaJ2NsShOilOpXcq7Z/53r0nss72XNkvfXgmitreYGOP5/e+cerFVVBfDf4oIggg8cSBsz0cmBGwpmI6kpoRmiGVDYmA1GlI5aPqY0SGsydMa0sbEhH1PNSIwvlNIccuoPKw0bxwbNyMckipqJZgookCiw+mPt797j18f97pV7zlnnnvWbOcN5fZffXufcfb+z99lrA9/GErLdBXyq6fi5wOFpfTRwNzCtbO8W5TgOmwR+LDAVWAscmjk+ArgQuCWzryNiHLGM+A6s+PZ1qds3/HeA5UAn8BAwVUTGNg6q6vWqujKtvwq8DowqQ7QNQ4BHVXWNqv4B+DFwZeb4JmAZsFFELheRq4EDCnKrWowjlvkS8XVErSp8tTbCf6jqJmApsB9whIgMhe5Xw0RklIhcAxwK/KUs3x4YDuwtIsMAVPUHwL4icmraVuAtrEPsHOBVVX2mCLEKxjhimS8RX0cM2Ap/R69RqeqW9O9zwApgCjAu7dP0F34p9s1kiqquLkS4BdmOrmzbptrk8QcBn86cfjXwjcz2lcDjwP6q+sOC/dzFWERGZdY9xnJHfu5i2QoRGd9qv6P47sivEvHtN8puU+rvBZgB/AKY1LRfgEFpvSP9uzuwCDgdmAOckvbvXXIZpmPvAC8BLs3s7wCGpvXTsLbRA9L2/sB1wMi0PawkP1cxBk5McVoCXJPZP8hJLHvycxXLHsqwCFjTiJ+n+Lbxq0R8+zUWZQv00wVtjCeYCvwNWIk9Hu6VPZ7WDwT2zGyfD6wDVgMnlVmGVGGejT02ngRMxtoY5zWde2A6fyHwc+BrwG+BGx35lRbjjOtZWNvsjFTJ/BGY7iiWvfVzdb9mf5/S9i3AI8BXG5V82fHto5+r+OZ67coW6M+Li3X27Iu9GbAYewRrHBsELMDeEpiebsJxwLPAJY7KcBLwocz2hdjAFFIlsQB4FTgG2AM4GnuiudiRX2kxbnLtBAan9THAHalibXyju7TkWPbGz9X92uTf8DwPOBN76puQOT4fm/6vrPj2xu9lT/HNexlMhRGRrwPHi8gDwG1q7XAAa0VkGjBFRFar6r+AfYANQKeqrkuffw44RK3TphQyZfgTsERV7xWRDhEZrKpbscEfT6XTx2BlOLhRBuBBEXlIVbc58islxk33w+2q+kTafxjWhDAY+yX/L3AR5cWyL35u7teM//3AHar6kojsgjVLfQn7snWaiDyMver4BvbloOj49sVvvJf4FkLZf3He6wLMwpoWpgI3AT8BJmaOTwRuBma1+Ozgsv17KMOkrCP2pHJUi8920PTo6syv0Bj3dD9gj+37p/URWEV6mINY9tav9Pt1B/4fSce+n/79AlaJPkmm3bvE+PbWr/T4FrVU+S2dycANau/2XoZ1ynQlYlLVx7Ab4BAROS6N9mvkwthagm8rWpXhfABV3ZpeD/sAsFJE9hORM6GrDNs03a1O/YqOcSvXC5Prs6r6QlrfiL11MSrjWlYse+vn4X5t5X9uOnZyegKcD/wa65fYBKXHt7d+HuJbCO4r/ObXKzPbz2K96ajq88BvgN1E5DOZ02/DOmqWAnvnb9ua91CGGen4OMz7AuAechr04d1vJ1yHN90PiMh3gA9jrwHS3xWRd7929NF/TxE5EhtM9WdVnaSqc7Dm0/Hp3DLjW7ifd9xX+Nj7r11kLtAyYHOm8lmLveXQKcYI7EKvwoZyX9z0+SLpaxnGpxv5QOzGHAucrKpXNX2+Ln4749oJICLTRWQFcDAwW1VfrqlfO/ri/3vgWCwtwvzMx2ap6qM19XON2wpfRI4UkTuxhEWd0j1RQqOjeR2W/+Kc9Fi2AWv/HJZugreAC1T1ZFVdW7Ey7JrKsBr4uKqek0cZvPv1l2s6/iRwtqqe4S2WRfjl5L8b9vu2XawjfxCAqr5VN7+q4LLCF5ExWKfLvcBrWJPBPLC243Tarlg+7bXAT0Xk/dhkBe80zlPVfxes3kU/lWGVquYylNu7Xz+6vp3Oe05V/15Hv3bspP/WdN42zSm9sXe/SqF97OUtYgFOwF6zBPsrPQ0b4DMu7bsCu7iHYe3GV2CPx9dTUKa9qpfBu1+VXL37Vd3fu1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X6rLYb4QcGBpKamoras7p1bG1tCQwMbNFrVMBXFDOSZVx05W1Vqh2ok8P3dHbgoOxJaMauNuuDlZUVYWFhbXZ9pWEqpaMoZiTbuOjK3cJYnrfOCN/D0ZpthnA88uO0WTxKt6ICvqKYkexibYTvKqoDfu0RvpWFjkTHaHQY4Mz29u6e0sZUwFcUM1I9wnemWDtQJ6UDYB0ynHKskIkb27NrSjtQAV9RzEhWUQVWFgJbvbbJRt2UDkBEmC979H2oTFABv7tRAV9RzEhGQRneTraIsjywsAEru0vOiQ52ZZshHOusOCjO7oBeKm1FBXxFMSPpeaX4u9oayypcOroH6OvjxD6LCO1B0uZ27J3S1lTAVxQzkp5fir+rXb1lFapZWuiwCIimBDtI3NTOPVTakgr4imImDAbJufwy/Fzs6q2UWVNUqCc7DX0xnFZ5/O5EBXxFMRNZReVU6mWNlE79I3yA6GA3turD0eUkQEF6O/ZSaUsq4CuKmUjP18op+LsYUzqNjPAHB7ux3aBtbk6iyuN3FyrgK4qZOJunlVPwqx7hN5DDB3B3sKbUvT/FOieVx+9GVMBXFDORZgz4AS5Np3QABoW4s8MwQC3A6kZMEvCFEFOEEMeFEAlCiCfref4KIUS+EOKA8c+zpmhXUZTmO5tfhp2VBS4W5SD1jaZ0QMvjb6jsj8hPgdyk9umk0qZaXS1TCGEBvAdMAlKB3UKIn6SUR+ucullKeW1r21MU5fKczS/Fz9UWUV0UrZGUDmgB/5MLefxN4Bbath1U2pwpRvjDgAQp5WkpZQWwHJhugusqimJCaXllBLgap2RCkyP8vr5OnLMKotDSQ+XxuwlTBPwAIKXG41TjsbpGCCEOCiF+FUKEm6BdRVFa4GxeKX7V+XtoModvoRNEBbmxVzdQC/hqh6ouzxQBX9RzrO6/jH1AiJQyCngH+LHBiwmxQAixRwixR+2IoyimUVFlILOoXFt0Vdq8ET7A4GBXVpf0haIMyDrRxr1U2popAn4qEFTjcSBQa6WGlLJASllk/P4XwEoI4VnfxaSUi6WUMVLKGC8vLxN0T1GUjIIypMSY0mleDh+qF2D11x6otE6XZ4qAvxvoLYQIE0JYA7cAP9U8QQjhK4QQxu+HGdtVZfgUpZ2k15qD35IRvhsp0psCGz9Q0zO7vFbP0pFSVgkhHgDWABbA/6SUcUKIe4zPLwJmAvcKIaqAUuAWqbasV5R2c9a4ytbPxQ5S8wEBNk0HfHcHa8I8HTlEFKMTN4PBADq1fKerMskm5sY0zS91ji2q8f27wLumaEtRlJarXnTl72qr5fBtnJsduAcHu7L6WB9Gy9WQcRj8otqyq0obUm/VimIGzuaX4mpvhb21pZbSsWt6dF8tOtiN30r7aA9UHr9LUwFfUczA2TxjWWRoVlmFmqKD3cjAnULHMBXwuzgV8BXFDKTllRLgaqs9aKJSZl19fZ1wsLbgqM0gOLMN9JVt1EulramAryhm4Gx+nRF+M6ZkVtMWYLnye3lfqCiC9P1t1EulramAryjdXHF5FfmlldqUTGhyt6v6RAe78X1OmPZATc/sslTAV5Ru7my+sSyy6+Xl8AGiQ1zJMjhR7NZf5fG7MBXwFaWbS8+rMQe/qgIqS1oc8AcHuQFw0j4akndCZZnJ+6m0PRXwFaWbO5FRCGAsnGZcZduCHD6Am4M1PTwd2FTVH/TlkLrb1N1U2oEK+IrSjaXllfLW7yeJDnYl0K1GHZ0W5vBBK7PwbWYwUliotE4XpQK+onRTeoPk0a8OYDBI3rx5MEKIGpUyWzbCBy2Pn1xiSYV3lAr4XZQK+IrSTf130yl2Jebw/PSBBHvYawdbUCmzruhgLY+f5DwE0vZAeZGpuqq0ExXwFaUbOpSax+trT3BNhB83RtfYj6gFlTLr6uOjLcDaU9UTDFVw/piJequ0FxXwFaWbKamo4pHlB/BysuHlGwZirEyuKbv8lE71Aqw/cjy0A5kq4Hc1KuArSjfz4qpjJGYX89pNUbjaW9d+sgW7XdUnOtiNjZn2SEs7OB/fyp4q7U0FfEXpRtbGnWPZrmQWjO3ByJ71bCpXlgcWNmBle1nXjw5xpcogKHbuqUb4XZAK+IrSTZwvKOOJ7w4xMMCZv07qW/9JhefAyeey26hegJVqFaJG+F2QCviK0g0YDJK/fnOQ0ko9b948GGvLBv5r56WAS/Blt1O9AOtIhR8Upl9MESldggr4itINfLotic0ns3j6mgH08nZs+MS8ZHC9/IAP2gKszfnGdFGmGuV3JSrgK0oXF3+ugIWr47myvzdzhjcSzKsqoPAsuAa1qr3Bwa7sLfXVHqipmV2KCviK0oVJKXn0q4M421qx8MbI2lMw6ypIAyS4tC7g9/FxIk16orewUyP8LkYFfEXpwlJzSzl2toAHJ/TC09Gm8ZPzU7SvrRzh9/J2RKIjxz5MjfC7GBXwFaULO3q2AIDIwGbMq89L1r62coTv7mCNm70VZyxD1Qi/i1EBX1G6sKPpBegE9PN1bvrkvBRAgEtgq9vt6eXIsSp/KMqAkpxWX09pHyrgK0oXFpdeQA8vR+ysLZo+OT8FnHzBsonUTzP08nZkV7G39kCldboMFfAVpQs7draAAX7NGN2DltJpZTqnWk8vR/aWGGfqqBW3XYYK+IrSReUWV5CWV0q4fzMDfn5Kq2/YVuvp7UA6HuitHNWK2y5EBXxF6aKOGW/YDmhOwDfoIT/VZCP8Xl5OgCDPoYe6cduFqICvKF1U9QydZqV0Cs9pNexbucq2WoCbHdaWOlIsgyEjDgwGk1xXaVsq4CtKFxWXXoCvsy0eTc2/hxpz8E0T8C10gh6eDuwkAkpzIGmzSa6rtC2TBHwhxBQhxHEhRIIQ4sl6nhdCiLeNzx8SQkSbol1FMWdH0wual84B45RMTJbSAejp7ci3JYO02vr7PjfZdZW20+qAL4SwAN4DpgIDgNlCiAF1TpsK9Db+WQB80Np2FcWclVXqScgsasENW+OiKxPdtAVtps6pXD1VA2+CYyuhNNdk11bahilG+MOABCnlaSllBbAcmF7nnOnA51KzA3AVQviZoG1FMUsnMgrRG2TLpmTauYO1g8n60MvbEYOElNAbQV8Oh74x2bWVtmGKgB8ApNR4nGo81tJzFEVpprj0FszQAS2lY6L8fbWeXtqbx1FDKPgN0tI6Upq0DcW0TBHw6yvPV/dvvTnnaCcKsUAIsUcIsSczM7PVnVOU7uhoegFONpYEudk37wUmnINfrYenI0JAwvkiiJ4HGYfh7AGTtqGYlikCfipQ819SIJB+GecAIKVcLKWMkVLGeHl5maB7aKOOlF1QVW6a6ylKBzt6toD+fs7odI2UQ64mZat3uqqPnbUFAa52nMosgoEzwdJW3bzt5EwR8HcDvYUQYUIIa+AW4Kc65/wE3GacrRML5EspzzZ5ZVMEaH0VrHoUPp4EX94IZQWtv6aidKBKvYG49HzCA5qZzinJhqpSk4/wwXjjNrMI7Fyh37UQ96O2yEvplFod8KWUVcADwBrgGPC1lDJOCHGPEOIe42m/AKeBBOBD4L5mXbwku3WdqyiBr+fB3k84HzgZmbwdPr0Gis637rqK0oGOnS2grNJAdLBb816Qd0b7asIpmdV6eWsBv6LKAH2maHPyVVqn07I0xUWklL+gBfWaxxbV+F4C97f4uiU52mhB14xKgHUZ9LBkJvLMNr50u59nEkYx2z2Wl7P+je7DiRA+HXwiwHcgePYBC6uWt9Fcxdmg04FdM/+DKkoj9p3Rpj9GhzTz31PmCe2riW/aAgwNdePjLYnsT85leM/xgICEPyBgiMnbUlrPJAG/rQhDJZzeAL0mtvzFx1bCma28an0fi8+P5t4revDVbmvOVD3NBxZf4bxzMUJvTBlZWINX34tvAD4DwX8w2DbzI3NNBoNWWyRlp3bfIGUn5JwCJz9YsBGcfFp+TUWpYV9yHj7ONvi72DZ8UlU5xK+CvZ9B4kZtSqZ7mMn7MqKnJzoBWxKyGN6jL/gPgoR1MO5vJm9Lab1OHfD1WCAPLEW0NOBLSeWm10nDj+8MV7B8wTCGhLgxNzaEe76wIyotjBBXa6b3KOEKlwz6izPY5RyDU7/DwaXaNawdIfp2iL238dynlHBmKyRt1YJ76h4oz9ees/eEoOEQeRNsfQu+uR1uX9m2nyaUbm9fci7RwW7171+beQL2fQYHl2kpUZdgGP93GDwXbJxM3hcXOysiA13ZfDKLv17VF3pOhC1vQGmeltdXOpVOHfDzpAPy2EpES//xnN6AVcZB/ls1ny/vG0Uvb0cAAlzt+OaeEXyzN5XNJzL55KSBt8sCgAD6+01hdH8PxgUKYmxSsT36DexcBLv+C+OegHGP19/WjvdhzVOAAJ9wGDhDC/JBw8C9B1T/p/ToBd/dBWv+Dlf/u1W/F8V8nS8sIzW3lNtHhF48WFkKR1fA3k8heTvoLKHv1TDkdugx/vJSoi0wtrcn765PIL+kEpdeV8LmV7VPFQPqrr9UOlqnDvi5OKHT50PcDxDzp2a/rnT9qxRIV6wGz74Q7KvZWlkwLzaEebEhVOkNHEkvYGtCFltOZvHZtjN8qDdgZSGYHH4Pr9zzfzhteh7WvwwePWHgjbUbStkNvz1LZe+p5E9+B2lTJwVUVHHhW9cBM7BK2wc73tPSRYNmt/j3oSj7zuQBEB1iHAAZ9PDfcZB1XBtgXPk8DLoVHL3brU+je3vx9h8JbD+dxZT+MWDjDAm/q4DfCXXqgG9p48Bp4UzYgaWI5gb8tL3YpW7hHTmXB64Kb/z6FjoGBbkyKMiV+8f3orRCz54zOayPz+Tz7UkcTS/gv7e+Ru+CdFjxAHj21XL8ACU5VH51G/k6LyYcnkHB4V2NthXm6cDyu/4Pn3OHYOVDWj41OLZ5P5OiGO1PzsXKQhDub9y0vCBdC/ZXPKV9Cq0vzdPGBge74mBtweaTWUwZ6Ac9xmkBX8oO6Y/SsE4d8F3srVhWMYq/py6FlY/AqIebvPGUv2YhSHvsY+/E26mRm1r1sLO2YExvL8b09mJyuA/3L93H9Yt288pVC5mWdyti+a0w+Z/k5WaStfVzgorOc698gdnjIghsZMVjeaWeN347wdxP9vH1bR/htvRqWH4r3L1OG5UpSjPtT84j3N8FWytjmiY3UfsaNKzDgquVhY7YHh5sScjSDvScqE2ayDqhTYZQOo1OHfCd7az4islM9iwh5sASbRVfxEwY/Sh497/kfMPez3FJXsu7utncPiGyVW0P7+HBqgfH8MDSfTy06izrPB7ljeK/Y/HVHFwBJyn4NeRvfHDT3Xg2ox55uL8Ld3yyi9uWJbBs5lIcv5gCS26CO9eAg0er+qqYh0q9gUNpedw6LOTiwdwk7WsbzMBpidG9Pfk9/jwpOSUEVU+ySFinAn4n06k3QLEQgti+gdyXP4+CBXu1GTPHVsH7sbB8DqTuvXjyucMYfn6MLfpwfK5+Eifb1s+E8XWx5es/j+C9W6M5IvoypuQ1ple8xD97L+fcfSe49s6/NyvYA4zo6cEHc6M5draAa5ecZUvMW8jcJHhzIPxwDyRtUYWnlEZdWHAVUmMCQ06idpPWObDjOgaM6e0JwOaTWdp8f88+cGp9h/ZJuVSnHuEDzIoJYu3RDAa9eZiIgKlMjLyO6ytWEXTyc0T8KggbB7H3UbrqcfL19qzp9zIvxIQ0feFm0ukE10T6MTnch/XHMwn1sKe3z+VNb5vQz4fP7hzGCyuPMvc3S671eo07bX4n/PAKbA4uozDiDpxmvKnynkq9Liy4qrnCNjdJW0Fr0bH/lXt6OeLnYsvmk5ncOjwYQkbCkR+0dSm6Tj2uNCud/m9i0gAfvr9vJA9O6I21pY63t2czdncsQ4reYKnLfIrTjsCym7EqTGGhwxM8OXNM/fOTW8nSQsekAT6XHeyrjerlyS8Pj+GNm6M4Rgjzzt/KyKpFfKS/GqfDn1K06v/USF+p177kPHydbfF3tbt4MDexw9M5AEIIRvT0YFdiDlJKCIrV1qNkHuvorik1dPoRPmgjmuhgNx6d1Iei8ip2JWazNSGbzxM8eb5gFNdbbKVE58yDt8/Fwabz/2xfJmMAACAASURBVEgWOsENgwO5YfDFj+GHU8ax7KMFzN77AcU2Tjhc9fcO7KHSGSWcL6KfX50BR04iDBzcMR2qY3CQK9/vSyMtr5TA4OHaweQd2voUpVPo/NGxDkcbSyb082FCP61EwfnCMrafGkaAqx19Wjn67kgRQa6U3fE+P3zyJ27Y9m8yUg7jeP2rOHh0bG5W6RyklKTklBATWiOdU5oLZXng1vEjfICoIO3ewsGUfAIjwsDBW1t9PvSuDu6ZUq3Tp3Sa4u1ky/RBAcSEund0V1ptaJgnHrcu5h3DTbgmr8Pwdgwfvv4Ub649xu6kHCr1ho7uotJB8ksrKSyvIti9xvTfTjJDp1o/X2esLXQcSs3T7kMFD9dG+Eqn0eVG+N3d2H5+DHtmEUcOL8Bj41PMz3+PA1tW89T6uzhj1ZNhYe6M6uXJqF6e9PN1apP7FUrnk5JTClB7vUd1wHcLbff+1MfaUkd/f2cOpGirgQmK1ebjF54DJ9+O7ZwCqIDfKdlaWTAkeigMXguHvyVy9f+xqvQZtnvO4uWs63npuLb1o6ejNSN6ejIvNoRhYV3/E47SsOScEoDaI/wc46KrThLwAQYFuvDN3lT0BolF9Ury5B0Qfn3HdkwBukFKp1sTAiJnoXtwN7ro2xiVuZxfLB5j36wK/jMzktG9PNmWkMWfPtml7TrUVpJ3wAejtOJcSodIydUCfpB7zRk6SVpF1jaognm5ooJcKanQa/vc+kZq2x6m7OzobilGKuB3BXZuMO1NuHMt2DrjvvIOZp18gjeneLDqodFYW+q4f8k+SitMvLWcwQCbX4dProbzx+DnxyBtn2nbUJolJacEV3ur2gsKO8mUzJou3rjNA0trbSMUlcfvNFTA70qCh8OfN2kVEU+vh3eH4Xfofd6cOYDjGYU899MR07b3/d3w+/Mw4Dp4aB84+sC3f4KyfNO2ozQpOaekdjoHtBF+J5mhUy3MwwEnW0sOpFbn8YfBuUPadqNKh1MBv6uxsILRj8D9u6D3lfD7C4z7ZQJfh/7Cgb3bWXEgzTTtnD8GR77TCtbN/ETLE8/8GPJSYOXDanFYO0vNLSWo5g3bqgrIT+1U+XvQVqZHBrpoM3VAu3FrqIK0vY2/UGkX6qZtV+UaBDd/Caf+gF0fEXNyOWttvuTDVQeoingfS4tWvpfv+xyps+JY6B3o0wqw0An6BQ5HN+FpbdSPgCkL1ZaN7UBvkKTmlnBVeI3fdX4KSEOnS+kARAW6snjTacoq9dgGDdMOHv0RQkfXWzakrFLPvjO5bEnIYuupbDLyy4gJdWN0L08m9PducdVbpWEq4Hd1PSdAzwmIokwyly5gTtr3rN19P1fHRlz+NSvLMBxYxu8yhvkfX1waPyM6gNdmPozQV2q7Gp36XUsvRd+u6qW0oYyCMir1ss4c/M43Q6daVJArVQZJXHoBQ0Lcte0Vd38EOiuY/E/0CI6mF2gBPiGL3Uk5lFcZsNAJBge5MjTMnZ2ns1l16Cy9vB1Z95dxHf0jdRsq4HcXjl54XL8Q3h9O4fo3MAz7GJ3uMufox69CV5bLl5VX8NqsKFzsrNiSkMWn25IYHubOzVc8AeE3wKpHYdUj2v6p174JPgMavqa+CtY+rdVWcQ4El0CImAWevS6vj2YkxTgls1ZK58KUzM43wh9U48btkBA35LS3KdTb4LzzA3bHHeeeorvJLtPO7ePjyK3DgxnV05PhPdwv3JSWUvLvNcf5YMMpSiqqsLdWocoU1G+xG9F59yUlYCrXpq5i88F4xg2+dM+A5sjf8iH5Bi/CR1/HjUO00g7j+3mTcL6IZ1fEERnoSn+/PnDHKjiwFNb+Hf47Rsv3j/0bWNnVvqCU8MtjsPcTbape5nFtMc7Wt2DySxBzl6oQ2oh65+DnJmlTHjvhgiYfZ1t8nW1ZuDqet34/icEgKSyfwL0WxTxRtJxlDgWcuPoDhvULxtu5/nSNEOLCG8fJjKILs3+U1lGfw7sZv2nPYCcqyPztDa1qYQuVZ5zEJWMHa2yu4qErL25eYaETvHnLIFzsrLhvyT7ySyq1ID14DjywByJugs2vaXsVnPqj9kU3v6oF+9GPwj2b4a/x2p+QkfDzX2HpTdrmNvG/QMouyD4FpXnqxrBRSm4pQlCnSmaSls7ppG+Uz08P59ZhwdwwOIAbhwTy4vURzHrkNeR179KnZD/X7p+Pt66w0Wv0NdbGOp7R+HlK86kRfjdj6TuAJN9JTD77E3vin2Bo/54tev3Bn94hWuqIuvb+i9voGXk62vDO7MHM+WgnU9/axOs3DyK2hwc4eMINH2gbs698BL64AULHaBtpSwlx30PkLTDxuYsXc/KFud/Brg/ht2fh5NpLO6OzAnvj9X3CYdKLZnmTODWnBD9nW6wta4zPOuGUzJomh/syObyeTx9e88DBC765A/53Fcz9vsEbz0Hu9tha6ThxTgV8U1Ej/G7Ib9qzOIlSMv94v0Wv01dVEZq2kjj7YQyLqr+k7fAeHnx770isLXXM/nAHC3+Np6LKWNQtbCzcuw2u+D9trn76ATizDcJnwHXvUFKpv3guaKPT4QvgiSR45DDMXw9zvoXrF8FVL8GI+7Wppy5BcHSF9unh2MrL/K10Xck5JQTVTOdICblnwM10G/20q75T4PafoCQHPr4Kzh6q9zQLnaC3t5Ma4ZuQGuF3QzYBEZx0GELU+RWUlb+CrY11s16XsHs1fcnhzMBZjZ43KMiVnx8aw0s/H2XRxlNsOpHJ27MH0cvbCaxs4YontT815BRXMO2dTVQZDDw8sQ+zYgKxqp46amWrbYvnGtxwo5nH4fv58NVc6DMV+l0DvSaCkx/oK6CyBGxdO22KozVScksY09vr4oHSXKgoBNcuGvBBW5B15xr4coa2knv2Um3AUEcfHye2JGR2QAe7JzXC76aqBt9GgMjkyKYfmv2a0r3LKZJ29B17U5PnOthY8sqMSBbPG8K5gjKueXsLn29Pqve+gcEgeeSrA2QWlePrYsdTPxxm0usb2ZqQ1fwfyKsv3LUOxj0BZw/ATw/A6/3hRU94yRv+FQq/PdP863URZZV6MgrKa8/QyTujfW3sDbIr8O4Hd/0GLgHwxQzY8gYYapcH6evrSEZBOXklFR3Uye5FBfxuqvfYW8jGBcsDnzfrfFlZSq+s39nvOBpnJ+dmt3NVuC+rHxlDbA8Pnl0Rx58+3c35wrJa53xg/BTw3LQB/HjfSD66LQadTjDv450s2niq+TeXLa1h/FPwl2Na6mjSizDyIZjwDPSaBDs+gKyEZve9K0jN1coiB3vUvGFrDPhdNaVTk0sA3LlaS/Os+wd8MlW7aW9UvanRiYw2LA5oRlqV0hFCuANfAaFAEnCTlDK3nvOSgEJAD1RJKWNa067SNEtrW+K8pzEyYyklWSnYewY1en767hUEUELFgJktbsvbyZZP/zSUz7ef4Z+/HGPKm5t5aEIvnO2syCmu4LW1x5k+yJ9bhwUjhODKAT6M6OnB498eYuGv8RxMyWPSgMZvxga52zO0epMbIbSbuDW3zivMgHeiYd1zcMuSFv8MndWFKpm1RvjJ2teuPsKvZucGN30Bh7/Rpu8uGgPXvwfhN9DX9+JMHVUCvPVam8N/EvhdSrlQCPGk8fETDZw7XkrZgs/wSmu5jLoLyx++5OTvi+l/84uNnlu6dznnpSsRo6ddVltCCG4fGcrInh48vPwA/1h59MJzfXwc+ecNEbU2a3GwseTdWwcTucmFf62O59cj55psY/6YMJ6Y0q/+shFOPtq0zz9ehMTNEDbmsn6Ozialvjn4ecna/Qpblw7qVRsQAiJvgpBR2gyeb+6A1D34XvkPnGwtu+1MndTcEn7cn8a9V/TC4nIXSrZAawP+dOAK4/efARtoOOAr7SwiYjC7foyk54nloH8OLBr46y7NJSR7M6vtr2Wai0Or2uzt48TKB0eTlluKREvV+LrYYmNpccm5Qgj+PK4nM4cEUlRe1eA1pYT/bU3kw82JxKUX8M7swXg42lx64oj7Yc8nsOYpWLCxW5R7SMkpwcZSh5dTjZ8370z3Gd3X5RIAd/wMa/4Ptr+LyEumr8+93XamzrMr4vgj/jzRwW6M7OXZ5u21NuD7SCnPAkgpzwohvBs4TwJrhRAS+K+UcnEr21WaQacTpITdxLDTTyNf9MRg7Qy2LujsXRDVI0RbV0rzM7Gj6rLSOfWx0AmCPeybPtHIw9Gm/gBewwvTBxIR4MLffzzCpDc28cD4XsyJDa79RmJlB1f+QyvrvHGhNj20i8/aOVdQjq+Lbe2tLPOSwaMbl6SwtIZrXtM2dtnyBsP638bS4xIpZbfa0nPH6Wz+iD8PwMpD6Z0j4Ash1gH1rd/+ewvaGSWlTDe+IfwmhIiXUm5qoL0FwAKA4OBuOoppR33Hz+H/jifiK3JwrirBubQYl9wS3C2ycNMl4yyKsTcUs9vQh+jY8R3d3UbNigki3N+Fl34+ygurjvLxlkTmjQhhdC9PBvg5a7WDImZqK303/gsqirX5/F04SGQVluNZ881QSi3g97qy4zrVXobcAVveYGLVZt4vGU5mYXmDpRi6GiklC3+Nx9fZlkFBrvx65BzPXzew9uK6NtBkwJdSNvgvSwiRIYTwM47u/YDzDVwj3fj1vBDiB2AYUG/AN47+FwPExMSotfWtNDDInfv++hKpuaVkF5eTVVhOUnEFWUXlZBZWaMeKyunr48xHXo4d3d0mDfB3Zun8WLaczOK1346z8Nd4ANwdrHn+unCmRfnD9Pe00eH2d6EsD6a9DbpLU0pdQVZROT28aqTZirO0NQfdNaVTk1soBA6lX9YaYDjHMwq7TcBffeQcB1Ly+PeNkXg4WrM67hxbE7IY36+hJIlptDal8xNwO7DQ+HVF3ROEEA6ATkpZaPz+KuCFVrartECQu33tlZrdwOjenozu7UlGQRnbTmXx8ZZEnvr+MMPC3PFxtoWp/wI7V22kX1YAN34Elo2njTqjrKLy2rNTLszB7wZTMptj4EwcVj9BL5HK8XP9ay9A66Iq9Qb+s+Y4fXwcuXFIIHqDxNnWkpUH09s84Lf288NCYJIQ4iQwyfgYIYS/EOIX4zk+wBYhxEFgF/CzlHJ1K9tVFECrzHjD4EDemR1Nud7A8yvjtCeE0ObsT34Fjv0Ey27RUjxdSKXeQG5JZe2UTndZdNVc4TeA0HGL7S6Od5OZOq+uPc7prGIen9wPC53A2lLH1IF+rD2aQVmlifelrqNVAV9KmS2lnCil7G38mmM8ni6lvNr4/WkpZZTxT7iU8mVTdFxRagrzdOChCb345fA51h3NuPjEiPu0FM/pDVpRt9JLlol0WjnF2upSz1ozdLrZHPymOPlA2Fim6bYRf7ago3vTaisOpPHfjaeZMzyYK2usPZkW5U9ReRUbjtebFTeZrj9vTVGMFoztSR8fR55dcaT2NM/Bc2HWp5C2Dz69Fora9j+VqWQWlgPg5VijFlLuGa2CqE3nv99iMgNn4qNPx+b8ASr1hqbP76SOpOXzxHeHGBrqxnPTahcnjO3hjqejNSsPnm3TPqiAr3Qb1pY6XpkRwdmCMu78ZHft+isDpsOtX0HOafjf5Isj5U4sq0gL+LVTOsnmM7qv1n8aep0VV7OF05ldKy1XLauonAWf78Hd3pr35wy5ZDaOpYWOayP9+e1oBqcz266MhAr4SrcyJMSdt24ZzIGUPG78YNuFlaqAVl1z3o9Qkq1VaCzL77iONkNWkTGlUzeHby43bKvZuVLcYyozLTZyIqnzv1HXVVFl4L4v95FdXMF/58XUXkRXw31X9MTGSsfffzhyWZsXNYcK+Eq3c12UP5/fNYzMwnJmfLCNI2k1AnvwcK3mfn4q/N54uYmOdmGEXx0gDAbISzG/ET5gP+FxnEUpLgc/6uiutNgLq+LYlZTDv2dGEhHYcDkMb2dbnpzaj+2ns/l2b2qb9EUFfKVbijVu1GKlE9z03+21b4YFDYNhC2D3R5C6p/4L6Cs7fIvFrMJybK10OFgb1xAUZYC+3CwDvqV/BNusRzLk3Ffa9pddxNKdyXy5I5k/j+3B9EEBTZ4/e2gwMSFuvPzLMbKNb/impAK+0m318XHih/tHEeLhwF2f7eHr3SkXn5zwtLZ5ysqHteBerbwINv4b/hWm7bfbgbKKtFW2F8oJVN93cAvtsD51pD0h83GQxcgdH3R0V5plT1IOz/10hLF9vHh8Sr9mvUanE/xzRgTF5VU8uyIOg8G0gw4V8JVuzcfZlq//HMvInh48/t0h3vjthJYftXWGq/8NGUfgpwdhw7/g1yfhrShY/7I2HXDPx9p0zg6SVVRx6Q1bMMsRPoBbzyGs1Q/RAn4nv/9yNr+Ue77cR4CrHe/cMrhFlTD7+Djxl0l9+fnwWV5YddSk+XwV8JVuz8nWiv/dMZSZQwJ56/eTPP7tIW16X/9pEHETHFwGG/4J+z7TauzftQ7u2QLuPeGnhzpswVb1CP+CvCTtq5kG/AF+zrxVNQNdeT58fRucXHfJDlkNKi+Edc/DmpaUALs8ZZV6/vzFXkorqvjwthhc7K3qP1FKOPQ1/HgfVNVO39wzrgd3jQ7j021JvPHbCZP1Te1pq5gFKwsd/5kZib+rHW//fpLs4go+vC0GixmL4er/gLXjpeWjp7+r7cD0x0sw5ZV273NWUTmDg90uHshLBgdvrSqoGerv58RRwtgU+hBjzy2BJTdqG9wPnguD5oBrPZv8VAfV356FIuOeC4Pngnf/Nuvn8yvjOJSaz4e3xdDbuGPXJYrOw6pHIX6V9rjv1dD/2gtPCyF4+pr+FJVV8fYfCVToJY9O6l1vmfGWUCN8xWwIIfjLpD48N20Af8SfZ8nOM1oJBjvX+vcKCBkJQ+/Wtk5M2d2ufdUbJDnFFbUXXZnjHPwa7K0tCfN04EvddPhLPMz6DDz7wIaF8GYEfHkjHP0JqozrL5J3wEcT4YcF4OwPt34NOivY90Wb9VFKycqDZ5k5JLDhXdzObIP3hsPJ32DSC2DnDnHfX3KaEFo+/5ahQSzaeIqpb25u2T7Q9VABXzE7d4wMZUxvT/69+jjn8ssaP/nKf4CTr7YhRzvO2skprsAg65ZVSKl/FGtGBvg5E5deoNXMD78e5n0PDx+EcY/D+WPw9Tx4YwB8dp22wK4gHaa/D3f/Dn0mQ7+rtRRelelnwACk55dRVF7FoCDX+k849Ye2Ybu9h5Y2HPWwtijw+GqoKLnkdAudYOGNkXxx1zAMUjLno508vHz/JftGN5cK+IrZEULw0vUDqdQb+MdPcY2fbOOkFWFL3Q1HLykG22YuWWUrpbZ2wCWw3frQGYX7u5CWV0p+SY2ZVW4h2t/RI4e1NRZBwyHrBFzxFDy4FwbPubj7WfRtUJpzMZViYtVbMVbvxVtL/C+w9Gbw6Al/+hW8+mjHB86AymI4uabB647p7cXqR8by8MTe/Hr4HBNf28gXO86gb+EsHhXwFbMU4uHAw1f2ZnXcOb7anUz8uQLizxXUDiTVBs0B7wGw7h8X0wVt7JKAX5ypzcF3Md+UDmj7IQDEna1nlo7OAnpP0jax/2s8XPEEWNfZsrPHBO13uO/zNulf9VaMfbzrBPwj38FXc8E3Am5fCY41yjyHjAJHHzhyaVqnJlsrCx6d1IfVj4whMtCFZ348cunCwiaogK+YrfljetDP14knvjvMlDc3M+XNzQx6cS3T3tnCwl/jySgwfmzWWWi51txE2PO/dunbxYBvzOHnG9cQmPkIf4CfFvAPp17mtEydTrtpe3oD5CaZrF/VTpwrxNfZtvbMnP1fwnd3a5885v0I9u61X6Sz0NI6J9dqs4ma0MPLkS/vGs5btwwiLbeE697dwj9+iiMuPb/Jeftqlo5itqwsdCxfEMv2U9mAtvHyyYwitiZk8dHm0xxJy+fLu4drJ/e6EsLGaRuq9JkM7mFt2reswjqlkfOMAd/Mc/heTjb08XHkP2uOk19ayYMTemNn3cKZK4Pnanse7/sCJj5j0v4dzyikT810zq4P4ZfHoOcEuHkJWDewEVH4DNi1WMvlR85qsh0hBNMHBXBFX29eXXOcz7Yn8em2JDwcrBt9nRrhK2bN1d6aqRF+TI3w4+oIPx6+sjdf3zOCxyb3ZUtCFodSjcv4hdD2x60shXeGwPcL4Hx8m/Urq6gca0sdTjbGMVm+sbaKmY/wAZbNj+X6wQG8v+EUk97YyPr4Fpa7dgmA3lfB3k+1v08T0RskJ88X0dfHWLp6y5tasO97Dcxe3nCwB2307+Rf72ydxrjYWfHi9QPZ/uREXpsVxbg+je8IpgK+otRjzvBgnG0teX/9qYsH/SLhoX0Qey8cWwnvD4flc7Q6+yaWWVSOV82yCvmp2loB2wZmf5gRD0cbXp0VxfIFsdhaWfCnT3dz75d7OZvfguA98kEoydLSLSZyJruYiioDfbwd4Y+XYd1zMPBGuOmzprfX1OkgYiacWA2Jm1vctq+LLTcOCeT1mwc13kyLr6woZsDJ1orbRoSy5ug5Es7XqE/u7A+TX4ZH42DcE5C0GT4cD1/N06pZmkhmYfnF/D1oOXyXIO2ThgJoBfJ+eWgMf5vclz/iz3Plaxv5eEsiVXU2SanUG9h7Joe31p3kix3GLSJDRkFADGx7B/RV9Vy95U4Yb9iOTVsMm/6tpY5mfAgWDay0rWvc49rq7m/vhMJzJulTXSrgK0oD/jQqFBtLHYs2nrr0SXt341TAI9po8dhPkLTJZG1fUkcnP0Wlc+phbanj/vG9+O3RcQwNc+fFVUe57t2trDuawcdbErnr090MfuE3bvxgO2+sO8E/forTbogLAaMf0fYXOPqjSfpy/FwR7qIA70OLIGIWTHtHuyHbXDZOcPMXUFGkBX0TvRHVpAK+ojTAw9GGW4YG8+P+NNLyGkgX2DrD+Ke1VIsJV3BeWkdHLbpqTLCHPZ/cMZQP5kSTXVzO3Z/v4cVVRzmVWcR1g/z5YE40yxfEojdIfj1iHD33vQY8esPWN02yqO5ERiF3OO1BGCph1CMX5/63hHd/mPYWnNkK619qdZ/qUrN0FKUR88f2YMnOM7z7RwKvzIio/yQrW4i8CfZ+pm2SbudW/3nNZDCWVfB0MqZ0Koq1xUJqhN8oIQRTI/wY08eL7aeyGeDvTIDrxbpDUkp6ezuy8mA682JDtIA86iGtWuqm/4CDF1SVaTdy6361sNbObaQ09fGMQh5nA/hFge/Ay/9BIm+CxE2w9W1tDYhn78u/Vh0q4CtKIwJc7ZgzPIQvdpzh7jFh9PRqYPPwwXO1aXWHv4Vh81vVZm5JBXqDvDjCz0/TvrqoEX5zONpY1lvHRgjBtCh/3lh3grP5pfi52EHkzdr+B+tfvvRCFjbam7mlnVaO+dBXMPmf2mrdOvdSyqv02GQfJcQqAQb9p/U/xMTnIO5H+O05mL209dczUikdRWnCAxN6YWup4z+rjzd8kl+UtorSBLM+LtnLNt9YB18F/Fa7NtIPKeHnQ2e1A5Y2cM9meHAfPHoUHk+Ep87Cs7nwzHl4MhkeOw4P7IaAaFj5kDYzq05+/XRmMTPERvQ6K222TWs5emn3GI7/rBVbMxEV8BWlCZ6ONiwY25PVcefYeya34RMHz4OzB+Dc4Va1d0lZBTUH32R6eDkyMMCZldUBH7QUnEdPbX6+vbs2X75u/t01COatgInPakH44LJaT588m831FlsoCb3q0pW0lyv2Pm1u/tqnm3+PofrTYANUwFeUZrh7TBiejjYs/PUYeSUVl/wpr9JrMzMsrGH/kla1VR3wvapz+PmpICy0LRmVVpsW6c/BlDySsy+tTtkonQ5G/0WbzrnhFai8WLGyKn4tHqIQ26HzTNdRa3ttJXDaXvjjRUhYB5nHtW046xP3A3wwstFLqhy+ojSDg40lD1/Zm2d+PMKgF3675HlPR2u2PDEB2/7XaTtnDZ6jpXguQ2quNiPIy9FWO5CXos3/r69mv9Ji10T68cqv8aw8lM7943u17MVCwJXPwWfTtC0wR9wPZflEJ31EtnDDo88k03Y28mZt9tfm17Q/1WxdtBSfc4D2yaQkR5teGjAESG7wcupfkKI0063DgnGxsyK7qHYt9dTcUj7eksj209mMn/yylnNdNhvmr69dFbEZpJSsOJDGoCDXiwW4VFlkkwp0sycmxI3v9qVy3xU9L65mbq6wsdBjPGx6FfpPQ359GwEVp/ky8Hn+ZOo3ZZ0F3LEKCtK0dE1+KhSkal/z07TvU3dpM7nGPq4t3lrQcD0dFfAVpZksdILrovwvOV5WqWfpzmTWx59nfN+BWnneT6Zq+67etkLbrKOZ9qfkcSKjiIU1p4DmJ0NQrCl+BMXo1uHB/OXrg2xNyGZ0b8+WX2Dis9oK6/diQV/JnyseYWLENNN3FLSg7xrc+G5nBkOz5v2rHL6itJKtlQWjennwR/x5pJTabI7p70HyNvjx3hbV0P9qVwoO1hZMq35jMei1XZvUCN+kro7ww93Bms+3J13eBQKitTo50sDWYe/yhyGa6ODWrb9olWYu8mpVwBdCzBJCxAkhDEKImEbOmyKEOC6ESBBCPNmaNhWlM7qirzepuaWcyizWDkTM1OZSH/kWvrhey7E2oai8ipWH0pkW5Y9DdZXMogwwVKmAb2K2VhbcMjSIdccyGl5F3ZTrF8Ejh1hdNgBHG0v6NLRheSfS2hH+EWAG0GARESGEBfAeMBUYAMwWQgxoZbuK0qmM7+cNULtU75i/wIyPIHUPfDgBsuupyVPDyoPplFTouXlojfn2F+rgm/dOV21hTmwIAEuqC6q1lKU1OHqz70weUUEuWOg6f2G7VgV8KeUxKWUjq1EAGAYkSClPSykrgOXA9Na0qyidTYCrHX19nFh/vE5t9shZ2k23khz4o/HaKMt3p9DXx6n2DXCIlwAAEhpJREFUBthqp6s2E+Bqx5X9fVi+O4WySv1lXaO4vIr4cwUdm85pgfbI4QcAKTUepxqP1UsIsUAIsUcIsSczM7PNO6copjK+nze7EnMoLKuzL27QMOg7RSul3MACmmNnCziYksfNQ4NqzxpRAb9N3T4ylJziCl5YdZTv9qay8UQmcen5ZBSUUalvutz1wdQ8DJIuE/CbnKUjhFgH+Nbz1N+llCua0UZ9n3MaXDYmpVwMLAaIiYlpfQk7RWkn4/t6sWjjKbYmZDFlYJ1FUqGjtVosWSfAq+8lr/1qdwrWFjpuGFxnLJSXrFXitOn8+eGuaGRPDwYFubJ0ZzJLd146f93N3goPRxuC3Ox4cmp/+vrW/nvYn6ztiDY4uGtsTNNkwJdSXtnKNlKBmkVAAoH0Vl5TUTqdISFuONla8kf8+foDPkDSlksCflmlnh/2pzF5oC9udfckzTxe7xuEYhpCCH64byRF5VVkFVWQXVROVlE5WUUVZBWVk238uisxhxve38p/ZkZxTeTFv9v9ybn08HLA1b75U287UnvMw98N9BZChAFpwC3Are3QrqK0K0sLHWP7eLHxRCZSytqpGbcwbVVk0hYYelet162JO0d+aSWzh9YpjiYlZMRB+PXt0HvzJYTAydYKJ1srwjwd6j0no6CMe7/cy/1L93EorQd/u6ovFjrBvuQ8Jhhv2HcFrZ2WeYMQIhUYAfwshFhjPO4vhPgFQEpZBTwArAGOAV9LKeNa121F6ZxG9fQko6CcxKzi2k8IoW2r9//tnX+YFdV5gN9vd2EXFhB3A8TKL5dKgIiCoohGKJqIolaoGhNTKKHVYhpF0yRSTZuU2BrNY5o8NsbYpBJiIiopaR605Q+1KkYKbDChCimwoFCRn6KwKMju1z/OubvD5S67K3funNn53ueZZ+fOzL37nm/mfnfmzDlntiw/ph5/0cqtDK7pyfl1tUe/Z/9b8P4+6P/xmK2N9hjQp4pFN03gc+MH88PnG5j1yCpe2bqPvY2HU1N/DyfeSmeJqg5U1UpVHaCqU/zyN1V1amS7p1V1uKoOU9UCA08bRtfg/Do3UuKKhgLt7od+Ahp3wu4NLYu27G7k5YY9XH/uIMrym/XtfM397T8yLl2jE3SvKOMfpo/mvmvOZOXmvVz/8AoAzh6Sjvp7sJ62hlFUTvtINQP6VPJyw55jV7bU47/YsuiJ1VspE7j2nAKtcFoSvnVbCYlPnzuIJ+dMoLa6OzXV3Tm9f3puqNtYOoZRRESE8+tq+fWmPcfW49fUufHNfT3+kaZmnqzfxsUj+jOgT9WxH7ZzHfQaANW1x64zEuWsQX1ZdvtE3n3vg1R0uMphZ/iGUWQm1NWya/+h1mEWcojA0NZ6/Od+v4td+w9x/blt9KLd8aqd3QdMn6puDDy5Z9IancISvmEUmdzN1xVtVes07oQ9G3l81Rv0713J5I8VGEK5uck1ybSEbxQRS/iGUWSG1PbklJOq2qjHvwiAxuUP8ez6HVw3biAV5QW+hm9vgSPvwQBL+EbxsDp8wygyuXr8FzcUaI9fUwdj/5TqNT/i/op1nD3mp4U/xFroGDFgZ/iGEQMT6mrZfeAwG3fmPX9UhOYrH+Dhbjcwvfwlhiy9ARp3H/sBO9cBAv1GlMTXyAaW8A0jBo5Xj//y5r384/4rWXXu/bD9FTd08s71R2+041U4eSh0L9zz0zA+DJbwDSMGBtX04NS+PXhq7XaamvN61q7aSt+e3Rh96edh1lPwwUH48aWw6bnWjXaugwHWw9YoLpbwDSMGRIS/nFTHioa9zPvF72j2SX9x/Tb+Y+12po89lapu5TBwHNz4LJx0Kjx6Daz+VzhyCPZstPp7o+jYTVvDiImZE4ay58BhvvfMBqorK2hqVn664nUuGFbL3EtOb92w72CYvQwWz4alt8PGZ0CbrEmmUXQs4RtGjNz2ydM5cOgIP16+GYCbJtbx1SkfO7YpZlUf+OwiWHYnrPyhW2YJ3ygylvANI0ZEhK9dMZJ+vSsZWlvNZWcUepaQp7wCpt4H/YZDw/NQ+4elEzUygWgbj1wLgXHjxunq1auT1jAMw0gNIlKvquMKrbObtoZhGBnBEr5hGEZGsIRvGIaRESzhG4ZhZARL+IZhGBnBEr5hGEZGsIRvGIaREYJuhy8iu4DXk/bI4yNAgfFsU0Po/qH7RQndNXS/jhB6GUL0G6KqBR6jFnjCDxERWd1Wp4Y0ELp/6H5RQncN3a8jhF6G0P3ysSodwzCMjGAJ3zAMIyNYwu88DyctcIKE7h+6X5TQXUP36wihlyF0v6OwOnzDMIyMYGf4hmEYGcESfhuIiCTt0NWxGBcPi2W8dJX4WsKPII7bRWSgpriuS0TK/d/gDtK0xdhiGS8W39JiCd8jIjOB54CxwLshHoDtISKzRGQNMDdpl0KkKcYWy3ix+CaD3bQFRORC4EXgPFVdnbdO0vDrLiIjgIXAMmA08CVVbRCRMlVtTtYuXTG2WMaLxTc5MnuGn7uUBFDVl4D/Bkb6dfNE5CoR6RXyzhWR3rl5VV0PzAT+CXgN+KJfntgXKE0xtljGi8U3DDKZ8EVkPvB3IhIdb2IO8BMReQXoC9wCfNufjQSHiMwD1ojIvSIyyy/+varuBZYAw0Rkot+25Ps5TTG2WMaLxTcgVDUzE1AJ/A1uQLYlwKV5678AnOPn+wG/BKYk7V2gHBcDLwCnAZOB7cCZkfW9gNuAn0WWlVuMLZYW364V385OWTvD/wBYCowCVgCTReS03EpVfVBV6/38LmAvUJOEaDt0A9ao6mZVfQ74HnBPZH0jsBg4ICLfFJH7gKElcktbjC2W8WLxDYhMJXx1dYT/q6qNwOPAQOA8EamE1qZhIlIjIvcDZwKrkvI9Dj2BWhGpAlDVbwGniMh1/rUC7+NuiN0M7FLVTaUQS2GMLZbxYvENiC6b8NtqRqWqh/zfLcByYBIwwi9T/wv/OO7MZJKqbiyJcAGiN7qidZuqugQYBlwZ2fw+4EuR1/cArwKDVfXbJfYLLsYiUhOZDzGWbfkFF8tCiMjIQssDim9bfqmIb9FIuk6p2BNwNfATYEzecgHK/Hy5/9sHeAC4AZgBXOWX1yZchstxbYAXAndFlpcDlX7+M7i60aH+9WDg+0Bv/7oqIb+gYgxc5uO0ELg/srwskFgezy+oWB6nDA8Am3PxCym+7filIr5FjUXSAkXaobn+BJOB3wH1uMvDk6Pr/Xwd0Dfy+lbgbWAjMDXJMviEOQd32TgVGI+rY5ydt22d334+8CPgr4D/BB4KyC+xGEdcb8LVzV7tk8x/AZcHFMuO+gV1vEa/T/71z4DfAH+RS/JJx7eTfkHFN9Z9l7RAMXcu7mbPKbiWAQtwl2C5dWXAPFwrgcv9QTgCaADuDKgMU4HTI69vw3VMwSeJecAu4CLgJOBC3BXNVwLySyzGea6jgAo/3x94wifW3BndXQnHsiN+QR2vef45z1uAG3FXfWdE1t+Be/xfUvHtiN9bIcU37qmCFCMiXwQuEZEXgMfU1cMBbBeRKcAkEdmoqv8HfBR4Bxilqm/7928BRqu7aZMIkTK8CCxU1adFpFxEKlT1CK7zx3q/eX9cGYbnygC8JCIrVLUpIL9EYpx3PCxS1df88rG4KoQK3Jf8PeDLJBfLzvgFc7xG/J8HnlDVN0WkO65a6s9wJ1ufEZGVuKaO7+JODkod3874jQwlviUh6V+cDzsB03FVC5OBR4B/Bs6KrD8LeBSYXuC9FUn7H6cMY6KOuCuVCwq8t5y8S9fA/Eoa4+MdD7jL9sF+vhcukY4NIJYd9Uv8eG3D/2y/7u/938/ikug6IvXeCca3o36Jx7dUU5pb6YwHfqCube83cDdlWgZiUtXf4g6A0SJyse/tlxsL40gCvoUoVIZbAVT1iG8eNgioF5GBInIjtJShSf3RGqhfqWNcyPU279qgqm/4+QO4Vhc1EdekYtlRvxCO10L+X/DrrvBXgHcA/467L9EIice3o34hxLckBJ/w85tXRl434O6mo6qvA08B1SLyx5HNH8PdqHkcqI3ftjAfogxX+/UjcN5zgV8RU6eP0P1OwLVn3vGAiHwN+DiuGSDFTkSh+7VHJ/37isgEXGeqX6vqGFWdgas+Hem3TTK+JfcLneATPq79awuRHbQYOBhJPttxrRxGiaMXbkevxXXl/kre+0tJZ8sw0h/IdbgD8zTgClW9N+/9WfE7EddRACJyuYgsB4YD16rqWxn1a4/O+D8LTMQNi3BH5G3TVXVNRv2CJtiELyITRORJ3IBFo6T1QQm5G81v48a/uNlflr2Dq/+s8gfB+8BcVb1CVbenrAw9fBk2Ap9Q1ZvjKEPofsVy9evXAXNUdWZosSyFX0z+1bjvW7O4G/llAKr6ftb80kKQCV9E+uNuujwN7MFVGcwGV3fsN+uBG097O/CwiPwB7mEFH+S2U9WdJVZvoUhlWKuqsXTlDt2viK6H/XZbVPV/sujXHifof8Rv16QxDW8cul+q0E7e5S3FBHwK18wS3K/0FFwHnxF+2d24nTsWV298N+7y+EFKNNJe2ssQul+aXEP3S7t/6H5pmhIX8DtsGnAnrh4Y3FCkG4Bh/nUN8HXgXtxgTD/PrYt8Rk8rQ3r90uQaul/a/UP3S/OUaJWOiPQTkV/iBlLaCzwiIteqG4r0F7gecgD7gGdwO7pKVW9Q1U1y9CBTB0usD4RfhtD90uQaul97hO4ful9XIOk6/GHAS6o6UVUfAv6a1lH0HgNGiMgn1dW97QEGAIfAjSioYdTJhV6G0P3S5Bq6X3uE7h+6X+op+dAK4p4G/wawEjfI2Wa/vBz3fMtX/aZrgUXAd0VkGnAJbryLbpD48y+DLkPofmlyDd2vPUL3D92vq1GShC8iguvs8HOgGdiEG8xorqruEJFyVW0SN2b1SdCyAxf4O/TzcJ18blTVfaVwTlsZQvdLk2vofmn3D92vSxP3TQJaR6wbDjzq5ytw407/W942C4FP+/mPRj6je9yeaS5D6H5pcg3dL+3+oft19Sm2M3zfIWI+UC4iT+MeLtAELeOw3Aq8KSKTVPV5/7YDwGZxT5H/ExG5TFW3qerhuDzTXIbQ/dLkGrpf2v1D98sKsdy0FZFJuPq4k3G9Mb+J66wzWUTOg5Yu0fNxAx3l6uxm47pI9wEmq+q2OPw6QuhlCN0vTa6h+7VH6P6h+2WKOC4bcA88mBF5/SDuCVSzgHq/rAxXj/cEMAR3h/67+CFNk55CL0PofmlyDd0v7f6h+2VpimsH9wQqaa2L+xxwj59/BbjFz4/DPQgi8UCkrQyh+6XJNXS/tPuH7pelKZYqHVU9qKqHtPXJNp/CPUoO4PO40RaX4trW1sOxw54mTehlCN0vSuiuofu1R+j+oftliVibZfp6OMV1kPiVX7wf1236DGCzuscPov4nPjRCL0PoflFCdw3drz1C9w/dLwvE3dO2GdcxYjdwpv8V/1ugWVWX53Zu4IRehtD9ooTuGrpfe4TuH7pf1yfuOiPgfNyOXg78edJ1WF2xDKH7pck1dL+0+4fu19Un8TshNkRkIDAD+I6qHor1n8VE6GUI3S9K6K6h+7VH6P6h+3V1Yk/4hmEYRhgkPVqmYRiGUSIs4RuGYWQES/iGYRgZwRK+YRhGRrCEbxiGkREs4RtGG4jIN0Tky8dZP01ERpXSyTBOBEv4hvHhmQZYwjdSg7XDN4wIInIXMBPYihvgqx54B7gJ6I4bz30GMAZY6te9A1zjP+L7QD/gIO4RfOtL6W8Yx8MSvmF4ROQcYAEwHjew4G+Ah4BHVHWP3+ZuYIeqPiAiC4ClqrrYr3sGmKOqG0RkPG4I4ItLXxLDKExJHmJuGCnhImCJqh4EEJHciI5n+ETfF+gFLMt/o4j0Ai4AnoyM7FsZu7FhdAJL+IZxNIUueRcA01T1tyIyC/ijAtuUAftUdUx8aoZxYthNW8No5QVguoj0EJHewFV+eW9gu4h0wz2tKcd+vw5VfRf3wO3rwD3AQ0TOKp26YbSP1eEbRoTITdvXgW3Aa0Aj8FW/bC3QW1VniciFwL8Ah4BrccP+/gA4BTfu+yJVnV/yQhhGG1jCNwzDyAhWpWMYhpERLOEbhmFkBEv4hmEYGcESvmEYRkawhG8YhpERLOEbhmFkBEv4hmEYGcESvmEYRkb4f50eP1Y3ILwYAAAAAElFTkSuQmCC\\n\",\n 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\\n\",\n 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1VVgCrn4dtC8DVB8Y9BQNvN4b2ilZBEr7tSMJvw7LOlHLLO5vJLSzn2alxTB3QpfY79aoqYdUzsGme8drRDXqOB2cvyD0MJw9C2RljZMtVT0Ln/g0Lonqyn/kVdGujc4hk7oMlfzC6lUIHwTWvQtjglo5KIAnfliTht3EZp0u47+Od7EnLZ0iEL89c34feod61H5D8ExRlG4m9+pj98mLY9g5seB1KT4NXZ+OmteA+ENLn0ouvZjPs/RxWPm38objtK4gYZdf3andawy9fwvInofAkDJoFY58Gjxquk4hmIwnfdiThtwNms+aLHam8vOwQ+SUVzBrejd+Pj6GTWxPG4pecNubuydhltHqrz+zp5A5BvY0/AJn7IH2H0Rq+9u/QZZBt31RLKj0D616GrW+Ds6fxjSd+duNGGgmbaY8J/+xY+4yMDH7729/y1Vdf1Vr29ddfZ86cObi7N3wo8dq1a3n11Vf5/vvvL9jeEpOnCRszmRTTh4YzsU8If1+eyH+2HOP7vSd4dGIvbhoc1rhFYNx8YPh9519XlkH2ISPBZ+0zLsLu/58ximbq20a/fw393bmFZZwqLre8UnRyc8LX3QlHh6b1jecUlnHahvXVydXbuJN54CzjprYlj8C+r+HOJdK3L2pVVVWFg0PjGgWhoaF1JnswEv7MmTMblfBtRRJ+K+bj7sxzU/twy5CuPPXtfv749V4+3XacZ6fE0S+siXecOroY/fnV+/TPfsur4XqB1pqPNh/j+R8OUFF14bdBpcDX3Zg+ws/DmQDLdBJ+Hs6E+bozvLsfYb4X/qNOzStm/urDfP1zOlXmS+vzcXMypqXwcLZMTWHU2cXHjeHd/c/NX9QkQb3g9m+NOYuWPwkp66H76KbXJ9qslJQUJk6cyLBhw9i1axc9e/bko48+onfv3syePZvly5fzwAMPMGTIEO6//36ys7Nxd3fn3XffpVevXiQnJzNjxgwqKyuZOHHiBfVOnjyZffv2UVVVxaOPPsqPP/6IUop77rkHrTUZGRmMGTOGgIAA1qxZw/Lly3nqqacoKysjKiqKDz74AE9PT5YtW8bvfvc7AgICGDTINt+4JeG3AX26dOKruSNYvCudF5ccYsqbG5k1vBuPXxOLq5MNuiVquTBcWFbJY1/v5fu9JxgTE8iNg8IAMGtNfknFuZlAcwuN2UEPZZ4ht+jCSeS6+rkRHeiJUoqKKjObj+RiMilmDe92biSSWWvOWOrLLSojr6icnMJyEjILyCvK5VS1+rr4uDEiyp+RUf6MiPKnc6dGziyqFAy5B9a9Ars/lYTfgSUkJLBw4UJGjRrF7NmzeeuttwBjquMNGzYAMHbsWN5++2169OjB1q1b+c1vfsPq1at56KGHuO+++7j99tt58803a6x/wYIFJCcns2vXLhwdHc/Nz/OPf/yDNWvWEBAQQE5ODs8//zwrV67Ew8ODl19+mX/84x/88Y9/5J577mH16tVER0dzyy232OQ9S8JvI5RS3DgojHG9g3ltRSIfbExh57FTvHWbMQGb2axJyi6kk5sTwd6u544rragiIbMAVycH/D2d8XV3btDEcQmZBdz3yU5Scor448QY5l4R1eCupIoqM0eyC9l8JJfNR3I5kX9+1sDbhoVz3+hoQjq51lHDhSqrzCTnFLH5aC6bknJZeTCLr3amATC4my8Pj+vJqOhGLIrt5Ap9fwW7P4NrXjHuLxAtY+ljRreiLYX0hUl/rbdY165dGTXKGJgwc+ZM5s0zRrydTa6FhYVs2rSJadOmnTumrKwMgI0bN/L1118DMGvWLB599NFL6l+5ciVz587F0dFIszVNubxlyxYOHDhwLo7y8nJGjBjBoUOHiIyMpEePHufiW7BgQcPefx0k4bcx3q5OPHVdHJf3CODhz/cwef4GRnT3Z3tK3rmWcPcADwZ38yX1VDE/Hz9NeaX53PFKgZ+787mul7Oze3b1c2d4d39iO3vzze50Hl/8C54uTnz862GMjGrcQi9ODiZ6hXjTK8Sbu0ZFWv2eHR1M9Aj2okewF7ePiMBs1hzMPMOGwzl8uCmFmQu3MjTSj+em9CEmpIFLXg6YCTveN65fDL7D6hhF23NxA+Hsaw8PY6ELs9mMj48Pu3fvbtDxF9NaN6jM1VdfzWeffXbB9t27d9tl8RRJ+G3UVb2C+f7By/i/L/ewP+MMY2ODGRbpx+niCjYdyWH5gSzCfN24fXg34iN8qajS5BZaukuKysktNLpiDmacIaewjDOlxsgdTxdHCssqGRrpxxu3DiTIu+Et8eZiMiniQjsRF9qJO0dF8Pn2VOatOsz0BZv5bM5weoXUMYz1rC6DjGkYdn8qCb8lNaAlbi/Hjx9n8+bNjBgxgs8++4zLLruMXbt2ndvv7e1NZGQkX375JdOmTUNrzd69e+nfvz+jRo1i0aJFzJw5k08++aTG+sePH8/bb7/N6NGjL+jS8fLyoqCggICAAIYPH879999PUlIS0dHRFBcXk5aWdu46wZEjR4iKirrkD0KTaa1b7WPw4MFaNI8Tp0v04p/T9GNf79FvrD6sKyqrWjqkRknJKdTDXlipBz27XCdmnmnYQT+9pvVT3lpnH7ZvcOICBw4caOkQdHJyso6NjdX33nuv7tu3r77xxht1UVGR7tatm87Ozj5X7ujRo3rChAm6X79+OjY2Vj/zzDPntg8fPlzHx8frl156SXt4eJyrNy4uTmutdUVFhX744Yd1bGys7tevn54/f77WWut58+bpmJgYPXr0aK211qtWrdLx8fG6b9++um/fvvqbb77RWmu9dOlSHRMTo0eNGqUfffRRfe21117yPmr6LIEdupacKuPwRbuRnFPELe9sxqzhz5NjGRcbjIdLHV9iz5yA13obi82M/UvzBdrBtYZx+NVH07RljR2HL4OQRbsRGeDBp/cMx93ZgYcW7WbQcyu47+Od/LD3BCXlVZce4N0ZoscZi7EUZDV/wEI0M+nDF+1KdJAnax8ZzY5jp/h+bwZLfslk6b5M3J0dGBsbzOR+nbmyZ+D54azjn4d3rjDm65/xRdtdYEY0SkRERJtv3TeFJHzR7phMiqGRfgyN9OOp6+LYejSX7/aeYNm+E3y3JwMvF0duHtKVJ6+NRQXGGIvKLP2jsSJX/OyWDl8Iu5EuHdGuOZgUI6MDeOnGvmx7Yhwfzh7KZT0CWLghmXWJ2UahIfdA1FXw4xPGYirC7lrztcO2oimfoSR80WE4OZi4smcg/5w+kNBOrsxbddj4T2MywZS3jGknfni4pcNs91xdXcnNzZWkbwWtNbm5ubi6Nm7YtHTpiA7H2dHEfaOj+PM3+9l0JNdYS9i7M1zxB/jxcTi+pfnXDe5AwsLCSEtLIzs7u6VDadNcXV0JCwtr1DEyLFN0SKUVVVz5yhoi/D34/F7LqmDlxfB6X2NiuVn/bdkAhWgiGZYpxEVcnRyYe2UUW5Pz2Ho019jo7A4jH4AjqyBtZ8sGKIQdSMIXHdatQ8MJ8HTh78sTz0/VPOTXxlq4619p2eCEsANJ+KLDcnVy4I8TYtiWksfrKxONjS5eMPw3kLgUTuxt2QCFsDFJ+KJDmxYfxs3xYcxfncSKA5a7bYfdaywGv+2dlg1OCBuThC86NKUUz07pQ98unfj957vZmJTDvjzF6a5XoROWgrmGKRmEaKOsSvhKKT+l1Aql1GHLs28t5VKUUr8opXYrpWTYjWhVXJ0c+NfMQTg6KG57byuT52/giYPdUMW5kLq1pcMTwmasbeE/BqzSWvcAVlle12aM1npAbcOFhGhJYb7u/PDby1kwazALZg2mOPxKKnBEH/qhpUMTwmasTfhTgA8tP38ITLWyPiFaTKiPG+PjQhgfF8Kk+Bg2VsVRvv+784u8N1ZVJVSW2zZIIaxgbcIP1lqfALA8B9VSTgPLlVI7lVJzrDynEHZ3Va8gVpgH43LmGJw82LiDS04bwzr/HgMvd4PPbjWWUywrtE+wQjRQvVMrKKVWAiE17HqiEecZpbXOUEoFASuUUoe01utrOd8cYA5AeHh4I04hhO0EeLqQGTIGct+HhB8guHfDDtz2Lqx8BsoLIPpq8O0Gh5dDwhI4ug5u/rD+OoSwk3oTvtZ6XG37lFJZSqnOWusTSqnOwMla6siwPJ9USi0GhgI1Jnyt9QJgARhTK9T/FoSwj0F9erN7TRRx+7/H6Yo/1H/Ahtdh5VMQNRbGPQ2d+xnbtYblT8KWt+B0Kvh0tWfYQtTK2i6db4GzK0DfAXxzcQGllIdSyuvsz8B4oOOtPCDanHGxwSyviscpazfkp9ddeP0rRrLv8ytjIZWzyR6MRVWG3Wv8vGOh/QIWoh7WJvy/AlcrpQ4DV1teo5QKVUotsZQJBjYopfYA24AftNbLrDyvEHbXM9iTPZ6jjBe7Pq65UEUp/PAIrH4e+k2HGxaAQw1fnH3CIeYa2PkhVJTYL2gh6mBVwtda52qtx2qte1ie8yzbM7TW11h+Pqq17m95xGmtX7BF4ELYm1KKHnHxLDUPR//0KmQduLBA1gF4dwxsfxdGPABT36o52Z817F4oyYN9X9s3cCFqIXfaClGHcbHBPFF+JxWOnrD4XqiqMPrkt71rJPuiHJj5NUx4AUwOdVcWcTkE9Yat7zR9qKcQVpCEL0Qdhkb6UeHqx6dBD0PmXljxlDHMcskjEHkF3LcJomsd13AhpWDoHKOe45vtG7gQNZCEL0QdnB1NXB0bzGtpvTD3uQm2vGnMlz/xZePirGdgrcduPpLLtuS8Czf2uxnc/WHNi9LKF81OEr4Q9ZjUtzP5JRVs6fUnY+rke9bA8LlGi70We1JPc8f725i1cCv70vPP73D2gNF/gpSfjLH5QjQjSfhC1OPyHgF4ODvwXWIxTHwJQvrUWT63sIz7Pt5JoJcLfh7O3PfJTk4XV5tiYfBdEBADy/8sUy+IZiUJX4h6uDo5cFVsMMv3Z1FZZa6zbGWVmd8u2kVOUTlvzxzMW7cNIjO/lIcW7T6/qpaDI4x/HvKOyLh80awk4QvRANf0CSG3qJxtKXm1ltFa8/wPB9mYlMvzU/rQN6wTA8N9eeq6ONYlZvP+huTzhXtcDd3HwNq/QnHtdQphS5LwhWiAK2MCcXUysfSXzBr3a615eVkC/96UwuxRkdw85Pz0CbcNC2dguA/f7804f4BSxlDOsjOyfq5oNpLwhWgAd2dHxsQEsWx/JmbzpaNr/rEikbfXHeG2YeH8eXLsBfuUUlzeI5Bf0vPJL6k4vyM4DgbOgm0LICfJ3m9BiPonTxNCGCb17czSfZnEPfUjpmoDdDRQXF7F9CFdeW5KH1QNo3dGRvkzb9VhthzNZUJctclnxzxh3Hm78imY/on934To0CThC9FAE+KCeWhsD4rKKi/ZF+brxu0jIjCZah6qOTDcB1cnE5uSci5M+F7BcNnDsPo5SP4JIi+3V/hCSMIXoqFcHB14+OqeTT52SIQfG4/kXrpzxP2w4wP48XGYsw5M0tMq7EP+ZQnRTEZFB5B0spCsM6UX7nByM+bPz9wLexe1RGiig5CEL0QzGRUVAMCmIzmX7uzzK+gyGFY9C+VFzRyZ6Cgk4QvRTHqHetPJzYmNSTV065hMMOFFKDgBm+Y3f3CiQ5CEL0QzcTApRnT3Z/ORXHRNE6eFD4feU2HjP+FMxqX7hbCSJHwhmtGoaH/ST5dwLLe45gLjngZzpbGClhA2JglfiGY0Mtrox9+QVEM/PoBfpDFn/u5P4cyJZoxMdASS8IVoRt0DPOji48a6xOzaCw24DdBweHmzxSU6Bkn4QjQjpRRXxgSyKSmHssqqmgsFxUKncEhc1rzBiXZPEr4QzWxMTBBF5VXsSDlVcwGlIGYiHFkDFSXNG5xo1yThC9HMRkb54+xgYm3CydoL9ZwIlSWQvL75AhPtniR8IZqZh4sjQyP9WJNQRz9+xGXg7AkJS5svMNHuScIXogWMjgkk6WQhqXm1DM90dIGoMZD4oyx2LmxGEr4QLWB0TBAAa+sardNzEhRkGHPsCGEDkvCFaAFRgR6E+bqxrq5+/B7jAQUJMlpH2IYkfCFagFKKMTFBbEzKrX14pmcghMXD3s8h53DzBijaJUn4QrSQ0XJXAyoAACAASURBVDGBlFRUsS25jkXML3sYCjLhzWHw7W+Nn0XLykuGvKOQnw6VZS0dTaNYlfCVUtOUUvuVUmalVHwd5SYqpRKUUklKqcesOacQ7cWIKH+cHU2srWu0Tq9r4aE9MPQeY7qF/85pvgDFJcxrXoJ5A2DeQHitN/xrJJTXcuG9uZ1OhXV/q7OItS38fcCNQK2DhZVSDsCbwCSgN3CrUqq3lecVos1zd3ZkWKQfa+rqxweja2fSyxB/F6T/LKN2WsrJg5jXv8oKczzpo1+DsX+B3CRjEfqWUlYIexbBR1Ph9b6w5oU6i1uV8LXWB7XWCfUUGwokaa2Paq3LgUXAFGvOK0R7MSYmiKPZRRyvbfbM6gJ7QXkB5KfZPzBxIbOZM189QIHZlUfLf80fk3qjL/s9RF8NG16D0vzmi6WqAhKXw1d3w6s9YPG9kHcErnzU+DZYh+bow+8CpFZ7nWbZJkSHNzomEIC1ifW08gGCLF+MTx60Y0SiJuU7PsL75A7ecbmTX08cwsakXOOb2VVPQulp2PSGfQPQGtJ2wJI/wN97wafTIGkl9J8Os3+Eh/bCmD+Bb0Sd1dS7iLlSaiUQUsOuJ7TW3zQgVFVT+HWcbw4wByA8PLwB1QvRdkUGeNDN3521CdncPiKi7sJBvYznkweg53i7x9YhaW0k8IJMY/Uxy3PV2tfZau7FFTf/jiGR/ny1I40Xlxziiocux7H3VNj8Jgy+E46shu3vgkeQ8ccgdIB18ZirYOs7Rp15R8HBBWImQb9bIHocODo3qrp6E77WelyTgzWkAV2rvQ4Dal3OR2u9AFgAEB8fL52Vol1TSjG6ZyCf70iltKIKVyeH2gu7+YJXKGQfar4A2zutYf0rxkR1ZxN85aUT1uXqADbFPsnD0cY3sscm9WLOf3by2fZUZo15Ag5+a1zMrSqHoDhI3wkLrjTWKr7qSfDr3vjY8pJh8VxI3QLdRsHl/wex14Frpya/3XoTvg1sB3oopSKBdGA6MKMZzitEmzC6VxAfbj7G1uQ8ruwZWHfhoF5GC1/YxuY3jAudXQZTFDiANB9vDhZ5siPXhYRiT7LwBc9gRsR05cnJsecOu7p3MMMi/XhzdRKzho+Fyx+BjF0w/D6IugrKzsDGeUbL/8A3ED8brvgDeAY1LK4D38L/7gNlghsWQL+bjVlUrWRVwldK3QDMBwKBH5RSu7XWE5RSocB7WutrtNaVSqkHgB8BB+B9rfV+qyMXop0Y0d0fF0cTaw6dbEDC7w3b3zO+6pvq+DYg6ndkNXrFX0j0u4q7cx8g7UgpAP4ezgyP8mdqlD8juvsTGeCBuijZKqUYGxvEi0sOcbq4HJ+rnriwbtdOMPbPxnDadS/D9oWw6xMY+SCMfABcvGqPKzvRGH4b3BumfQg+XWsv20hWJXyt9WJgcQ3bM4Brqr1eAiyx5lxCtFeuTg6MiPKvexWss4JiobIUTqWAf5TdY2u38pLRX95FlnMEN2TMZFiMF7++vDsjowPoEeR5SYKvSfcATwCO5hQxKLyWvnSvEJj8Ggy/H1Y/C+v+avzBHv0YDPn1pa32ynL4+m5wcoNbPgHvzta+0wvInbZCtAKjewaSnFNESk5R3QWDLN0KMlKn6bRGL55LSUUVN595kNlj+vD+nUO4c1QkPYO9GpTsASIDPQA4ml3P7wwgIBpu/gh+vdr4HS55BBJqaAOvfs6YLG/KGzZP9iAJX4hW4dzsmfXdhBUQYzxLwm+6o2tQqVt4oXQak64Ywf+N79ngJF9duJ87DiZFck5hww8KGwyz/geewcad09Ulr4dN82DwXcYd1nYgCV+IViAiwIPIAI+6p0sGcPEEn25y4baptKbgxxfI0H6ogbfx2MReTUr2AE4OJsL93BvWwq/OwdG4CJu4DIpyzsXF8j8bv9sJdd8taw1J+EK0EqNjAtl8JJfSilpmzzwrqLcMzWyi8qS1eJ3cwSLnX/Gn6wY0Odmf1T3Ag+T6uuFq0n8GmCvhl6+M10dWwYndxtBLZw+rYqqLJHwhWonRMUGUVZrZfDS37oJBsZCTaFzgE42S+e0zZGpfht/0MB4u1o9K7x5oJHyzuZG3DAX3hs4DYPcnxuv1r4J3F+h/q9Ux1UUSvhCtxLBIP1ydTKw9VE8/flBvo3WYd6R5AmsnjmxfRnjBLraF3s7IGNvM7hIZ4ElZpZn005ferFWvATOMC7RbF8DxzTDqoUbfOdtYkvCFaCVcnRwYGRXAmoRsdF0zYlafYkE0SH5RKeVLnyQbX0bPeMRm9Xa3jNRpUrdOn5vA5ATLHgWPQBh0u83iqo0kfCFakTExgRzPK647gfj3AOUAJ6UfvyHMZs33C58j1nyYgiufxtvL22Z1dw84OzSzESN1zvLwh54TQJthxAPG2Hs7k4QvRCtyfnhmHaN1nFyNfvyUDc0UVdv2wbLNXJ+7kAy/4XQffYdN6w70csHTxbFpLXyAUb8zplgecrdN46qNJHwhWpGufu5EBXrUvyhK3FQ4vslY5UjUatORHEI2P42rqZLOt71lk/loqlNK0T3Qg6NNTfhdh8DMr+qeaqER1tczrFcSvhCtzOiYILYm59W+uDkY/b8A+75qnqDaqDU/LOJah61w+SMoO01FERng0fix+DZWZda8tiKROz7YVmc5SfhCtDJxod6UV5pJP1XHyA+/SAgbcn4ct7jE/ox8hmd/RZFzIE5XPGy383QP8CQjv6T++yfsJL+kgjs/2MY/Vx3mhoF1jz6ShC9EKxPm6w5Aal0JH6DvzZC1D7JktE5Nvlz3M1ea9uA4YDo4utjtPN0DPdC6iSN1rFRQWsEd729jy9FcXrqxL3+f1r/O8pLwhWhluvoZozXSTtWzzm3cVGO0zi9fNkNUbUt2QRkuB77GUZlxiZ9p13NFBlgxNNMKhWWV3PnBdval5/PGjEHcOjS83juHJeEL0coEebni5KBIq6+F7xkE3Ucb3Tpmc3OE1mZ8uvU4U9U6SoP6n79vwU4irRma2UTF5ZXM/vd2dqeeZt6tA5kQV9MqtJeShC9EK+NgUoT6uJGaV08LH4xJuPKPQ+pW+wfWRpRVVrF181piTcdxHWzf1j2Ah4sjId6uTR+p00gl5VX8+sMd7EjJ47VbBnBN34ZPoywJX4hWqKuve/0tfDCm0XXxNu7WLG/ZkSKtgdmseea7A1xVtgqzyQn63tQs540K8iDppP1b+KUVVcz5zw42H83l7zf35/r+oY06XhK+EK1QmK9bwxK+ixfc+C5k/gJf32MsfdhBVVSZ+d3nu/li61Fudd2C6jkB3P2a5dx9unTi0ImCuofSWqmssoq5H+9kQ1IOf/tVP24YGNboOiThC9EKhfm6kVNYRkl5AxJIzESY+FdI+MGYU70t0xoqGj8RWWlFFff+Zyc/7TnEdxFf4lF5CjVghh0CrNnArr6UV5nZn3HGLvWXV5q5/5OfWZuQzUs39GVafNPWuZWEL0Qr1NXPGJqZfroB/fgAw+6FoffCljeNNVPbovJiWHQb/CMW8tMbfFhBaQV3LtxMl8OfsMXrj8RmLTHmpuk50Y7BXmhguA8Au46ftnndFVVmHvzsZ1YePMlzU/swfWh4k+uyfkJoIYTNhfkaQzNT80qIDmrgbfcTXzIWN1/yB2PlpB5X2y9AWys5BZ9ONy4+O1hmkLzl43oPyysq5+V3PuAvp9+kt9Mx6HI5XPPK+bV/m0mwtytdfNzYdfwUEGmzerXWPPLlHn7cn8XT1/Vm1vBuVtUnCV+IVujszVf1jsWvzuQAN70PH0yEL++E2T9CSB/7BFiP47nFuDiZCPZ2Pb9Ra+PCcnEuFOdA0dnnHNjzGeQmwbQPIC8ZVj0DCUshZlKt58hKT+aXfz/EyxXrKPUIgcn/ht5TbT5fTkMN6Opj8xb+uz8d5ZvdGfxhQgx3jrL+D4kkfCFaoUBPF5wdTQ27cFudiyfc+jm8NxY+vQXuWQVeDRujbStVZs2t726hq58bi2YPhv/NheNbjeReWVrzQe7+cNuXxn0FVRXGzWRL/gARlxvvqbrKcvJW/xOvTa9yha4krd/9hF33hF2XBmyIgeE+/PDLCU6eKSWo+h+6Jtp0JIe/Lj3ENX1D+M1o28wDJAlfiFbIZFKE+biR2pgW/lmdusCMz+H9SUbSv2tJsybD9YnZpJ8uIfNMKaXLn8F139cQdyN4h4JHALgHVHv2N55dvM63zB2cYPLr8P54WPFnuOZV49sLQHYipZ/NxC8vgXUMJuTm14iJq3s6geZyrh8/9XSDb4SqzYn8Eh78dBfdAz352039rV579yxJ+EK0UmF+DRyLX5PO/eGmhbBohjFc85b/nE+adrZo+3GcHUwM07tx3fYGDL4Lrnu9cZWED4Nhc2Hr23BiD1w3D3IS0d8+SEm5iScd/8TcOfc3/PpGM4gL7YSTg2K3DRL+k4v3UVpRxdszB+Npg7V3z5JROkK0Ug0ei1+bmEkw4SVjuOaKv9gusDqcLChl1cGT/GaIF685v02Gc4RxMbkpJv4VbnwPTh2Ddy6Hr+6izC+GSaUvMOjqGa0q2YOxRGVsZ2/LhdumO5JdyKpDJ5lzRRTRQZ71H9AIkvCFaKXCfN3IKyqnqKyy6ZUMn2u0sDe/AflptguuFl/vTKfSrPl1wZt0UsX8puxBylUTZ6pUCvpNgwe2Q/zdcNnDfD/oPTLxJz7C17aB28jArj7sTcunsqrpcxt9uCkFZwcTM4Y1ffhlbaxK+EqpaUqp/Uops1Iqvo5yKUqpX5RSu5VSO6w5pxAdRddzI3WsaOUDDJxlPKfvtDKiummt+Xz7ccZ0c8Lz6I+k9byd3WWd2Z6SZ13F7n5w7asw7ml2pBbi7epIdKBtW762MjDcl+LyKhKzmjbNQn5JBV/tTOP6AaEEetl+SmdrW/j7gBuB9Q0oO0ZrPUBrXesfBiHEeWfH4jdqaGZNQvqAycnuCX/L0TxScou5v3Mi6Co6j5yOs6OJlQezbHaOHcdOMbibLyZTywy9rM/5C7dN69b5YnsqxeVV3DUqwoZRnWdVwtdaH9RaJ9gqGCHEeecWQmnIrJl1cXSBkL6Q/rMNoqrd4l1peLk6MrBoA3iH4Roez6gof1YdPInW2ur6TxeXk3SykPiI5pkfpynC/dzx93Dms23HyTpTyxDUWlRWmfn3phSGRfoRF9rJLvE1Vx++BpYrpXYqpeY00zmFaNMCPJ1xdWrCWPyadBkMGbvsOrnaocwChnVxweHoaoi9DpRibGwwx/OKbTKT5M5jRqt5cLfW2X8PxqLmz0/tw5GTRVw77yc2Hclp8LErD2aRfrqEu2xwg1Vt6k34SqmVSql9NTymNOI8o7TWg4BJwP1KqSvqON8cpdQOpdSO7Oy6V2AXoj1TShHm6960sfgX6zIYygshJ9H6umqgtSY5p4irHfdAVZmR8IGxsUEAfLEj1epz7Dh2CkeTon+Yj9V12dOkvp359oFRdHJzYuZ7W3lzTRJmc/3fcL7amU7nTq5c3TvYbrHVm/C11uO01n1qeHzT0JNorTMszyeBxcDQOsou0FrHa63jAwMDG3oKIdqlMF83UvNs1MIHu/Xj5xaVU1BayZDSjcaNVOHDAejcyY1fDQrj3Z+See+no1adY2fKKeK6dMLNuXnuJ7BGj2Avvn3gMq7tF8orPyZw94fbOVVUXmv5gtIK1h/OZlKfzjjY8fqE3bt0lFIeSimvsz8D4zEu9goh6tEntBMHTpzhue8PUGHFUD/8o42FUuzUj5+SU4QL5YTnbjAWZal2k9fLv+rLNX1DeP6Hg3y4KaVJ9ZdXmtmTdpr4VtydczEPF0fmTR/Ac1Pi2JCUw+T5G9idWvNcO6sPnaS80sykvvadBsPaYZk3KKXSgBHAD0qpHy3bQ5VSSyzFgoENSqk9wDbgB631MmvOK0RH8duxPbhzZAQLNyRzyzubOZHfxNa+yQShA+3Wwk/OKWKUaR+OlUUQe/0F+xwdTPxz+kDG9w7mqW/38/GWY42uf19GPmWV5jaV8MHolps1IoKv5o4EYNrbm/hoc8olF7GX7cskyMuFweH2fX/WjtJZrLUO01q7aK2DtdYTLNsztNbXWH4+qrXub3nEaa1fsEXgQnQEzo4mnr4+jjdmDCQhs4C7PtjeoP7gGnUZDFn7oKJxo0caIiW3iAkOO9EuXhB56SU6JwcTb8wYxNheQTz5v30s2na8UfXvTLFcsG2lN1zVp39XH75/8DIuiw7gL9/s57eLdlNaYVxALy6vZE3CSSbEhdh9uKncaStEGzC5Xygv3tiXQ5kFLD+Q2bRKugwCc6WxHKKNpeQUM9IxARVxOTg611jG2dHEWzMHcWXPQP60+Be+2tnwO393HMujm787QV7Wz0LZUnw9nFl4xxD+MCGG7/Zk8NQ3+wFYm5BNaYX9u3NAEr4QbcbkfqF0D/Bg3qqkpo1rt+OF29yTGXTVGdC11vEYALg4OvDOrMGMigrgD1/t4X+76l/ZSmvN9pRTxHdrvePvG8pkUtw/JpoHxkTz+Y5UFm07ztJ9mfh7ODO0Ge4vkIQvRBvhYFL8Zkw0B06cYdXBk42vwDsUvDrbPOFrrfE7tcd4EVZ3wgdjkrF3b49neKQ/v/9iN9/tyaizfNLJQvKKyhnWve0n/LMevronl/cwundWHMhkfFwwjg72T8eS8IVoQ6YMCCXcz515qw83vZWfts1YfcpGThaUEWc+hFk5GheGG8DN2YGFd8YT382P332+m6W/nKi17JZkYy6eYZHtJ+E7mBTzpg8k0MvF6M7p07lZzisJX4g2xMnBxG9GR7E3LZ91iU24MbHXtca6t3s/t1lMyTlFDFJJFPnGgrN7g49zd3bk/buGMKCrDw9+touEzIIay209mkuItyvhfg2vuy3w9XDmg7uG8NurohkZ5d8s55SEL0Qbc+OgMLr4uPHikoOUVTZyqoR+06FLPCz/M5Tm2ySeYyfz6W86Um//fU08XRx57/Z4PFwceWHJwUv2a63ZlpzHsO5+Nlv1qTXpGezF78fHNEt3DkjCF6LNcXY08dzUOBKzCnlzzZHGHWwyGVMNF2XDmiYuTHKRorQ9uKsyPKJGNul4Xw9nHrwqmvWJ2axNuPDaREpuMScLyhjajrpzWpIkfCHaoKt6BXPDwC68tSaJAxlnGndw6ECInw3b3rHJEE23TOMisCl8WJPruH1EBBH+7ry45OAFi4dsPZoLwLDI5unyaO8k4QvRRv1lcm983J3449d7Gr/C0lVPgqsPLH/S6jiC8/dyysEfOoU1uQ5nRxOPTepFYlYhX+w4Pz5/a3IeAZ7ORAU23yLs7ZkkfCHaKF8PZ56d0od96Wd4b0Ny4w5294Phv4Gja401Y5vIbNb0LD9Apnc/Y0lCK0yIC2FohB+vLk8gOacIgG3JeQyNbJ/99y1BEr4Qbdg1fTszvncwr69MbPxCKf1uNp73ftHk82edOEaYyqY4eHCT6zhLKcWLN/YFYMa7W9iUlEP66RLpzrEhSfhCtHFPXx+HSSme+nZ/48bm+3aDbpfB3kVNHpd/OmEjAE7dhjfp+ItFB3ny8d3DKKmo4o4PtgG0qxuuWpokfCHauFAfN35/dU9WHzrJj/sbOc9O/+mQm9Tku28dktdQqp0I7DmkScfXpHeoNx/fPQw3Jwd83Z3oGeRls7o7Okn4QrQDd46MILazN09/e4DCssqGH9h7Cji6wp7PGn/S0nwi0r9niR5JsK9t12Dt06UT3z14GR/NHtZqFyxviyThC9EOODqYePGGPmQVlPKP5Y1YxtDV27j7dt/XUFn7ikw12rMIZ3MJG/xusEtS7ubvQd8w+yzm3VFJwheinRgY7suMoeH8e1My+9IbcRdt/1uh5BQc/rHhx5jN6G0L2EtPnMKsv2ArmockfCHakT9O7IWfhzNPLP6FqoYulNJ9DHiGwFd3w6e3wM8fGfPt1HUh9+gaVG4SC8vH0auz9LG3FY4tHYAQwnY6uTnx58m9eWjRbj7deoxZIyLqP8jBEW7/Bn7+EA5+D4mWFUjd/Y3ZNbsMNubf6TLIGL8PsO1dyl39WVo6jFtCJOG3FZLwhWhnru8fypc70vjbsgQmxIUQ5N2AVaKCesHEl2DCi5C1H1K3Gguep++EwysAS2vfNwI6D4DEZeyLuJvy0070CvG259sRNiQJX4h2RinFc1P7MOH19Tz3w0Hm39qwOeotB0NIH+Mx5G5jW1kBZOw2kn/6DkjbDi5efOc0gWBvE34eNS9pKFofSfhCtEORAR7cPzqa11YmMm1wGFf0DGx6ZS5eEHm58TjLbGbr/I3EhLhYH6xoNnLRVoh2au7o7nQP8ODP3+yjtKKR8+bXo1IbSw/GSv99myIJX4h2ysXRgeen9uFYbjFvrUmyad3JOUWUV5mJkYTfpkjCF6IdGxkdwNQBofxr3RHOlFbYrN6DluUI5YJt2yIJX4h27tp+oVRUaY5mF9mszoTMMziaFFFBMk99WyIJX4h2LjLAWPw7Jcd2Cf/QiQK6B3rg4uhgszqF/UnCF6Kd6+rnjknBUVsm/MwC6c5pgyThC9HOuTg6EOrjZrMW/pnSCtJPl8iUCm2QVQlfKfWKUuqQUmqvUmqxUsqnlnITlVIJSqkkpdRj1pxTCNF4kQEepOTaJuEnnLtgKwm/rbG2hb8C6KO17gckAn+6uIBSygF4E5gE9AZuVUr1tvK8QohGiAzwIDmnqHErYtViW3IeALGdpUunrbEq4Wutl2utz662sAWoadn6oUCS1vqo1rocWARMsea8QojGifD3oKC0ktyiRs55fxGtNV/uSGVIhC+dO7nZKDrRXGzZhz8bWFrD9i5AarXXaZZtQohmEhlgDJ+0th9/a3IeKbnFTB8SbouwRDOrN+ErpVYqpfbV8JhSrcwTQCXwSU1V1LCt1u+VSqk5SqkdSqkd2dnZDXkPQoh6RFgSfrKVCX/RtuN4uTpyTd/OtghLNLN6J0/TWo+ra79S6g5gMjBW19xBmAZ0rfY6DMio43wLgAUA8fHx1nc4CiEI83XDwaSsunCbX1zBkn2Z3BLfFTdnGX/fFlk7Smci8Chwvda6uJZi24EeSqlIpZQzMB341przCiEax8nBRLifOyk5tf03rd//dqdTXmnmliFd6y8sWiVr+/DfALyAFUqp3UqptwGUUqFKqSUAlou6DwA/AgeBL7TW+608rxCikSL83Zt885XWms+2Hadvl0706SILi7dVVs2Hr7WOrmV7BnBNtddLgCXWnEsIYZ2IAA+2JuehtUapmi6t1W5vWj6HMgt4fmofO0UnmoPcaStEBxEZ4EFxeRUnC8oafeyi7cdxc3Lg+gGhdohMNBdJ+EJ0EBH+TRupU1RWybe7M7i2X2e8XZ3sEZpoJpLwheggIps4NPOHvScoKq9iulysbfMk4QvRQYT6uOHsYGr0zVefbT9OdJAng7v52iky0Vwk4QvRQTiYFOH+7o1q4SdkFrDr+GmmD+na6Au9ovWRhC9EBxLh37hZMz/fnoqTg+KGgTIbSnsgCV+IDqRXiBdHs4vIKax/pE5pRRX/3ZXG+LgQ/D1dmiE6YW+S8IXoQKYODKXSrPnvz2n1ll1+IIvTxRVysbYdkYQvRAcSHeRFfDdfFm1PrXdu/M+3HyfM141RUQHNFJ2wN0n4QnQwtwzpytHsIrannKq1zLHcIjYm5XJLfFdMJrlY215Iwheig7m2X2e8XBxZtP14rWW+2JGKScG0eOnOaU8k4QvRwbg7O3L9gFCW/HKC/JKKS/ZXVpn5ckcaY2KCCOnk2gIRCnuRhC9EB3Tr0HBKK8x8uzv9kn1rErI5WVAm0yC3Q5LwheiA+nTpRFyoN1/uvHS0zufbjxPk5cJVvYJaIDJhT5Lwheigru3Xmb1p+WSdKT23La+onDUJ2dw4KAxHB0kP7Y38RoXooMbFBgOw6uDJc9uW78+kyqy5rr+sWdseScIXooPqEeRJVz83Vh7MOrdt6b5Mwv3c6d3ZuwUjE/YiCV+IDkopxdhewWxMyqGkvIr84go2JuUwqW+ITJTWTknCF6IDGxcbTFmlmQ1JOaw4mEWlWXNNH+nOaa+sWtNWCNG2DY30w8vFkVUHs8guKKOLjxv9wmSR8vZKEr4QHZizo4krYgJZcSCLgtJKZo3oJt057Zh06QjRwY2LDSK3qJzyKjPX9A1p6XCEHUkLX4gObnTPIEwKAr1cGNhVljFszyThC9HB+Xo4c8fICML93GVmzHZOEr4Qgqeui2vpEEQzkD58IYToICThCyFEB2FVl45S6hXgOqAcOALcpbU+XUO5FKAAqAIqtdbx1pxXCCFE41nbwl8B9NFa9wMSgT/VUXaM1nqAJHshhGgZViV8rfVyrXWl5eUWIMz6kIQQQtiDLfvwZwNLa9mngeVKqZ1KqTk2PKcQQogGqrcPXym1Eqjp9rsntNbfWMo8AVQCn9RSzSitdYZSKghYoZQ6pLVeX8v55gBzAMLDwxvwFoQQQjSE0lpbV4FSdwBzgbFa6+IGlH8aKNRav9qAstnAMasCtL0AIKelg7BCa4+/tcdXXWuPtbXH1xCt/T20xvi6aa0Da9ph7SidicCjwJW1JXullAdg0loXWH4eDzzbkPprC7olKaV2tOULz609/tYeX3WtPdbWHl9DtPb30Nrju5i1ffhvAF4Y3TS7lVJvAyilQpVSSyxlgoENSqk9wDbgB631MivPK4QQopGsauFrraNr2Z4BXGP5+SjQ35rzCCGEsJ7cadt4C1o6ACu19vhbe3zVtfZYW3t8DdHa30Nrj+8CVl+0FUII0TZIC18IIToISfi1ULLOm93JZ2w78lnaV3v5fCXhV6MMDyulwnQb7utSSjlYnlvdP9K29hnLZ2lf8vk2L0n4Fkqp24E1wEDgTGv8B1gfpdSdSqldwEMtHUtN2tJnLJ+lfcnn2zLkoi2gpenpMAAAC2lJREFUlBoF/AQM1VrvuGifagt/3ZVSvYCPgB+BvsDvtdZHlVImrbW5ZaNrW5+xfJb2JZ9vy+mwLfyzXyUBtNYbga1ArGXfY0qp65RSnq35l6uU8jr7s9b6EHA78BpwAHjAsr3F/gO1pc9YPkv7ks+3deiQCV8p9SzwF6VU9akb5gIfKqV2Az7Ag8ArltZIq6OUegzYpZR6WSl1p2VzgtY6D1gMRCmlrrCUbfbfc1v6jOWztC/5fFsRrXWHeQAuGIu0HMP4hzb+ov2/AQb/f3vnH+tVWQbwz8NFQQR/wKBsRv5YDm4oUE1SU0JziGZCYTMbRhQOKn+sNJjWMnQzbTqbE121SczfWpojVn9YSdiaDc3IH0sQNBKNFFEgUODpj+f93nu8fbnfe+Wec55zz/PZzji/vpfP+5xz3/s973ve503rI4GHgKllezcpx6nACuBIYAqwETguc3wocClwZ2ZfW8Q4Yhnx7V/x7e1St2/47wDLgHZswpYpInJk46CqLlbVVWl9E/A6MLwM0RbsBzypqutU9ffAj4FrM8e3AQ8AW0XkahG5HjiiILeqxThimS8RX0fUqsJXayP8h6puA+7FZug6XkQGQeerYSIyXERuAI4D/lKWbzcMAUaIyGAAVf0hcJiInJu2FdiBdYjNBzap6toixCoY44hlvkR8HdFvK/y9vUalqjvTv+uBlcBkYEzap+kv/L3YN5PJqrqmEOEmZDu6sm2bqvogcDTwmczp1wPfymxfCzwNjFbVHxXs5y7GIjI8s+4xlnvzcxfLZojI2Gb7HcV3b36ViG+fUXabUl8vwDnAz4EJXfYLlpcfUhshcBBwM3A+MAs4O+0fUXIZpmHvAC/FZhZr7G8DBqX187C20SPS9mjgFmBY2h5ckp+rGANnpDgtBW7I7B/gJJbd+bmKZTdluBlY14ifp/i28KtEfPs0FmUL9NEFbYwnmAL8DViFPR4emj2e1o8CDslsXwxsBtYAZ5ZZhlRhzsMeG88EJmFtjHO6nHtUOn8R8DPgG8BvgNsc+ZUW44zrhVjb7DmpkvkDMM1RLHvq5+p+zf4+pe07gSeArzUq+bLj20s/V/HN9dqVLdCXFxfr7DkMezNgCfYI1jg2AFiIvSUwLd2EY4AXgCscleFM4MOZ7UuxgSmkSmIhsAk4GTgYOAl7ornckV9pMe7i2g4MTOujgPtSxdr4RndlybHsiZ+r+7WLf8PzImAu9tQ3LnN8ATb9X1nx7YnfK57im/eyTxOglI2IfBM4TURWAHertcMBbBSRqcBkEVmjqv/CJmLfArSr6ub0+fXAsWqdNqWQKcMfgaWqulxE2kRkoKruwgZ/PJdOH4WV4ZhGGYDHROTPqrrbkV8pMe5yP9yjqs+k/ROxJoSB2C/5f4HLKC+WvfFzc79m/B8F7lPVl0Vkf6xZ6svYl63zRORx7FXHN7EvB0XHtzd+Y73EtxDK/ovzXhdgBta0MAW4HZtucXzm+HjgDmBGk88OLNu/mzJMyDpiTyonNvlsG10eXZ35FRrj7u4H7LF9dFofilWkEx3Esqd+pd+ve/H/aDr2g/TvF7FK9Fky7d4lxrenfqXHt6ilym/pTAJuVXu39yqsU6YjEZOqPoXdAMeKyKlptF8jF8auEnyb0awMFwOo6q70etgHgVUicriIzIWOMuzWdLc69Ss6xs1cL02uL6jqS2l9K/bWxfCMa1mx7Kmfh/u1mf/X07Gz0hPgAuBXWL/ENig9vj318xDfQnBf4Xd9vTKz/QLWm46qvgj8GjhQRD6bOf1urKPmXmBE/rbNeQ9lOCcdH4N5XwI8TE6DPrz77YPrkC73AyLyXeAj2GuA9HVF5N2vFb30P0RETsAGU/1JVSeo6iys+XRsOrfM+Bbu5x33FT72/msHmQv0ALA9U/lsxN5yaBdjKHahV2NDuS/v8vki6W0ZxqYb+SjsxjwSOEtVr+vy+br47YtrO4CITBORlcAxwExVfaWmfq3ojf/vgFOwtAgLMh+boapP1tTPNW4rfBE5QUTuxxIWtUvnRAmNjubNWP6L+emxbAvW/jk43QQ7gEtU9SxV3VixMhyQyrAG+KSqzs+jDN79+so1HX8WmKeqF3iLZRF+OfkfiP2+7RHryB8AoKo76uZXFVxW+CIyCut0WQ68hjUZzAFrO06nHYDl094I/EREPoBNVvBO4zxV/XfB6h30URlWq2ouQ7m9+/Wh69vpvPWq+vc6+rViH/13pfN2a07pjb37VQrtZS9vEQtwOvaaJdhf6anYAJ8xad812MWdiLUbX4M9Hi+moEx7VS+Dd78quXr3q7q/d78qLaULpAs2HbgCawcGS0X6PHB02h4OfB+4DkvGdFfjWOZnDIkyVNevSq7e/aru792vykupTToiMlJEHsISKb0O3C4iM9VSkf4CGyEH8AbwCHahB6vq+aq6Vt6dZGp7wfqA/zJ496uSq3e/Vnj39+7XHyi7Df9o4DFVPUVVbwO+TWcWvbuBMSLyabW2t9eA9wE7wTIKqo82Oe9l8O5XJVfvfq3w7u/dr/IUnlpBbDb4l4DHsSRn69L+Nmx+y6fTqauBe4CbRGQ6cBqW72I/KH3+S9dl8O5XJVfvfq3w7u/dr79RSIUvIoINdrgL2AOsxZIZXaKqr4pIm6ruFstZfTB0XMAlqYd+ITbIZ66qvlGEc9XK4N2vSq7e/aru792vX5N3JwGdGeuOAe5I6wOxvNO/7HLOUuALaf39mZ+xf96eVS6Dd78quXr3q7q/d7/+vuT2DT8NiFgEtInIcmxygd3QkYflYuBlEZmsqo+mj20F1onNIv85ETlDVTeo6tt5eVa5DN79quTq3a/q/t796kIunbYiMhlrjzsUG415NTZYZ4qIHA8dQ6IXYYmOGm12c7Ah0gcBU1R1Qx5+PcF7Gbz7VcnVu18rvPt796sVeTw2YBMezMpsL8ZmoJoNrEr7BmDtePcBH8J66G8ipTQte/FeBu9+VXL17ld1f+9+dVryusBDgEF0tsV9Cbg2rf8VuCitfxybCKL0QFStDN79quTq3a/q/t796rTk0qSjqttVdad2zmxzOjaVHMBXsGyLy7B3a1fB/6c9LRvvZfDul8W7q3e/Vnj39+5XJ3J9LTO1wyk2QOLhtPstbNj0OGCd2vSDaPoT7w3vZfDul8W7q3e/Vnj39+5XB/IeabsHGxjxH+C49Ff8e8AeVV3ZuLjO8V4G735ZvLt692uFd3/vfv2fvNuMgE9gF3ol8NWy27D6Yxm8+1XJ1btf1f29+/X3RdJFyA0RORyYBdyoqjtz/c9ywnsZvPtl8e7q3a8V3v29+/V3cq/wgyAIAh+UnS0zCIIgKIio8IMgCGpCVPhBEAQ1ISr8IAiCmhAVfhAEQU2ICj8I9oKIXCUil3VzfLqItBfpFAT7QlT4QfDemQ5EhR9UhngPPwgyiMiVwAXAP7EEX6uALcCFwP5YPvdZwARgWTq2Bfh8+hG3ACOB7dgUfM8V6R8E3REVfhAkRORjwBJgEpZY8AngNuB2VX0tnXMN8Kqq3iwiS4BlqvpAOvYIME9VnxeRSVgK4FOLL0kQNKeQScyDoCKcDDyoqtsBRKSR0XFcqugPAYYCv+36QREZCpwI3J/J7Dsod+Mg6AVR4QfBu2n2yLsEmK6qT4nIbOBTTc4ZALyhqhPyUwuCfSM6bYOgkxXADBE5QESGAWen/cOAjSKyHzZbU4O30jFU9U1swu1zwSbwEJHxxakHQWuiDT8IMmQ6bV8ENgDPANuA76R9q4FhqjpbRE4CfgrsBGZiaX9vBQ7D8r7fo6qLCi9EEOyFqPCDIAhqQjTpBEEQ1ISo8IMgCGpCVPhBEAQ1ISr8IAiCmhAVfhAEQU2ICj8IgqAmRIUfBEFQE6LCD4IgqAn/A1aNeNxjB5RCAAAAAElFTkSuQmCC\\n\",\n 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\\n\",\n 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\\n\",\n 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0JP08PxbCewSQfL6Le0v6BOyeLK/nj8t0M79mVBy4Z2KrX3HR+HOU19SzfntXu93U3ksCFcDNHThk9ULw97fPvPWNIDw5kn2bea5s5cqqsXet4bOU+auosvHz9SHy9PFv1msReIYyMDeHdLcexdODLw51IAhfCzRzKLaO/Hdq/G9wyIY4XrxvBsfxyLn1pA/9al9amo/ENh/P4Zl8uv53ar83dAm8+P45j+eWsPXSqrWG7JUngQriRhh4oA2xwgrE5SinmjOjJqt9fyOQBkTz1VSpXvbqZI6dKW3xtbb2FRz/fR+9wf269oO2jNy8ZFkVMaBee//aw1F5BErgQbuXIKaMHSn87nMA8W7cgP/61YDQvXjeC9IJyLn1pI6+1cDT+zuZ00vLK+d9ZQ1rddNKYj5cH90wfwJ4TJXyzL6cj4bsFSeBCuJHDp+zXA6UpDUfj3/5+ElMGRvL0OY7Gc0qqeGH1YSYPjGTqoG7tfs8rRvakb2QAz6461KETqe5AErgQbsSePVDOJTLIl9duHM1L14/kuPVo/MMfMs48X1Vbz23vb8eiNY9cPhRrEbx28fRQ3HvRQI6cKuPTnSdsEb7LkgQuhBs5nFtKn4hAu/VAORelFLMTo1n1+0mcFx/Gg5/s4bV1aWiteWjFXlIyi3numkTibVDPZObQHgzrGczzqw9RU2exQfSuSRK4EG7k8Kky+jmo+aQ5kUG+vHHTGGYlRPH0V6lc//pWPt6RxV3T+tusiqGHh+L30weQVVTJ96mdt0eKJHAh3IQjeqC0lo+XBy9eN5Lrx8ay9WghM4Z0555p/W36Hhf0j6SLtydb0vJtul5X0poJHd7EmDrtlNZ6mPWxMOAjIA5IB67RWhfZL0whREsaeqA46gRmSzw9FE9eMYzLE6IY1TvUZtULG/h4eZAUF8qWowU2Xa8rac0R+NvAzLMeexD4TmvdH/jOel8I4UQ/pBcCMCTadpM4dJRSivP7ReDn3fYug60xvm84h3LLyCuttsv6za7FBK61Xg8UnvXwHOAd6+13gLk2jksI0UafpZxgWM9gh/dAcabz+0YAsLWTHoW3tw28u9Y6G8B63f5OnUKIDjuaV8burBLmjrB9DXAzGxYdTJCvF5vTJIHbhVJqkVIqWSmVLLNWC2Efn6acRCm4PDHa2aE4lJenB2Pjw+QIvI1ylVJRANbrZvvxaK0Xa62TtNZJkZGR7Xw7IURztNZ8uvMEE/pG0D3Yz9nhONz4vuEcyy8nu6TS2aE4XHsT+OfATdbbNwGf2SYcIURb7cwsJqOwgjkjOtfRd4PxfcMB2NIJm1FaTOBKqaXAFmCgUipLKfUb4GlghlLqMDDDel8I4QSf7jyBr5cHM4ede1YbdzW4RzAh/t6dsh28xX7gWuvrm3lqmo1jEUK0UW29hS92ZzN9SHeC/LydHY5TeHgoxsWHsyWtAK11h+qsuBoZiSmEC0vJLKawvIbLE2wzRN1Vnd8vnBPFlWQUVjg7FIeSBC6EC9uZYQyATooLc3IkzjWxn9EffP3hzjWsXhK4EC4sJbOYmNAuRAT6OjsUp4qPCCAmtAvrD3WursqSwIVwYbsySxjRK8TZYTidUooLB0SyJa2A2vrOU15WErgQLupUaRUniislgVtd2D+Ssuo6dhzvPHX1JIEL4aJSMooBGBkrCRyME5meHor1hztPM4okcCFcVEpmMV4eiqHRXZ0diikE+3kzKjaE9Yc6z4lMSeBCuKiUzGIGRQXZrVSrK7qwfyR7T5ZQUNY5ystKAhfCBVksmt1ZcgLzbBcMiERr2HjEdY/Cy6vr2H68kPWH8khOL2T/ydPNLtviSEwhhPmk5ZVRVl3HiF6hzg7FVIb37EqIvzfrDuUxxwVK65ZW1bL/5Gn2nChh74kS9p48TVqeMbNSa0gCF8IF7cw0TmDKEfjPeXooJvaLYPX+XN7edIzZI3oSFuDj7LB+Zk9WCf/eeJQ9WSUczS8/83iPYD+G9QxmVkIUw6K70tXfm4qaeipr6rjkb02vSxK4EC4oJbOYID8v+kR0ntl3WuuOyf04mlfOoyv388R/DzBlUDeuGtWTKYO64evlvPMFWmve35bBX1buJ8DXkzFxYcwd2ZPhPbsyrGdXIoPaPhhLErgQLiglo5jEmBCbTxTsDoZEB/Pl3RdwIPs0n+zI4tOUk3y7P5cQf28uT4jmylE9GdErxO5Fr/JKq3l05T4AeoX6k1FYzpd7cpg8MJLnrxlBqA1+GSjd2sYWG0hKStLJyckOez8h3FFGQQVT/rGW2yf15b6LBzo7HNOrq7ew8Ug+H+84wap9OVTXWegTEcCTVw5nXJ9wu7yn1ppfv/0jm9MKiO7qx4niSiwafj+9P3dM7tfmL16l1HatddLZj8sRuBAupKbOwu+W7iDAx5PrxvZydjguwcvTg8kDuzF5YDdOV9Xy1Z5sXlx9mL/+9wArfzfRLu/5zuZ01h7M4/E5Q/nV+DgsFk11nYUuPrZtwpFuhEK4kGe+SWVXVgl/n5dATKi/s8NxOcF+3lw7JpaFF/Zhz4mSc3bRa6/UnNM8+VUqUwd1Y8G43oBRs9zWyRs6mMCVUr9XSu1TSu1VSi1VSnW+CfmEcJA1qad4fcMxFozrzcxhnbv+d0fNHdETH08P/pOcadP11ls093yYQrCfN3+fl2D3dvZ2J3ClVE/gLiBJaz0M8ASus1VgQoif5J6u4t5luxjUI4iHLhvs7HBcXmiADzOGdufTlBNU19XbbL3rDp0iNaeUh2cNdkiJ3442oXgBXZRSXoA/cLLjIQkhGms4qqusqef/5o+SofM2cm1SL4oralm1L/cXz9XUWdiVWUxbO3l8sC2TiEBfLh3umF9I7U7gWusTwLNABpANlGitV9kqMCGE4ZU1R9hytIDH5wylX7dAZ4fjNib2i6BnSJdfNKNU1daz8N1k5ryyqU1NLDklVXyfmss1STF4ezrm9GJHmlBCgTlAPBANBCilbmxiuUVKqWSlVHJeXucp8yiELfxwrJAXVh/iipE9mTc6xtnhuBUPD8W80TFsPJLPkVNlAFTW1HPrO8msP5xHfEQAj36+n6N5Za1a30c/ZmLRcN2YWHuG/TMd+ZqYDhzTWudprWuBT4Dzz15Ia71Ya52ktU6KjIzswNsJ0bkUlddw94c7iQ3z5y9zh3Wq2dYd5eqkGLw9PJj+3Douen4dV766mc1p+Tw7L5GlC8fh6+3B3R+mUFN37ll+6i2aj37M4IL+EcSGO653UEcSeAYwTinlr4xP1jTggG3CEqJz01rzx+W7KCir4f/mjyLQV4Zs2ENMqD//vWsi988cSPdgP05X1vL8tSO4anQMPbr68fSVCew5UcJjK/exI6OIjIIKyqvrftE2vv5QHidLqrh+rOOOvqEDA3m01tuUUsuBHUAdsBNYbKvAhOjM3tqUzuoDp3jk8iEM6ykTNthT/+5B9O8exB2T+/3iuZnDejD/vFiWbMtgybaMM4/7enkQEehLeKAP4QE+HC+oICLQh+mDuzsy9I6NxNRaPwI8YqNYhBDA3hMlPPXVAaYP7s7N58c5O5xO769zhzF/bCx5pdUUlNdQUGZc55dVU1BWQ35ZDdV1Fm6f3A8fL8eOjZTfZUKYzLOrDhLi78MzDhgIIlqmlDLtryAZSi+EiRSV17DxcD5XjYqxSbU64d4kgQthIqv251Bn0cxKkKHyomWSwIUwkS92Z9M73J+h0cHODkW4AEngQphEYXkNm9MKuGx4lLR9i1aRBC6ESXy9N4d6i2ZWQrSzQxEuQhK4ECbx3z0n6RMRwOCoIMe+8akDsPZvxrVwKdKNUAgTyC+rZktaAXdO6eeY5pOKQtizHFKWQHaK8dj2t2Dh9xAsvwBchRyBC2EC3+zLwaLhMnv2PqmvhdQv4cMb4NkB8NUfQVtg5tNw00qoLoUProXq1hVvEs4nR+BCmMDWo4X0CPZjYHc7NJ8UHoMfFsPu/0BFPgREwnn/A4nXQ49hPy037y1Yei18shCufR88pO642UkCF8IEdhwvYnRcqG2bTywWI3GvfhR0PQy8BBLnQ79p4On9y+UHXGQcjX91P+xaCiN/UR1amIw0oQjhZNkllZwormR0bKjtVlpyAt6ZBV8/AHET4a4UuOZdGDiz6eTdYOwiiBgA29+xXSzCbiSBC+FkO44XAzC6t40SeHUZfHANZO+COa/ADcuga8/WvVYpGLkAsn6AvIO2iUfYjSRwIZxs+/Ei/Lw9GGKL0ZcWC6z4Hzi1H655x2gGaWuzTOL14OEFO97teDzCrqQNXAgn255RREJMSPvmUSw6bjSVdB8Ow6+C3H2Q+gVc/CT0m96+gAIjYcBM2PUhTHsEvKSolll16AhcKRWilFqulEpVSh1QSo23VWBCdAZVtfXsO1HS/uaT5DeN9u6TO2D5LbDhHzDiBhh3R8cCG/Uro8fKoa87th5hVx09An8R+FprPU8p5QM4bjI4IdzA7qwS6iy6fScw62pg5/tG75Jr3oOMLUa795jftL3Z5Gx9p0FQNOx8D4bM7ti6hN10ZFb6YOBC4A0ArXWN1rrYVoEJ0RlsP14EwKj2HIGnrjSOkkf/Gjw8IG4CjL8DvHw7HpinF4yYD0dWQ1F6x9cn7KIjTSh9gDzgLaXUTqXUv5VSATaKS4hOYfvxIvpEBBDWnskbkt+CkFjoO9X2gQEk3QJeXeC/98FZk/gKc+hIAvcCRgGvaq1HAuXAg2cvpJRapJRKVkol5+XldeDthHAPdfUWwJh5fkdGUfuOvvOPQPoGGHWTcfRtD117wrSH4ci3Rt0UWyo8ZowMrTpt2/V2Mh1pA88CsrTW26z3l9NEAtdaL8Y6W31SUpJ8jYtO7WBOKbNe3kCfiEDGxIdSWF7TvhOY298yuvqNXGD7IBsbuwj2LDMGBPWdCgHhHVtfeT6sfwZ+fAMsteAXAuPvNIb2+5lz3kkza/dXt9Y6B8hUSg20PjQN2G+TqIRwU5/vOoFFQ2iANx/+kAnA2Piwtq3k5E7j5OWgyyCoux2ibMTDE2a/DFUlRvGrmvL2ryt9E7w00hjeP2I+LFgBseNhzV/huaHwxR8gZ4/tYu8EOtoL5XfAEmsPlKPArzsekhDu65t9uYyNC2PponGUVNaSXVJJ38jA1r3YUg+bX4Lvn4CAbjDpFz947aP7ULjgXlj3N6OaYf8Z0HuC0dNFW4y4tMWot6ItxmAiMHrHNBTLOpUKH14PQT2MHjPdBhmP950KJ1Ng66vGl1LyGxAzxjgxO/QK8JGObeeitANPTiQlJenk5GSHvZ8QZnLkVBnTn1vHY7OHctP5cW17cUkWrLjNaPceMgdmvQD+bTxy7wit4dh6OPA5HFgJZbktv0Z5woS7jD7l78yG+hr4zbcQ2rvp5SsKjcFDyW9CwWGjSSXxeiOZNyT8TkoptV1rnfSLxyWBC+EYr6w5wjPfHGTLn6YS1bVL61+49xP44h6or4NLnzGaH5w5Z6bFAhUFRvOKUkaiVh7W+x7G/erT8O3DxlG18gBvf/j1lxCV2PL6tYbjm4xEvv9zo608djxc8rfWvd4NNZfAZSi9EA7yzb4cEnuFtD55V5fCl/fDrg+g52i48nUI72vfIFvDw8MYbn8u/mFGIa3h18CGZ2HiH1qffJUyKijGTTROeqYsgc0vwyeL4LZNRh91AUgCF8IhThRXsjurhAdmtrIpIPNH+ORWKM6AC++HSfefuwysWfWZZFzaKyACJtwNYX3hoxuM3jdjF9ouPhcn1QiFcIBV+3IAuHhoK3qNbFsMb15sNFXc/CVMfcg1k7ctDboM4i6ANU9CZZGzozENSeBCOMA3+3IY0D2QPi31ONnxrtFdb8DFcNsG6C314QCjWeVia/Je/6yzozENSeBC2Fnu6Sp+OFbIxUN7nHvBfZ/CyruNQlJXvwNdQhwToKuISoBRC2DbazLZhJUkcCHs7P2tx9HAvNExzS+UsRU+vtXoA33te1KDuzlTHwbfYHj/KqMWeicnCVwIO6qqreeDbRlMG9SN3uHnqPX2w2Kj3/P8j8BHasI1K7CbMYKz+jS8c7nRP74TkwQuhB19vuskBeU1/HpCfPML1dcZZVsHzIQuNpzY2F1FjzCSeGWRkZz1H/EAACAASURBVMRLWzGoyFG0hh//bYw8dQBJ4ELYidaatzalM7B7EOf3PUcRqKwfjFoj/Wc4LjhX13M03PiJkbyXXtuxGi22tGcZ/PdeeGumUSLAziSBC2En244VciD7NDdPiEOda+TkoW+MyoJ9pzguOHfQawzMe9OYhejjW42aLM5UWQzfPAQ9hoNPkFE+IMu+I88lgQthJ29tOkaIvzdzR/Q894KHVxlDxaWcatsNnAkz/wYHv4SvH/ypkJYzrPmrMUPS7JeNsgH+YfDuHDi82m5vKQlcCDvILKzg2/25XD82li4+ns0vWJwJp/Yb/b5F+5y3CMbdaZwIfmumc0rSntxptH2PuRWiR0JILyOJh8bBknmw9mm7fLnIUHoh7OC9rcdRSrFgXDOV9xocXmVc95cE3iEX/xW6D4Fv/xf+NQmGz4OuMUaXQ98g49rvrNtdezVfFKy69KcTy97N1K4pLzCKbh3fZFRo9I+AKQ/99HxwtFF98Yvfw9qnIPMHoyRCr/NsVoxMErgQNlZRU8eHP2Qwc1gPokOa+Oevr/upINPhVRDSGyL6OzZId6MUjLwRBl5q1Evf94lxYlif46i333SjNvnZNcerSox+5lk/GnOOXvQEDJ4N5XlGsk63Ju1T1vlrvLoY7fEX3v/LwVc+/nDFa8bzqx42SiSE9IZhVxq10GPGgrdf+ze7o+VklVKeQDJwQms961zLSjlZ0Rm8t/U4D3+6l+W3jScp7qya3Rnb4J1ZEDvOmM/ys98aowsvfcY5wbozraG2wjiarjptXFeXGNd5B42j4l7nGX3vG84/VBbBe1dAzl6Y8ifY8zGc2geBPaDMqGeDdwDEnmdMahE3EaJHtW7gVXUppP4Xdn8ER9cZE2B4+hrrip9kXKJHNllt0W71wJVSfwCSgGBJ4KKzs1g0M55fh7+PF5//dsLPe59Y6mHxZCjNBi8/KDGmVOOGj6H/dKfE26nt/QQ+WQjdhxmz/1QUwOFvoTDNODIfONP4tbTjbWMyi+hRRsKOSux4cbGqEji+xVjvsfWQa2239wmC3udD/IVGt9JIY8ZKu9QDV0rFAJcBfwX+0JF1CeEONhzJJy2vnOeuSfxl18Gd70PObrjqDSNhHP7W6ALXkXKrov2GXWmMev3PTbD6EeNoODgarlv60xeqp5dxYnLMrbZ9b7+uxhfEwJnG/fICY7alhoR++BtY9RBEDDBmYGpGR9vAXwDuB4I6uB4h3MJbm44REejLZQlRP3+iqgS+e9zoLjjsKqPNtvE/sHCOARfD/WlGc4tPgPNmOgoIh6FzjQtAyQmja+T+z2DDP5p9Wbu7ESqlZgGntNbbW1hukVIqWSmVnJeX1963E8L00vLKWHswjxvHxeLrdVbXwXV/N36iz3zaudOhiV/yCQDfQHP9Xbr2NCauuPkLuO9ws4t1pB/4BGC2Uiod+BCYqpR6/+yFtNaLtdZJWuukyMgWpmESwoW9szkdH08PbjjvrK6DJ3YYJVBHLTDqeAjRFgERzT7V7gSutf6T1jpGax0HXAd8r7W+sb3rE8KVlVTWsnx7FrMSo4gM8v3piaoSWP5rCIqC6Y85L0DhlqQfuBA2sCw5k4qaem5pXHVQa/j8d8Zoy1u+NoZWC2FDNkngWuu1wFpbrEsIV1Nv0by9OZ0xcaEM69monsmP/zZOQk1/DHqNdV6Awm1JLRQhOmj1gVyyiiq5+fxGR9/Zu+GbP0O/GXD+Xc4LTrg1SeBCdNCy5Cx6BPv9NON8dSksu9mojXHFv8BD/s2EfcgnS4gOqK23sPVoAVMHd8PL08No9155DxQdg3lvGP17hbATSeBCdMDurGLKquuY2M/a1WvHu7B3OUz5szEkWgg7kgQuRAdsPFyAUjC+Tzjk7oOv7oc+U2Divc4OTXQCksCF6IBNafkMjQ4m1KvGaPf26wpXLpZ2b+EQ8ikTop3Kq+vYmVHEhH4R8OV9kH8YrnwdArs5OzTRSUgCF51adV09VbXtmwz3h/RCaus1V3psgF1LYdIDUllQOJSMxBSdVl29hdkvb+JgbinRXf2IjwygT0Qg8REB9O0WyPl9w/H2bP4YZ9PhfAK86hmw9zljZpVJ9zsweiEkgYtObNn2LA7mlnL92F5U1Vo4ml/OpyknKK2qA2DywEhevWF0s5MSb0or4O7w7aiSbJjzCnicY/JiIexAErjolCpr6nlh9SGujCrkyQlxqO4JAGitKSyvYeWukzz2xX5+9eY23rh5DMF+P5+BJb+smoPZxVwT+rExQ0vfqc7YDNHJSRu46JTe3pxOVOk+nj19L+rV8+HduXBkNQoID/Tl5gnxvHz9SFIyi7l+8Vbyy6p/9vpNR/K51GMbIZWZcMG95qolLToNSeCi0ympqOXztZt5p8tzeAT1gKn/D04dMGYif3UC7FwCddXMSohm8a+SSMsr45p/beFkcSUAOzKKePSzvdzluxId3h8GXe7kLRKdlSRw0em8/X0KL1meItBLww3L4cI/wj17YO6rgIbP7oAXEmDVw0wJzOK9W8aSd7qaea9u5r0t6fzm9bXc6v0lA3Q6auI90udbOE2HZ6VvC5mVXjib1ppVf7mcaZYteN30KcRfcPYCkPYdbPsXpH0Pljro2ovSwHjWnPSkvq6GmZ7b6UIVRI+EW1aBl49zNkZ0GnaZlV4IV3PwyGGm1m8mre+NDDw7eYPRlt1vunGpKITU/8LhVQSdPsGl/ieora7Ea+jVMGo+9BonR9/CqdqdwJVSvYB3gR6ABVistX7RVoEJYQ+FG95gkKoncsrtLS/sH2bMYzlqAWD8s8gRjzCTjhw+1AH3aq0HA+OAO5VSQ2wTlhB2YKmnX9bH7PIZSVivwc6ORogO68ikxtla6x3W26XAAaCnrQITwtYKUr6gmyWPnAHznR2KEDZhkwY8pVQcMBLYZov1CWEPlZtfJ1eH0HfC1c4ORQib6HACV0oFAh8D92itTzfx/CKlVLJSKjkvL6+jbydE+xQdJzp/I9/4XETfHiHOjkYIm+hQAldKeWMk7yVa60+aWkZrvVhrnaS1ToqMjOzI2wnRbjUbXkJrKB48HyWjJoWbaHcCV8Z/wRvAAa31c7YLSQgbS9+I9443WFI/nXEjE50djRA205Ej8AnAAmCqUirFernURnEJYRvVpehPbyfHM4rFvjczKlaaT4T7aHe3Vq31RkB+iwpz++bPUJLFnVX/y29mDTVmjhfCTci4BOG+9iyHHe+yzHcep/wSmX9erLMjEsKm5HBEuKe9H8MniygIG8X/K7mcey8agK+XTLgg3IscgQv3s3sZrFiEpdc45uf9jj49ApmTKGPMhPuRI3DhXjK2wopF6N4T+EfkXzlYpHlg5iA8POR0jXA/ksCFe1nzJPhH8FL3J3hlUzbzz4tl8kAZfyDckyRw4T4yf4Bj61gfOZ/n153g2qRePDFnmAzcEW5LErhwH+ufocYnlNtSE7hqVAxPXTlcmk6EW5MELtzDyRQ4vIqPfecQGhLK01dJ8hbuT3qhuKDDuaXsPVlCTZ2F6jrLmevqOguWmgr8Kk4QqCu4aMalRIcGODtcx9jwLPU+wTyZN5G7L4vDWwbsiE5AErjJ1NZbyCysoE9kYJPPf7w9i2c/Wc8gfZQYlUdPlU+MyiNG5dNT5RGpfioIuW3/MI7PfY3xI4Y7KnzHK0qHNU/BgZV8E7YAaoK5bqwM2BGdgyRwEzlZXMmdH+xgZ0Yxd03tx+9nDDhzAs5i0bzwzX6qNr7MWp9P8NXVAGhPXyzBPVEhsaiQCRAaC11jyc/LIXHjX6lcMZPPUx9h1jUL3atJoSwPNjwLP74BHp6cHnUHf9gylpsujCXQVz7WonOQT7pJrDuUxz0f7iSmPou/Rh9h+ZrDHM8/zaNzEtm46yAHtn3NrKL3GOJ9HEv/S2DiPRAajwqIxLOJiXUjgKqhF1P2zgJmp/6Rla8d5ZL/edL1a4FUnYYtr8CW/4PaShh5I0x+kOfWFlGnjvPr8+OdHaEQDqO01g57s6SkJJ2cnOyw93MFZdV1PPN1Kl9tTeHRoM+5pHY1StcDcFp3IV+H0McjG4By3274z/kHasjsVq9f11Vz6LUbGJj/LV+GzGfqHS/j5+OC39t11cbR9oZnoaIAhsyBqQ9DRH/WpJ7izg92MHNYD567ZoSzIxXC5pRS27XWSWc/7oL/ye5j9f5c3l2xkplV/+WhLpvxrqtDjbkVxi6EnD2U7via2oITZPa5gZgR0wjoORq8fNv0HsrLl4F3fMTBNxZy6YkP+P6FAgYveI6oqJhzv7C2EkpzoCwXSrOhsgi6xkLkQOgaA47qW6017FkG3z0OJZkQPwmmPwI9R3PkVBlPvPUDaw/m0ScigHumDXBMTEKYhByBO8GpomK++vA1hmcvY5THESyefngkzIML7oWwPvZ5U605+MEfGXj4dSq0L2lx1zJk0tV4lmRCYRqUZGEpzcFSko0qz8WzuqT5dXUJg3F3wLjbwDfIPvEC5B2C//4B0jdAVCJMfwz6TuF0VS0vrT7M25vT6eLtyd3T+/Or8XH4eLl485AQzWjuCLxDCVwpNRN4EfAE/q21fvpcy3f2BK4L0kj94iV6HPuYUEop6tKboIn/g9eo+dAl1CExZB/eydEVf2Fc+fd4KuNvX4cHp3QYOTqEXB3KKet1Hg33QynRAfRSp7ioWzGz/ffSLXsN+EfA+b81mjPa8sVjqYfqUqNuybF1Rh9uS531qF6BrjeWydkDPv4w7REYfTP1eLAsOZNnvjlIYUUN143pxb0XDSQisG2/SoRwNTZP4EopT+AQMAPIAn4Ertda72/uNW1K4Fo77me6PWkNh7+lYsP/4Z+5jlrtSXKX8fS++HdEj7jYKduoteb7LT9w/PAeCn16UuTTA18fX0L8vQnx96ZrF29C/H0I6WLcD+niQ53FwrLtWby35Tgniiu5JS6fB3yW45ux3lhpxADoNgQqC6E8H6rLwFIL9bXW67qf7tPoM+flB1EjwNvP2FdoUJ7g4QmhcTDpAQjsxo/phTz6+T72nTzNmLhQHrl8KMN6dnX4vhPCGeyRwMcDj2qtL7be/xOA1vqp5l5zJoFrbfyTl2RCSVajS6P75XnGP7d3F/DrCmHxENYXug+FvlMhtHe74naojK3obx9BZW4lR4fxsZpOz6n/w+yJo122S1+9RfPelnSe/joVXy9PnpwcwEXeu/A+sgqKMyAgEgIijKYVDy/w9AYPb+t1o/tevtBzFMSMNZJ3E2rrLaxJPcVHP2byXeoporr68adLB3N5QpTUNxGdij0S+Dxgptb6Vuv9BcB5WuvfNveaQf3i9Mr7zicmfyNd6n7exlqrfCn26UGRd3eKvbtR4RNGr67e9ArU+FQXQ+FR41JtHagS3h96nQfhfY1LYHcjaTRcfILA08HnaGsqIGMzpG8y2m2zfqTEM4y/V82lcOC1PDZ3BN2Cm05WruZYfjn3/ieFHRnFBPp6MWNIdyYPjKR3eAAxoV0I8/dp8Usqp6SKlMxiDuaUUlZdS0VNPZW19VTW1FNRU8++k6fJL6smItCXG8fFsujCPvi7Yg8aITrIHr1Qmvrv/MW3gVJqEbAIYHSUBxHZ6/jKMoLdlr7kqAhyVQS5KpIyjyA8az3wrPfAswbKC+spO16Ht6fivPhwpg7txrRBkfTWJyHtOziyGo58CynvNx+ht/9PCd2vK4TEQmg8+IcZPSxOnzS6pwV2M74AIgcYvRwCIlq/F2rKIe172LcCDn4NteWgPKnqlsASv5t4rmQSv790JE9MjHero8b4iACW3XY+m9Py+WJXNl/vy2HFzhM/W0Yp8FQKTw+Fl4fCw3rt6eFBvcVCUUXtmWW7eHvi7+NJF5+Gay/Gxody5cgYJg2MlKHxQjTBoU0oI4YO1Ot/TMHf17fFASV19RZ2ZBTzXWou3x04xZFTZQD0DvdnRK8QEmNC6NctkEifGrrVncSvthjPmlI8a8vwqCnDo6YUVVNqnCyrLoWKQuMnfnGG0Rbr1QWCo4zrslyoyP/pzXsMh/B+RhOOl6+xjJevtUnHz7iuq4Kja+H4ZqivAf9wGDybjO5TeflIOJ/sLSHQ14tX5o9iYv82fCG4qJo6C0fzy8gqrCSzqIKSylosFk2dRVOvNfX1xm2LNq4B+ncLJLFXCEOigvHzlunOhGiOPZpQvDBOYk4DTmCcxJyvtd7X3Gs60gslo6CC71Jz2Xa0kJTMYnJOV7X4Gg8Fnh7GEaCPpwfRIV3oFeJL/xCIjerBgKhg+nULJNjP2zi5lr0bjn4PR9cZSb22ykjUddVQV2kk6sYiBkL/Geh+M/hBD+a1DcdZczCPAB9P5p8Xy60X9KG7mzSZCCGcx17dCC8FXsDoRvim1vqv51relt0Ic0qqyCyqoKCsmvyyGqpq66lv5miv3qKpqq3nZHElmYWVHC8sp6rWcmZdwX5e9Arzp29koHF03yuE2DB/gvy88PXy+Knpw2KB+mpjkAtQooJYtS+HpT9ksCOjmPAAH24+P44F43sT4u9jk+0UQgi7JPC2Mks/cItFk1VUycHcUo7mlZFVZPzsP5hTSnbJz4/svT0Vgb5eBPl5W6+NS3Wdha1HC6it1/QO9+c3E+O5enQvuvhIU4AQwrZkKH0jHh6K2HB/YsP9ge4/ey73dBW7MovJPV1FaXUdpVV1lFXVUVpVS1l1Haer6jhZXEWdxcJN4+OYlRhNYkxXtzpBKYRwDZ0ygZ9L92A/Lhraw9lhCCFEi6RvlhBCuChJ4EII4aIkgQshhIuSBC6EEC5KErgQQrgoSeBCCOGiJIELIYSLcuhITKVUHnDcYW/YOhFAfotLmZvZt8Hs8TVm9ljNHl9LXCF+M8bYW2sdefaDDk3gZqSUSm5qiKorMfs2mD2+xsweq9nja4krxO8KMTaQJhQhhHBRksCFEMJFSQKHxc4OwAbMvg1mj68xs8dq9vha4grxu0KMgLSBCyGEy5IjcCGEcFGdJoErKdhtd7KPbUf2pf25wz526wSuDL9XSsVoF24rUkp5Wq9N94FztX0s+9L+ZB87jtsmcKXUr4A1wEjgtBk/TC1RSt2slNoJ3O3sWJriSvtY9qX9yT52PLc8iamUmgBsAMZqrZPPek65wjevUmoQ8C7wDTAc+IPW+qhSykNrbTn3q+3Plfax7Ev7k33sHG5zBN7wsw1Aa70J2AYMtj73oFLqcqVUoJn/UEqpoIbbWutU4FfA88B+4LfWx532z+BK+1j2pf3JPnY+t0jgSqnHgf9VSjWuFXAb8I5SKgUIAX4HPGM9UjAdpdSDwE6l1N+UUjdbHz6otS4EVgB9lVIXWpd1+N/Nlfax7Ev7k31sElprl70AvsCfMApkrQAuOuv5O4DR1tuRwKfAxc6Ou4ntmAqsB+KBKUA2kNDo+UDgHmBJo8c8ZR/LvpR97J77uLUXVz8CrwW+AIYAW4EpSqn4hie11v/UWm+33s4DCoEwZwTaAm9gp9b6mNZ6DfAi8FSj58uB5UCZUuovSqm/A3EOis3V9rHsS/uTfWwSLp3AtdG+dkhrXQ58BMQAY5VSvvBTNyalVJhS6h9AAvCjs+I9B38gXCnlB6C1fhqIUkpdbb2vgSqMk0O3A3la6zRHBOaC+1j2pf3JPjYJl0ngzXX50VpXW6/TgY3AJGCQ9TFt/fb9COOoYZLW+ohDAm5C45M+jdsFtdYrgL7ArEaL/x34Q6P7TwH7gFit9TMOjs90+1gpFdbothn3ZXPxmW5fNkcpNbipx020j5uLz2X2cYc5uw2npQswB3gHGHHW4wrw0I3a14Bg4GVgPrAAuNz6eLiTt+ESjP6n7wIPNXrcE/C13r4Oo10xzno/FngFCLLe93NSfKbax8BM6356F/hHo8c9TLIvzxWfqfZlC9vxMnCsYR+aaR+3EJ/L7GOb7AdnB9DMH6ehf/oUYDewHeOnWGjj5623+wAhje7fBRQBR4BLnbkN1gR4G8ZPtEuB8zDa5245a9k+1uUfB/4N3Al8Dbxmovicto8bxboIo11zjjVhrAUuMdG+bG18pvy8nnV/CbADuLUhaTt7H7cxPtPtY7vsF2cHcK4/FMaJjyiMs95vY/zcaXjOA3gQ4wz4JdYP1CDgKPBnE23DpUD/RvfvwRjkgPWf/kEgD7gA6ApMwPjF8UcTxee0fXxWrEMAL+vtbsB/rImy4WjrISfvy9bEZ/bPa0OsvwMWYvwyG9bo+Qcwphtz1j5uTXw5ZtvH9rp4YSJKqd8C05RS64Gl2mjDAshWSl0MTFJKHdFanwB6ACXAEK11kfX16cBwbZzAcIpG27ABeFdr/aVSylMp5aW1rsMYSJBqXbwbxjYMaNgGYJNSaqvWut5E8TllH5/1efhQa73f+vhIjJ/rXhj/sJXAfThvX7YlPrN+XtcB/9Fan1RK+WA0Bd2EcQB1nVLqB4yueacxvvAdvY/bEt9gM+1ju3L2N0ijb84rMH7KTwHeAv4PSGz0fCLwPnBFE6/1cnb859iGEY1jxPglcX4Tr/XkrJ+JJovPofv4XJ8HjJ/IsdbbgRiJcaQJ9mVr4zPz53WU9bnHrNfXYyTFAzRqN3biPm5tfKbYx/a+mKkXynnAq9roV/ooxgmKM0VxtNa7MP6Yw5VSU60jwRrqGNQ5Id6mNLUNdwForeusXZl6AduVUjFKqYVwZhvqtfWTZ9L4HL2Pm4r1HmusR7XWGdbbZRg9CsIaxeqsfdna+Mz8eb3D+txl1l9pDwCfYbTtl4PT93Fr4zPLPrYrZwxxVc3cP4pxphit9XHgv0CAUmp2o8WXYpy0+AgIt3+0TWvHNsyxPj8II+67gc+x0wACs8fXgVj9z/o8oJT6f8BQjC5r2DqpmD2+1mjjNoQopcZjDM7ZrLUeobVegNFkOdi6rDP3scPjMzNnHIF7N77TaGcvByoaJZNsjLP4Q5QhEOOPtgdj2O4fz3q9I7V1GwZbP5R9MD5k8cBlWuu/nfX6zhJfR2IdAqCUukQptREYAMzTWud00vhaoy3b8D1wIcYw+AcavewKrfXOThqfaTksgSulxiullmEUjxmifir63nAitQijdsHt1p9AJRjth37WP2gVcLfW+jKtdbaj4rbRNnSxbsMRYKLW+nZ7bIPZ47NVrNbnDwC3aa1/ZbZ96Yj4WqOd2xCA8T9nUcbJbQ8ArXVVZ4vPFTgkgSulumGcgPgSKMD4iX4LGG2v1sW6YNQSzgYWK6WiMQqv1zYsp7U+5Yh4m2KjbdijtbbLsF2zx2fDWGusy6Vrrfd2xvhao4PbUGddrl7bqRys2eNzGbqNZz3bcwFmYHQLBOMb9GKMASODrI89gfGHGonR7voExs/Rf+KgKmauvg1mj8+VYjV7fO6wDWaPz1Uu9vrjzAX+jNGOCkbZxsNAX+v9MOAR4G8YhXE+aHiu0Tr8nbpjTL4NZo/PlWI1e3zusA1mj89VLzZtQlFKRSqlPsUoalMIvKWUmqeNso0fY4yeAigGvrP+0fy01vO11mnq50V/KmwZW2uZfRvMHp8rxWr2+FrD7Ntg9vhcna3bwPsCm7TWF2qtXwPu5acKZUuBQUqp6dpotyoAugPVYFRs0+ZozzL7Npg9PleK1ezxtYbZt8Hs8bm0Dg+lV8ZMzxnADxhFp45ZH/fEmBtvn3XRPcCHwAtKqbnANIxaBd7g9LnzTL0NZo/PlWI1e3ytYfZtMHt87qRdCVwppTA6zn8AWIA0jMIyd2utc5VSnlrremXU6+0KZ/4Yb1vPPj+IMWhkoda62Abb4XbbYPb4XClWs8fnDttg9vjcVlsbzfmpGtgA4H3rbS+MmrufnLXMu8A11ts9Gq3Dx1aN+O25mH0bzB6fK8Vq9vjcYRvMHp87X1p9BG7tXP844KmU+hKjUHo9nKmjcRdwUik1SWu9zvqyMuCYMmaIvlIpNVNrnaW1rmnt+9qS2bfB7PG5Uqxmj88dtsHs8XUGrTqJqZSahNGWFYoxWu8vGIM/piilxsKZ4a+PYxSdaWjvugVjOGwwMEVrnWXj+FvN7Ntg9vhcKVazx9caZt8Gs8fXabTmMB2jePuCRvf/iTFDzs3AdutjHhhtYP8BemOcfX4Ba/lHZ1/Mvg1mj8+VYjV7fO6wDWaPr7NcWvvH8gd8+akd6wbgKevtFOB31ttJGIXtnb5hrrYNZo/PlWI1e3zusA1mj6+zXFrVhKK1rtBaV+ufZt2YgTF1FcCvMarZfYHRr3M7/LJEpLOZfRvMHl9jZo/V7PG1htm3wezxdRZt6kZobcPSGJ3tP7c+XIoxRHYYcEwb052hrV+/ZmP2bTB7fI2ZPVazx9caZt8Gs8fn7to6EtOC0ck+H0iwfsM+DFi01hsb/lAmZ/ZtMHt8jZk9VrPH1xpm3wazx+fe2trmAozD+KNtBH7j7Dag9lzMvg1mj8+VYjV7fO6wDWaPz50vyvoHaDWlVAywAHhOa13d9q8M5zP7Npg9vsbMHqvZ42sNs2+D2eNzZ21O4EIIIczBTLPSCyGEaANJ4EII4aIkgQshhIuSBC6EEC5KErgQQrgoSeCiU1FKPaqUuu8cz89VSg1xZExCtJckcCF+bi4gCVy4BOkHLtyeUuoh4FdAJkbBpe1ACbAI8MGoZ70AGAF8YX2uBLjKuopXgEigAmPKr1RHxi9EcySBC7emlBoNvA2ch1G8bQfwGvCW1rrAuswTQK7W+mWl1NvAF1rr5dbnvgNu01ofVkqdh1Eydarjt0SIX+rwrPRCmNwFwAqtdQWAUqqhYt4wa+IOAQKBb85+oVIqEDgfWNaoEqqv3SMWopUkgYvOoKmfmW8Dc7XWu5RSNwOTm1jGAyjWWo+wX2hCtJ+cxBTubj1whVKqi1IqCLjc+ngQkK2U8saYTaZBqfU5tNanMSbgvRqMCQmUUomOC12Ic5M2cOH2Gp3EPA5kAfuBcuB+jii/dAAAAGJJREFU62N7gCCt9c1KqQnA60A1MA+jTOqrQBRG3esPtdaPO3wjhGiCJHAhhHBR0oQihBAuShK4EEK4KEngQgjhoiSBCyGEi5IELoQQLkoSuBBCuChJ4EII4aIkgQshhIv6/w9NWOiORlj4AAAAAElFTkSuQmCC\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# compare each of the variables since beginning of the year\\n\",\n \"since = '2020-01-01'\\n\",\n \"predicted = transform(res, since)\\n\",\n \"actual = transform(frame_t, since)\\n\",\n \"\\n\",\n \"for asset in frame_t.columns:\\n\",\n \" compare_plot(asset, actual, predicted)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What's New\\n\",\n \"\\n\",\n \"* New `IRCMSSpreadOption` instrument (`from gs_quant.instruments import IRCMSSpreadOption`)\\n\",\n \"* New tunable epidemiological models (SIR, SEIR, age-stratified SEIR) (`from gs_quant.models.epidemiology import SIR, SEIR, SEIRCMAgeStratified`) \\n\",\n \"* New timeseries functions (percentile, conditional funcs, exponential_moving_average) (`from gs_quant.timeseries import *`)\\n\",\n \"* Added discount factor information to the `Cashflows` measure\\n\",\n \"* `to_frame()` function for portfolios now supports nested portfolios\\n\",\n \"* `gs-quant` now compatible with `pandas` versions > 1.0.0\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/made_with_gs_quant/7-Predicting Performance and Live Risk.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Live Risk and Performance Predict\\n\",\n \"\\n\",\n \"### Summary \\n\",\n \"\\n\",\n \"While we are working on delivering live risk in gs-quant, computing risk real-time for large portfolios can be computationaly expensive. In this notebook, I team up with FICC Derivatives' David Vanden Bon to introduce a simple and lightweight framework for approximating it. This means you can compute your end of day risk and connect a live market feed to this code to estimate how performance, delta and gamma risk are changing for your book in real time. \\n\",\n \"\\n\",\n \"You can also use this framework to predict performance - that is, estimate impact of a hypothetical market move, which can be a useful risk management tool.\\n\",\n \"\\n\",\n \"More specifically, we do this by calculating a delta ladder for our portfolio via applying a number of spot shocks. We use the delta values to fit a polynomial for each instrument in the curve and then use this polynomial to appriximate delta, gamma and pnl for any given market move in these instruments.\\n\",\n \"\\n\",\n \"Let's get started!\\n\",\n \"\\n\",\n \"Don't forget to check out the [What's New](#What's-New) section for the latest gs-quant additions.\\n\",\n \"\\n\",\n \"The content of this notebook is split into:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Create or load portfolio](#2---Create-or-load-portfolio)\\n\",\n \"* [3 - Calculate delta ladder](#3---Calculate-delta-ladder)\\n\",\n \"* [4 - Fit polynomial](#4---Fit-polynomial)\\n\",\n \"* [5 - Estimate performance and risk for any market move](#5---Estimate-performance-and-risk-for-any-market-move)\\n\",\n \"* [What's New](#What's-New)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example. Below produced using gs-quant version 0.8.130.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Create or load portfolio\\n\",\n \"\\n\",\n \"The goal for this section is to load your own book. If you are an internal user please reach out for ways to connect your book.\\n\",\n \"\\n\",\n \"If you are an external user, we have additional natural language processing tools to help map your book coming soon! \\n\",\n \"\\n\",\n \"For this note, I'll create a sample portfolio of a combination of 3m, 1y, 2y and 3y expiry payer and receiver swaptions on 5y, 10y, 15y and 20y tails struck 10, 20 and 30bps OTMF and resolve it to fix all the relative parameters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"\\n\",\n \"ccy = Currency.USD\\n\",\n \"payers = [IRSwaption(PayReceive.Pay, termination_date='{}y'.format(t), notional_currency=ccy, \\n\",\n \" expiration_date='{}m'.format(e), strike='a+{}'.format(s)) \\n\",\n \" for t in [5, 10, 15, 20] for e in [3, 12, 24, 36] for s in [10, 20, 30]]\\n\",\n \"\\n\",\n \"receivers = [IRSwaption(PayReceive.Receive, termination_date='{}y'.format(t), notional_currency=ccy, \\n\",\n \" expiration_date='{}m'.format(e), strike='a-{}'.format(s)) \\n\",\n \" for t in [5, 10, 15, 20] for e in [3, 12, 24, 36] for s in [10, 20, 30]]\\n\",\n \"\\n\",\n \"portfolio = Portfolio(payers + receivers)\\n\",\n \"portfolio.resolve()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Calculate Delta Ladder\\n\",\n \"\\n\",\n \"Now let's take our portfolio and calculate a delta ladder by shocking spot with varying shifts (where 0 our portfolio delta without any shocks applied). Each row in the resulting dataframe corresponds to a different instrument. We will use this delta information to fit a polynomial for each of these instruments in the next step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
mkt_typemkt_assetmkt_classmkt_point-50-40-30-20-10-5-20251020304050
0IRUSDCASHO/N-96.652300-87.508851-80.140622-74.702595-71.280237-70.330161-70.001395-69.881709-69.840876-69.925542-70.448960-72.866759-76.972091-82.564403-89.423074
1IRUSDCASHCASH STUB19236.46265815938.26419812100.0098237851.2264803375.4121261107.540696-251.889772-1155.410051-2055.450143-3397.138702-5601.994693-9838.668869-13744.821189-17227.777252-20248.264302
2IRUSDFRAJUN2058295.74476248164.75397436542.37939623781.78665510376.9656343584.071217-489.994901-3198.958126-5898.759200-9925.239905-16549.585996-29314.223366-41151.872378-51799.726695-61135.816203
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" mkt_type mkt_asset mkt_class mkt_point -50 -40 \\\\\\n\",\n \"0 IR USD CASH O/N -96.652300 -87.508851 \\n\",\n \"1 IR USD CASH CASH STUB 19236.462658 15938.264198 \\n\",\n \"2 IR USD FRA JUN20 58295.744762 48164.753974 \\n\",\n \"\\n\",\n \" -30 -20 -10 -5 -2 \\\\\\n\",\n \"0 -80.140622 -74.702595 -71.280237 -70.330161 -70.001395 \\n\",\n \"1 12100.009823 7851.226480 3375.412126 1107.540696 -251.889772 \\n\",\n \"2 36542.379396 23781.786655 10376.965634 3584.071217 -489.994901 \\n\",\n \"\\n\",\n \" 0 2 5 10 20 \\\\\\n\",\n \"0 -69.881709 -69.840876 -69.925542 -70.448960 -72.866759 \\n\",\n \"1 -1155.410051 -2055.450143 -3397.138702 -5601.994693 -9838.668869 \\n\",\n \"2 -3198.958126 -5898.759200 -9925.239905 -16549.585996 -29314.223366 \\n\",\n \"\\n\",\n \" 30 40 50 \\n\",\n \"0 -76.972091 -82.564403 -89.423074 \\n\",\n \"1 -13744.821189 -17227.777252 -20248.264302 \\n\",\n \"2 -41151.872378 -51799.726695 -61135.816203 \"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType, Price, IRDeltaLocalCcy\\n\",\n \"\\n\",\n \"# calculate the base delta case (current delta of portfolio - 0bp shift in the rates)\\n\",\n \"delta = portfolio.calc(IRDeltaLocalCcy).aggregate().rename(columns = {'value': 0})\\n\",\n \"\\n\",\n \"# calculate delta ladder (local delta for a number of shifts)\\n\",\n \"shifts = [-50, -40, -30, -20, -10, -5, -2, 0, 2, 5, 10, 20, 30, 40, 50]\\n\",\n \"results = {}\\n\",\n \"with PricingContext(is_async=True):\\n\",\n \" for shift in shifts:\\n\",\n \" with MarketDataShockBasedScenario({\\n\",\n \" MarketDataPattern('IR', ccy): MarketDataShock(MarketDataShockType.Absolute, shift / 10000),\\n\",\n \" MarketDataPattern('IR Reset', '{}-3m'.format(ccy)): MarketDataShock(MarketDataShockType.Absolute, shift / 10000),\\n\",\n \" MarketDataPattern('IR Reset', '{}-6m'.format(ccy)): MarketDataShock(MarketDataShockType.Absolute, shift / 10000)\\n\",\n \" }):\\n\",\n \" results[shift] = portfolio.calc(IRDeltaLocalCcy)\\n\",\n \"\\n\",\n \"# aggregate delta and insert into dataframe\\n\",\n \"for shift in shifts:\\n\",\n \" delta[shift] = results[shift].aggregate().value\\n\",\n \"\\n\",\n \"# insert base delta into sorted numerical column order \\n\",\n \"cols = list(delta.columns)\\n\",\n \"column_list = cols[:4] + sorted(cols[4:])\\n\",\n \"delta = delta[column_list]\\n\",\n \"delta.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Fit Polynomial\\n\",\n \"\\n\",\n \"With delta accross various shocks for each instrument in hand (the frame above), we can calculate a polynomial which fits the evolution of delta across the shifts for each instrument.\\n\",\n \"\\n\",\n \"`coeffs` will give us the polynomial (order number of shifts-1) coefficients for each instrument, thus `coeffs` is a matrix of size `len(delta)` x `len(shifts)` where each matrix row is a polynomial corresponding to the coefficients for each instrument. In this example it will be 30x15.\\n\",\n \"\\n\",\n \"We'll use this polynomial in the next step to extrapolate delta, gamma and performance to any market move.\\n\",\n \"\\n\",\n \"Note polynomial fitting is as much as art it is a science and additional techniques can be used here to avoid overfitting which can produce kinks and other undesirable effects.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"coeffs = [np.polynomial.polynomial.polyfit(shifts, row.values, len(shifts) - 1) for _, row in delta[shifts].iterrows()]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now visualize what the polynomial looks for a particular point in the curve - '5Y' in the example below. The points in the graph are the shocks we fit the polynomial on for the '5Y' instrument.\\n\",\n \"\\n\",\n \"This means that for '5Y' instrument (and the rest of instruments in the table above) we can plug in any market move and get the expected delta (plug in x=spot and get y=delta, which we'll do in the next step).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'Delta')\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"point = '5Y'\\n\",\n \"\\n\",\n \"x = shifts\\n\",\n \"y = delta[delta['mkt_point'] == point][shifts].values[0]\\n\",\n \"coefficients = np.polyfit(x, y, len(x)-1)\\n\",\n \"poly = np.poly1d(coefficients)\\n\",\n \"\\n\",\n \"new_x = np.linspace(x[0], x[-1], 100)\\n\",\n \"new_y = poly(new_x)\\n\",\n \"\\n\",\n \"plt.plot(x, y, \\\"o\\\", new_x, new_y)\\n\",\n \"plt.xlim([x[0]-1, x[-1] + 1])\\n\",\n \"plt.title('Delta curve for {}'.format(point))\\n\",\n \"plt.xlabel('Spot Move (bps)')\\n\",\n \"plt.ylabel('Delta')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5 - Predicting Performance and Risk\\n\",\n \"\\n\",\n \"We can already get expected delta for any market move using our polynomial above - with a big of simple math, we can also get gamma (first derivative) and pnl approximation. Let's define a few functions to help us to that.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def delta_f(inst_coeff, market_move):\\n\",\n \" return sum([c*market_move**(i) for i, c in enumerate(inst_coeff)])\\n\",\n \"\\n\",\n \"def gamma_f(inst_coeff, market_move):\\n\",\n \" return sum([(i + 1)*c*market_move**(i) for i, c in enumerate(inst_coeff[1:])])\\n\",\n \" \\n\",\n \"def pnl_f(inst_coeff, market_move):\\n\",\n \" return sum([(c*market_move**(i + 1))/(i + 1) for i, c in enumerate(inst_coeff)])\\n\",\n \" \\n\",\n \"def get_derived_values(inst_coeff, market_move):\\n\",\n \" b_delta = inst_coeff[0]\\n\",\n \" b_gamma = gamma_f(inst_coeff, 0)\\n\",\n \" \\n\",\n \" new_delta = delta_f(inst_coeff, market_move)\\n\",\n \" new_gamma = gamma_f(inst_coeff, market_move)\\n\",\n \"\\n\",\n \" delta_pnl = b_delta * market_move\\n\",\n \" gamma_pnl = pnl_f(inst_coeff, market_move) - delta_pnl\\n\",\n \" \\n\",\n \" return {'current_delta': b_delta, 'current_gamma': b_gamma, 'new_delta': new_delta, 'new_gamma': new_gamma,\\n\",\n \" 'delta_pnl': delta_pnl, 'gamma_pnl': gamma_pnl}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now imagine you have a live market data feed - you can plug that in for `market_moves` and get live risk on your book. You can also plug in any market move to predict pnl and risk for your book. \\n\",\n \"\\n\",\n \"Here I apply a flat 22bp move to all instruments but you can specify different moves for each instrument using the `market_moves` dictionary. As an example, I'll update O/N to 9bps and 10Y swap to 25.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# plug in move (bps) - flat accross all instruments or construct custom dictionary\\n\",\n \"move = 22\\n\",\n \"market_moves = dict(zip(delta.mkt_point.values, len(delta.mkt_point)*[move]))\\n\",\n \"market_moves['O/N'] = 9\\n\",\n \"market_moves['10Y'] = 25\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's now calculate estimated delta, gamma and pnl given these market moves.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
current_deltacurrent_gammanew_deltanew_gammadelta_pnlgamma_pnl
mkt_point
O/N-69.8817090.040065-70.306373-0.133143-628.935377-0.742775
CASH STUB-1155.410051-450.968775-10675.092828-418.613276-25419.021126-106376.459182
JUN20-3198.958126-1352.429189-31787.348638-1230.087633-70377.078769-319716.680302
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" current_delta current_gamma new_delta new_gamma \\\\\\n\",\n \"mkt_point \\n\",\n \"O/N -69.881709 0.040065 -70.306373 -0.133143 \\n\",\n \"CASH STUB -1155.410051 -450.968775 -10675.092828 -418.613276 \\n\",\n \"JUN20 -3198.958126 -1352.429189 -31787.348638 -1230.087633 \\n\",\n \"\\n\",\n \" delta_pnl gamma_pnl \\n\",\n \"mkt_point \\n\",\n \"O/N -628.935377 -0.742775 \\n\",\n \"CASH STUB -25419.021126 -106376.459182 \\n\",\n \"JUN20 -70377.078769 -319716.680302 \"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd \\n\",\n \"\\n\",\n \"# calculate values for all the instruments\\n\",\n \"instruments = delta['mkt_point']\\n\",\n \"results = pd.DataFrame([get_derived_values(coeffs[delta[delta['mkt_point'] == instrument].index[0]], \\n\",\n \" market_moves[instrument]) for instrument in list(instruments)])\\n\",\n \"results.index = instruments\\n\",\n \"results.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We now have delta, gamma and delta given a specified move in each instrument. Current delta and gamma are values for the current portfolio and new delta and gamma are values after the market move is applied.\\n\",\n \"\\n\",\n \"Now to better understand the results, let's examine them visually! Note all of these values are approximations and may differ from actual performance and risk for the book.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"results[['delta_pnl', 'gamma_pnl']].plot(kind='bar', stacked=True, title='Gamma and Delta PnL given a {}bp move'.format(move), figsize=(10, 5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"risk_changes = pd.DataFrame([results.new_delta - results.current_delta, results.new_gamma - results.current_gamma]).T\\n\",\n \"risk_changes.columns = ['delta change', 'gamma change']\\n\",\n \"risk_changes.plot(kind='bar', stacked=True, figsize=(10, 6), title='Risk changes given a {}bp move'.format(move))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Please reach out with your thoughts and feedback!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What's New\\n\",\n \"\\n\",\n \"* New `IRXccySwapFixFlt` and `IRXccySwapFixFix` instruments (`from gs_quant.instruments import IRXccySwapFixFlt, IRXccySwapFixFix`)\\n\",\n \"* Improved error handling\\n\",\n \"* Improved computational efficiency - see `4-Delta Hedging` for examples\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/made_with_gs_quant/8-What's New Internal.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# What's New - Internal \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Summary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This new edition of `made-with-gs-quant` is tailored exclusively for our internal users and showcases some of the latest features of the internal gs-quant toolkit. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook we find solutions for some the most popular questions we get such as:\\n\",\n \"+ How do I generate an FX dual binary pricing grid in gs_quant? \\n\",\n \"+ How do I load the trade that my client did with us? \\n\",\n \"+ How do I price this rates structure live?\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The content of this notebook is split into:\\n\",\n \"1. [Let's get started with gs-quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"2. [TDAPI package](#2---TDAPI-package)\\n\",\n \"3. [Load trade from SecDb](#3---Load-trade-from-SecDb)\\n\",\n \"4. [Live Pricing](#4---Live-Pricing)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs-quant\\n\",\n \"Upon installing the `gs-quant` and `gs-quant[internal]` packages ([see here](https://gitlab.gs.com/marquee/analytics/gs_quant_internal#internal-users) for instructions), start your session using your kerberos entitlements directly (no need to register an app). \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"GsSession.use() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - TDAPI package\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"On top of the [externalised instruments](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/instruments/) supported in gs-quant, TDAPI enables you to directly leverage a wider range of instruments from SecDb, considerably widening the available universe for pricing and risk analytics.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To see which instruments are supported, simply import the package and type `tdapi.[tab]`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import gs_quant_internal.tdapi as tdapi\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from IPython.display import Image\\n\",\n \"Image(filename='images/tdapi_package.png')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook we showcase the use of the TDAPI package using the example of Dual FX Binary Options. \\n\",\n \"Let's consider 2 months options with payoff of $10M if strike 1 < EURUSD and GBPUSD < strike 2. These options are composed of 2 nested legs built as below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"\\n\",\n \"def FXDualBinaryOption(pair_1, pair_2, strikes_1, strikes_2, expiry, size):\\n\",\n \" portfolio = Portfolio()\\n\",\n \" for strike_1 in strikes_1:\\n\",\n \" for strike_2 in strikes_2:\\n\",\n \" over, under = pair_1.split('/')\\n\",\n \" leg_1 = tdapi.FXMultiCrossBinaryChildBuilder(over=over, under=under, strike=strike_1)\\n\",\n \" over, under = pair_2.split('/')\\n\",\n \" leg_2 = tdapi.FXMultiCrossBinaryChildBuilder(over=over, under=under, strike=strike_2, striketype='Binary Put')\\n\",\n \" dual = tdapi.FXMultiCrossBinaryBuilder(expiry=expiry, size=size, legs=(leg_1, leg_2), premium=0)\\n\",\n \" portfolio.append(dual)\\n\",\n \" return portfolio\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For assistance while building the instrument, `shif+tab` on the instrument name displays the list of parameters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from IPython.display import Image\\n\",\n \"Image(filename='images/instrument_help.png')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For each leg we consider a range of 9 possible strikes varying around the current spot rate of the associated currency pair. We build a portfolio of 81 options (9 USD/EUR strikes x 9 USD/GBP strikes) and compute the price for each option as a percentage of the payoff. Please note that the code uses internal SecDB notation for FX crosses (e.g. USD/EUR).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eur_strikes = [\\\"s{:+}%\\\".format(x) for x in range(-4, 5)]\\n\",\n \"gbp_strikes = [\\\"s{:+}%\\\".format(x) for x in range(-4, 5)]\\n\",\n \"\\n\",\n \"dual_options = FXDualBinaryOption('USD/EUR', 'USD/GBP', eur_strikes, gbp_strikes, '2m', 10e6)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"with PricingContext():\\n\",\n \" dual_prices = dual_options.dollar_price()\\n\",\n \"\\n\",\n \"prices = pd.DataFrame(index=eur_strikes, columns=gbp_strikes)\\n\",\n \"for dual in dual_options:\\n\",\n \" prices.loc[dual.legs[0].strike][dual.legs[1].strike] = dual_prices[dual] / 10e6\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now visualise the price repartition of the options on the strike grid.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"plt.subplots(figsize=(14, 8))\\n\",\n \"ax = sns.heatmap(prices.apply(pd.to_numeric), annot=True, fmt='.2%', cmap='YlGn')\\n\",\n \"ax.set(ylabel='USD/EUR Strikes', xlabel='USD/GBP Strikes', title='Option prices depending on Strike')\\n\",\n \"ax.xaxis.tick_top()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By looking at prices as a % of premium, we can assimilate this grid of binary prices to the discounted value of the risk neutral probability of both crosses being in the money in two months time. This begs the question of how correlated they are and what premium we are saving from the correlation effect of USD/EUR and USD/GBP for the various different events.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's investigate by building a similar price grid for the individual binary options represented by the two legs. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def FXBinaryOptionPrice(pair, strike, expiry, size, optiontype='Call'):\\n\",\n \" over, under = pair.split('/')\\n\",\n \" with PricingContext():\\n\",\n \" option = tdapi.FXBinaryBuilder(over=over, under=under, strike=strike, expiry=expiry, size=size,\\n\",\n \" premium=0, optiontype=optiontype)\\n\",\n \" price = option.dollar_price()\\n\",\n \" return price.result()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"indep_prices = pd.DataFrame(index=eur_strikes, columns=gbp_strikes)\\n\",\n \"for dual in dual_options:\\n\",\n \" eurusd_price = FXBinaryOptionPrice('USD/EUR', dual.legs[0].strike, expiry='2m', size=10e6)\\n\",\n \" gbpusd_price = FXBinaryOptionPrice('USD/GBP', dual.legs[1].strike, expiry='2m', size=10e6,\\n\",\n \" optiontype='Put')\\n\",\n \" indep_prices.loc[dual.legs[0].strike][dual.legs[1].strike] = eurusd_price/10e6 * gbpusd_price/10e6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.subplots(figsize=(14, 8))\\n\",\n \"ax = sns.heatmap(indep_prices.apply(pd.to_numeric)-prices.apply(pd.to_numeric), annot=True, fmt='.2%', cmap='YlGn')\\n\",\n \"ax.set(ylabel='USD/EUR Strikes', xlabel='USD/GBP Strikes', title='Option price probabilities assuming independance of xccy pairs')\\n\",\n \"ax.xaxis.tick_top()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By comparing the prices of the individual binary options in the above table to that of the dual binary option, you can see an approximation for the premium saved (or cost) from the correlation factor of the events.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 - Load trade from SecDb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Going one step further into leveraging the resources of SecDb, as an internal users you are now able to load real SecDb trades or portfolio of trades referenced by their SecDb ETI or Book ID.\\n\",\n \"With this feature you can now run concrete risk analysis your own book. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With `from_book()` you can easily create a portfolio from your existing SecDb book:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio.from_book('my Book ID') # replace with your secDb book ID e.g. '76037320'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Your gs-quant portfolio now contains instruments based on your SecDb book leaves:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(SDBBZCJ333333WQR3D (CashPaymentBuilder),\\n\",\n \" SDBBZCJ333333WQR3D (CashPaymentBuilder),\\n\",\n \" LTAABBCS3333PGQGCZ7A (IRSwaptionBuilder),\\n\",\n \" LTAABBCS3333PGQGCZ7A (CashPaymentBuilder),\\n\",\n \" LTAABBCS3333NRX9CCJA (CashPaymentBuilder),\\n\",\n \" LTAABBCS3333NRX9CCJA (IRSwaptionBuilder))\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"portfolio.as_dict()['instruments'][:5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can use all the gs-quant functionalities to price and calculate any of the available risk measures on your own portfolio:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import DollarPrice, IRDeltaParallel\\n\",\n \"results = portfolio.calc((DollarPrice, IRDeltaParallel))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To view risk results, you may directly reference the SecDb leaf:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 167,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"({DollarPrice (RiskMeasure): -191826.0084344892,\\n\",\n \" IRDeltaParallel (RiskMeasure): -13401.772371713341},\\n\",\n \" {DollarPrice (RiskMeasure): 701555.9420614609,\\n\",\n \" IRDeltaParallel (RiskMeasure): -14.823504145097733})\"\n ]\n },\n \"execution_count\": 167,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"results[portfolio['my leaf name']] # replace with your SecDb leaf e.g. 'LTAABBCS3333PGQGCZ7A'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or view book level results:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"122075528.76810677\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"results[DollarPrice].aggregate()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For trades involving supported instruments, `from.eti()` creates a gs-quant portfolio directly from the existing SecDb trade referenced by the ETI number:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio.from_eti('my ETI number') # Replace with your ETI trade number\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With `from_book` and `from_eti`, you can now fully leverage the gs-quant risk package on your own SecDb books! **Please note that all portfolio types are not yet handled.** Should you experience any issue, reach-out to the `gs-quant` distro.\\n\",\n \"For more examples of what you can do with Portfolios, refer to the [examples folder](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/examples/) and [previous made_with_gs_quant notebooks](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/tree/master/gs_quant/made_with_gs_quant/) of `made_with_gs_quant`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - Live Pricing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, another powerful new feature brought to our internal users is the ability to get live prices.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Re-using the dual option portfolio built in the first section, let's compare our portfolio price under yesterday's market data at close and live pricing environments.\\n\",\n \"Here we use a PricingContext with batch to group the requests together and avoid timeouts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"160,206,436\\n\",\n \"158,675,150\\n\"\n ]\n }\n ],\n \"source\": [\n \"from gs_quant.markets import LiveMarket\\n\",\n \"\\n\",\n \"live_pricing = PricingContext(market=LiveMarket('LDN'), market_data_location='LDN')\\n\",\n \"close_pricing = PricingContext(is_batch=True, is_async=True)\\n\",\n \"\\n\",\n \"with live_pricing:\\n\",\n \" prices_live = dual_options.dollar_price()\\n\",\n \"with close_pricing:\\n\",\n \" prices_close = dual_options.dollar_price()\\n\",\n \" \\n\",\n \"print('{:,.0f}'.format(prices_live.aggregate()))\\n\",\n \"print('{:,.0f}'.format(prices_close.aggregate()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`LiveMarket` accesses the latest market data available for the specified market location. This means you can now use gs-quant to compute real-time prices and intra-day risk on your portfolio.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This closes this first internal edition of `made_with_gs_quant` please continue sharing your feedback on to make `gs-quant` even more useful to you, and look out for the next editions of `What's New` in `made_with_gs_quant`. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Happy hacking!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/made_with_gs_quant/9-Hedging using Machine Learning Techniques.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Hedging using Machine Learning Techniques\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Summary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As systematic and macro factors dominate the investment landscape, we see equity investors move away from one-size-fits all hedging strategies to more precise ways to separate intended and unintended risks and isolate alpha. \\n\",\n \"\\n\",\n \"If you are a fundamental equity investor, take concentrated positions in single names, or have significant idiosyncratic risk that is otherwise difficult to hedge, this highly customizable correlation-based approach is for you. Traditional factor based hedges can fare poorly when most of the risk cannot be explained by factors – in those instances this approach can allow for much better correlation to offset risk as well as a number of ways to guide which names get included while ensuring a high level of tradability by controlling liquidity and borrow costs.\\n\",\n \"\\n\",\n \"In this notebook, we will showcase how to leverage this approach through one of our most popular tools, the [marquee performance hedger](https://marquee.gs.com/s/hedging/performance).\\n\",\n \"\\n\",\n \"Additionally, based on feedback we have received from top users, we are adding the ability to easily run and compare hedges in python through [gs quant](https://developer.gs.com/docs/gsquant/) as well as new modeling techniques, two of whose key advantages are:\\n\",\n \"\\n\",\n \"* **Increased accuracy** through reduced overfitting\\n\",\n \"* **More control** by allowing the user to specify how concentrated or diversified the hedge portfolio is\\n\",\n \"\\n\",\n \"\\n\",\n \"The contents of this notebook are as follows:\\n\",\n \"* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\\n\",\n \"* [2 - Calculate a hedge](#2---Calculate-a-hedge)\\n\",\n \"* [3 - Increased accuracy](#3---Increased-accuracy)\\n\",\n \"* [4 - More control](#4---More-control)\\n\",\n \"* [5 - Customizing your optimization](#5---Customizing-your-optimization)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Let's get started with gs quant\\n\",\n \"Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below, you can set the logging level of the notebook. By default, we set the level to INFO to show informative statements about the hedging functions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import logging\\n\",\n \"\\n\",\n \"logging.basicConfig(level=logging.INFO)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - Calculate a Hedge\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's start by calculating a hedge for Amazon with some starting parameters.\\n\",\n \"\\n\",\n \"To leverage the (machine learning-based) techniques in the enhanced performance hedger, please ensure:\\n\",\n \"* `use_machine_learning` parameter is true\\n\",\n \"\\n\",\n \"Note: the hedge result utilizes optimal values found (using grid search) for the ML parameters, which are known as Concentration (`lasso_weight`) and Diversity (`ridge_weight`)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
countrynametransactionCostsectorsharesassetIdbbidcurrencyindustrymarginalCostadvPercentagepriceweightborrowCost
0United StatesAdobe Inc20.263329Information Technology-832.032329MA4B66MW5E27U9XPV7XADBE UWUSDSoftware0.2613260.000149310.00-0.01289740
1United StatesAutodesk Inc20.628566Information Technology-1387.449548MA4B66MW5E27U9XPVBTADSK UWUSDSoftware0.2146010.000393149.96-0.01040340
2United StatesAkamai Technologies Inc26.810420Information Technology-4678.311848MA4B66MW5E27U9XPVVMAKAM UWUSDIT Services0.5642980.00205289.98-0.02104840
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" country name transactionCost \\\\\\n\",\n \"0 United States Adobe Inc 20.263329 \\n\",\n \"1 United States Autodesk Inc 20.628566 \\n\",\n \"2 United States Akamai Technologies Inc 26.810420 \\n\",\n \"\\n\",\n \" sector shares assetId bbid currency \\\\\\n\",\n \"0 Information Technology -832.032329 MA4B66MW5E27U9XPV7X ADBE UW USD \\n\",\n \"1 Information Technology -1387.449548 MA4B66MW5E27U9XPVBT ADSK UW USD \\n\",\n \"2 Information Technology -4678.311848 MA4B66MW5E27U9XPVVM AKAM UW USD \\n\",\n \"\\n\",\n \" industry marginalCost advPercentage price weight borrowCost \\n\",\n \"0 Software 0.261326 0.000149 310.00 -0.012897 40 \\n\",\n \"1 Software 0.214601 0.000393 149.96 -0.010403 40 \\n\",\n \"2 IT Services 0.564298 0.002052 89.98 -0.021048 40 \"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from gs_quant.api.gs.hedges import GsHedgeApi\\n\",\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier\\n\",\n \"from datetime import date\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"target_asset = SecurityMaster.get_asset('AMZN UW', AssetIdentifier.BLOOMBERG_ID).get_marquee_id()\\n\",\n \"universe = SecurityMaster.get_asset('SPX', AssetIdentifier.BLOOMBERG_ID).get_marquee_id()\\n\",\n \"hedge_query_example = GsHedgeApi.construct_performance_hedge_query(hedge_target=target_asset, \\n\",\n \" universe=(universe, ),\\n\",\n \" notional=10e6,\\n\",\n \" observation_start_date=date(2019, 3, 25), \\n\",\n \" observation_end_date=date(2020, 3, 24), \\n\",\n \" backtest_start_date=date(2020, 3, 25), \\n\",\n \" backtest_end_date=date(2020, 4, 24),\\n\",\n \" use_machine_learning=True)\\n\",\n \"\\n\",\n \"# Calculate the hedge using the hedge_query_example as input\\n\",\n \"results = GsHedgeApi.calculate_hedge(hedge_query_example)\\n\",\n \"pd.DataFrame(results['result']['hedgedTarget']['constituents']).head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Increased Accuracy\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's compare results with and without using the new machine learning parameters and examine the first advantage - improved accuracy.\\n\",\n \"\\n\",\n \"We can do this by looking at the out-of-sample differences in cumulative returns of the two hedges over a hedge backtest period. Note here that we fit the hedge to the observation window and use the backtest window to see how both hedges perform. \\n\",\n \"\\n\",\n \"Note for this example: the ML parameters, Concentration (lasso_weight) and Diversity (ridge_weight), are being manually passed in, which is an alternative way to run the new performance hedger (compared to running grid search to find the optimal pair)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"observation_start = date(2019, 3, 25)\\n\",\n \"observation_end = date(2020, 3, 24)\\n\",\n \"backtest_start = observation_end\\n\",\n \"backtest_end = date(2020, 4, 24)\\n\",\n \"\\n\",\n \"standard_hedger_query = GsHedgeApi.construct_performance_hedge_query(hedge_target=target_asset, \\n\",\n \" universe=(universe, ), \\n\",\n \" notional=10e6,\\n\",\n \" observation_start_date=observation_start, \\n\",\n \" observation_end_date=observation_end, \\n\",\n \" backtest_start_date=backtest_start, \\n\",\n \" backtest_end_date=backtest_end,\\n\",\n \" max_return_deviation=20)\\n\",\n \"\\n\",\n \"new_hedger_query = GsHedgeApi.construct_performance_hedge_query(hedge_target=target_asset, \\n\",\n \" universe=(universe, ), \\n\",\n \" notional=10e6,\\n\",\n \" observation_start_date=observation_start,\\n\",\n \" observation_end_date=observation_end,\\n\",\n \" backtest_start_date=backtest_start,\\n\",\n \" backtest_end_date=backtest_end,\\n\",\n \" use_machine_learning=True,\\n\",\n \" lasso_weight=5.0,\\n\",\n \" ridge_weight=5.0,\\n\",\n \" max_return_deviation=20)\\n\",\n \"\\n\",\n \"standard_results = GsHedgeApi.calculate_hedge(standard_hedger_query)\\n\",\n \"new_hedger_results = GsHedgeApi.calculate_hedge(new_hedger_query)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"def compare_backtests_against_target_asset(new_results, standard_results, figsize=(10, 6)):\\n\",\n \" dates, target_returns = zip(*new_results['result']['target']['backtestPerformance'])\\n\",\n \" _, new_hedge_returns = zip(*new_results['result']['hedge']['backtestPerformance'])\\n\",\n \" _, standard_hedge_returns = zip(*standard_results['result']['hedge']['backtestPerformance'])\\n\",\n \" \\n\",\n \" results = pd.DataFrame([pd.Series(target_returns, dates, name='Target'), \\n\",\n \" pd.Series(new_hedge_returns, dates, name='New Hedger'),\\n\",\n \" pd.Series(standard_hedge_returns, dates, name='Standard Hedger')]).T\\n\",\n \" \\n\",\n \" results.plot(figsize=(10, 6))\\n\",\n \" plt.legend(fontsize=18)\\n\",\n \" plt.xlabel('Backtest Period', size=13)\\n\",\n \" plt.ylabel('Cumulative Returns (% change)', size=13)\\n\",\n \" plt.title('Hedge Performance Against Asset', size=22)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, we plot the cumulative returns of the target asset against the cumulative returns of each hedge.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"compare_backtests_against_target_asset(new_hedger_results, standard_results)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As you can see above, the New Performance Hedger more closely tracks the target asset in the backtest, or \\\"simulated future\\\", period (and thus is more accurate).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4 - More Control\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, let's demonstrate the second advantage, namely the enhanced control we have over the performance hedger results.\\n\",\n \"\\n\",\n \"Below, you can plot the effects of varying Concentration and Diversity on your hedge query from section 2 above. \\n\",\n \"\\n\",\n \"In this example, if the values for Concentration are 'None' and the values for Diversity are [10, 20], then the plotting function would run the hedge query passed in for Diversity values of 10% and 20% and then plot the weight/number of asset distributions on the y-axis and x-axis, respectively. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# For now, use ONLY Concentration or Diversity when plotting to view the effects of this hyperparameter value changing on a hedge (set the hyperparam you aren't \\n\",\n \"# using to None)\\n\",\n \"hyperparams = {'Concentration': None, 'Diversity': [10, 20]}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from gs_quant.markets.hedge import Hedge\\n\",\n \"\\n\",\n \"# Plot results of the hedge - with emphasis on the effects that either the Concentration or Diversity hyperparameter has on the hedge\\n\",\n \"hedge_plot = Hedge.plot_weights_against_number_of_assets(hedge_query_example, hyperparams, figsize=(10, 6))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As you can see above, varying the value of a single hyperparameter has significant effects on the weights & total number of assets of the hedge portfolio that is constructed to hedge the single target asset.\\n\",\n \"\\n\",\n \"For this particular example, the effect is that the weights become more balanced the more the \\\"Diversity\\\" hyperparameter increases.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5 - Customizing your Optimization\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since we know how to run a basic hedge, let's experiment with the concentration and diversity metrics to find an optimal hedge.\\n\",\n \"\\n\",\n \"Note, the hedger is flexible and allows you to choose which metric to optimize - for example, you may want to run to optimize correlation (in which case you would maximize r-squared) or to optimize transaction costs (in which case you would minimize total transaction costs).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Modify hyperparameter grid to the values you want to use to find the optimal hedge . As mentioned before, the terminology used for Concentration and Diversity\\n\",\n \"# seen below relates to the terminology used for each term in the machine learning literature.\\n\",\n \"# Concentration = Lasso (as a percentage)\\n\",\n \"# Diversity = Ridge (as a percentage)\\n\",\n \"hyperparams = {'Concentration': [0, 20, 40], 'Diversity': [10, 20]}\\n\",\n \"\\n\",\n \"# Modify this to optimize a metric (maximize or minimize depending on the metric. \\n\",\n \"# See the create_optimization_mappings function for how metrics are optimized\\n\",\n \"metric_to_optimize = 'rSquared'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's now run the optimization through the custom grid of concentration/diversity values we specified, and optimize for the specified metric. In this case, we will look for the combination of concentration and diversity values that maximizes correlation, or r-squared.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:gs_quant.markets.hedge:We are trying to maximize rSquared and will return the optimized hedge & metric value...\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge is using the following values for Concentration/Diversity: (0, 10)\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge value for rSquared: 67.6%\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge is using the following values for Concentration/Diversity: (0, 20)\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge value for rSquared: 65.3%\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge is using the following values for Concentration/Diversity: (20, 10)\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge value for rSquared: 65.6%\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge is using the following values for Concentration/Diversity: (20, 20)\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge value for rSquared: 63.4%\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge is using the following values for Concentration/Diversity: (40, 10)\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge value for rSquared: 65.0%\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge is using the following values for Concentration/Diversity: (40, 20)\\n\",\n \"INFO:gs_quant.markets.hedge:Current Hedge value for rSquared: 63.1%\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The optimal pair of hyperparameters was (0, 10), achieving a value for rSquared of 67.6% during the out of sample period.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from gs_quant.markets.hedge import Hedge\\n\",\n \"\\n\",\n \"opt_hedge, opt_metric_val, opt_hyperparams = Hedge.find_optimal_hedge(hedge_query_example, hyperparams, metric_to_optimize)\\n\",\n \"print(f'The optimal pair of hyperparameters was {opt_hyperparams}, achieving a value for {metric_to_optimize} '\\n\",\n \" f'of {opt_metric_val*100:.3}% during the out of sample period.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Stay tuned for additional ways to use the New Performance Hedger using `gs-quant`. For any questions/comments, feel free to reach out to the email distro **gs-data-ml**!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/reports_and_screens/00_fx/0000_vol_screen.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.timeseries import percentiles, volatility, last_value, Returns\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"import seaborn as sns\\n\",\n \"import pandas as pd\\n\",\n \"pd.options.display.float_format = '{:,.2f}'.format \\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from scipy import stats\\n\",\n \"import warnings\\n\",\n \"from datetime import date\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"sns.set(style=\\\"darkgrid\\\", color_codes=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Screen Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def format_df(data_dict):\\n\",\n \" df = pd.concat(data_dict, axis=1)\\n\",\n \" df.columns = data_dict.keys()\\n\",\n \" return df.fillna(method='ffill').dropna()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def volatility_screen(crosses, start_date, end_date, tenor='3m', history='2y', plot=True):\\n\",\n \" #replace with premium dataset for more history\\n\",\n \" fxspot_dataset, fxvol_dataset = Dataset('FXSPOT'), Dataset('FXIMPLIEDVOL')\\n\",\n \" spot_data, impvol_data, spot_fx, data = {}, {}, {}, {}\\n\",\n \" for cross in crosses:\\n\",\n \" spot_fx[cross] = fxspot_dataset.get_data(start_date, end_date, bbid=cross)[['spot']].drop_duplicates(keep='last')['spot']\\n\",\n \" spot_data[cross] = volatility(spot_fx[cross], tenor) # realized vol \\n\",\n \" impvol_data[cross] = fxvol_dataset.get_data(start_date, end_date, bbid=cross, tenor=tenor, deltaStrike='DN', location='NYC')[['impliedVolatility']]* 100\\n\",\n \"\\n\",\n \" spdata, ivdata = format_df(spot_data), format_df(impvol_data)\\n\",\n \" diff = ivdata.subtract(spdata).dropna()\\n\",\n \" for cross in crosses:\\n\",\n \" data[cross] = {'Spot': last_value(spot_fx[cross]),\\n\",\n \" f'{tenor} Implied': last_value(ivdata[cross]),\\n\",\n \" f'{tenor} Realized': last_value(spdata[cross]),\\n\",\n \" 'Diff': last_value(diff[cross]),\\n\",\n \" f'{history} Implied Low': min(ivdata[cross]),\\n\",\n \" f'{history} Implied High': max(ivdata[cross]),\\n\",\n \" '%-ile': last_value(percentiles(ivdata[cross]))\\n\",\n \" }\\n\",\n \" df = pd.DataFrame(data)\\n\",\n \" vol_screen = df.transpose()\\n\",\n \" \\n\",\n \" if plot:\\n\",\n \" for fx in vol_screen.index:\\n\",\n \" plt.scatter(vol_screen.loc[fx]['%-ile'], vol_screen.loc[fx]['Diff'])\\n\",\n \" plt.legend(vol_screen.index,loc='best', bbox_to_anchor=(0.9, -0.13), ncol=3)\\n\",\n \" \\n\",\n \" plt.xlabel('Percentile of Current Implied Vol')\\n\",\n \" plt.ylabel('Implied vs Realized Vol')\\n\",\n \" plt.title('Entry Point vs Richness')\\n\",\n \" plt.show()\\n\",\n \" return vol_screen.sort_values(by=['Diff']).style.background_gradient(subset=['Diff']).format(\\\"{:.1f}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### FX Implied Volatility Screen\\n\",\n \"Let's pull [GS FX Spot](https://marquee.gs.com/s/developer/datasets/FXSPOT) and [GS FX Implied Volatility](https://marquee.gs.com/s/developer/datasets/FXIMPLIEDVOL) and look at implied vs realized vol as well as current implied level as percentile relative to the last 2 years. Note, FX Spot uses GS NYC closes.\\n\",\n \"\\n\",\n \"\\n\",\n \"If you are looking for additional history or coverage, please see our premium version [link](https://marquee.gs.com/s/developer/datasets/FXIMPLIEDVOL_PREMIUM).\\n\",\n \"\\n\",\n \"The FX Structuring team uses this analysis to screen for the most attractive vols to buy in the 3m tenor by looking at where implied trades in its own history and where realized trades in relationship with implieds.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Entry Point vs Richness\\n\",\n \"\\n\",\n \"Note: Lower left corner shows currencies with low and cheap vol. Upper right corner\\n\",\n \"shows currencies with high and rich vol.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"\\n\",\n \"end = business_day_offset(date.today(), -1, roll='forward')\\n\",\n \"start = business_day_offset(end - relativedelta(years=2), -1, roll='forward')\\n\",\n \"\\n\",\n \"tenor = '3m'\\n\",\n \"history = '2y'\\n\",\n \"\\n\",\n \"screen = volatility_screen(g10, start, end, tenor, history, plot=True)\\n\",\n \"screen\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/reports_and_screens/00_fx/0001_vol_calendar_screen.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Vol Calendar Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.timeseries import last_value, forward_vol, percentiles\\n\",\n \"from gs_quant.markets.securities import AssetIdentifier, SecurityMaster\\n\",\n \"import pandas as pd\\n\",\n \"pd.options.display.float_format = '{:,.2f}'.format \\n\",\n \"import warnings\\n\",\n \"from datetime import date\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"\\n\",\n \"def vol_calendar_screen(crosses, long_tenor, short_tenor, start_date, end_date, history='2y',boxplot=True, deltaStrike='DN', location='NYC'):\\n\",\n \" impvol = Dataset('FXIMPLIEDVOL')\\n\",\n \" data = {}\\n\",\n \" boxplt = pd.DataFrame()\\n\",\n \" for bbid in crosses:\\n\",\n \" asset = SecurityMaster.get_asset(bbid, AssetIdentifier.BLOOMBERG_ID)\\n\",\n \" long_implied = impvol.get_data(start_date , end_date, bbid=bbid, tenor=long_tenor, deltaStrike=deltaStrike, location=location)[['impliedVolatility']]\\n\",\n \" short_implied = impvol.get_data(start_date , end_date, bbid=bbid, tenor=short_tenor, deltaStrike=deltaStrike, location=location)[['impliedVolatility']]\\n\",\n \" spread = (long_implied - short_implied)*100\\n\",\n \" spread = spread.dropna()\\n\",\n \" \\n\",\n \" boxplt[bbid] = spread['impliedVolatility']\\n\",\n \" data[bbid] = {'Current': last_value(spread['impliedVolatility']),\\n\",\n \" f'{history} Min': min(spread['impliedVolatility']),\\n\",\n \" f'{history} Max': max(spread['impliedVolatility']),\\n\",\n \" '%-ile': last_value(percentiles(spread['impliedVolatility'])),\\n\",\n \" }\\n\",\n \" df = pd.DataFrame(data)\\n\",\n \" vol_cal_screen = df.transpose()\\n\",\n \" vol_cal_screen = vol_cal_screen.sort_values(by=['Current'], ascending=False).style.background_gradient(subset=['Current']).format(\\\"{:.2f}\\\")\\n\",\n \" if boxplot:\\n\",\n \" f, ax = plt.subplots(figsize=(7, 6))\\n\",\n \" ax = sns.boxplot(data=boxplt, orient=\\\"h\\\", palette=\\\"mako\\\").set_title('Spread Ranges')\\n\",\n \" return vol_cal_screen\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#assumes tenors are ordered from shortest to longest\\n\",\n \"def steepest_flattest(crosses, tenors, top=10):\\n\",\n \" impvol = Dataset('FXIMPLIEDVOL')\\n\",\n \" vols = pd.DataFrame()\\n\",\n \" screen = pd.DataFrame(columns=['Cross', 'Tenors', 'Vol Far', 'Vol Near', 'Abs Spread', '%-ile'])\\n\",\n \" for bbid in crosses:\\n\",\n \" asset = SecurityMaster.get_asset(bbid, AssetIdentifier.BLOOMBERG_ID)\\n\",\n \" for tenor in tenors:\\n\",\n \" imp_vol = impvol.get_data(start , end, bbid=bbid, tenor=tenor, deltaStrike='DN', location='NYC')['impliedVolatility']\\n\",\n \" vols[bbid + f'_{tenor}'] = imp_vol\\n\",\n \" for x in tenors[:-1]:\\n\",\n \" for y in tenors[1:]:\\n\",\n \" if(x != y):\\n\",\n \" vol_far = vols[bbid + f'_{y}']\\n\",\n \" vol_near = vols[bbid + f'_{x}']\\n\",\n \" spread = vol_far - vol_near\\n\",\n \" row = {'Cross' : bbid, 'Tenors': f'{y}' +f'- {x}', 'Vol Far': last_value(vol_far)*100, 'Vol Near': last_value(vol_near)*100, 'Abs Spread' : last_value(spread), \\n\",\n \" '%-ile': last_value(percentiles(spread))}\\n\",\n \" screen = screen.append(row, ignore_index=True) \\n\",\n \" display(screen.sort_values(by=['%-ile'], ascending=False).head(top).style.set_caption(f'{top} Steepest Calendars').background_gradient(subset=['%-ile']))\\n\",\n \" display(screen.sort_values(by=['%-ile'], ascending=True).head(top).style.set_caption(f'{top} Flattest Calendars').background_gradient(subset=['%-ile']))\\n\",\n \" return screen\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## FX Volatility Calendar Spread\\n\",\n \"\\n\",\n \"The FX Structuring team uses this analysis to screen for the steepest and flattest spreads between 1y implied volatility - 3m implied volatility.\\n\",\n \"\\n\",\n \"We are pulling [GS FX Implied Volatility](https://marquee.gs.com/s/developer/datasets/FXIMPLIEDVOL) by default. If you are looking for additional history or coverage, please see our premium version [link](https://marquee.gs.com/s/developer/datasets/FXIMPLIEDVOL_PREMIUM).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"# Inputs to modify\\n\",\n \"\\n\",\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"\\n\",\n \"end = business_day_offset(date.today(), -1, roll='forward')\\n\",\n \"start = business_day_offset(end - relativedelta(years=2), -1, roll='forward')\\n\",\n \"\\n\",\n \"\\n\",\n \"long_tenor = '1y'\\n\",\n \"short_tenor = '3m'\\n\",\n \"history = '2y'\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"screen = vol_calendar_screen(crosses=g10, long_tenor='1y', short_tenor='3m', start_date=start, end_date=end, boxplot=True)\\n\",\n \"screen\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Steepest & Flattest Calendars\\n\",\n \"\\n\",\n \"The FX Structuring team uses this analysis to screen for the steepest calendars, both in absolute terms and in percentile terms across multiple tenors. The steepest and flattest calendars are sorted by the percentile of calendar vs. its own history.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tenors = ['3m', '6m', '1y', '2y']\\n\",\n \"result = steepest_flattest(crosses=g10, tenors=tenors, top=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/reports_and_screens/00_fx/0002_forward_vol_screen.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Forward Volatility Screen Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.timeseries import last_value, forward_vol, VolReference\\n\",\n \"from gs_quant.markets.securities import AssetIdentifier, SecurityMaster\\n\",\n \"import pandas as pd\\n\",\n \"pd.options.display.float_format = '{:,.2f}'.format \\n\",\n \"import warnings\\n\",\n \"from datetime import date\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"\\n\",\n \"def forward_volatility_screen(crosses, tenor, forward_tenor, start_date, end_date, plot=True):\\n\",\n \" data = {}\\n\",\n \" #replace with premium dataset for more history\\n\",\n \" impvol = Dataset('FXIMPLIEDVOL')\\n\",\n \" for bbid in crosses:\\n\",\n \" asset = SecurityMaster.get_asset(bbid, AssetIdentifier.BLOOMBERG_ID)\\n\",\n \" fwd_vol = forward_vol(asset, tenor=tenor, forward_start_date=forward_tenor, strike_reference=VolReference.DELTA_NEUTRAL, relative_strike=0)\\n\",\n \" change = fwd_vol.diff(5)\\n\",\n \" implied = impvol.get_data(start_date , end_date, bbid=bbid, tenor=tenor, deltaStrike='DN', location='NYC')[['impliedVolatility']]\\n\",\n \" data[bbid] = {f'{tenor}{forward_tenor} Fwd Vol': last_value(fwd_vol),\\n\",\n \" '1w Change': last_value(change),\\n\",\n \" f'{tenor} Implied Vol': last_value(implied['impliedVolatility'])*100,\\n\",\n \" 'Roll-down': last_value(fwd_vol) - last_value(implied['impliedVolatility'])*100,\\n\",\n \" 'Strike to Lows': min(implied['impliedVolatility'])*100 - last_value(fwd_vol),\\n\",\n \" 'Strike to Highs': max(implied['impliedVolatility'])*100 - last_value(fwd_vol),\\n\",\n \" 'Diff' : (max(implied['impliedVolatility'])*100 - last_value(fwd_vol)) + (min(implied['impliedVolatility'])*100 - last_value(fwd_vol))\\n\",\n \" }\\n\",\n \" df = pd.DataFrame(data)\\n\",\n \" fva_screen = df.transpose()\\n\",\n \" fva_screen = fva_screen.sort_values(by=['Roll-down'])\\n\",\n \" \\n\",\n \" if plot:\\n\",\n \" for fx in fva_screen.index:\\n\",\n \" plt.scatter(fva_screen.loc[fx]['Roll-down'], fva_screen.loc[fx]['Diff'])\\n\",\n \" plt.legend(fva_screen.index,loc='best', bbox_to_anchor=(0.9, -0.13), ncol=3)\\n\",\n \" plt.xlabel('Carry (vols)')\\n\",\n \" plt.ylabel('Upside vs Downside')\\n\",\n \" plt.title('Carry vs Upside/Downside')\\n\",\n \" plt.show()\\n\",\n \" return fva_screen.style.background_gradient(subset=['Roll-down']).format(\\\"{:.2f}\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Forward Volatility\\n\",\n \"The FX Structuring desk uses this analysis to screen for the most attractive 6m forward 6m vols in FVA format\\n\",\n \"\\n\",\n \"We are pulling [GS FX Implied Volatility](https://marquee.gs.com/s/developer/datasets/FXIMPLIEDVOL) by default. If you are looking for additional history or coverage, please see our premium version [link](https://marquee.gs.com/s/developer/datasets/FXIMPLIEDVOL_PREMIUM).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Carry vs Upside/Downside\\n\",\n \"\\n\",\n \"Note: Upper left corner shows attractive long vol crosses with low rolldown and high\\n\",\n \"upside vs. downside difference. Lower right corner shows attractive short vol crosses\\n\",\n \"with high carry and low upside vs. downside difference.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"\\n\",\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"EM = ['USDBRL', 'USDZAR', 'USDMXN', 'USDCLP','USDPLN', 'USDCZK', 'USDHUF', 'USDSGD', 'USDINR','USDKRW','USDSGD', 'USDINR',\\n\",\n \" 'USDTWD', 'USDTRY', 'USDPHD']\\n\",\n \"\\n\",\n \"\\n\",\n \"forward_tenor = '6m'\\n\",\n \"tenor = '6m'\\n\",\n \"\\n\",\n \"end = business_day_offset(date.today(), -1, roll='forward')\\n\",\n \"start = business_day_offset(end - relativedelta(years=2), -1, roll='forward')\\n\",\n \"\\n\",\n \"screen = forward_volatility_screen(g10, tenor, forward_tenor, start, end, plot=True)\\n\",\n \"screen\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/reports_and_screens/00_fx/0003_10x_binary_screen.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 10x Binaries\\n\",\n \"\\n\",\n \"This screen shows how far a OTMS a 3m binary option must be struck to have a payout ratio of approximately 10:1. \\n\",\n \"Normalized = % OTMS / 3m realized vol\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.timeseries import last_value, realized_volatility\\n\",\n \"from gs_quant.markets.securities import AssetIdentifier, SecurityMaster\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.instrument import FXBinary\\n\",\n \"from gs_quant.target.common import OptionType\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import FXSpot, Price\\n\",\n \"from gs_quant.data import DataContext\\n\",\n \"from gs_quant.api.gs.data import * \\n\",\n \"import pandas as pd\\n\",\n \"import warnings\\n\",\n \"from datetime import date\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"pd.set_option('display.precision', 3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Functions\\n\",\n \"\\n\",\n \"1) Calculating payout for binaries using defaults of 3m and 10:1\\n\",\n \"\\n\",\n \"\\n\",\n \"2) Plot historical spot with call and put strikes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def payout_struck_binaries(crosses, payout_ratio='10%', tenor='3m', normalize=True, start_date=business_day_offset(business_day_offset(date.today(), -1, roll='forward') - relativedelta(years=1), -1, roll='forward')):\\n\",\n \" \\\"\\\"\\\"Prices FX Binaries for inputted crosses strike price using inputted payout ratio\\n\",\n \" \\n\",\n \" Screens for % OTMS for a given payout ratio\\n\",\n \" \\n\",\n \" : param crosses: array of string FX pairs e.g. ['USDJPY', 'USDNOK']\\n\",\n \" : param payout_ratio: string with % used to solve for strike price e.g 10% solves for a 10:1 payout ratio\\n\",\n \" : param tenor: FX Binary expiration date\\n\",\n \" : param normalize: divides % OTMS by realized volatility \\n\",\n \" : param start_date: start date for volatility history, if normalizing % OTMS, defaults to 1yr\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" binaries = Portfolio([FXBinary(pair=cross, expiration_date=tenor, option_type=direction, \\n\",\n \" strike_price=f'p={payout_ratio}', premium=0) \\n\",\n \" for direction in ('Call', 'Put') for cross in crosses])\\n\",\n \" with PricingContext(): \\n\",\n \" binaries.resolve()\\n\",\n \" spot = binaries.calc(FXSpot) \\n\",\n \" bf = binaries.to_frame()\\n\",\n \" bf.index = bf.index.droplevel(0)\\n\",\n \" bf = bf[['pair', 'option_type', 'strike_price']]\\n\",\n \" bf['spot'] = list(spot)\\n\",\n \" bf['% otms'] = abs(bf.strike_price/bf.spot-1)*100\\n\",\n \" \\n\",\n \" #normalizing otms\\n\",\n \" if normalize:\\n\",\n \" df_vol = pd.DataFrame(columns=['pair','implied_vol'])\\n\",\n \" for cross in crosses:\\n\",\n \" asset = SecurityMaster.get_asset(cross, AssetIdentifier.BLOOMBERG_ID)\\n\",\n \" with DataContext(start=start_date, end=business_day_offset(date.today(), -1, roll='forward')): \\n\",\n \" vol = last_value(realized_volatility(asset, w=tenor))\\n\",\n \" df_vol = df_vol.append({'pair': f'{cross[:3]} {cross[3:]}', 'implied_vol': vol}, ignore_index=True)\\n\",\n \" bf = bf.merge(df_vol, left_on='pair', right_on='pair')\\n\",\n \" bf['normalized'] = bf['% otms'] / bf['implied_vol']\\n\",\n \" return bf.set_index(['option_type', 'pair']).sort_values(by=['option_type', '% otms'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def plot_strikes(crosses, binaries, start_date=business_day_offset(business_day_offset(date.today(), -1, roll='forward') - relativedelta(years=2), -1, roll='forward')):\\n\",\n \" \\\"\\\"\\\"Plots historical spot for each FX pair and strike prices from FX Binary \\n\",\n \" \\n\",\n \" : param crosses: array of string FX pairs e.g. ['USDJPY', 'USDNOK']\\n\",\n \" : param binaries: Dataframe with crosses as index with strikes for both call and put FX Binary\\n\",\n \" : param start: start date for spot history, defaults to 2y\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" for cross in binaries.index.get_level_values('pair'):\\n\",\n \" with DataContext(start=start_date, end=business_day_offset(date.today(), -1, roll='forward')): \\n\",\n \" asset = SecurityMaster.get_asset(cross.replace(\\\" \\\", \\\"\\\"), AssetIdentifier.BLOOMBERG_ID)\\n\",\n \" q = GsDataApi.build_market_data_query(\\n\",\n \" [asset.get_marquee_id()],\\n\",\n \" QueryType.SPOT,\\n\",\n \" source=None,\\n\",\n \" real_time=False\\n\",\n \" )\\n\",\n \" spot = GsDataApi.get_market_data(q)\\n\",\n \" spot.plot()\\n\",\n \" plt.axhline(y=binaries.loc[(OptionType.Call, f'{cross}')]['strike_price'], color='g', label='Call Strike')\\n\",\n \" plt.axhline(y=binaries.loc[(OptionType.Put, f'{cross}')]['strike_price'], color='r', label='Put Strike')\\n\",\n \" plt.title(f'{cross} Binary')\\n\",\n \" plt.legend()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 10X Binary Screen\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"\\n\",\n \"results = payout_struck_binaries(crosses=g10)\\n\",\n \"results.style.background_gradient(subset=['% otms'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"plot_strikes(g10, results, start_date=start_date)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/reports_and_screens/00_fx/0004_bear_put_spread_screen.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Costless 1x2 Put Spreads \\n\",\n \"\\n\",\n \"This screen looks for max payouts and breakeven levels for 1x2 zero cost put spreads for a given tenor. Top strike is fixed at the specified delta and the bottom strike is solved to make the structure zero-cost.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.risk import FXSpot\\n\",\n \"from gs_quant.common import BuySell\\n\",\n \"import pandas as pd; pd.set_option('display.precision', 2)\\n\",\n \"\\n\",\n \"def get_screen(df_portfolio, strike_label='40d'):\\n\",\n \" screen = pd.pivot_table(df_portfolio, values='strike_price', index=['pair', 'Spot'], columns=['buy_sell'])\\n\",\n \" screen = screen.reset_index('Spot')\\n\",\n \" upper_label, lower_label = f'Upper Strike ({strike_label})', 'Lower Strike (Mid)*'\\n\",\n \" screen = screen.rename(columns={BuySell.Buy: upper_label, BuySell.Sell: lower_label})\\n\",\n \" screen['Max Payout'] = screen[upper_label] / screen[lower_label] - 1\\n\",\n \" screen['Lower Breakeven (Mid)'] = 2 * screen[lower_label] - screen[upper_label]\\n\",\n \" screen['Lower Breakeven OTMS'] = abs(screen['Lower Breakeven (Mid)'] / screen['Spot'] - 1)\\n\",\n \" return screen\\n\",\n \"\\n\",\n \"def calculate_bear_put_spreads(upper_strike='40d', crosses=['USDNOK'], tenor='3m'):\\n\",\n \" portfolio = Portfolio()\\n\",\n \" for cross in crosses:\\n\",\n \" option_type = 'Put' if cross[:3] == 'USD' else 'Call'\\n\",\n \" upper_leg = FXOption(pair=f'{cross}', strike_price=upper_strike, notional_amount=10e6, \\n\",\n \" option_type=option_type, buy_sell=BuySell.Buy, expiration_date=tenor, premium_currency=cross[:3])\\n\",\n \" lower_leg = FXOption(pair=f'{cross}', strike_price=f'P={abs(upper_leg.premium)}', notional_amount=20e6,\\n\",\n \" option_type=option_type, buy_sell=BuySell.Sell, expiration_date=tenor)\\n\",\n \" portfolio.append((upper_leg, lower_leg))\\n\",\n \" with PricingContext():\\n\",\n \" portfolio.resolve()\\n\",\n \" spot = portfolio.calc(FXSpot)\\n\",\n \" summary = portfolio.to_frame()\\n\",\n \" summary['Spot'] = list(spot.result())\\n\",\n \" return get_screen(summary, upper_strike)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
buy_sell Spot Upper Strike (40d) Lower Strike (Mid)* Max Payout Lower Breakeven (Mid) Lower Breakeven OTMS
pair
USD NOK8.498.378.182.29%7.995.81%
USD SEK8.338.248.091.82%7.944.70%
USD JPY105.79104.84103.031.75%101.234.31%
USD CHF0.900.890.871.55%0.864.05%
USD CAD1.271.261.241.45%1.223.66%
NZD USD0.720.730.74-1.10%0.743.34%
AUD USD0.770.780.79-1.28%0.803.77%
EUR USD1.211.221.24-1.85%1.264.73%
GBP USD1.391.411.44-2.33%1.475.74%
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"\\n\",\n \"result = calculate_bear_put_spreads(upper_strike='40d', crosses=g10, tenor='3m')\\n\",\n \"result.sort_values(by='Max Payout', ascending=False).style.format({\\n\",\n \" 'Max Payout': '{:,.2%}'.format, 'Lower Breakeven OTMS': '{:,.2%}'.format}).background_gradient(\\n\",\n \" subset=['Max Payout', 'Lower Breakeven OTMS'])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/reports_and_screens/00_fx/0005_vol_skew.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Volatility Skew & Smile Screen\\n\",\n \"\\n\",\n \"This screen looks at historical implied volatility for 3m 25 delta options. The screen shows either the volatility smile or skew. The delta strike, option tenor and history can be adjusted. Relative refers to the vol adjusted results.\\n\",\n \"\\n\",\n \"Volatility skew or risk reversal refers to 25d call vol - 25d put vol. Volatility smile or butterflys refer to (25d call vol + 25d put vol) / 2 - ATM vol.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"import pandas as pd\\n\",\n \"pd.set_option('display.precision', 2)\\n\",\n \"\\n\",\n \"def volatility_skew(bbids, start_date, end_date, tenor='3m', history='2y', strike_level='25D', smile=False, relative=False, plot=True):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Screens for Volatility Skew \\n\",\n \" defaults to risk reversal (25d call vol - 25d put vol)\\n\",\n \" \\n\",\n \" : param crosses: array of string FX pairs e.g. ['USDJPY', 'USDNOK']\\n\",\n \" : param start_date: start date for volatility history\\n\",\n \" : param end_date: end date for volatility history\\n\",\n \" : param tenor: volatility tenor\\n\",\n \" : param history: label for historical calculations \\n\",\n \" : param strike_level: defaults to 25D\\n\",\n \" : param smile: if true, calculates butterfly else displays risk reversal\\n\",\n \" : param relative: if true, skew is divided by ATM Vol\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" impvol_data = {}\\n\",\n \" data = {}\\n\",\n \" strikes = [f'{strike_level}C', f'{strike_level}P', 'DN']\\n\",\n \" for cross in bbids:\\n\",\n \" for strike in strikes:\\n\",\n \" #replace with premium dataset for more history\\n\",\n \" impvol_data[f'{cross}_{strike}'] = Dataset('FXIMPLIEDVOL').get_data(start_date, end_date, bbid=cross, tenor=tenor, deltaStrike=strike, location='NYC')['impliedVolatility']*100\\n\",\n \" if smile:\\n\",\n \" data[cross] = (impvol_data[f'{cross}_{strike_level}C'] + impvol_data[f'{cross}_{strike_level}P'])/2 - impvol_data[f'{cross}_DN']\\n\",\n \" #risk reversal\\n\",\n \" else:\\n\",\n \" data[cross] = (impvol_data[f'{cross}_{strike_level}C'] - impvol_data[f'{cross}_{strike_level}P'])\\n\",\n \" if relative:\\n\",\n \" data[cross] = data[cross] / impvol_data[f'{cross}_DN']\\n\",\n \" df = pd.DataFrame(data)\\n\",\n \" return get_screen(df, plot=plot, smile=smile, history=history)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.timeseries import last_value, change, percentiles\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"def get_screen(data, strike_label='25D', history='2y', plot=True, smile=False):\\n\",\n \" screen = {}\\n\",\n \" for cross in data.columns:\\n\",\n \" screen[cross] = {'Current': last_value(data[cross]),\\n\",\n \" '1w Change': last_value(change(data[cross].tail(5))),\\n\",\n \" f'{history} Min': min(data[cross]),\\n\",\n \" f'{history} Max': max(data[cross]),\\n\",\n \" '%-ile': last_value(percentiles(data[cross]))\\n\",\n \" }\\n\",\n \" vol_screen = pd.DataFrame(screen).T\\n\",\n \" \\n\",\n \" if plot:\\n\",\n \" fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\\n\",\n \" vol_screen = vol_screen.sort_values('%-ile')\\n\",\n \" label = 'Smile' if smile else 'Skew'\\n\",\n \" top = vol_screen.iloc[-1]\\n\",\n \" data[top.name].plot(ax=axes[0], title=f'Highest %-ile {label} ({top.name})')\\n\",\n \" bottom = vol_screen.iloc[0]\\n\",\n \" data[bottom.name].plot(ax=axes[1], title=f'Lowest %-ile {label} ({bottom.name})')\\n\",\n \" return vol_screen\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Risk Reversal\\n\",\n \"\\n\",\n \"Risk reversals refer to 25d call vol - 25d put vol. Relative risk reversals are computed as risk reversals divided by ATM vol.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3m 25D Risk Reversal\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Current 1w Change 2y Min 2y Max %-ile
USDJPY-0.990.06-9.03-0.7899.21
GBPUSD-0.640.29-4.210.2187.72
USDSEK1.13-0.05-0.733.8379.21
EURUSD0.350.03-3.372.4978.42
USDCHF-0.60-0.09-3.26-0.2376.53
USDNOK1.520.13-0.296.1672.18
AUDUSD-1.11-0.03-8.08-0.4440.20
NZDUSD-1.00-0.07-8.12-0.3337.52
USDCAD0.250.040.144.7012.97
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3m 25D Risk Reversal (Relative)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Current 1w Change 2y Min 2y Max %-ile
USDJPY-0.160.01-0.65-0.1499.31
GBPUSD-0.080.03-0.270.0287.62
USDCHF-0.09-0.02-0.32-0.0580.50
USDSEK0.120.00-0.060.2477.92
USDNOK0.120.01-0.030.2971.39
EURUSD0.050.01-0.270.2670.79
AUDUSD-0.11-0.01-0.47-0.0751.58
NZDUSD-0.10-0.01-0.48-0.0444.26
USDCAD0.040.010.020.393.66
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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from datetime import date\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"end = business_day_offset(date.today(), -1, roll='forward')\\n\",\n \"start = business_day_offset(end - relativedelta(years=2), -1, roll='forward')\\n\",\n \"\\n\",\n \"\\n\",\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"tenor='3m'\\n\",\n \"strike_level='25D'\\n\",\n \"\\n\",\n \"risk_reversal = volatility_skew(g10, start_date=start, end_date=end, tenor=tenor, strike_level=strike_level)\\n\",\n \"relative_rr = volatility_skew(g10, start_date=start, end_date=end, tenor=tenor, strike_level=strike_level, relative=True, plot=False)\\n\",\n \"print(f'{tenor} {strike_level} Risk Reversal')\\n\",\n \"display(risk_reversal.sort_values('%-ile', ascending=False).style.background_gradient(subset=['%-ile']))\\n\",\n \"\\n\",\n \"print(f'{tenor} {strike_level} Risk Reversal (Relative)')\\n\",\n \"display(relative_rr.sort_values('%-ile', ascending=False).style.background_gradient(subset=['%-ile']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Smile \\n\",\n \"\\n\",\n \"Butterflys refer to (25d call vol + 25d put vol) / 2 - ATM vol. Relative BF are computed as BF divided by ATM vol.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3m 25D Smile\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Current 1w Change 2y Min 2y Max %-ile
USDNOK0.44-0.030.141.5063.27
NZDUSD0.350.010.191.5765.94
AUDUSD0.340.000.191.5765.15
USDSEK0.34-0.010.131.1180.69
GBPUSD0.28-0.010.161.3623.17
USDJPY0.270.000.211.3338.61
USDCHF0.240.000.151.2568.22
USDCAD0.23-0.010.111.1458.91
EURUSD0.22-0.030.121.4262.08
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Current 1w Change 2y Min 2y Max %-ile
USDJPY0.040.000.030.1141.88
USDCHF0.040.000.020.1164.85
NZDUSD0.040.000.020.0869.01
AUDUSD0.040.000.020.0866.83
USDNOK0.04-0.000.020.0559.50
USDSEK0.040.000.020.0780.30
USDCAD0.03-0.000.020.1058.32
EURUSD0.03-0.000.020.1250.79
GBPUSD0.030.000.020.0839.90
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hJZUc+P3zXf85L/fvPB47wZw0jPkvX/q6SBKxjryWRzjOzq4ALnHOz2j1YEsbMfglsdc7dlOhYusvMBuF9+DnIec1bREREOszM/gU87Jx7PNGx9BQzOwtva4JPJzoW6RolbNJjzGwoXinG28BEvBmsW/aGREBEREREJBFa3AxYpIuy8Kbbx+KVODxE29sAiIiIiIhIGzTDJiIiIiIikqTUdERERERERCRJJUVJ5MCBA92YMWMSHYaIiMTZ/PnztzvnihMdR6rQ+Cgikj5aGyOTImEbM2YM8+bNa/9AERFJaWa2V+6PZ2Z3A2fidVxta0Pbg/HanX/GOfff9h5X46OISPpobYxUSaSIiEj33QOc2tYBZhbE21vr+d4ISERE9g5K2ERERLrJOfc63qa1bfka8AiwNf4RiYjI3kIJm4iISJyZ2XDgPOD2Dhx7uZnNM7N527Zti39wIiKS1JSwiYiIxN9NwHXOuVB7Bzrn7nTOzXTOzSwuVn8WEZF0lxRNR0RERPZyM4GHzAxgIHC6mTU65x5PaFQiIpL0lLCJiIjEmXNubOR7M7sHeErJmoiIdIQSNhERkW4ysweBY4GBZlYC/BjIBHDOtbtuTUREpDVK2ERERLrJOffZThx7SRxDERGRvYyajoiksYraBj7eXIFzLtGhiIiIJJWqukYqahsSHYaIEjaRdPale+dxyk2vs3hjeaJDERERSSozbnyR/X/yQqLDEFHCJpLOPthQBsC2yroERyIiIpJcahvCiQ5BBFDCJpLWcjKDANTUt7s1lIiIiIgkgBI2kTSWq4RNREREJKkpYRNJY9mZ3ltAdYMSNhEREZFkpIRNJI3lZHgzbLWaYRMRERFJSkrYRNJYTmSGTQmbiIiISFJSwiaSxswMgOqGxgRHIiIiIiItUcImksYiG2arJFJEREQkOSlhE0ljzv9XJZEiIiIiyUkJm0gaC/sZW426RIqIiIgkJSVsIunML4nUPmwiIiIiyUkJm0gai8yw7ayuT2wgIiIiSSqy3lskUZSwiaQx569i21ZZl+BIREREklMorIRNEksJm0gaC4e9f7eW1+kMooiISAsalbBJgilhE0ljkSGorjFMRZ32YhMREWlOCZskmhI2kTQWO6u2rUJlkSIiIs01hsKJDkHSnBI2kTTmHBTmZABeWaSIiIg0pRk2STQlbCJpzOEY3CcHUOMRERGRljSGlLBJYilhE0ljYQeD+2QDsLW8NsHRiIiIJJ/GsEoiJbGUsImkMeccfXMzyQoGNMMmIiLSAs2wSaIpYRNJYw4ImFFcmM02rWETERHZg9awSaJ1KmEzs7vNbKuZfdjK7WZmN5vZCjNbZGbTeyZMEYkH58AiCZtm2ERERPagkkhJtM7OsN0DnNrG7acBE/2vy4G/di0sEekNzjkCBsWF2eoSKSIi0gKVREqidSphc869DpS2ccg5wD+dZw5QZGZDuxOgiMRP2IEBgzTDJiIiEhW7T6lKIiXRenoN23BgfczlEv86EUlCDhctiSytqqdBm4OKiIgQiknSQiqJlATr6YTNWriuxdMSZna5mc0zs3nbtm3r4TBEpCPCYTC/JBJgR2V9giMSERFJvFDMDFuDSiIlwXo6YSsBRsZcHgFsbOlA59ydzrmZzrmZxcXFPRyGiHSUYeRmBgGobQglOBqR1NSBplwX+s24FpnZW2Z2QG/HKCIdFzupVqOxURKspxO2J4GL/W6RhwFlzrlNPfwzRKSHRJqOBAPe5HjsGUUR6ZR7aLsp12pglnNuGvBz4M7eCEpEuiZ2PNxSVpvASEQgozMHm9mDwLHAQDMrAX4MZAI4524HngFOB1YA1cClPRmsiPSssPNKIgPmJ2xaWC3SJc65181sTBu3vxVzcQ5eBYqIJKnY8XDjrpoERiLSyYTNOffZdm53wFe7FZGI9BqHI2BGRkAJm0gv+iLwbGs3mtnleFvjMGrUqN6KSURihGPGwxIlbJJgPV0SKSIpJDrDpoRNpFeY2XF4Cdt1rR2jNd4iiRdbEqkZNkm0Ts2wicjexRuPjKBfEhnWGjaRuDGzacBdwGnOuR2JjkdEWhduUhKpNWySWJphE0lrzZqOaIZNJC7MbBTwKHCRc25ZouMRkbZFNsvul5fJprKaJgmcSG/TDJtIGmteEqkZNpGu6UBTrh8BA4DbzJvRbnTOzUxMtCLSnsgJzFH981hYUsb2yjoG9clJcFSSrpSwiaQxr63/7pLIULidO4hIizrQlOtLwJd6KRwR6abICcwRfsK2YVeNEjZJGJVEiqSxsAMDAv47gUoiRUREdo+HI/vlAVrHJomlhE0kjTnnsCYzbErYREREIjNsI/vnAuoUKYmlhE0kjTm8NWwZQT9h0xo2ERGR6BKBotwsCrMz2KCETRJICZtIGnMODCMQaeuvGTYREZFoxUkwAIP75rClXCWRkjhK2ETSmNd0RG39RUREYkVKIgNmZGcEqG9UVy5JHCVsImks2tbfVBIpIiISERkOA2ZkBAM06ISmJJASNpE05vDb+gdUEikiIhIRnWELQEbACIU1wyaJo4RNJI2FHRBbEqkZNhERkeh4aGZkBIyGkMZHSRwlbCLprFnTEa1hExER8dZ4AwTNyAwGaAxphk0SRwmbSBrzSiJ3z7CFNcMmIiJCuMkaNqNRJzQlgZSwiaSxSNORyMbZjSr5EBERia7pDhhkBAIqiZSEUsImksa8tv5GMKgZNhERkYjma9jUdEQSSQmbSBoLOzB2z7CpRF9ERGR3W/9gwC+J1AybJJASNpF0Z0bAfydQl0gREZHYjbMhMxigQTNskkBK2ETSlIsZjCIzbNqHTUREZHfTkUhJpGbYJJGUsImkqehgxO6Ns9XWX0REpFnTkWCA2oYQK7ZWJjgqSVdK2ETSVOwMW0Bt/UVERKIi42Ew4M2w7axu4MQ/vsaW8toERybpSAmbSJraXe4R23RECZuIiEjzfdgiquoaExSRpDMlbCJpyrG7ZXGkJFIbg4qIiOyeYTO/6UhETUMoUSFJGlPCJpKmXOwMW0BNR0RERCJ2r2HzSiIjquuVsEnvU8ImkqZcbNORSEmk1rCJiIhESyK9fdh2f1xWSaQkghI2kTQVKYls0nREM2wiIiJN9mGLnWGr0QybJIASNpE0Fdt0BLyziJphExERiV3D1qzpiBI2SQAlbCJpandbf28gCpoRCicyIhERkeQQjhkjQzGbZlfXqyRSep8SNpE01bz6MRDQPmwiIiIAYf8EZtCM6pjOkFV1mmGT3qeETSRdRUsiY2fYlLCJiIjEtvWPXbemGTZJBCVsImkqtukIeI1HlLCJiIjs7qQcCFiTzpCaYZNEUMImkqaiTUf8yxlK2ERERIDd29wErOnea2U1DYkKSdKYEjaRNBVtOuJPsalLpEjXmdndZrbVzD5s5XYzs5vNbIWZLTKz6b0do4h0XKQkMmjGQaOKAC9521Vdn8CoJF0pYRNJU3vOsAVoaFSbSJEuugc4tY3bTwMm+l+XA3/thZhEpIvCMeu8LztyLK9951gOHz+AnUrYJAGUsImkKUfTjdj652exo0oDkUhXOOdeB0rbOOQc4J/OMwcoMrOhvROdiHSWiymJDASM0QPyKcrLYpdKIiUBlLCJpKvIgmp/im1I3xy2lNcmLh6RvdtwYH3M5RL/uj2Y2eVmNs/M5m3btq1XghORpiJruiN7lQIU5Wayq1oJm/S+TiVsZnaqmX3s1+Bf38Ltfc3s/8xsoZktNrNLey5UEelJu0sivcFocJ9sJWwi8WMtXNfiolHn3J3OuZnOuZnFxcVxDktEWhKO6RIZ0S8vi13V9YTVoEt6WYcTNjMLArfi1eFPBT5rZlObHfZVYIlz7gDgWOAPZpbVQ7GKSA9q3tZ/cJ8ctlfW0xDSOjaROCgBRsZcHgFsTFAsItKO2JLIiKK8TMIOKuq0F5v0rs7MsB0CrHDOrXLO1QMP4dXkx3JAoXk78Rbg1fPrVS2ShMJNl7AxsCAbgJ1axyYSD08CF/vdIg8DypxzmxIdlIi0LOxaKInM8+Yg1ClSeltnEraO1N/fAuyDd9bwA+Ba51yLp+tVoy+SWJGzh+YPRoU5GQAsWLczYTGJpCozexB4G5hsZiVm9kUzu9LMrvQPeQZYBawA/gZ8JUGhikgHRIpNYhO2fnmZAFrHJr0uoxPHdqT+/hTgfeB4YDzwopm94Zwr3+OOzt0J3Akwc+ZMFQOL9DLXrK1/nxxvILry/gUs/dmp5GYFExOYSApyzn22ndsd3rIBEUkB0Rm2mKmNIj9hU2t/6W2dmWHrSP39pcCjftviFcBqYEr3QhSReHAxe8zA7hk2gM1qPiIiImnMtVkSqRk26V2dSdjmAhPNbKzfSOQCvJr8WOuAEwDMbDAwGa8ERESSTPOmI4X+DBvApl01iQhJREQkKUS7RFrTLpGgNWzS+zpcEumcazSzq4HngSBwt3NucaQ+3zl3O/Bz4B4z+wCv0uo659z2OMQtIt3UvOlI7AzbxjLNsImISPravQ/b7uv6+OPkTs2wSS/rzBo2nHPP4C2cjr3u9pjvNwIn90xoIhJPzcs9+uRqhk1ERAS8MdJs97IBgIxggD45GZphk17XqY2zRWTv0Xzfz/yYJiOaYRMRkXQWdk3LISOK8rLYVaMZNuldSthE0lbTtv5mxrGTiwHYXKYZNhERSV9h55qUQ0b0y8tUSaT0OiVsImnKRRdU777unksP4cR9BrNJM2wiIpLGQs41KYeMKMrLUkmk9DolbCJpqqYhBIA122JxWFEOG7WGTURE0phzEGwxYctUW3/pdUrYRNLU2be8CbBHycfQvrmU1zZSVdeYgKhEREQSLxxurSQySxtnS69TwiaS5pqfQBxWlAPAJq1jExGRNNVa05E+ORlU1DZGOy2L9AYlbCJprq4x3OTy0L65AGzcpXVsIiKSnsJ+W//mcrO8HbFqG8J73igSJ0rYRNLctoq6JpeH9tUMm4iIpLewcwRbqInMzfQ+OlfXa9mA9B4lbCJpbmuzhK1ffhYAZdpnRkRE0pTX1n/PhC3Pn2Grrg/1dkiSxpSwiaSh2Nr7Cw8d1eS2nAzvbaFO5R4iIpKmwo4W2/rnZgWB3Z2WRXqDEjaRNBQZaK4/bQqjB+Q3uS0jGCAYMGobNRiJiEh6aq1LZJ6fsGmGTXqTEjaRNFRe49Xe98nJbPH2nIyAZthERCRttbqGLZqwaQ2b9B4lbCJpqLzWW5/WJzejxduzM4N7dI8UERFJF6219c+LdonUDJv0HiVsImmoIpKwtTLDlp0R0GAkIiJpq7W2/rElkc455q0pJRzWnmwSX0rYRNJQpCSyMKflGbYczbCJiEga89awtdTWf3fCNnvFdj55+9u8s7q0t8OTNKOETSQN7S6J1AybiIhIc2FHi2vYIjNsNfUhnl+8GdA2OBJ/SthE0lB5TTslkZphExGRNBZqpUtkbkxJ5EtLtgLQENJ4KfGlhE0kDZXXtl0SqRk2ERFJZ3WNYbIzgntcnxX0Pjq/t24nm8trAajXCU6JMyVsImmovKaB7IwAOZl7DkagNWwiIpLe6hpDZGXs+TE5slfpC0u2RK+r1wybxJkSNpE0VF7b0Or6NdAMm4iIpDdvhq3lj8mR6/v4VSqaYZN4U8ImkobKaxqjA01LcjKDGoBERCRt1TeGyW6lCiWSsBXlZUWPFYknJWwiaUgzbCIiIq2rawxH16s1lxVN2LxxVCWREm9K2ETSUHltI4WtdIgEyMkMaA2biIikrbrGENmZrZVEejNvff0Tn5phk3hTwiaShipqGtosiczOCGqGTURE0lZ9B9aw5WUFyQyaZtgk7pSwiaShjpREaoZNpHPM7FQz+9jMVpjZ9S3c3tfM/s/MFprZYjO7NBFxikj72mo6khVN2DLICgY0wyZxp4RNJM045/ymI22VRAZpDDsaddZQpEPMLAjcCpwGTAU+a2ZTmx32VWCJc+4A4FjgD2aW1auBikiH1DWEWtyHDXbPsOVmBcnKUMIm8aeETSTN1DWGqQ+F6ZPbVklkIHqsiHTIIcAK59wq51w98BBwTrNjHFBoZgYUAKVAY++GKSIdUR9qqyTSS+TyMr2ErUEnNyXOlLCJpJny2gYACrPbbusPaB2bSMcNB9bHXC7xr4t1C7APsBH4ALjWObfHJz0zu9zM5pnZvG3btsUrXhFphXPO6xLZbkmkZtikdyhhE0kzdQ3ewNLa/jKwe4atRgmbSEdZC9e5ZpdPAd4HhgEHAreYWZ897uTcnc65mc65mcXFxT0dp4i0oyHkcI5WZ9gi/7Fz/TVsdZphkzhTwiaSZiJljq0NRLB7hu2o37zCO6t29EpcIimuBBgZc3kE3kxarEuBR51nBbAamNJL8YlIB9U1eicrW1vDFg57KVtuZoBMNR2RXqCETSTNRAainA7MsAG8uGRL3GMS2QvMBSaa2Vi/kcgFwJPNjlkHnABgZoOBycCqXo1SRNoVScBaK4ncXF4LwKA+OWSrJFJ6gRI2kTTTmRk2gDmrNcMm0h7nXCNwNfA8sBR42Dm32MyuNLMr/cN+DhxhZh8A/wOuc85tT0zEItKa9sbJ1durADhkbH+tYZNe0XrXARHZK0UaibRW6uHdtnuQWrKx3Nu3rY1tAEQEnHPPAM80u+72mO83Aif3dlwi0jnV9d44mZvV8jj5j0sO5u1VOxhYkE1OZpDKOjV7lfjSDJtImomeOcxs/b9/bEOSsIN5a0rjHpeIiEgyqIh0U85peV7jmEnFXHeqt/w0LytITb0adEl8KWETSTPRLpFtlETG3pYVDDBnlRI2ERFJD5EZs8IOVJbkZ2VQVa8ZNokvlUSKpJn2ul8BZAa9hG1k/1wG5GfzQUlZr8QmIiKSaJW1XgJW0MZ+pRF52UGq6zTDJvGlGTaRNBMpicxpoyQyP9tL5s4+YBj987Moq2noldhEREQSraI2MsPWfsKmGTbpDZ1K2MzsVDP72MxWmNn1rRxzrJm9b2aLzey1nglTRHrK7u5Xrc+wjeiXx6vfPpZvnTSZPjkZVNQpYRMRkfRQESmJzG6/JDIvK4PahjChsGv3WJGu6nBJpJkFgVuBk/A2CJ1rZk8655bEHFME3Aac6pxbZ2aDejheEemmukiXyDZm2ADGDMwHvBr+yNlGERGRvV2k6Uik2qQteX4nyer6xg6teRPpis7MsB0CrHDOrXLO1QMPAec0O+ZzwKPOuXUAzrmtPROmiPSUjuzDFqswJ4OK2kac09lDERHZ+1XWNpKXFSQj2P44mZcdSdi0jk3ipzMJ23BgfczlEv+6WJOAfmb2qpnNN7OLW3swM7vczOaZ2bxt27Z1IgwR6Y7IDFtWBwYi8GbYQmFHTYMGIxER2ftV1DZ2qOEIeGvYAKq0F5vEUWcSNmvhuuan3DOAGcAZwCnAD81sUksP5py70zk30zk3s7i4uBNhiEh31DWGyc4IYNbSf+k9RRZdv7liRzzDEhERSQqVdY0dajgCsSWROqkp8dOZhK0EGBlzeQSwsYVjnnPOVTnntgOvAwd0L0QR6Ul1jWGyOlgOCRDwE7sv/3NevEISERFJGuW1DRR0cD1arp+wqQpF4qkzCdtcYKKZjTWzLOAC4MlmxzwBHG1mGWaWBxwKLO2ZUEWkJ4SdIxjo2OwawJShhdHv1QVLRET2dpV1jfTp4AxbZHlBvb8+XCQeOpywOecagauB5/GSsIedc4vN7Eozu9I/ZinwHLAIeBe4yzn3Yc+HLSJdFQo7gh0shwSYPqoff/qMN1F+2T1zWbO9Kl6hiYiIJFxn1rBFKlbqGjXDJvHT4bb+AM65Z4Bnml13e7PLvwN+1/3QRCQews4R6MQMG8A5BwyntKqBP724jPP/+hZzvn8CmR1sWiIiIpJKKms7voYtsqepZtgknvSJSyTNdHaGDSAQML541Fi+fuJEdlTVa3G1iIjstSrrGinowKbZEDvDpoRN4kcJm0iaCYXp1Bq2WJGBqSGkgUlERPY+obDrVJfIyJ6mmmGTeFLCJpJmOtt0JFZkcbUSNhER2RtV1Xv7qXU0YYucyKzXuChxpIRNJM00hruesGWqG5aIiOzFKmo7l7BFZtjqGjQuSvwoYRNJM+Gwo4v5GpkqiRQRkb1YpZ+wdXYNm2bYJJ6UsImkmVA3Ztiygt796hu1H5uIiOx9KmobgE6URKryRHqBEjaRNBNyjkAnu0RGqOmIiIjszSrq/Bm2DiZsGcEAAVPCJvGlhE0kzYR7YA2bEjYREdkbRdewdXDjbPD2YtPG2RJPSthE0kyoG10i1XRERET2ZpXRpiMdW8MGXvWJxkWJJyVsImkmFO56SWQ0YdMMm4iI7IUia9g6WhIJfsKmcVHiSAmbSJoJO0dGF2fYsqNr2NR0RERE9j6VdY2YQX5WsMP3yQoGqNMMm8SREjaRNBMKOwJawyYiIrKH2oYQORlBrBOVKNmZKomU+FLCJpJmQmFHsMslkd79lLCJiMjeqDHc+SqUrGCAspqGOEUkooRNJO10Zx+2yAybSj9ERGRvFAo7gsHOjZGzJhXzxvLtvLlie5yiknSnhE0kzYQcXS6JzNY+bCIishcLdWGG7RsnTWLcwHy++99F0aYlIj1JCZtImgmHHZ08eRgVXcOmGTaRPZjZqWb2sZmtMLPrWznmWDN738wWm9lrvR2jiLStK52UczKD/P7TB7CprIa/vb4qTpFJOut4z1IR2St0qyRSXSJFWmRmQeBW4CSgBJhrZk8655bEHFME3Aac6pxbZ2aDEhKsiLSqK2vYAKaP6sfI/nms2VEdh6gk3WmGTSTNhF139mHz7qf9ZkT2cAiwwjm3yjlXDzwEnNPsmM8Bjzrn1gE457b2cowi0o5wF9awRRTlZbGzur6HIxJRwiaSdrozw5bll0T+7vmPWbKxvCfDEkl1w4H1MZdL/OtiTQL6mdmrZjbfzC5u6YHM7HIzm2dm87Zt2xancEWkJY3d6KTcLy+TDbtqND5Kj1PCJpJmQq7rCVvsvjSn3/xGT4Uksjdo6T9V89rhDGAGcAZwCvBDM5u0x52cu9M5N9M5N7O4uLjnIxWRVnVnjCzKzWTVtipOv/kNNeeSHqWETSTNhLsxw9ZcbUOoRx5HZC9QAoyMuTwC2NjCMc8556qcc9uB14EDeik+EemAUKgbCVteVvT7nVUqjZSeo4RNJM2EXNfLPZpbukllHyK+ucBEMxtrZlnABcCTzY55AjjazDLMLA84FFjay3GKSBsaw45goGsfj4vyMqPf71DCJj1ICZtImgmFXJf3YWtuUUlZjzyOSKpzzjUCVwPP4yVhDzvnFpvZlWZ2pX/MUuA5YBHwLnCXc+7DRMUsInsKu651iQSvU2TEjkolbNJzlLCJpJnuzrDNHO0NSP3zs1hYsquHohJJfc65Z5xzk5xz451zv/Cvu905d3vMMb9zzk11zu3nnLspYcGKSIsaw10/qXnMpGJe+uYsAHZU1fVkWJLmlLCJpJlQmG7NsN196cE8fc1RHDSyiEUlZXzv0UXs88PnWLG1sgejFBGR2oYQs373CvfNWZvoUNJGKBzu8gwbwMACbx3bds2wSQ9SwiaSZsLOEezG//w+OZnsO6wv00YUsXJbJQ++u56ahhD/t7B5fwUREemON1dsZ+2Oan74+IfU1KvJU2/oztY3AH1zM8nLClKyUxtoS89RwiaSZkLd2GMm1rSRfXExTctfXLKl248pIiK7vbF8e/T7B95peZatvjHMjkqV3/WU7o6RZsbYgfms2lbVg1FJulPCJpJmwt3ogBXrkDH9mTWpmEPH9ueSI8awZFO5ziiKiPSgRSW7OGRMfw4fN4A7Xl/V4lYqX3twATNufAnnmm/7J10RCjsygt07qTm+uIBV27VMQHqOEjaRNBPqZklkRH52Bvdedgj/vuJwvnDEGECzbCIiPWnppgr2Hd6Ha06YyLaKOp5sofT8+cXe+255bWNvh7dXCoUdgW5WoYwrzqdkZw1LNpYz5vqneW3Zth6KTtKVEjaRNBPqRges1owdmM/wolwWrNvVo48rIpKuGkJhahpC9M/L4rBx/RnSJ4dXP97a6vHbKlQW2RMaw11v6x8xrrgA5+Dx9zcA8OiCkp4ITdKYEjaRNBPuwY2zY/XNzaS6Tmd4RUR6QnWdV/6Yn52BmTFjTD+WbCxv9fitFbW9FdperbtNRwDGDcwHYMPOGgA1jAGWb6kgHFbZblcpYRNJMz0xGLUkLytITQvrK0REpPOq6r0TYPnZQQCKcjOblD065/jHm6ujlzsyw7a5rJbV29UMoy09krAVewnb4o1lANQ2hrsdVypbsbWSk/70Oje9tCzRoaQsJWwiacQ5R9jR7fr8luRmBanWWUQRkR5R7SdseVkZAPTJzaSitiHaXOSBd9bx0/9bwszR/YD2E7Z3V5dy2K/+x3G/fzV+Qe8FeiJhy8vKYGjfHNbs8Bpx1ab52Fha5e1J99bKHQmOJHUpYRNJIyG/HCEeM2y5mcEWO5ilu+c+3MTxv3+Vd1ZpoBKRjquKlkR6M2yFORk0hBzPL97Mv95Zx11vrOKAEX15+IrDycoItJuwffqOt+Me894g5Lq/hg12z7IBbNhVk9ZdPCNdN2sb9Rmhq5SwiaSRxjgmbHmaYWvRnFWlrNpexbMfbk50KCKSQqr8NcH5kRm2nEwArrx/Ad9/7APW7KjmpKmDCQSM4oJsNR3pIY2hnmnMNWlwYfT7DbtqWLYlfdv81zV4JaG1DeldGtodSthE0sgvnl4K7F4Q3ZNytYatRY1hb4BaWLIrsYGISEqpqt/ddAS8GTaAkf1zGZCfBcCUIX0AKC7MZqsSth4R7qEZtkPHDmhy+aWl6bvtTaT6RlU4XdephM3MTjWzj81shZld38ZxB5tZyMw+2f0QRaQn1DWGuG/OWgBOnDq4xx8/NzNDnbBa0BjyZjUXbyjXYCUiHbZ7DVvQ/9dP2Prl8fdLDuaAkUUcMq4/AIMKd8+wLd5Yxt9nr2ZNs+YikYSvKC+zV+JPVY091JjryAkDmDG6H18+eiwHjCxK631Ka6IJm2bYuiqjoweaWRC4FTgJKAHmmtmTzrklLRz3G+D5ngxURLpnS5k3mP/2/Glk9sTO2c1EukQ657A4NDVJVfWhcPTf99bt4vDxA9q5h4iks+89uoinF23isqPGArtn2CInfAYWZHPgyCKe+OqR0fsMK8pl9ortnP/Xt5i/dicAd89ezevfPY5H5pfw+xc+psLvMBk5iSQt66lOyoU5mTxy1REA3PLycn7/wjK2VtQyqDCn24+dajTD1n2d+dR2CLDCObfKOVcPPASc08JxXwMeAVrf3VFEet3GMm8/mKFF8RkscrOChMIumqCIpzHkGJCfhRm8vnybZiFF0tS6HdX86IkP92ir75zjr6+u5NZXVuCc48F311Ne28jcNaXA7hm2E/YZxCcOGs4NZ+yzx2Ofut8QqutD0WQNvHVTT3+wiScXbqS6PkR2RoApQwqpT/MW8+0JhR0ZgZ49qTlzjDcT+vHmih593FQRmWGrrGvUXmxd1JlX5HBgfczlEv+6KDMbDpwH3N790ESkJ22KJGx9c+Py+LmZ3oeK2np9GIjVEAozoCCLfYf14a+vrmT6z19M625hIunqR09+yD/fXsu5t77JpB88y+l/foPKukbWlVbzm+c+4nfPf8zXHnwvevzSTd6H+0gpZF5WBn/8zIEM6rPnSbdDx/bnjotm8LlDRwFwxTHjmDiogGsfeo85q3bw6Zkj+ejnp3Ly1MHUh8J6D2rBi0u2MO0nz1NZ19jjW9+M7J8HwEV/f5fKusZ2jt77xJ6oXFtancBIUldnEraWXr3N/8ffBFznnGv3FLKZXW5m88xs3rZt2zoRhoh0xaayWgCGxWmGLXIWuLI+/QajtjSEwmQEAtEF6DUNIb7z30UJjkpEepNzjoXrd3HE+AGYeSXSSzaVc8xvX+G0P78BwLQRfXlq0abofUqr6snNDHaoPM/MOGXfIQzr672/O+BLR4/FOW9N1hWzxmFm0XL4Rs1y7OF3z39EeW2jN8MW7NmEbUhMkr1uR/olLHUxs7qRzcSlczqTsJUAI2MujwA2NjtmJvCQma0BPgncZmbntvRgzrk7nXMznXMzi4uLOxGGiHTFpl219M3NjJ6t7WmD/Q8Km3bVxOXxU1VDyJHplyJFrNhayYJ1O/n0HW9TXtuQwOhEpDdsLq9lZ3UDp+43JNql9/gpgxg7MD+6HcqfLziI2y6czjdPmsRY/5jIHmwddfwUr6HUKfsO4ZCYLoWD/YQhK8P72Neg0vU9RNYKQs9vfRP7eOE0nN2MzLBlBIzFG8sTHE1q6kzCNheYaGZjzSwLuAB4MvYA59xY59wY59wY4L/AV5xzj/dUsCLSdZvKahjaN36LnUf7JR9r0vDsYVsaQmEyA8bZBw7jmhMmcsykYspqGnjwnXW8u7qUh95dl+gQRSTONuz0TmSNHpDPt0+eTG5mkN+cP40vHDEmeszQvjmcvv9QrjlhIuOLCwA6fYJt6rA+rPn1GcwY3Y8xA7z35GMn7z4pHplha2hMv6ShPQWxCVscGmd977QpQHo23qhtCJGfFWTi4EIlbF3U4YTNOdcIXI3X/XEp8LBzbrGZXWlmV8YrQBHpGRt31TKsKD7r1wBG9MsjYLBuR1X7B6eRxpAjMxggOyPIN0+axKj+ueyqrmeInzyv3No7z9e9b61hg2Y/RRJiR1U9AAPyszhiwkCW/vxUiguzoyWMADmZu2fTRvvJVqTUvCvMjAU/PInbPz8jel2mP8NWF0q/pKE9+Vnxm2EDOHBkEdC0PDBd1DSEyM0Ksu+wPizZWKY1lF3QqTY4zrlnnHOTnHPjnXO/8K+73Tm3R5MR59wlzrn/9lSgItI98Z5hy8oIMKAgm22V2rw1Vn0o3GQ9RFFuFmU1DZTXeKWQpdX1cY9hR2UdP35yMRf//R0Almws58MN3jqCXdX1vLB4c9xjEElnpX7C1t/f8DpiaCsn0Ub5FQuxsz5d0T8/q0kimOW/FzWotf8ecmOS48weXsMGuxPydJxhq2kIkZPpJWzbK+ujewYmk3dW7aCsJnmXKPT8ZkwiknRq6kPsrG6I6wwbQHZGIC3PHralMRwmK2bfu6K8TMIONvpNYCIf5OIp8jNWba9iW0Udp9/8Bmf+ZTYNoTA/f2opl983n/fW7WznUUSkq1pL2Ib0yeHcA4dF9+uKGBWZYetmwtbc7pJIvU83F7vdQf/87B5//OxM77lPx82j6xrC5GQGmTTYW8u9YltlgiNqqrYhxOfueocv/3MejUm6vlMJm0ga2FzuJQfxnGEDb5ZNCVtTDY2uyUblfXIzAVjvtzbubMJW2xBiR2UddY0dP0sbKcdyDg7+xUvR66+8bz5vLPe69N78v+U45yitqle5ikgPCoUdf3xxGdC07BG80rubLjiIGaP7Nbk+MsOW342SyJao6UjrqmM6HA8oyGrjyK7JyfD+lp15795b1DSEyM0M0ifHG/+q65LrOajwu4O+u7o0+n812ShhE0kDkc6NQ+KcsGVnBLUpazMN4aYlkQP9DwKRzXO3+sl0e+oaQ9Q2hPjk7W8x48aXOPLXL7Nw/a4O3belpLBPTgavLdvGVr805ZWPt3Hz/1Zw6C9f4lO3v83STVoYLtIT3li+jVAn2+iP6JeLWeebjrQncvKoXgnbHqpj9gobkB+HhC1aEpl+z31NfYiczAB5ftfTqiTb/qfK3xuvMDuDf769NilPWiphE0kDu2fYVBLZ2xpCTUsij5wwkPHF+dHnqao+xAV3vs2Y659mzPVP87+lWwD41O1vcZ2/X9vN/1vO5BueY8oPn+PDDV4itb2ynnNufZNbXl7ebgyRGbafnr0vp+zrtf3++omTeObao/nrhdOZ+4MTmTq0D396aRmGsXJbJWf9ZTYrtlb03BMhkqae9vdWu/eyQzp8n+yMICfuM3iPmbfuirwX6cTanmLXlg0o6PmSyJxoSWRyzS71htpGbw1bpLFLbHKcDCKbmR80uh+VdY3RJQvJRAmbSBqo8QeIzu7p01lZGQHq0nAwaktDY9NNWLMzgvzivP0BOHriQC46bDRzVpVGb//Gv9/nuv8uYu6anfx73nq2lNfyjzdXc+DIImZN8tpzTxpcwJzvnQDA3DXtrz0rrfQSts8dOoq/XjiDP19wIBcdPppJgws5bf+hFBdm8/dLZnL9aVO44+IZPPHVo2gMO0784+vc/L/2E0IRaVl9Y5jnF2/mE9OHR///dtTfLp7J5w4d1aPxRNewqenIHmKTiOZrDXtCdrQkMv2S5Zp6ryQyOsNWl5wzbAf5nTyXbdl9svIv/1vOyx9tSURYTcRnB10RSSqN/uCcEYjvOZrsjED0TJV4GsPhJmvYAA4bN4DfnL8/kwYXctCofpy872Au+vu7AJTXNvLveeujx371gQXsrG7gulNHcs6Bw7n9tZVcdtRY+uZmcsb+Q3n6g018+z8L+e350wi00op6w65qiguzo3Gcc+DwPY4Z2jeXK2eNB8A5R25mkJqGEH98cRlnThvKOH9fKBHpGOccZ98ym/LaRs6cNjTR4QC7ux9qDdueahpCTBxUwDGTiumT0/Mfj7Mz0niGze8SmeeXhSbbDFukRPOgUUUALNtcwXGTB9EQCvMHf03bml+fkajwAM2wiaSFyOCcEYdWxbGyMwLUpWF9flvqG/dM2AA+c/AoDhrllTsdPbGY2z8/nW+dNIn9hveJHvPlo8cyb603gzZ6QD65WUG+cdIk+vqNSyID/3/nl/CB36Y/1hPvb+CVj7eyZkd1dGPzjjAzvn3KZA7wzzZ++Z/z+N6ji3hxSeLPMoqkipqGEB9t9s7UHzWhc7Nr8ZLtf2Ces2pHUq7TSaSa+hCHjO3PD8+cisVh4+xAwMjKCFCbhk1HahvC5GYGyQgGyMoIJN0atkq/CcqIfrkUF2azbIvXxfLCu95JZFhNaIZNJA1EFrxnxGEz0FjZGcGUWcy+taKWxRvKOW7KoLj+nMaw69CePqfuN5RT94PjpgziB499wJ0Xz6RvbiZ/e2M1AGMG7plwXTFrPBt21fDR5gpeWLKZjzdXMLxfLsGA8UFJGb957iMa/b/9+dNHdCruLx41li8eNZa//G85f3hxGSu3VTFes2xtMrNTgT8DQeAu59yvWznuYGAO8BntV7r3qqj1PpT+5Kyp0e6MibbfsD6cuM8g/vLyCgC+dfLkBEeUPGoaQt3aqLwjWjupOW9NKV//9/vcc+nBTBhUGNcYEiGycTZ4nU+TrUtkpCQyPzuDyYMLeWRBCet3VvPu6t3LFSbf8Cz7DuvDo185MiExKmETSQON4d4pifTa+ifXG3FrLrtnLh9uKGfhj06mb14m76/fxbThfVstK+yqhlDLM2yt2W94X564+qjo5W+dNIl/zlnL4MI9O3weMrY/z339GC64821ufWVli493yJj+vLumlJljuta84EtHj+Ox9zZw/owRfOnocV16jHRgZkHgVuAkoASYa2ZPOueWtHDcb4Dnez9K6U3l/ia8/ePQwKKrMoIB/nbxTM64eTbvrduV6HCShnMu2no+nnIyg3uURK7ZXsUnb38bgD++uIxbPzedusbwHltApLKahlB0H7q8rIykm2GLTdgmDi5g9ort0WTtoFFFHDF+AHNWlbJg3U6cc3GZgW2PEjaRNBAtiYz7DFtqlESGwi7abfGDDWUsLNnF757/mJs/exBnHzCsx36Oc46GkCOjEwlbc187YSJfO2Fim8ecPHVIk8YlR08cyBvLtwPwzy8eQsCsy2f4c7OCvPztY7t03zRzCLDCObcKwMweAs4BljQ77mvAI8DBvRVYOOx6/EREV4XDjvNue5NhRbn88MypDO6Tg0HSxNeTyv0ZtsI4rIfqDjOjMCdDnSJj1DaEcQ5ye3gbheYKczKoaLbOO1I2m5UR4KUlW/nxk4v559tree07xzJ6QH5c4+kN4bCjvjEcTYbzs4PUJNkatshseF5mMLoH4vFTBnHeQcM5c9pQzIzbXl3B/LU7E5ZMJ8ccvYjEVSjsCFj8PxRlZQRSoiTyztdXRb+/6oH5/O75jwHY5u9J1lMi3Tmz41wOddJUr1V/ZtB4/0cn8U+/fXhOZoCczGDSlGPt5YYD62Mul/jXRZnZcOA84PbeCmr+2p2c8ZfZbCqr6a0f2aZN5bUsLCnj2Q838/m73uHK++dz4h9fY+2OqkSH1uMqar0Ztng0sOiurIwAdSnwXt1bIu/V8S6J7JOTGZ15jdha4bWQv+vimdSHwvzz7bUAzPrdq3ztwfeYv3YnC9btJNzJvfySRWTNXk40YctIuuZk2yrr6J+fRUYwwKTBXknqcVMGcdYBw6KzaZGGKYlKNjWKi6SB7s7ydFSqzLD9e+46RvTL5Zfn7R89swawo7JpwvanF5cx5vqnu7w4f8lGbxYvMgDEy8j+eTz6lSN4+pqjKcrLwsyYfd1xzL7u+Lj+XGmipbMhzV84NwHXOefaHPHN7HIzm2dm87Zt29atoHIyA6wvreaSu+dS1uyDYiKs2uYt5p81qZhV26t4cckWVm2v4vy/vs3cNaXt3Du1RN5b+uRkJjiSPWVnBDXDFqPaL9GLd0lkn9w9E7Yt5bUEA8ZREwbSvNLu5aVbOP+vb/GJ297ijRXb4xpbvJTX7C43BOiXl0WpvzdosthaXsvgPt6ygyMnDOT/rj6KzzfbUiOyBq86QV0+lbCJpIFQOBz3ckhoOsP20Lvr+Oq/FsT9Z3ZWXWOINTuq+dSMkXzu0FG89M1jePiKwxnWN4ct5U0Ttj/7e5B1dnB5Z9UOjvjV/7j0nrkATBvRt2eCb8P0Uf2aJIYj+uUxMInWzqSBEmBkzOURwMZmx8wEHjKzNcAngdvM7NzmD+Scu9M5N9M5N7O4uHvdBfcd1pc7LprBym2V3PbKim49Vk9Yvd2bSTtu8u7f6x+XHkxVXSN/fGFZosKKi3J/hq0wKRO2APUpst64N0TWleXGfYYtI1oq65zj2Q828e7qUgYVZhMIGN85xWsC8+hXjmDZjafxwJcPi973wxY6AaeCj/09zSYO8ppWDcjPYkdlciVsW8rrGNxn93i5/4i+e6xTi5TLJmqGLfnm6UWkxzWEXK8kbNkZQUJhR2MozPWPfgDArz/RkFQfWDbu8spPRvTLBYh25BrUJ4cNu6pbvM+aHdUMaCf5efy9DWwsq+Erx07gr6+tpLYxzJnThjFmQF70zJ3s1eYCE81sLLABuAD4XOwBzrmxke/N7B7gKefc4/EO7MgJAxk9II+SnYkvi4x8UJs+encTnGMnFXPa/kOYs3JHosKKi4okXcMGqVO+3lsi+4LFe4atb8wM26+f+4g7XvPK84/1T2BcNWs8nz14FP38jbsPHFnE/BtO5Oxb3myymXNLFqzbSVYwwH7D43+CsDMilSb7DPG2rBlQkE1pVX3Cmne0ZEt5LVOH9mnzmNwEl0Qm37uIiPS4xnC4V0oiI2ulvvvfRdHrPtxQzuHjB8T9Z3fUBv9DayRhizhywgBufWUl89aUMqRvDv+ZVxK9bV1pFTNGt91l8ev/fh+AFVsreW3ZNq45fiLfOGlSzwYvScs512hmV+N1fwwCdzvnFpvZlf7tvbZurSX98xNThvThhjJqG0LMHNMf8ErPcjODTBpcyKxJxXz+sNGYGaP75/Pogg3RDXb3BlvKa8nLCsZ9XVRXZAVTo3y9t0QStrivYcvNpLy2gTXbq7jjtVXkZwV56VuzotUQZhZN1iIGFGQzcXBBdG+wllTWNfKJ294CEr/Bc3NLN5UzvCiXvnneidsB+VnUh8JU1DUmRbmwc44dVfUMKMhq87howpagkkglbCJpIBTunRm2A0cWMap/Hi9/vDV63aKSXUmVsJXs9GbRRjTbSPqrx03g8fc28rUH38OAjWW10dvW7mh55q0lc1buYNKgQi5sVv8uez/n3DPAM82uazFRc85d0hsxRfTLy2Jdacdfxz3lrFtm4xws+dkp5GVlUFkXIj87g5zMIPf6zXEARg/w/j+W7KxhwqC9Y7+/dTuqGdU/L2lmEWJlZ2qGLVbkQ3hOLzQdaQg5rnvEO6n5xNVHMbRvbjv3gsmDC3lr5Q4aQy2ffH0/ibdoWLKpnH1iZq8iidGOynr65GRy1xurmDWpmIlxXuvdmtqGMKGwo6CdmfDoGrYEbUmgNWwiaaC3SiIPGzeA1797HC9+Y1Z0tm1RSffq7iNn6HtKyc4aggFjcGHTEse8rAx+eOZUNpXVsrGsloNGFUXb+65rJ2GLvIF/++RJvPW9E3j+G8cwSGWQkkQSNcMW6dczZ5VX7lhd30h+9p4fiiMf4pKtGUFnldU0cMdrK6lvDLO2tDr6HpJssoIBNR2JUdNLM2wzx/SLlicfO7mY8cUda9s/cXBh9DXVksh6SaDLTbLiobYhxKptlUwdujsZG+TvKbq5rJaSndXc+PRSTvrT63zp3nl86d553Ddnba/GGOlYWZDdTsLmz7D15OeRztAMm0gaCIV7p0tkRHFhNstuPI1rHnyP2Su2U9cYIjuj8wPh6u1VnPmX2ew/3GucMKyo/TOR7SnZWc3QvjktPh8nTx1MUV4mU4YU8tDlhwNwwZ1vsyam5XhZdQPrd1ZTlJfJiH67ZwXA69Yokoz65WextaKO9aXVvfo6HdInh83ltazZ7n3QrKprJL+Fva765XkJ287q1E7Yfvf8R9w/Zx2vL9/Giq2VnLrvkESH1KKsDCVssWp6aQ3bwWP689p3juv0/SYN9madl2+pYHzxnjPQsR1gv/7v96muD9EvL5PT9x/KsZMHdT3gbvp4cwVhB1OH7Z5hiyxHKNlZHa14AVi7o4qd1Q3MXVPKhYeM6rW9GaObZrezB18kmU9USaRm2ETSQEMoTEaw98tyzp8xgtKqep77cDOhsKOuk13JlvuLrJduKueK++b3yJnDkp01e6xfiwgEjDevO75JqdbYgQUs31JJbUMI5xyX3PMuZ/5lNsf+7lV2VtUTDjs2++WTHSltEUmEyBqZo3/7Sq/ugZSd6X3MiOyzVlUXanGGrchf37IrxRO2yAb2c1aVcu6Bw7jy2PEJjqhlWRkBGsMuZff26mnVvdQlsqsiZcJX3r8gOpvWEApzxX3zeG/dziZbBTzx/kZeXLKFh+eVcMk/5ka30kiEpZv8hiMxJZHDinIxg/U7a6KdLy87ciwvfOMYbjhjH8pqGli6ubzXYozOsHW4JFIJm4jESWMvlUQ2d7Tfne7+OWv51O1vccBPX+A/89a3f0dfZM3NN06axAcbyliyqftv4l7C1voMQ352RpPZwDP2H0pFXSPn3vomY7/3DO+t28Wwvjk0hh03Pr2Uo3/7CvP8/aNi2wKLJJOT/c3VwdtMu7dEPtxESrmq6huj+zHFisywXffIB7y2bFtKJhKby2pZsbWSS44Yw+zrjuOmCw5qt8wqUSIl61rH5qntpRm2rsqLmf1ZuH4X4I1lzy/ewpf/OS+axH3pqLG89M1ZfOPESZwxbSh9czP55sMLeyXGjbtqmPW7V7jh8Q+i1y3dVE5BdgYjY8bcrIwAQ/vkUFJazbItlRw0qogfnTUVM4smpr3Z0baqoyWRWYntEqmETSQNNIYdGYHe/+8eCBgXHjqKuWt2smDdLmobwnznv4s4+5bZhDrwgWztjmr65GRwyr7eh8322hq3p74xzJaKWoZ3orTyyAkD+NJRY/lo8+6ffZjfROWRBSVs2FXDzS97+1upfb8kq5H987j989MB+Mebqzs8W72too6v/mtBkzUynRH5cLPTX5vWWklk7NqhL9z9LjN/8RIfb+7e//feFkmEzztoeNLPtkdOStWpLBKI7RKZnAk2wE/P3hfwEiPYPRu9vbKe8ppG+uVlcsOZU5kwqIBrT5zIrZ+bzhcOH82ikl09Xv7aGApTXtvAprIajvz1y4z//jMc8euXWbujmvvnrIuuWV2yqZwpQwr3KG8cW5zPog1lLN5YxqRBu9e39c31Ztqbby4eT1X1TTf2bk2i2/orYRNJA15b/8R0KvvcoaP58tFjuWLWOBb/9BT2H96XRSVl7Kisa/e+m8pqGFaUy4h+eZh1rltja4/n3J4t/dtiZtxw5lT+euH06HWHju0f/f6LR40lGDD65mbuNe3IZe906n5D+enZ+/Lqx9v417vrOnSfW15eztOLNvHo/JL2D27GORdtyLPL/wDWWklkbCfFH5y+D6VV9Ty/eHOnf2YiLd1UTjBgTB6SmG53nRGdYVPCBkB1QyNZGQGCCahE6agLDx1FMGCsL/USttgGPQ+8s5Y+uXu2yB/ZP4+w88a+7mgMhdlW4Y3ZW8trOePm2Uz7yQsc/quX2bCrhi8eFd1ikrED87nmwfdYt6OaJRvLm6xfi5g0uJAVWyupaQjxuZiOypE2/5HNxXtDZZ2XgBW08L4UKzMYICNgausvIvGTqJJI8MoMfnDG1Ojlrx0/gcvvm8/m8tp2OyluKa9jSN8ccjKDDO2Tw00vLeeqY8d3qYEJ7C6zaKsksjWn7T80+v1xUwYRMG/N2vdP34dpI/omxabEIu256LDRvLhkCzc+tZRjJha324CkwZ8J78oH2brGMJGJ9LKaBpxzlNc2UJDd8t5LPz5rKiP75XHi1MH8+rmPOr3mtbm3Vmxn9ortfOeUyXFtrV9Z18hl/5jLwpJdTCguSIkTN9lBlUTGqq0PJW05ZERGMMCwohw+8Nd9RTahL8rLZFd1AwPy99xHLLYx1ugBHetI2ZIbHv+Qh+au57yDhvPYexsAr/xySN8cRvXP4+R9h/CpGSOorg+RlRHg3FvfZNbvX8E5OOfA4Xs8XmRN2w1nTOWAkUXR6wtzMjDbPcO2o7KOgpyMDo/51fWN1DeGKcpre0+1WNGmIx0oX87NCiZsDZsSNpE00BgOJ6QksiVD+npJ2gNz1jHtk0V73F5aVc87q3Zw2v5D2Vxey1T/jX3qsL5sLKvl/XW7OHRc1/Z1e+WjrWQELNpxq7O+ceIkQs4xqDCH8w4awRHjBxAMWIsDkkgyCgSMn52zL8f/4TVe/XgrFx0+ps3jQyE/4+pCwhMpHSrMyaC8poEdVfVU14cY3soM96VH7j5Ln5MRoLabGzs/smADjywoYfqofhw9aSBvrtjO0ROLyezhjrmzl2/j3TWlnD99BJ85eGSPPna8RGbY6hI0W5BsqutDSbnBeXPnTx/BTS8t58MNZWyv8ma8Xv/ucby1YnuL+xeO7O/9X1u7o5ojJ3TtZzrnePkjb2/VSLJ282cP4qxpQ5ucCIndR+3AkUW8s7qUvKwg00cV7fGYZx8wjBFFuXvs0RoIGAXZGZTVNBAOO2bc+BL7De/Dd0+ZQkbAmDmmf/S1G2v5lgo2ldXy2+c/4sMN5bz+neMYNSCPjbtqKMjJaHOD7jXbvYZIHdnEOzczmLC2/snxCU5E4qox5BJWEtlcZJ3Xv+etp7GFs7vXPvQeVz2wgPWl1WyvrGOwn+D98Mx9gN3NCxZvLOt0t7vnl2zmuCmDGFDQteYg1544kW+eNAmAP3z6AM6fMaJLjyOSSGMG5JMZtCabw7em0Z8iq+jCGrbI2pChfXMIu917sXVkb7KcHvhgtKXc+/1+9exSvvfIB1x2zzyufei9Ft93uqq2IcQPHvuQvKwgv/rE/hwSUy6dzLLVdKSJmobkn2EDuOyosRTlZXLx3e9y1xur6ZubSZ+cTE7dbygTBu1Zijusby65mUGWb+38etAlG8vZVV3PvLU72VpRx/dOm8KIfrkcNWEgZx8wrM1Z68jM/Yh+uS0el5MZ5IgJA1u8rU9OJuW1DTz+vpccfrihnIvvfpfP3fUOt7y8fI/j6xvDnPmX2Vx897t8uMFrTPa1B711t0f8+mWuvG9+q3HurKrn/jlrOWP/oR2aYctL4AybEjaRNNDYy/uwtWVgTLK0vbKe0qp6Kmoboh8IV23zzna9tXI7zu3uvDi8KJeMgLF2RxXltQ2ccfNsvvOfjne/2lFZx/rSGmaO7teDv41I6gkEjMF9cqLNC9oSKRfaVd3AP95czYJ17XeYfHLhRs7/61ts90u2hvgNOK7+13sAjB7Q0YSte8nE5vJaBhZksXJbFY++t4GsYIBnPtjMNx5e2GObCy/bUsGOqnq+dNTYFs/8J6tIx7uqOs2wgTcbnKwt/WP1ycnk4SsOZ2S/XEqr6rn48NFtHh/wK0reWrGDe99aw4PvruOFDqwNLdlZzek3v8G3Hl7Iw3PX0zc3k4sPH8PTXzuaW2PWc7cmsk48skl2Z/TJ9Uo8f/p/SwC446IZPHLV4RwzqZgH5+7ZZbq0qp66xjBXzhrPE189kuMmF/PR5gpuf3UlAG+t3MH60mrmrinlmQ82NXkPu+P1VVQ3hLj2xIkdii0nM0iNvxn40h7oWt0ZKokUSQNeSWRyzLAFA8afLziQax96n9teXcE/314LeGfh3/7eCdFB87Vl2wBv413w6vdHDcjjrZU7OHiMdxZ7UUlZh3/uIr/uf9qIop76VURS1rC+uWza1f4M2w6/7Or5xZtZu6OawpwMnvv6MW12Wr1/zlrmr93Jube+SVYwwHGTi3l92TZyMgMcNWEgYwe2v5YmOzNAbTfXsG0uq+WTM0YwtG8OG3bV8L3T9uH211by5/8t55IjxjBjdD8aQmEemLOW0/cf2u6a2pYs3+LtcXV2ipVFR1qYV/XinnzJrDoF1rBFTBpcyKNfOZINO2taLS+OdfCY/tw1ezU/fnJx9LpHv3IE00e1fvLyuQ+9pO7VZduYPLiQ/Yf3JTcr2OGkdtqIvoBXGtlZEwcV8OTCjQBcf9oUTvE3nz9m4kBeX7aN0qp6+ses19vuNzA7cGQRB4ws4rT9hvLKx9u4zU/YwNt/MtZjXzmCkf3zuPetNZx9wDAmDe5Yo6DcrCA19SGuuG8+WRkBnr7m6E7/fl2lhE0kDSSy6UhLxg30au0jyRrAprJaFq7fFV1s/NrHXsIW2yr/imPGcd0jH3DJP+YCUFzY8dLGD0rKMIP9/YFEJJ0NLcrhvXW72j0u0tgg0qG1oraRI3/9MlOGFHL18RM4c9qwPe4zJOb/7PWnTeELh49h7MB8jhg/sMOzUNkZwW6tr6qqa6SyrpHBfXK4YtbuzasvPXIMf3l5Oa99vJWsYIC7Zq/iifc3cveba/j3FYdRlJtFRtA6vM5t+dZKMoPGmA7MGiaTfCVsTdQ0hChsZ+PkZBIMGKM6+Jr7wRn78JXjJlDfGOawX/0P8Do9Any0uZzFG8r3KO/f7JdLh8KOJZvKufTIMZ2K7/gpg/ngJyd3aR/CX5+/P8WF2Tz34WY+cdDuEyGRNXKzV2ynZGc1lxwxhrysDHb43TIHFnhJ3KHj+jOkTw6fPngklx8zjs/c8TbBgDU5wfvVBxZw5ISB1DWGuOaEjs2ugVcS+d66nVTVh5okjb0hdebvRaTLvJLI5EnYBvf1Eq1R/fN46PLD+P7pU8gMGvfPWcuuar/9t18nPihmM+pPzRjJoJgkrTN7CC1Yt5PxxQVJu5GtSG8aWJAdPTPdlu2VdZwwZRAvfXMW//ryodHr60NhrvvvohY3uI7s2XbFrHFceuQYAgHj2MmDOlUymJPZvaYjkZbnAwqafqgqysti9IB8bn55BWfdMpunF21i3MB8dlTWccqfXmefHz3HtztRar1iawXjBhYkTcl5R0XeByPrgJ1z3D17dfSDerqpSaEZts4yM/rnZzGkbw5nTvO6HS/ZWM4fXviYU296g2/9ZyHbK+v43fMfUeafMN1SUceYAXncedEM9hnah5P2Gdzpn1uYk9ml7qx5WRn88MypvHn98U1mvQ8cUcTAgiyuefA9fvvcx9z2ijeDVupXAUTWpo8ekM+c75/AN0+aREF2Bk9fczQPX3F49HFuu3A6G8tq+c/8Es49aDjjizvehCw3Mxj9bOKVYvZeSXFqvcOISJc0hpKnSyR4de0/OnMq9152CIeNG8Dlx4znkzNG8p/5JXssgh+YvztBCwSMR646gh+cvg8XHDySlVsr+fTtb7e7we7W8lreWL6dE6YMisvvI5JqBhZkU10fiu6T1pLnPtxEeW0jB4wsYsKgAg4e05+sYIDvnDKZK48ZT1V9iDU7qva4X1lNA0dPHMj3Ttuny+30czLabzpSXd/Iebe9yfy1pXvcFk3YWjgLHukG+OmZI5j7gxN5+dvH8sTVR0ZbjT/x/sYOx7l8ayUTu9h1NpGaz7BtLKvlZ08t4fL75rV4fDjsuH/OWv704rJei7E3VTc0dqjpRKq75XPTKcjO4OaXV/CXl1dEr//xk4u59ZWV0b/vFn/bnZP3HcKz1x7NERMGJirkqL55mTx77THRWbd7315DeW1DtAqg+cmZWLFbbZw8dTBXzhrPlCGFfOPESZ2KIdffWD0ym3fna6s6df/uSJ5PcCISN5V1jS1uVptIlx01tslalpOm7k6mZsQ0Bgk0K+Uc2T+PLx8zjvMOGs4REwYwd20p3/nvQp7wO0q1ZPnWSkJhx7GTlbCJwO4PHNsr6ve4rbYhxPcf+4Ar718AEC39yQwGWPaL0/jqcROipcXH/+E11jZL2sprGjrUIrstOR1Yw7Z4YznvrdvFtx72ZsTCYUdZdQM7q+qja2BaKltq8E8KHTt5EP382ycMKuTfVxzOV48bj5n3ntkQCrPDn4X8cEMZB/7sBVZuq4w+zqayGtaVVnd4/UsyiYwHkdmCnX6Cu3JrJe+uLuUzd7zNPW+uxjnH9x5dxLjvP8MNj3/In/+3nO89uihhccdLaxu6740uOnw0x0wq5tsnT+LMaUPpn58VXbMW6ay6tby2yXKEZFFcmM0fP3Mg93/xUCpqG7n91ZXc+PRScjIDFLaTcN918UyuOGYcGcEA1582hee+fky7+1A2l5vppU2X+RuF/6EXT2Ds/acTRNLU4o1lVNQ2csiY/uysbmBAftda2feW2MFh5ph+zF+7k8w2yjgPHTeAQ8cN4OQ/vcaikjKufeh9jpsyqMUPiq2VR4mkq4F+afHLH21hZ3UD4wcVMGtSMX1yMrj2ofd4fvEW+uZmUlbTQH0LpcdThhSyz9A+LN1UztcefI9HrzoiWhZYXttIn9zufbyI7RL5jzdXR7vAeY/fQEFWRrTL5Zod1Xz9ofd45sPNe8TaUsIWOWZkvz0/rB0/ZRC3vrKS6/67iKK8TB54Zx1vf+94LrzrHcpqGnh75Y5oCdXN/1tBRsD4xPTUajgC3hrBzKBFSyIj75FV9SH++OLHvLO6lNKqekYPzOfBd73OfN85ZTJ/n72af89dzw1nTN2rZqS8k5p7z+/TlutOndLk8t9nr+bnT3kdGXdU1VPfGKZkZw2n7z80EeF1yJSh3kmSSGOR2oZwu7P5J04dzIlTO1/aGevcA4czsl8eFx02mt8+9zEAv372IxaV7OL2i2Z0+0RVW9Lj1SmShn7//MfMX7uT575+DKGw6/UFsp0Vm7AdNLKIvrmZ3HDGPu3eb//hRSzzO7U9PHc9B4wsinaRjNhZ7X0Y6ZeX3M+BSG8Z7Z9Z/onfOhu8zZR/dd7+PL94C985ZTIXHT6aXz/7UYsJiZnx0OWH8eiCEn76f0s4+5Y3ueaECZy639AemmHzSiIbQuFoe++pQ/vw3rpd/OOt1Riw01/vesKUQTy1aFN0z7hYLb3vnbb/UP766spo6/FYM0b35wen78MvnlkavW7W716NJnmR2bk126t4eN56Pn/oKEa0kPilgvzsDKqbJWwAK7Z676ert1fxzKJNAHzl2PFcNWs800b05aK/v8vcNaV7TcVCQyhMfWOYgqz0/Eh84aGjuPP1lWwpr+Pd1aU8+6H3fymZZ44HFmQzID+L2oYQVfWhJo2O4umICQOj5aE3feZAvv7v97n9NS9pnPaTF1jys1PIi9PrKD1fnSJpoLS6gfLaRu59aw2Q/LNL/WOSqQEF2Sz88ckdut/nDh3FIwtKALjxae9D1uNfPbJJO+HIh5GivPid/RJJJeOKC/jHpQezrbyOd9eU8t/5JdQ3hvnWfxaSmxnkosNH0ycnk1+et3+rj9E3N5NLjxzL+tIa7n5zNTc8/iEzRvenrjHcZL/FrsjJDFDXGG6yPvXiu99tcszAgixG9c/j75ccTFl1A099sJHigmyG9s3lifc3cNfs1S02Gfr2yZO59Mgx0XLI5r509FjMvK1Fjp08iEUlu+ibm8n9c9ZG18vc9NIyMoPGV4+f0K3fM5HyszKo8BO2HTEJ2/bKeoYX5bJhVw3/mV/CcZOL+a4/KzNzdH8yg8bbq3bsNQlbZB1fusywNZeTGeQvn53O+tJqfvLkYq596H2ApE7YAG48dz/652exYVdNQrbrOWXfIXznlMmcsu8Q3l1dyrItFQTj2I07PV+dElcrtlZw71tr+eGZU6NdwdbtqCbsHGP8NUvffPh9po/qx+cPa3vTR+m6SHv8O173FsUme0lk7Fq1zrRXnjG6Hx/+9BSe+3BztLvbko3lTRK2nVX19MnJ6HCrbpF0cJz/gfuMaUM5euJACrIz+OK98zhz2tBOzZD96Kyp7DusD9/6z0Lue3sNANNHF3UrtuyMILX1oWgny/HF+Vx65FhuePxDAFb+8vQmH4765mVy4aG7x5P9hvfhB2e03PQkGLA2N/Q1M7509Di+dPS4Jtc/88FmdlTV8/HmCp5YuJErjhnfpY2Bk8WQvjmUlHplpWt3VJERMI6fMogXlmzh4sNH4/DWM8aWxuVmBTlwZBFzVu5IUNQ9L1IWms4dhA8Z259DxvZnaN8c3l1TSv/8LPYZmtwJ22kJLtnMzQry1eO8EzYTBsW/8VD6vjqlx63cVskJf3gtenl4v1zO2H8ouVlBjvndK/TPz+Lw8QMYVJjNows28OiCDVx46Cjuf2cdx00upiA7A+do9ayndM6u6noKczKoqPUGo87sWZYoh47tzzurSynK7dxroCA7gzOnDY0mbPPWlPK5Q0dFb99Z3ZD0JaEiiZKfncE5/sbPj1x1OJOH9On0Y0z3GwXd8soKBuRnsd/w7u132C8vi4q6RraWewnb3y6eybjiAl79eBszRvdr90x2V7tTtmVgQRbbKmr544sfU5CVwZWzxrV/pyS237A+/Hd+CTur6nnw3XWcvv9QLj9mHK8u857jmc1KyyMOHzeAW15ZQXlt90tfk0FVndd4JV1n2GLFlvxJctGrU3rMKx9tbXL5189+xK+f/YhzD/Q2Vi2tqudpvx4+Yt7anfzQP2OanRFg8pBCnrz6qN4JeC8WDjvKahr48tHjGFecT3ZGkEkp0Hr6nksPYc6qHQzp2/mz1jmZQZ762lH84umlPPb+Bl5auoXJQwr5z5VHsLO6XicCRDpgxuiWP6S3Z3T/PPrnZ5ERMO774qFkZ3Sv417kBNPyrV5JZOSEy11fmNmtx+2OqcP68OiCDZjBlbPGU5Tia2KnDutD1dsh7puzloaQ45Ijx7Df8L588JOT2/z7HTZ+ADe/vIK5q0s5oQv7cyWbymhJZHp0iZTU1Kn6IDM71cw+NrMVZnZ9C7dfaGaL/K+3zOyAngtVklFjKMwuv6HDJn/DzV99oumah8f9PW2OmjCQmz5zYJPbrvLbRoPXrv3DDWVt7gsk7atrDHHpPXMJO29h7mcOHsW5Bw2PyxnnnpabFeS4buyVtt/wvvzh0wfwxSPHMnFwIXPX7KS+MUxpVb0ajojEUSBg/OfKw3nm2qOZPKT7pVSRhG3ZlkoCRlLM5PzyvP357CEjyc0M8qkZIxIdTreNGeAtUbjz9VUML8rlQH8dUHvJ9vRR/cjKCPB2O2WR63ZURzt5tqa2IcSCdTs7HnQPW7ejmsUby4D0LomU5NfhhM3MgsCtwGnAVOCzZja12WGrgVnOuWnAz4E7eypQaZtzjoXrd/GPN1dz9b8W8MJib0+Nldsq+e5/F3LD4x9E95PpSZ++420O+eX/CIcd89buZP/hffnsIbtL0Zb/4jQuOWIMR00YyP1fOpRzDxrOt0/evVHh9so6PnvIKJb+7FSuO3UKYQcfbigHvC5cf3zhY8ItdP4Sz67qen71zNLoommA+Wt28tqybYC3riPdDCvK5YYzp3LWNK++vaLW25dJCZtIfI0vLuh2s5GI3QlbBUV5WXvsx5gIOZlBfvWJaSz88cmMK07+ioX2jBrgdbesrGvkqmPHd/g5zskMMn1UEW+vaj1hq20IcczvXuGIX7/MVn9vr5aOOf3mN/jEbW/xQUlZ53+BHnDyTa/xoycWA1CYBCcFRFrTmdMJhwArnHOrAMzsIeAcINoT2Dn3Vszxc4BeOQVVXd9IRiBAVkaA6vpGcjODKTGb0B0vLtnCnFU7uOyoseyqrueMm2c3uf2FJVu477JD+Nsbq3lp6RYA1myv5t7LDonW/i/ZWM78taVcdPiYLsWwq7qeBet2AfDEwg0sXL+Ln569LwBPfe0oBvXJJjMY4Cf+dRFXHz+RrIwAv3zmI8BbTJ6bFeSQMf3JCBhf/dcCnv/6MXz93+/z/vpdnH3gMCYMSu7Fr4ny8kdbueP1VQztm8Pp04by7upSNpftHhx7YyFssooMvhW1jZRW19M/X4OxSKqIJGybymqZnGTd6vaW5kWDC3PICgbol5/JJzs5Y3j4uIHc9L9l7Kqub7E0dP7a3bNmX/7nPGZNKuYT00cwZmA+H24o45WPtjK0KJdV27xN119auiW6GXtvKa9tiO71BzAoBdZ5S/rqTMI2HFgfc7kEOLSN478IPNvajWZ2OXA5wKhRo1o7rF21DSHO/+vbHDiyL987fR+m/eQFvnb8BL518uQuP2ayKq9t4NH5JdQ0hPnNc16y8/fZq5sc88q3jyUzaBz1m1f4z/wSVm+vZOzAfL589Di+/9gH3P7aymhXm8/+bQ5lNQ2cfcDwLs3ElOzcXerww8cXkxUMcJ6/X097C85jG2BkZ3rlF33zvEHjobnr+fxd70RLLf+3dCvz1uxkytA+TTr/CazdUQ3AfXPW8uyHm3lndWl0P5KDx/TjoDR+viKdJrdV1lHbENYaNpEUMqxvDtedOgUzOHZycaLD2SsFAsZVx45n32F9yMns3PqtYycX86eXlvHf+SV7dNOE3XtfXn3cBO5+czULS8qoqg/xwzOncuPTS5izqjS6LnHKkEL+/L/lbKusY3hRLpvKasgKBvnqceMZ0EMzti2Zu7q0yWVt+yLJrDMJW0tTVi3WqpnZcXgJW6vdI5xzd+KXTM6cObPLNW85mUGOm1zMba+uZMkmb3HyX15ewSn7DiEvK7hXlC1EPDx3fXSfqYmDCljub245ZUghH22u4NCx/Rnrt80/csIAFqzdybrSaq6YNY7PHjKS5xdv5nfPf8wLi73WxGV+2/fP3Pk2T19zdKf3jyjZ6SULh43rT5+cTA4e07/D6wxiy2ZyYwaKX58/jdP2H8qX751Hvb9B6a+e/Sh6+x0XzeCUfYd0Ks692dod3tnJlduqWOmfqdxcXss/LzuEYyal94ecyAxbJKntr5JIkZRh5iUTEl/fOGlS+we14ICRRRw1YSB/fXUlFx46mtyspglfeY1Xpv/5w0bz7VMmc+gvX6KqrtFfs7YL8BqRFeZk8NhXjuSXzyzlvjlrAa/BTGlVPWMH5nW5Aqgj3l65g4yARTdc39srsyS1dWZevwQYGXN5BLCx+UFmNg24CzjHOdcrG3V8++TJnDR1MAvX7wK8PVbO/Mtsjv/Da1TUNvRGCL1izirvbNBPzprK09cczU/OmsrZBwzjua8fw6pfns5Dlx8WPXbS4EJWba8iKyPAyVOHYGZ8aqZX8rCwpKzJ4tqPNlfw2+c/ojX3vb2GP7+0HPDWnJVVN1BW0xCdYbv98zO48+KZfPmYjrc4jp1hy8ls+jKcNamYy2Me69MzR3CF3z755v8t59EFJYS0rg2AdaXVTB9V1OTM4FkHDEv7ZA2gT673Gl+93TuxoRk2EZGe87lDR7Gjqj7ayTNWuf/ZK/I+nJeVQVV9iGVbKqhvDHP9aVMIBowR/fLIzQry47Omcua0ofzyvP2Zf8OJFGZnsGxLZVzifnvlDv780nLeXrWDmWP6xeVniPS0zsywzQUmmtlYYANwAfC52APMbBTwKHCRc25Zj0XZjkDA+MtnD+Lx9zZw+PgBPLJgAzf/z0sw/j13fYvT9eA16liyqZznF29hRFEup+w3hM1ltT3S4ao9lXWNnH3LbG48dz+OGN/+nhfhsGPumlI+M3Mklxw5FoBLjhzLJUd6tzdfLPzlo8cxaXAhp+83NFrueMb+Qyn6YhbvrinlsiPHcODPXuQ7p0xmc1ktd7y2imWbK8jOCDJ5SCH987M4f8YI3l65gx/6C3LPO2g459/+Ftsq6sgMGvsM7UNRXiZ9cztfRtDaDFvEF44Ywy2vrGD6qCJ++0mv2eio/nn86pmP+ObDCykuzOboiUpKtpTXcejY/hw8pj93vL6KoycO5PefmpbosJJCZLb31ldWAqjpiIhID4qceN1VveeJ8fKaBjICFh3f87KCVNc18vFmL7k7aepg73Z/Zi4jGOCWz02P3n/i4AJeXbaVL907l1+etz+D+vTcBuWf/duc6PffOHESp+03VHuwSdLr8CvUOddoZlcDzwNB4G7n3GIzu9K//XbgR8AA4DZ/arnROdcrm6bkZAa5wO9O+LXjJ1CUm8kD76zlX++s44tHjY1OdYfCjt8+9xGfOXgkLy7Z0qTc7mdPLaGyrpEnrz6SaX57264IhR2VtY1trgubv3Ynq7ZVceNTS3nm2qOb3Pb32au56cVlXHXceL541FgefGcd763fRVlNA4eM7dgeOcOKcpt0awRvuv+oiQM5aqKXIH5846lkBQOEwo5V2yt55eNt5GYGec7vMHnTS8vYGfNGfMzvXol+3xByLCop47Bx/btURtAvL4tgwAiFXYu188WF2Tz+1SOj67EALjx0NKfsO4SZN77ERX9/l+e/fkyvJNeJ5pzj4rvf5ZAx/fnaCROj14fDjq0VtQzqk8NXjhvP2IH5HDdlULf3P9pbNC/PVdMRiTczOxX4M94YeZdz7tfNbr8QuM6/WAlc5Zxb2LtRivSMIv9kbWS9WkRpVT23veqdKIt8PsjPyqCqvpHlWyvJyggwun9eqyfTAY6dPIg/vriM9aU19M39mD98Oj67RB00qkgVKZISOnVKwTn3DPBMs+tuj/n+S8CXeia0rssMBrjsqLEU5WXyzYcX8uiCDXxi+nAaQo4PN5Zxx+uruOP1VXvcLyczQGUdXPPge/zry4cxrCi31Z8xf+1Ovvnw+/zqvP2ZProfzy/ezFnThhEIGL94eikPzV3Hvy8/nEF9sjFgbWk1T76/kcUbyxhalMsGv5wwM2isL63mG/9+n+tPm8LfZ6/m2Q+9hOm3z33Mn19aTl1jmIEF2Uwd2odZPbj4OvLBPiNo3HbhDB56dx0XHDKKUNjx8eYKbnppGQeOKuKa4yfyzuodPPH+Rl5csoUnrz6SrRV1fO5v77DP0D5d+tnBgNE/P4ttFXWtLnZuqcHIgJiytlNuep2fn7Mv5xw0PCn26OmMnVX1fOe/C/nJ2fsyol9em8d+/d/v88by7byxfDsnTh3MPkP78EFJGT/5v8U0hBxD+mTTJyczesJCPH3zMvnxWVP56f95jWw1wybxFLP1zUl4SwjmmtmTzrklMYdFtr7ZaWan4a3jbqt5l0jSinSHjKyHj4h0po6Vlx2ktKqeZVsqGF9cQEY7nTY/d+go/viiV6j19srthMOuR7Z2aAyFm1xOh5O+snfYq+eAzz5gGA/NXc8Nj3/Ix1squPP1VXvsETNtRF9yMoIcNq4/lx45llm/e4U1O6o55abXufHc/TjnwOEtPvZfX13B2h3V3Pj0UtbuqKKqPkRNfYhT9h3Cv95dS21DmLNumd3ifQeW1rDd3xNt+dZKfvXsUuat3cknb387esxvz59GTUOIpxdt4qLDR3PWAcN66FlpWd/cTK6YtXuB9+HjB3D4+MOjl4+fMpjjpwyOXp4wqJD7v3gok4Z0valLcUE22yrqWiyJbI2ZUZiTQUWtt6D5h08s5odPLOaKY8bxvdP36XIsve2d1Tt4aelWXlq6lbsunsmJUwe3eNyqbZU88f5GBhZkU9sQ4gePfcA1J0zkivvmU9foDTxD+vZcqcje5tIjx0YTtq6U7op0QtJufSMSD5H31J1VDdQ2hNhSXssFd84hI7hnYpWflcH60mq2V9R1qFIo9rPaxrJa5qzawRET2l8+0pZnPtjEV/+1oMl1auUvqWKvTtgyggFu+exBnH7zbO70Z9QOG9efjzZXYMANZ05lVrOp8KnD+jBnVSnjigu49qH3WbyxnO+fvg8vf7SF/84v4apZE9h/RN/o5s5LNpVH73v9ox9w/aMfAPDHTx/AdY8soiHkNcf45kmT2G94H8YNLGBEv1z+O7+E8YMKuOr++TzzgTejdsjY/rzrt5mdPKSQA0YW8YUjxsTzKeqWSGllVw0szIZNezYdac+b1x+Pc979vvfoBzy6YAN3vL6KrRV11DaEuOVz06MdL8NhR2V9I31yMtlSXsugwmzMjDeWb+PtlTs4Y9pQ9h3W/b1fnlq0kWnDi6Ibkbanuj4U/f5rD77HP794CG8s3855Bw1n7MB8Fq7fRUVtIy9/tJWMgPHMtUfx0pKtfP+xD7jkH3MZXpTLhl3eLO2UIV2b5Uw37Z3RFemmHt36RiTZZWUEKMjOYPHGMo7+7Stsq/BOREfG3yevPjJ6bF5WkC3ldVTWNTKpg7NaT33tKGoaQlx2z1yeeH9jtxO2+Wt3khUMcO2JEzloZD/CzqkzpKSMvTphAxjUJ4fbPz+dHz6xmIsPH73Huq7mbvncdFZtq2L6qCKufeh97p+zllP2HcxXHlhAbUOYZVsqGV6Uy+byWr598iR+/4I3ZX/y1MEM7ZvDvW97bWk/MX0E5x00vNU3g0j52h0XzeCaB9/nylnjuOjwMTy5cCM/eOwDJiXZRqHxUOyfQWveDrg9seWPf/z0gZy67xAuv28+j723AYAv3P0u44vzeWd1KdX1ITaX1/LVYyfwp5eWcfTEgVw5azxfeWABFbWN/Gd+Cb/+xP7kZAY5spODwYqtXpOW+lCYq//1HgALf3zyHjM5VXWNLCzZ1aS5TGmVV/P/7LVH84nb3uJT/uzq2yu386kZI/nuI4sAyM4IcOa0oQwqzOG8g4bzs6cWU9sQZnhRLqfvP4SSnTWM8bdykJZ96aixzI3ZxFUkTnps65ue2qdUJN765GTwwpLdJZAnTR3Md0+ZTNg1LTfMz86gsq6RzKBx1rSOVQxF9nM9cGQRizeVdTvW9aXVjOqfx1eOndDtxxLpbXt9wgYwc0x/nm3W2KM1Awuyo1PxZx84jKc/2MT5f32bgQVZXHDwMO55aw1by2s5YERfLj1yLCP759EYcpw/YwS1DSHufXst50/3qlw6cuZmxuj+vHn98dHLZx8wjLPjXP6YLAYWevXvOd1sknH8lEF8euYIjplUzKsfb+O/80uYvWJ7k2P+9JKXWEfWggF84fDR3Pv2Wr547zwAXv32sR1OfpZvqeCkP70OwLdi9rG58K45PPjlw9hSXktpVQP52UHeXLGdXz7zEXdeNIMDRxUxqDCHHVX1ZAaNKUMKue7UyfzEL9ubu2Ync9fsTi4ygwGuP80r9czNCvLKt4/l8F+9zCemD9eatQ664cypiQ5B0kNnt745rbWtb3pqn1KRePvMwaP400vLuP3zMxhWlMOEQQXkZe350TLPPzH76ZkjGdm/Y5UoEZMHF3LX7NXsqKwjYEZpdT3ju7DHbsnOmk7/bJFkkRYJW1edMGUQXzt+AsGAcdlRYynIyuDKWeMZ3Cc7mozFrnHLyQzy/o9OUnvYDjpoZBFjBuRRkNO95ysjGIi2/p82vIiF63fxm09Oo09OJgvW7eTwcQO4+l8LuHLWeGaM6cfiDeW8t24nVx47noq6Rh5d4M3M3fPWGq45YSIZQaPWL1kcWJAdXehc1xgiKxjgqUWb+NqD70V//p0xDWw+3FDO/j95oUl8+f5Adfl98wF44EuHsnFXDf3ysjAzLjlyLGceMIx+eVlsragl7ODjzeVcds88vnXypCZr1Ib2zeWjn59KdobK+0SSTNJufSMSL9ecMIFPTB/ebiI0tCiXvKwgXz2u87Nb0/wGZEf95hXqQ2FCYcfdl8xssq6+I9bvrNa+a5KyzLnEn7ybOXOmmzdvXqLDkDTUGAqztrSaP76wjKc/2LTH7Z89ZBS/+sT+lFbVM+PGF7nm+Ik88M66aNOY8cX5rNxWxVkHDONbJ03i8vvmRTf7/MzMkTy/ZDO7qhuYOKiA5VubbgK63/A+PPW11md+V2ytZHxxvmrsZa9iZvN7a7uX3mZmpwM3sXvrm1/Ebn1jZncB5wNr/bu0u/WNxkfZG4TCjvKaBvrld75br3OOXz6zlL+9sZqBBVlsr6ynMCeD9354EhnBAFV1jby4ZAun7jck2nXaOceTCzcytG8utQ0hDhhRxAE/e4EfnL4PXz6m9e0ERBKttTFSCZsIsLWiNtrUA+Dn5+7Hv95Zx9JN5eRmBgk5R73flTFg8NhXjmRHVR3HTR7Ee+t3MbJfHsWF2WytqOXVj7eRlxXklH2HsGRjOR9uLOPYyYNYu6OK/vlZLFi7C4fjwJFFPdLwRCSV7M0JWzxofBTxErBtFXUMLMjmX++u44bHP+Sui2eyeGM5c9eUMnvFdsYOzOekqYMpyM6gKC+THz2xOHp/M3AObv/8dE7db2gCfxORtilhE+mAhet3sbWijpOmDuZbDy/kkQUlHDauP9NGFFHXECInM8j00f04Zd8hiQ5VJCUpYescjY8iTe2orGPGjS8BXiKWGQwwdkA+H2+paPH4zx82ivvnrAO8zpORZiYiyai1MVKLrURiHBCzWXdto7eO7TMHj+S8g7RdkoiISKINKMjmO6dMZu6aUn545tRoA5LnPtzElfd7+6ydsu9g+udn86tP7A/AafsN5Y8vLutSsxKRZKCETaQV3zppEnUNIU6aqtk0ERGRZNFS85JT9xvK3ZfM5OlFm/n9p6Y1Wf995ISBnd66RySZKGETacW44gLu+sLBiQ5DREREOuD4KYM73T1SJBWoN7iIiIiIiEiSUsImIiIiIiKSpJSwiYiIiIiIJCklbCIiIiIiIklKCZuIiIiIiEiSUsImIiIiIiKSpJSwiYiIiIiIJCklbCIiIiIiIklKCZuIiIiIiEiSMudcomPAzLYBa7t494HA9h4MJ54Ua3ykUqyQWvEq1vhI51hHO+eKe/Dx9mrdHB8hdV5rqRInpFaskFrxKtb4SKVYIbXi7ZUxMikStu4ws3nOuZmJjqMjFGt8pFKskFrxKtb4UKzSW1Ll75cqcUJqxQqpFa9ijY9UihVSK97eilUlkSIiIiIiIklKCZuIiIiIiEiS2hsStjsTHUAnKNb4SKVYIbXiVazxoVilt6TK3y9V4oTUihVSK17FGh+pFCukVry9EmvKr2ETERERERHZW+0NM2wiIiIiIiJ7JSVsIiIiIiIiSUoJm4iIiIiISJJKiYTNzCzRMXSEme2T6Bg6ysy+ZWYn+98n/fNrZn1jvk/qeJM9vlh6zcZHKr1eITVilJal0t9O7zfxofeb+NFrNj5S6TWbLPEldcJmZueY2b3AAYmOpT1m9hfgGTMbk+hY2mJmJ5vZ88B1wMUALok7z5jZ8Wb2PvBXM/s+JG+8qfR6Bb1m4yGVXq+Qeq9Z2S3V/nZ6v+l5er+JL71me14qvWaT7fWakegAmjMzc845MzsO+DnQABxuZmudczsTHF5UJM6Yq/oDO4ETzew+51xdgkLbg392IBP4ETAL+BWQBRxsZplAYzL+hzGzAuD7eK+Dd4F7zSzPOXdDYiPbLVVer6DXbLylwusVUus1K02l0t9O7zfxpfebnqfXbHylwms2mV+vSTXD1uw/y2rgFOA7wKHAtIQF1kxsnGYW9K+eA/wVuBCYmKjYmovE6pyrB55wzh3tnHsG703oAudcQ7L9pwYwswBQAKwH3nPOrQe+BHzGzKYkNDhfqrxeQa/ZeEuF1yuk1mtWmkqlv53eb+JL7zc9T6/Z+EqF12yyv16TJmEzs6uBR83sG2Y2xDm3xjm3yTn3MrAFmGVmwxMcZmycXzezYc65kJllAacCjwGvABeY2SfMrDhJYv2GmQ11zs31r890zr0GrDKz0xIZYywz+4qZnQ/gnAsDDijG+0+Oc24V3nP8M//4hNUVp8rrFfSajZdUer36Pz9lXrPSVCr97fR+Ex96v4kfvWbjI5Ves6nwek2KhM3MzgO+ANyMl8XeYGYHxhzyADAJL8uNvV+v/nGbxXkA8H0zm+Gf5ZjnnNsOLAeuAX4BJPLF1/w5/YGZRepwG82sP7AWCCUoxCgzKzSz2/Gm9u81swwA59wWYAnw9ZjDrwcONbN9E3UGKVVer/7P1Gu2h6Xa6xVS6zUrTaXS307vNz1P7zfxpddsz0u112yqvF6TImHDexL+6px7BfgJ3lTkNZEbnXOLgLnAfuYtWLzOv763/7gtxXmVf9sZZvYG3qLPx/Gm0st7Ob5YLcV6LXjPm3OuFMgFjoPodHVCOOcqgNecc0OAp4BbY27+GXCgmZ1uZtn+WZqn8Gq3EyVVXq+txarXbDek4OsVUus1K02l0t9O7zc9TO83CYlVr9luSMHXbEq8Xnv1j9k8G425vAr4HIBzbi3wNJBvZmfHHP4gXr3rv4GBLT1eguIsMrPDgT8DbznnDnTOXQwMAeLeDrabz+n9wCFmluP/p4m7NuJ90v/368BnzWwigHOuEvgtcAHema+fAUcDmxIYa1K9XrsQq16z3Y816V6vLUnm16w0pfebpIhV7zc9E69es70Xq16zXZTMr9eW9Hb23aQrZUx2+l+g2szO8S9vAl4FppqnAO8/zgfANOfcd5rdP5FxvgwcAzzgnLsu5m7nOefei1N8sbr0nPrX5QIP0bvT5y3G65yrMrOAc24zcBtwV8wxDwG/xCtFKAZO86fW463JGZ8kfr12NtZEv2a79Lz61/X2a7bFWJP09Qo0WTCf7K9ZaSpVxsfOxpro95tUGiNTaXwEjZHxojEyTlJ5fOyVhM3MDjOzB4CfmtnEyBNmfl0rXmebx4CrzMycc2V4ixJz/CekFrjWOXeGcy5uWXgX48z34wybWdD8KWjnXG284uxGrAVAdsyL7Ann3N+ccw3xjLWdeKPPWYRz7npgrJkdbmZDzOxQ59xHwI+dc1c55zbEOdbDzew/wO/MbGqyvl67EWuiXrNdfV57/TXbRqxJ93qNifdnfjyhmOsjg3jSvGalqVQZH7sRq8bIrseazO83GiOTI1aNkR2LNeXHx7gnbGa2H/AXvBrVrcDl7N7Yr9E/LBd4Hi+rvdPMhgEH4e1/gHOu0Tm3NYnjbPSPC7nemYLudqyReOMdawfiDflvigVA35i7/QZ4E3gdyPGPjfvZDDMbBNwCPAPswKsPv8z/+Unzeu2BWHv7NdvtWCPxJjjWpHq9+vF+AbgXb6H0p/3rIou8IzEkxWtWmkqV8bEHYtUY2bVYk/H9RmNkksYaiTfBsSbVa3avGh+dc3H9wqv5vM//Ph/4KfASMM6/7ud4T9RBeJsU3og3FXkbEIx3fKkWZ6rF2ol4nwOO9i+fBnwE/B7I7OVYTwIejIn1FLyBdIp/3Y3J8twq1oTFmjSvV//nnwiMAE4G1sVcH/T//UmyPLf62uNvlzLv5Yo1obEm0/vN3vRerljjE2vSvGbZi8bHeDw5s4BDYy4f4P/yE/zLP8arF/2p/4f+FzC+2WPk9cIfMSXiTLVYeyJeYCowspdiPRf4PnCGf7kYr4XveP9yfz/e3wB5CX4dKNYkjLU3X6/N4j3TvxzEHwSB2cDPY44dlOj3A301ed5T5r1csSZnrAl8v9nr38sVa3xi7c3XLHvx+NiTT1Ih8ChQCtwN9PevL8DrCPM6XlvUp/C6w/weCMTcP9BTsewNcaZarD0Ub2/OqBb7sbwOXIlXivJJ/7ZfAzdFnkPgKOBvkd8nAa8DxZqcsfb2Wc2W4j3Pvy3L/3dfoAwY3ML9e/X9QF9NnvuUeS9XrEkbazK83+yt7+WKNT6xJvoz3V41Pvbkk5UNXA2cjjeleEWz26cB5/jfzwSeTdALMCXiTLVYUy1e4DDgOzGXL8Jr3wveGc/ngBP9y/vgtajNV6yKNRGxthevfzlS4nEX8A//+9N6O059tfi3S6X3RsWa5rH6PzNl3h8Vq2JtK1b/csqPj01ayHaWmV2Mt6v6QufcLjO7Cwjj7VFwlJlNcs4tg+jGc4v8ux4PzPG7sTgX5wWdqRJnqsWaavH6sa4D3gXm422OGGnzugRY7B/6AV5b3JvM7FzgBLz2s5n+76FYFWtv/v9qK94P/MsGOD+2L5lZ2I/7N2b2fG/FK7ul4HujYk3jWGPiTYn3R8WqWNNtfDQ/y+z4HbxffAhe3WcYWIlXZ32tc267f8xE4AtArXPuxpj7zgD+gLc/xOXOuZU98UukcpypFmuqxdterGYWdM6FzOzzwNnOuU/H3Pe7wCRgCvBl59xSxapY4xlrD8Q7GvgTMAD4qnPuw3jHK7vtTe+NinXvj7Uj8SbT+6NiVaxpPT66zk05RqYUJwH3+99n4LWkfaTZsefhdVmZAOT61w0AZnXmZ3blK1XiTLVYUy3edmJ9tNkx/wQ+7X8/JOYxshSrYu2NWLsZb7H/bxFwSG/Fq68O/+1S6b1RsaZBrB2IN6neHxWrYu1GrHvF+Nihkkjz9iz4GRA0s2eAPvi7qDvnGs3sGmCjmc1yzr3mX/+Yme2DV+NaYGbHO+eWAK915Gd2RarEmWqxplq8XYkVqARWm7e54ifM7FTnXIlzrl6xKtZ4xtqD8Z7unIuUh0gv2dvfGxXr3hVrV+NF7+WKNbVjTf3xsb2MDq8F7ULgr8CX8TqwnIpXN3pIzHFXAa/EXP4UUIXXNWZQvDPPVIkz1WJNtXi7Eite29davLUGN+GfjVGsilXx6qun/3b+5ZR4b1Sse1esXY1X7+WKVbEm/qsjT9bRwEUxl2/zn5hLgPn+dQG8mtKHgbEx9zu6136RFIkz1WJNtXi7EOtoYLz/n3q6YlWsvRlrKsarr2797VLpvVGx7mWxdjFevZcrVsWaBF8debLy8NrRRupCLwR+5X//PvA1//uZ+DufJ+QXSZE4Uy3WVIu3k7E+pFgVayJjTcV49dXlv10qvTcq1r0w1i7Eq/dyxapYk+QrQDucc9XOuTrnXMi/6iRgm//9pcA+ZvYU8CCwoL3Hi5dUiRNSK1ZIrXg7Get8iHYd6nWKNT5SKVZIvXhlt734vVGxdlAqxQqp9X6jWONDsaamDu/DZt6+Bg4YjLc5HkAF8H1gP2C1c25Dj0fYSakSJ6RWrJBa8XYmVuefnkkUxRofqRQrpF68stve+t6YaIo1flLp/UaxxodiTS3tzrDFCONtiLcdmOZntD8Ews652Un0RpQqcUJqxQqpFa9ijQ/FGj+pFq/slkp/O8UaH6kUK6RWvIo1PhRrKnGdqyU9DO9Jmw18sTP37c2vVIkz1WJNtXgVq2JNpVhTMV59pebfTrEq1lSLV7Eq1lSKNR5f5j8JHWJmI4CLgD865+o6fMdelipxQmrFCqkVr2KND8UaP6kWr+yWSn87xRofqRQrpFa8ijU+FGvq6FTCJiIiIiIiIr2nM2vYREREREREpBcpYRMREREREUlSSthERERERESSlBI2ERERERGRJKWETUREREREJEkpYROJIzP7iZl9u43bzzWzqb0Zk4iISDLQGCnSMUrYRBLrXECDkYiIyJ7ORWOkiPZhE+lpZvYD4GJgPbANmA+UAZcDWcAKvM0fDwSe8m8rA873H+JWoBioBr7snPuoF8MXERGJG42RIp2nhE2kB5nZDOAe4FAgA1gA3A78wzm3wz/mRmCLc+4vZnYP8JRz7r/+bf8DrnTOLTezQ4FfOeeO7/3fREREpGdpjBTpmoxEByCylzkaeMw5Vw1gZk/61+/nD0JFQAHwfPM7mlkBcATwHzOLXJ0d74BFRER6icZIkS5QwibS81qatr4HONc5t9DMLgGObeGYALDLOXdg3CITERFJLI2RIp2kpiMiPet14DwzyzWzQuAs//pCYJOZZQIXxhxf4d+Gc64cWG1mnwIwzwG9F7qIiEhcaYwU6QKtYRPpYTELqtcCJcASoAr4rn/dB0Chc+4SMzsS+BtQB3wSCAN/BYYCmcBDzrmf9fovISIiEgcaI0U6TwmbiIiIiIhIklJJpIiIiIiISJJSwiYiIiIiIpKklLCJiIiIiIgkKSVsIiIiIiIiSUoJm4iIiIiISJJSwiYiIiIiIpKklLCJiIiIiIgkqf8H8E7aBNY8btgAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"vol_smile = volatility_skew(g10, start_date=start, end_date=end, tenor=tenor,smile=True)\\n\",\n \"relative_smile = volatility_skew(g10, start_date=start, end_date=end, tenor=tenor, strike_level=strike_level, smile=True, relative=True, plot=False)\\n\",\n \"print(f'{tenor} {strike_level} Smile')\\n\",\n \"display(vol_smile.sort_values('Current', ascending=False).style.background_gradient(subset=['Current']))\\n\",\n \"\\n\",\n \"print(f'{tenor} {strike_level} Smile (Relative)')\\n\",\n \"display(relative_smile.sort_values('Current', ascending=False).style.background_gradient(subset=['Current']))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/content/reports_and_screens/00_fx/0006_vol_swap_spread.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics')) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Volatility Swap Spread Screen\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.timeseries import implied_volatility, VolReference, last_value\\n\",\n \"from datetime import date\\n\",\n \"from gs_quant.data import Dataset, DataContext \\n\",\n \"def get_data(bbids=['USDJPY', 'AUDUSD'], long_tenor='6m', end=business_day_offset(date.today(), -1, roll='forward'), start=RelativeDate('-5y').apply_rule()):\\n\",\n \" \\\"\\\"\\\"Pulls historical implied volatility and realized volatility data \\n\",\n \" \\n\",\n \" : param bbids: array of string FX pairs e.g. ['USDJPY', 'USDNOK']\\n\",\n \" : param long_tenor: implied vol tenor\\n\",\n \" : param end: end date for historical data\\n\",\n \" : param start: start date for historical data\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" #implied vol\\n\",\n \" vol_data = Dataset('FXIMPLIEDVOL').get_data(start, end, bbid=bbids, tenor=long_tenor, deltaStrike='DN', location='NYC')\\n\",\n \" vol_df = pd.pivot_table(vol_data, values='impliedVolatility', index=['date'], columns=['bbid'])\\n\",\n \" vol_df = vol_df*100\\n\",\n \" \\n\",\n \" shift_start = RelativeDate(f'-{long_tenor[0]}v', base_date=start).apply_rule()\\n\",\n \" #internal users replace with 'WMFXSPOT'\\n\",\n \" spot_data = Dataset('FXSPOT').get_data(shift_start, end, bbid=bbids)\\n\",\n \" #replace with 'midPrice' if using WMFXSPOT\\n\",\n \" spot_df = pd.pivot_table(spot_data, values='spot', index=['date'], columns=['bbid'])\\n\",\n \" return vol_df, spot_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.timeseries import returns, percentile, filter_, FilterOperator, sum_, sqrt, union\\n\",\n \"def robust_volatility(series, tenor='6m', cutoff=5, pct=[90, 10]):\\n\",\n \" ret = returns(1/series, 1)\\n\",\n \" std_rob = last_value((percentile(ret, pct[0], tenor) - percentile(ret, pct[1], tenor))/2)\\n\",\n \" zap = filter_(abs(ret), FilterOperator.GREATER, 5*std_rob)\\n\",\n \" combined = union([zap, (0*ret)/ret])\\n\",\n \" zap_count = sum_(zap, tenor)\\n\",\n \" days = int(tenor[0])*21\\n\",\n \" var = sqrt((252/(days - zap_count))*sum_(combined**2, tenor))*100\\n\",\n \" return var\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.timeseries import volatility, returns, Returns, exponential_std, annualize, std\\n\",\n \"def calculate_realized_vol(spot_df, tenor='6m'):\\n\",\n \" \\\"\\\"\\\"Calculates realized vol using historical spot data\\n\",\n \" \\n\",\n \" : param tenor: realized vol tenor\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" weighting = (22-1)/(22+1)\\n\",\n \" short_vol, long_vol, robust_vol = {}, {}, {}\\n\",\n \" \\n\",\n \" for ccy, row in spot_df.iteritems():\\n\",\n \" long_vol[ccy] = volatility(row, tenor, returns_type=Returns.LOGARITHMIC)\\n\",\n \" short_vol[ccy] = annualize(returns(row,1, Returns.LOGARITHMIC).ewm(alpha=1-weighting, adjust=True).std())*100\\n\",\n \" robust_vol[ccy] = robust_volatility(row, tenor)\\n\",\n \" \\n\",\n \" return pd.DataFrame.from_dict(long_vol), pd.DataFrame.from_dict(short_vol), pd.DataFrame.from_dict(robust_vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.timeseries import LinearRegression\\n\",\n \"import pandas as pd\\n\",\n \"pd.set_option('display.precision', 2)\\n\",\n \"import warnings\\n\",\n \"import itertools\\n\",\n \"def calculate_vol_swap_screen(bbids=['USDJPY', 'AUDUSD'], long_tenor='6m', \\n\",\n \" end=business_day_offset(date.today(), -1, roll='forward'), \\n\",\n \" start=RelativeDate('-5y').apply_rule()):\\n\",\n \" \\\"\\\"\\\"Calculates volatility swap spread screen\\n\",\n \" \\n\",\n \" : param bbids: array of string FX pairs e.g. ['USDJPY', 'USDNOK']\\n\",\n \" : param long_tenor: implied vol and realized vol tenor\\n\",\n \" : param short_tenor: shorter realized vol tenor\\n\",\n \" : param end: end date for historical data\\n\",\n \" : param start: start date for historical data\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" vol_df, spot_df = get_data(bbids, long_tenor, end, start)\\n\",\n \" long_rvol, short_rvol, robust_vol = calculate_realized_vol(spot_df, long_tenor)\\n\",\n \" results = pd.DataFrame(columns=['crosses', '6m Implied spread', 'Beta', f'Entry vs {long_tenor} Z-score', \\n\",\n \" 'Avg Carry Z-score', 'Score', f'{long_tenor} Realized Vol', f'{long_tenor} Carry', f'{long_tenor} 5y Avg', f'{long_tenor} 10th', \\n\",\n \" f'{long_tenor} 90th', '1m Realized Vol', '1m Carry'])\\n\",\n \" pairs = itertools.combinations(crosses, 2)\\n\",\n \" for pair in pairs:\\n\",\n \" short, long = pair[0], pair[1]\\n\",\n \" beta = LinearRegression(vol_df[short], vol_df[long], fit_intercept=False).coefficient(1)\\n\",\n \" iv_spread = vol_df.iloc[-1][long] - beta*vol_df.iloc[-1][short]\\n\",\n \" rv_long_spread = long_rvol[long] - beta*long_rvol[short] \\n\",\n \" rv_short_spread = short_rvol[long] - beta*short_rvol[short]\\n\",\n \" robust_spread = robust_vol[long] - beta*robust_vol[short]\\n\",\n \" z_score = (robust_spread.mean() - iv_spread)/robust_spread.std()\\n\",\n \" carry_long = rv_long_spread[-1] - iv_spread\\n\",\n \" carry_short = rv_short_spread[-1] - iv_spread\\n\",\n \" carry_avg = (carry_long + carry_short)/2\\n\",\n \" carry_zscore = carry_avg / robust_spread.std()\\n\",\n \" results = results.append({'crosses': f'{long} vs. {short}', '6m Implied spread': iv_spread, 'Beta': beta, f'Entry vs {long_tenor} Z-score': z_score,\\n\",\n \" 'Avg Carry Z-score': carry_zscore, 'Score': z_score + carry_zscore, f'{long_tenor} Realized Vol': rv_long_spread[-1], \\n\",\n \" f'{long_tenor} Carry': carry_long,f'{long_tenor} 5y Avg': robust_spread.mean(), \\n\",\n \" f'{long_tenor} 10th': rv_long_spread.quantile(0.1), f'{long_tenor} 90th': rv_long_spread.quantile(0.9), \\n\",\n \" '1m Realized Vol': rv_short_spread[-1], '1m Carry': carry_short}, ignore_index=True)\\n\",\n \" return results.set_index('crosses').sort_values('6m Implied spread')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Vol Swap Spread Screen\\n\",\n \"\\n\",\n \"This screen compares the historical realized volatility spread between two crosses with the implied volatility spread. The spread is beta adjusted on the short leg. The score is the combination of the entry vs 6m z-score and the average carry z-score (long carry + 1m carry). The volatility spreads with highest score are more attractive to buy.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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6m Implied spread Beta Entry vs 6m Z-score Avg Carry Z-score Score 6m Realized Vol 6m Carry 6m 5y Avg 6m 10th 6m 90th 1m Realized Vol 1m Carry
crosses
GBPUSD vs. AUDUSD-1.961.001.571.022.60-0.041.920.52-2.263.87-0.651.30
GBPUSD vs. NZDUSD-1.550.951.020.982.000.281.840.07-1.623.63-0.251.31
USDNOK vs. AUDUSD0.951.07-0.421.961.543.922.970.41-0.924.073.112.15
USDJPY vs. AUDUSD-2.750.901.29-0.061.22-2.400.35-0.30-3.143.56-3.34-0.59
USDSEK vs. USDMXN-0.670.680.640.391.030.331.000.21-3.091.49-0.580.09
GBPUSD vs. USDCHF0.281.290.290.690.991.291.010.79-1.484.771.641.35
USDJPY vs. NZDUSD-2.430.850.98-0.090.89-2.150.28-0.73-2.742.81-3.01-0.58
USDSEK vs. AUDUSD-0.610.960.610.260.870.020.630.29-1.862.16-0.460.15
GBPUSD vs. USDCAD-0.791.290.520.250.770.931.720.15-1.863.90-1.60-0.81
USDNOK vs. NZDUSD1.401.01-0.901.560.664.292.89-0.05-1.314.463.552.15
GBPUSD vs. EURUSD0.391.270.150.390.551.531.130.69-1.524.540.760.37
USDSEK vs. NZDUSD-0.220.910.060.220.280.330.55-0.13-2.271.90-0.070.15
USDJPY vs. USDCHF-0.841.170.73-0.530.19-1.30-0.47-0.16-1.272.49-1.37-0.53
USDJPY vs. USDCAD-1.801.170.85-0.91-0.06-1.630.17-0.72-1.931.92-4.28-2.48
USDMXN vs. AUDUSD0.241.380.01-0.13-0.12-0.31-0.560.26-2.804.080.310.07
USDNOK vs. USDCHF3.411.36-1.020.81-0.215.422.010.78-2.007.055.602.19
USDCHF vs. USDCAD-0.821.000.32-0.54-0.21-0.270.55-0.48-1.821.08-2.49-1.67
USDNOK vs. USDJPY4.471.15-1.140.90-0.257.022.551.07-3.327.717.252.78
USDSEK vs. USDNOK-1.400.890.76-1.05-0.28-3.42-2.02-0.02-4.301.88-3.17-1.77
USDCAD vs. EURUSD0.930.98-0.620.29-0.330.48-0.450.44-0.371.711.840.91
USDNOK vs. USDCAD2.271.36-0.910.56-0.365.022.750.09-2.045.932.18-0.09
USDJPY vs. EURUSD-0.741.140.48-0.86-0.38-1.10-0.35-0.25-1.612.51-2.16-1.42
USDNOK vs. EURUSD3.521.33-1.120.64-0.475.662.140.66-2.196.544.671.15
USDMXN vs. NZDUSD0.821.31-0.52-0.13-0.650.16-0.66-0.33-3.424.570.890.07
USDSEK vs. USDJPY2.471.04-0.940.28-0.662.720.250.81-3.103.053.210.74
USDSEK vs. USDCHF1.511.23-0.65-0.01-0.661.28-0.240.54-1.362.771.710.20
USDCHF vs. EURUSD0.090.98-0.25-0.54-0.790.180.10-0.07-0.790.91-0.67-0.76
USDJPY vs. GBPUSD-0.880.880.17-0.99-0.82-2.22-1.34-0.62-2.502.57-2.64-1.76
USDSEK vs. USDCAD0.501.23-0.37-0.47-0.830.940.44-0.06-3.021.72-1.36-1.86
NZDUSD vs. USDCHF1.951.35-0.73-0.25-0.981.08-0.870.77-1.072.881.990.04
USDNOK vs. GBPUSD3.221.04-1.540.42-1.124.190.960.08-4.915.053.990.76
USDMXN vs. USDJPY4.731.50-1.10-0.03-1.123.64-1.101.05-4.257.455.630.90
USDMXN vs. USDCHF3.381.77-0.86-0.27-1.131.58-1.800.68-2.966.793.500.12
USDSEK vs. EURUSD1.611.21-0.87-0.32-1.191.49-0.120.44-1.412.210.88-0.73
AUDUSD vs. USDCHF2.271.28-1.12-0.24-1.361.37-0.900.31-1.713.222.310.04
NZDUSD vs. USDCAD0.811.36-0.53-0.86-1.390.68-0.130.08-1.692.11-1.41-2.22
USDMXN vs. USDCAD1.881.77-0.74-0.65-1.391.05-0.83-0.22-4.865.62-0.96-2.84
NZDUSD vs. EURUSD2.061.32-0.89-0.55-1.451.32-0.740.67-1.212.671.07-0.98
USDMXN vs. EURUSD3.511.73-1.03-0.49-1.521.88-1.630.53-2.786.602.28-1.22
USDMXN vs. USDNOK-0.941.290.40-1.97-1.57-5.33-4.39-0.22-2.633.07-3.66-2.72
USDSEK vs. GBPUSD1.360.94-1.00-0.79-1.790.19-1.18-0.06-5.312.030.27-1.09
AUDUSD vs. EURUSD2.371.25-1.31-0.52-1.831.59-0.780.20-1.822.891.44-0.93
AUDUSD vs. NZDUSD0.400.95-1.82-0.08-1.900.33-0.08-0.44-1.040.560.410.00
AUDUSD vs. USDCAD1.181.28-1.11-0.84-1.950.99-0.20-0.35-2.022.59-0.92-2.10
USDMXN vs. GBPUSD3.141.35-1.35-0.99-2.34-0.01-3.16-0.20-5.664.471.41-1.73
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"crosses = ['EURUSD', 'USDCAD', 'USDCHF', 'NZDUSD', 'AUDUSD', 'GBPUSD', 'USDJPY' , 'USDNOK', 'USDMXN', 'USDSEK']\\n\",\n \"screen = calculate_vol_swap_screen(crosses)\\n\",\n \"screen.sort_values('Score', ascending=False).style.background_gradient(subset=['Score'])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/content/reports_and_screens/00_fx/0007_eq_fx_hedges.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"changing-buyer\",\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None,scopes=('read_product_data', 'run_analytics')) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"prostate-identifier\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXOption, EqOption\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.timeseries import last_value, correlation, percentiles, volatility, Returns\\n\",\n \"from gs_quant.risk import FXSpot\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"pd.set_option('display.precision', 2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"mounted-atlas\",\n \"metadata\": {},\n \"source\": [\n \"## EQ FX Hedging Screen\\n\",\n \"\\n\",\n \"We screen for the most attractive FX options to hedge an equity index such as S&P. For each 3m FX option, we the direction is chosen based on historical 1y correlations using weekly returns. The strikes and notionals of the FX options are adjusted by the 6m realized vol ratio to an equity index. \\n\",\n \"\\n\",\n \"Realized Correlation uses weekly returns with the currency value in USD and percentile uses a 5y history. 6m realized volatility ratio uses weekly returns. Strike price is spot + FX / SPX vol ratio. Strike and Spot are in normal spot convention. Discount to SPX is FX Option premium / SPX price. Past performance is not indicative of future results. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"signal-ground\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calculate_screen(eq_ric, fx_crosses, start=RelativeDate('-5y').apply_rule(), end=RelativeDate('-1b').apply_rule()):\\n\",\n \" fxspot_data = Dataset('WMFXSPOT').get_data(start, end, bbid=fx_crosses)\\n\",\n \" fxspot_df = pd.pivot_table(fxspot_data, values='midPrice', index=['date'], columns=['bbid']).resample('W-FRI').last()\\n\",\n \" eq = Dataset('TREOD').get_data(start, end, ric=eq_ric).closePrice.resample('W-FRI').last()\\n\",\n \" eq_vol = last_value(volatility(eq, 24))\\n\",\n \" cors = pd.DataFrame({bbid: correlation(eq, 1/fxspot_df[bbid] if bbid[0] == 'U' else fxspot_df[bbid] , 52) for bbid in fx_crosses})\\n\",\n \" cur_cor = pd.Series(cors.tail(1).squeeze()*100, name='1y Realized Correlation (%)')\\n\",\n \" pct_cor = pd.Series({bbid: last_value(percentiles(cors[bbid])) for bbid in fx_crosses}, name='Corr %-ile')\\n\",\n \" vol_ = pd.DataFrame({bbid: volatility(fxspot_df[bbid], 24, Returns.LOGARITHMIC) for bbid in fx_crosses})\\n\",\n \" vol_cur = pd.Series(vol_.tail(1).squeeze() / eq_vol , name=f'6m Realized Vol Ratio (FX / {eq_ric})')\\n\",\n \" table = pd.concat([cur_cor, pct_cor, vol_cur ], axis=1)\\n\",\n \" \\n\",\n \" #price options\\n\",\n \" eqo = EqOption(option_type='Put', underlier=eq_ric, exchange='NYSE', strike_price='90%', expiration_date='3m', buy_sell='Buy', premium=0)\\n\",\n \" eqo.resolve()\\n\",\n \" notional = eqo.strike_price * eqo.multiplier\\n\",\n \" portfolio = Portfolio()\\n\",\n \" for cross in fx_crosses:\\n\",\n \" ratio = table.loc[cross][f'6m Realized Vol Ratio (FX / {eq_ric})']\\n\",\n \" if cross[0] != 'U':\\n\",\n \" cross = f'USD{cross[:3]}'\\n\",\n \" portfolio.append(FXOption(pair=cross, option_type='Call', expiration_date='3m', strike_price=f's+{ratio*10}%', buy_sell='Buy', \\n\",\n \" notional_amount=notional/ratio))\\n\",\n \" portfolio.resolve()\\n\",\n \" port_df = portfolio.to_frame()\\n\",\n \" port_df['Cost in bps'] = (port_df['premium'] / port_df['notional_amount'])*1e4\\n\",\n \" port_df[f'Discount to {eq_ric} (%)'] =(abs(port_df['premium'])/eqo.price() - 1)*100\\n\",\n \" port_df['Spot'] = list(portfolio.calc(FXSpot).result())\\n\",\n \" port_df['pair'] = port_df['pair'].str.replace(' ', '')\\n\",\n \" port_df['pair_'] = g10\\n\",\n \" port_df = port_df.set_index('pair_')\\n\",\n \" port_df['Strike'] = [1 / port_df.loc[x]['strike_price'] if x != port_df.loc[x]['pair'] else port_df.loc[x]['strike_price'] for x in port_df.index]\\n\",\n \" result = table.join([port_df['Strike'], port_df['Spot'],port_df['Cost in bps'],port_df[f'Discount to {eq_ric} (%)']])\\n\",\n \" return result.sort_values(by=['1y Realized Correlation (%)', f'Discount to {eq_ric} (%)'], ascending=(False, True))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"effective-validity\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
1y Realized Correlation (%) Corr %-ile 6m Realized Vol Ratio (FX / .N225) Strike Spot Cost in bps Discount to .N225 (%)
AUDUSD73.2280.710.670.720.77-34.22-67.38
USDNOK69.6582.620.949.318.51-36.53-75.05
USDCAD69.5083.570.411.331.28-24.64-60.91
NZDUSD67.3890.710.570.680.72-44.89-49.14
GBPUSD58.7587.860.581.301.37-18.03-79.88
USDSEK58.5378.810.698.978.39-22.40-79.09
EURUSD50.3086.430.451.151.20-10.95-84.34
USDCHF41.0787.380.460.940.90-9.08-87.23
USDJPY15.1199.760.33108.65105.18-17.39-66.10
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"start = RelativeDate('-5y').apply_rule()\\n\",\n \"end = RelativeDate('-1b').apply_rule()\\n\",\n \"g10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\\n\",\n \"\\n\",\n \"#example equity index rics '.FTSE','.N225','.SPX' or '.STOXX50E'\\n\",\n \"eq_ric = '.N225'\\n\",\n \"table = calculate_screen(eq_ric='.N225', fx_crosses=g10)\\n\",\n \"table.style.background_gradient(\\n\",\n \" subset=['1y Realized Correlation (%)', f'Discount to {eq_ric} (%)'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"labeled-language\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.scatterplot(x=table['1y Realized Correlation (%)'], y=table[f'Discount to {eq_ric} (%)'], hue=table.index, s=60)\\n\",\n \"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/content/reports_and_screens/00_fx/__init__.py", "content": "" }, { "path": "gs_quant/content/reports_and_screens/00_fx/vol_screen_app.py", "content": "from gs_quant.data import Dataset\nfrom gs_quant.timeseries import percentiles, volatility, last_value\nfrom gs_quant.datetime import business_day_offset\nimport seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport warnings\nfrom datetime import date\nimport streamlit as st\nfrom gs_quant.session import GsSession\n\nwarnings.filterwarnings('ignore')\nsns.set(style=\"darkgrid\", color_codes=True)\n\n# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\nGsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\n\n\ndef format_df(data_dict):\n df = pd.concat(data_dict, axis=1)\n df.columns = data_dict.keys()\n return df.ffill().dropna()\n\n\ndef volatility_screen(crosses, start_date, end_date, tenor='3m', plot=True):\n fxspot_dataset, fxvol_dataset = Dataset('FXSPOT'), Dataset('FXIMPLIEDVOL')\n spot_data, impvol_data, spot_fx, data = {}, {}, {}, {}\n for cross in crosses:\n spot = fxspot_dataset.get_data(start_date, end_date, bbid=cross)[['spot']].drop_duplicates(keep='last')\n spot_fx[cross] = spot['spot']\n spot_data[cross] = volatility(spot['spot'], tenor) # realized vol\n vol = fxvol_dataset.get_data(start_date, end_date, bbid=cross, tenor=tenor, deltaStrike='DN', location='NYC')[\n ['impliedVolatility']]\n impvol_data[cross] = vol.drop_duplicates(keep='last') * 100\n\n spdata, ivdata = format_df(spot_data), format_df(impvol_data)\n diff = ivdata.subtract(spdata).dropna()\n for cross in crosses:\n data[cross] = {'Spot': last_value(spot_fx[cross]),\n '{} Implied'.format(tenor): last_value(ivdata[cross]),\n '{} Realized'.format(tenor): last_value(spdata[cross]),\n 'Diff': last_value(diff[cross]),\n 'Historical Implied Low': min(ivdata[cross]),\n 'Historical Implied High': max(ivdata[cross]),\n '%-ile': last_value(percentiles(ivdata[cross]))\n }\n df = pd.DataFrame(data)\n vol_screen = df.transpose()\n st.write(st.dataframe(vol_screen.style.highlight_max(axis=0)))\n if plot:\n for fx in vol_screen.index:\n plt.scatter(vol_screen.loc[fx]['%-ile'], vol_screen.loc[fx]['Diff'])\n plt.legend(vol_screen.index, loc='best', bbox_to_anchor=(0.9, -0.13), ncol=3)\n\n plt.xlabel('Percentile of Current Implied Vol')\n plt.ylabel('Implied vs Realized Vol')\n plt.title('Entry Point vs Richness')\n st.pyplot(plt)\n return\n\n\ng10 = ['USDJPY', 'EURUSD', 'AUDUSD', 'GBPUSD', 'USDCAD', 'USDNOK', 'NZDUSD', 'USDSEK', 'USDCHF']\n\nend = business_day_offset(date.today(), -1, roll='preceding')\n\nst.title('Vol Monitor')\n\ncrosses = st.multiselect('FX Crosses',\n g10, default='EURUSD')\ntenors = st.selectbox('Tenor', ('1m', '3m', '6m', '1y'))\n\nstart = st.slider(\"Start Date\", value=(date(2018, 1, 1), end), format=\"MM/DD/YY\")\n\nvolatility_screen(crosses, start[0], start[1], tenors)\n" }, { "path": "gs_quant/content/reports_and_screens/02_rates/0000_vol_fixed_strike_grid.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"potential-davis\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"developmental-private\",\n \"metadata\": {},\n \"source\": [\n \"### IR Implied Volatility Screen (Fixed Strike)\\n\",\n \"\\n\",\n \"In this screen, we compare the implied volatility of swaptions at a fixed strike across a range of expiries and tenors in a grid.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"tight-resistance\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.common import PayReceive\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import IRAnnualImpliedVol\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"pd.options.display.float_format = '{:,.0f}'.format\\n\",\n \"\\n\",\n \"def implied_vol_grid(expiries, termination, pay_rec, ccy, moneyness):\\n\",\n \" portfolios = Portfolio([Portfolio([IRSwaption(pay_or_receive=pay_rec, notional_currency=ccy, termination_date=t, \\n\",\n \" expiration_date=e, strike=f'ATMF+{moneyness}') for e in expiries]) for t in termination])\\n\",\n \" portfolios.resolve()\\n\",\n \" results = portfolios.calc(IRAnnualImpliedVol)\\n\",\n \"\\n\",\n \" frame = results.to_frame('value','portfolio_name_0','instrument_name') * 10000\\n\",\n \" frame.columns=expiries\\n\",\n \" frame.index=termination\\n\",\n \" plt.subplots(figsize=(16, 10))\\n\",\n \" ax = sns.heatmap(frame, annot=True, fmt='.2f', cmap='coolwarm')\\n\",\n \" ax.set(ylabel='Expiries', xlabel='Tenor', title='Implied Vol Grid (bps) w. '+ str(moneyness)+'bps moneyness')\\n\",\n \" ax.xaxis.tick_top()\\n\",\n \" ax.xaxis.set_label_position('top') \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"ceramic-marketing\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"expiries = ['1m', '3m' ,'6m', '9m', '1y', '2y', '3y', '4y', '5y', '6y', '7y', '8y', '9y', '10y', '15y', '20y', '25y', '30y']\\n\",\n \"termination = ['1y', '2y', '3y', '4y', '5y', '6y', '7y', '8y', '9y', '10y', '15y', '20y', '25y', '30y']\\n\",\n \"pay_rec = PayReceive.Pay\\n\",\n \"ccy = 'EUR'\\n\",\n \"moneyness = 0\\n\",\n \"\\n\",\n \"implied_vol_grid(expiries, termination, pay_rec, ccy, moneyness)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/content/reports_and_screens/02_rates/0001_vol_moneyness_screen.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"biblical-positive\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"identified-radical\",\n \"metadata\": {},\n \"source\": [\n \"### IR Implied Volatility Across Strikes\\n\",\n \"\\n\",\n \"We look at how the implied vol changes for swaptions with varying strikes but fixed expiries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"expired-continuity\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.common import PayReceive\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import IRAnnualImpliedVol\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"pd.options.display.float_format = '{:,.0f}'.format\\n\",\n \"\\n\",\n \"def moneyness_curve(expiration, termination, pay_rec, ccy, min_bps, max_bps, step_size):\\n\",\n \" eval_range =np.arange(min_bps, max_bps + step_size, step_size).tolist()\\n\",\n \" num_instr = len(termination)\\n\",\n \" results = pd.DataFrame(index = eval_range)\\n\",\n \" for i in range(num_instr):\\n\",\n \" portfolios = Portfolio([IRSwaption(pay_or_receive=pay_rec, notional_currency=ccy, termination_date=termination[i], \\n\",\n \" expiration_date=expiration[i], strike=f'ATMF+{eval_K}') for eval_K in eval_range])\\n\",\n \" portfolios.resolve()\\n\",\n \" name_i = expiration[i] + termination[i] +' '+ ccy +' '+ pay_rec\\n\",\n \" results[name_i] = portfolios.calc(IRAnnualImpliedVol).to_frame().values * 10000 \\n\",\n \"\\n\",\n \" plt.figure(figsize=(8, 5))\\n\",\n \" plt.plot(results)\\n\",\n \" plt.xlabel('Moneyness (bps)')\\n\",\n \" plt.ylabel('Implied Ann. Volatility (bps)')\\n\",\n \" plt.legend(results.columns)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"confused-medication\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"expiration = ['10y','15y']\\n\",\n \"termination = ['10y', '15y']\\n\",\n \"pay_rec = 'Pay'\\n\",\n \"ccy = 'USD'\\n\",\n \"min_bps = -300\\n\",\n \"max_bps = 300\\n\",\n \"step_size = 20\\n\",\n \"\\n\",\n \"moneyness_curve(expiration, termination, pay_rec, ccy, min_bps, max_bps, step_size)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/content/reports_and_screens/02_rates/0002_swaption_carry_grid.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"separated-loading\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bacterial-tutorial\",\n \"metadata\": {},\n \"source\": [\n \"### Swaption Carry Grid\\n\",\n \"\\n\",\n \"In this screen, we compare carry of swaptions for a fixed strike and for a fixed horizon across a range of expiries and tenors.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"objective-superintendent\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import RollFwd\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"pd.options.display.float_format = '{:,.0f}'.format\\n\",\n \"\\n\",\n \"def carry_grid(horizon, expiries, termination, pay_rec, ccy, moneyness):\\n\",\n \" portfolios = Portfolio([Portfolio([IRSwaption(pay_or_receive=pay_rec, notional_currency=ccy, termination_date=t, \\n\",\n \" expiration_date=e, strike=f'ATMF+{moneyness}') for e in expiries]) for t in termination])\\n\",\n \" portfolios.resolve()\\n\",\n \"\\n\",\n \" price = portfolios.price()\\n\",\n \" price_df = price.to_frame()\\n\",\n \"\\n\",\n \" with RollFwd(horizon):\\n\",\n \" price_fwd = portfolios.price()\\n\",\n \"\\n\",\n \" price_fwd_df = price_fwd.to_frame()\\n\",\n \" carry = 100*(price_fwd_df - price_df)/price_df\\n\",\n \" carry.index = termination #Keep ordering\\n\",\n \" carry.columns= expiries\\n\",\n \" \\n\",\n \" ##plot\\n\",\n \" carry = carry.T\\n\",\n \" plt.subplots(figsize=(12, 6))\\n\",\n \" ax = sns.heatmap(carry, annot=True, fmt='.2f', cmap='coolwarm')\\n\",\n \" ax.set(ylabel='Expiries', xlabel='Tenor', title= horizon + ' carry w. '+ str(moneyness)+'bps moneyness, %')\\n\",\n \" ax.xaxis.tick_top()\\n\",\n \" return carry\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"better-saint\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"horizon = '1y'\\n\",\n \"expiries = ['4y', '5y', '6y', '7y', '8y', '9y', '10y', '15y', '20y', '25y', '30y']\\n\",\n \"termination = ['1y', '2y', '3y', '4y', '5y', '6y', '7y', '8y', '9y', '10y', '15y', '20y', '25y', '30y']\\n\",\n \"pay_rec = 'Pay'\\n\",\n \"ccy = 'USD'\\n\",\n \"moneyness = 100\\n\",\n \"\\n\",\n \"carry = carry_grid(horizon, expiries, termination, pay_rec, ccy, moneyness)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/reports_and_screens/02_rates/0003_spot_vol_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"funny-creature\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"placed-uzbekistan\",\n \"metadata\": {},\n \"source\": [\n \"#### Combined IR and Vol Shock\\n\",\n \"\\n\",\n \"In this screen, we look at how the price fo a swaption changes under a range of spot and vol rate shocks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"cardiac-knitting\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.common import PayReceive\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType, Price\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"def IR_scenario_grid(ccy, IR_min=-200, IR_max=200, IR_iter=50, vol_min=-10, vol_max=10, vol_iter=2):\\n\",\n \" portfolio = Portfolio(IRSwaption(pay_or_receive=pay_rec, notional_currency=ccy, termination_date=termination, expiration_date=expiry, strike=f'ATMF+{moneyness}'))\\n\",\n \" portfolio.resolve()\\n\",\n \"\\n\",\n \" IR_shocks = list(range(IR_min, IR_max + IR_iter, IR_iter))\\n\",\n \" IR_vol_shocks = list(range(vol_min, vol_max + vol_iter, vol_iter))\\n\",\n \" ir_vol_grid = pd.DataFrame(index = IR_vol_shocks, columns = IR_shocks)\\n\",\n \" grid = {}\\n\",\n \"\\n\",\n \" base_price = portfolio.price().to_frame()\\n\",\n \"\\n\",\n \" with PricingContext(is_batch=True, is_async=True):\\n\",\n \" for IR_shock in IR_shocks:\\n\",\n \" vals = []\\n\",\n \" for IR_vol_shock in IR_vol_shocks:\\n\",\n \" shocks = MarketDataShockBasedScenario(shocks={\\n\",\n \" MarketDataPattern('IR', ccy) : MarketDataShock(shock_type=MarketDataShockType.Absolute, value= IR_shock/10000),\\n\",\n \" MarketDataPattern('IR Vol'): MarketDataShock(MarketDataShockType.Absolute, IR_vol_shock/10000)\\n\",\n \" })\\n\",\n \"\\n\",\n \" with shocks:\\n\",\n \" shock_price = portfolio.price()\\n\",\n \" vals.append(shock_price)\\n\",\n \" grid[IR_shock] = vals\\n\",\n \"\\n\",\n \" for k,v in grid.items():\\n\",\n \" ir_vol_grid[k] = pd.DataFrame(v, index=IR_vol_shocks)\\n\",\n \" result = 100*(ir_vol_grid/base_price[Price][0] -1)\\n\",\n \" plt.subplots(figsize=(12, 6))\\n\",\n \" ax = sns.heatmap(result.astype(float), annot=True, fmt='.2f', cmap='coolwarm')\\n\",\n \" ax.set(ylabel='IR shocks (bps)', xlabel='IR Vol shocks (bps)', title=' Scenario Analysis: PV effect of IR and IR Vol shocks, %')\\n\",\n \" ax.xaxis.tick_top()\\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"classified-rates\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"expiry = '10y'\\n\",\n \"termination = '10y'\\n\",\n \"pay_rec = 'Pay'\\n\",\n \"ccy = 'USD'\\n\",\n \"horizon = '0b'\\n\",\n \"moneyness = 50\\n\",\n \"\\n\",\n \"#Specify shocks\\n\",\n \"IR_min = -200\\n\",\n \"IR_max = 200\\n\",\n \"IR_iter = 50\\n\",\n \"vol_min = -10\\n\",\n \"vol_max = 10\\n\",\n \"vol_iter = 2\\n\",\n \"\\n\",\n \"result = IR_scenario_grid(ccy, IR_min, IR_max, IR_iter, vol_min, vol_max, vol_iter)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/reports_and_screens/03_prime/0001_gs_pb_data_case_study_sector_positioning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91187d9d\",\n \"metadata\": {},\n \"source\": [\n \"# Hedge Fund Positioning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18550fb1\",\n \"metadata\": {},\n \"source\": [\n \"The Federal Reserve signalled a major shift in monetary policy stance at the end of 2021. How has hedge fund positioning changed in a rising rates environment?\\n\",\n \"We start by fetching global positioning data from January 2020 from our Prime Book Flows and Exposures (PBFA) dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"649e79c0\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T10:58:01.243652Z\",\n \"start_time\": \"2023-06-28T10:57:55.650450Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Import libraries\\n\",\n \"import datetime\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c593d046\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T10:58:01.258652Z\",\n \"start_time\": \"2023-06-28T10:58:01.247656Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Graphical formatting\\n\",\n \"import matplotlib.dates as mdates\\n\",\n \"import matplotlib.ticker as mtick\\n\",\n \"half_year_locator = mdates.MonthLocator(interval=6)\\n\",\n \"year_month_formatter = mdates.DateFormatter(\\\"%Y-%m\\\") # four digits for year, two for month\\n\",\n \"pct_formatter = mtick.PercentFormatter(xmax=1,decimals=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"587c8116\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:34:58.144247Z\",\n \"start_time\": \"2023-06-28T11:34:57.852239Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Authentication\\n\",\n \"client_id = # \\n\",\n \"client_secret = # \\n\",\n \"GsSession.use(Environment.PROD, client_id, client_secret, ('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ba1401e0\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:35:05.797524Z\",\n \"start_time\": \"2023-06-28T11:34:59.373295Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Fetch positioning data from January 2020\\n\",\n \"ds = Dataset('PBFA')\\n\",\n \"data = ds.get_data(datetime.date(2020, 1, 1))\\n\",\n \"data = data.reset_index()\\n\",\n \"data.date = data.date.apply(lambda x: x.date())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f563a2c6\",\n \"metadata\": {},\n \"source\": [\n \"We can then focus on sector rotation. We expect rising rates to have a significant impact on growth companies because their valuation is sensitive to the discount rate applied to earnings expected in the distant future.\\n\",\n \"Sector positioning is available by selecting the appropriate GICS level 1 sectors and displaying the values in the “clientNetWeight” column.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e11201f1\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:35:20.353939Z\",\n \"start_time\": \"2023-06-28T11:35:19.948926Z\"\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Plot weight of Technology, Energy and Financials sectors\\n\",\n \"glob_pos = data.loc[(data.clientStrategy==\\\"All\\\") & (data.region==\\\"Global\\\") & (data.sectorGICS2==\\\"All\\\") & \\\\\\n\",\n \" (data.sectorGICS1.isin(['Information Technology','Financials','Energy'])) ]\\n\",\n \"glob_pos = glob_pos.pivot(index=\\\"date\\\", columns=\\\"sectorGICS1\\\", values=\\\"clientNetWeight\\\")\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"ax.plot(glob_pos.index, glob_pos['Financials'], 'g-',label='Financials')\\n\",\n \"ax.plot(glob_pos.index, glob_pos['Information Technology'], 'r-',label='Technology')\\n\",\n \"ax.plot(glob_pos.index, glob_pos['Energy'], 'b-',label='Energy')\\n\",\n \"ax.set_title('Global Hedge Fund Positioning')\\n\",\n \"ax.set_xlabel('Time')\\n\",\n \"ax.set_ylabel('Prime book weight', color='black')\\n\",\n \"ax.xaxis.set_major_locator(half_year_locator)\\n\",\n \"ax.xaxis.set_major_formatter(year_month_formatter)\\n\",\n \"ax.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"fig.autofmt_xdate()\\n\",\n \"ax.legend(loc='upper right')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"15676525\",\n \"metadata\": {},\n \"source\": [\n \"The results suggest that global hedge funds rotated out of Technology stocks from the end of 2021, bringing the sector weight down from 22% to 15%.\\n\",\n \"The rotation benefitted Financials, which briefly overtook Technology in terms of net weight at the end of 2022, and Energy. Thelatter was driven by soaring energy prices, particularly as the conflict in Ukraine started in February 2022.\\n\",\n \"The beginning of 2023 saw a partial reversal of this trend, arguably due to cooling inflation expectations and a general improvement in the global macroeconomic outlook.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fdddb30d\",\n \"metadata\": {},\n \"source\": [\n \"Is the pattern driven by any region in particular?\\n\",\n \"The previous chart considered positions in global equities but we can restrict the analysis to a specific region, e.g. Europe, i.e. focus on equities issued by European firms.\\n\",\n \"Here we rescale sector weights by the total net weight allocated to Europe in the global PB book so that sector weights sum up to one and are comparable to those displayed in the previous graph.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7e22eee5\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:38:30.931869Z\",\n \"start_time\": \"2023-06-28T11:38:30.657856Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Plot weight of Technology, Energy and Financials sectors in Europe\\n\",\n \"eur_pos = data.loc[(data.clientStrategy==\\\"All\\\") & (data.region==\\\"Europe\\\") & (data.sectorGICS2==\\\"All\\\") & (data.country==\\\"All\\\") &\\\\\\n\",\n \" (data.sectorGICS1.isin(['Information Technology','Financials','Energy','All'])) ]\\n\",\n \"eur_pos = eur_pos.pivot(index=\\\"date\\\", columns=\\\"sectorGICS1\\\", values=\\\"clientNetWeight\\\")\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"ax.plot(eur_pos.index, eur_pos['Financials']/eur_pos['All'], 'g-',label='Financials')\\n\",\n \"ax.plot(eur_pos.index, eur_pos['Information Technology']/eur_pos['All'], 'r-',label='Technology')\\n\",\n \"ax.plot(eur_pos.index, eur_pos['Energy']/eur_pos['All'], 'b-',label='Energy')\\n\",\n \"ax.set_title('Hedge Fund Positioning - European Stocks')\\n\",\n \"ax.set_xlabel('Time')\\n\",\n \"ax.set_ylabel('Prime book weight', color='black')\\n\",\n \"ax.xaxis.set_major_locator(half_year_locator)\\n\",\n \"ax.xaxis.set_major_formatter(year_month_formatter)\\n\",\n \"ax.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"fig.autofmt_xdate()\\n\",\n \"ax.legend(loc='lower right')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8fdaa067\",\n \"metadata\": {},\n \"source\": [\n \"The chart shows that the weight of Technology declined in Europe between the end of 2021 and the end of 2022, in line with our global results.\\n\",\n \"Exposure to Energy also displays a marked increase, particularly from February 2022.\\n\",\n \"Sector allocation is not as concentrated as in the global chart, where Technology exceeds 20% of the total net weight.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c5b99e40\",\n \"metadata\": {},\n \"source\": [\n \"So far we have looked at portfolio weights, which can be affected by differences in return across sectors even if managers keep their holdings unchanged. For example, sustained outperformance of Energy stocks tends to boost the weight of the Energy sector in the portfolio.\\n\",\n \"Our datasets contain information on trading flows, split into long flows (long buying and long selling) and short flows (short covering and short selling).\\n\",\n \"Flows are converted into a z-score, which gives an indication of the intensity compared to history.\\n\",\n \"We extract flows for the Technology sector from January 2020 and display the data using a daily bar plot.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aaca6f02\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:38:56.904856Z\",\n \"start_time\": \"2023-06-28T11:38:53.825749Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Plot long and short flow indicators for global Technology stocks\\n\",\n \"glob_long_flow = data.loc[(data.clientStrategy==\\\"All\\\") & (data.region==\\\"Global\\\") & (data.sectorGICS2==\\\"All\\\") & \\\\\\n\",\n \" (data.sectorGICS1.isin(['Information Technology','Financials','Energy'])) ]\\n\",\n \"glob_long_flow = glob_long_flow.pivot(index=\\\"date\\\", columns=\\\"sectorGICS1\\\", values=\\\"longLevel\\\")\\n\",\n \"glob_short_flow = data.loc[(data.clientStrategy==\\\"All\\\") & (data.region==\\\"Global\\\") & (data.sectorGICS2==\\\"All\\\") & \\\\\\n\",\n \" (data.sectorGICS1.isin(['Information Technology','Financials','Energy'])) ]\\n\",\n \"glob_short_flow = glob_short_flow.pivot(index=\\\"date\\\", columns=\\\"sectorGICS1\\\", values=\\\"shortLevel\\\")\\n\",\n \"\\n\",\n \"fig, (ax1,ax2) = plt.subplots(2,1,figsize=(10, 6),sharey=True)\\n\",\n \"ax1.bar(glob_long_flow.index,glob_long_flow['Information Technology'],color=\\\"blue\\\",width=4)\\n\",\n \"ax1.set_title('Long Flows - Technology')\\n\",\n \"ax1.set_ylabel('Z-score', color='black')\\n\",\n \"ax2.bar(glob_short_flow.index,glob_short_flow['Information Technology'],color=\\\"red\\\",width=4)\\n\",\n \"ax2.set_ylabel('Z-score', color='black')\\n\",\n \"ax2.set_title('Short flows - Technology')\\n\",\n \"fig.autofmt_xdate()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e5da9ff7\",\n \"metadata\": {},\n \"source\": [\n \"Short flows throughout 2021 and 2022 appear strongly tilted towards short selling, while long flows are more balanced.\\n\",\n \"The short squeeze of January 2021 is evident in the chart, with risk-off flows (both long selling and short covering) that exceed 5 standard deviations compared to history.\\n\",\n \"In early 2023 we detect a sharp reversal in the directionality of the flows, with strong short covering in Technology stocks andmuted long flows.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1a085043\",\n \"metadata\": {},\n \"source\": [\n \"## Disclaimers:\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. 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The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone:\\n\",\n \"(i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"(ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"(iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"(iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act.\\n\",\n \"No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"venv311\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"title_cell\": \"Table of Contents\",\n \"title_sidebar\": \"Contents\",\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": true,\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/reports_and_screens/03_prime/0002_gs_pb_data_case_study_flows.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e5c22c5b\",\n \"metadata\": {},\n \"source\": [\n \"# Short Covering Episodes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"76a78ef7\",\n \"metadata\": {},\n \"source\": [\n \"What was the strongest episode of short covering in our data?\\n\",\n \"To answer this question we can load the full dataset on hedge fund positioning (PBFA).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"085ef504\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:40:55.678837Z\",\n \"start_time\": \"2023-06-28T11:40:51.175685Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Import libraries\\n\",\n \"import datetime\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d5ecb4de\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:40:55.694836Z\",\n \"start_time\": \"2023-06-28T11:40:55.681841Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Graphical formatting\\n\",\n \"import matplotlib.dates as mdates\\n\",\n \"import matplotlib.ticker as mtick\\n\",\n \"from matplotlib.patches import Ellipse\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c0df50d2\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:40:56.066851Z\",\n \"start_time\": \"2023-06-28T11:40:55.697839Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Authentication\\n\",\n \"client_id = # \\n\",\n \"client_secret = # \\n\",\n \"GsSession.use(Environment.PROD, client_id, client_secret, ('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8147ac4a\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:41:04.973171Z\",\n \"start_time\": \"2023-06-28T11:40:56.069853Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Fetch positioning data from 2016\\n\",\n \"ds = Dataset('PBFA')\\n\",\n \"data = ds.get_data(datetime.date(2016, 9, 16))\\n\",\n \"data = data.reset_index()\\n\",\n \"data.date = data.date.apply(lambda x: x.date())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0466390b\",\n \"metadata\": {},\n \"source\": [\n \"We then create a rolling one-month average of the short flow indicator (column ‘shortLevel’) and plot its evolution over time, highlighting the all-time high.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f940ed09\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:41:07.969281Z\",\n \"start_time\": \"2023-06-28T11:41:04.975173Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Plot moving average of short flows\\n\",\n \"long_short_flows = data.loc[(data.clientStrategy==\\\"All\\\") & (data.region==\\\"Global\\\") & (data.sectorGICS2==\\\"All\\\") & \\\\\\n\",\n \" (data.sectorGICS1==\\\"All\\\"),['date','longLevel','shortLevel']]\\n\",\n \"long_short_flows = long_short_flows.set_index('date')\\n\",\n \"long_short_flows_roll = long_short_flows.rolling(21).mean()\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"ax.bar(long_short_flows_roll.index, long_short_flows_roll.shortLevel,color='black',width=4)\\n\",\n \"ax.axhline(y=0,linestyle='-', color='black')\\n\",\n \"ax.axhline(y=long_short_flows_roll.shortLevel.max(),linestyle='--',color='blue')\\n\",\n \"ax.set_ylabel('Z-score')\\n\",\n \"ax.set_title('Average 21-day Short Flows')\\n\",\n \"ax.add_patch(Ellipse((long_short_flows_roll.shortLevel.idxmax(),long_short_flows_roll.shortLevel.max()),\\\\\\n\",\n \" height=0.5,width=100,color='aquamarine',zorder=0))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cbe80988\",\n \"metadata\": {},\n \"source\": [\n \"Peaks in the series represent periods of intense short covering, including the sharp portfolio rotation of November 2018, the short squeeze of January 2021 and a further peak in November 2022.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74eb5036\",\n \"metadata\": {},\n \"source\": [\n \"By calling long_short_flows_roll.shortLevel.idxmax() we can find out when the highest peak occurred.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0d1fcae5\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2023-06-28T11:41:07.984281Z\",\n \"start_time\": \"2023-06-28T11:41:07.972284Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# When did the peak occur?\\n\",\n \"long_short_flows_roll.shortLevel.idxmax()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4836c17d\",\n \"metadata\": {},\n \"source\": [\n \"Disclaimers:\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"Please refer to https://marquee.gs.com/studio/ for price information of corporate equity securities.\\n\",\n \"Notice to Australian Investors: When this document is disseminated in Australia by Goldman Sachs & Co. LLC (\\\"GSCO\\\"), Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\"), or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied the GS entities that:\\n\",\n \"• the person is a Sophisticated or Professional Investor for the purposes of section 708 of the Corporations Act of Australia; and\\n\",\n \"• the person is a wholesale client for the purpose of section 761G of the Corporations Act of Australia.\\n\",\n \"To the extent that the GS entities are providing a financial service in Australia, the GS entities are each exempt from the requirement to hold an Australian financial services licence for the financial services they provide in Australia. Each of the GS entities are regulated by a foreign regulator under foreign laws which differ from Australian laws, specifically:\\n\",\n \"• GSCO is regulated by the US Securities and Exchange Commission under US laws;\\n\",\n \"• GSI is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, under UK laws;\\n\",\n \"• GSBE is subject to direct prudential supervision by the European Central Bank and in other respects is supervised by the German Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufischt, BaFin) and Deutsche Bundesbank;\\n\",\n \"• GSALLC is regulated by the Hong Kong Securities and Futures Commission under Hong Kong laws; and\\n\",\n \"• GSSP is regulated by the Monetary Authority of Singapore under Singapore laws.\\n\",\n \"Notice to Brazilian Investors\\n\",\n \"Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"The offer of any securities mentioned in this message may not be made to the general public in Brazil. 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These terms will apply on every access to Marquee.\\n\",\n \"Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \"Note to Investors in Israel: GS is not licensed to provide investment advice or investment management services under Israeli law.\\n\",\n \"Notice to Investors in Japan\\n\",\n \"Marquee is made available in Japan by Goldman Sachs Japan Co., Ltd.\\n\",\n \"本書は情報の提供を目的としております。また、売却・購入が違法となるような法域での有価証券その他の売却若しくは購入を勧めるものでもありません。ゴールドマン・サックスは本書内の取引又はストラクチャーの勧誘を行うものではございません。これらの取引又はストラクチャーは、社内及び法規制等の承認等次第で実際にはご提供できない場合がございます。\\n\",\n \"<適格機関投資家限定　転売制限>\\n\",\n \"ゴールドマン・サックス証券株式会社が適格機関投資家のみを相手方として取得申込みの勧誘（取得勧誘）又は売付けの申込み若しくは買付けの申込みの勧誘(売付け勧誘等)を行う本有価証券には、適格機関投資家に譲渡する場合以外の譲渡が禁止される旨の制限が付されています。本有価証券は金融商品取引法第４条に基づく財務局に対する届出が行われておりません。なお、本告知はお客様によるご同意のもとに、電磁的に交付させていただいております。\\n\",\n \"＜適格機関投資家用資料＞\\n\",\n \"本資料は、適格機関投資家のお客さまのみを対象に作成されたものです。本資料における金融商品は適格機関投資家のお客さまのみがお取引可能であり、適格機関投資家以外のお客さまからのご注文等はお受けできませんので、ご注意ください。 商号等/ゴールドマン・サックス証券株式会社 金融商品取引業者　関東財務局長（金商）第６９号\\n\",\n \"加入協会/　日本証券業協会、一般社団法人金融先物取引業協会、一般社団法人第二種金融商品取引業協会\\n\",\n \"本書又はその添付資料に信用格付が記載されている場合、日本格付研究所（JCR）及び格付投資情報センター（R&I）による格付は、登録信用格付業者による格付（登録格付）です。その他の格付は登録格付である旨の記載がない場合は、無登録格付です。無登録格付を投資判断に利用する前に、「無登録格付に関する説明書」（http://www.goldmansachs.com/disclaimer/ratings.html）を十分にお読みください。\\n\",\n \"If any credit ratings are contained in this material or any attachments, those that have been issued by Japan Credit Rating Agency, Ltd. (JCR) or Rating and Investment Information, Inc. (R&I) are credit ratings that have been issued by a credit rating agency registered in Japan (registered credit ratings). Other credit ratings are unregistered unless denoted as being registered. Before using unregistered credit ratings to make investment decisions, please carefully read \\\"Explanation Regarding Unregistered Credit Ratings\\\" (http://www.goldmansachs.com/disclaimer/ratings.html).\\n\",\n \"Notice to Mexican Investors: Information contained herein is not meant for the general public in Mexico. The services or products provided by or through Goldman Sachs Mexico, Casa de Bolsa, S.A. de C.V. (GS Mexico) may not be offered or sold to the general public in Mexico. You have received information herein exclusively due to your existing relationship with a GS Mexico or any other Goldman Sachs business. The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone:\\n\",\n \"(i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"(ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"(iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"(iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act.\\n\",\n \"No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"64d217eb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"toc\": {\n \"base_numbering\": 1,\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"title_cell\": \"Table of Contents\",\n \"title_sidebar\": \"Contents\",\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": true,\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/reports_and_screens/03_prime/0003_gs_pb_data_case_study_factors.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Hedge Fund Style Rotation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"How can we use our data to track hedge fund style exposures over time?\\n\",\n \"To answer this question we load the dataset on hedge fund flows and exposures by factor (PBFF).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import libraries\\n\",\n \"import datetime\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Graphical formatting\\n\",\n \"import matplotlib.dates as mdates\\n\",\n \"import matplotlib.ticker as mtick\\n\",\n \"from matplotlib.patches import Ellipse\\n\",\n \"half_year_locator = mdates.MonthLocator(interval=6)\\n\",\n \"year_month_formatter = mdates.DateFormatter(\\\"%Y-%m\\\") # four digits for year, two for month\\n\",\n \"pct_formatter = mtick.PercentFormatter(xmax=1,decimals=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Authentication\\n\",\n \"client_id = # \\n\",\n \"client_secret = # \\n\",\n \"GsSession.use(Environment.PROD, client_id, client_secret, ('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Fetch flow and exposure data by factor \\n\",\n \"ds = Dataset('PBFF')\\n\",\n \"data = ds.get_data(datetime.date(2021, 1, 1)).reset_index()\\n\",\n \"data['date'] = data['date'].apply(lambda x: x.date())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We then focus on the rotation from growth to value stocks that has characterised the evolution of style exposures between 2021 and 2022.\\n\",\n \"A few lines of code generate a time series plot of the aggregate beta to each of the two factors.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot hedge funds' exposure to Growth and Value over time\\n\",\n \"style_pos_flows = data.loc[(data.riskModel==\\\"Prime v45\\\") & ((data.factor==\\\"Growth\\\") | (data.factor==\\\"Value\\\")), \\\\\\n\",\n \" ['date','clientExposure','level','factor']]\\n\",\n \"style_pos = style_pos_flows.pivot(index='date',columns='factor',values='clientExposure')\\n\",\n \"\\n\",\n \"fig, ax1 = plt.subplots(figsize=(10, 6))\\n\",\n \"ax2 = ax1.twinx()\\n\",\n \"ax1.plot(style_pos.index, style_pos.Growth, 'g-')\\n\",\n \"ax2.plot(style_pos.index, style_pos.Value, 'b-')\\n\",\n \"ax1.set_title('Growth vs. Value, Factor Exposure')\\n\",\n \"ax1.set_xlabel('Time')\\n\",\n \"ax1.set_ylabel('Growth', color='g')\\n\",\n \"ax2.set_ylabel('Value', color='b')\\n\",\n \"ax1.xaxis.set_major_locator(half_year_locator)\\n\",\n \"ax1.xaxis.set_major_formatter(year_month_formatter)\\n\",\n \"ax1.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"ax2.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"fig.autofmt_xdate()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The chart illustrates how long-short managers rotated from Growth to Value starting in 2021 as rates increased.\\n\",\n \"Towards the end of 2022, as inflation concerns abated and a less hawkish monetary policy outlook gained traction, we saw a reversal in this trend. Hedge funds started to increase their exposure to the Growth factor and reduce their Value tilt.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we use a bar plot to display the flow indicator for the two styles over the same period.\\n\",\n \"As in the previous example, we create 21-day rolling averages of the t-stat that represents the intensity of signed flows in the Growth and Value factors.\\n\",\n \"Most of the Growth flows are negative during this period, as expected, which indicates that managers actively decided to reduce their Growth exposure by trading out of the factor.\\n\",\n \"The first two months of 2023 have seen the first period of rotation from Value into Growth in more than a year. This can be seen from the fact that the two indicators have opposite signs: positive for Growth (green area) and negative for Value (blue area).\\n\",\n \"Rising geopolitical tensions in February 2022 led to negative flows in both Growth and Value.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot moving average of style flows\\n\",\n \"style_flow = style_pos_flows.pivot(index='date',columns='factor',values='level')\\n\",\n \"style_flow_roll = style_flow.rolling(21).mean()\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"ax.bar(style_flow_roll.index, style_flow_roll.Growth,color='g',width=4,label='Growth')\\n\",\n \"ax.bar(style_flow_roll.index, style_flow_roll.Value,color='b',width=4,label='Value')\\n\",\n \"ax.axhline(y=0,linestyle='-', color='black')\\n\",\n \"ax.set_ylabel('Z-score')\\n\",\n \"ax.set_title('Average 21-day Flows, Growth vs. Value')\\n\",\n \"ax.legend(loc='upper right')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Disclaimers:\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"Please refer to https://marquee.gs.com/studio/ for price information of corporate equity securities.\\n\",\n \"Notice to Australian Investors: When this document is disseminated in Australia by Goldman Sachs & Co. LLC (\\\"GSCO\\\"), Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\"), or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied the GS entities that:\\n\",\n \"• the person is a Sophisticated or Professional Investor for the purposes of section 708 of the Corporations Act of Australia; and\\n\",\n \"• the person is a wholesale client for the purpose of section 761G of the Corporations Act of Australia.\\n\",\n \"To the extent that the GS entities are providing a financial service in Australia, the GS entities are each exempt from the requirement to hold an Australian financial services licence for the financial services they provide in Australia. Each of the GS entities are regulated by a foreign regulator under foreign laws which differ from Australian laws, specifically:\\n\",\n \"• GSCO is regulated by the US Securities and Exchange Commission under US laws;\\n\",\n \"• GSI is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, under UK laws;\\n\",\n \"• GSBE is subject to direct prudential supervision by the European Central Bank and in other respects is supervised by the German Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufischt, BaFin) and Deutsche Bundesbank;\\n\",\n \"• GSALLC is regulated by the Hong Kong Securities and Futures Commission under Hong Kong laws; and\\n\",\n \"• GSSP is regulated by the Monetary Authority of Singapore under Singapore laws.\\n\",\n \"Notice to Brazilian Investors\\n\",\n \"Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \"Note to Investors in Israel: GS is not licensed to provide investment advice or investment management services under Israeli law.\\n\",\n \"Notice to Investors in Japan\\n\",\n \"Marquee is made available in Japan by Goldman Sachs Japan Co., Ltd.\\n\",\n \"本書は情報の提供を目的としております。また、売却・購入が違法となるような法域での有価証券その他の売却若しくは購入を勧めるものでもありません。ゴールドマン・サックスは本書内の取引又はストラクチャーの勧誘を行うものではございません。これらの取引又はストラクチャーは、社内及び法規制等の承認等次第で実際にはご提供できない場合がございます。\\n\",\n \"<適格機関投資家限定　転売制限>\\n\",\n \"ゴールドマン・サックス証券株式会社が適格機関投資家のみを相手方として取得申込みの勧誘（取得勧誘）又は売付けの申込み若しくは買付けの申込みの勧誘(売付け勧誘等)を行う本有価証券には、適格機関投資家に譲渡する場合以外の譲渡が禁止される旨の制限が付されています。本有価証券は金融商品取引法第４条に基づく財務局に対する届出が行われておりません。なお、本告知はお客様によるご同意のもとに、電磁的に交付させていただいております。\\n\",\n \"＜適格機関投資家用資料＞\\n\",\n \"本資料は、適格機関投資家のお客さまのみを対象に作成されたものです。本資料における金融商品は適格機関投資家のお客さまのみがお取引可能であり、適格機関投資家以外のお客さまからのご注文等はお受けできませんので、ご注意ください。 商号等/ゴールドマン・サックス証券株式会社 金融商品取引業者　関東財務局長（金商）第６９号\\n\",\n \"加入協会/　日本証券業協会、一般社団法人金融先物取引業協会、一般社団法人第二種金融商品取引業協会\\n\",\n \"本書又はその添付資料に信用格付が記載されている場合、日本格付研究所（JCR）及び格付投資情報センター（R&I）による格付は、登録信用格付業者による格付（登録格付）です。その他の格付は登録格付である旨の記載がない場合は、無登録格付です。無登録格付を投資判断に利用する前に、「無登録格付に関する説明書」（http://www.goldmansachs.com/disclaimer/ratings.html）を十分にお読みください。\\n\",\n \"If any credit ratings are contained in this material or any attachments, those that have been issued by Japan Credit Rating Agency, Ltd. (JCR) or Rating and Investment Information, Inc. (R&I) are credit ratings that have been issued by a credit rating agency registered in Japan (registered credit ratings). Other credit ratings are unregistered unless denoted as being registered. Before using unregistered credit ratings to make investment decisions, please carefully read \\\"Explanation Regarding Unregistered Credit Ratings\\\" (http://www.goldmansachs.com/disclaimer/ratings.html).\\n\",\n \"Notice to Mexican Investors: Information contained herein is not meant for the general public in Mexico. The services or products provided by or through Goldman Sachs Mexico, Casa de Bolsa, S.A. de C.V. (GS Mexico) may not be offered or sold to the general public in Mexico. You have received information herein exclusively due to your existing relationship with a GS Mexico or any other Goldman Sachs business. The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone:\\n\",\n \"(i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"(ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"(iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"(iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act.\\n\",\n \"No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/reports_and_screens/03_prime/0004_gs_pb_data_case_study_country_positioning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# China Reopening and Hedge Fund Positioning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We recently added country-level positioning and flow metrics to our data offering. In this notebook we use the dataset PBFA to investigate how the hedge fund community reacted to news that China was removing key Covid-era restrictions starting in late 2022.
\\n\",\n \"In particular, our researchers speak of an 'abrupt reopening' in December 2022, after a proprietary GS indicator of the severity of covid-related restrictions peaked in November.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import libraries\\n\",\n \"import datetime\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Graphical formatting\\n\",\n \"import matplotlib.dates as mdates\\n\",\n \"import matplotlib.ticker as mtick\\n\",\n \"from matplotlib.patches import Ellipse\\n\",\n \"quarter_locator = mdates.MonthLocator(interval=3)\\n\",\n \"year_month_formatter = mdates.DateFormatter(\\\"%Y-%m\\\") # four digits for year, two for month\\n\",\n \"pct_formatter = mtick.PercentFormatter(xmax=1,decimals=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Authentication\\n\",\n \"client_id = # \\n\",\n \"client_secret = # \\n\",\n \"GsSession.use(Environment.PROD, client_id, client_secret, ('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Fetch positioning data from 2022 Q4 \\n\",\n \"ds = Dataset('PBFA')\\n\",\n \"data = ds.get_data(datetime.date(2022, 10, 1)).reset_index()\\n\",\n \"data['date'] = data['date'].apply(lambda x: x.date())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, we gather data on the aggregate net weight of positions in Chinese equities present in our overall Prime book.\\n\",\n \"A few lines of code generate a time series plot of the aggregate net exposure.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"china_weight = data.loc[(data.country=='CHINA'),:].pivot(columns='country',index='date',values='clientNetWeight')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot hedge funds' exposure to Chinese stocks over time\\n\",\n \"fig, ax1 = plt.subplots(figsize=(10, 6))\\n\",\n \"ax1.plot(china_weight.index, china_weight['CHINA'], color='black', linestyle='solid')\\n\",\n \"ax1.set_title('Net weight of Chinese equities in the Book')\\n\",\n \"ax1.set_xlabel('Time')\\n\",\n \"ax1.xaxis.set_major_locator(quarter_locator)\\n\",\n \"ax1.xaxis.set_major_formatter(year_month_formatter)\\n\",\n \"ax1.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"fig.autofmt_xdate()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The chart illustrates how hedge funds ramped up their exposure to China in December 2022 as restrictions were dropped. The weight in our book almost doubled from 7% to over 13% between October 2022 and January 2023.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"However, portfolio weights are driven both by trading activity and by market prices. If Chinese equities outperform, then even in the absence of any active decision to buy more China, the country weight is bound to increase. Our data can be used to disentangle market dynamics from active rotation into Chinese names. In particular, we focus on our indicator of long flows.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot moving average of country-level long flows\\n\",\n \"china_flow = data.loc[(data.country=='CHINA'),:].pivot(columns='country',index='date',values='longLevel')\\n\",\n \"china_flow_roll = china_flow.rolling(21).mean()\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"ax.bar(china_flow_roll.index, china_flow_roll['CHINA'],color='black',width=4,label='China')\\n\",\n \"ax.axhline(y=0,linestyle='-', color='black')\\n\",\n \"ax.set_ylabel('Z-score')\\n\",\n \"ax.set_title('Average 21-day Flows, Chinese equities')\\n\",\n \"#ax.legend(loc='upper right')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The chart suggests that hedge funds started adding Chinese equities (positive flows) to their longs in November-December 2022 and the buying trend accelerated in January 2023. At the end of February a quick reversal appears in the data and flows remain negative for most of 2023.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Disclaimers:\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"Please refer to https://marquee.gs.com/studio/ for price information of corporate equity securities.\\n\",\n \"Notice to Australian Investors: When this document is disseminated in Australia by Goldman Sachs & Co. LLC (\\\"GSCO\\\"), Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\"), or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied the GS entities that:\\n\",\n \"• the person is a Sophisticated or Professional Investor for the purposes of section 708 of the Corporations Act of Australia; and\\n\",\n \"• the person is a wholesale client for the purpose of section 761G of the Corporations Act of Australia.\\n\",\n \"To the extent that the GS entities are providing a financial service in Australia, the GS entities are each exempt from the requirement to hold an Australian financial services licence for the financial services they provide in Australia. Each of the GS entities are regulated by a foreign regulator under foreign laws which differ from Australian laws, specifically:\\n\",\n \"• GSCO is regulated by the US Securities and Exchange Commission under US laws;\\n\",\n \"• GSI is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, under UK laws;\\n\",\n \"• GSBE is subject to direct prudential supervision by the European Central Bank and in other respects is supervised by the German Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufischt, BaFin) and Deutsche Bundesbank;\\n\",\n \"• GSALLC is regulated by the Hong Kong Securities and Futures Commission under Hong Kong laws; and\\n\",\n \"• GSSP is regulated by the Monetary Authority of Singapore under Singapore laws.\\n\",\n \"Notice to Brazilian Investors\\n\",\n \"Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \"Note to Investors in Israel: GS is not licensed to provide investment advice or investment management services under Israeli law.\\n\",\n \"Notice to Investors in Japan\\n\",\n \"Marquee is made available in Japan by Goldman Sachs Japan Co., Ltd.\\n\",\n \"本書は情報の提供を目的としております。また、売却・購入が違法となるような法域での有価証券その他の売却若しくは購入を勧めるものでもありません。ゴールドマン・サックスは本書内の取引又はストラクチャーの勧誘を行うものではございません。これらの取引又はストラクチャーは、社内及び法規制等の承認等次第で実際にはご提供できない場合がございます。\\n\",\n \"<適格機関投資家限定　転売制限>\\n\",\n \"ゴールドマン・サックス証券株式会社が適格機関投資家のみを相手方として取得申込みの勧誘（取得勧誘）又は売付けの申込み若しくは買付けの申込みの勧誘(売付け勧誘等)を行う本有価証券には、適格機関投資家に譲渡する場合以外の譲渡が禁止される旨の制限が付されています。本有価証券は金融商品取引法第４条に基づく財務局に対する届出が行われておりません。なお、本告知はお客様によるご同意のもとに、電磁的に交付させていただいております。\\n\",\n \"＜適格機関投資家用資料＞\\n\",\n \"本資料は、適格機関投資家のお客さまのみを対象に作成されたものです。本資料における金融商品は適格機関投資家のお客さまのみがお取引可能であり、適格機関投資家以外のお客さまからのご注文等はお受けできませんので、ご注意ください。 商号等/ゴールドマン・サックス証券株式会社 金融商品取引業者　関東財務局長（金商）第６９号\\n\",\n \"加入協会/　日本証券業協会、一般社団法人金融先物取引業協会、一般社団法人第二種金融商品取引業協会\\n\",\n \"本書又はその添付資料に信用格付が記載されている場合、日本格付研究所（JCR）及び格付投資情報センター（R&I）による格付は、登録信用格付業者による格付（登録格付）です。その他の格付は登録格付である旨の記載がない場合は、無登録格付です。無登録格付を投資判断に利用する前に、「無登録格付に関する説明書」（http://www.goldmansachs.com/disclaimer/ratings.html）を十分にお読みください。\\n\",\n \"If any credit ratings are contained in this material or any attachments, those that have been issued by Japan Credit Rating Agency, Ltd. (JCR) or Rating and Investment Information, Inc. (R&I) are credit ratings that have been issued by a credit rating agency registered in Japan (registered credit ratings). Other credit ratings are unregistered unless denoted as being registered. Before using unregistered credit ratings to make investment decisions, please carefully read \\\"Explanation Regarding Unregistered Credit Ratings\\\" (http://www.goldmansachs.com/disclaimer/ratings.html).\\n\",\n \"Notice to Mexican Investors: Information contained herein is not meant for the general public in Mexico. The services or products provided by or through Goldman Sachs Mexico, Casa de Bolsa, S.A. de C.V. (GS Mexico) may not be offered or sold to the general public in Mexico. You have received information herein exclusively due to your existing relationship with a GS Mexico or any other Goldman Sachs business. The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone:\\n\",\n \"(i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"(ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"(iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"(iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act.\\n\",\n \"No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"venv311\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/reports_and_screens/03_prime/0005_gs_pb_data_case_study_leverage.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# The 2020 COVID Selloff and Hedge Fund Leverage\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From mid February 2020, news of the spread of COVID-19, coupled with a surge in the volatility of commodity prices, triggered a deep selloff in global equities.
\\n\",\n \"The most acute phase started on February 24, as Italian authorities adopted restrictive measures to try and curb the diffusion of the infection. After plunging for the best part of three weeks, stock prices entered a more stable phase, recovering a small fraction of the previous losses.
\\n\",\n \"In this case study we build on the PBFA dataset to analyse how hedge fund leverage changed as a result of the sudden shock inflicted on the financial system by the spread of COVID-19.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import libraries\\n\",\n \"import datetime\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Graphical formatting\\n\",\n \"import matplotlib.dates as mdates\\n\",\n \"import matplotlib.ticker as mtick\\n\",\n \"year_month_formatter = mdates.DateFormatter(\\\"%Y-%m\\\") # four digits for year, two for month\\n\",\n \"pct_formatter = mtick.PercentFormatter(xmax=1,decimals=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Authentication\\n\",\n \"client_id = # \\n\",\n \"client_secret = # \\n\",\n \"GsSession.use(Environment.PROD, client_id, client_secret, ('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Fetch positioning data for 2020\\n\",\n \"ds = Dataset('PBFA')\\n\",\n \"data = ds.get_data(datetime.date(2020, 1, 1),datetime.date(2021, 1, 1))\\n\",\n \"data = data.reset_index()\\n\",\n \"data.date = data.date.apply(lambda x: x.date())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After fetching the data from Marquee, we use some simple filters to focus on the broadest aggregates (region=global and sectors=all) and create dataframe which contain gross and net leverage figures by date and strategy.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Gather gross and net leverage data\\n\",\n \"glob_pos = data.loc[(data.region==\\\"Global\\\") & \\\\\\n\",\n \" (data.sectorGICS1==\\\"All\\\"), ['date','clientStrategy','grossLeverage','netLeverage'] ]\\n\",\n \"\\n\",\n \"gross_lev= glob_pos.pivot(index=\\\"date\\\", columns=\\\"clientStrategy\\\", values=\\\"grossLeverage\\\").fillna(method='ffill')\\n\",\n \"net_lev= glob_pos.pivot(index=\\\"date\\\", columns=\\\"clientStrategy\\\", values=\\\"netLeverage\\\").fillna(method='ffill')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, we plot gross and net leverage for the overall book.
\\n\",\n \"As expected, from the end February both measures of leverage declined sharply. However, net leverage (black line) subsequently recovered and ended 2020 at around 85%, higher than the 70-75% level seen before the selloff.
\\n\",\n \"In contrast, gross leverage (green) reached a trough at around 215% and remained below pre-COVID levels for most of 2020.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot book-level gross and net leverage\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"ax2 = ax.twinx()\\n\",\n \"ax.plot(gross_lev.index, gross_lev['All'], 'g-',label='Gross')\\n\",\n \"ax2.plot(net_lev.index, net_lev['All'], color='black',linestyle='solid',label='Net')\\n\",\n \"ax.set_title('Gross vs. Net Leverage, Prime Book')\\n\",\n \"ax.set_xlabel('Time')\\n\",\n \"ax.set_ylabel('Gross Leverage', color='green')\\n\",\n \"ax2.set_ylabel('Net Leverage', color='black')\\n\",\n \"ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\\n\",\n \"ax.xaxis.set_major_formatter(year_month_formatter)\\n\",\n \"ax.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"ax2.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"fig.autofmt_xdate()\\n\",\n \"ax.legend(loc='center left')\\n\",\n \"ax2.legend(loc='center right')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also use the data to track leverage for a specific category of hedge fund strategies, i.e. fundamental long-short\\n\",\n \"The chart suggests that the evolution of gross leverage for fundamental long-short strategies (black line) was similar to that of the overall book (green line). If anything, fundamental managers seem to have returned to the pre-COVID level of risk exposure even faster than other types of investor.
\\n\",\n \"Leverage is typically lower for fundamental strategies in our book, compared to other hedge fund categories, due to the concentrated nature of their typical portfolios.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot gross leverage of fundamental L/S managers\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"ax2 = ax.twinx()\\n\",\n \"ax.plot(gross_lev.index, gross_lev['All'], 'g-',label='Prime Book')\\n\",\n \"ax2.plot(gross_lev.index, gross_lev['Fundamental L/S v2'], color='black',label='Fundamental L/S')\\n\",\n \"ax.set_title('Gross Leverage: Prime Book vs. Fundamental L/S')\\n\",\n \"ax.set_xlabel('Time')\\n\",\n \"ax.set_ylabel('Gross Leverage', color='green')\\n\",\n \"ax2.set_ylabel('Gross Leverage', color='black')\\n\",\n \"ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\\n\",\n \"ax.xaxis.set_major_formatter(year_month_formatter)\\n\",\n \"ax.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"ax2.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"fig.autofmt_xdate()\\n\",\n \"ax.legend(loc='center right')\\n\",\n \"ax2.legend(loc='lower right')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Within the fundamental category, we can drill down further into the numbers by considering strategies with a regional focus\\n\",\n \"Managers within a European and an Asia-focused mandate saw their leverage decline earlier than US-focused manager – a result that is intuitive given the staggered geographic spread of COVID.
\\n\",\n \"After reaching very similar lows in gross leverage (slightly above 150%), US-focused managers (blue line) re-grossed more cautiously than their Asia and Europe-focused counterparts (green and red line, respectively).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot gross leverage of regionally focused fundamental L/S managers\\n\",\n \"fig, ax = plt.subplots(figsize=(10, 6))\\n\",\n \"ax.plot(gross_lev.index, gross_lev['Asia-Focused Fundamental L/S v2'], color='green',label='Asia-Focused')\\n\",\n \"ax.plot(gross_lev.index, gross_lev['Europe-Focused Fundamental L/S v2'], color='red',label='Europe-Focused')\\n\",\n \"ax.plot(gross_lev.index, gross_lev['US-Focused Fundamental L/S v2'], 'blue',label='US-Focused')\\n\",\n \"ax.set_title('Gross Leverage: Regionally Focused Fundamental L/S Strategies')\\n\",\n \"ax.set_xlabel('Time')\\n\",\n \"ax.set_ylabel('Gross Leverage', color='black')\\n\",\n \"ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\\n\",\n \"ax.xaxis.set_major_formatter(year_month_formatter)\\n\",\n \"ax.yaxis.set_major_formatter(pct_formatter)\\n\",\n \"fig.autofmt_xdate()\\n\",\n \"ax.legend(loc='lower right')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Disclaimers:\\n\",\n \"Indicative Terms/Pricing Levels: This material may contain indicative terms only, including but not limited to pricing levels. There is no representation that any transaction can or could have been effected at such terms or prices. Proposed terms and conditions are for discussion purposes only. Finalized terms and conditions are subject to further discussion and negotiation.\\n\",\n \"www.goldmansachs.com/disclaimer/sales-and-trading-invest-rec-disclosures.html If you are not accessing this material via Marquee ContentStream, a list of the author's investment recommendations disseminated during the preceding 12 months and the proportion of the author's recommendations that are 'buy', 'hold', 'sell' or other over the previous 12 months is available by logging into Marquee ContentStream using the link below. Alternatively, if you do not have access to Marquee ContentStream, please contact your usual GS representative who will be able to provide this information to you.\\n\",\n \"Please refer to https://marquee.gs.com/studio/ for price information of corporate equity securities.\\n\",\n \"Notice to Australian Investors: When this document is disseminated in Australia by Goldman Sachs & Co. LLC (\\\"GSCO\\\"), Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\"), or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied the GS entities that:\\n\",\n \"• the person is a Sophisticated or Professional Investor for the purposes of section 708 of the Corporations Act of Australia; and\\n\",\n \"• the person is a wholesale client for the purpose of section 761G of the Corporations Act of Australia.\\n\",\n \"To the extent that the GS entities are providing a financial service in Australia, the GS entities are each exempt from the requirement to hold an Australian financial services licence for the financial services they provide in Australia. Each of the GS entities are regulated by a foreign regulator under foreign laws which differ from Australian laws, specifically:\\n\",\n \"• GSCO is regulated by the US Securities and Exchange Commission under US laws;\\n\",\n \"• GSI is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority, under UK laws;\\n\",\n \"• GSBE is subject to direct prudential supervision by the European Central Bank and in other respects is supervised by the German Federal Financial Supervisory Authority (Bundesanstalt für Finanzdienstleistungsaufischt, BaFin) and Deutsche Bundesbank;\\n\",\n \"• GSALLC is regulated by the Hong Kong Securities and Futures Commission under Hong Kong laws; and\\n\",\n \"• GSSP is regulated by the Monetary Authority of Singapore under Singapore laws.\\n\",\n \"Notice to Brazilian Investors\\n\",\n \"Marquee is not meant for the general public in Brazil. The services or products provided by or through Marquee, at any time, may not be offered or sold to the general public in Brazil. You have received a password granting access to Marquee exclusively due to your existing relationship with a GS business located in Brazil. The selection and engagement with any of the offered services or products through Marquee, at any time, will be carried out directly by you. Before acting to implement any chosen service or products, provided by or through Marquee you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Any steps necessary in order to implement the chosen service or product, including but not limited to remittance of funds, shall be carried out at your discretion. Accordingly, such services and products have not been and will not be publicly issued, placed, distributed, offered or negotiated in the Brazilian capital markets and, as a result, they have not been and will not be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários), nor have they been submitted to the foregoing agency for approval. Documents relating to such services or products, as well as the information contained therein, may not be supplied to the general public in Brazil, as the offering of such services or products is not a public offering in Brazil, nor used in connection with any offer for subscription or sale of securities to the general public in Brazil.\\n\",\n \"The offer of any securities mentioned in this message may not be made to the general public in Brazil. Accordingly, any such securities have not been nor will they be registered with the Brazilian Securities and Exchange Commission (Comissão de Valores Mobiliários) nor has any offer been submitted to the foregoing agency for approval. Documents relating to the offer, as well as the information contained therein, may not be supplied to the public in Brazil, as the offer is not a public offering of securities in Brazil. These terms will apply on every access to Marquee.\\n\",\n \"Ouvidoria Goldman Sachs Brasil: 0800 727 5764 e/ou ouvidoriagoldmansachs@gs.com\\n\",\n \"Horário de funcionamento: segunda-feira à sexta-feira (exceto feriados), das 9hs às 18hs.\\n\",\n \"Ombudsman Goldman Sachs Brazil: 0800 727 5764 and / or ouvidoriagoldmansachs@gs.com\\n\",\n \"Available Weekdays (except holidays), from 9 am to 6 pm.\\n\",\n \"Note to Investors in Israel: GS is not licensed to provide investment advice or investment management services under Israeli law.\\n\",\n \"Notice to Investors in Japan\\n\",\n \"Marquee is made available in Japan by Goldman Sachs Japan Co., Ltd.\\n\",\n \"本書は情報の提供を目的としております。また、売却・購入が違法となるような法域での有価証券その他の売却若しくは購入を勧めるものでもありません。ゴールドマン・サックスは本書内の取引又はストラクチャーの勧誘を行うものではございません。これらの取引又はストラクチャーは、社内及び法規制等の承認等次第で実際にはご提供できない場合がございます。\\n\",\n \"<適格機関投資家限定　転売制限>\\n\",\n \"ゴールドマン・サックス証券株式会社が適格機関投資家のみを相手方として取得申込みの勧誘（取得勧誘）又は売付けの申込み若しくは買付けの申込みの勧誘(売付け勧誘等)を行う本有価証券には、適格機関投資家に譲渡する場合以外の譲渡が禁止される旨の制限が付されています。本有価証券は金融商品取引法第４条に基づく財務局に対する届出が行われておりません。なお、本告知はお客様によるご同意のもとに、電磁的に交付させていただいております。\\n\",\n \"＜適格機関投資家用資料＞\\n\",\n \"本資料は、適格機関投資家のお客さまのみを対象に作成されたものです。本資料における金融商品は適格機関投資家のお客さまのみがお取引可能であり、適格機関投資家以外のお客さまからのご注文等はお受けできませんので、ご注意ください。 商号等/ゴールドマン・サックス証券株式会社 金融商品取引業者　関東財務局長（金商）第６９号\\n\",\n \"加入協会/　日本証券業協会、一般社団法人金融先物取引業協会、一般社団法人第二種金融商品取引業協会\\n\",\n \"本書又はその添付資料に信用格付が記載されている場合、日本格付研究所（JCR）及び格付投資情報センター（R&I）による格付は、登録信用格付業者による格付（登録格付）です。その他の格付は登録格付である旨の記載がない場合は、無登録格付です。無登録格付を投資判断に利用する前に、「無登録格付に関する説明書」（http://www.goldmansachs.com/disclaimer/ratings.html）を十分にお読みください。\\n\",\n \"If any credit ratings are contained in this material or any attachments, those that have been issued by Japan Credit Rating Agency, Ltd. (JCR) or Rating and Investment Information, Inc. (R&I) are credit ratings that have been issued by a credit rating agency registered in Japan (registered credit ratings). Other credit ratings are unregistered unless denoted as being registered. Before using unregistered credit ratings to make investment decisions, please carefully read \\\"Explanation Regarding Unregistered Credit Ratings\\\" (http://www.goldmansachs.com/disclaimer/ratings.html).\\n\",\n \"Notice to Mexican Investors: Information contained herein is not meant for the general public in Mexico. The services or products provided by or through Goldman Sachs Mexico, Casa de Bolsa, S.A. de C.V. (GS Mexico) may not be offered or sold to the general public in Mexico. You have received information herein exclusively due to your existing relationship with a GS Mexico or any other Goldman Sachs business. The selection and engagement with any of the offered services or products through GS Mexico will be carried out directly by you at your own risk. Before acting to implement any chosen service or product provided by or through GS Mexico you should consider, at your sole discretion, whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Information contained herein related to GS Mexico services or products, as well as any other information, shall not be considered as a product coming from research, nor it contains any recommendation to invest, not to invest, hold or sell any security and may not be supplied to the general public in Mexico.\\n\",\n \"Notice to New Zealand Investors: When this document is disseminated in New Zealand by Goldman Sachs & Co. LLC (\\\"GSCO\\\") , Goldman Sachs International (\\\"GSI\\\"), Goldman Sachs Bank Europe SE (\\\"GSBE\\\"), Goldman Sachs (Asia) L.L.C. (\\\"GSALLC\\\") or Goldman Sachs (Singapore) Pte (\\\"GSSP\\\") (collectively the \\\"GS entities\\\"), this document, and any access to it, is intended only for a person that has first satisfied; the GS entities that the person is someone:\\n\",\n \"(i) who is an investment business within the meaning of clause 37 of Schedule 1 of the Financial Markets Conduct Act 2013 (New Zealand) (the \\\"FMC Act\\\");\\n\",\n \"(ii) who meets the investment activity criteria specified in clause 38 of Schedule 1 of the FMC Act;\\n\",\n \"(iii) who is large within the meaning of clause 39 of Schedule 1 of the FMC Act; or\\n\",\n \"(iv) is a government agency within the meaning of clause 40 of Schedule 1 of the FMC Act.\\n\",\n \"No offer to acquire the interests is being made to you in this document. Any offer will only be made in circumstances where disclosure is not required under the Financial Markets Conducts Act 2013 or the Financial Markets Conduct Regulations 2014.\\n\",\n \"Notice to Swiss Investors: This is marketing material for financial instruments or services. The information contained in this material is for general informational purposes only and does not constitute an offer, solicitation, invitation or recommendation to buy or sell any financial instruments or to provide any investment advice or service of any kind.\\n\",\n \"THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSITUTE, AND IS NOT INTENDED TO CONSTITUTE, A PUBLIC OFFER OF SECURITIES IN THE UNITED ARAB EMIRATES IN ACCORDANCE WITH THE COMMERCIAL COMPANIES LAW (FEDERAL LAW NO. 2 OF 2015), ESCA BOARD OF DIRECTORS' DECISION NO. (9/R.M.) OF 2016, ESCA CHAIRMAN DECISION NO 3/R.M. OF 2017 CONCERNING PROMOTING AND INTRODUCING REGULATIONS OR OTHERWISE UNDER THE LAWS OF THE UNITED ARAB EMIRATES. ACCORDINGLY, THE INTERESTS IN THE SECURITIES MAY NOT BE OFFERED TO THE PUBLIC IN THE UAE (INCLUDING THE DUBAI INTERNATIONAL FINANCIAL CENTRE AND THE ABU DHABI GLOBAL MARKET). THIS DOCUMENT HAS NOT BEEN APPROVED BY, OR FILED WITH THE CENTRAL BANK OF THE UNITED ARAB EMIRATES, THE SECURITIES AND COMMODITIES AUTHORITY, THE DUBAI FINANCIAL SERVICES AUTHORITY, THE FINANCIAL SERVICES REGULATORY AUTHORITY OR ANY OTHER RELEVANT LICENSING AUTHORITIES IN THE UNITED ARAB EMIRATES. IF YOU DO NOT UNDERSTAND THE CONTENTS OF THIS DOCUMENT, YOU SHOULD CONSULT WITH A FINANCIAL ADVISOR. THIS DOCUMENT IS PROVIDED TO THE RECIPIENT ONLY AND SHOULD NOT BE PROVIDED TO OR RELIED ON BY ANY OTHER PERSON.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/content/reports_and_screens/README.md", "content": "### How to use Screen Apps\n\nTo use the screeners in a browser format,\n\n1) Install the open source streamlit package \n
\"pip install streamlit\"\n \n2) Navigate to the folder with the python app file and type \"streamlit run python_file_name\"\n
Ex. \\gs_quant\\gs_quant\\content\\reports_and_screens\\00_fx>streamlit run vol_screen_app.py\n \n3) Launch the Streamlit app in your browser by clicking the local or Network URL\n\n\n" }, { "path": "gs_quant/content/reports_and_screens/__init__.py", "content": "" }, { "path": "gs_quant/context_base.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport threading\n\nfrom gs_quant.errors import MqUninitialisedError, MqValueError\n\nthread_local = threading.local()\n\n\nclass ContextMeta(type):\n @property\n def __path_key(cls) -> str:\n return '{}_path'.format(cls.__name__)\n\n @property\n def __default_key(cls) -> str:\n return '{}_default'.format(cls.__name__)\n\n @classmethod\n def default_value(mcs) -> object:\n return None\n\n @property\n def path(cls) -> tuple:\n return getattr(thread_local, cls.__path_key, ())\n\n @property\n def current(cls):\n \"\"\"\n The current instance of this context\n \"\"\"\n path = cls.path\n current = cls.__default if not path else next(iter(path))\n if current is None:\n raise MqUninitialisedError('{} is not initialised'.format(cls.__name__))\n\n return current\n\n @current.setter\n def current(cls, current):\n path = cls.path\n if cls.has_prior:\n raise MqValueError('Cannot set current while in a nested context {}'.format(cls.__name__))\n\n if len(path) == 1:\n cur = cls.current\n try:\n if cur.is_entered:\n raise MqValueError('Cannot set current while in a nested context {}'.format(cls.__name__))\n except AttributeError:\n pass\n\n setattr(thread_local, cls.__path_key, (current,))\n\n @property\n def current_is_set(cls) -> bool:\n return bool(cls.path) or cls.__default is not None\n\n @property\n def __default(cls):\n default = getattr(thread_local, cls.__default_key, None)\n if default is None:\n default = cls.default_value()\n if default is not None:\n setattr(thread_local, cls.__default_key, default)\n\n return default\n\n @property\n def prior(cls):\n path = cls.path\n if len(path) < 2:\n raise MqValueError('Current {} has no prior'.format(cls.__name__))\n\n return path[1]\n\n @property\n def has_prior(cls):\n path = cls.path\n return len(path) >= 2\n\n def push(cls, context):\n setattr(thread_local, cls.__path_key, (context,) + cls.path)\n\n def pop(cls):\n path = cls.path\n setattr(thread_local, cls.__path_key, path[1:])\n return path[0]\n\n\nclass ContextBase(metaclass=ContextMeta):\n def __enter__(self):\n self._cls.push(self)\n setattr(thread_local, self.__entered_key, True)\n self._on_enter()\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n try:\n self._on_exit(exc_type, exc_val, exc_tb)\n finally:\n self._cls.pop()\n setattr(thread_local, self.__entered_key, False)\n\n async def __aenter__(self):\n self._cls.push(self)\n setattr(thread_local, self.__entered_key, True)\n await self._on_aenter()\n return self\n\n async def __aexit__(self, exc_type, exc_val, exc_tb):\n try:\n await self._on_aexit(exc_type, exc_val, exc_tb)\n finally:\n self._cls.pop()\n setattr(thread_local, self.__entered_key, False)\n\n @property\n def __entered_key(self) -> str:\n return '{}_entered'.format(id(self))\n\n @property\n def _cls(self) -> ContextMeta:\n seen = set()\n stack = [self.__class__]\n cls = None\n\n while stack:\n base = stack.pop()\n if ContextBase in base.__bases__ or ContextBaseWithDefault in base.__bases__:\n cls = base\n break\n\n if base not in seen:\n seen.add(base)\n stack.extend(b for b in base.__bases__ if issubclass(b, ContextBase))\n\n return cls or self.__class__\n\n @property\n def is_entered(self) -> bool:\n return getattr(thread_local, self.__entered_key, False)\n\n def _on_enter(self):\n pass\n\n def _on_exit(self, exc_type, exc_val, exc_tb):\n pass\n\n async def _on_aenter(self):\n pass\n\n async def _on_aexit(self, exc_type, exc_val, exc_tb):\n pass\n\n\nclass ContextBaseWithDefault(ContextBase):\n @classmethod\n def default_value(cls) -> object:\n return cls()\n\n\ntry:\n from contextlib import nullcontext\nexcept ImportError:\n from contextlib import AbstractContextManager\n\n class nullcontext(AbstractContextManager):\n def __init__(self, enter_result=None):\n self.enter_result = enter_result\n\n def __enter__(self):\n return self.enter_result\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n pass\n" }, { "path": "gs_quant/data/__init__.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .coordinate import DataCoordinate\nfrom .core import DataContext, DataFrequency\nfrom .dataset import Dataset, PTPDataset\nfrom .fields import DataMeasure, DataDimension, Fields, AssetMeasure\n\n__name__ = 'data'\n" }, { "path": "gs_quant/data/coordinate.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport json\nimport uuid\nfrom abc import ABCMeta\nfrom enum import Enum\nfrom typing import Union, Dict, Tuple, Optional, List\n\nimport pandas as pd\n\nfrom .core import DataContext, DataFrequency, DataAggregationOperator\nfrom .dataset import Dataset\nfrom .fields import DataMeasure, DataDimension\n\nDataDimensions = Dict[Union[DataDimension, str], Union[str, float]]\nDateOrDatetime = Union[dt.date, dt.datetime]\n\n\nclass BaseDataCoordinate(metaclass=ABCMeta):\n \"\"\"Base class for data coordinates\"\"\"\n\n __slots__ = ['__measure', '__dimensions']\n\n def __init__(self, measure: Union[DataMeasure, str], dimensions: Optional[DataDimensions] = None):\n self.__measure = measure\n # Sorted so different dimension orders doesn't matter\n if dimensions is None:\n self.__dimensions = tuple()\n else:\n self.__dimensions = tuple(\n sorted({k.value if isinstance(k, Enum) else k: v for k, v in dimensions.items()}.items())\n )\n\n @property\n def measure(self) -> DataMeasure:\n return self.__measure\n\n @property\n def dimensions(self) -> Dict:\n return dict(self.__dimensions)\n\n def get_series(self, start: Optional[DateOrDatetime] = None, end: Optional[DateOrDatetime] = None):\n pass\n\n def set_dimensions(self, dimensions: DataDimensions):\n \"\"\"Set further dimensions that weren't defined at initialization\n\n :param dimensions: Additional dimensions to add\n\n ** Usage **\n\n For datagrids, we may not know dimension asset_id at point of initialization\n \"\"\"\n _dimensions = self.dimensions\n for key, v in dimensions.items():\n _dimensions[key.value if isinstance(key, Enum) else key] = v\n self.__dimensions = tuple(sorted(_dimensions.items()))\n\n\nclass DataCoordinate(BaseDataCoordinate):\n \"\"\"A coordinate which locates a given datapoint through time\"\"\"\n\n __slots__ = ['__dataset_id', '__frequency', '__id']\n\n def __init__(\n self,\n measure: Union[DataMeasure, str],\n dataset_id: Optional[str] = None,\n dimensions: Optional[DataDimensions] = None,\n frequency: Optional[DataFrequency] = None,\n ):\n \"\"\"Initialize data coordinate\n\n :param dataset_id: Unique identifier for dataset\n :param measure: Unique identifier for data measure (dataset numerical field to query)\n :param dimensions: dict of dimensions to uniquely query dataset (e.g. keys)\n\n ** Usage **\n\n A DataCoordinate uniquely identifies a single timeseries on the GS Data Platform. Any dataset has a given\n set of dimensions (keys or identifiers over which to query) and measures (values which can be aggregated).\n\n A single coordinate specifies the identifier of the dataset, values of each required dimension through a\n dict, and a specific data measure (field). This can be used as a unique reference to query a timeseries\n on the platform over a given time range or sample point-in-time.\n\n DataCoordinates are immutable and can therefore be compared for equality\n \"\"\"\n super().__init__(measure, dimensions)\n self.__dataset_id = dataset_id\n self.__frequency = frequency\n self.__id = str(uuid.uuid4())\n\n @property\n def dataset_id(self) -> str:\n return self.__dataset_id\n\n @property\n def frequency(self) -> DataFrequency:\n return self.__frequency\n\n @property\n def id(self) -> str:\n return self.__id\n\n @id.setter\n def id(self, value):\n self.__id = value\n\n def __eq__(self, other):\n \"\"\"Determine if coordinates are equal\n\n :param other: other object\n\n ** Usage **\n\n Equality check for two coordinates. Validates if the dataset id, data measure and dimensions are equivalent.\n\n \"\"\"\n measure = self.measure if isinstance(self.measure, str) else self.measure.value\n other_measure = other.measure if isinstance(other.measure, str) else other.measure.value\n return (self.dataset_id, measure, self.dimensions) == (other.dataset_id, other_measure, other.dimensions)\n\n def get_dimensions(self) -> Tuple:\n return tuple(self.dimensions.items())\n\n def __hash__(self):\n return hash((self.dataset_id, self.measure, tuple(self.dimensions)))\n\n def __str__(self):\n return (\n f'Dataset Id: ({self.dataset_id}) '\n f'Measure: ({self.measure if isinstance(self.measure, str) else self.measure.value}) '\n f'Dimensions: ({json.dumps(self.dimensions)})'\n )\n\n def get_range(\n self, start: Optional[DateOrDatetime] = None, end: Optional[DateOrDatetime] = None\n ) -> Tuple[Optional[DateOrDatetime], Optional[DateOrDatetime]]:\n if start is None:\n start = (\n DataContext.current.start_time\n if self.frequency is DataFrequency.REAL_TIME\n else DataContext.current.start_date\n )\n\n if end is None:\n end = (\n DataContext.current.end_time\n if self.frequency is DataFrequency.REAL_TIME\n else DataContext.current.end_date\n )\n\n return start, end\n\n def get_series(\n self,\n start: Optional[DateOrDatetime] = None,\n end: Optional[DateOrDatetime] = None,\n dates: List[dt.date] = None,\n operator: DataAggregationOperator = None,\n ) -> Union[pd.Series, None]:\n \"\"\"Get timeseries of coordinate\"\"\"\n\n if not self.dataset_id:\n return None\n\n dataset = Dataset(self.dataset_id)\n start, end = self.get_range(start, end)\n\n measure = self.measure\n if operator:\n measure = f'{operator}({measure})'\n\n return dataset.get_data_series(measure, start=start, end=end, dates=dates, **self.dimensions)\n\n def last_value(self, before: Optional[DateOrDatetime] = None) -> Union[float, None]:\n \"\"\"Return the last available value\n\n Returns the last value prior to to the specified date / time. If `before` argument is not provided, will\n return the last value prior to the end date / time of the current data context\n\n \"\"\"\n if not self.dataset_id:\n return None\n\n start, end = self.get_range(None, before)\n\n dataset = Dataset(self.dataset_id)\n measure = self.measure.value if isinstance(self.measure, Enum) else self.measure\n\n return dataset.get_data_last(end, fields=[measure], **self.dimensions).get(measure, default=None)\n\n def as_dict(self):\n dimensions = {}\n for key, value in self.dimensions.items():\n if isinstance(key, Enum):\n dimensions[key.value] = value\n else:\n dimensions[key] = value\n coordinate = {\n 'measure': self.measure.value if isinstance(self.measure, Enum) else self.measure,\n 'frequency': self.frequency.value,\n 'id': self.id,\n }\n\n if self.dataset_id:\n coordinate['datasetId'] = self.dataset_id\n\n if dimensions:\n coordinate['dimensions'] = dimensions\n return coordinate\n\n @classmethod\n def from_dict(cls, obj):\n measure = obj.get('measure')\n dimensions = obj.get('dimensions', {})\n frequency = obj.get('frequency')\n dataset_id = obj.get('datasetId')\n id_ = obj.get('id')\n\n measure = DataMeasure(measure) if measure in DataMeasure._value2member_map_ else measure\n\n parsed_dimensions = {}\n data_dimension_map = DataDimension._value2member_map_\n for key, value in dimensions.items():\n if key in data_dimension_map:\n parsed_dimensions[DataDimension(key)] = value\n else:\n parsed_dimensions[key] = value\n\n coordinate = (\n DataCoordinate(\n dataset_id=dataset_id, measure=measure, dimensions=parsed_dimensions, frequency=DataFrequency(frequency)\n )\n if dataset_id\n else DataCoordinate(measure=measure, dimensions=parsed_dimensions, frequency=DataFrequency(frequency))\n )\n if id_:\n coordinate.id = id_\n\n return coordinate\n" }, { "path": "gs_quant/data/core.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport re\nfrom enum import Enum\n\nfrom gs_quant.context_base import ContextBaseWithDefault\nfrom gs_quant.errors import MqTypeError, MqValueError\n\n\ndef _now():\n return dt.datetime.now(dt.timezone.utc)\n\n\nclass DataFrequency(Enum):\n \"\"\"Data Frequency enumeration\n\n Enumeration of different data frequencies for field subscription\n\n \"\"\"\n\n #: Data subscription for series updating daily\n DAILY = 'daily'\n\n #: Data subscription for real-time or intraday series\n REAL_TIME = 'realTime'\n\n #: Data subscription for real-time or daily series\n ANY = 'any'\n\n\nclass DataAggregationOperator:\n MIN = 'min'\n MAX = 'max'\n FIRST = 'first'\n LAST = 'last'\n\n\nclass IntervalFrequency(Enum):\n DAILY = 'daily'\n WEEKLY = 'weekly'\n MONTHLY = 'monthly'\n YEARLY = 'yearly'\n\n\nclass DataContext(ContextBaseWithDefault):\n def __init__(self, start=None, end=None, interval=None):\n super().__init__()\n self.__start = start\n self.__end = end\n if interval is None:\n self.__interval = None\n return\n\n if not isinstance(interval, str):\n raise MqTypeError('interval must be a str')\n if not re.fullmatch('[1-9]\\\\d{0,2}[a-z]', interval):\n raise MqValueError('interval must be a valid str e.g. 1m, 2h, 3d')\n self.__interval = interval\n\n @staticmethod\n def _get_date(o, default):\n if o is None:\n return default\n elif isinstance(o, dt.datetime):\n # note that datetime objects are also instances of date\n return o.date()\n elif isinstance(o, dt.date):\n return o\n elif isinstance(o, str):\n loc = o.find('T')\n ds = o[:loc] if loc != -1 else o\n return dt.datetime.strptime(ds, '%Y-%m-%d').date()\n else:\n raise ValueError(f'{o} is not a valid date')\n\n @staticmethod\n def _get_datetime(o, default):\n if o is None:\n return default\n elif isinstance(o, dt.datetime):\n return o\n elif isinstance(o, dt.date):\n return dt.datetime.combine(o, dt.time(tzinfo=dt.timezone.utc))\n elif isinstance(o, str):\n tmp = dt.datetime.strptime(o, '%Y-%m-%dT%H:%M:%SZ')\n return tmp.replace(tzinfo=dt.timezone.utc)\n else:\n raise ValueError(f'{o} is not a valid date')\n\n @property\n def start_date(self):\n return self._get_date(self.__start, dt.date.today() - dt.timedelta(days=30))\n\n @property\n def end_date(self):\n return self._get_date(self.__end, dt.date.today())\n\n @property\n def start_time(self):\n return self._get_datetime(self.__start, _now() - dt.timedelta(days=1))\n\n @property\n def end_time(self):\n return self._get_datetime(self.__end, _now())\n\n @property\n def interval(self):\n return self.__interval\n\n\nif __name__ == '__main__':\n with DataContext(dt.date(2019, 1, 1), dt.datetime(2019, 2, 1, tzinfo=dt.timezone.utc)) as dc:\n print(f'{dc.start_date}, {dc.end_date}')\n print(f'{dc.start_time}, {dc.end_time}')\n with DataContext(None, '2019-01-01T00:00:00Z') as dc2:\n print(f'{dc2.start_date}, {dc2.end_date}')\n print(f'{dc2.start_time}, {dc2.end_time}')\n" }, { "path": "gs_quant/data/dataset.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport re\nimport webbrowser\nfrom enum import Enum\nfrom typing import Iterable, Optional, Union, List, Dict, Callable\nfrom urllib.parse import quote\n\nimport inflection\nimport numpy as np\nimport pandas as pd\nfrom pydash import camel_case, snake_case\n\nfrom gs_quant.api.data import DataApi\nfrom gs_quant.api.gs.users import GsUsersApi\nfrom gs_quant.data.fields import Fields\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.session import GsSession\nfrom functools import partial\nfrom gs_quant.data.utilities import Utilities\nfrom gs_quant.target.data import (\n DataSetEntity,\n DataSetParameters,\n DataSetDimensions,\n FieldColumnPair,\n DataSetFieldEntity,\n DBConfig,\n DataSetType,\n)\n\n\nclass InvalidInputException(Exception):\n pass\n\n\nclass Dataset:\n \"\"\"A collection of related data\"\"\"\n\n class Vendor(Enum):\n pass\n\n class GS(Vendor):\n HOLIDAY = 'HOLIDAY'\n HOLIDAY_CURRENCY = 'HOLIDAY_CURRENCY'\n EDRVOL_PERCENT_INTRADAY = 'EDRVOL_PERCENT_INTRADAY'\n EDRVOL_PERCENT_STANDARD = 'EDRVOL_PERCENT_STANDARD'\n MA_RANK = 'MA_RANK'\n EDRVS_INDEX_SHORT = 'EDRVS_INDEX_SHORT'\n EDRVS_INDEX_LONG = 'EDRVS_INDEX_LONG'\n\n # Baskets\n CBGSSI = 'CBGSSI'\n CB = 'CB'\n\n # STS\n STSLEVELS = 'STSLEVELS'\n\n # Central Bank Watch\n CENTRAL_BANK_WATCH = 'CENTRAL_BANK_WATCH_PREMIUM'\n IR_SWAP_RATES_INTRADAY_CALC_BANK = 'IR_SWAP_RATES_INTRADAY_CALC_BANK'\n RETAIL_FLOW_DAILY_V2_PREMIUM = 'RETAIL_FLOW_DAILY_V2_PREMIUM'\n FX_EVENTS_JUMPS = 'FX_EVENTS_JUMPS'\n FXSPOT_INTRADAY2 = 'FXSPOT_INTRADAY2'\n FXFORWARDPOINTS_PREMIUM = 'FXFORWARDPOINTS_PREMIUM'\n FXFORWARDPOINTS_INTRADAY = 'FXFORWARDPOINTS_INTRADAY'\n # Test Datasets\n WEATHER = 'WEATHER'\n\n # TCA\n QES_INTRADAY_COVARIANCE = 'QES_INTRADAY_COVARIANCE_PREMIUM'\n\n class TR(Vendor):\n TREOD = 'TREOD'\n TR = 'TR'\n TR_FXSPOT = 'TR_FXSPOT'\n\n class FRED(Vendor):\n GDP = 'GDP'\n\n class TradingEconomics(Vendor):\n MACRO_EVENTS_CALENDAR = 'MACRO_EVENTS_CALENDAR'\n\n def __init__(self, dataset_id: Union[str, Vendor], provider: Optional[DataApi] = None):\n \"\"\"\n\n :param dataset_id: The dataset's identifier\n :param provider: The data provider\n \"\"\"\n self.__id = self._get_dataset_id_str(dataset_id)\n self.__provider = provider\n\n def _get_dataset_id_str(self, dataset_id):\n return dataset_id.value if isinstance(dataset_id, Dataset.Vendor) else dataset_id\n\n @property\n def id(self) -> str:\n \"\"\"\n The dataset's identifier\n \"\"\"\n return self.__id\n\n @property\n def name(self):\n pass\n\n @property\n def provider(self):\n from gs_quant.api.gs.data import GsDataApi\n\n return self.__provider or GsDataApi\n\n def _build_data_query(\n self,\n start: Union[dt.date, dt.datetime],\n end: Union[dt.date, dt.datetime],\n as_of: dt.datetime,\n since: dt.datetime,\n fields: Iterable[Union[str, Fields]],\n empty_intervals: bool,\n **kwargs,\n ):\n field_names = None if fields is None else list(map(lambda f: f if isinstance(f, str) else f.value, fields))\n # check whether a function is called e.g. difference(tradePrice)\n schema_varies = field_names is not None and any(map(lambda s: re.match(\"\\\\w+\\\\(\", s), field_names))\n if kwargs and \"date\" in kwargs:\n d = kwargs[\"date\"]\n if type(d) is str:\n try:\n kwargs[\"date\"] = dt.datetime.strptime(d, \"%Y-%m-%d\").date()\n except ValueError:\n pass # Ignore error if date parameter is in some other format\n if \"dates\" not in kwargs and start is None and end is None:\n kwargs[\"dates\"] = (kwargs[\"date\"],)\n return self.provider.build_query(\n start=start,\n end=end,\n as_of=as_of,\n since=since,\n fields=field_names,\n empty_intervals=empty_intervals,\n **kwargs,\n ), schema_varies\n\n def _build_data_frame(self, data, schema_varies, standard_fields) -> pd.DataFrame:\n if type(data) is tuple:\n df = self.provider.construct_dataframe_with_types(\n self.id, data[0], schema_varies, standard_fields=standard_fields\n )\n return df.groupby(data[1], group_keys=True).apply(lambda x: x)\n else:\n return self.provider.construct_dataframe_with_types(\n self.id, data, schema_varies, standard_fields=standard_fields\n )\n\n def get_data(\n self,\n start: Optional[Union[dt.date, dt.datetime]] = None,\n end: Optional[Union[dt.date, dt.datetime]] = None,\n as_of: Optional[dt.datetime] = None,\n since: Optional[dt.datetime] = None,\n fields: Optional[Iterable[Union[str, Fields]]] = None,\n asset_id_type: Optional[str] = None,\n empty_intervals: Optional[bool] = None,\n standard_fields: Optional[bool] = False,\n **kwargs,\n ) -> pd.DataFrame:\n \"\"\"\n Get data for the given range and parameters\n\n :param start: Requested start date/datetime for data\n :param end: Requested end date/datetime for data\n :param as_of: Request data as_of\n :param since: Request data since\n :param fields: DataSet fields to include\n :param empty_intervals: whether to request empty intervals\n :param standard_fields: If set, will use fields api instead of catalog api to get fieldTypes\n :param kwargs: Extra query arguments, e.g. ticker='EDZ19'\n :return: A Dataframe of the requested data\n\n **Examples**\n\n >>> from gs_quant.data import Dataset\n >>> import datetime as dt\n >>>\n >>> weather = Dataset('WEATHER')\n >>> weather_data = weather.get_data(dt.date(2016, 1, 15), dt.date(2016, 1, 16), city=('Boston', 'Austin'))\n \"\"\"\n\n query, schema_varies = self._build_data_query(start, end, as_of, since, fields, empty_intervals, **kwargs)\n data = self.provider.query_data(query, self.id, asset_id_type=asset_id_type)\n return self._build_data_frame(data, schema_varies, standard_fields)\n\n async def get_data_async(\n self,\n start: Optional[Union[dt.date, dt.datetime]] = None,\n end: Optional[Union[dt.date, dt.datetime]] = None,\n as_of: Optional[dt.datetime] = None,\n since: Optional[dt.datetime] = None,\n fields: Optional[Iterable[Union[str, Fields]]] = None,\n empty_intervals: Optional[bool] = None,\n standard_fields: Optional[bool] = False,\n **kwargs,\n ) -> pd.DataFrame:\n \"\"\"\n Get data for the given range and parameters\n\n :param start: Requested start date/datetime for data\n :param end: Requested end date/datetime for data\n :param as_of: Request data as_of\n :param since: Request data since\n :param fields: DataSet fields to include\n :param empty_intervals: whether to request empty intervals\n :param standard_fields: If set, will use fields api instead of catalog api to get fieldTypes\n :param kwargs: Extra query arguments, e.g. ticker='EDZ19'\n :return: A Dataframe of the requested data\n\n **Examples**\n\n >>> from gs_quant.data import Dataset\n >>> import datetime as dt\n >>>\n >>> weather = Dataset('WEATHER')\n >>> weather_data = await weather.get_data_async(dt.date(2016, 1, 15), dt.date(2016, 1, 16),\n >>> city=('Boston', 'Austin'))\n \"\"\"\n\n query, schema_varies = self._build_data_query(start, end, as_of, since, fields, empty_intervals, **kwargs)\n data = await self.provider.query_data_async(query, self.id)\n return self._build_data_frame(data, schema_varies, standard_fields)\n\n def _build_data_series_query(\n self,\n field: Union[str, Fields],\n start: Union[dt.date, dt.datetime],\n end: Union[dt.date, dt.datetime],\n as_of: dt.datetime,\n since: dt.datetime,\n dates: List[dt.date],\n **kwargs,\n ):\n field_value = field if isinstance(field, str) else field.value\n query = self.provider.build_query(\n start=start, end=end, as_of=as_of, since=since, fields=(field_value,), dates=dates, **kwargs\n )\n symbol_dimensions = self.provider.symbol_dimensions(self.id)\n if len(symbol_dimensions) != 1:\n raise MqValueError('get_data_series only valid for symbol_dimensions of length 1')\n symbol_dimension = symbol_dimensions[0]\n return field_value, query, symbol_dimension\n\n def _build_data_series(self, data, field_value, symbol_dimension, standard_fields: bool) -> pd.Series:\n df = self.provider.construct_dataframe_with_types(self.id, data, standard_fields=standard_fields)\n\n from gs_quant.api.gs.data import GsDataApi\n\n if isinstance(self.provider, GsDataApi):\n gb = df.groupby(symbol_dimension)\n if len(gb.groups) > 1:\n raise MqValueError('Not a series for a single {}'.format(symbol_dimension))\n if df.empty:\n return pd.Series(dtype=float)\n if '(' in field_value:\n field_value = field_value.replace('(', '_')\n field_value = field_value.replace(')', '')\n return pd.Series(index=df.index, data=df.loc[:, field_value].values)\n\n def get_data_series(\n self,\n field: Union[str, Fields],\n start: Optional[Union[dt.date, dt.datetime]] = None,\n end: Optional[Union[dt.date, dt.datetime]] = None,\n as_of: Optional[dt.datetime] = None,\n since: Optional[dt.datetime] = None,\n dates: Optional[List[dt.date]] = None,\n standard_fields: Optional[bool] = False,\n **kwargs,\n ) -> pd.Series:\n \"\"\"\n Get a time series of data for a field of a dataset\n\n :param field: The DataSet field to use\n :param start: Requested start date/datetime for data\n :param end: Requested end date/datetime for data\n :param as_of: Request data as_of\n :param since: Request data since\n :param dates: Requested dates for data\n :param standard_fields: If set, will use fields api instead of catalog api to get fieldTypes\n :param kwargs: Extra query arguments, e.g. ticker='EDZ19'\n :return: A Series of the requested data, indexed by date or time, depending on the DataSet\n\n **Examples**\n\n >>> from gs_quant.data import Dataset\n >>> import datetime as dt\n >>>\n >>> weather = Dataset('WEATHER')\n >>> dew_point = weather\n >>>> .get_data_series('dewPoint', dt.date(2016, 1, 15), dt.date(2016, 1, 16), city=('Boston', 'Austin'))\n \"\"\"\n field_value, query, symbol_dimension = self._build_data_series_query(\n field, start, end, as_of, since, dates, **kwargs\n )\n data = self.provider.query_data(query, self.id)\n return self._build_data_series(data, field_value, symbol_dimension, standard_fields)\n\n async def get_data_series_async(\n self,\n field: Union[str, Fields],\n start: Optional[Union[dt.date, dt.datetime]] = None,\n end: Optional[Union[dt.date, dt.datetime]] = None,\n as_of: Optional[dt.datetime] = None,\n since: Optional[dt.datetime] = None,\n dates: Optional[List[dt.date]] = None,\n standard_fields: Optional[bool] = False,\n **kwargs,\n ) -> pd.Series:\n \"\"\"\n Get a time series of data for a field of a dataset\n\n :param field: The DataSet field to use\n :param start: Requested start date/datetime for data\n :param end: Requested end date/datetime for data\n :param as_of: Request data as_of\n :param since: Request data since\n :param dates: Requested dates for data\n :param standard_fields: If set, will use fields api instead of catalog api to get fieldTypes\n :param kwargs: Extra query arguments, e.g. ticker='EDZ19'\n :return: A Series of the requested data, indexed by date or time, depending on the DataSet\n\n **Examples**\n\n >>> from gs_quant.data import Dataset\n >>> import datetime as dt\n >>>\n >>> weather = Dataset('WEATHER')\n >>> dew_point = await weather.get_data_series_async('dewPoint', dt.date(2016, 1, 15), dt.date(2016, 1, 16),\n >>> city=('Boston', 'Austin'))\n \"\"\"\n field_value, query, symbol_dimension = self._build_data_series_query(\n field, start, end, as_of, since, dates, **kwargs\n )\n data = await self.provider.query_data_async(query, self.id)\n return self._build_data_series(data, field_value, symbol_dimension, standard_fields)\n\n def get_data_last(\n self,\n as_of: Optional[Union[dt.date, dt.datetime]],\n start: Optional[Union[dt.date, dt.datetime]] = None,\n fields: Optional[Iterable[str]] = None,\n standard_fields: Optional[bool] = False,\n **kwargs,\n ) -> pd.DataFrame:\n \"\"\"\n Get the last point for this DataSet, at or before as_of\n\n :param as_of: The date or time as of which to query\n :param start: The start of the range to query\n :param fields: The fields for which to query\n :param standard_fields: If set, will use fields api instead of catalog api to get fieldTypes\n :param kwargs: Additional query parameters, e.g., city='Boston'\n :return: A Dataframe of values\n\n **Examples**\n\n >>> from gs_quant.data import Dataset\n >>> import datetime as dt\n >>>\n >>> weather = Dataset('WEATHER')\n >>> last = weather.get_data_last(dt.datetime.now())\n \"\"\"\n query = self.provider.build_query(start=start, end=as_of, fields=fields, format='JSON', **kwargs)\n query.format = None # \"last\" endpoint does not support MessagePack\n\n data = self.provider.last_data(query, self.id)\n return self.provider.construct_dataframe_with_types(self.id, data, standard_fields=standard_fields)\n\n def get_coverage(\n self,\n limit: Optional[int] = None,\n offset: Optional[int] = None,\n fields: Optional[List[str]] = None,\n include_history: bool = False,\n **kwargs,\n ) -> pd.DataFrame:\n \"\"\"\n Get the assets covered by this DataSet\n\n :param limit: The maximum number of assets to return\n :param offset: The offset\n :param fields: The fields to return, e.g. assetId\n :param include_history: Return column for historyStartDate\n :return: A Dataframe of the assets covered\n\n **Examples**\n\n >>> from gs_quant.data import Dataset\n >>>\n >>> weather = Dataset('WEATHER')\n >>> cities = weather.get_coverage()\n \"\"\"\n coverage = self.provider.get_coverage(\n self.id, limit=limit, offset=offset, fields=fields, include_history=include_history, **kwargs\n )\n\n return pd.DataFrame(coverage)\n\n async def get_coverage_async(\n self,\n limit: Optional[int] = None,\n offset: Optional[int] = None,\n fields: Optional[List[str]] = None,\n include_history: bool = False,\n **kwargs,\n ) -> pd.DataFrame:\n \"\"\"\n Get the assets covered by this DataSet\n\n :param limit: The maximum number of assets to return\n :param offset: The offset\n :param fields: The fields to return, e.g. assetId\n :param include_history: Return column for historyStartDate\n :return: A Dataframe of the assets covered\n\n **Examples**\n\n >>> from gs_quant.data import Dataset\n >>>\n >>> weather = Dataset('WEATHER')\n >>> cities = await weather.get_coverage_async()\n \"\"\"\n coverage = await self.provider.get_coverage_async(\n self.id, limit=limit, offset=offset, fields=fields, include_history=include_history, **kwargs\n )\n\n return pd.DataFrame(coverage)\n\n def delete(self) -> Dict:\n \"\"\"\n Delete dataset definition.\n\n Needs 'modify_profuct_data' to execute operation.\n\n >>> from gs_quant.data import Dataset\n >>>\n >>> test_dataset = Dataset('TEST')\n >>> test_dataset.delete()\n \"\"\"\n return self.provider.delete_dataset(self.id)\n\n def undelete(self) -> Dict:\n \"\"\"\n Un-delete dataset definition.\n\n Needs 'modify_profuct_data' to execute operation.\n\n >>> from gs_quant.data import Dataset\n >>>\n >>> test_dataset = Dataset('TEST')\n >>> test_dataset.undelete()\n \"\"\"\n return self.provider.undelete_dataset(self.id)\n\n def delete_data(self, delete_query: Dict):\n \"\"\"\n Delete data from dataset. You must have admin access to the dataset to delete data.\n All data deleted is not recoverable.\n\n :param delete_query: Query to specify data to be deleted.\n\n >>> from gs_quant.data import Dataset\n >>>\n >>> test_dataset = Dataset('TEST')\n >>> delete_query = {'startDate': dt.date.today(), 'endDate': dt.date.today(), 'deleteAll': True}\n >>> test_dataset.delete_data(delete_query)\n \"\"\"\n return self.provider.delete_data(self.id, delete_query)\n\n def upload_data(self, data: Union[pd.DataFrame, list, tuple]) -> Dict:\n \"\"\"\n Upload data to this DataSet\n\n :param data: data to be uploaded to the dataset\n\n **Examples**\n\n >>> from gs_quant.data import Dataset\n >>>\n >>> weather = Dataset('WEATHER')\n >>> data = [{\n >>> \"date\": \"2016-12-31\",\n >>> \"city\": \"Chicago\",\n >>> \"maxTemperature\": 40.0,\n >>> \"minTemperature\": 23.0,\n >>> \"dewPoint\": 21.0,\n >>> \"windSpeed\": 11.4,\n >>> \"precipitation\": 0.0,\n >>> \"snowfall\": 0.0,\n >>> \"pressure\": 29.03,\n >>> \"updateTime\": \"2017-03-06T16:49:39.493Z\"\n >>> }]\n >>> upload_response = weather.upload_data(data)\n \"\"\"\n return self.provider.upload_data(self.id, data)\n\n def get_data_bulk(\n self,\n request_batch_size,\n original_start: dt.datetime,\n final_end: Optional[dt.datetime] = None,\n identifier=\"bbid\",\n symbols_per_csv: int = 1000,\n datetime_delta_override: Optional[int] = None,\n handler: Optional[Callable[[pd.DataFrame], None]] = None,\n ):\n \"\"\"\n Extracts data from dataset by running parallel queries in the background\n\n :param request_batch_size: Used to group number of symbols per batch run (> 0 and <5)\n :param original_start: Start date to fetch the data (mandatory)\n :param final_end: End date to fetch the data. If not entered defaults to current datetime\n :param identifier: Use specific identifier as per dataset configuration. ex. cusip, bbid, clusterRegion\n :param symbols_per_csv: Number of symbols per CSV (recommended value = 1000)\n :param datetime_delta_override: A numeric parameter to increment start date and fetch data in batches\n units are days for daily datasets and hours for intraday\n :param handler: A callable function, if provided, to handle dataframe instead of writing to CSV\n\n\n **Examples**\n\n >>> import datetime as dt\n >>> from gs_quant.data import Dataset\n >>> dataset_id = \"EQTRADECLUSTERS\"\n >>> original_start = dt.datetime(2023, 3, 1,0,0,0)\n >>> final_end = dt.datetime(2023, 3, 1,0,0,0)\n >>> c = Dataset(dataset_id)\n >>> c.get_data_bulk(original_start=original_start,\n >>> final_end=final_end,\n >>> datetime_delta_override=1,\n >>> request_batch_size=4,\n >>> identifier=\"clusterRegion\")\n \"\"\"\n\n try:\n authenticate = partial(\n GsSession.use, client_id=GsSession.current.client_id, client_secret=GsSession.current.client_secret\n )\n except AttributeError:\n authenticate = partial(GsSession.use)\n\n time_field, history_time, symbol_dimension, timedelta = Utilities.get_dataset_parameter(self)\n final_end = final_end or dt.datetime.now()\n write_to_csv = handler is None\n final_end, target_dir_result = Utilities.pre_checks(\n final_end, original_start, time_field, datetime_delta_override, request_batch_size, write_to_csv\n )\n if write_to_csv:\n print(\"Target Destination Folder: \", target_dir_result)\n\n if time_field == 'date':\n original_start = max(original_start.date(), history_time.date())\n final_end = max(final_end.date(), history_time.date())\n datetime_delta_override = (\n timedelta if datetime_delta_override is None else dt.timedelta(days=datetime_delta_override)\n )\n elif time_field == 'time':\n original_start = max(original_start.astimezone(dt.timezone.utc), history_time.astimezone(dt.timezone.utc))\n final_end = max(final_end.astimezone(dt.timezone.utc), history_time.astimezone(dt.timezone.utc))\n datetime_delta_override = (\n timedelta if datetime_delta_override is None else dt.timedelta(hours=datetime_delta_override)\n )\n\n original_end = min(original_start + datetime_delta_override, final_end)\n coverage = Utilities.get_dataset_coverage(identifier, symbol_dimension, self)\n coverage_batches = Utilities.batch(coverage, n=symbols_per_csv)\n batch_number = 1\n coverage_length = len(coverage)\n\n for coverage_batch in coverage_batches:\n Utilities.iterate_over_series(\n self,\n coverage_batch,\n original_start,\n original_end,\n datetime_delta_override,\n identifier,\n request_batch_size,\n authenticate,\n final_end,\n write_to_csv,\n target_dir_result,\n batch_number,\n coverage_length,\n symbols_per_csv,\n handler,\n )\n\n batch_number += 1\n\n\nclass PTPDataset(Dataset):\n \"\"\"\n Special class for and viewing PTP-style datasets.\n\n PTP provides a wrapper around the data service that makes it easy to define datasets, upload data, and plot them in\n PlotTool Pro. PTP datasets can only contain numeric data with a pd.DatetimeIndex (will throw an error otherwise).\n Currently, we only support EOD data; realtime data is not allowed.\n\n (Technically, PTP datasets are datasets with one symbol dimension, which is the dataset ID. Tags indicate that they\n are PTP-style datasets and can be viewed in the 'Datasets' tab in PTP).\n\n :param series: pd.DataFrame or pd.Series with a pd.DatetimeIndex containing EOD numeric data.\n :param name: Name of dataset (default \"GSQ Default\")\n\n **Example**\n\n >>> from gs_quant.data import PTPDataset\n >>> import datetime\n >>> a = pd.Series(range(50), index=pd.date_range(start=datetime.date(2021, 1, 1), periods=50, freq='D'))\n >>> dataset = PTPDataset(a, 'My Dataset')\n >>> dataset.sync()\n >>> print(dataset.plot())\n >>> dataset.delete()\n \"\"\"\n\n def __init__(self, series: Union[pd.Series, pd.DataFrame], name: Optional[str] = None):\n if isinstance(series, pd.Series):\n series = pd.DataFrame({series.attrs.get('name', 'values'): series})\n if not isinstance(series.index, pd.DatetimeIndex):\n raise MqValueError('PTP datasets require a Pandas object with a DatetimeIndex.')\n if isinstance(series, pd.DataFrame) and len(series.select_dtypes(include=np.number).columns) != len(\n series.columns\n ):\n raise MqValueError('PTP datasets must contain only numbers.')\n\n self._series = series\n self._name = name\n super(PTPDataset, self).__init__('', None)\n\n def sync(self):\n \"\"\"\n Upload data and save dataset.\n \"\"\"\n temp_ser = self._series.assign(date=self._series.index.to_series().apply(dt.date.isoformat))\n data = temp_ser.to_dict('records')\n kwargs = dict(data=data, name=self._name if self._name else 'GSQ Default', fields=list(self._series.columns))\n res = GsSession.current.sync.post('/plots/datasets', payload=kwargs)\n self._fields = {\n key: inflection.underscore(field)\n for key, field in res['fieldMap'].items()\n if field not in ['updateTime', 'date', 'datasetId']\n }\n self._id = res['dataset']['id']\n super(PTPDataset, self).__init__(self._id, None)\n\n def plot(self, open_in_browser: bool = True, field: Optional[str] = None) -> str:\n \"\"\"\n Generate transient plot expression to view dataset in PTP. Copying and pasting the transient expression\n will show your data in PTP.\n\n :param open_in_browser: whether to use webbrowser to open the generated plot expression in your default browser\n (default True)\n :param field: if passed, only this field will be included in the transient plot (default None)\n :return: transient plot expression.\n \"\"\"\n if not field:\n fields = self._fields.values()\n else:\n fields = [field]\n fields = [inflection.underscore(re.sub(r'([a-zA-Z])(\\d)', r'\\1_\\2', f)) for f in fields]\n domain = GsSession.current.domain.replace('marquee.web', 'marquee') # remove .web from prod domain\n expression = f'{domain}/s/plottool/transient?expr=Dataset(\"{self._id}\").{fields[0]}()'\n for f in fields[1:]:\n expression += quote(\"\\n\") + f'Dataset(\"{self._id}\").{f}()'\n if open_in_browser:\n webbrowser.open(expression)\n return expression\n\n\nclass MarqueeDataIngestionLibrary:\n \"\"\"\n A class to create a native snowflake datasets and upload data from dataframe.\n\n\n **Example Usage:**\n >>> from gs_quant.data import MarqueeDataIngestionLibrary\n >>> import pandas as pd\n >>> data = pd.DataFrame({\n >>> \"date\": [\"2023-01-01\", \"2023-01-02\", \"2023-01-03\"],\n >>> \"symbol\": [\"AAPL\", \"GOOG\", \"MSFT\"],\n >>> \"price\": [150.0, 2800.0, 310.0],\n >>> \"volume\": [1000, 2000, 1500]\n >>> })\n >>> ingestion_lib = MarqueeDataIngestionLibrary()\n >>> dataset = ingestion_lib.create_dataset(data, \"DATASET_NAME\", \"SYMBOL_DIMENSION\", \"date\",\n >>> dimensions=[\"volume\", \"measure2\", \"measure3\"])\n \"\"\"\n\n def __init__(self, provider: Optional[DataApi] = None):\n \"\"\"\n :param provider: The data provider\n \"\"\"\n self.__provider = provider\n self.user = GsUsersApi.get_current_user_info()\n self.managers = GsUsersApi.get_current_app_managers()\n\n @property\n def provider(self):\n from gs_quant.api.gs.data import GsDataApi\n\n return self.__provider or GsDataApi\n\n def _create_parameters(\n self, time_dimension: str, symbol_dimension: str, is_internal_user: bool\n ) -> DataSetParameters:\n \"\"\"\n Create the parameters for the dataset.\n\n :return: A DataSetParameters object.\n \"\"\"\n parameters = DataSetParameters()\n parameters.frequency = 'Daily'\n parameters.snowflake_config = DBConfig()\n parameters.snowflake_config.db = \"TIMESERIES\" if is_internal_user else \"EXTERNAL\"\n parameters.snowflake_config.date_time_column = self.to_upper_underscore(time_dimension)\n parameters.snowflake_config.id_column = self.to_upper_underscore(symbol_dimension)\n return parameters\n\n def _check_and_create_field(self, fieldMap: Dict[str, str], dataframe: pd.DataFrame) -> None:\n fields_to_create = []\n for column, field_name in fieldMap.items():\n if not (self.provider.get_dataset_fields(names=field_name)):\n data_type = pd.api.types.infer_dtype(dataframe[column])\n api_data_type = 'number' if data_type in ['floating', 'integer'] else data_type\n fields_to_create.append(\n DataSetFieldEntity(\n name=field_name, type_=api_data_type, description=f'field {field_name} created from GSQuant'\n )\n )\n\n if fields_to_create:\n return self.provider.create_dataset_fields(fields_to_create)\n\n def _create_dimensions(\n self,\n data: pd.DataFrame,\n symbol_dimension: str,\n time_dimension: str,\n dimensions: Optional[List[str]],\n measures: List[str],\n internal_user: bool,\n ) -> DataSetDimensions:\n \"\"\"\n Create the dimensions for the dataset.\n\n :param data: DataFrame containing the data to be ingested.\n :param symbol_dimension: List of columns that represent the symbol dimension.\n :param time_dimension: Column that represents the time dimension.\n :param dimensions: Optional List of non-symbol dimensions.\n :return: A DataSetDimensions object.\n \"\"\"\n\n if len(measures) > 25:\n raise ValueError(\"The number of measures exceeds the allowed limit of 25.\")\n\n dataset_dimensions = DataSetDimensions()\n dataset_dimensions.time_field = time_dimension\n dataset_dimensions.symbol_dimensions = [symbol_dimension]\n\n if internal_user:\n fieldMap = {field: self.to_camel_case(field) for field in (dimensions + measures)}\n else:\n drgName = self.user.get(\"drgName\")\n if drgName is None:\n raise InvalidInputException(\"drgName is required but was not found.\")\n INVALID_DRG_NAME_CHARS = r\"Pvt Ltd.*|Private Ltd.*|Limited.*|Ltd.*|Inc.*|LP$|LLP$|[^a-zA-Z0-9]\"\n drgName = re.sub(INVALID_DRG_NAME_CHARS, \"\", drgName)\n\n fieldMap = {\n field: self.to_camel_case((field if field == 'updateTime' else f\"{field}Org{drgName}\")[:64])\n for field in (dimensions + measures)\n }\n\n self._check_and_create_field(fieldMap, data)\n\n dataset_dimensions.non_symbol_dimensions = tuple(\n FieldColumnPair(field_=fieldMap.get(dim), column=self.to_upper_underscore(dim))\n for dim in (dimensions or [])\n )\n dataset_dimensions.measures = tuple(\n FieldColumnPair(\n field_=fieldMap.get(mea),\n column=self.to_upper_underscore(mea),\n resolvable=True if mea != 'updateTime' else None,\n )\n for mea in measures\n )\n return dataset_dimensions\n\n def to_upper_underscore(self, name: str) -> str:\n return snake_case(name).upper()\n\n def to_camel_case(self, name: str) -> str:\n return camel_case(name)\n\n def create_dataset(\n self,\n data: pd.DataFrame,\n dataset_id: str,\n symbol_dimension: str,\n time_dimension: str,\n dimensions: Optional[List[str]] = [],\n ) -> DataSetEntity:\n \"\"\"\n Create a dataset using the provided data and metadata.\n\n This method creates a dataset in the Marquee platform using the provided DataFrame and metadata.\n The dataset is defined by its unique identifier, symbol dimension, time dimension, and optional\n non-symbol dimensions. The data is validated to ensure compatibility with the platform's requirements.\n\n :param data: DataFrame containing the data to be ingested. The DataFrame must include the symbol\n and time dimensions, along with any additional dimensions or measures.\n :param dataset_id: The unique identifier for the dataset. This identifier is used to reference\n the dataset in the platform.\n :param symbol_dimension: Column name in the DataFrame that represents the symbol dimension.\n Valid symbol dimensions are: {\"isin\", \"bbid\", \"ric\", \"sedol\", \"cusip\", \"ticker\",\n \"countryId\", \"currency\"}.\n :param time_dimension: Column name in the DataFrame that represents the time dimension. This\n must be a date column, as intraday timestamps are not supported.\n :param dimensions: Optional list of column names in the DataFrame that represent non-symbol dimensions.\n :return: A DataSetEntity object representing the dataset specification.\n \"\"\"\n\n if data.empty or not dataset_id or not symbol_dimension or not time_dimension:\n print(\"Error: 'data', 'dataset_id', 'symbol_dimension', and 'time_dimension' are all required.\")\n raise InvalidInputException(\"One or more required parameters are empty or null.\")\n\n if not pd.api.types.is_datetime64_any_dtype(pd.to_datetime(data[time_dimension])) or not all(\n pd.to_datetime(data[time_dimension]).dt.time == dt.time(0, 0)\n ):\n raise InvalidInputException(\n f\"Snowflake doesn't support intraday data. The time_dimension \"\n f\"'{time_dimension}' must be a date, not a timestamp.\"\n )\n\n dataset_definition = DataSetEntity()\n dataset_definition.id_ = dataset_id\n dataset_definition.name = dataset_id.replace('_', ' ').title()\n dataset_definition.description = \"Dataset created from GSQuant\"\n\n all_columns = set(data.columns)\n specified_columns = set([symbol_dimension] + [time_dimension] + (dimensions or []))\n measures = list(all_columns - specified_columns)\n\n VALID_TIME_DIMENSION = { # we do not support intraday data for snowflake datasets\n \"date\"\n }\n VALID_SYMBOL_DIMENSION = {\"isin\", \"bbid\", \"ric\", \"sedol\", \"cusip\", \"ticker\", \"countryId\", \"currency\"}\n\n internal_user = self.user.get(\"internal\")\n\n custom_symbol_dimension = (\n \"customId\"\n if (not internal_user and self.to_camel_case(symbol_dimension) not in VALID_SYMBOL_DIMENSION)\n else self.to_camel_case(symbol_dimension)\n )\n custom_time_dimensions = \"date\" if time_dimension not in VALID_TIME_DIMENSION else time_dimension\n\n dataset_definition.parameters = self._create_parameters(time_dimension, symbol_dimension, internal_user)\n dataset_definition.dimensions = self._create_dimensions(\n data, custom_symbol_dimension, custom_time_dimensions, dimensions, measures, internal_user\n )\n dataset_definition.type_ = DataSetType.NativeSnowflake\n\n result = self.provider.create(dataset_definition)\n url = self.provider.get_catalog_url(dataset_id)\n print(f\"Dataset created successfully. Access it on Catalog: {url}\")\n return result\n\n def write_data(self, df: pd.DataFrame, dataset_id: str) -> None:\n \"\"\"\n Write data from a DataFrame to the specified dataset.\n\n :param df: DataFrame containing the data to be written.\n :param dataset_id: The identifier of the dataset to write data to.\n \"\"\"\n if df.empty:\n raise ValueError(\"The DataFrame is empty. No data to write.\")\n\n if not dataset_id:\n raise ValueError(\"Dataset ID is required.\")\n\n # Upload data to the dataset\n dataset = self.provider.get_definition(dataset_id)\n allFields = (\n dataset.dimensions.non_symbol_dimensions\n + dataset.dimensions.measures\n + (\n FieldColumnPair(\n field_=dataset.dimensions.time_field, column=dataset.parameters.snowflake_config.date_time_column\n ),\n FieldColumnPair(\n field_=dataset.dimensions.symbol_dimensions[0], column=dataset.parameters.snowflake_config.id_column\n ),\n )\n )\n\n renamed_df = df.copy().rename(\n columns={\n column: field.field_\n for column in df.columns\n for field in allFields\n if self.to_upper_underscore(column) == field.column\n }\n )\n\n data = renamed_df.to_dict('records')\n\n return self.provider.upload_data(dataset_id, data)\n" }, { "path": "gs_quant/data/fields.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom aenum import Enum, extend_enum\nfrom typing import Optional\nfrom dataclasses_json import LetterCase, dataclass_json\nfrom dataclasses import dataclass, field\n\n\nclass DataMeasure(Enum):\n \"\"\"Data measure definitions\n\n Enumeration of measures available through data APIs. Measures are facts that are usually quantities and that can\n be aggregated over a defined period of time. For example: tradePrice and volume are measures\n\n \"\"\"\n\n ASK_PRICE = 'askPrice'\n BID_PRICE = 'bidPrice'\n HIGH_PRICE = 'highPrice'\n MID_PRICE = 'midPrice'\n LOW_PRICE = 'lowPrice'\n OPEN_PRICE = 'openPrice'\n CLOSE_PRICE = 'closePrice'\n TRADE_PRICE = 'tradePrice'\n SPOT_PRICE = 'spot'\n VOLUME = 'volume'\n ADJUSTED_ASK_PRICE = 'adjustedAskPrice'\n ADJUSTED_BID_PRICE = 'adjustedBidPrice'\n ADJUSTED_HIGH_PRICE = 'adjustedHighPrice'\n ADJUSTED_LOW_PRICE = 'adjustedLowPrice'\n ADJUSTED_OPEN_PRICE = 'adjustedOpenPrice'\n ADJUSTED_CLOSE_PRICE = 'adjustedClosePrice'\n ADJUSTED_TRADE_PRICE = 'adjustedTradePrice'\n ADJUSTED_VOLUME = 'adjustedVolume'\n IMPLIED_VOLATILITY = 'impliedVolatility'\n VAR_SWAP = 'varSwap'\n PRICE = 'price'\n NAV_PRICE = 'navPrice'\n SPREAD = 'spread'\n NAV_SPREAD = 'navSpread'\n IMPLIED_VOLATILITY_BY_DELTA_STRIKE = 'impliedVolatilityByDeltaStrike'\n FORWARD_POINT = 'forwardPoint'\n DIVIDEND_YIELD = 'Dividend Yield'\n EARNINGS_PER_SHARE = 'Earnings per Share'\n EARNINGS_PER_SHARE_POSITIVE = 'Earnings per Share Positive'\n NET_DEBT_TO_EBITDA = 'Net Debt to EBITDA'\n PRICE_TO_BOOK = 'Price to Book'\n PRICE_TO_CASH = 'Price to Cash'\n PRICE_TO_EARNINGS = 'Price to Earnings'\n PRICE_TO_EARNINGS_POSITIVE = 'Price to Earnings Positive'\n PRICE_TO_EARNINGS_POSITIVE_EXCLUSIVE = 'Price to Earnings Positive Exclusive'\n PRICE_TO_SALES = 'Price to Sales'\n RETURN_ON_EQUITY = 'Return on Equity'\n SALES_PER_SHARE = 'Sales per Share'\n CURRENT_CONSTITUENTS_DIVIDEND_YIELD = 'Current Constituents Dividend Yield'\n CURRENT_CONSTITUENTS_EARNINGS_PER_SHARE = 'Current Constituents Earnings per Share'\n CURRENT_CONSTITUENTS_EARNINGS_PER_SHARE_POSITIVE = 'Current Constituents Earnings per Share Positive'\n CURRENT_CONSTITUENTS_NET_DEBT_TO_EBITDA = 'Current Constituents Net Debt to EBITDA'\n CURRENT_CONSTITUENTS_PRICE_TO_BOOK = 'Current Constituents Price to Book'\n CURRENT_CONSTITUENTS_PRICE_TO_CASH = 'Current Constituents Price to Cash'\n CURRENT_CONSTITUENTS_PRICE_TO_EARNINGS = 'Current Constituents Price to Earnings'\n CURRENT_CONSTITUENTS_PRICE_TO_EARNINGS_POSITIVE = 'Current Constituents Price to Earnings Positive'\n CURRENT_CONSTITUENTS_PRICE_TO_SALES = 'Current Constituents Price to Sales'\n CURRENT_CONSTITUENTS_RETURN_ON_EQUITY = 'Current Constituents Return on Equity'\n CURRENT_CONSTITUENTS_SALES_PER_SHARE = 'Current Constituents Sales per Share'\n ONE_YEAR = '1y'\n TWO_YEARS = '2y'\n THREE_YEARS = '3y'\n FORWARD = 'forward'\n TRAILING = 'trailing'\n\n def __repr__(self):\n return self.value\n\n @classmethod\n def list_fundamentals(cls):\n return [\n metric.value\n for metric in [\n cls.DIVIDEND_YIELD,\n cls.EARNINGS_PER_SHARE,\n cls.EARNINGS_PER_SHARE_POSITIVE,\n cls.NET_DEBT_TO_EBITDA,\n cls.PRICE_TO_BOOK,\n cls.PRICE_TO_CASH,\n cls.PRICE_TO_EARNINGS,\n cls.PRICE_TO_EARNINGS_POSITIVE,\n cls.PRICE_TO_EARNINGS_POSITIVE_EXCLUSIVE,\n cls.PRICE_TO_SALES,\n cls.RETURN_ON_EQUITY,\n cls.SALES_PER_SHARE,\n cls.CURRENT_CONSTITUENTS_DIVIDEND_YIELD,\n cls.CURRENT_CONSTITUENTS_EARNINGS_PER_SHARE,\n cls.CURRENT_CONSTITUENTS_EARNINGS_PER_SHARE_POSITIVE,\n cls.CURRENT_CONSTITUENTS_NET_DEBT_TO_EBITDA,\n cls.CURRENT_CONSTITUENTS_PRICE_TO_BOOK,\n cls.CURRENT_CONSTITUENTS_PRICE_TO_CASH,\n cls.CURRENT_CONSTITUENTS_PRICE_TO_EARNINGS,\n cls.CURRENT_CONSTITUENTS_PRICE_TO_EARNINGS_POSITIVE,\n cls.CURRENT_CONSTITUENTS_PRICE_TO_SALES,\n cls.CURRENT_CONSTITUENTS_RETURN_ON_EQUITY,\n cls.CURRENT_CONSTITUENTS_SALES_PER_SHARE,\n ]\n ]\n\n\nclass DataDimension(Enum):\n \"\"\"Data dimension definitions\n\n Enumeration of dimensions available through data APIs. Dimensions describe or provide context to measures, and can\n be used to select or group data. For example: ticker and exchange are dimensions\n\n \"\"\"\n\n ASSET_ID = 'assetId'\n NAME = 'name'\n RIC = 'ric'\n TENOR = 'tenor'\n STRIKE_REFERENCE = 'strikeReference'\n RELATIVE_STRIKE = 'relativeStrike'\n EXPIRATION_DATE = 'expirationDate'\n UPDATE_TIME = 'updateTime'\n\n\nclass Fields(Enum):\n \"\"\"Data field enumeration\n\n Enumeration of fields available through data APIs\n\n \"\"\"\n\n @property\n def unit(self) -> Optional[str]:\n # TODO: Define units and look up appropriate unit for self\n return None\n\n\nfor enum in DataMeasure:\n extend_enum(Fields, enum.name, enum.value)\n\nfor enum in DataDimension:\n extend_enum(Fields, enum.name, enum.value)\n\n\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(frozen=True)\nclass AssetMeasure:\n dataset_field: str = field(default=None)\n frequency: str = field(default=None)\n type: str = field(default=None)\n" }, { "path": "gs_quant/data/log.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nNO_REQUEST_ID = 'no-request-id'\n\n\ndef log_debug(request_id, logger, fmt_str, *args, **kwargs):\n logger.debug(f'{request_id or NO_REQUEST_ID}: {fmt_str}', *args, **kwargs)\n\n\ndef log_warning(request_id, logger, fmt_str, *args, **kwargs):\n logger.warning(f'{request_id or NO_REQUEST_ID}: {fmt_str}', *args, **kwargs)\n\n\ndef log_info(request_id, logger, fmt_str, *args, **kwargs):\n logger.info(f'{request_id or NO_REQUEST_ID}: {fmt_str}', *args, **kwargs)\n" }, { "path": "gs_quant/data/query.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom enum import Enum\nfrom typing import Union\n\nimport pandas as pd\n\nfrom gs_quant.data import DataCoordinate\nfrom gs_quant.data.coordinate import DateOrDatetime\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom .stream import DataSeries\n\n\nclass DataQueryType(Enum):\n LAST = 'LAST'\n RANGE = 'RANGE'\n\n\nclass DataQuery:\n \"\"\"Defines a query on a coordinate\"\"\"\n\n def __init__(\n self,\n coordinate: DataCoordinate,\n start: Union[DateOrDatetime, RelativeDate] = None,\n end: Union[DateOrDatetime, RelativeDate] = None,\n query_type: DataQueryType = DataQueryType.RANGE,\n ):\n \"\"\"Initialize data query\"\"\"\n\n self.coordinate = coordinate\n self.start = start\n self.end = end\n self.query_type = query_type\n\n def get_series(self) -> Union[pd.Series, None]:\n \"\"\"Execute query and return series\"\"\"\n\n if self.query_type is DataQueryType.RANGE:\n return self.coordinate.get_series(self.start, self.end)\n\n if self.query_type is DataQueryType.LAST:\n return self.coordinate.last_value(self.end)\n\n def get_data_series(self) -> DataSeries:\n return DataSeries(self.get_series(), self.coordinate)\n\n def get_range_string(self) -> str:\n return f'start={self.start}|end={self.end}'\n" }, { "path": "gs_quant/data/stream.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom typing import Union\n\nimport pandas as pd\n\nfrom gs_quant.data import DataCoordinate\n\n\nclass DataSeries:\n \"\"\"Represents a data series update\"\"\"\n\n def __init__(self, series: pd.Series, coordinate: DataCoordinate):\n self.series = series\n self.coordinate = coordinate\n\n\nclass DataEvent:\n \"\"\"Represents a data update event\"\"\"\n\n def __init__(self, time: dt.datetime, value: Union[None, str, float], coordinate: DataCoordinate = None):\n self.time = time\n self.value = value\n self.coordinate = coordinate\n" }, { "path": "gs_quant/data/utilities.py", "content": "\"\"\"\nCopyright 2023 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport os\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom itertools import groupby\nfrom functools import partial\nfrom concurrent.futures import ThreadPoolExecutor\nfrom gs_quant.target.assets import FieldFilterMap, EntityQuery\nfrom gs_quant.session import GsSession\nimport math\nimport pandas as pd\nimport datetime as dt\nfrom typing import Dict, List, Any, Union, Tuple\n\n\nclass Utilities:\n class AssetApi:\n IdList = Union[Tuple[str, ...], List]\n\n @classmethod\n def __create_query(\n cls,\n fields: Union[List, Tuple] = None,\n as_of: dt.datetime = None,\n limit: int = None,\n scroll: str = None,\n scroll_id: str = None,\n order_by: List[str] = None,\n **kwargs,\n ) -> EntityQuery:\n keys = set(kwargs.keys())\n valid = keys.intersection(FieldFilterMap.properties())\n invalid = keys.difference(valid)\n\n if invalid:\n bad_args = ['{}={}'.format(k, kwargs[k]) for k in invalid]\n raise KeyError('Invalid asset query argument(s): {}'.format(', '.join(bad_args)))\n\n return EntityQuery(\n where=FieldFilterMap(**kwargs),\n fields=fields,\n asOfTime=as_of or dt.datetime.utcnow(),\n limit=limit,\n scroll=scroll,\n scroll_id=scroll_id,\n order_by=order_by,\n )\n\n @classmethod\n def get_many_assets_data(\n cls, fields: IdList = None, as_of: dt.datetime = None, limit: int = None, **kwargs\n ) -> dict:\n query = cls.__create_query(fields, as_of, limit, **kwargs)\n response = GsSession.current.sync.post('/assets/data/query', payload=query)\n return response['results']\n\n @staticmethod\n def target_folder():\n get_cwd = os.getcwd()\n target_dir = 'data_extract_' + dt.datetime.now().strftime(\"%d_%m_%Y_%H_%M_%S\")\n\n if os.path.exists(get_cwd + \"\\\\\" + target_dir):\n final_dir = get_cwd + \"\\\\\" + target_dir\n return final_dir\n\n elif os.access(get_cwd, os.W_OK):\n try:\n os.makedirs(target_dir)\n final_dir = get_cwd + \"\\\\\" + target_dir\n return final_dir\n except Exception as ex:\n print(ex)\n return 1\n\n @staticmethod\n def pre_checks(final_end, original_start, time_field, datetime_delta_override, request_batch_size, write_to_csv):\n if write_to_csv:\n target_dir_result = Utilities.target_folder()\n if target_dir_result == 1:\n raise ValueError(\"Current working directory doesn't have write permissions to save data. Exiting .\")\n else:\n target_dir_result = None\n\n if request_batch_size is None or not (0 < request_batch_size < 5):\n raise ValueError(\"Enter request batch size beteen 1-5\")\n\n if datetime_delta_override is not None:\n if not isinstance(datetime_delta_override, int):\n raise ValueError(\"Time delta override must be greater than 0 and 1 - 5 for intraday dataset\")\n elif isinstance(datetime_delta_override, int) and (datetime_delta_override < 0):\n raise ValueError(\"Time delta override must be greater than 0 and 1 - 5 for intraday dataset\")\n elif (time_field == \"time\") and (datetime_delta_override > 5):\n raise ValueError(\"Time delta override must be greater than 0 and 1 - 5 for intraday dataset\")\n\n if final_end is not None:\n if not isinstance(final_end, dt.datetime):\n raise ValueError(\"End date must of datetime.datetime format. Existing program....\")\n elif not isinstance(original_start, dt.datetime):\n raise ValueError(\"Start date must be of datetime.datetime format. Exiting program......\")\n elif original_start > final_end:\n raise ValueError(\"Start date cannot be greater than end date. Exiting program......\")\n elif (time_field == \"time\") and ((final_end - original_start).total_seconds() / 3600 > 5):\n raise ValueError(\"For intraday datasets diff between start & end date should be <= 5 hrs\")\n\n return final_end, target_dir_result\n\n @staticmethod\n def batch(iterable, n=1):\n iter_len = len(iterable)\n for ndx in range(0, iter_len, n):\n yield iterable[ndx : min(ndx + n, iter_len)]\n\n @staticmethod\n def fetch_data(dataset, symbols, start=dt.datetime.now(), end=dt.datetime.now(), dimension=\"assetId\", auth=None):\n if auth is not None:\n auth()\n try:\n return dataset.get_data(start, end, **{dimension: symbols})\n except Exception as ex:\n print(ex)\n return pd.DataFrame()\n\n @staticmethod\n def execute_parallel_query(\n dataset, coverage, start, end, symbol_dimension, parallel_factor, batch_size, authenticate, retry=0\n ):\n bound_get_data = partial(\n Utilities.fetch_data, dataset, start=start, end=end, dimension=symbol_dimension, auth=authenticate\n )\n\n print(f\"{len(coverage)} symbols, will run in {math.ceil(len(coverage) / batch_size)} batches\")\n with ThreadPoolExecutor(max_workers=parallel_factor) as e:\n try:\n df = pd.concat(e.map(bound_get_data, Utilities.batch(coverage, n=batch_size)))\n print(f\"Fetched {len(df.index)} rows in {parallel_factor} concurrent process from {start} - {end}\")\n except Exception as e:\n print(f\"Failure getting data with error {e}, retrying...\")\n if retry > 3:\n raise Exception(\"retry failure\")\n\n retry += 1\n df = Utilities.execute_parallel_query(\n dataset, coverage, start, end, symbol_dimension, parallel_factor, batch_size, authenticate, retry\n )\n\n return df\n\n @staticmethod\n def get_dataset_parameter(dataset):\n dimensions = dataset.provider.symbol_dimensions(dataset.id)\n symbol_dimension = dimensions[0]\n dataset_definition = dataset.provider.get_definition(dataset.id)\n history_time = dataset_definition.parameters.history_date\n time_field = dataset_definition.dimensions.time_field\n timedelta = dt.timedelta(days=180) if time_field == \"date\" else dt.timedelta(hours=1)\n return [time_field, history_time, symbol_dimension, timedelta]\n\n @staticmethod\n def write_consolidated_results(\n data_frame, target_dir_result, dataset, batch_number, handler, write_to_csv, coverage_length, symbols_per_csv\n ):\n if data_frame.shape[0] > 0:\n if write_to_csv:\n data_frame.to_csv(f\"{target_dir_result}\\\\{dataset.id}-batch {batch_number}.csv\", \",\")\n else:\n handler(data_frame)\n\n print(f\"Wrote batch {batch_number} file out of {math.ceil(coverage_length / symbols_per_csv)}\")\n\n @staticmethod\n def iterate_over_series(\n dataset,\n coverage_batch,\n original_start,\n original_end,\n datetime_delta_override,\n symbol_dimension,\n request_batch_size,\n authenticate,\n final_end,\n write_to_csv,\n target_dir_result,\n batch_number,\n coverage_length,\n symbols_per_csv,\n handler,\n parallel_factor=5,\n ):\n start = original_start\n end = original_end\n data_frame = pd.DataFrame()\n\n while True:\n batch_frame = Utilities.execute_parallel_query(\n dataset, coverage_batch, start, end, symbol_dimension, parallel_factor, request_batch_size, authenticate\n )\n\n data_frame = pd.concat([data_frame, batch_frame], axis=0)\n\n start += datetime_delta_override\n end += datetime_delta_override\n\n if end > final_end:\n Utilities.write_consolidated_results(\n data_frame,\n target_dir_result,\n dataset,\n batch_number,\n handler,\n write_to_csv,\n coverage_length,\n symbols_per_csv,\n )\n break\n\n del data_frame\n return None\n\n @staticmethod\n def extract_xref(assets, out_type):\n # if multiple assets match, return highest ranking\n return sorted(assets, key=lambda x: x.get(\"rank\", 0), reverse=True)[0].get(out_type, \"\")\n\n @staticmethod\n def map_identifiers(input_type: str, output_type: str, ids, as_of=dt.datetime.now()):\n asset_batches = Utilities.batch(ids, n=1000)\n all_assets = []\n for asset_batch in asset_batches:\n assets = Utilities.AssetApi.get_many_assets_data(\n **{input_type: asset_batch},\n limit=min(5000, 5 * len(asset_batch)),\n as_of=as_of,\n fields=[input_type, output_type, \"listed\", \"assetClassificationsIsPrimary\", \"rank\"],\n asset_classifications_is_primary=[True],\n )\n\n assets = sorted(assets, key=lambda x: x[input_type])\n all_assets = all_assets + assets\n return {\n inp_id: Utilities.extract_xref(grouped_assets, output_type)\n for inp_id, grouped_assets in groupby(all_assets, key=lambda x: x[input_type])\n }\n\n @staticmethod\n def get_dataset_coverage(identifier, symbol_dimension, dataset):\n if symbol_dimension == \"assetId\":\n cov = dataset.get_coverage(fields=[identifier])\n coverage = cov[cov[identifier].notna()][identifier].tolist()\n elif symbol_dimension == \"gsid\":\n coverage = dataset.get_coverage()[symbol_dimension].tolist()\n coverage = list(Utilities.map_identifiers(symbol_dimension, identifier, coverage).values())\n else:\n coverage = dataset.get_coverage()[symbol_dimension].tolist()\n\n return coverage\n\n\nclass SecmasterXrefFormatter:\n class EventType(Enum):\n START = \"start\"\n END = \"end\"\n\n @dataclass\n class Event:\n date: str\n event_type: 'SecmasterXrefFormatter.EventType'\n record: Dict[str, Any]\n\n def __post_init__(self):\n # For end events, we want them to be processed after start events on the same date\n # This ensures proper handling of adjacent periods\n self.priority = 1 if self.event_type == SecmasterXrefFormatter.EventType.END else 0\n\n INFINITY_DATE = \"9999-12-31\"\n INFINITY_MARKER = \"9999-99-99\"\n\n @staticmethod\n def convert(data: Dict[str, Any]) -> Dict[str, Dict[str, List[Dict[str, Any]]]]:\n results = {}\n for entity_key, records in data.items():\n xrefs = SecmasterXrefFormatter._convert_entity_records(records)\n results[entity_key] = {\"xrefs\": xrefs}\n return results\n\n @staticmethod\n def _convert_entity_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:\n \"\"\"\n Converts records using sweep-line algorithm for optimal time complexity.\n \"\"\"\n if not records:\n return []\n\n # Filter and normalize records\n normalized_records = []\n for record in records:\n normalized_record = record.copy()\n if normalized_record['endDate'] == SecmasterXrefFormatter.INFINITY_MARKER:\n normalized_record['endDate'] = SecmasterXrefFormatter.INFINITY_DATE\n normalized_records.append(normalized_record)\n\n if not normalized_records:\n return []\n\n # Create events for sweep-line algorithm\n events = SecmasterXrefFormatter._create_events(normalized_records)\n\n # Sort events by date, then by priority (end events after start events on same date)\n events.sort(key=lambda e: (SecmasterXrefFormatter._date_sort_key(e.date), e.priority))\n\n # Process events with sweep-line\n return SecmasterXrefFormatter._process_events(events)\n\n @staticmethod\n def _create_events(records: List[Dict[str, Any]]) -> List[Event]:\n \"\"\"\n Creates start and end events for each record.\n \"\"\"\n events = []\n\n for record in records:\n # Create start event\n events.append(\n SecmasterXrefFormatter.Event(\n date=record['startDate'], event_type=SecmasterXrefFormatter.EventType.START, record=record\n )\n )\n\n # Create end event (day after the actual end date)\n if record['endDate'] != SecmasterXrefFormatter.INFINITY_DATE:\n next_day = SecmasterXrefFormatter._add_one_day(record['endDate'])\n if next_day:\n events.append(\n SecmasterXrefFormatter.Event(\n date=next_day, event_type=SecmasterXrefFormatter.EventType.END, record=record\n )\n )\n\n return events\n\n @staticmethod\n def _process_events(events: List[Event]) -> List[Dict[str, Any]]:\n \"\"\"\n Processes events using sweep-line algorithm to generate time periods.\n \"\"\"\n periods = []\n active_identifiers = {} # type -> record mapping\n current_period_start = None\n\n i = 0\n while i < len(events):\n current_date = events[i].date\n current_date_events = []\n\n # Collect all events for the current date\n while i < len(events) and events[i].date == current_date:\n current_date_events.append(events[i])\n i += 1\n\n # Close current period if we have active identifiers\n if active_identifiers and current_period_start is not None:\n period_end = SecmasterXrefFormatter._subtract_one_day(current_date)\n periods.append(\n {\n \"startDate\": current_period_start,\n \"endDate\": period_end,\n \"identifiers\": {record['type']: record['value'] for record in active_identifiers.values()},\n }\n )\n\n # Process all events for this date\n # First process END events, then START events\n end_events = [e for e in current_date_events if e.event_type == SecmasterXrefFormatter.EventType.END]\n start_events = [e for e in current_date_events if e.event_type == SecmasterXrefFormatter.EventType.START]\n\n # Remove ending identifiers\n for event in end_events:\n identifier_type = event.record['type']\n if identifier_type in active_identifiers:\n del active_identifiers[identifier_type]\n\n # Add starting identifiers\n for event in start_events:\n identifier_type = event.record['type']\n active_identifiers[identifier_type] = event.record\n\n # Start new period if we have active identifiers\n if active_identifiers:\n current_period_start = current_date\n\n # Handle final period extending to infinity or latest end date\n if active_identifiers and current_period_start is not None:\n # Check if any active identifier has infinity end date\n has_infinity = any(\n record['endDate'] == SecmasterXrefFormatter.INFINITY_DATE for record in active_identifiers.values()\n )\n\n if has_infinity:\n period_end = SecmasterXrefFormatter.INFINITY_DATE\n else:\n # Find latest end date among active identifiers\n latest_end = max(record['endDate'] for record in active_identifiers.values())\n period_end = latest_end\n\n periods.append(\n {\n \"startDate\": current_period_start,\n \"endDate\": period_end,\n \"identifiers\": {record['type']: record['value'] for record in active_identifiers.values()},\n }\n )\n\n return periods\n\n @staticmethod\n def _date_sort_key(date_str: str) -> dt.datetime:\n if date_str == SecmasterXrefFormatter.INFINITY_DATE:\n return dt.datetime(9999, 12, 31)\n return dt.datetime.strptime(date_str, '%Y-%m-%d')\n\n @staticmethod\n def _add_one_day(date_str: str) -> str:\n try:\n if date_str == SecmasterXrefFormatter.INFINITY_DATE:\n return None\n date_obj = dt.datetime.strptime(date_str, '%Y-%m-%d')\n if date_obj.year == 9999 and date_obj.month == 12 and date_obj.day == 31:\n return None\n\n next_day = date_obj + dt.timedelta(days=1)\n return next_day.strftime('%Y-%m-%d')\n\n except (ValueError, OverflowError):\n return None\n\n @staticmethod\n def _subtract_one_day(date_str: str) -> str:\n try:\n date_obj = dt.datetime.strptime(date_str, '%Y-%m-%d')\n prev_day = date_obj - dt.timedelta(days=1)\n return prev_day.strftime('%Y-%m-%d')\n except (ValueError, OverflowError):\n return date_str\n" }, { "path": "gs_quant/datetime/__init__.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\nfrom .date import *\nfrom .time import *\nfrom .gscalendar import *\nfrom .point import *\n\n__name__ = 'datetime'\n" }, { "path": "gs_quant/datetime/date.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport calendar as cal\nimport datetime as dt\nimport zoneinfo\nfrom enum import Enum, IntEnum\nfrom typing import Iterable, Optional, Tuple, Union\n\nimport numpy as np\n\nfrom gs_quant.common import PricingLocation\nfrom gs_quant.datetime.gscalendar import GsCalendar\n\nDateOrDates = Union[dt.date, Iterable[dt.date]]\n\nlocation_to_tz_mapping = {\n PricingLocation.NYC: zoneinfo.ZoneInfo(\"America/New_York\"),\n PricingLocation.LDN: zoneinfo.ZoneInfo(\"Europe/London\"),\n PricingLocation.HKG: zoneinfo.ZoneInfo(\"Asia/Hong_Kong\"),\n PricingLocation.TKO: zoneinfo.ZoneInfo(\"Asia/Tokyo\"),\n}\n\n\nclass PaymentFrequency(IntEnum):\n \"\"\"Payment frequency enumeration\n\n Provides an enumeration of different payment frequencies used to to discount cashflows and accrue interest\n\n \"\"\"\n\n DAILY = 252\n WEEKLY = 52\n SEMI_MONTHLY = 26\n MONTHLY = 12\n SEMI_QUARTERLY = 6\n QUARTERLY = 4\n TRI_ANNUALLY = 3\n SEMI_ANNUALLY = 2\n ANNUALLY = 1\n\n\nclass DayCountConvention(Enum):\n \"\"\"Day Count Convention enumeration\n\n Provides an enumeration of different day count conventions for determining how interest accrues over payment periods\n for financial securities\n\n \"\"\"\n\n # Actual/360: Number of days between dates divided by 360\n ACTUAL_360 = \"ACTUAL_360\"\n\n # Actual/364: Number of days between dates divided by 364\n ACTUAL_364 = \"ACTUAL_364\"\n\n # Actual/365_25: Number of days between dates divided by 365.25\n ACTUAL_365_25 = \"ACTUAL_365_25\"\n\n # Actual/365 FIXED: Number of days between dates divided by 365\n ACTUAL_365F = \"ACTUAL_365F\"\n\n # Actual/365 LEAP: Number of days between dates divided by 365 or 366 in leap years\n ACTUAL_365L = \"ACTUAL_365L\"\n\n # ONE_ONE: Always returns a day count fraction of 1\n ONE_ONE = \"ONE_ONE\"\n\n\ndef is_business_day(\n dates: DateOrDates, calendars: Union[str, Tuple[str, ...]] = (), week_mask: Optional[str] = None\n) -> Union[bool, Tuple[bool, ...]]:\n \"\"\"\n Determine whether each date in dates is a business day\n\n :param dates: The input date or dates\n :param calendars: Calendars to use for holidays\n :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n :return: True/False if dates is a single date. A tuple indicating True/False for each date if dates is an iterable\n\n **Examples**\n\n >>> import datetime as dt\n >>> is_business_day(dt.date.today())\n >>> is_business_day(dt.date(2019, 7, 4), calendars=('NYSE',))\n \"\"\"\n calendar = GsCalendar.get(calendars)\n res = np.is_busday(dates, busdaycal=calendar.business_day_calendar(week_mask))\n return tuple(res) if isinstance(res, np.ndarray) else res\n\n\ndef business_day_offset(\n dates: DateOrDates,\n offsets: Union[int, Iterable[int]],\n roll: str = 'raise',\n calendars: Union[str, Tuple[str, ...]] = (),\n week_mask: Optional[str] = None,\n) -> DateOrDates:\n \"\"\"\n Apply offsets to the dates and move to the nearest business date\n\n :param dates: The input date or dates\n :param offsets: The number of days by which to adjust the dates\n :param roll: Which direction to roll, in order to get to the nearest business date\n :param calendars: Calendars to use for holidays\n :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n :return: A date (if dates is a single date) or tuple of dates, adjusted by the offsets\n\n **Examples**\n\n >>> import datetime as dt\n >>> prev_bus_date = business_day_offset(dt.date.today(), -1, roll='forward')\n \"\"\"\n calendar = GsCalendar.get(calendars)\n res = np.busday_offset(dates, offsets, roll, busdaycal=calendar.business_day_calendar(week_mask)).astype(dt.date)\n return tuple(res) if isinstance(res, np.ndarray) else res\n\n\ndef prev_business_date(\n dates: DateOrDates = dt.date.today(), calendars: Union[str, Tuple[str, ...]] = (), week_mask: Optional[str] = None\n) -> DateOrDates:\n \"\"\"\n Returns the previous business date for a given date or date series, defaulting to today.\n\n :param dates: The input date or dates, defaults to today\n :param calendars: Calendars to use for holidays\n :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n :return: A date (if dates is a single date) or tuple of dates, adjusted by the offset of one day.\n\n **Example**\n\n >>> import datetime as dt\n >>> prev_bus_date = prev_business_date()\n \"\"\"\n return business_day_offset(dates, -1, roll='forward', calendars=calendars, week_mask=week_mask)\n\n\ndef business_day_count(\n begin_dates: DateOrDates,\n end_dates: DateOrDates,\n calendars: Union[str, Tuple[str, ...]] = (),\n week_mask: Optional[str] = None,\n) -> Union[int, Tuple[int, ...]]:\n \"\"\"\n Determine the number of business days between begin_dates and end_dates\n\n :param begin_dates: A date or collection of beginning dates\n :param end_dates: A date or collection of end dates\n :param calendars: Calendars to use for holidays\n :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n :return: An int or tuple of ints, representing the number of business days between begin_dates and end_dates\n\n **Examples**\n\n >>> import datetime as dt\n >>> today = dt.date.today()\n >>> bus_days = business_day_count(today, today + dt.timedelta(days=7))\n \"\"\"\n calendar = GsCalendar.get(calendars)\n res = np.busday_count(begin_dates, end_dates, busdaycal=calendar.business_day_calendar(week_mask))\n return tuple(res) if isinstance(res, np.ndarray) else res\n\n\ndef date_range(\n begin: Union[int, dt.date],\n end: Union[int, dt.date],\n calendars: Union[str, Tuple[str, ...]] = (),\n week_mask: Optional[str] = None,\n) -> Iterable[dt.date]:\n \"\"\"\n Construct a range of dates\n\n :param begin: Beginning date or int. An int will be interpreted as the number of business days before end\n (which must be a date)\n :param end: End date or int. An int will be interpreted as the number of business days after begin\n (which must be a date)\n :param calendars: Calendars to use for holidays\n :param week_mask: Which days are considered weekends (defaults to Saturday and Sunday)\n :return: A generator of dates\n\n >>> import datetime as dt\n >>> today = dt.date.today()\n >>> dates = tuple(date_range(5, today))\n >>>\n >>> for date in date_range(dt.date(2019, 1, 1), dt.date(2019, 2, 1)):\n >>> print(date)\n \"\"\"\n if isinstance(begin, dt.date):\n if isinstance(end, dt.date):\n\n def f():\n prev = begin\n if prev > end:\n raise ValueError('begin must be <= end')\n\n while prev <= end:\n yield prev\n prev = business_day_offset(prev, 1, calendars=calendars, week_mask=week_mask)\n\n return (d for d in f())\n elif isinstance(end, int):\n return (business_day_offset(begin, i, calendars=calendars, week_mask=week_mask) for i in range(end))\n else:\n raise ValueError('end must be a date or int')\n elif isinstance(begin, int):\n if isinstance(end, dt.date):\n return (\n business_day_offset(end, -i, roll='preceding', calendars=calendars, week_mask=week_mask)\n for i in range(begin)\n )\n else:\n raise ValueError('end must be a date if begin is an int')\n else:\n raise ValueError('begin must be a date or int')\n\n\ndef today(location: Optional[PricingLocation] = None) -> dt.date:\n if not location:\n return dt.date.today()\n\n tz = location_to_tz_mapping.get(location, None)\n\n if tz is None:\n raise ValueError(f'Unrecognized timezone {location}')\n\n return dt.datetime.now(tz).date()\n\n\ndef has_feb_29(start: dt.date, end: dt.date):\n \"\"\"\n Determine if date range has a leap day (29Feb)\n\n :param start: first date\n :param end: second date\n\n **Usage**\n\n Determine if a given date range contains a leap day (Feb 29). Used for various day count convention calculations\n which alter behaviour for leap years. Start date is exclusive and end date is inclusive\n\n **Examples**\n\n Determine if a given date range contains 29Feb\n\n >>> start = date(2020, 1, 1)\n >>> end = date(2020, 3, 15)\n >>> has_feb_29(start, end)\n \"\"\"\n feb_29 = False\n for x in range(1, (end - start).days + 1):\n date = start + dt.timedelta(days=x)\n feb_29 = feb_29 | (date.month == 2 and date.day == 29)\n\n return feb_29\n\n\ndef day_count_fraction(\n start: dt.date, # First payment date\n end: dt.date, # Second payment date\n convention: DayCountConvention = DayCountConvention.ACTUAL_360,\n frequency: PaymentFrequency = PaymentFrequency.MONTHLY,\n):\n \"\"\"\n Compute day count fraction between dates\n\n :param start: first date\n :param end: second date\n :param convention: day count convention\n :param frequency: payment frequency of instrument\n :return: day count fraction between dates per convention\n\n **Usage**\n\n Compute day count fraction between dates, based on the value of *convention*. For more information on the available\n day count conventions, see the\n `Day Count Conventions `_ guide.\n\n **Examples**\n\n Compute day count fraction between two dates using Actual/360 convention:\n\n >>> start = date(2015, 11, 12)\n >>> end = date(2017, 12, 15)\n >>> day_count_fraction(start, end, DayCountConvention.ACTUAL_360)\n\n \"\"\"\n\n if convention == DayCountConvention.ACTUAL_360:\n return (end - start).days / 360\n elif convention == DayCountConvention.ACTUAL_364:\n return (end - start).days / 364\n elif convention == DayCountConvention.ACTUAL_365F:\n return (end - start).days / 365\n elif convention == DayCountConvention.ACTUAL_365L:\n if frequency == PaymentFrequency.ANNUALLY:\n days_in_year = 366 if has_feb_29(start, end) else 365\n else:\n days_in_year = 366 if cal.isleap(end.year) else 365\n return (end - start).days / days_in_year\n elif convention == DayCountConvention.ACTUAL_365_25:\n return (end - start).days / 365.25\n elif convention == DayCountConvention.ONE_ONE:\n return 1\n else:\n raise ValueError('Unknown day count convention: ' + convention.value)\n" }, { "path": "gs_quant/datetime/gscalendar.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom enum import Enum, EnumMeta\nfrom threading import Lock\nfrom typing import Tuple, Union, List\n\nimport numpy as np\nfrom cachetools import TTLCache, cached\nfrom cachetools.keys import hashkey\n\nfrom gs_quant.common import PricingLocation, Currency\nfrom gs_quant.data import Dataset\nfrom gs_quant.errors import MqRequestError\n\n_logger = logging.getLogger(__name__)\n\n_calendar_cache = TTLCache(maxsize=128, ttl=600)\n_coverage_cache = TTLCache(maxsize=128, ttl=3600)\n\n\ndef _split_list(items, predicate) -> Tuple[Tuple[str, ...], Tuple[str, ...]]:\n true_res = []\n false_res = []\n for item in items:\n item_str = item.value if isinstance(item, (Enum, EnumMeta)) else item.upper()\n if predicate(item):\n true_res.append(item_str)\n else:\n false_res.append(item_str)\n return tuple(true_res), tuple(false_res)\n\n\nclass GsCalendar:\n DATE_LOW_LIMIT = dt.date(1952, 1, 1)\n DATE_HIGH_LIMIT = dt.date(2052, 12, 31)\n DEFAULT_WEEK_MASK = '1111100' # Default to Sat, Sun weekend days\n\n def __init__(self, calendars: Union[str, PricingLocation, Currency, Tuple[str, ...]] = (), skip_valid_check=True):\n if isinstance(calendars, (str, PricingLocation, Currency)):\n calendars = (calendars,)\n if calendars is None:\n calendars = ()\n self.__calendars = calendars\n self.__business_day_calendars = {}\n self._skip_valid_check = skip_valid_check\n\n @staticmethod\n def get(calendars: Union[str, Tuple], skip_valid_check=True):\n return GsCalendar(calendars, skip_valid_check)\n\n @staticmethod\n def reset():\n _calendar_cache.clear()\n\n def calendars(self) -> Tuple:\n return self.__calendars\n\n @staticmethod\n def is_currency(currency: Union[str, PricingLocation, Currency]) -> bool:\n if isinstance(currency, Currency):\n return True\n if isinstance(currency, PricingLocation):\n return False\n try:\n _ = Currency(currency.upper())\n return True\n except (ValueError, AttributeError):\n return False\n\n @cached(_coverage_cache, key=lambda s, d, q: d.id, lock=Lock())\n def _get_dataset_coverage(self, dataset: Dataset, query_key: str):\n coverage_df = dataset.get_coverage()\n coverage = set() if coverage_df.empty else set(coverage_df[query_key])\n return coverage\n\n def holidays_from_dataset(self, dataset: Dataset, query_key: str, query_values: Tuple[str, ...]) -> List[dt.date]:\n if not len(query_values):\n return []\n coverage = self._get_dataset_coverage(dataset, query_key)\n for item in query_values:\n if item not in coverage:\n if self._skip_valid_check:\n _logger.warning(\n f'Ignoring invalid calendar {item}. This will throw in future versions of gs-quant.'\n )\n else:\n raise ValueError(f'Invalid calendar {item}')\n try:\n data = dataset.get_data(**{query_key: query_values}, start=self.DATE_LOW_LIMIT, end=self.DATE_HIGH_LIMIT)\n if not data.empty:\n return [d.date() for d in data.index.to_pydatetime()]\n except MqRequestError:\n pass\n return []\n\n @property\n @cached(_calendar_cache, key=lambda s: hashkey(str(s.__calendars)), lock=Lock())\n def holidays(self) -> Tuple[dt.date, ...]:\n currencies, exchanges = _split_list(self.__calendars, GsCalendar.is_currency)\n holidays = self.holidays_from_dataset(Dataset(Dataset.GS.HOLIDAY), 'exchange', exchanges)\n holidays = holidays + self.holidays_from_dataset(Dataset(Dataset.GS.HOLIDAY_CURRENCY), 'currency', currencies)\n holidays = tuple(set(holidays))\n return holidays\n\n def business_day_calendar(self, week_mask: str = None) -> np.busdaycalendar:\n return self.__business_day_calendars.setdefault(\n week_mask,\n np.busdaycalendar(\n weekmask=week_mask or self.DEFAULT_WEEK_MASK,\n holidays=tuple([np.datetime64(d.isoformat()) for d in self.holidays]),\n ),\n )\n" }, { "path": "gs_quant/datetime/point.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport functools\nimport re\nimport string\nfrom typing import Optional\n\nfrom gs_quant.errors import MqValueError\n\nConstPoints = {\n \"O/N\": 0,\n \"T/N\": 0.1,\n \"OIS FIX\": 1,\n \"CASH STUB\": 1.1,\n \"CASHSTUB\": 1.1,\n \"DEFAULT\": 0,\n \"IN\": 0.1,\n \"OUT\": 0.2,\n}\n\n# Regular expression for different types of market data coordinate points\nEuroOrFraReg = (\n r\"^(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec|JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)+([0-9][0-9])$\"\n)\nNumberReg = r\"^([0-9]*)$\"\nMMMYYYYReg = r\"^([a-zA-Z]{3}[0-9]{4})$\"\nDDMMMYYYYReg = r\"^([1-3]*[0-9]{1}[a-zA-Z]{3}[0-9]{4})$\"\nSpikeQEReg = r\"^(QE[0-9])-([0-9]{4})$\"\nFRAxReg = r\"^([0-9]+)x([0-9]+)$\"\nRDatePartReg = r\"^([-]*[0-9]+[mydwbfMYDWBF])([-]*[0-9]+[mydwbfMYDWBF])?$\"\nCashFXReg = r\"^([-]*[0-9]+[mydwbfMYDWBF])([-]*[0-9]+[mydwbfMYDWBF])? XC$\"\nPricerCoordRegI = r\"^(No )([0-9]*)$\"\nPricerCoordRegII = r\"^(Pricer )([0-9]*)$\"\nPricerBFReg = r\"^([-]*[0-9]+[mydwbfMYDWBFM])([-]*[0-9]+[mydwbfMYDWBF])([-]*[0-9]+[mydwbfMYDWBF])?$\"\nPricerBondSpreadReg = r\"^[0-9][SQHT]([0-9]{2})[/][0-9][SQHT]([0-9]{2})\"\nSeasonalFrontReg = r\"(Front|Back)\"\ninfl_volReg = r\"(Caplet|ZCCap|Swaption|ZCSwo)\"\nMMMReg = r\"^(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)$\"\nMMMYYReg = r\"^(JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC) ([0-9]{2})\"\nDatePairReg = r\"^([0-9]{8})/([0-9]{8})$\"\nDatePairReg2 = r\"^([0-9]{8}) ([0-9]{8})$\"\nFXVolAddonParmsReg = (\n r\"(Spread Addon|Spread Init|Spread Final|Rho Addon|Rho Init|Rho Final|Vol Addon|Vol Init|Vol Final|HL|Addon HL)\"\n)\nCopulaReg = r\"(Rho$|Rho Rate|Rho Vol|RR|BF|Alpha|Beta|KO$|K0=L|K0=S)\"\nBondCoordReg = r\"^[0-9]* ([0-9.]*) ([0-9]{2}/[0-9]{2}/[0-9]{4})$\"\nBondFutReg = r\"^[A-Z]{3}([FGHJKMNQUVXZ])([0-9])$\"\nFFFutReg = r\"^FF([FGHJKMNQUVXZ])([0-9])$\"\nRepoGCReg = r\"^(ON|SN|TN|[0-9]+) (|Month |Week |Year |Day )GC$\"\nFloatingYear = r\"^([0-9]*\\.[0-9])[yY]$\"\nRelativeReg = r\"^([0-9]+) (day|week|month|year|DAY|WEEK|MONTH|YEAR)$\"\n\nLYYReg = r\"([FGHJKMNQUVXZ])([0-9]{2})\"\nDateRuleReg = r\"^([-]*[0-9]+[mydwbfMYDWBFM])+$\"\nDDMMMYYReg = r\"^([0-3]*[0-9]{1}[a-zA-Z]{3}[0-9]{2})$\"\n\nFutMonth = r\"FGHJKMNQUVXZ\"\n\nDictDayRule = {\n 'Month': 30,\n 'MONTH': 30,\n 'Week': 7,\n 'WEEK': 7,\n 'Year': 365,\n 'YEAR': 365,\n 'Day': 1,\n 'DAY': 1,\n 'd': 1,\n 'D': 1,\n 'w': 7,\n 'W': 7,\n 'b': 1,\n 'B': 1,\n 'f': 30,\n 'F': 30,\n 'm': 30,\n 'M': 30,\n 'y': 365,\n 'Y': 365,\n}\n\n\ndef relative_date_add(date_rule: str, strict: bool = False) -> float:\n \"\"\"Change the string in date rule format to the number of days. E.g 1d to 1, 1y to 365, 1m to 30, -1w to -7\"\"\"\n days = ''\n\n if re.search(DateRuleReg, date_rule) is not None:\n res = re.search(DateRuleReg, date_rule)\n date_str = res.group(1)\n if date_str[0] == '-':\n num = float(date_str[1:-1])\n days = '-'\n else:\n num = float(date_str[:-1])\n rule = date_str[-1:]\n if rule in DictDayRule:\n scale = DictDayRule[rule]\n days = days + str(num * scale)\n d = float(days)\n return d\n else:\n raise MqValueError('There are no valid day rule for the point provided.')\n\n if strict:\n raise MqValueError(f'invalid date rule {date_rule}')\n return 0\n\n\n@functools.lru_cache(maxsize=None)\ndef point_sort_order(point: str, ref_date: Optional[dt.date] = None) -> Optional[float]:\n \"\"\"\n Calculates a number that can be used to sort Mkt Points by it.\n\n :param point: The point string from MarketDataCoordinate.\n :param ref_date: Reference date, normally the pricing date.\n :return: The number of days from the reference date to the date specified by the point string\n\n **Examples**\n\n >>> import datetime as dt\n >>> days = point_sort_order(point = 'Dec20', ref_date=dt.date.today())\n \"\"\"\n\n ref_date = dt.date.today() if ref_date is None else ref_date\n\n if not point or not isinstance(point, str):\n return 0\n\n const_value = ConstPoints.get(point.upper())\n if const_value is not None:\n return const_value\n\n parts = [p.strip() for p in point.split(';')]\n if len(parts) > 1:\n first = point_sort_order(parts[0])\n if not first:\n return 0\n\n return first + (0.1 * sum(point_sort_order(p, ref_date) for p in parts[1:]) / first)\n\n days = None\n\n if point == 'o/n':\n days = 0\n elif point == 't/n':\n days = 0.1\n elif point == 'Cash Stub':\n days = 1.1\n elif point == 'CashStub':\n days = 1.1\n elif point == 'Default':\n days = 0\n elif point == 'In':\n days = 0.1\n elif point == 'Out':\n days = 0.2\n elif re.search(infl_volReg, point) is not None:\n res = re.search(infl_volReg, point)\n infl_vol = res.group(1)\n if infl_vol == 'Caplet':\n days = 0\n elif infl_vol == 'ZCCap':\n days = 1\n elif infl_vol == 'Swaption':\n days = 2\n elif infl_vol == 'ZCSwo':\n days = 3\n elif re.search(CopulaReg, point) is not None:\n pass\n elif re.search(SeasonalFrontReg, point) is not None:\n res = re.search(SeasonalFrontReg, point)\n if res.group(1) == 'Front':\n days = 0\n else:\n days = 1\n elif re.search(MMMReg, point) is not None:\n res = re.search(MMMReg, point)\n date_str = '1' + res.group(1) + '2000'\n format_str = '%d%b%Y'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(EuroOrFraReg, point) is not None:\n res = re.search(EuroOrFraReg, point)\n date_str = '15' + res.group(1) + res.group(2)\n format_str = '%d%b%y'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(RDatePartReg, point) is not None:\n res = re.search(RDatePartReg, point)\n date_str = res.group(1)\n days = relative_date_add(date_str)\n elif re.search(CashFXReg, point) is not None:\n res = re.search(CashFXReg, point)\n date_str = res.group(1)\n days = relative_date_add(date_str)\n elif re.search(PricerBFReg, point) is not None:\n res = re.search(PricerBFReg, point)\n date_str = res.group(1)\n days = relative_date_add(date_str)\n elif re.search(FRAxReg, point) is not None:\n res = re.search(FRAxReg, point)\n date_str = res.group(1) + 'm'\n days = relative_date_add(date_str)\n elif re.search(SpikeQEReg, point) is not None:\n res = re.search(SpikeQEReg, point)\n qe = res.group(1)\n if qe == 'QE1':\n month = \"Mar\"\n elif qe == 'QE2':\n month = \"Jun\"\n elif qe == 'QE3':\n month = \"Sep\"\n elif qe == 'QE4':\n month = 'Dec'\n else:\n month = 'Dec'\n date_str = \"1\" + month + res.group(2)\n format_str = '%d%b%Y'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(MMMYYYYReg, point) is not None:\n res = re.search(MMMYYYYReg, point)\n date_str = '1' + res.group(1)\n format_str = '%d%b%Y'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(DDMMMYYYYReg, point) is not None:\n res = re.search(DDMMMYYYYReg, point)\n date_str = res.group(1)\n format_str = '%d%b%Y'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(NumberReg, point) is not None:\n res = re.search(NumberReg, point)\n days = float(res.group(1))\n elif re.search(FloatingYear, point) is not None:\n res = re.search(FloatingYear, point)\n year = float(res.group(1))\n days = 365 * year\n elif re.search(PricerCoordRegI, point) is not None:\n res = re.search(PricerCoordRegI, point)\n days = float(res.group(2))\n elif re.search(PricerCoordRegII, point) is not None:\n res = re.search(PricerCoordRegII, point)\n days = float(res.group(2))\n elif re.search(PricerBondSpreadReg, point) is not None:\n pass\n elif re.search(LYYReg, point) is not None:\n res = re.search(LYYReg, point)\n month = FutMonth.find(res.group(1)) + 1\n year = res.group(2)\n date_str = year + '-' + str(month) + '-1'\n format_str = '%y-%m-%d'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(DatePairReg, point) is not None:\n res = re.search(DatePairReg, point)\n date_str = res.group(2)\n format_str = '%Y%m%d'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(DatePairReg2, point) is not None:\n res = re.search(DatePairReg2, point)\n date_str = res.group(2)\n format_str = '%Y%m%d'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(MMMYYReg, point) is not None:\n res = re.search(MMMYYReg, point)\n date_str = '1' + res.group(1) + res.group(2)\n format_str = '%d%b%y'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(FXVolAddonParmsReg, point) is not None:\n pass\n elif re.search(BondCoordReg, point) is not None:\n res = re.search(BondCoordReg, point)\n date_str = res.group(2)\n format_str = '%d/%m/%Y'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(BondFutReg, point) is not None:\n res = re.search(BondFutReg, point)\n month = FutMonth.find(res.group(1)) + 1\n date_str = str(ref_date.year) + '-' + str(month) + '-1'\n format_str = '%Y-%m-%d'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(FFFutReg, point) is not None:\n res = re.search(FFFutReg, point)\n month = FutMonth.find(res.group(1)) + 1\n date_str = str(ref_date.year) + '-' + str(month) + '-1'\n format_str = '%Y-%m-%d'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n elif re.search(RepoGCReg, point) is not None:\n res = re.search(RepoGCReg, point)\n if point == 'ON GC':\n days = 0\n elif point == 'TN GC':\n days = 1\n elif point == 'SN GC':\n days = 2\n elif res.group(2).strip() in DictDayRule:\n scale = DictDayRule[res.group(2).strip()]\n num = float(res.group(1))\n days = num * scale\n elif re.search(RelativeReg, point) is not None:\n res = re.search(RelativeReg, point)\n rule = string.capwords(res.group(2))\n if rule in DictDayRule:\n scale = DictDayRule[rule]\n num = float(res.group(1))\n days = num * scale\n elif re.search(DDMMMYYReg, point) is not None:\n res = re.search(DDMMMYYReg, point)\n date_str = res.group(1)\n format_str = '%d%b%y'\n days = (dt.datetime.strptime(date_str, format_str).date() - ref_date).days\n else:\n days = 0\n return days\n" }, { "path": "gs_quant/datetime/relative_date.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport logging\nfrom copy import copy\nfrom typing import Union, Optional, List\n\nimport pandas as pd\n\nimport gs_quant.datetime.rules as rules\nfrom gs_quant.common import Currency\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.securities import ExchangeCode\n\n_logger = logging.getLogger(__name__)\n\n\nclass RelativeDate:\n \"\"\"\n RelativeDates are objects which provide utilities for getting dates given a relative date rule.\n Some rules require a business day calendar.\n\n :param rule: Rule to use\n :param base_date: Base date to use (Optional).\n :return: new RelativeDate object\n\n **Usage**\n\n Create a RelativeDate object and then call `apply_rule` to get a date back.\n\n **Examples**\n\n RelativeDate to return relative previous day:\n\n >>> my_date: date = RelativeDate('-1d').apply_rule()\n\n **Documentation**\n\n Full Documentation and examples can be found here:\n https://developer.gs.com/docs/gsquant/api/datetime.html\n\n \"\"\"\n\n def __init__(self, rule: str, base_date: Optional[dt.date] = None):\n self.rule = rule\n self.base_date_passed_in = False\n if base_date:\n self.base_date = base_date\n self.base_date_passed_in = True\n elif PricingContext.current.is_entered:\n pricing_date = PricingContext.current.pricing_date\n self.base_date = pricing_date\n else:\n self.base_date = dt.date.today()\n self.base_date = (\n self.base_date.date() if isinstance(self.base_date, (dt.datetime, pd.Timestamp)) else self.base_date\n )\n\n def apply_rule(\n self,\n currencies: List[Union[Currency, str]] = None,\n exchanges: List[Union[ExchangeCode, str]] = None,\n holiday_calendar: List[dt.date] = None,\n week_mask: str = '1111100',\n **kwargs,\n ) -> dt.date:\n \"\"\"\n Applies business date logic on the rule using the given holiday calendars for rules that use business\n day logic. week_mask is based off\n https://numpy.org/doc/stable/reference/generated/numpy.busdaycalendar.weekmask.html.\n\n :param holiday_calendar: Optional list of date to use for holiday calendar. This parameter takes precedence over\n currencies/exchanges.\n :param currencies: List of currency holiday calendars to use. (GS Internal only)\n :param exchanges: List of exchange holiday calendars to use.\n :param week_mask: String of seven-element boolean mask indicating valid days. Default weekend is Sat and Sun.\n :return: dt.date\n \"\"\"\n\n result = copy(self.base_date)\n\n for rule in self._get_rules():\n result = self.__handle_rule(\n rule,\n result,\n week_mask,\n currencies=currencies,\n exchanges=exchanges,\n holiday_calendar=holiday_calendar,\n **kwargs,\n )\n\n return result\n\n def _get_rules(self) -> List[str]:\n rule_list = []\n current_rule = ''\n if not len(self.rule):\n raise MqValueError('Invalid Rule \"\"')\n current_alpha = self.rule[0].isalpha()\n for c in self.rule:\n is_alpha = c.isalpha()\n if current_alpha and not is_alpha:\n if current_rule.startswith('+'):\n rule_list.append(current_rule[1:])\n else:\n rule_list.append(current_rule)\n current_rule = ''\n current_alpha = False\n if is_alpha:\n current_alpha = True\n current_rule += c\n if current_rule.startswith('+'):\n rule_list.append(current_rule[1:])\n else:\n rule_list.append(current_rule)\n return rule_list\n\n def __handle_rule(\n self,\n rule: str,\n result: dt.date,\n week_mask: str,\n currencies: List[Union[Currency, str]] = None,\n exchanges: List[Union[ExchangeCode, str]] = None,\n holiday_calendar: List[dt.date] = None,\n **kwargs,\n ) -> dt.date:\n sign = \"+\"\n if rule.startswith('-'):\n index = 1\n while index != len(rule) and rule[index].isdigit():\n index += 1\n number = int(rule[1:index]) * -1 if index < len(rule) else 0\n rule_str = rule[index]\n sign = \"-\"\n else:\n index = 0\n if not rule[0].isdigit():\n rule_str = rule\n number = 0\n else:\n while index != len(rule) and rule[index].isdigit():\n index += 1\n if index < len(rule):\n number = int(rule[0:index])\n rule_str = rule[index]\n else:\n rule_str = rule\n number = 0\n\n if not rule_str:\n raise MqValueError(f'Invalid rule \"{rule}\"')\n\n roll = kwargs.get('roll_convention')\n\n try:\n rule_class = getattr(rules, f'{rule_str}Rule')\n return rule_class(\n result,\n results=result,\n number=number,\n week_mask=week_mask,\n currencies=currencies,\n exchanges=exchanges,\n holiday_calendar=holiday_calendar,\n usd_calendar=kwargs.get('usd_calendar'),\n roll=roll,\n sign=sign,\n ).handle()\n except AttributeError:\n raise NotImplementedError(f'Rule {rule} not implemented')\n\n def as_dict(self):\n rdate_dict = {'rule': self.rule}\n if self.base_date_passed_in:\n rdate_dict['baseDate'] = str(self.base_date)\n return rdate_dict\n\n\nclass RelativeDateSchedule:\n \"\"\"\n RelativeDatesSchedules are objects which wrap a RelativeDate to provide a schedule between two dates\n Some rules require a business day calendar.\n\n :param rule: Rule to use\n :param base_date: Base date to use (Optional).\n :param end_date: No dates past this date will be returned (Optional).\n :return: new RelativeDateSchedule object\n\n **Usage**\n\n Create a RelativeDateSchedule object and then call `apply_rule` to get a date schedule back.\n\n **Examples**\n\n RelativeDateSchedule to return a schedule from today to 1w in the future\n\n >>> my_date: date = RelativeDateSchedule('1w', datetime.date.today(), ).apply_rule()\n\n \"\"\"\n\n def __init__(self, rule: str, base_date: Optional[dt.date] = None, end_date: Optional[dt.date] = None):\n self.rule = rule\n self.base_date_passed_in = False\n if base_date:\n self.base_date = base_date\n self.base_date_passed_in = True\n elif PricingContext.current.is_entered:\n pricing_date = PricingContext.current.pricing_date\n self.base_date = (\n pricing_date.date() if isinstance(pricing_date, (dt.datetime, pd.Timestamp)) else pricing_date\n )\n else:\n self.base_date = dt.date.today()\n self.end_date = end_date\n\n def apply_rule(\n self,\n currencies: List[Union[Currency, str]] = None,\n exchanges: List[Union[ExchangeCode, str]] = None,\n holiday_calendar: List[dt.date] = None,\n week_mask: str = '1111100',\n **kwargs,\n ) -> List[dt.date]:\n \"\"\"\n Applies business date logic on the rule using the given holiday calendars for rules that use business\n day logic. week_mask is based off\n https://numpy.org/doc/stable/reference/generated/numpy.busdaycalendar.weekmask.html.\n\n :param holiday_calendar: Optional list of date to use for holiday calendar. This parameter takes precedence over\n currencies/exchanges.\n :param currencies: List of currency holiday calendars to use. (GS Internal only)\n :param exchanges: List of exchange holiday calendars to use.\n :param week_mask: String of seven-element boolean mask indicating valid days. Default weekend is Sat and Sun.\n :return: dt.date\n \"\"\"\n\n i = 1\n schedule = [self.base_date]\n while True:\n rule = f'{int(self.rule[:-1]) * i}{self.rule[-1]}'\n result = RelativeDate(rule, self.base_date).apply_rule(\n currencies, exchanges, holiday_calendar, week_mask, **kwargs\n )\n if self.end_date is None or result > self.end_date:\n break\n i += 1\n schedule.append(result)\n\n return schedule\n\n def as_dict(self):\n rdate_dict = {'rule': self.rule}\n if self.base_date_passed_in:\n rdate_dict['baseDate'] = str(self.base_date)\n rdate_dict['endDate'] = str(self.end_date)\n return rdate_dict\n" }, { "path": "gs_quant/datetime/rules.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport calendar\nimport logging\nfrom abc import ABC, abstractmethod\nfrom typing import List, Union, Optional\n\nfrom dateutil.relativedelta import relativedelta, FR, SA, SU, TH, TU, WE, MO\nimport numpy as np\nimport pandas as pd\n\nfrom gs_quant.datetime.gscalendar import GsCalendar\nfrom gs_quant.markets.securities import ExchangeCode\nfrom gs_quant.common import Currency\n\n_logger = logging.getLogger(__name__)\n\n\nclass RDateRule(ABC):\n result: dt.date\n number: int\n week_mask: str\n currencies: List[Union[Currency, str]] = None\n exchanges: List[Union[ExchangeCode, str]] = None\n holiday_calendar: List[dt.date] = None\n sign: Optional[str] = None\n\n def __init__(self, result: dt.date, **params):\n self.result = result\n self.number = params.get('number')\n self.week_mask = params.get('week_mask')\n self.currencies = params.get('currencies')\n self.exchanges = params.get('exchanges')\n self.holiday_calendar = params.get('holiday_calendar')\n self.usd_calendar = params.get('usd_calendar')\n self.roll = params.get('roll')\n self.sign = params.get('sign')\n super().__init__()\n\n @abstractmethod\n def handle(self) -> dt.date:\n \"\"\"\n Handle RDate Rule. Use any available class field to compute.\n :return: date\n \"\"\"\n pass\n\n def _get_holidays(self) -> List[dt.date]:\n if self.holiday_calendar is not None:\n if self.usd_calendar is None:\n return self.holiday_calendar\n return list(set().union(self.holiday_calendar, self.usd_calendar))\n try:\n currencies = (\n []\n if self.currencies is None\n else [self.currencies]\n if isinstance(self.currencies, str)\n else self.currencies\n )\n exchanges = (\n []\n if self.exchanges is None\n else [self.exchanges]\n if isinstance(self.exchanges, str)\n else self.exchanges\n )\n cal = GsCalendar(exchanges + currencies)\n return cal.holidays\n except Exception as e:\n _logger.warning('Unable to fetch holiday calendar. Try passing your own when applying a rule. {}'.format(e))\n return []\n\n def _apply_business_days_logic(self, holidays: List[dt.date], offset: int = None, roll: str = 'preceding'):\n if offset is not None:\n offset_to_use = offset\n else:\n offset_to_use = self.number if self.number else 0\n return pd.to_datetime(\n np.busday_offset(self.result, offset_to_use, roll, holidays=holidays, weekmask=self.week_mask)\n ).date()\n\n def _get_nth_day_of_month(self, calendar_day):\n temp = self.result.replace(day=1)\n adj = (calendar_day - temp.weekday()) % 7\n temp += relativedelta(days=adj)\n temp += relativedelta(weeks=self.number - 1)\n return temp\n\n def add_years(self, holidays: List[dt.date]):\n self.result = self.result + relativedelta(years=self.number)\n if self.result.isoweekday() in {6, 7}:\n self.result += dt.timedelta(days=self.result.isoweekday() % 5)\n return self._apply_business_days_logic(holidays, offset=0)\n\n @staticmethod\n def is_weekend(d: dt.date):\n return False if d.weekday() < 5 else True # 5 Sat, 6 Sun\n\n def roll_convention(self, default=None):\n return self.roll or default\n\n\nclass ARule(RDateRule):\n def handle(self) -> dt.date:\n result = self.result.replace(month=1, day=1)\n return result + relativedelta(year=self.number)\n\n\nclass bRule(RDateRule):\n def handle(self) -> dt.date:\n holidays = self._get_holidays()\n roll = self.roll_convention('forward' if self.number <= 0 else 'preceding')\n return self._apply_business_days_logic(holidays, offset=self.number, roll=roll)\n\n\nclass dRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result + relativedelta(days=self.number)\n\n\nclass eRule(RDateRule):\n def handle(self) -> dt.date:\n month_range = calendar.monthrange(self.result.year, self.result.month)\n return self.result.replace(day=month_range[1])\n\n\nclass FRule(RDateRule):\n def handle(self) -> dt.date:\n return self._get_nth_day_of_month(calendar.FRIDAY)\n\n\nclass gRule(RDateRule):\n def handle(self) -> dt.date:\n self.result = self.result + relativedelta(weeks=self.number)\n holidays = self._get_holidays()\n return self._apply_business_days_logic(holidays, offset=0, roll=self.roll_convention('backward'))\n\n\nclass NRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result + relativedelta(weekday=MO(self.number))\n\n\nclass GRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result + relativedelta(weekday=FR(self.number))\n\n\nclass IRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result + relativedelta(weekday=SA(self.number))\n\n\nclass JRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result.replace(day=1)\n\n\nclass kRule(RDateRule):\n def handle(self) -> dt.date:\n self.result = self.result + relativedelta(years=self.number)\n while self.week_mask[self.result.isoweekday() - 1] == '0':\n self.result += relativedelta(days=1)\n holidays = self._get_holidays()\n return self._apply_business_days_logic(holidays, offset=0, roll=self.roll_convention('backward'))\n\n\nclass mRule(RDateRule):\n def handle(self) -> dt.date:\n self.result = self.result + relativedelta(months=self.number)\n return self._apply_business_days_logic(self._get_holidays(), offset=0, roll=self.roll_convention('forward'))\n\n\nclass MRule(RDateRule):\n def handle(self) -> dt.date:\n return self._get_nth_day_of_month(calendar.MONDAY)\n\n\nclass PRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result + relativedelta(weekday=SU(self.number))\n\n\nclass rRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result.replace(month=12, day=31) + relativedelta(years=self.number)\n\n\nclass RRule(RDateRule):\n def handle(self) -> dt.date:\n return self._get_nth_day_of_month(calendar.THURSDAY)\n\n\nclass SRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result + relativedelta(weekday=TH(self.number))\n\n\nclass TRule(RDateRule):\n def handle(self) -> dt.date:\n return self._get_nth_day_of_month(calendar.TUESDAY)\n\n\nclass uRule(RDateRule):\n def handle(self) -> dt.date:\n holidays = self._get_holidays()\n roll = 'preceding' if self.sign == \"-\" and self.number == 0 else 'forward' if self.number <= 0 else 'preceding'\n return self._apply_business_days_logic(holidays, offset=self.number, roll=roll)\n\n\nclass URule(RDateRule):\n def handle(self) -> dt.date:\n return self.result + relativedelta(weekday=TU(self.number))\n\n\nclass vRule(RDateRule):\n def handle(self) -> dt.date:\n self.result = self.result + relativedelta(months=self.number) if self.number else self.result\n month_range = calendar.monthrange(self.result.year, self.result.month)\n self.result = self.result.replace(day=month_range[1])\n holidays = self._get_holidays()\n return self._apply_business_days_logic(holidays, offset=0, roll=self.roll_convention('backward'))\n\n\nclass VRule(RDateRule):\n def handle(self) -> dt.date:\n return self._get_nth_day_of_month(calendar.SATURDAY)\n\n\nclass WRule(RDateRule):\n def handle(self) -> dt.date:\n return self._get_nth_day_of_month(calendar.WEDNESDAY)\n\n\nclass wRule(RDateRule):\n def handle(self) -> dt.date:\n self.result = self.result + relativedelta(weeks=self.number)\n holidays = self._get_holidays()\n roll = 'forward' if self.number >= 0 else 'backward'\n return self._apply_business_days_logic(holidays, offset=0, roll=self.roll_convention(roll))\n\n\nclass xRule(RDateRule):\n def handle(self) -> dt.date:\n month_range = calendar.monthrange(self.result.year, self.result.month)\n self.result = self.result.replace(day=month_range[1])\n holidays = self._get_holidays()\n return self._apply_business_days_logic(holidays, offset=0, roll=self.roll_convention('backward'))\n\n\nclass XRule(RDateRule):\n def handle(self) -> dt.date:\n return self.result + relativedelta(weekday=WE(self.number))\n\n\nclass yRule(RDateRule):\n def handle(self) -> dt.date:\n self.result = self.result + relativedelta(years=self.number)\n while self.week_mask[self.result.isoweekday() - 1] == '0':\n self.result += relativedelta(days=1)\n holidays = self._get_holidays()\n return self._apply_business_days_logic(holidays, offset=0, roll=self.roll_convention('backward'))\n\n\nclass ZRule(RDateRule):\n def handle(self) -> dt.date:\n return self._get_nth_day_of_month(calendar.SUNDAY)\n" }, { "path": "gs_quant/datetime/time.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\n\nimport numpy as np\n\nfrom gs_quant.errors import MqValueError\n\n_logger = logging.getLogger(__name__)\n\nDAYS_IN_YEAR = 365.25\nDAYS_IN_WEEK = 7\nHOURS_IN_DAY = 24\nMINS_IN_HOUR = 60\nSECS_IN_MIN = 60\n\nSECS_IN_HOUR = SECS_IN_MIN * MINS_IN_HOUR\nMINS_IN_DAY = MINS_IN_HOUR * HOURS_IN_DAY\nSECS_IN_DAY = SECS_IN_MIN * MINS_IN_DAY\n\nHOURS_IN_WEEK = HOURS_IN_DAY * DAYS_IN_WEEK\nMINS_IN_WEEK = HOURS_IN_WEEK * MINS_IN_HOUR\nSECS_IN_WEEK = MINS_IN_WEEK * SECS_IN_HOUR\n\nSECS_IN_YEAR = SECS_IN_MIN * MINS_IN_HOUR * HOURS_IN_DAY * DAYS_IN_YEAR\n\n\nclass Timer:\n def __init__(self, print_on_exit: bool = True, label: str = 'Execution', threshold: int = None):\n self.__print_on_exit = print_on_exit\n self.__label = label\n self.__threshold = threshold\n\n def __enter__(self):\n self.__start = dt.datetime.now()\n\n def __exit__(self, *args):\n self.__elapsed = dt.datetime.now() - self.__start\n\n if self.__print_on_exit:\n if self.__threshold is None or self.__elapsed.seconds > self.__threshold:\n _logger.warning(\n f'{self.__label} took {self.__elapsed.seconds + self.__elapsed.microseconds / 1000000} seconds'\n )\n\n\ndef to_zulu_string(time: dt.datetime):\n return time.isoformat()[:-3] + 'Z'\n\n\ndef time_difference_as_string(time_delta: np.timedelta64, resolution: str = 'Second') -> str:\n times = [SECS_IN_YEAR, SECS_IN_WEEK, SECS_IN_DAY, SECS_IN_HOUR, SECS_IN_MIN, 1]\n time_strings = ['Year', 'Week', 'Day', 'Hour', 'Minute', 'Second']\n\n if resolution not in time_strings:\n raise MqValueError('incorrect resolution passed in \"s\"' % resolution)\n\n times_mapped = zip(times, time_strings)\n\n diff: int = abs(time_delta / np.timedelta64(1, 's'))\n result = ''\n\n for time, time_string in times_mapped:\n m = diff // time\n if m > 0:\n added = time_string\n if m != 1:\n added += 's'\n\n result += '%d %s ' % (m, added)\n diff -= m * time\n\n if time_string == resolution:\n break\n\n return result.strip()\n" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0000_query_dataset.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. External clients need to substitute thier own client id and client secret below. Please refer to [Authentication](https://developer.gs.com/p/docs/institutional/platform/authentication/) for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## How to query data \\n\",\n \"The Data APIs support many ways to query datasets to intuitively fetch only the data users need.\\n\",\n \"More details on [Querying Data](https://developer.gs.com/p/docs/services/data/data-access/query-data/) can be found in the documentation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date, timedelta, datetime\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"import pydash\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Data in Marquee is available in the form of Datasets (collections of homogenous data). Each Dataset has a set of entitlements, a fixed schema, and assets in coverage.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dataset_id = 'FXIVOL_STANDARD' # https://marquee.gs.com/s/developer/datasets/FXIVOL_STANDARD\\n\",\n \"ds = Dataset(dataset_id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Data for limited number of assets or spanning a small time frame can be queried in one go by specifying the assets to query and date/time range.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"start_date = date(2024, 1, 15)\\n\",\n \"end_date = date(2024, 1, 18)\\n\",\n \"\\n\",\n \"data = ds.get_data(start_date, end_date, bbid=[\\\"EURUSD\\\"])\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Instead of a range, one can also specify a set of date/times to get data for just those specific date/times\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = ds.get_data(dates=[date(2025, 1, 15), date(2025, 1, 18)], bbid=['EURCAD'])\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For larger number of assets or for longer time ranges, \\n\",\n \"we recommend iterating over assets and time to avoid hitting API query limits. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# loop over assets\\n\",\n \"def iterate_over_assets(\\n\",\n \" dataset, coverage, start, end, batch_size=5, query_dimension='assetId', delta=timedelta(days=6)\\n\",\n \"):\\n\",\n \" for ids in pydash.chunk(coverage[query_dimension].tolist(), size=batch_size):\\n\",\n \" print('iterate over assets', ids)\\n\",\n \" iterate_over_time(start, end, ids, dataset, delta=delta, query_dimension=query_dimension)\\n\",\n \"\\n\",\n \"\\n\",\n \"# loop over time\\n\",\n \"def iterate_over_time(start, end, ids, dataset, delta=timedelta(days=6), query_dimension='assetId'):\\n\",\n \" iter_start = start\\n\",\n \" while iter_start < end:\\n\",\n \" iter_end = min(iter_start + delta, end)\\n\",\n \" print('time iteration since', iter_start, 'until', iter_end)\\n\",\n \" data = dataset.get_data(iter_start, iter_end, **{query_dimension: ids})\\n\",\n \" # Add your code here to make use of fetched data\\n\",\n \"\\n\",\n \" iter_start = iter_end\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"dataset_id = 'EDRVOL_PERCENT_V1_STANDARD' # https://marquee.gs.com/s/developer/datasets/EDRVOL_PERCENT_V1_STANDARD\\n\",\n \"ds = Dataset(dataset_id)\\n\",\n \"\\n\",\n \"coverage = ds.get_coverage()\\n\",\n \"\\n\",\n \"iterate_over_assets(ds, coverage, date(2025, 3, 1), date(2021, 3, 31), batch_size=5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Similar approach can be used to download all data of a dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"coverage = ds.get_coverage(include_history=True)\\n\",\n \"coverage = coverage.sort_values(by='historyStartDate', axis=0)\\n\",\n \"start_date = datetime.strptime(coverage['historyStartDate'].values[0], '%Y-%m-%d').date()\\n\",\n \"\\n\",\n \"# warning: long running operation if ran on whole of coverage for all time\\n\",\n \"iterate_over_assets(ds, coverage.head(), date.today() - timedelta(days=90), date.today())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0001_get_dataset_coverage.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. External clients need to substitute thier own client id and client secret below. Please refer to [Authentication](https://developer.gs.com/p/docs/institutional/platform/authentication/) for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# get the dataset\\n\",\n \"dataset = Dataset('EDRVOL_PERCENT_STANDARD')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dataset Coverage\\n\",\n \"Dataset coverage is a list of the symbol dimensions in a dataset. This allows users to quickly see what is available in a dataset.\\n\",\n \"Coverage can by fetched via API as below or in the [dataset product page](https://marquee.gs.com/s/developer/datasets/EDRVOL_PERCENT_STANDARD/coverage)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(25, 4)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# get the coverage\\n\",\n \"coverage = dataset.get_coverage(include_history=True)\\n\",\n \"\\n\",\n \"# show the number of records and columns\\n\",\n \"coverage.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['bbid', 'assetId', 'name', 'historyStartDate'], dtype='object')\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# show the columns names\\n\",\n \"coverage.keys()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
bbidassetIdnamehistoryStartDate
0AEXMA4B66MW5E27U8NN8LSAEX Index2018-06-03
1IBOVMA4B66MW5E27U8NN8N6Brazil Ibovespa Index2018-06-03
2INDUMA4B66MW5E27U8NN8SPDow Jones Industrial Average2018-06-03
3CACMA4B66MW5E27U8NN922CAC 40 Index2018-06-03
4UKXMA4B66MW5E27U8NN95PFTSE 100 Index2018-06-03
5DAXMA4B66MW5E27U8NPZQYDAX Index2018-06-03
6HSCEIMA4B66MW5E27U8P3294Hang Seng China Ent Index2018-06-03
7HSIMA4B66MW5E27U8P3295Hang Seng Index2018-06-03
8KOSPI2MA4B66MW5E27U8P32DMKOSPI 200 Index2018-06-03
9MXEAMA4B66MW5E27U8P32GBMSCI EAFE2018-06-03
10MXEFMA4B66MW5E27U8P32KKMSCI Emerging Markets Index2018-06-03
11MEXBOLMA4B66MW5E27U8P32LES&P/bmv Ipc2018-06-03
12NKYMA4B66MW5E27U8P32LHNikkei 2252018-06-03
13NDXMA4B66MW5E27U8P32LQNasdaq 100 Stock Index2018-06-03
14RTYMA4B66MW5E27U8P32PCRussell 2000 Index2018-06-03
15SPTSX60MA4B66MW5E27U8P32S9S&P/TSX 60 Index2018-06-03
16SPXMA4B66MW5E27U8P32SBS&P 500 Index2018-06-03
17SMIMA4B66MW5E27U8P32SRSwiss Market Index2018-06-03
18SX5EMA4B66MW5E27U8P32SYEuro Stoxx 50 Price Return2018-06-03
19TPXMA4B66MW5E27U8P32XGTOPIX Index (Tokyo)2018-06-03
20EEM UPMA4B66MW5E27UAEP4R5iShares MSCI Emerging Markets2018-06-03
21EFA UPMA4B66MW5E27UAEP4RTiShares MSCI EAFE ETF2018-06-03
22GLD UPMA4B66MW5E27UAGYYRRSPDR Gold Shares2018-06-03
23XLF UPMA4B66MW5E27UANZH2RFinancial Select Sector SPDR2018-06-03
24XLK UPMA4B66MW5E27UANZH2TTechnology Select Sect SPDR2018-06-03
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" bbid assetId name \\\\\\n\",\n \"0 AEX MA4B66MW5E27U8NN8LS AEX Index \\n\",\n \"1 IBOV MA4B66MW5E27U8NN8N6 Brazil Ibovespa Index \\n\",\n \"2 INDU MA4B66MW5E27U8NN8SP Dow Jones Industrial Average \\n\",\n \"3 CAC MA4B66MW5E27U8NN922 CAC 40 Index \\n\",\n \"4 UKX MA4B66MW5E27U8NN95P FTSE 100 Index \\n\",\n \"5 DAX MA4B66MW5E27U8NPZQY DAX Index \\n\",\n \"6 HSCEI MA4B66MW5E27U8P3294 Hang Seng China Ent Index \\n\",\n \"7 HSI MA4B66MW5E27U8P3295 Hang Seng Index \\n\",\n \"8 KOSPI2 MA4B66MW5E27U8P32DM KOSPI 200 Index \\n\",\n \"9 MXEA MA4B66MW5E27U8P32GB MSCI EAFE \\n\",\n \"10 MXEF MA4B66MW5E27U8P32KK MSCI Emerging Markets Index \\n\",\n \"11 MEXBOL MA4B66MW5E27U8P32LE S&P/bmv Ipc \\n\",\n \"12 NKY MA4B66MW5E27U8P32LH Nikkei 225 \\n\",\n \"13 NDX MA4B66MW5E27U8P32LQ Nasdaq 100 Stock Index \\n\",\n \"14 RTY MA4B66MW5E27U8P32PC Russell 2000 Index \\n\",\n \"15 SPTSX60 MA4B66MW5E27U8P32S9 S&P/TSX 60 Index \\n\",\n \"16 SPX MA4B66MW5E27U8P32SB S&P 500 Index \\n\",\n \"17 SMI MA4B66MW5E27U8P32SR Swiss Market Index \\n\",\n \"18 SX5E MA4B66MW5E27U8P32SY Euro Stoxx 50 Price Return \\n\",\n \"19 TPX MA4B66MW5E27U8P32XG TOPIX Index (Tokyo) \\n\",\n \"20 EEM UP MA4B66MW5E27UAEP4R5 iShares MSCI Emerging Markets \\n\",\n \"21 EFA UP MA4B66MW5E27UAEP4RT iShares MSCI EAFE ETF \\n\",\n \"22 GLD UP MA4B66MW5E27UAGYYRR SPDR Gold Shares \\n\",\n \"23 XLF UP MA4B66MW5E27UANZH2R Financial Select Sector SPDR \\n\",\n \"24 XLK UP MA4B66MW5E27UANZH2T Technology Select Sect SPDR \\n\",\n \"\\n\",\n \" historyStartDate \\n\",\n \"0 2018-06-03 \\n\",\n \"1 2018-06-03 \\n\",\n \"2 2018-06-03 \\n\",\n \"3 2018-06-03 \\n\",\n \"4 2018-06-03 \\n\",\n \"5 2018-06-03 \\n\",\n \"6 2018-06-03 \\n\",\n \"7 2018-06-03 \\n\",\n \"8 2018-06-03 \\n\",\n \"9 2018-06-03 \\n\",\n \"10 2018-06-03 \\n\",\n \"11 2018-06-03 \\n\",\n \"12 2018-06-03 \\n\",\n \"13 2018-06-03 \\n\",\n \"14 2018-06-03 \\n\",\n \"15 2018-06-03 \\n\",\n \"16 2018-06-03 \\n\",\n \"17 2018-06-03 \\n\",\n \"18 2018-06-03 \\n\",\n \"19 2018-06-03 \\n\",\n \"20 2018-06-03 \\n\",\n \"21 2018-06-03 \\n\",\n \"22 2018-06-03 \\n\",\n \"23 2018-06-03 \\n\",\n \"24 2018-06-03 \"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# show the coverage\\n\",\n \"coverage\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
bbidassetIdnamehistoryStartDate
16SPXMA4B66MW5E27U8P32SBS&P 500 Index2018-06-03
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\"\n ],\n \"text/plain\": [\n \" bbid assetId name historyStartDate\\n\",\n \"16 SPX MA4B66MW5E27U8P32SB S&P 500 Index 2018-06-03\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# find specific asset\\n\",\n \"coverage[coverage['bbid'] == 'SPX']\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0002_using_data_contexts.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"\\n\",\n \"import gs_quant.timeseries as ts\\n\",\n \"from gs_quant.data import DataContext\\n\",\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier, ExchangeCode\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Create a data context covering 2018\\n\",\n \"data_ctx = DataContext(start=date(2018, 1, 1), end=date(2018, 12, 31))\\n\",\n \"\\n\",\n \"# Lookup S&P 500 Index via Security Master\\n\",\n \"spx = SecurityMaster.get_asset('SPX', AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE)\\n\",\n \"\\n\",\n \"# Use the data context\\n\",\n \"with data_ctx:\\n\",\n \" # Get 25 delta call implied volatility\\n\",\n \" vol = ts.implied_volatility(spx, '1m', ts.VolReference.DELTA_CALL, 25)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"vol.tail()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0003_get_data_using_coordinate.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import DataMeasure\\n\",\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier, ExchangeCode\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterh\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Lookup S&P 500 Index via Security Master\\n\",\n \"spx = SecurityMaster.get_asset('SPX', AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE)\\n\",\n \"\\n\",\n \"# Resolve the data coordinate for SPX without explicitly specifying the dataset\\n\",\n \"spot_data_coordinate = spx.get_data_coordinate(DataMeasure.SPOT_PRICE)\\n\",\n \"\\n\",\n \"# Fetch the series\\n\",\n \"series = spot_data_coordinate.get_series()\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"series.tail()\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n }\n ],\n \"metadata\": {\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"language\": \"python\",\n \"display_name\": \"Python 3\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0004_get_all_assets_from_region.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. External clients need to substitute thier own client id and client secret below. Please refer to [Authentication](https://developer.gs.com/p/docs/institutional/platform/authentication/) for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.api.gs.assets import GsAssetApi\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"ds = Dataset('EDRVOL_PERCENT_STANDARD') # https://marquee.gs.com/s/developer/datasets/EDRVOL_PERCENT_STANDARD\\n\",\n \"ids = ds.get_coverage().get('assetId')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Filtering coverage by Asset properties\\n\",\n \"A lot of datasets use Marquee Asset Id as symbol dimension. Marquee Asset Service to get more properties for these assets and/or filter by them.\\n\",\n \"Below example showcases how to filter assets by Region\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# find all assets from given region (One of \\\"Asia\\\", \\\"Europe\\\", \\\"Americas\\\")\\n\",\n \"def get_assets_in_region(region, asset_ids):\\n\",\n \" step = 500\\n\",\n \" size = asset_ids.shape[0]\\n\",\n \" assets = []\\n\",\n \" # go over all assets in the dataset in batches of 500 and query for region\\n\",\n \" for i in range(0, size, step):\\n\",\n \" end = min(i + step, size)\\n\",\n \" # prepare the query\\n\",\n \" query = {'id': asset_ids.values[i:end].tolist(), 'region': region}\\n\",\n \" # run the query\\n\",\n \" results = GsAssetApi.get_many_assets_data(limit=step, **query)\\n\",\n \" assets.extend(results)\\n\",\n \" return assets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"filtered_assets = get_assets_in_region('Europe', ids)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idbbidregioncurrencyexchange
0MA4B66MW5E27U8P32SRSMIEuropeCHFEBS
1MA4B66MW5E27U8P32SYSX5EEuropeEURDTBX
2MA4B66MW5E27U8NPZQYDAXEuropeEURXETR
3MA4B66MW5E27U8P32GBMXEAEuropeUSDDTBX
4MA4B66MW5E27U8NN95PUKXEuropeGBPLNSE
5MA4B66MW5E27U8NN922CACEuropeEURPARE
6MA4B66MW5E27U8NN8LSAEXEuropeEURAMSE
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\"\n ],\n \"text/plain\": [\n \" id bbid region currency exchange\\n\",\n \"0 MA4B66MW5E27U8P32SR SMI Europe CHF EBS\\n\",\n \"1 MA4B66MW5E27U8P32SY SX5E Europe EUR DTBX\\n\",\n \"2 MA4B66MW5E27U8NPZQY DAX Europe EUR XETR\\n\",\n \"3 MA4B66MW5E27U8P32GB MXEA Europe USD DTBX\\n\",\n \"4 MA4B66MW5E27U8NN95P UKX Europe GBP LNSE\\n\",\n \"5 MA4B66MW5E27U8NN922 CAC Europe EUR PARE\\n\",\n \"6 MA4B66MW5E27U8NN8LS AEX Europe EUR AMSE\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.DataFrame(filtered_assets, columns=['id', 'bbid', 'region', 'currency', 'exchange'])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0005_get_vol_surface.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"increasing-model\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. External clients need to substitute thier own client id and client secret below. Please refer to [Authentication](https://developer.gs.com/p/docs/institutional/platform/authentication/) for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"color-legislation\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"destroyed-saying\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"labeled-tobacco\",\n \"metadata\": {},\n \"source\": [\n \"The entire vol surface is published for each snap of the data, so we don’t have to pull diffs and reconstruct the surface\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"accepted-jungle\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dataset_id = 'EDRVOL_PERCENT_V1_STANDARD' # https://marquee.gs.com/s/developer/datasets/EDRVOL_PERCENT_STANDARD\\n\",\n \"ds = Dataset(dataset_id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"smoking-musical\",\n \"metadata\": {},\n \"source\": [\n \"Get the latest available datapoint.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"digital-korean\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
assetIdstrikeReferencetenorrelativeStrikeabsoluteStrikeimpliedVolatilityupdateTimebbid
date
2021-06-07MA4B66MW5E27U8P32SBforward9m1.56298.5827570.1547952021-06-07 23:12:59SPX
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" assetId strikeReference tenor relativeStrike \\\\\\n\",\n \"date \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 9m 1.5 \\n\",\n \"\\n\",\n \" absoluteStrike impliedVolatility updateTime bbid \\n\",\n \"date \\n\",\n \"2021-06-07 6298.582757 0.154795 2021-06-07 23:12:59 SPX \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"last = ds.get_data_last(as_of=dt.date.today(), bbid=['SPX'])\\n\",\n \"last\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"twenty-relief\",\n \"metadata\": {},\n \"source\": [\n \"We can use this to identify the timestamp of the latest surface and then query for the full surface matching this timestamp\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"finnish-consultancy\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
assetIdstrikeReferencetenorrelativeStrikeabsoluteStrikeimpliedVolatilityupdateTimebbid
date
2021-06-07MA4B66MW5E27U8P32SBforward18m0.401668.1519250.3936842021-06-07 23:12:59SPX
2021-06-07MA4B66MW5E27U8P32SBforward18m0.502085.1899070.3517152021-06-07 23:12:59SPX
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2021-06-07MA4B66MW5E27U8P32SBforward18m0.652710.7468790.2992882021-06-07 23:12:59SPX
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" assetId strikeReference tenor relativeStrike \\\\\\n\",\n \"date \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.40 \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.50 \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.55 \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.60 \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.65 \\n\",\n \"\\n\",\n \" absoluteStrike impliedVolatility updateTime bbid \\n\",\n \"date \\n\",\n \"2021-06-07 1668.151925 0.393684 2021-06-07 23:12:59 SPX \\n\",\n \"2021-06-07 2085.189907 0.351715 2021-06-07 23:12:59 SPX \\n\",\n \"2021-06-07 2293.708897 0.333246 2021-06-07 23:12:59 SPX \\n\",\n \"2021-06-07 2502.227888 0.315852 2021-06-07 23:12:59 SPX \\n\",\n \"2021-06-07 2710.746879 0.299288 2021-06-07 23:12:59 SPX \"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"snap_time = last.index[0].date() # the dataframe is indexed on date\\n\",\n \"df = ds.get_data(bbid=['SPX'], start=snap_time, end=snap_time, strikeReference='forward')\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"vulnerable-pipeline\",\n \"metadata\": {},\n \"source\": [\n \"The dataframe can be consumed directly or can be serialized for consumption by a different process\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"growing-gibson\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open(f'SPX_{snap_time}_curve.csv', 'w') as f:\\n\",\n \" df.to_csv(f)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"frozen-twenty\",\n \"metadata\": {},\n \"source\": [\n \"Above process can be abstracted to a function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"negative-hampton\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_latest_vol_surface(dataset, bbid, strike_reference='delta', intraday=False):\\n\",\n \" # Get the date/time of the most recent snap\\n\",\n \" as_of = dt.datetime.now() if intraday else dt.date.today()\\n\",\n \" last_data = dataset.get_data_last(as_of=as_of, bbid=bbid)\\n\",\n \"\\n\",\n \" # Pull the surface with the date/time of the most recent snap\\n\",\n \" last_time = last_data.index[0] if intraday else last_data.index[0].date()\\n\",\n \" df = ds.get_data(bbid=bbid, start=last_time, end=last_time, strikeReference=strike_reference)\\n\",\n \"\\n\",\n \" # Write latest vol surface to CSV\\n\",\n \" with open(f'{bbid}_{last_time}.csv', 'w') as f:\\n\",\n \" print(f'Writing latest vol surface for {bbid} to {bbid}_{last_time}.csv')\\n\",\n \" df.to_csv(f)\\n\",\n \"\\n\",\n \" return df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"english-western\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Writing latest vol surface for SPX to SPX_2021-06-07.csv\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
assetIdstrikeReferencetenorrelativeStrikeabsoluteStrikeimpliedVolatilityupdateTimebbid
date
2021-06-07MA4B66MW5E27U8P32SBforward18m0.401668.1519250.3936842021-06-07 23:12:59SPX
2021-06-07MA4B66MW5E27U8P32SBforward18m0.502085.1899070.3517152021-06-07 23:12:59SPX
2021-06-07MA4B66MW5E27U8P32SBforward18m0.552293.7088970.3332462021-06-07 23:12:59SPX
2021-06-07MA4B66MW5E27U8P32SBforward18m0.602502.2278880.3158522021-06-07 23:12:59SPX
2021-06-07MA4B66MW5E27U8P32SBforward18m0.652710.7468790.2992882021-06-07 23:12:59SPX
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" assetId strikeReference tenor relativeStrike \\\\\\n\",\n \"date \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.40 \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.50 \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.55 \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.60 \\n\",\n \"2021-06-07 MA4B66MW5E27U8P32SB forward 18m 0.65 \\n\",\n \"\\n\",\n \" absoluteStrike impliedVolatility updateTime bbid \\n\",\n \"date \\n\",\n \"2021-06-07 1668.151925 0.393684 2021-06-07 23:12:59 SPX \\n\",\n \"2021-06-07 2085.189907 0.351715 2021-06-07 23:12:59 SPX \\n\",\n \"2021-06-07 2293.708897 0.333246 2021-06-07 23:12:59 SPX \\n\",\n \"2021-06-07 2502.227888 0.315852 2021-06-07 23:12:59 SPX \\n\",\n \"2021-06-07 2710.746879 0.299288 2021-06-07 23:12:59 SPX \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_latest_vol_surface(ds, 'SPX', strike_reference='forward').head()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0006_ptp_dataset.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"76da2538\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. External clients need to substitute thier own client id and client secret below. Please refer to [Authentication](https://developer.gs.com/p/docs/institutional/platform/authentication/) for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"a2dd9568\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from datetime import date\\n\",\n \"from gs_quant.data import PTPDataset\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"402e5482\",\n \"metadata\": {},\n \"source\": [\n \"# PTP Datasets\\n\",\n \"\\n\",\n \"PlotTool Pro provides a way to easily upload and visualize your data stored with Marquee Data Services, and you can use `gs_quant`'s `PTPDataset` class to upload, save, and view your `pandas` data structures. \\n\",\n \"\\n\",\n \"For now, PTP datasets are only available for internal Marquee users.\\n\",\n \"\\n\",\n \"Data uploaded to a PTP dataset must be\\n\",\n \"1. Numeric\\n\",\n \"1. Indexed by a `pd.DatetimeIndex`\\n\",\n \"1. EOD (as opposed to real-time)\\n\",\n \"\\n\",\n \"For further information, see the `PTPDataset` class. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e325a576\",\n \"metadata\": {},\n \"source\": [\n \"## Workflow\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"41a9f73e\",\n \"metadata\": {},\n \"source\": [\n \"#### Create a PTP Dataset\\n\",\n \"\\n\",\n \"When creating a new PTP dataset, you first initialize an object, passing in a `pandas` Series or DataFrame, and an optional name. If you don't provide a name, the dataset's name will default to \\\"GSQ Default.\\\" \\n\",\n \"\\n\",\n \"If you pass a DataFrame, the field names in your dataset will be derived from the DataFrame's column names; if you provide a Series, the field name will be taken from the series name (if it has one), or otherwise \\\"values\\\" by de\\n\",\n \"\\n\",\n \"**Note**: your data is not *saved* until you call `.sync()` on your PTP dataset object. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"b52dae72\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.DataFrame(\\n\",\n \" {'col': range(50), 'fieldb': range(100, 150)}, index=pd.date_range(start=date(2021, 1, 1), periods=50, freq='D')\\n\",\n \")\\n\",\n \"dataset = PTPDataset(df, 'Test Dataset')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fb2aad49\",\n \"metadata\": {},\n \"source\": [\n \"#### Sync\\n\",\n \"\\n\",\n \"Sync your dataset to save your data. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"4e0cf7f6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dataset.sync()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b25a2be3\",\n \"metadata\": {},\n \"source\": [\n \"#### Plot\\n\",\n \"\\n\",\n \"Generate a transient plot expression, which will bring you to a plot displaying your data. \\n\",\n \"\\n\",\n \"Make sure to hit \\\"Copy to My Plots\\\" to save the plot (although you can always re-generate another transient plot expression). \\n\",\n \"\\n\",\n \"If you're running `gs_quant` on a device with a default browser, `.plot()` will automatically open the plot for you. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"afd058d6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'https://marquee.web.gs.com/s/plottool/transient?expr=Dataset(\\\"PTP_TEST_DATASET_LOAK1\\\").col()%0ADataset(\\\"PTP_TEST_DATASET_LOAK1\\\").fieldb()'\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dataset.plot(open_in_browser=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fb870105\",\n \"metadata\": {},\n \"source\": [\n \"#### Other dataset functions\\n\",\n \"\\n\",\n \"Since `PTPDataset` inherits from the `Dataset` class, it has all the functionality of a normal `Dataset` object in addition to the methods above. \\n\",\n \"\\n\",\n \"For example, to delete a dataset: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"853c639d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Successfully deleted dataset.'\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dataset.delete()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0007_query_fxvol.script.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"dc1b5843-eca4-47f5-8e3c-4e4dc83775f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"from functools import lru_cache\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.api.gs.assets import GsAssetApi\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fde1622d-5e57-4f4e-9602-7ac6aa75f23a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Internals\\n\",\n \"@lru_cache(maxsize=128)\\n\",\n \"def _get_coverage(ds):\\n\",\n \" print(\\\"Fetching coverage\\\", end=\\\" \\\")\\n\",\n \" cov = ds.get_coverage()[\\\"assetId\\\"].to_list()\\n\",\n \" print(\\\"[DONE]\\\")\\n\",\n \" return cov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"34341f3d-cf64-4519-b808-335d874b3cd0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def _nest_list(flat_list, nested_size):\\n\",\n \" return [flat_list[i : i + nested_size] for i in range(0, len(flat_list), nested_size)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f73538c6-a774-4ae4-bacd-fa584428cd9f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def _get_data_by_asset_parameters(ds, start_date, end_date, pricing_location=\\\"NYC\\\", chunk_size=100, **kwargs):\\n\",\n \" coverage = _get_coverage(ds)\\n\",\n \" assets = []\\n\",\n \" asset_chunks = _nest_list(coverage, 5000)\\n\",\n \" print(\\\"Reading Assets:\\\", end=\\\" \\\")\\n\",\n \" for ch in asset_chunks:\\n\",\n \" print(\\\"#\\\", end=\\\"\\\")\\n\",\n \" kwargs[\\\"id\\\"] = ch\\n\",\n \" assets += GsAssetApi.get_many_assets([\\\"id\\\"], limit=10000, **kwargs)\\n\",\n \" print(\\\" [DONE]\\\")\\n\",\n \"\\n\",\n \" ids = [a.id for a in assets]\\n\",\n \" print(\\\"Assets to fetch \\\" + str(len(ids)))\\n\",\n \" chunks = _nest_list(ids, chunk_size)\\n\",\n \" frames = [pd.DataFrame()]\\n\",\n \" print(\\\"Reading Data:\\\", end=\\\" \\\")\\n\",\n \" for ch in chunks:\\n\",\n \" print(\\\"#\\\", end=\\\"\\\")\\n\",\n \" frames.append(ds.get_data(start_date, end_date, assetId=ch, pricingLocation=pricing_location))\\n\",\n \" print(\\\" [DONE]\\\")\\n\",\n \" return pd.concat(frames)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"93546060-b2a4-42c1-94f9-87939b07e1e1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_fxivol(\\n\",\n \" ds,\\n\",\n \" bbid: str = None,\\n\",\n \" tenor=None,\\n\",\n \" strike_reference=None,\\n\",\n \" relative_strike=None,\\n\",\n \" pricing_location=\\\"NYC\\\",\\n\",\n \" start_date=datetime.date.today(),\\n\",\n \" end_date=datetime.date.today(),\\n\",\n \"):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" :param ds: Dataset object\\n\",\n \" :param bbid: selected identifier i.e. EURUSD\\n\",\n \" :param tenor: tenor of the option i.e. 1y, 5y\\n\",\n \" :param strike_reference: one of \\\"delta\\\", \\\"forward\\\", \\\"spot\\\", one supported at at time\\n\",\n \" :param relative_strike: moneyness of the option i.e. -5, 0, 5 ... etc Mixing negative and positive are not supported\\n\",\n \" :param pricing_location:\\n\",\n \" :param start_date: start date of the query\\n\",\n \" :param end_date: end date of the query\\n\",\n \" :return: DataFrame with data\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" query = dict()\\n\",\n \" if bbid is not None:\\n\",\n \" query[\\\"asset_parameters_call_currency\\\"] = [bbid[0:3], bbid[3:]]\\n\",\n \" query[\\\"asset_parameters_put_currency\\\"] = [bbid[0:3], bbid[3:]]\\n\",\n \" if strike_reference is not None:\\n\",\n \" relative_strike_l = relative_strike\\n\",\n \" if strike_reference == \\\"delta\\\" and relative_strike is not None:\\n\",\n \" if not isinstance(relative_strike, list):\\n\",\n \" relative_strike_l = [relative_strike]\\n\",\n \" if min(relative_strike_l) < 0 < max(relative_strike_l):\\n\",\n \" print(\\\"ERROR: Relative strikes should be same sign in the query to work properly\\\")\\n\",\n \" return None\\n\",\n \" query[\\\"asset_parameters_option_type\\\"] = \\\"Put\\\" if relative_strike_l[0] < 0 else \\\"Call\\\"\\n\",\n \" query[\\\"asset_parameters_strike_price_relative\\\"] = [\\n\",\n \" str(abs(s)) + \\\"D\\\" if s != 0 else \\\"DN\\\" for s in relative_strike_l\\n\",\n \" ]\\n\",\n \"\\n\",\n \" if strike_reference == \\\"forward\\\":\\n\",\n \" query[\\\"asset_parameters_strike_price_relative\\\"] = \\\"ATMF\\\"\\n\",\n \" if strike_reference == \\\"spot\\\":\\n\",\n \" query[\\\"asset_parameters_strike_price_relative\\\"] = [\\\"ATMS\\\", \\\"Spot\\\", \\\"ATM\\\"]\\n\",\n \"\\n\",\n \" if tenor is not None:\\n\",\n \" query[\\\"asset_parameters_expiration_date\\\"] = tenor\\n\",\n \"\\n\",\n \" print(query)\\n\",\n \" return _get_data_by_asset_parameters(ds, start_date, end_date, pricing_location=pricing_location, **query)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"516cd795-dcf1-40ee-bf59-cb7189d3a204\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_fx_fwd(\\n\",\n \" ds,\\n\",\n \" bbid: str = None,\\n\",\n \" tenor=None,\\n\",\n \" pricing_location=\\\"NYC\\\",\\n\",\n \" start_date=datetime.date.today(),\\n\",\n \" end_date=datetime.date.today(),\\n\",\n \"):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" :param ds: Dataset object\\n\",\n \" :param bbid: selected identifier i.e. EURUSD\\n\",\n \" :param tenor: tenor of the option i.e. 1y, 5y\\n\",\n \" :param pricing_location:\\n\",\n \" :param start_date: start date of the query\\n\",\n \" :param end_date: end date of the query\\n\",\n \" :return: DataFrame with data\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" args = dict()\\n\",\n \" if bbid is not None:\\n\",\n \" args[\\\"asset_parameters_pair\\\"] = bbid\\n\",\n \" if tenor is not None:\\n\",\n \" args[\\\"asset_parameters_expiration_date\\\"] = tenor\\n\",\n \" return _get_data_by_asset_parameters(ds, start_date, end_date, pricing_location=pricing_location, **args)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"28f5b009-3302-4829-b2a3-d6f56c61577e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# USER DETAILS\\n\",\n \"CLIENT_ID = None\\n\",\n \"SECRET = None\\n\",\n \"GsSession.use(Environment.PROD, client_id=CLIENT_ID, client_secret=SECRET)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5739dad6-aee6-4dde-b504-a355233fc4cd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# SELECT DATASET\\n\",\n \"FX_VOL_DS = Dataset(\\\"FXIVOL_V2_PREMIUM\\\")\\n\",\n \"start_date = datetime.date(2021, 12, 17)\\n\",\n \"end_date = datetime.date(2022, 12, 17)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3528c97b-9f4a-4381-a92f-5bf71a3d304a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vol = get_fxivol(\\n\",\n \" FX_VOL_DS,\\n\",\n \" start_date=start_date,\\n\",\n \" end_date=end_date,\\n\",\n \" bbid=\\\"EURUSD\\\",\\n\",\n \" relative_strike=[-10],\\n\",\n \" tenor=\\\"5y\\\",\\n\",\n \" strike_reference=\\\"delta\\\",\\n\",\n \")\\n\",\n \"print(vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"992630f6-7614-4165-94f8-18ebca34e25e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vol = get_fxivol(\\n\",\n \" FX_VOL_DS,\\n\",\n \" start_date=start_date,\\n\",\n \" end_date=end_date,\\n\",\n \" bbid=\\\"EURUSD\\\",\\n\",\n \" relative_strike=[10],\\n\",\n \" tenor=\\\"5y\\\",\\n\",\n \" strike_reference=\\\"delta\\\",\\n\",\n \")\\n\",\n \"print(vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a5730f24-8624-44c8-93a5-5cf4683b2423\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vol = get_fxivol(\\n\",\n \" FX_VOL_DS, start_date=start_date, end_date=end_date, bbid=\\\"USDPLN\\\", strike_reference=\\\"delta\\\", tenor=\\\"3m\\\"\\n\",\n \")\\n\",\n \"print(vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3d62c755-55c9-42e7-9407-9f78ab1f10b2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vol = get_fxivol(\\n\",\n \" FX_VOL_DS,\\n\",\n \" start_date=start_date,\\n\",\n \" end_date=end_date,\\n\",\n \" bbid=\\\"EURUSD\\\",\\n\",\n \" relative_strike=[0, 10, 15],\\n\",\n \" strike_reference=\\\"delta\\\",\\n\",\n \" tenor=\\\"1y\\\",\\n\",\n \")\\n\",\n \"print(vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ac617f52-0004-487a-b6a2-e67757846a51\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vol = get_fxivol(\\n\",\n \" FX_VOL_DS, start_date=start_date, end_date=end_date, bbid=\\\"USDPLN\\\", strike_reference=\\\"forward\\\", tenor=\\\"5y\\\"\\n\",\n \")\\n\",\n \"print(vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d4637647-6cd0-4695-b081-8a22b75081bb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vol = get_fxivol(\\n\",\n \" FX_VOL_DS, start_date=start_date, end_date=end_date, bbid=\\\"USDPLN\\\", strike_reference=\\\"spot\\\", tenor=\\\"1y\\\"\\n\",\n \")\\n\",\n \"print(vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6ba12394-7b8d-4136-974e-5d100ad4c091\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/00_data/00_datasets/examples/0008_s3_partners_short_interest.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c6b8ac530007ea21\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 1: Authentication\\n\",\n \"\\n\",\n \"The first step is to initialize a GsSession with your application ID. For more information on how to create and register an application, check this [tutorial](https://developer.gs.com/p/docs/institutional/platform/getting-started/). In the snippet below, substitute client_id and client_secret with your application ID and secret respectively.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"db1c365daf064da\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2024-08-20T20:55:42.569859800Z\",\n \"start_time\": \"2024-08-20T20:55:39.923882800Z\"\n },\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dcd6b4e12461dea8\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Define parameters\\n\",\n \"\\n\",\n \"Next, we import all required modules and define required parameters:\\n\",\n \"\\n\",\n \"1. **The Dataset ID**: S3_PARTNERS_EQUITY_SHORT_INTEREST\\n\",\n \"2. **A time range** \\n\",\n \"3. **A list of assets** to query data for\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b35aecebdb1a47d9\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"dataset_id = \\\"S3_PARTNERS_EQUITY_SHORT_INTEREST\\\"\\n\",\n \"\\n\",\n \"dataset = Dataset(dataset_id)\\n\",\n \"start_date = dt.date(2024, 5, 1)\\n\",\n \"end_date = dt.date(2024, 8, 1)\\n\",\n \"bbids = [\\\"META UW\\\", \\\"NVDA UW\\\", \\\"AAPL UW\\\", \\\"MSFT UW\\\", \\\"AMZN UW\\\", \\\"GOOG UW\\\", \\\"TSLA UW\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dbe6305116d93bac\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"**Tip**: You can use the get_coverage function to see the full list of equity securities for which there is data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"291c364a533d9dc\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"coverage = dataset.get_coverage()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1361ea71b49d9289\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Step 3: Get data\\n\",\n \"\\n\",\n \"Now, it's time to get data. We will just have to plug in the parameters we defined above\\n\",\n \"\\n\",\n \"1. **start**: The start date for the query\\n\",\n \"2. **end**: The end date for the query\\n\",\n \"3. **bbid**: Bloomberg IDs of assets. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"26ce1a7acf1eb902\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"s3_data = dataset.get_data(start=start_date, end=end_date, bbid=bbids)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cb8ffba3955000af\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### What if I want to pass in other identifiers, such as SEDOLs or ISINs instead of BBIDs? \\n\",\n \"\\n\",\n \"You can also pass other identifiers (such as ticker, SEDOL, ISIN, CUSIP, etc).\\n\",\n \"\\n\",\n \"Please note certain identifiers may be subject to licensing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c914b3b91cd89679\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"s3_data = dataset.get_data(start=end_date, end=end_date, sedol=[\\\"2857817\\\", \\\"2206657\\\"])\\n\",\n \"s3_data = dataset.get_data(start=end_date, end=end_date, isin=[\\\"US7134481081\\\", \\\"US5801351017\\\"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"703802dc2f6a0ec6\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Step 4: Get individual fields\\n\",\n \"\\n\",\n \"You many also want to query a timeseries for a subset of fields in the dataset. Below is a 3-months timeseries daily short interest and short interest percent data for a group of stocks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6eed59910d32b3\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"daily_short_interest = dataset.get_data(\\n\",\n \" start=start_date, end=end_date, bbid=bbids, fields=[\\\"dailyShortInterest\\\", \\\"shortInterestPercent\\\"]\\n\",\n \")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/00_data/01_analytics/examples/0000_charting_data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"market_date = PricingContext.current.market.date # Determine current market date\\n\",\n \"\\n\",\n \"vol_dataset = Dataset('EDRVOL_PERCENT_STANDARD') # Initialize the equity implied volatility dataset\\n\",\n \"vol_data = vol_dataset.get_data(market_date, market_date, ticker='SPX', tenor='1m', strikeReference='forward')\\n\",\n \"\\n\",\n \"strikes = vol_data['relativeStrike']\\n\",\n \"vols = vol_data['impliedVolatility'] * 100\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"plt.plot(strikes, vols, label='Implied Volatility by Strike')\\n\",\n \"plt.xlabel('Relative Strike')\\n\",\n \"plt.ylabel('Implied Volatility')\\n\",\n \"plt.title('Implied Volatility by Strike')\\n\",\n \"\\n\",\n \"plt.show()\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n }\n ],\n \"metadata\": {\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"language\": \"python\",\n \"display_name\": \"Python 3\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/00_data/01_analytics/examples/0001_exporting_data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# fetch the data\\n\",\n \"weather_ds = Dataset(Dataset.GS.WEATHER)\\n\",\n \"panda_df = weather_ds.get_data(dt.date(2016, 1, 15), dt.date(2016, 1, 16), city=['Boston', 'Austin'])\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"panda_df.to_csv('my_data.csv')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n }\n ],\n \"metadata\": {\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"language\": \"python\",\n \"display_name\": \"Python 3\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/00_data/02_workspaces/examples/0001_creating_a_workspace.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.api.gs.users import GsUsersApi\\n\",\n \"from gs_quant.api.gs.workspaces import GsWorkspacesMarketsApi as ws_api\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.target.workspaces_markets import *\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Initializing your Workspace\\n\",\n \"\\n\",\n \"Determine the basics of your Workspace (i.e name, description and entitlements over who can view and edit)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"name = 'Example Workspace'\\n\",\n \"alias = 'example-workspace' # This needs to be unique across all users as to not have conflicting URLs\\n\",\n \"description = 'This workspace was created as an example.'\\n\",\n \"user_id = GsUsersApi.get_my_guid()\\n\",\n \"\\n\",\n \"# Entitle everyone internal to view but only yourself to edit and change entitlements\\n\",\n \"entitlements = Entitlements(view=(user_id,), edit=(user_id,), admin=(user_id,))\\n\",\n \"\\n\",\n \"components = [] # Empty list of components with some to be added below\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Add Components\\n\",\n \"\\n\",\n \"Workspaces currently support many components such as Promo (text), Plots, DataGrids, and Commentary. See all available components\\n\",\n \"[here](https://developer.gs.com/p/docs/services/data/workspaces-markets/#components).\\n\",\n \"\\n\",\n \"To create a component, create the parameters object for the component such as PromoComponentParameters, fill out the required and optional fields.\\n\",\n \"\\n\",\n \"Then add the component using these parameters. Let's start with a simple Promo Component.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### Add a Promo Component \\n\",\n \"\\n\",\n \"If you want to add simple text to a workspace, this can be done by adding a Promo Component, as seen below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"promo_params = PromoComponentParameters(height=52, body='Your text here!', transparent=False)\\n\",\n \"components.append(WorkspaceComponent(id_='promo-1', type_=ComponentType.promo, parameters=promo_params))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Create the Workspace\\n\",\n \"\\n\",\n \"Now you are ready to create your workspace. Let us put everything together.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"layout = 'r(c12($0))'\\n\",\n \"parameters = WorkspaceParameters(layout=layout, components=components)\\n\",\n \"workspace = Workspace(parameters=parameters, name=name, alias=alias, entitlements=entitlements, description=description)\\n\",\n \"workspace = ws_api.create_workspace(workspace)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"The above snippet will create a workspace that is now viewable at the URL https://marquee.gs.com/s/markets/{alias}. \\n\",\n \"Substitute {alias} with the alias you set. Remember to change the alias since the example one probably already exists.\\n\",\n \"\\n\",\n \"The layout string controls the layout of your components. In this case, a simple layout with a single component that spans a single row.\\n\",\n \"Learn more about layouts [here](https://developer.gs.com/p/docs/services/data/workspaces-markets/#layouts). \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Updating the Workspace with Additional Components\\n\",\n \"\\n\",\n \"Now let us create a Workspace with some plots and a commentary component.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### Add a plot \\n\",\n \"\\n\",\n \"Start by creating plots in PlotTool Pro [here](https://marquee.gs.com/s/plottool/new) \\n\",\n \"\\n\",\n \"After you have created your plot, grab the plot id from the browser.\\n\",\n \"* For example, the id for [this plot](https://marquee.gs.com/s/plottool/CH5RJJ9APZMRQ7B7) is CHYYNR2YSD8W21GA\\n\",\n \" \\n\",\n \"You want all the underlying components that have entitlements to have the same entitlements as the Workspace, so all components are visible on the Workspace for the intended audience.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Create the plot component parameters, setting the height to the desired height in pixels. Also, other configurations can be set such as hiding the legend.\\n\",\n \"plot_params = PlotComponentParameters(height=300, hideLegend=False)\\n\",\n \"plot_id = 'CHYYNR2YSD8W21GA' # example plot id\\n\",\n \"\\n\",\n \"# Add the plot component to the list of components\\n\",\n \"components.append(WorkspaceComponent(id_=plot_id, type_=ComponentType.plot, parameters=plot_params))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### Add a commentary stream\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"channel_1 = 'EQUITIES MACRO'\\n\",\n \"channel_2 = 'EQUITIES GS BASKETS'\\n\",\n \"commentary_params = CommentaryComponentParameters(height=300, commentary_channels=(channel_1, channel_2))\\n\",\n \"components.append(\\n\",\n \" WorkspaceComponent(id_='streaming-commentary-1', type_=ComponentType.commentary, parameters=commentary_params)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Update the Workspace\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"MqRequestError\",\n \"evalue\": \"context: POST https://marquee.web.gs.com/v1/workspaces/markets\\nstatus: 400, message: \\n\\n\\n\\nRequest Body Validation Error\\n\\n\\n\\n

400 Bad Request - Request Body Validation Error

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Problem Description

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\\r\\n The request did not match any of the requests defined for this endpoint.\\r\\n

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Details

\\r\\n\\r\\n

Request Definition 1

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The JSON object in the body does not conform to the schema

\\r\\n
    \\r\\n \\r\\n
  • error: ECMA 262 regex "^(?:\\\\w{1,256}:){0,10}[\\\\w.]{1,256}$$" does not match input string "insert your user id"\\n level: "error"\\n schema: {"loadingURI":"definition:/Token#","pointer":""}\\n instance: {"pointer":"/entitlements/admin/0"}\\n domain: "validation"\\n keyword: "pattern"\\n regex: "^(?:\\\\\\\\w{1,256}:){0,10}[\\\\\\\\w.]{1,256}$$"\\n string: "insert your user id"\\n
    • \\r\\n \\r\\n
\\r\\n\\r\\n

Troubleshooting

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    \\r\\n
  • Verify that the content type of the request matches the service definition.
    • \\r\\n
  • Check that the JSON document you are sending matches the schema.
    • \\r\\n
  • If the service does not define any requests, make sure you are not sending a request body.
    • \\r\\n
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Error ID: 7001873889772119441

Dash Services Framework 62.3 [SVN build 8011 at 2021-01-22 16:07]

\\n\\n\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\n\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mMqRequestError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32m\\u001b[0m in \\u001b[0;36m\\u001b[1;34m\\u001b[0m\\n\\u001b[0;32m 3\\u001b[0m \\u001b[0mparameters\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mWorkspaceParameters\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mlayout\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mlayout\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mcomponents\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mcomponents\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdisclaimer\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mdisclaimer\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 4\\u001b[0m \\u001b[0mnew_workspace\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mWorkspace\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mparameters\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[1;34m'cashboard'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mname\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0malias\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0malias\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mentitlements\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mentitle\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mdescription\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mdescription\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m----> 5\\u001b[1;33m \\u001b[0mcreated_workspace\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mwk\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mcreate_workspace\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mnew_workspace\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 6\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32mH:\\\\_repos\\\\gs_quant\\\\gs_quant\\\\api\\\\gs\\\\workspaces.py\\u001b[0m in \\u001b[0;36mcreate_workspace\\u001b[1;34m(cls, workspace)\\u001b[0m\\n\\u001b[0;32m 49\\u001b[0m \\u001b[1;33m@\\u001b[0m\\u001b[0mclassmethod\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 50\\u001b[0m \\u001b[1;32mdef\\u001b[0m \\u001b[0mcreate_workspace\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mcls\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mworkspace\\u001b[0m\\u001b[1;33m:\\u001b[0m \\u001b[0mWorkspace\\u001b[0m\\u001b[1;33m)\\u001b[0m \\u001b[1;33m->\\u001b[0m \\u001b[0mWorkspace\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 51\\u001b[1;33m \\u001b[1;32mreturn\\u001b[0m \\u001b[0mGsSession\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mcurrent\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m_post\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34mf'{API}'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mworkspace\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mcls\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mWorkspace\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mrequest_headers\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mWORKSPACES_MARKETS_HEADERS\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 52\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 53\\u001b[0m \\u001b[1;33m@\\u001b[0m\\u001b[0mclassmethod\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;32mH:\\\\_repos\\\\gs_quant\\\\gs_quant\\\\session.py\\u001b[0m in \\u001b[0;36m_post\\u001b[1;34m(self, path, payload, request_headers, cls, include_version, timeout)\\u001b[0m\\n\\u001b[0;32m 230\\u001b[0m include_version: bool = True, timeout: int = DEFAULT_TIMEOUT) -> Union[Base, tuple, dict]:\\n\\u001b[0;32m 231\\u001b[0m return self.__request('POST', path, payload=payload, request_headers=request_headers,\\n\\u001b[1;32m--> 232\\u001b[1;33m cls=cls, include_version=include_version, timeout=timeout)\\n\\u001b[0m\\u001b[0;32m 233\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 234\\u001b[0m def _delete(self, path: str, payload: Optional[Union[dict, Base]] = None, request_headers: Optional[dict]\\n\",\n \"\\u001b[1;32mH:\\\\_repos\\\\gs_quant\\\\gs_quant\\\\session.py\\u001b[0m in \\u001b[0;36m__request\\u001b[1;34m(self, method, path, payload, request_headers, cls, try_auth, include_version, timeout)\\u001b[0m\\n\\u001b[0;32m 195\\u001b[0m \\u001b[1;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0m__request\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mmethod\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mpath\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mpayload\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mpayload\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mcls\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0mcls\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mtry_auth\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;32mFalse\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 196\\u001b[0m \\u001b[1;32melif\\u001b[0m \\u001b[1;32mnot\\u001b[0m \\u001b[1;36m199\\u001b[0m \\u001b[1;33m<\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mstatus_code\\u001b[0m \\u001b[1;33m<\\u001b[0m \\u001b[1;36m300\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m--> 197\\u001b[1;33m \\u001b[1;32mraise\\u001b[0m \\u001b[0mMqRequestError\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mresponse\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mstatus_code\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtext\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mcontext\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;34m'{} {}'\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mformat\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mmethod\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0murl\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 198\\u001b[0m \\u001b[1;32melif\\u001b[0m \\u001b[1;34m'Content-Type'\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mheaders\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 199\\u001b[0m \\u001b[1;32mif\\u001b[0m \\u001b[1;34m'application/x-msgpack'\\u001b[0m \\u001b[1;32min\\u001b[0m \\u001b[0mresponse\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mheaders\\u001b[0m\\u001b[1;33m[\\u001b[0m\\u001b[1;34m'Content-Type'\\u001b[0m\\u001b[1;33m]\\u001b[0m\\u001b[1;33m:\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;31mMqRequestError\\u001b[0m: context: POST https://marquee.web.gs.com/v1/workspaces/markets\\nstatus: 400, message: \\n\\n\\n\\nRequest Body Validation Error\\n\\n\\n\\n

400 Bad Request - Request Body Validation Error

\\n\\r\\n

Problem Description

\\r\\n

\\r\\n The request did not match any of the requests defined for this endpoint.\\r\\n

\\r\\n

Details

\\r\\n\\r\\n

Request Definition 1

\\r\\n

The JSON object in the body does not conform to the schema

\\r\\n
    \\r\\n \\r\\n
  • error: ECMA 262 regex "^(?:\\\\w{1,256}:){0,10}[\\\\w.]{1,256}$$" does not match input string "insert your user id"\\n level: "error"\\n schema: {"loadingURI":"definition:/Token#","pointer":""}\\n instance: {"pointer":"/entitlements/admin/0"}\\n domain: "validation"\\n keyword: "pattern"\\n regex: "^(?:\\\\\\\\w{1,256}:){0,10}[\\\\\\\\w.]{1,256}$$"\\n string: "insert your user id"\\n
    • \\r\\n \\r\\n
\\r\\n\\r\\n

Troubleshooting

\\r\\n
    \\r\\n
  • Verify that the content type of the request matches the service definition.
    • \\r\\n
  • Check that the JSON document you are sending matches the schema.
    • \\r\\n
  • If the service does not define any requests, make sure you are not sending a request body.
    • \\r\\n
\\r\\n

Error ID: 7001873889772119441

Dash Services Framework 62.3 [SVN build 8011 at 2021-01-22 16:07]

\\n\\n\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\n\"\n ]\n }\n ],\n \"source\": [\n \"workspace.parameters.layout = 'r(c12($0))r(c6($1)c6($2))'\\n\",\n \"workspace.parameters.components = components\\n\",\n \"workspace = ws_api.update_workspace(workspace)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/00_data/03_visualizations/examples/0001_creating_a_visualization.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Introduction to Data Visualizations\\n\",\n \"\\n\",\n \"The Data Visualizations class `PlotlyViz` allows you to create and visualize different chart objects in Marquee.\\n\",\n \"This class has been created to provide flexibility to our users when creating and visualizing charts. All while \\n\",\n \"maintaining Marquee style and utilizing the proper Marquee workflow to persist and entitle these objects.\\n\",\n \"\\n\",\n \"At the moment we support 5 different chart types:\\n\",\n \"\\n\",\n \" * Bar Charts\\n\",\n \" * Scatter Charts\\n\",\n \" * Bubble Charts\\n\",\n \" * Pie Charts\\n\",\n \" * Radar Charts\\n\",\n \" \\n\",\n \"Let's take a look at a few example and concepts to better understand how to work and create these visualizations.\\n\",\n \"\\n\",\n \"First, let's import all necessary libraries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.analytics.datagrid import DataRow, DataColumn, DataGrid\\n\",\n \"from gs_quant.analytics.dataviz import PlotlyViz\\n\",\n \"from gs_quant.analytics.processors import EntityProcessor, VolatilityProcessor\\n\",\n \"from gs_quant.analytics.processors.utility_processors import LastProcessor, DivisionProcessor\\n\",\n \"from gs_quant.api.gs.users import GsUsersApi\\n\",\n \"from gs_quant.data import DataCoordinate, DataMeasure, DataFrequency, DataDimension\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.entities.entitlements import Entitlements, EntitlementBlock, User\\n\",\n \"from gs_quant.markets.securities import Asset, AssetIdentifier\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Next, let's create our Datagrid, all the visualization example we will be using the following [Datagrid](https://developer.gs.com/docs/gsquant/data/data-analytics/datagrid/overview/) \\n\",\n \"as data source:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"h:\\\\virtual\\\\gs-quant\\\\lib\\\\site-packages\\\\urllib3\\\\connectionpool.py:1020: InsecureRequestWarning: Unverified HTTPS request is being made to host 'marquee.web.gs.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\\n InsecureRequestWarning,\\n\",\n \"h:\\\\virtual\\\\gs-quant\\\\lib\\\\site-packages\\\\urllib3\\\\connectionpool.py:1020: InsecureRequestWarning: Unverified HTTPS request is being made to host 'marquee.web.gs.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\\n InsecureRequestWarning,\\n\",\n \"h:\\\\virtual\\\\gs-quant\\\\lib\\\\site-packages\\\\urllib3\\\\connectionpool.py:1020: InsecureRequestWarning: Unverified HTTPS request is being made to host 'marquee.web.gs.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\\n InsecureRequestWarning,\\nh:\\\\virtual\\\\gs-quant\\\\lib\\\\site-packages\\\\urllib3\\\\connectionpool.py:1020: InsecureRequestWarning: Unverified HTTPS request is being made to host 'marquee.web.gs.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\\n InsecureRequestWarning,\\nh:\\\\virtual\\\\gs-quant\\\\lib\\\\site-packages\\\\urllib3\\\\connectionpool.py:1020: InsecureRequestWarning: Unverified HTTPS request is being made to host 'marquee.web.gs.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\\n InsecureRequestWarning,\\n\",\n \"h:\\\\virtual\\\\gs-quant\\\\lib\\\\site-packages\\\\urllib3\\\\connectionpool.py:1020: InsecureRequestWarning: Unverified HTTPS request is being made to host 'marquee.web.gs.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\\n InsecureRequestWarning,\\nh:\\\\virtual\\\\gs-quant\\\\lib\\\\site-packages\\\\urllib3\\\\connectionpool.py:1020: InsecureRequestWarning: Unverified HTTPS request is being made to host 'marquee.web.gs.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\\n InsecureRequestWarning,\\nh:\\\\virtual\\\\gs-quant\\\\lib\\\\site-packages\\\\urllib3\\\\connectionpool.py:1020: InsecureRequestWarning: Unverified HTTPS request is being made to host 'marquee.web.gs.com'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\\n InsecureRequestWarning,\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\nDatagrid is currently experimental. The API is likely to change. For questions contact gs-marquee-markets@gs.com\\n\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": \"
\\n\\n\\n \\n \\n \\n \\n \\n \\n \\n
\\n
\",\n \"text/plain\": \"Empty DataFrame\\nColumns: []\\nIndex: []\"\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Fetching assets to work with\\n\",\n \"SPX = Asset.get(\\\"MA4B66MW5E27U8P32SB\\\", AssetIdentifier.MARQUEE_ID)\\n\",\n \"SX5E = Asset.get(\\\"MA4B66MW5E27U8P32SY\\\", AssetIdentifier.MARQUEE_ID)\\n\",\n \"UKX = Asset.get(\\\"MA4B66MW5E27U8NN95P\\\", AssetIdentifier.MARQUEE_ID)\\n\",\n \"HSCEI = Asset.get(\\\"MA4B66MW5E27U8P3294\\\", AssetIdentifier.MARQUEE_ID)\\n\",\n \"NKY = Asset.get(\\\"MA4B66MW5E27U8P32LH\\\", AssetIdentifier.MARQUEE_ID)\\n\",\n \"KS200 = Asset.get(\\\"MA4B66MW5E27U8P32DM\\\", AssetIdentifier.MARQUEE_ID)\\n\",\n \"IBOV = Asset.get(\\\"MA4B66MW5E27U8NN8N6\\\", AssetIdentifier.MARQUEE_ID)\\n\",\n \"\\n\",\n \"# Defining Datagrid rows\\n\",\n \"rows = [DataRow(SPX), DataRow(SX5E), DataRow(UKX), DataRow(HSCEI), DataRow(NKY), DataRow(KS200), DataRow(IBOV)]\\n\",\n \"\\n\",\n \"# Defining column operations\\n\",\n \"spx_spot = DataCoordinate(measure=DataMeasure.CLOSE_PRICE, frequency=DataFrequency.DAILY)\\n\",\n \"\\n\",\n \"implied_vol = DataCoordinate(\\n\",\n \" measure=DataMeasure.IMPLIED_VOLATILITY,\\n\",\n \" frequency=DataFrequency.DAILY,\\n\",\n \" dimensions={DataDimension.TENOR: \\\"3m\\\", DataDimension.STRIKE_REFERENCE: \\\"delta\\\", DataDimension.RELATIVE_STRIKE: 0.5},\\n\",\n \")\\n\",\n \"\\n\",\n \"implied_spot = LastProcessor(implied_vol)\\n\",\n \"realized_vol = LastProcessor(\\n\",\n \" DivisionProcessor(VolatilityProcessor(spx_spot, w=63, start=RelativeDate(\\\"-1y\\\")), dividend=100)\\n\",\n \")\\n\",\n \"\\n\",\n \"col_0 = DataColumn(name=\\\"Name\\\", processor=EntityProcessor(field=\\\"short_name\\\"))\\n\",\n \"col_1 = DataColumn(name=\\\"Implied Volatility\\\", processor=implied_spot)\\n\",\n \"col_2 = DataColumn(name=\\\"Realized Vol\\\", processor=realized_vol)\\n\",\n \"\\n\",\n \"columns = [col_0, col_1, col_2]\\n\",\n \"\\n\",\n \"# Entitlements\\n\",\n \"# Entitle everyone internal to view but only yourself to edit and change entitlements\\n\",\n \"user_id = GsUsersApi.get_my_guid()\\n\",\n \"self_entitlement_block = EntitlementBlock(users=[User.get(user_id=user_id)])\\n\",\n \"entitlements = Entitlements(view=self_entitlement_block, edit=self_entitlement_block, admin=self_entitlement_block)\\n\",\n \"\\n\",\n \"# Datagrid creation\\n\",\n \"datagrid = DataGrid(name=\\\"Datagrid For Visualization\\\", rows=rows, columns=columns, entitlements=entitlements)\\n\",\n \"\\n\",\n \"datagrid_id = datagrid.save()\\n\",\n \"datagrid.initialize()\\n\",\n \"datagrid.poll()\\n\",\n \"datagrid.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Once we have our Datagrid we can use it as our data source to create the visualizations. Let's start with a Bar Chart\\n\",\n \"example:\\n\",\n \"\\n\",\n \"### Bar Charts\\n\",\n \"\\n\",\n \"To create a bar chart, simply pass in your datagrid and entitlements to the `PlotlyViz` class to initialize the class.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"visualization = PlotlyViz(datagrid=datagrid, entitlements=entitlements)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Then, call the bar function, passing the parameters that you require for your visualization. In this case we're simply\\n\",\n \"passing x, y and title. We're passing the column names from our Datagrid to our `x` and `y` parameters as well as a \\n\",\n \"visualization title:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"visualization.bar(x='Name', y='Realized Vol', title=\\\"Realized Vol Bar Chart Example\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"*Note: All parameters used in our visualization functions are the same parameters you'd use in [Plotly Express](https://plotly.com/python/plotly-express/).\\n\",\n \"\\n\",\n \"Next, we can persist our chart and visualize it in Marquee like so:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"viz_id = visualization.save()\\n\",\n \"# The open() method does not work in Jupyter, but it works fine from your own IDE.\\n\",\n \"visualization.open()\\n\",\n \"print(f'Your data visualization should be viewable at: https://marquee.gs.com/s/markets/visualizations/{viz_id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Scatter Charts\\n\",\n \"\\n\",\n \"Similarly, we can create scatter charts like so:\\n\",\n \"\\n\",\n \"In our previous Bar chart example, we passed the Datagrid variable directly to `PlotlyViz`. Now, since that Datagrid\\n\",\n \"is already created, let's simply use it's ID as reference:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"visualization = PlotlyViz(datagrid_id=datagrid_id, entitlements=entitlements)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Next, use the scatter function and pass the required variables. After that, you should be able to save and open it to\\n\",\n \"visualize in Marquee:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"visualization.scatter(x='Implied Volatility', y='Realized Vol', color=\\\"Name\\\", title=\\\"Scatter Chart Example\\\")\\n\",\n \"viz_id = visualization.save()\\n\",\n \"# The open() method does not work in Jupyter, but it works fine from your own IDE.\\n\",\n \"visualization.open()\\n\",\n \"print(f'Your data visualization should be viewable at: https://marquee.gs.com/s/markets/visualizations/{viz_id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Bubble Charts\\n\",\n \"\\n\",\n \"Bubble charts are actually scatter charts but with a third variable that specifies the dot size. Here's an example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"visualization = PlotlyViz(datagrid_id=datagrid_id, entitlements=entitlements)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"Now, call the scatter function with the required variables. After that, you should be able to save and open it to\\n\",\n \"visualize in Marquee:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Using Realized Vol as the column to use for dot size\\n\",\n \"visualization.scatter(\\n\",\n \" x='Implied Volatility', y='Realized Vol', size='Realized Vol', color=\\\"Name\\\", title=\\\"Bubble Chart Example\\\"\\n\",\n \")\\n\",\n \"viz_id = visualization.save()\\n\",\n \"# The open() method does not work in Jupyter, but it works fine from your own IDE.\\n\",\n \"visualization.open()\\n\",\n \"print(f'Your data visualization should be viewable at: https://marquee.gs.com/s/markets/visualizations/{viz_id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Radar Charts\\n\",\n \"\\n\",\n \"A useful chart in different scenarios is the Radar Chart. This chart can be created like so:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"visualization = PlotlyViz(datagrid_id=datagrid_id, entitlements=entitlements)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"The function to call this time is called `line_polar`. Call this function with the required variables. After that, you should be able to save and open it to\\n\",\n \"visualize in Marquee:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"visualization.line_polar(r='Implied Volatility', theta=\\\"Name\\\", line_close=True, title=\\\"Radar Chart\\\")\\n\",\n \"viz_id = visualization.save()\\n\",\n \"# The open() method does not work in Jupyter, but it works fine from your own IDE.\\n\",\n \"visualization.open()\\n\",\n \"print(f'Your data visualization should be viewable at: https://marquee.gs.com/s/markets/visualizations/{viz_id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Again, all the parameters used in these visualizations are the same that [Plotly Express](https://plotly.com/python/plotly-express/) uses,\\n\",\n \"You can also see some useful examples on their site for the visualizations we support: [Bar](https://plotly.com/python/bar-charts/), \\n\",\n \"[Scatter](https://plotly.com/python/line-and-scatter/), [Bubble](https://plotly.com/python/line-and-scatter/#bubble-scatter-plots), \\n\",\n \"[Pie](https://plotly.com/python/pie-charts/), [Radar](https://plotly.com/python/radar-chart/)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/00_data/04_screens/examples/00_base_screener_api_tutorial.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"49804acd\",\n \"metadata\": {},\n \"source\": [\n \"# GS Quant Base Screener Tutorial\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"614cf75b\",\n \"metadata\": {},\n \"source\": [\n \"Base screeners are tools used to explore and compare information about specific assets. These screeners act as permanent data tables containing rows of information about different assets and indices. Each base screener is recomputed once a day, and `gs_quant` screens act as persistent filters for screeners. Each base screener has two main components: the screener schema, which determines what data is permitted to be in the screener, and the screener data, which can be published to the screener after the schema is fixed. \\n\",\n \"\\n\",\n \"New features in the `gs_quant` library now allow users with the requisite permissions to create and manipulate screeners using only library functions. Below, we demonstrate how to use each library function and provide examples of common use cases. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f91951a\",\n \"metadata\": {},\n \"source\": [\n \"### Imports\\n\",\n \"\\n\",\n \"First, we import the basic functions we need to begin manipulating base screeners in `gs_quant`. Additional imports are also used for easier visualization of screener data. \\n\",\n \"\\n\",\n \"\\n\",\n \"To use any of the screener functions in `GsBaseScreenerApi`, users must have an active `GsSession`. Users must also be members of the `PlotScreenerAdmins` group in `gs_quant` in order to use functions such as `create_screener()`, `edit_screener()`, `clear_screener()`, `delete_screener()`, and `publish_to_screener()`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8d926374\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Required imports\\n\",\n \"from gs_quant.session import GsSession, Environment # Authenticate user\\n\",\n \"from gs_quant.api.gs.base_screener import GsBaseScreenerApi # Base screener functions\\n\",\n \"from gs_quant.target.base_screener import (\\n\",\n \" Screener,\\n\",\n \" ScreenerParameters,\\n\",\n \" ScreenerRow,\\n\",\n \" ScreenerColumn,\\n\",\n \") # Screener data classes\\n\",\n \"from gs_quant.target.common import FilterRequest\\n\",\n \"\\n\",\n \"# Visualization imports\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.api.gs.data_screen import GsDataScreenApi\\n\",\n \"from gs_quant.target.data_screen import AnalyticsScreen\\n\",\n \"from time import sleep\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD) # Initialize GsSession\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1838f1c\",\n \"metadata\": {},\n \"source\": [\n \"## Overview\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"20958ee6\",\n \"metadata\": {},\n \"source\": [\n \"Below is a brief overview of a typical use case: getting a screener, creating a new screener, editing a schema, publishing data to a screener, viewing screener data, clearing a screener, and deleting a screener. For more detail on these actions, continue to the sections below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5f3a10dc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"existing_schema = GsBaseScreenerApi.get_screener('BSW3GT9DS8A86MTSH3')\\n\",\n \"\\n\",\n \"\\n\",\n \"existing_schema.name = 'Tutorial Overview Screener'\\n\",\n \"\\n\",\n \"new_screener = GsBaseScreenerApi.create_screener(existing_schema)\\n\",\n \"\\n\",\n \"\\n\",\n \"new_col = ScreenerColumn(column_name='1m IV', expression='ASSET.implied_volatility(1m, spot, 100)')\\n\",\n \"\\n\",\n \"new_screener.parameters.columns = new_screener.parameters.columns + (new_col,)\\n\",\n \"\\n\",\n \"\\n\",\n \"data = {\\n\",\n \" 'rows': [\\n\",\n \" {'id': 0, 'Name': 'Obalon Therapeutics Inc', 'BBID': 'DSE', '1m IV': 0.15},\\n\",\n \" {'id': 1, 'Name': 'Disney Inc', 'BBID': 'DIS', '1m IV': 0.16},\\n\",\n \" ]\\n\",\n \"}\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.publish_to_screener(new_screener.id, data)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"\\n\",\n \"screen_schema = AnalyticsScreen(\\n\",\n \" name='Overview Screener View', filter_parameters=FilterRequest(), base_screener=new_screener.id\\n\",\n \")\\n\",\n \"\\n\",\n \"screen = GsDataScreenApi.create_screen(screen_schema)\\n\",\n \"\\n\",\n \"display(pd.DataFrame(GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)))\\n\",\n \"\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.clear_screener(new_screener.id)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"display(pd.DataFrame(GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)))\\n\",\n \"\\n\",\n \"\\n\",\n \"GsDataScreenApi.delete_screen(screen.id)\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.delete_screener(new_screener.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"27fefdea\",\n \"metadata\": {},\n \"source\": [\n \"## Viewing Existing Base Screeners\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"97804a6a\",\n \"metadata\": {},\n \"source\": [\n \"### Class `Screener`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b0c719c\",\n \"metadata\": {},\n \"source\": [\n \"In `gs_quant`, a screener is represented by an object of class `Screener`. More explicitly, a `Screener` object describes the schema of an existing base screener. These objects are often returned when retrieving a screener using its ID or creating a new screener with a specific schema. Some notable fields of this type include: \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`name`** *(str, Required)*: The name of the base screener.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`parameters`** *(ScreenerParameters, Required)*: An object of type ScreenerParameters that specifies the exact rows (ex. assets or indices) and columns (attributes) that should be visible in this screener. Only data conforming to the ScreenerParameters object of the screener can be published to the screener. This `ScreenerParameters` object also determines how values in the screener are updated over time. \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`id`** *(str, Optional)*: An ID that uniquely identifies this screener, which is permanent. As long as this screener is not deleted, this ID will reference the same screener. This ID can be used to retrieve screener information and data from permanent storage. \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`cron_schedule`** *(str, Required)*: A cron schedule expression to schedule a cron job using this screener. Currently, this feature is not implemented; however, the functions using `Screener` objects still expect a valid cron schedule expression in this location.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1b4ecf8e\",\n \"metadata\": {},\n \"source\": [\n \"### Function `get_screeners()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e38bfcc4\",\n \"metadata\": {},\n \"source\": [\n \"When new screeners are created, they are stored persistently. Function `get_screeners()` returns a list of all base screeners available to the user. Each screener or entry in this tuple is a `Screener` object that can be used to retrieve the screener's data, manipulate the screener, or create a new screener. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fe113420\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: get_screeners()\\n\",\n \"\\n\",\n \"all_screeners = GsBaseScreenerApi.get_screeners()\\n\",\n \"print(all_screeners)\\n\",\n \"\\n\",\n \"screener = all_screeners[0]\\n\",\n \"\\n\",\n \"print(\\n\",\n \" '\\\\nBase Screener Name: %s, Base Screener Column 1: %s, Base Screener ID: %s'\\n\",\n \" % (screener.name, screener.parameters.columns[0].column_name, screener.id)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"29e18ed4\",\n \"metadata\": {},\n \"source\": [\n \"### Function `get_screener()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9ab25b7b\",\n \"metadata\": {},\n \"source\": [\n \"Use function `get_screener(screener_id)`to retrieve the `Screener` object for a specific screener using its ID. This function allows a user to search for a specific screener to view its schema and information. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b1dbe4fa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: get_screener()\\n\",\n \"\\n\",\n \"screener = GsBaseScreenerApi.get_screener('BSW3GT9DS8A86MTSH3')\\n\",\n \"\\n\",\n \"print(screener)\\n\",\n \"\\n\",\n \"print(\\n\",\n \" '\\\\nBase Screener Name: %s, Base Screener Column 1: %s, Base Screener ID: %s'\\n\",\n \" % (screener.name, screener.parameters.columns[0].column_name, screener.id)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"289b201a\",\n \"metadata\": {},\n \"source\": [\n \"## Creating and Deleting Base Screeners \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"22db9ccd\",\n \"metadata\": {},\n \"source\": [\n \"### Class `ScreenerRow`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b0129a76\",\n \"metadata\": {},\n \"source\": [\n \"Class `ScreenerRow` defines a row or set of rows (i.e. assets or indices) that can be viewed in this screener. Note that this class does not publish data to a screener. Rather, `ScreenerRow` objects contain information about possible rows that the base screener's schema should accept. Tuples of `ScreenerRow` objects are added to the `rows` field of `ScreenerParameter` instances to define the rowwise schema of a screener; it is up to the user to then publish corresponding data to the screener in order to make this data visible. Notable fields of this type include: \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`entity_id`**, *(str, Required)*: The unique ID of the entity that this `ScreenerRow` adds to the rowwise schema.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`entity_type`**, *(str, Required)*: The type of the entity specified in this `ScreenerRow` object. Currently, this field is not in use and can just be set to the string `'asset'`.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`expand`**, *(bool, Optional)*: In the event that the asset is a positioned entity, such as an index or a mutual fund, a user can opt to enter all assets contained in this entity as their own rows in the schema by setting the `expand` field to be `True`. \\n\",\n \"\\n\",\n \"**Note:** Currently, although users specify individual assets for each row in the schema, these specifications are not strictly enforced. The screener will not reject data that is not one of the permitted assets specified in the schema, but it is best practice to create a schema that matches the intended data closely.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e2a8f007\",\n \"metadata\": {},\n \"source\": [\n \"### Class `ScreenerColumn`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4582c75d\",\n \"metadata\": {},\n \"source\": [\n \"Class `ScreenerColumn` defines a single column to be added to a base screener's schema. Similarly to `ScreenerRow`, this class does not publish data to a screener. Rather, `ScreenerColumn` objects contain information about possible columns that the base screener's schema should accept. Tuples of `ScreenerColumn` objects are added to the `columns` field of `ScreenerParameter` instances to define the columnwise schema of a screener; it is up to the user to then publish corresponding data to the screener in order to make this data visible. Notable fields of this type include: \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`column_name`** *(str, Required)*: The name of the column to add to the base screener's schema.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`entity_parameter`** *(str, Optional)*: The entity parameter name corresponding to the values in this column, if applicable. Specifying this field allows the screener to be automatically updated once per day with the correct value of this column for each asset. Possible `entity_parameter` values include `id`, `bbid`, `region`, `assetClass`, `currency`, `description`, `exchange`, `name`, `type`, `ric`, `region`, `assetClassificationsCountryName`, `assetClassificationsGicsSector`, and `assetClassificationGicsIndustry`.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`expression`** *(str, Optional)*: The PlotTool expression to calculate this column, if applicable. For calculated columns, providing a PlotTool expression allows the screener to update once per day with the most recent value of this expression. No specific asset should be provided with this expression, as it will be applied individually to each asset in each row of the screener's data. Use the placeholder `ASSET` for an asset name in the expression where needed (ex. `\\\"ASSET.implied_volatility(1m, delta_call, 50)\\\"`).\\n\",\n \"\\n\",\n \"\\n\",\n \"**Note**: Although both the `entity_parameter` and `expression` fields are optional, each individual column must fill out exactly one of these fields. Currently, base screeners do not support entering custom asset values not tied to expressions or entity information. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"29186dd9\",\n \"metadata\": {},\n \"source\": [\n \"### Class `ScreenerParameters`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"34dbfe0c\",\n \"metadata\": {},\n \"source\": [\n \"Class `ScreenerParameters` gathers and defines an entire schema for a base_screener. This schema object is then assigned to the `parameters` field in a `Screener` object when creating a new screen with this specific schema. Notable fields of this type include: \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`name`** *(str, Optional)*: The name of this schema. \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`rows`** *(ScreenerRow tuple, Required)*: A tuple of `ScreenerRow` objects that define each allowed data row in the schema.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`columns`** *(ScreenerColumn tuple, Required)*: A tuple of `ScreenerColumn` objects that define the allowed columns in the schema.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"188c1e26\",\n \"metadata\": {},\n \"source\": [\n \"### Function `create_screener()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"743b9be8\",\n \"metadata\": {},\n \"source\": [\n \"To create a new base screener, pass a `Screener` object into the function `create_screener(screener)`. The `Screener` object should have the correct specifications for the new base screener, including a `parameters` field with a `ScreenParameters` object populated with the correct schema. The `id` field of the input `Screener` object can be left blank, as this function will return an identical `Screener` object to the input, except it will have its `id` field populated with the ID for the new base screener. \\n\",\n \"\\n\",\n \"The returning of the identical `Screener` objects confirms that the new base screener has the intended schema, which is also visible by using the `get_screener()` function with the new screener ID.\\n\",\n \"\\n\",\n \"**Note:** Only users who are members of the `PlotScreenerAdmins` group can use the `create_screener()` function to make new screeners.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1cb13147\",\n \"metadata\": {},\n \"source\": [\n \"### Function `delete_screener()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5684a951\",\n \"metadata\": {},\n \"source\": [\n \"Pass a screener's unique ID into the function `delete_screener(screener_id)` to permanently delete a base screener and all of its associated data. The screener will no longer be accessible to any user after this action. \\n\",\n \"\\n\",\n \"**Note:** Only users who are members of the `PlotScreenerAdmins` group can use the `delete_screener()` function to remove screeners.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"63078318\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: create_screener(), delete_screener()\\n\",\n \"\\n\",\n \"# Create a Schema\\n\",\n \"\\n\",\n \"rows = (\\n\",\n \" ScreenerRow(entity_id='MAYDMZQXXVXD9JK7', entity_type='asset'),\\n\",\n \" ScreenerRow(entity_id='MAQJQTVRMKVKX9FZ', entity_type='asset'),\\n\",\n \")\\n\",\n \"\\n\",\n \"columns = (\\n\",\n \" ScreenerColumn(column_name='Name', entity_parameter='name'),\\n\",\n \" ScreenerColumn(column_name='BBID', entity_parameter='bbid'),\\n\",\n \" ScreenerColumn(column_name='RSI 14 DAY', expression='relative_strength_index(ASSET.spot(), 14d)'),\\n\",\n \" ScreenerColumn(column_name='Skew', expression='ASSET.skew(1m, delta, 25, normalized)'),\\n\",\n \")\\n\",\n \"\\n\",\n \"params = ScreenerParameters(rows=rows, columns=columns)\\n\",\n \"\\n\",\n \"cron_schedule = '5 4 * * *'\\n\",\n \"\\n\",\n \"schema = Screener(name='My New Screener', parameters=params, cron_schedule=cron_schedule)\\n\",\n \"\\n\",\n \"# Create a New Screener\\n\",\n \"\\n\",\n \"new_screener = GsBaseScreenerApi.create_screener(schema)\\n\",\n \"\\n\",\n \"print('Screener Name: %s, Screener ID: %s\\\\n' % (new_screener.name, new_screener.id))\\n\",\n \"\\n\",\n \"# Delete Screener\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.delete_screener(new_screener.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"142d742b\",\n \"metadata\": {},\n \"source\": [\n \"Additionally, we can use existing `Screener` objects and function `create_screener(screener)` to create new screeners by copying or modifying existing screeners. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5b36b194\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example: Create a New Screener from an Existing Schema and Delete It\\n\",\n \"\\n\",\n \"# Retrieve Existing Screener\\n\",\n \"\\n\",\n \"screener = GsBaseScreenerApi.get_screener('BSW3GT9DS8A86MTSH3')\\n\",\n \"\\n\",\n \"print(\\n\",\n \" 'Screener ID: %s, Screener Columns: %s\\\\n'\\n\",\n \" % (screener.id, [column.column_name for column in screener.parameters.columns])\\n\",\n \")\\n\",\n \"\\n\",\n \"# Modify Schema\\n\",\n \"\\n\",\n \"screener.parameters.columns = (screener.parameters.columns[0],)\\n\",\n \"\\n\",\n \"# Create a New Screener\\n\",\n \"\\n\",\n \"new_screener = GsBaseScreenerApi.create_screener(screener)\\n\",\n \"\\n\",\n \"print(\\n\",\n \" 'New Screener ID: %s, New Screener Columns: %s'\\n\",\n \" % (new_screener.id, [column.column_name for column in new_screener.parameters.columns])\\n\",\n \")\\n\",\n \"\\n\",\n \"# Delete Screener\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.delete_screener(new_screener.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18404d92\",\n \"metadata\": {},\n \"source\": [\n \"## Viewing Base Screener Data\\n\",\n \"\\n\",\n \"\\n\",\n \"To view data from a base screener, please create a `gs_quant` screen that sources data from this base screener and uses any desired filters. More information on how to create a screen can be found in the `gs_quant` screens tutorial. \\n\",\n \"\\n\",\n \"**Note**: Please wait a short amount of time between alternating operations of editing the base screener and viewing its data to allow time for the base screener to update. Using the python `sleep(seconds)` function in the `time` module between operations is a simple way to ensure consistency when viewing recently edited data from a base screener. Between nearby operations, `sleep(1)` should be sufficient.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d87d18ef\",\n \"metadata\": {},\n \"source\": [\n \"## Publishing Data to Base Screeners and Clearing Screeners\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bca147f8\",\n \"metadata\": {},\n \"source\": [\n \"### Function `clear_screener()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"de202b51\",\n \"metadata\": {},\n \"source\": [\n \"To clear a base screener of its data, use the function `clear_screener(screener_id)` with the screener ID of the base screener being cleared. The base screener schema will still be permanently stored and accessible with the same ID, but the screener will be empty.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d65fae34\",\n \"metadata\": {},\n \"source\": [\n \"### Data Publishing Format\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5871eff3\",\n \"metadata\": {},\n \"source\": [\n \"After a base screener is created using a schema, data following this schema can be added to the screener. \\n\",\n \"\\n\",\n \"\\n\",\n \"`gs_quant`'s publishing function accepts data as a dictionary containing a single list of dictionaries. Each dictionary in the list identifies a single row (or asset) of data. The keys of the dictionary must match the names of columns in the schema, and the value corresponding to a specific key will be the value entered at this column for a particular asset. Including additional keys within data rows will cause an error, and no data will be published to the screener. Including fewer keys within data rows will cause unspecified columns to have an empty value for this asset. This complete list of dictionaries is then inserted as the value of the `'rows'` key in an outer dictionary. \\n\",\n \"\\n\",\n \"\\n\",\n \"In addition to column name-value pairs within row dictionaries, users can also optionally include a unique `'id'` key and value in each row of data. This `'id'` key allows users to overwrite specific rows with new data after earlier data has already been published to the screener. If no `'id'` key is specified when data is first published, each row will automatically be given a unique ID that can be retrieved when the screener's data is viewed. If a data row is re-published with the same `'id'` value, this data row will be overwritten in favor of newer data. If a data row is re-published without specifying an ID, a new data row will be appended to the screener regardless of whether or not this data already exists in the base screener. \\n\",\n \"\\n\",\n \"\\n\",\n \"Below is an example of a correctly formatted data dictionary. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"94543cdd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example: Data Format\\n\",\n \"\\n\",\n \"{\\n\",\n \" 'rows': [\\n\",\n \" {\\n\",\n \" 'id': 0, # Specifies a unique ID\\n\",\n \" 'Name': 'Obalon Therapeutics Inc',\\n\",\n \" 'Entity ID': 'ABDFDSCD',\\n\",\n \" 'BBID': 'DSE',\\n\",\n \" 'Impliedretailbuynotional': 4.0,\\n\",\n \" 'Notional': 450.3,\\n\",\n \" 'Implied / realized': 14.2,\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'Name': 'Disney Inc', # Does not specify a unique ID\\n\",\n \" 'Entity ID': 'ABDFDSCe',\\n\",\n \" 'BBID': 'DIS',\\n\",\n \" 'Impliedretailbuynotional': 4.0,\\n\",\n \" 'Notional': 45.0,\\n\",\n \" 'Implied / realized': 14.0,\\n\",\n \" },\\n\",\n \" ]\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"02a0004d\",\n \"metadata\": {},\n \"source\": [\n \"### Function `publish_to_screener()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"56c9df1c\",\n \"metadata\": {},\n \"source\": [\n \"Use function `publish_to_screener(screener_id, data)` to publish the specified data to the base screener with the corresponding screener ID. This function returns a copy of the data published, along with additional storage information. \\n\",\n \"\\n\",\n \"Any data that includes an `'id'` field will overwrite any existing row with the same `'id'` value or add a new row with this ID if none exists. Any data that is provided without a specified `'id'` value will be appended to any existing screener data. Publishing to a screener mulitple times will not erase any previously published data unless it is overwritten through the use of row IDs. \\n\",\n \"\\n\",\n \"**Note:** Only users who are members of the `PlotScreenerAdmins` group can use the `publish_to_screener()` function to add data to existing screeners.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7bf59ad5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: publish_to_screener(), clear_screener()\\n\",\n \"\\n\",\n \"# Create Screener\\n\",\n \"\\n\",\n \"rows = (ScreenerRow(entity_id='MAYDMZQXXVXD9JK7', entity_type='asset'),)\\n\",\n \"\\n\",\n \"columns = (ScreenerColumn(column_name='Name', entity_parameter='name'),)\\n\",\n \"\\n\",\n \"params = ScreenerParameters(rows=rows, columns=columns)\\n\",\n \"\\n\",\n \"cron_schedule = '5 4 * * *'\\n\",\n \"\\n\",\n \"schema = Screener(name='My New Screener', parameters=params, cron_schedule=cron_schedule)\\n\",\n \"\\n\",\n \"screener = GsBaseScreenerApi.create_screener(schema)\\n\",\n \"\\n\",\n \"# Publish data to screener\\n\",\n \"\\n\",\n \"data = {\\n\",\n \" 'rows': [\\n\",\n \" {\\n\",\n \" 'id': 0, # Specifies a unique ID\\n\",\n \" 'Name': 'Obalon Therapeutics Inc',\\n\",\n \" }\\n\",\n \" ]\\n\",\n \"}\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.publish_to_screener(screener.id, data)\\n\",\n \"\\n\",\n \"sleep(1) # Sleep to Ensure Viewing Consistency\\n\",\n \"\\n\",\n \"# Create an Empty Screen to View Screener Data\\n\",\n \"\\n\",\n \"screen_schema = AnalyticsScreen(name='My View', filter_parameters=FilterRequest(), base_screener=screener.id)\\n\",\n \"\\n\",\n \"screen = GsDataScreenApi.create_screen(screen_schema)\\n\",\n \"\\n\",\n \"view_data = GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)\\n\",\n \"\\n\",\n \"print('Screener %s with Initial Data:\\\\n' % (screener.id))\\n\",\n \"\\n\",\n \"display(pd.DataFrame(view_data))\\n\",\n \"\\n\",\n \"# Re-Publish to a Screener\\n\",\n \"\\n\",\n \"new_data = {\\n\",\n \" 'rows': [\\n\",\n \" {\\n\",\n \" 'id': 0, # Overwrite this Row\\n\",\n \" 'Name': 'MY NEW NAME',\\n\",\n \" },\\n\",\n \" {'Name': 'Disney Inc'},\\n\",\n \" ]\\n\",\n \"}\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.publish_to_screener(screener.id, new_data)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"# View Screener Data\\n\",\n \"\\n\",\n \"view_data = GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)\\n\",\n \"\\n\",\n \"print('\\\\nScreener %s with Re-Published Data:\\\\n' % (screener.id))\\n\",\n \"\\n\",\n \"display(pd.DataFrame(view_data))\\n\",\n \"\\n\",\n \"# Clear Screener\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.clear_screener(screener.id)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"view_data = GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)\\n\",\n \"\\n\",\n \"print('\\\\nCleared Screener %s Data:\\\\n' % (screener.id))\\n\",\n \"\\n\",\n \"display(pd.DataFrame(view_data))\\n\",\n \"\\n\",\n \"# Delete Screen and Screener\\n\",\n \"\\n\",\n \"GsDataScreenApi.delete_screen(screen.id)\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.delete_screener(screener.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"81af2906\",\n \"metadata\": {},\n \"source\": [\n \"## Editing Base Screeners\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1ca52c15\",\n \"metadata\": {},\n \"source\": [\n \"### Function `edit_screener()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"452b52a5\",\n \"metadata\": {},\n \"source\": [\n \"To modify the schema of a base screener, use the function `edit_screener(screener_id, screener)` with the screener ID of the screener to modify and a `Screener` object specifying the schema that this screener should now have. \\n\",\n \"\\n\",\n \"\\n\",\n \"It is best practice to clear the screener of any existing data before making modifications to its schema. Then, users can re-publish their data to take full advantage of the new schema without needing to manually adjust individual rows. \\n\",\n \"\\n\",\n \"\\n\",\n \"`edit_screener(screener_id, screener)` will throw an error if the screener ID provided does not match the ID field in the `Screener` object provided.\\n\",\n \"\\n\",\n \"\\n\",\n \"**Note:** Only users who are members of the `PlotScreenerAdmins` group can use the `edit_screener()` function to modify existing screeners.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"739eedee\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: edit_screener()\\n\",\n \"\\n\",\n \"# Create Screener\\n\",\n \"\\n\",\n \"rows = (ScreenerRow(entity_id='MAYDMZQXXVXD9JK7', entity_type='asset'),)\\n\",\n \"\\n\",\n \"columns = (ScreenerColumn(column_name='Name', entity_parameter='name'),)\\n\",\n \"\\n\",\n \"params = ScreenerParameters(rows=rows, columns=columns)\\n\",\n \"\\n\",\n \"cron_schedule = '5 4 * * *'\\n\",\n \"\\n\",\n \"schema = Screener(name='My New Screener', parameters=params, cron_schedule=cron_schedule)\\n\",\n \"\\n\",\n \"screener = GsBaseScreenerApi.create_screener(schema)\\n\",\n \"\\n\",\n \"print('Screener ID: %s, Columns: %s\\\\n' % (screener.id, [col.column_name for col in screener.parameters.columns]))\\n\",\n \"\\n\",\n \"# Create Screen\\n\",\n \"\\n\",\n \"screen_schema = AnalyticsScreen(name='My View', filter_parameters=FilterRequest(), base_screener=screener.id)\\n\",\n \"\\n\",\n \"screen = GsDataScreenApi.create_screen(screen_schema)\\n\",\n \"\\n\",\n \"# Publish Data and View\\n\",\n \"\\n\",\n \"data = {\\n\",\n \" 'rows': [\\n\",\n \" {\\n\",\n \" 'id': 0,\\n\",\n \" 'Name': 'Obalon Therapeutics Inc',\\n\",\n \" }\\n\",\n \" ]\\n\",\n \"}\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.publish_to_screener(screener.id, data)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"view_data = GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)\\n\",\n \"\\n\",\n \"print('Screener %s Data:\\\\n' % (screener.id))\\n\",\n \"\\n\",\n \"display(pd.DataFrame(view_data))\\n\",\n \"\\n\",\n \"# Clear Data\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.clear_screener(screener.id)\\n\",\n \"\\n\",\n \"# Edit Screener Schema (Columns)\\n\",\n \"\\n\",\n \"screener.parameters.columns = screener.parameters.columns + (\\n\",\n \" ScreenerColumn(column_name='BBID', entity_parameter='bbid'),\\n\",\n \")\\n\",\n \"\\n\",\n \"updated_screener = GsBaseScreenerApi.edit_screener(screener.id, screener)\\n\",\n \"\\n\",\n \"# View Screener Columns\\n\",\n \"\\n\",\n \"print(\\n\",\n \" '\\\\nScreener ID: %s, Columns: %s'\\n\",\n \" % (updated_screener.id, [col.column_name for col in updated_screener.parameters.columns])\\n\",\n \")\\n\",\n \"\\n\",\n \"# Publish Data and View\\n\",\n \"\\n\",\n \"data = {'rows': [{'id': 0, 'Name': 'Obalon Therapeutics Inc', 'BBID': 'DSE'}]}\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.publish_to_screener(screener.id, data)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"view_data = GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)\\n\",\n \"\\n\",\n \"print('\\\\nScreener %s Data:\\\\n' % (updated_screener.id))\\n\",\n \"\\n\",\n \"display(pd.DataFrame(view_data))\\n\",\n \"\\n\",\n \"# Delete Screen and Screener\\n\",\n \"\\n\",\n \"GsDataScreenApi.delete_screen(screen.id)\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.delete_screener(updated_screener.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"97f8519d\",\n \"metadata\": {},\n \"source\": [\n \"If an additional column is added to the schema without clearing the screener, all previously existing rows will have no value in the new column. The old data in these rows will remain the same. To fill in data for existing rows in the new column, users will need to manually re-publish data for all columns of each existing row using the corresponding row IDs. This method will completely overwrite the data in each specified row to better fit the new schema. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ea77601c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example: Manually Editing Rows\\n\",\n \"\\n\",\n \"# Create Screener\\n\",\n \"\\n\",\n \"rows = (ScreenerRow(entity_id='MAYDMZQXXVXD9JK7', entity_type='asset'),)\\n\",\n \"\\n\",\n \"columns = (ScreenerColumn(column_name='Name', entity_parameter='name'),)\\n\",\n \"\\n\",\n \"params = ScreenerParameters(rows=rows, columns=columns)\\n\",\n \"\\n\",\n \"cron_schedule = '5 4 * * *'\\n\",\n \"\\n\",\n \"schema = Screener(name='My New Screener', parameters=params, cron_schedule=cron_schedule)\\n\",\n \"\\n\",\n \"screener = GsBaseScreenerApi.create_screener(schema)\\n\",\n \"\\n\",\n \"print('Screener ID: %s, Columns: %s\\\\n' % (screener.id, [col.column_name for col in screener.parameters.columns]))\\n\",\n \"\\n\",\n \"# Create Screen\\n\",\n \"\\n\",\n \"screen_schema = AnalyticsScreen(name='My View', filter_parameters=FilterRequest(), base_screener=screener.id)\\n\",\n \"\\n\",\n \"screen = GsDataScreenApi.create_screen(screen_schema)\\n\",\n \"\\n\",\n \"# Publish Data and View\\n\",\n \"\\n\",\n \"data = {\\n\",\n \" 'rows': [\\n\",\n \" {\\n\",\n \" 'id': 0,\\n\",\n \" 'Name': 'Obalon Therapeutics Inc',\\n\",\n \" }\\n\",\n \" ]\\n\",\n \"}\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.publish_to_screener(screener.id, data)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"view_data = GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)\\n\",\n \"\\n\",\n \"print('Screener %s Data:\\\\n' % (screener.id))\\n\",\n \"\\n\",\n \"display(pd.DataFrame(view_data))\\n\",\n \"\\n\",\n \"# Edit Screener Schema\\n\",\n \"\\n\",\n \"screener.parameters.columns = screener.parameters.columns + (\\n\",\n \" ScreenerColumn(column_name='BBID', entity_parameter='bbid'),\\n\",\n \")\\n\",\n \"\\n\",\n \"updated_screener = GsBaseScreenerApi.edit_screener(screener.id, screener)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"# View Screener Columns\\n\",\n \"\\n\",\n \"print(\\n\",\n \" '\\\\nScreener ID: %s, Columns: %s'\\n\",\n \" % (updated_screener.id, [col.column_name for col in updated_screener.parameters.columns])\\n\",\n \")\\n\",\n \"\\n\",\n \"# Publish Additional Data\\n\",\n \"\\n\",\n \"data = {'rows': [{'id': 1, 'Name': 'Disney Inc', 'BBID': 'DIS'}]}\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.publish_to_screener(updated_screener.id, data)\\n\",\n \"\\n\",\n \"sleep(1)\\n\",\n \"\\n\",\n \"# View Screener Data\\n\",\n \"\\n\",\n \"view_data = GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)\\n\",\n \"\\n\",\n \"print('\\\\nScreener %s Data:\\\\n' % (updated_screener.id))\\n\",\n \"\\n\",\n \"display(pd.DataFrame(view_data))\\n\",\n \"\\n\",\n \"# Delete Screen and Screener\\n\",\n \"\\n\",\n \"GsDataScreenApi.delete_screen(screen.id)\\n\",\n \"\\n\",\n \"GsBaseScreenerApi.delete_screener(updated_screener.id)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/00_data/04_screens/examples/01_screen_api_tutorial.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fed9c09f\",\n \"metadata\": {},\n \"source\": [\n \"# GS Quant Screens Tutorial\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bb98323a\",\n \"metadata\": {},\n \"source\": [\n \"Marquee data screens persistent views of asset data that can be filtered, modified, created, and deleted. New features in the `gs_quant` library now allow users to view the screens provisioned to them, make new screens based on existing screens, filter screens, delete screens, and modify existing screens using only library functions. Below, we demonstrate how to use each library function and provide examples of common use cases. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bdafbb35\",\n \"metadata\": {},\n \"source\": [\n \"### Imports\\n\",\n \"\\n\",\n \"First, we import the basic functions we need to begin manipulating screens in `gs_quant`. Additional imports are also used for easier visualization of screen data. To use any of the screen functions in `GsDataScreenApi`, users must have an active `GsSession`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"47085e25\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Required imports\\n\",\n \"from gs_quant.session import GsSession, Environment # Authenticate user\\n\",\n \"from gs_quant.api.gs.data_screen import GsDataScreenApi # Screen functions\\n\",\n \"from gs_quant.target.data_screen import AnalyticsScreen, FilterRequest, OrderByBody # Screen data classes\\n\",\n \"\\n\",\n \"# Visualization imports\\n\",\n \"import pandas as pd\\n\",\n \"import pprint\\n\",\n \"import copy\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD) # Initialize GsSession\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e7113ed9\",\n \"metadata\": {},\n \"source\": [\n \"## Overview\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"401abf11\",\n \"metadata\": {},\n \"source\": [\n \"Below is a brief overview of a typical use case: getting, modifying, filtering, saving, and deleting a screen. For more detail on these actions, continue to the sections below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"44177740\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"existing_screen = GsDataScreenApi.get_screens()[0]\\n\",\n \"\\n\",\n \"column_info = GsDataScreenApi.get_column_info(existing_screen.id)\\n\",\n \"\\n\",\n \"new_screen_specs = copy.deepcopy(existing_screen)\\n\",\n \"\\n\",\n \"new_screen_specs.name = 'New Screen'\\n\",\n \"new_screen_specs.filter_parameters.filters = ({'columnName': 'Name', 'type': 'Substring', 'q': 'Z'},)\\n\",\n \"new_screen_specs.filter_parameters.limit = 3\\n\",\n \"new_screen_specs.filter_parameters.include_columns = ('Name', 'Skew')\\n\",\n \"\\n\",\n \"new_screen = GsDataScreenApi.create_screen(new_screen_specs)\\n\",\n \"\\n\",\n \"data = GsDataScreenApi.filter_screen(new_screen.id, new_screen.filter_parameters)\\n\",\n \"\\n\",\n \"display(pd.DataFrame(data))\\n\",\n \"\\n\",\n \"new_screen_specs = copy.deepcopy(existing_screen)\\n\",\n \"\\n\",\n \"new_screen_specs.name = 'New Screen'\\n\",\n \"new_screen_specs.filter_parameters.filters = ({'columnName': 'Name', 'type': 'Substring', 'q': 'Z'},)\\n\",\n \"new_screen_specs.filter_parameters.limit = 3\\n\",\n \"new_screen_specs.filter_parameters.include_columns = ('Name', 'Skew')\\n\",\n \"\\n\",\n \"new_screen = GsDataScreenApi.create_screen(new_screen_specs)\\n\",\n \"\\n\",\n \"data = GsDataScreenApi.filter_screen(new_screen.id, new_screen.filter_parameters)\\n\",\n \"\\n\",\n \"display(pd.DataFrame(data))\\n\",\n \"\\n\",\n \"new_screen.filter_parameters.include_columns = new_screen.filter_parameters.include_columns + ('BBG Ticker',)\\n\",\n \"\\n\",\n \"new_screen = GsDataScreenApi.update_screen(new_screen.id, new_screen)\\n\",\n \"\\n\",\n \"data = GsDataScreenApi.filter_screen(new_screen.id, new_screen.filter_parameters)\\n\",\n \"\\n\",\n \"display(pd.DataFrame(data))\\n\",\n \"\\n\",\n \"GsDataScreenApi.delete_screen(new_screen.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c436463e\",\n \"metadata\": {},\n \"source\": [\n \"## Viewing Screens\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"598e0687\",\n \"metadata\": {},\n \"source\": [\n \"### Class `AnalyticsScreen`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c82baae6\",\n \"metadata\": {},\n \"source\": [\n \"Each instance of class `AnalyticsScreen` represents a screen and contains all information about the screen's applied filters, ordering, columns, identifiers, data sources, and other properties. `AnalyticsScreen` objects are commonly returned when gathering data on existing screens or created by the user to generate or update screens. Some notable fields of this type include: \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`name`** *(str, Required)*: The name of this screen. \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`filter_parameters`** *(FilterRequest, Required)*: The filters applied to this screen's data currently.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`base_screener`** *(str, Required)*: An identifier of the data source that this screen uses. All data available in a screen comes directly from the corresponding base screener. The screen also applies all specified filters to this data before displaying it to the user.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`id`** *(str, Optional)*: The unique identifier of this screen. This ID can be used to retrieve screen information from permanent storage. \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`hidden_columns`** *(Tuple[str, ...], Optional)*: Available columns that are currently not shown in this screen. Filtering on a screen allows users to display or hide specific columns. The `hidden_columns` field lists which columns have been excluded from view by the user. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bf115fe9\",\n \"metadata\": {},\n \"source\": [\n \"### Function `get_screens()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fc0e1d35\",\n \"metadata\": {},\n \"source\": [\n \"When new screens are created, they are stored persistently. Function `get_screens()` returns a list of all screens available to the user. Each screen or entry in this list is an `AnalyticsScreen` object that can be used to retrieve the screen's data, manipulate the screen, or create a new screen. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fb2215f0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: get_screens()\\n\",\n \"\\n\",\n \"all_screens = GsDataScreenApi.get_screens()\\n\",\n \"print(all_screens)\\n\",\n \"\\n\",\n \"screen = all_screens[0]\\n\",\n \"\\n\",\n \"print('\\\\nScreen Name: %s, Base Screener: %s, Screen ID: %s' % (screen.name, screen.base_screener, screen.id))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"67be928b\",\n \"metadata\": {},\n \"source\": [\n \"### Function `get_screen()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"24972be6\",\n \"metadata\": {},\n \"source\": [\n \"Use function `get_screen(screen_id)`to retrieve the `AnalyticsScreen` object for a specific screen using its screen ID. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d75c9e2a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: get_screen()\\n\",\n \"\\n\",\n \"screen = GsDataScreenApi.get_screen('SCQC82C1C960G1I30P') # REMOVE:Keep permanent screen for this demo? in prod/dev?\\n\",\n \"\\n\",\n \"print(screen)\\n\",\n \"print('\\\\nScreen Name: %s, Base Screener: %s, Screen ID: %s' % (screen.name, screen.base_screener, screen.id_))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fc83e909\",\n \"metadata\": {},\n \"source\": [\n \"## Filtering and Viewing Screen Data \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bed56e08\",\n \"metadata\": {},\n \"source\": [\n \"### Function `get_column_info()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ea24d1ae\",\n \"metadata\": {},\n \"source\": [\n \"Before filtering a screen's data for specific assets, we may want to view a description of all columns available in this screen and their properties. Use function `get_column_info(screen_id)` to get information about all columns in a screen, including previously hidden columns. Descriptions of certain column types also include other metrics: \\n\",\n \"\\n\",\n \"- For `Enum` type columns, column descriptions include each possible value for the `Enum` as well as the number of times each value appears in the unfiltered data. \\n\",\n \"\\n\",\n \"\\n\",\n \"- For `Number` type columns, column descriptions include the maximum and minimum numeric values present in the unfiltered data.\\n\",\n \"\\n\",\n \"\\n\",\n \"- For `String` type columns, column descriptions are currently empty. If an `Enum` type column has more than 20 values, it is automatically converted to a `String` type column. \\n\",\n \"\\n\",\n \"These column properties can help users create more effective filters. The keys of the dictionary returned from `get_column_info()` correspond to the names of each column, and the corresponding values are the description of each column. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"77faa83d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: get_column_info() #ASK - is any string coluymn w/ <20 values immediately converted to enum? or no?\\n\",\n \"# Do some enum cols not show the values?\\n\",\n \"\\n\",\n \"column_info = GsDataScreenApi.get_column_info('SCQC82C1C960G1I30P')\\n\",\n \"pprint.pprint(column_info)\\n\",\n \"\\n\",\n \"# Example: Number type column description\\n\",\n \"\\n\",\n \"num_colname = 'Skew'\\n\",\n \"print('\\\\033[4m\\\\nNumeric Column:\\\\033[0m %s\\\\n ' % num_colname)\\n\",\n \"pprint.pprint(column_info[num_colname])\\n\",\n \"\\n\",\n \"# Example: Enum type column description\\n\",\n \"\\n\",\n \"enum_colname = 'Entity ID'\\n\",\n \"print('\\\\033[4m\\\\nEnum Column:\\\\033[0m %s\\\\n ' % enum_colname)\\n\",\n \"pprint.pprint(column_info[enum_colname])\\n\",\n \"\\n\",\n \"# Example: String type column description\\n\",\n \"\\n\",\n \"str_colname = 'Name'\\n\",\n \"print('\\\\033[4m\\\\nString Column:\\\\033[0m %s\\\\n ' % str_colname)\\n\",\n \"pprint.pprint(column_info[str_colname])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9a58e4b5\",\n \"metadata\": {},\n \"source\": [\n \"### Creating Filters\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0bb26cdd\",\n \"metadata\": {},\n \"source\": [\n \"Using information about our column types and values, we can now create filters that we will apply to screens to view their filtered data. Additionally, we can permanently filter screens by assigning filters to the `filter_parameters` field in screen objects.\\n\",\n \"\\n\",\n \"A configuration of filters for a single screen is stored in a `FilterRequest` object. Some notable fields of the `FilterRequest` class include: \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`include_columns`** *(Tuple[str, ...], Optional)*: A tuple of column names. The columns included in this field will be visible in the screen's data. All other columns will be hidden. If nothing is supplied in this field, all columns will be visible.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`filters`** *(Tuple[dict, ...], Optional)*: A tuple of dictionaries. Each dictionary defines a filter to be applied to the data. Each filter pertains to data in a single specified column. All assets returned from the screen data must meet the requirements of all filters in `filters`. If nothing is supplied in this field, all screen data will be visible. \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`order_by`** *(OrderByBody, Optional)*: An `OrderByBody` object. An instance of `OrderByBody` specifies how the returned data rows of the screen should be ordered after filtering. \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`limit`** *(float, Optional)*: A value specifying the maximum number of results that should be returned from the screen. If the `limit` field has value `n`, the first `n` rows of data will be retrieved based on the ordering in `order_by`.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"02f743c6\",\n \"metadata\": {},\n \"source\": [\n \"Different filters are available based on the different columns and column types in the screen. `Number` type columns support `Range` filters, `Enum` type columns support `Include` filters (which enums to include), and `String` type columns support `Substring` filters. Below is an example of how to generate each type of filter:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e43eb480\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Number Columns: Range Filtering\\n\",\n \"\\n\",\n \"num_col = \\\"Skew\\\"\\n\",\n \"\\n\",\n \"range_filter = {'columnName': num_col, 'type': 'Range', 'greaterThanEqual': 0, 'lessThanEqual': 1}\\n\",\n \"\\n\",\n \"print('\\\\033[4mRange Filter Example:\\\\033[0m\\\\n')\\n\",\n \"pprint.pprint(range_filter)\\n\",\n \"\\n\",\n \"\\n\",\n \"# Enum Columns: Include Filtering\\n\",\n \"\\n\",\n \"enum_col = \\\"Entity ID\\\"\\n\",\n \"\\n\",\n \"include_filter = {\\n\",\n \" 'columnName': enum_col,\\n\",\n \" 'type': 'Include',\\n\",\n \" 'values': ['VALUE1', 'VALUE2'],\\n\",\n \"} # find a screen w/ actual enums\\n\",\n \"\\n\",\n \"print('\\\\n\\\\033[4mInclude Filter Example:\\\\033[0m\\\\n')\\n\",\n \"pprint.pprint(include_filter)\\n\",\n \"\\n\",\n \"# String Columns: Substring Filtering\\n\",\n \"\\n\",\n \"str_col = \\\"Name\\\"\\n\",\n \"\\n\",\n \"substring_filter = {'columnName': str_col, 'type': 'Substring', 'q': 'D'}\\n\",\n \"\\n\",\n \"print('\\\\n\\\\033[4mSubstring Filter Example:\\\\033[0m\\\\n')\\n\",\n \"pprint.pprint(substring_filter)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"da8d51ee\",\n \"metadata\": {},\n \"source\": [\n \"When creating a `FilterRequest` object, include an `OrderByBody` object to enforce an ordering of the retrieved data rows. Notable fields in these instances include: \\n\",\n \"\\n\",\n \"\\n\",\n \"- **`column_name`** *(str, Optional)*: The name of the column to order by.\\n\",\n \"\\n\",\n \"\\n\",\n \"- **`type`** *(str, Optional)*: The type of ordering to enforce. Must be either `Ascending` or `Descending`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e24a4956\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example: Ascending Order by Skew\\n\",\n \"\\n\",\n \"order_by = OrderByBody(column_name='Skew', type='Ascending')\\n\",\n \"print('Column Name: %s, Row Ordering: %s' % (order_by.column_name, order_by.type))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d24113d1\",\n \"metadata\": {},\n \"source\": [\n \"### Function `filter_screen()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"81116675\",\n \"metadata\": {},\n \"source\": [\n \"Use function `filter_screen(screen_id, filter_request)` to view filtered data from a screen. The `filters` parameter of this function is a filter configuration that temporarily overrides the existing filter configuration associated with the given screen. All existing filters applied to this screen are temporarily removed, and the filters supplied in this function will be applied instead.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92f2184d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: filter_screen()\\n\",\n \"\\n\",\n \"# Create a FilterRequest\\n\",\n \"\\n\",\n \"filters = (substring_filter, range_filter)\\n\",\n \"\\n\",\n \"include_columns = ('Name', 'Skew', 'BBG Ticker', 'Vol Premia')\\n\",\n \"\\n\",\n \"limit = 3\\n\",\n \"\\n\",\n \"filter_request = FilterRequest(filters=filters, include_columns=include_columns, order_by=order_by, limit=limit)\\n\",\n \"\\n\",\n \"# Get Filtered Data\\n\",\n \"\\n\",\n \"filtered_data = GsDataScreenApi.filter_screen('SCQC82C1C960G1I30P', filter_request)\\n\",\n \"\\n\",\n \"pprint.pprint(filtered_data)\\n\",\n \"\\n\",\n \"# Visualize Filtered Data as Dataframe\\n\",\n \"\\n\",\n \"df = pd.DataFrame(filtered_data)\\n\",\n \"\\n\",\n \"display(df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88ab5796\",\n \"metadata\": {},\n \"source\": [\n \"To view filtered data using the screen's existing configuration, pass the value stored in the `filter_parameters` field of the screen's object into `filter_screen()`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3d54ff9e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example: View Current Configuration Data\\n\",\n \"\\n\",\n \"screen = GsDataScreenApi.get_screen('SCQC82C1C960G1I30P')\\n\",\n \"\\n\",\n \"filtered_data = GsDataScreenApi.filter_screen(screen.id, screen.filter_parameters)\\n\",\n \"\\n\",\n \"pprint.pprint(filtered_data[0:3])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f368479e\",\n \"metadata\": {},\n \"source\": [\n \"## Creating and Deleting Screens\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eb42e422\",\n \"metadata\": {},\n \"source\": [\n \"### Function `delete_screen()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7bad7dc4\",\n \"metadata\": {},\n \"source\": [\n \"Use function `delete_screen()` to permanently delete an existing screen using its ID. Once a screen is deleted, its information and data cannot be retrieved again. However, it is possible that users may still have access to stale data or `AnalyticsScreen` objects remaining from the old screen.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"68f2fa05\",\n \"metadata\": {},\n \"source\": [\n \"### Function `create_screen()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9dddc7fb\",\n \"metadata\": {},\n \"source\": [\n \"Use `create_screen()` to create new permanent screens using `AnalyticsScreen` objects. To specify a new screen's data source, applied filters, and other attributes, edit the information in the `AnalyticsScreen` instance provided to create the screen. All screens with the same `base_screener` field value will reference the same data source.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b3f2efc0\",\n \"metadata\": {},\n \"source\": [\n \"### Examples: Screen Creation and Deletion\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7c4866d7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: create_screen() and delete_screen()\\n\",\n \"\\n\",\n \"# Create new screen\\n\",\n \"\\n\",\n \"filter_request = FilterRequest(filters=({'columnName': 'Name', 'type': 'Substring', 'q': 'A'},), limit=3)\\n\",\n \"\\n\",\n \"screen_specs = AnalyticsScreen(name='New Screen', filter_parameters=filter_request, base_screener='BS88M7XNRA1D1FL3OM')\\n\",\n \"\\n\",\n \"print('Name: %s, Screen ID: %s' % (screen_specs.name, screen_specs.id))\\n\",\n \"\\n\",\n \"new_screen = GsDataScreenApi.create_screen(screen_specs)\\n\",\n \"\\n\",\n \"print('Name: %s, Screen ID: %s' % (new_screen.name, new_screen.id))\\n\",\n \"\\n\",\n \"# View screen data\\n\",\n \"\\n\",\n \"data = GsDataScreenApi.filter_screen(new_screen.id, new_screen.filter_parameters)\\n\",\n \"\\n\",\n \"display(pd.DataFrame(data))\\n\",\n \"\\n\",\n \"# Delete screen\\n\",\n \"\\n\",\n \"GsDataScreenApi.delete_screen(new_screen.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"64c97ba6\",\n \"metadata\": {},\n \"source\": [\n \"Additionally, create copies of existing screens by passing their screen objects directly into `create_screen()`. These existing screen objects can also be modified and passed to `create_screen()` to generate new screens that only differ marginally from an existing screen. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1fe6783c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example: Create and Delete a Copy of an Existing Screen\\n\",\n \"\\n\",\n \"# Retrieve an Existing Screen\\n\",\n \"\\n\",\n \"existing_screen = GsDataScreenApi.get_screens()[0]\\n\",\n \"\\n\",\n \"print('\\\\033[4mExisting Screen ID and Filters:\\\\033[0m %s\\\\n' % existing_screen.id)\\n\",\n \"pprint.pprint(existing_screen.filter_parameters.filters)\\n\",\n \"\\n\",\n \"# Make a Copy\\n\",\n \"\\n\",\n \"new_screen = GsDataScreenApi.create_screen(existing_screen)\\n\",\n \"\\n\",\n \"print('\\\\n\\\\033[4mCopy Screen ID and Filters:\\\\033[0m %s\\\\n' % new_screen.id)\\n\",\n \"pprint.pprint(new_screen.filter_parameters.filters)\\n\",\n \"\\n\",\n \"\\n\",\n \"# Delete Copy\\n\",\n \"\\n\",\n \"GsDataScreenApi.delete_screen(new_screen.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6549b18d\",\n \"metadata\": {},\n \"source\": [\n \"## Updating Existing Screens\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8e2ac731\",\n \"metadata\": {},\n \"source\": [\n \"### Function `update_screen()`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9e931fd6\",\n \"metadata\": {},\n \"source\": [\n \"Use function `update_screen()` to permanently update an existing screen using the ID of the screen and an `AnalyticsScreen` object specifying how the screen should be changed. Note that all previous specifications for the existing screen will be replaced by the information in the `AnalyticsScreen` instance passed into `update_screen()`, including screen names, filters, and data sources. Only the screen ID will remain the same after updating. \\n\",\n \"\\n\",\n \"Additionally, `update_screen()` requires that the screen ID passed in must match the ID field of the screen object passed in. An error is thrown if these two values do not match.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9f5ff8a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Usage Example: update_screen()\\n\",\n \"\\n\",\n \"# Create a New Screen\\n\",\n \"\\n\",\n \"filter_request = FilterRequest()\\n\",\n \"\\n\",\n \"screen_spec = AnalyticsScreen(\\n\",\n \" name='Screen to Update', base_screener='BS88M7XNRA1D1FL3OM', filter_parameters=filter_request\\n\",\n \")\\n\",\n \"\\n\",\n \"screen = GsDataScreenApi.create_screen(screen_spec)\\n\",\n \"\\n\",\n \"print('Name: %s, ID: %s\\\\n' % (screen.name, screen.id))\\n\",\n \"pprint.pprint(screen.filter_parameters.filters)\\n\",\n \"\\n\",\n \"# Update the New Screen\\n\",\n \"\\n\",\n \"updated_filter_request = FilterRequest(filters=({'columnName': 'Name', 'type': 'Substring', 'q': 'A'},))\\n\",\n \"\\n\",\n \"screen.filter_parameters = updated_filter_request\\n\",\n \"\\n\",\n \"screen.name = 'Updated Screen'\\n\",\n \"\\n\",\n \"screen = GsDataScreenApi.update_screen(screen.id, screen)\\n\",\n \"\\n\",\n \"print('\\\\nName: %s, ID: %s\\\\n' % (screen.name, screen.id))\\n\",\n \"\\n\",\n \"pprint.pprint(screen.filter_parameters.filters)\\n\",\n \"\\n\",\n \"# Delete the New Screen\\n\",\n \"\\n\",\n \"GsDataScreenApi.delete_screen(screen.id)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/00_data/covid/Comparing, Reconciling, and Combining COVID-19 Data Sources.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparing, Reconciling, and Combining COVID-19 Data Sources\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Summary\\n\",\n \"\\n\",\n \"In this note we use `gs-quant` to compare COVID-19 data sources. To do this, we first retrieve COVID-19-related time \\n\",\n \"series data and preprocess it, joining different sources together to analyze confirmed cases. \\n\",\n \"\\n\",\n \"The contents of this notebook are as follows:\\n\",\n \"\\n\",\n \"- [1 - Getting Started](#1---Getting-Started)\\n\",\n \"- [2 - COVID-19 Data](#2---COVID-19-Data)\\n\",\n \"- [3 - Comparing Global Sources](#3---Comparing-Global-Sources)\\n\",\n \"- [4 - Comparing US Sources](#4---Comparing-US-Sources)\\n\",\n \"- [5 - Comparing subregions, combining with mobility data](#5---Comparing-subregions,-combining-with-mobility-data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1 - Getting Started\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Start every session with authenticating with your unique client id and secret. For information on\\n\",\n \"how to get setup on GS Quant, see [Getting Started](/covid/guides/getting-started). Below produced\\n\",\n \"using gs-quant version 0.8.126\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2 - COVID-19 Data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll start by defining a general function to load various datasets, which includes regional data, for the past week:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data import Dataset\\n\",\n \"import datetime\\n\",\n \"\\n\",\n \"\\n\",\n \"# Note: There is data going back to 2019-12-31, you will need to write your own code to batch data fetching\\n\",\n \"def get_datasets(datasets):\\n\",\n \" ds_dict = {}\\n\",\n \" end = datetime.date(2020, 7, 9)\\n\",\n \" start = end - datetime.timedelta(weeks=1)\\n\",\n \"\\n\",\n \" for dataset in datasets:\\n\",\n \" try:\\n\",\n \" df = Dataset(dataset).get_data(start, end)\\n\",\n \"\\n\",\n \" keys = [x for x in ['countryId', 'subdivisionId'] if x in df.columns] + ['date']\\n\",\n \" val_map = {'newConfirmed': 'totalConfirmed', 'newFatalities': 'totalFatalities'}\\n\",\n \" vals = [x for x in list(val_map.keys()) if x in df.columns]\\n\",\n \"\\n\",\n \" df_t = df.groupby(keys).sum().groupby(level=0).cumsum().reset_index()[keys + vals].rename(columns=val_map)\\n\",\n \" ds_dict[dataset] = df.reset_index().merge(df_t, on=keys, suffixes=('', '_y')).set_index('date')\\n\",\n \"\\n\",\n \" except Exception as err:\\n\",\n \" print(f'Failed to obtain {dataset} with {getattr(err, \\\"message\\\", repr(err))}')\\n\",\n \" return ds_dict\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We create a list of some of the available datasets, and fetch all of them, so that we can compare them.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"country_datasets = [\\n\",\n \" 'COVID19_COUNTRY_DAILY_ECDC',\\n\",\n \" 'COVID19_COUNTRY_DAILY_WHO',\\n\",\n \" 'COVID19_COUNTRY_DAILY_WIKI',\\n\",\n \" 'COVID19_US_DAILY_CDC',\\n\",\n \"]\\n\",\n \"df = get_datasets(country_datasets)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next we look at the date ranges of each dataset to determine how much history they have, and ensure they are \\n\",\n \"up-to-date:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"for name, ds in df.items():\\n\",\n \" print('{:<30} {} {}'.format(name, ds.index[0].date(), ds.index[-1].date()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3 - Comparing Global Sources\\n\",\n \"\\n\",\n \"Below is a general function to compare the time series of certain columns across datasets:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import re\\n\",\n \"from typing import Union\\n\",\n \"\\n\",\n \"\\n\",\n \"def compare_time_series(\\n\",\n \" df, datasets, columns: Union[str, list], grouping: str = 'countryId', suffix_identifier: float = 1\\n\",\n \"):\\n\",\n \" columns = [columns] if isinstance(columns, str) else columns\\n\",\n \" suffixes = list(map(lambda ds_name: '_' + re.findall('\\\\_([A-Z]+)', ds_name)[-suffix_identifier], datasets))\\n\",\n \" df_combo = None\\n\",\n \"\\n\",\n \" for ds_name in datasets:\\n\",\n \" ds = df[ds_name]\\n\",\n \" df_combo = ds if df_combo is None else df_combo\\n\",\n \" df_suffixes = ('', '_' + re.findall('\\\\_([A-Z]+)', ds_name)[-suffix_identifier])\\n\",\n \" df_combo = df_combo.merge(ds, on=['date', grouping], suffixes=df_suffixes)\\n\",\n \"\\n\",\n \" return df_combo[[grouping] + [column + suffix for suffix in suffixes for column in columns]]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For example, if we want to compare the time series for total confirmed cases across the WHO, ECDC, and Wikipedia \\n\",\n \"datasets globally, we can do the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"datasets = ['COVID19_COUNTRY_DAILY_ECDC', 'COVID19_COUNTRY_DAILY_WHO', 'COVID19_COUNTRY_DAILY_WIKI']\\n\",\n \"df_to_compare = compare_time_series(df, datasets, columns='totalConfirmed')\\n\",\n \"\\n\",\n \"df_to_compare.describe().style.background_gradient(cmap='Blues', axis=1).format('{:,.2f}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"This shows statistical properties for each dataset for all common countries and dates. As we can see, there's some \\n\",\n \"variation in the data sources. Let's dig in a little further and plot the relationship between the WHO and ECDC for \\n\",\n \"a number of countries: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"select_countries = ['GB', 'DE', 'IT', 'ES', 'FR', 'RU']\\n\",\n \"to_plot = df_to_compare[df_to_compare.countryId.isin(select_countries)]\\n\",\n \"\\n\",\n \"sns.lmplot(\\n\",\n \" x=\\\"totalConfirmed_ECDC\\\", y=\\\"totalConfirmed_WHO\\\", col=\\\"countryId\\\", data=to_plot, col_wrap=3, height=3, fit_reg=False\\n\",\n \");\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"As we can see, there is some dispersion between sources for certain countries. For information on the various ISO\\n\",\n \"country codes, see [this guide](https://developer.gs.com/docs/covid/guides/standards/iso-countries/).\\n\",\n \"\\n\",\n \"### 4 - Comparing US Sources\\n\",\n \"\\n\",\n \"Now let's take a closer look at the US data, adding in the CDC dataset:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"datasets = [\\n\",\n \" 'COVID19_US_DAILY_CDC',\\n\",\n \" 'COVID19_COUNTRY_DAILY_ECDC',\\n\",\n \" 'COVID19_COUNTRY_DAILY_WHO',\\n\",\n \" 'COVID19_COUNTRY_DAILY_WIKI',\\n\",\n \"]\\n\",\n \"df_to_compare = compare_time_series(df, datasets, columns='totalConfirmed')\\n\",\n \"\\n\",\n \"df_to_compare.describe().style.background_gradient(cmap='Blues', axis=1).format('{:,.2f}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As of 21 of May 2020, CDC had the most confirmed cases, followed by Wikipedia, and then ECDC and WHO. This is not \\n\",\n \"overly surprising given the information collection and validation flows. Now let's examine the last few points:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's compare all the series side by side:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"df_to_compare.plot(figsize=(10, 6), title='US')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"(df_to_compare['totalConfirmed_WHO'] - df_to_compare['totalConfirmed_ECDC']).plot(\\n\",\n \" figsize=(10, 6), title='Differences vs WHO', label='ECDC'\\n\",\n \")\\n\",\n \"(df_to_compare['totalConfirmed_WHO'] - df_to_compare['totalConfirmed_CDC']).plot(label='CDC')\\n\",\n \"(df_to_compare['totalConfirmed_WHO'] - df_to_compare['totalConfirmed_WIKI']).plot(label='WIKI')\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This chart illustrates how the ECDC and CDC map cases versus the WHO. At the start of the epidemic these sources were\\n\",\n \"much closer, and diverged over time, with CDC leading in reporting for the US versus the ECDC and WHO. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5 - Comparing subregions, combining with mobility data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we illustrate how to compare datasets for specific countries (in this case, Italy) at different level of granularity (region, province, etc.) and how to ccombine epidemic data with mobility data from Google.\\n\",\n \"\\n\",\n \"As before, we fetch data for Italy, at three levels of granularity.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"datasets = ['COVID19_ITALY_DAILY_DPC', 'COVID19_REGION_DAILY_DPC', 'COVID19_PROVINCE_DAILY_DPC']\\n\",\n \"df = get_datasets(datasets)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_to_compare = compare_time_series(df, datasets, columns='totalConfirmed', suffix_identifier=3)\\n\",\n \"\\n\",\n \"df_to_compare.describe().style.background_gradient(cmap='Blues', axis=1).format('{:,.2f}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We write a function to compare the data across different geographic subdivisions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from functools import reduce\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"\\n\",\n \"def compare_totals_across_breakdowns(df, data1, data2, column_to_check):\\n\",\n \" # pick the common indices between the data being compared\\n\",\n \" common_idx_province = reduce(lambda x, y: x & y, df[data1[0]].groupby(data1[1]).apply(lambda x: x.index).tolist())\\n\",\n \" common_idx_region = reduce(lambda x, y: x & y, df[data2[0]].groupby(data2[1]).apply(lambda x: x.index).tolist())\\n\",\n \" idx = common_idx_province & common_idx_region\\n\",\n \"\\n\",\n \" # calculate the difference, and rename column\\n\",\n \" diff = df[data1[0]].groupby(data1[1]).apply(lambda x: x.loc[idx][column_to_check]).T.apply(sum, axis=1) - df[\\n\",\n \" data2[0]\\n\",\n \" ].groupby(data2[1]).apply(lambda x: x.loc[idx][column_to_check]).T.apply(sum, axis=1)\\n\",\n \" diff = pd.DataFrame(diff).rename(columns={0: f'{data1[0]}-{data2[0]}'})\\n\",\n \" return diff\\n\",\n \"\\n\",\n \"\\n\",\n \"diff1 = compare_totals_across_breakdowns(\\n\",\n \" df, ('COVID19_ITALY_DAILY_DPC', 'countryId'), ('COVID19_REGION_DAILY_DPC', 'subdivisionId'), 'totalConfirmed'\\n\",\n \")\\n\",\n \"diff2 = compare_totals_across_breakdowns(\\n\",\n \" df,\\n\",\n \" ('COVID19_REGION_DAILY_DPC', 'subdivisionId'),\\n\",\n \" ('COVID19_PROVINCE_DAILY_DPC', 'administrativeRegion'),\\n\",\n \" 'totalConfirmed',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We plot the discrepancies below...\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"to_plot = diff1.join(diff2)\\n\",\n \"\\n\",\n \"sns.lmplot(\\n\",\n \" x=\\\"COVID19_ITALY_DAILY_DPC-COVID19_REGION_DAILY_DPC\\\",\\n\",\n \" y=\\\"COVID19_REGION_DAILY_DPC-COVID19_PROVINCE_DAILY_DPC\\\",\\n\",\n \" data=to_plot,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"... and interestingly, this indicates there is no discrepancy at all when we compare country-level aggregate data with region-level aggregate data, but we do see discrepancies when we compate province-level with region-level data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we illustrate how to join region-level data from Italy with mobility data from Google, which allows us to check, for example, how the increase in cases of COVID-19 affected mobility patterns in the population. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime\\n\",\n \"\\n\",\n \"df_mob = Dataset('COVID19_SUBDIVISION_DAILY_GOOGLE').get_data(start_date=datetime(2020, 2, 1).date(), countryId='IT')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_mob.head(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We now join mobility data with region-level data in Italy, for the subdivision of Liguria.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def join_dfs(subdivision, mobility, column_to_compare):\\n\",\n \" df_red = df['COVID19_REGION_DAILY_DPC'][df['COVID19_REGION_DAILY_DPC']['subdivisionId'] == subdivision]\\n\",\n \" subdivision_name = df_red['subdivisionName'].unique()[0]\\n\",\n \" df1 = df_red[[column_to_compare]]\\n\",\n \" df2 = df_mob[df_mob.subdivisionId.isin([subdivision]) & df_mob.group.isin([mobility])]\\n\",\n \" df_joint = df1.merge(df2, on='date')\\n\",\n \" return df_joint, mobility, subdivision_name\\n\",\n \"\\n\",\n \"\\n\",\n \"df_joint, mobility, subdivision_name = join_dfs('IT-42', 'retailRecreation', 'newConfirmed')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, we plot a chart comparing mobility data with data on the growth of the epidemic.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_joint['Change in Mobility'] = df_joint['value'].diff()\\n\",\n \"df_joint[['newConfirmed', 'value']].rename(\\n\",\n \" columns={'newConfirmed': 'New Confirmed Cases', 'value': f'Change in {mobility} mobility'}\\n\",\n \").plot(\\n\",\n \" figsize=(\\n\",\n \" 10,\\n\",\n \" 7,\\n\",\n \" ),\\n\",\n \" grid=True,\\n\",\n \" title=f'Comparison of new confirmed cases and mobility in {subdivision_name}',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can observe a dramatic drop in moblity as the rate of new cases began to increase, a pattern that persisted during the peak of the epidemic in the region of Liguria.\\n\",\n \"\\n\",\n \"Please reach out to `covid-data@gs.com` with any questions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Disclaimer\\n\",\n \"This website may contain links to websites and the content of third parties (\\\"Third Party Content\\\"). We do not monitor, \\n\",\n \"review or update, and do not have any control over, any Third Party Content or third party websites. We make no \\n\",\n \"representation, warranty or guarantee as to the accuracy, completeness, timeliness or reliability of any \\n\",\n \"Third Party Content and are not responsible for any loss or damage of any sort resulting from the use of, or for any \\n\",\n \"failure of, products or services provided at or from a third party resource. If you use these links and the \\n\",\n \"Third Party Content, you acknowledge that you are doing so entirely at your own risk.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/01_markets/00_securities/examples/0000_get_an_asset_from_identifier.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterh\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"asset = SecurityMaster.get_asset('2407966', AssetIdentifier.SEDOL, sort_by_rank=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"asset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"asset.get_identifiers() # get all identifiers of the asset as of now\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/01_markets/02_rdates/examples/0000_get_a_date_relative_from_today.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterh\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"my_date = RelativeDate('-1d').apply_rule() # Returns previous day\\n\",\n \"my_date\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/01_markets/02_rdates/examples/0001_get_a_date_relative_from_a_base_date.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import date, timedelta\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterh\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"yesterday = date.today() - timedelta(days=1)\\n\",\n \"my_date = RelativeDate('-1d', base_date=yesterday).apply_rule() # Returns 2 days ago\\n\",\n \"my_date\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/01_markets/02_rdates/examples/0002_get_a_date_relative_using_pricing_context.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"from datetime import timedelta\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterh\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"datetime.date(2021, 1, 3)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"yesterday = date.today() - timedelta(days=1)\\n\",\n \"with PricingContext(pricing_date=yesterday):\\n\",\n \" my_date = RelativeDate('-1d').apply_rule() # Returns 2 days ago\\n\",\n \"my_date\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/01_markets/02_rdates/examples/0003_get_a_date_using_a_business_day_calendar.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.target.common import Currency\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.markets.securities import ExchangeCode\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterh\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"date_a = RelativeDate('-1b').apply_rule(\\n\",\n \" exchanges=[ExchangeCode.NYSE]\\n\",\n \") # Returns previous business day base on NYSE calendar\\n\",\n \"date_b = RelativeDate('-1b').apply_rule(currencies=[Currency.BRL]) # Returns previous business day base on BRL Currency\\n\",\n \"print(date_a, date_b)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/01_markets/02_rdates/examples/0004_chaining_relativedate_rules.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.markets.securities import ExchangeCode\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"outputs\": [\n {\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32m\\u001b[0m in \\u001b[0;36m\\u001b[1;34m\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[1;33m \\u001b[0myesterday\\u001b[0m\\u001b[1;33m:\\u001b[0m \\u001b[0mdate\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mdate\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mtoday\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m \\u001b[1;33m-\\u001b[0m \\u001b[0mtimedelta\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[0mdays\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[1;36m1\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 2\\u001b[0m \\u001b[0mdate\\u001b[0m\\u001b[1;33m:\\u001b[0m \\u001b[0mdate\\u001b[0m \\u001b[1;33m=\\u001b[0m \\u001b[0mRelativeDate\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;34m'-1d'\\u001b[0m\\u001b[1;33m,\\u001b[0m \\u001b[0mbase_date\\u001b[0m\\u001b[1;33m=\\u001b[0m\\u001b[0myesterday\\u001b[0m\\u001b[1;33m)\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mapply_rule\\u001b[0m\\u001b[1;33m(\\u001b[0m\\u001b[1;33m)\\u001b[0m \\u001b[1;31m# Returns 2 days ago\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'date' is not defined\"\n ],\n \"ename\": \"NameError\",\n \"evalue\": \"name 'date' is not defined\",\n \"output_type\": \"error\"\n }\n ],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterh\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\",\n \"is_executing\": false\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Date Rule conventions\\n\",\n \"A: first day of th year\\n\",\n \"b: business days of calendar passed, USD by default\\n\",\n \"d: Gregorian calendar days\\n\",\n \"e: end of month (ignores number)\\n\",\n \"m: month\\n\",\n \"r: end of year\\n\",\n \"u: business days ignoring USD holidays\\n\",\n \"v: gets last business day of month (does not ignore number)\\n\",\n \"x: gets last business day of month (ignores the number)\\n\",\n \"y: add years, Result will be moved to the next week if falling on a weekend\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# Returns four business days after the first business day on or after the 15th calendar day of the month\\n\",\n \"date: date = RelativeDate('14d+0u+4u').apply_rule(exchanges=[ExchangeCode.NYSE])\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n }\n ],\n \"metadata\": {\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"language\": \"python\",\n \"display_name\": \"Python 3\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/01_markets/02_rdates/examples/0005_passing_your_own_holiday_calendar.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterh\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"holiday_calendar = [date(2020, 12, 15)]\\n\",\n \"my_date = RelativeDate('-1b', base_date=date(2020, 12, 16)).apply_rule(holiday_calendar=holiday_calendar)\\n\",\n \"my_date\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/01_view-trade-properties.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# View IRSwap instrument properties\\n\",\n \"IRSwap.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# When you create an instance of IRSwap, properties can be set\\n\",\n \"my_swap = IRSwap(PayReceive.Pay, '5y', Currency.USD)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# View these property values by calling 'as_dict' on the swap instance\\n\",\n \"print(my_swap.as_dict())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/02_get-a-property.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"my_swap = IRSwap(PayReceive.Receive, '13m', Currency.GBP, notional_amount=5e6)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"print(my_swap.termination_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# you can also see the properties of an instrument by converting that instrument to a dictionary and looking at that\\n\",\n \"\\n\",\n \"my_swap.to_dict()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/03_set-a-property.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"my_swap = IRSwap(PayReceive.Pay, '7y', Currency.EUR)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"my_swap.termination_date = '10y'\\n\",\n \"print(my_swap.termination_date)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/04_enum-property.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Some available properties are enums available in the common modules. 'Currency' and 'PayReceive' in this examples are enums. To see possible values in an IDE please use autocomplete (tab).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Instrument Properties\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"my_swap = IRSwap(PayReceive.Receive, '5y', Currency.GBP, effective_date='1m')\\n\",\n \"print(list(PayReceive))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Most properties which take an enum also take a string representation of that enum\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"my_swap = IRSwap('Receive', '5y', Currency.GBP, effective_date='1m')\\n\",\n \"print(my_swap.pay_or_receive)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/05_resolve-a-trade.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Calling the `resolve()` method will resolve any relative parameters to absolute values and fill in unspecified parameters.\\n\",\n \"* `resolve()` will change the state of the instrument object in place by default unless argument `in_place` is `False`.\\n\",\n \"* `resolve` can be used to \\\"fix\\\" the instrument parameters when evaluating pricing & risk.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"my_swap = IRSwap(PayReceive.Pay, '5y', fixed_rate='atm+30')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - Resolve\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# note fixed_rate and termination_date are resolved to a float and date, respectively\\n\",\n \"my_swap.resolve()\\n\",\n \"my_swap.to_dict()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are resolving under a historical pricing context you will need to set the in_place parameter to False to\\n\",\n \"return a dictionary of the resolved trades on each date in the pricing context.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - Resolve\",\n \"Contexts - HistoricalPricingContext\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"with HistoricalPricingContext(dt.date(2025, 3, 25), dt.date(2025, 3, 27)):\\n\",\n \" resolved_swaps = my_swap.resolve(in_place=False)\\n\",\n \"\\n\",\n \"resolved_swaps.result()[dt.date(2025, 3, 25)].to_dict()\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Tags\",\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import Currency, PayReceive\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.markets import PricingContext, OverlayMarket, MarketDataCoordinate\\n\",\n \"from gs_quant.session import GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Retrieve market data \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Let's create a swaption and retrieve the market data our instrument is sensitive to. To do so we can call `market()` on \\n\",\n \"our instrument. This will give us the `OverlayMarket` object which contains the market data used to price our \\n\",\n \"instrument. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Market Data - Retrieval\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"swap = IRSwap(PayReceive.Receive, '10y', Currency.EUR, fixed_rate=-0.025)\\n\",\n \"swap.resolve()\\n\",\n \"market = swap.market()\\n\",\n \"\\n\",\n \"print(f'Base price: {swap.price()}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Then, using the `market_data` attribute, we can access the market data coordinates and values directly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Market Data - Retrieval\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"print(f'The value of the coordinate, {market.market_data[0].coordinate} is {market.market_data[0].value}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can also get a dictionary of all market data coordinates\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Market Data - Retrieval\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"market.market_data_dict\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Overwrite market data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"We can also amend the market data of our instrument's `OverlayMarket` to pass-in our own market data value. \\n\",\n \"To do so, we simply overwrite the `MarketDataCoordinate` of the instrument `OverlayMarket` to a given value.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Market Data - Overwriting\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"c_10y = MarketDataCoordinate.from_string('IR_EUR_SWAP_10Y.ATMRATE')\\n\",\n \"\\n\",\n \"print(f'Current value of the EUR 10yr swap point is {market[c_10y]}')\\n\",\n \"\\n\",\n \"market[c_10y] = -0.02\\n\",\n \"\\n\",\n \"print(f'New value of the EUR 10yr swap point is {market[c_10y]}')\\n\",\n \"\\n\",\n \"with PricingContext(market=market):\\n\",\n \" price_f = swap.price()\\n\",\n \"\\n\",\n \"print(f'New price: {price_f.result()}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"... or pass in an new `OverlayMarket` all together! Here we create a bespoke market with our own values for the 3m5y \\n\",\n \"implied volatility and 10y swap rate. Note that the values that are not overwritten will be defaulted to their original \\n\",\n \"value.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Market Data - Overwriting\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"\\n\",\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.EUR, expiration_date='3m')\\n\",\n \"swaption.resolve()\\n\",\n \"\\n\",\n \"print(f'Base price: {swaption.price()}')\\n\",\n \"\\n\",\n \"vol_3m5y = MarketDataCoordinate.from_string('IR VOL_EUR-EURIBOR-TELERATE_SWAPTION_5Y,3M')\\n\",\n \"market_data = {c_10y: 0.01, vol_3m5y: 40 / 1e4}\\n\",\n \"new_market = OverlayMarket(market_data)\\n\",\n \"\\n\",\n \"with PricingContext(market=new_market):\\n\",\n \" price_f = swaption.price()\\n\",\n \"\\n\",\n \"print(f'Price from new market data: {price_f.result()}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/07_float-with-info.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Risk calculation results in gs_quant extend the ResultInfo Class, which contain helpful information about \\n\",\n \"the risk calculations themselves. \\n\",\n \"\\n\",\n \"While you don't need to use these datatypes directly, they contain information about the returned result that \\n\",\n \"might be useful to you. One of these datatypes is 'FloatWithInfo'.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swap_5y = IRSwap(PayReceive.Receive, '5y', Currency.EUR, fixed_rate='atm+10')\\n\",\n \"swap_10y = IRSwap(PayReceive.Receive, '10y', Currency.EUR, fixed_rate='atm+10')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": true,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# When 'price' is called on an instrument, a 'FloatWithInfo' is returned\\n\",\n \"res_5y = swap_5y.price()\\n\",\n \"type(res_5y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# This datatype has a few members - most simply, the raw value:\\n\",\n \"res_5y.raw_value\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The risk unit\\n\",\n \"res_5y.unit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The risk_key, which helpfully provides information of what market, date, scenario and risk_measure were used\\n\",\n \"# to produce the results\\n\",\n \"res_5y.risk_key\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# You can add two instances of FloatWithInfo together when units are consistent\\n\",\n \"res_10y = swap_10y.price()\\n\",\n \"res_total = res_5y + res_10y\\n\",\n \"type(res_total)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# You can also add FloatWithInfo and other types of objects, but units won't be checked\\\\n\\\",\\n\",\n \"# For example, adding a FloatWithInfo and float returns a float\\\\n\\\",\\n\",\n \"res_total = res_5y + res_10y.raw_value\\n\",\n \"type(res_total)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"tags\": [\n \"Results - FloatWithInfo\"\n ],\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/01_view_swap_definition.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Typing question mark after any object, function or method will return docstring\\n\",\n \"IRSwap?\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency, DayCountFraction, BusinessDayConvention\\n\",\n \"from gs_quant.target.common import SwapClearingHouse\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Typing question mark after any object, function or method will return docstring\\n\",\n \"IRSwap?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get list of properties of an interest rate swap\\n\",\n \"IRSwap.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaps = Portfolio()\\n\",\n \"\\n\",\n \"# you don't need to specify any parameters to get a valid trade. All properties have defaults\\n\",\n \"swaps.append(IRSwap())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - PayReceive\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# pay_or_receive can be a string of 'pay' or 'receive' or the PayReceive enum\\n\",\n \"# and relates to paying or receiving the fixed leg. defaults to a receiver swap\\n\",\n \"swaps.append(IRSwap(pay_or_receive=PayReceive.Pay))\\n\",\n \"swaps.append(IRSwap(pay_or_receive='Pay'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# termination_date is the end date of the swap. It may be a tenor relative to effective_date or a datetime.date.\\n\",\n \"# defaults to 10y\\n\",\n \"swaps.append(IRSwap(termination_date=date(2025, 11, 12)))\\n\",\n \"swaps.append(IRSwap(termination_date='1y'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - Currency\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# notional currency may be a string or the Currency enum. defaults to USD\\n\",\n \"swaps.append(IRSwap(notional_currency=Currency.USD))\\n\",\n \"swaps.append(IRSwap(notional_currency='EUR'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# the effective date is the start date of the swap and may be a tenor relative\\n\",\n \"# to the active PricingContext.pricing_date or a datetime.date, default is pricing date\\n\",\n \"swaps.append(IRSwap(effective_date='1y'))\\n\",\n \"swaps.append(IRSwap(effective_date=date(2019, 11, 12)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Properties - Solving - Swap fixed rate\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# fixed_rate is the interest rate on the fixed leg of the swap. Defaults to Par Rate (ATM).\\n\",\n \"# Can be expressed as 'ATM', 'ATM+25' for 25bp above par, a-100 for 100bp below par, 0.01 for 1%, can also be solved for a PV\\n\",\n \"swaps.append(IRSwap(fixed_rate='ATM'))\\n\",\n \"swaps.append(IRSwap(fixed_rate='ATM+50'))\\n\",\n \"swaps.append(IRSwap(fixed_rate='a-100'))\\n\",\n \"swaps.append(IRSwap(fixed_rate=0.01))\\n\",\n \"swaps.append(IRSwap(fixed_rate='10000/pv'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# floating_rate_for_the_initial_calculation_period sets the first fixing on the trade.\\n\",\n \"# It should be a float in absolute terms so 0.0075 is 75bp. Defaults to the value derived from fwd curve\\n\",\n \"swaps.append(IRSwap(floating_rate_for_the_initial_calculation_period=0.0075))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Properties - floating_rate_option\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# floating rate option is the index that is being observed, defaults to LIBOR style index for each ccy,\\n\",\n \"# 'OIS' will give the default overnight index for the notional ccy\\n\",\n \"swaps.append(IRSwap(notional_currency='USD', floating_rate_option='USD-ISDA-SWAP RATE'))\\n\",\n \"swaps.append(IRSwap(notional_currency='USD', floating_rate_option='USD-LIBOR-BBA'))\\n\",\n \"swaps.append(IRSwap(notional_currency='EUR', floating_rate_option='EUR-EONIA-OIS-COMPOUND'))\\n\",\n \"swaps.append(IRSwap(notional_currency='GBP', floating_rate_option='OIS'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# floating_rate_designated_maturity is the index term. defaults to the frequency of the floating leg\\n\",\n \"swaps.append(IRSwap(notional_currency='GBP', floating_rate_designated_maturity='3m'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# floating_rate_spread is a float spread over the index. eg. pay euribor + 1%. defaults to 0\\n\",\n \"swaps.append(IRSwap(pay_or_receive='receive', notional_currency='EUR', floating_rate_spread=0.01))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# floating_rate_frequency is the accrual frequency of the floating leg defined as a tenor.\\n\",\n \"# It will drive the floating_rate_designated_maturity if that has not been independently set.\\n\",\n \"# Defaults to ccy/tenor market standard defaults\\n\",\n \"swaps.append(IRSwap(floating_rate_frequency='6m'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - DayCountFraction\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# floating_rate_day_count_fraction can be the enum used here or a string. defaults to ccy market standard defaults\\n\",\n \"swaps.append(IRSwap(floating_rate_day_count_fraction=DayCountFraction.ACT_OVER_365L_ISDA))\\n\",\n \"swaps.append(IRSwap(floating_rate_day_count_fraction=DayCountFraction._30E_OVER_360))\\n\",\n \"swaps.append(IRSwap(floating_rate_day_count_fraction='30/360'))\\n\",\n \"swaps.append(IRSwap(floating_rate_day_count_fraction='ACT/360'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - BusinessDayConvention\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# floating_rate_business_day_convention can be the enum used here a the equivalent string\\n\",\n \"swaps.append(IRSwap(floating_rate_business_day_convention=BusinessDayConvention.Following))\\n\",\n \"swaps.append(IRSwap(floating_rate_business_day_convention='Modified Following'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# fee is an amount paid. A positive fee will have a negative impact on the PV. Defaults to 0\\n\",\n \"swaps.append(IRSwap(fee=50000))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# you can specify fee currency and fee date. trades where the fee is paid in a different currency to the\\n\",\n \"# notional currency are supported. Default fee currency is notional currency\\n\",\n \"# fee date can be a datetime.date or a tenor. Default fee date is spot dates from the PricingContext.pricing_date\\n\",\n \"swaps.append(IRSwap(notional_currency=Currency.GBP, fee=50000, fee_currency=Currency.GBP, fee_payment_date='1y'))\\n\",\n \"swaps.append(\\n\",\n \" IRSwap(notional_currency=Currency.GBP, fee=1e5, fee_currency=Currency.USD, fee_payment_date=date(2020, 1, 30))\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - SwapClearingHouse\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# valid clearinghouses are held in the SwapClearingHouse enum\\n\",\n \"swaps.append(IRSwap(clearing_house=SwapClearingHouse.LCH))\\n\",\n \"swaps.append(IRSwap(clearing_house=SwapClearingHouse.EUREX))\\n\",\n \"swaps.append(IRSwap(clearing_house='CME'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# you can specify a name for a trade. This has no economic effect but is useful when extracting results\\n\",\n \"# from a portfolio object\\n\",\n \"swaps.append(IRSwap(PayReceive.Receive, '5y', 'gbp', name='GBP5y'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaps.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# you can express a swap as a dictionary\\n\",\n \"swap = IRSwap(termination_date='10y', notional_currency='EUR', fixed_rate='ATM+50')\\n\",\n \"swap_dict = swap.as_dict()\\n\",\n \"swap_dict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - create from dictionary\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# and you can construct a swap from a dictionary\\n\",\n \"new_swap = IRSwap.from_dict(swap_dict)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = IRSwap(effective_date='1y')\\n\",\n \"swap.resolve()\\n\",\n \"swap.as_dict()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/03_calc_swap_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = IRSwap(PayReceive.Receive, date(2055, 1, 15), Currency.EUR)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics - Price\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"swap.price()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import DollarPrice, IRDelta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = IRSwap(PayReceive.Pay, '10y', Currency.GBP)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"result = swap.calc((DollarPrice, IRDelta(aggregation_level='Type')))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Results\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"print(result) # all results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Results\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"print(result[IRDelta(aggregation_level='Type')]) # single measure\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/05_calc_swap_price_in_pricing_context.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = IRSwap(PayReceive.Pay, '10y', notional_currency=Currency.GBP)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Contexts - PricingContext\",\n \"Market Data - location\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# price as of a specific date using nyc close\\n\",\n \"with PricingContext(pricing_date=date(2019, 1, 15), market_data_location='NYC'):\\n\",\n \" pv_f = swap.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(pv_f.result())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/06_calc_swap_price_historically.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - Resolve\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# create 2 swaps and fix swap_b parameters by resolving. Swap_a parameters will resolve in HistoricalPricingContext\\n\",\n \"swap_a = IRSwap(PayReceive.Pay, '30y', Currency.USD, fixed_rate='atm+5')\\n\",\n \"swap_b = IRSwap(PayReceive.Pay, '30y', Currency.USD, fixed_rate='atm+5')\\n\",\n \"swap_b.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Contexts - HistoricalPricingContext\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# HistoricalPricingContexts can take two dates or you can specify a number of historic business days as we do here\\n\",\n \"with HistoricalPricingContext(150):\\n\",\n \" res_a_f = swap_a.price()\\n\",\n \" res_b_f = swap_b.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"res_a = res_a_f.result()\\n\",\n \"res_b = res_b_f.result()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"plt.plot(res_a, 'g-', label='Rolling 30y GBP Payer struck atm+5bps')\\n\",\n \"plt.plot(res_b, 'b-', label='30y GBP Payer struck atm+5bps over time')\\n\",\n \"plt.xlabel('time')\\n\",\n \"plt.ylabel('PV ($)')\\n\",\n \"plt.title('PV over Time')\\n\",\n \"plt.legend()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import DollarPrice, IRDelta\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = IRSwap(PayReceive.Pay, '12y', notional_currency=Currency.GBP)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics - aggregation level\",\n \"Metrics\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# if you set an aggregation level for a risk ladder like IRDelta it will change to be filtered at the market coordinate level\\n\",\n \"# levels are: type, asset, class, point and quoting style. type is at the metric level.\\n\",\n \"# in this case when setting the aggregation level to Type the model will do a simple parallel shift rather than calculating\\n\",\n \"# the perturbated risk and aggregating.\\n\",\n \"\\n\",\n \"with PricingContext(pricing_date=date(2019, 1, 15)):\\n\",\n \" res_f = swap.calc((DollarPrice, IRDelta, IRDelta(aggregation_level='Type')))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(res_f.result()) # retrieve all measures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Results - Retrieve Specific Measure\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"print(res_f[DollarPrice]) # retrieve specific measure\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/08_calc_swap_risk_historically.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import DollarPrice, IRDelta\\n\",\n \"from gs_quant.common import AggregationLevel\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap_10bps = IRSwap(PayReceive.Receive, '5y', Currency.EUR, fixed_rate='atm+10')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Contexts - HistoricalPricingContext\",\n \"Metrics - aggregation_level\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"with HistoricalPricingContext(date(2020, 3, 2), date(2020, 4, 1), show_progress=True):\\n\",\n \" res_f = swap_10bps.calc((DollarPrice, IRDelta, IRDelta(aggregation_level=AggregationLevel.Type, currency='local')))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(res_f.result()) # retrieve all results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Results - historical results\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"print(res_f[DollarPrice]) # retrieve historical prices\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"\\n\",\n \"from gs_quant.common import PayReceive\\n\",\n \"from gs_quant.instrument import IRSwap, IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import SwapSettlement\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaptions = Portfolio()\\n\",\n \"\\n\",\n \"# you don't need to specify any parameters to get a valid trade. All properties have defaults\\n\",\n \"swaptions.append(IRSwaption())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get list of properties of an interest rate swaption\\n\",\n \"# Many of these properties overlap with the IRSwap properties (outlined in example '010001_swap_trade_construction')\\n\",\n \"IRSwaption.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - PayReceive\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# pay_or_receive can be a string of 'pay', 'receive', 'straddle' (an option strategy\\n\",\n \"# where you enter into both a payer and receiver) or the PayReceive enum\\n\",\n \"# relates to whether you expect to pay/receive fixed for the underlying. default is 'straddle'\\n\",\n \"swaptions.append(IRSwaption(pay_or_receive=PayReceive.Receive))\\n\",\n \"swaptions.append(IRSwaption(pay_or_receive='Receive'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# expiration_date is the date the option expires and may be a tenor relative\\n\",\n \"# to the active PricingContext.pricing_date or a datetime.date, default is '10y'\\n\",\n \"swaptions.append(IRSwaption(expiration_date='6m'))\\n\",\n \"swaptions.append(IRSwaption(expiration_date=date(2022, 2, 12)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties - Solving - IRSwaption strike\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# strike is the rate at which the option can be exercised\\n\",\n \"# It also represents the interest rate on the fixed leg of the swap if the swaption expires ITM. Defaults to Par Rate (ATM).\\n\",\n \"# Can be expressed as 'ATM', 'ATM+25' for 25bp above par, a-100 for 100bp below par, 0.01 for 1%, you can also solve\\n\",\n \"# for a specific delta or pv\\n\",\n \"swaptions.append(IRSwaption(strike='ATM'))\\n\",\n \"swaptions.append(IRSwaption(strike='ATM+50'))\\n\",\n \"swaptions.append(IRSwaption(strike='a-100'))\\n\",\n \"swaptions.append(IRSwaption(strike=0.02))\\n\",\n \"swaptions.append(IRSwaption(strike='10000/pv', notional_amount=10000))\\n\",\n \"swaptions.append(IRSwaption(strike='25d', notional_amount=10000))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# effective_date is the start date of the underlying swap and may be a tenor relative\\n\",\n \"# to the expiration_date or a datetime.date. Default is spot dates from expiration\\n\",\n \"# For example, for a swaption w/ notional_currency as GBP, spot date is T+0, so effective_date = expiration_date.\\n\",\n \"# for a swaption w/ notional_currency USD, spot is T+2 days and the effective_date is 2b after expiration_date\\n\",\n \"swaptions.append(IRSwaption(effective_date='5b'))\\n\",\n \"swaptions.append(IRSwaption(expiration_date=date(2031, 2, 10), effective_date=date(2031, 2, 12)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# An IRSwaption's strike will resolve to an IRSwap's fixed_rate if the swaps' paramaters match and\\n\",\n \"# the swaption's effective_date is equivalent to the swap's effective_date\\n\",\n \"swap = IRSwap(notional_currency='GBP', effective_date='10y')\\n\",\n \"swap.resolve()\\n\",\n \"swaption = IRSwaption(notional_currency='GBP', expiration_date='10y', effective_date='0b')\\n\",\n \"swaption.resolve()\\n\",\n \"print(swap.fixed_rate * 100)\\n\",\n \"print(swaption.strike * 100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - PayReceive\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# settlement is the settlement convention of the swaption and can be a string of:\\n\",\n \"# 'Phys.CLEARED' (enter into cleared swap), 'Cash.PYU' (PYU - Par Yield Unadjusted,\\n\",\n \"# cash payment calculated with PYU), 'Physical' (enter into a uncleared swap),\\n\",\n \"# 'Cash.CollatCash' (collateralized, cash settled at expiry) or the SwapSettlement enum\\n\",\n \"swaptions.append(IRSwaption(settlement=SwapSettlement.Phys_CLEARED))\\n\",\n \"swaptions.append(IRSwaption(settlement='Cash.PYU'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# premium is the amount to be exchanged for the option contract. A positive premium will have a\\n\",\n \"# negative impact on the PV. premium is a default is 0.\\n\",\n \"swaptions.append(IRSwaption(premium=1e4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# premium_payment_date is the date when premium is exchanged.\\n\",\n \"# premium_payment_date can be a datetime.date or a tenor. defaulted to spot dates from the\\n\",\n \"# PricingContext.pricing_date\\n\",\n \"swaptions.append(IRSwaption(premium_payment_date='5d'))\\n\",\n \"swaptions.append(IRSwaption(premium_payment_date=date(2020, 2, 13)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# in some markets, the convention is for premium to be exchanged at expiration\\n\",\n \"# this can be expressed by changing the premium_payment_date to the swaption's expiration_date\\n\",\n \"swaptions.append(IRSwaption(premium_payment_date='5y', expiration_date='5y'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaptions.price().to_frame()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"tags\": [\n \"Instrument - IRSwaption\"\n ],\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/10_straddle_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# A swaption straddle represents the purchase of a payer swaption and\\n\",\n \"# receiver swaption with the same strike and expiration_date\\n\",\n \"straddle = IRSwaption(pay_or_receive='Straddle')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The price of a straddle is sum of the price of the payer and receiver swaptions\\n\",\n \"payer = IRSwaption(pay_or_receive=PayReceive.Pay)\\n\",\n \"receiver = IRSwaption(pay_or_receive=PayReceive.Receive)\\n\",\n \"\\n\",\n \"print(straddle.price())\\n\",\n \"print(payer.price() + receiver.price())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"tags\": [\n \"Instrument - Swaption Straddle\"\n ],\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/11_midcurve_swaption_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import IRSwaption\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Construct a midcurve swaption: 6m 2y1y\\n\",\n \"# expiration_date='6m' - option expires in 6m\\n\",\n \"# effective_date='2y' - swap starting 2y after expiry (2.5y after trade)\\n\",\n \"# termination_date='1y' - swap tenor is 1y (swap matures 3.5y post trade)\\n\",\n \"midcurve = IRSwaption(pay_or_receive='Pay', expiration_date='6m', effective_date='2y', termination_date='1y')\\n\",\n \"midcurve.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# expiration_date can also be specified as IMM date\\n\",\n \"midcurve = IRSwaption(pay_or_receive='Pay', expiration_date='IMM1', effective_date='2y', termination_date='1y')\\n\",\n \"midcurve.price()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n },\n \"tags\": [\n \"Instrument - Rates Midcurve Swaption\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/12_swap_future_cashflows.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.risk import Cashflows\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = IRSwap(PayReceive.Receive, '5y', Currency.EUR, fixed_rate='atm+10')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics - Cashflows\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# returns a dataframe of future cashflows\\n\",\n \"# note this feature will be expanded to cover portfolios in future releases\\n\",\n \"swap.calc(Cashflows)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/13_calc_xccy_swap_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import Currency\\n\",\n \"from gs_quant.instrument import IRXccySwap, IRXccySwapFltFlt\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# IRXccySwap is a MTM cross currency swap. IRXccySwapFltFlt doesn't exchange on each payment date\\n\",\n \"\\n\",\n \"# spreads are absolute so 0.0005 is 5bp\\n\",\n \"xswap = IRXccySwap(\\n\",\n \" payer_currency=Currency.EUR,\\n\",\n \" receiver_currency=Currency.USD,\\n\",\n \" effective_date='3m',\\n\",\n \" termination_date='10y',\\n\",\n \" payer_spread=-0.0005,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(xswap.price())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xswap_mtm = IRXccySwapFltFlt(\\n\",\n \" payer_currency=Currency.EUR,\\n\",\n \" receiver_currency=Currency.USD,\\n\",\n \" effective_date='3m',\\n\",\n \" termination_date='10y',\\n\",\n \" payer_spread=-0.0005,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(xswap_mtm.price())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - XCcy Swap\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/14_calc_xccy_swap_risk_measures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import Currency\\n\",\n \"from gs_quant.instrument import IRXccySwap\\n\",\n \"from gs_quant.risk import IRDelta, IRXccyDelta, IRXccyDeltaParallel\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xswap = IRXccySwap(\\n\",\n \" payer_currency=Currency.EUR, receiver_currency=Currency.USD, effective_date='3m', termination_date='10y'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics - XCcy\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"delta = xswap.calc((IRDelta, IRXccyDelta))\\n\",\n \"parallel_delta = xswap.calc(IRXccyDeltaParallel)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(delta) # all results for delta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(parallel_delta) # all results for parallel delta\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/15_spread_option_grid_pricing.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRCMSSpreadOption\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import IRAnnualImpliedVol, Price\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"pd.options.display.float_format = '{:,.4f}'.format\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - IRCMSSpreadOption\",\n \"Portfolio - Nested Portfolios\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# define a set of pairs and option expiries\\n\",\n \"pairs = [('5y', '2y'), ('10y', '2y')]\\n\",\n \"expiries = ['3m', '6m', '1y', '2y', '5y', '10y']\\n\",\n \"portfolios = Portfolio(\\n\",\n \" [\\n\",\n \" Portfolio(\\n\",\n \" [\\n\",\n \" IRCMSSpreadOption(\\n\",\n \" termination_date=e,\\n\",\n \" notional_currency='EUR',\\n\",\n \" notional_amount=10000,\\n\",\n \" index1_tenor=p[0],\\n\",\n \" index2_tenor=p[1],\\n\",\n \" name=e,\\n\",\n \" )\\n\",\n \" for e in expiries\\n\",\n \" ],\\n\",\n \" name=p,\\n\",\n \" )\\n\",\n \" for p in pairs\\n\",\n \" ]\\n\",\n \")\\n\",\n \"\\n\",\n \"# price our list of portfolios\\n\",\n \"with PricingContext():\\n\",\n \" result_p = portfolios.calc(Price)\\n\",\n \" result_v = portfolios.calc(IRAnnualImpliedVol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"prices = result_p.to_frame()\\n\",\n \"prices\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vols = result_v.to_frame() * 10000\\n\",\n \"vols\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/16_change_discount_curve.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.EUR, settlement='Cash.CollatCash')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"CSA\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# price using default OIS rate\\n\",\n \"with PricingContext(pricing_date=dt.date.today(), csa_term='EUR-OIS'):\\n\",\n \" price = swaption.price()\\n\",\n \"\\n\",\n \"print(price.result())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# price using ESTR\\n\",\n \"with PricingContext(pricing_date=dt.date.today(), csa_term='EUR-EuroSTR'):\\n\",\n \" price = swaption.price()\\n\",\n \"\\n\",\n \"print(price.result())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/17_calc_xccy_swap_cashflows.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRXccySwap, IRXccySwapFixFix\\n\",\n \"from gs_quant.risk import Cashflows\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"# external users should substitute their client id and secret\\n\",\n \"client_id = None # Supply your application id\\n\",\n \"client_secret = None # Supply your client secret\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client_id, client_secret=client_secret, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Create a Xccy float vs float Swap and a Xccy fix vs fix swap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"tags\": [\n \"Instrument - XccySwap\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"mtm_swap = IRXccySwap(payer_currency='EUR', receiver_currency='USD', effective_date='3m', termination_date='10y')\\n\",\n \"fix_swap = IRXccySwapFixFix(\\n\",\n \" payer_currency='EUR', receiver_currency='USD', termination_date='10y', payer_rate=0.01, receiver_rate=0.015\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Compute cashflows for the 10y EURUSD fix swap \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"tags\": [\n \"Metrics - Cashflows\"\n ]\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
currencypayment_dateset_dateaccrual_start_dateaccrual_end_datepayment_amountnotionalpayment_typefloating_rate_optionfloating_rate_designated_maturityday_count_fractionspreadratediscount_factor
0EUR2025-07-07None2025-04-022025-07-02-250000.0100000000.0FIXNANA0.25None0.010.993976
1EUR2025-10-06None2025-07-022025-10-02-250000.0100000000.0FIXNANA0.25None0.010.989072
2EUR2026-01-06None2025-10-022026-01-02-250000.0100000000.0FIXNANA0.25None0.010.984584
3EUR2026-04-08None2026-01-022026-04-02-250000.0100000000.0FIXNANA0.25None0.010.980044
4EUR2026-07-06None2026-04-022026-07-02-250000.0100000000.0FIXNANA0.25None0.010.975711
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" currency payment_date set_date accrual_start_date accrual_end_date \\\\\\n\",\n \"0 EUR 2025-07-07 None 2025-04-02 2025-07-02 \\n\",\n \"1 EUR 2025-10-06 None 2025-07-02 2025-10-02 \\n\",\n \"2 EUR 2026-01-06 None 2025-10-02 2026-01-02 \\n\",\n \"3 EUR 2026-04-08 None 2026-01-02 2026-04-02 \\n\",\n \"4 EUR 2026-07-06 None 2026-04-02 2026-07-02 \\n\",\n \"\\n\",\n \" payment_amount notional payment_type floating_rate_option \\\\\\n\",\n \"0 -250000.0 100000000.0 FIX NA \\n\",\n \"1 -250000.0 100000000.0 FIX NA \\n\",\n \"2 -250000.0 100000000.0 FIX NA \\n\",\n \"3 -250000.0 100000000.0 FIX NA \\n\",\n \"4 -250000.0 100000000.0 FIX NA \\n\",\n \"\\n\",\n \" floating_rate_designated_maturity day_count_fraction spread rate \\\\\\n\",\n \"0 NA 0.25 None 0.01 \\n\",\n \"1 NA 0.25 None 0.01 \\n\",\n \"2 NA 0.25 None 0.01 \\n\",\n \"3 NA 0.25 None 0.01 \\n\",\n \"4 NA 0.25 None 0.01 \\n\",\n \"\\n\",\n \" discount_factor \\n\",\n \"0 0.993976 \\n\",\n \"1 0.989072 \\n\",\n \"2 0.984584 \\n\",\n \"3 0.980044 \\n\",\n \"4 0.975711 \"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"cf_fix = fix_swap.calc(Cashflows)\\n\",\n \"cf_fix.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Compute cashflows for the forward starting 10y EURUSD float float swap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
currencypayment_dateset_dateaccrual_start_dateaccrual_end_datepayment_amountnotionalpayment_typefloating_rate_optionfloating_rate_designated_maturityday_count_fractionspreadratediscount_factor
0EUR2025-10-062025-07-022025-07-022025-10-02-501686.798657100000000.0FltEUR-EuroSTR-COMPOUND3m0.255556-0.0002040.0198350.989072
1EUR2026-01-062025-10-022025-10-022026-01-02-471412.499356100000000.0FltEUR-EuroSTR-COMPOUND3m0.255556-0.0002040.0186510.984584
2EUR2026-04-082026-01-022026-01-022026-04-02-446566.760037100000000.0FltEUR-EuroSTR-COMPOUND3m0.250000-0.0002040.0180670.980044
3EUR2026-07-062026-04-022026-04-022026-07-02-451174.776800100000000.0FltEUR-EuroSTR-COMPOUND3m0.252778-0.0002040.0180530.975711
4EUR2026-10-062026-07-022026-07-022026-10-02-464690.352983100000000.0FltEUR-EuroSTR-COMPOUND3m0.255556-0.0002040.0183870.971167
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" currency payment_date set_date accrual_start_date accrual_end_date \\\\\\n\",\n \"0 EUR 2025-10-06 2025-07-02 2025-07-02 2025-10-02 \\n\",\n \"1 EUR 2026-01-06 2025-10-02 2025-10-02 2026-01-02 \\n\",\n \"2 EUR 2026-04-08 2026-01-02 2026-01-02 2026-04-02 \\n\",\n \"3 EUR 2026-07-06 2026-04-02 2026-04-02 2026-07-02 \\n\",\n \"4 EUR 2026-10-06 2026-07-02 2026-07-02 2026-10-02 \\n\",\n \"\\n\",\n \" payment_amount notional payment_type floating_rate_option \\\\\\n\",\n \"0 -501686.798657 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"1 -471412.499356 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"2 -446566.760037 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"3 -451174.776800 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"4 -464690.352983 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"\\n\",\n \" floating_rate_designated_maturity day_count_fraction spread rate \\\\\\n\",\n \"0 3m 0.255556 -0.000204 0.019835 \\n\",\n \"1 3m 0.255556 -0.000204 0.018651 \\n\",\n \"2 3m 0.250000 -0.000204 0.018067 \\n\",\n \"3 3m 0.252778 -0.000204 0.018053 \\n\",\n \"4 3m 0.255556 -0.000204 0.018387 \\n\",\n \"\\n\",\n \" discount_factor \\n\",\n \"0 0.989072 \\n\",\n \"1 0.984584 \\n\",\n \"2 0.980044 \\n\",\n \"3 0.975711 \\n\",\n \"4 0.971167 \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mtm_swap.calc(Cashflows).head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Clone the float float swap keeping spread constant but modifying the rate applied at initiation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"tags\": [\n \"Instrument - Cloning\"\n ]\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
currencypayment_dateset_dateaccrual_start_dateaccrual_end_datepayment_amountnotionalpayment_typefloating_rate_optionfloating_rate_designated_maturityday_count_fractionspreadratediscount_factor
0EUR2025-10-062025-07-022025-07-022025-10-02-501685.517234100000000.0FltEUR-EuroSTR-COMPOUND3m0.255556-0.0002040.0198350.989072
1EUR2026-01-062025-10-022025-10-022026-01-02-471411.217933100000000.0FltEUR-EuroSTR-COMPOUND3m0.255556-0.0002040.0186510.984584
2EUR2026-04-082026-01-022026-01-022026-04-02-446565.506472100000000.0FltEUR-EuroSTR-COMPOUND3m0.250000-0.0002040.0180670.980044
3EUR2026-07-062026-04-022026-04-022026-07-02-451173.509306100000000.0FltEUR-EuroSTR-COMPOUND3m0.252778-0.0002040.0180530.975711
4EUR2026-10-062026-07-022026-07-022026-10-02-464689.071560100000000.0FltEUR-EuroSTR-COMPOUND3m0.255556-0.0002040.0183870.971167
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" currency payment_date set_date accrual_start_date accrual_end_date \\\\\\n\",\n \"0 EUR 2025-10-06 2025-07-02 2025-07-02 2025-10-02 \\n\",\n \"1 EUR 2026-01-06 2025-10-02 2025-10-02 2026-01-02 \\n\",\n \"2 EUR 2026-04-08 2026-01-02 2026-01-02 2026-04-02 \\n\",\n \"3 EUR 2026-07-06 2026-04-02 2026-04-02 2026-07-02 \\n\",\n \"4 EUR 2026-10-06 2026-07-02 2026-07-02 2026-10-02 \\n\",\n \"\\n\",\n \" payment_amount notional payment_type floating_rate_option \\\\\\n\",\n \"0 -501685.517234 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"1 -471411.217933 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"2 -446565.506472 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"3 -451173.509306 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"4 -464689.071560 100000000.0 Flt EUR-EuroSTR-COMPOUND \\n\",\n \"\\n\",\n \" floating_rate_designated_maturity day_count_fraction spread rate \\\\\\n\",\n \"0 3m 0.255556 -0.000204 0.019835 \\n\",\n \"1 3m 0.255556 -0.000204 0.018651 \\n\",\n \"2 3m 0.250000 -0.000204 0.018067 \\n\",\n \"3 3m 0.252778 -0.000204 0.018053 \\n\",\n \"4 3m 0.255556 -0.000204 0.018387 \\n\",\n \"\\n\",\n \" discount_factor \\n\",\n \"0 0.989072 \\n\",\n \"1 0.984584 \\n\",\n \"2 0.980044 \\n\",\n \"3 0.975711 \\n\",\n \"4 0.971167 \"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mtm_swap = mtm_swap.clone(initial_fx_rate=1.2, payer_spread=mtm_swap.payer_spread)\\n\",\n \"mtm_swap.calc(Cashflows).head()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/18_solve_present_value.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Solve for Present Value\\n\",\n \"Using 1x2 receiver structure - solve for strike on second leg such that pv of leg2 = pv of leg1\\n\",\n \"\\n\",\n \"This example takes advantage of intra-portfolio formulae. You could do this in two API requests with:\\n\",\n \"```python\\n\",\n \"swaption_1 = IRSwaption( ... )\\n\",\n \"price = swaption_1.price()\\n\",\n \"swaption_2 = IRSwaption( ..., strike=f'{price}/pv', ... )\\n\",\n \"zero_cost_portfolio = Portfolio((swaption_1, swaption_2))\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%# buy 1x atmf payer, sell 2x atmf+X payer solving for 0 cost strike\\n\"\n },\n \"tags\": [\n \"Instrument - Solving across multiple instruments\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"zero_cost_portfolio = Portfolio(\\n\",\n \" (\\n\",\n \" IRSwaption(\\n\",\n \" 'Receive',\\n\",\n \" '30y',\\n\",\n \" 'USD',\\n\",\n \" notional_amount=10e6,\\n\",\n \" expiration_date='3m',\\n\",\n \" strike='atmf',\\n\",\n \" buy_sell='Buy',\\n\",\n \" name='30y_buy',\\n\",\n \" ),\\n\",\n \" IRSwaption(\\n\",\n \" 'Receive',\\n\",\n \" '30y',\\n\",\n \" 'USD',\\n\",\n \" notional_amount=20e6,\\n\",\n \" expiration_date='3m',\\n\",\n \" strike='=solvefor([30y_buy].risk.Price,pv)',\\n\",\n \" buy_sell='Sell',\\n\",\n \" ),\\n\",\n \" )\\n\",\n \")\\n\",\n \"\\n\",\n \"# see the strikes and prices\\n\",\n \"print([s.strike * 1e4 for s in zero_cost_portfolio.resolve(in_place=False)])\\n\",\n \"print([s for s in zero_cost_portfolio.price()])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/19_solve_delta.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption, IRSwap\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import IRDelta\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Solve for Delta\\n\",\n \"Size swap to match swaption delta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - Solving - Size one trade by the risk of another trade\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"port = Portfolio(\\n\",\n \" (\\n\",\n \" IRSwaption('Pay', '5y', 'USD', expiration_date='1y', buy_sell='Sell', name='payer_swaption'),\\n\",\n \" IRSwap(\\n\",\n \" 'Receive',\\n\",\n \" '5y',\\n\",\n \" 'USD',\\n\",\n \" fixed_rate='atm',\\n\",\n \" notional_amount='=solvefor([payer_swaption].risk.IRDeltaParallel,bp)',\\n\",\n \" name='hedge1',\\n\",\n \" ),\\n\",\n \" )\\n\",\n \")\\n\",\n \"port.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"port_delta = port.calc(IRDelta(aggregation_level='Type'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# check that delta is approximately equal\\n\",\n \"port_delta['payer_swaption'] - port_delta['hedge1']\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRFixedLeg, IRFloatLeg, IRSwap\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.session import GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - IRFixedLeg\",\n \"Instrument - IRFloatLeg\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"fixed_leg = IRFixedLeg('Buy', fixed_rate=0.05, notional_currency='USD', termination_date='5y', notional_amount=100e6)\\n\",\n \"\\n\",\n \"float_leg = IRFloatLeg(\\n\",\n \" 'Sell', notional_currency='USD', termination_date='5y', floating_rate_spread=40 / 1e4, notional_amount=100e6\\n\",\n \")\\n\",\n \"\\n\",\n \"swap = IRSwap(\\n\",\n \" pay_or_receive='Receive',\\n\",\n \" termination_date='5y',\\n\",\n \" notional_currency='USD',\\n\",\n \" fixed_rate=0.05,\\n\",\n \" floating_rate_spread=40 / 1e4,\\n\",\n \" principal_exchange='None',\\n\",\n \" notional_amount=100e6,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"CSA\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"with PricingContext(csa_term='USD-SOFR'):\\n\",\n \" fixed_price = fixed_leg.price()\\n\",\n \" float_price = float_leg.price()\\n\",\n \" swap_price = swap.price()\\n\",\n \"\\n\",\n \"print(fixed_price.result() + float_price.result())\\n\",\n \"print(swap_price.result())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/21_asset_swap_definition.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import IRAssetSwapFxdFlt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - IRAssetSwapFxdFlt\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# The default instrument is a 0 PV asset swap on a US10Y Bond, which price is defaulted to 100\\n\",\n \"default = IRAssetSwapFxdFlt()\\n\",\n \"default.resolve()\\n\",\n \"default.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"default.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We may change the underlier bond and price which will resolve the other attributes accordingly\\n\",\n \"asset_swap = IRAssetSwapFxdFlt(identifier='GB5Y', traded_clean_price=99)\\n\",\n \"asset_swap.resolve()\\n\",\n \"asset_swap.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"asset_swap.price()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/22_cap_floor.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-07-21T12:55:44.710864900Z\",\n \"start_time\": \"2025-07-21T12:55:44.699341200Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"from gs_quant.instrument import IRCapFloor, IRCap, IRFloor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-07-21T12:47:57.166676100Z\",\n \"start_time\": \"2025-07-21T12:47:55.177673500Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - IRAssetSwapFxdFlt\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# Create an interest rate cap using IRCapFloor, setting cap_floor to \\\"Cap\\\"\\n\",\n \"cap = IRCapFloor(\\n\",\n \" cap_floor=\\\"Cap\\\",\\n\",\n \" notional_currency=\\\"USD\\\",\\n\",\n \" notional_amount=1000,\\n\",\n \" effective_date='2025-07-18',\\n\",\n \" termination_date='2030-07-18',\\n\",\n \" floating_rate_option='USD-LIBOR-BBA',\\n\",\n \" floating_rate_designated_maturity='3m',\\n\",\n \" floating_rate_frequency='3m',\\n\",\n \" strike=0.015, # 1. 5% floor rate\\n\",\n \")\\n\",\n \"cap.resolve(in_place=False).to_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cap.price()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"Compared that with the output of IRCap, we have similar result.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"ircap = IRCap(\\n\",\n \" notional_currency=\\\"USD\\\",\\n\",\n \" notional_amount=1000,\\n\",\n \" effective_date='2025-07-15',\\n\",\n \" termination_date='2030-07-15',\\n\",\n \" floating_rate_option='USD-LIBOR-BBA',\\n\",\n \" floating_rate_designated_maturity='3m',\\n\",\n \" floating_rate_frequency='3m',\\n\",\n \" cap_rate=0.015, # 1.5% cap rate\\n\",\n \")\\n\",\n \"ircap.resolve(in_place=False).to_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"ircap.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create an interest rate cap using IRCapFloor, setting cap_floor to \\\"Floor\\\"\\n\",\n \"floor = IRFloor(\\n\",\n \" notional_currency=\\\"USD\\\",\\n\",\n \" notional_amount=1000,\\n\",\n \" effective_date='2025-07-15',\\n\",\n \" termination_date='2030-07-15',\\n\",\n \" floating_rate_option='USD-LIBOR-BBA',\\n\",\n \" floating_rate_designated_maturity='3m',\\n\",\n \" floating_rate_frequency='3m',\\n\",\n \" floor_rate=0.015, # 1.5% floor rate\\n\",\n \")\\n\",\n \"floor.resolve(in_place=False).to_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"floor.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Compare output with IRFloor function\\n\",\n \"irfloor = IRFloor(\\n\",\n \" notional_currency=\\\"USD\\\",\\n\",\n \" notional_amount=1000,\\n\",\n \" effective_date='2025-07-15',\\n\",\n \" termination_date='2030-07-15',\\n\",\n \" floating_rate_option='USD-LIBOR-BBA',\\n\",\n \" floating_rate_designated_maturity='3m',\\n\",\n \" floating_rate_frequency='3m',\\n\",\n \" floor_rate=0.015, # 1.5% cap rate\\n\",\n \")\\n\",\n \"irfloor.resolve(in_place=False).to_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"irfloor.price()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.1\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-08-11T12:19:09.294055700Z\",\n \"start_time\": \"2025-08-11T12:19:03.376662500Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption, IRSwap, IRCapFloor\\n\",\n \"from gs_quant.common import AggregationLevel\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import IRVanna, IRVolga\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"ExecuteTime\": {\n \"end_time\": \"2025-08-11T12:19:16.393303500Z\",\n \"start_time\": \"2025-08-11T12:19:11.738322600Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Solve for Vanna and Volga\\n\",\n \"Size swap to match swaption vanna\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"tags\": [\n \"Instrument - Solving - Size one trade by the risk of another trade\"\n ],\n \"ExecuteTime\": {\n \"end_time\": \"2025-08-11T12:19:21.346721500Z\",\n \"start_time\": \"2025-08-11T12:19:18.268449900Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"port = Portfolio(\\n\",\n \" (\\n\",\n \" IRSwaption('Pay', '5y', 'USD', expiration_date='1y', buy_sell='Sell', name='payer_swaption'),\\n\",\n \" IRSwap(\\n\",\n \" 'Receive',\\n\",\n \" '5y',\\n\",\n \" 'USD',\\n\",\n \" fixed_rate='atm',\\n\",\n \" notional_amount='=solvefor([payer_swaption].risk.IRDeltaParallel,bp)',\\n\",\n \" name='hedge1',\\n\",\n \" ),\\n\",\n \" )\\n\",\n \")\\n\",\n \"port.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"port.calc(IRVanna).to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"port.calc(IRVolga).to_frame()\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Swaption (return) is non-linear (to interest change), that's why we can calculate Vanna and Volga for it, while IRSwap is linear and have it as zero.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"We can also check IRVanna and IRVolga with IRCapFloor instruments, which return new options for every defined period. \"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"cap = IRCapFloor(\\n\",\n \" cap_floor=\\\"Cap\\\",\\n\",\n \" notional_currency=\\\"USD\\\",\\n\",\n \" notional_amount=1000,\\n\",\n \" effective_date='2025-07-18',\\n\",\n \" termination_date='2030-07-18',\\n\",\n \" floating_rate_option='USD-LIBOR-BBA',\\n\",\n \" floating_rate_designated_maturity='3m',\\n\",\n \" floating_rate_frequency='3m',\\n\",\n \" strike=0.015, # 1. 5% floor rate\\n\",\n \")\\n\",\n \"cap.resolve(in_place=False).to_dict()\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"cap.calc(IRVanna)\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"cap.calc(IRVanna(aggregation_level=AggregationLevel.Type))\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"cap.calc(IRVolga)\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"cap.calc(IRVolga(aggregation_level=AggregationLevel.Type))\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/01_fx_fwd_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import FXForward\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from datetime import date\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get list of properties of an fx forward\\n\",\n \"FXForward.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# in this example we will construct and price a portfolio of FXForwards\\n\",\n \"fx_fwds = Portfolio()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# pair is the forward's currency pair. It a string of two ccy iso codes, optionally separated with a space (' ')\\n\",\n \"# The first currency is the base currency and the second is the quote currency\\n\",\n \"\\n\",\n \"# In this case, base currency is 'EUR' and quote currency is 'GBP'.\\n\",\n \"fx_fwds.append(FXForward(pair='EUR GBP', settlement_date='3y'))\\n\",\n \"fx_fwds.append(FXForward(pair='EURGBP', settlement_date='3y'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# notional_amount is the quantity of the base currency to be exchanged in the future; it can be a string (eg: '100m')\\n\",\n \"# or a double (10e8)\\n\",\n \"\\n\",\n \"# For these two forwards - some amount of GBP will be exchanged for 100mm EUR at a future date\\n\",\n \"fx_fwds.append(FXForward(pair='EURGBP', notional_amount='100m', settlement_date='3y'))\\n\",\n \"fx_fwds.append(FXForward(pair='EURGBP', notional_amount=10e8, settlement_date='3y'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# settlement_date is the contract settlement date. It can either be a date or string\\n\",\n \"fx_fwds.append(FXForward(settlement_date='3y'))\\n\",\n \"fx_fwds.append(FXForward(settlement_date=date(2023, 9, 25)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# forward rate is the exchange rate which will be used on the settlement date. It is a double.\\n\",\n \"# If not set then it will resolve to the fair fx fwd rate\\n\",\n \"\\n\",\n \"# In this example, the individual long the FXForward will pay 95mm GBP in exchange for 100mm EUR on the settlement date\\n\",\n \"fx_fwds.append(FXForward(pair='EURGBP', notional_amount=10e8, forward_rate=0.95, settlement_date='3y'))\\n\",\n \"\\n\",\n \"# If not set then it will resolve to the fair fx fwd rate\\n\",\n \"fx_fwds.append(FXForward(pair='EURGBP', notional_amount=10e8, settlement_date='3y'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame(fx_fwds.price())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - FXForward\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/02_fx_option_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# get list of properties of an fx option\\n\",\n \"FXOption.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# in this example we will construct and price a portfolio of FXOptions\\n\",\n \"fx_options = Portfolio()\\n\",\n \"\\n\",\n \"# you don't need to specify any parameters to get a valid trade. All properties have defaults\\n\",\n \"fx_options.append(FXOption())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# buy_sell indicates whether the option is bought (long) or sold (short)\\n\",\n \"# It can be represented by the BuySell enum or a string\\n\",\n \"fx_options.append(FXOption(buy_sell='Buy'))\\n\",\n \"\\n\",\n \"from gs_quant.common import BuySell\\n\",\n \"\\n\",\n \"fx_options.append(FXOption(buy_sell=BuySell.Sell))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# put_call indicates whether the option is a put or call; it can be represented by the OptionType enum or a string\\n\",\n \"fx_options.append(FXOption(option_type='Put'))\\n\",\n \"\\n\",\n \"from gs_quant.common import OptionType\\n\",\n \"\\n\",\n \"fx_options.append(FXOption(option_type=OptionType.Call))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# pair is the FXOption's underlying currency pair. It is a string of two ccy iso codes, optionally separated\\n\",\n \"# with a space (' '). The first currency is the base (transaction) currency and the second is the quote currency\\n\",\n \"\\n\",\n \"# In this case, base currency is 'EUR' and quote currency is 'GBP'\\n\",\n \"# This option gives the purchasor the option to buy EUR at expiration\\n\",\n \"fx_options.append(FXOption(buy_sell=BuySell.Buy, option_type=OptionType.Call, pair='EUR GBP'))\\n\",\n \"\\n\",\n \"# Here, base currency is 'GBP' and quote currency is 'USD'\\n\",\n \"# This option gives the purchasor the option to sell GBP at expiration\\n\",\n \"fx_options.append(FXOption(buy_sell=BuySell.Buy, option_type=OptionType.Put, pair='GBPUSD'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# notional_currency is the notional amount's denominated currency\\n\",\n \"# It can be a Currency enum or a string. By default, notional_currency will match the base currency\\n\",\n \"\\n\",\n \"# In this case, notional_currency of a EURGBP Call is EUR.\\n\",\n \"from gs_quant.common import Currency\\n\",\n \"\\n\",\n \"fx_options.append(FXOption(option_type=OptionType.Call, pair='EUR GBP', notional_currency=Currency.EUR))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# notional_amount is the quantity of the base currency to be exchanged in the future; it can be a string (eg: '100mm')\\n\",\n \"# or a double (10e8). By definition, notional_amount_other_currency is the notional amount in the quoted currency. It can be a Currency enum or a string\\n\",\n \"# by default, notional_amount_other_currency will be the product of 'notional_amount' and 'strike_price'\\n\",\n \"\\n\",\n \"# Here, a EURGBP Call has a notional of 10mm EUR\\n\",\n \"fx_options.append(\\n\",\n \" FXOption(option_type=OptionType.Call, pair='EUR GBP', notional_currency=Currency.EUR, notional_amount='10m')\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"strike_price is the exchange rate stuck for the FXOption's underlying currency pair, which can be specified by a double \\n\",\n \"or a string. If a string is used, it represents a relative value. When the trade is resolved, we solve for the strike_price \\n\",\n \"\\n\",\n \"The specific solver keys are: \\n\",\n \" - 'S' - current spot rate\\n\",\n \" - 'F' - forward\\n\",\n \" - 'ATM' - At the Money \\n\",\n \" - 'ATMF' - At the Money Forward\\n\",\n \" - 'D' - Delta Strikes\\n\",\n \" - 'P' - Premium\\n\",\n \"\\n\",\n \"You can use these keys to strike_price with the following formats: \\n\",\n \" - For S, F, ATM, ATMF: 's*1.1', 'F+10%', '1.05*ATMF+.01'\\n\",\n \" - For Delta Strikes, specify the option delta: '25D', '20D-.01', etc.\\n\",\n \" - You can also solve for Premium: P=, P= P=,%, PPct= \\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"# Here the option is bought to purchase 95mm EUR at expiration\\n\",\n \"fx_options.append(\\n\",\n \" FXOption(buy_sell=BuySell.Buy, pair='EURGBP', option_type='Call', notional_amount=10e8, strike_price=0.95)\\n\",\n \")\\n\",\n \"\\n\",\n \"# Here the option is sold to sell 100k of EUR at the current spot rate on expiration\\n\",\n \"fx_options.append(\\n\",\n \" FXOption(buy_sell=BuySell.Sell, pair='EURGBP', option_type=OptionType.Put, notional_amount='100k', strike_price='S')\\n\",\n \")\\n\",\n \"\\n\",\n \"# The option is sold to purchase AUD at the Forward rate + 1%\\n\",\n \"fx_options.append(FXOption(buy_sell=BuySell.Sell, pair='AUDJPY', option_type=OptionType.Call, strike_price='F+1%'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# notional_amount is the quantity of the base currency to be exchanged in the future; it can be a string (eg: '100mm')\\n\",\n \"# or a double (10e8). By definition, notional_amount_other_currency is the notional amount in the quoted currency. It\\n\",\n \"# can be a Currency enum or a string\\n\",\n \"# by default, notional_amount_other_currency will be the product of 'notional_amount' and 'strike_price'\\n\",\n \"\\n\",\n \"# Here, a EURGBP Call has a notional of 10mm EUR\\n\",\n \"fx_options.append(\\n\",\n \" FXOption(option_type=OptionType.Call, pair='EUR GBP', notional_currency=Currency.EUR, notional_amount='10m')\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# method_of_settlement indicates whether the option is cash or physically settled and can be either the\\n\",\n \"# OptionSettlementMethod Enum or a string\\n\",\n \"from gs_quant.common import OptionSettlementMethod\\n\",\n \"\\n\",\n \"fx_options.append(FXOption(method_of_settlement=OptionSettlementMethod.Cash))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# premium_currency is the premium amount's denominated currency\\n\",\n \"# It can be a Currency enum or a string. By default, premium_currency will match the base currency\\n\",\n \"\\n\",\n \"# In this case, premium_currency of a EURGBP Put is EUR\\n\",\n \"fx_options.append(FXOption(option_type=OptionType.Put, pair='EUR GBP', premium_currency=Currency.EUR))\\n\",\n \"\\n\",\n \"# premium_payment_date is the date the premium is exchanged. It can either be a date or string\\n\",\n \"# It can be set to 's' which indicates spot premium\\n\",\n \"fx_options.append(FXOption(option_type=OptionType.Call, pair='EUR GBP', premium_payment_date='spot'))\\n\",\n \"\\n\",\n \"# or set to 'fwd' or 'forward' to indicate forward premium\\n\",\n \"fx_options.append(FXOption(option_type=OptionType.Call, pair='EUR GBP', premium_payment_date='fwd'))\\n\",\n \"\\n\",\n \"# premium is the price of the option contract. It can be a float or a string\\n\",\n \"fx_options.append(FXOption(option_type=OptionType.Call, premium=-5e6))\\n\",\n \"\\n\",\n \"# It is possible to solve for the strike_price based on a certain Premium\\n\",\n \"# The below resolves the strike_price such that the option premium is -£5mm\\n\",\n \"fx_options.append(\\n\",\n \" FXOption(pair='AUD JPY', option_type=OptionType.Call, strike_price='P=-5mm', premium_currency=Currency.AUD)\\n\",\n \")\\n\",\n \"\\n\",\n \"# The below resolves the strike_price such that the option premium is 5% of the total amount exchanged if the option\\n\",\n \"# is exercised\\n\",\n \"fx_options.append(\\n\",\n \" FXOption(pair='AUD JPY', option_type=OptionType.Call, strike_price='Premium=5%', premium_currency=Currency.JPY)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame(fx_options.price())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXVolatilitySwap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get list of properties of an fx vol swap\\n\",\n \"FXVolatilitySwap.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = FXVolatilitySwap(pair='GBPUSD', last_fixing_date='3m', buy_sell='Buy')\\n\",\n \"swap.resolve()\\n\",\n \"print(swap.strike_vol * 100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# forward starting vol swap\\n\",\n \"fwd_6m6m_swap = FXVolatilitySwap(pair='USDJPY', first_fixing_date='6m', last_fixing_date='1y', buy_sell='Buy')\\n\",\n \"fwd_6m6m_swap.resolve()\\n\",\n \"print(fwd_6m6m_swap.as_dict())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"**Fixing frequency** defaults to 'Daily/Business Days' but many other conventions are supported such as:\\n\",\n \"* 'Daily/All Days'\\n\",\n \"* 'Daily/Holiday'\\n\",\n \"* 'Daily/MonToSat'\\n\",\n \"* 'Daily/Weighted Lagging'\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties - Fixing Frequency\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"weighted_swap = FXVolatilitySwap(\\n\",\n \" pair='GBPUSD', last_fixing_date='1y', buy_sell='Buy', fixing_frequency='Daily/Weighted Lagging'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"**Fixing source** defaults to 'WM Company LDN 4pm Mid' but other options include:\\n\",\n \"* 'GS NYC 3PM'\\n\",\n \"* 'WM Company NYC 10am Mid'\\n\",\n \"* 'BFIX LDN 4PM'\\n\",\n \"* 'WMC Company LDN 1pm Mid'\\n\",\n \"\\n\",\n \"We also have bloomberg options as well such as 'BFIX TKO 3pm-m'. \\n\",\n \"We're happy to confirm the syntax if you are looking for a fixing source that is not mentioned here.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties - Fixing Source\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"bfix = FXVolatilitySwap(pair='GBPUSD', last_fixing_date='1y', buy_sell='Buy', fixing_source='BFIX LDN 4PM')\\n\",\n \"print(bfix.strike_vol)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"**Strike vol** defaults to ATM but the following solvers are supported:\\n\",\n \"\\n\",\n \"* zero cost vol : p=0\\n\",\n \"* % Vol = {%}N (i.e. '2N')\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - Solving - Solving FXVolSwap strike vol\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# zero cost vol\\n\",\n \"zero_swap = FXVolatilitySwap(pair='USDNOK', last_fixing_date='3m', buy_sell='Sell', strike_vol='p=0')\\n\",\n \"zero_swap.resolve()\\n\",\n \"zero_swap.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# 1.25*atm\\n\",\n \"swap_25 = FXVolatilitySwap(pair='EURUSD', last_fixing_date='3m', buy_sell='Sell', strike_vol='1.25N')\\n\",\n \"swap_25.resolve()\\n\",\n \"print(swap_25.strike_vol * 100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# current spot\\n\",\n \"swap_spot = FXVolatilitySwap(pair='EURUSD', last_fixing_date='3m', buy_sell='Sell', strike_vol='S')\\n\",\n \"swap_spot.resolve()\\n\",\n \"print(swap_spot.strike_vol * 100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# current spot\\n\",\n \"swap_spot = FXVolatilitySwap(pair='EURUSD', last_fixing_date='3m', buy_sell='Sell', strike_vol='S')\\n\",\n \"swap_spot.resolve()\\n\",\n \"print(swap_spot.strike_vol * 100)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Zero Cost or Fair Vol Strike** - This can be calculated using the measure `FairVolStrike` from the risk package and will calculate the volatility strike that would give 0 premium\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics - FairVolStrike\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import FairVolStrike\\n\",\n \"\\n\",\n \"vol_swap = FXVolatilitySwap(pair='EURUSD', last_fixing_date='3m', strike_vol=0.1)\\n\",\n \"print(vol_swap.calc(FairVolStrike))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - FXVolatilitySwap\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXBinary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"FXBinary?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"binary = FXBinary(pair='USDJPY', buy_sell='Buy', expiration_date='3m')\\n\",\n \"binary.resolve()\\n\",\n \"print(binary.as_dict())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Strike Price \\n\",\n \"\\n\",\n \"If the strike price is not specified, the current spot is used as the default. Similar to FXOption, the strike price can be specified by a double or a string.\\n\",\n \"\\n\",\n \"\\n\",\n \"The specific solver keys are: \\n\",\n \"\\n\",\n \" - 'S' - current spot rate\\n\",\n \" - 'F' - forward\\n\",\n \" - 'P' - Premium\\n\",\n \" - 'D' - Delta \\n\",\n \"\\n\",\n \"You can use these keys to strike_price with the following formats: \\n\",\n \"\\n\",\n \" - For S, F: 's*1.1', 'F+10%' \\n\",\n \" - For Delta Strikes, specify the option delta: '25D', '20D-.01', etc.\\n\",\n \" - You can also solve for Premium: P={target}%\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - Solving - FX Binary strike solving\",\n \"Metrics - FXSpot\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import FXSpot, FXFwd\\n\",\n \"\\n\",\n \"# solve for strike price by setting payout ratio to 10%\\n\",\n \"binary_10x = FXBinary(\\n\",\n \" pair='EURUSD', buy_sell='Buy', option_type='Put', expiration_date='1m', strike_price='p=10%', premium=0\\n\",\n \")\\n\",\n \"binary_10x.resolve()\\n\",\n \"print('strike price:', binary_10x.strike_price)\\n\",\n \"print('spot level:', binary_10x.calc(FXSpot))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"binary_itm = FXBinary(pair='AUDUSD', buy_sell='Buy', expiration_date='3m', strike_price='1.1*s')\\n\",\n \"binary_itm.resolve()\\n\",\n \"print('strike price:', binary_itm.strike_price)\\n\",\n \"print('spot level:', binary_itm.calc(FXSpot))\\n\",\n \"binary_itm.calc(FXSpot) * 1.1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics - FXFwd\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"binary_otm = FXBinary(pair='AUDUSD', buy_sell='Buy', expiration_date='3m', notional_amount='100k', strike_price='F-2%')\\n\",\n \"binary_otm.resolve()\\n\",\n \"print('strike price:', binary_otm.strike_price)\\n\",\n \"print('fwd level:', binary_otm.calc(FXFwd))\\n\",\n \"binary_otm.calc(FXFwd) * 0.98\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - FXBinary\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/05_calc_option_measures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.risk import FXAnnualImpliedVol, FXAnnualATMImpliedVol, Price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Metrics - FXAnnualImpliedVol, Metrics - FXAnnualATMImpliedVol\",\n \"Results - Multiple Measures\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"fx_option = FXOption(pair='USDJPY', option_type='Put', expiration_date='3m', premium=0)\\n\",\n \"result = fx_option.calc((Price, FXAnnualImpliedVol, FXAnnualATMImpliedVol)).to_frame()\\n\",\n \"result\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/06_fx_var_swap_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXVarianceSwap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get list of properties of an fx var swap\\n\",\n \"FXVarianceSwap.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = FXVarianceSwap(pair='GBPUSD', last_fixing_date='3m', buy_sell='Buy')\\n\",\n \"swap.resolve()\\n\",\n \"print(swap.strike_vol * 100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pprint\\n\",\n \"\\n\",\n \"pp = pprint.PrettyPrinter(indent=4)\\n\",\n \"\\n\",\n \"# forward starting var swap\\n\",\n \"fwd_6m6m_swap = FXVarianceSwap(pair='USDJPY', first_fixing_date='6m', last_fixing_date='1y', buy_sell='Buy')\\n\",\n \"fwd_6m6m_swap.resolve()\\n\",\n \"pp.pprint(fwd_6m6m_swap.as_dict())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"**Fixing frequency** defaults to 'Daily/Business Days' but many other conventions are supported such as:\\n\",\n \"* 'Daily/All Days'\\n\",\n \"* 'Daily/Holiday'\\n\",\n \"* 'Daily/MonToSat'\\n\",\n \"* 'Daily/Weighted Lagging'\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"weighted_swap = FXVarianceSwap(\\n\",\n \" pair='GBPUSD', last_fixing_date='1y', buy_sell='Buy', fixing_frequency='Daily/Weighted Lagging'\\n\",\n \")\\n\",\n \"weighted_swap.resolve()\\n\",\n \"pp.pprint(weighted_swap.as_dict())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"**Fixing source** defaults to 'WM Company LDN 4pm Mid' but other options include:\\n\",\n \"* 'GS NYC 3PM'\\n\",\n \"* 'WM Company NYC 10am Mid'\\n\",\n \"* 'BFIX LDN 4PM'\\n\",\n \"* 'WMC Company LDN 1pm Mid'\\n\",\n \"\\n\",\n \"We also have bloomberg options as well such as 'BFIX TKO 3pm-m'. \\n\",\n \"We're happy to confirm the syntax if you are looking for a fixing source that is not mentioned here.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bfix = FXVarianceSwap(pair='GBPUSD', last_fixing_date='1y', buy_sell='Buy', fixing_source='BFIX LDN 4PM')\\n\",\n \"bfix.resolve()\\n\",\n \"print(bfix.strike_vol)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Zero Cost or Fair Vol Strike** - This can be calculated using the measure `FairVolStrike` from the risk package and will calculate the strike in volatility terms that would give 0 premium\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import FairVolStrike\\n\",\n \"\\n\",\n \"vol_swap = FXVarianceSwap(pair='GBPUSD', last_fixing_date='3m', strike_vol=0.1)\\n\",\n \"print(vol_swap.calc(FairVolStrike))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import FXKnockout\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# get list of properties of an fx knockout - Note the knockout instrument will also do RKO (Reverse Knockout)\\n\",\n \"FXKnockout.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# in this example we will construct and price a portfolio of FXKnockouts\\n\",\n \"fx_kos = Portfolio()\\n\",\n \"\\n\",\n \"# you don't need to specify any parameters to get a valid trade. All properties have defaults\\n\",\n \"fx_kos.append(FXKnockout())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - Knock In / Knock Out\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# knock_in_or_out controls whether the option exits if the barrier_level is breached (knockout) or whether the option\\n\",\n \"# only starts once the barrier_level is breached. Switch from a KO to an RKO.\\n\",\n \"\\n\",\n \"from gs_quant.common import InOut\\n\",\n \"\\n\",\n \"fx_kos.append(FXKnockout(barrier_level=0.95, knock_in_or_out=InOut.Out))\\n\",\n \"fx_kos.append(FXKnockout(barrier_level=0.6, knock_in_or_out='In'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - Upper barrier / Lower barrier\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# knock_up_or_down controls whether which direction breaches are measured from\\n\",\n \"\\n\",\n \"from gs_quant.common import UpDown\\n\",\n \"\\n\",\n \"fx_kos.append(FXKnockout(barrier_level=0.95, knock_up_or_down=UpDown.Up))\\n\",\n \"fx_kos.append(FXKnockout(barrier_level=0.6, knock_up_or_down='Down'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# buy_sell indicates whether the option is bought (long) or sold (short)\\n\",\n \"# It can be represented by the BuySell enum or a string\\n\",\n \"fx_kos.append(FXKnockout(buy_sell='Buy'))\\n\",\n \"\\n\",\n \"from gs_quant.common import BuySell\\n\",\n \"\\n\",\n \"fx_kos.append(FXKnockout(buy_sell=BuySell.Sell))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# put_call indicates whether the option is a put or call; it can be represented by the OptionType enum or a string\\n\",\n \"fx_kos.append(FXKnockout(option_type='Put'))\\n\",\n \"\\n\",\n \"from gs_quant.common import OptionType\\n\",\n \"\\n\",\n \"fx_kos.append(FXKnockout(option_type=OptionType.Call))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# pair is the FXKnockout's underlying currency pair. It is a string of two ccy iso codes, optionally separated\\n\",\n \"# with a space (' '). The first currency is the base (transaction) currency and the second is the quote currency\\n\",\n \"\\n\",\n \"# In this case, base currency is 'EUR' and quote currency is 'GBP'\\n\",\n \"# This option gives the purchasor the option to buy EUR at expiration\\n\",\n \"fx_kos.append(FXKnockout(buy_sell=BuySell.Buy, option_type=OptionType.Call, pair='EUR GBP'))\\n\",\n \"\\n\",\n \"# Here, base currency is 'GBP' and quote currency is 'USD'\\n\",\n \"# This option gives the purchasor the option to sell GBP at expiration\\n\",\n \"fx_kos.append(FXKnockout(buy_sell=BuySell.Buy, option_type=OptionType.Put, pair='GBPUSD'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# notional_currency is the notional amount's denominated currency\\n\",\n \"# It can be a Currency enum or a string. By default, notional_currency will match the base currency\\n\",\n \"\\n\",\n \"# In this case, notional_currency of a EURGBP Call is EUR.\\n\",\n \"from gs_quant.common import Currency\\n\",\n \"\\n\",\n \"fx_kos.append(FXKnockout(option_type=OptionType.Call, pair='EUR GBP', notional_currency=Currency.EUR))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# notional_amount is the quantity of the base currency to be exchanged in the future; it can be a string (eg: '100mm')\\n\",\n \"# or a double (10e8). By definition, notional_amount_other_currency is the notional amount in the quoted currency. It can be a Currency enum or a string\\n\",\n \"# by default, notional_amount_other_currency will be the product of 'notional_amount' and 'strike_price'\\n\",\n \"\\n\",\n \"# Here, a EURGBP Call has a notional of 10mm EUR\\n\",\n \"fx_kos.append(\\n\",\n \" FXKnockout(option_type=OptionType.Call, pair='EUR GBP', notional_currency=Currency.EUR, notional_amount='10m')\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Instrument Solving - FXKO Strike Solving\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"\\\"\\\"\\\"\\n\",\n \"strike_price is the exchange rate stuck for the FXOption's underlying currency pair, which can be specified by a double \\n\",\n \"or a string. If a string is used, it represents a relative value. When the trade is resolved, we solve for the strike_price \\n\",\n \"\\n\",\n \"The specific solver keys are: \\n\",\n \" - 'S' - current spot rate\\n\",\n \" - 'F' - forward\\n\",\n \" - 'ATM' - At the Money \\n\",\n \" - 'ATMF' - At the Money Forward\\n\",\n \" - 'D' - Delta Strikes\\n\",\n \" - 'P' - Premium\\n\",\n \"\\n\",\n \"You can use these keys to strike_price with the following formats: \\n\",\n \" - For S, F, ATM, ATMF: 's*1.1', 'F+10%', '1.05*ATMF+.01'\\n\",\n \" - For Delta Strikes, specify the option delta: '25D', '20D-.01', etc.\\n\",\n \" - You can also solve for Premium: P=, P= P=,%, PPct= \\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"# Here the option is bought to purchase 95mm EUR at expiration\\n\",\n \"fx_kos.append(\\n\",\n \" FXKnockout(buy_sell=BuySell.Buy, pair='EURGBP', option_type='Call', notional_amount=10e8, strike_price=0.95)\\n\",\n \")\\n\",\n \"\\n\",\n \"# Here the option is sold to sell 100k of EUR at the current spot rate on expiration\\n\",\n \"fx_kos.append(\\n\",\n \" FXKnockout(\\n\",\n \" buy_sell=BuySell.Sell, pair='EURGBP', option_type=OptionType.Put, notional_amount='100k', strike_price='S'\\n\",\n \" )\\n\",\n \")\\n\",\n \"\\n\",\n \"# The option is sold to purchase AUD at the Forward rate + 1%\\n\",\n \"fx_kos.append(FXKnockout(buy_sell=BuySell.Sell, pair='AUDJPY', option_type=OptionType.Call, strike_price='F+1%'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Enums - OptionSettlementMethod\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# method_of_settlement indicates whether the option is cash or physically settled and can be either the\\n\",\n \"# OptionSettlementMethod Enum or a string\\n\",\n \"from gs_quant.common import OptionSettlementMethod\\n\",\n \"\\n\",\n \"fx_kos.append(FXKnockout(method_of_settlement=OptionSettlementMethod.Cash))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# premium_currency is the premium amount's denominated currency\\n\",\n \"# It can be a Currency enum or a string. By default, premium_currency will match the base currency\\n\",\n \"\\n\",\n \"# In this case, premium_currency of a EURGBP Put is EUR\\n\",\n \"fx_kos.append(FXKnockout(option_type=OptionType.Put, pair='EUR GBP', premium_currency=Currency.EUR))\\n\",\n \"\\n\",\n \"# premium_payment_date is the date the premium is exchanged. It can either be a date or string\\n\",\n \"# It can be set to 's' which indicates spot premium\\n\",\n \"fx_kos.append(FXKnockout(option_type=OptionType.Call, pair='EUR GBP', premium_payment_date='spot'))\\n\",\n \"\\n\",\n \"# or set to 'fwd' or 'forward' to indicate forward premium\\n\",\n \"fx_kos.append(FXKnockout(option_type=OptionType.Call, pair='EUR GBP', premium_payment_date='fwd'))\\n\",\n \"\\n\",\n \"# premium is the price of the option contract. It can be a float or a string\\n\",\n \"fx_kos.append(FXKnockout(option_type=OptionType.Call, premium=-5e6))\\n\",\n \"\\n\",\n \"# It is possible to solve for the strike_price based on a certain Premium\\n\",\n \"# The below resolves the strike_price such that the option premium is -£5mm\\n\",\n \"fx_kos.append(\\n\",\n \" FXKnockout(pair='AUD JPY', option_type=OptionType.Call, strike_price='P=-5mm', premium_currency=Currency.AUD)\\n\",\n \")\\n\",\n \"\\n\",\n \"# The below resolves the strike_price such that the option premium is 5% of the total amount exchanged if the option\\n\",\n \"# is exercised\\n\",\n \"fx_kos.append(\\n\",\n \" FXKnockout(pair='AUD JPY', option_type=OptionType.Call, strike_price='Premium=5%', premium_currency=Currency.JPY)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame(fx_kos.price())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n },\n \"tags\": [\n \"Instrument - FXKnockout\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import FXDoubleOneTouch\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Basic Details\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# get list of properties of an fx knockout - Note the knockout instrument will also do RKO (Reverse Knockout)\\n\",\n \"FXDoubleOneTouch.properties()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# in this example we will construct and price a portfolio of FX DNTs\\n\",\n \"fx_dbl_touches = Portfolio()\\n\",\n \"\\n\",\n \"# you don't need to specify any parameters to get a valid trade. All properties have defaults\\n\",\n \"# Note that touch_or_no_touch defaults to \\\"No Touch\\\", so the default trade is a DNT not a DOT (see below cell)\\n\",\n \"fx_dbl_touches.append(FXDoubleOneTouch())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Double **No** Touch vs Double **One** touch\\n\",\n \"`touch_or_no_touch` controls whether the option pays if either barrier_level is breached (called a Double One Touch or DOT for short). \\n\",\n \"Or whether the option only pays if neither barrier_level has been breached by the `expiration_date` (called a Double No Touch or DNT).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import TouchNoTouch\\n\",\n \"\\n\",\n \"fx_dbl_touches.append(\\n\",\n \" FXDoubleOneTouch(lower_barrier_level=0.9, upper_barrier_level=1.2, touch_or_no_touch=TouchNoTouch.No_Touch)\\n\",\n \")\\n\",\n \"fx_dbl_touches.append(\\n\",\n \" FXDoubleOneTouch(lower_barrier_level=0.9, upper_barrier_level=1.2, touch_or_no_touch=TouchNoTouch.Touch)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Buying vs Selling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# buy_sell indicates whether the option is bought (long) or sold (short)\\n\",\n \"# It can be represented by the BuySell enum or a string\\n\",\n \"fx_dbl_touches.append(FXDoubleOneTouch(buy_sell='Buy'))\\n\",\n \"\\n\",\n \"from gs_quant.common import BuySell\\n\",\n \"\\n\",\n \"fx_dbl_touches.append(FXDoubleOneTouch(buy_sell=BuySell.Sell))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Currencies involved\\n\",\n \"`pair` is the `FXDoubleOneTouch`'s underlying currency pair. It is a string of two ccy iso codes, optionally separated with a space (' '). \\n\",\n \"The first currency is the base (transaction) currency and the second is the quote currency.\\n\",\n \"The upper and lower barrier levels are then levels corresponding to this FX rate\\n\",\n \"The option also has a `notional_currency` and `notional_amount` field, this corresponds to the conditional option payout.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import Currency\\n\",\n \"\\n\",\n \"# In this case, base currency is 'EUR' and quote currency is 'USD'\\n\",\n \"# This option gives the purchasor a 100k EUR payout if the barrier levels are not breached\\n\",\n \"fx_dbl_touches.append(\\n\",\n \" FXDoubleOneTouch(buy_sell=BuySell.Buy, pair='EUR USD', notional_currency=Currency.EUR, notional_amount=100e3)\\n\",\n \")\\n\",\n \"\\n\",\n \"# Here, base currency is 'GBP' and quote currency is 'USD'\\n\",\n \"# This option gives the purchasor a 100k USD payout if either barrier is breached\\n\",\n \"fx_dbl_touches.append(\\n\",\n \" FXDoubleOneTouch(\\n\",\n \" buy_sell=BuySell.Buy,\\n\",\n \" touch_or_no_touch=\\\"Touch\\\",\\n\",\n \" pair='GBPUSD',\\n\",\n \" notional_currency=Currency.EUR,\\n\",\n \" notional_amount=\\\"100k\\\",\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"tags\": [\n \"Instrument - Solving - DNT strikes\"\n ]\n },\n \"source\": [\n \"#### Barrier Levels\\n\",\n \"`upper_barrier_level` and `lower_barrier_level` are the exchange rates stuck for the DOT/DNT's underlying currency pair, which can be specified by a double \\n\",\n \"or a string with a keyword/letter. If a string is used, it represents a relative value. When the trade is resolved, we solve for the strike_price \\n\",\n \"\\n\",\n \"The specific solver keys are: \\n\",\n \"* 'S' - current spot rate\\n\",\n \"* 'F' - forward\\n\",\n \"* 'D' - Delta Strikes of a vanilla option\\n\",\n \"* 'P' - Premium\\n\",\n \"\\n\",\n \"You can use these keys with the following formats: \\n\",\n \"* For S, F, ATM, ATMF: 's*1.05', 'F+10%', '1.05*ATMF+.01'\\n\",\n \"* For Delta Strikes, specify the option delta: '25D', '20D-.01', etc.\\n\",\n \"* You can also solve for Premium: P=, P= P=,%, PPct=\\n\",\n \"\\n\",\n \"To have the upper barrier 2% above the current spot either of the following syntaxs would be valid: \\n\",\n \"`upper_barrier_level=\\\"s+2%\\\"` or `upper_barrier_level=\\\"s*1.02\\\"`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Here the option is set to have fixed barrier levels\\n\",\n \"fx_dbl_touches.append(\\n\",\n \" FXDoubleOneTouch(pair='EURGBP', expiration_date=\\\"1m\\\", lower_barrier_level=0.8, upper_barrier_level=0.95)\\n\",\n \")\\n\",\n \"\\n\",\n \"# The option is sold have the lower barrier 1% below spot and upper barrier 1% above the fwd\\n\",\n \"fx_dbl_touches.append(\\n\",\n \" FXDoubleOneTouch(pair='EURGBP', expiration_date=\\\"1m\\\", lower_barrier_level=\\\"s-1%\\\", upper_barrier_level=\\\"f+1%\\\")\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties - FX Option Premium\"\n ]\n },\n \"source\": [\n \"#### Option Premium\\n\",\n \"`premium_currency` is the premium amount's denominated currency. \\n\",\n \"It can be a Currency enum or a string. By default, `premium_currency` will match the `notional_currency`. \\n\",\n \"The `premium` will **default to the fair premium** s.t. that the PresentValue of the option + premium = 0. \\n\",\n \"You therefore may find it helpful to set the premium explictly to 0 s.t. the PresentValue of the DOT/DNT is just the value of the option. \\n\",\n \"`premium_payment_date` is the date the premium is exchanged. It can either be a date or string such as \\\"fwd\\\" or \\\"spot\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# In this case, the payout and premium currency of the EURGBP DNt will be EUR\\n\",\n \"fx_dbl_touches.append(FXDoubleOneTouch(pair='EUR GBP', notional_currency='EUR', premium_currency='EUR'))\\n\",\n \"\\n\",\n \"# Here we explictly set the premium to 0 so measures like DollarPrice or PresentValue only include the option value\\n\",\n \"fx_dbl_touches.append(FXDoubleOneTouch(pair='EUR GBP', premium=0, notional_currency='EUR'))\\n\",\n \"\\n\",\n \"# Here we set it the option to have forward premium by setting `premium_payment_date` to 'fwd' or 'forward'\\n\",\n \"fx_dbl_touches.append(FXDoubleOneTouch(pair='EUR GBP', premium_payment_date='fwd'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Premium Solving\\n\",\n \"It is possible to solve for the level of a single barrier to reach a certain Premium (in % or absolute value) \\n\",\n \"You can also sovle both barrier levels for a target premium in which case they will be symmetrical spaced from current spot. \\n\",\n \"Common syntax for solving for permium in % or absolute level include `p=10%` and `p=-200k`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import gs_quant.risk as risk\\n\",\n \"\\n\",\n \"# e.g. Solve X in a \\\"1m USDJPY spot-3%/X DNT\\\" s.t. that the fair premium is 10% of the notional\\n\",\n \"upper_solve = FXDoubleOneTouch(\\n\",\n \" pair='USD JPY', expiration_date='1m', lower_barrier_level='spot-3%', upper_barrier_level=\\\"p=10%\\\"\\n\",\n \")\\n\",\n \"calced = upper_solve.calc((risk.FairPremium, risk.ResolvedInstrumentValues))\\n\",\n \"print(calced[risk.FairPremium], calced[risk.ResolvedInstrumentValues].upper_barrier_level)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# e.g. Solve for a 1m USDJPY DNT with symmetric strikes aorund spot s.t. the premium is 5%\\n\",\n \"double_solve = FXDoubleOneTouch(\\n\",\n \" pair='USD JPY', expiration_date='1m', lower_barrier_level='p=5%', upper_barrier_level='p=5%', premium=0\\n\",\n \")\\n\",\n \"calced = double_solve.calc((risk.FairPremiumInPercent, risk.FXSpot, risk.ResolvedInstrumentValues))\\n\",\n \"resolved = calced[risk.ResolvedInstrumentValues]\\n\",\n \"print(\\n\",\n \" calced[risk.FairPremiumInPercent] * 100,\\n\",\n \" resolved.lower_barrier_level,\\n\",\n \" calced[risk.FXSpot],\\n\",\n \" resolved.upper_barrier_level,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame(fx_dbl_touches.price())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n },\n \"tags\": [\n \"Instrument - FXDoubleOneTouch\",\n \"Instrument - FX DNT\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"98fd95ce-17e8-46e4-b27a-698538c3c003\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import Currency, OptionType\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.instrument import FXOption, FXForward\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant import risk\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3b2c11bd-c938-4852-b9db-86d4251453fd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c97dffb-4d87-4063-81b5-73cdcd9305c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cross = \\\"GBPJPY\\\"\\n\",\n \"premium_currency = Currency.GBP\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aeed27d1-dde1-499d-b288-bebc2dc8a06e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"option = FXOption(\\n\",\n \" cross,\\n\",\n \" expiration_date=\\\"2y\\\",\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" strike_price=\\\"25d\\\",\\n\",\n \" premium_currency=premium_currency,\\n\",\n \" premium_payment_date=\\\"spot\\\",\\n\",\n \")\\n\",\n \"option.resolve()\\n\",\n \"# Here premium will have resolved to equal the option value\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8dda27a5-9667-4e6f-9b40-d56e3d9b6acc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spot = option.calc(risk.FXSpot)\\n\",\n \"spot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2b5e92c6-72bc-40f7-b464-7357e01e8a4f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The CSA terms for FX Fwds won't always be USD-OIS like for derrivs, so we force everything to USD-OIS to be consistent\\n\",\n \"PricingContext.current = PricingContext(csa_term='USD-OIS')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"83146b2a-33c1-418c-99d1-4725edff74e9\",\n \"metadata\": {\n \"tags\": [\n \"Metrics - Black-Scholes Delta\"\n ]\n },\n \"source\": [\n \"### Black-Scholes Delta\\n\",\n \"`FXQuotedDelta` will calculate Black-Scholes delta in % terms and is the delta metric that is used to solve if you specify the option strike in delta like above\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"736eccbb-12fe-4e33-8628-f102668f4711\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"option.calc(risk.FXQuotedDelta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"54966f17-18a0-4c24-8f60-6ff01d01f23e\",\n \"metadata\": {\n \"tags\": [\n \"Metrics - FXDelta\"\n ]\n },\n \"source\": [\n \"### Delta PnL\\n\",\n \"The `FXDelta` measure captures the USD present value change from the move in spot rates for each USD cross (in normal convention). i.e. `PnL(USD) = dSpot * FXDelta` \\n\",\n \"Note: USD can be changed for another currency by parameterising the measure `FXDelta(currency='EUR')`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1be64972-841e-42f8-a7c2-b691e1272ece\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_fxdelta = option.calc(risk.FXDelta)\\n\",\n \"opt_fxdelta.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"53871c7e-324e-4680-9f55-b30315b8f054\",\n \"metadata\": {\n \"tags\": [\n \"Metrics - Delta as Notional of fwd\"\n ]\n },\n \"source\": [\n \"### Delta as Notional of Fwd\\n\",\n \"To compute the wing delta as the notional amount of an equivalent FX Fwd there are the measures `FXCalcDelta` and `FXDeltaHedge`. \\n\",\n \"`FXCalcDelta` returns a percentage factor of the option notional amount for the option cross. \\n\",\n \"`FXDeltaHedge` returns notional amounts of FX Fwds on USD crosses to hedge the option.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"afa2372f-3bed-4895-b82c-2ea5153b4438\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Here this will return a number close to the 0.25 B-S delta\\n\",\n \"opt_fxcalc_notional = option.calc(risk.FXCalcDelta)\\n\",\n \"opt_fxcalc_notional\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"99994eeb-757c-4031-a3fa-dfe7b3f509df\",\n \"metadata\": {},\n \"source\": [\n \"We can build the FXForward settling on spot date, with that `-1 * FXCalcDelta * notional` and check the FXDelta offsets, i.e. this is the spot trade on the option cross to hedge delta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f10b1365-c509-43af-9157-f4df14774d91\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fwd = FXForward(cross, \\\"0b\\\", \\\"f\\\", notional_amount=-1 * opt_fxcalc_notional * option.notional_amount)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"560ba96b-6da0-403c-8cd7-ebb95549fe0a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fwd_risk = fwd.calc(risk.FXDelta)\\n\",\n \"fwd_risk\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e360ec53-d58b-4fd5-b2b7-cf7d5eb84250\",\n \"metadata\": {},\n \"source\": [\n \"**Alternatively**: that hedge can be broken down into the two equivalent USDJPY and GBPUSD hedges, the `FXDeltaHedge` measure is already scaled by `-1 * option notional`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"dc44c7b8-4513-4fc4-966c-7821f1e561b7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_fxhedge = option.calc(risk.FXDeltaHedge)\\n\",\n \"opt_fxhedge\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4a52955c-1d57-4fd0-ace4-5c8fc71ee878\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"usd_fwds = [\\n\",\n \" FXForward(\\n\",\n \" opt_fxhedge.iloc[i].mkt_asset[4:] + opt_fxhedge.iloc[i].mkt_asset[:3], \\\"0b\\\", \\\"f\\\", opt_fxhedge.iloc[i].value\\n\",\n \" )\\n\",\n \" for i in range(len(opt_fxhedge))\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1ca8402b-d5ee-4607-a4f0-33323fbea298\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Portfolio(usd_fwds).calc(risk.FXDelta).aggregate()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Metrics - FX Delta Measures\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/10_fx_gamma_measures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"98fd95ce-17e8-46e4-b27a-698538c3c003\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"from scipy.stats import norm\\n\",\n \"from gs_quant.common import Currency, OptionType\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant import risk\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3b2c11bd-c938-4852-b9db-86d4251453fd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c97dffb-4d87-4063-81b5-73cdcd9305c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cross = \\\"USDJPY\\\"\\n\",\n \"premium_currency = Currency.JPY\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aeed27d1-dde1-499d-b288-bebc2dc8a06e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"option = FXOption(\\n\",\n \" cross,\\n\",\n \" expiration_date=\\\"2y\\\",\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" strike_price=\\\"25d\\\",\\n\",\n \" premium_currency=premium_currency,\\n\",\n \" premium_payment_date=\\\"spot\\\",\\n\",\n \" notional_amount=10e6,\\n\",\n \" notional_currency=Currency.USD,\\n\",\n \" premium=0,\\n\",\n \")\\n\",\n \"option.resolve()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"83146b2a-33c1-418c-99d1-4725edff74e9\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"### FXGamma\\n\",\n \"The only gamma measure currently available for FX is `FXGamma`. It represents the change in delta - wing delta in the underlying currency, scaled by the notional amount - for a 100% move in spot.\\n\",\n \"\\n\",\n \"This measure is computed using a central finite difference approximation, where spot shifts for repricing are done under the sticky-delta market model.\\n\",\n \"\\n\",\n \"In can be used in the following ways:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1fc5cf1f-1611-4dd7-9416-46b858600f42\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"gamma = option.calc(risk.FXGamma)\\n\",\n \"spot = option.calc(risk.FXSpot)\\n\",\n \"delta = option.calc(risk.FXCalcDelta) * option.notional_amount\\n\",\n \"print(f\\\"\\\"\\\"\\n\",\n \"Spot : {spot:.4f}\\n\",\n \"FXGamma (change in wing delta for a 100% move in spot) : {gamma:,.0f} {cross[3:]} / {cross}\\n\",\n \"FXDelta : {delta:,.0f} {cross[:3]}\\n\",\n \"\\\"\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9c8a4116-dc15-4cc3-8cc4-7c2eb7b275f4\",\n \"metadata\": {},\n \"source\": [\n \"#### 1) Computing change in delta for a unit spot change\\n\",\n \"Shock spot by 1 and recompute delta; the difference is approximated by `FXGamma / FXSpot`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8efcd37d-3bd1-47bf-a04d-41a9a5666c0a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mkt_shock = risk.MarketDataShockBasedScenario(\\n\",\n \" shocks={risk.MarketDataPattern('FX', 'JPY/USD'): risk.MarketDataShock(risk.MarketDataShockType.Absolute, 1)}\\n\",\n \")\\n\",\n \"with mkt_shock:\\n\",\n \" shocked_spot = option.calc(risk.FXSpot)\\n\",\n \" shocked_delta = option.calc(risk.FXCalcDelta) * option.notional_amount\\n\",\n \"\\n\",\n \"shocked_spot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cdea113c-18c0-48b7-9977-8064cad87f16\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(f\\\"\\\"\\\"\\n\",\n \"Shocked Delta : {shocked_delta:,.0f}\\n\",\n \"Delta Diff : {shocked_delta - delta:,.0f}\\n\",\n \"FXGamma/spot : {gamma / spot:,.0f}\\n\",\n \"\\\"\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8f60aca9-66ac-4bf0-adcb-917e22600ba1\",\n \"metadata\": {},\n \"source\": [\n \"#### 2) Computing change in delta for a percentage spot change\\n\",\n \"Shock spot by X% and recompute delta; the difference is approximated by `FXGamma * X / 100`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"247303e9-18e8-4577-b43f-00b4693af8df\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"shock_size = 0.01\\n\",\n \"mkt_shock = risk.MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" risk.MarketDataPattern('FX', 'JPY/USD'): risk.MarketDataShock(risk.MarketDataShockType.Proportional, shock_size)\\n\",\n \" }\\n\",\n \")\\n\",\n \"with mkt_shock:\\n\",\n \" shocked_spot = option.calc(risk.FXSpot)\\n\",\n \" shocked_delta = option.calc(risk.FXCalcDelta) * option.notional_amount\\n\",\n \"\\n\",\n \"shocked_spot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"caba1236-22d8-40c4-a26c-992e01b9509c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(f\\\"\\\"\\\"\\n\",\n \"Shocked Delta : {shocked_delta:,.0f}\\n\",\n \"Delta Diff : {shocked_delta - delta:,.0f}\\n\",\n \"FXGamma * {shock_size} : {gamma * shock_size:,.0f}\\n\",\n \"\\\"\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b49a616c-dc04-48cd-bd7e-bfbaa3c95020\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"shock_size = 0.0001 # small bump size approximates the gamma FDM calculation, so the difference will match FXGamma more closely now\\n\",\n \"mkt_shock = risk.MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" risk.MarketDataPattern('FX', 'JPY/USD'): risk.MarketDataShock(risk.MarketDataShockType.Proportional, shock_size)\\n\",\n \" }\\n\",\n \")\\n\",\n \"with mkt_shock:\\n\",\n \" shocked_spot = option.calc(risk.FXSpot)\\n\",\n \" shocked_delta = option.calc(risk.FXCalcDelta) * option.notional_amount\\n\",\n \"\\n\",\n \"shocked_spot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1b61e11a-bb16-4a09-8dc7-312803f27e4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(f\\\"\\\"\\\"\\n\",\n \"Shocked Delta : {shocked_delta:,.0f}\\n\",\n \"Delta Diff : {shocked_delta - delta:,.0f}\\n\",\n \"FXGamma * {shock_size} : {gamma * shock_size:,.0f}\\n\",\n \"\\\"\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fbe1612e-478c-47cd-ad0e-4f3531196f75\",\n \"metadata\": {},\n \"source\": [\n \"#### 3) Validating raw gamma (FXGamma/FXSpot) against Black-Scholes gamma\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"47689765-356e-4beb-967f-04b8540c9dac\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"raw_gamma = gamma / spot\\n\",\n \"print(f\\\"\\\"\\\"\\n\",\n \"Raw Gamma (scaled by notional): {raw_gamma:,.0f}\\n\",\n \"\\\"\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"60743f54-7bc1-4624-9fa9-313f8890e8b9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def black_sholes_raw_gamma(spot, strike, T, df_1, df_2, volatility):\\n\",\n \" r_1 = -np.log(df_1) / T\\n\",\n \" r_2 = -np.log(df_2) / T\\n\",\n \" d1 = (np.log(spot / strike) + (r_2 - r_1 + 0.5 * volatility**2) * T) / (volatility * np.sqrt(T))\\n\",\n \" n_prime_d1 = norm.pdf(d1)\\n\",\n \" bs_gamma = (np.exp(-r_1 * T) * n_prime_d1) / (spot * volatility * np.sqrt(T))\\n\",\n \" return float(bs_gamma)\\n\",\n \"\\n\",\n \"\\n\",\n \"vol = option.calc(risk.FXAnnualImpliedVol)\\n\",\n \"df_jpy = option.calc(risk.FXDiscountFactorOver)\\n\",\n \"df_usd = option.calc(risk.FXDiscountFactorUnder)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cb1837f1-bbed-47aa-ae92-52c5044f39df\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bs_raw_gamma = black_sholes_raw_gamma(spot, option.strike_price, 2.0, df_usd, df_jpy, vol)\\n\",\n \"print(f\\\"\\\"\\\"\\n\",\n \"Black-Scholes Raw Gamma (scaled by notional): {bs_raw_gamma * option.notional_amount:,.0f}\\n\",\n \"\\\"\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"58fe9c4a-03e2-48c3-98b3-6300dec78e31\",\n \"metadata\": {},\n \"source\": [\n \"#### 4) Computing Gamma PnL of holding the option for one day\\n\",\n \"Raw gamma can be used in the Taylor series expansion term to derive Gamma PnL in the Overlying currency.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e0768c74-af08-4f19-b698-3fde13e64598\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spot_price = option.calc(risk.FairPremium) # in JPY, as option has premium_currency = JPY\\n\",\n \"spot_shocks = [i / 100 for i in range(-8, 12)]\\n\",\n \"prices = []\\n\",\n \"# wrap in PricingContext to send calculations in parallel\\n\",\n \"with PricingContext():\\n\",\n \" for spot_shock in spot_shocks:\\n\",\n \" with risk.MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" risk.MarketDataPattern('FX', 'JPY/USD'): risk.MarketDataShock(\\n\",\n \" risk.MarketDataShockType.Proportional, spot_shock\\n\",\n \" )\\n\",\n \" }\\n\",\n \" ):\\n\",\n \" prices.append(option.calc(risk.FairPremium))\\n\",\n \"# prices are in JPY, as option has premium_currency = JPY\\n\",\n \"prices = [p.result() for p in prices]\\n\",\n \"ds_array = [spot * shock for shock in spot_shocks]\\n\",\n \"spots = [float(spot) + ds for ds in ds_array]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a456c5b9-e525-45f4-a050-1f781f4f274a\",\n \"metadata\": {},\n \"source\": [\n \"Delta is in the notional currency (USD), so `delta * ds` is in JPY, like `spot_price`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ebdbefd7-61b5-4483-b144-5fe7f4cd762c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"prices_delta_approx = [spot_price + float(delta * ds) for ds in ds_array]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5caf3b53-a90f-49b5-b9c9-596a3c44a96b\",\n \"metadata\": {},\n \"source\": [\n \"`gamma` is in the underlying currency (USD) and `raw_gamma = gamma / spot`.\\n\",\n \"\\n\",\n \"Therefore `raw_gamma * ds` is in the underlying currency and `raw_gamma * (ds ** 2)` is in the overlying currency (JPY) like the rest of the terms.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f085b733-851f-425f-918d-fd5a0a8a942a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"prices_delta_gamma_approx = [spot_price + float(delta * ds) + float(0.5 * raw_gamma * ds**2) for ds in ds_array]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"eedacd93-46fe-4b67-8763-7bad35c1a581\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib.ticker import FuncFormatter\\n\",\n \"\\n\",\n \"prices_millions = [p / 1e6 for p in prices]\\n\",\n \"prices_delta_approx_millions = [p / 1e6 for p in prices_delta_approx]\\n\",\n \"prices_delta_gamma_approx_millions = [p / 1e6 for p in prices_delta_gamma_approx]\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12, 8))\\n\",\n \"\\n\",\n \"plt.plot(spots, prices_millions, '-', linewidth=2.5, label='Actual Option Price', color='#2E86AB', zorder=3)\\n\",\n \"plt.plot(\\n\",\n \" spots,\\n\",\n \" prices_delta_approx_millions,\\n\",\n \" '--',\\n\",\n \" linewidth=2,\\n\",\n \" label='Delta Approximation (Linear)',\\n\",\n \" color='#A23B72',\\n\",\n \" alpha=0.8,\\n\",\n \" zorder=2,\\n\",\n \")\\n\",\n \"plt.plot(\\n\",\n \" spots,\\n\",\n \" prices_delta_gamma_approx_millions,\\n\",\n \" ':',\\n\",\n \" linewidth=2.5,\\n\",\n \" label='Delta-Gamma Approximation, using FXGamma/FXSpot (Quadratic)',\\n\",\n \" color='#F18F01',\\n\",\n \" alpha=0.8,\\n\",\n \" zorder=2,\\n\",\n \")\\n\",\n \"\\n\",\n \"current_idx = spot_shocks.index(0.0)\\n\",\n \"current_spot = spots[current_idx]\\n\",\n \"current_color = '#FF4444'\\n\",\n \"marker_style = dict(s=120, color=current_color, marker='o', edgecolors='darkred', linewidths=2, zorder=5)\\n\",\n \"\\n\",\n \"plt.scatter([current_spot], [prices_millions[current_idx]], label='Current Spot', **marker_style)\\n\",\n \"plt.scatter([current_spot], [prices_delta_approx_millions[current_idx]], **marker_style)\\n\",\n \"plt.scatter([current_spot], [prices_delta_gamma_approx_millions[current_idx]], **marker_style)\\n\",\n \"\\n\",\n \"plt.axvline(x=current_spot, color=current_color, linestyle='--', alpha=0.3, linewidth=1.5)\\n\",\n \"\\n\",\n \"plt.xlabel('Spot Price (USDJPY)', fontsize=12, fontweight='bold')\\n\",\n \"plt.ylabel('Option Premium (Million JPY)', fontsize=12, fontweight='bold')\\n\",\n \"plt.title('Option Price: Actual vs Delta & Delta-Gamma Approximations', fontsize=14, fontweight='bold', pad=20)\\n\",\n \"plt.legend(loc='best', fontsize=10, framealpha=0.9, shadow=True)\\n\",\n \"plt.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)\\n\",\n \"\\n\",\n \"ax = plt.gca()\\n\",\n \"ax.yaxis.set_major_formatter(FuncFormatter(lambda x, p: f'{x:.2f}'))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.4\"\n },\n \"tags\": [\n \"Metrics - FX Gamma Measures\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/000306_calc_eq_option_greeks.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"\\n\",\n \"from gs_quant.datetime.date import prev_business_date\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.markets import PricingContext, HistoricalPricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import EqDelta, EqGamma, EqVega, EqSpot, DollarPrice\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Create Eq Options Portfolios\\n\",\n \"\\n\",\n \"Let's now create a put-spread and a butterfly using `EqOption`. The underlier is entered as Reuters Instrument Code and \\n\",\n \"can be one of .SPX, .STOXX50E, .FTSE, .N225. \\n\",\n \"Strike can be specified as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF.\\n\",\n \"Other parameters we can instantiate can be uncovers by pressing `shift+tab` on `EqOption`. \"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"put_1 = EqOption('.SPX', expiration_date = '3m', strike_price = '95%', option_type = OptionType.Put, \\n\",\n \" option_style = OptionStyle.European, buy_sell = 'Buy')\\n\",\n \"put_2 = EqOption('.SPX', expiration_date = '3m', strike_price = '105%', option_type = OptionType.Put, \\n\",\n \" option_style = OptionStyle.European, buy_sell = 'Sell')\\n\",\n \"put_spread = Portfolio((put_1, put_2))\\n\",\n \"\\n\",\n \"itm_call = EqOption('.SPX', expiration_date = '3m', strike_price = '90%', option_type = OptionType.Call, \\n\",\n \" option_style=OptionStyle.European, buy_sell = 'Buy')\\n\",\n \"otm_call = EqOption('.SPX', expiration_date = '3m', strike_price = '110%', option_type = OptionType.Call, \\n\",\n \" option_style = OptionStyle.European, buy_sell = 'Buy')\\n\",\n \"atm_call = EqOption('.SPX', expiration_date = '3m', strike_price = 'atm', option_type = OptionType.Call, \\n\",\n \" number_of_options = 2, option_style = OptionStyle.European, buy_sell = 'Sell')\\n\",\n \"butterfly = Portfolio((itm_call, otm_call, atm_call))\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Compute greeks for both our portfolios at inception and at expiry\\n\",\n \"\\n\",\n \"Let's now compute greeks on our portfolios. For an exhaustive list of supported metrics for `EqOption` please refer to \\n\",\n \"the [Measures](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/measures/) guide.\\n\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"trade_date = dt.date.today() - relativedelta(months = 3)\\n\",\n \"trade_date = prev_business_date(trade_date)\\n\",\n \"\\n\",\n \"with PricingContext(pricing_date = trade_date):\\n\",\n \" put_spread.resolve(in_place = True)\\n\",\n \" ps_greeks = put_spread.calc((EqDelta, EqGamma, EqVega))\\n\",\n \" butterfly.resolve(in_place = True)\\n\",\n \" b_greeks = butterfly.calc((EqDelta, EqGamma, EqVega))\\n\",\n \"\\n\",\n \"expiry = prev_business_date(put_1.expiration_date)\\n\",\n \"\\n\",\n \"with PricingContext(pricing_date = expiry):\\n\",\n \" ps_exp = put_spread.calc((EqDelta, EqGamma, EqVega))\\n\",\n \" b_exp = butterfly.calc((EqDelta, EqGamma, EqVega))\\n\",\n \" \"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"print(f'Put Spread Delta at inception: {ps_greeks[EqDelta].aggregate():.0f}')\\n\",\n \"print(f'Put Spread Gamma at inception: {ps_greeks[EqGamma].aggregate():.0f}')\\n\",\n \"print(f'Put Spread Vega at inception: {ps_greeks[EqVega].aggregate():.0f}')\\n\",\n \"\\n\",\n \"print(f'Butterfly Delta at inception: {b_greeks[EqDelta].aggregate():.0f}')\\n\",\n \"print(f'Butterfly Gamma at inception: {b_greeks[EqGamma].aggregate():.0f}')\\n\",\n \"print(f'Butterfly Vega at inception: {b_greeks[EqVega].aggregate():.0f}')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Now let's look at our portfolios' PV vs Spot price\\n\",\n \"\\n\",\n \"Using `EqSpot` we can track the performance of the index vs our portfolio.\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"with HistoricalPricingContext(start = trade_date, end = expiry):\\n\",\n \" ps_perf = put_spread.calc((EqSpot, DollarPrice))\\n\",\n \" b_perf = butterfly.calc((EqSpot, DollarPrice))\\n\",\n \" \"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"from pandas.plotting import register_matplotlib_converters\\n\",\n \"register_matplotlib_converters()\\n\",\n \"\\n\",\n \"ps_perf = ps_perf.aggregate()\\n\",\n \"fig, ax1 = plt.subplots(figsize = (10, 6))\\n\",\n \"plt.figure(figsize = (24, 20))\\n\",\n \"color='tab:blue'\\n\",\n \"ax1.set_ylabel('Spot', color = color)\\n\",\n \"ax1.plot(ps_perf[EqSpot], color = color)\\n\",\n \"ax1.set_title('Put Spread')\\n\",\n \"ax2 = ax1.twinx()\\n\",\n \"color = 'tab:orange'\\n\",\n \"ax2.set_ylabel('$PV', color = color)\\n\",\n \"ax2.plot(ps_perf[DollarPrice], color = color)\\n\",\n \"\\n\",\n \"plt.show()\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"b_perf = b_perf.aggregate()\\n\",\n \"fig, ax1 = plt.subplots(figsize=(10, 6))\\n\",\n \"plt.figure(figsize=(24, 20))\\n\",\n \"color='tab:blue'\\n\",\n \"ax1.set_ylabel('Spot', color=color)\\n\",\n \"ax1.plot(b_perf[EqSpot], color=color)\\n\",\n \"ax1.set_title('Butterfly')\\n\",\n \"ax2 = ax1.twinx()\\n\",\n \"color = 'tab:orange'\\n\",\n \"ax2.set_ylabel('$PV', color=color)\\n\",\n \"ax2.plot(b_perf[DollarPrice], color=color)\\n\",\n \"\\n\",\n \"plt.show()\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n }\n ],\n \"metadata\": {\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"language\": \"python\",\n \"display_name\": \"Python 3\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle, UnderlierType\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create a .STOXX50E 3m call option striking at-the-money spot\\n\",\n \"eq_option = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='ATMS',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# calculate local price and dollar price\\n\",\n \"print('Local price: {:,.4f}'.format(eq_option.price()))\\n\",\n \"print('Dollar price: {:,.4f}'.format(eq_option.dollar_price()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Underlier Syntax\\n\",\n \"\\n\",\n \"The underlier accepts an underlier as a RIC or BBID identifier. The default is RIC.\\n\",\n \"\\n\",\n \"| Syntax | Defintion |\\n\",\n \"|---------|---------------------|\\n\",\n \"| 'RIC' | Reuters identifier |\\n\",\n \"| 'BBID' | Bloomberg identifier |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Enums - UnderlyierType\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# resolve using a Bloomberg ID\\n\",\n \"eq_option_bbid = EqOption(\\n\",\n \" 'SX5E',\\n\",\n \" underlier_type=UnderlierType.BBID,\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='ATMS',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"eq_option_bbid.resolve()\\n\",\n \"eq_option_bbid.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"tags\": [\n \"Instrument - Solving - EqOption strike solving\"\n ]\n },\n \"source\": [\n \"#### Strike Syntax\\n\",\n \"The strike_price syntax allows for an int or a string. The absolute level can be specified using an integer.\\n\",\n \"\\n\",\n \"The following solver keys using a string format are accepted: \\n\",\n \"\\n\",\n \"| Syntax | Defintion |\\n\",\n \"|---------|---------------------|\\n\",\n \"| '%' | Percent of Spot |\\n\",\n \"| 'ATMS' | At the Money |\\n\",\n \"| 'ATMF' | At the Money Forward|\\n\",\n \"| 'D' | Delta Strikes |\\n\",\n \"| 'P' | Premium |\\n\",\n \"\\n\",\n \" - For ATM, ATMF: '1.05*ATMF+.01'\\n\",\n \" - For Delta Strikes, specify the option delta: '25D', '20D-.01', etc.\\n\",\n \" - You can also solve for Premium: P=,% \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# resolve with strike at 110% of spot\\n\",\n \"eq_atm_solver = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='ATMS+10%',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"eq_atm_solver.resolve()\\n\",\n \"eq_atm_solver.strike_price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# resolve with strike at 94.5% of spot\\n\",\n \"eq_spot_pct = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='94.5%',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"eq_spot_pct.resolve()\\n\",\n \"eq_spot_pct.strike_price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# resolve with strike at spot minus 10\\n\",\n \"eq_atmf_solver = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='1m',\\n\",\n \" strike_price='ATMF-10',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"eq_atmf_solver.resolve()\\n\",\n \"eq_atmf_solver.strike_price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# resolve with strike solving for 10% premium\\n\",\n \"eq_10x = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='6m',\\n\",\n \" strike_price='P=10%',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"eq_10x.resolve()\\n\",\n \"eq_10x.strike_price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# resolve with strike solving for absolute premium\\n\",\n \"# 'PriceLocal' and 'PriceUSD' are also valid\\n\",\n \"eq_abs_prem = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='6m',\\n\",\n \" strike_price='Price=1000',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"eq_abs_prem.resolve()\\n\",\n \"print(eq_abs_prem.price())\\n\",\n \"eq_abs_prem.strike_price\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - EqOption\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"import gs_quant.risk as risk\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create eq option\\n\",\n \"eq_option = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='ATMS',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"eq_option.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# single measure\\n\",\n \"dollar_delta = eq_option.calc(risk.EqDelta)\\n\",\n \"dollar_delta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics - Equity Risks\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# multiple measures\\n\",\n \"risks = eq_option.calc((risk.EqDelta, risk.EqGamma, risk.EqVega))\\n\",\n \"risks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Results - slice multi risk results\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# result for a single measure\\n\",\n \"risks[risk.EqDelta]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Metrics - parameterized currency\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# single measure with parameterized currency\\n\",\n \"euro_delta = eq_option.calc(risk.EqDelta(currency='EUR'))\\n\",\n \"euro_delta\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/03_calc_eq_option_price_and_risk_in_pricing_context.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create Option\\n\",\n \"eq_option = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='ATMS',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"eq_option.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Contexts - PricingContext\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# price as of a specific date using LDN close\\n\",\n \"with PricingContext(pricing_date=date(2020, 2, 20), market_data_location='LDN'):\\n\",\n \" price = eq_option.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get the result\\n\",\n \"price_result = price.result()\\n\",\n \"print(price_result)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# calculate the delta as of a specific date using NYC close\\n\",\n \"with PricingContext(pricing_date=date(2020, 2, 20), market_data_location='NYC'):\\n\",\n \" delta = eq_option.calc(risk.EqDelta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get the result\\n\",\n \"delta_results = delta.result()\\n\",\n \"print(delta_results)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/04_calc_eq_option_price_and_risk_historically.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create Option\\n\",\n \"eq_option = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='ATMS',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \")\\n\",\n \"eq_option.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Contexts - HistoricalPricingContext\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# price daily for dates between 14Jan20 and 21Jan20\\n\",\n \"with HistoricalPricingContext(date(2020, 1, 14), date(2020, 1, 21)):\\n\",\n \" previous_prices_holder = eq_option.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get the results\\n\",\n \"previous_prices = previous_prices_holder.result()\\n\",\n \"print(previous_prices)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# plot the instrument price over time\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from pandas.plotting import register_matplotlib_converters\\n\",\n \"\\n\",\n \"register_matplotlib_converters()\\n\",\n \"\\n\",\n \"plt.plot(previous_prices)\\n\",\n \"plt.xlabel('time')\\n\",\n \"plt.ylabel('price')\\n\",\n \"plt.title('Price over time')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# calculate delta daily for dates between 20Jan20 and 27Jan20\\n\",\n \"with HistoricalPricingContext(date(2020, 1, 20), date(2020, 1, 27)):\\n\",\n \" previous_dollar_deltas_holder = eq_option.calc(risk.EqDelta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# get the results\\n\",\n \"previous_dollar_deltas = previous_dollar_deltas_holder.result()\\n\",\n \"print(previous_dollar_deltas)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# plot the instrument delta over time\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from pandas.plotting import register_matplotlib_converters\\n\",\n \"\\n\",\n \"register_matplotlib_converters()\\n\",\n \"\\n\",\n \"plt.plot(previous_dollar_deltas)\\n\",\n \"plt.xlabel('time')\\n\",\n \"plt.ylabel('$delta')\\n\",\n \"plt.title('$delta over time')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from dateutil.relativedelta import relativedelta\\n\",\n \"\\n\",\n \"from gs_quant.datetime.date import prev_business_date\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.markets import PricingContext, HistoricalPricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import EqDelta, EqGamma, EqVega, EqSpot, DollarPrice\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Contexts - Market Data Based Shock Scenario\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# This example uses instrument resolving. For external users this requires a reference spot override using a market scenario when resolving\\n\",\n \"from gs_quant.risk import MarketDataPattern, MarketDataShock, MarketDataShockType, MarketDataShockBasedScenario\\n\",\n \"\\n\",\n \"eq_spot_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern('Eq', '.SPX', 'Reference Spot', mkt_quoting_style='Price'): MarketDataShock(\\n\",\n \" MarketDataShockType.Override, 3900\\n\",\n \" )\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Create Eq Options Portfolios\\n\",\n \"\\n\",\n \"Let's now create a put-spread and a butterfly using `EqOption`. The underlier is entered as Reuters Instrument Code and \\n\",\n \"can be one of .SPX, .STOXX50E, .FTSE, .N225. \\n\",\n \"Strike can be specified as value, percent or at-the-money e.g. 62.5, 95%, ATM-25, ATMF.\\n\",\n \"Other parameters we can instantiate can be uncovers by pressing `shift+tab` on `EqOption`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Portfolios\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"put_1 = EqOption(\\n\",\n \" '.SPX',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='95%',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" buy_sell='Buy',\\n\",\n \")\\n\",\n \"put_2 = EqOption(\\n\",\n \" '.SPX',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='105%',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" buy_sell='Sell',\\n\",\n \")\\n\",\n \"put_spread = Portfolio((put_1, put_2))\\n\",\n \"\\n\",\n \"itm_call = EqOption(\\n\",\n \" '.SPX',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='90%',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" buy_sell='Buy',\\n\",\n \")\\n\",\n \"otm_call = EqOption(\\n\",\n \" '.SPX',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='110%',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" buy_sell='Buy',\\n\",\n \")\\n\",\n \"atm_call = EqOption(\\n\",\n \" '.SPX',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='atm',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" number_of_options=2,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" buy_sell='Sell',\\n\",\n \")\\n\",\n \"butterfly = Portfolio((itm_call, otm_call, atm_call))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Compute greeks for both our portfolios at inception and at expiry\\n\",\n \"\\n\",\n \"Let's now compute greeks on our portfolios. For an exhaustive list of supported metrics for `EqOption` please refer to \\n\",\n \"the [Measures](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/measures/) guide.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"tags\": [\n \"Metrics - Equity Greeks\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"trade_date = dt.date.today() - relativedelta(months=3)\\n\",\n \"trade_date = prev_business_date(trade_date)\\n\",\n \"\\n\",\n \"# apply spot override when resolving\\\"\\n\",\n \"with PricingContext(pricing_date=trade_date), eq_spot_scenario:\\n\",\n \" put_spread.resolve(in_place=True)\\n\",\n \" ps_greeks = put_spread.calc((EqDelta, EqGamma, EqVega))\\n\",\n \" butterfly.resolve(in_place=True)\\n\",\n \" b_greeks = butterfly.calc((EqDelta, EqGamma, EqVega))\\n\",\n \"\\n\",\n \"expiry = prev_business_date(put_1.expiration_date)\\n\",\n \"\\n\",\n \"with PricingContext(pricing_date=expiry):\\n\",\n \" ps_exp = put_spread.calc((EqDelta, EqGamma, EqVega))\\n\",\n \" b_exp = butterfly.calc((EqDelta, EqGamma, EqVega))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print(f'Put Spread Delta at inception: {ps_greeks[EqDelta].aggregate():.0f}')\\n\",\n \"print(f'Put Spread Gamma at inception: {ps_greeks[EqGamma].aggregate():.0f}')\\n\",\n \"print(f'Put Spread Vega at inception: {ps_greeks[EqVega].aggregate():.0f}')\\n\",\n \"\\n\",\n \"print(f'Butterfly Delta at inception: {b_greeks[EqDelta].aggregate():.0f}')\\n\",\n \"print(f'Butterfly Gamma at inception: {b_greeks[EqGamma].aggregate():.0f}')\\n\",\n \"print(f'Butterfly Vega at inception: {b_greeks[EqVega].aggregate():.0f}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Now let's look at our portfolios' PV vs Spot price\\n\",\n \"\\n\",\n \"Using `EqSpot` we can track the performance of the index vs our portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"with HistoricalPricingContext(start=trade_date, end=expiry):\\n\",\n \" ps_perf = put_spread.calc((EqSpot, DollarPrice))\\n\",\n \" b_perf = butterfly.calc((EqSpot, DollarPrice))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"from pandas.plotting import register_matplotlib_converters\\n\",\n \"\\n\",\n \"register_matplotlib_converters()\\n\",\n \"\\n\",\n \"ps_perf = ps_perf.aggregate()\\n\",\n \"fig, ax1 = plt.subplots(figsize=(10, 6))\\n\",\n \"plt.figure(figsize=(24, 20))\\n\",\n \"color = 'tab:blue'\\n\",\n \"ax1.set_ylabel('Spot', color=color)\\n\",\n \"ax1.plot(ps_perf[EqSpot], color=color)\\n\",\n \"ax1.set_title('Put Spread')\\n\",\n \"ax2 = ax1.twinx()\\n\",\n \"color = 'tab:orange'\\n\",\n \"ax2.set_ylabel('$PV', color=color)\\n\",\n \"ax2.plot(ps_perf[DollarPrice], color=color)\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"b_perf = b_perf.aggregate()\\n\",\n \"fig, ax1 = plt.subplots(figsize=(10, 6))\\n\",\n \"plt.figure(figsize=(24, 20))\\n\",\n \"color = 'tab:blue'\\n\",\n \"ax1.set_ylabel('Spot', color=color)\\n\",\n \"ax1.plot(b_perf[EqSpot], color=color)\\n\",\n \"ax1.set_title('Butterfly')\\n\",\n \"ax2 = ax1.twinx()\\n\",\n \"color = 'tab:orange'\\n\",\n \"ax2.set_ylabel('$PV', color=color)\\n\",\n \"ax2.plot(b_perf[DollarPrice], color=color)\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2a21a3b4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import CDIndexOption\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.risk import CDATMSpread, CDFwdSpread, CDImpliedVolatility\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5b9fa24b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1f5ffbdf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View properties of a CD Index Option\\n\",\n \"CDIndexOption.properties()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ba34f6c7\",\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties - credit underlying index\"\n ]\n },\n \"source\": [\n \"Available properties (defaults) for determining the underlying index are:\\n\",\n \"- index_family (iTraxx Europe): 'iTraxx Europe', 'iTraxx Europe FNSE', 'iTraxx Europe XOVER', 'CDX.NA.IG', 'CDX.NA.HY'\\n\",\n \"- index_series (OTR): OTR\\n\",\n \"- index_version (latest): latest\\n\",\n \"\\n\",\n \"Available properties for determining the option are:\\n\",\n \"- option_type (Call): 'Call', 'Put'\\n\",\n \"- strike (0.01): Any\\n\",\n \"- strike_type (Spread): 'Spread', 'Price'\\n\",\n \"- expriation_date (next market expiry - third Wednesday of month): Between 1m and 6m\\n\",\n \"- termination_date (5y next IMM date): 5y\\n\",\n \"- notional_amount (10000000): Any\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9a073ff4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a 3 month call on an OTR iTraxx Main 5y underlier with a strike of 65\\n\",\n \"cd_option = CDIndexOption(\\n\",\n \" strike=0.0065,\\n\",\n \" option_type='Call',\\n\",\n \" expiration_date='19Jan22',\\n\",\n \" termination_date='5y',\\n\",\n \" index_family='iTraxx Europe FNSE',\\n\",\n \" notional_amount=10000000,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"216d883e\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"with PricingContext(market_data_location='NYC'):\\n\",\n \" cd_option.resolve()\\n\",\n \"cd_option.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"76b7bb37\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(cd_option.price())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d7cb184b\",\n \"metadata\": {\n \"tags\": [\n \"Metrics - Credit metrics\"\n ]\n },\n \"source\": [\n \"For index options we can also calculate the ATM (reference) spread, forward spread and implied annual volatility used for pricing the option\\n\",\n \"\\n\",\n \"- CDATMSpread: At the money value of the index in spread terms\\n\",\n \"- CDFwdSpread: Expected forward value of the underlying index at option expiry, in spread terms\\n\",\n \"- CDImpliedVolatility: Annual volatility for the underlying index hazard rate \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"864b04e5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cd_option.calc(CDATMSpread)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b476065f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cd_option.calc(CDFwdSpread)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4389869f\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"cd_option.calc(CDImpliedVolatility)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - CDIndexOption\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/02_cdindex_option_xover_payer_spread.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7fb27b941602401d91542211134fc71a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import CDIndexOption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"acae54e37e7d407bbb7b55eff062a284\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9a63283cbaf04dbcab1f6479b197f3a8\",\n \"metadata\": {},\n \"source\": [\n \"Here we price a payer spread on the iTraxx Crossover OTR index\\n\",\n \"\\n\",\n \"We buy a XOVER call and sell an offsetting XOVER call with a higher strike\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8dd0d8092fe74a7c96281538738b07e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Buy 3m XO call k=300\\n\",\n \"long_leg = CDIndexOption(\\n\",\n \" index_family='iTraxx Europe XOVER',\\n\",\n \" strike=0.0300,\\n\",\n \" option_type='Call',\\n\",\n \" expiration_date='3m',\\n\",\n \" termination_date='5y',\\n\",\n \" buy_sell='Buy',\\n\",\n \" name='Long_XOVER_Call',\\n\",\n \")\\n\",\n \"# Sell 3m XO call k=350\\n\",\n \"short_leg = CDIndexOption(\\n\",\n \" index_family='iTraxx Europe XOVER',\\n\",\n \" strike=0.0350,\\n\",\n \" option_type='Call',\\n\",\n \" expiration_date='3m',\\n\",\n \" termination_date='5y',\\n\",\n \" buy_sell='Sell',\\n\",\n \" name='Short_XOVER_Call',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"72eea5119410473aa328ad9291626812\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"payer_spread = Portfolio((long_leg, short_leg))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8edb47106e1a46a883d545849b8ab81b\",\n \"metadata\": {\n \"tags\": [\n \"Contexts - Historical Pricing Context\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"with HistoricalPricingContext(10, show_progress=True):\\n\",\n \" prices = payer_spread.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"10185d26023b46108eb7d9f57d49d2b3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"prices.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8763a12b2bbd4a93a75aff182afb95dc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot both legs of the payer spraed as well as the overall PV\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"ax.plot(prices.to_frame()['Long_XOVER_Call'], label='Buy leg, k=300')\\n\",\n \"ax.plot(prices.to_frame()['Short_XOVER_Call'], label='Sell leg, k=350')\\n\",\n \"ax.plot(prices.aggregate(), label='Overall cost')\\n\",\n \"ax.set_xlabel('time')\\n\",\n \"ax.set_ylabel('PV ($)')\\n\",\n \"ax.set_title('PV over Time')\\n\",\n \"ax.legend(bbox_to_anchor=(1, 0.5))\\n\",\n \"fig.autofmt_xdate()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7623eae2785240b9bd12b16a66d81610\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"prices.aggregate()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - Credit Payer Spread\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/03_cdindex_option_risks.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"810b3f51\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import CDIndexOption\\n\",\n \"from gs_quant.risk import CDDelta, CDVega, CDTheta, CDGamma\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets import PricingContext, HistoricalPricingContext\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a01bc09d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"075f0ab2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cd_option = CDIndexOption(\\n\",\n \" index_family='iTraxx Europe XOVER',\\n\",\n \" strike=0.0300,\\n\",\n \" option_type='Call',\\n\",\n \" buy_sell='Sell',\\n\",\n \" expiration_date='1m',\\n\",\n \" termination_date='5y',\\n\",\n \" notional_amount=1,\\n\",\n \")\\n\",\n \"cd_option.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c392b31c\",\n \"metadata\": {\n \"tags\": [\n \"Metrics - Credit Greeks\"\n ]\n },\n \"source\": [\n \"Index Option specific risk measures are:\\n\",\n \"- CDDelta - amount of underlying index in local notional to buy in order to hedge the change in Dollar Price due to a 1bp shift in the underlying index spread\\n\",\n \"- CDGamma - amount of underlying index in local notional to buy in order to hedge the change in option delta due to a 1bp shift in the underlying index spread\\n\",\n \"- CDVega - change in option Dollar Price due to a 1bp shift in the implied volatility of the underlying index \\n\",\n \"- CDTheta - change in option Dollar Price over one day, assuming constant vol\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5b9e49d0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with PricingContext(market_data_location='LDN'):\\n\",\n \" greeks = cd_option.calc((CDDelta, CDGamma, CDVega, CDTheta))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8a42c52b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"greeks.result()[CDDelta]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9508a54e\",\n \"metadata\": {},\n \"source\": [\n \"View historic delta:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"71dd4b37\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with HistoricalPricingContext(10, show_progress=True, is_batch=True):\\n\",\n \" previous_option_deltas_holder = cd_option.calc(CDDelta)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f2752b5d\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"previous_option_deltas = previous_option_deltas_holder.result()\\n\",\n \"print(previous_option_deltas.value)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"edcb96c4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# plot the instrument delta over time\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from pandas.plotting import register_matplotlib_converters\\n\",\n \"\\n\",\n \"register_matplotlib_converters()\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"ax.plot(previous_option_deltas.value)\\n\",\n \"ax.set_xlabel('time')\\n\",\n \"ax.set_ylabel('delta')\\n\",\n \"ax.set_title('delta over time')\\n\",\n \"fig.autofmt_xdate()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/04_cdindex_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8388f063\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import CDIndex\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"from gs_quant.risk import CDATMSpread\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f7993feb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"34576b63\",\n \"metadata\": {\n \"tags\": [\n \"Instrument Properties - Credit Index Families\"\n ]\n },\n \"source\": [\n \"#### Available index families for CDIndex\\n\",\n \"- iTraxx Europe OTR: 3y, 5y, 7y, 10y\\n\",\n \"- iTraxx Europe XOVER OTR: 5y\\n\",\n \"- CDX.NA.IG OTR: 5y\\n\",\n \"- CDX.NA.HY OTR: 3y, 5y, 7y, 10y\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1c092ad7\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Create an index product on S42 main\\n\",\n \"itraxx_main = CDIndex(index_family='iTraxx Europe', termination_date='5y', index_series=42)\\n\",\n \"\\n\",\n \"itraxx_main.resolve()\\n\",\n \"itraxx_main.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"76106f9b\",\n \"metadata\": {},\n \"source\": [\n \"Also create an index product on iTraxx Xover and price historically alongside iTraxx Main.\\n\",\n \"\\n\",\n \"Instead of pricing these, we will calculate the ATM spread\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"22776a0d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"itraxx_xover = CDIndex(index_family='iTraxx Europe XOVER', index_series=42)\\n\",\n \"\\n\",\n \"with HistoricalPricingContext(10, show_progress=True):\\n\",\n \" itraxx_main_res_f = itraxx_main.calc(CDATMSpread)\\n\",\n \" itraxx_xover_res_f = itraxx_xover.calc(CDATMSpread)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ac6ff3fe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"itraxx_main_res_f.result()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d761f1b6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"itraxx_main_res = itraxx_main_res_f.result()\\n\",\n \"itraxx_xover_res = itraxx_xover_res_f.result()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9ebae46a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"ax.plot(itraxx_main_res, label=\\\"iTraxx Main\\\")\\n\",\n \"ax.plot(itraxx_xover_res, label=\\\"iTraxx XOVER\\\")\\n\",\n \"ax.set_xlabel('time')\\n\",\n \"ax.set_ylabel('ATM Spread ($)')\\n\",\n \"ax.set_title('ATM Spread over Time')\\n\",\n \"ax.legend()\\n\",\n \"fig.autofmt_xdate()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - CDIndex\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/05_cdindex_roll_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ba142b5d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import CDIndex\\n\",\n \"from gs_quant.risk import CDATMSpread\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"84b1dfa3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"378e5921\",\n \"metadata\": {},\n \"source\": [\n \"# Rolling a position on XOVER from S41 to S42\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c17f3f58\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xover_41 = CDIndex(\\n\",\n \" index_family='iTraxx Europe XOVER', index_series=41, buy_sell='Sell', termination_date='5y', name='sell41'\\n\",\n \")\\n\",\n \"xover_42 = CDIndex(\\n\",\n \" index_family='iTraxx Europe XOVER', index_series=42, buy_sell='Buy', termination_date='5y', name='buy42'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a017b8d5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xover_roll = Portfolio((xover_41, xover_42))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aced6560\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"with PricingContext(market_data_location='LDN'):\\n\",\n \" roll_spreads = xover_roll.calc(CDATMSpread)\\n\",\n \" roll_price = xover_roll.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"465eb4ee\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = roll_spreads.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d5698ddf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df.loc['buy42'] - df.loc['sell41']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"77f522ef\",\n \"metadata\": {\n \"tags\": [\n \"Results - aggregating\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"roll_price.aggregate()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"tags\": [\n \"Instrument - Credit Index Rolling\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/tutorials/Instruments.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. `run_analytics` scope is required for the functionality covered in this tutorial. External clients need to substitute thier own client id and client secret below. Please refer to Sessions for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What is an Instrument\\n\",\n \"\\n\",\n \"[`Instrument`](https://developer.gs.com/docs/gsquant/api/instrument.html) is a class that inherits from the [`Priceable`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html)\\n\",\n \"class and is used to represent financial objects that can be priced, such as derivative instruments.\\n\",\n \"\\n\",\n \"[`Priceable`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable/) exposes several methods common to all instruments, such as [`as_dict()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.as_dict) which returns a dictionary of all the public, non-null properties and values and\\n\",\n \"[`calc(risk_measure)`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.calc) which is used to evaluate various risk measures. More about the latter in the [Measures](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/measures/) guide.\\n\",\n \"\\n\",\n \"`gs-quant` offers a number of [`Instrument`](https://developer.gs.com/docs/gsquant/api/instrument.html#instruments/) implementations, such as [equity options](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.EqOption.html#gs_quant.instrument.EqOption/), [interest rate swaptions](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRSwaption.html#gs_quant.instrument.IRSwaption/) and [commodity swaps](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.CommodSwap.html).\\n\",\n \"Please refer to [supported instruments](#supported-instruments) for a list of externally supported instruments.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## How to Create an Instrument\\n\",\n \"\\n\",\n \"Let's now create an instance of an instrument implementation.\\n\",\n \"For this example, we will create an Interest Rate Swaption, which the [`IRSwaption`](/gsquant/api/classes/gs_quant.instrument.IRSwaption.html#gs_quant.instrument.IRSwaption/) class implements.\\n\",\n \"\\n\",\n \"We will start by importing [`IRSwaption`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRSwaption.html#gs_quant.instrument.IRSwaption/) from the [`Instrument`](https://developer.gs.com/docs/gsquant/api/instrument.html#instruments/) package as well as `PayReceive`, `Currency` to represent commonly used constants.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.common import PayReceive, Currency\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We will now instantiate an [`IRSwaption`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRSwaption.html#gs_quant.instrument.IRSwaption/) object. Note that all [`Instruments`](https://developer.gs.com/docs/gsquant/api/instrument.html#instruments/), including [`IRSwaption`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRSwaption.html#gs_quant.instrument.IRSwaption/), take some non-keyworded required arguments (first 3 in this example),\\n\",\n \"and some optional keyworded arguments (\\\\*\\\\*kwargs). If the optional arguments are not supplied, default market conventions will be used as described in each\\n\",\n \"instrument's signature. Signatures can be found in the [the Instrument Package](/gsquant/api/instrument.html#instruments/) by clicking\\n\",\n \"on the desired instrument.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(\\n\",\n \" PayReceive.Receive, '5y', Currency.USD, expiration_date='13m', strike='atm+40', notional_amount=1e8\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now use the [`as_dict()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.as_dict) method inherited from [`Priceable`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#) to view all public non-null properties and values of this swaption instance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Instrument Resolution\\n\",\n \"\\n\",\n \"The above output shows only the inputs specified, many of which are relative (i.e. expiration date, strike). Calling the [`resolve()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.resolve) method will resolve these\\n\",\n \"parameters to absolute values as well as fill in any defaulted parameters using the [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html). Please refer to the above mentioned [Instrument Package](/gsquant/api/instrument.html#instruments/) for each instrument's available parameters and to the [Pricing Context guide](/gsquant/guides/Pricing-and-Risk/pricing-context) for further details on [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption.resolve()\\n\",\n \"swaption.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note [`resolve()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.resolve) will change the state of the instrument object. In the code snippet above, calling [`resolve()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.resolve) mutates several specified relative parameters, for example:\\n\",\n \"\\n\",\n \"- `expiration_date`, specified as '13m', was resolved to '2020-11-02'\\n\",\n \"- `strike` specified as 'atm+40', was resolved to '0.017845989434194357'\\n\",\n \"\\n\",\n \"[`resolve()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.resolve) will also add any unspecified default parameters - note the additions when calling [`as_dict()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.as_dict) before and after [`resolve()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.resolve). For example:\\n\",\n \"\\n\",\n \"- 'fixed_rate_frequency': '6m'\\n\",\n \"- 'premium_payment_date': '2019-10-04'\\n\",\n \"\\n\",\n \"Accessing any of the unspecified parameters on the unresolved swaption will resolve the swaption in place.\\n\",\n \"\\n\",\n \"Additionally, as discussed in the [measures guide](/gsquant/guides/Pricing-and-Risk/measures), if [`resolve()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.resolve) is not called prior to calling [`price()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.price) or calculating risk, the instrument object will be copied and resolved on the fly without mutating the original swaption object.\\n\",\n \"\\n\",\n \"The preferred behavior may depend on the [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html#gs_quant.markets.PricingContext/) - more on this in the [Pricing Context](/gsquant/guides/Pricing-and-Risk/pricing-context) guide.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"## Supported Instruments\\n\",\n \"\\n\",\n \"Below are the instruments covered, names they are referred to as in gs_quant and brief definitions as well as links to\\n\",\n \" the technical documentation for each. Each instrument corresponds to a model maintained by Goldman Sachs Securities Division.\\n\",\n \"\\n\",\n \"| Instrument | gs_quant name | Description |\\n\",\n \"| ------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------|\\n\",\n \"| Eq Option | [EqOption](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.EqOption.html) | An option on an underlying equity security |\\n\",\n \"| FX Forward | [FXForward](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.FXForward.html) | An exchange of cashflows in different currencies at a determined future time |\\n\",\n \"| FX Option | [FXOption](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.FXOption.html) | An option on an FX Forward |\\n\",\n \"| FX Binary | [FXBinary](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.FXBinary.html) | An option where the buyer receives a fixed amount if a certain currency pair fixes above or below a specified level on a specified date. |\\n\",\n \"| FX Multi Cross Binary | [FXMultiCrossBinary](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.FXMultiCrossBinary.html) | An option where the buyer receives a fixed amount if each of the currency pairs fixes above or below a specified level on a specified date. |\\n\",\n \"| FX Volatility Swap | [FXVolatilitySwap](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.FXVolatilitySwap.html) | An exchange of cashflows based on the realized volatility of the underlying FX cross and a pre-determined fixed volatility level |\\n\",\n \"| Inflation Swap | [InflationSwap](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.InflationSwap.html) | A zero coupon vanilla inflation swap of fixed vs floating cashflows adjusted to inflation rate |\\n\",\n \"| Interest Rate Swap | [IRSwap](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRSwap.html) | A vanilla interest rate swap of fixed vs floating cashflows in the same currency |\\n\",\n \"| Interest Rate Basis Swap | [IRBasisSwap](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRBasisSwap.html) | An exchange of cashflows from different interest rate indices in the same currency |\\n\",\n \"| Interest Rate Xccy Swap | [IRXccySwap](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRXccySwap.html) | An exchange of cashflows from different interest rate indices in different currencies |\\n\",\n \"| Interest Rate Xccy Swap Fix Fix | [IRXccySwapFixFix](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRXccySwapFixFix.html) | An exchange of fixed cashflows in different currencies |\\n\",\n \"| Interest Rate Xccy Swap Fix Float | [IRXccySwapFixFlt](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRXccySwapFixFlt.html) | A vanilla interest rate swap of fixed vs floating cashflows in different currencies |\\n\",\n \"| Interest Rate Swaption | [IRSwaption](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRSwaption.html) | An option to enter into a vanilla interest rate swap of fixed vs floating cashflows |\\n\",\n \"| Interest Rate Cap | [IRCap](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRCap.html) | An instrument in which the buyer receives payments at the end of each period in which the interest rate exceeds the agreed strike price |\\n\",\n \"| Interest Rate Floor | [IRFloor](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRFloor.html) | An instrument in which the buyer receives payments at the end of each period in which the interest rate is below the agreed strike price |\\n\",\n \"| Interest Rate CMS Option | [IRCMSOption](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRCMSOption.html) | An option on a single date where the payoff is based on the CMS rate |\\n\",\n \"| Interest Rate CMS Option Strip | [IRCMSOptionStrip](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRCMSOptionStrip.html) | A strip of CMS Options |\\n\",\n \"| Interest Rate CMS Spread Option | [IRCMSSpreadOption](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRCMSSpreadOption.html) | An option on a single date where the payoff is dependent on the spread of two CMS rates compared to the strike of the option. |\\n\",\n \"| Interest Rate CMS Spread Option Strip | [IRCMSSpreadOptionStrip](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRCMSSpreadOptionStrip.html) | A strip of CMS Spread Options |\\n\",\n \"\\n\",\n \"Note that `IRDelta` is additional available for Interest Rate Futures and Bond Futures upon request.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Disclaimer\\n\",\n \"This website may contain links to websites and the content of third parties (\\\"Third Party Content\\\"). We do not monitor, review or update, and do not have any control over, any Third Party Content or third party websites. We make no representation, warranty or guarantee as to the accuracy, completeness, timeliness or reliability of any Third Party Content and are not responsible for any loss or damage of any sort resulting from the use of, or for any failure of, products or services provided at or from a third party resource. If you use these links and the Third Party Content, you acknowledge that you are doing so entirely at your own risk.\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/00_instruments_and_measures/tutorials/Measures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. `run_analytics` scope is required for the functionality covered in this tutorial. External clients need to substitute thier own client id and client secret below. Please refer to Sessions for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Measures\\n\",\n \"\\n\",\n \"A measure is a metric that can be calculated on an instrument, like a dollar price. Below is a table of supported measures and their definitions.\\n\",\n \"\\n\",\n \"| Measure | Definition |\\n\",\n \"| -------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\\n\",\n \"| [Annuity*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.Annuity) | Annuity of instrument in local currency (unless currency specified in parameters) |\\n\",\n \"| [Dollar Price](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.DollarPrice) | Price of the instrument in US Dollars |\\n\",\n \"| [Price*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.Price) | Price of the instrument (In local currency unless currency specified in parameters) |\\n\",\n \"| [ForwardPrice](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.ForwardPrice) | Price of the instrument at expiry in the local currency |\\n\",\n \"| [CDDelta](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.CDDelta) | Change in option Dollar Price relative to the change in underlying index Dollar Price, due to a 1bp shift in the underlying index spread |\\n\",\n \"| [CDGamma](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.CDGamma) | Change in option Delta relative to the change in underlying index Delta, due to a 1bp shift in the underlying index spread |\\n\",\n \"| [CDTheta](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.CDTheta) | Change in option Dollar Price over one day |\\n\",\n \"| [CDVega](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.CDVega) | Change in option Dollar Price due to a 1bp shift in the implied volatility of the underlying index |\\n\",\n \"| [EqDelta](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.EqDelta) | Change in Dollar Price (USD present value) due to individual 1% move in the spot price of underlying equity security |\\n\",\n \"| [EqGamma](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.EqGamma) | Change in EqDelta for a 1% move in the price of the underlying equity security |\\n\",\n \"| [EqVega](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.EqVega) | Change in Dollar Price (USD present value) due to individual 1bp moves in the implied volatility of the underlying equity security |\\n\",\n \"| [FairPremium](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.FairPremium) | Fair Premium to be paid on the premium settlement date (in the premium currency) such that the present value would be zero\\n\",\n \"| [FairVolStrike](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.FairVolstrike) | Fair Volatility Strike of a Var or Vol Swap such that the present value would be zero |\\n\",\n \"| [FXDelta*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.FXDelta) | Dollar Price sensitivity of the instrument to a move in the underlying spot such that dSpot \\\\* FXDelta = PnL |\\n\",\n \"| [FXGamma*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.FXGamma) | FXDelta sensitivity of the instrument to a move in the underlying spot such that dSpot \\\\* FXGamma = dDelta |\\n\",\n \"| [FXAnnualImpliedVol](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.FXAnnualImpliedVol) | FX daily implied volatility (in percent) |\\n\",\n \"| [FXAnnualATMImpliedVol](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.FXAnnualATMImpliedVol) | FX daily implied volatility (in basis points) |\\n\",\n \"| [FXSpot](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.FXSpot) | FX spot reference |\\n\",\n \"| [FXVega*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.FXVega) | Change in Dollar Price due to a 1 vol move in the implied volatility of ATM instruments used to build the volatility surface |\\n\",\n \"| [IRBasis*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRBasis) | Change in Dollar Price (USD present value) due to individual 1bp moves in the interest rate instruments used to build the basis curve(s) |\\n\",\n \"| [IRDelta*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRDelta) | Change in Dollar Price (USD present value) due to individual 1bp moves in the interest rate instruments used to build the underlying discount curve |\\n\",\n \"| [IRGamma*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRGamma) | Change in aggregated IRDelta for a aggregated 1bp shift in the interest rate instruments used to build the underlying discount curve |\\n\",\n \"| [IRVega*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRVega) | Change in Dollar Price (USD present value) due to individual 1bp moves in the implied volatility (IRAnnualImpliedVol) of instruments used to build the volatility surface |\\n\",\n \"| [IRAnnualImpliedVol](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRAnnualImpliedVol) | Interest rate annual implied volatility (in percent) |\\n\",\n \"| [IRAnnualATMImpliedVol](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRAnnualATMImpliedVol) | Interest rate annual implied at-the-money volatility (in percent) |\\n\",\n \"| [IRDailyImpliedVol](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRDailyImpliedVol) | Interest rate daily implied volatility (in basis points) |\\n\",\n \"| [IRSpotRate](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRSpotRate) | Interest rate at-the-money spot rate (in percent) |\\n\",\n \"| [IRFwdRate](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRFwdRate) | Interest rate par rate (in percent) |\\n\",\n \"| [IRXccyDelta*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.IRXccyDelta) | Change in Price due to 1bp move in cross currency rates. |\\n\",\n \"| [InflationDelta*](https://developer.gs.com/docs/gsquant/api/risk.html#gs_quant.risk.InflationDelta) | Change in Price due to 1bp move in inflation curve. |\\n\",\n \"\\n\",\n \"Note - * indicates parameterised risk measures. See Parameterised Risk Measure section below.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"## Calculating Price Measures\\n\",\n \"\\n\",\n \"Let's price an instrument. For information on how to define an instrument, please refer to the [Instruments](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/instruments/) guide.\\n\",\n \"\\n\",\n \"Note, below we resolve the swaption parameters that will be used to price the swaption, thereby mutating the swaption object. If [`resolve()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.resolve), is\\n\",\n \"not called prior to calling [`price()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.price), the object will be copied and resolved on the fly without mutating the original swaption object.\\n\",\n \"The preferred behavior may depend on the [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) - more on this in the [Pricing Context](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/pricing-context/) guide.\\n\",\n \"\\n\",\n \"[`price()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.price) method will return price in the local currency.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### IR Swaps\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"\\n\",\n \"# Creating Swaps - spot starting, forward starting\\n\",\n \"swap = IRSwap(PayReceive.Receive, '10y', 'GBP', fixed_rate='atm+50', notional_amount=1e8) # running\\n\",\n \"swap_fwd_start = IRSwap(PayReceive.Pay, '5y', 'EUR', fixed_rate='atm+20', effective_date='3y') # fwd starting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Instrument w/ specified relative parameters\\n\",\n \"swap_fwd_start.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Resolve Instrument, View fixed parameters\\n\",\n \"swap_fwd_start.resolve()\\n\",\n \"swap_fwd_start.as_dict()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### IR Swaptions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.common import PayReceive\\n\",\n \"\\n\",\n \"swaption = IRSwaption(\\n\",\n \" PayReceive.Receive, '5y', Currency.USD, expiration_date='13m', strike='atm+40', notional_amount=1e8\\n\",\n \")\\n\",\n \"swaption.resolve()\\n\",\n \"swaption.price() # local is USD\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"All instruments can also priced in dollars.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption.dollar_price() # USD price\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Calculating Risk Measures\\n\",\n \"\\n\",\n \"We can also calculate risk measures for the defined instrument. Please refer to [the Measures Guide](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/measures/) for the supported risk measures.\\n\",\n \"Calling [`calc(risk_measure)`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.calc) calculates the value of the risk measure and can return a float, a dataframe or a future thereof, depending on how [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) is being used - more on this in the [Pricing Context guide](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/pricing-context/).\\n\",\n \"\\n\",\n \"Calculate a scalar value like implied volatility:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import gs_quant.risk as risk\\n\",\n \"\\n\",\n \"swaption.calc(risk.IRAnnualImpliedVol) * 10000\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Calculate a structured value like vega:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption.calc(risk.IRVega)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Calculate IR Delta (Aggregated Scalar and Ladder) for Swaps\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import AggregationLevel\\n\",\n \"\\n\",\n \"ir_risk = swap.calc(\\n\",\n \" (\\n\",\n \" risk.IRDelta(aggregation_level=AggregationLevel.Type),\\n\",\n \" risk.IRDelta(aggregation_level=AggregationLevel.Type, currency='local'),\\n\",\n \" risk.IRDelta,\\n\",\n \" )\\n\",\n \")\\n\",\n \"\\n\",\n \"# Print Risks\\n\",\n \"print(ir_risk[risk.IRDelta(aggregation_level=AggregationLevel.Type)])\\n\",\n \"print(ir_risk[risk.IRDelta(aggregation_level=AggregationLevel.Type, currency='local')])\\n\",\n \"print(ir_risk[risk.IRDelta])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Calculate a conditional risk measure. Show IRDelta Ladder only where exposure >1e-2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"ird_ladder = ir_risk[risk.IRDelta]\\n\",\n \"print(ird_ladder[abs(ird_ladder.value) > 1e-2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"See [measures](#Measures) table for information on units.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Parameterised Risk Measures\\n\",\n \"\\n\",\n \"Some risk measures now support extra parameters\\n\",\n \"\\n\",\n \"You can now specify which currency you want the price of an instrument to be expressed in\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eur_swap = IRSwap(PayReceive.Pay, '5y', 'EUR', fixed_rate='atm+20')\\n\",\n \"price = eur_swap.price(currency='PLN')\\n\",\n \"print(f'{price} {list(price.unit.keys())[0]}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For some finite difference risk measures (noted * in the table above), you can now pass in the specifics of the calculation methodology.\\n\",\n \"\\n\",\n \"\\n\",\n \"**Parameters supported in finite difference risk measures**\\n\",\n \"\\n\",\n \"|Parameter name\\t |Type\\t |Description|\\n\",\n \"|-------------------------|--------------------------------------------------|------------------------------------------------------------\\n\",\n \"|currency\\t |string\\t |Currency of risk result|\\n\",\n \"|aggregation_level |gs_quant.target.common.AggregationLevel\\t |Level of aggregate shift|\\n\",\n \"|local_curve\\t |bool \\t |Change in Price (present value in the denominated currency)|\\n\",\n \"|finite_difference_method |gs_quant.target.common.FiniteDifferenceParameter\\t |Direction and dimension of finite difference|\\n\",\n \"|mkt_marking_options |gs_quant.target.common.MktMarkingOptions |Market marking mode|\\n\",\n \"|bump_size\\t |float\\t |Bump size|\\n\",\n \"|scale_factor\\t |float\\t |Scale factor|\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(swap.calc(risk.IRDelta))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Calculate delta using an aggregate 1bp shift on the asset level and using change in Price only in the denominated currency\\n\",\n \"print(swap.calc(risk.IRDelta(aggregation_level=AggregationLevel.Type, currency='local')))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Disclaimer\\n\",\n \"This website may contain links to websites and the content of third parties (\\\"Third Party Content\\\"). We do not monitor, review or update, and do not have any control over, any Third Party Content or third party websites. We make no representation, warranty or guarantee as to the accuracy, completeness, timeliness or reliability of any Third Party Content and are not responsible for any loss or damage of any sort resulting from the use of, or for any failure of, products or services provided at or from a third party resource. If you use these links and the Third Party Content, you acknowledge that you are doing so entirely at your own risk.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010000_market_data_in_gs_quant.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# There are currently two ways to access data in gs_quant:\\n\",\n \"# 1 - gs_quant.data module - which provides programmatic access to The Marquee Data Catalogue,\\n\",\n \"# which includes a range of market data, gs product data, GIR Data and Execution & Liquidity Data.\\n\",\n \"# More info here: https://marquee.gs.com/s/discover/data-services\\n\",\n \"# 2 - Market Objects - which can be used to get both close and live market data\\n\",\n \"#\\n\",\n \"# The notebooks in this 01_market_objects directory focusing on accessing data via the Market Object\\n\",\n \"#\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"# querying data from Market Objects requires the 'run_analytics' scope\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010001_market_objects.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Each PricingContext has a Market Object\\n\",\n \"mkt = PricingContext.current.market\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# By default, the PricingContext's market is a CloseMarket object\\n\",\n \"print(mkt.market_type)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The CloseMarket Object is paramaterized by a location and date\\n\",\n \"\\n\",\n \"# The default location is the PricingContext.current.market_data_location\\n\",\n \"print(mkt.location)\\n\",\n \"\\n\",\n \"# The default date is the prior business date to the current pricing date\\n\",\n \"print(mkt.date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# There are five types of Market Objects currently supported in gs_quant\\n\",\n \"from gs_quant.markets import LiveMarket, TimestampedMarket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The Live Market, which is paramaterized by a location\\n\",\n \"mkt = LiveMarket()\\n\",\n \"print(mkt.location)\\n\",\n \"\\n\",\n \"# You can set the location by using the PricingLocation Enum or a string\\n\",\n \"from gs_quant.common import PricingLocation\\n\",\n \"\\n\",\n \"mkt.location = PricingLocation.HKG\\n\",\n \"print(mkt.location)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The TimestampedMarket has a required timestamp argument and optional location\\n\",\n \"import time as t\\n\",\n \"\\n\",\n \"mkt = TimestampedMarket(t.time(), location=PricingLocation.TKO)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"\\n\"\n ]\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010002_marketdatapattern_basics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Market Data Representation\\n\",\n \"Market data is a key part of gs_quant. The `MarketDataPattern` class allows the user to select a collection of this \\n\",\n \"observable market data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import MarketDataPattern\\n\",\n \"\\n\",\n \"MarketDataPattern?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A `MarketDataPattern` can be instantiated with any combination of the following attributes:\\n\",\n \"\\n\",\n \"- `mkt_type` - natural category of risk representation\\n\",\n \"- `mkt_asset` - marked observable\\n\",\n \"- `mkt_class` - market priceable\\n\",\n \"- `mkt_point` - unique identifier for the marked observable within a mkt_class\\n\",\n \"\\n\",\n \"The highest level of market data classification is the `mkt_type`. Examples of mkt_type include: 'IR', 'IR Vol' and 'FX'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# you can instantiate a MarketDataPattern with keyword arguments\\n\",\n \"ir_mdp = MarketDataPattern(mkt_type='IR')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# or positional arguments\\n\",\n \"fx_mdp2 = MarketDataPattern('FX')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010003_marketdatapattern_construction_ir.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern class allows the user to select a collection of observable market data\\n\",\n \"# MarketDataPattern arguments include: mkt_type, mkt_asset, mkt_class, mkt_point and mkt_quoting_style\\n\",\n \"from gs_quant.risk import MarketDataPattern\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# A MarketDataPattern defined with 'IR' mkt_type contains market data related to IR Delta for rates instruments\\n\",\n \"# (such as swaps, eurodollar futures, fras, etc)\\n\",\n \"\\n\",\n \"ir = MarketDataPattern(mkt_type='IR')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The collection of 'IR' market data can be further constrained by setting the'mkt_asset'\\n\",\n \"# For data with mkt_type 'IR', the mkt_asset represents a rate curve's denominated currency\\n\",\n \"ir_gbp = MarketDataPattern(mkt_type='IR', mkt_asset='GBP')\\n\",\n \"ir_jpy = MarketDataPattern('IR', 'JPY')\\n\",\n \"ir_usd = MarketDataPattern('IR', 'USD')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010004_marketdatapattern_construction_ir_vol.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern class allows the user to select a collection of observable market data\\n\",\n \"# MarketDataPattern arguments include: mkt_type, mkt_asset, mkt_class, mkt_point and mkt_quoting_style\\n\",\n \"from gs_quant.risk import MarketDataPattern\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# A MarketDataPattern defined with 'IR Vol' mkt_type contains market data related to implied volatility for\\n\",\n \"# IR Swaptions/Caplets\\n\",\n \"ir_vol = MarketDataPattern(mkt_type='IR_Vol')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The collection of 'IR Vol' market data can be further constrained by setting the'mkt_asset'\\n\",\n \"# For data with mkt_type 'IR Vol', the mkt_asset is a benchmark rate\\n\",\n \"ir_vol_eur_euribor = MarketDataPattern(mkt_type='IR Vol', mkt_asset='EUR-EURIBOR-TELERATE')\\n\",\n \"ir_vol_gbp_libor = MarketDataPattern('IR Vol', 'GBP-LIBOR-BBA')\\n\",\n \"ir_vol_chf_libor = MarketDataPattern('IR Vol', 'CHF-LIBOR-BBA')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Additionally we can select subset of curve instruments using the 'mkt_class'\\n\",\n \"# valid mkt_classes for 'IR Vol' data are 'swaption' and 'caplet'\\n\",\n \"ir_vol_gbp_libor_swaption = MarketDataPattern(mkt_type='IR Vol', mkt_asset='GBP-LIBOR-BBA', mkt_class='swaption')\\n\",\n \"ir_vol_gbp_libor_caplet = MarketDataPattern('IR Vol', 'GBP-LIBOR-BBA', 'caplet')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010005_marketdatapattern_construction_ir_basis.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern class allows the user to select a collection of observable market data\\n\",\n \"# MarketDataPattern arguments include: mkt_type, mkt_asset, mkt_class, mkt_point and mkt_quoting_style\\n\",\n \"from gs_quant.risk import MarketDataPattern\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# A MarketDataPattern defined with 'IR Basis' mkt_type contains market data for rates spreads such as tenor basis,\\n\",\n \"# funding/libor basis, etc.\\n\",\n \"ir_basis = MarketDataPattern(mkt_type='IR Basis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The collection of 'IR Basis' market data can be further constrained by setting the 'mkt_asset'\\n\",\n \"# For data with mkt_type 'IR Basis', the mkt_asset is the basis curve, represented by two currency/tenor pairs\\n\",\n \"ir_basis_usd_ois1m = MarketDataPattern(mkt_type='IR Basis', mkt_asset='USD OIS/USD-1M')\\n\",\n \"ir_basis_eur_3m6m = MarketDataPattern('IR Basis', 'EUR-3M/EUR-6M')\\n\",\n \"ir_basis_gbp_3m6m = MarketDataPattern('IR Basis', 'GBP-3M/GBP-6M')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010006_marketdatapattern_construction_ir_xc.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern class allows the user to select a collection of observable market data\\n\",\n \"# MarketDataPattern arguments include: mkt_type, mkt_asset, mkt_class, mkt_point and mkt_quoting_style\\n\",\n \"from gs_quant.risk import MarketDataPattern\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# A MarketDataPattern defined with 'IR XC' mkt_type contains market data related to cross currency risk\\n\",\n \"ir_xc = MarketDataPattern(mkt_type='IR XC')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The collection of 'IR XC' market data can be further constrained by setting the 'mkt_asset'\\n\",\n \"# For data with mkt_type 'IR XC', mkt_asset is a single currency, which denotes the cross-currency curve\\n\",\n \"# (the large majority of which are vs. USD)\\n\",\n \"ir_xc_eur = MarketDataPattern(mkt_type='IR XC', mkt_asset='EUR')\\n\",\n \"ir_xc_gbp = MarketDataPattern('IR XC', 'GBP')\\n\",\n \"ir_xc_chf = MarketDataPattern('IR XC', 'CHF')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010007_marketdatapattern_construction_fx.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern class allows the user to select a collection of observable market data\\n\",\n \"# MarketDataPattern arguments include: mkt_type, mkt_asset, mkt_class, mkt_point and mkt_quoting_style\\n\",\n \"from gs_quant.risk import MarketDataPattern\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# A MarketDataPattern defined with 'FX' mkt_type contains FX Spot rate data\\n\",\n \"fx = MarketDataPattern(mkt_type='FX')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The collection of 'FX' market data can be further constrained by setting the 'mkt_asset'\\n\",\n \"# For data with mkt_type 'FX', the mkt_asset is a cross against USD\\n\",\n \"fx_eur = MarketDataPattern(mkt_type='FX', mkt_asset='USD/EUR')\\n\",\n \"fx_gbp = MarketDataPattern('FX', 'USD/GBP')\\n\",\n \"fx_chf = MarketDataPattern('FX', 'CHF/USD')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010008_marketdatapattern_construction_fx_fwd.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern class allows the user to select a collection of observable market data\\n\",\n \"# MarketDataPattern arguments include: mkt_type, mkt_asset, mkt_class, mkt_point and mkt_quoting_style\\n\",\n \"from gs_quant.risk import MarketDataPattern\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# A MarketDataPattern defined with 'FX Fwd' mkt_type contains FX Forward curve data\\n\",\n \"fx_fwd = MarketDataPattern(mkt_type='FX Fwd')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The collection of 'FX Fwd' market data can be further constrained by setting the 'mkt_asset'\\n\",\n \"# For data with mkt_type 'FX Fwd', the mkt_asset is a cross\\n\",\n \"# Note - FX Fwd market data are represented as USD crosses\\n\",\n \"\\n\",\n \"fx_fwd_eur = MarketDataPattern(mkt_type='FX Fwd', mkt_asset='USD/EUR')\\n\",\n \"fx_fwd_dkk = MarketDataPattern('FX Fwd', 'DKK/USD')\\n\",\n \"fx_fwd_jpy = MarketDataPattern('FX Fwd', 'JPY/USD')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010009_marketdatapattern_construction_fx_vol.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern class allows the user to select a collection of observable market data\\n\",\n \"# MarketDataPattern arguments include: mkt_type, mkt_asset, mkt_class, mkt_point and mkt_quoting_style\\n\",\n \"from gs_quant.risk import MarketDataPattern\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# A MarketDataPattern defined with 'FX Vol' mkt_type contains market data related to the implied volatility for\\n\",\n \"# FX instruments\\n\",\n \"fx_vol = MarketDataPattern(mkt_type='FX Vol')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The collection of 'FX Vol' market data can be further constrained by setting the'mkt_asset'\\n\",\n \"# For data with mkt_type 'FX Vol', the mkt_asset is a cross\\n\",\n \"fx_vol_gbp_eur = MarketDataPattern(mkt_type='FX Vol', mkt_asset='GBP/EUR', mkt_class='ATM Vol')\\n\",\n \"fx_vol_dkk_usd = MarketDataPattern('FX Vol', 'DKK/USD')\\n\",\n \"fx_vol_jpy_aud = MarketDataPattern('FX Vol', 'JPY/AUD')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010010_marketdatapattern_construction_cd.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern class allows the user to select a collection of observable market data\\n\",\n \"# MarketDataPattern arguments include: mkt_type, mkt_asset, mkt_class, mkt_point and mkt_quoting_style\\n\",\n \"from gs_quant.risk import MarketDataPattern\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# A MarketDataPattern defined with 'CD' mkt_type contains market data related to credit instruments\\n\",\n \"# (such as CDS, Index Spreads, Recovery Rates, etc.)\\n\",\n \"\\n\",\n \"cd = MarketDataPattern(mkt_type='CD')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The collection of 'CD' market data can be further constrained by setting the 'mkt_class'\\n\",\n \"\\n\",\n \"# CDS Index Products are selected by the 'INDEX' mkt_class\\n\",\n \"index = MarketDataPattern(mkt_type='CD', mkt_class='INDEX')\\n\",\n \"# The mkt_asset isolates the series and mt_point indicates maturity\\n\",\n \"index_series = MarketDataPattern(mkt_type='CD', mkt_asset='ITRAXX EUROPE`36', mkt_class='INDEX', mkt_point='5Y')\\n\",\n \"\\n\",\n \"# CDS are selected by the 'CDS' mkt_class and mkt_asset isolates the specific cds curve\\n\",\n \"cds = MarketDataPattern(mkt_type='CD', mkt_class='CDS')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/01_rollfwd_for_trade.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import RollFwd\\n\",\n \"from gs_quant.session import GsSession, Environment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.EUR, expirationDate=dt.date(2029, 10, 8), strike='atm')\\n\",\n \"base_price = swaption.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Contexts - RollFwd\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"swaption.resolve() # fix expiry and maturity\\n\",\n \"\\n\",\n \"# RollFwd Scenario - Roll forward 1 month\\n\",\n \"with RollFwd(date='1m', holiday_calendar='NYC'):\\n\",\n \" fwd_price = swaption.price()\\n\",\n \"\\n\",\n \"print('Base price: {:,.2f}'.format(base_price))\\n\",\n \"print('Scenario price: {:,.2f}'.format(fwd_price))\\n\",\n \"print('Diff: {:,.2f}'.format(fwd_price - base_price))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/02_basic_use_of_rollfwd_scenario.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.risk import RollFwd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Context - RollFwd\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# basic usage of RollFwd scenario\\n\",\n \"eur1y10y = IRSwaption('Pay', '10y', 'EUR', expiration_date='1y')\\n\",\n \"\\n\",\n \"# care needs to be taken when creating relative trades like the one above.\\n\",\n \"# If you don't resolve the trade, the resolution of the trade parameters will be done with\\n\",\n \"# reference to the active pricing context. Under the RollFwd scenario this means that\\n\",\n \"# if you don't resolve the trade will be a different trade when priced under the rollfwd scenario.\\n\",\n \"eur1y10y.resolve()\\n\",\n \"\\n\",\n \"# Roll forward 1 month\\n\",\n \"rollfwd_scenario = RollFwd(date='1m', holiday_calendar='LDN')\\n\",\n \"with rollfwd_scenario:\\n\",\n \" fwd_price = eur1y10y.price()\\n\",\n \"\\n\",\n \"print('Base price: {:,.2f}'.format(eur1y10y.price()))\\n\",\n \"print('Scenario price: {:,.2f}'.format(fwd_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Context - RollFwd\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# show how the option value will roll down moving forward 66 business days assuming either fwds\\n\",\n \"# or spot rates are realised.\\n\",\n \"\\n\",\n \"short_swaption = IRSwaption('Pay', '5y', 'USD', expirationDate='6m', notionalAmount=1e8)\\n\",\n \"short_swaption.resolve()\\n\",\n \"\\n\",\n \"prices = []\\n\",\n \"roll_spot_prices = []\\n\",\n \"with PricingContext():\\n\",\n \" for bus_days in range(66):\\n\",\n \" with RollFwd(date=f'{bus_days}b', holiday_calendar='NYC', realise_fwd=True):\\n\",\n \" prices.append(short_swaption.price())\\n\",\n \" with RollFwd(date=f'{bus_days}b', holiday_calendar='NYC', realise_fwd=False):\\n\",\n \" roll_spot_prices.append(short_swaption.price())\\n\",\n \"\\n\",\n \"pd.Series([p.result() for p in prices], dtype=np.dtype(float)).plot(\\n\",\n \" figsize=(10, 6), title=\\\"Swaption Price Forward in Time\\\", label='roll to fwd'\\n\",\n \")\\n\",\n \"pd.Series([rp.result() for rp in roll_spot_prices], dtype=np.dtype(float)).plot(figsize=(10, 6), label='roll to spot')\\n\",\n \"plt.xlabel('Business Days from Pricing Date')\\n\",\n \"plt.ylabel('PV')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create a grid of expiry by tenor swaptions showing the pv under the rollfwd scenario minus the base pv.\\n\",\n \"def calc_risk_matrix(ccy, strike, pay_rec, date, roll_to_fwds, expiries, tenors):\\n\",\n \" portfolio = Portfolio(\\n\",\n \" [\\n\",\n \" IRSwaption(pay_rec, tenor, ccy, expiration_date=expiry, strike=strike, name='{}_{}'.format(expiry, tenor))\\n\",\n \" for expiry in expiries\\n\",\n \" for tenor in tenors\\n\",\n \" ]\\n\",\n \" )\\n\",\n \" portfolio.resolve()\\n\",\n \" with RollFwd(date=date, holiday_calendar='LDN', realise_fwd=roll_to_fwds):\\n\",\n \" rollfwd_results = portfolio.price()\\n\",\n \"\\n\",\n \" base_results = portfolio.price()\\n\",\n \"\\n\",\n \" rollfwd_records = [\\n\",\n \" (rollfwd_results[t.name] - base_results[t.name], t.name.split('_')[0], t.name.split('_')[1]) for t in portfolio\\n\",\n \" ]\\n\",\n \" rollfwd_df = pd.DataFrame(rollfwd_records, columns=['Value', 'Expiry', 'Tenor'])\\n\",\n \"\\n\",\n \" pivot_df = rollfwd_df.pivot_table(values='Value', index='Expiry', columns='Tenor')\\n\",\n \" return pivot_df[tenors].reindex(expiries)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ccy = 'EUR'\\n\",\n \"strike = 'ATM'\\n\",\n \"pay_rec = 'Pay' # or 'Receive' or 'Straddle'\\n\",\n \"date = '1m' # enter relative or actual date\\n\",\n \"roll_to_fwds = True\\n\",\n \"expiries = ['1m', '2m', '3m', '6m', '9m', '1y', '18m', '2y', '3y', '5y', '7y', '10y']\\n\",\n \"tenors = ['1y', '2y', '3y', '5y', '7y', '10y', '15y', '20y', '25y', '30y']\\n\",\n \"\\n\",\n \"calc_risk_matrix(ccy, strike, pay_rec, date, roll_to_fwds, expiries, tenors)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.risk import RollFwd\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.instrument import IRSwap, IRSwaption\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import gs_quant.risk as risk\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Context - RollFwd\",\n \"Metrics - Cashflows\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# create a swap which has a 1m floating frequency\\n\",\n \"swap = IRSwap('Pay', '10y', 'EUR', fixed_rate='ATM-5', floating_rate_frequency='1m', name='EUR10y')\\n\",\n \"\\n\",\n \"# resolve the trade as of today to fix the dates and rate\\n\",\n \"swap.resolve()\\n\",\n \"\\n\",\n \"# roll daily for 66 business days assuming both forward curve is realised and spot curve is realised\\n\",\n \"fwd_price = []\\n\",\n \"fwd_cash = []\\n\",\n \"spot_price = []\\n\",\n \"spot_cash = []\\n\",\n \"r = range(0, 66, 6)\\n\",\n \"# by wrapping all the scenarios into one PricingContext we package all the requests into one call to GS\\n\",\n \"with PricingContext():\\n\",\n \" for bus_days in r:\\n\",\n \" with PricingContext(is_async=True), RollFwd(date=f'{bus_days}b', holiday_calendar='LDN', realise_fwd=True):\\n\",\n \" fwd_price.append(swap.price())\\n\",\n \" fwd_cash.append(swap.calc(risk.Cashflows))\\n\",\n \" with PricingContext(is_async=True), RollFwd(date=f'{bus_days}b', holiday_calendar='LDN', realise_fwd=False):\\n\",\n \" spot_price.append(swap.price())\\n\",\n \" spot_cash.append(swap.calc(risk.Cashflows))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Carry\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"fwd_pv = pd.Series([p.result() for p in fwd_price], index=r)\\n\",\n \"spot_pv = pd.Series([p.result() for p in spot_price], index=r)\\n\",\n \"\\n\",\n \"# The output of the cashflows measure is a dataframe of the past and implied future cashflows. We could filter by payment date\\n\",\n \"# but conviently the discount factor is 0 for paid cashflows\\n\",\n \"cash_fwd = pd.Series([c.result()[c.result().discount_factor == 0].payment_amount.sum() for c in fwd_cash], index=r)\\n\",\n \"cash_spot = pd.Series([c.result()[c.result().discount_factor == 0].payment_amount.sum() for c in spot_cash], index=r)\\n\",\n \"\\n\",\n \"fwd_pv.plot(figsize=(10, 6), title='Swap Carry', label='{} Realise Fwd'.format(swap.name))\\n\",\n \"spot_pv.plot(label='{} Realise Spot'.format(swap.name))\\n\",\n \"(fwd_pv + cash_fwd).plot(label='{} Realise Fwd (inc. cash)'.format(swap.name))\\n\",\n \"(spot_pv + cash_spot).plot(label='{} Realise Spot (inc. cash)'.format(swap.name))\\n\",\n \"\\n\",\n \"plt.xlabel('Business Days from Pricing Date')\\n\",\n \"plt.ylabel('PV')\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\\n\",\n \"# note that the steps represent the move in MTM as the cashflows are paid. The libor fixing is implied from the fwd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Context - Nested Contexts\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"itm_swaption = IRSwaption('Receive', '10y', 'EUR', strike='ATM+20', expiration_date='1m', name='ITM swaption')\\n\",\n \"otm_swaption = IRSwaption('Receive', '10y', 'EUR', strike='ATM-20', expiration_date='1m', name='OTM swaption')\\n\",\n \"port = Portfolio([itm_swaption, otm_swaption])\\n\",\n \"port.resolve()\\n\",\n \"\\n\",\n \"# roll daily for 44 business days assuming both forward curve is realised and spot curve is realised\\n\",\n \"fwd_results = []\\n\",\n \"spot_results = []\\n\",\n \"r = range(0, 44, 4)\\n\",\n \"# by wrapping all the scenarios into one PricingContext we package all the requests into one call to GS\\n\",\n \"with PricingContext():\\n\",\n \" for bus_days in r:\\n\",\n \" with PricingContext(is_async=True), RollFwd(date=f'{bus_days}b', holiday_calendar='LDN', realise_fwd=True):\\n\",\n \" fwd_results.append(port.price())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.DataFrame.from_records(\\n\",\n \" [[p['ITM swaption'], p['OTM swaption']] for p in fwd_results], index=r, columns=['ITM', 'OTM']\\n\",\n \")\\n\",\n \"df.plot(figsize=(10, 6), secondary_y=['OTM'], title='Swaption Carry', xlabel='Business Days', ylabel='PV')\\n\",\n \"# note that the OTM swaption prices at 0 post expiry whereas the ITM swaption prices at the value of the swap.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.risk import IRFwdRate, RollFwd\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"import matplotlib.pylab as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Instrument - FRA\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"ccy = 'EUR'\\n\",\n \"# construct a series of 6m FRAs going out 20y or so\\n\",\n \"fras = Portfolio(\\n\",\n \" [\\n\",\n \" IRSwap(\\n\",\n \" 'Pay',\\n\",\n \" '6m',\\n\",\n \" ccy,\\n\",\n \" effective_date='{}m'.format(i - 6),\\n\",\n \" fixed_rate_frequency='6m',\\n\",\n \" floating_rate_frequency='6m',\\n\",\n \" )\\n\",\n \" for i in range(6, 123, 6)\\n\",\n \" ]\\n\",\n \")\\n\",\n \"fras.resolve()\\n\",\n \"results = fras.calc(IRFwdRate)\\n\",\n \"\\n\",\n \"# get the fwd rates for these fras under the base sceneraio (no shift in time)\\n\",\n \"base = {fras[i].termination_date: res for i, res in enumerate(results)}\\n\",\n \"base_series = pd.Series(base, name='base', dtype=np.dtype(float))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": [\n \"Context - RollFwd\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# calculate the fwd rates with a shift forward of 6m. This shift keeps fwd rates constant.\\n\",\n \"# So 5.5y rate today will be 5y rate under the scenario of pricing 6m in the future.\\n\",\n \"with RollFwd(date='6m', realise_fwd=True, holiday_calendar='LDN'):\\n\",\n \" results_fwd = fras.calc(IRFwdRate)\\n\",\n \"with RollFwd(date='6m', realise_fwd=False, holiday_calendar='LDN'):\\n\",\n \" results_spot = fras.calc(IRFwdRate)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"roll_to_fwds = {fras[i].termination_date: res for i, res in enumerate(results_fwd)}\\n\",\n \"roll_to_fwds_series = pd.Series(roll_to_fwds, name='roll to fwd', dtype=np.dtype(float))\\n\",\n \"\\n\",\n \"roll_to_spot = {fras[i].termination_date: res for i, res in enumerate(results_spot)}\\n\",\n \"roll_to_spot_series = pd.Series(roll_to_spot, name='roll to spot', dtype=np.dtype(float))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# show the curves, the base in blue, the roll to fwd in green and the roll to spot in orange.\\n\",\n \"# note blue and green curves are not exactly on top of each other as we aren't using the curve instruments themselves\\n\",\n \"# but instead using FRAs to show a smooth curve.\\n\",\n \"base_series.plot(figsize=(20, 10))\\n\",\n \"roll_to_spot_series.plot()\\n\",\n \"roll_to_fwds_series.plot()\\n\",\n \"plt.legend()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n },\n \"tags\": [\n \"Market Data - Yield Curve\"\n ]\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/05_rollfwd_swap_fwd_rates.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.risk import RollFwd, IRFwdRate\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true,\n \"tags\": [\n \"Context - RollFwd\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# construct a portfolio of swaps with tenors 6m apart and resolve these trades\\n\",\n \"\\n\",\n \"tenor = ['42m', '4y', '54m', '5y']\\n\",\n \"swap_port = [\\n\",\n \" IRSwap('Pay', t, 'EUR', fixed_rate='ATM', floating_rate_frequency='6m', effective_date='6m', name='EUR6m' + t)\\n\",\n \" for t in tenor\\n\",\n \"]\\n\",\n \"port = Portfolio(swap_port)\\n\",\n \"port.resolve()\\n\",\n \"\\n\",\n \"fwd_results = []\\n\",\n \"spot_results = []\\n\",\n \"\\n\",\n \"# calculate the fwd rate under the assumption spot rates are held constant and that fwd rates are held constant.\\n\",\n \"# here we calculate the fwd rate every week forward in time.\\n\",\n \"\\n\",\n \"with PricingContext():\\n\",\n \" for week in range(0, 12):\\n\",\n \" with PricingContext(is_async=True), RollFwd(date=f'{week}w', realise_fwd=True, holiday_calendar='LDN'):\\n\",\n \" fwd_results.append(port.calc(IRFwdRate))\\n\",\n \" with PricingContext(is_async=True), RollFwd(date=f'{week}w', realise_fwd=False, holiday_calendar='LDN'):\\n\",\n \" spot_results.append(port.calc(IRFwdRate))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true,\n \"tags\": [\n \"Carry\"\n ]\n },\n \"outputs\": [],\n \"source\": [\n \"# graph the results. Note that under the fwd rate constant assumption the fwd rate doesn't change,\\n\",\n \"# whereas you see the effect of the rolldown of the curve in the spot rate constant assumption.\\n\",\n \"for swap in swap_port:\\n\",\n \" s = pd.Series([p[swap.name] for p in spot_results], dtype=np.dtype(float))\\n\",\n \" s.plot(figsize=(20, 12), title='Swap Carry', label='{} spot curve carry'.format(swap.name))\\n\",\n \" f = pd.Series([p[swap.name] for p in fwd_results], dtype=np.dtype(float))\\n\",\n \" f.plot(label='{} fwd curve carry'.format(swap.name))\\n\",\n \"\\n\",\n \"plt.xlabel('Weeks')\\n\",\n \"plt.ylabel('Rate')\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010200_marketdatashockbasedscenario_basics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The gs_quant risk package contains Scenarios, which allow the user to perform scenario analysis on a set of trades\\n\",\n \"# 'MarketDataShockBasedScenario' is the most flexible scenario, which allows users to construct bespoke market data\\n\",\n \"# scenarios\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario\\n\",\n \"\\n\",\n \"MarketDataShockBasedScenario?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"is_executing\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# MarketDataShockBasedScenarios has one property: 'shocks', a mapping of MarketDataPattern to MarketDataShock\\n\",\n \"MarketDataShockBasedScenario.shocks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# The MarketDataPattern defines a collection of observable Market Data. Examples for the MarketDataPattern class are\\n\",\n \"# in gs_quant/examples/01_pricing_and_risk/01_market_objects.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010201_rate_spot_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ir_spot_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('IR', 'EUR'): MarketDataShock(MarketDataShockType.Absolute, 50 / 10000)}\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.EUR, expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\\n\",\n \"\\n\",\n \"with ir_spot_scenario:\\n\",\n \" swaption_scenario_price = swaption.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"print('Base price: {:,.2f}'.format(swaption.price()))\\n\",\n \"print('Scenario price: {:,.2f}'.format(swaption_scenario_price))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010202_rate_curve_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveScenario\\n\",\n \"\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.EUR, expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Price the swaption under a curve move of 5bp parallel shift, 1bp slope shift pivoted at 5y point (up to 50y)\\n\",\n \"curve_scenario = CurveScenario(annualised_parallel_shift=5, \\n\",\n \" annualised_slope_shift=1, pivot_point=5, cutoff=50)\\n\",\n \"\\n\",\n \"with curve_scenario:\\n\",\n \" swaption_scenario_price = swaption.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"print('Base price: {:,.2f}'.format(swaption.price()))\\n\",\n \"print('Scenario price: {:,.2f}'.format(swaption_scenario_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010203_rate_vol_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ir_vol_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('IR Vol'): MarketDataShock(MarketDataShockType.Absolute, 1e-4)}\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.EUR, expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\\n\",\n \"\\n\",\n \"with ir_vol_scenario:\\n\",\n \" swaption_scenario_price = swaption.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"print('Base price: {:,.2f}'.format(swaption.price()))\\n\",\n \"print('Scenario price: {:,.2f}'.format(swaption_scenario_price))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010204_delta_shock_equivalent.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import (\\n\",\n \" MarketDataShockBasedScenario,\\n\",\n \" MarketDataPattern,\\n\",\n \" MarketDataShock,\\n\",\n \" MarketDataShockType,\\n\",\n \" IRDeltaParallel,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Risk measures are short-hand for a set of predefined scenarios. Let's look at how at to get the `IRDeltaParallel` value using a `MarketDataShockBasedScenario`. We calculate delta as a 2-sided 1bp bump.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap = IRSwap(PayReceive.Pay, '10y', Currency.USD, notional_amount=10e6)\\n\",\n \"swap.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"r_shock_bp = 1 / 10000\\n\",\n \"ir_scenario_up = MarketDataShockBasedScenario(\\n\",\n \" {MarketDataPattern('IR', mkt_asset=Currency.USD): MarketDataShock(MarketDataShockType.Absolute, r_shock_bp)}\\n\",\n \")\\n\",\n \"ir_scenario_down = MarketDataShockBasedScenario(\\n\",\n \" {MarketDataPattern('IR', mkt_asset=Currency.USD): MarketDataShock(MarketDataShockType.Absolute, -r_shock_bp)}\\n\",\n \")\\n\",\n \"with PricingContext(market_data_location='NYC'):\\n\",\n \" delta = swap.calc(IRDeltaParallel)\\n\",\n \" with ir_scenario_up:\\n\",\n \" up = swap.dollar_price()\\n\",\n \" with ir_scenario_down:\\n\",\n \" down = swap.dollar_price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"combined = (up.result() - down.result()) / 2\\n\",\n \"# should give the same value\\n\",\n \"print(f'Delta direct={delta.result():.0f}, Delta by shocks={((up.result() - down.result()) / 2):.0f}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010205_vega_shock_equivalent.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import (\\n\",\n \" MarketDataShockBasedScenario,\\n\",\n \" MarketDataPattern,\\n\",\n \" MarketDataShock,\\n\",\n \" MarketDataShockType,\\n\",\n \" IRVegaParallel,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Risk measures are short-hand for a set of predefined scenarios. Let's look at how at to get the `IRVegaParallel` value using a `MarketDataShockBasedScenario`. We calculate delta as a 2-sided 1bp bump.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Pay, '10y', Currency.USD, notional_amount=10e6)\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"r_shock_bp = 1 / 10000\\n\",\n \"ir_scenario_up = MarketDataShockBasedScenario(\\n\",\n \" {MarketDataPattern('IR Vol'): MarketDataShock(MarketDataShockType.Absolute, r_shock_bp)}\\n\",\n \")\\n\",\n \"ir_scenario_down = MarketDataShockBasedScenario(\\n\",\n \" {MarketDataPattern('IR Vol'): MarketDataShock(MarketDataShockType.Absolute, -r_shock_bp)}\\n\",\n \")\\n\",\n \"with PricingContext():\\n\",\n \" vega = swaption.calc(IRVegaParallel)\\n\",\n \" with ir_scenario_up:\\n\",\n \" up = swaption.dollar_price()\\n\",\n \" with ir_scenario_down:\\n\",\n \" down = swaption.dollar_price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"combined = (up.result() - down.result()) / 2\\n\",\n \"# should give the same value\\n\",\n \"combined - delta.result()\\n\",\n \"\\n\",\n \"print(f'Vega direct={vega.result():.0f}, Vega by shocks={((up.result() - down.result()) / 2):.0f}')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010206_eq_spot_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import EqOption\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType\\n\",\n \"from gs_quant.risk import EqDelta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"o = EqOption('.SPX')\\n\",\n \"o.resolve()\\n\",\n \"p = o.price()\\n\",\n \"d1 = o.calc(EqDelta)\\n\",\n \"print(f'Base price: {p:,.2f}')\\n\",\n \"print(f'Equity Delta: {d1:,.2f}')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# shock equity spot up and down\\n\",\n \"with MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern(mkt_type='Eq', mkt_class='Spot', mkt_quoting_style='PropSpread'): MarketDataShock(\\n\",\n \" MarketDataShockType.Override, 0.005\\n\",\n \" )\\n\",\n \" }\\n\",\n \"):\\n\",\n \" price_up = o.price()\\n\",\n \"with MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern(mkt_type='Eq', mkt_class='Spot', mkt_quoting_style='PropSpread'): MarketDataShock(\\n\",\n \" MarketDataShockType.Override, -0.005\\n\",\n \" )\\n\",\n \" }\\n\",\n \"):\\n\",\n \" price_down = o.price()\\n\",\n \"d2 = (price_up - price_down) / 0.01\\n\",\n \"\\n\",\n \"print(f'Equity Delta via shocking: {d2:,.2f}')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010207_fx_shock_examples.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c1f25a7c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType\\n\",\n \"from gs_quant.risk import FXSpot, Price, FXAnnualImpliedVol, FXAnnualATMImpliedVol\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5efa07c6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fxoption = FXOption(pair='EURUSD', expiration_date='3m', premium=0)\\n\",\n \"unshifted_res = fxoption.calc((FXSpot, Price, FXAnnualImpliedVol, FXAnnualATMImpliedVol))\\n\",\n \"unshifted_res.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a62b8561\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# set FX Spot to X\\n\",\n \"\\n\",\n \"x = 1.1\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('FX', 'USD/EUR'): MarketDataShock(MarketDataShockType.Override, x)}\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_spot = fxoption.calc(FXSpot)\\n\",\n \"shifted_spot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b55ee1cf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Bump FX spot by x\\n\",\n \"\\n\",\n \"# relative - 5%\\n\",\n \"x = 0.05\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('FX', 'USD/EUR'): MarketDataShock(MarketDataShockType.Proportional, x)}\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_spot = fxoption.calc(FXSpot)\\n\",\n \"print(shifted_spot)\\n\",\n \"print(unshifted_res[FXSpot] * (1 + x))\\n\",\n \"\\n\",\n \"# absolute - 0.08\\n\",\n \"x = 0.08\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('FX', 'USD/EUR'): MarketDataShock(MarketDataShockType.Absolute, x)}\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_spot = fxoption.calc(FXSpot)\\n\",\n \"print(shifted_spot)\\n\",\n \"print(unshifted_res[FXSpot] + x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0ffacf30\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set Strike Vol to x & Bump Strike Vol to x\\n\",\n \"\\n\",\n \"x = 0.06\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern('FX VOL', 'USD/EUR', 'ATM VOL'): MarketDataShock(MarketDataShockType.Override, x),\\n\",\n \" MarketDataPattern('FX VOL', 'USD/EUR', 'BF 25'): MarketDataShock(MarketDataShockType.Override, 0),\\n\",\n \" MarketDataPattern('FX VOL', 'USD/EUR', 'RR 25'): MarketDataShock(MarketDataShockType.Override, 0),\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_vol = fxoption.calc(FXAnnualImpliedVol)\\n\",\n \"print(shifted_vol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6defbc3d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Parallel Shift the ATMVol Curve by x\\n\",\n \"\\n\",\n \"# relative - 5%\\n\",\n \"x = 0.05\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('FX VOL', 'USD/EUR', 'ATM VOL'): MarketDataShock(MarketDataShockType.Proportional, x)}\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_vol = fxoption.calc(FXAnnualATMImpliedVol)\\n\",\n \"print(shifted_vol)\\n\",\n \"print(unshifted_res[FXAnnualATMImpliedVol] * (1 + x))\\n\",\n \"\\n\",\n \"# absolute - 0.01\\n\",\n \"x = 0.01\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('FX VOL', 'USD/EUR', 'ATM VOL'): MarketDataShock(MarketDataShockType.Absolute, x)}\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_vol = fxoption.calc(FXAnnualATMImpliedVol)\\n\",\n \"print(shifted_vol)\\n\",\n \"print(unshifted_res[FXAnnualATMImpliedVol] + x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ecaf3874\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# set atmvol at expiry X to y\\n\",\n \"\\n\",\n \"x = '3m'\\n\",\n \"y = 0.62\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern('FX VOL', 'USD/EUR', 'ATM VOL', mkt_point=[x]): MarketDataShock(\\n\",\n \" MarketDataShockType.Override, y\\n\",\n \" )\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_atmvol = fxoption.calc(FXAnnualATMImpliedVol)\\n\",\n \"shifted_atmvol\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d53e3f2c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Parallel Shift the ATMVol at expiry x by y\\n\",\n \"\\n\",\n \"x = '3m'\\n\",\n \"# relative - 5%\\n\",\n \"y = 0.05\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern('FX VOL', 'USD/EUR', 'ATM VOL', mkt_point=[x]): MarketDataShock(\\n\",\n \" MarketDataShockType.Proportional, y\\n\",\n \" )\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_vol = fxoption.calc(FXAnnualATMImpliedVol)\\n\",\n \"print(shifted_vol)\\n\",\n \"print(unshifted_res[FXAnnualATMImpliedVol] * (1 + y))\\n\",\n \"\\n\",\n \"# absolute - 0.01\\n\",\n \"y = 0.01\\n\",\n \"\\n\",\n \"mkt_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern('FX VOL', 'USD/EUR', 'ATM VOL', mkt_point=[x]): MarketDataShock(\\n\",\n \" MarketDataShockType.Absolute, y\\n\",\n \" )\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"with mkt_shock:\\n\",\n \" shifted_vol = fxoption.calc(FXAnnualATMImpliedVol)\\n\",\n \"print(shifted_vol)\\n\",\n \"print(unshifted_res[FXAnnualATMImpliedVol] + y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3c14d137\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# parallel shift the yield curve\\n\",\n \"\\n\",\n \"ir_spot_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('IR', 'EUR'): MarketDataShock(MarketDataShockType.Absolute, 50 / 10000)}\\n\",\n \")\\n\",\n \"\\n\",\n \"with ir_spot_scenario:\\n\",\n \" yield_curve_shift = fxoption.price()\\n\",\n \"\\n\",\n \"print(yield_curve_shift)\\n\",\n \"print(unshifted_res[Price])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b93b3651\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/03_composite_shocks/010300_rate_composite_shocks.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.risk import RollFwd, MarketDataPattern, MarketDataShock, MarketDataShockBasedScenario, MarketDataShockType\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# define swap\\n\",\n \"swap = IRSwap(termination_date='10y', notional_currency=Currency.USD, pay_or_receive=PayReceive.Pay, fixed_rate=0.007)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 1bp Spot Shock\\n\",\n \"ir_1bp_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern('IR', 'USD'): MarketDataShock(shock_type=MarketDataShockType.Absolute, value=1e-4),\\n\",\n \" MarketDataPattern('IR Reset', 'USD-3m'): MarketDataShock(shock_type=MarketDataShockType.Absolute, value=1e-4),\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"# Roll Forward 22b\\n\",\n \"rollfwd_22b_scenario = RollFwd(date='22b', holiday_calendar='NYC')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Roll Forward 22b and apply 1bp Spot Shock\\n\",\n \"with rollfwd_22b_scenario, ir_1bp_scenario:\\n\",\n \" rollfwd_22b_then_ir_1bp_price = swap.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"print('Base price: {:,.2f}'.format(swap.price()))\\n\",\n \"print('Scenario price: {:,.2f}'.format(rollfwd_22b_then_ir_1bp_price))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/03_composite_shocks/010301_multiple_shocks.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk import MarketDataPattern, MarketDataShock, MarketDataShockBasedScenario, MarketDataShockType\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# define two swaps\\n\",\n \"swap1 = IRSwap(\\n\",\n \" termination_date='10y', notional_currency=Currency.USD, pay_or_receive=PayReceive.Pay, fixed_rate=0.007, name='10y'\\n\",\n \")\\n\",\n \"swap2 = IRSwap(\\n\",\n \" termination_date='15y', notional_currency=Currency.USD, pay_or_receive=PayReceive.Pay, fixed_rate=0.007, name='15y'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# create a couple of scenarios\\n\",\n \"\\n\",\n \"# 1bp Spot Shock\\n\",\n \"ir_1bp_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('IR', 'USD'): MarketDataShock(shock_type=MarketDataShockType.Absolute, value=1e-4)}\\n\",\n \")\\n\",\n \"\\n\",\n \"# 2bp Spot Shock\\n\",\n \"ir_2bp_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('IR', 'USD'): MarketDataShock(shock_type=MarketDataShockType.Absolute, value=1e-4)}\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# if we want to apply one scenario to many trades it is best to join those trades into a portfolio and send this in one calc\\n\",\n \"\\n\",\n \"portfolio = Portfolio([swap1, swap2], name='portfolio')\\n\",\n \"\\n\",\n \"# note you can append (and pop) trades into a portfolio but this is generally less efficient than just creating with list comprehension\\n\",\n \"# also portfolios can themselves contain portfolios\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"H:\\\\code\\\\gs_quant\\\\gs_quant\\\\risk\\\\results.py:80: FutureWarning: The provided callable is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \\\"sum\\\" instead.\\n\",\n \" pivot_df = df.pivot_table(values=values, index=index, columns=columns, aggfunc=aggfunc)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
risk_measurePrice
instrument_name
10y2.295177e+07
15y3.253489e+07
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \"risk_measure Price\\n\",\n \"instrument_name \\n\",\n \"10y 2.295177e+07\\n\",\n \"15y 3.253489e+07\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# many trades with one scenario\\n\",\n \"\\n\",\n \"with ir_1bp_scenario:\\n\",\n \" prices = portfolio.price()\\n\",\n \"\\n\",\n \"prices.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"22951766.87812186\\n\",\n \"32534888.041928925\\n\"\n ]\n }\n ],\n \"source\": [\n \"# now I want to apply 1 scenario to 1 trade and the other scenario to the 2nd trade\\n\",\n \"# you could do this sequentially and that will work but be slow. Better to nest the scenarios in a overarching context\\n\",\n \"\\n\",\n \"with PricingContext(): # this context just wraps the calcs so that they will be run in parallel\\n\",\n \" with ir_1bp_scenario:\\n\",\n \" p1 = swap1.price()\\n\",\n \" with ir_2bp_scenario:\\n\",\n \" p2 = swap2.price()\\n\",\n \"\\n\",\n \"print(p1.result())\\n\",\n \"print(p2.result())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010400_parallel_curve_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveScenario, MarketDataPattern\\n\",\n \"from gs_quant.markets import MarketDataCoordinate\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption('Receive', '5y', 'USD', expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"original_price = swaption.price()\\n\",\n \"market_data = swaption.market().market_data_dict\\n\",\n \"print('Base price: {:,.2f}'.format(original_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Price the swaption under a curve move of 10bp parallel shift\\n\",\n \"parallel_shift_scenario = CurveScenario(market_data_pattern=MarketDataPattern('IR', 'USD'), parallel_shift=10)\\n\",\n \"\\n\",\n \"with parallel_shift_scenario:\\n\",\n \" swaption_parallel_shift = swaption.price()\\n\",\n \" market_data_parallel_shift = swaption.market().market_data_dict\\n\",\n \"print('Price under parallel shift: {:,.2f}'.format(swaption_parallel_shift))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compare swap rate market data coordinates before and after curve scenario shock\\n\",\n \"coord = MarketDataCoordinate(mkt_type=\\\"IR\\\", mkt_asset=\\\"USD\\\")\\n\",\n \"market_data_df = pd.DataFrame(\\n\",\n \" [\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data_parallel_shift.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" ],\\n\",\n \" index=['Values', 'Shocked values'],\\n\",\n \").transpose()\\n\",\n \"market_data_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plotting swap rate market data before and after curve scenario shock\\n\",\n \"swap_curve = pd.DataFrame.from_dict(\\n\",\n \" {int(''.join(list(filter(str.isdigit, str(v))))): market_data_df.loc[v] for v in market_data_df.index},\\n\",\n \" orient='index',\\n\",\n \")\\n\",\n \"\\n\",\n \"swap_curve['Shock'] = swap_curve['Shocked values'] - swap_curve['Values']\\n\",\n \"swap_curve.plot(figsize=(12, 8), title='USD Swap Curve Before and After Parallel Shock')\\n\",\n \"plt.xlabel('Tenor (years)')\\n\",\n \"plt.ylabel('bp')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010401_curve_shock_midpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.common import PayReceive\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveScenario, MarketDataPattern\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', 'USD', expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"original_price = swaption.price()\\n\",\n \"# retrieve the market data our instrument is sensitive to.\\n\",\n \"market_data = swaption.market().market_data_dict\\n\",\n \"print('Base price: {:,.2f}'.format(original_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Price the swaption under a curve shift of 10bp\\n\",\n \"# Pivot point is the tenor at which curve shift =0 and influences the type and shape of curve shift\\n\",\n \"# When pivot_point is not set, pivot_point is defaulted to the mid of tenor_end and tenor_start\\n\",\n \"# tenor_start and tenor_end are mandatory fields to be specified with curve_shift\\n\",\n \"curve_shift_scenario = CurveScenario(\\n\",\n \" market_data_pattern=MarketDataPattern('IR', 'USD'), curve_shift=10, tenor_start=15, tenor_end=25\\n\",\n \")\\n\",\n \"\\n\",\n \"with curve_shift_scenario:\\n\",\n \" swaption_curve_shift = swaption.price()\\n\",\n \" market_data_curve_shift = swaption.market().market_data_dict\\n\",\n \"\\n\",\n \"print('Price under curve shift: {:,.2f}'.format(swaption_curve_shift))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compare swap rate market data coordinates before and after curve scenario shock\\n\",\n \"market_data_df = pd.DataFrame(\\n\",\n \" [\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data_curve_shift.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" ],\\n\",\n \" index=['Values', 'Shocked values'],\\n\",\n \").transpose()\\n\",\n \"market_data_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plotting swap rate market data before and after curve scenario shock\\n\",\n \"swap_curve = pd.DataFrame.from_dict(\\n\",\n \" {int(''.join(list(filter(str.isdigit, str(v))))): market_data_df.loc[v] for v in market_data_df.index},\\n\",\n \" orient='index',\\n\",\n \")\\n\",\n \"\\n\",\n \"swap_curve['Shock'] = swap_curve['Shocked values'] - swap_curve['Values']\\n\",\n \"swap_curve_plot = swap_curve.plot(figsize=(12, 8), title='USD Swap Curve Before and After Midpoint Curve Shock')\\n\",\n \"plt.xlabel('Tenor (years)')\\n\",\n \"plt.ylabel('bp')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010402_bear_steepener_curve_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveScenario, MarketDataPattern\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.USD, expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"original_price = swaption.price()\\n\",\n \"# retrieve the market data our instrument is sensitive to.\\n\",\n \"market_data = swaption.market().market_data_dict\\n\",\n \"print('Base price: {:,.2f}'.format(original_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Pivot point is the tenor at which curve shift =0 and influences the type and shape of curve shift\\n\",\n \"# Price the swaption under a bear steepener scenario of 10bp\\n\",\n \"bear_steepener_scenario = CurveScenario(\\n\",\n \" market_data_pattern=MarketDataPattern('IR', 'USD'), curve_shift=10, tenor_start=15, tenor_end=25, pivot_point=15\\n\",\n \")\\n\",\n \"\\n\",\n \"with bear_steepener_scenario:\\n\",\n \" swaption_bear_steepener = swaption.price()\\n\",\n \" market_data_bear_steepener = swaption.market().market_data_dict\\n\",\n \"\\n\",\n \"print('Price under bear steepener curve shift: {:,.2f}'.format(swaption_bear_steepener))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Compare swap rate market data coordinates before and after curve scenario shock\\n\",\n \"market_data_df = pd.DataFrame(\\n\",\n \" [\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data_bear_steepener.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" ],\\n\",\n \" index=['Values', 'Shocked values'],\\n\",\n \").transpose()\\n\",\n \"market_data_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Plotting swap rate market data before and after curve scenario shock\\n\",\n \"swap_curve = pd.DataFrame.from_dict(\\n\",\n \" {int(''.join(list(filter(str.isdigit, str(v))))): market_data_df.loc[v] for v in market_data_df.index},\\n\",\n \" orient='index',\\n\",\n \")\\n\",\n \"\\n\",\n \"swap_curve['Shock'] = swap_curve['Shocked values'] - swap_curve['Values']\\n\",\n \"swap_curve.plot(\\n\",\n \" figsize=(12, 8),\\n\",\n \" title='USD Swap Curve Before and After {}bp Bear Steepening Shock'.format(bear_steepener_scenario.curve_shift),\\n\",\n \")\\n\",\n \"plt.xlabel('Tenor (years)')\\n\",\n \"plt.ylabel('bp')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010403_bull_steepener_curve_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveScenario, MarketDataPattern\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.USD, expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"original_price = swaption.price()\\n\",\n \"# retrieve the market data our instrument is sensitive to.\\n\",\n \"market_data = swaption.market().market_data_dict\\n\",\n \"print('Base price: {:,.2f}'.format(original_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Pivot point is the tenor at which curve shift =0 and influences the type and shape of curve shift\\n\",\n \"# Price the swaption under a bull steepener scenario of 10bp\\n\",\n \"bull_steepener_scenario = CurveScenario(\\n\",\n \" market_data_pattern=MarketDataPattern('IR', 'USD'), curve_shift=10, tenor_start=15, tenor_end=25, pivot_point=25\\n\",\n \")\\n\",\n \"\\n\",\n \"with bull_steepener_scenario:\\n\",\n \" swaption_bull_steepener = swaption.price()\\n\",\n \" market_data_bull_steepener = swaption.market().market_data_dict\\n\",\n \"\\n\",\n \"print('Price under bull steepener curve shift: {:,.2f}'.format(swaption_bull_steepener))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compare swap rate market data coordinates before and after curve scenario shock\\n\",\n \"market_data_df = pd.DataFrame(\\n\",\n \" [\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data_bull_steepener.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" ],\\n\",\n \" index=['Values', 'Shocked values'],\\n\",\n \").transpose()\\n\",\n \"market_data_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plotting swap rate market data before and after curve scenario shock\\n\",\n \"swap_curve = pd.DataFrame.from_dict(\\n\",\n \" {int(''.join(list(filter(str.isdigit, str(v))))): market_data_df.loc[v] for v in market_data_df.index},\\n\",\n \" orient='index',\\n\",\n \")\\n\",\n \"\\n\",\n \"swap_curve['Shock'] = swap_curve['Shocked values'] - swap_curve['Values']\\n\",\n \"swap_curve.plot(\\n\",\n \" figsize=(12, 8),\\n\",\n \" title='USD Swap Curve Before and After {}bp bull Steepening Shock'.format(bull_steepener_scenario.curve_shift),\\n\",\n \")\\n\",\n \"plt.xlabel('Tenor (years)')\\n\",\n \"plt.ylabel('bp')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010404_bear_flattener_curve_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveScenario, MarketDataPattern\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.USD, expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"original_price = swaption.price()\\n\",\n \"# retrieve the market data our instrument is sensitive to.\\n\",\n \"market_data = swaption.market().market_data_dict\\n\",\n \"print('Base price: {:,.2f}'.format(original_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Pivot point is the tenor at which curve shift =0 and influences the type and shape of curve shift\\n\",\n \"# Price the swaption under a bear flattener scenario of -10bp\\n\",\n \"bear_flattener_scenario = CurveScenario(\\n\",\n \" market_data_pattern=MarketDataPattern('IR', 'USD'), curve_shift=-10, tenor_start=15, tenor_end=25, pivot_point=25\\n\",\n \")\\n\",\n \"\\n\",\n \"with bear_flattener_scenario:\\n\",\n \" swaption_bear_flattener = swaption.price()\\n\",\n \" market_data_bear_flattener = swaption.market().market_data_dict\\n\",\n \"\\n\",\n \"print('Price under bear flattener curve shift: {:,.2f}'.format(swaption_bear_flattener))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compare swap rate market data coordinates before and after curve scenario shock\\n\",\n \"market_data_df = pd.DataFrame(\\n\",\n \" [\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data_bear_flattener.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" ],\\n\",\n \" index=['Values', 'Shocked values'],\\n\",\n \").transpose()\\n\",\n \"market_data_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plotting swap rate market data before and after curve scenario shock\\n\",\n \"swap_curve = pd.DataFrame.from_dict(\\n\",\n \" {int(''.join(list(filter(str.isdigit, str(v))))): market_data_df.loc[v] for v in market_data_df.index},\\n\",\n \" orient='index',\\n\",\n \")\\n\",\n \"\\n\",\n \"swap_curve['Shock'] = swap_curve['Shocked values'] - swap_curve['Values']\\n\",\n \"swap_curve.plot(\\n\",\n \" figsize=(12, 8),\\n\",\n \" title='USD Swap Curve Before and After {}bp Bear flattening Shock'.format(bear_flattener_scenario.curve_shift),\\n\",\n \")\\n\",\n \"plt.xlabel('Tenor (years)')\\n\",\n \"plt.ylabel('bp')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010405_bull_flattener_curve_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveScenario, MarketDataPattern\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption(PayReceive.Receive, '5y', Currency.USD, expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"original_price = swaption.price()\\n\",\n \"# retrieve the market data our instrument is sensitive to.\\n\",\n \"market_data = swaption.market().market_data_dict\\n\",\n \"print('Base price: {:,.2f}'.format(original_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Pivot point is the tenor at which curve shift =0 and influences the type and shape of curve shift\\n\",\n \"# Price the swaption under a bull flattener scenario of -10bp\\n\",\n \"bull_flattener_scenario = CurveScenario(\\n\",\n \" market_data_pattern=MarketDataPattern('IR', 'USD'), curve_shift=-10, tenor_start=15, tenor_end=25, pivot_point=15\\n\",\n \")\\n\",\n \"\\n\",\n \"with bull_flattener_scenario:\\n\",\n \" swaption_bull_flattener = swaption.price()\\n\",\n \" market_data_bull_flattener = swaption.market().market_data_dict\\n\",\n \"\\n\",\n \"print('Price under bull flattener curve shift: {:,.2f}'.format(swaption_bull_flattener))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Compare swap rate market data coordinates before and after curve scenario shock\\n\",\n \"market_data_df = pd.DataFrame(\\n\",\n \" [\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" {\\n\",\n \" mkt_data: value * 1e4\\n\",\n \" for mkt_data, value in market_data_bull_flattener.items()\\n\",\n \" if (mkt_data.mkt_type == \\\"IR\\\" and mkt_data.mkt_asset == \\\"USD\\\" and mkt_data.mkt_class == \\\"SWAP OIS\\\")\\n\",\n \" },\\n\",\n \" ],\\n\",\n \" index=['Values', 'Shocked values'],\\n\",\n \").transpose()\\n\",\n \"market_data_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plotting swap rate market data before and after curve scenario shock\\n\",\n \"swap_curve = pd.DataFrame.from_dict(\\n\",\n \" {int(''.join(list(filter(str.isdigit, str(v))))): market_data_df.loc[v] for v in market_data_df.index},\\n\",\n \" orient='index',\\n\",\n \")\\n\",\n \"\\n\",\n \"swap_curve['Shock'] = swap_curve['Shocked values'] - swap_curve['Values']\\n\",\n \"swap_curve.plot(\\n\",\n \" figsize=(12, 8),\\n\",\n \" title='USD Swap Curve Before and After {}bp bull flattening Shock'.format(bull_flattener_scenario.curve_shift),\\n\",\n \")\\n\",\n \"plt.xlabel('Tenor (years)')\\n\",\n \"plt.ylabel('bp')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/05_market_override/010501_eq_spot_override.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import OptionType, OptionStyle\\n\",\n \"from gs_quant.instrument import EqOption\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eq_spot_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern('Eq', '.STOXX50E', 'Reference Spot', mkt_quoting_style='Price'): MarketDataShock(\\n\",\n \" MarketDataShockType.Override, 3800\\n\",\n \" )\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"option = EqOption(\\n\",\n \" '.STOXX50E', expirationDate='3m', strikePrice='ATM', optionType=OptionType.Call, optionStyle=OptionStyle.European\\n\",\n \")\\n\",\n \"\\n\",\n \"with eq_spot_scenario:\\n\",\n \" option_spot_scenario_price = option.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"print('Base price: {:,.2f}'.format(option.price()))\\n\",\n \"print('Spot scenario price: {:,.2f}'.format(option_spot_scenario_price))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/05_market_override/010502_eq_future_override.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import OptionType, OptionStyle\\n\",\n \"from gs_quant.instrument import EqOption\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.risk import MarketDataShockBasedScenario, MarketDataPattern, MarketDataShock, MarketDataShockType\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"eq_future_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern(\\n\",\n \" 'Eq Future', '.STOXX50E', 'Reference Spot', ['2021-12-17'], mkt_quoting_style='Price'\\n\",\n \" ): MarketDataShock(MarketDataShockType.Override, 3800)\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"option = EqOption(\\n\",\n \" '.STOXX50E', expirationDate='3m', strikePrice='ATM', optionType=OptionType.Call, optionStyle=OptionStyle.European\\n\",\n \")\\n\",\n \"\\n\",\n \"with eq_future_scenario:\\n\",\n \" option_future_scenario_price = option.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"print('Base price: {:,.2f}'.format(option.price()))\\n\",\n \"print('Future scenario price: {:,.2f}'.format(option_future_scenario_price))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/06_vol_market_override/010601_eq_vol_override_eod.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import OptionType, OptionStyle\\n\",\n \"from gs_quant.instrument import EqOption\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.markets.securities import AssetIdentifier\\n\",\n \"from gs_quant.risk.scenario_utils import *\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.target.common import UnderlierType\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Construct a vol override scenario.\\n\",\n \"\\n\",\n \"# This example uses a sample vol dataset EDRVOL_PERCENT_EXPIRY_PREMIUM_SAMPLE\\n\",\n \"# Full dataset and additional info available in the data catalog\\n\",\n \"# https://marquee.gs.com/s/developer/datasets/EDRVOL_PERCENT_EXPIRY_PREMIUM\\n\",\n \"\\n\",\n \"eq_vol_scenario = build_eq_vol_scenario_eod(\\n\",\n \" 'TMK UN',\\n\",\n \" 'EDRVOL_PERCENT_EXPIRY_PREMIUM_SAMPLE',\\n\",\n \" vol_date=date(2019, 6, 3),\\n\",\n \" asset_name_type=AssetIdentifier.BLOOMBERG_ID,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define an option and price with and without the vol override scenario\\n\",\n \"\\n\",\n \"option = EqOption(\\n\",\n \" 'TMK UN',\\n\",\n \" underlierType=UnderlierType.BBID,\\n\",\n \" expirationDate='3m',\\n\",\n \" strikePrice=95,\\n\",\n \" optionType=OptionType.Call,\\n\",\n \" optionStyle=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"with PricingContext(date(2019, 6, 10)):\\n\",\n \" historic_option_price = option.price()\\n\",\n \"\\n\",\n \"with PricingContext(date(2019, 6, 10)), eq_vol_scenario:\\n\",\n \" historic_option_vol_scenario_price = option.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"\\n\",\n \"print('Base price: {:,.4f}'.format(historic_option_price.result()))\\n\",\n \"print('Scenario price: {:,.4f}'.format(historic_option_vol_scenario_price.result()))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/06_vol_market_override/010601_eq_vol_override_intraday.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import OptionType, OptionStyle\\n\",\n \"from gs_quant.instrument import EqOption\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.markets.securities import AssetIdentifier\\n\",\n \"from gs_quant.risk.scenario_utils import *\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.target.common import UnderlierType\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# construct a vol override scenario.\\n\",\n \"\\n\",\n \"# This example uses a sample vol dataset EDRVOL_PERCENT_EXPIRY_INTRADAY_PREMIUM_SAMPLE\\n\",\n \"# Full dataset and additional info available in the data catalog:\\n\",\n \"# https://marquee.gs.com/s/developer/datasets/EDRVOL_PERCENT_EXPIRY_INTRADAY_PREMIUM\\n\",\n \"\\n\",\n \"eq_vol_scenario = build_eq_vol_scenario_intraday(\\n\",\n \" 'TMK UN',\\n\",\n \" 'EDRVOL_PERCENT_EXPIRY_INTRADAY_PREMIUM_SAMPLE',\\n\",\n \" start_time=datetime(2019, 6, 3, 0, 0, 0),\\n\",\n \" end_time=datetime(2019, 6, 4, 0, 0, 0),\\n\",\n \" asset_name_type=AssetIdentifier.BLOOMBERG_ID,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define an option and price with and without the vol override scenario\\n\",\n \"\\n\",\n \"option = EqOption(\\n\",\n \" 'TMK UN',\\n\",\n \" underlierType=UnderlierType.BBID,\\n\",\n \" expirationDate='3m',\\n\",\n \" strikePrice='ATM',\\n\",\n \" optionType=OptionType.Call,\\n\",\n \" optionStyle=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"with PricingContext(date(2019, 6, 10)):\\n\",\n \" historic_option_price = option.price()\\n\",\n \"\\n\",\n \"with PricingContext(date(2019, 6, 10)), eq_vol_scenario:\\n\",\n \" historic_option_vol_scenario_price = option.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"\\n\",\n \"print('Base price: {:,.2f}'.format(historic_option_price.result()))\\n\",\n \"print('Scenario price: {:,.2f}'.format(historic_option_vol_scenario_price.result()))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/07_curve_overlay/010700_discount_curve_overlay.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Curve Overlay Context\\n\",\n \"Scenario contexts enable the users to price and calculate risk using their own curves.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveOverlay\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from datetime import datetime\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create and price a swaption\\n\",\n \"swaption = IRSwaption('Pay', '5y', 'USD', expiration_date='3m')\\n\",\n \"base_price = swaption.price()\\n\",\n \"print('Original price: {:,.2f}'.format(base_price))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Curve Overlay Scenario \\n\",\n \"A predefined scenario used to overlay discount curve or index curve. This allows the user to use customized discount factors to overwrite existing curves on graph.\\n\",\n \"* discount_factors - Discount factors\\n\",\n \"* dates - Dates of the discount factors.\\n\",\n \"* curve_type - Discount curve or index curve.\\n\",\n \"* rate_option - Rate option of the index curve.\\n\",\n \"* tenor - Tenor of the index curve.\\n\",\n \"* csa_term - The funding scenario of the curve.\\n\",\n \"* denominated - The denominated currency of the curve.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant.common import MarketBehaviour\\n\",\n \"\\n\",\n \"# Read discount factors from a csv file\\n\",\n \"df = pd.read_csv(r'CurveExample.csv', sep=\\\",\\\")\\n\",\n \"dates = df.MaturityDate.tolist()\\n\",\n \"dates_reformat = [datetime.strptime(date, \\\"%d-%b-%Y\\\").strftime(\\\"%Y-%m-%d\\\") for date in dates]\\n\",\n \"discount_factors = df.DiscountFactor.tolist()\\n\",\n \"\\n\",\n \"curve_overlay_scenario = CurveOverlay(\\n\",\n \" dates=dates_reformat,\\n\",\n \" discount_factors=discount_factors,\\n\",\n \" denominated=\\\"USD\\\",\\n\",\n \" csa_term=\\\"USD-SOFR\\\",\\n\",\n \" curve_type=\\\"Discount Curve\\\",\\n\",\n \")\\n\",\n \"with PricingContext(market_behaviour=MarketBehaviour.Calibrated):\\n\",\n \" with curve_overlay_scenario:\\n\",\n \" price_with_overlay = swaption.price()\\n\",\n \"p = price_with_overlay.result()\\n\",\n \"print('Scenario price: {:,.2f}'.format(p))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/07_curve_overlay/CurveExample.csv", "content": "Curve,CloseDate,MaturityDate,DiscountFactor\nUSD_SOFR_EOD,5/12/2021,13-May-2021,0.999999722\nUSD_SOFR_EOD,5/12/2021,14-May-2021,0.999999444\nUSD_SOFR_EOD,5/12/2021,17-May-2021,0.999998794\nUSD_SOFR_EOD,5/12/2021,18-May-2021,0.999998634\nUSD_SOFR_EOD,5/12/2021,19-May-2021,0.999998496\nUSD_SOFR_EOD,5/12/2021,20-May-2021,0.999998379\nUSD_SOFR_EOD,5/12/2021,21-May-2021,0.999998278\nUSD_SOFR_EOD,5/12/2021,24-May-2021,0.999997972\nUSD_SOFR_EOD,5/12/2021,25-May-2021,0.99999781\nUSD_SOFR_EOD,5/12/2021,26-May-2021,0.999997585\nUSD_SOFR_EOD,5/12/2021,27-May-2021,0.999997281\nUSD_SOFR_EOD,5/12/2021,28-May-2021,0.999996878\nUSD_SOFR_EOD,5/12/2021,31-May-2021,0.999995114\nUSD_SOFR_EOD,5/12/2021,1-Jun-2021,0.999994438\nUSD_SOFR_EOD,5/12/2021,2-Jun-2021,0.999993771\nUSD_SOFR_EOD,5/12/2021,3-Jun-2021,0.999993145\nUSD_SOFR_EOD,5/12/2021,4-Jun-2021,0.99999259\nUSD_SOFR_EOD,5/12/2021,7-Jun-2021,0.999991475\nUSD_SOFR_EOD,5/12/2021,8-Jun-2021,0.999991248\nUSD_SOFR_EOD,5/12/2021,9-Jun-2021,0.999991072\nUSD_SOFR_EOD,5/12/2021,10-Jun-2021,0.999990934\nUSD_SOFR_EOD,5/12/2021,11-Jun-2021,0.99999082\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/08_multi_scenario/010801_multi_scenario_shock.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"da2a5ed8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import CurveScenario, MarketDataPattern, MultiScenario\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"71b4f043\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2bf27b8b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption = IRSwaption('Receive', '5y', 'USD', expiration_date='13m', strike='atm')\\n\",\n \"swaption.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5956a6da\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"original_price = swaption.price()\\n\",\n \"market_data = swaption.market().market_data_dict\\n\",\n \"print('Base price: {:,.2f}'.format(original_price))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"88163749\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create multiple curve scenarios (Multi Scenario only supports a list of the same type of scenario)\\n\",\n \"shifts = [5, 10, 15]\\n\",\n \"list_of_scenarios = [\\n\",\n \" CurveScenario(market_data_pattern=MarketDataPattern('IR', 'USD'), parallel_shift=p, name=f\\\"parallel shift {p}\\\")\\n\",\n \" for p in shifts\\n\",\n \"]\\n\",\n \"multi_scenario = MultiScenario(scenarios=list_of_scenarios)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"159cd685\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Price the swaption under a multiple curve scenarios\\n\",\n \"with multi_scenario:\\n\",\n \" scenario_price = swaption.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c0c69ce2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"scenario_price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"946b62d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010901_fx_override_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ce061973367468ad\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.common import MeasureScenario\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"from gs_quant import risk\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8b13e419dcb97542\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-07-10T09:24:18.566753800Z\",\n \"start_time\": \"2025-07-10T09:24:12.780395500Z\"\n },\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2e9968f7e25e8115\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"source\": [\n \"MeasureScenario class allows us to override inputs such as spot or volatility when pricing an instrument. Here are some examples of how to use it with FX Options.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1a647b948e0ccc30\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Initial FX Option\\n\",\n \"fx_option = FXOption(pair='EURJPY', expiration_date='3m', strike_price='185')\\n\",\n \"base_measures = fx_option.calc((risk.FairPremiumInPercent, risk.FXAnnualImpliedVol, risk.FXSpot))\\n\",\n \"base_measures.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"952616a7c77ff7c2\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Override a single input (Spot/Strike Price) with MeasureScenario context manager\\n\",\n \"with MeasureScenario(FXSpot, 180):\\n\",\n \" scenario_measures = fx_option.calc((risk.FairPremiumInPercent, risk.FXAnnualImpliedVol, risk.FXSpot))\\n\",\n \"scenario_measures.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bec831f54e74bd45\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"source\": [\n \"We can see that the FXSpot value was overridden, and other variables are re-calculated to account for the new spot rate.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d5aebf58ca8fa787\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Override multiple inputs (Spot rate and Price) with MeasureScenario context manager\\n\",\n \"with MeasureScenario(FXSpot, 180), MeasureScenario(FXAnnualImpliedVol, 0.08):\\n\",\n \" multi_scenario_measures = fx_option.calc((risk.FairPremiumInPercent, risk.FXAnnualImpliedVol, risk.FXSpot))\\n\",\n \"multi_scenario_measures.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"85d3535611a8379\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"source\": [\n \"We can also combine the measure override with other context managers, such as PricingContext\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3fdad1581e368a05\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Set up time context for pricing, and override the input\\n\",\n \"with PricingContext(dt.date(2025, 7, 5)):\\n\",\n \" with MeasureScenario(FXSpot, 170):\\n\",\n \" historic_scen_measures = fx_option.calc((risk.FairPremiumInPercent, risk.FXAnnualImpliedVol, risk.FXSpot))\\n\",\n \"historic_scen_measures.result().to_frame()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010902_rates_override_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"ce061973367468ad\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-07-10T09:28:07.778220600Z\",\n \"start_time\": \"2025-07-10T09:28:07.757061300Z\"\n },\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import IRAnnualImpliedVol, Price, IRFwdRate, IRAnnualATMImpliedVol, IRDelta\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.common import MeasureScenario\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"8b13e419dcb97542\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-07-10T09:28:14.582241900Z\",\n \"start_time\": \"2025-07-10T09:28:08.144756700Z\"\n },\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2e9968f7e25e8115\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"MeasureScenario class allows us to override inputs such as spot or volatility when pricing an instrument. Here are some examples of how to use it with Interest rate swaption.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1a647b948e0ccc30\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Initial IRSwaption\\n\",\n \"swaption = IRSwaption(notional_currency='EUR', effective_date='1y', termination_date='10y')\\n\",\n \"swaption.calc((IRAnnualImpliedVol, Price, IRFwdRate, IRAnnualATMImpliedVol)).to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"952616a7c77ff7c2\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Override a single input with MeasureScenario context manager\\n\",\n \"with MeasureScenario(IRFwdRate, 0.024):\\n\",\n \" risk = swaption.calc((IRAnnualImpliedVol, Price, IRFwdRate, IRAnnualATMImpliedVol))\\n\",\n \"risk.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bec831f54e74bd45\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"We can see that the Interest rate forward rate value was overridden, and other variables are re-calculated to account for the new rate.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e566074c80751e50\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"We might also be interested in comparing the forward rate between the original, up, and down override scenarios.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"72d934f4c7eaa031\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"init = swaption.calc((IRDelta(aggregation_level='Type'), Price, IRFwdRate))\\n\",\n \"init.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"b9f77b4037c3b693\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-07-10T09:28:32.540887600Z\",\n \"start_time\": \"2025-07-10T09:28:29.404805700Z\"\n },\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"with MeasureScenario(IRFwdRate, init[IRFwdRate] + 0.0001):\\n\",\n \" up = swaption.calc((Price, IRFwdRate))\\n\",\n \"\\n\",\n \"with MeasureScenario(IRFwdRate, init[IRFwdRate] - 0.0001):\\n\",\n \" down = swaption.calc((Price, IRFwdRate))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f5839afc1037abd1\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"print(init[IRFwdRate])\\n\",\n \"print(up[IRFwdRate])\\n\",\n \"print(down[IRFwdRate])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d24dcf9e6bef345f\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"The forward rates are close in 3 scenarios, which is expected as the swaption expiry date is in 1 year only. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b1e9c04ed2bb53be\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"We can also override multiple inputs.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2e7cec45c7b0756a\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Override multiple inputs (with nested context managers)\\n\",\n \"with MeasureScenario(IRFwdRate, 0.028):\\n\",\n \" with MeasureScenario(IRAnnualImpliedVol, 0.009):\\n\",\n \" risk = swaption.calc((IRAnnualImpliedVol, Price, IRFwdRate, IRAnnualATMImpliedVol))\\n\",\n \"risk.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"52670824ba328019\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"We can also combine the measure override with other context managers, such as PricingContext\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a36367b5758d79fd\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Combine with PricingContext context manager\\n\",\n \"with PricingContext(dt.date(2025, 7, 5)):\\n\",\n \" with MeasureScenario(IRAnnualImpliedVol, 0.002):\\n\",\n \" risk = swaption.calc((IRAnnualImpliedVol, Price, IRFwdRate, IRAnnualATMImpliedVol))\\n\",\n \"risk.result().to_frame()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010903_eq_override_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"ce061973367468ad\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-07-09T16:53:54.617771Z\",\n \"start_time\": \"2025-07-09T16:53:46.811176300Z\"\n },\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import EqOption\\n\",\n \"from gs_quant.risk import EqAnnualImpliedVol, Price, EqSpot, EqDelta\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.common import MeasureScenario\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"8b13e419dcb97542\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2025-07-09T16:54:13.152886800Z\",\n \"start_time\": \"2025-07-09T16:54:04.718992900Z\"\n },\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2e9968f7e25e8115\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"MeasureScenario class allows us to override inputs such as spot or volatility when pricing an instrument. Here are some examples of how to use it with FX Options.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1a647b948e0ccc30\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Initial FX Option\\n\",\n \"eq_option = EqOption('.SPX')\\n\",\n \"init_eq_option = eq_option.calc((EqSpot, EqDelta, Price))\\n\",\n \"init_eq_option.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"353a1c995d13edc9\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"We can override inputs such as spot or volatility when pricing an instrument. Here are some examples of how to use it with Equities Options. We might be also interested in comparing delta calculated by the system and the approximation derived from the price change. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"952616a7c77ff7c2\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Override a single input (Spot) with MeasureScenario context manager\\n\",\n \"with MeasureScenario(EqSpot, init_eq_option[EqSpot] * 1.01):\\n\",\n \" eq_risks_up = eq_option.calc((EqSpot, EqDelta, Price))\\n\",\n \"eq_risks_up.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"68bc3ab1301e268a\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"with MeasureScenario(EqSpot, init_eq_option[EqSpot] * 0.99):\\n\",\n \" eq_risks_down = eq_option.calc((EqSpot, EqDelta, Price))\\n\",\n \"eq_risks_down.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fa5824ef58b1450d\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"print(init_eq_option[EqDelta] / 100)\\n\",\n \"print(((eq_risks_up[Price] - eq_risks_down[Price]) / 2))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bec831f54e74bd45\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"We can see that the delta values were close, which is expected.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6230da08bd5b48b9\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"We can override multiple inputs this way.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d5aebf58ca8fa787\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Override multiple inputs (Spot rate and Annual Implied Volatility) with MeasureScenario context manager and return multiple outputs\\n\",\n \"with MeasureScenario(EqSpot, 6000):\\n\",\n \" with MeasureScenario(EqAnnualImpliedVol, 0.2):\\n\",\n \" eq_risks = eq_option.calc((Price, EqAnnualImpliedVol, EqSpot))\\n\",\n \"eq_risks.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"85d3535611a8379\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"We can also combine the measure override with other context managers, such as PricingContext\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3fdad1581e368a05\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Set up time context for pricing, and override the input\\n\",\n \"with PricingContext(dt.date(2025, 7, 5)):\\n\",\n \" with MeasureScenario(EqAnnualImpliedVol, 0.2):\\n\",\n \" eq_risks = eq_option.calc((Price, EqAnnualImpliedVol, EqSpot))\\n\",\n \"eq_risks.result().to_frame()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/tutorials/Pricing_Context.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. External clients need to substitute thier own client id and client secret below. Please refer to Sessions for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What is Pricing Context\\n\",\n \"\\n\",\n \"[`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) is a class used for controlling pricing, market data and computation behavior when pricing instruments and calculating risk.\\n\",\n \"It can be used to provide a common context which can be reused for a number of different data access or manipulation functions.\\n\",\n \"\\n\",\n \"More specifically, [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) can be used to define pricing date, date for sourcing market data, market data location as well as whether the calculation should be processed\\n\",\n \"asynchronously or batched.\\n\",\n \"\\n\",\n \"[`HistoricalPricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.HistoricalPricingContext.html) is also available to produce valuations over multiple dates. Both [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) and [`HistoricalPricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.HistoricalPricingContext.html) are distinct from the `MarketDataContext` which provides a market state as of a given date.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Evaluating Pricing and Risk Using A Context\\n\",\n \"\\n\",\n \"Pricing date is used to compute the expiration date and discounting rules for a derivative instrument. For example, the expiration date of a 1-month forward will be 1 month from the current pricing date.\\n\",\n \"`pricing_date` is different from `market_data_as_of` which is the date for sourcing market data and is defaulted to 1 business day before `pricing_date`.\\n\",\n \"\\n\",\n \" For information on how to define an instrument and compute price and risk for it, please refer to Instruments and Measures, respectively, for details. \\n\",\n \"\\n\",\n \"Let's import [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) and examine the current pricing date. It is defaulted to today when pricing an instrument.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import PricingContext\\n\",\n \"\\n\",\n \"PricingContext.current.pricing_date\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you wish to change the default behaviour, you can change the default pricing context:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"\\n\",\n \"PricingContext.current = PricingContext(\\n\",\n \" pricing_date=business_day_offset(dt.date.today(), -1, roll='preceding'), market_data_location='NYC'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We define an [`IRSwaption`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.instrument.IRSwaption.html#gs_quant.instrument.IRSwaption/) below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.common import Currency, PayReceive\\n\",\n \"\\n\",\n \"swaption = IRSwaption(\\n\",\n \" PayReceive.Receive, '5y', Currency.USD, expiration_date='13m', strike='atm+40', notional_amount=1e8\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) may also be used as a context manager, to temporarily change pricing parameters for the scope of a computation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"custom_date = dt.date(2019, 5, 31)\\n\",\n \"\\n\",\n \"with PricingContext(pricing_date=custom_date):\\n\",\n \" swaption.resolve()\\n\",\n \" price_f = swaption.price()\\n\",\n \"\\n\",\n \"print(swaption.as_dict())\\n\",\n \"print(price_f.result())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that using a [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) as a context manager has two extra effects:\\n\",\n \"\\n\",\n \"1. All calls to [`price()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.price), [`calc()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.calc) are dispatched as a single request, on context manager exit. This allows for the communication overhead to be borne only once for multiple calculations.\\n\",\n \"2. The results of these calls will be futures, whose result is the original data type. Thus, `calc(risk.IRDelta)` will return a future whose result will be a pandas DataFrame and\\n\",\n \"[`dollar_price()`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable.dollar_price), will return a future whose result will be a float.\\n\",\n \"\\n\",\n \"PricingContext has optional parameters of `is_async` and `is_batch`, which are discussed [below](#Controlling-Computation-Behavior).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Historical Pricing Context\\n\",\n \"\\n\",\n \"[`HistoricalPricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.HistoricalPricingContext.html) can be used to evaluate instruments for a range of parameters, like the date range below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"import gs_quant.risk as risk\\n\",\n \"\\n\",\n \"start_date = dt.date(2019, 5, 1)\\n\",\n \"end_date = dt.date(2019, 5, 31)\\n\",\n \"\\n\",\n \"with HistoricalPricingContext(start_date, end_date):\\n\",\n \" swaption_vega_f = swaption.calc(risk.IRVega)\\n\",\n \"\\n\",\n \"swaption_vega = swaption_vega_f.result()\\n\",\n \"swaption_vega.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"## Controlling Computation Behavior\\n\",\n \"\\n\",\n \"There are two parameters of [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) which determine how the calculation call should be made: `is_async` and `is_batch`. There are several considerations when deciding what the preferred behavior is.\\n\",\n \"\\n\",\n \"### Async\\n\",\n \"\\n\",\n \"`is_async` determines whether the request is processed asynchronously. This allows a unit of work to run separately from the primary application thread.\\n\",\n \"\\n\",\n \"If `False` (the default) the `__exit__` method of [`PricingContext`](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.PricingContext.html) will block until the results are returned. If `True`, it will return immediately and the user should check the status of the returned futures, or add a callback to them in order to handle results.\\n\",\n \"\\n\",\n \"Setting `is_async=True` allows other work to be done while waiting for calculation results. It is best used for processing independent data; asynchronously updating records that are dependent or depended upon may lead to unexpected results.\\n\",\n \"\\n\",\n \"### Batch\\n\",\n \"\\n\",\n \"Calculation requests are handled by HTTP calls. The gateway which processes these calls has a maximum timeout of 3 minutes.\\n\",\n \"For calculations that are expected to last longer than 3 minutes, `is_batch` should be set to `True`.\\n\",\n \"In this mode, the computation is run asynchronously on Goldman Sachs' servers and a thread is started to listen for the results.\\n\",\n \"\\n\",\n \"`is_batch` is independent of `is_async` and may be used with `is_async` set either to `True` or `False`.\\n\",\n \"\\n\",\n \"Currently `is_batch=True` adds a small overhead to calculations. This will be eliminated in the future, at which point we will likely to make all calculations execute this way, and eliminate the `is_batch` option.\\n\",\n \"\\n\",\n \"\\n\",\n \"### Pricing Context with Async and Batch\\n\",\n \"\\n\",\n \"In the example below we will examine the behavior of async and batch. We will price and calculate vega for an Interest Rate Swaption over May 2019 and do\\n\",\n \"some 'work' while waiting for the result. Based on the output, one can see that some useful work can be done by setting `is_async=True` but changing `is_batch=True` doesn't\\n\",\n \"offer any additional speed-up.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from time import sleep\\n\",\n \"\\n\",\n \"# create swaption\\n\",\n \"swaption = IRSwaption(\\n\",\n \" PayReceive.Receive, '5y', Currency.USD, expiration_date='13m', strike='atm+40', notional_amount=1e8\\n\",\n \")\\n\",\n \"\\n\",\n \"start_date = dt.date(2019, 5, 1)\\n\",\n \"end_date = dt.date(2019, 5, 31)\\n\",\n \"\\n\",\n \"# price over date range with both async and batch = True\\n\",\n \"with HistoricalPricingContext(start_date, end_date, is_async=True, is_batch=True):\\n\",\n \" swaption_price = swaption.price()\\n\",\n \" swaption_vega = swaption.calc(risk.IRVega)\\n\",\n \"\\n\",\n \"# Do some work while waiting for results. All futures will be completed on exit of PricingContext\\n\",\n \"n = 0\\n\",\n \"while not swaption_price.done():\\n\",\n \" print(n)\\n\",\n \" n += 1\\n\",\n \" sleep(1)\\n\",\n \"\\n\",\n \"swaption_prices = swaption_price.result()\\n\",\n \"swaption_vegas = swaption_vega.result()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Disclaimer\\n\",\n \"This website may contain links to websites and the content of third parties (\\\"Third Party Content\\\"). We do not monitor, review or update, and do not have any control over, any Third Party Content or third party websites. We make no representation, warranty or guarantee as to the accuracy, completeness, timeliness or reliability of any Third Party Content and are not responsible for any loss or damage of any sort resulting from the use of, or for any failure of, products or services provided at or from a third party resource. If you use these links and the Third Party Content, you acknowledge that you are doing so entirely at your own risk.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/01_scenarios_and_contexts/tutorials/Scenarios.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Scenario Context\\n\",\n \"Scenario contexts enable the user to price and calculate risk under varying market states and pricing environments.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import (\\n\",\n \" MarketDataPattern,\\n\",\n \" MarketDataShock,\\n\",\n \" MarketDataShockType,\\n\",\n \" MarketDataShockBasedScenario,\\n\",\n \" RollFwd,\\n\",\n \" CurveScenario,\\n\",\n \" IndexCurveShift,\\n\",\n \")\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create and price a swaption\\n\",\n \"swaption = IRSwaption('Pay', '5y', 'USD', expiration_date='3m')\\n\",\n \"base_price = swaption.price()\\n\",\n \"base_price\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### MarketDataShockBasedScenario\\n\",\n \"Allows the user to create a bespoke market data shock\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"MarketDataShockBasedScenario?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Scenario Creation: Shock all points on the Vol Curve by 1bp\\n\",\n \"ir_vol_scenario = MarketDataShockBasedScenario(\\n\",\n \" shocks={MarketDataPattern('IR Vol'): MarketDataShock(MarketDataShockType.Absolute, 1e-4)}\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Price swaption under scenario\\n\",\n \"with ir_vol_scenario:\\n\",\n \" scenario_price = swaption.price()\\n\",\n \"\\n\",\n \"scenario_price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Swaption Price Comparison: Base vs. Shocked Vol Curves\\n\",\n \"diff = scenario_price - base_price\\n\",\n \"diff\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Comparing Parallel Bump Scenario w/ Vega\\n\",\n \"from gs_quant.risk import IRVegaParallel\\n\",\n \"\\n\",\n \"vega = swaption.calc(IRVegaParallel)\\n\",\n \"vega\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Curve Scenario \\n\",\n \"A predefined scenario used to modify the shape of the curve with bespoke transformations - by applying both parallel and slope shifts. \\n\",\n \"* market_data_pattern - Market pattern for matching curve assets (required parameter)\\n\",\n \"* parallel_shift - A constant (X bps) which shifts all points by the same amount\\n\",\n \"* curve_shift - A double which represents the net rate change (X bps) between tenorStart and tenorEnd\\n\",\n \"* pivot_point – The tenor in years (float) at which there is zero rate change, which is between tenor_start and tenor_end inclusive, informing the type of curve shift. If not specified, pivot_point is the midpoint of tenor_start and tenor_end\\n\",\n \"* tenor_start – The tenor, in years, (float) which is the starting point of the curve shift (required parameter is curve_shift is specified)\\n\",\n \"* tenor_end – The tenor, in years, (float) which is the end point of the curve shift (required parameter is curve_shift is specified)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"CurveScenario?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Scenario Creation: modify the Swap Curve by a 5bp parallel shift, 1bp slope shift pivoted at 5y point (up to 50y)\\n\",\n \"curve_scenario = CurveScenario(\\n\",\n \" market_data_pattern=MarketDataPattern('IR', 'USD'),\\n\",\n \" parallel_shift=5,\\n\",\n \" curve_shift=1,\\n\",\n \" pivot_point=5,\\n\",\n \" tenor_end=50,\\n\",\n \" tenor_start=0,\\n\",\n \")\\n\",\n \"\\n\",\n \"with curve_scenario:\\n\",\n \" swaption_scenario_price = swaption.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"print('Base price: {:,.2f}'.format(base_price))\\n\",\n \"print('Scenario price: {:,.2f}'.format(swaption_scenario_price))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### RollFwd Scenario\\n\",\n \"A predefined scenario used to evolve market data and trades over a period of time\\n\",\n \"* date - Absolute or Relative Date to shift markets to\\n\",\n \"* realise_fwd - Roll along the forward curve or roll in spot space\\n\",\n \"* holiday_calendar - Calendar to use if relative date is specified in the date parameter\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"RollFwd?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# RollFwd Scenario - Roll forward 1 month\\n\",\n \"base_price = swaption.price()\\n\",\n \"with RollFwd(date='1m', holiday_calendar='NYC', realise_fwd=False):\\n\",\n \" fwd_price = swaption.price()\\n\",\n \"\\n\",\n \"print('Base price: {:,.2f}'.format(base_price))\\n\",\n \"print('Scenario price: {:,.2f}'.format(fwd_price))\\n\",\n \"print('Diff: {:,.2f}'.format(fwd_price - base_price))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Index Curve Shift Scenario \\n\",\n \"A predefined scenario used to modify the shape of the index curve. This allows the user to easily shock the curve including parallel shift and slope shift. Users can even specify which part of the curve for parallel shift through custom bucket. \\n\",\n \"* market_data_pattern - Market pattern for matching curve assets\\n\",\n \"* rate_option - Rate option of the index curve.\\n\",\n \"* tenor - Tenor of the index curve.\\n\",\n \"* floor - Floor size in bps of the index curve.\\n\",\n \"* annualised_parallel_shift – Parallel shift in bps.\\n\",\n \"* annualised_slope_shift – Annual slope shift in bps with pivot = 0.\\n\",\n \"* cutoff – Cutoff time in years to stop applying the shift.\\n\",\n \"* bucket_start - Start date for the custom bucket.\\n\",\n \"* bucket_end - End date for the custom bucket.\\n\",\n \"* bucket_shift - Bucket shift in bps for the custom bucket.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"IndexCurveShift?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Scenario Creation: modify the index curve of this swaption by a 1bp parallel shift, 1bp slope shift, pivoted default to 0 year with floor value -1bp (up to 50y)\\n\",\n \"index_curve_shift = IndexCurveShift(\\n\",\n \" rate_option=\\\"USD-LIBOR-BBA\\\", tenor=\\\"3m\\\", annualised_parallel_shift=1, annualised_slope_shift=1, floor=-1, cutoff=50\\n\",\n \")\\n\",\n \"\\n\",\n \"with index_curve_shift:\\n\",\n \" swaption_scenario_price = swaption.price()\\n\",\n \"\\n\",\n \"# Look at the difference between scenario and base prices\\n\",\n \"print('Base price: {:,.2f}'.format(base_price))\\n\",\n \"print('Scenario price: {:,.2f}'.format(swaption_scenario_price))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/02_pricing_and_risk/External Demo v1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c6fc1426\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"63822e50\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create what if trade\\n\",\n \"\\n\",\n \"from gs_quant.instrument import FXOption, FXEuropeanKnockout\\n\",\n \"from gs_quant.markets.portfolio import Portfolio, Grid\\n\",\n \"from gs_quant.markets import PricingContext, LiveMarket\\n\",\n \"from gs_quant.risk import FXSpot, FXAnnualImpliedVol\\n\",\n \"import datetime as dt\\n\",\n \"import pprint\\n\",\n \"\\n\",\n \"pp = pprint.PrettyPrinter(indent=4)\\n\",\n \"\\n\",\n \"# shift+tab to preview what the parameters should be\\n\",\n \"leg = FXOption(\\n\",\n \" pair='EURUSD',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='25D',\\n\",\n \" notional_amount='50m',\\n\",\n \" premium=0,\\n\",\n \" option_type='Call',\\n\",\n \" buy_sell='Buy',\\n\",\n \")\\n\",\n \"pp.pprint(leg.as_dict())\\n\",\n \"\\n\",\n \"leg.price()\\n\",\n \"\\n\",\n \"# by default the pricing is done as of today with last nights close rolled to today\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6a273f03\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# if we wanted to price a trade historically in time we can do that using an historical pricing context\\n\",\n \"\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"\\n\",\n \"with HistoricalPricingContext(\\n\",\n \" start=dt.date(2025, 1, 3), end=dt.date(2025, 4, 1), visible_to_gs=True, market_data_location='NYC'\\n\",\n \"):\\n\",\n \" h = leg.price()\\n\",\n \"h.result().plot()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ecafb3e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# when we price an Spot+1% trade historically what does that mean. Well that depends on whether you have resolved the trade\\n\",\n \"# or not.\\n\",\n \"# If not then the trade will be moving in time, on each day a new 3m spot+1% option was priced. We may want to fix the\\n\",\n \"# trade as at the start date\\n\",\n \"\\n\",\n \"with PricingContext(pricing_date=dt.date(2025, 1, 3)):\\n\",\n \" leg2 = leg.resolve(in_place=False)\\n\",\n \"leg2 = leg2.result()\\n\",\n \"\\n\",\n \"# compare the unresolved trade to the resolved one.\\n\",\n \"pp.pprint(leg2.as_dict())\\n\",\n \"\\n\",\n \"\\n\",\n \"from gs_quant.risk import Price\\n\",\n \"\\n\",\n \"with HistoricalPricingContext(start=dt.date(2025, 1, 3), end=dt.date(2025, 4, 1)):\\n\",\n \" h2 = leg2.calc(Price)\\n\",\n \"h2.result().plot()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1cfeef04-117b-42e6-ab15-16b982e06d87\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We can price live by amending the pricing context as well\\n\",\n \"\\n\",\n \"with PricingContext(market=LiveMarket()):\\n\",\n \" s = leg.calc(FXSpot)\\n\",\n \"s.result()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7530294f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we have a concept of a portfolio which behaves similarly to a trade so you can pack many trades into one object and price that. Also shows how other metrics can be referenced and called.\\n\",\n \"\\n\",\n \"from gs_quant.common import InOut\\n\",\n \"from gs_quant.risk import FXQuotedDelta\\n\",\n \"\\n\",\n \"port = Portfolio(\\n\",\n \" [\\n\",\n \" FXEuropeanKnockout(\\n\",\n \" pair='GBPUSD',\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='1.25',\\n\",\n \" knock_in_or_out=InOut.Out,\\n\",\n \" barrier_level=f'{1.10 + x / 100}',\\n\",\n \" name=f'{x}',\\n\",\n \" )\\n\",\n \" for x in range(25, 35)\\n\",\n \" ]\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"print(len(port))\\n\",\n \"r = port.calc((FXQuotedDelta, FXAnnualImpliedVol))\\n\",\n \"r.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2f8da928-96b4-4881-a06f-571b31251fe0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# screeners are a popular usecase and there is a type of portfolio called a grid which can aid in construction of 2d grids of prices\\n\",\n \"\\n\",\n \"# grids take an example trade and then two parameters and the possible values of each\\n\",\n \"grid = Grid(FXOption(pair='EURUSD', premium=0), 'strike_price', ['10d', '20d'], 'expiration_date', ['1m', '3m', '6m'])\\n\",\n \"\\n\",\n \"r = grid.price()\\n\",\n \"r.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"573b76f2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can define and apply scenarios to our pricing\\n\",\n \"\\n\",\n \"from gs_quant.risk import MarketDataPattern, MarketDataShock, MarketDataShockType, MarketDataShockBasedScenario\\n\",\n \"\\n\",\n \"parallel_shock = MarketDataShockBasedScenario(\\n\",\n \" shocks={\\n\",\n \" MarketDataPattern(mkt_type='FX Vol', mkt_asset='USD/EUR', mkt_class='ATM VOL'): MarketDataShock(\\n\",\n \" MarketDataShockType.Absolute, 0.0001\\n\",\n \" )\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"with PricingContext(dt.date(2025, 2, 18)):\\n\",\n \" orig_price = leg.price()\\n\",\n \" with parallel_shock:\\n\",\n \" res = leg.price()\\n\",\n \"print(f'original price: {orig_price.result():.4f}')\\n\",\n \"print(f'shock price: {res.result():.4f}')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6ffe240d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# as well as pricing historically we can project the market forward and price in the future.\\n\",\n \"\\n\",\n \"from gs_quant.risk import RollFwd\\n\",\n \"\\n\",\n \"carry_scenario = RollFwd(date=dt.date(2025, 3, 15), realise_fwd=True)\\n\",\n \"with carry_scenario:\\n\",\n \" res = leg.price()\\n\",\n \"\\n\",\n \"print(f'roll fwd price: {res:.4f}')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e4672f11\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# sometimes you may want to look at the market data used in a pricing request.\\n\",\n \"\\n\",\n \"from gs_quant.markets import PricingContext\\n\",\n \"\\n\",\n \"with PricingContext(dt.date(2024, 4, 4)):\\n\",\n \" market = leg.market()\\n\",\n \"market.result().market_data_dict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"39bc9d3e\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"# loading from a dataset\\n\",\n \"\\n\",\n \"\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"\\n\",\n \"dataset = Dataset('MACRO_EVENTS_CALENDAR')\\n\",\n \"dataset.get_data(dt.datetime(2024, 12, 1), dt.datetime(2025, 12, 31), eventName='ISM Manufacturing PMI')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"82f92f0e-a699-4835-953e-8f0d0b8b9f67\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"irfwd_eur = Dataset('IR_SWAP_RATES').get_data(\\n\",\n \" dt.date(2025, 1, 3),\\n\",\n \" dt.date(2025, 1, 29),\\n\",\n \" currency='EUR',\\n\",\n \" effectiveTenor='1y',\\n\",\n \" terminationTenor='10y',\\n\",\n \" floatingRateOption='EUR-EURIBOR-TELERATE',\\n\",\n \" floatingRateDesignatedMaturity='6m',\\n\",\n \" clearingHouse='LCH',\\n\",\n \")\\n\",\n \"irfwd_eur['rate']\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/.ipynb_checkpoints/030000_create_portfolio-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ModuleNotFoundError\",\n \"evalue\": \"No module named 'gs_quant_analytics_internal'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mModuleNotFoundError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[1;32mC:\\\\Users\\\\kwokdo\\\\AppData\\\\Local\\\\Temp/ipykernel_1004/1815385159.py\\u001b[0m in \\u001b[0;36m\\u001b[1;34m\\u001b[0m\\n\\u001b[0;32m 9\\u001b[0m \\u001b[1;32mimport\\u001b[0m \\u001b[0mdatetime\\u001b[0m \\u001b[1;32mas\\u001b[0m \\u001b[0mdt\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 10\\u001b[0m \\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m---> 11\\u001b[1;33m \\u001b[1;32mfrom\\u001b[0m \\u001b[0mgs_quant_analytics_internal\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mcomponents\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfx\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mvanilla_contour_pricer\\u001b[0m \\u001b[1;32mimport\\u001b[0m \\u001b[0mGenericPayoffContourPricer\\u001b[0m\\u001b[1;33m,\\u001b[0m\\u001b[0mVanillaPayoffContourPricer\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[0;32m 12\\u001b[0m \\u001b[1;32mfrom\\u001b[0m \\u001b[0mgs_quant_analytics_internal\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mcomponents\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mfx\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mexotic_contour_pricer\\u001b[0m \\u001b[1;32mimport\\u001b[0m \\u001b[0mExoticPayoffContourPricer\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m 13\\u001b[0m \\u001b[1;32mfrom\\u001b[0m \\u001b[0mgs_quant_analytics_internal\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mcomponents\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mxasset\\u001b[0m\\u001b[1;33m.\\u001b[0m\\u001b[0mpayoff_contour_pricer\\u001b[0m \\u001b[1;32mimport\\u001b[0m \\u001b[0mPayoffContourPricerParams\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[1;33m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[1;31mModuleNotFoundError\\u001b[0m: No module named 'gs_quant_analytics_internal'\"\n ]\n }\n ],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"from gs_quant.markets import PricingContext,LiveMarket\\n\",\n \"from gs_quant.datetime import date_range\\n\",\n \"from gs_quant.markets import PricingContext,LiveMarket\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.risk.results import PricingFuture, PortfolioRiskResult\\n\",\n \"from gs_quant.target.common import MapParameter\\n\",\n \"from gs_quant_internal.boltweb import valuation\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant_analytics_internal.components.fx.vanilla_contour_pricer import GenericPayoffContourPricer,VanillaPayoffContourPricer\\n\",\n \"from gs_quant_analytics_internal.components.fx.exotic_contour_pricer import ExoticPayoffContourPricer\\n\",\n \"from gs_quant_analytics_internal.components.xasset.payoff_contour_pricer import PayoffContourPricerParams\\n\",\n \"from gs_quant_analytics_internal.components.xasset.payoff_contours import PayoffContours\\n\",\n \"from gs_quant.session import GsSession,Environment\\n\",\n \"from gs_quant.common import AssetClass\\n\",\n \"from gs_quant_analytics_internal.xasset.utils import builder_to_priceable_asset, quick_entry_to_portfolio\\n\",\n \"GsSession.use()\\n\",\n \"live_pricing = PricingContext(market=LiveMarket('LDN'),market_data_location='LDN',)\\n\",\n \"opt = tdapi.FXOptionBuilder(\\n\",\n \" over=\\\"JPY\\\",\\n\",\n \" under='USD',\\n\",\n \" expiry='2021-12-29',\\n\",\n \" size='5M',\\n\",\n \" strike='113.465',\\n\",\n \" hedgetype='spot')\\n\",\n \"with live_pricing:\\n\",\n \" spot=opt.calc(valuation('FXSpot'))\\n\",\n \" fwd=opt.calc(valuation('FXFwd'))\\n\",\n \" pricepct = opt.calc(valuation('PricePct'))\\n\",\n \" volatility = opt.calc(valuation('FXVol'))\\n\",\n \" winghedge = opt.calc(valuation('FXCalcHedge'))\\n\",\n \"\\n\",\n \"opt.valuation_overrides = {\\n\",\n \" 'FXSpot':spot.result(), \\n\",\n \" 'FXFwd':fwd.result(),\\n\",\n \" 'FXVol':volatility.result(),\\n\",\n \" 'PricePct':pricepct.result()}\\n\",\n \"opt.hedge = winghedge.result()\\n\",\n \"with live_pricing:\\n\",\n \" opt.resolve()\\n\",\n \" new_portfolio = Portfolio(opt)\\n\",\n \" new_portfolio.save_as_quote()\\n\",\n \"print(new_portfolio.id) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption1 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='3m', name='EUR-3m5y')\\n\",\n \"swaption2 = IRSwaption(PayReceive.Pay, '7y', Currency.EUR, expiration_date='6m', name='EUR-6m7y')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio((swaption1, swaption2))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030000_create_portfolio.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption1 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='3m', name='EUR-3m5y')\\n\",\n \"swaption2 = IRSwaption(PayReceive.Pay, '7y', Currency.EUR, expiration_date='6m', name='EUR-6m7y')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio((swaption1, swaption2))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030001_modify_instruments.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption1 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='3m', name='EUR-3m5y Payer')\\n\",\n \"swaption2 = IRSwaption(PayReceive.Pay, '7y', Currency.EUR, expiration_date='9m', name='EUR-9m7y Payer')\\n\",\n \"swaption_midcurve = IRSwaption(\\n\",\n \" PayReceive.Receive, '10y', Currency.EUR, effective_date='10y', expiration_date='6m', name='EUR-6m10y10y Receiver'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio((swaption1, swaption2))\\n\",\n \"print(portfolio.instruments)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio.pricables = (swaption1, swaption2, swaption_midcurve)\\n\",\n \"print(portfolio.instruments)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/03_portfolios/examples/030002_extracting_instruments_and_results.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import IRVega, IRDelta, Price\\n\",\n \"from gs_quant.markets import HistoricalPricingContext, PricingContext\\n\",\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"pd.options.display.float_format = '{:,.0f}'.format\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# instantiate session if not running in jupyter hub\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Extract instruments \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with PricingContext(pricing_date=dt.date(2019, 4, 24)):\\n\",\n \" trade1 = IRSwaption(\\n\",\n \" pay_or_receive='Pay',\\n\",\n \" notional_currency='EUR',\\n\",\n \" expiration_date='6m',\\n\",\n \" termination_date='3y',\\n\",\n \" strike='ATMF',\\n\",\n \" name='payer',\\n\",\n \" )\\n\",\n \" trade2 = IRSwaption(\\n\",\n \" pay_or_receive='Receive',\\n\",\n \" notional_currency='EUR',\\n\",\n \" expiration_date='6m',\\n\",\n \" termination_date='3y',\\n\",\n \" strike='ATMF+50',\\n\",\n \" name='receiver',\\n\",\n \" )\\n\",\n \" trade2.resolve()\\n\",\n \"portfolio = Portfolio((trade1, trade2))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# We can reference the instruments in the portfolio in different ways\\n\",\n \"print(portfolio[1]) # by index\\n\",\n \"print(portfolio[trade2]) # by instrument object - - note instrument object changes once you resolve it\\n\",\n \"print(portfolio['receiver']) # by instrument name\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Extract risk results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with PricingContext(pricing_date=dt.date(2019, 4, 24)):\\n\",\n \" results = portfolio.calc((IRVega, IRDelta, Price))\\n\",\n \"type(results)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can extract all the risks for a single trade and it will return a dictionary of results keyed by the risk type\\n\",\n \"# note that the risks can come back as a number of formats depending on what the risk is. So it could come back as a\\n\",\n \"# scalar float or as a panda series or as a panda dataframe\\n\",\n \"results[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can extract a single risk for a single trade in a number of ways\\n\",\n \"print('{:,.0f}'.format(results[Price]['receiver'])) # by risk name and trade name\\n\",\n \"print('{:,.0f}'.format(results[Price][1])) # by risk name and index of trade in portfolio\\n\",\n \"print('{:,.0f}'.format(results[Price][trade2])) # by risk name and trade object\\n\",\n \"\\n\",\n \"# order doesn't matter when extracting results from a PorfolioRiskResult\\n\",\n \"print('{:,.0f}'.format(results['receiver'][Price]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can aggregate risks\\n\",\n \"a = results[Price]['payer']\\n\",\n \"b = results[Price]['receiver']\\n\",\n \"print('{:,.0f}'.format(a + b))\\n\",\n \"print('{:,.0f}'.format(results[Price].aggregate()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we could return the results as a dataframe\\n\",\n \"pd.DataFrame(results[Price])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# if the risk is not scalar as in the case of IRDelta we can use a similar pattern but the result will be a dataframe\\n\",\n \"results[IRDelta]['payer']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# if we wanted to see the total IRDelta for this trade we could do\\n\",\n \"results[IRDelta]['payer'].value.sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can aggregate dataframe results\\n\",\n \"print('{:,.0f}'.format(results[IRDelta][0].value[1]))\\n\",\n \"print('{:,.0f}'.format(results[IRDelta][1].value[1]))\\n\",\n \"results[IRDelta].aggregate()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# when we price with an HistoricalPricingContext we introduce a new dimension to our results\\n\",\n \"\\n\",\n \"with HistoricalPricingContext(dt.date(2019, 4, 24), dt.date(2019, 5, 24)):\\n\",\n \" hist_results = portfolio.calc((IRVega, IRDelta, Price))\\n\",\n \"\\n\",\n \"type(hist_results)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# now if we ask for the price of a single trade instead of a float we will receieve a panda series of floats indexed by date\\n\",\n \"hist_results[Price][trade1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can still aggregate in the same way and the series will be aggregated by date\\n\",\n \"hist_results[Price].aggregate()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# to get a single price for a specific date we can do\\n\",\n \"hist_results[Price].aggregate().at[dt.date(2019, 4, 30)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# if we return a dataframe then the result will be concatinated with an index of the date.\\n\",\n \"hist_results[IRDelta][0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030003_resolve_portfolio.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency # import constants\\n\",\n \"from gs_quant.instrument import IRSwaption # import instruments\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import Environment, GsSession # import sessions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption1 = IRSwaption(PayReceive.Pay, '12m', Currency.EUR, expiration_date='3m', strike='atm+50', name='EUR-3m5y')\\n\",\n \"swaption2 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='6m', notional_amount=5e7, name='EUR-6m5y')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio((swaption1, swaption2))\\n\",\n \"portfolio.resolve()\\n\",\n \"print(portfolio['EUR-3m5y'].as_dict()) # resolved instrument in the portfolio\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030004_price_portfolio.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption1 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='3m', name='EUR-3m5y')\\n\",\n \"swaption2 = IRSwaption(PayReceive.Pay, '8y', Currency.EUR, expiration_date='5w', name='EUR-5w8y')\\n\",\n \"portfolio = Portfolio((swaption1, swaption2))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"price_result = portfolio.price()\\n\",\n \"print(price_result.aggregate()) # aggregate portfolio price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print(price_result['EUR-3m5y']) # price of specific instrument\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030005_calculate_portfolio_risk.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import PayReceive, Currency # import constants\\n\",\n \"from gs_quant.instrument import IRSwaption # import instruments\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"import gs_quant.risk as risk\\n\",\n \"from gs_quant.session import Environment, GsSession # import sessions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"swaption1 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='3m', name='EUR-3m5y')\\n\",\n \"swaption2 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='6m', name='EUR-6m5y')\\n\",\n \"portfolio = Portfolio((swaption1, swaption2))\\n\",\n \"result = portfolio.calc((risk.DollarPrice, risk.IRDelta))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# portfolio risk\\n\",\n \"print(result[risk.IRDelta].aggregate())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# instrument risk\\n\",\n \"print(result[risk.DollarPrice]['EUR-3m5y'])\\n\",\n \"print(result['EUR-3m5y'][risk.DollarPrice])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030006_portfolio_grid_calc.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.risk import IRAnnualImpliedVol\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tails = ['1y', '3y', '5y', '10y', '15y', '20y', '25y', '30y']\\n\",\n \"expiries = ['3m', '6m', '9m', '1y', '18m', '2y', '3y', '4y', '5y']\\n\",\n \"pay_rec = 'Pay'\\n\",\n \"ccy = 'EUR'\\n\",\n \"moneyness = 25\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolios = Portfolio(\\n\",\n \" [\\n\",\n \" Portfolio(\\n\",\n \" [\\n\",\n \" IRSwaption(\\n\",\n \" pay_or_receive=pay_rec,\\n\",\n \" notional_currency=ccy,\\n\",\n \" termination_date=t,\\n\",\n \" expiration_date=e,\\n\",\n \" strike='ATM+{}'.format(moneyness),\\n\",\n \" name=e,\\n\",\n \" )\\n\",\n \" for e in expiries\\n\",\n \" ],\\n\",\n \" name=t,\\n\",\n \" )\\n\",\n \" for t in tails\\n\",\n \" ]\\n\",\n \")\\n\",\n \"results = portfolios.calc(IRAnnualImpliedVol)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"frame = results.to_frame('value', 'portfolio_name_0', 'instrument_name') * 10000\\n\",\n \"frame\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.subplots(figsize=(12, 8))\\n\",\n \"ax = sns.heatmap(frame, annot=True, fmt='.2f', cmap='coolwarm')\\n\",\n \"ax.set(ylabel='Expiries', xlabel='Tails', title='Implied Vol Grid')\\n\",\n \"ax.xaxis.tick_top()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030007_pnl_explain.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"client_id = None # Supply your application id\\n\",\n \"client_secret = None # Supply your client secret\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id, client_secret, ('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Create a dummy portfolio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwap, IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"\\n\",\n \"swap = IRSwap(notional_currency='EUR', termination_date='10y', pay_or_receive='Pay')\\n\",\n \"swaption = IRSwaption(notional_currency='EUR', termination_date='10y', expiration_date='1y', pay_or_receive='Receive')\\n\",\n \"\\n\",\n \"portfolio = Portfolio((swap, swaption))\\n\",\n \"portfolio.resolve()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Calculate PnlExplain from 1 week ago (5 business days) to today, compare with the dollar price difference and examine the breakdown\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.datetime.date import business_day_offset\\n\",\n \"from gs_quant.markets import CloseMarket, PricingContext, close_market_date\\n\",\n \"from gs_quant.risk import DollarPrice, PnlExplain\\n\",\n \"\\n\",\n \"to_date = close_market_date()\\n\",\n \"\\n\",\n \"# 5 business days ago\\n\",\n \"from_date = business_day_offset(to_date, -5)\\n\",\n \"\\n\",\n \"# A risk measure for calculating PnlExplain from that date\\n\",\n \"explain = PnlExplain(CloseMarket(date=to_date))\\n\",\n \"\\n\",\n \"# Calculate PnlExplain and dollar price from 1 week ago\\n\",\n \"with PricingContext(pricing_date=from_date):\\n\",\n \" result = portfolio.calc((DollarPrice, explain))\\n\",\n \"\\n\",\n \"# Calculate dollar price with the \\\"to\\\" market but \\\"from\\\" pricing date\\n\",\n \"with PricingContext(pricing_date=from_date, market=CloseMarket(date=to_date)):\\n\",\n \" to_market_price = portfolio.dollar_price()\\n\",\n \"\\n\",\n \"# Calculate dollar price with the \\\"to\\\" market and pricing date\\n\",\n \"with PricingContext(pricing_date=to_date):\\n\",\n \" to_price = portfolio.dollar_price()\\n\",\n \"\\n\",\n \"# Compute the time component (PnlExplain does not do this)\\n\",\n \"time_value = to_price.aggregate() - to_market_price.aggregate()\\n\",\n \"\\n\",\n \"print(f'Dollar price difference: {to_price.aggregate() - result[DollarPrice].aggregate():.0f}')\\n\",\n \"print(f'Pnl explain + time value total: {result[explain].aggregate().value.sum() + time_value:.0f}')\\n\",\n \"print(f'Pnl explain total: {result[explain].aggregate().value.sum():.0f}')\\n\",\n \"print(f'Time value total: {time_value:.0f}')\\n\",\n \"\\n\",\n \"# Show the PnlExplain breakdown\\n\",\n \"explain_all = result[explain].aggregate()\\n\",\n \"explain_all[explain_all.value.abs() > 1.0].round(0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030008_portflio_from_frame.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Import Portfolios from Excel\\n\",\n \"\\n\",\n \"This example will demonstrate how to create a `Portfolio` from an excel file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's assume you have an excel file with many different trades on which you would like to compute `gs-quant` analytics.\\n\",\n \"You can import you file into a pandas dataframe with the below command: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# import pandas as pd\\n\",\n \"# data = pd.read_excel(r'path_to_my_file.csv', sheet_name='my_sheet', usecols=None)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"For the benefit of the analysis here I simulated a dummy dataframe representing the result of your file import.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"data = pd.DataFrame.from_dict(\\n\",\n \" {\\n\",\n \" 'name': {0: 'my favourite swap', 1: 'my favourite swaption', 2: None, 3: None, 4: None, 5: None},\\n\",\n \" 'trade_type': {0: 'Swap', 1: 'Swaption', 2: 'Swaption', 3: 'Swaption', 4: 'Swaption', 5: 'Swaption'},\\n\",\n \" 'rate': {0: 0.01, 1: None, 2: None, 3: None, 4: None, 5: None},\\n\",\n \" 'strike': {0: None, 1: '2%', 2: '0.02', 3: '0.02', 4: '0.02', 5: '0.02'},\\n\",\n \" 'ccy': {0: 'EUR', 1: 'GBP', 2: 'GBP', 3: 'GBP', 4: 'GBP', 5: 'GBP'},\\n\",\n \" 'freq': {0: '3m/6m', 1: '3m/6m', 2: '3m/6m', 3: '3m/6m', 4: '3m/6m', 5: '3m/6m'},\\n\",\n \" 'index': {\\n\",\n \" 0: 'EURIBOR-TELERATE',\\n\",\n \" 1: 'LIBOR-BBA',\\n\",\n \" 2: 'LIBOR-BBA',\\n\",\n \" 3: 'LIBOR-BBA',\\n\",\n \" 4: 'LIBOR-BBA',\\n\",\n \" 5: 'LIBOR-BBA',\\n\",\n \" },\\n\",\n \" 'expiration_date': {0: '30/06/2021', 1: '30/06/2021', 2: '3d', 3: '30/06/2021', 4: '30/06/2021', 5: '3m'},\\n\",\n \" 'asset_class': {0: 'rates', 1: 'rates', 2: 'rates', 3: 'rates', 4: 'rates', 5: 'rates'},\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"In order to support various excel files and formats, the `from_frame` function takes a mapping argument which enables you \\n\",\n \"to specify which columns of your excel file correspond to the associated `gs_quant` instrument attribute. \\n\",\n \"You may also specify date formats to expand the list of the ones supported by default.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"\\n\",\n \"mapper = {\\n\",\n \" 'type': 'trade_type',\\n\",\n \" 'fixed_rate': 'rate',\\n\",\n \" 'pay_ccy': 'ccy',\\n\",\n \" 'fixed_rate_frequency': lambda row: row['freq'][: row['freq'].index(\\\"/\\\")],\\n\",\n \" 'floating_rate_frequency': lambda row: row['freq'][row['freq'].index(\\\"/\\\") + 1 :],\\n\",\n \" 'floating_rate_option': lambda row: row['ccy'] + '-' + row['index'],\\n\",\n \"}\\n\",\n \"\\n\",\n \"portfolio = Portfolio.from_frame(data, mappings=mapper)\\n\",\n \"portfolio.to_frame().reset_index(drop=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"If you excel file is in csv format, you may also use the `from_csv` command which executes the two above steps \\n\",\n \"all together and converts your csv to a `Portfolio` directly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# portfolio = Portfolio.from_csv(r'path_to_my_file.csv', mappings=mapper)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"You can now leverage all the risk and pricing functionality of `gs-quant` on your Excel built portfolio!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/03_portfolios/examples/030009_portfolio_risk_result_to_frame.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e415037a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"import gs_quant.risk as risk\\n\",\n \"from gs_quant.instrument import IRSwap, IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.config import DisplayOptions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8e001d1f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e1558474\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# portfolio construction\\n\",\n \"swap_1 = IRSwap('Pay', '5y', 'EUR', fixed_rate=-0.005, name='5y')\\n\",\n \"swap_2 = IRSwap('Pay', '10y', 'EUR', fixed_rate=-0.005, name='10y')\\n\",\n \"swap_3 = IRSwap('Pay', '5y', 'USD', fixed_rate=-0.005, name='5y')\\n\",\n \"swap_4 = IRSwap('Pay', '10y', 'USD', fixed_rate=-0.005, name='10y')\\n\",\n \"swaption_1 = IRSwaption('Pay', '5y', 'USD', expiration_date='1y', name='5y')\\n\",\n \"eur_port = Portfolio([swap_1, swap_2], name='EUR')\\n\",\n \"usd_port = Portfolio([swap_3, swap_4], name='USD')\\n\",\n \"nested_port = Portfolio([eur_port, usd_port, swaption_1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8b770f93\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# risk calculations\\n\",\n \"eur_port_price = eur_port.price()\\n\",\n \"nested_port_price = nested_port.price()\\n\",\n \"nested_port_vega = nested_port.calc(risk.IRVegaParallel)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fbf14a17\",\n \"metadata\": {},\n \"source\": [\n \"Pivot Parameters\\n\",\n \"\\n\",\n \"- inherited from pandas.pivot_table()\\n\",\n \"- index and column values are the names of instruments and portfolios\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3dd1164b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# default to_frame()\\n\",\n \"nested_port_price.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6469cb11\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# to_frame() with no pivot\\n\",\n \"nested_port_price.to_frame(values=None, columns=None, index=None)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"454e1851\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# modify to_frame() with custom pivot parameters - similar to pandas.pivot_table()\\n\",\n \"nested_port_price.to_frame(values='value', columns='portfolio_name_0', index='instrument_name')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"65d6a2f6\",\n \"metadata\": {},\n \"source\": [\n \"Aggregation\\n\",\n \"- inherited from pandas.pivot_table()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"03514616\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swap_5 = IRSwap('Pay', '5y', 'EUR', fixed_rate=-0.005, name='5y')\\n\",\n \"swap_6 = IRSwap('Pay', '10y', 'EUR', fixed_rate=-0.005, name='5y')\\n\",\n \"port = Portfolio([swap_5, swap_6])\\n\",\n \"res = port.price()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7e441762\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# when instruments have the same name, the values are summed by default\\n\",\n \"res.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"267db8cd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# change aggregation of values\\n\",\n \"res.to_frame(aggfunc='mean')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4ae17333\",\n \"metadata\": {},\n \"source\": [\n \"Show N/A values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d15564e6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# to_frame() by default eliminates N/A values from the dataframe result\\n\",\n \"nested_port_vega.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a9797837\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# pass in display_options to show N/A values\\n\",\n \"nested_port_vega.to_frame(display_options=DisplayOptions(show_na=True))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030010_portfolio_intra_leg_dependencies.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0af5ba20\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.common import PayReceive, Currency\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d5bf201e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6ea95f36\",\n \"metadata\": {},\n \"source\": [\n \"### Reference other leg properties in instrument fields\\n\",\n \"This would be the equivalent of making two requests to resolve the legs serialially\\n\",\n \"```python\\n\",\n \"swaption_1y5y = IRSwaption( ... )\\n\",\n \"swaption_1y4y = IRSwaption( ... )\\n\",\n \"\\n\",\n \"swaption_1y5y.resolve()\\n\",\n \"swaption_1y4y.strike = swaption_1y5y.strike\\n\",\n \"swaption_1y4y.resolve()\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"96cac46c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"swaption_1y5y = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='1y', strike=\\\"atm\\\", name=\\\"foo\\\")\\n\",\n \"swaption_1y4y = IRSwaption(\\n\",\n \" PayReceive.Pay, '4y', Currency.EUR, expiration_date='1y', strike=\\\"=[foo].strike + 5bp\\\", name=\\\"bar\\\"\\n\",\n \")\\n\",\n \"port = Portfolio((swaption_1y5y, swaption_1y4y))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"00f45d28\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"port.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c0913ba1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(port['foo'].strike * 1e4)\\n\",\n \"print(port['bar'].strike * 1e4)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/030011_portfolio_from_csv.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5690b929\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.instrument import IRSwap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c1e82503\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"40ca1564\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"IRSwap?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e14778db\",\n \"metadata\": {},\n \"source\": [\n \"The Portfolio.from_csv mappings is a dictionary mapping a gs-quant instrument field to your csv column name (columns must not have duplicates). Mappings can also be a callable lambda function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"eb9febfb\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mappers = {\\n\",\n \" 'type': lambda row: IRSwap.type_.value,\\n\",\n \" 'asset_class': lambda row: IRSwap.asset_class.value,\\n\",\n \" 'effective_date': 'EffectiveDate',\\n\",\n \" 'pay_or_receive': lambda row: 'Pay' if float(row['Notional'].replace(',', '')) < 0 else 'Receive',\\n\",\n \" 'termination_date': 'EndDate',\\n\",\n \" 'fixed_rate': 'Coupon/Spread',\\n\",\n \" 'notional_amount': 'Notional',\\n\",\n \" 'notional_currency': 'CCY1',\\n\",\n \" 'roll_convention': lambda row: 'IMM' if row['Roll Conv'] == 'IMM' else None,\\n\",\n \" 'fixed_rate_frequency': lambda row: '3m' if row['Frequency'] == 'QUARTERLY' else '6m',\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"704dabf0\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"p = Portfolio.from_csv('my_excel_portfolio.csv', mappers)\\n\",\n \"p.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b0881ba6\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"p[0].as_dict()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/examples/my_excel_portfolio.csv", "content": "Count,CCY1,Notional,EffectiveDate,Roll Conv,EndDate,LegName,Frequency,RateFixingInfo,RollDate\n1,USD,\"50,000,000\",27-Jan-15,27,27-Jan-25,FIXED,QUARTERLY,NONE,27-Jan-15\n2,USD,\"-10,500,000\",16-Sep-15,16,16-Sep-25,FIXED,QUARTERLY,NONE,16-Sep-15\n3,USD,\"-500,000\",16-Sep-15,16,16-Sep-25,FIXED,QUARTERLY,NONE,16-Sep-15\n4,USD,\"-1,200,000\",16-Sep-15,16,16-Sep-25,FIXED,SEMI,NONE,16-Sep-15\n5,USD,\"-8,100,000\",16-Sep-15,16,16-Sep-25,FIXED,SEMI,NONE,16-Sep-15\n6,USD,\"-9,000,000\",16-Sep-15,16,16-Sep-25,FIXED,SEMI,NONE,16-Sep-15\n7,USD,\"-2,000,000\",16-Sep-15,16,16-Sep-25,FIXED,SEMI,NONE,16-Sep-15\n8,USD,\"50,000,000\",16-Mar-16,IMM,18-Mar-26,FIXED,QUARTERLY,NONE,15-Jun-16\n9,USD,\"-27,200,000\",16-Sep-15,IMM,16-Sep-25,FIXED,QUARTERLY,NONE,15-Jun-16\n" }, { "path": "gs_quant/documentation/03_portfolios/tutorials/Create New Portfolio.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Create a Marquee Portfolio with GS Quant\\n\",\n \"\\n\",\n \"The Marquee Portfolio Service provides a powerful framework for uploading portfolio positions and retrieving analytics including historical performance, factor risk exposure, ESG analytics, and more. GS Quant makes operating the suite of Portfolio Service API's intuitive and fast.\\n\",\n \"\\n\",\n \"## Permission Prerequisites\\n\",\n \"\\n\",\n \"To execute all the code in this tutorial, you will need the following application scopes:\\n\",\n \"- **read_product_data**\\n\",\n \"- **read_financial_data**\\n\",\n \"- **modify_product_data** (must be requested)\\n\",\n \"- **modify_financial_data** (must be requested)\\n\",\n \"- **run_analytics** (must be requested)\\n\",\n \"- **read_user_profile**\\n\",\n \"\\n\",\n \"If you are not yet permissioned for these scopes, please request them on your [My Applications Page](https://developer.gs.com/go/apps/view). If you have any other questions please reach out to the [Marquee sales team](mailto:gs-marquee-sales@gs.com).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.entities.entitlements import Entitlements, EntitlementBlock, User\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import CustomAUMDataPoint\\n\",\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.target.portfolios import RiskAumSource\\n\",\n \"\\n\",\n \"client = 'ENTER CLIENT ID'\\n\",\n \"secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \" scopes=(\\n\",\n \" 'read_product_data read_financial_data modify_product_data modify_financial_data run_analytics read_user_profile',\\n\",\n \" ),\\n\",\n \")\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 2: Create the Portfolio\\n\",\n \"\\n\",\n \"The first step is to create a new, empty portfolio in Marquee.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio(name='My New Portfolio')\\n\",\n \"portfolio.save()\\n\",\n \"\\n\",\n \"print(f\\\"Created portfolio '{portfolio.name}' with ID: {portfolio.id}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Once your portfolio has been saved to Marquee, the `PortfolioManager` class allows users to interact with their Marquee portfolios directly from GS Quant. We will be using `PortfolioManager` to update portfolio positions, entitlements, update custom AUM, and run reports.\\n\",\n \"\\n\",\n \"## Step 3: Define Portfolio Entitlements\\n\",\n \"\\n\",\n \"By default, an application will have all entitlement permissions to a portfolio it makes. However, if you want to see the portfolio in the Marquee Webpage, you will have to share it with yourself (since your app is a separate user from you). \\n\",\n \"\\n\",\n \"Similarly, if you would like others to have access to your portfolio, either Marquee users or other applications, you will need to share the portfolio with them. Let's walk through how to do that:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n },\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_admin_emails = ['LIST OF ADMIN EMAILS']\\n\",\n \"portfolio_viewer_emails = ['LIST OF VIEWER EMAILS']\\n\",\n \"\\n\",\n \"pm = PortfolioManager(portfolio.id)\\n\",\n \"pm.share(portfolio_admin_emails, admin=True)\\n\",\n \"pm.share(portfolio_viewer_emails, admin=False)\\n\",\n \"\\n\",\n \"print(f\\\"Updated entitlements for '{portfolio.name}'\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"If you'd like more control on the entitlements (e.g. you want to remove someone), you can use the `Entitlements` class directly. Here's an example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_admin_emails = ['LIST OF ADMIN EMAILS']\\n\",\n \"portfolio_viewer_emails = ['LIST OF VIEWER EMAILS']\\n\",\n \"\\n\",\n \"admin_entitlements = EntitlementBlock(users=User.get_many(emails=portfolio_admin_emails))\\n\",\n \"view_entitlements = EntitlementBlock(users=User.get_many(emails=portfolio_viewer_emails))\\n\",\n \"\\n\",\n \"entitlements = Entitlements(view=view_entitlements, admin=admin_entitlements)\\n\",\n \"\\n\",\n \"print(f'Entitlements:\\\\n{entitlements.to_dict()}')\\n\",\n \"\\n\",\n \"pm = PortfolioManager(portfolio.id)\\n\",\n \"pm.set_entitlements(entitlements)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 4: Define Portfolio Positions\\n\",\n \"\\n\",\n \"Portfolio positions in Marquee are stored on a holding basis, when means you only upload positions for days where you are rebalancing your portfolio. Take the following set of positions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet(\\n\",\n \" date=dt.date(day=3, month=5, year=2021),\\n\",\n \" positions=[Position(identifier='AAPL UW', quantity=25), Position(identifier='GS UN', quantity=50)],\\n\",\n \" ),\\n\",\n \" PositionSet(\\n\",\n \" date=dt.date(day=1, month=7, year=2021),\\n\",\n \" positions=[Position(identifier='AAPL UW', quantity=26), Position(identifier='GS UN', quantity=51)],\\n\",\n \" ),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"If these positions were to be uploaded correctly, this portfolio would hold 50 shares of GS UN and 25 shares of AAPL UW from May 3, 2021 to June 30, 2021, and it would hold 51 shares of GS UN and 26 shares of AAPL UW from July 1, 2021 to today.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm.update_positions(portfolio_position_sets)\\n\",\n \"\\n\",\n \"print(f\\\"Updated positions for '{portfolio.name}'\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 5: Schedule Reports\\n\",\n \"By default, creating a portfolio will automatically create a corresponding Performance Report for it as well. If you would like to create a Factor Risk Report for it as well, follow the steps [here](../examples/marquee/00_create_factor_risk_report.ipynb). Then, remember to schedule all the portfolio reports.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm.schedule_reports()\\n\",\n \"\\n\",\n \"print('All portfolio reports scheduled.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 6: Update Custom AUM (Optional)\\n\",\n \"The `CustomAUMDataPoint` class is used to represent custom AUM data for a specific date. A list of them can be posted to Marquee using our initialized `PortfolioManager`. If you do not upload custom AUM data for your portfolio and change your portfolio's AUM Source to `Custom AUM`, by default the \\\"AUM\\\" (which is used for calculating risk as percent values) will be your portfolio's long exposure.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"performance_report = pm.get_performance_report()\\n\",\n \"performance_report.set_aum_source(RiskAumSource.Custom_AUM)\\n\",\n \"custom_aum = [\\n\",\n \" CustomAUMDataPoint(date=dt.date(2021, 5, 1), aum=100000),\\n\",\n \" CustomAUMDataPoint(date=dt.date(2021, 7, 1), aum=200000),\\n\",\n \"]\\n\",\n \"performance_report.upload_custom_aum(custom_aum, clear_existing_data=False)\\n\",\n \"\\n\",\n \"print(f\\\"Custom AUM for '{portfolio.name} successfully uploaded'\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### You're all set, Congrats! What's next?\\n\",\n \"\\n\",\n \"* [Creating and scheduling a factor risk report](../examples/marquee/00_create_factor_risk_report.ipynb)\\n\",\n \"\\n\",\n \"* [Updating the portfolio with new positions](../tutorials/Update%20Historical%20Portfolio.ipynb)\\n\",\n \"\\n\",\n \"* [Retrieving the portfolio's performance analytics](../tutorials/Pull%20Portfolio%20Performance%20Data.ipynb)\\n\",\n \"\\n\",\n \"* [Retrieving the portfolio's factor risk and attribution analytics](../tutorials/Pull%20Portfolio%20Factor%20Risk%20Data.ipynb)\\n\",\n \"\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/tutorials/Portfolios.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. `run_analytics` scope is required for the functionality covered in this tutorial. External clients need to substitute thier own client id and client secret below. Please refer to Sessions for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What is a Portfolio\\n\",\n \"\\n\",\n \"Portfolios are collections of instruments. They can be used to simplify calculations and syntax when working with many instruments. [All `Priceable` methods](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.base.Priceable.html#gs_quant.base.Priceable/#methods/) like `price`, `as_dict`, and `calc` are also available for a `Portfolio`. When evaluated on a portfolio, a user can still access results for individual instruments in that portfolio.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## How to Create a Portfolio\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's first create a few instruments that will be included in our portfolio. To learn more about instruments and how to create them please see the [instruments guide](https://developer.gs.com/docs/gsquant/guides/Pricing-and-Risk/instruments/) and [tutorial](https://developer.gs.com/docs/gsquant/tutorials/Pricing-and-Risk/1-creating-an-instrument/). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"\\n\",\n \"swaption1 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='3m', name='EUR-5y3m')\\n\",\n \"swaption2 = IRSwaption(PayReceive.Pay, '5y', Currency.EUR, expiration_date='6m', name='EUR-5y6m')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now import `Portfolio` and add the two swaptions we created.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"\\n\",\n \"portfolio = Portfolio((swaption1, swaption2))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Portfolio constituents can also be set (or reset) by assiging to instruments.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio.pricables = (swaption1, swaption2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## How to Refer to Specific Instruments in a Portfolio\\n\",\n \"\\n\",\n \"Instruments can be referred to by position, by instrument object and by name. Examples of each below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio[0] # by position\\n\",\n \"portfolio[swaption2] # by instrument object\\n\",\n \"portfolio['EUR-5y6m'] # by instrument name\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## How to Work with Portfolios\\n\",\n \"\\n\",\n \"As mentioned above, working with portfolios is same as working with instruments and the same methods are available including `resolve`, `price`, `calc`. Any calculations on a portfolio can be aggregated on a portfolio level using `aggregate.` Let's look at each below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio.resolve() # resolve\\n\",\n \"portfolio.price().aggregate() # price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# calculate risk measures\\n\",\n \"import gs_quant.risk as risk\\n\",\n \"\\n\",\n \"result = portfolio.calc((risk.DollarPrice, risk.IRDelta))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Instrument-level results can be accessed through either indexing into the measure or the instrument first. See [above](#How-to-Refer-to-Specific-Instruments-in-a-Portfolio) on ways the instruments can be referred to.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"result[risk.DollarPrice]['EUR-5y3m'] # or\\n\",\n \"result['EUR-5y3m'][risk.DollarPrice]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Portfolio-level results can be aggregated using `aggregate`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"price = result[risk.DollarPrice].aggregate()\\n\",\n \"delta = result[risk.IRDelta].aggregate()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Disclaimer\\n\",\n \"This website may contain links to websites and the content of third parties (\\\"Third Party Content\\\"). We do not monitor, review or update, and do not have any control over, any Third Party Content or third party websites. We make no representation, warranty or guarantee as to the accuracy, completeness, timeliness or reliability of any Third Party Content and are not responsible for any loss or damage of any sort resulting from the use of, or for any failure of, products or services provided at or from a third party resource. If you use these links and the Third Party Content, you acknowledge that you are doing so entirely at your own risk.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/03_portfolios/tutorials/Pull Portfolio Factor Risk Data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Pull Portfolio Factor Risk and Attribution Data\\n\",\n \"\\n\",\n \"When you create and upload positions to a portfolio in Marquee, GS Quant makes it possible to pull factor risk analytics in just a few lines of code.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"Before you begin this tutorial, you must have a portfolio in Marquee with positions and a factor risk report that has already been created, scheduled, and completed. If that's not the case, please navigate to our tutorial on [creating a new portfolio](../tutorials/Create%20New%20Portfolio.ipynb) and/or [updating positions and running reports](../tutorials/Update%20Historical%20Portfolio.ipynb).\\n\",\n \"\\n\",\n \"Your application also needs to have the **run_analytics** scope. If it doesn't, please request it on your [My Applications Page](https://developer.gs.com/go/apps/view). If you have any other questions please reach out to the [Marquee sales team](mailto:gs-marquee-sales@gs.com).\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.api.gs.risk_models import GsRiskModelApi\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import FactorRiskReport\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"client = 'ENTER CLIENT ID'\\n\",\n \"secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('run_analytics',))\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Step 2: Get all Portfolio Reports\\n\",\n \"\\n\",\n \"The `PortfolioManager` class allows for easy retrieval and scheduling of portfolio reports. Simply running:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"outputs\": [],\n \"source\": [\n \"all_reports = PortfolioManager('ENTER PORTFOLIO ID').get_reports()\\n\",\n \"\\n\",\n \"print(all_reports)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"will return a list of `Report` objects that represent the reports associated with the portfolio.\\n\",\n \"\\n\",\n \"## Step 3: Retrieve the Relevant Factor Risk Report\\n\",\n \"\\n\",\n \"The GS Quant `Report` class is inherited by report subclasses, like `FactorRiskReport` and `PerformanceReport`, each of which corresponds to a type of Marquee report. Each subclass then has additional functions specific to its report type. In this case, we'd like to find the `FactorRiskReport` associated with this portfolio, and leverage its functions to retrieve data. First let's retrieve the list of all risk models we can choose from.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"outputs\": [],\n \"source\": [\n \"list_of_risk_models = []\\n\",\n \"for report in all_reports:\\n\",\n \" if isinstance(report, FactorRiskReport):\\n\",\n \" list_of_risk_models.append(report.get_risk_model_id())\\n\",\n \"\\n\",\n \"print(f'This portfolio has scheduled factor risk reports with the following risk models:\\\\n {list_of_risk_models}')\\n\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"If you would like to pull factor risk data off of a risk model not on this list, you can [create and schedule a new factor risk report](../examples/marquee/00_create_factor_risk_report.ipynb).\\n\",\n \"\\n\",\n \"### Quick Tip!\\n\",\n \"If you would like to see all available risk model IDs to choose from, simply run the following\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"outputs\": [],\n \"source\": [\n \"risk_models = GsRiskModelApi.get_risk_models(limit=1000)\\n\",\n \"for risk_model in risk_models:\\n\",\n \" print(f'{risk_model}\\\\n')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Now let's pick one risk model ID from the list above and find its correlating `FactorRiskReport`.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"outputs\": [],\n \"source\": [\n \"risk_model_id = 'ENTER RISK MODEL ID'\\n\",\n \"factor_risk_reports = list(filter(lambda report: isinstance(report, FactorRiskReport) and report.get_risk_model_id() == risk_model_id, all_reports))\\n\",\n \"if len(factor_risk_reports) == 0:\\n\",\n \" print(f'This portfolio does not have a factor risk report with the risk model {risk_model_id}. Please create a new factor risk report and schedule it before proceeding.')\\n\",\n \"factor_risk_report = factor_risk_reports[0]\\n\",\n \"\\n\",\n \"print(f'Factor risk model found with ID: \\\"{factor_risk_report.id}\\\".')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"will return a list of `Report` objects that represent the reports associated with the portfolio.\\n\",\n \"\\n\",\n \"## Step 4: Pull Report Data\\n\",\n \"\\n\",\n \"Now that we have our factor risk report, we can leverage the functionality of the `FactorRiskReport` class to pull attribution and risk data. In this example, let's pull historical data on factor, specific, and total PnL for the year 2020:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"outputs\": [],\n \"source\": [\n \"factor_pnl = factor_risk_report.get_factor_pnl(factor_name='Factor', start_date=dt.date(2020, 1, 1), end_date=dt.date(2021, 1, 1))\\n\",\n \"specific_pnl = factor_risk_report.get_factor_pnl(factor_name='Specific', start_date=dt.date(2020, 1, 1), end_date=dt.date(2021, 1, 1))\\n\",\n \"total_pnl = factor_risk_report.get_factor_pnl(factor_name='Total', start_date=dt.date(2020, 1, 1), end_date=dt.date(2021, 1, 1))\\n\",\n \"\\n\",\n \"print(f'Factor: \\\\n{factor_pnl.__str__()}')\\n\",\n \"print(f'Specific: \\\\n{specific_pnl.__str__()}')\\n\",\n \"print(f'Total: \\\\n{total_pnl.__str__()}')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Now let's pull the breakdown of proportion of risk among the different factor types over time:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"outputs\": [],\n \"source\": [\n \"market_prop_of_risk = factor_risk_report.get_factor_proportion_of_risk(factor_name='Market', start_date=dt.date(2020, 1, 1), end_date=dt.date(2021, 1, 1))\\n\",\n \"style_prop_of_risk = factor_risk_report.get_factor_proportion_of_risk(factor_name='Style', start_date=dt.date(2020, 1, 1), end_date=dt.date(2021, 1, 1))\\n\",\n \"industry_prop_of_risk = factor_risk_report.get_factor_proportion_of_risk(factor_name='Industry', start_date=dt.date(2020, 1, 1), end_date=dt.date(2021, 1, 1))\\n\",\n \"country_prop_of_risk = factor_risk_report.get_factor_proportion_of_risk(factor_name='Country', start_date=dt.date(2020, 1, 1), end_date=dt.date(2021, 1, 1))\\n\",\n \"\\n\",\n \"print(f'Market: \\\\n{market_prop_of_risk.__str__()}')\\n\",\n \"print(f'Style: \\\\n{style_prop_of_risk.__str__()}')\\n\",\n \"print(f'Industry: \\\\n{industry_prop_of_risk.__str__()}')\\n\",\n \"print(f'Country: \\\\n{country_prop_of_risk.__str__()}')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Quick Tip!\\n\",\n \"If you would like to pull all factor risk data for a list of different factors, you can use the `get_results` function:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"factor_and_total_results = factor_risk_report.get_results(factors=['Factor', 'Specific'], start_date=dt.date(2020, 1, 1), end_date=dt.date(2021, 1, 1))\\n\",\n \"print(factor_and_total_results.__str__())\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"\\n\",\n \"### You're all set, Congrats! What's next?\\n\",\n \"\\n\",\n \"* [Creating and scheduling a new factor risk report](../examples/marquee/00_create_factor_risk_report.ipynb)\\n\",\n \"\\n\",\n \"* [Updating the portfolio with new positions](../tutorials/Update%20Historical%20Portfolio.ipynb)\\n\",\n \"\\n\",\n \"* [Retrieving the portfolio's performance analytics](../tutorials/Pull%20Portfolio%20Performance%20Data.ipynb)\\n\",\n \"\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/03_portfolios/tutorials/Pull Portfolio Performance Data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Pull Portfolio Performance Data\\n\",\n \"\\n\",\n \"When you create and upload positions to a portfolio in Marquee, GS Quant makes it easy to pull historical performance data with just a few lines of code.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"Before you begin this tutorial, you must have a portfolio in Marquee with positions and reports that have already been scheduled and completed. If that's not the case, please navigate to our tutorial on [creating a new portfolio](../tutorials/Create%20New%20Portfolio.ipynb) and/or [updating positions and running reports](../tutorials/Update%20Historical%20Portfolio.ipynb).\\n\",\n \"\\n\",\n \"Your application also needs to have the **run_analytics** scope. If it doesn't, please request it on your [My Applications Page](https://developer.gs.com/go/apps/view). If you have any other questions please reach out to the [Marquee sales team](mailto:gs-marquee-sales@gs.com).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import PerformanceReport\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"client = 'ENTER CLIENT ID'\\n\",\n \"secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('run_analytics',))\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Step 2: Get all Portfolio Reports\\n\",\n \"\\n\",\n \"The `PortfolioManager` class allows for easy retrieval and scheduling of portfolio reports. Simply running:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"outputs\": [],\n \"source\": [\n \"all_reports = PortfolioManager('ENTER PORTFOLIO ID').get_reports()\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"will return a list of `Report` objects that represent the reports associated with the portfolio.\\n\",\n \"\\n\",\n \"## Step 3: Find Portfolio Performance Analytics Report and Pull Data\\n\",\n \"\\n\",\n \"The GS Quant `Report` class is inherited by report subclasses, like `FactorRiskReport` and `PerformanceReport`, each of which corresponds to a type of Marquee report. Each subclass then has additional functions specific to its report type. In this case, we'd like to find the `PerformanceReport` associated with this portfolio, and leverage its functions to retrieve data. In this example, we will pull all historical PnL, gross exposure, and net exposure available.\\n\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"outputs\": [],\n \"source\": [\n \"performance_report = list(filter(lambda report: isinstance(report, PerformanceReport), all_reports))[0]\\n\",\n \"pnl = performance_report.get_pnl()\\n\",\n \"gross_exposure = performance_report.get_gross_exposure()\\n\",\n \"net_exposure = performance_report.get_net_exposure()\\n\",\n \"\\n\",\n \"print(f'PnL: \\\\n{pnl.__str__()}')\\n\",\n \"print(f'Gross Exposure: \\\\n{gross_exposure.__str__()}')\\n\",\n \"print(f'Net Exposure: \\\\n{net_exposure.__str__()}')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Quick Tip!\\n\",\n \"If you would like to pull multiple performance measures in one dataframe, you can use the `get_many_measures` function:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"aggregated_performance_results = performance_report.get_many_measures(measures=['pnl', 'grossExposure', 'netExposure'])\\n\",\n \"print(aggregated_performance_results)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"\\n\",\n \"### Quick Tip!\\n\",\n \"If you would only like to pull data for a specific date range, all the functions used above also take in function parameters `start_date` and `end_date`, in which you can pass `datetime.date` objects to specify the range you would like.\\n\",\n \"\\n\",\n \"\\n\",\n \"### You're all set, Congrats! What's next?\\n\",\n \"\\n\",\n \"* [Creating and scheduling a new factor risk report](../examples/marquee/00_create_factor_risk_report.ipynb)\\n\",\n \"\\n\",\n \"* [Updating the portfolio with new positions](../tutorials/Update%20Historical%20Portfolio.ipynb)\\n\",\n \"\\n\",\n \"* [Retrieving the portfolio's performance analytics](../tutorials/Pull%20Portfolio%20Performance%20Data.ipynb)\\n\",\n \"\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/03_portfolios/tutorials/Pull Portfolio Risk Data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Pull Portfolio Performance and Factor Risk Data\\n\",\n \"\\n\",\n \"This tutorial uses a pre-populated portfolio (accessible to all internal users) and walks through pulling performance and factor risk data *(for demo, not client distribution, purposes)*.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import PerformanceReport, FactorRiskReport\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD)\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Step 2: Get all Portfolio Reports\\n\",\n \"\\n\",\n \"The `PortfolioManager` class allows for easy retrieval and scheduling of portfolio reports. Simply running:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"all_reports = PortfolioManager('MPZV9A0F1EMQGG79').get_reports()\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"will return a list of `Report` objects that represent the reports associated with the portfolio.\\n\",\n \"\\n\",\n \"## Step 3: Find Portfolio Performance Analytics Report and Pull Data\\n\",\n \"\\n\",\n \"The GS Quant `Report` class is inherited by report subclasses, like `FactorRiskReport` and `PerformanceReport`, each of which corresponds to a type of Marquee report. Each subclass then has additional functions specific to its report type. In this case, we'd like to find the `PerformanceReport` associated with this portfolio, and leverage its functions to retrieve data. In this example, we will pull all historical PnL, gross exposure, and net exposure available.\\n\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"performance_report = list(filter(lambda report: isinstance(report, PerformanceReport), all_reports))[0]\\n\",\n \"pnl = performance_report.get_pnl(start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"gross_exposure = performance_report.get_gross_exposure(start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"net_exposure = performance_report.get_net_exposure(start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"\\n\",\n \"print(f'PnL: \\\\n{pnl.__str__()}')\\n\",\n \"print(f'Gross Exposure: \\\\n{gross_exposure.__str__()}')\\n\",\n \"print(f'Net Exposure: \\\\n{net_exposure.__str__()}')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Step 4: Find Factor Risk Report and Pull Data\\n\",\n \"Now let's find a factor risk report and pull its correlating `FactorRiskReport`.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"risk_model_id = 'BARRA_USSLOWL'\\n\",\n \"factor_risk_reports = list(filter(lambda report: isinstance(report, FactorRiskReport) and report.get_risk_model_id() == risk_model_id, all_reports))\\n\",\n \"if len(factor_risk_reports) == 0:\\n\",\n \" print(f'This portfolio does not have a factor risk report with the risk model {risk_model_id}. Please create a new factor risk report and schedule it before proceeding.')\\n\",\n \"factor_risk_report = factor_risk_reports[0]\\n\",\n \"\\n\",\n \"print(f'Factor risk model found with ID: \\\"{factor_risk_report.id}\\\".')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Now that we have our factor risk report, we can leverage the functionality of the `FactorRiskReport` class to pull attribution and risk data. In this example, let's pull historical data on factor, specific, and total PnL for the first five months of 2021:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"factor_pnl = factor_risk_report.get_factor_pnl(factor_name='Factor', start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"specific_pnl = factor_risk_report.get_factor_pnl(factor_name='Specific', start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"total_pnl = factor_risk_report.get_factor_pnl(factor_name='Total', start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"\\n\",\n \"print(f'Factor: \\\\n{factor_pnl.__str__()}')\\n\",\n \"print(f'Specific: \\\\n{specific_pnl.__str__()}')\\n\",\n \"print(f'Total: \\\\n{total_pnl.__str__()}')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Now let's pull the breakdown of proportion of risk among the different factor types over time:\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"market_prop_of_risk = factor_risk_report.get_factor_proportion_of_risk(factor_name='Market', start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"style_prop_of_risk = factor_risk_report.get_factor_proportion_of_risk(factor_name='Style', start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"industry_prop_of_risk = factor_risk_report.get_factor_proportion_of_risk(factor_name='Industry', start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"country_prop_of_risk = factor_risk_report.get_factor_proportion_of_risk(factor_name='Country', start_date=dt.date(2021, 1, 1), end_date=dt.date(2021, 6, 1))\\n\",\n \"\\n\",\n \"print(f'Market: \\\\n{market_prop_of_risk.__str__()}')\\n\",\n \"print(f'Style: \\\\n{style_prop_of_risk.__str__()}')\\n\",\n \"print(f'Industry: \\\\n{industry_prop_of_risk.__str__()}')\\n\",\n \"print(f'Country: \\\\n{country_prop_of_risk.__str__()}')\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"\\n\",\n \"### You're all set, Congrats! What's next?\\n\",\n \"\\n\",\n \"* [Creating and scheduling a new factor risk report](../examples/marquee/00_create_factor_risk_report.ipynb)\\n\",\n \"\\n\",\n \"* [Updating the portfolio with new positions](../tutorials/Update%20Historical%20Portfolio.ipynb)\\n\",\n \"\\n\",\n \"* [Retrieving the portfolio's performance analytics](../tutorials/Pull%20Portfolio%20Performance%20Data.ipynb)\\n\",\n \"\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ],\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n }\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/03_portfolios/tutorials/Update Historical Portfolio.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Updating a Historical Marquee Portfolio\\n\",\n \"\\n\",\n \"If you already have a portfolio in Marquee, the GS Quant SDK provides a simple and intuitive workflow to update positions and rerun reports.\\n\",\n \"\\n\",\n \"## Permission Prerequisites\\n\",\n \"\\n\",\n \"To execute all the code in this tutorial, you will need the following application scopes:\\n\",\n \"- **read_product_data**\\n\",\n \"- **read_financial_data**\\n\",\n \"- **modify_financial_data** (must be requested)\\n\",\n \"- **run_analytics** (must be requested)\\n\",\n \"\\n\",\n \"If you are not yet permissioned for these scopes, please request them on your [My Applications Page](https://developer.gs.com/go/apps/view). If you have any other questions please reach out to the [Marquee sales team](mailto:gs-marquee-sales@gs.com).\\n\",\n \"\\n\",\n \"You will also need to be an admin on the portfolio you would like to update. If you are not an admin, please ask a portfolio admin to [edit the portfolio's entitlements](../examples/marquee/01_edit_portfolio_entitlements.ipynb) to include you.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import itertools\\n\",\n \"from time import sleep\\n\",\n \"\\n\",\n \"from gs_quant.api.gs.assets import GsAssetApi\\n\",\n \"from gs_quant.api.gs.portfolios import GsPortfolioApi\\n\",\n \"from gs_quant.common import PositionSet\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.target.portfolios import Position\\n\",\n \"\\n\",\n \"client = 'ENTER CLIENT ID'\\n\",\n \"secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \" scopes=('read_product_data read_financial_data modify_financial_data run_analytics',),\\n\",\n \")\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Define Your Portfolio ID and the Positions You Would Like to Upload\\n\",\n \"\\n\",\n \"Portfolio positions in Marquee are stored on a holding basis, when means you only upload positions for days where you are rebalancing your portfolio. Take the following set of positions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_id = 'ENTER PORTFOLIO ID'\\n\",\n \"positions = [\\n\",\n \" {'identifier': 'GS UN', 'quantity': 50, 'positionDate': '2020-05-01'},\\n\",\n \" {'identifier': 'AAPL UW', 'quantity': 25, 'positionDate': '2020-05-01'},\\n\",\n \" {'identifier': 'GS UN', 'quantity': 51, 'positionDate': '2020-07-01'},\\n\",\n \" {'identifier': 'AAPL UW', 'quantity': 26, 'positionDate': '2020-07-01'},\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If these positions were to be uploaded correctly, that portfolio would hold 50 shares of GS UN and 25 shares of AAPL UW from May 1, 2020 to June 30, 2020, and it would hold 51 shares of GS UN and 26 shares of AAPL UW from July 1, 2020 to today (assuming the portfolio was initially empty).\\n\",\n \"\\n\",\n \"## Step 3: Format positions\\n\",\n \"\\n\",\n \"Now let's proceed with updating these positions to our portfolio. The first step is to resolve the identifiers provided into their corresponding unique Marquee identifiers. In this case, positions are identified by Bloomberg ID, but other identifiers can be used and resolved by adding them to the `fields` parameter in the function `GsAssetApi.resolve_assets`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"all_identifiers = list(set([p['identifier'] for p in positions]))\\n\",\n \"results = GsAssetApi.resolve_assets(identifier=all_identifiers, fields=['bbid', 'id'], limit=1)\\n\",\n \"try:\\n\",\n \" identifier_to_marquee_id = dict(zip(results.keys(), [a[0]['id'] for a in results.values()]))\\n\",\n \"except:\\n\",\n \" unmapped_assets = {k for k, v in results.items() if not v}\\n\",\n \" raise ValueError('Error in resolving the following identifiers: ' + ', '.join(unmapped_assets))\\n\",\n \"\\n\",\n \"print('Position identifiers successfully mapped as the following:')\\n\",\n \"for mq_id in identifier_to_marquee_id:\\n\",\n \" print(f'{mq_id} --> {identifier_to_marquee_id[mq_id]}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next we need to rearrange the data in the positions to fit the format expected in the Marquee API.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = []\\n\",\n \"\\n\",\n \"for position_date, positions_on_date in itertools.groupby(positions, lambda x: x['positionDate']):\\n\",\n \" formatted_positions = tuple(\\n\",\n \" Position(asset_id=identifier_to_marquee_id[p['identifier']], quantity=p['quantity']) for p in positions_on_date\\n\",\n \" )\\n\",\n \" position_set = PositionSet(position_date=position_date, positions=formatted_positions)\\n\",\n \" portfolio_position_sets.append(position_set)\\n\",\n \"\\n\",\n \"print('Portfolio positions successfully formatted.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Post Positions to the Marquee Portfolio\\n\",\n \"We're finally ready to update the portfolio with the positions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"GsPortfolioApi.update_positions(portfolio_id, portfolio_position_sets)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Rerun All Portfolio Reports\\n\",\n \"\\n\",\n \"Once your portfolio has new positions, it's time to rerun all reports associated with the portfolio. You can do that easily using the `PortfolioManager` class.\\n\",\n \"\\n\",\n \"When running reports, you have the option of running them synchronously (meaning the function `run_reports()` will wait for all reports to finish running to return their results), or asynchronously (meaning the function will return a list of `ReportJobFuture` objects that will store the result of the report job once it is available). This tutorial will leverage the asyncronous feature.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"\\n\",\n \"report_results = pm.run_reports(backcast=False, is_async=True)\\n\",\n \"for future in report_results:\\n\",\n \" while not future.done():\\n\",\n \" # you can use this time to perform other calculations or run other functions\\n\",\n \" sleep(5)\\n\",\n \" print('~~~~~~~~ Results ~~~~~~~~')\\n\",\n \" report_result = future.result()\\n\",\n \" print(report_result.__str__())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Quick Tip!\\n\",\n \"If you would only like to run reports on a specified range of dates, pass `start_date` and `end_date` datetime.date objects into the `run_reports()` function parameters.\\n\",\n \"\\n\",\n \"### You're all set, Congrats! What's next?\\n\",\n \"\\n\",\n \"* [Creating and scheduling a new factor risk report](../examples/marquee/00_create_factor_risk_report.ipynb)\\n\",\n \"\\n\",\n \"* [Retrieving the portfolio's performance analytics](../tutorials/Pull%20Portfolio%20Performance%20Data.ipynb)\\n\",\n \"\\n\",\n \"* [Retrieving the portfolio's factor risk and attribution analytics](../tutorials/Pull%20Portfolio%20Factor%20Risk%20Data.ipynb)\\n\",\n \"\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/01_PredefinedAssetEngine/040100_simple_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"44d93b81\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.backtests.triggers import DateTriggerRequirements, DateTrigger\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction\\n\",\n \"from gs_quant.backtests.core import ValuationFixingType\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.predefined_asset_engine import PredefinedAssetEngine\\n\",\n \"from gs_quant.backtests.data_sources import DataManager\\n\",\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.data import DataFrequency\\n\",\n \"\\n\",\n \"from random import random\\n\",\n \"import pandas as pd\\n\",\n \"from datetime import datetime, timedelta\\n\",\n \"\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2b213057\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trigger_times = [\\n\",\n \" datetime(2021, 1, 4, 14, 43, 0),\\n\",\n \" datetime(2021, 1, 6, 14, 43, 0),\\n\",\n \" datetime(2021, 1, 6, 14, 58, 0),\\n\",\n \" datetime(2021, 1, 7, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 7, 14, 58, 0),\\n\",\n \" datetime(2021, 1, 8, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 13, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 14, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 15, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 15, 14, 13, 0),\\n\",\n \" datetime(2021, 1, 15, 14, 58, 0),\\n\",\n \" datetime(2021, 1, 21, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 22, 14, 43, 0),\\n\",\n \" datetime(2021, 1, 22, 14, 58, 0),\\n\",\n \" datetime(2021, 1, 26, 14, 58, 0),\\n\",\n \" datetime(2021, 1, 27, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 27, 18, 58, 0),\\n\",\n \" datetime(2021, 1, 28, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 28, 14, 58, 0),\\n\",\n \" datetime(2021, 1, 29, 13, 28, 0),\\n\",\n \" datetime(2021, 1, 29, 14, 58, 0),\\n\",\n \" datetime(2021, 2, 1, 14, 43, 0),\\n\",\n \" datetime(2021, 2, 1, 14, 58, 0),\\n\",\n \" datetime(2021, 2, 3, 14, 43, 0),\\n\",\n \" datetime(2021, 2, 3, 14, 58, 0),\\n\",\n \" datetime(2021, 2, 4, 13, 28, 0),\\n\",\n \" datetime(2021, 2, 4, 14, 58, 0),\\n\",\n \" datetime(2021, 2, 5, 13, 28, 0),\\n\",\n \" datetime(2021, 2, 10, 13, 28, 0),\\n\",\n \" datetime(2021, 2, 11, 13, 28, 0),\\n\",\n \" datetime(2021, 2, 12, 14, 58, 0),\\n\",\n \" datetime(2021, 2, 17, 13, 28, 0),\\n\",\n \" datetime(2021, 2, 17, 14, 13, 0),\\n\",\n \" datetime(2021, 2, 18, 13, 28, 0),\\n\",\n \" datetime(2021, 2, 19, 14, 43, 0),\\n\",\n \" datetime(2021, 2, 19, 14, 58, 0),\\n\",\n \" datetime(2021, 2, 23, 14, 58, 0),\\n\",\n \" datetime(2021, 2, 24, 14, 58, 0),\\n\",\n \" datetime(2021, 2, 25, 13, 28, 0),\\n\",\n \" datetime(2021, 2, 26, 13, 28, 0),\\n\",\n \" datetime(2021, 2, 26, 14, 58, 0),\\n\",\n \" datetime(2021, 3, 1, 14, 43, 0),\\n\",\n \" datetime(2021, 3, 1, 14, 58, 0),\\n\",\n \" datetime(2021, 3, 3, 14, 43, 0),\\n\",\n \" datetime(2021, 3, 3, 14, 58, 0),\\n\",\n \" datetime(2021, 3, 4, 13, 28, 0),\\n\",\n \" datetime(2021, 3, 4, 14, 58, 0),\\n\",\n \" datetime(2021, 3, 5, 13, 28, 0),\\n\",\n \" datetime(2021, 3, 10, 13, 28, 0),\\n\",\n \"]\\n\",\n \"\\n\",\n \"start_date, end_date = min(trigger_times), max(trigger_times)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6e26342c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Asset and Prices of asset\\n\",\n \"# the asset defined here could be anything as we are going to provide the valuation of that asset.\\n\",\n \"asset = FXOption(buy_sell='Buy', option_type='Put', pair='EURUSD', notional_amount='10m', name='EURUSD')\\n\",\n \"\\n\",\n \"\\n\",\n \"# we need to provide the price of the asset in a data_manager\\n\",\n \"\\n\",\n \"data_manager = DataManager()\\n\",\n \"index = pd.date_range(start_date, end_date.date() + timedelta(days=1), freq='T')\\n\",\n \"# Here I am constructing random prices for the asset\\n\",\n \"price = [100]\\n\",\n \"for i in range(1, len(index)):\\n\",\n \" price.append(price[i - 1] + (-1 if random() < 0.5 else 1))\\n\",\n \"prices = pd.Series(price, index=index)\\n\",\n \"\\n\",\n \"eod_prices = prices.at_time('16:00').dropna()\\n\",\n \"\\n\",\n \"data_manager.add_data_source(prices, DataFrequency.REAL_TIME, asset, ValuationFixingType.PRICE)\\n\",\n \"\\n\",\n \"data_manager.add_data_source(eod_prices, DataFrequency.DAILY, asset, ValuationFixingType.PRICE)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"991db60a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# define the triggers, actions and strategy - this is common across all backtest engine implementations\\n\",\n \"\\n\",\n \"# the DateTrigger takes a list of dates when the an action should be taken\\n\",\n \"trig_req = DateTriggerRequirements(trigger_times)\\n\",\n \"# we are going to add our asset and hold it for 30 minutes\\n\",\n \"trigger = DateTrigger(trig_req, AddTradeAction(asset, pd.Timedelta(minutes=30)))\\n\",\n \"strategy = Strategy(None, trigger)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ddee6af3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# construct a PredefinedAssetEngine including our asset prices and then run the backtest\\n\",\n \"engine = PredefinedAssetEngine(data_manager)\\n\",\n \"bt = engine.run_backtest(strategy, start=start_date, end=end_date, frequency='D')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"468d4f3e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can look at the performance of the backtest\\n\",\n \"\\n\",\n \"bt.performance.plot()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"efeaa099\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# or we can look at a trade ledge which shows the trades that were entered and their individual pnls\\n\",\n \"bt.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f77f1e1d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6f12331a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/02_EquityVolEngine/040200_strategy_simple.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from gs_quant.target.common import UnderlierType\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2019, 9, 4)\\n\",\n \"end_date = date(2020, 9, 4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define instrument for strategy. EqOption or EqVarianceSwap are supported\\n\",\n \"\\n\",\n \"# Equity Option\\n\",\n \"instrument = EqOption(\\n\",\n \" 'SX5E',\\n\",\n \" underlier_type=UnderlierType.BBID,\\n\",\n \" expirationDate='3m',\\n\",\n \" strikePrice='ATM',\\n\",\n \" optionType=OptionType.Call,\\n\",\n \" optionStyle=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"# Uncomment to backtest Equity Variance Swap\\n\",\n \"# instrument = EqVarianceSwap('.STOXX50E', expirationDate='3m', strikePrice='ATM')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define a periodic trigger and action. Trade and roll the option instrument every 1m\\n\",\n \"trade_action = EnterPositionQuantityScaledAction(\\n\",\n \" priceables=instrument,\\n\",\n \" trade_duration='1m',\\n\",\n \" trade_quantity=1000,\\n\",\n \" trade_quantity_type=BacktestTradingQuantityType.quantity,\\n\",\n \")\\n\",\n \"trade_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create strategy\\n\",\n \"strategy = Strategy(initial_portfolio=None, triggers=[trade_trigger])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run backtest\\n\",\n \"backtest = EquityVolEngine.run_backtest(strategy, start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot the performance\\n\",\n \"pnl = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"pnl.plot(legend=True, label='PNL')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/02_EquityVolEngine/040201_strategy_delta_hedged.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from gs_quant.target.common import UnderlierType\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2019, 9, 4)\\n\",\n \"end_date = date(2020, 9, 4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define option instruments for strategy\\n\",\n \"option = EqOption(\\n\",\n \" 'SX5E',\\n\",\n \" underlierType=UnderlierType.BBID,\\n\",\n \" expirationDate='3m',\\n\",\n \" strikePrice='ATM',\\n\",\n \" optionType=OptionType.Call,\\n\",\n \" optionStyle=OptionStyle.European,\\n\",\n \")\\n\",\n \"\\n\",\n \"long_call = EqOption(\\n\",\n \" 'SX5E',\\n\",\n \" underlierType=UnderlierType.BBID,\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='ATM',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" buy_sell=BuySell.Buy,\\n\",\n \")\\n\",\n \"short_put = EqOption(\\n\",\n \" 'SX5E',\\n\",\n \" underlierType=UnderlierType.BBID,\\n\",\n \" expiration_date='3m',\\n\",\n \" strike_price='ATM',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" buy_sell=BuySell.Sell,\\n\",\n \")\\n\",\n \"\\n\",\n \"hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define a periodic trade trigger and action.\\n\",\n \"\\n\",\n \"# Trade and roll 1000 units of the option instrument every 1m\\n\",\n \"trade_action = EnterPositionQuantityScaledAction(\\n\",\n \" priceables=option,\\n\",\n \" trade_duration='1m',\\n\",\n \" trade_quantity=1000,\\n\",\n \" trade_quantity_type=BacktestTradingQuantityType.quantity,\\n\",\n \")\\n\",\n \"trade_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action,\\n\",\n \")\\n\",\n \"\\n\",\n \"# Hedge option delta every day using the synthetic forward\\n\",\n \"hedge_action = HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b')\\n\",\n \"hedge_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\\n\",\n \" actions=hedge_action,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create strategy\\n\",\n \"strategy = Strategy(initial_portfolio=None, triggers=[trade_trigger, hedge_trigger])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run backtest\\n\",\n \"backtest = EquityVolEngine.run_backtest(strategy, start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot the performance\\n\",\n \"pnl = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"pnl.plot(legend=True, label='PNL')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/02_EquityVolEngine/040202_strategy_pnl_decomposition.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from gs_quant.target.common import UnderlierType\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2019, 9, 4)\\n\",\n \"end_date = date(2020, 9, 4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define option instrument for strategy\\n\",\n \"option = EqOption(\\n\",\n \" 'SX5E',\\n\",\n \" underlierType=UnderlierType.BBID,\\n\",\n \" expirationDate='3m',\\n\",\n \" strikePrice='ATM',\\n\",\n \" optionType=OptionType.Call,\\n\",\n \" optionStyle=OptionStyle.European,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define a periodic trade trigger and action. Trade and roll 1000 units of the option instrument every 1m\\n\",\n \"trade_action = EnterPositionQuantityScaledAction(\\n\",\n \" priceables=option,\\n\",\n \" trade_duration='1m',\\n\",\n \" trade_quantity=1000,\\n\",\n \" trade_quantity_type=BacktestTradingQuantityType.quantity,\\n\",\n \")\\n\",\n \"trade_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create strategy\\n\",\n \"strategy = Strategy(initial_portfolio=None, triggers=[trade_trigger])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run backtest\\n\",\n \"backtest = EquityVolEngine.run_backtest(strategy, start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot the performance\\n\",\n \"pnl = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"pnl.plot(legend=True, label='PNL Total')\\n\",\n \"\\n\",\n \"pnl_carry = backtest.get_measure_series(FlowVolBacktestMeasure.PNL_carry)\\n\",\n \"pnl_carry.plot(legend=True, label='PNL Carry')\\n\",\n \"\\n\",\n \"pnl_vol = backtest.get_measure_series(FlowVolBacktestMeasure.PNL_vol)\\n\",\n \"pnl_vol.plot(legend=True, label='PNL Vol')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/02_EquityVolEngine/040203_strategy_with_signals.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from gs_quant.target.common import UnderlierType\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2019, 9, 4)\\n\",\n \"end_date = date(2020, 9, 4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define option instrument for strategy\\n\",\n \"option = EqOption(\\n\",\n \" 'SX5E',\\n\",\n \" underlierType=UnderlierType.BBID,\\n\",\n \" expirationDate='3m',\\n\",\n \" strikePrice='ATM',\\n\",\n \" optionType=OptionType.Call,\\n\",\n \" optionStyle=OptionStyle.European,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define a periodic trade action. Trade 1000 units of the option instrument and hold for 1m\\n\",\n \"trade_action = EnterPositionQuantityScaledAction(\\n\",\n \" priceables=option,\\n\",\n \" trade_duration='1m',\\n\",\n \" trade_quantity=1000,\\n\",\n \" trade_quantity_type=BacktestTradingQuantityType.quantity,\\n\",\n \")\\n\",\n \"\\n\",\n \"# Define an entry signal trigger. This is a list of dates when the entry signal is true.\\n\",\n \"# When entry signal is true, the strategy will enter a position (only if no position is already held)\\n\",\n \"entry_dates = (\\n\",\n \" date(2019, 9, 20),\\n\",\n \" date(2019, 10, 18),\\n\",\n \" date(2019, 11, 15),\\n\",\n \" date(2019, 12, 20),\\n\",\n \" date(2020, 1, 17),\\n\",\n \" date(2020, 2, 21),\\n\",\n \")\\n\",\n \"entry_trigger = AggregateTrigger(\\n\",\n \" trigger_requirements=AggregateTriggerRequirements(\\n\",\n \" [DateTriggerRequirements(dates=entry_dates), PortfolioTriggerRequirements('len', 0, TriggerDirection.EQUAL)]\\n\",\n \" ),\\n\",\n \" actions=trade_action,\\n\",\n \")\\n\",\n \"\\n\",\n \"# Define an exit signal trigger. This is a list of dates when the exit signal is true.\\n\",\n \"# When the exit signal is true the strategy will exit the full position (only if already holding a position)\\n\",\n \"exit_dates = (date(2019, 11, 4), date(2020, 3, 2))\\n\",\n \"exit_trigger = AggregateTrigger(\\n\",\n \" trigger_requirements=AggregateTriggerRequirements(\\n\",\n \" [DateTriggerRequirements(dates=exit_dates), PortfolioTriggerRequirements('len', 0, TriggerDirection.ABOVE)]\\n\",\n \" ),\\n\",\n \" actions=ExitTradeAction(),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create strategy\\n\",\n \"strategy = Strategy(initial_portfolio=None, triggers=[entry_trigger, exit_trigger])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run backtest\\n\",\n \"backtest = EquityVolEngine.run_backtest(strategy, start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot the performance\\n\",\n \"pnl = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"pnl.plot(legend=True, label='PNL Total')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/02_EquityVolEngine/040204_strategy_market_model.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3285e18c-43b9-403a-b9b4-c1b9d364ebfe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.instrument import EqOption, OptionType, OptionStyle\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from gs_quant.target.common import UnderlierType\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b745f15f-2962-4930-b3f2-6c90aee2b79a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3c167482-fd99-4a2b-8972-d235ca583c1f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2019, 9, 4)\\n\",\n \"end_date = date(2020, 9, 4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"997f3de3-1f8a-4f07-939c-f8ba74323351\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define option instruments for strategy\\n\",\n \"instrument = EqOption(\\n\",\n \" 'SX5E',\\n\",\n \" underlierType=UnderlierType.BBID,\\n\",\n \" expirationDate='3m',\\n\",\n \" strikePrice='ATM',\\n\",\n \" optionType=OptionType.Call,\\n\",\n \" optionStyle=OptionStyle.European,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2fc22284-6ef6-455d-bcfa-050583f5dd7d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define a periodic trigger and action. Trade and roll the option instrument every 1m\\n\",\n \"trade_action = EnterPositionQuantityScaledAction(\\n\",\n \" priceables=instrument,\\n\",\n \" trade_duration='1m',\\n\",\n \" trade_quantity=1000,\\n\",\n \" trade_quantity_type=BacktestTradingQuantityType.quantity,\\n\",\n \")\\n\",\n \"trade_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ee16b5f8-ada2-4ce1-bf71-508a2dcfe9ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create strategy\\n\",\n \"strategy = Strategy(initial_portfolio=None, triggers=[trade_trigger])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0b8a48e8-eb9b-49e2-a817-19acc28e20cb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run backtest with SFK market model (the default). Other options are SD and SVR\\n\",\n \"backtest = EquityVolEngine.run_backtest(strategy, start=start_date, end=end_date, market_model='SFK')\\n\",\n \"pnl_sfk = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"delta_sfk = backtest.get_measure_series(FlowVolBacktestMeasure.delta)\\n\",\n \"gamma_sfk = backtest.get_measure_series(FlowVolBacktestMeasure.gamma)\\n\",\n \"vega_sfk = backtest.get_measure_series(FlowVolBacktestMeasure.vega)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"31536684-42dd-41d5-ad87-a287e484dfb6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run backtest with SVR market model\\n\",\n \"backtest = EquityVolEngine.run_backtest(strategy, start=start_date, end=end_date, market_model='SVR')\\n\",\n \"pnl_svr = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"delta_svr = backtest.get_measure_series(FlowVolBacktestMeasure.delta)\\n\",\n \"gamma_svr = backtest.get_measure_series(FlowVolBacktestMeasure.gamma)\\n\",\n \"vega_svr = backtest.get_measure_series(FlowVolBacktestMeasure.vega)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fb3d4a7b-6a57-4519-bf9f-6495011464c8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Market model does not affect performance calculation\\n\",\n \"relative_pnl_diff = (pnl_svr - pnl_sfk) / pnl_sfk\\n\",\n \"relative_pnl_diff.plot(legend=True, label='Relative PnL diff')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"dd264604-3072-4ade-881f-7b4ef1836525\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Market model affects delta and gamma calculations\\n\",\n \"relative_delta_diff = (delta_svr - delta_sfk) / delta_sfk\\n\",\n \"relative_delta_diff.plot(legend=True, label='Relative delta diff')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"585b3d09-4675-41ed-9032-39ccb76704a1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"relative_gamma_diff = (gamma_svr - gamma_sfk) / gamma_sfk\\n\",\n \"relative_gamma_diff.plot(legend=True, label='Relative gamma diff')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"27a00835-d940-4960-9498-536298674603\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Market model does not affect the calculation of any other risk measures\\n\",\n \"relative_vega_diff = (vega_svr - vega_sfk) / vega_sfk\\n\",\n \"relative_vega_diff.plot(legend=True, label='Relative vega diff')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/02_EquityVolEngine/040205_strategy_scaled_add_action.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a53fde7e-f24f-4cb0-9962-9ed075790628\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6a9bc9fb-3914-49fc-b3cf-4e59d22fd8a2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import OptionStyle\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from datetime import datetime, date\\n\",\n \"\\n\",\n \"import gs_quant.risk as risk\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2a6175aa-bd47-4cd3-bead-dcebe4f5ea8d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5e2c6435-b2a0-4824-ae5c-4d4ef0f42ee4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2024, 4, 1)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"62435b59-ec26-4d5b-9b74-fd72396a468d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instruments for strategy\\n\",\n \"# Portfolio of two eq options\\n\",\n \"call = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='1m',\\n\",\n \" strike_price='ATM',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" name='call',\\n\",\n \")\\n\",\n \"put = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='1m',\\n\",\n \" strike_price='ATM',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" name='put',\\n\",\n \")\\n\",\n \"portfolio = Portfolio(name='portfolio', priceables=[call, put])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ea1412f0-055e-4954-9d05-527e93018181\",\n \"metadata\": {},\n \"source\": [\n \"### Entering a position monthly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"adb4fc9d-1c23-40a6-b109-3ca8425ad623\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Trade the position monthly without any scaling\\n\",\n \"trade_action = AddTradeAction(priceables=portfolio, trade_duration='1m', name='act')\\n\",\n \"trade_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"engine = EquityVolEngine\\n\",\n \"backtest = engine.run_backtest(strategy, start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"932678aa-cf61-4c71-b057-55a3ab81c241\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pnl = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"pnl.plot(legend=True, label='PNL')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d3c2217-f45b-4c84-a64f-afdc9e91940f\",\n \"metadata\": {},\n \"source\": [\n \"### Scaling quantity\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7998da4c-0a08-4698-97af-169654f1f11a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Scale the position quantity (no. of options) by 2\\n\",\n \"trade_action_scaled = AddScaledTradeAction(\\n\",\n \" priceables=portfolio,\\n\",\n \" trade_duration='1m',\\n\",\n \" scaling_type=ScalingActionType.size,\\n\",\n \" scaling_risk=None,\\n\",\n \" scaling_level=2,\\n\",\n \" name='scaled_act',\\n\",\n \")\\n\",\n \"trade_trigger_scaled = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action_scaled,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger_scaled)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"engine = EquityVolEngine\\n\",\n \"backtest = engine.run_backtest(strategy, start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"14d7e2f3-8f06-4a89-9486-9f5fb35363f1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# note the performance is twice the above graph\\n\",\n \"pnl = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"pnl.plot(legend=True, label='PNL')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b52d3f47-ab87-4074-ae34-3ef3bb7c54c9\",\n \"metadata\": {},\n \"source\": [\n \"### Scaling a risk measure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2dff6a31-583b-4858-8c7c-ef259f1a6659\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initial and final Vega of the portfolio\\n\",\n \"with PricingContext(start_date):\\n\",\n \" start_vega = portfolio.calc(risk.EqVega)\\n\",\n \"\\n\",\n \"with PricingContext(end_date):\\n\",\n \" end_vega = portfolio.calc(risk.EqVega)\\n\",\n \"\\n\",\n \"print(start_vega.aggregate())\\n\",\n \"print(end_vega.aggregate())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"01384407-a6c2-4083-8577-f8a98468a429\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Scale the position vega to 100\\n\",\n \"trade_action_scaled = AddScaledTradeAction(\\n\",\n \" priceables=portfolio,\\n\",\n \" trade_duration='1m',\\n\",\n \" scaling_type=ScalingActionType.risk_measure,\\n\",\n \" scaling_risk=risk.EqVega,\\n\",\n \" scaling_level=100,\\n\",\n \" name='scaled_vega_act',\\n\",\n \")\\n\",\n \"trade_trigger_scaled = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action_scaled,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger_scaled)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"engine = EquityVolEngine\\n\",\n \"backtest = engine.run_backtest(strategy, start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"10ef8a2a-b290-40dc-ad05-e070897652c3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# note the scaling should be approx the 100/vega\\n\",\n \"pnl = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"pnl.plot(legend=True, label='PNL')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b5984b12-10ce-498a-9f78-335bd135aabe\",\n \"metadata\": {},\n \"source\": [\n \"### NAV Trading\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"417169e2-addf-4c0b-a355-cff4f35b106a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a NAV-based action that starts with 100 available to spend\\n\",\n \"start_date = date(2024, 1, 1)\\n\",\n \"end_date = datetime.today().date()\\n\",\n \"\\n\",\n \"trade_action_scaled = EnterPositionQuantityScaledAction(\\n\",\n \" priceables=portfolio,\\n\",\n \" trade_duration='1m',\\n\",\n \" trade_quantity=100,\\n\",\n \" trade_quantity_type=BacktestTradingQuantityType.NAV,\\n\",\n \" name='nav_act',\\n\",\n \")\\n\",\n \"trade_trigger_scaled = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action_scaled,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger_scaled)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"engine = EquityVolEngine\\n\",\n \"backtest = engine.run_backtest(strategy, start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3832acf6-370d-4a77-ab57-219134a9ee6f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pnl = backtest.get_measure_series(FlowVolBacktestMeasure.PNL)\\n\",\n \"pnl.plot(legend=True, label='PNL')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"19ba18f5-e905-4183-b47c-cd6cc7c74b0d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040300_strategy_periodic_trigger.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime, date\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.common import BuySell, OptionType\\n\",\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.risk import Price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Buy $100k 2y USDJPY ATMF call, roll monthly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2021, 6, 1)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instrument for strategy\\n\",\n \"\\n\",\n \"# FX Option\\n\",\n \"call = FXOption(\\n\",\n \" buy_sell=BuySell.Buy,\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" pair='USDJPY',\\n\",\n \" strike_price='ATMF',\\n\",\n \" expiration_date='2y',\\n\",\n \" name='2y_call',\\n\",\n \" premium=0,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Periodic trigger: based on frequency\\n\",\n \"freq = '1m'\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency=freq)\\n\",\n \"\\n\",\n \"# hold the trade for 1m\\n\",\n \"actions = AddTradeAction(call, freq)\\n\",\n \"\\n\",\n \"# starting with empty portfolio (first arg to Strategy), apply actions on trig_req\\n\",\n \"triggers = PeriodicTrigger(trig_req, actions)\\n\",\n \"strategy = Strategy(None, triggers)\\n\",\n \"\\n\",\n \"# run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040301_strategy_risk_trigger.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime, date\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.instrument import FXOption, FXForward\\n\",\n \"from gs_quant.common import BuySell, OptionType, AggregationLevel\\n\",\n \"from gs_quant.backtests.triggers import (\\n\",\n \" PeriodicTrigger,\\n\",\n \" PeriodicTriggerRequirements,\\n\",\n \" StrategyRiskTrigger,\\n\",\n \" RiskTriggerRequirements,\\n\",\n \" TriggerDirection,\\n\",\n \")\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction, HedgeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.risk import Price, FXDelta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Buy $100k 1y EURUSD ATMF call, roll monthly, delta hedge when breach of +50k\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2021, 6, 1)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instrument for strategy\\n\",\n \"\\n\",\n \"# FX Option\\n\",\n \"call = FXOption(\\n\",\n \" buy_sell=BuySell.Buy,\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" pair='EURUSD',\\n\",\n \" strike_price='ATMF',\\n\",\n \" expiration_date='1y',\\n\",\n \" notional_amount=1e5,\\n\",\n \" name='1y_call',\\n\",\n \" premium=0,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Let's look at FX Delta for strategy without hedging\\n\",\n \"\\n\",\n \"# Define frequency for adding trade\\n\",\n \"freq_add = '1m'\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency=freq_add)\\n\",\n \"action_add = AddTradeAction(call, freq_add)\\n\",\n \"\\n\",\n \"# Starting with empty portfolio (first arg to Strategy), apply actions in order on trig_req\\n\",\n \"triggers = PeriodicTrigger(trig_req, action_add)\\n\",\n \"strategy = Strategy(None, triggers)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(\\n\",\n \" strategy,\\n\",\n \" start=start_date,\\n\",\n \" end=end_date,\\n\",\n \" frequency='1b',\\n\",\n \" show_progress=True,\\n\",\n \" risks=[Price, FXDelta(aggregation_level=AggregationLevel.Type, currency='USD')],\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View FX Delta risk\\n\",\n \"b1 = pd.DataFrame(\\n\",\n \" {'No Hedge': backtest.result_summary[FXDelta(aggregation_level=AggregationLevel.Type, currency='USD')]}\\n\",\n \")\\n\",\n \"b1.plot(figsize=(10, 6), title='FX Delta')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Let's hedge delta when it is above +50k\\n\",\n \"\\n\",\n \"# Define frequency for adding trade\\n\",\n \"freq_add = '1m'\\n\",\n \"trig_req_add = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency=freq_add)\\n\",\n \"action_add = AddTradeAction(call, freq_add)\\n\",\n \"trigger_add = PeriodicTrigger(trig_req_add, action_add)\\n\",\n \"\\n\",\n \"# Define risk to hedge\\n\",\n \"hedge_risk = FXDelta(aggregation_level='Type', currency='USD')\\n\",\n \"\\n\",\n \"# Define FX Forward to hedge with\\n\",\n \"fwd_hedge = FXForward(pair='EURUSD', settlement_date='1y', notional_amount=1e5, name='1y_forward')\\n\",\n \"action_hedge = HedgeAction(hedge_risk, fwd_hedge)\\n\",\n \"\\n\",\n \"# Define hedge triggers\\n\",\n \"trig_req_hedge = RiskTriggerRequirements(risk=hedge_risk, trigger_level=50e3, direction=TriggerDirection.ABOVE)\\n\",\n \"trigger_risk = StrategyRiskTrigger(trig_req_hedge, action_hedge)\\n\",\n \"\\n\",\n \"# Starting with empty portfolio (first arg to Strategy), apply actions in order on trig_req\\n\",\n \"strategy = Strategy(None, [trigger_add, trigger_risk])\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View FX Delta risk\\n\",\n \"b2 = pd.DataFrame(\\n\",\n \" {'Hedge above +50k': backtest.result_summary[FXDelta(aggregation_level=AggregationLevel.Type, currency='USD')]}\\n\",\n \")\\n\",\n \"b2.plot(figsize=(10, 6), title='FX Delta')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Let's compare the two results\\n\",\n \"b1.columns = ['No Hedge']\\n\",\n \"b2.columns = ['Hedge above +50k']\\n\",\n \"pd.concat([b1, b2], axis=1).plot(figsize=(10, 6))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040302_strategy_delta_hedge_FX.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime, date\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.instrument import FXOption, FXForward\\n\",\n \"from gs_quant.common import BuySell, OptionType, AggregationLevel\\n\",\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction, HedgeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.risk import Price, FXDelta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Buy $100k 2y USDJPY ATMF call, roll monthly, delta hedge monthly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2021, 6, 1)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instrument for strategy\\n\",\n \"\\n\",\n \"# FX Option\\n\",\n \"call = FXOption(\\n\",\n \" buy_sell=BuySell.Buy,\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" pair='USDJPY',\\n\",\n \" strike_price='ATMF',\\n\",\n \" expiration_date='2y',\\n\",\n \" notional_amount=1e6,\\n\",\n \" name='2y_call',\\n\",\n \" premium=0,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Risk Trigger: based on frequency threshold, delta hedge by Forward trade\\n\",\n \"\\n\",\n \"# Define frequency for adding trade\\n\",\n \"freq_add = '1m'\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency=freq_add)\\n\",\n \"action_add = AddTradeAction(call, freq_add)\\n\",\n \"\\n\",\n \"# Define trade to hedge FX Delta\\n\",\n \"freq_hedge = '1m'\\n\",\n \"fwd_hedge = FXForward(pair='USDJPY', settlement_date='2y', name='2y_forward')\\n\",\n \"hedge_risk = FXDelta(currency='USD', aggregation_level='Type')\\n\",\n \"action_hedge = HedgeAction(hedge_risk, fwd_hedge, freq_hedge)\\n\",\n \"\\n\",\n \"# starting with empty portfolio (first arg to Strategy), apply actions in order on trig_req\\n\",\n \"triggers = PeriodicTrigger(trig_req, [action_add, action_hedge])\\n\",\n \"strategy = Strategy(None, triggers)\\n\",\n \"\\n\",\n \"# run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View FX Delta risk\\n\",\n \"pd.DataFrame(\\n\",\n \" {'Generic backtester': backtest.result_summary[FXDelta(aggregation_level=AggregationLevel.Type, currency='USD')]}\\n\",\n \").plot(figsize=(10, 6), title='FX Delta')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040303_strategy_delta_hedge_Rates.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime, date\\n\",\n \"from gs_quant.instrument import IRSwap, IRSwaption\\n\",\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction, HedgeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.risk import Price, IRDelta\\n\",\n \"from gs_quant.common import Currency\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Sell swaption monthly, delta hedge monthly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2021, 6, 1)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instrument for strategy\\n\",\n \"\\n\",\n \"# IR Swaption\\n\",\n \"option = IRSwaption(expiration_date='1m', termination_date='30y', notional_currency=Currency.EUR, buy_sell='Sell')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Risk Trigger: based on frequency threshold, delta hedge by swap trade\\n\",\n \"\\n\",\n \"# Define frequency for adding trade\\n\",\n \"freq_add = '1m'\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency=freq_add)\\n\",\n \"action_add = AddTradeAction(option, 'expiration_date')\\n\",\n \"\\n\",\n \"# Define trade to hedge Delta\\n\",\n \"freq_hedge = '1m'\\n\",\n \"swap_hedge = IRSwap(termination_date='30y', notional_currency=Currency.EUR, notional_amount=100e6, name='30yhedge')\\n\",\n \"hedge_risk = IRDelta(aggregation_level='Type', currency=Currency.USD)\\n\",\n \"action_hedge = HedgeAction(hedge_risk, swap_hedge)\\n\",\n \"\\n\",\n \"# Starting with empty portfolio (first arg to Strategy), apply actions in order on trig_req\\n\",\n \"triggers = PeriodicTrigger(trig_req, [action_add, action_hedge])\\n\",\n \"strategy = Strategy(None, triggers)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"# The results will be in local ccy, EUR in this case. To change them to USD, specify result_ccy in the backtest args\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Plot results\\n\",\n \"df = backtest.result_summary\\n\",\n \"df['Performance'] = df[Price] + df['Cumulative Cash']\\n\",\n \"df.plot(figsize=(10, 10)).legend(bbox_to_anchor=(1, 1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040304_strategy_mean_reversion.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime, date\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.backtests.triggers import MeanReversionTrigger, MeanReversionTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.data_sources import GenericDataSource, MissingDataStrategy\\n\",\n \"from gs_quant.risk import Price\\n\",\n \"from gs_quant.common import Currency, PayReceive\\n\",\n \"from gs_quant.data import Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Buy 10y EUR payers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2021, 6, 1)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instrument for strategy\\n\",\n \"\\n\",\n \"# IR Swap\\n\",\n \"swap = IRSwap(\\n\",\n \" pay_or_receive=PayReceive.Pay,\\n\",\n \" termination_date='10y',\\n\",\n \" notional_currency=Currency.EUR,\\n\",\n \" notional_amount=1e4,\\n\",\n \" fixed_rate='ATM',\\n\",\n \" name='swap_10y',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Dataset from Marquee Data Catalog: https://marquee.gs.com/s/developer/datasets/SWAPRATES_STANDARD\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get data set for 10y EUR swap rates\\n\",\n \"ds = Dataset('SWAPRATES_STANDARD')\\n\",\n \"data = ds.get_data(start_date, assetId=['MA5WM2QWRVMYKDK0'])\\n\",\n \"data_10y = data.loc[data['tenor'] == '10y']\\n\",\n \"data_10y.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Mean Reversion Trigger\\n\",\n \"\\n\",\n \"# Define our actions: add the swap\\n\",\n \"action = AddTradeAction(swap)\\n\",\n \"\\n\",\n \"# Define data source\\n\",\n \"s = pd.Series(data_10y['rate'].to_dict())\\n\",\n \"data_source = GenericDataSource(s, MissingDataStrategy.fill_forward)\\n\",\n \"\\n\",\n \"# Define bounds\\n\",\n \"z_score_bound = 2\\n\",\n \"rolling_mean_window = 30\\n\",\n \"rolling_std_window = 30\\n\",\n \"\\n\",\n \"# Build trigger requirements\\n\",\n \"# Sell when value hits z_score_bound on up side, Buy when value hits z_score_bound on down side\\n\",\n \"# Close out position when value crosses mean for the rolling_mean_window\\n\",\n \"trig_req = MeanReversionTriggerRequirements(data_source, z_score_bound, rolling_mean_window, rolling_std_window)\\n\",\n \"trigger = MeanReversionTrigger(trig_req, action)\\n\",\n \"strategy = Strategy(None, trigger)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040305_strategy_exit_trade_action.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date, datetime\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.common import BuySell, AggregationLevel, Currency\\n\",\n \"from gs_quant.backtests.triggers import StrategyRiskTrigger, RiskTriggerRequirements, TriggerDirection\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction, ExitTradeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.risk import Price, IRDelta\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2021, 6, 1)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instruments for strategy\\n\",\n \"\\n\",\n \"# USD swaption\\n\",\n \"swaption = IRSwaption(\\n\",\n \" expiration_date='6m',\\n\",\n \" termination_date='10y',\\n\",\n \" notional_currency=Currency.USD,\\n\",\n \" buy_sell=BuySell.Sell,\\n\",\n \" strike='=solvefor(25e3,bp)',\\n\",\n \" name='swaption10y',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Start with USD swaption on start_date, rebalance when IR Delta breaches +75k\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define risk to hedge\\n\",\n \"rebal_risk = IRDelta(aggregation_level=AggregationLevel.Type, currency='USD')\\n\",\n \"\\n\",\n \"# Define trade actions when breach\\n\",\n \"action_exit = ExitTradeAction()\\n\",\n \"action_add = AddTradeAction(swaption)\\n\",\n \"\\n\",\n \"# Define rebalance triggers\\n\",\n \"trig_req = RiskTriggerRequirements(risk=rebal_risk, trigger_level=1e3, direction=TriggerDirection.ABOVE)\\n\",\n \"# Order is important here, we first want to exit all positions in portfolio, then add the new trade\\n\",\n \"trigger_risk = StrategyRiskTrigger(trig_req, [action_exit, action_add])\\n\",\n \"\\n\",\n \"# Starting with a swaption (first arg to Strategy), apply actions in order\\n\",\n \"strategy = Strategy(swaption, trigger_risk)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger - this includes all trades that were added and removed excluding starting portfolio\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040306_strategy_mkt_trigger.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.common import BuySell, OptionType\\n\",\n \"from gs_quant.backtests.triggers import MktTrigger, MktTriggerRequirements, TriggerDirection\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.data_sources import GenericDataSource, MissingDataStrategy\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.risk import Price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Buy 1m USDCNH Put if spot below 6.42\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2021, 8, 1)\\n\",\n \"end_date = date(2022, 2, 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Define instrument for strategy\\n\",\n \"\\n\",\n \"# FX Option\\n\",\n \"put = FXOption(\\n\",\n \" buy_sell=BuySell.Buy,\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" pair='USDCNH',\\n\",\n \" strike_price='ATMF',\\n\",\n \" expiration_date='1m',\\n\",\n \" name='1m_put',\\n\",\n \" premium=0,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Dataset from Marquee Data Catalog: https://marquee.gs.com/s/developer/datasets/FXSPOT_PREMIUM\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"is_executing\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Get data set for USDCNH spot\\n\",\n \"ds = Dataset('FXSPOT_PREMIUM')\\n\",\n \"data = ds.get_data(start_date, assetId=['MAEFRJZ9NYGDDR41'])\\n\",\n \"s = pd.Series(data['spot'].to_dict())\\n\",\n \"data_source = GenericDataSource(s, MissingDataStrategy.fill_forward)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View historical spot data\\n\",\n \"s.plot(figsize=(10, 6), title='USDCNH Spot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define trade to add\\n\",\n \"action_add = AddTradeAction(put)\\n\",\n \"\\n\",\n \"# Define market trigger strategy\\n\",\n \"mkt_trigger = MktTrigger(MktTriggerRequirements(data_source, 6.42, TriggerDirection.BELOW), action_add)\\n\",\n \"strategy = Strategy(None, mkt_trigger)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040307_strategy_seagull_bullish.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import AggregationLevel\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.markets import HistoricalPricingContext\\n\",\n \"from gs_quant.risk import IRDelta, Price, IRFwdRate, IRDailyImpliedVol, DollarPrice\\n\",\n \"from datetime import date\\n\",\n \"from gs_quant.datetime import business_day_offset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Seagull params\\n\",\n \"start = date(2020, 1, 1)\\n\",\n \"end = business_day_offset(date.today(), -1, roll='preceding')\\n\",\n \"\\n\",\n \"# Add trades at what frequency\\n\",\n \"freq_int = 1\\n\",\n \"ramp_up_freq = 'w'\\n\",\n \"freq = str(freq_int) + ramp_up_freq\\n\",\n \"ramp_up_mult = 5\\n\",\n \"ramp_up_period = ramp_up_mult * freq_int - 1\\n\",\n \"holding_period = str(ramp_up_period) + ramp_up_freq\\n\",\n \"strategy_notional = 100e6\\n\",\n \"\\n\",\n \"print(freq, holding_period)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Buy call spread, sell out-of-the-money put\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Portfolio def\\n\",\n \"\\n\",\n \"ccy = 'USD'\\n\",\n \"expiry = '1y'\\n\",\n \"tail = '10y'\\n\",\n \"rate_option = 'USD-LIBOR-BBA'\\n\",\n \"pay_rec = ['Receiver', 'Receive', 'Pay']\\n\",\n \"strikes = ['A-50', 'A-25', 'A+50']\\n\",\n \"direction = [-1, 1, -1]\\n\",\n \"notional_inception = round(strategy_notional / (ramp_up_period + 1), 0)\\n\",\n \"\\n\",\n \"port = []\\n\",\n \"for i in range(len(pay_rec)):\\n\",\n \" opt = IRSwaption(\\n\",\n \" pay_or_receive=pay_rec[i],\\n\",\n \" termination_date=tail,\\n\",\n \" notional_currency=ccy,\\n\",\n \" expiration_date=expiry,\\n\",\n \" notional_amount=direction[i] * notional_inception,\\n\",\n \" strike=strikes[i],\\n\",\n \" floating_rate_option=rate_option,\\n\",\n \" name='swaption' + str(i),\\n\",\n \" )\\n\",\n \" port.append(opt)\\n\",\n \"\\n\",\n \"csa_string = ccy + '-1'\\n\",\n \"\\n\",\n \"seagull = Portfolio(port, name='Seagull')\\n\",\n \"seagull.to_frame().transpose()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define deltas\\n\",\n \"myParallelDelta = IRDelta(currency='local', aggregation_level=AggregationLevel.Type)\\n\",\n \"\\n\",\n \"# Define backtest\\n\",\n \"trade_action = AddTradeAction(seagull, holding_period)\\n\",\n \"trigger = PeriodicTrigger(PeriodicTriggerRequirements(start_date=start, end_date=end, frequency=freq), [trade_action])\\n\",\n \"strategy = Strategy(None, trigger)\\n\",\n \"\\n\",\n \"# Select an engine\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(\\n\",\n \" strategy,\\n\",\n \" start=start,\\n\",\n \" end=end,\\n\",\n \" frequency=freq,\\n\",\n \" risks=[Price, myParallelDelta],\\n\",\n \" show_progress=True,\\n\",\n \" csa_term=csa_string,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"backtest_summary = backtest.result_summary\\n\",\n \"backtest_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"backtest_dates_reduced = backtest_summary.index\\n\",\n \"\\n\",\n \"with HistoricalPricingContext(dates=backtest_dates_reduced, show_progress=True):\\n\",\n \" ir_rates = seagull[0].calc([IRFwdRate, IRDailyImpliedVol])\\n\",\n \" entry = seagull.calc([DollarPrice])\\n\",\n \"\\n\",\n \"\\n\",\n \"premium = entry.result().to_frame()\\n\",\n \"raw_data = ir_rates.result().to_frame()\\n\",\n \"raw_data[IRFwdRate] *= 1e4\\n\",\n \"raw_data['entry_premium'] = premium.iloc[:, 0] + premium.iloc[:, 1] + premium.iloc[:, 2]\\n\",\n \"raw_data.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(backtest_summary['Cumulative Cash'] / 1e6).plot(\\n\",\n \" title='Rolling Seagull vs Delta Proxy', legend=True, label='Strategy', figsize=(20, 10)\\n\",\n \")\\n\",\n \"(backtest_summary.iloc[5, 1] * (raw_data[IRFwdRate] - raw_data[IRFwdRate][0]) / 1e6).plot(\\n\",\n \" legend=True, label=f'Delta Proxy ({round(backtest_summary.iloc[5, 1] / 1e3, 0)}k per bp)'\\n\",\n \")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040308_rebalance_action.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"66f09374-d47a-440a-919a-33eeb87cf4da\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import OptionStyle\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.target.measures import Price\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4711538f-454f-43a4-ab94-687d39782579\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"eb25d59a-8c1d-4e53-a92f-e28937ca34e6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2022, 1, 1)\\n\",\n \"end_date = date(2022, 2, 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"09e44a2e-fdb5-4781-b65a-aa4b968948f5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instruments for strategy\\n\",\n \"# Portfolio of two eq options\\n\",\n \"spx_opt = EqOption(\\n\",\n \" '.SPX',\\n\",\n \" expiration_date='2m',\\n\",\n \" strike_price='ATM',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" name='spx',\\n\",\n \")\\n\",\n \"ndx_opt = EqOption(\\n\",\n \" '.NDX',\\n\",\n \" expiration_date='2m',\\n\",\n \" strike_price='ATM',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" name='ndx',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f6068e9e-e104-4816-ab42-609ed3be3adf\",\n \"metadata\": {},\n \"source\": [\n \"## RebalanceAction rebalances a trade's quantity according to a custom function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"12c81ab7-d0ab-462f-be30-fd1f45edf12a\",\n \"metadata\": {},\n \"source\": [\n \"#### This function returns the no. of .NDX options held\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"95e58596-1fdd-4e35-b057-49193b180830\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def match_ndx_holding(state, backtest, info):\\n\",\n \" port = backtest.portfolio_dict\\n\",\n \" current_ndx_notional = sum(\\n\",\n \" [x.number_of_options for x in port[state].all_instruments if isinstance(x, EqOption) and x.underlier == '.NDX']\\n\",\n \" )\\n\",\n \"\\n\",\n \" return current_ndx_notional\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9fdebe41-933b-4ee5-a6cf-d0b3cac8ff79\",\n \"metadata\": {},\n \"source\": [\n \"#### AddTradeAction adds .NDX options daily, RebalanceAction rebalances the .SPX option according to the total number of .NDX options held weekly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4fdd6cdd-7368-4c68-bb25-3c1fcc112e40\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Add NDX options daily\\n\",\n \"add_trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b')\\n\",\n \"add_ndx = AddTradeAction(ndx_opt, '2m', name='Action1')\\n\",\n \"add_trigger = PeriodicTrigger(add_trig_req, add_ndx)\\n\",\n \"\\n\",\n \"# Rebalance SPX option holdings weekly\\n\",\n \"rebalance_trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1w')\\n\",\n \"# Need to resolve an option before rebalancing it. In this case we want to rebalance the option we are starting with\\n\",\n \"with PricingContext(start_date):\\n\",\n \" spx_opt.resolve()\\n\",\n \"rebalance_spx = RebalanceAction(spx_opt, 'number_of_options', match_ndx_holding)\\n\",\n \"rebalance_trigger = PeriodicTrigger(rebalance_trig_req, rebalance_spx)\\n\",\n \"\\n\",\n \"strategy = Strategy(spx_opt, [add_trigger, rebalance_trigger])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c03ab015-0f64-4ef0-b2b7-0c69381e8e7d\",\n \"metadata\": {},\n \"source\": [\n \"#### Run the strategy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1005273b-7d8e-4170-9ba0-42317b0deb5d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, risks=[Price], end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"892047e1-7813-4b87-afe0-9f192f1f03c2\",\n \"metadata\": {},\n \"source\": [\n \"#### See total no. of options by underlier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"376e40c5-5341-4ebf-b304-86b1b2a7d2ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ts = backtest.strategy_as_time_series()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a3ca6a7e-87b7-44cd-ad97-6ca4b30ffb42\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ndx = ts[~ts.index.get_level_values('Instrument Name').str.contains('spx')]\\n\",\n \"ndx = ndx.groupby('Pricing Date').agg({('Static Instrument Data', 'number_of_options'): ['sum']})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"de82e4d0-7086-4c8f-a8c2-fef3803ad52e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spx = ts[~ts.index.get_level_values('Instrument Name').str.contains('ndx')]\\n\",\n \"spx = spx.groupby('Pricing Date').agg({('Static Instrument Data', 'number_of_options'): ['sum']})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"54e4bd65-3f5b-439e-aad6-6e2c8b397e02\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"plt.plot(ndx['Static Instrument Data']['number_of_options']['sum'], label='NDX')\\n\",\n \"plt.plot(spx['Static Instrument Data']['number_of_options']['sum'], label='SPX')\\n\",\n \"\\n\",\n \"plt.legend(prop={'size': 15})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5b32a18f-6377-43c2-9868-eedb3a40e8af\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"578c4bf6-f080-4e34-ab1d-ac2a6831d5ab\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040309_inflation_hedging_strategy.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime, date\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.instrument import InflationSwap\\n\",\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction, HedgeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.risk import Price, InflationDelta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Inflation strategy hedging a 50y inflation swap with a 30y inflation swap.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2024, 1, 3)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instruments for strategy\\n\",\n \"\\n\",\n \"# 50y inflation swap, 30y inflation swap which we will use to hedge\\n\",\n \"infl_hedge = InflationSwap(\\n\",\n \" pay_or_receive='Pay', termination_date='30y', notional_currency='EUR', notional_amount=100e6, name='30yhedge'\\n\",\n \")\\n\",\n \"infl = InflationSwap(\\n\",\n \" pay_or_receive='Receive', termination_date='50y', notional_currency='EUR', notional_amount=100e6, name='50y'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Periodic trigger: based on frequency\\n\",\n \"freq = '1b'\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency=freq)\\n\",\n \"\\n\",\n \"actions = [AddTradeAction(infl, '1m'), HedgeAction(InflationDelta(aggregation_level='Type'), infl_hedge, freq)]\\n\",\n \"triggers = PeriodicTrigger(trig_req, actions)\\n\",\n \"strategy = Strategy(None, triggers)\\n\",\n \"\\n\",\n \"# run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040310_initial_swap_hedging_strategy_varying_CSA.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import datetime, date\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import HedgeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.risk import Price, IRDelta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Inflation strategy hedging a 50y inflation swap with a 30y inflation swap.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2024, 1, 3)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instruments for strategy\\n\",\n \"\\n\",\n \"# initial holding of 50y swap hedge with daily 30y swaps where the CMS of the main swap and the hedge are different\\n\",\n \"swap_hedge = IRSwap(\\n\",\n \" pay_or_receive='Receive',\\n\",\n \" termination_date='30y',\\n\",\n \" notional_currency='EUR',\\n\",\n \" notional_amount=100e6,\\n\",\n \" fixed_rate='ATM',\\n\",\n \" name='30yhedge',\\n\",\n \")\\n\",\n \"swap = IRSwap(\\n\",\n \" pay_or_receive='Pay',\\n\",\n \" termination_date='50y',\\n\",\n \" notional_currency='EUR',\\n\",\n \" notional_amount=100e6,\\n\",\n \" fixed_rate='ATM',\\n\",\n \" name='50y',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Periodic trigger: based on frequency\\n\",\n \"freq = \\\"1b\\\"\\n\",\n \"\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency=freq)\\n\",\n \"actions_hedge = HedgeAction(IRDelta(aggregation_level='Type'), swap_hedge, csa_term='EUR-OIS')\\n\",\n \"triggers = PeriodicTrigger(trig_req, actions_hedge)\\n\",\n \"strategy = Strategy([swap], triggers)\\n\",\n \"\\n\",\n \"# run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040311_handling_holidays.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"from gs_quant.instrument import IRSwap\\n\",\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Simple backtest with no holiday calendar handling.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2024, 5, 3)\\n\",\n \"end_date = date(2024, 6, 3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define strategy -- this strategy adds a swap every day holding forever.\\n\",\n \"\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b')\\n\",\n \"irswap = IRSwap('Pay', '10y', 'USD', notional_amount=10000, name='10y')\\n\",\n \"actions = AddTradeAction(irswap)\\n\",\n \"triggers = PeriodicTrigger(trig_req, actions)\\n\",\n \"strategy = Strategy(None, triggers)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\\n\",\n \"backtest.result_summary\\n\",\n \"\\n\",\n \"# Note that May 27th appears in result summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can see that a trade was opened on 27th May 2024 dispite it being a holiday because we didn't say anything about holidays in our strategy\\n\",\n \"date(2024, 5, 27) in list(backtest.trade_ledger()['Open'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# now change the trigger not to trigger on exclude May 27th as it was memorial day\\n\",\n \"holiday = (date(2024, 5, 27),)\\n\",\n \"\\n\",\n \"trig_req_exclude_hol = PeriodicTriggerRequirements(\\n\",\n \" start_date=start_date, end_date=end_date, frequency='1b', calendar=holiday\\n\",\n \")\\n\",\n \"triggers_exc_hol = PeriodicTrigger(trig_req_exclude_hol, actions)\\n\",\n \"strategy_exc_hol = Strategy(None, triggers_exc_hol)\\n\",\n \"\\n\",\n \"backtest2 = GE.run_backtest(strategy_exc_hol, start=start_date, end=end_date, frequency='1b', show_progress=True)\\n\",\n \"backtest2.result_summary\\n\",\n \"\\n\",\n \"# note May 27th still appears on result summary as valuing trades is done on the schedule created by run_backtest which didn't include this holiday\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# we can see that no trade was opened on 27th May 2024 because the trigger knew it wasn't a valid day\\n\",\n \"date(2024, 5, 27) in list(backtest2.trade_ledger()['Open'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# now lets run the strategy above excluding holidays at the run_backtest level\\n\",\n \"backtest3 = GE.run_backtest(\\n\",\n \" strategy_exc_hol, start=start_date, end=end_date, frequency='1b', show_progress=True, holiday_calendar=holiday\\n\",\n \")\\n\",\n \"backtest3.result_summary\\n\",\n \"\\n\",\n \"# now we can see that May 27th is excluded from the result_summary. No trades were created on this date and no risks run.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Hang on. What if the action specifies an exit date for a trade and that falls on a holiday!?\\n\",\n \"\\n\",\n \"actions_roll_daily = AddTradeAction(irswap, '1b')\\n\",\n \"triggers_roll_daily = PeriodicTrigger(trig_req_exclude_hol, actions_roll_daily)\\n\",\n \"strategy_roll_daily = Strategy(None, triggers_roll_daily)\\n\",\n \"\\n\",\n \"backtest4 = GE.run_backtest(\\n\",\n \" strategy_roll_daily, start=start_date, end=end_date, frequency='1b', show_progress=True, holiday_calendar=holiday\\n\",\n \")\\n\",\n \"backtest4.result_summary\\n\",\n \"\\n\",\n \"# now we see that May 27th is back... That is because the action didn't know that it couldn't exit a trade on that date.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"date(2024, 5, 27) in list(backtest4.trade_ledger()['Open'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"date(2024, 5, 27) in list(backtest4.trade_ledger()['Close'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# so now lets tell the action that 27th May is a holiday\\n\",\n \"\\n\",\n \"actions_roll_daily_hol = AddTradeAction(irswap, '1b', holiday_calendar=holiday)\\n\",\n \"triggers_roll_daily_hol = PeriodicTrigger(trig_req_exclude_hol, actions_roll_daily_hol)\\n\",\n \"strategy_roll_daily_hol = Strategy(None, triggers_roll_daily_hol)\\n\",\n \"\\n\",\n \"backtest5 = GE.run_backtest(\\n\",\n \" strategy_roll_daily_hol,\\n\",\n \" start=start_date,\\n\",\n \" end=end_date,\\n\",\n \" frequency='1b',\\n\",\n \" show_progress=True,\\n\",\n \" holiday_calendar=holiday,\\n\",\n \")\\n\",\n \"backtest5.result_summary\\n\",\n \"\\n\",\n \"\\n\",\n \"# success\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040312_strategy_scaled_add_action.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a53fde7e-f24f-4cb0-9962-9ed075790628\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6a9bc9fb-3914-49fc-b3cf-4e59d22fd8a2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import OptionStyle\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.risk import Price\\n\",\n \"from datetime import datetime, date\\n\",\n \"\\n\",\n \"import gs_quant.risk as risk\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2a6175aa-bd47-4cd3-bead-dcebe4f5ea8d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5e2c6435-b2a0-4824-ae5c-4d4ef0f42ee4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2024, 4, 1)\\n\",\n \"end_date = datetime.today().date()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"62435b59-ec26-4d5b-9b74-fd72396a468d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define instruments for strategy\\n\",\n \"# Portfolio of two eq options\\n\",\n \"call = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='1m',\\n\",\n \" strike_price='ATM',\\n\",\n \" option_type=OptionType.Call,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" name='call',\\n\",\n \")\\n\",\n \"put = EqOption(\\n\",\n \" '.STOXX50E',\\n\",\n \" expiration_date='1m',\\n\",\n \" strike_price='ATM',\\n\",\n \" option_type=OptionType.Put,\\n\",\n \" option_style=OptionStyle.European,\\n\",\n \" name='put',\\n\",\n \")\\n\",\n \"portfolio = Portfolio(name='portfolio', priceables=[call, put])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ea1412f0-055e-4954-9d05-527e93018181\",\n \"metadata\": {},\n \"source\": [\n \"### Entering a position monthly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"adb4fc9d-1c23-40a6-b109-3ca8425ad623\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Trade the position monthly without any scaling\\n\",\n \"trade_action = AddTradeAction(priceables=portfolio, trade_duration='1m', name='act')\\n\",\n \"trade_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"932678aa-cf61-4c71-b057-55a3ab81c241\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9c126b67-aecf-4493-9ac6-397b984852ed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3b612c5d-3855-4a23-bff9-cca08fdfd767\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a1d2cf05-0622-4899-af32-be9dc0fad840\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d3c2217-f45b-4c84-a64f-afdc9e91940f\",\n \"metadata\": {},\n \"source\": [\n \"### Scaling quantity\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7998da4c-0a08-4698-97af-169654f1f11a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Scale the position quantity (no. of options) by 2\\n\",\n \"trade_action_scaled = AddScaledTradeAction(\\n\",\n \" priceables=portfolio,\\n\",\n \" trade_duration='1m',\\n\",\n \" scaling_type=ScalingActionType.size,\\n\",\n \" scaling_risk=None,\\n\",\n \" scaling_level=2,\\n\",\n \" name='scaled_act',\\n\",\n \")\\n\",\n \"trade_trigger_scaled = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action_scaled,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger_scaled)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"14d7e2f3-8f06-4a89-9486-9f5fb35363f1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary. Note that MtM and cash payments are double\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f1d811a8-2ca1-467d-8d1a-a8b164fbfd23\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger. Note that all values are double\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9f805bfe-9f5f-402e-a21e-0e08e40a55de\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bd2576e9-de1b-4955-80fa-632c02897e68\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b52d3f47-ab87-4074-ae34-3ef3bb7c54c9\",\n \"metadata\": {},\n \"source\": [\n \"### Scaling a risk measure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2dff6a31-583b-4858-8c7c-ef259f1a6659\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initial and final Vega of the portfolio\\n\",\n \"with PricingContext(start_date):\\n\",\n \" start_vega = portfolio.calc(risk.EqVega)\\n\",\n \"\\n\",\n \"with PricingContext(end_date):\\n\",\n \" end_vega = portfolio.calc(risk.EqVega)\\n\",\n \"\\n\",\n \"print(start_vega.aggregate())\\n\",\n \"print(end_vega.aggregate())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"01384407-a6c2-4083-8577-f8a98468a429\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Scale the position vega to 100\\n\",\n \"trade_action_scaled = AddScaledTradeAction(\\n\",\n \" priceables=portfolio,\\n\",\n \" trade_duration='1m',\\n\",\n \" scaling_type=ScalingActionType.risk_measure,\\n\",\n \" scaling_risk=risk.EqVega,\\n\",\n \" scaling_level=100,\\n\",\n \" name='scaled_vega_act',\\n\",\n \")\\n\",\n \"trade_trigger_scaled = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action_scaled,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger_scaled)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(\\n\",\n \" strategy, start=start_date, end=end_date, frequency='1b', show_progress=True, risks=risk.EqVega\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"10ef8a2a-b290-40dc-ad05-e070897652c3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary. Note that vega on first of month is 100\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"25b76af9-3b23-4b56-9307-b88dc7c3c5ef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger. Note that all values are around 10x larger than without scaling\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4ccd5bcd-bd52-4fb2-ba29-e2c72c0d3892\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f14aabf9-1964-4002-9a4a-48032438f53d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b5984b12-10ce-498a-9f78-335bd135aabe\",\n \"metadata\": {},\n \"source\": [\n \"### NAV Trading\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"417169e2-addf-4c0b-a355-cff4f35b106a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create a NAV-based action that starts with 100 available to spend\\n\",\n \"start_date = date(2024, 1, 1)\\n\",\n \"end_date = datetime.today().date()\\n\",\n \"\\n\",\n \"trade_action_scaled = EnterPositionQuantityScaledAction(\\n\",\n \" priceables=portfolio,\\n\",\n \" trade_duration='1m',\\n\",\n \" trade_quantity=100,\\n\",\n \" trade_quantity_type=BacktestTradingQuantityType.NAV,\\n\",\n \" name='nav_act',\\n\",\n \")\\n\",\n \"trade_trigger_scaled = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action_scaled,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger_scaled)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3832acf6-370d-4a77-ab57-219134a9ee6f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary. The action trades the starting cash and only uses proceeds from selling options after that: note that Cumulative Cash is always the initial trade quantity\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3050b635-46dc-4725-bebb-e017b69efc47\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aeaf8396-b874-40b3-a4e3-78c9959aeb9a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market. If zero right before an order, future orders are all zero\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a0db5cf7-1e40-4505-8324-740028aebc30\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Performance. Maximum loss is 100 if there is no short selling\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"600d7d36-eb13-4099-ab3d-7525683836b4\",\n \"metadata\": {},\n \"source\": [\n \"### Add Scaled Action with Transaction Cost\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"38f96947-0729-410d-95e8-169b0e6a0fe0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Scale the position quantity (no. of options) by 2 with a transaction cost of 1 per trade\\n\",\n \"trade_action_scaled_tc = AddScaledTradeAction(\\n\",\n \" priceables=portfolio,\\n\",\n \" trade_duration='1m',\\n\",\n \" scaling_type=ScalingActionType.size,\\n\",\n \" scaling_risk=None,\\n\",\n \" scaling_level=2,\\n\",\n \" name='scaled_act',\\n\",\n \" transaction_cost=ConstantTransactionModel(1),\\n\",\n \")\\n\",\n \"trade_trigger_scaled_tc = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\\n\",\n \" actions=trade_action_scaled_tc,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger_scaled_tc)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f9fca96e-a17f-41b1-9a40-502036aa6753\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View results summary. we see the transaction costs\\n\",\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0b954e01-bd97-43f1-9188-fad34a2c149a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bcbd056e-8760-48dc-885f-3e1a1413d44f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View Mark to Market\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c633620b-85e5-4862-9172-1dd39bf5e162\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame(\\n\",\n \" [backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price], backtest.result_summary['Total']],\\n\",\n \" index=(['Without Transaction Cost', 'With Transaction Cost']),\\n\",\n \").T.plot()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c0c84855-26c1-4b29-9b18-af611b3e0f0c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040313_chained_actions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"431469a8-b15b-4a5d-8559-f8561ccb75f1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"\\n\",\n \"warnings.filterwarnings('ignore')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a53fde7e-f24f-4cb0-9962-9ed075790628\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6a9bc9fb-3914-49fc-b3cf-4e59d22fd8a2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXOption\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.triggers import *\\n\",\n \"from gs_quant.backtests.actions import *\\n\",\n \"from gs_quant.backtests.equity_vol_engine import *\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from datetime import date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2a6175aa-bd47-4cd3-bead-dcebe4f5ea8d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3911f5d4-3a4b-4fca-9404-315dfa03aea9\",\n \"metadata\": {},\n \"source\": [\n \"#### If we have a periodic trigger which triggers every 1w and has an add trade action which has a trade duration of 1w sometimes you can end up with a situation where you have 2 trades or 0 trades. \\n\",\n \"#### This is because the schedule of 1w dates for the trigger will derive a schedule of 1w dates and then adjust each date for holidays. Where as if you take a specific date and add a week this will not take note of the holiday and therefore may give a different date.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5e2c6435-b2a0-4824-ae5c-4d4ef0f42ee4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = date(2024, 3, 24)\\n\",\n \"end_date = date(2024, 5, 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2f30447d-103c-4792-adec-8c7f2516b617\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"call = FXOption(pair='EURUSD', expiration_date='3m', option_type='Call', name='call')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f62f44c9-d432-4212-9313-966c9e7b52ec\",\n \"metadata\": {},\n \"source\": [\n \"### Entering a position weekly\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"adb4fc9d-1c23-40a6-b109-3ca8425ad623\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# To demonstrate create add trade action with a duration of 1w on a periodic trigger of 1w across a holiday\\n\",\n \"hol_cal = (dt.date(2024, 3, 29), dt.date(2024, 4, 1))\\n\",\n \"trade_action = AddTradeAction(priceables=call, trade_duration='1w', name='weekly_duration', holiday_calendar=hol_cal)\\n\",\n \"trade_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(\\n\",\n \" start_date=start_date, end_date=end_date, frequency='1w', calendar=hol_cal\\n\",\n \" ),\\n\",\n \" actions=trade_action,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9c126b67-aecf-4493-9ac6-397b984852ed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2fd8f5f6-7521-4182-a0ec-193f39553860\",\n \"metadata\": {},\n \"source\": [\n \"#### note that the second trade closes on the 4th April but the third trade opens on the 5th.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7248340c-13b8-4c98-b837-1a5e4a12b88f\",\n \"metadata\": {},\n \"source\": [\n \"In order to fix this we can set the trade duration to \\\"next schedule\\\".\\n\",\n \"This will have the effect of forcing the close date to line up with the open date of the following trade.\\n\",\n \"\\n\",\n \"This should only be used if you want the trade duration of the trade and the frequency of the trigger to line up \\n\",\n \"and the frequency is not daily. Thereby chaining (or rolling) the positions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a1d2cf05-0622-4899-af32-be9dc0fad840\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hol_cal = (dt.date(2024, 3, 29), dt.date(2024, 4, 1))\\n\",\n \"trade_action = AddTradeAction(\\n\",\n \" priceables=call, trade_duration='next schedule', name='weekly_duration', holiday_calendar=hol_cal\\n\",\n \")\\n\",\n \"trade_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(\\n\",\n \" start_date=start_date, end_date=end_date, frequency='1w', calendar=hol_cal\\n\",\n \" ),\\n\",\n \" actions=trade_action,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(None, trade_trigger)\\n\",\n \"\\n\",\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"13b2bdc3-c710-4cd3-a020-23e8f1351151\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# View backtest trade ledger\\n\",\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ca734a86-f37f-497e-bd96-34044a95d70a\",\n \"metadata\": {},\n \"source\": [\n \"#### now note that all the trades start and end dates line up\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"294b7063-3aff-4322-a400-070bc0b1ce35\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/examples/03_GenericEngine/040314_gradual_entries.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2fac05c0-0169-4681-87b1-8abc3130be69\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\",\n \"from gs_quant.instrument import EqOption\\n\",\n \"from gs_quant.backtests.triggers import (\\n\",\n \" MktTriggerRequirements,\\n\",\n \" MktTrigger,\\n\",\n \" TriggerDirection,\\n\",\n \" AggregateTrigger,\\n\",\n \" AggregateTriggerRequirements,\\n\",\n \" AggType,\\n\",\n \" TradeCountTriggerRequirements,\\n\",\n \")\\n\",\n \"from gs_quant.backtests.actions import AddScaledTradeAction, ScalingActionType, ExitAllPositionsAction\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.backtests.data_sources import GenericDataSource, MissingDataStrategy\\n\",\n \"from gs_quant.data import Dataset\\n\",\n \"from gs_quant.risk import EqVega\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c12af18a-fd16-46cd-bcaa-7abc1bb79472\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialize session\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f7a11125-e78c-483d-9e0d-e776c7b3653e\",\n \"metadata\": {},\n \"source\": [\n \"#### This notebook shows to do partial/ gradual buys of instruments based on signal. \\n\",\n \"\\n\",\n \"E.g. “backtest buying a VIX 3m put where I buy 100k vega for each day that the SPX is above 6500 upto a max of 1m vega, exit portfolio if SPX below 6500” \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"95ba3c63-8214-4ad7-90c6-9ca0fb0c95f0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define backtest dates\\n\",\n \"start_date = dt.date(2024, 9, 1)\\n\",\n \"end_date = dt.date(2025, 9, 25)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5820ab11-e661-4896-b9c0-0c1b92211054\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get data set for SPX spot\\n\",\n \"ds = Dataset('TREOD')\\n\",\n \"data = ds.get_data(start_date, assetId=['MA4B66MW5E27U8P32SB'])\\n\",\n \"s = pd.Series(data['closePrice'].to_dict())\\n\",\n \"data_source = GenericDataSource(s, MissingDataStrategy.fill_forward)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"52abb547-ccff-44d6-becb-c463ac8fadb9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt = EqOption(underlier='.VIX', expiration_date='3m', option_type='put')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6e5b276d-0301-4ad4-8e7e-237c36bd3752\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define trade to add\\n\",\n \"trade_action_scaled = AddScaledTradeAction(\\n\",\n \" priceables=opt,\\n\",\n \" trade_duration='3m',\\n\",\n \" scaling_type=ScalingActionType.risk_measure,\\n\",\n \" scaling_risk=EqVega,\\n\",\n \" scaling_level=100000,\\n\",\n \" name='EnterAction',\\n\",\n \")\\n\",\n \"\\n\",\n \"# Define a trigger which will trigger if the SPX is above 6500 and the number of trades is less than 10\\n\",\n \"\\n\",\n \"spx_above_6500 = MktTriggerRequirements(data_source, 6500, TriggerDirection.ABOVE)\\n\",\n \"trade_count_below_10 = TradeCountTriggerRequirements(10, TriggerDirection.BELOW)\\n\",\n \"enter_trigger = AggregateTrigger(\\n\",\n \" AggregateTriggerRequirements([spx_above_6500, trade_count_below_10], AggType.ALL_OF), trade_action_scaled\\n\",\n \")\\n\",\n \"\\n\",\n \"# Define action to sell all instruments\\n\",\n \"exit_action = ExitAllPositionsAction(name='ExitAction')\\n\",\n \"\\n\",\n \"# Define a trigger which will trigger if the SPX is below 6500\\n\",\n \"exit_trigger = MktTrigger(MktTriggerRequirements(data_source, 6500, TriggerDirection.BELOW), exit_action)\\n\",\n \"\\n\",\n \"strategy = Strategy(None, [enter_trigger, exit_trigger])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"30cb7401-9f16-489f-bb8c-ba2cd3be53e7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run backtest daily\\n\",\n \"GE = GenericEngine()\\n\",\n \"backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0f226b56-3d7e-4c14-8804-195da95e134b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0969d751-ca47-4130-9f99-f706841c35ac\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"s.plot()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/04_backtesting/tutorials/Backtesting.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Examples require an initialized GsSession and relevant entitlements. External clients need to substitute thier own client id and client secret below. Please refer to [Sessions](https://developer.gs.com/docs/gsquant/Authentication/gs-session/) for details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Backtesting\\n\",\n \"\\n\",\n \"The backtesting framework is a language for describing backtests that's (1) intuitive (2) cross-asset and instrument agnostic (3) available natively in python for users to change and extend. It's compatible with a variety of calculation engines - eliminating need for specific syntax knowledge - and uses the gs risk API as the default calculation engine.\\n\",\n \"\\n\",\n \"The language consists of several basic components:\\n\",\n \"* [Trigger](#Trigger)\\n\",\n \"* [Action](#Action)\\n\",\n \"* [Strategy](#Strategy)\\n\",\n \"* [Calculation Engine](#Calculation-Engine)\\n\",\n \"* [Backtest](#Backtest)\\n\",\n \"\\n\",\n \"In this tutorial, we will examine the components in the context of looking at a basic vol selling strategy where we sell a 1m10y USD straddle daily.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Trigger\\n\",\n \"\\n\",\n \"A trigger can be used to amend a strategy based on a set of requirements and the current state of the backtest. Each trigger has an associated action or set of actions that are applied when the trigger is active. A backtest can have multiple triggers.\\n\",\n \"\\n\",\n \"Below are the triggers we currently offer:\\n\",\n \"* `PeriodicTrigger`: triggers at a regular interval (time/date). e.g.: every month end\\n\",\n \"* `MktTrigger`: triggers when market data, absolute or change, hits some target e.g. whenever SPX 1m vol crosses 10. Check out our [data catalog](https://marquee.gs.com/s/discover/data-services/catalog) for sources of market data that can be leveraged here\\n\",\n \"* `StrategyRiskTrigger`: triggers when the current risk of the strategy hits some target e.g. delta crosses $10m\\n\",\n \"\\n\",\n \"Since in our example, we are selling an instrument periodically e.g. (daily), we will use a single `PeriodicTrigger.` Let's define the `PeriodicTriggerRequirements` first and then talk about actions, since we need to specify at least a single action to happen when this trigger is triggered.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.backtests.triggers import PeriodicTrigger, PeriodicTriggerRequirements\\n\",\n \"from datetime import date\\n\",\n \"\\n\",\n \"start_date, end_date = date(2020, 1, 1), date(2020, 12, 1)\\n\",\n \"# define dates on which actions will be triggered (1b=every business day here)\\n\",\n \"trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Action\\n\",\n \"\\n\",\n \"Actions define how the portfolio is updated when a trigger is triggered. Below are the actions we currently offer:\\n\",\n \"* `AddTradeAction`: adds trade to a strategy\\n\",\n \"* `ExitTradeAction`: removes trade from a strategy\\n\",\n \"* `HedgeAction`: add trade that's a hedge for a risk\\n\",\n \"\\n\",\n \"Since in our example we are selling a straddle, we only want a single `AddTradeAction` to supply to our trigger. Let's define it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.backtests.actions import AddTradeAction\\n\",\n \"from gs_quant.common import PayReceive, Currency\\n\",\n \"from gs_quant.instrument import IRSwaption\\n\",\n \"\\n\",\n \"# straddle is the position we'll be adding (note it's a short position since buy_sell='Sell')\\n\",\n \"straddle = IRSwaption(\\n\",\n \" PayReceive.Straddle, '10y', Currency.USD, expiration_date='1m', notional_amount=1e8, buy_sell='Sell'\\n\",\n \")\\n\",\n \"\\n\",\n \"# this action specifies we will add the straddle when this action is triggered and hold it until expiration_date\\n\",\n \"action = AddTradeAction(straddle, 'expiration_date')\\n\",\n \"\\n\",\n \"# we will now combine our trigger requirement and action to produce a PeriodicTrigger\\n\",\n \"# Note, action can be a list if there are multiple actions\\n\",\n \"trigger = PeriodicTrigger(trig_req, action)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Strategy\\n\",\n \"\\n\",\n \"A `Strategy` combines all the information needed to run a backtest. It has 2 components: an initial trade or portfolio and a trigger or set of triggers. \\n\",\n \"\\n\",\n \"In our example, we don't have a starting portfolio and we have a single trigger we defined in the previous step. Let's put these together.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"\\n\",\n \"# in this case we have a single trigger but this can be a list of triggers as well\\n\",\n \"strategy = Strategy(None, trigger)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Calculation engine\\n\",\n \"\\n\",\n \"The final piece is calculate engine - the underlying engine/source of calculations you want to run your backtest. \\n\",\n \"\\n\",\n \"Once you have defined your backtest in the steps above, you can view which available engines support your strategy. There may be multiple as different engines have been developed and optimized for certain types of backtesting. For example, the `EquityVolEngine` is optimized for fast volatility backtesting for a specific set of equity underlyers. That said, `GenericEngine` will support majority of use cases, running calculations using gs-quant's risk apis. You can check which available engines support your strategy using the function below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"strategy.get_available_engines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Alternatively, you check whether a particular engine supports your strategy:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"\\n\",\n \"ge = GenericEngine()\\n\",\n \"ge.supports_strategy(strategy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As a final step, let's put everything together to run the backtest. The frequency here indicates the frequency of calculations. You can also supply additional risks you may want to calculate as part of your backtest (greeks or dollar price, for example).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"backtest = ge.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\\n\",\n \"backtest\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Backtest\\n\",\n \"\\n\",\n \"* to view the results of `backtest.risks` (by default, `Price`) (all in local ccy) and the cumulative cashflows evaluated on each backtested date: `backtest.results_summary`\\n\",\n \"* to view status for each trade: `backtest.trade_ledger()`\\n\",\n \"* to view and further evaluate the portfolio on any given backtested date: `backtest.portfolio_dict`\\n\",\n \"* to view the number of total api calls and number of calculations performed: `backtest.calc_calls` and `backtest.calculations`\\n\",\n \"\\n\",\n \"Here we'll use `backtest.results_summary` to plot mark to market and performance of the strategy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from gs_quant.risk import Price\\n\",\n \"\\n\",\n \"pd.DataFrame({'Generic backtester': backtest.result_summary[Price]}).plot(figsize=(10, 6), title='Mark to market')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame({'Generic backtester': backtest.result_summary['Cumulative Cash'] + backtest.result_summary[Price]}).plot(\\n\",\n \" figsize=(10, 6), title='Performance'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you have feedback on further expanding this framework, please reach out to `gs-quant-dev@gs.com`\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/04_backtesting/tutorials/Basic backtest walkthrough.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"be4586c8-61a8-4380-910c-0631a7f19ff6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.instrument import FXOption, FXForward\\n\",\n \"from gs_quant.backtests.triggers import (\\n\",\n \" PeriodicTrigger,\\n\",\n \" PeriodicTriggerRequirements,\\n\",\n \" StrategyRiskTrigger,\\n\",\n \" RiskTriggerRequirements,\\n\",\n \" TriggerDirection,\\n\",\n \")\\n\",\n \"from gs_quant.backtests.actions import AddTradeAction, HedgeAction\\n\",\n \"from gs_quant.backtests.backtest_objects import ScaledTransactionModel\\n\",\n \"from gs_quant.backtests.generic_engine import GenericEngine\\n\",\n \"from gs_quant.backtests.strategy import Strategy\\n\",\n \"from gs_quant.risk import FXDelta\\n\",\n \"\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"fcc30104-6f26-48cd-837c-da5021936cfe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e6626743-dc9f-427c-bd2a-9b4e7ffb0708\",\n \"metadata\": {},\n \"source\": [\n \"Simplest backtest. Buy a trade on a periodic basis. Hold that trade for some number of days and exit the trade.\\n\",\n \"\\n\",\n \"Here we buy an option every day between 4 Aug and 4 Sep and we hold each of those options for 1 week. We convert this idea into a Strategy object.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"5663a431-1acf-4c51-8c4f-ac8063163038\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt = FXOption(pair='EURUSD', expiration_date='3m', strike_price='25d', option_type='Call', premium=0)\\n\",\n \"\\n\",\n \"action = AddTradeAction(priceables=opt, trade_duration='1w')\\n\",\n \"trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(\\n\",\n \" start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1b'\\n\",\n \" ),\\n\",\n \" actions=action,\\n\",\n \")\\n\",\n \"\\n\",\n \"strategy = Strategy(initial_portfolio=None, triggers=trigger)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0b286ba1-9a01-410e-b6ed-cefed2c8f530\",\n \"metadata\": {},\n \"source\": [\n \"strategy objects can be run by Engines such as the GenericEngine\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"e37d9bba-31e0-451d-8767-2c8bce8c6e50\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|███████████████████████████████████████████████████████████████████████████████████| 110/110 [00:18<00:00, 5.83it/s]\\n\",\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 19/19 [00:06<00:00, 3.09it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"ge = GenericEngine()\\n\",\n \"backtest = ge.run_backtest(strategy=strategy, start=dt.date(2025, 8, 4), end=dt.date(2025, 9, 4), frequency='1b')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2c8d461d-59cb-4768-a7c1-d4c3405628aa\",\n \"metadata\": {},\n \"source\": [\n \"when we run a backtest the engine will move through time from the start to the end and for each date determine if a trigger has triggered and if\\n\",\n \"it has it will apply the action(s) associated with the trigger. We can see that the backtest result object hold a dictionary of every day in the\\n\",\n \"backtest and the portfolio holding on each date.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"ecedf61f-3f16-46bf-bb81-3745539dc28c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"defaultdict(gs_quant.markets.portfolio.Portfolio,\\n\",\n \" {datetime.date(2025, 8, 4): Portfolio(1 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 5): Portfolio(2 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 6): Portfolio(3 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 7): Portfolio(4 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 8): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 11): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 12): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 13): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 14): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 15): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 18): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 19): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 20): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 21): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 22): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 25): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 26): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 27): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 28): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 8, 29): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 9, 1): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 9, 2): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 9, 3): Portfolio(5 instrument(s)),\\n\",\n \" datetime.date(2025, 9, 4): Portfolio(5 instrument(s))})\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"backtest.portfolio_dict\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b8619115-7cad-4c04-8368-0b345994d1db\",\n \"metadata\": {},\n \"source\": [\n \"you can see here that on the first day we have a single option then over the week we add an option but this tops out at 5 options as after this\\n\",\n \"point the we are exiting the positions.\\n\",\n \"\\n\",\n \"the backtest object contains all the positions as above but also all the risks and pvs required for the backtest in the results. It also has\\n\",\n \"some useful utilities like the result_summary and the trade_ledger\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"2b4b4237-0685-4762-aa8e-11e29bb80dde\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PriceCumulative CashTransaction CostsTotal
2025-08-047.039538e+05-7.039538e+0500.000000e+00
2025-08-051.384686e+06-1.401329e+060-1.664271e+04
2025-08-062.438426e+06-2.120211e+0603.182143e+05
2025-08-073.064023e+06-2.849922e+0602.141012e+05
2025-08-083.987985e+06-3.570582e+0604.174028e+05
2025-08-112.955388e+06-3.617959e+060-6.625704e+05
2025-08-123.478294e+06-3.509605e+060-3.131080e+04
2025-08-133.906875e+06-3.460997e+0604.458788e+05
2025-08-143.141030e+06-3.517508e+060-3.764782e+05
2025-08-153.605119e+06-3.542721e+0606.239791e+04
2025-08-182.817288e+06-3.498368e+060-6.810794e+05
2025-08-192.913192e+06-3.585282e+060-6.720894e+05
2025-08-202.956274e+06-3.771344e+060-8.150702e+05
2025-08-212.624439e+06-3.935025e+060-1.310585e+06
2025-08-223.989135e+06-3.956594e+0603.254059e+04
2025-08-253.759709e+06-3.873152e+060-1.134430e+05
2025-08-263.234885e+06-3.920626e+060-6.857406e+05
2025-08-272.738868e+06-4.047281e+060-1.308413e+06
2025-08-283.193984e+06-3.945057e+060-7.510734e+05
2025-08-293.582485e+06-4.036871e+060-4.543858e+05
2025-09-013.694458e+06-4.100455e+060-4.059970e+05
2025-09-023.208602e+06-4.153800e+060-9.451980e+05
2025-09-033.280696e+06-4.037686e+060-7.569907e+05
2025-09-042.838070e+06-4.147867e+060-1.309797e+06
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Price Cumulative Cash Transaction Costs Total\\n\",\n \"2025-08-04 7.039538e+05 -7.039538e+05 0 0.000000e+00\\n\",\n \"2025-08-05 1.384686e+06 -1.401329e+06 0 -1.664271e+04\\n\",\n \"2025-08-06 2.438426e+06 -2.120211e+06 0 3.182143e+05\\n\",\n \"2025-08-07 3.064023e+06 -2.849922e+06 0 2.141012e+05\\n\",\n \"2025-08-08 3.987985e+06 -3.570582e+06 0 4.174028e+05\\n\",\n \"2025-08-11 2.955388e+06 -3.617959e+06 0 -6.625704e+05\\n\",\n \"2025-08-12 3.478294e+06 -3.509605e+06 0 -3.131080e+04\\n\",\n \"2025-08-13 3.906875e+06 -3.460997e+06 0 4.458788e+05\\n\",\n \"2025-08-14 3.141030e+06 -3.517508e+06 0 -3.764782e+05\\n\",\n \"2025-08-15 3.605119e+06 -3.542721e+06 0 6.239791e+04\\n\",\n \"2025-08-18 2.817288e+06 -3.498368e+06 0 -6.810794e+05\\n\",\n \"2025-08-19 2.913192e+06 -3.585282e+06 0 -6.720894e+05\\n\",\n \"2025-08-20 2.956274e+06 -3.771344e+06 0 -8.150702e+05\\n\",\n \"2025-08-21 2.624439e+06 -3.935025e+06 0 -1.310585e+06\\n\",\n \"2025-08-22 3.989135e+06 -3.956594e+06 0 3.254059e+04\\n\",\n \"2025-08-25 3.759709e+06 -3.873152e+06 0 -1.134430e+05\\n\",\n \"2025-08-26 3.234885e+06 -3.920626e+06 0 -6.857406e+05\\n\",\n \"2025-08-27 2.738868e+06 -4.047281e+06 0 -1.308413e+06\\n\",\n \"2025-08-28 3.193984e+06 -3.945057e+06 0 -7.510734e+05\\n\",\n \"2025-08-29 3.582485e+06 -4.036871e+06 0 -4.543858e+05\\n\",\n \"2025-09-01 3.694458e+06 -4.100455e+06 0 -4.059970e+05\\n\",\n \"2025-09-02 3.208602e+06 -4.153800e+06 0 -9.451980e+05\\n\",\n \"2025-09-03 3.280696e+06 -4.037686e+06 0 -7.569907e+05\\n\",\n \"2025-09-04 2.838070e+06 -4.147867e+06 0 -1.309797e+06\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"13f6a66e-eec9-4b4b-b35d-5909dcf7a7f8\",\n \"metadata\": {},\n \"source\": [\n \"Now we are going to take this basic example and extend it. I want to extend the strategy I had above to also hedge the delta risk. In order\\n\",\n \"to do this I am going to add a hedge action. A hedge action required the risk you wish to hedge and the instrument you wish to hedge that\\n\",\n \"risk with. When the hedge action is triggered the engine will calculate the risk and scale the hedge instrument such that the risk is hedged.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"3d9cc0e0-9104-441f-88e1-3d588f1e5b0b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hedge_action = HedgeAction(\\n\",\n \" risk=FXDelta(aggregation_level='Type'),\\n\",\n \" priceables=FXForward(pair='EURUSD', settlement_date='2b'),\\n\",\n \" trade_duration='1b',\\n\",\n \") # we are holding our hedge for one day. So each day a new fwd is being added as a hedge.\\n\",\n \"\\n\",\n \"# Note that we supplied an aggregation level to the risk to ensure the risk comes back as a scalar value. Finer control of risks can be\\n\",\n \"# achieved by using a risk transformer and supplying this.\\n\",\n \"# Having created our hedge action we can now add it to the list of actions to be run when our trigger is triggered.\\n\",\n \"\\n\",\n \"hedge_trigger = PeriodicTrigger(\\n\",\n \" trigger_requirements=PeriodicTriggerRequirements(\\n\",\n \" start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1b'\\n\",\n \" ),\\n\",\n \" actions=[action, hedge_action],\\n\",\n \") # now when the trigger triggers both actions will be done in order.\\n\",\n \"\\n\",\n \"hedge_strategy = Strategy(initial_portfolio=None, triggers=hedge_trigger)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"859e2ef8-79de-4991-861f-a16bf84bddb6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|███████████████████████████████████████████████████████████████████████████████████| 268/268 [00:22<00:00, 11.77it/s]\\n\",\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 42/42 [00:09<00:00, 4.42it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"hedge_backtest = ge.run_backtest(\\n\",\n \" strategy=hedge_strategy, start=dt.date(2025, 8, 4), end=dt.date(2025, 9, 4), frequency='1b'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"985cd029-741c-44c1-9bce-6e850401b483\",\n \"metadata\": {},\n \"source\": [\n \"if we check the result summary for this backtest we can check that the delta is 0 on all dates.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"e2bec6c2-72ea-4913-876f-e4f4f86d28de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PriceFXDelta(aggregation_level:Type)Cumulative CashTransaction CostsTotal
2025-08-047.039538e+050.000000e+00-7.039538e+0500.000000
2025-08-051.384686e+060.000000e+00-1.408141e+060-23454.304906
2025-08-062.438426e+060.000000e+00-2.382851e+06055574.368854
2025-08-073.064023e+060.000000e+00-3.024605e+06039417.710660
2025-08-083.987985e+06-1.490116e-08-4.028740e+060-40754.340909
2025-08-112.955388e+060.000000e+00-3.261150e+060-305761.320428
2025-08-123.478294e+06-1.490116e-08-3.831561e+060-353266.819324
2025-08-133.906875e+060.000000e+00-4.270443e+060-363567.721780
2025-08-143.141030e+060.000000e+00-3.617677e+060-476646.930316
2025-08-153.605119e+061.490116e-08-4.124625e+060-519505.288948
2025-08-182.817288e+060.000000e+00-3.624033e+060-806744.825889
2025-08-192.913192e+060.000000e+00-3.791480e+060-878288.088792
2025-08-202.956274e+061.490116e-08-3.816132e+060-859858.766961
2025-08-212.624439e+060.000000e+00-3.433863e+060-809423.092240
2025-08-223.989135e+060.000000e+00-4.499546e+060-510410.859932
2025-08-253.759709e+061.490116e-08-4.406246e+060-646536.348574
2025-08-263.234885e+06-1.490116e-08-3.690361e+060-455476.104749
2025-08-272.738868e+060.000000e+00-3.241891e+060-503023.803534
2025-08-283.193984e+060.000000e+00-3.653487e+060-459503.049236
2025-08-293.582485e+060.000000e+00-4.064240e+060-481755.283012
2025-09-013.694458e+060.000000e+00-4.205189e+060-510730.793778
2025-09-023.208602e+06-1.490116e-08-3.698486e+060-489883.853533
2025-09-033.280696e+060.000000e+00-3.764776e+060-484079.900233
2025-09-042.838070e+060.000000e+00-3.413359e+060-575289.380348
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Price FXDelta(aggregation_level:Type) Cumulative Cash \\\\\\n\",\n \"2025-08-04 7.039538e+05 0.000000e+00 -7.039538e+05 \\n\",\n \"2025-08-05 1.384686e+06 0.000000e+00 -1.408141e+06 \\n\",\n \"2025-08-06 2.438426e+06 0.000000e+00 -2.382851e+06 \\n\",\n \"2025-08-07 3.064023e+06 0.000000e+00 -3.024605e+06 \\n\",\n \"2025-08-08 3.987985e+06 -1.490116e-08 -4.028740e+06 \\n\",\n \"2025-08-11 2.955388e+06 0.000000e+00 -3.261150e+06 \\n\",\n \"2025-08-12 3.478294e+06 -1.490116e-08 -3.831561e+06 \\n\",\n \"2025-08-13 3.906875e+06 0.000000e+00 -4.270443e+06 \\n\",\n \"2025-08-14 3.141030e+06 0.000000e+00 -3.617677e+06 \\n\",\n \"2025-08-15 3.605119e+06 1.490116e-08 -4.124625e+06 \\n\",\n \"2025-08-18 2.817288e+06 0.000000e+00 -3.624033e+06 \\n\",\n \"2025-08-19 2.913192e+06 0.000000e+00 -3.791480e+06 \\n\",\n \"2025-08-20 2.956274e+06 1.490116e-08 -3.816132e+06 \\n\",\n \"2025-08-21 2.624439e+06 0.000000e+00 -3.433863e+06 \\n\",\n \"2025-08-22 3.989135e+06 0.000000e+00 -4.499546e+06 \\n\",\n \"2025-08-25 3.759709e+06 1.490116e-08 -4.406246e+06 \\n\",\n \"2025-08-26 3.234885e+06 -1.490116e-08 -3.690361e+06 \\n\",\n \"2025-08-27 2.738868e+06 0.000000e+00 -3.241891e+06 \\n\",\n \"2025-08-28 3.193984e+06 0.000000e+00 -3.653487e+06 \\n\",\n \"2025-08-29 3.582485e+06 0.000000e+00 -4.064240e+06 \\n\",\n \"2025-09-01 3.694458e+06 0.000000e+00 -4.205189e+06 \\n\",\n \"2025-09-02 3.208602e+06 -1.490116e-08 -3.698486e+06 \\n\",\n \"2025-09-03 3.280696e+06 0.000000e+00 -3.764776e+06 \\n\",\n \"2025-09-04 2.838070e+06 0.000000e+00 -3.413359e+06 \\n\",\n \"\\n\",\n \" Transaction Costs Total \\n\",\n \"2025-08-04 0 0.000000 \\n\",\n \"2025-08-05 0 -23454.304906 \\n\",\n \"2025-08-06 0 55574.368854 \\n\",\n \"2025-08-07 0 39417.710660 \\n\",\n \"2025-08-08 0 -40754.340909 \\n\",\n \"2025-08-11 0 -305761.320428 \\n\",\n \"2025-08-12 0 -353266.819324 \\n\",\n \"2025-08-13 0 -363567.721780 \\n\",\n \"2025-08-14 0 -476646.930316 \\n\",\n \"2025-08-15 0 -519505.288948 \\n\",\n \"2025-08-18 0 -806744.825889 \\n\",\n \"2025-08-19 0 -878288.088792 \\n\",\n \"2025-08-20 0 -859858.766961 \\n\",\n \"2025-08-21 0 -809423.092240 \\n\",\n \"2025-08-22 0 -510410.859932 \\n\",\n \"2025-08-25 0 -646536.348574 \\n\",\n \"2025-08-26 0 -455476.104749 \\n\",\n \"2025-08-27 0 -503023.803534 \\n\",\n \"2025-08-28 0 -459503.049236 \\n\",\n \"2025-08-29 0 -481755.283012 \\n\",\n \"2025-09-01 0 -510730.793778 \\n\",\n \"2025-09-02 0 -489883.853533 \\n\",\n \"2025-09-03 0 -484079.900233 \\n\",\n \"2025-09-04 0 -575289.380348 \"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"hedge_backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"747fe3e4-55cd-4d69-ac17-ed7f74641e9c\",\n \"metadata\": {},\n \"source\": [\n \"now we are going to change this so that instead of buying an option every day we are going to buy every week but still hedge every day. In\\n\",\n \"order to do this we are going to have two different triggers, one daily and one weekly with different actions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"3477dd7e-e8ba-4fe4-8606-a24867073829\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"weekly_trigger = PeriodicTrigger(\\n\",\n \" PeriodicTriggerRequirements(start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1w'),\\n\",\n \" actions=action,\\n\",\n \")\\n\",\n \"daily_trigger = PeriodicTrigger(\\n\",\n \" PeriodicTriggerRequirements(start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1b'),\\n\",\n \" actions=hedge_action,\\n\",\n \")\\n\",\n \"\\n\",\n \"hedge_weekly_option_strategy = Strategy(initial_portfolio=None, triggers=[weekly_trigger, daily_trigger])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"41a2bdf3-d5f7-4918-99db-aa09d9768511\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:13<00:00, 7.00it/s]\\n\",\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 27/27 [00:03<00:00, 7.41it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"hedge_weekly_option_backtest = ge.run_backtest(\\n\",\n \" strategy=hedge_weekly_option_strategy, start=dt.date(2025, 8, 4), end=dt.date(2025, 9, 4), frequency='1b'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"862b0f31-cad5-444a-89cc-470147508535\",\n \"metadata\": {},\n \"source\": [\n \"if we look at the trade ledger for this backtest we can see that options are bought weekly but hedges are still done daily\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"68924907-74d0-4b8c-88ab-6f2e9b6b8d5e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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OpenCloseOpen ValueClose ValueLong ShortStatusTrade PnL
Action1_Priceable0_2025-08-042025-08-042025-08-11-703953.846673637865.502946-1closed-66088.343728
Action1_Priceable0_2025-08-112025-08-112025-08-18-685241.75833685891.313071-1closed649.554742
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Action1_Priceable0_2025-08-252025-08-252025-09-01-664120.360966608390.108042-1closed-55730.252924
Action1_Priceable0_2025-09-012025-09-01None-671974.3047190-1openNone
Action2_Priceable0_2025-08-042025-08-042025-08-050.000042-6811.598662-1closed-6811.59862
Action2_Priceable0_2025-08-052025-08-052025-08-06-0.000076-127463.975776-1closed-127463.975852
Action2_Priceable0_2025-08-062025-08-062025-08-07-0.00006830876.67724-1closed30876.677172
Action2_Priceable0_2025-08-072025-08-072025-08-080.000019-76216.870412-1closed-76216.870393
Action2_Priceable0_2025-08-082025-08-082025-08-110.000098180086.64404-1closed180086.644138
Action2_Priceable0_2025-08-112025-08-112025-08-120.000069-153650.797851-1closed-153650.797782
Action2_Priceable0_2025-08-122025-08-122025-08-13-0.000055-114033.030923-1closed-114033.030979
Action2_Priceable0_2025-08-132025-08-132025-08-140.000085168857.202827-1closed168857.202912
Action2_Priceable0_2025-08-142025-08-142025-08-15-0.000076-114516.597115-1closed-114516.597192
Action2_Priceable0_2025-08-152025-08-152025-08-180.000118108502.142566-1closed108502.142684
Action2_Priceable0_2025-08-182025-08-182025-08-190.000105-17902.333232-1closed-17902.333127
Action2_Priceable0_2025-08-192025-08-192025-08-200.00009735228.088101-1closed35228.088198
Action2_Priceable0_2025-08-202025-08-202025-08-21-0.000089113097.124562-1closed113097.124474
Action2_Priceable0_2025-08-212025-08-212025-08-220.000051-205754.054176-1closed-205754.054125
Action2_Priceable0_2025-08-222025-08-222025-08-250.0000221953.316272-1closed1953.316294
Action2_Priceable0_2025-08-252025-08-252025-08-260.000049133874.524524-1closed133874.524574
Action2_Priceable0_2025-08-262025-08-262025-08-270.000089100261.657657-1closed100261.657746
Action2_Priceable0_2025-08-272025-08-272025-08-28-0.000054-85779.392115-1closed-85779.392169
Action2_Priceable0_2025-08-282025-08-282025-08-29-0.000085-55443.513444-1closed-55443.513529
Action2_Priceable0_2025-08-292025-08-292025-09-01-0.000073-13212.896855-1closed-13212.896928
Action2_Priceable0_2025-09-012025-09-012025-09-02-0.000107101496.323024-1closed101496.322917
Action2_Priceable0_2025-09-022025-09-022025-09-03-0.00001-32662.887524-1closed-32662.887534
Action2_Priceable0_2025-09-032025-09-032025-09-040.00007986025.151973-1closed86025.152052
Action2_Priceable0_2025-09-042025-09-04None-0.0000770-1openNone
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\"\n ],\n \"text/plain\": [\n \" Open Close Open Value \\\\\\n\",\n \"Action1_Priceable0_2025-08-04 2025-08-04 2025-08-11 -703953.846673 \\n\",\n \"Action1_Priceable0_2025-08-11 2025-08-11 2025-08-18 -685241.75833 \\n\",\n \"Action1_Priceable0_2025-08-18 2025-08-18 2025-08-25 -641537.745143 \\n\",\n \"Action1_Priceable0_2025-08-25 2025-08-25 2025-09-01 -664120.360966 \\n\",\n \"Action1_Priceable0_2025-09-01 2025-09-01 None -671974.304719 \\n\",\n \"Action2_Priceable0_2025-08-04 2025-08-04 2025-08-05 0.000042 \\n\",\n \"Action2_Priceable0_2025-08-05 2025-08-05 2025-08-06 -0.000076 \\n\",\n \"Action2_Priceable0_2025-08-06 2025-08-06 2025-08-07 -0.000068 \\n\",\n \"Action2_Priceable0_2025-08-07 2025-08-07 2025-08-08 0.000019 \\n\",\n \"Action2_Priceable0_2025-08-08 2025-08-08 2025-08-11 0.000098 \\n\",\n \"Action2_Priceable0_2025-08-11 2025-08-11 2025-08-12 0.000069 \\n\",\n \"Action2_Priceable0_2025-08-12 2025-08-12 2025-08-13 -0.000055 \\n\",\n \"Action2_Priceable0_2025-08-13 2025-08-13 2025-08-14 0.000085 \\n\",\n \"Action2_Priceable0_2025-08-14 2025-08-14 2025-08-15 -0.000076 \\n\",\n \"Action2_Priceable0_2025-08-15 2025-08-15 2025-08-18 0.000118 \\n\",\n \"Action2_Priceable0_2025-08-18 2025-08-18 2025-08-19 0.000105 \\n\",\n \"Action2_Priceable0_2025-08-19 2025-08-19 2025-08-20 0.000097 \\n\",\n \"Action2_Priceable0_2025-08-20 2025-08-20 2025-08-21 -0.000089 \\n\",\n \"Action2_Priceable0_2025-08-21 2025-08-21 2025-08-22 0.000051 \\n\",\n \"Action2_Priceable0_2025-08-22 2025-08-22 2025-08-25 0.000022 \\n\",\n \"Action2_Priceable0_2025-08-25 2025-08-25 2025-08-26 0.000049 \\n\",\n \"Action2_Priceable0_2025-08-26 2025-08-26 2025-08-27 0.000089 \\n\",\n \"Action2_Priceable0_2025-08-27 2025-08-27 2025-08-28 -0.000054 \\n\",\n \"Action2_Priceable0_2025-08-28 2025-08-28 2025-08-29 -0.000085 \\n\",\n \"Action2_Priceable0_2025-08-29 2025-08-29 2025-09-01 -0.000073 \\n\",\n \"Action2_Priceable0_2025-09-01 2025-09-01 2025-09-02 -0.000107 \\n\",\n \"Action2_Priceable0_2025-09-02 2025-09-02 2025-09-03 -0.00001 \\n\",\n \"Action2_Priceable0_2025-09-03 2025-09-03 2025-09-04 0.000079 \\n\",\n \"Action2_Priceable0_2025-09-04 2025-09-04 None -0.000077 \\n\",\n \"\\n\",\n \" Close Value Long Short Status Trade PnL \\n\",\n \"Action1_Priceable0_2025-08-04 637865.502946 -1 closed -66088.343728 \\n\",\n \"Action1_Priceable0_2025-08-11 685891.313071 -1 closed 649.554742 \\n\",\n \"Action1_Priceable0_2025-08-18 747562.463969 -1 closed 106024.718826 \\n\",\n \"Action1_Priceable0_2025-08-25 608390.108042 -1 closed -55730.252924 \\n\",\n \"Action1_Priceable0_2025-09-01 0 -1 open None \\n\",\n \"Action2_Priceable0_2025-08-04 -6811.598662 -1 closed -6811.59862 \\n\",\n \"Action2_Priceable0_2025-08-05 -127463.975776 -1 closed -127463.975852 \\n\",\n \"Action2_Priceable0_2025-08-06 30876.67724 -1 closed 30876.677172 \\n\",\n \"Action2_Priceable0_2025-08-07 -76216.870412 -1 closed -76216.870393 \\n\",\n \"Action2_Priceable0_2025-08-08 180086.64404 -1 closed 180086.644138 \\n\",\n \"Action2_Priceable0_2025-08-11 -153650.797851 -1 closed -153650.797782 \\n\",\n \"Action2_Priceable0_2025-08-12 -114033.030923 -1 closed -114033.030979 \\n\",\n \"Action2_Priceable0_2025-08-13 168857.202827 -1 closed 168857.202912 \\n\",\n \"Action2_Priceable0_2025-08-14 -114516.597115 -1 closed -114516.597192 \\n\",\n \"Action2_Priceable0_2025-08-15 108502.142566 -1 closed 108502.142684 \\n\",\n \"Action2_Priceable0_2025-08-18 -17902.333232 -1 closed -17902.333127 \\n\",\n \"Action2_Priceable0_2025-08-19 35228.088101 -1 closed 35228.088198 \\n\",\n \"Action2_Priceable0_2025-08-20 113097.124562 -1 closed 113097.124474 \\n\",\n \"Action2_Priceable0_2025-08-21 -205754.054176 -1 closed -205754.054125 \\n\",\n \"Action2_Priceable0_2025-08-22 1953.316272 -1 closed 1953.316294 \\n\",\n \"Action2_Priceable0_2025-08-25 133874.524524 -1 closed 133874.524574 \\n\",\n \"Action2_Priceable0_2025-08-26 100261.657657 -1 closed 100261.657746 \\n\",\n \"Action2_Priceable0_2025-08-27 -85779.392115 -1 closed -85779.392169 \\n\",\n \"Action2_Priceable0_2025-08-28 -55443.513444 -1 closed -55443.513529 \\n\",\n \"Action2_Priceable0_2025-08-29 -13212.896855 -1 closed -13212.896928 \\n\",\n \"Action2_Priceable0_2025-09-01 101496.323024 -1 closed 101496.322917 \\n\",\n \"Action2_Priceable0_2025-09-02 -32662.887524 -1 closed -32662.887534 \\n\",\n \"Action2_Priceable0_2025-09-03 86025.151973 -1 closed 86025.152052 \\n\",\n \"Action2_Priceable0_2025-09-04 0 -1 open None \"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"hedge_weekly_option_backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2ea54ad1-a7a7-4013-aa29-b21b4f6cd58f\",\n \"metadata\": {},\n \"source\": [\n \"it would be useful if the name of the trades was more human readable.\\n\",\n \"We can control this by naming the trade and the actions. Then the name of the instrument would be the action name_trade name_date the trade was\\n\",\n \"entered.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"dd2448dd-e335-42a2-b659-2248e66f0280\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:17<00:00, 5.57it/s]\\n\",\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 27/27 [00:08<00:00, 3.21it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"opt = FXOption(pair='EURUSD', expiration_date='3m', strike_price='25d', premium=0, name='EURUSD call')\\n\",\n \"add_action = AddTradeAction(priceables=opt, trade_duration='1w', name='add action')\\n\",\n \"fwd = FXForward(pair='EURUSD', settlement_date='2b', name='spot hedge')\\n\",\n \"named_hedge_action = HedgeAction(\\n\",\n \" risk=FXDelta(aggregation_level='Type'), priceables=fwd, trade_duration='1b', name='hedge action'\\n\",\n \")\\n\",\n \"\\n\",\n \"weekly_trigger = PeriodicTrigger(\\n\",\n \" PeriodicTriggerRequirements(start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1w'),\\n\",\n \" actions=add_action,\\n\",\n \")\\n\",\n \"daily_trigger = PeriodicTrigger(\\n\",\n \" PeriodicTriggerRequirements(start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1b'),\\n\",\n \" actions=named_hedge_action,\\n\",\n \")\\n\",\n \"\\n\",\n \"hedge_weekly_option_strategy = Strategy(initial_portfolio=None, triggers=[weekly_trigger, daily_trigger])\\n\",\n \"hedge_weekly_option_backtest = ge.run_backtest(\\n\",\n \" strategy=hedge_weekly_option_strategy, start=dt.date(2025, 8, 4), end=dt.date(2025, 9, 4), frequency='1b'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"2691a8a3-c784-4d3d-be4c-42c1be7a40d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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OpenCloseOpen ValueClose ValueLong ShortStatusTrade PnL
add action_EURUSD call_2025-08-042025-08-042025-08-11-703953.846673637865.502946-1closed-66088.343728
add action_EURUSD call_2025-08-112025-08-112025-08-18-685241.75833685891.313071-1closed649.554742
add action_EURUSD call_2025-08-182025-08-182025-08-25-641537.745143747562.463969-1closed106024.718826
add action_EURUSD call_2025-08-252025-08-252025-09-01-664120.360966608390.108042-1closed-55730.252924
add action_EURUSD call_2025-09-012025-09-01None-671974.3047190-1openNone
hedge action_spot hedge_2025-08-042025-08-042025-08-050.000042-6811.598662-1closed-6811.59862
hedge action_spot hedge_2025-08-052025-08-052025-08-06-0.000076-127463.975776-1closed-127463.975852
hedge action_spot hedge_2025-08-062025-08-062025-08-07-0.00006830876.67724-1closed30876.677172
hedge action_spot hedge_2025-08-072025-08-072025-08-080.000019-76216.870412-1closed-76216.870393
hedge action_spot hedge_2025-08-082025-08-082025-08-110.000098180086.64404-1closed180086.644138
hedge action_spot hedge_2025-08-112025-08-112025-08-120.000069-153650.797851-1closed-153650.797782
hedge action_spot hedge_2025-08-122025-08-122025-08-13-0.000055-114033.030923-1closed-114033.030978
hedge action_spot hedge_2025-08-132025-08-132025-08-140.000085168857.202827-1closed168857.202912
hedge action_spot hedge_2025-08-142025-08-142025-08-15-0.000076-114516.597122-1closed-114516.597199
hedge action_spot hedge_2025-08-152025-08-152025-08-180.000118108502.142565-1closed108502.142683
hedge action_spot hedge_2025-08-182025-08-182025-08-190.000104-17902.333232-1closed-17902.333128
hedge action_spot hedge_2025-08-192025-08-192025-08-200.00009735228.088102-1closed35228.088199
hedge action_spot hedge_2025-08-202025-08-202025-08-21-0.000088113097.124563-1closed113097.124475
hedge action_spot hedge_2025-08-212025-08-212025-08-220.000054-205754.054176-1closed-205754.054122
hedge action_spot hedge_2025-08-222025-08-222025-08-250.0000221953.316272-1closed1953.316294
hedge action_spot hedge_2025-08-252025-08-252025-08-260.000049133874.524524-1closed133874.524573
hedge action_spot hedge_2025-08-262025-08-262025-08-270.000088100261.657657-1closed100261.657745
hedge action_spot hedge_2025-08-272025-08-272025-08-28-0.000054-85779.392115-1closed-85779.392169
hedge action_spot hedge_2025-08-282025-08-282025-08-29-0.000085-55443.513444-1closed-55443.513529
hedge action_spot hedge_2025-08-292025-08-292025-09-01-0.000073-13212.896855-1closed-13212.896928
hedge action_spot hedge_2025-09-012025-09-012025-09-020.000107101496.32281-1closed101496.322917
hedge action_spot hedge_2025-09-022025-09-022025-09-03-0.00001-32662.887524-1closed-32662.887534
hedge action_spot hedge_2025-09-032025-09-032025-09-040.00007986025.151973-1closed86025.152052
hedge action_spot hedge_2025-09-042025-09-04None-0.0000770-1openNone
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Open Close Open Value \\\\\\n\",\n \"add action_EURUSD call_2025-08-04 2025-08-04 2025-08-11 -703953.846673 \\n\",\n \"add action_EURUSD call_2025-08-11 2025-08-11 2025-08-18 -685241.75833 \\n\",\n \"add action_EURUSD call_2025-08-18 2025-08-18 2025-08-25 -641537.745143 \\n\",\n \"add action_EURUSD call_2025-08-25 2025-08-25 2025-09-01 -664120.360966 \\n\",\n \"add action_EURUSD call_2025-09-01 2025-09-01 None -671974.304719 \\n\",\n \"hedge action_spot hedge_2025-08-04 2025-08-04 2025-08-05 0.000042 \\n\",\n \"hedge action_spot hedge_2025-08-05 2025-08-05 2025-08-06 -0.000076 \\n\",\n \"hedge action_spot hedge_2025-08-06 2025-08-06 2025-08-07 -0.000068 \\n\",\n \"hedge action_spot hedge_2025-08-07 2025-08-07 2025-08-08 0.000019 \\n\",\n \"hedge action_spot hedge_2025-08-08 2025-08-08 2025-08-11 0.000098 \\n\",\n \"hedge action_spot hedge_2025-08-11 2025-08-11 2025-08-12 0.000069 \\n\",\n \"hedge action_spot hedge_2025-08-12 2025-08-12 2025-08-13 -0.000055 \\n\",\n \"hedge action_spot hedge_2025-08-13 2025-08-13 2025-08-14 0.000085 \\n\",\n \"hedge action_spot hedge_2025-08-14 2025-08-14 2025-08-15 -0.000076 \\n\",\n \"hedge action_spot hedge_2025-08-15 2025-08-15 2025-08-18 0.000118 \\n\",\n \"hedge action_spot hedge_2025-08-18 2025-08-18 2025-08-19 0.000104 \\n\",\n \"hedge action_spot hedge_2025-08-19 2025-08-19 2025-08-20 0.000097 \\n\",\n \"hedge action_spot hedge_2025-08-20 2025-08-20 2025-08-21 -0.000088 \\n\",\n \"hedge action_spot hedge_2025-08-21 2025-08-21 2025-08-22 0.000054 \\n\",\n \"hedge action_spot hedge_2025-08-22 2025-08-22 2025-08-25 0.000022 \\n\",\n \"hedge action_spot hedge_2025-08-25 2025-08-25 2025-08-26 0.000049 \\n\",\n \"hedge action_spot hedge_2025-08-26 2025-08-26 2025-08-27 0.000088 \\n\",\n \"hedge action_spot hedge_2025-08-27 2025-08-27 2025-08-28 -0.000054 \\n\",\n \"hedge action_spot hedge_2025-08-28 2025-08-28 2025-08-29 -0.000085 \\n\",\n \"hedge action_spot hedge_2025-08-29 2025-08-29 2025-09-01 -0.000073 \\n\",\n \"hedge action_spot hedge_2025-09-01 2025-09-01 2025-09-02 0.000107 \\n\",\n \"hedge action_spot hedge_2025-09-02 2025-09-02 2025-09-03 -0.00001 \\n\",\n \"hedge action_spot hedge_2025-09-03 2025-09-03 2025-09-04 0.000079 \\n\",\n \"hedge action_spot hedge_2025-09-04 2025-09-04 None -0.000077 \\n\",\n \"\\n\",\n \" Close Value Long Short Status \\\\\\n\",\n \"add action_EURUSD call_2025-08-04 637865.502946 -1 closed \\n\",\n \"add action_EURUSD call_2025-08-11 685891.313071 -1 closed \\n\",\n \"add action_EURUSD call_2025-08-18 747562.463969 -1 closed \\n\",\n \"add action_EURUSD call_2025-08-25 608390.108042 -1 closed \\n\",\n \"add action_EURUSD call_2025-09-01 0 -1 open \\n\",\n \"hedge action_spot hedge_2025-08-04 -6811.598662 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-05 -127463.975776 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-06 30876.67724 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-07 -76216.870412 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-08 180086.64404 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-11 -153650.797851 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-12 -114033.030923 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-13 168857.202827 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-14 -114516.597122 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-15 108502.142565 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-18 -17902.333232 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-19 35228.088102 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-20 113097.124563 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-21 -205754.054176 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-22 1953.316272 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-25 133874.524524 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-26 100261.657657 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-27 -85779.392115 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-28 -55443.513444 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-29 -13212.896855 -1 closed \\n\",\n \"hedge action_spot hedge_2025-09-01 101496.32281 -1 closed \\n\",\n \"hedge action_spot hedge_2025-09-02 -32662.887524 -1 closed \\n\",\n \"hedge action_spot hedge_2025-09-03 86025.151973 -1 closed \\n\",\n \"hedge action_spot hedge_2025-09-04 0 -1 open \\n\",\n \"\\n\",\n \" Trade PnL \\n\",\n \"add action_EURUSD call_2025-08-04 -66088.343728 \\n\",\n \"add action_EURUSD call_2025-08-11 649.554742 \\n\",\n \"add action_EURUSD call_2025-08-18 106024.718826 \\n\",\n \"add action_EURUSD call_2025-08-25 -55730.252924 \\n\",\n \"add action_EURUSD call_2025-09-01 None \\n\",\n \"hedge action_spot hedge_2025-08-04 -6811.59862 \\n\",\n \"hedge action_spot hedge_2025-08-05 -127463.975852 \\n\",\n \"hedge action_spot hedge_2025-08-06 30876.677172 \\n\",\n \"hedge action_spot hedge_2025-08-07 -76216.870393 \\n\",\n \"hedge action_spot hedge_2025-08-08 180086.644138 \\n\",\n \"hedge action_spot hedge_2025-08-11 -153650.797782 \\n\",\n \"hedge action_spot hedge_2025-08-12 -114033.030978 \\n\",\n \"hedge action_spot hedge_2025-08-13 168857.202912 \\n\",\n \"hedge action_spot hedge_2025-08-14 -114516.597199 \\n\",\n \"hedge action_spot hedge_2025-08-15 108502.142683 \\n\",\n \"hedge action_spot hedge_2025-08-18 -17902.333128 \\n\",\n \"hedge action_spot hedge_2025-08-19 35228.088199 \\n\",\n \"hedge action_spot hedge_2025-08-20 113097.124475 \\n\",\n \"hedge action_spot hedge_2025-08-21 -205754.054122 \\n\",\n \"hedge action_spot hedge_2025-08-22 1953.316294 \\n\",\n \"hedge action_spot hedge_2025-08-25 133874.524573 \\n\",\n \"hedge action_spot hedge_2025-08-26 100261.657745 \\n\",\n \"hedge action_spot hedge_2025-08-27 -85779.392169 \\n\",\n \"hedge action_spot hedge_2025-08-28 -55443.513529 \\n\",\n \"hedge action_spot hedge_2025-08-29 -13212.896928 \\n\",\n \"hedge action_spot hedge_2025-09-01 101496.322917 \\n\",\n \"hedge action_spot hedge_2025-09-02 -32662.887534 \\n\",\n \"hedge action_spot hedge_2025-09-03 86025.152052 \\n\",\n \"hedge action_spot hedge_2025-09-04 None \"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"hedge_weekly_option_backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"98566c1f-97aa-4034-8288-ac36ac9ce788\",\n \"metadata\": {},\n \"source\": [\n \"that's great but in reality we are trading all these instrument at mid which isn't very likely. We should add some transaction costs\\n\",\n \"into the backtest to get something which mirrors reality better\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"058fca5d-8068-48d7-ada6-b819e96bd64c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:18<00:00, 5.26it/s]\\n\",\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 27/27 [00:13<00:00, 1.95it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"t_cost = ScaledTransactionModel(\\n\",\n \" 'notional_amount', 0.00005\\n\",\n \") # We are going to add a 0.5bp transaction cost. We could have charge scaled by risk as well\\n\",\n \"add_action_tcost = AddTradeAction(priceables=opt, trade_duration='1w', name='add action', transaction_cost=t_cost)\\n\",\n \"named_hedge_action_tcost = HedgeAction(\\n\",\n \" risk=FXDelta(aggregation_level='Type'),\\n\",\n \" priceables=fwd,\\n\",\n \" trade_duration='1b',\\n\",\n \" name='hedge action',\\n\",\n \" transaction_cost=t_cost,\\n\",\n \")\\n\",\n \"\\n\",\n \"weekly_trigger_tcost = PeriodicTrigger(\\n\",\n \" PeriodicTriggerRequirements(start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1w'),\\n\",\n \" actions=add_action_tcost,\\n\",\n \")\\n\",\n \"daily_trigger_tcost = PeriodicTrigger(\\n\",\n \" PeriodicTriggerRequirements(start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1b'),\\n\",\n \" actions=named_hedge_action_tcost,\\n\",\n \")\\n\",\n \"\\n\",\n \"hedge_weekly_option_strategy_tcost = Strategy(\\n\",\n \" initial_portfolio=None, triggers=[weekly_trigger_tcost, daily_trigger_tcost]\\n\",\n \")\\n\",\n \"hedge_weekly_option_backtest_tcost = ge.run_backtest(\\n\",\n \" strategy=hedge_weekly_option_strategy_tcost, start=dt.date(2025, 8, 4), end=dt.date(2025, 9, 4), frequency='1b'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"1c5dca41-84cf-4f4e-849c-c89846c1f85a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PriceFXDelta(aggregation_level:Type)Cumulative CashTransaction CostsTotal
2025-08-04703953.8466310.000000e+00-7.039538e+05-6051.326333-6051.326333
2025-08-05687311.1404640.000000e+00-7.107654e+05-8152.522301-31606.827207
2025-08-06854096.726246-3.725290e-09-8.382294e+05-10459.8432805407.461751
2025-08-07816927.5581110.000000e+00-8.073527e+05-12918.673655-3343.859500
2025-08-08872578.0985123.725290e-09-8.835696e+05-15413.046675-26404.562434
2025-08-11685241.7582610.000000e+00-7.508592e+05-27756.869337-93374.336621
2025-08-12828486.3446213.725290e-09-9.045100e+05-30066.308412-106089.987243
2025-08-13941680.8965670.000000e+00-1.018543e+06-32748.850904-109611.008628
2025-08-14749260.4694480.000000e+00-8.496859e+05-35363.040760-135788.422853
2025-08-15856628.3845190.000000e+00-9.642024e+05-37917.845073-145491.909092
2025-08-18641537.7450390.000000e+00-8.113467e+05-50344.674601-220153.667502
2025-08-19644700.8710140.000000e+00-8.292491e+05-52490.475935-237038.675995
2025-08-20613688.2059250.000000e+00-7.940210e+05-54613.777845-234946.554981
2025-08-21511073.2686070.000000e+00-6.809239e+05-56539.135316-226389.725156
2025-08-22776543.772222-3.725290e-09-8.866779e+05-58690.329946-168824.470327
2025-08-25664120.3609160.000000e+00-8.012825e+05-71027.812708-208189.945069
2025-08-26564999.5365620.000000e+00-6.674080e+05-73018.588822-175427.020925
2025-08-27455386.8550340.000000e+00-5.671463e+05-74700.472260-186459.928287
2025-08-28548765.1590180.000000e+00-6.529257e+05-76370.066582-180530.610827
2025-08-29600466.9539450.000000e+00-7.083692e+05-78259.895976-186162.158810
2025-09-01671974.3046120.000000e+00-7.851663e+05-90314.567163-203506.572755
2025-09-02573496.5792880.000000e+00-6.836700e+05-92322.836654-202496.244769
2025-09-03607131.2232360.000000e+00-7.163329e+05-94248.276676-203449.928287
2025-09-04503559.3305160.000000e+00-6.303077e+05-96084.215916-222832.608351
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Price FXDelta(aggregation_level:Type) Cumulative Cash \\\\\\n\",\n \"2025-08-04 703953.846631 0.000000e+00 -7.039538e+05 \\n\",\n \"2025-08-05 687311.140464 0.000000e+00 -7.107654e+05 \\n\",\n \"2025-08-06 854096.726246 -3.725290e-09 -8.382294e+05 \\n\",\n \"2025-08-07 816927.558111 0.000000e+00 -8.073527e+05 \\n\",\n \"2025-08-08 872578.098512 3.725290e-09 -8.835696e+05 \\n\",\n \"2025-08-11 685241.758261 0.000000e+00 -7.508592e+05 \\n\",\n \"2025-08-12 828486.344621 3.725290e-09 -9.045100e+05 \\n\",\n \"2025-08-13 941680.896567 0.000000e+00 -1.018543e+06 \\n\",\n \"2025-08-14 749260.469448 0.000000e+00 -8.496859e+05 \\n\",\n \"2025-08-15 856628.384519 0.000000e+00 -9.642024e+05 \\n\",\n \"2025-08-18 641537.745039 0.000000e+00 -8.113467e+05 \\n\",\n \"2025-08-19 644700.871014 0.000000e+00 -8.292491e+05 \\n\",\n \"2025-08-20 613688.205925 0.000000e+00 -7.940210e+05 \\n\",\n \"2025-08-21 511073.268607 0.000000e+00 -6.809239e+05 \\n\",\n \"2025-08-22 776543.772222 -3.725290e-09 -8.866779e+05 \\n\",\n \"2025-08-25 664120.360916 0.000000e+00 -8.012825e+05 \\n\",\n \"2025-08-26 564999.536562 0.000000e+00 -6.674080e+05 \\n\",\n \"2025-08-27 455386.855034 0.000000e+00 -5.671463e+05 \\n\",\n \"2025-08-28 548765.159018 0.000000e+00 -6.529257e+05 \\n\",\n \"2025-08-29 600466.953945 0.000000e+00 -7.083692e+05 \\n\",\n \"2025-09-01 671974.304612 0.000000e+00 -7.851663e+05 \\n\",\n \"2025-09-02 573496.579288 0.000000e+00 -6.836700e+05 \\n\",\n \"2025-09-03 607131.223236 0.000000e+00 -7.163329e+05 \\n\",\n \"2025-09-04 503559.330516 0.000000e+00 -6.303077e+05 \\n\",\n \"\\n\",\n \" Transaction Costs Total \\n\",\n \"2025-08-04 -6051.326333 -6051.326333 \\n\",\n \"2025-08-05 -8152.522301 -31606.827207 \\n\",\n \"2025-08-06 -10459.843280 5407.461751 \\n\",\n \"2025-08-07 -12918.673655 -3343.859500 \\n\",\n \"2025-08-08 -15413.046675 -26404.562434 \\n\",\n \"2025-08-11 -27756.869337 -93374.336621 \\n\",\n \"2025-08-12 -30066.308412 -106089.987243 \\n\",\n \"2025-08-13 -32748.850904 -109611.008628 \\n\",\n \"2025-08-14 -35363.040760 -135788.422853 \\n\",\n \"2025-08-15 -37917.845073 -145491.909092 \\n\",\n \"2025-08-18 -50344.674601 -220153.667502 \\n\",\n \"2025-08-19 -52490.475935 -237038.675995 \\n\",\n \"2025-08-20 -54613.777845 -234946.554981 \\n\",\n \"2025-08-21 -56539.135316 -226389.725156 \\n\",\n \"2025-08-22 -58690.329946 -168824.470327 \\n\",\n \"2025-08-25 -71027.812708 -208189.945069 \\n\",\n \"2025-08-26 -73018.588822 -175427.020925 \\n\",\n \"2025-08-27 -74700.472260 -186459.928287 \\n\",\n \"2025-08-28 -76370.066582 -180530.610827 \\n\",\n \"2025-08-29 -78259.895976 -186162.158810 \\n\",\n \"2025-09-01 -90314.567163 -203506.572755 \\n\",\n \"2025-09-02 -92322.836654 -202496.244769 \\n\",\n \"2025-09-03 -94248.276676 -203449.928287 \\n\",\n \"2025-09-04 -96084.215916 -222832.608351 \"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"hedge_weekly_option_backtest_tcost.result_summary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28b1eed4-a466-4ca8-9976-3c17427ab54e\",\n \"metadata\": {},\n \"source\": [\n \"lets plot the pnl of the strategy with and without the transaction costs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"f9652b0c-6a39-4cf3-ab6d-99536594c7a6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"hedge_weekly_option_backtest_tcost.result_summary['Total'].plot()\\n\",\n \"hedge_weekly_option_backtest.result_summary['Total'].plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5698cf7f-7503-4857-bb09-faaea78b1cb9\",\n \"metadata\": {},\n \"source\": [\n \"we can see that even a relatively low transaction cost of 0.5bp is having a significant effect. Perhaps we are hedging too frequently.\\n\",\n \"We should go back to basics and look at exactly how much delta the options are producing.\\n\",\n \"We construct a backtest which just buys the calls and hedges on inception and when running the backtest we add the FXDelta risk to the risks to be calculated so\\n\",\n \"we can look at how much delta we are running\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"5bb34a4c-e30b-489f-9119-59ab4c96265d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:14<00:00, 6.69it/s]\\n\",\n \"100%|███████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:01<00:00, 4.68it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"weekly_hedge_action = HedgeAction(\\n\",\n \" risk=FXDelta(aggregation_level='Type'),\\n\",\n \" priceables=FXForward(pair='EURUSD', settlement_date='1w'),\\n\",\n \" trade_duration='1w',\\n\",\n \" transaction_cost=t_cost,\\n\",\n \" name='initial hedge',\\n\",\n \")\\n\",\n \"# Note we are now hedging with a 1w fwd and holding for a week to tie in to the option schedule\\n\",\n \"weekly_option_and_hedge_trigger = PeriodicTrigger(\\n\",\n \" PeriodicTriggerRequirements(start_date=dt.date(2025, 8, 4), end_date=dt.date(2025, 9, 4), frequency='1w'),\\n\",\n \" actions=[add_action_tcost, initial_hedge_action],\\n\",\n \")\\n\",\n \"weekly_option_strategy = Strategy(initial_portfolio=None, triggers=weekly_option_and_hedge_trigger)\\n\",\n \"weekly_option_backtest = ge.run_backtest(\\n\",\n \" strategy=weekly_option_strategy,\\n\",\n \" start=dt.date(2025, 8, 4),\\n\",\n \" end=dt.date(2025, 9, 4),\\n\",\n \" frequency='1b',\\n\",\n \" risks=[FXDelta(aggregation_level='Type')],\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"dfc4c7c5-2176-4bf0-9ff5-ab50954fa897\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PriceFXDelta(aggregation_level:Type)Cumulative CashTransaction CostsTotal
2025-08-04703953.8466590.000000e+00-703953.846659-6051.957748-6051.957748
2025-08-05680489.942334-3.667943e+04-703953.846659-6051.957748-29515.862072
2025-08-06719584.6303784.109436e+06-703953.846659-6051.9577489578.825971
2025-08-07708224.3415582.986270e+06-703953.846659-6051.957748-1781.462848
2025-08-08697124.3846184.814721e+06-703953.846659-6051.957748-12881.419788
2025-08-11685241.7582730.000000e+00-780309.659995-18155.376168-113223.277890
2025-08-12674788.1548014.145061e+06-780309.659995-18155.376168-123676.881362
2025-08-13692719.8344447.446583e+06-780309.659995-18155.376168-105745.201719
2025-08-14624955.1887572.773939e+06-780309.659995-18155.376168-173509.847406
2025-08-15631145.4206496.254668e+06-780309.659995-18155.376168-167319.615514
2025-08-18641537.7451130.000000e+00-877905.875414-30269.758474-266637.888774
2025-08-19626809.6763304.167375e+05-877905.875414-30269.758474-281365.957557
2025-08-20630341.541006-4.598743e+05-877905.875414-30269.758474-277834.092882
2025-08-21643348.160861-3.544384e+06-877905.875414-30269.758474-264827.473027
2025-08-22661875.6829494.048457e+06-877905.875414-30269.758474-246299.950939
2025-08-25664120.3610720.000000e+00-907536.570217-42405.183716-285821.392861
2025-08-26698915.609748-3.066615e+06-907536.570217-42405.183716-251026.144184
2025-08-27706335.164311-6.185495e+06-907536.570217-42405.183716-243606.589622
2025-08-28679163.920997-3.317354e+06-907536.570217-42405.183716-270777.832936
2025-08-29665284.655358-1.787266e+06-907536.570217-42405.183716-284657.098575
2025-09-01671974.3047510.000000e+00-920687.388437-54549.799385-303262.883071
2025-09-02674996.386998-2.699085e+06-920687.388437-54549.799385-300240.800823
2025-09-03671249.433688-1.665711e+06-920687.388437-54549.799385-303987.754133
2025-09-04660967.206777-4.492958e+06-920687.388437-54549.799385-314269.981044
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Price FXDelta(aggregation_level:Type) Cumulative Cash \\\\\\n\",\n \"2025-08-04 703953.846659 0.000000e+00 -703953.846659 \\n\",\n \"2025-08-05 680489.942334 -3.667943e+04 -703953.846659 \\n\",\n \"2025-08-06 719584.630378 4.109436e+06 -703953.846659 \\n\",\n \"2025-08-07 708224.341558 2.986270e+06 -703953.846659 \\n\",\n \"2025-08-08 697124.384618 4.814721e+06 -703953.846659 \\n\",\n \"2025-08-11 685241.758273 0.000000e+00 -780309.659995 \\n\",\n \"2025-08-12 674788.154801 4.145061e+06 -780309.659995 \\n\",\n \"2025-08-13 692719.834444 7.446583e+06 -780309.659995 \\n\",\n \"2025-08-14 624955.188757 2.773939e+06 -780309.659995 \\n\",\n \"2025-08-15 631145.420649 6.254668e+06 -780309.659995 \\n\",\n \"2025-08-18 641537.745113 0.000000e+00 -877905.875414 \\n\",\n \"2025-08-19 626809.676330 4.167375e+05 -877905.875414 \\n\",\n \"2025-08-20 630341.541006 -4.598743e+05 -877905.875414 \\n\",\n \"2025-08-21 643348.160861 -3.544384e+06 -877905.875414 \\n\",\n \"2025-08-22 661875.682949 4.048457e+06 -877905.875414 \\n\",\n \"2025-08-25 664120.361072 0.000000e+00 -907536.570217 \\n\",\n \"2025-08-26 698915.609748 -3.066615e+06 -907536.570217 \\n\",\n \"2025-08-27 706335.164311 -6.185495e+06 -907536.570217 \\n\",\n \"2025-08-28 679163.920997 -3.317354e+06 -907536.570217 \\n\",\n \"2025-08-29 665284.655358 -1.787266e+06 -907536.570217 \\n\",\n \"2025-09-01 671974.304751 0.000000e+00 -920687.388437 \\n\",\n \"2025-09-02 674996.386998 -2.699085e+06 -920687.388437 \\n\",\n \"2025-09-03 671249.433688 -1.665711e+06 -920687.388437 \\n\",\n \"2025-09-04 660967.206777 -4.492958e+06 -920687.388437 \\n\",\n \"\\n\",\n \" Transaction Costs Total \\n\",\n \"2025-08-04 -6051.957748 -6051.957748 \\n\",\n \"2025-08-05 -6051.957748 -29515.862072 \\n\",\n \"2025-08-06 -6051.957748 9578.825971 \\n\",\n \"2025-08-07 -6051.957748 -1781.462848 \\n\",\n \"2025-08-08 -6051.957748 -12881.419788 \\n\",\n \"2025-08-11 -18155.376168 -113223.277890 \\n\",\n \"2025-08-12 -18155.376168 -123676.881362 \\n\",\n \"2025-08-13 -18155.376168 -105745.201719 \\n\",\n \"2025-08-14 -18155.376168 -173509.847406 \\n\",\n \"2025-08-15 -18155.376168 -167319.615514 \\n\",\n \"2025-08-18 -30269.758474 -266637.888774 \\n\",\n \"2025-08-19 -30269.758474 -281365.957557 \\n\",\n \"2025-08-20 -30269.758474 -277834.092882 \\n\",\n \"2025-08-21 -30269.758474 -264827.473027 \\n\",\n \"2025-08-22 -30269.758474 -246299.950939 \\n\",\n \"2025-08-25 -42405.183716 -285821.392861 \\n\",\n \"2025-08-26 -42405.183716 -251026.144184 \\n\",\n \"2025-08-27 -42405.183716 -243606.589622 \\n\",\n \"2025-08-28 -42405.183716 -270777.832936 \\n\",\n \"2025-08-29 -42405.183716 -284657.098575 \\n\",\n \"2025-09-01 -54549.799385 -303262.883071 \\n\",\n \"2025-09-02 -54549.799385 -300240.800823 \\n\",\n \"2025-09-03 -54549.799385 -303987.754133 \\n\",\n \"2025-09-04 -54549.799385 -314269.981044 \"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"weekly_option_backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"05eeadc9-9634-44ae-88b6-58400a547d0f\",\n \"metadata\": {},\n \"source\": [\n \"maybe we can try a strategy which only hedges if the abs(delta) is greater than 4e6.\\n\",\n \"To do this instead of hedging on a periodic basis we need a different type of trigger which triggers dependent on the risk on the strategy.\\n\",\n \"This is the StrategyRiskTrigger\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"f20bed2a-2d0d-4657-ad4c-d60982154b80\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:16<00:00, 5.91it/s]\\n\",\n \"100%|█████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:10<00:00, 1.98it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"risk_hedge_trigger_up = StrategyRiskTrigger(\\n\",\n \" RiskTriggerRequirements(\\n\",\n \" risk=FXDelta(aggregation_level='Type'), trigger_level=4e6, direction=TriggerDirection.ABOVE\\n\",\n \" ),\\n\",\n \" actions=named_hedge_action_tcost,\\n\",\n \")\\n\",\n \"risk_hedge_trigger_down = StrategyRiskTrigger(\\n\",\n \" RiskTriggerRequirements(\\n\",\n \" risk=FXDelta(aggregation_level='Type'), trigger_level=-4e6, direction=TriggerDirection.BELOW\\n\",\n \" ),\\n\",\n \" actions=named_hedge_action_tcost,\\n\",\n \")\\n\",\n \"risk_hedge_strategy = Strategy(\\n\",\n \" initial_portfolio=None, triggers=[weekly_option_and_hedge_trigger, risk_hedge_trigger_up, risk_hedge_trigger_down]\\n\",\n \")\\n\",\n \"risk_hedge_backtest = ge.run_backtest(\\n\",\n \" strategy=risk_hedge_strategy, start=dt.date(2025, 8, 4), end=dt.date(2025, 9, 4), frequency='1b'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"8eb40fed-96b5-4ce5-b6a0-068ffa6099ae\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
OpenCloseOpen ValueClose ValueLong ShortStatusTrade PnL
Action6_Priceable0_2025-08-042025-08-042025-08-110.000015-28979.558009-1closed-28979.557994
Action6_Priceable0_2025-08-112025-08-112025-08-180.000056-141949.783377-1closed-141949.78332
Action6_Priceable0_2025-08-182025-08-182025-08-250.00003-113072.7977-1closed-113072.79767
Action6_Priceable0_2025-08-252025-08-252025-09-01-0.00010650433.378489-1closed50433.378383
Action6_Priceable0_2025-09-012025-09-01None-0.0000320-1openNone
add action_EURUSD call_2025-08-042025-08-042025-08-11-703953.846673637865.502946-1closed-66088.343728
add action_EURUSD call_2025-08-112025-08-112025-08-18-685241.75833685891.313071-1closed649.554742
add action_EURUSD call_2025-08-182025-08-182025-08-25-641537.745143747562.463969-1closed106024.718826
add action_EURUSD call_2025-08-252025-08-252025-09-01-664120.360966608390.108042-1closed-55730.252924
add action_EURUSD call_2025-09-012025-09-01None-671974.3047190-1openNone
hedge action_spot hedge_2025-08-042025-08-042025-08-050.00.0-1closed0.0
hedge action_spot hedge_2025-08-062025-08-062025-08-07-0.0000115048.991118-1closed5048.991107
hedge action_spot hedge_2025-08-082025-08-082025-08-110.00001833553.625264-1closed33553.625282
hedge action_spot hedge_2025-08-112025-08-112025-08-120.00.0-1closed0.0
hedge action_spot hedge_2025-08-122025-08-122025-08-13-0.000009-18791.163959-1closed-18791.163968
hedge action_spot hedge_2025-08-132025-08-132025-08-140.00002244184.569597-1closed44184.56962
hedge action_spot hedge_2025-08-152025-08-152025-08-180.00002724885.006478-1closed24885.006505
hedge action_spot hedge_2025-08-182025-08-182025-08-190.00.0-1closed0.0
hedge action_spot hedge_2025-08-222025-08-222025-08-250.000003312.639056-1closed312.639059
hedge action_spot hedge_2025-08-252025-08-252025-08-260.00.0-1closed0.0
hedge action_spot hedge_2025-08-272025-08-272025-08-280.00002234800.42608-1closed34800.426102
hedge action_spot hedge_2025-09-012025-09-012025-09-020.00.0-1closed0.0
hedge action_spot hedge_2025-09-042025-09-04None0.000020-1openNone
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Open Close Open Value \\\\\\n\",\n \"Action6_Priceable0_2025-08-04 2025-08-04 2025-08-11 0.000015 \\n\",\n \"Action6_Priceable0_2025-08-11 2025-08-11 2025-08-18 0.000056 \\n\",\n \"Action6_Priceable0_2025-08-18 2025-08-18 2025-08-25 0.00003 \\n\",\n \"Action6_Priceable0_2025-08-25 2025-08-25 2025-09-01 -0.000106 \\n\",\n \"Action6_Priceable0_2025-09-01 2025-09-01 None -0.000032 \\n\",\n \"add action_EURUSD call_2025-08-04 2025-08-04 2025-08-11 -703953.846673 \\n\",\n \"add action_EURUSD call_2025-08-11 2025-08-11 2025-08-18 -685241.75833 \\n\",\n \"add action_EURUSD call_2025-08-18 2025-08-18 2025-08-25 -641537.745143 \\n\",\n \"add action_EURUSD call_2025-08-25 2025-08-25 2025-09-01 -664120.360966 \\n\",\n \"add action_EURUSD call_2025-09-01 2025-09-01 None -671974.304719 \\n\",\n \"hedge action_spot hedge_2025-08-04 2025-08-04 2025-08-05 0.0 \\n\",\n \"hedge action_spot hedge_2025-08-06 2025-08-06 2025-08-07 -0.000011 \\n\",\n \"hedge action_spot hedge_2025-08-08 2025-08-08 2025-08-11 0.000018 \\n\",\n \"hedge action_spot hedge_2025-08-11 2025-08-11 2025-08-12 0.0 \\n\",\n \"hedge action_spot hedge_2025-08-12 2025-08-12 2025-08-13 -0.000009 \\n\",\n \"hedge action_spot hedge_2025-08-13 2025-08-13 2025-08-14 0.000022 \\n\",\n \"hedge action_spot hedge_2025-08-15 2025-08-15 2025-08-18 0.000027 \\n\",\n \"hedge action_spot hedge_2025-08-18 2025-08-18 2025-08-19 0.0 \\n\",\n \"hedge action_spot hedge_2025-08-22 2025-08-22 2025-08-25 0.000003 \\n\",\n \"hedge action_spot hedge_2025-08-25 2025-08-25 2025-08-26 0.0 \\n\",\n \"hedge action_spot hedge_2025-08-27 2025-08-27 2025-08-28 0.000022 \\n\",\n \"hedge action_spot hedge_2025-09-01 2025-09-01 2025-09-02 0.0 \\n\",\n \"hedge action_spot hedge_2025-09-04 2025-09-04 None 0.00002 \\n\",\n \"\\n\",\n \" Close Value Long Short Status \\\\\\n\",\n \"Action6_Priceable0_2025-08-04 -28979.558009 -1 closed \\n\",\n \"Action6_Priceable0_2025-08-11 -141949.783377 -1 closed \\n\",\n \"Action6_Priceable0_2025-08-18 -113072.7977 -1 closed \\n\",\n \"Action6_Priceable0_2025-08-25 50433.378489 -1 closed \\n\",\n \"Action6_Priceable0_2025-09-01 0 -1 open \\n\",\n \"add action_EURUSD call_2025-08-04 637865.502946 -1 closed \\n\",\n \"add action_EURUSD call_2025-08-11 685891.313071 -1 closed \\n\",\n \"add action_EURUSD call_2025-08-18 747562.463969 -1 closed \\n\",\n \"add action_EURUSD call_2025-08-25 608390.108042 -1 closed \\n\",\n \"add action_EURUSD call_2025-09-01 0 -1 open \\n\",\n \"hedge action_spot hedge_2025-08-04 0.0 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-06 5048.991118 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-08 33553.625264 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-11 0.0 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-12 -18791.163959 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-13 44184.569597 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-15 24885.006478 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-18 0.0 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-22 312.639056 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-25 0.0 -1 closed \\n\",\n \"hedge action_spot hedge_2025-08-27 34800.42608 -1 closed \\n\",\n \"hedge action_spot hedge_2025-09-01 0.0 -1 closed \\n\",\n \"hedge action_spot hedge_2025-09-04 0 -1 open \\n\",\n \"\\n\",\n \" Trade PnL \\n\",\n \"Action6_Priceable0_2025-08-04 -28979.557994 \\n\",\n \"Action6_Priceable0_2025-08-11 -141949.78332 \\n\",\n \"Action6_Priceable0_2025-08-18 -113072.79767 \\n\",\n \"Action6_Priceable0_2025-08-25 50433.378383 \\n\",\n \"Action6_Priceable0_2025-09-01 None \\n\",\n \"add action_EURUSD call_2025-08-04 -66088.343728 \\n\",\n \"add action_EURUSD call_2025-08-11 649.554742 \\n\",\n \"add action_EURUSD call_2025-08-18 106024.718826 \\n\",\n \"add action_EURUSD call_2025-08-25 -55730.252924 \\n\",\n \"add action_EURUSD call_2025-09-01 None \\n\",\n \"hedge action_spot hedge_2025-08-04 0.0 \\n\",\n \"hedge action_spot hedge_2025-08-06 5048.991107 \\n\",\n \"hedge action_spot hedge_2025-08-08 33553.625282 \\n\",\n \"hedge action_spot hedge_2025-08-11 0.0 \\n\",\n \"hedge action_spot hedge_2025-08-12 -18791.163968 \\n\",\n \"hedge action_spot hedge_2025-08-13 44184.56962 \\n\",\n \"hedge action_spot hedge_2025-08-15 24885.006505 \\n\",\n \"hedge action_spot hedge_2025-08-18 0.0 \\n\",\n \"hedge action_spot hedge_2025-08-22 312.639059 \\n\",\n \"hedge action_spot hedge_2025-08-25 0.0 \\n\",\n \"hedge action_spot hedge_2025-08-27 34800.426102 \\n\",\n \"hedge action_spot hedge_2025-09-01 0.0 \\n\",\n \"hedge action_spot hedge_2025-09-04 None \"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"risk_hedge_backtest.trade_ledger()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1b3ef088-41e6-4a7e-be2c-70b30776935d\",\n \"metadata\": {},\n \"source\": [\n \"so now we can see that we hedging on a sparse schedule and if we look at the result summary we can see that the delta is not always zero but that\\n\",\n \"the cumulative transaction costs are lower.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"8589b96c-582f-4b8b-bf12-78caee06eff8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PriceFXDelta(aggregation_level:Type)Cumulative CashTransaction CostsTotal
2025-08-04703953.8466590.000000e+00-703953.846659-6051.957748-6051.957748
2025-08-05680489.942334-3.667943e+04-703953.846659-6051.957748-29515.862072
2025-08-06719584.6303890.000000e+00-703953.846670-6257.5776939373.206026
2025-08-07708224.3415582.986270e+06-698904.855552-6463.1976372856.288369
2025-08-08697124.3846000.000000e+00-698904.855533-6704.107429-8484.578362
2025-08-11685241.7582730.000000e+00-741707.043607-19048.435640-75513.720973
2025-08-12674788.1548100.000000e+00-741707.043616-19255.838305-86174.727110
2025-08-13692719.8344229.313226e-10-760498.207553-19835.838794-87614.211925
2025-08-14624955.1887572.773939e+06-716313.637955-20208.436618-111566.885817
2025-08-15631145.4206219.313226e-10-716313.637928-20521.395873-105689.613179
2025-08-18641537.7451130.000000e+00-789024.846869-32948.737433-180435.839189
2025-08-19626809.6763314.167375e+05-789024.846869-32948.737433-195163.907971
2025-08-20630341.541006-4.598743e+05-789024.846869-32948.737433-191632.043296
2025-08-21643348.160860-3.544384e+06-789024.846869-32948.737433-178625.423442
2025-08-22661875.6829460.000000e+00-789024.846865-33151.307018-160300.470937
2025-08-25664120.3610720.000000e+00-818342.902612-45489.301845-199711.843385
2025-08-26698915.609748-3.066615e+06-818342.902612-45489.301845-164916.594709
2025-08-27706335.1642890.000000e+00-818342.902590-45798.836086-157806.574388
2025-08-28679163.920997-3.317354e+06-783542.476510-46108.370327-150486.925841
2025-08-29665284.655358-1.787266e+06-783542.476510-46108.370327-164366.191480
2025-09-01671974.3047510.000000e+00-796693.294730-58252.985996-182971.975975
2025-09-02674996.386999-2.699085e+06-796693.294730-58252.985996-179949.893727
2025-09-03671249.433689-1.665711e+06-796693.294730-58252.985996-183696.847037
2025-09-04660967.2067570.000000e+00-796693.294710-58477.796095-194203.884048
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Price FXDelta(aggregation_level:Type) Cumulative Cash \\\\\\n\",\n \"2025-08-04 703953.846659 0.000000e+00 -703953.846659 \\n\",\n \"2025-08-05 680489.942334 -3.667943e+04 -703953.846659 \\n\",\n \"2025-08-06 719584.630389 0.000000e+00 -703953.846670 \\n\",\n \"2025-08-07 708224.341558 2.986270e+06 -698904.855552 \\n\",\n \"2025-08-08 697124.384600 0.000000e+00 -698904.855533 \\n\",\n \"2025-08-11 685241.758273 0.000000e+00 -741707.043607 \\n\",\n \"2025-08-12 674788.154810 0.000000e+00 -741707.043616 \\n\",\n \"2025-08-13 692719.834422 9.313226e-10 -760498.207553 \\n\",\n \"2025-08-14 624955.188757 2.773939e+06 -716313.637955 \\n\",\n \"2025-08-15 631145.420621 9.313226e-10 -716313.637928 \\n\",\n \"2025-08-18 641537.745113 0.000000e+00 -789024.846869 \\n\",\n \"2025-08-19 626809.676331 4.167375e+05 -789024.846869 \\n\",\n \"2025-08-20 630341.541006 -4.598743e+05 -789024.846869 \\n\",\n \"2025-08-21 643348.160860 -3.544384e+06 -789024.846869 \\n\",\n \"2025-08-22 661875.682946 0.000000e+00 -789024.846865 \\n\",\n \"2025-08-25 664120.361072 0.000000e+00 -818342.902612 \\n\",\n \"2025-08-26 698915.609748 -3.066615e+06 -818342.902612 \\n\",\n \"2025-08-27 706335.164289 0.000000e+00 -818342.902590 \\n\",\n \"2025-08-28 679163.920997 -3.317354e+06 -783542.476510 \\n\",\n \"2025-08-29 665284.655358 -1.787266e+06 -783542.476510 \\n\",\n \"2025-09-01 671974.304751 0.000000e+00 -796693.294730 \\n\",\n \"2025-09-02 674996.386999 -2.699085e+06 -796693.294730 \\n\",\n \"2025-09-03 671249.433689 -1.665711e+06 -796693.294730 \\n\",\n \"2025-09-04 660967.206757 0.000000e+00 -796693.294710 \\n\",\n \"\\n\",\n \" Transaction Costs Total \\n\",\n \"2025-08-04 -6051.957748 -6051.957748 \\n\",\n \"2025-08-05 -6051.957748 -29515.862072 \\n\",\n \"2025-08-06 -6257.577693 9373.206026 \\n\",\n \"2025-08-07 -6463.197637 2856.288369 \\n\",\n \"2025-08-08 -6704.107429 -8484.578362 \\n\",\n \"2025-08-11 -19048.435640 -75513.720973 \\n\",\n \"2025-08-12 -19255.838305 -86174.727110 \\n\",\n \"2025-08-13 -19835.838794 -87614.211925 \\n\",\n \"2025-08-14 -20208.436618 -111566.885817 \\n\",\n \"2025-08-15 -20521.395873 -105689.613179 \\n\",\n \"2025-08-18 -32948.737433 -180435.839189 \\n\",\n \"2025-08-19 -32948.737433 -195163.907971 \\n\",\n \"2025-08-20 -32948.737433 -191632.043296 \\n\",\n \"2025-08-21 -32948.737433 -178625.423442 \\n\",\n \"2025-08-22 -33151.307018 -160300.470937 \\n\",\n \"2025-08-25 -45489.301845 -199711.843385 \\n\",\n \"2025-08-26 -45489.301845 -164916.594709 \\n\",\n \"2025-08-27 -45798.836086 -157806.574388 \\n\",\n \"2025-08-28 -46108.370327 -150486.925841 \\n\",\n \"2025-08-29 -46108.370327 -164366.191480 \\n\",\n \"2025-09-01 -58252.985996 -182971.975975 \\n\",\n \"2025-09-02 -58252.985996 -179949.893727 \\n\",\n \"2025-09-03 -58252.985996 -183696.847037 \\n\",\n \"2025-09-04 -58477.796095 -194203.884048 \"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"risk_hedge_backtest.result_summary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8a64c520-9803-47f5-b1e9-9672676f9c12\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/05_factor_models/01_Factor_Models.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0674ed16\",\n \"metadata\": {},\n \"source\": [\n \"# Querying Factor Models\\n\",\n \"\\n\",\n \"The GS Quant `FactorRiskModel` class allows users to access vendor factor models such as Barra. The `FactorRiskModel` interface supports date-based querying of the factor risk model outputs such as factor returns, covariance matrix and specific risk for assets.\\n\",\n \"\\n\",\n \"In this tutorial, we’ll look at querying available risk models, their coverage universe, and how to access the returns and volatility of factors in the model. We also show how to query factor exposures (z-scores), specific risk and total risk for a given set of assets in the model's universe.\\n\",\n \"\\n\",\n \"The factor returns represent the regression outputs of the model for each day. The definitions of each factor vary depending on the model. More details can be found in the [Marquee Data Catalog](https://marquee.gs.com/s/discover/data-services/catalog?query=factor+risk+model).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c995452e\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ae1681e6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import warnings\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.ticker as mtick\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"from gs_quant.timeseries import beta\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.models.risk_model import FactorRiskModel\\n\",\n \"from gs_quant.models.risk_model import Measure, RiskModelUniverseIdentifierRequest as Identifier, DataAssetsRequest\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the lines below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"floral-apollo\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Pull Risk Model Coverage\\n\",\n \"\\n\",\n \"Popular third party risk models that have been onboarded onto Marquee for programmatic access are below.\\n\",\n \"\\n\",\n \"| Risk Model Name | Risk Model Id | Description|\\n\",\n \"|-------------------------------------|--------------------|-------------\\n\",\n \"| Barra USMED Short (BARRA_USMEDS) | BARRA_USMEDS |Barra (MSCI) US Total Market Equity Model for medium-term investors. Includes all styles from the long-term model plus additional factors for investment horizons between 1 month and 1 year (responsive variant).|\\n\",\n \"| Barra USSLOW Long (BARRA_USSLOWL) | BARRA_USSLOWL |Barra (MSCI) US Total Market Equity Model for long-term investors. Designed with a focus on portfolio construction and reporting for long investment horizons (stable variant).|\\n\",\n \"| Barra GEMLT Long (BARRA_GEMLTL) | BARRA_GEMLTL |Barra (MSCI) Global Total Market Equity Model for long-term investors. Designed with a focus on portfolio construction and reporting for global equity investors (stable variant).|\\n\",\n \"| Barra US Fast (BARRA_USFAST) | BARRA_USFAST |Barra (MSCI) US Equity Trading Model for short-term investors. Includes all styles from the medium-term model plus additional factors for shorter investment horizons.|\\n\",\n \"| Wolfe Developed Markets All-Cap v1 | WOLFE_QES_DM_AC_1 | Wolfe's Developed Markets All-Cap model is intended for global portfolios with an emphasis on Developed Markets. The model combines next generation factors like short interest and interest rate sensitivity with conventional factors like value and growth.|\\n\",\n \"| Wolfe US TMT v2 | WOLFE_QES_US_TMT_2 | Wolfe's US TMT model is intended for sector portfolios. The model uses sector-specific factors in a TMT estimation universe to explain more systematic risk and return relative to a broad market model.|\\n\",\n \"| Wolfe Europe All-Cap v2.1 | WOLFE_QES_EU_AC_21 | Wolfe's Europe All-Cap model is intended for European portfolios with a focus on the developed markets. The model combines next generation factors like short interest and interest rate sensitivity with conventional factors like value and growth.|\\n\",\n \"| Wolfe US Healthcare v2 | WOLFE_QES_US_HC_2 | Wolfe's US Healthcare model is intended for sector portfolios. The model uses sector-specific factors in a Healthcare estimation universe to explain more systematic risk and return relative to a broad market model.|\\n\",\n \"\\n\",\n \"After selecting a risk model, we can create an instance of the risk model to pull information on the model coverage such as the available dates, asset coverage universe, available factors and model description. The `RiskModelCoverage` enum of the model indicates whether the scope of the universe is Global, Region or Country and the `Term` enum refers to the horizon of the model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"social-attendance\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Check available history for a factor model to decide start and end dates\\n\",\n \"available_days = factor_model.get_dates()\\n\",\n \"\\n\",\n \"print(f'Data available for {model_id} from {available_days[0]} to {available_days[-1]}')\\n\",\n \"print(\\n\",\n \" f'{model_id}:\\\\n - Name: {factor_model.name}\\\\n - Description: {factor_model.description}\\\\n - Coverage: {factor_model.coverage.value}\\\\n - Horizon: {factor_model.term.value}'\\n\",\n \")\\n\",\n \"print(f'For all info https://marquee.gs.com/v1/risk/models/{model_id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"median-individual\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Query Factor Data\\n\",\n \"\\n\",\n \"The following parameters are required for querying factor data:\\n\",\n \"\\n\",\n \"* `start_date` - date or datetime that is a business day\\n\",\n \"* `end_date` - date or datetime that is a business day. If an end date is not specified, it will default to the last available date\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"documentary-trail\",\n \"metadata\": {},\n \"source\": [\n \"##### Get Available Factors\\n\",\n \"For each model, we can retrieve a list of factors available. Each factor has a `name`, `id`, `type` and `factorCategory`.\\n\",\n \"\\n\",\n \"A factor's `factorCategory` can be one of the following:\\n\",\n \"* Style - balance sheet and market metrics\\n\",\n \"* Industry - an asset's line of business (i.e. Barra uses GICS classification)\\n\",\n \"* Country - reference an asset’s exchange country location\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"improving-recycling\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"start_date = dt.date(2021, 1, 4) # Set your start date, e.g: dt.date(2021, 1, 4)\\n\",\n \"end_date = dt.date(2021, 1, 26) # Set your end date, e.g: dt.date(2021, 1, 26)\\n\",\n \"\\n\",\n \"available_factors = factor_model.get_factor_data(start_date, end_date).set_index('identifier')\\n\",\n \"available_factors.sort_values(by=['factorCategory'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cosmetic-contrary\",\n \"metadata\": {},\n \"source\": [\n \"##### Get All Factor Returns\\n\",\n \"\\n\",\n \"To query factor returns, we can either use `get_factor_returns_by_name` to retrieve the returns with names or `get_factor_returns_by_id` to get the returns with factor ids. We can leverage [the timeseries package](https://developer.gs.com/docs/gsquant/data/data-analytics/timeseries/) to transform and visualize the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"equal-thailand\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"date = dt.date(2020, 1, 4) # Set your date, e.g. dt.date(2020, 1, 4)\\n\",\n \"\\n\",\n \"factor_returns = factor_model.get_factor_returns_by_name(date)\\n\",\n \"fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\\n\",\n \"\\n\",\n \"factor_returns[['Growth', 'Momentum', 'Size']].cumsum().plot(\\n\",\n \" title='Factor Performance over Time for Risk Model', ax=ax[0]\\n\",\n \")\\n\",\n \"factor_beta = beta(factor_returns['Growth'], factor_returns['Momentum'], 63, prices=False)\\n\",\n \"factor_beta.plot(title='3m Rolling Beta of Growth to Momentum', ax=ax[1])\\n\",\n \"fig.autofmt_xdate()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"removable-boards\",\n \"metadata\": {},\n \"source\": [\n \"##### Covariance Matrix\\n\",\n \"\\n\",\n \"The covariance matrix represents an N-factor by N-factor matrix with the diagonal representing the variance of each factor for each day. The covariance matrix is in daily variance units.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bibliographic-medicare\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"start_date = dt.date(2021, 1, 4) # Set your start date, e.g: dt.date(2021, 1, 4)\\n\",\n \"end_date = dt.date(2021, 1, 26) # Set your end date, e.g: dt.date(2021, 1, 26)\\n\",\n \"\\n\",\n \"cov_matrix = factor_model.get_covariance_matrix(start_date, end_date) * 100\\n\",\n \"\\n\",\n \"# set display options below--set max_rows and max_columns to None to return full dataframe\\n\",\n \"max_rows = 10\\n\",\n \"max_columns = 7\\n\",\n \"pd.set_option('display.max_rows', max_rows)\\n\",\n \"pd.set_option('display.max_columns', max_columns)\\n\",\n \"\\n\",\n \"# get the last available matrix\\n\",\n \"round(cov_matrix.loc['DATE: e.g. 2021-02-26'], 6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"operating-hearing\",\n \"metadata\": {},\n \"source\": [\n \"##### Factor Correlation and Volatility\\n\",\n \"\\n\",\n \"The `Factor` Class allows for quick analytics for a specified factor to easily support comparing one factor across different models or to another factor.\\n\",\n \"\\n\",\n \"The factor volatility and correlation functions use the covariance matrix for calculations:\\n\",\n \"* Volatility is the square root of the diagonal\\n\",\n \"* Correlation is derived from the covariance matrix by dividing the cov(x,y) by the vol(x) * vol(y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"outdoor-security\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"momentum = factor_model.get_factor('Momentum')\\n\",\n \"growth = factor_model.get_factor('Growth')\\n\",\n \"\\n\",\n \"start_date = dt.date(2021, 1, 4) # Set your start date, e.g: dt.date(2021, 1, 4)\\n\",\n \"end_date = dt.date(2021, 1, 26) # Set your end date, e.g: dt.date(2021, 1, 26)\\n\",\n \"\\n\",\n \"vol = momentum.volatility(start_date, end_date)\\n\",\n \"corr = momentum.correlation(growth, start_date, end_date)\\n\",\n \"\\n\",\n \"# plot\\n\",\n \"fig, ax1 = plt.subplots()\\n\",\n \"ax2 = ax1.twinx()\\n\",\n \"ax1.plot(vol.index, corr * 100, 'g-', label='Momentum vs Growth Correlation (LHS)')\\n\",\n \"ax1.yaxis.set_major_formatter(mtick.PercentFormatter())\\n\",\n \"ax2.yaxis.set_major_formatter(mtick.PercentFormatter())\\n\",\n \"ax2.plot(vol.index, vol * 1e4, 'b-', label='Momentum Volatility (RHS)')\\n\",\n \"plt.xticks(vol.index.values[::30])\\n\",\n \"fig.legend(loc=\\\"lower right\\\", bbox_to_anchor=(0.75, -0.10))\\n\",\n \"fig.autofmt_xdate()\\n\",\n \"plt.title('Momentum vs Growth Historical Factor Analysis')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"21845fe4\",\n \"metadata\": {},\n \"source\": [\n \"#### Quick Hint:\\n\",\n \"\\n\",\n \"You can also get factor volatility for many factors all at once!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"10df2a94\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"factor_model.get_factor_volatility(start_date, end_date)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"identified-terminology\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Query Asset Data\\n\",\n \"\\n\",\n \"The factor risk represents the beta coefficient that can be attributed to the model, whereas the specific (residual) risk refers to the error term that is not explained by the model.\\n\",\n \"\\n\",\n \"| Measure | Definition |\\n\",\n \"|----------------------------|---------------|\\n\",\n \"| `Specific Risk` | Annualized idiosyncratic risk or error term which is not attributable to factors in percent units |\\n\",\n \"| `Total Risk` | Annualized risk which is the sum of specific and factor risk in percent units |\\n\",\n \"| `Historical Beta` | The covariance of the residual returns relative to the model's estimation universe or benchmark (i.e results of a one factor model) |\\n\",\n \"| `Residual Variance` | Daily error variance that is not explained by the model which is equal to $$\\\\frac{({\\\\frac{\\\\text{Specific Risk}}{100}})^2}{252}$$ |\\n\",\n \"| `Universe Factor Exposure` | Z-score for each factor relative to the model's estimation universe |\\n\",\n \"| `Predicted Beta` | The beta coefficient derived from predictive models; it estimates the asset’s expected sensitivity to market or factor fluctuations, reflecting forecast risk exposure |\\n\",\n \"| `Daily Return` | The daily percentage change in an asset’s price, representing short-term performance fluctuations |\\n\",\n \"| `R Squared` | The proportion of asset return variance explained by the model’s risk factors, expressed as a percentage |\\n\",\n \"| `Factor Return` | The portion of the asset return that is attributed to the risk factor(s) |\\n\",\n \"| `Dividend Yield` | The annual dividend income expressed as a percentage of the asset's current price |\\n\",\n \"\\n\",\n \"We can retrieve an asset universe on a given date by passing in an empty list and a `RiskModelUniverseIdentifierRequest` to the `DataAssetsRequest`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"spatial-problem\",\n \"metadata\": {},\n \"source\": [\n \"##### Get Risk Model Universe Coverage\\n\",\n \"\\n\",\n \"Note: \\n\",\n \"You can query asset-related risk model data with the folloing identifiers:\\n\",\n \"|Identifier |\\n\",\n \"|-----------|\\n\",\n \"|BBID |\\n\",\n \"|BCID |\\n\",\n \"|SEDOL |\\n\",\n \"| CUSIP|\\n\",\n \"|ISIN |\\n\",\n \"|GSID |, bcid, sedol, cusip, isin, gsid\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"short-kennedy\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"date = dt.date(2021, 1, 4) # Set your date, e.g: dt.date(2021, 1, 4)\\n\",\n \"\\n\",\n \"asset_universe_for_request = DataAssetsRequest(Identifier.gsid, []) # entire universe\\n\",\n \"universe_on_date = factor_model.get_asset_universe(date, assets=asset_universe_for_request)\\n\",\n \"\\n\",\n \"# set display options below--set max_rows to None to return full list of identifiers\\n\",\n \"max_rows = 10\\n\",\n \"pd.set_option('display.max_rows', max_rows)\\n\",\n \"universe_on_date\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"relevant-brooklyn\",\n \"metadata\": {},\n \"source\": [\n \"##### Query Aggregated Risk\\n\",\n \"\\n\",\n \"For asset data, we can query for a specific measure or pull data for a list of measures over a range of dates.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"copyrighted-elements\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"asset_bbid = 'ASSET BBID (e.g. \\\"AAPL UW\\\")'\\n\",\n \"\\n\",\n \"start_date = dt.date(2021, 1, 4) # Set your start date, e.g: dt.date(2021, 1, 4)\\n\",\n \"end_date = dt.date(2021, 1, 26) # Set your end date, e.g: dt.date(2021, 1, 26)\\n\",\n \"\\n\",\n \"# get risk\\n\",\n \"universe_for_request = DataAssetsRequest(Identifier.bbid, [asset_bbid])\\n\",\n \"specific_risk = factor_model.get_specific_risk(start_date, end_date, universe_for_request)\\n\",\n \"total_risk = factor_model.get_total_risk(start_date, end_date, universe_for_request)\\n\",\n \"factor_risk = total_risk - specific_risk\\n\",\n \"\\n\",\n \"plt.stackplot(\\n\",\n \" total_risk.index, specific_risk[asset_bbid], factor_risk[asset_bbid], labels=['Specific Risk', 'Factor Risk']\\n\",\n \")\\n\",\n \"plt.title(f'{asset_bbid} Risk')\\n\",\n \"plt.xticks(total_risk.index.values[::50])\\n\",\n \"plt.legend(loc='upper right')\\n\",\n \"plt.gcf().autofmt_xdate()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"frequent-lithuania\",\n \"metadata\": {},\n \"source\": [\n \"##### Query Factor Exposures (z-scores)\\n\",\n \"\\n\",\n \"When querying the asset factor exposures, set the `limit_factor` to True to receive only non zero exposures.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"micro-processor\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"asset_bbid = 'ASSET BBID (e.g. \\\"AAPL UW\\\")'\\n\",\n \"universe_for_request = DataAssetsRequest(Identifier.bbid, [asset_bbid])\\n\",\n \"\\n\",\n \"start_date = dt.date(2021, 1, 4) # Set your start date, e.g: dt.date(2021, 1, 4)\\n\",\n \"end_date = dt.date(2021, 1, 26) # Set your end date, e.g: dt.date(2021, 1, 26)\\n\",\n \"\\n\",\n \"factor_exposures = factor_model.get_universe_factor_exposure(start_date, end_date, universe_for_request)\\n\",\n \"\\n\",\n \"available_factors = factor_model.get_factor_data(dt.date(2020, 1, 4)).set_index('identifier')\\n\",\n \"available_factors.sort_values(by=['factorCategory']).tail()\\n\",\n \"\\n\",\n \"factor_exposures.columns = [available_factors.loc[x]['name'] for x in factor_exposures.columns]\\n\",\n \"\\n\",\n \"sns.boxplot(data=factor_exposures[['Beta', 'Momentum', 'Growth', 'Profitability']])\\n\",\n \"plt.title(f'Distribution of {asset_bbid} Factor Exposures since 1/4/20')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"greatest-graham\",\n \"metadata\": {},\n \"source\": [\n \"##### Query Multiple Asset Measures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"subtle-young\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'RISK MODEL ID'\\n\",\n \"factor_model = FactorRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# get multiple measures across a date range for a universe specified\\n\",\n \"start_date = dt.date(2021, 1, 4) # Set your start date, e.g: dt.date(2021, 1, 4)\\n\",\n \"end_date = dt.date(2021, 1, 26) # Set your end date, e.g: dt.date(2021, 1, 26)\\n\",\n \"\\n\",\n \"asset_bbid = 'ASSET BBID (e.g. \\\"AAPL UW\\\")'\\n\",\n \"universe_for_request = DataAssetsRequest(Identifier.bbid, [asset_bbid])\\n\",\n \"\\n\",\n \"data_measures = [\\n\",\n \" Measure.Universe_Factor_Exposure,\\n\",\n \" Measure.Asset_Universe,\\n\",\n \" Measure.Historical_Beta,\\n\",\n \" Measure.Specific_Risk,\\n\",\n \" Measure.Factor_Name,\\n\",\n \" Measure.Factor_Id,\\n\",\n \"]\\n\",\n \"asset_risk_data = factor_model.get_data(data_measures, start_date, end_date, universe_for_request, limit_factors=True)\\n\",\n \"\\n\",\n \"for i in range(len(asset_risk_data.get('results'))):\\n\",\n \" date = asset_risk_data.get('results')[i].get('date')\\n\",\n \" universe = asset_risk_data.get('results')[i].get('assetData').get('universe')\\n\",\n \" factor_exposure = asset_risk_data.get('results')[i].get('assetData').get('factorExposure')\\n\",\n \" factor_id_to_name = asset_risk_data.get('results')[i].get('factorData')\\n\",\n \" # Create a mapping of factorId to factorName\\n\",\n \" factor_id_to_name_map = {item['factorId']: item['factorName'] for item in factor_id_to_name}\\n\",\n \" # Replace factor IDs with factor names in factor_exposure\\n\",\n \" factor_exposure_with_names = [\\n\",\n \" {factor_id_to_name_map.get(fid, fid): value for fid, value in exposure.items()} for exposure in factor_exposure\\n\",\n \" ]\\n\",\n \" historical_beta = asset_risk_data.get('results')[i].get('assetData').get('historicalBeta')\\n\",\n \" specific_risk = asset_risk_data.get('results')[i].get('assetData').get('specificRisk')\\n\",\n \" print(f'date: {date}')\\n\",\n \" print(f'universe: {universe}')\\n\",\n \" print(f'factor id to factor exposure: {factor_exposure}')\\n\",\n \" print(f'factor name to factor exposure: {factor_exposure_with_names}')\\n\",\n \" print(f'historical beta: {historical_beta}')\\n\",\n \" print(f'specific risk: {specific_risk}')\\n\",\n \" print('\\\\n')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/05_factor_models/02_Upload_Factor_Models.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Uploading Factor Risk Models\\n\",\n \"\\n\",\n \"The GS Quant `FactorRiskModel` class gives users the power to upload their own risk models to Marquee for seamless integration with the Marquee Portfolio Analytics and Plot Tool Pro suite. After uploading a custom `FactorRiskModel`, users can access their factor model data programmatically using GS Quant, visualize their factor risk model data with Plot Tool Pro, or run historical factor attribution analysis on equity portfolios through the lens of their uploaded factor risk model with GS Quant's `Portfolio` class.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.models.risk_model import (\\n\",\n \" FactorRiskModel,\\n\",\n \" RiskModelCalendar,\\n\",\n \" Term,\\n\",\n \" CoverageType,\\n\",\n \" UniverseIdentifier,\\n\",\n \" FactorType,\\n\",\n \" RiskModelFactor,\\n\",\n \")\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the lines below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Create a Factor Model\\n\",\n \"\\n\",\n \"Input fields to create the initial Factor Risk Model object\\n\",\n \"\\n\",\n \"| Attribute | Can be Modified? | Description \\n\",\n \"|----------------------------------|------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\\n\",\n \"| id | No | Model ID |\\n\",\n \"| name | Yes | Name of model |\\n\",\n \"| description | Yes | Longer description of model |\\n\",\n \"| term | Yes | Term or horizon of model. One of: Long, Medium, Short, Trading, Daily |\\n\",\n \"| coverage | Yes | Geographical coverage of assets within model universe. One of: Global, Region, Region Excluding Countries, Country |\\n\",\n \"| vendor | Yes | Who creates the model |\\n\",\n \"| version | Yes | Version of model |\\n\",\n \"| universe_identifier | No | Identifier used to upload the model's asset universe. One of: sedol, cusip, isin, bcid, gsid |\\n\",\n \"| expected_update_time (optional) | Yes | Time (in UTC) data is expected to be published. |\\n\",\n \"| entitlements (optional) | Yes | Who can view, execute (display access in Marquee), query (via API), or upload data to the risk model. By default, the application that creates the risk model is granted admin entitlements, which include all these rights and the ability to edit entitlements. |\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'VENDOR_COVERAGE_MODELNAME_TERM_VERSION'\\n\",\n \"model_name = 'My Risk Model'\\n\",\n \"description = 'My Custom Factor Risk Model'\\n\",\n \"term = Term.Medium\\n\",\n \"coverage = CoverageType.Country\\n\",\n \"universe_identifier = UniverseIdentifier.sedol\\n\",\n \"expected_update_time = dt.time(6, 0, 0)\\n\",\n \"vendor = 'Goldman Sachs'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create the Factor Risk Model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = FactorRiskModel(\\n\",\n \" id_=model_id,\\n\",\n \" name=model_name,\\n\",\n \" description=description,\\n\",\n \" coverage=coverage,\\n\",\n \" term=term,\\n\",\n \" universe_identifier=universe_identifier,\\n\",\n \" vendor=vendor,\\n\",\n \" expected_update_time=expected_update_time,\\n\",\n \" version=1,\\n\",\n \")\\n\",\n \"\\n\",\n \"model.save()\\n\",\n \"print(f\\\"Successfully create a factor risk model with ID: {model.id}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### A note on Model IDs\\n\",\n \"\\n\",\n \"Model IDs are unique identifiers in Marquee that adhere to a standardized format, ensuring clarity and consistency for clients. The format is:\\n\",\n \"\\n\",\n \"`VENDOR_COVERAGE_MODELNAME_TERM_VERSION`\\n\",\n \"\\n\",\n \"- **Vendor**: The name of the entity or organization creating the model.\\n\",\n \"- **Coverage**: The geographical asset coverage of the model (e.g., \\\"GLOBAL\\\", \\\"APAC\\\", \\\"EU\\\", \\\"US\\\", \\\"UK\\\", \\\"EM\\\" (or Emerging), \\\"DM\\\" (for Developed Markets), \\\"WW\\\" (for World Wide) etc).\\n\",\n \"- **Term**: 1-3 character(s) denoting the time horizon of the model (e.g., \\\"L\\\", \\\"S\\\", \\\"M\\\", \\\"TRD\\\" for \\\"Long\\\", \\\"Short\\\", \\\"Medium\\\", or \\\"Trading\\\" respectively). Variations of these can also take the form of \\\"MH\\\" or \\\"MT\\\" for Medium Horizon and Medium term. \\n\",\n \"- **Version**: The version number of the model, starting from 1 and incrementing with updates.\\n\",\n \"\\n\",\n \"This format is designed to be intuitive and easily recognizable for clients, making it simple to identify the purpose and scope of a model at a glance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Upload a Calendar To Your Model\\n\",\n \"\\n\",\n \"The calendar associated with the Factor Risk Model contains the dates which the risk model should have posted data on to be considered \\\"complete.\\\" The calendar can go further back as well as forward in time than the data that is currently posted for the calendar, but there cannot be any gaps in the data posted to the risk model according to the calendar. Please note that the `upload_calendar` function overwrites the previous calendar if one exists, so be sure to include all dates you want in the calendar.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"calendar = RiskModelCalendar(\\n\",\n \" [\\n\",\n \" '2021-01-29',\\n\",\n \" '2021-01-28',\\n\",\n \" '2021-01-27',\\n\",\n \" '2021-01-26',\\n\",\n \" '2021-01-25',\\n\",\n \" '2021-01-22',\\n\",\n \" '2021-01-21',\\n\",\n \" '2021-01-20',\\n\",\n \" '2021-01-19',\\n\",\n \" '2021-01-18',\\n\",\n \" '2021-01-15',\\n\",\n \" '2021-01-14',\\n\",\n \" '2021-01-13',\\n\",\n \" '2021-01-12',\\n\",\n \" '2021-01-11',\\n\",\n \" '2021-01-08',\\n\",\n \" '2021-01-07',\\n\",\n \" '2021-01-06',\\n\",\n \" '2021-01-05',\\n\",\n \" '2021-01-04',\\n\",\n \" '2021-01-01',\\n\",\n \" ]\\n\",\n \")\\n\",\n \"\\n\",\n \"model.upload_calendar(calendar)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### How to update a calendar? \\n\",\n \"To update a calendar, you can use the same `upload_calendar` method with a new list of dates. The new list will replace the existing calendar. If you want to add new dates without removing the existing ones, you must first retrieve the current calendar using `model.get_calendar()` and then append the new dates before uploading.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the existing calendar\\n\",\n \"existing_calendar = model.get_calendar().business_dates\\n\",\n \"\\n\",\n \"# Append new dates\\n\",\n \"new_dates = ['2021-01-30', '2021-01-31']\\n\",\n \"updated_business_dates = list(existing_calendar) + new_dates\\n\",\n \"\\n\",\n \"model.upload_calendar(RiskModelCalendar(updated_business_dates))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Upload Data To Your Model\\n\",\n \"\\n\",\n \"Once the calendar is posted for a model, we can start uploading data to it.\\n\",\n \"\\n\",\n \"The data must be uploaded in a specific format, which is defined in the `RiskModelData` class. The data must be uploaded for each date in the calendar. \\n\",\n \"\\n\",\n \"Below are the components of RiskModelData\\n\",\n \"\\n\",\n \"#### 1. Factor Data\\n\",\n \"\\n\",\n \"\\n\",\n \"| Field | Description \\n\",\n \"|------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------\\n\",\n \"| factorId | Unique ID of the factor (must be < 20 characters). It must map consistently to the same factor across every date. |\\n\",\n \"| factorName | Name of the factor. It can be any string and should be consistent across every date. |\\n\",\n \"| factorCategoryId | Unique Id of the category that the factor belongs to (must be < 20 characters). It must map consistently to the same factor category across every date. |\\n\",\n \"| factorCategory | Name of the category that the factor belongs to (Style, Industry, Market, Currency, etc.). |\\n\",\n \"| factorReturn | Daily return of the factor in percent units (i.e. for 10%, use 10). |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"factor_data = [\\n\",\n \" {\\n\",\n \" \\\"factorId\\\": \\\"1\\\",\\n\",\n \" \\\"factorName\\\": \\\"Factor 1\\\",\\n\",\n \" \\\"factorCategoryId\\\": \\\"RI\\\",\\n\",\n \" \\\"factorCategory\\\": \\\"Style\\\",\\n\",\n \" \\\"factorReturn\\\": 0.39,\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"factorId\\\": \\\"2\\\",\\n\",\n \" \\\"factorName\\\": \\\"Factor 2\\\",\\n\",\n \" \\\"factorCategoryId\\\": \\\"RI\\\",\\n\",\n \" \\\"factorCategory\\\": \\\"Style\\\",\\n\",\n \" \\\"factorReturn\\\": 1.99,\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"factorId\\\": \\\"3\\\",\\n\",\n \" \\\"factorName\\\": \\\"Factor 3\\\",\\n\",\n \" \\\"factorCategoryId\\\": \\\"RI\\\",\\n\",\n \" \\\"factorCategory\\\": \\\"Style\\\",\\n\",\n \" \\\"factorReturn\\\": 0.29,\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"factorId\\\": \\\"4\\\",\\n\",\n \" \\\"factorName\\\": \\\"Factor 4\\\",\\n\",\n \" \\\"factorCategoryId\\\": \\\"MKT\\\",\\n\",\n \" \\\"factorCategory\\\": \\\"Market\\\",\\n\",\n \" \\\"factorReturn\\\": -0.9,\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"factorId\\\": \\\"5\\\",\\n\",\n \" \\\"factorName\\\": \\\"Factor 5\\\",\\n\",\n \" \\\"factorCategoryId\\\": \\\"IND\\\",\\n\",\n \" \\\"factorCategory\\\": \\\"Industry\\\",\\n\",\n \" \\\"factorReturn\\\": 0.2,\\n\",\n \" },\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. Asset Data\\n\",\n \"\\n\",\n \"The asset data can be categorized into different components: \\n\",\n \" - **Factor Exposures**: factor loadings/zscores of assets in the universe.\\n\",\n \" - **Asset risk data**: Total risk, specific risk and specific return\\n\",\n \" - **Asset betas**: Historical beta and model-predicted beta\\n\",\n \" - **Market data**: Daily return, price, market capitalization, trading volumes, and dividend yields\\n\",\n \" \\n\",\n \" \\n\",\n \"| Field | Optional? | Description \\n\",\n \"|--------------------------|-----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\\n\",\n \"| universe | No | The model universe uploaded as an array of identifiers. The identifier type must match the model's universe identifier |\\n\",\n \"| factorExposure | No | Array of dictionaries of factorId to the factor exposure of each asset in the universe. There is no need to include a factorId in the map if the asset has zero exposure to the factor.|\\n\",\n \"| specificRisk | No | Array of annualized specific risk. Represented in percent units (i.e. for 10%, use 10). The order in this array must match the order of the universe array. |\\n\",\n \"| totalRisk | No | Array of annualized total risk. Represented in percent units (e.g., for 10%, use 10). The order in this array must match the order of the universe array. |\\n\",\n \"| specificReturn | No | Array of specific returns. Assign `None` for missing or N/A values. The order of this array must match the order of the universe array.|\\n\",\n \"| estimationUniverseWeight | No | Array of weights in the estimation universe. The order of this array must match the order of the universe array. Assign `None` if an asset is not in the estimation universe.|\\n\",\n \"| predictedBeta | No | Array of predicted betas. Assign `None` for missing or N/A values. The order of this array must match the order of the universe array.|\\n\",\n \"| historicalBeta | No | Array of historical betas. Assign `None` for missing or N/A values. The order of this array must match the order of the universe array.|\\n\",\n \"| globalPredictedBeta | Yes | Array of predicted betas relative to a global market. This measure is not applicable to all models. Assign `None` for missing or N/A values. The order of this array must match the order of the universe array. |\\n\",\n \"| dailyReturn | Yes | Array of asset daily returns. Represented in percent units (e.g., for 10%, use 10). Assign `None` for missing or N/A values. The order in this array must match the order of the universe array. |\\n\",\n \"| price | Yes | Array of asset prices. Assign `None` for missing or N/A values. The order in this array must match the order of the universe array. |\\n\",\n \"| currency | Yes | Array of currencies. Assign `None` for missing or N/A values. The order in this array must match the order of the universe array.|\\n\",\n \"| capitalization | Yes | Array of asset market capitalizations in trading currency. Assign `None` for missing or N/A values. The order in this array must match the order of the universe array. |\\n\",\n \"| dividendYield | Yes | Array of dividend yields. Assign `None` for missing or N/A values. The ordering of this array must correspond to the ordering of the universe array |\\n\",\n \"| tradingVolume | Yes | Array of asset daily trading volumes. Assign `None` for missing or N/A values. The ordering of this array must correspond to the ordering of the universe array |\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"asset_data = {\\n\",\n \" \\\"universe\\\": [\\\"ASSET1\\\", \\\"ASSET2\\\", \\\"ASSET3\\\", \\\"ASSET4\\\", \\\"ASSET5\\\"],\\n\",\n \" \\\"factorExposure\\\": [\\n\",\n \" {\\\"1\\\": 0.48760401340992243, \\\"2\\\": -0.590034096365603},\\n\",\n \" {\\\"3\\\": -0.8017494714963063, \\\"4\\\": 0.7862451506530919},\\n\",\n \" {\\n\",\n \" \\\"5\\\": -0.253403252938764,\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"1\\\": 1.7,\\n\",\n \" \\\"2\\\": 0.08459557023760955,\\n\",\n \" \\\"3\\\": 1.8459557023760955,\\n\",\n \" \\\"4\\\": 0.8459557023760955,\\n\",\n \" \\\"5\\\": -1.8459557023760955,\\n\",\n \" },\\n\",\n \" {\\\"2\\\": -0.607987433173778},\\n\",\n \" ],\\n\",\n \" \\\"totalRisk\\\": [14.324714301179963, 17.071151010504597, 13.874122072602503, 14.479219370106968, 15.6372307010737],\\n\",\n \" \\\"specificRisk\\\": [6.267696111058748, 7.348880522500647, 7.866871501833286, 5.835214680084606, 11.76129276971361],\\n\",\n \" \\\"specificReturn\\\": [\\n\",\n \" 0.5659048855060385,\\n\",\n \" 0.37595374172320617,\\n\",\n \" -0.24473722135505294,\\n\",\n \" -0.9616000320458231,\\n\",\n \" -0.7699674832309915,\\n\",\n \" ],\\n\",\n \" \\\"predictedBeta\\\": [\\n\",\n \" 0.8376437537112529,\\n\",\n \" 0.8825284596124832,\\n\",\n \" 0.8658162312132138,\\n\",\n \" 1.1663832117848132,\\n\",\n \" 1.1638549355761407,\\n\",\n \" ],\\n\",\n \" \\\"historicalBeta\\\": [\\n\",\n \" 0.9699683237602925,\\n\",\n \" 1.184349884914826,\\n\",\n \" 1.1478458610614193,\\n\",\n \" 1.1073962331683949,\\n\",\n \" 0.8610028132979528,\\n\",\n \" ],\\n\",\n \" \\\"estimationUniverseWeight\\\": [None, 0.5, 0.2, 0.3, None],\\n\",\n \" \\\"dailyReturn\\\": [\\n\",\n \" 0.36864638955606754,\\n\",\n \" -0.16247541720716152,\\n\",\n \" -0.14959565439528988,\\n\",\n \" 0.3449230613583272,\\n\",\n \" 0.4624063629113383,\\n\",\n \" ],\\n\",\n \" \\\"currency\\\": [\\\"USD\\\", \\\"USD\\\", \\\"USD\\\", \\\"USD\\\", \\\"USD\\\"],\\n\",\n \" \\\"price\\\": [146.85268872446377, 112.66584268302705, 102.8189727997599, 122.91632409053749, 131.95674622727046],\\n\",\n \" \\\"capitalization\\\": [2310528821.941124, 8391014828.775409, 7744577643.201652, 1873806225.329452, 6606600945.467762],\\n\",\n \" \\\"issuerMarketCap\\\": [\\n\",\n \" 8694261819.837845,\\n\",\n \" 7063154593.331768,\\n\",\n \" 1761291245.7391672,\\n\",\n \" 4251457616.7812405,\\n\",\n \" 5264161805.272642,\\n\",\n \" ],\\n\",\n \" \\\"dividendYield\\\": [4.578537816528908, 3.7261774596135204, 3.4343027973124207, 3.413227950682553, 2.175273338412861],\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3. Covariance Matrix\\n\",\n \"\\n\",\n \"The covariance matrix is represented as a 2D array, where each row corresponds to a factor, and the elements in the inner arrays represent the covariances (in daily variance units) between that factor and every other factor. The ordering of both the rows and columns aligns with the ordering of the factor data list in the payload. The first array below contains variance-covariance data for the factor with ID \\\"1\\\" and the first element is the variance of the factor itself. The second element is the covariance between the first and second factor, and so on. The covariance matrix must be a square matrix, meaning it has the same number of rows and columns.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"covariance_matrix = [\\n\",\n \" [0.9864, 0.026, 0.0965, 0.067, 0.0502],\\n\",\n \" [0.026, 0.6192, 0.0272, 0.0437, 0.0185],\\n\",\n \" [0.0965, 0.0272, 0.8771, 0.0863, 0.0407],\\n\",\n \" [0.067, 0.0437, 0.0863, 0.9589, 0.0333],\\n\",\n \" [0.0502, 0.0185, 0.0407, 0.0333, 0.5216],\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 4. Risk Free Rates \\n\",\n \"\\n\",\n \"The currency rates data is represented as an object containing information about currency exchange rates and risk-free rates. It includes the following components: `currency`, an array of strings representing the currency tickers; `exchangeRate`, an array of daily USD exchange rates, where the ordering corresponds to the ordering of the currencies; and `riskFreeRate`, an array of annualized monthly risk-free rates (in percentage), also ordered to match the currencies. If the model has a numeraire different from USD, the exchangeRate will be an array of daily exchange rates in that currency\\n\",\n \"\\n\",\n \"- currency: Array of currency tickers\\n\",\n \"- exchangeRate: Array of daily USD (or the risk model numeraire if not USD) exchange rates, ordered to match the currencies\\n\",\n \"- riskFreeRate: Array of annualized monthly risk-free rates (in percentage), ordered to match the currencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"currency_data = {\\n\",\n \" 'currency': ['USD', 'EUR', 'GBP'],\\n\",\n \" 'exchangeRate': [1.0, 0.85, 0.75],\\n\",\n \" 'riskFreeRate': [0.01, 0.005, 0.007],\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 5. Issuer Specific Covariance\\n\",\n \"\\n\",\n \"The issuer-specific covariance data is represented as an object that captures the covariance between two assets in daily variance units. It consists of three components: universeId1, an array of strings representing the first set of universe identifiers, which must exist in the asset universe; universeId2, an array of strings representing the second set of universe identifiers, also required to exist in the asset universe; and covariance, an array of numbers where each value represents the covariance between the corresponding identifiers at the same index in universeId1 and universeId2.\\n\",\n \"\\n\",\n \"- universeId1: Array of assets with issuer specific covariance to the asset in universeId2 at the same index. Each asset must also be present in the Asset Data universe\\n\",\n \"- universeId1: Array of assets with issuer specific covariance to the asset in universeId1 at the same index. Each asset must also be present in the Asset Data universe\\n\",\n \"- covariance: Array of the covariance between universeId1 and universeId2 at the same index. In daily variance units\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"issuer_specific_covariance = {\\n\",\n \" 'universeId1': ['ASSET1', 'ASSET2'],\\n\",\n \" 'universeId2': ['ASSET3', 'ASSET5'],\\n\",\n \" 'covariance': [0.03754, 0.01234],\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6. Factor Portfolios\\n\",\n \"\\n\",\n \"The factor portfolios data is represented as an object that defines a set of assets combining to form a portfolio representing a single factor. This portfolio has unit exposure to the specified factor and zero exposure to all other factors, making it useful for mapping intraday factor return data. It consists of two components: `universe`, an array of strings representing the universe of the factor portfolios, which must be a subset of the model asset universe; and `portfolio`, an array of dictionaries containing factor identifiers and their corresponding portfolio weights.\\n\",\n \"\\n\",\n \"The portfolios array contains objects with the following fields:\\n\",\n \"- factorId: The ID of the factor corresponding to the factor data's factorIds\\n\",\n \"- weights: An array of weights for each asset ID in the universe, corresponding to the ordering of the universe. The weights must sum to 1 and can include zero values (null values are not allowed).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"factor_portfolios = {\\n\",\n \" \\\"universe\\\": [\\\"ASSET2\\\", \\\"ASSET3\\\", \\\"ASSET4\\\"],\\n\",\n \" \\\"portfolio\\\": [\\n\",\n \" {\\\"factorId\\\": \\\"1\\\", \\\"weights\\\": [0.3545899480088869, 0.2880743474246568, 0.35733570456645636]},\\n\",\n \" {\\\"factorId\\\": \\\"2\\\", \\\"weights\\\": [0.4390053364378467, 0.2907941789867976, 0.2702004845753557]},\\n\",\n \" {\\\"factorId\\\": \\\"3\\\", \\\"weights\\\": [0.3547375024699844, 0.29918415302858037, 0.3460783445014351]},\\n\",\n \" {\\\"factorId\\\": \\\"4\\\", \\\"weights\\\": [0.5821881487398893, 0.06066318669175055, 0.35714866456836014]},\\n\",\n \" {\\\"factorId\\\": \\\"5\\\", \\\"weights\\\": [0.4078609724431924, 0.39878265902389126, 0.1933563685329163]},\\n\",\n \" ],\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"### Upload Data\\n\",\n \"\\n\",\n \"Now we are ready to upload risk model data. Note you can only upload on dates that are in the model's calendar.\\n\",\n \"The data must be uploaded in a specific format, which is defined in the `RiskModelData` class. The data must be uploaded for each date in the calendar, one date at a time.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = {\\n\",\n \" 'date': '2021-01-13',\\n\",\n \" 'factorData': factor_data,\\n\",\n \" 'covarianceMatrix': covariance_matrix,\\n\",\n \" 'assetData': asset_data,\\n\",\n \" 'currencyRatesData': currency_data,\\n\",\n \" 'issuerSpecificCovariance': issuer_specific_covariance,\\n\",\n \" 'factorPortfolios': factor_portfolios,\\n\",\n \"}\\n\",\n \"\\n\",\n \"risk_model = FactorRiskModel.get(model_id)\\n\",\n \"risk_model.upload_data(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Important\\n\",\n \"\\n\",\n \"For large payloads, use the `max_asset_batch_size` parameter to split the data into smaller batches. This is particularly important for large models (asset universe larger than 10,000), as it helps prevent timeouts during the upload process. Typically, a batch_size of `10000` works well. \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"risk_model.upload_data(data, max_asset_batch_size=1000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Check which days have data posted\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `get_dates` method returns a list of dates for which data has been posted to the risk model. This is useful for verifying whether the data upload was successful. Upon completing the historical data backfill, all dates returned by this method should match the dates in the model's calendar. If any dates are missing, it may indicate that the data upload was incomplete or has not occurred yet.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dates_posted = risk_model.get_dates()\\n\",\n \"print(dates_posted)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 6: Uploading Partial data\\n\",\n \"The `upload_data` method can be used to upload partial data, which is useful for updating subsets of data without re-uploading the entire dataset. Data that can be uploaded partially includes `factorData` and `covarianceMatrix`, `assetData`, `factorPortfolios`, and `issuerSpecificCovariance`. Note that `factorData` and `covarianceMatrix` must already be uploaded when uploading partial data. If not, you must upload them first. The partial data must conform to the same structure as the full data upload but can represent a subset of the complete dataset.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Uploading factor portfolios separately\\n\",\n \"factor_portfolios = {'date': '2021-01-13', 'factorPortfolios': factor_portfolios}\\n\",\n \"\\n\",\n \"risk_model.upload_data(factor_portfolios, max_asset_batch_size=1000)\\n\",\n \"\\n\",\n \"# Uploading issuer specific covariance separately\\n\",\n \"issuer_specific_covariance = {'date': '2021-01-13', 'issuerSpecificCovariance': issuer_specific_covariance}\\n\",\n \"risk_model.upload_data(issuer_specific_covariance, max_asset_batch_size=1000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 7: Enhance Factor Descriptions and Tool Tips\\n\",\n \"\\n\",\n \"The last step is adding tooltips and descriptions to the risk model factors. We highly encourage you to do this for every non-binary factor in your model (such as style factors) so that Marquee UI users of your model can leverage the tooltips and descriptions to better understand how the factors were constructed and what they represent. The snippet below should be repeated for each factor for which you are updating descriptions and tooltips. The `identifier` field must match the `factorId` in the factor data you uploaded earlier.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"identifier = '3'\\n\",\n \"tooltip = 'Short description that appears when you hover over the factor name on our UI.'\\n\",\n \"description = 'Longer description that appears on the portfolio drill-down page of this factor.'\\n\",\n \"glossary_description = 'Longest description to describe the factor in depth on our risk model glossary page.'\\n\",\n \"\\n\",\n \"factor = RiskModelFactor(\\n\",\n \" identifier=identifier,\\n\",\n \" type_=FactorType.Factor,\\n\",\n \" tooltip=tooltip,\\n\",\n \" description=description,\\n\",\n \" glossary_description=glossary_description,\\n\",\n \")\\n\",\n \"\\n\",\n \"risk_model.save_factor_metadata(factor)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/01_basket_composition_data/0000_get_latest_basket_composition.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_latest_position_set().to_frame()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/01_basket_composition_data/0001_get_basket_composition_for_date.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_position_set_for_date(dt.date(2021, 1, 7)).to_frame()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/01_basket_composition_data/0002_get_basket_composition_for_date_range.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_sets = basket.get_position_sets(dt.date(2021, 1, 7), dt.date(2021, 1, 7))\\n\",\n \"position_sets = pd.concat([position_set.to_frame() for position_set in position_sets])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/01_basket_composition_data/0003_get_full_basket_composition_history.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_sets = basket.get_position_sets()\\n\",\n \"position_sets = pd.concat([position_set.to_frame() for position_set in position_sets])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/01_basket_composition_data/0004_get_basket_composition_dates.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_position_dates()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/01_basket_composition_data/0005_get_basket_composition_dataset_coverage.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import AssetClass, BasketType, get_constituents_dataset_coverage\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"client = 'CLIENT_ID'\\n\",\n \"secret = 'CLIENT_SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Equity Custom Baskets\\n\",\n \"GSBASKETCONSTITUENTS_COVERAGE = get_constituents_dataset_coverage()\\n\",\n \"\\n\",\n \"# Equity Research Baskets\\n\",\n \"GIRBASKETCONSTITUENTS_COVERAGE = get_constituents_dataset_coverage(basket_type=BasketType.RESEARCH_BASKET)\\n\",\n \"\\n\",\n \"# Credit Custom Baskets\\n\",\n \"GSCREDITBASKETCONSTITUENTS_COVERAGE = get_constituents_dataset_coverage(asset_class=AssetClass.Credit)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/02_basket_pricing_data/0000_get_latest_basket_close_price.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_latest_close_price()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/02_basket_pricing_data/0001_get_basket_close_price_for_date.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_close_price_for_date(dt.date(2021, 1, 7))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/02_basket_pricing_data/0002_get_basket_close_price_for_dates.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_close_prices(dt.date(2021, 1, 7), dt.date(2021, 3, 27))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/02_basket_pricing_data/0003_get_full_basket_close_price_history.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_close_prices()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/0000_clone_basket_position_set.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.markets.indices_utils import ReturnType\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"parent_basket = Basket.get('GSMBXXXX')\\n\",\n \"new_basket = parent_basket.clone()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you'd like, you may now create a new basket in Marquee using this composition. See the Basket Create Tutorial for a more nuanced example/explanation on how to do this (you may skip step 3 in this case).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"new_basket.ticker = 'GSMBCLNE'\\n\",\n \"new_basket.name = 'Clone of GSMBXXXX'\\n\",\n \"new_basket.currency = 'USD'\\n\",\n \"new_basket.return_type = ReturnType.PRICE_RETURN\\n\",\n \"\\n\",\n \"new_basket.create()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0000_create_position_set_using_position_weights.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"positions = [Position(identifier='AAPL UW', weight=0.5), Position(identifier='MSFT UW', weight=0.5)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet(positions)\\n\",\n \"position_set.resolve()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0001_create_position_set_using_position_quantities.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"positions = [Position(identifier='AAPL UW', quantity=100), Position(identifier='MSFT UW', quantity=100)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet(positions)\\n\",\n \"position_set.resolve()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0002_create_position_set_of_equal_weight_from_list_of_identifiers.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"identifiers = ['AAPL UW', 'MSFT UW']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet.from_list(identifiers)\\n\",\n \"position_set.resolve()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0003_equalize_position_set_weights.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet([Position(identifier='AAPL UW', weight=0.3), Position(identifier='MSFT UW', weight=0.7)])\\n\",\n \"position_set.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set.equalize_position_weights()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0004_add_position_to_existing_position_set.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet([Position(identifier='MSFT UW', quantity=200)])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set.positions += Position(\\n\",\n \" identifier='AAPL UW', weight=800\\n\",\n \") # can alternatively specify 'quantity' instead of 'weight'\\n\",\n \"position_set.resolve()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0005_create_position_set_from_list_of_dictionaries.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# can alternatively specify 'quantity' instead of 'weight'\\n\",\n \"positions = [{'identifier': 'AAPL UW', 'weight': 0.5}, {'identifier': 'MSFT UW', 'weight': 0.5}]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet.from_dicts(positions)\\n\",\n \"position_set.resolve()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0006_create_position_set_from_dataframe.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Note:** You may create/upload your positions in dataframe format however you'd like (e.g., import from a local excel file). This is a simplified example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# can alternatively specify 'quantity' instead of 'weight'\\n\",\n \"positions = [{'identifier': 'AAPL UW', 'weight': 0.5}, {'identifier': 'MSFT UW', 'weight': 0.5}]\\n\",\n \"positions_df = pd.DataFrame(positions)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet.from_frame(positions_df)\\n\",\n \"position_set.resolve()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0007_create_position_set_from_csv.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"positions_df = pd.read_csv('positions_data.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet.from_frame(positions_df)\\n\",\n \"position_set.resolve()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0008_create_position_set_from_excel.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Your excel file must be formatted in one of three ways (column names included). The third example will assign each position equal weight.\\n\",\n \"\\n\",\n \"\\n\",\n \"
1. Position Weight2. Position Quantity3. Position Identifiers
\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
identifierweight
AAPL UW0.4
MSFT UW0.6
\\n\",\n \"		\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
identifierquantity
AAPL UW100
MSFT UW100
\\n\",\n \"		\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
identifier
AAPL UW
MSFT UW
\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"positions_df = pd.read_excel('positions_data.xlsx')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet.from_frame(positions_df)\\n\",\n \"position_set.resolve()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0009_fetch_position_quantities_from_weights.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"\\n\",\n \"from gs_quant.markets.position_set import Currency, Position, PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"positions = [Position(identifier='AAPL UW', weight=0.3), Position(identifier='MSFT UW', weight=0.7)]\\n\",\n \"notional = 10000000\\n\",\n \"date = date(2023, 3, 14)\\n\",\n \"\\n\",\n \"position_set = PositionSet(positions=positions, reference_notional=notional, date=date)\\n\",\n \"position_set.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"position_set.price(\\n\",\n \" currency=Currency.EUR, use_unadjusted_close_price=False\\n\",\n \") # Currency is an optional parameters and will default to USD\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0010_fetch_position_weights_from_quantities.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from datetime import date\\n\",\n \"\\n\",\n \"from gs_quant.markets.position_set import Position, PositionSet, PositionSetWeightingStrategy\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"positions = [Position(identifier='AAPL UW', quantity=100), Position(identifier='MSFT UW', quantity=100)]\\n\",\n \"date = date(2023, 3, 14)\\n\",\n \"\\n\",\n \"position_set = PositionSet(positions=positions, date=date)\\n\",\n \"position_set.resolve()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"position_set.price(\\n\",\n \" use_unadjusted_close_price=False, weighting_strategy=PositionSetWeightingStrategy.Quantity\\n\",\n \") # Weighting strategy is an optional parameter and will default based on the position information if not passed in\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0011_redistribute_position_weights.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"position_set = PositionSet(\\n\",\n \" positions=[Position(identifier='AAPL UW', weight=0.3), Position(identifier='MSFT UW', weight=0.3)]\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"If you have a weighted position set that does not add up to 1, you can call `redistribute_weights` in order to\\n\",\n \"redistribute the remaining or additional weights proportionally among each position.\\n\",\n \"\\n\",\n \"#### Note: This function assumes the position set is one-sided.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"position_set.redistribute_weights()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/03_basket_creation/position_set/0012_create_historical_position_sets_from_excel.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"If you have historical positions that you'd like to bring into Marquee, you should create an individual position\\n\",\n \"set per each position date. The below examples show the three valid ways to do this (column names/format included)\\n\",\n \"\\n\",\n \"### Step 1: Define your file format\\n\",\n \"#### Option 1: Create historical position sets by weight\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
identifierweightdate
AAPL UW0.42022-06-03
MSFT UW0.62022-06-03
AAPL UW0.42023-01-04
MSFT UW0.22023-01-04
IBM UN0.42023-01-04
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"columns = ['identifier', 'weight', 'date']\\n\",\n \"equalize = False\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### Option 2: Create historical position sets by quantity\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
identifierquantitydate
AAPL UW1002022-06-03
MSFT UW1002022-06-03
AAPL UW2002023-01-04
MSFT UW3002023-01-04
IBM UN2002023-01-04
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"columns = ['identifier', 'quantity', 'date']\\n\",\n \"equalize = False\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### Option 3: Create equally-weighted historical position sets\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
identifierdate
AAPL UW2022-06-03
MSFT UW2022-06-03
AAPL UW2023-01-04
MSFT UW2023-01-04
IBM UN2023-01-04
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"columns = ['identifier', 'date']\\n\",\n \"equalize = True\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Step 2: Upload your excel file and convert your positions into position sets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"positions_data = pd.read_excel('positions_data.xlsx', engine='openpyxl')\\n\",\n \"all_positions = positions_data[columns]\\n\",\n \"\\n\",\n \"positions_dict, position_sets = {}, []\\n\",\n \"for i, row in all_positions.iterrows():\\n\",\n \" date = row.get('date')\\n\",\n \" if date not in positions_dict:\\n\",\n \" positions_dict[date] = set()\\n\",\n \" positions_dict[date].add(\\n\",\n \" Position(identifier=row.get('identifier'), weight=row.get('weight', None), quantity=row.get('quantity', None))\\n\",\n \" )\\n\",\n \"\\n\",\n \"for date in positions_dict:\\n\",\n \" position_date = dt.datetime.strptime(str(date), '%Y-%m-%d %H:%M:%S').date()\\n\",\n \" position_set = PositionSet(positions=positions_dict[date], date=position_date)\\n\",\n \" if equalize:\\n\",\n \" position_set.equalize_position_weights()\\n\",\n \" position_sets.append(position_set)\\n\",\n \"\\n\",\n \"print(position_sets)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/04_basket_corporate_actions_data/0000_get_full_basket_corporate_actions_history.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_corporate_actions()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/04_basket_corporate_actions_data/0001_get_basket_corporate_actions_for_dates.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_corporate_actions(start=dt.date(2021, 1, 7), end=dt.date(2021, 3, 27))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/04_basket_corporate_actions_data/0002_get_basket_upcoming_corporate_actions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_corporate_actions(start=dt.date.today())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/04_basket_corporate_actions_data/0003_get_specific_corporate_actions_types.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.markets.indices_utils import CorporateActionType\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose any combination of the following corporate action types:\\n\",\n \"\\n\",\n \"* **Aquisition:** CorporateActionType.*ACQUISITION*\\n\",\n \"* **Cash Dividend:** CorporateActionType.*CASH_DIVIDEND*\\n\",\n \"* **Identifier Change:** CorporateActionType.*IDENTIFIER_CHANGE*\\n\",\n \"* **Rights Issue:** CorporateActionType.*RIGHTS_ISSUE*\\n\",\n \"* **Share Change:** CorporateActionType.*SHARE_CHANGE*\\n\",\n \"* **Special Dividend:** CorporateActionType.*SPECIAL_DIVIDEND*\\n\",\n \"* **Spin Off:** CorporateActionType.*SPIN_OFF*\\n\",\n \"* **Stock Dividend:** CorporateActionType.*STOCK_DIVIDEND*\\n\",\n \"* **Stock Split:** CorporateActionType.*STOCK_SPLIT*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_corporate_actions(ca_type=[CorporateActionType.IDENTIFIER_CHANGE, CorporateActionType.RIGHTS_ISSUE])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/05_basket_fundamentals_data/0000_get_basket_fundamentals_history.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_fundamentals() # default period is one year, default period direction is forward\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/05_basket_fundamentals_data/0001_get_basket_fundamentals_for_dates.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_fundamentals(start=dt.date(2021, 1, 7), end=dt.date(2021, 3, 27))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/05_basket_fundamentals_data/0001_get_basket_fundamentals_specific_period_and_direction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_financial_data read_product_data read_content',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_fundamentals(start=dt.date(2021, 1, 7), end=dt.date(2021, 3, 27))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/05_basket_fundamentals_data/0002_get_basket_fundamentals_for_date_range.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.markets.indices_utils import FundamentalMetricPeriod, FundamentalMetricPeriodDirection\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_financial_data read_product_data read_content',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose one of the following periods:\\n\",\n \"\\n\",\n \"* **1 year:** FundamentalMetricPeriod.*ONE_YEAR*\\n\",\n \"* **2 years:** FundamentalMetricPeriod.*TWO_YEARS*\\n\",\n \"* **3 years:** FundamentalMetricPeriod.*THREE_YEARS*\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose one of the following period directions:\\n\",\n \"\\n\",\n \"* **Forward:** FundamentalMetricPeriodDirection.*FORWARD*\\n\",\n \"* **Trailing:** FundamentalMetricPeriodDirection.*TRAILING*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_fundamentals(period=FundamentalMetricPeriod.TWO_YEARS, direction=FundamentalMetricPeriodDirection.TRAILING)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/05_basket_fundamentals_data/0002_get_basket_fundamentals_specific_period_and_direction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data.fields import DataMeasure\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose one of the following periods:\\n\",\n \"\\n\",\n \"* **1 year:** DataMeasure.*ONE_YEAR*\\n\",\n \"* **2 years:** DataMeasure.*TWO_YEARS*\\n\",\n \"* **3 years:** DataMeasure.*THREE_YEARS*\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose one of the following period directions:\\n\",\n \"\\n\",\n \"* **Forward:** DataMeasure.*FORWARD*\\n\",\n \"* **Trailing:** DataMeasure.*TRAILING*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_fundamentals(period=DataMeasure.TWO_YEARS, direction=DataMeasure.TRAILING)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/05_basket_fundamentals_data/0003_get_specific_basket_fundamentals_metrics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.data.fields import DataMeasure\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose any combinations of the following metrics:\\n\",\n \"\\n\",\n \"* **Dividend Yield:** DataMeasure.*DIVIDEND_YIELD*\\n\",\n \"* **Earnings per Share:** DataMeasure.*EARNINGS_PER_SHARE*\\n\",\n \"* **Earnings per Share Positive:** DataMeasure.*EARNINGS_PER_SHARE_POSITIVE*\\n\",\n \"* **Net Debt to EBITDA:** DataMeasure.*NET_DEBT_TO_EBITDA*\\n\",\n \"* **Price to Book:** DataMeasure.*PRICE_TO_BOOK*\\n\",\n \"* **Price to Cash:** DataMeasure.*PRICE_TO_CASH*\\n\",\n \"* **Price to Earnings:** DataMeasure.*PRICE_TO_EARNINGS*\\n\",\n \"* **Price to Earnings Positive:** DataMeasure.*PRICE_TO_EARNINGS_POSITIVE*\\n\",\n \"* **Price to Sales:** DataMeasure.*PRICE_TO_SALES*\\n\",\n \"* **Return on Equity:** DataMeasure.*RETURN_ON_EQUITY*\\n\",\n \"* **Sales per Share:** DataMeasure.*SALES_PER_SHARE*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_fundamentals(metrics=[DataMeasure.PRICE_TO_CASH, DataMeasure.SALES_PER_SHARE])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/examples/06_basket_reports/0000_create_new_factor_risk_report.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.add_factor_risk_report(\\n\",\n \" risk_model_id='AXUS4M', fx_hedged=True\\n\",\n \") # substitute desired risk model and fx_hedged value\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/06_basket_reports/0001_delete_existing_factor_risk_report.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.delete_factor_risk_report(\\n\",\n \" risk_model_id='AXUS4M'\\n\",\n \") # substitute risk model id for the one corresponding to the report you'd like to delete\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/06_basket_reports/0002_get_existing_factor_risk_report.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_factor_risk_report(\\n\",\n \" risk_model_id='AXUS4M'\\n\",\n \") # substitute risk model id for the one corresponding to the report you'd like to retrieve\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/06_basket_reports/0003_get_all_existing_basket_reports.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_reports()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/06_basket_reports/0004_get_status_of_all_existing_basket_reports.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.get_status_of_reports()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/06_basket_reports/0005_poll_status_of_most_recent_basket_create_report.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.poll_status(\\n\",\n \" timeout=300, step=20\\n\",\n \") # timeout/step are optional - default behavior will be to check status every 30 sec for <= 10 min\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/06_basket_reports/0006_poll_report_status_using_report_id.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.poll_report(\\n\",\n \" report_id='R1234567890', timeout=300, step=20\\n\",\n \") # timeout/step are optional - default behavior will be to check status every 30 sec for <= 10 min\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/07_basket_permissions/0000_get_basket_permissions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### What are the four levels of basket permissions?\\n\",\n \"\\n\",\n \"1. **Admin:** Basket admins can view all basket information, edit metadata, submit requests to update basket composition, and permission other users to the basket\\n\",\n \"2. **Edit:** Editors may view basket data and edit details such as name, description, etc.\\n\",\n \"3. **Rebalance:** Rebalance permissions enable a user to view basket data and submit and approve rebalance submissions\\n\",\n \"4. **View:** Viewers are able to see most basket information, but are not able to modify the basket in any way\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.entitlements.to_frame()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/07_basket_permissions/0001_permission_application_to_basket.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.entities.entitlements import User\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD, client_id=client, client_secret=secret, scopes=('read_user_profile modify_product_data',)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, retrieve the user object by inserting the app id that you'd like to permission. Then, you may update the entitlements block corresponding to the level of permissioning you'd like to modify (e.g., *'entitlements.admin'*, *'entitlements.edit'*, etc.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"application = User.get(user_id='application_id')\\n\",\n \"\\n\",\n \"basket.entitlements.view.users += [application] # update the entitlements block 'users' property\\n\",\n \"\\n\",\n \"basket.update()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/07_basket_permissions/0002_permission_user_to_basket_by_email.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.entities.entitlements import User\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_user_profile ',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, retrieve the user object by inputting the user email that you'd like to permission. Then, you may update the entitlements block corresponding to the level of permissioning you'd like to modify (e.g., *'entitlements.view'*, *'entitlements.rebalance'*, etc.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"user = User.get(email='user_email@example.com')\\n\",\n \"\\n\",\n \"basket.entitlements.admin.users += [user] # update the entitlements block 'users' property\\n\",\n \"\\n\",\n \"basket.update()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/07_basket_permissions/0003_permission_group_to_basket.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.entities.entitlements import Group\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD, client_id=client, client_secret=secret, scopes=('read_user_profile modify_product_data',)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, retrieve the group object by inputting the group id that you'd like to permission. Then, you may update the entitlements block corresponding to the level of permissioning you'd like to modify (e.g., *'entitlements.view'*, *'entitlements.rebalance'*, etc.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"group = Group.get(group_id='example.group')\\n\",\n \"\\n\",\n \"basket.entitlements.admin.groups += [group] # update the entitlements block 'groups' property\\n\",\n \"\\n\",\n \"basket.update()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/07_basket_permissions/0004_remove_basket_permissions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.entities.entitlements import User\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD, client_id=client, client_secret=secret, scopes=('read_user_profile modify_product_data',)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX') # substitute input with any identifier for a basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, retrieve the user object by inputting the user id that you'd like to unpermission (you may alternatively retrieve a user by email, or can retrieve a group instead. Refer to the other examples in this folder for examples of this). Then, you may update the entitlements block corresponding to the level of permissioning you'd like to modify (e.g., *'entitlements.view'*, *'entitlements.rebalance'*, etc.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"user = User.get(user_id='user_id')\\n\",\n \"\\n\",\n \"basket.entitlements.admin.users.remove(user) # update the entitlements block 'groups' property\\n\",\n \"\\n\",\n \"basket.update()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/07_basket_permissions/0005_get_your_permissioned_baskets.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import get_my_baskets\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_my_baskets()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0000_get_flagship_baskets.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import get_flagship_baskets\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagship_baskets()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0001_get_flagship_baskets_on_date.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.markets.indices_utils import get_flagship_baskets\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagship_baskets(as_of=dt.datetime(2021, 1, 7, 11, 0, 0))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0002_get_flagship_baskets_containing_assets.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import get_flagships_with_assets\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagships_with_assets(\\n\",\n \" identifiers=['AAPL UW', 'MSFT UW']\\n\",\n \") # insert list of assets using any common identifier (bbid, ric, etc.)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0003_get_flagship_baskets_containing_assets_on_date.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.markets.indices_utils import get_flagships_with_assets\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagships_with_assets(\\n\",\n \" identifiers=['AAPL UW, MSFT UW'], as_of=dt.date(2021, 1, 7)\\n\",\n \") # insert list of assets using any common identifier (bbid, ric, etc.)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0004_get_flagship_baskets_prices.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import get_flagships_performance\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagships_performance()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0005_get_flagship_baskets_prices_for_date_range.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.markets.indices_utils import get_flagships_performance\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagships_performance(start=dt.date(2021, 1, 7), end=dt.date(2021, 3, 27))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0006_get_flagship_baskets_compositions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import get_flagships_constituents\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagships_constituents()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0007_get_flagship_baskets_compositions_for_date_range.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.markets.indices_utils import get_flagships_constituents\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagships_constituents(start=dt.date(2021, 1, 7), end=dt.date(2021, 3, 27))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0008_filter_flagship_baskets_by_basket_type.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import *\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose any combination of the following basket types:\\n\",\n \"\\n\",\n \"* **Custom Basket:** BasketType.*CUSTOM_BASKET*\\n\",\n \"* **Research Basket:** BasketType.*RESEARCH_BASKET*\\n\",\n \"\\n\",\n \"These options will work with any of the following functions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagship_baskets(basket_type=[BasketType.CUSTOM_BASKET])\\n\",\n \"\\n\",\n \"get_flagships_with_assets(identifiers=['AAPL UW'], basket_type=[BasketType.CUSTOM_BASKET])\\n\",\n \"\\n\",\n \"get_flagships_performance(basket_type=[BasketType.RESEARCH_BASKET])\\n\",\n \"\\n\",\n \"get_flagships_constituents(basket_type=[BasketType.RESEARCH_BASKET])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0009_filter_flagship_baskets_by_region.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import *\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose any combination of the following regions:\\n\",\n \"\\n\",\n \"* **Americas:** Region.*AMERICAS*\\n\",\n \"* **Asia:** Region.*ASIA*\\n\",\n \"* **EM:** Region.*EM*\\n\",\n \"* **Europe:** Region.*EUROPE*\\n\",\n \"* **Global:** Region.*GLOBAL*\\n\",\n \"\\n\",\n \"These options will work with any of the following functions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagship_baskets(region=[Region.ASIA])\\n\",\n \"\\n\",\n \"get_flagships_with_assets(identifiers=['AAPL UW'], region=[Region.AMERICAS])\\n\",\n \"\\n\",\n \"get_flagships_performance(region=[Region.EUROPE, Region.GLOBAL])\\n\",\n \"\\n\",\n \"get_flagships_constituents(region=[Region.EM])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0010_filter_flagship_baskets_by_styles.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import *\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You may choose any combination of the following styles:\\n\",\n \"\\n\",\n \"#### Custom Basket Styles\\n\",\n \"* **Ad Hoc Desk Work:** CustomBasketStyles.*AD_HOC_DESK_WORK*\\n\",\n \"* **Client Constructed/Wrapper:** CustomBasketStyles.*CLIENT_CONSTRUCTED_WRAPPER*\\n\",\n \"* **Consumer:** CustomBasketStyles.*CONSUMER*\\n\",\n \"* **Energy:** CustomBasketStyles.*ENERGY*\\n\",\n \"* **Enhanced Index Solutions:** CustomBasketStyles.*ENHANCED_INDEX_SOLUTIONS*\\n\",\n \"* **ESG:** CustomBasketStyles.*ESG*\\n\",\n \"* **Factors:** CustomBasketStyles.*FACTORS*\\n\",\n \"* **Financials:** CustomBasketStyles.*FINANCIALS*\\n\",\n \"* **Flagship:** CustomBasketStyles.*FLAGSHIP*\\n\",\n \"* **Geographic:** CustomBasketStyles.*GEOGRAPHIC*\\n\",\n \"* **Growth:** CustomBasketStyles.*GROWTH*\\n\",\n \"* **Health Care:** CustomBasketStyles.*HEALTHCARE*\\n\",\n \"* **Hedging:** CustomBasketStyles.*HEDGING*\\n\",\n \"* **Industrials:** CustomBasketStyles.*INDUSTRIALS*\\n\",\n \"* **Materials:** CustomBasketStyles.*MATERIALS*\\n\",\n \"* **Momentum:** CustomBasketStyles.*MOMENTUM*\\n\",\n \"* **PIPG:** CustomBasketStyles.*PIPG*\\n\",\n \"* **Sectors/Industries:** CustomBasketStyles.*SECTORS_INDUSTRIES*\\n\",\n \"* **Size:** CustomBasketStyles.*SIZE*\\n\",\n \"* **Structured One Delta:** CustomBasketStyles.*STRUCTURED_ONE_DELTA*\\n\",\n \"* **Thematic:** CustomBasketStyles.*THEMATIC*\\n\",\n \"* **TMT:** CustomBasketStyles.*TMT*\\n\",\n \"* **Utilities:** CustomBasketStyles.*UTILITIES*\\n\",\n \"* **Value:** CustomBasketStyles.*VALUE*\\n\",\n \"* **Volatility:** CustomBasketStyles.*VOLATILITY*\\n\",\n \"\\n\",\n \"#### Research Basket Styles\\n\",\n \"* **Asia ex-Japan:** ResearchBasketStyles.*ASIA_EX_JAPAN*\\n\",\n \"* **Equity Thematic:** ResearchBasketStyles.*EQUITY_THEMATIC*\\n\",\n \"* **Europe:** ResearchBasketStyles.*EUROPE*\\n\",\n \"* **Fund Ownership:** ResearchBasketStyles.*FUND_OWNERSHIP*\\n\",\n \"* **Fundamentals:** ResearchBasketStyles.*FUNDAMENTALS*\\n\",\n \"* **FX/Oil:** ResearchBasketStyles.*FX_OIL*\\n\",\n \"* **Geographical Exposure:** ResearchBasketStyles.*GEOGRAPHICAL_EXPOSURE*\\n\",\n \"* **Hedge Fund:** ResearchBasketStyles.*HEDGE_FUND*\\n\",\n \"* **Investment Profile (IP) Factors:** ResearchBasketStyles.*IP_FACTORS*\\n\",\n \"* **Japan:** ResearchBasketStyles.*JAPAN*\\n\",\n \"* **Macro:** ResearchBasketStyles.*MACRO*\\n\",\n \"* **Macro Slice/Styles:** ResearchBasketStyles.*MACRO_SLICE_STYLES*\\n\",\n \"* **Mutual Fund:** ResearchBasketStyles.*MUTUAL_FUND*\\n\",\n \"* **Positioning:** ResearchBasketStyles.*POSITIONING*\\n\",\n \"* **Portfolio Strategy:** ResearchBasketStyles.*PORTFOLIO_STRATEGY*\\n\",\n \"* **Risk & Liquidity:** ResearchBasketStyles.*RISK_AND_LIQUIDITY*\\n\",\n \"* **Sector:** ResearchBasketStyles.*SECTOR*\\n\",\n \"* **Shareholder Return:** ResearchBasketStyles.*SHAREHOLDER_RETURN*\\n\",\n \"* **Style, Factor and Fundamental:** ResearchBasketStyles.*STYLE_FACTOR_AND_FUNDAMENTAL*\\n\",\n \"* **Style/Themes:** ResearchBasketStyles.*STYLES_THEMES*\\n\",\n \"* **Tactical Research:** ResearchBasketStyles.*TACTICAL_RESEARCH*\\n\",\n \"* **Thematic:** ResearchBasketStyles.*THEMATIC*\\n\",\n \"* **US:** ResearchBasketStyles.*US*\\n\",\n \"* **Wavefront Components:** ResearchBasketStyles.*WAVEFRONT_COMPONENTS*\\n\",\n \"* **Wavefront Pairs:** ResearchBasketStyles.*WAVEFRONT_PAIRS*\\n\",\n \"* **Wavefronts:** ResearchBasketStyles.*WAVEFRONTS*\\n\",\n \"\\n\",\n \"These options will work with any of the following functions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"get_flagship_baskets(styles=[CustomBasketStyles.FACTORS, ResearchBasketStyles.WAVEFRONTS])\\n\",\n \"\\n\",\n \"get_flagships_with_assets(\\n\",\n \" identifiers=['AAPL UW'],\\n\",\n \" region=[Region.AMERICAS],\\n\",\n \" styles=[CustomBasketStyles.FACTORS, ResearchBasketStyles.WAVEFRONTS],\\n\",\n \")\\n\",\n \"\\n\",\n \"get_flagships_performance(\\n\",\n \" region=[Region.EUROPE, Region.GLOBAL], styles=[CustomBasketStyles.FACTORS, ResearchBasketStyles.WAVEFRONTS]\\n\",\n \")\\n\",\n \"\\n\",\n \"get_flagships_constituents(region=[Region.EM], styles=[CustomBasketStyles.FACTORS, ResearchBasketStyles.WAVEFRONTS])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/examples/08_flagship_baskets/0011_filter_flagship_baskets_by_ticker.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.indices_utils import *\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"tickers = ['GSTHHVIP', 'GSCBLMOM '] # Insert list of basket tickers\\n\",\n \"\\n\",\n \"get_flagship_baskets(ticker=tickers)\\n\",\n \"\\n\",\n \"get_flagships_with_assets(identifiers=['AAPL UW'], ticker=tickers)\\n\",\n \"\\n\",\n \"get_flagships_performance(ticker=tickers)\\n\",\n \"\\n\",\n \"get_flagships_constituents(ticker=tickers)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "gs_quant/documentation/06_baskets/tutorials/Basket Backcast.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Uploading Custom History when Creating a Custom Basket\\n\",\n \"\\n\",\n \"When creating a custom basket, Marquee by default will publish up to five years of pricing and composition history based\\n\",\n \"on the composition entered during creation. If you choose to bypass this behavior by setting the `default_backcast`\\n\",\n \"parameter to `False`, you may upload your own custom history any time after the basket has been created. This tutorial\\n\",\n \"will show you how to submit a basket backcast, including best practices to avoid mapping errors, and more!\\n\",\n \"\\n\",\n \"**Note:** You must specify this setting during creation in order to perform the below steps. If you'd like to submit\\n\",\n \"custom history for a basket that has already been created and did not originally select this option, you'll need to\\n\",\n \"create a new basket.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 1: Authenticate & Initialize your session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.api.gs.reports import GsReportApi\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.markets.position_set import Currency, Position, PositionSet, PositionSetWeightingStrategy\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('modify_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Extract your positions into dated position sets\\n\",\n \"\\n\",\n \"Next you'll need to create a PositionSet for each date your basket had position changes historically. Examples of\\n\",\n \"position changes are anything that affect the price or composition of your basket. For example, periodic weighted\\n\",\n \"rebalances, identifier changes or other corporate actions, adds/drops, etc.\\n\",\n \"\\n\",\n \"When Marquee goes to upload your history, we'll convert your position weights into quantities and carry those forward\\n\",\n \"until the next date with position changes occurs. If you do not have position weights and only have quantities at the\\n\",\n \"time of upload, make sure to follow step #4.\\n\",\n \"\\n\",\n \"For the sake of simplicity in this tutorial, we'll show a very basic example of position sets that may represent\\n\",\n \"a basket's composition history. However based on your own personal Input/Output preferences and setup, you'll likely want to make\\n\",\n \"some adjustments in order to aggregate your positions into unique PositionSet objects per date. See\\n\",\n \"[position_set examples](../examples/03_basket_creation/position_set/0012_create_historical_position_sets_from_excel.ipynb)\\n\",\n \"for alternate methods.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"positions = [Position(identifier='AAPL UW', weight=0.5), Position(identifier='MSFT UW', weight=0.5)]\\n\",\n \"\\n\",\n \"pos_set_1 = PositionSet(positions, dt.date(2021, 6, 3))\\n\",\n \"pos_set_2 = PositionSet(positions, dt.date(2022, 1, 2))\\n\",\n \"pos_set_3 = PositionSet(positions, dt.date(2022, 6, 1))\\n\",\n \"pos_set_4 = PositionSet(positions, dt.date(2023, 1, 4))\\n\",\n \"\\n\",\n \"position_sets = [pos_set_1, pos_set_2, pos_set_3, pos_set_4]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Resolve your position sets and check for unmapped assets\\n\",\n \"\\n\",\n \"Now that you have your position sets created, it's time to resolve your positions historically. This will help confirm\\n\",\n \"that each asset identifier can be mapped as of the provided position date.\\n\",\n \"\\n\",\n \"If you face any mapping issues, double check that your identifiers are valid. Consider identifier changes (e.g.,\\n\",\n \"FB UW -> META UW), listed status, IPOs, etc. If you believe you have corrected all assets with these issues and are still\\n\",\n \"experiencing problems mapping your positions, you may remove these identifiers or email the\\n\",\n \"[baskets support team](mailto:gs-marquee-baskets-support@gs.com?subject=Failed%20To%20Map%20Positions%20for%20Basket%20Backcast)\\n\",\n \"for assistance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"for position_set in position_sets:\\n\",\n \" position_set.resolve()\\n\",\n \" if position_set.unresolved_positions is not None and len(position_set.unresolved_positions):\\n\",\n \" print(\\n\",\n \" f'Error resolving assets on {position_set.date.strftime(\\\"%Y-%m-%d\\\")}: {position_set.unresolved_positions}'\\n\",\n \" )\\n\",\n \" \\\"\\\"\\\" Uncomment the below to removed unresolved positions from your position set \\\"\\\"\\\"\\n\",\n \" # position_set.remove_unresolved_positions()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 4 (Optional): Convert position quantities to weights\\n\",\n \"\\n\",\n \"Basket backcasts require each position to have a specified weight. If you only have historical quantities, you may\\n\",\n \"call the `price` function on each position set in order to extract these weights via Marquee. Otherwise you may skip\\n\",\n \"this step.\\n\",\n \"\\n\",\n \"Similar to step #3, you can double check if any assets are unable to be priced and either remove these from your\\n\",\n \"position set or contact the [baskets support team](mailto:gs-marquee-baskets-support@gs.com?subject=Failed%20To%20Price%20Positions%20for%20Basket%20Backcast)\\n\",\n \"for help.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"currency = Currency.USD # replace with desired currency\\n\",\n \"\\n\",\n \"for position_set in position_sets:\\n\",\n \" position_set.price(\\n\",\n \" currency=currency, use_unadjusted_close_price=False, weighting_strategy=PositionSetWeightingStrategy.Quantity\\n\",\n \" )\\n\",\n \" if position_set.unpriced_positions is not None and len(position_set.unpriced_positions):\\n\",\n \" print(f'Error pricing assets on {position_set.date.strftime(\\\"%Y-%m-%d\\\")}: {position_set.unpriced_positions}')\\n\",\n \" \\\"\\\"\\\" Uncomment the below to removed unpriced positions from your position set \\\"\\\"\\\"\\n\",\n \" # position_set.remove_unpriced_positions()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Quick Tip!\\n\",\n \"If you had to remove positions from any position set for one of the reasons described above, the total weight of\\n\",\n \"each position might no longer add up to 1. If this is the case, you can call `redistribute_weights` on any misweighted\\n\",\n \"position sets, which will redistribute the remaining weights proportionally among each position.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"for position_set in position_sets:\\n\",\n \" position_set.redistribute_weights()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 5: Fetch your basket and upload your history\\n\",\n \"\\n\",\n \"Once your positions have all been mapped and assigned weights, you're ready to submit to Marquee! Fetch your basket\\n\",\n \"and call `upload_position_history` using the position sets from above.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX')\\n\",\n \"basket.upload_position_history(position_sets)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 6 (Optional): Reschedule or Cancel failed report jobs\\n\",\n \"\\n\",\n \"There are several cases that can cause a backcast report to fail, including long history length, large number of\\n\",\n \"positions/rebalance frequency, historical errors for assets in Marquee, and more. The above script will catch as\\n\",\n \"many issues as possible prior to upload but there are sometimes still failures that require investigation from the\\n\",\n \"support team to fix.\\n\",\n \"\\n\",\n \"If you find that your backcasts are failing, you can reschedule any failed report jobs using the below snippet to catch\\n\",\n \"for intermittent errors such as timeouts or memory issues on Marquee side. If the errors persist, you may reach out\\n\",\n \"to the [baskets support team](mailto:gs-marquee-baskets-support@gs.com) for further information.\\n\",\n \"\\n\",\n \"If your backcast reports are failing due a non-Marquee issue and you need to re-upload your position history, you'll\\n\",\n \"need to cancel any failed report jobs in order to resubmit your adjusted positions. Then you may repeat steps #1-5.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"backcast_reports = GsReportApi.get_reports(position_source_id=basket.get_marquee_id(), report_type='Basket Backcast')\\n\",\n \"report_ids, report_job_ids = [r.id for r in backcast_reports], []\\n\",\n \"for report_id in report_ids:\\n\",\n \" report_jobs = GsReportApi.get_report_jobs(report_id)\\n\",\n \" report_job_ids = [rj.get('id') for rj in report_jobs if rj.get('status') == 'error']\\n\",\n \"\\n\",\n \"\\\"\\\"\\\" Use this to RESCHEDULE failed report jobs \\\"\\\"\\\"\\n\",\n \"# for report_job in report_job_ids:\\n\",\n \"# print(f'Rescheduling report job {report_job.get(\\\"id\\\")}')\\n\",\n \"# GsReportApi.reschedule_report_job(report_job.get('id'))\\n\",\n \"\\n\",\n \"\\\"\\\"\\\" Use this to CANCEL failed report jobs \\\"\\\"\\\"\\n\",\n \"# for report_job in report_job_ids:\\n\",\n \"# print(f'Cancelling report job {report_job.get(\\\"id\\\")}')\\n\",\n \"# GsReportApi.cancel_report_job(report_job.get('id'))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/06_baskets/tutorials/Basket Create.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Creating a Custom Basket\\n\",\n \"\\n\",\n \"Welcome to the basket creation tutorial! Marquee allows you to create your own tradable basket ticker and manage it through the platform. When you create a basket it automatically gets published to Marquee, and you may also publish it to Bloomberg, Reuters, and Factset. This basket will tick live.\\n\",\n \"\\n\",\n \"Creating a basket requires enhanced levels of permissioning. If you are not yet permissioned to create baskets please reach out to your sales coverage or to the [Marquee sales team](mailto:gs-marquee-sales@gs.com).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate & Initialize your session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.markets.indices_utils import ReturnType\\n\",\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \" scopes=('read_product_data read_user_profile modify_product_data',),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Define your basket metadata, publishing options, pricing options, & return type\\n\",\n \"\\n\",\n \"In this step you are going to define all the specifications needed to create your basket. First, instantiate an empty basket object and then you may begin defining it's settings. The below list contains all the parameters you may set.\\n\",\n \"\\n\",\n \"| Parameter Name | Required? | Default Value | Description |\\n\",\n \"|:-------------------|:-----------|:--------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\\n\",\n \"|name |**Required**|-- | Display name of the basket |\\n\",\n \"|ticker |**Required**|-- | Associated 8-character basket identifier (must be prefixed with \\\"GS\\\" in order to publish to Bloomberg). If you would like to request a custom prefix instead of using the default GSMB prefix please reach out to the [baskets team](mailto:gs-marquee-baskets@gs.com) |\\n\",\n \"|currency |**Required**|-- | Denomination you want your basket to tick in. This can not be changed once your basket has been created |\\n\",\n \"|return_type |**Required**|-- | Determines the index calculation methodology with respect to dividend reinvestment. One of Price Return, Gross Return, Total Return |\\n\",\n \"|position_set |**Required**|-- | Information of constituents associated with the basket. You may provide the weight or quantity for each position. If neither is provided we will distribute the total weight evenly among each position. Please also note that any fractional shares will be rounded up to whole numbers. |\\n\",\n \"|cash_reinvestment_treatment|Optional|Reinvest At Open| How to treat cash acquisitions, regular dividends, and special dividends for basket underliers. |\\n\",\n \"|description |Optional |-- | Free text description of basket. Description provided will be indexed in the search service for free text relevance match. |\\n\",\n \"|divisor |Optional |-- | Divisor to be applied to the overall position set. You need not set this unless you want to change the divisor to a specific number, which will in turn change the basket price (current notional/divisor). This might impact price continuity. |\\n\",\n \"|initial_price |Optional |100 | Initial price the basket should start ticking at |\\n\",\n \"|target_notional |Optional |10,000,000 | Target notional for the position set |\\n\",\n \"|publish_to_bloomberg|Optional |True | If you'd like us to publish your basket to Bloomberg |\\n\",\n \"|publish_to_reuters |Optional |False | If you'd like us to publish your basket to Reuters |\\n\",\n \"|publish_to_factset |Optional |False | If you'd like us to publish your basket to Factset |\\n\",\n \"|historical_methodology|Optional |Backcast | Preferred methodology to be applied when publishing basket history.
  • **Backcast**: Apply current composition backwards up to 5 years, assuming constituent shares remained constant
  • **Backtest**: Reset composition based on the selected criteria and rebalance frequency over selected period of time. Must specify these options in `backtest_parameters`.
  • **Custom History**: Upload your own basket history after the basket has been created. *Note: Previously this was indicated by setting* `default_backcast = False`
|\\n\",\n \"|backtest_parameters |Optional |-- | Rules applied when performing a historical backtest.
  • **Weighting Mechanism**: On each rebalance date, whether to reset positions to have equal weights or restore based on original position set values.
  • **Time Horizon**: Time horizon for which to generate historical backtest. (5 years is the only supported option at this time.)
  • **Frequency**: Frequency at which to recalculate position shares (Weekly, Monthly, Quarterly, Annually.)
|\\n\",\n \"|reweight |Optional |False | If you'd like us to reweight positions if input weights don't add up to 1 upon submission |\\n\",\n \"|weighting_strategy |Optional |-- | Strategy used to price the position set (will be inferred if not indicated). One of Equal, Market Capitalization, Quantity, Weight |\\n\",\n \"|allow_ca_restricted_assets|Optional|False | Allow your basket to have constituents that will not be corporate action adjusted in the future (You will recieve a message indicating if this action is needed when attempting to create your basket) |\\n\",\n \"|allow_limited_access_assets|Optional|False | Allow basket to have constituents that GS has limited access to (You will recieve a message indicating if this action is needed when attempting to create your basket) |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"my_basket = Basket()\\n\",\n \"\\n\",\n \"my_basket.name = 'My New Custom Basket'\\n\",\n \"my_basket.ticker = 'GSMBXXXX'\\n\",\n \"my_basket.currency = 'USD'\\n\",\n \"my_basket.publish_to_reuters = True\\n\",\n \"\\n\",\n \"my_basket.return_type = ReturnType.PRICE_RETURN\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Quick Tip!\\n\",\n \"At any point, you may call the get_details() method on your basket, which will print the current state of the basket object. We recommend doing this throughout the creation process to ensure there are not any discrepancies between your preferences and the current basket settings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"my_basket.get_details() # prints out each parameters on the basket\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Define your basket's composition\\n\",\n \"\\n\",\n \"Now you will decide what your basket composition is. If you'd like to include several positions, you may define the composition using your preferred input method (e.g., uploading an excel file) but it must then be converted to a dictionary or pandas dataframe.\\n\",\n \"\\n\",\n \"Your dataframe must have a column entitled 'identifier', which holds any commonly accepted identifier such as BloombergId, Cusip, Ticker, etc. for each position. You may also have a column entitled 'quantity' to store the number of shares for each position, or a column named 'weight' to represent the weight of each. If the second column is missing, we will later assign equal weight to each position when you submit your basket for creation.\\n\",\n \"\\n\",\n \"After uploading your composition and converting it to a dataframe, make sure to rename your columns to match our specifications if they aren't in the correct format already, and then you may use it to create a valid Position Set. You should then call get_positions() to make sure that your positions have all been mapped correctly, and can then store this composition on the basket.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"positions_df = pd.read_excel('path/to/excel.xlsx') # example of uploading composition from excel document\\n\",\n \"positions_df.columns = ['identifier', 'weight'] # replace weight column with 'quantity' if using number of shares\\n\",\n \"position_set = PositionSet.from_frame(positions_df)\\n\",\n \"position_set.resolve()\\n\",\n \"\\n\",\n \"position_set.get_positions() # returns a dataframe with each position's identifier, name, Marquee unique identifier, and weight/quantity\\n\",\n \"\\n\",\n \"my_basket.position_set = position_set\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Quick Tip!\\n\",\n \"Wanting to quickly add one or two positions to a position set without having to modify your dataframe? You can add to a position set by inputting an identifier and an optional weight/quantity to a Position object and modify the position set directly, like below. Refer to the [position_set examples](../examples/03_basket_creation/position_set/0004_add_position_to_existing_position_set.ipynb) section for more tips like this!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"positions_to_add = [Position('AAPL UW', weight=0.1), Position('MSFT UW', weight=0.1)]\\n\",\n \"position_set.positions += positions_to_add\\n\",\n \"\\n\",\n \"my_basket.position_set = position_set\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Create your basket\\n\",\n \"\\n\",\n \"Once you've ensured that your basket has been set up to your satisfaction, you're ready to officially create and publish to Marquee! Once you call create on your new basket, you may poll its status to make sure that it has processed successfully. This will check the report status every 30 seconds for 10 minutes by default, but you can override this option if you prefer as shown below. If you'd like to view your basket on the Marquee site, you can retrieve the link to your page by calling get_url().\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"my_basket.get_details() # we highly recommend verifying the basket state looks correct before calling create!\\n\",\n \"\\n\",\n \"my_basket.create()\\n\",\n \"\\n\",\n \"my_basket.poll_status(\\n\",\n \" timeout=120, step=20\\n\",\n \") # optional: constantly checks create status until report succeeds, fails, or the poll times out (this example checks every 20 seconds for 2 minutes)\\n\",\n \"\\n\",\n \"my_basket.get_url() # will return a url to your Marquee basket page ex. https://marquee.gs.com/s/products/MA9B9TEMQ2RW16K9/summary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Update your basket's entitlements\\n\",\n \"\\n\",\n \"The application you use to create your basket will initially be the only one permissioned to view, edit, and submit rebalance requests. If you'd like to entitle other users or groups with view or admin access, you may update your basket's permissions at any time.\\n\",\n \"\\n\",\n \"In order to add or remove permissions for a specific user, you will need either their Marquee user id or email. You may also permission groups using their group id. See the snippet below, or refer to the [baskets permissions examples](../examples/07_basket_permissions/0001_permission_application_to_basket.ipynb) for more options.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.entities.entitlements import User\\n\",\n \"\\n\",\n \"user = User.get(user_id='application_id')\\n\",\n \"basket.entitlements.view.users += [\\n\",\n \" user\\n\",\n \"] # update the desired entitlements block ('edit', 'admin', etc) 'users' property\\n\",\n \"\\n\",\n \"basket.update()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### You're all set, Congrats! What's next?\\n\",\n \"\\n\",\n \"* [How do I upload my basket's historical composition?](./Basket%20Backcast.ipynb)\\n\",\n \"\\n\",\n \"* [How do I retrieve composition data for my basket?](../examples/01_basket_composition_data/0000_get_latest_basket_composition.ipynb)\\n\",\n \"\\n\",\n \"* [How do I retrieve pricing data for my basket?](../examples/02_basket_pricing_data/0000_get_latest_basket_close_price.ipynb)\\n\",\n \"\\n\",\n \"* [How do I change my basket's current composition?](./Basket%20Rebalance.ipynb)\\n\",\n \" \\n\",\n \"* [How do I make other changes to my basket (name, description, etc.)?](./Basket%20Edit.ipynb)\\n\",\n \"\\n\",\n \"* [What else can I do with my basket?](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.baskets.Basket.html#gs_quant.markets.baskets.Basket)\\n\",\n \"\\n\",\n \"Other questions? Reach out to the [baskets team](mailto:gs-marquee-baskets-support@gs.com) anytime!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/06_baskets/tutorials/Basket Edit.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Editing a Custom Basket\\n\",\n \"\\n\",\n \"Editing a custom basket is very similar to the creation process. You simply fetch your basket, set its properties to match your preferences, and call update! Please note that not all settings are modifiable after creation (see the list of options below). Additionally, if you'd like to adjust your basket's composition or price settings, please refer to the [rebalance tutorial](./Basket%20Rebalance.ipynb) instead.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate & Initialize your session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \" scopes=('read_product_data read_user_profile modify_product_data',),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Fetch your basket and set your changes\\n\",\n \"\\n\",\n \"Next you will fetch the basket you'd like to edit by passing in any of its identifiers such as BloombergId, Cusip, Ticker, etc. If this is a basket you or someone from your organization created, please make sure your application has edit/admin entitlements or you will not be able to modify it. You will then be ready to change any of the properties listed below.\\n\",\n \"\\n\",\n \"| Parameter Name |Description |\\n\",\n \"|:--------------------|:-----------|\\n\",\n \"|name |Display name of the basket|\\n\",\n \"|description |Free text description of basket. Description provided will be indexed in the search service for free text relevance match.|\\n\",\n \"|publish_to_bloomberg |If you'd like us to publish your basket to Bloomberg|\\n\",\n \"|publish_to_reuters |If you'd like us to publish your basket to Reuters |\\n\",\n \"|publish_to_factset |If you'd like us to publish your basket to Factset |\\n\",\n \"|include_price_history|Republish price history based on current composition|\\n\",\n \"|cash_reinvestment_treatment|How to treat cash acquisitions, regular dividends, and special dividends for basket underliers.|\\n\",\n \"|historical_methodology|Preferred methodology to be applied when publishing basket history.
  • **Backcast**: Apply current composition backwards up to 5 years, assuming constituent shares remained constant
  • **Backtest**: Reset composition based on the selected criteria and rebalance frequency over selected period of time. Must specify these options in `backtest_parameters`.
  • **Custom History**: Upload your own basket history after the basket has been created. *Note: Previously this was indicated by setting* `default_backcast = False`
|\\n\",\n \"|backtest_parameters |Rules applied when performing a historical backtest.
  • **Weighting Mechanism**: On each rebalance date, whether to reset positions to have equal weights or restore based on original position set values.
  • **Time Horizon**: Time horizon for which to generate historical backtest. (5 years is the only supported option at this time.)
  • **Frequency**: Frequency at which to recalculate position shares (Weekly, Monthly, Quarterly, Annually.)
|\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX')\\n\",\n \"\\n\",\n \"basket.description = 'My New and Improved basket description'\\n\",\n \"basket.publish_to_reuters = True\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Update your basket's entitlements\\n\",\n \"\\n\",\n \"The application you use to create your basket will initially be the only one permissioned to view, edit, and submit rebalance requests. If you'd like to entitle other users or groups with view or admin access, you may update your basket's permissions at any time.\\n\",\n \"\\n\",\n \"In order to add or remove permissions for a specific user, you will need either their Marquee user id or email. You may also permission groups using their group id. See the snippet below, or refer to the [baskets permissions examples](../examples/07_basket_permissions/0001_permission_application_to_basket.ipynb) for more options.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.entities.entitlements import Group, User\\n\",\n \"\\n\",\n \"user = User.get(user_id='application_id')\\n\",\n \"basket.entitlements.view.users.remove(user)\\n\",\n \"\\n\",\n \"group = Group.get(group_id='group_id')\\n\",\n \"basket.entitlements.admin.groups += [group]\\n\",\n \"\\n\",\n \"basket.entitlements.to_frame() # call to verify the entitlement changes are now reflected properly\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Submit your changes\\n\",\n \"\\n\",\n \"Once you've ensured that your basket has been updated to your satisfaction, you're ready to officially submit these changes to Marquee! Once you call update on the basket, you may poll its status to make sure that it has processed successfully. This will check the report status every 30 seconds for 10 minutes by default, but you can override this option if you prefer as shown below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"my_basket.get_details() # call to make sure that your changes are all reflected properly\\n\",\n \"\\n\",\n \"my_basket.update()\\n\",\n \"\\n\",\n \"my_basket.poll_status(\\n\",\n \" timeout=120, step=20\\n\",\n \") # optional: constantly checks edit status until report succeeds, fails, or the poll times out (this example checks every 20 seconds for 2 minutes)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "gs_quant/documentation/06_baskets/tutorials/Basket Rebalance.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Rebalancing a Custom Basket\\n\",\n \"\\n\",\n \"Here we will go through how to modify your basket's current composition. You may concurrently modify basket details, pricing options, and publishing preferences.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate & Initialize your session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"client = 'CLIENT ID'\\n\",\n \"secret = 'CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret, scopes=('read_product_data read_user_profile',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Fetch your basket and set your changes\\n\",\n \"\\n\",\n \"Next you will fetch the basket you'd like to update by passing in any of its identifiers such as BloombergId, Cusip, Ticker, etc. If this is a basket you or someone from your organization created, please make sure your application has admin entitlements or you will not be able to submit the rebalance request. You will then be ready to change any of the properties listed below.\\n\",\n \"\\n\",\n \"| Parameter Name | Required? | Description |\\n\",\n \"|:-------------------|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\\n\",\n \"|position_set |**Required**| Information of constituents associated with the basket. You may provide the weight or quantity for each position. If neither is provided we will distribute the total weight evenly among each position. Please also note that any fractional shares will be rounded up to whole numbers. |\\n\",\n \"|divisor |Optional | Divisor to be applied to the overall position set. Ideally, you should not to change this as it will cause a price deviation on the basket. |\\n\",\n \"|initial_price |Optional | Price you'd like to reset the basket to. Ideally, you should not to do this as it will cause a price deviation on the basket. |\\n\",\n \"|publish_to_bloomberg|Optional | If you'd like us to publish your basket to Bloomberg |\\n\",\n \"|publish_to_reuters |Optional | If you'd like us to publish your basket to Reuters |\\n\",\n \"|publish_to_factset |Optional | If you'd like us to publish your basket to Factset |\\n\",\n \"|include_price_history|Optional | Republish price history based on current composition when publishing to Bloomberg |\\n\",\n \"|reweight |Optional | If you'd like us to reweight positions if input weights don't add up to 1 upon submission |\\n\",\n \"|weighting_strategy |Optional | Strategy used to price the position set (will be inferred if not indicated). One of Equal, Market Capitalization, Quantity, Weight |\\n\",\n \"|allow_ca_restricted_assets|Optional| Allow your basket to have constituents that will not be corporate action adjusted in the future (You will recieve a message indicating if this action is needed when attempting to rebalance your basket) |\\n\",\n \"|allow_limited_access_assets|Optional| Allow basket to have constituents that GS has limited access to (You will recieve a message indicating if this action is needed when attempting to rebalance your basket) |\\n\",\n \"|cash_reinvestment_treatment|How to treat cash acquisitions, regular dividends, and special dividends for basket underliers.|\\n\",\n \"|historical_methodology|Preferred methodology to be applied when publishing basket history.
  • **Backcast**: Apply current composition backwards up to 5 years, assuming constituent shares remained constant
  • **Backtest**: Reset composition based on the selected criteria and rebalance frequency over selected period of time. Must specify these options in `backtest_parameters`.
  • **Custom History**: Upload your own basket history after the basket has been created. *Note: Previously this was indicated by setting* `default_backcast = False`
|\\n\",\n \"|backtest_parameters |Rules applied when performing a historical backtest.
  • **Weighting Mechanism**: On each rebalance date, whether to reset positions to have equal weights or restore based on original position set values.
  • **Time Horizon**: Time horizon for which to generate historical backtest. (5 years is the only supported option at this time.)
  • **Frequency**: Frequency at which to recalculate position shares (Weekly, Monthly, Quarterly, Annually.)
|\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket = Basket.get('GSMBXXXX')\\n\",\n \"\\n\",\n \"basket.publish_to_bloomberg = True\\n\",\n \"\\n\",\n \"positions_df = pd.read_excel('path/to/excel.xlsx') # example composition upload from a local excel file\\n\",\n \"positions_df.columns = ['identifier', 'weight'] # replace weight column with 'quantity' if using number of shares\\n\",\n \"position_set = PositionSet.from_frame(positions_df)\\n\",\n \"position_set.resolve()\\n\",\n \"\\n\",\n \"position_set.get_positions() # returns a dataframe with each position's identifier, name, Marquee unique identifier, and weight/quantity\\n\",\n \"\\n\",\n \"my_basket.position_set = position_set\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Submit your changes\\n\",\n \"\\n\",\n \"Once you've ensured that your basket composition has been adjusted to your satisfaction, you're ready to officially submit these changes to Marquee! Once you call update on the basket, this request will be sent for approval. You can check on the approval status by calling get_rebalance_approval_status(). The rebalance will begin processing once the request is approved, where you can then poll its status to make sure that it has processed successfully. This will check the report status every 30 seconds for 10 minutes by default, but you can override this option if you prefer as shown below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.update() # submits the rebalance request to Marquee\\n\",\n \"\\n\",\n \"basket.get_rebalance_approval_status() # check approval status of most recent rebalance submission\\n\",\n \"\\n\",\n \"basket.poll_status(\\n\",\n \" timeout=120, step=20\\n\",\n \") # optional: constantly checks rebalance status after request is approved until report succeeds, fails, or the poll times out (this example checks every 20 seconds for 2 minutes)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Not happy with the new composition you submitted?\\n\",\n \"\\n\",\n \"If your most recent rebalance request is not yet approved, you may either update your composition and submit a new rebalance request using the steps listed above, or you can simply cancel the request.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"basket.cancel_rebalance()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/07_index/README.md", "content": "## Pre Requisites for STS Indices\nTo use the Index functionality on **STS Indices**, your application needs to have the following access\n\n#### Accessing STS Indices\nYour app should have *view access* to the STS index you intend to use.\nTo request access, please [send an email](mailto:gs-marquee-sts-support@ny.email.gs.com?subject=Request%20to%20access%20%20via%20API&body=I%20would%20like%20to%20access%20the%20index%20%20via%20my%20app%20) to the Marquee STS Team.\n\n#### Accessing STS Datasets\nYour app needs to have access to the following datasets:\n1. [STSLEVELS](https://marquee.gs.com/s/developer/datasets/STSLEVELS) - Official Values of STS Indices\n2. [STS_INDICATIVE_LEVELS](https://marquee.gs.com/s/developer/datasets/STS_INDICATIVE_LEVELS) - Indicative Values of STS Indices\n3. [STS_FUNDAMENTALS](https://marquee.gs.com/s/developer/datasets/STS_FUNDAMENTALS) - Fundamental metrics of STS Indices\n4. [STS_UNDERLIER_WEIGHTS](https://marquee.gs.com/s/developer/datasets/STS_UNDERLIER_WEIGHTS) - Weights of index underliers of STS Indices\n5. [STS_UNDERLIER_ATTRIBUTION](https://marquee.gs.com/s/developer/datasets/STS_UNDERLIER_ATTRIBUTION) - Attribution of index underliers\n6. [STSCONSTITUENTS](https://marquee.gs.com/s/developer/datasets/STSCONSTITUENTS) - Bottom level index constituents and associated weights\n\nYou can request access by going to the Dataset Catalog Page linked above.\n\nNote - Please skip this if you are an internal user" }, { "path": "gs_quant/documentation/07_index/examples/0000_get_index_Marquee_url.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c2ec89ec\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.index import Index\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"de70c758\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f384ea49\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = Index.get('GSXXXXXX') # substitute input with any identifier for an Index\\n\",\n \"index.get_url() # url to view index on Marquee\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7fb27b941602401d91542211134fc71a\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"*Have any other questions? Reach out to the [Marquee STS team](mailto:gs-marquee-sts-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/07_index/examples/0001_get_index_close_prices.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d78f4f5c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.index import Index, PriceType\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7fb27b941602401d91542211134fc71a\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Pre Requisites\\n\",\n \"To use below functionality on **STS Indices**, your application needs to have access to the following datasets:\\n\",\n \"1. [STSLEVELS](https://marquee.gs.com/s/developer/datasets/STSLEVELS) - Official Values of STS Indices\\n\",\n \"2. [STS_INDICATIVE_LEVELS](https://marquee.gs.com/s/developer/datasets/STS_INDICATIVE_LEVELS) - Indicative Values of STS Indices\\n\",\n \"\\n\",\n \"You can request access by going to the Dataset Catalog Page linked above.\\n\",\n \"\\n\",\n \"Note - Please skip this if you are an internal user\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c6eff76d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3ac14d1f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = Index.get('GSXXXXXX') # substitute input with any identifier for an index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"97a0847c\",\n \"metadata\": {},\n \"source\": [\n \"#### Close price functions supports the following price types\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ad9bf45e\",\n \"metadata\": {},\n \"source\": [\n \"You may choose one of the following price types:\\n\",\n \"\\n\",\n \"- **Official Price:** PriceType.OFFICIAL_PRICE\\n\",\n \"- **Indicative Price** PriceType.INDICATIVE_CLOSE_PRICE - Currently supports STS indices only.\\n\",\n \"\\n\",\n \"Default returns the official close price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7e037f7a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_latest_close_price(\\n\",\n \" price_type=[PriceType.OFFICIAL_CLOSE_PRICE]\\n\",\n \") # returns latest official levels for the index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5e179014\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_close_price_for_date(\\n\",\n \" dt.date(2021, 1, 7), price_type=[PriceType.OFFICIAL_CLOSE_PRICE]\\n\",\n \") # returns official levels for a given date.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bdd5f86d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_close_prices(\\n\",\n \" start=dt.date(2021, 1, 7), end=dt.date(2021, 3, 27), price_type=[PriceType.OFFICIAL_CLOSE_PRICE]\\n\",\n \") # returns official levels for a given date range.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f7f9f147\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_close_prices(price_type=[PriceType.OFFICIAL_CLOSE_PRICE]) # returns all the official levels of the index.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b5a88445\",\n \"metadata\": {},\n \"source\": [\n \"#### STS indices can use PriceType.INDICATIVE_CLOSE_PRICE as well to get the indicative levels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8ceaa6e6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_latest_close_price(\\n\",\n \" price_type=[PriceType.OFFICIAL_CLOSE_PRICE, PriceType.INDICATIVE_CLOSE_PRICE]\\n\",\n \") # returns latest indicative and official levels of the index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3b129435\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_close_price_for_date(\\n\",\n \" dt.date(2021, 1, 7), price_type=[PriceType.OFFICIAL_CLOSE_PRICE, PriceType.INDICATIVE_CLOSE_PRICE]\\n\",\n \") # returns both indicative and official levels of the index for a given date.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"371146cb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_close_prices(\\n\",\n \" start=dt.date(2021, 1, 7),\\n\",\n \" end=dt.date(2021, 3, 27),\\n\",\n \" price_type=[PriceType.OFFICIAL_CLOSE_PRICE, PriceType.INDICATIVE_CLOSE_PRICE],\\n\",\n \") # returns both indicative and official levels of the index for a given date range.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ebc1ad33\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_close_prices(\\n\",\n \" price_type=[PriceType.OFFICIAL_CLOSE_PRICE, PriceType.INDICATIVE_CLOSE_PRICE]\\n\",\n \") # returns all the indicative and official levels of the index.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"acae54e37e7d407bbb7b55eff062a284\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"*Have any other questions? Reach out to the [Marquee STS team](mailto:gs-marquee-sts-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/07_index/examples/0002_get_index_fundamentals_data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c2ec89ec\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.data.fields import DataMeasure\\n\",\n \"from gs_quant.markets.index import Index\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9eb4d9e4\",\n \"metadata\": {},\n \"source\": [\n \"## Pre Requisites\\n\",\n \"To use below functionality on **STS Indices**, your application needs to have access to the following datasets:\\n\",\n \"1. [STS_FUNDAMENTALS](https://marquee.gs.com/s/developer/datasets/STS_FUNDAMENTALS) - Fundamental metrics of STS Indices\\n\",\n \"\\n\",\n \"You can request access by going to the Dataset Catalog Page linked above.\\n\",\n \"\\n\",\n \"Note - Please skip this if you are an internal user\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"de70c758\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f384ea49\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = Index.get('GSXXXXXX') # substitute input with any identifier for a single stock STS index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d619009b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_fundamentals() # default period is one year, default period direction is forward, default metrics are all available metrics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8c98e5fc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_fundamentals(\\n\",\n \" start=dt.date(2021, 1, 7), end=dt.date(2021, 3, 27)\\n\",\n \") # get fundamentals for the specified date range\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e4024bc8\",\n \"metadata\": {},\n \"source\": [\n \"You may choose one of the following periods:\\n\",\n \"\\n\",\n \"- **1 year:** DataMeasure.ONE_YEAR\\n\",\n \"- **2 years:** DataMeasure.TWO_YEARS\\n\",\n \"- **3 years:** DataMeasure.THREE_YEARS\\n\",\n \"\\n\",\n \"You may choose one of the following period directions:\\n\",\n \"\\n\",\n \"- **Forward:** DataMeasure.FORWARD\\n\",\n \"- **Trailing:** DataMeasure.TRAILING\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b2257277\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_fundamentals(period=DataMeasure.TWO_YEARS, direction=DataMeasure.TRAILING)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"369b0fed\",\n \"metadata\": {},\n \"source\": [\n \"You may choose any combinations of the following metrics:\\n\",\n \"\\n\",\n \"- **Dividend Yield:** DataMeasure.DIVIDEND_YIELD\\n\",\n \"- **Earnings per Share:** DataMeasure.EARNINGS_PER_SHARE\\n\",\n \"- **Earnings per Share Positive:** DataMeasure.EARNINGS_PER_SHARE_POSITIVE\\n\",\n \"- **Net Debt to EBITDA:** DataMeasure.NET_DEBT_TO_EBITDA\\n\",\n \"- **Price to Book:** DataMeasure.PRICE_TO_BOOK\\n\",\n \"- **Price to Cash:** DataMeasure.PRICE_TO_CASH\\n\",\n \"- **Price to Earnings:** DataMeasure.PRICE_TO_EARNINGS\\n\",\n \"- **Price to Earnings Positive:** DataMeasure.PRICE_TO_EARNINGS_POSITIVE\\n\",\n \"- **Price to Sales:** DataMeasure.PRICE_TO_SALES\\n\",\n \"- **Return on Equity:** DataMeasure.RETURN_ON_EQUITY\\n\",\n \"- **Sales per Share:** DataMeasure.SALES_PER_SHARE\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"37118eed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_fundamentals(metrics=[DataMeasure.PRICE_TO_CASH, DataMeasure.SALES_PER_SHARE])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"55047ebb\",\n \"metadata\": {},\n \"source\": [\n \"*Have any other questions? Reach out to the [Marquee STS team](mailto:gs-marquee-sts-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/07_index/examples/0003_get_index_constituents.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d78f4f5c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from gs_quant.markets.index import Index\\n\",\n \"from gs_quant.session import Environment, GsSession\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b50c33ff-4e90-4ba3-828c-1ca0f06e05f0\",\n \"metadata\": {},\n \"source\": [\n \"## Pre Requisites\\n\",\n \"To use below functionality on **STS Indices**, your application needs to have access to the following datasets:\\n\",\n \"1. [STSCONSTITUENTS](https://marquee.gs.com/s/developer/datasets/STSCONSTITUENTS) - Bottom level index constituents and associated weights\\n\",\n \"\\n\",\n \"You can request access by going to the Dataset Catalog Page linked above.\\n\",\n \"\\n\",\n \"Note - Please skip this if you are an internal user\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c6eff76d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3ac14d1f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = Index.get('GSXXXXXX') # substitute input with any identifier for an index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"61adac51\",\n \"metadata\": {},\n \"source\": [\n \"## Get Constituents\\n\",\n \"\\n\",\n \"Returns a pandas dataframe of the constituents\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"109fef12\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_latest_constituents() # returns the latest constituents of the index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a6cf3b37\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_constituents_for_date(dt.date(2021, 7, 1)) # returns the constituents of the index for the specified date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"da4c497a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_constituents(\\n\",\n \" dt.date(2021, 6, 1), dt.date(2021, 6, 10)\\n\",\n \") # returns the constituents sets of the index for the specified date range\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"69d5708d\",\n \"metadata\": {},\n \"source\": [\n \"## Get Constituents as Instruments\\n\",\n \"\\n\",\n \"Returns the constituents as Instrument objects\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d0644e0a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_latest_constituent_instruments() # returns the latest constituents of the index as instrument objects.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"18d10cd3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_constituent_instruments_for_date(\\n\",\n \" dt.date(2021, 7, 1)\\n\",\n \") # returns the constituents of the index for the specified date as Instrument objects\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"79a818b8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index.get_constituent_instruments(\\n\",\n \" dt.date(2021, 6, 1), dt.date(2021, 6, 10)\\n\",\n \") # returns the constituents sets of the index for the specified date range as Instrument objects\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3d66e679\",\n \"metadata\": {},\n \"source\": [\n \"*Have any other questions? Reach out to the [Marquee STS team](mailto:gs-marquee-sts-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/07_index/examples/0004_get_index_weights_and_attribution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9e4d4a4b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets.index import Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3027d017\",\n \"metadata\": {},\n \"source\": [\n \"## Pre Requisites\\n\",\n \"To use below functionality on **STS Indices**, your application needs to have access to the following datasets:\\n\",\n \"4. [STS_UNDERLIER_WEIGHTS](https://marquee.gs.com/s/developer/datasets/STS_UNDERLIER_WEIGHTS) - Weights of index underliers of STS Indices\\n\",\n \"5. [STS_UNDERLIER_ATTRIBUTION](https://marquee.gs.com/s/developer/datasets/STS_UNDERLIER_ATTRIBUTION) - Attribution of index underliers\\n\",\n \"\\n\",\n \"You can request access by going to the Dataset Catalog Page linked above.\\n\",\n \"\\n\",\n \"Note - Please skip this if you are an internal user\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f6a8d144\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"827dc4fc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"strategy_index = Index.get('GSXXXXXX') # substitute input with any identifier for an index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f88343e4\",\n \"metadata\": {},\n \"source\": [\n \"### These functions currently supports STS indices only\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d39c29b5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns a pandas dataframe containing latest weights of the immediate underliers of the index\\n\",\n \"strategy_index.get_underlier_weights()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b3758a0e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns a pandas dataframe containing latest attribution of the immediate underliers of the index\\n\",\n \"strategy_index.get_underlier_attribution()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ae9d3263\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the latest weights and attribution of the underliers at all levels\\n\",\n \"strategy_index.get_underlier_tree().to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6994f631\",\n \"metadata\": {},\n \"source\": [\n \"*Have any other questions? Reach out to the [Marquee STS team](mailto:gs-marquee-sts-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/07_index/examples/0005_get_index_as_a_tree.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"18a55a4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.markets.index import Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7fb27b941602401d91542211134fc71a\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Pre Requisites\\n\",\n \"To use below functionality on **STS Indices**, your application needs to have access to the following datasets:\\n\",\n \"1. [STS_UNDERLIER_WEGHTS](https://marquee.gs.com/s/developer/datasets/STS_UNDERLIER_WEGHTS) - Weights of index underliers of STS Indices\\n\",\n \"5. [STS_UNDERLIER_WEGHTS](https://marquee.gs.com/s/developer/datasets/STS_UNDERLIER_WEGHTS) - Attribution of index underliers\\n\",\n \"\\n\",\n \"You can request access by going to the Dataset Catalog Page linked above.\\n\",\n \"\\n\",\n \"Note - Please skip this if you are an internal user\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"14915132\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"315d7921\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"strategy_index = Index.get('GSXXXXXX') # substitute input with any identifier for an index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"20b48849\",\n \"metadata\": {},\n \"source\": [\n \"### These functions currently supports STS indices only\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5e5df9ff\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns the root node of the underlier tree formed by the index\\n\",\n \"strategy_index.get_underlier_tree()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9dcdf9da\",\n \"metadata\": {},\n \"source\": [\n \"To know more about how to use the AssetTreeNode object, please refer to the example notebook for the Tree Entity class, found in gs_quant/documentation/08_tree_entity\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"15dfe760\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns the full tree formed by the the index as a pandas dataframe\\n\",\n \"strategy_index.get_underlier_tree().to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"acae54e37e7d407bbb7b55eff062a284\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# This functionality requires treelib to be installed\\n\",\n \"# Prints the tree structure formed by the Index for easy visualisation\\n\",\n \"strategy_index.visualise_tree(visualise_by='name')\\n\",\n \"\\n\",\n \"# If data is missing for any field, then assetId will be used instead\\n\",\n \"strategy_index.visualise_tree(visualise_by='bbid')\\n\",\n \"\\n\",\n \"strategy_index.visualise_tree(visualise_by='id')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"900625d8\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# To refresh and rebuild the tree\\n\",\n \"strategy_index.get_underlier_tree(refresh_tree=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9a63283cbaf04dbcab1f6479b197f3a8\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"*Have any other questions? Reach out to the [Marquee STS team](mailto:gs-marquee-sts-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/07_index/tutorials/GS Quant STS Index Tutorial.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fcb3ad26\",\n \"metadata\": {},\n \"source\": [\n \"# GS Quant STS Index Tutorial\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f178e4f\",\n \"metadata\": {},\n \"source\": [\n \"Welcome to the GS Quant Index tutorial! This tutorial will walk you through using the Index class functionality in GS Quant with a focus on STS Indices.\\n\",\n \"\\n\",\n \"Marquee [Systematic Trading Strategy (STS)](https://marquee.gs.com/welcome/products/index-solutions/systematic-trading-strategies) offering provides a variety of information for STS Indices including prices, compositions and PnL attribution.\\n\",\n \"GS Quant makes accessing this data via API intuitive and fast.\\n\",\n \"\\n\",\n \"In this tutorial, you will fetch an STS Index and learn how to:\\n\",\n \"1. Get strategy values\\n\",\n \"3. Get bottom level composition\\n\",\n \"4. Get full underlier tree\\n\",\n \"5. Get factor risk data\\n\",\n \"2. Get fundamentals metrics\\n\",\n \"\\n\",\n \"\\n\",\n \"Please ensure that you have followed the setup instructions for GS Quant mentioned [here](https://developer.gs.com/docs/gsquant/getting-started/). \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5e4f17b6\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Pre Requisites\\n\",\n \"To use below functionality on **STS Indices**, your application needs to have access to the following datasets:\\n\",\n \"1. [STSLEVELS](https://marquee.gs.com/s/developer/datasets/STSLEVELS) - Official Values of STS Indices\\n\",\n \"2. [STS_INDICATIVE_LEVELS](https://marquee.gs.com/s/developer/datasets/STS_INDICATIVE_LEVELS) - Indicative Values of STS Indices\\n\",\n \"3. [STS_FUNDAMENTALS](https://marquee.gs.com/s/developer/datasets/STS_FUNDAMENTALS) - Fundamental metrics of STS Indices\\n\",\n \"4. [STS_UNDERLIER_WEIGHTS](https://marquee.gs.com/s/developer/datasets/STS_UNDERLIER_WEIGHTS) - Weights of index underliers of STS Indices\\n\",\n \"5. [STS_UNDERLIER_ATTRIBUTION](https://marquee.gs.com/s/developer/datasets/STS_UNDERLIER_ATTRIBUTION) - Attribution of index underliers\\n\",\n \"6. [STSCONSTITUENTS](https://marquee.gs.com/s/developer/datasets/STSCONSTITUENTS) - Bottom level index constituents and associated weights\\n\",\n \"\\n\",\n \"You can request access by going to the Dataset Catalog Page linked above.\\n\",\n \"\\n\",\n \"**Note** - If you're using GS Quant as an internal user, you need to raise the above for yourself. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b88245b\",\n \"metadata\": {},\n \"source\": [\n \"# First Steps\\n\",\n \"### 1. Authenticate & Initialize your session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret. To checke your app's authentication, follow our [Getting Started guide.](https://developer.gs.com/docs/gsquant/authentication/gs-session/)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5171db43\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.index import Index\\n\",\n \"from gs_quant.markets.indices_utils import *\\n\",\n \"from gs_quant.data.fields import DataMeasure\\n\",\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"\\n\",\n \"import datetime as dt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f758e77f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4069e261\",\n \"metadata\": {},\n \"source\": [\n \"### 2. Fetch the index information\\n\",\n \"\\n\",\n \"#### Load the index\\n\",\n \"Next you will fetch the index you'd like to work with, by passing in any of its identifiers such as Bloomberg Id, Ticker, etc.\\n\",\n \"In this tutorial, we're using some sample STS indices:\\n\",\n \"1. GSMBMA5S - A multi-asset strategy\\n\",\n \"2. GSISM37E - A strategy based on single stocks\\n\",\n \"\\n\",\n \"To access any STS Index, your app should have access to view that index. Please note that **apps do not automatically inherit the access of their owners**. This means that your apps need to be separately granted access to the required STS Indices.\\n\",\n \"\\n\",\n \"To request access to the above samples, please [send an email](mailto:gs-sts-marquee-permissioning-global@gs.com?cc=gs-marquee-sts-support@gs.com&subject=Request%20to%20access%20%20via%20API&body=I%20would%20like%20to%20access%20the%20index%20%20via%20my%20app%20) to our team mentioning your app's id.\\n\",\n \"\\n\",\n \"If you app already has access to some other STS indices, you can use them as well. Just replace the ticker below with the ticker of your index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b5d12812\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ma_index = Index.get('GSMBMA5S') # Substitute input with the identifier for an Index\\n\",\n \"sstk_index = Index.get('GSISM37E')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b9ea63af\",\n \"metadata\": {},\n \"source\": [\n \"#### Get the Index URL on Marquee\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5bcff23b-fd83-45e8-9b36-7cb3d0d51bc2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Retrieves the url of the product page of the index on Marquee\\n\",\n \"ma_index.get_url()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a7580ce9\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2022, 3, 1)\\n\",\n \"end_date = dt.date(2022, 4, 1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6aca8516\",\n \"metadata\": {},\n \"source\": [\n \"# Strategy Values of an STS Index\\n\",\n \"\\n\",\n \"In general, all Indices support official close prices but for STS Indices, official and indicatvive strategy values are supported. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"29b00c8b\",\n \"metadata\": {},\n \"source\": [\n \"The strategy values of an STS Index can be obtained by using the `get_close_prices` function. You may choose one of the following price types:\\n\",\n \"\\n\",\n \"**Official Strategy Values**: PriceType.OFFICIAL_PRICE
\\n\",\n \"**Indicative Strategy Values**: PriceType.INDICATIVE_CLOSE_PRICE - _Currently available for STS indices only._\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dad27eeb-a6b2-4d73-8702-4d34625576b8\",\n \"metadata\": {},\n \"source\": [\n \"### Get Official Close Prices\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cc5ac3e0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns strategy values between start and end date. When no price_type is passed, it defaults to official strategy values\\n\",\n \"ma_index.get_close_prices(start=start_date, end=end_date)\\n\",\n \"\\n\",\n \"# Returns official strategy values between start and end date\\n\",\n \"sstk_index.get_close_prices(start=start_date, end=end_date, price_type=[PriceType.OFFICIAL_CLOSE_PRICE])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"66a19bca-d28b-4c1d-ad60-1d5e9b0c4e0a\",\n \"metadata\": {},\n \"source\": [\n \"### Get Indicative Close Prices\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cb897e4e-e5a9-4a19-adec-bafb22a198a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns indicative strategy values between start and end date\\n\",\n \"ma_index.get_close_prices(start=start_date, end=end_date, price_type=[PriceType.INDICATIVE_CLOSE_PRICE])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5ecd4b35-7d4e-4c35-9903-76c6aea3c067\",\n \"metadata\": {},\n \"source\": [\n \"### Get all close prices\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6b204b40-50b9-458b-b130-7ca636eebac8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns official and indicative strategy values between start and end date for the STS index\\n\",\n \"ma_index.get_close_prices(\\n\",\n \" start=start_date, end=end_date, price_type=[PriceType.OFFICIAL_CLOSE_PRICE, PriceType.INDICATIVE_CLOSE_PRICE]\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3236cb73-6ed7-4ba1-96c3-39836cce872f\",\n \"metadata\": {},\n \"source\": [\n \"# Composition Data of STS Index\\n\",\n \"\\n\",\n \"The composition of an STS Index can be understood as a tree structure - the index is the topmost node, and it has some child nodes.\\n\",\n \"These child nodes can either be bottom level assets or have child nodes of their own.\\n\",\n \"The intermediate child nodes (i.e. those which have their own children) are called **underliers**. While the bottom level assets are called **constituents**.\\n\",\n \"\\n\",\n \"![Tree](./images/index_composition_diagram.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d1f82b0\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"### Get Constituents\\n\",\n \"\\n\",\n \"The constituents of an Index are the bottom level assets. Fetch the constituents of the index using the `get_constituents` method.\\n\",\n \"\\n\",\n \"You can also get convert the constituents of an index into Instrument objects.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"96b365a6-1dce-48e2-b3dc-ec2b078f5312\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns constituents of the index as a pandas DataFrame object\\n\",\n \"sstk_index.get_constituents_for_date(date=start_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"16cf7e9b-470e-489c-89be-100049a2459e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns constituents of the index as a list of instrument class objects\\n\",\n \"sstk_instruments = sstk_index.get_constituent_instruments_for_date(date=start_date)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bbb6eb5b\",\n \"metadata\": {},\n \"source\": [\n \"### Get Underlier Tree\\n\",\n \"\\n\",\n \"You can fetch the underlier tree of an index using the `get_underlier_tree` method. This method returns the composition tree alongwith the weights and attributions of the nodes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"31d01dda\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Returns the top node of the tree structure formed by the index\\n\",\n \"ma_index.get_underlier_tree()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b548eec0\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Returns the tree structure formed by the index as a pandas dataframe\\n\",\n \"ma_index.get_underlier_tree().to_frame()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"df0d7595-7936-4602-8fca-fd889193748b\",\n \"metadata\": {},\n \"source\": [\n \"We proVisualisation functions require installing the `treelib` package, so let's go ahead and install it.\\n\",\n \"\\n\",\n \"*Note:* This is part of GS Quant's `notebook` dependencies. You can run `pip install gs-quant[notebook]` to install all of them.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f62f6e5b-f674-4b72-a8f5-f702949fd794\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!pip install treelib\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5c9b479f\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Prints the tree structure formed by the Index for easy visualisation\\n\",\n \"ma_index.visualise_tree(visualise_by='name')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7b8717fb\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# If data is missing for any field, then assetId will be used instead\\n\",\n \"ma_index.visualise_tree(visualise_by='bbid')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8df35d26-0238-45af-bf04-dd532685f7c4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ma_index.visualise_tree(visualise_by='id')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8bddb2cb\",\n \"metadata\": {},\n \"source\": [\n \"### Get Underlier Weights and Attribution\\n\",\n \"The underliers of an Index are the intermediate nodes in the composition tree.\\n\",\n \"\\n\",\n \"You can fetch the weights and attribution of the underliers one level down using `get_underlier_weights` and `get_underlier_attribution` methods.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b14ecab1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns immediate underlier weights (one level down) as a pandas dataframe\\n\",\n \"ma_index.get_underlier_weights()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8cdbbf43-62c8-4f9e-b486-b42d970cec58\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns immediate underlier attribution (one level down) as a pandas dataframe\\n\",\n \"ma_index.get_underlier_attribution()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1c35b26\",\n \"metadata\": {},\n \"source\": [\n \"# Fundamental Metrics of STS Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c76799d2\",\n \"metadata\": {},\n \"source\": [\n \"Single Stock STS Indices offer Fundamental Metrics data via API, which can be obtained using the `get_fundamentals` function.\\n\",\n \"\\n\",\n \"You may choose one of the following periods:\\n\",\n \"\\n\",\n \"- **1 year:** DataMeasure.ONE_YEAR\\n\",\n \"- **2 years:** DataMeasure.TWO_YEARS\\n\",\n \"- **3 years:** DataMeasure.THREE_YEARS\\n\",\n \"\\n\",\n \"You may choose one of the following period directions:\\n\",\n \"\\n\",\n \"- **Forward:** DataMeasure.FORWARD\\n\",\n \"- **Trailing:** DataMeasure.TRAILING\\n\",\n \"\\n\",\n \"You may choose any combination of the following metrics:\\n\",\n \"\\n\",\n \"- **Dividend Yield:** DataMeasure.DIVIDEND_YIELD\\n\",\n \"- **Earnings per Share:** DataMeasure.EARNINGS_PER_SHARE\\n\",\n \"- **Earnings per Share Positive:** DataMeasure.EARNINGS_PER_SHARE_POSITIVE\\n\",\n \"- **Net Debt to EBITDA:** DataMeasure.NET_DEBT_TO_EBITDA\\n\",\n \"- **Price to Book:** DataMeasure.PRICE_TO_BOOK\\n\",\n \"- **Price to Cash:** DataMeasure.PRICE_TO_CASH\\n\",\n \"- **Price to Earnings:** DataMeasure.PRICE_TO_EARNINGS\\n\",\n \"- **Price to Earnings Positive:** DataMeasure.PRICE_TO_EARNINGS_POSITIVE\\n\",\n \"- **Price to Sales:** DataMeasure.PRICE_TO_SALES\\n\",\n \"- **Return on Equity:** DataMeasure.RETURN_ON_EQUITY\\n\",\n \"- **Sales per Share:** DataMeasure.SALES_PER_SHARE\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"92be04e7\",\n \"metadata\": {},\n \"source\": [\n \"### Get Fundamentals\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7899fcb1\",\n \"metadata\": {\n \"collapsed\": false,\n \"jupyter\": {\n \"outputs_hidden\": false\n },\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Returns fundamentals data between start and end date for the STS index\\n\",\n \"sstk_index.get_fundamentals(start=start_date, end=end_date)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"45d2bce5-df5a-4ce9-a7d1-a35da7685db3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Returns fundamentals data between start and end date for the STS index for one year period with trailing direction and Price to Cash metric\\n\",\n \"sstk_index.get_fundamentals(\\n\",\n \" start=start_date,\\n\",\n \" end=end_date,\\n\",\n \" period=DataMeasure.ONE_YEAR,\\n\",\n \" direction=DataMeasure.TRAILING,\\n\",\n \" metrics=[DataMeasure.PRICE_TO_CASH, DataMeasure.SALES_PER_SHARE],\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3b52580e\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### You're all set, Congrats!\\n\",\n \"\\n\",\n \"*Have any other questions? Reach out to the [Marquee STS team](mailto:gs-marquee-sts-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/08_tree_entity/tree_entity.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"14962dd6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import Environment, GsSession\\n\",\n \"from gs_quant.entities.tree_entity import TreeHelper\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"d6001d21\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# external users should substitute their client id and secret; please skip this step if using internal jupyterhub\\n\",\n \"GsSession.use(Environment.PROD, client_id=None, client_secret=None, scopes=('read_product_data',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bd085370\",\n \"metadata\": {},\n \"source\": [\n \"### Tree class usage\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f7e8ba3b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Initialise the TreeHelper with the MarqueeID of the root Asset and the dataset to be used to build the tree.\\n\",\n \"# In this example, we are building an STS index tree, so we use STS index datasets.\\n\",\n \"\\n\",\n \"tree = TreeHelper(\\n\",\n \" 'MAM9TYYXXXXXXXXX', tree_underlier_dataset='STS_UNDERLIER_WEIGHTS', underlier_column='underlyingAssetId'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b9069bde\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Builds the tree with the underlier dataset passed\\n\",\n \"tree.build_tree()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8b287a90\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Populate weights and attributions of the asset with the appropriate dataset and column name.\\n\",\n \"# In this example, we use the datasets for STS indices.\\n\",\n \"\\n\",\n \"tree.populate_weights(dataset='STS_UNDERLIER_WEIGHTS', weight_column='weight')\\n\",\n \"\\n\",\n \"tree.populate_attribution(dataset='STS_UNDERLIER_ATTRIBUTION', attribution_column='absoluteAttribution')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"79f3f22e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get tree as a df with the contents stored at this point\\n\",\n \"tree.to_frame()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7fb27b941602401d91542211134fc71a\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Prints the tree structure formed by the Index for easy visualisation\\n\",\n \"tree.get_visualisation(visualise_by='asset_name')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"acae54e37e7d407bbb7b55eff062a284\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# This feature needs treelib to be installed.\\n\",\n \"tree.get_visualisation(visualise_by='bbid')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"528eb668\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the time at which the tree was built\\n\",\n \"tree.update_time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4e77945d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# This feature needs the notebook package of gs-quant to be installed.\\n\",\n \"# You can install it using 'pip install gs-quant[notebook]'\\n\",\n \"tree.get_visualisation(visualise_by='bbid')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0fc17c69\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get the time at which the tree was built\\n\",\n \"tree.update_time\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"de5a16b6\",\n \"metadata\": {},\n \"source\": [\n \"### Accessing the child nodes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a4a2dbce\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Accessing the root node of the tree\\n\",\n \"root_node = tree.root\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d13fdcd8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Accessing the child nodes of the root node.\\n\",\n \"child_nodes_depth_1 = root_node.direct_underlier_assets_as_nodes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f8a15958\",\n \"metadata\": {},\n \"source\": [\n \"Each of these child nodes are TreeNode objects themselves, so their children can be accessed using the same technique. This can be used to recursively explore the full tree. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"13230d97\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Accessing the child nodes of the first child node of the root.\\n\",\n \"child_nodes_depth_2 = child_nodes_depth_1[0].direct_underlier_assets_as_nodes\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}" }, { "path": "gs_quant/documentation/09_security_master/Security Master SDK.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"34a4c60d-fc51-494f-91f2-b90815ad05d4\",\n \"metadata\": {},\n \"source\": [\n \"# Goldman Sachs Marquee Security Master\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"22e2f1c2-e053-4f21-849d-ba7f88ea8667\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"## Introduction\\n\",\n \"This notebook provides examples using the GS-Quant Security Master SDK. The examples will help users resolve current identifiers to historical identifiers, retrieve detailed security data, and perform various queries.

\\n\",\n \"**Security Master developer documentation is available here:**\\n\",\n \"[https://developer.gs.com/p/docs/services/data/securities-master/](https://developer.gs.com/p/docs/services/data/securities-master/)

\\n\",\n \"**GS-Quant SDK documentation is available here:**\\n\",\n \"[https://developer.gs.com/docs/gsquant/data/accessing-data/secmaster_sdk/](https://developer.gs.com/docs/gsquant/data/accessing-data/secmaster_sdk/)

\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"695687a2-dce9-4d4d-bd90-cf90b98d19c7\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from IPython.display import Markdown\\n\",\n \"from pprint import pprint\\n\",\n \"from gs_quant.target.secmaster import SecMasterAssetType\\n\",\n \"from gs_quant.api.gs.secmaster import GsSecurityMasterApi\\n\",\n \"from gs_quant.markets.securities import SecurityIdentifier\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id=\\\"INSERT HERE\\\", client_secret=\\\"INSERT HERE\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b1f49cfd-2469-4ab5-bc3f-e41266b2d8e1\",\n \"metadata\": {},\n \"source\": [\n \"## Sec Master ID Sorter\\n\",\n \"In the following example is a function using the Get Many Securities method where the user can provide any identifier and have them resolved to an immutable Sec Master ID ('id') along with other market standard symbology.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"48a840db-28f4-4d9e-ac89-e6930b92727a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def sec_master_id_sorter(identifiers):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Fetch security data and normalize the identifiers.\\n\",\n \"\\n\",\n \" :param identifiers: List of security identifiers\\n\",\n \" :return: DataFrame containing normalized identifiers\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" data = GsSecurityMasterApi.get_many_securities(identifier=identifiers, isPrimary=True)\\n\",\n \" results = pd.DataFrame(data['results'])\\n\",\n \" return pd.json_normalize(results['identifiers'])\\n\",\n \"\\n\",\n \"\\n\",\n \"# Example usage\\n\",\n \"identifiers = [\\\"GOOGL UW\\\", \\\"VOD.N\\\", \\\"US67066G1040\\\"]\\n\",\n \"normalized_identifiers = sec_master_id_sorter(identifiers)\\n\",\n \"pd.DataFrame(normalized_identifiers)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2eb80c58-12e4-41bc-aa05-cdf96a1f5de8\",\n \"metadata\": {},\n \"source\": [\n \"## Map Current Symbology to Historical Symbology\\n\",\n \"The following example is a function using the Get Many Securities method that resolves current symbology to historical symbology, ensuring that the user has correct identifiers as of a given point in time.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"adb107a1-493c-45c2-af05-070ab40871fa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_historical_ids(identifiers, date, listed=True, id_type='bbid'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Fetch historical identifiers for multiple securities.\\n\",\n \"\\n\",\n \" :param identifiers: List of current identifiers of the securities.\\n\",\n \" :param date: The date for which the historical identifiers are required.\\n\",\n \" :param listed: Whether to filter by listed securities.\\n\",\n \" :param id_type: The type of identifier to retrieve.\\n\",\n \" :return: Dictionary mapping each identifier to its historical identifier.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" historical_ids = {}\\n\",\n \" for identifier in identifiers:\\n\",\n \" try:\\n\",\n \" data = GsSecurityMasterApi.get_many_securities(identifier=[identifier], isPrimary=True)\\n\",\n \" if not data.get('results'):\\n\",\n \" raise ValueError(f\\\"No security found for identifier {identifier}\\\")\\n\",\n \"\\n\",\n \" secm_id = data['results'][0]['id']\\n\",\n \" historical_data = GsSecurityMasterApi.get_identifiers(secmaster_id=secm_id)\\n\",\n \"\\n\",\n \" match = next(\\n\",\n \" (\\n\",\n \" record['value']\\n\",\n \" for record in historical_data\\n\",\n \" if dt.datetime.strptime(record['startDate'], '%Y-%m-%d').date()\\n\",\n \" <= date\\n\",\n \" <= (\\n\",\n \" dt.datetime.strptime(record['endDate'], '%Y-%m-%d').date()\\n\",\n \" if record['endDate'] != '9999-99-99'\\n\",\n \" else dt.date.today()\\n\",\n \" )\\n\",\n \" and (not listed or record['type'] == id_type)\\n\",\n \" ),\\n\",\n \" None,\\n\",\n \" )\\n\",\n \" historical_ids[identifier] = match or identifier\\n\",\n \" except Exception as e:\\n\",\n \" print(f\\\"Error fetching historical ID for {identifier}: {e}\\\")\\n\",\n \" historical_ids[identifier] = None\\n\",\n \" return historical_ids\\n\",\n \"\\n\",\n \"\\n\",\n \"# Example usage\\n\",\n \"identifiers = [\\\"META UW\\\", \\\"GOOG UW\\\", \\\"XYZ UN\\\"]\\n\",\n \"query_date = dt.date(2017, 1, 1)\\n\",\n \"\\n\",\n \"try:\\n\",\n \" historical_ids = get_historical_ids(identifiers, query_date)\\n\",\n \" for identifier, historical_id in historical_ids.items():\\n\",\n \" print(f\\\"Historical ID for {identifier} as of {query_date}: {historical_id}\\\")\\n\",\n \"except ValueError as e:\\n\",\n \" print(f\\\"Error: {e}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bfd14a6a-7927-4f67-bda5-7b67f2ae699e\",\n \"metadata\": {},\n \"source\": [\n \"## Query by Identifier\\n\",\n \"This example allows the user to quickly retrieve the latest available information about a specific security.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"263c5bf5-9338-4c11-a6a8-cd7c28c778b4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = GsSecurityMasterApi.get_security_data(id_value=\\\"GS UN\\\", id_type=SecurityIdentifier.BBID)\\n\",\n \"pprint(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c4a68e7-5e14-4dbb-8d35-8162a32d8e21\",\n \"metadata\": {},\n \"source\": [\n \"## Query by Attribute\\n\",\n \"This example allows the user to specify an asset type or set of identifiers to retrieve matching securities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3c01274f-20b5-48f2-a050-3068ce3e0b8c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = GsSecurityMasterApi.get_many_securities(type_=SecMasterAssetType.Common_Stock, limit=1)\\n\",\n \"results = data['results']\\n\",\n \"pprint(results)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e398e263-5c5c-4517-9617-1d9e36ffd731\",\n \"metadata\": {},\n \"source\": [\n \"## Get All Securities\\n\",\n \"This example is an expansive layer over 'Query by Attribute' and is tailored for retrieving an extensive list of assets within a specific type.\\n\",\n \"It simplifies the user experience by automatically handling the scrolling.\\n\",\n \"The output below shows the first two results for Dutch Certificates. This example was selected to optimize speed since there are comparably few assets of this type.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e0e00289-a159-43a6-a4aa-e288f7934d49\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = GsSecurityMasterApi.get_all_securities(type_=SecMasterAssetType.Dutch_Cert)\\n\",\n \"pprint(data['results'][0:2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c3b9d49e-5c55-4548-b696-277bcb7f0a94\",\n \"metadata\": {},\n \"source\": [\n \"## Get Identifier History\\n\",\n \"This example provides a chronological account of how a particular identifier has changed for a security over time.\\n\",\n \"This method only accepts 'id' as an input, which is the immutable Sec Master ID.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cc3acf88-109f-4a61-959c-e31fe78055a8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"secm_id = \\\"GSPD227284E459\\\"\\n\",\n \"pd.DataFrame(GsSecurityMasterApi.get_identifiers(secmaster_id=secm_id))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fb699965-a231-476d-ae8e-2de27b0dee77\",\n \"metadata\": {},\n \"source\": [\n \"## Map Securities\\n\",\n \"This example converts between different types of security identifiers and accounts for historical changes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0391f211-7631-4d46-bcb5-241dcd5925ca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame(\\n\",\n \" GsSecurityMasterApi.map(\\n\",\n \" SecurityIdentifier.GSID,\\n\",\n \" [\\\"40602012\\\"],\\n\",\n \" start_date=\\\"2007-01-01\\\",\\n\",\n \" end_date=\\\"2024-10-29\\\",\\n\",\n \" output_types=[SecurityIdentifier.BBID, SecurityIdentifier.CUSIP, SecurityIdentifier.SEDOL],\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"29a1a54c-9286-41b0-afd6-a2eda9d49ebf\",\n \"metadata\": {},\n \"source\": [\n \"## Search Securities\\n\",\n \"This example allows for full-text searches across many fields, including identifiers and company names.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5cf5fc0b-53c7-478c-9f6c-55d6331edff8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = GsSecurityMasterApi.search(\\n\",\n \" q=\\\"TESLA\\\", is_primary=True, active_listing=True, type_=SecMasterAssetType.Common_Stock\\n\",\n \")\\n\",\n \"[\\n\",\n \" f\\\"The bbid is: {data['identifiers']['bbid']} and the issuer name is: {data['issuer']['name']} and the type is: {data['type']}.\\\"\\n\",\n \" for data in results\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6cc58f33-3768-4c4f-b6d7-15594f4e8c9c\",\n \"metadata\": {},\n \"source\": [\n \"## Deltas - Get Recent Changes\\n\",\n \"This example retrieves the list of recent identifier changes within a specified time range.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"29454b40-bc16-444b-9541-3dfa1596c6af\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"start_time = dt.datetime.now() - dt.timedelta(hours=1)\\n\",\n \"data = GsSecurityMasterApi.get_deltas(raw=False, start_time=start_time)\\n\",\n \"pd.DataFrame(data['results'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"450cac0b-7360-4a83-a366-0b66bb412248\",\n \"metadata\": {},\n \"source\": [\n \"## Issuers - Capital Structure\\n\",\n \"This example provides all listed securities types for a given issuer. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"54f6356a-a632-4357-b451-e9bb20ac76ac\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bbid = \\\"META UW\\\"\\n\",\n \"data = GsSecurityMasterApi.get_capital_structure(id_value=bbid, id_type=SecurityIdentifier.BBID)\\n\",\n \"res = data['results'][0]\\n\",\n \"display(Markdown(f\\\"Capital structure for {bbid} matched {res['issuerName']}\\\"))\\n\",\n \"display(pd.DataFrame([data['assetTypesTotal']]))\\n\",\n \"for t in res['types']:\\n\",\n \" display(Markdown(f\\\"**{t}**\\\"))\\n\",\n \" display(pd.DataFrame(res['types'][t][:5]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7351a972-cc65-4387-b18b-a3e210a104dd\",\n \"metadata\": {},\n \"source\": [\n \"## Exchanges\\n\",\n \"This example retrieves detailed information about exchanges.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9b5df6a8-547b-4c2b-bbf4-8064b8433e75\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = GsSecurityMasterApi.get_exchanges()\\n\",\n \"pd.DataFrame(data['results'][:5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4986c8c5-d0ee-436e-ad08-919b72721a05\",\n \"metadata\": {},\n \"source\": [\n \"### Exchanges - Query by Exchange Code\\n\",\n \"This example retrieves detailed information about exchanges by selected exchange codes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1ea8dcb6-5f57-47ca-b20a-7bc59f87c9e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = GsSecurityMasterApi.get_exchanges(mic=\\\"XBUE\\\")\\n\",\n \"pd.DataFrame(data['results'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ef9b840f-0688-489e-b207-cf4327f584bc\",\n \"metadata\": {},\n \"source\": [\n \"### Exchanges - Get Exchange Code History\\n\",\n \"This example provides the history of exchange codes.\\n\",\n \"This method only accepts gsExchangeId as an input.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2106a6f0-8578-458c-83cf-73af2500ccf0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = GsSecurityMasterApi.get_exchange_identifiers_history(gs_exchange_id=2)\\n\",\n \"pd.DataFrame(results)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5ae9dd3a3e0d75ac\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Listed Derivatives by Underlyer\\n\",\n \"This example provides the listed derivatives for a given underlying security.\\n\",\n \"This method only accepts 'id' as an input, which is the immutable Sec Master ID.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cf9bfbdf004b8c63\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"underlyers = GsSecurityMasterApi.get_securities_by_underlyers(\\n\",\n \" id_value=\\\"GSPD100E0\\\", limit=5, type_=SecMasterAssetType.Equity_Option\\n\",\n \")\\n\",\n \"print(f\\\"Offset key is: {underlyers.get('offsetKey', 'No offset key returned')}.\\\")\\n\",\n \"display(pd.DataFrame(underlyers['results']))\\n\",\n \"\\n\",\n \"if 'offsetKey' in underlyers:\\n\",\n \" underlyers = GsSecurityMasterApi.get_securities_by_underlyers(\\n\",\n \" id_value=\\\"GSPD100E0\\\", limit=5, type_=SecMasterAssetType.Equity_Option, offset_key=underlyers['offsetKey']\\n\",\n \" )\\n\",\n \" print(f\\\"Offset key is: {underlyers.get('offsetKey', 'No offset key returned')}.\\\")\\n\",\n \" display(pd.DataFrame(underlyers['results']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae4b7b1c7bf29b1a\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Listed Derivatives by Underlyer - fetch all matching securities\\n\",\n \"This example gets all matching listed derivatives\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2043ebbfaf7ddff7\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"underlyers = GsSecurityMasterApi.get_securities_by_underlyers(\\n\",\n \" id_value=\\\"GSPD100E0\\\", type_=SecMasterAssetType.Equity_Option, scroll_all_pages=True\\n\",\n \")\\n\",\n \"print(f\\\"Total assets count is: {underlyers.get('totalResults', 'No totalResults returned')}.\\\")\\n\",\n \"display(pd.DataFrame(underlyers['results']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f7705fd742bf8278\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Listed Derivatives by Underlyer - Currency and Country Code Filter\\n\",\n \"This example filters listed derivatives by currency and country code.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aeb1d0666440513c\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"underlyers = GsSecurityMasterApi.get_securities_by_underlyers(\\n\",\n \" id_value=\\\"GSPD100E0\\\", limit=5, type_=SecMasterAssetType.Equity_Option, currency='USD', country_code='US'\\n\",\n \")\\n\",\n \"\\n\",\n \"display(pd.DataFrame(underlyers['results']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"935b7da8ea4c1b50\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Listed Derviatives by Underlyer - Effective Date Filter\\n\",\n \"This example filters listed derivatives by effective date.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"753dc7694abe28ff\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"underlyers = GsSecurityMasterApi.get_securities_by_underlyers(\\n\",\n \" id_value=\\\"GSPD100E0\\\",\\n\",\n \" limit=5,\\n\",\n \" type_=SecMasterAssetType.Equity_Option,\\n\",\n \" effective_date='2023-01-01',\\n\",\n \" country_code='US',\\n\",\n \")\\n\",\n \"display(pd.DataFrame(underlyers['results']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3bf6098b8a896034\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Listed Derivatives by Underlyer - Inactive Filter\\n\",\n \"This example includes inactive listed derivatives in the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"599c4f6601e35096\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"underlyers = GsSecurityMasterApi.get_securities_by_underlyers(\\n\",\n \" id_value=\\\"GSPD100E0\\\", limit=5, type_=SecMasterAssetType.Equity_Option, include_inactive=True, country_code='US'\\n\",\n \")\\n\",\n \"display(pd.DataFrame(underlyers['results']))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e59a4ce1bcdf63d1\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/09_security_master/Security Master.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b9e06507\",\n \"metadata\": {},\n \"source\": [\n \"**NOTE**: Legacy documentation\\n\",\n \"\\n\",\n \"## Introduction\\n\",\n \"A Security Master provides financial product reference data and is a foundational building block of any quantitative financial analytics engine. The core component of Goldman Sachs' Security Master is symbology, the data necessary to map from one identifier to another. Clients can retrieve this data via the GS Quant SDK.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d9844f7\",\n \"metadata\": {},\n \"source\": [\n \"## Getting Started\\n\",\n \"Set up a GsSession. You must input the client_id and client_secret of an application with access to Security Master. For more information on the product and on how to get access, please contact [GS Data Services](mailto:data-services@gs.com).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"333f29f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession\\n\",\n \"\\n\",\n \"GsSession.use(client_id='client_id', client_secret='client_secret')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"be357aad\",\n \"metadata\": {},\n \"source\": [\n \"Set source to Security Master (or else it will default to Asset Service).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a9288aa9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.securities import SecurityMaster, SecurityMasterSource\\n\",\n \"\\n\",\n \"SecurityMaster.set_source(SecurityMasterSource.SECURITY_MASTER)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e23cde3a\",\n \"metadata\": {},\n \"source\": [\n \"## Querying\\n\",\n \"Retrieve a security by identifier. The get_asset method returns identifiers at a point in time specified by the `as_of` parameter (which should be set to 2021-01-01 or later, given the history available in Security Master).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"08cc971f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"from gs_quant.markets.securities import SecurityIdentifier, SecurityMaster\\n\",\n \"\\n\",\n \"asset = SecurityMaster.get_asset('GS UN', SecurityIdentifier.BBID, as_of=datetime.date(2021, 1, 5))\\n\",\n \"print(asset, '\\\\n')\\n\",\n \"asset.get_identifiers()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a00d2241\",\n \"metadata\": {},\n \"source\": [\n \"You can use SecurityIdentifier members to pull out identifier values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"31696f61\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"asset.get_identifiers()[SecurityIdentifier.BBID.value]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"397888ae\",\n \"metadata\": {},\n \"source\": [\n \"## Bulk Querying\\n\",\n \"\\n\",\n \"#### History\\n\",\n \"You can retrieve identifier history (from 2021-01-01 onwards) for a list of assets. The following example also shows a ticker change.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"002a5ab4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"from gs_quant.markets.securities import SecurityIdentifier, SecurityMaster\\n\",\n \"\\n\",\n \"ids = ['CTLP UW', 'TSLA UW', 'FB UW']\\n\",\n \"as_of = datetime.datetime(2021, 6, 5) # as-of-date used to map input IDs to assets\\n\",\n \"start = datetime.datetime(2000, 1, 5) # get identifier entries with update time after this time\\n\",\n \"end = as_of # get identifier entries with update time before this time\\n\",\n \"identifiers = SecurityMaster.get_identifiers(ids, SecurityIdentifier.BBID, as_of=as_of, start=start, end=end)\\n\",\n \"[identifier for identifier in identifiers['CTLP UW'] if identifier['type'] == SecurityIdentifier.TICKER.value]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"93597208\",\n \"metadata\": {},\n \"source\": [\n \"#### Point in Time\\n\",\n \"\\n\",\n \"You can retrieve point-in-time identifiers without specifying individual assets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5629db2b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"from gs_quant.markets.securities import AssetType, SecurityMaster\\n\",\n \"from gs_quant.target.common import AssetClass\\n\",\n \"\\n\",\n \"as_of = datetime.datetime(2021, 5, 1)\\n\",\n \"identifiers = SecurityMaster.get_all_identifiers(AssetClass.Equity, [AssetType.STOCK], as_of=as_of)\\n\",\n \"identifiers[next(iter(identifiers))] # show identifiers for one of the retrieved securities\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7fb27b941602401d91542211134fc71a\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"You can also use a generator to retrieve each page of results when needed - just call `next()`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"acae54e37e7d407bbb7b55eff062a284\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"from gs_quant.markets.securities import SecurityMaster\\n\",\n \"\\n\",\n \"generator = SecurityMaster.get_all_identifiers_gen(as_of=datetime.datetime(2021, 5, 1))\\n\",\n \"page_1 = next(generator)\\n\",\n \"page_2 = next(generator)\\n\",\n \"page_3 = next(generator)\\n\",\n \"# and so on... (keep in mind that a generator will raise StopIteration when it is done)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9a63283cbaf04dbcab1f6479b197f3a8\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Mapping Between Identifiers\\n\",\n \"\\n\",\n \"To map from one ID to other IDs for the same security, use the `map_identifiers` function.\\n\",\n \"For example, if we have a security \\\"GS UN\\\" and want to find its CUSIP over a certain date range: \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8dd0d8092fe74a7c96281538738b07e2\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"from gs_quant.markets.securities import SecurityMaster\\n\",\n \"\\n\",\n \"start = datetime.date(2021, 10, 11)\\n\",\n \"end = datetime.date(2021, 10, 15)\\n\",\n \"SecurityMaster.map_identifiers([\\\"GS UN\\\"], [SecurityIdentifier.CUSIP], start, end)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.10\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": []\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Hedger/01_Performance_Hedger.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"# Performance Hedge\\n\",\n \"\\n\",\n \"The Performance Hedge provides a framework to replicate an asset's performance without\\n\",\n \"direct exposure by using historical performance correlation.\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from IPython.display import display\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"from gs_quant.markets.hedge import (\\n\",\n \" HedgeConstraints,\\n\",\n \" HedgeExclusions,\\n\",\n \" Constraint,\\n\",\n \" PerformanceHedge,\\n\",\n \" PerformanceHedgeParameters,\\n\",\n \")\\n\",\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.timeseries.helper import Window\\n\",\n \"from gs_quant.timeseries.econometrics import correlation\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the lines below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Define Your Positions to Hedge and Your Hedge Universe\\n\",\n \"\\n\",\n \"The hedger takes in the initial portfolio as a `PositionSet` object. You can define your positions\\n\",\n \"as a list of identifiers with quantities or alternatively, as a list of identifiers with weights, along with a\\n\",\n \"reference notional value. The `date` corresponds to the hedge date.\\n\",\n \"\\n\",\n \"In addition, you need to define your universe of candidates for hedge constituents as a list of identifiers.\\n\",\n \"\\n\",\n \"*GS Quant will resolve all identifiers (Bloomberg IDs, SEDOLs, RICs, etc) historically as of the hedge date*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"positions = PositionSet(\\n\",\n \" date=dt.date(day=9, month=4, year=2025),\\n\",\n \" positions=[Position(identifier='AAPL UW', quantity=26), Position(identifier='GS UN', quantity=51)],\\n\",\n \")\\n\",\n \"\\n\",\n \"positions.resolve()\\n\",\n \"\\n\",\n \"universe = ['SPY']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Define Your Hedge Exclusions\\n\",\n \"\\n\",\n \"The `HedgeExclusions` class offers a clean way to specify any assets, countries, regions, sectors, and/or industries you\\n\",\n \"would like to exclude from your hedge. Each attribute takes in a list of strings. In this example, let's try excluding\\n\",\n \"Goldman Sachs and all assets in Mexico, Europe, the Utilities sector, the Airlines industry.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"exclusions = HedgeExclusions(\\n\",\n \" assets=['GS UN'], countries=['Mexico'], regions=['Europe'], sectors=['Utilities'], industries=['Airlines']\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 4: Define Your Hedge Constraints\\n\",\n \"\\n\",\n \"Rather than excluding an industry or region entirely, you can also constrain how much each makes up in\\n\",\n \"your hedge, and you can do so by leveraging the `HedgeConstraints` class. Each attribute takes in a list of `Constraint`\\n\",\n \"objects, each of which has a `name` attribute, along with `minimum` and `maximum` that should be expressed as positive\\n\",\n \"numbers between 0 and 100.\\n\",\n \"\\n\",\n \"In addition to constraining assets, countries, regions, sectors, and industries, it's also\\n\",\n \"possible to constrain your hedge to only include assets that have an ESG score between a specified range. All data is\\n\",\n \"pulled as of your hedge date from the GIR SUSTAIN ESG Headline Metrics Dataset. For more information on the various\\n\",\n \"ESG metrics available, please visit the dataset page\\n\",\n \"[here](https://marquee.gs.com/s/developer/datasets/ESG_HEADLINE_METRICS).\\n\",\n \"\\n\",\n \"In this example, let's constrain our hedge to only include at most 20% Software assets by weight. In addition, let's\\n\",\n \"request a hedge with only assets that have a G Headline Percentile Score of above 75%.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"constraints = HedgeConstraints(\\n\",\n \" sectors=[Constraint(constraint_name='Software', minimum=0, maximum=100)],\\n\",\n \" esg=[Constraint(constraint_name='gPercentile', minimum=0, maximum=100)],\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 5: Define Any Other Parameters\\n\",\n \"\\n\",\n \"The `PerformanceHedgeParameters` wraps all the performance hedge parameters, including the positions, exclusions, and\\n\",\n \"constraints, into an object to be passed into a `PerformanceHedge`. Along with the parameters defined above, the\\n\",\n \"following optional parameters can also be passed in:\\n\",\n \"\\n\",\n \"| Parameter | Description | Type| Default Value|\\n\",\n \"|-----------------|---------------|-------------|-------------\\n\",\n \"| `observation_start_date` | Date on which to start the observation of historical performance correlation |`datetime.date`| One year before the hedge date |\\n\",\n \"| `sampling_period` | The length of time in between return samples |`str`| 'Daily' |\\n\",\n \"| `max_leverage` | Maximum percentage of the notional that can be used to hedge | `float` | 100 |\\n\",\n \"| `percentage_in_cash` | Percentage of the hedge notional that will be in cash | `float` | None |\\n\",\n \"| `explode_universe` | Explode the assets in the universe into their underliers to be used as the hedge universe | `boolean` | True |\\n\",\n \"| `exclude_target_assets` | Exclude assets in the target composition from being in the hedge |`boolean`| True |\\n\",\n \"| `exclude_corporate_actions_types` | Set of of corporate actions to be excluded in the hedge |`List[CorporateActionsTypes]`| None |\\n\",\n \"| `exclude_hard_to_borrow_assets` | Whether hard to borrow assets should be excluded in the universe | `boolean` | False |\\n\",\n \"| `exclude_restricted_assets` | Whether to exclude assets in restricted trading lists | `float` | False |\\n\",\n \"| `max_adv_percentage` | Maximum percentage notional to average daily dollar volume allowed for any hedge constituent | `float` | 15 |\\n\",\n \"| `max_return_deviation` | Maximum percentage difference in annualized return between the target and the hedge result |`float`| 5 |\\n\",\n \"| `max_weight` | Maximum weight of any constituent in hedge |`float`| 100 |\\n\",\n \"| `min_market_cap` | Lowest market cap allowed for any hedge constituent | `float` | None |\\n\",\n \"| `max_market_cap` | Highest market cap allowed for any hedge constituent | `float` | None |\\n\",\n \"| `market_participation_rate` | Maximum market participation rate used to estimate the cost of trading a portfolio of stocks | `float` | 10 |\\n\",\n \"| `lasso_weight` | Value of the lasso hyperparameter for machine learning hedges |`float`| 0 |\\n\",\n \"| `ridge_weight` | Value of the ridge hyperparameter for machine learning hedges |`float`| 0 |\\n\",\n \"| `benchmarks` | List of benchmarks to compare the backtest of your hedge against for correlation and performance( eg ['SPX', 'RAY'] ) |`List[String]`| None |\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"parameters = PerformanceHedgeParameters(\\n\",\n \" initial_portfolio=positions,\\n\",\n \" universe=universe,\\n\",\n \" exclusions=exclusions,\\n\",\n \" constraints=constraints,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 6: Calculate Your Hedge\\n\",\n \"\\n\",\n \"It's finally time to run the parameters into the Marquee Hedger. Once defined, a `PerformanceHedge` can be calculated\\n\",\n \"in just one line.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"hedge = PerformanceHedge(parameters)\\n\",\n \"all_results = hedge.calculate()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Note:\\n\",\n \"The above code can fail with an error message along the lines of:\\n\",\n \"\\\"Error calculating hedge: Size( Universe Price Curves ) : Universe has no price curves.. Please adjust your constraints and try again.\\\"\\n\",\n \"\\n\",\n \"This means that no optimization was found, modify your hedge parameters by loosening your constraints and try again.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 7: Pull Hedge Results\\n\",\n \"\\n\",\n \"That's it! Once calculated, you can pull the results right from the `PerformanceHedge` object.\\n\",\n \"\\n\",\n \"### Constituents\\n\",\n \"Let's pull the constituents metadata of the resulting hedge:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"hedge_constituents = hedge.get_constituents()\\n\",\n \"display(hedge_constituents)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Stats\\n\",\n \"\\n\",\n \"Next let's pull a table of general stats like transaction cost, annualized volatility, annualized return, and more:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"stats = hedge.get_statistics()\\n\",\n \"display(stats)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Backtest Performance\\n\",\n \"\\n\",\n \"It's also possible to pull a timeseries of the performance of both the initial portfolio and the hedge for the\\n\",\n \"observation period:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"backtest_performance = hedge.get_backtest_performance()\\n\",\n \"\\n\",\n \"backtest_performance.plot(title='Backtest Performance')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"If benchmarks are provided the performance curves for the benchmarks are returned as well\\n\",\n \"\\n\",\n \"### Backtest Correlation\\n\",\n \"\\n\",\n \"It's also possible to pull a timeseries of the correlation between the hedge and portfolio for the\\n\",\n \"observation period by leveraging GS Quant econometric function `correlation`:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"backtest_correlation = correlation(backtest_performance['Portfolio'], backtest_performance['Hedge'], Window(44, 0))\\n\",\n \"backtest_correlation.plot(title='Backtest Correlation')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"If benchmarks are provided the correlation curves for the benchmarks are returned as well\\n\",\n \"\\n\",\n \"### You're all set; Congrats!\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"venv312 (3.12.1)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Hedger/02_Factor_Hedger_(Axioma_Portfolio_Optimizer).ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"jupyter\": {\n \"outputs_hidden\": true\n },\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Portfolio Optimizer\\n\",\n \"\\n\",\n \"The portfolio optimizer brings together the power of the Axioma Portfolio Optimizer with Marquee's risk analytics infrastructure\\n\",\n \"to make minimizing your portfolio's factor risk possible within the same ecosystem.\\n\",\n \"\\n\",\n \"The optimizer supports two hedging modes:\\n\",\n \"- **Unidirectional Hedger**: Generates only short positions to hedge your portfolio (default mode)\\n\",\n \"- **Bidirectional Hedger**: Generates both long and short positions for more flexible factor risk management\\n\",\n \"\\n\",\n \"To use the optimizer, you must have a license to the Axioma Portfolio Optimizer. Please reach out to the\\n\",\n \"[Marquee sales team](mailto:gs-marquee-sales@ny.email.gs.com?Subject=Portfolio Optimizer Trial Request)\\n\",\n \"to learn more about how to get a license or how to bring an existing license to Marquee.\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from IPython.display import display\\n\",\n \"import warnings\\n\",\n \"from gs_quant.markets.optimizer import (\\n\",\n \" OptimizerUniverse,\\n\",\n \" FactorConstraint,\\n\",\n \" AssetConstraint,\\n\",\n \" SectorConstraint,\\n\",\n \" IndustryConstraint,\\n\",\n \" OptimizerSettings,\\n\",\n \" OptimizerStrategy,\\n\",\n \" OptimizerConstraints,\\n\",\n \" OptimizerObjective,\\n\",\n \" OptimizerType,\\n\",\n \" OptimizerObjectiveTerm,\\n\",\n \" OptimizerObjectiveParameters,\\n\",\n \" ConstraintPriorities,\\n\",\n \" PrioritySetting,\\n\",\n \" CountryConstraint,\\n\",\n \" HedgeTarget,\\n\",\n \")\\n\",\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.markets.securities import Asset, AssetIdentifier\\n\",\n \"from gs_quant.models.risk_model import FactorRiskModel\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.target.hedge import CorporateActionsTypes\\n\",\n \"from gs_quant.markets.factor_analytics import FactorAnalytics\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import ReturnFormat\\n\",\n \"from gs_quant.api.gs.hedges import GsHedgeApi\\n\",\n \"\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Define Your Initial Position Set\\n\",\n \"\\n\",\n \"You have two options for defining the initial holdings to optimize:\\n\",\n \"\\n\",\n \"**Option A**: Create positions manually using identifiers and weights/quantities\\n\",\n \"**Option B**: Load positions from an existing Marquee portfolio\\n\",\n \"\\n\",\n \"Choose the option that best fits your workflow. Both approaches will create a `PositionSet` object that can be used in the optimization.\\n\",\n \"\\n\",\n \"*GS Quant will resolve all identifiers (Bloomberg IDs, SEDOLs, RICs, etc) historically as of the optimization date.*\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Option A: Create New Position Set\\n\",\n \"\\n\",\n \"Use the `PositionSet` class to define positions manually with identifiers and weights or quantities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Option A: Create positions manually\\n\",\n \"position_set = PositionSet(\\n\",\n \" date=dt.date(day=23, month=4, year=2024),\\n\",\n \" reference_notional=10_000_000,\\n\",\n \" positions=[Position(identifier='AAPL UW', weight=0.4), Position(identifier='GS UN', weight=0.6)],\\n\",\n \")\\n\",\n \"\\n\",\n \"position_set.resolve()\\n\",\n \"print(f'Created position set with {len(position_set.positions)} positions')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Option B: Load Positions from Marquee Portfolio\\n\",\n \"\\n\",\n \"Load positions from an existing Marquee portfolio using the portfolio ID. This is useful when you want to optimize an existing portfolio that's already managed in Marquee.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Option B: Load from existing Marquee portfolio using positions data endpoint\\n\",\n \"portfolio_id = 'YOUR PORTFOLIO ID HERE'\\n\",\n \"optimization_date = dt.date(day=11, month=11, year=2025)\\n\",\n \"\\n\",\n \"# Load portfolio positions using the positions data endpoint\\n\",\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"\\n\",\n \"# Get positions data for the specific date with all relevant fields\\n\",\n \"portfolio_report = pm.get_performance_report()\\n\",\n \"\\n\",\n \"positions = portfolio_report.get_portfolio_constituents(\\n\",\n \" start_date=optimization_date,\\n\",\n \" end_date=optimization_date,\\n\",\n \" fields=['grossWeight', 'quantity'],\\n\",\n \" return_format=ReturnFormat.JSON,\\n\",\n \")\\n\",\n \"\\n\",\n \"# Create position set from the filtered positions\\n\",\n \"position_set = PositionSet(\\n\",\n \" date=optimization_date,\\n\",\n \" positions=[\\n\",\n \" Position(\\n\",\n \" asset_id=row['assetId'],\\n\",\n \" identifier=row['assetId'],\\n\",\n \" quantity=row.get('quantity'),\\n\",\n \" weight=row.get('grossWeight'),\\n\",\n \" )\\n\",\n \" for row in positions\\n\",\n \" ],\\n\",\n \")\\n\",\n \"print(f'Created position set with {len(position_set.positions)} positions')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 2.1: Analyze Initial Portfolio Risk Exposures\\n\",\n \"\\n\",\n \"Analyze the risk exposures of the initial portfolio using the Marquee PreTrade.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set parameters for factor analysis in PreTrade\\n\",\n \"risk_model_id = 'AXIOMA_AXUS4S'\\n\",\n \"currency = 'USD'\\n\",\n \"participation_rate = 0.15\\n\",\n \"\\n\",\n \"analytics = FactorAnalytics(risk_model_id=risk_model_id, currency=currency, participation_rate=participation_rate)\\n\",\n \"\\n\",\n \"print('Running risk analysis on initial portfolio...')\\n\",\n \"try:\\n\",\n \" initial_analysis = analytics.get_factor_analysis(position_set)\\n\",\n \"\\n\",\n \" print('\\\\nInitial Portfolio Summary:')\\n\",\n \" summary_table = analytics.create_exposure_summary_table(initial_analysis)\\n\",\n \" display(summary_table)\\n\",\n \"except Exception as e:\\n\",\n \" print('\\\\nERROR: Factor analysis failed\\\\n')\\n\",\n \" print(f'{str(e)}')\\n\",\n \" raise\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 2.2: Visualize Initial Portfolio Style Factor Exposures\\n\",\n \"\\n\",\n \"View the top contributing style factors in your initial portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fig = analytics.create_style_factor_chart(initial_analysis, rows=5, title='Initial Portfolio - Style Factor Exposures')\\n\",\n \"fig.show()\\n\",\n \"\\n\",\n \"print('\\\\nStyle factors displayed (e.g., Volatility, Market Sensitivity, Momentum, Size, Value, etc.)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 2.3: Historical Performance Metrics\\n\",\n \"\\n\",\n \"View dynamic performance metrics for the initial portfolio over the past year.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create dynamic performance chart with time series data from liquidity endpoint\\n\",\n \"# Shows cumulative PnL and normalized performance over time\\n\",\n \"performance_chart = analytics.create_dynamic_performance_chart(\\n\",\n \" initial_analysis, title='Initial Portfolio - Historical Performance Metrics'\\n\",\n \")\\n\",\n \"performance_chart.show()\\n\",\n \"\\n\",\n \"print('\\\\nUse the dropdown menu above to toggle between:')\\n\",\n \"print('- Cumulative PnL')\\n\",\n \"print('- Normalized Performance')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Define Your Optimizer Universe\\n\",\n \"\\n\",\n \"An optimizer universe corresponds to the assets that can be used when constructing an optimization, which can be created\\n\",\n \"using the `OptimizerUniverse` class:\\n\",\n \"\\n\",\n \"| Parameter | Description | Type| Default Value|\\n\",\n \"|-----------------|---------------|-------------|-------------\\n\",\n \"| `assets` | Assets to include in the universe. |`List[Asset]`| N/A |\\n\",\n \"| `explode_composites` | Explode indices, ETFs, and baskets and include their underliers in the universe. |`boolean`| `True` |\\n\",\n \"| `exclude_initial_position_set_assets` | Exclude assets in the initial holdings from the optimization. | `boolean` | `False` |\\n\",\n \"| `exclude_corporate_actions_types` | Set of of corporate actions to be excluded in the universe. |`List[CorporateActionsTypes]`| `[]` |\\n\",\n \"| `exclude_hard_to_borrow_assets` | Exclude hard to borrow assets from the universe. | `boolean` | `False` |\\n\",\n \"| `exclude_restricted_assets` | Exclude assets on restricted trading lists from the universe. | `boolean` | `False` |\\n\",\n \"| `min_market_cap` | Lowest market cap allowed for any universe constituent. | `float` | `None` |\\n\",\n \"| `max_market_cap` | Highest market cap allowed for any universe constituent. | `float` | `None` |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"asset_list = ['SPY US']\\n\",\n \"asset_list_resolved = [Asset.get(x, AssetIdentifier.BLOOMBERG_COMPOSITE_ID) for x in asset_list]\\n\",\n \"\\n\",\n \"universe = OptimizerUniverse(\\n\",\n \" assets=asset_list_resolved,\\n\",\n \" explode_composites=True,\\n\",\n \" exclude_initial_position_set_assets=True,\\n\",\n \" exclude_restricted_assets=True,\\n\",\n \" exclude_hard_to_borrow_assets=True,\\n\",\n \" exclude_corporate_actions_types=[CorporateActionsTypes.Mergers],\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Define Your Risk Model and Factor Risk Constraints\\n\",\n \"\\n\",\n \"You can run the optimizer using a factor risk model of your choice, so long as you have a license for it, by leveraging\\n\",\n \"the `FactorRiskModel` class. For any factor in the risk model, you can set more granular constraints on the optimized\\n\",\n \"portfolio's exposure to the factor.\\n\",\n \"\\n\",\n \"In this example, let's use the Axioma AXUS4S model and limit the final exposure to Volatility be $10,000 and\\n\",\n \"the final exposure of Market Sensitivity to be 5,000.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Pulling Factor Names from Risk Models\\n\",\n \"\\n\",\n \"You can run the code below to pull the factor names for your model. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"risk_model = FactorRiskModel.get('AXIOMA_AXUS4S')\\n\",\n \"\\n\",\n \"risk_model_factors = risk_model.get_many_factors(dt.date(2025, 4, 9))\\n\",\n \"risk_model_factors_names = pd.DataFrame([x.name for x in risk_model_factors])\\n\",\n \"pd.set_option('display.max_rows', 87)\\n\",\n \"print(risk_model_factors_names)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Set your Factor Constraints\\n\",\n \"\\n\",\n \"Note that different models might have different sets of factor names, if the code below doesn't work please confirm that the factor name used exists in the Risk Model as shown above. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"factor_constraints = [\\n\",\n \" FactorConstraint(risk_model.get_factor('Volatility'), 10000),\\n\",\n \" FactorConstraint(risk_model.get_factor('Market Sensitivity'), 5000),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Define Other Optimization Constraints\\n\",\n \"\\n\",\n \"Outside factor-specific constraints, it's also possible to limit the holding value of individual assets, assets\\n\",\n \"belonging to a particular GICS sector, and/or assets in a particular country of domicile in the optimization.\\n\",\n \"\\n\",\n \"In this example, let's constrain the optimization to have 0-50% Microsoft and limit the optimization's notional\\n\",\n \"coming from Energy and Health Care assets to each be 0-80%. We also limit exposure from gics industry (one level more granular than sector) to be from [0, 50%]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"asset_constraints = [AssetConstraint(Asset.get('MSFT UW', AssetIdentifier.BLOOMBERG_ID), 0, 50)]\\n\",\n \"\\n\",\n \"sector_constraints = [SectorConstraint('Energy', 0, 80), SectorConstraint('Health Care', 0, 80)]\\n\",\n \"industry_constraints = [IndustryConstraint('Energy Equipment & Services', 0, 50)]\\n\",\n \"country_constraints = [CountryConstraint('United States', 0, 100)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 6: Configure Your Optimization Settings\\n\",\n \"\\n\",\n \"All other settings for the optimization can be set via the `OptimizerSettings` class:\\n\",\n \"\\n\",\n \"### Unidirectional Hedger (Default)\\n\",\n \"Returns only short positions to hedge the target portfolio. Use `allow_long_short=False` (default).\\n\",\n \"\\n\",\n \"### Bidirectional Hedger\\n\",\n \"Returns both long and short positions for more flexible hedging. Use `allow_long_short=True` and specify `gross_notional` and `net_notional`.\\n\",\n \"\\n\",\n \"| Parameter | Description | Type| Default Value|\\n\",\n \"|--------------------|---------------|-------------|-------------\\n\",\n \"| `notional` | For unidirectional hedges: max notional of hedge (all short positions). For bidirectional: overridden by `gross_notional` |`float`| `10000000` |\\n\",\n \"| `allow_long_short` | Enable bidirectional hedge mode with both long and short positions |`boolean`| `False` |\\n\",\n \"| `gross_notional` | Total absolute notional (\\\\|long\\\\| + \\\\|short\\\\|). Only for bidirectional mode. |`float`| `None` |\\n\",\n \"| `net_notional` | Net notional (long - short). Required for bidirectional mode. Use 0 for market neutral. |`float`| `None` |\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Define Constraint Priorities\\n\",\n \"\\n\",\n \"Priority of the constraint range from 0-5 (prioritized in that order). \\n\",\n \"The optimization will fail if it cannot meet a constraint with 0 priority. \\n\",\n \"A constraint with priority of 1-5 can be called a relaxed constraint, which means that the optimization will make its best effort to meet the constraint but will not fail if it cannot. \\\\\\n\",\n \"A constraint with a lower priority will take precedence over a constraint with a higher priority. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"constraint_priorities = ConstraintPriorities(\\n\",\n \" style_factor_exposures=PrioritySetting.ZERO,\\n\",\n \" min_sector_weights=PrioritySetting.ONE,\\n\",\n \" max_sector_weights=PrioritySetting.ONE,\\n\",\n \" min_industry_weights=PrioritySetting.ONE,\\n\",\n \" max_industry_weights=PrioritySetting.ONE,\\n\",\n \" min_country_weights=PrioritySetting.ONE,\\n\",\n \" max_country_weights=PrioritySetting.ONE,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### OPTION A: Unidirectional Hedger Configuration\\n\",\n \"\\n\",\n \"Let's put above parameters in action. In the cell below we configure a **unidirectional hedger** that:\\n\",\n \"\\n\",\n \" * Uses tight style exposure constraints\\n\",\n \" * Can relax sector, industry, country weight constraints if needed to find a feasible solution\\n\",\n \" * Allows 0-100 names in the hedge\\n\",\n \" * Sets weight range for constituents from hedge from [1%, 99%]\\n\",\n \" * Limits max daily ADV to less than 15%\\n\",\n \" * Uses one-sided hedge (short positions only) as allow_long_short is turned off\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Configure unidirectional hedger (short positions only)\\n\",\n \"settings = OptimizerSettings(\\n\",\n \" allow_long_short=False, # unidirectional mode: hedge contains only short positions\\n\",\n \" constraint_priorities=constraint_priorities,\\n\",\n \" min_names=0,\\n\",\n \" max_names=100,\\n\",\n \" min_weight_per_constituent=0.01,\\n\",\n \" max_weight_per_constituent=0.99,\\n\",\n \" max_adv=15,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### OPTION B: Bidirectional Hedger Configuration\\n\",\n \"\\n\",\n \"For a **bidirectional hedge** that can take both long and short positions, configure the settings as follows:\\n\",\n \"\\n\",\n \"**Key differences:**\\n\",\n \"- Set `allow_long_short=True` to enable both long and short positions\\n\",\n \"- Use `gross_notional` to control total hedge size (|long| + |short|)\\n\",\n \"- Use `net_notional` to control net exposure (long - short)\\n\",\n \" - Set `net_notional=0` for market neutral hedge\\n\",\n \" - Set `net_notional > 0` for net long bias\\n\",\n \" - Set `net_notional < 0` for net short bias\\n\",\n \"\\n\",\n \"\\n\",\n \"**Examples:**\\n\",\n \"- For a net long bias example: gross_notional=20M, net_notional=10M (e.g., 15M long + 5M short)\\n\",\n \"- For a net short bias example: gross_notional=20M, net_notional=-10M (e.g., 5M long + 15M short)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Example: Bidirectional Hedger with Market Neutral Configuration\\n\",\n \"settings = OptimizerSettings(\\n\",\n \" allow_long_short=True, # Enable bidirectional mode\\n\",\n \" gross_notional=20_000_000, # Total |long| + |short| = 20M\\n\",\n \" net_notional=0, # Market neutral: long - short = 0 (e.g., 10M long + 10M short)\\n\",\n \" constraint_priorities=constraint_priorities,\\n\",\n \" max_names=600,\\n\",\n \" max_adv=15,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 7: Create and Run a Strategy\\n\",\n \"\\n\",\n \"It's finally time to take all these parameters and construct an optimizer strategy using the `OptimizerStrategy` class in which all the building blocks configured from previous steps are put together. \\n\",\n \"\\n\",\n \" * initial portfolio\\n\",\n \" * style, sector, industry and country constraints\\n\",\n \" * constraint priorities\\n\",\n \" * optimization settings with number of names, weight limits and liquitdy limits on a one sided hedge\\n\",\n \" * optimization objectives to minimize on factor risk only\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"constraints = OptimizerConstraints(\\n\",\n \" asset_constraints=asset_constraints,\\n\",\n \" sector_constraints=sector_constraints,\\n\",\n \" factor_constraints=factor_constraints,\\n\",\n \" industry_constraints=industry_constraints,\\n\",\n \")\\n\",\n \"\\n\",\n \"# define specific risk penalty to be 0 to optimize on factor risk only\\n\",\n \"risk_term = OptimizerObjectiveTerm(params={'specific_weight': 0, 'factor_weight': 1})\\n\",\n \"objective_parameters = OptimizerObjectiveParameters(terms=[risk_term])\\n\",\n \"\\n\",\n \"strategy = OptimizerStrategy(\\n\",\n \" initial_position_set=position_set,\\n\",\n \" constraints=constraints,\\n\",\n \" settings=settings,\\n\",\n \" universe=universe,\\n\",\n \" risk_model=risk_model,\\n\",\n \" objective=OptimizerObjective.MINIMIZE_FACTOR_RISK,\\n\",\n \" objective_parameters=objective_parameters,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Option A: Run the hedge with option to save and share it\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run the optimization and capture the request/response for saving later\\n\",\n \"strategy_request, optimization_response = strategy.run_save_share(\\n\",\n \" optimizer_type=OptimizerType.AXIOMA_PORTFOLIO_OPTIMIZER\\n\",\n \")\\n\",\n \"\\n\",\n \"# Get hedge and hedged portfolio\\n\",\n \"hedge = strategy.get_optimization() # Returns just the optimization results as a PositionSet object\\n\",\n \"hedged_portfolio = strategy.get_optimized_position_set()\\n\",\n \"\\n\",\n \"print(f'\\\\nHedge Portfolio: {len(hedge.positions)} positions')\\n\",\n \"hedge_df = pd.DataFrame(\\n\",\n \" [\\n\",\n \" {\\n\",\n \" 'Asset': p.identifier,\\n\",\n \" 'Quantity': f'{p.quantity:.0f}' if p.quantity else '',\\n\",\n \" 'Weight': f'{p.weight:.2%}' if p.weight else '',\\n\",\n \" }\\n\",\n \" for p in hedge.positions\\n\",\n \" ]\\n\",\n \")\\n\",\n \"display(hedge_df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Option B: Run the hedge with NO option to save and share it\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Run the optimization and capture the request/response for saving later\\n\",\n \"strategy.run(optimizer_type=OptimizerType.AXIOMA_PORTFOLIO_OPTIMIZER)\\n\",\n \"\\n\",\n \"# Get hedge and hedged portfolio\\n\",\n \"hedge = strategy.get_optimization() # Returns just the optimization results as a PositionSet object\\n\",\n \"hedged_portfolio = strategy.get_optimized_position_set()\\n\",\n \"\\n\",\n \"print(f'\\\\nHedge Portfolio: {len(hedge.positions)} positions')\\n\",\n \"hedge_df = pd.DataFrame(\\n\",\n \" [\\n\",\n \" {\\n\",\n \" 'Asset': p.identifier,\\n\",\n \" 'Quantity': f'{p.quantity:.0f}' if p.quantity else '',\\n\",\n \" 'Weight': f'{p.weight:.2%}' if p.weight else '',\\n\",\n \" }\\n\",\n \" for p in hedge.positions\\n\",\n \" ]\\n\",\n \")\\n\",\n \"display(hedge_df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Analyze Hedge Composition\\n\",\n \"\\n\",\n \"View detailed exposure metrics for the hedge, including gross/net/long/short exposures. This is especially useful for long/short hedges.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get detailed exposure summary\\n\",\n \"exposure_summary = strategy.get_hedge_exposure_summary()\\n\",\n \"\\n\",\n \"print('=' * 80)\\n\",\n \"print('HEDGE EXPOSURE SUMMARY')\\n\",\n \"print('=' * 80)\\n\",\n \"\\n\",\n \"# Display hedge information\\n\",\n \"hedge_info = exposure_summary['hedge']\\n\",\n \"print(f\\\"\\\\nHedge Mode: {hedge_info.get('mode', 'N/A')}\\\")\\n\",\n \"print(f\\\"Number of Positions: {hedge_info.get('number_of_positions', 0)}\\\")\\n\",\n \"print(f\\\"Gross Exposure: ${hedge_info.get('gross_exposure', 0):,.0f}\\\")\\n\",\n \"print(f\\\"Net Exposure: ${hedge_info.get('net_exposure', 0):,.0f}\\\")\\n\",\n \"print(f\\\"Long Exposure: ${hedge_info.get('long_exposure', 0):,.0f}\\\")\\n\",\n \"print(f\\\"Short Exposure: ${hedge_info.get('short_exposure', 0):,.0f}\\\")\\n\",\n \"\\n\",\n \"# Display target portfolio information\\n\",\n \"print('\\\\n' + '-' * 80)\\n\",\n \"print('TARGET PORTFOLIO')\\n\",\n \"print('-' * 80)\\n\",\n \"target_info = exposure_summary['target']\\n\",\n \"print(f\\\"Number of Positions: {target_info.get('number_of_positions', 0)}\\\")\\n\",\n \"print(f\\\"Gross Exposure: ${target_info.get('gross_exposure', 0):,.0f}\\\")\\n\",\n \"print(f\\\"Net Exposure: ${target_info.get('net_exposure', 0):,.0f}\\\")\\n\",\n \"\\n\",\n \"# Display hedged portfolio information\\n\",\n \"print('\\\\n' + '-' * 80)\\n\",\n \"print('HEDGED TARGET PORTFOLIO (Target + Hedge)')\\n\",\n \"print('-' * 80)\\n\",\n \"hedged_info = exposure_summary['hedged_target']\\n\",\n \"print(f\\\"Number of Positions: {hedged_info.get('number_of_positions', 0)}\\\")\\n\",\n \"print(f\\\"Gross Exposure: ${hedged_info.get('gross_exposure', 0):,.0f}\\\")\\n\",\n \"print(f\\\"Net Exposure: ${hedged_info.get('net_exposure', 0):,.0f}\\\")\\n\",\n \"print(f\\\"Long Exposure: ${hedged_info.get('long_exposure', 0):,.0f}\\\")\\n\",\n \"print(f\\\"Short Exposure: ${hedged_info.get('short_exposure', 0):,.0f}\\\")\\n\",\n \"print('=' * 80)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Split Hedge Constituents by Direction\\n\",\n \"\\n\",\n \"For bidirectional hedges, view the long and short positions separately to understand each side of the hedge.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Split hedge into long and short positions\\n\",\n \"constituents_by_direction = strategy.get_hedge_constituents_by_direction()\\n\",\n \"\\n\",\n \"summary = constituents_by_direction['summary']\\n\",\n \"long_positions = constituents_by_direction['long_positions']\\n\",\n \"short_positions = constituents_by_direction['short_positions']\\n\",\n \"\\n\",\n \"print(\\\"\\\\nHedge Breakdown:\\\")\\n\",\n \"print(f\\\" Long Positions: {summary['num_long']} positions, Total Notional: ${summary['total_long_notional']:,.0f}\\\")\\n\",\n \"print(f\\\" Short Positions: {summary['num_short']} positions, Total Notional: ${summary['total_short_notional']:,.0f}\\\")\\n\",\n \"\\n\",\n \"if len(long_positions) > 0:\\n\",\n \" print(\\\"\\\\nTop 10 Long Positions:\\\")\\n\",\n \" display(long_positions[['assetId', 'name', 'notional']].head(10))\\n\",\n \"else:\\n\",\n \" print(\\\"\\\\nNo long positions in hedge (Unidirectional mode: short positions only)\\\")\\n\",\n \"\\n\",\n \"if len(short_positions) > 0:\\n\",\n \" print(\\\"\\\\nTop 10 Short Positions:\\\")\\n\",\n \" display(short_positions[['assetId', 'name', 'notional']].head(10))\\n\",\n \"else:\\n\",\n \" print(\\\"\\\\nNo short positions in hedge\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 7.1: Analyze Hedged Portfolio Performance\\n\",\n \"\\n\",\n \"View the cumulative PnL performance of the hedged portfolio (target + hedge combined).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"performance_df = strategy.get_cumulative_pnl_performance(HedgeTarget.HEDGED_TARGET)\\n\",\n \"\\n\",\n \"if not performance_df.empty:\\n\",\n \" hedge_mode = 'Bidirectional' if settings.allow_long_short else 'Unidirectional (Short Positions Only)'\\n\",\n \" print(f'\\\\nHedge Mode: {hedge_mode}')\\n\",\n \" print('Hedged Portfolio Cumulative PnL Performance:')\\n\",\n \" fig = analytics.create_performance_chart(\\n\",\n \" performance_df, metric='cumulativePnl', title=f'Hedged Portfolio - Cumulative PnL ({hedge_mode})'\\n\",\n \" )\\n\",\n \" fig.show()\\n\",\n \"else:\\n\",\n \" print('No performance data available')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 7.2: Visualize Hedged Portfolio Style Factor Exposures\\n\",\n \"\\n\",\n \"View the style factor exposures after hedging.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create style factor chart (excludes GICS classification data)\\n\",\n \"hedged_factor_exposures = strategy.get_style_factor_exposures(HedgeTarget.HEDGED_TARGET)\\n\",\n \"hedged_analysis = analytics.convert_hedge_factor_exposures(hedged_factor_exposures)\\n\",\n \"\\n\",\n \"hedge_mode = 'Bidirectional' if settings.allow_long_short else 'Unidirectional'\\n\",\n \"fig = analytics.create_style_factor_chart(\\n\",\n \" hedged_analysis, rows=5, title=f'Hedged Portfolio - Style Factor Exposures ({hedge_mode} Hedge)'\\n\",\n \")\\n\",\n \"fig.show()\\n\",\n \"\\n\",\n \"print('\\\\nStyle factors displayed (e.g., Volatility, Market Sensitivity, Momentum, Size, Value, etc.)')\\n\",\n \"print(f'Hedge Mode: {hedge_mode}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 7.3: Compare Initial vs Hedged Portfolio\\n\",\n \"\\n\",\n \"Compare key risk metrics between the initial and hedged portfolios.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Display performance summary from strategy in separate tables\\n\",\n \"hedge_mode = 'Bidirectional Hedger' if settings.allow_long_short else 'Unidirectional Hedger (Short Positions Only)'\\n\",\n \"print(f'\\\\nPerformance Summary from Hedge Calculation ({hedge_mode}):')\\n\",\n \"try:\\n\",\n \" summary = strategy.get_performance_summary()\\n\",\n \"\\n\",\n \" # Optionally display all metrics in one combined table\\n\",\n \" print('\\\\n--- All Metrics (Combined) ---')\\n\",\n \" display(summary['combined'])\\n\",\n \"\\n\",\n \" if settings.allow_long_short:\\n\",\n \" print('\\\\nNote: Bidirectional hedge includes both long and short positions for more flexible factor management.')\\n\",\n \" else:\\n\",\n \" print('\\\\nNote: Unidirectional hedge contains only short positions to offset the target portfolio.')\\n\",\n \"\\n\",\n \"except Exception as e:\\n\",\n \" print(f'Could not retrieve performance summary: {str(e)}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Side-by-Side Factor Comparison\\n\",\n \"\\n\",\n \"Compare all style factor exposures between initial and hedged portfolios using a grouped bar chart.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create side-by-side comparison of all style factors\\n\",\n \"hedge_mode = 'Bidirectional' if settings.allow_long_short else 'Unidirectional'\\n\",\n \"print(f'Creating Style Factor Comparison Chart ({hedge_mode} Hedge)...\\\\n')\\n\",\n \"comparison_chart = analytics.create_factor_heatmap_comparison(\\n\",\n \" initial_analysis, hedged_analysis, title=f'Style Factor Comparison: Initial vs Hedged Target ({hedge_mode})'\\n\",\n \")\\n\",\n \"comparison_chart.show()\\n\",\n \"\\n\",\n \"print('\\\\nThis chart displays all style factors with side-by-side bars for easy comparison.')\\n\",\n \"print('Blue bars = Initial Portfolio | Green bars = Hedged Target Portfolio')\\n\",\n \"print('Factors are sorted by initial portfolio exposure (largest to smallest by absolute value).')\\n\",\n \"print('A vertical line at zero helps identify positive vs negative exposures.')\\n\",\n \"print(f'\\\\nHedge Mode: {hedge_mode}')\\n\",\n \"if settings.allow_long_short:\\n\",\n \" print(' - Bidirectional hedge can take both long and short positions')\\n\",\n \"else:\\n\",\n \" print(' - Unidirectional hedge contains only short positions')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Note:\\n\",\n \"If code above fails to find optimization, relax the constraints and try again.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 9: Save Your Hedge to Marquee\\n\",\n \"\\n\",\n \"Once you're satisfied with your hedge results, you can save them to Marquee for future reference and analysis. This will create a hedge group that can be accessed through the Marquee UI.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Save the hedge using the strategy's built-in save_to_marquee method\\n\",\n \"# This method handles building the payload and posting to the API\\n\",\n \"\\n\",\n \"saved_hedge_response = strategy.save_to_marquee(\\n\",\n \" strategy_request=strategy_request,\\n\",\n \" optimization_response=optimization_response,\\n\",\n \" hedge_name=\\\"Factor Hedge\\\",\\n\",\n \" group_name=\\\"My Factor Hedge Group\\\",\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 9.1: Share Your Hedge with Other Users (Optional)\\n\",\n \"\\n\",\n \"After saving your hedge, you can share it with other users or groups by updating the entitlements. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# For view-only access:\\n\",\n \"hedge_view_emails = [\\\"YOUR EMAIL\\\"]\\n\",\n \"\\n\",\n \"# Uncomment the following lines to share the hedge:\\n\",\n \"if saved_hedge_response and saved_hedge_response.get('id'):\\n\",\n \" hedge_group_id = saved_hedge_response['id']\\n\",\n \" share_response = GsHedgeApi.share_hedge_group(\\n\",\n \" hedge_group_id=hedge_group_id,\\n\",\n \" view_emails=hedge_view_emails,\\n\",\n \" strategy_request=strategy_request,\\n\",\n \" optimization_response=optimization_response,\\n\",\n \" admin_emails=[], # Add admin emails here if needed\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 10: Create a Basket or Portfolio With Your Results\\n\",\n \"\\n\",\n \"Now that you have saved your hedge, you can also create a basket or portfolio with your optimization results by following these tutorials:\\n\",\n \"\\n\",\n \"- [Create a Basket](https://nbviewer.org/github/goldmansachs/gs-quant/blob/master/gs_quant/documentation/06_baskets/tutorials/Basket%20Create.ipynb)\\n\",\n \"- [Create a Portfolio](https://nbviewer.org/github/goldmansachs/gs-quant/blob/master/gs_quant/documentation/10_one_delta/scripts/portfolios/01_Create%20New%20Historical%20Portfolio.ipynb)\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"venv312 (3.12.1)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Hedger/03_Continuous_Optimization.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Quant Backtesting Workflow in Marquee\\n\",\n \"\\n\",\n \"This notebook demonstrates how to implement continuous portfolio optimizations using GS Quant.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize your Session \\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import time\\n\",\n \"from math import copysign\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from gs_quant.datetime.relative_date import RelativeDate\\n\",\n \"from gs_quant.markets.position_set import Position, PositionSet\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.securities import Asset, AssetIdentifier\\n\",\n \"from gs_quant.markets.report import FactorRiskReport, ReturnFormat\\n\",\n \"from gs_quant.models.risk_model import FactorRiskModel\\n\",\n \"from gs_quant.session import GsSession\\n\",\n \"from gs_quant.target.common import PositionSetWeightingStrategy\\n\",\n \"\\n\",\n \"from gs_quant.target.hedge import CorporateActionsTypes\\n\",\n \"from gs_quant.markets.optimizer import (\\n\",\n \" OptimizerStrategy,\\n\",\n \" OptimizerUniverse,\\n\",\n \" AssetConstraint,\\n\",\n \" FactorConstraint,\\n\",\n \" SectorConstraint,\\n\",\n \" OptimizerSettings,\\n\",\n \" OptimizerConstraints,\\n\",\n \" OptimizerObjective,\\n\",\n \" OptimizerType,\\n\",\n \")\\n\",\n \"\\n\",\n \"pd.set_option('display.width', 1000)\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Quick Hint:\\n\",\n \"If you don't already have a portfolio set up, follow the instructions in [01_Create_Backcasted_Portfolio](../Portfolios/01_Create_Backcasted_Portfolio.ipynb) to create a snapshot portfolio and backcast it. \\n\",\n \"Alternatively, if you have a historical position set, you can also create a historical portfolio in [02_Create_New_Historical_Portfolio](../Portfolios/02_Create_New_Historical_Portfolio.ipynb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Load your Portfolio\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"Load your portfolio and define hedge parameters\\n\",\n \"\\n\",\n \"##### Note - The start date for your continuous optimization must be **after** the first position set uploaded in your source portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"port_id = 'YOUR PORTFOLIO ID' # put in your existing portfolio id\\n\",\n \"start_date = dt.date(2024, 1, 2)\\n\",\n \"hedge_notional_pct = 1.0\\n\",\n \"universe = ['SPX']\\n\",\n \"rebalance_freq = '1m'\\n\",\n \"risk_model_id = 'AXIOMA_AXWW4M'\\n\",\n \"apply_factor_constraints_on_total = True\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"port_object = Portfolio.get(port_id)\\n\",\n \"port_manager = PortfolioManager(port_id)\\n\",\n \"print(f'Using portfolio {port_object.name} with id {port_id} as a source portfolio')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Create a Portfolio for your Optimization\\n\",\n \"\\n\",\n \"\\n\",\n \"You will first create a portfolio with a custom name and share it with a list of emails.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_port_object = Portfolio(name=f'{port_object.name} - L/S Hedge')\\n\",\n \"opt_port_object.save()\\n\",\n \"opt_port_id = opt_port_object.id\\n\",\n \"print(f'Created portfolio {opt_port_object.name} with id {opt_port_id}')\\n\",\n \"opt_port_manager = PortfolioManager(opt_port_id)\\n\",\n \"opt_port_manager.share(['your.email@yourcompany.com', 'your.colleague@yourcompany.com'], admin=True)\\n\",\n \"opt_port_manager.set_tag_name_hierarchy(['source'])\\n\",\n \"\\n\",\n \"opt_risk_report = FactorRiskReport(risk_model_id=risk_model_id)\\n\",\n \"opt_risk_report.set_position_source(opt_port_id)\\n\",\n \"opt_risk_report.save()\\n\",\n \"\\n\",\n \"opt_port_manager.update_portfolio_tree()\\n\",\n \"print(f'Using portfolio {opt_port_object.name} with id {opt_port_id} as the optimization portfolio')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Risk and performance information about your source portfolio is required in order to run the continuous optimization. \\n\",\n \"\\n\",\n \"We'll first make sure that your source portfolio's factor risk and performance reports have finished successfully.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print('Ensuring performance and risk calculations are complete on the source...')\\n\",\n \"perf_report = port_manager.get_performance_report()\\n\",\n \"perf_report.get_most_recent_job().wait_for_completion()\\n\",\n \"\\n\",\n \"risk_report = port_manager.get_factor_risk_report(risk_model_id=risk_model_id)\\n\",\n \"risk_report.get_most_recent_job().wait_for_completion()\\n\",\n \"\\n\",\n \"factor_exposure_data = risk_report.get_factor_exposure(start_date=start_date, end_date=start_date)\\n\",\n \"factor_exposure_map = factor_exposure_data.to_dict(orient='records')[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Setting Up Optimization Parameters\\n\",\n \"\\n\",\n \"Now that you have a position set, you can get a hedge according to your liking.\\n\",\n \"`prepare_factor_constraints` below will pull constraints defined at a portfolio level to apply on the hedge.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def prepare_factor_constraints(factor_constraints, port_factor_exposure_map):\\n\",\n \" \\\"\\\"\\\"Given factor constraints defined on the total portfolio and factor exposures of the core portfolio,\\n\",\n \" return constraints to be applied on the hedge\\\"\\\"\\\"\\n\",\n \" new_constraints = []\\n\",\n \" for fc in factor_constraints:\\n\",\n \" old = fc.max_exposure\\n\",\n \" new = port_factor_exposure_map.get(fc.factor.name, 0) - fc.max_exposure\\n\",\n \" print('Changing factor constraint for ', fc.factor.name, 'from ', old, 'to ', new)\\n\",\n \" new_constraints.append(\\n\",\n \" FactorConstraint(fc.factor, port_factor_exposure_map.get(fc.factor.name, 0) - fc.max_exposure)\\n\",\n \" )\\n\",\n \" return new_constraints\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that you have a position set, you can get a hedge according to your liking. We have put in some sample settings below. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hedge_universe = OptimizerUniverse(\\n\",\n \" assets=[Asset.get(a, AssetIdentifier.BLOOMBERG_ID) for a in universe],\\n\",\n \" explode_composites=True,\\n\",\n \" exclude_corporate_actions_types=[CorporateActionsTypes.Mergers],\\n\",\n \")\\n\",\n \"\\n\",\n \"risk_model = FactorRiskModel.get(risk_model_id)\\n\",\n \"\\n\",\n \"asset_constraints = [\\n\",\n \" AssetConstraint(Asset.get('MSFT UW', AssetIdentifier.BLOOMBERG_ID), 0, 5),\\n\",\n \" AssetConstraint(Asset.get('AAPL UW', AssetIdentifier.BLOOMBERG_ID), 0, 5),\\n\",\n \"]\\n\",\n \"\\n\",\n \"# Specify the constraints on factor exposure of the Total Optimized Portfolio\\n\",\n \"factor_constraints = [\\n\",\n \" FactorConstraint(risk_model.get_factor('Size'), 0),\\n\",\n \"]\\n\",\n \"\\n\",\n \"if apply_factor_constraints_on_total:\\n\",\n \" hedge_factor_constraints = prepare_factor_constraints(factor_constraints, factor_exposure_map)\\n\",\n \"else:\\n\",\n \" hedge_factor_constraints = factor_constraints\\n\",\n \"\\n\",\n \"sector_constraints = [SectorConstraint('Energy', 0, 30), SectorConstraint('Health Care', 0, 30)]\\n\",\n \"constraints = OptimizerConstraints(\\n\",\n \" asset_constraints=asset_constraints,\\n\",\n \" factor_constraints=hedge_factor_constraints,\\n\",\n \" sector_constraints=sector_constraints,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Continuous Optimization Loop\\n\",\n \"\\n\",\n \"With our initial setup done and settings configured, we are now ready to launch a flow that will continuously optimize our portfolio at our desired frequency.\\n\",\n \"\\n\",\n \"We will take the positions from the previous rebalance, utilize performance analytics to get the latest positions, and then optimize the portfolio again. We will then update the portfolio with the new positions and schedule reports for the next rebalance date.\\n\",\n \"\\n\",\n \"This operation of moving your portfolio forward using performance analytics relies solely on availability of the underlying assets. \\n\",\n \"\\n\",\n \"##### Note - The Optimizer is designed to work with prices adjusted for events such as dividends, splits, and corporate actions. This means it reflects the true economic value of assets after such adjustments. In contrast, our portfolio analytics operate on the raw (unadjusted) closing prices to provide a more detailed, day‐to‐day picture of historical trade activity. To avoid inconsistencies, we recommend passing position sets based solely on weight (which is independent of price adjustments) to derive the optimization output also in terms of weights\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"port_manager = PortfolioManager(port_id)\\n\",\n \"start = start_date\\n\",\n \"max_end = RelativeDate(\\\"-1b\\\", dt.date.today()).apply_rule(exchanges=['NYSE'])\\n\",\n \"start_time = time.time()\\n\",\n \"rebal = start_date\\n\",\n \"\\n\",\n \"optimizer_runs = []\\n\",\n \"\\n\",\n \"while rebal < max_end:\\n\",\n \" print(f'Moving to rebalance date {rebal}')\\n\",\n \"\\n\",\n \" source_port_perf_report = port_manager.get_performance_report()\\n\",\n \" latest_source_port_pos_data = source_port_perf_report.get_portfolio_constituents(\\n\",\n \" start_date=rebal,\\n\",\n \" end_date=rebal,\\n\",\n \" fields=['quantity', 'grossWeight'],\\n\",\n \" prefer_rebalance_positions=True,\\n\",\n \" return_format=ReturnFormat.JSON,\\n\",\n \" )\\n\",\n \" latest_source_port_exp = source_port_perf_report.get_gross_exposure(start_date=rebal, end_date=rebal)[\\n\",\n \" 'grossExposure'\\n\",\n \" ][0]\\n\",\n \" latest_source_position_set = PositionSet(\\n\",\n \" date=rebal,\\n\",\n \" reference_notional=latest_source_port_exp,\\n\",\n \" positions=[\\n\",\n \" Position(\\n\",\n \" asset_id=p['assetId'],\\n\",\n \" identifier=p['assetId'],\\n\",\n \" # We recommend using gross weight to find your reference weight, like below\\n\",\n \" weight=copysign(p.get('grossWeight', 0), p.get('quantity', 0)),\\n\",\n \" tags=[{'source': 'Portfolio'}],\\n\",\n \" )\\n\",\n \" for p in latest_source_port_pos_data\\n\",\n \" ],\\n\",\n \" )\\n\",\n \" settings = OptimizerSettings(\\n\",\n \" notional=hedge_notional_pct * latest_source_position_set.reference_notional, # 40% of your original portfolio\\n\",\n \" allow_long_short=True,\\n\",\n \" )\\n\",\n \"\\n\",\n \" if factor_constraints and apply_factor_constraints_on_total:\\n\",\n \" source_port_risk_report = port_manager.get_factor_risk_report(risk_model_id=risk_model_id)\\n\",\n \"\\n\",\n \" factor_exposure_data = risk_report.get_factor_exposure(\\n\",\n \" start_date=latest_source_position_set.date, end_date=latest_source_position_set.date\\n\",\n \" )\\n\",\n \" factor_exposure_map = factor_exposure_data.to_dict(orient='records')[0]\\n\",\n \" hedge_factor_constraints = prepare_factor_constraints(factor_constraints, factor_exposure_map)\\n\",\n \" else:\\n\",\n \" hedge_factor_constraints = factor_constraints\\n\",\n \"\\n\",\n \" constraints = OptimizerConstraints(\\n\",\n \" asset_constraints=asset_constraints,\\n\",\n \" factor_constraints=hedge_factor_constraints,\\n\",\n \" sector_constraints=sector_constraints,\\n\",\n \" )\\n\",\n \" strategy = OptimizerStrategy(\\n\",\n \" initial_position_set=latest_source_position_set,\\n\",\n \" constraints=constraints,\\n\",\n \" settings=settings,\\n\",\n \" universe=hedge_universe,\\n\",\n \" risk_model=risk_model,\\n\",\n \" objective=OptimizerObjective.MINIMIZE_FACTOR_RISK,\\n\",\n \" )\\n\",\n \" print('Optimizing...')\\n\",\n \" # Using adjusted prices in the Optimizer to reflect post-corporate action values\\n\",\n \" strategy.run(optimizer_type=OptimizerType.AXIOMA_PORTFOLIO_OPTIMIZER)\\n\",\n \" print('Optimization complete')\\n\",\n \" optimizer_runs.append(strategy)\\n\",\n \" optimization_result = strategy._OptimizerStrategy__result['hedge']\\n\",\n \" optimization = PositionSet(\\n\",\n \" date=strategy.initial_position_set.date,\\n\",\n \" reference_notional=optimization_result['netExposure'],\\n\",\n \" positions=[\\n\",\n \" Position(identifier=asset.get('bbid', asset['name']), asset_id=asset['assetId'], weight=asset['weight'])\\n\",\n \" for asset in optimization_result['constituents']\\n\",\n \" ],\\n\",\n \" )\\n\",\n \" # Applying unadjusted close prices for portfolio analytics to preserve historical price granularity\\n\",\n \" optimization.price(\\n\",\n \" use_unadjusted_close_price=True, weighting_strategy=PositionSetWeightingStrategy.Weight, fallbackDate='5d'\\n\",\n \" )\\n\",\n \" for p in optimization.positions:\\n\",\n \" p.add_tag('source', 'Optimization')\\n\",\n \" # Again using unadjusted prices on source positions for consistency in analytics\\n\",\n \" latest_source_position_set.price(\\n\",\n \" use_unadjusted_close_price=True, weighting_strategy=PositionSetWeightingStrategy.Weight, fallbackDate='5d'\\n\",\n \" )\\n\",\n \" combined_pset = PositionSet(\\n\",\n \" date=optimization.date,\\n\",\n \" positions=[\\n\",\n \" Position(identifier=p.identifier, asset_id=p.asset_id, quantity=p.quantity, tags=p.tags)\\n\",\n \" for p in latest_source_position_set.positions + optimization.positions\\n\",\n \" ],\\n\",\n \" )\\n\",\n \" opt_port_manager.update_positions([combined_pset])\\n\",\n \" if not opt_port_manager.get_portfolio_tree().sub_portfolios:\\n\",\n \" opt_port_manager.update_portfolio_tree()\\n\",\n \" start = rebal\\n\",\n \" rebal = min(max_end, RelativeDate(rebalance_freq, start).apply_rule())\\n\",\n \" print(f'Scheduling reports to calculate performance till {rebal}...')\\n\",\n \" opt_port_manager.schedule_reports(start_date=start, end_date=rebal)\\n\",\n \" time.sleep(2)\\n\",\n \"\\n\",\n \"print(f'Done! Processing completed in {time.time() - start_time} seconds')\\n\",\n \"\\n\",\n \"print(f'As a reminder your optimization can be found in your \\\"{opt_port_object.name}\\\" portfolio with id {opt_port_id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You have successfully completed a basic run of our Quant Backtesting Workflow. \\n\",\n \"\\n\",\n \"For questions, please reach out to [Marquee Sales](mailto:gs-marquee-sales@gs.com)!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"tutorial-env\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/01_Create_Backcasted_Portfolio.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Create a Marquee Backcasted Portfolio with GS Quant\\n\",\n \"\\n\",\n \"The Marquee Portfolio Service provides a powerful framework for uploading portfolio positions and retrieving analytics including historical performance, factor risk exposure, ESG analytics, and more. GS Quant makes operating the suite of Portfolio Service API's intuitive and fast.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"from gs_quant.datetime import business_day_offset\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"from gs_quant.markets.report import FactorRiskReport, ThematicReport\\n\",\n \"from gs_quant.models.risk_model import FactorRiskModel\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.entities.entitlements import Entitlements, EntitlementBlock, User\\n\",\n \"\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Create the Portfolio\\n\",\n \"\\n\",\n \"The first step is to create a new, empty portfolio in Marquee.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio(name='My New Backcasted Portfolio')\\n\",\n \"portfolio.save()\\n\",\n \"\\n\",\n \"print(f\\\"Created portfolio '{portfolio.name}' with ID: {portfolio.id}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once your portfolio has been saved to Marquee, the `PortfolioManager` class allows users to interact with their Marquee portfolios directly from GS Quant. We will be using `PortfolioManager` to update portfolio positions, entitlements, update custom AUM, and run reports.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pm = PortfolioManager(portfolio.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Define Portfolio Entitlements (Optional)\\n\",\n \"\\n\",\n \"By default, an application will have all entitlement permissions to a portfolio it makes.\\n\",\n \"However, if you would like to share the portfolio with others, either Marquee users or other\\n\",\n \"applications, you will need to specify them in the entitlements parameter of the portfolio.\\n\",\n \"Let's walk through how we convert a list of admin and viewer emails into an `Entitlements` object:\\n\",\n \"\\n\",\n \"*Note: If you would like to see this portfolio on your Marquee webpage, you'll need to add your account\\n\",\n \"email address into the `portfolio_admin_emails` list*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio_admin_emails = ['LIST OF ADMIN EMAILS']\\n\",\n \"portfolio_viewer_emails = ['LIST OF VIEWER EMAILS']\\n\",\n \"\\n\",\n \"admin_entitlements = EntitlementBlock(users=User.get_many(emails=portfolio_admin_emails))\\n\",\n \"view_entitlements = EntitlementBlock(users=User.get_many(emails=portfolio_viewer_emails))\\n\",\n \"\\n\",\n \"entitlements = Entitlements(view=view_entitlements, admin=admin_entitlements)\\n\",\n \"\\n\",\n \"print(f'Entitlements:\\\\n{entitlements.to_dict()}')\\n\",\n \"\\n\",\n \"pm.set_entitlements(entitlements)\\n\",\n \"\\n\",\n \"print(f\\\"Updated entitlements for '{portfolio.name}'\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Define Portfolio Positions\\n\",\n \"\\n\",\n \"Portfolio positions in Marquee are stored on a holding basis, when means you only upload positions for days where you are rebalancing your portfolio. Depending on your quantity type, take the following example positions:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Uploading Positions Using Shares\\n\",\n \"Portfolio quantity type uses share counts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"positions = [\\n\",\n \" {'identifier': 'AAPL UW', 'quantity': 25},\\n\",\n \" {'identifier': 'GS UN', 'quantity': 50},\\n\",\n \" {'identifier': 'META UW', 'quantity': 25},\\n\",\n \" {'identifier': 'AMZN UN', 'quantity': 50},\\n\",\n \" {'identifier': 'MSFT UW', 'quantity': 25},\\n\",\n \" {'identifier': 'AZN UW', 'quantity': 50},\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Uploading Positions Using Exposures\\n\",\n \"Portfolio quantity type uses notional values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"positions = [\\n\",\n \" {'identifier': 'AAPL UW', 'notional': 25000},\\n\",\n \" {'identifier': 'GS UN', 'notional': 50000},\\n\",\n \" {'identifier': 'META UW', 'notional': 25000},\\n\",\n \" {'identifier': 'AMZN UN', 'notional': 50000},\\n\",\n \" {'identifier': 'MSFT UW', 'notional': 25000},\\n\",\n \" {'identifier': 'AZN UW', 'notional': 50000},\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"For uploading using quantities or exposures, resolve the positions in your portfolio as of last business day.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet.from_dicts(date=business_day_offset(dt.date.today(), -1, roll='forward'), positions=positions)\\n\",\n \"]\\n\",\n \"\\n\",\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.resolve()\\n\",\n \"\\n\",\n \"print(\\\"All positions have been resolved and saved.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Uploading Positions Using Weights\\n\",\n \"Portfolio quantity type uses weight values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"positions = [\\n\",\n \" {'identifier': 'AAPL UW', 'weight': 0.25},\\n\",\n \" {'identifier': 'GS UN', 'weight': 0.25},\\n\",\n \" {'identifier': 'META UW', 'weight': 0.25},\\n\",\n \" {'identifier': 'AMZN UN', 'weight': 0.25},\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For uploading using weights, resolve the positions in your portfolio as of last business day and using a desired target notional.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet.from_dicts(\\n\",\n \" date=business_day_offset(dt.date.today(), -1, roll='forward'), reference_notional=15000, positions=positions\\n\",\n \" )\\n\",\n \"]\\n\",\n \"\\n\",\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.resolve()\\n\",\n \"\\n\",\n \"print(\\\"All positions have been resolved and saved.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Price Positions (Weights & Exposures)\\n\",\n \"When uploading positions by weights or exposures they must priced and converted to share quantities before being uploaded to the portfolio.\\n\",\n \"Select the currency you want to price your positions with. If you do not specify, default is USD.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"currency = 'USD'\\n\",\n \"\\n\",\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.price(currency)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Define Currency for portfolio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.common import Currency\\n\",\n \"\\n\",\n \"pm = PortfolioManager(portfolio.id)\\n\",\n \"pm.set_currency(Currency.USD) # Set the currency using PortfolioManager\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 5: Upload the positions onto the portfolio:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pm.update_positions(portfolio_position_sets)\\n\",\n \"\\n\",\n \"print(f\\\"Successfully updated positions for portfolio {pm.portfolio_id}\\\\n\\\")\\n\",\n \"print(f\\\"Latest updated set of positions for {pm.portfolio_id}: \\\\n{pm.get_latest_position_set().get_positions()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 6: Create Factor Risk and/or Thematic Reports (Optional)\\n\",\n \"\\n\",\n \"By default, creating a portfolio will automatically create a corresponding performance report for it as well.\\n\",\n \"If you would like to create a factor risk and/or thematic report (more documentation on reports found [here](https://developer.gs.com/p/docs/services/portfolio/programmatic-access/reports/))\\n\",\n \"for it as well, run the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"risk_model_id = 'RISK MODEL ID'\\n\",\n \"\\n\",\n \"# Add a factor risk report\\n\",\n \"risk_report = FactorRiskReport(risk_model_id=risk_model_id)\\n\",\n \"risk_report.set_position_source(portfolio.id)\\n\",\n \"risk_report.save()\\n\",\n \"\\n\",\n \"# Add a thematic report\\n\",\n \"thematic_report = ThematicReport()\\n\",\n \"thematic_report.set_position_source(portfolio.id)\\n\",\n \"thematic_report.save()\\n\",\n \"\\n\",\n \"print('All portfolio reports created.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Quick Tip!\\n\",\n \"\\n\",\n \"*Explore the different factor risk models available in Marquee in our [Data Catalog](https://marquee.gs.com/s/discover/data-services/catalog?Category=Factor+Risk+Model)*\\n\",\n \"\\n\",\n \"*Premium clients get access to many more risk models (including premium vendors like MSCI Barra),\\n\",\n \"while non-premium clients get access to a limited suite of models. To see which models you have access to,\\n\",\n \"simply run the following:*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"risk_models = FactorRiskModel.get_many(limit=100)\\n\",\n \"for risk_model in risk_models:\\n\",\n \" print(f'{risk_model.name}: {risk_model.id}\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 7: Schedule Reports\\n\",\n \"\\n\",\n \"Now, it's schedule all the portfolio reports. Once this is done and reports are completed, you can programmatically retrieve factor risk and attribution data for your portfolio.\\n\",\n \"\\n\",\n \"When scheduling reports, you have two options:\\n\",\n \"- Backcast the report: Take the earliest date with positions in the portfolio / basket and run the report on the positions held then with a start date before the earliest position date and an end date\\n\",\n \" of the earliest position date. This option is ideal for snapshot portfolios.\\n\",\n \"- Do not backcast the report: Set the start date as a date that has positions in the portfolio or basket and an end date after that (best practice is to set it to T-1). In this case the\\n\",\n \" report will run on positions held as of each day in the date range. This option is ideal for historical portfolios.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm.schedule_reports(backcast=True)\\n\",\n \"\\n\",\n \"print('All portfolio reports scheduled.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note: Cannot Schedule backcasted report with no position sets on the report job end date.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/02_Create_New_Historical_Portfolio.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Create a Marquee Historical Portfolio with GS Quant\\n\",\n \"\\n\",\n \"The Marquee Portfolio Service provides a powerful framework for uploading portfolio positions and retrieving analytics including historical performance, factor risk exposure, ESG analytics, and more. GS Quant makes operating the suite of Portfolio Service API's intuitive and fast.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.entities.entitlements import Entitlements, EntitlementBlock, User\\n\",\n \"from gs_quant.markets.portfolio import Portfolio\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.position_set import PositionSet, Position\\n\",\n \"from gs_quant.markets.report import FactorRiskReport, ThematicReport, CustomAUMDataPoint\\n\",\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.target.portfolios import RiskAumSource\\n\",\n \"from gs_quant.models.risk_model import FactorRiskModel\\n\",\n \"from gs_quant.common import PositionTag\\n\",\n \"\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Create the Portfolio\\n\",\n \"\\n\",\n \"The first step is to create a new, empty portfolio in Marquee.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio = Portfolio(name='My New Historical Portfolio')\\n\",\n \"portfolio.save()\\n\",\n \"\\n\",\n \"print(f\\\"Created portfolio '{portfolio.name}' with ID: {portfolio.id}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once your portfolio has been saved to Marquee, the `PortfolioManager` class allows users to interact with their Marquee portfolios directly from GS Quant. We will be using `PortfolioManager` to update portfolio positions, entitlements, update custom AUM, and run reports.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pm = PortfolioManager(portfolio.id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Define Portfolio Entitlements (Optional)\\n\",\n \"\\n\",\n \"By default, an application will have all entitlement permissions to a portfolio it makes.\\n\",\n \"However, if you would like to share the portfolio with others, either Marquee users or other\\n\",\n \"applications, you will need to specify them in the entitlements parameter of the portfolio.\\n\",\n \"Let's walk through how we convert a list of admin and viewer emails into an `Entitlements` object:\\n\",\n \"\\n\",\n \"*Note: If you would like to see this portfolio on your Marquee webpage, you'll need to add your account\\n\",\n \"email address into the `portfolio_admin_emails` list*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio_admin_emails = ['LIST OF ADMIN EMAILS']\\n\",\n \"portfolio_viewer_emails = ['LIST OF VIEWER EMAILS']\\n\",\n \"\\n\",\n \"admin_entitlements = EntitlementBlock(users=User.get_many(emails=portfolio_admin_emails))\\n\",\n \"view_entitlements = EntitlementBlock(users=User.get_many(emails=portfolio_viewer_emails))\\n\",\n \"\\n\",\n \"entitlements = Entitlements(view=view_entitlements, admin=admin_entitlements)\\n\",\n \"\\n\",\n \"print(f'Entitlements:\\\\n{entitlements.to_dict()}')\\n\",\n \"\\n\",\n \"pm.set_entitlements(entitlements)\\n\",\n \"\\n\",\n \"print(f\\\"Updated entitlements for '{portfolio.name}'\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Define Portfolio Positions\\n\",\n \"\\n\",\n \"Portfolio positions in Marquee are stored on a holding basis, when means you only upload positions for days where you are rebalancing your portfolio. Take the following set of positions:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Uploading Positions Using Shares\\n\",\n \"Portfolio quantity type uses share counts.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet(\\n\",\n \" date=dt.date(day=3, month=5, year=2021),\\n\",\n \" positions=[\\n\",\n \" Position(identifier='AAPL UW', quantity=25, tags=[PositionTag(name='Analyst', value='Marcus Goldman')]),\\n\",\n \" Position(identifier='GS UN', quantity=50, tags=[PositionTag(name='Analyst', value='Samuel Sachs')]),\\n\",\n \" ],\\n\",\n \" ),\\n\",\n \" PositionSet(\\n\",\n \" date=dt.date(day=1, month=7, year=2021),\\n\",\n \" positions=[\\n\",\n \" Position(identifier='AAPL UW', quantity=25, tags=[PositionTag(name='Analyst', value='Marcus Goldman')]),\\n\",\n \" Position(identifier='GS UN', quantity=50, tags=[PositionTag(name='Analyst', value='Samuel Sachs')]),\\n\",\n \" ],\\n\",\n \" ),\\n\",\n \"]\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Uploading Positions Using Exposures\\n\",\n \"Portfolio quantity type uses notional values.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet(\\n\",\n \" date=dt.date(day=3, month=5, year=2021),\\n\",\n \" positions=[\\n\",\n \" Position(identifier='AAPL UW', notional=25000, tags=[PositionTag(name='Analyst', value='Marcus Goldman')]),\\n\",\n \" Position(identifier='GS UN', notional=50000, tags=[PositionTag(name='Analyst', value='Samuel Sachs')]),\\n\",\n \" ],\\n\",\n \" ),\\n\",\n \" PositionSet(\\n\",\n \" date=dt.date(day=1, month=7, year=2021),\\n\",\n \" positions=[\\n\",\n \" Position(identifier='AAPL UW', notional=30000, tags=[PositionTag(name='Analyst', value='Marcus Goldman')]),\\n\",\n \" Position(identifier='GS UN', notional=75000, tags=[PositionTag(name='Analyst', value='Samuel Sachs')]),\\n\",\n \" ],\\n\",\n \" ),\\n\",\n \"]\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Uploading Positions Using Weights\\n\",\n \"Portfolio quantity type uses weight values.\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet(\\n\",\n \" date=dt.date(day=3, month=5, year=2021),\\n\",\n \" reference_notional=15000,\\n\",\n \" positions=[\\n\",\n \" Position(identifier='AAPL UW', weight=0.5, tags=[PositionTag(name='Analyst', value='Marcus Goldman')]),\\n\",\n \" Position(identifier='GS UN', weight=0.5, tags=[PositionTag(name='Analyst', value='Samuel Sachs')]),\\n\",\n \" ],\\n\",\n \" ),\\n\",\n \" PositionSet(\\n\",\n \" date=dt.date(day=1, month=7, year=2021),\\n\",\n \" reference_notional=20000,\\n\",\n \" positions=[\\n\",\n \" Position(identifier='AAPL UW', weight=0.5, tags=[PositionTag(name='Analyst', value='Marcus Goldman')]),\\n\",\n \" Position(identifier='GS UN', weight=0.5, tags=[PositionTag(name='Analyst', value='Samuel Sachs')]),\\n\",\n \" ],\\n\",\n \" ),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Resolve the positions in your portfolio as of last business day.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.resolve()\\n\",\n \" if len(pos_set.unresolved_positions) > 0:\\n\",\n \" print(\\n\",\n \" f'The following positions on {pos_set.date} could not be resolved: {[p.identifier for p in pos_set.unresolved_positions]}'\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" print(f\\\"All positions on {pos_set.date} have been resolved and saved.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Price Positions (Weights & Exposures)\\n\",\n \"When uploading positions by weights or exposures they must priced and converted to share quantities before being uploaded to the portfolio.\\n\",\n \"Select the currency you want to price your positions with. If you do not specify, default is USD.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"currency = 'USD'\\n\",\n \"\\n\",\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.price(currency)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Step 5: Update Portfolio\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"pm.update_positions(portfolio_position_sets)\\n\",\n \"\\n\",\n \"print(f\\\"Successfully updated positions for portfolio {pm.portfolio_id}\\\\n\\\")\\n\",\n \"print(f\\\"Latest updated set of positions for {pm.portfolio_id}: \\\\n{pm.get_latest_position_set().get_positions()}\\\")\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If these positions were to be uploaded correctly using share quantities, this portfolio would hold 50 shares of GS UN and 25 shares of AAPL UW from May 3, 2021 to June 30, 2021, and it would hold 51 shares of GS UN and 26 shares of AAPL UW from July 1, 2021 to today.\\n\",\n \"\\n\",\n \"#### Do you have your positions as a dataframe?\\n\",\n \"\\n\",\n \"If you have a day's positions in a dataframe with columns `identifer` (string values), `quantity` (float values),\\n\",\n \"and optionally `tags` (dictionary values), you can turn them into a `PositionSet` object by using the\\n\",\n \"`PositionSet.from_frame()` function:\\n\",\n \"\\n\",\n \"`position_set = PositionSet.from_frame(positions_df, datetime_date)`\\n\",\n \"\\n\",\n \"You can also substitute the `quantity` column with either `notional` (float values) or `weight` (float values) columns.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# positions_df = DATAFRAME WITH POSITIONS\\n\",\n \"# datetime_date = POSITIONS' DATE\\n\",\n \"\\n\",\n \"portfolio_position_sets = []\\n\",\n \"portfolio_position_sets.append(PositionSet.from_frame(positions_df, datetime_date))\\n\",\n \"\\n\",\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.resolve()\\n\",\n \" if len(pos_set.unresolved_positions) > 0:\\n\",\n \" print(\\n\",\n \" f'The following positions on {pos_set.date} could not be resolved: {[p.identifier for p in pos_set.unresolved_positions]}'\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" print(f\\\"All positions on {pos_set.date} have been resolved and saved.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 6: Create Factor Risk and/or Thematic Reports (Optional)\\n\",\n \"\\n\",\n \"By default, creating a portfolio will automatically create a corresponding performance report for it as well.\\n\",\n \"If you would like to create a factor risk and/or thematic report (more documentation on reports found [here](https://developer.gs.com/p/docs/services/portfolio/programmatic-access/reports/))\\n\",\n \"for it as well, run the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"risk_model_id = 'RISK_MODEL_ID_HERE'\\n\",\n \"risk_report = FactorRiskReport(risk_model_id=risk_model_id)\\n\",\n \"risk_report.set_position_source(portfolio.id)\\n\",\n \"risk_report.save()\\n\",\n \"\\n\",\n \"# Add a thematic report\\n\",\n \"thematic_report = ThematicReport()\\n\",\n \"thematic_report.set_position_source(portfolio.id)\\n\",\n \"thematic_report.save()\\n\",\n \"\\n\",\n \"print('All portfolio reports created.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"If you want to analyze your portfolio's tracking error relative to a benchmark and its active return against a benchmark, you can create an active factor risk report by including an additional parameter, `benchmark_id`, when creating a `FactorRiskReport`, as shown below::\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"outputs\": [],\n \"source\": [\n \"benchmark = SecurityMaster.get_asset(id_value='BENCHMARK TICKER HERE', id_type=AssetIdentifier.TICKER)\\n\",\n \"\\n\",\n \"# Add an active factor risk report with a benchmark of your choice\\n\",\n \"active_risk_report = FactorRiskReport(risk_model_id=risk_model_id, benchmark_id=benchmark.get_marquee_id())\\n\",\n \"active_risk_report.set_position_source(portfolio.id)\\n\",\n \"active_risk_report.save()\"\n ],\n \"metadata\": {\n \"collapsed\": false\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Quick Tip!\\n\",\n \"\\n\",\n \"*Explore the different factor risk models available in Marquee in our [Data Catalog](https://marquee.gs.com/s/discover/data-services/catalog?Category=Factor+Risk+Model)*\\n\",\n \"\\n\",\n \"*Premium clients get access to many more risk models from industry-leading commercial \\n\",\n \"risk model vendors such as MSCI Barra, Axioma (SimCorp), and Wolfe_Research, \\n\",\n \"while non-premium clients get access to a limited suite of models. To see which models \\n\",\n \"you have access to, simply run the following:*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"risk_models = FactorRiskModel.get_many(limit=100)\\n\",\n \"for risk_model in risk_models:\\n\",\n \" print(f'{risk_model.name}: {risk_model.id}\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 7: Schedule Reports\\n\",\n \"\\n\",\n \"Now, let's schedule all the portfolio reports. Once this is done and reports are completed, you can programmatically retrieve factor risk and attribution data for your portfolio.\\n\",\n \"\\n\",\n \"When scheduling reports, you have two options:\\n\",\n \"- Backcast the report: Take the earliest date with positions in the portfolio and run the report on the positions held then with a start date before the earliest position date and an end date\\n\",\n \" of the earliest position date. This option is ideal for snapshot portfolios.\\n\",\n \"- Do not backcast the report: Set the start date as a date that has positions in the portfolio and an end date after that (best practice is to set it to T-1). In this case the\\n\",\n \" report will run on positions held as of each day in the date range. This option is ideal for historical portfolios.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pm.schedule_reports(backcast=False)\\n\",\n \"\\n\",\n \"print('All portfolio reports have been scheduled.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 8: Update Custom AUM/NAV (Optional)\\n\",\n \"The `CustomAUMDataPoint` class is used to represent custom AUM data for a specific date. A list of them can be posted to Marquee using our initialized `PortfolioManager`. If you do not upload custom AUM data for your portfolio and change your portfolio's AUM Source to `Custom AUM`, by default the \\\"AUM\\\" (which is used for calculating risk as percent values) will be your portfolio's long exposure.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"performance_report = pm.get_performance_report()\\n\",\n \"performance_report.set_aum_source(RiskAumSource.Custom_AUM)\\n\",\n \"custom_aum = [\\n\",\n \" CustomAUMDataPoint(date=dt.date(2021, 5, 1), aum=100000),\\n\",\n \" CustomAUMDataPoint(date=dt.date(2021, 7, 1), aum=200000),\\n\",\n \"]\\n\",\n \"performance_report.upload_custom_aum(custom_aum, clear_existing_data=False)\\n\",\n \"\\n\",\n \"print(f\\\"Custom AUM for '{portfolio.name} successfully uploaded'\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"tutorial-env\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/03_Update_Historical_Portfolio.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Updating a Historical Marquee Portfolio\\n\",\n \"\\n\",\n \"If you already have a portfolio in Marquee, the GS Quant SDK provides a simple and intuitive workflow to update positions and rerun reports.\\n\",\n \"\\n\",\n \"You will need to be an admin on the portfolio you would like to update. If you are not an admin, please ask a portfolio admin to [edit the portfolio's entitlements](../examples/marquee/01_edit_portfolio_entitlements.ipynb) to include you.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.markets.position_set import PositionSet\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"scopes = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \")\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Define Your Portfolio ID and the Positions You Would Like to Upload\\n\",\n \"\\n\",\n \"Portfolio positions in Marquee are stored on a holding basis, when means you only upload positions for days where you are rebalancing your portfolio. \\n\",\n \"\\n\",\n \"Positions can be uploaded using the following identifiers:\\n\",\n \"\\n\",\n \"| Identifier | Descriptions |\\n\",\n \"|-----------------------------------|---------------------------------------------------------------------------|\\n\",\n \"| BLOOMBERG ID = \\\"BBID\\\" | Bloomberg identifier and exchange code (GS UN) |\\n\",\n \"| SEDOL = \\\"SEDOL\\\" | LSE Stock Exchange Daily Official List code (2407966) |\\n\",\n \"| CUSIP = \\\"CUSIP\\\" | Committee on Uniform Security Identification Procedures code (38141G104) |\\n\",\n \"| ISIN = \\\"ISIN\\\" | International Securities Identification Number (US38141G1040) |\\n\",\n \"| GSID = \\\"GSID\\\" | Goldman Sachs Identifier |\\n\",\n \"| REUTERS ID = \\\"RIC\\\" | Thompson Reuters Instrument Code (RIC), (GS.N) |\\n\",\n \"| TICKER = \\\"TICKER\\\" | Exchange ticker (GS) |\\n\",\n \"\\n\",\n \" Depending on your quantity type, you can upload by shares, weights, or exposures.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_id = 'PORTFOLIO ID'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Uploading Positions Using Shares\\n\",\n \"Portfolio quantity type uses share counts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet.from_dicts(\\n\",\n \" date=dt.date(day=3, month=5, year=2021),\\n\",\n \" positions=[{'identifier': 'AAPL UW', 'quantity': 25}, {'identifier': 'GS UN', 'quantity': 50}],\\n\",\n \" ),\\n\",\n \" PositionSet.from_dicts(\\n\",\n \" date=dt.date(day=1, month=7, year=2021),\\n\",\n \" positions=[{'identifier': 'AAPL UW', 'quantity': 25}, {'identifier': 'GS UN', 'quantity': 50}],\\n\",\n \" ),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Uploading Positions Using Exposures\\n\",\n \"Portfolio quantity type uses notional values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet.from_dicts(\\n\",\n \" date=dt.date(day=3, month=5, year=2021),\\n\",\n \" positions=[{'identifier': 'AAPL UW', 'notional': 2500}, {'identifier': 'GS UN', 'notional': 5000}],\\n\",\n \" ),\\n\",\n \" PositionSet.from_dicts(\\n\",\n \" date=dt.date(day=1, month=7, year=2021),\\n\",\n \" positions=[{'identifier': 'AAPL UW', 'notional': 2500}, {'identifier': 'GS UN', 'notional': 500}],\\n\",\n \" ),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Uploading Positions Using Weights\\n\",\n \"Portfolio quantity type uses weight values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_position_sets = [\\n\",\n \" PositionSet.from_dicts(\\n\",\n \" date=dt.date(day=3, month=5, year=2021),\\n\",\n \" reference_notional=10000,\\n\",\n \" positions=[{'identifier': 'AAPL UW', 'weight': 0.5}, {'identifier': 'GS UN', 'weight': 0.5}],\\n\",\n \" ),\\n\",\n \" PositionSet.from_dicts(\\n\",\n \" date=dt.date(day=1, month=7, year=2021),\\n\",\n \" reference_notional=10000,\\n\",\n \" positions=[{'identifier': 'AAPL UW', 'weight': 0.5}, {'identifier': 'GS UN', 'weight': 0.5}],\\n\",\n \" ),\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Resolve Positions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.resolve()\\n\",\n \" if len(pos_set.unresolved_positions) > 0:\\n\",\n \" print(\\n\",\n \" f'The following positions on {pos_set.date} could not be resolved: {[p.identifier for p in pos_set.unresolved_positions]}'\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" print(f\\\"All positions on {pos_set.date} have been resolved and saved.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Price Positions (Weights & Exposures)\\n\",\n \"When uploading positions by weights or exposures they must priced and converted to share quantities before being uploaded to the portfolio.\\n\",\n \"Select the currency you want to price your positions with. If you do not specify, default is USD.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"currency = 'USD'\\n\",\n \"\\n\",\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.price(currency)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Post Positions to the Marquee Portfolio\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"pm.update_positions(portfolio_position_sets)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Have your positions as a dataframe?\\n\",\n \"\\n\",\n \"If you have a day's positions in a dataframe with columns `identifer` (string values), `quantity` (float values),\\n\",\n \"and optionally `tags` (dictionary values), you can turn them into a `PositionSet` object by using the\\n\",\n \"`PositionSet.from_frame()` function:\\n\",\n \"\\n\",\n \"`position_set = PositionSet.from_frame(positions_df, datetime_date)`\\n\",\n \"\\n\",\n \"You can also substitute the `quantity` column with either `notional` (float values) or `weight` (float values) columns.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# positions_df = DATAFRAME WITH POSITIONS\\n\",\n \"# datetime_date = POSITIONS' DATE\\n\",\n \"\\n\",\n \"portfolio_position_sets = []\\n\",\n \"portfolio_position_sets.append(PositionSet.from_frame(positions_df, datetime_date))\\n\",\n \"\\n\",\n \"for pos_set in portfolio_position_sets:\\n\",\n \" pos_set.resolve()\\n\",\n \" if len(pos_set.unresolved_positions) > 0:\\n\",\n \" print(\\n\",\n \" f'The following positions on {pos_set.date} could not be resolved: {[p.identifier for p in pos_set.unresolved_positions]}'\\n\",\n \" )\\n\",\n \" else:\\n\",\n \" print(f\\\"All positions on {pos_set.date} have been resolved and saved.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Reschedule All Portfolio Reports\\n\",\n \"\\n\",\n \"Now that the portfolio has new positions, it's time to rerun all reports associated with the portfolio so your performance, risk, and other analytics reflect these new positions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm.schedule_reports()\\n\",\n \"\\n\",\n \"print('All portfolio reports scheduled.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/04_Get_Factor_Attribution_Data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Step 1: Pull Portfolio Factor Attribution Data with GS Quant\\n\",\n \"\\n\",\n \"## Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from IPython.display import display\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import FactorRiskUnit, FactorRiskTableMode\\n\",\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Get Portfolio Factor Risk Report\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"risk_report = PortfolioManager('ENTER PORTFOLIO ID').get_factor_risk_report(\\n\",\n \" risk_model_id='ENTER RISK MODEL ID', benchmark_id=None\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f'Factor risk report found with ID: {risk_report.id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"Want to query data for an active risk report? Leverage the `SecurityMaster` class to retrieve the benchmark identifier and\\n\",\n \"pass it into the `get_factor_risk_report` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"benchmark = SecurityMaster.get_asset(id_value='SPX', id_type=AssetIdentifier.BLOOMBERG_ID)\\n\",\n \"\\n\",\n \"risk_report = PortfolioManager('ENTER PORTFOLIO ID').get_factor_risk_report(\\n\",\n \" risk_model_id='ENTER RISK MODEL ID', benchmark_id=benchmark.get_marquee_id()\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f'Factor risk report found with ID: {risk_report.id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Get Current Portfolio Attribution\\n\",\n \"\\n\",\n \"Once your risk report is scheduled as of the latest business day, you can view updated attribution broken down by factor category:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"attr_data = risk_report.get_view(\\n\",\n \" start_date=risk_report.earliest_start_date, end_date=risk_report.latest_end_date, unit=FactorRiskUnit.Notional\\n\",\n \")\\n\",\n \"category_table = attr_data.get('factorCategoriesTable')\\n\",\n \"category_df = pd.DataFrame(category_table).filter(items=['name', 'pnl', 'minExposure', 'maxExposure', 'avgExposure'])\\n\",\n \"\\n\",\n \"display(category_df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"It is also possible to get a similar table for all the factors in a factor category. In this case, let's drill down into the Style factors:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"factor_tables = attr_data.get('factorCategoryTables')\\n\",\n \"factor_tables = [f for f in factor_tables if f.get('factorCategory') == 'Style']\\n\",\n \"factor_df = pd.DataFrame(factor_tables[0].get('factors')).filter(\\n\",\n \" items=['name', 'pnl', 'minExposure', 'maxExposure', 'avgExposure']\\n\",\n \")\\n\",\n \"\\n\",\n \"display(factor_df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"You can also generate a table that shows you the factor PnL over a date range at the asset level. Let's see the factor PnL for each asset for the factors Country, Beta, and Earnings Quality:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pnl_table = risk_report.get_table(\\n\",\n \" mode=FactorRiskTableMode.Pnl,\\n\",\n \" start_date=risk_report.earliest_start_date,\\n\",\n \" end_date=risk_report.latest_end_date,\\n\",\n \" factors=[\\n\",\n \" \\\"Country\\\",\\n\",\n \" \\\"Beta\\\",\\n\",\n \" \\\"Earnings Quality\\\",\\n\",\n \" ], # Skip passing in a value here to get a table with all model factors\\n\",\n \")\\n\",\n \"\\n\",\n \"display(pd.DataFrame(pnl_table))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 4: Historical Portfolio Factor Performance\\n\",\n \"\\n\",\n \"`get_factor_pnl` allows you to pull historical factor performance for a list of factors, as well as aggregations like factor, specific, and total risk:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pnl = risk_report.get_factor_pnl(\\n\",\n \" factor_names=['Factor', 'Specific', 'Total', 'Market', 'Country', 'Industry', 'Style'],\\n\",\n \" start_date=risk_report.earliest_start_date,\\n\",\n \" end_date=risk_report.latest_end_date,\\n\",\n \" unit=FactorRiskUnit.Notional,\\n\",\n \")\\n\",\n \"pnl_overview = pnl.filter(items=['Date', 'Total']).set_index('Date')\\n\",\n \"\\n\",\n \"pnl_overview.cumsum().plot(title='PnL')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"This makes it easy to break down PnL over time and how it was attributed to various systematic risk factors:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pnl_overview = pnl.filter(items=['Date', 'Factor', 'Specific', 'Total']).set_index('Date')\\n\",\n \"pnl_overview.cumsum().plot(title='PnL Overview')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"And dissect that further by factor attribution further by category...\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pnl_by_type = pnl.filter(items=['Date', 'Market', 'Country', 'Industry', 'Style', 'Specific']).set_index('Date')\\n\",\n \"\\n\",\n \"pnl_by_type.cumsum().plot(title='PnL by Factor Category')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 5: Historical Factor Exposure\\n\",\n \"\\n\",\n \"For each day, it's possible to pull your portfolio's exposure to specific factors...\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"category_exposures = risk_report.get_factor_exposure(\\n\",\n \" factor_names=['Market', 'Industry', 'Style'],\\n\",\n \" start_date=risk_report.earliest_start_date,\\n\",\n \" end_date=risk_report.latest_end_date,\\n\",\n \" unit=FactorRiskUnit.Notional,\\n\",\n \").set_index('Date')\\n\",\n \"\\n\",\n \"category_exposures.plot(title='Exposures to Factor Categories')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"Or get the exposures to all factors in a given category:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"category_exposures = risk_report.get_factor_exposure(\\n\",\n \" factor_categories=['Style'],\\n\",\n \" start_date=risk_report.earliest_start_date,\\n\",\n \" end_date=risk_report.latest_end_date,\\n\",\n \" unit=FactorRiskUnit.Notional,\\n\",\n \").set_index('Date')\\n\",\n \"\\n\",\n \"category_exposures.plot(title='Exposures to Style Factors')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/05_Manage_a_Fund_of_Funds.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Managing a Fund of Funds\\n\",\n \"\\n\",\n \"If you have a Marquee portfolio with position tags, you can easily turn it into a fund of funds,\\n\",\n \"with which you can create and leverage performance, risk, and thematic reports for a subset of portfolio\\n\",\n \"positions based off their tags.\\n\",\n \"\\n\",\n \"This tutorial assumes you already have a portfolio with positions tags. To learn more about how to create a portfolio\\n\",\n \"in Marquee using GS Quant, visit our tutorial\\n\",\n \"[here](https://developer.gs.com/p/docs/services/portfolio/programmatic-access/upload/).\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Turn your portfolio into a fund of funds\\n\",\n \"\\n\",\n \"Let's imagine your portfolio has the following tags on its positions:\\n\",\n \"\\n\",\n \"| Tag Name | Possible Tag Values |\\n\",\n \"|-----------------|---------------\\n\",\n \"| `Sector` | `Fintech` , `Banks` , `Software` |\\n\",\n \"| `Analyst` | `Marcus` , `Samuel` |\\n\",\n \"\\n\",\n \"When creating a fund of funds, the portfolio's tag name hierarchy is a list of the portfolio's\\n\",\n \"tag names in an order that determines how the fund of funds is segmented. In this example, say we want to analyze\\n\",\n \"the portfolio by `Sector`, and then `Analyst`:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm = PortfolioManager(portfolio_id='YOUR PORTFOLIO ID')\\n\",\n \"pm.set_tag_name_hierarchy(['Sector', 'Analyst'])\\n\",\n \"\\n\",\n \"print(f\\\"Your portfolio has the following Tag Hierarchy: {pm.get_tag_name_hierarchy()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"The result will be the following list of sub-portfolios:\\n\",\n \"\\n\",\n \"| Sub-Portfolios |\\n\",\n \"|-----------------\\n\",\n \"| `Sector`: `Fintech` |\\n\",\n \"| `Sector`: `Banks` |\\n\",\n \"| `Sector`: `Software` |\\n\",\n \"| `Sector`: `Fintech` , `Analyst`: `Marcus` |\\n\",\n \"| `Sector`: `Banks` , `Analyst`: `Marcus` |\\n\",\n \"| `Sector`: `Software` , `Analyst`: `Marcus` |\\n\",\n \"| `Sector`: `Fintech` , `Analyst`: `Samuel` |\\n\",\n \"| `Sector`: `Banks` , `Analyst`: `Samuel` |\\n\",\n \"| `Sector`: `Software` , `Analyst`: `Samuel` |\\n\",\n \"\\n\",\n \"You can retrieve this list as a dictionary using the following function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print(pm.get_all_fund_of_fund_tags())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Retrieve Sub-Portfolio Analytics\\n\",\n \"\\n\",\n \"Now we have a list of dictionaries in which each dictionary\\n\",\n \"corresponds to a particular sub-portfolio. These dictionaries can be passed to retrieve the relevant\\n\",\n \"reports for the sub-portfolio:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"subportfolio_reports = pm.get_reports(tags={'Sector': 'Banks', 'Analyst': 'Marcus'})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"From here, you can pull analytics from these reports as you would for any other portfolio. For more information\\n\",\n \"on leveraging reports, visit our tutorials [here](https://developer.gs.com/p/docs/services/portfolio/programmatic-access/reports/).\\n\",\n \"\\n\",\n \"At any point, if you create/edit/delete reports on your main portfolio or change the tag name hierarchy on\\n\",\n \"the portfolio, it's important to update your fund of funds so all your sub-portfolio reports are in sync\\n\",\n \"with your main portfolio reports. You can do so by running this line:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm.update_portfolio_tree()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/06_Get_Factor_Risk_Data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Pull Portfolio Factor Risk Data with GS Quant\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from IPython.display import display\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import FactorRiskUnit, FactorRiskTableMode\\n\",\n \"from gs_quant.markets.securities import SecurityMaster, AssetIdentifier\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \")\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Get Portfolio Factor Risk Report\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"risk_report = PortfolioManager('ENTER PORTFOLIO ID').get_factor_risk_report(risk_model_id='RISK MODEL ID')\\n\",\n \"\\n\",\n \"print(f'Factor risk report found with ID: {risk_report.id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"Want to query data for an active risk report? Leverage the `SecurityMaster` class to retrieve the benchmark identifier and\\n\",\n \"pass it into the `get_factor_risk_report` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"benchmark = SecurityMaster.get_asset(id_value='SPX', id_type=AssetIdentifier.BLOOMBERG_ID)\\n\",\n \"\\n\",\n \"risk_report = PortfolioManager('ENTER PORTFOLIO ID').get_factor_risk_report(\\n\",\n \" risk_model_id='AXIOMA_AXUSWW4', benchmark_id=benchmark.get_marquee_id()\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f'Factor risk report found with ID: {risk_report.id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Current Portfolio Risk\\n\",\n \"\\n\",\n \"Once your risk report is scheduled as of the latest business day, you can view updated risk data broken down by factor category:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"category_table = risk_report.get_view(\\n\",\n \" start_date=risk_report.latest_end_date, end_date=risk_report.latest_end_date, unit=FactorRiskUnit.Notional\\n\",\n \").get('factorCategoriesTable')\\n\",\n \"\\n\",\n \"category_df = pd.DataFrame(category_table).filter(\\n\",\n \" items=[\\n\",\n \" 'name',\\n\",\n \" 'proportionOfRisk',\\n\",\n \" 'marginalContributionToRiskPercent',\\n\",\n \" 'relativeMarginalContributionToRisk',\\n\",\n \" 'exposure',\\n\",\n \" 'avgProportionOfRisk',\\n\",\n \" ]\\n\",\n \")\\n\",\n \"category_df.rename(\\n\",\n \" columns={\\n\",\n \" 'proportionOfRisk': 'Prop. of Risk',\\n\",\n \" 'marginalContributionToRiskPercent': 'MCTR Percent',\\n\",\n \" 'relativeMarginalContributionToRisk': 'MCTR (USD)',\\n\",\n \" 'exposure': 'Exposure (USD)',\\n\",\n \" 'avgProportionOfRisk': 'Avg Prop. of Risk',\\n\",\n \" },\\n\",\n \" inplace=True,\\n\",\n \")\\n\",\n \"\\n\",\n \"display(category_df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"It is also possible to get a similar table for all the factors in a factor category. In this case, let's pull risk data for all the Style factors:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"factor_table = risk_report.get_view(\\n\",\n \" factor_category='Style',\\n\",\n \" start_date=risk_report.latest_end_date,\\n\",\n \" end_date=risk_report.latest_end_date,\\n\",\n \" unit=FactorRiskUnit.Notional,\\n\",\n \").get('factorsTable')\\n\",\n \"\\n\",\n \"factor_df = pd.DataFrame(factor_table).filter(\\n\",\n \" items=[\\n\",\n \" 'name',\\n\",\n \" 'proportionOfRisk',\\n\",\n \" 'marginalContributionToRiskPercent',\\n\",\n \" 'relativeMarginalContributionToRisk',\\n\",\n \" 'exposure',\\n\",\n \" 'avgProportionOfRisk',\\n\",\n \" ]\\n\",\n \")\\n\",\n \"factor_df.rename(\\n\",\n \" columns={\\n\",\n \" 'proportionOfRisk': 'Prop. of Risk',\\n\",\n \" 'marginalContributionToRiskPercent': 'MCTR %',\\n\",\n \" 'relativeMarginalContributionToRisk': 'MCTR (USD)',\\n\",\n \" 'exposure': 'Exposure (USD)',\\n\",\n \" 'avgProportionOfRisk': 'Avg Prop. of Risk',\\n\",\n \" },\\n\",\n \" inplace=True,\\n\",\n \")\\n\",\n \"\\n\",\n \"display(factor_df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"For an asset-level risk breakdown for a set of factors, leverage the get_table() function on the FactorRiskReport class. In this case, let's see the Z-Scores for the factors Beta, Dividend Yield, and Downside Risk:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"zscore_table = risk_report.get_table(\\n\",\n \" mode=FactorRiskTableMode.ZScore,\\n\",\n \" factors=[\\\"Beta\\\", \\\"Dividend Yield\\\", \\\"Downside Risk\\\"],\\n\",\n \" date=dt.date(2025, 1, 29), # Define the date for which you are looking to query the report on\\n\",\n \")\\n\",\n \"\\n\",\n \"display(pd.DataFrame(zscore_table))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 4: Historical Portfolio Risk\\n\",\n \"\\n\",\n \"First let's pull the daily annualized risk across the duration of your portfolio:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"risk_data = risk_report.get_results(\\n\",\n \" start_date=risk_report.earliest_start_date, end_date=risk_report.latest_end_date, unit=FactorRiskUnit.Notional\\n\",\n \")\\n\",\n \"\\n\",\n \"historical_risk = risk_data[risk_data['factor'] == 'Total'][['date', 'annualRisk']].set_index('date')\\n\",\n \"\\n\",\n \"historical_risk = pd.DataFrame(historical_risk)\\n\",\n \"\\n\",\n \"historical_risk.plot(title='Annualized Risk % (ex-ante)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"For each day, you can see what percent of your risk is contributed to factor risk and what percent is idiosyncratic:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"historical_risk = risk_data[risk_data['factor'].isin(['Specific', 'Factor'])][\\n\",\n \" ['date', 'factor', 'annualRisk']\\n\",\n \"].set_index('date')\\n\",\n \"\\n\",\n \"# Formatting the data for plotting\\n\",\n \"historical_risk = historical_risk.pivot(columns='factor', values='annualRisk')\\n\",\n \"historical_risk.reset_index(inplace=True)\\n\",\n \"historical_risk.rename(columns={'Factor': 'Factor Risk', 'Specific': 'Specific Risk'}, inplace=True)\\n\",\n \"\\n\",\n \"historical_risk.plot(x='date', y=['Factor Risk', 'Specific Risk'], title='Factor and Specific Risk')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"It's even possible to break down that factor risk further by category.\\n\",\n \"\\n\",\n \"Let's start by just seeing the universe of **Factor Categories** in our risk report.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"risk_data = risk_report.get_results(\\n\",\n \" start_date=risk_report.earliest_start_date, end_date=risk_report.latest_end_date, unit=FactorRiskUnit.Notional\\n\",\n \")\\n\",\n \"\\n\",\n \"print(risk_data[\\\"factorCategory\\\"].unique())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Factor categories allow to plot factor risk that falls under the specificied universe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"factor_categories = ['Style', 'Market']\\n\",\n \"\\n\",\n \"prop_of_risk = risk_report.get_factor_proportion_of_risk(\\n\",\n \" factor_categories=factor_categories,\\n\",\n \" start_date=risk_report.earliest_start_date,\\n\",\n \" end_date=risk_report.latest_end_date,\\n\",\n \").set_index('Date')\\n\",\n \"\\n\",\n \"prop_of_risk.plot(title=f'Proportion of Risk By Factor Categories: {factor_categories}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"And by factors within a category. In this case, let's try the Style factors:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"factor_categories = ['Style']\\n\",\n \"factor_names = ['Dividend Yield', 'Earnings Yield', 'Growth']\\n\",\n \"\\n\",\n \"prop_of_risk = risk_report.get_factor_proportion_of_risk(\\n\",\n \" factor_categories=factor_categories,\\n\",\n \" factor_names=factor_names,\\n\",\n \" start_date=risk_report.earliest_start_date,\\n\",\n \" end_date=risk_report.latest_end_date,\\n\",\n \").set_index('Date')\\n\",\n \"\\n\",\n \"prop_of_risk.plot(title='Proportion of Risk of Style Factors')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/07_Get_Portfolio_Performance_Analytics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Pull Performance Report Results with GS Quant\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from IPython.display import display\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.securities import Asset, AssetIdentifier\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \")\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm = PortfolioManager('ENTER PORTFOLIO ID')\\n\",\n \"performance_report = pm.get_performance_report()\\n\",\n \"start_date = performance_report.earliest_start_date\\n\",\n \"end_date = performance_report.latest_end_date\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Step 2: Pull daily exposures and PnL data: \\n\",\n \"\\n\",\n \"Now that we have our performance report, we can leverage the unique functionalities of the PerformanceReport class to pull daily exposure and PnL data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"all_exposures = performance_report.get_many_measures(\\n\",\n \" start_date=start_date, end_date=end_date, measures=['pnl', 'grossExposure', 'netExposure']\\n\",\n \")\\n\",\n \"\\n\",\n \"print(all_exposures)\\n\",\n \"all_exposures.plot(title='Performance Breakdown')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Step 3: Pull Brinson Attribution Data \\n\",\n \"Now let's pull Brinson Attribution data to analyze the PnL of your portfolio compared to a benchmark, which can be any equity ETF, Index, or Basket in Marquee:\\n\",\n \"\\n\",\n \"*Important Note: An error in the code below might occur if time period for which you are querying is too long*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"asset = Asset.get(id_value='MXWO', id_type=AssetIdentifier.TICKER)\\n\",\n \"\\n\",\n \"brinson_attribution_results = performance_report.get_brinson_attribution(\\n\",\n \" benchmark=asset.get_marquee_id(), include_interaction=True, start_date=start_date, end_date=end_date\\n\",\n \")\\n\",\n \"\\n\",\n \"display(pd.DataFrame(brinson_attribution_results))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Step 4: Pull Asset Level Performance Data\\n\",\n \"\\n\",\n \"We can pull asset level data from the performance report.\\n\",\n \"\\n\",\n \"You can extract asset level data of quantity, netWeight, netExposure, etc.., using `get_positions_data`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"asset_level_data = performance_report.get_positions_data(\\n\",\n \" start=performance_report.earliest_start_date, end=performance_report.earliest_start_date\\n\",\n \")\\n\",\n \"\\n\",\n \"asset_level_data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can also pull asset level contribution to PnL using the `pnl_contribution` function used below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pnl_contribution = performance_report.get_pnl_contribution(\\n\",\n \" start_date=performance_report.earliest_start_date, end_date=performance_report.latest_end_date\\n\",\n \")\\n\",\n \"\\n\",\n \"pnl_contribution\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/08_Get_ESG_Analytics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Pull Marquee ESG Analytics with GS Quant\\n\",\n \"\\n\",\n \"Portfolio ESG Analytics are driven by the Goldman Sachs GIR SUSTAIN Headline Metrics dataset. Using data from third-party ESG data providers, the GIR SUSTAIN team has identified what it believes to be the most relevant Environmental & Social (E&S) exposures for a company’s sector and Governance (G) exposure relative to its region and the global SUSTAIN ESG universe. These metrics form the framework through which to evaluate corporate ESG engagement. We caution at the outset that this data should not be viewed as signalling 'good' or 'bad' ESG or overall performance. Instead, the framework highlights a company’s peer-relative performance on the key ESG metrics that the team believes to be the most relevant for that sector as a basis for further analysis.\\n\",\n \"\\n\",\n \"Please note that for companies with multiple share classes, SUSTAIN ESG data will only be provided for the primary share class. If other share classes are included in your portfolio, the ESG metrics will be represented as N/A and aggregate metrics will not include them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"from gs_quant.api.gs.esg import ESGMeasure, ESGCard\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from IPython.display import display\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Define Your Entity\\n\",\n \"\\n\",\n \"ESG can be pulled from any object that inherits from the `PositionedEntity` class, such as `PortfolioManager`, `Index` or `Basket`. In this example, we will get ESG data for a Marquee portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm = PortfolioManager('ENTER YOUR PORTFOLIO ID')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"Now we will walk through some ESG analytics you can pull from your portfolio. For all of the following examples, if no date is provided, the results will be as of the last previous business day.\\n\",\n \"\\n\",\n \"## Step 3: Get Portfolio's Weight Averaged ESG Percentile Values\\n\",\n \"\\n\",\n \"Easily pull your weighted average percentile values of your portfolio on a given day:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Portfolio Summmary:')\\n\",\n \"display(pm.get_esg_summary(pricing_date=dt.date(2021, 9, 1)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Quintile Breakdown\\n\",\n \"\\n\",\n \"If you want to see what percent of your portfolio has an ESG percentile value between 0-20%, 20%-40%,\\n\",\n \"and so on, it's possible to pull that information for your requested ESG measure:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Quintile Breakdown:')\\n\",\n \"display(pm.get_esg_quintiles(measure=ESGMeasure.ES_PERCENTILE, pricing_date=dt.date(2021, 9, 1)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Breakdown by Region and Sector\\n\",\n \"\\n\",\n \"View your portfolio's weighted average ESG percentile value by GIR SUSTAIN subsector...\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Sector Breakdown:')\\n\",\n \"display(pm.get_esg_by_sector(measure=ESGMeasure.ES_PERCENTILE, pricing_date=dt.date(2021, 9, 1)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Breakdown by Region and Sector\\n\",\n \"\\n\",\n \"and by GIR SUSTAIN region:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Region Breakdown:')\\n\",\n \"display(pm.get_esg_by_region(measure=ESGMeasure.ES_PERCENTILE, pricing_date=dt.date(2021, 9, 1)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Top Ten and Bottom Ten Ranked\\n\",\n \"\\n\",\n \"Get a list of your ten positions with the highest ESG percentile values...\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Top Ten Ranked:')\\n\",\n \"display(pm.get_esg_top_ten(measure=ESGMeasure.ES_PERCENTILE, pricing_date=dt.date(2021, 9, 1)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"and a list of your ten positions with the lowest ESG percentile values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Bottom Ten Ranked:')\\n\",\n \"display(pm.get_esg_bottom_ten(measure=ESGMeasure.ES_PERCENTILE, pricing_date=dt.date(2021, 9, 1)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Quick Tip!\\n\",\n \"If you would like to pull data for multiple measures at once, you can leverage the `get_all_esg_data` function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"aggregated_esg_results = pm.get_all_esg_data(\\n\",\n \" measures=[ESGMeasure.ES_PERCENTILE, ESGMeasure.G_PERCENTILE],\\n\",\n \" cards=[ESGCard.QUINTILES, ESGCard.MEASURES_BY_SECTOR],\\n\",\n \" pricing_date=dt.date(2021, 9, 1),\\n\",\n \")\\n\",\n \"print(aggregated_esg_results)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### You're all set, Congrats!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/09_Get_Carbon_Analytics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Pull Marquee Carbon Analytics Data with GS Quant\\n\",\n \"\\n\",\n \"### Permission Prerequisites\\n\",\n \"\\n\",\n \"To execute all the code in this tutorial, you will need the following application scopes:\\n\",\n \"- **read_financial_data**\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"\\n\",\n \"from IPython.display import display\\n\",\n \"from gs_quant.api.gs.assets import GsAssetApi\\n\",\n \"from gs_quant.api.gs.carbon import CarbonCoverageCategory, CarbonEmissionsIntensityType\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \")\\n\",\n \"\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Define Your Entity\\n\",\n \"\\n\",\n \"Carbon Analytics can be pulled from any object that inherits from the `PositionedEntity` class, such as `PortfolioManager`, `Index` or `Basket`. In this example, we will get analytics for a Marquee portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pm = PortfolioManager('YOUR PORTFOLIO ID')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Pull Carbon Analytics\\n\",\n \"\\n\",\n \"There are various parameters each of these methods take in.\\n\",\n \" - reporting_year - takes 'Latest' and last 4 complete years i.e., from T-2 to T-5. defaults to Latest\\n\",\n \" - currency - uses 'Currency' enum, defaults to the entity currency.\\n\",\n \" - include_estimates - Parameter to choose if estimated emissions are to be included or not, defaults to false.\\n\",\n \" - use_historical_data - Parameter to choose historical positions or backcast latest composition, defaults to false.\\n\",\n \" - normalize_emissions - Parameter to normalize entity notional to 1,000,000 in denominated currency passed.\\n\",\n \" - analytics_view - Parameter to view analytics using long component or short component of the portfolio\\n\",\n \" - scope - uses 'CarbonScope' enum with totalGHG, scope1, scope2 values, defaults to totalGHG.\\n\",\n \" - coverage_category - category for data coverage, uses 'CarbonCoverageCategory' enum with weights and numberOfCompanies, defaults to weights.\\n\",\n \" - target_coverage_category - category for SBTI and Net Zero Targets, uses 'CarbonTargetCoverageCategory' enum with portfolioEmissions and capitalAllocated, defaults to portfolioEmissions.\\n\",\n \" - classification - classification to group financed emissions, uses CarbonEmissionsAllocationCategory with sector, industry and region, defaults to sector\\n\",\n \" - intensity_metric - intensity metric to query attribution for, uses CarbonEmissionsIntensityType with enterprise value, marketcap and revenue. defaults to enterprise value.\\n\",\n \" - benchmark_id - Marquee identifier for the benchmark to do attribution analysis with.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Data Coverage\\n\",\n \"\\n\",\n \"Pull the data coverage for a reporting year based on weights or number of companies in the entity. You can choose to include estimated emissions, choose between using historical compositions or backcasting latest composition\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Data Coverage:')\\n\",\n \"display(pm.get_carbon_coverage(include_estimates=True, coverage_category=CarbonCoverageCategory.NUMBER_OF_COMPANIES))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### SBTI and Net Zero Emissions Target Coverage\\n\",\n \"\\n\",\n \"Pull Science Based Target Coverage and Net Zero Emissions Target Coverage for a reporting year based on capital allocated or portfolio emissions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Science Based Target and Net Zero Emissions Target Coverage:')\\n\",\n \"display(pm.get_carbon_sbti_netzero_coverage(include_estimates=True))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Financed Emissions and Emissions Intensity profile\\n\",\n \"\\n\",\n \"Pull Financed Emissions profile and intensity metrics for a reporting year in respective denomination. Other parameters are to include estimates, use historical data, normalize emissions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Financed Emissions and Intensity Profile:')\\n\",\n \"display(pm.get_carbon_emissions(include_estimates=True))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Financed Emissions by sector, industry and region\\n\",\n \"\\n\",\n \"Aggregate financed emissions and capital for each of the categories for a reporting year in respective denomination. We can pass a scope to look at specific scope.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"print('Financed Emissions by Sector:')\\n\",\n \"display(pm.get_carbon_emissions_allocation(include_estimates=True))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Attribution analysis with benchmark\\n\",\n \"\\n\",\n \"Pull up brinson attribution analysis for sector allocation and security selection with respect to benchmark. Pass in the benchmark id, intensity type.\\n\",\n \"\\n\",\n \"Benchmark can be either an asset id or a portfolio id. For an asset, get the asset id by resolving the identifier and, for a portfolio use the portfolio id.\\n\",\n \"\\n\",\n \"Resolve asset id from identifier:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"identifier = 'SPX'\\n\",\n \"mqids = GsAssetApi.resolve_assets(identifier=[identifier], fields=['id'], limit=1)\\n\",\n \"try:\\n\",\n \" benchmark_id = mqids[identifier][0]['id']\\n\",\n \"except:\\n\",\n \" raise ValueError('Error in resolving the following identifier: ' + identifier)\\n\",\n \"\\n\",\n \"print('Attribution Analysis:')\\n\",\n \"display(\\n\",\n \" pm.get_carbon_attribution_table(\\n\",\n \" benchmark_id=benchmark_id, include_estimates=True, intensity_metric=CarbonEmissionsIntensityType.EI_REVENUE\\n\",\n \" )\\n\",\n \")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Portfolios/10_Get_Thematic_Analytics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Pull Portfolio Thematic Analytics with GS Quant\\n\",\n \"\\n\",\n \"## Permission Prerequisites\\n\",\n \"\\n\",\n \"To execute all the code in this tutorial, you will need the following application scopes:\\n\",\n \"- **read_product_data**\\n\",\n \"- **read_financial_data**\\n\",\n \"- **run_analytics** (must be requested)\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from IPython.display import display\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Get Portfolio Thematic Report\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"thematic_report = PortfolioManager('ENTER YOUR PORTFOLI ID').get_thematic_report()\\n\",\n \"\\n\",\n \"print(f'Thematic report found with ID: {thematic_report.id}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Current Thematic Exposure to All Baskets\\n\",\n \"\\n\",\n \"Once your thematic report is scheduled as of the latest business day, you can view your portfolio's current exposure and\\n\",\n \"beta to every flagship basket in our thematic factor model in just a few lines of code:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"thematic_exposures = thematic_report.get_all_thematic_exposures(\\n\",\n \" start_date=thematic_report.latest_end_date, end_date=thematic_report.latest_end_date\\n\",\n \")\\n\",\n \"\\n\",\n \"pd.set_option('display.max_colwidth', 0)\\n\",\n \"\\n\",\n \"display(thematic_exposures)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Get Thematic Exposure Breakdown for a Basket\\n\",\n \"\\n\",\n \"Interested in a more granular breakdown of your exposure to a particular basket? Pull your thematic breakdown by asset\\n\",\n \"on a desired date:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"date = dt.date(\\n\",\n \" 2024, 12, 12\\n\",\n \") # If date is not in range of portfolio's position data there will not be any thematic data returned.\\n\",\n \"\\n\",\n \"thematic_breakdown = thematic_report.get_thematic_breakdown(date, \\\"BASKET'S MARQUEE ID\\\")\\n\",\n \"\\n\",\n \"display(thematic_breakdown)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Historical Thematic Exposure\\n\",\n \"\\n\",\n \"You can also pull the historical change in your thematic exposure to a basket:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"historical_exposures = thematic_report.get_all_thematic_exposures(\\n\",\n \" start_date=thematic_report.earliest_start_date,\\n\",\n \" end_date=thematic_report.latest_end_date,\\n\",\n \" basket_ids=[\\\"BASKET'S MARQUEE ID\\\"],\\n\",\n \")[['Date', 'Thematic Exposure']]\\n\",\n \"\\n\",\n \"\\n\",\n \"historical_exposures.plot(title='Historical Exposure to GSXUSTAY')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Reports/01_Factor_Risk_Report.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Factor Risk Reports\\n\",\n \"\\n\",\n \"Factor risk reports run historical factor risk analyses for your portfolio or basket over a specified date range by leveraging a factor risk model of your choice.\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from time import sleep\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from gs_quant.markets.report import FactorRiskReport\\n\",\n \"from gs_quant.models.risk_model import FactorRiskModel\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"scopes = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(\\n\",\n \" Environment.PROD,\\n\",\n \" client_id=client,\\n\",\n \" client_secret=secret,\\n\",\n \")\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Create a New Factor Risk Report\\n\",\n \"\\n\",\n \"#### Already have a factor risk report?\\n\",\n \"\\n\",\n \"If you want to skip creating a new report and continue this tutorial with an existing factor risk report, run the following and skip to Step 3:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"risk_report_id = 'FACTOR RISK REPORT ID'\\n\",\n \"\\n\",\n \"risk_report = FactorRiskReport.get(risk_report_id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When creating a factor risk report, you must specify the risk model you would like to use.\\n\",\n \"\\n\",\n \"\\n\",\n \"If you would like to see all available risk model IDs to choose from, run the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"risk_models = FactorRiskModel.get_many()\\n\",\n \"for risk_model in risk_models:\\n\",\n \" print(f'{risk_model.id}\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we'll create a factor risk report leveraging the Barra USSLOW Long model. If you would like to calculate\\n\",\n \"risk in relation to a benchmark, you can add an index, basket, or ETF to your `FactorRiskReport` object:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"entity_id = 'PORTFOLIO ID'\\n\",\n \"risk_model_id = 'RISK MODEL ID'\\n\",\n \"\\n\",\n \"\\n\",\n \"risk_report = FactorRiskReport(\\n\",\n \" risk_model_id=risk_model_id,\\n\",\n \" fx_hedged=True,\\n\",\n \")\\n\",\n \"\\n\",\n \"risk_report.set_position_source(entity_id)\\n\",\n \"risk_report.save()\\n\",\n \"\\n\",\n \"print(f'A new factor risk report for entity \\\"{entity_id}\\\" has been made with ID \\\"{risk_report.id}\\\".')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Schedule the Report\\n\",\n \"\\n\",\n \"When scheduling reports, you have two options:\\n\",\n \"- Backcast the report: Take the earliest date with positions in the portfolio / basket and run the report on the positions held then with a start date before the earliest position date and an end date\\n\",\n \" of the earliest position date\\n\",\n \"- Do not backcast the report: Set the start date as a date that has positions in the portfolio or basket and an end date after that (best practice is to set it to T-1). In this case the\\n\",\n \" report will run on positions held as of each day in the date range\\n\",\n \"\\n\",\n \"In this case, let's try scheduling the report without backcasting:\\n\",\n \"\\n\",\n \"### Note:\\n\",\n \"Cannot Schedule backcasted report with no position sets on the report job end date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2025, 4, 1)\\n\",\n \"end_date = dt.date(2025, 4, 17)\\n\",\n \"\\n\",\n \"risk_report.schedule(start_date=start_date, end_date=end_date, backcast=True)\\n\",\n \"\\n\",\n \"print(f'Report \\\"{risk_report.id}\\\" has been scheduled.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Alternative Step 3: Run the Report\\n\",\n \"\\n\",\n \"Depending on the size of your portfolio and the length of the schedule range, it usually takes anywhere from a couple seconds to a couple minutes for your report to finish executing.\\n\",\n \"Only after that can you successfully pull the results from that report. If you would rather run the report and pull the results immediately after they are ready, you can leverage the `run`\\n\",\n \"function.\\n\",\n \"\\n\",\n \"You can run a report synchronously or asynchronously.\\n\",\n \"- Synchronous: the Python script will stall at the `run` function line and wait for the report to finish. The `run` function will then return a dataframe with the report results\\n\",\n \"- Asynchronously: the Python script will not stall at the `run` function line. The `run` function will return a `ReportJobFuture` object that will contain the report results when they are ready.\\n\",\n \"\\n\",\n \"In this example, let's run the report asynchronously and wait for the results:\\n\",\n \"\\n\",\n \"### Note:\\n\",\n \"Cannot Schedule historical report with no position sets before the report job start date\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2025, 4, 2)\\n\",\n \"end_date = dt.date(2025, 4, 7)\\n\",\n \"\\n\",\n \"report_result_future = risk_report.run(start_date=start_date, end_date=end_date, backcast=False, is_async=True)\\n\",\n \"\\n\",\n \"while not report_result_future.done():\\n\",\n \" print('Waiting for report results...')\\n\",\n \" sleep(5)\\n\",\n \"\\n\",\n \"print('\\\\nReport results done! Here they are...')\\n\",\n \"print(report_result_future.result())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2025, 2, 6)\\n\",\n \"end_date = dt.date(2025, 3, 31)\\n\",\n \"\\n\",\n \"report_result_future = risk_report.run(start_date=start_date, end_date=end_date, backcast=False, is_async=True)\\n\",\n \"\\n\",\n \"while not report_result_future.done():\\n\",\n \" print('Waiting for report results...')\\n\",\n \" sleep(5)\\n\",\n \"\\n\",\n \"print('\\\\nReport results done! Here they are...')\\n\",\n \"print(report_result_future.result())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Pull Report Results\\n\",\n \"\\n\",\n \"Now that we have our completed factor risk report, we can leverage the unique functionalities of the `FactorRiskReport` class to pull attribution and risk data. \\n\",\n \"\\n\",\n \"This `get_results` function will return the date indexed data as shown in the dummy table below:\\n\",\n \"\\n\",\n \"|date |factor |factorCategory|exposure|pnl|dailyRisk|annualRisk|proportionOfRisk|relativeMarginalContributionToRisk|aum|marginalContributionToRiskPercent|\\n\",\n \"|----------|------------|---------------|-----------|--------------|--------------|------------|------------|-------------|----|-------|\\n\",\n \"|2025-04-10|\\tSpecific|\\tAggregations|\\t0.000000|\\t-186.921292|\\t116.350041|\\t1846.999635|\\t0.235141|\\t895.635113|\\t0.0|\\tNaN|\\n\",\n \"|2025-04-11|\\tSpecific|\\tAggregations|\\t0.000000|\\t-17.822047|\\t117.014467|\\t1857.547084|\\t0.230338|\\t891.503322|\\t0.0|\\tNaN|\\n\",\n \"|2025-04-14|\\tSpecific|\\tAggregations|\\t0.000000|\\t73.498315|\\t118.745143|\\t1885.020702|\\t0.225550|\\t895.235643|\\t0.0|\\tNaN|\\n\",\n \"|2025-04-15|\\tSpecific|\\tAggregations|\\t0.000000|\\t58.400802|\\t119.321131|\\t1894.164234|\\t0.223980|\\t896.442371|\\t2000000.0|\\t0.044822|\\n\",\n \"|2025-04-16|\\tSpecific|\\tAggregations|\\t0.000000|\\t23.749292|\\t116.631423|\\t1851.466441|\\t0.222957|\\t874.230618|\\t1000000.0|\\t0.087423|\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"factor_and_total_results = risk_report.get_results(\\n\",\n \" factors=['Factor', 'Specific'], start_date=dt.date(2025, 4, 10), end_date=dt.date(2025, 4, 17)\\n\",\n \")\\n\",\n \"factor_and_total_results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Quick Tip & Plotting!\\n\",\n \"You can pull historical data on factor, specific, and total PnL:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"pnl = risk_report.get_factor_pnl(factor_names=['Factor', 'Specific', 'Total'], start_date=start_date, end_date=end_date)\\n\",\n \"pnl.set_index('Date', inplace=True)\\n\",\n \"pnl.index = pd.to_datetime(pnl.index)\\n\",\n \"\\n\",\n \"pnl.cumsum().plot(title='Risk Attribution Breakdown')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's pull the breakdown of proportion of risk among the different factor types over time:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"prop_of_risk = risk_report.get_factor_proportion_of_risk(\\n\",\n \" factor_names=['Market', 'Style', 'Industry', 'Country'], start_date=start_date, end_date=end_date\\n\",\n \")\\n\",\n \"prop_of_risk.set_index('Date', inplace=True)\\n\",\n \"prop_of_risk.index = pd.to_datetime(prop_of_risk.index)\\n\",\n \"\\n\",\n \"prop_of_risk.plot(title='Factor Proportion of Risk Breakdown')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Please find more functions to pull specific factor risk data available [here](https://developer.gs.com/docs/gsquant/api/classes/gs_quant.markets.report.FactorRiskReport.html#gs_quant.markets.report.FactorRiskReport).**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### You're all set; Congrats!\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Reports/02_Performance_Report.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"# Performance Reports\\n\",\n \"\\n\",\n \"Performance reports run historical analyses on measures like exposure and PnL for your portfolio over a specified date range.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\",\n \"from time import sleep\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import PerformanceReport\\n\",\n \"from gs_quant.markets.securities import Asset, AssetIdentifier\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Get Performance Report\\n\",\n \"\\n\",\n \"When creating a Marquee portfolio, a corresponding performance report for it is automatically created. Thus, we can leverage the `PortfolioManager` class to pull all the reports associated with your portfolio and find the performance report.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_id = 'PORTFOLIO ID'\\n\",\n \"\\n\",\n \"all_reports = PortfolioManager(portfolio_id).get_reports()\\n\",\n \"performance_report = list(filter(lambda report: isinstance(report, PerformanceReport), all_reports))[0]\\n\",\n \"\\n\",\n \"print(f'Performance report for portfolio \\\"{portfolio_id}\\\" has been found with ID \\\"{performance_report.id}\\\".')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Schedule the Report\\n\",\n \"\\n\",\n \"When scheduling reports, you have two options:\\n\",\n \"- Backcast the report: Take the earliest date with positions in the portfolio / basket and run the report on the positions held then with a start date before the earliest position date and an end date\\n\",\n \" of the earliest position date\\n\",\n \"- Do not backcast the report: Set the start date as a date that has positions in the portfolio or basket and an end date after that (best practice is to set it to T-1). In this case the\\n\",\n \" report will run on positions held as of each day in the date range\\n\",\n \"\\n\",\n \"In this case, let's try scheduling the report without backcasting:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2025, 4, 18)\\n\",\n \"end_date = dt.date(2025, 4, 23)\\n\",\n \"\\n\",\n \"performance_report.schedule(start_date=start_date, end_date=end_date, backcast=False)\\n\",\n \"\\n\",\n \"print(\\n\",\n \" f'A new performance report for portfolio \\\"{performance_report.position_source_id}\\\" has been made with ID \\\"{performance_report.id}\\\".'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Alternative Step 3: Run the Report\\n\",\n \"\\n\",\n \"Depending on the size of your portfolio and the length of the schedule range, it usually takes anywhere from a couple seconds to a couple minutes for your report to finish executing.\\n\",\n \"Only after that can you successfully pull the results from that report. If you would rather run the report and pull the results immediately after they are ready, you can leverage the `run`\\n\",\n \"function of the `Report` class.\\n\",\n \"\\n\",\n \"You can run a report synchronously or asynchronously.\\n\",\n \"- Synchronous: the Python script will stall at the `run` function line and wait for the report to finish. The `run` function will then return a dataframe with the report results\\n\",\n \"- Asynchronously: the Python script will not stall at the `run` function line. The `run` function will return a `ReportJobFuture` object that will contain the report results when they are ready.\\n\",\n \"\\n\",\n \"In this example, let's run the report asynchronously and wait for the results:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2025, 2, 10)\\n\",\n \"end_date = dt.date(2025, 2, 12)\\n\",\n \"\\n\",\n \"report_result_future = performance_report.run(start_date=start_date, end_date=end_date, backcast=False, is_async=True)\\n\",\n \"\\n\",\n \"while not report_result_future.done():\\n\",\n \" print('Waiting for report results...')\\n\",\n \" sleep(5)\\n\",\n \"\\n\",\n \"print('\\\\nReport results done! Here they are...')\\n\",\n \"print(report_result_future.result())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 4: Pull Report Results\\n\",\n \"\\n\",\n \"Now that we have our performance report, we can leverage the unique functionalities of the `PerformanceReport` class to pull daily exposure and PnL data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"all_exposures = performance_report.get_many_measures(\\n\",\n \" measures=['pnl', 'grossExposure', 'netExposure'],\\n\",\n \" start_date=performance_report.earliest_start_date,\\n\",\n \" end_date=performance_report.latest_end_date,\\n\",\n \")\\n\",\n \"\\n\",\n \"print(all_exposures)\\n\",\n \"all_exposures.plot(title='Performance Breakdown')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"Leverage the Brinson Attribution function in the `PerformanceReport` class to compare the PnL of your portfolio to a benchmark, which can be any equity ETF, Index, or Basket in Marquee:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = performance_report.earliest_start_date # Can also set to: dt.date(YYYY, MM, DD)\\n\",\n \"end_date = performance_report.latest_end_date # Can also set to: dt.date(YYYY, MM, DD)\\n\",\n \"\\n\",\n \"asset = Asset.get(id_value='SPY UP', id_type=AssetIdentifier.BLOOMBERG_ID)\\n\",\n \"\\n\",\n \"brinson_attribution_results = performance_report.get_brinson_attribution(\\n\",\n \" benchmark=asset.get_marquee_id(), include_interaction=True, start_date=start_date, end_date=end_date\\n\",\n \")\\n\",\n \"\\n\",\n \"display(pd.DataFrame(brinson_attribution_results))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can extract asset level data of quantity, netWeight, netExposure, etc.., using `get_positions_data`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"asset_level_data = performance_report.get_positions_data(\\n\",\n \" start=performance_report.earliest_start_date, end=performance_report.earliest_start_date\\n\",\n \")\\n\",\n \"\\n\",\n \"asset_level_data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can also pull asset level contribution to PnL using the `pnl_contribution` function used below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pnl_contribution = performance_report.get_pnl_contribution(\\n\",\n \" start_date=performance_report.earliest_start_date, end_date=performance_report.latest_end_date\\n\",\n \")\\n\",\n \"\\n\",\n \"pnl_contribution\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### You're all set; Congrats!\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/10_one_delta/Reports/03_Thematic_Report.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# Thematic Reports\\n\",\n \"\\n\",\n \"Thematic reports run historical analyses on the exposure of a portfolio to various Goldman Sachs Flagship Thematic baskets over a specified date range.\\n\",\n \"\\n\",\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import datetime as dt\\n\",\n \"from time import sleep\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.report import ThematicReport\\n\",\n \"from gs_quant.markets.securities import Asset, AssetIdentifier\\n\",\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Create a New Thematic Report\\n\",\n \"\\n\",\n \"#### Already have a thematic report?\\n\",\n \"\\n\",\n \"If you want to skip creating a new report and continue this tutorial with an existing thematic report, run the following and skip to Step 3:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_id = 'ENTER PORTFOLIO ID'\\n\",\n \"\\n\",\n \"thematic_report = PortfolioManager(portfolio_id).get_thematic_report()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The only parameter necessary in creating a new thematic report is the unique Marquee identifier of the portfolio on which you would like to run thematic analytics.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_id = 'ENTER PORTFOLIO ID'\\n\",\n \"\\n\",\n \"thematic_report = ThematicReport()\\n\",\n \"thematic_report.set_position_source(portfolio_id)\\n\",\n \"thematic_report.save()\\n\",\n \"\\n\",\n \"print(f'A new thematic report for portfolio \\\"{portfolio_id}\\\" has been made with ID \\\"{thematic_report.id}\\\".')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Schedule the Report\\n\",\n \"\\n\",\n \"When scheduling reports, you have two options:\\n\",\n \"- Backcast the report: Take the earliest date with positions in the portfolio / basket and run the report on the positions held then with a start date before the earliest position date and an end date\\n\",\n \" of the earliest position date\\n\",\n \"- Do not backcast the report: Set the start date as a date that has positions in the portfolio or basket and an end date after that (best practice is to set it to T-1). In this case the\\n\",\n \" report will run on positions held as of each day in the date range\\n\",\n \"\\n\",\n \"In this case, let's try scheduling the report without backcasting:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2021, 1, 4)\\n\",\n \"end_date = dt.date(2021, 8, 4)\\n\",\n \"\\n\",\n \"thematic_report.schedule(start_date=start_date, end_date=end_date, backcast=False)\\n\",\n \"\\n\",\n \"print(f'Report \\\"{thematic_report.id}\\\" has been scheduled.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Alternative Step 3: Run the Report\\n\",\n \"\\n\",\n \"Depending on the size of your portfolio and the length of the schedule range, it usually takes anywhere from a couple seconds to half a minute for your report to finish executing.\\n\",\n \"Only after that can you successfully pull the results from that report. If you would rather run the report and pull the results immediately after they are ready, you can leverage the `run`\\n\",\n \"function.\\n\",\n \"\\n\",\n \"You can run a report synchronously or asynchronously.\\n\",\n \"- Synchronous: the Python script will stall at the `run` function line and wait for the report to finish. The `run` function will then return a dataframe with the report results\\n\",\n \"- Asynchronously: the Python script will not stall at the `run` function line. The `run` function will return a `ReportJobFuture` object that will contain the report results when they are ready.\\n\",\n \"\\n\",\n \"In this example, let's run the report asynchronously and wait for the results:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2021, 1, 4)\\n\",\n \"end_date = dt.date(2021, 8, 4)\\n\",\n \"\\n\",\n \"report_result_future = thematic_report.run(start_date=start_date, end_date=end_date, backcast=False, is_async=True)\\n\",\n \"\\n\",\n \"while not report_result_future.done():\\n\",\n \" print('Waiting for report results...')\\n\",\n \" sleep(5)\\n\",\n \"\\n\",\n \"print('\\\\nReport results done! Here they are...')\\n\",\n \"print(report_result_future.result())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 3: Pull Report Results\\n\",\n \"\\n\",\n \"Now that we have our factor risk report, we can leverage the unique functionalities of the `ThematicReport` class to pull exposure and PnL data. Let's get the historical changes in thematic exposure and beta to a GS basket:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"basket = Asset.get(id_value='BASKET ID', id_type=AssetIdentifier.TICKER)\\n\",\n \"thematic_exposures = thematic_report.get_thematic_data(\\n\",\n \" start_date=start_date, end_date=end_date, basket_ids=[basket.get_marquee_id()]\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f'Thematic Exposures: \\\\n{thematic_exposures.__str__()}')\\n\",\n \"thematic_exposures.plot(title='Thematic Data Breakdown')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### You're all set; Congrats!\\n\",\n \"\\n\",\n \"*Other questions? Reach out to the [Portfolio Analytics team](mailto:gs-marquee-analytics-support@gs.com)!*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "gs_quant/documentation/11_macro_models/01_Query_Macro_Models.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"# Discover your Macro risk with Quant Insight models\\n\",\n \"\\n\",\n \"Quant Insight's Macro risk models provide an insight in the relationship between movement in an asset price and\\n\",\n \"macroeconomic factors such as economic growth, monetary policy and commodity prices. The goal is to understand how much\\n\",\n \"of the movement in the asset price is attributable to those macroeconomic factors. The GS quant class `MacroRiskModel`\\n\",\n \"provides an array of functions that query macro risk model data. In this tutorial, we will look at querying all the\\n\",\n \"available macro risk model data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"from gs_quant.models.risk_model import (\\n\",\n \" MacroRiskModel,\\n\",\n \" DataAssetsRequest,\\n\",\n \" RiskModelUniverseIdentifierRequest as UniverseIdentifier,\\n\",\n \" FactorType,\\n\",\n \" RiskModelUniverseIdentifierRequest,\\n\",\n \" Unit,\\n\",\n \")\\n\",\n \"\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.markets.factor import Factor\\n\",\n \"\\n\",\n \"\\n\",\n \"import datetime as dt\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"from itertools import groupby, cycle\\n\",\n \"import seaborn as sns\\n\",\n \"import warnings\\n\",\n \"from IPython.display import display\\n\",\n \"from typing import Dict\\n\",\n \"\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'YOUR CLIENT ID'\\n\",\n \"# secret = 'YOUR SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Get All Available Macro Factor Categories\\n\",\n \"\\n\",\n \"We can leverage `get_factor_data` in the `MacroRiskModel` class to get all the available macro factor categories\\n\",\n \"in the model.\\n\",\n \"\\n\",\n \"#### Macro Factor Data\\n\",\n \"\\n\",\n \"Macro Factors are grouped within a factor category.\\n\",\n \"Here are some examples of macro factor categories along with a list of macro factors in that category:\\n\",\n \"\\n\",\n \"| Macro Factor Category | Macro Factors |\\n\",\n \"|-----------------------|------------------------------------------------------------|\\n\",\n \"| Inflation | US 5Y Infl. Expec., US 2Y Infl. Expec., US 10Y Infl. Expec.|\\n\",\n \"| Economic Growth | Japan GDP, Euro GDP, China GDP |\\n\",\n \"| CB Rate Expectations | Fed Rate Expectations |\\n\",\n \"| Energy | WTI |\\n\",\n \"| Risk Aversion | VDAX, VIX, VXEEM, Gold Silver Ratio |\\n\",\n \"\\n\",\n \"For more macro factor categories and their descriptions, see the factor glossary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"factor_category_data = model.get_factor_data(start_date=start_date, factor_type=FactorType.Factor)\\n\",\n \"\\n\",\n \"factor_category_data_reshaped = (\\n\",\n \" factor_category_data.set_index(\\\"factorCategoryId\\\")\\n\",\n \" .drop(columns={\\\"name\\\", \\\"type\\\", \\\"identifier\\\"})\\n\",\n \" .drop_duplicates()\\n\",\n \" .rename_axis(\\\"Factor Category Id\\\")\\n\",\n \" .rename(columns={\\\"factorCategory\\\": \\\"Factor Category\\\"})\\n\",\n \")\\n\",\n \"\\n\",\n \"display(factor_category_data_reshaped)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Get All Available Macro Factors\\n\",\n \"\\n\",\n \"Within each macro factor category, we have several macro factors that are grouped together.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"\\n\",\n \"model_id = \\\"RISK_MODEL_ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Get the factors in dataframe with name, and type\\n\",\n \"factor_data = model.get_factor_data(start_date=start_date, factor_type=FactorType.Factor)\\n\",\n \"\\n\",\n \"factor_data_reshaped = (\\n\",\n \" factor_data.rename(columns={\\\"name\\\": \\\"Factor\\\", \\\"factorCategory\\\": \\\"Factor Category\\\"})\\n\",\n \" .sort_values(by=[\\\"Factor Category\\\"])\\n\",\n \" .drop(columns={\\\"type\\\", \\\"factorCategoryId\\\", \\\"identifier\\\"})\\n\",\n \" .set_index(\\\"Factor Category\\\")\\n\",\n \" .stack()\\n\",\n \" .to_frame()\\n\",\n \" .droplevel(level=1)\\n\",\n \" .rename(columns={0: \\\"Factor\\\"})\\n\",\n \")\\n\",\n \"display(factor_data_reshaped)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"Further, for a more granular view, we can get all the macro factors that are grouped within a factor category.\\n\",\n \"Below is a list of all macro factors in the factorCategory \\\"Risk Aversion\\\".\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"macro_factors_in_risk_aversion = factor_data_reshaped.groupby(\\\"Factor Category\\\").get_group(\\\"Risk Aversion\\\")\\n\",\n \"display(macro_factors_in_risk_aversion)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 4: Get your Portfolio Exposure to Macro Factors and Categories\\n\",\n \"\\n\",\n \"The metrics below are instrumental in providing insight in the relationship between movement of asset price and macro\\n\",\n \"factors. These key metrics are outlined below:\\n\",\n \"\\n\",\n \"| Metric | Description |\\n\",\n \"|----------------------------|---------------|\\n\",\n \"| `Universe Macro Factor Sensitivity` | Percentage change in asset price for 1 standard deviation move up in the macro factor. |\\n\",\n \"| `R Squared (Model Confidence)` | Gauge of how sensitive the asset is to macroeconomic forces. Typically values above 65% are considered to show strong explanatory power or ‘confidence’. |\\n\",\n \"| `Fair Value Gap` | The difference between the actual price of an asset and the Qi Model Value. This is quoted both in absolute (in percentage) and in standard deviation terms. |\\n\",\n \"| `Specific Risk` | Annualized asset volatility. |\\n\",\n \"\\n\",\n \"\\n\",\n \"Given a portfolio, we can get its cash exposure to a 1 standard deviation move up in each macro factor. The exposures are\\n\",\n \"calculated by multiplying each asset notional amount to their sensitivity to each factor, which gives us individual asset\\n\",\n \" exposure to each macro factor. We then aggregate per asset exposures to get portfolio cash exposure to each macro \\\\\\n\",\n \" factor.\\n\",\n \"We can leverage the function `get_macro_exposure_table` of the `PortfolioManager` class. Note that exposure is expressed\\n\",\n \"in the currency of the notional value of the portfolio.\\n\",\n \"\\n\",\n \"We can get portfolio exposure to both macro factor categories and macro factors. We can thus gain an insight\\n\",\n \"in which factor categories and macro factors within these categories are driving the portfolio.\\n\",\n \"\\n\",\n \"#### Exposure to Macro Factor Categories\\n\",\n \"\\n\",\n \"We need to pass the following parameters to the function:\\n\",\n \"* `macro_risk_model`: The model to base your exposure data on\\n\",\n \"* `date`: date for which to get exposure\\n\",\n \"* `group_by_factor_category`: whether to get exposure per factor category.\\n\",\n \"\\n\",\n \"The result will be sorted from top positive drivers to top negative drivers\\n\",\n \"\\n\",\n \"Note that, in the result, the macro factor categories are ranked from most positive exposure to most negative portfolio exposure\\n\",\n \"\\n\",\n \"Below:\\n\",\n \"* We get asset level exposure to each factor category.\\n\",\n \"* The last row of the resulting dataframe is the portfolio exposure to each factor category (asset level exposure aggregated).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Get the model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Input the portfolio id\\n\",\n \"portfolio_id = \\\"YOUR PORTFOLIO ID\\\"\\n\",\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"date = dt.date(2025, 2, 4)\\n\",\n \"\\n\",\n \"# Get Per Asset Factor Category Exposure Dataframe\\n\",\n \"exposure_factor_category_df = pm.get_macro_exposure(model=model, date=date, factor_type=FactorType.Category)\\n\",\n \"exposure_factor_category_df.loc[\\\"Total Factor Category Exposure\\\", \\\"Asset Name\\\"] = \\\"Total\\\"\\n\",\n \"exposure_factor_category_df = exposure_factor_category_df.set_index(\\\"Asset Name\\\", drop=True)\\n\",\n \"\\n\",\n \"# Get the Portfolio Total\\n\",\n \"total = exposure_factor_category_df.loc[\\\"Total\\\", :].to_frame().drop(\\\"Notional\\\")\\n\",\n \"\\n\",\n \"# Display the Dataframes.\\n\",\n \"styles = [\\n\",\n \" {'selector': \\\"caption\\\", 'props': [(\\\"font-size\\\", \\\"200%\\\"), (\\\"font-weight\\\", \\\"bold\\\"), (\\\"color\\\", 'black')]},\\n\",\n \" {'selector': 'thead', 'props': [(\\\"background-color\\\", \\\"silver\\\"), (\\\"font-size\\\", \\\"13px\\\")]},\\n\",\n \" {\\n\",\n \" 'selector': 'tr',\\n\",\n \" 'props': [\\n\",\n \" (\\\"background-color\\\", \\\"whitesmoke\\\"),\\n\",\n \" (\\\"font-size\\\", \\\"13px\\\"),\\n\",\n \" (\\\"border\\\", \\\"solid\\\"),\\n\",\n \" (\\\"border-width\\\", \\\"0.001em\\\"),\\n\",\n \" ],\\n\",\n \" },\\n\",\n \" {'selector': 'td', 'props': [(\\\"background-color\\\", \\\"aliceblue\\\"), (\\\"font-size\\\", \\\"13px\\\")]},\\n\",\n \"]\\n\",\n \"exposure_styler = (\\n\",\n \" exposure_factor_category_df.style.set_caption(\\\"Portfolio Cash Exposure to Factor Categories\\\")\\n\",\n \" .format('${:,.0f}')\\n\",\n \" .applymap(lambda v: \\\"color:red;\\\" if v < 0 else None)\\n\",\n \" .set_table_styles(styles)\\n\",\n \")\\n\",\n \"styles.pop(0)\\n\",\n \"total_styler = (\\n\",\n \" total.style.format('${:,.0f}').applymap(lambda v: \\\"color:red;\\\" if v < 0 else None).set_table_styles(styles)\\n\",\n \")\\n\",\n \"display(exposure_styler)\\n\",\n \"display(total_styler)\\n\",\n \"\\n\",\n \"# Plot\\n\",\n \"fig, ax = plt.subplots(constrained_layout=True)\\n\",\n \"sns.barplot(data=total.reset_index(), x=\\\"Factor Category\\\", y='Total', ax=ax)\\n\",\n \"ax.set_xlabel(\\\"Factor Category\\\")\\n\",\n \"ax.set_ylabel(\\\"Portfolio Notional Change for 1 std Move in the Factor Category\\\", fontsize='medium')\\n\",\n \"ax.set_title(\\\"Portfolio Exposure to Factor Categories\\\")\\n\",\n \"ax.tick_params(axis='x', labelrotation=50)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### Exposure to Macro Factors\\n\",\n \"Once we get portfolio exposure to factor categories, we can get a granular view of the portfolio exposure to individual macro factors within that factor category.\\n\",\n \"\\n\",\n \"Here are the user inputs:\\n\",\n \"* `model`: The model we are using.\\n\",\n \"* `factor_categories`: A list of factor categories whose factors we want exposure for. If empty, it will default to returning exposure to the top 2 positive and negative macro drivers\\n\",\n \"* `portfolio_id`: The portfolio we are getting macro exposure for.\\n\",\n \"* `date`: Date \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Get the model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Input the portfolio id\\n\",\n \"portfolio_id = \\\"YOUR PORTFOLIO ID\\\"\\n\",\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"date = dt.date(2025, 2, 4)\\n\",\n \"\\n\",\n \"# Add Factor categories whose factors you want exposure for\\n\",\n \"factor_categories = []\\n\",\n \"factor_categories_data = model.get_many_factors(\\n\",\n \" start_date=date, end_date=date, factor_names=factor_categories, factor_type=FactorType.Category\\n\",\n \")\\n\",\n \"\\n\",\n \"# Get Per Asset Factor Exposure dataframe\\n\",\n \"exp_factor_df = pm.get_macro_exposure(\\n\",\n \" model=model, date=date, factor_type=FactorType.Factor, factor_categories=factor_categories_data\\n\",\n \")\\n\",\n \"exp_factor_df.loc[\\\"Total Factor Exposure\\\", (\\\"Asset Information\\\", \\\"Asset Name\\\")] = \\\"Total\\\"\\n\",\n \"exp_factor_df = exp_factor_df.set_index((\\\"Asset Information\\\", \\\"Asset Name\\\"), drop=True)\\n\",\n \"\\n\",\n \"# Total Portfolio Exposure\\n\",\n \"total = exp_factor_df.loc[\\\"Total\\\", :].to_frame().drop((\\\"Asset Information\\\", \\\"Notional\\\"))\\n\",\n \"\\n\",\n \"# Display the Dataframes.\\n\",\n \"styles = [\\n\",\n \" {'selector': \\\"caption\\\", 'props': [(\\\"font-size\\\", \\\"200%\\\"), (\\\"font-weight\\\", \\\"bold\\\"), (\\\"color\\\", 'black')]},\\n\",\n \" {'selector': 'thead', 'props': [(\\\"background-color\\\", \\\"silver\\\"), (\\\"font-size\\\", \\\"13px\\\")]},\\n\",\n \" {\\n\",\n \" 'selector': 'tr',\\n\",\n \" 'props': [\\n\",\n \" (\\\"background-color\\\", \\\"whitesmoke\\\"),\\n\",\n \" (\\\"font-size\\\", \\\"13px\\\"),\\n\",\n \" (\\\"border\\\", \\\"solid\\\"),\\n\",\n \" (\\\"border-width\\\", \\\"0.001em\\\"),\\n\",\n \" ],\\n\",\n \" },\\n\",\n \" {'selector': 'td', 'props': [(\\\"background-color\\\", \\\"aliceblue\\\"), (\\\"font-size\\\", \\\"13px\\\")]},\\n\",\n \"]\\n\",\n \"exposure_styler = (\\n\",\n \" exp_factor_df.style.set_caption(\\\"Portfolio Cash Exposure to Macro Factors\\\")\\n\",\n \" .format('${:,.0f}')\\n\",\n \" .applymap(lambda v: \\\"color:red;\\\" if v < 0 else None)\\n\",\n \" .set_table_styles(styles)\\n\",\n \")\\n\",\n \"styles.pop(0)\\n\",\n \"total_styler = (\\n\",\n \" total.style.format('${:,.0f}').applymap(lambda v: \\\"color:red;\\\" if v < 0 else None).set_table_styles(styles)\\n\",\n \")\\n\",\n \"display(exposure_styler)\\n\",\n \"display(total_styler)\\n\",\n \"\\n\",\n \"# Plot the portfolio percent change if every factor category shifted 1 std\\n\",\n \"fig, ax = plt.subplots(constrained_layout=True)\\n\",\n \"sns.barplot(data=total.sort_values(by=[\\\"Total\\\"], ascending=False).reset_index(), x=\\\"Factor\\\", y='Total', ax=ax)\\n\",\n \"ax.set_xlabel(\\\"Macro Factor\\\")\\n\",\n \"ax.set_ylabel(\\\"Portfolio Notional Change for 1 std Move in the Macro Factor\\\", fontsize='medium')\\n\",\n \"ax.set_title(\\\"Portfolio Exposure to Macro Factors\\\")\\n\",\n \"ax.tick_params(axis='x', labelrotation=60)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"\\n\",\n \"## Step 5: Run empirical stress tests to observe impact on portfolio\\n\",\n \"\\n\",\n \"We can bump up or down any combination of macro factors and see the impact on the portfolio. Since your portfolio's\\n\",\n \"sensitivities to each macro factor are independent of each other, we can isolate a (or a combination of) macro factor categories and factors,\\n\",\n \"and assign a shift up or down in standard deviation and observe the change in the portfolio notional value.\\n\",\n \"\\n\",\n \"Below, we define a class `MacroScenario` that takes in a date and a dictionary of macro factor categories or macro factors and their suggested standard\\n\",\n \"deviation shifts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"class MacroScenario:\\n\",\n \" def __init__(self, date: dt.date, factor_std_shifts: Dict[Factor, float]):\\n\",\n \" self.date = date\\n\",\n \" self.factor_std_shifts = factor_std_shifts\\n\",\n \"\\n\",\n \"\\n\",\n \"def macro_stress_test(\\n\",\n \" exposure_df: pd.DataFrame, scenario: MacroScenario, is_factor_category_stress: bool\\n\",\n \") -> pd.DataFrame:\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" For a given portfolio, find how shifts in standard deviation in the selected macro factors affects\\n\",\n \" the portfolio's notional value.\\n\",\n \" :param exposure_df: exposure of the portfolio to the requested macro factors/factor categories\\n\",\n \" :param scenario: scenario to stress test for. Contains date and a dict of factors and respective shifts in standard\\n\",\n \" deviation\\n\",\n \" :is_factor_category_stress: whether we are stress testing for shifts in factor categories or shifts in factors.\\n\",\n \" :return: portfolio notional change and change in portfolio exposure for each macro factor\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" factors_and_std = scenario.factor_std_shifts\\n\",\n \" factors_to_shift = {factor.name: factor.category for factor in factors_and_std.keys()}\\n\",\n \" total_row_name = \\\"Total Factor Category Exposure\\\" if is_factor_category_stress else \\\"Total Factor Exposure\\\"\\n\",\n \" notional_column_name = \\\"Notional\\\" if is_factor_category_stress else (\\\"Asset Information\\\", \\\"Notional\\\")\\n\",\n \" portfolio_notional = exposure_df.loc[total_row_name, notional_column_name]\\n\",\n \" exposure_df = (\\n\",\n \" exposure_df[list(factors_to_shift.keys())]\\n\",\n \" if is_factor_category_stress\\n\",\n \" else exposure_df[list(zip(factors_to_shift.values(), factors_to_shift.keys()))]\\n\",\n \" )\\n\",\n \"\\n\",\n \" fact_name = 'Factor Category' if is_factor_category_stress else 'Factor'\\n\",\n \" col_1_name = f\\\"Exposure to {fact_name} Before Stress Test\\\"\\n\",\n \" col_2_name = f\\\"Exposure to {fact_name} After Stress Test\\\"\\n\",\n \" portfolio_change_df = pd.DataFrame(\\n\",\n \" np.zeros((4, len(set(exposure_df.columns.tolist())))),\\n\",\n \" index=[\\\"Standard Deviation Shift\\\", col_1_name, col_2_name, \\\"Percent Change After Stress Test\\\"],\\n\",\n \" columns=exposure_df.columns.tolist(),\\n\",\n \" )\\n\",\n \"\\n\",\n \" for factor, std_shift in factors_and_std.items():\\n\",\n \" factor_name = factor.name if is_factor_category_stress else (factors_to_shift[factor.name], factor.name)\\n\",\n \" old_exposure = exposure_df.loc[total_row_name, factor_name]\\n\",\n \" new_exposure = old_exposure * std_shift\\n\",\n \" portfolio_change_df.loc[\\\"Standard Deviation Shift\\\", factor_name] = std_shift\\n\",\n \" portfolio_change_df.loc[col_1_name, factor_name] = old_exposure\\n\",\n \" portfolio_change_df.loc[col_2_name, factor_name] = new_exposure\\n\",\n \" portfolio_change_df.loc[\\\"Percent Change After Stress Test\\\", factor_name] = (\\n\",\n \" new_exposure / portfolio_notional\\n\",\n \" ) * 100\\n\",\n \"\\n\",\n \" total_name = \\\"Total\\\" if is_factor_category_stress else (\\\"Total\\\", \\\"Total\\\")\\n\",\n \" total_sum = portfolio_change_df.agg(np.sum, axis=\\\"columns\\\").to_frame().rename(columns={0: total_name})\\n\",\n \"\\n\",\n \" portfolio_change_df = pd.concat([portfolio_change_df, total_sum], axis='columns')\\n\",\n \" portfolio_change_df.loc[\\\"Standard Deviation Shift\\\", total_name] = np.nan\\n\",\n \"\\n\",\n \" return portfolio_change_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Factor Standard Deviation\\n\",\n \"\\n\",\n \"Before the stress test, we can first determine, for each factor, the value of one shift in standard deviation in absolute terms. We can leverage the function `get_factor_standard_deviation` of the `MacroRiskModel` class.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.models.risk_model import MacroRiskModel\\n\",\n \"import datetime as dt\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"end_date = dt.date(2025, 2, 5)\\n\",\n \"\\n\",\n \"date = model.get_dates(start_date, end_date)[-1]\\n\",\n \"\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"factor_category_shift_std = {\\\"Inflation\\\": 3, \\\"Real Rates\\\": 3, \\\"Economic Growth\\\": -3}\\n\",\n \"\\n\",\n \"factor_data = model.get_factor_data(date, date)\\n\",\n \"factor_data = factor_data.loc[factor_data['factorCategory'].isin(list(factor_category_shift_std.keys())), :]\\n\",\n \"factor_data = factor_data.set_index(\\\"name\\\")\\n\",\n \"\\n\",\n \"factor_std_df = model.get_factor_standard_deviation(start_date=date, end_date=date, factors=factor_data.index.tolist())\\n\",\n \"\\n\",\n \"factor_std_df = (\\n\",\n \" factor_std_df.set_axis(\\n\",\n \" pd.MultiIndex.from_tuples([(factor_data.loc[f, 'factorCategory'], f) for f in factor_std_df.columns.values]),\\n\",\n \" axis=1,\\n\",\n \" )\\n\",\n \" .rename_axis((\\\"Factor Category\\\", \\\"Factor\\\"), axis=1)\\n\",\n \" .sort_index(level=0, axis=1)\\n\",\n \" .round(3)\\n\",\n \" .T.rename(columns={date.isoformat(): \\\"Value of 1 STD\\\"})\\n\",\n \")\\n\",\n \"\\n\",\n \"display(\\n\",\n \" (\\n\",\n \" factor_std_df.style.set_caption(f\\\"One Standard Deviation of Factors on {date.isoformat()}\\\")\\n\",\n \" .format(\\\"{:.3f}\\\", na_rep=\\\"N/A\\\")\\n\",\n \" .background_gradient()\\n\",\n \" .set_table_styles(\\n\",\n \" [dict(selector=\\\"caption\\\", props=[(\\\"font-size\\\", \\\"120%\\\"), (\\\"font-weight\\\", \\\"bold\\\"), (\\\"color\\\", 'black')])]\\n\",\n \" )\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Stress Test Shifts in Factor Categories\\n\",\n \"\\n\",\n \"We can stress test for shifts in factor categories and observe the impact on our portfolio. \\n\",\n \"\\n\",\n \"Here are the user inputs:\\n\",\n \"* `model_id`: The model we are using\\n\",\n \"* `portfolio_id`: Portfolio we are stress testing for.\\n\",\n \"* `factors_shifts_std`: Map of factor categories and their standard deviation scenario shift\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"date = dt.date(2025, 4, 23)\\n\",\n \"\\n\",\n \"model_id = \\\"RISK MODEL ID\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"portfolio_id = \\\"YOUR PORTFOLIO ID\\\"\\n\",\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"\\n\",\n \"factor_category_shift_std = {\\\"Inflation\\\": 0.5, \\\"Real Rates\\\": 3, \\\"Economic Growth\\\": -2}\\n\",\n \"many_factor_categories = model.get_many_factors(\\n\",\n \" start_date=date, end_date=date, factor_names=list(factor_category_shift_std.keys()), factor_type=FactorType.Category\\n\",\n \")\\n\",\n \"std_dict = {factor: factor_category_shift_std[factor.name] for factor in many_factor_categories}\\n\",\n \"\\n\",\n \"# Create a MacroScenario object\\n\",\n \"scenario = MacroScenario(date, std_dict)\\n\",\n \"\\n\",\n \"# First, get the portfolio factor category exposure\\n\",\n \"exposure_df = pm.get_macro_exposure(\\n\",\n \" model=model, date=scenario.date, factor_type=FactorType.Category, factor_categories=many_factor_categories\\n\",\n \")\\n\",\n \"\\n\",\n \"# Then calculate portfolio change given std shifts in the factor categories in `scenario`\\n\",\n \"portfolio_change = macro_stress_test(exposure_df, scenario, is_factor_category_stress=True).T.rename_axis(\\n\",\n \" \\\"Factor Category\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"# Display\\n\",\n \"styles = [dict(selector=\\\"caption\\\", props=[(\\\"font-size\\\", \\\"120%\\\"), (\\\"font-weight\\\", \\\"bold\\\"), (\\\"color\\\", 'black')])]\\n\",\n \"display(\\n\",\n \" (\\n\",\n \" portfolio_change.style.set_caption(\\\"Stress Test: Shifts in Factor Categories and their Impact on Portfolio\\\")\\n\",\n \" .format(\\n\",\n \" {\\n\",\n \" \\\"Percent Change After Stress Test\\\": \\\"{:.3f}%\\\",\\n\",\n \" \\\"Exposure to Factor Category Before Stress Test\\\": \\\"${:,.0f}\\\",\\n\",\n \" \\\"Exposure to Factor Category After Stress Test\\\": \\\"${:,.0f}\\\",\\n\",\n \" \\\"Standard Deviation Shift\\\": \\\"{:.2f}\\\",\\n\",\n \" },\\n\",\n \" na_rep=\\\"N/A\\\",\\n\",\n \" )\\n\",\n \" .background_gradient()\\n\",\n \" .set_table_styles(styles)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Stress Test Shifts in Factors\\n\",\n \"\\n\",\n \"We can also stress test for shifts in individual factors and observe the impact on our portfolio. \\n\",\n \"\\n\",\n \"Here are the user inputs:\\n\",\n \"* `model_id`: The model we are using\\n\",\n \"* `portfolio_id`: Portfolio we are stress testing for.\\n\",\n \"* `factors_shifts_std`: Map of factors and their standard deviation scenario shifts\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Get the model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Input the portfolio id\\n\",\n \"portfolio_id = \\\"YOUR PORTFOLIO ID\\\"\\n\",\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"date = dt.date(2025, 2, 4)\\n\",\n \"\\n\",\n \"factors_shift_std = {\\n\",\n \" \\\"US 2Y Infl. Expec.\\\": 0.5,\\n\",\n \" \\\"US 5Y Infl. Expec.\\\": 0.5,\\n\",\n \" \\\"US 10Y Infl. Expec.\\\": 0.5,\\n\",\n \" \\\"USD 10Y Real Rate\\\": 3.0,\\n\",\n \" \\\"JPY 10Y Real Rate\\\": 3.0,\\n\",\n \" \\\"EUR 10Y Real Rate\\\": 3.0,\\n\",\n \"}\\n\",\n \"\\n\",\n \"many_factors = model.get_many_factors(\\n\",\n \" start_date=date, end_date=date, factor_names=list(factors_shift_std.keys()), factor_type=FactorType.Factor\\n\",\n \")\\n\",\n \"std_dict = {factor: factors_shift_std[factor.name] for factor in many_factors}\\n\",\n \"\\n\",\n \"# MacroScenario object\\n\",\n \"scenario = MacroScenario(date, std_dict)\\n\",\n \"\\n\",\n \"# First, get the portfolio factor exposure\\n\",\n \"exposure_df = pm.get_macro_exposure(model=model, date=scenario.date, factor_type=FactorType.Factor).sort_values(\\n\",\n \" by=[\\\"Total Factor Exposure\\\"], axis=1, ascending=False\\n\",\n \")\\n\",\n \"\\n\",\n \"# Then calculate portfolio change given std shifts in the factors in `scenario`\\n\",\n \"portfolio_change = macro_stress_test(exposure_df, scenario, is_factor_category_stress=False).T.rename_axis(\\n\",\n \" [\\\"Factor Category\\\", \\\"Factor\\\"]\\n\",\n \")\\n\",\n \"\\n\",\n \"# Display\\n\",\n \"styles = [dict(selector=\\\"caption\\\", props=[(\\\"font-size\\\", \\\"130%\\\"), (\\\"font-weight\\\", \\\"bold\\\"), (\\\"color\\\", 'black')])]\\n\",\n \"display(\\n\",\n \" (\\n\",\n \" portfolio_change.style.set_caption(\\\"Stress Test: Shifts in Factors and their Impact on Portfolio\\\")\\n\",\n \" .format(\\n\",\n \" {\\n\",\n \" \\\"Percent Change After Stress Test\\\": \\\"{:.3f}%\\\",\\n\",\n \" \\\"Exposure to Factor Before Stress Test\\\": \\\"${:,.0f}\\\",\\n\",\n \" \\\"Exposure to Factor After Stress Test\\\": \\\"${:,.0f}\\\",\\n\",\n \" \\\"Standard Deviation Shift\\\": \\\"{:.2f}\\\",\\n\",\n \" },\\n\",\n \" na_rep=\\\"N/A\\\",\\n\",\n \" )\\n\",\n \" .background_gradient()\\n\",\n \" .set_table_styles(styles)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 6: Which assets in the portfolio are in a Macro Regime?\\n\",\n \"\\n\",\n \"In a macro regime, macro factors are the most significant in explaining the movement in asset price. To determine which\\n\",\n \"assets are in a macro regime, we look at the R Squared value, the proportion of the movement in asset price that is explained\\n\",\n \"by the macro factors. Assets with R Squared values above 65% are in a macro regime while values below 65% are driven\\n\",\n \"primarily by micro and idiosyncratic risk.\\n\",\n \"\\n\",\n \"Once an asset is in a macro regime, we can look at the fair value gap to determine if it is rich or cheap.\\n\",\n \"The fair value gap metric is the difference between the spot price of an asset and the asset model value. An asset is\\n\",\n \" undervalued when its fair value gap is negative while it is overvalued when it is a positive value.\\n\",\n \"\\n\",\n \"Given a portfolio, we can find out how many assets are in a macro regime at a specific date. We can leverage the function\\n\",\n \"`get_fair_value_gap` and `get_r_squared` to determine the regime of each asset in the portfolio as well as its fair value\\n\",\n \"gap. Note that the fair value gap data is available both in absolute terms (in percentage) and in standard deviation terms.\\n\",\n \"\\n\",\n \"Here are the user inputs:\\n\",\n \"* `PORTFOLIO ID`: Portfolio we are using. \\n\",\n \"* `RISK MODEL ID`: The model we are using to get r_squared and fair value gap data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Time range\\n\",\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"end_date = dt.date(2025, 4, 23)\\n\",\n \"\\n\",\n \"# Model\\n\",\n \"model_id = \\\"QI_US_EQUITY_LT\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Input the portfolio id\\n\",\n \"portfolio_id = \\\"MPPP95AWA1E5V24D\\\"\\n\",\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"positions = pm.get_latest_position_set().positions\\n\",\n \"\\n\",\n \"universe = [position.identifier for position in positions]\\n\",\n \"universe_for_request = DataAssetsRequest(RiskModelUniverseIdentifierRequest.bbid, universe)\\n\",\n \"\\n\",\n \"# Get fair value gap and r squared for the assets in the portfolio\\n\",\n \"fvg_std_df = model.get_fair_value_gap(\\n\",\n \" start_date=start_date, end_date=end_date, fair_value_gap_unit=Unit.STANDARD_DEVIATION, assets=universe_for_request\\n\",\n \")\\n\",\n \"r_squared_df = model.get_r_squared(start_date=start_date, end_date=end_date, assets=universe_for_request)\\n\",\n \"\\n\",\n \"date = dt.date(2024, 4, 20).strftime(\\\"%Y-%m-%d\\\")\\n\",\n \"fvg_one_date_df = fvg_std_df.loc[date, :].to_frame().rename(columns={date: \\\"Fair Value Gap\\\"})\\n\",\n \"r_squared_one_date_df = r_squared_df.loc[date, :].to_frame().rename(columns={date: \\\"R Squared\\\"})\\n\",\n \"fvg_rsq_df = r_squared_one_date_df.join(fvg_one_date_df).reset_index().round(2)\\n\",\n \"\\n\",\n \"# Get the Macro cheap assets\\n\",\n \"macro_cheap_df = (\\n\",\n \" fvg_rsq_df[(fvg_rsq_df[\\\"R Squared\\\"] > 65) & (fvg_rsq_df[\\\"Fair Value Gap\\\"] < 0)]\\n\",\n \" .set_index(\\\"index\\\")\\n\",\n \" .rename_axis(\\\"Asset\\\")\\n\",\n \" .sort_values(by=\\\"Fair Value Gap\\\", ascending=True)\\n\",\n \")\\n\",\n \"\\n\",\n \"# Plot Quadrant of assets that are macro cheap/rich and micro\\n\",\n \"fig, ax = plt.subplots(figsize=(20, 15), constrained_layout=True)\\n\",\n \"sns.scatterplot(data=fvg_rsq_df, x=\\\"R Squared\\\", y='Fair Value Gap', hue='index', legend=False)\\n\",\n \"ax.set_title(f\\\"Macro Rich vs Macro Cheap Assets on {date}\\\")\\n\",\n \"ax.set_xlabel(\\\"R Squared (Model Confidence) in %\\\")\\n\",\n \"ax.set_ylabel(\\\"Fair Value Gap (sigma)\\\")\\n\",\n \"ax.set_xlim(0, 100)\\n\",\n \"ax.set_ylim(-3, 3)\\n\",\n \"\\n\",\n \"\\n\",\n \"def annotate_plot(data, ax, index):\\n\",\n \" for i in range(data.shape[0]):\\n\",\n \" if data[index][i] == 'DHR UN':\\n\",\n \" ax.text(data[\\\"R Squared\\\"][i], data[\\\"Fair Value Gap\\\"][i], s=data[index][i], size='x-large', color='r')\\n\",\n \" else:\\n\",\n \" ax.text(data[\\\"R Squared\\\"][i], data[\\\"Fair Value Gap\\\"][i], s=data[index][i], size='large')\\n\",\n \"\\n\",\n \"\\n\",\n \"annotate_plot(fvg_rsq_df, ax, \\\"index\\\")\\n\",\n \"\\n\",\n \"for pair, label in {\\n\",\n \" (75, 2.2): \\\"Macro Rich\\\",\\n\",\n \" (25, 2.2): \\\"Micro Regime\\\",\\n\",\n \" (25, -2.2): \\\"Micro Regime\\\",\\n\",\n \" (72, -2.2): \\\"Macro Cheap\\\",\\n\",\n \"}.items():\\n\",\n \" ax.text(x=pair[0], y=pair[1], s=label, alpha=0.7, fontsize='xx-large', color='g', fontweight='bold')\\n\",\n \"ax.axhline(y=0, color='k', linestyle='dashed')\\n\",\n \"ax.axvline(x=65, color='k', linestyle='dashed')\\n\",\n \"\\n\",\n \"# Plot Just macro cheap assets\\n\",\n \"fig, ax = plt.subplots(figsize=(18, 7), constrained_layout=True)\\n\",\n \"sns.scatterplot(data=macro_cheap_df.reset_index(), x=\\\"R Squared\\\", y=\\\"Fair Value Gap\\\", hue=\\\"Asset\\\", legend=False)\\n\",\n \"annotate_plot(macro_cheap_df.reset_index(), ax, \\\"Asset\\\")\\n\",\n \"ax.set_title(f\\\"Macro Cheap Assets on {end_date}\\\")\\n\",\n \"ax.set_ylabel(\\\"Fair Value Gap\\\")\\n\",\n \"ax.set_xlabel(\\\"R Squared (in %)\\\")\\n\",\n \"ax.text(x=75, y=-2.0, s=\\\"Macro Cheap\\\", alpha=0.7, fontsize='xx-large', color='g', fontweight='bold')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"The first plot above classifies assets into 3 groups: macro cheap, macro rich and assets in a micro regime. The second plot shows the bottom right of the first\\n\",\n \"plot, i.e all the assets that are macro cheap. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"\\n\",\n \"## Step 7: Asset Sensitivity to Factors\\n\",\n \"\\n\",\n \"The asset Sensitivity to a macro factor is the percentage change in the asset price for 1 standard deviation move\\n\",\n \"in that macro factor. We can leverage the function `get_universe_sensitivity` of the `MacroRiskModel` class to get a\\n\",\n \"time series of daily asset sensitivity to macro factors for a list of assets.\\n\",\n \"\\n\",\n \"Here are the parameters that are needed:\\n\",\n \"* `start_date` and `end_date`: Time range for which to get sensitivity for. \\n\",\n \"* `model_id`: The model we are using.\\n\",\n \"* `asset`: Asset to get macro sensitivity for.\\n\",\n \"\\n\",\n \"Below, we are querying a time series of daily sensitivity data over the time range for one of the macro cheap assets in the portfolio. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### Asset Sensitivity to Factor Categories\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Time range\\n\",\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"end_date = dt.date(2025, 2, 5)\\n\",\n \"\\n\",\n \"# Model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Universe\\n\",\n \"asset = \\\"DHR UN\\\"\\n\",\n \"universe = [asset]\\n\",\n \"universe_for_request = DataAssetsRequest(UniverseIdentifier.bbid, universe)\\n\",\n \"\\n\",\n \"# Get asset sensitivity to factor categories\\n\",\n \"factor_sens_df = model.get_universe_sensitivity(\\n\",\n \" start_date=start_date,\\n\",\n \" end_date=end_date,\\n\",\n \" assets=universe_for_request,\\n\",\n \" factor_type=FactorType.Category,\\n\",\n \" get_factors_by_name=True,\\n\",\n \")\\n\",\n \"factor_sens_df = (\\n\",\n \" factor_sens_df.droplevel(1).sort_values(by=[asset], ascending=False, axis=1).rename_axis(\\\"Factor Category\\\", axis=1)\\n\",\n \")\\n\",\n \"\\n\",\n \"# Display the dataframe\\n\",\n \"factor_sens_to_display = factor_sens_df.T.rename(\\n\",\n \" columns={asset: f\\\"Percent Change in Asset Price for 1 STD shift in each Factor Category for {asset}\\\"}\\n\",\n \")\\n\",\n \"display(\\n\",\n \" (\\n\",\n \" factor_sens_to_display.style.set_caption(f\\\"Sensitivity of {asset} to Factor Categories on {start_date}\\\")\\n\",\n \" .format(\\\"{:.2f}%\\\")\\n\",\n \" .applymap(lambda v: \\\"color:red;\\\" if v < 0 else None)\\n\",\n \" .set_table_styles(\\n\",\n \" [\\n\",\n \" {'selector': \\\"caption\\\", 'props': [(\\\"font-size\\\", \\\"100%\\\"), (\\\"font-weight\\\", \\\"bold\\\"), (\\\"color\\\", 'black')]},\\n\",\n \" {'selector': 'table', 'props': [('width', '100px')]},\\n\",\n \" {\\n\",\n \" 'selector': 'thead',\\n\",\n \" 'props': [(\\\"background-color\\\", \\\"silver\\\"), (\\\"font-size\\\", \\\"12px\\\"), (\\\"width\\\", \\\"400px\\\")],\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'selector': 'tr',\\n\",\n \" 'props': [\\n\",\n \" (\\\"background-color\\\", \\\"gainsboro\\\"),\\n\",\n \" (\\\"font-size\\\", \\\"12px\\\"),\\n\",\n \" (\\\"border\\\", \\\"solid\\\"),\\n\",\n \" (\\\"border-width\\\", \\\"1px\\\"),\\n\",\n \" ],\\n\",\n \" },\\n\",\n \" {'selector': 'td', 'props': [(\\\"background-color\\\", \\\"aliceblue\\\"), (\\\"font-size\\\", \\\"12px\\\")]},\\n\",\n \" ]\\n\",\n \" )\\n\",\n \" )\\n\",\n \")\\n\",\n \"\\n\",\n \"# Plot the portfolio percent change if every factor category shifted 1 std\\n\",\n \"fig, ax = plt.subplots(constrained_layout=True)\\n\",\n \"sns.barplot(data=factor_sens_df.T.reset_index(), x=\\\"Factor Category\\\", y=asset, ax=ax, palette=exp_palette)\\n\",\n \"ax.set_title(f\\\"Expected % change in {asset} Price for 1 std Move in Factor Category on {end_date}\\\")\\n\",\n \"ax.set_xlabel(\\\"Factor Category\\\")\\n\",\n \"ax.set_ylabel(\\\"\\\")\\n\",\n \"ax.set_yticks([], [])\\n\",\n \"ax.tick_params(axis='x', labelrotation=30)\\n\",\n \"\\n\",\n \"labls = factor_sens_df.T.reset_index()[\\\"Factor Category\\\"].values.tolist()\\n\",\n \"values = factor_sens_df.T.reset_index()[asset].values.tolist()\\n\",\n \"for i in range(len(labls)):\\n\",\n \" if values[i] > 0:\\n\",\n \" ax.text(i, values[i] + 0.01, s=f'{round(values[i], 1)}%', ha='center', fontsize='x-small')\\n\",\n \" else:\\n\",\n \" ax.text(i, values[i] - 0.08, s=f'{round(values[i], 1)}%', ha='center', fontsize='x-small')\\n\",\n \"\\n\",\n \"sns.despine(left=True)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"#### Asset Sensitivity to Factors\\n\",\n \"\\n\",\n \"Once we know an asset sensitivity to factor categories, we can also query its sensitivity to individual macro factors within a factor category.\\n\",\n \"\\n\",\n \"Here are the user inputs:\\n\",\n \"* `model`: The model we are using.\\n\",\n \"* `factor_categories`: A list of factor categories whose factors we want sensitivity for. If empty, it will default to returning sensitivity to the top 2 positive and negative macro drivers\\n\",\n \"* `start_date` and `end_date`: Time range to get sensitivity data for an asset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Time range\\n\",\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"end_date = dt.date(2025, 2, 5)\\n\",\n \"\\n\",\n \"# Model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Select factor_categories\\n\",\n \"factor_categories = []\\n\",\n \"\\n\",\n \"# Universe\\n\",\n \"asset = \\\"DHR UN\\\"\\n\",\n \"universe = [asset]\\n\",\n \"universe_for_request = DataAssetsRequest(UniverseIdentifier.bbid, universe)\\n\",\n \"factor_data = model.get_factor_data(start_date=start_date, end_date=end_date).set_index('name')\\n\",\n \"\\n\",\n \"# Get asset sensitivity to factors as a dataframe\\n\",\n \"factor_sens_df = model.get_universe_sensitivity(\\n\",\n \" start_date=start_date, end_date=end_date, assets=universe_for_request, get_factors_by_name=True\\n\",\n \")\\n\",\n \"factor_sens_df = factor_sens_df.set_axis(\\n\",\n \" pd.MultiIndex.from_tuples([(factor_data.loc[x, 'factorCategory'], x) for x in factor_sens_df.columns]), axis=1\\n\",\n \").droplevel(1)\\n\",\n \"\\n\",\n \"if not factor_categories:\\n\",\n \" # Get top drivers\\n\",\n \" top_drivers = (\\n\",\n \" model.get_universe_sensitivity(\\n\",\n \" start_date=start_date,\\n\",\n \" end_date=end_date,\\n\",\n \" assets=universe_for_request,\\n\",\n \" factor_type=FactorType.Category,\\n\",\n \" get_factors_by_name=True,\\n\",\n \" )\\n\",\n \" .droplevel(1)\\n\",\n \" .sort_values(by=asset, axis=1, ascending=False)\\n\",\n \" .columns.values.tolist()\\n\",\n \" )\\n\",\n \" factor_categories = top_drivers[0:2] + top_drivers[-2:None]\\n\",\n \"\\n\",\n \"factor_sens_df = (\\n\",\n \" factor_sens_df[factor_categories]\\n\",\n \" .T.rename_axis([\\\"Factor Category\\\", \\\"Factor\\\"])\\n\",\n \" .sort_values(by=asset, ascending=False)\\n\",\n \" .rename(columns={asset: f\\\"Percent Change in Asset Price for 1 STD shift in each Factor for {asset}\\\"})\\n\",\n \")\\n\",\n \"\\n\",\n \"# Display the dataframe\\n\",\n \"display(\\n\",\n \" (\\n\",\n \" factor_sens_df.style.set_caption(f\\\"Sensitivity of {asset} to Factors on {start_date}\\\")\\n\",\n \" .format(\\\"{:.2f}%\\\")\\n\",\n \" .applymap(lambda v: \\\"color:red;\\\" if v < 0 else None)\\n\",\n \" .set_table_styles(\\n\",\n \" [\\n\",\n \" {'selector': \\\"caption\\\", 'props': [(\\\"font-size\\\", \\\"100%\\\"), (\\\"font-weight\\\", \\\"bold\\\"), (\\\"color\\\", 'black')]},\\n\",\n \" {'selector': 'table', 'props': [('width', '100px')]},\\n\",\n \" {\\n\",\n \" 'selector': 'thead',\\n\",\n \" 'props': [(\\\"background-color\\\", \\\"silver\\\"), (\\\"font-size\\\", \\\"12px\\\"), (\\\"width\\\", \\\"400px\\\")],\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'selector': 'tr',\\n\",\n \" 'props': [\\n\",\n \" (\\\"background-color\\\", \\\"gainsboro\\\"),\\n\",\n \" (\\\"font-size\\\", \\\"12px\\\"),\\n\",\n \" (\\\"border\\\", \\\"solid\\\"),\\n\",\n \" (\\\"border-width\\\", \\\"1px\\\"),\\n\",\n \" ],\\n\",\n \" },\\n\",\n \" {'selector': 'td', 'props': [(\\\"background-color\\\", \\\"aliceblue\\\"), (\\\"font-size\\\", \\\"12px\\\")]},\\n\",\n \" ]\\n\",\n \" )\\n\",\n \" )\\n\",\n \")\\n\",\n \"\\n\",\n \"# Plot the portfolio percent change if every factor category shifted 1 std\\n\",\n \"fig, ax = plt.subplots(constrained_layout=True)\\n\",\n \"sns.barplot(\\n\",\n \" data=factor_sens_df.reset_index(),\\n\",\n \" x=\\\"Factor\\\",\\n\",\n \" y=f\\\"Percent Change in Asset Price for 1 STD shift in each Factor for {asset}\\\",\\n\",\n \" ax=ax,\\n\",\n \" palette=exp_palette,\\n\",\n \")\\n\",\n \"ax.set_title(f\\\"Expected % change in {asset} Price for 1 std Move in Factor Category on {end_date}\\\")\\n\",\n \"ax.set_xlabel(\\\"Macro Factor\\\")\\n\",\n \"ax.set_ylabel(\\\"\\\")\\n\",\n \"ax.set_yticks([], [])\\n\",\n \"ax.tick_params(axis='x', labelrotation=60)\\n\",\n \"\\n\",\n \"labls = factor_sens_df.reset_index()[\\\"Factor\\\"].values.tolist()\\n\",\n \"values = factor_sens_df.reset_index()[\\n\",\n \" f\\\"Percent Change in Asset Price for 1 STD shift in each Factor for {asset}\\\"\\n\",\n \"].values.tolist()\\n\",\n \"for i in range(len(labls)):\\n\",\n \" if values[i] > 0:\\n\",\n \" ax.text(i, values[i] + 0.01, s=f'{round(values[i], 1)}%', ha='center', fontsize='x-small')\\n\",\n \" else:\\n\",\n \" ax.text(i, values[i] - 0.03, s=f'{round(values[i], 1)}%', ha='center', fontsize='x-small')\\n\",\n \"\\n\",\n \"sns.despine(left=True)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Historical Asset Sensitivity to Factor Categories\\n\",\n \"\\n\",\n \"We can query asset sensitivity to factor categories over time. To get sensitivity to factor categories, make sure that the factor_type parameter in the function `get_universe_sensitivity` is set to `FactorType.Category`.\\n\",\n \"\\n\",\n \"If the factor_categories parameter is not specified, the top drivers of the asset will be shown instead.\\n\",\n \"\\n\",\n \"On the plot, she shaded area represents the time range when the asset was in a macro regime.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Time range\\n\",\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"end_date = dt.date(2025, 2, 5)\\n\",\n \"\\n\",\n \"# Model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Asset and factor categories\\n\",\n \"asset = \\\"DHR UN\\\"\\n\",\n \"factor_categories = []\\n\",\n \"\\n\",\n \"universe = [asset]\\n\",\n \"universe_for_request = DataAssetsRequest(UniverseIdentifier.bbid, universe)\\n\",\n \"\\n\",\n \"# Get asset sensitivity to factor category (note that factor_type is set to `Category`)\\n\",\n \"factor_category_sens_df = model.get_universe_sensitivity(\\n\",\n \" start_date=start_date,\\n\",\n \" end_date=end_date,\\n\",\n \" assets=universe_for_request,\\n\",\n \" factor_type=FactorType.Category,\\n\",\n \" get_factors_by_name=True,\\n\",\n \")\\n\",\n \"\\n\",\n \"# If no factor categories passed in, get the top drivers as of end_date\\n\",\n \"if not factor_categories:\\n\",\n \" top_drivers = (\\n\",\n \" factor_category_sens_df.loc[(asset, end_date.strftime(\\\"%Y-%m-%d\\\")), :]\\n\",\n \" .to_frame()\\n\",\n \" .droplevel(1, axis=1)\\n\",\n \" .sort_values(by=asset, ascending=False)\\n\",\n \" .index.values.tolist()\\n\",\n \" )\\n\",\n \" factor_categories = top_drivers[0:2] + top_drivers[-2:None]\\n\",\n \"factor_category_sens_df = factor_category_sens_df[factor_categories].droplevel(0)\\n\",\n \"\\n\",\n \"# Get date range when `asset` was in a macro regime\\n\",\n \"r_squared_historical = (\\n\",\n \" model.get_r_squared(\\n\",\n \" start_date=start_date, end_date=end_date, assets=DataAssetsRequest(UniverseIdentifier.bbid, [asset])\\n\",\n \" )\\n\",\n \" .reset_index()\\n\",\n \" .rename(columns={\\\"index\\\": \\\"Date\\\"})\\n\",\n \")\\n\",\n \"macro_regime_dates = r_squared_historical.loc[r_squared_historical[asset] > 65].index.tolist()\\n\",\n \"temp_list = cycle(macro_regime_dates)\\n\",\n \"next(temp_list)\\n\",\n \"groups = groupby(macro_regime_dates, key=lambda j: j + 1 == next(temp_list))\\n\",\n \"macro_regime_date_range = [list(v) + [next(next(groups)[1])] for k, v in groups if k]\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(constrained_layout=True)\\n\",\n \"factor_category_sens_df.reset_index().rename(columns={\\\"index\\\": \\\"Date\\\"}).plot(ax=ax, x=\\\"Date\\\")\\n\",\n \"ax.set_title(f\\\"Sensitivity of {universe[0]} to Factor Categories on {end_date} Over Time\\\")\\n\",\n \"ax.set_ylabel(\\\"Percentage change in asset price for 1 std shift\\\")\\n\",\n \"ax.axhline(y=0, linestyle='dashed')\\n\",\n \"for date_range in macro_regime_date_range:\\n\",\n \" ax.axvspan(date_range[0], date_range[-1], color='aliceblue')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Historical Asset Sensitivity to Factors\\n\",\n \"\\n\",\n \"We can query asset sensitivity to indiviual factors within a factor category over time. The `factor_type` parameter in the function `get_universe_sensitivity` is by default set to `FactorType.Factor` to specify that we want exposure to individual factors.\\n\",\n \"\\n\",\n \"If the `factors` parameter is not specified, the factors to which the asset has the most exposure will be returned. On the plot, the shaded area represents a time period when the asset was in a macro regime.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Time range\\n\",\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"end_date = dt.date(2025, 2, 5)\\n\",\n \"\\n\",\n \"# Model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"# Asset and factors\\n\",\n \"asset = \\\"DHR UN\\\"\\n\",\n \"factors = []\\n\",\n \"\\n\",\n \"universe = [asset]\\n\",\n \"universe_for_request = DataAssetsRequest(UniverseIdentifier.bbid, universe)\\n\",\n \"\\n\",\n \"# Get asset factor sensitivity data\\n\",\n \"factor_sens_df = model.get_universe_sensitivity(\\n\",\n \" start_date=start_date, end_date=end_date, assets=universe_for_request, get_factors_by_name=True\\n\",\n \")\\n\",\n \"\\n\",\n \"# If no factors passed, get the factors with the highest positive/negative sensitivity\\n\",\n \"if not factors:\\n\",\n \" top_drivers = (\\n\",\n \" factor_sens_df.loc[(asset, end_date.strftime(\\\"%Y-%m-%d\\\")), :]\\n\",\n \" .to_frame()\\n\",\n \" .droplevel(1, axis=1)\\n\",\n \" .sort_values(by=asset, ascending=False)\\n\",\n \" .index.values.tolist()\\n\",\n \" )\\n\",\n \" factors = top_drivers[0:2] + top_drivers[-2:None]\\n\",\n \"\\n\",\n \"factor_sens_df = factor_sens_df[factors].droplevel(0)\\n\",\n \"\\n\",\n \"# Get date range where this asset was in a macro regime\\n\",\n \"r_squared_historical = (\\n\",\n \" model.get_r_squared(\\n\",\n \" start_date=start_date, end_date=end_date, assets=DataAssetsRequest(UniverseIdentifier.bbid, [asset])\\n\",\n \" )\\n\",\n \" .reset_index()\\n\",\n \" .rename(columns={\\\"index\\\": \\\"Date\\\"})\\n\",\n \")\\n\",\n \"macro_regime_dates = r_squared_historical.loc[r_squared_historical[asset] > 65].index.tolist()\\n\",\n \"temp_list = cycle(macro_regime_dates)\\n\",\n \"next(temp_list)\\n\",\n \"groups = groupby(macro_regime_dates, key=lambda j: j + 1 == next(temp_list))\\n\",\n \"macro_regime_date_range = [list(v) + [next(next(groups)[1])] for k, v in groups if k]\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(constrained_layout=True)\\n\",\n \"factor_sens_df.reset_index().rename(columns={\\\"index\\\": \\\"Date\\\"}).plot(ax=ax, x='Date')\\n\",\n \"ax.set_title(f\\\"Sensitivity of {universe[0]} to Macro Factors on {end_date} Over Time\\\")\\n\",\n \"ax.set_ylabel(\\\"Percentage change in asset price for 1 std shift\\\")\\n\",\n \"ax.axhline(y=0, linestyle='dashed')\\n\",\n \"for date_range in macro_regime_date_range:\\n\",\n \" ax.axvspan(date_range[0], date_range[-1], color='aliceblue')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"### Regime Shift over Time\\n\",\n \"\\n\",\n \"In a time range, we can pinpoint points in time when macro factors have primarily explained the variance in asset price over a period of time for some assets in our portfolio. \\n\",\n \"In other words, we can observe when R Squared for an asset was above 65% and when it was below 65%. \\n\",\n \"\\n\",\n \"Here are the user inputs:\\n\",\n \"* `model_id`: The model we are using.\\n\",\n \"* `universe`: A list of assets to get historical R Squared data for.\\n\",\n \"* `start_date` and `end_date`: Time range to get R Squared data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Time range\\n\",\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"end_date = dt.date(2025, 2, 5)\\n\",\n \"\\n\",\n \"# Model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"universe = [\\\"DHR UN\\\", \\\"AMZN UN\\\"]\\n\",\n \"universe_for_request = DataRequest(RiskModelUniverseIdentifierRequest.bbid, universe)\\n\",\n \"\\n\",\n \"r_squared_df = model.get_r_squared(start_date=start_date, end_date=end_date, assets=universe_for_request)\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(constrained_layout=True)\\n\",\n \"r_squared_df.plot(ax=ax)\\n\",\n \"ax.set_title(f\\\"Regime Shift from {start_date} to {end_date}\\\")\\n\",\n \"ax.set_ylabel(\\\"RSquared (in %)\\\")\\n\",\n \"ax.axhline(y=65, linestyle='dashed')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Historical Fair Value Gap\\n\",\n \"\\n\",\n \"The fair value gap is the difference between the model value and asset price. Note that a negative fair value gap (model value < asset price) means that the asset is rich, while a model value > asset price means that the asset is cheap.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Time range\\n\",\n \"start_date = dt.date(2025, 2, 4)\\n\",\n \"end_date = dt.date(2025, 2, 5)\\n\",\n \"\\n\",\n \"# Model\\n\",\n \"model_id = \\\"RISK MODEL ID\\\" # Such as \\\"QI_US_EQUITY_LT\\\"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"\\n\",\n \"asset_name = \\\"DHR UN\\\"\\n\",\n \"universe = [asset_name]\\n\",\n \"universe_for_request = DataRequest(RiskModelUniverseIdentifierRequest.bbid, universe)\\n\",\n \"\\n\",\n \"fair_value_gap_std_df = model.get_fair_value_gap(\\n\",\n \" start_date=start_date, end_date=end_date, fair_value_gap_unit=Unit.STANDARD_DEVIATION, assets=universe_for_request\\n\",\n \")\\n\",\n \"\\n\",\n \"# Get date range where this asset was in a macro regime\\n\",\n \"r_squared_historical = (\\n\",\n \" model.get_r_squared(\\n\",\n \" start_date=start_date, end_date=end_date, assets=DataAssetsRequest(UniverseIdentifier.bbid, universe)\\n\",\n \" )\\n\",\n \" .reset_index()\\n\",\n \" .rename(columns={\\\"index\\\": \\\"Date\\\"})\\n\",\n \")\\n\",\n \"macro_regime_dates = r_squared_historical.loc[r_squared_historical[asset_name] > 65].index.tolist()\\n\",\n \"temp_list = cycle(macro_regime_dates)\\n\",\n \"next(temp_list)\\n\",\n \"groups = groupby(macro_regime_dates, key=lambda j: j + 1 == next(temp_list))\\n\",\n \"macro_regime_date_range = [list(v) + [next(next(groups)[1])] for k, v in groups if k]\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(figsize=(15, 7), dpi=150, constrained_layout=True)\\n\",\n \"fair_value_gap_std_df.reset_index().rename(columns={\\\"index\\\": \\\"Date\\\"}).iloc[:, 0:4].plot(ax=ax, x='Date')\\n\",\n \"ax.set_title(f\\\"Fair Value Gap from {start_date} to {end_date}\\\", fontsize='x-large', fontweight='bold')\\n\",\n \"ax.set_ylabel(\\\"Fair Value Gap (sigma)\\\", fontsize='large')\\n\",\n \"ax.axhline(y=0, linestyle='dashed')\\n\",\n \"\\n\",\n \"for date_range in macro_regime_date_range:\\n\",\n \" ax.axvspan(date_range[0], date_range[-1], color='aliceblue')\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/11_macro_models/02_Upload_Macro_Models.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"# Macro Models\\n\",\n \"\\n\",\n \"The GS Quant `MacroRiskModel` class gives users the power to upload their own risk models to Marquee for seamless integration with the Marquee Portfolio Analytics and Plot Tool Pro suite. After uploading a custom `MacroRiskModel`, users can access their Macro model data programmatically using GS Quant.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Authenticate and Initialize Your Session\\n\",\n \"\\n\",\n \"First you will import the necessary modules and add your client id and client secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"\\n\",\n \"from gs_quant.models.risk_model import MacroRiskModel, RiskModelCalendar, Term, CoverageType, UniverseIdentifier\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the line below\\n\",\n \"\\n\",\n \"# client = 'YOUR CLIENT ID'\\n\",\n \"# secret = 'YOUR SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 2: Create a Macro Model\\n\",\n \"\\n\",\n \"Input fields to create the initial Macro Risk Model object\\n\",\n \"\\n\",\n \"| Attribute |Can be Modified |Description\\n\",\n \"|-----------------|-------------------|-------------\\n\",\n \"| id | No |Model id|\\n\",\n \"| name | Yes |Name of model|\\n\",\n \"| description | Yes |Longer description of model|\\n\",\n \"| term | Yes |Term or horizon of model. One of: Long, Medium, Short|\\n\",\n \"| coverage | Yes |Geographical coverage of assets within model universe. One of: Global, Region, Region Excluding Countries, Country|\\n\",\n \"| vendor | Yes |Who creates the model|\\n\",\n \"| version | Yes |Version of model|\\n\",\n \"| identifier | No |Identifier used to upload the model's asset universe. One of: sedol, cusip, bcid, gsid|\\n\",\n \"| entitlements | Yes |Who can manage, edit, and view the risk model|\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model_id = 'MY_MODEL'\\n\",\n \"model_name = 'My Risk Model'\\n\",\n \"description = 'My Custom Macro Risk Model'\\n\",\n \"term = Term.Medium\\n\",\n \"coverage = CoverageType.Country\\n\",\n \"universe_identifier = UniverseIdentifier.sedol\\n\",\n \"vendor = 'Goldman Sachs'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"model = MacroRiskModel(\\n\",\n \" id_=model_id,\\n\",\n \" name=model_name,\\n\",\n \" description=description,\\n\",\n \" coverage=coverage,\\n\",\n \" term=term,\\n\",\n \" universe_identifier=universe_identifier,\\n\",\n \" vendor=vendor,\\n\",\n \" version=1,\\n\",\n \")\\n\",\n \"\\n\",\n \"model.save()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 3: Upload a Calendar To Your Model\\n\",\n \"The calendar associated with the Macro Risk Model contains the dates which the risk model should have posted data on to be considered \\\"complete.\\\" The calendar can go further back as well as forward in time than the data that is currently posted for the calendar, but there cannot be any gaps in the data posted to the risk model according to the calendar.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"calendar = RiskModelCalendar(\\n\",\n \" [\\n\",\n \" '2021-01-29',\\n\",\n \" '2021-01-28',\\n\",\n \" '2021-01-27',\\n\",\n \" '2021-01-26',\\n\",\n \" '2021-01-25',\\n\",\n \" '2021-01-22',\\n\",\n \" '2021-01-21',\\n\",\n \" '2021-01-20',\\n\",\n \" '2021-01-19',\\n\",\n \" '2021-01-18',\\n\",\n \" '2021-01-15',\\n\",\n \" '2021-01-14',\\n\",\n \" '2021-01-13',\\n\",\n \" '2021-01-12',\\n\",\n \" '2021-01-11',\\n\",\n \" '2021-01-08',\\n\",\n \" '2021-01-07',\\n\",\n \" '2021-01-06',\\n\",\n \" '2021-01-05',\\n\",\n \" '2021-01-04',\\n\",\n \" '2021-01-01',\\n\",\n \" ]\\n\",\n \")\\n\",\n \"\\n\",\n \"model.upload_calendar(calendar)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 4: Upload Data To Your Model\\n\",\n \"\\n\",\n \"Once the calendar is posted for a model, we can start uploading data to it. We can supply data multiple ways:\\n\",\n \"\\n\",\n \"1. Upload total data one day at a time\\n\",\n \"2. Upload partial data one day at a time\\n\",\n \"\\n\",\n \"For a complete day of data, we need three things, defined in `RiskModelData`\\n\",\n \"1. Factor Data\\n\",\n \" - factorId: Can be any string, but needs to map consistently to the same factor across every date\\n\",\n \" - factorName: Can be any string, will be the display name of the factor, should be consistent across every date\\n\",\n \" - factorCategoryId: Id of the category that the factor belongs to\\n\",\n \" - factorCategory: Name of the category that the factor belongs to, will be the display name of the category (Style, Industry, Market, Currency, ect.)\\n\",\n \" - factorReturn: Daily return of the factor in percent units\\n\",\n \"2. Asset Data\\n\",\n \" - universe: Array of assets in the universe\\n\",\n \" - factorExposure: Array of dictionaries that map factorId to the factor exposure of that asset, corresponds to ordering of asset universe\\n\",\n \" - specificRisk: Array of annualized specific risk in percent units, corresponds to ordering of asset universe (null values not allowed)\\n\",\n \" - totalRisk: (optional) Array of total risk in percent units, corresponds to ordering of asset universe (null values not allowed)\\n\",\n \" - historicalBeta: (optional) Array of historical beta, corresponds to ordering of asset universe (null values not allowed)\\n\",\n \"\\n\",\n \"### Step 5: Upload Full Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"data = {\\n\",\n \" 'date': '2021-01-13', # Note: You can only upload to dates in your risk model's calendar\\n\",\n \" 'assetData': {\\n\",\n \" 'universe': ['B02V2Q0', '6560713', 'B3Q15X5', '0709954'],\\n\",\n \" 'specificRisk': [12.09, 45.12, 3.09, 1.0],\\n\",\n \" 'factorExposure': [\\n\",\n \" {'1': 0.23, '2': 0.023},\\n\",\n \" {'1': 0.023, '2': 2.09, '3': 0.3},\\n\",\n \" {'1': 0.063, '2': 2.069, '3': 0.73},\\n\",\n \" {'2': 0.067, '3': 0.93},\\n\",\n \" ],\\n\",\n \" 'totalRisk': [12.7, 45.5, 12.7, 10.3],\\n\",\n \" },\\n\",\n \" 'factorData': [\\n\",\n \" {\\n\",\n \" 'factorId': '1',\\n\",\n \" 'factorName': 'USD',\\n\",\n \" 'factorCategory': 'Currency',\\n\",\n \" 'factorCategoryId': 'CUR',\\n\",\n \" 'factorReturn': 0.5,\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'factorId': '2',\\n\",\n \" 'factorName': 'JPY 1Y Basis Swap',\\n\",\n \" 'factorCategory': 'GDP',\\n\",\n \" 'factorCategoryId': 'GDP',\\n\",\n \" 'factorReturn': 0.3,\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'factorId': '3',\\n\",\n \" 'factorName': 'US HY',\\n\",\n \" 'factorCategory': 'Credit Spreads',\\n\",\n \" 'factorCategoryId': 'CDS',\\n\",\n \" 'factorReturn': 0.2,\\n\",\n \" },\\n\",\n \" ],\\n\",\n \"}\\n\",\n \"\\n\",\n \"model.upload_data(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Step 6: Query Data From Model\\n\",\n \"\\n\",\n \"Once the data is uploaded, you can query it back using the same class\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.models.risk_model import Measure, DataAssetsRequest\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"model = MacroRiskModel.get(model_id)\\n\",\n \"# get multiple measures across a date range for a universe specified\\n\",\n \"start_date = dt.date(2021, 1, 13)\\n\",\n \"end_date = dt.date(2021, 1, 13)\\n\",\n \"\\n\",\n \"universe_for_request = DataAssetsRequest(\\n\",\n \" universe_identifier.value, []\\n\",\n \") # an empty assets request returns the full universe\\n\",\n \"data_measures = [\\n\",\n \" Measure.Universe_Factor_Exposure,\\n\",\n \" Measure.Asset_Universe,\\n\",\n \" Measure.Specific_Risk,\\n\",\n \" Measure.Total_Risk,\\n\",\n \" Measure.Factor_Id,\\n\",\n \" Measure.Factor_Name,\\n\",\n \" Measure.Factor_Category,\\n\",\n \" Measure.Factor_Category_Id,\\n\",\n \" Measure.Factor_Return,\\n\",\n \"]\\n\",\n \"\\n\",\n \"macro_factor_data = model.get_data(data_measures, start_date, end_date, universe_for_request, limit_factors=True)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/12_scenarios/01_Custom_Factor_Shocks.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"# What is a Scenario?\\n\",\n \"\\n\",\n \"A scenario is a set of parameters that define a market condition. Those parameters drive the factor returns that define\\n\",\n \"that scenario, and we can then use those factor returns, along with the current factor exposures of a portfolio,\\n\",\n \"to calculate the theoretical factor performance.\\n\",\n \"\\n\",\n \"What Types of Scenarios Exist?\\n\",\n \"\\n\",\n \"## Custom Factor Shocks\\n\",\n \"\\n\",\n \"This type of scenario shocks explicitly defined risk model factors up or down a certain percent. These scenarios must be risk-model specific and have two variations:\\n\",\n \"\\n\",\n \"1. **No Propagation**: This means the factors are shocked exactly as specified\\n\",\n \"2. **Propagation**: all the factor shocks defined in the scenario are applied normally, but we also shock all other factors by a calculable amount as defined by the correlation between that factor and the factors that are shocked\\n\",\n \"\\n\",\n \"### How do I create a custom factor shock scenario?\\n\",\n \"\\n\",\n \"Let's create a custom factor shock scenario in which the Value factor in the global Axioma risk model rises by 5%,\\n\",\n \"the Growth factor moves down by -5%, and the Medium-Term Momentum drops by -2%.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Initialize a GsSession\\n\",\n \"\\n\",\n \"The initial step is to import all required modules and initialize a GsSession with your application ID and secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.markets.scenario import (\\n\",\n \" FactorScenario,\\n\",\n \" FactorShockParameters,\\n\",\n \" FactorShock,\\n\",\n \" FactorScenarioType,\\n\",\n \")\\n\",\n \"from gs_quant.entities.entity import ScenarioCalculationMeasure\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.entities.entitlements import Entitlements, EntitlementBlock, User\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the lines below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 2: Define custom factor shocks parameters\\n\",\n \"\\n\",\n \"Next, we define the set of parameters that encapsulate the key components of our custom factor shock scenario:\\n\",\n \" - A valid risk model ID (see [full list of available risk models here](https://marquee.gs.com/s/discover/data-services/catalog?Category=Factor+Risk+Model)).\\n\",\n \" - A set of factors and their corresponding shocks in %, defined as a list of `FactorShock` object.\\n\",\n \" - Whether these shocks are propagated according to the correlation of factors in the risk model\\n\",\n \"\\n\",\n \"These three components will be encapsulated in a `FactorShockParameters` object. You make take a quick look at [historical factor returns](https://developer.gs.com/p/docs/services/risk/factor-models/get-factor-model-data/#:~:text=Get%20All%20Factor%20Returns) to guide you in\\n\",\n \"determining factor shocks. Below, we are defining two sets of factor shock parameters with similar attributes\\n\",\n \"except for the `propagate_shocks` component.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"risk_model_id = \\\"RISK MODEL ID\\\"\\n\",\n \"factor_1 = \\\"FACTOR 1\\\" # E.g. Value\\n\",\n \"factor_2 = \\\"FACTOR 2\\\" # E.g. Growth\\n\",\n \"factor_3 = \\\"FACTOR 3\\\" # E.g. Medium-Term Momentum\\n\",\n \"\\n\",\n \"factor_shocks = [\\n\",\n \" FactorShock(factor=factor_1, shock=5),\\n\",\n \" FactorShock(factor=factor_2, shock=-5),\\n\",\n \" FactorShock(factor=factor_3, shock=-2),\\n\",\n \"]\\n\",\n \"\\n\",\n \"\\n\",\n \"factor_shock_parameters_no_propagation = FactorShockParameters(\\n\",\n \" factor_shocks=factor_shocks, risk_model=risk_model_id, propagate_shocks=False\\n\",\n \")\\n\",\n \"\\n\",\n \"factor_shock_parameters_with_propagation = FactorShockParameters(\\n\",\n \" factor_shocks=factor_shocks, risk_model=risk_model_id, propagate_shocks=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 3: Set Scenario entitlements\\n\",\n \"\\n\",\n \"By default, an application will have all entitlement permissions to a scenario it creates. If you would like\\n\",\n \"to share the scenario with other Marquee users at your firm or other applications, you will need to specify them in the\\n\",\n \" `entitlements` attribute of the scenario. Let's walk through how we convert a list of admin, edit and viewer emails\\n\",\n \" into an `Entitlements` object:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"admin_emails = [\\\"ADMIN EMAILS\\\"]\\n\",\n \"edit_emails = [\\\"EDIT EMAILS\\\"]\\n\",\n \"view_emails = [\\\"VIEW EMAILS\\\"]\\n\",\n \"\\n\",\n \"admin_entitlements = EntitlementBlock(users=User.get_many(emails=admin_emails))\\n\",\n \"edit_entitlements = EntitlementBlock(users=User.get_many(emails=edit_emails))\\n\",\n \"view_entitlements = EntitlementBlock(users=User.get_many(emails=view_emails))\\n\",\n \"\\n\",\n \"scenario_entitlements = Entitlements(view=view_entitlements, edit=edit_entitlements, admin=admin_entitlements)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## Step 4: Create a custom factor shock scenario\\n\",\n \"\\n\",\n \"Now we are ready to create a factor shock scenario. We will need:\\n\",\n \"\\n\",\n \"- A name for the scenario\\n\",\n \"- The type of the scenario. Since we are creating a factor shock scenario, the type will be `FactorScenarioType.Factor_Shock`\\n\",\n \"- The parameters of the scenario\\n\",\n \"- Scenario entitlements\\n\",\n \"- A description for the scenario\\n\",\n \"\\n\",\n \"Below, we will create two scenarios: one factor shock scenario with no propagation, the second one with propagation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"factor_shock_scenario_no_propagation_name = f\\\"Sample factor shock scenario with {risk_model_id} with no propagation\\\"\\n\",\n \"factor_shock_scenario_no_propagation = FactorScenario(\\n\",\n \" name=factor_shock_scenario_no_propagation_name,\\n\",\n \" type=FactorScenarioType.Factor_Shock,\\n\",\n \" parameters=factor_shock_parameters_no_propagation,\\n\",\n \" description=f\\\"Sample custom shock scenario with {risk_model_id}\\\",\\n\",\n \" entitlements=scenario_entitlements,\\n\",\n \")\\n\",\n \"factor_shock_scenario_no_propagation.save()\\n\",\n \"print(factor_shock_scenario_no_propagation)\\n\",\n \"\\n\",\n \"factor_shock_scenario_propagation_name = f\\\"Sample factor shock scenario with {risk_model_id} with propagation\\\"\\n\",\n \"factor_shock_scenario_with_propagation = FactorScenario(\\n\",\n \" name=factor_shock_scenario_propagation_name,\\n\",\n \" type=FactorScenarioType.Factor_Shock,\\n\",\n \" parameters=factor_shock_parameters_with_propagation,\\n\",\n \" description=f\\\"Sample custom shock scenario with {risk_model_id}\\\",\\n\",\n \" entitlements=scenario_entitlements,\\n\",\n \")\\n\",\n \"factor_shock_scenario_with_propagation.save()\\n\",\n \"print(factor_shock_scenario_with_propagation)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"## FAQs:\\n\",\n \"\\n\",\n \"### How do I get my Scenarios or Scenarios that are shared with me?\\n\",\n \"\\n\",\n \"You can get saved scenarios that you created or that were shared with you:\\n\",\n \"- By its unique Marquee ID\\n\",\n \"- By its name\\n\",\n \"- By applying filters such as `risk_model`, `type` of the scenario, the `shocked_factors`, or whether the shocks are propagated\\n\",\n \"- For a historical simulation scenario, you can filter by a `start_date` and `end_date`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Get by id\\n\",\n \"scenario_by_id = FactorScenario.get(\\\"SCENARIO ID\\\")\\n\",\n \"\\n\",\n \"# Get all my factor Shock scenarios (and those that are shared with me) with risk model AXIOMA_AXWW4M and whose\\n\",\n \"# factor shocks are propagated to all factors\\n\",\n \"many_scenarios = FactorScenario.get_many(\\n\",\n \" risk_model=risk_model_id, propagated_shocks=True, type=FactorScenarioType.Factor_Shock\\n\",\n \")\\n\",\n \"# Get all my factor Shock scenarios (and those that are shared with me) with risk model AXIOMA_AXWW4M that shocks the\\n\",\n \"# Value factor\\n\",\n \"many_scenarios = FactorScenario.get_many(\\n\",\n \" risk_model=risk_model_id, shocked_factors=[\\\"Value\\\"], type=FactorScenarioType.Factor_Shock\\n\",\n \")\\n\",\n \"\\n\",\n \"# Get all my historical simulation scenarios that are within the requested start and end date\\n\",\n \"historical_simulation_scenarios = FactorScenario.get_many(\\n\",\n \" type=\\\"Factor Historical Simulation\\\", start_date=dt.date(2023, 1, 1), end_date=dt.date(2024, 1, 1)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### How do I get GS pre-scanned scenarios?\\n\",\n \"\\n\",\n \"To get GS pre-scanned scenarios, you can additionally filter by the tag \\\"GS\\\". See below for some examples\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"# Get 3 factor shocks scenarios with risk model AXIOMA_AXWW4M that propagate factor shocks\\n\",\n \"pre_canned_factor_shock_scenarios = FactorScenario.get_many(\\n\",\n \" risk_model=risk_model_id, propagated_shocks=True, type=FactorScenarioType.Factor_Shock, tags=[\\\"GS\\\"], limit=3\\n\",\n \")\\n\",\n \"print(pre_canned_factor_shock_scenarios)\\n\",\n \"\\n\",\n \"# Get 3 historical simulation that are within selected start and end date\\n\",\n \"pre_canned_historical_simulation_scenarios = FactorScenario.get_many(\\n\",\n \" type=\\\"Factor Historical Simulation\\\",\\n\",\n \" start_date=dt.date(2010, 1, 1),\\n\",\n \" end_date=dt.date(2024, 1, 1),\\n\",\n \" tags=[\\\"GS\\\"],\\n\",\n \" limit=3,\\n\",\n \")\\n\",\n \"print(pre_canned_historical_simulation_scenarios)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Running Scenario(s) on a Portfolio\\n\",\n \"\\n\",\n \"To see the estimated impact of scenarios on your portfolio, run the `get_factor_scenario_analytics` function in the\\n\",\n \"`PortfolioManager` class, which is the interface for managing your portfolios in Marquee.\\n\",\n \"\\n\",\n \"A scenario calculation request requires the following components:\\n\",\n \"\\n\",\n \"- A date: date on which to run a scenario. We will run the scenario(s) on your portfolio holdings as of this date.\\n\",\n \"- Scenarios: List of scenarios to run. Can either be a list of unique Marquee IDs, or a scenario object itself.\\n\",\n \"- Risk Model: The risk model to run a scenario with.\\n\",\n \"- Measures: Metrics to return, which include:\\n\",\n \" 1. Total portfolio estimated factor PnL\\n\",\n \" 2. Total portfolio PnL by GICS sector & industry\\n\",\n \" 3. Total portfolio PnL by region\\n\",\n \" 4. Total portfolio PnL by the direction (LONG/SHORT) of your portfolio holdings,\\n\",\n \" 5. Estimated performance of each asset in your portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"portfolio_id = \\\"PORTFOLIO ID\\\"\\n\",\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"\\n\",\n \"scenario_analytics = pm.get_factor_scenario_analytics(\\n\",\n \" scenarios=[\\\"SCENARIO ID\\\"],\\n\",\n \" date=dt.date(2024, 4, 10),\\n\",\n \" measures=[\\n\",\n \" ScenarioCalculationMeasure.SUMMARY,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_FACTOR_PNL,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_SECTOR,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_REGION,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_DIRECTION,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_ASSET,\\n\",\n \" ],\\n\",\n \" risk_model=risk_model_id,\\n\",\n \")\\n\",\n \"summary = scenario_analytics.get('summary')\\n\",\n \"factor_pnl = scenario_analytics.get('factorPnl')\\n\",\n \"sector_aggregations = scenario_analytics.get('bySectorAggregations')\\n\",\n \"region_aggregations = scenario_analytics.get(\\\"byRegionAggregations\\\")\\n\",\n \"by_asset_aggregations = scenario_analytics.get('byAsset')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%% md\\n\"\n }\n },\n \"source\": [\n \"### Run Scenario(s) on a Basket\\n\",\n \"\\n\",\n \"We can also run one or multiple scenarios on an existing equity custom basket and pull scenario analytics data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"\\n\",\n \"basket_ticker = \\\"BASKET TICKER\\\"\\n\",\n \"basket = Basket.get(basket_ticker)\\n\",\n \"\\n\",\n \"basket_scenario_analytics = basket.get_factor_scenario_analytics(\\n\",\n \" scenarios=[factor_shock_scenario_no_propagation, factor_shock_scenario_with_propagation, \\\"YOUR SCENARIO\\\"],\\n\",\n \" date=dt.date(2024, 4, 1),\\n\",\n \" measures=[\\n\",\n \" ScenarioCalculationMeasure.SUMMARY,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_FACTOR_PNL,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_SECTOR,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_REGION,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_DIRECTION,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_ASSET,\\n\",\n \" ],\\n\",\n \" risk_model=risk_model_id,\\n\",\n \")\\n\",\n \"\\n\",\n \"summary = basket_scenario_analytics.get('summary')\\n\",\n \"factor_pnl = basket_scenario_analytics.get('factorPnl')\\n\",\n \"sector_aggregations = basket_scenario_analytics.get('bySectorAggregations')\\n\",\n \"region_aggregations = basket_scenario_analytics.get(\\\"byRegionAggregations\\\")\\n\",\n \"by_asset_aggregations = basket_scenario_analytics.get('byAsset')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "gs_quant/documentation/12_scenarios/02_Historical_Scenarios.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Historical Factor Scenario\\n\",\n \"\\n\",\n \"In a historical factor scenario, the aim is to replicate the market conditions during a significant or\\n\",\n \"topical historical event and estimate the impact on your portfolio today. The first step is to define the time period\\n\",\n \" of the historical event. Then, factor returns across the time period are geometrically aggregated, and these aggregated\\n\",\n \" returns are the factor shocks that will be used to derive the estimated impact on the performance of your portfolio.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Initialize a GsSession\\n\",\n \"\\n\",\n \"The initial step is to import all required modules and initialize a GsSession with your application ID and secret.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.session import GsSession, Environment\\n\",\n \"from gs_quant.markets.scenario import FactorScenario, FactorScenarioType, HistoricalSimulationParameters\\n\",\n \"from gs_quant.entities.entity import ScenarioCalculationMeasure\\n\",\n \"from gs_quant.markets.portfolio_manager import PortfolioManager\\n\",\n \"from gs_quant.entities.entitlements import Entitlements, EntitlementBlock, User\\n\",\n \"import datetime as dt\\n\",\n \"\\n\",\n \"client = None\\n\",\n \"secret = None\\n\",\n \"\\n\",\n \"## External users must fill in their client ID and secret below and comment out the lines below\\n\",\n \"\\n\",\n \"# client = 'ENTER CLIENT ID'\\n\",\n \"# secret = 'ENTER CLIENT SECRET'\\n\",\n \"\\n\",\n \"GsSession.use(Environment.PROD, client_id=client, client_secret=secret)\\n\",\n \"\\n\",\n \"print('GS Session initialized.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: How do I create a historical factor scenario?\\n\",\n \"\\n\",\n \"Similar to factor shock scenarios, we will need to provide:\\n\",\n \"- A name for the scenario\\n\",\n \"- The type of the scenario. In this case, the scenario type is `FactorScenarioType.Factor_Historical_Simulation`\\n\",\n \"- The parameters of the scenario\\n\",\n \"- Scenario entitlements\\n\",\n \"- A description for the scenario\\n\",\n \"\\n\",\n \"The parameters are defined in a `HistoricalSimulationParameters` object and will define the time\\n\",\n \"period of the historical simulation scenario:\\n\",\n \"\\n\",\n \"- Start Date: The start date of the historical event simulation period\\n\",\n \"- End Date: The end date of the historical event simulation period.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"start_date = dt.date(2023, 1, 1)\\n\",\n \"end_date = dt.date(2024, 1, 1)\\n\",\n \"\\n\",\n \"admin_emails = [\\\"ADMIN EMAILS\\\"]\\n\",\n \"edit_emails = [\\\"EDIT EMAILS\\\"]\\n\",\n \"view_emails = [\\\"VIEW EMAILS\\\"]\\n\",\n \"\\n\",\n \"admin_entitlements = EntitlementBlock(users=User.get_many(emails=admin_emails))\\n\",\n \"edit_entitlements = EntitlementBlock(users=User.get_many(emails=edit_emails))\\n\",\n \"view_entitlements = EntitlementBlock(users=User.get_many(emails=view_emails))\\n\",\n \"\\n\",\n \"scenario_entitlements = Entitlements(view=view_entitlements, edit=edit_entitlements, admin=admin_entitlements)\\n\",\n \"\\n\",\n \"date_range_parameters = HistoricalSimulationParameters(start_date=start_date, end_date=end_date)\\n\",\n \"historical_scenario_name = (\\n\",\n \" f\\\"Example historical simulation scenario from {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}\\\"\\n\",\n \")\\n\",\n \"historical_replay_scenario = FactorScenario(\\n\",\n \" name=historical_scenario_name,\\n\",\n \" type=FactorScenarioType.Factor_Historical_Simulation,\\n\",\n \" parameters=date_range_parameters,\\n\",\n \" description=f\\\"Sample historical simulation scenario from {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}\\\",\\n\",\n \" entitlements=scenario_entitlements,\\n\",\n \")\\n\",\n \"\\n\",\n \"historical_replay_scenario.save()\\n\",\n \"print(historical_replay_scenario)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## FAQs:\\n\",\n \"\\n\",\n \"### How do I get my Scenarios or Scenarios that are shared with me?\\n\",\n \"\\n\",\n \"You can get saved scenarios that you created or that were shared with you:\\n\",\n \"- By its unique Marquee ID\\n\",\n \"- By its name\\n\",\n \"- By applying filters such as `risk_model`, `type` of the scenario, the `shocked_factors`, or whether the shocks are propagated\\n\",\n \"- For a historical simulation scenario, you can filter by a `start_date` and `end_date`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"risk_model_id = \\\"RISK MODEL ID\\\"\\n\",\n \"\\n\",\n \"# Get by id\\n\",\n \"scenario_by_id = FactorScenario.get(\\\"SCENARIO ID\\\")\\n\",\n \"\\n\",\n \"# Get all my factor Shock scenarios (and those that are shared with me) with risk model AXIOMA_AXWW4M and whose\\n\",\n \"# factor shocks are propagated to all factors\\n\",\n \"many_scenarios = FactorScenario.get_many(\\n\",\n \" risk_model=risk_model_id, propagated_shocks=True, type=FactorScenarioType.Factor_Shock\\n\",\n \")\\n\",\n \"# Get all my factor Shock scenarios (and those that are shared with me) with risk model AXIOMA_AXWW4M that shocks the\\n\",\n \"# Value factor\\n\",\n \"many_scenarios = FactorScenario.get_many(\\n\",\n \" risk_model=risk_model_id, shocked_factors=[\\\"Value\\\"], type=FactorScenarioType.Factor_Shock\\n\",\n \")\\n\",\n \"\\n\",\n \"# Get all my historical simulation scenarios that are within the requested start and end date\\n\",\n \"historical_simulation_scenarios = FactorScenario.get_many(\\n\",\n \" type=\\\"Factor Historical Simulation\\\", start_date=dt.date(2023, 1, 1), end_date=dt.date(2024, 1, 1)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### How do I get GS pre-scanned scenarios?\\n\",\n \"\\n\",\n \"To get GS pre-scanned scenarios, you can additionally filter by the tag \\\"GS\\\". See below for some examples\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get 3 factor shocks scenarios with risk model AXIOMA_AXWW4M that propagate factor shocks\\n\",\n \"pre_canned_factor_shock_scenarios = FactorScenario.get_many(\\n\",\n \" risk_model=risk_model_id, propagated_shocks=True, type=FactorScenarioType.Factor_Shock, tags=[\\\"GS\\\"], limit=3\\n\",\n \")\\n\",\n \"print(pre_canned_factor_shock_scenarios)\\n\",\n \"\\n\",\n \"# Get 3 historical simulation that are within selected start and end date\\n\",\n \"pre_canned_historical_simulation_scenarios = FactorScenario.get_many(\\n\",\n \" type=\\\"Factor Historical Simulation\\\",\\n\",\n \" start_date=dt.date(2010, 1, 1),\\n\",\n \" end_date=dt.date(2024, 1, 1),\\n\",\n \" tags=[\\\"GS\\\"],\\n\",\n \" limit=3,\\n\",\n \")\\n\",\n \"print(pre_canned_historical_simulation_scenarios)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Running Scenario(s) on a Portfolio\\n\",\n \"\\n\",\n \"To see the estimated impact of scenarios on your portfolio, run the `get_factor_scenario_analytics` function in the\\n\",\n \"`PortfolioManager` class, which is the interface for managing your portfolios in Marquee.\\n\",\n \"\\n\",\n \"A scenario calculation request requires the following components:\\n\",\n \"\\n\",\n \"- A date: date on which to run a scenario. We will run the scenario(s) on your portfolio holdings as of this date.\\n\",\n \"- Scenarios: List of scenarios to run. Can either be a list of unique Marquee IDs, or a scenario object itself.\\n\",\n \"- Risk Model: The risk model to run a scenario with.\\n\",\n \"- Measures: Metrics to return, which include:\\n\",\n \" 1. Total portfolio estimated factor PnL\\n\",\n \" 2. Total portfolio PnL by GICS sector & industry\\n\",\n \" 3. Total portfolio PnL by region\\n\",\n \" 4. Total portfolio PnL by the direction (LONG/SHORT) of your portfolio holdings,\\n\",\n \" 5. Estimated performance of each asset in your portfolio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"portfolio_id = \\\"PORTFOLIO ID\\\"\\n\",\n \"pm = PortfolioManager(portfolio_id)\\n\",\n \"\\n\",\n \"scenario_analytics = pm.get_factor_scenario_analytics(\\n\",\n \" scenarios=[\\\"SCENARIO ID\\\"],\\n\",\n \" date=dt.date(2024, 4, 10),\\n\",\n \" measures=[\\n\",\n \" ScenarioCalculationMeasure.SUMMARY,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_FACTOR_PNL,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_SECTOR,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_REGION,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_DIRECTION,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_ASSET,\\n\",\n \" ],\\n\",\n \" risk_model=risk_model_id,\\n\",\n \")\\n\",\n \"summary = scenario_analytics.get('summary')\\n\",\n \"factor_pnl = scenario_analytics.get('factorPnl')\\n\",\n \"sector_aggregations = scenario_analytics.get('bySectorAggregations')\\n\",\n \"region_aggregations = scenario_analytics.get(\\\"byRegionAggregations\\\")\\n\",\n \"by_asset_aggregations = scenario_analytics.get('byAsset')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Run Scenario(s) on a Basket\\n\",\n \"\\n\",\n \"We can also run one or multiple scenarios on an existing equity custom basket and pull scenario analytics data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from gs_quant.markets.baskets import Basket\\n\",\n \"\\n\",\n \"basket_ticker = \\\"BASKET TICKER\\\"\\n\",\n \"basket = Basket.get(basket_ticker)\\n\",\n \"\\n\",\n \"basket_scenario_analytics = basket.get_factor_scenario_analytics(\\n\",\n \" scenarios=[\\\"YOUR SCENARIOS\\\"],\\n\",\n \" date=dt.date(2024, 4, 1),\\n\",\n \" measures=[\\n\",\n \" ScenarioCalculationMeasure.SUMMARY,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_FACTOR_PNL,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_SECTOR,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_REGION,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_DIRECTION,\\n\",\n \" ScenarioCalculationMeasure.ESTIMATED_PNL_BY_ASSET,\\n\",\n \" ],\\n\",\n \" risk_model=risk_model_id,\\n\",\n \")\\n\",\n \"\\n\",\n \"summary = basket_scenario_analytics.get('summary')\\n\",\n \"factor_pnl = basket_scenario_analytics.get('factorPnl')\\n\",\n \"sector_aggregations = basket_scenario_analytics.get('bySectorAggregations')\\n\",\n \"region_aggregations = basket_scenario_analytics.get(\\\"byRegionAggregations\\\")\\n\",\n \"by_asset_aggregations = basket_scenario_analytics.get('byAsset')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"tutorial-env\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "gs_quant/documentation/Contents.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67001\",\n \"metadata\": {},\n \"source\": [\n \"# Contents\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67002\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 00_data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67003\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **00_datasets**\\n\",\n \" - 📁 examples\\n\",\n \" - 📄 [0000_query_dataset.ipynb](00_data/00_datasets/examples/0000_query_dataset.ipynb)\\n\",\n \" - 📄 [0001_get_dataset_coverage.ipynb](00_data/00_datasets/examples/0001_get_dataset_coverage.ipynb)\\n\",\n \" - 📄 [0002_using_data_contexts.ipynb](00_data/00_datasets/examples/0002_using_data_contexts.ipynb)\\n\",\n \" - 📄 [0003_get_data_using_coordinate.ipynb](00_data/00_datasets/examples/0003_get_data_using_coordinate.ipynb)\\n\",\n \" - 📄 [0004_get_all_assets_from_region.ipynb](00_data/00_datasets/examples/0004_get_all_assets_from_region.ipynb)\\n\",\n \" - 📄 [0005_get_vol_surface.ipynb](00_data/00_datasets/examples/0005_get_vol_surface.ipynb)\\n\",\n \" - 📄 [0006_ptp_dataset.ipynb](00_data/00_datasets/examples/0006_ptp_dataset.ipynb)\\n\",\n \" - 📄 [0007_query_fxvol.script.ipynb](00_data/00_datasets/examples/0007_query_fxvol.script.ipynb)\\n\",\n \" - 📄 [0008_s3_partners_short_interest.ipynb](00_data/00_datasets/examples/0008_s3_partners_short_interest.ipynb)\\n\",\n \"- 📁 **01_analytics**\\n\",\n \" - 📁 examples\\n\",\n \" - 📄 [0000_charting_data.ipynb](00_data/01_analytics/examples/0000_charting_data.ipynb)\\n\",\n \" - 📄 [0001_exporting_data.ipynb](00_data/01_analytics/examples/0001_exporting_data.ipynb)\\n\",\n \"- 📁 **02_workspaces**\\n\",\n \" - 📁 examples\\n\",\n \" - 📄 [0001_creating_a_workspace.ipynb](00_data/02_workspaces/examples/0001_creating_a_workspace.ipynb)\\n\",\n \"- 📁 **03_visualizations**\\n\",\n \" - 📁 examples\\n\",\n \" - 📄 [0001_creating_a_visualization.ipynb](00_data/03_visualizations/examples/0001_creating_a_visualization.ipynb)\\n\",\n \"- 📁 **04_screens**\\n\",\n \" - 📁 examples\\n\",\n \" - 📄 [00_base_screener_api_tutorial.ipynb](00_data/04_screens/examples/00_base_screener_api_tutorial.ipynb)\\n\",\n \" - 📄 [01_screen_api_tutorial.ipynb](00_data/04_screens/examples/01_screen_api_tutorial.ipynb)\\n\",\n \"- 📁 **covid**\\n\",\n \" - 📄 [Comparing, Reconciling, and Combining COVID-19 Data Sources.ipynb](00_data/covid/Comparing,%20Reconciling,%20and%20Combining%20COVID-19%20Data%20Sources.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67004\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 01_markets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67005\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **00_securities**\\n\",\n \" - 📁 examples\\n\",\n \" - 📄 [0000_get_an_asset_from_identifier.ipynb](01_markets/00_securities/examples/0000_get_an_asset_from_identifier.ipynb)\\n\",\n \"- 📁 **02_rdates**\\n\",\n \" - 📁 examples\\n\",\n \" - 📄 [0000_get_a_date_relative_from_today.ipynb](01_markets/02_rdates/examples/0000_get_a_date_relative_from_today.ipynb)\\n\",\n \" - 📄 [0001_get_a_date_relative_from_a_base_date.ipynb](01_markets/02_rdates/examples/0001_get_a_date_relative_from_a_base_date.ipynb)\\n\",\n \" - 📄 [0002_get_a_date_relative_using_pricing_context.ipynb](01_markets/02_rdates/examples/0002_get_a_date_relative_using_pricing_context.ipynb)\\n\",\n \" - 📄 [0003_get_a_date_using_a_business_day_calendar.ipynb](01_markets/02_rdates/examples/0003_get_a_date_using_a_business_day_calendar.ipynb)\\n\",\n \" - 📄 [0004_chaining_relativedate_rules.ipynb](01_markets/02_rdates/examples/0004_chaining_relativedate_rules.ipynb)\\n\",\n \" - 📄 [0005_passing_your_own_holiday_calendar.ipynb](01_markets/02_rdates/examples/0005_passing_your_own_holiday_calendar.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67006\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 02_pricing_and_risk\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67007\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **00_instruments_and_measures**\\n\",\n \" - 📁 examples\\n\",\n \" - 📁 00_instrument_basics\\n\",\n \" - 📄 [01_view-trade-properties.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/01_view-trade-properties.ipynb)\\n\",\n \" - 📄 [02_get-a-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/02_get-a-property.ipynb)\\n\",\n \" - 📄 [03_set-a-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/03_set-a-property.ipynb)\\n\",\n \" - 📄 [04_enum-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/04_enum-property.ipynb)\\n\",\n \" - 📄 [05_resolve-a-trade.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/05_resolve-a-trade.ipynb)\\n\",\n \" - 📄 [06_market-data.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb)\\n\",\n \" - 📄 [07_float-with-info.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/07_float-with-info.ipynb)\\n\",\n \" - 📁 01_rates\\n\",\n \" - 📄 [01_view_swap_definition.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/01_view_swap_definition.ipynb)\\n\",\n \" - 📄 [02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb)\\n\",\n \" - 📄 [03_calc_swap_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/03_calc_swap_price.ipynb)\\n\",\n \" - 📄 [04_calc_swap_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb)\\n\",\n \" - 📄 [05_calc_swap_price_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/05_calc_swap_price_in_pricing_context.ipynb)\\n\",\n \" - 📄 [06_calc_swap_price_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/06_calc_swap_price_historically.ipynb)\\n\",\n \" - 📄 [07_calc_swap_risks_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb)\\n\",\n \" - 📄 [08_calc_swap_risk_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/08_calc_swap_risk_historically.ipynb)\\n\",\n \" - 📄 [09_swaption_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb)\\n\",\n \" - 📄 [10_straddle_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/10_straddle_price.ipynb)\\n\",\n \" - 📄 [11_midcurve_swaption_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/11_midcurve_swaption_price.ipynb)\\n\",\n \" - 📄 [12_swap_future_cashflows.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/12_swap_future_cashflows.ipynb)\\n\",\n \" - 📄 [13_calc_xccy_swap_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/13_calc_xccy_swap_price.ipynb)\\n\",\n \" - 📄 [14_calc_xccy_swap_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/14_calc_xccy_swap_risk_measures.ipynb)\\n\",\n \" - 📄 [15_spread_option_grid_pricing.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/15_spread_option_grid_pricing.ipynb)\\n\",\n \" - 📄 [16_change_discount_curve.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/16_change_discount_curve.ipynb)\\n\",\n \" - 📄 [17_calc_xccy_swap_cashflows.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/17_calc_xccy_swap_cashflows.ipynb)\\n\",\n \" - 📄 [18_solve_present_value.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/18_solve_present_value.ipynb)\\n\",\n \" - 📄 [19_solve_delta.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/19_solve_delta.ipynb)\\n\",\n \" - 📄 [20_fix_float_legs_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb)\\n\",\n \" - 📄 [21_asset_swap_definition.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/21_asset_swap_definition.ipynb)\\n\",\n \" - 📄 [22_cap_floor.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/22_cap_floor.ipynb)\\n\",\n \" - 📄 [23_solve_vanna_&_volga.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb)\\n\",\n \" - 📁 02_fx\\n\",\n \" - 📄 [01_fx_fwd_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/01_fx_fwd_trade_construction.ipynb)\\n\",\n \" - 📄 [02_fx_option_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/02_fx_option_trade_construction.ipynb)\\n\",\n \" - 📄 [03_fx_vol_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb)\\n\",\n \" - 📄 [04_fx_binary_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb)\\n\",\n \" - 📄 [05_calc_option_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/05_calc_option_measures.ipynb)\\n\",\n \" - 📄 [06_fx_var_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/06_fx_var_swap_trade_construction.ipynb)\\n\",\n \" - 📄 [07_fx_knockout_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb)\\n\",\n \" - 📄 [08_fx_double_touch_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb)\\n\",\n \" - 📄 [09_fx_delta_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb)\\n\",\n \" - 📄 [10_fx_gamma_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/10_fx_gamma_measures.ipynb)\\n\",\n \" - 📁 03_eq\\n\",\n \" - 📄 [01_calc_eq_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb)\\n\",\n \" - 📄 [02_calc_eq_option_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb)\\n\",\n \" - 📄 [03_calc_eq_option_price_and_risk_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/03_calc_eq_option_price_and_risk_in_pricing_context.ipynb)\\n\",\n \" - 📄 [04_calc_eq_option_price_and_risk_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/04_calc_eq_option_price_and_risk_historically.ipynb)\\n\",\n \" - 📄 [05_calc_eq_option_portfolio_greeks.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb)\\n\",\n \" - 📁 04_credit\\n\",\n \" - 📄 [01_cdindex_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb)\\n\",\n \" - 📄 [02_cdindex_option_xover_payer_spread.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/02_cdindex_option_xover_payer_spread.ipynb)\\n\",\n \" - 📄 [03_cdindex_option_risks.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/03_cdindex_option_risks.ipynb)\\n\",\n \" - 📄 [04_cdindex_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/04_cdindex_price.ipynb)\\n\",\n \" - 📄 [05_cdindex_roll_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/05_cdindex_roll_price.ipynb)\\n\",\n \" - 📁 tutorials\\n\",\n \" - 📄 [Instruments.ipynb](02_pricing_and_risk/00_instruments_and_measures/tutorials/Instruments.ipynb)\\n\",\n \" - 📄 [Measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/tutorials/Measures.ipynb)\\n\",\n \"- 📁 **01_scenarios_and_contexts**\\n\",\n \" - 📁 examples\\n\",\n \" - 📁 00_market_objects\\n\",\n \" - 📄 [010000_market_data_in_gs_quant.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010000_market_data_in_gs_quant.ipynb)\\n\",\n \" - 📄 [010001_market_objects.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010001_market_objects.ipynb)\\n\",\n \" - 📄 [010002_marketdatapattern_basics.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010002_marketdatapattern_basics.ipynb)\\n\",\n \" - 📄 [010003_marketdatapattern_construction_ir.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010003_marketdatapattern_construction_ir.ipynb)\\n\",\n \" - 📄 [010004_marketdatapattern_construction_ir_vol.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010004_marketdatapattern_construction_ir_vol.ipynb)\\n\",\n \" - 📄 [010005_marketdatapattern_construction_ir_basis.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010005_marketdatapattern_construction_ir_basis.ipynb)\\n\",\n \" - 📄 [010006_marketdatapattern_construction_ir_xc.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010006_marketdatapattern_construction_ir_xc.ipynb)\\n\",\n \" - 📄 [010007_marketdatapattern_construction_fx.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010007_marketdatapattern_construction_fx.ipynb)\\n\",\n \" - 📄 [010008_marketdatapattern_construction_fx_fwd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010008_marketdatapattern_construction_fx_fwd.ipynb)\\n\",\n \" - 📄 [010009_marketdatapattern_construction_fx_vol.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010009_marketdatapattern_construction_fx_vol.ipynb)\\n\",\n \" - 📄 [010010_marketdatapattern_construction_cd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010010_marketdatapattern_construction_cd.ipynb)\\n\",\n \" - 📁 01_rollfwd_shock\\n\",\n \" - 📄 [01_rollfwd_for_trade.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/01_rollfwd_for_trade.ipynb)\\n\",\n \" - 📄 [02_basic_use_of_rollfwd_scenario.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/02_basic_use_of_rollfwd_scenario.ipynb)\\n\",\n \" - 📄 [03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb)\\n\",\n \" - 📄 [04_yield_curves_with_rollfwd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb)\\n\",\n \" - 📄 [05_rollfwd_swap_fwd_rates.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/05_rollfwd_swap_fwd_rates.ipynb)\\n\",\n \" - 📁 02_market_shock\\n\",\n \" - 📄 [010200_marketdatashockbasedscenario_basics.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010200_marketdatashockbasedscenario_basics.ipynb)\\n\",\n \" - 📄 [010201_rate_spot_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010201_rate_spot_shock.ipynb)\\n\",\n \" - 📄 [010203_rate_vol_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010203_rate_vol_shock.ipynb)\\n\",\n \" - 📄 [010204_delta_shock_equivalent.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010204_delta_shock_equivalent.ipynb)\\n\",\n \" - 📄 [010205_vega_shock_equivalent.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010205_vega_shock_equivalent.ipynb)\\n\",\n \" - 📄 [010206_eq_spot_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010206_eq_spot_shock.ipynb)\\n\",\n \" - 📄 [010207_fx_shock_examples.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010207_fx_shock_examples.ipynb)\\n\",\n \" - 📁 03_composite_shocks\\n\",\n \" - 📄 [010300_rate_composite_shocks.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/03_composite_shocks/010300_rate_composite_shocks.ipynb)\\n\",\n \" - 📄 [010301_multiple_shocks.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/03_composite_shocks/010301_multiple_shocks.ipynb)\\n\",\n \" - 📁 04_curve_shock\\n\",\n \" - 📄 [010400_parallel_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010400_parallel_curve_shock.ipynb)\\n\",\n \" - 📄 [010401_curve_shock_midpoint.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010401_curve_shock_midpoint.ipynb)\\n\",\n \" - 📄 [010402_bear_steepener_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010402_bear_steepener_curve_shock.ipynb)\\n\",\n \" - 📄 [010403_bull_steepener_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010403_bull_steepener_curve_shock.ipynb)\\n\",\n \" - 📄 [010404_bear_flattener_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010404_bear_flattener_curve_shock.ipynb)\\n\",\n \" - 📄 [010405_bull_flattener_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010405_bull_flattener_curve_shock.ipynb)\\n\",\n \" - 📁 05_market_override\\n\",\n \" - 📄 [010501_eq_spot_override.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/05_market_override/010501_eq_spot_override.ipynb)\\n\",\n \" - 📄 [010502_eq_future_override.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/05_market_override/010502_eq_future_override.ipynb)\\n\",\n \" - 📁 06_vol_market_override\\n\",\n \" - 📄 [010601_eq_vol_override_eod.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/06_vol_market_override/010601_eq_vol_override_eod.ipynb)\\n\",\n \" - 📄 [010601_eq_vol_override_intraday.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/06_vol_market_override/010601_eq_vol_override_intraday.ipynb)\\n\",\n \" - 📁 07_curve_overlay\\n\",\n \" - 📄 [010700_discount_curve_overlay.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/07_curve_overlay/010700_discount_curve_overlay.ipynb)\\n\",\n \" - 📁 08_multi_scenario\\n\",\n \" - 📄 [010801_multi_scenario_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/08_multi_scenario/010801_multi_scenario_shock.ipynb)\\n\",\n \" - 📁 09_measure_scenario\\n\",\n \" - 📄 [010901_fx_override_example.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010901_fx_override_example.ipynb)\\n\",\n \" - 📄 [010902_rates_override_example.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010902_rates_override_example.ipynb)\\n\",\n \" - 📄 [010903_eq_override_example.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010903_eq_override_example.ipynb)\\n\",\n \" - 📁 tutorials\\n\",\n \" - 📄 [Pricing_Context.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/tutorials/Pricing_Context.ipynb)\\n\",\n \" - 📄 [Scenarios.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/tutorials/Scenarios.ipynb)\\n\",\n \"- 📄 [External Demo v1.ipynb](02_pricing_and_risk/External%20Demo%20v1.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67008\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 03_portfolios\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67009\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **examples**\\n\",\n \" - 📄 [030000_create_portfolio.ipynb](03_portfolios/examples/030000_create_portfolio.ipynb)\\n\",\n \" - 📄 [030001_modify_instruments.ipynb](03_portfolios/examples/030001_modify_instruments.ipynb)\\n\",\n \" - 📄 [030002_extracting_instruments_and_results.ipynb](03_portfolios/examples/030002_extracting_instruments_and_results.ipynb)\\n\",\n \" - 📄 [030003_resolve_portfolio.ipynb](03_portfolios/examples/030003_resolve_portfolio.ipynb)\\n\",\n \" - 📄 [030004_price_portfolio.ipynb](03_portfolios/examples/030004_price_portfolio.ipynb)\\n\",\n \" - 📄 [030005_calculate_portfolio_risk.ipynb](03_portfolios/examples/030005_calculate_portfolio_risk.ipynb)\\n\",\n \" - 📄 [030006_portfolio_grid_calc.ipynb](03_portfolios/examples/030006_portfolio_grid_calc.ipynb)\\n\",\n \" - 📄 [030007_pnl_explain.ipynb](03_portfolios/examples/030007_pnl_explain.ipynb)\\n\",\n \" - 📄 [030008_portflio_from_frame.ipynb](03_portfolios/examples/030008_portflio_from_frame.ipynb)\\n\",\n \" - 📄 [030009_portfolio_risk_result_to_frame.ipynb](03_portfolios/examples/030009_portfolio_risk_result_to_frame.ipynb)\\n\",\n \" - 📄 [030010_portfolio_intra_leg_dependencies.ipynb](03_portfolios/examples/030010_portfolio_intra_leg_dependencies.ipynb)\\n\",\n \" - 📄 [030011_portfolio_from_csv.ipynb](03_portfolios/examples/030011_portfolio_from_csv.ipynb)\\n\",\n \"- 📁 **tutorials**\\n\",\n \" - 📄 [Create New Portfolio.ipynb](03_portfolios/tutorials/Create%20New%20Portfolio.ipynb)\\n\",\n \" - 📄 [Portfolios.ipynb](03_portfolios/tutorials/Portfolios.ipynb)\\n\",\n \" - 📄 [Update Historical Portfolio.ipynb](03_portfolios/tutorials/Update%20Historical%20Portfolio.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67010\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 04_backtesting\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67011\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **examples**\\n\",\n \" - 📁 01_PredefinedAssetEngine\\n\",\n \" - 📄 [040100_simple_example.ipynb](04_backtesting/examples/01_PredefinedAssetEngine/040100_simple_example.ipynb)\\n\",\n \" - 📁 02_EquityVolEngine\\n\",\n \" - 📄 [040200_strategy_simple.ipynb](04_backtesting/examples/02_EquityVolEngine/040200_strategy_simple.ipynb)\\n\",\n \" - 📄 [040201_strategy_delta_hedged.ipynb](04_backtesting/examples/02_EquityVolEngine/040201_strategy_delta_hedged.ipynb)\\n\",\n \" - 📄 [040202_strategy_pnl_decomposition.ipynb](04_backtesting/examples/02_EquityVolEngine/040202_strategy_pnl_decomposition.ipynb)\\n\",\n \" - 📄 [040203_strategy_with_signals.ipynb](04_backtesting/examples/02_EquityVolEngine/040203_strategy_with_signals.ipynb)\\n\",\n \" - 📄 [040204_strategy_market_model.ipynb](04_backtesting/examples/02_EquityVolEngine/040204_strategy_market_model.ipynb)\\n\",\n \" - 📄 [040205_strategy_scaled_add_action.ipynb](04_backtesting/examples/02_EquityVolEngine/040205_strategy_scaled_add_action.ipynb)\\n\",\n \" - 📁 03_GenericEngine\\n\",\n \" - 📄 [040300_strategy_periodic_trigger.ipynb](04_backtesting/examples/03_GenericEngine/040300_strategy_periodic_trigger.ipynb)\\n\",\n \" - 📄 [040301_strategy_risk_trigger.ipynb](04_backtesting/examples/03_GenericEngine/040301_strategy_risk_trigger.ipynb)\\n\",\n \" - 📄 [040302_strategy_delta_hedge_FX.ipynb](04_backtesting/examples/03_GenericEngine/040302_strategy_delta_hedge_FX.ipynb)\\n\",\n \" - 📄 [040303_strategy_delta_hedge_Rates.ipynb](04_backtesting/examples/03_GenericEngine/040303_strategy_delta_hedge_Rates.ipynb)\\n\",\n \" - 📄 [040304_strategy_mean_reversion.ipynb](04_backtesting/examples/03_GenericEngine/040304_strategy_mean_reversion.ipynb)\\n\",\n \" - 📄 [040305_strategy_exit_trade_action.ipynb](04_backtesting/examples/03_GenericEngine/040305_strategy_exit_trade_action.ipynb)\\n\",\n \" - 📄 [040306_strategy_mkt_trigger.ipynb](04_backtesting/examples/03_GenericEngine/040306_strategy_mkt_trigger.ipynb)\\n\",\n \" - 📄 [040307_strategy_seagull_bullish.ipynb](04_backtesting/examples/03_GenericEngine/040307_strategy_seagull_bullish.ipynb)\\n\",\n \" - 📄 [040308_rebalance_action.ipynb](04_backtesting/examples/03_GenericEngine/040308_rebalance_action.ipynb)\\n\",\n \" - 📄 [040309_inflation_hedging_strategy.ipynb](04_backtesting/examples/03_GenericEngine/040309_inflation_hedging_strategy.ipynb)\\n\",\n \" - 📄 [040310_initial_swap_hedging_strategy_varying_CSA.ipynb](04_backtesting/examples/03_GenericEngine/040310_initial_swap_hedging_strategy_varying_CSA.ipynb)\\n\",\n \" - 📄 [040311_handling_holidays.ipynb](04_backtesting/examples/03_GenericEngine/040311_handling_holidays.ipynb)\\n\",\n \" - 📄 [040312_strategy_scaled_add_action.ipynb](04_backtesting/examples/03_GenericEngine/040312_strategy_scaled_add_action.ipynb)\\n\",\n \" - 📄 [040313_chained_actions.ipynb](04_backtesting/examples/03_GenericEngine/040313_chained_actions.ipynb)\\n\",\n \" - 📄 [040314_gradual_entries.ipynb](04_backtesting/examples/03_GenericEngine/040314_gradual_entries.ipynb)\\n\",\n \"- 📁 **tutorials**\\n\",\n \" - 📄 [Backtesting.ipynb](04_backtesting/tutorials/Backtesting.ipynb)\\n\",\n \" - 📄 [Basic backtest walkthrough.ipynb](04_backtesting/tutorials/Basic%20backtest%20walkthrough.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67012\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 05_factor_models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67013\",\n \"metadata\": {},\n \"source\": [\n \"- 📄 [01_Factor_Models.ipynb](05_factor_models/01_Factor_Models.ipynb)\\n\",\n \"- 📄 [02_Upload_Factor_Models.ipynb](05_factor_models/02_Upload_Factor_Models.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67014\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 06_baskets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67015\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **examples**\\n\",\n \" - 📁 01_basket_composition_data\\n\",\n \" - 📄 [0000_get_latest_basket_composition.ipynb](06_baskets/examples/01_basket_composition_data/0000_get_latest_basket_composition.ipynb)\\n\",\n \" - 📄 [0001_get_basket_composition_for_date.ipynb](06_baskets/examples/01_basket_composition_data/0001_get_basket_composition_for_date.ipynb)\\n\",\n \" - 📄 [0002_get_basket_composition_for_date_range.ipynb](06_baskets/examples/01_basket_composition_data/0002_get_basket_composition_for_date_range.ipynb)\\n\",\n \" - 📄 [0003_get_full_basket_composition_history.ipynb](06_baskets/examples/01_basket_composition_data/0003_get_full_basket_composition_history.ipynb)\\n\",\n \" - 📄 [0004_get_basket_composition_dates.ipynb](06_baskets/examples/01_basket_composition_data/0004_get_basket_composition_dates.ipynb)\\n\",\n \" - 📄 [0005_get_basket_composition_dataset_coverage.ipynb](06_baskets/examples/01_basket_composition_data/0005_get_basket_composition_dataset_coverage.ipynb)\\n\",\n \" - 📁 02_basket_pricing_data\\n\",\n \" - 📄 [0000_get_latest_basket_close_price.ipynb](06_baskets/examples/02_basket_pricing_data/0000_get_latest_basket_close_price.ipynb)\\n\",\n \" - 📄 [0001_get_basket_close_price_for_date.ipynb](06_baskets/examples/02_basket_pricing_data/0001_get_basket_close_price_for_date.ipynb)\\n\",\n \" - 📄 [0002_get_basket_close_price_for_dates.ipynb](06_baskets/examples/02_basket_pricing_data/0002_get_basket_close_price_for_dates.ipynb)\\n\",\n \" - 📄 [0003_get_full_basket_close_price_history.ipynb](06_baskets/examples/02_basket_pricing_data/0003_get_full_basket_close_price_history.ipynb)\\n\",\n \" - 📁 03_basket_creation\\n\",\n \" - 📄 [0000_clone_basket_position_set.ipynb](06_baskets/examples/03_basket_creation/0000_clone_basket_position_set.ipynb)\\n\",\n \" - 📁 position_set\\n\",\n \" - 📄 [0000_create_position_set_using_position_weights.ipynb](06_baskets/examples/03_basket_creation/position_set/0000_create_position_set_using_position_weights.ipynb)\\n\",\n \" - 📄 [0001_create_position_set_using_position_quantities.ipynb](06_baskets/examples/03_basket_creation/position_set/0001_create_position_set_using_position_quantities.ipynb)\\n\",\n \" - 📄 [0002_create_position_set_of_equal_weight_from_list_of_identifiers.ipynb](06_baskets/examples/03_basket_creation/position_set/0002_create_position_set_of_equal_weight_from_list_of_identifiers.ipynb)\\n\",\n \" - 📄 [0003_equalize_position_set_weights.ipynb](06_baskets/examples/03_basket_creation/position_set/0003_equalize_position_set_weights.ipynb)\\n\",\n \" - 📄 [0004_add_position_to_existing_position_set.ipynb](06_baskets/examples/03_basket_creation/position_set/0004_add_position_to_existing_position_set.ipynb)\\n\",\n \" - 📄 [0005_create_position_set_from_list_of_dictionaries.ipynb](06_baskets/examples/03_basket_creation/position_set/0005_create_position_set_from_list_of_dictionaries.ipynb)\\n\",\n \" - 📄 [0006_create_position_set_from_dataframe.ipynb](06_baskets/examples/03_basket_creation/position_set/0006_create_position_set_from_dataframe.ipynb)\\n\",\n \" - 📄 [0007_create_position_set_from_csv.ipynb](06_baskets/examples/03_basket_creation/position_set/0007_create_position_set_from_csv.ipynb)\\n\",\n \" - 📄 [0008_create_position_set_from_excel.ipynb](06_baskets/examples/03_basket_creation/position_set/0008_create_position_set_from_excel.ipynb)\\n\",\n \" - 📄 [0009_fetch_position_quantities_from_weights.ipynb](06_baskets/examples/03_basket_creation/position_set/0009_fetch_position_quantities_from_weights.ipynb)\\n\",\n \" - 📄 [0010_fetch_position_weights_from_quantities.ipynb](06_baskets/examples/03_basket_creation/position_set/0010_fetch_position_weights_from_quantities.ipynb)\\n\",\n \" - 📄 [0011_redistribute_position_weights.ipynb](06_baskets/examples/03_basket_creation/position_set/0011_redistribute_position_weights.ipynb)\\n\",\n \" - 📄 [0012_create_historical_position_sets_from_excel.ipynb](06_baskets/examples/03_basket_creation/position_set/0012_create_historical_position_sets_from_excel.ipynb)\\n\",\n \" - 📁 04_basket_corporate_actions_data\\n\",\n \" - 📄 [0000_get_full_basket_corporate_actions_history.ipynb](06_baskets/examples/04_basket_corporate_actions_data/0000_get_full_basket_corporate_actions_history.ipynb)\\n\",\n \" - 📄 [0001_get_basket_corporate_actions_for_dates.ipynb](06_baskets/examples/04_basket_corporate_actions_data/0001_get_basket_corporate_actions_for_dates.ipynb)\\n\",\n \" - 📄 [0002_get_basket_upcoming_corporate_actions.ipynb](06_baskets/examples/04_basket_corporate_actions_data/0002_get_basket_upcoming_corporate_actions.ipynb)\\n\",\n \" - 📄 [0003_get_specific_corporate_actions_types.ipynb](06_baskets/examples/04_basket_corporate_actions_data/0003_get_specific_corporate_actions_types.ipynb)\\n\",\n \" - 📁 05_basket_fundamentals_data\\n\",\n \" - 📄 [0000_get_basket_fundamentals_history.ipynb](06_baskets/examples/05_basket_fundamentals_data/0000_get_basket_fundamentals_history.ipynb)\\n\",\n \" - 📄 [0001_get_basket_fundamentals_for_dates.ipynb](06_baskets/examples/05_basket_fundamentals_data/0001_get_basket_fundamentals_for_dates.ipynb)\\n\",\n \" - 📄 [0002_get_basket_fundamentals_specific_period_and_direction.ipynb](06_baskets/examples/05_basket_fundamentals_data/0002_get_basket_fundamentals_specific_period_and_direction.ipynb)\\n\",\n \" - 📄 [0003_get_specific_basket_fundamentals_metrics.ipynb](06_baskets/examples/05_basket_fundamentals_data/0003_get_specific_basket_fundamentals_metrics.ipynb)\\n\",\n \" - 📁 06_basket_reports\\n\",\n \" - 📄 [0000_create_new_factor_risk_report.ipynb](06_baskets/examples/06_basket_reports/0000_create_new_factor_risk_report.ipynb)\\n\",\n \" - 📄 [0001_delete_existing_factor_risk_report.ipynb](06_baskets/examples/06_basket_reports/0001_delete_existing_factor_risk_report.ipynb)\\n\",\n \" - 📄 [0002_get_existing_factor_risk_report.ipynb](06_baskets/examples/06_basket_reports/0002_get_existing_factor_risk_report.ipynb)\\n\",\n \" - 📄 [0003_get_all_existing_basket_reports.ipynb](06_baskets/examples/06_basket_reports/0003_get_all_existing_basket_reports.ipynb)\\n\",\n \" - 📄 [0004_get_status_of_all_existing_basket_reports.ipynb](06_baskets/examples/06_basket_reports/0004_get_status_of_all_existing_basket_reports.ipynb)\\n\",\n \" - 📄 [0005_poll_status_of_most_recent_basket_create_report.ipynb](06_baskets/examples/06_basket_reports/0005_poll_status_of_most_recent_basket_create_report.ipynb)\\n\",\n \" - 📄 [0006_poll_report_status_using_report_id.ipynb](06_baskets/examples/06_basket_reports/0006_poll_report_status_using_report_id.ipynb)\\n\",\n \" - 📁 07_basket_permissions\\n\",\n \" - 📄 [0000_get_basket_permissions.ipynb](06_baskets/examples/07_basket_permissions/0000_get_basket_permissions.ipynb)\\n\",\n \" - 📄 [0001_permission_application_to_basket.ipynb](06_baskets/examples/07_basket_permissions/0001_permission_application_to_basket.ipynb)\\n\",\n \" - 📄 [0002_permission_user_to_basket_by_email.ipynb](06_baskets/examples/07_basket_permissions/0002_permission_user_to_basket_by_email.ipynb)\\n\",\n \" - 📄 [0003_permission_group_to_basket.ipynb](06_baskets/examples/07_basket_permissions/0003_permission_group_to_basket.ipynb)\\n\",\n \" - 📄 [0004_remove_basket_permissions.ipynb](06_baskets/examples/07_basket_permissions/0004_remove_basket_permissions.ipynb)\\n\",\n \" - 📄 [0005_get_your_permissioned_baskets.ipynb](06_baskets/examples/07_basket_permissions/0005_get_your_permissioned_baskets.ipynb)\\n\",\n \" - 📁 08_flagship_baskets\\n\",\n \" - 📄 [0000_get_flagship_baskets.ipynb](06_baskets/examples/08_flagship_baskets/0000_get_flagship_baskets.ipynb)\\n\",\n \" - 📄 [0001_get_flagship_baskets_on_date.ipynb](06_baskets/examples/08_flagship_baskets/0001_get_flagship_baskets_on_date.ipynb)\\n\",\n \" - 📄 [0002_get_flagship_baskets_containing_assets.ipynb](06_baskets/examples/08_flagship_baskets/0002_get_flagship_baskets_containing_assets.ipynb)\\n\",\n \" - 📄 [0003_get_flagship_baskets_containing_assets_on_date.ipynb](06_baskets/examples/08_flagship_baskets/0003_get_flagship_baskets_containing_assets_on_date.ipynb)\\n\",\n \" - 📄 [0004_get_flagship_baskets_prices.ipynb](06_baskets/examples/08_flagship_baskets/0004_get_flagship_baskets_prices.ipynb)\\n\",\n \" - 📄 [0005_get_flagship_baskets_prices_for_date_range.ipynb](06_baskets/examples/08_flagship_baskets/0005_get_flagship_baskets_prices_for_date_range.ipynb)\\n\",\n \" - 📄 [0006_get_flagship_baskets_compositions.ipynb](06_baskets/examples/08_flagship_baskets/0006_get_flagship_baskets_compositions.ipynb)\\n\",\n \" - 📄 [0007_get_flagship_baskets_compositions_for_date_range.ipynb](06_baskets/examples/08_flagship_baskets/0007_get_flagship_baskets_compositions_for_date_range.ipynb)\\n\",\n \" - 📄 [0008_filter_flagship_baskets_by_basket_type.ipynb](06_baskets/examples/08_flagship_baskets/0008_filter_flagship_baskets_by_basket_type.ipynb)\\n\",\n \" - 📄 [0009_filter_flagship_baskets_by_region.ipynb](06_baskets/examples/08_flagship_baskets/0009_filter_flagship_baskets_by_region.ipynb)\\n\",\n \" - 📄 [0010_filter_flagship_baskets_by_styles.ipynb](06_baskets/examples/08_flagship_baskets/0010_filter_flagship_baskets_by_styles.ipynb)\\n\",\n \" - 📄 [0011_filter_flagship_baskets_by_ticker.ipynb](06_baskets/examples/08_flagship_baskets/0011_filter_flagship_baskets_by_ticker.ipynb)\\n\",\n \"- 📁 **tutorials**\\n\",\n \" - 📄 [Basket Backcast.ipynb](06_baskets/tutorials/Basket%20Backcast.ipynb)\\n\",\n \" - 📄 [Basket Create.ipynb](06_baskets/tutorials/Basket%20Create.ipynb)\\n\",\n \" - 📄 [Basket Edit.ipynb](06_baskets/tutorials/Basket%20Edit.ipynb)\\n\",\n \" - 📄 [Basket Rebalance.ipynb](06_baskets/tutorials/Basket%20Rebalance.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67016\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 07_index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67017\",\n \"metadata\": {},\n \"source\": [\n \"- 📄 [README.md](07_index/README.md)\\n\",\n \"- 📁 **examples**\\n\",\n \" - 📄 [0000_get_index_Marquee_url.ipynb](07_index/examples/0000_get_index_Marquee_url.ipynb)\\n\",\n \" - 📄 [0001_get_index_close_prices.ipynb](07_index/examples/0001_get_index_close_prices.ipynb)\\n\",\n \" - 📄 [0002_get_index_fundamentals_data.ipynb](07_index/examples/0002_get_index_fundamentals_data.ipynb)\\n\",\n \" - 📄 [0003_get_index_constituents.ipynb](07_index/examples/0003_get_index_constituents.ipynb)\\n\",\n \" - 📄 [0004_get_index_weights_and_attribution.ipynb](07_index/examples/0004_get_index_weights_and_attribution.ipynb)\\n\",\n \" - 📄 [0005_get_index_as_a_tree.ipynb](07_index/examples/0005_get_index_as_a_tree.ipynb)\\n\",\n \"- 📁 **tutorials**\\n\",\n \" - 📄 [GS Quant STS Index Tutorial.ipynb](07_index/tutorials/GS%20Quant%20STS%20Index%20Tutorial.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67018\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 08_tree_entity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67019\",\n \"metadata\": {},\n \"source\": [\n \"- 📄 [tree_entity.ipynb](08_tree_entity/tree_entity.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67020\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 09_security_master\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67021\",\n \"metadata\": {},\n \"source\": [\n \"- 📄 [Security Master SDK.ipynb](09_security_master/Security%20Master%20SDK.ipynb)\\n\",\n \"- 📄 [Security Master.ipynb](09_security_master/Security%20Master.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67022\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 10_one_delta\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67023\",\n \"metadata\": {},\n \"source\": [\n \"- 📁 **Hedger**\\n\",\n \" - 📄 [01_Performance_Hedger.ipynb](10_one_delta/Hedger/01_Performance_Hedger.ipynb)\\n\",\n \" - 📄 [02_Factor_Hedger_(Axioma_Portfolio_Optimizer).ipynb](10_one_delta/Hedger/02_Factor_Hedger_(Axioma_Portfolio_Optimizer).ipynb)\\n\",\n \" - 📄 [03_Continuous_Optimization.ipynb](10_one_delta/Hedger/03_Continuous_Optimization.ipynb)\\n\",\n \"- 📁 **Portfolios**\\n\",\n \" - 📄 [01_Create_Backcasted_Portfolio.ipynb](10_one_delta/Portfolios/01_Create_Backcasted_Portfolio.ipynb)\\n\",\n \" - 📄 [02_Create_New_Historical_Portfolio.ipynb](10_one_delta/Portfolios/02_Create_New_Historical_Portfolio.ipynb)\\n\",\n \" - 📄 [03_Update_Historical_Portfolio.ipynb](10_one_delta/Portfolios/03_Update_Historical_Portfolio.ipynb)\\n\",\n \" - 📄 [04_Get_Factor_Attribution_Data.ipynb](10_one_delta/Portfolios/04_Get_Factor_Attribution_Data.ipynb)\\n\",\n \" - 📄 [05_Manage_a_Fund_of_Funds.ipynb](10_one_delta/Portfolios/05_Manage_a_Fund_of_Funds.ipynb)\\n\",\n \" - 📄 [06_Get_Factor_Risk_Data.ipynb](10_one_delta/Portfolios/06_Get_Factor_Risk_Data.ipynb)\\n\",\n \" - 📄 [07_Get_Portfolio_Performance_Analytics.ipynb](10_one_delta/Portfolios/07_Get_Portfolio_Performance_Analytics.ipynb)\\n\",\n \" - 📄 [08_Get_ESG_Analytics.ipynb](10_one_delta/Portfolios/08_Get_ESG_Analytics.ipynb)\\n\",\n \" - 📄 [09_Get_Carbon_Analytics.ipynb](10_one_delta/Portfolios/09_Get_Carbon_Analytics.ipynb)\\n\",\n \" - 📄 [10_Get_Thematic_Analytics.ipynb](10_one_delta/Portfolios/10_Get_Thematic_Analytics.ipynb)\\n\",\n \"- 📁 **Reports**\\n\",\n \" - 📄 [01_Factor_Risk_Report.ipynb](10_one_delta/Reports/01_Factor_Risk_Report.ipynb)\\n\",\n \" - 📄 [02_Performance_Report.ipynb](10_one_delta/Reports/02_Performance_Report.ipynb)\\n\",\n \" - 📄 [03_Thematic_Report.ipynb](10_one_delta/Reports/03_Thematic_Report.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67024\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 11_macro_models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67025\",\n \"metadata\": {},\n \"source\": [\n \"- 📄 [01_Query_Macro_Models.ipynb](11_macro_models/01_Query_Macro_Models.ipynb)\\n\",\n \"- 📄 [02_Upload_Macro_Models.ipynb](11_macro_models/02_Upload_Macro_Models.ipynb)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67026\",\n \"metadata\": {},\n \"source\": [\n \"## 📂 12_scenarios\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d1e67027\",\n \"metadata\": {},\n \"source\": [\n \"- 📄 [01_Custom_Factor_Shocks.ipynb](12_scenarios/01_Custom_Factor_Shocks.ipynb)\\n\",\n \"- 📄 [02_Historical_Scenarios.ipynb](12_scenarios/02_Historical_Scenarios.ipynb)\\n\"\n ]\n }\n ],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/Index.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"06d212c0\",\n \"metadata\": {},\n \"source\": [\n \"# Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8bdc7267\",\n \"metadata\": {},\n \"source\": [\n \"- **CSA** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/16_change_discount_curve.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/16_change_discount_curve.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb) cell 3\\n\",\n \"- **Carry** - \\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/05_rollfwd_swap_fwd_rates.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/05_rollfwd_swap_fwd_rates.ipynb) cell 3\\n\",\n \"- **Context - Nested Contexts** - \\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb) cell 4\\n\",\n \"- **Context - RollFwd** - \\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/02_basic_use_of_rollfwd_scenario.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/02_basic_use_of_rollfwd_scenario.ipynb) cell 2\\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/02_basic_use_of_rollfwd_scenario.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/02_basic_use_of_rollfwd_scenario.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb) cell 2\\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/05_rollfwd_swap_fwd_rates.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/05_rollfwd_swap_fwd_rates.ipynb) cell 2\\n\",\n \"- **Contexts - Historical Pricing Context** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/02_cdindex_option_xover_payer_spread.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/02_cdindex_option_xover_payer_spread.ipynb) cell 5\\n\",\n \"- **Contexts - HistoricalPricingContext** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/04_calc_eq_option_price_and_risk_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/04_calc_eq_option_price_and_risk_historically.ipynb) cell 3\\n\",\n \"- **Contexts - Market Data Based Shock Scenario** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb) cell 2\\n\",\n \"- **Contexts - PricingContext** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/03_calc_eq_option_price_and_risk_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/03_calc_eq_option_price_and_risk_in_pricing_context.ipynb) cell 3\\n\",\n \"- **Contexts - RollFwd** - \\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/01_rollfwd_for_trade.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/01_rollfwd_for_trade.ipynb) cell 3\\n\",\n \"- **Enums - BusinessDayConvention** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb) cell 16\\n\",\n \"- **Enums - Currency** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb) cell 7\\n\",\n \"- **Enums - DayCountFraction** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb) cell 15\\n\",\n \"- **Enums - Knock In / Knock Out** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb) cell 4\\n\",\n \"- **Enums - OptionSettlementMethod** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb) cell 12\\n\",\n \"- **Enums - PayReceive** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb) cell 5\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb) cell 4\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb) cell 9\\n\",\n \"- **Enums - SwapClearingHouse** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb) cell 19\\n\",\n \"- **Enums - UnderlyierType** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb) cell 5\\n\",\n \"- **Enums - Upper barrier / Lower barrier** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb) cell 5\\n\",\n \"- **Instrument - CDIndex** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/04_cdindex_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/04_cdindex_price.ipynb)\\n\",\n \"- **Instrument - CDIndexOption** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb)\\n\",\n \"- **Instrument - Cloning** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/17_calc_xccy_swap_cashflows.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/17_calc_xccy_swap_cashflows.ipynb) cell 8\\n\",\n \"- **Instrument - Credit Index Rolling** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/05_cdindex_roll_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/05_cdindex_roll_price.ipynb)\\n\",\n \"- **Instrument - Credit Payer Spread** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/02_cdindex_option_xover_payer_spread.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/02_cdindex_option_xover_payer_spread.ipynb)\\n\",\n \"- **Instrument - EqOption** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb)\\n\",\n \"- **Instrument - FRA** - \\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb) cell 2\\n\",\n \"- **Instrument - FX DNT** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb)\\n\",\n \"- **Instrument - FXBinary** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb)\\n\",\n \"- **Instrument - FXDoubleOneTouch** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb)\\n\",\n \"- **Instrument - FXForward** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/01_fx_fwd_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/01_fx_fwd_trade_construction.ipynb)\\n\",\n \"- **Instrument - FXKnockout** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb)\\n\",\n \"- **Instrument - FXVolatilitySwap** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb)\\n\",\n \"- **Instrument - IRAssetSwapFxdFlt** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/21_asset_swap_definition.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/21_asset_swap_definition.ipynb) cell 2\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/22_cap_floor.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/22_cap_floor.ipynb) cell 2\\n\",\n \"- **Instrument - IRCMSSpreadOption** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/15_spread_option_grid_pricing.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/15_spread_option_grid_pricing.ipynb) cell 2\\n\",\n \"- **Instrument - IRFixedLeg** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb) cell 2\\n\",\n \"- **Instrument - IRFloatLeg** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb) cell 2\\n\",\n \"- **Instrument - Rates Midcurve Swaption** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/11_midcurve_swaption_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/11_midcurve_swaption_price.ipynb)\\n\",\n \"- **Instrument - Resolve** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/06_calc_swap_price_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/06_calc_swap_price_historically.ipynb) cell 2\\n\",\n \"- **Instrument - Solving - DNT strikes** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb) cell 11\\n\",\n \"- **Instrument - Solving - EqOption strike solving** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb) cell 6\\n\",\n \"- **Instrument - Solving - FX Binary strike solving** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb) cell 5\\n\",\n \"- **Instrument - Solving - Size one trade by the risk of another trade** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/19_solve_delta.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/19_solve_delta.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb) cell 3\\n\",\n \"- **Instrument - Solving - Solving FXVolSwap strike vol** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb) cell 10\\n\",\n \"- **Instrument - Solving across multiple instruments** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/18_solve_present_value.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/18_solve_present_value.ipynb) cell 3\\n\",\n \"- **Instrument - XCcy Swap** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/13_calc_xccy_swap_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/13_calc_xccy_swap_price.ipynb)\\n\",\n \"- **Instrument - XccySwap** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/17_calc_xccy_swap_cashflows.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/17_calc_xccy_swap_cashflows.ipynb) cell 2\\n\",\n \"- **Instrument - create from dictionary** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb) cell 23\\n\",\n \"- **Instrument Properties** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/01_view-trade-properties.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/01_view-trade-properties.ipynb) cell 2\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/01_view-trade-properties.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/01_view-trade-properties.ipynb) cell 4\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/02_get-a-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/02_get-a-property.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/02_get-a-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/02_get-a-property.ipynb) cell 4\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/03_set-a-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/03_set-a-property.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/04_enum-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/04_enum-property.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/04_enum-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/04_enum-property.ipynb) cell 5\\n\",\n \"- **Instrument Properties - Credit Index Families** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/04_cdindex_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/04_cdindex_price.ipynb) cell 2\\n\",\n \"- **Instrument Properties - FX Option Premium** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb) cell 13\\n\",\n \"- **Instrument Properties - Fixing Frequency** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb) cell 6\\n\",\n \"- **Instrument Properties - Fixing Source** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb) cell 8\\n\",\n \"- **Instrument Properties - Solving - IRSwaption strike** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb) cell 6\\n\",\n \"- **Instrument Properties - credit underlying index** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb) cell 3\\n\",\n \"- **Instrument Solving - FXKO Strike Solving** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb) cell 11\\n\",\n \"- **Market Data - Overwriting** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb) cell 11\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb) cell 13\\n\",\n \"- **Market Data - Retrieval** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb) cell 4\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb) cell 6\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb) cell 8\\n\",\n \"- **Market Data - Yield Curve** - \\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb)\\n\",\n \"- **Market Data - location** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/05_calc_swap_price_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/05_calc_swap_price_in_pricing_context.ipynb) cell 3\\n\",\n \"- **Metrics** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb) cell 3\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb) cell 3\\n\",\n \"- **Metrics - Black-Scholes Delta** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb) cell 6\\n\",\n \"- **Metrics - Cashflows** - \\n\",\n \" [02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb) cell 2\\n\",\n \"- **Metrics - Credit Greeks** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/03_cdindex_option_risks.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/03_cdindex_option_risks.ipynb) cell 3\\n\",\n \"- **Metrics - Credit metrics** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb) cell 7\\n\",\n \"- **Metrics - Delta as Notional of fwd** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb) cell 10\\n\",\n \"- **Metrics - Equity Greeks** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb) cell 6\\n\",\n \"- **Metrics - Equity Risks** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb) cell 4\\n\",\n \"- **Metrics - FX Delta Measures** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb)\\n\",\n \"- **Metrics - FX Gamma Measures** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/10_fx_gamma_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/10_fx_gamma_measures.ipynb)\\n\",\n \"- **Metrics - FXAnnualImpliedVol, Metrics - FXAnnualATMImpliedVol** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/05_calc_option_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/05_calc_option_measures.ipynb) cell 2\\n\",\n \"- **Metrics - FXDelta** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb) cell 8\\n\",\n \"- **Metrics - FXFwd** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb) cell 7\\n\",\n \"- **Metrics - FXSpot** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb) cell 5\\n\",\n \"- **Metrics - FairVolStrike** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb) cell 15\\n\",\n \"- **Metrics - Price** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/03_calc_swap_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/03_calc_swap_price.ipynb) cell 3\\n\",\n \"- **Metrics - XCcy** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/14_calc_xccy_swap_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/14_calc_xccy_swap_risk_measures.ipynb) cell 3\\n\",\n \"- **Metrics - aggregation level** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb) cell 3\\n\",\n \"- **Metrics - aggregation_level** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/08_calc_swap_risk_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/08_calc_swap_risk_historically.ipynb) cell 3\\n\",\n \"- **Metrics - parameterized currency** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb) cell 6\\n\",\n \"- **Portfolio - Nested Portfolios** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/15_spread_option_grid_pricing.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/15_spread_option_grid_pricing.ipynb) cell 2\\n\",\n \"- **Portfolios** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb) cell 4\\n\",\n \"- **Properties - Solving - Swap fixed rate** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb) cell 9\\n\",\n \"- **Properties - floating_rate_option** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb) cell 11\\n\",\n \"- **Results** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb) cell 4\\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb) cell 5\\n\",\n \"- **Results - Multiple Measures** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/05_calc_option_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/05_calc_option_measures.ipynb) cell 2\\n\",\n \"- **Results - Retrieve Specific Measure** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb) cell 5\\n\",\n \"- **Results - aggregating** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/05_cdindex_roll_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/05_cdindex_roll_price.ipynb) cell 8\\n\",\n \"- **Results - historical results** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/08_calc_swap_risk_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/08_calc_swap_risk_historically.ipynb) cell 5\\n\",\n \"- **Results - slice multi risk results** - \\n\",\n \" [02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb) cell 5\\n\"\n ]\n }\n ],\n \"metadata\": {},\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "gs_quant/documentation/README.md", "content": "# Documentation Overview\n\ngs_quant documentation is organized by core functionality outlined below.\nWithin each group, you will find tutorials introducing core ideas and functions (this is a great place to start) and\nexamples providing a searchable library of short specific snippets.\n\n- 📁 **00_data**\n - 📁 00_datasets\n - 📁 examples\n - 📄 [0000_query_dataset.ipynb](00_data/00_datasets/examples/0000_query_dataset.ipynb)\n - 📄 [0001_get_dataset_coverage.ipynb](00_data/00_datasets/examples/0001_get_dataset_coverage.ipynb)\n - 📄 [0002_using_data_contexts.ipynb](00_data/00_datasets/examples/0002_using_data_contexts.ipynb)\n - 📄 [0003_get_data_using_coordinate.ipynb](00_data/00_datasets/examples/0003_get_data_using_coordinate.ipynb)\n - 📄 [0004_get_all_assets_from_region.ipynb](00_data/00_datasets/examples/0004_get_all_assets_from_region.ipynb)\n - 📄 [0005_get_vol_surface.ipynb](00_data/00_datasets/examples/0005_get_vol_surface.ipynb)\n - 📄 [0006_ptp_dataset.ipynb](00_data/00_datasets/examples/0006_ptp_dataset.ipynb)\n - 📄 [0007_query_fxvol.script.ipynb](00_data/00_datasets/examples/0007_query_fxvol.script.ipynb)\n - 📄 [0008_s3_partners_short_interest.ipynb](00_data/00_datasets/examples/0008_s3_partners_short_interest.ipynb)\n - 📁 01_analytics\n - 📁 examples\n - 📄 [0000_charting_data.ipynb](00_data/01_analytics/examples/0000_charting_data.ipynb)\n - 📄 [0001_exporting_data.ipynb](00_data/01_analytics/examples/0001_exporting_data.ipynb)\n - 📁 02_workspaces\n - 📁 examples\n - 📄 [0001_creating_a_workspace.ipynb](00_data/02_workspaces/examples/0001_creating_a_workspace.ipynb)\n - 📁 03_visualizations\n - 📁 examples\n - 📄 [0001_creating_a_visualization.ipynb](00_data/03_visualizations/examples/0001_creating_a_visualization.ipynb)\n - 📁 04_screens\n - 📁 examples\n - 📄 [00_base_screener_api_tutorial.ipynb](00_data/04_screens/examples/00_base_screener_api_tutorial.ipynb)\n - 📄 [01_screen_api_tutorial.ipynb](00_data/04_screens/examples/01_screen_api_tutorial.ipynb)\n - 📁 covid\n - 📄 [Comparing, Reconciling, and Combining COVID-19 Data Sources.ipynb](00_data/covid/Comparing,%20Reconciling,%20and%20Combining%20COVID-19%20Data%20Sources.ipynb)\n- 📁 **01_markets**\n - 📁 00_securities\n - 📁 examples\n - 📄 [0000_get_an_asset_from_identifier.ipynb](01_markets/00_securities/examples/0000_get_an_asset_from_identifier.ipynb)\n - 📁 02_rdates\n - 📁 examples\n - 📄 [0000_get_a_date_relative_from_today.ipynb](01_markets/02_rdates/examples/0000_get_a_date_relative_from_today.ipynb)\n - 📄 [0001_get_a_date_relative_from_a_base_date.ipynb](01_markets/02_rdates/examples/0001_get_a_date_relative_from_a_base_date.ipynb)\n - 📄 [0002_get_a_date_relative_using_pricing_context.ipynb](01_markets/02_rdates/examples/0002_get_a_date_relative_using_pricing_context.ipynb)\n - 📄 [0003_get_a_date_using_a_business_day_calendar.ipynb](01_markets/02_rdates/examples/0003_get_a_date_using_a_business_day_calendar.ipynb)\n - 📄 [0004_chaining_relativedate_rules.ipynb](01_markets/02_rdates/examples/0004_chaining_relativedate_rules.ipynb)\n - 📄 [0005_passing_your_own_holiday_calendar.ipynb](01_markets/02_rdates/examples/0005_passing_your_own_holiday_calendar.ipynb)\n- 📁 **02_pricing_and_risk**\n - 📁 00_instruments_and_measures\n - 📁 examples\n - 📁 00_instrument_basics\n - 📄 [01_view-trade-properties.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/01_view-trade-properties.ipynb)\n - 📄 [02_get-a-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/02_get-a-property.ipynb)\n - 📄 [03_set-a-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/03_set-a-property.ipynb)\n - 📄 [04_enum-property.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/04_enum-property.ipynb)\n - 📄 [05_resolve-a-trade.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/05_resolve-a-trade.ipynb)\n - 📄 [06_market-data.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/06_market-data.ipynb)\n - 📄 [07_float-with-info.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/00_instrument_basics/07_float-with-info.ipynb)\n - 📁 01_rates\n - 📄 [01_view_swap_definition.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/01_view_swap_definition.ipynb)\n - 📄 [02_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/02_swap_trade_construction.ipynb)\n - 📄 [03_calc_swap_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/03_calc_swap_price.ipynb)\n - 📄 [04_calc_swap_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/04_calc_swap_risk_measures.ipynb)\n - 📄 [05_calc_swap_price_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/05_calc_swap_price_in_pricing_context.ipynb)\n - 📄 [06_calc_swap_price_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/06_calc_swap_price_historically.ipynb)\n - 📄 [07_calc_swap_risks_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/07_calc_swap_risks_in_pricing_context.ipynb)\n - 📄 [08_calc_swap_risk_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/08_calc_swap_risk_historically.ipynb)\n - 📄 [09_swaption_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/09_swaption_trade_construction.ipynb)\n - 📄 [10_straddle_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/10_straddle_price.ipynb)\n - 📄 [11_midcurve_swaption_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/11_midcurve_swaption_price.ipynb)\n - 📄 [12_swap_future_cashflows.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/12_swap_future_cashflows.ipynb)\n - 📄 [13_calc_xccy_swap_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/13_calc_xccy_swap_price.ipynb)\n - 📄 [14_calc_xccy_swap_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/14_calc_xccy_swap_risk_measures.ipynb)\n - 📄 [15_spread_option_grid_pricing.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/15_spread_option_grid_pricing.ipynb)\n - 📄 [16_change_discount_curve.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/16_change_discount_curve.ipynb)\n - 📄 [17_calc_xccy_swap_cashflows.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/17_calc_xccy_swap_cashflows.ipynb)\n - 📄 [18_solve_present_value.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/18_solve_present_value.ipynb)\n - 📄 [19_solve_delta.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/19_solve_delta.ipynb)\n - 📄 [20_fix_float_legs_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/20_fix_float_legs_price.ipynb)\n - 📄 [21_asset_swap_definition.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/21_asset_swap_definition.ipynb)\n - 📄 [22_cap_floor.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/22_cap_floor.ipynb)\n - 📄 [23_solve_vanna_&_volga.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/01_rates/23_solve_vanna_&_volga.ipynb)\n - 📁 02_fx\n - 📄 [01_fx_fwd_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/01_fx_fwd_trade_construction.ipynb)\n - 📄 [02_fx_option_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/02_fx_option_trade_construction.ipynb)\n - 📄 [03_fx_vol_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/03_fx_vol_swap_trade_construction.ipynb)\n - 📄 [04_fx_binary_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/04_fx_binary_trade_construction.ipynb)\n - 📄 [05_calc_option_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/05_calc_option_measures.ipynb)\n - 📄 [06_fx_var_swap_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/06_fx_var_swap_trade_construction.ipynb)\n - 📄 [07_fx_knockout_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/07_fx_knockout_trade_construction.ipynb)\n - 📄 [08_fx_double_touch_trade_construction.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/08_fx_double_touch_trade_construction.ipynb)\n - 📄 [09_fx_delta_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/09_fx_delta_measures.ipynb)\n - 📄 [10_fx_gamma_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/02_fx/10_fx_gamma_measures.ipynb)\n - 📁 03_eq\n - 📄 [01_calc_eq_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/01_calc_eq_option_price.ipynb)\n - 📄 [02_calc_eq_option_risk_measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/02_calc_eq_option_risk_measures.ipynb)\n - 📄 [03_calc_eq_option_price_and_risk_in_pricing_context.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/03_calc_eq_option_price_and_risk_in_pricing_context.ipynb)\n - 📄 [04_calc_eq_option_price_and_risk_historically.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/04_calc_eq_option_price_and_risk_historically.ipynb)\n - 📄 [05_calc_eq_option_portfolio_greeks.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/03_eq/05_calc_eq_option_portfolio_greeks.ipynb)\n - 📁 04_credit\n - 📄 [01_cdindex_option_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/01_cdindex_option_price.ipynb)\n - 📄 [02_cdindex_option_xover_payer_spread.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/02_cdindex_option_xover_payer_spread.ipynb)\n - 📄 [03_cdindex_option_risks.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/03_cdindex_option_risks.ipynb)\n - 📄 [04_cdindex_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/04_cdindex_price.ipynb)\n - 📄 [05_cdindex_roll_price.ipynb](02_pricing_and_risk/00_instruments_and_measures/examples/04_credit/05_cdindex_roll_price.ipynb)\n - 📁 tutorials\n - 📄 [Instruments.ipynb](02_pricing_and_risk/00_instruments_and_measures/tutorials/Instruments.ipynb)\n - 📄 [Measures.ipynb](02_pricing_and_risk/00_instruments_and_measures/tutorials/Measures.ipynb)\n - 📁 01_scenarios_and_contexts\n - 📁 examples\n - 📁 00_market_objects\n - 📄 [010000_market_data_in_gs_quant.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010000_market_data_in_gs_quant.ipynb)\n - 📄 [010001_market_objects.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010001_market_objects.ipynb)\n - 📄 [010002_marketdatapattern_basics.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010002_marketdatapattern_basics.ipynb)\n - 📄 [010003_marketdatapattern_construction_ir.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010003_marketdatapattern_construction_ir.ipynb)\n - 📄 [010004_marketdatapattern_construction_ir_vol.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010004_marketdatapattern_construction_ir_vol.ipynb)\n - 📄 [010005_marketdatapattern_construction_ir_basis.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010005_marketdatapattern_construction_ir_basis.ipynb)\n - 📄 [010006_marketdatapattern_construction_ir_xc.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010006_marketdatapattern_construction_ir_xc.ipynb)\n - 📄 [010007_marketdatapattern_construction_fx.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010007_marketdatapattern_construction_fx.ipynb)\n - 📄 [010008_marketdatapattern_construction_fx_fwd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010008_marketdatapattern_construction_fx_fwd.ipynb)\n - 📄 [010009_marketdatapattern_construction_fx_vol.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010009_marketdatapattern_construction_fx_vol.ipynb)\n - 📄 [010010_marketdatapattern_construction_cd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/00_market_objects/010010_marketdatapattern_construction_cd.ipynb)\n - 📁 01_rollfwd_shock\n - 📄 [01_rollfwd_for_trade.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/01_rollfwd_for_trade.ipynb)\n - 📄 [02_basic_use_of_rollfwd_scenario.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/02_basic_use_of_rollfwd_scenario.ipynb)\n - 📄 [03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/03_rollfwd-showing-lifecycling-effects-on-swaps-and-swaptions.ipynb)\n - 📄 [04_yield_curves_with_rollfwd.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/04_yield_curves_with_rollfwd.ipynb)\n - 📄 [05_rollfwd_swap_fwd_rates.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/01_rollfwd_shock/05_rollfwd_swap_fwd_rates.ipynb)\n - 📁 02_market_shock\n - 📄 [010200_marketdatashockbasedscenario_basics.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010200_marketdatashockbasedscenario_basics.ipynb)\n - 📄 [010201_rate_spot_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010201_rate_spot_shock.ipynb)\n - 📄 [010203_rate_vol_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010203_rate_vol_shock.ipynb)\n - 📄 [010204_delta_shock_equivalent.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010204_delta_shock_equivalent.ipynb)\n - 📄 [010205_vega_shock_equivalent.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010205_vega_shock_equivalent.ipynb)\n - 📄 [010206_eq_spot_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010206_eq_spot_shock.ipynb)\n - 📄 [010207_fx_shock_examples.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/02_market_shock/010207_fx_shock_examples.ipynb)\n - 📁 03_composite_shocks\n - 📄 [010300_rate_composite_shocks.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/03_composite_shocks/010300_rate_composite_shocks.ipynb)\n - 📄 [010301_multiple_shocks.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/03_composite_shocks/010301_multiple_shocks.ipynb)\n - 📁 04_curve_shock\n - 📄 [010400_parallel_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010400_parallel_curve_shock.ipynb)\n - 📄 [010401_curve_shock_midpoint.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010401_curve_shock_midpoint.ipynb)\n - 📄 [010402_bear_steepener_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010402_bear_steepener_curve_shock.ipynb)\n - 📄 [010403_bull_steepener_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010403_bull_steepener_curve_shock.ipynb)\n - 📄 [010404_bear_flattener_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010404_bear_flattener_curve_shock.ipynb)\n - 📄 [010405_bull_flattener_curve_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/04_curve_shock/010405_bull_flattener_curve_shock.ipynb)\n - 📁 05_market_override\n - 📄 [010501_eq_spot_override.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/05_market_override/010501_eq_spot_override.ipynb)\n - 📄 [010502_eq_future_override.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/05_market_override/010502_eq_future_override.ipynb)\n - 📁 06_vol_market_override\n - 📄 [010601_eq_vol_override_eod.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/06_vol_market_override/010601_eq_vol_override_eod.ipynb)\n - 📄 [010601_eq_vol_override_intraday.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/06_vol_market_override/010601_eq_vol_override_intraday.ipynb)\n - 📁 07_curve_overlay\n - 📄 [010700_discount_curve_overlay.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/07_curve_overlay/010700_discount_curve_overlay.ipynb)\n - 📁 08_multi_scenario\n - 📄 [010801_multi_scenario_shock.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/08_multi_scenario/010801_multi_scenario_shock.ipynb)\n - 📁 09_measure_scenario\n - 📄 [010901_fx_override_example.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010901_fx_override_example.ipynb)\n - 📄 [010902_rates_override_example.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010902_rates_override_example.ipynb)\n - 📄 [010903_eq_override_example.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/examples/09_measure_scenario/010903_eq_override_example.ipynb)\n - 📁 tutorials\n - 📄 [Pricing_Context.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/tutorials/Pricing_Context.ipynb)\n - 📄 [Scenarios.ipynb](02_pricing_and_risk/01_scenarios_and_contexts/tutorials/Scenarios.ipynb)\n - 📄 [External Demo v1.ipynb](02_pricing_and_risk/External%20Demo%20v1.ipynb)\n- 📁 **03_portfolios**\n - 📁 examples\n - 📄 [030000_create_portfolio.ipynb](03_portfolios/examples/030000_create_portfolio.ipynb)\n - 📄 [030001_modify_instruments.ipynb](03_portfolios/examples/030001_modify_instruments.ipynb)\n - 📄 [030002_extracting_instruments_and_results.ipynb](03_portfolios/examples/030002_extracting_instruments_and_results.ipynb)\n - 📄 [030003_resolve_portfolio.ipynb](03_portfolios/examples/030003_resolve_portfolio.ipynb)\n - 📄 [030004_price_portfolio.ipynb](03_portfolios/examples/030004_price_portfolio.ipynb)\n - 📄 [030005_calculate_portfolio_risk.ipynb](03_portfolios/examples/030005_calculate_portfolio_risk.ipynb)\n - 📄 [030006_portfolio_grid_calc.ipynb](03_portfolios/examples/030006_portfolio_grid_calc.ipynb)\n - 📄 [030007_pnl_explain.ipynb](03_portfolios/examples/030007_pnl_explain.ipynb)\n - 📄 [030008_portflio_from_frame.ipynb](03_portfolios/examples/030008_portflio_from_frame.ipynb)\n - 📄 [030009_portfolio_risk_result_to_frame.ipynb](03_portfolios/examples/030009_portfolio_risk_result_to_frame.ipynb)\n - 📄 [030010_portfolio_intra_leg_dependencies.ipynb](03_portfolios/examples/030010_portfolio_intra_leg_dependencies.ipynb)\n - 📄 [030011_portfolio_from_csv.ipynb](03_portfolios/examples/030011_portfolio_from_csv.ipynb)\n - 📁 tutorials\n - 📄 [Create New Portfolio.ipynb](03_portfolios/tutorials/Create%20New%20Portfolio.ipynb)\n - 📄 [Portfolios.ipynb](03_portfolios/tutorials/Portfolios.ipynb)\n - 📄 [Update Historical Portfolio.ipynb](03_portfolios/tutorials/Update%20Historical%20Portfolio.ipynb)\n- 📁 **04_backtesting**\n - 📁 examples\n - 📁 01_PredefinedAssetEngine\n - 📄 [040100_simple_example.ipynb](04_backtesting/examples/01_PredefinedAssetEngine/040100_simple_example.ipynb)\n - 📁 02_EquityVolEngine\n - 📄 [040200_strategy_simple.ipynb](04_backtesting/examples/02_EquityVolEngine/040200_strategy_simple.ipynb)\n - 📄 [040201_strategy_delta_hedged.ipynb](04_backtesting/examples/02_EquityVolEngine/040201_strategy_delta_hedged.ipynb)\n - 📄 [040202_strategy_pnl_decomposition.ipynb](04_backtesting/examples/02_EquityVolEngine/040202_strategy_pnl_decomposition.ipynb)\n - 📄 [040203_strategy_with_signals.ipynb](04_backtesting/examples/02_EquityVolEngine/040203_strategy_with_signals.ipynb)\n - 📄 [040204_strategy_market_model.ipynb](04_backtesting/examples/02_EquityVolEngine/040204_strategy_market_model.ipynb)\n - 📄 [040205_strategy_scaled_add_action.ipynb](04_backtesting/examples/02_EquityVolEngine/040205_strategy_scaled_add_action.ipynb)\n - 📁 03_GenericEngine\n - 📄 [040300_strategy_periodic_trigger.ipynb](04_backtesting/examples/03_GenericEngine/040300_strategy_periodic_trigger.ipynb)\n - 📄 [040301_strategy_risk_trigger.ipynb](04_backtesting/examples/03_GenericEngine/040301_strategy_risk_trigger.ipynb)\n - 📄 [040302_strategy_delta_hedge_FX.ipynb](04_backtesting/examples/03_GenericEngine/040302_strategy_delta_hedge_FX.ipynb)\n - 📄 [040303_strategy_delta_hedge_Rates.ipynb](04_backtesting/examples/03_GenericEngine/040303_strategy_delta_hedge_Rates.ipynb)\n - 📄 [040304_strategy_mean_reversion.ipynb](04_backtesting/examples/03_GenericEngine/040304_strategy_mean_reversion.ipynb)\n - 📄 [040305_strategy_exit_trade_action.ipynb](04_backtesting/examples/03_GenericEngine/040305_strategy_exit_trade_action.ipynb)\n - 📄 [040306_strategy_mkt_trigger.ipynb](04_backtesting/examples/03_GenericEngine/040306_strategy_mkt_trigger.ipynb)\n - 📄 [040307_strategy_seagull_bullish.ipynb](04_backtesting/examples/03_GenericEngine/040307_strategy_seagull_bullish.ipynb)\n - 📄 [040308_rebalance_action.ipynb](04_backtesting/examples/03_GenericEngine/040308_rebalance_action.ipynb)\n - 📄 [040309_inflation_hedging_strategy.ipynb](04_backtesting/examples/03_GenericEngine/040309_inflation_hedging_strategy.ipynb)\n - 📄 [040310_initial_swap_hedging_strategy_varying_CSA.ipynb](04_backtesting/examples/03_GenericEngine/040310_initial_swap_hedging_strategy_varying_CSA.ipynb)\n - 📄 [040311_handling_holidays.ipynb](04_backtesting/examples/03_GenericEngine/040311_handling_holidays.ipynb)\n - 📄 [040312_strategy_scaled_add_action.ipynb](04_backtesting/examples/03_GenericEngine/040312_strategy_scaled_add_action.ipynb)\n - 📄 [040313_chained_actions.ipynb](04_backtesting/examples/03_GenericEngine/040313_chained_actions.ipynb)\n - 📄 [040314_gradual_entries.ipynb](04_backtesting/examples/03_GenericEngine/040314_gradual_entries.ipynb)\n - 📁 tutorials\n - 📄 [Backtesting.ipynb](04_backtesting/tutorials/Backtesting.ipynb)\n - 📄 [Basic backtest walkthrough.ipynb](04_backtesting/tutorials/Basic%20backtest%20walkthrough.ipynb)\n- 📁 **05_factor_models**\n - 📄 [01_Factor_Models.ipynb](05_factor_models/01_Factor_Models.ipynb)\n - 📄 [02_Upload_Factor_Models.ipynb](05_factor_models/02_Upload_Factor_Models.ipynb)\n- 📁 **06_baskets**\n - 📁 examples\n - 📁 01_basket_composition_data\n - 📄 [0000_get_latest_basket_composition.ipynb](06_baskets/examples/01_basket_composition_data/0000_get_latest_basket_composition.ipynb)\n - 📄 [0001_get_basket_composition_for_date.ipynb](06_baskets/examples/01_basket_composition_data/0001_get_basket_composition_for_date.ipynb)\n - 📄 [0002_get_basket_composition_for_date_range.ipynb](06_baskets/examples/01_basket_composition_data/0002_get_basket_composition_for_date_range.ipynb)\n - 📄 [0003_get_full_basket_composition_history.ipynb](06_baskets/examples/01_basket_composition_data/0003_get_full_basket_composition_history.ipynb)\n - 📄 [0004_get_basket_composition_dates.ipynb](06_baskets/examples/01_basket_composition_data/0004_get_basket_composition_dates.ipynb)\n - 📄 [0005_get_basket_composition_dataset_coverage.ipynb](06_baskets/examples/01_basket_composition_data/0005_get_basket_composition_dataset_coverage.ipynb)\n - 📁 02_basket_pricing_data\n - 📄 [0000_get_latest_basket_close_price.ipynb](06_baskets/examples/02_basket_pricing_data/0000_get_latest_basket_close_price.ipynb)\n - 📄 [0001_get_basket_close_price_for_date.ipynb](06_baskets/examples/02_basket_pricing_data/0001_get_basket_close_price_for_date.ipynb)\n - 📄 [0002_get_basket_close_price_for_dates.ipynb](06_baskets/examples/02_basket_pricing_data/0002_get_basket_close_price_for_dates.ipynb)\n - 📄 [0003_get_full_basket_close_price_history.ipynb](06_baskets/examples/02_basket_pricing_data/0003_get_full_basket_close_price_history.ipynb)\n - 📁 03_basket_creation\n - 📄 [0000_clone_basket_position_set.ipynb](06_baskets/examples/03_basket_creation/0000_clone_basket_position_set.ipynb)\n - 📁 position_set\n - 📄 [0000_create_position_set_using_position_weights.ipynb](06_baskets/examples/03_basket_creation/position_set/0000_create_position_set_using_position_weights.ipynb)\n - 📄 [0001_create_position_set_using_position_quantities.ipynb](06_baskets/examples/03_basket_creation/position_set/0001_create_position_set_using_position_quantities.ipynb)\n - 📄 [0002_create_position_set_of_equal_weight_from_list_of_identifiers.ipynb](06_baskets/examples/03_basket_creation/position_set/0002_create_position_set_of_equal_weight_from_list_of_identifiers.ipynb)\n - 📄 [0003_equalize_position_set_weights.ipynb](06_baskets/examples/03_basket_creation/position_set/0003_equalize_position_set_weights.ipynb)\n - 📄 [0004_add_position_to_existing_position_set.ipynb](06_baskets/examples/03_basket_creation/position_set/0004_add_position_to_existing_position_set.ipynb)\n - 📄 [0005_create_position_set_from_list_of_dictionaries.ipynb](06_baskets/examples/03_basket_creation/position_set/0005_create_position_set_from_list_of_dictionaries.ipynb)\n - 📄 [0006_create_position_set_from_dataframe.ipynb](06_baskets/examples/03_basket_creation/position_set/0006_create_position_set_from_dataframe.ipynb)\n - 📄 [0007_create_position_set_from_csv.ipynb](06_baskets/examples/03_basket_creation/position_set/0007_create_position_set_from_csv.ipynb)\n - 📄 [0008_create_position_set_from_excel.ipynb](06_baskets/examples/03_basket_creation/position_set/0008_create_position_set_from_excel.ipynb)\n - 📄 [0009_fetch_position_quantities_from_weights.ipynb](06_baskets/examples/03_basket_creation/position_set/0009_fetch_position_quantities_from_weights.ipynb)\n - 📄 [0010_fetch_position_weights_from_quantities.ipynb](06_baskets/examples/03_basket_creation/position_set/0010_fetch_position_weights_from_quantities.ipynb)\n - 📄 [0011_redistribute_position_weights.ipynb](06_baskets/examples/03_basket_creation/position_set/0011_redistribute_position_weights.ipynb)\n - 📄 [0012_create_historical_position_sets_from_excel.ipynb](06_baskets/examples/03_basket_creation/position_set/0012_create_historical_position_sets_from_excel.ipynb)\n - 📁 04_basket_corporate_actions_data\n - 📄 [0000_get_full_basket_corporate_actions_history.ipynb](06_baskets/examples/04_basket_corporate_actions_data/0000_get_full_basket_corporate_actions_history.ipynb)\n - 📄 [0001_get_basket_corporate_actions_for_dates.ipynb](06_baskets/examples/04_basket_corporate_actions_data/0001_get_basket_corporate_actions_for_dates.ipynb)\n - 📄 [0002_get_basket_upcoming_corporate_actions.ipynb](06_baskets/examples/04_basket_corporate_actions_data/0002_get_basket_upcoming_corporate_actions.ipynb)\n - 📄 [0003_get_specific_corporate_actions_types.ipynb](06_baskets/examples/04_basket_corporate_actions_data/0003_get_specific_corporate_actions_types.ipynb)\n - 📁 05_basket_fundamentals_data\n - 📄 [0000_get_basket_fundamentals_history.ipynb](06_baskets/examples/05_basket_fundamentals_data/0000_get_basket_fundamentals_history.ipynb)\n - 📄 [0001_get_basket_fundamentals_for_dates.ipynb](06_baskets/examples/05_basket_fundamentals_data/0001_get_basket_fundamentals_for_dates.ipynb)\n - 📄 [0002_get_basket_fundamentals_specific_period_and_direction.ipynb](06_baskets/examples/05_basket_fundamentals_data/0002_get_basket_fundamentals_specific_period_and_direction.ipynb)\n - 📄 [0003_get_specific_basket_fundamentals_metrics.ipynb](06_baskets/examples/05_basket_fundamentals_data/0003_get_specific_basket_fundamentals_metrics.ipynb)\n - 📁 06_basket_reports\n - 📄 [0000_create_new_factor_risk_report.ipynb](06_baskets/examples/06_basket_reports/0000_create_new_factor_risk_report.ipynb)\n - 📄 [0001_delete_existing_factor_risk_report.ipynb](06_baskets/examples/06_basket_reports/0001_delete_existing_factor_risk_report.ipynb)\n - 📄 [0002_get_existing_factor_risk_report.ipynb](06_baskets/examples/06_basket_reports/0002_get_existing_factor_risk_report.ipynb)\n - 📄 [0003_get_all_existing_basket_reports.ipynb](06_baskets/examples/06_basket_reports/0003_get_all_existing_basket_reports.ipynb)\n - 📄 [0004_get_status_of_all_existing_basket_reports.ipynb](06_baskets/examples/06_basket_reports/0004_get_status_of_all_existing_basket_reports.ipynb)\n - 📄 [0005_poll_status_of_most_recent_basket_create_report.ipynb](06_baskets/examples/06_basket_reports/0005_poll_status_of_most_recent_basket_create_report.ipynb)\n - 📄 [0006_poll_report_status_using_report_id.ipynb](06_baskets/examples/06_basket_reports/0006_poll_report_status_using_report_id.ipynb)\n - 📁 07_basket_permissions\n - 📄 [0000_get_basket_permissions.ipynb](06_baskets/examples/07_basket_permissions/0000_get_basket_permissions.ipynb)\n - 📄 [0001_permission_application_to_basket.ipynb](06_baskets/examples/07_basket_permissions/0001_permission_application_to_basket.ipynb)\n - 📄 [0002_permission_user_to_basket_by_email.ipynb](06_baskets/examples/07_basket_permissions/0002_permission_user_to_basket_by_email.ipynb)\n - 📄 [0003_permission_group_to_basket.ipynb](06_baskets/examples/07_basket_permissions/0003_permission_group_to_basket.ipynb)\n - 📄 [0004_remove_basket_permissions.ipynb](06_baskets/examples/07_basket_permissions/0004_remove_basket_permissions.ipynb)\n - 📄 [0005_get_your_permissioned_baskets.ipynb](06_baskets/examples/07_basket_permissions/0005_get_your_permissioned_baskets.ipynb)\n - 📁 08_flagship_baskets\n - 📄 [0000_get_flagship_baskets.ipynb](06_baskets/examples/08_flagship_baskets/0000_get_flagship_baskets.ipynb)\n - 📄 [0001_get_flagship_baskets_on_date.ipynb](06_baskets/examples/08_flagship_baskets/0001_get_flagship_baskets_on_date.ipynb)\n - 📄 [0002_get_flagship_baskets_containing_assets.ipynb](06_baskets/examples/08_flagship_baskets/0002_get_flagship_baskets_containing_assets.ipynb)\n - 📄 [0003_get_flagship_baskets_containing_assets_on_date.ipynb](06_baskets/examples/08_flagship_baskets/0003_get_flagship_baskets_containing_assets_on_date.ipynb)\n - 📄 [0004_get_flagship_baskets_prices.ipynb](06_baskets/examples/08_flagship_baskets/0004_get_flagship_baskets_prices.ipynb)\n - 📄 [0005_get_flagship_baskets_prices_for_date_range.ipynb](06_baskets/examples/08_flagship_baskets/0005_get_flagship_baskets_prices_for_date_range.ipynb)\n - 📄 [0006_get_flagship_baskets_compositions.ipynb](06_baskets/examples/08_flagship_baskets/0006_get_flagship_baskets_compositions.ipynb)\n - 📄 [0007_get_flagship_baskets_compositions_for_date_range.ipynb](06_baskets/examples/08_flagship_baskets/0007_get_flagship_baskets_compositions_for_date_range.ipynb)\n - 📄 [0008_filter_flagship_baskets_by_basket_type.ipynb](06_baskets/examples/08_flagship_baskets/0008_filter_flagship_baskets_by_basket_type.ipynb)\n - 📄 [0009_filter_flagship_baskets_by_region.ipynb](06_baskets/examples/08_flagship_baskets/0009_filter_flagship_baskets_by_region.ipynb)\n - 📄 [0010_filter_flagship_baskets_by_styles.ipynb](06_baskets/examples/08_flagship_baskets/0010_filter_flagship_baskets_by_styles.ipynb)\n - 📄 [0011_filter_flagship_baskets_by_ticker.ipynb](06_baskets/examples/08_flagship_baskets/0011_filter_flagship_baskets_by_ticker.ipynb)\n - 📁 tutorials\n - 📄 [Basket Backcast.ipynb](06_baskets/tutorials/Basket%20Backcast.ipynb)\n - 📄 [Basket Create.ipynb](06_baskets/tutorials/Basket%20Create.ipynb)\n - 📄 [Basket Edit.ipynb](06_baskets/tutorials/Basket%20Edit.ipynb)\n - 📄 [Basket Rebalance.ipynb](06_baskets/tutorials/Basket%20Rebalance.ipynb)\n- 📁 **07_index**\n - 📄 [README.md](07_index/README.md)\n - 📁 examples\n - 📄 [0000_get_index_Marquee_url.ipynb](07_index/examples/0000_get_index_Marquee_url.ipynb)\n - 📄 [0001_get_index_close_prices.ipynb](07_index/examples/0001_get_index_close_prices.ipynb)\n - 📄 [0002_get_index_fundamentals_data.ipynb](07_index/examples/0002_get_index_fundamentals_data.ipynb)\n - 📄 [0003_get_index_constituents.ipynb](07_index/examples/0003_get_index_constituents.ipynb)\n - 📄 [0004_get_index_weights_and_attribution.ipynb](07_index/examples/0004_get_index_weights_and_attribution.ipynb)\n - 📄 [0005_get_index_as_a_tree.ipynb](07_index/examples/0005_get_index_as_a_tree.ipynb)\n - 📁 tutorials\n - 📄 [GS Quant STS Index Tutorial.ipynb](07_index/tutorials/GS%20Quant%20STS%20Index%20Tutorial.ipynb)\n- 📁 **08_tree_entity**\n - 📄 [tree_entity.ipynb](08_tree_entity/tree_entity.ipynb)\n- 📁 **09_security_master**\n - 📄 [Security Master SDK.ipynb](09_security_master/Security%20Master%20SDK.ipynb)\n - 📄 [Security Master.ipynb](09_security_master/Security%20Master.ipynb)\n- 📁 **10_one_delta**\n - 📁 Hedger\n - 📄 [01_Performance_Hedger.ipynb](10_one_delta/Hedger/01_Performance_Hedger.ipynb)\n - 📄 [02_Factor_Hedger_(Axioma_Portfolio_Optimizer).ipynb](10_one_delta/Hedger/02_Factor_Hedger_(Axioma_Portfolio_Optimizer).ipynb)\n - 📄 [03_Continuous_Optimization.ipynb](10_one_delta/Hedger/03_Continuous_Optimization.ipynb)\n - 📁 Portfolios\n - 📄 [01_Create_Backcasted_Portfolio.ipynb](10_one_delta/Portfolios/01_Create_Backcasted_Portfolio.ipynb)\n - 📄 [02_Create_New_Historical_Portfolio.ipynb](10_one_delta/Portfolios/02_Create_New_Historical_Portfolio.ipynb)\n - 📄 [03_Update_Historical_Portfolio.ipynb](10_one_delta/Portfolios/03_Update_Historical_Portfolio.ipynb)\n - 📄 [04_Get_Factor_Attribution_Data.ipynb](10_one_delta/Portfolios/04_Get_Factor_Attribution_Data.ipynb)\n - 📄 [05_Manage_a_Fund_of_Funds.ipynb](10_one_delta/Portfolios/05_Manage_a_Fund_of_Funds.ipynb)\n - 📄 [06_Get_Factor_Risk_Data.ipynb](10_one_delta/Portfolios/06_Get_Factor_Risk_Data.ipynb)\n - 📄 [07_Get_Portfolio_Performance_Analytics.ipynb](10_one_delta/Portfolios/07_Get_Portfolio_Performance_Analytics.ipynb)\n - 📄 [08_Get_ESG_Analytics.ipynb](10_one_delta/Portfolios/08_Get_ESG_Analytics.ipynb)\n - 📄 [09_Get_Carbon_Analytics.ipynb](10_one_delta/Portfolios/09_Get_Carbon_Analytics.ipynb)\n - 📄 [10_Get_Thematic_Analytics.ipynb](10_one_delta/Portfolios/10_Get_Thematic_Analytics.ipynb)\n - 📁 Reports\n - 📄 [01_Factor_Risk_Report.ipynb](10_one_delta/Reports/01_Factor_Risk_Report.ipynb)\n - 📄 [02_Performance_Report.ipynb](10_one_delta/Reports/02_Performance_Report.ipynb)\n - 📄 [03_Thematic_Report.ipynb](10_one_delta/Reports/03_Thematic_Report.ipynb)\n- 📁 **11_macro_models**\n - 📄 [01_Query_Macro_Models.ipynb](11_macro_models/01_Query_Macro_Models.ipynb)\n - 📄 [02_Upload_Macro_Models.ipynb](11_macro_models/02_Upload_Macro_Models.ipynb)\n- 📁 **12_scenarios**\n - 📄 [01_Custom_Factor_Shocks.ipynb](12_scenarios/01_Custom_Factor_Shocks.ipynb)\n - 📄 [02_Historical_Scenarios.ipynb](12_scenarios/02_Historical_Scenarios.ipynb)\n- 📄 [Contents.ipynb](Contents.ipynb)\n- 📄 [Index.ipynb](Index.ipynb)\n- 📄 [README.md](README.md)\n" }, { "path": "gs_quant/documentation/git", "content": "" }, { "path": "gs_quant/entities/__init__.py", "content": "" }, { "path": "gs_quant/entities/entitlements.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom typing import List, Dict\n\nimport pandas as pd\nfrom pydash import get\n\nfrom gs_quant.api.gs.groups import GsGroupsApi\nfrom gs_quant.api.gs.users import GsUsersApi\nfrom gs_quant.common import Entitlements as TargetEntitlements\nfrom gs_quant.errors import MqValueError, MqRequestError\nfrom gs_quant.target.groups import Group as TargetGroup\n\n_logger = logging.getLogger(__name__)\n\n\nclass User:\n def __init__(self, user_id: str, name: str = None, email: str = None, company: str = None):\n self.__id = user_id\n self.__email = email\n self.__name = name\n self.__company = company\n\n def __eq__(self, other):\n return self.id == other.id\n\n def __hash__(self):\n return hash(self.id) ^ hash(self.name)\n\n @property\n def id(self):\n return self.__id\n\n @property\n def email(self):\n return self.__email\n\n @property\n def name(self):\n return self.__name\n\n @property\n def company(self):\n return self.__company\n\n @classmethod\n def get(cls, user_id: str = None, name: str = None, email: str = None):\n \"\"\"\n Resolve a user ID, name, email, and/or company into a single User object\n\n :param user_id: User's unique GS Marquee User ID\n :param name: User's name (formatted 'Last Name, First Name')\n :param email: User's email address\n :return: A Marquee User object that corresponds to requested parameters\n \"\"\"\n if all(arg is None for arg in [user_id, name, email]):\n raise MqValueError('Please specify a user id, name, or email address')\n user_id = user_id[5:] if user_id and user_id.startswith('guid:') else user_id\n results = GsUsersApi.get_users(\n user_ids=[user_id] if user_id else None,\n user_names=[name] if name else None,\n user_emails=[email] if email else None,\n )\n if len(results) > 1:\n raise MqValueError('Error: This request resolves to more than one user in Marquee')\n if len(results) == 0:\n raise MqValueError('Error: No user found')\n return User(user_id=results[0].id, name=results[0].name, email=results[0].email, company=results[0].company)\n\n @classmethod\n def get_many(\n cls, user_ids: List[str] = None, names: List[str] = None, emails: List[str] = None, companies: List[str] = None\n ):\n \"\"\"\n Resolve requested parameters into a list of User objects\n\n :param user_ids: User's unique GS Marquee User IDs\n :param names: User's names (formatted 'Last Name, First Name')\n :param emails: User's email addresses\n :param companies: User's companies\n :return: A list of User objects that corresponds to requested parameters\n \"\"\"\n user_ids = user_ids if user_ids else []\n names = names if names else []\n emails = [email.lower() for email in emails] if emails else []\n companies = companies if companies else []\n\n if not user_ids + names + emails + companies:\n return []\n user_ids = [id_[5:] if id_.startswith('guid:') else id_ for id_ in user_ids]\n results = GsUsersApi.get_users(\n user_ids=user_ids, user_names=names, user_emails=emails, user_companies=companies\n )\n\n all_users = []\n for user in results:\n all_users.append(User(user_id=user.id, name=user.name, email=user.email, company=user.company))\n return all_users\n\n def save(self):\n raise NotImplementedError\n\n\nclass Group:\n def __init__(self, group_id: str, name: str, entitlements=None, description: str = None, tags: List = None):\n self.__id = group_id\n self.__name = name\n self.__entitlements = entitlements\n self.__description = description\n self.__tags = tags\n\n def __eq__(self, other):\n return self.id == other.id\n\n def __hash__(self):\n return hash(self.id) ^ hash(self.name)\n\n @property\n def id(self):\n return self.__id\n\n @property\n def name(self):\n return self.__name\n\n @name.setter\n def name(self, value: str):\n self.__name = value\n\n @property\n def entitlements(self):\n return self.__entitlements\n\n @entitlements.setter\n def entitlements(self, value):\n self.__entitlements = value\n\n @property\n def description(self):\n return self.__description\n\n @description.setter\n def description(self, value: str):\n self.__description = value\n\n @property\n def tags(self):\n return self.__tags\n\n @tags.setter\n def tags(self, value: List):\n self.__tags = value\n\n @classmethod\n def get(cls, group_id: str):\n \"\"\"\n Resolve a group ID into a single Group object\n\n :param group_id: Group's unique GS Marquee ID\n :return: A Group object that corresponds to requested ID\n \"\"\"\n group_id = group_id[6:] if group_id and group_id.startswith('group:') else group_id\n result = GsGroupsApi.get_group(group_id=group_id)\n return Group(\n group_id=result.id,\n name=result.name,\n entitlements=Entitlements.from_target(result.entitlements)\n if result.entitlements\n else None\n if result.entitlements\n else None,\n description=result.description,\n tags=result.tags,\n )\n\n @classmethod\n def get_many(cls, group_ids: List[str] = None, names: List[str] = None):\n \"\"\"\n Resolve requested parameters into a list of Group objects\n\n :param group_ids: Group's unique GS Marquee IDs\n :param names: Group's names\n :return: A list of Group objects that corresponds to requested parameters\n \"\"\"\n group_ids = group_ids if group_ids else []\n names = names if names else []\n if not group_ids + names:\n return []\n group_ids = [id_[6:] if id_.startswith('group:') else id_ for id_ in group_ids]\n results = GsGroupsApi.get_groups(ids=group_ids, names=names)\n all_groups = []\n for group in results:\n all_groups.append(\n Group(\n group_id=group.id,\n name=group.name,\n entitlements=Entitlements.from_target(group.entitlements) if group.entitlements else None,\n description=group.description,\n tags=group.tags,\n )\n )\n return all_groups\n\n def save(self):\n \"\"\"\n If the group id already exists in Marquee, update it. If not, create a new Marquee Group\n \"\"\"\n if self._group_exists():\n _logger.info(f'Updating group \"{self.id}\"')\n result = GsGroupsApi.update_group(group_id=self.id, group=self.to_target())\n else:\n _logger.info(f'Creating group \"{self.id}\"')\n result = GsGroupsApi.create_group(group=self.to_target())\n return Group(\n group_id=result.id,\n name=result.name,\n entitlements=Entitlements.from_target(result.entitlements)\n if result.entitlements\n else None\n if result.entitlements\n else None,\n description=result.description,\n tags=result.tags,\n )\n\n def _group_exists(self):\n try:\n Group.get(self.id)\n return True\n except MqRequestError as e:\n if e.status == 404:\n return False\n else:\n raise e\n\n def delete(self):\n \"\"\"\n Delete this group from Marquee\n \"\"\"\n GsGroupsApi.delete_group(self.id)\n _logger.info(f'Group \"{self.id}\" deleted from Marquee.')\n\n def get_users(self) -> List[User]:\n \"\"\"\n Get a list of all users in this group\n \"\"\"\n users = GsGroupsApi.get_users_in_group(self.id)\n return [\n User(user_id=user.get('id'), name=user.get('name'), email=user.get('email'), company=user.get('company'))\n for user in users\n ]\n\n def add_users(self, users: List[User]):\n \"\"\"\n Add a list of users to a group\n :param users: List of User objects\n \"\"\"\n user_ids = [user.id for user in users]\n GsGroupsApi.add_users_to_group(group_id=self.id, user_ids=user_ids)\n _logger.info(f'Users added to \"{self.name}\".')\n\n def delete_users(self, users: List[User]):\n \"\"\"\n Remove a list of users to a group\n :param users: List of User objects\n \"\"\"\n user_ids = [user.id for user in users]\n GsGroupsApi.delete_users_from_group(group_id=self.id, user_ids=user_ids)\n _logger.info(f'Users removed from \"{self.name}\".')\n\n def to_dict(self):\n \"\"\"\n Return a Group object as a dictionary\n \"\"\"\n return {\n 'name': self.name,\n 'id': self.id,\n 'description': self.description,\n 'entitlements': self.entitlements.to_dict() if self.entitlements else None,\n 'tags': self.tags,\n }\n\n def to_target(self):\n \"\"\"\n Return a Group object as a target object\n \"\"\"\n return TargetGroup(\n name=self.name,\n id=self.id,\n description=self.description,\n entitlements=self.entitlements.to_target() if self.entitlements else None,\n tags=self.tags,\n )\n\n\nclass EntitlementBlock:\n def __init__(\n self,\n users: List[User] = None,\n groups: List[Group] = None,\n roles: List[str] = None,\n unconverted_tokens: List[str] = None,\n ):\n self.__users = list(set(users)) if users else []\n self.__groups = list(set(groups)) if groups else []\n self.__roles = list(set(roles)) if roles else []\n self.__unconverted_tokens = unconverted_tokens\n\n def __eq__(self, other) -> bool:\n if not isinstance(other, EntitlementBlock):\n return False\n for prop in ['users', 'groups', 'roles']:\n slf, oth = get(self, prop), get(other, prop)\n if not (slf is None and oth is None) and not slf == oth:\n return False\n return True\n\n @property\n def users(self):\n return self.__users\n\n @users.setter\n def users(self, value: List[User]):\n self.__users = list(set(value))\n\n @property\n def groups(self):\n return self.__groups\n\n @groups.setter\n def groups(self, value: List[Group]):\n self.__groups = list(set(value))\n\n @property\n def roles(self):\n return self.__roles\n\n @roles.setter\n def roles(self, value: List[str]):\n self.__roles = list(set(value))\n\n @property\n def unconverted_tokens(self) -> List[str]:\n return self.__unconverted_tokens\n\n def is_empty(self):\n return len(self.users + self.groups + self.roles) == 0\n\n def to_list(self, as_dicts: bool = False, action: str = None, include_all_tokens: bool = False):\n if as_dicts:\n all_entitled = []\n for user in self.users:\n all_entitled.append(dict(action=action, type='user', name=user.name, id=user.id))\n for group in self.groups:\n all_entitled.append(dict(action=action, type='group', name=group.name, id=group.id))\n for role in self.roles:\n all_entitled.append(dict(action=action, type='role', name=role, id=role))\n return all_entitled\n else:\n unconverted_tokens = self.unconverted_tokens or [] if include_all_tokens else []\n return (\n [f'guid:{user.id}' for user in self.users]\n + [f'group:{group.id}' for group in self.groups]\n + [f'role:{role}' for role in self.roles]\n + unconverted_tokens\n )\n\n\nclass Entitlements:\n def __init__(\n self,\n admin: EntitlementBlock = None,\n delete: EntitlementBlock = None,\n display: EntitlementBlock = None,\n upload: EntitlementBlock = None,\n edit: EntitlementBlock = None,\n execute: EntitlementBlock = None,\n plot: EntitlementBlock = None,\n query: EntitlementBlock = None,\n rebalance: EntitlementBlock = None,\n trade: EntitlementBlock = None,\n view: EntitlementBlock = None,\n ):\n self.__admin = admin if admin else EntitlementBlock()\n self.__delete = delete if delete else EntitlementBlock()\n self.__display = display if display else EntitlementBlock()\n self.__upload = upload if upload else EntitlementBlock()\n self.__edit = edit if edit else EntitlementBlock()\n self.__execute = execute if execute else EntitlementBlock()\n self.__plot = plot if plot else EntitlementBlock()\n self.__query = query if query else EntitlementBlock()\n self.__rebalance = rebalance if rebalance else EntitlementBlock()\n self.__trade = trade if trade else EntitlementBlock()\n self.__view = view if view else EntitlementBlock()\n\n def __eq__(self, other) -> bool:\n if not isinstance(other, Entitlements):\n return False\n for prop in [\n 'admin',\n 'delete',\n 'display',\n 'upload',\n 'edit',\n 'execute',\n 'plot',\n 'query',\n 'rebalance',\n 'view',\n 'trade',\n ]:\n slf, oth = get(self, prop), get(other, prop)\n if not (slf is None and oth is None) and not slf == oth:\n return False\n return True\n\n @property\n def admin(self):\n return self.__admin\n\n @admin.setter\n def admin(self, value: EntitlementBlock):\n self.__admin = value\n\n @property\n def delete(self):\n return self.__delete\n\n @delete.setter\n def delete(self, value: EntitlementBlock):\n self.__delete = value\n\n @property\n def display(self):\n return self.__display\n\n @display.setter\n def display(self, value: EntitlementBlock):\n self.__display = value\n\n @property\n def upload(self):\n return self.__upload\n\n @upload.setter\n def upload(self, value: EntitlementBlock):\n self.__upload = value\n\n @property\n def edit(self):\n return self.__edit\n\n @edit.setter\n def edit(self, value: EntitlementBlock):\n self.__edit = value\n\n @property\n def execute(self):\n return self.__execute\n\n @execute.setter\n def execute(self, value: EntitlementBlock):\n self.__execute = value\n\n @property\n def plot(self):\n return self.__plot\n\n @plot.setter\n def plot(self, value: EntitlementBlock):\n self.__plot = value\n\n @property\n def query(self):\n return self.__query\n\n @query.setter\n def query(self, value: EntitlementBlock):\n self.__query = value\n\n @property\n def rebalance(self):\n return self.__rebalance\n\n @rebalance.setter\n def rebalance(self, value: EntitlementBlock):\n self.__rebalance = value\n\n @property\n def trade(self):\n return self.__trade\n\n @trade.setter\n def trade(self, value: EntitlementBlock):\n self.__trade = value\n\n @property\n def view(self):\n return self.__view\n\n @view.setter\n def view(self, value: EntitlementBlock):\n self.__view = value\n\n def to_target(self, include_all_tokens: bool = False) -> TargetEntitlements:\n \"\"\"\n Return Entitlement object as a target object\n :return: Entitlements as a target object\n \"\"\"\n target_entitlements = TargetEntitlements.default_instance()\n if not self.admin.is_empty():\n target_entitlements.admin = self.admin.to_list(include_all_tokens=include_all_tokens)\n if not self.delete.is_empty():\n target_entitlements.delete = self.delete.to_list(include_all_tokens=include_all_tokens)\n if not self.display.is_empty():\n target_entitlements.display = self.display.to_list(include_all_tokens=include_all_tokens)\n if not self.upload.is_empty():\n target_entitlements.upload = self.upload.to_list(include_all_tokens=include_all_tokens)\n if not self.edit.is_empty():\n target_entitlements.edit = self.edit.to_list(include_all_tokens=include_all_tokens)\n if not self.execute.is_empty():\n target_entitlements.execute = self.execute.to_list(include_all_tokens=include_all_tokens)\n if not self.plot.is_empty():\n target_entitlements.plot = self.plot.to_list(include_all_tokens=include_all_tokens)\n if not self.query.is_empty():\n target_entitlements.query = self.query.to_list(include_all_tokens=include_all_tokens)\n if not self.rebalance.is_empty():\n target_entitlements.rebalance = self.rebalance.to_list(include_all_tokens=include_all_tokens)\n if not self.trade.is_empty():\n target_entitlements.trade = self.trade.to_list(include_all_tokens=include_all_tokens)\n if not self.view.is_empty():\n target_entitlements.view = self.view.to_list(include_all_tokens=include_all_tokens)\n return target_entitlements\n\n def to_dict(self) -> Dict:\n \"\"\"\n Return Entitlement object as a dictionary\n :return: Entitlements as a dictionary\n \"\"\"\n return self.to_target().as_dict()\n\n def to_frame(self) -> pd.DataFrame:\n all_entitled = []\n all_entitled += self.admin.to_list(True, 'admin')\n all_entitled += self.delete.to_list(True, 'delete')\n all_entitled += self.display.to_list(True, 'display')\n all_entitled += self.upload.to_list(True, 'upload')\n all_entitled += self.edit.to_list(True, 'edit')\n all_entitled += self.execute.to_list(True, 'execute')\n all_entitled += self.plot.to_list(True, 'plot')\n all_entitled += self.query.to_list(True, 'query')\n all_entitled += self.rebalance.to_list(True, 'rebalance')\n all_entitled += self.trade.to_list(True, 'trade')\n all_entitled += self.view.to_list(True, 'view')\n return pd.DataFrame(all_entitled)\n\n @classmethod\n def from_target(cls, entitlements: TargetEntitlements):\n \"\"\"\n Create an Entitlement object from a target object\n :param entitlements: Entitlements as a target object\n :return: A new Entitlements object with all specified entitlements\n \"\"\"\n entitlements = TargetEntitlements.default_instance() if entitlements is None else entitlements\n return cls.from_dict(entitlements.as_dict())\n\n @classmethod\n def from_dict(cls, entitlements: Dict):\n \"\"\"\n Create an Entitlement object from a dictionary object\n :param entitlements: Entitlements as a dictionary object\n :return: A new Entitlements object with all specified entitlements\n \"\"\"\n entitlement_kwargs, token_map = {}, {}\n user_ids, group_ids = set(), set()\n for token_set in entitlements.values():\n for t in token_set:\n if t.startswith('guid:'):\n user_ids.add(t)\n elif t.startswith('group:'):\n group_ids.add(t)\n elif t.startswith('role:'):\n token_map[t] = t[5:]\n all_users = User.get_many(user_ids=list(user_ids))\n all_groups = Group.get_many(group_ids=list(group_ids))\n for u in all_users:\n token_map[f'guid:{u.id}'] = u\n for g in all_groups:\n token_map[f'group:{g.id}'] = g\n for action, token_set in entitlements.items():\n users, groups, roles, unconverted_tokens = [], [], [], []\n for t in token_set:\n if t in token_map.keys():\n if t in user_ids:\n users.append(token_map.get(t))\n elif t in group_ids:\n groups.append(token_map.get(t))\n else:\n roles.append(token_map.get(t))\n else:\n unconverted_tokens.append(t)\n unconverted_tokens = unconverted_tokens if len(unconverted_tokens) else None\n if users or groups or roles or unconverted_tokens:\n entitlement_kwargs[action] = EntitlementBlock(\n users=users, groups=groups, roles=roles, unconverted_tokens=unconverted_tokens\n )\n return Entitlements(**entitlement_kwargs)\n" }, { "path": "gs_quant/entities/entity.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport time\nfrom abc import ABCMeta, abstractmethod\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Dict, List, Optional, Tuple, Union\n\nimport deprecation\nimport pandas as pd\nfrom pydash import get\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.carbon import (\n CarbonCard,\n GsCarbonApi,\n CarbonTargetCoverageCategory,\n CarbonScope,\n CarbonEmissionsAllocationCategory,\n CarbonEmissionsIntensityType,\n CarbonCoverageCategory,\n CarbonEntityType,\n CarbonAnalyticsView,\n)\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.api.gs.esg import ESGMeasure, GsEsgApi, ESGCard\nfrom gs_quant.api.gs.indices import GsIndexApi\nfrom gs_quant.api.gs.portfolios import GsPortfolioApi\nfrom gs_quant.api.gs.reports import GsReportApi\nfrom gs_quant.api.gs.thematics import ThematicMeasure, GsThematicApi, Region\nfrom gs_quant.api.gs.scenarios import GsFactorScenarioApi\nfrom gs_quant.common import DateLimit, PositionType, Currency\nfrom gs_quant.data import DataCoordinate, DataFrequency, DataMeasure\nfrom gs_quant.data.coordinate import DataDimensions\nfrom gs_quant.entities.entitlements import Entitlements\nfrom gs_quant.errors import MqError, MqValueError\nfrom gs_quant.markets.indices_utils import BasketType, IndicesDatasets\nfrom gs_quant.markets.position_set import PositionSet\nfrom gs_quant.markets.report import (\n PerformanceReport,\n FactorRiskReport,\n Report,\n ThematicReport,\n flatten_results_into_df,\n get_thematic_breakdown_as_df,\n ReturnFormat,\n)\nfrom gs_quant.markets.scenario import Scenario\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.data import DataQuery\nfrom gs_quant.target.reports import ReportStatus, ReportType\nfrom gs_quant.entities.entity_utils import _explode_data\n\n_logger = logging.getLogger(__name__)\n\n\nclass EntityType(Enum):\n ASSET = 'asset'\n BACKTEST = 'backtest'\n COUNTRY = 'country'\n HEDGE = 'hedge'\n KPI = 'kpi'\n PORTFOLIO = 'portfolio'\n REPORT = 'report'\n RISK_MODEL = 'risk_model'\n SUBDIVISION = 'subdivision'\n DATASET = 'dataset'\n SCENARIO = 'scenario'\n\n\n@dataclass\nclass EntityKey:\n id_: str\n entity_type: EntityType\n\n\nclass EntityIdentifier(Enum):\n pass\n\n\nclass ScenarioCalculationType(Enum):\n FACTOR_SCENARIO = \"Factor Scenario\"\n\n\nclass ScenarioCalculationMeasure(Enum):\n SUMMARY = \"Summary\"\n ESTIMATED_FACTOR_PNL = \"Factor Pnl\"\n ESTIMATED_PNL_BY_SECTOR = \"By Sector Pnl Aggregations\"\n ESTIMATED_PNL_BY_REGION = \"By Region Pnl Aggregations\"\n ESTIMATED_PNL_BY_DIRECTION = \"By Direction Pnl Aggregations\"\n ESTIMATED_PNL_BY_ASSET = \"By Asset Pnl\"\n\n\nclass Entity(metaclass=ABCMeta):\n \"\"\"Base class for any first-class entity\"\"\"\n\n _entity_to_endpoint = {\n EntityType.ASSET: 'assets',\n EntityType.COUNTRY: 'countries',\n EntityType.SUBDIVISION: 'countries/subdivisions',\n EntityType.KPI: 'kpis',\n EntityType.PORTFOLIO: 'portfolios',\n EntityType.RISK_MODEL: 'risk/models',\n EntityType.DATASET: 'data/datasets',\n }\n\n def __init__(self, id_: str, entity_type: EntityType, entity: Optional[Dict] = None):\n self.__id: str = id_\n self.__entity_type: EntityType = entity_type\n self.__entity: Dict = entity\n\n @property\n @abstractmethod\n def data_dimension(self) -> str:\n pass\n\n @classmethod\n @abstractmethod\n def entity_type(cls) -> EntityType:\n pass\n\n @classmethod\n def get(\n cls, id_value: str, id_type: Union[EntityIdentifier, str], entity_type: Optional[Union[EntityType, str]] = None\n ):\n id_type = id_type.value if isinstance(id_type, Enum) else id_type\n\n if entity_type is None:\n entity_type = cls.entity_type()\n endpoint = cls._entity_to_endpoint[entity_type]\n else:\n entity_type = entity_type.value if isinstance(entity_type, Enum) else entity_type\n endpoint = cls._entity_to_endpoint[EntityType(entity_type)]\n\n if entity_type == 'asset':\n from gs_quant.markets.securities import SecurityMaster, AssetIdentifier\n\n return SecurityMaster.get_asset(id_value, AssetIdentifier.MARQUEE_ID)\n\n if id_type == 'MQID':\n result = GsSession.current.sync.get(f'/{endpoint}/{id_value}')\n else:\n result = get(GsSession.current.sync.get(f'/{endpoint}?{id_type.lower()}={id_value}'), 'results.0')\n if result:\n return cls._get_entity_from_type(result, EntityType(entity_type))\n\n @classmethod\n def _get_entity_from_type(cls, entity: Dict, entity_type: EntityType = None):\n id_ = entity.get('id')\n entity_type = entity_type or cls.entity_type()\n if entity_type == EntityType.COUNTRY:\n return Country(id_, entity=entity)\n if entity_type == EntityType.KPI:\n return KPI(id_, entity=entity)\n if entity_type == EntityType.SUBDIVISION:\n return Subdivision(id_, entity=entity)\n if entity_type == EntityType.RISK_MODEL:\n return RiskModelEntity(id_, entity=entity)\n\n def get_marquee_id(self) -> str:\n return self.__id\n\n def get_entity(self) -> Optional[Dict]:\n return self.__entity\n\n def get_unique_entity_key(self) -> EntityKey:\n return EntityKey(self.get_marquee_id(), self.__entity_type)\n\n def get_data_coordinate(\n self,\n measure: Union[DataMeasure, str],\n dimensions: Optional[DataDimensions] = None,\n frequency: DataFrequency = DataFrequency.DAILY,\n availability=None,\n ) -> DataCoordinate:\n id_ = self.get_marquee_id()\n dimensions = dimensions or {}\n dimensions[self.data_dimension] = id_\n measure = measure if isinstance(measure, str) else measure.value\n available: Dict = GsDataApi.get_data_providers(id_, availability).get(measure, {})\n\n if frequency == DataFrequency.DAILY:\n daily_dataset_id = available.get(DataFrequency.DAILY)\n return DataCoordinate(\n dataset_id=daily_dataset_id, measure=measure, dimensions=dimensions, frequency=frequency\n )\n if frequency == DataFrequency.REAL_TIME:\n rt_dataset_id = available.get(DataFrequency.REAL_TIME)\n return DataCoordinate(dataset_id=rt_dataset_id, measure=measure, dimensions=dimensions, frequency=frequency)\n\n def get_entitlements(self):\n entitlements_dict = self.get_entity().get('entitlements')\n if entitlements_dict is None:\n raise ValueError('This entity does not have entitlements.')\n return Entitlements.from_dict(entitlements_dict)\n\n\nclass Country(Entity):\n class Identifier(EntityIdentifier):\n MARQUEE_ID = 'MQID'\n NAME = 'name'\n\n def __init__(self, id_: str, entity: Optional[Dict] = None):\n super().__init__(id_, EntityType.COUNTRY, entity)\n\n @property\n def data_dimension(self) -> str:\n return 'countryId'\n\n @classmethod\n def entity_type(cls) -> EntityType:\n return EntityType.COUNTRY\n\n @classmethod\n def get_by_identifier(cls, id_value: str, id_type: Identifier) -> Optional['Entity']:\n super().get(id_value, id_type)\n\n def get_name(self) -> Optional[str]:\n return get(self.get_entity(), 'name')\n\n def get_region(self) -> Optional[str]:\n return get(self.get_entity(), 'region')\n\n def get_sub_region(self):\n return get(self.get_entity(), 'subRegion')\n\n def get_region_code(self):\n return get(self.get_entity(), 'regionCode')\n\n def get_sub_region_code(self):\n return get(self.get_entity(), 'subRegionCode')\n\n def get_alpha3(self):\n return get(self.get_entity(), 'xref.alpha3')\n\n def get_bbid(self):\n return get(self.get_entity(), 'xref.bbid')\n\n def get_alpha2(self):\n return get(self.get_entity(), 'xref.alpha2')\n\n def get_country_code(self):\n return get(self.get_entity(), 'xref.countryCode')\n\n\nclass Subdivision(Entity):\n class Identifier(EntityIdentifier):\n MARQUEE_ID = 'MQID'\n name = 'name'\n\n def __init__(self, id_: str, entity: Optional[Dict] = None):\n super().__init__(id_, EntityType.SUBDIVISION, entity)\n\n @property\n def data_dimension(self) -> str:\n return 'subdivisionId'\n\n @classmethod\n def entity_type(cls) -> EntityType:\n return EntityType.SUBDIVISION\n\n @classmethod\n def get_by_identifier(cls, id_value: str, id_type: Identifier) -> Optional['Entity']:\n super().get(id_value, id_type)\n\n def get_name(self) -> Optional[str]:\n return get(self.get_entity(), 'name')\n\n\nclass KPI(Entity):\n class Identifier(EntityIdentifier):\n MARQUEE_ID = \"MQID\"\n name = 'name'\n\n def __init__(self, id_: str, entity: Optional[Dict] = None):\n super().__init__(id_, EntityType.KPI, entity)\n\n @property\n def data_dimension(self) -> str:\n return 'kpiId'\n\n @classmethod\n def entity_type(cls) -> EntityType:\n return EntityType.KPI\n\n @classmethod\n def get_by_identifier(cls, id_value: str, id_type: Identifier) -> Optional['Entity']:\n super().get(id_value, id_type)\n\n def get_name(self) -> Optional[str]:\n return get(self.get_entity(), 'name')\n\n def get_category(self) -> Optional[str]:\n return get(self.get_entity(), 'category')\n\n def get_sub_category(self):\n return get(self.get_entity(), 'subCategory')\n\n\nclass RiskModelEntity(Entity):\n class Identifier(EntityIdentifier):\n MARQUEE_ID = \"MQID\"\n name = 'name'\n\n def __init__(self, id_: str, entity: Optional[Dict] = None):\n super().__init__(id_, EntityType.RISK_MODEL, entity)\n\n @property\n def data_dimension(self) -> str:\n return 'riskModel'\n\n @classmethod\n def entity_type(cls) -> EntityType:\n return EntityType.RISK_MODEL\n\n @classmethod\n def get_by_identifier(cls, id_value: str, id_type: Identifier) -> Optional['Entity']:\n super().get(id_value, id_type)\n\n def get_name(self) -> Optional[str]:\n return get(self.get_entity(), 'name')\n\n def get_coverage(self) -> Optional[str]:\n return get(self.get_entity(), 'coverage')\n\n def get_term(self) -> Optional[str]:\n return get(self.get_entity(), 'term')\n\n def get_vendor(self) -> Optional[str]:\n return get(self.get_entity(), 'vendor')\n\n\nclass PositionedEntity(metaclass=ABCMeta):\n def __init__(self, id_: str, entity_type: EntityType):\n self.__id: str = id_\n self.__entity_type: EntityType = entity_type\n\n @property\n def id(self) -> str:\n return self.__id\n\n @property\n def positioned_entity_type(self) -> EntityType:\n return self.__entity_type\n\n def get_entitlements(self) -> Entitlements:\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n response = GsPortfolioApi.get_portfolio(self.id)\n elif self.positioned_entity_type == EntityType.ASSET:\n response = GsAssetApi.get_asset(self.id)\n else:\n raise NotImplementedError\n return Entitlements.from_target(response.entitlements)\n\n def get_latest_position_set(self, position_type: PositionType = PositionType.CLOSE) -> PositionSet:\n if self.positioned_entity_type == EntityType.ASSET:\n response = GsAssetApi.get_latest_positions(self.id, position_type)\n return PositionSet.from_target(response)\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n response = GsPortfolioApi.get_latest_positions(portfolio_id=self.id, position_type=position_type.value)\n return PositionSet.from_target(response)\n raise NotImplementedError\n\n def get_position_set_for_date(self, date: dt.date, position_type: PositionType = PositionType.CLOSE) -> PositionSet:\n if self.positioned_entity_type == EntityType.ASSET:\n response = GsAssetApi.get_asset_positions_for_date(self.id, date, position_type)\n if len(response) == 0:\n _logger.info(\"No positions available for {}\".format(date))\n return PositionSet([], date=date)\n return PositionSet.from_target(response[0])\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n response = GsPortfolioApi.get_positions_for_date(\n portfolio_id=self.id, position_date=date, position_type=position_type.value\n )\n return PositionSet.from_target(response) if response else None\n raise NotImplementedError\n\n def get_position_sets(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n position_type: PositionType = PositionType.CLOSE,\n ) -> List[PositionSet]:\n if self.positioned_entity_type == EntityType.ASSET:\n response = GsAssetApi.get_asset_positions_for_dates(self.id, start, end, position_type)\n if len(response) == 0:\n _logger.info(\"No positions available in the date range {} - {}\".format(start, end))\n return []\n return [PositionSet.from_target(position_set) for position_set in response]\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n response = GsPortfolioApi.get_positions(portfolio_id=self.id, start_date=start, end_date=end)\n return [PositionSet.from_target(position_set) for position_set in response]\n raise NotImplementedError\n\n def update_positions(self, position_sets: List[PositionSet], net_positions: bool = True):\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n if not position_sets:\n return\n currency = GsPortfolioApi.get_portfolio(self.id).currency\n new_sets = []\n for pos_set in position_sets:\n positions_are_missing_quantities = len([p for p in pos_set.positions if p.quantity is None]) > 0\n if positions_are_missing_quantities:\n pos_set.price(currency)\n new_sets.append(pos_set)\n GsPortfolioApi.update_positions(\n portfolio_id=self.id, position_sets=[p.to_target() for p in new_sets], net_positions=net_positions\n )\n time.sleep(3)\n else:\n raise NotImplementedError\n\n def get_positions_data(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n fields: [str] = None,\n position_type: PositionType = PositionType.CLOSE,\n ) -> List[Dict]:\n if self.positioned_entity_type == EntityType.ASSET:\n return GsIndexApi.get_positions_data(self.id, start, end, fields, position_type)\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n raise MqError(\n 'Please use the get_positions_data function on the portfolio performance report using the '\n 'PerformanceReport class'\n )\n raise NotImplementedError\n\n def get_last_positions_data(\n self, fields: [str] = None, position_type: PositionType = PositionType.CLOSE\n ) -> List[Dict]:\n if self.positioned_entity_type == EntityType.ASSET:\n return GsIndexApi.get_last_positions_data(self.id, fields, position_type)\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n raise MqError(\n 'Please use the get_positions_data function on the portfolio performance report using the '\n 'PerformanceReport class'\n )\n raise NotImplementedError\n\n def get_position_dates(self) -> Tuple[dt.date, ...]:\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n return GsPortfolioApi.get_position_dates(portfolio_id=self.id)\n if self.positioned_entity_type == EntityType.ASSET:\n return GsAssetApi.get_position_dates(asset_id=self.id)\n raise NotImplementedError\n\n def get_reports(self, tags: Dict = None) -> List[Report]:\n if self.positioned_entity_type == EntityType.PORTFOLIO:\n reports_as_target = GsPortfolioApi.get_reports(portfolio_id=self.id, tags=tags)\n elif self.positioned_entity_type == EntityType.ASSET:\n reports_as_target = GsAssetApi.get_reports(asset_id=self.id)\n else:\n raise NotImplementedError\n report_objects = []\n for report in reports_as_target:\n if report.type == ReportType.Portfolio_Performance_Analytics:\n report_objects.append(PerformanceReport.from_target(report))\n elif report.type in [ReportType.Portfolio_Factor_Risk, ReportType.Asset_Factor_Risk]:\n report_objects.append(FactorRiskReport.from_target(report))\n elif report.type in [ReportType.Portfolio_Thematic_Analytics, ReportType.Asset_Thematic_Analytics]:\n report_objects.append(ThematicReport.from_target(report))\n else:\n report_objects.append(Report.from_target(report))\n return report_objects\n\n def get_status_of_reports(self, tags: Dict = None) -> pd.DataFrame:\n reports = self.get_reports(tags)\n reports_dict = {\n 'Name': [r.name for r in reports],\n 'ID': [r.id for r in reports],\n 'Latest Execution Time': [r.latest_execution_time for r in reports],\n 'Latest End Date': [r.latest_end_date for r in reports],\n \"Status\": [r.status for r in reports],\n 'Percentage Complete': [r.percentage_complete for r in reports],\n }\n\n return pd.DataFrame.from_dict(reports_dict)\n\n def get_factor_risk_reports(self, fx_hedged: bool = None, tags: Dict = None) -> List[FactorRiskReport]:\n if self.positioned_entity_type in [EntityType.PORTFOLIO, EntityType.ASSET]:\n position_source_type = self.positioned_entity_type.value.capitalize()\n reports = GsReportApi.get_reports(\n limit=100,\n position_source_type=position_source_type,\n position_source_id=self.id,\n report_type=f'{position_source_type} Factor Risk',\n tags=tags,\n scroll='1m',\n )\n if fx_hedged:\n reports = [report for report in reports if report.parameters.fx_hedged == fx_hedged]\n if len(reports) == 0:\n raise MqError(f'This {position_source_type} has no factor risk reports that match your parameters.')\n return [FactorRiskReport.from_target(report) for report in reports]\n raise NotImplementedError\n\n def get_factor_risk_report(\n self, risk_model_id: str = None, fx_hedged: bool = None, benchmark_id: str = None, tags: Dict = None\n ) -> FactorRiskReport:\n position_source_type = self.positioned_entity_type.value.capitalize()\n reports = self.get_factor_risk_reports(fx_hedged=fx_hedged, tags=tags)\n if risk_model_id:\n reports = [report for report in reports if report.parameters.risk_model == risk_model_id]\n reports = [report for report in reports if report.parameters.benchmark == benchmark_id]\n if len(reports) == 0:\n raise MqError(\n f'This {position_source_type} has no factor risk reports that match '\n 'your parameters. Please edit the risk model ID, fxHedged, and/or benchmark value in the '\n 'function parameters.'\n )\n if len(reports) > 1:\n raise MqError(\n f'This {position_source_type} has more than one factor risk report that matches '\n 'your parameters. Please specify the risk model ID, fxHedged, and/or benchmark value in the '\n 'function parameters.'\n )\n return reports[0]\n\n def get_thematic_report(self, tags: Dict = None) -> ThematicReport:\n if self.positioned_entity_type in [EntityType.PORTFOLIO, EntityType.ASSET]:\n position_source_type = self.positioned_entity_type.value.capitalize()\n reports = GsReportApi.get_reports(\n limit=100,\n position_source_type=position_source_type,\n position_source_id=self.id,\n report_type=f'{position_source_type} Thematic Analytics',\n tags=tags,\n )\n reports = [report for report in reports if report.parameters.tags == tags]\n if len(reports) == 0:\n raise MqError(f'This {position_source_type} has no thematic analytics report.')\n return ThematicReport.from_target(reports[0])\n raise NotImplementedError\n\n def poll_report(self, report_id: str, timeout: int = 600, step: int = 30) -> ReportStatus:\n poll = True\n timeout = 1800 if timeout > 1800 else timeout\n step = 15 if step < 15 else step\n end = dt.datetime.now() + dt.timedelta(seconds=timeout)\n\n while poll and dt.datetime.now() <= end:\n try:\n status = Report.get(report_id).status\n if status not in {ReportStatus.error, ReportStatus.cancelled, ReportStatus.done}:\n _logger.info(f'Report is {status} as of {dt.datetime.now().isoformat()}')\n time.sleep(step)\n else:\n poll = False\n if status == ReportStatus.error:\n raise MqError(\n f'Report {report_id} has failed for {self.id}. \\\n Please reach out to the Marquee team for assistance.'\n )\n elif status == ReportStatus.cancelled:\n _logger.info(\n f'Report {report_id} has been cancelled. Please reach out to the \\\n Marquee team if you believe this is a mistake.'\n )\n return status\n else:\n _logger.info(f'Report {report_id} is now complete')\n return status\n except Exception as err:\n raise MqError(f'Could not fetch report status with error {err}')\n\n raise MqError(\n 'The report is taking longer than expected to complete. \\\n Please check again later or reach out to the Marquee team for assistance.'\n )\n\n def get_all_esg_data(\n self,\n measures: List[ESGMeasure] = None,\n cards: List[ESGCard] = None,\n pricing_date: dt.date = None,\n benchmark_id: str = None,\n ) -> Dict:\n \"\"\"\n Get all ESG Data\n :param measures: list of ESG Measures to include in results\n :param cards: list of ESG Cards to include in results\n :param pricing_date: optional pricing date; defaults to last previous business day\n :param benchmark_id: optional benchmark asset ID to include in results\n :return: a dictionary of results\n \"\"\"\n return GsEsgApi.get_esg(\n entity_id=self.id,\n pricing_date=pricing_date,\n cards=cards if cards else [c for c in ESGCard],\n measures=measures if measures else [m for m in ESGMeasure],\n benchmark_id=benchmark_id,\n )\n\n def get_esg_summary(self, pricing_date: dt.date = None) -> pd.DataFrame:\n summary_data = GsEsgApi.get_esg(entity_id=self.id, pricing_date=pricing_date, cards=[ESGCard.SUMMARY]).get(\n 'summary'\n )\n return pd.DataFrame(summary_data)\n\n def get_esg_quintiles(self, measure: ESGMeasure, pricing_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get breakdown of entity by weight in each percentile quintile for requested ESG measure\n :param measure: ESG Measure\n :param pricing_date: optional pricing date; defaults to last previous business day\n :return: a Pandas DataFrame with results\n \"\"\"\n quintile_data = (\n GsEsgApi.get_esg(\n entity_id=self.id, pricing_date=pricing_date, cards=[ESGCard.QUINTILES], measures=[measure]\n )\n .get('quintiles')[0]\n .get('results')\n )\n df = pd.DataFrame(quintile_data)\n return df.filter(items=['description', 'gross', 'long', 'short'])\n\n def get_esg_by_sector(self, measure: ESGMeasure, pricing_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get breakdown of entity by sector, along with the weighted average score of the compositions in each sector\n :param measure: ESG Measure\n :param pricing_date: optional pricing date; defaults to last previous business day\n :return: a Pandas DataFrame with results\n \"\"\"\n return self._get_esg_breakdown(ESGCard.MEASURES_BY_SECTOR, measure, pricing_date)\n\n def get_esg_by_region(self, measure: ESGMeasure, pricing_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get breakdown of entity by region, along with the weighted average score of the compositions in each region\n :param measure: ESG Measure\n :param pricing_date: optional pricing date; defaults to last previous business day\n :return: a Pandas DataFrame with results\n \"\"\"\n return self._get_esg_breakdown(ESGCard.MEASURES_BY_REGION, measure, pricing_date)\n\n def get_esg_top_ten(self, measure: ESGMeasure, pricing_date: dt.date = None):\n \"\"\"\n Get entity constituents with the ten highest ESG percentile values\n :param measure: ESG Measure\n :param pricing_date: optional pricing date; defaults to last previous business day\n :return: a Pandas DataFrame with results\n \"\"\"\n return self._get_esg_ranked_card(ESGCard.TOP_TEN_RANKED, measure, pricing_date)\n\n def get_esg_bottom_ten(self, measure: ESGMeasure, pricing_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get entity constituents with the ten lowest ESG percentile values\n :param measure: ESG Measure\n :param pricing_date: optional pricing date; defaults to last previous business day\n :return: a Pandas DataFrame with results\n \"\"\"\n return self._get_esg_ranked_card(ESGCard.BOTTOM_TEN_RANKED, measure, pricing_date)\n\n def _get_esg_ranked_card(self, card: ESGCard, measure: ESGMeasure, pricing_date: dt.date = None) -> pd.DataFrame:\n data = (\n GsEsgApi.get_esg(entity_id=self.id, pricing_date=pricing_date, cards=[card], measures=[measure])\n .get(card.value)[0]\n .get('results')\n )\n return pd.DataFrame(data)\n\n def _get_esg_breakdown(self, card: ESGCard, measure: ESGMeasure, pricing_date: dt.date = None) -> pd.DataFrame:\n sector_data = (\n GsEsgApi.get_esg(entity_id=self.id, pricing_date=pricing_date, cards=[card], measures=[measure])\n .get(card.value)[0]\n .get('results')\n )\n return pd.DataFrame(sector_data)\n\n def get_carbon_analytics(\n self,\n benchmark_id: str = None,\n reporting_year: str = 'Latest',\n currency: Currency = None,\n include_estimates: bool = False,\n use_historical_data: bool = False,\n normalize_emissions: bool = False,\n cards: List[CarbonCard] = [c for c in CarbonCard],\n analytics_view: CarbonAnalyticsView = CarbonAnalyticsView.LONG,\n ) -> Dict:\n return GsCarbonApi.get_carbon_analytics(\n entity_id=self.id,\n benchmark_id=benchmark_id,\n reporting_year=reporting_year,\n currency=currency,\n include_estimates=include_estimates,\n use_historical_data=use_historical_data,\n normalize_emissions=normalize_emissions,\n cards=cards,\n analytics_view=analytics_view,\n )\n\n def get_carbon_coverage(\n self,\n reporting_year: str = 'Latest',\n include_estimates: bool = False,\n use_historical_data: bool = False,\n coverage_category: CarbonCoverageCategory = CarbonCoverageCategory.WEIGHTS,\n analytics_view: CarbonAnalyticsView = CarbonAnalyticsView.LONG,\n ) -> pd.DataFrame:\n coverage = (\n self.get_carbon_analytics(\n reporting_year=reporting_year,\n include_estimates=include_estimates,\n use_historical_data=use_historical_data,\n cards=[CarbonCard.COVERAGE],\n analytics_view=analytics_view,\n )\n .get(CarbonCard.COVERAGE.value)\n .get(coverage_category.value, {})\n .get(CarbonEntityType.PORTFOLIO.value, {})\n )\n return pd.DataFrame(coverage)\n\n def get_carbon_sbti_netzero_coverage(\n self,\n reporting_year: str = 'Latest',\n include_estimates: bool = False,\n use_historical_data: bool = False,\n target_coverage_category: CarbonTargetCoverageCategory = CarbonTargetCoverageCategory.PORTFOLIO_EMISSIONS,\n analytics_view: CarbonAnalyticsView = CarbonAnalyticsView.LONG,\n ) -> pd.DataFrame:\n coverage = (\n self.get_carbon_analytics(\n reporting_year=reporting_year,\n include_estimates=include_estimates,\n use_historical_data=use_historical_data,\n cards=[CarbonCard.SBTI_AND_NET_ZERO_TARGETS],\n analytics_view=analytics_view,\n )\n .get(CarbonCard.SBTI_AND_NET_ZERO_TARGETS.value)\n .get(target_coverage_category.value, {})\n )\n coverage = {\n target: target_coverage.get(CarbonEntityType.PORTFOLIO.value, {})\n for target, target_coverage in coverage.items()\n }\n return pd.DataFrame(coverage)\n\n def get_carbon_emissions(\n self,\n currency: Currency = None,\n include_estimates: bool = False,\n use_historical_data: bool = False,\n normalize_emissions: bool = False,\n scope: CarbonScope = CarbonScope.TOTAL_GHG,\n analytics_view: CarbonAnalyticsView = CarbonAnalyticsView.LONG,\n ) -> pd.DataFrame:\n emissions = (\n self.get_carbon_analytics(\n currency=currency,\n include_estimates=include_estimates,\n use_historical_data=use_historical_data,\n normalize_emissions=normalize_emissions,\n cards=[CarbonCard.EMISSIONS],\n analytics_view=analytics_view,\n )\n .get(CarbonCard.EMISSIONS.value)\n .get(scope.value, {})\n .get(CarbonEntityType.PORTFOLIO.value, [])\n )\n return pd.DataFrame(emissions)\n\n def get_carbon_emissions_allocation(\n self,\n reporting_year: str = 'Latest',\n currency: Currency = None,\n include_estimates: bool = False,\n use_historical_data: bool = False,\n normalize_emissions: bool = False,\n scope: CarbonScope = CarbonScope.TOTAL_GHG,\n classification: CarbonEmissionsAllocationCategory = CarbonEmissionsAllocationCategory.GICS_SECTOR,\n analytics_view: CarbonAnalyticsView = CarbonAnalyticsView.LONG,\n ) -> pd.DataFrame:\n allocation = (\n self.get_carbon_analytics(\n reporting_year=reporting_year,\n currency=currency,\n include_estimates=include_estimates,\n use_historical_data=use_historical_data,\n normalize_emissions=normalize_emissions,\n cards=[CarbonCard.ALLOCATIONS],\n analytics_view=analytics_view,\n )\n .get(CarbonCard.ALLOCATIONS.value)\n .get(scope.value, {})\n .get(CarbonEntityType.PORTFOLIO.value, {})\n .get(classification.value)\n )\n return pd.DataFrame(allocation).rename(columns={'name': classification.value})\n\n def get_carbon_attribution_table(\n self,\n benchmark_id: str,\n reporting_year: str = 'Latest',\n currency: Currency = None,\n include_estimates: bool = False,\n use_historical_data: bool = False,\n scope: CarbonScope = CarbonScope.TOTAL_GHG,\n intensity_metric: CarbonEmissionsIntensityType = CarbonEmissionsIntensityType.EI_ENTERPRISE_VALUE,\n analytics_view: CarbonAnalyticsView = CarbonAnalyticsView.LONG,\n ) -> pd.DataFrame:\n attribution = (\n self.get_carbon_analytics(\n benchmark_id=benchmark_id,\n reporting_year=reporting_year,\n currency=currency,\n include_estimates=include_estimates,\n use_historical_data=use_historical_data,\n cards=[CarbonCard.ATTRIBUTION],\n analytics_view=analytics_view,\n )\n .get(CarbonCard.ATTRIBUTION.value)\n .get(scope.value, [])\n )\n attribution_table = []\n for entry in attribution:\n new_entry = {\n 'sector': entry.get('sector'),\n 'weightPortfolio': entry.get('weightPortfolio'),\n 'weightBenchmark': entry.get('weightBenchmark'),\n 'weightComparison': entry.get('weightComparison'),\n }\n new_entry.update(entry.get(intensity_metric.value, {}))\n attribution_table.append(new_entry)\n return pd.DataFrame(attribution_table)\n\n def get_thematic_exposure(\n self,\n basket_identifier: str,\n notional: int = 10000000,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n ) -> pd.DataFrame:\n if not self.positioned_entity_type == EntityType.ASSET:\n raise NotImplementedError\n response = GsAssetApi.resolve_assets(identifier=[basket_identifier], fields=['id', 'type'], limit=1)[\n basket_identifier\n ]\n _id, _type = get(response, '0.id'), get(response, '0.type')\n if len(response) == 0 or _id is None:\n raise MqValueError(f'Basket could not be found using identifier {basket_identifier}.')\n if _type not in BasketType.to_list():\n raise MqValueError(f'Asset {basket_identifier} of type {_type} is not a Custom or Research Basket.')\n query = DataQuery(where={'assetId': self.id, 'basketId': _id}, start_date=start, end_date=end)\n response = GsDataApi.query_data(query=query, dataset_id=IndicesDatasets.COMPOSITE_THEMATIC_BETAS.value)\n df = []\n for r in response:\n df.append(\n {\n 'date': r['date'],\n 'assetId': r['assetId'],\n 'basketId': r['basketId'],\n 'thematicExposure': r['beta'] * notional,\n }\n )\n df = pd.DataFrame(df)\n return df.set_index('date')\n\n def get_thematic_beta(\n self, basket_identifier: str, start: dt.date = DateLimit.LOW_LIMIT.value, end: dt.date = dt.date.today()\n ) -> pd.DataFrame:\n if not self.positioned_entity_type == EntityType.ASSET:\n raise NotImplementedError\n response = GsAssetApi.resolve_assets(identifier=[basket_identifier], fields=['id', 'type'], limit=1)[\n basket_identifier\n ]\n _id, _type = get(response, '0.id'), get(response, '0.type')\n if len(response) == 0 or _id is None:\n raise MqValueError(f'Basket could not be found using identifier {basket_identifier}.')\n if _type not in BasketType.to_list():\n raise MqValueError(f'Asset {basket_identifier} of type {_type} is not a Custom or Research Basket.')\n query = DataQuery(where={'assetId': self.id, 'basketId': _id}, start_date=start, end_date=end)\n response = GsDataApi.query_data(query=query, dataset_id=IndicesDatasets.COMPOSITE_THEMATIC_BETAS.value)\n df = []\n for r in response:\n df.append(\n {'date': r['date'], 'assetId': r['assetId'], 'basketId': r['basketId'], 'thematicBeta': r['beta']}\n )\n df = pd.DataFrame(df)\n return df.set_index('date')\n\n @deprecation.deprecated(\n deprecated_in=\"0.9.110\", details=\"Please use the same function using the ThematicReport class\"\n )\n def get_all_thematic_exposures(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n basket_ids: List[str] = None,\n regions: List[Region] = None,\n ) -> pd.DataFrame:\n results = GsThematicApi.get_thematics(\n entity_id=self.id,\n start_date=start_date,\n end_date=end_date,\n basket_ids=basket_ids,\n regions=regions,\n measures=[ThematicMeasure.ALL_THEMATIC_EXPOSURES],\n )\n return flatten_results_into_df(results)\n\n @deprecation.deprecated(\n deprecated_in=\"0.9.110\", details=\"Please use the same function using the ThematicReport class\"\n )\n def get_top_five_thematic_exposures(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n basket_ids: List[str] = None,\n regions: List[Region] = None,\n ) -> pd.DataFrame:\n results = GsThematicApi.get_thematics(\n entity_id=self.id,\n start_date=start_date,\n end_date=end_date,\n basket_ids=basket_ids,\n regions=regions,\n measures=[ThematicMeasure.TOP_FIVE_THEMATIC_EXPOSURES],\n )\n return flatten_results_into_df(results)\n\n @deprecation.deprecated(\n deprecated_in=\"0.9.110\", details=\"Please use the same function using the ThematicReport class\"\n )\n def get_bottom_five_thematic_exposures(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n basket_ids: List[str] = None,\n regions: List[Region] = None,\n ) -> pd.DataFrame:\n results = GsThematicApi.get_thematics(\n entity_id=self.id,\n start_date=start_date,\n end_date=end_date,\n basket_ids=basket_ids,\n regions=regions,\n measures=[ThematicMeasure.BOTTOM_FIVE_THEMATIC_EXPOSURES],\n )\n return flatten_results_into_df(results)\n\n def get_thematic_breakdown(self, date: dt.date, basket_id: str) -> pd.DataFrame:\n \"\"\"\n Get a by-asset breakdown of a portfolio or basket's thematic exposure to a particular flagship basket on a\n particular date\n :param date: date\n :param basket_id: GS flagship basket's unique Marquee ID\n :return: a Pandas DataFrame with results\n \"\"\"\n return get_thematic_breakdown_as_df(entity_id=self.id, date=date, basket_id=basket_id)\n\n def get_factor_scenario_analytics(\n self,\n scenarios: List[Scenario],\n date: dt.date,\n measures: List[ScenarioCalculationMeasure],\n risk_model: str = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, Union[Dict, pd.DataFrame]]:\n \"\"\"Given a list of factor scenarios (historical simulation and/or custom shocks), return the estimated pnl\n of the given positioned entity.\n :param scenarios: List of factor-based scenarios\n :param date: date to run scenarios.\n :param measures: which metrics to return\n :param risk_model: valid risk model ID\n :param return_format: whether to return data formatted in a dataframe or as a dict\n\n **Examples**\n\n >>> from gs_quant.session import GsSession, Environment\n >>> from gs_quant.markets.portfolio_manager import PortfolioManager, ReturnFormat\n >>> from gs_quant.entities.entity import ScenarioCalculationMeasure, PositionedEntity\n >>> from gs_quant.markets.scenario import Scenario\n\n Get scenarios\n >>> covid_19_omicron = Scenario.get_by_name(\"Covid 19 Omicron (v2)\") # historical simulation\n >>> custom_shock = Scenario.get_by_name(\"Shocking factor by x% (Propagated)\") # custom shock\n >>> risk_model = \"RISK_MODEL_ID\" # valid risk model ID\n\n Instantiate your positionedEntity. Here, we are using one of its subclasses, PortfolioManager\n\n >>> pm = PortfolioManager(portfolio_id=\"PORTFOLIO_ID\")\n\n Set the date you wish to run your scenario on\n\n >>> date = dt.date(2023, 3, 7)\n\n Run scenario and get estimated impact on your positioned entity\n\n >>> scenario_analytics = pm.get_factor_scenario_analytics(\n ... scenarios=[covid_19_omicron, beta_propagated],\n ... date=date,\n ... measures=[ScenarioCalculationMeasure.SUMMARY,\n ... ScenarioCalculationMeasure.ESTIMATED_FACTOR_PNL,\n ... ScenarioCalculationMeasure.ESTIMATED_PNL_BY_SECTOR,\n ... ScenarioCalculationMeasure.ESTIMATED_PNL_BY_REGION,\n ... ScenarioCalculationMeasure.ESTIMATED_PNL_BY_DIRECTION,\n ... ScenarioCalculationMeasure.ESTIMATED_PNL_BY_ASSET],\n ... risk_model=risk_model)\n\n By default, the result will be returned in a dict with keys as the measures/metrics requested and values as\n the scenario calculation results formatted in a dataframe. To get the results in a dict, specify the return\n format as JSON\n \"\"\"\n risk_report = self.get_factor_risk_report(risk_model_id=risk_model)\n\n id_to_scenario_map = {scenario.id: scenario for scenario in scenarios}\n scenario_ids = list(id_to_scenario_map.keys())\n\n calculation_request = {\n \"date\": date,\n \"scenarioIds\": scenario_ids,\n \"reportId\": risk_report.id,\n \"measures\": [m.value for m in measures],\n \"riskModel\": risk_model,\n \"type\": \"Factor Scenario\",\n }\n\n results = GsFactorScenarioApi.calculate_scenario(calculation_request)\n\n scenarios = [id_to_scenario_map.get(sc_id) for sc_id in results.get('scenarios')]\n calculation_results = results.get('results')\n\n if return_format == ReturnFormat.JSON:\n return dict(zip(scenarios, calculation_results))\n\n result = {}\n\n all_data = {}\n [\n all_data.update({result_type: []})\n for result_type in [\n 'summary',\n 'factorPnl',\n 'bySectorAggregations',\n 'byRegionAggregations',\n 'byDirectionAggregations',\n 'byAsset',\n ]\n ]\n\n for i, calc_result in enumerate(calculation_results):\n for result_type in [\n 'summary',\n 'factorPnl',\n 'bySectorAggregations',\n 'byRegionAggregations',\n 'byDirectionAggregations',\n 'byAsset',\n ]:\n scenario_metadata_map = {\n \"scenarioId\": scenarios[i].id,\n \"scenarioName\": scenarios[i].name,\n \"scenarioType\": scenarios[i].type.value,\n }\n if result_type == 'summary':\n calc_result.get(result_type).update(scenario_metadata_map)\n all_data.get(result_type).append(calc_result.get(result_type))\n else:\n [data_map.update(scenario_metadata_map) for data_map in calc_result.get(result_type, [])]\n [all_data.get(result_type).append(element) for element in calc_result.get(result_type, [])]\n\n for result_type, result_label in {\n \"summary\": \"summary\",\n \"factorPnl\": \"factorCategories\",\n \"bySectorAggregations\": \"sectors\",\n \"byRegionAggregations\": \"countries\",\n \"byDirectionAggregations\": \"direction\",\n \"byAsset\": \"byAsset\",\n }.items():\n estimated_pnl_results_as_json = all_data.get(result_type)\n if estimated_pnl_results_as_json:\n estimated_pnl_df = pd.DataFrame.from_dict(estimated_pnl_results_as_json)\n estimated_pnl_df = estimated_pnl_df.apply(_explode_data, axis=1, parent_label=result_label)\n if isinstance(estimated_pnl_df, pd.Series):\n estimated_pnl_df = pd.concat(estimated_pnl_df.values, ignore_index=True)\n\n estimated_pnl_df.columns = estimated_pnl_df.columns.map(\n lambda x: {\n \"factorCategories\": \"Factor Category\",\n \"factors\": \"Factor\",\n \"sectors\": \"Sector\",\n \"countries\": \"Country\",\n \"industries\": \"Industry\",\n \"direction\": \"Direction\",\n \"scenarioId\": \"Scenario ID\",\n \"scenarioName\": \"Scenario Name\",\n \"scenarioType\": \"Scenario Type\",\n \"assetId\": \"Asset ID\",\n \"name\": \"Asset Name\",\n \"bbid\": \"BBID\",\n \"factorExposure\": \"Factor Exposure\",\n \"factorShock\": \"Factor Shock (%)\",\n \"exposure\": \"Exposure\",\n \"estimatedPnl\": \"Estimated Pnl\",\n \"estimatedPerformance\": \"Estimated Performance (%)\",\n \"stressedMarketValue\": \"Stressed Market Value\",\n }.get(x, x)\n )\n column_order = [\n col\n for col in [\n \"Scenario ID\",\n \"Scenario Name\",\n \"Scenario Type\",\n \"Asset ID\",\n \"BBID\",\n \"Asset Name\",\n \"Factor Category\",\n \"Factor\",\n \"Factor Exposure\",\n \"Factor Shock (%)\",\n \"Sector\",\n \"Industry\",\n \"Country\",\n \"Direction\",\n \"Exposure\",\n \"Estimated Pnl\",\n \"Estimated Performance (%)\",\n \"Stressed Market Value\",\n ]\n if col in estimated_pnl_df.columns.tolist()\n ]\n estimated_pnl_df = estimated_pnl_df[column_order]\n result[result_type] = estimated_pnl_df\n\n return result\n" }, { "path": "gs_quant/entities/entity_utils.py", "content": "\"\"\"\nCopyright 2024 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Union\n\nimport pandas as pd\n\n\ndef _explode_data(data: pd.Series, parent_label: str) -> Union[pd.DataFrame, pd.Series]:\n parent_to_child_map = {\n \"factorCategories\": \"factors\",\n \"factors\": \"byAsset\",\n \"sectors\": \"industries\",\n \"industries\": None,\n \"countries\": None,\n \"direction\": None,\n }\n\n labels_to_ignore_map = {\n \"factorCategories\": [\"factorExposure\", \"estimatedPnl\", \"factors\"],\n \"factors\": [\"factorExposure\", \"estimatedPnl\", \"byAsset\"],\n \"sectors\": [\"exposure\", \"estimatedPnl\", \"industries\"],\n \"industries\": [],\n \"countries\": [],\n \"direction\": [],\n \"byAsset\": [],\n }\n\n data = data.rename({'name': parent_label}) if parent_label in parent_to_child_map.keys() else data\n child_label = parent_to_child_map.get(parent_label)\n\n if child_label and child_label in data.index.values:\n child_df = pd.DataFrame(data[child_label])\n child_df = child_df.apply(_explode_data, axis=1, parent_label=child_label)\n\n data = data.drop(labels=labels_to_ignore_map.get(parent_label))\n if isinstance(child_df, pd.Series):\n child_df = pd.concat(child_df.values, ignore_index=True)\n child_df = child_df.assign(**data.to_dict())\n\n return child_df\n\n return data\n" }, { "path": "gs_quant/entities/tree_entity.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport pandas as pd\nimport datetime as dt\n\nfrom typing import Optional\nfrom pydash import get\n\nfrom gs_quant.api.gs.assets import GsAsset, GsAssetApi\nfrom gs_quant.data import Dataset\nfrom gs_quant.errors import MqValueError\n\n\nclass AssetTreeNode:\n def __init__(self, id, depth: Optional[int] = 0, date: Optional[dt.date] = None, asset: Optional[GsAsset] = None):\n self.id = id\n self.date = date # date for which the Tree is constructed. If None, it is set to the latest available date.\n self.depth = depth # depth of the node with respect to the root node\n self.asset = asset\n self.name = get(self.asset, 'name')\n self.bbid = get(get(self.asset, 'xref'), 'bbid')\n self.asset_type = get(self.asset, 'type')\n self.data = {}\n self.constituents_df = pd.DataFrame()\n self.direct_underlier_assets_as_nodes = [] # holds the child nodes as AssetTreeNode objects.\n\n def __str__(self):\n result = self.bbid if self.bbid is not None else self.id\n return f'Tree Node - {result}'\n\n def to_frame(self) -> pd.DataFrame:\n if len(self.constituents_df) > 0:\n return self.constituents_df\n else:\n self.constituents_df = (\n self.__build_constituents_df(pd.DataFrame())\n .drop_duplicates()\n .sort_values(by='depth')\n .reset_index(drop=True)\n )\n return self.constituents_df\n\n def populate_values(self, dataset, value_column, underlier_column):\n ds = Dataset(dataset)\n query = ds.get_data(start=self.date, end=self.date, assetId=[self.id])\n if len(query) > 0:\n for node in self.direct_underlier_assets_as_nodes:\n value = query.loc[query[underlier_column] == node.id][value_column].iloc[0]\n node.data[value_column] = value\n node.populate_values(dataset, value_column, underlier_column)\n\n def build_tree(self, dataset, underlier_column):\n \"\"\"\n Build the full tree and return the root node\n \"\"\"\n query = self.__get_direct_underliers(self.id, dataset)\n if len(query) > 0:\n all_ids = query[underlier_column].tolist()\n all_assets = GsAssetApi.get_many_assets(id=all_ids)\n asset_lookup = {mq_id: asset_obj for mq_id, asset_obj in zip(all_ids, all_assets)}\n for i_, row in query.iterrows():\n underlier = row[underlier_column]\n if underlier not in asset_lookup:\n raise Exception(\"Unable to find {}\".format(underlier))\n child_node = AssetTreeNode(underlier, self.depth + 1, self.date, asset_lookup[underlier])\n child_node.build_tree(dataset, underlier_column)\n self.direct_underlier_assets_as_nodes.append(child_node)\n\n def __get_direct_underliers(self, asset_id, dataset) -> pd.DataFrame:\n \"\"\"\n Queries the dataset for the date passed during initialisation. If date isn't passed, returns the data of the\n latest available date.\n \"\"\"\n ds = Dataset(dataset)\n if self.date:\n query = ds.get_data(start=self.date, end=self.date, assetId=[asset_id]).drop_duplicates()\n else:\n query = ds.get_data(assetId=[asset_id]).drop_duplicates()\n\n if len(query) > 0:\n self.date = query.index.max().date()\n query = query[query.index == query.index.max()].reset_index()\n return query\n\n def __build_constituents_df(self, constituents_df) -> pd.DataFrame:\n for node in self.direct_underlier_assets_as_nodes:\n data = {\n 'date': self.date,\n 'assetName': self.name,\n 'assetId': self.id,\n 'assetBbid': self.bbid,\n 'underlyingAssetName': node.name,\n 'underlyingAssetId': node.id,\n 'underlyingAssetBbid': node.bbid,\n 'depth': node.depth,\n }\n\n for key, value in node.data.items():\n data[key] = value\n constituents_df = constituents_df.append(pd.DataFrame(data, index=[0]))\n d = node.__build_constituents_df(pd.DataFrame())\n if len(d) > 0:\n constituents_df = constituents_df.append(d)\n return constituents_df\n\n\nclass TreeHelper:\n def __init__(\n self,\n id,\n date: Optional[dt.date] = None,\n tree_underlier_dataset: Optional[str] = None,\n underlier_column: Optional[str] = 'underlyingAssetId',\n ):\n self.id = id\n self.root = AssetTreeNode(self.id, 0, date, GsAssetApi.get_asset(asset_id=self.id))\n self.date = self.root.date\n self.update_time = dt.datetime.now()\n self.constituents_df = pd.DataFrame()\n self.tree_built = False\n self.__tree_underlier_dataset = tree_underlier_dataset\n self.__underlier_column = underlier_column\n\n def populate_weights(self, dataset, weight_column: Optional[str] = 'weight'):\n if not self.tree_built:\n self.build_tree()\n\n self.root.data['weight'] = 1\n self.root.populate_values(dataset, weight_column, self.__underlier_column)\n\n def populate_attribution(self, dataset, attribution_column: Optional[str] = 'absoluteAttribution'):\n if not self.tree_built:\n self.build_tree()\n\n self.root.data['absoluteAttribution'] = 1\n self.root.populate_values(dataset, attribution_column, self.__underlier_column)\n\n def to_frame(self) -> pd.DataFrame:\n \"\"\"\n Retrieve constituents of the full tree. If it has already been fetched once, it is stored and returned when\n called later in the future.\n\n :return: dataframe with constituents of the full tree, with parent AssetID, underlying AssetID, depth and weight\n\n **Usage**\n\n Retrieve constituents of the full tree.\n \"\"\"\n\n if not self.tree_built:\n self.build_tree()\n\n self.constituents_df = self.root.to_frame()\n if len(self.constituents_df) > 0:\n return self.constituents_df\n else:\n raise MqValueError('No constituents found for the asset')\n\n def build_tree(self):\n if not self.tree_built:\n self.root.build_tree(self.__tree_underlier_dataset, self.__underlier_column)\n self.tree_built = True\n self.update_time = dt.datetime.now()\n\n def get_tree(self) -> AssetTreeNode:\n \"\"\"\n Build the full tree and return the root node of the full-fledged tree.\n If the tree has been built already, return it on future calls.\n\n :return: AssetTreeNode object of the root node, with a list attribute direct_underlier_assets_as_nodes that\n holds the child AssetTreeNode object.\n\n **Usage**\n\n Root AssetTreeNode object of the tree entity\n \"\"\"\n\n if not self.tree_built:\n self.build_tree()\n\n return self.root\n\n def get_visualisation(self, visualise_by: str = 'name'):\n try:\n from treelib import Tree\n except ModuleNotFoundError:\n raise RuntimeError('You must install treelib to be able use this function.')\n\n if not self.tree_built:\n self.build_tree()\n\n if visualise_by in ['name', 'bbid', 'id']:\n # Each entry in the BFS queue is an array having the node and the prefix value for that node.\n # The prefix is the path from root to the parent of that node, and is empty for the root node.\n # This definition of prefix allows using the same node in multiple branches.\n bfs_queue = [[self.root, '']]\n\n tree_vis = Tree()\n while len(bfs_queue) != 0:\n node, prefix = bfs_queue.pop(0)\n node_id = prefix + '-' + node.id\n node_name = getattr(node, visualise_by)\n if str(node_name) == 'None':\n node_name = f'NA ({node.id})'\n if prefix == '':\n tree_vis.create_node(node_name, node_id)\n else:\n tree_vis.create_node(node_name, node_id, parent=prefix)\n for c in node.direct_underlier_assets_as_nodes:\n bfs_queue.append([c, node_id])\n\n else:\n raise MqValueError('visualise_by argument has to be either name, id or bbid')\n\n return tree_vis.show()\n" }, { "path": "gs_quant/errors.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport sys\n\n\nclass MqError(Exception):\n \"\"\"Base class for errors in this module\"\"\"\n\n pass\n\n\nclass MqValueError(MqError, ValueError):\n pass\n\n\nclass MqTypeError(MqError):\n pass\n\n\nclass MqWrappedError(MqError):\n pass\n\n\nclass MqRequestError(MqError):\n def __init__(self, status, message, context=None):\n self.status = status\n self.message = message\n self.context = context\n\n def __str__(self):\n prepend = 'context: {}\\n'.format(self.context) if self.context else ''\n result = '{}status: {}, message: {}'.format(prepend, self.status, self.message)\n if sys.version_info.major < 3:\n result = result.encode('ascii', 'ignore')\n return result\n\n\nclass MqAuthenticationError(MqRequestError):\n pass\n\n\nclass MqAuthorizationError(MqRequestError):\n pass\n\n\nclass MqUninitialisedError(MqError):\n pass\n\n\n# Creating errors based on status code to be able to effectively use backoff decorator\nclass MqRateLimitedError(MqRequestError):\n pass\n\n\nclass MqTimeoutError(MqRequestError):\n pass\n\n\nclass MqInternalServerError(MqRequestError):\n pass\n\n\ndef error_builder(status, message, context=None):\n if status == 401:\n return MqAuthenticationError(status, message, context)\n elif status == 403:\n return MqAuthorizationError(status, message, context)\n elif status == 429:\n return MqRateLimitedError(status, message, context)\n elif status == 500:\n return MqInternalServerError(status, message, context)\n elif status == 504:\n return MqTimeoutError(status, message, context)\n else:\n return MqRequestError(status, message, context)\n" }, { "path": "gs_quant/instrument/__init__.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\nfrom .core import Instrument, Security, DummyInstrument\nfrom gs_quant.target.instrument import *\nfrom gs_quant.target.common import SwapClearingHouse, SwapSettlement\nfrom .overrides import *" }, { "path": "gs_quant/instrument/core.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport inspect\nimport logging\nimport warnings\nfrom copy import deepcopy\nfrom typing import Iterable, Optional, Tuple, Union\n\nfrom dataclasses_json import global_config\n\nfrom gs_quant.api.gs.parser import GsParserApi\nfrom gs_quant.api.gs.risk import GsRiskApi\nfrom gs_quant.base import get_enum_value, InstrumentBase, Priceable, Scenario\nfrom gs_quant.common import AssetClass, AssetType, XRef, RiskMeasure, MultiScenario\nfrom gs_quant.markets import HistoricalPricingContext, MarketDataCoordinate, PricingContext\nfrom gs_quant.priceable import PriceableImpl\nfrom gs_quant.risk import (\n FloatWithInfo,\n DataFrameWithInfo,\n SeriesWithInfo,\n ResolvedInstrumentValues,\n DEPRECATED_MEASURES,\n)\nfrom gs_quant.risk.results import ErrorValue, MultipleRiskMeasureFuture, PricingFuture, MultipleScenarioFuture\n\n_logger = logging.getLogger(__name__)\n\n\nclass Instrument(PriceableImpl, InstrumentBase):\n PROVIDER = GsRiskApi\n __instrument_mappings = {}\n\n def __repr__(self):\n return f'{self.__class__.__name__}{\"(\" + self.name + \")\" if self.name else \"\"}'\n\n @classmethod\n def __asset_class_and_type_to_instrument(cls):\n if not cls.__instrument_mappings:\n import gs_quant.target.instrument as instrument_ # noqa\n\n instrument_classes = [\n c\n for _, c in inspect.getmembers(instrument_, inspect.isclass)\n if issubclass(c, Instrument) and c is not Instrument\n ]\n\n cls.__instrument_mappings[(AssetClass.Cash, AssetType.Currency)] = instrument_.Forward\n\n for clazz in instrument_classes:\n instrument = clazz.default_instance()\n cls.__instrument_mappings[(instrument.asset_class, instrument.type)] = clazz\n\n return cls.__instrument_mappings\n\n @property\n def provider(self):\n return self.PROVIDER\n\n def resolve(self, in_place: bool = True) -> Optional[Union[PriceableImpl, PricingFuture, dict]]:\n \"\"\"\n Resolve non-supplied properties of an instrument\n\n **Examples**\n\n >>> from gs_quant.instrument import IRSwap\n >>>\n >>> swap = IRSwap('Pay', '10y', 'USD')\n >>> rate = swap.fixedRate\n\n rate is None\n\n >>> swap.resolve()\n >>> rate = swap.fixedRate\n\n rates is now the solved fixed rate\n \"\"\"\n\n is_historical = isinstance(PricingContext.current, HistoricalPricingContext)\n\n def handle_result(result: Optional[Union[ErrorValue, InstrumentBase]]) -> Optional[PriceableImpl]:\n ret = None if in_place else result\n if isinstance(result, ErrorValue):\n _logger.error('Failed to resolve instrument fields: ' + result.error)\n ret = {result.risk_key.date: None} if is_historical else None\n elif result is None:\n _logger.error('Unknown error resolving instrument fields')\n ret = {dt.date.today(): self} if is_historical else self\n elif in_place:\n self.from_instance(result)\n\n return ret\n\n if in_place and is_historical:\n raise RuntimeError('Cannot resolve in place under a HistoricalPricingContext')\n\n if in_place and len([i for i in Scenario.path if isinstance(i, MultiScenario)]):\n raise RuntimeError('Cannot resolve in place under a MultiScenario Context')\n\n return self.calc(ResolvedInstrumentValues, fn=handle_result)\n\n def calc(\n self, risk_measure: Union[RiskMeasure, Iterable[RiskMeasure]], fn=None\n ) -> Union[\n DataFrameWithInfo,\n ErrorValue,\n FloatWithInfo,\n PriceableImpl,\n PricingFuture,\n SeriesWithInfo,\n Tuple[MarketDataCoordinate, ...],\n ]:\n \"\"\"\n Calculate the value of the risk_measure\n\n :param risk_measure: the risk measure to compute, e.g. IRDelta (from gs_quant.risk)\n :param fn: post-processing function (optional)\n :return: a float or dataframe, depending on whether the value is scalar or structured, or a future thereof\n (depending on how PricingContext is being used)\n\n **Examples**\n\n >>> from gs_quant.instrument import IRCap\n >>> from gs_quant.risk import IRDelta\n >>>\n >>> cap = IRCap('1y', 'USD')\n >>> delta = cap.calc(IRDelta)\n\n delta is a dataframe\n\n >>> from gs_quant.instrument import EqOption\n >>> from gs_quant.risk import EqDelta\n >>>\n >>> option = EqOption('.SPX', '3m', 'ATMF', 'Call', 'European')\n >>> delta = option.calc(EqDelta)\n\n delta is a float\n\n >>> from gs_quant.markets import PricingContext\n >>>\n >>> cap_usd = IRCap('1y', 'USD')\n >>> cap_eur = IRCap('1y', 'EUR')\n\n >>> with PricingContext():\n >>> usd_delta_f = cap_usd.calc(IRDelta)\n >>> eur_delta_f = cap_eur.calc(IRDelta)\n >>>\n >>> usd_delta = usd_delta_f.result()\n >>> eur_delta = eur_delta_f.result()\n\n usd_delta_f and eur_delta_f are futures, usd_delta and eur_delta are dataframes\n \"\"\"\n\n def get_inst_futures(curr_measure):\n return (\n MultipleScenarioFuture(\n self, multi_scenario.scenarios, (curr_measure.pricing_context.calc(self, curr_measure),)\n )\n if multi_scenario\n else curr_measure.pricing_context.calc(self, curr_measure)\n )\n\n single_measure = isinstance(risk_measure, RiskMeasure)\n multi_scenario = next((i for i in Scenario.path if isinstance(i, MultiScenario)), None)\n\n with self._pricing_context:\n future = (\n get_inst_futures(risk_measure)\n if single_measure\n else MultipleRiskMeasureFuture(self, {r: get_inst_futures(r) for r in risk_measure})\n )\n\n # Warn on use of deprecated measures\n def warning_on_one_line(msg, category, _filename, _lineno, _file=None, _line=None):\n return f'{category.__name__}:{msg}'\n\n for measure in (risk_measure,) if single_measure else risk_measure:\n if measure.name in DEPRECATED_MEASURES.keys():\n message = (\n '{0} risk measure is deprecated. Please use {1} instead and pass in arguments to describe '\n 'risk measure specifics.\\n'.format(measure.name, DEPRECATED_MEASURES[measure.name])\n )\n warnings.simplefilter('once')\n warnings.formatwarning = warning_on_one_line\n warnings.warn(message, DeprecationWarning)\n warnings.simplefilter('ignore')\n\n if fn is not None:\n ret = PricingFuture()\n\n def cb(f):\n try:\n ret.set_result(fn(f.result()))\n except Exception as e:\n ret.set_exception(e)\n\n future.add_done_callback(cb)\n future = ret\n\n return future if self._return_future else future.result()\n\n @classmethod\n def from_dict(cls, values: dict):\n if not values:\n return\n\n instrument = cls if hasattr(cls, 'asset_class') else None\n if instrument is None:\n builder_type = values.get('$type') or values.get('builder', values.get('defn', {})).get('$type')\n values_used = values.get('builder', values.get('defn', values))\n if builder_type:\n from gs_quant_internal.base import decode_quill_value\n\n return decode_quill_value(values_used)\n\n asset_class_field = next((f for f in ('asset_class', 'assetClass') if f in values), None)\n if not asset_class_field:\n raise ValueError('assetClass/asset_class not specified')\n if 'type' not in values:\n raise ValueError('type not specified')\n\n asset_type = values.pop('type')\n asset_class = values.pop(asset_class_field)\n security_types = (None, '', 'Security')\n default_type = Security if asset_type in security_types and asset_class in security_types else None\n\n instrument = Instrument.__asset_class_and_type_to_instrument().get(\n (get_enum_value(AssetClass, asset_class), get_enum_value(AssetType, asset_type)), default_type\n )\n\n if instrument is None:\n raise ValueError('unable to build instrument')\n\n return instrument.from_dict(values)\n\n @classmethod\n def from_quick_entry(cls, text: str, asset_class: Optional[AssetClass] = None):\n if not asset_class:\n try:\n inst = cls.default_instance()\n asset_class = inst.asset_class\n except AttributeError:\n pass\n\n if not asset_class:\n res = GsParserApi.get_instrument_from_text(text)\n if len(res): # multiple instruments returned\n instrument = res.pop(0)\n else:\n raise ValueError('Could not resolve instrument')\n else:\n instrument = GsParserApi.get_instrument_from_text_asset_class(text, asset_class.value)\n try:\n return cls.from_dict(instrument)\n except AttributeError:\n raise ValueError('Invalid instrument specification')\n\n @classmethod\n def from_asset_ids(cls, asset_ids: Tuple[str, ...]) -> Tuple[InstrumentBase, ...]:\n from gs_quant.api.gs.assets import GsAssetApi\n\n instruments = GsAssetApi.get_instruments_for_asset_ids(asset_ids)\n\n try:\n inst = cls.default_instance()\n asset_class = inst.asset_class\n asset_type = inst.type\n\n if not all(i.asset_class == asset_class and i.type == asset_type for i in instruments):\n raise ValueError(f'Instrument(s) not all of type {cls.__name__}')\n except AttributeError:\n pass\n\n return instruments\n\n @classmethod\n def from_asset_id(cls, asset_id: str) -> InstrumentBase:\n return cls.from_asset_ids((asset_id,))[0]\n\n @staticmethod\n def compose(components: Iterable):\n return {c.risk_key.date if isinstance(c, ErrorValue) else c.resolution_key.date: c for c in components}\n\n def flip(self, in_place: bool = True):\n return self.scale(-1, in_place)\n\n def scale(self, scaling: float, in_place: bool = True, check_resolved=True):\n if scaling is None:\n return self\n if not hasattr(self, 'scale_in_place'):\n raise NotImplementedError(f'scale_in_place not implemented on {type(self).__name__}')\n if in_place:\n self.scale_in_place(scaling, check_resolved=check_resolved)\n return\n new_inst = deepcopy(self)\n new_inst.scale(scaling, check_resolved=check_resolved)\n return new_inst\n\n\nclass DummyInstrument(Instrument):\n def __init__(self, dummy_result: Union[str, float] = None):\n super().__init__()\n self.dummy_result = dummy_result\n\n @property\n def dummy_result(self) -> Union[str, float]:\n return self.__dummy_result\n\n @dummy_result.setter\n def dummy_result(self, value: Union[str, float]):\n self.__dummy_result = value\n\n @property\n def type(self) -> AssetType:\n return AssetType.Any\n\n\nclass Security(XRef, Instrument):\n \"\"\"A security, specified by a well-known identifier\"\"\"\n\n def __init__(\n self,\n ticker: str = None,\n bbid: str = None,\n ric: str = None,\n isin: str = None,\n cusip: str = None,\n prime_id: str = None,\n quantity: float = 1,\n ):\n \"\"\"\n Create a security by passing one identifier only and, optionally, a quantity\n\n :param ticker: Exchange ticker\n :param bbid: Bloomberg identifier\n :param isin: International Security Number\n :param cusip: CUSIP\n :param prime_id: Prime (GS internal) identifier\n :param quantity: Quantity (number of contracts for exchange-traded instruments, notional for bonds)\n \"\"\"\n if len(tuple(filter(None, (f is not None for f in (ticker, bbid, isin, cusip, prime_id))))) > 1:\n raise ValueError('Only specify one identifier')\n\n XRef.__init__(self, ticker=ticker, bbid=bbid, ric=ric, isin=isin, cusip=cusip, prime_id=prime_id)\n Instrument.__init__(self)\n self.quantity_ = quantity\n\n @classmethod\n def from_dict(cls, env):\n return cls(**{k: v for k, v in env.items() if k in inspect.signature(cls).parameters})\n\n\ndef encode_instrument(instrument: Optional[Instrument]) -> Optional[dict]:\n if instrument is not None:\n return instrument.to_dict()\n\n\ndef encode_instruments(instruments: Optional[Iterable[Instrument]]) -> Optional[Iterable[Optional[dict]]]:\n if instruments is not None:\n return [encode_instrument(i) for i in instruments]\n\n\nglobal_config.decoders[Instrument] = Instrument.from_dict\nglobal_config.decoders[InstrumentBase] = Instrument.from_dict\nglobal_config.encoders[Instrument] = encode_instrument\nglobal_config.encoders[Priceable] = encode_instrument\nglobal_config.encoders[Optional[Priceable]] = encode_instrument\nglobal_config.encoders[Optional[Instrument]] = encode_instrument\nglobal_config.encoders[Optional[InstrumentBase]] = encode_instrument\n" }, { "path": "gs_quant/instrument/overrides.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n" }, { "path": "gs_quant/interfaces/__init__.py", "content": "" }, { "path": "gs_quant/interfaces/algebra.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport abc\n\n\nclass AlgebraicType(abc.ABC):\n @abc.abstractmethod\n def __add__(self, other): ...\n\n def __radd__(self, other):\n return self.__add__(other)\n\n @abc.abstractmethod\n def __sub__(self, other): ...\n\n @abc.abstractmethod\n def __mul__(self, other): ...\n\n def __rmul__(self, other):\n return self.__mul__(other)\n\n @abc.abstractmethod\n def __div__(self, other): ...\n" }, { "path": "gs_quant/json_convertors.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport re\nfrom dataclasses import MISSING, fields\nfrom typing import Optional, Union, Iterable, Dict, Tuple, Any\n\nimport pandas as pd\nfrom dataclasses_json import config\nfrom dateutil.parser import isoparse\n\n__valid_date_formats = (\n '%Y-%m-%d', # '2020-07-28'\n '%d%b%y', # '28Jul20'\n '%d%b%Y', # '28Jul2020'\n '%d-%b-%y', # '28-Jul-20'\n '%d/%m/%Y',\n) # '28/07/2020\n\nDateOrDateTime = Union[dt.date, dt.datetime]\n\n\ndef encode_date_or_str(value: Optional[Union[str, dt.date]]) -> Optional[str]:\n return value.isoformat() if isinstance(value, dt.date) else value\n\n\ndef decode_optional_date(value: Optional[str]) -> Optional[dt.date]:\n # from dataclasses-json 0.6.5 onwards the global config for type T will be applied to Optional[T]\n # So this decoder would become redundant, to allow any version we simply return if it's already a date\n if value is None or isinstance(value, dt.date):\n return value\n elif isinstance(value, str):\n decoded_date_str = __try_decode_valid_date_formats(value)\n if decoded_date_str is not None:\n return decoded_date_str\n\n raise ValueError(f'Cannot convert {value} to date')\n\n\ndef decode_optional_time(value: Optional[str]) -> Optional[dt.time]:\n # from dataclasses-json 0.6.5 onwards the global config for type T will be applied to Optional[T]\n # So this decoder would become redundant, to allow any version we simply return if it's already a time\n if value is None or isinstance(value, dt.time):\n return value\n elif isinstance(value, str):\n return dt.time.fromisoformat(value)\n\n raise ValueError(f'Cannot convert {value} to date')\n\n\ndef encode_optional_time(value: Optional[Union[str, dt.time]]) -> Optional[str]:\n return value.isoformat() if isinstance(value, dt.time) else value\n\n\ndef decode_date_tuple(blob: Tuple[str, ...]):\n return tuple(decode_optional_date(s) for s in blob) if isinstance(blob, (tuple, list)) else None\n\n\ndef encode_date_tuple(values: Tuple[Optional[Union[str, dt.date]], ...]):\n return (\n tuple(encode_date_or_str(value) if isinstance(value, (str, dt.date)) else None for value in values)\n if values is not None\n else None\n )\n\n\ndef decode_iso_date_or_datetime(value: Any) -> Union[Tuple[DateOrDateTime, ...], DateOrDateTime]:\n if isinstance(value, (tuple, list)):\n return tuple(decode_iso_date_or_datetime(v) for v in value)\n if isinstance(value, (dt.date, dt.datetime)):\n return value\n elif isinstance(value, str):\n if len(value) == 10:\n return decode_optional_date(value)\n else:\n return optional_from_isodatetime(value)\n raise TypeError(f'Cannot convert {value} to date or datetime')\n\n\ndef optional_from_isodatetime(datetime: Union[str, dt.datetime, None]) -> Optional[dt.datetime]:\n if datetime is None or isinstance(datetime, dt.datetime):\n return datetime\n return dt.datetime.fromisoformat(datetime.replace('Z', ''))\n\n\ndef optional_to_isodatetime(datetime: Optional[dt.datetime]):\n return f'{dt.datetime.isoformat(datetime, timespec=\"seconds\")}Z' if datetime is not None else None\n\n\noptional_datetime_config = config(encoder=optional_to_isodatetime, decoder=optional_from_isodatetime)\noptional_date_config = config(encoder=encode_date_or_str, decoder=decode_optional_date)\n\n\ndef decode_dict_date_key(value):\n return {dt.date.fromisoformat(d): v for d, v in value.items()} if value is not None else None\n\n\ndef decode_dict_date_key_or_float(value):\n if value is not None:\n return decode_dict_date_key(value) if isinstance(value, dict) else decode_float_or_str(value)\n return None\n\n\ndef decode_dict_dict_date_key(value):\n return (\n {\n k: {dt.date.fromisoformat(d): v for d, v in val.items()} if val is not None else None\n for k, val in value.items()\n }\n if value is not None\n else None\n )\n\n\ndef decode_dict_date_value(value):\n return {k: dt.date.fromisoformat(d) for k, d in value.items()} if value is not None else None\n\n\ndef decode_datetime_tuple(blob: Tuple[str, ...]):\n return tuple(optional_from_isodatetime(s) for s in blob) if isinstance(blob, (tuple, list)) else None\n\n\ndef __try_decode_valid_date_formats(value: str) -> Optional[dt.date]:\n for fmt in __valid_date_formats:\n try:\n return dt.datetime.strptime(value, fmt).date()\n except ValueError:\n pass\n return None\n\n\ndef decode_date_or_str(value: Union[dt.date, float, str]) -> Optional[Union[dt.date, str]]:\n if value is None or isinstance(value, dt.date):\n return value\n elif isinstance(value, float):\n # Assume it's an Excel date\n if value > 59:\n value -= 1 # Excel leap year bug, 1900 is not a leap year!\n return (dt.datetime(1899, 12, 31) + dt.timedelta(days=value)).date()\n elif isinstance(value, str):\n # Try the supported string date formats\n decoded_date_str = __try_decode_valid_date_formats(value)\n\n # Assume it's a tenor\n return value if decoded_date_str is None else decoded_date_str\n\n raise TypeError(f'Cannot convert {value} to date')\n\n\ndef encode_datetime(value: Optional[dt.datetime]) -> Optional[str]:\n if value is None:\n return value\n\n try:\n iso_formatted = value.isoformat(timespec='milliseconds')\n except TypeError:\n # Pandas Timestamp objects don't take timespec, will raise TypeError (as of 1.2.4)\n iso_formatted = value.isoformat()\n\n return iso_formatted if value.tzinfo else iso_formatted + 'Z' # Make sure to be explict about timezone\n\n\ndef decode_datetime(value: Optional[Union[int, str]]) -> Optional[dt.datetime]:\n if value is None or isinstance(value, dt.datetime):\n return value\n if isinstance(value, int):\n return dt.datetime.fromtimestamp(value / 1000)\n elif isinstance(value, str):\n matcher = re.search('\\\\.([0-9]*)Z$', value)\n if matcher:\n sub_seconds = matcher.group(1)\n if len(sub_seconds) > 6:\n value = re.sub(matcher.re, '.{}Z'.format(sub_seconds[:6]), value)\n\n return isoparse(value)\n\n raise TypeError(f'Cannot convert {value} to datetime')\n\n\ndef decode_float_or_str(value: Optional[Union[float, int, str]]) -> Optional[Union[float, str]]:\n if value is None:\n return value\n elif isinstance(value, float):\n return value\n elif isinstance(value, int):\n return float(value)\n elif isinstance(value, str):\n try:\n return float(value)\n except ValueError:\n # Assume it's a strike or similar, e.g. 'ATM'\n return value\n\n raise TypeError(f'Cannot convert {value} to float')\n\n\ndef decode_instrument(value: Optional[Dict]):\n from gs_quant.instrument import Instrument\n\n return Instrument.from_dict(value) if value else None\n\n\ndef decode_named_instrument(value: Optional[Union[Iterable[Dict], dict]]):\n from gs_quant.instrument import Instrument\n\n if isinstance(value, (list, tuple)):\n return tuple(decode_named_instrument(v) for v in value)\n elif isinstance(value, dict) and 'portfolio_name' in value.keys():\n return decode_named_portfolio(value)\n return Instrument.from_dict(value) if value else None\n\n\ndef decode_named_portfolio(value):\n from gs_quant.markets.portfolio import Portfolio\n\n return Portfolio([decode_named_instrument(v) for v in value['instruments']], name=value['portfolio_name'])\n\n\ndef encode_named_instrument(obj):\n from gs_quant.markets.portfolio import Portfolio\n\n if isinstance(obj, (list, tuple)):\n return tuple(encode_named_instrument(o) for o in obj)\n elif isinstance(obj, Portfolio):\n return encode_named_portfolio(obj)\n return obj.as_dict()\n\n\ndef encode_named_portfolio(obj):\n return {'portfolio_name': obj.name, 'instruments': tuple(encode_named_instrument(o) for o in obj.all_instruments)}\n\n\ndef encode_pandas_series(obj):\n series_dict = pd.Series.to_dict(obj)\n if isinstance(next(iter(series_dict)), (dt.date, dt.datetime)):\n series_dict = {k.isoformat(): v for k, v in series_dict.items()}\n return series_dict\n\n\ndef decode_pandas_series(value: dict):\n dated_dict = {decode_iso_date_or_datetime(k): v for k, v in value.items()}\n return pd.Series(dated_dict)\n\n\ndef decode_quote_report(value: Optional[dict]):\n from gs_quant.quote_reports.core import quote_report_from_dict\n\n return quote_report_from_dict(value) if value else None\n\n\ndef decode_quote_reports(value: Optional[Iterable[Dict]]):\n from gs_quant.quote_reports.core import quote_reports_from_dicts\n\n return quote_reports_from_dicts(value) if value else None\n\n\ndef decode_custom_comment(value: Optional[dict]):\n from gs_quant.quote_reports.core import custom_comment_from_dict\n\n return custom_comment_from_dict(value) if value else None\n\n\ndef decode_custom_comments(value: Optional[Iterable[Dict]]):\n from gs_quant.quote_reports.core import custom_comments_from_dicts\n\n return custom_comments_from_dicts(value) if value else None\n\n\ndef decode_hedge_type(value: Optional[dict]):\n from gs_quant.quote_reports.core import hedge_type_from_dict\n\n return hedge_type_from_dict(value) if value else None\n\n\ndef decode_hedge_types(value: Optional[Iterable[Dict]]):\n from gs_quant.quote_reports.core import hedge_type_from_dicts\n\n return hedge_type_from_dicts(value) if value else None\n\n\ndef encode_dictable(o):\n return o if o is None else o.to_dict()\n\n\ndef encode_named_dictable(o):\n d = encode_dictable(o)\n if d is not None:\n d['type'] = type(o).__name__\n return d\n\n\ndef _get_dc_type(cls, name_field: str, allow_missing: bool):\n type_field = list(filter(lambda f: f.name in (name_field, f'{name_field}_'), fields(cls)))\n if len(type_field) == 0:\n if allow_missing:\n return None\n raise ValueError(f'Class {cls} has no \"{name_field}\" property')\n def_value = type_field[0].default\n if def_value == MISSING or def_value is None:\n raise ValueError('No default value for \"class_type\" field on class')\n return def_value\n\n\ndef _value_decoder(type_to_cls_map, explicit_cls=None, str_mapper=None):\n def decode_value(value):\n if value is None or 'null' == value:\n return None\n if isinstance(value, (list, tuple)):\n return tuple(decode_value(v) for v in value)\n if isinstance(value, str):\n return str_mapper(value) if str_mapper is not None else value\n if isinstance(value, (float, int, dt.date)):\n return value\n if not isinstance(value, dict):\n raise TypeError(f'Cannot decode object of type: {type(value)}')\n if explicit_cls is not None:\n return explicit_cls.from_dict(value)\n else:\n if 'class_type' not in value:\n raise ValueError(f'Object has no \"class_type\" property {value}')\n obj_type = value['class_type']\n if obj_type not in type_to_cls_map:\n raise ValueError(f'No class mapping for object type: \"{obj_type}\"')\n try:\n return type_to_cls_map[obj_type].from_dict(value)\n except Exception as e:\n raise ValueError(f'Failed to de-serialise {type_to_cls_map[obj_type]} from value {value}') from e\n\n return decode_value\n\n\ndef dc_decode(*classes, name_field='class_type', allow_missing=False):\n mappings = ((_get_dc_type(cls, name_field, allow_missing), cls) for cls in classes)\n type_to_cls_map = dict((k, v) for k, v in mappings if k is not None)\n return _value_decoder(type_to_cls_map, None)\n" }, { "path": "gs_quant/json_convertors_common.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\n\nfrom enum import Enum\nfrom typing import Tuple, Dict, Optional, Union\n\nfrom gs_quant.base import RiskMeasureParameter\nfrom gs_quant.common import RiskMeasure, ParameterisedRiskMeasure\n\nfrom gs_quant import common\nfrom gs_quant import risk\n\n\ndef gsq_rm_for_name(name: str) -> Optional[RiskMeasure]:\n if name is None or name not in dir(risk):\n return None\n return getattr(risk, name)\n\n\ndef encode_risk_measure(rm: RiskMeasure) -> Dict:\n result = rm.as_dict(as_camel_case=True)\n if rm.parameters is not None:\n result['parameters'] = rm.parameters.as_dict(as_camel_case=True)\n return result\n\n\ndef encode_risk_measure_tuple(blob: Tuple[RiskMeasure, ...]) -> Tuple[Dict, ...]:\n return tuple(encode_risk_measure(rm) for rm in blob)\n\n\ndef _decode_param(data: dict) -> Optional[RiskMeasureParameter]:\n params = data.get('parameters', None)\n if params is not None and isinstance(params, dict) and 'parameterType' in params:\n cls_name = params['parameterType'] + 'Parameter'\n parameter_cls = getattr(common, cls_name)\n parameter = parameter_cls(**{k: v for k, v in params.items() if k != 'parameterType'})\n return parameter\n return None\n\n\ndef _decode_gsq_risk_measure(data: dict) -> Optional[RiskMeasure]:\n def _enum_or_str_equal(a: Optional[Union[Enum, str]], b: Optional[Union[Enum, str]]):\n return (a is None and b is None) or (str(a).lower() == str(b).lower())\n\n name = data.get('name', None)\n gsq_rm = gsq_rm_for_name(name)\n if gsq_rm is None:\n return None\n asset_class = data.get('assetClass', None)\n measure_type = data.get('measureType', None)\n if _enum_or_str_equal(asset_class, gsq_rm.asset_class) and _enum_or_str_equal(measure_type, gsq_rm.measure_type):\n result = copy.copy(gsq_rm)\n param = _decode_param(data)\n if param:\n result.parameters = param\n return result\n return None\n\n\ndef decode_risk_measure(data: Dict) -> RiskMeasure:\n result = _decode_gsq_risk_measure(data)\n if result is not None:\n return result\n if 'parameters' in data:\n result = ParameterisedRiskMeasure.from_dict(data)\n result.parameters = _decode_param(data)\n else:\n result = RiskMeasure.from_dict(data)\n return result\n\n\ndef decode_risk_measure_tuple(blob: Tuple[Dict, ...]) -> Tuple[RiskMeasure, ...]:\n return tuple(decode_risk_measure(s) for s in blob) if isinstance(blob, (tuple, list)) else None\n" }, { "path": "gs_quant/json_encoder.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom enum import Enum\nimport json\nimport pandas as pd\n\nfrom gs_quant.base import Base, Market\nfrom gs_quant.json_convertors import encode_date_or_str, encode_datetime\n\n\ndef encode_default(o):\n if isinstance(o, dt.datetime):\n return encode_datetime(o)\n if isinstance(o, dt.date):\n return encode_date_or_str(o)\n elif isinstance(o, dt.time):\n return o.isoformat(timespec='milliseconds')\n elif isinstance(o, Enum):\n return o.value\n elif isinstance(o, (Base, Market)):\n return o.to_dict()\n elif isinstance(o, pd.DataFrame):\n return o.to_json()\n\n\nclass JSONEncoder(json.JSONEncoder):\n def default(self, o):\n ret = encode_default(o)\n return super().default(o) if ret is None else ret\n" }, { "path": "gs_quant/markets/__init__.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\nfrom .core import PricingCache, PricingContext, PositionContext\nfrom .historical import HistoricalPricingContext, BackToTheFuturePricingContext\nfrom .markets import *\n" }, { "path": "gs_quant/markets/baskets.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport json\nimport logging\nfrom copy import deepcopy\nfrom enum import Enum\nfrom functools import wraps\nfrom typing import List, Optional, Union, Tuple\n\nimport pandas as pd\nfrom pydash import has, get, set_\n\nfrom gs_quant.api.gs.assets import GsAsset, GsAssetApi\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.api.gs.indices import GsIndexApi\nfrom gs_quant.api.gs.reports import GsReportApi\nfrom gs_quant.api.gs.users import GsUsersApi\nfrom gs_quant.common import (\n DateLimit,\n PositionType,\n EqBasketBacktestParameters,\n EqBasketHistoryMethodology,\n BloombergPublishParameters,\n CashReinvestmentTreatment,\n CashReinvestmentTreatmentType,\n EqBasketRebalanceCalendar,\n AssetClass,\n)\nfrom gs_quant.data.fields import DataMeasure\nfrom gs_quant.entities.entitlements import Entitlements as BasketEntitlements\nfrom gs_quant.entities.entity import EntityType, PositionedEntity\nfrom gs_quant.errors import MqError, MqValueError\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.markets.indices_utils import (\n BasketType,\n IndicesDatasets,\n ReturnType,\n WeightingStrategy,\n CorporateActionType,\n)\nfrom gs_quant.markets.position_set import PositionSet\nfrom gs_quant.markets.securities import Asset, AssetType as SecAssetType\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.data import DataQuery\nfrom gs_quant.target.indices import (\n CustomBasketsCreateInputs,\n CustomBasketsPricingParameters,\n PublishParameters,\n IndicesPositionInput,\n IndicesPositionSet,\n CustomBasketsBackcastInputs,\n CustomBasketsRebalanceAction,\n CustomBasketRiskParams,\n IndicesCurrency,\n CustomBasketsEditInputs,\n CustomBasketsResponse,\n CustomBasketsRebalanceInputs,\n)\nfrom gs_quant.target.reports import Report, ReportStatus\n\n_logger = logging.getLogger(__name__)\n\n\nclass ErrorMessage(Enum):\n NON_ADMIN = 'You are not permitted to perform this action on this basket. Please make sure \\\n the basket owner has entitled your application properly if you believe this is a mistake'\n NON_INTERNAL = 'You are not permitted to access this basket setting.'\n RESTRICTED_ATTRIBUTE = 'You are not permitted to access this basket setting'\n UNINITIALIZED = 'Basket class object must be initialized using one of an existing basket\\'s \\\n identifiers to perform this action'\n UNMODIFIABLE = 'This property can not be modified since the basket has already been created'\n\n\ndef _validate(*error_msgs):\n \"\"\"Confirms initialization is complete and checks for errors before calling function\"\"\"\n\n def _outer(fn):\n @wraps(fn)\n def _inner(self, *args, **kwargs):\n if has(self, '_Basket__error_messages') and self._Basket__error_messages is not None:\n if len(self._Basket__error_messages) < 1:\n self._Basket__finish_initialization()\n for error_msg in error_msgs:\n if error_msg in self._Basket__error_messages:\n raise MqError(error_msg.value)\n return fn(self, *args, **kwargs)\n\n return _inner\n\n return _outer\n\n\nclass Basket(Asset, PositionedEntity):\n \"\"\"\n Basket which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets\n \"\"\"\n\n def __init__(self, gs_asset: GsAsset = None, **kwargs):\n self.__error_messages = None\n if gs_asset:\n if gs_asset.type.value not in BasketType.to_list():\n raise MqValueError(f'Failed to initialize. Asset {gs_asset.id} is not a basket')\n self.__id = gs_asset.id\n self.__initial_entitlements = gs_asset.entitlements\n asset_entity: dict = json.loads(json.dumps(gs_asset.as_dict(), cls=JSONEncoder))\n Asset.__init__(\n self,\n gs_asset.id,\n gs_asset.asset_class,\n gs_asset.name,\n exchange=gs_asset.exchange,\n currency=gs_asset.currency,\n entity=asset_entity,\n )\n PositionedEntity.__init__(self, gs_asset.id, EntityType.ASSET)\n self.__populate_current_attributes_for_existing_basket(gs_asset)\n else:\n self.__populate_default_attributes_for_new_basket(**kwargs)\n self.__error_messages = set([])\n if get(kwargs, '_finish_init', False):\n self.__finish_initialization()\n\n @classmethod\n def get(cls, identifier: str, **kwargs):\n \"\"\"\n Fetch an existing basket\n\n :param identifier: Any common identifier for a basket (ric, ticker, etc.)\n :return: Basket object\n\n **Usage**\n\n Get existing basket instance\n\n **Examples**\n\n Get basket details:\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n \"\"\"\n gs_asset = cls.__get_gs_asset(identifier)\n return cls(gs_asset=gs_asset, _finish_init=get(kwargs, '_finish_init', True))\n\n @_validate()\n def get_details(self) -> pd.DataFrame:\n \"\"\"\n Get basket details\n\n :return: dataframe containing current basket properties\n\n **Usage**\n\n Get basket's current state\n\n **Examples**\n\n Get basket details:\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_details()\n \"\"\"\n props = list(\n CustomBasketsPricingParameters.properties().union(\n PublishParameters.properties(), CustomBasketsCreateInputs.properties()\n )\n )\n props = sorted(props)\n details = [{'name': k, 'value': get(self, k)} for k in props if has(self, k)]\n return pd.DataFrame(details)\n\n def create(self) -> dict:\n \"\"\"\n Create a new custom basket in Marquee\n\n :return: dictionary containing asset id and report id\n\n **Usage**\n\n Create a new custom basket in Marquee\n\n **See also**\n\n :func:`get_details` :func:`poll_status` :func:`update`\n\n \"\"\"\n inputs, pricing, publish = {}, {}, {}\n for prop in CustomBasketsCreateInputs.properties():\n set_(inputs, prop, get(self, prop))\n for prop in CustomBasketsPricingParameters.properties():\n set_(pricing, prop, get(self, prop))\n for prop in PublishParameters.properties():\n set_(publish, prop, get(self, prop))\n set_(inputs, 'position_set', self.position_set.to_target(common=False))\n set_(inputs, 'pricing_parameters', CustomBasketsPricingParameters(**pricing))\n set_(inputs, 'publish_parameters', PublishParameters(**publish))\n create_inputs = CustomBasketsCreateInputs(**inputs)\n\n response = GsIndexApi.create(create_inputs)\n gs_asset = GsAssetApi.get_asset(response.asset_id)\n self.__latest_create_report = GsReportApi.get_report(response.report_id)\n self.__init__(gs_asset=gs_asset, _finish_init=True)\n return response.as_dict()\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def clone(self):\n \"\"\"\n Retrieve a clone of an existing basket\n\n :return: New basket instance with position set identical to current basket\n\n **Usage**\n\n Clone an existing basket's position set in a new basket instance prior to creation\n\n **Examples**\n\n Clone current basket:\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> parent_basket = Basket.get(\"GSMBXXXX\")\n >>> clone = parent_basket.clone()\n\n **See also**\n\n :func:`create`\n \"\"\"\n position_set = deepcopy(self.position_set)\n return Basket(position_set=position_set, clone_parent_id=self.id, parent_basket=self.ticker)\n\n @_validate(ErrorMessage.UNINITIALIZED, ErrorMessage.NON_ADMIN)\n def update(self) -> dict:\n \"\"\"\n Update your custom basket\n\n :return: dictionary containing asset id and report id\n\n **Usage**\n\n Make updates to your basket's metadata, pricing options, publishing options, or composition\n\n **See also**\n\n :func:`get_details` :func:`poll_status` :func:`create`\n\n \"\"\"\n edit_inputs, rebal_inputs = self.__get_updates()\n response = None\n\n init_entitlements = BasketEntitlements.from_target(self.__initial_entitlements)\n if not init_entitlements == self.__entitlements:\n response = GsAssetApi.update_asset_entitlements(\n self.id, self.__entitlements.to_target(include_all_tokens=True)\n )\n if edit_inputs is None and rebal_inputs is None:\n if response:\n return response\n raise MqValueError('Update failed: Nothing on the basket was changed')\n elif edit_inputs is not None and rebal_inputs is None:\n response = GsIndexApi.edit(self.id, edit_inputs)\n elif rebal_inputs is not None and edit_inputs is None:\n response = GsIndexApi.rebalance(self.id, rebal_inputs)\n else:\n response = self.__edit_and_rebalance(edit_inputs, rebal_inputs)\n gs_asset = GsAssetApi.get_asset(self.id)\n self.__latest_create_report = GsReportApi.get_report(response.report_id)\n self.__init__(gs_asset=gs_asset, _finish_init=True)\n return response.as_dict()\n\n @_validate(ErrorMessage.UNINITIALIZED, ErrorMessage.NON_ADMIN)\n def upload_position_history(self, position_sets: List[PositionSet]) -> dict:\n \"\"\"\n Upload basket composition history\n\n :param position_sets: list of dated position sets\n :return: dictionary containing asset id and report id\n\n **Usage**\n\n Upload your basket's historical composition after it's been created\n\n **Examples**\n\n Upload composition history from a list of identifiers:\n\n >>> from datetime import date\n >>> from gs_quant.markets.baskets import Basket\n >>> from gs_quant.markets.position_set import PositionSet\n >>>\n >>> first_position_set = PositionSet.from_list(['BBID1', 'BBID2'], date(2020, 1, 1))\n >>> second_position_set = PositionSet.from_list(['BBID1','BBID2', 'BBID3'], date(2021, 1, 1))\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.upload_position_history([first_position_set, second_position_set])\n\n **See also**\n\n :class:`PositionSet`\n \"\"\"\n if self.default_backcast:\n raise MqValueError('Unable to upload position history: option must be set during basket creation')\n historical_position_sets = []\n for position_set in position_sets:\n self.__validate_position_set(position_set)\n positions = [IndicesPositionInput(p.asset_id, p.weight) for p in position_set.positions]\n historical_position_sets.append(IndicesPositionSet(tuple(positions), position_set.date))\n response = GsIndexApi.backcast(self.id, CustomBasketsBackcastInputs(tuple(historical_position_sets)))\n return response.as_dict()\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def poll_status(self, timeout: int = 600, step: int = 30) -> ReportStatus:\n \"\"\"\n Polls the status of the basket's most recent create/edit/rebalance report\n\n :param timeout: how many seconds you'd like to poll for (default is 600 sec)\n :param step: how frequently you'd like to check the report's status (default is every 30 sec)\n :return: Report status\n\n **Usage**\n\n Poll the status of a newly created or updated basket\n\n **Examples**\n\n Poll most recent create/update report status:\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.poll_status(timeout=120, step=20)\n\n **See also**\n\n :func:`create` :func:`update`\n \"\"\"\n report = get(self, '__latest_create_report', self.__get_latest_create_report())\n report_id = get(report, 'id')\n return self.poll_report(report_id, timeout, step)\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_latest_rebalance_data(self) -> dict:\n \"\"\"\n Retrieve the most recent rebalance data for a basket\n\n **Usage**\n\n Retrieve the most recent rebalance data for a basket\n\n **Examples**\n\n Retrieve the most recent rebalance data for a basket\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_latest_rebalance_data()\n\n **See also**\n\n :func:`get_latest_rebalance_date`\n \"\"\"\n return GsIndexApi.last_rebalance_data(self.id)\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_latest_rebalance_date(self) -> dt.date:\n \"\"\"\n Retrieve the most recent rebalance date for a basket\n\n :return: dictionary\n\n **Usage**\n\n Retrieve the most recent rebalance date for a basket\n\n **Examples**\n\n Retrieve the most recent rebalance date for a basket\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_latest_rebalance_date()\n\n **See also**\n\n :func:`get_latest_rebalance_data`\n \"\"\"\n last_rebalance = GsIndexApi.last_rebalance_data(self.id)\n return dt.datetime.strptime(last_rebalance['date'], '%Y-%m-%d').date()\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_rebalance_approval_status(self) -> str:\n \"\"\"\n Retrieve the most recent rebalance submission's approval status\n\n :return: current approval status\n\n **Usage**\n\n Retrieve the most recent rebalance submission's approval status\n\n **Examples**\n\n Retrieve the most recent rebalance submission's approval status\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_rebalance_approval_status()\n\n **See also**\n\n :func:`cancel_rebalance` :func:`poll_report`\n \"\"\"\n last_approval = GsIndexApi.last_rebalance_approval(self.id)\n return get(last_approval, 'status')\n\n @_validate(ErrorMessage.NON_ADMIN)\n def cancel_rebalance(self) -> dict:\n \"\"\"\n Cancel the most recent rebalance submission\n\n **Usage**\n\n Cancel the basket's most recent rebalance submission if it has not yet been approved\n\n **Examples**\n\n Cancel the basket's most recent rebalance submission\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.cancel_rebalance()\n\n **See also**\n\n :func:`get_rebalance_approval_status` :func:`update`\n \"\"\"\n return GsIndexApi.cancel_rebalance(self.id, CustomBasketsRebalanceAction.default_instance())\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_corporate_actions(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today() + dt.timedelta(days=10),\n ca_type: List[CorporateActionType] = CorporateActionType.to_list(),\n ) -> pd.DataFrame:\n \"\"\"\n Retrieve corporate actions for a basket across a date range\n\n :param start: start date (default minimum date value)\n :param end: end date (default is maximum date value)\n :param ca_type: list of corporate action types (default is all)\n :return: dataframe with corporate actions information\n\n **Usage**\n\n Retrieve corporate actions for a basket across a date range\n\n **Examples**\n\n Retrieve historical acquisition corporate actions for a basket\n\n >>> from gs_quant.markets.baskets import Basket\n >>> from gs_quant.markets.indices_utils import CorporateActionType\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_corporate_actions(ca_type=[CorporateActionType.ACQUISITION])\n\n **See also**\n\n :func:`get_fundamentals`\n \"\"\"\n where = dict(assetId=self.id, corporateActionType=ca_type)\n query = DataQuery(where=where, start_date=start, end_date=end)\n response = GsDataApi.query_data(query=query, dataset_id=IndicesDatasets.CORPORATE_ACTIONS.value)\n return pd.DataFrame(response)\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_fundamentals(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n period: DataMeasure = DataMeasure.ONE_YEAR.value,\n direction: DataMeasure = DataMeasure.FORWARD.value,\n metrics: List[DataMeasure] = DataMeasure.list_fundamentals(),\n ) -> pd.DataFrame:\n \"\"\"\n Retrieve fundamentals data for a basket across a date range\n\n :param start: start date (default minimum date value)\n :param end: end date (default is today)\n :param period: period for the relevant metric (default is 1y)\n :param direction: direction of the outlook period (default is forward)\n :param metrics: list of fundamentals metrics (default is all)\n :return: dataframe with fundamentals information\n\n **Usage**\n\n Retrieve fundamentals data for a basket across a date range\n\n **Examples**\n\n Retrieve historical dividend yield data for a basket\n\n >>> from gs_quant.data.fields import DataMeasure\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_fundamentals(metrics=[DataMeasure.DIVIDEND_YIELD])\n\n **See also**\n\n :func:`get_corporate_actions`\n \"\"\"\n where = dict(assetId=self.id, period=period, periodDirection=direction, metric=metrics)\n query = DataQuery(where=where, start_date=start, end_date=end)\n response = GsDataApi.query_data(query=query, dataset_id=IndicesDatasets.BASKET_FUNDAMENTALS.value)\n return pd.DataFrame(response)\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_live_date(self) -> Optional[dt.date]:\n \"\"\"\n Retrieve basket's live date\n\n **Usage**\n\n Retrieve basket's live date\n\n **Examples**\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_live_date()\n \"\"\"\n return self.__live_date\n\n def get_type(self) -> Optional[SecAssetType]:\n \"\"\"\n Retrieve basket type\n\n **Usage**\n\n Retrieve basket type\n\n **Examples**\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_type()\n \"\"\"\n if self.__gs_asset_type:\n return SecAssetType[self.__gs_asset_type.name.upper()]\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_latest_position_set(\n self, position_type: PositionType = PositionType.CLOSE, source: str = \"Basket\"\n ) -> PositionSet:\n if self.positioned_entity_type == EntityType.ASSET:\n response = GsAssetApi.get_latest_positions(self.id, position_type)\n return PositionSet.from_target(response, source=source)\n raise NotImplementedError\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_position_set_for_date(\n self, date: dt.date, position_type: PositionType = PositionType.CLOSE, source: str = \"Basket\"\n ) -> PositionSet:\n if self.positioned_entity_type == EntityType.ASSET:\n response = GsAssetApi.get_asset_positions_for_date(self.id, date, position_type)\n if len(response) == 0:\n _logger.info(\"No positions available for {}\".format(date))\n return PositionSet([], date=date)\n return PositionSet.from_target(response[0], source=source)\n raise NotImplementedError\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_position_sets(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n position_type: PositionType = PositionType.CLOSE,\n source: str = \"Basket\",\n ) -> List[PositionSet]:\n if self.positioned_entity_type == EntityType.ASSET:\n response = GsAssetApi.get_asset_positions_for_dates(self.id, start, end, position_type)\n if len(response) == 0:\n _logger.info(\"No positions available in the date range {} - {}\".format(start, end))\n return []\n return [PositionSet.from_target(position_set, source=source) for position_set in response]\n raise NotImplementedError\n\n @_validate(ErrorMessage.UNINITIALIZED)\n def get_url(self) -> str:\n \"\"\"\n Retrieve url to basket's product page in Marquee\n\n **Usage**\n\n Retrieve url to basket's product page in Marquee\n\n **Examples**\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.get_url()\n \"\"\"\n env = '-dev' if 'dev' in get(GsSession, 'current.domain', '') else ''\n env = '-qa' if 'qa' in get(GsSession, 'current.domain', '') else env\n return f'https://marquee{env}.gs.com/s/products/{self.id}/summary'\n\n @_validate(ErrorMessage.UNINITIALIZED, ErrorMessage.NON_ADMIN)\n def add_factor_risk_report(self, risk_model_id: str, fx_hedged: bool):\n \"\"\"\n Create and schedule a new factor risk report for your basket\n\n :param risk_model_id: risk model identifier\n :param fx_hedged: Assume basket is FX hedged\n\n **Usage**\n\n Create and schedule a new factor risk report for your basket\n\n **Examples**\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.add_factor_risk_report('AXUS4M', True)\n\n **See also**\n\n :func:`delete_factor_risk_report`\n \"\"\"\n payload = CustomBasketRiskParams(risk_model=risk_model_id, fx_hedged=fx_hedged)\n return GsIndexApi.update_risk_reports(payload)\n\n @_validate(ErrorMessage.UNINITIALIZED, ErrorMessage.NON_ADMIN)\n def delete_factor_risk_report(self, risk_model_id: str):\n \"\"\"\n Delete an existing factor risk report for your basket\n\n :param risk_model_id: risk model identifier for the report you'd like to delete\n\n **Usage**\n\n Delete an existing factor risk report for your basket\n\n **Examples**\n\n >>> from gs_quant.markets.baskets import Basket\n >>>\n >>> basket = Basket.get(\"GSMBXXXX\")\n >>> basket.delete_factor_risk_report('AXUS4M')\n\n **See also**\n\n :func:`add_factor_risk_report`\n \"\"\"\n payload = CustomBasketRiskParams(risk_model=risk_model_id, delete=True)\n return GsIndexApi.update_risk_reports(payload)\n\n @property\n def allow_ca_restricted_assets(self) -> Optional[bool]:\n \"\"\"Allow basket to have constituents that will not be corporate action adjusted in the future\"\"\"\n return self.__allow_ca_restricted_assets\n\n @allow_ca_restricted_assets.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def allow_ca_restricted_assets(self, value: bool):\n self.__allow_ca_restricted_assets = value\n\n @property\n def allow_limited_access_assets(self) -> Optional[bool]:\n \"\"\"Allow basket to have constituents that GS has limited access to\"\"\"\n return self.__allow_limited_access_assets\n\n @allow_limited_access_assets.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def allow_limited_access_assets(self, value: bool):\n self.__allow_limited_access_assets = value\n\n @property\n def asset_class(self) -> Optional[AssetClass]:\n \"\"\"Asset class of the basket\"\"\"\n return self.__asset_class\n\n @asset_class.setter\n @_validate(ErrorMessage.UNMODIFIABLE)\n def asset_class(self, value: AssetClass):\n self.__asset_class = value\n\n @property\n def benchmark(self) -> Optional[str]:\n \"\"\"Benchmark for a basket\"\"\"\n return self.__benchmark\n\n @benchmark.setter\n @_validate(ErrorMessage.NON_ADMIN, ErrorMessage.NON_INTERNAL)\n def benchmark(self, value: str):\n self.__benchmark = value\n\n @property\n def backtest_parameters(self) -> Optional[EqBasketBacktestParameters]:\n \"\"\"Backtest parameters for a basket\"\"\"\n return self.__backtest_parameters\n\n @backtest_parameters.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def backtest_parameters(self, value: EqBasketBacktestParameters):\n if value is not None:\n self.__historical_methodology = EqBasketHistoryMethodology.Backtest\n self.__backtest_parameters = value\n\n @property\n def bloomberg_publish_parameters(self) -> Optional[BloombergPublishParameters]:\n \"\"\"Bloomberg publish overrides for a basket\"\"\"\n return self.__bloomberg_publish_parameters\n\n @bloomberg_publish_parameters.setter\n @_validate(ErrorMessage.NON_ADMIN, ErrorMessage.NON_INTERNAL)\n def bloomberg_publish_parameters(self, value: BloombergPublishParameters):\n self.__bloomberg_publish_parameters = value\n\n @property\n def cash_reinvestment_treatment(self) -> Optional[CashReinvestmentTreatment]:\n \"\"\"Cash reinvestment treatment options for a basket\"\"\"\n return self.__cash_reinvestment_treatment\n\n @cash_reinvestment_treatment.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def cash_reinvestment_treatment(self, value: Union[CashReinvestmentTreatment, CashReinvestmentTreatmentType]):\n if isinstance(value, CashReinvestmentTreatmentType):\n self.__cash_reinvestment_treatment = CashReinvestmentTreatment(\n cash_acquisition_treatment=value, regular_dividend_treatment=value, special_dividend_treatment=value\n )\n else:\n self.__cash_reinvestment_treatment = value\n\n @property\n def clone_parent_id(self) -> Optional[str]:\n \"\"\"Marquee Id of the source basket, in case basket composition is sourced from another marquee basket\"\"\"\n return self.__clone_parent_id\n\n @property\n def currency(self) -> Optional[IndicesCurrency]:\n \"\"\"Denomination of the basket\"\"\"\n return self.__currency\n\n @currency.setter\n @_validate(ErrorMessage.UNMODIFIABLE)\n def currency(self, value: IndicesCurrency):\n self.__currency = value\n\n @property\n def default_backcast(self) -> Optional[bool]:\n \"\"\"If basket should be backcasted using the current composition\"\"\"\n return self.__default_backcast\n\n @default_backcast.setter\n @_validate(ErrorMessage.UNMODIFIABLE)\n def default_backcast(self, value: bool):\n if not value:\n self.__historical_methodology = EqBasketHistoryMethodology.Custom\n self.__default_backcast = value\n\n @property\n def description(self) -> Optional[str]:\n \"\"\"Free text description of basket\"\"\"\n return self.__description\n\n @description.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def description(self, value: str):\n self.__description = value\n\n @property\n @_validate()\n def divisor(self) -> Optional[float]:\n \"\"\"Divisor to be applied to the overall position set\"\"\"\n return self.__divisor\n\n @divisor.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def divisor(self, value: float):\n self.__initial_price = None\n self.__divisor = value\n\n @property\n @_validate()\n def entitlements(self) -> Optional[BasketEntitlements]:\n \"\"\"Basket entitlements\"\"\"\n return self.__entitlements\n\n @entitlements.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def entitlements(self, value: BasketEntitlements):\n self.__entitlements = value\n\n @property\n def flagship(self) -> Optional[bool]:\n \"\"\"If the basket is flagship (internal only)\"\"\"\n return self.__flagship\n\n @flagship.setter\n @_validate(ErrorMessage.NON_INTERNAL)\n def flagship(self, value: bool):\n self.__flagship = value\n\n @property\n def hedge_id(self) -> Optional[str]:\n \"\"\"Marquee Id of the source hedge, in case current basket composition is sourced from marquee hedge\"\"\"\n return self.__hedge_id\n\n @property\n def historical_methodology(self) -> Optional[EqBasketHistoryMethodology]:\n \"\"\"Historical methodology for a basket\"\"\"\n return self.__historical_methodology\n\n @historical_methodology.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def historical_methodology(self, value: EqBasketHistoryMethodology):\n self.__default_backcast = value != EqBasketHistoryMethodology.Custom\n self.__historical_methodology = value\n\n @property\n def include_price_history(self) -> Optional[bool]:\n \"\"\"Include full price history when publishing to Bloomberg\"\"\"\n return self.__include_price_history\n\n @include_price_history.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def include_price_history(self, value: bool):\n self.__include_price_history = value\n\n @property\n @_validate()\n def initial_price(self) -> Optional[float]:\n \"\"\"Initial price the basket it should start ticking at\"\"\"\n return self.__initial_price\n\n @initial_price.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def initial_price(self, value: float):\n self.__divisor = None\n self.__initial_price = value\n\n @property\n def name(self) -> Optional[str]:\n \"\"\"Display name of the basket (must be <= 24 characters)\"\"\"\n return self.__name\n\n @name.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def name(self, value: str):\n if len(value) > 24:\n _logger.info(f'Basket name of {len(value)} characters is too long (must be <= 24 characters).')\n self.__name = value\n\n @property\n def parent_basket(self) -> Optional[str]:\n \"\"\"Ticker of the source basket, in case current basket composition is sourced from another marquee basket\"\"\"\n if has(self, '__clone_parent_id') and not has(self, '__parent_basket'):\n self.__parent_basket = get(GsAssetApi.get_asset(self.__clone_parent_id), 'id')\n return self.__parent_basket\n\n @parent_basket.setter\n @_validate(ErrorMessage.UNMODIFIABLE)\n def parent_basket(self, value: str):\n self.__clone_parent_id = get(self.__get_gs_asset(value), 'id')\n self.__parent_basket = value\n\n @property\n @_validate()\n def position_set(self) -> Optional[PositionSet]:\n \"\"\"Information of constituents associated with the basket\"\"\"\n return self.__position_set\n\n @position_set.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def position_set(self, value: PositionSet):\n self.__validate_position_set(value)\n self.__position_set = value\n\n @property\n def preferred_risk_model(self) -> Optional[str]:\n \"\"\"Preferred risk model for a basket\"\"\"\n return self.__preferred_risk_model\n\n @preferred_risk_model.setter\n @_validate(ErrorMessage.NON_ADMIN, ErrorMessage.NON_INTERNAL)\n def preferred_risk_model(self, value: str):\n self.__preferred_risk_model = value\n\n @property\n @_validate()\n def publish_to_bloomberg(self) -> Optional[bool]:\n \"\"\"If the basket should be published to Bloomberg\"\"\"\n return self.__publish_to_bloomberg\n\n @publish_to_bloomberg.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def publish_to_bloomberg(self, value: bool):\n self.__publish_to_bloomberg = value\n\n @property\n @_validate()\n def publish_to_factset(self) -> Optional[bool]:\n \"\"\"If the basket should be published to Factset\"\"\"\n return self.__publish_to_factset\n\n @publish_to_factset.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def publish_to_factset(self, value: bool):\n self.__publish_to_factset = value\n\n @property\n def publish_to_reuters(self) -> Optional[bool]:\n \"\"\"If the basket should be published to Reuters\"\"\"\n return self.__publish_to_reuters\n\n @publish_to_reuters.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def publish_to_reuters(self, value: bool):\n self.__publish_to_reuters = value\n\n @property\n def rebalance_calendar(self) -> Optional[EqBasketRebalanceCalendar]:\n \"\"\"Expected rebalance calender/frequency a basket\"\"\"\n return self.__rebalance_calendar\n\n @rebalance_calendar.setter\n @_validate(ErrorMessage.NON_ADMIN, ErrorMessage.NON_INTERNAL)\n def rebalance_calendar(self, value: EqBasketRebalanceCalendar):\n self.__rebalance_calendar = value\n\n @property\n def return_type(self) -> Optional[ReturnType]:\n \"\"\"Determines the index calculation methodology with respect to dividend reinvestment\"\"\"\n return self.__return_type\n\n @return_type.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def return_type(self, value: ReturnType):\n self.__return_type = value\n\n @property\n def reweight(self) -> Optional[bool]:\n \"\"\"To reweight positions if input weights don't add up to 1\"\"\"\n return self.__reweight\n\n @reweight.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def reweight(self, value: bool):\n self.__reweight = value\n\n @property\n def target_notional(self) -> Optional[float]:\n \"\"\"Target notional for the position set\"\"\"\n return self.__target_notional\n\n @target_notional.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def target_notional(self, value: float):\n self.__target_notional = value\n\n @property\n def ticker(self) -> Optional[str]:\n \"\"\"Associated 8-character basket identifier\"\"\"\n return self.__ticker\n\n @ticker.setter\n @_validate(ErrorMessage.UNMODIFIABLE)\n def ticker(self, value: str):\n self.__validate_ticker(value)\n self.__ticker = value\n\n @property\n def weighting_strategy(self) -> Optional[WeightingStrategy]:\n \"\"\"Strategy used to price the position set\"\"\"\n return self.__weighting_strategy\n\n @weighting_strategy.setter\n @_validate(ErrorMessage.NON_ADMIN)\n def weighting_strategy(self, value: WeightingStrategy):\n self.__weighting_strategy = value\n\n @property\n def pricing_date(self) -> Optional[dt.date]:\n \"\"\"Pricing date for a rebalance, default to prior day\"\"\"\n return self.__pricing_date\n\n @pricing_date.setter\n @_validate(ErrorMessage.NON_ADMIN, ErrorMessage.RESTRICTED_ATTRIBUTE)\n def pricing_date(self, value: dt.date):\n self.__pricing_date = value\n\n @property\n def action_date(self) -> Optional[dt.date]:\n \"\"\"Basket action date (user's current date based on timezone)\"\"\"\n return self.__action_date\n\n @action_date.setter\n @_validate(ErrorMessage.NON_ADMIN, ErrorMessage.RESTRICTED_ATTRIBUTE)\n def action_date(self, value: dt.date):\n self.__action_date = value\n\n @property\n def allow_system_approval(self) -> Optional[bool]:\n \"\"\"To allow system to verify whether the basket is in position or not and approve rebalance without\n manual intervention when the basket is not in position. Default is false.\"\"\"\n return self.__allow_system_approval\n\n @allow_system_approval.setter\n @_validate(ErrorMessage.NON_ADMIN, ErrorMessage.RESTRICTED_ATTRIBUTE)\n def allow_system_approval(self, value: bool):\n self.__allow_system_approval = value\n\n def __edit_and_rebalance(\n self, edit_inputs: CustomBasketsEditInputs, rebal_inputs: CustomBasketsRebalanceInputs\n ) -> CustomBasketsResponse:\n \"\"\"If updates require edit and rebalance, rebal will not be scheduled until/if edit report succeeds\"\"\"\n _logger.info(\n 'Current update request requires multiple reports. Your rebalance request will be submitted \\\n once the edit report has completed. Submitting basket edits now...'\n )\n response = GsIndexApi.edit(self.id, edit_inputs)\n report_id = response.report_id\n self.__latest_create_report = GsReportApi.get_report(report_id)\n report_status = self.poll_report(report_id, timeout=600, step=15)\n if report_status != ReportStatus.done:\n raise MqError(\n f'The basket edit report\\'s status is {report_status}. The current rebalance request will \\\n not be submitted in the meantime.'\n )\n _logger.info('Your basket edits have completed successfully. Submitting rebalance request now...')\n response = GsIndexApi.rebalance(self.id, rebal_inputs)\n return response\n\n def __finish_initialization(self):\n \"\"\"Fetches remaining data not retrieved during basket initialization\"\"\"\n if has(self, 'id'):\n if not has(self, '__initial_positions'):\n position_set = GsAssetApi.get_latest_positions(self.id, PositionType.ANY)\n position_set = PositionSet.from_target(position_set)\n self.__position_set = position_set\n self.__divisor = get(position_set, 'divisor')\n self.__initial_positions = set(deepcopy(self.__position_set.positions))\n set_(self.__initial_state, 'divisor', self.__divisor)\n set_(self.__initial_state, 'position_set', self.__position_set)\n if not has(self.__initial_state, 'initial_price'):\n initial_price = GsIndexApi.initial_price(self.id, dt.date.today())\n self.__initial_price = get(initial_price, 'price')\n set_(self.__initial_state, 'initial_price', self.__initial_price)\n if not has(self.__initial_state, 'publish_to_bloomberg'):\n report_params = self.__get_latest_create_report().parameters\n self.__publish_to_bloomberg = report_params.publish_to_bloomberg\n self.__publish_to_factset = report_params.publish_to_factset\n self.__publish_to_reuters = report_params.publish_to_reuters\n self.__backtest_parameters = report_params.backtest_parameters\n self.__bloomberg_publish_parameters = report_params.bloomberg_publish_parameters\n set_(self.__initial_state, 'publish_to_bloomberg', self.__publish_to_bloomberg)\n set_(self.__initial_state, 'publish_to_factset', self.__publish_to_factset)\n set_(self.__initial_state, 'publish_to_reuters', self.__publish_to_reuters)\n set_(self.__initial_state, 'bloomberg_publish_parameters', self.__bloomberg_publish_parameters)\n set_(self.__initial_state, 'backtest_parameters', self.__backtest_parameters)\n if not has(self, '__entitlements'):\n self.__entitlements = BasketEntitlements.from_target(self.__initial_entitlements)\n self.__set_error_messages()\n\n @staticmethod\n def __get_gs_asset(identifier: str) -> GsAsset:\n \"\"\"Resolves basket identifier during initialization\"\"\"\n response = GsAssetApi.resolve_assets(identifier=[identifier], fields=['id'], limit=1)[identifier]\n if len(response) == 0 or get(response, '0.id') is None:\n raise MqValueError(f'Basket could not be found using identifier {identifier}')\n return GsAssetApi.get_asset(get(response, '0.id'))\n\n def __get_latest_create_report(self) -> Report:\n \"\"\"Used to find basket's most recent price/publish info\"\"\"\n report = GsReportApi.get_reports(\n limit=1, position_source_id=self.id, report_type='Basket Create', order_by='>latestExecutionTime'\n )\n return get(report, '0')\n\n def __get_updates(self) -> Tuple[Optional[CustomBasketsEditInputs], Optional[CustomBasketsRebalanceInputs]]:\n \"\"\"Compares initial and current basket state to determine if updates require edit/rebalance\"\"\"\n edit_inputs, eligible_for_edit = {}, False\n rebal_inputs, eligible_for_rebal = {}, False\n pricing, pricing_updated = {}, False\n publish, publish_updated = {}, False\n\n positions = set(self.position_set.positions)\n positions_updated = self.__initial_positions != positions\n\n for prop in CustomBasketsEditInputs.properties():\n if prop not in ['action_date'] and get(self.__initial_state, prop) != get(self, prop):\n eligible_for_edit = True\n set_(edit_inputs, prop, get(self, prop))\n for prop in CustomBasketsRebalanceInputs.properties():\n if prop not in ['position_set', 'allow_system_approval', 'action_date'] and get(\n self.__initial_state, prop\n ) != get(self, prop):\n eligible_for_rebal = True\n if prop != 'position_set':\n set_(rebal_inputs, prop, get(self, prop))\n for prop in CustomBasketsPricingParameters.properties():\n if get(self.__initial_state, prop) != get(self, prop):\n pricing_updated = True\n set_(pricing, prop, get(self, prop))\n for prop in PublishParameters.properties():\n if get(self.__initial_state, prop) != get(self, prop):\n publish_updated = True\n set_(publish, prop, get(self, prop))\n\n # Since edit and rebalance have some shared inputs, we need to decide which action(s) to perform\n # 1. rebalance if pricing/positions have changed or if rebalance specific metadata has been updated\n # 2. edit if edit metadata has changed, or if only publishing options have changed (rebal not required)\n should_rebal = pricing_updated or positions_updated or (eligible_for_rebal and not eligible_for_edit)\n should_edit = (publish_updated and not should_rebal) or eligible_for_edit\n\n # handle nested objects and make sure required/default values are input correctly\n if should_rebal:\n if positions_updated:\n set_(rebal_inputs, 'position_set', self.position_set.to_target(common=False))\n if pricing_updated:\n set_(rebal_inputs, 'pricing_parameters', CustomBasketsPricingParameters(**pricing))\n if publish_updated:\n set_(rebal_inputs, 'publish_parameters', PublishParameters(**publish))\n if should_edit:\n set_(edit_inputs, 'publish_parameters', PublishParameters(**publish))\n\n edit_inputs = CustomBasketsEditInputs(**edit_inputs) if should_edit else None\n rebal_inputs = CustomBasketsRebalanceInputs(**rebal_inputs) if should_rebal else None\n return edit_inputs, rebal_inputs\n\n def __populate_current_attributes_for_existing_basket(self, gs_asset: GsAsset):\n \"\"\"Current basket settings for existing basket\"\"\"\n self.__benchmark = get(gs_asset, 'parameters.benchmark')\n self.__cash_reinvestment_treatment = get(gs_asset, 'parameters.cashReinvestmentTreatment')\n self.__clone_parent_id = get(gs_asset, 'parameters.cloneParentId')\n self.__default_backcast = get(gs_asset, 'parameters.defaultBackcast')\n self.__description = get(gs_asset, 'description')\n self.__flagship = get(gs_asset, 'parameters.flagship')\n self.__gs_asset_type = get(gs_asset, 'type')\n self.__hedge_id = get(gs_asset, 'parameters.hedgeId')\n self.__historical_methodology = get(gs_asset, 'parameters.historicalMethodology')\n self.__include_price_history = False\n self.__live_date = get(gs_asset, 'liveDate')\n self.__preferred_risk_model = get(gs_asset, 'parameters.preferredRiskModel')\n self.__rebalance_calendar = get(gs_asset, 'parameters.rebalanceFrequency')\n self.__return_type = get(gs_asset, 'parameters.indexCalculationType')\n self.__ticker = get(gs_asset, 'xref.ticker')\n\n self.__initial_state = {}\n for prop in CustomBasketsEditInputs.properties().union(\n CustomBasketsRebalanceInputs.properties(),\n CustomBasketsPricingParameters.properties(),\n PublishParameters.properties(),\n ):\n set_(self.__initial_state, prop, get(self, prop))\n\n def __populate_default_attributes_for_new_basket(self, **kwargs):\n \"\"\"Default basket settings prior to creation\"\"\"\n self.__allow_ca_restricted_assets = get(kwargs, 'allow_ca_restricted_assets')\n self.__allow_limited_access_assets = get(kwargs, 'allow_limited_access_assets')\n self.__backtest_parameters = get(kwargs, 'backtest_parameters')\n self.__benchmark = get(kwargs, 'benchmark')\n self.__cash_reinvestment_treatment = get(\n kwargs,\n 'cash_reinvestment_treatment',\n CashReinvestmentTreatment(\n cash_acquisition_treatment=CashReinvestmentTreatmentType.Reinvest_At_Open,\n regular_dividend_treatment=CashReinvestmentTreatmentType.Reinvest_At_Open,\n special_dividend_treatment=CashReinvestmentTreatmentType.Reinvest_At_Open,\n ),\n )\n self.__clone_parent_id = get(kwargs, 'clone_parent_id')\n self.__currency = get(kwargs, 'currency')\n self.__default_backcast = get(kwargs, 'default_backcast', True)\n self.__description = get(kwargs, 'description')\n self.__divisor = get(kwargs, 'divisor')\n self.__hedge_id = get(kwargs, 'hedge_id')\n self.__historical_methodology = get(kwargs, 'historical_methodology', EqBasketHistoryMethodology.Backcast)\n self.__include_price_history = get(kwargs, 'include_price_history', False)\n self.__initial_price = get(kwargs, 'initial_price', 100) if self.__divisor is None else None\n self.__name = get(kwargs, 'name')\n self.__parent_basket = get(kwargs, 'parent_basket')\n if self.__parent_basket is not None and self.__clone_parent_id is None:\n self.__clone_parent_id = get(self.__get_gs_asset(self.__parent_basket), 'id')\n self.__position_set = get(kwargs, 'position_set')\n self.__preferred_risk_model = get(kwargs, 'preferred_risk_model')\n self.__publish_to_bloomberg = get(kwargs, 'publish_to_bloomberg', True)\n self.__publish_to_factset = get(kwargs, 'publish_to_factset', False)\n self.__publish_to_reuters = get(kwargs, 'publish_to_reuters', False)\n self.__rebalance_calendar = get(kwargs, 'rebalance_calendar')\n self.__return_type = get(kwargs, 'return_type')\n self.__target_notional = get(kwargs, 'target_notional', 10000000)\n self.__ticker = get(kwargs, 'ticker')\n\n def __set_error_messages(self):\n \"\"\"Errors to check for based on current user/basket state\"\"\"\n if len(get(self, '__error_messages', [])) > 0:\n return\n errors = []\n user_tokens = get(GsUsersApi.get_current_user_info(), 'tokens', [])\n if 'internal' not in user_tokens:\n errors.append(ErrorMessage.NON_INTERNAL)\n if 'internal' not in user_tokens and 'group:EqBasketRestrictedAttributes' not in user_tokens:\n errors.append(ErrorMessage.RESTRICTED_ATTRIBUTE)\n if not has(self, 'id'):\n errors.append(ErrorMessage.UNINITIALIZED)\n else:\n errors.append(ErrorMessage.UNMODIFIABLE)\n tokens = set(get(self.__initial_entitlements, 'admin', []))\n if not any(t in user_tokens for t in tokens):\n errors.append(ErrorMessage.NON_ADMIN)\n self.__error_messages = set(errors)\n\n @staticmethod\n def __validate_position_set(position_set: PositionSet):\n position_set.resolve()\n neg_pos = [p.identifier for p in position_set.positions if (p.weight or 1) < 0 or (p.quantity or 1) < 0]\n if len(neg_pos):\n raise MqValueError(\n f'Position weights/quantities must be positive. Found negative values for date \\\n {position_set.date}: {neg_pos}'\n )\n if position_set.unresolved_positions is not None and len(position_set.unresolved_positions):\n raise MqValueError(\n f'Error in resolving the following identifiers for date {position_set.date}: \\\n {[p.identifier for p in position_set.unresolved_positions]}'\n )\n\n @staticmethod\n def __validate_ticker(ticker: str):\n \"\"\"Blocks ticker setter if entry is invalid\"\"\"\n if not len(ticker) == 8:\n raise MqValueError('Invalid ticker: must be 8 characters')\n GsIndexApi.validate_ticker(ticker)\n if not ticker[:2] == 'GS':\n _logger.info(\n 'Remember to prefix your ticker with \\'GS\\' if you\\'d like to \\\n publish your basket to Bloomberg'\n )\n" }, { "path": "gs_quant/markets/core.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport asyncio\nimport datetime as dt\nimport logging\nimport sys\nimport weakref\nfrom abc import ABCMeta\nfrom concurrent.futures import ThreadPoolExecutor\nfrom inspect import signature\nfrom itertools import zip_longest, takewhile\nfrom typing import Optional, Union, Type\n\nfrom tqdm import tqdm\n\nfrom gs_quant.base import InstrumentBase, RiskKey, Scenario, get_enum_value\nfrom gs_quant.common import PricingLocation, RiskMeasure, PricingDateAndMarketDataAsOf\nfrom gs_quant.context_base import ContextBaseWithDefault\nfrom gs_quant.datetime.date import business_day_offset, today\nfrom gs_quant.risk import (\n CompositeScenario,\n DataFrameWithInfo,\n ErrorValue,\n FloatWithInfo,\n MarketDataScenario,\n StringWithInfo,\n)\nfrom gs_quant.risk.results import PricingFuture\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.risk import RiskPosition, RiskRequest, RiskRequestParameters\nfrom gs_quant.tracing import Tracer\nfrom .markets import CloseMarket, LiveMarket, Market, close_market_date, OverlayMarket, RelativeMarket\nfrom ..api.risk import GenericRiskApi\n\n_logger = logging.getLogger(__name__)\n\nCacheResult = Union[DataFrameWithInfo, FloatWithInfo, StringWithInfo]\n\n\nclass PricingCache(metaclass=ABCMeta):\n \"\"\"\n Weakref cache for instrument calcs\n \"\"\"\n\n __cache = weakref.WeakKeyDictionary()\n\n @classmethod\n def clear(cls):\n __cache = weakref.WeakKeyDictionary()\n\n @classmethod\n def get(cls, risk_key: RiskKey, instrument: InstrumentBase) -> Optional[CacheResult]:\n return cls.__cache.get(instrument, {}).get(risk_key)\n\n @classmethod\n def put(cls, risk_key: RiskKey, instrument: InstrumentBase, result: CacheResult):\n if not isinstance(result, ErrorValue) and not isinstance(risk_key.market, LiveMarket):\n cls.__cache.setdefault(instrument, {})[risk_key] = result\n\n @classmethod\n def drop(cls, instrument: InstrumentBase):\n if instrument in cls.__cache:\n cls.__cache.pop(instrument)\n\n\nclass PricingContext(ContextBaseWithDefault):\n \"\"\"\n A context for controlling pricing and market data behaviour\n \"\"\"\n\n def __init__(\n self,\n pricing_date: Optional[dt.date] = None,\n market_data_location: Optional[Union[PricingLocation, str]] = None,\n is_async: bool = None,\n is_batch: bool = None,\n use_cache: bool = None,\n visible_to_gs: Optional[bool] = None,\n request_priority: Optional[int] = None,\n csa_term: Optional[str] = None,\n timeout: Optional[int] = None,\n market: Optional[Market] = None,\n show_progress: Optional[bool] = None,\n use_server_cache: Optional[bool] = None,\n market_behaviour: Optional[str] = 'ContraintsBased',\n set_parameters_only: bool = False,\n use_historical_diddles_only: Optional[bool] = None,\n provider: Optional[Type[GenericRiskApi]] = None,\n ):\n \"\"\"\n The methods on this class should not be called directly. Instead, use the methods on the instruments,\n as per the examples\n\n :param pricing_date: the date for pricing calculations. Default is today\n :param market_data_location: the location for sourcing market data ('NYC', 'LDN' or 'HKG' (defaults to LDN)\n :param is_async: if True, return (a future) immediately. If False, block (defaults to False)\n :param is_batch: use for calculations expected to run longer than 3 mins, to avoid timeouts.\n It can be used with is_async=True|False (defaults to False)\n :param use_cache: store results in the pricing cache (defaults to False)\n :param visible_to_gs: are the contents of risk requests visible to GS (defaults to False)\n :param request_priority: the priority of risk requests\n :param csa_term: the csa under which the calculations are made. Default is local ccy ois index\n :param timeout: the timeout for batch operations\n :param market: a Market object\n :param show_progress: add a progress bar (tqdm)\n :param use_server_cache: cache query results on the GS servers\n :param market_behaviour: the behaviour to build the curve for pricing ('ContraintsBased' or 'Calibrated'\n (defaults to ContraintsBased))\n :param set_parameters_only: if true don't stop embedded pricing contexts submitting their jobs.\n :param use_historical_diddles_only: if true only use historical diddles\n :param provider: GenericRiskApi implementation to use for pricing requests\n\n **Examples**\n\n To change the market data location of the default context:\n\n >>> from gs_quant.markets import PricingContext\n >>>\n >>> PricingContext.current = PricingContext(market_data_location='LDN')\n\n For a blocking, synchronous request:\n\n >>> from gs_quant.instrument import IRCap\n >>> cap = IRCap('5y', 'GBP')\n >>>\n >>> with PricingContext():\n >>> price_f = cap.dollar_price()\n >>>\n >>> price = price_f.result()\n\n For an asynchronous request:\n\n >>> with PricingContext(is_async=True):\n >>> price_f = cap.dollar_price()\n >>>\n >>> while not price_f.done:\n >>> ...\n \"\"\"\n super().__init__()\n\n if (\n market\n and market_data_location\n and market.location is not get_enum_value(PricingLocation, market_data_location)\n ):\n raise ValueError('market.location and market_data_location cannot be different')\n\n if not market and pricing_date and pricing_date > dt.date.today() + dt.timedelta(5):\n # We allow a small tolerance to rolling over weekends/holidays\n # We should use a calendar but not everyone has access\n raise ValueError(\n 'The PricingContext does not support a pricing_date in the future. Please use the RollFwd Scenario '\n 'to roll the pricing_date to a future date'\n )\n\n if market:\n market_date = None\n if isinstance(market, OverlayMarket) or isinstance(market, CloseMarket):\n market_date = getattr(market, 'date', None) or getattr(market.market, 'date', None)\n\n if isinstance(market, RelativeMarket):\n market_date = (\n market.market.from_market.date\n if market.market.from_market.date > dt.date.today()\n else market.market.to_market.date\n )\n\n if market_date:\n if market_date > dt.date.today():\n raise ValueError(\n 'The PricingContext does not support a market dated in the future. Please use the RollFwd '\n 'Scenario to roll the pricing_date to a future date'\n )\n\n if not market_data_location:\n if market:\n market_data_location = market.location\n\n market_data_location = get_enum_value(PricingLocation, market_data_location)\n\n self.__pricing_date = pricing_date\n self.__csa_term = csa_term\n self.__market_behaviour = market_behaviour\n self.__is_async = is_async\n self.__is_batch = is_batch\n self.__timeout = timeout\n self.__use_cache = use_cache\n self.__visible_to_gs = visible_to_gs\n self.__request_priority = request_priority\n self.__market_data_location = market_data_location\n self.__market = market\n self.__show_progress = show_progress\n self.__use_server_cache = use_server_cache\n self.__provider = provider\n self.__max_per_batch = None\n self.__max_concurrent = None\n self.__dates_per_batch = None\n self.__use_historical_diddles_only = use_historical_diddles_only\n self.__set_parameters_only = set_parameters_only\n\n self.__pending = {}\n self._group_by_date = True\n\n self.__attrs_on_entry = {}\n\n def __save_attrs_to(self, attr_dict):\n attr_dict['pricing_date'] = self.__pricing_date\n attr_dict['csa_term'] = self.__csa_term\n attr_dict['market_behaviour'] = self.__market_behaviour\n attr_dict['is_batch'] = self.__is_batch\n attr_dict['is_async'] = self.__is_async\n attr_dict['timeout'] = self.__timeout\n attr_dict['use_cache'] = self.__use_cache\n attr_dict['visible_to_gs'] = self.__visible_to_gs\n attr_dict['request_priority'] = self.__request_priority\n attr_dict['market_data_location'] = self.__market_data_location\n attr_dict['market'] = self.__market\n attr_dict['show_progress'] = self.__show_progress\n attr_dict['use_server_cache'] = self.__use_server_cache\n attr_dict['provider'] = self.__provider\n attr_dict['_max_concurrent'] = self.__max_concurrent\n attr_dict['_max_per_batch'] = self.__max_per_batch\n attr_dict['_dates_per_batch'] = self.__dates_per_batch\n attr_dict['use_historical_diddles_only'] = self.__use_historical_diddles_only\n\n def _inherited_val(self, parameter, default=None, from_active=False):\n if from_active:\n # some properties are inherited from the active context\n if self != self.active_context and getattr(self.active_context, parameter) is not None:\n return getattr(self.active_context, parameter)\n if not self.is_entered and (not PricingContext.has_prior or self is not PricingContext.prior):\n # if not yet entered, get property from current (would-be prior) so that getters still display correctly\n if (\n PricingContext.current is not self\n and PricingContext.current\n and getattr(PricingContext.current, parameter) is not None\n ):\n return getattr(PricingContext.current, parameter)\n else:\n # if entered, inherit from the prior\n if (\n PricingContext.has_prior\n and PricingContext.prior is not self\n and getattr(PricingContext.prior, parameter) is not None\n ):\n return getattr(PricingContext.prior, parameter)\n # default if nothing to inherit\n return default\n\n def _on_enter(self):\n self.__save_attrs_to(self.__attrs_on_entry)\n\n self.__market_data_location = self.market_data_location\n self.__pricing_date = self.pricing_date\n self.__market = self.market\n self.__csa_term = self.csa_term\n self.__market_behaviour = self.market_behaviour\n self.__is_async = self.is_async\n self.__is_batch = self.is_batch\n self.__timeout = self.timeout\n self.__use_cache = self.use_cache\n self.__visible_to_gs = self.visible_to_gs\n self.__request_priority = self.request_priority\n self.__show_progress = self.show_progress\n self.__use_server_cache = self.use_server_cache\n self.__provider = self.provider\n self.__max_concurrent = self._max_concurrent\n self.__max_per_batch = self._max_per_batch\n self.__dates_per_batch = self._dates_per_batch\n self.__use_historical_diddles_only = self.use_historical_diddles_only\n\n def __reset_atts(self):\n self.__pricing_date = self.__attrs_on_entry.get('pricing_date')\n self.__csa_term = self.__attrs_on_entry.get('csa_term')\n self.__market_behaviour = self.__attrs_on_entry.get('market_behaviour')\n self.__is_async = self.__attrs_on_entry.get('is_async')\n self.__is_batch = self.__attrs_on_entry.get('is_batch')\n self.__timeout = self.__attrs_on_entry.get('timeout')\n self.__use_cache = self.__attrs_on_entry.get('use_cache')\n self.__visible_to_gs = self.__attrs_on_entry.get('visible_to_gs')\n self.__request_priority = self.__attrs_on_entry.get('request_priority')\n self.__market_data_location = self.__attrs_on_entry.get('market_data_location')\n self.__market = self.__attrs_on_entry.get('market')\n self.__show_progress = self.__attrs_on_entry.get('show_progress')\n self.__use_server_cache = self.__attrs_on_entry.get('use_server_cache')\n self.__provider = self.__attrs_on_entry.get('provider')\n self.__max_concurrent = self.__attrs_on_entry.get('_max_concurrent')\n self.__max_per_batch = self.__attrs_on_entry.get('_max_per_batch')\n self.__dates_per_batch = self.__attrs_on_entry.get('_dates_per_batch')\n\n self.__attrs_on_entry = {}\n\n def _on_exit(self, exc_type, exc_val, exc_tb):\n try:\n if exc_val:\n raise exc_val\n else:\n self.__calc()\n finally:\n self.__reset_atts()\n\n def __calc(self):\n def run_requests(requests_: list, provider_, create_event_loop: bool, pc_attrs: dict, span):\n if create_event_loop:\n asyncio.set_event_loop(asyncio.new_event_loop())\n provider_.populate_pending_futures(\n requests_,\n session,\n self.__pending,\n max_concurrent=pc_attrs['_max_concurrent'],\n progress_bar=progress_bar,\n timeout=pc_attrs['timeout'],\n span=span,\n cache_impl=PricingCache if pc_attrs['use_cache'] else None,\n is_async=pc_attrs['is_async'],\n )\n\n # Group requests optimally\n requests_by_provider = {}\n for key, instrument in self.__pending.keys():\n dates_markets, measures = (\n requests_by_provider.setdefault(key.provider, {})\n .setdefault((key.params, key.scenario), {})\n .setdefault(instrument, (set(), set()))\n )\n dates_markets.add((key.date, key.market))\n measures.add(key.risk_measure)\n\n requests_for_provider = {}\n if requests_by_provider:\n session = GsSession.current\n request_visible_to_gs = session.is_internal() if self.__visible_to_gs is None else self.__visible_to_gs\n\n for provider, by_params_scenario in requests_by_provider.items():\n grouped_requests = {}\n\n for (params, scenario), positions_by_dates_markets_measures in by_params_scenario.items():\n for instrument, (dates_markets, risk_measures) in positions_by_dates_markets_measures.items():\n grouped_requests.setdefault(\n (params, scenario, tuple(sorted(dates_markets)), tuple(sorted(risk_measures))), []\n ).append(instrument)\n\n requests = []\n\n # Restrict to 1,000 instruments and 1 date in a batch, until server side changes are made\n\n for (params, scenario, dates_markets, risk_measures), instruments in grouped_requests.items():\n date_chunk_size = (\n (self._dates_per_batch if self._group_by_date else self._max_per_batch)\n if provider.batch_dates\n else len(dates_markets)\n )\n for insts_chunk in [\n tuple(filter(None, i)) for i in zip_longest(*[iter(instruments)] * self._max_per_batch)\n ]:\n for dates_chunk in [\n tuple(filter(None, i)) for i in zip_longest(*[iter(dates_markets)] * date_chunk_size)\n ]:\n requests.append(\n RiskRequest(\n tuple(\n RiskPosition(\n instrument=i, quantity=i.instrument_quantity, instrument_name=i.name\n )\n for i in insts_chunk\n ),\n risk_measures,\n parameters=params,\n wait_for_results=not self.__is_batch,\n scenario=scenario,\n pricing_and_market_data_as_of=tuple(\n PricingDateAndMarketDataAsOf(pricing_date=d, market=m) for d, m in dates_chunk\n ),\n request_visible_to_gs=request_visible_to_gs,\n use_cache=self.__use_server_cache,\n priority=self.__request_priority,\n )\n )\n\n requests_for_provider[provider] = requests\n\n show_status = self.__show_progress and (\n len(requests_for_provider) > 1 or len(next(iter(requests_for_provider.values()))) > 1\n )\n request_pool = (\n ThreadPoolExecutor(len(requests_for_provider))\n if len(requests_for_provider) > 1 or self.__is_async\n else None\n )\n progress_bar = (\n tqdm(total=len(self.__pending), position=0, maxinterval=1, file=sys.stdout) if show_status else None\n )\n completion_futures = []\n\n # Requests might get dispatched asynchronously and the PricingContext gets cleaned up on exit.\n # We should use a saved state of the object when dispatching async requests, except for self.__pending\n # All attributes are immutable, so a shared dictionary is sufficient. __pending remains shared.\n attrs_for_request = {}\n self.__save_attrs_to(attrs_for_request)\n all_futures_count = len(requests_for_provider)\n span = Tracer.active_span()\n if self.__is_async and span and span.is_recording():\n # if we are in async mode we can't be certain that the `span` here will still be recording by the time\n # the request_pool threads execute. So we create a sub-span to track the dispatch activity.\n # We only finish that span when all futures are complete\n # e.g. would have been a problem if you did\n # with Tracer('blah'), PricingContext(is_async=True):\n # something.calc(risk.SomeRisk)\n sub_scope = Tracer.start_active_span('async-request-dispatch')\n span = sub_scope.span\n\n def handle_fut_res(f):\n nonlocal all_futures_count\n all_futures_count -= 1\n if all_futures_count == 0:\n Tracer.activate_span(span, finish_on_close=True).close()\n\n for provider, requests in requests_for_provider.items():\n if request_pool:\n completion_future = request_pool.submit(\n run_requests, requests, provider, True, attrs_for_request, span\n )\n if self.__is_async:\n completion_future.add_done_callback(handle_fut_res)\n else:\n # We append to this list when not async as these are the ones we wait on before returning\n completion_futures.append(completion_future)\n\n else:\n run_requests(requests, provider, False, attrs_for_request, span)\n\n # Wait on results if not async, so exceptions are surfaced\n if request_pool:\n request_pool.shutdown(False)\n all(f.result() for f in completion_futures)\n\n def __risk_key(self, risk_measure: RiskMeasure, provider: type) -> RiskKey:\n return RiskKey(provider, self.__pricing_date, self.__market, self._parameters, self._scenario, risk_measure)\n\n @property\n def _parameters(self) -> RiskRequestParameters:\n return RiskRequestParameters(\n csa_term=self.__csa_term,\n raw_results=True,\n market_behaviour=self.__market_behaviour,\n use_historical_diddles_only=self.use_historical_diddles_only,\n )\n\n @property\n def _scenario(self) -> Optional[MarketDataScenario]:\n scenarios = Scenario.path\n if not scenarios:\n return None\n\n return MarketDataScenario(\n scenario=scenarios[0] if len(scenarios) == 1 else CompositeScenario(scenarios=tuple(reversed(scenarios)))\n )\n\n @property\n def active_context(self):\n # active context cannot be below self on the stack - this also prevents infinite recursion when inheriting\n path = takewhile(lambda x: x != self, reversed(PricingContext.path))\n return next((c for c in path if c.is_entered and not c.set_parameters_only), self)\n\n @property\n def is_current(self) -> bool:\n return self == PricingContext.current\n\n @property\n def _max_concurrent(self) -> int:\n return self.__max_concurrent if self.__max_concurrent else self._inherited_val('_max_concurrent', default=1000)\n\n @_max_concurrent.setter\n def _max_concurrent(self, value):\n self.__max_concurrent = value\n\n @property\n def _max_per_batch(self) -> int:\n return self.__max_per_batch if self.__max_per_batch else self._inherited_val('_max_per_batch', default=1000)\n\n @_max_per_batch.setter\n def _max_per_batch(self, value):\n self.__max_per_batch = value\n\n @property\n def _dates_per_batch(self) -> int:\n return self.__dates_per_batch if self.__dates_per_batch else self._inherited_val('_dates_per_batch', default=1)\n\n @_dates_per_batch.setter\n def _dates_per_batch(self, value):\n self.__dates_per_batch = value\n\n @property\n def is_async(self) -> bool:\n if self.__is_async is not None:\n return self.__is_async\n return self._inherited_val('is_async', default=False)\n\n @property\n def is_batch(self) -> bool:\n return self.__is_batch if self.__is_batch else self._inherited_val('is_batch', default=False)\n\n @property\n def market(self) -> Market:\n return (\n self.__market\n if self.__market\n else CloseMarket(\n date=close_market_date(self.market_data_location, self.pricing_date, CloseMarket.roll_hr_and_min),\n location=self.market_data_location,\n )\n )\n\n @property\n def market_data_location(self) -> PricingLocation:\n return (\n self.__market_data_location\n if self.__market_data_location\n else self._inherited_val('market_data_location', from_active=True, default=PricingLocation.LDN)\n )\n\n @property\n def csa_term(self) -> str:\n return self.__csa_term if self.__csa_term else self._inherited_val('csa_term')\n\n @property\n def show_progress(self) -> bool:\n return self.__show_progress if self.__show_progress else self._inherited_val('show_progress', default=False)\n\n @property\n def timeout(self) -> int:\n return self.__timeout if self.__timeout else self._inherited_val('timeout')\n\n @property\n def request_priority(self) -> int:\n return self.__request_priority if self.__request_priority else self._inherited_val('request_priority')\n\n @property\n def use_server_cache(self) -> bool:\n return (\n self.__use_server_cache\n if self.__use_server_cache is not None\n else self._inherited_val('use_server_cache', False)\n )\n\n @property\n def provider(self) -> Type[GenericRiskApi]:\n return self.__provider if self.__provider is not None else self._inherited_val('provider', None)\n\n @property\n def market_behaviour(self) -> str:\n return (\n self.__market_behaviour\n if self.__market_behaviour\n else self._inherited_val('market_behaviour', default='ContraintsBased')\n )\n\n @property\n def pricing_date(self) -> dt.date:\n \"\"\"Pricing date\"\"\"\n if self.__pricing_date is not None:\n return self.__pricing_date\n default_pricing_date = business_day_offset(today(self.market_data_location), 0, roll='preceding')\n return self._inherited_val('pricing_date', default=default_pricing_date)\n\n @property\n def use_cache(self) -> bool:\n \"\"\"Cache results\"\"\"\n return self.__use_cache if self.__use_cache else self._inherited_val('use_cache', default=False)\n\n @property\n def visible_to_gs(self) -> Optional[bool]:\n \"\"\"Request contents visible to GS\"\"\"\n return self.__visible_to_gs if self.__visible_to_gs else self._inherited_val('visible_to_gs')\n\n @property\n def set_parameters_only(self) -> bool:\n return self.__set_parameters_only\n\n @property\n def use_historical_diddles_only(self) -> bool:\n if self.__use_historical_diddles_only is not None:\n return self.__use_historical_diddles_only\n return self._inherited_val('use_historical_diddles_only', default=False)\n\n def clone(self, **kwargs):\n clone_kwargs = {k: getattr(self, k, None) for k in signature(self.__init__).parameters.keys()}\n clone_kwargs.update(kwargs)\n return self.__class__(**clone_kwargs)\n\n def _calc(self, instrument: InstrumentBase, risk_key: RiskKey) -> PricingFuture:\n pending = self.active_context.__pending\n\n from gs_quant.instrument import DummyInstrument\n\n if isinstance(instrument, DummyInstrument):\n return PricingFuture(StringWithInfo(value=instrument.dummy_result, risk_key=risk_key))\n\n future = pending.get((risk_key, instrument))\n\n if future is None:\n future = PricingFuture()\n cached_result = PricingCache.get(risk_key, instrument) if self.use_cache else None\n\n if cached_result is not None:\n future.set_result(cached_result)\n else:\n pending[(risk_key, instrument)] = future\n\n return future\n\n def calc(self, instrument: InstrumentBase, risk_measure: RiskMeasure) -> PricingFuture:\n \"\"\"\n Calculate the risk measure for the instrument. Do not use directly, use via instruments\n\n :param instrument: The instrument\n :param risk_measure: The measure we wish to calculate\n :return: A PricingFuture whose result will be the calculation result\n\n **Examples**\n\n >>> from gs_quant.instrument import IRSwap\n >>> from gs_quant.risk import IRDelta\n >>>\n >>> swap = IRSwap('Pay', '10y', 'USD', fixed_rate=0.01)\n >>> delta = swap.calc(IRDelta)\n \"\"\"\n provider = instrument.provider if self.provider is None else self.provider\n return self._calc(instrument, self.__risk_key(risk_measure, provider))\n\n\nclass PositionContext(ContextBaseWithDefault):\n \"\"\"\n A context for controlling portfolio position behaviour\n \"\"\"\n\n def __init__(self, position_date: Optional[dt.date] = None):\n \"\"\"\n The methods on this class should not be called directly. Instead, use the methods on the portfolios,\n as per the examples\n\n :param position_date: the date for pricing calculations. Default is today\n\n **Examples**\n\n To change the position date of the default context:\n\n >>> from gs_quant.markets import PositionContext\n >>> import datetime\n >>>\n >>> PricingContext.current = PositionContext(datetime.date(2021, 1, 2))\n\n For a pricing a portfolio with positions held on a specific date:\n\n >>> from gs_quant.markets.portfolio import Portfolio\n >>> portfolio = Portfolio.get(portfolio_id='MQPORTFOLIOID')\n >>>\n >>> with PositionContext():\n >>> portfolio.price()\n >>>\n\n For an asynchronous request:\n\n >>> with PositionContext(), PricingContext(is_async=True):\n >>> price_f = portfolio.price()\n >>>\n >>> while not price_f.done:\n >>> ...\n \"\"\"\n super().__init__()\n\n if position_date:\n if position_date > dt.date.today():\n raise ValueError(\"The PositionContext does not support a position_date in the future\")\n\n self.__position_date = (\n position_date if position_date else business_day_offset(dt.date.today(), 0, roll='preceding')\n )\n\n @property\n def position_date(self):\n return self.__position_date\n\n @classmethod\n def default_value(cls) -> object:\n return PositionContext()\n\n def clone(self, **kwargs):\n clone_kwargs = {k: getattr(self, k, None) for k in signature(self.__init__).parameters.keys()}\n clone_kwargs.update(kwargs)\n return self.__class__(**clone_kwargs)\n" }, { "path": "gs_quant/markets/factor.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport math\nfrom enum import auto, Enum\nfrom typing import Dict, Union\n\nimport numpy as np\nimport pandas as pd\n\nfrom gs_quant.api.gs.risk_models import (\n GsFactorRiskModelApi,\n RiskModelDataMeasure,\n RiskModelDataAssetsRequest,\n IntradayFactorDataSource,\n)\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.datetime import date, time\nfrom gs_quant.models.risk_model_utils import (\n get_covariance_matrix_dataframe,\n build_factor_volatility_dataframe,\n build_factor_data_map,\n build_pfp_data_dataframe,\n)\nfrom gs_quant.target.risk_models import RiskModelUniverseIdentifierRequest\n\n\nclass ReturnFormat(Enum):\n \"\"\"Alternative format for data to be returned from get_data functions\"\"\"\n\n JSON = auto()\n DATA_FRAME = auto()\n\n\nclass Factor:\n def __init__(\n self,\n risk_model_id: str,\n id_: str,\n type_: str,\n name: str = None,\n category: str = None,\n tooltip: str = None,\n description: str = None,\n glossary_description: str = None,\n ):\n self.__risk_model_id = risk_model_id\n self.__id = id_\n self.__name = name\n self.__type = type_\n self.__category = category\n self.__tooltip = tooltip\n self.__description = description\n self.__glossary_description = glossary_description\n\n @property\n def id(self):\n return self.__id\n\n @property\n def name(self):\n return self.__name\n\n @property\n def type(self) -> str:\n return self.__type\n\n @property\n def category(self) -> str:\n return self.__category\n\n @property\n def tooltip(self):\n return self.__tooltip\n\n @property\n def description(self):\n return self.__description\n\n @property\n def glossary_description(self):\n return self.__glossary_description\n\n @property\n def risk_model_id(self):\n return self.__risk_model_id\n\n def covariance(\n self,\n factor: 'Factor',\n start_date: date = DataContext.current.start_date,\n end_date: date = DataContext.current.end_date,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Retrieve a Dataframe or Dictionary of date->covariance values between this factor and another for a date\n range\"\"\"\n covariance_data_raw = GsFactorRiskModelApi.get_risk_model_data(\n self.risk_model_id,\n start_date,\n end_date,\n measures=[\n RiskModelDataMeasure.Covariance_Matrix,\n RiskModelDataMeasure.Factor_Name,\n RiskModelDataMeasure.Factor_Id,\n ],\n factors=list({self.name, factor.name}),\n limit_factors=False,\n ).get('results')\n\n covariance_data_raw = get_covariance_matrix_dataframe(covariance_data_raw)\n\n covariance_data_df = covariance_data_raw.stack().loc[pd.IndexSlice[:, self.name, factor.name]] * 252\n\n if format == ReturnFormat.JSON:\n return covariance_data_df.to_dict()\n\n return covariance_data_df.to_frame(name=\"covariance\")\n\n def variance(\n self,\n start_date: date = DataContext.current.start_date,\n end_date: date = DataContext.current.end_date,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Retrieve a Dataframe or Dictionary of date->variance values for a factor over a date range\"\"\"\n variance_raw_data = self.covariance(self, start_date, end_date, ReturnFormat.DATA_FRAME).rename(\n columns={\"covariance\": \"variance\"}\n )\n\n if format == ReturnFormat.JSON:\n return variance_raw_data.squeeze().to_dict()\n\n return variance_raw_data\n\n def volatility(\n self,\n start_date: date = DataContext.current.start_date,\n end_date: date = DataContext.current.end_date,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Retrieve a Dataframe or Dictionary of date->volatility values for a factor over a date range\"\"\"\n volatility_raw_data = GsFactorRiskModelApi.get_risk_model_data(\n self.risk_model_id,\n start_date,\n end_date,\n measures=[\n RiskModelDataMeasure.Factor_Volatility,\n RiskModelDataMeasure.Factor_Id,\n RiskModelDataMeasure.Factor_Name,\n ],\n factors=[self.name],\n limit_factors=False,\n ).get('results')\n\n volatility_data_df = build_factor_volatility_dataframe(volatility_raw_data, True, None) * math.sqrt(252)\n if format == ReturnFormat.JSON:\n return volatility_data_df.squeeze(axis=1).to_dict()\n\n return volatility_data_df\n\n def correlation(\n self,\n other_factor,\n start_date: date = DataContext.current.start_date,\n end_date: date = DataContext.current.end_date,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Retrieve a Dataframe or Dictionary of date->correlation values between this factor and another for a date\n range\"\"\"\n\n raw_data = GsFactorRiskModelApi.get_risk_model_data(\n self.risk_model_id,\n start_date,\n end_date,\n measures=[\n RiskModelDataMeasure.Covariance_Matrix,\n RiskModelDataMeasure.Factor_Name,\n RiskModelDataMeasure.Factor_Id,\n ],\n factors=[self.name, other_factor.name],\n limit_factors=False,\n ).get('results')\n\n covariance_data_raw = get_covariance_matrix_dataframe(raw_data) * 252\n numerator = covariance_data_raw.stack().loc[pd.IndexSlice[:, self.name, other_factor.name]]\n denominator = np.sqrt(\n covariance_data_raw.stack().loc[pd.IndexSlice[:, self.name, self.name]]\n * covariance_data_raw.stack().loc[pd.IndexSlice[:, other_factor.name, other_factor.name]]\n )\n\n correlation_df = numerator / denominator\n\n if format == ReturnFormat.JSON:\n return correlation_df.to_dict()\n return correlation_df.to_frame(name=\"correlation\")\n\n def returns(\n self,\n start_date: date = DataContext.current.start_date,\n end_date: date = DataContext.current.end_date,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Retrieve a Dataframe or Dictionary of date->factor return values for a date range\"\"\"\n factor_returns_raw = GsFactorRiskModelApi.get_risk_model_data(\n self.risk_model_id,\n start_date,\n end_date,\n measures=[\n RiskModelDataMeasure.Factor_Return,\n RiskModelDataMeasure.Factor_Name,\n RiskModelDataMeasure.Factor_Id,\n ],\n factors=[self.name],\n limit_factors=False,\n ).get('results')\n\n factor_returns_formatted = build_factor_data_map(\n factor_returns_raw, 'factorName', self.risk_model_id, RiskModelDataMeasure.Factor_Return, None\n )\n factor_returns_df = factor_returns_formatted.rename(columns={self.name: 'return'}).rename_axis(None, axis=1)\n\n if format == ReturnFormat.JSON:\n return factor_returns_df.squeeze().to_dict()\n\n return factor_returns_df\n\n def intraday_returns(\n self,\n start_time: time = DataContext.current.start_time,\n end_time: time = DataContext.current.end_time,\n data_source: Union[IntradayFactorDataSource, str] = IntradayFactorDataSource.GS_FMP,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Retrieve a Dataframe or Dictionary of timestamp->factor return values for a time range\"\"\"\n\n max_interval = dt.timedelta(hours=23, minutes=59, seconds=59)\n current_start = start_time\n all_data = []\n\n while current_start < end_time:\n current_end = min(current_start + max_interval, end_time)\n factor_returns_batch = GsFactorRiskModelApi.get_risk_model_factor_data_intraday(\n self.risk_model_id, current_start, current_end, data_source=data_source, factors=[self.name]\n )\n all_data.extend(factor_returns_batch)\n current_start = current_end + dt.timedelta(seconds=1) # Move to the next interval\n\n factor_returns_df = pd.DataFrame(all_data)\n try:\n factor_returns_df.set_index(['time'], inplace=True)\n except KeyError:\n factor_returns_df = pd.DataFrame()\n\n factor_returns_df.drop(columns=[\"factorCategory\", \"factor\", \"factorId\"], errors='ignore', inplace=True)\n\n if format == ReturnFormat.JSON:\n return factor_returns_df.squeeze().to_dict()\n\n return factor_returns_df\n\n def mimicking_portfolio(\n self,\n start_date: date = DataContext.current.start_date,\n end_date: date = DataContext.current.end_date,\n assets: RiskModelDataAssetsRequest = RiskModelDataAssetsRequest(\n identifier=RiskModelUniverseIdentifierRequest.bbid, universe=()\n ),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ):\n \"\"\"Retrieves a timeseries of factor mimicking portfolios for a date range.\"\"\"\n\n results = GsFactorRiskModelApi.get_risk_model_data(\n self.risk_model_id,\n start_date,\n end_date,\n assets=assets,\n measures=[\n RiskModelDataMeasure.Factor_Portfolios,\n RiskModelDataMeasure.Factor_Id,\n RiskModelDataMeasure.Factor_Name,\n ],\n factors=[self.name],\n limit_factors=False,\n ).get('results')\n\n factor_portfolio_df = build_pfp_data_dataframe(results, True)\n\n factor_portfolio_df = factor_portfolio_df.reset_index()\n factor_portfolio_df = factor_portfolio_df.pivot(index=\"date\", columns=\"identifier\", values=self.name)\n\n if format.value == ReturnFormat.JSON.value:\n return factor_portfolio_df.to_dict(orient='index')\n return factor_portfolio_df\n" }, { "path": "gs_quant/markets/factor_analytics.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom typing import Dict, List\nimport pandas as pd\nimport plotly.graph_objects as go\n\nfrom gs_quant.api.gs.risk import GsRiskApi\nfrom gs_quant.markets.position_set import PositionSet\nfrom gs_quant.errors import MqValueError\n\n_logger = logging.getLogger(__name__)\n\n\nclass FactorAnalytics:\n \"\"\"\n Helper class for performing style factor analysis and visualizations.\n Provides methods to analyze style factor exposures, performance metrics, and create visualizations.\n \"\"\"\n\n def __init__(self, risk_model_id: str, currency: str = 'USD', participation_rate: float = 0.1):\n \"\"\"\n Initialize FactorAnalytics helper.\n\n :param risk_model_id: Risk model identifier (e.g., 'AXIOMA_AXUS4S', 'BARRA_EFM_USALTL')\n :param currency: Currency for analysis (default: USD)\n :param participation_rate: Market participation rate (default: 0.1 = 10%)\n \"\"\"\n self.risk_model_id = risk_model_id\n self.currency = currency\n self.participation_rate = participation_rate\n\n def get_factor_analysis(self, position_set: PositionSet) -> Dict:\n \"\"\"\n Get style factor analysis for a position set using the liquidity endpoint.\n Returns risk metrics and style factor exposures.\n\n :param position_set: Portfolio positions to analyze\n :return: Factor analysis results dictionary with style factors\n \"\"\"\n if not position_set or not position_set.positions:\n raise MqValueError(\"Position set is empty\")\n\n if not position_set.date:\n raise MqValueError(\"Position set must have a date\")\n\n unresolved = [p for p in position_set.positions if not p.asset_id]\n if unresolved:\n _logger.info(f\"Resolving {len(unresolved)} unresolved positions...\")\n position_set.resolve()\n\n api_positions = []\n position_mapping = {}\n\n for position in position_set.positions:\n if not position.asset_id:\n _logger.warning(f\"Skipping unresolved position: {position.identifier}\")\n continue\n\n position_mapping[position.asset_id] = position.identifier\n\n if position.quantity is not None:\n api_positions.append({\"assetId\": position.asset_id, \"quantity\": position.quantity})\n elif position.weight is not None:\n api_positions.append({\"assetId\": position.asset_id, \"weight\": position.weight * 100})\n else:\n _logger.warning(f\"Position {position.identifier} has no quantity or weight\")\n\n if not api_positions:\n raise MqValueError(\"No valid positions to analyze\")\n\n notional = position_set.reference_notional if position_set.reference_notional else None\n\n try:\n results = GsRiskApi.get_liquidity_and_factor_analysis(\n positions=api_positions,\n risk_model=self.risk_model_id,\n date=position_set.date,\n currency=self.currency,\n participation_rate=self.participation_rate,\n notional=notional,\n measures=[\n \"Time Series Data\",\n \"Risk Buckets\",\n \"Factor Risk Buckets\",\n \"Factor Exposure Buckets\",\n \"Exposure Buckets\",\n ],\n )\n return results\n except MqValueError as e:\n error_msg = str(e)\n if 'missing in marquee' in error_msg.lower():\n import re\n\n asset_ids_match = re.findall(r'MA[A-Z0-9]+', error_msg)\n if asset_ids_match:\n problematic_positions = []\n for asset_id in asset_ids_match:\n identifier = position_mapping.get(asset_id, f\"Unknown (Asset ID: {asset_id})\")\n problematic_positions.append(f\"{identifier} ({asset_id})\")\n\n raise MqValueError(\n f\"Factor analysis failed due to asset resolution issues.\\n\\n\"\n f\"The following positions could not be found in Marquee:\\n\"\n f\"{chr(10).join([' - ' + pos for pos in problematic_positions])}\\n\\n\"\n f\"Please verify:\\n\"\n f\"1. Asset identifiers are correct (e.g., 'AAPL UW' for Bloomberg tickers)\\n\"\n f\"2. Positions are available in the Marquee system\\n\"\n f\"3. Asset IDs were resolved correctly\\n\\n\"\n f\"Original error: {error_msg}\"\n )\n raise\n except Exception as e:\n _logger.error(f\"Factor analysis failed: {str(e)}\")\n raise\n\n def convert_hedge_factor_exposures(self, style_factors: List) -> Dict:\n \"\"\"\n Convert hedge result style factor exposures to factor analysis format.\n This allows reusing visualization methods with data from the hedge API.\n\n :param style_factors: Style factor exposures from hedge result (style, sector, country)\n :return: Dictionary in the same format as get_factor_analysis() for visualization\n \"\"\"\n if not style_factors:\n raise MqValueError(\"Style factor exposures data is empty\")\n\n if not style_factors:\n _logger.warning(\"No style factor data in hedge result\")\n\n sub_factors = [{'name': item['factor'], 'value': item['exposure']} for item in style_factors]\n\n return {\n 'factorExposureBuckets': [{'name': 'Style', 'subFactors': sub_factors}],\n 'notional': 0,\n 'currency': 'USD',\n 'riskBuckets': [],\n }\n\n def create_exposure_bar_chart(self, exposures: Dict[str, float], title: str, horizontal: bool = True) -> go.Figure:\n \"\"\"\n Create a bar chart for style factor exposures with color coding.\n Note: This method is intended for style factors only.\n\n :param exposures: Dictionary of factor names to exposure values\n :param title: Chart title\n :param horizontal: If True, creates horizontal bars; otherwise vertical\n :return: Plotly figure\n \"\"\"\n if not exposures:\n return go.Figure().add_annotation(text=\"No data available\", showarrow=False)\n\n names = list(exposures.keys())\n values = list(exposures.values())\n\n colors = ['green' if v >= 0 else 'red' for v in values]\n\n fig = go.Figure()\n\n if horizontal:\n fig.add_trace(\n go.Bar(\n y=names,\n x=values,\n orientation='h',\n marker_color=colors,\n text=[f'{v:,.0f}' for v in values],\n textposition='outside',\n showlegend=False,\n )\n )\n fig.update_xaxes(title=\"Exposure\")\n fig.update_yaxes(title=\"\")\n else:\n fig.add_trace(\n go.Bar(\n x=names,\n y=values,\n marker_color=colors,\n text=[f'{v:,.0f}' for v in values],\n textposition='outside',\n showlegend=False,\n )\n )\n fig.update_xaxes(title=\"\")\n fig.update_yaxes(title=\"Exposure\")\n\n if horizontal:\n chart_height = max(300, len(names) * 40 + 150)\n else:\n chart_height = 500\n\n fig.update_layout(title=title, height=chart_height, margin=dict(l=200 if horizontal else 50, r=50, t=80, b=50))\n\n return fig\n\n def create_style_factor_chart(\n self, factor_analysis: Dict, rows: int = None, title: str = \"Style Factor Exposures\"\n ) -> go.Figure:\n \"\"\"\n Create a bar chart showing positive and negative style factor exposures.\n\n :param factor_analysis: Factor analysis results from liquidity endpoint\n :param title: Chart title\n :return: Plotly figure showing top positive and negative style factors\n \"\"\"\n if 'factorExposureBuckets' not in factor_analysis:\n return go.Figure().add_annotation(text=\"No style factor data available\", showarrow=False)\n\n style_factors = {}\n for bucket in factor_analysis['factorExposureBuckets']:\n if bucket.get('name') == 'Style':\n for sub_factor in bucket.get('subFactors', []):\n factor_name = sub_factor.get('name')\n factor_value = sub_factor.get('value', 0)\n if factor_name:\n style_factors[factor_name] = factor_value\n break\n\n if not style_factors:\n return go.Figure().add_annotation(text=\"No style factor data available\", showarrow=False)\n\n positive_factors = {k: v for k, v in style_factors.items() if v > 0}\n negative_factors = {k: v for k, v in style_factors.items() if v < 0}\n\n # most negative first - ascending order by value)\n top_negative_limit = rows if rows is not None else None\n top_negative = dict(sorted(negative_factors.items(), key=lambda x: x[1])[:top_negative_limit])\n\n # descending order - highest first)\n top_positive_limit = rows if rows is not None else None\n top_positive_items = sorted(positive_factors.items(), key=lambda x: x[1], reverse=True)[:top_positive_limit]\n # Reverse to get ascending order for display (lowest to highest)\n top_positive = dict(reversed(top_positive_items))\n\n selected_factors = {**top_negative, **top_positive}\n\n if not selected_factors:\n return go.Figure().add_annotation(text=\"No style factor data available\", showarrow=False)\n\n total_factors = len(style_factors)\n\n subset_title = title\n if rows is not None:\n subset_title = f\"{title} (Top {rows} Positive & Top {rows} Negative, {total_factors} Total)\"\n\n return self.create_exposure_bar_chart(selected_factors, subset_title, horizontal=True)\n\n def create_exposure_summary_table(self, factor_analysis: Dict) -> pd.DataFrame:\n \"\"\"\n Create a summary table of key portfolio metrics.\n\n :param factor_analysis: Factor analysis results from liquidity endpoint\n :return: Pandas DataFrame with summary metrics\n \"\"\"\n notional = factor_analysis.get('notional', 0)\n currency = factor_analysis.get('currency', 'USD')\n\n risk_buckets = {bucket['name']: bucket['value'] for bucket in factor_analysis.get('riskBuckets', [])}\n\n data = {\n 'Metric': ['Notional', 'Currency', 'Market Risk', 'Specific Risk', 'Sector Risk', 'Style Risk'],\n 'Value': [\n f\"${notional:,.0f}\",\n currency,\n f\"{risk_buckets.get('Market', 0):.4f}\",\n f\"{risk_buckets.get('Specific', 0):.4f}\",\n f\"{risk_buckets.get('Sector', 0):.4f}\",\n f\"{risk_buckets.get('Style', 0):.4f}\",\n ],\n }\n\n return pd.DataFrame(data, index=None)\n\n def create_performance_chart(\n self, performance_data: pd.DataFrame, metric: str = 'cumulativePnl', title: str = \"Performance\"\n ) -> go.Figure:\n \"\"\"\n Create a time series performance chart.\n\n :param performance_data: DataFrame with date and performance columns\n :param metric: Column name to plot\n :param title: Chart title\n :return: Plotly figure\n \"\"\"\n if performance_data.empty:\n return go.Figure().add_annotation(text=\"No performance data available\", showarrow=False)\n\n fig = go.Figure()\n\n fig.add_trace(\n go.Scatter(\n x=performance_data['date'] if 'date' in performance_data.columns else performance_data.index,\n y=performance_data[metric] if metric in performance_data.columns else performance_data.iloc[:, 0],\n mode='lines',\n name=metric,\n line=dict(width=2),\n )\n )\n\n fig.update_layout(\n title=title,\n xaxis_title=\"Date\",\n yaxis_title=metric.replace('_', ' ').title(),\n height=500,\n hovermode='x unified',\n )\n\n return fig\n\n def create_dynamic_performance_chart(\n self, factor_analysis: Dict, title: str = \"Portfolio Performance Metrics\"\n ) -> go.Figure:\n \"\"\"\n Create a dynamic chart with toggleable performance metrics from timeseriesData.\n Shows cumulative PnL and normalized performance.\n\n :param factor_analysis: Factor analysis results from liquidity endpoint\n :param title: Chart title\n :return: Plotly figure with dropdown menu for cumulative PnL and normalized performance\n \"\"\"\n timeseries_data = factor_analysis.get('timeseriesData', [])\n\n if not timeseries_data:\n return go.Figure().add_annotation(\n text=\"No time series data available. Ensure 'Time Series Data' measure is included.\",\n showarrow=False,\n font=dict(size=14),\n )\n\n total_data = None\n for item in timeseries_data:\n if item.get('name') == 'total':\n total_data = item\n break\n\n cumulative_pnl_raw = total_data.get('cumulativePnl', [])\n normalized_performance_raw = total_data.get('normalizedPerformance', [])\n\n if not cumulative_pnl_raw and not normalized_performance_raw:\n return go.Figure().add_annotation(\n text=\"No cumulative PnL or normalized performance data available.\", showarrow=False, font=dict(size=14)\n )\n\n cumulative_dates = []\n cumulative_values = []\n for item in cumulative_pnl_raw:\n if len(item) == 2 and isinstance(item[0], str):\n cumulative_dates.append(item[0])\n cumulative_values.append(item[1])\n\n normalized_dates = []\n normalized_values = []\n for item in normalized_performance_raw:\n if len(item) == 2 and isinstance(item[0], str):\n normalized_dates.append(item[0])\n normalized_values.append(item[1])\n\n if not cumulative_dates and cumulative_values:\n cumulative_dates = list(range(len(cumulative_values)))\n if not normalized_dates and normalized_values:\n normalized_dates = list(range(len(normalized_values)))\n\n fig = go.Figure()\n\n if cumulative_values:\n fig.add_trace(\n go.Scatter(\n x=cumulative_dates,\n y=cumulative_values,\n mode='lines',\n name='Cumulative PnL',\n visible=True,\n line=dict(width=2, color='blue'),\n )\n )\n\n if normalized_values:\n fig.add_trace(\n go.Scatter(\n x=normalized_dates,\n y=normalized_values,\n mode='lines',\n name='Normalized Performance',\n visible=False,\n line=dict(width=2, color='green'),\n )\n )\n\n buttons = [\n dict(\n label=\"Cumulative PnL\",\n method=\"update\",\n args=[{\"visible\": [True, False]}, {\"yaxis.title.text\": \"Cumulative PnL ($)\"}],\n ),\n dict(\n label=\"Normalized Performance\",\n method=\"update\",\n args=[{\"visible\": [False, True]}, {\"yaxis.title.text\": \"Normalized Performance\"}],\n ),\n ]\n\n fig.update_layout(\n title=title,\n xaxis_title=\"Date\",\n yaxis_title=\"Cumulative PnL ($)\",\n height=500,\n hovermode='x unified',\n updatemenus=[\n dict(\n buttons=buttons,\n direction=\"down\",\n pad={\"r\": 10, \"t\": 10},\n showactive=True,\n x=0.11,\n xanchor=\"left\",\n y=1.15,\n yanchor=\"top\",\n )\n ],\n annotations=[\n dict(text=\"Select Metric:\", showarrow=False, x=0.01, xref=\"paper\", y=1.13, yref=\"paper\", align=\"left\")\n ],\n )\n\n return fig\n\n def create_factor_heatmap_comparison(\n self, initial_analysis: Dict, hedged_analysis: Dict, title: str = \"Style Factor Comparison: Initial vs Hedged\"\n ) -> go.Figure:\n \"\"\"\n Create a grouped bar chart comparing style factor exposures.\n Shows side-by-side bars for easy comparison.\n\n :param initial_analysis: Factor analysis for initial portfolio\n :param hedged_analysis: Factor analysis for hedged portfolio\n :param title: Chart title\n :return: Plotly figure with grouped bar chart\n \"\"\"\n\n def extract_style_factors(analysis):\n for bucket in analysis.get('factorExposureBuckets', []):\n if bucket.get('name') == 'Style':\n return {sf['name']: sf['value'] for sf in bucket.get('subFactors', [])}\n return {}\n\n initial_factors = extract_style_factors(initial_analysis)\n hedged_factors = extract_style_factors(hedged_analysis)\n\n all_factors = set(initial_factors.keys()) | set(hedged_factors.keys())\n\n if not all_factors:\n return go.Figure().add_annotation(text=\"No factor data available\", showarrow=False)\n\n sorted_factors = sorted(all_factors, key=lambda f: abs(initial_factors.get(f, 0)), reverse=True)\n\n initial_values = [initial_factors.get(f, 0) for f in sorted_factors]\n hedged_values = [hedged_factors.get(f, 0) for f in sorted_factors]\n\n fig = go.Figure()\n\n fig.add_trace(\n go.Bar(\n name='Initial Portfolio',\n y=sorted_factors,\n x=initial_values,\n orientation='h',\n marker_color='#4472C4',\n text=[f'{v:,.0f}' for v in initial_values],\n textposition='outside',\n textfont=dict(size=10),\n )\n )\n\n # Hedged portfolio bars\n fig.add_trace(\n go.Bar(\n name='Hedged Portfolio',\n y=sorted_factors,\n x=hedged_values,\n orientation='h',\n marker_color='#70AD47',\n text=[f'{v:,.0f}' for v in hedged_values],\n textposition='outside',\n textfont=dict(size=10),\n )\n )\n\n # Calculate dynamic height based on number of factors\n chart_height = max(500, len(sorted_factors) * 35 + 150)\n\n fig.update_layout(\n title=title,\n xaxis_title=\"Exposure Value\",\n yaxis_title=\"\",\n height=chart_height,\n barmode='group',\n bargap=0.15,\n bargroupgap=0.1,\n legend=dict(orientation=\"h\", yanchor=\"bottom\", y=1.02, xanchor=\"right\", x=1),\n margin=dict(l=200, r=100, t=100, b=50),\n )\n\n # Add vertical line at x=0 for reference\n fig.add_vline(x=0, line_width=1, line_dash=\"dash\", line_color=\"gray\")\n\n return fig\n" }, { "path": "gs_quant/markets/hedge.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom collections import defaultdict\nfrom enum import Enum\nfrom typing import Union, List, Dict, Optional\n\nimport numpy as np\nimport pandas as pd\nfrom dateutil.relativedelta import relativedelta\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.hedges import GsHedgeApi\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.position_set import PositionSet\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.hedge import HedgeObjective, CorporateActionsTypes\n\n_logger = logging.getLogger(__name__)\n\n\nclass FactorExposureCategory(Enum):\n COUNTRY = 'country'\n SECTOR = 'sector'\n INDUSTRY = 'industry'\n STYLE = 'style'\n\n\nclass ConstraintType(Enum):\n ASSET = \"Asset\"\n COUNTRY = \"Country\"\n REGION = \"Region\"\n SECTOR = \"Sector\"\n INDUSTRY = \"Industry\"\n ESG = \"Esg\"\n\n\nclass HedgeExclusions:\n \"\"\"\n List of assets, countries, regions, sectors, and industries to exclude from the hedge universe\n \"\"\"\n\n def __init__(\n self,\n assets: List[str] = None,\n countries: List[str] = None,\n regions: List[str] = None,\n sectors: List[str] = None,\n industries: List[str] = None,\n ):\n self.__assets = assets\n self.__countries = countries\n self.__regions = regions\n self.__sectors = sectors\n self.__industries = industries\n\n @property\n def assets(self) -> List[str]:\n return self.__assets\n\n @assets.setter\n def assets(self, value: List[str]):\n self.__assets = value\n\n @property\n def countries(self) -> List[str]:\n return self.__countries\n\n @countries.setter\n def countries(self, value: List[str]):\n self.__countries = value\n\n @property\n def regions(self) -> List[str]:\n return self.__regions\n\n @regions.setter\n def regions(self, value: List[str]):\n self.__regions = value\n\n @property\n def sectors(self) -> List[str]:\n return self.__sectors\n\n @sectors.setter\n def sectors(self, value: List[str]):\n self.__sectors = value\n\n @property\n def industries(self) -> List[str]:\n return self.__industries\n\n @industries.setter\n def industries(self, value: List[str]):\n self.__industries = value\n\n def to_dict(self):\n response = {}\n all_constraints = []\n if self.countries:\n all_constraints = all_constraints + self._get_exclusions(self.countries, ConstraintType.COUNTRY)\n if self.regions:\n all_constraints = all_constraints + self._get_exclusions(self.regions, ConstraintType.REGION)\n if self.sectors:\n all_constraints = all_constraints + self._get_exclusions(self.sectors, ConstraintType.SECTOR)\n if self.industries:\n all_constraints = all_constraints + self._get_exclusions(self.industries, ConstraintType.INDUSTRY)\n if len(all_constraints) > 0:\n response['classificationConstraints'] = all_constraints\n if self.assets:\n response['assetConstraints'] = self._get_exclusions(self.assets, ConstraintType.ASSET)\n return response\n\n @staticmethod\n def _get_exclusions(exclusions_list: List, constraint_type: ConstraintType):\n return [\n Constraint(constraint_name=exclusion, constraint_type=constraint_type, minimum=0, maximum=0).to_dict()\n for exclusion in exclusions_list\n ]\n\n\nclass Constraint:\n def __init__(\n self,\n constraint_name: str,\n minimum: float = 0,\n maximum: float = 100,\n constraint_type: Optional[ConstraintType] = None,\n ):\n self.__constraint_name = constraint_name\n self.__minimum = minimum\n self.__maximum = maximum\n self.__constraint_type = constraint_type\n\n @property\n def constraint_name(self) -> str:\n return self.__constraint_name\n\n @constraint_name.setter\n def constraint_name(self, value: str):\n self.__constraint_name = value\n\n @property\n def minimum(self) -> float:\n return self.__minimum\n\n @minimum.setter\n def minimum(self, value: float):\n self.__minimum = value\n\n @property\n def maximum(self) -> float:\n return self.__maximum\n\n @maximum.setter\n def maximum(self, value: float):\n self.__maximum = value\n\n @property\n def constraint_type(self) -> Optional[ConstraintType]:\n return self.__constraint_type\n\n @constraint_type.setter\n def constraint_type(self, value: ConstraintType):\n self.__constraint_type = value\n\n @classmethod\n def from_dict(cls, as_dict: Dict):\n if as_dict.get('type') is not None:\n constraint_type = ConstraintType(as_dict.get('type'))\n elif as_dict.get('assetId') is not None:\n constraint_type = ConstraintType.ASSET\n else:\n constraint_type = ConstraintType.ESG\n return Constraint(\n constraint_name=as_dict.get('name') or as_dict.get('assetId'),\n constraint_type=constraint_type,\n minimum=as_dict.get('min'),\n maximum=as_dict.get('max'),\n )\n\n def to_dict(self):\n response = {'name': self.constraint_name, 'min': self.minimum, 'max': self.maximum}\n if self.constraint_type != ConstraintType.ESG and self.constraint_type != ConstraintType.ASSET:\n response['type'] = self.constraint_type.value\n if self.constraint_type == ConstraintType.ASSET:\n response['assetId'] = response['name']\n response.pop('name')\n\n return response\n\n\nclass HedgeConstraints:\n \"\"\"\n List of assets, countries, sectors, and industries to constrain in the hedge universe\n \"\"\"\n\n def __init__(\n self,\n assets: List[Constraint] = None,\n countries: List[Constraint] = None,\n regions: List[Constraint] = None,\n sectors: List[Constraint] = None,\n industries: List[Constraint] = None,\n esg: List[Constraint] = None,\n ):\n for con in assets or []:\n con.constraint_type = ConstraintType.ASSET\n for con in regions or []:\n con.constraint_type = ConstraintType.REGION\n for con in countries or []:\n con.constraint_type = ConstraintType.COUNTRY\n for con in sectors or []:\n con.constraint_type = ConstraintType.SECTOR\n for con in industries or []:\n con.constraint_type = ConstraintType.INDUSTRY\n for con in esg or []:\n con.constraint_type = ConstraintType.ESG\n self.__assets = assets\n self.__countries = countries\n self.__regions = regions\n self.__sectors = sectors\n self.__industries = industries\n self.__esg = esg\n\n @property\n def assets(self) -> List[Constraint]:\n return self.__assets\n\n @assets.setter\n def assets(self, value: List[Constraint]):\n self.__assets = value\n\n @property\n def countries(self) -> List[Constraint]:\n return self.__countries\n\n @countries.setter\n def countries(self, value: List[Constraint]):\n self.__countries = value\n\n @property\n def regions(self) -> List[Constraint]:\n return self.__regions\n\n @regions.setter\n def regions(self, value: List[Constraint]):\n self.__regions = value\n\n @property\n def sectors(self) -> List[Constraint]:\n return self.__sectors\n\n @sectors.setter\n def sectors(self, value: List[Constraint]):\n self.__sectors = value\n\n @property\n def industries(self) -> List[Constraint]:\n return self.__industries\n\n @industries.setter\n def industries(self, value: List[Constraint]):\n self.__industries = value\n\n @property\n def esg(self) -> List[Constraint]:\n return self.__esg\n\n @esg.setter\n def esg(self, value: List[Constraint]):\n self.__esg = value\n\n def to_dict(self):\n response = {}\n\n classification_constraints = []\n for constraint_type in [self.countries, self.regions, self.sectors, self.industries]:\n if constraint_type:\n classification_constraints += [con.to_dict() for con in constraint_type]\n if len(classification_constraints) > 0:\n response['classificationConstraints'] = classification_constraints\n\n esg_constraints = [con.to_dict() for con in self.esg] if self.esg else []\n if len(esg_constraints) > 0:\n response['esgConstraints'] = esg_constraints\n\n asset_constraints = [con.to_dict() for con in self.assets] if self.assets else []\n if len(asset_constraints) > 0:\n response['assetConstraints'] = asset_constraints\n\n return response\n\n\nclass PerformanceHedgeParameters:\n \"\"\"Parameters for a performance replication hedge calculation.\"\"\"\n\n def __init__(\n self,\n initial_portfolio: PositionSet,\n universe: List[str],\n exclusions: Optional[HedgeExclusions] = None,\n constraints: Optional[HedgeConstraints] = None,\n observation_start_date: dt.date = None,\n sampling_period: str = 'Daily',\n max_leverage: float = 100,\n percentage_in_cash: Optional[float] = None,\n explode_universe: bool = True,\n exclude_target_assets: bool = True,\n exclude_corporate_actions_types: Optional[List[Union[CorporateActionsTypes, str]]] = None,\n exclude_hard_to_borrow_assets: bool = False,\n exclude_restricted_assets: bool = False,\n max_adv_percentage: float = 15,\n max_return_deviation: float = 5,\n max_weight: float = 100,\n min_market_cap: Optional[float] = None,\n max_market_cap: Optional[float] = None,\n market_participation_rate: float = 10,\n lasso_weight: float = 0,\n ridge_weight: float = 0,\n benchmarks: List[str] = None,\n ):\n self.__initial_portfolio = initial_portfolio\n self.__universe = universe\n self.__exclusions = exclusions\n self.__constraints = constraints\n self.__observation_start_date = observation_start_date\n self.__sampling_period = sampling_period\n self.__max_leverage = max_leverage\n self.__percentage_in_cash = percentage_in_cash\n self.__explode_universe = explode_universe\n self.__exclude_target_assets = exclude_target_assets\n self.__exclude_corporate_actions_types = exclude_corporate_actions_types\n self.__exclude_hard_to_borrow_assets = exclude_hard_to_borrow_assets\n self.__exclude_restricted_assets = exclude_restricted_assets\n self.__max_adv_percentage = max_adv_percentage\n self.__max_return_deviation = max_return_deviation\n self.__max_weight = max_weight\n self.__min_market_cap = min_market_cap\n self.__max_market_cap = max_market_cap\n self.__market_participation_rate = market_participation_rate\n self.__lasso_weight = lasso_weight\n self.__ridge_weight = ridge_weight\n self.__benchmarks = benchmarks\n\n @property\n def initial_portfolio(self) -> PositionSet:\n \"\"\"The set of positions that make up the hedge target.\"\"\"\n return self.__initial_portfolio\n\n @initial_portfolio.setter\n def initial_portfolio(self, value: PositionSet):\n self.__initial_portfolio = value\n\n @property\n def universe(self) -> List[str]:\n \"\"\"A list of asset identifiers (asset IDs, bloomberg IDs, tickers, SEDOLs, etc) that make up the universe,\n which are resolved as of the observation end date.\"\"\"\n return self.__universe\n\n @universe.setter\n def universe(self, value: List[str]):\n self.__universe = value\n\n @property\n def observation_start_date(self) -> dt.date:\n \"\"\"ISO 8601-formatted date\"\"\"\n return self.__observation_start_date\n\n @observation_start_date.setter\n def observation_start_date(self, value: dt.date):\n self.__observation_start_date = value\n\n @property\n def exclusions(self) -> Optional[HedgeExclusions]:\n \"\"\"Assets, countries, sectors, and industries to exclude from the hedge\"\"\"\n return self.__exclusions\n\n @exclusions.setter\n def exclusions(self, value: HedgeExclusions):\n self.__exclusions = value\n\n @property\n def constraints(self) -> Optional[HedgeConstraints]:\n \"\"\"Assets, countries, sectors, and industries to constrain in the hedge\"\"\"\n return self.__constraints\n\n @constraints.setter\n def constraints(self, value: HedgeConstraints):\n self.__constraints = value\n\n @property\n def sampling_period(self) -> str:\n \"\"\"The length of time in between return samples.\"\"\"\n return self.__sampling_period\n\n @sampling_period.setter\n def sampling_period(self, value: str):\n self.__sampling_period = value\n\n @property\n def max_leverage(self) -> float:\n \"\"\"Maximum percentage of the notional that can be used to hedge.\"\"\"\n return self.__max_leverage\n\n @max_leverage.setter\n def max_leverage(self, value: float):\n self.__max_leverage = value\n\n @property\n def percentage_in_cash(self) -> Optional[float]:\n \"\"\"Percentage of the hedge notional that will be in cash.\"\"\"\n return self.__percentage_in_cash\n\n @percentage_in_cash.setter\n def percentage_in_cash(self, value: float):\n self.__percentage_in_cash = value\n\n @property\n def explode_universe(self) -> bool:\n \"\"\"Explode the assets in the universe into their underliers to be used as the hedge\n universe.\"\"\"\n return self.__explode_universe\n\n @explode_universe.setter\n def explode_universe(self, value: bool):\n self.__explode_universe = value\n\n @property\n def exclude_target_assets(self) -> bool:\n \"\"\"Exclude assets in the target composition from being in the hedge.\"\"\"\n return self.__exclude_target_assets\n\n @exclude_target_assets.setter\n def exclude_target_assets(self, value: bool):\n self.__exclude_target_assets = value\n\n @property\n def exclude_corporate_actions_types(self) -> Optional[List[Union[CorporateActionsTypes, str]]]:\n \"\"\"Set of of corporate actions to be excluded in the hedge\"\"\"\n return self.__exclude_corporate_actions_types\n\n @exclude_corporate_actions_types.setter\n def exclude_corporate_actions_types(self, value: List[Union[CorporateActionsTypes, str]]):\n self.__exclude_corporate_actions_types = value\n\n @property\n def exclude_hard_to_borrow_assets(self) -> bool:\n \"\"\"Whether hard to borrow assets should be excluded in the universe or not. True\n for exclude.\"\"\"\n return self.__exclude_hard_to_borrow_assets\n\n @exclude_hard_to_borrow_assets.setter\n def exclude_hard_to_borrow_assets(self, value: bool):\n self.__exclude_hard_to_borrow_assets = value\n\n @property\n def exclude_restricted_assets(self) -> bool:\n \"\"\"Whether to include assets in restricted trading lists or not.\"\"\"\n return self.__exclude_restricted_assets\n\n @exclude_restricted_assets.setter\n def exclude_restricted_assets(self, value: bool):\n self.__exclude_restricted_assets = value\n\n @property\n def max_adv_percentage(self) -> float:\n \"\"\"Maximum percentage notional to average daily dollar volume allowed for any hedge\n constituent.\"\"\"\n return self.__max_adv_percentage\n\n @max_adv_percentage.setter\n def max_adv_percentage(self, value: float):\n self.__max_adv_percentage = value\n\n @property\n def max_return_deviation(self) -> float:\n \"\"\"Maximum percentage difference in annualized return between the target and the\n hedge result.\"\"\"\n return self.__max_return_deviation\n\n @max_return_deviation.setter\n def max_return_deviation(self, value: float):\n self.__max_return_deviation = value\n\n @property\n def max_weight(self) -> float:\n \"\"\"Maximum weight of any constituent in hedge.\"\"\"\n return self.__max_weight\n\n @max_weight.setter\n def max_weight(self, value: float):\n self.__max_weight = value\n\n @property\n def min_market_cap(self) -> Optional[float]:\n \"\"\"Lowest market cap allowed for any hedge constituent.\"\"\"\n return self.__min_market_cap\n\n @min_market_cap.setter\n def min_market_cap(self, value: float):\n self.__min_market_cap = value\n\n @property\n def max_market_cap(self) -> Optional[float]:\n \"\"\"Highest market cap allowed for any hedge constituent.\"\"\"\n return self.__max_market_cap\n\n @max_market_cap.setter\n def max_market_cap(self, value: float):\n self.__max_market_cap = value\n\n @property\n def market_participation_rate(self) -> float:\n \"\"\"Maximum market participation rate used to estimate the cost of trading a\n portfolio of stocks. This does not effect the optimization.\"\"\"\n return self.__market_participation_rate\n\n @market_participation_rate.setter\n def market_participation_rate(self, value: float):\n self.__market_participation_rate = value\n\n @property\n def benchmarks(self) -> List[str]:\n \"\"\"Marquee unique identifiers of assets to be used as benchmarks.\"\"\"\n return self.__benchmarks\n\n @benchmarks.setter\n def benchmarks(self, value: List[str]):\n self.__benchmarks = value\n\n @property\n def lasso_weight(self) -> float:\n \"\"\"Value of the lasso hyperparameter for machine learning hedges.\"\"\"\n return self.__lasso_weight\n\n @lasso_weight.setter\n def lasso_weight(self, value: float):\n self.__lasso_weight = value\n\n @property\n def ridge_weight(self) -> float:\n \"\"\"Value of the ridge hyperparameter for machine learning hedges\"\"\"\n return self.__ridge_weight\n\n @ridge_weight.setter\n def ridge_weight(self, value: float):\n self.__ridge_weight = value\n\n def to_dict(self, resolved_identifiers):\n positions_to_price = []\n for position in self.initial_portfolio.positions:\n pos_to_price = {'assetId': position.asset_id}\n if position.quantity:\n pos_to_price['quantity'] = position.quantity\n if position.weight:\n pos_to_price['weight'] = position.weight\n positions_to_price.append(pos_to_price)\n payload = {\n 'positions': positions_to_price,\n 'parameters': {\n 'currency': 'USD',\n 'pricingDate': self.initial_portfolio.date.strftime('%Y-%m-%d'),\n 'useUnadjustedClosePrice': True,\n 'frequency': 'End Of Day',\n },\n }\n if self.initial_portfolio.reference_notional:\n payload['parameters']['targetNotional'] = self.initial_portfolio.reference_notional\n payload['parameters']['weightingStrategy'] = \"Weight\"\n\n try:\n price_results = GsSession.current.sync.post('/price/positions', payload)\n except Exception as e:\n raise MqValueError(f'There was an error pricing your positions: {e}')\n if 'errorMessage' in price_results:\n raise MqValueError(f'There was an error pricing your positions: {price_results[\"errorMessage\"]}')\n\n if self.initial_portfolio.reference_notional is None:\n self.initial_portfolio.reference_notional = price_results.get('actualNotional')\n positions_as_dict = [\n {'assetId': p['assetId'], 'quantity': p['quantity']} for p in price_results.get('positions', [])\n ]\n\n # Resolve any assets in the hedge universe, asset constraints, and asset exclusions\n hedge_date = self.initial_portfolio.date\n self.universe = [resolved_identifiers.get(asset, [{'id': asset}])[0].get('id') for asset in self.universe]\n if self.benchmarks is not None:\n self.benchmarks = [\n resolved_identifiers.get(asset, [{'id': asset}])[0].get('id') for asset in self.benchmarks\n ]\n if self.exclusions is not None:\n if self.exclusions.assets is not None:\n self.exclusions.assets = [\n resolved_identifiers.get(asset, [{'id': asset}])[0].get('id') for asset in self.exclusions.assets\n ]\n if self.constraints is not None and self.constraints.assets is not None:\n for con in self.constraints.assets:\n if len(resolved_identifiers.get(con.constraint_name, [])) > 0:\n con.constraint_name = resolved_identifiers.get(con.constraint_name)[0].get(\n 'id', con.constraint_name\n )\n\n # Parse and return dictionary\n observation_start_date = self.observation_start_date or hedge_date - relativedelta(years=1)\n as_dict = {\n 'hedgeTarget': {'positions': positions_as_dict},\n 'universe': self.universe,\n 'notional': self.initial_portfolio.reference_notional,\n 'observationStartDate': observation_start_date.strftime(\"%Y-%m-%d\"),\n 'observationEndDate': hedge_date.strftime(\"%Y-%m-%d\"),\n 'backtestStartDate': observation_start_date.strftime(\"%Y-%m-%d\"),\n 'backtestEndDate': hedge_date.strftime(\"%Y-%m-%d\"),\n 'samplingPeriod': self.sampling_period,\n 'maxLeverage': self.max_leverage,\n 'explodeUniverse': self.explode_universe,\n 'excludeTargetAssets': self.exclude_target_assets,\n 'excludeHardToBorrowAssets': self.exclude_hard_to_borrow_assets,\n 'excludeRestrictedAssets': self.exclude_hard_to_borrow_assets,\n 'maxAdvPercentage': self.max_adv_percentage,\n 'maxReturnDeviation': self.max_return_deviation,\n 'maxWeight': self.max_weight,\n 'marketParticipationRate': self.market_participation_rate,\n 'useMachineLearning': True,\n 'lassoWeight': self.lasso_weight,\n 'ridgeWeight': self.ridge_weight,\n }\n\n exclusions_as_dict = self.exclusions.to_dict() if self.exclusions else {}\n constraints_as_dict = self.constraints.to_dict() if self.constraints else {}\n if 'classificationConstraints' in exclusions_as_dict or 'classificationConstraints' in constraints_as_dict:\n exclusions = exclusions_as_dict.get('classificationConstraints', [])\n constraints = constraints_as_dict.get('classificationConstraints', [])\n as_dict['classificationConstraints'] = exclusions + constraints\n if 'assetConstraints' in exclusions_as_dict or 'assetConstraints' in constraints_as_dict:\n exclusions = exclusions_as_dict.get('assetConstraints', [])\n constraints = constraints_as_dict.get('assetConstraints', [])\n as_dict['assetConstraints'] = exclusions + constraints\n if 'esgConstraints' in constraints_as_dict:\n as_dict['esgConstraints'] = constraints_as_dict.get('esgConstraints', [])\n if self.percentage_in_cash is not None:\n as_dict['percentageInCash'] = self.percentage_in_cash\n if self.exclude_corporate_actions_types:\n as_dict['excludeCorporateActionTypes'] = [x.value for x in self.exclude_corporate_actions_types]\n if self.min_market_cap is not None:\n as_dict['minMarketCap'] = self.min_market_cap\n if self.max_market_cap is not None:\n as_dict['maxMarketCap'] = self.max_market_cap\n if self.benchmarks is not None and len(self.benchmarks):\n as_dict['benchmarks'] = self.benchmarks\n\n return as_dict\n\n def resolve_identifiers_in_payload(self, hedge_date) -> tuple:\n \"\"\"\n The hedge payload has identifiers which need to be resolved\n The resolved values here are used to convert back from asset Id to provided identifier for benchmark curves\n \"\"\"\n identifiers = [identifier for identifier in self.universe]\n if self.exclusions is not None and self.exclusions.assets is not None:\n identifiers = identifiers + [asset for asset in self.exclusions.assets]\n if self.benchmarks is not None:\n identifiers = identifiers + [asset for asset in self.benchmarks]\n if self.constraints is not None:\n if self.constraints.assets is not None:\n identifiers = identifiers + [asset.constraint_name for asset in self.constraints.assets]\n resolver = GsAssetApi.resolve_assets(identifier=identifiers, fields=['id'], as_of=hedge_date)\n return resolver\n\n\nclass Hedge:\n \"\"\"\n A Marquee hedge.\n \"\"\"\n\n def __init__(self, parameters, objective: HedgeObjective):\n self.__parameters = parameters\n self.__objective = objective\n self.__result = {}\n\n @property\n def parameters(self):\n return self.__parameters\n\n @parameters.setter\n def parameters(self, value):\n self.__parameters = value\n\n @property\n def objective(self):\n return self.__objective\n\n @property\n def result(self) -> Dict:\n return self.__result\n\n def calculate(self) -> Dict:\n \"\"\"\n Calculates the hedge\n :return: a dictionary with calculation results\n \"\"\"\n resolved_identifiers = self.parameters.resolve_identifiers_in_payload(self.parameters.initial_portfolio.date)\n params = self.parameters.to_dict(resolved_identifiers)\n results = GsHedgeApi.calculate_hedge({'objective': self.objective.value, 'parameters': params})\n if 'errorMessage' in results and 'result' not in results:\n raise MqValueError(\n f\"Error calculating hedge: {results['errorMessage']}. Please adjust your constraints and try again.\"\n )\n calculation_results = results.get('result')\n formatted_results = self._format_hedge_calculate_results(calculation_results)\n formatted_results = self._enhance_result_with_benchmark_curves(\n formatted_results, calculation_results.get('benchmarks', []), resolved_identifiers\n )\n\n self.__result = formatted_results\n return formatted_results\n\n def get_constituents(self) -> pd.DataFrame:\n \"\"\"\n Get metadata for hedge constituents\n :return: a DataFrame with results\n \"\"\"\n constituents = self.result.get('Hedge', {}).get('Constituents', [])\n formatted_constituents = []\n for row in constituents:\n formatted_row = {}\n for key in row:\n formatted_row[key[0].capitalize() + ''.join(map(lambda x: x if x.islower() else f' {x}', key[1:]))] = (\n row[key]\n )\n formatted_constituents.append(formatted_row)\n return pd.DataFrame(formatted_constituents)\n\n def get_statistics(self) -> pd.DataFrame:\n \"\"\"\n Get all statistics available for the portfolio, hedge, and hedged portfolio\n :return: a Pandas DataFrame with results\n \"\"\"\n results = {'Portfolio': {}, 'Hedge': {}, 'Hedged Portfolio': {}}\n for key in self.result:\n for inner_key in self.result[key]:\n if isinstance(self.result[key][inner_key], float):\n results[key][inner_key] = self.result[key][inner_key]\n return pd.DataFrame(results)\n\n def get_backtest_performance(self) -> pd.DataFrame:\n \"\"\"\n Get the backtest performance timeseries\n :return: a Pandas DataFrame with results\n \"\"\"\n return self._get_timeseries('Backtest Performance')\n\n def get_backtest_correlation(self) -> pd.DataFrame:\n \"\"\"\n Get the backtest_correlation timeseries of the hedge\n :return: a Pandas DataFrame with results\n \"\"\"\n return self._get_timeseries('Backtest Correlation')\n\n def _get_timeseries(self, timeseries_name: str) -> pd.DataFrame:\n results = {}\n for key in self.result:\n if timeseries_name in self.result[key]:\n ts = self.result[key][timeseries_name]\n for data in ts:\n date = data[0]\n if date in results:\n results[date][key] = data[1]\n else:\n results[date] = {'Date': date, key: data[1]}\n return pd.DataFrame(results.values()).set_index('Date')\n\n @staticmethod\n def _format_hedge_calculate_results(calculation_results):\n renamed_results = {\n 'Portfolio': calculation_results.get('target'),\n 'Hedge': calculation_results.get('hedge'),\n 'Hedged Portfolio': calculation_results.get('hedgedTarget'),\n }\n\n formatted_results = {}\n for key in renamed_results:\n formatted_results[key] = Hedge.format_dictionary_key_to_readable_format(renamed_results[key])\n\n return formatted_results\n\n @staticmethod\n def _enhance_result_with_benchmark_curves(formatted_results, benchmark_results, resolver):\n asset_id_to_provided_identifier_map = dict(\n (x['id'], provided_identifier)\n for provided_identifier, marquee_assets in resolver.items()\n for x in marquee_assets\n )\n\n if len(benchmark_results):\n for x in benchmark_results:\n benchmark_asset_id = asset_id_to_provided_identifier_map[x['assetId']]\n formatted_results[benchmark_asset_id] = Hedge.format_dictionary_key_to_readable_format(x)\n\n return formatted_results\n\n @staticmethod\n def format_dictionary_key_to_readable_format(renamed_results):\n formatted_results = {}\n for inner_key in renamed_results:\n formatted_results[\n inner_key[0].capitalize() + ''.join(map(lambda x: x if x.islower() else f' {x}', inner_key[1:]))\n ] = renamed_results[inner_key]\n return formatted_results\n\n @staticmethod\n def find_optimal_hedge(hedge_query: dict, hyperparams: dict, metric: str) -> Union[dict, float]:\n \"\"\"\n This function is designed to find the 'best' hedge from a list of hedges that are computed using a grid\n search over all hyperparameters passed in - where 'best' is defined by the metric argument passed in and\n whether we want to minimize or maximize this metric.\n\n :param hedge_query: dict, hedge data that is sent to the Marquee API as input to the new performance hedger\n :param hyperparams: dict, keys are hyperparameters (Concentration or Diversity) that map to lists of the values\n to use for one of these hyperparameters when running the new performance hedger.\n :param metric: str, the metric we want to optimize i.e. 'holdingError' or 'rSquared'\n :return: dict, float, and List, the best hedge found using the algorithm, the value of the metric being\n optimized, and the List of optimal hyperparameters\n \"\"\"\n hedge_results = {}\n opt_map = Hedge.create_optimization_mappings()\n optimization_type = opt_map[metric]\n _logger.info(\n f'We are trying to {optimization_type} {metric} and will return the optimized hedge & metric value...'\n )\n hyperparam_grid = [(x, y) for x in hyperparams['Concentration'] for y in hyperparams['Diversity']]\n for pair in hyperparam_grid:\n hedge_params = hedge_query['parameters']\n hedge_params.lasso_weight, hedge_params.ridge_weight = pair[0], pair[1]\n hedge_query['parameters'] = hedge_params\n results = GsHedgeApi.calculate_hedge(hedge_query)\n _logger.info(f'Current Hedge is using the following values for Concentration/Diversity: {pair}')\n curr_results, curr_pair = results['result']['hedge'], pair\n hedge_results[curr_results[metric]] = (curr_results, curr_pair)\n _logger.info(f'Current Hedge value for {metric}: {curr_results[metric] * 100:.3}%')\n optimized_metric = min(hedge_results.keys()) if optimization_type == 'minimize' else max(hedge_results.keys())\n optimized_hedge, optimized_hyperparams = hedge_results[optimized_metric][0], hedge_results[optimized_metric][1]\n return optimized_hedge, optimized_metric, optimized_hyperparams\n\n @staticmethod\n def create_optimization_mappings() -> dict:\n \"\"\"\n This function is designed to construct a mapping between metrics a user can choose to optimize when calling the\n New Performance Hedger and the way the metric should be optimized.\n\n :param none:\n :return: dict, the dictionary containing a mapping between metrics to optimize and how they should be optimized\n \"\"\"\n opt_dict = {\n 'rSquared': 'maximize',\n 'correlation': 'maximize',\n 'holdingError': 'minimize',\n 'trackingError': 'minimize',\n 'transactionCost': 'minimize',\n 'annualizedReturn': 'maximize',\n }\n return opt_dict\n\n @staticmethod\n def construct_portfolio_weights_and_asset_numbers(results: dict) -> Union[dict, List]:\n \"\"\"\n Function used to retrieve the constructed portfolio from a performance hedge, sort it, then calculate the\n weights for all assets and total number of assets and return these results.\n\n :param results: dict, the results of the performance hedge request made to the Marquee API\n :return: dict, list, list - the portfolio, portfolio weights, and number of assets\n \"\"\"\n portfolio = results[\"result\"][\"hedge\"][\"constituents\"]\n portfolio.sort(key=lambda x: x['weight'], reverse=True)\n weights = [asset['weight'] for asset in portfolio]\n asset_numbers = list(range(len(portfolio)))\n return portfolio, weights, asset_numbers\n\n @staticmethod\n def asset_id_diffs(portfolio_asset_ids, thomson_reuters_asset_ids):\n \"\"\"\n Function designed to find the assets that are contained in the portfolio but that we don't have\n Thomson Reuters data for.\n\n :param portfolio_asset_ids: list, the list of MQIDs representing all of the assets that we are computing\n rebalance costs for\n :param thomson_reuters_asset_ids: list, the list of MQIDs representing all of the assets that we have Thomson\n Reuters data for\n :return: list, the assets that we don't have Thomson Reuters data for and should exclude in the transaction cost\n calculations\n \"\"\"\n diffs = list(set(portfolio_asset_ids) - set(thomson_reuters_asset_ids))\n return diffs\n\n @staticmethod\n def create_transaction_cost_data_structures(\n portfolio_asset_ids, portfolio_quantities, thomson_reuters_eod_data, backtest_dates\n ):\n \"\"\"\n Function designed to create the data structures necessary to compute transaction costs based on rebalancing a\n portfolio of assets.\n\n :param portfolio_asset_ids: list, the asset_ids for each asset in the underlying portfolio that we want to\n compute transaction costs for\n :param portfolio_quantities: list, the number of shares for each asset in the underlying portfolio that we want\n to compute transaction costs for\n :param thomson_reuters_eod_data: Dataset, the data used to fetch prices for assets - in this case from Thomson\n Reuters\n :param backtest_dates: list, the dates that the portfolio is held for (that we want to compute rebalance costs\n for)\n :return: Union[list, dict], the data structures necessary for computing transaction (rebalance) costs\n \"\"\"\n thomson_reuters_asset_ids = [\n asset_id\n for asset_id in thomson_reuters_eod_data.get_data(\n backtest_dates[-1], backtest_dates[-1], assetId=portfolio_asset_ids\n )['assetId']\n ]\n diffs = Hedge.asset_id_diffs(portfolio_asset_ids, thomson_reuters_asset_ids)\n for diff in diffs:\n portfolio_asset_ids.remove(diff)\n\n # Map asset_id to quantity of shares from portfolio, while excluding assets that are found in the diffs since\n # there is no Thomson Reuters data on them\n id_quantity_map = {}\n for idx, asset_id in enumerate(portfolio_asset_ids):\n if asset_id not in diffs:\n id_quantity_map[asset_id] = portfolio_quantities[idx]\n\n # Map asset_id to list of prices of that asset over the transaction_cost_dates we want\n id_prices_map = defaultdict(lambda: list())\n prices_df = pd.DataFrame()\n for date in backtest_dates:\n data = thomson_reuters_eod_data.get_data(date, date, assetId=portfolio_asset_ids)\n prices_df = prices_df.append(data)\n for asset_id in portfolio_asset_ids:\n id_prices_map[asset_id] = list(prices_df.loc[prices_df['assetId'] == asset_id]['closePrice'])\n\n # Create list representing notional of each day in transaction_cost_days and map asset_ids to notional of each\n # asset on each day\n id_to_notional_map = {}\n notionals_assets = [\n abs(np.asarray(id_prices_map[asset_id]) * id_quantity_map[asset_id]) for asset_id in portfolio_asset_ids\n ]\n # Mapping asset_id to notionals of each day of that asset_id\n for idx, asset_id in enumerate(portfolio_asset_ids):\n id_to_notional_map[asset_id] = list(notionals_assets[idx])\n total_notionals_each_day = list(np.sum(notionals_assets, axis=0))\n\n # Create map of asset_ids to weights of total portfolio on each day\n id_to_weight_map = {}\n for idx, asset_id in enumerate(portfolio_asset_ids):\n id_to_weight_map[asset_id] = [\n i / j for i, j in zip(id_to_notional_map[portfolio_asset_ids[idx]], total_notionals_each_day)\n ]\n return id_quantity_map, id_prices_map, id_to_notional_map, id_to_weight_map\n\n @staticmethod\n def t_cost(basis_points, notional_traded):\n \"\"\"\n Function designed to compute the transaction costs associated with trading a notional\n amount of an asset to rebalance a portfolio.\n\n :param basis_points: float, the number of basis points to use as an approximation when computing transaction\n costs to trade each asset that is rebalanced and that will be converted to a\n percentage (e.g. 20.43)\n :param notional_traded: float, notional amount of the asset that is being traded to rebalance the portfolio\n :return: float, the total transaction cost of trading a particular notional amount of an asset\n \"\"\"\n return (basis_points * 1e-4) * notional_traded\n\n @staticmethod\n def compute_notional_traded(notional_on_the_day, prev_weight, curr_weight):\n \"\"\"\n Function used to compute the notional amount (USD) of an asset that will be traded on a particular day, using\n the weights of the asset from the previous day and the weights & notional from the current day.\n\n :param notional_on_the_day: float, notional amount of the asset that the portfolio contains on the current day\n :param prev_weight: float, the weighting of the corresponding asset (of the entire portfolio) on the previous\n day\n :param curr_weight: float, the weighting of the corresponding asset (of the entire portfolio) on the current day\n :return: float, the net notional amount of the asset traded on the current day\n \"\"\"\n return sum([np.abs(curr_weight - prev_weight) * notional_on_the_day])\n\n @staticmethod\n def compute_tcosts(basis_points, asset_weights, asset_notionals, backtest_dates, portfolio_asset_ids):\n \"\"\"\n Function to compute cumulative transaction costs associated with rebalancing a portfolio. In particular, for\n each day on which we compute the cumulative the rebalancing costs (USD), the weights of the constituents of the\n portfolio on the previous day are used to calculate how much of the notional amount of each asset is traded to\n execute the rebalance.\n\n :param basis_points: float, the number of basis points to use as an approximation when computing transaction\n costs to trade each asset that is rebalanced and that will be converted to a\n percentage (e.g. 20.43)\n :param asset_weights: dict, the dictionary mapping of asset_ids (MQIDs) to a list of weights where each weight\n represents the weighting of the corresponding asset (of the entire portfolio) on\n each day in the backtest period\n :param asset_notionals: dict, the dictionary mapping of asset_ids (MQIDs) to a list of floats where each float\n represents the notional amount of the corresponding asset (of the entire\n portfolio) on each day in the backtest period\n :param backtest_dates: list, the dates that the portfolio is held for (that we want to compute rebalance costs\n for)\n :param portfolio_asset_ids: list, the list of MQIDs representing all of the assets that we are computing\n rebalancing costs for\n :return: pd.Series, the cumulative transaction costs associated with rebalancing the portfolio across the\n backtest period\n \"\"\"\n tcosts_each_day = []\n for idx, date in enumerate(backtest_dates):\n tcost_today = 0\n for asset_id in portfolio_asset_ids:\n prev_weights = asset_weights[asset_id][0] if idx == 0 else asset_weights[asset_id][idx - 1]\n notional_on_the_day, curr_weights = asset_notionals[asset_id][idx], asset_weights[asset_id][idx]\n notional_to_trade = Hedge.compute_notional_traded(notional_on_the_day, prev_weights, curr_weights)\n transaction_cost = Hedge.t_cost(basis_points, notional_to_trade)\n tcost_today += transaction_cost\n tcosts_each_day.append(abs(tcost_today))\n cum_tcosts = pd.Series(np.cumsum(tcosts_each_day))\n return cum_tcosts\n\n\nclass PerformanceHedge(Hedge):\n def __init__(self, parameters: PerformanceHedgeParameters = None, **kwargs):\n super().__init__(parameters, HedgeObjective.Replicate_Performance)\n" }, { "path": "gs_quant/markets/historical.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom typing import Iterable, Optional, Tuple, Union, Type\n\nfrom gs_quant.base import InstrumentBase, RiskKey\nfrom gs_quant.common import RiskMeasure\nfrom gs_quant.datetime.date import date_range\nfrom gs_quant.risk import RollFwd, MarketDataScenario\nfrom gs_quant.risk.results import HistoricalPricingFuture, PricingFuture\nfrom .core import PricingContext\nfrom .markets import CloseMarket\nfrom ..api.risk import GenericRiskApi\n\n\nclass HistoricalPricingContext(PricingContext):\n \"\"\"\n A context for producing valuations over multiple dates\n \"\"\"\n\n def __init__(\n self,\n start: Optional[Union[int, dt.date]] = None,\n end: Optional[Union[int, dt.date]] = None,\n calendars: Union[str, Tuple] = (),\n dates: Optional[Iterable[dt.date]] = None,\n is_async: bool = None,\n is_batch: bool = None,\n use_cache: bool = None,\n visible_to_gs: bool = None,\n request_priority: Optional[int] = None,\n csa_term: str = None,\n market_data_location: Optional[str] = None,\n timeout: Optional[int] = None,\n show_progress: Optional[bool] = None,\n use_server_cache: Optional[bool] = None,\n provider: Optional[Type[GenericRiskApi]] = None,\n ):\n \"\"\"\n A context for producing valuations over multiple dates\n\n :param start: start date\n :param end: end date (defaults to today)\n :param calendars: holiday calendars\n :param dates: a custom iterable of dates\n :param is_async: return immediately (True) or wait for results (False) (defaults to False)\n :param is_batch: use for calculations expected to run longer than 3 mins, to avoid timeouts.\n It can be used with is_async=True|False (defaults to False)\n :param use_cache: store results in the pricing cache (defaults to False)\n :param visible_to_gs: are the contents of risk requests visible to GS (defaults to False)\n :param csa_term: the csa under which the calculations are made. Default is local ccy ois index\n :param market_data_location: the location for sourcing market data ('NYC', 'LDN' or 'HKG' (defaults to LDN)\n :param timeout: the timeout for batch operations\n :param show_progress: add a progress bar (tqdm)\n :param use_server_cache: cache query results on the GS servers\n\n **Examples**\n\n >>> from gs_quant.instrument import IRSwap\n >>>\n >>> ir_swap = IRSwap('Pay', '10y', 'DKK')\n >>> with HistoricalPricingContext(10):\n >>> price_f = ir_swap.price()\n >>>\n >>> price_series = price_f.result()\n \"\"\"\n super().__init__(\n is_async=is_async,\n is_batch=is_batch,\n use_cache=use_cache,\n visible_to_gs=visible_to_gs,\n request_priority=request_priority,\n csa_term=csa_term,\n market_data_location=market_data_location,\n timeout=timeout,\n show_progress=show_progress,\n use_server_cache=use_server_cache,\n use_historical_diddles_only=True,\n provider=provider,\n )\n\n if start is not None:\n if dates is not None:\n raise ValueError('Must supply start or dates, not both')\n\n if end is None:\n end = dt.date.today()\n\n self.__date_range = tuple(date_range(start, end, calendars=calendars))\n elif dates is not None:\n self.__date_range = tuple(dates)\n else:\n raise ValueError('Must supply start or dates')\n\n def _market(self, date: dt.date, location: str) -> CloseMarket:\n return CloseMarket(location=location, date=date, check=True)\n\n def calc(self, instrument: InstrumentBase, risk_measure: RiskMeasure) -> PricingFuture:\n futures = []\n\n provider = instrument.provider if self.provider is None else self.provider\n scenario = self._scenario\n parameters = self._parameters\n location = self.market.location\n\n for date in self.__date_range:\n risk_key = RiskKey(provider, date, self._market(date, location), parameters, scenario, risk_measure)\n futures.append(self._calc(instrument, risk_key))\n\n return HistoricalPricingFuture(futures)\n\n @property\n def date_range(self):\n return self.__date_range\n\n\nclass BackToTheFuturePricingContext(HistoricalPricingContext):\n \"\"\"\n A context for producing valuations over multiple dates both in the past and into the future\n \"\"\"\n\n def __init__(\n self,\n start: Optional[Union[int, dt.date]] = None,\n end: Optional[Union[int, dt.date]] = None,\n calendars: Union[str, Tuple] = (),\n dates: Optional[Iterable[dt.date]] = None,\n roll_to_fwds: bool = True,\n is_async: bool = None,\n is_batch: bool = None,\n use_cache: bool = None,\n visible_to_gs: bool = None,\n csa_term: str = None,\n market_data_location: Optional[str] = None,\n timeout: Optional[int] = None,\n show_progress: Optional[bool] = None,\n name: Optional[str] = None,\n provider: Optional[Type[GenericRiskApi]] = None,\n ):\n \"\"\"\n A context for producing valuations over multiple dates\n\n :param start: start date\n :param end: end date (defaults to today)\n :param calendars: holiday calendars\n :param dates: a custom iterable of dates\n :param roll_to_fwds: if True then for future dates assume fwd curve is realised. If False assume spot rates\n are realised. (defaults to True)\n :param is_async: return immediately (True) or wait for results (False) (defaults to False)\n :param is_batch: use for calculations expected to run longer than 3 mins, to avoid timeouts.\n It can be used with is_async=True|False (defaults to False)\n :param use_cache: store results in the pricing cache (defaults to False)\n :param visible_to_gs: are the contents of risk requests visible to GS (defaults to False)\n :param csa_term: the csa under which the calculations are made. Default is local ccy ois index\n :param market_data_location: the location for sourcing market data ('NYC', 'LDN' or 'HKG' (defaults to LDN)\n\n **Examples** assuming today is between dt.date(2020, 7, 6) and dt.date(2020, 7, 14)\n\n >>> from gs_quant.instrument import IRSwap\n >>>\n >>> ir_swap = IRSwap('Pay', '10y', 'DKK')\n >>> with BackToTheFuturePricingContext(dt.date(2020, 7, 6), dt.date(2020, 7, 14), roll_to_fwds=True):\n >>> price_f = ir_swap.price()\n >>>\n >>> price_series = price_f.result()\n \"\"\"\n super().__init__(\n start=start,\n end=end,\n calendars=calendars,\n dates=dates,\n is_async=is_async,\n is_batch=is_batch,\n use_cache=use_cache,\n visible_to_gs=visible_to_gs,\n csa_term=csa_term,\n market_data_location=market_data_location,\n timeout=timeout,\n show_progress=show_progress,\n provider=provider,\n )\n self._roll_to_fwds = roll_to_fwds\n self.name = name\n if start is not None:\n if dates is not None:\n raise ValueError('Must supply start or dates, not both')\n\n if end is None:\n end = dt.date.today()\n\n self.__date_range = tuple(date_range(start, end, calendars=calendars))\n elif dates is not None:\n self.__date_range = tuple(dates)\n else:\n raise ValueError('Must supply start or dates')\n\n def calc(self, instrument: InstrumentBase, risk_measure: RiskMeasure) -> PricingFuture:\n futures = []\n\n provider = instrument.provider if self.provider is None else self.provider\n base_scenario = self._scenario\n parameters = self._parameters\n location = self.market.location\n base_market = self.market\n\n for date in self.__date_range:\n if date > self.pricing_date:\n scenario = MarketDataScenario(RollFwd(date=date, realise_fwd=self._roll_to_fwds, name=self.name))\n risk_key = RiskKey(provider, date, base_market, parameters, scenario, risk_measure)\n futures.append(self._calc(instrument, risk_key))\n else:\n risk_key = RiskKey(\n provider, date, self._market(date, location), parameters, base_scenario, risk_measure\n )\n futures.append(self._calc(instrument, risk_key))\n\n return HistoricalPricingFuture(futures)\n" }, { "path": "gs_quant/markets/index.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport json\nimport pandas as pd\n\nfrom typing import Dict, Optional, List, Tuple\nfrom pydash import get\n\nfrom gs_quant.api.gs.assets import GsAsset, GsAssetApi\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.common import AssetClass, Currency, DateLimit\nfrom gs_quant.data.fields import DataMeasure\nfrom gs_quant.entities.entity import EntityType, PositionedEntity\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.instrument import Instrument\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.markets.securities import Asset, AssetType\nfrom gs_quant.markets.indices_utils import ReturnType, STSIndexType, IndicesDatasets, PriceType\nfrom gs_quant.target.data import DataQuery\nfrom gs_quant.entities.tree_entity import AssetTreeNode, TreeHelper\n\n\nclass Index(Asset, PositionedEntity):\n \"\"\"\n Index which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets.\n Includes support for STS indices.\n \"\"\"\n\n def __init__(\n self,\n id_: str,\n asset_class: AssetClass,\n name: str,\n exchange: Optional[str] = None,\n currency: Optional[Currency] = None,\n entity: Optional[Dict] = None,\n ):\n Asset.__init__(self, id_, asset_class, name, exchange, currency, entity=entity)\n PositionedEntity.__init__(self, id_, EntityType.ASSET)\n\n if entity:\n self.asset_type = AssetType(entity['type'])\n else:\n self.asset_type = AssetType.INDEX\n\n if self.__is_sts_index():\n self.tree_helper = TreeHelper(id_, tree_underlier_dataset='STS_UNDERLIER_WEIGHTS')\n self.tree_df = pd.DataFrame()\n\n def __str__(self):\n return self.name\n\n def get_type(self) -> AssetType:\n return self.asset_type\n\n def get_currency(self) -> Optional[Currency]:\n return self.currency\n\n def get_return_type(self) -> ReturnType:\n if self.parameters is None or self.parameters.index_return_type is None:\n return ReturnType.TOTAL_RETURN\n\n return ReturnType(self.parameters.index_return_type)\n\n @classmethod\n def get(cls, identifier: str) -> Optional['Index']:\n \"\"\"\n Fetch an existing index\n\n :param identifier: Any common identifier for an index(ric, ticker, etc.)\n :return: Index object\n\n **Usage**\n\n Get existing Index instance\n\n **Examples**\n\n Get index details:\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n \"\"\"\n gs_asset = cls.__get_gs_asset(identifier)\n asset_entity: Dict = json.loads(json.dumps(gs_asset.as_dict(), cls=JSONEncoder))\n if gs_asset.type.value in STSIndexType.to_list() or gs_asset.type.value == 'Index':\n return cls(\n gs_asset.id,\n gs_asset.asset_class,\n gs_asset.name,\n exchange=gs_asset.exchange,\n currency=gs_asset.currency,\n entity=asset_entity,\n )\n else:\n raise MqValueError(f'{identifier} is not an Index identifier')\n\n def get_fundamentals(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n period: Optional[DataMeasure] = None,\n direction: DataMeasure = DataMeasure.FORWARD.value,\n metrics: List[DataMeasure] = DataMeasure.list_fundamentals(),\n ) -> pd.DataFrame:\n \"\"\"\n Retrieve fundamentals data for an index across a date range. Currently supports STS indices only\n\n :param start: start date (default is 1 January, 1970)\n :param end: end date (default is today)\n :param period: period for the relevant metric. Can be one of ONE_YEAR('1y'), TWO_YEARS('2y'), \\\n THREE_YEARS('3y') (default is all periods)\n :param direction: direction of the outlook period. Can be one of 'forward' or 'trailing' (default is forward)\n :param metrics: list of fundamentals metrics. (default is all)\n :return: dataframe with fundamentals information\n\n **Usage**\n\n Retrieve fundamentals data for an index across a date range\n\n **Examples**\n\n Retrieve historical dividend yield data for an index\n\n >>> from gs_quant.markets.index import Index\n >>> from gs_quant.data.fields import DataMeasure\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_fundamentals(metrics=[DataMeasure.DIVIDEND_YIELD])\n \"\"\"\n if self.__is_sts_index():\n where = dict(assetId=self.id, periodDirection=direction, metric=metrics)\n if period:\n where[\"period\"] = period\n query = DataQuery(where=where, start_date=start, end_date=end)\n response = GsDataApi.query_data(query=query, dataset_id=IndicesDatasets.STS_FUNDAMENTALS.value)\n return pd.DataFrame(response)\n\n else:\n raise MqValueError('This method currently supports STS indices only')\n\n def get_latest_close_price(self, price_type: List[PriceType] = None) -> pd.DataFrame:\n \"\"\"\n Retrieve latest close prices for an index. Only STS indices support indicative prices.\n\n :param price_type: Type of prices to return. Default returns official close price\n :return: dataframe with latest close price\n\n **Usage**\n\n Retrieve latest close prices for an index\n\n **Examples**\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_latest_close_price([PriceType.OFFICIAL_CLOSE_PRICE, PriceType.INDICATIVE_CLOSE_PRICE])\n \"\"\"\n if (not price_type) or (price_type == [PriceType.OFFICIAL_CLOSE_PRICE]):\n return super().get_latest_close_price()\n\n prices = pd.DataFrame()\n if PriceType.OFFICIAL_CLOSE_PRICE in price_type:\n official_level = super().get_latest_close_price()\n prices['date'] = official_level.index\n prices['closePrice'] = official_level[0]\n\n if PriceType.INDICATIVE_CLOSE_PRICE in price_type:\n if self.__is_sts_index():\n where = dict(assetId=self.id)\n query = DataQuery(where=where)\n response = GsDataApi.last_data(query=query, dataset_id=IndicesDatasets.STS_INDICATIVE_LEVELS.value)\n indicative_level = pd.DataFrame(response).iloc[-1:][['date', 'indicativeClosePrice']]\n prices['date'] = indicative_level['date'].iat[0]\n prices['indicativeClosePrice'] = indicative_level['indicativeClosePrice'].iat[0]\n\n else:\n raise MqValueError('PriceType.INDICATIVE_CLOSE_PRICE currently supports STS indices only')\n\n return prices\n\n def get_close_price_for_date(\n self, date: dt.date = dt.date.today(), price_type: List[PriceType] = None\n ) -> pd.DataFrame:\n \"\"\"\n Retrieve close prices for an index. Only STS indices support indicative prices.\n\n :param date: date of the required prices (default is today)\n :param price_type: Type of prices to return. Default returns official close price\n :return: dataframe with date's close prices\n\n **Usage**\n\n Retrieve the close prices for an index for a given date\n\n **Examples**\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_close_price_for_date(dt.date(2021, 1, 7), \\\n [PriceType.OFFICIAL_CLOSE_PRICE, PriceType.INDICATIVE_CLOSE_PRICE])\n \"\"\"\n if (not price_type) or (price_type == [PriceType.OFFICIAL_CLOSE_PRICE]):\n return super().get_close_price_for_date(date)\n\n prices = pd.DataFrame()\n if PriceType.OFFICIAL_CLOSE_PRICE in price_type:\n official_level = super().get_close_price_for_date(date)\n prices['date'] = official_level.index\n prices['closePrice'] = official_level[0]\n\n if PriceType.INDICATIVE_CLOSE_PRICE in price_type:\n if self.__is_sts_index():\n response = self.__query_indicative_levels_dataset(start=date, end=date)\n indicative_level = pd.DataFrame(response)\n prices['date'] = indicative_level['date'].iat[0]\n prices['indicativeClosePrice'] = indicative_level['indicativeClosePrice'].iat[0]\n\n else:\n raise MqValueError('PriceType.INDICATIVE_CLOSE_PRICE currently supports STS indices only')\n\n return prices\n\n def get_close_prices(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n price_type: List[PriceType] = None,\n ) -> pd.DataFrame:\n \"\"\"\n Retrieve close prices for an index for a date range. Only STS indices support indicative prices.\n\n :param start: start date (default is 1 January, 1970)\n :param end: end date (default is today)\n :param price_type: Type of prices to return. Default returns official close price\n :return: dataframe with the close price between start and end date\n\n **Usage**\n\n Retrieve the close prices for an index for a given date range.\n\n **Examples**\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_close_prices(dt.date(2021, 1, 7), dt.date(2021, 3, 27), \\\n [PriceType.OFFICIAL_CLOSE_PRICE, PriceType.INDICATIVE_CLOSE_PRICE])\n \"\"\"\n if (not price_type) or (price_type == [PriceType.OFFICIAL_CLOSE_PRICE]):\n return super().get_close_prices(start, end)\n\n prices = pd.DataFrame()\n\n if self.__is_sts_index():\n if price_type == [PriceType.INDICATIVE_CLOSE_PRICE]:\n indicative_level = self.__query_indicative_levels_dataset(start=start, end=end)\n indicative_level = indicative_level.drop(['updateTime', 'assetId'], axis=1)\n indicative_level = indicative_level.astype({'date': 'datetime64[ns]'})\n prices['date'] = indicative_level['date']\n prices['indicativeClosePrice'] = indicative_level['indicativeClosePrice']\n return prices\n\n official_level = super().get_close_prices(start=start, end=end).to_frame('closePrice')\n indicative_level = self.__query_indicative_levels_dataset(start=start, end=end)\n\n official_level = official_level.reset_index()\n indicative_level = indicative_level.drop(['updateTime', 'assetId'], axis=1)\n indicative_levels = indicative_level.astype({'date': official_level.dtypes['date']})\n merged = pd.merge(official_level, indicative_levels, on='date', how='outer')\n return merged\n\n else:\n raise MqValueError('PriceType.INDICATIVE_CLOSE_PRICE currently supports STS indices only')\n\n def get_underlier_tree(self, refresh_tree: Optional[bool] = False) -> AssetTreeNode:\n \"\"\"\n Get the root node of the tree formed by the Index, as an AssetTreeNode object.\n\n :param refresh_tree: Refresh the underliers information of the entire tree\n\n Please refer to the documenation of the AssetTreeNode class for more information.\n Currently supports STS indices only.\n\n :return: root node of the tree formed by the Index, as an AssetTreeNode object.\n\n **Examples**\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_underlier_tree()\n \"\"\"\n if self.__is_sts_index():\n if (not self.tree_helper.tree_built) or refresh_tree:\n self.tree_helper.build_tree()\n self.tree_helper.populate_weights('STS_UNDERLIER_WEIGHTS')\n self.tree_helper.populate_attribution('STS_UNDERLIER_ATTRIBUTION')\n self.tree_df = self.tree_helper.to_frame()\n\n return self.tree_helper.root\n\n else:\n raise MqValueError('This method currently supports STS indices only')\n\n def get_underlier_weights(self) -> pd.DataFrame:\n \"\"\"\n Get the weights of the immediate (one-level-down) underliers of the Index.\n Currently supports STS indices only.\n\n :return: pandas DataFrame with the weights of the immediate underliers.\n\n **Examples**\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_underlier_weights()\n \"\"\"\n if self.__is_sts_index():\n if len(self.tree_df) == 0:\n self.get_underlier_tree()\n\n return self.tree_df.loc[self.tree_df.depth == 1].drop(\n columns=['absoluteAttribution', 'assetId', 'assetName', 'depth']\n )\n else:\n raise MqValueError('This method currently supports STS indices only')\n\n def get_underlier_attribution(self) -> pd.DataFrame:\n \"\"\"\n Get the attribution of the immediate (one-level-down) underliers of the Index.\n Currently supports STS indices only.\n\n :return: pandas DataFrame with the attribution of the immediate underliers.\n\n **Examples**\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_underlier_attribution()\n \"\"\"\n if self.__is_sts_index():\n if len(self.tree_df) == 0:\n self.get_underlier_tree()\n\n return self.tree_df.loc[self.tree_df.depth == 1].drop(columns=['weight', 'assetId', 'assetName', 'depth'])\n\n else:\n raise MqValueError('This method currently supports STS indices only')\n\n def visualise_tree(self, visualise_by: Optional[str] = 'asset_name'):\n \"\"\"\n Visualise the tree by printing the structure of the entire tree.\n The visualise_by argument can be either 'asset_name' or 'bbid'. Currently supports STS indices only.\n\n :param visualise_by: Parameter to visualise by. Default uses Asset Name to label the nodes\n :return: treelib Tree object, which can be printed to see the tree structure.\n\n **Examples**\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> print(index.visualise_tree())\n \"\"\"\n\n if self.__is_sts_index():\n return self.tree_helper.get_visualisation(visualise_by)\n\n else:\n raise MqValueError('This method currently supports STS indices only')\n\n def get_latest_constituents(self) -> pd.DataFrame:\n \"\"\"\n Fetch the latest constituents of the index in a pandas dataframe.\n\n :return: pandas dataframe with the index constituents, weights and other details.\n\n **Usage**\n\n Get the latest constituents of the index\n\n **Examples**\n\n Get latest index constituents:\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_latest_constituents()\n \"\"\"\n\n return self.get_latest_position_set().get_positions()\n\n def get_constituents_for_date(self, date: dt.date = dt.date.today()) -> pd.DataFrame:\n \"\"\"\n Fetch the constituents of the index in a pandas dataframe for a the given date.\n\n :return: pandas dataframe with the index constituents, weights and other details.\n\n **Usage**\n\n Get the constituents of the index for the given date\n\n **Examples**\n\n Get index constituents:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_constituents_for_date(dt.date(2021, 7, 1))\n \"\"\"\n\n return self.get_position_set_for_date(date).get_positions()\n\n def get_constituents(\n self, start: dt.date = DateLimit.LOW_LIMIT.value, end: dt.date = dt.date.today()\n ) -> List[pd.DataFrame]:\n \"\"\"\n Fetch the constituents of the index in a pandas dataframe for the given date range\n\n :return: pandas dataframe with the index constituents, weights and other details.\n\n **Usage**\n\n Get the constituents of the index for the given date range\n\n **Examples**\n\n Get index constituents:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_constituents(dt.date(2021, 6, 1), dt.date(2021, 6, 10))\n \"\"\"\n\n return [position_set.get_positions() for position_set in self.get_position_sets(start, end)]\n\n def get_latest_constituent_instruments(self) -> Tuple[Instrument, ...]:\n \"\"\"\n Fetch the latest constituents of the index as instrument objects.\n\n :return: A Tuple of instrument objects\n\n **Usage**\n\n Get the latest constituents of the index\n\n **Examples**\n\n Get latest index constituent instruments:\n\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_latest_constituent_instruments()\n \"\"\"\n\n return GsAssetApi.get_instruments_for_positions(self.get_latest_position_set().to_target().positions)\n\n def get_constituent_instruments_for_date(self, date: dt.date = dt.date.today()) -> Tuple[Instrument, ...]:\n \"\"\"\n Fetch the constituents of the index for a given date as instrument objects.\n\n :return: A Tuple of instrument objects\n\n **Usage**\n\n Get the constituents of the index for the given date as instrument objects\n\n **Examples**\n\n Get index constituent instruments:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_constituent_instruments_for_date(dt.date(2021, 7, 1))\n \"\"\"\n\n return GsAssetApi.get_instruments_for_positions(self.get_position_set_for_date(date).to_target().positions)\n\n def get_constituent_instruments(\n self, start: dt.date = DateLimit.LOW_LIMIT.value, end: dt.date = dt.date.today()\n ) -> Tuple[Tuple[Instrument, ...], ...]:\n \"\"\"\n Fetch the constituents of the index as instrument objects for the given date range\n\n :return: Tuple of Tuples, containing all instrument objects for each date.\n\n **Usage**\n\n Get the constituents of the index for the given date range as instrument objects.\n\n **Examples**\n\n Get index constituent instruments:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.index import Index\n >>>\n >>> index = Index.get(\"GSMBXXXX\")\n >>> index.get_constituent_instruments(dt.date(2021, 6, 1), dt.date(2021, 6, 10))\n \"\"\"\n position_sets = self.get_position_sets(start, end)\n return [\n GsAssetApi.get_instruments_for_positions(position_set.to_target().positions)\n for position_set in position_sets\n ]\n\n def __is_sts_index(self) -> bool:\n \"\"\"Checks if is an STS get_index\"\"\"\n\n if self.get_type().value in STSIndexType.to_list():\n return True\n return False\n\n def __query_indicative_levels_dataset(self, start=None, end=None) -> pd.DataFrame:\n where = dict(assetId=self.id)\n if start is None:\n query = DataQuery(where=where)\n else:\n query = DataQuery(where=where, start_date=start, end_date=end)\n\n response = GsDataApi.query_data(query=query, dataset_id=IndicesDatasets.STS_INDICATIVE_LEVELS.value)\n indicative_level = pd.DataFrame(response)\n if len(indicative_level) == 0:\n indicative_level['date'] = ''\n indicative_level['assetId'] = ''\n indicative_level['updateTime'] = ''\n indicative_level['indicativeClosePrice'] = ''\n return indicative_level\n\n @staticmethod\n def __get_gs_asset(identifier: str) -> GsAsset:\n \"\"\"Resolves index identifier during initialization\"\"\"\n response = GsAssetApi.resolve_assets(identifier=[identifier], fields=['id'], limit=1)[identifier]\n if len(response) == 0 or get(response, '0.id') is None:\n raise MqValueError(f'Asset could not be found using identifier {identifier}')\n return GsAssetApi.get_asset(get(response, '0.id'))\n" }, { "path": "gs_quant/markets/indices_utils.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport pandas as pd\n\nfrom enum import Enum\nfrom functools import partial, reduce\nfrom pydash import get\nfrom time import sleep\nfrom typing import Dict, List, Optional, Union\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.api.gs.monitors import GsMonitorsApi\nfrom gs_quant.api.utils import ThreadPoolManager\nfrom gs_quant.base import EnumBase\nfrom gs_quant.common import AssetClass\nfrom gs_quant.datetime.date import prev_business_date\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.data import DataQuery\n\n\nQUERY_LIMIT = 1000\n\n\nclass BasketType(EnumBase, Enum):\n \"\"\"Basket Types\"\"\"\n\n CUSTOM_BASKET = 'Custom Basket'\n RESEARCH_BASKET = 'Research Basket'\n\n def __repr__(self):\n return self.value\n\n @classmethod\n def to_list(cls):\n return [basket_type.value for basket_type in cls]\n\n\nclass CorporateActionType(EnumBase, Enum):\n \"\"\"Different types of corporate actions\"\"\"\n\n ACQUISITION = 'Acquisition'\n CASH_DIVIDEND = 'Cash Dividend'\n IDENTIFIER_CHANGE = 'Identifier Change'\n RIGHTS_ISSUE = 'Rights Issue'\n SHARE_CHANGE = 'Share Change'\n SPECIAL_DIVIDEND = 'Special Dividend'\n SPIN_OFF = 'Spin Off'\n STOCK_DIVIDEND = 'Stock Dividend'\n STOCK_SPLIT = 'Stock Split'\n\n def __repr__(self):\n return self.value\n\n @classmethod\n def to_list(cls):\n return [ca_type.value for ca_type in cls]\n\n\nclass CustomBasketStyles(EnumBase, Enum):\n \"\"\"Styles for Custom Baskets\"\"\"\n\n AD_HOC_DESK_WORK = 'Ad Hoc Desk Work'\n CLIENT_CONSTRUCTED_WRAPPER = 'Client Constructed/Wrapper'\n CONSUMER = 'Consumer'\n ENERGY = 'Energy'\n ENHANCED_INDEX_SOLUTIONS = 'Enhanced Index Solutions'\n ESG = 'ESG'\n FACTORS = 'Factors'\n FINANCIALS = 'Financials'\n FLAGSHIP = 'Flagship'\n GEOGRAPHIC = 'Geographic'\n GROWTH = 'Growth'\n HEALTHCARE = 'Health Care'\n HEDGING = 'Hedging'\n INDUSTRIALS = 'Industrials'\n MATERIALS = 'Materials'\n MOMENTUM = 'Momentum'\n PIPG = 'PIPG'\n SECTORS_INDUSTRIES = 'Sectors/Industries'\n SIZE = 'Size'\n STRUCTURED_ONE_DELTA = 'Structured One Delta'\n THEMATIC = 'Thematic'\n TMT = 'TMT'\n UTILITIES = 'Utilities'\n VALUE = 'Value'\n VOLATILITY = 'Volatility'\n\n def __repr__(self):\n return self.value\n\n\nclass IndicesDatasets(EnumBase, Enum):\n \"\"\"Indices Datasets\"\"\"\n\n BASKET_FUNDAMENTALS = 'BASKET_FUNDAMENTALS'\n COMPOSITE_THEMATIC_BETAS = 'COMPOSITE_THEMATIC_BETAS'\n CREDIT_EOD_PRICING_V1_STANDARD = 'CREDIT_EOD_PRICING_V1_STANDARD'\n CORPORATE_ACTIONS = 'CA'\n GIRBASKETCONSTITUENTS = 'GIRBASKETCONSTITUENTS'\n GSBASKETCONSTITUENTS = 'GSBASKETCONSTITUENTS'\n GSCB_FLAGSHIP = 'GSCB_FLAGSHIP'\n GSCREDITBASKETCONSTITUENTS = 'GSCREDITBASKETCONSTITUENTS'\n STS_FUNDAMENTALS = 'STS_FUNDAMENTALS'\n STS_INDICATIVE_LEVELS = 'STS_INDICATIVE_LEVELS'\n THEMATIC_FACTOR_BETAS_STANDARD = 'THEMATIC_FACTOR_BETAS_V2_STANDARD'\n\n def __repr__(self):\n return self.value\n\n\nclass PriceType(EnumBase, Enum):\n \"\"\"Index Price Types\"\"\"\n\n INDICATIVE_CLOSE_PRICE = 'indicativeClosePrice'\n OFFICIAL_CLOSE_PRICE = 'officialClosePrice'\n\n def __repr__(self):\n return self.value\n\n @classmethod\n def to_list(cls):\n return [price_type for price_type in cls]\n\n\nclass Region(EnumBase, Enum):\n \"\"\"Region of the index\"\"\"\n\n AMERICAS = 'Americas'\n ASIA = 'Asia'\n EM = 'EM'\n EUROPE = 'Europe'\n GLOBAL = 'Global'\n\n def __repr__(self):\n return self.value\n\n\nclass ResearchBasketStyles(EnumBase, Enum):\n \"\"\"Styles for Research Baskets\"\"\"\n\n ASIA_EX_JAPAN = 'Asia ex-Japan'\n EQUITY_THEMATIC = 'Equity Thematic'\n EUROPE = 'Europe'\n FUND_OWNERSHIP = 'Fund Ownership'\n FUNDAMENTALS = 'Fundamentals'\n FX_OIL = 'FX/Oil'\n GEOGRAPHICAL_EXPOSURE = 'Geographical Exposure'\n HEDGE_FUND = 'Hedge Fund'\n IP_FACTORS = 'Investment Profile (IP) Factors'\n JAPAN = 'Japan'\n MACRO = 'Macro'\n MACRO_SLICE_STYLES = 'Macro Slice/Styles'\n MUTUAL_FUND = 'Mutual Fund'\n POSITIONING = 'Positioning'\n PORTFOLIO_STRATEGY = 'Portfolio Strategy'\n RISK_AND_LIQUIDITY = 'Risk & Liquidity'\n SECTOR = 'Sector'\n SHAREHOLDER_RETURN = 'Shareholder Return'\n STYLE_FACTOR_AND_FUNDAMENTAL = 'Style, Factor and Fundamental'\n STYLES_THEMES = 'Style/Themes'\n TACTICAL_RESEARCH = 'Tactical Research'\n THEMATIC = 'Thematic'\n US = 'US'\n WAVEFRONT_COMPONENTS = 'Wavefront Components'\n WAVEFRONT_PAIRS = 'Wavefront Pairs'\n WAVEFRONTS = 'Wavefronts'\n\n def __repr__(self):\n return self.value\n\n\nclass ReturnType(EnumBase, Enum):\n \"\"\"Determines the index calculation methodology with respect to dividend reinvestment\"\"\"\n\n GROSS_RETURN = 'Gross Return'\n PRICE_RETURN = 'Price Return'\n TOTAL_RETURN = 'Total Return'\n\n def __repr__(self):\n return self.value\n\n\nclass STSIndexType(EnumBase, Enum):\n \"\"\"STS Types\"\"\"\n\n ACCESS = 'Access'\n MULTI_ASSET_ALLOCATION = 'Multi-Asset Allocation'\n RISK_PREMIA = 'Risk Premia'\n SYSTEMATIC_HEDGING = 'Systematic Hedging'\n\n def __repr__(self):\n return self.value\n\n @classmethod\n def to_list(cls):\n return [sts_type.value for sts_type in cls]\n\n\nclass WeightingStrategy(EnumBase, Enum):\n \"\"\"Strategy used to price the index's position set\"\"\"\n\n EQUAL = 'Equal'\n MARKET_CAPITALIZATION = 'Market Capitalization'\n QUANTITY = 'Quantity'\n WEIGHT = 'Weight'\n\n def __repr__(self):\n return self.value\n\n\ndef get_my_baskets(user_id: str = None) -> Optional[pd.DataFrame]:\n \"\"\"\n Retrieve a list of baskets a user is permissioned to\n\n :param user_id: Marquee user/app ID (default is current application's id)\n :return: dataframe of baskets user has access to\n\n **Usage**\n\n Retrieve a list of baskets a user is permissioned to\n\n **Examples**\n\n Retrieve a list of baskets the current user is permissioned to\n\n >>> from gs_quant.markets.indices_utils import *\n >>>\n >>> get_my_baskets()\n \"\"\"\n user_id = user_id if user_id is not None else GsSession.current.client_id\n tag = f'Custom Basket:{user_id}'\n response = GsMonitorsApi.get_monitors(tags=tag)\n if len(response):\n row_groups = get(response, '0.parameters.row_groups')\n my_baskets = []\n for row_group in row_groups:\n entity_ids = [entity.id for entity in row_group.entity_ids]\n baskets = GsAssetApi.get_many_assets_data(id=entity_ids, fields=['id', 'ticker', 'name', 'liveDate'])\n my_baskets += [\n dict(\n monitor_name=row_group.name,\n id=get(basket, 'id'),\n ticker=get(basket, 'ticker'),\n name=get(basket, 'name'),\n live_date=get(basket, 'liveDate'),\n )\n for basket in baskets\n ]\n return pd.DataFrame(my_baskets)\n\n\ndef __get_baskets(\n fields: List[str] = [],\n basket_type: List[BasketType] = BasketType.to_list(),\n asset_class: List[AssetClass] = [AssetClass.Equity],\n region: List[Region] = None,\n styles: List[Union[CustomBasketStyles, ResearchBasketStyles]] = None,\n as_of: dt.datetime = None,\n **kwargs,\n) -> Dict:\n default_fields = set(['id', 'name', 'ticker', 'region', 'type', 'description', 'styles', 'liveDate', 'assetClass'])\n query, fields = {}, list(set(fields).union(default_fields))\n for k, v in kwargs.items():\n query[k] = v\n if region:\n query['region'] = region\n if styles:\n query['styles'] = styles\n query = dict(\n fields=fields, type=basket_type, asset_class=asset_class, is_pair_basket=[False], flagship=[True], **query\n )\n return GsAssetApi.get_many_assets_data_scroll(**query, as_of=as_of, limit=QUERY_LIMIT, scroll='1m')\n\n\ndef __get_dataset_id(asset_class: AssetClass, basket_type: BasketType, data_type: str) -> str:\n if asset_class == AssetClass.Equity or asset_class == AssetClass.Equity.value:\n if data_type == 'price':\n return IndicesDatasets.GSCB_FLAGSHIP.value\n elif basket_type == BasketType.CUSTOM_BASKET or basket_type == BasketType.CUSTOM_BASKET.value:\n return IndicesDatasets.GSBASKETCONSTITUENTS.value\n else:\n return IndicesDatasets.GIRBASKETCONSTITUENTS.value\n raise NotImplementedError(f'{data_type} data for {asset_class} baskets is unsupported at this time')\n\n\ndef get_flagship_baskets(\n fields: List[str] = [],\n basket_type: List[BasketType] = BasketType.to_list(),\n asset_class: List[AssetClass] = [AssetClass.Equity],\n region: List[Region] = None,\n styles: List[Union[CustomBasketStyles, ResearchBasketStyles]] = None,\n as_of: dt.datetime = None,\n **kwargs,\n) -> pd.DataFrame:\n \"\"\"\n Retrieve flagship baskets\n\n :param fields: Fields to retrieve in addition to mqid, name, ticker, region, basket type, \\\n description, styles, live date, and asset class\n :param basket_type: Basket type(s)\n :param asset_class: Asset class (defaults to Equity)\n :param region: Basket region(s)\n :param styles: Basket style(s)\n :param as_of: Datetime for which to retrieve baskets (defaults to current time)\n :return: flagship baskets\n\n **Usage**\n\n Retrieve a list of flagship baskets\n\n **Examples**\n\n Retrieve a list of flagship baskets\n\n >>> from gs_quant.markets.indices_utils import *\n >>>\n >>> get_flagship_baskets()\n\n **See also**\n\n :func:`get_flagships_with_assets` :func:`get_flagships_performance` :func:`get_flagships_constituents`\n \"\"\"\n response = __get_baskets(\n fields=fields,\n as_of=as_of,\n basket_type=basket_type,\n asset_class=asset_class,\n region=region,\n styles=styles,\n **kwargs,\n )\n return pd.DataFrame(response)\n\n\ndef get_flagships_with_assets(\n identifiers: List[str],\n fields: List[str] = [],\n basket_type: List[BasketType] = BasketType.to_list(),\n asset_class: List[AssetClass] = [AssetClass.Equity],\n region: List[Region] = None,\n styles: List[Union[CustomBasketStyles, ResearchBasketStyles]] = None,\n as_of: dt.datetime = None,\n **kwargs,\n) -> pd.DataFrame:\n \"\"\"\n Retrieve a list of flagship baskets containing specified assets\n\n :param identifiers: List of asset identifiers\n :param fields: Fields to retrieve in addition to mqid, name, ticker, region, basket type, \\\n description, styles, live date, and asset class\n :param basket_type: Basket type(s)\n :param asset_class: Asset class (defaults to Equity)\n :param region: Basket region(s)\n :param styles: Basket style(s)\n :param as_of: Datetime for which to retrieve baskets (defaults to current time)\n :return: flagship baskets containing specified assets\n\n **Usage**\n\n Retrieve a list of flagship baskets containing specified assets\n\n **Examples**\n\n Retrieve a list of flagship custom baskets containing 'AAPL UW' single stock\n\n >>> from gs_quant.markets.indices_utils import *\n >>>\n >>> get_flagships_with_assets(identifiers=['AAPL UW'], basket_type=[BasketType.CUSTOM_BASKET])\n\n **See also**\n\n :func:`get_flagship_baskets` :func:`get_flagships_performance` :func:`get_flagships_constituents`\n \"\"\"\n response = GsAssetApi.resolve_assets(identifier=identifiers, fields=['id'], limit=1)\n mqids = [get(asset, '0.id') for asset in response.values()]\n response = __get_baskets(\n fields=fields,\n as_of=as_of,\n basket_type=basket_type,\n asset_class=asset_class,\n region=region,\n styles=styles,\n underlying_asset_ids=mqids,\n **kwargs,\n )\n return pd.DataFrame(response)\n\n\ndef get_flagships_performance(\n fields: List[str] = [],\n basket_type: List[BasketType] = BasketType.to_list(),\n asset_class: List[AssetClass] = [AssetClass.Equity],\n region: List[Region] = None,\n styles: List[Union[CustomBasketStyles, ResearchBasketStyles]] = None,\n start: dt.date = None,\n end: dt.date = None,\n **kwargs,\n) -> pd.DataFrame:\n \"\"\"\n Retrieve performance data for flagship baskets\n\n :param fields: Fields to retrieve in addition to bbid, mqid, name, region, basket type, \\\n styles, live date, and asset class\n :param basket_type: Basket type(s)\n :param asset_class: Asset class (defaults to Equity)\n :param region: Basket region(s)\n :param styles: Basket style(s)\n :param start: Date for which to retrieve pricing (defaults to previous business day)\n :param end: Date for which to retrieve pricing (defaults to previous business day)\n :return: pricing data for flagship baskets\n\n **Usage**\n\n Retrieve performance data for flagship baskets\n\n **Examples**\n\n Retrieve performance data for flagship Asia custom baskets\n\n >>> from gs_quant.markets.indices_utils import *\n >>>\n >>> get_flagships_performance(basket_type=[BasketType.CUSTOM_BASKET], region=[Region.ASIA])\n\n **See also**\n\n :func:`get_flagships_with_assets` :func:`get_flagship_baskets` :func:`get_flagships_constituents`\n \"\"\"\n start, end = start or prev_business_date(), end or prev_business_date()\n assets = __get_baskets(\n fields=fields, basket_type=basket_type, asset_class=asset_class, region=region, styles=styles, **kwargs\n )\n baskets = {b.get('id'): b for b in assets}\n dataset_id = __get_dataset_id(asset_class=asset_class[0], basket_type=basket_type[0], data_type='price')\n coverage = GsDataApi.get_coverage(dataset_id=dataset_id, fields=['id'])\n mqids = [b.get('assetId') for b in coverage if b.get('assetId') in baskets.keys()]\n batches = [mqids[i * 500 : (i + 1) * 500] for i in range((len(mqids) + 500 - 1) // 500)]\n response, performance = [], []\n for b in batches:\n response += GsDataApi.query_data(\n query=DataQuery(where={'assetId': b}, startDate=start, endDate=end), dataset_id=dataset_id\n )\n for b in response:\n data = baskets.get(b.get('assetId'))\n b.update(data)\n b.pop('assetId')\n b.pop('updateTime')\n performance.append(b)\n return pd.DataFrame(performance)\n\n\ndef get_flagships_constituents(\n fields: List[str] = [],\n basket_type: List[BasketType] = BasketType.to_list(),\n asset_class: List[AssetClass] = [AssetClass.Equity],\n region: List[Region] = None,\n styles: List[Union[CustomBasketStyles, ResearchBasketStyles]] = None,\n start: dt.date = None,\n end: dt.date = None,\n **kwargs,\n) -> pd.DataFrame:\n \"\"\"\n Retrieve flagship baskets constituents\n\n :param fields: Fields to retrieve in addition to mqid, name, ticker, region, basket type, \\\n styles, live date, and asset class\n :param basket_type: Basket type(s)\n :param asset_class: Asset class (defaults to Equity)\n :param region: Basket region(s)\n :param styles: Basket style(s)\n :param start: Start date for which to retrieve constituents (defaults to previous business day)\n :param end: End date for which to retrieve constituents (defaults to previous business day)\n :return: flagship baskets constituents\n\n **Usage**\n\n Retrieve flagship baskets constituents\n\n **Examples**\n\n Retrieve a list of flagship baskets constituents\n\n >>> from gs_quant.markets.indices_utils import *\n >>>\n >>> get_flagships_constituents()\n\n **See also**\n\n :func:`get_flagships_with_assets` :func:`get_flagships_performance` :func:`get_flagship_baskets`\n \"\"\"\n start, end = start or prev_business_date(), end or prev_business_date()\n basket_fields = list(\n set(fields).union(set(['id', 'name', 'ticker', 'region', 'type', 'styles', 'liveDate', 'assetClass']))\n )\n fields = list(set(fields).union(set(['id'])))\n response = __get_baskets(\n fields=['id'], basket_type=basket_type, asset_class=asset_class, region=region, styles=styles, **kwargs\n )\n basket_ids = [b.get('id') for b in response]\n cov_dataset_id = __get_dataset_id(asset_class=asset_class[0], basket_type=basket_type[0], data_type='price')\n coverage = GsDataApi.get_coverage(dataset_id=cov_dataset_id, fields=basket_fields, include_history=True)\n basket_map = {b['assetId']: {**b, 'constituents': []} for b in coverage if b['assetId'] in basket_ids}\n\n basket_dataset_query_map, constituents_data, tasks = {}, [], []\n # get appropriate dataset for each basket\n for b in basket_map.values():\n dataset_id = __get_dataset_id(asset_class=b['assetClass'], basket_type=b['type'], data_type='constituents')\n if dataset_id:\n basket_dataset_query_map[dataset_id] = basket_dataset_query_map.get(dataset_id, []) + [b['assetId']]\n\n # query constituents in batches of 25\n for ds, ids in basket_dataset_query_map.items():\n batches = [ids[i * 25 : (i + 1) * 25] for i in range((len(ids) + 25 - 1) // 25)]\n for batch in batches:\n tasks.append(\n partial(\n GsDataApi.query_data,\n query=DataQuery(where={'assetId': batch}, startDate=start, endDate=end),\n dataset_id=ds,\n )\n )\n\n # run 5 parallel dataset queries at a time\n tasks = [tasks[i * 2 : (i + 1) * 2] for i in range((len(tasks) + 2 - 1) // 2)]\n for task in tasks:\n constituents_data += ThreadPoolManager.run_async(task)\n sleep(1)\n constituents_data = reduce(lambda a, b: a + b, constituents_data)\n\n # fetch asset positions data\n # fetch asset positions data in batches to avoid exceeding API limits\n asset_ids = list(set([row['underlyingAssetId'] for row in constituents_data]))\n asset_data = []\n # Process in batches of 100 to stay below scroll size limit\n batch_size = 100\n for i in range(0, len(asset_ids), batch_size):\n batch = asset_ids[i : i + batch_size]\n asset_data += GsAssetApi.get_many_assets_data_scroll(\n id=batch, fields=fields, limit=QUERY_LIMIT, scroll='1m', source=\"Basket\"\n )\n asset_data_map = {get(asset, 'id'): asset for asset in asset_data}\n\n for row in constituents_data:\n basket_id = get(row, 'assetId', '')\n asset_id = get(row, 'underlyingAssetId', '')\n asset_id_map = get(asset_data_map, asset_id, {})\n for f in fields:\n row[f] = get(asset_id_map, f)\n basket_map[basket_id]['constituents'].append(row)\n\n return pd.DataFrame([r for r in basket_map.values() if r is not None])\n\n\ndef get_constituents_dataset_coverage(\n basket_type: BasketType = BasketType.CUSTOM_BASKET,\n asset_class: AssetClass = AssetClass.Equity,\n as_of: dt.datetime = None,\n) -> pd.DataFrame:\n \"\"\"\n Retrieve a list of baskets covered by constituents datasets\n\n :param basket_type: Basket type\n :param asset_class: Asset class (defaults to Equity)\n :param as_of: Date for which to retrieve coverage\n :return: baskets covered by the constituents dataset\n\n **Usage**\n\n Retrieve a list of baskets covered by constituents datasets\n\n **Examples**\n\n Retrieve basket constituent dataset coverage\n\n >>> from gs_quant.markets.indices_utils import *\n >>>\n >>> GSBASKETCONSTITUENTS_COVERAGE = get_constituents_dataset_coverage()\n >>> GIRBASKETCONSTITUENTS_COVERAGE = get_constituents_dataset_coverage(basket_type=BasketType.RESEARCH_BASKET)\n >>> GSCREDITBASKETCONSTITUENTS_COVERAGE = get_constituents_dataset_coverage(asset_class=AssetClass.Credit)\n\n **See also**\n\n :func:`get_flagships_constituents`\n \"\"\"\n query = dict(\n fields=['id', 'name', 'region', 'ticker', 'type', 'assetClass'],\n type=[basket_type],\n asset_class=[asset_class],\n is_pair_basket=[False],\n listed=[True],\n )\n if asset_class != AssetClass.Equity:\n query.pop('is_pair_basket')\n response = GsAssetApi.get_many_assets_data_scroll(**query, as_of=as_of, limit=QUERY_LIMIT, scroll='1m')\n return pd.DataFrame(response)\n" }, { "path": "gs_quant/markets/markets.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport re\nfrom typing import Mapping, Optional, Tuple, Union\n\nfrom gs_quant.base import Market, RiskKey\nfrom gs_quant.common import (\n CloseMarket as _CloseMarket,\n LiveMarket as _LiveMarket,\n OverlayMarket as _OverlayMarket,\n RelativeMarket as _RelativeMarket,\n TimestampedMarket as _TimestampedMarket,\n RefMarket as _RefMarket,\n PricingLocation,\n)\nfrom gs_quant.datetime.date import prev_business_date, location_to_tz_mapping\nfrom gs_quant.target.data import (\n MarketDataCoordinate as __MarketDataCoordinate,\n MarketDataCoordinateValue as __MarketDataCoordinateValue,\n)\n\n\ndef historical_risk_key(risk_key: RiskKey) -> RiskKey:\n market = LocationOnlyMarket(risk_key.market.location)\n return RiskKey(risk_key.provider, None, market, risk_key.params, risk_key.scenario, risk_key.risk_measure)\n\n\ndef market_location(location: Optional[PricingLocation] = None) -> PricingLocation:\n \"\"\"Determine PricingLocation and ensure passed optional 'location' does not\n conflict with the current PricingContext's market_data_location\n\n :param location: optional PricingLocation\n :return: PricingLocation\n \"\"\"\n from .core import PricingContext\n\n default = PricingContext.current.market_data_location\n\n if location is None:\n return default or PricingLocation.LDN\n else:\n return location\n\n\ndef close_market_date(\n location: Optional[Union[PricingLocation, str]] = None,\n date: Optional[dt.date] = None,\n roll_hr_and_min: Tuple[int, int] = (24, 0),\n) -> dt.date:\n \"\"\"Determine market data date based on current location (to infer calendar)\n and current pricing date\n\n :param location: date location, used for the roll timezone\n :param date: pricing date\n :param roll_hr_and_min: tuple of the hour and minute of the expected market data availability time\n in the location timezone\n :return: close market date\n \"\"\"\n from .core import PricingContext\n\n date = date or PricingContext.current.pricing_date\n\n location_tz = location_to_tz_mapping[PricingLocation(location)]\n now_time = dt.datetime.now().astimezone(location_tz).replace(tzinfo=None)\n hr_offset = roll_hr_and_min[0]\n min_offset = roll_hr_and_min[1]\n roll_time = dt.datetime(date.year, date.month, date.day).replace(tzinfo=None) + dt.timedelta(\n hours=hr_offset, minutes=min_offset\n )\n if now_time < roll_time:\n # Don't use the calendars argument here as external users do not (yet) have access to that dataset\n date = prev_business_date(date)\n\n return date\n\n\nclass MarketDataCoordinate(__MarketDataCoordinate):\n def __repr__(self):\n ret = \"_\".join(f or '' for f in (self.mkt_type, self.mkt_asset, self.mkt_class))\n\n if self.mkt_point:\n ret += '_' + ','.join(self.mkt_point)\n\n if self.mkt_quoting_style:\n ret += f'.{self.mkt_quoting_style}'\n\n return ret\n\n @classmethod\n def from_string(cls, value: str):\n from gs_quant.api.gs.data import GsDataApi\n\n ret = GsDataApi._coordinate_from_str(value)\n if len(ret.mkt_point) == 1:\n # Unfortunately _,; have all been used as delimiters in various places\n ret.mkt_point = tuple(re.split('[,_;]', ret.mkt_point[0]))\n\n return ret\n\n\nclass MarketDataCoordinateValue(__MarketDataCoordinateValue):\n def __repr__(self):\n return f'{self.coordinate} --> {self.value}'\n\n\nCoordinates = Tuple[MarketDataCoordinate, ...]\nMarketDataMap = Mapping[MarketDataCoordinate, float]\n\n\nclass LocationOnlyMarket(Market):\n def __init__(self, location: Optional[Union[str, PricingLocation]]):\n self.__location = (\n location if isinstance(location, PricingLocation) or location is None else PricingLocation(location)\n )\n\n @property\n def market(self):\n return None\n\n @property\n def location(self) -> PricingLocation:\n return self.__location\n\n\nclass CloseMarket(Market):\n \"\"\"Market Object which captures market data based on market_location\n and close_market_date\n \"\"\"\n\n __date_cache = {}\n roll_hr_and_min = (24, 0) # tuple of today's expected hr/min of market data availability in the location tz\n\n def __init__(\n self,\n date: Optional[dt.date] = None,\n location: Optional[Union[str, PricingLocation]] = None,\n check: Optional[bool] = True,\n ):\n self.__date = date\n self.__location = (\n location if isinstance(location, PricingLocation) or location is None else PricingLocation(location)\n )\n self.check = check\n\n def __repr__(self):\n return f'{self.date} ({self.location.value})'\n\n @property\n def market(self):\n return _CloseMarket(date=self.date, location=self.location)\n\n def to_dict(self):\n return {'date': self.date, 'location': self.location, 'marketType': 'CloseMarket'}\n\n def __hash__(self):\n return hash((self.date, self.location))\n\n def __eq__(self, other):\n return isinstance(other, CloseMarket) and self.date == other.date and self.location == other.location\n\n @property\n def location(self) -> PricingLocation:\n if self.__location is not None and not self.check:\n return self.__location\n else:\n return market_location(self.__location)\n\n @property\n def date(self) -> dt.date:\n if self.__date is not None and not self.check:\n return self.__date\n else:\n return close_market_date(self.location, self.__date, self.roll_hr_and_min)\n\n\nclass TimestampedMarket(Market):\n \"\"\"Market Object which captures market data based on location\n and timestamp\n \"\"\"\n\n def __init__(self, timestamp: dt.datetime, location: Optional[Union[str, PricingLocation]] = None):\n self.__timestamp = timestamp\n self.__location = (\n location if isinstance(location, PricingLocation) or location is None else PricingLocation(location)\n )\n\n def __repr__(self):\n return f'{self.__timestamp} ({self.location.value})'\n\n @property\n def market(self):\n return _TimestampedMarket(timestamp=self.__timestamp, location=self.location)\n\n @property\n def location(self) -> PricingLocation:\n return market_location(self.__location)\n\n\nclass LiveMarket(Market):\n \"\"\"Market Object which captures market data based on location\n and time at runtime\n \"\"\"\n\n def __init__(self, location: Optional[Union[str, PricingLocation]] = None):\n self.__location = (\n location if isinstance(location, PricingLocation) or location is None else PricingLocation(location)\n )\n\n def __repr__(self):\n return f'Live ({self.location.value})'\n\n @property\n def location(self) -> PricingLocation:\n return market_location(self.__location)\n\n @property\n def market(self):\n return _LiveMarket(location=self.location)\n\n\nclass OverlayMarket(Market):\n \"\"\"Market Object which overlays a base Market object (eg: CloseMarket, LiveMarket or\n TimestampedMarket) with a MarketDataMap (a map of market coordinate to float)\n \"\"\"\n\n def __init__(\n self,\n market_data: Optional[MarketDataMap] = None,\n base_market: Optional[Market] = None,\n binary_mkt_data: Optional[str] = None,\n ):\n market_data = market_data or {}\n\n self.__base_market = base_market or CloseMarket()\n self.__market_data = dict(filter(lambda elem: elem[1] != 'redacted', market_data.items()))\n self.__market_model_data = binary_mkt_data\n self.__redacted_coordinates = tuple(key for (key, value) in market_data.items() if value == 'redacted')\n\n def __getitem__(self, item):\n if isinstance(item, str):\n item = MarketDataCoordinate.from_string(item)\n\n return self.__market_data.get(item)\n\n def __setitem__(self, key, value):\n if isinstance(key, str):\n key = MarketDataCoordinate.from_string(key)\n\n if key in self.redacted_coordinates:\n raise KeyError(f'{key} cannot be overridden')\n\n self.__market_data[key] = value\n\n def __repr__(self):\n return f'Overlay ({id(self)}): {repr(self.__base_market)}'\n\n @property\n def market_data(self) -> Tuple[MarketDataCoordinateValue, ...]:\n return tuple(MarketDataCoordinateValue(coordinate=c, value=v) for c, v in self.__market_data.items())\n\n @property\n def market_model_data(self) -> str:\n return self.__market_model_data\n\n @property\n def market_data_dict(self) -> MarketDataMap:\n return {p.coordinate: p.value for p in self.market_data}\n\n @property\n def location(self) -> PricingLocation:\n return self.__base_market.location\n\n @property\n def market(self):\n return _OverlayMarket(\n base_market=self.__base_market.market,\n market_data=self.market_data,\n market_model_data=self.market_model_data,\n )\n\n @property\n def coordinates(self) -> Coordinates:\n return tuple(self.__market_data.keys())\n\n @property\n def redacted_coordinates(self) -> Coordinates:\n return self.__redacted_coordinates\n\n\nclass RefMarket(Market):\n \"\"\"Market Object which represents a Reference to a Market\"\"\"\n\n def __init__(self, market_ref: str):\n self.__market = _RefMarket(market_ref=str(market_ref))\n\n def __repr__(self):\n return f'Market Ref ({self.__market.market_ref})'\n\n @property\n def market(self):\n return self.__market\n\n @property\n def location(self) -> PricingLocation:\n return market_location()\n\n\nclass RelativeMarket(Market):\n \"\"\"Market Object which captures the change between two Market Objects\n (to_market and from_market)\n \"\"\"\n\n def __init__(self, from_market: Market, to_market: Market):\n self.__from_market = from_market\n self.__to_market = to_market\n\n def __repr__(self):\n return f'{repr(self.__from_market)} -> {repr(self.__to_market)}'\n\n @property\n def market(self):\n return _RelativeMarket(from_market=self.__from_market.market, to_market=self.__to_market.market)\n\n @property\n def location(self) -> PricingLocation:\n return self.__from_market.location if self.__from_market.location == self.__to_market.location else None\n" }, { "path": "gs_quant/markets/optimizer.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom enum import Enum\nfrom functools import wraps\nfrom typing import List, Dict, Optional, Union, Final\n\nfrom dateutil.relativedelta import relativedelta\n\nfrom gs_quant.api.gs.hedges import GsHedgeApi\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.factor import Factor\nfrom gs_quant.markets.position_set import PositionSet, Position\nfrom gs_quant.markets.securities import Asset\nfrom gs_quant.models.risk_model import FactorRiskModel\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.hedge import CorporateActionsTypes\nimport pandas as pd\nimport numpy as np\nimport math\nimport datetime as dt\n\n_logger = logging.getLogger(__name__)\n\n\ndef resolve_assets_in_batches(\n identifiers: List[str],\n fields: List[str] = None,\n as_of_date: dt.date = dt.date.today(),\n batch_size: int = 100,\n **kwargs,\n) -> List[Dict]:\n all_fields = [\"id\", \"name\", \"bbid\"]\n if fields:\n all_fields += fields\n identifiers_batches = (\n np.array_split(identifiers, math.ceil(len(identifiers) / batch_size))\n if len(identifiers) > batch_size\n else [identifiers]\n )\n\n all_assets_resolved = {}\n for batch in identifiers_batches:\n res = GsAssetApi.resolve_assets(\n identifier=list(batch),\n as_of=dt.datetime.combine(as_of_date, dt.datetime.min.time()),\n fields=all_fields,\n limit=1,\n **kwargs,\n )\n all_assets_resolved = {**all_assets_resolved, **res}\n\n assets_resolved_as_records = []\n for identifier in all_assets_resolved:\n if all_assets_resolved[identifier]:\n assets_resolved_as_records.append({\"identifier\": identifier, **all_assets_resolved[identifier][0]})\n\n return assets_resolved_as_records\n\n\nclass OptimizationConstraintUnit(Enum):\n DECIMAL = 'Decimal'\n NOTIONAL = 'Notional'\n PERCENT = 'Percent'\n\n\nclass HedgeTarget(Enum):\n HEDGED_TARGET = \"hedgedTarget\"\n HEDGE = \"hedge\"\n TARGET = \"target\"\n\n\nclass OptimizerObjective(Enum):\n MINIMIZE_FACTOR_RISK = 'Minimize Factor Risk'\n\n\nclass OptimizerRiskType(Enum):\n VARIANCE = 'Variance'\n\n\nclass OptimizerObjectiveTerm:\n DEFAULT_RISK_PARAMS: Final = {\n 'factor_weight': 1,\n 'specific_weight': 1,\n 'risk_type': OptimizerRiskType.VARIANCE,\n }\n\n def __init__(self, weight: float = 1, params: Dict[str, float] = DEFAULT_RISK_PARAMS):\n self.__weight = weight\n self.__params = {**self.DEFAULT_RISK_PARAMS, **params}\n\n @property\n def params(self) -> Dict:\n return self.__params\n\n @params.setter\n def params(self, params: Dict[str, float]):\n self.__params = {**self.DEFAULT_RISK_PARAMS, **params}\n\n @property\n def weight(self) -> float:\n return self.__weight\n\n @weight.setter\n def weight(self, weight: float):\n self.__weight = weight\n\n def to_dict(self) -> Dict:\n payload = {\n 'factorWeight': self.__params['factor_weight'],\n 'specificWeight': self.__params['specific_weight'],\n 'riskType': self.__params['risk_type'].value,\n 'weight': self.__weight,\n }\n return payload\n\n\nclass OptimizerObjectiveParameters:\n def __init__(\n self,\n objective: OptimizerObjective = OptimizerObjective.MINIMIZE_FACTOR_RISK,\n terms: List[OptimizerObjectiveTerm] = [OptimizerObjectiveTerm.DEFAULT_RISK_PARAMS],\n ):\n self.__objective = objective\n self.__terms = terms\n\n @property\n def objective(self):\n return self.__objective\n\n @objective.setter\n def objective(self, objective: OptimizerObjective):\n self.__objective = objective\n\n @property\n def terms(self):\n return self.__terms\n\n @terms.setter\n def terms(self, terms: List[OptimizerObjectiveTerm]):\n self.__terms = terms\n\n def to_dict(self):\n if len(self.__terms) != 1:\n raise MqValueError('Only single risk term is supported')\n return {'parameters': self.__terms[0].to_dict()}\n\n\nclass OptimizerType(Enum):\n AXIOMA_PORTFOLIO_OPTIMIZER = 'Axioma Portfolio Optimizer'\n\n\nclass PrioritySetting(Enum):\n ZERO = '0'\n ONE = '1'\n TWO = '2'\n THREE = '3'\n FOUR = '4'\n FIVE = '5'\n\n\nclass TurnoverNotionalType(Enum):\n NET = 'Net'\n LONG = 'Long'\n GROSS = 'Gross'\n\n\nclass AssetUniverse:\n def __init__(self, identifiers: List[str], asset_ids: List[str] = None, as_of_date: dt.date = dt.date.today()):\n self.__identifiers = identifiers\n self.__as_of_date = as_of_date\n self.__asset_ids = asset_ids\n\n @property\n def identifiers(self):\n return self.__identifiers\n\n @identifiers.setter\n def identifiers(self, identifiers: List[str]):\n self.__identifiers = identifiers\n\n @property\n def asset_ids(self):\n return self.__asset_ids\n\n @asset_ids.setter\n def asset_ids(self, asset_ids: List[str]):\n self.__asset_ids = asset_ids\n\n @property\n def as_of_date(self):\n return self.__as_of_date\n\n @as_of_date.setter\n def as_of_date(self, date: dt.date):\n self.__as_of_date = date\n\n def resolve(self):\n if not self.__asset_ids:\n assets_resolved_as_records = resolve_assets_in_batches(\n identifiers=self.identifiers, as_of_date=self.as_of_date, batch_size=250\n )\n\n assets_resolved_df = (\n pd.DataFrame(assets_resolved_as_records).set_index(\"identifier\").reindex(self.identifiers)\n )\n\n self.asset_ids = assets_resolved_df['id'].values.tolist()\n\n\nclass AssetConstraint:\n def __init__(\n self,\n asset: Union[Asset, str],\n minimum: float = 0,\n maximum: float = 100,\n unit: OptimizationConstraintUnit = OptimizationConstraintUnit.PERCENT,\n ):\n self.__asset = asset\n self.__minimum = minimum\n self.__maximum = maximum\n self.__unit = unit\n\n @property\n def asset(self) -> Union[Asset, str]:\n return self.__asset\n\n @asset.setter\n def asset(self, value: Union[Asset, str]):\n self.__asset = value\n\n @property\n def minimum(self) -> float:\n return self.__minimum\n\n @minimum.setter\n def minimum(self, value: float):\n self.__minimum = value\n\n @property\n def maximum(self) -> float:\n return self.__maximum\n\n @maximum.setter\n def maximum(self, value: float):\n self.__maximum = value\n\n @property\n def unit(self) -> OptimizationConstraintUnit:\n return self.__unit\n\n @unit.setter\n def unit(self, value: OptimizationConstraintUnit):\n self.__unit = value\n\n def to_dict(self):\n return {\n 'assetId': self.asset if isinstance(self.asset, str) else self.asset.get_marquee_id(),\n 'min': self.minimum * 100 if self.unit == OptimizationConstraintUnit.DECIMAL else self.minimum,\n 'max': self.maximum * 100 if self.unit == OptimizationConstraintUnit.DECIMAL else self.maximum,\n }\n\n @classmethod\n def build_many_constraints(\n cls,\n asset_constraints: Union[pd.DataFrame, List[Dict]],\n as_of_date: dt.date = dt.date.today(),\n fail_on_unresolved_positions: bool = True,\n **kwargs,\n ):\n \"\"\"Create many asset constraints from a dataframe or a list of dictionaries\n :param asset_constraints: dataframe or list of dictionaries containing the asset constraints\n :param as_of_date: the date on which to resolve the assets\n :param fail_on_unresolved_positions: whether to raise an error if any assets cannot be resolved\n :param kwargs: additional arguments to pass to the resolve_assets_in_batches function.\n\n :return: list of AssetConstraint objects\n\n :raises MqValueError: if the input is missing required columns \"identifier\", \"minimum\", \"maximum\", or \"unit\"\n :raises MqValueError: if any assets cannot be resolved and fail_on_unresolved_positions is True\n :raises MqValueError: if the input asset constraints are in more than one unit\n\n **Examples**\n\n 1. The input is a list of dictionaries\n >>> asset_constraints = AssetConstraint.build_many_constraints(\n >>> [{\"identifier\": \"AAPL UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"identifier\": \"MSFT UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"identifier\": \"NVDA UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}\n >>> ],\n >>> as_of_date=dt.date(2025, 2, 24))\n\n 2. The input is a dataframe\n\n >>> asset_constraints = AssetConstraint.build_many_constraints(\n >>> pd.DataFrame([{\"identifier\": \"AAPL UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"identifier\": \"MSFT UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"identifier\": \"NVDA UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}]),\n >>> as_of_date=dt.date(2025, 2, 24)\n >>> )\n\n Additional arguments can also be provided to the function and these will be used to resolve assets.\n Below we are adding a `type` and `assetClass` argument to tell the internal GS Security master to only return\n Single Stocks, Equity Indices, and ETFs.\n\n >>> asset_constraints = AssetConstraint.build_many_constraints(\n >>> pd.DataFrame([{\"identifier\": \"AAPL UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"identifier\": \"MSFT UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"identifier\": \"NVDA UW\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}]),\n >>> as_of_date=dt.date(2025, 2, 24),\n >>> fail_on_unresolved_positions=True,\n >>> type=[\"Single Stock\", \"Index\", \"ETF\"],\n >>> assetClass=[\"Equity\"]\n >>> )\n\n \"\"\"\n\n asset_constraints_df = (\n pd.DataFrame(asset_constraints) if isinstance(asset_constraints, list) else asset_constraints\n )\n missing_columns = [\n col for col in ['identifier', 'minimum', \"maximum\", \"unit\"] if col not in asset_constraints_df.columns\n ]\n if missing_columns:\n raise MqValueError(f\"The input is missing required columns: {', '.join(missing_columns)}\")\n\n if len(set(asset_constraints_df['unit'].values.tolist())) > 1:\n raise MqValueError('All asset constraints must be in the same unit')\n\n if 'assetId' not in asset_constraints_df:\n identifiers = asset_constraints_df['identifier'].values.tolist()\n\n assets_resolved_as_records = resolve_assets_in_batches(\n identifiers=identifiers, as_of_date=as_of_date, batch_size=250, **kwargs\n )\n\n asset_constraints_df = pd.merge(\n asset_constraints_df, pd.DataFrame(assets_resolved_as_records), on='identifier', how='left'\n )\n\n if fail_on_unresolved_positions and asset_constraints_df['id'].isna().any():\n missing_ids = asset_constraints_df[asset_constraints_df['id'].isna()]['identifier'].values.tolist()\n raise MqValueError(\n f\"The following identifiers could not be resolved on {as_of_date.strftime('%Y-%m-%d')}: \"\n f\"{', '.join(missing_ids)}\"\n )\n else:\n asset_constraints_df = asset_constraints_df[asset_constraints_df['id'].notna()]\n\n asset_constraints_df = asset_constraints_df.rename(columns={'id': 'assetId'})[\n ['assetId', 'minimum', 'maximum', 'unit']\n ]\n\n asset_constraints_df = asset_constraints_df.to_dict(orient='records')\n\n return [\n cls(\n asset=row.get('assetId'),\n minimum=row.get('minimum'),\n maximum=row.get('maximum'),\n unit=OptimizationConstraintUnit(row.get('unit')),\n )\n for row in asset_constraints_df\n ]\n\n\nclass CountryConstraint:\n def __init__(\n self,\n country_name: str,\n minimum: float = 0,\n maximum: float = 100,\n unit: OptimizationConstraintUnit = OptimizationConstraintUnit.PERCENT,\n ):\n \"\"\"\n Constrain notional held in any particular country in the resulting optimization\n\n :param country_name: country name\n :param minimum: minimum\n :param maximum: maximum\n :param unit: the unit in which the min and max values are passed in with (defaults to percent)\n \"\"\"\n if unit not in [OptimizationConstraintUnit.PERCENT, OptimizationConstraintUnit.DECIMAL]:\n raise MqValueError('Country constraints can only be set by percent or decimal.')\n self.__country_name = country_name\n self.__minimum = minimum\n self.__maximum = maximum\n self.__unit = unit\n\n @property\n def country_name(self) -> str:\n return self.__country_name\n\n @country_name.setter\n def country_name(self, value: str):\n self.__country_name = value\n\n @property\n def minimum(self) -> float:\n return self.__minimum\n\n @minimum.setter\n def minimum(self, value: float):\n self.__minimum = value\n\n @property\n def maximum(self) -> float:\n return self.__maximum\n\n @maximum.setter\n def maximum(self, value: float):\n self.__maximum = value\n\n @property\n def unit(self) -> OptimizationConstraintUnit:\n return self.__unit\n\n @unit.setter\n def unit(self, value: OptimizationConstraintUnit):\n if value not in [OptimizationConstraintUnit.PERCENT, OptimizationConstraintUnit.DECIMAL]:\n raise MqValueError('Country constraints can only be set by percent or decimal.')\n self.__unit = value\n\n def to_dict(self):\n return {\n 'type': 'Country',\n 'name': self.country_name,\n 'min': self.minimum * 100 if self.unit == OptimizationConstraintUnit.DECIMAL else self.minimum,\n 'max': self.maximum * 100 if self.unit == OptimizationConstraintUnit.DECIMAL else self.maximum,\n }\n\n @classmethod\n def build_many_constraints(cls, country_constraints: Union[pd.DataFrame, List[Dict]]):\n \"\"\"\n Create many country constraints from a dataframe or a list of dictionaries\n :param country_constraints: dataframe or list of dictionaries containing the country constraints\n\n :return: list of CountryConstraint objects\n\n :raises MqValueError: if the input is missing required columns \"country\", \"minimum\", \"maximum\", or \"unit\"\n :raises MqValueError: if the input country constraints are in more than one unit\n\n **Examples**\n\n 1. The input is a list of dictionaries\n >>> country_constraints = CountryConstraint.build_many_constraints(\n >>> [{\"country\": \"USA\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"country\": \"Canada\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"country\": \"Germany\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}\n >>> ])\n\n 2. The input is a dataframe\n >>> country_constraints = CountryConstraint.build_many_constraints(\n >>> pd.DataFrame([{\"country\": \"USA\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"country\": \"Canada\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"country\": \"Germany\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}])\n >>> )\n \"\"\"\n country_constraints = (\n pd.DataFrame(country_constraints) if isinstance(country_constraints, list) else country_constraints\n )\n\n missing_columns = [\n col for col in ['country', 'minimum', 'maximum', 'unit'] if col not in country_constraints.columns\n ]\n if missing_columns:\n raise MqValueError(f\"The input is missing required columns: {', '.join(missing_columns)}\")\n country_constraints_as_records = country_constraints.to_dict(orient='records')\n\n return [\n cls(\n country_name=row.get('country'),\n minimum=row.get('minimum'),\n maximum=row.get('maximum'),\n unit=OptimizationConstraintUnit(row.get('unit')),\n )\n for row in country_constraints_as_records\n ]\n\n\nclass SectorConstraint:\n def __init__(\n self,\n sector_name: str,\n minimum: float = 0,\n maximum: float = 100,\n unit: OptimizationConstraintUnit = OptimizationConstraintUnit.PERCENT,\n ):\n \"\"\"\n Constrain notional held in any particular GICS Sector in the resulting optimization\n\n :param sector_name: sector name\n :param minimum: minimum\n :param maximum: maximum\n :param unit: the unit in which the min and max values are passed in with (defaults to percent)\n \"\"\"\n if unit not in [OptimizationConstraintUnit.PERCENT, OptimizationConstraintUnit.DECIMAL]:\n raise MqValueError('Sector constraints can only be set by percent or decimal.')\n self.__sector_name = sector_name\n self.__minimum = minimum\n self.__maximum = maximum\n self.__unit = unit\n\n @property\n def sector_name(self) -> str:\n return self.__sector_name\n\n @sector_name.setter\n def sector_name(self, value: str):\n self.__sector_name = value\n\n @property\n def minimum(self) -> float:\n return self.__minimum\n\n @minimum.setter\n def minimum(self, value: float):\n self.__minimum = value\n\n @property\n def maximum(self) -> float:\n return self.__maximum\n\n @maximum.setter\n def maximum(self, value: float):\n self.__maximum = value\n\n @property\n def unit(self) -> OptimizationConstraintUnit:\n return self.__unit\n\n @unit.setter\n def unit(self, value: OptimizationConstraintUnit):\n if value not in [OptimizationConstraintUnit.PERCENT, OptimizationConstraintUnit.DECIMAL]:\n raise MqValueError('Sector constraints can only be set by percent.')\n self.__unit = value\n\n def to_dict(self):\n return {\n 'type': 'Sector',\n 'name': self.sector_name,\n 'min': self.minimum * 100 if self.unit == OptimizationConstraintUnit.DECIMAL else self.minimum,\n 'max': self.maximum * 100 if self.unit == OptimizationConstraintUnit.DECIMAL else self.maximum,\n }\n\n @classmethod\n def build_many_constraints(cls, sector_constraints: Union[pd.DataFrame, List[Dict]]):\n \"\"\"\n Create many sector constraints from a dataframe or a list of dictionaries\n :param sector_constraints: dataframe or list of dictionaries containing the sector constraints\n\n :return: list of SectorConstraint objects\n\n :raises MqValueError: if the input is missing required columns \"sector\", \"minimum\", \"maximum\", or \"unit\"\n :raises MqValueError: if the input sector constraints are in more than one unit\n\n **Examples**\n\n 1. The input is a list of dictionaries\n >>> sector_constraints = SectorConstraint.build_many_constraints(\n >>> [{\"sector\": \"Technology\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"sector\": \"Healthcare\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"sector\": \"Finance\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}\n >>> ])\n\n 2. The input is a dataframe\n >>> sector_constraints = SectorConstraint.build_many_constraints(\n >>> pd.DataFrame([{\"sector\": \"Technology\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"sector\": \"Healthcare\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"sector\": \"Finance\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}])\n >>> )\n \"\"\"\n sector_constraints = (\n pd.DataFrame(sector_constraints) if isinstance(sector_constraints, list) else sector_constraints\n )\n\n missing_columns = [\n col for col in ['sector', 'minimum', 'maximum', 'unit'] if col not in sector_constraints.columns\n ]\n if missing_columns:\n raise MqValueError(f\"The input is missing required columns: {', '.join(missing_columns)}\")\n sector_constraints_as_records = sector_constraints.to_dict(orient='records')\n\n return [\n cls(\n sector_name=row.get('sector'),\n minimum=row.get('minimum'),\n maximum=row.get('maximum'),\n unit=OptimizationConstraintUnit(row.get('unit')),\n )\n for row in sector_constraints_as_records\n ]\n\n\nclass IndustryConstraint:\n def __init__(\n self,\n industry_name: str,\n minimum: float = 0,\n maximum: float = 100,\n unit: OptimizationConstraintUnit = OptimizationConstraintUnit.PERCENT,\n ):\n \"\"\"\n Constrain notional held in any particular GICS Industry in the resulting optimization\n\n :param industry_name: industry name\n :param minimum: minimum\n :param maximum: maximum\n :param unit: the unit in which the min and max values are passed in with (defaults to percent)\n \"\"\"\n if unit not in [OptimizationConstraintUnit.PERCENT, OptimizationConstraintUnit.DECIMAL]:\n raise MqValueError('Industry constraints can only be set by percent or decimal.')\n self.__industry_name = industry_name\n self.__minimum = minimum\n self.__maximum = maximum\n self.__unit = unit\n\n @property\n def industry_name(self) -> str:\n return self.__industry_name\n\n @industry_name.setter\n def industry_name(self, value: str):\n self.__industry_name = value\n\n @property\n def minimum(self) -> float:\n return self.__minimum\n\n @minimum.setter\n def minimum(self, value: float):\n self.__minimum = value\n\n @property\n def maximum(self) -> float:\n return self.__maximum\n\n @maximum.setter\n def maximum(self, value: float):\n self.__maximum = value\n\n @property\n def unit(self) -> OptimizationConstraintUnit:\n return self.__unit\n\n @unit.setter\n def unit(self, value: OptimizationConstraintUnit):\n if value not in [OptimizationConstraintUnit.PERCENT, OptimizationConstraintUnit.DECIMAL]:\n raise MqValueError('Industry constraints can only be set by percent.')\n self.__unit = value\n\n def to_dict(self):\n return {\n 'type': 'Industry',\n 'name': self.industry_name,\n 'min': self.minimum * 100 if self.unit == OptimizationConstraintUnit.DECIMAL else self.minimum,\n 'max': self.maximum * 100 if self.unit == OptimizationConstraintUnit.DECIMAL else self.maximum,\n }\n\n @classmethod\n def build_many_constraints(cls, industry_constraints: Union[pd.DataFrame, List[Dict]]):\n \"\"\"\n Create many industry constraints from a dataframe or a list of dictionaries\n :param industry_constraints: dataframe or list of dictionaries containing the industry constraints\n\n :return: list of IndustryConstraint objects\n\n :raises MqValueError: if the input is missing required columns \"industry\", \"minimum\", \"maximum\", or \"unit\"\n :raises MqValueError: if the input industry constraints are in more than one unit\n\n **Examples**\n\n 1. The input is a list of dictionaries\n >>> industry_constraints = IndustryConstraint.build_many_constraints(\n >>> [{\"industry\": \"Software\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"industry\": \"Pharmaceuticals\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"industry\": \"Banking\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}\n >>> ])\n\n 2. The input is a dataframe\n >>> industry_constraints = IndustryConstraint.build_many_constraints(\n >>> pd.DataFrame([{\"industry\": \"Software\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"industry\": \"Pharmaceuticals\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"},\n >>> {\"industry\": \"Banking\", \"minimum\": 0, \"maximum\": 5, \"unit\": \"Percent\"}])\n >>> )\n \"\"\"\n industry_constraints = (\n pd.DataFrame(industry_constraints) if isinstance(industry_constraints, list) else industry_constraints\n )\n\n missing_columns = [\n col for col in ['industry', 'minimum', 'maximum', 'unit'] if col not in industry_constraints.columns\n ]\n if missing_columns:\n raise MqValueError(f\"The input is missing required columns: {', '.join(missing_columns)}\")\n industry_constraints_as_records = industry_constraints.to_dict(orient='records')\n\n return [\n cls(\n industry_name=row.get('industry'),\n minimum=row.get('minimum'),\n maximum=row.get('maximum'),\n unit=OptimizationConstraintUnit(row.get('unit')),\n )\n for row in industry_constraints_as_records\n ]\n\n\nclass FactorConstraint:\n def __init__(self, factor: Factor, max_exposure: float):\n \"\"\"\n Constrain a factor by a max exposure\n\n :param factor: the factor to constrain\n :param max_exposure: the maximum exposure to the factor in the final portfolio\n \"\"\"\n self.__factor = factor\n self.__max_exposure = max_exposure\n\n @property\n def factor(self) -> Factor:\n return self.__factor\n\n @factor.setter\n def factor(self, value: Factor):\n self.__factor = value\n\n @property\n def max_exposure(self) -> float:\n return self.__max_exposure\n\n @max_exposure.setter\n def max_exposure(self, value: float):\n self.__max_exposure = value\n\n def to_dict(self):\n return {'factor': self.factor.name, 'exposure': self.max_exposure}\n\n @classmethod\n def build_many_constraints(cls, factor_constraints: Union[pd.DataFrame, List[Dict]], risk_model_id: str):\n \"\"\"\n Create many factor constraints from a dataframe or a list of dictionaries\n :param factor_constraints: dataframe or list of dictionaries containing the factor constraints\n :param risk_model_id: the id of the risk model\n\n :return: list of FactorConstraint objects\n :raises MqValueError: if the input is missing required columns \"factor\" or \"exposure\"\n\n **Examples**\n 1. The input is a list of dictionaries\n >>> factor_constraints = FactorConstraint.build_many_constraints(\n >>> [{\"factor\": \"Value\", \"exposure\": 5000},\n >>> {\"factor\": \"Growth\", \"exposure\": 1000},\n >>> {\"factor\": \"Beta\", \"exposure\": 10000}\n >>> ], \"BARRA_USFAST\")\n\n 2. The input is a dataframe\n >>> factor_constraints = FactorConstraint.build_many_constraints(\n >>> pd.DataFrame([{\"factor\": \"Value\", \"exposure\": 5000},\n >>> {\"factor\": \"Growth\", \"exposure\": 1000},\n >>> {\"factor\": \"Beta\", \"exposure\": 10000}\n >>> ]), \"BARRA_USFAST\")\n\n \"\"\"\n factor_constraints_df = (\n pd.DataFrame(factor_constraints) if isinstance(factor_constraints, list) else factor_constraints\n )\n\n missing_columns = [col for col in ['factor', 'exposure'] if col not in factor_constraints_df.columns]\n if missing_columns:\n raise MqValueError(f\"The input is missing required columns: {', '.join(missing_columns)}\")\n\n risk_model = FactorRiskModel.get(risk_model_id)\n factors = risk_model.get_many_factors(factor_names=factor_constraints_df['factor'].values.tolist())\n\n name_to_factor_obj = [{\"factor\": f.name, \"factorObj\": f} for f in factors]\n name_to_factor_obj_df = pd.DataFrame(name_to_factor_obj)\n\n factor_constraints_df = factor_constraints_df.merge(name_to_factor_obj_df, on='factor', how='inner')\n\n factor_constraints_df = factor_constraints_df[[\"factorObj\", \"exposure\"]].rename(columns={\"factorObj\": \"factor\"})\n\n all_constraints = factor_constraints_df.to_dict(orient='records')\n\n return [cls(factor=row.get('factor'), max_exposure=row.get('exposure')) for row in all_constraints]\n\n\nclass OptimizerUniverse:\n def __init__(\n self,\n assets: Union[List[Asset], AssetUniverse] = None,\n explode_composites: bool = True,\n exclude_initial_position_set_assets: bool = True,\n exclude_corporate_actions_types: List[CorporateActionsTypes] = [],\n exclude_hard_to_borrow_assets: bool = False,\n exclude_restricted_assets: bool = False,\n min_market_cap: float = None,\n max_market_cap: float = None,\n ):\n \"\"\"\n The universe of assets with which to construct an optimization\n\n :param assets: list of assets to include in the universe\n :param explode_composites: explode composites in the universe to include their constituents in the universe\n :param exclude_initial_position_set_assets: exclude assets in the initial holdings\n :param exclude_corporate_actions_types: exclude assets included under the list of corporate action types\n :param exclude_hard_to_borrow_assets: exclude assets with a borrow cost greater than or equal to 200 bps\n :param exclude_restricted_assets: exclude restricted assets\n :param min_market_cap: exclude assets below the requested minimum market cap\n :param max_market_cap: exclude assets above the requested maximum market cap\n specify the identifier type\n \"\"\"\n self.__assets = assets\n self.__explode_composites = explode_composites\n self.__exclude_initial_position_set_assets = exclude_initial_position_set_assets\n self.__exclude_corporate_actions_types = exclude_corporate_actions_types\n self.__exclude_hard_to_borrow_assets = exclude_hard_to_borrow_assets\n self.__exclude_restricted_assets = exclude_restricted_assets\n self.__min_market_cap = min_market_cap\n self.__max_market_cap = max_market_cap\n\n @property\n def assets(self) -> List[Asset]:\n return self.__assets\n\n @assets.setter\n def assets(self, assets: List[Asset]):\n self.__assets = assets\n\n @property\n def explode_composites(self) -> bool:\n return self.__explode_composites\n\n @explode_composites.setter\n def explode_composites(self, value: bool):\n self.__explode_composites = value\n\n @property\n def exclude_initial_position_set_assets(self) -> bool:\n return self.__exclude_initial_position_set_assets\n\n @exclude_initial_position_set_assets.setter\n def exclude_initial_position_set_assets(self, value: bool):\n self.__exclude_initial_position_set_assets = value\n\n @property\n def exclude_corporate_actions_types(self) -> List[CorporateActionsTypes]:\n return self.__exclude_corporate_actions_types\n\n @exclude_corporate_actions_types.setter\n def exclude_corporate_actions_types(self, value: List[CorporateActionsTypes]):\n self.__exclude_corporate_actions_types = value\n\n @property\n def exclude_hard_to_borrow_assets(self) -> bool:\n return self.__exclude_hard_to_borrow_assets\n\n @exclude_hard_to_borrow_assets.setter\n def exclude_hard_to_borrow_assets(self, value: bool):\n self.__exclude_hard_to_borrow_assets = value\n\n @property\n def exclude_restricted_assets(self) -> bool:\n return self.__exclude_restricted_assets\n\n @exclude_restricted_assets.setter\n def exclude_restricted_assets(self, value: bool):\n self.__exclude_restricted_assets = value\n\n @property\n def min_market_cap(self) -> float:\n return self.__min_market_cap\n\n @min_market_cap.setter\n def min_market_cap(self, value: float):\n self.__min_market_cap = value\n\n @property\n def max_market_cap(self) -> float:\n return self.__max_market_cap\n\n @max_market_cap.setter\n def max_market_cap(self, value: float):\n self.__max_market_cap = value\n\n def to_dict(self):\n if isinstance(self.assets, AssetUniverse):\n self.assets.resolve()\n asset_ids = self.assets.asset_ids\n else:\n asset_ids = [asset.get_marquee_id() for asset in self.assets]\n as_dict = {\n 'hedgeUniverse': {'assetIds': asset_ids, 'assetTypes': []},\n 'excludeCorporateActions': len(self.exclude_corporate_actions_types) != 0,\n 'excludeCorporateActionsTypes': [x.value for x in self.exclude_corporate_actions_types],\n 'excludeHardToBorrowAssets': self.exclude_hard_to_borrow_assets,\n 'excludeRestrictedAssets': self.exclude_restricted_assets,\n 'excludeTargetAssets': self.exclude_initial_position_set_assets,\n 'explodeUniverse': self.explode_composites,\n }\n if self.min_market_cap:\n as_dict['minMarketCap'] = self.min_market_cap\n if self.max_market_cap:\n as_dict['maxMarketCap'] = self.max_market_cap\n return as_dict\n\n\nclass MaxFactorProportionOfRiskConstraint:\n def __init__(\n self,\n max_factor_proportion_of_risk: float,\n unit: OptimizationConstraintUnit = OptimizationConstraintUnit.PERCENT,\n ):\n if unit not in [OptimizationConstraintUnit.PERCENT, OptimizationConstraintUnit.DECIMAL]:\n raise MqValueError('Max Factor Proportion of Risk can only be set by percent or decimal.')\n if unit == OptimizationConstraintUnit.PERCENT:\n max_factor_proportion_of_risk = max_factor_proportion_of_risk / 100\n self.__max_factor_proportion_of_risk = max_factor_proportion_of_risk\n self.__unit = unit\n\n @property\n def max_factor_proportion_of_risk(self) -> float:\n return self.__max_factor_proportion_of_risk\n\n @max_factor_proportion_of_risk.setter\n def max_factor_proportion_of_risk(self, value: float):\n self.__max_factor_proportion_of_risk = value\n\n\nclass MaxProportionOfRiskByGroupConstraint:\n def __init__(\n self,\n factors: List[Factor],\n max_factor_proportion_of_risk: float,\n unit: OptimizationConstraintUnit = OptimizationConstraintUnit.PERCENT,\n ):\n \"\"\"\n Constrain the maximum proportion of risk coming from a group of factors in the final optimized result.\n\n :param factors: the list of factors\n :param max_factor_proportion_of_risk: the maximum proportion of risk\n :param unit: unit of proportion of risk\n \"\"\"\n if unit not in [OptimizationConstraintUnit.PERCENT, OptimizationConstraintUnit.DECIMAL]:\n raise MqValueError('Max Factor Proportion of Risk can only be set by percent or decimal.')\n if unit == OptimizationConstraintUnit.PERCENT:\n max_factor_proportion_of_risk = max_factor_proportion_of_risk / 100\n self.__factors = factors\n self.__max_factor_proportion_of_risk = max_factor_proportion_of_risk\n self.__unit = unit\n\n @property\n def factors(self) -> List[Factor]:\n return self.__factors\n\n @factors.setter\n def factors(self, value: List[Factor]):\n self.__factors = value\n\n @property\n def max_factor_proportion_of_risk(self) -> float:\n return self.__max_factor_proportion_of_risk\n\n @max_factor_proportion_of_risk.setter\n def max_factor_proportion_of_risk(self, value: float):\n self.__max_factor_proportion_of_risk = value\n\n def to_dict(self):\n return {'factors': [f.name for f in self.factors], 'max': self.max_factor_proportion_of_risk}\n\n\nclass OptimizerConstraints:\n def __init__(\n self,\n asset_constraints: List[AssetConstraint] = [],\n country_constraints: List[CountryConstraint] = [],\n sector_constraints: List[SectorConstraint] = [],\n industry_constraints: List[IndustryConstraint] = [],\n factor_constraints: List[FactorConstraint] = [],\n max_factor_proportion_of_risk: MaxFactorProportionOfRiskConstraint = None,\n max_proportion_of_risk_by_groups: List[MaxProportionOfRiskByGroupConstraint] = None,\n ):\n \"\"\"Set of Constraints for the optimizer\n\n :param asset_constraints: list of asset constraints\n :param country_constraints: list of country constraints\n :param sector_constraints: list of sector constraints\n :param industry_constraints: list of industry constraints\n :param factor_constraints: list of factor constraints\n :param max_factor_proportion_of_risk: maximum proportion of risk\n :param max_proportion_of_risk_by_groups: maximum proportion of risk by groups\n \"\"\"\n self.__asset_constraints = asset_constraints\n self.__country_constraints = country_constraints\n self.__sector_constraints = sector_constraints\n self.__industry_constraints = industry_constraints\n self.__factor_constraints = factor_constraints\n self.__max_factor_proportion_of_risk = max_factor_proportion_of_risk\n self.__max_proportion_of_risk_by_groups = max_proportion_of_risk_by_groups\n\n @property\n def asset_constraints(self) -> List[AssetConstraint]:\n return self.__asset_constraints\n\n @asset_constraints.setter\n def asset_constraints(self, value: List[AssetConstraint]):\n self.__asset_constraints = value\n\n @property\n def country_constraints(self) -> List[CountryConstraint]:\n return self.__country_constraints\n\n @country_constraints.setter\n def country_constraints(self, value: List[CountryConstraint]):\n self.__country_constraints = value\n\n @property\n def sector_constraints(self) -> List[SectorConstraint]:\n return self.__sector_constraints\n\n @sector_constraints.setter\n def sector_constraints(self, value: List[SectorConstraint]):\n self.__sector_constraints = value\n\n @property\n def industry_constraints(self) -> List[IndustryConstraint]:\n return self.__industry_constraints\n\n @industry_constraints.setter\n def industry_constraints(self, value: List[IndustryConstraint]):\n self.__industry_constraints = value\n\n @property\n def factor_constraints(self) -> List[FactorConstraint]:\n return self.__factor_constraints\n\n @factor_constraints.setter\n def factor_constraints(self, value: List[FactorConstraint]):\n self.__factor_constraints = value\n\n @property\n def max_factor_proportion_of_risk(self) -> MaxFactorProportionOfRiskConstraint:\n return self.__max_factor_proportion_of_risk\n\n @max_factor_proportion_of_risk.setter\n def max_factor_proportion_of_risk(self, value: MaxFactorProportionOfRiskConstraint):\n self.__max_factor_proportion_of_risk = value\n\n @property\n def max_proportion_of_risk_by_groups(self) -> List[MaxProportionOfRiskByGroupConstraint]:\n return self.__max_proportion_of_risk_by_groups\n\n @max_proportion_of_risk_by_groups.setter\n def max_proportion_of_risk_by_groups(self, value: List[MaxProportionOfRiskByGroupConstraint]):\n self.__max_proportion_of_risk_by_groups = value\n\n def to_dict(self):\n types = set([c.unit for c in self.asset_constraints])\n if len(types) > 1:\n raise MqValueError('All asset constraints need to have the same unit')\n constrain_by_notional = len(self.asset_constraints) > 0 and types.pop() == OptimizationConstraintUnit.NOTIONAL\n classification_constraints = self.country_constraints + self.sector_constraints + self.industry_constraints\n as_dict = {\n 'assetConstraints': [c.to_dict() for c in self.asset_constraints],\n 'classificationConstraints': [c.to_dict() for c in classification_constraints],\n 'factorConstraints': [c.to_dict() for c in self.factor_constraints],\n 'constrainAssetsByNotional': constrain_by_notional,\n }\n\n if self.max_factor_proportion_of_risk:\n as_dict['maxFactorMCTR'] = self.max_factor_proportion_of_risk.max_factor_proportion_of_risk\n\n if self.max_proportion_of_risk_by_groups:\n as_dict['maxFactorMCTRByGroup'] = [g.to_dict() for g in self.max_proportion_of_risk_by_groups]\n\n return as_dict\n\n\nclass ConstraintPriorities:\n def __init__(\n self,\n min_sector_weights: PrioritySetting = None,\n max_sector_weights: PrioritySetting = None,\n min_industry_weights: PrioritySetting = None,\n max_industry_weights: PrioritySetting = None,\n min_region_weights: PrioritySetting = None,\n max_region_weights: PrioritySetting = None,\n min_country_weights: PrioritySetting = None,\n max_country_weights: PrioritySetting = None,\n style_factor_exposures: PrioritySetting = None,\n ):\n \"\"\"\n Priority of the constraint from 0-5 (prioritized in that order). The optimization will fail if it cannot meet a\n constraint with 0 priority. A constraint with priority of 1-5 can be called a relaxed constraint, which means\n that the optimization will make its best effort to meet the constraint but will not fail if it cannot. A\n constraint with a lower priority will take precedence over a constraint with a higher priority.\n :param min_sector_weights: constraint priority of the minimum sector weight constraints\n :param max_sector_weights: constraint priority of the maximum sector weight constraints\n :param min_industry_weights: constraint priority of the minimum industry weight constraints\n :param max_industry_weights: constraint priority of the maximum industry weight constraints\n :param min_region_weights: constraint priority of the minimum region weight constraints\n :param max_region_weights: constraint priority of the maximum region weight constraints\n :param min_country_weights: constraint priority of the minimum country weight constraints\n :param max_country_weights: constraint priority of the maximum country weight constraints\n :param style_factor_exposures: constraint priority of the style factor exposure constraints\n \"\"\"\n self.__min_sector_weights = min_sector_weights\n self.__max_sector_weights = max_sector_weights\n self.__min_industry_weights = min_industry_weights\n self.__max_industry_weights = max_industry_weights\n self.__min_region_weights = min_region_weights\n self.__max_region_weights = max_region_weights\n self.__min_country_weights = min_country_weights\n self.__max_country_weights = max_country_weights\n self.__style_factor_exposures = style_factor_exposures\n\n @property\n def min_sector_weights(self) -> PrioritySetting:\n return self.__min_sector_weights\n\n @min_sector_weights.setter\n def min_sector_weights(self, value: PrioritySetting):\n self.__min_sector_weights = value\n\n @property\n def max_sector_weights(self) -> PrioritySetting:\n return self.__max_sector_weights\n\n @max_sector_weights.setter\n def max_sector_weights(self, value: PrioritySetting):\n self.__max_sector_weights = value\n\n @property\n def min_industry_weights(self) -> PrioritySetting:\n return self.__min_industry_weights\n\n @min_industry_weights.setter\n def min_industry_weights(self, value: PrioritySetting):\n self.__min_industry_weights = value\n\n @property\n def max_industry_weights(self) -> PrioritySetting:\n return self.__max_industry_weights\n\n @max_industry_weights.setter\n def max_industry_weights(self, value: PrioritySetting):\n self.__max_industry_weights = value\n\n @property\n def min_region_weights(self) -> PrioritySetting:\n return self.__min_region_weights\n\n @min_region_weights.setter\n def min_region_weights(self, value: PrioritySetting):\n self.__min_region_weights = value\n\n @property\n def max_region_weights(self) -> PrioritySetting:\n return self.__max_region_weights\n\n @max_region_weights.setter\n def max_region_weights(self, value: PrioritySetting):\n self.__max_region_weights = value\n\n @property\n def min_country_weights(self) -> PrioritySetting:\n return self.__min_country_weights\n\n @min_country_weights.setter\n def min_country_weights(self, value: PrioritySetting):\n self.__min_country_weights = value\n\n @property\n def max_country_weights(self) -> PrioritySetting:\n return self.__max_country_weights\n\n @max_country_weights.setter\n def max_country_weights(self, value: PrioritySetting):\n self.__max_country_weights = value\n\n @property\n def style_factor_exposures(self) -> PrioritySetting:\n return self.__style_factor_exposures\n\n @style_factor_exposures.setter\n def style_factor_exposures(self, value: PrioritySetting):\n self.__style_factor_exposures = value\n\n def to_dict(self) -> Dict:\n as_dict = (\n {\n 'minSectorWeights': self.min_sector_weights,\n 'maxSectorWeights': self.max_sector_weights,\n 'minIndustryWeights': self.min_industry_weights,\n 'maxIndustryWeights': self.max_industry_weights,\n 'minRegionWeights': self.min_region_weights,\n 'maxRegionWeights': self.max_region_weights,\n 'minCountryWeights': self.min_country_weights,\n 'maxCountryWeights': self.max_country_weights,\n 'styleExposures': self.style_factor_exposures,\n }\n if self is not None\n else {}\n )\n as_dict = {k: as_dict[k].value for k in as_dict.keys() if as_dict[k] is not None}\n return as_dict if len(as_dict.keys()) > 0 else None\n\n\nclass OptimizerSettings:\n def __init__(\n self,\n notional: float = 10000000,\n allow_long_short: bool = False,\n gross_notional: float = None,\n net_notional: float = None,\n min_names: float = 0,\n max_names: float = 100,\n min_weight_per_constituent: float = None,\n max_weight_per_constituent: float = None,\n max_adv: float = 15,\n constraint_priorities: ConstraintPriorities = None,\n ):\n \"\"\"\n Optimizer settings for factor hedging.\n\n **Unidirectional Hedger** (default):\n - Returns only short positions to hedge the target portfolio\n - Use when you want to add short-only positions to reduce factor risk\n - Set `allow_long_short=False` (default)\n - Only `notional` parameter is used (gross = net for short-only positions)\n\n **Bidirectional Hedger**:\n - Returns both long and short positions to hedge the target portfolio\n - Provides more flexibility in hedging factor exposures\n - Set `allow_long_short=True`\n - Specify `gross_notional` to control total hedge size\n - Must specify `net_notional` for target net exposure (use 0 for market neutral)\n\n :param notional: For unidirectional hedger: the notional of the hedge (all short positions).\n For bidirectional hedger: will be overridden by gross_notional if\n allow_long_short=True.\n :param allow_long_short: Enable bidirectional hedge mode. When True, the hedge can contain both\n long and short positions. When False (default), hedge contains only\n short positions.\n :param gross_notional: Total absolute notional (|long| + |short|) for the hedge.\n Only applicable when allow_long_short=True. If not specified when\n allow_long_short=True, defaults to the value of `notional`.\n Example: gross_notional=20M with net_notional=10M means 15M long + 5M\n short\n :param net_notional: Net notional (long - short) for the hedge. Required when\n allow_long_short=True. Use 0 for market neutral hedge.\n :param min_names: Minimum number of assets in the hedge.\n :param max_names: Maximum number of assets in the hedge.\n :param min_weight_per_constituent: Minimum absolute weight of each constituent in the hedge\n (as decimal, positive value). The optimizer uses this as\n an absolute value constraint. Example: 0.01 means each\n position must be at least 1% (in absolute value)\n :param max_weight_per_constituent: Maximum absolute weight of each constituent in the hedge\n (as decimal, positive value). The optimizer uses this as\n an absolute value constraint. Example: 0.05 means each\n position cannot exceed 5% (in absolute value)\n :param max_adv: Maximum percentage of average daily volume that can be traded for any constituent.\n :param constraint_priorities: Priority settings for classification constraints (sectors, countries, etc.)\n \"\"\"\n self.__notional = notional\n self.__allow_long_short = allow_long_short\n self.__gross_notional = gross_notional\n self.__net_notional = net_notional\n self.__min_names = min_names\n self.__max_names = max_names\n self.__min_weight_per_constituent = min_weight_per_constituent\n self.__max_weight_per_constituent = max_weight_per_constituent\n self.__max_adv = max_adv\n self.__constraint_priorities = constraint_priorities\n\n # Validate settings\n self._validate_settings()\n\n def _validate_settings(self):\n \"\"\"Validate optimizer settings for consistency and proper usage\"\"\"\n\n # Validate weight constraints are positive\n if self.__min_weight_per_constituent is not None and self.__min_weight_per_constituent < 0:\n self.__min_weight_per_constituent = 0\n raise Warning(\"min_weight_per_constituent cannot be negative. Setting to 0.\")\n\n if self.__max_weight_per_constituent is not None and self.__max_weight_per_constituent < 0:\n raise MqValueError(\n \"max_weight_per_constituent must be a positive value (absolute weight constraint). \"\n f\"Current value: {self.__max_weight_per_constituent}. \"\n \"The optimizer interprets weight constraints as absolute values.\"\n )\n\n if (\n self.__min_weight_per_constituent is not None\n and self.__max_weight_per_constituent is not None\n and self.__min_weight_per_constituent > self.__max_weight_per_constituent\n ):\n raise MqValueError(\n f\"min_weight_per_constituent ({self.__min_weight_per_constituent}) cannot be greater than \"\n f\"max_weight_per_constituent ({self.__max_weight_per_constituent})\"\n )\n\n # Validation for bidirectional mode\n if self.__allow_long_short:\n # When both gross_notional and net_notional are set, validate the relationship\n if self.__gross_notional is not None and self.__net_notional is not None:\n if abs(self.__net_notional) > self.__gross_notional:\n raise MqValueError(\n \"Invalid notional configuration: |net_notional| ({abs(self.__net_notional)}) \"\n f\"cannot be greater than gross_notional ({self.__gross_notional}). \"\n \"Formula: gross_notional = |long| + |short|, net_notional = long - short\"\n )\n # Allow bidirectional mode with only notional set (hedge notional use case)\n # No additional validation needed for this case\n\n else:\n # Unidirectional hedger mode validation\n # Prevent setting long/short parameters in unidirectional mode\n if (\n self.__gross_notional is not None\n and self.__net_notional is not None\n and self.__gross_notional != self.__net_notional\n ):\n raise MqValueError(\n \"Cannot set gross_notional != net_notional when allow_long_short=False. \"\n \"Use 'notional' parameter for unidirectional hedger mode, or set allow_long_short=True \"\n \"to enable bidirectional hedging with gross_notional and net_notional.\"\n )\n\n @property\n def notional(self) -> float:\n return self.__notional\n\n @notional.setter\n def notional(self, value: float):\n self.__notional = value\n self._validate_settings()\n\n @property\n def allow_long_short(self) -> bool:\n return self.__allow_long_short\n\n @allow_long_short.setter\n def allow_long_short(self, value: bool):\n self.__allow_long_short = value\n self._validate_settings()\n\n @property\n def gross_notional(self) -> float:\n \"\"\"\n Total absolute notional for the hedge (|long| + |short|).\n Only applicable when allow_long_short=True.\n \"\"\"\n return self.__gross_notional\n\n @gross_notional.setter\n def gross_notional(self, value: float):\n self.__gross_notional = value\n self._validate_settings()\n\n @property\n def net_notional(self) -> float:\n \"\"\"\n Net notional for the hedge (long - short).\n Only applicable when allow_long_short=True.\n Set to 0 for market neutral hedge.\n \"\"\"\n return self.__net_notional\n\n @net_notional.setter\n def net_notional(self, value: float):\n self.__net_notional = value\n self._validate_settings()\n\n @property\n def min_names(self) -> float:\n return self.__min_names\n\n @min_names.setter\n def min_names(self, value: float):\n self.__min_names = value\n\n @property\n def min_weight_per_constituent(self) -> float:\n return self.__min_weight_per_constituent\n\n @min_weight_per_constituent.setter\n def min_weight_per_constituent(self, value: float):\n self.__min_weight_per_constituent = value\n self._validate_settings()\n\n @property\n def max_weight_per_constituent(self) -> float:\n return self.__max_weight_per_constituent\n\n @max_weight_per_constituent.setter\n def max_weight_per_constituent(self, value: float):\n self.__max_weight_per_constituent = value\n self._validate_settings()\n\n @property\n def max_names(self) -> float:\n return self.__max_names\n\n @max_names.setter\n def max_names(self, value: float):\n self.__max_names = value\n\n @property\n def max_adv(self) -> float:\n return self.__max_adv\n\n @max_adv.setter\n def max_adv(self, value: float):\n self.__max_adv = value\n\n @property\n def constraint_priorities(self) -> ConstraintPriorities:\n return self.__constraint_priorities\n\n @constraint_priorities.setter\n def constraint_priorities(self, value: ConstraintPriorities):\n self.__constraint_priorities = value\n\n def to_dict(self):\n \"\"\"\n Convert optimizer settings to dictionary format for API payload.\n Handles both unidirectional and bidirectional hedger configurations.\n \"\"\"\n # Start with common parameters\n as_dict = {'minNames': self.min_names, 'maxNames': self.max_names, 'maxAdvPercentage': self.max_adv}\n\n # Configure notional parameters based on hedger mode\n if self.__allow_long_short:\n # Bidirectional Hedger mode\n as_dict['allowLongShort'] = self.__allow_long_short\n\n # Use grossNotional and netNotional if both are set\n if self.__gross_notional is not None and self.__net_notional is not None:\n as_dict['grossNotional'] = self.__gross_notional\n as_dict['netNotional'] = self.__net_notional\n # Otherwise, use hedgeNotional if only notional is set\n elif self.__notional is not None:\n as_dict['hedgeNotional'] = self.__notional\n\n else:\n # Unidirectional Hedger mode (default)\n # In unidirectional mode, use hedgeNotional (all short positions)\n as_dict['hedgeNotional'] = self.__notional\n # Explicitly set allowLongShort to False for clarity\n as_dict['allowLongShort'] = False\n\n # Add weight constraints if specified\n if self.min_weight_per_constituent is not None:\n as_dict['minWeight'] = self.min_weight_per_constituent * 100\n if self.max_weight_per_constituent is not None:\n as_dict['maxWeight'] = self.max_weight_per_constituent * 100\n\n # Add constraint priorities if specified\n if self.constraint_priorities:\n as_dict['constraintPrioritySettings'] = self.constraint_priorities.to_dict()\n\n return as_dict\n\n\nclass TurnoverConstraint:\n def __init__(\n self,\n turnover_portfolio: PositionSet,\n max_turnover_percent: float,\n turnover_notional_type: Optional[TurnoverNotionalType] = None,\n ):\n \"\"\"\n Specifying a list of positions and max turnover from those positions in the optimization result\n\n :param turnover_portfolio: turnover portfolio\n :param max_turnover_percent: max turnover as a percent (ex: 80 = a minimal overlap of 20% in notional of the\n specified positions and the optimization\n \"\"\"\n self.__turnover_portfolio = turnover_portfolio\n self.__max_turnover_percent = max_turnover_percent\n self.__turnover_notional_type = turnover_notional_type\n\n @property\n def turnover_portfolio(self) -> PositionSet:\n return self.__turnover_portfolio\n\n @turnover_portfolio.setter\n def turnover_portfolio(self, value: PositionSet):\n self.__turnover_portfolio = value\n\n @property\n def max_turnover_percent(self) -> float:\n return self.__max_turnover_percent\n\n @max_turnover_percent.setter\n def max_turnover_percent(self, value: float):\n self.__max_turnover_percent = value\n\n @property\n def turnover_notional_type(self):\n return self.__turnover_notional_type\n\n @turnover_notional_type.setter\n def turnover_notional_type(self, value: Optional[TurnoverNotionalType]):\n self.__turnover_notional_type = value\n\n def to_dict(self):\n positions = self.turnover_portfolio.positions\n payload = {\n 'turnoverPortfolio': [{'assetId': p.asset_id, 'quantity': p.quantity} for p in positions],\n 'maxTurnoverPercentage': self.max_turnover_percent,\n }\n if self.turnover_notional_type:\n payload['turnoverNotionalType'] = self.turnover_notional_type.value\n return payload\n\n\ndef _ensure_completed(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n self = args[0]\n if self._OptimizerStrategy__result is None:\n raise MqValueError('Please run the optimization before calling this method')\n return func(*args, **kwargs)\n\n return wrapper\n\n\nclass OptimizerStrategy:\n VERBOSE_ERROR_MSG: Final = {\n 'Missing asset xref': lambda e: f\"We noticed some underlying asset meta data error: {e}\",\n 'ERROR: Could not find solution.': lambda e: (\n f\"Potential infeasible inputs. {e}. Please relax your constraint or contact Marquee team for assistance.\"\n ),\n }\n\n def __init__(\n self,\n initial_position_set: PositionSet,\n universe: OptimizerUniverse,\n risk_model: FactorRiskModel,\n constraints: OptimizerConstraints = None,\n turnover: TurnoverConstraint = None,\n settings: OptimizerSettings = None,\n objective: OptimizerObjective = OptimizerObjective.MINIMIZE_FACTOR_RISK,\n objective_parameters: OptimizerObjectiveParameters = None,\n ):\n \"\"\"\n A strategy that can be passed into the optimizer and run\n\n :param initial_position_set: a position set correlating to your original holdings as of a specific date\n :param universe: universe from which to choose optimization assets\n :param risk_model: risk model with which to calculate risk\n :param constraints: constraints for the optimization\n :param turnover: turnover constraints for the optimization\n :param settings: settings for the optimization\n :param objective: objective for the optimization:\n \"\"\"\n self.__initial_position_set = initial_position_set\n self.__universe = universe\n self.__risk_model = risk_model\n self.__constraints = constraints\n self.__turnover = turnover\n self.__settings = settings\n self.__objective = objective\n self.__result = None\n self.__objective_parameters = objective_parameters\n\n @property\n def initial_position_set(self) -> PositionSet:\n return self.__initial_position_set\n\n @initial_position_set.setter\n def initial_position_set(self, value: PositionSet):\n self.__initial_position_set = value\n\n @property\n def universe(self) -> OptimizerUniverse:\n return self.__universe\n\n @universe.setter\n def universe(self, value: OptimizerUniverse):\n self.__universe = value\n\n @property\n def risk_model(self) -> FactorRiskModel:\n return self.__risk_model\n\n @risk_model.setter\n def risk_model(self, value: FactorRiskModel):\n self.__risk_model = value\n\n @property\n def constraints(self) -> OptimizerConstraints:\n return self.__constraints\n\n @constraints.setter\n def constraints(self, value: OptimizerConstraints):\n self.__constraints = value\n\n @property\n def turnover(self) -> TurnoverConstraint:\n return self.__turnover\n\n @turnover.setter\n def turnover(self, value: TurnoverConstraint):\n self.__turnover = value\n\n @property\n def settings(self) -> OptimizerSettings:\n return self.__settings\n\n @settings.setter\n def settings(self, value: OptimizerSettings):\n self.__settings = value\n\n @property\n def objective(self) -> OptimizerObjective:\n return self.__objective\n\n @objective.setter\n def objective(self, value: OptimizerObjective):\n self.__objective = value\n\n @property\n def objective_parameters(self):\n return self.__objective_parameters\n\n @objective_parameters.setter\n def objective_parameters(self):\n return self.__objetive_parameters\n\n def to_dict(self, fail_on_unpriced_positions: bool = True):\n \"\"\"Converts input to suitable json payload for optimizer. Does not modify initial_position_set\"\"\"\n if self.constraints is None:\n self.constraints = OptimizerConstraints()\n if self.settings is None:\n self.settings = OptimizerSettings()\n\n backtest_start_date = self.initial_position_set.date - relativedelta(years=1)\n positions_frame = self.initial_position_set.to_frame()\n if self.initial_position_set.reference_notional:\n positions_as_dict = positions_frame[['asset_id', 'weight']]\n else:\n positions_as_dict = positions_frame[['asset_id', 'quantity']]\n\n positions_as_dict = positions_as_dict.rename(columns={'asset_id': 'assetId'}).to_dict(orient='records')\n\n parameters = {\n 'hedgeTarget': {'positions': positions_as_dict},\n 'hedgeDate': self.initial_position_set.date.strftime('%Y-%m-%d'),\n 'backtestStartDate': backtest_start_date.strftime('%Y-%m-%d'),\n 'backtestEndDate': self.initial_position_set.date.strftime('%Y-%m-%d'),\n 'comparisons': [],\n 'fxHedged': False,\n 'marketParticipationRate': 10,\n }\n constraints = self.constraints.to_dict()\n for key in constraints:\n if constraints[key] is not None:\n parameters[key] = constraints[key]\n settings = self.settings.to_dict()\n for key in settings:\n if settings[key] is not None:\n parameters[key] = settings[key]\n universe = self.universe.to_dict()\n for key in universe:\n if universe[key] is not None:\n parameters[key] = universe[key]\n parameters['riskModel'] = self.risk_model.id\n if self.turnover:\n if self.turnover.turnover_portfolio.reference_notional is not None:\n self.turnover.turnover_portfolio.price()\n turnover_dict = self.turnover.to_dict()\n for key in turnover_dict:\n if turnover_dict[key] is not None:\n parameters[key] = turnover_dict[key]\n\n # Price initial_position_set if needed\n if self.initial_position_set.reference_notional is not None:\n parameters['targetNotional'] = self.initial_position_set.reference_notional\n\n if self.__objective_parameters is not None:\n parameters['hedgeObjectiveParameters'] = self.__objective_parameters.to_dict()\n\n payload = {\n 'positions': positions_as_dict,\n 'parameters': {\n 'currency': 'USD',\n 'pricingDate': self.initial_position_set.date.strftime('%Y-%m-%d'),\n 'useUnadjustedClosePrice': False, # Optimizer uses adjusted prices for all calculations\n 'frequency': 'End Of Day',\n 'priceRegardlessOfAssetsMissingPrices': not fail_on_unpriced_positions,\n 'fallbackDate': '5d',\n },\n }\n if self.initial_position_set.reference_notional is not None:\n payload['parameters']['targetNotional'] = self.initial_position_set.reference_notional\n try:\n price_results = GsSession.current.sync.post('/price/positions', payload)\n except Exception as e:\n raise MqValueError(f'There was an error pricing your positions: {e}')\n if 'errorMessage' in price_results:\n if len(price_results.get('assetIdsMissingPrices', [])) > 0:\n _logger.warning(\n f'Marquee is missing prices on {self.initial_position_set.date} for '\n f'the following assets: {price_results[\"assetIdsMissingPrices\"]}. '\n )\n raise MqValueError(f'There was an error pricing your positions: {price_results[\"errorMessage\"]}')\n if self.initial_position_set.reference_notional is None:\n parameters['targetNotional'] = price_results.get('actualNotional')\n else:\n parameters['hedgeTarget']['positions'] = [\n {'assetId': p['assetId'], 'quantity': p['quantity']} for p in price_results.get('positions', [])\n ]\n return {'objective': self.objective.value, 'parameters': parameters}\n\n def handle_error(self, error_message: str) -> List:\n for key, val in self.VERBOSE_ERROR_MSG.items():\n if error_message.startswith(key):\n return [val(error_message), True] # predefined\n return [error_message, False]\n\n def run(\n self,\n optimizer_type: OptimizerType = OptimizerType.AXIOMA_PORTFOLIO_OPTIMIZER,\n fail_on_unpriced_positions: bool = True,\n ):\n \"\"\"\n Run an optimization strategy, after which you can use the .get_optimization or get_optimized_position_set\n functions to pull results\n\n :param optimizer_type: optimizer type\n :param fail_on_unpriced_positions: whether to fail the calculations if some of the portfolio positions do not\n have pricing data in Marquee. If set to false, unpriced assets will be sifted out before the optimization is run\n \"\"\"\n if optimizer_type is None:\n raise MqValueError('You must pass an optimizer type.')\n if optimizer_type == OptimizerType.AXIOMA_PORTFOLIO_OPTIMIZER:\n strategy_as_dict = self.to_dict(fail_on_unpriced_positions)\n counter = 5\n predefined_error = False\n while counter > 0:\n try:\n optimization_results = GsHedgeApi.calculate_hedge(strategy_as_dict)\n if optimization_results.get('result') is None:\n if 'errorMessage' in optimization_results:\n error_message = optimization_results['errorMessage']\n verbose_message, predefined_error = self.handle_error(error_message)\n if predefined_error:\n counter = 0\n raise MqValueError(f\"The optimizer returns an error: {verbose_message}. \")\n else:\n raise MqValueError(\n f\"The optimizer returned an error: \"\n f\"{optimization_results.get('errorMessage')}. \"\n f\"Please adjust the constraints\"\n f\" or contact the Marquee team for assistance\"\n )\n elif counter == 1:\n raise MqValueError(\n 'Error calculating an optimization. Please contact the Marquee team for assistance.'\n )\n counter -= 1\n else:\n self.__result = optimization_results['result']\n counter = 0\n except Exception as e:\n if predefined_error:\n raise e\n if counter == 1:\n raise MqValueError(\n 'Error calculating an optimization. Please contact the Marquee team for assistance.'\n )\n counter -= 1\n\n def run_save_share(\n self,\n optimizer_type: OptimizerType = OptimizerType.AXIOMA_PORTFOLIO_OPTIMIZER,\n fail_on_unpriced_positions: bool = True,\n ):\n \"\"\"\n Run an optimization strategy, after which you can use the .get_optimization or get_optimized_position_set\n functions to pull results\n\n :param optimizer_type: optimizer type\n :param fail_on_unpriced_positions: whether to fail the calculations if some of the portfolio positions do not\n have pricing data in Marquee. If set to false, unpriced assets will be sifted out before the optimization is run\n :return: tuple of (strategy_as_dict, optimization_results) - the request payload and response from the optimizer\n \"\"\"\n if optimizer_type is None:\n raise MqValueError('You must pass an optimizer type.')\n if optimizer_type == OptimizerType.AXIOMA_PORTFOLIO_OPTIMIZER:\n strategy_as_dict = self.to_dict(fail_on_unpriced_positions)\n counter = 5\n predefined_error = False\n while counter > 0:\n try:\n optimization_results = GsHedgeApi.calculate_hedge(strategy_as_dict)\n if optimization_results.get('result') is None:\n if 'errorMessage' in optimization_results:\n error_message = optimization_results['errorMessage']\n verbose_message, predefined_error = self.handle_error(error_message)\n if predefined_error:\n counter = 0\n raise MqValueError(f\"The optimizer returns an error: {verbose_message}. \")\n else:\n raise MqValueError(\n f\"The optimizer returned an error: \"\n f\"{optimization_results.get('errorMessage')}. \"\n f\"Please adjust the constraints\"\n f\" or contact the Marquee team for assistance\"\n )\n elif counter == 1:\n raise MqValueError(\n 'Error calculating an optimization. Please contact the Marquee team for assistance.'\n )\n counter -= 1\n else:\n self.__result = optimization_results['result']\n counter = 0\n except Exception as e:\n if predefined_error:\n raise e\n if counter == 1:\n raise MqValueError(\n 'Error calculating an optimization. Please contact the Marquee team for assistance.'\n )\n counter -= 1\n\n # Return both the request and response for easy hedge saving\n return strategy_as_dict, optimization_results\n\n def __construct_position_set_from_hedge_result(self, result_key: str, by_weight: bool = True):\n result = self.__result[result_key]\n return PositionSet(\n date=self.initial_position_set.date,\n reference_notional=result['netExposure'] if by_weight else None,\n positions=[\n Position(\n identifier=asset.get('bbid', asset['name']),\n asset_id=asset['assetId'],\n quantity=asset['shares'] if not by_weight else None,\n weight=asset['weight'],\n )\n for asset in result['constituents']\n ],\n )\n\n @_ensure_completed\n def get_optimization(self, by_weight: bool = False):\n \"\"\"\n Get the optimization results\n\n :param by_weight: whether to return position set with weights instead of quantities\n \"\"\"\n return self.__construct_position_set_from_hedge_result('hedge', by_weight)\n\n @_ensure_completed\n def get_optimized_position_set(self, by_weight: bool = False):\n \"\"\"\n Get the optimized position set, which is a result of applying the optimization to the target\n\n :param by_weight: whether to return position set with weights instead of quantities\n \"\"\"\n return self.__construct_position_set_from_hedge_result('hedgedTarget', by_weight)\n\n @_ensure_completed\n def get_hedge_exposure_summary(self) -> Dict:\n \"\"\"\n Get a summary of the hedge exposures including gross, net, long, and short exposures.\n\n This is particularly useful for bidirectional hedges to understand the composition.\n\n :return: Dictionary containing exposure metrics for target, hedge, and hedged target\n\n **Example Output for bidirectional Hedger:**\n {\n 'hedge': {\n 'gross_exposure': 20000000,\n 'net_exposure': 10000000,\n 'long_exposure': 15000000,\n 'short_exposure': 5000000,\n 'number_of_positions': 25,\n 'mode': 'bidirectional'\n },\n 'target': {\n 'gross_exposure': 10000000,\n 'net_exposure': 10000000,\n 'long_exposure': 10000000,\n 'short_exposure': 0,\n 'number_of_positions': 1\n },\n 'hedged_target': {\n 'gross_exposure': 30000000,\n 'net_exposure': 20000000,\n 'long_exposure': 25000000,\n 'short_exposure': 5000000,\n 'number_of_positions': 26\n }\n }\n \"\"\"\n if self.__result is None:\n raise MqValueError('Please run the optimization before calling this method')\n\n def get_exposure_dict(result_key: str) -> Dict:\n \"\"\"Extract exposure metrics from result\"\"\"\n if result_key not in self.__result:\n return None\n\n result = self.__result[result_key]\n exposure_dict = {\n 'gross_exposure': result.get('grossExposure'),\n 'net_exposure': result.get('netExposure'),\n 'long_exposure': result.get('longExposure', 0),\n 'short_exposure': result.get('shortExposure', 0),\n 'number_of_positions': result.get('numberOfPositions'),\n }\n\n # Add mode information for hedge\n if result_key == 'hedge':\n if result.get('longExposure', 0) > 0:\n exposure_dict['mode'] = 'bidirectional'\n else:\n exposure_dict['mode'] = 'unidirectional (short positions only)'\n\n return exposure_dict\n\n summary = {\n 'hedge': get_exposure_dict('hedge'),\n 'target': get_exposure_dict('target'),\n 'hedged_target': get_exposure_dict('hedgedTarget'),\n }\n\n return summary\n\n @_ensure_completed\n def get_hedge_constituents_by_direction(self) -> Dict:\n \"\"\"\n For bidirectional hedges, split the hedge constituents into long and short positions.\n\n This is useful to analyze the long and short sides of the hedge separately.\n\n :return: Dictionary with 'long_positions' and 'short_positions' DataFrames\n\n **Example:**\n {\n 'long_positions': DataFrame with positive notional positions,\n 'short_positions': DataFrame with negative notional positions,\n 'summary': {\n 'num_long': 12,\n 'num_short': 13,\n 'total_long_notional': 15000000,\n 'total_short_notional': -5000000\n }\n }\n \"\"\"\n if self.__result is None:\n raise MqValueError('Please run the optimization before calling this method')\n if self.__result.get('hedge') is None:\n raise MqValueError('The optimization result does not contain hedge data')\n\n constituents = self.__result.get('hedge').get('constituents', [])\n\n if not constituents:\n return {\n 'long_positions': pd.DataFrame(),\n 'short_positions': pd.DataFrame(),\n 'summary': {'num_long': 0, 'num_short': 0, 'total_long_notional': 0, 'total_short_notional': 0},\n }\n\n # Convert to DataFrame\n df = pd.DataFrame(constituents)\n\n # Split by notional sign\n long_positions = df[df['notional'] > 0].copy() if 'notional' in df.columns else pd.DataFrame()\n short_positions = df[df['notional'] < 0].copy() if 'notional' in df.columns else pd.DataFrame()\n\n # Calculate summary statistics\n summary = {\n 'num_long': len(long_positions),\n 'num_short': len(short_positions),\n 'total_long_notional': long_positions['notional'].sum() if len(long_positions) > 0 else 0,\n 'total_short_notional': short_positions['notional'].sum() if len(short_positions) > 0 else 0,\n }\n\n return {'long_positions': long_positions, 'short_positions': short_positions, 'summary': summary}\n\n def get_cumulative_pnl_performance(self, target: HedgeTarget = HedgeTarget.HEDGED_TARGET) -> Dict:\n \"\"\"\n Get the cumulative PnL performance results of the optimization\n\n :param target: the target to get performance for (hedgedTarget, hedge, or target)\n :return: a pandas dataframe with performance results with columns ['date', 'cumulativePnl']\n \"\"\"\n if self.__result is None:\n raise MqValueError('Please run the optimization before calling this method')\n if self.__result.get(target.value) is None:\n raise MqValueError(f'The optimization result does not contain {target.value} data')\n cumulative_pnl = self.__result.get(target.value).get('cumulativePnl')\n df = pd.DataFrame(cumulative_pnl, columns=['date', 'cumulativePnl'])\n df['date'] = pd.to_datetime(df['date'])\n return df\n\n def get_style_factor_exposures(self, target: HedgeTarget = HedgeTarget.HEDGED_TARGET) -> Dict:\n \"\"\"\n Get the style factor exposures from the hedge result\n\n :param target: the target to get factor exposures for (hedgedTarget, hedge, or target)\n :return: a dictionary with style factor exposures\n \"\"\"\n if self.__result is None:\n raise MqValueError('Please run the optimization before calling this method')\n if self.__result.get(target.value) is None:\n raise MqValueError(f'The optimization result does not contain {target.value} data')\n if self.__result.get(target.value).get('factorExposures') is None:\n raise MqValueError(f'The optimization result does not contain factor exposures for {target.value}')\n factor_exposures = self.__result.get(target.value).get('factorExposures').get('style', [])\n return factor_exposures\n\n def get_risk_buckets(self, target: HedgeTarget = HedgeTarget.HEDGED_TARGET) -> Dict:\n \"\"\"\n Get the risk buckets from the hedge result\n\n :param target: the target to get risk buckets for (hedgedTarget, hedge, or target)\n :return: a dictionary with risk buckets\n \"\"\"\n if self.__result is None:\n raise MqValueError('Please run the optimization before calling this method')\n if self.__result.get(target.value) is None:\n raise MqValueError(f'The optimization result does not contain {target.value} data')\n if self.__result.get(target.value).get('riskBuckets') is None:\n raise MqValueError(f'The optimization result does not contain risk buckets for {target.value}')\n risk_buckets = self.__result.get(target.value).get('riskBuckets')\n return {\"risk_buckets\": risk_buckets}\n\n def get_transaction_and_liquidity_constituents_performance(\n self, target: HedgeTarget = HedgeTarget.HEDGED_TARGET\n ) -> Dict:\n \"\"\"\n Get the constituents performance results of the optimization\n\n :param target: the target to get performance for (hedgedTarget, hedge, or target)\n :return: a pandas dataframe with performance results\n \"\"\"\n if self.__result is None:\n raise MqValueError('Please run the optimization before calling this method')\n if self.__result.get(target.value) is None:\n raise MqValueError(f'The optimization result does not contain {target.value} data')\n constituents = self.__result.get(target.value).get('constituents')\n filtered_constituents = []\n keys_to_keep = {\n \"name\",\n \"assetId\",\n \"bbid\",\n \"notional\",\n \"shares\",\n \"price\",\n \"weight\",\n \"currency\",\n \"transactionCost\",\n \"marginalCost\",\n \"advPercentage\",\n \"borrowCost\",\n }\n for constituent in constituents:\n filtered_constituent = {key: value for key, value in constituent.items() if key in keys_to_keep}\n filtered_constituents.append(filtered_constituent)\n return pd.DataFrame(filtered_constituents)\n\n def get_performance_summary(self) -> Dict:\n \"\"\"\n Get the performance summary results of the optimization.\n\n Returns a dictionary containing multiple DataFrames for easy display:\n - 'risk': Risk metrics comparison\n - 'performance': Performance metrics comparison\n - 'transaction_cost': Transaction cost metrics comparison\n - 'comparison': Comparison with initial portfolio\n - 'combined': All metrics in a single table\n \"\"\"\n if self.__result is None:\n raise MqValueError('Please run the optimization before calling this method')\n if self.__result.get(HedgeTarget.HEDGED_TARGET.value) is None:\n raise MqValueError('The optimization result does not contain hedgedTarget data')\n if self.__result.get(HedgeTarget.TARGET.value) is None:\n raise MqValueError('The optimization result does not contain target data')\n\n target = self.__result.get(HedgeTarget.TARGET.value)\n hedged_target = self.__result.get(HedgeTarget.HEDGED_TARGET.value)\n\n # Build individual DataFrames for each category\n\n # Risk Metrics Table\n risk_df = pd.DataFrame(\n {\n 'Metric': ['Annualized Volatility', 'Specific Risk', 'Factor Risk', 'Factor Risk Delta'],\n 'Initial Portfolio': [\n target.get(\"volatility\"),\n target.get(\"specificExposure\"),\n target.get(\"systematicExposure\"),\n None,\n ],\n 'Hedged Portfolio': [\n hedged_target.get(\"volatility\"),\n hedged_target.get(\"specificExposure\"),\n hedged_target.get(\"systematicExposure\"),\n (\n hedged_target.get(\"systematicExposure\") - target.get(\"systematicExposure\")\n if hedged_target.get(\"systematicExposure\") is not None\n and target.get(\"systematicExposure\") is not None\n else None\n ),\n ],\n }\n )\n\n # Performance Metrics Table\n performance_df = pd.DataFrame(\n {\n 'Metric': ['PnL', 'PnL Delta'],\n 'Initial Portfolio': [target.get(\"totalPnl\"), None],\n 'Hedged Portfolio': [\n hedged_target.get(\"totalPnl\"),\n (\n hedged_target.get(\"totalPnl\") - target.get(\"totalPnl\")\n if hedged_target.get(\"totalPnl\") is not None and target.get(\"totalPnl\") is not None\n else None\n ),\n ],\n }\n )\n\n # Transaction Cost Table\n transaction_cost_df = pd.DataFrame(\n {\n 'Metric': ['Market Impact', 'Borrow Cost (bps)'],\n 'Initial Portfolio': [target.get(\"transactionCost\"), None],\n 'Hedged Portfolio': [hedged_target.get(\"transactionCost\"), hedged_target.get(\"borrowCostBps\")],\n }\n )\n\n # Comparison Table\n comparison_df = pd.DataFrame(\n {\n 'Metric': ['Overlap with Core'],\n 'Initial Portfolio': [None],\n 'Hedged Portfolio': [hedged_target.get(\"exposureOverlapWithTarget\")],\n }\n )\n\n # Combined Table\n combined_df = pd.concat(\n [\n risk_df.assign(Category='Risk'),\n performance_df.assign(Category='Performance'),\n transaction_cost_df.assign(Category='Transaction Cost'),\n comparison_df.assign(Category='Comparison'),\n ],\n ignore_index=True,\n )\n combined_df = combined_df[['Category', 'Metric', 'Initial Portfolio', 'Hedged Portfolio']]\n\n return {\n 'risk': risk_df,\n 'performance': performance_df,\n 'transaction_cost': transaction_cost_df,\n 'comparison': comparison_df,\n 'combined': combined_df,\n }\n\n def build_hedge_payload(\n self,\n strategy_request: Dict,\n optimization_response: Dict,\n hedge_name: str = \"Custom Hedge\",\n group_name: str = \"New Hedge Group\",\n ) -> Dict:\n \"\"\"\n Build the payload for saving a hedge to Marquee API.\n\n This method takes the request sent to the optimizer and the response received,\n and constructs the proper payload for the save hedge API.\n\n :param strategy_request: The strategy_as_dict from run() - the request payload sent to optimizer\n :param optimization_response: The optimization_results from run() - the response from optimizer\n :param hedge_name: Name for the individual hedge\n :param group_name: Name for the hedge group\n :return: Payload ready to POST to /v1/hedges/groups\n \"\"\"\n # The save hedge payload is simply:\n # - parameters: the request that was sent to the optimizer\n # - result: the response received from the optimizer\n\n payload = {\n \"name\": group_name,\n \"objective\": strategy_request.get(\"objective\", \"Minimize Factor Risk\"),\n \"hedges\": [\n {\n \"name\": hedge_name,\n \"objective\": strategy_request.get(\"objective\", \"Minimize Factor Risk\"),\n \"parameters\": strategy_request.get(\"parameters\", {}),\n \"result\": optimization_response.get(\"result\", {}),\n }\n ],\n }\n\n return payload\n\n def save_to_marquee(\n self,\n strategy_request: Dict,\n optimization_response: Dict,\n hedge_name: str = \"Custom Hedge\",\n group_name: str = \"New Hedge Group\",\n ) -> Dict:\n \"\"\"\n Save the hedge to Marquee via API POST.\n\n :param strategy_request: The strategy_as_dict from run() - the request payload sent to optimizer\n :param optimization_response: The optimization_results from run() - the response from optimizer\n :param hedge_name: Name for the individual hedge\n :param group_name: Name for the hedge group\n :return: API response with hedge group details including ID\n\n Example:\n >>> strategy_request, optimization_response = strategy.run()\n >>> saved_hedge = strategy.save_to_marquee(\n ... strategy_request,\n ... optimization_response,\n ... hedge_name=\"Factor Hedge\",\n ... group_name=\"My Hedge Group\"\n ... )\n >>> print(f\"Saved hedge ID: {saved_hedge['id']}\")\n \"\"\"\n # Build the payload\n payload = self.build_hedge_payload(\n strategy_request=strategy_request,\n optimization_response=optimization_response,\n hedge_name=hedge_name,\n group_name=group_name,\n )\n\n url = \"/hedges/groups\"\n\n try:\n response = GsSession.current.sync.post(url, payload=payload)\n\n result = response\n hedge_group_id = result.get('id', 'N/A')\n\n print(\"Hedge saved successfully!\")\n print(f\" Hedge Group ID: {hedge_group_id}\")\n print(f\" Name: {result.get('name', 'N/A')}\")\n print(f\" Created Time: {result.get('createdTime', 'N/A')}\")\n\n # Display link to Marquee UI\n if hedge_group_id != 'N/A':\n marquee_url = f\"https://marquee.gs.com/s/hedging/multi/{hedge_group_id}\"\n print(\"\\n View in Marquee UI:\")\n print(f\" {marquee_url}\")\n\n return result\n\n except Exception as e:\n print(f\"✗ Failed to save hedge: {e}\")\n if hasattr(e, 'response'):\n print(f\" Response: {e.response.text}\")\n raise MqValueError(f\"Failed to save hedge to Marquee: {e}\")\n" }, { "path": "gs_quant/markets/portfolio.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicablNe law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport re\nfrom dataclasses import dataclass\nfrom itertools import chain\nfrom typing import Iterable, Optional, Tuple, Union\nfrom urllib.parse import quote\n\nimport deprecation\nimport numpy as np\nimport pandas as pd\nfrom more_itertools import unique_everseen\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.portfolios import GsPortfolioApi\nfrom gs_quant.base import InstrumentBase\nfrom gs_quant.common import RiskMeasure, RiskPosition\nfrom gs_quant.instrument import Instrument, AssetType\nfrom gs_quant.markets import HistoricalPricingContext, OverlayMarket, PricingContext, PositionContext\nfrom gs_quant.priceable import PriceableImpl\nfrom gs_quant.risk import ResolvedInstrumentValues\nfrom gs_quant.risk.results import CompositeResultFuture, PortfolioRiskResult, PortfolioPath, PricingFuture\nfrom gs_quant.target.portfolios import Portfolio as MarqueePortfolio\nfrom gs_quant.target.portfolios import Position, PositionSet, RiskRequest, PricingDateAndMarketDataAsOf\n\n_logger = logging.getLogger(__name__)\n\n\n@dataclass\nclass Portfolio(PriceableImpl):\n \"\"\"A collection of instruments\n\n Portfolio holds a collection of instruments in order to run pricing and risk scenarios\n\n \"\"\"\n\n def __init__(\n self, priceables: Optional[Union[PriceableImpl, Iterable[PriceableImpl], dict]] = (), name: Optional[str] = None\n ):\n \"\"\"\n Creates a portfolio object which can be used to hold instruments\n\n :param priceables: constructed with an instrument, portfolio, iterable of either, or a dictionary where\n key is name and value is a priceable\n \"\"\"\n super().__init__()\n if isinstance(priceables, dict):\n priceables_list = []\n for name, priceable in priceables.items():\n priceable.name = name\n priceables_list.append(priceable)\n self.priceables = priceables_list\n else:\n self.priceables = priceables\n\n self.name = name\n self.__id = None\n self.__quote_id = None\n\n def __repr__(self):\n name = 'Portfolio'\n inst_desc = f'{len(self.all_instruments) if self.priceables else \"0\"} instrument(s)'\n if self.name:\n name += f'({self.name}, {inst_desc})'\n else:\n name += f'({inst_desc})'\n return name\n\n def _to_records(self):\n def get_name(obj, idx):\n if isinstance(obj, InstrumentBase) and hasattr(obj, 'type_'):\n type_name = obj.type_.name if isinstance(obj.type_, AssetType) else obj.type_\n else:\n type_name = 'Portfolio'\n return f'{type_name}_{idx}' if obj.name is None else obj.name\n\n stack = [(None, self)]\n records = []\n while stack:\n temp_records = []\n parent, portfolio = stack.pop()\n current_record = {} if len(records) == 0 else records.pop(0)\n for idx, priceable in enumerate(portfolio.__priceables):\n path = parent + PortfolioPath(idx) if parent is not None else PortfolioPath(idx)\n priceable_name = get_name(priceable, idx)\n if isinstance(priceable, Portfolio):\n stack.insert(0, (path, priceable))\n temp_records.append({**current_record, f'portfolio_name_{len(path) - 1}': priceable_name})\n else:\n temp_records.append({**current_record, 'instrument_name': priceable_name})\n records.extend(temp_records)\n return records\n\n def __getitem__(self, item):\n if isinstance(item, (int, slice)):\n return self.__priceables[item]\n elif isinstance(item, PortfolioPath):\n return item(self, rename_to_parent=True)\n else:\n values = (\n tuple(self[p] for it in item for p in self.paths(it))\n if isinstance(item, list)\n else tuple(self[p] for p in self.paths(item))\n )\n return values[0] if len(values) == 1 else values\n\n def __contains__(self, item):\n if isinstance(item, PriceableImpl):\n return any(item in p.__priceables for p in self.all_portfolios + (self,))\n elif isinstance(item, str):\n return any(item in p.__priceables_by_name for p in self.all_portfolios + (self,))\n else:\n return False\n\n def __len__(self):\n return len(self.__priceables)\n\n def __iter__(self):\n return iter(self.__priceables)\n\n def __hash__(self):\n hash_code = hash(self.name) ^ hash(self.__id)\n for priceable in self.__priceables:\n hash_code ^= hash(priceable)\n\n return hash_code\n\n def __eq__(self, other):\n if not isinstance(other, Portfolio):\n return False\n\n for path in self.all_paths:\n try:\n if path(self) != path(other):\n return False\n except (IndexError, TypeError):\n # indexerror occurs when two portfolios are of different lengths\n # typeerror: instrument is not subscriptable occurs when two portfolios are of different depths\n return False\n\n return True\n\n def __add__(self, other):\n if not isinstance(other, Portfolio):\n raise ValueError('Can only add instances of Portfolio')\n\n return Portfolio(self.__priceables + other.__priceables)\n\n @property\n def __position_context(self) -> PositionContext:\n return PositionContext.current if PositionContext.current.is_entered else PositionContext.default_value()\n\n @property\n def id(self) -> str:\n return self.__id\n\n @property\n def quote_id(self) -> str:\n return self.__quote_id\n\n @property\n def priceables(self) -> Tuple[PriceableImpl, ...]:\n return self.__priceables\n\n @priceables.setter\n def priceables(self, priceables: Union[PriceableImpl, Iterable[PriceableImpl]]):\n self.__priceables = (priceables,) if isinstance(priceables, PriceableImpl) else tuple(priceables)\n self.__priceables_by_name = {}\n\n for idx, i in enumerate(self.__priceables):\n if i and i.name:\n self.__priceables_by_name.setdefault(i.name, []).append(idx)\n\n @priceables.deleter\n def priceables(self):\n self.__priceables = None\n self.__priceables_by_name = None\n\n @property\n def instruments(self) -> Tuple[Instrument, ...]:\n return tuple(unique_everseen(i for i in self.__priceables if isinstance(i, Instrument)))\n\n @property\n def all_instruments(self) -> Tuple[Instrument, ...]:\n instr = chain(self.instruments, chain.from_iterable(p.all_instruments for p in self.all_portfolios))\n return tuple(unique_everseen(instr))\n\n @property\n def portfolios(self) -> Tuple[PriceableImpl, ...]:\n return tuple(i for i in self.__priceables if isinstance(i, Portfolio))\n\n @property\n def all_portfolios(self) -> Tuple[PriceableImpl, ...]:\n stack = list(self.portfolios)\n portfolios = list(unique_everseen(stack))\n\n while stack:\n portfolio = stack.pop()\n if portfolio in portfolios:\n continue\n\n sub_portfolios = portfolio.portfolios\n portfolios.extend(sub_portfolios)\n stack.extend(sub_portfolios)\n\n return tuple(unique_everseen(portfolios))\n\n def subset(self, paths: Iterable[PortfolioPath], name=None):\n # Do our paths represent a single portfolio?\n paths_tuple = tuple(paths)\n if len(paths_tuple) == 1 and isinstance(self[paths_tuple[0]], Portfolio):\n return self[paths_tuple[0]]\n else:\n return Portfolio(tuple(self[p] for p in paths_tuple), name=name)\n\n @staticmethod\n def __from_internal_positions(id_type: str, positions_id, activity_type: str):\n instruments = GsPortfolioApi.get_instruments_by_position_type(id_type, positions_id, activity_type)\n return Portfolio(instruments, name=positions_id)\n\n @staticmethod\n def from_eti(eti: str):\n return Portfolio.__from_internal_positions('ETI', quote(eti, safe=''), 'trade')\n\n @staticmethod\n def from_book(book: str, book_type: str = 'risk', activity_type: str = 'position'):\n return Portfolio.__from_internal_positions(book_type, book, activity_type)\n\n @staticmethod\n def from_asset_id(asset_id: str, date=None):\n asset = GsAssetApi.get_asset(asset_id)\n response = (\n GsAssetApi.get_asset_positions_for_date(asset_id, date)\n if date\n else GsAssetApi.get_latest_positions(asset_id)\n )\n response = response[0] if isinstance(response, tuple) else response\n positions = response.positions if isinstance(response, PositionSet) else response['positions']\n instruments = GsAssetApi.get_instruments_for_positions(positions)\n ret = Portfolio(instruments, name=asset.name)\n ret.__id = asset_id\n return ret\n\n @staticmethod\n def from_asset_name(name: str):\n asset = GsAssetApi.get_asset_by_name(name)\n return Portfolio.load_from_portfolio_id(asset.id)\n\n @classmethod\n def get(cls, portfolio_id: str = None, portfolio_name: str = None, query_instruments: Optional[bool] = False):\n if portfolio_name:\n portfolio = GsPortfolioApi.get_portfolio_by_name(portfolio_name)\n portfolio_id = portfolio.id\n position_date = PositionContext.current.position_date if PositionContext.is_entered else dt.date.today()\n portfolio = GsPortfolioApi.get_portfolio(portfolio_id)\n ret = Portfolio(name=portfolio.name)\n ret.__id = portfolio_id\n if query_instruments:\n ret._get_instruments(position_date, True)\n return ret\n\n @classmethod\n @deprecation.deprecated(\n deprecated_in='0.8.293',\n details='from_portfolio_id is now deprecated, please use Portfolio.get(portfolio_id=portfolio_id) instead.',\n )\n def from_portfolio_id(cls, portfolio_id: str):\n return cls.get(portfolio_id=portfolio_id)\n\n @classmethod\n @deprecation.deprecated(\n deprecated_in='0.8.293',\n details='from_portfolio_name is now deprecated, please use '\n 'Portfolio.get(portfolio_name=portfolio_name) instead.',\n )\n def from_portfolio_name(cls, name: str):\n return cls.get(portfolio_name=name)\n\n @staticmethod\n def from_quote(quote_id: str):\n instruments = GsPortfolioApi.get_instruments_by_workflow_id(quote_id)\n ret = Portfolio(instruments, name=quote_id)\n ret.__quote_id = quote_id\n return ret\n\n def save(self, overwrite: Optional[bool] = False):\n if self.portfolios:\n raise ValueError('Cannot save portfolios with nested portfolios')\n\n if self.__id:\n if not overwrite:\n raise ValueError(f'Portfolio with id {id} already exists. Use overwrite=True to overwrite')\n else:\n if not self.name:\n raise ValueError('name not set')\n self.__id = GsPortfolioApi.create_portfolio(MarqueePortfolio('USD', self.name)).id\n _logger.info(f'Created Marquee portfolio {self.name} with id {self.__id}')\n\n position_set = PositionSet(\n position_date=self.__position_context.position_date,\n positions=tuple(\n Position(asset_id=GsAssetApi.get_or_create_asset_from_instrument(i)) for i in self.instruments\n ),\n )\n if len(position_set.positions) > 0:\n GsPortfolioApi.update_positions(self.__id, [position_set])\n\n def save_as_quote(self, overwrite: Optional[bool] = False) -> str:\n if self.portfolios:\n raise ValueError('Cannot save portfolios with nested portfolios')\n\n pricing_context = self._pricing_context\n with pricing_context:\n pricing_date = PricingContext.current.pricing_date\n market = PricingContext.current.market\n\n request = RiskRequest(\n tuple(RiskPosition(instrument=i, quantity=i.instrument_quantity) for i in self.instruments),\n (ResolvedInstrumentValues,),\n pricing_and_market_data_as_of=(PricingDateAndMarketDataAsOf(pricing_date=pricing_date, market=market),),\n )\n\n if self.__quote_id:\n if not overwrite:\n raise ValueError(f'Quote with id {self.__quote_id} already exists. Use overwrite=True to overwrite')\n else:\n GsPortfolioApi.update_quote(self.__quote_id, request)\n _logger.info(f'Updated quote with id {self.__quote_id}')\n else:\n self.__quote_id = GsPortfolioApi.save_quote(request)\n _logger.info(f'Created quote with id {self.__quote_id}')\n return self.__quote_id\n\n def save_to_shadowbook(self, name: str):\n if self.portfolios:\n raise ValueError('Cannot save portfolios with nested portfolios')\n\n pricing_context = self._pricing_context\n with pricing_context:\n pricing_date = PricingContext.current.pricing_date\n market = PricingContext.current.market\n\n request = RiskRequest(\n tuple(RiskPosition(instrument=i, quantity=i.instrument_quantity) for i in self.instruments),\n (ResolvedInstrumentValues,),\n pricing_and_market_data_as_of=(PricingDateAndMarketDataAsOf(pricing_date=pricing_date, market=market),),\n )\n status = GsPortfolioApi.save_to_shadowbook(request, name)\n print(f'Save to shadowbook status - {status}')\n\n @classmethod\n def from_frame(cls, data: pd.DataFrame, mappings: dict = None):\n def get_value(this_row: pd.Series, attribute: str):\n value = mappings.get(attribute, attribute)\n return value(this_row) if callable(value) else this_row.get(value)\n\n instruments = []\n mappings = mappings or {}\n data = data.replace({np.nan: None})\n\n for row in (r for _, r in data.iterrows() if any(v for v in r.values if v is not None)):\n instrument = None\n for init_keys in (('asset_class', 'type'), ('$type',)):\n init_values = tuple(filter(None, (get_value(row, k) for k in init_keys)))\n if len(init_keys) == len(init_values):\n instrument = Instrument.from_dict(dict(zip(init_keys, init_values)))\n instrument = instrument.from_dict({p: get_value(row, p) for p in instrument.properties()})\n break\n\n if instrument:\n instruments.append(instrument)\n else:\n raise ValueError('Neither asset_class/type nor $type specified')\n\n return cls(instruments)\n\n @classmethod\n def from_csv(cls, csv_file: str, mappings: Optional[dict] = None):\n data = pd.read_csv(csv_file, skip_blank_lines=True).replace({np.nan: None})\n reg = re.compile(r'\\.[0-9]')\n dupelist = [re.sub(reg, '', word) for word in data.columns if reg.search(word)]\n if len(dupelist):\n raise ValueError(f'Duplicate column values {dupelist}')\n return cls.from_frame(data, mappings)\n\n def scale(self, scaling: int, in_place: bool = True):\n instruments = self._get_instruments(self.__position_context.position_date, in_place, False)\n if in_place:\n for inst in self.all_instruments:\n inst.scale(scaling, in_place)\n else:\n return Portfolio([inst.scale(scaling, in_place) for inst in instruments])\n\n def append(self, priceables: Union[PriceableImpl, Iterable[PriceableImpl]]):\n self.priceables += (priceables,) if isinstance(priceables, PriceableImpl) else tuple(priceables)\n\n def pop(self, item) -> PriceableImpl:\n priceable = self[item]\n self.priceables = [inst for inst in self.instruments if inst != priceable]\n return priceable\n\n def extend(self, portfolio: Iterable):\n self.priceables += tuple([p for p in portfolio])\n\n def to_frame(self, mappings: Optional[dict] = None) -> pd.DataFrame:\n def to_records(portfolio: Portfolio) -> list:\n records = []\n\n for priceable in portfolio.priceables:\n if isinstance(priceable, Portfolio):\n records.extend(to_records(priceable))\n else:\n as_dict = priceable.as_dict()\n if not hasattr(priceable, 'asset_class'):\n as_dict['$type'] = priceable.type_\n\n records.append(\n dict(chain(as_dict.items(), (('instrument', priceable), ('portfolio', portfolio.name))))\n )\n\n return records\n\n df = pd.DataFrame.from_records(to_records(self)).set_index(['portfolio', 'instrument'])\n all_columns = df.columns.to_list()\n columns = sorted(c for c in all_columns if c not in ('asset_class', 'type', '$type'))\n\n for asset_column in ('$type', 'type', 'asset_class'):\n if asset_column in all_columns:\n columns = [asset_column] + columns\n\n df = df[columns]\n mappings = mappings or {}\n\n for key, value in mappings.items():\n if isinstance(value, str):\n df[key] = df[value]\n elif callable(value):\n df[key] = len(df) * [None]\n df[key] = df.apply(value, axis=1)\n\n return df\n\n def to_csv(self, csv_file: str, mappings: Optional[dict] = None, ignored_cols: Optional[list] = None):\n port_df = self.to_frame(mappings or {})\n port_df = port_df[np.setdiff1d(port_df.columns, ignored_cols or [])]\n port_df = port_df.reset_index(drop=True)\n\n port_df.to_csv(csv_file)\n\n @property\n def all_paths(self) -> Tuple[PortfolioPath, ...]:\n paths = ()\n stack = [(None, self)]\n while stack:\n parent, portfolio = stack.pop()\n\n for idx, priceable in enumerate(portfolio.__priceables):\n path = parent + PortfolioPath(idx) if parent is not None else PortfolioPath(idx)\n if isinstance(priceable, Portfolio):\n stack.insert(0, (path, priceable))\n else:\n paths += (path,)\n\n return paths\n\n def paths(self, key: Union[str, PriceableImpl]) -> Tuple[PortfolioPath, ...]:\n if not isinstance(key, (str, Instrument, Portfolio)):\n raise ValueError('key must be a name or Instrument or Portfolio')\n\n if isinstance(key, str):\n idx = self.__priceables_by_name.get(key)\n else:\n idx = []\n for p_idx, p in enumerate(self.__priceables):\n if p == key or getattr(p, \"unresolved\", None) == key:\n idx.append(p_idx)\n\n paths = tuple(PortfolioPath(i) for i in idx) if idx else ()\n\n for path, porfolio in (\n (PortfolioPath(i), p) for i, p in enumerate(self.__priceables) if isinstance(p, Portfolio)\n ):\n paths += tuple(path + sub_path for sub_path in porfolio.paths(key))\n\n return paths\n\n def resolve(self, in_place: bool = True) -> Optional[Union[PricingFuture, PriceableImpl, dict]]:\n priceables = self._get_instruments(self.__position_context.position_date, in_place, True)\n pricing_context = self._pricing_context\n with pricing_context:\n futures = [p.resolve(in_place) for p in priceables]\n\n if not in_place:\n ret = {} if isinstance(PricingContext.current, HistoricalPricingContext) else Portfolio(name=self.name)\n result_future = PricingFuture() if self._return_future else None\n\n def cb(future: CompositeResultFuture):\n if isinstance(ret, Portfolio):\n ret.priceables = [f.result() for f in future.futures]\n else:\n priceables_by_date = {}\n for future in futures:\n for date, priceable in future.result().items():\n priceables_by_date.setdefault(date, []).append(priceable)\n\n for date, priceables in priceables_by_date.items():\n if any(p for p in priceables if not isinstance(p, PriceableImpl)):\n _logger.error(f'Error resolving on {date}, skipping that date')\n else:\n ret[date] = Portfolio(priceables, name=self.name)\n\n if result_future:\n result_future.set_result(ret)\n\n CompositeResultFuture(futures).add_done_callback(cb)\n return result_future or ret\n\n def market(self) -> Union[OverlayMarket, PricingFuture, dict]:\n \"\"\"\n Market Data map of coordinates and values. Note that this is not yet supported on all instruments\n\n ***Examples**\n\n >>> from gs_quant.markets.portfolio import Portfolio\n >>>\n >>> portfolio = Portfolio(...)\n >>> market = portfolio.market()\n \"\"\"\n pricing_context = self._pricing_context\n instruments = self._get_instruments(self.__position_context.position_date, False, False)\n with pricing_context:\n futures = [i.market() for i in instruments]\n\n result_future = PricingFuture()\n\n def cb(future: CompositeResultFuture):\n def update_market_data(all_market_data, this_market_data):\n for coordinate, value in this_market_data.items():\n existing_value = all_market_data.setdefault(coordinate, value)\n if abs(existing_value - value) > 1e-6:\n raise ValueError(f'Conflicting values for {coordinate}: {existing_value} vs {value}')\n\n results = [f.result() for f in future.futures]\n is_historical = isinstance(results[0], dict)\n market_data = None if is_historical else {}\n overlay_markets = {} if is_historical else None\n\n for result in results:\n if market_data is not None:\n update_market_data(market_data, result.market_data_dict)\n else:\n for market in result.values():\n update_market_data(overlay_markets.setdefault(market, {}), market.market_data)\n\n if market_data:\n ret = OverlayMarket(base_market=results[0], market_data=market_data)\n else:\n ret = {\n base_market.date: OverlayMarket(base_market=base_market, market_data=market_data)\n for base_market, market_data in overlay_markets.items()\n }\n\n if result_future:\n result_future.set_result(ret)\n\n CompositeResultFuture(futures).add_done_callback(cb)\n return result_future if self._return_future else result_future.result()\n\n def calc(self, risk_measure: Union[RiskMeasure, Iterable[RiskMeasure]], fn=None) -> PortfolioRiskResult:\n priceables = self._get_instruments(self.__position_context.position_date, False, True)\n with self._pricing_context:\n # PortfolioRiskResult should hold a copy of the portfolio instead of a reference to the portfolio\n # this is to prevent the portfolio object within portfolioriskresult to hold a reference to the portfolio\n # object should it later be modified in place (eg: resolution)\n return PortfolioRiskResult(\n self.clone(),\n (risk_measure,) if isinstance(risk_measure, RiskMeasure) else risk_measure,\n [p.calc(risk_measure, fn=fn) for p in priceables],\n )\n\n def _get_instruments(self, position_date: dt.date, in_place: bool, return_priceables: bool = True):\n if self.id:\n dates_prior = list(filter(lambda date: date < position_date, GsPortfolioApi.get_position_dates(self.id)))\n if len(dates_prior) == 0:\n raise ValueError('Your portfolio has no positions on the PositionContext date')\n date = max(dates_prior)\n response = GsPortfolioApi.get_positions_for_date(self.id, date)\n positions = response.positions if response else []\n instruments = GsAssetApi.get_instruments_for_positions(positions)\n if in_place:\n self.__priceables = instruments\n return instruments\n return self.__priceables if return_priceables else self.all_instruments\n\n def clone(self, clone_instruments: bool = False):\n portfolio_clone = Portfolio(\n [\n p.clone(clone_instruments) if isinstance(p, Portfolio) else p.clone() if clone_instruments else p\n for p in self.__priceables\n ],\n name=self.name,\n )\n portfolio_clone.__id = self.__id\n portfolio_clone.__quote_id = self.__quote_id\n return portfolio_clone\n\n\n@dataclass\nclass Grid(Portfolio):\n \"\"\"A grid of instruments\n\n A grid is a type of portfolio which represents a grid of similar instruments\n\n \"\"\"\n\n def __init__(\n self,\n priceable: PriceableImpl,\n x_param: str,\n x_values: Iterable,\n y_param: str,\n y_values: Iterable,\n name: Optional[str] = None,\n ):\n x_overrides = [{x_param: v, 'name': v} for v in x_values]\n y_overrides = [{y_param: v} for v in y_values]\n super().__init__(\n [\n Portfolio([priceable.clone(**{**x, **y}) for x in x_overrides], name=next(iter(y.values())))\n for y in y_overrides\n ],\n name,\n )\n" }, { "path": "gs_quant/markets/portfolio_manager.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport traceback\nfrom time import sleep\nfrom typing import List, Union, Dict\n\nimport deprecation\nimport numpy as np\nimport pandas as pd\n\nfrom gs_quant.api.gs.portfolios import GsPortfolioApi\nfrom gs_quant.api.gs.reports import GsReportApi\nfrom gs_quant.common import Currency, PositionType\nfrom gs_quant.entities.entitlements import Entitlements, EntitlementBlock, User\nfrom gs_quant.entities.entity import PositionedEntity, EntityType, ScenarioCalculationMeasure\nfrom gs_quant.errors import MqError\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.factor import Factor\nfrom gs_quant.markets.portfolio_manager_utils import (\n build_exposure_df,\n build_portfolio_constituents_df,\n build_sensitivity_df,\n get_batched_dates,\n)\nfrom gs_quant.markets.report import PerformanceReport, ReportJobFuture\nfrom gs_quant.markets.scenario import FactorScenario\nfrom gs_quant.models.risk_model import MacroRiskModel, ReturnFormat, FactorType, FactorRiskModel\nfrom gs_quant.target.portfolios import RiskAumSource, PortfolioTree\nfrom gs_quant.target.risk_models import RiskModelDataAssetsRequest, RiskModelUniverseIdentifierRequest\n\n_logger = logging.getLogger(__name__)\n\n\n@deprecation.deprecated(\n deprecated_in='1.0.10',\n details='portfolio_manager.CustomAUMDataPoint is now deprecated, please use report.CustomAUMDataPoint instead.',\n)\nclass CustomAUMDataPoint:\n \"\"\"\n\n Custom AUM Data Point represents a portfolio's AUM value for a specific date\n\n \"\"\"\n\n def __init__(self, date: dt.date, aum: float):\n self.__date = date\n self.__aum = aum\n\n @property\n def date(self) -> dt.date:\n return self.__date\n\n @date.setter\n def date(self, value: dt.date):\n self.__date = value\n\n @property\n def aum(self) -> float:\n return self.__aum\n\n @aum.setter\n def aum(self, value: float):\n self.__aum = value\n\n\nclass PortfolioManager(PositionedEntity):\n \"\"\"\n Portfolio Manager is used to manage Marquee portfolios (setting entitlements, running and retrieving reports, etc)\n \"\"\"\n\n def __init__(self, portfolio_id: str):\n \"\"\"\n Initialize a Portfolio Manager\n\n :param portfolio_id: Portfolio ID\n \"\"\"\n self.__portfolio_id = portfolio_id\n PositionedEntity.__init__(self, portfolio_id, EntityType.PORTFOLIO)\n\n @property\n def portfolio_id(self) -> str:\n return self.__portfolio_id\n\n @portfolio_id.setter\n def portfolio_id(self, value: str):\n self.__portfolio_id = value\n\n def get_performance_report(self, tags: Dict = None) -> PerformanceReport:\n \"\"\"\n Get performance report associated with a portfolio\n\n :param tags: If the portfolio is a fund of funds, pass in a dictionary corresponding to the tag values\n to retrieve results for a sub-portfolio\n\n :return: returns the PerformanceReport associated with portfolio if one exists\n \"\"\"\n reports = GsReportApi.get_reports(\n limit=500,\n position_source_type='Portfolio',\n position_source_id=self.id,\n report_type='Portfolio Performance Analytics',\n tags=tags,\n scroll='1m',\n )\n\n # If tags is set to None, it returns all PPA reports for the portfolio,\n # and returning reports[0] can return any one PPA, so specifically if tags is None it means we are\n # looking for the root PPA, we need to explicitly filter out and get the one report where tags is None\n if tags is None:\n reports = [report for report in reports if report.parameters.tags is None]\n if len(reports) == 0:\n raise MqError('No performance report found.')\n return PerformanceReport.from_target(reports[0])\n\n def schedule_reports(\n self, start_date: dt.date = None, end_date: dt.date = None, backcast: bool = False, months_per_batch: int = None\n ):\n \"\"\"\n Schedule all reports associated with a portfolio. If months_per_batch is passed and reports are historical, then\n schedule them in batches of bathc_period months. Backcasted reported can't be batched.\n\n :param start_date: start date of report job (optional)\n :param end_date: end date of report job (optional)\n :param backcast: true if reports should be backcasted; defaults to false\n :param months_per_batch: batch size of historical report job schedules in number of months; defaults to false\n if set, historical reports are scheduled in batches of size less than or equal to the given number of months\n\n **Examples**\n\n >>> pm = PortfolioManager(\"PORTFOLIO ID\")\n >>> pm.schedule_reports(backcast=False)\n\n for a portfolio having hisotry greater than 1 yr, use months_per_batch to schedule reports in batches of\n 6 months\n >>> pm = PortfolioManager(\"PORTFOLIO ID\")\n >>> pm.schedule_reports(backcast=False, months_per_batch=6)\n \"\"\"\n if months_per_batch is None or backcast:\n if backcast:\n print(\n 'Batching of schedule reports is only supported for historical reports. '\n 'Backcasted reports will be scheduled for the full date range'\n )\n GsPortfolioApi.schedule_reports(self.__portfolio_id, start_date, end_date, backcast=backcast)\n else:\n # Process input\n if months_per_batch <= 0:\n raise MqValueError(f'Invalid input, months_per_batch {months_per_batch} should be greater than 0')\n\n if end_date is None or start_date is None:\n hints = self.get_schedule_dates(backcast=backcast)\n start_date = hints[0] if start_date is None else start_date\n end_date = hints[1] if end_date is None else end_date\n\n if start_date >= end_date:\n raise MqValueError(f'Invalid input, start date {start_date} should be before end date {end_date}')\n\n # Validate input date range against position dates\n position_dates = self.get_position_dates()\n position_dates = [p for p in position_dates if end_date >= p >= start_date]\n\n if len(position_dates) == 0:\n raise MqValueError('Portfolio does not have any positions in the given date range')\n\n if start_date not in position_dates:\n # There should be positions on the start date for historical reports\n raise MqError(\n 'Cannot schedule historical report because the first set of positions within the '\n f'scheduling date range need to be on this date {start_date}'\n )\n else:\n # preparation for the first batch\n position_dates.remove(start_date)\n\n if end_date not in position_dates:\n # make sure to process the last batch\n position_dates.append(end_date)\n\n # Create batches using sliding window\n batch_boundaries = [start_date]\n prev_date = start_date\n for i, d in enumerate(position_dates):\n current_batch = d - prev_date\n if current_batch.days > months_per_batch * 30 and i > 0:\n # split the current window at the previous date\n prev_date = position_dates[i - 1]\n batch_boundaries.append(prev_date)\n\n batch_boundaries.append(end_date)\n\n # Schedule reports in batches\n print(f'Scheduling reports from {start_date} to {end_date} in {len(batch_boundaries) - 1} batches')\n\n for i in range(len(batch_boundaries) - 1):\n print(f'Scheduling for {batch_boundaries[i]} to {batch_boundaries[i + 1]}')\n GsPortfolioApi.schedule_reports(\n self.__portfolio_id, batch_boundaries[i], batch_boundaries[i + 1], backcast=backcast\n )\n if i > 0 and i % 10 == 0:\n sleep(6)\n\n def run_reports(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n backcast: bool = False,\n is_async: bool = True,\n months_per_batch: int = None,\n ) -> List[Union[pd.DataFrame, ReportJobFuture]]:\n \"\"\"\n Run all reports associated with a portfolio\n\n :param start_date: start date of report job\n :param end_date: end date of report job\n :param backcast: true if reports should be backcasted; defaults to false\n :param is_async: true if reports should run asynchronously; defaults to true\n :param months_per_batch: batch size of historical report job schedules in number of months; defaults to false\n if set, historical reports are scheduled in batches of size less than or equal to the given number of months\n :return: if is_async is true, returns a list of ReportJobFuture objects; if is_async is false, returns a list\n of dataframe objects containing report results for all portfolio results\n\n **Examples**\n\n >>> pm = PortfolioManager(\"PORTFOLIO ID\")\n >>> report_results = pm.run_reports(backcast=True)\n\n for longer hisotry, use months_per_batch to schedule reports in batches of 6 months\n >>> pm = PortfolioManager(\"PORTFOLIO ID\")\n >>> pm.schedule_reports(backcast=False, months_per_batch=6)\n \"\"\"\n self.schedule_reports(start_date, end_date, backcast, months_per_batch)\n reports = self.get_reports()\n report_futures = [report.get_most_recent_job() for report in reports]\n if is_async:\n return report_futures\n counter = 100\n while counter > 0:\n is_done = [future.done() for future in report_futures]\n if False not in is_done:\n return [job_future.result() for job_future in report_futures]\n sleep(6)\n raise MqValueError(\n f'Your reports for Portfolio {self.__portfolio_id} are taking longer than expected '\n f'to finish. Please contact the Marquee Analytics team at '\n f'gs-marquee-analytics-support@gs.com'\n )\n\n def set_entitlements(self, entitlements: Entitlements):\n \"\"\"\n Set the entitlements of a portfolio\n\n :param entitlements: Entitlements object\n\n **Examples**\n\n >>> portfolio_admin_emails = ['LIST OF ADMIN EMAILS']\n >>> portfolio_viewer_emails = ['LIST OF VIEWER EMAILS']\n >>> admin_entitlements = EntitlementBlock(users=User.get_many(emails=portfolio_admin_emails))\n >>> view_entitlements = EntitlementBlock(users=User.get_many(emails=portfolio_viewer_emails))\n >>>\n >>> entitlements = Entitlements(\n >>> view=view_entitlements,\n >>> admin=admin_entitlements\n >>> )\n >>>\n >>> pm = PortfolioManager(\"PORTFOLIO ID\")\n >>> pm.set_entitlements(entitlements)\n\n **See Also**\n :func: PortfolioManager.share\n :func: PortfolioManager.get_entitlements\n \"\"\"\n entitlements_as_target = entitlements.to_target()\n portfolio_as_target = GsPortfolioApi.get_portfolio(self.__portfolio_id)\n portfolio_as_target.entitlements = entitlements_as_target\n GsPortfolioApi.update_portfolio(portfolio_as_target)\n\n def share(self, emails: List[str], admin: bool = False):\n \"\"\"\n Share a portfolio with a list of emails\n\n :param emails: list of emails to share the portfolio with\n :param admin: true if the users should have admin access; defaults to false\n\n **Examples**\n\n >>> pm = PortfolioManager(\"PORTFOLIO ID\")\n >>> pm.share(['EMAIL1', 'EMAIL2'])\n \"\"\"\n current_entitlements = self.get_entitlements()\n users = User.get_many(emails=emails)\n found_emails = [user.email for user in users]\n if len(found_emails) != len(emails):\n missing_emails = [email for email in emails if email not in found_emails]\n raise MqValueError(f'Users with emails {missing_emails} not found')\n if admin:\n current_entitlements.admin = EntitlementBlock(users=set(current_entitlements.admin.users + users))\n current_entitlements.view = EntitlementBlock(users=set(current_entitlements.view.users + users))\n self.set_entitlements(current_entitlements)\n\n def set_currency(self, currency: Currency):\n \"\"\"\n Set the currency of a portfolio\n\n :param currency: Currency\n \"\"\"\n portfolio_as_target = GsPortfolioApi.get_portfolio(self.__portfolio_id)\n portfolio_as_target.currency = currency\n GsPortfolioApi.update_portfolio(portfolio_as_target)\n\n def get_tag_name_hierarchy(self) -> List:\n \"\"\"\n Get the list of tags by name by which a portfolio's fund of funds are structured in that order\n\n :return: a list of tag names\n \"\"\"\n portfolio = GsPortfolioApi.get_portfolio(self.portfolio_id)\n return list(portfolio.tag_name_hierarchy) if portfolio.tag_name_hierarchy else None\n\n def set_tag_name_hierarchy(self, tag_names: List):\n \"\"\"\n Set the list of tags by name by which a portfolio's fund of funds are structured in that order\n\n :param tag_names: a list of tag names in order of the new fund of funds structure\n \"\"\"\n portfolio = GsPortfolioApi.get_portfolio(self.portfolio_id)\n portfolio.tag_name_hierarchy = tag_names\n GsPortfolioApi.update_portfolio(portfolio)\n\n def update_portfolio_tree(self):\n \"\"\"\n After a modification is made on your portfolio (reports are added/modified, the tag name hierarchy is changed,\n etc), run this function so those changes reflect across all the portfolio's sub-portfolios\n \"\"\"\n GsPortfolioApi.update_portfolio_tree(self.portfolio_id)\n\n def get_portfolio_tree(self) -> PortfolioTree:\n \"\"\"\n Get the portfolio tree of a fund of funds portfolio\n \"\"\"\n return GsPortfolioApi.get_portfolio_tree(self.portfolio_id)\n\n def get_all_fund_of_fund_tags(self) -> List:\n \"\"\"\n If the portfolio is a fund of funds, this function will retrieve a list of dictionaries of all the tag sets\n associated with the sub-portfolios in the portfolio.\n\n :return: a list of tags as dictionaries\n \"\"\"\n tag_dicts = []\n for r in self.get_reports():\n if r.parameters.tags is not None:\n tags_as_dict = {tag.name: tag.value for tag in r.parameters.tags}\n if tags_as_dict not in tag_dicts:\n tag_dicts.append(tags_as_dict)\n tag_dicts.sort(key=lambda dictionary: len(dictionary.keys()))\n return tag_dicts\n\n def get_schedule_dates(self, backcast: bool = False) -> List[dt.date]:\n \"\"\"\n Get recommended start and end dates for a portfolio report scheduling job\n\n :param backcast: true if reports should be backcasted\n :return: a list of two dates, the first is the suggested start date and the second is the suggested end date\n \"\"\"\n return GsPortfolioApi.get_schedule_dates(self.id, backcast)\n\n @deprecation.deprecated(\n deprecated_in='1.0.10',\n details='PortfolioManager.get_aum_source is now deprecated, please use '\n 'PerformanceReport.get_aum_source instead.',\n )\n def get_aum_source(self) -> RiskAumSource:\n \"\"\"\n Get portfolio AUM Source\n\n :return: aum source\n \"\"\"\n portfolio = GsPortfolioApi.get_portfolio(self.portfolio_id)\n return portfolio.aum_source if portfolio.aum_source is not None else RiskAumSource.Long\n\n @deprecation.deprecated(\n deprecated_in='1.0.10',\n details='PortfolioManager.set_aum_source is now deprecated, please use '\n 'PerformanceReport.set_aum_source instead.',\n )\n def set_aum_source(self, aum_source: RiskAumSource):\n \"\"\"\n Set portfolio AUM Source\n\n :param aum_source: aum source for portfolio\n :return: aum source\n \"\"\"\n portfolio = GsPortfolioApi.get_portfolio(self.portfolio_id)\n portfolio.aum_source = aum_source\n GsPortfolioApi.update_portfolio(portfolio)\n\n @deprecation.deprecated(\n deprecated_in='1.0.10',\n details='PortfolioManager.get_custom_aum is now deprecated, please use '\n 'PerformanceReport.get_custom_aum instead.',\n )\n def get_custom_aum(self, start_date: dt.date = None, end_date: dt.date = None) -> List[CustomAUMDataPoint]:\n \"\"\"\n Get AUM data for portfolio\n\n :param start_date: start date\n :param end_date: end date\n :return: list of AUM data between the specified range\n \"\"\"\n aum_data = GsPortfolioApi.get_custom_aum(self.portfolio_id, start_date, end_date)\n return [\n CustomAUMDataPoint(date=dt.datetime.strptime(data['date'], '%Y-%m-%d'), aum=data['aum'])\n for data in aum_data\n ]\n\n @deprecation.deprecated(\n deprecated_in='1.0.10',\n details='PortfolioManager.get_aum is now deprecated, please use PerformanceReport.get_aum instead.',\n )\n def get_aum(self, start_date: dt.date, end_date: dt.date):\n \"\"\"\n Get AUM data for portfolio\n\n :param start_date: start date\n :param end_date: end date\n :return: dictionary of dates with corresponding AUM values\n \"\"\"\n aum_source = self.get_aum_source()\n if aum_source == RiskAumSource.Custom_AUM:\n aum = self.get_custom_aum(start_date=start_date, end_date=end_date)\n return {aum_point.date.strftime('%Y-%m-%d'): aum_point.aum for aum_point in aum}\n if aum_source == RiskAumSource.Long:\n aum = self.get_performance_report().get_long_exposure(start_date=start_date, end_date=end_date)\n return {row['date']: row['longExposure'] for index, row in aum.iterrows()}\n if aum_source == RiskAumSource.Short:\n aum = self.get_performance_report().get_short_exposure(start_date=start_date, end_date=end_date)\n return {row['date']: row['shortExposure'] for index, row in aum.iterrows()}\n if aum_source == RiskAumSource.Gross:\n aum = self.get_performance_report().get_gross_exposure(start_date=start_date, end_date=end_date)\n return {row['date']: row['grossExposure'] for index, row in aum.iterrows()}\n if aum_source == RiskAumSource.Net:\n aum = self.get_performance_report().get_net_exposure(start_date=start_date, end_date=end_date)\n return {row['date']: row['netExposure'] for index, row in aum.iterrows()}\n\n @deprecation.deprecated(\n deprecated_in='1.0.10',\n details='PortfolioManager.upload_custom_aum is now deprecated, please use '\n 'PerformanceReport.upload_custom_aum instead.',\n )\n def upload_custom_aum(self, aum_data: List[CustomAUMDataPoint], clear_existing_data: bool = None):\n \"\"\"\n Add AUM data for portfolio\n\n :param aum_data: list of AUM data to upload\n :param clear_existing_data: delete all previously uploaded AUM data for the portfolio (defaults to false)\n \"\"\"\n formatted_aum_data = [{'date': data.date.strftime('%Y-%m-%d'), 'aum': data.aum} for data in aum_data]\n GsPortfolioApi.upload_custom_aum(self.portfolio_id, formatted_aum_data, clear_existing_data)\n\n @deprecation.deprecated(\n deprecated_in=\"0.9.110\",\n details=\"Please use the get_pnl_contribution on your portfolio's performance report using\"\n \"the PerformanceReport class\",\n )\n def get_pnl_contribution(\n self, start_date: dt.date = None, end_date: dt.date = None, currency: Currency = None, tags: Dict = None\n ) -> pd.DataFrame:\n \"\"\"\n Get PnL Contribution of your portfolio broken down by constituents\n\n :param start_date: optional start date\n :param end_date: optional end date\n :param currency: optional currency; defaults to your portfolio's currency\n :param tags: If the portfolio is a fund of funds, pass in a dictionary corresponding to the tag values\n to retrieve results for a sub-portfolio\n :return: a Pandas DataFrame of results\n \"\"\"\n performance_report_id = None if tags is None else self.get_performance_report(tags).id\n return pd.DataFrame(\n GsPortfolioApi.get_attribution(self.portfolio_id, start_date, end_date, currency, performance_report_id)\n )\n\n def get_macro_exposure(\n self,\n model: MacroRiskModel,\n date: dt.date,\n factor_type: FactorType,\n factor_categories: List[Factor] = [],\n get_factors_by_name: bool = True,\n tags: Dict = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"\n Get portfolio and asset exposure to macro factors or macro factor categories\n\n :param model: the macro risk model\n :param date: date for which to get exposure\n :param factor_type: whether to get exposure to factor categories or factors.\n :param factor_categories: Get portfolio exposure to these factor categories. Must be valid Factor Categories.\n If factor_type is Factor, get exposure to factors that are grouped in these factor categories.\n If empty, return exposure to all factor categories/factors.\n :param get_factors_by_name: whether to identify factors by their name or identifier\n :param tags: If the portfolio is a fund of funds, pass in a dictionary corresponding to the tag values\n to retrieve results for a sub-portfolio\n :param return_format: whether to return a dict or a pandas dataframe\n\n :return: a Pandas Dataframe or a Dict of portfolio exposure to macro factors\n\n **Examples**\n\n >>> model = MacroRiskModel.get(\\\"MODEL\\\")\n >>> pm = PortfolioManager(\"PORTFOLIO ID\")\n >>> exposure_dataframe = pm.get_macro_exposure(\n >>> model=model,\n >>> date=dt.date(2022, 1, 1),\n >>> factor_type=FactorType.Factor\n >>> ).sort_values(\n >>> by=[\\\"Total Factor Exposure\\\"],\n >>> axis=1,\n >>> ascending=False\n >>> )\n \"\"\"\n performance_report = self.get_performance_report(tags)\n\n # Get portfolio constituents\n constituents_and_notional_df = build_portfolio_constituents_df(performance_report, date).rename(\n columns={\"name\": \"Asset Name\", \"netExposure\": \"Notional\"}\n )\n\n # Query universe sensitivity\n universe = constituents_and_notional_df.index.dropna().tolist()\n universe_sensitivities_df = build_sensitivity_df(universe, model, date, factor_type, get_factors_by_name)\n\n # Remove assets without exposure\n assets_with_exposure = list(universe_sensitivities_df.index.values)\n if not assets_with_exposure:\n logging.warning(\"The Portfolio is not exposed to any of the requested macro factors\")\n return pd.DataFrame()\n\n constituents_and_notional_df = constituents_and_notional_df.loc[assets_with_exposure]\n\n factor_data = (\n model.get_factor_data(date, date, factor_type=FactorType.Factor)\n if factor_type == FactorType.Factor\n else pd.DataFrame()\n )\n exposure_df = build_exposure_df(\n constituents_and_notional_df, universe_sensitivities_df, factor_categories, factor_data, get_factors_by_name\n )\n\n if return_format == ReturnFormat.JSON:\n return exposure_df.to_dict()\n\n return exposure_df\n\n def get_factor_scenario_analytics(\n self,\n scenarios: List[FactorScenario],\n date: dt.date,\n measures: List[ScenarioCalculationMeasure],\n risk_model: str = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, Union[Dict, pd.DataFrame]]:\n \"\"\"Given a list of factor scenarios (historical simulation and/or custom shocks), return the estimated pnl\n of the given positioned entity.\n :param scenarios: List of factor-based scenarios\n :param date: date to run scenarios.\n :param measures: which metrics to return\n :param risk_model: valid risk model ID\n :param return_format: whether to return data formatted in a dataframe or as a dict\n\n **Examples**\n\n >>> from gs_quant.session import GsSession, Environment\n >>> from gs_quant.markets.portfolio_manager import PortfolioManager, ReturnFormat\n >>> from gs_quant.entities.entity import ScenarioCalculationMeasure, PositionedEntity\n >>> from gs_quant.markets.scenario import Scenario\n\n Get scenarios\n >>> covid_19_omicron = Scenario.get_by_name(\"Covid 19 Omicron (v2)\") # historical simulation\n >>> custom_shock = Scenario.get_by_name(\"Shocking factor by x% (Propagated)\") # custom shock\n >>> risk_model = \"RISK_MODEL_ID\" # valid risk model ID\n\n Instantiate your positionedEntity. Here, we are using one of its subclasses, PortfolioManager\n\n >>> pm = PortfolioManager(portfolio_id=\"PORTFOLIO_ID\")\n\n Set the date you wish to run your scenario on\n\n >>> date = dt.date(2023, 3, 7)\n\n Run scenario and get estimated impact on your positioned entity\n\n >>> scenario_analytics = pm.get_factor_scenario_analytics(\n ... scenarios=[covid_19_omicron, beta_propagated],\n ... date=date,\n ... measures=[ScenarioCalculationMeasure.SUMMARY,\n ... ScenarioCalculationMeasure.ESTIMATED_FACTOR_PNL,\n ... ScenarioCalculationMeasure.ESTIMATED_PNL_BY_SECTOR,\n ... ScenarioCalculationMeasure.ESTIMATED_PNL_BY_REGION,\n ... ScenarioCalculationMeasure.ESTIMATED_PNL_BY_DIRECTION,\n ... ScenarioCalculationMeasure.ESTIMATED_PNL_BY_ASSET],\n ... risk_model=risk_model)\n\n By default, the result will be returned in a dict with keys as the measures/metrics requested and values as\n the scenario calculation results formatted in a dataframe. To get the results in a dict, specify the return\n format as JSON\n \"\"\"\n\n return super().get_factor_scenario_analytics(scenarios, date, measures, risk_model, return_format)\n\n def get_risk_model_predicted_beta(\n self, start_date: dt.date, end_date: dt.date, risk_model_id: str, default_beta_value: int = 1, tags: Dict = None\n ) -> pd.DataFrame:\n \"\"\"\n Get the predicted beta of a portfolio as estimated by the provided risk model.\n The beta is calculated using the factor risk model data. The function retrieves the factor risk report\n associated with the portfolio and the risk model ID, and then calculates the predicted beta for each date\n in the specified range.\n\n :param start_date: Start date for the beta calculation\n :param end_date: End date for the beta calculation\n :param risk_model_id: Risk model ID to be used for the calculation\n :param default_beta_value: Default beta value to be used if no data is available for a specific date\n :param tags: If the portfolio is a fund of funds, pass in a dictionary corresponding to the tag values\n to retrieve results for a sub-portfolio\n\n :return: A DataFrame containing the predicted beta for each date in the specified range\n\n **Examples**\n >>> pm = PortfolioManager(\"PORTFOLIO ID\")\n >>> beta_df = pm.get_risk_model_predicted_beta(start_date=dt.date(2023, 1, 1),\n ... end_date=dt.date(2023, 12, 31), risk_model_id=\"RISK_MODEL_ID\")\n >>> print(beta_df)\n >>> # Output:\n\n date beta\n 2023-01-01 1.05\n 2023-01-02 1.10\n 2023-01-03 1.02\n \"\"\"\n performance_report = self.get_performance_report(tags=tags)\n risk_model = FactorRiskModel.get(risk_model_id)\n risk_model_dates: List[dt.date] = risk_model.get_dates(start_date=start_date, end_date=end_date)\n batched_dates = get_batched_dates(risk_model_dates, batch_size=10)\n risk_model_predicted_beta_timeseries = pd.DataFrame()\n asset_level_betas = pd.DataFrame()\n portfolio_position_net_weights = pd.DataFrame()\n for batch in batched_dates:\n start_date, end_date = batch[0], batch[-1]\n try:\n date_wise_net_weight = performance_report.get_position_net_weights(\n start_date=start_date,\n end_date=end_date,\n asset_metadata_fields=[\"gsid\"],\n include_all_business_days=True,\n position_type=PositionType.CLOSE,\n )\n date_wise_net_weight = date_wise_net_weight.dropna().pivot_table(\n index='positionDate', columns='gsid', values='netWeight', aggfunc='sum'\n )\n portfolio_position_gsids = [gsid for gsid in date_wise_net_weight.columns.tolist() if gsid is not None]\n asset_level_betas_batch = risk_model.get_predicted_beta(\n start_date=start_date,\n end_date=end_date,\n assets=RiskModelDataAssetsRequest(\n identifier=RiskModelUniverseIdentifierRequest.gsid, universe=tuple(portfolio_position_gsids)\n ),\n )\n if asset_level_betas_batch.isna().all().all():\n logging.warning(\n f\"Risk model predicted beta not available for risk model {risk_model_id} for dates: {batch}\"\n )\n continue\n else:\n asset_level_betas = pd.concat([asset_level_betas, asset_level_betas_batch], axis=0)\n portfolio_position_net_weights = pd.concat(\n [portfolio_position_net_weights, date_wise_net_weight], axis=0\n )\n except Exception as ex:\n raise MqError(\n f\"Risk model predicted beta cannot be calculated for risk model {risk_model_id} \"\n f\"from {start_date} to {end_date} because of exception: {ex}, traceback: {traceback.format_exc()}\"\n )\n if asset_level_betas.empty:\n return pd.DataFrame()\n asset_level_betas = asset_level_betas.fillna(default_beta_value)\n risk_model_predicted_beta_timeseries = asset_level_betas * portfolio_position_net_weights\n risk_model_predicted_beta_timeseries = (\n risk_model_predicted_beta_timeseries.sum(axis=1).rename('beta').replace({0: np.nan}).ffill()\n )\n return risk_model_predicted_beta_timeseries.reset_index().rename(columns={'index': 'date', 0: 'beta'})\n" }, { "path": "gs_quant/markets/portfolio_manager_utils.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.markets.report import PerformanceReport\nfrom gs_quant.models.risk_model import (\n MacroRiskModel,\n DataAssetsRequest,\n RiskModelUniverseIdentifierRequest as UniverseIdentifierRequest,\n FactorType,\n)\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.errors import MqValueError\nfrom typing import List, Dict\nimport numpy as np\nimport pandas as pd\nimport datetime as dt\n\n\ndef build_macro_portfolio_exposure_df(\n df_constituents_and_notional: pd.DataFrame,\n universe_sensitivities_df: pd.DataFrame,\n factor_dict: Dict,\n factor_category_dict: Dict,\n factors_by_name: bool,\n) -> pd.DataFrame:\n if factors_by_name:\n universe_sensitivities_df = universe_sensitivities_df.rename(columns=factor_dict)\n\n columns_to_keep = factor_dict.values() if factors_by_name else factor_dict.keys()\n columns_to_drop = list(set(universe_sensitivities_df.columns.values) - set(columns_to_keep))\n universe_sensitivities_df = universe_sensitivities_df.drop(columns=columns_to_drop)\n\n notional_df = df_constituents_and_notional.rename(columns={\"name\": \"Asset Name\", \"netExposure\": \"Notional\"})\n gsids_with_exposure = list(universe_sensitivities_df.index.values)\n if not gsids_with_exposure:\n print(f\"The portfolio is not exposed to any of the requested macro factors {', '.join(columns_to_keep)}\")\n return pd.DataFrame()\n\n notional_df = notional_df.loc[gsids_with_exposure]\n\n # Multiply asset notional by asset sensitivity for each factor\n column_names = universe_sensitivities_df.columns.values.tolist()\n universe_sensitivities_df /= 100\n for column in column_names:\n universe_sensitivities_df[column] = universe_sensitivities_df[column] * notional_df['Notional']\n\n universe_sensitivities_df = notional_df.merge(universe_sensitivities_df, on='Asset Identifier')\n\n if factor_category_dict:\n # In case we want to group factors by the macro factor categories they belong in\n # Two-level column labels (Macro Factor Category -> Macro Factor)\n universe_sensitivities_df.columns = pd.MultiIndex.from_tuples(\n map(\n lambda factor: (factor_category_dict.get(factor, \"Asset Information\"), factor),\n universe_sensitivities_df.columns,\n ),\n names=(\"Macro Factor Category\", \"Macro Factor\"),\n )\n # Aggregate columns to get portfolio total exposure to each macro factor\n sum_over_factors_df = (\n universe_sensitivities_df.loc[:, universe_sensitivities_df.columns != (\"Asset Information\", \"Asset Name\")]\n .agg(np.sum, axis=\"index\")\n .to_frame()\n .transpose()\n )\n\n # Append total portfolio exposure to universe_sensitivities df\n portfolio_exposure_df = pd.concat([universe_sensitivities_df, sum_over_factors_df])\n\n # Group and Aggregate by Factor Category to get portfolio-level exposure to each macro Factor Category\n sum_over_categories_df = (\n sum_over_factors_df.transpose().groupby(\"Macro Factor Category\").apply(np.sum).transpose()\n )\n factor_and_category_zip = dict(\n zip(portfolio_exposure_df.columns.get_level_values(1), portfolio_exposure_df.columns.get_level_values(0))\n )\n portfolio_exposure_per_category = list(\n map(lambda x: sum_over_categories_df.loc[0, factor_and_category_zip[x]], factor_and_category_zip.keys())\n )\n\n # Append exposure df with total portfolio macro Factor Category exposure\n portfolio_exposure_df = pd.concat(\n [\n portfolio_exposure_df,\n pd.DataFrame(\n portfolio_exposure_per_category,\n index=portfolio_exposure_df.columns.tolist(),\n columns=[\"Portfolio Exposure Per Macro Factor Category\"],\n ).transpose(),\n ]\n )\n portfolio_exposure_df = portfolio_exposure_df.rename(index={0: \"Portfolio Exposure Per Macro Factor\"})\n portfolio_exposure_df.loc[\"Portfolio Exposure Per Macro Factor\", (\"Asset Information\", \"Asset Name\")] = np.nan\n portfolio_exposure_df.loc[\n \"Portfolio Exposure Per Macro Factor Category\", (\"Asset Information\", \"Asset Name\")\n ] = np.nan\n\n # Sort macro Factor Categories by total exposure\n portfolio_exposure_df = portfolio_exposure_df.sort_values(\n by=[\"Portfolio Exposure Per Macro Factor Category\"], axis='columns', ascending=False\n )\n\n name_col = portfolio_exposure_df.loc[:, (\"Asset Information\", \"Asset Name\")]\n notional_col = portfolio_exposure_df.loc[:, (\"Asset Information\", \"Notional\")]\n portfolio_exposure_df = portfolio_exposure_df.drop(\n columns=[(\"Asset Information\", \"Asset Name\"), (\"Asset Information\", \"Notional\")]\n )\n portfolio_exposure_df = pd.concat([name_col, notional_col, portfolio_exposure_df], axis=\"columns\")\n else:\n sum_over_factors_df = (\n universe_sensitivities_df.loc[:, universe_sensitivities_df.columns != 'Asset Name']\n .agg(np.sum, axis=\"index\")\n .to_frame()\n .transpose()\n )\n portfolio_exposure_df = pd.concat([universe_sensitivities_df, sum_over_factors_df])\n\n portfolio_exposure_df = portfolio_exposure_df.rename(index={0: \"Portfolio Exposure Per Macro Factor\"})\n portfolio_exposure_df.loc[\"Portfolio Exposure Per Macro Factor\", \"Asset Name\"] = np.nan\n portfolio_exposure_df = portfolio_exposure_df.sort_values(\n by=[\"Portfolio Exposure Per Macro Factor\"], axis='columns', ascending=False\n )\n\n name_col = portfolio_exposure_df['Asset Name']\n portfolio_exposure_df = portfolio_exposure_df.drop(columns=['Asset Name'])\n portfolio_exposure_df.insert(loc=0, column='Asset Name', value=name_col)\n\n portfolio_exposure_df.index.name = 'Asset Identifier'\n\n return portfolio_exposure_df\n\n\ndef build_portfolio_constituents_df(performance_report: PerformanceReport, date: dt.date) -> pd.DataFrame:\n constituents_df = performance_report.get_portfolio_constituents(\n fields=['netExposure'], start_date=date, end_date=date\n )\n if constituents_df.empty:\n raise MqValueError(\n f\"Macro Exposure can't be calculated as the portfolio constituents could not be found on\"\n f\" the requested date {date}. Make sure the portfolio performance report is up to date.\"\n )\n\n constituents_df = constituents_df[[\"assetId\", \"netExposure\"]]\n constituents_df = constituents_df.dropna().set_index(\"assetId\").rename_axis(\"Asset Identifier\")\n assets_data = GsAssetApi.get_many_assets_data_scroll(\n fields=['name', 'gsid', 'id'],\n as_of=dt.datetime(date.year, date.month, date.day),\n limit=1000,\n id=constituents_df.index.tolist(),\n )\n assets_data_df = (\n pd.DataFrame.from_records(assets_data)\n .set_index(\"id\")\n .fillna(value={\"name\": \"Name not available\"})\n .rename_axis(\"Asset Identifier\")\n )\n\n # Merge the constituents dataframe and asset data dataframe to get gsid, asset name, notional\n constituents_and_notional_df = (\n assets_data_df.merge(constituents_df, on='Asset Identifier')\n .reset_index(drop=True)\n .set_index(\"gsid\")\n .rename_axis(\"Asset Identifier\")\n .sort_index()\n )\n\n return constituents_and_notional_df\n\n\ndef build_sensitivity_df(\n universe: List, model: MacroRiskModel, date: dt.date, factor_type: FactorType, by_name: bool\n) -> pd.DataFrame:\n universe_sensitivities_df = model.get_universe_sensitivity(\n start_date=date,\n end_date=date,\n assets=DataAssetsRequest(UniverseIdentifierRequest.gsid, universe),\n factor_type=factor_type,\n get_factors_by_name=by_name,\n )\n\n if universe_sensitivities_df.empty:\n print(f\"None of the assets in the portfolio are exposed to the factors in model {model.id} \")\n return pd.DataFrame()\n\n universe_sensitivities_df = (\n universe_sensitivities_df.reset_index(level=1, drop=True).rename_axis(\"Asset Identifier\").sort_index()\n )\n\n return universe_sensitivities_df\n\n\ndef build_exposure_df(\n notional_df: pd.DataFrame,\n universe_sensitivities_df: pd.DataFrame,\n factor_categories: List,\n factor_data: pd.DataFrame,\n by_name: bool,\n) -> pd.DataFrame:\n # Multiply sensitivity with notional\n columns = universe_sensitivities_df.columns.values.tolist()\n universe_sensitivities_df /= 100\n for column in columns:\n universe_sensitivities_df[column] = universe_sensitivities_df[column] * notional_df['Notional']\n\n if factor_data.empty:\n if factor_categories:\n categories_names = [f.name for f in factor_categories] if by_name else [f.id for f in factor_categories]\n universe_sensitivities_df = universe_sensitivities_df[categories_names]\n\n universe_sensitivities_df = pd.concat(\n [\n universe_sensitivities_df,\n universe_sensitivities_df.agg(\"sum\").to_frame().rename(columns={0: \"Total Factor Category Exposure\"}).T,\n ]\n )\n\n universe_sensitivities_df = universe_sensitivities_df.sort_values(\n by=\"Total Factor Category Exposure\", axis=1, ascending=False\n )\n notional_df = pd.concat(\n [\n notional_df,\n notional_df[[\"Notional\"]].agg(\"sum\").to_frame().rename(columns={0: \"Total Factor Category Exposure\"}).T,\n ]\n )\n\n exposure_df = notional_df.join(universe_sensitivities_df).rename_axis(\"Factor Category\", axis=1)\n else:\n factor_data = factor_data.set_index(\"name\") if by_name else factor_data.set_index(\"identifier\")\n new_columns = (\n [(factor_data.loc[f, 'factorCategory'], f) for f in universe_sensitivities_df.columns.values]\n if by_name\n else [(factor_data.loc[f, 'factorCategoryId'], f) for f in universe_sensitivities_df.columns.values]\n )\n universe_sensitivities_df = universe_sensitivities_df.set_axis(\n pd.MultiIndex.from_tuples(new_columns), axis=1\n ).rename_axis((\"Factor Category\", \"Factor\"), axis=1)\n universe_sensitivities_df = pd.concat(\n [\n universe_sensitivities_df,\n universe_sensitivities_df.agg(\"sum\").to_frame().rename(columns={0: \"Total Factor Exposure\"}).T,\n ]\n )\n universe_sensitivities_df = universe_sensitivities_df.sort_values(\n by=[\"Total Factor Exposure\"], axis=1, ascending=False\n )\n\n # Only return factors that are grouped in the factor categories that we passed; if empty return all factors\n if factor_categories:\n categories_names = [f.name for f in factor_categories] if by_name else [f.id for f in factor_categories]\n universe_sensitivities_df = universe_sensitivities_df[categories_names]\n\n notional_df = pd.concat(\n [\n notional_df,\n notional_df[[\"Notional\"]].agg(\"sum\").to_frame().rename(columns={0: \"Total Factor Exposure\"}).T,\n ]\n ).set_axis(\n pd.MultiIndex.from_tuples([(\"Asset Information\", \"Asset Name\"), (\"Asset Information\", \"Notional\")]), axis=1\n )\n # Merge universe sensitivity with notional df\n exposure_df = notional_df.join(universe_sensitivities_df).rename_axis((\"Factor Category\", \"Factor\"), axis=1)\n\n return exposure_df\n\n\ndef get_batched_dates(dates: List[dt.date], batch_size: int = 90) -> List[List[dt.date]]:\n \"\"\"\n Split a list of dates into batches of a specified size.\n\n :param dates: List of dates to be split into batches\n :param batch_size: Size of each batch\n :return: List of lists, where each sublist contains a batch of dates\n \"\"\"\n\n return [dates[i : i + batch_size] for i in range(0, len(dates), batch_size)]\n" }, { "path": "gs_quant/markets/position_set.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport math\nfrom time import time\nfrom typing import Dict, List, Union, Optional\n\nimport numpy as np\nimport pandas as pd\nfrom pydash import get\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.price import GsPriceApi\nfrom gs_quant.common import (\n Position as CommonPosition,\n PositionPriceInput,\n PositionSet as CommonPositionSet,\n PositionTag as PositionTagTarget,\n Currency,\n PositionSetWeightingStrategy,\n MarketDataFrequency,\n)\nfrom gs_quant.errors import MqValueError, MqRequestError\nfrom gs_quant.markets.position_set_utils import (\n _get_asset_temporal_xrefs,\n _group_temporal_xrefs_into_discrete_time_ranges,\n _resolve_many_assets,\n)\nfrom gs_quant.models.risk_model_utils import _repeat_try_catch_request\nfrom gs_quant.target.positions_v2_pricing import (\n PositionsPricingParameters,\n PositionsRequest,\n PositionSetRequest,\n PositionsPricingRequest,\n)\nfrom gs_quant.target.price import PriceParameters, PositionSetPriceInput, PositionPriceResponse\n\n_logger = logging.getLogger(__name__)\n\n\nclass PositionTag(PositionTagTarget):\n @classmethod\n def from_dict(cls, tag_dict: Dict):\n if len(tag_dict) > 1:\n raise MqValueError('PositionTag.from_dict only accepts a single key-value pair')\n return cls(name=list(tag_dict.keys())[0], value=list(tag_dict.values())[0])\n\n\nclass Position:\n def __init__(\n self,\n identifier: str,\n weight: float = None,\n quantity: float = None,\n notional: float = None,\n name: str = None,\n asset_id: str = None,\n tags: Optional[List[Union[PositionTag, Dict]]] = None,\n ):\n self.__identifier = identifier\n self.__weight = weight\n self.__quantity = quantity\n self.__notional = notional\n self.__name = name\n self.__asset_id = asset_id\n if tags is not None:\n self.__tags = [PositionTag.from_dict(tag) if isinstance(tag, dict) else tag for tag in tags]\n else:\n self.__tags = tags\n self.__restricted, self.__hard_to_borrow = None, None\n\n def __eq__(self, other) -> bool:\n if not isinstance(other, Position):\n return False\n for prop in ['asset_id', 'weight', 'notional', 'quantity', 'tags']:\n # Calculations from V2 sometimes add insignificant decimals places\n slf = get(self, prop)\n oth = get(other, prop)\n if prop in ['weight', 'notional', 'quantity']:\n if not (slf is None or oth is None) and not round(slf, 5) == round(oth, 5):\n return False\n elif not (slf is None and oth is None) and not slf == oth:\n return False\n return True\n\n def __hash__(self):\n return hash(self.asset_id) ^ hash(self.identifier)\n\n @property\n def identifier(self) -> str:\n return self.__identifier\n\n @identifier.setter\n def identifier(self, value: str):\n self.__identifier = value\n\n @property\n def weight(self) -> float:\n return self.__weight\n\n @weight.setter\n def weight(self, value: float):\n self.__weight = value\n\n @property\n def quantity(self) -> float:\n return self.__quantity\n\n @quantity.setter\n def quantity(self, value: float):\n self.__quantity = value\n\n @property\n def notional(self) -> float:\n return self.__notional\n\n @notional.setter\n def notional(self, value: float):\n self.__notional = value\n\n @property\n def name(self) -> str:\n return self.__name\n\n @name.setter\n def name(self, value: str):\n self.__name = value\n\n @property\n def asset_id(self) -> str:\n return self.__asset_id\n\n @asset_id.setter\n def asset_id(self, value: str):\n self.__asset_id = value\n\n @property\n def tags(self) -> List[PositionTag]:\n return self.__tags\n\n @tags.setter\n def tags(self, value: List[PositionTag]):\n self.__tags = value\n\n @property\n def hard_to_borrow(self) -> bool:\n return self.__hard_to_borrow\n\n @hard_to_borrow.setter\n def _hard_to_borrow(self, value: bool):\n self.__hard_to_borrow = value\n\n @property\n def restricted(self) -> bool:\n return self.__restricted\n\n @restricted.setter\n def _restricted(self, value: bool):\n self.__restricted = value\n\n def add_tag(self, name: str, value: str):\n if self.tags is None:\n self.tags = []\n if not any(tag.name == name for tag in self.tags):\n self.tags.append(PositionTag(name=name, value=value))\n else:\n raise MqValueError(f'Position already has tag with name {name}')\n\n def tags_as_dict(self):\n return {tag.name: tag.value for tag in self.tags}\n\n def as_dict(self, tags_as_keys: bool = False) -> Dict:\n position_dict = dict(\n identifier=self.identifier,\n weight=self.weight,\n quantity=self.quantity,\n notional=self.notional,\n name=self.name,\n asset_id=self.asset_id,\n restricted=self.restricted,\n )\n if self.tags and tags_as_keys:\n position_dict.update(self.tags_as_dict())\n else:\n position_dict['tags'] = self.tags\n return {k: v for k, v in position_dict.items() if v is not None}\n\n @classmethod\n def from_dict(cls, position_dict: Dict, add_tags: bool = True):\n fields = [k.lower() for k in position_dict.keys()]\n if 'id' in fields and 'asset_id' in fields:\n raise MqValueError('Position cannot have both id and asset_id')\n if 'id' in fields:\n position_dict['asset_id'] = position_dict.pop('id')\n position_fields = ['identifier', 'weight', 'quantity', 'notional', 'name', 'asset_id']\n tag_dict = {k: v for k, v in position_dict.items() if k not in position_fields}\n return cls(\n identifier=position_dict['identifier'],\n weight=position_dict.get('weight'),\n notional=position_dict.get('notional'),\n quantity=position_dict.get('quantity'),\n name=position_dict.get('name'),\n asset_id=position_dict.get('asset_id'),\n tags=[PositionTag(name=k, value=v) for k, v in tag_dict.items()] if add_tags else position_dict.get('tags'),\n )\n\n def clone(self):\n return Position.from_dict(self.as_dict(tags_as_keys=True), add_tags=True)\n\n def to_target(self, common: bool = True) -> Union[CommonPosition, PositionPriceInput]:\n \"\"\"Returns Position type defined in target file for API payloads\"\"\"\n if common:\n tags_as_target = self.tags if self.tags else None\n return CommonPosition(self.asset_id, quantity=self.quantity, tags=tags_as_target)\n return PositionPriceInput(self.asset_id, quantity=self.quantity, weight=self.weight, notional=self.notional)\n\n\nclass PositionSet:\n \"\"\"\n\n Position Sets hold a collection of positions associated with a particular date\n\n \"\"\"\n\n def __init__(\n self,\n positions: List[Position],\n date: dt.date = dt.date.today(),\n divisor: float = None,\n reference_notional: float = None,\n unresolved_positions: List[Position] = None,\n unpriced_positions: List[Position] = None,\n ):\n if reference_notional is not None:\n for p in positions:\n if p.weight is None:\n raise MqValueError('Position set with reference notionals must have weights for every position.')\n if p.notional is not None:\n raise MqValueError('Position sets with reference notionals cannot have positions with notional.')\n if p.quantity is not None:\n raise MqValueError('Position sets with reference notionals cannot have positions with quantities.')\n self.__positions = positions\n self.__date = date\n self.__divisor = divisor\n self.__reference_notional = reference_notional\n self.__unresolved_positions = unresolved_positions if unresolved_positions is not None else []\n self.__unpriced_positions = unpriced_positions if unpriced_positions is not None else []\n\n def __eq__(self, other) -> bool:\n if len(self.positions) != len(other.positions):\n return False\n if self.date != other.date:\n return False\n if self.reference_notional != other.reference_notional:\n return False\n positions = self.positions\n positions.sort(key=lambda position: position.asset_id)\n other_positions = other.positions\n other_positions.sort(key=lambda position: position.asset_id)\n for i in range(0, len(positions)):\n if positions[i] != other_positions[i]:\n return False\n return True\n\n @property\n def positions(self) -> List[Position]:\n return self.__positions\n\n @positions.setter\n def positions(self, value: List[Position]):\n self.__positions = value\n\n @property\n def date(self) -> dt.date:\n return self.__date\n\n @date.setter\n def date(self, value: dt.date):\n self.__date = value\n\n @property\n def divisor(self) -> float:\n return self.__divisor\n\n @property\n def reference_notional(self) -> float:\n return self.__reference_notional\n\n @reference_notional.setter\n def reference_notional(self, value: float):\n self.__reference_notional = value\n\n @property\n def unresolved_positions(self) -> List[Position]:\n return self.__unresolved_positions\n\n @property\n def unpriced_positions(self) -> List[Position]:\n return self.__unpriced_positions\n\n def clone(self, keep_reference_notional: bool = False):\n \"\"\"Create a clone of the current position set\n\n :param keep_reference_notional: Whether to keep the reference notional of the original position set in case it\n has both quantity and reference notional\n \"\"\"\n frame = self.to_frame(add_tags=True)\n ref_notional = self.reference_notional\n if 'quantity' in frame.columns and ref_notional is not None:\n if keep_reference_notional:\n frame = frame.drop(columns=['quantity'])\n else:\n ref_notional = None\n return PositionSet.from_frame(\n frame, date=self.date, reference_notional=ref_notional, divisor=self.divisor, add_tags=True\n )\n\n def get_positions(self) -> pd.DataFrame:\n \"\"\"\n Retrieve formatted positions\n\n :return: DataFrame of positions for position set\n\n **Usage**\n\n View position set position info\n\n **Examples**\n\n Get position set positions:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW'), Position(identifier='MSFT UW')]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.get_positions()\n\n **See also**\n\n :func:`get_unresolved_positions` :func:`get_unpriced_positions` :func:`resolve` :func:`price`\n \"\"\"\n positions = [p.as_dict() for p in self.positions]\n return pd.DataFrame(positions)\n\n def get_unresolved_positions(self) -> pd.DataFrame:\n \"\"\"\n Retrieve formatted unresolved positions\n\n :return: DataFrame of unresolved positions for position set\n\n **Usage**\n\n View position set unresolved position info\n\n **Examples**\n\n Get position set unresolved positions:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW'), Position(identifier='MSFT UW')]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n >>> position_set.get_unresolved_positions()\n\n **See also**\n\n :func:`get_positions` :func:`get_unpriced_positions` :func:`resolve` :func:`price`\n \"\"\"\n positions = [p.as_dict() for p in self.unresolved_positions]\n return pd.DataFrame(positions)\n\n def remove_unresolved_positions(self):\n \"\"\"\n Remove unresolved positions from your position set\n\n **Usage**\n\n Remove unresolved positions from your position set\n\n **Examples**\n\n Remove unresolved positions from your position set:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW'), Position(identifier='MSFT UW')]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n >>> position_set.remove_unresolved_positions()\n\n **See also**\n\n :func:`get_positions` :func:`get_unpriced_positions` :func:`resolve` :func:`price`\n :func:`remove_unpriced_positions` :func:`get_unresolved_positions`\n \"\"\"\n self.positions = [p for p in self.positions if p.asset_id is not None]\n self.__unresolved_positions = None\n\n def get_unpriced_positions(self) -> pd.DataFrame:\n \"\"\"\n Retrieve formatted unpriced positions\n\n :return: DataFrame of unpriced positions for position set\n\n **Usage**\n\n View position set unpriced position info\n\n **Examples**\n\n Get position set unpriced positions:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW', quantity=100), Position(identifier='MSFT UW', quantity=100)]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n >>> position_set.price()\n >>> position_set.get_unpriced_positions()\n\n **See also**\n\n :func:`get_positions` :func:`get_unresolved_positions` :func:`resolve` :func:`price`\n \"\"\"\n positions = [p.as_dict() for p in self.unpriced_positions]\n return pd.DataFrame(positions)\n\n def remove_unpriced_positions(self):\n \"\"\"\n Remove unpriced positions from your position set\n\n **Usage**\n\n Remove unpriced positions from your position set\n\n **Examples**\n\n Remove unpriced positions from your position set:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW'), Position(identifier='MSFT UW')]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n >>> position_set.remove_unpriced_positions()\n\n **See also**\n\n :func:`get_positions` :func:`get_unpriced_positions` :func:`resolve` :func:`price`\n :func:`get_unresolved_positions` :func:`remove_unresolved_positions`\n \"\"\"\n self.__unpriced_positions = None\n\n def get_restricted_positions(self) -> pd.DataFrame:\n \"\"\"\n Retrieve formatted RTL positions\n\n :return: DataFrame of RTL positions for position set\n\n **Usage**\n\n View position set RTL position info\n\n **Examples**\n\n Get position set RTL positions:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW', quantity=100), Position(identifier='MSFT UW', quantity=100)]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n >>> position_set.get_restricted_positions()\n\n **See also**\n\n :func:`get_positions` :func:`resolve` :func:`price` :func:`remove_restricted_positions`\n :func:`get_hard_to_borrow_positions` :func:`remove_hard_to_borrow_positions`\n \"\"\"\n positions = [p.as_dict() for p in self.positions if p.restricted]\n return pd.DataFrame(positions)\n\n def remove_restricted_positions(self):\n \"\"\"\n Remove RTL positions from your position set\n\n **Usage**\n\n Remove RTL positions from your position set\n\n **Examples**\n\n Remove RTL positions from your position set:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW'), Position(identifier='MSFT UW')]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n >>> position_set.remove_restricted_positions()\n\n **See also**\n\n :func:`get_positions` :func:`resolve` :func:`price` :func:`get_restricted_positions`\n :func:`get_hard_to_borrow_positions` :func:`remove_hard_to_borrow_positions`\n \"\"\"\n self.positions = [p for p in self.positions if p.restricted is not True]\n\n def get_hard_to_borrow_positions(self) -> pd.DataFrame:\n \"\"\"\n Retrieve formatted htb positions\n\n :return: DataFrame of htb positions for position set\n\n **Usage**\n\n View position set htb position info\n\n **Examples**\n\n Get position set htb positions:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW', quantity=100), Position(identifier='MSFT UW', quantity=100)]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n >>> position_set.price()\n >>> position_set.get_hard_to_borrow_positions()\n\n **See also**\n\n :func:`get_positions` :func:`resolve` :func:`price` :func:`get_restricted_positions`\n :func:`remove_restricted_positions` :func:`remove_hard_to_borrow_positions`\n \"\"\"\n positions = [p.as_dict() for p in self.positions if p.hard_to_borrow]\n return pd.DataFrame(positions)\n\n def remove_hard_to_borrow_positions(self):\n \"\"\"\n Remove hard to borrow positions from your position set\n\n **Usage**\n\n Remove hard to borrow positions from your position set\n\n **Examples**\n\n Remove hard to borrow positions from your position set:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW'), Position(identifier='MSFT UW')]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n >>> position_set.price()\n >>> position_set.remove_hard_to_borrow_positions()\n\n **See also**\n\n :func:`get_positions` :func:`resolve` :func:`price` :func:`get_restricted_positions`\n :func:`remove_restricted_positions` :func:`get_hard_to_borrow_positions`\n \"\"\"\n self.positions = [p for p in self.positions if p.hard_to_borrow is not True]\n\n def equalize_position_weights(self):\n \"\"\"\n Assigns equal weight to each position in position set\n\n **Usage**\n\n Assigns equal weight to each position in position set\n\n **Examples**\n\n Assign equal weight to each position in position set:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW'), Position(identifier='MSFT UW')]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.equalize_position_weights()\n\n **See also**\n\n :func:`get_positions` :func:`redistribute_weights`\n \"\"\"\n weight = 1 / len(self.positions)\n equally_weighted_positions = []\n for p in self.positions:\n p.weight = weight\n p.quantity = None\n p.notional = None\n equally_weighted_positions.append(p)\n self.positions = equally_weighted_positions\n\n def to_frame(self, add_tags: bool = False) -> pd.DataFrame:\n \"\"\"\n Retrieve formatted position set\n\n :return: DataFrame of position set info\n\n **Usage**\n\n View position set info\n\n **Examples**\n\n Retrieve formatted position set:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW', quantity=100), Position(identifier='MSFT UW', quantity=100)]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.to_frame()\n\n Retrieve tags in the pd DataFrame:\n\n >>> from gs_quant.markets.position_set import PositionSet\n >>> pset = PositionSet.from_dicts([\n >>> {'identifier': 'AAPL UW', 'quantity': 100, 'MyTag': 'Name 1'},\n >>> {'identifier': 'AAPL UW', 'quantity': 100, 'MyTag': 'Name 2'},\n >>> {'identifier': 'META UW', 'quantity': 100, 'MyTag': 'Name 1'}\n >>> ], add_tags=True)\n >>> pset.to_frame(add_tags=True)\n\n **See also**\n\n :func:`from_frame` :func:`from_dicts` :func:`from_list`\n \"\"\"\n positions = []\n for p in self.positions:\n position = dict(date=self.date.isoformat())\n if self.divisor is not None:\n position.update(dict(divisor=self.divisor))\n position.update(p.as_dict(tags_as_keys=add_tags))\n positions.append(position)\n return pd.DataFrame(positions)\n\n def resolve(self, **kwargs):\n \"\"\"\n Resolve any unmapped positions\n\n **Usage**\n\n Resolve any unmapped positions\n\n **Examples**\n\n Resolve any unmapped positions:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW'), Position(identifier='MSFT UW')]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.resolve()\n\n **See also**\n\n :func:`get_positions` :func:`get_unresolved_positions` :func:`get_unpriced_positions` :func:`price`\n \"\"\"\n unresolved_positions = [p.identifier for p in self.positions if p.asset_id is None]\n if len(unresolved_positions):\n [id_map, unresolved_positions] = self.__resolve_identifiers(unresolved_positions, self.date, **kwargs)\n self.__unresolved_positions = [p for p in self.positions if p.identifier in unresolved_positions]\n resolved_positions = []\n for p in self.positions:\n if p.identifier in id_map:\n asset = get(id_map, p.identifier.replace('.', '\\\\.'))\n p.asset_id = get(asset, 'id')\n p.name = get(asset, 'name')\n p._restricted = get(asset, 'restricted')\n if p.asset_id is not None:\n resolved_positions.append(p)\n self.positions = resolved_positions\n\n def redistribute_weights(self):\n \"\"\"\n Redistribute position weights proportionally for a one-sided position set\n\n **Usage**\n\n Redistribute position weights proportionally for a one-sided position set\n\n **Examples**\n\n Redistribute position weights proportionally for a one-sided position set:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>>\n >>> my_positions = [Position(identifier='AAPL UW', weight=0.3), Position(identifier='MSFT UW', weight=0.3)]\n >>> position_set = PositionSet(positions=my_positions)\n >>> position_set.redistribute_weights()\n\n **See also**\n\n :func:`get_positions` :func:`equalize_position_weights` :func:`get_unpriced_positions` :func:`price`\n \"\"\"\n total_weight = 0\n new_weights, unweighted = [], []\n for p in self.positions:\n if p.weight is None:\n unweighted.append(p.identifier)\n else:\n total_weight += p.weight\n if len(unweighted):\n raise MqValueError(f'Cannot reweight as some positions are missing weights: {unweighted}')\n\n weight_to_distribute = 1 - total_weight if total_weight < 0 else total_weight - 1\n for p in self.positions:\n p.weight = p.weight - (p.weight / total_weight) * weight_to_distribute\n p.quantity = None\n p.notional = None\n new_weights.append(p)\n self.positions = new_weights\n\n def price(\n self,\n currency: Optional[Currency] = Currency.USD,\n use_unadjusted_close_price: bool = True,\n weighting_strategy: Optional[PositionSetWeightingStrategy] = None,\n handle_long_short: bool = False,\n fail_on_unpriced_positions: bool = False,\n **kwargs,\n ):\n \"\"\"\n Fetch positions weights from quantities, or vice versa\n\n :param currency: Reference notional currency (defaults to USD if not passed in)\n :param use_unadjusted_close_price: Use adjusted or unadjusted close prices (defaults to unadjusted)\n :param weighting_strategy: Quantity or Weighted weighting strategy (defaults based on positions info)\n :param handle_long_short: Whether to handle the loss of directionality in weights that comes from pricing using\n gross notional. Useful when input position iset is a long/short. Note, this also sets the reference notional to\n Gross Notional if not already so\n :param fail_on_unpriced_positions: Whether to raise an exception if any positions are unpriced\n :param kwargs: Additional parameters to pass to the pricing API\n\n **Usage**\n\n Fetch positions weights from quantities, or vice versa\n\n **Examples**\n\n Fetch position weights from quantities:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.position_set import Position, PositionSet, PositionSetWeightingStrategy\n >>>\n >>> my_positions = [Position(identifier='AAPL UW', quantity=100), Position(identifier='MSFT UW', quantity=100)]\n >>> position_set = PositionSet(positions=my_positions, date= dt.date(2023, 3, 16))\n >>> position_set.resolve()\n >>> position_set.price(weighting_strategy=PositionSetWeightingStrategy.Quantity)\n\n Fetch position quantities from weights:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.position_set import Position, PositionSet, PositionSetWeightingStrategy\n >>>\n >>> my_positions = [Position(identifier='AAPL UW', weight=0.5), Position(identifier='MSFT UW', weight=0.5)]\n >>> position_set = PositionSet(positions=my_positions, date= dt.date(2023, 3, 16), reference_notional=10000000)\n >>> position_set.resolve()\n >>> position_set.price(weighting_strategy=PositionSetWeightingStrategy.Weight)\n\n Fetch position weights from exposures:\n\n >>> import datetime as dt\n >>> from gs_quant.markets.position_set import Position, PositionSet, PositionSetWeightingStrategy\n >>>\n >>> my_positions = [Position(identifier='AAPL UW', notional=10000),\n >>> Position(identifier='MSFT UW', notional=10000)]\n >>> position_set = PositionSet(positions=my_positions, date= dt.date(2023, 3, 16))\n >>> position_set.resolve()\n >>> position_set.price(weighting_strategy=PositionSetWeightingStrategy.Notional)\n\n **See also**\n\n :func:`get_unpriced_positions` :func:`get_unresolved_positions` :func:`resolve`\n \"\"\"\n weighting_strategy = self.__get_default_weighting_strategy(\n self.positions, self.reference_notional, weighting_strategy\n )\n positions = self.__convert_positions_for_pricing(self.positions, weighting_strategy)\n\n if 'fractional_shares' not in kwargs:\n should_allow_fractional_shares = (\n True if weighting_strategy == PositionSetWeightingStrategy.Notional else False\n )\n else:\n should_allow_fractional_shares = kwargs.pop('fractional_shares')\n\n price_parameters = PriceParameters(\n currency=currency,\n divisor=self.divisor,\n frequency=MarketDataFrequency.End_Of_Day,\n target_notional=self.reference_notional,\n notional_type='Gross',\n pricing_date=self.date,\n price_regardless_of_assets_missing_prices=True,\n weighting_strategy=weighting_strategy,\n use_unadjusted_close_price=use_unadjusted_close_price,\n fractional_shares=should_allow_fractional_shares,\n )\n\n if 'dataset' in kwargs:\n price_parameters.asset_data_set_id = kwargs['dataset']\n price_parameters.frequency = None\n\n for k, v in kwargs.items():\n price_parameters.__setattr__(k, v)\n results = GsPriceApi.price_positions(PositionSetPriceInput(positions=positions, parameters=price_parameters))\n position_result_map = {f'{p.asset_id}{self.__hash_position_tag_list(p.tags)}': p for p in results.positions}\n priced_positions, unpriced_positions = [], []\n for p in self.positions:\n asset_key = f'{p.asset_id}{self.__hash_position_tag_list(p.tags)}'\n if asset_key in position_result_map:\n pos: PositionPriceResponse = position_result_map.get(asset_key)\n p.quantity = pos.quantity\n w = pos.weight\n p.notional = pos.notional\n if handle_long_short:\n # In case of long/short positions, we need to convert the returned gross weight to reference weight\n w = math.copysign(w, pos.notional)\n p.weight = w\n p._hard_to_borrow = pos.hard_to_borrow\n priced_positions.append(p)\n else:\n unpriced_positions.append(p)\n\n if fail_on_unpriced_positions and unpriced_positions:\n raise MqValueError(\n f'Failed to price positions: '\n f'{\", \".join([p.identifier for p in unpriced_positions])} on {self.date}. Please'\n f'contat Marquee Support for assistance.'\n )\n self.positions = priced_positions\n self.__unpriced_positions = unpriced_positions\n if handle_long_short:\n # Set notional to gross notional because in case of L/S pricing the API normalizes all weights wrt gross\n self.reference_notional = results.gross_notional\n\n def get_subset(self, copy: bool = True, **kwargs):\n \"\"\"Extract a subset of the position set based on values of tags.\n\n Not that weights are returned with respect to original position set. Use redistribute_weights function.\n For more advanced filtering, use .to_frame() to get the frame as a pandas DataFrame.\n\n **Usage**\n Given a position set that has tags, extract a subset of the positions based on the values of one or more of the\n tags.\n\n **Examples**\n Extract a subset of the position set based on the value of a single tag:\n\n >>> from gs_quant.markets.position_set import Position, PositionSet\n >>> from gs_quant.target.common import PositionTag\n >>> pset = PositionSet.from_dicts([\n >>> {'identifier': 'AAPL UW', 'quantity': 1000, 'MyTag': 'Name 1', 'MyOtherTag': 'Class 1'},\n >>> {'identifier': 'MSFT UW', 'quantity': 2000, 'MyTag': 'Name 2', 'MyOtherTag': 'Class 1'},\n >>> {'identifier': 'GOOGL UW', 'quantity': 3000, 'MyTag': 'Name 1', 'MyOtherTag': 'Class 2'}\n >>> ])\n >>>\n >>> subset = pset.get_subset(MyTag='Name 1')\n\n Extract a subset of the position set based on the values of multiple tags:\n\n >>> subset = pset.get_subset(MyTag='Name 1', MyOtherTag='Class 2')\n\n \"\"\"\n subset = []\n for p in self.positions:\n if not p.tags:\n raise MqValueError(f'PositionSet has position {p.identifier} that does not have tags')\n tags_dict = p.tags_as_dict()\n if all(tags_dict.get(k) == v for k, v in kwargs.items()):\n subset.append(p if not copy else p.clone())\n return PositionSet(positions=subset, date=self.date, reference_notional=self.reference_notional)\n\n def to_target(self, common: bool = True) -> Union[CommonPositionSet, List[PositionPriceInput]]:\n \"\"\"Returns PostionSet type defined in target file for API payloads\"\"\"\n positions = tuple(p.to_target(common) for p in self.positions)\n return CommonPositionSet(positions, self.date) if common else list(positions)\n\n @classmethod\n def from_target(cls, position_set: CommonPositionSet, source: Optional[str] = None):\n \"\"\"Create PostionSet instance from PostionSet type defined in target file\"\"\"\n positions = position_set.positions\n mqids = [position.asset_id for position in positions]\n position_data = cls.__get_positions_data(mqids, source=source)\n converted_positions = []\n for p in positions:\n asset = get(position_data, p.asset_id)\n tags = p.tags if p.tags else None\n position = Position(\n identifier=get(asset, 'bbid'),\n name=get(asset, 'name'),\n asset_id=p.asset_id,\n quantity=p.quantity,\n tags=tags,\n )\n converted_positions.append(position)\n return cls(converted_positions, position_set.position_date, position_set.divisor)\n\n @classmethod\n def from_list(cls, positions: List[str], date: dt.date = dt.date.today()):\n \"\"\"\n Create equally-weighted PostionSet instance from a list of identifiers\n\n **Usage**\n\n Create equally-weighted PostionSet instance from a list of identifiers\n\n **Examples**\n\n Create equally-weighted PostionSet instance from a list of identifiers:\n\n >>> from gs_quant.markets.position_set import PositionSet\n >>>\n >>> identifiers = ['AAPL UW', 'MSFT UW']\n >>> position_set = PositionSet.from_list(positions=identifiers)\n\n **See also**\n\n :func:`get_positions` :func:`resolve` :func:`from_dicts` :func:`from_frame` :func:`to_frame`\n \"\"\"\n weight = 1 / len(positions)\n converted_positions = [Position(identifier=p, weight=weight) for p in positions]\n return cls(converted_positions, date)\n\n @classmethod\n def from_dicts(\n cls,\n positions: List[Dict],\n date: dt.date = dt.date.today(),\n reference_notional: float = None,\n add_tags: bool = False,\n ):\n \"\"\"\n Create PostionSet instance from a list of position-object-like dictionaries\n\n **Usage**\n\n Create PostionSet instance from a list of position-object-like dictionaries\n\n **Examples**\n\n Create PostionSet instance from a list of position-object-like dictionaries:\n\n >>> from gs_quant.markets.position_set import PositionSet\n >>>\n >>> my_positions = [{'identifier': 'AAPL UW', 'weight': 0.5}, {'identifier': 'AAPL UW', 'weight': 0.5}]\n >>> position_set = PositionSet.from_dicts(positions=my_positions)\n\n **See also**\n\n :func:`get_positions` :func:`resolve` :func:`from_list` :func:`from_frame` :func:`to_frame`\n \"\"\"\n positions_df = pd.DataFrame(positions)\n return cls.from_frame(positions_df, date, reference_notional, add_tags=add_tags)\n\n @classmethod\n def from_frame(\n cls,\n positions: pd.DataFrame,\n date: dt.date = dt.date.today(),\n reference_notional: float = None,\n divisor: float = None,\n add_tags: bool = False,\n ):\n \"\"\"\n Create PostionSet instance from a dataframe of positions\n\n **Usage**\n\n Create PostionSet instance from a dataframe of positions\n\n **Examples**\n\n Create PostionSet instance from a dataframe of positions:\n\n >>> import pandas as pd\n >>> from gs_quant.markets.position_set import PositionSet\n >>>\n >>> my_positions = [{'identifier': 'AAPL UW', 'weight': 0.5}, {'identifier': 'AAPL UW', 'weight': 0.5}]\n >>> positions_df = pd.DataFrame(my_positions)\n >>> position_set = PositionSet.from_frame(positions=positions_df)\n\n **See also**\n\n :func:`get_positions` :func:`resolve` :func:`from_list` :func:`from_dicts` :func:`to_frame`\n \"\"\"\n positions.columns = cls.__normalize_position_columns(positions)\n tag_columns = cls.__get_tag_columns(positions) if add_tags else []\n positions = positions[~positions['identifier'].isna()]\n equalize = not (\n 'quantity' in positions.columns.str.lower()\n or 'weight' in positions.columns.str.lower()\n or 'notional' in positions.columns.str.lower()\n )\n equal_weight = 1 / len(positions)\n\n positions_list = []\n for row in positions.to_dict(orient='records'):\n positions_list.append(\n Position(\n identifier=row.get('identifier'),\n asset_id=row.get('id'),\n name=row.get('name'),\n weight=equal_weight if equalize else row.get('weight'),\n quantity=None if equalize else row.get('quantity'),\n notional=None if equalize else row.get('notional'),\n tags=list(PositionTag(tag, get(row, tag)) for tag in tag_columns) if len(tag_columns) else None,\n )\n )\n\n return cls(positions_list, date, reference_notional=reference_notional, divisor=divisor)\n\n @staticmethod\n def __get_tag_columns(positions: pd.DataFrame) -> List[str]:\n return [\n c\n for c in positions.columns\n if c.lower() not in ['identifier', 'id', 'quantity', 'notional', 'weight', 'date', 'restricted']\n ]\n\n @staticmethod\n def __normalize_position_columns(positions: pd.DataFrame) -> List[str]:\n columns = []\n if 'asset_id' in positions.columns and 'id' not in positions.columns:\n positions = positions.rename(columns={'asset_id': 'id'})\n for c in positions.columns:\n columns.append(\n c.lower()\n if c.lower() in ['identifier', 'id', 'quantity', 'notional', 'weight', 'date', 'restricted']\n else c\n )\n return columns\n\n @staticmethod\n def __resolve_identifiers(identifiers: List[str], date: dt.date, **kwargs) -> List:\n unmapped_assets = []\n id_map = {}\n batch_size = 500\n logging.debug(f'Resolving positions in {len(identifiers) / batch_size} batches')\n for i in range(0, len(identifiers), batch_size):\n identifier_batch = identifiers[i : i + batch_size]\n response = GsAssetApi.resolve_assets(\n identifier=identifier_batch, fields=['name', 'id', 'tradingRestriction'], limit=1, as_of=date, **kwargs\n )\n\n for identifier in response:\n if response[identifier] is not None and len(response[identifier]) > 0:\n id_map[identifier] = {\n 'id': response[identifier][0]['id'],\n 'name': response[identifier][0]['name'],\n 'restricted': response[identifier][0].get('tradingRestriction'),\n }\n else:\n unmapped_assets.append(identifier)\n\n if len(unmapped_assets) > 0:\n logging.info(\n f'Error in resolving the following identifiers: {unmapped_assets}. Sifting them out and '\n f'resolving the rest...'\n )\n\n return [id_map, unmapped_assets]\n\n @staticmethod\n def __get_positions_data(mqids: List[str], source: Optional[str] = None) -> Dict:\n response = GsAssetApi.get_many_assets_data(id=mqids, fields=['id', 'name', 'bbid'], source=source)\n data = {}\n for asset in response:\n data[get(asset, 'id')] = dict(name=get(asset, 'name'), bbid=get(asset, 'bbid'))\n return data\n\n @staticmethod\n def __get_default_weighting_strategy(\n positions: List[Position],\n reference_notional: float = None,\n weighting_strategy: Optional[PositionSetWeightingStrategy] = None,\n ) -> PositionSetWeightingStrategy:\n missing_weights = [p.identifier for p in positions if p.weight is None]\n missing_quantities = [p.identifier for p in positions if p.quantity is None]\n missing_exposures = [p.identifier for p in positions if p.notional is None]\n if weighting_strategy is None:\n if len(missing_weights) and len(missing_quantities) and len(missing_exposures):\n raise MqValueError(\n f'Unable to determine weighting strategy due to missing weights for \\\n {missing_weights}, missing quantities for {missing_quantities}, and missing exposures \\\n for {missing_exposures}'\n )\n if not len(missing_weights) and (reference_notional is not None or len(missing_quantities)):\n weighting_strategy = PositionSetWeightingStrategy.Weight\n elif not len(missing_exposures):\n weighting_strategy = PositionSetWeightingStrategy.Notional\n else:\n weighting_strategy = PositionSetWeightingStrategy.Quantity\n use_quantity = weighting_strategy == PositionSetWeightingStrategy.Quantity\n use_weight = weighting_strategy == PositionSetWeightingStrategy.Weight\n use_exposure = weighting_strategy == PositionSetWeightingStrategy.Notional\n if (\n (use_weight and len(missing_weights))\n or (use_quantity and len(missing_quantities))\n or (use_exposure and len(missing_exposures))\n ):\n raise MqValueError(\n f'You must input a {weighting_strategy.value} for the following positions: \\\n {missing_weights if use_weight else (missing_exposures if use_exposure else missing_quantities)}'\n )\n if use_weight and reference_notional is None:\n raise MqValueError('You must specify a reference notional in order to price by weight.')\n return weighting_strategy\n\n @staticmethod\n def __convert_positions_for_pricing(\n positions: List[Position], weighting_strategy: PositionSetWeightingStrategy\n ) -> List[PositionPriceInput]:\n position_inputs, missing_ids = [], []\n use_quantity = weighting_strategy == PositionSetWeightingStrategy.Quantity\n use_weight = weighting_strategy == PositionSetWeightingStrategy.Weight\n use_exposure = weighting_strategy == PositionSetWeightingStrategy.Notional\n for p in positions:\n if p.asset_id is None:\n missing_ids.append(p.identifier)\n else:\n position_inputs.append(\n PositionPriceInput(\n asset_id=p.asset_id,\n weight=p.weight if use_weight else None,\n notional=p.notional if use_exposure else None,\n quantity=p.quantity if use_quantity else None,\n tags=p.tags,\n )\n )\n if len(missing_ids):\n raise MqValueError(\n f'Positions: {missing_ids} are missing asset ids. Resolve your position \\\n set or remove unmapped identifiers.'\n )\n return position_inputs\n\n @staticmethod\n def __hash_position_tag_list(position_tags: List[PositionTag]) -> str:\n hashed_results = ''\n if position_tags is not None:\n for tag in position_tags:\n hashed_results = hashed_results + tag.name + '-' + tag.value\n return hashed_results\n\n @staticmethod\n def to_frame_many(position_sets: List['PositionSet']) -> pd.DataFrame:\n \"\"\"Returns dataframe of position sets\"\"\"\n position_sets = pd.DataFrame(position_sets, columns=[\"position_sets\"])\n\n for field in ['date', 'divisor', 'reference_notional']:\n position_sets[field] = [getattr(pos, field, None) for pos in position_sets['position_sets']]\n\n position_sets['positions'] = [pos.positions for pos in position_sets['position_sets']]\n\n position_sets = position_sets[position_sets['positions'].apply(lambda x: len(x) > 0)]\n\n position_sets = position_sets.explode('positions')\n position_sets['positions'] = [pos.as_dict() for pos in position_sets['positions']]\n\n for field in ['name', 'asset_id', 'identifier', 'weight', 'notional', 'restricted', 'quantity', 'tags']:\n position_sets[field] = [pos.get(field) for pos in position_sets['positions']]\n\n columns_to_drop = [\"position_sets\", \"positions\"]\n position_sets = position_sets.drop(columns=columns_to_drop)\n return position_sets\n\n @staticmethod\n @np.vectorize\n def __build_positions_from_frame(\n names: pd.Series = None,\n identifiers: pd.Series = None,\n asset_ids: pd.Series = None,\n weights: pd.Series = None,\n notionals: pd.Series = None,\n quantities: pd.Series = None,\n restricted: pd.Series = None,\n hard_to_borrow: pd.Series = None,\n tags: pd.Series = None,\n ):\n position = Position(\n asset_id=asset_ids,\n name=names,\n identifier=identifiers,\n weight=weights if weights else None,\n notional=notionals if notionals else None,\n quantity=quantities if quantities else None,\n tags=tags,\n )\n\n position._restricted = restricted\n position._hard_to_borrow = hard_to_borrow\n\n return position\n\n @classmethod\n def resolve_many(cls, position_sets: List['PositionSet'], **kwargs):\n \"\"\"\n Resolve positions on each holding date into Marquee assets. Positions sets will be updated inplace.\n Each resolved position will have a unique Marquee ID.\n\n :param position_sets: Positions sets in a list.\n :param kwargs: Additional parameters to send to the GS Resolver API.\n\n **Usage**\n\n >>> from gs_quant.markets.position_set import PositionSet, Position, PositionTag\n >>> import datetime as dt\n >>> import pandas as pd\n\n The input to the function can be a list of `PositionSet` object.\n\n >>> position_set_list = [\n ... PositionSet(date=dt.date(2024, 5, 1),\n ... reference_notional=1000,\n ... positions=[Position(identifier='GS UN',\n ... weight=0.5,\n ... tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")]),\n ... Position(identifier='AAPL UW',\n ... weight=0.5,\n ... ags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")])]),\n ... PositionSet(date=dt.date(2024, 5, 1),\n ... reference_notional=1000,\n ... positions=[Position(identifier='GS UN',\n ... weight=0.5,\n ... tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")]),\n ... Position(identifier='AAPL UW',\n ... weight=0.5,\n ... tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")])])\n ... ]\n\n >>> PositionSet.resolve_many(position_set_list)\n\n **See also**\n\n :func:`price_many`\n\n \"\"\"\n\n position_sets_df = cls.to_frame_many(position_sets)\n if \"name\" in position_sets_df.columns.tolist():\n position_sets_df = position_sets_df.drop(columns=\"name\")\n if \"asset_id\" in position_sets_df.columns.tolist():\n position_sets_df = position_sets_df.drop(columns=\"asset_id\")\n position_sets_df = position_sets_df.dropna(how='all', axis=1)\n\n position_sets_attributes = position_sets_df.columns.tolist()\n if \"quantity\" in position_sets_attributes and \"weight\" in position_sets_attributes:\n raise MqValueError(\"Cannot have both weight and quantity in position sets\")\n if \"quantity\" in position_sets_attributes and \"notional\" in position_sets_attributes:\n raise MqValueError(\"Cannot have both weight and notional in position sets\")\n\n asset_temporal_xrefs_df, asset_identifier_type = _get_asset_temporal_xrefs(position_sets_df)\n _group_temporal_xrefs_into_discrete_time_ranges(asset_temporal_xrefs_df)\n resolved_assets_results_df = _resolve_many_assets(asset_temporal_xrefs_df, asset_identifier_type, **kwargs)\n\n position_sets_df = pd.merge(\n position_sets_df,\n resolved_assets_results_df[\n [\"assetId\", asset_identifier_type, \"name\", \"asOfDate\", \"tradingRestriction\", \"startDate\", \"endDate\"]\n ],\n how=\"left\",\n left_on=\"identifier\",\n right_on=asset_identifier_type,\n )\n position_sets_df[\"date\"] = pd.to_datetime(position_sets_df[\"date\"])\n\n # Fill N/A for startDate and endDate so they are not filtered out (for instance if some positions\n # did not have xrefs or were not resolved. These should be included in the unresolved positions group\"\n position_sets_df['startDate'] = position_sets_df['startDate'].fillna(position_sets_df['date'])\n position_sets_df['endDate'] = position_sets_df['endDate'].fillna(position_sets_df['date'])\n\n position_sets_df = position_sets_df[\n (position_sets_df[\"startDate\"] <= position_sets_df[\"date\"])\n & (position_sets_df[\"date\"] <= position_sets_df[\"endDate\"])\n ]\n position_sets_df = (\n position_sets_df.drop(columns=[asset_identifier_type, \"asOfDate\", \"startDate\", \"endDate\"])\n .rename(columns={\"tradingRestriction\": \"restricted\"})\n .fillna(np.nan)\n .replace([np.nan], [None])\n )\n\n # Build position sets\n if 'reference_notional' in position_sets_df.columns.tolist():\n if \"quantity\" in position_sets_df.columns.tolist():\n position_sets_df = position_sets_df.drop(columns='quantity')\n\n weights_df = position_sets_df['weight'] if 'weight' in position_sets_df.columns.tolist() else None\n quantities_df = position_sets_df['quantity'] if 'quantity' in position_sets_df.columns.tolist() else None\n notionals_df = position_sets_df['notional'] if 'notional' in position_sets_df.columns.tolist() else None\n tags_df = position_sets_df['tags'] if 'tags' in position_sets_df.columns.tolist() else None\n\n all_positions = cls.__build_positions_from_frame(\n names=position_sets_df['name'],\n identifiers=position_sets_df['identifier'],\n asset_ids=position_sets_df['assetId'],\n weights=weights_df,\n notionals=notionals_df,\n quantities=quantities_df,\n restricted=position_sets_df['restricted'],\n tags=tags_df,\n )\n\n position_sets_df['positions'] = all_positions\n position_sets_grouped_by_date = position_sets_df.groupby('date')\n for position_set in position_sets:\n if not isinstance(position_set.date, dt.date):\n position_set.date = pd.Timestamp(position_set.date).to_pydatetime().date()\n positions_on_holding_date_df = position_sets_grouped_by_date.get_group(position_set.date)\n position_set.positions = positions_on_holding_date_df.loc[\n ~positions_on_holding_date_df['assetId'].isna(), 'positions'\n ].tolist()\n unresolved_positions = positions_on_holding_date_df.loc[\n positions_on_holding_date_df['assetId'].isna(), 'positions'\n ].tolist()\n if unresolved_positions:\n position_set.__unresolved_positions = unresolved_positions\n\n @classmethod\n def price_many(\n cls,\n position_sets: List['PositionSet'],\n currency: Optional[Currency] = Currency.USD,\n weighting_strategy: PositionSetWeightingStrategy = None,\n carryover_positions_for_missing_dates: bool = False,\n should_reweight: bool = False,\n allow_fractional_shares: bool = False,\n allow_partial_pricing: bool = False,\n batch_size: int = 20,\n **kwargs,\n ):\n \"\"\"Fetch position weights from quantities or vice versa for a list of position sets. This function modifies the\n input position sets inplace\n\n :param position_sets: Positions sets in a list.\n :param currency: Currency to use to price. Defaults to USD.\n :param weighting_strategy: The weighting strategy to use. Should be weight or quantity. If None, infers\n weighting strategy from positions metadata\n :param carryover_positions_for_missing_dates: Broadcast previous positions onto dates with missing\n positions. Defaults to False\n :param should_reweight: Ensures total weight across positions on a holding date equals 1. Defaults to False\n :param allow_fractional_shares: Whether to allow fractional shares. Defaults to False if the weighting\n strategy is Quantity or Weight. Set to True if the weighting strategy is Notional.\n :param allow_partial_pricing: whether to price a subset of positions in case of errors\n :param batch_size: Size of position sets to send to the pricing API per request. Defaults to 30\n :param kwargs: Additional parameters to pass to the GS pricing API\n\n **Usage**\n\n The function expects a required input of positions sets, which can be a dataframe of the format below or\n a list of `PositionSet` object\n\n >>> from gs_quant.markets.position_set import PositionSet, Position, PositionTag\n >>> import datetime as dt\n >>> import pandas as pd\n\n The input to the function is a list of `PositoinSet`\n\n >>> position_set_list = [\n ... PositionSet(date=dt.date(2024, 5, 1),\n ... reference_notional=1000,\n ... positions=[Position(identifier='GS UN',\n ... weight=0.5,\n ... tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")]),\n ... Position(identifier='AAPL UW',\n ... weight=0.5,\n ... tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")])]),\n ... PositionSet(date=dt.date(2024, 5, 1),\n ... reference_notional=1000,\n ... positions=[Position(identifier='GS UN',\n ... weight=0.5,\n ... tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")]),\n ... Position(identifier='AAPL UW',\n ... weight=0.5,\n ... tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")])])\n ... ]\n\n >>> PositionSet.price_many(position_set_list)\n\n :func:`resolve_many`\n\n \"\"\"\n position_sets_to_price_df = cls.to_frame_many(position_sets)\n position_sets_to_price_df = position_sets_to_price_df.dropna(how='all', axis=1)\n position_sets_column_attributes = position_sets_to_price_df.columns.tolist()\n\n if \"quantity\" in position_sets_column_attributes and \"weight\" in position_sets_column_attributes:\n raise MqValueError(\"Cannot have both weight and quantity in position sets\")\n if \"notional\" in position_sets_column_attributes and \"weight\" in position_sets_column_attributes:\n raise MqValueError(\"Cannot have both weight and notional in position sets\")\n\n if not weighting_strategy:\n if \"weight\" in position_sets_column_attributes and \"reference_notional\" in position_sets_column_attributes:\n weighting_strategy = PositionSetWeightingStrategy.Weight\n elif \"notional\" in position_sets_column_attributes:\n weighting_strategy = PositionSetWeightingStrategy.Notional\n else:\n weighting_strategy = PositionSetWeightingStrategy.Quantity\n\n if weighting_strategy not in [\n PositionSetWeightingStrategy.Quantity,\n PositionSetWeightingStrategy.Weight,\n PositionSetWeightingStrategy.Notional,\n ]:\n raise MqValueError(\"Can only specify a weighting strategy of weight, notional, or quantity\")\n\n if (\n weighting_strategy == PositionSetWeightingStrategy.Quantity\n and \"quantity\" not in position_sets_column_attributes\n ):\n raise MqValueError(\n \"Unable to price positions without position weights and daily reference notional or position quantities\"\n )\n should_allow_fractional_shares = (\n True if weighting_strategy == PositionSetWeightingStrategy.Notional else allow_fractional_shares\n )\n\n position_pricing_parameters = PositionsPricingParameters(\n currency=currency.value,\n weighting_strategy=weighting_strategy.value,\n carryover_positions_for_missing_dates=carryover_positions_for_missing_dates,\n should_reweight=should_reweight,\n allow_fractional_shares=should_allow_fractional_shares,\n )\n\n if kwargs:\n [setattr(position_pricing_parameters, arg, value) for arg, value in kwargs.items()]\n\n if weighting_strategy == PositionSetWeightingStrategy.Weight:\n positions_with_missing_weights = position_sets_to_price_df[position_sets_to_price_df['weight'].isna()]\n if not positions_with_missing_weights.empty:\n _logger.warning(\"Some positions do not have weights. These will be filtered out\")\n elif weighting_strategy == PositionSetWeightingStrategy.Notional:\n positions_with_missing_exposures = position_sets_to_price_df[position_sets_to_price_df['notional'].isna()]\n if not positions_with_missing_exposures.empty:\n _logger.warning(\"Some positions do not have exposures. These will be filtered out\")\n elif weighting_strategy == PositionSetWeightingStrategy.Quantity:\n positions_with_missing_quantities = position_sets_to_price_df[position_sets_to_price_df['quantity'].isna()]\n if not positions_with_missing_quantities.empty:\n _logger.warning(\"Some positions do not have quantities. These will be filtered out\")\n\n if \"weight\" not in position_sets_column_attributes:\n position_sets_to_price_df['weight'] = None\n if \"notional\" not in position_sets_column_attributes:\n position_sets_to_price_df['notional'] = None\n if \"quantity\" not in position_sets_column_attributes:\n position_sets_to_price_df['quantity'] = None\n\n position_sets_to_price_df['positions'] = np.vectorize(\n lambda asset_id, weight, quantity, notional: PositionsRequest(\n asset_id=asset_id, weight=weight, quantity=quantity, notional=notional\n )\n )(\n position_sets_to_price_df['asset_id'],\n position_sets_to_price_df['weight'],\n position_sets_to_price_df['quantity'],\n position_sets_to_price_df['notional'],\n )\n\n # build positionSets requests\n position_sets_grouped_by_date = position_sets_to_price_df.groupby(\"date\")\n\n all_pos_sets = []\n for date, pos_df in position_sets_grouped_by_date:\n all_pos_sets.append(\n PositionSetRequest(\n date=date,\n positions=pos_df['positions'].tolist(),\n target_notional=pos_df['reference_notional'].iat[0]\n if weighting_strategy == PositionSetWeightingStrategy.Weight\n else None,\n )\n )\n\n batches = np.array_split(all_pos_sets, math.ceil(len(all_pos_sets) / batch_size))\n all_pricing_results = []\n\n start = time()\n for batch_idx, batch in enumerate(batches):\n _logger.info(f\"Pricing batch {batch_idx} of {len(batches)}\")\n try:\n payload = PositionsPricingRequest(parameters=position_pricing_parameters, position_sets=tuple(batch))\n pricing_results = _repeat_try_catch_request(\n GsPriceApi.price_many_positions,\n number_retries=3,\n return_result=True,\n verbose=False,\n pricing_request=payload,\n )\n all_pricing_results += pricing_results\n except MqRequestError as request_exception:\n earliest_pos_set_in_batch = batch[0]\n latest_pos_set_in_batch = batch[-1]\n _logger.error(\n f\"An error occurred while pricing positions on holding dates \"\n f\"{earliest_pos_set_in_batch.date} to {latest_pos_set_in_batch.date}: \"\n f\"{request_exception}.Consider batching position sets or reducing the batch\"\n f\"size\"\n )\n if not allow_partial_pricing:\n raise request_exception\n _logger.info(f\"Total time to price positions is {time() - start} seconds\")\n\n next_start = time()\n date_to_priced_position_sets = {\n dt.datetime.strptime(pos_set.get('date'), '%Y-%m-%d').date(): pos_set for pos_set in all_pricing_results\n }\n\n for input_position_set in position_sets:\n if not isinstance(input_position_set.date, dt.date):\n # use pandas to infer format of date\n input_position_set.date = pd.to_datetime(input_position_set.date).date()\n if not date_to_priced_position_sets.get(input_position_set.date):\n input_position_set.__unpriced_positions = list(input_position_set.positions)\n input_position_set.positions = None\n continue\n\n priced_position_set = date_to_priced_position_sets.get(input_position_set.date)\n position_date = dt.datetime.strptime(priced_position_set.get('date'), '%Y-%m-%d').date()\n priced_positions_df = pd.DataFrame(priced_position_set.get('positions'))\n if weighting_strategy == PositionSetWeightingStrategy.Weight:\n column_from_initial_position_sets_to_merge_by = \"weight\"\n column_from_priced_positions_results_to_merge_by = \"referenceWeight\"\n elif weighting_strategy == PositionSetWeightingStrategy.Notional:\n column_from_initial_position_sets_to_merge_by = \"notional\"\n column_from_priced_positions_results_to_merge_by = \"notional\"\n else:\n column_from_initial_position_sets_to_merge_by = \"quantity\"\n column_from_priced_positions_results_to_merge_by = \"quantity\"\n\n priced_positions_df = priced_positions_df.drop_duplicates(\n subset=['assetId', column_from_priced_positions_results_to_merge_by]\n )\n\n df_to_merge_left = position_sets_to_price_df.loc[\n position_sets_to_price_df['date'] == position_date, :\n ].copy()\n df_to_merge_right = priced_positions_df.copy()\n\n # Rounding column quantities to avoid minute discrepancies in price/bulk calculations.\n df_to_merge_left[column_from_initial_position_sets_to_merge_by] = df_to_merge_left[\n column_from_initial_position_sets_to_merge_by\n ].round(5)\n\n df_to_merge_right[column_from_priced_positions_results_to_merge_by] = df_to_merge_right[\n column_from_priced_positions_results_to_merge_by\n ].round(5)\n\n priced_positions_df = pd.merge(\n df_to_merge_left,\n df_to_merge_right,\n how=\"left\",\n left_on=['asset_id', column_from_initial_position_sets_to_merge_by],\n right_on=['assetId', column_from_priced_positions_results_to_merge_by],\n suffixes=(\"_original\", None),\n )\n\n if weighting_strategy == PositionSetWeightingStrategy.Weight:\n unpriced_positions_df = priced_positions_df[priced_positions_df['weight'].isna()]\n priced_positions_df = priced_positions_df[~priced_positions_df['weight'].isna()]\n else:\n unpriced_positions_df = priced_positions_df[priced_positions_df['quantity'].isna()]\n priced_positions_df = priced_positions_df[~priced_positions_df[\"quantity\"].isna()]\n\n positions = []\n for record in priced_positions_df.to_dict('records'):\n curr_position = Position(\n asset_id=record.get('assetId'),\n identifier=record.get('identifier'),\n name=record.get('name'),\n weight=record.get('weight'),\n notional=record.get('notional'),\n quantity=record.get('quantity'),\n tags=record.get('tags'),\n )\n curr_position._restricted = record.get('restricted')\n positions.append(curr_position)\n\n unpriced_positions = [\n Position(\n asset_id=unpriced_record.get('asset_id'),\n identifier=unpriced_record.get('identifier'),\n name=unpriced_record.get('name'),\n weight=unpriced_record.get('weight_original'),\n quantity=unpriced_record.get('quantity_original'),\n notional=unpriced_record.get('notional_original'),\n )\n for unpriced_record in unpriced_positions_df.to_dict('records')\n ]\n\n input_position_set.positions = positions\n input_position_set.__unpriced_positions = unpriced_positions\n\n _logger.info(f\"Total time to process pricing results is {time() - next_start} seconds\")\n" }, { "path": "gs_quant/markets/position_set_utils.py", "content": "\"\"\"\nCopyright 2024 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom typing import Tuple\n\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nimport math\n\n\ndef _get_asset_temporal_xrefs(position_sets_df: pd.DataFrame) -> Tuple[pd.DataFrame, str]:\n \"\"\"Helper function to get temporal xrefs for assets in a position set\"\"\"\n universe = list(set(position_sets_df['identifier'].tolist()))\n batches = np.array_split(universe, math.ceil(len(universe) / 500))\n earliest_position_date = position_sets_df['date'].min()\n earliest_position_date = pd.Timestamp(earliest_position_date).to_pydatetime()\n\n results = []\n for batch in batches:\n cur_result = GsAssetApi.get_many_asset_xrefs(list(batch), limit=500)\n results += cur_result\n\n asset_xrefs_final = []\n for res in results:\n xrefs_list = res.get('xrefs')\n if not xrefs_list:\n continue\n all_xrefs = []\n for item in xrefs_list:\n if dt.datetime.strptime(item.get('endDate'), '%Y-%m-%d') >= earliest_position_date:\n new_xref_map = {\n 'assetId': res.get('assetId'),\n 'startDate': item.get('startDate'),\n 'endDate': item.get('endDate'),\n }\n new_xref_map.update(**item.get('identifiers'))\n all_xrefs.append(new_xref_map)\n asset_xrefs_final += all_xrefs\n\n xref_df = pd.DataFrame(asset_xrefs_final)\n\n # Infer identifier type\n all_possible_identifier_types = [\n \"ticker\",\n \"bbid\",\n \"bcid\",\n \"ric\",\n \"cusip\",\n \"isin\",\n \"sedol\",\n \"gss\",\n \"gsid\",\n \"primeId\",\n \"gsn\",\n ]\n identifiers_found = set(xref_df.columns.tolist()) & set(all_possible_identifier_types)\n inferred_identifier_type = None\n largest_count = 0\n\n for each_id_type in identifiers_found:\n number_of_matches = len(set(xref_df[each_id_type]) & set(universe))\n\n if number_of_matches > largest_count:\n inferred_identifier_type = each_id_type\n largest_count = number_of_matches\n\n xref_df = xref_df.dropna(subset=inferred_identifier_type)\n xref_df = xref_df.fillna(value={'delisted': 'no'})\n xref_df = xref_df.loc[xref_df['delisted'] == 'no', :]\n\n return xref_df, inferred_identifier_type\n\n\ndef _group_temporal_xrefs_into_discrete_time_ranges(xref_df: pd.DataFrame):\n \"\"\"Helper function that group asset xref data with overlapping temporal history\"\"\"\n\n def group_fn(df):\n # Find where the next group should start\n df = df.sort_values(by=\"endDate\")\n\n # Groups should have non-overlapping time intervals/start_date, end_date\n where_next_group_should_start = df['startDate'].shift(-1) > df['endDate']\n\n # Assign group numbers based on where the next groups starts\n group_numbers = where_next_group_should_start.cumsum().shift(1).fillna(0).astype(int)\n\n return group_numbers\n\n xref_df['startDate'] = [dt.datetime.strptime(x, '%Y-%m-%d') for x in xref_df['startDate']]\n xref_df['endDate'] = [dt.datetime.strptime(x, '%Y-%m-%d') for x in xref_df['endDate']]\n\n groups = group_fn(xref_df)\n xref_df['group'] = groups\n\n\ndef _resolve_many_assets(historical_xref_df: pd.DataFrame, identifier_type: str, **kwargs) -> pd.DataFrame:\n \"\"\"Given a dataframe with temporal xref asset data, resolve\"\"\"\n all_dfs = []\n xref_group_by = historical_xref_df.groupby('group')\n for _, grouped_df in xref_group_by:\n unmapped = []\n all_results = []\n as_of = min(grouped_df['endDate'])\n identifiers = grouped_df[identifier_type].tolist()\n batches = np.array_split(identifiers, math.ceil(len(identifiers) / 500))\n\n resolved_positions = {}\n\n for batch in batches:\n curr_batch_map = GsAssetApi.resolve_assets(\n identifier=list(batch),\n as_of=as_of,\n limit=500,\n fields=['name', 'id', identifier_type, 'tradingRestriction'],\n **kwargs,\n )\n resolved_positions = {**resolved_positions, **curr_batch_map}\n\n for asset_identifier, asset_resolved_data in resolved_positions.items():\n if asset_resolved_data:\n all_results.append(asset_resolved_data[0])\n else:\n unmapped += [{identifier_type: asset_identifier}]\n\n df = pd.DataFrame(all_results + unmapped)\n df['asOfDate'] = as_of\n df = pd.merge(\n df, grouped_df, how=\"inner\", left_on=[\"id\", identifier_type], right_on=[\"assetId\", identifier_type]\n )[[\"assetId\", \"name\", identifier_type, \"tradingRestriction\", \"asOfDate\", \"startDate\", \"endDate\"]]\n all_dfs.append(df)\n\n final_df = pd.concat(all_dfs) if all_dfs else pd.DataFrame()\n\n return final_df\n" }, { "path": "gs_quant/markets/report.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom enum import Enum, auto\nimport numpy as np\nfrom time import sleep\nfrom typing import Tuple, Union, List, Dict, OrderedDict\nimport scipy.stats as st\n\nimport pandas as pd\nfrom dateutil.relativedelta import relativedelta\nfrom inflection import titleize\n\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.api.gs.portfolios import GsPortfolioApi\nfrom gs_quant.api.gs.reports import GsReportApi, FactorRiskTableMode\nfrom gs_quant.api.gs.thematics import Region, GsThematicApi, ThematicMeasure\nfrom gs_quant.common import PositionType, ReportParameters, Currency, PositionTag\nfrom gs_quant.datetime import business_day_offset, prev_business_date\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.report_utils import _get_ppaa_batches\nfrom gs_quant.target.coordinates import MDAPIDataBatchResponse\nfrom gs_quant.target.data import DataQuery, DataQueryResponse\nfrom gs_quant.target.reports import Report as TargetReport, ReportType, PositionSourceType, ReportStatus\nfrom gs_quant.target.portfolios import RiskAumSource\n\n\nclass ReturnFormat(Enum):\n \"\"\"Alternative format for data to be returned from get_data functions\"\"\"\n\n JSON = auto()\n DATA_FRAME = auto()\n\n\nclass ReportDataset(Enum):\n PPA_DATASET = \"PPA\"\n PPAA_DATASET = \"PPAA\"\n PFR_DATASET = \"PFR\"\n PFRA_DATASET = \"PFRA\"\n AFR_DATASET = \"AFR\"\n AFRA_DATASET = \"AFRA\"\n ATA_DATASET = \"ATA\"\n ATAA_DATASET = \"ATAA\"\n PTA_DATASET = \"PTA\"\n PTAA_DATASET = \"PTAA\"\n PORTFOLIO_CONSTITUENTS = \"PORTFOLIO_CONSTITUENTS\"\n\n\nclass FactorRiskViewsMode(Enum):\n Risk = 'Risk'\n Attribution = 'Attribution'\n\n\nclass FactorRiskResultsMode(Enum):\n Portfolio = 'Portfolio'\n Positions = 'Positions'\n\n\nclass FactorRiskUnit(Enum):\n Percent = 'Percent'\n Notional = 'Notional'\n\n\nclass AttributionAggregationType(Enum):\n Arithmetic = 'arithmetic'\n Geometric = 'geometric'\n\n\nclass AggregationCategoryType(Enum):\n Sector = 'assetClassificationsGicsSector'\n Industry = 'assetClassificationsGicsIndustry'\n Region = 'region'\n Country = 'assetClassificationsCountryName'\n\n\nclass CustomAUMDataPoint:\n \"\"\"\n\n Custom AUM Data Point represents a portfolio's AUM value for a specific date\n\n \"\"\"\n\n def __init__(self, date: dt.date, aum: float):\n self.__date = date\n self.__aum = aum\n\n @property\n def date(self) -> dt.date:\n return self.__date\n\n @date.setter\n def date(self, value: dt.date):\n self.__date = value\n\n @property\n def aum(self) -> float:\n return self.__aum\n\n @aum.setter\n def aum(self, value: float):\n self.__aum = value\n\n\nclass ReportJobFuture:\n \"\"\"Report job future that monitors report status and results\"\"\"\n\n def __init__(self, report_id: str, job_id: str, report_type: ReportType, start_date: dt.date, end_date: dt.date):\n self.__report_id = report_id\n self.__job_id = job_id\n self.__report_type = report_type\n self.__start_date = start_date\n self.__end_date = end_date\n\n @property\n def job_id(self) -> str:\n return self.__job_id\n\n @property\n def end_date(self) -> dt.date:\n return self.__end_date\n\n def status(self) -> ReportStatus:\n \"\"\"\n :return: the status of the report job\n \"\"\"\n job = GsReportApi.get_report_job(self.__job_id)\n return ReportStatus(job.get('status'))\n\n def done(self) -> bool:\n \"\"\"\n :return: true if the report job is in the following states: \"done\", \"error\", or \"cancelled\". Returns\n false otherwise\n \"\"\"\n return self.status() in [ReportStatus.done, ReportStatus.error, ReportStatus.cancelled]\n\n def result(self):\n \"\"\"\n :return: a Pandas DataFrame containing the results of the report job\n \"\"\"\n status = self.status()\n if status == ReportStatus.cancelled:\n raise MqValueError('This report job in status \"cancelled\". Cannot retrieve results.')\n if status == ReportStatus.error:\n raise MqValueError('This report job is in status \"error\". Cannot retrieve results.')\n if status != ReportStatus.done:\n raise MqValueError('This report job is not done. Cannot retrieve results.')\n if self.__report_type in [ReportType.Portfolio_Factor_Risk, ReportType.Asset_Factor_Risk]:\n results = GsReportApi.get_factor_risk_report_results(\n risk_report_id=self.__report_id, start_date=self.__start_date, end_date=self.__end_date\n )\n return pd.DataFrame(results)\n if self.__report_type == ReportType.Portfolio_Performance_Analytics:\n query = DataQuery(\n where={'reportId': self.__report_id}, start_date=self.__start_date, end_date=self.__end_date\n )\n results = GsDataApi.query_data(query=query, dataset_id=ReportDataset.PPA_DATASET.value)\n return pd.DataFrame(results)\n return None\n\n def wait_for_completion(self, sleep_time: int = 10, max_retries: int = 10, error_on_timeout: bool = True) -> bool:\n \"\"\"Periodically query status and sleep till the status become done. If error_on_timeout is false, returns\n boolean value indicating if the job is done or not\"\"\"\n retries = 0\n while not self.done() and retries < max_retries:\n sleep(sleep_time)\n retries += 1\n if retries == max_retries:\n if error_on_timeout:\n raise MqValueError(\n f'Report job {self.__job_id} is taking longer than expected to finish. '\n f'Please contact the Marquee Analytics team at gs-marquee-analytics-support@gs.com '\n 'if the issue persists.'\n )\n else:\n print(f'Report job {self.__job_id} is taking longer than expected to finish.')\n return False\n return True\n\n def reschedule(self):\n GsReportApi.reschedule_report_job(self.__job_id)\n\n\nclass Report:\n \"\"\"General report class\"\"\"\n\n def __init__(\n self,\n report_id: str = None,\n name: str = None,\n position_source_id: str = None,\n position_source_type: Union[str, PositionSourceType] = None,\n report_type: Union[str, ReportType] = None,\n parameters: ReportParameters = None,\n earliest_start_date: dt.date = None,\n latest_end_date: dt.date = None,\n latest_execution_time: dt.datetime = None,\n status: Union[str, ReportStatus] = ReportStatus.new,\n percentage_complete: float = None,\n ):\n self.__id = report_id\n self.__name = name\n self.__position_source_id = position_source_id\n self.__position_source_type = (\n position_source_type\n if isinstance(position_source_type, PositionSourceType) or position_source_type is None\n else PositionSourceType(position_source_type)\n )\n self.__type = (\n report_type if isinstance(report_type, ReportType) or report_type is None else ReportType(report_type)\n )\n self.__parameters = parameters\n self.__earliest_start_date = earliest_start_date\n self.__latest_end_date = latest_end_date\n self.__latest_execution_time = latest_execution_time\n self.__status = status if isinstance(status, ReportStatus) else ReportStatus(status)\n self.__percentage_complete = percentage_complete\n\n @property\n def id(self) -> str:\n return self.__id\n\n @property\n def name(self) -> str:\n return self.__name\n\n @property\n def position_source_id(self) -> str:\n return self.__position_source_id\n\n @position_source_id.setter\n def position_source_id(self, value: str):\n self.__position_source_id = value\n\n @property\n def position_source_type(self) -> PositionSourceType:\n return self.__position_source_type\n\n @position_source_type.setter\n def position_source_type(self, value: Union[str, PositionSourceType]):\n self.__position_source_type = value if isinstance(value, PositionSourceType) else PositionSourceType(value)\n\n @property\n def type(self) -> ReportType:\n return self.__type\n\n @type.setter\n def type(self, value: Union[str, ReportType]):\n self.__type = value if isinstance(value, ReportType) else ReportType(value)\n\n @property\n def parameters(self) -> ReportParameters:\n return self.__parameters\n\n @parameters.setter\n def parameters(self, value: ReportParameters):\n self.__parameters = value\n\n @property\n def earliest_start_date(self) -> dt.date:\n return self.__earliest_start_date\n\n @property\n def latest_end_date(self) -> dt.date:\n return self.__latest_end_date\n\n @property\n def latest_execution_time(self) -> dt.datetime:\n return self.__latest_execution_time\n\n @property\n def status(self) -> ReportStatus:\n return self.__status\n\n @property\n def percentage_complete(self) -> float:\n return self.__percentage_complete\n\n @classmethod\n def get(cls, report_id: str, acceptable_types: List[ReportType] = None):\n return cls.from_target(GsReportApi.get_report(report_id))\n\n @classmethod\n def from_target(cls, report: TargetReport):\n return Report(\n report_id=report.id,\n name=report.name,\n position_source_id=report.position_source_id,\n position_source_type=report.position_source_type,\n report_type=report.type,\n parameters=report.parameters,\n earliest_start_date=report.earliest_start_date,\n latest_end_date=report.latest_end_date,\n latest_execution_time=report.latest_execution_time,\n status=report.status,\n percentage_complete=report.percentage_complete,\n )\n\n def save(self):\n \"\"\"Create a report in Marquee if it doesn't exist. Update the report if it does.\"\"\"\n target_report = TargetReport(\n name=self.name,\n position_source_id=self.position_source_id,\n position_source_type=self.position_source_type,\n type_=self.type,\n parameters=self.parameters if self.parameters else ReportParameters(),\n )\n if self.id:\n target_report.id = self.id\n GsReportApi.update_report(target_report)\n else:\n report = GsReportApi.create_report(target_report)\n self.__id = report.id\n\n def delete(self):\n \"\"\"Delete a report from Marquee\"\"\"\n GsReportApi.delete_report(self.id)\n\n def set_position_source(self, entity_id: str):\n \"\"\"Set position source type and position source ID\"\"\"\n is_portfolio = entity_id.startswith('MP')\n self.position_source_type = 'Portfolio' if is_portfolio else 'Asset'\n self.position_source_id = entity_id\n if isinstance(self, FactorRiskReport):\n self.type = ReportType.Portfolio_Factor_Risk if is_portfolio else ReportType.Asset_Factor_Risk\n if isinstance(self, ThematicReport):\n self.type = ReportType.Portfolio_Thematic_Analytics if is_portfolio else ReportType.Asset_Thematic_Analytics\n\n def get_most_recent_job(self):\n \"\"\"Retrieve the most current report job\"\"\"\n jobs = GsReportApi.get_report_jobs(self.id)\n most_current_job = sorted(jobs, key=lambda i: i['createdTime'], reverse=True)[0]\n return ReportJobFuture(\n report_id=self.id,\n job_id=most_current_job.get('id'),\n report_type=ReportType(most_current_job.get('reportType')),\n start_date=dt.datetime.strptime(most_current_job.get('startDate'), \"%Y-%m-%d\").date(),\n end_date=dt.datetime.strptime(most_current_job.get('endDate'), \"%Y-%m-%d\").date(),\n )\n\n def schedule(self, start_date: dt.date = None, end_date: dt.date = None, backcast: bool = None):\n \"\"\"\n Schedule a report with the given date range\n\n :param start_date: start date (optional)\n :param end_date: end date (optional)\n :param backcast: set to true if the report should be backcasted\n \"\"\"\n if None in [self.id, self.__position_source_id]:\n raise MqValueError('Can only schedule reports with valid IDs and Position Source IDs.')\n if self.position_source_type != PositionSourceType.Portfolio and None in [start_date, end_date]:\n raise MqValueError('Must specify schedule start and end dates for report.')\n if None in [start_date, end_date]:\n position_dates = GsPortfolioApi.get_position_dates(self.position_source_id)\n if len(position_dates) == 0:\n raise MqValueError('Cannot schedule reports for a portfolio with no positions.')\n if start_date is None:\n start_date = (\n business_day_offset(min(position_dates) - relativedelta(years=1), -1, roll='forward')\n if backcast\n else min(position_dates)\n )\n if end_date is None:\n end_date = min(position_dates) if backcast else business_day_offset(dt.date.today(), -1, roll='forward')\n GsReportApi.schedule_report(report_id=self.id, start_date=start_date, end_date=end_date, backcast=backcast)\n\n def run(self, start_date: dt.date = None, end_date: dt.date = None, backcast: bool = False, is_async: bool = True):\n \"\"\"\n Run a report with the given date range\n\n :param start_date: start date (optional)\n :param end_date: end date (optional)\n :param backcast: set to true if the report should be backcasted; defaults to false\n :param is_async: return immediately (true) or wait for results (false); defaults to true\n \"\"\"\n self.schedule(start_date, end_date, backcast)\n counter = 5\n while counter > 0:\n try:\n job_future = self.get_most_recent_job()\n if is_async:\n return job_future\n counter = 100\n while counter > 0:\n if job_future.done():\n return job_future.result()\n sleep(6)\n raise MqValueError(\n f'Your report {self.id} is taking longer than expected to finish. Please contact the '\n 'Marquee Analytics team at gs-marquee-analytics-support@gs.com'\n )\n except IndexError:\n counter -= 1\n status = Report.get(self.id).status\n if status == ReportStatus.waiting:\n raise MqValueError(\n f'Your report {self.id} is stuck in \"waiting\" status and therefore cannot be run at this time.'\n )\n raise MqValueError(\n f'Your report {self.id} is taking longer to run than expected. '\n 'Please reach out to the Marquee Analytics team at gs-marquee-analytics-support@gs.com '\n 'for assistance.'\n )\n\n\nclass PerformanceReport(Report):\n \"\"\"\n Historical analyses on measures like PnL and exposure of a position source over a date range\n \"\"\"\n\n def __init__(\n self,\n report_id: str = None,\n name: str = None,\n position_source_id: str = None,\n position_source_type: Union[str, PositionSourceType] = None,\n parameters: ReportParameters = None,\n earliest_start_date: dt.date = None,\n latest_end_date: dt.date = None,\n latest_execution_time: dt.datetime = None,\n status: Union[str, ReportStatus] = ReportStatus.new,\n percentage_complete: float = None,\n **kwargs,\n ):\n \"\"\"\n Historical analyses on measures like PnL and exposure of a portfolio over a date range\n\n :param report_id: Marquee report ID\n :param name: report name\n :param position_source_id: position source ID\n :param position_source_type: position source (i.e. 'Portfolio', 'Asset', or 'Hedge')\n :param parameters: parameters of the report\n :param earliest_start_date: start date of report\n :param latest_end_date: end date of report\n :param latest_execution_time: date of the latest execution\n :param status: status of of report (i.e. 'ready', 'executing', or 'done')\n :param percentage_complete: percent of the report that is complete\n\n **Examples**\n\n >>> performance_report = PerformanceReport(\n >>> position_source_type=PositionSourceType.Portfolio,\n >>> position_source_id='PORTFOLIOID'\n >>> )\n \"\"\"\n super().__init__(\n report_id,\n name,\n position_source_id,\n position_source_type,\n ReportType.Portfolio_Performance_Analytics,\n parameters,\n earliest_start_date,\n latest_end_date,\n latest_execution_time,\n status,\n percentage_complete,\n )\n\n @classmethod\n def get(cls, report_id: str, **kwargs):\n \"\"\"\n Get a performance report from the unique report identifier\n\n :param report_id: Marquee report ID\n :return: returns a PerformanceReport object that correlates to the Marquee report\n \"\"\"\n return cls.from_target(GsReportApi.get_report(report_id))\n\n @classmethod\n def from_target(cls, report: TargetReport):\n if report.type != ReportType.Portfolio_Performance_Analytics:\n raise MqValueError('This report is not a performance report.')\n return PerformanceReport(\n report_id=report.id,\n name=report.name,\n position_source_id=report.position_source_id,\n position_source_type=report.position_source_type,\n report_type=report.type,\n parameters=report.parameters,\n earliest_start_date=report.earliest_start_date,\n latest_end_date=report.latest_end_date,\n latest_execution_time=report.latest_execution_time,\n status=report.status,\n percentage_complete=report.percentage_complete,\n )\n\n def get_pnl(\n self, start_date: dt.date = None, end_date: dt.date = None, unit: FactorRiskUnit = FactorRiskUnit.Notional\n ) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio PnL\n\n :param start_date: start date\n :param end_date: end date\n :param unit: return the results in terms of notional or percent (defaults to notional)\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_pnl_measure(\"pnl\", unit, start_date, end_date)\n\n def get_long_exposure(self, start_date: dt.date = None, end_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio long exposure\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"longExposure\", start_date, end_date)\n\n def get_short_exposure(self, start_date: dt.date = None, end_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio short exposure\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"shortExposure\", start_date, end_date)\n\n def get_asset_count(self, start_date: dt.date = None, end_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio asset count\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"assetCount\", start_date, end_date)\n\n def get_turnover(self, start_date: dt.date = None, end_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio turnover\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"turnover\", start_date, end_date)\n\n def get_asset_count_long(self, start_date: dt.date = None, end_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio long asset count\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"assetCountLong\", start_date, end_date)\n\n def get_asset_count_short(\n self, start_date: dt.date = None, end_date: dt.date = None\n ) -> Union[MDAPIDataBatchResponse, DataQueryResponse, tuple, list]:\n \"\"\"\n Get historical portfolio short asset count\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"assetCountShort\", start_date, end_date)\n\n def get_net_exposure(self, start_date: dt.date = None, end_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio net exposure\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"netExposure\", start_date, end_date)\n\n def get_gross_exposure(self, start_date: dt.date = None, end_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio gross exposure\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"grossExposure\", start_date, end_date)\n\n def get_position_net_weights(\n self,\n start_date: dt.date,\n end_date: dt.date,\n asset_metadata_fields: List[str] = [\"id\", \"name\", \"ticker\"],\n include_all_business_days: bool = True,\n position_type: PositionType = None,\n ) -> pd.DataFrame:\n \"\"\"\n Get the net weight of each position in the portfolio for the given date range.\n :param start_date: start date from which to retrieve the net weight data\n :param end_date: end date until which to retrieve the net weight data\n :param asset_metadata_fields: List of fields to include in the result. Default is [\"id\", \"name\", \"ticker\"].\n :param include_all_business_days: If True, include all business days in the date range, even if there is no\n position set uploaded on those days. If False, only include dates where there are uploaded positions.\n :param position_type: The type of position to retrieve. If None, all positions are retrieved.\n :return: A DataFrame with the dates as the index and the positions as the columns. The values in the DataFrame\n are the net weights of the positions on the corresponding dates.\n\n \"\"\"\n asset_metadata_fields.append(\"netWeight\")\n try:\n return pd.DataFrame(\n self.get_positions_data(\n start=start_date,\n end=end_date,\n fields=asset_metadata_fields,\n include_all_business_days=include_all_business_days,\n position_type=position_type,\n )\n )\n except Exception as e:\n raise MqValueError(f\"Error retrieving net weight data: {e}\")\n\n def get_trading_pnl(\n self, start_date: dt.date = None, end_date: dt.date = None, unit: FactorRiskUnit = FactorRiskUnit.Notional\n ) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio trading PnL\n\n :param start_date: start date\n :param end_date: end date\n :param unit: return the results in terms of notional or percent (defaults to notional)\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_pnl_measure(\"tradingPnl\", unit, start_date, end_date)\n\n def get_trading_cost_pnl(\n self, start_date: dt.date = None, end_date: dt.date = None, unit: FactorRiskUnit = FactorRiskUnit.Notional\n ) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio trading cost PnL\n\n :param start_date: start date\n :param end_date: end date\n :param unit: return the results in terms of notional or percent (defaults to notional)\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_pnl_measure(\"tradingCostPnl\", unit, start_date, end_date)\n\n def get_servicing_cost_long_pnl(\n self, start_date: dt.date = None, end_date: dt.date = None, unit: FactorRiskUnit = FactorRiskUnit.Notional\n ) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio servicing cost long PnL\n\n :param start_date: start date\n :param end_date: end date\n :param unit: return the results in terms of notional or percent (defaults to notional)\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_pnl_measure(\"servicingCostLongPnl\", unit, start_date, end_date)\n\n def get_servicing_cost_short_pnl(\n self, start_date: dt.date = None, end_date: dt.date = None, unit: FactorRiskUnit = FactorRiskUnit.Notional\n ) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio servicing cost short PnL\n\n :param start_date: start date\n :param end_date: end date\n :param unit: return the results in terms of notional or percent (defaults to notional)\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_pnl_measure(\"servicingCostShortPnl\", unit, start_date, end_date)\n\n def get_asset_count_priced(self, start_date: dt.date = None, end_date: dt.date = None) -> pd.DataFrame:\n \"\"\"\n Get historical portfolio asset count priced\n\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n return self.get_measure(\"assetCountPriced\", start_date, end_date)\n\n def get_measure(\n self,\n field: str,\n start_date: dt.date = None,\n end_date: dt.date = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"\n Get historical portfolio metrics\n\n :param field: the entity property to be returned\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n fields = (field,)\n where = {'reportId': self.id}\n query = DataQuery(where=where, fields=fields, start_date=start_date, end_date=end_date)\n results = GsDataApi.query_data(query=query, dataset_id=ReportDataset.PPA_DATASET.value)\n return pd.DataFrame(results) if return_format == ReturnFormat.DATA_FRAME else results\n\n def get_pnl_measure(self, field: str, unit: FactorRiskUnit, start_date: dt.date, end_date: dt.date):\n measure = self.get_measure(field, start_date, end_date)\n if unit == FactorRiskUnit.Notional:\n return measure\n else:\n aggregated_pnl = get_pnl_percent(self, measure, field, start_date, end_date)\n return pd.merge(measure.drop(columns=[field]), aggregated_pnl, left_on='date', right_index=True).rename(\n columns={'return': field}\n )\n\n def get_many_measures(\n self,\n measures: Tuple[str, ...] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"\n Get many historical portfolio metrics\n\n :param measures: a list of metrics\n :param start_date: start date\n :param end_date: end date\n :return: returns a Pandas DataFrame with the results\n \"\"\"\n if measures is None:\n measures = []\n fields = tuple(measure for measure in measures)\n where = {'reportId': self.id}\n query = DataQuery(where=where, fields=fields, start_date=start_date, end_date=end_date)\n results = GsDataApi.query_data(query=query, dataset_id=ReportDataset.PPA_DATASET.value)\n return pd.DataFrame(results) if return_format == ReturnFormat.DATA_FRAME else results\n\n def get_aum_source(self) -> RiskAumSource:\n \"\"\"\n Get AUM Source for the portfolio associated with the performance report\n\n :return: aum source\n \"\"\"\n portfolio = GsPortfolioApi.get_portfolio(self.position_source_id)\n return portfolio.aum_source if portfolio.aum_source is not None else RiskAumSource.Long\n\n def set_aum_source(self, aum_source: RiskAumSource):\n \"\"\"\n Set AUM Source for the portfolio associated with the performance report\n\n :param aum_source: aum source for portfolio\n :return: aum source\n \"\"\"\n portfolio = GsPortfolioApi.get_portfolio(self.position_source_id)\n portfolio.aum_source = aum_source\n GsPortfolioApi.update_portfolio(portfolio)\n\n def get_custom_aum(self, start_date: dt.date = None, end_date: dt.date = None) -> List[CustomAUMDataPoint]:\n \"\"\"\n Get AUM data for performance report\n\n :param start_date: start date\n :param end_date: end date\n :return: list of AUM data between the specified range\n \"\"\"\n aum_data = GsReportApi.get_custom_aum(self.id, start_date, end_date)\n return [\n CustomAUMDataPoint(date=dt.datetime.strptime(data['date'], '%Y-%m-%d'), aum=data['aum'])\n for data in aum_data\n ]\n\n def get_aum(self, start_date: dt.date, end_date: dt.date):\n \"\"\"\n Get AUM data for performance report\n\n :param start_date: start date\n :param end_date: end date\n :return: dictionary of dates with corresponding AUM values\n \"\"\"\n aum_source = self.get_aum_source()\n if aum_source == RiskAumSource.Custom_AUM:\n aum = self.get_custom_aum(start_date=start_date, end_date=end_date)\n return {aum_point.date.strftime('%Y-%m-%d'): aum_point.aum for aum_point in aum}\n if aum_source == RiskAumSource.Long:\n aum = self.get_long_exposure(start_date=start_date, end_date=end_date)\n return {row['date']: row['longExposure'] for index, row in aum.iterrows()}\n if aum_source == RiskAumSource.Short:\n aum = self.get_short_exposure(start_date=start_date, end_date=end_date)\n return {row['date']: row['shortExposure'] for index, row in aum.iterrows()}\n if aum_source == RiskAumSource.Gross:\n aum = self.get_gross_exposure(start_date=start_date, end_date=end_date)\n return {row['date']: row['grossExposure'] for index, row in aum.iterrows()}\n if aum_source == RiskAumSource.Net:\n aum = self.get_net_exposure(start_date=start_date, end_date=end_date)\n return {row['date']: row['netExposure'] for index, row in aum.iterrows()}\n\n def upload_custom_aum(self, aum_data: List[CustomAUMDataPoint], clear_existing_data: bool = None):\n \"\"\"\n Add AUM data for portfolio corresponding to the performance report\n\n :param aum_data: list of AUM data to upload\n :param clear_existing_data: delete all previously uploaded AUM data for the portfolio (defaults to false)\n \"\"\"\n formatted_aum_data = [{'date': data.date.strftime('%Y-%m-%d'), 'aum': data.aum} for data in aum_data]\n GsReportApi.upload_custom_aum(self.id, formatted_aum_data, clear_existing_data)\n\n def get_positions_data(\n self,\n start: dt.date = None,\n end: dt.date = dt.date.today(),\n fields: [str] = None,\n include_all_business_days: bool = False,\n position_type: PositionType = None,\n ) -> List[Dict]:\n return GsPortfolioApi.get_positions_data(\n self.position_source_id,\n start,\n end,\n fields,\n performance_report_id=self.id,\n include_all_business_days=include_all_business_days,\n position_type=position_type,\n )\n raise NotImplementedError\n\n def get_portfolio_constituents(\n self,\n fields: List[str] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n prefer_rebalance_positions: bool = False,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"\n Get historical portfolio constituents\n\n :param fields: list of fields to include in the results\n :param start_date: start date\n :param end_date: end date\n :param prefer_rebalance_positions: If both Holding and Rebalance entries are present, prefer rebalance entries\n :param return_format: return format; defaults to a Pandas DataFrame, but can be manually\n set to ReturnFormat.JSON\n :return: Portfolio constituent data for each day in the requested date range\n \"\"\"\n where = {'reportId': self.id}\n asset_count = self.get_asset_count(start_date, end_date)\n if asset_count.empty:\n return pd.DataFrame() if return_format == ReturnFormat.DATA_FRAME else {}\n date_batches = _get_ppaa_batches(asset_count, 3000000)\n queries = [\n DataQuery(where=where, fields=fields, start_date=dates_batch[0], end_date=dates_batch[1])\n for dates_batch in date_batches\n ]\n results = [\n GsDataApi.query_data(query=query, dataset_id=ReportDataset.PORTFOLIO_CONSTITUENTS.value)\n for query in queries\n ]\n results = sum(results, [])\n if prefer_rebalance_positions:\n rebalance_dates = set()\n for result in results:\n if result['entryType'] == 'Rebalance':\n rebalance_dates.add(result['date'])\n\n results = [\n result\n for result in results\n if result['date'] not in rebalance_dates or result['entryType'] == 'Rebalance'\n ]\n return pd.DataFrame(results) if return_format == ReturnFormat.DATA_FRAME else results\n\n def get_pnl_contribution(\n self, start_date: dt.date = None, end_date: dt.date = None, currency: Currency = None\n ) -> pd.DataFrame:\n \"\"\"\n Get PnL Contribution broken down by constituents\n\n :param start_date: optional start date\n :param end_date: optional end date\n :param currency: optional currency; defaults to your portfolio's currency\n :return: a Pandas DataFrame of results\n \"\"\"\n return pd.DataFrame(\n GsPortfolioApi.get_attribution(self.position_source_id, start_date, end_date, currency, self.id)\n )\n\n def get_brinson_attribution(\n self,\n benchmark: str = None,\n currency: Currency = None,\n include_interaction: bool = False,\n aggregation_type: AttributionAggregationType = AttributionAggregationType.Arithmetic,\n aggregation_category: AggregationCategoryType = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"\n Get PnL analytics called Brinson Attribution\n\n :param benchmark: benchmark's unique Marquee identifier\n :param currency: currency of results; if none passed results default to your portfolio's currency\n :param include_interaction: show interaction independent of security selection\n :param aggregation_type: either artihmetic or geometric\n :param aggregation_category: aggregate results by sector, industry, region or country name\n :param start_date: start date\n :param end_date: end date\n :param return_format: return format; defaults to a Pandas DataFrame, but can be manually\n set to ReturnFormat.JSON\n :return: Portfolio Brinson Attribution data for the requested date range\n\n **Examples**\n\n >>> brinson_attribution_results = performance_report.get_brinson_attribution (\n >>> benchmark=asset.get_marquee_id(),\n >>> include_interaction=True,\n >>> start_date=performance_report.earliest_start_date,\n >>> end_date=performance_report.latest_end_date,\n >>> )\n >>> display(pd.DataFrame(brinson_attribution_results))\n \"\"\"\n results = GsReportApi.get_brinson_attribution_results(\n portfolio_id=self.position_source_id,\n benchmark=benchmark,\n currency=currency,\n include_interaction=include_interaction,\n aggregation_type=aggregation_type.value,\n aggregation_category=aggregation_category.value if aggregation_category else None,\n start_date=start_date,\n end_date=end_date,\n )\n if return_format == ReturnFormat.DATA_FRAME:\n rows = results.get('results')\n rows_data_frame = pd.DataFrame(rows)\n rows_data_frame = rows_data_frame.rename(columns=lambda c: titleize(c))\n return rows_data_frame\n return results\n\n\nclass FactorRiskReport(Report):\n \"\"\"\n Historical analyses on both the risk and attribution of a portfolio or asset to various factors determined by the\n specified risk model\n \"\"\"\n\n def __init__(\n self,\n risk_model_id: str = None,\n fx_hedged: bool = True,\n benchmark_id: str = None,\n report_id: str = None,\n name: str = None,\n position_source_id: str = None,\n position_source_type: Union[str, PositionSourceType] = None,\n report_type: Union[str, ReportType] = None,\n earliest_start_date: dt.date = None,\n latest_end_date: dt.date = None,\n latest_execution_time: dt.datetime = None,\n status: Union[str, ReportStatus] = ReportStatus.new,\n percentage_complete: float = None,\n tags: Tuple[PositionTag, ...] = None,\n **kwargs,\n ):\n \"\"\"\n Historical analyses on both the risk and attribution of a portfolio or asset to various factors determined by\n the specified risk model\n\n :param risk_model_id: risk model ID\n :param fx_hedged: if position source is FX hedged\n :param benchmark_id: optional benchmark asset ID to include in results\n :param report_id: Marquee report ID\n :param name: report name\n :param position_source_id: position source ID\n :param position_source_type: position source (i.e. 'Portfolio', 'Asset', or 'Hedge')\n :param report_type: report type (i.e. 'Asset Factor Risk' or 'Portfolio Factor Risk')\n :param earliest_start_date: start date of report\n :param latest_end_date: end date of report\n :param latest_execution_time: date of the latest execution\n :param status: status of of report (i.e. 'ready', 'executing', or 'done')\n :param percentage_complete: percent of the report that is complete\n :param tags: tags of the report\n\n **Examples**\n\n >>> risk_report = FactorRiskReport(\n >>> risk_model_id='RISKMODELID',\n >>> fx_hedged=True,\n >>> benchmark_id=benchmark.get_marquee_id(),\n >>> position_source_id='PORTFOLIOID',\n >>> position_source_type=PositionSourceType.Portfolio\n >>> )\n \"\"\"\n if position_source_id and not position_source_type:\n position_source_type = (\n PositionSourceType.Portfolio if position_source_id.startswith('MP') else PositionSourceType.Asset\n )\n\n if position_source_type and not report_type:\n report_type = (\n ReportType.Portfolio_Factor_Risk\n if position_source_type is PositionSourceType.Portfolio\n else ReportType.Asset_Factor_Risk\n )\n\n super().__init__(\n report_id,\n name,\n position_source_id,\n position_source_type,\n report_type,\n ReportParameters(risk_model=risk_model_id, fx_hedged=fx_hedged, benchmark=benchmark_id, tags=tags),\n earliest_start_date,\n latest_end_date,\n latest_execution_time,\n status,\n percentage_complete,\n )\n\n @classmethod\n def get(cls, report_id: str, **kwargs):\n \"\"\"\n Get a factor risk report from the unique report identifier\n\n :param report_id: Marquee report ID\n :return: returns a FactorRiskReport object that correlates to the Marquee report\n \"\"\"\n return cls.from_target(GsReportApi.get_report(report_id))\n\n @classmethod\n def from_target(cls, report: TargetReport):\n if report.type not in [ReportType.Portfolio_Factor_Risk, ReportType.Asset_Factor_Risk]:\n raise MqValueError('This report is not a factor risk report.')\n return FactorRiskReport(\n risk_model_id=report.parameters.risk_model,\n fx_hedged=report.parameters.fx_hedged,\n benchmark_id=report.parameters.benchmark,\n report_id=report.id,\n position_source_id=report.position_source_id,\n position_source_type=report.position_source_type,\n report_type=report.type,\n earliest_start_date=report.earliest_start_date,\n latest_end_date=report.latest_end_date,\n status=report.status,\n percentage_complete=report.percentage_complete,\n tags=report.parameters.tags,\n )\n\n def get_risk_model_id(self) -> str:\n \"\"\"\n :return: the ID of the risk model associated with the factor risk report\n \"\"\"\n return self.parameters.risk_model\n\n def get_benchmark_id(self) -> str:\n \"\"\"\n :return: the unique Marquee identifier of the benchmark associated with the factor risk report\n \"\"\"\n return self.parameters.benchmark\n\n def get_results(\n self,\n mode: FactorRiskResultsMode = FactorRiskResultsMode.Portfolio,\n factors: List[str] = None,\n factor_categories: List[str] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n unit: FactorRiskUnit = FactorRiskUnit.Notional,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"\n Get the raw results associated with the factor risk report\n\n :param mode: results mode; defaults to the portfolio level\n :param factors: optional list of factors; defaults to all of them\n :param factor_categories: optional list of factor categories; defaults to all of them\n :param start_date: start date\n :param end_date: end date\n :param currency: currency\n :param return_format: return format; defaults to a Pandas DataFrame, but can be manually\n set to ReturnFormat.JSON\n :param: unit: return the results in terms of notional or percent (defaults to notional)\n :return: risk report results\n\n **Examples**\n\n >>> factor_and_total_results = risk_report.get_results(\n >>> factors=['Factor', 'Specific'],\n >>> start_date=dt.date(2022, 1, 1),\n >>> end_date=dt.date(2021, 1, 1)\n >>> )\n >>> print(factor_and_total_results)\n \"\"\"\n results = GsReportApi.get_factor_risk_report_results(\n risk_report_id=self.id,\n view=mode.value,\n factors=factors,\n factor_categories=factor_categories,\n currency=currency,\n start_date=start_date,\n end_date=end_date,\n unit=unit.value,\n )\n return pd.DataFrame(results) if return_format == ReturnFormat.DATA_FRAME else results\n\n def get_view(\n self,\n factor: str = None,\n factor_category: str = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n unit: FactorRiskUnit = FactorRiskUnit.Notional,\n ) -> Dict:\n \"\"\"\n Get the results associated with the factor risk report as seen on the Marquee user interface\n\n :param factor: optional factor name\n :param factor_category: optional factor category\n :param start_date: start date\n :param end_date: end date\n :param currency: currency\n :param: unit: return the results in terms of notional or percent (defaults to notional)\n :return: risk report results\n\n\n\n **Examples**\n\n >>> category_table = risk_report.get_view(\n >>> start_date=risk_report.latest_end_date,\n >>> end_date=risk_report.latest_end_date,\n >>> unit=FactorRiskUnit.Notional\n >>> ).get('factorCategoriesTable')\n >>> category_df = pd.DataFrame(category_table).filter(items=[\n >>> 'name',\n >>> 'proportionOfRisk',\n >>> 'marginalContributionToRiskPercent',\n >>> 'relativeMarginalContributionToRisk',\n >>> 'exposure',\n >>> 'avgProportionOfRisk'\n >>> ])\n >>> display(category_df)\n \"\"\"\n return GsReportApi.get_factor_risk_report_view(\n risk_report_id=self.id,\n factor=factor,\n factor_category=factor_category,\n currency=currency,\n start_date=start_date,\n end_date=end_date,\n unit=unit.value,\n )\n\n def get_table(\n self,\n mode: FactorRiskTableMode,\n factors: List[str] = None,\n factor_categories: List[str] = None,\n date: dt.date = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n unit: FactorRiskUnit = None,\n currency: Currency = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"\n Get the results associated with the factor risk report formatted for the asset level table on the interface\n\n :param mode: tables mode\n :param factors: optional list of factors to filter by; defaults to all\n :param factor_categories: optional list of factor categories to filter by; defaults to all\n :param date: date for modes requiring snapshot data (defaults to the latest available date)\n :param start_date: start date for modes requiring date range (defaults to 1 month before the end date)\n :param end_date: end date for modes requiring date range (defaults to the latest available date)\n :param unit: return the results in terms of notional or percent (defaults to notional)\n :param currency: currency\n :return: risk report table at asset level\n\n **Examples**\n\n >>> pnl_table = risk_report.get_table(\n >>> mode=FactorRiskTableMode.Pnl,\n >>> start_date=risk_report.earliest_start_date,\n >>> end_date=risk_report.latest_end_date\n >>> )\n >>> display(pd.DataFrame(pnl_table))\n \"\"\"\n # setting default values to start_date and end_date if they are None\n if start_date is None and end_date is None:\n start_date = (\n self.latest_end_date - relativedelta(months=1)\n if mode == FactorRiskTableMode.Pnl\n else self.latest_end_date\n )\n end_date = self.latest_end_date\n elif start_date is None:\n start_date = end_date - relativedelta(months=1) if mode == FactorRiskTableMode.Pnl else end_date\n elif end_date is None:\n end_date = start_date if mode != FactorRiskTableMode.Pnl else self.latest_end_date\n\n table = GsReportApi.get_factor_risk_report_table(\n risk_report_id=self.id,\n mode=mode,\n unit=unit.value if unit else None,\n currency=currency,\n date=date,\n start_date=start_date,\n end_date=end_date,\n )\n if 'table' not in table and 'warning' in table:\n raise MqValueError(table.get('warning'))\n if return_format == ReturnFormat.DATA_FRAME:\n column_info = table.get('table').get('metadata').get('columnInfo')\n column_info[0].update({'columns': ['name', 'symbol', 'sector']})\n rows = table.get('table').get('rows')\n sorted_columns = _filter_table_by_factor_and_category(column_info, factors, factor_categories)\n sorted_columns = list(OrderedDict.fromkeys(sorted_columns))\n rows_data_frame = pd.DataFrame(rows)\n rows_data_frame = rows_data_frame.reindex(columns=sorted_columns)\n rows_data_frame = rows_data_frame.set_index('name')\n return rows_data_frame\n return table\n\n def get_factor_pnl(\n self,\n mode: FactorRiskResultsMode = FactorRiskResultsMode.Portfolio,\n factor_names: List[str] = None,\n factor_categories: List[str] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n unit: FactorRiskUnit = FactorRiskUnit.Notional,\n ) -> pd.DataFrame:\n \"\"\"\n Get historical factor PnL\n\n :param mode: results mode; defaults to the portfolio level\n :param factor_names: optional list of factor names; defaults to all of them\n :param factor_categories: optional list of factor categories; defaults to all of them\n :param start_date: start date\n :param end_date: end date\n :param currency: currency\n :param unit: return the results in terms of notional or percent (defaults to notional)\n :return: a Pandas DataFrame with the results\n \"\"\"\n\n factor_names_to_query = factor_names\n if unit == FactorRiskUnit.Percent and factor_names_to_query is not None:\n factor_names_to_query.append('Total')\n\n factor_data = self.get_results(\n mode=mode,\n factors=factor_names_to_query,\n factor_categories=factor_categories,\n start_date=start_date,\n end_date=end_date,\n currency=currency,\n return_format=ReturnFormat.JSON,\n unit=unit if self.position_source_type != PositionSourceType.Portfolio else FactorRiskUnit.Notional,\n )\n\n if unit == FactorRiskUnit.Notional or self.position_source_type != PositionSourceType.Portfolio:\n return _format_multiple_factor_table(factor_data, 'pnl')\n else:\n if factor_names is None:\n factor_names = list(set([x.get('factor') for x in factor_data]))\n\n all_reports = GsPortfolioApi.get_reports(self.position_source_id, None)\n performance_reports = [\n PerformanceReport.get(r.id)\n for r in all_reports\n if r.type_ == ReportType.Portfolio_Performance_Analytics\n ]\n performance_report = [r for r in performance_reports if r.parameters.tags == self.parameters.tags][0]\n aum_df = format_aum_for_return_calculation(performance_report, start_date, end_date)\n\n total_data = [d for d in factor_data if d.get('factor') == 'Total']\n # Total pnl must be retieved when using smoothening to calculate Pnl %\n if len(total_data) == 0:\n total_data = self.get_results(\n mode=mode,\n factors=['Total'],\n start_date=start_date,\n end_date=end_date,\n currency=currency,\n return_format=ReturnFormat.JSON,\n unit=FactorRiskUnit.Notional,\n )\n\n smoothened_factor_data = {}\n for factor_name in factor_names:\n selected_factor_data = [d for d in factor_data if d.get('factor') == factor_name]\n start_date = dt.datetime.strptime(min([d['date'] for d in selected_factor_data]), '%Y-%m-%d').date()\n smoothened_factor_data[factor_name] = get_factor_pnl_percent_for_single_factor(\n selected_factor_data, total_data, aum_df, start_date\n )\n\n result = pd.DataFrame(smoothened_factor_data).reset_index().rename(columns={'date': 'Date'})\n return result.loc[result['Date'] >= start_date.strftime(\"%Y-%m-&d\")]\n\n def get_factor_exposure(\n self,\n mode: FactorRiskResultsMode = FactorRiskResultsMode.Portfolio,\n factor_names: List[str] = None,\n factor_categories: List[str] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n unit: FactorRiskUnit = FactorRiskUnit.Notional,\n ) -> pd.DataFrame:\n \"\"\"\n Get historical factor exposure\n\n :param mode: results mode; defaults to the portfolio level\n :param factor_names: optional list of factor names; defaults to all of them\n :param factor_categories: optional list of factor categories; defaults to all of them\n :param start_date: start date\n :param end_date: end date\n :param currency: currency\n :param: unit: return the results in terms of notional or percent (defaults to notional)\n :return: a Pandas DataFrame with the results\n \"\"\"\n factor_data = self.get_results(\n mode=mode,\n factors=factor_names,\n factor_categories=factor_categories,\n start_date=start_date,\n end_date=end_date,\n currency=currency,\n return_format=ReturnFormat.JSON,\n unit=unit,\n )\n return _format_multiple_factor_table(factor_data, 'exposure')\n\n def get_factor_proportion_of_risk(\n self,\n factor_names: List[str] = None,\n factor_categories: List[str] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n ) -> pd.DataFrame:\n \"\"\"\n Get historical factor proportion of risk\n\n :param factor_names: optional list of factor names; defaults to all of them\n :param factor_categories: optional list of factor categories; defaults to all of them\n :param start_date: start date\n :param end_date: end date\n :param currency: currency\n :return: a Pandas DataFrame with the results\n \"\"\"\n factor_data = self.get_results(\n factors=factor_names,\n factor_categories=factor_categories,\n start_date=start_date,\n end_date=end_date,\n currency=currency,\n return_format=ReturnFormat.JSON,\n )\n return _format_multiple_factor_table(factor_data, 'proportionOfRisk')\n\n def get_annual_risk(\n self,\n factor_names: List[str] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n ) -> pd.DataFrame:\n \"\"\"\n Get historical annual risk\n\n :param factor_names: optional list of factor names; must be from the following: \"Factor\", \"Specific\", \"Total\"\n :param start_date: start date\n :param end_date: end date\n :param currency: currency\n :return: a Pandas DataFrame with the results\n \"\"\"\n factor_data = self.get_results(\n factors=factor_names,\n start_date=start_date,\n end_date=end_date,\n currency=currency,\n return_format=ReturnFormat.JSON,\n )\n return _format_multiple_factor_table(factor_data, 'annualRisk')\n\n def get_daily_risk(\n self,\n factor_names: List[str] = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n ) -> pd.DataFrame:\n \"\"\"\n Get historical daily risk\n\n :param factor_names: optional list of factor names; must be from the following: \"Factor\", \"Specific\", \"Total\"\n :param start_date: start date\n :param end_date: end date\n :param currency: currency\n :return: a Pandas DataFrame with the results\n \"\"\"\n factor_data = self.get_results(\n factors=factor_names,\n start_date=start_date,\n end_date=end_date,\n currency=currency,\n return_format=ReturnFormat.JSON,\n )\n return _format_multiple_factor_table(factor_data, 'dailyRisk')\n\n def get_ex_ante_var(\n self,\n confidence_interval: float = 95.0,\n start_date: dt.date = None,\n end_date: dt.date = None,\n currency: Currency = None,\n ) -> pd.DataFrame:\n \"\"\"\n Get ex-ante Value at Risk as defined by the risk model\n\n :param confidence_interval: the VaR confidence interval as a percent\n :param start_date: start date\n :param end_date: end date\n :param currency: currency\n :return: a Pandas DataFrame with the results\n \"\"\"\n factor_data = self.get_results(\n factors=['Total'],\n start_date=start_date,\n end_date=end_date,\n currency=currency,\n return_format=ReturnFormat.JSON,\n )\n z_score = st.norm.ppf(confidence_interval / 100)\n for data in factor_data:\n data['var'] = data['dailyRisk'] * z_score\n return _format_multiple_factor_table(factor_data, 'var')\n\n\ndef _format_multiple_factor_table(factor_data: List[Dict], key: str) -> pd.DataFrame:\n formatted_data = {}\n for data in factor_data:\n date = data['date']\n if date in formatted_data:\n formatted_data[date][data['factor']] = data[key]\n else:\n formatted_data[date] = {'Date': date, data['factor']: data[key]}\n\n return pd.DataFrame(formatted_data.values())\n\n\nclass ThematicReport(Report):\n \"\"\"\n Historical analyses on the exposure of a portfolio to various GS Flagship Thematic baskets over a date range\n \"\"\"\n\n def __init__(\n self,\n report_id: str = None,\n name: str = None,\n position_source_id: str = None,\n parameters: ReportParameters = None,\n position_source_type: Union[str, PositionSourceType] = None,\n report_type: Union[str, ReportType] = None,\n earliest_start_date: dt.date = None,\n latest_end_date: dt.date = None,\n latest_execution_time: dt.datetime = None,\n status: Union[str, ReportStatus] = ReportStatus.new,\n percentage_complete: float = None,\n **kwargs,\n ):\n \"\"\"\n Historical analyses on the exposure of a portfolio to various GS Flagship Thematic baskets over a date range\n\n :param report_id: Marquee report ID\n :param name: report name\n :param position_source_id: position source ID\n :param parameters: parameters of the report\n :param position_source_type: position source (i.e. 'Portfolio', 'Asset', or 'Hedge')\n :param report_type: report type (i.e. 'Portfolio Thematic Analytics' or 'Asset Thematic Analytics')\n :param earliest_start_date: start date of report\n :param latest_end_date: end date of report\n :param latest_execution_time: date of the latest execution\n :param status: status of of report (i.e. 'ready', 'executing', or 'done')\n :param percentage_complete: percent of the report that is complete\n\n **Examples**\n\n >>> thematic_report = ThematicReport(\n >>> report_id='REPORTID',\n >>> position_source_type=PositionSourceType.Portfolio,\n >>> position_source_id='PORTFOLIOID',\n >>> parameters=None\n >>> )\n \"\"\"\n if position_source_id and not position_source_type:\n position_source_type = (\n PositionSourceType.Portfolio if position_source_id.startswith('MP') else PositionSourceType.Asset\n )\n\n if position_source_type and not report_type:\n report_type = (\n ReportType.Portfolio_Thematic_Analytics\n if (position_source_type is PositionSourceType.Portfolio)\n else (ReportType.Asset_Thematic_Analytics)\n )\n\n super().__init__(\n report_id,\n name,\n position_source_id,\n position_source_type,\n report_type,\n parameters,\n earliest_start_date,\n latest_end_date,\n latest_execution_time,\n status,\n percentage_complete,\n )\n\n @classmethod\n def get(cls, report_id: str, **kwargs):\n \"\"\"\n Get a thematic report from the unique report identifier\n\n :param report_id: Marquee report ID\n :return: returns a ThematicReport object that correlates to the Marquee report\n \"\"\"\n return cls.from_target(GsReportApi.get_report(report_id))\n\n @classmethod\n def from_target(cls, report: TargetReport):\n if report.type not in [ReportType.Portfolio_Thematic_Analytics, ReportType.Asset_Thematic_Analytics]:\n raise MqValueError('This report is not a thematic report.')\n return ThematicReport(\n report_id=report.id,\n name=report.name,\n position_source_id=report.position_source_id,\n parameters=report.parameters,\n position_source_type=report.position_source_type,\n report_type=report.type,\n earliest_start_date=report.earliest_start_date,\n latest_end_date=report.latest_end_date,\n latest_execution_time=report.latest_execution_time,\n status=report.status,\n percentage_complete=report.percentage_complete,\n )\n\n def get_thematic_data(\n self, start_date: dt.date = None, end_date: dt.date = None, basket_ids: List[str] = None\n ) -> pd.DataFrame:\n \"\"\"\n Get all results from the thematic report for a date range\n\n :param start_date: start date\n :param end_date: end date\n :param basket_ids: optional list of thematic basket IDs to include; defaults to all of them\n :return: a Pandas DataFrame with results\n \"\"\"\n\n results = self._get_measures(\n [\"thematicExposure\", \"grossExposure\"], start_date, end_date, basket_ids, ReturnFormat.JSON\n )\n for result in results:\n result['thematicBeta'] = result['thematicExposure'] / result['grossExposure']\n return pd.DataFrame(results).filter(items=['date', 'thematicExposure', 'thematicBeta'])\n\n def get_thematic_exposure(\n self, start_date: dt.date = None, end_date: dt.date = None, basket_ids: List[str] = None\n ) -> pd.DataFrame:\n \"\"\"\n Get portfolio historical exposure to GS Flagship Thematic baskets\n\n :param start_date: start date\n :param end_date: end date\n :param basket_ids: optional list of thematic basket IDs to include; defaults to all of them\n :return: a Pandas DataFrame with results\n \"\"\"\n return self._get_measures([\"thematicExposure\"], start_date, end_date, basket_ids)\n\n def get_thematic_betas(\n self, start_date: dt.date = None, end_date: dt.date = None, basket_ids: List[str] = None\n ) -> pd.DataFrame:\n \"\"\"\n Get portfolio historical beta to GS Flagship Thematic baskets\n\n :param start_date: start date\n :param end_date: end date\n :param basket_ids: optional list of thematic basket IDs to include; defaults to all of them\n :return: a Pandas DataFrame with results\n \"\"\"\n results = self._get_measures(\n [\"thematicExposure\", \"grossExposure\"], start_date, end_date, basket_ids, ReturnFormat.JSON\n )\n for result in results:\n result['thematicBeta'] = result['thematicExposure'] / result['grossExposure']\n result.pop('thematicExposure')\n result.pop('grossExposure')\n return pd.DataFrame(results)\n\n def _get_measures(\n self,\n fields: List,\n start_date: dt.date = None,\n end_date: dt.date = None,\n basket_ids: List[str] = None,\n return_format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n where = {'reportId': self.id}\n if basket_ids:\n where['basketId'] = basket_ids\n dataset = (\n ReportDataset.PTA_DATASET.value\n if self.position_source_type == PositionSourceType.Portfolio\n else ReportDataset.ATA_DATASET.value\n )\n query = DataQuery(where=where, fields=fields, start_date=start_date, end_date=end_date)\n results = GsDataApi.query_data(query=query, dataset_id=dataset)\n return pd.DataFrame(results) if return_format == ReturnFormat.DATA_FRAME else results\n\n def get_all_thematic_exposures(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n basket_ids: List[str] = None,\n regions: List[Region] = None,\n ) -> pd.DataFrame:\n \"\"\"\n Get all portfolio thematic analytics for GS Flagshop Thematic baskets\n\n :param start_date: start date\n :param end_date: end date\n :param basket_ids: optional list of thematic basket IDs to include; defaults to all of them\n :param regions: regions by which to filter flagship thematic baskets; defaults to all regions\n :return: a Pandas DataFrame with results\n \"\"\"\n results = GsThematicApi.get_thematics(\n entity_id=self.position_source_id,\n start_date=start_date,\n end_date=end_date,\n basket_ids=basket_ids,\n regions=regions,\n measures=[ThematicMeasure.ALL_THEMATIC_EXPOSURES],\n )\n return flatten_results_into_df(results)\n\n def get_top_five_thematic_exposures(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n basket_ids: List[str] = None,\n regions: List[Region] = None,\n ) -> pd.DataFrame:\n \"\"\"\n Get portfolio thematic analytics for the five GS Flagship Thematic baskets with the highest thematic exposures\n\n :param start_date: start date\n :param end_date: end date\n :param basket_ids: optional list of thematic basket IDs to include; defaults to all of them\n :param regions: regions by which to filter flagship thematic baskets; defaults to all regions\n :return: a Pandas DataFrame with the top 5 results\n \"\"\"\n results = GsThematicApi.get_thematics(\n entity_id=self.position_source_id,\n start_date=start_date,\n end_date=end_date,\n basket_ids=basket_ids,\n regions=regions,\n measures=[ThematicMeasure.TOP_FIVE_THEMATIC_EXPOSURES],\n )\n return flatten_results_into_df(results)\n\n def get_bottom_five_thematic_exposures(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n basket_ids: List[str] = None,\n regions: List[Region] = None,\n ) -> pd.DataFrame:\n \"\"\"\n Get portfolio thematic analytics for the five GS Flagship Thematic baskets with the lowest thematic exposures\n\n :param start_date: start date\n :param end_date: end date\n :param basket_ids: optional list of thematic basket IDs to include; defaults to all of them\n :param regions: regions by which to filter flagship thematic baskets; defaults to all regions\n :return: a Pandas DataFrame with the bottom 5 results\n \"\"\"\n results = GsThematicApi.get_thematics(\n entity_id=self.position_source_id,\n start_date=start_date,\n end_date=end_date,\n basket_ids=basket_ids,\n regions=regions,\n measures=[ThematicMeasure.BOTTOM_FIVE_THEMATIC_EXPOSURES],\n )\n return flatten_results_into_df(results)\n\n def get_thematic_breakdown(self, date: dt.date, basket_id: str) -> pd.DataFrame:\n \"\"\"\n Get a by-asset breakdown of a portfolio or basket's thematic exposure to a particular flagship basket on a\n particular date\n\n :param date: date\n :param basket_id: GS flagship basket's unique Marquee ID\n :return: a Pandas DataFrame with results\n \"\"\"\n return get_thematic_breakdown_as_df(entity_id=self.position_source_id, date=date, basket_id=basket_id)\n\n\ndef get_thematic_breakdown_as_df(entity_id: str, date: dt.date, basket_id: str) -> pd.DataFrame:\n \"\"\"\n Get a by-asset breakdown of a portfolio or basket's thematic exposure to a particular flagship basket on a\n particular data and return as a Pandas DataFrame\n\n :param entity_id: position source ID; i.e. portfolio or basket\n :param date: date\n :param basket_id: GS flagship basket's unique Marquee ID\n :return: a Pandas DataFrame with results\n \"\"\"\n results = GsThematicApi.get_thematics(\n entity_id=entity_id,\n start_date=date,\n end_date=date,\n basket_ids=[basket_id],\n measures=[ThematicMeasure.THEMATIC_BREAKDOWN_BY_ASSET],\n )\n breakdown = (\n results[0]\n .get(ThematicMeasure.THEMATIC_BREAKDOWN_BY_ASSET.value, [{}])[0]\n .get(ThematicMeasure.THEMATIC_BREAKDOWN_BY_ASSET.value, [])\n )\n formatted_breakdown = []\n for data in breakdown:\n formatted_data = {titleize(k): data[k] for k in data}\n formatted_breakdown.append(formatted_data)\n return pd.DataFrame(formatted_breakdown)\n\n\ndef flatten_results_into_df(results: List):\n \"\"\"\n Flatten a list of thematic data into a Pandas DataFrame\n\n :param results: a list of thematic data by date\n :return: Pandas DataFrame with all results formatted\n \"\"\"\n all_results = []\n for result in results:\n date = result['date']\n for key in result:\n if isinstance(result[key], list):\n for thematic_data in result[key]:\n all_results.append({'Date': date, **{titleize(k): thematic_data[k] for k in thematic_data}})\n all_results = pd.DataFrame(all_results).rename(columns={'Basket': 'Basket Id'})\n return pd.DataFrame(all_results)\n\n\ndef get_pnl_percent(\n performance_report: PerformanceReport,\n pnl_df: pd.DataFrame,\n field: str,\n start_date: dt.datetime.date,\n end_date: dt.datetime.date,\n):\n aum_df = format_aum_for_return_calculation(performance_report, start_date, end_date)\n is_first_data_point_on_start_date = pnl_df['date'].iloc[[0]].values[0] == start_date.strftime('%Y-%m-%d')\n return_series = generate_daily_returns(aum_df, pnl_df, 'aum', field, is_first_data_point_on_start_date)\n return (return_series.add(1).cumprod() - 1).multiply(100)\n\n\ndef get_factor_pnl_percent_for_single_factor(factor_data, total_data, aum_df, start_date):\n pnl_df = format_factor_pnl_for_return_calculation(factor_data, total_data)\n is_start_date_first_data_point = pnl_df['date'].iloc[[0]].values[0] == start_date.strftime('%Y-%m-%d')\n return generate_daily_returns(aum_df, pnl_df, 'aum', 'pnl', is_start_date_first_data_point)\n\n\ndef format_factor_pnl_for_return_calculation(factor_data: list, total_data: list):\n pnl_df = pd.DataFrame(factor_data)[['date', 'pnl']]\n total_returns_df = pd.DataFrame(total_data)[['date', 'pnl']]\n total_returns_df = total_returns_df.rename(columns={'pnl': 'totalPnl'})\n pnl_df = pd.merge(pnl_df, total_returns_df, how='inner', on=['date'])\n return pnl_df\n\n\ndef format_aum_for_return_calculation(\n performance_report: PerformanceReport, start_date: dt.datetime.date, end_date: dt.datetime.date\n):\n aum_as_dict = performance_report.get_aum(start_date=prev_business_date(start_date), end_date=end_date)\n aum_df = pd.DataFrame(aum_as_dict.items(), columns=['date', 'aum'])\n return aum_df\n\n\ndef generate_daily_returns(\n aum_df: pd.DataFrame, pnl_df: pd.DataFrame, aum_col_key: str, pnl_col_key: str, is_start_date_first_data_point: bool\n):\n # Returns are defined as Pnl today divided by AUM yesterday.\n if is_start_date_first_data_point:\n pnl_df.loc[0, pnl_col_key] = 0\n if 'totalPnl' in pnl_df.columns:\n pnl_df.loc[0, 'totalPnl'] = 0\n df = pd.merge(pnl_df, aum_df, how='outer', on='date')\n df = df.set_index('date')\n df = df.sort_index()\n df[aum_col_key] = df[aum_col_key].ffill()\n df['return'] = df[pnl_col_key].div(df[aum_col_key].shift(1))\n if 'totalPnl' in df.columns:\n df['totalPnl'] = df['totalPnl'].div(df[aum_col_key].shift(1))\n df = df.fillna(0)\n df['return'] = __smooth_percent_returns(df['return'].to_numpy(), df['totalPnl'].to_numpy()).tolist()\n return_series = pd.Series(df['return'], name=\"return\").dropna()\n return return_series\n\n\ndef __smooth_percent_returns(daily_factor_returns: np.array, daily_total_returns: np.array) -> np.array:\n \"\"\"\n When attribution (in weights) are decomposed among multiple factors (like a group of risk model factors or\n categories), simple geometric aggregation will not preserve additivity. In other words, the geometric sum of factor\n PnL from Factor and Specific will NOT add up to the factor PnL from Total. The Carino log linking formula\n calculates the coefficients for each node, and after that multiplication is done, the results can be aggregated\n to calculate cumulative PnL:\n https://rdrr.io/github/R-Finance/PortfolioAttribution/man/Carino.html\n\n βt = A * αt\n where βt is the linking coefficient on date t\n where A is the log scaling factor (which is constant throughout the timeseries)\n where αt is the perturbation factor on date t\n\n A = (Rp - Rb) / (ln(1 + Rp) - ln(1 + Rb)) where Rp is the total portfolio return ; Rb is the total benchmark return\n αt = (ln(1 + Rpt) - ln(1 + Rbt)) / (Rpt - Rbt) where Rpt is portfolio return on t and Rbp is benchmark return on t\n\n For this use case benchmark returns are set to 0 for every day.\n \"\"\"\n total_return = np.prod(daily_total_returns + 1) - 1\n log_scaling_factor = total_return / (np.log(1 + total_return)) if total_return != 0 else 1\n perturbation_factors = np.log(1 + daily_total_returns) / daily_total_returns\n perturbation_factors = np.nan_to_num(perturbation_factors, nan=1)\n return np.cumsum(daily_factor_returns * log_scaling_factor * perturbation_factors * 100)\n\n\ndef _filter_table_by_factor_and_category(column_info: Dict, factors: List, factor_categories: List):\n if factors is None and factor_categories is None:\n sorted_columns = []\n for column_group in column_info:\n sorted_columns = sorted_columns + column_group.get('columns')\n else:\n sorted_columns = column_info[0].get('columns') + column_info[1].get('columns')\n if factors is not None:\n sorted_columns = sorted_columns + factors\n if factor_categories is not None:\n for column_group in [\n column_group for column_group in column_info if column_group.get('columnGroup') in factor_categories\n ]:\n sorted_columns = sorted_columns + column_group.get('columns')\n return sorted_columns\n" }, { "path": "gs_quant/markets/report_utils.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport pandas as pd\nimport math\nfrom typing import List\nfrom pandas.tseries.offsets import BDay\n\n\ndef _get_ppaa_batches(asset_count: pd.DataFrame, max_row_limit: int) -> List[List[dt.date]]:\n start_row = asset_count.iloc[0]\n end_row = asset_count.iloc[-1]\n avg_positions = start_row['assetCount'] + end_row['assetCount'] / 2\n start_date = dt.datetime.strptime(start_row['date'], '%Y-%m-%d').date()\n end_date = dt.datetime.strptime(end_row['date'], '%Y-%m-%d').date()\n # multiply by 5 because of # fields: pnl, exposure, asset id, report id, date\n days_per_batch = math.ceil(max_row_limit / (avg_positions * 5))\n return _batch_dates(start_date, end_date, days_per_batch)\n\n\ndef _batch_dates(start_date: dt.date, end_date: dt.date, batch_size: int) -> List[List[dt.date]]:\n if (start_date - end_date).days < batch_size:\n return [[start_date, end_date]]\n date_list = []\n curr_end = start_date\n while end_date > curr_end:\n curr_end = (start_date + BDay(batch_size)).date()\n curr_end = curr_end if curr_end < end_date else end_date\n date_batches = [start_date, curr_end]\n date_list.append(date_batches)\n start_date = curr_end + dt.timedelta(days=1)\n return date_list\n" }, { "path": "gs_quant/markets/scenario.py", "content": "\"\"\"\nCopyright 2024 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.api.gs.scenarios import GsFactorScenarioApi\nfrom gs_quant.markets.factor import Factor\nfrom gs_quant.target.risk import Scenario as TargetScenario, FactorScenarioType\nfrom gs_quant.entities.entitlements import Entitlements\nfrom gs_quant.errors import MqValueError\nfrom enum import Enum\nfrom typing import List, Union, Dict\nfrom pydash import get\nfrom copy import deepcopy\n\nimport datetime as dt\nimport pandas as pd\n\n\nclass ScenarioCalculationType(Enum):\n FACTOR_SCENARIO = \"Factor Scenario\"\n\n\nclass FactorShock:\n \"\"\"Marquee Factor Shock\"\"\"\n\n def __init__(self, factor: Union[str, Factor], shock: float):\n self.__factor = factor\n self.__shock = shock\n\n def __eq__(self, other) -> bool:\n if not isinstance(other, FactorShock):\n return False\n\n factor_name = self.factor.name if isinstance(self.factor, Factor) else self.factor\n other_factor_name = other.factor.name if isinstance(other.factor, Factor) else other.factor\n\n return factor_name == other_factor_name and self.shock == other.shock\n\n def __repr__(self):\n return '%r(factor=%r, shock=%r)' % (self.__class__.__name__, self.factor, self.shock)\n\n @property\n def factor(self) -> Union[str, Factor]:\n \"\"\"Get factor being shocked\"\"\"\n return self.__factor\n\n @property\n def shock(self) -> float:\n \"\"\"Get factor being shocked\"\"\"\n return self.__shock\n\n @shock.setter\n def shock(self, shock: float):\n self.__shock = shock\n\n def to_dict(self):\n return {\"factor\": self.factor.name if isinstance(self.factor, Factor) else self.factor, \"shock\": self.shock}\n\n @classmethod\n def from_dict(cls, obj):\n return FactorShock(factor=obj.get(\"factor\"), shock=obj.get(\"shock\"))\n\n\nclass FactorShockParameters:\n def __init__(self, factor_shocks: List[FactorShock] = None, propagate_shocks: bool = None, risk_model: str = None):\n self.__factor_shocks = factor_shocks\n self.__propagate_shocks = propagate_shocks\n self.__risk_model = risk_model\n\n def __eq__(self, other) -> bool:\n if not isinstance(other, FactorShockParameters):\n return False\n\n return (\n self.factor_shocks == other.factor_shocks\n and self.propagate_shocks == other.propagate_shocks\n and self.risk_model == other.risk_model\n )\n\n def __repr__(self):\n return '%r(risk_model=%r, propagate_shocks=%r, factor_shocks=%r)' % (\n self.__class__.__name__,\n self.risk_model,\n self.propagate_shocks,\n self.factor_shocks,\n )\n\n @property\n def factor_shocks(self) -> List[FactorShock]:\n return self.__factor_shocks\n\n @factor_shocks.setter\n def factor_shocks(self, factor_shocks: Union[List[FactorShock], Dict, pd.DataFrame]):\n if isinstance(factor_shocks, pd.DataFrame):\n factor_shocks_as_dict = factor_shocks.to_dict(orient='split')\n self.__factor_shocks = [\n FactorShock(factor=f, shock=s)\n for f, s in zip(factor_shocks_as_dict.get('columns'), factor_shocks_as_dict.get('data'))\n ]\n elif isinstance(factor_shocks, Dict):\n self.__factor_shocks = [FactorShock(factor=k, shock=v) for k, v in factor_shocks.items()]\n else:\n self.__factor_shocks = factor_shocks\n\n @property\n def propagate_shocks(self) -> bool:\n return self.__propagate_shocks\n\n @propagate_shocks.setter\n def propagate_shocks(self, propagate_shocks: bool):\n self.__propagate_shocks = propagate_shocks\n\n @property\n def risk_model(self) -> str:\n return self.__risk_model\n\n @classmethod\n def from_dict(cls, obj: Dict) -> 'FactorShockParameters':\n return cls(\n factor_shocks=[FactorShock.from_dict(f_shock) for f_shock in obj.get('factorShocks')],\n risk_model=obj.get(\"riskModel\"),\n propagate_shocks=obj.get(\"propagateShocks\"),\n )\n\n def to_dict(self) -> Dict:\n return {\n \"riskModel\": self.risk_model,\n \"propagateShocks\": self.propagate_shocks,\n \"factorShocks\": [f_shock.to_dict() for f_shock in self.factor_shocks],\n }\n\n\nclass HistoricalSimulationParameters:\n def __init__(self, start_date: dt.date = None, end_date: dt.date = None):\n self.__start_date = start_date\n self.__end_date = end_date\n\n def __eq__(self, other) -> bool:\n if not isinstance(other, HistoricalSimulationParameters):\n return False\n\n return self.start_date == other.start_date and self.end_date == other.end_date\n\n def __repr__(self):\n return '%r(start_date=%r, end_date=%r)' % (self.__class__.__name__, self.start_date, self.end_date)\n\n @property\n def start_date(self) -> dt.date:\n return self.__start_date\n\n @start_date.setter\n def start_date(self, start_date: dt.date):\n self.__start_date = start_date\n\n @property\n def end_date(self) -> dt.date:\n return self.__end_date\n\n @end_date.setter\n def end_date(self, end_date: dt.date):\n self.__end_date = end_date\n\n @classmethod\n def from_dict(cls, obj: Dict) -> 'HistoricalSimulationParameters':\n return cls(\n start_date=dt.datetime.strptime(obj.get('startDate'), \"%Y-%m-%d\").date(),\n end_date=dt.datetime.strptime(obj.get('endDate'), \"%Y-%m-%d\").date(),\n )\n\n def to_dict(self) -> Dict:\n return {\"startDate\": self.start_date, \"endDate\": self.end_date}\n\n\nScenarioParameters = Union[FactorShockParameters, HistoricalSimulationParameters]\n\n\nclass FactorScenario:\n \"\"\"Marquee Factor-based Scenario\"\"\"\n\n def __init__(\n self,\n name: str,\n type: Union[str, FactorScenarioType],\n parameters: Union[Dict, HistoricalSimulationParameters, FactorShockParameters],\n entitlements: Union[Dict, Entitlements] = None,\n id_: str = None,\n description: str = None,\n tags: List[str] = None,\n ):\n self.__id = id_\n self.__name = name\n self.__type = type\n self.__description = description\n self.__parameters = (\n parameters\n if any(\n [isinstance(parameters, FactorShockParameters), isinstance(parameters, HistoricalSimulationParameters)]\n )\n else FactorShockParameters.from_dict(parameters)\n if type == FactorScenarioType.Factor_Shock\n else HistoricalSimulationParameters.from_dict(parameters)\n )\n self.__entitlements = entitlements\n self.__tags = tags\n\n def __repr__(self):\n instance_repr = '%r(id=%r, name=%r, description=%r, type=%r, parameters=%r, entitlements=%r, tags=%r)' % (\n self.__class__.__name__,\n self.id,\n self.name,\n self.description,\n self.type,\n self.parameters.__repr__(),\n self.entitlements.__repr__(),\n self.tags,\n )\n\n return instance_repr\n\n def __str__(self):\n s = \"{}('id={}', 'name={}', 'description={}', 'type={}', 'parameters={}'\".format(\n self.__class__.__name__, self.id, self.name, self.description, self.type.value, self.parameters.__repr__()\n )\n\n s += \")\"\n return s\n\n @property\n def id(self) -> str:\n return self.__id\n\n @property\n def name(self) -> str:\n return self.__name\n\n @name.setter\n def name(self, name: str):\n self.__name = name\n\n @property\n def type(self) -> Union[str, FactorScenarioType]:\n return self.__type\n\n @property\n def description(self) -> str:\n return self.__description\n\n @description.setter\n def description(self, description: str):\n self.__description = description\n\n @property\n def parameters(self) -> ScenarioParameters:\n return self.__parameters\n\n @parameters.setter\n def parameters(self, parameters: ScenarioParameters):\n self.__parameters = parameters\n\n @property\n def entitlements(self) -> Entitlements:\n return self.__entitlements\n\n @entitlements.setter\n def entitlements(self, entitlements: Union[Dict, Entitlements]):\n self.__entitlements = entitlements\n\n @property\n def tags(self) -> List[str]:\n return self.__tags\n\n @tags.setter\n def tags(self, tags: List[str]):\n self.__tags = tags\n\n @classmethod\n def from_dict(cls, scenario_as_dict: Dict) -> 'FactorScenario':\n scenario_data = {\n \"name\": scenario_as_dict.get('name'),\n \"description\": scenario_as_dict.get('description'),\n \"id_\": scenario_as_dict.get('id'),\n \"type\": scenario_as_dict.get('type'),\n \"parameters\": get(scenario_as_dict, 'parameters', None),\n \"entitlements\": get(scenario_as_dict, 'entitlements', None),\n \"tags\": scenario_as_dict.get('tags'),\n }\n return cls(**scenario_data)\n\n @classmethod\n def from_target(cls, target_scenario: TargetScenario):\n parameters = (\n FactorShockParameters.from_dict(target_scenario.parameters)\n if target_scenario.type == FactorScenarioType.Factor_Shock\n else HistoricalSimulationParameters.from_dict(target_scenario.parameters)\n )\n scenario = cls(\n id_=target_scenario.id,\n name=target_scenario.name,\n type=target_scenario.type,\n parameters=parameters,\n entitlements=Entitlements.from_target(target_scenario.entitlements),\n description=target_scenario.description,\n tags=target_scenario.tags,\n )\n\n return scenario\n\n @classmethod\n def get(cls, scenario_id: str) -> 'FactorScenario':\n \"\"\"\n Get a scenario by its name\n\n :param scenario_id: The ID of the scenario\n\n :return: Instance of factor scenario\n\n :func:`get_by_name` :func:`get_many`\n \"\"\"\n scenario = GsFactorScenarioApi.get_scenario(scenario_id)\n return cls.from_target(scenario)\n\n @classmethod\n def get_by_name(cls, scenario_name: str) -> 'FactorScenario':\n \"\"\"\n Get a scenario by its name\n\n :param scenario_name: The name of the scenario\n\n :return: Instance of factor scenario\n\n :func:`get_many` :func:`get`\n \"\"\"\n scenario = GsFactorScenarioApi.get_scenario_by_name(scenario_name)\n return cls.from_target(scenario)\n\n @classmethod\n def get_many(\n cls,\n ids: List[str] = None,\n names: List[str] = None,\n type: Union[str, FactorScenarioType] = None,\n risk_model: str = None,\n shocked_factors: List[str] = None,\n shocked_factor_categories: List[str] = None,\n propagated_shocks: bool = None,\n start_date: dt.date = None,\n end_date: dt.date = None,\n tags: List[str] = None,\n limit: int = 100,\n ) -> List['FactorScenario']:\n \"\"\"\n Get many factor scenarios from Marquee\n\n :param ids: List of unique Marquee Identifiers\n :param names: Names of requested scenarios\n :param type: The type of the scenario\n :param risk_model: Filter by the risk model\n :param shocked_factors: Return scenarios that shock requested factors.\n :param shocked_factor_categories: Filter scenarios that shock factors in the requested factor categories\n :param propagated_shocks: Return factor shock scenarios that propagate initial factor shocks to other\n non-shocked factors\n :param start_date: Return historical simulation scenarios with an start date that is greater or equal than this\n date. This is only applicable to historical simulation\n :param end_date: Return historical simulation scenarios with an end date that is lower or equal than this date.\n This is only applicable to historical simulation\n :param tags: Returns scenario with any of the requested tags\n :param limit: limit of number of models in response\n\n :return: list of Factor Shock scenario\n\n **Usage**\n\n >>> many_factor_shock_scenarios = FactorScenario.get_many(risk_model=\"MODEL_ID\",\n ... propagated_shocks=True,\n ... type=\"Factor Shock\")\n\n >>> value_shocks_scenarios = FactorScenario.get_many(risk_model=\"AXIOMA_AXWW4M\",\n ... shocked_factors=[\"Value\"],\n ... type=\"Factor Shock\")\n\n >>> many_historical_simulation_scenarios = FactorScenario.get_many(type=\"Factor Historical Simulation\",\n ... start_date=dt.date(2010, 1, 1),\n ... end_date=dt.date(2024, 1, 1))\n\n **See also**\n\n :func:`get_by_name` :func:`get`\n \"\"\"\n many_scenarios_as_dict = GsFactorScenarioApi.get_many_scenarios(\n ids=ids,\n names=names,\n type=type.value if isinstance(type, FactorScenarioType) else type,\n risk_model=risk_model,\n shocked_factors=shocked_factors,\n shocked_factor_categories=shocked_factor_categories,\n start_date=start_date,\n end_date=end_date,\n tags=tags,\n limit=limit,\n )\n\n all_scenarios = [cls.from_target(scenario_as_target) for scenario_as_target in many_scenarios_as_dict]\n\n if propagated_shocks is not None:\n return [\n scenario\n for scenario in all_scenarios\n if (scenario.parameters.propagate_shocks == propagated_shocks)\n or scenario.type != FactorScenarioType.Factor_Shock\n ]\n\n return all_scenarios\n\n def save(self):\n \"\"\"Update factor scenario or Create it if it does not exist\"\"\"\n target_scenario = TargetScenario(\n name=self.name,\n type_=self.type,\n description=self.description if self.description else None,\n parameters=self.parameters.to_dict(),\n entitlements=self.entitlements.to_target() if self.entitlements else None,\n tags=tuple(self.tags) if self.tags else (),\n )\n\n if self.id:\n target_scenario.id_ = self.id\n GsFactorScenarioApi.update_scenario(target_scenario)\n else:\n scenario = GsFactorScenarioApi.create_scenario(target_scenario)\n self.__id = scenario.id\n\n def delete(self):\n \"\"\"Deletes factor scenario from Marquee\"\"\"\n if not self.id:\n raise MqValueError(\"Cannot delete scenario that has not been created in Marquee\")\n\n GsFactorScenarioApi.delete_scenario(self.id)\n\n def clone(self):\n \"\"\"\n Clones an existing scenario\n\n :return: New scenario instance with identical parameters\n\n **Usage**\n\n\n >>> from gs_quant.markets.scenario import FactorScenario\n >>>\n >>> initial_scenario = FactorScenario.get(\"SCENARIO_ID\")\n >>> clone = initial_scenario.clone()\n >>> clone.save()\n\n **See also**\n\n :func:`save`\n \"\"\"\n parameters = deepcopy(self.parameters)\n\n return FactorScenario(\n name=f\"{self.name} copy\", description=self.description, type=self.type, parameters=parameters\n )\n\n\nScenario = Union[FactorScenario]\n" }, { "path": "gs_quant/markets/screens.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom enum import Enum, unique\nfrom typing import Union, Tuple\n\nimport pandas as pd\nfrom pydash import set_, get\n\nfrom gs_quant.api.gs.screens import GsScreenApi\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.target.assets_screener import (\n AssetScreenerCreditRequestFilters,\n AssetScreenerRequest,\n AssetScreenerRequestFilterLimits,\n AssetScreenerRequestStringOptions,\n)\nfrom gs_quant.target.screens import Screen as TargetScreen, ScreenParameters as TargetScreenParameters\nfrom gs_quant.common import Currency as CurrencyImport\n\nlogging.root.setLevel('INFO')\n\n\nclass RangeFilter:\n \"\"\"Respresents asset filters that are ranges\"\"\"\n\n def __init__(self, min_: Union[float, str] = None, max_: Union[float, str] = None):\n self.__min = min_\n self.__max = max_\n\n def __str__(self) -> str:\n range_filter = f'{{Min: {self.min}, Max: {self.max}}}'\n return range_filter\n\n @property\n def min(self) -> Union[float, str]:\n return self.__min\n\n @min.setter\n def min(self, value: Union[float, str]):\n self.__min = value\n\n @property\n def max(self) -> Union[float, str]:\n return self.__max\n\n @max.setter\n def max(self, value: Union[float, str]):\n self.__max = value\n\n\n@unique\nclass CheckboxType(Enum):\n INCLUDE = \"Include\"\n EXCLUDE = \"Exclude\"\n\n\n@unique\nclass Sector(Enum):\n COMMUNICATION_SERVICES = \"Communication Services\"\n CONSUMER_DISCRETIONARY = \"Consumer Discretionary\"\n CONSUMER_STAPLES = \"Consumer Staples\"\n ENERGY = \"Energy\"\n FINANCIALS = \"Financials\"\n HEALTH_CARE = \"Health Care\"\n INDUSTRIALS = \"Industrials\"\n INFORMATION_TECHNOLOGY = \"Information Technology\"\n MATERIALS = \"Materials\"\n REAL_ESTATE = \"Real Estate\"\n UTILITIES = \"Utilities\"\n\n\n@unique\nclass Seniority(Enum):\n JUNIOR_SUBORDINATE = \"Junior Subordinate\"\n SENIOR = \"Senior\"\n SENIOR_SUBORDINATE = \"Senior Subordinate\"\n SUBORDINATE = \"Subordinate\"\n\n\n@unique\nclass Direction(Enum):\n BUY = \"Buy\"\n SELL = \"Sell\"\n\n\n@unique\nclass Currency(CurrencyImport, Enum):\n pass\n\n\nclass CheckboxFilter:\n \"\"\"Represents asset filters that have multiple enumerated options\"\"\"\n\n def __init__(self, checkbox_type: CheckboxType = None, selections: Tuple[Enum, ...] = None):\n self.__selections = selections\n self.__checkbox_type = checkbox_type\n\n def __str__(self) -> str:\n checkbox_filter = f'{{Type: {self.checkbox_type}, Selections: {self.selections}}}'\n return checkbox_filter\n\n @property\n def checkbox_type(self) -> CheckboxType:\n return self.__checkbox_type\n\n @checkbox_type.setter\n def checkbox_type(self, value: CheckboxType):\n self.__checkbox_type = value\n\n @property\n def selections(self) -> Tuple[Enum, ...]:\n return self.__selections\n\n @selections.setter\n def selections(self, value: Tuple[Enum, ...]):\n self.__selections = value\n\n def add(self, new_selections: Tuple[Enum, ...]):\n new_selections = set(new_selections)\n old_selections = set(self.selections)\n self.selections = tuple(set(new_selections).union(set(old_selections)))\n\n def remove(self, remove_selections: Tuple[Enum, ...]):\n remove_selections = set(remove_selections)\n old_selections = set(self.selections)\n self.selections = tuple(old_selections.difference(remove_selections))\n\n\nclass ScreenFilters:\n def __init__(\n self,\n face_value: float = 1000000,\n direction: str = \"Buy\",\n liquidity_score: RangeFilter = RangeFilter(),\n gs_charge_bps: RangeFilter = RangeFilter(),\n gs_charge_dollars: RangeFilter = RangeFilter(),\n duration: RangeFilter = RangeFilter(),\n yield_: RangeFilter = RangeFilter(),\n spread: RangeFilter = RangeFilter(),\n z_spread: RangeFilter = RangeFilter(),\n g_spread: RangeFilter = RangeFilter(),\n mid_price: RangeFilter = RangeFilter(),\n maturity: RangeFilter = RangeFilter(),\n amount_outstanding: RangeFilter = RangeFilter(),\n letter_rating: RangeFilter = RangeFilter(),\n seniority: CheckboxFilter = CheckboxFilter(),\n currency: CheckboxFilter = CheckboxFilter(),\n sector: CheckboxFilter = CheckboxFilter(),\n ):\n self.__face_value = face_value\n self.__direction = direction\n self.__liquidity_score = liquidity_score\n self.__gs_charge_bps = gs_charge_bps\n self.__gs_charge_dollars = gs_charge_dollars\n self.__duration = duration\n self.__yield_ = yield_\n self.__spread = spread\n self.__z_spread = z_spread\n self.__g_spread = g_spread\n self.__mid_price = mid_price\n self.__maturity = maturity\n self.__amount_outstanding = amount_outstanding\n self.__rating = letter_rating\n self.__seniority = seniority\n self.__currency = currency\n self.__sector = sector\n\n def __str__(self) -> str:\n to_return = {}\n filter_names = self.__dict__.keys()\n for name in filter_names:\n if self.__dict__[name]:\n to_return[name] = self.__dict__[name].__str__()\n return str(to_return)\n\n @property\n def face_value(self) -> float:\n \"\"\"Face value of the bond.\"\"\"\n return self.__face_value\n\n @face_value.setter\n def face_value(self, value: float):\n self.__face_value = value\n\n @property\n def direction(self) -> str:\n \"\"\"Whether the position is a buy or sell.\"\"\"\n return self.__direction\n\n @direction.setter\n def direction(self, value: str):\n self.__direction = value\n\n @property\n def liquidity_score(self) -> RangeFilter:\n \"\"\"Liquidity score assigned to buying/selling the bond.\"\"\"\n return self.__liquidity_score\n\n @liquidity_score.setter\n def liquidity_score(self, value: RangeFilter):\n self.__validate_range_settings(min_=1, max_=6, value=self.__liquidity_score)\n self.__liquidity_score = value\n\n @property\n def gs_charge_bps(self) -> RangeFilter:\n \"\"\"Goldman Sachs' indicative charge of the bond (bps).\"\"\"\n return self.__gs_charge_bps\n\n @gs_charge_bps.setter\n def gs_charge_bps(self, value: RangeFilter):\n self.__validate_range_settings(min_=0, max_=10, value=self.__gs_charge_bps)\n self.__gs_charge_bps = value\n\n @property\n def gs_charge_dollars(self) -> RangeFilter:\n \"\"\"Goldman Sachs' indicative charge of the bond (dollars).\"\"\"\n return self.__gs_charge_dollars\n\n @gs_charge_dollars.setter\n def gs_charge_dollars(self, value: RangeFilter):\n self.__validate_range_settings(min_=0, max_=2, value=self.__gs_charge_dollars)\n self.__gs_charge_dollars = value\n\n @property\n def duration(self) -> RangeFilter:\n \"\"\"Measure of a bond's price sensitivity to changes in interest rates.\"\"\"\n return self.__duration\n\n @duration.setter\n def duration(self, value: RangeFilter):\n self.__validate_range_settings(min_=0, max_=20, value=self.__duration)\n self.__duration = value\n\n @property\n def yield_(self) -> RangeFilter:\n \"\"\"Return an investor realizes on a bond sold at the mid price.\"\"\"\n return self.__yield_\n\n @yield_.setter\n def yield_(self, value: RangeFilter):\n self.__validate_range_settings(min_=0, max_=10, value=self.__yield_)\n self.__yield_ = value\n\n @property\n def spread(self) -> RangeFilter:\n \"\"\"Spread between the yields of a debt security and its benchmark when both are\n purchased at bid price.\"\"\"\n return self.__spread\n\n @spread.setter\n def spread(self, value: RangeFilter):\n self.__validate_range_settings(min_=0, max_=1000, value=self.__spread)\n self.__spread = value\n\n @property\n def z_spread(self) -> RangeFilter:\n \"\"\"Zero volatility spread of a bond.\"\"\"\n return self.__z_spread\n\n @z_spread.setter\n def z_spread(self, value: RangeFilter):\n self.__z_spread = value\n\n @property\n def g_spread(self) -> RangeFilter:\n \"\"\"Difference between yield on treasury bonds and yield on corporate bonds of same\n maturity.\"\"\"\n return self.__g_spread\n\n @g_spread.setter\n def g_spread(self, value: RangeFilter):\n self.__g_spread = value\n\n @property\n def mid_price(self) -> RangeFilter:\n \"\"\"Mid price.\"\"\"\n return self.__mid_price\n\n @mid_price.setter\n def mid_price(self, value: RangeFilter):\n self.__validate_range_settings(min_=0, max_=200, value=self.__mid_price)\n self.__mid_price = value\n\n @property\n def maturity(self) -> RangeFilter:\n \"\"\"Length of time bond owner will receive interest payments on the investment.\"\"\"\n return self.__maturity\n\n @maturity.setter\n def maturity(self, value: RangeFilter):\n self.__validate_range_settings(min_=0, max_=40, value=self.__maturity)\n self.__maturity = value\n\n @property\n def amount_outstanding(self) -> RangeFilter:\n \"\"\"Aggregate principal amount of the total number of bonds not redeemed or\n otherwise discharged.\"\"\"\n return self.__amount_outstanding\n\n @amount_outstanding.setter\n def amount_outstanding(self, value: RangeFilter):\n self.__validate_range_settings(min_=0, max_=1000000000, value=self.__amount_outstanding)\n self.__amount_outstanding = value\n\n @property\n def rating(self) -> RangeFilter:\n \"\"\"S&P rating given to a bond.\"\"\"\n return self.__rating\n\n @rating.setter\n def rating(self, value: RangeFilter):\n self.__rating = value\n\n @property\n def seniority(self) -> CheckboxFilter:\n \"\"\"Seniority of the bond.\"\"\"\n return self.__seniority\n\n @seniority.setter\n def seniority(self, value: CheckboxFilter):\n self.__seniority = value\n\n @property\n def currency(self) -> CheckboxFilter:\n \"\"\"Currency of the bond.\"\"\"\n return self.__currency\n\n @currency.setter\n def currency(self, value: CheckboxFilter):\n self.__currency = value\n\n @property\n def sector(self) -> CheckboxFilter:\n \"\"\"Sector / industry of the bond.\"\"\"\n return self.__sector\n\n @sector.setter\n def sector(self, value: CheckboxFilter):\n self.__sector = value\n\n @staticmethod\n def __validate_range_settings(min_: int, max_: int, value: RangeFilter):\n if value.min is None and value.max is None:\n return\n if value.min < min_ or value.max > max_:\n raise MqValueError(f'Please ensure your min and max values are in the range of {min} <= x <= {max}')\n\n\nclass Screen:\n \"\"\" \"Private variables\"\"\"\n\n def __init__(self, filters: ScreenFilters = None, screen_id: str = None, name: str = None):\n if not filters:\n self.__filters = ScreenFilters()\n else:\n self.__filters = filters\n\n self.__id = screen_id\n self.__name = name if name is not None else f\"Screen {dt.date.today().strftime('%d-%b-%Y')}\"\n\n @property\n def id(self) -> str:\n return self.__id\n\n @property\n def name(self) -> str:\n return self.__name\n\n @name.setter\n def name(self, name: str):\n self.__name = name\n\n @property\n def filters(self) -> ScreenFilters:\n return self.__filters\n\n @filters.setter\n def filters(self, filters: ScreenFilters):\n self.__filters = filters\n\n @classmethod\n def get(cls, screen_id: str):\n screen = GsScreenApi.get_screen(screen_id=screen_id)\n return Screen.__from_target(screen)\n\n def calculate(self, format_: str = None):\n \"\"\"Applies screen filters, returning assets that satisfy the condition(s)\"\"\"\n filters = self.__to_target_filters()\n payload = AssetScreenerRequest(filters=filters)\n assets = GsScreenApi.calculate(payload)\n dataframe = pd.DataFrame(assets)\n if format_ == 'json':\n return dataframe['results'].to_json(indent=4)\n if format_ == 'csv':\n return dataframe.to_csv()\n return dataframe\n\n def save(self):\n \"\"\"Create a screen using GsScreenApi if it doesn't exist. Update the report if it does.\"\"\"\n parameters = self.__to_target_parameters()\n target_screen = TargetScreen(name=self.name, parameters=parameters)\n if self.id:\n target_screen.id = self.id\n GsScreenApi.update_screen(target_screen)\n else:\n screen = GsScreenApi.create_screen(target_screen)\n self.__id = screen.id\n logging.info(f'New screen created with ID: {self.id} \\n')\n\n def delete(self):\n \"\"\"Hits GsScreensApi to delete a report\"\"\"\n GsScreenApi.delete_screen(self.id)\n\n @classmethod\n def __from_target(cls, screen):\n return Screen(filters=screen.parameters, screen_id=screen.id, name=screen.name)\n\n def __to_target_filters(self) -> AssetScreenerCreditRequestFilters:\n payload = {}\n filters = self.__set_up_filters()\n\n for name in filters:\n if name == 'face_value' or name == 'direction':\n payload[name] = filters[name]\n elif isinstance(filters[name], RangeFilter):\n payload[name] = AssetScreenerRequestFilterLimits(min_=filters[name].min, max_=filters[name].max)\n elif isinstance(filters[name], CheckboxFilter):\n if filters[name].selections and filters[name].checkbox_type:\n payload[name] = AssetScreenerRequestStringOptions(\n options=filters[name].selections, type_=filters[name].checkbox_type\n )\n return AssetScreenerCreditRequestFilters(**payload)\n\n def __set_up_filters(self) -> dict:\n filters = {}\n for prop in AssetScreenerCreditRequestFilters.properties():\n set_(filters, prop, get(self.__filters, prop))\n return filters\n\n def __to_target_parameters(self) -> TargetScreenParameters:\n payload = {}\n parameters = self.__set_up_parameters()\n\n for name in parameters:\n if name == 'face_value' or name == 'direction':\n payload[name] = parameters[name]\n elif isinstance(parameters[name], RangeFilter):\n payload[name] = AssetScreenerRequestFilterLimits(min_=parameters[name].min, max_=parameters[name].max)\n elif isinstance(parameters[name], CheckboxFilter):\n if parameters[name].selections and parameters[name].checkbox_type:\n payload[name] = parameters[name].selections\n return TargetScreenParameters(**payload)\n\n def __set_up_parameters(self) -> dict:\n filter_to_parameter = {\n 'face_value': 'face_value',\n 'direction': 'direction',\n 'gs_liquidity_score': 'liquidity_score',\n 'gs_charge_bps': 'gs_charge_bps',\n 'gs_charge_dollars': 'gs_charge_dollars',\n 'modified_duration': 'duration',\n 'yield_to_convention': 'yield_',\n 'spread_to_benchmark': 'spread',\n 'z_spread': 'z_spread',\n 'g_spread': 'g_spread',\n 'bval_mid_price': 'mid_price',\n 'maturity': 'maturity',\n 'amount_outstanding': 'amount_outstanding',\n 'rating_standard_and_poors': 'rating',\n 'seniority': 'seniority',\n 'currency': 'currency',\n 'sector': 'sector',\n 'issue_date': 'issue_date',\n }\n\n parameters = {}\n for prop in TargetScreenParameters.properties():\n set_(parameters, prop, get(self.__filters, filter_to_parameter[prop]))\n return parameters\n" }, { "path": "gs_quant/markets/securities.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport calendar\nimport datetime as dt\nimport json\nimport logging\nimport threading\nimport time\nfrom abc import ABCMeta, abstractmethod\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom enum import auto, Enum\nfrom functools import partial\nfrom typing import Tuple, Generator, Iterable, Optional, Dict, List, Union\n\nimport backoff\nimport cachetools\nimport pandas as pd\nfrom dateutil.relativedelta import relativedelta\nfrom pydash import get\n\nfrom gs_quant.api.gs.assets import GsAsset, GsIdType, GsAssetApi\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.api.utils import ThreadPoolManager\nfrom gs_quant.base import get_enum_value\nfrom gs_quant.common import AssetClass, AssetParameters, AssetType as GsAssetType, Currency, DateLimit\nfrom gs_quant.context_base import nullcontext\nfrom gs_quant.data import DataMeasure, DataFrequency, Dataset, AssetMeasure\nfrom gs_quant.data.coordinate import DataDimensions, DateOrDatetime\nfrom gs_quant.data.core import IntervalFrequency, DataAggregationOperator\nfrom gs_quant.entities.entity import Entity, EntityIdentifier, EntityType, PositionedEntity\nfrom gs_quant.errors import MqValueError, MqTypeError, MqRequestError\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.indices_utils import BasketType, IndicesDatasets\nfrom gs_quant.session import GsSession\nfrom gs_quant.target.data import DataQuery\nfrom gs_quant.tracing import Tracer\n\n_logger = logging.getLogger(__name__)\n\n\nclass ExchangeCode(Enum):\n \"\"\"Exchange enumeration\n\n Exchange codes representing global venues where Securities are listed and traded\n\n \"\"\"\n\n NASDAQ = \"NASD\" # Nasdaq Global Stock Market\n NYSE = \"NYSE\" # New York Stock Exchange\n\n\nclass AssetType(Enum):\n \"\"\"Asset type enumeration\n\n Enumeration of different types of asset or security.\n\n \"\"\"\n\n #: Index which tracks an evolving portfolio of securities, and can be traded through cash or derivatives markets\n INDEX = \"Index\"\n\n #: Exchange traded fund which tracks an evolving portfolio of securities and is listed on an exchange to be\n #: traded as a security\n ETF = \"ETF\"\n\n #: Bespoke basket which provides exposure to a customized collection of assets with levels published daily; can be\n #: traded on swap and rebalanced programmatically\n CUSTOM_BASKET = \"Custom Basket\"\n\n #: Bespoke basket which provides exposure to a customized collection of assets with levels published daily;\n #: basket composition maintained by Goldman Sachs Investment Research\n RESEARCH_BASKET = \"Research Basket\"\n\n #: Listed equities which provide access to equity holding in a company and participation in dividends and other\n #: distributions in common, preferred or other variants which provide different investor rights\n STOCK = \"Single Stock\"\n\n #: Standardized listed contract which provides delivery of an asset at a pre-defined forward date and can be\n #: settled in cash or physical form\n FUTURE = \"Future\"\n\n #: FX cross or currency pair\n CROSS = \"Cross\"\n\n #: Currency\n CURRENCY = \"Currency\"\n\n #: Rate\n RATE = \"Rate\"\n\n #: Cash\n CASH = \"Cash\"\n\n #: Weather Index\n WEATHER_INDEX = \"Weather Index\"\n\n #: Swap\n SWAP = \"Swap\"\n\n #: Swaption\n SWAPTION = \"Swaption\"\n\n #: Option\n OPTION = \"Option\"\n\n #: Binary\n BINARY = \"Binary\"\n\n #: Commodity Reference Price\n COMMODITY_REFERENCE_PRICE = \"Commodity Reference Price\"\n\n # COMMODITY NATURAL GAS Hub\n COMMODITY_NATURAL_GAS_HUB = \"Commodity Natural Gas Hub\"\n\n # COMMODITY EU NATURAL GAS Hub\n COMMODITY_EU_NATURAL_GAS_HUB = \"Commodity EU Natural Gas Hub\"\n\n #: Commodity Power Node\n COMMODITY_POWER_NODE = \"Commodity Power Node\"\n\n #: Commodity Power Aggregated Nodes\n COMMODITY_POWER_AGGREGATED_NODES = \"Commodity Power Aggregated Nodes\"\n\n #: Bond\n BOND = \"Bond\"\n\n #: Future Market\n FUTURE_MARKET = \"Future Market\"\n\n #: Future Contract\n FUTURE_CONTRACT = \"Future Contract\"\n\n #: Commodity\n COMMODITY = \"Commodity\"\n\n #: Crypto\n CRYPTOCURRENCY = \"Cryptocurrency\"\n\n #: Forward\n FORWARD = \"Forward\"\n\n #: Fund\n FUND = \"Fund\"\n\n #: Default Swap\n DEFAULT_SWAP = \"Default Swap\"\n\n #: Systematic Hedging\n SYSTEMATIC_HEDGING = 'Systematic Hedging'\n\n #: Access\n ACCESS = 'Access'\n\n #: Risk Premia\n RISK_PREMIA = 'Risk Premia'\n\n #: Multi Asset Allocation\n MULTI_ASSET_ALLOCATION = 'Multi-Asset Allocation'\n\n # Sec Master types\n\n ADR = 'ADR'\n GDR = 'GDR'\n DUTCH_CERT = 'Dutch Cert'\n NYRS = 'NY Reg Shrs'\n RECEIPT = 'Receipt'\n UNIT = 'Unit'\n MUTUAL_FUND = 'Mutual Fund'\n RIGHT = 'Right'\n PREFERRED = 'Preferred'\n MISC = 'Misc.'\n REIT = 'REIT'\n PRIVATE_COMP = 'Private Comp'\n PREFERENCE = 'Preference'\n LIMITED_PARTNERSHIP = 'Ltd Part'\n TRACKING_STOCK = 'Tracking Stk'\n ROYALTY_TRUST = 'Royalty Trst'\n CLOSED_END_FUND = 'Closed-End Fund'\n OPEN_END_FUND = 'Open-End Fund'\n FUND_OF_FUNDS = 'Fund of Funds'\n MLP = 'MLP'\n STAPLED_SECURITY = 'Stapled Security'\n SAVINGS_SHARE = 'Savings Share'\n EQUITY_WRT = 'Equity WRT'\n # ETF already defined\n SAVINGS_PLAN = 'Savings Plan'\n EQUITY_INDEX = 'Equity Index'\n COMMON_STOCK = 'Common Stock'\n\n\nclass AssetIdentifier(EntityIdentifier):\n \"\"\"Asset type enumeration\n\n Enumeration of different security identifiers\n\n \"\"\"\n\n MARQUEE_ID = \"MQID\" #: Goldman Sachs Marquee identifier code (MA4B66MW5E27UAHKG34)\n REUTERS_ID = \"RIC\" #: Thompson Reuters Instrument Code (RIC), (GS.N)\n BLOOMBERG_ID = \"BBID\" #: Bloomberg identifier and exchange code (GS UN)\n BLOOMBERG_COMPOSITE_ID = \"BCID\" #: Bloomberg composite identifier and exchange code (GS US)\n CUSIP = \"CUSIP\" #: Committee on Uniform Security Identification Procedures code (38141G104)\n ISIN = \"ISIN\" #: International Securities Identification Number (US38141G1040)\n SEDOL = \"SEDOL\" #: LSE Stock Exchange Daily Official List code (2407966)\n TICKER = \"TICKER\" #: Exchange ticker (GS)\n PLOT_ID = \"PLOT_ID\" #: ID for Marquee PlotTool\n GSID = \"GSID\"\n NAME = 'NAME' #: Name of the asset ('US Treasury 20y GOVN')\n\n\nclass SecurityIdentifier(EntityIdentifier):\n GSID = \"gsid\"\n RCIC = \"rcic\"\n RIC = \"ric\"\n ID = \"id\"\n CUSIP = \"cusip\"\n CUSIP8 = \"cusip8\"\n CINS = \"cins\"\n SEDOL = \"sedol\"\n ISIN = \"isin\"\n TICKER = \"ticker\"\n BBID = \"bbid\"\n BCID = \"bcid\"\n GSS = \"gss\"\n PRIMEID = \"primeId\"\n BBG = \"bbg\"\n ASSET_ID = \"assetId\"\n ANY = \"identifiers\"\n BARRA_ID = \"barraId\"\n AXIOMA_ID = \"axiomaId\"\n\n\nclass ReturnType(Enum):\n \"\"\"Index return type\n\n Represents different index return types or funding models\n\n \"\"\"\n\n EXCESS_RETURN = \"Excess Return\" # Returns are excess of funding rate in denominated currency\n TOTAL_RETURN = \"Total Return\" # Returns are inclusive of funding rate in denominated currency\n\n\nclass Asset(Entity, metaclass=ABCMeta):\n def __init__(\n self,\n id_: str,\n asset_class: AssetClass,\n name: str,\n exchange: Optional[str] = None,\n currency: Optional[str] = None,\n parameters: AssetParameters = None,\n entity: Optional[Dict] = None,\n ):\n super().__init__(id_, EntityType.ASSET, entity=entity)\n self.__id = id_\n self.asset_class = asset_class\n self.name = name\n self.exchange = exchange\n self.currency = currency\n self.parameters = parameters\n self.entity = entity\n\n def get_marquee_id(self):\n return self.__id\n\n def get_url(self) -> str:\n \"\"\"\n Retrieve url to asset's product page on Marquee\n \"\"\"\n env = '-dev-ext.web' if 'dev' in get(GsSession, 'current.domain', '') else ''\n env = '-qa' if 'qa' in get(GsSession, 'current.domain', '') else env\n return f'https://marquee{env}.gs.com/s/products/{self.get_marquee_id()}/summary'\n\n def get_identifiers(self, as_of: dt.date = None) -> dict:\n \"\"\"\n Get asset identifiers\n\n :param as_of: As of date for query\n :return: dict of identifiers\n\n **Usage**\n\n Get asset identifiers as of a given date. Where the identifiers are temporal (and can change over time), this\n function will return the identifiers as of that point in time. If no date is provided as a parameter, will use\n the current PricingContext.\n\n **Examples**\n\n Get current asset identifiers:\n\n >>> gs = SecurityMaster.get_asset(\"GS\", AssetIdentifier.TICKER)\n >>> gs.get_identifiers()\n\n Get identifiers as of 1Jan18:\n\n >>> gs.get_identifiers(dt.date(2018,1,1))\n\n Use PricingContext to determine as of date:\n\n >>> with PricingContext(dt.date(2018,1,1)) as ctx:\n >>> gs.get_identifiers()\n\n **See also**\n\n :class:`AssetIdentifier`\n :func:`get_asset`\n \"\"\"\n if not as_of:\n current = PricingContext.current\n if not current.is_entered:\n with current:\n as_of = current.pricing_date\n else:\n as_of = current.pricing_date\n\n if isinstance(as_of, dt.datetime):\n as_of = as_of.date()\n\n valid_ids = set(item.value for item in AssetIdentifier)\n xrefs = GsAssetApi.get_asset_xrefs(self.get_marquee_id())\n identifiers = {}\n\n for xref in xrefs:\n start_date = xref.startDate\n end_date = xref.endDate\n\n if start_date <= as_of <= end_date:\n identifiers = {k.upper(): v for k, v in xref.identifiers.as_dict().items() if k.upper() in valid_ids}\n\n return identifiers\n\n @cachetools.cached(\n cachetools.TTLCache(256, 600),\n lambda s, id_type, as_of=None: cachetools.keys.hashkey(s.get_marquee_id(), id_type, as_of),\n threading.RLock(),\n )\n def get_identifier(self, id_type: AssetIdentifier, as_of: dt.date = None):\n \"\"\"\n Get asset identifier\n\n :param as_of: As of date for query\n :param id_type: requested id type\n :return: identifier value\n\n **Usage**\n\n Get asset identifier as of a given date. Where the identifiers are temporal (and can change over time), this\n function will return the identifier as of that point in time. If no date is provided as a parameter, will use\n the current PricingContext.\n\n **Examples**\n\n Get current SEDOL:\n\n >>> import datetime as dt\n >>>\n >>> gs = SecurityMaster.get_asset(\"GS\", AssetIdentifier.TICKER)\n >>> gs.get_identifier(AssetIdentifier.SEDOL)\n\n Get SEDOL as of 1Jan18:\n\n >>> gs.get_identifier(AssetIdentifier.SEDOL, as_of=dt.date(2018,1,1))\n\n Use PricingContext to determine as of date:\n\n >>> with PricingContext(dt.date(2018,1,1)) as ctx:\n >>> gs.get_identifier(AssetIdentifier.SEDOL)\n\n **See also**\n\n :class:`AssetIdentifier`\n :func:`get_asset_identifiers`\n\n \"\"\"\n if id_type == AssetIdentifier.MARQUEE_ID:\n return self.get_marquee_id()\n\n ids = self.get_identifiers(as_of=as_of)\n return ids.get(id_type.value)\n\n def get_asset_measures(self) -> List[AssetMeasure]:\n \"\"\"\n Get asset measures\n\n :return: A list consisting of the following measures available for an asset: type, frequency, datasetField.\n For more details check the fields defined in class: 'AssetMeasures'\n\n **Usage**\n\n Get list of measures available for an asset\n\n **Examples**\n\n >>> from gs_quant.markets.securities import SecurityMaster,AssetIdentifier\n >>>\n >>> asset = SecurityMaster.get_asset(\"USDJPY\", AssetIdentifier.BLOOMBERG_ID)\n >>> asset.get_asset_measures()\n\n **See also**\n\n :class:`AssetMeasures`\n\n \"\"\"\n\n availability_response = GsSession.current.sync.get(f'/data/measures/{self.get_marquee_id()}/availability')\n final_measure_set = set()\n\n if availability_response['data']:\n for measure_set in availability_response['data']:\n asset_measures = AssetMeasure.from_dict(measure_set)\n\n if {'type', 'frequency', 'datasetField'} <= measure_set.keys():\n final_measure_set.add(asset_measures)\n\n return list(final_measure_set)\n\n def get_data_series(\n self,\n measure: DataMeasure,\n dimensions: Optional[DataDimensions] = None,\n frequency: Optional[DataFrequency] = None,\n start: Optional[DateOrDatetime] = None,\n end: Optional[DateOrDatetime] = None,\n dates: List[dt.date] = None,\n operator: DataAggregationOperator = None,\n ) -> pd.Series:\n \"\"\"\n Get asset series\n\n :param measure: measure to get as series\n :param dimensions: dimensions to query (e.g. tenor)\n :param frequency: data frequency to query\n :param start: start of the series\n :param end: end of the series\n :return: timeseries of given measure\n\n **Usage**\n\n Get a given timeseries for the asset\n\n **Examples**\n\n Get close price series:\n\n >>> from gs_quant.markets.securities import SecurityMaster\n >>> from gs_quant.data import DataMeasure\n >>>\n >>> gs = SecurityMaster.get_asset(\"GS\", AssetIdentifier.TICKER)\n >>> gs.get_data_series(DataMeasure.CLOSE_PRICE)\n\n **See also**\n\n :class:`DataMeasure`\n\n \"\"\"\n\n coordinate = self.get_data_coordinate(measure, dimensions, frequency)\n if coordinate is None:\n raise MqValueError(f\"No data coordinate found for parameters: {measure, dimensions, frequency}\")\n elif coordinate.dataset_id is None:\n raise MqValueError(f\"Measure '{measure.value}' not found for asset: {self.__id}\")\n return coordinate.get_series(start=start, end=end, dates=dates, operator=operator)\n\n def get_latest_close_price(self) -> float:\n coordinate = self.get_data_coordinate(DataMeasure.CLOSE_PRICE, None, DataFrequency.DAILY)\n if coordinate is None:\n raise MqValueError(\n f\"No data co-ordinate found for these parameters: \\\n {DataMeasure.CLOSE_PRICE, None, DataFrequency.DAILY}\"\n )\n return coordinate.last_value()\n\n def get_close_price_for_date(self, date: dt.date) -> pd.Series:\n return self.get_data_series(DataMeasure.CLOSE_PRICE, None, DataFrequency.DAILY, date, date)\n\n def get_close_prices(self, start: dt.date = DateLimit.LOW_LIMIT.value, end: dt.date = dt.date.today()) -> pd.Series:\n \"\"\"\n Get close price series\n\n :return: timeseries of close prices\n\n **Usage**\n\n Get close prices for an asset\n\n **Examples**\n\n Get close price series:\n\n >>> from gs_quant.markets.securities import SecurityMaster\n >>>\n >>> gs = SecurityMaster.get_asset(\"GS\", AssetIdentifier.TICKER)\n >>> gs.get_close_prices()\n\n **See also**\n\n :class:`DataMeasure`\n :func:`get_data_series`\n \"\"\"\n return self.get_data_series(DataMeasure.CLOSE_PRICE, None, DataFrequency.DAILY, start, end)\n\n def get_hloc_prices(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n interval_frequency: IntervalFrequency = IntervalFrequency.DAILY,\n ) -> pd.DataFrame:\n \"\"\"\n Get high, low, open, close (hloc) prices\n\n :return: dataframe indexed by datetimes bucketed by the given interval_frequency with High, Low, Open, and Close\n columns\n\n **Usage**\n\n Get high, low, open, and close prices for an asset for the given interval frequency.\n\n **Examples**\n\n Get hloc price series:\n\n >>> from gs_quant.markets.securities import SecurityMaster\n >>>\n >>> gs = SecurityMaster.get_asset(\"GS\", AssetIdentifier.TICKER)\n >>> gs.get_hloc_prices()\n\n **See also**\n\n :class:`DataMeasure`\n :func:`get_close_prices`\n \"\"\"\n\n if self.asset_class == AssetClass.Equity:\n if interval_frequency == IntervalFrequency.DAILY:\n dates = None\n use_field = False\n elif interval_frequency == IntervalFrequency.MONTHLY:\n d = dt.date(start.year, start.month, 1)\n dates = [d, dt.date(d.year, d.month, calendar.monthrange(d.year, d.month)[-1])]\n d += relativedelta(months=1)\n while d < end:\n dates.append(dt.date(d.year, d.month, calendar.monthrange(d.year, d.month)[-1]))\n d += relativedelta(months=1)\n dates.append(end)\n use_field = True\n else:\n raise MqValueError(f'Unsupported IntervalFrequency {interval_frequency.value} for get_hloc_prices')\n\n tasks = [\n partial(\n self.get_data_series,\n DataMeasure.ADJUSTED_HIGH_PRICE,\n None,\n DataFrequency.DAILY,\n start,\n end,\n dates=dates,\n operator=DataAggregationOperator.MAX if use_field else None,\n ),\n partial(\n self.get_data_series,\n DataMeasure.ADJUSTED_LOW_PRICE,\n None,\n DataFrequency.DAILY,\n start,\n end,\n dates=dates,\n operator=DataAggregationOperator.MIN if use_field else None,\n ),\n partial(\n self.get_data_series,\n DataMeasure.ADJUSTED_OPEN_PRICE,\n None,\n DataFrequency.DAILY,\n start,\n end,\n dates=dates,\n operator=DataAggregationOperator.FIRST if use_field else None,\n ),\n partial(\n self.get_data_series,\n DataMeasure.ADJUSTED_CLOSE_PRICE,\n None,\n DataFrequency.DAILY,\n start,\n end,\n dates=dates,\n operator=DataAggregationOperator.LAST if use_field else None,\n ),\n ]\n\n results = ThreadPoolManager.run_async(tasks)\n df = pd.DataFrame({'high': results[0], 'low': results[1], 'open': results[2], 'close': results[3]})\n elif self.asset_class == AssetClass.FX:\n if interval_frequency != IntervalFrequency.DAILY:\n raise MqValueError('Unsupported IntervalFrequency for FX asset class.')\n ds = Dataset('FX_HLOC')\n df = ds.get_data(start=start, end=end, assetId=self.get_marquee_id())\n df = df.drop(columns=['assetId', 'updateTime']).reindex(columns=['high', 'low', 'open', 'close'])\n else:\n raise MqValueError('Unsupported AssetClass for HLOC data.')\n\n return df.dropna()\n\n @abstractmethod\n def get_type(self) -> AssetType:\n \"\"\"Overridden by sub-classes to return security type\"\"\"\n\n @classmethod\n def entity_type(cls) -> EntityType:\n return EntityType.ASSET\n\n @property\n def data_dimension(self) -> str:\n return 'assetId'\n\n @classmethod\n def get(\n cls,\n id_value: str,\n id_type: AssetIdentifier,\n as_of: Union[dt.date, dt.datetime] = None,\n exchange_code: ExchangeCode = None,\n asset_type: AssetType = None,\n sort_by_rank: bool = False,\n ) -> Optional['Asset']:\n asset = SecurityMaster.get_asset(id_value, id_type, as_of, exchange_code, asset_type, sort_by_rank)\n return asset\n\n\nclass SecMasterAsset(Asset):\n def __init__(\n self,\n id_: str,\n asset_type: AssetType,\n asset_class: AssetClass,\n name: str,\n exchange: Optional[str] = None,\n currency: Optional[str] = None,\n parameters: AssetParameters = None,\n entity: Optional[Dict] = None,\n ):\n Asset.__init__(\n self,\n id_,\n asset_class=asset_class,\n name=name,\n exchange=exchange,\n currency=currency,\n parameters=parameters,\n entity=entity,\n )\n self.__asset_type = asset_type\n self.__cached_identifiers = None\n\n def get_type(self) -> AssetType:\n return self.__asset_type\n\n def get_marquee_id(self):\n marquee_id = self.get_identifier(SecurityIdentifier.ASSET_ID)\n self.__id = marquee_id # Updates Marquee Id in case it changes from context change\n if marquee_id is None:\n current = PricingContext.current\n if not current.is_entered:\n with current:\n current_pricing_date = current.pricing_date\n else:\n current_pricing_date = current.pricing_date\n raise MqValueError(\n f\"Current SecMasterAsset does not have a Marquee Id as of {current_pricing_date}. \"\n f\"Perhaps asset did not exist at that time, or is a not an exchange-level asset.\"\n )\n return marquee_id\n\n def get_identifier(self, id_type: Union[AssetIdentifier, SecurityIdentifier], as_of: dt.date = None):\n # Add an exception since original get_identifier() takes id_type: AssetIdentifier\n if not isinstance(id_type, SecurityIdentifier):\n raise MqTypeError(\n f\"\"\"Expected id_type: SecurityIdentifier.enum for Assets sourced from SecurityMaster.\n Received: {id_type}\"\"\"\n )\n if id_type == SecurityIdentifier.GSID:\n return self.entity['identifiers'].get(SecurityIdentifier.GSID.value)\n if id_type == SecurityIdentifier.ID:\n return self.entity['id']\n ids = self.get_identifiers(as_of=as_of)\n return ids.get(id_type.value, None)\n\n def get_identifiers(self, as_of: dt.date = None) -> dict:\n # Cache identifiers if not already there\n if self.__cached_identifiers is None:\n self.__load_identifiers()\n # Retrieve from cached identifiers\n if as_of is None:\n current = PricingContext.current\n if not current.is_entered:\n with current:\n as_of = current.pricing_date\n else:\n as_of = current.pricing_date\n identifiers = dict()\n for id_type in SecurityIdentifier:\n id_history = self.__cached_identifiers.get(id_type.value)\n if id_history is not None:\n for xref in id_history:\n if xref[\"start_date\"] <= as_of <= xref[\"end_date\"]:\n identifiers[id_type.value] = xref[\"value\"]\n break\n # Add GSID and ID as it is not exposed in Get Identifiers History\n identifiers[SecurityIdentifier.ID.value] = self.entity.get('id')\n identifiers[SecurityIdentifier.GSID.value] = self.entity.get('identifiers').get(SecurityIdentifier.GSID.value)\n # Mainly for currencies, where assetId is not exposed in Get Identifiers History\n if SecurityIdentifier.ASSET_ID.value not in identifiers and self.__asset_type == AssetType.CURRENCY:\n identifiers[SecurityIdentifier.ASSET_ID.value] = self.entity.get(\"identifiers\").get(\"assetId\")\n # TODO: BCID and BBID are not exposed in Get Identifiers History.\n return identifiers\n\n def get_data_series(\n self,\n measure: DataMeasure,\n dimensions: Optional[DataDimensions] = None,\n frequency: Optional[DataFrequency] = None,\n start: Optional[DateOrDatetime] = None,\n end: Optional[DateOrDatetime] = None,\n dates: List[dt.date] = None,\n operator: DataAggregationOperator = None,\n ) -> pd.Series:\n \"\"\"\n Will be also called by Asset.get_close_prices(), Asset.get_close_price_for_date().\n \"\"\"\n\n coordinate = self.get_data_coordinate(measure, dimensions, frequency)\n if coordinate is None:\n raise MqValueError(f\"No data coordinate found for parameters:{measure, dimensions, frequency}\")\n range_start, range_end = coordinate.get_range(start, end)\n\n if self.__is_validate_range(start=range_start, end=range_end):\n with PricingContext(range_start):\n return super(SecMasterAsset, self).get_data_series(\n measure=measure,\n dimensions=dimensions,\n frequency=frequency,\n start=range_start,\n end=range_end,\n dates=dates,\n operator=operator,\n )\n\n def get_hloc_prices(\n self,\n start: dt.date = DateLimit.LOW_LIMIT.value,\n end: dt.date = dt.date.today(),\n interval_frequency: IntervalFrequency = IntervalFrequency.DAILY,\n ) -> pd.DataFrame:\n if self.__is_validate_range(start=start, end=end):\n with PricingContext(start):\n return super(SecMasterAsset, self).get_hloc_prices(\n start=start, end=end, interval_frequency=interval_frequency\n )\n\n def __is_validate_range(self, start: DateOrDatetime, end: DateOrDatetime = dt.date.today()) -> bool:\n \"\"\"\n Validates that only one Marquee Id exist in start and end.\n - This function will return True if only one Marquee id exists in range.\n - This function will raise MqValueError if either many Marquee Ids exists in or none exist in input range or\n the Id at start_date is not equal to id at end_date.\n\n Example:\n __cached_identifiers = {\"assetId\" : [{start: 2020-01-01, end: 2020-01-05, value: \"marqueeId1\"}\n {start: 2020-01-06, end: 2020-10-10, value: \"marqueeId2\"}]}\n args = {start: 2020-01-01, end: 2020-01-07}\n\n return: MqValueError\n \"\"\"\n if self.__cached_identifiers is None:\n self.__load_identifiers()\n\n if isinstance(start, dt.datetime):\n start_date = start.date\n else:\n start_date = start\n\n if isinstance(end, dt.datetime):\n end_date = end.date\n else:\n end_date = end\n\n with PricingContext(start_date):\n start_marquee_id = self.get_marquee_id()\n with PricingContext(end_date):\n end_marquee_id = self.get_marquee_id()\n if start_marquee_id is None or end_marquee_id is None or start_marquee_id != end_marquee_id:\n raise MqValueError(\n f\"Asset's Marquee Id is either none or different. start:[{start_date}->{start_marquee_id}] to \"\n f\"end=[{end_date}->{end_marquee_id}].\"\n )\n\n marquee_id_xref = self.__cached_identifiers.get(SecurityIdentifier.ASSET_ID.value)\n marquee_ids = set()\n output_range_start = None\n output_range_end = None\n overlap_ranges = defaultdict(list)\n for xref in marquee_id_xref:\n if end_date < xref['start_date'] or start_date > xref['end_date']: # Skip xrefs that are outside range\n continue\n marquee_id = xref.get(\"value\")\n range_start = max(start_date, xref['start_date'])\n range_end = min(end_date, xref['end_date'])\n\n if range_start <= range_end:\n marquee_ids.add(marquee_id)\n overlap_ranges[marquee_id].append([range_start.strftime(\"%Y-%m-%d\"), range_end.strftime(\"%Y-%m-%d\")])\n output_range_start = (\n min(output_range_start, range_start) if output_range_start is not None else output_range_start\n )\n output_range_end = (\n max(output_range_end, range_end) if output_range_end is not None else output_range_end\n )\n\n if len(marquee_ids) > 1:\n raise MqValueError(\n f\"Asset has multiple Marquee ids between [start,end]=[{start_date},{end_date}] due to corporate \"\n f\"actions. Try limiting the range over a single Marquee id. Marquee Ids found: {overlap_ranges}.\"\n )\n if len(marquee_ids) == 0:\n raise MqValueError(\n f\"Asset was not assigned Marquee Id over range [start,end]=[{start_date},{end_date}]. \"\n f\"Perhaps asset did not exist at that range, or is a not an exchange-level asset.\"\n )\n return True\n\n def __load_identifiers(self) -> None:\n if self.__cached_identifiers is None:\n r = GsSession.current.sync.get(f'/markets/securities/{self.entity[\"id\"]}/identifiers')\n results = r['results']\n xrefs = defaultdict(list)\n for temporal_xref in results:\n id_type = temporal_xref['type']\n xref_dict = {\n \"start_date\": dt.datetime.strptime(temporal_xref['startDate'], \"%Y-%m-%d\").date(),\n \"update_date\": temporal_xref['updateTime'],\n \"value\": temporal_xref['value'],\n }\n if temporal_xref['endDate'] == \"9999-99-99\":\n xref_dict['end_date'] = dt.datetime.max.date()\n else:\n xref_dict['end_date'] = dt.datetime.strptime(temporal_xref['endDate'], \"%Y-%m-%d\").date()\n xrefs[id_type].append(xref_dict)\n self.__cached_identifiers = xrefs\n\n\nclass Stock(Asset):\n \"\"\"Base Security Type\n\n Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n referenced in a derivative transaction\n\n \"\"\"\n\n def __init__(\n self,\n id_: str,\n name: str,\n exchange: Optional[str] = None,\n currency: Optional[Currency] = None,\n entity: Optional[Dict] = None,\n ):\n Asset.__init__(self, id_, AssetClass.Equity, name, exchange, currency, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.STOCK\n\n def get_currency(self) -> Optional[Currency]:\n return self.currency\n\n def get_thematic_beta(\n self, basket_identifier: str, start: dt.date = DateLimit.LOW_LIMIT.value, end: dt.date = dt.date.today()\n ) -> pd.DataFrame:\n response = GsAssetApi.resolve_assets(identifier=[basket_identifier], fields=['id', 'type'], limit=1)[\n basket_identifier\n ]\n _id, _type = get(response, '0.id'), get(response, '0.type')\n if len(response) == 0 or _id is None:\n raise MqValueError(f'Basket could not be found using identifier {basket_identifier}.')\n if _type not in BasketType.to_list():\n raise MqValueError(f'Asset {basket_identifier} of type {_type} is not a Custom or Research Basket.')\n\n query = DataQuery(\n where={'gsid': self.get_identifier(AssetIdentifier.GSID, end), 'basketId': _id},\n start_date=start,\n end_date=end,\n )\n response = GsDataApi.query_data(query=query, dataset_id=IndicesDatasets.THEMATIC_FACTOR_BETAS_STANDARD.value)\n df = []\n for r in response:\n df.append({'date': r['date'], 'gsid': r['gsid'], 'basketId': r['basketId'], 'thematicBeta': r['beta']})\n df = pd.DataFrame(df)\n return df.set_index('date')\n\n\nclass Cross(Asset):\n \"\"\"Base Security Type\n\n Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n referenced in a derivative transaction\n\n \"\"\"\n\n def __init__(\n self,\n id_: str,\n name: str,\n entity: Optional[Dict] = None,\n asset_class: Optional[Union[AssetClass, str]] = AssetClass.FX,\n ):\n if isinstance(asset_class, str):\n asset_class = get_enum_value(AssetClass, asset_class)\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.CROSS\n\n\nclass Future(Asset):\n \"\"\"Future Security Type\n\n Represents a standardized listed contract which provides delivery of an asset at a pre-defined forward date and can\n be settled in cash or physical form\n\n \"\"\"\n\n def __init__(\n self,\n id_: str,\n asset_class: Union[AssetClass, str],\n name: str,\n currency: Optional[Currency] = None,\n entity: Optional[Dict] = None,\n ):\n if isinstance(asset_class, str):\n asset_class = get_enum_value(AssetClass, asset_class)\n\n Asset.__init__(self, id_, asset_class, name, currency=currency, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.FUTURE\n\n def get_currency(self) -> Optional[Currency]:\n return self.currency\n\n\nclass Currency(Asset):\n \"\"\"Base Security Type\n\n Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n referenced in a derivative transaction\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Cash, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.CURRENCY\n\n\nclass Rate(Asset):\n \"\"\"Base Security Type\n\n Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n referenced in a derivative transaction\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Rates, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.RATE\n\n\nclass Cash(Asset):\n \"\"\"Cash Security Type\n\n Represents a financial asset which can be held in a portfolio, or has an observable price fixing which can be\n referenced in a derivative transaction\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Cash, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.CASH\n\n\nclass WeatherIndex(Asset):\n \"\"\"Weather Index Type\n\n Represents an underlying index on a weather derivative, including where the data (e.g. CPD) has been collected,\n an actual physical reference point (weather station) and various fall back arrangements.\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.WEATHER_INDEX\n\n\nclass CommodityReferencePrice(Asset):\n \"\"\"Commodity Reference Price\n\n Represents an underlying index for commodities in the event that no ISDA Commodity Reference Price exists.\n Includes base, details, unit, currency and exchange id or publication etc.\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.COMMODITY_REFERENCE_PRICE\n\n\nclass CommodityNaturalGasHub(Asset):\n \"\"\"Commodity Natural Gas Hub\n\n Represents a distinct location in commodity Natural Gas markets\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.COMMODITY_NATURAL_GAS_HUB\n\n\nclass CommodityEUNaturalGasHub(Asset):\n \"\"\"Commodity EU Natural Gas Hub\n\n Represents a virtual/physical hub in EU Natural Gas markets\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.COMMODITY_EU_NATURAL_GAS_HUB\n\n\nclass Cryptocurrency(Asset):\n \"\"\"Cryptocurrency\n\n Represents a cryptocurrency\n\n \"\"\"\n\n def __init__(self, id_: str, asset_class: Union[AssetClass, str], name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.CRYPTOCURRENCY\n\n\nclass CommodityPowerNode(Asset):\n \"\"\"Commodity Power Node\n\n Represents a distinct location in commodity power markets\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.COMMODITY_POWER_NODE\n\n\nclass CommodityPowerAggregatedNodes(Asset):\n \"\"\"Commodity Power Aggregated Nodes\n\n Represents a group of locations in commodity power markets\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.COMMODITY_POWER_AGGREGATED_NODES\n\n\nclass Commodity(Asset):\n \"\"\"Commodity\n\n Represents a commodity.\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Commod, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.COMMODITY\n\n\nclass Bond(Asset):\n \"\"\"Bond\n\n Represents a bond.\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, asset_class: AssetClass = AssetClass.Credit, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.BOND\n\n\nclass Fund(Asset):\n \"\"\"Fund\n Represents a fund.\n \"\"\"\n\n def __init__(self, id_: str, name: str, asset_class: AssetClass, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.FUND\n\n\nclass FutureMarket(Asset):\n \"\"\"Future Market\n\n Represents a future market\n\n \"\"\"\n\n def __init__(self, id_: str, asset_class: Union[AssetClass, str], name: str, entity: Optional[Dict] = None):\n if isinstance(asset_class, str):\n asset_class = get_enum_value(AssetClass, asset_class)\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.FUTURE_MARKET\n\n\nclass FutureContract(Asset):\n \"\"\"Future Contract\n\n Represents a future contract\n\n \"\"\"\n\n def __init__(self, id_: str, asset_class: Union[AssetClass, str], name: str, entity: Optional[Dict] = None):\n if isinstance(asset_class, str):\n asset_class = get_enum_value(AssetClass, asset_class)\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.FUTURE_CONTRACT\n\n\nclass Swap(Asset):\n \"\"\"Swap Instrument Type\n\n Represents a Swap Instrument\n\n \"\"\"\n\n def __init__(self, id_: str, asset_class: Union[AssetClass, str], name: str, entity: Optional[Dict] = None):\n if isinstance(asset_class, str):\n asset_class = get_enum_value(AssetClass, asset_class)\n\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.SWAP\n\n\nclass Option(Asset):\n \"\"\"Option Instrument Type\n\n Represents an Option Instrument\n\n \"\"\"\n\n def __init__(self, id_: str, asset_class: Union[AssetClass, str], name: str, entity: Optional[Dict] = None):\n if isinstance(asset_class, str):\n asset_class = get_enum_value(AssetClass, asset_class)\n\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.OPTION\n\n\nclass Forward(Asset):\n def __init__(self, id_: str, asset_class: Union[AssetClass, str], name: str, entity: Optional[Dict] = None):\n if isinstance(asset_class, str):\n asset_class = get_enum_value(AssetClass, asset_class)\n\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.FORWARD\n\n\nclass ETF(Asset, PositionedEntity):\n \"\"\"ETF Asset\n\n ETF which tracks an evolving portfolio of securities, and can be traded on exchange\n \"\"\"\n\n def __init__(\n self,\n id_: str,\n asset_class: AssetClass,\n name: str,\n exchange: Optional[str] = None,\n currency: Optional[Currency] = None,\n entity: Optional[Dict] = None,\n ):\n Asset.__init__(self, id_, asset_class, name, exchange, currency, entity=entity)\n PositionedEntity.__init__(self, id_, EntityType.ASSET)\n\n def get_type(self) -> AssetType:\n return AssetType.ETF\n\n def get_currency(self) -> Optional[Currency]:\n return self.currency\n\n\nclass Swaption(Asset):\n \"\"\"Swaption\n\n Represents a swaption.\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Rates, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.SWAPTION\n\n\nclass Binary(Asset):\n \"\"\"Binary\n Represents a binary.\n \"\"\"\n\n def __init__(self, id_: str, name: str, asset_class: AssetClass, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.BINARY\n\n\nclass DefaultSwap(Asset):\n \"\"\"DefaultSwap\n\n Represents a default swap.\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Credit, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.DEFAULT_SWAP\n\n\nclass XccySwapMTM(Asset):\n \"\"\"XccySwapMTM\n\n Represents a cross-currency mark-to-market swap.\n\n \"\"\"\n\n def __init__(self, id_: str, name: str, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, AssetClass.Rates, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.XccySwapMTM\n\n\nclass MutualFund(Asset):\n \"\"\"MutualFund\n\n Represents a mutual fund asset.\n \"\"\"\n\n def __init__(self, id_: str, name: str, asset_class: AssetClass, entity: Optional[Dict] = None):\n Asset.__init__(self, id_, asset_class, name, entity=entity)\n\n def get_type(self) -> AssetType:\n return AssetType.MUTUAL_FUND\n\n\nclass SecurityMasterSource(Enum):\n ASSET_SERVICE = auto()\n SECURITY_MASTER = auto()\n\n\nclass Security:\n def __init__(self, json: dict):\n for k, v in json.items():\n if k == 'identifiers':\n self._ids = {inner_k: inner_v for inner_k, inner_v in v.items()}\n else:\n setattr(self, k, v)\n\n def __str__(self):\n return str({k: v for k, v in self.__dict__.items() if not k.startswith(\"_\")})\n\n def get_identifiers(self):\n return deepcopy(self._ids)\n\n\n@backoff.on_exception(backoff.expo, MqRequestError, giveup=lambda e: e.status != 429)\ndef _get_with_retries(url, payload):\n return GsSession.current.sync.get(url, payload=payload)\n\n\nclass SecurityMaster:\n \"\"\"Security Master\n\n The SecurityMaster class provides an interface to security lookup functions. This allows querying and retrieval of\n different security types (assets) based on a variety of different identifiers through point-in-time lookups.\n\n Uses the current PricingContext to provide as of dates if optional arguments are not provided. Will return\n the relevant asset subclass depending on the type of the security\n\n **See also**\n\n :class:`Asset`\n\n \"\"\"\n\n _source = SecurityMasterSource.ASSET_SERVICE\n _page_size = 1000\n\n @classmethod\n def __gs_asset_to_asset(cls, gs_asset: GsAsset) -> Asset:\n asset_type = gs_asset.type.value\n asset_entity: Dict = json.loads(json.dumps(gs_asset.as_dict(), cls=JSONEncoder))\n\n if asset_type in (GsAssetType.Single_Stock.value,):\n return Stock(gs_asset.id, gs_asset.name, gs_asset.exchange, gs_asset.currency, entity=asset_entity)\n\n if asset_type in (GsAssetType.ETF.value,):\n return ETF(\n gs_asset.id,\n gs_asset.assetClass,\n gs_asset.name,\n gs_asset.exchange,\n gs_asset.currency,\n entity=asset_entity,\n )\n\n if asset_type in (\n GsAssetType.Index.value,\n GsAssetType.Access.value,\n GsAssetType.Multi_Asset_Allocation.value,\n GsAssetType.Risk_Premia.value,\n GsAssetType.Systematic_Hedging.value,\n ):\n from gs_quant.markets.index import Index\n\n return Index(\n gs_asset.id,\n gs_asset.assetClass,\n gs_asset.name,\n gs_asset.exchange,\n gs_asset.currency,\n entity=asset_entity,\n )\n\n if asset_type in (GsAssetType.Custom_Basket.value, GsAssetType.Research_Basket.value):\n from gs_quant.markets.baskets import Basket\n\n return Basket(gs_asset=gs_asset)\n\n if asset_type in (GsAssetType.Future.value,):\n return Future(gs_asset.id, gs_asset.assetClass, gs_asset.name, gs_asset.currency, entity=asset_entity)\n\n if asset_type in (GsAssetType.Cross.value,):\n return Cross(gs_asset.id, gs_asset.name, entity=asset_entity, asset_class=gs_asset.assetClass)\n\n if asset_type in (GsAssetType.Currency.value,):\n return Currency(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.Rate.value,):\n return Rate(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.Cash.value,):\n return Cash(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.WeatherIndex.value,):\n return WeatherIndex(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.Swap.value,):\n return Swap(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.Option.value,):\n return Option(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.CommodityReferencePrice.value,):\n return CommodityReferencePrice(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.CommodityNaturalGasHub.value,):\n return CommodityNaturalGasHub(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.CommodityEUNaturalGasHub.value,):\n return CommodityEUNaturalGasHub(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.CommodityPowerNode.value,):\n return CommodityPowerNode(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.CommodityPowerAggregatedNodes.value,):\n return CommodityPowerAggregatedNodes(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.Bond.value,):\n return Bond(gs_asset.id, gs_asset.name, gs_asset.assetClass or AssetClass.Credit, entity=asset_entity)\n\n if asset_type in (GsAssetType.Commodity.value,):\n return Commodity(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.FutureMarket.value,):\n return FutureMarket(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n if asset_type in (GsAssetType.FutureContract.value,):\n return FutureContract(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n # workaround as casing is being migrated\n if asset_type == GsAssetType.Cryptocurrency.value:\n return Cryptocurrency(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n if asset_type == GsAssetType.Forward.value:\n return Forward(gs_asset.id, gs_asset.assetClass, gs_asset.name, entity=asset_entity)\n\n if asset_type == GsAssetType.Fund.value:\n return Fund(gs_asset.id, gs_asset.name, gs_asset.assetClass, entity=asset_entity)\n\n if asset_type == GsAssetType.Default_Swap.value:\n return DefaultSwap(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type == GsAssetType.Swaption.value:\n return Swaption(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type == GsAssetType.Binary.value:\n return Binary(gs_asset.id, gs_asset.name, gs_asset.assetClass, entity=asset_entity)\n\n if asset_type == GsAssetType.XccySwapMTM.value:\n return XccySwapMTM(gs_asset.id, gs_asset.name, entity=asset_entity)\n\n if asset_type == GsAssetType.Mutual_Fund.value:\n return MutualFund(gs_asset.id, gs_asset.name, gs_asset.asset_class, entity=asset_entity)\n\n raise TypeError(f'unsupported asset type {asset_type}')\n\n @classmethod\n def __asset_type_to_gs_types(cls, asset_type: AssetType) -> Tuple[GsAssetType, ...]:\n asset_map = {\n AssetType.STOCK: (GsAssetType.Single_Stock,),\n AssetType.INDEX: (\n GsAssetType.Index,\n GsAssetType.Multi_Asset_Allocation,\n GsAssetType.Risk_Premia,\n GsAssetType.Access,\n ),\n AssetType.ETF: (GsAssetType.ETF, GsAssetType.ETN),\n AssetType.CUSTOM_BASKET: (GsAssetType.Custom_Basket,),\n AssetType.RESEARCH_BASKET: (GsAssetType.Research_Basket,),\n AssetType.FUTURE: (GsAssetType.Future,),\n AssetType.RATE: (GsAssetType.Rate,),\n }\n\n return asset_map.get(asset_type)\n\n @classmethod\n def set_source(cls, source: SecurityMasterSource):\n cls._source = source\n\n @classmethod\n def get_asset_query(\n cls,\n id_value: Union[str, List[str]],\n id_type: Union[AssetIdentifier, SecurityIdentifier],\n as_of: Union[dt.date, dt.datetime] = None,\n exchange_code: ExchangeCode = None,\n asset_type: AssetType = None,\n ) -> Tuple[Dict, dt.datetime]:\n if not as_of:\n current = PricingContext.current\n if not current.is_entered:\n with current:\n as_of = current.pricing_date\n else:\n as_of = current.pricing_date\n if isinstance(as_of, dt.date):\n as_of = dt.datetime.combine(as_of, dt.time(0, 0), dt.timezone.utc)\n query = {\"id\" if id_type == AssetIdentifier.MARQUEE_ID else id_type.value.lower(): id_value}\n if exchange_code is not None:\n query['exchange'] = exchange_code.value\n if asset_type is not None:\n query['type'] = [t.value for t in cls.__asset_type_to_gs_types(asset_type)]\n return query, as_of\n\n @classmethod\n def _get_asset_results(cls, results, sort_by_rank) -> Asset:\n if sort_by_rank:\n result = get(results, '0')\n if result:\n result = GsAsset.from_dict(result)\n else:\n result = next(iter(results), None)\n if result:\n return cls.__gs_asset_to_asset(result)\n return None\n\n @classmethod\n def _get_many_assets_results(cls, results) -> List[Asset]:\n if results is not None:\n return [cls.__gs_asset_to_asset(result) for result in results]\n return []\n\n @classmethod\n def get_asset(\n cls,\n id_value: str,\n id_type: Union[AssetIdentifier, SecurityIdentifier],\n as_of: Union[dt.date, dt.datetime] = None,\n exchange_code: ExchangeCode = None,\n asset_type: AssetType = None,\n sort_by_rank: bool = True,\n fields: Optional[List[str]] = None,\n ) -> Asset:\n \"\"\"\n Get an asset by identifier and identifier type\n\n :param id_value: identifier value\n :param id_type: identifier type\n :param exchange_code: exchange code\n :param asset_type: asset type\n :param as_of: As of date for query\n :param sort_by_rank: whether to sort assets by rank. This flag is ignored when using SecMasterContext\n :param fields: asset fields to return\n :return: Asset object or None\n\n **Usage**\n\n Get asset object using a specified identifier and identifier type. Where the identifiers are temporal (and can\n change over time), will use the current MarketContext to evaluate based on the specified date.\n\n **Examples**\n\n Get asset by bloomberg id:\n\n >>> gs = SecurityMaster.get_asset(\"GS UN\", AssetIdentifier.BLOOMBERG_ID)\n\n Get asset by ticker and exchange code:\n\n >>> gs = SecurityMaster.get_asset(\"GS\", AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE)\n\n Get asset by ticker and asset type:\n\n >>> spx = SecurityMaster.get_asset(\"SPX\", AssetIdentifier.TICKER, asset_type=AssetType.INDEX)\n\n **See also**\n\n :class:`AssetIdentifier`\n :func:`get_many_assets`\n\n \"\"\"\n if cls._source == SecurityMasterSource.SECURITY_MASTER:\n if not isinstance(id_type, SecurityIdentifier):\n raise MqTypeError('expected a security identifier')\n if exchange_code or asset_type:\n raise NotImplementedError('argument not implemented for Security Master (supported in Asset Service)')\n return cls._get_security_master_asset(id_value, id_type, as_of=as_of, fields=fields)\n\n if id_type is AssetIdentifier.MARQUEE_ID:\n gs_asset = GsAssetApi.get_asset(id_value)\n return cls.__gs_asset_to_asset(gs_asset)\n\n query, as_of = cls.get_asset_query(id_value, id_type, as_of, exchange_code, asset_type)\n if sort_by_rank:\n results = GsAssetApi.get_many_assets(as_of=as_of, return_type=dict, order_by=['>rank'], **query)\n return cls._get_asset_results(results, sort_by_rank)\n else:\n results = GsAssetApi.get_many_assets(as_of=as_of, **query)\n return cls._get_asset_results(results, sort_by_rank)\n\n @classmethod\n async def get_asset_async(\n cls,\n id_value: str,\n id_type: Union[AssetIdentifier, SecurityIdentifier],\n as_of: Union[dt.date, dt.datetime] = None,\n exchange_code: ExchangeCode = None,\n asset_type: AssetType = None,\n sort_by_rank: bool = True,\n fields: Optional[List[str]] = None,\n ) -> Asset:\n \"\"\"\n Get an asset by identifier and identifier type\n\n :param id_value: identifier value\n :param id_type: identifier type\n :param exchange_code: exchange code\n :param asset_type: asset type\n :param as_of: As of date for query\n :param sort_by_rank: whether to sort assets by rank. This flag is ignored when using SecMasterContext\n :param fields: asset fields to return\n :return: Asset object or None\n\n **Usage**\n\n Get asset object using a specified identifier and identifier type. Where the identifiers are temporal (and can\n change over time), will use the current MarketContext to evaluate based on the specified date.\n\n **Examples**\n\n Get asset by bloomberg id:\n\n >>> gs = await SecurityMaster.get_asset_async(\"GS UN\", AssetIdentifier.BLOOMBERG_ID)\n\n Get asset by ticker and exchange code:\n\n >>> gs = await SecurityMaster.get_asset_async(\"GS\", AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE)\n\n Get asset by ticker and asset type:\n\n >>> spx = await SecurityMaster.get_asset_async(\"SPX\", AssetIdentifier.TICKER, asset_type=AssetType.INDEX)\n\n **See also**\n\n :class:`AssetIdentifier`\n :func:`get_many_assets`\n\n \"\"\"\n if cls._source == SecurityMasterSource.SECURITY_MASTER:\n if not isinstance(id_type, SecurityIdentifier):\n raise MqTypeError('expected a security identifier')\n if exchange_code or asset_type:\n raise NotImplementedError('argument not implemented for Security Master (supported in Asset Service)')\n return await cls._get_security_master_asset_async(id_value, id_type, as_of=as_of, fields=fields)\n\n if id_type is AssetIdentifier.MARQUEE_ID:\n gs_asset = await GsAssetApi.get_asset_async(id_value)\n return cls.__gs_asset_to_asset(gs_asset)\n\n query, as_of = cls.get_asset_query(id_value, id_type, as_of, exchange_code, asset_type)\n if sort_by_rank:\n results = await GsAssetApi.get_many_assets_async(as_of=as_of, return_type=dict, order_by=['>rank'], **query)\n return cls._get_asset_results(results, sort_by_rank)\n else:\n results = await GsAssetApi.get_many_assets_async(as_of=as_of, **query)\n return cls._get_asset_results(results, sort_by_rank)\n\n @classmethod\n def get_many_assets(\n cls,\n id_values: List[str],\n id_type: AssetIdentifier,\n limit: int = 100,\n as_of: Union[dt.date, dt.datetime] = None,\n exchange_code: ExchangeCode = None,\n sort_by_rank: bool = True,\n ) -> List[Asset]:\n \"\"\"\n Get an asset by identifier and identifier type\n\n :param id_values: identifier values\n :param id_type: identifiers type\n :param limit: max number of results\n :param exchange_code: exchange code\n :param as_of: As of date for query\n :param sort_by_rank: whether to sort assets by rank. This flag is ignored when using SecMasterContext\n :return: list of Asset objects or None\n\n **Usage**\n\n Get asset object using a specified identifier and identifier type. Where the identifiers are temporal (and can\n change over time), will use the current MarketContext to evaluate based on the specified date.\n\n **Examples**\n\n Get asset by bloomberg id:\n\n >>> gs = SecurityMaster.get_many_assets([\"GS UN\", \"MSFT UW\"], AssetIdentifier.BLOOMBERG_ID)\n\n Get asset by ticker and exchange code:\n\n >>> gs = SecurityMaster.get_many_assets([\"GS\", \"MSFT\"], AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE)\n\n Get asset by ticker and asset type:\n\n >>> spx = SecurityMaster.get_many_assets([\"SPX\"], AssetIdentifier.TICKER, asset_type=AssetType.INDEX)\n\n **See also**\n\n :class:`AssetIdentifier`\n :func:`get_many_assets`\n\n \"\"\"\n span = Tracer.active_span()\n tracer = Tracer('SecurityMaster.get_many_assets') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n if scope and scope.span:\n scope.span.set_tag(f'request.ids.{id_type.value}', len(id_values) if id_values else 0)\n query, as_of = cls.get_asset_query(id_values, id_type, as_of, exchange_code)\n if sort_by_rank:\n results = GsAssetApi.get_many_assets(as_of=as_of, order_by=['>rank'], limit=limit, **query)\n else:\n results = GsAssetApi.get_many_assets(as_of=as_of, limit=limit, **query)\n return cls._get_many_assets_results(results)\n\n @classmethod\n async def get_many_assets_async(\n cls,\n id_values: List[str],\n id_type: AssetIdentifier,\n limit: int = 100,\n as_of: Union[dt.date, dt.datetime] = None,\n exchange_code: ExchangeCode = None,\n sort_by_rank: bool = True,\n ) -> List[Asset]:\n \"\"\"\n Get an asset by identifier and identifier type\n\n :param id_values: identifier values\n :param id_type: identifiers type\n :param limit: max number of results\n :param exchange_code: exchange code\n :param as_of: As of date for query\n :param sort_by_rank: whether to sort assets by rank. This flag is ignored when using SecMasterContext\n :return: list of Asset objects or None\n\n **Usage**\n\n Get asset object using a specified identifier and identifier type. Where the identifiers are temporal (and can\n change over time), will use the current MarketContext to evaluate based on the specified date.\n\n **Examples**\n\n Get asset by bloomberg id:\n\n >>> gs = await SecurityMaster.get_many_assets_async([\"GS UN\"], AssetIdentifier.BLOOMBERG_ID)\n\n Get asset by ticker and exchange code:\n\n >>> gs = await SecurityMaster.get_many_assets_async([\"GS\"], AssetIdentifier.TICKER,\n >>> exchange_code=ExchangeCode.NYSE)\n\n Get asset by ticker and asset type:\n\n >>> spx = await SecurityMaster.get_many_assets_async([\"SPX\"], AssetIdentifier.TICKER,\n >>> asset_type=AssetType.INDEX)\n\n **See also**\n\n :class:`AssetIdentifier`\n :func:`get_many_assets`\n\n \"\"\"\n span = Tracer.active_span()\n tracer = Tracer('SecurityMaster.get_many_assets_async') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n if scope and scope.span:\n scope.span.set_tag(f'request.ids.{id_type.value}', len(id_values) if id_values else 0)\n query, as_of = cls.get_asset_query(id_values, id_type, as_of, exchange_code)\n if sort_by_rank:\n results = await GsAssetApi.get_many_assets_async(as_of=as_of, order_by=['>rank'], limit=limit, **query)\n else:\n results = await GsAssetApi.get_many_assets_async(as_of=as_of, limit=limit, **query)\n return cls._get_many_assets_results(results)\n\n @classmethod\n def _get_security_master_asset_params(\n cls,\n id_value: str,\n id_type: SecurityIdentifier,\n as_of: Union[dt.date, dt.datetime] = None,\n fields: Optional[List[str]] = None,\n ) -> dict:\n as_of = as_of or dt.datetime(2100, 1, 1)\n type_ = id_type.value\n params = {\n type_: id_value,\n 'asOfDate': as_of.strftime('%Y-%m-%d'), # TODO: update endpoint to take times\n }\n if fields is not None:\n request_fields = {'identifiers', 'assetClass', 'type', 'currency', 'exchange', 'id'}\n request_fields.update(fields)\n params['fields'] = request_fields\n return params\n\n @classmethod\n def _get_security_master_asset_response(cls, response) -> SecMasterAsset:\n if response['totalResults'] == 0:\n return None\n asset_dict = response['results'][0]\n asset_id = asset_dict['identifiers'].get(\"assetId\", None)\n # Converting dict to Asset Class\n asset_name = asset_dict.get('name', None)\n asset_exchange = asset_dict.get(\"exchange\").get(\"name\", None) if \"exchange\" in asset_dict else None\n asset_currency = asset_dict.get('currency', None)\n try:\n asset_type = AssetType(asset_dict['type'])\n asset_class = AssetClass(asset_dict['assetClass'])\n return SecMasterAsset(\n id_=asset_id,\n asset_type=asset_type,\n asset_class=asset_class,\n name=asset_name,\n exchange=asset_exchange,\n currency=asset_currency,\n entity=asset_dict,\n )\n except ValueError:\n raise NotImplementedError(\n f\"Not yet implemented for AssetType={asset_dict['type']}, AssetClass={asset_dict['assetClass']}.\"\n )\n\n @classmethod\n def _get_security_master_asset(\n cls,\n id_value: str,\n id_type: SecurityIdentifier,\n as_of: Union[dt.date, dt.datetime] = None,\n fields: Optional[List[str]] = None,\n ) -> SecMasterAsset:\n params = cls._get_security_master_asset_params(id_value, id_type, as_of, fields)\n response = GsSession.current.sync.get('/markets/securities', payload=params)\n return cls._get_security_master_asset_response(response)\n\n @classmethod\n async def _get_security_master_asset_async(\n cls,\n id_value: str,\n id_type: SecurityIdentifier,\n as_of: Union[dt.date, dt.datetime] = None,\n fields: Optional[List[str]] = None,\n ) -> SecMasterAsset:\n params = cls._get_security_master_asset_params(id_value, id_type, as_of, fields)\n response = await GsSession.current.async_.get('/markets/securities', payload=params)\n return cls._get_security_master_asset_response(response)\n\n @classmethod\n def get_identifiers(\n cls,\n id_values: List[str],\n id_type: SecurityIdentifier,\n as_of: dt.datetime = None,\n start: dt.datetime = None,\n end: dt.datetime = None,\n ) -> dict:\n \"\"\"\n Get identifiers for given assets.\n\n :param id_values: identifier values e.g. ['GS UN']\n :param id_type: identifier type e.g. BBID\n :param as_of: point in time to use for resolving given ids to assets\n :param start: restrict results to ids updated after this time\n :param end: restrict results to ids updated before this time\n :return: dict from IDs (of id_type) to available identifiers\n \"\"\"\n if cls._source != SecurityMasterSource.SECURITY_MASTER:\n raise NotImplementedError(\"method not available when using Asset Service\")\n\n as_of = as_of or dt.datetime.now()\n start = start or dt.datetime(1970, 1, 1)\n end = end or dt.datetime(2100, 1, 1)\n\n type_ = id_type.value\n params = {\n type_: id_values,\n 'fields': ['id', 'identifiers'],\n 'asOfDate': as_of.strftime('%Y-%m-%d'), # TODO: update endpoint to take times\n }\n\n r = GsSession.current.sync.get('/markets/securities', payload=params)\n id_map = {}\n for asset in r['results']:\n id_map[asset['identifiers'][type_]] = asset['id']\n\n if len(id_map) == 0:\n return {}\n\n output = {}\n for k, v in id_map.items():\n r = GsSession.current.sync.get(f'/markets/securities/{v}/identifiers')\n\n piece = []\n for e in r['results']:\n piece.append(e)\n output[k] = piece\n\n return output\n\n @staticmethod\n def asset_type_to_str(asset_class: AssetClass, asset_type: AssetType):\n if asset_type == AssetType.STOCK:\n return \"Common Stock\"\n if asset_type == AssetType.INDEX and asset_class == AssetClass.Equity:\n return \"Equity Index\"\n return asset_type.value\n\n @classmethod\n def get_all_identifiers_gen(\n cls,\n class_: AssetClass = None,\n types: Optional[List[AssetType]] = None,\n as_of: dt.datetime = None,\n *,\n id_type: SecurityIdentifier = SecurityIdentifier.ID,\n use_offset_key=True,\n sleep=0.5,\n ) -> Generator[dict, None, None]:\n \"\"\"\n Get identifiers for all matching assets. Returns a generator iterator so that the caller can load each page of\n results using next().\n\n :param class_: if not None, restrict results to assets of given class e.g. Equity\n :param types: if not None, restrict results to given types e.g. Stock\n :param as_of: point in time for which identifiers are fetched\n :param id_type: identifier type to use for keys of results\n :param use_offset_key: whether to use offset keys for pagination (required for large result sets)\n :param sleep: seconds to sleep between API calls (to avoid server-side throttling)\n :return: a generator iterator that yields dicts from id (of the id_type) to available identifiers\n \"\"\"\n if cls._source != SecurityMasterSource.SECURITY_MASTER:\n raise NotImplementedError(\"method not available when using Asset Service\")\n\n as_of = as_of or dt.datetime.now()\n if types is not None:\n p = partial(cls.asset_type_to_str, class_)\n types = set(map(p, types))\n\n params = {\n 'fields': ['id', 'identifiers', 'assetClass', 'type'],\n 'asOfDate': as_of.date(),\n 'limit': cls._page_size,\n 'type': types,\n }\n\n while True:\n r = _get_with_retries('/markets/securities', params)\n if r['totalResults'] == 0:\n return\n\n output = {}\n\n for e in r['results']:\n # TODO: perform assetClass filtering on server side once assetClass is supported (just Equties is\n # supported for now)\n if class_ is None or e['assetClass'] == class_.value:\n box = e['identifiers']\n box['id'] = e['id'] # copy top-level security id into result\n key = box[id_type.value]\n if key in box:\n _logger.debug(f'encountered duplicate key {key}')\n output[key] = box\n\n yield output\n if use_offset_key:\n if 'offsetKey' not in r:\n return\n params['offsetKey'] = r['offsetKey']\n else:\n params['offset'] = params.get('offset', 0) + cls._page_size\n if params['offset'] + params['limit'] > 10000:\n _logger.warning('reached result size limit; enable use of offset keys to retrieve all results')\n return\n time.sleep(sleep)\n\n @classmethod\n def get_all_identifiers(\n cls,\n class_: AssetClass = None,\n types: Optional[List[AssetType]] = None,\n as_of: dt.datetime = None,\n *,\n id_type: SecurityIdentifier = SecurityIdentifier.ID,\n use_offset_key=True,\n sleep=0.5,\n ) -> Dict[str, dict]:\n \"\"\"\n Get identifiers for all matching assets.\n\n :param class_: if not None, restrict results to assets of given class e.g. Equity\n :param types: if not None, restrict results to given types e.g. Stock\n :param as_of: point in time for which identifiers are fetched\n :param id_type: identifier type to use for keys of results\n :param use_offset_key: whether to use offset keys for pagination (required for large result sets)\n :param sleep: seconds to sleep between API calls (to avoid server-side throttling)\n :return: dict from id (of the id_type) to available identifiers\n \"\"\"\n gen = cls.get_all_identifiers_gen(\n class_, types, as_of, id_type=id_type, use_offset_key=use_offset_key, sleep=sleep\n )\n accumulator = dict()\n while True:\n try:\n accumulator.update(next(gen))\n except StopIteration:\n return accumulator\n\n @classmethod\n def map_identifiers(\n cls,\n input_type: SecurityIdentifier,\n ids: Iterable[str],\n output_types: Iterable[SecurityIdentifier] = frozenset([SecurityIdentifier.GSID]),\n start_date: dt.date = None,\n end_date: dt.date = None,\n as_of_date: dt.date = None,\n ) -> Dict[dt.date, dict]:\n \"\"\"\n Map to other identifier types, from given IDs.\n\n :param input_type: type of input IDs\n :param ids: security IDs\n :param output_types: types of IDs to map to\n :param start_date: first as-of date (defaults to current date)\n :param end_date: last as-of date (defaults to current date)\n :param as_of_date: an exact as-of date for mapping\n :return: dict containing mappings for as-of date(s)\n\n **Examples**\n\n Get CUSIP for GS UN:\n >>> result = SecurityMaster.map_identifiers(SecurityIdentifier.BBID, [\"GS UN\"], [SecurityIdentifier.CUSIP])\n\n Get Bloomberg ticker for 104563 as-of a past date:\n >>> result = SecurityMaster.map_identifiers(SecurityIdentifier.GSID, [\"104563\"], [SecurityIdentifier.BBG],\n ... as_of_date=dt.date(2021, 4, 19))\n \"\"\"\n if isinstance(ids, str):\n raise MqTypeError(\"expected an iterable of strings e.g. list of strings\")\n\n def get_asset_id_type(type_: SecurityIdentifier):\n try:\n return GsIdType[type_.value]\n except KeyError:\n raise MqValueError(f'unsupported type {type_.value}')\n\n if cls._source == SecurityMasterSource.ASSET_SERVICE:\n output_types = list(output_types)\n if len(output_types) != 1:\n raise MqValueError('provide exactly one output type')\n if (start_date or end_date) is not None:\n raise MqValueError('use as_of_date instead of start_date and/or end_date')\n if as_of_date is None:\n as_of_date = dt.date.today()\n\n input_type = get_asset_id_type(input_type)\n output_type = get_asset_id_type(output_types[0])\n as_of = None if as_of_date is None else dt.datetime.combine(as_of_date, dt.time(tzinfo=dt.timezone.utc))\n result = GsAssetApi.map_identifiers(input_type, output_type, list(ids), as_of=as_of, multimap=True)\n if len(result) == 0:\n return result\n inner = {k: {output_type.name: v} for k, v in result.items()}\n return {as_of.strftime('%Y-%m-%d'): inner}\n\n assert cls._source == SecurityMasterSource.SECURITY_MASTER\n params = {\n input_type.value: list(ids),\n 'toIdentifiers': [identifier.value for identifier in output_types],\n 'compact': True,\n }\n if as_of_date is not None:\n if (start_date or end_date) is not None:\n raise MqValueError('provide (start date / end date) or as-of date, but not both')\n params['startDate'] = as_of_date\n params['endDate'] = as_of_date\n\n if start_date is not None:\n params['startDate'] = start_date\n if end_date is not None:\n params['endDate'] = end_date\n r = _get_with_retries('/markets/securities/map', params)\n\n results = r['results']\n if isinstance(results, dict):\n return results\n\n output = dict()\n date_format = '%Y-%m-%d'\n date_delta = dt.timedelta(days=1)\n for row in results:\n current = dt.datetime.strptime(row['startDate'], date_format)\n end = dt.datetime.strptime(row['endDate'], date_format)\n while current <= end:\n outer = output.setdefault(current, dict())\n inner = outer.setdefault(row[\"input\"], dict())\n output_type = row[\"outputType\"]\n output_value = row[\"outputValue\"]\n if output_type == \"ric\":\n if SecurityIdentifier.RIC in output_types:\n values = inner.setdefault('ric', [])\n if output_value not in values:\n values.append(output_value)\n if SecurityIdentifier.ASSET_ID in output_types and \"assetId\" in row:\n values = inner.setdefault('assetId', [])\n asset_id = row['assetId']\n if asset_id not in values:\n values.append(asset_id)\n elif output_type == \"bbg\":\n if SecurityIdentifier.BBG in output_types:\n if SecurityIdentifier.BBG.value not in inner:\n inner[SecurityIdentifier.BBG.value] = []\n inner[SecurityIdentifier.BBG.value].append(output_value)\n if SecurityIdentifier.BBID in output_types:\n exchange = row.get('exchange')\n if SecurityIdentifier.BBID.value not in inner:\n inner[SecurityIdentifier.BBID.value] = []\n if exchange is not None:\n inner[SecurityIdentifier.BBID.value].append(f\"{output_value} {exchange}\")\n else:\n inner[SecurityIdentifier.BBID.value].append(f\"{output_value}\")\n if SecurityIdentifier.BCID in output_types:\n composite_exchange = row.get('compositeExchange')\n if composite_exchange is not None:\n if SecurityIdentifier.BCID.value not in inner:\n inner[SecurityIdentifier.BCID.value] = []\n inner[SecurityIdentifier.BCID.value].append(f\"{output_value} {composite_exchange}\")\n else:\n if SecurityIdentifier(output_type) in output_types:\n if output_type not in inner:\n inner[output_type] = []\n if output_value not in inner[output_type]:\n inner[output_type].append(output_value)\n\n current += date_delta\n\n # much faster to run strftime (once for each date) at the end\n return {k.strftime(date_format): v for k, v in output.items()}\n" }, { "path": "gs_quant/models/__init__.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"" }, { "path": "gs_quant/models/epidemiology.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import Type, Union\n\nimport numpy as np\nfrom lmfit import minimize, Parameters, report_fit\nfrom scipy.integrate import odeint\n\n\"\"\"\nStatistical models for the transmission of infectious diseases\n\"\"\"\n\n\nclass CompartmentalModel(ABC):\n @classmethod\n @abstractmethod\n def calibrate(cls, xs, t, parameters) -> tuple:\n \"\"\"Should be of form callable(y, t, ...)\n See https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.odeint.html\"\"\"\n raise NotImplementedError\n\n @classmethod\n @abstractmethod\n def get_parameters(cls, *args, **kwargs) -> tuple:\n raise NotImplementedError\n\n\nclass SIR(CompartmentalModel):\n \"\"\"SIR Model\"\"\"\n\n @classmethod\n def calibrate(cls, xs: tuple, t: float, parameters: Union[Parameters, tuple]) -> tuple:\n \"\"\"\n SIR model derivatives at t.\n\n :param xs: variables that we are solving for, i.e. [S]usceptible, [I]nfected, [R]emoved\n :param t: time parameter, inactive for this model\n :param parameters: parameters of the model (not including initial conditions), i.e. beta, gamma, N\n :return: tuple, the derivatives dSdt, dIdt, dRdt of each of the S, I, R variables\n \"\"\"\n s, i, r = xs\n\n if isinstance(parameters, Parameters):\n beta = parameters['beta'].value\n gamma = parameters['gamma'].value\n N = parameters['N'].value\n elif isinstance(parameters, tuple):\n beta, gamma, N = parameters\n else:\n raise ValueError(\"Cannot recognize parameter input\")\n\n dSdt = -beta * s * i / N\n dIdt = beta * s * i / N - gamma * i\n dRdt = gamma * i\n\n return dSdt, dIdt, dRdt\n\n @classmethod\n def get_parameters(\n cls,\n S0: float,\n I0: float,\n R0: float,\n N: float,\n beta: float = 0.2,\n gamma: float = 0.1,\n beta_max: float = 10,\n gamma_max: float = 1,\n S0_fixed: bool = True,\n S0_max: float = 1e6,\n beta_fixed: bool = False,\n gamma_fixed: bool = False,\n R0_fixed: bool = True,\n R0_max: float = 1e6,\n I0_fixed: bool = True,\n I0_max: float = 1e6,\n ) -> tuple:\n \"\"\"\n Produce a set of parameters for the SIR model.\n\n :param S0: initial number of susceptible in the population\n :param I0: initial number of infected in the population, usually set to 1\n :param R0: initial number of recovered/removed in the population, usually set to 0\n :param N: size of the population\n :param beta: transmission rate parameter\n :param gamma: recovery rate parameter\n :param beta_max: maximum value to consider for beta during parameter fitting\n :param gamma_max: maximum value of gamma to consider during parameter fitting\n :param S0_fixed: whether to keep S0 fixed during fitting\n :param S0_max: maximum value of S0 to consider during parameter fitting\n :param R0_fixed: whether to keep R0 fixed during fitting\n :param R0_max: maximum value of R0 to consider during parameter fitting\n :param I0_fixed: whether to keep I0 fixed during fitting\n :param I0_max: maximum value of I0 to consider during parameter fitting\n :return: tuple[Parameters, list]: (parameters, a list of the names of the variables for initial conditions)\n \"\"\"\n parameters = Parameters()\n parameters.add('N', value=N, min=0, max=N, vary=False)\n parameters.add('S0', value=S0, min=0, max=S0_max, vary=not S0_fixed)\n parameters.add('I0', value=I0, min=0, max=I0_max, vary=not I0_fixed)\n parameters.add('R0', value=R0, min=0, max=R0_max, vary=not R0_fixed)\n parameters.add('beta', value=beta, min=0, max=beta_max, vary=not beta_fixed)\n parameters.add('gamma', value=gamma, min=0, max=gamma_max, vary=not gamma_fixed)\n initial_conditions = ['S0', 'I0', 'R0']\n\n return parameters, initial_conditions\n\n\nclass SEIR(CompartmentalModel):\n \"\"\"SEIR Model\"\"\"\n\n @classmethod\n def calibrate(cls, xs: tuple, t: float, parameters: Union[Parameters, tuple]) -> tuple:\n \"\"\"\n SEIR model derivatives at t.\n\n :param xs: variables that we are solving for, i.e. [S]usceptible, [E]xposed, [I]nfected, [R]emoved\n :param t: time parameter, inactive for this model\n :param parameters: parameters of the model (not including initial conditions), i.e. beta, gamma, sigma, N\n :return: tuple, the derivatives dSdt, dEdt, dIdt, dRdt of each of the S, E, I, R variables\n \"\"\"\n s, e, i, r = xs\n\n if isinstance(parameters, Parameters):\n beta = parameters['beta'].value\n gamma = parameters['gamma'].value\n sigma = parameters['sigma'].value\n N = parameters['N'].value\n elif isinstance(parameters, tuple):\n beta, gamma, sigma, N = parameters\n else:\n raise ValueError(\"Cannot recognize parameter input\")\n\n dSdt = -beta * s * i / N\n dEdt = beta * s * i / N - sigma * e\n dIdt = sigma * e - gamma * i\n dRdt = gamma * i\n\n return dSdt, dEdt, dIdt, dRdt\n\n @classmethod\n def get_parameters(\n cls,\n S0: float,\n E0: float,\n I0: float,\n R0: float,\n N: float,\n beta: float = 0.2,\n gamma: float = 0.1,\n sigma: float = 0.2,\n beta_max: float = 10,\n gamma_max: float = 1,\n sigma_max: float = 1,\n beta_fixed: bool = False,\n gamma_fixed: bool = False,\n sigma_fixed: bool = False,\n S0_fixed: bool = True,\n S0_max: float = 1e6,\n R0_fixed: bool = True,\n R0_max: float = 1e6,\n I0_fixed: bool = True,\n I0_max: float = 1e6,\n E0_fixed: bool = True,\n E0_max: float = 1e6,\n ) -> tuple:\n \"\"\"\n Produce a set of parameters for the SIR model.\n\n :param S0: initial number of susceptible in the population\n :param E0: initial number of exposed in the population\n :param I0: initial number of infected in the population, usually set to 1\n :param R0: initial number of recovered/removed in the population, usually set to 0\n :param N: size of the population\n :param beta: transmission rate parameter\n :param gamma: recovery rate parameter\n :param sigma: parameter controlling transition from exposed to infectious\n :param beta_max: maximum value to consider for beta during parameter fitting\n :param gamma_max: maximum value of gamma to consider during parameter fitting\n :param sigma_max: maximum value of gamma to consider during parameter fitting\n :param S0_fixed: whether to keep S0 fixed during fitting\n :param S0_max: maximum value of S0 to consider during parameter fitting\n :param E0_fixed: whether to keep E0 fixed during fitting\n :param E0_max: maximum value of E0 to consider during parameter fitting\n :param R0_fixed: whether to keep R0 fixed during fitting\n :param R0_max: maximum value of R0 to consider during parameter fitting\n :param I0_fixed: whether to keep I0 fixed during fitting\n :param I0_max: maximum value of I0 to consider during parameter fitting\n :return: tuple[Parameters, list]: (parameters, a list of the names of the variables for initial conditions)\n \"\"\"\n parameters = Parameters()\n parameters.add('N', value=N, min=0, max=N, vary=False)\n parameters.add('S0', value=S0, min=0, max=S0_max, vary=not S0_fixed)\n parameters.add('E0', value=E0, min=0, max=E0_max, vary=not E0_fixed)\n parameters.add('I0', value=I0, min=0, max=I0_max, vary=not I0_fixed)\n parameters.add('R0', value=R0, min=0, max=R0_max, vary=not R0_fixed)\n parameters.add('beta', value=beta, min=0, max=beta_max, vary=not beta_fixed)\n parameters.add('gamma', value=gamma, min=0, max=gamma_max, vary=not gamma_fixed)\n parameters.add('sigma', value=sigma, min=0, max=sigma_max, vary=not sigma_fixed)\n initial_conditions = ['S0', 'E0', 'I0', 'R0']\n\n return parameters, initial_conditions\n\n\ndef switch(t: float, T: float, eta: float = 0, xi: float = 0.1, nu: float = 0) -> float:\n \"\"\"\n Return a time-dependent factor that decreases exponentially, to scale parameters that are affected at some\n time point T.\n\n Ex. quarantine control measures were instated at some point T > t0, where t0 is the first day in available data.\n After this point, transmission rate was effectively reduced. This is modeled as a time-varying exponential decrease\n from 1 (no effect) to some fixed coefficient eta (the full effect) (ie. at time t0 it is 1 * beta, and by\n time T + nu, it is around eta * beta). If quarantine reduces transmission rate by 60%, eta would be 0.4. The\n paramater xi controls the steepness of this decrease, the nu controls the shift relative to time T at which the\n decrease peaks (ie. effects from quarantine measures at time T are visible at T+5 so nu=5). If nu=0, steepest\n point is at time T.\n\n To disable the 'switch', set eta = 1.0\n\n :param t: current time step\n :param T: some fixed time step at which the regime 'switches' - T is relative to t0, which is positioned at 0\n NOTE: this is not necessarily the steepest point of decrease. That is controlled by nu.\n :param eta: optional - the proportional reduction in transmission rate after control measures (ie. quarantine)\n :param xi: optional - the slope of the exponential decrease\n :param nu: optional - the shift of the exponential decrease\n :return:\n \"\"\"\n return eta + (1 - eta) / (1 + np.exp(xi * (t - T - nu)))\n\n\nclass SEIRCM(CompartmentalModel):\n \"\"\"SEIR Model from https://www.medrxiv.org/content/10.1101/2020.03.04.20031104v1.full.pdf\n with cumulative cases (C) and cumulative fatalities (M)\"\"\"\n\n @classmethod\n def calibrate(cls, xs: tuple, t: float, parameters: Union[Parameters, tuple]) -> tuple:\n \"\"\"\n SEIRCM model derivatives at t.\n\n :param xs: variables that we are solving for\n i.e. [S]usceptible, [E]xposed, [I]nfected, [R]emoved, [C]ases, [M]ortality\n :param t: time parameter\n :param parameters: parameters of the model (not including initial conditions)\n i.e. beta, gamma, sigma, eta, epsilon\n :return: the derivatives of each of the S, E, I, R, C, M variables\n \"\"\"\n s, e, i, r, c, m = xs\n\n if isinstance(parameters, Parameters):\n beta = parameters['beta'].value # transmission rate\n gamma = parameters['gamma'].value # removal rate\n sigma = parameters['sigma'].value # infection rate\n eta = parameters['eta'].value # control measure redux\n epsilon = parameters['sigma'].value # case fatality rate\n T_quarantine = parameters['T'].value # time of quarantine policy, relative to t=0 (first reported case)\n elif isinstance(parameters, tuple):\n beta, gamma, sigma, eta, epsilon, T_quarantine = parameters\n else:\n raise ValueError(\"Cannot recognize parameter input\")\n\n # total population\n N = s + e + i + r\n\n # if T_quarantine is 0, we are not considering effect of quarantine policy so scale factor is fixed at 1\n quarantine_factor = switch(t, T_quarantine, eta=eta) if T_quarantine else 1\n\n dSdt = -(quarantine_factor * beta) * s * i / N # susceptible -> exposed\n dEdt = (quarantine_factor * beta) * s * i / N - sigma * e # exposed -> infected AND recorded cases\n dIdt = sigma * e - gamma * i # infected -> removed (recovered AND dead)\n dRdt = (1 - epsilon) * gamma * i # recovered (from removed)\n dCdt = sigma * e # recorded cases (from infected)\n dMdt = epsilon * gamma * i # dead (from removed)\n\n return dSdt, dEdt, dIdt, dRdt, dCdt, dMdt\n\n @classmethod\n def get_parameters(\n cls,\n S0: float,\n E0: float,\n I0: float,\n R0: float,\n C0: float,\n M0: float,\n T_quarantine: float = 0,\n beta: float = 0.2,\n gamma: float = 0.1,\n sigma: float = 0.2,\n eta: float = 0.6,\n epsilon: float = 0.02,\n beta_max: float = 10,\n gamma_max: float = 1,\n sigma_max: float = 1,\n eta_max: float = 1,\n epsilon_max: float = 1,\n beta_fixed: bool = False,\n gamma_fixed: bool = False,\n sigma_fixed: bool = False,\n eta_fixed: bool = False,\n epsilon_fixed: bool = False,\n S0_fixed: bool = True,\n S0_max: float = 1e6,\n R0_fixed: bool = True,\n R0_max: float = 1e6,\n I0_fixed: bool = True,\n I0_max: float = 1e6,\n E0_fixed: bool = True,\n E0_max: float = 1e6,\n C0_fixed: bool = True,\n C0_max: float = 1e6,\n M0_fixed: bool = True,\n M0_max: float = 1e6,\n ) -> tuple:\n \"\"\"\n Produce a set of parameters for the SIERCM model.\n\n :param S0: initial number of susceptible in the population\n :param E0: initial number of exposed in the population\n :param I0: initial number of infected in the population, usually set to 1\n :param R0: initial number of recovered/removed in the population, usually set to 0\n :param C0: initial number of cumulative infected cases\n :param M0: initial number of cumulative fatalities\n :param T_quarantine: relative time at which quarantine policy goes in effect. If 0, not used.\n :param beta: transmission rate parameter\n :param gamma: recovery rate parameter\n :param sigma: parameter controlling transition from exposed to infectious\n :param eta: parameter controlling proportion by which quarantine measure reduces rate of transmission\n :param epsilon: parameter controlling rate of death\n :param beta_max: maximum value to consider for beta during parameter fitting\n :param gamma_max: maximum value of gamma to consider during parameter fitting\n :param sigma_max: maximum value to consider for sigma during parameter fitting\n :param eta_max: maximum value to consider for eta during parameter fitting\n :param epsilon_max: maximum value to consider for epsilon during parameter fitting\n :param beta_fixed: whether to keep beta fixed during fitting\n :param gamma_fixed: whether to keep gamma fixed during fitting\n :param sigma_fixed: whether to keep sigma fixed during fitting\n :param eta_fixed: whether to keep eta fixed during fitting\n :param epsilon_fixed: whether to keep epsilon fixed during fitting\n :param S0_fixed: whether to keep S0 fixed during fitting\n :param S0_max: maximum value of S0 to consider during parameter fitting\n :param E0_fixed: whether to keep E0 fixed during fitting\n :param E0_max: maximum value of E0 to consider during parameter fitting\n :param R0_fixed: whether to keep R0 fixed during fitting\n :param R0_max: maximum value of R0 to consider during parameter fitting\n :param I0_fixed: whether to keep I0 fixed during fitting\n :param I0_max: maximum value of I0 to consider during parameter fitting\n :param C0_fixed: whether to keep C0 fixed during fitting\n :param C0_max: maximum value of C0 to consider during parameter fitting\n :param M0_fixed: whether to keep M0 fixed during fitting\n :param M0_max: maximum value of M0 to consider during parameter fitting\n :return: tuple[Parameters, list]: (parameters, a list of the names of the variables for initial conditions)\n \"\"\"\n parameters = Parameters()\n parameters.add('T', value=T_quarantine, min=0, max=T_quarantine, vary=False)\n parameters.add('S0', value=S0, min=0, max=S0_max, vary=not S0_fixed)\n parameters.add('E0', value=E0, min=0, max=E0_max, vary=not E0_fixed)\n parameters.add('I0', value=I0, min=0, max=I0_max, vary=not I0_fixed)\n parameters.add('R0', value=R0, min=0, max=R0_max, vary=not R0_fixed)\n parameters.add('C0', value=C0, min=0, max=C0_max, vary=not C0_fixed)\n parameters.add('M0', value=M0, min=0, max=M0_max, vary=not M0_fixed)\n parameters.add('beta', value=beta, min=0, max=beta_max, vary=not beta_fixed)\n parameters.add('gamma', value=gamma, min=0, max=gamma_max, vary=not gamma_fixed)\n parameters.add('sigma', value=sigma, min=0, max=sigma_max, vary=not sigma_fixed)\n parameters.add('eta', value=eta, min=0, max=eta_max, vary=not eta_fixed)\n parameters.add('epsilon', value=epsilon, min=0, max=epsilon_max, vary=not epsilon_fixed)\n\n initial_conditions = ['S0', 'E0', 'I0', 'R0', 'C0', 'M0']\n\n return parameters, initial_conditions\n\n\nclass SEIRCMAgeStratified(CompartmentalModel):\n \"\"\"Age-structured SEIRCM Model from https://www.medrxiv.org/content/10.1101/2020.03.04.20031104v1.full.pdf\"\"\"\n\n @classmethod\n def calibrate(cls, y: list, t: float, parameters: Parameters) -> list:\n \"\"\"\n SEIR model derivatives at t.\n\n :param y: variables that we are solving for\n i.e. [S]usceptible, [E]xposed, [I]nfected, [R]emoved, [C]ases, [M]ortality\n :param t: time parameter\n :param parameters: parameters of the model (not including initial conditions)\n i.e. beta, gamma, sigma, eta, epsilon\n :return: the derivatives dydt of each of the variable in y\n \"\"\"\n beta = parameters['beta'].value # transmission rate\n gamma = parameters['gamma'].value # removal rate\n sigma = parameters['sigma'].value # infection rate\n eta = parameters['eta'].value # control measure redux\n T_quarantine = parameters['T'].value # time of quarantine policy, relative to t=0 (first reported case)\n K = parameters['K'].value # number of age groups\n\n # y is of dimension (6 * K) x 1, where the first K are 'S', next K are 'E', and so on for each age group...\n assert len(y) == 6 * K, f'Error: SEIRCM states not organized into {K} age groups!'\n dydt = [0] * len(y)\n\n def epsilon(k): # case fatality rate per age group\n return parameters[f'epsilon_{k}'].value\n\n N = sum(y[: 4 * K]) # sum(S) + sum(E) + sum(I) + sum(R) where sum is over age groups\n I_total = sum(y[2 * K : 3 * K]) # total number of infectious people across age groups\n s, e, i = lambda k: y[k], lambda k: y[K + k], lambda k: y[2 * K + k]\n\n # if T_quarantine is 0, we are not considering effect of quarantine policy so scale factor is fixed at 1\n quarantine_factor = switch(t, T_quarantine, eta=eta) if T_quarantine else 1\n\n for k in range(K):\n dydt[k] = -(quarantine_factor * beta) * s(k) * I_total / N # susceptible -> exposed\n dydt[K + k] = (quarantine_factor * beta) * s(k) * I_total / N - sigma * e(k) # exposed -> infected\n dydt[2 * K + k] = sigma * e(k) - gamma * i(k) # infected -> removed\n dydt[3 * K + k] = (1 - epsilon(k)) * gamma * i(k) # -> cumulative recovered (from removed)\n dydt[4 * K + k] = sigma * e(k) # -> cumulative recorded cases (from infected)\n dydt[5 * K + k] = epsilon(k) * gamma * i(k) # -> cumulative fatalities (from removed)\n\n return dydt\n\n @classmethod\n def get_parameters(\n cls,\n S0: np.ndarray,\n E0: np.ndarray,\n I0: np.ndarray,\n R0: np.ndarray,\n C0: np.ndarray,\n M0: np.ndarray,\n K: int,\n T_quarantine: float = 0,\n beta: float = 0.2,\n gamma: float = 0.1,\n sigma: float = 0.2,\n eta: float = 0.6,\n epsilon: np.ndarray = None,\n beta_max: float = 10,\n gamma_max: float = 1,\n sigma_max: float = 1,\n eta_max: float = 1,\n epsilon_max: float = 1,\n beta_fixed: bool = False,\n gamma_fixed: bool = False,\n sigma_fixed: bool = False,\n eta_fixed: bool = False,\n epsilon_fixed: bool = False,\n S0_fixed: bool = True,\n S0_max: float = 1e6,\n R0_fixed: bool = True,\n R0_max: float = 1e6,\n I0_fixed: bool = True,\n I0_max: float = 1e6,\n E0_fixed: bool = True,\n E0_max: float = 1e6,\n C0_fixed: bool = True,\n C0_max: float = 1e6,\n M0_fixed: bool = True,\n M0_max: float = 1e6,\n ) -> tuple:\n \"\"\"\n Produce a set of parameters for the age-stratified SIERCM model.\n\n :param S0: initial number of susceptible in the population per age group\n :param E0: initial number of exposed in the population per age group\n :param I0: initial number of infected in the population per age group\n :param R0: initial number of recovered/removed in the population per age group\n :param C0: initial number of cumulative infected cases per age group\n :param M0: initial number of cumulative fatalities per age group\n :param K: number of age groups\n :param T_quarantine: relative time at which quarantine policy goes in effect. If 0, not used.\n :param beta: transmission rate parameter\n :param gamma: removal rate parameter\n :param sigma: parameter controlling transition from exposed to infectious\n :param eta: parameter controlling proportion by which quarantine measure reduces rate of transmission\n :param epsilon: parameter controlling rate of death per age group. Recovery rate == 1 - epsilon\n :param beta_max: maximum value to consider for beta during parameter fitting\n :param gamma_max: maximum value of gamma to consider during parameter fitting\n :param sigma_max: maximum value to consider for sigma during parameter fitting\n :param eta_max: maximum value to consider for eta during parameter fitting\n :param epsilon_max: maximum value to consider for epsilon during parameter fitting\n :param beta_fixed: whether to keep beta fixed during fitting\n :param gamma_fixed: whether to keep gamma fixed during fitting\n :param sigma_fixed: whether to keep sigma fixed during fitting\n :param eta_fixed: whether to keep eta fixed during fitting\n :param epsilon_fixed: whether to keep epsilon fixed during fitting\n :param S0_fixed: whether to keep S0 fixed during fitting\n :param S0_max: maximum value of S0 to consider during parameter fitting\n :param E0_fixed: whether to keep E0 fixed during fitting\n :param E0_max: maximum value of E0 to consider during parameter fitting\n :param R0_fixed: whether to keep R0 fixed during fitting\n :param R0_max: maximum value of R0 to consider during parameter fitting\n :param I0_fixed: whether to keep I0 fixed during fitting\n :param I0_max: maximum value of I0 to consider during parameter fitting\n :param C0_fixed: whether to keep C0 fixed during fitting\n :param C0_max: maximum value of C0 to consider during parameter fitting\n :param M0_fixed: whether to keep M0 fixed during fitting\n :param M0_max: maximum value of M0 to consider during parameter fitting\n :return: tuple[Parameters, list]: (parameters, a list of the names of the variables for initial conditions)\n \"\"\"\n parameters = Parameters()\n parameters.add('K', value=K, min=0, max=K, vary=False)\n parameters.add('T', value=T_quarantine, min=-1, max=T_quarantine, vary=False)\n parameters.add('beta', value=beta, min=0, max=beta_max, vary=not beta_fixed)\n parameters.add('gamma', value=gamma, min=0, max=gamma_max, vary=not gamma_fixed)\n parameters.add('sigma', value=sigma, min=0, max=sigma_max, vary=not sigma_fixed)\n parameters.add('eta', value=eta, min=0, max=eta_max, vary=not eta_fixed)\n\n # add parameters that vary by age group\n for k in range(K):\n parameters.add(f'epsilon_{k}', value=epsilon[k], min=0, max=epsilon_max, vary=not epsilon_fixed)\n\n # add initial state conditions that vary by age group\n initial_conditions = []\n for param, param_value, param_value_max, param_fixed in [\n ('S0', S0, S0_max, S0_fixed),\n ('E0', E0, E0_max, E0_fixed),\n ('I0', I0, I0_max, I0_fixed),\n ('R0', R0, R0_max, R0_fixed),\n ('C0', C0, C0_max, C0_fixed),\n ('M0', M0, M0_max, M0_fixed),\n ]:\n for k in range(K):\n parameters.add(f'{param}_{k}', value=param_value[k], min=0, max=param_value_max, vary=not param_fixed)\n initial_conditions.append(f'{param}_{k}')\n\n return parameters, initial_conditions\n\n\nclass EpidemicModel:\n \"\"\"Class to perform solutions and parameter-fitting of epidemic models\"\"\"\n\n def __init__(\n self,\n model: Type[CompartmentalModel],\n parameters: tuple = None,\n data: np.array = None,\n initial_conditions: list = None,\n fit_method: str = 'leastsq',\n error: callable = None,\n fit_period: float = None,\n ):\n \"\"\"\n A class to standardize fitting and solving epidemiological models.\n\n :param model: the model to use, currently a class in the form of SIR, SEIR above\n :param parameters: tuple, parameters to use for the model, defaults to the output of [model].get_parameters\n :param data: np.array, data that can be used to calibrate the model\n :param initial_conditions: list, initial conditions for the model\n :param fit_method: str, the method to use to minimize the (given) error. Available methods are those in the\n lmfit.minimizer.minimize function. Default is Levenberg-Marquardt least squares minimization.\n :param error: callable, control which residuals (and in what form) to minimize for fitting.\n :param fit_period: float, how far back to fit the data, defaults to fitting all data\n \"\"\"\n self.model = model\n self.parameters = parameters\n self.data = data\n self.initial_conditions = initial_conditions\n self.fit_method = fit_method\n self.error = error\n self.fit_period = fit_period\n self.result = None\n self.fitted_parameters = None\n\n def solve(self, time_range: np.ndarray, initial_conditions: Union[list, tuple], parameters) -> np.ndarray:\n \"\"\"\n Integrate the model ODEs to get a solution.\n\n :param time_range: the time range to solve for\n :param initial_conditions: the initial conditions for the solution\n :param parameters: the parameters for the solution\n :return:\n \"\"\"\n x = odeint(self.model.calibrate, initial_conditions, time_range, args=(parameters,))\n x = np.array(x)\n return x\n\n def residual(self, parameters: Parameters, time_range: np.arange, data: np.ndarray) -> np.ndarray:\n \"\"\"\n Obtain fit error (to minimize).\n\n :param parameters: parameters to use (which we are usually minimizing the residual for)\n :param time_range: time range for solution (over which we obtain the residual)\n :param data: data to fit the models too (i.e. compute residuals in terms of)\n :return:\n \"\"\"\n initial_conditions = []\n for variable in self.initial_conditions:\n initial_conditions.append(parameters[variable].value)\n\n # obtain solution given current initial conditions and parameters\n solution = self.solve(time_range, initial_conditions, parameters)\n\n # compute residual, using custom error function if it has been passed in\n residual = solution - data if self.error is None else self.error(solution, data, parameters)\n\n if self.fit_period is not None:\n residual = residual[-self.fit_period :]\n\n return residual.ravel()\n\n def fit(\n self,\n time_range: np.arange = None,\n parameters: Union[Parameters, tuple] = None,\n initial_conditions: list = None,\n residual=None,\n verbose: bool = False,\n data: np.array = None,\n fit_period: float = None,\n ):\n \"\"\"\n Fit the model based on data in the form np.array([X1,...,Xn])\n \"\"\"\n if data is None:\n if self.data is None:\n raise ValueError(\"No data to fit the model on!\")\n data = self.data\n if initial_conditions is not None:\n self.initial_conditions = initial_conditions\n if self.initial_conditions is None:\n raise ValueError(\"No initial conditions to fit the model with!\")\n if parameters is None:\n if self.parameters is None:\n raise ValueError(\"No parameters to fit the model with!\")\n parameters = self.parameters\n if time_range is None:\n time_range = np.arange(data.shape[0])\n if fit_period is not None:\n self.fit_period = fit_period\n if residual is None:\n residual = self.residual\n\n result = minimize(residual, parameters, args=(time_range, data), method=self.fit_method)\n self.result = result\n self.fitted_parameters = result.params.valuesdict()\n\n if verbose:\n report_fit(result)\n\n return result\n" }, { "path": "gs_quant/models/risk_model.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport math\nfrom enum import Enum, auto\nfrom typing import List, Dict, Tuple, Union\nimport pandas as pd\nimport numpy as np\nimport logging\nimport deprecation\nfrom gs_quant.common import Currency\n\nfrom gs_quant.api.gs.risk_models import GsFactorRiskModelApi, GsRiskModelApi, IntradayFactorDataSource\nfrom gs_quant.base import EnumBase\nfrom gs_quant.data import DataMeasure\nfrom gs_quant.errors import MqValueError, MqRequestError\nfrom gs_quant.markets.factor import Factor\nfrom gs_quant.markets.securities import SecurityMaster, AssetIdentifier\nfrom gs_quant.models.risk_model_utils import (\n build_pfp_data_dataframe,\n get_closest_date_index,\n upload_model_data,\n get_optional_data_as_dataframe,\n get_universe_size,\n get_covariance_matrix_dataframe,\n build_factor_data_map,\n build_asset_data_map,\n build_factor_id_to_name_map,\n only_factor_data_is_present,\n batch_and_upload_partial_data,\n build_factor_volatility_dataframe,\n batch_and_upload_coverage_data,\n)\nfrom gs_quant.target.risk_models import (\n RiskModel as RiskModelBuilder,\n RiskModelEventType,\n RiskModelData,\n RiskModelCalendar,\n RiskModelDataAssetsRequest as DataAssetsRequest,\n RiskModelDataMeasure as Measure,\n RiskModelCoverage as CoverageType,\n RiskModelUniverseIdentifier as UniverseIdentifier,\n Entitlements,\n RiskModelTerm as Term,\n RiskModelUniverseIdentifierRequest,\n Factor as RiskModelFactor,\n RiskModelType,\n RiskModelDataMeasure,\n RiskModelDataAssetsRequest,\n)\n\n\nclass ReturnFormat(Enum):\n \"\"\"Alternative format for data to be returned from get_data functions\"\"\"\n\n JSON = auto()\n DATA_FRAME = auto()\n\n\nclass Unit(Enum):\n \"\"\"Units in which to return a risk model data measure\"\"\"\n\n PERCENT = auto()\n STANDARD_DEVIATION = auto()\n\n\nclass FactorType(EnumBase, Enum):\n \"\"\"Factor represents a risk factor and Category represents a risk factor category\"\"\"\n\n Factor = 'Factor'\n Category = 'Category'\n\n def __repr__(self):\n return self.value\n\n\nclass RiskModel:\n \"\"\"Risk Model Class\"\"\"\n\n def __init__(self, id_: str, name: str):\n self.__id: str = id_\n self.__name: str = name\n\n @property\n def id(self) -> str:\n \"\"\"Get risk model id\"\"\"\n return self.__id\n\n @property\n def name(self) -> str:\n \"\"\"Get risk model name\"\"\"\n return self.__name\n\n @name.setter\n def name(self, name: str):\n \"\"\"Set risk model name\"\"\"\n self.__name = name\n\n def __str__(self):\n return self.id\n\n def __repr__(self):\n s = \"{}('{}','{}'\".format(self.__class__.__name__, self.id, self.name)\n\n s += \")\"\n return s\n\n\nclass MarqueeRiskModel(RiskModel):\n \"\"\"Marquee Risk Model Class\"\"\"\n\n def __init__(\n self,\n id_: str,\n name: str,\n type_: Union[str, RiskModelType],\n vendor: str,\n version: float,\n coverage: CoverageType,\n universe_identifier: UniverseIdentifier,\n term: Term,\n universe_size: int = None,\n entitlements: Union[Dict, Entitlements] = None,\n description: str = None,\n expected_update_time: dt.time = None,\n ):\n super().__init__(id_, name)\n self.__type: RiskModelType = type_ if type_ and isinstance(type_, RiskModelType) else RiskModelType(type_)\n self.__vendor = vendor\n self.__version = version\n self.__coverage = coverage\n self.__universe_identifier = universe_identifier\n self.__term = term\n self.__universe_size = universe_size\n self.__entitlements: Entitlements = (\n entitlements\n if entitlements and isinstance(entitlements, Entitlements)\n else Entitlements.from_dict(entitlements)\n if entitlements and isinstance(entitlements, Dict)\n else None\n )\n self.__description: str = description\n self.__expected_update_time = expected_update_time\n\n @property\n def type(self) -> RiskModelType:\n \"\"\"Get risk model type\"\"\"\n return self.__type\n\n @type.setter\n def type(self, type_: RiskModelType):\n \"\"\"Set risk model type\"\"\"\n self.__type = type_\n\n @property\n def vendor(self) -> str:\n \"\"\"Get risk model vendor\"\"\"\n return self.__vendor\n\n @vendor.setter\n def vendor(self, vendor):\n \"\"\"Set risk model vendor\"\"\"\n self.__vendor = vendor\n\n @property\n def version(self) -> float:\n \"\"\"Get risk model version\"\"\"\n return self.__version\n\n @version.setter\n def version(self, version: float):\n \"\"\"Set risk model version\"\"\"\n self.__version = version\n\n @property\n def coverage(self) -> CoverageType:\n \"\"\"Get risk model coverage\"\"\"\n return self.__coverage\n\n @coverage.setter\n def coverage(self, coverage: CoverageType):\n \"\"\"Set risk model coverage\"\"\"\n self.__coverage = coverage\n\n @property\n def universe_identifier(self) -> UniverseIdentifier:\n \"\"\"Get risk model universe identifier\"\"\"\n return self.__universe_identifier\n\n @property\n def term(self) -> Term:\n \"\"\"Get risk model term\"\"\"\n return self.__term\n\n @term.setter\n def term(self, term: Term):\n \"\"\"Set risk model term\"\"\"\n self.__term = term\n\n @property\n def description(self) -> str:\n \"\"\"Get risk model description\"\"\"\n return self.__description\n\n @description.setter\n def description(self, description: str):\n \"\"\"Set risk model description\"\"\"\n self.__description = description\n\n @property\n def universe_size(self) -> int:\n \"\"\"Get risk model universe size\"\"\"\n return self.__universe_size\n\n @universe_size.setter\n def universe_size(self, universe_size: int):\n \"\"\"Set risk model universe size\"\"\"\n self.__universe_size = universe_size\n\n @property\n def entitlements(self) -> Entitlements:\n \"\"\"Get risk model entitlements\"\"\"\n return self.__entitlements\n\n @entitlements.setter\n def entitlements(self, entitlements: Union[Entitlements, Dict]):\n \"\"\"Set risk model entitlements\"\"\"\n self.__entitlements = entitlements\n\n @property\n def expected_update_time(self) -> dt.time:\n \"\"\"Get risk model expected update time\"\"\"\n return self.__expected_update_time\n\n @expected_update_time.setter\n def expected_update_time(self, expected_update_time: dt.time):\n \"\"\"Set expected update time\"\"\"\n self.__expected_update_time = expected_update_time\n\n def delete(self):\n \"\"\"Delete existing risk model object from Marquee\"\"\"\n return GsRiskModelApi.delete_risk_model(self.id)\n\n def get_dates(\n self, start_date: dt.date = None, end_date: dt.date = None, event_type: RiskModelEventType = None\n ) -> List[dt.date]:\n \"\"\"Get dates between start_date and end_date for which risk model data is present\n\n :param start_date: List returned including and after start_date\n :param end_date: List returned up to and including end_date\n :param event_type: Which event type to retrieve dates for\n\n :return: A list of dates where risk model data is present\n\n **Usage**\n\n Get all the dates for which risk model data is present over start_date and end_date (inclusive).\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel, RiskModelEventType\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> dates = model.get_dates(start_date, end_date)\n\n **See also**\n\n :func:`get_missing_dates` :func:`get_calendar` :func:`get_most_recent_date_from_calendar`\n \"\"\"\n return [\n dt.datetime.strptime(date, \"%Y-%m-%d\").date()\n for date in GsRiskModelApi.get_risk_model_dates(self.id, start_date, end_date, event_type=event_type)\n ]\n\n def get_calendar(self, start_date: dt.date = None, end_date: dt.date = None) -> RiskModelCalendar:\n \"\"\"Get risk model calendar for existing risk model between start and end date\n\n :param start_date: List returned including and after start_date\n :param end_date: List returned up to and including end_date\n\n :return: RiskModelCalendar for model\n\n **Usage**\n\n Get the risk model calendar.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> model_calendar = model.get_calendar(start_date, end_date)\n\n **See also**\n\n :func:`get_most_recent_date_from_calendar` :func:`get_dates` :func:`get_missing_dates`\n \"\"\"\n calendar = GsRiskModelApi.get_risk_model_calendar(self.id)\n if not start_date and not end_date:\n return calendar\n start_idx = get_closest_date_index(start_date, calendar.business_dates, 'after') if start_date else 0\n end_idx = (\n get_closest_date_index(end_date, calendar.business_dates, 'before')\n if end_date\n else len(calendar.business_dates)\n )\n return RiskModelCalendar(calendar.business_dates[start_idx : end_idx + 1])\n\n def upload_calendar(self, calendar: RiskModelCalendar):\n \"\"\"Upload risk model calendar to existing risk model\n\n :param calendar: RiskModelCalendar containing list of dates where model data is expected\n \"\"\"\n return GsRiskModelApi.upload_risk_model_calendar(self.id, calendar)\n\n def get_missing_dates(self, start_date: dt.date = None, end_date: dt.date = None) -> List[dt.date]:\n \"\"\"Get any dates where data is not published according to expected days returned from the risk model calendar\n\n :param start_date: Date to truncate missing dates at\n :param end_date: Date to truncate missing dates at\n\n If no end_date is provided, end_date defaults to T-1 date according\n to the risk model calendar\n\n **Usage**\n\n Get dates with missing data between start date and end date.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> dates = model.get_missing_dates(start_date, end_date)\n\n **See also**\n\n :func:`get_dates` :func:`get_calendar`\n \"\"\"\n\n posted_dates = self.get_dates()\n if not start_date:\n start_date = posted_dates[0]\n if not end_date:\n end_date = dt.date.today() - dt.timedelta(days=1)\n calendar = [\n dt.datetime.strptime(date, \"%Y-%m-%d\").date()\n for date in self.get_calendar(start_date=start_date, end_date=end_date).business_dates\n ]\n return [date for date in calendar if date not in posted_dates]\n\n def get_most_recent_date_from_calendar(self) -> dt.date:\n \"\"\"Get T-1 date according to risk model calendar\"\"\"\n yesterday = dt.date.today() - dt.timedelta(1)\n calendar = self.get_calendar(end_date=yesterday).business_dates\n return dt.datetime.strptime(calendar[len(calendar) - 1], '%Y-%m-%d').date()\n\n def save(self):\n \"\"\"Upload current Risk Model object to Marquee\"\"\"\n model = RiskModelBuilder(\n self.coverage,\n self.id,\n self.name,\n self.term,\n self.universe_identifier,\n self.vendor,\n self.version,\n type_=self.type,\n description=self.description,\n entitlements=self.entitlements,\n universe_size=self.universe_size,\n expected_update_time=self.expected_update_time.strftime('%H:%M:%S') if self.expected_update_time else None,\n )\n try:\n GsRiskModelApi.create_risk_model(model)\n except MqRequestError:\n GsRiskModelApi.update_risk_model(model)\n\n @classmethod\n def get(cls, model_id: str):\n \"\"\"Get a risk model from Marquee\n :param model_id: risk model id corresponding to Marquee Risk Model\n\n :return: Risk Model object\n \"\"\"\n model = GsRiskModelApi.get_risk_model(model_id)\n return cls.from_target(model)\n\n def get_asset_universe(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get asset universe data for existing risk model\n\n :param start_date: Start date for data request. Must be equal to end_date if universe array\n in DataAssetsRequest is empty.\n :param end_date: End date for data request. Must be equal to start_date if universe array\n in DataAssetsRequest is empty.\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Risk model universe data in a pandas dataframe or dict\n\n **Usage**\n\n Get the assets covered by the model between start date and end date.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier, ReturnFormat\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> universe = ['GS UN']\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> asset_universe = model.get_asset_universe(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, universe))\n\n **See also**\n\n :func:`get_specific_risk` :func:`get_universe_factor_exposure` :func:`get_total_risk`\n \"\"\"\n if not assets.universe and not end_date:\n end_date = start_date\n results = self.get_data(\n start_date=start_date,\n end_date=end_date,\n assets=assets,\n measures=[Measure.Asset_Universe],\n limit_factors=False,\n ).get('results')\n dates = [dt.datetime.strptime((data.get('date')), '%Y-%m-%d').date() for data in results]\n universe = [data.get('assetData').get('universe') for data in results]\n dates_to_universe = dict(zip(dates, universe))\n if format == ReturnFormat.DATA_FRAME:\n return pd.DataFrame.from_dict(dates_to_universe, orient='index').T\n return dates_to_universe\n\n def get_factor(self, name: str, start_date: dt.date = None, end_date: dt.date = None) -> Factor:\n \"\"\"Get risk model factor from its name\n\n :param name: Factor name associated with risk model\n :param start_date: Start date of when to search for factor (optional, default to last month)\n :param end_date: End date of when to search for factor (optional, default to today)\n\n :return: Factor object\n \"\"\"\n name_matches = [\n f\n for f in self.get_factor_data(start_date=start_date, end_date=end_date, format=ReturnFormat.JSON)\n if f['name'] == name\n ]\n\n if not name_matches:\n raise MqValueError(f'Factor with name {name} does not in exist in risk model {self.id}')\n\n factor = name_matches.pop()\n return Factor(\n risk_model_id=self.id,\n id_=factor['identifier'],\n type_=factor['type'],\n name=factor.get('name'),\n category=factor.get('factorCategory'),\n tooltip=factor.get('tooltip'),\n description=factor.get('description'),\n glossary_description=factor.get('glossaryDescription'),\n )\n\n def get_many_factors(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n factor_names: List[str] = None,\n factor_ids: List[str] = None,\n factor_type: FactorType = None,\n ) -> List[Factor]:\n \"\"\"Get risk model factors\n :param start_date: Start date of when to search for factors (optional, default to last month)\n :param end_date: End date of when to search for factors\n :param factor_names: The list of names of factors to get. All names must be valid factor names. If both\n factor_names and factor_ids are empty, all the factors will be returned.\n :param factor_ids: The list of ids of factors to get. All ids must be valid factor ids. If both factor_names and\n factor_ids are empty, all the factors will be returned\n :param factor_type: Whether to return factors or factor categories. If unspecified, all the factors of all types\n will be returned\n\n :return: A list of Factor objects\n\n **Usage**\n\n Given a list of factor names and/or ids, return the factor objects that represent the factor requested. If no\n factor names or ids are provided, all the factors will be returned.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel, FactorType\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> many_factors = model.get_many_factors(start_date, end_date, factor_names=[\"factor1\", \"factor2\"],\n ... factor_type=FactorType.Factor)\n >>>\n\n **See also**\n\n :func:`get_factor` :func:`get_factor_data`\n \"\"\"\n factors_from_model = self.get_factor_data(\n start_date=start_date, end_date=end_date, factor_type=factor_type, format=ReturnFormat.JSON\n )\n name_matches = []\n if not factor_names and not factor_ids:\n name_matches = factors_from_model\n else:\n for f in factors_from_model:\n if factor_names:\n if f[\"name\"] in factor_names:\n name_matches.append(f)\n factor_names.remove(f[\"name\"])\n if factor_ids:\n if f[\"identifier\"] in factor_ids:\n name_matches.append(f)\n factor_ids.remove(f[\"identifier\"])\n\n if factor_names or factor_ids:\n raise MqValueError(\n f'Factor names: {factor_names} and factor ids: {factor_ids} not in model'\n f' {self.id} for date range requested'\n )\n\n return [\n Factor(\n risk_model_id=self.id,\n id_=f['identifier'],\n type_=f['type'],\n name=f.get('name'),\n category=f.get('factorCategory'),\n tooltip=f.get('tooltip'),\n description=f.get('description'),\n glossary_description=f.get('glossaryDescription'),\n )\n for f in name_matches\n ]\n\n def save_factor_metadata(self, factor_metadata: RiskModelFactor):\n \"\"\"Add metadata to a factor in a risk model\n\n :param factor_metadata: factor metadata object\n \"\"\"\n try:\n GsFactorRiskModelApi.update_risk_model_factor(self.id, factor_metadata)\n except MqRequestError:\n GsFactorRiskModelApi.create_risk_model_factor(self.id, factor_metadata)\n\n def delete_factor_metadata(self, factor_id: str):\n \"\"\"Delete a factor's metadata from a risk model\n\n :param factor_id: factor id associated with risk model's factor\n \"\"\"\n GsFactorRiskModelApi.delete_risk_model_factor(self.id, factor_id)\n\n def get_intraday_factor_data(\n self,\n start_time: dt.datetime = dt.datetime.now() - dt.timedelta(hours=3),\n end_time: dt.datetime = dt.datetime.now(),\n factors: List[str] = None,\n factor_ids: List[str] = None,\n data_source: Union[IntradayFactorDataSource, str] = None,\n category_filter: List[str] = None,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get intraday factor data for existing risk model\n\n :param start_time: Start time for data request\n :param end_time: End time for data request\n :param factors: Filter the results based on factor name.\n :param factor_ids: List of factor ids associated with risk model\n :param data_source: Data source to use for the request.\n :param category_filter: Filter the results to those having one of the specified categories. \\\n Default is to return all results\n :param format: Which format to return the results in\n :return: Risk model intraday factor data\n\n **Usage**\n\n **Examples**\n\n This will get the factor data for the past 3 hours.\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_intraday_factor_data()\n\n This will get the factor data within the range start_time and end_time\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_intraday_factor_data(start_time=dt.datetime(), end_date=dt.date(\"2023-02-03\"))\n\n This will return factor data for factors with the specified identifiers\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_intraday_factor_data(factorIds=[\"1\", \"2\", \"3\"])\n\n This will filter factor data based on factor names\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_intraday_factor_data(factors=[\"Factor name a\", \"Factor name b\", \"Factor name c\"])\n\n This will return factor data for factors in the specified factor categories\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_intraday_factor_data(category_filter=[\"Factor category 1\", \"Factor category 2\"])\n\n **See also**\n\n :func:`get_many_factors :func:`get_factor_returns_by_name`\n \"\"\"\n factor_categories = category_filter if category_filter else []\n intraday_factor_data = GsFactorRiskModelApi.get_risk_model_factor_data_intraday(\n self.id,\n start_time=start_time,\n end_time=end_time,\n factor_ids=factor_ids,\n factors=factors,\n data_source=data_source,\n factor_categories=factor_categories,\n )\n\n if format == ReturnFormat.DATA_FRAME:\n intraday_factor_data = pd.DataFrame(intraday_factor_data)\n return intraday_factor_data\n\n def get_factor_data(\n self,\n start_date: dt.date = None,\n end_date: dt.date = None,\n identifiers: List[str] = None,\n include_performance_curve: bool = False,\n category_filter: List[str] = None,\n name_filter: List[str] = None,\n factor_type: FactorType = None,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get factor data for existing risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param identifiers: List of factor ids associated with risk model\n :param include_performance_curve: Include the performance curve of the factors\n :param category_filter: Filter the results to those having one of the specified categories. \\\n Default is to return all results\n :param factor_type: The type of factor.\n :param format: which format to return the results in\n :param name_filter: Filter the results based on factor name.\n :return: Risk model factor data\n\n **Usage**\n\n Get factor data for factors whose ids are specified in identifiers.\n\n **Examples**\n\n This will get the factor data for the past 1 month.\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_factor_data()\n\n This will get the factor data within the range start_date and end_date\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_factor_data(start_date=dt.date(\"2023-01-01\"), end_date=dt.date(\"2023-02-03\"))\n\n This will return factor data for factors with the specified identifiers\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_factor_data(identifiers=[\"1\", \"2\", \"3\"])\n\n This will filter factor data based on factor names\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_factor_data(name_filter=[\"Factor name a\", \"Factor name b\", \"Factor name c\"])\n\n This will return factor data for factors in the specified factor categories\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_factor_data(category_filter=[\"Factor category 1\", \"Factor category 2\"])\n\n This will return all the factors of type \"Factor\" (won't return data on factor categories)\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_factor_data(factor_type=FactorType.Factor)\n\n This will only return higher level factor category data.\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_data = model.get_factor_data(factor_type=FactorType.Category)\n\n **See also**\n\n :func:`get_many_factors :func:`get_factor_returns_by_name`\n \"\"\"\n factor_categories = category_filter if category_filter else []\n if factor_type == FactorType.Category:\n if factor_categories:\n raise ValueError('Category filter is not applicable while requesting the FactorType as Category')\n factor_categories = ['Aggregations']\n factor_data = GsFactorRiskModelApi.get_risk_model_factor_data(\n self.id,\n start_date,\n end_date,\n identifiers,\n include_performance_curve,\n factor_categories,\n name_filter,\n )\n if factor_type == FactorType.Factor:\n if 'Aggregations' in factor_categories:\n raise ValueError(\"Aggregations should not be passed while requesting the FactorType as Factor\")\n factor_data = [factor for factor in factor_data if factor['type'] == factor_type.value]\n if format == ReturnFormat.DATA_FRAME:\n factor_data = pd.DataFrame(factor_data)\n return factor_data\n\n def get_factor_returns_by_name(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n factors: List[str] = [],\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Get factor return data for existing risk model keyed by name\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the factors by\n :param factors: The factors to get factor return data for. If empty, the data for all factors is returned\n :param format: Which format to return the results in\n\n :return: Factor returns by name\n\n **Usage**\n\n Get factor returns between start date and end date.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_returns = model.get_factor_returns_by_name(start_date, end_date)\n\n **See also**\n\n :func:`get_factor_returns_by_id` :func:`get_factor_data`\n \"\"\"\n\n return self._get_factor_data_measure(Measure.Factor_Return, start_date, end_date, assets, True, factors, format)\n\n def get_factor_returns_by_id(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n factors: List[str] = [],\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Get factor return data for existing risk model keyed by factor id\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the factors by\n :param factors: The factors to get factor return data for. If empty, the data for all factors is returned\n :param format: Which format to return the results in\n\n :return: Factor returns by factor id\n\n **Usage**\n\n Get factor returns between start date and end date.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_returns = model.get_factor_returns_by_id(start_date, end_date)\n\n\n **See also**\n\n :func:`get_factor_returns_by_name` :func:`get_factor_data`\n \"\"\"\n\n return self._get_factor_data_measure(\n Measure.Factor_Return, start_date, end_date, assets, False, factors, format\n )\n\n def _get_factor_data_measure(\n self,\n requested_measure: RiskModelDataMeasure,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n factors_by_name=True,\n factors: List[str] = [],\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n limit_factors = False\n measures = [requested_measure, Measure.Factor_Name, Measure.Factor_Id]\n if assets:\n measures += [Measure.Universe_Factor_Exposure, Measure.Asset_Universe]\n limit_factors = True\n results = self.get_data(\n start_date=start_date, end_date=end_date, assets=assets, measures=measures, limit_factors=limit_factors\n ).get('results')\n identifier = 'factorName' if factors_by_name else 'factorId'\n factor_data_df = build_factor_data_map(results, identifier, self.id, requested_measure, factors=factors)\n return factor_data_df if format == ReturnFormat.DATA_FRAME else factor_data_df.to_dict()\n\n def _get_asset_data_measure(\n self,\n requested_measure: RiskModelDataMeasure,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n results = self.get_data(\n start_date=start_date,\n end_date=end_date,\n assets=assets,\n measures=[requested_measure, Measure.Asset_Universe],\n limit_factors=False,\n ).get('results')\n measure_data = build_asset_data_map(results, assets.universe, requested_measure, {})\n if format == ReturnFormat.DATA_FRAME:\n measure_data = pd.DataFrame(measure_data)\n return measure_data\n\n def get_universe_exposure(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n factors: List[Union[str, Factor]] = None,\n get_factors_by_name: bool = False,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get universe factor exposure data for existing risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param factors: List of factors to limit the exposure by. If empty, the data for all factors is returned.\n :param get_factors_by_name: Return results keyed by factor name instead of ID\n :param format: Which format to return the results in\n\n :return: Factor exposure for assets requested\n\n **Usage**\n\n Given a list of assets, return their exposure to factor between start date and end date.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> universe = ['GS UN']\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> universe_exposure = model.get_universe_exposure(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, universe))\n\n **See also**\n\n :func:`get_asset_universe` :func:`get_factor_returns_by_name` :func:`get_covariance_matrix`\n \"\"\"\n results = self.get_data(\n start_date=start_date,\n end_date=end_date,\n assets=assets,\n factors=factors,\n measures=[Measure.Factor_Name, Measure.Factor_Id, Measure.Universe_Factor_Exposure, Measure.Asset_Universe],\n limit_factors=False,\n ).get('results')\n factor_map = {}\n if get_factors_by_name:\n factor_map = build_factor_id_to_name_map(results)\n factor_exposure = build_asset_data_map(results, assets.universe, Measure.Universe_Factor_Exposure, factor_map)\n if format == ReturnFormat.DATA_FRAME:\n factor_exposure = pd.DataFrame.from_dict(\n {(i, j): factor_exposure[i][j] for i in factor_exposure.keys() for j in factor_exposure[i].keys()},\n orient='index',\n )\n return factor_exposure\n\n def get_specific_risk(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get specific risk data for existing risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Specific risk for assets requested\n\n **Usage**\n\n Get specific risk data for assets specified in `assets` between `start_date` and `end_date`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> specific_risk = model.get_specific_risk(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_specific_return` :func:`get_total_risk`\n \"\"\"\n return self._get_asset_data_measure(Measure.Specific_Risk, start_date, end_date, assets, format)\n\n def get_factor_standard_deviation(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n factors: List[str] = [],\n factors_by_name: bool = True,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get factor standard deviation data for existing risk model keyed by name or id\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the factors by\n :param factors: the factors to get standard deviation for. If empty, the data for all factors is returned\n :param factors_by_name: whether to identify factors by their name or id. If true, the list of factors must \\\n be a list of factor names. Otherwise, it should be a list of factor ids\n :param format: which format to return the results in\n\n :return: factor standard deviation\n\n **Usage**\n\n Get the value of one standard deviation of the factors specified in `factors`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MarqueeRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MarqueeRiskModel.get(\"MODEL_ID\")\n >>> factor_standard_deviation = model.get_factor_standard_deviation(start_date, end_date,\n ... factors=['factor1', 'factor2'])\n\n **See also**\n\n :func:`get_specific_risk` :func:`get_factor_mean`\n \"\"\"\n\n return self._get_factor_data_measure(\n Measure.Factor_Standard_Deviation, start_date, end_date, assets, factors_by_name, factors, format\n )\n\n def get_factor_mean(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n factors: List[str] = [],\n factors_by_name: bool = True,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get factor mean data for existing risk model keyed by name or id\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the factors by\n :param factors: the factors to get standard deviation for. If empty, the data for all factors is returned\n :param factors_by_name: whether to identify factors by their name or id. If true, the list of factors must \\\n be a list of factor names. Otherwise, it should be a list of factor ids\n :param format: which format to return the results in\n\n :return: factor mean\n\n **Usage**\n\n Get the value of one mean of the factors specified in `factors`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MarqueeRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MarqueeRiskModel.get(\"MODEL_ID\")\n >>> factor_mean = model.get_factor_mean(start_date, end_date, factors=['factor1', 'factor2'])\n\n **See also**\n\n :func:`get_specific_risk` :func:`get_factor_standard_deviation`\n \"\"\"\n\n return self._get_factor_data_measure(\n Measure.Factor_Mean, start_date, end_date, assets, factors_by_name, factors, format\n )\n\n def get_factor_cross_sectional_mean(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n factors: List[str] = [],\n factors_by_name: bool = True,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> (Union)[List[Dict], pd.DataFrame]:\n \"\"\" Get factor cross-sectional mean data for existing risk model keyed by name or id\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the factors by\n :param factors: the factors to get standard deviation for. If empty, the data for all factors is returned\n :param factors_by_name: whether to identify factors by their name or id. If true, the list of factors must \\\n be a list of factor names. Otherwise, it should be a list of factor ids\n :param format: which format to return the results in\n\n :return: factor cross-sectional mean\n\n **Usage**\n\n Get the value of one cross-sectional mean of the factors specified in `factors`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MarqueeRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MarqueeRiskModel.get(\"MODEL_ID\")\n >>> factor_cross_sectional_mean = model.get_factor_cross_sectional_mean(start_date, end_date,\n factors=['factor1', 'factor2'])\n\n **See also**\n\n :func:`get_factor_mean` :func:`get_factor_cross_sectional_standard_deviation`\n \"\"\"\n\n return self._get_factor_data_measure(\n Measure.Factor_Cross_Sectional_Mean, start_date, end_date, assets, factors_by_name, factors, format\n )\n\n def get_factor_cross_sectional_standard_deviation(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n factors: List[str] = [],\n factors_by_name: bool = True,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get factor cross-sectional standard deviation data for existing risk model keyed by name or id\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the factors by\n :param factors: the factors to get standard deviation for. If empty, the data for all factors is returned\n :param factors_by_name: whether to identify factors by their name or id. If true, the list of factors must \\\n be a list of factor names. Otherwise, it should be a list of factor ids\n :param format: which format to return the results in\n\n :return: factor cross-sectional standard deviation\n\n **Usage**\n\n Get the value of one cross-sectional standard deviation of the factors specified in `factors`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MarqueeRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MarqueeRiskModel.get(\"MODEL_ID\")\n >>> factor_cross_sectional_sd = model.get_factor_cross_sectional_standard_deviation(start_date, end_date,\n factors=['factor1',\n 'factor2'])\n\n **See also**\n\n :func:`get_factor_standard_deviation` :func:`get_factor_cross_sectional_mean`\n \"\"\"\n\n return self._get_factor_data_measure(\n Measure.Factor_Cross_Sectional_Standard_Deviation,\n start_date,\n end_date,\n assets,\n factors_by_name,\n factors,\n format,\n )\n\n def get_data(\n self,\n measures: List[Measure],\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n factors: List[Union[str, Factor]] = None,\n limit_factors: bool = True,\n ) -> Dict:\n \"\"\"Get data for multiple measures for existing risk model\n\n :param measures: list of measures for general risk model data request\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param factors: Only include data for these factors. If empty, all factors will be included.\n :param limit_factors: limit factors included in factorData and covariance matrix to only include factors\n which the input universe has non-zero exposure to\n\n :return: risk model data or MqRequestError if query is too large for the service\n \"\"\"\n if factors:\n factors = [f.name if isinstance(f, Factor) else f for f in factors]\n try:\n return GsFactorRiskModelApi.get_risk_model_data(\n model_id=self.id,\n start_date=start_date,\n end_date=end_date,\n assets=assets,\n factors=factors,\n measures=measures,\n limit_factors=limit_factors,\n )\n except MqRequestError as e:\n if e.status > 499:\n logging.warning(\n f\"Potential timeout in request for model {self.id}. Consider adding a retry or\"\n f\" batching request if error persists\"\n )\n raise MqRequestError(\n e.status,\n f\"timeout while getting model data between {start_date} and {end_date} \"\n f\" for {self.id}, consider batching request\",\n )\n raise e\n\n def upload_data(self, data: Union[RiskModelData, Dict], max_asset_batch_size: int = 10000, aws_upload: bool = True):\n \"\"\"Upload risk model data to existing risk model in Marquee\n\n :param data: complete or partial risk model data for uploading on given date\n includes: date, and one or more of: factorData, assetData, covarianceMatrix,\n issuerSpecificCovariance, factorPortfolios and currencyRatesData. Look at risk model upload\n documentation for further information on what data can be grouped together if asset data size is above\n the max asset batch size\n :param max_asset_batch_size: size of payload to batch with. Defaults to 20000 assets which works well for\n models that have factor ids ranging from 1- 3 characters in length. For models with longer factor ids,\n consider batching with a smaller max asset batch size\n If upload universe is over max_asset_batch_size, will batch data in chunks of max_asset_batch_size assets\n :param aws_upload: If true, uploads risk model data to AWS\n This function takes risk model data, and if partial requests are necessary, will upload data by\n 1. factor data (includes covariance matrix if factor model)\n 2. asset data in batches of max_asset_batch_size\n 3. issuer specific covariance data in batches of max_asset_batch_size / 2 due to the structure of this data\n 4. factor portfolio data in batches of max_asset_batch_size / 2 due to the structure of this data\n\n In the case of repeat identifiers on a given data, the repeated data will replace existing data\n \"\"\"\n\n data = data.as_dict() if type(data) is RiskModelData else data\n full_data_present = 'factorData' in data.keys() and 'assetData' in data.keys()\n only_factor_data_present = only_factor_data_is_present(self.type, data)\n target_universe_size = 0 if only_factor_data_present else get_universe_size(data)\n make_partial_request = target_universe_size > max_asset_batch_size or not full_data_present\n if target_universe_size:\n logging.info(f'Target universe size for upload: {target_universe_size}')\n if make_partial_request:\n batch_and_upload_partial_data(self.id, data, max_asset_batch_size, aws_upload=aws_upload)\n else:\n logging.info('Uploading model data in one request')\n upload_model_data(self.id, data, aws_upload=aws_upload)\n\n @deprecation.deprecated(deprecated_in=\"0.9.42\", details=\"Please use upload_data instead\")\n def upload_partial_data(self, data: Union[RiskModelData, dict], final_upload: bool = None):\n \"\"\"Upload partial risk model data to existing risk model in Marquee\n\n :param data: partial risk model data for uploading on given date\n :param final_upload: if this is the last upload for the batched subset of data\n\n The models factorData and covarianceMatrix must be uploaded first on given date if repeats in partial\n upload, newer posted data will replace existing data on upload day\n\n \"\"\"\n upload_model_data(self.id, data, partial_upload=True, final_upload=final_upload)\n\n def upload_asset_coverage_data(self, date: dt.date = None, batch_size: int = 100):\n \"\"\"Upload to the coverage dataset for given risk model and date\n\n :param date: Date to upload coverage data for, default date is last date from risk model calendar\n :param batch_size: Number of assets to upload in one request\n\n Posting to the coverage dataset within the last 5 days will enable the risk model to be seen in the\n Marquee UI dropdown for users with \"execute\" capabilities\n \"\"\"\n if not date:\n date = self.get_dates()[-1]\n gsid_list = self.get_asset_universe(\n date, assets=DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []), format=ReturnFormat.JSON\n ).get(date)\n if not gsid_list:\n raise MqRequestError(404, f'No asset data found on {date}')\n batch_and_upload_coverage_data(date, gsid_list, self.id, batch_size)\n\n @classmethod\n def from_target(cls, model):\n uid = model.universe_identifier\n return MarqueeRiskModel(\n id_=model.id,\n name=model.name,\n coverage=model.coverage if isinstance(model.coverage, CoverageType) else CoverageType(model.coverage),\n term=model.term if isinstance(model.term, Term) else Term(model.term),\n universe_identifier=uid\n if isinstance(uid, UniverseIdentifier)\n else UniverseIdentifier(uid)\n if uid\n else None,\n vendor=model.vendor,\n version=model.version,\n type_=model.type_\n if not model.type_ or isinstance(model.type_, RiskModelType)\n else RiskModelType(model.type_),\n entitlements=model.entitlements,\n description=model.description,\n expected_update_time=dt.datetime.strptime(model.expected_update_time, \"%H:%M:%S\").time()\n if model.expected_update_time\n else None,\n )\n\n @classmethod\n def from_many_targets(cls, models: Tuple[RiskModelBuilder, ...]):\n return [cls.from_target(model) for model in models]\n\n def __str__(self):\n return self.id\n\n def __repr__(self):\n s = \"{}('{}','{}','{}','{}','{}','{}','{}', '{}\".format(\n self.__class__.__name__,\n self.id,\n self.name,\n self.coverage,\n self.term,\n self.universe_identifier,\n self.vendor,\n self.version,\n self.type,\n )\n if self.universe_size:\n s += \", universe_size={}\".format(self)\n if self.entitlements:\n s += \", entitlements={}\".format(self)\n if self.description:\n s += \", description={}\".format(self)\n if self.expected_update_time:\n s += \", expected_update_time={}\".format(self)\n\n s += \")\"\n return s\n\n\nclass FactorRiskModel(MarqueeRiskModel):\n \"\"\"Factor Risk Model used for calculating asset level factor risk\"\"\"\n\n def __init__(\n self,\n id_: str,\n name: str,\n coverage: CoverageType,\n term: Term,\n universe_identifier: UniverseIdentifier,\n vendor: str,\n version: float,\n universe_size: int = None,\n entitlements: Union[Dict, Entitlements] = None,\n description: str = None,\n expected_update_time: dt.time = None,\n ):\n \"\"\"Create new factor risk model object\n\n :param id_: risk model id (cannot be changed)\n :param name: risk model name\n :param coverage: coverage of risk model asset universe\n :param term: horizon term\n :param universe_identifier: identifier used in asset universe upload (cannot be changed)\n :param vendor: risk model vendor\n :param version: version of model\n :param universe_size: total rough expected universe size (rounding up to nearest 1k)\n :param entitlements: entitlements associated with risk model\n :param description: risk model description\n :param expected_update_time: time when risk model daily data is expected to be uploaded\n\n :return: FactorRiskModel object\n \"\"\"\n super().__init__(\n id_,\n name,\n RiskModelType.Factor,\n vendor,\n version,\n coverage,\n universe_identifier,\n term,\n universe_size=universe_size,\n entitlements=entitlements,\n description=description,\n expected_update_time=expected_update_time,\n )\n\n @classmethod\n def from_target(cls, model: RiskModelBuilder):\n uid = model.universe_identifier\n return FactorRiskModel(\n model.id,\n model.name,\n model.coverage if isinstance(model.coverage, CoverageType) else CoverageType(model.coverage),\n model.term if isinstance(model.term, Term) else Term(model.term),\n uid if isinstance(uid, UniverseIdentifier) else UniverseIdentifier(uid) if uid else None,\n model.vendor,\n model.version,\n universe_size=model.universe_size,\n entitlements=model.entitlements,\n description=model.description,\n expected_update_time=dt.datetime.strptime(model.expected_update_time, \"%H:%M:%S\").time()\n if model.expected_update_time\n else None,\n )\n\n @classmethod\n def get_many(\n cls,\n ids: List[str] = None,\n terms: List[str] = None,\n vendors: List[str] = None,\n names: List[str] = None,\n coverages: List[str] = None,\n limit: int = None,\n ) -> list:\n \"\"\"Get many factor risk models from Marquee\n\n :param ids: list of model identifiers in Marquee\n :param terms: list of model terms\n :param vendors: list of model vendors\n :param names: list of model names\n :param coverages: list of model coverages\n :param limit: limit of number of models in response\n\n :return: list of Factor Risk Model object\n \"\"\"\n models = GsRiskModelApi.get_risk_models(\n ids=ids,\n terms=terms,\n vendors=vendors,\n names=names,\n coverages=coverages,\n limit=limit,\n types=[RiskModelType.Factor.value],\n )\n return cls.from_many_targets(models)\n\n def get_total_risk(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get total risk data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: which format to return the results in\n\n :return: total risk for assets requested\n\n **Usage**\n\n Get total risk data for assets specified in `assets` between `start_date` and `end_date`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier, ReturnFormat\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> total_risk = model.get_total_risk(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_specific_risk` :func:`get_specific_return` :func:`get_historical_beta`\n \"\"\"\n return self._get_asset_data_measure(Measure.Total_Risk, start_date, end_date, assets, format)\n\n def get_historical_beta(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get historical beta data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: which format to return the results in\n\n :return: historical beta for assets requested\n\n **Usage**\n\n Get historical beta data for assets specified in `assets` between `start_date` and `end_date`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier, ReturnFormat\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> historical_beta = model.get_historical_beta(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_predicted_beta` :func:`get_global_predicted_beta`\n \"\"\"\n return self._get_asset_data_measure(Measure.Historical_Beta, start_date, end_date, assets, format)\n\n def get_predicted_beta(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get predicted beta data for an existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: which format to return the results in\n\n :return: predicted beta for assets requested\n\n **Usage**\n\n Get predicted beta data for assets specified in `assets` between `start_date` and `end_date`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MacroRiskModel.get(\"MODEL_ID\")\n >>> predicted_beta = model.get_predicted_beta(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_historical_beta` :func:`get_global_predicted_beta`\n \"\"\"\n return self._get_asset_data_measure(Measure.Predicted_Beta, start_date, end_date, assets, format)\n\n def get_global_predicted_beta(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get global predicted beta data for an existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: which format to return the results in\n\n :return: global predicted beta for assets requested\n\n **Usage**\n\n Get global predicted beta for assets specified in `assets` between `start_date` and `end_date`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> global_predicted_beta = model.get_global_predicted_beta(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_predicted_beta` :func:`get_historical_beta`\n \"\"\"\n return self._get_asset_data_measure(Measure.Global_Predicted_Beta, start_date, end_date, assets, format)\n\n def get_daily_return(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get daily asset total return data\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Daily asset total return data\n\n **Usage**\n\n Get daily return data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> daily_returns = model.get_daily_return(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_specific_risk` :func:`get_universe_factor_exposure` :func: `get_specific_return`\n\n \"\"\"\n return self._get_asset_data_measure(Measure.Daily_Return, start_date, end_date, assets, format)\n\n def get_specific_return(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get specific return data for existing risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Specific return data\n\n **Usage**\n\n Get specific return data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> specific_returns = model.get_specific_return(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_specific_risk` :func:`get_universe_factor_exposure`\n\n \"\"\"\n return self._get_asset_data_measure(Measure.Specific_Return, start_date, end_date, assets, format)\n\n def get_bid_ask_spread(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n days: int = 0,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get bid ask spread data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param days: Get bid ask spread for a past average of specific days\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Bid Ask Spread Data\n\n **Usage**\n\n Get Bid Ask Spread data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> bid_ask_spread_30d = model.get_bid_ask_spread(start_date, end_date, 30, \\\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_composite_volume` :func:`get_trading_volume`\n\n \"\"\"\n try:\n requested_measure = Measure.Bid_Ask_Spread if not days else Measure[f'Bid_Ask_Spread_{days}d']\n return super()._get_asset_data_measure(requested_measure, start_date, end_date, assets, format)\n except KeyError:\n raise ValueError(f'Bid Ask Spread data is not available for the requested days: {days}')\n\n def get_trading_volume(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n days: int = 0,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get trading volume data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param days: Get trading volume for a past average of specific days\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Trading Volume data\n\n **Usage**\n\n Get trading volume data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> trading_volume_60d = model.get_trading_volume(start_date, end_date, 60,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_composite_volume` :func:`get_traded_value`\n\n \"\"\"\n try:\n requested_measure = Measure.Trading_Volume if not days else Measure[f'Trading_Volume_{days}d']\n return super()._get_asset_data_measure(requested_measure, start_date, end_date, assets, format)\n except KeyError:\n raise ValueError(f'Trading volume data is not available for the requested days: {days}')\n\n def get_traded_value(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n days: int = 30,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get traded value data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param days: Get traded value for a past average of specific days\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Traded Value Data\n\n **Usage**\n\n Get traded value data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> traded_value_30d = model.get_traded_value(start_date, end_date,\n ... assets = DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_composite_value` :func:`get_trading_volume`\n\n \"\"\"\n try:\n requested_measure = Measure.Traded_Value_30d if not days else Measure[f'Traded_Value_{days}d']\n return super()._get_asset_data_measure(requested_measure, start_date, end_date, assets, format)\n except KeyError:\n raise ValueError(f'Traded Value data is not available for the requested days: {days}')\n\n def get_composite_volume(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n days: int = 0,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get composite volume data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param days: Get composite volume for a past average of specific days\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Composite volume data\n\n **Usage**\n\n Get composite volume data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> composite_volume_30d = model.get_composite_volume(start_date, end_date, 30,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_composite_value` :func:`get_traded_value`\n\n \"\"\"\n try:\n requested_measure = Measure.Composite_Volume if not days else Measure[f'Composite_Volume_{days}d']\n return super()._get_asset_data_measure(requested_measure, start_date, end_date, assets, format)\n except KeyError:\n raise ValueError(f'Composite Volume data is not available for the requested days: {days}')\n\n def get_composite_value(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n days: int = 30,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get composite value data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param days: Get composite value for a past average of specific days\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Composite Value data\n\n **Usage**\n\n Get composite value data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> composite_value_30d = model.get_composite_value(start_date, end_date,\n ... assets=DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_traded_value` :func:`get_composite_volume`\n\n \"\"\"\n try:\n requested_measure = Measure.Composite_Value_30d if not days else Measure[f'Composite_Value_{days}d']\n return super()._get_asset_data_measure(requested_measure, start_date, end_date, assets, format)\n except KeyError:\n raise ValueError(f'Composite Value data is not available for the requested days: {days}')\n\n def get_issuer_market_cap(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get issuer market capitalization for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Issue Market cap data\n\n **Usage**\n\n Get issuer market capitalization for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> issuer_market_cap = model.get_issuer_market_cap(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_asset_price` :func:`get_asset_capitalization` :func:`get_currency`\n\n \"\"\"\n return super()._get_asset_data_measure(Measure.Issuer_Market_Cap, start_date, end_date, assets, format)\n\n def get_asset_price(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get asset price data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Asset price data\n\n **Usage**\n\n Get prices for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> asset_price = model.get_asset_price(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_asset_capitalization` :func:`get_currency`\n\n \"\"\"\n return super()._get_asset_data_measure(Measure.Price, start_date, end_date, assets, format)\n\n def get_asset_capitalization(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get asset capitalization data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Asset Capitalization data\n\n **Usage**\n\n Get asset capitalization data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> asset_capitalization = model.get_asset_capitalization(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_issuer_market_cap` :func:`get_currency`\n\n \"\"\"\n return super()._get_asset_data_measure(Measure.Capitalization, start_date, end_date, assets, format)\n\n def get_currency(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get currency data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Currency data\n\n **Usage**\n\n Get currency data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> currency = model.get_currency(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_asset_price` :func:`get_issuer_market_cap`\n\n \"\"\"\n return super()._get_asset_data_measure(Measure.Currency, start_date, end_date, assets, format)\n\n def get_unadjusted_specific_risk(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get asset unadjusted specific risk data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Unadjusted Specific Risk data\n\n **Usage**\n\n Get unadjusted specific risk data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> unadjusted_specific_risk = model.get_unadjusted_specific_risk(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_specific_risk` :func:`get_total_risk`\n\n \"\"\"\n return super()._get_asset_data_measure(Measure.Unadjusted_Specific_Risk, start_date, end_date, assets, format)\n\n def get_dividend_yield(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get dividend yield data for assets covered by the risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Asset Divident Yield\n\n **Usage**\n\n Get dividend yield data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> dividend_yield = model.get_dividend_yield(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_asset_capitalization` :func:`get_issuer_market_cap`\n\n \"\"\"\n return super()._get_asset_data_measure(Measure.Dividend_Yield, start_date, end_date, assets, format)\n\n def get_universe_factor_exposure(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n factors: List[Union[str, Factor]] = None,\n get_factors_by_name: bool = False,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get universe factor exposure data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param factors: list of factors to return exposure for. If left None, will return all applicable factors\n :param get_factors_by_name: return results keyed by factor name instead of ID\n :param format: which format to return the results in\n\n :return: factor exposure for assets requested\n\n **Usage**\n\n Given a list of assets, return their exposure to factors between start date and end date.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> universe = ['GS UN']\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> universe_exposure = model.get_universe_factor_exposure(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, universe))\n\n **See also**\n\n :func:`get_asset_universe` :func:`get_specific_risk`\n \"\"\"\n return super().get_universe_exposure(\n start_date, end_date, assets, factors=factors, get_factors_by_name=get_factors_by_name, format=format\n )\n\n def _build_covariance_matrix_measure(\n self,\n covariance_matrix_type: Measure,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n measure_to_field_name_map = {\n Measure.Covariance_Matrix: 'covarianceMatrix',\n Measure.Unadjusted_Covariance_Matrix: 'unadjustedCovarianceMatrix',\n Measure.Pre_VRA_Covariance_Matrix: 'preVRACovarianceMatrix',\n }\n limit_factors = True if assets else False\n measures = [covariance_matrix_type, Measure.Factor_Name, Measure.Factor_Id]\n if assets:\n measures += [Measure.Universe_Factor_Exposure, Measure.Asset_Universe]\n results = self.get_data(\n start_date=start_date, end_date=end_date, assets=assets, measures=measures, limit_factors=limit_factors\n ).get('results')\n covariance_data = (\n results\n if format == ReturnFormat.JSON\n else get_covariance_matrix_dataframe(\n results, covariance_matrix_key=measure_to_field_name_map[covariance_matrix_type]\n )\n )\n return covariance_data\n\n def get_covariance_matrix(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\" Get covariance matrix data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the covariance matrix by\n :param format: which format to return the results in\n\n :return: covariance matrix of daily factor returns\n\n **Usage**\n\n Get the covariance matrix of daily factor returns\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> covariance_matrix = model.get_covariance_matrix(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_factor_returns_by_name` :func:`get_factor_returns_by_id`\n \"\"\"\n return self._build_covariance_matrix_measure(Measure.Covariance_Matrix, start_date, end_date, assets, format)\n\n def get_unadjusted_covariance_matrix(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\" Get covariance matrix data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the covariance matrix by\n :param format: which format to return the results in\n\n :return: covariance matrix of daily factor returns\n\n **Usage**\n\n Get the covariance matrix of daily factor returns\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> covariance_matrix = model.get_unadjusted_covariance_matrix(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_factor_returns_by_name` :func:`get_factor_returns_by_id`\n \"\"\"\n return self._build_covariance_matrix_measure(\n Measure.Unadjusted_Covariance_Matrix, start_date, end_date, assets, format\n )\n\n def get_pre_vra_covariance_matrix(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\" Get covariance matrix data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the covariance matrix by\n :param format: which format to return the results in\n\n :return: covariance matrix of daily factor returns\n\n **Usage**\n\n Get the covariance matrix of daily factor returns\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> covariance_matrix = model.get_covariance_matrix(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_factor_returns_by_name` :func:`get_factor_returns_by_id`\n \"\"\"\n return self._build_covariance_matrix_measure(\n Measure.Pre_VRA_Covariance_Matrix, start_date, end_date, assets, format\n )\n\n def _build_currency_rates_data(\n self, rows: List[Dict], currencies: List[Currency], rates_key: str, format: ReturnFormat\n ) -> Union[Dict, pd.DataFrame]:\n currency_rates_key = \"currencyRatesData\"\n currency_rates_df = get_optional_data_as_dataframe(rows, currency_rates_key)\n if currencies:\n currency_rates_df = currency_rates_df.loc[\n currency_rates_df['currency'].isin([cur.value for cur in currencies])\n ]\n if format == ReturnFormat.DATA_FRAME:\n return currency_rates_df\n return currency_rates_df.to_dict()\n\n def get_risk_free_rate(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n currencies: List[Currency] = [],\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Get risk-free rates for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param currencies: return risk-free rates for these currencies. If empty returns data for all currencies\n :param format: which format to return the results in\n\n :return: risk-free rates data\n\n **Usage**\n\n Get risk-free rates data between `start_date` and `end_date`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> risk_free_rates = model.get_risk_free_rate(start_date, end_date))\n\n **See also**\n\n :func:`get_currency_exchange_rate`\n \"\"\"\n results = self.get_data(\n measures=[RiskModelDataMeasure.Risk_Free_Rate],\n start_date=start_date,\n end_date=end_date,\n limit_factors=False,\n ).get('results')\n return self._build_currency_rates_data(results, rates_key=\"riskFreeRate\", currencies=currencies, format=format)\n\n def get_currency_exchange_rate(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n currencies: List[Currency] = [],\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\"Get currency exchange rates for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param currencies: return currency exchange rates for these currencies. If empty returns data for all currencies\n :param format: which format to return the results in\n\n :return: currency exchange rates data\n\n **Usage**\n\n Get currency exchange rates data between `start_date` and `end_date`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> currency_exchange_rates = model.get_currency_exchange_rate(start_date, end_date))\n\n **See also**\n\n :func:`get_risk_free_rate`\n \"\"\"\n results = self.get_data(\n measures=[RiskModelDataMeasure.Currency_Exchange_Rate],\n start_date=start_date,\n end_date=end_date,\n limit_factors=False,\n ).get('results')\n return self._build_currency_rates_data(results, rates_key=\"exchangeRate\", currencies=currencies, format=format)\n\n def get_factor_volatility(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n factors: List[str] = None,\n get_factors_by_name: bool = True,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\" Get factor volatility data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param factors: The factors to get factor return data for. If empty, the data for all factors is returned\n :param get_factors_by_name: get results keyed by factor name instead of ID\n :param format: which format to return the results in\n\n :return: factor volatility data\n\n **Usage**\n\n Get the factor volatility data for daily factor returns\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_volatility_by_name = model.get_factor_volatility(start_date, end_date,\n ... factors=[\"factor1\", \"factor2\"])\n >>> factor_volatility_by_id = model.get_factor_volatility(start_date=start_date, end_date=end_date,\n ... factors=[\"1\", \"2\", \"3\"], get_factors_by_name=False)\n\n **See also**\n\n :func:`get_factor_returns_by_name` :func:`get_factor_returns_by_id`\n \"\"\"\n if factors is None:\n factors = []\n measures = [Measure.Factor_Volatility, Measure.Factor_Name, Measure.Factor_Id]\n results = self.get_data(start_date=start_date, end_date=end_date, measures=measures, limit_factors=False).get(\n 'results'\n )\n if format == ReturnFormat.JSON and not get_factors_by_name and not factors:\n return results\n else:\n factor_volatility_df = build_factor_volatility_dataframe(results, get_factors_by_name, factors)\n return factor_volatility_df if format == ReturnFormat.DATA_FRAME else factor_volatility_df.to_dict()\n\n def get_estimation_universe_weights(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\" Get estimation universe data for existing risk model\n\n :param start_date: Start date for data request. Must be equal to end_date if universe array\n in DataAssetsRequest is empty.\n :param end_date: End date for data request. Must be equal to start_date if universe array\n in DataAssetsRequest is empty.\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Estimation Universe data in a pandas dataframe or dict\n\n **Usage**\n\n Get the assets that are in the estimation universe of the model and their weights.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> universe = ['GS UN']\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> estimation_universe_weights = model.get_estimation_universe_weights(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, universe))\n\n **See also**\n\n :func:`get_asset_universe` :func:`get_specific_risk`\n \"\"\"\n return self._get_asset_data_measure(Measure.Estimation_Universe_Weight, start_date, end_date, assets, format)\n\n def get_issuer_specific_covariance(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\" Get issuer specific covariance data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: which format to return the results in\n\n :return: issuer specific covariance matrix (covariance of assets with the same issuer)\n\n **Usage**\n\n Get the covariance of assets with the same issuer.\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> issuer_specific_covariance = model.get_issuer_specific_covariance(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_factor_portfolios`\n \"\"\"\n isc = self.get_data(\n start_date=start_date,\n end_date=end_date,\n assets=assets,\n measures=[Measure.Issuer_Specific_Covariance],\n limit_factors=False,\n ).get('results')\n isc_data = (\n isc if format == ReturnFormat.JSON else get_optional_data_as_dataframe(isc, 'issuerSpecificCovariance')\n )\n return isc_data\n\n def get_factor_portfolios(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n factors: List[str] = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n get_factors_by_name: bool = False,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[Dict, pd.DataFrame]:\n \"\"\" Get factor portfolios data for existing risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request:\n :param factors: The factors to get factor portfolio data for. If empty, the data for all factors is returned\n :param get_factors_by_name: whether to denote the results by factor name (True) or ID (False)\n :param format: which format to return the results in\n\n :return: factor portfolios data\n\n **Usage**\n\n Get the factor portfolios data between `start_date` and `end_date`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> factor_portfolios = model.get_factor_portfolios(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_issuer_specific_covariance`\n \"\"\"\n results = self.get_data(\n start_date=start_date,\n end_date=end_date,\n assets=assets,\n factors=factors,\n measures=[Measure.Factor_Id, Measure.Factor_Name, Measure.Factor_Portfolios],\n limit_factors=False,\n ).get('results')\n pfp_data = build_pfp_data_dataframe(\n results, return_df=format is ReturnFormat.DATA_FRAME, get_factors_by_name=get_factors_by_name\n )\n return pfp_data\n\n def get_asset_contribution_to_risk(\n self,\n asset_identifier: str,\n date: dt.date,\n asset_identifier_type=RiskModelUniverseIdentifierRequest.bbid,\n get_factors_by_name: bool = True,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ):\n \"\"\"\n Get factor proportion of risk and marginal contribution to risk for each factor in the model\n\n :param asset_identifier: asset identifier\n :param date: date\n :param asset_identifier_type: type corresponding to the identifier passed in\n :param get_factors_by_name: whether to denote the results by factor name (True) or ID (False)\n :param format: which format to return the results in\n\n :return: factor proportion of risk and marginal contribution to risk breakdown\n\n **Usage**\n\n Get the factor contribution breakdown for any asset covered by the risk model\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> mctr_breakdown = model.get_asset_contribution_to_risk('AAPL UW', date)\n \"\"\"\n # Get spot price for the asset as of date\n security = SecurityMaster.get_asset(asset_identifier, AssetIdentifier(asset_identifier_type.value.upper()))\n spot_data_coordinate = security.get_data_coordinate(DataMeasure.SPOT_PRICE)\n series = spot_data_coordinate.get_series(start=date, end=date)\n if len(series) == 0:\n raise MqValueError(f'{asset_identifier} has no end of day price available on {date.strftime(\"%Y-%m-%d\")}')\n spot_price = series.iloc[0]\n\n # Get risk model data for the asset as of date\n data_measures = [\n RiskModelDataMeasure.Asset_Universe,\n RiskModelDataMeasure.Total_Risk,\n RiskModelDataMeasure.Universe_Factor_Exposure,\n RiskModelDataMeasure.Covariance_Matrix,\n RiskModelDataMeasure.Factor_Name,\n RiskModelDataMeasure.Factor_Id,\n RiskModelDataMeasure.Factor_Category,\n ]\n risk_results = self.get_data(\n data_measures,\n date,\n date,\n RiskModelDataAssetsRequest(identifier=asset_identifier_type, universe=[asset_identifier]),\n limit_factors=True,\n )\n if len(risk_results['results'][0]['assetData']['totalRisk']) == 0:\n raise MqValueError(f'{asset_identifier} is not covered by {self.id} on {date.strftime(\"%Y-%m-%d\")}')\n total_risk = risk_results['results'][0]['assetData']['totalRisk'][0]\n var = total_risk / 100 * spot_price / math.sqrt(252)\n covariance_matrix = np.array(risk_results['results'][0]['covarianceMatrix'])\n factor_z_scores = risk_results['results'][0]['assetData']['factorExposure'][0]\n\n # Calculate results\n factors = risk_results['results'][0]['factorData']\n factor_ids_in_order = [f['factorId'] for f in factors]\n exp = np.array([factor_z_scores[key] * spot_price for key in factor_ids_in_order])\n cov_x_exp = np.dot(covariance_matrix, exp)\n mctr = np.divide(cov_x_exp, var)\n rmctr = np.multiply(exp, mctr)\n annualized_rmctr = np.multiply(rmctr, math.sqrt(252) / spot_price * 100)\n annualized_factor_rmctr_sum = np.sum(annualized_rmctr)\n\n final_results = []\n for i in range(0, len(mctr)):\n final_results.append(\n {\n 'Factor': factors[i]['factorName'] if get_factors_by_name else factors[i]['factorId'],\n 'Factor Category': factors[i]['factorCategory'],\n 'Proportion of Risk (%)': annualized_rmctr[i] / total_risk * 100,\n 'MCTR (%)': annualized_rmctr[i],\n }\n )\n final_results.append(\n {\n 'Factor': 'Specific' if get_factors_by_name else 'SPC',\n 'Factor Category': 'Specific',\n 'Proportion of Risk (%)': (total_risk - annualized_factor_rmctr_sum) / total_risk * 100,\n 'MCTR (%)': total_risk - annualized_factor_rmctr_sum,\n }\n )\n\n return pd.DataFrame(final_results) if format == ReturnFormat.DATA_FRAME else final_results\n\n def get_asset_factor_attribution(\n self,\n asset_identifier: str,\n start_date: dt.date,\n end_date: dt.date,\n asset_identifier_type=RiskModelUniverseIdentifierRequest.bbid,\n get_factors_by_name: bool = True,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ):\n \"\"\"\n Get attribution by factor for an asset for a desired date range\n\n :param asset_identifier: asset identifier\n :param start_date: start date\n :param end_date: end date\n :param asset_identifier_type: type corresponding to the identifier passed in\n :param get_factors_by_name: whether to denote the results by factor name (True) or ID (False)\n :param format: which format to return the results in\n\n :return: factor attribution breakdown\n\n **Usage**\n\n Get the factor attribution (in percent) breakdown for any asset covered by the risk model\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import FactorRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 5, 2)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = FactorRiskModel.get(\"MODEL_ID\")\n >>> attribution_breakdown = model.get_asset_factor_attribution('AAPL UW', start_date, end_date)\n \"\"\"\n\n # Get risk model data for the asset\n data_measures = [\n RiskModelDataMeasure.Asset_Universe,\n RiskModelDataMeasure.Universe_Factor_Exposure,\n RiskModelDataMeasure.Factor_Volatility,\n RiskModelDataMeasure.Factor_Return,\n RiskModelDataMeasure.Factor_Name,\n RiskModelDataMeasure.Factor_Id,\n RiskModelDataMeasure.Factor_Category,\n ]\n risk_results = self.get_data(\n data_measures,\n start_date,\n end_date,\n RiskModelDataAssetsRequest(identifier=asset_identifier_type, universe=[asset_identifier]),\n limit_factors=True,\n )['results']\n if len(risk_results) == 0:\n raise MqValueError(f'{asset_identifier} is not covered by {self.id}')\n if len(risk_results) < 2:\n raise MqValueError('Attribution cannot be calculated with only one day of risk data')\n factor_id_to_name_map = {}\n factor_attribution = []\n for i in range(1, len(risk_results)):\n attribution_on_date = {'Date': risk_results[i]['date']}\n for factor_data in risk_results[i]['factorData']:\n factor = factor_data['factorName'] if get_factors_by_name else factor_data['factorId']\n attribution_on_date[factor] = factor_data['factorReturn']\n factor_id_to_name_map[factor_data['factorId']] = factor_data['factorName']\n\n previous_factor_exposures = risk_results[i - 1]['assetData']['factorExposure'][0]\n for factor_id in previous_factor_exposures:\n factor = factor_id_to_name_map[factor_id] if get_factors_by_name else factor_id\n factor_exposure = previous_factor_exposures[factor_id]\n attribution_on_date[factor] *= factor_exposure\n factor_attribution.append(attribution_on_date)\n\n return pd.DataFrame(factor_attribution) if format == ReturnFormat.DATA_FRAME else factor_attribution\n\n def __repr__(self):\n s = \"{}('{}','{}','{}','{}','{}','{}','{}'\".format(\n self.__class__.__name__,\n self.id,\n self.name,\n self.coverage,\n self.term,\n self.universe_identifier,\n self.vendor,\n self.version,\n )\n if self.universe_size:\n s += \", universe_size={}\".format(self)\n if self.entitlements:\n s += \", entitlements={}\".format(self)\n if self.description:\n s += \", description={}\".format(self)\n if self.expected_update_time:\n s += \", expected_update_time={}\".format(self)\n\n s += \")\"\n return s\n\n\nclass MacroRiskModel(MarqueeRiskModel):\n \"\"\"Macro Risk Model used for sensitivity analysis\"\"\"\n\n def __init__(\n self,\n id_: str,\n name: str,\n coverage: CoverageType,\n term: Term,\n universe_identifier: UniverseIdentifier,\n vendor: str,\n version: float,\n universe_size: int = None,\n entitlements: Union[Dict, Entitlements] = None,\n description: str = None,\n expected_update_time: dt.time = None,\n ):\n \"\"\"Create new Macro risk model object\n\n :param id_: risk model id (cannot be changed)\n :param name: risk model name\n :param coverage: coverage of risk model asset universe\n :param universe_identifier: identifier used in asset universe upload (cannot be changed)\n :param vendor: risk model vendor\n :param version: version of model\n :param universe_size: total rough expected universe size (rounding up to nearest 1k)\n :param entitlements: entitlements associated with risk model\n :param description: risk model description\n :param expected_update_time: time when risk model daily data is expected to be uploaded\n\n :return: MacroRiskModel object\n \"\"\"\n super().__init__(\n id_,\n name,\n RiskModelType.Macro,\n vendor,\n version,\n coverage,\n universe_identifier,\n term,\n universe_size=universe_size,\n entitlements=entitlements,\n description=description,\n expected_update_time=expected_update_time,\n )\n\n @classmethod\n def from_target(cls, model: RiskModelBuilder):\n uid = model.universe_identifier\n return MacroRiskModel(\n model.id,\n model.name,\n model.coverage if isinstance(model.coverage, CoverageType) else CoverageType(model.coverage),\n model.term if isinstance(model.term, Term) else Term(model.term),\n uid if isinstance(uid, UniverseIdentifier) else UniverseIdentifier(uid) if uid else None,\n model.vendor,\n model.version,\n universe_size=model.universe_size,\n entitlements=model.entitlements,\n description=model.description,\n expected_update_time=dt.datetime.strptime(model.expected_update_time, \"%H:%M:%S\").time()\n if model.expected_update_time\n else None,\n )\n\n @classmethod\n def get_many(\n cls,\n ids: List[str] = None,\n terms: List[str] = None,\n vendors: List[str] = None,\n names: List[str] = None,\n coverages: List[str] = None,\n limit: int = None,\n ):\n \"\"\"Get many Macro risk models from Marquee\n\n :param ids: list of model identifiers in Marquee\n :param terms: list of model terms\n :param vendors: list of model vendors\n :param names: list of model names\n :param coverages: list of model coverages\n :param limit: limit of number of models in response\n\n :return: list of Macro Risk Model object\n\n \"\"\"\n\n models = GsRiskModelApi.get_risk_models(\n ids=ids,\n terms=terms,\n vendors=vendors,\n names=names,\n coverages=coverages,\n types=[RiskModelType.Macro.value],\n limit=limit,\n )\n return cls.from_many_targets(models)\n\n def get_universe_sensitivity(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n factor_type: FactorType = FactorType.Factor,\n get_factors_by_name: bool = False,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get universe factor or factor category sensitivity data for existing macro risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param factor_type: If factor type is factor, return factor sensitivity. Otherwise, return factor category\n sensitivity.\n :param get_factors_by_name: return results keyed by factor name instead of ID\n :param format: which format to return the results in\n\n :return: Factor or Factor Category sensitivity for assets requested\n\n **Usage**\n\n Given a list of assets, return their sensitivity to macro factors (when the factor_type is `Factor`)\n or macro factor categories (when the factor_type is Category).\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MacroRiskModel.get(\"MODEL_ID\")\n >>> sensitivity = model.get_universe_sensitivity(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n\n :func:`get_r_squared` :func:`factor_standard_deviation` :func:`factor_z_score`\n \"\"\"\n\n sensitivity_df = (\n super().get_universe_exposure(\n start_date, end_date, assets, get_factors_by_name=get_factors_by_name, format=format\n )\n if factor_type == FactorType.Factor\n else super().get_universe_exposure(start_date, end_date, assets, get_factors_by_name=get_factors_by_name)\n )\n\n if factor_type == FactorType.Factor or sensitivity_df.empty:\n return sensitivity_df\n\n factor_data = self.get_factor_data(start_date, end_date)\n factor_data = factor_data.set_index(\"name\") if get_factors_by_name else factor_data.set_index(\"identifier\")\n columns = (\n [(factor_data.loc[f, \"factorCategory\"], f) for f in sensitivity_df.columns.values]\n if get_factors_by_name\n else [(factor_data.loc[f, \"factorCategoryId\"], f) for f in sensitivity_df.columns.values]\n )\n sensitivity_df = sensitivity_df.set_axis(pd.MultiIndex.from_tuples(columns), axis=1)\n\n factor_categories = list(set(sensitivity_df.columns.get_level_values(0).values))\n factor_category_sens_df = pd.concat(\n [\n sensitivity_df[factor_category].agg(np.sum, axis=1).to_frame().rename(columns={0: factor_category})\n for factor_category in factor_categories\n ],\n axis=1,\n )\n\n if format == ReturnFormat.JSON:\n factor_category_sens_df = factor_category_sens_df.to_dict(orient='index')\n factor_category_sens_dict = {}\n\n for key, value in factor_category_sens_df.items():\n temp_dict = factor_category_sens_dict.get(key[0], {})\n temp_dict[key[1]] = value\n factor_category_sens_dict[key[0]] = temp_dict\n\n return factor_category_sens_dict\n\n return factor_category_sens_df\n\n def get_r_squared(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\"Get R Squared data for existing macro risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: The format to return the results in (Dataframe or Dict)\n :return: R Squared for assets requested\n\n **Usage**\n\n Given a list of assets, return the r squared for each asset between `start_date` and `end_date`.\n\n **Examples**\n\n >>> start = dt.date(2022, 1, 1)\n >>> end = dt.date(2022, 5, 2)\n >>> model = MacroRiskModel.get(\"MODEL_ID\")\n >>> r_squared_df = model.get_r_squared(start, end, DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n To get the data in dict format, specify the return format to JSON\n\n >>> r_squared_dict = model.get_r_squared(start, end,\n ... DataAssetsRequest(RiskModelUniverseIdentifierRequest.bbid, ['GS UN'],\n ... ReturnFormat.JSON)\n\n **See also**\n\n :func:`get_fair_value_gap` :func:`factor_standard_deviation` :func:`factor_z_score`\n \"\"\"\n\n return self._get_asset_data_measure(Measure.R_Squared, start_date, end_date, assets, format)\n\n def get_fair_value_gap(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n fair_value_gap_unit: Unit = Unit.STANDARD_DEVIATION,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get fair value gap data for a list of assets between start date and end date\n\n :param start_date: The start date for data request.\n :param end_date: The end date for data request.\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request.\n :param fair_value_gap_unit: Return the fair value gap value expressed in this unit. The default is\n standard deviation.\n :param format: The format to return the data in. The default is pandas Dataframe.\n\n :return: Fair Value Gap for the assets requested\n\n **Usage**\n\n Given a list of assets, return the fair value gap for each asset between `start_date`\n and `end_date`. The data can be returned as a pandas DataFrame or as a dict.\n\n **Examples**\n\n Get the fair value gap data in a dataframe format between start and end.\n >>> from gs_quant.models.risk_model import MacroRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier, ReturnFormat\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MacroRiskModel.get(\"MODEL_ID\")\n >>> fvg_df = model.get_fair_value_gap(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n To get the data in percentage, specify the fair_value_gap_unit to PERCENT.\n\n >>> fvg_df = model.get_fair_value_gap(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']), Unit.PERCENT)\n\n **See also**\n\n :func:`get_r_squared` :func:`factor_standard_deviation` :func:`factor_z_score`\n \"\"\"\n\n return self._get_asset_data_measure(\n Measure.Fair_Value_Gap_Standard_Deviation\n if fair_value_gap_unit == Unit.STANDARD_DEVIATION\n else Measure.Fair_Value_Gap_Percent,\n start_date,\n end_date,\n assets,\n format,\n )\n\n def get_factor_z_score(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = None,\n factors: List[str] = [],\n factors_by_name: bool = True,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get factor z score data for existing risk model keyed by name or id\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to limit the factors by\n :param factors: the factors to get z score data for. If empty, the data for all factors is returned\n :param factors_by_name: whether to identify factors by their name or id. If true, the list of factors must \\\n be a list of factor names. Otherwise, it should be a list of factor ids\n :param format: which format to return the results in\n\n :return: factor z score\n\n **Usage**\n\n Get the z score of the factors specified in `factors`\n\n **Examples**\n\n >>> from gs_quant.models.risk_model import MacroRiskModel\n >>> import datetime as dt\n >>>\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MacroRiskModel.get(\"MODEL_ID\")\n >>> factor_z_score = model.get_factor_z_score(start_date, end_date, factors=['factor1', 'factor2'])\n\n **See also**\n\n :func:`get_r_squared` :func:`factor_standard_deviation` :func:`factor_z_score`\n \"\"\"\n\n return self._get_factor_data_measure(\n Measure.Factor_Z_Score, start_date, end_date, assets, factors_by_name, factors, format\n )\n\n def get_model_price(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\" Get model price data for existing risk model\n\n :param start_date: Start date for data request\n :param end_date: End date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param format: Which format to return the results in\n\n :return: Model price data\n\n **Usage**\n\n Get model price data for assets specified in `assets` between `start_date` and `end_date`\n\n **Example**\n >>> from gs_quant.models.risk_model import FactorRiskModel, DataAssetsRequest, \\\n ... RiskModelUniverseIdentifierRequest as UniverseIdentifier\n >>> import datetime as dt\n >>> start_date = dt.date(2022, 1, 1)\n >>> end_date = dt.date(2022, 5, 2)\n >>> model = MacroRiskModel.get(\"MODEL_ID\")\n >>> model_price = model.get_model_price(start_date, end_date,\n ... DataAssetsRequest(UniverseIdentifier.bbid, ['GS UN']))\n\n **See also**\n :func:`get_r_squared` :func:`get_fair_value_gap`\n\n \"\"\"\n return super()._get_asset_data_measure(Measure.Model_Price, start_date, end_date, assets, format)\n\n def __repr__(self):\n s = \"{}('{}','{}','{}','{}','{}','{}'\".format(\n self.__class__.__name__,\n self.id,\n self.name,\n self.coverage,\n self.universe_identifier,\n self.vendor,\n self.version,\n )\n if self.universe_size:\n s += \", universe_size={}\".format(self)\n if self.entitlements:\n s += \", entitlements={}\".format(self)\n if self.description:\n s += \", description={}\".format(self)\n if self.expected_update_time:\n s += \", expected_update_time={}\".format(self)\n\n s += \")\"\n return s\n\n\nclass ThematicRiskModel(MarqueeRiskModel):\n \"\"\"Thematic Risk Model used for calculating exposure to thematic flagship baskets\"\"\"\n\n def __init__(\n self,\n id_: str,\n name: str,\n coverage: CoverageType,\n term: Term,\n universe_identifier: UniverseIdentifier,\n vendor: str,\n version: float,\n universe_size: int = None,\n entitlements: Union[Dict, Entitlements] = None,\n description: str = None,\n expected_update_time: dt.time = None,\n ):\n \"\"\"Create new Thematic risk model object\n\n :param id_: risk model id (cannot be changed)\n :param name: risk model name\n :param coverage: coverage of risk model asset universe\n :param term: horizon term\n :param universe_identifier: identifier used in asset universe upload (cannot be changed)\n :param vendor: risk model vendor\n :param version: version of model\n :param universe_size: total rough expected universe size (rounding up to nearest 1k)\n :param entitlements: entitlements associated with risk model\n :param description: risk model description\n :param expected_update_time: time when risk model daily data is expected to be uploaded\n\n :return: Thematic Risk Model object\n \"\"\"\n super().__init__(\n id_,\n name,\n RiskModelType.Thematic,\n vendor,\n version,\n coverage,\n universe_identifier,\n term,\n universe_size=universe_size,\n entitlements=entitlements,\n description=description,\n expected_update_time=expected_update_time,\n )\n\n @classmethod\n def from_target(cls, model: RiskModelBuilder):\n uid = model.universe_identifier\n return ThematicRiskModel(\n model.id,\n model.name,\n model.coverage if isinstance(model.coverage, CoverageType) else CoverageType(model.coverage),\n model.term if isinstance(model.term, Term) else Term(model.term),\n uid if isinstance(uid, UniverseIdentifier) else UniverseIdentifier(uid) if uid else None,\n model.vendor,\n model.version,\n universe_size=model.universe_size,\n entitlements=model.entitlements,\n description=model.description,\n expected_update_time=dt.datetime.strptime(model.expected_update_time, \"%H:%M:%S\").time()\n if model.expected_update_time\n else None,\n )\n\n @classmethod\n def get_many(\n cls,\n ids: List[str] = None,\n terms: List[str] = None,\n vendors: List[str] = None,\n names: List[str] = None,\n coverages: List[str] = None,\n limit: int = None,\n ):\n \"\"\"Get a Thematic risk model from Marquee\n\n :param ids: list of model identifiers in Marquee\n :param terms: list of model terms\n :param vendors: list of model vendors\n :param names: list of model names\n :param coverages: list of model coverages\n :param limit: limit of number of models in response\n\n :return: Macro Risk Model object\n \"\"\"\n models = GsRiskModelApi.get_risk_models(\n ids=ids,\n terms=terms,\n vendors=vendors,\n names=names,\n coverages=coverages,\n types=[RiskModelType.Thematic.value],\n limit=limit,\n )\n return cls.from_many_targets(models)\n\n def get_universe_sensitivity(\n self,\n start_date: dt.date,\n end_date: dt.date = None,\n assets: DataAssetsRequest = DataAssetsRequest(RiskModelUniverseIdentifierRequest.gsid, []),\n get_factors_by_name: bool = False,\n format: ReturnFormat = ReturnFormat.DATA_FRAME,\n ) -> Union[List[Dict], pd.DataFrame]:\n \"\"\"Get universe sensitivity data for existing thematic risk model\n\n :param start_date: start date for data request\n :param end_date: end date for data request\n :param assets: DataAssetsRequest object with identifier and list of assets to retrieve for request\n :param get_factors_by_name: return results keyed by factor name instead of ID\n :param format: which format to return the results in\n\n :return: basket sensitivity for assets requested\n \"\"\"\n return super().get_universe_exposure(\n start_date, end_date, assets, get_factors_by_name=get_factors_by_name, format=format\n )\n\n def __repr__(self):\n s = \"{}('{}','{}','{}','{}','{}','{}'\".format(\n self.__class__.__name__,\n self.id,\n self.name,\n self.coverage,\n self.universe_identifier,\n self.vendor,\n self.version,\n )\n if self.universe_size:\n s += \", universe_size={}\".format(self)\n if self.entitlements:\n s += \", entitlements={}\".format(self)\n if self.description:\n s += \", description={}\".format(self)\n if self.expected_update_time:\n s += \", expected_update_time={}\".format(self)\n\n s += \")\"\n return s\n" }, { "path": "gs_quant/models/risk_model_utils.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport math\nimport pydash\nimport random\nfrom time import sleep\nfrom typing import List, Union\n\nimport pandas as pd\n\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.api.gs.risk_models import GsFactorRiskModelApi\nfrom gs_quant.errors import MqRequestError\nfrom gs_quant.target.risk_models import RiskModelData, RiskModelType as Type\nfrom gs_quant.target.risk_models import RiskModelDataMeasure as Measure\n\n\ndef _map_measure_to_field_name(measure: Measure):\n measure_to_field = {\n Measure.Specific_Risk: 'specificRisk',\n Measure.Total_Risk: 'totalRisk',\n Measure.Historical_Beta: 'historicalBeta',\n Measure.Predicted_Beta: 'predictedBeta',\n Measure.Global_Predicted_Beta: 'globalPredictedBeta',\n Measure.Daily_Return: 'dailyReturn',\n Measure.Specific_Return: 'specificReturn',\n Measure.Estimation_Universe_Weight: 'estimationUniverseWeight',\n Measure.R_Squared: 'rSquared',\n Measure.Fair_Value_Gap_Standard_Deviation: 'fairValueGapStandardDeviation',\n Measure.Fair_Value_Gap_Percent: 'fairValueGapPercent',\n Measure.Universe_Factor_Exposure: 'factorExposure',\n Measure.Factor_Return: 'factorReturn',\n Measure.Factor_Standard_Deviation: 'factorStandardDeviation',\n Measure.Factor_Z_Score: 'factorZScore',\n Measure.Bid_Ask_Spread: 'bidAskSpread',\n Measure.Bid_Ask_Spread_30d: 'bidAskSpread30d',\n Measure.Bid_Ask_Spread_60d: 'bidAskSpread60d',\n Measure.Bid_Ask_Spread_90d: 'bidAskSpread90d',\n Measure.Trading_Volume: 'tradingVolume',\n Measure.Trading_Volume_30d: 'tradingVolume30d',\n Measure.Trading_Volume_60d: 'tradingVolume60d',\n Measure.Trading_Volume_90d: 'tradingVolume90d',\n Measure.Traded_Value_30d: 'tradedValue30d',\n Measure.Composite_Volume: 'compositeVolume',\n Measure.Composite_Volume_30d: 'compositeVolume30d',\n Measure.Composite_Volume_60d: 'compositeVolume60d',\n Measure.Composite_Volume_90d: 'compositeVolume90d',\n Measure.Composite_Value_30d: 'compositeValue30d',\n Measure.Issuer_Market_Cap: 'issuerMarketCap',\n Measure.Capitalization: 'capitalization',\n Measure.Currency: 'currency',\n Measure.Dividend_Yield: 'dividendYield',\n Measure.Price: 'price',\n Measure.Unadjusted_Specific_Risk: 'unadjustedSpecificRisk',\n Measure.Model_Price: 'modelPrice',\n Measure.Factor_Mean: 'factorMean',\n Measure.Factor_Cross_Sectional_Mean: 'factorCrossSectionalMean',\n Measure.Factor_Cross_Sectional_Standard_Deviation: 'factorCrossSectionalStandardDeviation',\n }\n\n return measure_to_field.get(measure, '')\n\n\ndef build_factor_id_to_name_map(results: List) -> dict:\n risk_model_factor_data = {}\n for row in results:\n for factor in row.get('factorData', []):\n factor_id = factor['factorId']\n if not risk_model_factor_data.get(factor_id):\n risk_model_factor_data[factor_id] = factor['factorName']\n return risk_model_factor_data\n\n\ndef build_asset_data_map(\n results: List, requested_universe: tuple, requested_measure: Measure, factor_map: dict\n) -> dict:\n if not results:\n return {}\n data_field = _map_measure_to_field_name(requested_measure)\n # if full universe is requested then pull the universe from the results.\n universe = pydash.get(results, '0.assetData.universe', []) if not requested_universe else list(requested_universe)\n data_map = {}\n for asset in universe:\n date_list = {}\n for row in results:\n if asset in row.get('assetData').get('universe'):\n i = row.get('assetData').get('universe').index(asset)\n if data_field == 'factorExposure':\n exposures = row.get('assetData').get(data_field)[i]\n date_list[row.get('date')] = {factor_map.get(f, f): v for f, v in exposures.items()}\n else:\n date_list[row.get('date')] = row.get('assetData').get(data_field)[i]\n data_map[asset] = date_list\n return data_map\n\n\ndef build_factor_data_map(\n results: List, identifier: str, risk_model_id: str, requested_measure: Measure, factors: List[str] = []\n) -> Union[dict, pd.DataFrame]:\n field_name = _map_measure_to_field_name(requested_measure)\n if not field_name:\n raise NotImplementedError(f\"{requested_measure.value} is currently not yet supported\")\n\n data_list = []\n for row in results:\n date = row.get('date')\n factor_data = row.get('factorData')\n for factor_map in factor_data:\n data_list.append(\n {\"date\": date, identifier: factor_map.get(identifier), field_name: factor_map.get(field_name)}\n )\n\n factor_data_df = pd.DataFrame(data_list)\n factor_data_df = factor_data_df.pivot(index=\"date\", columns=identifier, values=field_name)\n\n # if factors, only return data for those factors\n if factors:\n missing_factors = set(factors) - set(factor_data_df.columns.tolist())\n if missing_factors:\n raise ValueError(\n f'Factors(s) with {identifier}(s) {\", \".join(missing_factors)} do not exist in '\n f'risk model {risk_model_id}. Make sure the factor {identifier}(s) are correct'\n )\n\n factor_data_df = factor_data_df[factors]\n\n return factor_data_df\n\n\ndef build_pfp_data_dataframe(\n results: List, return_df: bool = True, get_factors_by_name: bool = True\n) -> Union[pd.DataFrame, list]:\n factor_data_df = pd.DataFrame(results)[[\"date\", \"factorData\"]]\n factor_data_df = factor_data_df.explode('factorData')\n factor_data_df['factorId'] = factor_data_df['factorData'].apply(lambda x: x.get('factorId'))\n factor_data_df['factorName'] = factor_data_df['factorData'].apply(lambda x: x.get('factorName'))\n factor_data_df = factor_data_df.drop(columns='factorData').set_index(\"date\")\n\n pfp_list = []\n identifier_col_name = \"assetId\" if not get_factors_by_name else 'identifier'\n for row in results:\n factor_map_on_date = factor_data_df.loc[row.get('date'), ['factorId', 'factorName']]\n if isinstance(factor_map_on_date, pd.Series):\n factor_map_on_date = factor_map_on_date.to_frame().T\n\n factor_map_on_date = factor_map_on_date.to_dict(orient='list')\n factor_id_to_name_map = dict(zip(factor_map_on_date.get('factorId'), factor_map_on_date.get('factorName')))\n pfp_map = dict()\n pfp_map[identifier_col_name] = row.get('factorPortfolios').get('universe')\n for factor in row.get('factorPortfolios').get('portfolio'):\n factor_id = factor.get('factorId')\n if get_factors_by_name:\n pfp_map[factor_id_to_name_map.get(factor_id)] = factor.get('weights')\n else:\n pfp_map[f'factorId: {factor_id}'] = factor.get('weights')\n pfp_map['date'] = row.get('date')\n if not return_df:\n pfp_list.append(pfp_map)\n else:\n weights_df = pd.DataFrame(pfp_map)\n pfp_list.append(weights_df)\n if not return_df:\n return pfp_list\n\n results = pd.concat(pfp_list) if pfp_list else pd.DataFrame({})\n return results.set_index(\"date\")\n\n\ndef get_optional_data_as_dataframe(results: List, optional_data_key: str) -> pd.DataFrame:\n cov_list = []\n date_list = []\n for row in results:\n matrix_df = pd.DataFrame(row.get(optional_data_key))\n cov_list.append(matrix_df)\n date_list.append(row.get('date'))\n results = pd.concat(cov_list, keys=date_list) if cov_list else pd.DataFrame({})\n return results\n\n\ndef get_covariance_matrix_dataframe(\n results: List[dict], covariance_matrix_key: str = 'covarianceMatrix'\n) -> pd.DataFrame:\n cov_list = []\n date_list = []\n for row in results:\n matrix_df = pd.DataFrame(row.get(covariance_matrix_key))\n factor_names = [data.get('factorName') for data in row.get('factorData')]\n matrix_df.columns = factor_names\n matrix_df.index = factor_names\n cov_list.append(matrix_df)\n date_list.append(row.get('date'))\n results = pd.concat(cov_list, keys=date_list) if cov_list else pd.DataFrame({})\n return results\n\n\ndef build_factor_volatility_dataframe(results: List, group_by_name: bool, factors: List[str]) -> pd.DataFrame:\n data = []\n dates = []\n for row in results:\n dates.append(row.get('date'))\n data.append(row.get('factorVolatility'))\n\n df = pd.DataFrame(data, index=dates)\n if group_by_name:\n factor_id_to_name_map = build_factor_id_to_name_map(results)\n df = df.rename(columns=factor_id_to_name_map)\n if factors:\n missing_factors = set(factors) - set(df.columns.tolist())\n if missing_factors:\n raise ValueError(\n f'Factors(s): {\", \".join(missing_factors)} do not exist in the risk model. '\n f'Make sure the factors are correct.'\n )\n else:\n return df[factors]\n return df\n\n\ndef get_closest_date_index(date: dt.date, dates: List[str], direction: str) -> int:\n for i in range(50):\n for index in range(len(dates)):\n if direction == 'before':\n next_date = (date - dt.timedelta(days=i)).strftime('%Y-%m-%d')\n else:\n next_date = (date + dt.timedelta(days=i)).strftime('%Y-%m-%d')\n if next_date == dates[index]:\n return index\n return -1\n\n\ndef divide_request(data, n):\n for i in range(0, len(data), n):\n yield data[i : i + n]\n\n\ndef batch_and_upload_partial_data_use_target_universe_size(model_id: str, data: dict, max_asset_size: int):\n \"\"\"Takes in total risk model data for one day and batches requests according to\n asset data size, returns a list of messages from resulting post calls\"\"\"\n date = data.get('date')\n _upload_factor_data_if_present(model_id, data, date)\n sleep(random.uniform(3, 7))\n _batch_data_if_present(model_id, data, max_asset_size, date)\n\n\ndef _upload_factor_data_if_present(model_id: str, data: dict, date: str, **kwargs):\n aws_upload = kwargs.get('aws_upload', None)\n if data.get('factorData'):\n factor_data = {'date': date, 'factorData': data.get('factorData')}\n if data.get('covarianceMatrix'):\n factor_data['covarianceMatrix'] = data.get('covarianceMatrix')\n if data.get('unadjustedCovarianceMatrix') and aws_upload:\n factor_data['unadjustedCovarianceMatrix'] = data.get('unadjustedCovarianceMatrix')\n if data.get('preVRACovarianceMatrix') and aws_upload:\n factor_data['preVRACovarianceMatrix'] = data.get('preVRACovarianceMatrix')\n logging.info('Uploading factor data')\n _repeat_try_catch_request(\n GsFactorRiskModelApi.upload_risk_model_data,\n model_id=model_id,\n model_data=factor_data,\n partial_upload=True,\n **kwargs,\n )\n\n\ndef _batch_data_if_present(model_id: str, data, max_asset_size, date):\n if data.get('assetData'):\n asset_data_list, target_size = _batch_input_data({'assetData': data.get('assetData')}, max_asset_size)\n for asset_data in asset_data_list:\n _repeat_try_catch_request(\n GsFactorRiskModelApi.upload_risk_model_data,\n model_id=model_id,\n model_data={'assetData': asset_data, 'date': date},\n partial_upload=True,\n target_universe_size=target_size,\n )\n sleep(random.uniform(3, 7))\n\n if 'issuerSpecificCovariance' in data.keys() or 'factorPortfolios' in data.keys():\n for optional_key in ['issuerSpecificCovariance', 'factorPortfolios']:\n if data.get(optional_key):\n optional_data = data.get(optional_key)\n optional_data_list, target_size = _batch_input_data({optional_key: optional_data}, max_asset_size // 2)\n logging.info(f'{optional_key} being uploaded for {date}...')\n for optional_data in optional_data_list:\n _repeat_try_catch_request(\n GsFactorRiskModelApi.upload_risk_model_data,\n model_id=model_id,\n model_data={optional_key: optional_data, 'date': date},\n partial_upload=True,\n target_universe_size=target_size,\n )\n sleep(random.uniform(3, 7))\n\n\ndef only_factor_data_is_present(model_type: Type, data: dict) -> bool:\n if model_type == Type.Macro or model_type == Type.Thematic:\n if len(data.keys()) == 2 and 'factorData' in data.keys():\n return True\n else:\n if len(data.keys()) == 3 and 'factorData' in data.keys() and 'covarianceMatrix' in data.keys():\n return True\n return False\n\n\ndef batch_and_upload_partial_data(model_id: str, data: dict, max_asset_size: int, **kwargs):\n \"\"\"Takes in total risk model data for one day and batches requests according to\n asset data size, returns a list of messages from resulting post calls\"\"\"\n date = data.get('date')\n _upload_factor_data_if_present(model_id, data, date, **kwargs)\n sleep(random.uniform(3, 7))\n if data.get('currencyRatesData'):\n _repeat_try_catch_request(\n GsFactorRiskModelApi.upload_risk_model_data,\n model_id=model_id,\n model_data={\"currencyRatesData\": data.get('currencyRatesData'), 'date': date},\n partial_upload=True,\n final_upload=True,\n **kwargs,\n )\n sleep(random.uniform(3, 7))\n for risk_model_data_type in [\"assetData\", \"issuerSpecificCovariance\", \"factorPortfolios\"]:\n _repeat_try_catch_request(\n _batch_data_v2,\n model_id=model_id,\n data=data.get(risk_model_data_type),\n data_type=risk_model_data_type,\n max_asset_size=max_asset_size,\n date=date,\n **kwargs,\n )\n sleep(random.uniform(3, 7))\n\n\ndef _batch_data_v2(model_id: str, data: dict, data_type: str, max_asset_size: int, date: Union[str, dt.date], **kwargs):\n if data:\n if data_type in [\"issuerSpecificCovariance\", \"factorPortfolios\"]:\n max_asset_size //= 2\n data_list, _ = _batch_input_data({data_type: data}, max_asset_size)\n for i, data_chunk in enumerate(data_list):\n final_upload = i == len(data_list) - 1\n res = GsFactorRiskModelApi.upload_risk_model_data(\n model_id=model_id,\n model_data={data_type: data_chunk, 'date': date},\n partial_upload=True,\n final_upload=final_upload,\n **kwargs,\n )\n logging.info(res)\n\n\ndef batch_and_upload_coverage_data(date: dt.date, gsid_list: list, model_id: str, batch_size: int):\n update_time = dt.datetime.today().strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n request_array = [\n {'date': date.strftime('%Y-%m-%d'), 'gsid': gsid, 'riskModel': model_id, 'updateTime': update_time}\n for gsid in set(gsid_list)\n ]\n logging.info(f\"Uploading {len(request_array)} gsids to asset coverage dataset\")\n list_of_requests = list(divide_request(request_array, batch_size))\n logging.info(f\"Uploading in {len(list_of_requests)} batches of {batch_size} gsids\")\n [\n _repeat_try_catch_request(GsDataApi.upload_data, data=data, dataset_id=\"RISK_MODEL_ASSET_COVERAGE\")\n for data in list_of_requests\n ]\n\n\ndef upload_model_data(model_id: str, data: dict, **kwargs):\n _repeat_try_catch_request(GsFactorRiskModelApi.upload_risk_model_data, model_id=model_id, model_data=data, **kwargs)\n\n\ndef risk_model_data_to_json(risk_model_data: RiskModelData) -> dict:\n risk_model_data = risk_model_data.to_json()\n risk_model_data['assetData'] = risk_model_data.get('assetData').to_json()\n if risk_model_data.get('factorPortfolios'):\n risk_model_data['factorPortfolios'] = risk_model_data.get('factorPortfolios').to_json()\n risk_model_data['factorPortfolios']['portfolio'] = [\n portfolio.to_json() for portfolio in risk_model_data.get('factorPortfolios').get('portfolio')\n ]\n if risk_model_data.get('issuerSpecificCovariance'):\n risk_model_data['issuerSpecificCovariance'] = risk_model_data.get('issuerSpecificCovariance').to_json()\n return risk_model_data\n\n\ndef get_universe_size(data_to_split: dict) -> int:\n # takes any chunk of risk model data and returns the universe size\n if 'assetData' in data_to_split.keys():\n return len(data_to_split.get('assetData').get('universe'))\n data_splitted = list(data_to_split.values())\n for data in data_splitted:\n if isinstance(data, str):\n continue\n if 'universe' in data.keys():\n return len(data.get('universe'))\n if 'universeId1' in data.keys():\n return len(data.get('universeId1'))\n raise ValueError(f'No universe found for data {data_to_split}')\n\n\ndef _batch_input_data(input_data: dict, max_asset_size: int):\n data_key = list(input_data.keys())[0]\n target_universe_size = get_universe_size(input_data)\n split_num = (\n math.ceil(target_universe_size / max_asset_size) if math.ceil(target_universe_size / max_asset_size) else 1\n )\n split_idx = math.ceil(target_universe_size / split_num)\n batched_data_list = []\n for i in range(split_num):\n if data_key == 'assetData':\n data_batched = _batch_asset_input(\n input_data.get('assetData'), i, split_idx, split_num, target_universe_size\n )\n elif data_key == 'factorPortfolios':\n data_batched = _batch_pfp_input(\n input_data.get('factorPortfolios'), i, split_idx, split_num, target_universe_size\n )\n else:\n data_batched = _batch_isc_input(\n input_data.get('issuerSpecificCovariance'), i, split_idx, split_num, target_universe_size\n )\n batched_data_list.append(data_batched)\n return batched_data_list, target_universe_size\n\n\ndef _batch_asset_input(input_data: dict, i: int, split_idx: int, split_num: int, target_universe_size: int) -> dict:\n end_idx = (i + 1) * split_idx if split_num != i + 1 else target_universe_size + 1\n asset_data_subset = {\n 'universe': input_data.get('universe')[i * split_idx : end_idx],\n 'specificRisk': input_data.get('specificRisk')[i * split_idx : end_idx],\n 'factorExposure': input_data.get('factorExposure')[i * split_idx : end_idx],\n }\n\n optional_fields = list(input_data.keys())\n [optional_fields.remove(required_field) for required_field in [\"universe\", \"specificRisk\", \"factorExposure\"]]\n for optional_input in optional_fields:\n if input_data.get(optional_input):\n asset_data_subset[optional_input] = input_data.get(optional_input)[i * split_idx : end_idx]\n return asset_data_subset\n\n\ndef _batch_pfp_input(input_data: dict, i: int, split_idx: int, split_num: int, target_universe_size: int) -> dict:\n end_idx = (i + 1) * split_idx if split_num != i + 1 else target_universe_size + 1\n pfp_data_subset = dict()\n universe_slice = input_data.get('universe')[i * split_idx : end_idx]\n pfp_data_subset['universe'] = universe_slice\n portfolio_slice = list()\n for portfolio in input_data.get('portfolio'):\n factor_id = portfolio.get('factorId')\n weights_slice = portfolio.get('weights')[i * split_idx : end_idx]\n portfolio_slice.append({\"factorId\": factor_id, \"weights\": weights_slice})\n pfp_data_subset['portfolio'] = portfolio_slice\n return pfp_data_subset\n\n\ndef _batch_isc_input(input_data: dict, i: int, split_idx: int, split_num: int, target_universe_size: int) -> dict:\n end_idx = (i + 1) * split_idx if split_num != i + 1 else target_universe_size + 1\n return {\n 'universeId1': input_data.get('universeId1')[i * split_idx : end_idx],\n 'universeId2': input_data.get('universeId2')[i * split_idx : end_idx],\n 'covariance': input_data.get('covariance')[i * split_idx : end_idx],\n }\n\n\ndef _repeat_try_catch_request(\n input_function, number_retries: int = 5, return_result: bool = False, verbose: bool = True, **kwargs\n):\n t = 3.0\n errors = []\n for i in range(number_retries):\n try:\n result = input_function(**kwargs)\n if result:\n if return_result:\n return result\n logging.info(result)\n errors.clear()\n break\n except MqRequestError as e:\n errors.append(e)\n if e.status < 500 and e.status != 429:\n raise e\n elif i < number_retries - 1:\n sleep_time = math.pow(2.2, t)\n t += 1\n if verbose:\n logging.warning(f'Exception caught while making request: {e}, retrying in {int(sleep_time)}')\n sleep(sleep_time)\n else:\n if verbose:\n logging.warning(f'Maximum number of retries: {number_retries} triggered')\n except Exception as unknown_exception:\n errors.append(unknown_exception)\n if i < number_retries - 1:\n sleep_time = math.pow(2.2, t)\n t += 1\n if verbose:\n logging.warning(f'Unknown exception caught: {unknown_exception}, retrying in {int(sleep_time)}')\n sleep(sleep_time)\n else:\n if verbose:\n logging.warning(f'Maximum number of retries: {number_retries} triggered')\n if errors:\n raise errors.pop()\n" }, { "path": "gs_quant/priceable.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom abc import ABC\nfrom typing import Union, Optional\n\nfrom gs_quant.base import Priceable\nfrom gs_quant.context_base import nullcontext\nfrom gs_quant.markets import MarketDataCoordinate, PricingContext, CloseMarket, OverlayMarket\nfrom gs_quant.risk import DataFrameWithInfo, DollarPrice, FloatWithInfo, Price, SeriesWithInfo, MarketData\nfrom gs_quant.risk.results import PricingFuture, PortfolioRiskResult, ErrorValue\n\n__asset_class_and_type_to_instrument = {}\n_logger = logging.getLogger(__name__)\n\n\nclass PriceableImpl(Priceable, ABC):\n @property\n def _pricing_context(self) -> PricingContext:\n pricing_context = PricingContext.current\n return pricing_context if not (pricing_context.is_entered or pricing_context.is_async) else nullcontext()\n\n @property\n def _return_future(self) -> bool:\n pricing_context = self._pricing_context\n return not isinstance(pricing_context, PricingContext) or (\n pricing_context.is_async or pricing_context.is_entered\n )\n\n def dollar_price(self) -> Union[FloatWithInfo, PortfolioRiskResult, PricingFuture, SeriesWithInfo]:\n \"\"\"\n Present value in USD\n\n :return: a float or a future, depending on whether the current PricingContext is async, or has been entered\n\n **Examples**\n\n >>> from gs_quant.instrument import IRCap\n >>>\n >>> cap = IRCap('1y', 'EUR')\n >>> price = cap.dollar_price()\n\n price is the present value in USD (a float)\n\n >>> cap_usd = IRCap('1y', 'USD')\n >>> cap_eur = IRCap('1y', 'EUR')\n >>>\n >>> from gs_quant.markets import PricingContext\n >>>\n >>> with PricingContext():\n >>> price_usd_f = cap_usd.dollar_price()\n >>> price_eur_f = cap_eur.dollar_price()\n >>>\n >>> price_usd = price_usd_f.result()\n >>> price_eur = price_eur_f.result()\n\n price_usd_f and price_eur_f are futures, price_usd and price_eur are floats\n \"\"\"\n return self.calc(DollarPrice)\n\n def price(self, currency=None) -> Union[FloatWithInfo, PortfolioRiskResult, PricingFuture, SeriesWithInfo]:\n \"\"\"\n Present value in local currency. Note that this is not yet supported on all instruments\n\n ***Examples**\n\n >>> from gs_quant.instrument import IRSwap\n >>>\n >>> swap = IRSwap('Pay', '10y', 'EUR')\n >>> price = swap.price()\n\n price is the present value in EUR (a float)\n \"\"\"\n return self.calc(Price(currency=currency)) if currency else self.calc(Price)\n\n def market(self) -> Union[OverlayMarket, PricingFuture]:\n \"\"\"\n Market Data map of coordinates and values. Note that this is not yet supported on all instruments\n\n ***Examples**\n\n >>> from gs_quant.instrument import IRSwap\n >>>\n >>> swap = IRSwap('Pay', '10y', 'EUR')\n >>> market = swap.market()\n\n \"\"\"\n\n def handle_result(\n result: Optional[Union[DataFrameWithInfo, ErrorValue, PricingFuture]],\n ) -> [OverlayMarket, dict]:\n if isinstance(result, ErrorValue):\n return result\n\n properties = MarketDataCoordinate.properties()\n is_historical = result.index.name == 'date'\n location = PricingContext.current.market_data_location\n\n def extract_market_data(this_result: DataFrameWithInfo):\n market_data = {}\n\n for _, row in this_result.iterrows():\n coordinate_values = {p: row.get(p) for p in properties}\n mkt_point = coordinate_values.get('mkt_point')\n if mkt_point is not None:\n coordinate_values['mkt_point'] = tuple(coordinate_values['mkt_point'].split(';'))\n\n # return 'redacted' as coordinate value if its a redacted coordinate\n market_data[MarketDataCoordinate.from_dict(coordinate_values)] = (\n row['value'] if row['permissions'] == 'Granted' else 'redacted'\n )\n\n return market_data\n\n if is_historical:\n return {\n date: OverlayMarket(\n base_market=CloseMarket(date=date, location=location),\n market_data=extract_market_data(result.loc[date]),\n )\n for date in set(result.index)\n }\n else:\n return OverlayMarket(base_market=result.risk_key.market, market_data=extract_market_data(result))\n\n return self.calc(MarketData, fn=handle_result)\n" }, { "path": "gs_quant/quote_reports/__init__.py", "content": "" }, { "path": "gs_quant/quote_reports/core.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Dict, Any, Iterable, Union\n\nfrom dataclasses_json.cfg import _GlobalConfig\n\nfrom gs_quant.base import CustomComments\nfrom gs_quant.workflow import (\n VisualStructuringReport,\n BinaryImageComments,\n HyperLinkImageComments,\n CustomDeltaHedge,\n DeltaHedge,\n HedgeTypes,\n)\n\nglobal_config = _GlobalConfig()\n\n\ndef quote_report_from_dict(quote_report_dict: Union[Dict[str, Any], VisualStructuringReport]):\n if quote_report_dict is not None:\n if isinstance(quote_report_dict, VisualStructuringReport):\n return quote_report_dict\n type = quote_report_dict.get('reportType')\n if 'VisualStructuringReport' == type:\n report = VisualStructuringReport.from_dict(quote_report_dict)\n return report\n return None\n\n\ndef quote_reports_from_dicts(quote_report_dicts: Iterable[Dict[str, Any]]):\n if quote_report_dicts is not None:\n reports = []\n for quote_report_dict in quote_report_dicts:\n report = quote_report_from_dict(quote_report_dict)\n reports.append(report)\n return reports\n return None\n\n\ndef custom_comment_from_dict(in_dict: Union[Dict[str, Any], CustomComments]):\n if in_dict is not None:\n if isinstance(in_dict, CustomComments):\n return in_dict\n type = in_dict.get('commentType')\n if 'binaryImageComments' == type:\n out = BinaryImageComments.from_dict(in_dict)\n return out\n if 'hyperLinkImageComments' == type:\n out = HyperLinkImageComments.from_dict(in_dict)\n return out\n return None\n\n\ndef custom_comments_from_dicts(in_dicts: Iterable[Dict[str, Any]]):\n if in_dicts is not None:\n comments = []\n for in_dict in in_dicts:\n report = custom_comment_from_dict(in_dict)\n comments.append(report)\n return comments\n return None\n\n\ndef hedge_type_from_dict(hedge_type_dict: Union[Dict[str, Any], HedgeTypes]):\n if hedge_type_dict is not None:\n if isinstance(hedge_type_dict, HedgeTypes):\n return hedge_type_dict\n type = hedge_type_dict.get('type')\n if 'CustomDeltaHedge' == type:\n hedge_type = CustomDeltaHedge.from_dict(hedge_type_dict)\n return hedge_type\n if 'DeltaHedge' == type:\n hedge_type = DeltaHedge.from_dict(hedge_type_dict)\n return hedge_type\n return None\n\n\ndef hedge_type_from_dicts(in_dicts: Iterable[Dict[str, Any]]):\n if in_dicts is not None:\n hedge_types = []\n for in_dict in in_dicts:\n hedge_type = hedge_type_from_dict(in_dict)\n hedge_types.append(hedge_type)\n return hedge_types\n return None\n" }, { "path": "gs_quant/risk/__init__.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .core import *\nfrom .measures import *\nfrom .scenarios import MarketDataShockBasedScenario\nfrom gs_quant.target.portfolios import LiquidityRequest\nfrom gs_quant.target.risk import LiborFallbackScenario, CarryScenario, CompositeScenario, CurveScenario, IndexCurveShift,\\\n LiquidityResponse, MarketDataPattern, MarketDataScenario, MarketDataShock, MarketDataShockType, RiskRequest, RollFwd,\\\n CurveOverlay, MultiScenario\nfrom gs_quant.target.measures import *" }, { "path": "gs_quant/risk/core.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport itertools\nfrom abc import ABCMeta, abstractmethod\nfrom concurrent.futures import Future\nfrom copy import copy\nfrom dataclasses import dataclass, fields\nfrom typing import Iterable, Optional, Union, Tuple, Dict, Callable, List\n\nimport gs_quant\nimport pandas as pd\nfrom dataclasses_json import dataclass_json\nfrom gs_quant.base import RiskKey\nfrom gs_quant.config import DisplayOptions\nfrom gs_quant.datetime import point_sort_order\n\n__column_sort_fns = {'label1': point_sort_order, 'mkt_point': point_sort_order, 'point': point_sort_order}\n__risk_columns = ('date', 'time', 'mkt_type', 'mkt_asset', 'mkt_class', 'mkt_point')\n\n\nclass ResultInfo(metaclass=ABCMeta):\n def __init__(\n self,\n risk_key: RiskKey,\n unit: Optional[dict] = None,\n error: Optional[Union[str, dict]] = None,\n request_id: Optional[str] = None,\n ):\n self.__risk_key = risk_key\n self.__unit = unit\n self.__error = error\n self.__request_id = request_id\n\n @property\n @abstractmethod\n def raw_value(self): ...\n\n @property\n def risk_key(self) -> RiskKey:\n return self.__risk_key\n\n @property\n def unit(self) -> dict:\n \"\"\"The units of this result\"\"\"\n return self.__unit\n\n @property\n def error(self) -> Union[str, dict]:\n \"\"\"Any error associated with this result\"\"\"\n return self.__error\n\n @property\n def request_id(self) -> Optional[str]:\n \"\"\"The request Id associated with this result\"\"\"\n return self.__request_id\n\n @staticmethod\n def composition_info(components: Iterable):\n from gs_quant.markets.markets import historical_risk_key\n\n dates = []\n values = []\n errors = {}\n risk_key = None\n unit = None\n\n for component in components:\n date = component.risk_key.date\n risk_key = historical_risk_key(component.risk_key) if risk_key is None else risk_key\n\n if risk_key.market.location != component.risk_key.market.location:\n raise ValueError('Cannot compose results with different markets')\n\n if isinstance(component, (ErrorValue, Exception)):\n errors[date] = component\n elif isinstance(component, UnsupportedValue):\n values.append(component)\n dates.append(date)\n unit = None\n else:\n values.append(component.raw_value)\n dates.append(date)\n unit = unit or component.unit\n\n return dates, values, errors, risk_key, unit\n\n\nclass ErrorValue(ResultInfo):\n def __init__(self, risk_key: RiskKey, error: Union[str, dict], request_id: Optional[str] = None):\n super().__init__(risk_key, error=error, request_id=request_id)\n\n def __repr__(self):\n return self.error\n\n def __getattr__(self, item):\n # only called if self.item doesn't exist\n raise AttributeError(f'ErrorValue object has no attribute {item}. Error was {self.error}')\n\n @property\n def raw_value(self):\n return None\n\n def _to_records(self, extra_dict, display_options: DisplayOptions = None):\n return [{**extra_dict, 'value': self}]\n\n\nclass UnsupportedValue(ResultInfo):\n def __init__(self, risk_key: RiskKey, request_id: Optional[str] = None):\n super().__init__(risk_key, request_id=request_id)\n\n def __repr__(self):\n return 'Unsupported Value'\n\n @property\n def raw_value(self):\n return 'Unsupported Value'\n\n @staticmethod\n def compose(components: Iterable):\n dates, values, errors, risk_key, unit = ResultInfo.composition_info(components)\n return SeriesWithInfo(\n pd.Series(index=pd.DatetimeIndex(dates).date, data=values), risk_key=risk_key, unit=unit, error=errors\n )\n\n def _to_records(self, extra_dict, display_options: DisplayOptions = None):\n if display_options is not None and not isinstance(display_options, DisplayOptions):\n raise TypeError(\"display_options must be of type DisplayOptions\")\n\n options = display_options if display_options is not None else gs_quant.config.display_options\n show_na = options.show_na\n\n return [{**extra_dict, 'value': self}] if show_na else []\n\n\nclass ScalarWithInfo(ResultInfo, metaclass=ABCMeta):\n def __init__(\n self,\n risk_key: RiskKey,\n value: Union[float, str, dict],\n unit: Optional[dict] = None,\n error: Optional[Union[str, dict]] = None,\n request_id: Optional[str] = None,\n ):\n ResultInfo.__init__(self, risk_key, unit=unit, error=error, request_id=request_id)\n float.__init__(value)\n\n def __reduce__(self):\n return self.__class__, (self.risk_key, self.raw_value, self.unit, self.error, self.request_id)\n\n @property\n @abstractmethod\n def raw_value(self): ...\n\n @staticmethod\n def compose(components: Iterable):\n dates, values, errors, risk_key, unit = ResultInfo.composition_info(components)\n return SeriesWithInfo(\n pd.Series(index=pd.DatetimeIndex(dates).date, data=values), risk_key=risk_key, unit=unit, error=errors\n )\n\n def _to_records(self, extra_dict, display_options: DisplayOptions = None):\n return [{**extra_dict, 'value': self}]\n\n\nclass FloatWithInfo(ScalarWithInfo, float):\n def __new__(\n cls,\n risk_key: RiskKey,\n value: Union[float, str],\n unit: dict = None,\n error: Optional[str] = None,\n request_id: Optional[str] = None,\n ):\n return float.__new__(cls, value)\n\n @property\n def raw_value(self) -> float:\n return float(self)\n\n def __str__(self):\n return float.__repr__(self)\n\n def __repr__(self):\n if self.error:\n return self.error\n else:\n res = float.__repr__(self)\n if self.unit and isinstance(self.unit, dict):\n # Build unit string from dict like {'A': 1, 'B': -1} -> \"A/B\"\n numerator = []\n denominator = []\n\n for unit_name, power in self.unit.items():\n if power > 0:\n if power == 1:\n numerator.append(unit_name)\n else:\n numerator.append(f\"{unit_name}^{power}\")\n elif power < 0:\n if power == -1:\n denominator.append(unit_name)\n else:\n denominator.append(f\"{unit_name}^{abs(power)}\")\n\n if numerator and denominator:\n unit_str = f\"{'*'.join(numerator)}/{'*'.join(denominator)}\"\n elif numerator:\n unit_str = '*'.join(numerator)\n elif denominator:\n unit_str = f\"1/{'*'.join(denominator)}\"\n else:\n unit_str = \"\"\n\n return f\"{res} ({unit_str})\" if unit_str else res\n return res\n\n def __add__(self, other):\n if isinstance(other, FloatWithInfo):\n if self.unit == other.unit:\n return FloatWithInfo(\n combine_risk_key(self.risk_key, other.risk_key), self.raw_value + other.raw_value, self.unit\n )\n else:\n raise ValueError('FloatWithInfo unit mismatch')\n return super(FloatWithInfo, self).__add__(other)\n\n def __mul__(self, other):\n if isinstance(other, FloatWithInfo):\n return FloatWithInfo(\n combine_risk_key(self.risk_key, other.risk_key), self.raw_value * other.raw_value, self.unit\n )\n else:\n return FloatWithInfo(self.risk_key, self.raw_value * other, self.unit)\n\n def to_frame(self):\n return self\n\n\nclass StringWithInfo(ScalarWithInfo, str):\n def __new__(\n cls,\n risk_key: RiskKey,\n value: Union[float, str],\n unit: Optional[dict] = None,\n error: Optional[str] = None,\n request_id: Optional[str] = None,\n ):\n return str.__new__(cls, value)\n\n @property\n def raw_value(self) -> str:\n return str(self)\n\n def __repr__(self):\n return self.error if self.error else str.__repr__(self)\n\n\nclass DictWithInfo(ScalarWithInfo, dict):\n def __new__(\n cls,\n risk_key: RiskKey,\n value: Union[float, str, dict],\n unit: Optional[dict] = None,\n error: Optional[str] = None,\n request_id: Optional[str] = None,\n ):\n return dict.__new__(cls, value)\n\n def __init__(\n self,\n risk_key: RiskKey,\n value: Union[float, str, dict],\n unit: Optional[dict] = None,\n error: Optional[Union[str, dict]] = None,\n request_id: Optional[str] = None,\n ):\n dict.__init__(self, value)\n ResultInfo.__init__(self, risk_key, unit=unit, error=error, request_id=request_id)\n\n @property\n def raw_value(self) -> dict:\n return dict(self)\n\n def __repr__(self):\n return self.error if self.error else dict.__repr__(self)\n\n\nclass SeriesWithInfo(pd.Series, ResultInfo):\n _internal_names = pd.DataFrame._internal_names + [\n '_ResultInfo__' + i for i in dir(ResultInfo) if isinstance(getattr(ResultInfo, i), property)\n ]\n _internal_names_set = set(_internal_names)\n\n def __init__(\n self,\n *args,\n risk_key: Optional[RiskKey] = None,\n unit: Optional[dict] = None,\n error: Optional[Union[str, dict]] = None,\n request_id: Optional[str] = None,\n **kwargs,\n ):\n pd.Series.__init__(self, *args, **kwargs)\n ResultInfo.__init__(self, risk_key, unit=unit, error=error, request_id=request_id)\n\n def __repr__(self):\n if self.error:\n return pd.Series.__repr__(self) + \"\\nErrors: \" + str(self.error)\n return pd.Series.__repr__(self)\n\n @property\n def _constructor(self):\n return SeriesWithInfo\n\n @property\n def _constructor_expanddim(self):\n return DataFrameWithInfo\n\n @property\n def raw_value(self) -> pd.Series:\n return pd.Series(self)\n\n @staticmethod\n def compose(components: Iterable):\n dates, values, errors, risk_key, unit = ResultInfo.composition_info(components)\n return SeriesWithInfo(\n pd.Series(index=pd.DatetimeIndex(dates).date, data=values), risk_key=risk_key, unit=unit, error=errors\n )\n\n def _to_records(self, extra_dict, display_options: DisplayOptions = None):\n df = pd.DataFrame(self).reset_index()\n df.columns = ['dates', 'value']\n records = df.to_dict('records')\n records = [dict(item, **{**extra_dict}) for item in records]\n return records\n\n def __mul__(self, other):\n new_result = pd.Series.__mul__(self, other)\n ResultInfo.__init__(\n new_result, risk_key=self.risk_key, unit=self.unit, error=self.error, request_id=self.request_id\n )\n return new_result\n\n def copy_with_resultinfo(self, deep=True):\n return SeriesWithInfo(\n self.raw_value.copy(deep=deep),\n risk_key=self.risk_key,\n unit=self.unit,\n error=self.error,\n request_id=self.request_id,\n )\n\n\nclass DataFrameWithInfo(pd.DataFrame, ResultInfo):\n _internal_names = pd.DataFrame._internal_names + [\n '_ResultInfo__' + i for i in dir(ResultInfo) if isinstance(getattr(ResultInfo, i), property)\n ]\n _internal_names_set = set(_internal_names)\n\n def __init__(\n self,\n *args,\n risk_key: Optional[RiskKey] = None,\n unit: Optional[dict] = None,\n error: Optional[Union[str, dict]] = None,\n request_id: Optional[str] = None,\n **kwargs,\n ):\n pd.DataFrame.__init__(self, *args, **kwargs)\n ResultInfo.__init__(self, risk_key, unit=unit, error=error, request_id=request_id)\n\n def __repr__(self):\n if self.error:\n return pd.DataFrame.__repr__(self) + \"\\nErrors: \" + str(self.errors)\n return pd.DataFrame.__repr__(self)\n\n @property\n def _constructor(self):\n return DataFrameWithInfo\n\n @property\n def _constructor_sliced(self):\n return SeriesWithInfo\n\n @property\n def raw_value(self) -> pd.DataFrame:\n if self.empty:\n return pd.DataFrame(self)\n df = self.copy()\n if isinstance(self.index.values[0], dt.date):\n df.index.name = 'dates'\n df = df.reset_index()\n return pd.DataFrame(df)\n\n @staticmethod\n def compose(components: Iterable):\n dates, values, errors, risk_key, unit = ResultInfo.composition_info(components)\n df = pd.concat(v.assign(date=d) for d, v in zip(dates, values)).set_index('date')\n\n return DataFrameWithInfo(df, risk_key=risk_key, unit=unit, error=errors)\n\n def to_frame(self):\n return self\n\n def _to_records(self, extra_dict, display_options: DisplayOptions = None):\n if self.empty:\n if display_options is not None and not isinstance(display_options, DisplayOptions):\n raise TypeError(\"display_options must be of type DisplayOptions\")\n\n options = display_options if display_options is not None else gs_quant.config.display_options\n show_na = options.show_na\n\n return [{**extra_dict, 'value': None}] if show_na else []\n\n return [dict(item, **{**extra_dict}) for item in self.raw_value.to_dict('records')]\n\n def copy_with_resultinfo(self, deep=True):\n return DataFrameWithInfo(\n self.raw_value.copy(deep=deep),\n risk_key=self.risk_key,\n unit=self.unit,\n error=self.error,\n request_id=self.request_id,\n )\n\n def filter_by_coord(self, coordinate):\n from gs_quant.markets import MarketDataCoordinate\n\n df = self.copy_with_resultinfo()\n for att in [i.name for i in fields(MarketDataCoordinate)]:\n if getattr(coordinate, att) is not None:\n if isinstance(getattr(coordinate, att), str):\n df = df[getattr(df, att) == getattr(coordinate, att)]\n else:\n df = df[getattr(df, att).isin(getattr(coordinate, att))]\n return df\n\n\n@dataclass_json\n@dataclass\nclass MQVSValidationTarget:\n env: Optional[str] = None\n operator: Optional[str] = None\n mqGroups: Optional[Tuple[str, ...]] = None\n users: Optional[Tuple[str, ...]] = None\n assetClasses: Optional[Tuple[str, ...]] = None\n assets: Optional[Tuple[str, ...]] = None\n legTypes: Optional[Tuple[str, ...]] = None\n legFields: Optional[Dict[str, str]] = None\n\n\n@dataclass_json\n@dataclass\nclass MQVSValidatorDefn:\n validatorType: str\n targets: Tuple[MQVSValidationTarget, ...]\n args: Dict[str, str]\n groupId: Optional[str] = None\n groupIndex: Optional[int] = None\n groupMethod: Optional[str] = None\n\n\nclass MQVSValidatorDefnsWithInfo(ResultInfo):\n validators: Tuple[MQVSValidatorDefn, ...]\n\n def __init__(\n self,\n risk_key: RiskKey,\n value: Union[MQVSValidatorDefn, Tuple[MQVSValidatorDefn, ...]],\n unit: Optional[dict] = None,\n error: Optional[Union[str, dict]] = None,\n request_id: Optional[str] = None,\n ):\n ResultInfo.__init__(self, risk_key, unit=unit, error=error, request_id=request_id)\n if value and isinstance(value, tuple):\n self.validators = value\n elif value and isinstance(value, MQVSValidatorDefn):\n self.validators = tuple([value])\n\n @property\n def raw_value(self):\n return self.validators\n\n\ndef aggregate_risk(\n results: Iterable[Union[DataFrameWithInfo, Future]],\n threshold: Optional[float] = None,\n allow_heterogeneous_types: bool = False,\n) -> pd.DataFrame:\n \"\"\"\n Combine the results of multiple InstrumentBase.calc() calls, into a single result\n\n :param results: An iterable of Dataframes and/or Futures (returned by InstrumentBase.calc())\n :param threshold: exclude values whose absolute value falls below this threshold\n :param allow_heterogeneous_types: allow Series to be converted to DataFrames before aggregating\n :return: A Dataframe with the aggregated results\n\n **Examples**\n\n >>> from gs_quant.instrument import IRCap, IRFloor\n >>> from gs_quant.markets import PricingContext\n >>> from gs_quant.risk import IRDelta, IRVega\n >>>\n >>> cap = IRCap('5y', 'GBP')\n >>> floor = IRFloor('5y', 'GBP')\n >>> instruments = (cap, floor)\n >>>\n >>> with PricingContext():\n >>> delta_f = [inst.calc(IRDelta) for inst in instruments]\n >>> vega_f = [inst.calc(IRVega) for inst in (cap, floor)]\n >>>\n >>> delta = aggregate_risk(delta_f, threshold=0.1)\n >>> vega = aggregate_risk(vega_f)\n\n delta_f and vega_f are lists of futures, where the result will be a Dataframe\n delta and vega are Dataframes, representing the merged risk of the individual instruments\n \"\"\"\n\n def get_df(result_obj):\n if isinstance(result_obj, Future):\n result_obj = result_obj.result()\n if isinstance(result_obj, pd.Series) and allow_heterogeneous_types:\n return pd.DataFrame(result_obj.raw_value).T\n return result_obj.raw_value\n\n dfs = [get_df(r) for r in results]\n result = pd.concat(dfs).fillna(0)\n result = result.groupby([c for c in result.columns if c != 'value'], as_index=False).sum()\n\n if threshold is not None:\n result = result[result.value.abs() > threshold]\n\n return sort_risk(result)\n\n\nResultType = Union[None, dict, tuple, DataFrameWithInfo, FloatWithInfo, SeriesWithInfo]\n\n\ndef aggregate_results(\n results: Iterable[ResultType], allow_mismatch_risk_keys=False, allow_heterogeneous_types=False\n) -> ResultType:\n unit = None\n risk_key = None\n results = tuple(results)\n\n if not len(results):\n return None\n\n for result in results:\n if isinstance(result, Exception):\n raise Exception\n\n if result.error:\n raise ValueError('Cannot aggregate results in error')\n\n if not allow_heterogeneous_types and not isinstance(result, type(results[0])):\n raise ValueError(f'Cannot aggregate heterogeneous types: {type(result)} vs {type(results[0])}')\n\n if result.unit:\n if unit and unit != result.unit:\n raise ValueError(f'Cannot aggregate results with different units for {result.risk_key.risk_measure}')\n\n unit = unit or result.unit\n\n if (\n not allow_mismatch_risk_keys\n and risk_key\n and risk_key.ex_historical_diddle != result.risk_key.ex_historical_diddle\n ):\n raise ValueError('Cannot aggregate results with different pricing keys')\n\n risk_key = risk_key or result.risk_key\n\n inst = next(iter(results))\n if isinstance(inst, dict):\n return dict((k, aggregate_results([r[k] for r in results])) for k in inst.keys())\n elif isinstance(inst, tuple):\n return tuple(set(itertools.chain.from_iterable(results)))\n elif isinstance(inst, FloatWithInfo):\n return FloatWithInfo(risk_key, sum(results), unit=unit)\n elif isinstance(inst, SeriesWithInfo):\n return SeriesWithInfo(sum(results), risk_key=risk_key, unit=unit)\n elif isinstance(inst, DataFrameWithInfo):\n return DataFrameWithInfo(\n aggregate_risk(results, allow_heterogeneous_types=allow_heterogeneous_types), risk_key=risk_key, unit=unit\n )\n\n\ndef subtract_risk(left: DataFrameWithInfo, right: DataFrameWithInfo) -> pd.DataFrame:\n \"\"\"Subtract bucketed risk. Dimensions must be identical\n\n :param left: Results to substract from\n :param right: Results to substract\n\n **Examples**\n\n >>> from gs_quant.datetime.date import business_day_offset\n >>> from gs_quant.instrument IRSwap\n >>> from gs_quant.markets import PricingContext\n >>> from gs_quant.risk import IRDelta\n >>> import datetime as dt\n >>>\n >>> ir_swap = IRSwap('Pay', '10y', 'USD')\n >>> delta_today = ir_swap.calc(IRDelta)\n >>>\n >>> with PricingContext(pricing_date=business_day_offset(dt.date.today(), -1, roll='preceding')):\n >>> delta_yday_f = ir_swap.calc(IRDelta)\n >>>\n >>> delta_diff = subtract_risk(delta_today, delta_yday_f.result())\n \"\"\"\n assert left.columns.names == right.columns.names\n assert 'value' in left.columns.names\n\n right_negated = copy(right)\n right_negated.value *= -1\n\n return aggregate_risk((left, right_negated))\n\n\ndef sort_values(data: Iterable, columns: Tuple[str, ...], by: Tuple[str, ...]) -> Iterable:\n indices = tuple(columns.index(c) for c in by if c in columns)\n fns: List[Optional[Callable[[any], Optional[float]]]] = [None] * len(columns)\n for idx in indices:\n fns[idx] = __column_sort_fns.get(columns[idx])\n\n def cmp(row) -> tuple:\n return tuple((fns[i](row[i]) or 0) if fns[i] else row[i] for i in indices)\n\n return sorted(data, key=cmp)\n\n\ndef sort_risk(df: pd.DataFrame, by: Tuple[str, ...] = __risk_columns) -> pd.DataFrame:\n \"\"\"\n Sort bucketed risk\n\n :param df: Input Dataframe\n :param by: Columns to sort by\n :return: A sorted Dataframe\n \"\"\"\n columns = tuple(df.columns)\n data = sort_values(df.values, columns, by)\n df_fields = [f for f in by if f in columns]\n df_fields.extend(f for f in columns if f not in df_fields)\n\n result = pd.DataFrame.from_records(data, columns=columns)[df_fields]\n if 'date' in result:\n result = result.set_index('date')\n\n return result\n\n\ndef combine_risk_key(key_1: RiskKey, key_2: RiskKey) -> RiskKey:\n \"\"\"\n Combine two risk keys (key_1, key_2) into a new RiskKey\n\n :type key_1: RiskKey\n :type key_2: RiskKey\n \"\"\"\n\n def get_field_value(field_name: str):\n return getattr(key_1, field_name) if getattr(key_1, field_name) == getattr(key_2, field_name) else None\n\n return RiskKey(\n get_field_value(\"provider\"),\n get_field_value(\"date\"),\n get_field_value(\"market\"),\n get_field_value(\"params\"),\n get_field_value(\"scenario\"),\n get_field_value(\"risk_measure\"),\n )\n" }, { "path": "gs_quant/risk/measures.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Union\n\nfrom gs_quant.base import Market\nfrom gs_quant.common import AssetClass, AggregationLevel, RiskMeasure, RiskMeasureType, RiskMeasureUnit\nfrom gs_quant.target.measures import IRBasis, IRVega, IRDelta, IRXccyDelta, InflationDelta\n\nDEPRECATED_MEASURES = {}\n\n\nclass __RelativeRiskMeasure(RiskMeasure):\n def __init__(\n self,\n to_market: Market,\n asset_class: Union[AssetClass, str] = None,\n measure_type: Union[RiskMeasureType, str] = None,\n unit: Union[RiskMeasureUnit, str] = None,\n value: Union[float, str] = None,\n name: str = None,\n ):\n super().__init__(asset_class=asset_class, measure_type=measure_type, unit=unit, value=value)\n self.__to_market = to_market\n self.name = name\n\n @property\n def pricing_context(self):\n from gs_quant.markets import PricingContext, RelativeMarket\n\n current = PricingContext.current\n return current.clone(market=RelativeMarket(from_market=current.market, to_market=self.__to_market))\n\n\nclass PnlExplain(__RelativeRiskMeasure):\n \"\"\"Pnl Explained\"\"\"\n\n def __init__(self, to_market: Market):\n super().__init__(to_market, measure_type=RiskMeasureType.PnlExplain, name=RiskMeasureType.PnlExplain.value)\n\n\nclass PnlExplainClose(PnlExplain):\n def __init__(self):\n from gs_quant.markets import CloseMarket\n\n super().__init__(CloseMarket())\n\n\nclass PnlExplainLive(PnlExplain):\n def __init__(self):\n from gs_quant.markets import LiveMarket\n\n super().__init__(LiveMarket())\n\n\nclass PnlPredictLive(__RelativeRiskMeasure):\n \"\"\"Pnl Predicted\"\"\"\n\n def __init__(self):\n from gs_quant.markets import LiveMarket\n\n super().__init__(LiveMarket(), measure_type=RiskMeasureType.PnlPredict, name=RiskMeasureType.PnlPredict.value)\n\n\n# Defining Parameterised Risk Measures\nIRBasisParallel = IRBasis(aggregation_level=AggregationLevel.Asset, name='IRBasisParallel')\nInflationDeltaParallel = InflationDelta(aggregation_level=AggregationLevel.Type, name='InflationDeltaParallel')\nIRDeltaParallel = IRDelta(aggregation_level=AggregationLevel.Asset, name='IRDeltaParallel')\nIRDeltaLocalCcy = IRDelta(currency='local', name='IRDeltaLocalCcy')\nIRXccyDeltaParallel = IRXccyDelta(aggregation_level=AggregationLevel.Type, name='IRXccyDeltaParallel')\nIRVegaParallel = IRVega(aggregation_level=AggregationLevel.Asset, name='IRVegaParallel')\nIRVegaLocalCcy = IRVega(currency='local', name='IRVegaLocalCcy')\n" }, { "path": "gs_quant/risk/result_handlers.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom typing import Iterable, Optional, Union\n\nfrom gs_quant.base import InstrumentBase, RiskKey\nfrom gs_quant.common import RiskMeasure, AssetClass, RiskMeasureType\nfrom gs_quant.risk.measures import PnlExplain\nfrom .core import (\n DataFrameWithInfo,\n ErrorValue,\n UnsupportedValue,\n FloatWithInfo,\n SeriesWithInfo,\n StringWithInfo,\n sort_values,\n MQVSValidatorDefnsWithInfo,\n MQVSValidatorDefn,\n DictWithInfo,\n)\n\n_logger = logging.getLogger(__name__)\n\n\ndef __dataframe_handler(\n result: Iterable, mappings: tuple, risk_key: RiskKey, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n first_row = next(iter(result), None)\n if first_row is None:\n return DataFrameWithInfo(risk_key=risk_key, request_id=request_id)\n\n columns = ()\n indices = [False] * len(first_row.keys())\n mappings_lookup = {v: k for k, v in mappings}\n\n for idx, src in enumerate(first_row.keys()):\n if src in mappings_lookup:\n indices[idx] = True\n columns += ((mappings_lookup[src]),)\n\n records = tuple(\n sort_values((tuple(v for i, v in enumerate(r.values()) if indices[i]) for r in result), columns, columns)\n )\n\n df = DataFrameWithInfo(records, risk_key=risk_key, request_id=request_id)\n df.columns = columns\n\n return df\n\n\ndef __dataframe_handler_unsorted(\n result: Iterable, mappings: tuple, date_cols: tuple, risk_key: RiskKey, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n first_row = next(iter(result), None)\n if first_row is None:\n return DataFrameWithInfo(risk_key=risk_key, request_id=request_id)\n\n records = ([row.get(field_from) for field_to, field_from in mappings] for row in result)\n df = DataFrameWithInfo(records, risk_key=risk_key, request_id=request_id)\n df.columns = [m[0] for m in mappings]\n for dt_col in date_cols:\n df[dt_col] = df[dt_col].map(lambda x: dt.datetime.strptime(x, '%Y-%m-%d').date() if isinstance(x, str) else x)\n\n return df\n\n\ndef cashflows_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n mappings = (\n ('currency', 'currency'),\n ('payment_date', 'payDate'),\n ('set_date', 'setDate'),\n ('accrual_start_date', 'accStart'),\n ('accrual_end_date', 'accEnd'),\n ('payment_amount', 'payAmount'),\n ('notional', 'notional'),\n ('payment_type', 'paymentType'),\n ('floating_rate_option', 'index'),\n ('floating_rate_designated_maturity', 'indexTerm'),\n ('day_count_fraction', 'dayCountFraction'),\n ('spread', 'spread'),\n ('rate', 'rate'),\n ('discount_factor', 'discountFactor'),\n )\n date_cols = ('payment_date', 'set_date', 'accrual_start_date', 'accrual_end_date')\n return __dataframe_handler_unsorted(result['cashflows'], mappings, date_cols, risk_key, request_id=request_id)\n\n\ndef error_handler(\n result: dict, risk_key: RiskKey, instrument: InstrumentBase, request_id: Optional[str] = None\n) -> ErrorValue:\n error = result.get('errorString', 'Unknown error')\n if request_id:\n error += f'. request Id={request_id}'\n\n _logger.error(f'Error while computing {risk_key.risk_measure} on {instrument} for {risk_key.date}: {error}')\n return ErrorValue(risk_key, error, request_id=request_id)\n\n\ndef leg_definition_handler(\n result: dict, risk_key: RiskKey, instrument: InstrumentBase, request_id: Optional[str] = None\n) -> InstrumentBase:\n return instrument.resolved(result, risk_key)\n\n\ndef message_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> Union[StringWithInfo, ErrorValue]:\n message = result.get('message')\n if message is None:\n return ErrorValue(risk_key, \"No result returned\", request_id=request_id)\n else:\n return StringWithInfo(risk_key, message, request_id=request_id)\n\n\ndef number_and_unit_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> FloatWithInfo:\n return FloatWithInfo(risk_key, result.get('value', float('nan')), unit=result.get('unit'), request_id=request_id)\n\n\ndef required_assets_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n mappings = (('mkt_type', 'type'), ('mkt_asset', 'asset'))\n return __dataframe_handler(result['requiredAssets'], mappings, risk_key, request_id=request_id)\n\n\ndef dict_risk_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DictWithInfo:\n return DictWithInfo(risk_key, result, unit=None, request_id=request_id)\n\n\ndef risk_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> Union[DataFrameWithInfo, FloatWithInfo]:\n if result.get('children'):\n # result with leg valuations\n classes = []\n if result.get('val'):\n classes.append({'path': 'parent', 'value': result.get('val')})\n\n for key, val in result.get('children').items():\n classes.append({'path': key, 'value': val})\n mappings = (('path', 'path'), ('value', 'value'))\n return __dataframe_handler(classes, mappings, risk_key, request_id=request_id)\n else:\n return FloatWithInfo(risk_key, result.get('val', float('nan')), unit=result.get('unit'), request_id=request_id)\n\n\ndef risk_by_class_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> Union[DataFrameWithInfo, FloatWithInfo]:\n # TODO Remove this once we migrate parallel USD IRDelta measures\n types = [c['type'] for c in result['classes']]\n # list of risk by class measures exposed in gs-quant\n external_risk_by_class_val = ['IRBasisParallel', 'IRDeltaParallel', 'IRVegaParallel', 'PnlExplain']\n if str(risk_key.risk_measure.name) in external_risk_by_class_val and len(types) <= 2 and len(set(types)) == 1:\n return FloatWithInfo(\n risk_key, sum(result.get('values', (float('nan'),))), unit=result.get('unit'), request_id=request_id\n )\n else:\n classes = []\n skip = []\n\n crosses_idx = next((i for i, c in enumerate(result['classes']) if c['type'] == 'CROSSES'), None)\n for idx, (clazz, value) in enumerate(zip(result['classes'], result['values'])):\n mkt_type = clazz['type']\n if 'SPIKE' in mkt_type or 'JUMP' in mkt_type:\n skip.append(idx)\n\n if crosses_idx is not None:\n result['classes'][crosses_idx]['value'] += value\n\n clazz.update({'value': value})\n\n for idx, clazz in enumerate(result['classes']):\n if idx not in skip:\n classes.append(clazz)\n\n mappings = (('mkt_type', 'type'), ('mkt_asset', 'asset'), ('value', 'value'))\n\n if isinstance(risk_key.risk_measure, PnlExplain):\n return __dataframe_handler_unsorted(classes, mappings, (), risk_key, request_id=request_id)\n else:\n return __dataframe_handler(classes, mappings, risk_key, request_id=request_id)\n\n\ndef risk_vector_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n assets = result['asset']\n # Handle equity risk measures which are really scalars\n if len(assets) == 1 and risk_key.risk_measure.name.startswith('Eq'):\n return FloatWithInfo(risk_key, assets[0], request_id=request_id)\n\n for points, value in zip(result['points'], assets):\n points.update({'value': value})\n\n mappings = (\n ('mkt_type', 'type'),\n ('mkt_asset', 'asset'),\n ('mkt_class', 'class_'),\n ('mkt_point', 'point'),\n ('mkt_quoting_style', 'quoteStyle'),\n ('value', 'value'),\n )\n return __dataframe_handler(result['points'], mappings, risk_key, request_id=request_id)\n\n\ndef fixing_table_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> SeriesWithInfo:\n rows = result['fixingTableRows']\n\n dates = []\n values = []\n for row in rows:\n dates.append(dt.date.fromisoformat(row[\"fixingDate\"]))\n values.append(row[\"fixing\"])\n\n return SeriesWithInfo(values, index=dates, risk_key=risk_key, request_id=request_id)\n\n\ndef simple_valtable_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n def get_value(value):\n handler = result_handlers.get(value.get('$type'))\n return handler(value, risk_key, _instrument, request_id)\n\n raw_res = result['rows']\n # simplevaltable's values contain all the information on units which needs to be extracted into the dataframe\n df = DataFrameWithInfo(\n [(res['label'], get_value(res['value'])) for res in raw_res],\n risk_key=risk_key,\n request_id=request_id,\n unit=raw_res[0]['value'].get('unit'),\n )\n df.columns = ['label', 'value']\n return df\n\n\ndef canonical_projection_table_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n mappings = (\n ('asset_class', 'assetClass'),\n ('asset', 'asset'),\n ('asset_family', 'assetFamily'),\n ('asset_sub_family', 'assetSubFamily'),\n ('product', 'product'),\n ('product_family', 'productFamily'),\n ('product_sub_family', 'productSubFamily'),\n ('side', 'side'),\n ('size', 'size'),\n ('size_unit', 'sizeUnit'),\n ('quote_level', 'quoteLevel'),\n ('quote_unit', 'quoteUnit'),\n ('start_date', 'startDate'),\n ('end_date', 'endDate'),\n ('expiration_date', 'expiryDate'),\n ('strike', 'strike'),\n ('strike_unit', 'strikeUnit'),\n ('option_type', 'optionType'),\n ('option_style', 'optionStyle'),\n ('tenor', 'tenor'),\n ('tenor_unit', 'tenorUnit'),\n ('premium_currency', 'premiumCcy'),\n ('currency', 'currency'),\n )\n date_cols = ('start_date', 'end_date', 'expiration_date')\n return __dataframe_handler_unsorted(result['rows'], mappings, date_cols, risk_key, request_id)\n\n\ndef risk_float_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> FloatWithInfo:\n return FloatWithInfo(risk_key, result['values'][0], request_id=request_id)\n\n\ndef map_coordinate_to_column(coordinate_struct, tag):\n updated_struct = {\n tag + \"_\" + k: v\n for k, v in coordinate_struct.items()\n if k in ['type', 'asset', 'class_', 'point', 'quoteStyle']\n }\n raw_point = updated_struct.get('point', '')\n point = ';'.join(raw_point) if isinstance(raw_point, list) else raw_point\n updated_struct['point'] = point\n return updated_struct\n\n\ndef __is_single_row_2nd_order_risk(risk_key: RiskKey):\n return (\n risk_key is not None\n and isinstance(risk_key.risk_measure, RiskMeasure)\n and risk_key.risk_measure.asset_class == AssetClass.Rates\n and risk_key.risk_measure.measure_type in (RiskMeasureType.ParallelGamma, RiskMeasureType.ParallelGammaLocalCcy)\n )\n\n\ndef mdapi_second_order_table_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> Union[DataFrameWithInfo, FloatWithInfo]:\n if len(result['values']) == 1 and __is_single_row_2nd_order_risk(risk_key):\n return risk_float_handler(result, risk_key, _instrument, request_id)\n\n coordinate_pairs = []\n\n if len(result['innerPoints']) != len(result['outerPoints']):\n raise Exception(\"Found inner and outer points of different size\")\n\n for inner, outer, value in zip(result['innerPoints'], result['outerPoints'], result['values']):\n row_dict = dict(map_coordinate_to_column(inner, 'inner'), **map_coordinate_to_column(outer, 'outer'))\n row_dict.update({'value': value})\n coordinate_pairs.append(row_dict)\n\n mappings = (\n ('inner_mkt_type', 'inner_type'),\n ('inner_mkt_asset', 'inner_asset'),\n ('inner_mkt_class', 'inner_class_'),\n ('inner_mkt_point', 'inner_point'),\n ('inner_mkt_quoting_style', 'inner_quotingStyle'),\n ('outer_mkt_type', 'outer_type'),\n ('outer_mkt_asset', 'outer_asset'),\n ('outer_mkt_class', 'outer_class_'),\n ('outer_mkt_point', 'outer_point'),\n ('outer_mkt_quoting_style', 'outer_quotingStyle'),\n ('value', 'value'),\n ('permissions', 'permissions'),\n )\n\n return __dataframe_handler(coordinate_pairs, mappings, risk_key, request_id=request_id)\n\n\ndef mdapi_table_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n coordinates = []\n for r in result['rows']:\n raw_point = r['coordinate'].get('point', '')\n point = ';'.join(raw_point) if isinstance(raw_point, list) else raw_point\n r['coordinate'].update({'point': point})\n r['coordinate'].update({'value': r.get('value', None)})\n r['coordinate'].update({'permissions': r['permissions']})\n coordinates.append(r['coordinate'])\n\n mappings = (\n ('mkt_type', 'type'),\n ('mkt_asset', 'asset'),\n ('mkt_class', 'assetClass'),\n ('mkt_point', 'point'),\n ('mkt_quoting_style', 'quotingStyle'),\n ('value', 'value'),\n ('permissions', 'permissions'),\n )\n\n return __dataframe_handler(coordinates, mappings, risk_key, request_id=request_id)\n\n\ndef mmapi_table_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n coordinates = []\n for r in result['rows']:\n raw_point = r['modelCoordinate'].get('point', '')\n point = ';'.join(raw_point) if isinstance(raw_point, list) else raw_point\n r['modelCoordinate'].update({'point': point})\n raw_tags = r['modelCoordinate'].get('tags', '')\n tags = ';'.join(raw_tags) if isinstance(raw_tags, list) else raw_tags\n r['modelCoordinate'].update({'tags': tags})\n rows = r['value'].get('value', '')\n DataPoints = []\n for row in rows:\n DataPoints.append([dt.date.fromisoformat(row[\"date\"]), row[\"value\"]])\n r['modelCoordinate'].update({'value': DataPoints})\n coordinates.append(r['modelCoordinate'])\n\n mappings = (\n ('mkt_type', 'type'),\n ('mkt_asset', 'asset'),\n ('mkt_point', 'point'),\n ('mkt_tags', 'tags'),\n ('mkt_quoting_style', 'quotingStyle'),\n ('value', 'value'),\n )\n\n return __dataframe_handler(coordinates, mappings, risk_key, request_id=request_id)\n\n\ndef mmapi_pca_table_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n coordinates = []\n for r in result['rows']:\n raw_point = r['coordinate'].get('point', '')\n point = ';'.join(raw_point) if isinstance(raw_point, list) else raw_point\n r['coordinate'].update({'point': point})\n r['coordinate'].update({'value': r['value']})\n r['coordinate'].update({'layer1': r['layer1']})\n r['coordinate'].update({'layer2': r['layer2']})\n r['coordinate'].update({'layer3': r['layer3']})\n r['coordinate'].update({'layer4': r['layer4']})\n r['coordinate'].update({'level': r['level']})\n r['coordinate'].update({'sensitivity': r['sensitivity']})\n r['coordinate'].update({'irDelta': r['irDelta']})\n r['coordinate'].update({'endDate': r['endDate']})\n coordinates.append(r['coordinate'])\n\n mappings = (\n ('mkt_type', 'type'),\n ('mkt_asset', 'asset'),\n ('mkt_class', 'assetClass'),\n ('mkt_point', 'point'),\n ('mkt_quoting_style', 'quotingStyle'),\n ('value', 'value'),\n ('layer1', 'layer1'),\n ('layer2', 'layer2'),\n ('layer3', 'layer3'),\n ('layer4', 'layer4'),\n ('level', 'level'),\n ('sensitivity', 'sensitivity'),\n ('irDelta', 'irDelta'),\n ('endDate', 'endDate'),\n )\n\n return __dataframe_handler(coordinates, mappings, risk_key, request_id=request_id)\n\n\ndef mmapi_pca_hedge_table_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> DataFrameWithInfo:\n coordinates = []\n for r in result['rows']:\n raw_point = r['coordinate'].get('point', '')\n point = ';'.join(raw_point) if isinstance(raw_point, list) else raw_point\n r['coordinate'].update({'point': point})\n r['coordinate'].update({'size': r.get('size')})\n r['coordinate'].update({'fixedRate': r.get('fixedRate')})\n r['coordinate'].update({'irDelta': r.get('irDelta')})\n coordinates.append(r['coordinate'])\n mappings = (\n ('mkt_type', 'type'),\n ('mkt_asset', 'asset'),\n ('mkt_class', 'assetClass'),\n ('mkt_point', 'point'),\n ('mkt_quoting_style', 'quotingStyle'),\n ('size', 'size'),\n ('fixedRate', 'fixedRate'),\n ('irDelta', 'irDelta'),\n )\n\n return __dataframe_handler(coordinates, mappings, risk_key, request_id=request_id)\n\n\ndef mqvs_validators_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> MQVSValidatorDefnsWithInfo:\n validators = [MQVSValidatorDefn.from_dict(r) for r in result['validators']]\n return MQVSValidatorDefnsWithInfo(risk_key, tuple(validators), request_id=request_id)\n\n\ndef market_handler(\n result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> StringWithInfo:\n return StringWithInfo(risk_key, result.get('marketRef'), request_id=request_id)\n\n\ndef unsupported_handler(\n _result: dict, risk_key: RiskKey, _instrument: InstrumentBase, request_id: Optional[str] = None\n) -> UnsupportedValue:\n return UnsupportedValue(risk_key, request_id=request_id)\n\n\nresult_handlers = {\n 'Error': error_handler,\n 'IRPCashflowTable': cashflows_handler,\n 'LegDefinition': leg_definition_handler,\n 'Message': message_handler,\n 'MDAPITable': mdapi_table_handler,\n 'MMAPITable': mmapi_table_handler,\n 'MMAPIPCATable': mmapi_pca_table_handler,\n 'MMAPIPCAHedgeTable': mmapi_pca_hedge_table_handler,\n 'MQVSValidators': mqvs_validators_handler,\n 'NumberAndUnit': number_and_unit_handler,\n 'PriceGrid': dict_risk_handler,\n 'RequireAssets': required_assets_handler,\n 'Risk': risk_handler,\n 'RiskByClass': risk_by_class_handler,\n 'RiskVector': risk_vector_handler,\n 'FixingTable': fixing_table_handler,\n 'Table': simple_valtable_handler,\n 'CanonicalProjectionTable': canonical_projection_table_handler,\n 'RiskSecondOrderVector': mdapi_second_order_table_handler,\n 'RiskTheta': risk_float_handler,\n 'Market': market_handler,\n 'Unsupported': unsupported_handler,\n}\n" }, { "path": "gs_quant/risk/results.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport datetime as dt\nimport logging\nimport operator as op\nimport weakref\nfrom concurrent.futures import Future\nfrom itertools import chain\nfrom typing import Any, Iterable, Mapping, Optional, Tuple, Union\n\nimport pandas as pd\nfrom gs_quant.base import Priceable, RiskKey, Sentinel, InstrumentBase, is_instance_or_iterable, is_iterable, Scenario\nfrom gs_quant.common import RiskMeasure\nfrom gs_quant.config import DisplayOptions\nfrom gs_quant.risk import (\n DataFrameWithInfo,\n ErrorValue,\n UnsupportedValue,\n FloatWithInfo,\n SeriesWithInfo,\n ResultInfo,\n ScalarWithInfo,\n aggregate_results,\n)\nfrom gs_quant.risk.transform import Transformer\nfrom more_itertools import unique_everseen\n\n_logger = logging.getLogger(__name__)\n\n\ndef get_default_pivots(\n cls: str, has_dates: bool, multi_measures: bool, multi_scen: bool, simple_port: bool = None, ori_cols=None\n):\n if cls == 'MultipleScenarioResult':\n return 'value', 'scenario', 'dates' if has_dates else None\n elif cls == 'MultipleRiskMeasureResult':\n return 'value', ('risk_measure', 'scenario') if multi_scen else 'risk_measure', 'dates' if has_dates else None\n elif cls == 'PortfolioRiskResult':\n if ori_cols is None:\n raise ValueError('columns of dataframe required to get default pivots')\n portfolio_names = list(filter(lambda x: 'portfolio_name_' in x, ori_cols))\n port_and_inst_names = portfolio_names + ['instrument_name']\n pivot_rules = [\n # has_dates, multi_measures, simple_port, multi_scen\n # output: (value,index,columns)\n [True, True, None, False, ('value', 'dates', port_and_inst_names + ['risk_measure'])],\n [True, False, None, False, ('value', 'dates', port_and_inst_names)],\n [False, False, False, False, ('value', portfolio_names, 'instrument_name')],\n [False, None, None, False, ('value', port_and_inst_names, 'risk_measure')],\n [True, True, None, True, ('value', 'dates', port_and_inst_names + ['risk_measure', 'scenario'])],\n [True, False, None, True, ('value', 'dates', port_and_inst_names + ['scenario'])],\n [False, True, None, True, ('value', port_and_inst_names, ['risk_measure', 'scenario'])],\n [False, False, None, True, ('value', port_and_inst_names, 'scenario')],\n ]\n\n def match(rule_value, check_value) -> bool:\n if rule_value is None:\n return True\n elif callable(rule_value):\n return rule_value(check_value)\n else:\n return rule_value == check_value\n\n for rule in pivot_rules:\n [rule_has_dates, rule_multi_measures, rule_simple_port, rule_multi_scen, rule_output] = rule\n if (\n match(rule_has_dates, has_dates)\n and match(rule_multi_measures, multi_measures)\n and match(rule_simple_port, simple_port)\n and match(rule_multi_scen, multi_scen)\n ):\n return rule_output\n return None, None, None\n\n\ndef pivot_to_frame(df, values, index, columns, aggfunc):\n try:\n pivot_df = df.pivot_table(values=values, index=index, columns=columns, aggfunc=aggfunc)\n except ValueError:\n raise RuntimeError('Unable to successfully pivot data')\n\n try: # attempt to correct order of index\n if index is not None:\n idx = df.set_index(list(pivot_df.index.names)).index.unique()\n pivot_df = pivot_df.reindex(index=idx)\n if columns is not None:\n cols = df.set_index(list(pivot_df.columns.names)).index.unique()\n pivot_df = pivot_df.reindex(columns=cols)\n return pivot_df\n except KeyError:\n return pivot_df\n\n\ndef _compose(lhs: ResultInfo, rhs: ResultInfo) -> ResultInfo:\n if isinstance(lhs, ScalarWithInfo):\n if isinstance(rhs, ScalarWithInfo):\n return rhs if lhs.risk_key.date == rhs.risk_key.date else lhs.compose((lhs, rhs))\n elif isinstance(rhs, SeriesWithInfo):\n return lhs.compose((lhs,)).combine_first(rhs).sort_index()\n elif isinstance(lhs, SeriesWithInfo):\n if isinstance(rhs, SeriesWithInfo):\n return rhs.combine_first(lhs).sort_index()\n elif isinstance(rhs, ScalarWithInfo):\n return rhs.compose((rhs,)).combine_first(lhs).sort_index()\n elif isinstance(lhs, DataFrameWithInfo):\n if lhs.index.name != 'date':\n lhs = lhs.assign(date=lhs.risk_key.date).set_index('date')\n\n if isinstance(rhs, DataFrameWithInfo):\n if rhs.index.name != 'date':\n rhs = rhs.assign(date=rhs.risk_key.date).set_index('date')\n\n return lhs.loc[set(lhs.index) - set(rhs.index)].append(rhs).sort_index()\n elif isinstance(lhs, MultipleRiskMeasureResult):\n if isinstance(rhs, MultipleRiskMeasureResult):\n return lhs + rhs\n\n raise RuntimeError(f'{lhs} and {rhs} cannot be composed')\n\n\ndef _value_for_date(\n result: Union[DataFrameWithInfo, SeriesWithInfo], date: Union[Iterable, dt.date]\n) -> Union[DataFrameWithInfo, ErrorValue, FloatWithInfo, SeriesWithInfo]:\n from gs_quant.markets import CloseMarket\n\n if result.empty:\n return result\n\n # if the result is a dataframe try to preserve the type\n # if a dataframe has only 1 row selected it otherwise gets turned into a series\n raw_value = (\n result.loc[[date]] if isinstance(result, DataFrameWithInfo) and isinstance(date, dt.date) else result.loc[date]\n )\n key = result.risk_key\n\n risk_key = RiskKey(\n key.provider,\n date if isinstance(date, dt.date) else tuple(date),\n CloseMarket(date=date, location=key.market.location if isinstance(key.market, CloseMarket) else None),\n key.params,\n key.scenario,\n key.risk_measure,\n )\n\n unit = result.unit\n error = result.error\n if isinstance(raw_value, DataFrameWithInfo):\n raw_value = raw_value.raw_value.set_index('dates')\n raw_value = raw_value.reset_index(drop=True) if isinstance(date, dt.date) else raw_value\n elif isinstance(raw_value, float):\n unit = result.unit.get(date, result.unit) if unit else None\n return _get_value_with_info(raw_value, risk_key, unit, error)\n\n\ndef _get_value_with_info(value, risk_key, unit, error):\n if isinstance(value, (ErrorValue, UnsupportedValue)):\n return value\n elif isinstance(value, pd.DataFrame):\n return DataFrameWithInfo(value, risk_key=risk_key, unit=unit, error=error)\n elif isinstance(value, pd.Series):\n return SeriesWithInfo(value.raw_value, risk_key=risk_key, unit=unit, error=error)\n else:\n return FloatWithInfo(risk_key, value, unit=unit, error=error)\n\n\ndef _risk_keys_compatible(lhs, rhs) -> bool:\n from gs_quant.markets import historical_risk_key\n\n while isinstance(lhs, MultipleRiskMeasureResult):\n lhs = next(iter(lhs.values()))\n\n while isinstance(rhs, MultipleRiskMeasureResult):\n rhs = next(iter(rhs.values()))\n\n return historical_risk_key(lhs.risk_key).ex_measure == historical_risk_key(rhs.risk_key).ex_measure\n\n\ndef _value_for_measure_or_scen(res: dict, item: Union[Iterable, RiskMeasure, Scenario]) -> dict:\n result = copy.copy(res)\n if isinstance(item, Iterable):\n for value in list(result):\n if value not in item:\n del result[value]\n else:\n for value in list(result):\n if value != item:\n del result[value]\n return result\n\n\nclass PricingFuture(Future):\n __RESULT_SENTINEL = Sentinel('PricingFuture')\n\n def __init__(self, result: Optional[Any] = __RESULT_SENTINEL):\n super().__init__()\n self.__pricing_context = None\n\n if result is not self.__RESULT_SENTINEL:\n self.set_result(result)\n else:\n from gs_quant.markets import PricingContext\n\n self.__pricing_context = weakref.ref(PricingContext.current.active_context)\n\n def __add__(self, other):\n if isinstance(other, (int, float)):\n operand = other\n elif isinstance(other, self.__class__):\n operand = other.result()\n else:\n raise ValueError(f'Cannot add {self.__class__.__name__} and {other.__class__.name}')\n\n return self.__class__(_compose(self.result(), operand))\n\n def __mul__(self, other):\n if isinstance(other, (int, float)):\n return self.__class__(self.result() * other)\n else:\n raise ValueError('Can only multiply by an int or float')\n\n def result(self, timeout=None):\n \"\"\"Return the result of the call that the future represents.\n\n :param timeout: The number of seconds to wait for the result if the future isn't done.\n If None, then there is no limit on the wait time.\n\n Returns:\n The result of the call that the future represents.\n\n Raises:\n CancelledError: If the future was cancelled.\n TimeoutError: If the future didn't finish executing before the given timeout.\n\n Exception: If the call raised then that exception will be raised.\n \"\"\"\n if not self.done():\n pricing_context = self.__pricing_context() if self.__pricing_context else None\n if pricing_context is not None and pricing_context.is_entered:\n raise RuntimeError('Cannot evaluate results under the same pricing context being used to produce them')\n\n return super().result(timeout=timeout)\n\n\nclass CompositeResultFuture(PricingFuture):\n def __init__(self, futures: Iterable[PricingFuture]):\n super().__init__()\n self.__futures = tuple(futures)\n self.__pending = set()\n\n for future in self.__futures:\n if not future.done():\n future.add_done_callback(self.__cb)\n self.__pending.add(future)\n\n if not self.__pending:\n self._set_result()\n\n def __getitem__(self, item):\n return self.result()[item]\n\n def __cb(self, future: PricingFuture):\n self.__pending.discard(future)\n if not self.__pending:\n self._set_result()\n\n def _set_result(self):\n self.set_result([f.result() for f in self.__futures])\n\n @property\n def futures(self) -> Tuple[PricingFuture, ...]:\n return self.__futures\n\n\nclass MultipleRiskMeasureResult(dict):\n def __init__(self, instrument, dict_values: Iterable):\n super().__init__(dict_values)\n self.__instrument = instrument\n\n def __getitem__(self, item):\n if is_instance_or_iterable(item, dt.date):\n if all(isinstance(v, (DataFrameWithInfo, SeriesWithInfo)) for v in self.values()):\n return MultipleRiskMeasureResult(\n self.__instrument, ((k, _value_for_date(v, item)) for k, v in self.items())\n )\n elif all(isinstance(v, MultipleScenarioResult) for v in self.values()):\n return MultipleRiskMeasureResult(self.__instrument, ((k, v[item]) for k, v in self.items()))\n else:\n raise ValueError('Can only index by date on historical results')\n elif is_instance_or_iterable(item, Scenario):\n if all(isinstance(v, MultipleScenarioResult) for v in self.values()):\n return MultipleRiskMeasureResult(\n self.__instrument, ((k, _value_for_measure_or_scen(v, item)) for k, v in self.items())\n )\n else:\n raise ValueError('Can only index by scenario on multiple scenario results')\n\n else:\n return super().__getitem__(item)\n\n def __mul__(self, other):\n if isinstance(other, (int, float)):\n return self.__op(op.mul, other)\n else:\n return ValueError('Can only multiply by an int or float')\n\n def __add__(self, other):\n if isinstance(other, (int, float)):\n return self.__op(op.add, other)\n elif isinstance(other, MultipleRiskMeasureResult):\n if not _risk_keys_compatible(self, other):\n raise ValueError('Results must have matching scenario and location')\n\n instruments_equal = self.__instrument == other.__instrument\n\n self_dt = [list(self.values())[0].risk_key.date] if len(self.dates) == 0 else self.dates\n other_dt = [list(other.values())[0].risk_key.date] if len(other.dates) == 0 else other.dates\n dates_overlap = not set(self_dt).isdisjoint(other_dt)\n\n if not set(self.keys()).isdisjoint(other.keys()) and instruments_equal and dates_overlap:\n raise ValueError('Results overlap on risk measures, instruments or dates')\n\n all_keys = set(chain(self.keys(), other.keys()))\n\n if not instruments_equal:\n from gs_quant.markets.portfolio import Portfolio\n\n return PortfolioRiskResult(\n Portfolio((self.__instrument, other.__instrument)),\n all_keys,\n tuple(\n MultipleRiskMeasureFuture(\n r.__instrument, {k: PricingFuture(r[k]) if k in r else None for k in all_keys}\n )\n for r in (self, other)\n ),\n )\n else:\n results = {}\n for result in (self, other):\n for key in all_keys:\n if key in result:\n results[key] = _compose(results[key], result[key]) if key in results else result[key]\n\n return MultipleRiskMeasureResult(self.__instrument, results)\n else:\n raise ValueError('Can only add instances of MultipleRiskMeasureResult or int, float')\n\n def __op(self, operator, operand):\n values = {}\n for key, value in self.items():\n if isinstance(value, SeriesWithInfo) or isinstance(value, DataFrameWithInfo):\n new_value = value.copy_with_resultinfo()\n if not value.empty:\n new_value.value = operator(value.value, operand)\n elif isinstance(value, pd.DataFrame) or isinstance(value, pd.Series):\n new_value = value.copy()\n new_value.value = operator(value.value, operand)\n else:\n new_value = operator(value, operand)\n\n values[key] = new_value\n\n return MultipleRiskMeasureResult(self.__instrument, values)\n\n @property\n def instrument(self):\n return self.__instrument\n\n @property\n def dates(self) -> Tuple[dt.date, ...]:\n dates = set()\n for value in self.values():\n if isinstance(value, (DataFrameWithInfo, SeriesWithInfo)):\n if all([isinstance(i, dt.date) for i in value.index]):\n dates.update(value.index)\n\n return tuple(sorted(dates))\n\n @property\n def _multi_scen_key(self) -> Iterable[Scenario]:\n for value in self.values():\n if isinstance(value, MultipleScenarioResult):\n return tuple(value.scenarios)\n return tuple()\n\n def to_frame(\n self,\n values='default',\n index='default',\n columns='default',\n aggfunc=\"sum\",\n display_options: DisplayOptions = None,\n ):\n df = pd.DataFrame.from_records(self._to_records({}, display_options=display_options))\n if values is None and index is None and columns is None:\n return df\n elif values == 'default' and index == 'default' and columns == 'default':\n if 'mkt_type' in df.columns:\n return df.set_index('risk_measure')\n values, columns, index = get_default_pivots(\n 'MultipleRiskMeasureResult',\n multi_measures=True,\n has_dates='dates' in df.columns,\n multi_scen='scenario' in df.columns,\n )\n else:\n values = 'value' if values == 'default' or values == ['value'] else values\n index = None if index == 'default' else index\n columns = None if columns == 'default' else columns\n return pivot_to_frame(df, values, index, columns, aggfunc)\n\n def _to_records(self, extra_dict, display_options: DisplayOptions = None):\n return list(\n chain.from_iterable(\n [dict(item, **{'risk_measure': rm}) for item in self[rm]._to_records(extra_dict, display_options)]\n for rm in self\n )\n )\n\n\nclass MultipleRiskMeasureFuture(CompositeResultFuture):\n def __init__(self, instrument: InstrumentBase, measures_to_futures: Mapping[RiskMeasure, PricingFuture]):\n self.__measures_to_futures = measures_to_futures\n self.__instrument = instrument\n super().__init__(measures_to_futures.values())\n\n def __add__(self, other):\n result = self.result() + other.result() if isinstance(other, MultipleRiskMeasureFuture) else other\n return MultipleRiskMeasureFuture(self.__instrument, {k: PricingFuture(v) for k, v in result.items()})\n\n def _set_result(self):\n self.set_result(\n MultipleRiskMeasureResult(\n self.__instrument, zip(self.__measures_to_futures.keys(), (f.result() for f in self.futures))\n )\n )\n\n @property\n def measures_to_futures(self) -> Mapping[RiskMeasure, PricingFuture]:\n return self.__measures_to_futures\n\n\nclass MultipleScenarioFuture(CompositeResultFuture):\n def __init__(self, instrument: InstrumentBase, scenarios: Iterable[Scenario], futures: Iterable[PricingFuture]):\n self.__instrument = instrument\n self.__scenarios = scenarios\n super().__init__(futures)\n\n def _set_result(self):\n res = next(iter(self.futures)).result()\n values = (\n tuple(res[res['label'] == r]['value'] for r in tuple(unique_everseen(res['label'].values)))\n if res.index.name == 'date'\n else tuple(v for v in res['value'])\n )\n val_w_info = tuple(_get_value_with_info(v, res.risk_key, res.unit, res.error) for v in values)\n self.set_result(MultipleScenarioResult(self.__instrument, {k: v for k, v in zip(self.__scenarios, val_w_info)}))\n\n\nclass MultipleScenarioResult(dict):\n def __init__(self, instrument, dict_values: Iterable):\n super().__init__(dict_values)\n self.__instrument = instrument\n\n def __getitem__(self, item):\n if is_instance_or_iterable(item, dt.date):\n if all(isinstance(v, (DataFrameWithInfo, SeriesWithInfo)) for v in self.values()):\n return MultipleScenarioResult(\n self.__instrument, ((k, _value_for_date(v, item)) for k, v in self.items())\n )\n else:\n raise ValueError('Can only index by date on historical results')\n return super().__getitem__(item)\n\n def to_frame(\n self,\n values='default',\n index='default',\n columns='default',\n aggfunc=\"sum\",\n display_options: DisplayOptions = None,\n ):\n df = pd.DataFrame.from_records(self._to_records({}, display_options=display_options))\n if values is None and index is None and columns is None:\n return df\n elif values == 'default' and index == 'default' and columns == 'default':\n if 'mkt_type' in df.columns:\n return df.set_index('scenario')\n values, columns, index = get_default_pivots(\n 'MultipleScenarioResult', has_dates='dates' in df.columns, multi_measures=False, multi_scen=True\n )\n else:\n values = 'value' if values == 'default' or values == ['value'] else values\n index = None if index == 'default' else index\n columns = None if columns == 'default' else columns\n return pivot_to_frame(df, values, index, columns, aggfunc)\n\n @property\n def instrument(self):\n return self.__instrument\n\n @property\n def scenarios(self):\n return self.keys()\n\n def _to_records(self, extra_dict, display_options: DisplayOptions = None):\n return list(\n chain.from_iterable(\n [dict(item, **{'scenario': scen}) for item in self[scen]._to_records(extra_dict, display_options)]\n for scen in self\n )\n )\n\n\nclass HistoricalPricingFuture(CompositeResultFuture):\n def _set_result(self):\n results = [f.result() for f in self.futures]\n base = next((r for r in results if not isinstance(r, (ErrorValue, Exception))), None)\n\n if base is None:\n _logger.error(f'Historical pricing failed: {results[0]}')\n self.set_result(results[0])\n else:\n if isinstance(base, MultipleRiskMeasureResult):\n result = MultipleRiskMeasureResult(\n base.instrument, {k: base[k].compose(r[k] for r in results) for k in base.keys()}\n )\n else:\n result = base.compose(results)\n self.set_result(result)\n\n\nclass PortfolioPath:\n def __init__(self, path):\n self.__path = (path,) if isinstance(path, int) else path\n\n def __repr__(self):\n return repr(self.__path)\n\n def __iter__(self):\n return iter(self.__path)\n\n def __len__(self):\n return len(self.__path)\n\n def __add__(self, other):\n return PortfolioPath(self.__path + other.__path)\n\n def __eq__(self, other):\n return self.__path == other.__path\n\n def __hash__(self):\n return hash(self.__path)\n\n def __call__(self, target, rename_to_parent: Optional[bool] = False):\n parent = None\n path = list(self.__path)\n\n while path:\n elem = path.pop(0)\n parent = target if len(self) - len(path) > 1 else None\n target = target.futures[elem] if isinstance(target, CompositeResultFuture) else target[elem]\n\n if isinstance(target, PricingFuture) and path:\n target = target.result()\n\n if rename_to_parent and parent and getattr(parent, 'name', None) and not isinstance(target, InstrumentBase):\n target = copy.copy(target)\n target.name = parent.name\n\n return target\n\n @property\n def path(self):\n return self.__path\n\n\nclass PortfolioRiskResult(CompositeResultFuture):\n def __init__(self, portfolio, risk_measures: Iterable[RiskMeasure], futures: Iterable[PricingFuture]):\n super().__init__(futures)\n self.__portfolio = portfolio\n self.__risk_measures = tuple(risk_measures)\n\n def __getitem__(self, item):\n futures = []\n\n if is_instance_or_iterable(item, RiskMeasure):\n '''Slicing a list of risk measures'''\n if isinstance(item, Iterable):\n if any(it not in self.risk_measures for it in item):\n raise ValueError('{} not computed'.format(item))\n else:\n if item not in self.risk_measures:\n raise ValueError('{} not computed'.format(item))\n\n if len(self.risk_measures) == 1:\n return self\n else:\n for idx, priceable in enumerate(self.portfolio):\n result = self.__result(PortfolioPath(idx))\n if isinstance(result, PortfolioRiskResult):\n futures.append(result[item])\n else:\n futures.append(\n MultipleRiskMeasureFuture(\n priceable,\n {k: PricingFuture(v) for k, v in _value_for_measure_or_scen(result, item).items()},\n )\n )\n\n risk_measure = tuple(item) if isinstance(item, Iterable) else (item,)\n return PortfolioRiskResult(self.__portfolio, risk_measure, futures)\n\n elif is_instance_or_iterable(item, Scenario):\n '''Slicing a list of scenarios'''\n if isinstance(item, Iterable) and any(it not in self._multi_scen_key for it in item):\n raise ValueError('{} not computed'.format(item))\n else:\n if item not in self._multi_scen_key:\n raise ValueError('{} not computed'.format(item))\n\n if len(self._multi_scen_key) == 0: # field not used if not multiscenario\n return self\n else:\n for idx, priceable in enumerate(self.portfolio):\n result = self.__result(PortfolioPath(idx))\n if isinstance(result, PortfolioRiskResult):\n futures.append(result[item])\n elif isinstance(result, MultipleRiskMeasureResult):\n futures.append(\n MultipleRiskMeasureFuture(\n priceable,\n {k: PricingFuture(_value_for_measure_or_scen(result[k], item)) for k in result},\n )\n )\n elif isinstance(result, MultipleScenarioResult):\n futures.append(PricingFuture(_value_for_measure_or_scen(result, item)))\n return PortfolioRiskResult(self.__portfolio, self.risk_measures, futures)\n\n elif is_instance_or_iterable(item, dt.date):\n for idx, _ in enumerate(self.portfolio):\n result = self.__result(PortfolioPath(idx))\n if isinstance(result, (MultipleRiskMeasureResult, PortfolioRiskResult, MultipleScenarioResult)):\n futures.append(PricingFuture(result[item]))\n elif isinstance(result, (DataFrameWithInfo, SeriesWithInfo)):\n futures.append(PricingFuture(_value_for_date(result, item)))\n else:\n raise RuntimeError('Can only index by date on historical results')\n return PortfolioRiskResult(self.__portfolio, self.risk_measures, futures)\n\n elif is_iterable(item, InstrumentBase):\n '''Slicing a list/tuple of instruments (not an Portfolio iterable)'''\n return self.subset(item)\n\n # Inputs from excel always becomes a list\n # Catch list length = 1 so that it doesn't return a sub-PortfolioRiskResult\n elif isinstance(item, list) and len(item) == 1:\n return self.__results(items=item[0])\n\n else:\n return self.__results(items=item)\n\n def __contains__(self, item):\n if isinstance(item, RiskMeasure):\n return item in self.__risk_measures\n elif isinstance(item, dt.date):\n return item in self.dates\n else:\n return item in self.__portfolio\n\n def __repr__(self):\n ret = f'{self.__risk_measures} Results'\n if self.__portfolio.name:\n ret += f' for {self.__portfolio.name}'\n\n return ret + f' ({len(self)})'\n\n def __len__(self):\n return len(self.futures)\n\n def __iter__(self):\n return iter(self.__results())\n\n def __mul__(self, other):\n if isinstance(other, (int, float)):\n return PortfolioRiskResult(self.__portfolio, self.__risk_measures, [f * other for f in self.futures])\n else:\n return ValueError('Can only multiply by an int or float')\n\n def __add__(self, other):\n def as_multiple_result_futures(portfolio_result):\n if len(portfolio_result.__risk_measures) > 1:\n return portfolio_result\n mr_futures = []\n for p, f in zip(portfolio_result.__portfolio, portfolio_result.futures):\n if isinstance(f, PortfolioRiskResult):\n mr_futures.append(as_multiple_result_futures(f))\n elif isinstance(f, MultipleRiskMeasureFuture):\n mr_futures.append(f)\n else:\n mr_futures.append(MultipleRiskMeasureFuture(p, {portfolio_result.__risk_measures[0]: f}))\n return PortfolioRiskResult(portfolio_result.__portfolio, portfolio_result.__risk_measures, mr_futures)\n\n def set_value(dest_result, src_result, src_risk_measure):\n for priceable, future in zip(dest_result.__portfolio, dest_result.futures):\n if isinstance(future, PortfolioRiskResult):\n set_value(future, src_result, src_risk_measure)\n else:\n try:\n value = src_result[priceable]\n value = value[src_risk_measure] if isinstance(value, MultipleRiskMeasureResult) else value\n future.result()[src_risk_measure] = value\n except KeyError:\n pass\n\n def first_value(portfolio_result):\n if len(portfolio_result.__risk_measures) > 1:\n return next(iter(portfolio_result[next(iter(portfolio_result.portfolio.all_instruments))].values()))\n else:\n return portfolio_result[next(iter(portfolio_result.__portfolio.all_instruments))]\n\n if isinstance(other, (int, float)):\n return PortfolioRiskResult(self.__portfolio, self.__risk_measures, [f + other for f in self.futures])\n elif isinstance(other, PortfolioRiskResult):\n if not _risk_keys_compatible(first_value(self), first_value(other)) and not set(\n self.__portfolio.all_instruments\n ).isdisjoint(other.__portfolio.all_instruments):\n raise ValueError('Results must have matching scenario and location')\n\n self_dt = (first_value(self).risk_key.date,) if len(self.dates) == 0 else self.dates\n other_dt = (first_value(other).risk_key.date,) if len(other.dates) == 0 else other.dates\n dates_overlap = not set(self_dt).isdisjoint(other_dt)\n\n if (\n not set(self.__risk_measures).isdisjoint(other.__risk_measures)\n and dates_overlap\n and not set(self.__portfolio.all_instruments).isdisjoint(other.__portfolio.all_instruments)\n ):\n raise ValueError('Results overlap on risk measures, instruments or dates')\n\n self_futures = as_multiple_result_futures(self).futures\n other_futures = as_multiple_result_futures(other).futures\n\n if self.__portfolio is other.__portfolio or self.__portfolio == other.__portfolio:\n portfolio = self.__portfolio\n futures = [future + other_future for future, other_future in zip(self_futures, other_futures)]\n else:\n portfolio = self.__portfolio + other.__portfolio\n futures = self_futures + other_futures\n\n ret = PortfolioRiskResult(portfolio, set(chain(self.risk_measures, other.risk_measures)), futures)\n\n if portfolio is not self.__portfolio and len(ret.risk_measures) > 1:\n # Now fill in overlapping values\n for dest, src in ((self, other), (other, self)):\n for risk_measure in (m for m in src.risk_measures if dest == self or m not in dest.risk_measures):\n set_value(ret, src, risk_measure)\n\n return ret\n else:\n raise ValueError('Can only add instances of PortfolioRiskResult or int, float')\n\n @property\n def portfolio(self):\n return self.__portfolio\n\n @property\n def risk_measures(self) -> Tuple[RiskMeasure, ...]:\n return self.__risk_measures\n\n @property\n def dates(self) -> Tuple[dt.date, ...]:\n dates = set()\n for result in self.__results():\n if isinstance(result, (MultipleRiskMeasureResult, PortfolioRiskResult)):\n if all([isinstance(i, dt.date) for i in result.dates]):\n dates.update(result.dates)\n elif isinstance(result, (pd.DataFrame, pd.Series)):\n if all([isinstance(i, dt.date) for i in result.index]):\n dates.update(result.index)\n try:\n return tuple(sorted(dates))\n except TypeError:\n return tuple()\n\n @property\n def _multi_scen_key(self) -> Iterable[Scenario]:\n for result in self.__results():\n if isinstance(result, MultipleScenarioResult):\n return tuple(result.scenarios)\n elif isinstance(result, (MultipleRiskMeasureResult, PortfolioRiskResult)):\n return result._multi_scen_key\n return tuple()\n\n def result(self, timeout: Optional[int] = None):\n super().result(timeout=timeout)\n return self\n\n def subset(self, items: Iterable[Union[int, str, PortfolioPath, Priceable]], name: Optional[str] = None):\n paths = tuple(chain.from_iterable((i,) if isinstance(i, PortfolioPath) else self.__paths(i) for i in items))\n sub_portfolio = self.__portfolio.subset(paths, name=name)\n return PortfolioRiskResult(sub_portfolio, self.risk_measures, [p(self.futures) for p in paths])\n\n def transform(self, risk_transformation: Transformer = None):\n if risk_transformation is None:\n return self\n elif len(self.__risk_measures) > 1:\n return MultipleRiskMeasureResult(\n self.portfolio, ((r, self[r].transform(risk_transformation)) for r in self.__risk_measures)\n )\n elif len(self.__risk_measures) == 1:\n flattened_results = risk_transformation.apply(self.__results())\n futures = []\n for result in flattened_results:\n transformed_future = PricingFuture()\n transformed_future.set_result(result)\n transformed_future.done()\n futures.append(transformed_future)\n return PortfolioRiskResult(self.portfolio, self.risk_measures, futures)\n else:\n return self\n\n def aggregate(\n self, allow_mismatch_risk_keys=False, allow_heterogeneous_types=False\n ) -> Union[float, pd.DataFrame, pd.Series, MultipleRiskMeasureResult]:\n if len(self.__risk_measures) > 1:\n return MultipleRiskMeasureResult(self.portfolio, ((r, self[r].aggregate()) for r in self.__risk_measures))\n else:\n return aggregate_results(\n self.__results(),\n allow_mismatch_risk_keys=allow_mismatch_risk_keys,\n allow_heterogeneous_types=allow_heterogeneous_types,\n )\n\n def _to_records(self, display_options: DisplayOptions = None):\n def get_records(rec):\n temp = []\n for f in rec.futures:\n if (\n isinstance(f.result(), ResultInfo)\n or isinstance(f.result(), MultipleRiskMeasureResult)\n or isinstance(f.result(), MultipleScenarioResult)\n ):\n temp.append(f.result())\n else:\n temp.extend(get_records(f.result()))\n return temp\n\n future_records = get_records(self)\n portfolio_records = self.__portfolio._to_records()\n records = []\n if len(future_records) == len(portfolio_records):\n for i in range(len(future_records)):\n records.extend(future_records[i]._to_records({**portfolio_records[i]}, display_options))\n return records\n\n def to_frame(\n self,\n values='default',\n index='default',\n columns='default',\n aggfunc=\"sum\",\n display_options: DisplayOptions = None,\n ):\n final_records = self._to_records(display_options=display_options)\n if len(final_records) > 0:\n ori_df = pd.DataFrame.from_records(final_records)\n if 'risk_measure' not in ori_df.columns.values:\n ori_df['risk_measure'] = self.risk_measures[0]\n else:\n return\n df_cols = list(ori_df.columns.values)\n # fill n/a values for different sub-portfolio depths\n cols_except_value = [c for c in df_cols if c != 'value']\n ori_df[cols_except_value] = ori_df[cols_except_value].fillna(\"N/A\")\n\n has_dt = True if 'dates' in df_cols else False\n other_cols = sorted([p for p in df_cols if 'portfolio' in p]) + ['instrument_name', 'risk_measure']\n other_cols = other_cols + ['dates'] if has_dt else other_cols\n val_cols = [col for col in df_cols if col not in other_cols]\n if 'value' in val_cols and val_cols[-1] != 'value':\n val_cols = [col for col in val_cols if col != 'value'] + ['value']\n sorted_col = other_cols + val_cols\n ori_df = ori_df[sorted_col]\n\n if values is None and index is None and columns is None: # to_frame(None, None, None)\n return ori_df\n elif values == 'default' and index == 'default' and columns == 'default': # to_frame()\n multi_scen = len(self._multi_scen_key) > 1\n has_bucketed = True if 'mkt_type' in df_cols else False\n has_cashflows = True if 'payment_amount' in df_cols else False\n multi_rm = True if len(self.risk_measures) > 1 else False\n port_depth_one = True if len(max(self.portfolio.all_paths, key=len)) == 1 else False\n if has_bucketed or has_cashflows:\n return ori_df.set_index(other_cols)\n else:\n values, index, columns = get_default_pivots(\n 'PortfolioRiskResult',\n has_dates=has_dt,\n multi_measures=multi_rm,\n simple_port=port_depth_one,\n multi_scen=multi_scen,\n ori_cols=df_cols,\n )\n else: # user defined pivoting\n values = 'value' if values == 'default' or values == ['value'] else values\n\n return pivot_to_frame(ori_df, values, index, columns, aggfunc)\n\n def __paths(self, items: Union[int, slice, str, Priceable]) -> Tuple[PortfolioPath, ...]:\n if isinstance(items, int):\n return (PortfolioPath(items),)\n elif isinstance(items, slice):\n return tuple(PortfolioPath(i) for i in range(len(self.__portfolio))[items])\n elif isinstance(items, (str, Priceable)):\n paths = self.__portfolio.paths(items)\n # will enter in here only if trying to slice an unresolved portfolio with a resolved instrument\n if not paths and isinstance(items, InstrumentBase) and items.unresolved:\n paths = self.__portfolio.paths(items.unresolved)\n if not paths:\n raise KeyError(f'{items} not in portfolio')\n key = items.resolution_key.ex_measure\n paths = tuple(p for p in paths if self.__result(p, self.risk_measures[0]).risk_key.ex_measure == key)\n\n if not paths:\n raise KeyError(f'Cannot slice {items} which is resolved in a different pricing context')\n\n return paths\n\n def __results(self, items: Optional[Union[int, slice, str, Priceable]] = None):\n if items is None:\n return tuple(self.__result(p) for p in self.__portfolio.all_paths)\n\n paths = self.__paths(items)\n if not paths:\n raise KeyError(f'{items}')\n\n return self.__result(paths[0]) if not isinstance(items, slice) else self.subset(paths)\n\n def __result(self, path: PortfolioPath, risk_measure: Optional[RiskMeasure] = None):\n res = path(self.futures).result()\n\n if len(self.risk_measures) == 1 and not risk_measure:\n risk_measure = self.risk_measures[0]\n\n return (\n res[risk_measure]\n if risk_measure and isinstance(res, (MultipleRiskMeasureResult, PortfolioRiskResult))\n else res\n )\n\n def get(self, item, default):\n try:\n value = self.__getitem__(item)\n except (KeyError, ValueError):\n value = default\n return value\n" }, { "path": "gs_quant/risk/scenario_utils.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nfrom gs_quant.markets.securities import AssetIdentifier, SecurityMaster\nfrom gs_quant.risk.scenarios import MarketDataVolShockScenario\nfrom gs_quant.data import Dataset\n\n\ndef build_eq_vol_scenario_intraday(\n asset_name: str,\n source_dataset: str,\n ref_spot: float = None,\n asset_name_type: AssetIdentifier = AssetIdentifier.REUTERS_ID,\n start_time: dt.datetime = dt.datetime.now() - dt.timedelta(hours=1),\n end_time: dt.datetime = dt.datetime.now(),\n) -> MarketDataVolShockScenario:\n asset = SecurityMaster.get_asset(asset_name, asset_name_type)\n vol_dataset = Dataset(source_dataset)\n vol_data = vol_dataset.get_data(\n assetId=[asset.get_marquee_id()], strikeReference='forward', startTime=start_time, endTime=end_time\n )\n asset_ric = asset.get_identifier(AssetIdentifier.REUTERS_ID)\n return MarketDataVolShockScenario.from_dataframe(asset_ric, vol_data, ref_spot)\n\n\ndef build_eq_vol_scenario_eod(\n asset_name: str,\n source_dataset: str,\n ref_spot: float = None,\n asset_name_type: AssetIdentifier = AssetIdentifier.REUTERS_ID,\n vol_date: dt.date = dt.date.today(),\n) -> MarketDataVolShockScenario:\n asset = SecurityMaster.get_asset(asset_name, asset_name_type)\n vol_dataset = Dataset(source_dataset)\n vol_data = vol_dataset.get_data(\n assetId=[asset.get_marquee_id()], strikeReference='forward', startDate=vol_date, endDate=vol_date\n )\n asset_ric = asset.get_identifier(AssetIdentifier.REUTERS_ID)\n return MarketDataVolShockScenario.from_dataframe(asset_ric, vol_data, ref_spot)\n" }, { "path": "gs_quant/risk/scenarios.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Mapping, Optional\n\nimport pandas as pd\n\nfrom gs_quant.target.risk import (\n MarketDataPattern,\n MarketDataShock,\n MarketDataPatternAndShock,\n MarketDataShockBasedScenario as __MarketDataShockBasedScenario,\n MarketDataVolShockScenario as __MarketDataVolShockScenario,\n MarketDataVolSlice,\n MarketDataShockType,\n)\n\n\nclass MarketDataShockBasedScenario(__MarketDataShockBasedScenario):\n def __init__(self, shocks: Mapping[MarketDataPattern, MarketDataShock], name: Optional[str] = None):\n super().__init__(tuple(MarketDataPatternAndShock(p, s) for p, s in shocks.items()), name=name)\n\n\nclass MarketDataVolShockScenario(__MarketDataVolShockScenario):\n @classmethod\n def from_dataframe(cls, asset_ric: str, df: pd.DataFrame, ref_spot: float = None, name=None):\n \"\"\"\n Create a MarketDataVolShockScenario using an input DataFrame containing expiry dates, strikes and vol levels\n :param asset_ric: the RIC of the asset\n :param df: input data frame. Expects a DataFrame indexed by date/time and containing columns expirationDate,\n absoluteStrike and impliedVolatility.\n :param ref_spot: the current reference spot level\n :param name: name\n :return: MarketDataVolShockScenario\n \"\"\"\n last_datetime = max(list(df.index))\n df_filtered = df.loc[df.index == last_datetime]\n df_grouped = df_filtered.groupby(['expirationDate'])\n\n vol_slices = []\n for key in df_grouped.groups:\n value = df_grouped.get_group(key)\n df_sorted = value.sort_values(['absoluteStrike'])\n strikes = list(df_sorted.absoluteStrike)\n levels = list(df_sorted.impliedVolatility)\n vol_slice = MarketDataVolSlice(key.date(), strikes, levels)\n vol_slices.append(vol_slice)\n\n scenario = MarketDataVolShockScenario(\n MarketDataPattern('Eq Vol', asset_ric), MarketDataShockType.Override, vol_slices, ref_spot, name=name\n )\n return scenario\n" }, { "path": "gs_quant/risk/transform.py", "content": "\"\"\"\nCopyright 2022 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass\nfrom dataclasses_json import dataclass_json\nfrom typing import Generic, Callable, Sequence, Any, TypeVar, Iterable, Union, Optional\n\nfrom gs_quant.risk.core import ResultType, DataFrameWithInfo, SeriesWithInfo, FloatWithInfo\n\n_InputT = TypeVar('_InputT')\n_ResultT = TypeVar('_ResultT')\n\n\nclass Transformer(ABC, Generic[_InputT, _ResultT]):\n @abstractmethod\n def apply(self, data: _InputT, *args, **kwargs) -> _ResultT:\n pass\n\n\nclass GenericResultWithInfoTransformer(Transformer[ResultType, ResultType]):\n def __init__(self, fn: Callable[[ResultType, Sequence[Any]], ResultType]):\n self.__fn = fn\n\n def apply(self, data: ResultType, *args, **kwargs) -> ResultType:\n return self.__fn(data, *args, **kwargs)\n\n\n@dataclass_json\n@dataclass\nclass ResultWithInfoAggregator(Transformer[Iterable[ResultType], FloatWithInfo]):\n risk_col: str = 'value'\n filter_coord: Optional[object] = None\n\n def apply(\n self, results: Iterable[Union[float, FloatWithInfo, SeriesWithInfo, DataFrameWithInfo]], *args, **kwargs\n ) -> Iterable[Union[float, FloatWithInfo]]:\n flattened_results = []\n\n for result in results:\n if isinstance(result, float):\n flattened_results.append(result)\n else:\n if isinstance(result, FloatWithInfo):\n val = result.raw_value\n elif isinstance(result, SeriesWithInfo):\n val = getattr(result, self.risk_col).sum()\n elif isinstance(result, DataFrameWithInfo):\n if result.empty:\n val = 0\n elif self.filter_coord is not None:\n df = result.filter_by_coord(self.filter_coord)\n val = getattr(df, self.risk_col).sum()\n else:\n val = getattr(result, self.risk_col).sum()\n else:\n raise ValueError(f'Aggregation of {type(result).__name__} not currently supported.')\n risk_key = result.risk_key\n unit = result.unit\n error = result.error\n flattened_results.append(FloatWithInfo(value=val, risk_key=risk_key, unit=unit, error=error))\n\n return flattened_results\n" }, { "path": "gs_quant/session.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on ans\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport asyncio\nimport inspect\nimport itertools\nimport json\nimport logging\nimport os\nimport ssl\nimport sys\nfrom abc import abstractmethod\nfrom configparser import ConfigParser\nfrom contextlib import asynccontextmanager\nfrom enum import Enum, auto, unique\nfrom typing import Optional, Union, Iterable, Any\n\nimport backoff\nimport certifi\nimport httpx\nimport msgpack\nimport pandas as pd\nimport requests\nimport requests.adapters\nimport requests.cookies\nimport urllib3\n\nfrom gs_quant import version as APP_VERSION\nfrom gs_quant.base import Base\nfrom gs_quant.context_base import ContextBase, nullcontext\nfrom gs_quant.errors import MqError, MqRequestError, MqAuthenticationError, MqUninitialisedError, error_builder\nfrom gs_quant.json_encoder import JSONEncoder, encode_default\nfrom gs_quant.tracing import Tracer, TracingScope, Tags\n\nlogger = logging.getLogger(__name__)\n\nAPI_VERSION = 'v1'\nDEFAULT_APPLICATION = 'gs-quant'\nDEFAULT_TIMEOUT = 65\n\n\n@unique\nclass Environment(Enum):\n DEV = auto()\n QA = auto()\n PROD = auto()\n\n\nclass CustomHttpAdapter(requests.adapters.HTTPAdapter):\n # \"Transport adapter\" that allows us to use custom ssl_context.\n\n __ssl_ctx = None\n\n @classmethod\n def ssl_context(cls) -> ssl.SSLContext:\n if cls.__ssl_ctx is None:\n cls.__ssl_ctx = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)\n cls.__ssl_ctx.check_hostname = False\n cls.__ssl_ctx.verify_mode = 0\n cls.__ssl_ctx.options |= 0x4 # OP_LEGACY_SERVER_CONNECT\n cls.__ssl_ctx.load_default_certs()\n cls.__ssl_ctx.load_verify_locations(certifi.where())\n return cls.__ssl_ctx\n\n def init_poolmanager(self, connections, maxsize=100, block=False):\n self.poolmanager = urllib3.poolmanager.PoolManager(\n num_pools=connections, maxsize=maxsize, block=block, ssl_context=self.ssl_context()\n )\n\n\nclass Domain:\n MDS_US_EAST = \"MdsDomainEast\"\n MDS_WEB = \"MdsWebDomain\"\n APP = \"AppDomain\"\n\n\nclass _SyncSessionAPI:\n \"\"\"Synchronous HTTP interface for GsSession. Access via ``GsSession.current.sync``.\"\"\"\n\n def __init__(self, session: 'GsSession'):\n self._session = session\n\n def get(\n self,\n path: str,\n payload: Optional[Union[dict, 'Base']] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union['Base', tuple, dict]:\n return self._session._get(\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n def post(\n self,\n path: str,\n payload: Optional[Union[dict, bytes, 'Base', 'pd.DataFrame']] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union['Base', tuple, dict]:\n return self._session._post(\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n def put(\n self,\n path: str,\n payload: Optional[Union[dict, 'Base']] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union['Base', tuple, dict]:\n return self._session._put(\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n def delete(\n self,\n path: str,\n payload: Optional[Union[dict, 'Base']] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n use_body: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union['Base', tuple, dict]:\n return self._session._delete(\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n use_body=use_body,\n domain=domain,\n )\n\n\nclass _AsyncSessionAPI:\n \"\"\"Asynchronous HTTP interface for GsSession. Access via ``GsSession.current.async_``.\"\"\"\n\n def __init__(self, session: 'GsSession'):\n self._session = session\n\n async def get(\n self,\n path: str,\n payload: Optional[Union[dict, 'Base']] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union['Base', tuple, dict]:\n return await self._session._get_async(\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n async def post(\n self,\n path: str,\n payload: Optional[Union[dict, bytes, 'Base', 'pd.DataFrame']] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union['Base', tuple, dict]:\n return await self._session._post_async(\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n async def put(\n self,\n path: str,\n payload: Optional[Union[dict, 'Base']] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union['Base', tuple, dict]:\n return await self._session._put_async(\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n async def delete(\n self,\n path: str,\n payload: Optional[Union[dict, 'Base']] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n use_body: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union['Base', tuple, dict]:\n return await self._session._delete_async(\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n use_body=use_body,\n domain=domain,\n )\n\n @asynccontextmanager\n async def connect_websocket(\n self,\n path: str,\n headers: Optional[dict] = None,\n include_version=True,\n domain: Optional[str] = None,\n **kwargs: Any,\n ):\n async with self._session._connect_websocket(\n path, headers=headers, include_version=include_version, domain=domain, **kwargs\n ) as ws:\n yield ws\n\n\nclass GsSession(ContextBase):\n __config = None\n\n class Scopes(Enum):\n READ_CONTENT = 'read_content'\n READ_FINANCIAL_DATA = 'read_financial_data'\n READ_PRODUCT_DATA = 'read_product_data'\n READ_USER_PROFILE = 'read_user_profile'\n MODIFY_CONTENT = 'modify_content'\n MODIFY_FINANCIAL_DATA = 'modify_financial_data'\n MODIFY_PRODUCT_DATA = 'modify_product_data'\n MODIFY_USER_PROFILE = 'modify_user_profile'\n RUN_ANALYTICS = 'run_analytics'\n EXECUTE_TRADES = 'execute_trades'\n\n @classmethod\n def get_default(cls):\n return (\n cls.READ_CONTENT.value,\n cls.READ_PRODUCT_DATA.value,\n cls.READ_FINANCIAL_DATA.value,\n cls.READ_USER_PROFILE.value,\n )\n\n def __init__(\n self,\n domain: str,\n environment: str = None,\n api_version: str = API_VERSION,\n application: str = DEFAULT_APPLICATION,\n verify=True,\n http_adapter: requests.adapters.HTTPAdapter = None,\n application_version=APP_VERSION,\n proxies=None,\n redirect_to_mds=False,\n ):\n super().__init__()\n self._session = None\n self._session_async = None\n self._sync_api: Optional['_SyncSessionAPI'] = None\n self._async_api: Optional['_AsyncSessionAPI'] = None\n self.domain = domain\n self._orig_domain = domain\n if environment in tuple(x.name for x in Environment):\n self.environment = Environment[environment]\n elif isinstance(domain, Environment):\n self.environment = domain\n else:\n self.environment = Environment.DEV\n self.api_version = api_version\n self.application = application\n self.verify = verify\n if http_adapter is None:\n if ssl.OPENSSL_VERSION_INFO >= (3, 0, 0):\n self.http_adapter = CustomHttpAdapter()\n else:\n self.http_adapter = requests.adapters.HTTPAdapter(pool_maxsize=100)\n else:\n self.http_adapter = http_adapter\n self.application_version = application_version\n self.proxies = proxies\n self.mounts = {key: httpx.HTTPTransport(proxy=val) for key, val in proxies} if proxies else None\n self.redirect_to_mds = redirect_to_mds\n\n @backoff.on_exception(\n lambda: backoff.expo(factor=2), (requests.exceptions.HTTPError, requests.exceptions.Timeout), max_tries=5\n )\n @backoff.on_predicate(lambda: backoff.expo(factor=2), lambda x: x.status_code in (500, 502, 503, 504), max_tries=5)\n @abstractmethod\n def _authenticate(self):\n raise NotImplementedError(\"Must implement _authenticate\")\n\n def _authenticate_async(self):\n if self._has_async_session():\n self._session_async.headers.update([(k, v) for k, v in self._session.headers.items()])\n for cookie in self._session.cookies:\n self._session_async.cookies.set(cookie.name, cookie.value, domain=cookie.domain)\n\n def _authenticate_all_sessions(self):\n self._authenticate()\n self._authenticate_async()\n\n def _on_enter(self):\n self.__close_on_exit = self._session is None\n if not self._session:\n self.init()\n\n def _on_exit(self, exc_type, exc_val, exc_tb):\n if self.__close_on_exit:\n self._session = None\n self._session_async = None\n\n def _has_async_session(self) -> bool:\n return self._session_async and not self._session_async.is_closed\n\n def _init_async(self):\n if not self._has_async_session():\n self._session_async = httpx.AsyncClient(\n follow_redirects=True, verify=CustomHttpAdapter.ssl_context(), mounts=self.mounts\n )\n self._session_async.headers.update({'X-Application': self.application})\n self._session_async.headers.update({'X-Version': self.application_version})\n self._authenticate_async()\n\n async def _on_aenter(self):\n self.__close_on_exit = self._session is None\n self.__close_on_exit_async = not self._has_async_session()\n if self.__close_on_exit:\n self.init()\n if self.__close_on_exit_async:\n self._init_async()\n\n async def _on_aexit(self, exc_type, exc_val, exc_tb):\n if self.__close_on_exit_async:\n if self._has_async_session():\n await self._session_async.aclose()\n self._session_async = None\n if self.__close_on_exit:\n self._session = None\n\n def init(self):\n if not self._session:\n self._session = requests.Session()\n if self.http_adapter is not None:\n self._session.mount('https://', self.http_adapter)\n if self.proxies is not None:\n self._session.proxies = self.proxies\n self._session.verify = self.verify\n self._session.headers.update({'X-Application': self.application})\n self._session.headers.update({'X-Version': self.application_version})\n self._authenticate()\n if self._orig_domain == Domain.APP:\n self.post_to_activity_service()\n\n def close(self):\n self._session: requests.Session\n if self._session:\n # don't close a shared adapter\n if self.http_adapter is None:\n self._session.close()\n self._session = None\n if self._session_async:\n try:\n asyncio.run(self._close_async())\n except Exception:\n pass\n\n async def _close_async(self):\n if self._session_async:\n await self._session_async.aclose()\n self._session_async = None\n\n def __del__(self):\n self.close()\n\n @staticmethod\n def __unpack(results: Union[dict, list], cls: type) -> Union[Base, tuple, dict]:\n if issubclass(cls, Base):\n if isinstance(results, list):\n return tuple(None if r is None else cls.from_dict(r) for r in results)\n else:\n return None if results is None else cls.from_dict(results)\n else:\n if isinstance(results, list):\n return tuple(cls(**r) for r in results)\n else:\n return cls(**results)\n\n def _build_url(self, domain: Optional[str], path: str, include_version: Optional[bool]):\n if not domain:\n domain = self.domain\n url = '{}{}{}'.format(domain, '/' + self.api_version if include_version else '', path)\n return url\n\n def _build_request_params(\n self,\n method: str,\n path: str,\n url: str,\n payload: Optional[Union[dict, str, bytes, Base, pd.DataFrame]],\n request_headers: Optional[dict],\n timeout: Optional[int],\n use_body: bool,\n data_key: str,\n tracing_scope: Optional[TracingScope],\n ) -> dict:\n is_dataframe = isinstance(payload, pd.DataFrame)\n if not is_dataframe:\n payload = payload or {}\n kwargs = {'timeout': timeout}\n\n if tracing_scope:\n tracing_scope.span.set_tag('path', path)\n tracing_scope.span.set_tag('timeout', timeout)\n tracing_scope.span.set_tag(Tags.HTTP_URL, url)\n tracing_scope.span.set_tag(Tags.HTTP_METHOD, method)\n tracing_scope.span.set_tag('span.kind', 'client')\n\n if method in ('GET', 'DELETE') and not use_body:\n kwargs['params'] = payload\n if tracing_scope or request_headers:\n headers = self._session.headers.copy()\n if request_headers:\n headers.update(request_headers)\n if tracing_scope:\n Tracer.inject(headers)\n kwargs['headers'] = headers\n elif method in ('POST', 'PUT') or (method in ('GET', 'DELETE') and use_body):\n headers = self._session.headers.copy()\n\n if request_headers:\n headers.update(request_headers)\n\n if tracing_scope:\n Tracer.inject(headers)\n\n if 'Content-Type' not in headers:\n headers.update({'Content-Type': 'application/json; charset=utf-8'})\n\n if tracing_scope:\n tracing_scope.span.set_tag('request.content.type', headers.get('Content-Type'))\n\n use_msgpack = headers.get('Content-Type') == 'application/x-msgpack'\n if use_msgpack:\n headers['Accept'] = headers.get('Content-Type')\n kwargs['headers'] = headers\n\n if is_dataframe or payload:\n kwargs[data_key] = (\n payload\n if isinstance(payload, (str, bytes))\n else msgpack.dumps(payload, default=encode_default)\n if use_msgpack\n else json.dumps(payload, cls=JSONEncoder)\n )\n else:\n raise MqError('not implemented')\n return kwargs\n\n def _parse_response(\n self, request_id, response, method: str, url: str, cls: Optional[type], return_request_id: Optional[bool]\n ):\n if not 199 < response.status_code < 300:\n reason = response.reason if hasattr(response, 'reason') else response.reason_phrase\n err_msg = reason if response.headers.get('Content-Type') == 'text/html' else f'{reason}: {response.text}'\n raise error_builder(response.status_code, err_msg, context=f'{request_id}: {method} {url}')\n elif 'Content-Type' in response.headers:\n if 'application/x-msgpack' in response.headers['Content-Type']:\n ret = msgpack.unpackb(response.content, raw=False)\n elif 'application/json' in response.headers['Content-Type']:\n ret = json.loads(response.text)\n else:\n ret = {'raw': response}\n if cls and ret:\n if isinstance(ret, dict) and 'results' in ret:\n ret['results'] = self.__unpack(ret['results'], cls)\n else:\n ret = self.__unpack(ret, cls)\n return (ret, request_id) if return_request_id else ret\n else:\n ret = {'raw': response}\n if return_request_id:\n ret['request_id'] = request_id\n return ret\n\n def __request(\n self,\n method: str,\n path: str,\n payload: Optional[Union[dict, str, bytes, Base, pd.DataFrame]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n try_auth: Optional[bool] = True,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n use_body: bool = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n span = Tracer.active_span()\n url = self._build_url(domain, path, include_version)\n tracer = Tracer(url) if span and span.is_recording() else nullcontext()\n with tracer as scope:\n kwargs = self._build_request_params(\n method, path, url, payload, request_headers, timeout, use_body, \"data\", scope\n )\n response = self._session.request(method, url, **kwargs)\n request_id = response.headers.get('x-dash-requestid')\n logger.debug('Handling response for [Request ID]: %s [Method]: %s [URL]: %s', request_id, method, url)\n if scope:\n scope.span.set_tag(Tags.HTTP_STATUS_CODE, response.status_code)\n if response.status_code > 399:\n scope.span.set_tag('error', True)\n scope.span.set_tag('dash.request.id', request_id)\n scope.span.set_tag('response.content.type', response.headers.get('Content-Type'))\n if response.status_code == 401:\n # Expired token or other authorization issue\n if not try_auth:\n raise MqRequestError(response.status_code, response.text, context=f'{request_id}: {method} {url}')\n self._authenticate()\n return self.__request(\n method,\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n try_auth=False,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n use_body=use_body,\n domain=domain,\n )\n return self._parse_response(request_id, response, method, url, cls, return_request_id)\n\n async def __request_async(\n self,\n method: str,\n path: str,\n payload: Optional[Union[dict, str, bytes, Base, pd.DataFrame]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n try_auth: Optional[bool] = True,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n use_body: bool = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n self._init_async()\n span = Tracer.active_span()\n url = self._build_url(domain, path, include_version)\n tracer = Tracer(f'http:/{path}') if span and span.is_recording() else nullcontext()\n with tracer as scope:\n kwargs = self._build_request_params(\n method, path, url, payload, request_headers, timeout, use_body, \"content\", scope\n )\n response = await self._session_async.request(method, url, **kwargs)\n request_id = response.headers.get('x-dash-requestid')\n if scope:\n scope.span.set_tag(Tags.HTTP_STATUS_CODE, response.status_code)\n scope.span.set_tag('dash.request.id', request_id)\n\n logger.debug('Handling response for [Request ID]: %s [Method]: %s [URL]: %s', request_id, method, url)\n if response.status_code == 401:\n # Expired token or other authorization issue\n logger.debug(f\"request: {request_id}, status: {response.status_code}, message: {response.text}, URL: {url}\")\n if not try_auth:\n raise MqRequestError(response.status_code, response.text, context=f'{request_id}: {method} {url}')\n self._authenticate_all_sessions()\n res = await self.__request_async(\n method,\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n try_auth=False,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n use_body=use_body,\n domain=domain,\n )\n return res\n return self._parse_response(request_id, response, method, url, cls, return_request_id)\n\n def _get(\n self,\n path: str,\n payload: Optional[Union[dict, Base]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n return self.__request(\n 'GET',\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n async def _get_async(\n self,\n path: str,\n payload: Optional[Union[dict, Base]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n ret = await self.__request_async(\n 'GET',\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n return ret\n\n def _post(\n self,\n path: str,\n payload: Optional[Union[dict, bytes, Base, pd.DataFrame]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n return self.__request(\n 'POST',\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n async def _post_async(\n self,\n path: str,\n payload: Optional[Union[dict, bytes, Base, pd.DataFrame]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n ret = await self.__request_async(\n 'POST',\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n return ret\n\n def _delete(\n self,\n path: str,\n payload: Optional[Union[dict, Base]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n use_body: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n return self.__request(\n 'DELETE',\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n use_body=use_body,\n domain=domain,\n )\n\n async def _delete_async(\n self,\n path: str,\n payload: Optional[Union[dict, Base]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n use_body: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n ret = await self.__request_async(\n 'DELETE',\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n use_body=use_body,\n domain=domain,\n )\n return ret\n\n def _put(\n self,\n path: str,\n payload: Optional[Union[dict, Base]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n return self.__request(\n 'PUT',\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n\n async def _put_async(\n self,\n path: str,\n payload: Optional[Union[dict, Base]] = None,\n request_headers: Optional[dict] = None,\n cls: Optional[type] = None,\n include_version: Optional[bool] = True,\n timeout: Optional[int] = DEFAULT_TIMEOUT,\n return_request_id: Optional[bool] = False,\n domain: Optional[str] = None,\n ) -> Union[Base, tuple, dict]:\n ret = await self.__request_async(\n 'PUT',\n path,\n payload=payload,\n request_headers=request_headers,\n cls=cls,\n include_version=include_version,\n timeout=timeout,\n return_request_id=return_request_id,\n domain=domain,\n )\n return ret\n\n @asynccontextmanager\n async def _connect_websocket(\n self,\n path: str,\n headers: Optional[dict] = None,\n include_version=True,\n domain: Optional[str] = None,\n **kwargs: Any,\n ):\n span = Tracer.active_span()\n trace = Tracer(f'wss:/{path}') if span and span.is_recording() else nullcontext()\n with trace as scope:\n tracing_headers = {}\n Tracer.inject(tracing_headers)\n headers = {**headers, **tracing_headers} if headers else tracing_headers\n async with self._connect_websocket_raw(path, headers, include_version, domain, **kwargs) as websocket:\n if scope and scope.span:\n if hasattr(websocket, 'request_headers'): # For websockets < 14\n scope.span.set_tag('wss.host', websocket.request_headers.get('host'))\n else:\n scope.span.set_tag('wss.host', websocket.request.headers.get('host'))\n yield websocket\n\n def _connect_websocket_raw(\n self,\n path: str,\n headers: Optional[dict] = None,\n include_version=True,\n domain: Optional[str] = None,\n **kwargs: Any,\n ):\n import websockets\n\n _WEBSOCKETS_VERSION = tuple(int(x) for x in websockets.__version__.split(\".\"))\n\n version_path = '/' + self.api_version if include_version else ''\n url = f'{domain}{version_path}{path}' if domain else f'ws{self.domain[4:]}{version_path}{path}'\n extra_headers = self._headers() + list((headers or {}).items())\n # websockets 14 supports python >= 3.9 only. When we drop support for python 3.8 we can clean this up\n if _WEBSOCKETS_VERSION >= (14, 0):\n ws_library_headers = {\"additional_headers\": extra_headers}\n else:\n ws_library_headers = {\"extra_headers\": extra_headers, \"read_limit\": 2**32}\n return websockets.connect(\n url,\n max_size=2**32,\n ssl=CustomHttpAdapter.ssl_context() if url.startswith('wss') else None,\n **ws_library_headers,\n **kwargs,\n )\n\n def _headers(self):\n headers = []\n if self._session:\n for k, v in self._session.headers.items():\n if k.upper() in ('AUTHORIZATION', 'X-MARQUEE-CSRF-TOKEN', 'X-APPLICATION', 'X-VERSION'):\n headers.append((k, v))\n cookies = ()\n if self._session.cookies:\n if \"MarqueeLogin\" in self._session.cookies:\n cookies += (f\"MarqueeLogin={self._session.cookies['MarqueeLogin']}\",)\n if \"MARQUEE-CSRF-TOKEN\" in self._session.cookies:\n cookies += (f\"MARQUEE-CSRF-TOKEN={self._session.cookies['MARQUEE-CSRF-TOKEN']}\",)\n if 'GSSSO' in self._session.cookies:\n cookies += (f\"GSSSO={self._session.cookies['GSSSO']}\",)\n if cookies:\n headers += [('Cookie', \"; \".join(cookies))]\n return headers\n\n def _get_mds_domain(self):\n env_config = GsSession._config_for_environment(self.environment.name)\n current_domain = self.domain.replace('marquee.web', 'marquee') # remove .web from prod domain\n if self.environment.name == Environment.QA.name:\n current_domain = self.domain.replace('marquee-qa.web', 'marquee-qa') # remove .web from qa domain\n\n is_mds_web = current_domain == Domain.MDS_WEB\n is_env_mds_web = current_domain == env_config['MdsWebDomain']\n is_env_marquee_web = current_domain == env_config['MarqueeWebDomain']\n\n if is_mds_web or is_env_mds_web or is_env_marquee_web:\n return env_config['MdsWebDomain']\n else:\n return env_config['MdsDomainEast']\n\n def _get_web_domain(self):\n env_config = GsSession._config_for_environment(self.environment.name)\n return env_config['MarqueeWebDomain']\n\n @classmethod\n def _config_for_environment(cls, environment):\n if cls.__config is None:\n cls.__config = ConfigParser()\n cls.__config.read(os.path.join(os.path.dirname(inspect.getfile(cls)), 'config.ini'))\n\n return cls.__config[environment]\n\n @classmethod\n def use(\n cls,\n environment_or_domain: Union[Environment, str] = Environment.PROD,\n client_id: Optional[str] = None,\n client_secret: Optional[str] = None,\n scopes: Optional[Union[Iterable[Union[Scopes, str]], str]] = (),\n api_version: str = API_VERSION,\n application: str = DEFAULT_APPLICATION,\n http_adapter: requests.adapters.HTTPAdapter = None,\n use_mds: bool = False,\n domain: Domain = Domain.APP,\n ) -> None:\n environment_or_domain = (\n environment_or_domain.name if isinstance(environment_or_domain, Environment) else environment_or_domain\n )\n if domain is None:\n raise MqError(\"None is not a valid domain.\")\n domain = Domain.MDS_US_EAST if use_mds else domain\n session = cls.get(\n environment_or_domain,\n client_id=client_id,\n client_secret=client_secret,\n scopes=scopes,\n api_version=api_version,\n application=application,\n http_adapter=http_adapter,\n domain=domain,\n )\n\n session.init()\n cls.current = session\n\n def post_to_activity_service(self):\n params = {\n 'featureApplication': self.application,\n 'gsQuantVersion': self.application_version,\n 'pythonVersion': f'{sys.version_info.major}.{sys.version_info.minor}',\n }\n try:\n self._session.post(\n f'{self.domain}/{self.api_version}/activities',\n verify=self.verify,\n headers={'Content-Type': 'application/json; charset=utf-8'},\n data=json.dumps(\n {\n 'action': 'Initiated',\n 'kpis': [{'id': 'gsqInitiated', 'value': 1}],\n 'resource': 'GSQuant',\n 'parameters': params,\n }\n ),\n )\n except Exception:\n pass\n\n @classmethod\n def get(\n cls,\n environment_or_domain: Union[Environment, str] = Environment.PROD,\n client_id: Optional[str] = None,\n client_secret: Optional[str] = None,\n scopes: Optional[Union[Iterable[Union[Scopes, str]], str]] = (),\n token: str = '',\n is_gssso: bool = False,\n is_marquee_login: bool = False,\n api_version: str = API_VERSION,\n application: str = DEFAULT_APPLICATION,\n http_adapter: requests.adapters.HTTPAdapter = None,\n application_version: str = APP_VERSION,\n domain: Domain = Domain.APP,\n is_jwt_login: bool = False,\n ) -> 'GsSession':\n \"\"\"Return an instance of the appropriate session type for the given credentials\"\"\"\n\n environment_or_domain = (\n environment_or_domain.name if isinstance(environment_or_domain, Environment) else environment_or_domain\n )\n\n if client_id is not None:\n if isinstance(scopes, str):\n scopes = (scopes,)\n\n scopes = (scope if isinstance(scope, str) else scope.value for scope in scopes)\n scopes = tuple(set(itertools.chain(scopes, cls.Scopes.get_default())))\n\n return OAuth2Session(\n environment_or_domain,\n client_id,\n client_secret,\n scopes,\n api_version=api_version,\n application=application,\n http_adapter=http_adapter,\n domain=domain,\n )\n elif token:\n if is_gssso:\n try:\n return PassThroughGSSSOSession(\n environment_or_domain,\n token,\n api_version=api_version,\n application=application,\n http_adapter=http_adapter,\n )\n except NameError:\n raise MqUninitialisedError('This option requires gs_quant_auth to be installed')\n elif is_marquee_login:\n return MQLoginSession(\n environment_or_domain,\n domain=domain,\n api_version=api_version,\n http_adapter=http_adapter,\n application_version=application_version,\n application=application,\n mq_login_token=token,\n )\n elif is_jwt_login:\n return MQLoginSession(\n environment_or_domain,\n domain=domain,\n api_version=api_version,\n http_adapter=http_adapter,\n application_version=application_version,\n application=application,\n jwt_token=token,\n )\n else:\n return PassThroughSession(\n environment_or_domain,\n token,\n api_version=api_version,\n application=application,\n http_adapter=http_adapter,\n domain=domain,\n )\n else:\n try:\n return MQLoginSession(\n environment_or_domain,\n domain=domain,\n api_version=api_version,\n http_adapter=http_adapter,\n application_version=application_version,\n application=application,\n mq_login_token=token,\n )\n except NameError:\n raise MqUninitialisedError(\n 'Unable to obtain MarqueeLogin token. Please use client_id and client_secret to make the query'\n )\n\n def is_internal(self) -> bool:\n return False\n\n @property\n def sync(self) -> '_SyncSessionAPI':\n \"\"\"Synchronous HTTP interface. Use in non-async code.\"\"\"\n if self._sync_api is None:\n self._sync_api = _SyncSessionAPI(self)\n return self._sync_api\n\n @property\n def async_(self) -> '_AsyncSessionAPI':\n \"\"\"Asynchronous HTTP interface. Use with ``await`` inside async functions.\"\"\"\n if self._async_api is None:\n self._async_api = _AsyncSessionAPI(self)\n return self._async_api\n\n\nclass OAuth2Session(GsSession):\n def __init__(\n self,\n environment,\n client_id,\n client_secret,\n scopes,\n api_version=API_VERSION,\n application=DEFAULT_APPLICATION,\n http_adapter=None,\n domain=Domain.APP,\n ):\n if environment not in (Environment.PROD.name, Environment.QA.name, Environment.DEV.name):\n env_config = self._config_for_environment(Environment.DEV.name)\n url = environment\n else:\n env_config = self._config_for_environment(environment)\n url = env_config[domain]\n\n super().__init__(url, environment, api_version=api_version, application=application, http_adapter=http_adapter)\n self.auth_url = env_config['AuthURL']\n self.client_id = client_id\n self.client_secret = client_secret\n self.scopes = scopes\n self._orig_domain = domain\n\n if environment == Environment.DEV.name or (url != env_config['AppDomain'] and not domain == Domain.MDS_US_EAST):\n import urllib3\n\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n self.verify = False\n\n def _authenticate(self):\n auth_data = {\n 'grant_type': 'client_credentials',\n 'client_id': self.client_id,\n 'client_secret': self.client_secret,\n 'scope': ' '.join(self.scopes),\n }\n reply = self._session.post(self.auth_url, data=auth_data, verify=self.verify)\n if reply.status_code != 200:\n raise MqAuthenticationError(reply.status_code, reply.text, context=self.auth_url)\n\n response = json.loads(reply.text)\n self._session.headers.update({'Authorization': 'Bearer {}'.format(response['access_token'])})\n\n def _headers(self):\n return [('Authorization', self._session.headers['Authorization'])]\n\n\nclass PassThroughSession(GsSession):\n __config = None\n\n @classmethod\n def domain_and_verify(cls, environment_or_domain: str, domain: Optional[str]):\n if cls.__config is None:\n cls.__config = ConfigParser()\n cls.__config.read(os.path.join(os.path.dirname(inspect.getfile(cls)), 'config.ini'))\n\n verify = False\n try:\n domain = cls.__config[environment_or_domain][domain]\n verify = True\n except KeyError:\n domain = environment_or_domain\n return domain, verify\n\n def __init__(\n self,\n environment: str,\n token,\n api_version=API_VERSION,\n application=DEFAULT_APPLICATION,\n http_adapter=None,\n domain=None,\n ):\n domain = domain if domain is not None else 'AppDomain'\n domain, verify = self.domain_and_verify(environment, domain)\n\n super().__init__(\n domain,\n environment,\n api_version=api_version,\n application=application,\n verify=verify,\n http_adapter=http_adapter,\n )\n self._orig_domain = domain\n self.token = token\n\n def _authenticate(self):\n self._session.headers.update({'Authorization': 'Bearer {}'.format(self.token)})\n\n def _headers(self):\n return [('Authorization', self._session.headers['Authorization'])]\n\n\ntry:\n from gs_quant_auth.kerberos.session_kerberos import KerberosSessionMixin\n\n class KerberosSession(KerberosSessionMixin, GsSession):\n def __init__(\n self,\n environment_or_domain: str,\n api_version: str = API_VERSION,\n application: str = DEFAULT_APPLICATION,\n http_adapter: requests.adapters.HTTPAdapter = None,\n application_version: str = APP_VERSION,\n ):\n domain, verify = self.domain_and_verify(environment_or_domain)\n GsSession.__init__(\n self,\n domain,\n environment_or_domain,\n api_version=api_version,\n application=application,\n verify=verify,\n http_adapter=http_adapter,\n application_version=application_version,\n )\n\n class PassThroughGSSSOSession(KerberosSessionMixin, GsSession):\n def __init__(\n self,\n environment: str,\n token,\n api_version=API_VERSION,\n application=DEFAULT_APPLICATION,\n http_adapter=None,\n csrf_token=None,\n ):\n domain, verify = self.domain_and_verify(environment)\n GsSession.__init__(\n self,\n domain,\n environment,\n api_version=api_version,\n application=application,\n verify=verify,\n http_adapter=http_adapter,\n )\n\n self.token = token\n self.csrf_token = csrf_token\n\n def _authenticate(self):\n if not (self.token and self.csrf_token):\n self._handle_cookies(self.token)\n return\n\n cookie = requests.cookies.create_cookie(domain='.gs.com', name='GSSSO', value=self.token)\n self._session.cookies.set_cookie(cookie)\n if self.csrf_token:\n cookie = requests.cookies.create_cookie(\n domain='.gs.com', name='MARQUEE-CSRF-TOKEN', value=self.csrf_token\n )\n self._session.cookies.set_cookie(cookie)\n self._session.headers.update({'X-MARQUEE-CSRF-TOKEN': self.csrf_token})\n\nexcept ModuleNotFoundError:\n pass\n\ntry:\n from gs_quant_auth.kerberos.session_kerberos import MQLoginMixin\n\n class MQLoginSession(MQLoginMixin, GsSession):\n def __init__(\n self,\n environment_or_domain: str,\n domain: str = Domain.APP,\n api_version: str = API_VERSION,\n application: str = DEFAULT_APPLICATION,\n http_adapter: requests.adapters.HTTPAdapter = None,\n application_version: str = APP_VERSION,\n mq_login_token=None,\n jwt_token=None,\n ):\n selected_domain, verify = self.domain_and_verify(environment_or_domain)\n if domain == Domain.MDS_WEB:\n env_config = self._config_for_environment(environment_or_domain)\n selected_domain = env_config[domain]\n self.mq_login_token = mq_login_token\n self.jwt_token = jwt_token\n GsSession.__init__(\n self,\n selected_domain,\n environment_or_domain,\n api_version=api_version,\n application=application,\n verify=verify,\n http_adapter=http_adapter,\n application_version=application_version,\n )\n self._orig_domain = domain\n\n def _authenticate(self):\n if self.jwt_token:\n self._session.headers.update({'Authorization': 'Bearer {}'.format(self.jwt_token)})\n super()._authenticate()\n\nexcept ModuleNotFoundError:\n pass\n" }, { "path": "gs_quant/target/__init__.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\n__name__ = 'target'" }, { "path": "gs_quant/target/assets.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass AllocatorType(EnumBase, Enum): \n \n \"\"\"Allocator type defines the type of investor company managing an asset\"\"\"\n\n Advisor = 'Advisor'\n Consultant_Institutional = 'Consultant (Institutional)'\n Endowment = 'Endowment'\n Family_Office_Multi = 'Family Office (Multi)'\n Family_Office_Single = 'Family Office (Single)'\n Foundation = 'Foundation'\n Fund_of_Funds = 'Fund of Funds'\n Healthcare_System = 'Healthcare System'\n Insurance_Company = 'Insurance Company'\n Outsourced_CIO = 'Outsourced CIO'\n Pension_Private = 'Pension (Private)'\n Pension_Public = 'Pension (Public)'\n Platform = 'Platform'\n Private_Bank = 'Private Bank'\n Prop_Capital_OVER_Commercial_Bank = 'Prop Capital/Commercial Bank'\n Registered_Investment_Advisor = 'Registered Investment Advisor'\n Sovereign_Wealth_Fund = 'Sovereign Wealth Fund' \n\n\nclass Commodities(EnumBase, Enum): \n \n \"\"\"Commodity asset\"\"\"\n\n Aluminium = 'Aluminium'\n Aluminium_Alloy = 'Aluminium Alloy'\n Chicago_Ethanol = 'Chicago Ethanol'\n Coal = 'Coal'\n Coffee = 'Coffee'\n Copper = 'Copper'\n Corn = 'Corn'\n Cotton = 'Cotton'\n Crude_Palm_Oil = 'Crude Palm Oil'\n Diesel_Fuel = 'Diesel Fuel'\n Electricity = 'Electricity'\n Emissions = 'Emissions'\n Ethylene = 'Ethylene'\n Freight = 'Freight'\n Fuel_Oil = 'Fuel Oil'\n Gas_Oil = 'Gas Oil'\n Gasoline = 'Gasoline'\n Gold = 'Gold'\n Heating_Oil = 'Heating Oil'\n Iron_Ore = 'Iron Ore'\n Jet_Fuel = 'Jet Fuel'\n Lead = 'Lead'\n Lean_Hogs = 'Lean Hogs'\n NGL = 'NGL'\n Naphtha = 'Naphtha'\n Natural_Gas = 'Natural Gas'\n Nickel = 'Nickel'\n Oil = 'Oil'\n Palladium = 'Palladium'\n Platinum = 'Platinum'\n Polypropylene = 'Polypropylene'\n Primary_Aluminium = 'Primary Aluminium'\n Silver = 'Silver'\n Soybean_Meal = 'Soybean Meal'\n Soybean_Oil = 'Soybean Oil'\n Soybeans = 'Soybeans'\n Sugar = 'Sugar'\n Tin = 'Tin'\n Ultra_Low_Sulphur_Diesel = 'Ultra Low Sulphur Diesel'\n Wheat = 'Wheat'\n White_Sugar = 'White Sugar'\n Zinc = 'Zinc' \n\n\nclass CommodityFamily(EnumBase, Enum): \n \n \"\"\"Commodity Family\"\"\"\n\n Base_Metal = 'Base Metal'\n Gas = 'Gas'\n Oil = 'Oil'\n Oil_Products = 'Oil Products' \n\n\nclass CommoditySubFamily(EnumBase, Enum): \n \n \"\"\"Commodity SubFamily\"\"\"\n\n Crude = 'Crude'\n Fuel = 'Fuel'\n Heat = 'Heat'\n NG = 'NG' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetMetadata(Base):\n version_timestamp: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Benchmark(Base):\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n value: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodityEUNaturalGasHub(Base):\n region: Optional[str] = field(default=None, metadata=field_metadata)\n hub_type: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodityNaturalGasHub(Base):\n region: Optional[str] = field(default=None, metadata=field_metadata)\n pipelines: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n platts_codes: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodityPowerAggregatedNodes(Base):\n ISO: Optional[str] = field(default=None, metadata=field_metadata)\n aggregate_type: Optional[str] = field(default=None, metadata=field_metadata)\n location_name: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodityPowerNode(Base):\n ISO: Optional[str] = field(default=None, metadata=field_metadata)\n location_name: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodityReferencePriceParameters(Base):\n commodity_base: Optional[str] = field(default=None, metadata=field_metadata)\n commodity_details: Optional[str] = field(default=None, metadata=field_metadata)\n currency: Optional[str] = field(default=None, metadata=field_metadata)\n unit: Optional[str] = field(default=None, metadata=field_metadata)\n exchange_id: Optional[str] = field(default=None, metadata=field_metadata)\n publication: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FutureContract(Base):\n future_market_marquee_id: Optional[str] = field(default=None, metadata=field_metadata)\n contract: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FutureMarket(Base):\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n period_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n product_group: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass NumberRange(Base):\n lower_bound: Optional[float] = field(default=None, metadata=field_metadata)\n upper_bound: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass People(Base):\n portfolio_managers: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WeatherIndexParameters(Base):\n data_provider: Optional[str] = field(default=None, metadata=field_metadata)\n weather_station: Optional[str] = field(default=None, metadata=field_metadata)\n reference_level_amount: Optional[float] = field(default=None, metadata=field_metadata)\n reference_level_unit: Optional[str] = field(default=None, metadata=field_metadata)\n weather_station_fallback: Optional[str] = field(default=None, metadata=field_metadata)\n weather_station_second_fallback: Optional[str] = field(default=None, metadata=field_metadata)\n alternative_data_provider: Optional[str] = field(default=None, metadata=field_metadata)\n synoptic_data_fallback: Optional[str] = field(default=None, metadata=field_metadata)\n adjustment_to_fallback_weather_station: Optional[str] = field(default=None, metadata=field_metadata)\n primary_disruption_fallbacks: Optional[str] = field(default=None, metadata=field_metadata)\n secondary_disruption_fallbacks: Optional[str] = field(default=None, metadata=field_metadata)\n final_edited_data: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetStats(Base):\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n period: Optional[AssetStatsPeriod] = field(default=None, metadata=field_metadata)\n type_: Optional[AssetStatsType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodConfigParameters(Base):\n infra: str = field(default=None, metadata=field_metadata)\n field_history: Tuple[DictBase, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CreditBasketParameters(Base):\n index_calculation_type: Optional[IndexCalculationType] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n initial_pricing_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n index_notes: Optional[str] = field(default=None, metadata=field_metadata)\n on_behalf_of: Optional[str] = field(default=None, metadata=field_metadata)\n valuation_source: Optional[BasketValuationSource] = field(default=None, metadata=field_metadata)\n quote_time: Optional[str] = field(default=None, metadata=field_metadata)\n official_side: Optional[Side] = field(default=None, metadata=field_metadata)\n quoting_type: Optional[QuoteType] = field(default=None, metadata=field_metadata)\n weighting_type: Optional[WeightingType] = field(default=None, metadata=field_metadata)\n close_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n clone_parent_id: Optional[str] = field(default=None, metadata=field_metadata)\n hedge_id: Optional[str] = field(default=None, metadata=field_metadata)\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n index_approval_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n credit_basket_type: Optional[CreditBasketType] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HedgeFundParameters(Base):\n aum: Optional[float] = field(default=None, metadata=field_metadata)\n strategy_aum: Optional[float] = field(default=None, metadata=field_metadata)\n aum_range: Optional[NumberRange] = field(default=None, metadata=field_metadata)\n strategy_aum_range: Optional[NumberRange] = field(default=None, metadata=field_metadata)\n disclaimers: Optional[str] = field(default=None, metadata=field_metadata)\n market_cap_category: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n marketing_status: Optional[str] = field(default=None, metadata=field_metadata)\n preferences: Optional[DictBase] = field(default=None, metadata=field_metadata)\n regional_focus: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n risk_taking_model: Optional[str] = field(default=None, metadata=field_metadata)\n strategy: Optional[Strategy] = field(default=None, metadata=field_metadata)\n strategy_description: Optional[str] = field(default=None, metadata=field_metadata)\n targeted_gross_exposure: Optional[NumberRange] = field(default=None, metadata=field_metadata)\n targeted_net_exposure: Optional[NumberRange] = field(default=None, metadata=field_metadata)\n targeted_num_of_positions_short: Optional[NumberRange] = field(default=None, metadata=field_metadata)\n targeted_num_of_positions_long: Optional[NumberRange] = field(default=None, metadata=field_metadata)\n turnover: Optional[str] = field(default=None, metadata=field_metadata)\n vehicle_type: Optional[str] = field(default=None, metadata=field_metadata)\n last_returns_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n open_to_sma: Optional[bool] = field(default=None, metadata=config(field_name='openToSMA', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecuritiesLendingLoan(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n fund_id: str = field(default=None, metadata=field_metadata)\n lender_id: str = field(default=None, metadata=field_metadata)\n borrower_id: str = field(default=None, metadata=field_metadata)\n loan_status: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_status: Optional[str] = field(default=None, metadata=field_metadata)\n collateral_type: Optional[str] = field(default=None, metadata=field_metadata)\n loan_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n adjustment_ind: Optional[bool] = field(default=None, metadata=field_metadata)\n country_of_issue: Optional[str] = field(default=None, metadata=field_metadata)\n input_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n effective_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n security_settle_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n cash_settle_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n term_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n return_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ShareClassParameters(Base):\n active_liquidity_fee: Optional[float] = field(default=None, metadata=field_metadata)\n additional_provisions: Optional[str] = field(default=None, metadata=field_metadata)\n benchmark: Optional[Benchmark] = field(default=None, metadata=field_metadata)\n class_fees: Optional[float] = field(default=None, metadata=field_metadata)\n class_type: Optional[str] = field(default=None, metadata=field_metadata)\n early_redemption_fee: Optional[float] = field(default=None, metadata=field_metadata)\n expense_ratio_gross: Optional[float] = field(default=None, metadata=field_metadata)\n expense_ratio_net: Optional[float] = field(default=None, metadata=field_metadata)\n share_class_type: Optional[str] = field(default=None, metadata=field_metadata)\n gate: Optional[float] = field(default=None, metadata=field_metadata)\n gate_type: Optional[str] = field(default=None, metadata=field_metadata)\n hurdle: Optional[float] = field(default=None, metadata=field_metadata)\n hurdle_type: Optional[str] = field(default=None, metadata=field_metadata)\n investment_manager: Optional[str] = field(default=None, metadata=field_metadata)\n investment_type: Optional[str] = field(default=None, metadata=field_metadata)\n institutional_share_class: Optional[bool] = field(default=None, metadata=field_metadata)\n lockup: Optional[float] = field(default=None, metadata=field_metadata)\n lockup_type: Optional[str] = field(default=None, metadata=field_metadata)\n management_fee: Optional[float] = field(default=None, metadata=field_metadata)\n minimum_subscription: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n number_of_shares: Optional[float] = field(default=None, metadata=field_metadata)\n performance_fee: Optional[float] = field(default=None, metadata=field_metadata)\n redemption_notice_period: Optional[float] = field(default=None, metadata=field_metadata)\n redemption_period: Optional[str] = field(default=None, metadata=field_metadata)\n share_class_currency: Optional[str] = field(default=None, metadata=field_metadata)\n side_pocket: Optional[str] = field(default=None, metadata=field_metadata)\n status: Optional[str] = field(default=None, metadata=field_metadata)\n sub_category: Optional[str] = field(default=None, metadata=field_metadata)\n term_type: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TemporalPeople(Base):\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n people: Optional[People] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TemporalXRef(Base):\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n identifiers: Optional[XRef] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetGetRequestPathSchema(Base):\n limit: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n offset: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n scroll: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n scroll_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n as_of_time: Optional[Tuple[datetime.datetime, ...]] = field(default=None, metadata=field_metadata)\n field_: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\n fields: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n order_by: Optional[Tuple[Union[DictBase, str], ...]] = field(default=None, metadata=field_metadata)\n next_rebalance_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n asset_classifications_digital_asset_class: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n sectors: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ticker: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n valoren: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_payer_currency: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n styles: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\n short_name: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n currency: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n rating_moodys: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n identifier: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n rcic: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name_raw: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_receiver_currency: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n primary_country_ric: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n listed: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n delisted: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_floating_rate_option: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n pair_calculation: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n mic: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n rating_fitch: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n cusip: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n is_legacy_pair_basket: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_index: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_classifications_digital_asset_sector: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n display_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n isin: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_strike_type: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_cap_floor: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_classifications_underliers_asset_class: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n last_updated_since: Optional[Tuple[datetime.datetime, ...]] = field(default=None, metadata=field_metadata)\n option_type: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n exchange: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n coin_metrics_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ric: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n trading_restriction: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n bbid: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n underlying_asset_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n bbid_equivalent: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_index1_tenor: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n owner_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_classifications_is_primary: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n tsdb_shortname: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n live_date: Optional[Tuple[Union[Op, datetime.date], ...]] = field(default=None, metadata=field_metadata)\n prime_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n description: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_classifications_is_country_primary: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n sedol: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n default_backcast: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n wpk: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n id_: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n bcid: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n last_rebalance_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n rating_second_highest: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n region: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_payer_spread: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n rating_linear: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n type_: Optional[Tuple[Union[AssetType, RiskModelType], ...]] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n asset_parameters_index2_tenor: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n rating_standard_and_poors: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_classifications_digital_asset_subsector: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_fee_currency: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_parameters_receiver_spread: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Asset(Base):\n asset_class: AssetClass = field(default=None, metadata=field_metadata)\n type_: AssetType = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n active: Optional[bool] = field(default=None, metadata=field_metadata)\n classifications: Optional[AssetClassifications] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n default_quantity: Optional[float] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n economic_terms_hash: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n identifiers: Optional[Tuple[Identifier, ...]] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n listed: Optional[bool] = field(default=None, metadata=field_metadata)\n live_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n key: Optional[str] = field(default=None, metadata=field_metadata)\n key_map: Optional[DictBase] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n parameters: Optional[DictBase] = field(default=None, metadata=field_metadata)\n asset_stats: Optional[Tuple[AssetStats, ...]] = field(default=None, metadata=field_metadata)\n people: Optional[People] = field(default=None, metadata=field_metadata)\n people_history: Optional[Tuple[TemporalPeople, ...]] = field(default=None, metadata=field_metadata)\n rank: Optional[float] = field(default=None, metadata=field_metadata)\n region: Optional[Region] = field(default=None, metadata=field_metadata)\n report_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n sectors: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n short_name: Optional[str] = field(default=None, metadata=field_metadata)\n styles: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n underliers: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n underlying_asset_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n xrefs: Optional[Tuple[TemporalXRef, ...]] = field(default=None, metadata=field_metadata)\n xref: Optional[XRef] = field(default=None, metadata=field_metadata)\n metadata: Optional[AssetMetadata] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetToInstrumentResponse(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n instrument: InstrumentBase = field(default=None, metadata=field_metadata)\n size_field: Optional[str] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/assets_screener.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetScreenerCreditResponseItem(Base):\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n cusip: Optional[str] = field(default=None, metadata=field_metadata)\n isin: Optional[str] = field(default=None, metadata=field_metadata)\n bbid: Optional[str] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n region: Optional[Region] = field(default=None, metadata=field_metadata)\n industry: Optional[str] = field(default=None, metadata=field_metadata)\n seniority: Optional[str] = field(default=None, metadata=field_metadata)\n ticker: Optional[str] = field(default=None, metadata=field_metadata)\n rating_standard_and_poors: Optional[str] = field(default=None, metadata=field_metadata)\n gs_liquidity_score: Optional[float] = field(default=None, metadata=field_metadata)\n amount_outstanding: Optional[float] = field(default=None, metadata=field_metadata)\n maturity: Optional[float] = field(default=None, metadata=field_metadata)\n bval_mid_price: Optional[float] = field(default=None, metadata=field_metadata)\n yield_to_convention: Optional[float] = field(default=None, metadata=field_metadata)\n modified_duration: Optional[float] = field(default=None, metadata=field_metadata)\n spread_to_benchmark: Optional[float] = field(default=None, metadata=field_metadata)\n g_spread: Optional[float] = field(default=None, metadata=field_metadata)\n z_spread: Optional[float] = field(default=None, metadata=field_metadata)\n charge_in_dollars: Optional[str] = field(default=None, metadata=field_metadata)\n charge_in_bps: Optional[str] = field(default=None, metadata=field_metadata)\n gs_indicative_benchmark_isin: Optional[str] = field(default=None, metadata=field_metadata)\n gs_indicative_buy_price: Optional[float] = field(default=None, metadata=field_metadata)\n gs_indicative_buy_quantity: Optional[float] = field(default=None, metadata=field_metadata)\n gs_indicative_buy_spread: Optional[float] = field(default=None, metadata=field_metadata)\n gs_indicative_buy_yield: Optional[float] = field(default=None, metadata=field_metadata)\n gs_indicative_pricing_convention: Optional[str] = field(default=None, metadata=field_metadata)\n gs_indicative_sell_price: Optional[float] = field(default=None, metadata=field_metadata)\n gs_indicative_sell_quantity: Optional[float] = field(default=None, metadata=field_metadata)\n gs_indicative_sell_spread: Optional[float] = field(default=None, metadata=field_metadata)\n gs_indicative_sell_yield: Optional[float] = field(default=None, metadata=field_metadata)\n science_based_target: Optional[str] = field(default=None, metadata=field_metadata)\n net_zero_emissions_target: Optional[str] = field(default=None, metadata=field_metadata)\n emissions_intensity_enterprise_value: Optional[float] = field(default=None, metadata=field_metadata)\n emissions_intensity_revenue: Optional[float] = field(default=None, metadata=field_metadata)\n g_percentile: Optional[float] = field(default=None, metadata=field_metadata)\n g_regional_percentile: Optional[float] = field(default=None, metadata=field_metadata)\n es_percentile: Optional[float] = field(default=None, metadata=field_metadata)\n es_disclosure_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n es_momentum_percentile: Optional[float] = field(default=None, metadata=field_metadata)\n direction: Optional[str] = field(default=None, metadata=field_metadata)\n face_value: Optional[float] = field(default=None, metadata=field_metadata)\n indicative_short_financing_label: Optional[str] = field(default=None, metadata=field_metadata)\n indicative_long_financing_label: Optional[str] = field(default=None, metadata=field_metadata)\n country_of_risk: Optional[str] = field(default=None, metadata=field_metadata)\n payment_rank: Optional[str] = field(default=None, metadata=field_metadata)\n industry_sector: Optional[str] = field(default=None, metadata=field_metadata)\n industry_group: Optional[str] = field(default=None, metadata=field_metadata)\n industry_sub_group: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetScreenerCreditResponse(Base):\n total_results: int = field(default=None, metadata=field_metadata)\n results: Optional[Tuple[AssetScreenerCreditResponseItem, ...]] = field(default=None, metadata=field_metadata)\n scroll_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetScreenerRequest(Base):\n asset_class: Optional[AssetClass] = field(default=None, metadata=field_metadata)\n type_: Optional[AssetType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n scroll: Optional[str] = field(default=None, metadata=field_metadata)\n scroll_id: Optional[str] = field(default=None, metadata=field_metadata)\n limit: Optional[int] = field(default=None, metadata=field_metadata)\n offset: Optional[int] = field(default=None, metadata=field_metadata)\n filters: Optional[AssetScreenerCreditRequestFilters] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/backtests.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass BacktestRiskMeasureType(EnumBase, Enum): \n \n \"\"\"The type of measure to perform risk on. e.g. Greeks\"\"\"\n\n Price = 'Price'\n Price_Percent = 'Price Percent'\n Delta = 'Delta'\n Gamma = 'Gamma'\n Vega = 'Vega'\n Forward = 'Forward'\n Implied_Volatility = 'Implied Volatility'\n Fair_Variance = 'Fair Variance'\n Strike_Level = 'Strike Level'\n Spot = 'Spot'\n Price_ATMS = 'Price ATMS'\n Price_ATMF_Volatility = 'Price ATMF Volatility'\n Barrier_Level = 'Barrier Level'\n Strike_Level_Adj_Implied = 'Strike Level Adj Implied'\n Strike_Level_Adj_Realised = 'Strike Level Adj Realised' \n\n\nclass BacktestTradingQuantityType(EnumBase, Enum): \n \n \"\"\"The trading quantity unit of a backtest strategy\"\"\"\n\n notional = 'notional'\n quantity = 'quantity'\n vega = 'vega'\n gamma = 'gamma'\n NAV = 'NAV'\n premium = 'premium'\n vegaNotional = 'vegaNotional' \n\n\nclass BacktestType(EnumBase, Enum): \n \n \"\"\"Backtest type differentiates the backtest type.\"\"\"\n\n Basket = 'Basket'\n Volatility = 'Volatility'\n Volatility_Flow = 'Volatility Flow'\n Enhanced_Beta = 'Enhanced Beta'\n ISelect = 'ISelect' \n\n\nclass EquityMarketModel(EnumBase, Enum): \n \n \"\"\"Market model for pricing\"\"\"\n\n SFK = 'SFK'\n SD = 'SD'\n SVR = 'SVR' \n\n\nclass FlowVolBacktestMeasure(EnumBase, Enum): \n \n \"\"\"Metric which can be calculated using Flow Vol. Backtester\"\"\"\n\n ALL_MEASURES = 'ALL MEASURES'\n PNL_spot = 'PNL_spot'\n PNL_vol = 'PNL_vol'\n PNL_carry = 'PNL_carry'\n PNL_delta = 'PNL_delta'\n PNL_gamma = 'PNL_gamma'\n PNL_higher_order_spot = 'PNL_higher_order_spot'\n PNL_higher_order_vol = 'PNL_higher_order_vol'\n PNL_theta = 'PNL_theta'\n PNL_transactions = 'PNL_transactions'\n PNL_unexplained = 'PNL_unexplained'\n PNL_vega = 'PNL_vega'\n PNL = 'PNL'\n delta = 'delta'\n gamma = 'gamma'\n vega = 'vega'\n NAV = 'NAV' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestComparison(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n correlation: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestRebalanceParameters(Base):\n frequency_period: Optional[str] = field(default=None, metadata=field_metadata)\n frequency: Optional[int] = field(default=None, metadata=field_metadata)\n day_of_week: Optional[str] = field(default=None, metadata=field_metadata)\n day_of_month: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestSignalSeriesItem(Base):\n date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n value: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nBacktestStrategySignal = Dict[str, float]\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestStrategyTransactionCostModelFixed(Base):\n cost: Optional[float] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(init=False, default='FixedCostModel', metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestStrategyTransactionCostModelScaled(Base):\n scaling_level: Optional[float] = field(default=None, metadata=field_metadata)\n scaling_quantity_type: Optional[str] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(init=False, default='ScaledCostModel', metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BaseIndexRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BuySellRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DeltaHedgeParameters(Base):\n frequency: str = field(default=None, metadata=field_metadata)\n fixing_time: Optional[str] = field(default=None, metadata=field_metadata)\n notional: Optional[float] = field(default=None, metadata=field_metadata)\n delta_type: Optional[str] = field(init=False, default='BlackScholes', metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DeltaHedgingParameters(Base):\n enabled: bool = field(default=None, metadata=field_metadata)\n frequency: str = field(default=None, metadata=field_metadata)\n fixing_time: str = field(default=None, metadata=field_metadata)\n notional_percentage: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EnhancedBetaUnderlier(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n month_add: Optional[float] = field(default=None, metadata=field_metadata)\n valid_months: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n is_included: Optional[bool] = field(default=None, metadata=field_metadata)\n weight_scale: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EnhancedBetaUnderlierRefData(Base):\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n valid_months: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n current: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EntityCorrelation(Base):\n primary_id: Optional[str] = field(default=None, metadata=field_metadata)\n secondary_id: Optional[str] = field(default=None, metadata=field_metadata)\n correlation: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ExpirationRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FixingTimeRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FrequencyRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HistoricalUnderlier(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n weight: Optional[float] = field(default=None, metadata=field_metadata)\n date: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LookBackPeriodRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass NotionalPercentageRefData(Base):\n default: Optional[float] = field(default=None, metadata=field_metadata)\n min_: Optional[float] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\n max_: Optional[float] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptionStrikeTypeRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptionTypeRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScalingMethodRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass StrikeRefData(Base):\n default: Optional[float] = field(default=None, metadata=field_metadata)\n min_: Optional[float] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\n max_: Optional[float] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TradeInMethodRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TradeInTimeRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass VolatilityWeightedWeightingModifier(Base):\n em_aalpha: Optional[float] = field(default=None, metadata=config(field_name='EMAalpha', exclude=exclude_none))\n look_back_period: Optional[str] = field(default=None, metadata=field_metadata)\n use_log_return: Optional[bool] = field(default=False, metadata=field_metadata)\n name: Optional[str] = field(init=False, default='Volatility Weighted', metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass VolatilityWeightedWeightingModifierRefData(Base):\n em_aalpha: Optional[DictBase] = field(default=None, metadata=config(field_name='EMAalpha', exclude=exclude_none))\n look_back_period: Optional[DictBase] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestStrategyUnderlierHedge(Base):\n risk_details: Optional[DeltaHedgeParameters] = field(default=None, metadata=field_metadata)\n quantity_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BasketBacktestParameters(Base):\n underliers: Tuple[Union[float, str], ...] = field(default=None, metadata=field_metadata)\n rebalance_parameters: Optional[BacktestRebalanceParameters] = field(default=None, metadata=field_metadata)\n weighting_modifiers: Optional[Tuple[VolatilityWeightedWeightingModifier, ...]] = field(default=None, metadata=field_metadata)\n weighting_strategy: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CurrencyRefData(Base):\n default: Optional[Currency] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[Currency, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DeltaHedgingRefData(Base):\n fixing_time: Optional[FixingTimeRefData] = field(default=None, metadata=field_metadata)\n frequency: Optional[FrequencyRefData] = field(default=None, metadata=field_metadata)\n notional_percentage: Optional[NotionalPercentageRefData] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EnhancedBetaBacktestParameters(Base):\n underliers: Tuple[EnhancedBetaUnderlier, ...] = field(default=None, metadata=field_metadata)\n roll_start: float = field(default=None, metadata=field_metadata)\n roll_end: float = field(default=None, metadata=field_metadata)\n base_index: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectBacktestParameters(Base):\n max_leverage: float = field(default=None, metadata=field_metadata)\n underliers: Tuple[HistoricalUnderlier, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptionBacktestUnderlier(Base):\n buy_sell: str = field(default=None, metadata=field_metadata)\n expiration: str = field(default=None, metadata=field_metadata)\n option_type: str = field(default=None, metadata=field_metadata)\n option_strike_type: str = field(default=None, metadata=field_metadata)\n strike: float = field(default=None, metadata=field_metadata)\n underlying_asset_id: str = field(default=None, metadata=field_metadata)\n notional_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n delta_hedging: Optional[DeltaHedgingParameters] = field(default=None, metadata=field_metadata)\n trade_in_time: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PerformanceRange(Base):\n horizon: Optional[str] = field(default=None, metadata=field_metadata)\n stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass UnderlyingAssetIdDataRefData(Base):\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_time: Optional[FixingTimeRefData] = field(default=None, metadata=field_metadata)\n frequency: Optional[FrequencyRefData] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestPerformanceDecomposition(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n performance: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestRisk(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n timeseries: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestRiskPosition(Base):\n instrument: DictBase = field(default=None, metadata=field_metadata)\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\n market_model: Optional[EquityMarketModel] = field(default=None, metadata=field_metadata)\n expiry_date_mode: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestStrategyTransactionCostModelAggregate(Base):\n models: Optional[Tuple[Union[BacktestStrategyTransactionCostModelFixed, BacktestStrategyTransactionCostModelScaled], ...]] = field(default=None, metadata=field_metadata)\n aggregation_type: Optional[str] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(init=False, default='AggregateCostModel', metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestStrategyTransactionCost(Base):\n entry: Optional[Union[BacktestStrategyTransactionCostModelAggregate, BacktestStrategyTransactionCostModelFixed, BacktestStrategyTransactionCostModelScaled]] = field(default=None, metadata=field_metadata)\n exit_: Optional[Union[BacktestStrategyTransactionCostModelAggregate, BacktestStrategyTransactionCostModelFixed, BacktestStrategyTransactionCostModelScaled]] = field(default=None, metadata=config(field_name='exit', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestTradingParameters(Base):\n quantity_type: Optional[BacktestTradingQuantityType] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[BacktestStrategySignal, float]] = field(default=None, metadata=field_metadata)\n trade_in_method: Optional[str] = field(default=None, metadata=field_metadata)\n roll_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n roll_date_mode: Optional[str] = field(default=None, metadata=field_metadata)\n scaling_method: Optional[str] = field(default=None, metadata=field_metadata)\n trade_in_signals: Optional[Tuple[BacktestSignalSeriesItem, ...]] = field(default=None, metadata=field_metadata)\n trade_out_signals: Optional[Tuple[BacktestSignalSeriesItem, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BasketBacktestRefData(Base):\n currency: Optional[CurrencyRefData] = field(default=None, metadata=field_metadata)\n look_back_period: Optional[LookBackPeriodRefData] = field(default=None, metadata=field_metadata)\n weighting_strategy: Optional[DictBase] = field(default=None, metadata=field_metadata)\n weighting_modifiers: Optional[DictBase] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ComparisonBacktestResult(Base):\n stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)\n performance: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EnhancedBetaRefData(Base):\n look_back_period: Optional[LookBackPeriodRefData] = field(default=None, metadata=field_metadata)\n currency: Optional[CurrencyRefData] = field(default=None, metadata=field_metadata)\n base_index: Optional[BaseIndexRefData] = field(default=None, metadata=field_metadata)\n MASJ8W49Y02X9CGS: Optional[DictBase] = field(default=None, metadata=field_metadata)\n MAAHST8JED9B607H: Optional[DictBase] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass UnderlyingAssetIdRefData(Base):\n default: Optional[str] = field(default=None, metadata=field_metadata)\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n data: Optional[Tuple[UnderlyingAssetIdDataRefData, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass VolatilityBacktestParameters(Base):\n underliers: Tuple[OptionBacktestUnderlier, ...] = field(default=None, metadata=field_metadata)\n trade_in_method: Optional[str] = field(default=None, metadata=field_metadata)\n scaling_method: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestResult(Base):\n backtest_id: Optional[str] = field(default=None, metadata=field_metadata)\n performance: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n portfolio: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)\n performance_decompositions: Optional[Tuple[BacktestPerformanceDecomposition, ...]] = field(default=None, metadata=field_metadata)\n risks: Optional[Tuple[BacktestRisk, ...]] = field(default=None, metadata=field_metadata)\n events: Optional[Tuple[BacktestRisk, ...]] = field(default=None, metadata=field_metadata)\n history: Optional[Tuple[PerformanceRange, ...]] = field(default=None, metadata=field_metadata)\n underlier_correlation: Optional[Tuple[EntityCorrelation, ...]] = field(default=None, metadata=field_metadata)\n comparisons: Optional[Tuple[BacktestComparison, ...]] = field(default=None, metadata=field_metadata)\n backtest_version: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestRiskRequest(Base):\n positions: Tuple[BacktestRiskPosition, ...] = field(default=None, metadata=field_metadata)\n measures: Tuple[BacktestRiskMeasureType, ...] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n dates: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestStrategyUnderlierTransactionCost(Base):\n trade: Optional[BacktestStrategyTransactionCost] = field(default=None, metadata=field_metadata)\n hedge: Optional[BacktestStrategyTransactionCost] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass VolBacktestRefData(Base):\n buy_sell: Optional[BuySellRefData] = field(default=None, metadata=field_metadata)\n currency: Optional[CurrencyRefData] = field(default=None, metadata=field_metadata)\n delta_hedging: Optional[DeltaHedgingRefData] = field(default=None, metadata=field_metadata)\n delta_strike: Optional[StrikeRefData] = field(default=None, metadata=field_metadata)\n notional_percentage: Optional[NotionalPercentageRefData] = field(default=None, metadata=field_metadata)\n expiration: Optional[ExpirationRefData] = field(default=None, metadata=field_metadata)\n look_back_period: Optional[LookBackPeriodRefData] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionTypeRefData] = field(default=None, metadata=field_metadata)\n option_strike_type: Optional[OptionStrikeTypeRefData] = field(default=None, metadata=field_metadata)\n relative_strike: Optional[StrikeRefData] = field(default=None, metadata=field_metadata)\n strike: Optional[StrikeRefData] = field(default=None, metadata=field_metadata)\n scaling_method: Optional[ScalingMethodRefData] = field(default=None, metadata=field_metadata)\n underlying_asset_id: Optional[UnderlyingAssetIdRefData] = field(default=None, metadata=field_metadata)\n trade_in_method: Optional[TradeInMethodRefData] = field(default=None, metadata=field_metadata)\n trade_in_time: Optional[TradeInTimeRefData] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestRefData(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n volatility: Optional[DictBase] = field(default=None, metadata=field_metadata)\n enhanced_beta: Optional[EnhancedBetaRefData] = field(default=None, metadata=config(field_name='enhanced_beta', exclude=exclude_none))\n basket: Optional[BasketBacktestRefData] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BacktestStrategyUnderlier(Base):\n instrument: DictBase = field(default=None, metadata=field_metadata)\n market_model: EquityMarketModel = field(default=None, metadata=field_metadata)\n notional_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n expiry_date_mode: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n hedge: Optional[BacktestStrategyUnderlierHedge] = field(default=None, metadata=field_metadata)\n transaction_cost: Optional[BacktestStrategyUnderlierTransactionCost] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass VolatilityFlowBacktestParameters(Base):\n trading_parameters: BacktestTradingParameters = field(default=None, metadata=field_metadata)\n index_initial_value: float = field(default=None, metadata=field_metadata)\n underliers: Optional[Tuple[BacktestStrategyUnderlier, ...]] = field(default=None, metadata=field_metadata)\n measures: Optional[Tuple[FlowVolBacktestMeasure, ...]] = field(default=('ALL MEASURES',), metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Backtest(Base):\n name: str = field(default=None, metadata=field_metadata)\n type_: BacktestType = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n asset_class: AssetClass = field(default=None, metadata=field_metadata)\n cost_netting: Optional[bool] = field(default=False, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n mq_symbol: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n report_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n parameters: Optional[Union[BasketBacktestParameters, EnhancedBetaBacktestParameters, ISelectBacktestParameters, VolatilityBacktestParameters, VolatilityFlowBacktestParameters]] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n version: Optional[float] = field(default=None, metadata=field_metadata)\n cash_accrual: Optional[bool] = field(default=True, metadata=field_metadata)\n" }, { "path": "gs_quant/target/base_screener.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScreenerColumn(Base):\n column_name: str = field(default=None, metadata=field_metadata)\n entity_parameter: Optional[str] = field(default=None, metadata=field_metadata)\n expression: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScreenerRow(Base):\n entity_id: str = field(default=None, metadata=field_metadata)\n entity_type: str = field(default=None, metadata=field_metadata)\n universe: Optional[bool] = field(default=None, metadata=field_metadata)\n expand: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScreenerData(Base):\n rows: Optional[Tuple[DataRow, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScreenerParameters(Base):\n rows: Tuple[ScreenerRow, ...] = field(default=None, metadata=field_metadata)\n columns: Tuple[ScreenerColumn, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Screener(Base):\n name: str = field(default=None, metadata=field_metadata)\n parameters: ScreenerParameters = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n cron_schedule: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/charts.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass ChartAnnotationFontStyle(EnumBase, Enum): \n \n \"\"\"Style of the font.\"\"\"\n\n italic = 'italic'\n normal = 'normal' \n\n\nclass ChartAnnotationFontWeight(EnumBase, Enum): \n \n \"\"\"Weight of the font.\"\"\"\n\n bold = 'bold'\n normal = 'normal' \n\n\nclass ChartAnnotationLineType(EnumBase, Enum): \n \n \"\"\"Type of the line.\"\"\"\n\n dashed = 'dashed'\n solid = 'solid' \n\n\nclass ChartAnnotationRangeLabelType(EnumBase, Enum): \n \n \"\"\"Type of the range label.\"\"\"\n\n withPct = 'withPct'\n withoutPct = 'withoutPct' \n\n\nclass ChartAnnotationTextAlign(EnumBase, Enum): \n \n \"\"\"Alignment of the text.\"\"\"\n\n center = 'center'\n left = 'left'\n right = 'right' \n\n\nclass ChartAnnotationTextDecoration(EnumBase, Enum): \n \n \"\"\"Decoration of the text.\"\"\"\n\n none = 'none'\n underline = 'underline'\n strike_through = 'strike-through' \n\n\nclass ChartAnnotationType(EnumBase, Enum): \n \n \"\"\"Type of the annotation.\"\"\"\n\n arrow = 'arrow'\n circle = 'circle'\n line = 'line'\n oval = 'oval'\n range = 'range'\n rect = 'rect'\n text = 'text' \n\n\nclass ChartFill(EnumBase, Enum): \n \n \"\"\"Chart Fill Type\"\"\"\n\n _None = 'None'\n Solid = 'Solid'\n Gradient = 'Gradient' \n\n\nclass ChartLineDrawType(EnumBase, Enum): \n \n \"\"\"Line Draw Type\"\"\"\n\n Area = 'Area'\n Bars = 'Bars'\n Candlesticks = 'Candlesticks'\n Lines = 'Lines'\n _None = 'None'\n StepAfter = 'StepAfter'\n StepBefore = 'StepBefore'\n StepLinear = 'StepLinear'\n Volumes = 'Volumes' \n\n\nclass ChartLineType(EnumBase, Enum): \n \n \"\"\"Line Type\"\"\"\n\n Bubble = 'Bubble'\n Solid = 'Solid'\n Knotted = 'Knotted'\n Dashed = 'Dashed'\n OHLC = 'OHLC' \n\n\nclass ChartRegressionStrokeType(EnumBase, Enum): \n \n \"\"\"Chart Regression Stroke Type\"\"\"\n\n Line = 'Line'\n Dash = 'Dash' \n\n\nclass ChartRegressionType(EnumBase, Enum): \n \n \"\"\"Chart Regression Type\"\"\"\n\n Linear = 'Linear'\n Exponential = 'Exponential'\n PlotData = 'PlotData' \n\n\nclass ChartType(EnumBase, Enum): \n \n \"\"\"Chart Type\"\"\"\n\n bar = 'bar'\n line = 'line'\n scatter = 'scatter' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartDisplaySettings(Base):\n scatter_fill: Optional[bool] = field(default=None, metadata=field_metadata)\n bar_chart_type: Optional[str] = field(default=None, metadata=field_metadata)\n bar_padding: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartLabelSettings(Base):\n hide_last_value_labels: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartProperties(Base):\n x: Optional[str] = field(default=None, metadata=field_metadata)\n x_label: Optional[str] = field(default=None, metadata=field_metadata)\n y: Optional[str] = field(default=None, metadata=field_metadata)\n y_label: Optional[str] = field(default=None, metadata=field_metadata)\n color: Optional[str] = field(default=None, metadata=field_metadata)\n shape: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartShare(Base):\n guids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n version: Optional[int] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartTime(Base):\n start: Optional[str] = field(default=None, metadata=field_metadata)\n end: Optional[str] = field(default=None, metadata=field_metadata)\n timezone: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ParameterField(Base):\n field_: str = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\n values: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass XAxisSettings(Base):\n auto_fit_range_to_data: Optional[bool] = field(default=None, metadata=field_metadata)\n label: Optional[str] = field(default=None, metadata=field_metadata)\n show_grid_lines: Optional[bool] = field(default=None, metadata=field_metadata)\n ignore_nil_date: Optional[bool] = field(default=None, metadata=field_metadata)\n x_axis_date_format: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass YAxisSettings(Base):\n decimal_precision: Optional[int] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n label: Optional[str] = field(default=None, metadata=field_metadata)\n label_format: Optional[str] = field(default=None, metadata=field_metadata)\n max_: Optional[float] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\n min_: Optional[float] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\n show_grid_lines: Optional[bool] = field(default=None, metadata=field_metadata)\n hide: Optional[bool] = field(default=None, metadata=field_metadata)\n invert_axis: Optional[bool] = field(default=None, metadata=field_metadata)\n show_label: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartAnnotation(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n type_: Optional[ChartAnnotationType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n chart_type: Optional[ChartType] = field(default=None, metadata=field_metadata)\n start_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n end_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n start_value: Optional[str] = field(default=None, metadata=field_metadata)\n end_value: Optional[str] = field(default=None, metadata=field_metadata)\n color: Optional[str] = field(default=None, metadata=field_metadata)\n is_proportional: Optional[bool] = field(default=False, metadata=field_metadata)\n fill_color: Optional[str] = field(default=None, metadata=field_metadata)\n font_size: Optional[float] = field(default=None, metadata=field_metadata)\n font_style: Optional[ChartAnnotationFontStyle] = field(default=None, metadata=field_metadata)\n font_weight: Optional[ChartAnnotationFontWeight] = field(default=None, metadata=field_metadata)\n label: Optional[str] = field(default=None, metadata=field_metadata)\n line_height: Optional[float] = field(default=None, metadata=field_metadata)\n line_type: Optional[ChartAnnotationLineType] = field(default=None, metadata=field_metadata)\n line_width: Optional[float] = field(default=None, metadata=field_metadata)\n radius: Optional[float] = field(default=None, metadata=field_metadata)\n range_label_type: Optional[ChartAnnotationRangeLabelType] = field(default=None, metadata=field_metadata)\n text_align: Optional[ChartAnnotationTextAlign] = field(default=None, metadata=field_metadata)\n text_decoration: Optional[ChartAnnotationTextDecoration] = field(default=None, metadata=field_metadata)\n text_width: Optional[float] = field(default=None, metadata=field_metadata)\n text_height: Optional[float] = field(default=None, metadata=field_metadata)\n y_axis_index: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nclass ChartControls(DictBase):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartExpression(Base):\n axis: Optional[str] = field(default=None, metadata=field_metadata)\n color: Optional[str] = field(default=None, metadata=field_metadata)\n delete_gap: Optional[bool] = field(default=None, metadata=field_metadata)\n delete_weekends: Optional[bool] = field(default=None, metadata=field_metadata)\n digits: Optional[float] = field(default=None, metadata=field_metadata)\n disable: Optional[bool] = field(default=None, metadata=field_metadata)\n fill: Optional[ChartFill] = field(default=None, metadata=field_metadata)\n has_x_grid: Optional[bool] = field(default=None, metadata=field_metadata)\n has_y_grid: Optional[bool] = field(default=None, metadata=field_metadata)\n show_statistics: Optional[bool] = field(default=None, metadata=field_metadata)\n hide: Optional[bool] = field(default=None, metadata=field_metadata)\n label: Optional[str] = field(default=None, metadata=field_metadata)\n line_type: Optional[ChartLineType] = field(default=None, metadata=field_metadata)\n line_draw_type: Optional[ChartLineDrawType] = field(default=None, metadata=field_metadata)\n line_transparency: Optional[float] = field(default=None, metadata=field_metadata)\n line_width: Optional[float] = field(default=None, metadata=field_metadata)\n x_label: Optional[str] = field(default=None, metadata=field_metadata)\n y_label: Optional[str] = field(default=None, metadata=field_metadata)\n default_gradient: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartRegression(Base):\n type_: Optional[ChartRegressionType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n line_data_index: Optional[int] = field(default=None, metadata=field_metadata)\n is_visible: Optional[bool] = field(default=None, metadata=field_metadata)\n show_statistical_info: Optional[bool] = field(default=None, metadata=field_metadata)\n stroke_color: Optional[str] = field(default=None, metadata=field_metadata)\n stroke_type: Optional[ChartRegressionStrokeType] = field(default=None, metadata=field_metadata)\n stroke_width: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ConstructorParameter(Base):\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n values: Optional[Tuple[ParameterField, ...]] = field(default=None, metadata=field_metadata)\n options: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TemplateVariable(Base):\n name: str = field(default=None, metadata=field_metadata)\n display_name: str = field(default=None, metadata=field_metadata)\n constructor_type: str = field(default=None, metadata=field_metadata)\n parameters: ConstructorParameter = field(default=None, metadata=field_metadata)\n default_value: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n hide: Optional[bool] = field(default=False, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Chart(Base):\n name: str = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n title: Optional[str] = field(default=None, metadata=field_metadata)\n subtitle: Optional[str] = field(default=None, metadata=field_metadata)\n rank: Optional[int] = field(default=None, metadata=field_metadata)\n folder_name: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n description_history: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n expressions: Optional[Tuple[ChartExpression, ...]] = field(default=None, metadata=field_metadata)\n chart_type: Optional[ChartType] = field(default=None, metadata=field_metadata)\n chart_properties: Optional[Tuple[ChartProperties, ...]] = field(default=None, metadata=field_metadata)\n regression_properties: Optional[Tuple[ChartRegression, ...]] = field(default=None, metadata=field_metadata)\n real_time: Optional[bool] = field(default=None, metadata=field_metadata)\n conversation_id: Optional[str] = field(default=None, metadata=field_metadata)\n show_controls_toolbar: Optional[bool] = field(default=None, metadata=field_metadata)\n interval: Optional[str] = field(default=None, metadata=field_metadata)\n relative_start_date: Optional[str] = field(default=None, metadata=field_metadata)\n relative_end_date: Optional[str] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n start_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n end_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n auto_tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n show_statistics: Optional[bool] = field(default=None, metadata=field_metadata)\n copy_from_id: Optional[str] = field(default=None, metadata=field_metadata)\n version: Optional[int] = field(default=None, metadata=field_metadata)\n draft_view_id: Optional[str] = field(default=None, metadata=field_metadata)\n label_settings: Optional[ChartLabelSettings] = field(default=None, metadata=field_metadata)\n time_settings: Optional[ChartTime] = field(default=None, metadata=field_metadata)\n x_axis_settings: Optional[XAxisSettings] = field(default=None, metadata=field_metadata)\n display_settings: Optional[ChartDisplaySettings] = field(default=None, metadata=field_metadata)\n y_axes_settings: Optional[Tuple[YAxisSettings, ...]] = field(default=None, metadata=field_metadata)\n annotations: Optional[Tuple[ChartAnnotation, ...]] = field(default=None, metadata=field_metadata)\n template_variables: Optional[DictBase] = field(default=None, metadata=field_metadata)\n parameters: Optional[ChartControls] = field(default=None, metadata=field_metadata)\n controls: Optional[Tuple[Union[ChartControls, bool, float, str], ...]] = field(default=None, metadata=field_metadata)\n ai: Optional[str] = field(default=None, metadata=field_metadata)\n ai_user_prompt: Optional[str] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/common.py", "content": "\"\"\"\r\nCopyright 2019 Goldman Sachs.\r\nLicensed under the Apache License, Version 2.0 (the \"License\");\r\nyou may not use this file except in compliance with the License.\r\nYou may obtain a copy of the License at\r\n\r\n http://www.apache.org/licenses/LICENSE-2.0\r\n\r\nUnless required by applicable law or agreed to in writing,\r\nsoftware distributed under the License is distributed on an\r\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\r\nKIND, either express or implied. See the License for the\r\nspecific language governing permissions and limitations\r\nunder the License.\r\n\"\"\"\r\n\r\nfrom gs_quant.base import *\r\n\r\nimport datetime\r\nfrom typing import Optional, Tuple, Union\r\nfrom dataclasses import dataclass, field\r\nfrom dataclasses_json import LetterCase, config, dataclass_json\r\nfrom enum import Enum\r\n\r\n\r\nclass AccrualConvention(EnumBase, Enum): \r\n \r\n Adjusted = 'Adjusted'\r\n Unadjusted = 'Unadjusted' \r\n\r\n\r\nclass AccumOrDecum(EnumBase, Enum): \r\n \r\n Accum = 'Accum'\r\n Decum = 'Decum'\r\n Non_Standard = 'Non-Standard' \r\n\r\n\r\nclass AccumulatorType(EnumBase, Enum): \r\n \r\n Terminating = 'Terminating'\r\n Non_Terminating = 'Non-Terminating' \r\n\r\n\r\nclass AggregationLevel(EnumBase, Enum): \r\n \r\n \"\"\"Aggregation Level\"\"\"\r\n\r\n Type = 'Type'\r\n Asset = 'Asset'\r\n Class = 'Class'\r\n Point = 'Point' \r\n\r\n\r\nclass AssetClass(EnumBase, Enum): \r\n \r\n \"\"\"Asset classification of security. Assets are classified into broad groups which\r\n exhibit similar characteristics and behave in a consistent way under\r\n different market conditions\"\"\"\r\n\r\n Cash = 'Cash'\r\n Commod = 'Commod'\r\n Credit = 'Credit'\r\n Cross_Asset = 'Cross Asset'\r\n Digital_Asset = 'Digital Asset'\r\n Debt = 'Debt'\r\n Econ = 'Econ'\r\n Equity = 'Equity'\r\n Fund = 'Fund'\r\n FX = 'FX'\r\n ListedDerivative = 'ListedDerivative'\r\n Mortgage = 'Mortgage'\r\n Rates = 'Rates'\r\n Repo = 'Repo'\r\n Loan = 'Loan'\r\n Social = 'Social'\r\n Cryptocurrency = 'Cryptocurrency' \r\n\r\n\r\nclass AssetStatsPeriod(EnumBase, Enum): \r\n \r\n \"\"\"The period used to produce date range.\"\"\"\r\n\r\n _1y = '1y'\r\n _3y = '3y'\r\n _5y = '5y'\r\n _10y = '10y' \r\n\r\n\r\nclass AssetStatsType(EnumBase, Enum): \r\n \r\n \"\"\"Is it rolling, none etc.\"\"\"\r\n\r\n Rolling = 'Rolling'\r\n Calendar = 'Calendar'\r\n YTD = 'YTD' \r\n\r\n\r\nclass AssetType(EnumBase, Enum): \r\n \r\n \"\"\"Asset type differentiates the product categorization or contract type\"\"\"\r\n\r\n Access = 'Access'\r\n Accumulator = 'Accumulator'\r\n AccumulatorScheduleLeg = 'AccumulatorScheduleLeg'\r\n AssetSwapFxdFlt = 'AssetSwapFxdFlt'\r\n AssetSwapFxdFxd = 'AssetSwapFxdFxd'\r\n Any = 'Any'\r\n AsianOption = 'AsianOption'\r\n Autoroll = 'Autoroll'\r\n AveragePriceOption = 'AveragePriceOption'\r\n Barrier = 'Barrier'\r\n Basis = 'Basis'\r\n BasisSwap = 'BasisSwap'\r\n Benchmark = 'Benchmark'\r\n Benchmark_Rate = 'Benchmark Rate'\r\n Binary = 'Binary'\r\n BinaryDoubleKnockout = 'BinaryDoubleKnockout'\r\n Bond = 'Bond'\r\n Bond_Forward = 'Bond Forward'\r\n BondFuture = 'BondFuture'\r\n BondFutureOption = 'BondFutureOption'\r\n BondOption = 'BondOption'\r\n Calendar_Spread = 'Calendar Spread'\r\n Cap = 'Cap'\r\n CapOrFloor = 'CapOrFloor'\r\n Cash = 'Cash'\r\n Certificate = 'Certificate'\r\n CD = 'CD'\r\n Cliquet = 'Cliquet'\r\n CMSOption = 'CMSOption'\r\n CMSOptionStrip = 'CMSOptionStrip'\r\n CMSSpreadOption = 'CMSSpreadOption'\r\n CMSSpreadOptionStrip = 'CMSSpreadOptionStrip'\r\n Coin = 'Coin'\r\n Commodity = 'Commodity'\r\n CommodityReferencePrice = 'CommodityReferencePrice'\r\n CommodVarianceSwap = 'CommodVarianceSwap'\r\n CommodityPowerNode = 'CommodityPowerNode'\r\n CommodityPowerAggregatedNodes = 'CommodityPowerAggregatedNodes'\r\n CommodityEUNaturalGasHub = 'CommodityEUNaturalGasHub'\r\n CommodityNaturalGasHub = 'CommodityNaturalGasHub'\r\n Company = 'Company'\r\n ContractDivOption = 'ContractDivOption'\r\n Convertible = 'Convertible'\r\n CorrelationSwap = 'CorrelationSwap'\r\n CorrelationSwapLeg = 'CorrelationSwapLeg'\r\n Credit_Basket = 'Credit Basket'\r\n Cross = 'Cross'\r\n CSL = 'CSL'\r\n Currency = 'Currency'\r\n Custom_Basket = 'Custom Basket'\r\n Cryptocurrency = 'Cryptocurrency'\r\n Default_Swap = 'Default Swap'\r\n Digital = 'Digital'\r\n DiscreteLock = 'DiscreteLock'\r\n DoubleKnockout = 'DoubleKnockout'\r\n DoubleTouch = 'DoubleTouch'\r\n DualDoubleKnockout = 'DualDoubleKnockout'\r\n DualDoubleKnockoutLeg = 'DualDoubleKnockoutLeg'\r\n Economic = 'Economic'\r\n Endowment = 'Endowment'\r\n Equity_Basket = 'Equity Basket'\r\n EuropeanKnockout = 'EuropeanKnockout'\r\n ETF = 'ETF'\r\n ETN = 'ETN'\r\n Event = 'Event'\r\n FRA = 'FRA'\r\n FixedLeg = 'FixedLeg'\r\n Fixing = 'Fixing'\r\n FloatLeg = 'FloatLeg'\r\n Floor = 'Floor'\r\n Forward = 'Forward'\r\n ForwardVarianceSwap = 'ForwardVarianceSwap'\r\n ForwardVolatilityAgreement = 'ForwardVolatilityAgreement'\r\n Fund = 'Fund'\r\n Future = 'Future'\r\n FutureContract = 'FutureContract'\r\n FutureMarket = 'FutureMarket'\r\n FutureOption = 'FutureOption'\r\n FutureStrategy = 'FutureStrategy'\r\n FXForward = 'FXForward'\r\n Hedge_Fund = 'Hedge Fund'\r\n Index = 'Index'\r\n IndexOption = 'IndexOption'\r\n IndexSwap = 'IndexSwap'\r\n InflationSwap = 'InflationSwap'\r\n Inter_Commodity_Spread = 'Inter-Commodity Spread'\r\n InvoiceSpread = 'InvoiceSpread'\r\n Knockout = 'Knockout'\r\n ListedOption = 'ListedOption'\r\n ListedOptionPeriod = 'ListedOptionPeriod'\r\n ListedSwap = 'ListedSwap'\r\n ListedSwapPeriod = 'ListedSwapPeriod'\r\n LockedLadder = 'LockedLadder'\r\n MacroBasket = 'MacroBasket'\r\n Market_Location = 'Market Location'\r\n MLF = 'MLF'\r\n Multi_Asset_Allocation = 'Multi-Asset Allocation'\r\n MultiCrossBinary = 'MultiCrossBinary'\r\n MultiCrossBinaryLeg = 'MultiCrossBinaryLeg'\r\n MultiCrossDoubleBinary = 'MultiCrossDoubleBinary'\r\n MultiCrossDoubleBinaryLeg = 'MultiCrossDoubleBinaryLeg'\r\n MultiCrossDoubleTouch = 'MultiCrossDoubleTouch'\r\n MultiCrossDoubleTouchLeg = 'MultiCrossDoubleTouchLeg'\r\n Mutual_Fund = 'Mutual Fund'\r\n Native_Asset = 'Native Asset'\r\n Note = 'Note'\r\n OneTouch = 'OneTouch'\r\n Option = 'Option'\r\n OptionLeg = 'OptionLeg'\r\n OptionPeriod = 'OptionPeriod'\r\n OptionStrategy = 'OptionStrategy'\r\n Peer_Group = 'Peer Group'\r\n Pension_Fund = 'Pension Fund'\r\n Pivot = 'Pivot'\r\n PivotScheduleLeg = 'PivotScheduleLeg'\r\n Preferred_Stock = 'Preferred Stock'\r\n Physical = 'Physical'\r\n Precious_Metal = 'Precious Metal'\r\n Precious_Metal_Swap = 'Precious Metal Swap'\r\n Precious_Metal_RFQ = 'Precious Metal RFQ'\r\n QuantoOption = 'QuantoOption'\r\n Reference_Entity = 'Reference Entity'\r\n Research_Basket = 'Research Basket'\r\n Rate = 'Rate'\r\n Risk_Premia = 'Risk Premia'\r\n Roll = 'Roll'\r\n Securities_Lending_Loan = 'Securities Lending Loan'\r\n Share_Class = 'Share Class'\r\n Single_Stock = 'Single Stock'\r\n ShiftingBermForward = 'ShiftingBermForward'\r\n Swap = 'Swap'\r\n SwapData = 'SwapData'\r\n SwapLeg = 'SwapLeg'\r\n SwapLPeriod = 'SwapLPeriod'\r\n SwapPeriod = 'SwapPeriod'\r\n SwapStrategy = 'SwapStrategy'\r\n Swaption = 'Swaption'\r\n Synthetic = 'Synthetic'\r\n SyntheticDateInfo = 'SyntheticDateInfo'\r\n Systematic_Hedging = 'Systematic Hedging'\r\n Tarf = 'Tarf'\r\n TarfScheduleLeg = 'TarfScheduleLeg'\r\n Token = 'Token'\r\n VarianceSwap = 'VarianceSwap'\r\n VolatilityKnockout = 'VolatilityKnockout'\r\n VolatilitySwap = 'VolatilitySwap'\r\n VolVarSwap = 'VolVarSwap'\r\n WeatherIndex = 'WeatherIndex'\r\n WindowDoubleKnockout = 'WindowDoubleKnockout'\r\n WindowKnockout = 'WindowKnockout'\r\n WorstOf = 'WorstOf'\r\n WorstOfLeg = 'WorstOfLeg'\r\n WorstOfKO = 'WorstOfKO'\r\n WorstOfKOLeg = 'WorstOfKOLeg'\r\n XccySwap = 'XccySwap'\r\n XccySwapFixFix = 'XccySwapFixFix'\r\n XccySwapFixFlt = 'XccySwapFixFlt'\r\n XccySwapMTM = 'XccySwapMTM'\r\n SyntheticOETTerms = 'SyntheticOETTerms'\r\n SyntheticSchedule = 'SyntheticSchedule'\r\n SyntheticLeg = 'SyntheticLeg' \r\n\r\n\r\nclass AswType(EnumBase, Enum): \r\n \r\n \"\"\"Asset Swap Type\"\"\"\r\n\r\n Par = 'Par'\r\n Proceeds = 'Proceeds' \r\n\r\n\r\nclass BasketAction(EnumBase, Enum): \r\n \r\n \"\"\"Indicates what was the action taken on basket - create/edit/rebalance\"\"\"\r\n\r\n Create = 'Create'\r\n Edit = 'Edit'\r\n Rebalance = 'Rebalance' \r\n\r\n\r\nclass BasketValuationSource(EnumBase, Enum): \r\n \r\n \"\"\"The source of basket pricing\"\"\"\r\n\r\n GS = 'GS'\r\n BVAL = 'BVAL'\r\n CBBT = 'CBBT' \r\n\r\n\r\nclass BestWorst(EnumBase, Enum): \r\n \r\n Best = 'Best'\r\n Worst = 'Worst' \r\n\r\n\r\nclass BondStrikeType(EnumBase, Enum): \r\n \r\n \"\"\"The type of the bond strike - price, yield etc\"\"\"\r\n\r\n Price = 'Price'\r\n Yield = 'Yield' \r\n\r\n\r\nclass BusinessDayConvention(EnumBase, Enum): \r\n \r\n \"\"\"Business Day Convention\"\"\"\r\n\r\n Following = 'Following'\r\n Modified_Following = 'Modified Following'\r\n Previous = 'Previous'\r\n Unadjusted = 'Unadjusted' \r\n\r\n\r\nclass BuySell(EnumBase, Enum): \r\n \r\n \"\"\"Buy or Sell side of contract\"\"\"\r\n\r\n Buy = 'Buy'\r\n Sell = 'Sell' \r\n\r\n\r\nclass CDOptionType(EnumBase, Enum): \r\n \r\n \"\"\"Credit Option Type\"\"\"\r\n\r\n Call = 'Call'\r\n Put = 'Put'\r\n Straddle = 'Straddle'\r\n Payer = 'Payer'\r\n Receiver = 'Receiver'\r\n Digital_Call = 'Digital Call'\r\n Digital_Put = 'Digital Put' \r\n\r\n\r\nclass CapOrFloor(EnumBase, Enum): \r\n \r\n Cap = 'Cap'\r\n Floor = 'Floor'\r\n Straddle = 'Straddle' \r\n\r\n\r\nclass CashReinvestmentTreatmentType(EnumBase, Enum): \r\n \r\n \"\"\"Cash reinvestment treatment for cash acquisitions and dividends.\"\"\"\r\n\r\n Reinvest_At_Open = 'Reinvest At Open'\r\n Add_To_Index = 'Add To Index' \r\n\r\n\r\nclass CommodMeanRule(EnumBase, Enum): \r\n \r\n \"\"\"Commodity mean rule\"\"\"\r\n\r\n Do_Not_Remove = 'Do Not Remove'\r\n Remove_Calculated = 'Remove Calculated'\r\n Remove_Fixed = 'Remove Fixed' \r\n\r\n\r\nclass CommodUnit(EnumBase, Enum): \r\n \r\n \"\"\"A coding scheme value to identify the unit of measure (e.g. Therms) in which the\r\n undelryer is denominated.\"\"\"\r\n\r\n Lot = 'Lot'\r\n MegaWattHour = 'MegaWattHour'\r\n Metric_Ton = 'Metric Ton'\r\n Million_British_Thermal_Units = 'Million British Thermal Units'\r\n Oil_Barrel = 'Oil Barrel'\r\n Troy_Pound = 'Troy Pound'\r\n US_Gallon = 'US Gallon' \r\n\r\n\r\nclass CommoditySector(EnumBase, Enum): \r\n \r\n \"\"\"The sector of the commodity\"\"\"\r\n\r\n Base_metals = 'Base metals'\r\n Precious_metals = 'Precious metals'\r\n Energy = 'Energy'\r\n Agriculturals = 'Agriculturals'\r\n Power = 'Power' \r\n\r\n\r\nclass CountryCode(EnumBase, Enum): \r\n \r\n \"\"\"ISO Country code\"\"\"\r\n\r\n AU = 'AU'\r\n CX = 'CX'\r\n CC = 'CC'\r\n HM = 'HM'\r\n NF = 'NF'\r\n NZ = 'NZ'\r\n CK = 'CK'\r\n NU = 'NU'\r\n TK = 'TK'\r\n JP = 'JP'\r\n JN = 'JN'\r\n EU = 'EU'\r\n ER = 'ER'\r\n EZ = 'EZ'\r\n AT = 'AT'\r\n BE = 'BE'\r\n FI = 'FI'\r\n FR = 'FR'\r\n GF = 'GF'\r\n PF = 'PF'\r\n TF = 'TF'\r\n GP = 'GP'\r\n MQ = 'MQ'\r\n YT = 'YT'\r\n NC = 'NC'\r\n RE = 'RE'\r\n SH = 'SH'\r\n PM = 'PM'\r\n WF = 'WF'\r\n DE = 'DE'\r\n GE = 'GE'\r\n GR = 'GR'\r\n IE = 'IE'\r\n IT = 'IT'\r\n LU = 'LU'\r\n NL = 'NL'\r\n AW = 'AW'\r\n AN = 'AN'\r\n PT = 'PT'\r\n ES = 'ES'\r\n BY = 'BY'\r\n CH = 'CH'\r\n SE = 'SE'\r\n SW = 'SW'\r\n DK = 'DK'\r\n FO = 'FO'\r\n NO = 'NO'\r\n BV = 'BV'\r\n SJ = 'SJ'\r\n LI = 'LI'\r\n GB = 'GB'\r\n UK = 'UK'\r\n AI = 'AI'\r\n IO = 'IO'\r\n KY = 'KY'\r\n FK = 'FK'\r\n GI = 'GI'\r\n MS = 'MS'\r\n PN = 'PN'\r\n GS = 'GS'\r\n TC = 'TC'\r\n VG = 'VG'\r\n JE = 'JE'\r\n _02 = '02'\r\n US = 'US'\r\n AS = 'AS'\r\n GU = 'GU'\r\n MP = 'MP'\r\n PR = 'PR'\r\n UM = 'UM'\r\n VI = 'VI'\r\n CA = 'CA'\r\n AR = 'AR'\r\n BA = 'BA'\r\n BD = 'BD'\r\n BG = 'BG'\r\n BS = 'BS'\r\n BM = 'BM'\r\n BO = 'BO'\r\n BR = 'BR'\r\n CL = 'CL'\r\n CN = 'CN'\r\n CO = 'CO'\r\n CR = 'CR'\r\n CZ = 'CZ'\r\n DO = 'DO'\r\n EC = 'EC'\r\n EG = 'EG'\r\n GA = 'GA'\r\n GT = 'GT'\r\n HK = 'HK'\r\n HR = 'HR'\r\n HU = 'HU'\r\n IL = 'IL'\r\n IM = 'IM'\r\n IR = 'IR'\r\n IS = 'IS'\r\n JO = 'JO'\r\n KE = 'KE'\r\n KR = 'KR'\r\n KZ = 'KZ'\r\n LB = 'LB'\r\n LK = 'LK'\r\n LT = 'LT'\r\n MA = 'MA'\r\n MH = 'MH'\r\n ML = 'ML'\r\n MO = 'MO'\r\n MT = 'MT'\r\n MX = 'MX'\r\n MY = 'MY'\r\n NI = 'NI'\r\n OM = 'OM'\r\n PA = 'PA'\r\n PD = 'PD'\r\n PE = 'PE'\r\n PH = 'PH'\r\n PK = 'PK'\r\n PL = 'PL'\r\n QA = 'QA'\r\n RO = 'RO'\r\n RU = 'RU'\r\n SA = 'SA'\r\n SG = 'SG'\r\n SI = 'SI'\r\n SK = 'SK'\r\n SV = 'SV'\r\n TH = 'TH'\r\n TN = 'TN'\r\n TP = 'TP'\r\n TR = 'TR'\r\n TW = 'TW'\r\n UA = 'UA'\r\n UY = 'UY'\r\n VE = 'VE'\r\n VN = 'VN'\r\n ZA = 'ZA'\r\n BH = 'BH'\r\n EE = 'EE'\r\n GH = 'GH'\r\n ME = 'ME'\r\n RS = 'RS'\r\n ZM = 'ZM'\r\n ZW = 'ZW'\r\n TT = 'TT'\r\n AE = 'AE'\r\n KW = 'KW'\r\n BB = 'BB'\r\n LV = 'LV'\r\n GG = 'GG'\r\n CY = 'CY'\r\n CI = 'CI'\r\n MU = 'MU'\r\n PY = 'PY'\r\n HN = 'HN'\r\n BZ = 'BZ'\r\n NA = 'NA'\r\n FJ = 'FJ'\r\n BW = 'BW'\r\n DZ = 'DZ'\r\n MN = 'MN'\r\n SN = 'SN'\r\n TZ = 'TZ'\r\n AD = 'AD'\r\n AG = 'AG'\r\n AL = 'AL'\r\n AM = 'AM'\r\n AO = 'AO'\r\n AZ = 'AZ'\r\n BF = 'BF'\r\n BI = 'BI'\r\n BJ = 'BJ'\r\n BN = 'BN'\r\n BT = 'BT'\r\n CD = 'CD'\r\n CF = 'CF'\r\n CG = 'CG'\r\n CM = 'CM'\r\n CU = 'CU'\r\n CV = 'CV'\r\n CS = 'CS'\r\n DJ = 'DJ'\r\n DM = 'DM'\r\n EH = 'EH'\r\n ET = 'ET'\r\n FM = 'FM'\r\n GD = 'GD'\r\n GL = 'GL'\r\n GM = 'GM'\r\n GN = 'GN'\r\n GQ = 'GQ'\r\n GW = 'GW'\r\n GY = 'GY'\r\n HT = 'HT'\r\n ID = 'ID'\r\n IN = 'IN'\r\n IQ = 'IQ'\r\n JM = 'JM'\r\n KG = 'KG'\r\n KH = 'KH'\r\n KI = 'KI'\r\n KM = 'KM'\r\n KN = 'KN'\r\n KP = 'KP'\r\n LA = 'LA'\r\n LC = 'LC'\r\n LR = 'LR'\r\n LS = 'LS'\r\n LY = 'LY'\r\n MC = 'MC'\r\n MD = 'MD'\r\n MG = 'MG'\r\n MK = 'MK'\r\n MM = 'MM'\r\n MR = 'MR'\r\n MV = 'MV'\r\n MW = 'MW'\r\n MZ = 'MZ'\r\n NE = 'NE'\r\n NG = 'NG'\r\n NP = 'NP'\r\n NR = 'NR'\r\n PG = 'PG'\r\n PW = 'PW'\r\n RW = 'RW'\r\n SB = 'SB'\r\n SC = 'SC'\r\n SD = 'SD'\r\n SL = 'SL'\r\n SM = 'SM'\r\n SO = 'SO'\r\n SR = 'SR'\r\n ST = 'ST'\r\n SY = 'SY'\r\n SZ = 'SZ'\r\n TD = 'TD'\r\n TG = 'TG'\r\n TJ = 'TJ'\r\n TL = 'TL'\r\n TM = 'TM'\r\n TO = 'TO'\r\n TV = 'TV'\r\n UG = 'UG'\r\n UZ = 'UZ'\r\n VA = 'VA'\r\n VC = 'VC'\r\n VU = 'VU'\r\n WS = 'WS'\r\n YE = 'YE' \r\n\r\n\r\nclass CreditBasketType(EnumBase, Enum): \r\n \r\n \"\"\"Credit basket type with regards to its constituents\"\"\"\r\n\r\n Corporate_Bond = 'Corporate Bond'\r\n CDS = 'CDS'\r\n Treasury = 'Treasury'\r\n Mixed = 'Mixed'\r\n Unknown = 'Unknown' \r\n\r\n\r\nclass CreditOptionStrikeType(EnumBase, Enum): \r\n \r\n Spread_Adj = 'Spread Adj'\r\n Delta = 'Delta' \r\n\r\n\r\nclass CreditOptionType(EnumBase, Enum): \r\n \r\n Payer = 'Payer'\r\n Receiver = 'Receiver' \r\n\r\n\r\nclass Currency(EnumBase, Enum): \r\n \r\n \"\"\"Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)\"\"\"\r\n\r\n _ = ''\r\n ACU = 'ACU'\r\n ADP = 'ADP'\r\n AED = 'AED'\r\n AFA = 'AFA'\r\n ALL = 'ALL'\r\n AMD = 'AMD'\r\n ANG = 'ANG'\r\n AOA = 'AOA'\r\n AOK = 'AOK'\r\n AON = 'AON'\r\n ARA = 'ARA'\r\n ARS = 'ARS'\r\n ARZ = 'ARZ'\r\n ATS = 'ATS'\r\n AUD = 'AUD'\r\n AUZ = 'AUZ'\r\n AWG = 'AWG'\r\n AZM = 'AZM'\r\n AZN = 'AZN'\r\n B03 = 'B03'\r\n BAD = 'BAD'\r\n BAK = 'BAK'\r\n BAM = 'BAM'\r\n BBD = 'BBD'\r\n BDN = 'BDN'\r\n BDT = 'BDT'\r\n BEF = 'BEF'\r\n BGL = 'BGL'\r\n BGN = 'BGN'\r\n BHD = 'BHD'\r\n BIF = 'BIF'\r\n BMD = 'BMD'\r\n BND = 'BND'\r\n BOB = 'BOB'\r\n BR6 = 'BR6'\r\n BRE = 'BRE'\r\n BRF = 'BRF'\r\n BRL = 'BRL'\r\n BRR = 'BRR'\r\n BSD = 'BSD'\r\n BTC = 'BTC'\r\n BTN = 'BTN'\r\n BTR = 'BTR'\r\n BWP = 'BWP'\r\n BYN = 'BYN'\r\n BYR = 'BYR'\r\n BZD = 'BZD'\r\n C23 = 'C23'\r\n CAC = 'CAC'\r\n CAD = 'CAD'\r\n CAZ = 'CAZ'\r\n CCI = 'CCI'\r\n CDF = 'CDF'\r\n CFA = 'CFA'\r\n CHF = 'CHF'\r\n CHZ = 'CHZ'\r\n CLF = 'CLF'\r\n CLP = 'CLP'\r\n CLZ = 'CLZ'\r\n CNH = 'CNH'\r\n CNO = 'CNO'\r\n CNY = 'CNY'\r\n CNZ = 'CNZ'\r\n COP = 'COP'\r\n COZ = 'COZ'\r\n CPB = 'CPB'\r\n CPI = 'CPI'\r\n CRC = 'CRC'\r\n CUP = 'CUP'\r\n CVE = 'CVE'\r\n CYP = 'CYP'\r\n CZH = 'CZH'\r\n CZK = 'CZK'\r\n DAX = 'DAX'\r\n DEM = 'DEM'\r\n DIJ = 'DIJ'\r\n DJF = 'DJF'\r\n DJE = 'DJE'\r\n DKK = 'DKK'\r\n DOP = 'DOP'\r\n DZD = 'DZD'\r\n E51 = 'E51'\r\n E52 = 'E52'\r\n E53 = 'E53'\r\n E54 = 'E54'\r\n ECI = 'ECI'\r\n ECS = 'ECS'\r\n ECU = 'ECU'\r\n EEK = 'EEK'\r\n EF0 = 'EF0'\r\n EGP = 'EGP'\r\n ESP = 'ESP'\r\n ETB = 'ETB'\r\n ETH = 'ETH'\r\n EUR = 'EUR'\r\n EUZ = 'EUZ'\r\n F06 = 'F06'\r\n FED = 'FED'\r\n FIM = 'FIM'\r\n FJD = 'FJD'\r\n FKP = 'FKP'\r\n FRF = 'FRF'\r\n FT1 = 'FT1'\r\n GBP = 'GBP'\r\n GBZ = 'GBZ'\r\n GEK = 'GEK'\r\n GEL = 'GEL'\r\n GHC = 'GHC'\r\n GHS = 'GHS'\r\n GHY = 'GHY'\r\n GIP = 'GIP'\r\n GLD = 'GLD'\r\n GLR = 'GLR'\r\n GMD = 'GMD'\r\n GNF = 'GNF'\r\n GQE = 'GQE'\r\n GRD = 'GRD'\r\n GTQ = 'GTQ'\r\n GWP = 'GWP'\r\n GYD = 'GYD'\r\n HKB = 'HKB'\r\n HKD = 'HKD'\r\n HNL = 'HNL'\r\n HRK = 'HRK'\r\n HSI = 'HSI'\r\n HTG = 'HTG'\r\n HUF = 'HUF'\r\n IDB = 'IDB'\r\n IDO = 'IDO'\r\n IDR = 'IDR'\r\n IEP = 'IEP'\r\n IGP = 'IGP'\r\n ILS = 'ILS'\r\n INO = 'INO'\r\n INP = 'INP'\r\n INR = 'INR'\r\n IPA = 'IPA'\r\n IPX = 'IPX'\r\n IQD = 'IQD'\r\n IRR = 'IRR'\r\n IRS = 'IRS'\r\n ISI = 'ISI'\r\n ISK = 'ISK'\r\n ISO = 'ISO'\r\n ITL = 'ITL'\r\n J05 = 'J05'\r\n JMD = 'JMD'\r\n JNI = 'JNI'\r\n JOD = 'JOD'\r\n JPY = 'JPY'\r\n JPZ = 'JPZ'\r\n JZ9 = 'JZ9'\r\n KES = 'KES'\r\n KGS = 'KGS'\r\n KHR = 'KHR'\r\n KMF = 'KMF'\r\n KOR = 'KOR'\r\n KPW = 'KPW'\r\n KRW = 'KRW'\r\n KWD = 'KWD'\r\n KYD = 'KYD'\r\n KZT = 'KZT'\r\n LAK = 'LAK'\r\n LBA = 'LBA'\r\n LBP = 'LBP'\r\n LHY = 'LHY'\r\n LKR = 'LKR'\r\n LRD = 'LRD'\r\n LSL = 'LSL'\r\n LSM = 'LSM'\r\n LTL = 'LTL'\r\n LUF = 'LUF'\r\n LVL = 'LVL'\r\n LYD = 'LYD'\r\n MAD = 'MAD'\r\n MDL = 'MDL'\r\n MGA = 'MGA'\r\n MGF = 'MGF'\r\n MKD = 'MKD'\r\n MMK = 'MMK'\r\n MNT = 'MNT'\r\n MOP = 'MOP'\r\n MRO = 'MRO'\r\n MRU = 'MRU'\r\n MTL = 'MTL'\r\n MTP = 'MTP'\r\n MUR = 'MUR'\r\n MVR = 'MVR'\r\n MWK = 'MWK'\r\n MXB = 'MXB'\r\n MXN = 'MXN'\r\n MXP = 'MXP'\r\n MXW = 'MXW'\r\n MXZ = 'MXZ'\r\n MYO = 'MYO'\r\n MYR = 'MYR'\r\n MZM = 'MZM'\r\n MZN = 'MZN'\r\n NAD = 'NAD'\r\n ND3 = 'ND3'\r\n NGF = 'NGF'\r\n NGI = 'NGI'\r\n NGN = 'NGN'\r\n NIC = 'NIC'\r\n NIO = 'NIO'\r\n NLG = 'NLG'\r\n NOK = 'NOK'\r\n NOZ = 'NOZ'\r\n NPR = 'NPR'\r\n NZD = 'NZD'\r\n NZZ = 'NZZ'\r\n O08 = 'O08'\r\n OMR = 'OMR'\r\n PAB = 'PAB'\r\n PEI = 'PEI'\r\n PEN = 'PEN'\r\n PEZ = 'PEZ'\r\n PGK = 'PGK'\r\n PHP = 'PHP'\r\n PKR = 'PKR'\r\n PLN = 'PLN'\r\n PLZ = 'PLZ'\r\n PSI = 'PSI'\r\n PTE = 'PTE'\r\n PYG = 'PYG'\r\n QAR = 'QAR'\r\n QOF = 'QOF'\r\n R2K = 'R2K'\r\n ROL = 'ROL'\r\n RON = 'RON'\r\n RSD = 'RSD'\r\n RUB = 'RUB'\r\n RUF = 'RUF'\r\n RUR = 'RUR'\r\n RWF = 'RWF'\r\n SAR = 'SAR'\r\n SBD = 'SBD'\r\n SCR = 'SCR'\r\n SDG = 'SDG'\r\n SDP = 'SDP'\r\n SDR = 'SDR'\r\n SEK = 'SEK'\r\n SET = 'SET'\r\n SGD = 'SGD'\r\n SGS = 'SGS'\r\n SHP = 'SHP'\r\n SIL = 'SIL'\r\n SIT = 'SIT'\r\n SKK = 'SKK'\r\n SLE = 'SLE'\r\n SLL = 'SLL'\r\n SRD = 'SRD'\r\n SRG = 'SRG'\r\n SSI = 'SSI'\r\n STD = 'STD'\r\n SUR = 'SUR'\r\n SVC = 'SVC'\r\n SVT = 'SVT'\r\n SYP = 'SYP'\r\n SZL = 'SZL'\r\n T21 = 'T21'\r\n T51 = 'T51'\r\n T52 = 'T52'\r\n T53 = 'T53'\r\n T54 = 'T54'\r\n T55 = 'T55'\r\n T71 = 'T71'\r\n TE0 = 'TE0'\r\n TED = 'TED'\r\n TF9 = 'TF9'\r\n THB = 'THB'\r\n THO = 'THO'\r\n TMM = 'TMM'\r\n TND = 'TND'\r\n TNT = 'TNT'\r\n TOF = 'TOF'\r\n TOP = 'TOP'\r\n TPE = 'TPE'\r\n TPX = 'TPX'\r\n TRB = 'TRB'\r\n TRL = 'TRL'\r\n TRY = 'TRY'\r\n TRZ = 'TRZ'\r\n TTD = 'TTD'\r\n TWD = 'TWD'\r\n TZS = 'TZS'\r\n UAH = 'UAH'\r\n UCB = 'UCB'\r\n UDI = 'UDI'\r\n UFC = 'UFC'\r\n UFZ = 'UFZ'\r\n UGS = 'UGS'\r\n UGX = 'UGX'\r\n USB = 'USB'\r\n USD = 'USD'\r\n UVR = 'UVR'\r\n UYP = 'UYP'\r\n UYU = 'UYU'\r\n UZS = 'UZS'\r\n VAC = 'VAC'\r\n VEB = 'VEB'\r\n VEF = 'VEF'\r\n VES = 'VES'\r\n VND = 'VND'\r\n VUV = 'VUV'\r\n WST = 'WST'\r\n XAF = 'XAF'\r\n XAG = 'XAG'\r\n XAU = 'XAU'\r\n XPD = 'XPD'\r\n XPT = 'XPT'\r\n XCD = 'XCD'\r\n XDR = 'XDR'\r\n XEU = 'XEU'\r\n XOF = 'XOF'\r\n XPF = 'XPF'\r\n YDD = 'YDD'\r\n YER = 'YER'\r\n YUD = 'YUD'\r\n YUN = 'YUN'\r\n ZAL = 'ZAL'\r\n ZAR = 'ZAR'\r\n ZAZ = 'ZAZ'\r\n ZMK = 'ZMK'\r\n ZMW = 'ZMW'\r\n ZRN = 'ZRN'\r\n ZRZ = 'ZRZ'\r\n ZWD = 'ZWD'\r\n ZWL = 'ZWL'\r\n AUd = 'AUd'\r\n BWp = 'BWp'\r\n EUr = 'EUr'\r\n GBp = 'GBp'\r\n ILs = 'ILs'\r\n KWd = 'KWd'\r\n MWk = 'MWk'\r\n SGd = 'SGd'\r\n SZl = 'SZl'\r\n USd = 'USd'\r\n ZAr = 'ZAr' \r\n\r\n\r\nclass CurrencyName(EnumBase, Enum): \r\n \r\n \"\"\"Currency Names\"\"\"\r\n\r\n United_States_Dollar = 'United States Dollar'\r\n Australian_Dollar = 'Australian Dollar'\r\n Canadian_Dollar = 'Canadian Dollar'\r\n Swiss_Franc = 'Swiss Franc'\r\n Yuan_Renminbi_Hong_Kong = 'Yuan Renminbi (Hong Kong)'\r\n Czech_Republic_Koruna = 'Czech Republic Koruna'\r\n Euro = 'Euro'\r\n Pound_Sterling = 'Pound Sterling'\r\n Japanese_Yen = 'Japanese Yen'\r\n South_Korean_Won = 'South Korean Won'\r\n Malasyan_Ringgit = 'Malasyan Ringgit'\r\n Norwegian_Krone = 'Norwegian Krone'\r\n New_Zealand_Dollar = 'New Zealand Dollar'\r\n Polish_Zloty = 'Polish Zloty'\r\n Russian_Rouble = 'Russian Rouble'\r\n Swedish_Krona = 'Swedish Krona'\r\n South_African_Rand = 'South African Rand'\r\n Yuan_Renminbi_Onshore = 'Yuan Renminbi (Onshore)' \r\n\r\n\r\nclass DayCountFraction(EnumBase, Enum): \r\n \r\n \"\"\"Day Count Fraction\"\"\"\r\n\r\n ACT_OVER_360 = 'ACT/360'\r\n ACT_OVER_360_ISDA = 'ACT/360 ISDA'\r\n ACT_OVER_365_Fixed = 'ACT/365 (Fixed)'\r\n ACT_OVER_365_Fixed_ISDA = 'ACT/365 Fixed ISDA'\r\n ACT_OVER_365L_ISDA = 'ACT/365L ISDA'\r\n ACT_OVER_ACT_ISDA = 'ACT/ACT ISDA'\r\n ACT_OVER_ACT_ISMA = 'ACT/ACT ISMA'\r\n _30_OVER_360 = '30/360'\r\n _30E_OVER_360 = '30E/360' \r\n\r\n\r\nclass EqBasketHistoryMethodology(EnumBase, Enum):\r\n \r\n \"\"\"Whether basket history should be backcasted, backtested, or uploaded manually\r\n after creation.\"\"\"\r\n\r\n Backcast = 'Backcast'\r\n Backtest = 'Backtest'\r\n Custom = 'Custom' \r\n\r\n\r\nclass EqBasketHolidayCalendar(EnumBase, Enum): \r\n \r\n \"\"\"Holiday calendar by exchange.\"\"\"\r\n\r\n NYSE = 'NYSE'\r\n LNSE = 'LNSE'\r\n HKGE = 'HKGE' \r\n\r\n\r\nclass EqBasketRebalanceFrequencyRule(EnumBase, Enum): \r\n \r\n \"\"\"Expected rebalance frequency for a basket.\"\"\"\r\n\r\n First_Biz_Day_of_Month = 'First Biz Day of Month'\r\n Last_Biz_Day_of_Month = 'Last Biz Day of Month'\r\n First_Biz_Day_of_Quarter = 'First Biz Day of Quarter'\r\n Last_Biz_Day_of_Quarter = 'Last Biz Day of Quarter'\r\n Last_Biz_Day_of_Quarter_first_Month = 'Last Biz Day of Quarter first Month'\r\n Biweekly_Based_on_Last_Rebal = 'Biweekly Based on Last Rebal'\r\n Custom = 'Custom' \r\n\r\n\r\nclass FVAStrikeType(EnumBase, Enum): \r\n \r\n Delta_Neutral = 'Delta Neutral'\r\n ATM_Forward = 'ATM Forward'\r\n Delta = 'Delta' \r\n\r\n\r\nclass FVAUnderlyingOptionType(EnumBase, Enum): \r\n \r\n Straddle = 'Straddle'\r\n Call = 'Call'\r\n Put = 'Put' \r\n\r\n\r\nclass FallbackType(EnumBase, Enum): \r\n \r\n \"\"\"Different Rules for Libor Fallback\"\"\"\r\n\r\n RFR = 'RFR'\r\n LastFixing = 'LastFixing' \r\n\r\n\r\nclass Field(EnumBase, Enum): \r\n \r\n \"\"\"Field to be returned\"\"\"\r\n\r\n investmentRate = 'investmentRate'\r\n startingEmmaLegalEntityId = 'startingEmmaLegalEntityId'\r\n bvalMidPrice = 'bvalMidPrice'\r\n mdapiClass = 'mdapiClass'\r\n totalNotionalUSD = 'totalNotionalUSD'\r\n bidUnadjusted = 'bidUnadjusted'\r\n poRisk = 'poRisk'\r\n navTargetQuantity = 'navTargetQuantity'\r\n invertedCross = 'invertedCross'\r\n aggressiveFillsPercentage = 'aggressiveFillsPercentage'\r\n future10yrMarketCap = 'future10yrMarketCap'\r\n vehicleType = 'vehicleType'\r\n totalFatalitiesByState = 'totalFatalitiesByState'\r\n newActive = 'newActive'\r\n dailyRisk = 'dailyRisk'\r\n energy = 'energy'\r\n currentConstituentsNetDebtToEbitda = 'currentConstituentsNetDebtToEbitda'\r\n sunshineDailyForecast = 'sunshineDailyForecast'\r\n sentimentScore = 'sentimentScore'\r\n customerBuySell = 'customerBuySell'\r\n assetParametersUnderlierType = 'assetParametersUnderlierType'\r\n annualizedZCRate = 'annualizedZCRate'\r\n fxQuotedDeltaNoPremiumAdjustment = 'fxQuotedDeltaNoPremiumAdjustment'\r\n settlementRequested = 'settlementRequested'\r\n _0 = '0'\r\n _1 = '1'\r\n _2 = '2'\r\n _3 = '3'\r\n correlation = 'correlation'\r\n exposure = 'exposure'\r\n size = 'size'\r\n _4 = '4'\r\n _5 = '5'\r\n _6 = '6'\r\n _7 = '7'\r\n marketDataAsset = 'marketDataAsset'\r\n _8 = '8'\r\n _9 = '9'\r\n buy75cents = 'buy75cents'\r\n unadjustedHigh = 'unadjustedHigh'\r\n sourceImportance = 'sourceImportance'\r\n closingYield = 'closingYield'\r\n wind = 'wind'\r\n sc16 = 'sc16'\r\n sc15 = 'sc15'\r\n sc12 = 'sc12'\r\n sc11 = 'sc11'\r\n primaryVwapInLimitUnrealizedBps = 'primaryVwapInLimitUnrealizedBps'\r\n displayName = 'displayName'\r\n minutesToTrade100Pct = 'minutesToTrade100Pct'\r\n sc14 = 'sc14'\r\n cumulativeVolumeInShares = 'cumulativeVolumeInShares'\r\n sc13 = 'sc13'\r\n newFatalities = 'newFatalities'\r\n buy50bps = 'buy50bps'\r\n numStaffedBeds = 'numStaffedBeds'\r\n upfrontPayment = 'upfrontPayment'\r\n annualizedRMCTRisk = 'annualizedRMCTRisk'\r\n arrivalMidRealizedCash = 'arrivalMidRealizedCash'\r\n sc10 = 'sc10'\r\n lowLeverage = 'lowLeverage'\r\n sc05 = 'sc05'\r\n lastTradingDateRule = 'lastTradingDateRule'\r\n a = 'a'\r\n sc04 = 'sc04'\r\n manualPricingTrader = 'manualPricingTrader'\r\n b = 'b'\r\n sc07 = 'sc07'\r\n c = 'c'\r\n yieldToMaturity = 'yieldToMaturity'\r\n sc06 = 'sc06'\r\n address = 'address'\r\n sc01 = 'sc01'\r\n leg2PaymentFrequency = 'leg2PaymentFrequency'\r\n sc03 = 'sc03'\r\n sc02 = 'sc02'\r\n geographyName = 'geographyName'\r\n borrower = 'borrower'\r\n shareClassType = 'shareClassType'\r\n settlePrice = 'settlePrice'\r\n currentConstituentsDividendYield = 'currentConstituentsDividendYield'\r\n assetParametersPutAmount = 'assetParametersPutAmount'\r\n assetParametersUnderlier = 'assetParametersUnderlier'\r\n performanceContribution = 'performanceContribution'\r\n nextRebalanceDate = 'nextRebalanceDate'\r\n sc09 = 'sc09'\r\n assetClassificationsDigitalAssetClass = 'assetClassificationsDigitalAssetClass'\r\n mktClass = 'mktClass'\r\n sc08 = 'sc08'\r\n collateralization = 'collateralization'\r\n futureMonthU26 = 'futureMonthU26'\r\n fxCalcDelta = 'fxCalcDelta'\r\n futureMonthU25 = 'futureMonthU25'\r\n futureMonthU24 = 'futureMonthU24'\r\n futureMonthU23 = 'futureMonthU23'\r\n futureMonthU22 = 'futureMonthU22'\r\n statementId = 'statementId'\r\n futureMonthU21 = 'futureMonthU21'\r\n assetParametersSettlementDate = 'assetParametersSettlementDate'\r\n modifiedDuration = 'modifiedDuration'\r\n impliedRetailSellPctShares = 'impliedRetailSellPctShares'\r\n vol180d = 'vol180d'\r\n fxQuotedDelta = 'fxQuotedDelta'\r\n shortRatesContribution = 'shortRatesContribution'\r\n impliedNormalVolatility = 'impliedNormalVolatility'\r\n solarGeneration = 'solarGeneration'\r\n requestedSide = 'requestedSide'\r\n mtmPrice = 'mtmPrice'\r\n swapSpreadChange = 'swapSpreadChange'\r\n realizedArrivalPerformanceUSD = 'realizedArrivalPerformanceUSD'\r\n portfolioAssets = 'portfolioAssets'\r\n tcmCostHorizon3Hour = 'tcmCostHorizon3Hour'\r\n exchangeRate = 'exchangeRate'\r\n potentialBedCapInc = 'potentialBedCapInc'\r\n numberCovered = 'numberCovered'\r\n numberOfPositions = 'numberOfPositions'\r\n openUnadjusted = 'openUnadjusted'\r\n strikeTime = 'strikeTime'\r\n askPrice = 'askPrice'\r\n eventId = 'eventId'\r\n sectors = 'sectors'\r\n additionalPriceNotationType = 'additionalPriceNotationType'\r\n grossInvestmentQtd = 'grossInvestmentQtd'\r\n annualizedRisk = 'annualizedRisk'\r\n estimatedHoldingTimeShort = 'estimatedHoldingTimeShort'\r\n impliedRetailBuyShares = 'impliedRetailBuyShares'\r\n outrightBid = 'outrightBid'\r\n midcurvePremium = 'midcurvePremium'\r\n volumeComposite = 'volumeComposite'\r\n sharpeQtd = 'sharpeQtd'\r\n clearingExceptionOrExemptionIndicator = 'clearingExceptionOrExemptionIndicator'\r\n estimatedHoldingTimeLong = 'estimatedHoldingTimeLong'\r\n external = 'external'\r\n trackerName = 'trackerName'\r\n sell50cents = 'sell50cents'\r\n tradePrice = 'tradePrice'\r\n cleared = 'cleared'\r\n primeIdNumeric = 'primeIdNumeric'\r\n buy8bps = 'buy8bps'\r\n totalNotionalLocal = 'totalNotionalLocal'\r\n cid = 'cid'\r\n totalConfirmedSeniorHome = 'totalConfirmedSeniorHome'\r\n ctdFwdPrice = 'ctdFwdPrice'\r\n sinkFactor = 'sinkFactor'\r\n assetParametersNotionalAmountInOtherCurrency = 'assetParametersNotionalAmountInOtherCurrency'\r\n assetParametersPair = 'assetParametersPair'\r\n temperatureForecast = 'temperatureForecast'\r\n primaryAssetClass = 'primaryAssetClass'\r\n bidHigh = 'bidHigh'\r\n pnlQtd = 'pnlQtd'\r\n buy50cents = 'buy50cents'\r\n impliedRetailPctNotional = 'impliedRetailPctNotional'\r\n sell4bps = 'sell4bps'\r\n receiverDayCountFraction = 'receiverDayCountFraction'\r\n auctionClosePercentage = 'auctionClosePercentage'\r\n targetPrice = 'targetPrice'\r\n bosInBpsDescription = 'bosInBpsDescription'\r\n lowPrice = 'lowPrice'\r\n adv22DayPct = 'adv22DayPct'\r\n contractMonthLetters = 'contractMonthLetters'\r\n gateType = 'gateType'\r\n currentRatio = 'currentRatio'\r\n matchedMaturitySwapSpread12m = 'matchedMaturitySwapSpread12m'\r\n priceRangeInTicksLabel = 'priceRangeInTicksLabel'\r\n ticker = 'ticker'\r\n notionalUnit = 'notionalUnit'\r\n tcmCostHorizon1Day = 'tcmCostHorizon1Day'\r\n approval = 'approval'\r\n testMeasure = 'testMeasure'\r\n fwdSpreadMode = 'fwdSpreadMode'\r\n optionLockOutPeriod = 'optionLockOutPeriod'\r\n executionTime = 'executionTime'\r\n retailActivity = 'retailActivity'\r\n sourceValueForecast = 'sourceValueForecast'\r\n leg2Spread = 'leg2Spread'\r\n annualizedMCTRisk = 'annualizedMCTRisk'\r\n shortConvictionLarge = 'shortConvictionLarge'\r\n leg1FloatingRateIndex = 'leg1FloatingRateIndex'\r\n ccgName = 'ccgName'\r\n multiTags = 'multiTags'\r\n bidGSpread = 'bidGSpread'\r\n dollarExcessReturn = 'dollarExcessReturn'\r\n gsn = 'gsn'\r\n tradeEndDate = 'tradeEndDate'\r\n receiverRateOption = 'receiverRateOption'\r\n gss = 'gss'\r\n percentOfMediandv1m = 'percentOfMediandv1m'\r\n lendables = 'lendables'\r\n sell75cents = 'sell75cents'\r\n optionAdjustedSpread = 'optionAdjustedSpread'\r\n optionAdjustedSwapSpread = 'optionAdjustedSwapSpread'\r\n bosInTicksLabel = 'bosInTicksLabel'\r\n positionSourceId = 'positionSourceId'\r\n buy1bps = 'buy1bps'\r\n activeIDAtClient = 'activeIDAtClient'\r\n buy3point5bps = 'buy3point5bps'\r\n gsSustainRegion = 'gsSustainRegion'\r\n absoluteReturnWtd = 'absoluteReturnWtd'\r\n deploymentId = 'deploymentId'\r\n tradedActiveID = 'tradedActiveID'\r\n assetParametersSeniority = 'assetParametersSeniority'\r\n askSpread = 'askSpread'\r\n flow = 'flow'\r\n futureMonthH26 = 'futureMonthH26'\r\n loanRebate = 'loanRebate'\r\n futureMonthH25 = 'futureMonthH25'\r\n period = 'period'\r\n indexCreateSource = 'indexCreateSource'\r\n futureMonthH24 = 'futureMonthH24'\r\n futureMonthH23 = 'futureMonthH23'\r\n futureMonthH22 = 'futureMonthH22'\r\n futureMonthH21 = 'futureMonthH21'\r\n nonUsdOis = 'nonUsdOis'\r\n realTWIContribution = 'realTWIContribution'\r\n averageExposure = 'averageExposure'\r\n mktAsset = 'mktAsset'\r\n leg2IndexLocation = 'leg2IndexLocation'\r\n twapUnrealizedBps = 'twapUnrealizedBps'\r\n fwdEbookPointSpreadAllInMultBid = 'fwdEbookPointSpreadAllInMultBid'\r\n quantityUnitOfMeasure = 'quantityUnitOfMeasure'\r\n lastUpdatedMessage = 'lastUpdatedMessage'\r\n loanValue = 'loanValue'\r\n optionAdjustedOISSpread = 'optionAdjustedOISSpread'\r\n clientFwdSpreadMultiplier = 'clientFwdSpreadMultiplier'\r\n totalReturnPrice = 'totalReturnPrice'\r\n valueCurrency = 'valueCurrency'\r\n weightedPercentInModel = 'weightedPercentInModel'\r\n initLoanSpreadRequired = 'initLoanSpreadRequired'\r\n electionPeriod = 'electionPeriod'\r\n fundingAskPrice = 'fundingAskPrice'\r\n historicalBeta = 'historicalBeta'\r\n bondRiskPremiumIndex = 'bondRiskPremiumIndex'\r\n hitRateYtd = 'hitRateYtd'\r\n girGsdeerGsfeer = 'girGsdeerGsfeer'\r\n numUnits = 'numUnits'\r\n assetParametersReceiverFrequency = 'assetParametersReceiverFrequency'\r\n expenseRatioGrossBps = 'expenseRatioGrossBps'\r\n relativePayoffWtd = 'relativePayoffWtd'\r\n ctdPrice = 'ctdPrice'\r\n paceOfRollNow = 'paceOfRollNow'\r\n product = 'product'\r\n leg2ReturnType = 'leg2ReturnType'\r\n agentLenderFee = 'agentLenderFee'\r\n baseUSDFwdRevenue = 'baseUSDFwdRevenue'\r\n assetParametersTradeAs = 'assetParametersTradeAs'\r\n disseminationId = 'disseminationId'\r\n optionStrikePrice = 'optionStrikePrice'\r\n precipitationType = 'precipitationType'\r\n lowerBound = 'lowerBound'\r\n entity = 'entity'\r\n active1yrMarketCap = 'active1yrMarketCap'\r\n arrivalMidNormalized = 'arrivalMidNormalized'\r\n underlyingAsset2 = 'underlyingAsset2'\r\n underlyingAsset1 = 'underlyingAsset1'\r\n legalEntity = 'legalEntity'\r\n performanceFee = 'performanceFee'\r\n orderState = 'orderState'\r\n actualDataQuality = 'actualDataQuality'\r\n indexRatio = 'indexRatio'\r\n traderDescription = 'traderDescription'\r\n queueInLotsLabel = 'queueInLotsLabel'\r\n adv10DayPct = 'adv10DayPct'\r\n longConvictionMedium = 'longConvictionMedium'\r\n relativeHitRateWtd = 'relativeHitRateWtd'\r\n dailyTrackingError = 'dailyTrackingError'\r\n sell140cents = 'sell140cents'\r\n sell10bps = 'sell10bps'\r\n aggressiveOffsetFromLast = 'aggressiveOffsetFromLast'\r\n longitude = 'longitude'\r\n newIcu = 'newIcu'\r\n marketCap = 'marketCap'\r\n entryType = 'entryType'\r\n weightedAverageMid = 'weightedAverageMid'\r\n clusterRegion = 'clusterRegion'\r\n valoren = 'valoren'\r\n indexName = 'indexName'\r\n averageExecutionPrice = 'averageExecutionPrice'\r\n assetParametersNumberOfOptions = 'assetParametersNumberOfOptions'\r\n usdFwdRevenue = 'usdFwdRevenue'\r\n proceedsAssetOISSwapSpread1m = 'proceedsAssetOISSwapSpread1m'\r\n payoffWtd = 'payoffWtd'\r\n basis = 'basis'\r\n investmentRateTrend = 'investmentRateTrend'\r\n grossInvestmentMtd = 'grossInvestmentMtd'\r\n _200 = '200'\r\n hedgeId = 'hedgeId'\r\n _201 = '201'\r\n sharpeMtd = 'sharpeMtd'\r\n _202 = '202'\r\n _203 = '203'\r\n tcmCostHorizon8Day = 'tcmCostHorizon8Day'\r\n _204 = '204'\r\n residualVariance = 'residualVariance'\r\n _205 = '205'\r\n restrictInternalDerivedData = 'restrictInternalDerivedData'\r\n _206 = '206'\r\n _207 = '207'\r\n _208 = '208'\r\n adv5DayPct = 'adv5DayPct'\r\n _209 = '209'\r\n midpointFillsPercentage = 'midpointFillsPercentage'\r\n riskPackages = 'riskPackages'\r\n openInterest = 'openInterest'\r\n turnoverCompositeUnadjusted = 'turnoverCompositeUnadjusted'\r\n fwdPoints = 'fwdPoints'\r\n relativeReturnWtd = 'relativeReturnWtd'\r\n units = 'units'\r\n payerRateOption = 'payerRateOption'\r\n assetClassificationsRiskCountryName = 'assetClassificationsRiskCountryName'\r\n extMktPoint3 = 'extMktPoint3'\r\n _210 = '210'\r\n _211 = '211'\r\n matchedMaturitySwapSpread = 'matchedMaturitySwapSpread'\r\n _212 = '212'\r\n cityName = 'cityName'\r\n _213 = '213'\r\n hourlyBucket = 'hourlyBucket'\r\n _214 = '214'\r\n averageImpliedVolatility = 'averageImpliedVolatility'\r\n totalHospitalizedWithSymptoms = 'totalHospitalizedWithSymptoms'\r\n _215 = '215'\r\n _216 = '216'\r\n _217 = '217'\r\n daysOpenRealizedCash = 'daysOpenRealizedCash'\r\n _218 = '218'\r\n _219 = '219'\r\n adjustedHighPrice = 'adjustedHighPrice'\r\n proceedsAssetOISSwapSpread = 'proceedsAssetOISSwapSpread'\r\n m2RPrice = 'm2RPrice'\r\n extMktPoint1 = 'extMktPoint1'\r\n direction = 'direction'\r\n extMktPoint2 = 'extMktPoint2'\r\n subRegionCode = 'subRegionCode'\r\n assetParametersFixedRate = 'assetParametersFixedRate'\r\n tsdbSyncedSymbol = 'tsdbSyncedSymbol'\r\n factorProportionOfRisk = 'factorProportionOfRisk'\r\n isEstimatedReturn = 'isEstimatedReturn'\r\n valueForecast = 'valueForecast'\r\n totalIcu = 'totalIcu'\r\n positionSourceType = 'positionSourceType'\r\n previousCloseUnrealizedCash = 'previousCloseUnrealizedCash'\r\n minimumDenomination = 'minimumDenomination'\r\n assetParametersStrikePriceRelative = 'assetParametersStrikePriceRelative'\r\n futureValueNotional = 'futureValueNotional'\r\n participationRate = 'participationRate'\r\n obfr = 'obfr'\r\n _220 = '220'\r\n _221 = '221'\r\n _222 = '222'\r\n buy9point5bps = 'buy9point5bps'\r\n _223 = '223'\r\n specificReturn = 'specificReturn'\r\n _224 = '224'\r\n _225 = '225'\r\n optionLockPeriod = 'optionLockPeriod'\r\n _226 = '226'\r\n esMomentumPercentile = 'esMomentumPercentile'\r\n _227 = '227'\r\n _228 = '228'\r\n exchangeCurrency = 'exchangeCurrency'\r\n advPercentage = 'advPercentage'\r\n _229 = '229'\r\n leg1AveragingMethod = 'leg1AveragingMethod'\r\n activeIDLatest = 'activeIDLatest'\r\n turnoverComposite = 'turnoverComposite'\r\n forecastDate = 'forecastDate'\r\n internalIndexCalcRegion = 'internalIndexCalcRegion'\r\n positionType = 'positionType'\r\n subAssetClass = 'subAssetClass'\r\n shortInterest = 'shortInterest'\r\n referencePeriod = 'referencePeriod'\r\n adjustedVolume = 'adjustedVolume'\r\n underlyingAssetIdType = 'underlyingAssetIdType'\r\n ctdFwdYield = 'ctdFwdYield'\r\n assetParametersStart = 'assetParametersStart'\r\n secDB = 'secDB'\r\n memoryUsed = 'memoryUsed'\r\n bpeQualityStars = 'bpeQualityStars'\r\n leg = 'leg'\r\n _230 = '230'\r\n _231 = '231'\r\n _232 = '232'\r\n ctd = 'ctd'\r\n _233 = '233'\r\n _234 = '234'\r\n _235 = '235'\r\n _236 = '236'\r\n _237 = '237'\r\n _238 = '238'\r\n _239 = '239'\r\n intendedParticipationRate = 'intendedParticipationRate'\r\n leg1PaymentType = 'leg1PaymentType'\r\n tradingPnl = 'tradingPnl'\r\n collateralValueRequired = 'collateralValueRequired'\r\n buy45bps = 'buy45bps'\r\n freeFloatMarketCapRatio = 'freeFloatMarketCapRatio'\r\n priceToEarningsPositive = 'priceToEarningsPositive'\r\n outrightAsk = 'outrightAsk'\r\n assetParametersPayerCurrency = 'assetParametersPayerCurrency'\r\n forecast = 'forecast'\r\n forecastValue = 'forecastValue'\r\n meanDailyVolume5d = 'meanDailyVolume5d'\r\n _240 = '240'\r\n pnl = 'pnl'\r\n _241 = '241'\r\n _242 = '242'\r\n _243 = '243'\r\n factorZScore = 'factorZScore'\r\n volumeInLimit = 'volumeInLimit'\r\n _244 = '244'\r\n isTerritory = 'isTerritory'\r\n fwdEbookRiskDirClientSellUnder = 'fwdEbookRiskDirClientSellUnder'\r\n _245 = '245'\r\n meanDailyVolume22d = 'meanDailyVolume22d'\r\n leg2DeliveryPoint = 'leg2DeliveryPoint'\r\n _246 = '246'\r\n _247 = '247'\r\n _248 = '248'\r\n _249 = '249'\r\n tcmCostHorizon4Day = 'tcmCostHorizon4Day'\r\n styles = 'styles'\r\n shortName = 'shortName'\r\n resetFrequency1 = 'resetFrequency1'\r\n buy4bps = 'buy4bps'\r\n resetFrequency2 = 'resetFrequency2'\r\n currentConstituentsPriceToSales = 'currentConstituentsPriceToSales'\r\n otherPriceTerm = 'otherPriceTerm'\r\n bidGspread = 'bidGspread'\r\n tradedMktFwdPointsMid = 'tradedMktFwdPointsMid'\r\n openPrice = 'openPrice'\r\n rfqState = 'rfqState'\r\n psId = 'psId'\r\n hitRateMtd = 'hitRateMtd'\r\n _250 = '250'\r\n _251 = '251'\r\n _252 = '252'\r\n _253 = '253'\r\n fairVolatility = 'fairVolatility'\r\n _254 = '254'\r\n dollarCross = 'dollarCross'\r\n _255 = '255'\r\n portfolioType = 'portfolioType'\r\n _256 = '256'\r\n optionExpirationRule = 'optionExpirationRule'\r\n _257 = '257'\r\n _258 = '258'\r\n _259 = '259'\r\n currency = 'currency'\r\n clusterClass = 'clusterClass'\r\n sell50bps = 'sell50bps'\r\n futureMonthM21 = 'futureMonthM21'\r\n bidSize = 'bidSize'\r\n coordinateId = 'coordinateId'\r\n arrivalMid = 'arrivalMid'\r\n _260 = '260'\r\n _261 = '261'\r\n _262 = '262'\r\n marginalContributionToRiskPercent = 'marginalContributionToRiskPercent'\r\n _263 = '263'\r\n _264 = '264'\r\n _265 = '265'\r\n _266 = '266'\r\n assetParametersExchangeCurrency = 'assetParametersExchangeCurrency'\r\n _267 = '267'\r\n candidateName = 'candidateName'\r\n _268 = '268'\r\n _269 = '269'\r\n positionDate = 'positionDate'\r\n impliedLognormalVolatility = 'impliedLognormalVolatility'\r\n vwapInLimitUnrealizedCash = 'vwapInLimitUnrealizedCash'\r\n ratingMoodys = 'ratingMoodys'\r\n futureMonthM26 = 'futureMonthM26'\r\n futureMonthM25 = 'futureMonthM25'\r\n futureMonthM24 = 'futureMonthM24'\r\n futureMonthM23 = 'futureMonthM23'\r\n developmentStatus = 'developmentStatus'\r\n futureMonthM22 = 'futureMonthM22'\r\n flowPct = 'flowPct'\r\n source = 'source'\r\n _270 = '270'\r\n _271 = '271'\r\n assetClassificationsCountryCode = 'assetClassificationsCountryCode'\r\n _272 = '272'\r\n settleDrop = 'settleDrop'\r\n _273 = '273'\r\n _274 = '274'\r\n _275 = '275'\r\n _276 = '276'\r\n dataSetSubCategory = 'dataSetSubCategory'\r\n _277 = '277'\r\n _278 = '278'\r\n sell9point5bps = 'sell9point5bps'\r\n _279 = '279'\r\n quantityBucket = 'quantityBucket'\r\n optionStyleSDR = 'optionStyleSDR'\r\n assetParametersIdentifier = 'assetParametersIdentifier'\r\n initialIndexLevel = 'initialIndexLevel'\r\n cvaDollarChargeAsk = 'cvaDollarChargeAsk'\r\n oeName = 'oeName'\r\n given = 'given'\r\n leg2DayCountConvention = 'leg2DayCountConvention'\r\n liquidityScoreSell = 'liquidityScoreSell'\r\n delistingDate = 'delistingDate'\r\n weight = 'weight'\r\n accruedInterest = 'accruedInterest'\r\n businessScope = 'businessScope'\r\n _280 = '280'\r\n _281 = '281'\r\n _282 = '282'\r\n wtdDegreeDays = 'wtdDegreeDays'\r\n _283 = '283'\r\n _284 = '284'\r\n _285 = '285'\r\n absoluteWeight = 'absoluteWeight'\r\n _286 = '286'\r\n _287 = '287'\r\n _288 = '288'\r\n measure = 'measure'\r\n _289 = '289'\r\n return30d = 'return30d'\r\n temperatureHourlyForecast = 'temperatureHourlyForecast'\r\n icebergTipRateType = 'icebergTipRateType'\r\n sharpeYtd = 'sharpeYtd'\r\n windSpeedForecast = 'windSpeedForecast'\r\n grossInvestmentYtd = 'grossInvestmentYtd'\r\n yieldPrice = 'yieldPrice'\r\n paymentFrequencyPeriodMultiplier2 = 'paymentFrequencyPeriodMultiplier2'\r\n paymentFrequencyPeriodMultiplier1 = 'paymentFrequencyPeriodMultiplier1'\r\n leg1TotalNotionalUnit = 'leg1TotalNotionalUnit'\r\n absoluteAttribution = 'absoluteAttribution'\r\n issuePrice = 'issuePrice'\r\n quantityCcy = 'quantityCcy'\r\n askHigh = 'askHigh'\r\n expectedDataQuality = 'expectedDataQuality'\r\n regionName = 'regionName'\r\n _290 = '290'\r\n _291 = '291'\r\n _292 = '292'\r\n _293 = '293'\r\n _294 = '294'\r\n _295 = '295'\r\n _296 = '296'\r\n valueRevised = 'valueRevised'\r\n _297 = '297'\r\n _298 = '298'\r\n _299 = '299'\r\n discretionUpperBound = 'discretionUpperBound'\r\n adjustedTradePrice = 'adjustedTradePrice'\r\n forecastTime = 'forecastTime'\r\n isoSubdivisionCodeAlpha2 = 'isoSubdivisionCodeAlpha2'\r\n ctdConversionFactor = 'ctdConversionFactor'\r\n impliedRetailNotionalw5kFilter = 'impliedRetailNotionalw5kFilter'\r\n proceedsAssetSwapSpread = 'proceedsAssetSwapSpread'\r\n isADR = 'isADR'\r\n issueDate = 'issueDate'\r\n serviceId = 'serviceId'\r\n yes = 'yes'\r\n gScore = 'gScore'\r\n impliedRetailNotionalw10kFilter = 'impliedRetailNotionalw10kFilter'\r\n marketValue = 'marketValue'\r\n entityId = 'entityId'\r\n notionalCurrency1 = 'notionalCurrency1'\r\n netDebtToEbitda = 'netDebtToEbitda'\r\n numUnitsUpper = 'numUnitsUpper'\r\n notionalCurrency2 = 'notionalCurrency2'\r\n inLimitParticipationRate = 'inLimitParticipationRate'\r\n spotMarketBid = 'spotMarketBid'\r\n pressureForecast = 'pressureForecast'\r\n paid = 'paid'\r\n fixedRate = 'fixedRate'\r\n short = 'short'\r\n time = 'time'\r\n buy4point5bps = 'buy4point5bps'\r\n sell30cents = 'sell30cents'\r\n eventEndDateTime = 'eventEndDateTime'\r\n leg1PaymentFrequency = 'leg1PaymentFrequency'\r\n cmId = 'cmId'\r\n taxonomy = 'taxonomy'\r\n buy45cents = 'buy45cents'\r\n measures = 'measures'\r\n seasonalAdjustment = 'seasonalAdjustment'\r\n clientDescription = 'clientDescription'\r\n assetParametersNotionalAmount = 'assetParametersNotionalAmount'\r\n currentConstituentsEarningsPerShare = 'currentConstituentsEarningsPerShare'\r\n rankWtd = 'rankWtd'\r\n underlyer = 'underlyer'\r\n createdTime = 'createdTime'\r\n return1yr = 'return1yr'\r\n matchingOrderFwdPointBid = 'matchingOrderFwdPointBid'\r\n identifier = 'identifier'\r\n priceUnit = 'priceUnit'\r\n tradeReportRefId = 'tradeReportRefId'\r\n subdivisionId = 'subdivisionId'\r\n tokenId = 'tokenId'\r\n primaryMarketVolume = 'primaryMarketVolume'\r\n unadjustedLow = 'unadjustedLow'\r\n buy160cents = 'buy160cents'\r\n portfolioId = 'portfolioId'\r\n zSpread = 'zSpread'\r\n floatingRateResetFrequencyPeriod2 = 'floatingRateResetFrequencyPeriod2'\r\n capFloorAtmFwdRate = 'capFloorAtmFwdRate'\r\n esPercentile = 'esPercentile'\r\n tdapi = 'tdapi'\r\n floatingRateResetFrequencyPeriod1 = 'floatingRateResetFrequencyPeriod1'\r\n locationCode = 'locationCode'\r\n yieldToConvention = 'yieldToConvention'\r\n rcic = 'rcic'\r\n nameRaw = 'nameRaw'\r\n simonAssetTags = 'simonAssetTags'\r\n hitRateQtd = 'hitRateQtd'\r\n primaryVolumeInLimit = 'primaryVolumeInLimit'\r\n precipitationDailyForecastPercent = 'precipitationDailyForecastPercent'\r\n aumEnd = 'aumEnd'\r\n mktFwdPointMid = 'mktFwdPointMid'\r\n premium = 'premium'\r\n low = 'low'\r\n crossGroup = 'crossGroup'\r\n reportRunTime = 'reportRunTime'\r\n fiveDayPriceChangeBps = 'fiveDayPriceChangeBps'\r\n holdings = 'holdings'\r\n precipitationDailyForecast = 'precipitationDailyForecast'\r\n fixedRecoveryCDSFinalPrice = 'fixedRecoveryCDSFinalPrice'\r\n priceMethod = 'priceMethod'\r\n assetParametersFixedRateFrequency = 'assetParametersFixedRateFrequency'\r\n oisXccy = 'oisXccy'\r\n clientFwdPointsBid = 'clientFwdPointsBid'\r\n daysOpen = 'daysOpen'\r\n buy110cents = 'buy110cents'\r\n highInterestCoverage = 'highInterestCoverage'\r\n averageSpreadBps = 'averageSpreadBps'\r\n buy55cents = 'buy55cents'\r\n assetParametersReceiverCurrency = 'assetParametersReceiverCurrency'\r\n underlyingAssetIdSDR = 'underlyingAssetIdSDR'\r\n futureMonthQ26 = 'futureMonthQ26'\r\n issueSize = 'issueSize'\r\n futureMonthQ25 = 'futureMonthQ25'\r\n futureMonthQ24 = 'futureMonthQ24'\r\n futureMonthQ23 = 'futureMonthQ23'\r\n futureMonthQ22 = 'futureMonthQ22'\r\n pendingLoanCount = 'pendingLoanCount'\r\n futureMonthQ21 = 'futureMonthQ21'\r\n priceSpotStopLossUnit = 'priceSpotStopLossUnit'\r\n priceRangeInTicksDescription = 'priceRangeInTicksDescription'\r\n tradeVolume = 'tradeVolume'\r\n primaryCountryRic = 'primaryCountryRic'\r\n chargeInBps = 'chargeInBps'\r\n optionExpirationFrequency = 'optionExpirationFrequency'\r\n assetParametersIndexVersion = 'assetParametersIndexVersion'\r\n assetParametersCommodityReferencePrice = 'assetParametersCommodityReferencePrice'\r\n isActive = 'isActive'\r\n useMachineLearning = 'useMachineLearning'\r\n growthScore = 'growthScore'\r\n bufferThreshold = 'bufferThreshold'\r\n buy120cents = 'buy120cents'\r\n matchedMaturitySwapRate = 'matchedMaturitySwapRate'\r\n underlyingAssetName = 'underlyingAssetName'\r\n primaryVwap = 'primaryVwap'\r\n exchangeTypeId = 'exchangeTypeId'\r\n basisSwapRate = 'basisSwapRate'\r\n exchangeCode = 'exchangeCode'\r\n group = 'group'\r\n assetParametersTerminationDate = 'assetParametersTerminationDate'\r\n estimatedSpread = 'estimatedSpread'\r\n yieldChangeOnDay = 'yieldChangeOnDay'\r\n created = 'created'\r\n autoTags = 'autoTags'\r\n tcmCost = 'tcmCost'\r\n sustainJapan = 'sustainJapan'\r\n historyStartDate = 'historyStartDate'\r\n bidSpread = 'bidSpread'\r\n usdQuantity = 'usdQuantity'\r\n currentConstituentsPriceToEarningsPositive = 'currentConstituentsPriceToEarningsPositive'\r\n percentageComplete = 'percentageComplete'\r\n marketSymbol = 'marketSymbol'\r\n hedgeTrackingError = 'hedgeTrackingError'\r\n assetParametersPutCurrency = 'assetParametersPutCurrency'\r\n termStatus = 'termStatus'\r\n windSpeedType = 'windSpeedType'\r\n strikePrice = 'strikePrice'\r\n lowInterestCoverage = 'lowInterestCoverage'\r\n parAssetSwapSpread12m = 'parAssetSwapSpread12m'\r\n tradeReportId = 'tradeReportId'\r\n adjustedOpenPrice = 'adjustedOpenPrice'\r\n countryId = 'countryId'\r\n point = 'point'\r\n pnlMtd = 'pnlMtd'\r\n totalReturns = 'totalReturns'\r\n lender = 'lender'\r\n annReturn1Year = 'annReturn1Year'\r\n ctdFwdDv01 = 'ctdFwdDv01'\r\n effYield7Day = 'effYield7Day'\r\n meetingDate = 'meetingDate'\r\n alias = 'alias'\r\n calendarSpreadMispricing = 'calendarSpreadMispricing'\r\n buy140cents = 'buy140cents'\r\n priceNotation2Type = 'priceNotation2Type'\r\n fundFocus = 'fundFocus'\r\n relativeStrike = 'relativeStrike'\r\n fwdEbookRiskDirClientBuyOver = 'fwdEbookRiskDirClientBuyOver'\r\n flagship = 'flagship'\r\n additionalPriceNotation = 'additionalPriceNotation'\r\n factorCategory = 'factorCategory'\r\n equityDelta = 'equityDelta'\r\n grossWeight = 'grossWeight'\r\n listed = 'listed'\r\n sell7bps = 'sell7bps'\r\n earningsRecordType = 'earningsRecordType'\r\n mean = 'mean'\r\n askYield = 'askYield'\r\n shockStyle = 'shockStyle'\r\n impliedRetailShares = 'impliedRetailShares'\r\n methodology = 'methodology'\r\n buy25cents = 'buy25cents'\r\n amountOutstanding = 'amountOutstanding'\r\n marketPnl = 'marketPnl'\r\n sustainAsiaExJapan = 'sustainAsiaExJapan'\r\n sell6point5bps = 'sell6point5bps'\r\n neighbourAssetId = 'neighbourAssetId'\r\n countIdeasYtd = 'countIdeasYtd'\r\n simonIntlAssetTags = 'simonIntlAssetTags'\r\n path = 'path'\r\n preferredRiskModel = 'preferredRiskModel'\r\n vwapUnrealizedCash = 'vwapUnrealizedCash'\r\n payoffMtd = 'payoffMtd'\r\n spread2 = 'spread2'\r\n bosInBpsLabel = 'bosInBpsLabel'\r\n spread1 = 'spread1'\r\n bosInBps = 'bosInBps'\r\n pointClass = 'pointClass'\r\n fxSpot = 'fxSpot'\r\n currentConstituentsPriceToBook = 'currentConstituentsPriceToBook'\r\n restrictNamedIndividuals = 'restrictNamedIndividuals'\r\n pricedBy = 'pricedBy'\r\n hedgeVolatility = 'hedgeVolatility'\r\n tags = 'tags'\r\n population = 'population'\r\n underlyingAssetId = 'underlyingAssetId'\r\n realLongRatesContribution = 'realLongRatesContribution'\r\n pctprices_return = 'pctprices_return'\r\n domain = 'domain'\r\n buy80cents = 'buy80cents'\r\n forwardTenor = 'forwardTenor'\r\n averagePrice = 'averagePrice'\r\n assetParametersTotalQuantity = 'assetParametersTotalQuantity'\r\n impliedRetailPctShares = 'impliedRetailPctShares'\r\n expectedUpdateTime = 'expectedUpdateTime'\r\n targetPriceRealizedBps = 'targetPriceRealizedBps'\r\n leg2FixedRate = 'leg2FixedRate'\r\n shareClassAssets = 'shareClassAssets'\r\n annuity = 'annuity'\r\n totalCount = 'totalCount'\r\n quoteType = 'quoteType'\r\n corporateActionStatus = 'corporateActionStatus'\r\n peggedTipSize = 'peggedTipSize'\r\n uid = 'uid'\r\n esPolicyPercentile = 'esPolicyPercentile'\r\n usdOis = 'usdOis'\r\n term = 'term'\r\n restrictInternalGsNtk = 'restrictInternalGsNtk'\r\n fairValueGapStandardDeviation = 'fairValueGapStandardDeviation'\r\n tcmCostParticipationRate100Pct = 'tcmCostParticipationRate100Pct'\r\n relativeUniverse = 'relativeUniverse'\r\n measureIdx = 'measureIdx'\r\n executedQuantity = 'executedQuantity'\r\n fredId = 'fredId'\r\n twiContribution = 'twiContribution'\r\n cloudCoverType = 'cloudCoverType'\r\n delisted = 'delisted'\r\n currentConstituentsPriceToCash = 'currentConstituentsPriceToCash'\r\n regionalFocus = 'regionalFocus'\r\n volumePrimary = 'volumePrimary'\r\n assetParametersPayerDesignatedMaturity = 'assetParametersPayerDesignatedMaturity'\r\n buy30cents = 'buy30cents'\r\n numLegs = 'numLegs'\r\n fundingBidPrice = 'fundingBidPrice'\r\n series = 'series'\r\n sell3bps = 'sell3bps'\r\n settlementPrice = 'settlementPrice'\r\n quarter = 'quarter'\r\n outrightMarketBid = 'outrightMarketBid'\r\n sell18bps = 'sell18bps'\r\n assetParametersFloatingRateOption = 'assetParametersFloatingRateOption'\r\n TRSAskPrice = 'TRSAskPrice'\r\n realizedVwapPerformanceBps = 'realizedVwapPerformanceBps'\r\n voteShare = 'voteShare'\r\n servicingCostShortPnl = 'servicingCostShortPnl'\r\n totalConfirmed = 'totalConfirmed'\r\n isLive = 'isLive'\r\n currentConstituentsEarningsPerSharePositive = 'currentConstituentsEarningsPerSharePositive'\r\n economicForecast = 'economicForecast'\r\n plotId = 'plotId'\r\n clusterDescription = 'clusterDescription'\r\n concentrationLimit = 'concentrationLimit'\r\n windSpeed = 'windSpeed'\r\n observationHour = 'observationHour'\r\n signal = 'signal'\r\n borrowerId = 'borrowerId'\r\n dataProduct = 'dataProduct'\r\n buy7point5bps = 'buy7point5bps'\r\n limitPrice = 'limitPrice'\r\n bmPrimeId = 'bmPrimeId'\r\n dataType = 'dataType'\r\n count = 'count'\r\n conviction = 'conviction'\r\n benchmarkMaturity = 'benchmarkMaturity'\r\n grossFlowNormalized = 'grossFlowNormalized'\r\n buy14bps = 'buy14bps'\r\n factorId = 'factorId'\r\n futureMonthV26 = 'futureMonthV26'\r\n stsFxCurrency = 'stsFxCurrency'\r\n futureMonthV25 = 'futureMonthV25'\r\n bidChange = 'bidChange'\r\n month = 'month'\r\n futureMonthV24 = 'futureMonthV24'\r\n investmentWtd = 'investmentWtd'\r\n futureMonthV23 = 'futureMonthV23'\r\n fixOrderRoutingRegion = 'fixOrderRoutingRegion'\r\n futureMonthV22 = 'futureMonthV22'\r\n futureMonthV21 = 'futureMonthV21'\r\n expiration = 'expiration'\r\n leg2ResetFrequency = 'leg2ResetFrequency'\r\n controversyScore = 'controversyScore'\r\n proceedAssetSwapSpread = 'proceedAssetSwapSpread'\r\n debtEquityRatio = 'debtEquityRatio'\r\n concentrationLevel = 'concentrationLevel'\r\n weightOfFaceValue = 'weightOfFaceValue'\r\n importance = 'importance'\r\n assetClassificationsGicsSector = 'assetClassificationsGicsSector'\r\n stsAssetName = 'stsAssetName'\r\n figi = 'figi'\r\n netExposureClassification = 'netExposureClassification'\r\n settlementMethod = 'settlementMethod'\r\n receiverDesignatedMaturity = 'receiverDesignatedMaturity'\r\n title = 'title'\r\n xRefTypeId = 'xRefTypeId'\r\n duration = 'duration'\r\n load = 'load'\r\n highLeverage = 'highLeverage'\r\n alpha = 'alpha'\r\n datasetId = 'datasetId'\r\n company = 'company'\r\n settlementFrequency = 'settlementFrequency'\r\n distAvg7Day = 'distAvg7Day'\r\n inRiskModel = 'inRiskModel'\r\n dailyNetShareholderFlowsPercent = 'dailyNetShareholderFlowsPercent'\r\n filledNotionalLocal = 'filledNotionalLocal'\r\n everHospitalized = 'everHospitalized'\r\n lastRebalanceApprovalId = 'lastRebalanceApprovalId'\r\n meetingNumber = 'meetingNumber'\r\n midGspread = 'midGspread'\r\n daysOpenUnrealizedBps = 'daysOpenUnrealizedBps'\r\n longLevel = 'longLevel'\r\n dataDescription = 'dataDescription'\r\n temperatureType = 'temperatureType'\r\n isSpecialDay = 'isSpecialDay'\r\n tradedUSDDiscountFactor = 'tradedUSDDiscountFactor'\r\n gsideid = 'gsideid'\r\n repoRate = 'repoRate'\r\n division = 'division'\r\n cloudCoverDailyForecast = 'cloudCoverDailyForecast'\r\n windSpeedDailyForecast = 'windSpeedDailyForecast'\r\n executionVenueType = 'executionVenueType'\r\n assetParametersFloatingRateDayCountFraction = 'assetParametersFloatingRateDayCountFraction'\r\n firstCloseDate = 'firstCloseDate'\r\n tradeAction = 'tradeAction'\r\n action = 'action'\r\n ctdYield = 'ctdYield'\r\n arrivalHaircutVwapNormalized = 'arrivalHaircutVwapNormalized'\r\n priceComponent = 'priceComponent'\r\n queueClockTimeDescription = 'queueClockTimeDescription'\r\n assetParametersReceiverDayCountFraction = 'assetParametersReceiverDayCountFraction'\r\n percentMidExecutionQuantity = 'percentMidExecutionQuantity'\r\n deltaStrike = 'deltaStrike'\r\n cloudCover = 'cloudCover'\r\n assetParametersNotionalCurrency = 'assetParametersNotionalCurrency'\r\n buy18bps = 'buy18bps'\r\n valueActual = 'valueActual'\r\n upi = 'upi'\r\n tradeRejectionReason = 'tradeRejectionReason'\r\n shortInterestNotional = 'shortInterestNotional'\r\n fixedRate1 = 'fixedRate1'\r\n collateralCurrency = 'collateralCurrency'\r\n originalCountry = 'originalCountry'\r\n fixedRate2 = 'fixedRate2'\r\n field = 'field'\r\n dailyMarkToMarketPnl = 'dailyMarkToMarketPnl'\r\n geographicFocus = 'geographicFocus'\r\n daysOpenRealizedBps = 'daysOpenRealizedBps'\r\n hedgeGroupId = 'hedgeGroupId'\r\n fxRiskPremiumIndex = 'fxRiskPremiumIndex'\r\n skew = 'skew'\r\n status = 'status'\r\n notionalCurrency = 'notionalCurrency'\r\n sustainEmergingMarkets = 'sustainEmergingMarkets'\r\n eventDateTime = 'eventDateTime'\r\n leg1DesignatedMaturity = 'leg1DesignatedMaturity'\r\n clientName = 'clientName'\r\n totalPrice = 'totalPrice'\r\n onBehalfOf = 'onBehalfOf'\r\n testType = 'testType'\r\n accruedInterestStandard = 'accruedInterestStandard'\r\n futureMonthZ26 = 'futureMonthZ26'\r\n futureMonthZ25 = 'futureMonthZ25'\r\n ccgCode = 'ccgCode'\r\n shortExposure = 'shortExposure'\r\n leg1FixedPaymentCurrency = 'leg1FixedPaymentCurrency'\r\n map = 'map'\r\n arrivalHaircutVwap = 'arrivalHaircutVwap'\r\n executionDays = 'executionDays'\r\n recallDueDate = 'recallDueDate'\r\n mktFwdSpreadMultiplier = 'mktFwdSpreadMultiplier'\r\n impliedRetailNotionalw2kFilter = 'impliedRetailNotionalw2kFilter'\r\n forward = 'forward'\r\n strike = 'strike'\r\n spreadLimit = 'spreadLimit'\r\n sopr = 'sopr'\r\n otherPaymentAmount = 'otherPaymentAmount'\r\n productScope = 'productScope'\r\n redemptionPeriod = 'redemptionPeriod'\r\n assetParametersIssuerType = 'assetParametersIssuerType'\r\n currency1 = 'currency1'\r\n currency2 = 'currency2'\r\n _300 = '300'\r\n previousCloseRealizedBps = 'previousCloseRealizedBps'\r\n _301 = '301'\r\n _302 = '302'\r\n daysSinceReported = 'daysSinceReported'\r\n _303 = '303'\r\n eventStatus = 'eventStatus'\r\n _304 = '304'\r\n _305 = '305'\r\n _306 = '306'\r\n vwapInLimit = 'vwapInLimit'\r\n _307 = '307'\r\n fwdDuration = 'fwdDuration'\r\n _308 = '308'\r\n _309 = '309'\r\n _return = 'return'\r\n isPairBasket = 'isPairBasket'\r\n notionalAmount = 'notionalAmount'\r\n optionPremiumAmount = 'optionPremiumAmount'\r\n payOrReceive = 'payOrReceive'\r\n impliedRetailSellPctNotional = 'impliedRetailSellPctNotional'\r\n totalSevere = 'totalSevere'\r\n _310 = '310'\r\n unexecutedNotionalUSD = 'unexecutedNotionalUSD'\r\n _311 = '311'\r\n expectedResidualPercentage = 'expectedResidualPercentage'\r\n _312 = '312'\r\n _313 = '313'\r\n maturityDate = 'maturityDate'\r\n _314 = '314'\r\n traceAdvSell = 'traceAdvSell'\r\n _315 = '315'\r\n _316 = '316'\r\n eventName = 'eventName'\r\n _317 = '317'\r\n addressLine2 = 'addressLine2'\r\n _318 = '318'\r\n indicationOfOtherPriceAffectingTerm = 'indicationOfOtherPriceAffectingTerm'\r\n _319 = '319'\r\n unadjustedBid = 'unadjustedBid'\r\n backtestType = 'backtestType'\r\n gsdeer = 'gsdeer'\r\n assetParametersIssuer = 'assetParametersIssuer'\r\n gRegionalPercentile = 'gRegionalPercentile'\r\n coverageChecked = 'coverageChecked'\r\n oisXccyExSpike = 'oisXccyExSpike'\r\n chargeInEntityCurrency = 'chargeInEntityCurrency'\r\n totalRisk = 'totalRisk'\r\n mnav = 'mnav'\r\n marketVolume = 'marketVolume'\r\n swapAnnuity = 'swapAnnuity'\r\n parAssetSwapSpread = 'parAssetSwapSpread'\r\n currYield7Day = 'currYield7Day'\r\n pressure = 'pressure'\r\n shortDescription = 'shortDescription'\r\n factorProfile = 'factorProfile'\r\n daysToCover30day = 'daysToCover30day'\r\n futureMonthZ24 = 'futureMonthZ24'\r\n feed = 'feed'\r\n futureMonthZ23 = 'futureMonthZ23'\r\n _320 = '320'\r\n mktPoint1 = 'mktPoint1'\r\n futureMonthZ22 = 'futureMonthZ22'\r\n _321 = '321'\r\n futureMonthZ21 = 'futureMonthZ21'\r\n _322 = '322'\r\n futureMonthZ20 = 'futureMonthZ20'\r\n _323 = '323'\r\n _324 = '324'\r\n assetParametersCommoditySector = 'assetParametersCommoditySector'\r\n priceNotation2 = 'priceNotation2'\r\n _325 = '325'\r\n _326 = '326'\r\n _327 = '327'\r\n marketBufferThreshold = 'marketBufferThreshold'\r\n priceNotation3 = 'priceNotation3'\r\n _328 = '328'\r\n _329 = '329'\r\n mktPoint3 = 'mktPoint3'\r\n mktPoint2 = 'mktPoint2'\r\n leg2Type = 'leg2Type'\r\n mktPoint4 = 'mktPoint4'\r\n degreeDaysType = 'degreeDaysType'\r\n sentiment = 'sentiment'\r\n investmentIncome = 'investmentIncome'\r\n groupType = 'groupType'\r\n forwardPointImm = 'forwardPointImm'\r\n twap = 'twap'\r\n clientShortName = 'clientShortName'\r\n groupCategory = 'groupCategory'\r\n bidPlusAsk = 'bidPlusAsk'\r\n foreignCcyRate = 'foreignCcyRate'\r\n electionOdds = 'electionOdds'\r\n windDirectionForecast = 'windDirectionForecast'\r\n commoditiesRelated = 'commoditiesRelated'\r\n requireAnonClientName = 'requireAnonClientName'\r\n _330 = '330'\r\n _331 = '331'\r\n _332 = '332'\r\n _333 = '333'\r\n _334 = '334'\r\n _335 = '335'\r\n _336 = '336'\r\n _337 = '337'\r\n _338 = '338'\r\n _339 = '339'\r\n pricingLocation = 'pricingLocation'\r\n beta = 'beta'\r\n lastReturnsEndDate = 'lastReturnsEndDate'\r\n upfrontPaymentDate = 'upfrontPaymentDate'\r\n sell1point5bps = 'sell1point5bps'\r\n longExposure = 'longExposure'\r\n sell4point5bps = 'sell4point5bps'\r\n tcmCostParticipationRate20Pct = 'tcmCostParticipationRate20Pct'\r\n venueType = 'venueType'\r\n currentActivityIndicator = 'currentActivityIndicator'\r\n multiAssetClassSwap = 'multiAssetClassSwap'\r\n assetParametersMultiplier = 'assetParametersMultiplier'\r\n tradedPrice = 'tradedPrice'\r\n medianDailyVolume5d = 'medianDailyVolume5d'\r\n _340 = '340'\r\n _341 = '341'\r\n _342 = '342'\r\n _343 = '343'\r\n deltaChangeId = 'deltaChangeId'\r\n _344 = '344'\r\n implementationId = 'implementationId'\r\n _345 = '345'\r\n _346 = '346'\r\n _347 = '347'\r\n leg1FixedPayment = 'leg1FixedPayment'\r\n _348 = '348'\r\n esNumericScore = 'esNumericScore'\r\n _349 = '349'\r\n inBenchmark = 'inBenchmark'\r\n actionSDR = 'actionSDR'\r\n nearbyContractRule = 'nearbyContractRule'\r\n quantityFrequency = 'quantityFrequency'\r\n countIdeasQtd = 'countIdeasQtd'\r\n knockOutPrice = 'knockOutPrice'\r\n spreadCurrency1 = 'spreadCurrency1'\r\n spreadCurrency2 = 'spreadCurrency2'\r\n currentSupply = 'currentSupply'\r\n ctdAssetId = 'ctdAssetId'\r\n _350 = '350'\r\n _351 = '351'\r\n buy10bps = 'buy10bps'\r\n _352 = '352'\r\n precipitation = 'precipitation'\r\n _353 = '353'\r\n _354 = '354'\r\n _355 = '355'\r\n impliedRetailSellShares = 'impliedRetailSellShares'\r\n _356 = '356'\r\n _357 = '357'\r\n _358 = '358'\r\n valueType = 'valueType'\r\n _359 = '359'\r\n betaAdjustedNetExposure = 'betaAdjustedNetExposure'\r\n pairCalculation = 'pairCalculation'\r\n estimatedRodVolume = 'estimatedRodVolume'\r\n sell14bps = 'sell14bps'\r\n _10 = '10'\r\n _11 = '11'\r\n _12 = '12'\r\n _13 = '13'\r\n excessReturnPrice = 'excessReturnPrice'\r\n _14 = '14'\r\n _15 = '15'\r\n _16 = '16'\r\n _17 = '17'\r\n _18 = '18'\r\n _19 = '19'\r\n fxPnl = 'fxPnl'\r\n fixingDate = 'fixingDate'\r\n leg2FloatingRateIndex = 'leg2FloatingRateIndex'\r\n _360 = '360'\r\n _361 = '361'\r\n _362 = '362'\r\n _363 = '363'\r\n _364 = '364'\r\n _365 = '365'\r\n _366 = '366'\r\n assetClassificationsGicsIndustryGroup = 'assetClassificationsGicsIndustryGroup'\r\n _367 = '367'\r\n meanDailyVolume10d = 'meanDailyVolume10d'\r\n _368 = '368'\r\n indexConstituents = 'indexConstituents'\r\n _369 = '369'\r\n lendingSecId = 'lendingSecId'\r\n dollarDuration = 'dollarDuration'\r\n equityTheta = 'equityTheta'\r\n dv01 = 'dv01'\r\n startDate = 'startDate'\r\n _20 = '20'\r\n _21 = '21'\r\n fwdTier = 'fwdTier'\r\n _22 = '22'\r\n _23 = '23'\r\n mixedSwap = 'mixedSwap'\r\n swaptionPremium = 'swaptionPremium'\r\n _24 = '24'\r\n _25 = '25'\r\n _26 = '26'\r\n snowfall = 'snowfall'\r\n liquidityBucketBuy = 'liquidityBucketBuy'\r\n dayOpen = 'dayOpen'\r\n _27 = '27'\r\n mic = 'mic'\r\n hurdleType = 'hurdleType'\r\n _28 = '28'\r\n latitude = 'latitude'\r\n _29 = '29'\r\n mid = 'mid'\r\n impliedRepo = 'impliedRepo'\r\n _370 = '370'\r\n _371 = '371'\r\n long = 'long'\r\n _372 = '372'\r\n _373 = '373'\r\n firstExecutionTime = 'firstExecutionTime'\r\n shares = 'shares'\r\n _374 = '374'\r\n _375 = '375'\r\n coveredBond = 'coveredBond'\r\n regionCode = 'regionCode'\r\n buy20cents = 'buy20cents'\r\n _376 = '376'\r\n _377 = '377'\r\n _378 = '378'\r\n longWeight = 'longWeight'\r\n calculationTime = 'calculationTime'\r\n liquidityBucketSell = 'liquidityBucketSell'\r\n _379 = '379'\r\n daysOpenUnrealizedCash = 'daysOpenUnrealizedCash'\r\n temperature = 'temperature'\r\n averageRealizedVariance = 'averageRealizedVariance'\r\n leg1CommodityUnderlyerId = 'leg1CommodityUnderlyerId'\r\n ratingFitch = 'ratingFitch'\r\n financialReturnsScore = 'financialReturnsScore'\r\n transitionPlanTransparencyPercentage = 'transitionPlanTransparencyPercentage'\r\n yearOrQuarter = 'yearOrQuarter'\r\n _30 = '30'\r\n _31 = '31'\r\n _32 = '32'\r\n nonSymbolDimensions = 'nonSymbolDimensions'\r\n _33 = '33'\r\n commoditiesForecast = 'commoditiesForecast'\r\n _34 = '34'\r\n _35 = '35'\r\n covid19ByState = 'covid19ByState'\r\n _36 = '36'\r\n _37 = '37'\r\n _38 = '38'\r\n _39 = '39'\r\n percentageExpectedResidual = 'percentageExpectedResidual'\r\n hospitalName = 'hospitalName'\r\n _380 = '380'\r\n _381 = '381'\r\n _382 = '382'\r\n buy90cents = 'buy90cents'\r\n _383 = '383'\r\n _384 = '384'\r\n _385 = '385'\r\n _386 = '386'\r\n _387 = '387'\r\n periodType = 'periodType'\r\n _388 = '388'\r\n assetClassificationsCountryName = 'assetClassificationsCountryName'\r\n totalHospitalized = 'totalHospitalized'\r\n _389 = '389'\r\n peggedRefillInterval = 'peggedRefillInterval'\r\n fatalitiesProbable = 'fatalitiesProbable'\r\n tenorCurveBucket = 'tenorCurveBucket'\r\n _40 = '40'\r\n administrativeRegion = 'administrativeRegion'\r\n _41 = '41'\r\n open = 'open'\r\n _42 = '42'\r\n _43 = '43'\r\n _44 = '44'\r\n daysToCover10day = 'daysToCover10day'\r\n _45 = '45'\r\n cusip = 'cusip'\r\n totalConfirmedByState = 'totalConfirmedByState'\r\n _46 = '46'\r\n ideaActivityTime = 'ideaActivityTime'\r\n _47 = '47'\r\n crowdedScore = 'crowdedScore'\r\n _48 = '48'\r\n _49 = '49'\r\n tagsToExclude = 'tagsToExclude'\r\n windAttribute = 'windAttribute'\r\n spreadOptionAtmFwdRate = 'spreadOptionAtmFwdRate'\r\n netExposure = 'netExposure'\r\n optionEntitlement = 'optionEntitlement'\r\n _390 = '390'\r\n _391 = '391'\r\n _392 = '392'\r\n _393 = '393'\r\n _394 = '394'\r\n _395 = '395'\r\n _396 = '396'\r\n _397 = '397'\r\n _398 = '398'\r\n isLegacyPairBasket = 'isLegacyPairBasket'\r\n _399 = '399'\r\n issuerType = 'issuerType'\r\n buy70cents = 'buy70cents'\r\n strikeReference = 'strikeReference'\r\n assetCount = 'assetCount'\r\n matchingOrderFwdPointAsk = 'matchingOrderFwdPointAsk'\r\n _50 = '50'\r\n s3Float = 's3Float'\r\n _51 = '51'\r\n isOrderInLimit = 'isOrderInLimit'\r\n _52 = '52'\r\n _53 = '53'\r\n assetParametersLastFixingDate = 'assetParametersLastFixingDate'\r\n _54 = '54'\r\n fundamentalMetric = 'fundamentalMetric'\r\n _55 = '55'\r\n _56 = '56'\r\n quoteStatusId = 'quoteStatusId'\r\n assetParametersMethodOfSettlement = 'assetParametersMethodOfSettlement'\r\n _57 = '57'\r\n absoluteValue = 'absoluteValue'\r\n closingReport = 'closingReport'\r\n redemptionNoticePeriod = 'redemptionNoticePeriod'\r\n _58 = '58'\r\n previousTotalConfirmed = 'previousTotalConfirmed'\r\n _59 = '59'\r\n longTenor = 'longTenor'\r\n multiplier = 'multiplier'\r\n buy40cents = 'buy40cents'\r\n assetCountPriced = 'assetCountPriced'\r\n voteDirection = 'voteDirection'\r\n impliedRepoRate = 'impliedRepoRate'\r\n settlementCurrency = 'settlementCurrency'\r\n wtdDegreeDaysForecast = 'wtdDegreeDaysForecast'\r\n indicationOfCollateralization = 'indicationOfCollateralization'\r\n futureMonthN26 = 'futureMonthN26'\r\n _60 = '60'\r\n lendingPartnerFee = 'lendingPartnerFee'\r\n futureMonthN25 = 'futureMonthN25'\r\n _61 = '61'\r\n futureMonthN24 = 'futureMonthN24'\r\n _62 = '62'\r\n primaryVwapRealizedBps = 'primaryVwapRealizedBps'\r\n futureMonthN23 = 'futureMonthN23'\r\n _63 = '63'\r\n futureMonthN22 = 'futureMonthN22'\r\n _64 = '64'\r\n futureMonthN21 = 'futureMonthN21'\r\n _65 = '65'\r\n _66 = '66'\r\n _67 = '67'\r\n _68 = '68'\r\n _69 = '69'\r\n breakEvenInflation = 'breakEvenInflation'\r\n pnlYtd = 'pnlYtd'\r\n leg1ReturnType = 'leg1ReturnType'\r\n tenor2 = 'tenor2'\r\n resetFrequency = 'resetFrequency'\r\n assetParametersPayerFrequency = 'assetParametersPayerFrequency'\r\n degreeDaysForecast = 'degreeDaysForecast'\r\n isManuallySilenced = 'isManuallySilenced'\r\n buy3bps = 'buy3bps'\r\n lastUpdatedById = 'lastUpdatedById'\r\n legalEntityAcct = 'legalEntityAcct'\r\n targetShareholderMeetingDate = 'targetShareholderMeetingDate'\r\n assetParametersForwardPrice = 'assetParametersForwardPrice'\r\n _70 = '70'\r\n _71 = '71'\r\n _72 = '72'\r\n paceOfRollp0 = 'paceOfRollp0'\r\n _73 = '73'\r\n _74 = '74'\r\n controversyPercentile = 'controversyPercentile'\r\n leg1NotionalCurrency = 'leg1NotionalCurrency'\r\n _75 = '75'\r\n complianceEffectiveTime = 'complianceEffectiveTime'\r\n expirationDate = 'expirationDate'\r\n _76 = '76'\r\n _77 = '77'\r\n _78 = '78'\r\n _79 = '79'\r\n floatingRateDayCountFraction = 'floatingRateDayCountFraction'\r\n callLastDate = 'callLastDate'\r\n factorReturn = 'factorReturn'\r\n passiveFlowRatio = 'passiveFlowRatio'\r\n composite5DayAdv = 'composite5DayAdv'\r\n squeezeRisk = 'squeezeRisk'\r\n marginalContributionToRisk = 'marginalContributionToRisk'\r\n closeDate = 'closeDate'\r\n temperatureHourForecast = 'temperatureHourForecast'\r\n newIdeasWtd = 'newIdeasWtd'\r\n assetClassSDR = 'assetClassSDR'\r\n yieldToWorst = 'yieldToWorst'\r\n assetParametersForwardRate = 'assetParametersForwardRate'\r\n _80 = '80'\r\n closingPrice = 'closingPrice'\r\n clientFwdPointsAsk = 'clientFwdPointsAsk'\r\n _81 = '81'\r\n turnoverCompositeAdjusted = 'turnoverCompositeAdjusted'\r\n comment = 'comment'\r\n sourceSymbol = 'sourceSymbol'\r\n _82 = '82'\r\n _83 = '83'\r\n _84 = '84'\r\n askUnadjusted = 'askUnadjusted'\r\n appliedSpeedBump = 'appliedSpeedBump'\r\n _85 = '85'\r\n _86 = '86'\r\n restrictExternalDerivedData = 'restrictExternalDerivedData'\r\n _87 = '87'\r\n _88 = '88'\r\n _89 = '89'\r\n askChange = 'askChange'\r\n countIdeasMtd = 'countIdeasMtd'\r\n endDate = 'endDate'\r\n sunshine = 'sunshine'\r\n contractType = 'contractType'\r\n momentumType = 'momentumType'\r\n specificRisk = 'specificRisk'\r\n chargeInQuoteConventionTwo = 'chargeInQuoteConventionTwo'\r\n assetParametersIndex = 'assetParametersIndex'\r\n freeFloatMarketCap = 'freeFloatMarketCap'\r\n shortMomentum = 'shortMomentum'\r\n mdapi = 'mdapi'\r\n payoffQtd = 'payoffQtd'\r\n loss = 'loss'\r\n midcurveVol = 'midcurveVol'\r\n sell6bps = 'sell6bps'\r\n tradingCostPnl = 'tradingCostPnl'\r\n priceNotationType = 'priceNotationType'\r\n price = 'price'\r\n paymentQuantity = 'paymentQuantity'\r\n _90 = '90'\r\n strategyAum = 'strategyAum'\r\n defensive = 'defensive'\r\n _91 = '91'\r\n _92 = '92'\r\n _93 = '93'\r\n assetParametersCallAmount = 'assetParametersCallAmount'\r\n _94 = '94'\r\n _95 = '95'\r\n outrightMarketAsk = 'outrightMarketAsk'\r\n _96 = '96'\r\n _97 = '97'\r\n _98 = '98'\r\n redemptionDate = 'redemptionDate'\r\n _99 = '99'\r\n leg2NotionalCurrency = 'leg2NotionalCurrency'\r\n subRegion = 'subRegion'\r\n productId = 'productId'\r\n currentConstituentsSalesPerShare = 'currentConstituentsSalesPerShare'\r\n benchmark = 'benchmark'\r\n nvtAdj = 'nvtAdj'\r\n tcmCostParticipationRate15Pct = 'tcmCostParticipationRate15Pct'\r\n fiscalYear = 'fiscalYear'\r\n recallDate = 'recallDate'\r\n internal = 'internal'\r\n gender = 'gender'\r\n assetClassificationsGicsIndustry = 'assetClassificationsGicsIndustry'\r\n adjustedBidPrice = 'adjustedBidPrice'\r\n lowUnadjusted = 'lowUnadjusted'\r\n MACSSecondaryAssetClass = 'MACSSecondaryAssetClass'\r\n confirmedPerMillion = 'confirmedPerMillion'\r\n aggregatedUsdSpotExposure = 'aggregatedUsdSpotExposure'\r\n exchangeRateBasis = 'exchangeRateBasis'\r\n assetClassOrgDrgname = 'assetClassOrgDrgname'\r\n dataSourceId = 'dataSourceId'\r\n integratedScore = 'integratedScore'\r\n buy7bps = 'buy7bps'\r\n arrivalMidUnrealizedCash = 'arrivalMidUnrealizedCash'\r\n knockInPrice = 'knockInPrice'\r\n event = 'event'\r\n isIntradayAuction = 'isIntradayAuction'\r\n locationName = 'locationName'\r\n coupon = 'coupon'\r\n percentageAuctionExecutedQuantity = 'percentageAuctionExecutedQuantity'\r\n avgYield7Day = 'avgYield7Day'\r\n referenceRateEur = 'referenceRateEur'\r\n originalDisseminationId = 'originalDisseminationId'\r\n totalOnVent = 'totalOnVent'\r\n twapUnrealizedCash = 'twapUnrealizedCash'\r\n stsCreditMarket = 'stsCreditMarket'\r\n assetClassificationsDigitalAssetSector = 'assetClassificationsDigitalAssetSector'\r\n weightOfMarketValue = 'weightOfMarketValue'\r\n onsCode = 'onsCode'\r\n passiveTouchFillsPercentage = 'passiveTouchFillsPercentage'\r\n seniority = 'seniority'\r\n inflationDelta = 'inflationDelta'\r\n leg1Index = 'leg1Index'\r\n highUnadjusted = 'highUnadjusted'\r\n relativeMarginalContributionToRisk = 'relativeMarginalContributionToRisk'\r\n submissionEvent = 'submissionEvent'\r\n TVProductMnemonic = 'TVProductMnemonic'\r\n avgTradeRateLabel = 'avgTradeRateLabel'\r\n lastActivityDate = 'lastActivityDate'\r\n disseminationTime = 'disseminationTime'\r\n priceToCash = 'priceToCash'\r\n buy10cents = 'buy10cents'\r\n fwdEbookPointSpreadAllInMultAsk = 'fwdEbookPointSpreadAllInMultAsk'\r\n realizedMarketCapRatio = 'realizedMarketCapRatio'\r\n failed = 'failed'\r\n navSpread = 'navSpread'\r\n venueMIC = 'venueMIC'\r\n dollarTotalReturn = 'dollarTotalReturn'\r\n blockUnit = 'blockUnit'\r\n emissionsIntensityEnterpriseValue = 'emissionsIntensityEnterpriseValue'\r\n midSpread = 'midSpread'\r\n istatProvinceCode = 'istatProvinceCode'\r\n totalRecoveredByState = 'totalRecoveredByState'\r\n displayId = 'displayId'\r\n repurchaseRate = 'repurchaseRate'\r\n dataSource = 'dataSource'\r\n totalBeingTested = 'totalBeingTested'\r\n clearedOrBilateral = 'clearedOrBilateral'\r\n cvaMultiplier = 'cvaMultiplier'\r\n metricName = 'metricName'\r\n emissionsIntensityRevenue = 'emissionsIntensityRevenue'\r\n askGspread = 'askGspread'\r\n forecastHour = 'forecastHour'\r\n leg2PaymentType = 'leg2PaymentType'\r\n calSpreadMisPricing = 'calSpreadMisPricing'\r\n totalTestedNegative = 'totalTestedNegative'\r\n impliedRetailNotional = 'impliedRetailNotional'\r\n rate366 = 'rate366'\r\n currentConstituentsReturnOnEquity = 'currentConstituentsReturnOnEquity'\r\n platform = 'platform'\r\n rate365 = 'rate365'\r\n fixedRateFrequency = 'fixedRateFrequency'\r\n rate360 = 'rate360'\r\n medianDailyVolume22d = 'medianDailyVolume22d'\r\n dailyNetMarkToMarketPnl = 'dailyNetMarkToMarketPnl'\r\n globalPredictedBeta = 'globalPredictedBeta'\r\n notionalQuantity2 = 'notionalQuantity2'\r\n notionalQuantity1 = 'notionalQuantity1'\r\n isContinuous = 'isContinuous'\r\n value = 'value'\r\n payerDesignatedMaturity = 'payerDesignatedMaturity'\r\n productType = 'productType'\r\n mdv22Day = 'mdv22Day'\r\n nvtAdjFf90 = 'nvtAdjFf90'\r\n twapRealizedBps = 'twapRealizedBps'\r\n testMeasureLabel = 'testMeasureLabel'\r\n quantity = 'quantity'\r\n reportId = 'reportId'\r\n indexWeight = 'indexWeight'\r\n MACSPrimaryAssetClass = 'MACSPrimaryAssetClass'\r\n traded = 'traded'\r\n trader = 'trader'\r\n leg2PriceType = 'leg2PriceType'\r\n floatingRateResetFrequencyPeriodMultiplier2 = 'floatingRateResetFrequencyPeriodMultiplier2'\r\n totalActive = 'totalActive'\r\n floatingRateResetFrequencyPeriodMultiplier1 = 'floatingRateResetFrequencyPeriodMultiplier1'\r\n gsid2 = 'gsid2'\r\n matchedMaturityOISSwapSpread = 'matchedMaturityOISSwapSpread'\r\n currentConstituentsPriceToEarnings = 'currentConstituentsPriceToEarnings'\r\n valuationDate = 'valuationDate'\r\n restrictGsFederation = 'restrictGsFederation'\r\n positionSource = 'positionSource'\r\n tcmCostHorizon6Hour = 'tcmCostHorizon6Hour'\r\n commodityReferencePrice = 'commodityReferencePrice'\r\n buy200cents = 'buy200cents'\r\n vwapUnrealizedBps = 'vwapUnrealizedBps'\r\n priceToBook = 'priceToBook'\r\n isin = 'isin'\r\n fwdEbookRiskSpreadMultBid = 'fwdEbookRiskSpreadMultBid'\r\n transitionPlanTransparencyPercentile = 'transitionPlanTransparencyPercentile'\r\n assetParametersStrikeType = 'assetParametersStrikeType'\r\n plId = 'plId'\r\n lastReturnsStartDate = 'lastReturnsStartDate'\r\n collateralValueVariance = 'collateralValueVariance'\r\n year = 'year'\r\n forecastPeriod = 'forecastPeriod'\r\n callFirstDate = 'callFirstDate'\r\n dataSetIds = 'dataSetIds'\r\n economicTermsHash = 'economicTermsHash'\r\n numBeds = 'numBeds'\r\n sell20bps = 'sell20bps'\r\n clientType = 'clientType'\r\n percentageCloseExecutedQuantity = 'percentageCloseExecutedQuantity'\r\n averageFillPriceExcludingFees = 'averageFillPriceExcludingFees'\r\n macaulayDuration = 'macaulayDuration'\r\n availableInventory = 'availableInventory'\r\n est1DayCompletePct = 'est1DayCompletePct'\r\n relativeHitRateYtd = 'relativeHitRateYtd'\r\n gSpread = 'gSpread'\r\n rai = 'rai'\r\n impliedRetailBuyPctNotional = 'impliedRetailBuyPctNotional'\r\n createdById = 'createdById'\r\n marketDataType = 'marketDataType'\r\n realShortRatesContribution = 'realShortRatesContribution'\r\n metricCategory = 'metricCategory'\r\n assetParametersCapFloor = 'assetParametersCapFloor'\r\n annualizedCarry = 'annualizedCarry'\r\n valuePrevious = 'valuePrevious'\r\n transmissionClassification = 'transmissionClassification'\r\n avgTradeRate = 'avgTradeRate'\r\n shortLevel = 'shortLevel'\r\n version = 'version'\r\n categoryType = 'categoryType'\r\n policyRateExpectation = 'policyRateExpectation'\r\n uploadDate = 'uploadDate'\r\n blockOffFacility = 'blockOffFacility'\r\n unrealizedVwapPerformanceUSD = 'unrealizedVwapPerformanceUSD'\r\n paceOfRollp75 = 'paceOfRollp75'\r\n earningsPerSharePositive = 'earningsPerSharePositive'\r\n numIcuBeds = 'numIcuBeds'\r\n bucketVolumeInPercentage = 'bucketVolumeInPercentage'\r\n estimatedTradingCost = 'estimatedTradingCost'\r\n assetClassificationsUnderliersAssetClass = 'assetClassificationsUnderliersAssetClass'\r\n eid = 'eid'\r\n calculationRegion = 'calculationRegion'\r\n relativeReturnQtd = 'relativeReturnQtd'\r\n assessedTestMeasure = 'assessedTestMeasure'\r\n mktQuotingStyle = 'mktQuotingStyle'\r\n expirationTenor = 'expirationTenor'\r\n tradedPriceNoMarkup = 'tradedPriceNoMarkup'\r\n priceLimit = 'priceLimit'\r\n marketModelId = 'marketModelId'\r\n receiverFrequency = 'receiverFrequency'\r\n realizedCorrelation = 'realizedCorrelation'\r\n issueStatus = 'issueStatus'\r\n collateralValueActual = 'collateralValueActual'\r\n atmFwdRate = 'atmFwdRate'\r\n tcmCostParticipationRate75Pct = 'tcmCostParticipationRate75Pct'\r\n close = 'close'\r\n vol30d = 'vol30d'\r\n esProductImpactScore = 'esProductImpactScore'\r\n equityVega = 'equityVega'\r\n executedFillQuantity = 'executedFillQuantity'\r\n lenderPayment = 'lenderPayment'\r\n fiveDayMove = 'fiveDayMove'\r\n realizedMarketCap = 'realizedMarketCap'\r\n valueFormat = 'valueFormat'\r\n xrefs = 'xrefs'\r\n windChillForecast = 'windChillForecast'\r\n assetParametersTenor = 'assetParametersTenor'\r\n targetNotional = 'targetNotional'\r\n fillLegId = 'fillLegId'\r\n rationale = 'rationale'\r\n realizedTwapPerformanceBps = 'realizedTwapPerformanceBps'\r\n lastUpdatedSince = 'lastUpdatedSince'\r\n totalTests = 'totalTests'\r\n equitiesContribution = 'equitiesContribution'\r\n fwdEbookPointSpreadMultAsk = 'fwdEbookPointSpreadMultAsk'\r\n simonId = 'simonId'\r\n congestion = 'congestion'\r\n leg2CommodityInstrumentId = 'leg2CommodityInstrumentId'\r\n notes = 'notes'\r\n totalProbableSeniorHome = 'totalProbableSeniorHome'\r\n eventCategory = 'eventCategory'\r\n averageFillRate = 'averageFillRate'\r\n cins = 'cins'\r\n unadjustedOpen = 'unadjustedOpen'\r\n criticality = 'criticality'\r\n bidAskSpread = 'bidAskSpread'\r\n arrivalMidUnrealizedBps = 'arrivalMidUnrealizedBps'\r\n optionType = 'optionType'\r\n terminationDate = 'terminationDate'\r\n queriesPerSecond = 'queriesPerSecond'\r\n liquidityType = 'liquidityType'\r\n creditLimit = 'creditLimit'\r\n rankQtd = 'rankQtd'\r\n combinedKey = 'combinedKey'\r\n girFxForecast = 'girFxForecast'\r\n effectiveTenor = 'effectiveTenor'\r\n girCommoditiesForecast = 'girCommoditiesForecast'\r\n relativeHumidityDailyForecast = 'relativeHumidityDailyForecast'\r\n std30DaysSubsidizedYield = 'std30DaysSubsidizedYield'\r\n annualizedTrackingError = 'annualizedTrackingError'\r\n futureMonthF26 = 'futureMonthF26'\r\n futureMonthF25 = 'futureMonthF25'\r\n volSwap = 'volSwap'\r\n futureMonthF24 = 'futureMonthF24'\r\n heatIndexDailyForecast = 'heatIndexDailyForecast'\r\n futureMonthF23 = 'futureMonthF23'\r\n realFCI = 'realFCI'\r\n blockTradesAndLargeNotionalOffFacilitySwaps = 'blockTradesAndLargeNotionalOffFacilitySwaps'\r\n futureMonthF22 = 'futureMonthF22'\r\n buy1point5bps = 'buy1point5bps'\r\n futureMonthF21 = 'futureMonthF21'\r\n expirationSettlementDate = 'expirationSettlementDate'\r\n absoluteReturnQtd = 'absoluteReturnQtd'\r\n grossExposure = 'grossExposure'\r\n volume = 'volume'\r\n adv = 'adv'\r\n shortConvictionMedium = 'shortConvictionMedium'\r\n completeTestMeasure = 'completeTestMeasure'\r\n percentPricesReturn = 'percentPricesReturn'\r\n fxQuotedVega = 'fxQuotedVega'\r\n exchange = 'exchange'\r\n esPolicyScore = 'esPolicyScore'\r\n rollVolumeStd = 'rollVolumeStd'\r\n temperatureDailyForecast = 'temperatureDailyForecast'\r\n relativePayoffQtd = 'relativePayoffQtd'\r\n onLoanPercentage = 'onLoanPercentage'\r\n fxCalcDeltaNoPremiumAdjustment = 'fxCalcDeltaNoPremiumAdjustment'\r\n twapRemainingSlices = 'twapRemainingSlices'\r\n fairVariance = 'fairVariance'\r\n hitRateWtd = 'hitRateWtd'\r\n previousCloseRealizedCash = 'previousCloseRealizedCash'\r\n estimationUniverseWeight = 'estimationUniverseWeight'\r\n realizedVolatility = 'realizedVolatility'\r\n unexecutedQuantity = 'unexecutedQuantity'\r\n clientOutrightBid = 'clientOutrightBid'\r\n proceedsAssetSwapSpread1m = 'proceedsAssetSwapSpread1m'\r\n cloneParentId = 'cloneParentId'\r\n windSpeedHourlyForecast = 'windSpeedHourlyForecast'\r\n impliedRetailSellNotional = 'impliedRetailSellNotional'\r\n etfFlowRatio = 'etfFlowRatio'\r\n assetParametersReceiverRateOption = 'assetParametersReceiverRateOption'\r\n buy60cents = 'buy60cents'\r\n securitySubTypeId = 'securitySubTypeId'\r\n coinMetricsId = 'coinMetricsId'\r\n TRSNotional = 'TRSNotional'\r\n denominated = 'denominated'\r\n message = 'message'\r\n stsRatesCountry = 'stsRatesCountry'\r\n sell65cents = 'sell65cents'\r\n assetParametersPremiumPaymentDate = 'assetParametersPremiumPaymentDate'\r\n horizon = 'horizon'\r\n wouldIfGoodLevel = 'wouldIfGoodLevel'\r\n bufferThresholdRequired = 'bufferThresholdRequired'\r\n faceValue = 'faceValue'\r\n rollVolumeHist = 'rollVolumeHist'\r\n counterPartyStatus = 'counterPartyStatus'\r\n composite22DayAdv = 'composite22DayAdv'\r\n percentageFarExecutedQuantity = 'percentageFarExecutedQuantity'\r\n tradingCentre = 'tradingCentre'\r\n loanSpreadRequired = 'loanSpreadRequired'\r\n fixingRequested = 'fixingRequested'\r\n assetClass = 'assetClass'\r\n assetClassificationsVendor = 'assetClassificationsVendor'\r\n sovereignSpreadContribution = 'sovereignSpreadContribution'\r\n ric = 'ric'\r\n bucketEndTime = 'bucketEndTime'\r\n rateType = 'rateType'\r\n totalFatalitiesSeniorHome = 'totalFatalitiesSeniorHome'\r\n loanStatus = 'loanStatus'\r\n shortWeight = 'shortWeight'\r\n geographyId = 'geographyId'\r\n sell7point5bps = 'sell7point5bps'\r\n nav = 'nav'\r\n fiscalQuarter = 'fiscalQuarter'\r\n versionString = 'versionString'\r\n payoffYtd = 'payoffYtd'\r\n marketImpact = 'marketImpact'\r\n eventType = 'eventType'\r\n fillPrice = 'fillPrice'\r\n assetCountLong = 'assetCountLong'\r\n sell180cents = 'sell180cents'\r\n expirationDateRule = 'expirationDateRule'\r\n updateSeconds = 'updateSeconds'\r\n _400 = '400'\r\n _401 = '401'\r\n _402 = '402'\r\n spot = 'spot'\r\n _403 = '403'\r\n _404 = '404'\r\n _405 = '405'\r\n _406 = '406'\r\n _407 = '407'\r\n applicationId = 'applicationId'\r\n indicativeClosePrice = 'indicativeClosePrice'\r\n _408 = '408'\r\n swapSpread = 'swapSpread'\r\n _409 = '409'\r\n tradingRestriction = 'tradingRestriction'\r\n assetParametersPayOrReceive = 'assetParametersPayOrReceive'\r\n priceSpotEntryUnit = 'priceSpotEntryUnit'\r\n unrealizedArrivalPerformanceBps = 'unrealizedArrivalPerformanceBps'\r\n city = 'city'\r\n assetParametersIndexSeries = 'assetParametersIndexSeries'\r\n pnlWtd = 'pnlWtd'\r\n covariance = 'covariance'\r\n bucketVolumeInShares = 'bucketVolumeInShares'\r\n commodityForecast = 'commodityForecast'\r\n valid = 'valid'\r\n stsCommodity = 'stsCommodity'\r\n initialPricingDate = 'initialPricingDate'\r\n indicationOfEndUserException = 'indicationOfEndUserException'\r\n _410 = '410'\r\n _411 = '411'\r\n windDirectionHourlyForecast = 'windDirectionHourlyForecast'\r\n _412 = '412'\r\n esScore = 'esScore'\r\n _413 = '413'\r\n _yield = 'yield'\r\n _414 = '414'\r\n numberOfPositionsExploded = 'numberOfPositionsExploded'\r\n fatalitiesUnderlyingConditionsPresent = 'fatalitiesUnderlyingConditionsPresent'\r\n _415 = '415'\r\n priceRangeInTicks = 'priceRangeInTicks'\r\n swapPointsMarketAsk = 'swapPointsMarketAsk'\r\n _416 = '416'\r\n _417 = '417'\r\n _418 = '418'\r\n paceOfRollp25 = 'paceOfRollp25'\r\n _419 = '419'\r\n dayCloseRealizedUSD = 'dayCloseRealizedUSD'\r\n pctChange = 'pctChange'\r\n brightnessType = 'brightnessType'\r\n futureMonth3M = 'futureMonth3M'\r\n fwdEbookRiskSpreadMultAsk = 'fwdEbookRiskSpreadMultAsk'\r\n numberOfRolls = 'numberOfRolls'\r\n isoCountryCodeNumeric = 'isoCountryCodeNumeric'\r\n priceType = 'priceType'\r\n realizedVwapPerformanceUSD = 'realizedVwapPerformanceUSD'\r\n orderSide = 'orderSide'\r\n tradingDesk = 'tradingDesk'\r\n _420 = '420'\r\n _421 = '421'\r\n fuelType = 'fuelType'\r\n _422 = '422'\r\n bbid = 'bbid'\r\n _423 = '423'\r\n vegaNotionalAmount = 'vegaNotionalAmount'\r\n _424 = '424'\r\n _425 = '425'\r\n _426 = '426'\r\n _427 = '427'\r\n _428 = '428'\r\n _429 = '429'\r\n fatalitiesUnderlyingConditionsAbsent = 'fatalitiesUnderlyingConditionsAbsent'\r\n effectiveDate = 'effectiveDate'\r\n TRSBidPrice = 'TRSBidPrice'\r\n capped = 'capped'\r\n rating = 'rating'\r\n optionCurrency = 'optionCurrency'\r\n isCloseAuction = 'isCloseAuction'\r\n volatility = 'volatility'\r\n assetClassificationsDigitalAssetMarket = 'assetClassificationsDigitalAssetMarket'\r\n avgVentUtil = 'avgVentUtil'\r\n _430 = '430'\r\n underlyingAssetIds = 'underlyingAssetIds'\r\n buy6point5bps = 'buy6point5bps'\r\n vwapInLimitRealizedCash = 'vwapInLimitRealizedCash'\r\n _431 = '431'\r\n _432 = '432'\r\n estimatedClosingAuctionVolume = 'estimatedClosingAuctionVolume'\r\n _433 = '433'\r\n _434 = '434'\r\n _435 = '435'\r\n sell2bps = 'sell2bps'\r\n _436 = '436'\r\n _437 = '437'\r\n _438 = '438'\r\n annualRisk = 'annualRisk'\r\n _439 = '439'\r\n eti = 'eti'\r\n vwapInLimitRealizedBps = 'vwapInLimitRealizedBps'\r\n rankMtd = 'rankMtd'\r\n marketBuffer = 'marketBuffer'\r\n futureMonthJ24 = 'futureMonthJ24'\r\n lastUploadedTime = 'lastUploadedTime'\r\n futureMonthJ23 = 'futureMonthJ23'\r\n oeId = 'oeId'\r\n futureMonthJ22 = 'futureMonthJ22'\r\n futureMonthJ21 = 'futureMonthJ21'\r\n bbidEquivalent = 'bbidEquivalent'\r\n initBufferThresholdRequired = 'initBufferThresholdRequired'\r\n leg2DesignatedMaturity = 'leg2DesignatedMaturity'\r\n matchedMaturityOISSwapRate = 'matchedMaturityOISSwapRate'\r\n fairPrice = 'fairPrice'\r\n participationRateInLimit = 'participationRateInLimit'\r\n extMktClass = 'extMktClass'\r\n _440 = '440'\r\n _441 = '441'\r\n priceCurrency = 'priceCurrency'\r\n _442 = '442'\r\n failedCount = 'failedCount'\r\n _443 = '443'\r\n leg1IndexLocation = 'leg1IndexLocation'\r\n _444 = '444'\r\n _445 = '445'\r\n _446 = '446'\r\n supraStrategy = 'supraStrategy'\r\n _447 = '447'\r\n _448 = '448'\r\n dayCountConvention = 'dayCountConvention'\r\n _449 = '449'\r\n roundedNotionalAmount1 = 'roundedNotionalAmount1'\r\n roundedNotionalAmount2 = 'roundedNotionalAmount2'\r\n factorSource = 'factorSource'\r\n futureMonthJ26 = 'futureMonthJ26'\r\n lendingSecType = 'lendingSecType'\r\n futureMonthJ25 = 'futureMonthJ25'\r\n exposureOrgDrgname = 'exposureOrgDrgname'\r\n leverage = 'leverage'\r\n factorExposure = 'factorExposure'\r\n forecastDay = 'forecastDay'\r\n optionFamily = 'optionFamily'\r\n generatorOutput = 'generatorOutput'\r\n priceSpotStopLossValue = 'priceSpotStopLossValue'\r\n kpiId = 'kpiId'\r\n windGeneration = 'windGeneration'\r\n percentageMidExecutedQuantity = 'percentageMidExecutedQuantity'\r\n staticVolumeForecast = 'staticVolumeForecast'\r\n borrowCost = 'borrowCost'\r\n knockOutDirection = 'knockOutDirection'\r\n screenId = 'screenId'\r\n riskModel = 'riskModel'\r\n assetParametersVendor = 'assetParametersVendor'\r\n assetParametersIndex1Tenor = 'assetParametersIndex1Tenor'\r\n isPublic = 'isPublic'\r\n fairValue = 'fairValue'\r\n openTime = 'openTime'\r\n pressureHourlyForecast = 'pressureHourlyForecast'\r\n localCcyRate = 'localCcyRate'\r\n endUserException = 'endUserException'\r\n sell90cents = 'sell90cents'\r\n executionVenue = 'executionVenue'\r\n nonStandardizedPricingIndicator = 'nonStandardizedPricingIndicator'\r\n primaryVwapInLimitRealizedBps = 'primaryVwapInLimitRealizedBps'\r\n approveRebalance = 'approveRebalance'\r\n adjustedClosePrice = 'adjustedClosePrice'\r\n lmsId = 'lmsId'\r\n rebateRate = 'rebateRate'\r\n sell130cents = 'sell130cents'\r\n speedBumpDelay = 'speedBumpDelay'\r\n priceUnitOfMeasure1 = 'priceUnitOfMeasure1'\r\n sell32bps = 'sell32bps'\r\n paceOfRollp50 = 'paceOfRollp50'\r\n priceMoveVsArrival = 'priceMoveVsArrival'\r\n strikeRelative = 'strikeRelative'\r\n pressureType = 'pressureType'\r\n buy40bps = 'buy40bps'\r\n priceNotation = 'priceNotation'\r\n strategy = 'strategy'\r\n vintageName = 'vintageName'\r\n priceUnitOfMeasure2 = 'priceUnitOfMeasure2'\r\n issueStatusDate = 'issueStatusDate'\r\n lenderIncome = 'lenderIncome'\r\n settlementCcy = 'settlementCcy'\r\n pbClientId = 'pbClientId'\r\n istatRegionCode = 'istatRegionCode'\r\n sell9bps = 'sell9bps'\r\n shortInterestPercent = 'shortInterestPercent'\r\n ownerId = 'ownerId'\r\n composite10DayAdv = 'composite10DayAdv'\r\n maxLoanBalance = 'maxLoanBalance'\r\n ideaActivityType = 'ideaActivityType'\r\n sell60cents = 'sell60cents'\r\n ideaSource = 'ideaSource'\r\n compositeFigi = 'compositeFigi'\r\n everOnVent = 'everOnVent'\r\n otcVolume = 'otcVolume'\r\n buy15cents = 'buy15cents'\r\n unadjustedAsk = 'unadjustedAsk'\r\n dynamicVolumeForecast = 'dynamicVolumeForecast'\r\n margin = 'margin'\r\n contributionName = 'contributionName'\r\n givenPlusPaid = 'givenPlusPaid'\r\n lastFillPrice = 'lastFillPrice'\r\n soprOut = 'soprOut'\r\n clientSpotAsk = 'clientSpotAsk'\r\n shortConvictionSmall = 'shortConvictionSmall'\r\n daysToCover90day = 'daysToCover90day'\r\n upfrontPaymentCurrency = 'upfrontPaymentCurrency'\r\n spotSettlementDate = 'spotSettlementDate'\r\n matrixOrder = 'matrixOrder'\r\n dayClose = 'dayClose'\r\n dateIndex = 'dateIndex'\r\n payerDayCountFraction = 'payerDayCountFraction'\r\n assetClassificationsIsPrimary = 'assetClassificationsIsPrimary'\r\n breakEvenInflationChange = 'breakEvenInflationChange'\r\n buy130cents = 'buy130cents'\r\n dwiContribution = 'dwiContribution'\r\n asset2Id = 'asset2Id'\r\n economicForecasts = 'economicForecasts'\r\n averageFillPrice = 'averageFillPrice'\r\n depthSpreadScore = 'depthSpreadScore'\r\n sell10cents = 'sell10cents'\r\n secType = 'secType'\r\n subAccount = 'subAccount'\r\n buy65cents = 'buy65cents'\r\n bondCdsBasis = 'bondCdsBasis'\r\n s3Utilization = 's3Utilization'\r\n vendor = 'vendor'\r\n passMessage = 'passMessage'\r\n dataSet = 'dataSet'\r\n totalNotionalQuantity2 = 'totalNotionalQuantity2'\r\n totalNotionalQuantity1 = 'totalNotionalQuantity1'\r\n notionalAmount2 = 'notionalAmount2'\r\n notionalAmount1 = 'notionalAmount1'\r\n queueingTime = 'queueingTime'\r\n annReturn5Year = 'annReturn5Year'\r\n volumeStartOfDay = 'volumeStartOfDay'\r\n priceNotation3Type = 'priceNotation3Type'\r\n assetParametersFloatingRateDesignatedMaturity = 'assetParametersFloatingRateDesignatedMaturity'\r\n impliedRetailBuyPctShares = 'impliedRetailBuyPctShares'\r\n executedNotionalLocal = 'executedNotionalLocal'\r\n tsdbShortname = 'tsdbShortname'\r\n businessSponsor = 'businessSponsor'\r\n unexplained = 'unexplained'\r\n seasonalAdjustmentShort = 'seasonalAdjustmentShort'\r\n metric = 'metric'\r\n ask = 'ask'\r\n closePrice = 'closePrice'\r\n endTime = 'endTime'\r\n sell100cents = 'sell100cents'\r\n executionTimestamp = 'executionTimestamp'\r\n buy180cents = 'buy180cents'\r\n predictedBeta = 'predictedBeta'\r\n absoluteStrike = 'absoluteStrike'\r\n liquidity = 'liquidity'\r\n sell3point5bps = 'sell3point5bps'\r\n liquidityScoreBuy = 'liquidityScoreBuy'\r\n paymentFrequency = 'paymentFrequency'\r\n expenseRatioNetBps = 'expenseRatioNetBps'\r\n metricType = 'metricType'\r\n rankYtd = 'rankYtd'\r\n leg1Spread = 'leg1Spread'\r\n coverageRegion = 'coverageRegion'\r\n absoluteReturnYtd = 'absoluteReturnYtd'\r\n dayCountConvention2 = 'dayCountConvention2'\r\n degreeDays = 'degreeDays'\r\n fwdPointsAsk = 'fwdPointsAsk'\r\n turnoverAdjusted = 'turnoverAdjusted'\r\n priceSpotTargetValue = 'priceSpotTargetValue'\r\n marketDataPoint = 'marketDataPoint'\r\n numOfFunds = 'numOfFunds'\r\n ebcsOutrightMid = 'ebcsOutrightMid'\r\n tradeTime = 'tradeTime'\r\n executionId = 'executionId'\r\n turnoverUnadjusted = 'turnoverUnadjusted'\r\n leg1FloatingIndex = 'leg1FloatingIndex'\r\n hedgeAnnualizedVolatility = 'hedgeAnnualizedVolatility'\r\n benchmarkCurrency = 'benchmarkCurrency'\r\n futuresContract = 'futuresContract'\r\n name = 'name'\r\n aum = 'aum'\r\n leg1DayCountConvention = 'leg1DayCountConvention'\r\n cbsCode = 'cbsCode'\r\n folderName = 'folderName'\r\n apiUsage = 'apiUsage'\r\n twapInterval = 'twapInterval'\r\n factorPnl = 'factorPnl'\r\n parameters = 'parameters'\r\n paymentFrequencyPeriod1 = 'paymentFrequencyPeriod1'\r\n uniqueId = 'uniqueId'\r\n optionExpirationDate = 'optionExpirationDate'\r\n paymentFrequencyPeriod2 = 'paymentFrequencyPeriod2'\r\n swaptionAtmFwdRate = 'swaptionAtmFwdRate'\r\n liveDate = 'liveDate'\r\n volumeForecastAdjustment = 'volumeForecastAdjustment'\r\n corporateActionType = 'corporateActionType'\r\n primeId = 'primeId'\r\n description = 'description'\r\n assetClassificationsIsCountryPrimary = 'assetClassificationsIsCountryPrimary'\r\n rebateRateLimit = 'rebateRateLimit'\r\n spotAsk = 'spotAsk'\r\n swapPointsMarketBid = 'swapPointsMarketBid'\r\n extId = 'extId'\r\n underLyingAssetsCoverage = 'underLyingAssetsCoverage'\r\n factor = 'factor'\r\n daysOnLoan = 'daysOnLoan'\r\n longConvictionSmall = 'longConvictionSmall'\r\n sell40cents = 'sell40cents'\r\n relativePayoffYtd = 'relativePayoffYtd'\r\n gsfeer = 'gsfeer'\r\n relativeHitRateQtd = 'relativeHitRateQtd'\r\n wam = 'wam'\r\n wal = 'wal'\r\n backtestId = 'backtestId'\r\n dirtyPrice = 'dirtyPrice'\r\n darkWouldRefPrice = 'darkWouldRefPrice'\r\n corporateSpreadContribution = 'corporateSpreadContribution'\r\n relativeHumidityHourlyForecast = 'relativeHumidityHourlyForecast'\r\n multipleScore = 'multipleScore'\r\n betaAdjustedExposure = 'betaAdjustedExposure'\r\n momentum = 'momentum'\r\n isAnnualized = 'isAnnualized'\r\n dividendPoints = 'dividendPoints'\r\n brightness = 'brightness'\r\n factorStandardDeviation = 'factorStandardDeviation'\r\n assetParametersReceiverDesignatedMaturity = 'assetParametersReceiverDesignatedMaturity'\r\n bosInTicksDescription = 'bosInTicksDescription'\r\n testId = 'testId'\r\n risk = 'risk'\r\n impliedCorrelation = 'impliedCorrelation'\r\n normalizedPerformance = 'normalizedPerformance'\r\n overnightNewsEndTime = 'overnightNewsEndTime'\r\n bytesConsumed = 'bytesConsumed'\r\n swaptionVol = 'swaptionVol'\r\n estimatedClosingVolume = 'estimatedClosingVolume'\r\n issuer = 'issuer'\r\n dividendYield = 'dividendYield'\r\n marketType = 'marketType'\r\n numUnitsLower = 'numUnitsLower'\r\n sourceOrigin = 'sourceOrigin'\r\n proceedsAssetSwapSpread3m = 'proceedsAssetSwapSpread3m'\r\n totalQuantity = 'totalQuantity'\r\n internalUser = 'internalUser'\r\n factSetRegionalId = 'factSetRegionalId'\r\n sell40bps = 'sell40bps'\r\n redemptionOption = 'redemptionOption'\r\n notionalUnit2 = 'notionalUnit2'\r\n notionalUnit1 = 'notionalUnit1'\r\n sedol = 'sedol'\r\n roundingCostPnl = 'roundingCostPnl'\r\n midYield = 'midYield'\r\n unexecutedNotionalLocal = 'unexecutedNotionalLocal'\r\n classifications = 'classifications'\r\n sustainGlobal = 'sustainGlobal'\r\n endingDate = 'endingDate'\r\n proceedsAssetSwapSpread12m = 'proceedsAssetSwapSpread12m'\r\n rvtAdj90 = 'rvtAdj90'\r\n grossInvestmentWtd = 'grossInvestmentWtd'\r\n annReturn3Year = 'annReturn3Year'\r\n sharpeWtd = 'sharpeWtd'\r\n discountFactor = 'discountFactor'\r\n swapPointsBid = 'swapPointsBid'\r\n relativeReturnMtd = 'relativeReturnMtd'\r\n exchangeCalendar = 'exchangeCalendar'\r\n priceChangeOnDay = 'priceChangeOnDay'\r\n buy100cents = 'buy100cents'\r\n forwardPoint = 'forwardPoint'\r\n increment = 'increment'\r\n fci = 'fci'\r\n enabled = 'enabled'\r\n recallQuantity = 'recallQuantity'\r\n strikePriceCurrency = 'strikePriceCurrency'\r\n fxPositioning = 'fxPositioning'\r\n openToPortableAlpha = 'openToPortableAlpha'\r\n gsidEquivalent = 'gsidEquivalent'\r\n categories = 'categories'\r\n extMktAsset = 'extMktAsset'\r\n quotingStyle = 'quotingStyle'\r\n isInPosition = 'isInPosition'\r\n errorMessage = 'errorMessage'\r\n compoundedFixedRate = 'compoundedFixedRate'\r\n midPrice = 'midPrice'\r\n proceedsAssetSwapSpread6m = 'proceedsAssetSwapSpread6m'\r\n stsEmDm = 'stsEmDm'\r\n TimeinYears = 'TimeinYears'\r\n embeddedOption = 'embeddedOption'\r\n tcmCostHorizon2Day = 'tcmCostHorizon2Day'\r\n ageBand = 'ageBand'\r\n returnsEnabled = 'returnsEnabled'\r\n runId = 'runId'\r\n queueInLots = 'queueInLots'\r\n tenderOfferExpirationDate = 'tenderOfferExpirationDate'\r\n assetParametersExpirationTime = 'assetParametersExpirationTime'\r\n midcurveAnnuity = 'midcurveAnnuity'\r\n lendingFundNavTrend = 'lendingFundNavTrend'\r\n cloudCoverForecast = 'cloudCoverForecast'\r\n tcmCostParticipationRate5Pct = 'tcmCostParticipationRate5Pct'\r\n defaultBackcast = 'defaultBackcast'\r\n assetParametersNumberOfShares = 'assetParametersNumberOfShares'\r\n lockup = 'lockup'\r\n lockupType = 'lockupType'\r\n newsOnIntensity = 'newsOnIntensity'\r\n priceFormingContinuationData = 'priceFormingContinuationData'\r\n adjustedShortInterest = 'adjustedShortInterest'\r\n newHospitalized = 'newHospitalized'\r\n assetParametersStrike = 'assetParametersStrike'\r\n buy35cents = 'buy35cents'\r\n impliedRetailBuyNotional = 'impliedRetailBuyNotional'\r\n leg2TotalNotional = 'leg2TotalNotional'\r\n assetParametersEffectiveDate = 'assetParametersEffectiveDate'\r\n annReturn10Year = 'annReturn10Year'\r\n numAdultIcuBeds = 'numAdultIcuBeds'\r\n daysToExpiration = 'daysToExpiration'\r\n continuationEvent = 'continuationEvent'\r\n leg2CommodityUnderlyerId = 'leg2CommodityUnderlyerId'\r\n fillPriceExcludingFees = 'fillPriceExcludingFees'\r\n wiId = 'wiId'\r\n marketCapCategory = 'marketCapCategory'\r\n historicalVolume = 'historicalVolume'\r\n buy5cents = 'buy5cents'\r\n eventStartDate = 'eventStartDate'\r\n leg1FixedRate = 'leg1FixedRate'\r\n transitionPerformancePercentage = 'transitionPerformancePercentage'\r\n equityGamma = 'equityGamma'\r\n rptId = 'rptId'\r\n grossIncome = 'grossIncome'\r\n emId = 'emId'\r\n assetCountInModel = 'assetCountInModel'\r\n stsCreditRegion = 'stsCreditRegion'\r\n minTemperature = 'minTemperature'\r\n bucketStartTime = 'bucketStartTime'\r\n medianDailyVolume10d = 'medianDailyVolume10d'\r\n fillType = 'fillType'\r\n closeTime = 'closeTime'\r\n failPct = 'failPct'\r\n isoCountryCodeAlpha2 = 'isoCountryCodeAlpha2'\r\n isoCountryCodeAlpha3 = 'isoCountryCodeAlpha3'\r\n assetParametersOptionType = 'assetParametersOptionType'\r\n amount = 'amount'\r\n lendingFundAcct = 'lendingFundAcct'\r\n fwdPricingSource = 'fwdPricingSource'\r\n rebate = 'rebate'\r\n electionType = 'electionType'\r\n relativeHitRateMtd = 'relativeHitRateMtd'\r\n impliedVolatility = 'impliedVolatility'\r\n spread = 'spread'\r\n variance = 'variance'\r\n wtdDegreeDaysDailyForecast = 'wtdDegreeDaysDailyForecast'\r\n swaptionAnnuity = 'swaptionAnnuity'\r\n latestEndDate = 'latestEndDate'\r\n buy6bps = 'buy6bps'\r\n g10Currency = 'g10Currency'\r\n humidityForecast = 'humidityForecast'\r\n relativePeriod = 'relativePeriod'\r\n user = 'user'\r\n fwdEbookPointSpreadMultBid = 'fwdEbookPointSpreadMultBid'\r\n customer = 'customer'\r\n leg1ResetFrequency = 'leg1ResetFrequency'\r\n queueClockTimeLabel = 'queueClockTimeLabel'\r\n settlementResolved = 'settlementResolved'\r\n paceOfRollp100 = 'paceOfRollp100'\r\n assetClassificationsGicsSubIndustry = 'assetClassificationsGicsSubIndustry'\r\n dewPointHourlyForecast = 'dewPointHourlyForecast'\r\n locationType = 'locationType'\r\n facetDivisionalReportingGroupId = 'facetDivisionalReportingGroupId'\r\n realizedTwapPerformanceUSD = 'realizedTwapPerformanceUSD'\r\n swapRate = 'swapRate'\r\n algoExecutionStyle = 'algoExecutionStyle'\r\n mktFwdPointBid = 'mktFwdPointBid'\r\n clientContact = 'clientContact'\r\n minTemperatureHour = 'minTemperatureHour'\r\n tradingCurrency = 'tradingCurrency'\r\n totalByOnset = 'totalByOnset'\r\n agencySwapSpread = 'agencySwapSpread'\r\n rank = 'rank'\r\n mixedSwapOtherReportedSDR = 'mixedSwapOtherReportedSDR'\r\n humidity = 'humidity'\r\n dataSetCategory = 'dataSetCategory'\r\n vwapRealizedBps = 'vwapRealizedBps'\r\n buy9bps = 'buy9bps'\r\n totalTested = 'totalTested'\r\n fatalitiesConfirmed = 'fatalitiesConfirmed'\r\n universeId1 = 'universeId1'\r\n fwdPointsBid = 'fwdPointsBid'\r\n assetParametersPayerDayCountFraction = 'assetParametersPayerDayCountFraction'\r\n universeId2 = 'universeId2'\r\n bbg = 'bbg'\r\n bidLow = 'bidLow'\r\n bucketizePrice = 'bucketizePrice'\r\n fairVarianceVolatility = 'fairVarianceVolatility'\r\n cleanPrice = 'cleanPrice'\r\n covid19 = 'covid19'\r\n clientExposure = 'clientExposure'\r\n leg2TotalNotionalUnit = 'leg2TotalNotionalUnit'\r\n sell45cents = 'sell45cents'\r\n gsSustainSubSector = 'gsSustainSubSector'\r\n sinkable = 'sinkable'\r\n isReal = 'isReal'\r\n maxTemperatureHour = 'maxTemperatureHour'\r\n leg2AveragingMethod = 'leg2AveragingMethod'\r\n pricingDate = 'pricingDate'\r\n jsn = 'jsn'\r\n sell160cents = 'sell160cents'\r\n firstExerciseDate = 'firstExerciseDate'\r\n spotBid = 'spotBid'\r\n knockInDirection = 'knockInDirection'\r\n finalCloseDate = 'finalCloseDate'\r\n dayCloseUnrealizedUSD = 'dayCloseUnrealizedUSD'\r\n tenor = 'tenor'\r\n pricingConvention = 'pricingConvention'\r\n dealableAuto = 'dealableAuto'\r\n popularity = 'popularity'\r\n floatingRateOption = 'floatingRateOption'\r\n tradedNeutralSpotMid = 'tradedNeutralSpotMid'\r\n hedgeValueType = 'hedgeValueType'\r\n assetParametersClearingHouse = 'assetParametersClearingHouse'\r\n disclaimer = 'disclaimer'\r\n payerFrequency = 'payerFrequency'\r\n assetParametersOptionStyle = 'assetParametersOptionStyle'\r\n loanFee = 'loanFee'\r\n deploymentVersion = 'deploymentVersion'\r\n buy16bps = 'buy16bps'\r\n tradeDayCount = 'tradeDayCount'\r\n transitionPerformancePercentile = 'transitionPerformancePercentile'\r\n transactionType = 'transactionType'\r\n priceToSales = 'priceToSales'\r\n secMasterId = 'secMasterId'\r\n newIdeasQtd = 'newIdeasQtd'\r\n subdivisionName = 'subdivisionName'\r\n adjustedAskPrice = 'adjustedAskPrice'\r\n fwdPointsMarketAsk = 'fwdPointsMarketAsk'\r\n factorUniverse = 'factorUniverse'\r\n arrivalRt = 'arrivalRt'\r\n internalIndexCalcAgent = 'internalIndexCalcAgent'\r\n excessMarginValue = 'excessMarginValue'\r\n transactionCost = 'transactionCost'\r\n centralBankSwapRate = 'centralBankSwapRate'\r\n previousNewConfirmed = 'previousNewConfirmed'\r\n unrealizedVwapPerformanceBps = 'unrealizedVwapPerformanceBps'\r\n degreeDaysDailyForecast = 'degreeDaysDailyForecast'\r\n positionAmount = 'positionAmount'\r\n heatIndexHourlyForecast = 'heatIndexHourlyForecast'\r\n maRank = 'maRank'\r\n fxPositioningSource = 'fxPositioningSource'\r\n vol60d = 'vol60d'\r\n eventStartDateTime = 'eventStartDateTime'\r\n impliedVolatilityByDeltaStrike = 'impliedVolatilityByDeltaStrike'\r\n mqSymbol = 'mqSymbol'\r\n numTotalUnits = 'numTotalUnits'\r\n corporateAction = 'corporateAction'\r\n leg1PriceType = 'leg1PriceType'\r\n assetParametersPayerRateOption = 'assetParametersPayerRateOption'\r\n sell20cents = 'sell20cents'\r\n leg2FixedPaymentCurrency = 'leg2FixedPaymentCurrency'\r\n gRegionalScore = 'gRegionalScore'\r\n hardToBorrow = 'hardToBorrow'\r\n sell5bps = 'sell5bps'\r\n rollVwap = 'rollVwap'\r\n wpk = 'wpk'\r\n bespokeSwap = 'bespokeSwap'\r\n assetParametersExpirationDate = 'assetParametersExpirationDate'\r\n countryName = 'countryName'\r\n carry = 'carry'\r\n startingDate = 'startingDate'\r\n loanId = 'loanId'\r\n onboarded = 'onboarded'\r\n liquidityScore = 'liquidityScore'\r\n longRatesContribution = 'longRatesContribution'\r\n sourceDateSpan = 'sourceDateSpan'\r\n annYield6Month = 'annYield6Month'\r\n underlyingDataSetId = 'underlyingDataSetId'\r\n closeUnadjusted = 'closeUnadjusted'\r\n valueUnit = 'valueUnit'\r\n voiceCurveReason = 'voiceCurveReason'\r\n quantityUnit = 'quantityUnit'\r\n adjustedLowPrice = 'adjustedLowPrice'\r\n isMomentum = 'isMomentum'\r\n longConvictionLarge = 'longConvictionLarge'\r\n spotTier = 'spotTier'\r\n oad = 'oad'\r\n rate = 'rate'\r\n couponType = 'couponType'\r\n client = 'client'\r\n esgDetailedMetric = 'esgDetailedMetric'\r\n markToRT = 'markToRT'\r\n convictionList = 'convictionList'\r\n passiveEtfRatio = 'passiveEtfRatio'\r\n futureMonthG26 = 'futureMonthG26'\r\n futureMonthG25 = 'futureMonthG25'\r\n futureMonthG24 = 'futureMonthG24'\r\n futureMonthG23 = 'futureMonthG23'\r\n typeOfReturn = 'typeOfReturn'\r\n futureMonthG22 = 'futureMonthG22'\r\n servicingCostLongPnl = 'servicingCostLongPnl'\r\n excessMarginPercentage = 'excessMarginPercentage'\r\n futureMonthG21 = 'futureMonthG21'\r\n totalMild = 'totalMild'\r\n realizedArrivalPerformanceBps = 'realizedArrivalPerformanceBps'\r\n precipitationDailyForecastInches = 'precipitationDailyForecastInches'\r\n exchangeId = 'exchangeId'\r\n leg2FixedPayment = 'leg2FixedPayment'\r\n indicativeAvailability = 'indicativeAvailability'\r\n tcmCostHorizon20Day = 'tcmCostHorizon20Day'\r\n assetClassificationsDigitalAssetIndustry = 'assetClassificationsDigitalAssetIndustry'\r\n realm = 'realm'\r\n goneManual = 'goneManual'\r\n gate = 'gate'\r\n bid = 'bid'\r\n hedgeValue = 'hedgeValue'\r\n isSeasonallyAdjusted = 'isSeasonallyAdjusted'\r\n orderStartTime = 'orderStartTime'\r\n isAggressive = 'isAggressive'\r\n floatingRateDesignatedMaturity = 'floatingRateDesignatedMaturity'\r\n percentageNearExecutedQuantity = 'percentageNearExecutedQuantity'\r\n orderId = 'orderId'\r\n hospitalType = 'hospitalType'\r\n aggregatePnl = 'aggregatePnl'\r\n dayCloseRealizedBps = 'dayCloseRealizedBps'\r\n precipitationHourlyForecast = 'precipitationHourlyForecast'\r\n forwardPriceNg = 'forwardPriceNg'\r\n marketCapUSD = 'marketCapUSD'\r\n auctionFillsPercentage = 'auctionFillsPercentage'\r\n highPrice = 'highPrice'\r\n absoluteShares = 'absoluteShares'\r\n fixedRateDayCountFraction = 'fixedRateDayCountFraction'\r\n model = 'model'\r\n unrealizedTwapPerformanceUSD = 'unrealizedTwapPerformanceUSD'\r\n id = 'id'\r\n maturity = 'maturity'\r\n deltaChange = 'deltaChange'\r\n index = 'index'\r\n finalIndexLevel = 'finalIndexLevel'\r\n unrealizedArrivalPerformanceUSD = 'unrealizedArrivalPerformanceUSD'\r\n icebergSlippage = 'icebergSlippage'\r\n sell120cents = 'sell120cents'\r\n futureMonthX26 = 'futureMonthX26'\r\n assetTypes = 'assetTypes'\r\n futureMonthX25 = 'futureMonthX25'\r\n bcid = 'bcid'\r\n mktPoint = 'mktPoint'\r\n futureMonthX24 = 'futureMonthX24'\r\n restrictionStartDate = 'restrictionStartDate'\r\n touchLiquidityScore = 'touchLiquidityScore'\r\n futureMonthX23 = 'futureMonthX23'\r\n futureMonthX22 = 'futureMonthX22'\r\n factorCategoryId = 'factorCategoryId'\r\n securityTypeId = 'securityTypeId'\r\n futureMonthX21 = 'futureMonthX21'\r\n vintage = 'vintage'\r\n investmentYtd = 'investmentYtd'\r\n leg2Notional = 'leg2Notional'\r\n sell1bps = 'sell1bps'\r\n sell200cents = 'sell200cents'\r\n expectedCompletionDate = 'expectedCompletionDate'\r\n spreadOptionVol = 'spreadOptionVol'\r\n sell80cents = 'sell80cents'\r\n impliedRetailPctAdv = 'impliedRetailPctAdv'\r\n inflationSwapRate = 'inflationSwapRate'\r\n activeQueries = 'activeQueries'\r\n sell45bps = 'sell45bps'\r\n gsLiquidityScore = 'gsLiquidityScore'\r\n embededOption = 'embededOption'\r\n chargeInLocalCurrency = 'chargeInLocalCurrency'\r\n eventSource = 'eventSource'\r\n qisPermNo = 'qisPermNo'\r\n settlement = 'settlement'\r\n shareclassId = 'shareclassId'\r\n feature2 = 'feature2'\r\n feature3 = 'feature3'\r\n settlementCurrency2 = 'settlementCurrency2'\r\n stsCommoditySector = 'stsCommoditySector'\r\n exceptionStatus = 'exceptionStatus'\r\n overnightNewsIntensity = 'overnightNewsIntensity'\r\n salesCoverage = 'salesCoverage'\r\n feature1 = 'feature1'\r\n tcmCostParticipationRate10Pct = 'tcmCostParticipationRate10Pct'\r\n eventTime = 'eventTime'\r\n positionSourceName = 'positionSourceName'\r\n covid19Vaccine = 'covid19Vaccine'\r\n deliveryDate = 'deliveryDate'\r\n settlementCurrency1 = 'settlementCurrency1'\r\n cyclical = 'cyclical'\r\n interestRate = 'interestRate'\r\n side = 'side'\r\n dynamicHybridAggressiveStyle = 'dynamicHybridAggressiveStyle'\r\n complianceRestrictedStatus = 'complianceRestrictedStatus'\r\n borrowFee = 'borrowFee'\r\n everIcu = 'everIcu'\r\n noWorseThanLevel = 'noWorseThanLevel'\r\n updateTime = 'updateTime'\r\n loanSpread = 'loanSpread'\r\n tcmCostHorizon12Hour = 'tcmCostHorizon12Hour'\r\n dewPoint = 'dewPoint'\r\n researchCommission = 'researchCommission'\r\n buy2bps = 'buy2bps'\r\n assetClassificationsRiskCountryCode = 'assetClassificationsRiskCountryCode'\r\n newIdeasMtd = 'newIdeasMtd'\r\n varSwapByExpiry = 'varSwapByExpiry'\r\n sellDate = 'sellDate'\r\n aumStart = 'aumStart'\r\n fwdEbookRiskDirClientSellOver = 'fwdEbookRiskDirClientSellOver'\r\n feedbackType = 'feedbackType'\r\n assetParametersSettlement = 'assetParametersSettlement'\r\n maxTemperature = 'maxTemperature'\r\n acquirerShareholderMeetingDate = 'acquirerShareholderMeetingDate'\r\n countryExchange = 'countryExchange'\r\n cusip8 = 'cusip8'\r\n countIdeasWtd = 'countIdeasWtd'\r\n arrivalRtNormalized = 'arrivalRtNormalized'\r\n reportType = 'reportType'\r\n sourceURL = 'sourceURL'\r\n estimatedReturn = 'estimatedReturn'\r\n tradedFwdPoints = 'tradedFwdPoints'\r\n high = 'high'\r\n sourceLastUpdate = 'sourceLastUpdate'\r\n sunshineForecast = 'sunshineForecast'\r\n quantityMW = 'quantityMW'\r\n sell70cents = 'sell70cents'\r\n sell110cents = 'sell110cents'\r\n pnodeId = 'pnodeId'\r\n price1 = 'price1'\r\n price2 = 'price2'\r\n referenceRate = 'referenceRate'\r\n humidityType = 'humidityType'\r\n prevCloseAsk = 'prevCloseAsk'\r\n level = 'level'\r\n impliedVolatilityByExpiration = 'impliedVolatilityByExpiration'\r\n hurdle = 'hurdle'\r\n assetParametersFixedRateDayCountFraction = 'assetParametersFixedRateDayCountFraction'\r\n esMomentumScore = 'esMomentumScore'\r\n leg1CommodityInstrumentId = 'leg1CommodityInstrumentId'\r\n leg2Index = 'leg2Index'\r\n netWeight = 'netWeight'\r\n portfolioManagers = 'portfolioManagers'\r\n bosInTicks = 'bosInTicks'\r\n assetParametersCouponType = 'assetParametersCouponType'\r\n swapPointsAsk = 'swapPointsAsk'\r\n expectedResidualQuantity = 'expectedResidualQuantity'\r\n clientSwapPointsBid = 'clientSwapPointsBid'\r\n rollDate = 'rollDate'\r\n dynamicHybridSpeed = 'dynamicHybridSpeed'\r\n capFloorVol = 'capFloorVol'\r\n targetQuantity = 'targetQuantity'\r\n submitter = 'submitter'\r\n no = 'no'\r\n notional = 'notional'\r\n esDisclosurePercentage = 'esDisclosurePercentage'\r\n closeExecutedQuantityPercentage = 'closeExecutedQuantityPercentage'\r\n twapRealizedCash = 'twapRealizedCash'\r\n isOpenAuction = 'isOpenAuction'\r\n leg1Type = 'leg1Type'\r\n wetBulbTempHourlyForecast = 'wetBulbTempHourlyForecast'\r\n cleanupPrice = 'cleanupPrice'\r\n externalRejectReason = 'externalRejectReason'\r\n total = 'total'\r\n filledNotionalUSD = 'filledNotionalUSD'\r\n assetId = 'assetId'\r\n blockTradeElectionIndicator = 'blockTradeElectionIndicator'\r\n testStatus = 'testStatus'\r\n mktType = 'mktType'\r\n covidDisrupted = 'covidDisrupted'\r\n lastUpdatedTime = 'lastUpdatedTime'\r\n yield30Day = 'yield30Day'\r\n optionPutPremium = 'optionPutPremium'\r\n buy28bps = 'buy28bps'\r\n proportionOfRisk = 'proportionOfRisk'\r\n futureMonthK23 = 'futureMonthK23'\r\n futureMonthK22 = 'futureMonthK22'\r\n futureMonthK21 = 'futureMonthK21'\r\n primaryEntityId = 'primaryEntityId'\r\n cross = 'cross'\r\n ideaStatus = 'ideaStatus'\r\n inCode = 'inCode'\r\n contractSubtype = 'contractSubtype'\r\n sri = 'sri'\r\n fxForecast = 'fxForecast'\r\n fixingTimeLabel = 'fixingTimeLabel'\r\n isETF = 'isETF'\r\n _100 = '100'\r\n _101 = '101'\r\n _102 = '102'\r\n _103 = '103'\r\n _104 = '104'\r\n fillId = 'fillId'\r\n excessReturns = 'excessReturns'\r\n _105 = '105'\r\n _106 = '106'\r\n dollarReturn = 'dollarReturn'\r\n orderInLimit = 'orderInLimit'\r\n expiryTime = 'expiryTime'\r\n _107 = '107'\r\n returnOnEquity = 'returnOnEquity'\r\n _108 = '108'\r\n _109 = '109'\r\n futureMonthK26 = 'futureMonthK26'\r\n futureMonthK25 = 'futureMonthK25'\r\n futureMonthK24 = 'futureMonthK24'\r\n restrictionEndDate = 'restrictionEndDate'\r\n queueInLotsDescription = 'queueInLotsDescription'\r\n volumeLimit = 'volumeLimit'\r\n objective = 'objective'\r\n navPrice = 'navPrice'\r\n leg1UnderlyingAsset = 'leg1UnderlyingAsset'\r\n _110 = '110'\r\n bbgid = 'bbgid'\r\n _111 = '111'\r\n _112 = '112'\r\n _113 = '113'\r\n privatePlacementType = 'privatePlacementType'\r\n _114 = '114'\r\n hedgeNotional = 'hedgeNotional'\r\n _115 = '115'\r\n dailyReturn = 'dailyReturn'\r\n _116 = '116'\r\n askLow = 'askLow'\r\n intendedPRate = 'intendedPRate'\r\n _117 = '117'\r\n _118 = '118'\r\n _119 = '119'\r\n expiry = 'expiry'\r\n assetParametersIndexFamily = 'assetParametersIndexFamily'\r\n avgMonthlyYield = 'avgMonthlyYield'\r\n periodDirection = 'periodDirection'\r\n prevRptId = 'prevRptId'\r\n earningsPerShare = 'earningsPerShare'\r\n strikePercentage = 'strikePercentage'\r\n esProductImpactPercentile = 'esProductImpactPercentile'\r\n vwapRealizedCash = 'vwapRealizedCash'\r\n parAssetSwapSpread1m = 'parAssetSwapSpread1m'\r\n prevCloseBid = 'prevCloseBid'\r\n minimumIncrement = 'minimumIncrement'\r\n tcmCostHorizon16Day = 'tcmCostHorizon16Day'\r\n investmentMtd = 'investmentMtd'\r\n settlementDate = 'settlementDate'\r\n weightedAverageMidNormalized = 'weightedAverageMidNormalized'\r\n _120 = '120'\r\n windowLength = 'windowLength'\r\n _121 = '121'\r\n _122 = '122'\r\n salesPerShare = 'salesPerShare'\r\n _123 = '123'\r\n _124 = '124'\r\n _125 = '125'\r\n unadjustedClose = 'unadjustedClose'\r\n _126 = '126'\r\n _127 = '127'\r\n _128 = '128'\r\n _129 = '129'\r\n loanDate = 'loanDate'\r\n matchedMaturitySwapSpread1m = 'matchedMaturitySwapSpread1m'\r\n collateralPercentageActual = 'collateralPercentageActual'\r\n vwapInLimitUnrealizedBps = 'vwapInLimitUnrealizedBps'\r\n rSquared = 'rSquared'\r\n metricValue = 'metricValue'\r\n autoExecState = 'autoExecState'\r\n totalRecovered = 'totalRecovered'\r\n relativeReturnYtd = 'relativeReturnYtd'\r\n _130 = '130'\r\n tickServer = 'tickServer'\r\n _131 = '131'\r\n _132 = '132'\r\n _133 = '133'\r\n clientOutrightAsk = 'clientOutrightAsk'\r\n _134 = '134'\r\n cumulativeVolumeInPercentage = 'cumulativeVolumeInPercentage'\r\n _135 = '135'\r\n underlyingRic = 'underlyingRic'\r\n _136 = '136'\r\n _137 = '137'\r\n _138 = '138'\r\n _139 = '139'\r\n realTimeRestrictionStatus = 'realTimeRestrictionStatus'\r\n tradeType = 'tradeType'\r\n settlementType = 'settlementType'\r\n netChange = 'netChange'\r\n percentOfIssueOutstanding = 'percentOfIssueOutstanding'\r\n numberOfUnderliers = 'numberOfUnderliers'\r\n swapType = 'swapType'\r\n forecastType = 'forecastType'\r\n leg1Notional = 'leg1Notional'\r\n sellSettleDate = 'sellSettleDate'\r\n _140 = '140'\r\n _141 = '141'\r\n _142 = '142'\r\n _143 = '143'\r\n _144 = '144'\r\n _145 = '145'\r\n _146 = '146'\r\n _147 = '147'\r\n newIdeasYtd = 'newIdeasYtd'\r\n managementFee = 'managementFee'\r\n _148 = '148'\r\n _149 = '149'\r\n parAssetSwapSpread3m = 'parAssetSwapSpread3m'\r\n sell36bps = 'sell36bps'\r\n matchedMaturitySwapSpread3m = 'matchedMaturitySwapSpread3m'\r\n sourceId = 'sourceId'\r\n country = 'country'\r\n optionPremiumCurrency = 'optionPremiumCurrency'\r\n vwap = 'vwap'\r\n touchSpreadScore = 'touchSpreadScore'\r\n lastRebalanceDate = 'lastRebalanceDate'\r\n ratingSecondHighest = 'ratingSecondHighest'\r\n sell24bps = 'sell24bps'\r\n _150 = '150'\r\n _151 = '151'\r\n _152 = '152'\r\n frequency = 'frequency'\r\n _153 = '153'\r\n _154 = '154'\r\n activityId = 'activityId'\r\n _155 = '155'\r\n estimatedImpact = 'estimatedImpact'\r\n sell35cents = 'sell35cents'\r\n _156 = '156'\r\n loanSpreadBucket = 'loanSpreadBucket'\r\n _157 = '157'\r\n _158 = '158'\r\n coronavirusGlobalActivityTracker = 'coronavirusGlobalActivityTracker'\r\n _159 = '159'\r\n underlyers = 'underlyers'\r\n assetParametersPricingLocation = 'assetParametersPricingLocation'\r\n eventDescription = 'eventDescription'\r\n icebergMaxSize = 'icebergMaxSize'\r\n assetParametersCoupon = 'assetParametersCoupon'\r\n details = 'details'\r\n sector = 'sector'\r\n mktFwdPointAsk = 'mktFwdPointAsk'\r\n avgBedUtilRate = 'avgBedUtilRate'\r\n buy20bps = 'buy20bps'\r\n indexLevel = 'indexLevel'\r\n epidemic = 'epidemic'\r\n mctr = 'mctr'\r\n exchangeTime = 'exchangeTime'\r\n historicalClose = 'historicalClose'\r\n fipsCode = 'fipsCode'\r\n _160 = '160'\r\n chargeInQuoteConvention = 'chargeInQuoteConvention'\r\n _161 = '161'\r\n buy32bps = 'buy32bps'\r\n _162 = '162'\r\n _163 = '163'\r\n ideaId = 'ideaId'\r\n commentStatus = 'commentStatus'\r\n marginalCost = 'marginalCost'\r\n _164 = '164'\r\n _165 = '165'\r\n _166 = '166'\r\n _167 = '167'\r\n _168 = '168'\r\n clientWeight = 'clientWeight'\r\n _169 = '169'\r\n leg1DeliveryPoint = 'leg1DeliveryPoint'\r\n sell5cents = 'sell5cents'\r\n liqWkly = 'liqWkly'\r\n unrealizedTwapPerformanceBps = 'unrealizedTwapPerformanceBps'\r\n region = 'region'\r\n temperatureHour = 'temperatureHour'\r\n upperBound = 'upperBound'\r\n sell55cents = 'sell55cents'\r\n spreadToBenchmark = 'spreadToBenchmark'\r\n _170 = '170'\r\n _171 = '171'\r\n numPediIcuBeds = 'numPediIcuBeds'\r\n _172 = '172'\r\n bidYield = 'bidYield'\r\n _173 = '173'\r\n assetParametersStrikePrice = 'assetParametersStrikePrice'\r\n _174 = '174'\r\n expectedResidual = 'expectedResidual'\r\n _175 = '175'\r\n fairValueGapPercent = 'fairValueGapPercent'\r\n _176 = '176'\r\n optionPremium = 'optionPremium'\r\n _177 = '177'\r\n _178 = '178'\r\n _179 = '179'\r\n ownerName = 'ownerName'\r\n parAssetSwapSpread6m = 'parAssetSwapSpread6m'\r\n zScore = 'zScore'\r\n sell12bps = 'sell12bps'\r\n eventStartTime = 'eventStartTime'\r\n matchedMaturitySwapSpread6m = 'matchedMaturitySwapSpread6m'\r\n turnover = 'turnover'\r\n priceSpotTargetUnit = 'priceSpotTargetUnit'\r\n coverage = 'coverage'\r\n gPercentile = 'gPercentile'\r\n _180 = '180'\r\n rvtAdj = 'rvtAdj'\r\n _181 = '181'\r\n _182 = '182'\r\n cloudCoverHourlyForecast = 'cloudCoverHourlyForecast'\r\n _183 = '183'\r\n assetParametersPayerSpread = 'assetParametersPayerSpread'\r\n _184 = '184'\r\n lendingFundNav = 'lendingFundNav'\r\n sourceOriginalCategory = 'sourceOriginalCategory'\r\n percentCloseExecutionQuantity = 'percentCloseExecutionQuantity'\r\n _185 = '185'\r\n latestExecutionTime = 'latestExecutionTime'\r\n _186 = '186'\r\n _187 = '187'\r\n arrivalMidRealizedBps = 'arrivalMidRealizedBps'\r\n _188 = '188'\r\n _189 = '189'\r\n location = 'location'\r\n scenarioId = 'scenarioId'\r\n terminationTenor = 'terminationTenor'\r\n queueClockTime = 'queueClockTime'\r\n discretionLowerBound = 'discretionLowerBound'\r\n tcmCostParticipationRate50Pct = 'tcmCostParticipationRate50Pct'\r\n ratingLinear = 'ratingLinear'\r\n previousCloseUnrealizedBps = 'previousCloseUnrealizedBps'\r\n _190 = '190'\r\n _191 = '191'\r\n subAssetClassForOtherCommodity = 'subAssetClassForOtherCommodity'\r\n _192 = '192'\r\n forwardPrice = 'forwardPrice'\r\n _193 = '193'\r\n type = 'type'\r\n fwdPointsMarketBid = 'fwdPointsMarketBid'\r\n _194 = '194'\r\n strikeRef = 'strikeRef'\r\n _195 = '195'\r\n _196 = '196'\r\n _197 = '197'\r\n cumulativePnl = 'cumulativePnl'\r\n _198 = '198'\r\n shortTenor = 'shortTenor'\r\n sell28bps = 'sell28bps'\r\n fundClass = 'fundClass'\r\n _199 = '199'\r\n unadjustedVolume = 'unadjustedVolume'\r\n buy36bps = 'buy36bps'\r\n positionIdx = 'positionIdx'\r\n cvaDollarChargeBid = 'cvaDollarChargeBid'\r\n midZSpread = 'midZSpread'\r\n windChillHourlyForecast = 'windChillHourlyForecast'\r\n secName = 'secName'\r\n impliedVolatilityByRelativeStrike = 'impliedVolatilityByRelativeStrike'\r\n assetParametersIndex2Tenor = 'assetParametersIndex2Tenor'\r\n percentADV = 'percentADV'\r\n referenceRateUSD = 'referenceRateUSD'\r\n leg1TotalNotional = 'leg1TotalNotional'\r\n contract = 'contract'\r\n nvtAdj90 = 'nvtAdj90'\r\n paymentFrequency1 = 'paymentFrequency1'\r\n paymentFrequency2 = 'paymentFrequency2'\r\n bespoke = 'bespoke'\r\n repoTenor = 'repoTenor'\r\n sell15cents = 'sell15cents'\r\n quoteId = 'quoteId'\r\n investmentQtd = 'investmentQtd'\r\n openToSMA = 'openToSMA'\r\n heatIndexForecast = 'heatIndexForecast'\r\n ratingStandardAndPoors = 'ratingStandardAndPoors'\r\n qualityStars = 'qualityStars'\r\n leg2FloatingIndex = 'leg2FloatingIndex'\r\n sourceTicker = 'sourceTicker'\r\n primaryVwapUnrealizedBps = 'primaryVwapUnrealizedBps'\r\n assetParametersCreditIndexSeries = 'assetParametersCreditIndexSeries'\r\n gsid = 'gsid'\r\n lendingFund = 'lendingFund'\r\n assetClassificationsDigitalAssetSubsector = 'assetClassificationsDigitalAssetSubsector'\r\n sensitivity = 'sensitivity'\r\n clientSwapPointsAsk = 'clientSwapPointsAsk'\r\n embeddedOptionType = 'embeddedOptionType'\r\n domainsData = 'domainsData'\r\n dayCount = 'dayCount'\r\n sell16bps = 'sell16bps'\r\n relativeBreakEvenInflationChange = 'relativeBreakEvenInflationChange'\r\n sell25cents = 'sell25cents'\r\n varSwap = 'varSwap'\r\n buy5point5bps = 'buy5point5bps'\r\n blockLargeNotional = 'blockLargeNotional'\r\n sell2point5bps = 'sell2point5bps'\r\n capacity = 'capacity'\r\n sectorsRaw = 'sectorsRaw'\r\n chargeInDollars = 'chargeInDollars'\r\n primaryVwapInLimit = 'primaryVwapInLimit'\r\n shareclassPrice = 'shareclassPrice'\r\n fwdEbookRiskDirClientBuyUnder = 'fwdEbookRiskDirClientBuyUnder'\r\n tradeSize = 'tradeSize'\r\n priceSpotEntryValue = 'priceSpotEntryValue'\r\n buy8point5bps = 'buy8point5bps'\r\n symbolDimensions = 'symbolDimensions'\r\n buy24bps = 'buy24bps'\r\n auctionCloseQuantity = 'auctionCloseQuantity'\r\n sidePocket = 'sidePocket'\r\n observation = 'observation'\r\n optionTypeSDR = 'optionTypeSDR'\r\n isEntity = 'isEntity'\r\n scenarioGroupId = 'scenarioGroupId'\r\n averageImpliedVariance = 'averageImpliedVariance'\r\n avgTradeRateDescription = 'avgTradeRateDescription'\r\n fraction = 'fraction'\r\n assetCountShort = 'assetCountShort'\r\n collateralPercentageRequired = 'collateralPercentageRequired'\r\n spotMarketAsk = 'spotMarketAsk'\r\n sell5point5bps = 'sell5point5bps'\r\n date = 'date'\r\n zipCode = 'zipCode'\r\n totalStdReturnSinceInception = 'totalStdReturnSinceInception'\r\n sourceCategory = 'sourceCategory'\r\n volumeUnadjusted = 'volumeUnadjusted'\r\n passiveRatio = 'passiveRatio'\r\n priceToEarnings = 'priceToEarnings'\r\n orderDepth = 'orderDepth'\r\n annYield3Month = 'annYield3Month'\r\n netFlowStd = 'netFlowStd'\r\n eZeroPriceWhenTraded = 'eZeroPriceWhenTraded'\r\n assetParametersFeeCurrency = 'assetParametersFeeCurrency'\r\n encodedStats = 'encodedStats'\r\n buy5bps = 'buy5bps'\r\n runTime = 'runTime'\r\n askSize = 'askSize'\r\n absoluteReturnMtd = 'absoluteReturnMtd'\r\n std30DaysUnsubsidizedYield = 'std30DaysUnsubsidizedYield'\r\n assetParametersReceiverSpread = 'assetParametersReceiverSpread'\r\n resource = 'resource'\r\n averageRealizedVolatility = 'averageRealizedVolatility'\r\n traceAdvBuy = 'traceAdvBuy'\r\n newConfirmed = 'newConfirmed'\r\n tax = 'tax'\r\n sell8bps = 'sell8bps'\r\n bidPrice = 'bidPrice'\r\n optionCallPremium = 'optionCallPremium'\r\n s3ShortInterestPercentFloat = 's3ShortInterestPercentFloat'\r\n sell8point5bps = 'sell8point5bps'\r\n targetPriceUnrealizedBps = 'targetPriceUnrealizedBps'\r\n clientSpotBid = 'clientSpotBid'\r\n assetParametersCallCurrency = 'assetParametersCallCurrency'\r\n esNumericPercentile = 'esNumericPercentile'\r\n leg2UnderlyingAsset = 'leg2UnderlyingAsset'\r\n csaTerms = 'csaTerms'\r\n relativePayoffMtd = 'relativePayoffMtd'\r\n dailyNetShareholderFlows = 'dailyNetShareholderFlows'\r\n buy2point5bps = 'buy2point5bps'\r\n cai = 'cai'\r\n executedNotionalUSD = 'executedNotionalUSD'\r\n systemTime = 'systemTime'\r\n totalHomeIsolation = 'totalHomeIsolation'\r\n stationName = 'stationName'\r\n passPct = 'passPct'\r\n openingReport = 'openingReport'\r\n dailyShortInterest = 'dailyShortInterest'\r\n eventTimestamp = 'eventTimestamp'\r\n tcm = 'tcm'\r\n midcurveAtmFwdRate = 'midcurveAtmFwdRate'\r\n precipitationForecast = 'precipitationForecast'\r\n equityRiskPremiumIndex = 'equityRiskPremiumIndex'\r\n fatalitiesUnderlyingConditionsUnknown = 'fatalitiesUnderlyingConditionsUnknown'\r\n tradeDate = 'tradeDate'\r\n buy12bps = 'buy12bps'\r\n clearingHouse = 'clearingHouse'\r\n dayCloseUnrealizedBps = 'dayCloseUnrealizedBps'\r\n stsRatesMaturity = 'stsRatesMaturity'\r\n stsIncludeSstkAnalytics = 'stsIncludeSstkAnalytics'\r\n nonOwnerId = 'nonOwnerId'\r\n dividendsPerShare = 'dividendsPerShare'\r\n liqDly = 'liqDly'\r\n contributorRole = 'contributorRole'\r\n totalFatalities = 'totalFatalities'\r\n internalRejectReason = 'internalRejectReason'\r\n adjustedClose = 'adjustedClose'\r\n averageValue = 'averageValue'\r\n avgInterestRate = 'avgInterestRate'\r\n basisDuration = 'basisDuration'\r\n bestMonthDate = 'bestMonthDate'\r\n bloombergTicker = 'bloombergTicker'\r\n capexDepreciation = 'capexDepreciation'\r\n capexSales = 'capexSales'\r\n cashConversion = 'cashConversion'\r\n category = 'category'\r\n convexity = 'convexity'\r\n countryCode = 'countryCode'\r\n croci = 'croci'\r\n currentValue = 'currentValue'\r\n dacf = 'dacf'\r\n dailyVolatility = 'dailyVolatility'\r\n divYield = 'divYield'\r\n dpsGrowth = 'dpsGrowth'\r\n drawdownOverReturn = 'drawdownOverReturn'\r\n ebitdaGrowth = 'ebitdaGrowth'\r\n ebitdaMargin = 'ebitdaMargin'\r\n ebitGrowth = 'ebitGrowth'\r\n ebitMargin = 'ebitMargin'\r\n evGci = 'evGci'\r\n fcfConversion = 'fcfConversion'\r\n fcfYield = 'fcfYield'\r\n gci = 'gci'\r\n grossProfTotAssets = 'grossProfTotAssets'\r\n historicCPR = 'historicCPR'\r\n incrementalMargin = 'incrementalMargin'\r\n industry = 'industry'\r\n informationRatio = 'informationRatio'\r\n interestCover = 'interestCover'\r\n lastChange = 'lastChange'\r\n lastChangePct = 'lastChangePct'\r\n lastDate = 'lastDate'\r\n lastValue = 'lastValue'\r\n liborMatchedMaturitySwap = 'liborMatchedMaturitySwap'\r\n liborOAS = 'liborOAS'\r\n liborProceedsASW = 'liborProceedsASW'\r\n liborzSpread = 'liborzSpread'\r\n manEarningGrowthMeas = 'manEarningGrowthMeas'\r\n marginalRiskContribution = 'marginalRiskContribution'\r\n maxDrawdown = 'maxDrawdown'\r\n netDebtEbitda = 'netDebtEbitda'\r\n netDebtEquity = 'netDebtEquity'\r\n niGrowth = 'niGrowth'\r\n niMargin = 'niMargin'\r\n oisMatchedMaturitySwap = 'oisMatchedMaturitySwap'\r\n oisProceedsASW = 'oisProceedsASW'\r\n oiszSpread = 'oiszSpread'\r\n optionStyle = 'optionStyle'\r\n payup = 'payup'\r\n preTaxProfitGrowth = 'preTaxProfitGrowth'\r\n riskPremiaStyles = 'riskPremiaStyles'\r\n roce = 'roce'\r\n rolldown = 'rolldown'\r\n salesGrowth = 'salesGrowth'\r\n sharpeRatio = 'sharpeRatio'\r\n totalDebtCapital = 'totalDebtCapital'\r\n totalDebtTotalAsset = 'totalDebtTotalAsset'\r\n totalReturn = 'totalReturn'\r\n unleveredFcfYield = 'unleveredFcfYield'\r\n worstMonthDate = 'worstMonthDate' \r\n\r\n\r\nclass FiniteDifferenceMethod(EnumBase, Enum): \r\n \r\n \"\"\"Direction and dimension of finite difference\"\"\"\r\n\r\n Up = 'Up'\r\n Centered = 'Centered'\r\n Down = 'Down'\r\n CenteredSecondOrder = 'CenteredSecondOrder' \r\n\r\n\r\nclass Format(EnumBase, Enum): \r\n \r\n \"\"\"Alternative format for data to be returned in\"\"\"\r\n\r\n Json = 'Json'\r\n Excel = 'Excel'\r\n MessagePack = 'MessagePack'\r\n Pdf = 'Pdf' \r\n\r\n\r\nclass FrequencyInterval(EnumBase, Enum): \r\n \r\n \"\"\"Frequency interval.\"\"\"\r\n\r\n Weekly = 'Weekly'\r\n Monthly = 'Monthly'\r\n Quarterly = 'Quarterly'\r\n Annually = 'Annually' \r\n\r\n\r\nclass InOut(EnumBase, Enum): \r\n \r\n In = 'In'\r\n Out = 'Out' \r\n\r\n\r\nclass IndexCalculationType(EnumBase, Enum): \r\n \r\n \"\"\"Quote type that is used for the bond price\"\"\"\r\n\r\n Price_Return = 'Price Return' \r\n\r\n\r\nclass IndexNotTradingReasons(EnumBase, Enum): \r\n \r\n \"\"\"Reasons the index was not traded\"\"\"\r\n\r\n Cost = 'Cost'\r\n Client_does_not_like_the_construction = 'Client does not like the construction'\r\n Basket_created_prematurely = 'Basket created prematurely'\r\n Economics_of_the_basket_changed__client_no_longer_interested_in_trading = 'Economics of the basket changed: client no longer interested in trading'\r\n GS_booking_OVER_operational_issues = 'GS booking/operational issues'\r\n _ = '' \r\n\r\n\r\nclass KnockoutConvention(EnumBase, Enum): \r\n \r\n \"\"\"Knockout convention\"\"\"\r\n\r\n Continuous = 'Continuous'\r\n Brazil = 'Brazil'\r\n INR = 'INR'\r\n Korean = 'Korean'\r\n Malaysia = 'Malaysia'\r\n Philippines = 'Philippines'\r\n Taipei = 'Taipei' \r\n\r\n\r\nclass LiquidityMeasure(EnumBase, Enum): \r\n \r\n \"\"\"A list of the different liquidity measures to choose from.\"\"\"\r\n\r\n Summary = 'Summary'\r\n Constituent_Transaction_Costs = 'Constituent Transaction Costs'\r\n Constituents = 'Constituents'\r\n Largest_Holdings_By_Weight = 'Largest Holdings By Weight'\r\n Least_Liquid_Holdings = 'Least Liquid Holdings'\r\n ADV_Percent_Buckets = 'ADV Percent Buckets'\r\n Market_Cap_Buckets = 'Market Cap Buckets'\r\n Region_Buckets = 'Region Buckets'\r\n Country_Buckets = 'Country Buckets'\r\n Sector_Buckets = 'Sector Buckets'\r\n Industry_Buckets = 'Industry Buckets'\r\n Currency_Buckets = 'Currency Buckets'\r\n Risk_Buckets = 'Risk Buckets'\r\n Factor_Risk_Buckets = 'Factor Risk Buckets'\r\n Exposure_Buckets = 'Exposure Buckets'\r\n Factor_Exposure_Buckets = 'Factor Exposure Buckets'\r\n Percent_Of_Trade_Complete_Over_Time = 'Percent Of Trade Complete Over Time'\r\n Execution_Cost_With_Different_Time_Horizons = 'Execution Cost With Different Time Horizons'\r\n Participation_Rate_With_Different_Time_Horizons = 'Participation Rate With Different Time Horizons'\r\n Risk_With_Different_Time_Horizons = 'Risk With Different Time Horizons'\r\n Historical_ADV_Percent_Curve = 'Historical ADV Percent Curve'\r\n Time_Series_Data = 'Time Series Data' \r\n\r\n\r\nclass LongShort(EnumBase, Enum): \r\n \r\n \"\"\"Client long or short on tarf\"\"\"\r\n\r\n Long = 'Long'\r\n Short = 'Short' \r\n\r\n\r\nclass MarketBehaviour(EnumBase, Enum): \r\n \r\n ContraintsBased = 'ContraintsBased'\r\n Calibrated = 'Calibrated' \r\n\r\n\r\nclass MarketDataFrequency(EnumBase, Enum): \r\n \r\n Real_Time = 'Real Time'\r\n End_Of_Day = 'End Of Day' \r\n\r\n\r\nclass MarketDataShockType(EnumBase, Enum): \r\n \r\n \"\"\"Market data shock type\"\"\"\r\n\r\n Absolute = 'Absolute'\r\n Proportional = 'Proportional'\r\n Invalid = 'Invalid'\r\n Override = 'Override'\r\n StdDev = 'StdDev'\r\n AutoDefault = 'AutoDefault'\r\n CSWFFR = 'CSWFFR'\r\n StdVolFactor = 'StdVolFactor'\r\n StdVolFactorProportional = 'StdVolFactorProportional' \r\n\r\n\r\nclass MarketDataVendor(EnumBase, Enum): \r\n \r\n Goldman_Sachs = 'Goldman Sachs'\r\n Thomson_Reuters = 'Thomson Reuters'\r\n Solactive = 'Solactive'\r\n Bloomberg = 'Bloomberg'\r\n Axioma = 'Axioma'\r\n Goldman_Sachs_Prime_Services = 'Goldman Sachs Prime Services'\r\n Goldman_Sachs_Global_Investment_Research = 'Goldman Sachs Global Investment Research'\r\n National_Weather_Service = 'National Weather Service'\r\n WM = 'WM'\r\n Hedge_Fund_Research__Inc_ = 'Hedge Fund Research, Inc.'\r\n London_Stock_Exchange = 'London Stock Exchange'\r\n Goldman_Sachs_MDFarm = 'Goldman Sachs MDFarm'\r\n PredictIt = 'PredictIt'\r\n Iowa_Electronic_Markets = 'Iowa Electronic Markets'\r\n RealClearPolitics = 'RealClearPolitics'\r\n _538 = '538'\r\n FiveThirtyEight = 'FiveThirtyEight'\r\n Opinium = 'Opinium'\r\n YouGov = 'YouGov'\r\n MorningStar = 'MorningStar'\r\n Survation = 'Survation'\r\n Survation__YouGov = 'Survation, YouGov'\r\n European_Centre_for_Disease_Prevention_and_Control = 'European Centre for Disease Prevention and Control'\r\n Centers_for_Disease_Control_and_Prevention = 'Centers for Disease Control and Prevention'\r\n Johns_Hopkins_University = 'Johns Hopkins University'\r\n Google = 'Google'\r\n National_Health_Service = 'National Health Service'\r\n World_Health_Organization = 'World Health Organization'\r\n Wikipedia = 'Wikipedia'\r\n StarSchema = 'StarSchema'\r\n Covid_Working_Group = 'Covid Working Group'\r\n CovidTracking = 'CovidTracking'\r\n Bing = 'Bing'\r\n FRED = 'FRED'\r\n Institute_for_Health_Metrics_and_Evaluation = 'Institute for Health Metrics and Evaluation'\r\n Refinitiv = 'Refinitiv'\r\n Goldman_Sachs_Global_Investment_Research__Refinitiv = 'Goldman Sachs Global Investment Research, Refinitiv'\r\n EPFR = 'EPFR'\r\n Coin_Metrics = 'Coin Metrics'\r\n MSCI = 'MSCI'\r\n MuniNet = 'MuniNet'\r\n Rearc_via_AWS_Data_Exchange = 'Rearc via AWS Data Exchange'\r\n Bank_of_Japan = 'Bank of Japan'\r\n Wolfe_Research = 'Wolfe Research'\r\n Qontigo = 'Qontigo'\r\n Quant_Insight = 'Quant Insight'\r\n FactSet_via_AWS_Data_Exchange = 'FactSet via AWS Data Exchange'\r\n Rearc = 'Rearc'\r\n FactSet = 'FactSet'\r\n WorldScope = 'WorldScope'\r\n GS_Muni = 'GS Muni'\r\n BlackRock = 'BlackRock'\r\n S3_Partners = 'S3 Partners'\r\n Turnleaf_Analytics = 'Turnleaf Analytics'\r\n Alpharoc = 'Alpharoc'\r\n LSEG = 'LSEG'\r\n ETF_GLobal = 'ETF GLobal'\r\n Northfield = 'Northfield'\r\n Haver_Analytics = 'Haver Analytics'\r\n Fineon = 'Fineon'\r\n ICE = 'ICE'\r\n CME = 'CME'\r\n Cboe = 'Cboe'\r\n Rose_AI = 'Rose.AI'\r\n QuantCube = 'QuantCube'\r\n Mkt_MediaStats = 'Mkt MediaStats' \r\n\r\n\r\nclass NewOrUnwind(EnumBase, Enum): \r\n \r\n \"\"\"New or unwnd of product\"\"\"\r\n\r\n New = 'New'\r\n Unwind = 'Unwind'\r\n Non_Standard = 'Non-Standard' \r\n\r\n\r\nclass NotionalOrStrike(EnumBase, Enum): \r\n \r\n \"\"\"Notional or Strke on target adjustment\"\"\"\r\n\r\n Notional = 'Notional'\r\n Strike = 'Strike'\r\n NA = 'NA' \r\n\r\n\r\nclass OptionExerciseStyle(EnumBase, Enum): \r\n \r\n \"\"\"How the option is exercised (e.g. Auto, Manual)\"\"\"\r\n\r\n Auto = 'Auto'\r\n Manual = 'Manual' \r\n\r\n\r\nclass OptionExpiryType(EnumBase, Enum): \r\n \r\n _1m = '1m'\r\n _2m = '2m'\r\n _3m = '3m'\r\n _4m = '4m'\r\n _5m = '5m'\r\n _6m = '6m' \r\n\r\n\r\nclass OptionSettlementMethod(EnumBase, Enum): \r\n \r\n \"\"\"How the option is settled (e.g. Cash, Physical)\"\"\"\r\n\r\n Cash = 'Cash'\r\n Physical = 'Physical'\r\n ElectDfltCash = 'ElectDfltCash'\r\n ElectDfltPhys = 'ElectDfltPhys'\r\n NetShares = 'NetShares' \r\n\r\n\r\nclass OptionStrikeType(EnumBase, Enum): \r\n \r\n Relative = 'Relative'\r\n Delta = 'Delta' \r\n\r\n\r\nclass OptionStyle(EnumBase, Enum): \r\n \r\n \"\"\"Option Exercise Style\"\"\"\r\n\r\n European = 'European'\r\n American = 'American'\r\n Bermudan = 'Bermudan'\r\n Asian = 'Asian' \r\n\r\n\r\nclass OptionType(EnumBase, Enum): \r\n \r\n \"\"\"Option Type\"\"\"\r\n\r\n Call = 'Call'\r\n Put = 'Put'\r\n Forward = 'Forward'\r\n Binary_Call = 'Binary Call'\r\n Binary_Put = 'Binary Put'\r\n Digital_Call = 'Digital Call'\r\n Digital_Put = 'Digital Put' \r\n\r\n\r\nclass PCOActionType(EnumBase, Enum): \r\n \r\n \"\"\"Types of PCO Actions\"\"\"\r\n\r\n Generate_Orders = 'Generate Orders'\r\n Update_Parameters = 'Update Parameters'\r\n Update_Client_Data = 'Update Client Data'\r\n Update_Open_Hedge_Notional = 'Update Open Hedge Notional'\r\n Add_Order = 'Add Order' \r\n\r\n\r\nclass PCOCurrencyType(EnumBase, Enum): \r\n \r\n \"\"\"Currency Type Options for PCO\"\"\"\r\n\r\n Exposure = 'Exposure'\r\n Base = 'Base'\r\n Local = 'Local' \r\n\r\n\r\nclass PCOOrigin(EnumBase, Enum): \r\n \r\n \"\"\"Origin of PCO Report\"\"\"\r\n\r\n PCOGui = 'PCOGui'\r\n PCOBackend = 'PCOBackend' \r\n\r\n\r\nclass PayReceive(EnumBase, Enum): \r\n \r\n \"\"\"Pay or receive fixed\"\"\"\r\n\r\n Pay = 'Pay'\r\n Payer = 'Payer'\r\n Receive = 'Receive'\r\n Receiver = 'Receiver'\r\n Straddle = 'Straddle'\r\n Rec = 'Rec' \r\n\r\n\r\nclass PaymentFrequency(EnumBase, Enum): \r\n \r\n Every_Return = 'Every Return'\r\n Maturity = 'Maturity' \r\n\r\n\r\nclass PayoutType(EnumBase, Enum): \r\n \r\n \"\"\"Delayed or Immediate payout\"\"\"\r\n\r\n Delayed = 'Delayed'\r\n Immediate = 'Immediate' \r\n\r\n\r\nclass Period(EnumBase, Enum): \r\n \r\n \"\"\"A coding scheme to define a period corresponding to a quantity amount\"\"\"\r\n\r\n Month = 'Month'\r\n Quarter = 'Quarter'\r\n Hour = 'Hour'\r\n Day = 'Day'\r\n BusinessDay = 'BusinessDay' \r\n\r\n\r\nclass PositionSetWeightingStrategy(EnumBase, Enum): \r\n \r\n \"\"\"Strategy used to price the position set.\"\"\"\r\n\r\n Equal = 'Equal'\r\n Market_Capitalization = 'Market Capitalization'\r\n Quantity = 'Quantity'\r\n Weight = 'Weight'\r\n Notional = 'Notional' \r\n\r\n\r\nclass PricingLocation(EnumBase, Enum): \r\n \r\n \"\"\"Based on the location of the exchange. Called 'Native Region' in SecDB\"\"\"\r\n\r\n NYC = 'NYC'\r\n LDN = 'LDN'\r\n TKO = 'TKO'\r\n HKG = 'HKG' \r\n\r\n\r\nclass PrincipalExchange(EnumBase, Enum): \r\n \r\n \"\"\"How principal is exchanged\"\"\"\r\n\r\n _None = 'None'\r\n Both = 'Both'\r\n First = 'First'\r\n Last = 'Last' \r\n\r\n\r\nclass ProductCode(EnumBase, Enum): \r\n \r\n \"\"\"Override the clearing destination/symbol\"\"\"\r\n\r\n CME__BB = 'CME::BB'\r\n CME__BK = 'CME::BK'\r\n CME__BY = 'CME::BY'\r\n CME__BZ = 'CME::BZ'\r\n CME__CL = 'CME::CL'\r\n CME__CL_BZ = 'CME::CL-BZ'\r\n CME__CS = 'CME::CS'\r\n CME__CY = 'CME::CY'\r\n CME__HK = 'CME::HK'\r\n CME__HO = 'CME::HO'\r\n CME__HOB = 'CME::HOB'\r\n CME__HO_CL = 'CME::HO-CL'\r\n CME__MP = 'CME::MP'\r\n CME__NG = 'CME::NG'\r\n CME__NLS = 'CME::NLS'\r\n CME__RB = 'CME::RB'\r\n CME__RBB = 'CME::RBB'\r\n CME__RB_BZ = 'CME::RB-BZ'\r\n CME__RB_CL = 'CME::RB-CL'\r\n CME__RH = 'CME::RH'\r\n CME__RL = 'CME::RL'\r\n CME__RM = 'CME::RM'\r\n CME__WS = 'CME::WS'\r\n CME_ICE__RB_B = 'CME-ICE::RB-B'\r\n ICE__B = 'ICE::B'\r\n ICE__BNB = 'ICE::BNB'\r\n ICE__BTD = 'ICE::BTD'\r\n ICE__G = 'ICE::G'\r\n ICE__G_B = 'ICE::G-B'\r\n ICE__HBT = 'ICE::HBT'\r\n ICE__HNG = 'ICE::HNG'\r\n ICE__HOF = 'ICE::HOF'\r\n ICE__I = 'ICE::I'\r\n ICE__N = 'ICE::N'\r\n ICE__N_B = 'ICE::N-B'\r\n ICE__O = 'ICE::O'\r\n ICE__O_B = 'ICE::O-B'\r\n ICE__R = 'ICE::R'\r\n ICE__RBR = 'ICE::RBR'\r\n ICE__T = 'ICE::T'\r\n ICE__T_B = 'ICE::T-B'\r\n ICE__ULA = 'ICE::ULA'\r\n ICE__ULC = 'ICE::ULC'\r\n ICE__ULD = 'ICE::ULD'\r\n ICE__ULM = 'ICE::ULM'\r\n ICE__WTB = 'ICE::WTB'\r\n LME__MAL = 'LME::MAL'\r\n LME__MNI = 'LME::MNI'\r\n LME__MPB = 'LME::MPB'\r\n LME__MZN = 'LME::MZN'\r\n OTC__BRT = 'OTC::BRT'\r\n OTC__GO = 'OTC::GO'\r\n OTC__GO_BRT = 'OTC::GO-BRT'\r\n OTC__HO = 'OTC::HO'\r\n OTC__HO_BRT = 'OTC::HO-BRT'\r\n OTC__HO_GO = 'OTC::HO-GO'\r\n OTC__HO_WTI = 'OTC::HO-WTI'\r\n OTC__MAL = 'OTC::MAL'\r\n OTC__MNI = 'OTC::MNI'\r\n OTC__MPB = 'OTC::MPB'\r\n OTC__MZN = 'OTC::MZN'\r\n OTC__NG = 'OTC::NG'\r\n OTC__RB = 'OTC::RB'\r\n OTC__RB_BRT = 'OTC::RB-BRT'\r\n OTC__RB_HO = 'OTC::RB-HO'\r\n OTC__RB_WTI = 'OTC::RB-WTI'\r\n OTC__WTI = 'OTC::WTI'\r\n OTC__WTI_BRT = 'OTC::WTI-BRT' \r\n\r\n\r\nclass ProductType(EnumBase, Enum): \r\n \r\n \"\"\"Product type of basket\"\"\"\r\n\r\n Flow = 'Flow'\r\n MPS = 'MPS'\r\n PWM = 'PWM'\r\n Volatility = 'Volatility'\r\n Single_Stock = 'Single Stock'\r\n Indexified_Basket = 'Indexified Basket' \r\n\r\n\r\nclass QuoteType(EnumBase, Enum): \r\n \r\n \"\"\"Quote type that is used for the bond price\"\"\"\r\n\r\n Dirty_Price = 'Dirty Price'\r\n Clean_Price = 'Clean Price'\r\n BM_Spread = 'BM Spread'\r\n Yield = 'Yield'\r\n Z_Spread = 'Z-Spread'\r\n G_Spread = 'G-Spread'\r\n TRS = 'TRS'\r\n TRS_w_OVER__Funding = 'TRS w/ Funding'\r\n TRS_w_OVER__Full_Funding = 'TRS w/ Full Funding' \r\n\r\n\r\nclass Region(EnumBase, Enum): \r\n \r\n \"\"\"Regional classification for the asset\"\"\"\r\n\r\n _ = ''\r\n Americas = 'Americas'\r\n Asia = 'Asia'\r\n EM = 'EM'\r\n Europe = 'Europe'\r\n Global = 'Global' \r\n\r\n\r\nclass ReportJobPriority(EnumBase, Enum): \r\n \r\n \"\"\"Report job priority.\"\"\"\r\n\r\n High = 'High'\r\n Normal = 'Normal' \r\n\r\n\r\nclass ReturnStyle(EnumBase, Enum): \r\n \r\n \"\"\"Return calculation style\"\"\"\r\n\r\n Rate_of_Return = 'Rate of Return'\r\n Price_Return = 'Price Return' \r\n\r\n\r\nclass ReturnType(EnumBase, Enum): \r\n \r\n \"\"\"Sum or Product of periodic return, relevant only if paying at maturity\"\"\"\r\n\r\n Sum = 'Sum'\r\n Product = 'Product' \r\n\r\n\r\nclass RiskMeasureType(EnumBase, Enum): \r\n \r\n \"\"\"The type of measure to perform risk on. e.g. Greeks\"\"\"\r\n\r\n Annual_ATM_Implied_Volatility = 'Annual ATM Implied Volatility'\r\n Annual_ATMF_Implied_Volatility = 'Annual ATMF Implied Volatility'\r\n Annual_Implied_Volatility = 'Annual Implied Volatility'\r\n AnnuityLocalCcy = 'AnnuityLocalCcy'\r\n ATM_Spread = 'ATM Spread'\r\n BaseCPI = 'BaseCPI'\r\n Basis = 'Basis'\r\n BSPrice = 'BSPrice'\r\n BSPricePct = 'BSPricePct'\r\n CRIF_IRCurve = 'CRIF IRCurve'\r\n Cashflows = 'Cashflows'\r\n CDIForward = 'CDIForward'\r\n CDIIndexDelta = 'CDIIndexDelta'\r\n CDIIndexVega = 'CDIIndexVega'\r\n CDIOptionPremium = 'CDIOptionPremium'\r\n CDIOptionPremiumFlatFwd = 'CDIOptionPremiumFlatFwd'\r\n CDIOptionPremiumFlatVol = 'CDIOptionPremiumFlatVol'\r\n CDISpot = 'CDISpot'\r\n CDISpreadDV01 = 'CDISpreadDV01'\r\n CDIUpfrontPrice = 'CDIUpfrontPrice'\r\n Compounded_Fixed_Rate = 'Compounded Fixed Rate'\r\n Correlation = 'Correlation'\r\n Cross_Multiplier = 'Cross Multiplier'\r\n Cross = 'Cross'\r\n Daily_Implied_Volatility = 'Daily Implied Volatility'\r\n Delta = 'Delta'\r\n DeltaLocalCcy = 'DeltaLocalCcy'\r\n Description = 'Description'\r\n Dollar_Price = 'Dollar Price'\r\n DV01 = 'DV01'\r\n ExpiryInYears = 'ExpiryInYears'\r\n FairPremium = 'FairPremium'\r\n FairPremiumPct = 'FairPremiumPct'\r\n Fair_Price = 'Fair Price'\r\n FairVarStrike = 'FairVarStrike'\r\n FairVolStrike = 'FairVolStrike'\r\n FinalCPI = 'FinalCPI'\r\n Forward_Price = 'Forward Price'\r\n Forward_Rate = 'Forward Rate'\r\n Forward_Spread = 'Forward Spread'\r\n FX_BF_25_Vol = 'FX BF 25 Vol'\r\n FX_Calculated_Delta = 'FX Calculated Delta'\r\n FX_Calculated_Delta_No_Premium_Adjustment = 'FX Calculated Delta No Premium Adjustment'\r\n FX_Discount_Factor_Over = 'FX Discount Factor Over'\r\n FX_Discount_Factor_Under = 'FX Discount Factor Under'\r\n FX_Hedge_Delta = 'FX Hedge Delta'\r\n FX_Premium = 'FX Premium'\r\n FX_Premium_Pct = 'FX Premium Pct'\r\n FX_Premium_Pct_Flat_Fwd = 'FX Premium Pct Flat Fwd'\r\n FX_Quoted_Delta_No_Premium_Adjustment = 'FX Quoted Delta No Premium Adjustment'\r\n FX_Quoted_Vega = 'FX Quoted Vega'\r\n FX_Quoted_Vega_Bps = 'FX Quoted Vega Bps'\r\n FX_RR_25_Vol = 'FX RR 25 Vol'\r\n FXSpotVal = 'FXSpotVal'\r\n Price = 'Price'\r\n Gamma = 'Gamma'\r\n GammaLocalCcy = 'GammaLocalCcy'\r\n Implied_Volatility = 'Implied Volatility'\r\n InflationDelta = 'InflationDelta'\r\n Inflation_Compounding_Period = 'Inflation Compounding Period'\r\n Inflation_Delta_in_Bps = 'Inflation Delta in Bps'\r\n Local_Currency_Accrual_in_Cents = 'Local Currency Accrual in Cents'\r\n Local_Currency_Annuity = 'Local Currency Annuity'\r\n Market_Data = 'Market Data'\r\n Market = 'Market'\r\n Market_Data_Assets = 'Market Data Assets'\r\n MV = 'MV'\r\n NonUSDOisDomesticRate = 'NonUSDOisDomesticRate'\r\n OAS = 'OAS'\r\n OisFXSpreadRateExcludingSpikes = 'OisFXSpreadRateExcludingSpikes'\r\n OisFXSpreadRate = 'OisFXSpreadRate'\r\n ParallelBasis = 'ParallelBasis'\r\n ParallelDelta = 'ParallelDelta'\r\n ParallelDeltaLocalCcy = 'ParallelDeltaLocalCcy'\r\n ParallelDiscountDelta = 'ParallelDiscountDelta'\r\n ParallelDiscountDeltaLocalCcy = 'ParallelDiscountDeltaLocalCcy'\r\n ParallelInflationDelta = 'ParallelInflationDelta'\r\n ParallelIndexDelta = 'ParallelIndexDelta'\r\n ParallelIndexDeltaLocalCcy = 'ParallelIndexDeltaLocalCcy'\r\n ParallelInflationDeltaLocalCcy = 'ParallelInflationDeltaLocalCcy'\r\n ParallelXccyDelta = 'ParallelXccyDelta'\r\n ParallelXccyDeltaLocalCcy = 'ParallelXccyDeltaLocalCcy'\r\n ParallelGamma = 'ParallelGamma'\r\n ParallelGammaLocalCcy = 'ParallelGammaLocalCcy'\r\n ParallelVega = 'ParallelVega'\r\n ParallelVegaLocalCcy = 'ParallelVegaLocalCcy'\r\n Points = 'Points'\r\n Premium_In_Cents = 'Premium In Cents'\r\n Premium = 'Premium'\r\n Probability_Of_Exercise = 'Probability Of Exercise'\r\n Resolved_Instrument_Values = 'Resolved Instrument Values'\r\n PNL = 'PNL'\r\n PnlExplain = 'PnlExplain'\r\n PnlExplainLocalCcy = 'PnlExplainLocalCcy'\r\n PnlPredict = 'PnlPredict'\r\n PV = 'PV'\r\n QuotedDelta = 'QuotedDelta'\r\n RFRFXRate = 'RFRFXRate'\r\n RFRFXSpreadRate = 'RFRFXSpreadRate'\r\n RFRFXSpreadRateExcludingSpikes = 'RFRFXSpreadRateExcludingSpikes'\r\n Spot = 'Spot'\r\n Spot_Rate = 'Spot Rate'\r\n Spread = 'Spread'\r\n Strike = 'Strike'\r\n StrikePts = 'StrikePts'\r\n Theta = 'Theta'\r\n USDOisDomesticRate = 'USDOisDomesticRate'\r\n Vanna = 'Vanna'\r\n Vega = 'Vega'\r\n VegaLocalCcy = 'VegaLocalCcy'\r\n Volga = 'Volga'\r\n Volatility = 'Volatility'\r\n XccyDelta = 'XccyDelta' \r\n\r\n\r\nclass RiskMeasureUnit(EnumBase, Enum): \r\n \r\n \"\"\"The unit of change of underlying in the risk computation.\"\"\"\r\n\r\n Percent = 'Percent'\r\n Dollar = 'Dollar'\r\n BPS = 'BPS'\r\n Pips = 'Pips' \r\n\r\n\r\nclass RiskModelType(EnumBase, Enum): \r\n \r\n \"\"\"Marquee risk model type\"\"\"\r\n\r\n Factor = 'Factor'\r\n Macro = 'Macro'\r\n Thematic = 'Thematic' \r\n\r\n\r\nclass ScenarioType(EnumBase, Enum): \r\n \r\n \"\"\"Type of Scenario\"\"\"\r\n\r\n Spot_Vol = 'Spot Vol'\r\n Greeks = 'Greeks' \r\n\r\n\r\nclass SettlementType(EnumBase, Enum): \r\n \r\n \"\"\"Settlement Type\"\"\"\r\n\r\n Cash = 'Cash'\r\n Physical = 'Physical' \r\n\r\n\r\nclass Side(EnumBase, Enum): \r\n \r\n \"\"\"Official side of an index\"\"\"\r\n\r\n Bid = 'Bid'\r\n Ask = 'Ask'\r\n Mid = 'Mid' \r\n\r\n\r\nclass Strategy(EnumBase, Enum): \r\n \r\n \"\"\"More specific descriptor of a fund's investment approach. Same view permissions\r\n as the asset\"\"\"\r\n\r\n Active_Extension = 'Active Extension'\r\n Active_Trading = 'Active Trading'\r\n Activist = 'Activist'\r\n Asset_Backed_Lending = 'Asset Backed Lending'\r\n Buyout = 'Buyout'\r\n Co_Invest__OVER__SPV = 'Co-Invest / SPV'\r\n Commodity = 'Commodity'\r\n Commodities = 'Commodities'\r\n Composite = 'Composite'\r\n Conservative = 'Conservative'\r\n Convert_Arb = 'Convert Arb'\r\n Convertible_Arbitrage = 'Convertible Arbitrage'\r\n Credit_Arbitrage = 'Credit Arbitrage'\r\n Cross_Capital_Structure = 'Cross-Capital-Structure'\r\n CTA__OVER__Managed_Futures = 'CTA / Managed Futures'\r\n Currency = 'Currency'\r\n Direct_Lending = 'Direct Lending'\r\n Discretionary = 'Discretionary'\r\n Discretionary_Thematic = 'Discretionary Thematic'\r\n Distressed = 'Distressed'\r\n Distressed__OVER__Special_Situations = 'Distressed / Special Situations'\r\n Distressed_Securities = 'Distressed Securities'\r\n Distressed_OVER_Restructuring = 'Distressed/Restructuring'\r\n Diversified = 'Diversified'\r\n Equity_Hedge = 'Equity Hedge'\r\n Equity_Market_Neutral = 'Equity Market Neutral'\r\n Equity_Only = 'Equity Only'\r\n Event_Driven = 'Event-Driven'\r\n Fixed_Income_Arb = 'Fixed Income Arb'\r\n Fixed_Income_Asset_Backed = 'Fixed Income-Asset Backed'\r\n Fixed_Income_Corporate = 'Fixed Income-Corporate'\r\n Fixed_Income_Sovereign = 'Fixed Income-Sovereign'\r\n Fundamental_Growth = 'Fundamental Growth'\r\n Fundamental_Value = 'Fundamental Value'\r\n General = 'General'\r\n General_Multi_Strategy = 'General Multi-Strategy'\r\n Generalist = 'Generalist'\r\n Growth_Equity = 'Growth Equity'\r\n Hybrid__OVER__Illiquid = 'Hybrid / Illiquid'\r\n Long__OVER__Short = 'Long / Short'\r\n Macro = 'Macro'\r\n Market_Defensive = 'Market Defensive'\r\n Merger_Arb = 'Merger Arb'\r\n Merger_Arbitrage = 'Merger Arbitrage'\r\n Multi_Strategy = 'Multi-Strategy'\r\n Quantitative_Directional = 'Quantitative Directional'\r\n Real_Estate__OVER__Real_Asset = 'Real Estate / Real Asset'\r\n Real_Estate__OVER__Real_Asset_Credit = 'Real Estate / Real Asset Credit'\r\n Relative_Value_Arbitrage = 'Relative Value Arbitrage'\r\n Risk_Premia = 'Risk Premia'\r\n Sector___Energy_OVER_Basic_Materials = 'Sector - Energy/Basic Materials'\r\n Sector___Healthcare = 'Sector - Healthcare'\r\n Sector___Technology = 'Sector - Technology'\r\n Sector___Technology_OVER_Healthcare = 'Sector - Technology/Healthcare'\r\n Sector_Specific = 'Sector-Specific'\r\n Short_Bias = 'Short Bias'\r\n Special_Situations = 'Special Situations'\r\n Specialty_Finance = 'Specialty Finance'\r\n Stat_Arb = 'Stat Arb'\r\n Statistical_Arbitrage = 'Statistical Arbitrage'\r\n Strategic = 'Strategic'\r\n Structured = 'Structured'\r\n Systematic = 'Systematic'\r\n Systematic_Diversified = 'Systematic Diversified'\r\n Vol_Arb__OVER__Options = 'Vol Arb / Options'\r\n Volatility = 'Volatility'\r\n Volatility_Target_10 = 'Volatility Target 10'\r\n Volatility_Target_12 = 'Volatility Target 12'\r\n Volatility_Target_15 = 'Volatility Target 15'\r\n Yield_Alternative = 'Yield Alternative' \r\n\r\n\r\nclass StrikeMethodType(EnumBase, Enum): \r\n \r\n Spread = 'Spread'\r\n Delta = 'Delta'\r\n Percentage_of_Price = 'Percentage of Price'\r\n Fixed = 'Fixed' \r\n\r\n\r\nclass SwapClearingHouse(EnumBase, Enum): \r\n \r\n \"\"\"Swap Clearing House\"\"\"\r\n\r\n LCH = 'LCH'\r\n EUREX = 'EUREX'\r\n JSCC = 'JSCC'\r\n CME = 'CME'\r\n NONE = 'NONE' \r\n\r\n\r\nclass SwapSettlement(EnumBase, Enum): \r\n \r\n \"\"\"Swap Settlement Type\"\"\"\r\n\r\n Phys_CLEARED = 'Phys.CLEARED'\r\n Physical = 'Physical'\r\n Cash_CollatCash = 'Cash.CollatCash'\r\n Cash_PYU = 'Cash.PYU' \r\n\r\n\r\nclass SwapType(EnumBase, Enum): \r\n \r\n Eq_CFD_Standard = 'Eq CFD Standard'\r\n Eq_Swap = 'Eq Swap'\r\n Eq_Swap_OET_Asymmetric = 'Eq Swap OET Asymmetric'\r\n Eq_Swap_OET_Simple = 'Eq Swap OET Simple'\r\n Eq_Swap_Term = 'Eq Swap Term'\r\n Eq_Synthetic_OET = 'Eq Synthetic OET' \r\n\r\n\r\nclass TargetPaymentType(EnumBase, Enum): \r\n \r\n Capped = 'Capped'\r\n Full = 'Full'\r\n _None = 'None' \r\n\r\n\r\nclass TargetType(EnumBase, Enum): \r\n \r\n \"\"\"Target type for accural redemption forward\"\"\"\r\n\r\n Big_Figures = 'Big Figures'\r\n Amount = 'Amount'\r\n Num_Of_ITM_Fixes = 'Num Of ITM Fixes' \r\n\r\n\r\nclass TouchNoTouch(EnumBase, Enum): \r\n \r\n \"\"\"Indicates Touch or NoTouch\"\"\"\r\n\r\n Touch = 'Touch'\r\n No_Touch = 'No Touch' \r\n\r\n\r\nclass TradeAs(EnumBase, Enum): \r\n \r\n \"\"\"Option trade as (i.e. listed, otc, lookalike etc)\"\"\"\r\n\r\n Listed = 'Listed'\r\n Listed_Look_alike_OTC = 'Listed Look alike OTC'\r\n Flex = 'Flex'\r\n OTC = 'OTC' \r\n\r\n\r\nclass TradeType(EnumBase, Enum): \r\n \r\n \"\"\"Direction\"\"\"\r\n\r\n Buy = 'Buy'\r\n Sell = 'Sell' \r\n\r\n\r\nclass UnderlierType(EnumBase, Enum): \r\n \r\n \"\"\"Type of underlyer\"\"\"\r\n\r\n BBID = 'BBID'\r\n BID = 'BID'\r\n CUSIP = 'CUSIP'\r\n ISIN = 'ISIN'\r\n SEDOL = 'SEDOL'\r\n RIC = 'RIC'\r\n Ticker = 'Ticker' \r\n\r\n\r\nclass UpDown(EnumBase, Enum): \r\n \r\n Up = 'Up'\r\n Down = 'Down' \r\n\r\n\r\nclass ValuationTime(EnumBase, Enum): \r\n \r\n \"\"\"The time of valuation, e.g. for an option\"\"\"\r\n\r\n MktClose = 'MktClose'\r\n MktOpen = 'MktOpen'\r\n SQ = 'SQ'\r\n MktPreOpen = 'MktPreOpen'\r\n MktPrevClose = 'MktPrevClose'\r\n Hedge_Unwind = 'Hedge Unwind'\r\n TWAP_PM = 'TWAP PM' \r\n\r\n\r\nclass VarianceConvention(EnumBase, Enum): \r\n \r\n \"\"\"Specifies whether the variance is Annualized or Total\"\"\"\r\n\r\n Annualized = 'Annualized'\r\n Total = 'Total' \r\n\r\n\r\nclass WeightingType(EnumBase, Enum): \r\n \r\n \"\"\"Weighting type that is used for the bond price\"\"\"\r\n\r\n Notional = 'Notional'\r\n Market_Value = 'Market Value'\r\n Dollar_Duration = 'Dollar Duration' \r\n\r\n\r\nclass YesNo(EnumBase, Enum): \r\n \r\n Yes = 'Yes'\r\n No = 'No' \r\n\r\n\r\n@dataclass\r\nclass SingleMarket(Base):\r\n pass\r\n\r\n\r\n@dataclass\r\nclass BaseMarket(SingleMarket):\r\n pass\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetIdPriceable(Priceable):\r\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerCreditStandardAndPoorsRatingOptions(Base):\r\n min_: Optional[str] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\r\n max_: Optional[str] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerRequestFilterDateLimits(Base):\r\n min_: Optional[datetime.date] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\r\n max_: Optional[datetime.date] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerRequestFilterLimits(Base):\r\n min_: Optional[float] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\r\n max_: Optional[float] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerRequestStringOptions(Base):\r\n options: tuple = field(default=None, metadata=field_metadata)\r\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetValueParameters(Base):\r\n max_: Optional[float] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\r\n min_: Optional[float] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\r\n increment: Optional[float] = field(default=None, metadata=field_metadata)\r\n value: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass BasketPositionTradeOnMetadata(Base):\r\n pair_calculation: Optional[str] = field(default=None, metadata=field_metadata)\r\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\r\n direction: Optional[str] = field(default=None, metadata=field_metadata)\r\n underlier_close_price: Optional[float] = field(default=None, metadata=field_metadata)\r\n underlier_quantity: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass BlendedBenchmarkAttribute(Base):\r\n benchmark_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass BloombergPublishParameters(Base):\r\n description: Optional[str] = field(default=None, metadata=field_metadata)\r\n description_without_url: Optional[str] = field(default=None, metadata=field_metadata)\r\n long_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n close_precision: Optional[float] = field(default=2, metadata=field_metadata)\r\n tick_precision: Optional[float] = field(default=4, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLDate(Base):\r\n date_value: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLDouble(Base):\r\n double_value: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLFXCross(Base):\r\n string_value: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLIndex(Base):\r\n string_value: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLSimpleSchedule(Base):\r\n fixing_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n settlement_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLStock(Base):\r\n string_value: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLString(Base):\r\n string_value: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLSymCaseNamedParam(Base):\r\n sym_case_value: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=field_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False, order=True)\r\nclass CurrencyParameter(RiskMeasureParameter):\r\n value: Optional[str] = field(default=None, metadata=field_metadata)\r\n parameter_type: Optional[str] = field(init=False, default='Currency', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CurveOverlay(Scenario):\r\n dates: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\r\n discount_factors: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\r\n denominated: Optional[str] = field(default=None, metadata=field_metadata)\r\n csa_term: Optional[str] = field(default=None, metadata=field_metadata)\r\n tenor: Optional[str] = field(default=None, metadata=field_metadata)\r\n rate_option: Optional[str] = field(default=None, metadata=field_metadata)\r\n curve_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n subtract_base: Optional[bool] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='CurveOverlay', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\nclass DataRow(DictBase):\r\n pass\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass DateRange(Base):\r\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False, order=True)\r\nclass DoubleParameter(RiskMeasureParameter):\r\n value: Optional[float] = field(default=None, metadata=field_metadata)\r\n parameter_type: Optional[str] = field(init=False, default='Double', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass EqBasketRebalanceDateOverride(Base):\r\n new_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n existing_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass ISelectNewUnit(Base):\r\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\r\n new_units: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass ISelectNewWeight(Base):\r\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\r\n new_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass Identifier(Base):\r\n type_: Optional[str] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\r\n value: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass LiquidityReportParameters(Base):\r\n title: Optional[str] = field(default=None, metadata=field_metadata)\r\n email: Optional[str] = field(default=None, metadata=field_metadata)\r\n trading_desk: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False, order=True)\r\nclass ListOfNumberParameter(RiskMeasureParameter):\r\n values: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\r\n parameter_type: Optional[str] = field(init=False, default='ListOfNumber', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False, order=True)\r\nclass ListOfStringParameter(RiskMeasureParameter):\r\n values: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n parameter_type: Optional[str] = field(init=False, default='ListOfString', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False, order=True)\r\nclass MapParameter(RiskMeasureParameter):\r\n value: Optional[DictBase] = field(default=None, metadata=field_metadata)\r\n parameter_type: Optional[str] = field(init=False, default='Map', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MarketDataCoordinate(Base):\r\n mkt_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n mkt_asset: Optional[str] = field(default=None, metadata=field_metadata)\r\n mkt_class: Optional[str] = field(default=None, metadata=field_metadata)\r\n mkt_point: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n mkt_quoting_style: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MarketDataVolSlice(Base):\r\n date: datetime.date = field(default=None, metadata=field_metadata)\r\n strikes: Tuple[float, ...] = field(default=None, metadata=field_metadata)\r\n levels: Tuple[float, ...] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MktMarkingOptions(Base):\r\n mode: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass Op(Base):\r\n gte: Optional[Union[datetime.date, float]] = field(default=None, metadata=field_metadata)\r\n lte: Optional[Union[datetime.date, float]] = field(default=None, metadata=field_metadata)\r\n lt: Optional[Union[datetime.date, float]] = field(default=None, metadata=field_metadata)\r\n gt: Optional[Union[datetime.date, float]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass OrderByBody(Base):\r\n column_name: str = field(default=None, metadata=field_metadata)\r\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOBenchmarkOptions(Base):\r\n name: Optional[str] = field(default=None, metadata=field_metadata)\r\n target_ratio: Optional[str] = field(default=None, metadata=field_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOExposureAdjustments(Base):\r\n nav_adjustment: Optional[str] = field(default=None, metadata=field_metadata)\r\n net_subscription_redemption: Optional[str] = field(default=None, metadata=field_metadata)\r\n net_subscription_redemption_limits: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n adjustment_vs_subscription_redemption: Optional[str] = field(default=None, metadata=field_metadata)\r\n adjustment_vs_subscription_redemption_limits: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOMtMHistoricalData(Base):\r\n value: Optional[str] = field(default=None, metadata=field_metadata)\r\n timestamp: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCONetSubscription(Base):\r\n confirmed: Optional[str] = field(default=None, metadata=field_metadata)\r\n estimated: Optional[str] = field(default=None, metadata=field_metadata)\r\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOSettlementsData(Base):\r\n timestamp: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n settlement: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOTargetDeviationData(Base):\r\n value: Optional[str] = field(default=None, metadata=field_metadata)\r\n timestamp: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PatternPropertiesDateTime(Base):\r\n start: Optional[str] = field(default=None, metadata=field_metadata)\r\n end: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PerformanceStats(Base):\r\n alpha: Optional[float] = field(default=None, metadata=field_metadata)\r\n annualized_return: Optional[float] = field(default=None, metadata=field_metadata)\r\n annualized_volatility: Optional[float] = field(default=None, metadata=field_metadata)\r\n average_return: Optional[float] = field(default=None, metadata=field_metadata)\r\n average_value: Optional[float] = field(default=None, metadata=field_metadata)\r\n average_volume_last_month: Optional[float] = field(default=None, metadata=field_metadata)\r\n best_month: Optional[float] = field(default=None, metadata=field_metadata)\r\n best_month_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n beta: Optional[float] = field(default=None, metadata=field_metadata)\r\n close_price: Optional[float] = field(default=None, metadata=field_metadata)\r\n correlation: Optional[float] = field(default=None, metadata=field_metadata)\r\n cumulative_return: Optional[float] = field(default=None, metadata=field_metadata)\r\n current_value: Optional[float] = field(default=None, metadata=field_metadata)\r\n drawdown_over_return: Optional[float] = field(default=None, metadata=field_metadata)\r\n high: Optional[float] = field(default=None, metadata=field_metadata)\r\n high_eod: Optional[float] = field(default=None, metadata=field_metadata)\r\n last_change: Optional[float] = field(default=None, metadata=field_metadata)\r\n last_change_pct: Optional[float] = field(default=None, metadata=field_metadata)\r\n last_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n last_value: Optional[float] = field(default=None, metadata=field_metadata)\r\n low: Optional[float] = field(default=None, metadata=field_metadata)\r\n low_eod: Optional[float] = field(default=None, metadata=field_metadata)\r\n max_draw_down: Optional[float] = field(default=None, metadata=field_metadata)\r\n max_draw_down_duration: Optional[int] = field(default=None, metadata=field_metadata)\r\n open_price: Optional[float] = field(default=None, metadata=field_metadata)\r\n positive_months: Optional[float] = field(default=None, metadata=field_metadata)\r\n sharpe_ratio: Optional[float] = field(default=None, metadata=field_metadata)\r\n sortino_ratio: Optional[float] = field(default=None, metadata=field_metadata)\r\n worst_month: Optional[float] = field(default=None, metadata=field_metadata)\r\n worst_month_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n total_return: Optional[float] = field(default=None, metadata=field_metadata)\r\n volume: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PositionTag(Base):\r\n name: str = field(default=None, metadata=field_metadata)\r\n value: str = field(default=None, metadata=field_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass RefMarket(BaseMarket):\r\n market_ref: Optional[str] = field(default=None, metadata=field_metadata)\r\n market_type: Optional[str] = field(init=False, default='RefMarket', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass ReportSubscriptionParameters(Base):\r\n frequency: object = field(default=None, metadata=field_metadata)\r\n recipients: Tuple[str, ...] = field(default=None, metadata=field_metadata)\r\n pfr_report_id: str = field(default=None, metadata=field_metadata)\r\n day_of_week: Optional[float] = field(default=None, metadata=field_metadata)\r\n day_of_month: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass SimpleParty(Base):\r\n party_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n party_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n party_book: Optional[str] = field(default=None, metadata=field_metadata)\r\n party_oe_id: Optional[str] = field(default=None, metadata=config(field_name='partyOEId', exclude=exclude_none))\r\n party_oe_name: Optional[str] = field(default=None, metadata=config(field_name='partyOEName', exclude=exclude_none))\r\n party_root_oe_id: Optional[str] = field(default=None, metadata=config(field_name='partyRootOEId', exclude=exclude_none))\r\n party_root_oe_name: Optional[str] = field(default=None, metadata=config(field_name='partyRootOEName', exclude=exclude_none))\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False, order=True)\r\nclass StringParameter(RiskMeasureParameter):\r\n value: Optional[str] = field(default=None, metadata=field_metadata)\r\n parameter_type: Optional[str] = field(init=False, default='String', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass SystematicHedgeThreshold(Base):\r\n factor: str = field(default=None, metadata=field_metadata)\r\n condition: str = field(default=None, metadata=field_metadata)\r\n value: float = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass TimeFilter(Base):\r\n start_hours: str = field(default=None, metadata=field_metadata)\r\n end_hours: str = field(default=None, metadata=field_metadata)\r\n time_zone: str = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass UserTag(Base):\r\n name: str = field(default=None, metadata=field_metadata)\r\n added_on: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n added_by_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n removed: Optional[bool] = field(default=None, metadata=field_metadata)\r\n removed_on: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n removed_by_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n removal_reason: Optional[str] = field(default=None, metadata=field_metadata)\r\n category: Optional[str] = field(default=None, metadata=field_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass WeightedPosition(Base):\r\n asset_id: str = field(default=None, metadata=field_metadata)\r\n weight: float = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass XRef(Base):\r\n ric: Optional[str] = field(default=None, metadata=field_metadata)\r\n rcic: Optional[str] = field(default=None, metadata=field_metadata)\r\n eid: Optional[str] = field(default=None, metadata=field_metadata)\r\n gsideid: Optional[str] = field(default=None, metadata=field_metadata)\r\n gsid: Optional[str] = field(default=None, metadata=field_metadata)\r\n gsid_equivalent: Optional[str] = field(default=None, metadata=field_metadata)\r\n cid: Optional[str] = field(default=None, metadata=field_metadata)\r\n bbid: Optional[str] = field(default=None, metadata=field_metadata)\r\n bcid: Optional[str] = field(default=None, metadata=field_metadata)\r\n delisted: Optional[str] = field(default=None, metadata=field_metadata)\r\n bbid_equivalent: Optional[str] = field(default=None, metadata=field_metadata)\r\n cusip: Optional[str] = field(default=None, metadata=field_metadata)\r\n gss: Optional[str] = field(default=None, metadata=field_metadata)\r\n isin: Optional[str] = field(default=None, metadata=field_metadata)\r\n jsn: Optional[str] = field(default=None, metadata=field_metadata)\r\n prime_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n sedol: Optional[str] = field(default=None, metadata=field_metadata)\r\n ticker: Optional[str] = field(default=None, metadata=field_metadata)\r\n valoren: Optional[str] = field(default=None, metadata=field_metadata)\r\n wpk: Optional[str] = field(default=None, metadata=field_metadata)\r\n gsn: Optional[str] = field(default=None, metadata=field_metadata)\r\n sec_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n cross: Optional[str] = field(default=None, metadata=field_metadata)\r\n simon_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n em_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n cm_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n lms_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n tdapi: Optional[str] = field(default=None, metadata=field_metadata)\r\n mdapi: Optional[str] = field(default=None, metadata=field_metadata)\r\n mdapi_class: Optional[str] = field(default=None, metadata=field_metadata)\r\n mic: Optional[str] = field(default=None, metadata=field_metadata)\r\n sf_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n dollar_cross: Optional[str] = field(default=None, metadata=field_metadata)\r\n mq_symbol: Optional[str] = field(default=None, metadata=field_metadata)\r\n primary_country_ric: Optional[str] = field(default=None, metadata=field_metadata)\r\n pnode_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n wi_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n ps_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n pl_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n exchange_code: Optional[str] = field(default=None, metadata=field_metadata)\r\n plot_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n cins: Optional[str] = field(default=None, metadata=field_metadata)\r\n bbgid: Optional[str] = field(default=None, metadata=field_metadata)\r\n display_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n tsdb_shortname: Optional[str] = field(default=None, metadata=field_metadata)\r\n coin_metrics_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n sec_master_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetClassifications(Base):\r\n risk_country_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n risk_country_code: Optional[str] = field(default=None, metadata=field_metadata)\r\n country_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n country_code: Optional[str] = field(default=None, metadata=field_metadata)\r\n listing_country_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n is_primary: Optional[bool] = field(default=None, metadata=field_metadata)\r\n is_country_primary: Optional[bool] = field(default=None, metadata=field_metadata)\r\n gics_sector: Optional[str] = field(default=None, metadata=field_metadata)\r\n gics_industry_group: Optional[str] = field(default=None, metadata=field_metadata)\r\n gics_industry: Optional[str] = field(default=None, metadata=field_metadata)\r\n gics_sub_industry: Optional[str] = field(default=None, metadata=field_metadata)\r\n naics_classification_code: Optional[str] = field(default=None, metadata=field_metadata)\r\n naics_industry_description: Optional[str] = field(default=None, metadata=field_metadata)\r\n bbg_industry_sector: Optional[str] = field(default=None, metadata=field_metadata)\r\n bbg_industry_group: Optional[str] = field(default=None, metadata=field_metadata)\r\n bbg_industry_sub_group: Optional[str] = field(default=None, metadata=field_metadata)\r\n rating_moodys: Optional[str] = field(default=None, metadata=field_metadata)\r\n rating_fitch: Optional[str] = field(default=None, metadata=field_metadata)\r\n rating_standard_and_poors: Optional[str] = field(default=None, metadata=field_metadata)\r\n rating_second_highest: Optional[str] = field(default=None, metadata=field_metadata)\r\n rating_linear: Optional[float] = field(default=None, metadata=field_metadata)\r\n commod_template: Optional[str] = field(default=None, metadata=field_metadata)\r\n security_subtype: Optional[str] = field(default=None, metadata=field_metadata)\r\n region: Optional[Region] = field(default=None, metadata=field_metadata)\r\n vendor: Optional[str] = field(default=None, metadata=field_metadata)\r\n underliers_asset_class: Optional[AssetClass] = field(default=None, metadata=field_metadata)\r\n digital_asset_market: Optional[str] = field(default=None, metadata=field_metadata)\r\n digital_asset_sector: Optional[str] = field(default=None, metadata=field_metadata)\r\n digital_asset_industry: Optional[str] = field(default=None, metadata=field_metadata)\r\n digital_asset_class: Optional[str] = field(default=None, metadata=field_metadata)\r\n digital_asset_subsector: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerBbgRequestFilter(Base):\r\n country_of_risk: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n payment_rank: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n industry_sector: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n industry_group: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n industry_sub_group: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerCarbonRequestFilter(Base):\r\n science_based_target: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n net_zero_emissions_target: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n emissions_intensity_enterprise_value: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n emissions_intensity_revenue: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerESGRequestFilter(Base):\r\n g_percentile: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n g_regional_percentile: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n es_percentile: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n es_disclosure_percentage: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n es_momentum_percentile: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerPbRequestFilter(Base):\r\n indicative_short_financing_label: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n indicative_long_financing_label: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass BasketPositionTradeOn(Base):\r\n ticker: Optional[str] = field(default=None, metadata=field_metadata)\r\n marquee_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n metadata: Optional[BasketPositionTradeOnMetadata] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLCurrency(Base):\r\n string_value: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLDateArray(Base):\r\n date_values: Optional[Tuple[CSLDate, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLDateArrayNamedParam(Base):\r\n date_values: Optional[Tuple[CSLDate, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=field_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLDoubleArray(Base):\r\n double_values: Optional[Tuple[CSLDouble, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLFXCrossArray(Base):\r\n fx_cross_values: Optional[Tuple[CSLFXCross, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLIndexArray(Base):\r\n index_values: Optional[Tuple[CSLIndex, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLSimpleScheduleArray(Base):\r\n simple_schedule_values: Optional[Tuple[CSLSimpleSchedule, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLStockArray(Base):\r\n stock_values: Optional[Tuple[CSLStock, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLStringArray(Base):\r\n string_values: Optional[Tuple[CSLString, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CarryScenario(Scenario):\r\n date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\r\n time_shift: Optional[int] = field(default=None, metadata=field_metadata)\r\n roll_to_fwds: Optional[bool] = field(default=True, metadata=field_metadata)\r\n holiday_calendar: Optional[PricingLocation] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='CarryScenario', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CashReinvestmentTreatment(Base):\r\n regular_dividend_treatment: Optional[CashReinvestmentTreatmentType] = field(default=None, metadata=field_metadata)\r\n special_dividend_treatment: Optional[CashReinvestmentTreatmentType] = field(default=None, metadata=field_metadata)\r\n cash_acquisition_treatment: Optional[CashReinvestmentTreatmentType] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CloseMarket(BaseMarket):\r\n date: datetime.date = field(default=None, metadata=field_metadata)\r\n location: PricingLocation = field(default=None, metadata=field_metadata)\r\n market_type: Optional[str] = field(init=False, default='CloseMarket', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CommodPrice(Base):\r\n unit: Optional[CommodUnit] = field(default=None, metadata=field_metadata)\r\n price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\r\n currency: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass EntitlementExclusions(Base):\r\n view: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n edit: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n admin: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n rebalance: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n execute: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n trade: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n upload: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n query: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n performance_details: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n plot: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n delete: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass Entitlements(Base):\r\n view: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n edit: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n admin: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n rebalance: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n execute: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n trade: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n upload: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n query: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n performance_details: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n plot: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n delete: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n display: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass EqBasketBacktestParameters(Base):\r\n weighting_mechanism: str = field(default=None, metadata=field_metadata)\r\n time_horizon: str = field(default=None, metadata=field_metadata)\r\n frequency_interval: FrequencyInterval = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass EqBasketRebalanceCustomFrequencyRule(Base):\r\n frequency_interval: FrequencyInterval = field(default=None, metadata=field_metadata)\r\n every: float = field(default=1, metadata=field_metadata)\r\n on: Union[float, str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\nclass FieldValueMap(DictBase):\r\n pass\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass FilterRequest(Base):\r\n scroll: Optional[str] = field(default=None, metadata=field_metadata)\r\n scroll_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n include_columns: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n filters: Optional[Tuple[DictBase, ...]] = field(default=None, metadata=field_metadata)\r\n order_by: Optional[OrderByBody] = field(default=None, metadata=field_metadata)\r\n limit: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass FilteredData(Base):\r\n total_results: Optional[float] = field(default=None, metadata=field_metadata)\r\n results: Optional[Tuple[DataRow, ...]] = field(default=None, metadata=field_metadata)\r\n scroll: Optional[str] = field(default=None, metadata=field_metadata)\r\n scroll_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False, order=True)\r\nclass FiniteDifferenceParameter(RiskMeasureParameter):\r\n aggregation_level: Optional[AggregationLevel] = field(default=None, metadata=field_metadata)\r\n currency: Optional[str] = field(default=None, metadata=field_metadata)\r\n local_curve: Optional[bool] = field(default=None, metadata=field_metadata)\r\n bump_size: Optional[float] = field(default=None, metadata=field_metadata)\r\n finite_difference_method: Optional[FiniteDifferenceMethod] = field(default=None, metadata=field_metadata)\r\n scale_factor: Optional[float] = field(default=None, metadata=field_metadata)\r\n mkt_marking_options: Optional[MktMarkingOptions] = field(default=None, metadata=field_metadata)\r\n parameter_type: Optional[str] = field(init=False, default='FiniteDifference', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass ISelectNewParameter(Base):\r\n early_unwind_after: Optional[float] = field(default=None, metadata=field_metadata)\r\n early_unwind_applicable: Optional[str] = field(default=None, metadata=field_metadata)\r\n expiry_date_rule: Optional[str] = field(default=None, metadata=field_metadata)\r\n option_target_expiry_parameter: Optional[Union[AssetValueParameters, float]] = field(default=None, metadata=field_metadata)\r\n option_early_unwind_days: Optional[float] = field(default=None, metadata=field_metadata)\r\n in_alpha: Optional[Union[bool, int]] = field(default=None, metadata=field_metadata)\r\n is_fsr_target_factor: Optional[Union[bool, int]] = field(default=None, metadata=config(field_name='isFSRTargetFactor', exclude=exclude_none))\r\n fsr_max_ratio: Optional[float] = field(default=None, metadata=field_metadata)\r\n fsr_min_ratio: Optional[float] = field(default=None, metadata=field_metadata)\r\n module_enabled: Optional[Union[bool, int]] = field(default=None, metadata=field_metadata)\r\n trend_signal_0: Optional[Union[bool, int]] = field(default=None, metadata=config(field_name='trendSignal_0', exclude=exclude_none))\r\n trend_signal_1: Optional[Union[bool, int]] = field(default=None, metadata=config(field_name='trendSignal_1', exclude=exclude_none))\r\n trend_signal_2: Optional[Union[bool, int]] = field(default=None, metadata=config(field_name='trendSignal_2', exclude=exclude_none))\r\n trend_signal_3: Optional[Union[bool, int]] = field(default=None, metadata=config(field_name='trendSignal_3', exclude=exclude_none))\r\n module_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n target_strike: Optional[float] = field(default=None, metadata=field_metadata)\r\n strike_method: Optional[StrikeMethodType] = field(default=None, metadata=field_metadata)\r\n option_expiry: Optional[OptionExpiryType] = field(default=None, metadata=field_metadata)\r\n bloomberg_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\r\n stock_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n asset_class: Optional[str] = field(default=None, metadata=field_metadata)\r\n future_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n ric: Optional[str] = field(default=None, metadata=field_metadata)\r\n new_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n execution_participation_rate: Optional[float] = field(default=None, metadata=field_metadata)\r\n new_shares: Optional[float] = field(default=None, metadata=field_metadata)\r\n new_lots: Optional[float] = field(default=None, metadata=field_metadata)\r\n execution_start_time: Optional[DictBase] = field(default=None, metadata=field_metadata)\r\n execution_end_time: Optional[DictBase] = field(default=None, metadata=field_metadata)\r\n execution_style: Optional[str] = field(default=None, metadata=field_metadata)\r\n execution_timezone: Optional[str] = field(default=None, metadata=field_metadata)\r\n notional: Optional[Union[AssetValueParameters, float]] = field(default=None, metadata=field_metadata)\r\n unwind_pct: Optional[Union[AssetValueParameters, float]] = field(default=None, metadata=field_metadata)\r\n leverage: Optional[float] = field(default=None, metadata=field_metadata)\r\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\r\n hedge_ratio: Optional[float] = field(default=None, metadata=field_metadata)\r\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\r\n option_strike_type: Optional[OptionStrikeType] = field(default=None, metadata=field_metadata)\r\n credit_option_type: Optional[CreditOptionType] = field(default=None, metadata=field_metadata)\r\n credit_option_strike_type: Optional[CreditOptionStrikeType] = field(default=None, metadata=field_metadata)\r\n strike_relative: Optional[Union[AssetValueParameters, float]] = field(default=None, metadata=field_metadata)\r\n trade_type: Optional[TradeType] = field(default=None, metadata=field_metadata)\r\n signal: Optional[float] = field(default=None, metadata=field_metadata)\r\n new_signal: Optional[float] = field(default=None, metadata=field_metadata)\r\n new_min_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n new_max_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n min_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n max_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n weight_smoothing_window: Optional[float] = field(default=None, metadata=field_metadata)\r\n election: Optional[str] = field(default=None, metadata=field_metadata)\r\n base_date: Optional[str] = field(default=None, metadata=field_metadata)\r\n commodity: Optional[str] = field(default=None, metadata=field_metadata)\r\n component_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n contract_nearby_number: Optional[float] = field(default=None, metadata=field_metadata)\r\n expiration_schedule: Optional[str] = field(default=None, metadata=field_metadata)\r\n fixing_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n last_eligible_date: Optional[float] = field(default=None, metadata=field_metadata)\r\n num_roll_days: Optional[float] = field(default=None, metadata=field_metadata)\r\n roll_end: Optional[float] = field(default=None, metadata=field_metadata)\r\n roll_start: Optional[float] = field(default=None, metadata=field_metadata)\r\n roll_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n valid_contract_expiry: Optional[str] = field(default=None, metadata=field_metadata)\r\n rtl: Optional[float] = field(default=None, metadata=field_metadata)\r\n current_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n chg_weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n new_units: Optional[float] = field(default=None, metadata=field_metadata)\r\n country: Optional[str] = field(default=None, metadata=field_metadata)\r\n region: Optional[str] = field(default=None, metadata=field_metadata)\r\n adv: Optional[float] = field(default=None, metadata=field_metadata)\r\n tadv: Optional[float] = field(default=None, metadata=field_metadata)\r\n hadv: Optional[float] = field(default=None, metadata=field_metadata)\r\n market_cap: Optional[float] = field(default=None, metadata=field_metadata)\r\n market_classification: Optional[str] = field(default=None, metadata=field_metadata)\r\n new_signal_param_a: Optional[float] = field(default=None, metadata=config(field_name='NewSignalParamA', exclude=exclude_none))\r\n new_signal_param_b: Optional[float] = field(default=None, metadata=config(field_name='NewSignalParamB', exclude=exclude_none))\r\n signal_param_a: Optional[float] = field(default=None, metadata=config(field_name='SignalParamA', exclude=exclude_none))\r\n signal_param_b: Optional[float] = field(default=None, metadata=config(field_name='SignalParamB', exclude=exclude_none))\r\n inv_vol_cap: Optional[float] = field(default=None, metadata=field_metadata)\r\n newinv_vol_cap: Optional[float] = field(default=None, metadata=field_metadata)\r\n units_scaling_factor: Optional[float] = field(default=None, metadata=field_metadata)\r\n new_units_scaling_factor: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass LiborFallbackScenario(Scenario):\r\n date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n fallback_type: Optional[FallbackType] = field(default=FallbackType.RFR, metadata=field_metadata)\r\n discounting: Optional[bool] = field(default=False, metadata=field_metadata)\r\n cash_flows: Optional[bool] = field(default=True, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='LiborFallbackScenario', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass LiveMarket(BaseMarket):\r\n location: PricingLocation = field(default=None, metadata=field_metadata)\r\n market_type: Optional[str] = field(init=False, default='LiveMarket', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MarketDataCoordinateValue(Base):\r\n coordinate: MarketDataCoordinate = field(default=None, metadata=field_metadata)\r\n value: float = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MarketDataPattern(Base):\r\n mkt_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n mkt_asset: Optional[str] = field(default=None, metadata=field_metadata)\r\n mkt_class: Optional[str] = field(default=None, metadata=field_metadata)\r\n mkt_point: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n mkt_quoting_style: Optional[str] = field(default=None, metadata=field_metadata)\r\n is_active: Optional[bool] = field(default=None, metadata=field_metadata)\r\n is_investment_grade: Optional[bool] = field(default=None, metadata=field_metadata)\r\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n country_code: Optional[CountryCode] = field(default=None, metadata=field_metadata)\r\n gics_sector: Optional[str] = field(default=None, metadata=field_metadata)\r\n gics_industry_group: Optional[str] = field(default=None, metadata=field_metadata)\r\n gics_industry: Optional[str] = field(default=None, metadata=field_metadata)\r\n gics_sub_industry: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MarketDataShock(Base):\r\n shock_type: MarketDataShockType = field(default=None, metadata=field_metadata)\r\n value: float = field(default=None, metadata=field_metadata)\r\n precision: Optional[float] = field(default=None, metadata=field_metadata)\r\n cap: Optional[float] = field(default=None, metadata=field_metadata)\r\n floor: Optional[float] = field(default=None, metadata=field_metadata)\r\n coordinate_cap: Optional[float] = field(default=None, metadata=field_metadata)\r\n coordinate_floor: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOBenchmark(Base):\r\n selected: Optional[str] = field(default=None, metadata=field_metadata)\r\n options: Optional[Tuple[PCOBenchmarkOptions, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOCashBalance(Base):\r\n local_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n cash_balance_limits: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n cash_reserve: Optional[str] = field(default=None, metadata=field_metadata)\r\n long_threshold: Optional[str] = field(default=None, metadata=field_metadata)\r\n short_threshold: Optional[str] = field(default=None, metadata=field_metadata)\r\n holding: Optional[str] = field(default=None, metadata=field_metadata)\r\n t0: Optional[str] = field(default=None, metadata=field_metadata)\r\n t1: Optional[str] = field(default=None, metadata=field_metadata)\r\n t2: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOCurrencyWeight(Base):\r\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n weight: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOParameterValues(Base):\r\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n value: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOSettlements(Base):\r\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n data: Optional[Tuple[PCOSettlementsData, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOShareClass(Base):\r\n total_net_assets: Optional[str] = field(default=None, metadata=field_metadata)\r\n estimated_switch: Optional[str] = field(default=None, metadata=field_metadata)\r\n estimated_net_subscription_effective_date: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n confirmed_net_subscription_effective_date: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n estimated_net_dividend: Optional[str] = field(default=None, metadata=field_metadata)\r\n confirmed_net_dividend: Optional[str] = field(default=None, metadata=field_metadata)\r\n net_subscriptions: Optional[Tuple[PCONetSubscription, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOTargetDeviation(Base):\r\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n data: Optional[Tuple[PCOTargetDeviationData, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOUnrealisedMarkToMarket(Base):\r\n total: Optional[str] = field(default=None, metadata=field_metadata)\r\n next_settlement_date: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n next_settlement: Optional[str] = field(default=None, metadata=field_metadata)\r\n next_roll_date: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n historical_data: Optional[Tuple[PCOMtMHistoricalData, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PerformanceStatsRequest(Base):\r\n annualized_return: Optional[Op] = field(default=None, metadata=field_metadata)\r\n annualized_volatility: Optional[Op] = field(default=None, metadata=field_metadata)\r\n best_month: Optional[Op] = field(default=None, metadata=field_metadata)\r\n max_draw_down: Optional[Op] = field(default=None, metadata=field_metadata)\r\n max_draw_down_duration: Optional[Op] = field(default=None, metadata=field_metadata)\r\n positive_months: Optional[Op] = field(default=None, metadata=field_metadata)\r\n sharpe_ratio: Optional[Op] = field(default=None, metadata=field_metadata)\r\n sortino_ratio: Optional[Op] = field(default=None, metadata=field_metadata)\r\n worst_month: Optional[Op] = field(default=None, metadata=field_metadata)\r\n average_return: Optional[Op] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PositionPriceInput(Base):\r\n asset_id: str = field(default=None, metadata=field_metadata)\r\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\r\n weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n notional: Optional[float] = field(default=None, metadata=field_metadata)\r\n tags: Optional[Tuple[PositionTag, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PositionsRequest(Base):\r\n asset_id: str = field(default=None, metadata=field_metadata)\r\n weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\r\n notional: Optional[float] = field(default=None, metadata=field_metadata)\r\n tags: Optional[Tuple[PositionTag, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass RiskRequestParameters(Base):\r\n csa_term: Optional[str] = field(default=None, metadata=field_metadata)\r\n raw_results: Optional[bool] = field(default=False, metadata=field_metadata)\r\n use_historical_diddles_only: Optional[bool] = field(default=False, metadata=field_metadata)\r\n market_behaviour: Optional[MarketBehaviour] = field(default=MarketBehaviour.ContraintsBased, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass RollFwd(Scenario):\r\n date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\r\n realise_fwd: Optional[bool] = field(default=True, metadata=field_metadata)\r\n holiday_calendar: Optional[PricingLocation] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='RollFwd', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass TimestampedMarket(BaseMarket):\r\n timestamp: datetime.datetime = field(default=None, metadata=field_metadata)\r\n location: PricingLocation = field(default=None, metadata=field_metadata)\r\n base_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n market_type: Optional[str] = field(init=False, default='TimestampedMarket', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetScreenerCreditRequestFilters(Base):\r\n face_value: Optional[float] = field(default=None, metadata=field_metadata)\r\n direction: Optional[str] = field(default=None, metadata=field_metadata)\r\n liquidity_score: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_charge_bps: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_charge_dollars: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n duration: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n bbg_data: Optional[AssetScreenerBbgRequestFilter] = field(default=None, metadata=field_metadata)\r\n carbon_data: Optional[AssetScreenerCarbonRequestFilter] = field(default=None, metadata=field_metadata)\r\n esg_data: Optional[AssetScreenerESGRequestFilter] = field(default=None, metadata=field_metadata)\r\n issue_date: Optional[AssetScreenerRequestFilterDateLimits] = field(default=None, metadata=field_metadata)\r\n yield_: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=config(field_name='yield', exclude=exclude_none))\r\n spread: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n z_spread: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n g_spread: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n mid_price: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n maturity: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n pb_data: Optional[AssetScreenerPbRequestFilter] = field(default=None, metadata=field_metadata)\r\n amount_outstanding: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n rating_standard_and_poors: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n seniority: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n ticker: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n cusip: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n isin: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n currency: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n gs_indicative_buy_price: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_indicative_buy_quantity: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_indicative_buy_spread: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_indicative_buy_yield: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_indicative_sell_price: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_indicative_sell_quantity: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_indicative_sell_spread: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n gs_indicative_sell_yield: Optional[AssetScreenerRequestFilterLimits] = field(default=None, metadata=field_metadata)\r\n region: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n sector: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n industry: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n coupon_type: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n private_placement_type: Optional[AssetScreenerRequestStringOptions] = field(default=None, metadata=field_metadata)\r\n universe: Optional[tuple] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetStatsRequest(Base):\r\n last_updated_time: Optional[DateRange] = field(default=None, metadata=field_metadata)\r\n period: Optional[AssetStatsPeriod] = field(default=None, metadata=field_metadata)\r\n type_: Optional[AssetStatsType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\r\n stats: Optional[PerformanceStatsRequest] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLCurrencyArray(Base):\r\n currency_values: Optional[Tuple[CSLCurrency, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLSchedule(Base):\r\n first_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n last_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n calendar_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n period: Optional[str] = field(default=None, metadata=field_metadata)\r\n delay: Optional[str] = field(default=None, metadata=field_metadata)\r\n business_day_convention: Optional[str] = field(default=None, metadata=field_metadata)\r\n day_count_convention: Optional[str] = field(default=None, metadata=field_metadata)\r\n days_per_term: Optional[str] = field(default=None, metadata=field_metadata)\r\n delay_business_day_convention: Optional[str] = field(default=None, metadata=field_metadata)\r\n delay_calendar_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n has_reset_date: Optional[bool] = field(default=None, metadata=field_metadata)\r\n term_formula: Optional[str] = field(default=None, metadata=field_metadata)\r\n extra_dates: Optional[Tuple[CSLDateArrayNamedParam, ...]] = field(default=None, metadata=field_metadata)\r\n extra_dates_by_offset: Optional[Tuple[CSLSymCaseNamedParam, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CurveScenario(Scenario):\r\n market_data_pattern: Optional[MarketDataPattern] = field(default=None, metadata=field_metadata)\r\n parallel_shift: Optional[float] = field(default=None, metadata=field_metadata)\r\n curve_shift: Optional[float] = field(default=None, metadata=field_metadata)\r\n pivot_point: Optional[float] = field(default=None, metadata=field_metadata)\r\n tenor_start: Optional[float] = field(default=None, metadata=field_metadata)\r\n tenor_end: Optional[float] = field(default=None, metadata=field_metadata)\r\n shock_type: Optional[MarketDataShockType] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='CurveScenario', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass EqBasketRebalanceFrequency(Base):\r\n frequency_rule: Union[EqBasketRebalanceCustomFrequencyRule, EqBasketRebalanceFrequencyRule, str] = field(default=None, metadata=field_metadata)\r\n holiday_calendar: Optional[EqBasketHolidayCalendar] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass FieldFilterMap(Base):\r\n issue_status_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n internal_index_calc_region: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n last_returns_start_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n pl_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n amount_outstanding: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_gics_sub_industry: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mdapi_class: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n data_set_ids: Optional[Union[Tuple[Tuple[str, ...], ...], Tuple[str, ...]]] = field(default=None, metadata=field_metadata)\r\n call_first_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n pb_client_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_start: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n owner_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sec_db: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=config(field_name='secDB', exclude=exclude_none))\r\n economic_terms_hash: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n objective: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n simon_intl_asset_tags: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n private_placement_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n hedge_notional: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n rank: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n data_set_category: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n pair_calculation: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_index_family: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n created_by_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n vehicle_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n market_data_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_payer_day_count_fraction: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n point_class: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_cap_floor: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_underlier_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_payer_currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n minimum_increment: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n settlement_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n hedge_volatility: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n version: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n tags: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_gics_industry_group: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n market_data_asset: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_is_primary: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n styles: Optional[Union[Tuple[Tuple[str, ...], ...], Tuple[str, ...]]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_total_quantity: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n short_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_underliers_asset_class: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n eid: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n calculation_region: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n jsn: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mkt_quoting_style: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mic: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n hurdle_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n ps_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n final_close_date: Optional[Union[Tuple[Union[Op, datetime.date], ...], Union[Op, datetime.date]]] = field(default=None, metadata=field_metadata)\r\n issue_status: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n region_code: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n dollar_cross: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n portfolio_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n vendor: Optional[Union[Tuple[Union[MarketDataVendor, str], ...], Union[MarketDataVendor, str]]] = field(default=None, metadata=field_metadata)\r\n popularity: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n term: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n real_time_restriction_status: Optional[Union[Tuple[Tuple[str, ...], ...], Tuple[str, ...]]] = field(default=None, metadata=field_metadata)\r\n rating_fitch: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_clearing_house: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n non_symbol_dimensions: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_option_style: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n share_class_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_put_amount: Optional[Union[Tuple[Union[float, str], ...], Union[float, str]]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_underlier: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_floating_rate_designated_maturity: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n next_rebalance_date: Optional[Union[Op, Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n target_notional: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_tenor: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mkt_class: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_digital_asset_class: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n delisted: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n last_updated_since: Optional[Union[Tuple[datetime.datetime, ...], datetime.datetime]] = field(default=None, metadata=field_metadata)\r\n regional_focus: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_payer_designated_maturity: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n tsdb_shortname: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n seasonal_adjustment_short: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_exchange_currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_country_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n management_fee: Optional[Union[Tuple[Union[Op, float], ...], Union[Op, float]]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_settlement_date: Optional[Union[Op, Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n rating_moodys: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n simon_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n development_status: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n cusip: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n notes: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n tags_to_exclude: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_floating_rate_option: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n internal_index_calc_agent: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n last_rebalance_date: Optional[Union[Op, Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n rating_second_highest: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_country_code: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n frequency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n option_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n data_set_sub_category: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n is_live: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n is_legacy_pair_basket: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n issuer_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n plot_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_pricing_location: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_coupon: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_identifier: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_last_fixing_date: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n data_product: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mq_symbol: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sectors: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_method_of_settlement: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n redemption_notice_period: Optional[Union[Tuple[Union[Op, float], ...], Union[Op, float]]] = field(default=None, metadata=field_metadata)\r\n multiplier: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_payer_rate_option: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n market_data_point: Optional[Union[Tuple[Tuple[str, ...], ...], Tuple[str, ...]]] = field(default=None, metadata=field_metadata)\r\n external: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n sts_fx_currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n wpk: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n hedge_annualized_volatility: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n fix_order_routing_region: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n aum: Optional[Union[Tuple[Union[Op, float], ...], Union[Op, float]]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_expiration_date: Optional[Union[Op, Tuple[Union[datetime.date, str], ...], Union[datetime.date, str]]] = field(default=None, metadata=field_metadata)\r\n exchange: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n folder_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n region: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n cid: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n onboarded: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n live_date: Optional[Union[Tuple[Union[Op, datetime.date], ...], Union[Op, datetime.date]]] = field(default=None, metadata=field_metadata)\r\n issue_price: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n sink_factor: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n underlying_data_set_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_gics_sector: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n prime_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_payer_frequency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_notional_amount_in_other_currency: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n sts_asset_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_pair: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n description: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_is_country_primary: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n title: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n net_exposure_classification: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_strike_price: Optional[Union[Tuple[Union[float, str], ...], Union[float, str]]] = field(default=None, metadata=field_metadata)\r\n coupon_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n last_updated_by_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_forward_price: Optional[Union[Tuple[Union[float, str], ...], Union[float, str]]] = field(default=None, metadata=field_metadata)\r\n clone_parent_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n company: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n gate_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n issue_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n expiration_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n coverage: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n ticker: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_receiver_rate_option: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n coin_metrics_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n call_last_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n sts_rates_country: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_payer_spread: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_premium_payment_date: Optional[Union[Op, Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n latest_execution_time: Optional[Union[Tuple[datetime.datetime, ...], datetime.datetime]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_forward_rate: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_digital_asset_industry: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_receiver_designated_maturity: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n gate: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n multi_tags: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n gsn: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n gss: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n rating_linear: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n asset_class: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_vendor: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n cm_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_index: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n type_: Optional[Union[Tuple[Union[AssetType, RiskModelType], ...], Union[AssetType, RiskModelType]]] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\r\n gsideid: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n ric: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mdapi: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n issuer: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n position_source_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n measures: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_floating_rate_day_count_fraction: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n first_close_date: Optional[Union[Tuple[Union[Op, datetime.date], ...], Union[Op, datetime.date]]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_notional_amount: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n strategy_aum: Optional[Union[Tuple[Union[Op, float], ...], Union[Op, float]]] = field(default=None, metadata=field_metadata)\r\n action: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n id_: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\r\n asset_parameters_call_amount: Optional[Union[Tuple[Union[float, str], ...], Union[float, str]]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_seniority: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n redemption_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n identifier: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sec_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n index_create_source: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sub_region: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_index2_tenor: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_receiver_day_count_fraction: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_notional_currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sedol: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mkt_asset: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n open_to_sma: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=config(field_name='openToSMA', exclude=exclude_none))\r\n rating_standard_and_poors: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_types: Optional[Union[Tuple[AssetType, ...], Tuple[Tuple[AssetType, ...], ...]]] = field(default=None, metadata=field_metadata)\r\n bcid: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_credit_index_series: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n vintage: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n gsid: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_digital_asset_subsector: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n tdapi: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n last_updated_message: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n trading_restriction: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n rcic: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n status: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n name_raw: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_pay_or_receive: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n domains_data: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n client_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_index_series: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_gics_industry: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n on_behalf_of: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n increment: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n accrued_interest_standard: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n enabled: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sectors_raw: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sts_commodity: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sts_commodity_sector: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n open_to_portable_alpha: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_receiver_frequency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n position_source_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n gsid_equivalent: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n categories: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n symbol_dimensions: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n ext_mkt_asset: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_fixed_rate_frequency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n coupon: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n compliance_restricted_status: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n side_pocket: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n quoting_style: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n is_entity: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n scenario_group_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_trade_as: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n redemption_period: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sts_credit_market: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_issuer_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n bbid: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_risk_country_code: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_digital_asset_sector: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sts_em_dm: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_receiver_currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n issue_size: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n returns_enabled: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_settlement: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n seniority: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n primary_country_ric: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_expiration_time: Optional[Union[Op, Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n is_pair_basket: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_index_version: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_digital_asset_market: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_commodity_reference_price: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n default_backcast: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_number_of_shares: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n use_machine_learning: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n performance_fee: Optional[Union[Tuple[Union[Op, float], ...], Union[Op, float]]] = field(default=None, metadata=field_metadata)\r\n report_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n lockup: Optional[Union[Tuple[Union[Op, float], ...], Union[Op, float]]] = field(default=None, metadata=field_metadata)\r\n lockup_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n underlying_asset_ids: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n encoded_stats: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_fee_currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n pnode_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n backtest_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_issuer: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n exchange_code: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_strike: Optional[Union[Tuple[Union[float, str], ...], Union[float, str]]] = field(default=None, metadata=field_metadata)\r\n oe_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_termination_date: Optional[Union[Tuple[Union[datetime.date, str], ...], Union[datetime.date, str]]] = field(default=None, metadata=field_metadata)\r\n resource: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n bbid_equivalent: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_receiver_spread: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n valoren: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_effective_date: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n hurdle: Optional[Union[Tuple[Union[Op, float], ...], Union[Op, float]]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_fixed_rate_day_count_fraction: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_number_of_options: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n auto_tags: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n short_description: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n ext_mkt_class: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mkt_point1: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n portfolio_managers: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_commodity_sector: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n hedge_tracking_error: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n supra_strategy: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_coupon_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_put_currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n term_status: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n market_cap_category: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n wi_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_call_currency: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mkt_point3: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n display_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mkt_point2: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n strike_price: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n mkt_point4: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n risk_packages: Optional[Union[Tuple[Tuple[str, ...], ...], Tuple[str, ...]]] = field(default=None, metadata=field_metadata)\r\n units: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n em_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sts_credit_region: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n ext_mkt_point3: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_classifications_risk_country_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_vendor: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_index1_tenor: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n mkt_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n alias: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n is_public: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n ext_mkt_point1: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n product_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n ext_mkt_point2: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sub_region_code: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n last_returns_end_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_fixed_rate: Optional[Union[Tuple[Union[float, str], ...], Union[float, str]]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_option_type: Optional[Union[Tuple[Union[OptionType, str], ...], Union[OptionType, str]]] = field(default=None, metadata=field_metadata)\r\n tsdb_synced_symbol: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n position_source_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_multiplier: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n cross: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n lms_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n flagship: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n minimum_denomination: Optional[Union[Tuple[float, ...], float]] = field(default=None, metadata=field_metadata)\r\n in_code: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_strike_price_relative: Optional[Union[Tuple[Union[float, str], ...], Union[float, str]]] = field(default=None, metadata=field_metadata)\r\n sts_rates_maturity: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n sts_include_sstk_analytics: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n position_source: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n listed: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n non_owner_id: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n latest_end_date: Optional[Union[Tuple[datetime.date, ...], datetime.date]] = field(default=None, metadata=field_metadata)\r\n shock_style: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n g10_currency: Optional[Union[Tuple[bool, ...], bool]] = field(default=None, metadata=field_metadata)\r\n strategy: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n vintage_name: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n methodology: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n isin: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n asset_parameters_strike_type: Optional[Union[Tuple[str, ...], str]] = field(default=None, metadata=field_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass IndexCurveShift(Scenario):\r\n market_data_pattern: Optional[MarketDataPattern] = field(default=None, metadata=field_metadata)\r\n freeze_pattern: Optional[MarketDataPattern] = field(default=None, metadata=field_metadata)\r\n annualised_parallel_shift: Optional[float] = field(default=None, metadata=field_metadata)\r\n annualised_slope_shift: Optional[float] = field(default=None, metadata=field_metadata)\r\n cutoff: Optional[float] = field(default=None, metadata=field_metadata)\r\n floor: Optional[float] = field(default=None, metadata=field_metadata)\r\n tenor: Optional[str] = field(default=None, metadata=field_metadata)\r\n rate_option: Optional[str] = field(default=None, metadata=field_metadata)\r\n bucket_shift: Optional[float] = field(default=None, metadata=field_metadata)\r\n bucket_start: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n bucket_end: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='IndexCurveShift', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MarketDataPatternAndShock(Base):\r\n pattern: MarketDataPattern = field(default=None, metadata=field_metadata)\r\n shock: MarketDataShock = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MarketDataVolShockScenario(Scenario):\r\n pattern: MarketDataPattern = field(default=None, metadata=field_metadata)\r\n shock_type: MarketDataShockType = field(default=None, metadata=field_metadata)\r\n vol_levels: Tuple[MarketDataVolSlice, ...] = field(default=None, metadata=field_metadata)\r\n ref_spot: float = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='MarketDataVolShockScenario', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOExposureLeg(Base):\r\n local_to_base_rate: Optional[str] = field(default=None, metadata=field_metadata)\r\n local_nav_limits: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n base_nav_limits: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n all_approved_hedge_ratio: Optional[str] = field(default=None, metadata=field_metadata)\r\n show_all_approved_hedge_ratio: Optional[bool] = field(default=None, metadata=field_metadata)\r\n hedge_ratio: Optional[str] = field(default=None, metadata=field_metadata)\r\n exposure_ratio: Optional[str] = field(default=None, metadata=field_metadata)\r\n local_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n target_ratio: Optional[str] = field(default=None, metadata=field_metadata)\r\n benchmark: Optional[PCOBenchmark] = field(default=None, metadata=field_metadata)\r\n long_rebalance_threshold: Optional[str] = field(default=None, metadata=field_metadata)\r\n short_rebalance_threshold: Optional[str] = field(default=None, metadata=field_metadata)\r\n base_nav: Optional[str] = field(default=None, metadata=field_metadata)\r\n local_nav: Optional[str] = field(default=None, metadata=field_metadata)\r\n base_fx_forward: Optional[str] = field(default=None, metadata=field_metadata)\r\n local_fx_forward: Optional[str] = field(default=None, metadata=field_metadata)\r\n auto_roll: Optional[bool] = field(default=None, metadata=field_metadata)\r\n exposure_currencies: Optional[Tuple[Currency, ...]] = field(default=None, metadata=field_metadata)\r\n roll_date: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOSecurityWeight(Base):\r\n security_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n currency_weights: Optional[Tuple[PCOCurrencyWeight, ...]] = field(default=None, metadata=field_metadata)\r\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass User(Base):\r\n company: str = field(default=None, metadata=field_metadata)\r\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\r\n country: str = field(default=None, metadata=field_metadata)\r\n city: str = field(default=None, metadata=field_metadata)\r\n region: str = field(default=None, metadata=field_metadata)\r\n email: str = field(default=None, metadata=field_metadata)\r\n name: str = field(default=None, metadata=field_metadata)\r\n internal: Optional[bool] = field(default=None, metadata=field_metadata)\r\n system_user: Optional[bool] = field(default=None, metadata=field_metadata)\r\n app_user: Optional[bool] = field(default=None, metadata=field_metadata)\r\n analytics_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n eaa_company: Optional[str] = field(default=None, metadata=field_metadata)\r\n root_oe_id: Optional[str] = field(default=None, metadata=config(field_name='rootOEId', exclude=exclude_none))\r\n oe_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n drg_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n root_oe_name: Optional[str] = field(default=None, metadata=config(field_name='rootOEName', exclude=exclude_none))\r\n oe_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n oe_alias: Optional[int] = field(default=None, metadata=field_metadata)\r\n coverage: Optional[Tuple[DictBase, ...]] = field(default=None, metadata=field_metadata)\r\n internal_email: Optional[str] = field(default=None, metadata=field_metadata)\r\n kerberos: Optional[str] = field(default=None, metadata=field_metadata)\r\n first_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n last_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n internal_id: Optional[str] = field(default=None, metadata=config(field_name='internalID', exclude=exclude_none))\r\n mi_fidii_trade_idea_declined: Optional[str] = field(default=None, metadata=config(field_name='miFIDIITradeIdeaDeclined', exclude=exclude_none))\r\n department_code: Optional[str] = field(default=None, metadata=field_metadata)\r\n department_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n division_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n business_unit: Optional[str] = field(default=None, metadata=field_metadata)\r\n title: Optional[str] = field(default=None, metadata=field_metadata)\r\n pmd: Optional[bool] = field(default=None, metadata=field_metadata)\r\n login: Optional[str] = field(default=None, metadata=field_metadata)\r\n tokens: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n roles: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n groups: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\r\n app_managers: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n contact_sources: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass AssetParameters(Base):\r\n basket_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n style: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_calculation_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_return_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_divisor: Optional[float] = field(default=None, metadata=field_metadata)\r\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n quote_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n index_initial_price: Optional[float] = field(default=None, metadata=field_metadata)\r\n initial_pricing_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n expiration_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n expiration_location: Optional[str] = field(default=None, metadata=field_metadata)\r\n number_of_shares: Optional[float] = field(default=None, metadata=field_metadata)\r\n option_style: Optional[str] = field(default=None, metadata=field_metadata)\r\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\r\n settlement_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n settlement_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\r\n put_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n put_amount: Optional[float] = field(default=None, metadata=field_metadata)\r\n automatic_exercise: Optional[bool] = field(default=None, metadata=field_metadata)\r\n call_amount: Optional[float] = field(default=None, metadata=field_metadata)\r\n call_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n exercise_time: Optional[str] = field(default=None, metadata=field_metadata)\r\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\r\n premium_payment_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n premium: Optional[float] = field(default=None, metadata=field_metadata)\r\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n callable_: Optional[bool] = field(default=None, metadata=config(field_name='callable', exclude=exclude_none))\r\n puttable: Optional[bool] = field(default=None, metadata=field_metadata)\r\n perpetual: Optional[bool] = field(default=None, metadata=field_metadata)\r\n seniority: Optional[str] = field(default=None, metadata=field_metadata)\r\n coupon_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n index: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_term: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_margin: Optional[float] = field(default=None, metadata=field_metadata)\r\n coupon: Optional[float] = field(default=None, metadata=field_metadata)\r\n issue_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n issuer: Optional[str] = field(default=None, metadata=field_metadata)\r\n issuer_country_code: Optional[str] = field(default=None, metadata=field_metadata)\r\n issuer_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n issue_size: Optional[float] = field(default=None, metadata=field_metadata)\r\n commodity_sector: Optional[CommoditySector] = field(default=None, metadata=field_metadata)\r\n pricing_location: Optional[PricingLocation] = field(default=None, metadata=field_metadata)\r\n contract_months: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n g10_currency: Optional[bool] = field(default=None, metadata=field_metadata)\r\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n hedge_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n ultimate_ticker: Optional[str] = field(default=None, metadata=field_metadata)\r\n strategy: Optional[str] = field(default=None, metadata=field_metadata)\r\n exchange_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n region: Optional[str] = field(default=None, metadata=field_metadata)\r\n delivery_point: Optional[str] = field(default=None, metadata=field_metadata)\r\n pricing_index: Optional[str] = field(default=None, metadata=field_metadata)\r\n common_code: Optional[str] = field(default=None, metadata=field_metadata)\r\n issuer_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n contract_month: Optional[str] = field(default=None, metadata=field_metadata)\r\n bloomberg_collateral_classification: Optional[str] = field(default=None, metadata=field_metadata)\r\n bloomberg_publish_parameters: Optional[BloombergPublishParameters] = field(default=None, metadata=field_metadata)\r\n load_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n contract_unit: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_approval_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n is_pair_basket: Optional[bool] = field(default=None, metadata=field_metadata)\r\n is_legacy_pair_basket: Optional[bool] = field(default=None, metadata=field_metadata)\r\n fixed_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\r\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\r\n forward_price: Optional[float] = field(default=None, metadata=field_metadata)\r\n pair_calculation: Optional[str] = field(default=None, metadata=field_metadata)\r\n pay_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\r\n receive_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\r\n pay_frequency: Optional[str] = field(default=None, metadata=field_metadata)\r\n receive_frequency: Optional[str] = field(default=None, metadata=field_metadata)\r\n resettable_leg: Optional[PayReceive] = field(default=None, metadata=field_metadata)\r\n inflation_lag: Optional[str] = field(default=None, metadata=field_metadata)\r\n fx_index: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_notes: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_not_trading_reasons: Optional[IndexNotTradingReasons] = field(default=None, metadata=field_metadata)\r\n trade_as: Optional[str] = field(default=None, metadata=field_metadata)\r\n clone_parent_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n on_behalf_of: Optional[str] = field(default=None, metadata=field_metadata)\r\n index_calculation_agent: Optional[str] = field(default=None, metadata=field_metadata)\r\n product_type: Optional[ProductType] = field(default=None, metadata=field_metadata)\r\n vendor: Optional[str] = field(default=None, metadata=field_metadata)\r\n call_first_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n call_last_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n amount_outstanding: Optional[float] = field(default=None, metadata=field_metadata)\r\n covered_bond: Optional[bool] = field(default=None, metadata=field_metadata)\r\n issue_status: Optional[str] = field(default=None, metadata=field_metadata)\r\n issue_status_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n issue_price: Optional[float] = field(default=None, metadata=field_metadata)\r\n sinkable: Optional[bool] = field(default=None, metadata=field_metadata)\r\n sink_factor: Optional[float] = field(default=None, metadata=field_metadata)\r\n accrued_interest_standard: Optional[float] = field(default=None, metadata=field_metadata)\r\n redemption_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n redemption_price: Optional[float] = field(default=None, metadata=field_metadata)\r\n redemption_amount: Optional[float] = field(default=None, metadata=field_metadata)\r\n redemption_percent: Optional[float] = field(default=None, metadata=field_metadata)\r\n private_placement_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n minimum_piece: Optional[float] = field(default=None, metadata=field_metadata)\r\n minimum_increment: Optional[float] = field(default=None, metadata=field_metadata)\r\n next_coupon_payment: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n minimum_denomination: Optional[float] = field(default=None, metadata=field_metadata)\r\n default_backcast: Optional[bool] = field(default=None, metadata=field_metadata)\r\n index_precision: Optional[float] = field(default=None, metadata=field_metadata)\r\n official_side: Optional[Side] = field(default=None, metadata=field_metadata)\r\n valuation_source: Optional[BasketValuationSource] = field(default=None, metadata=field_metadata)\r\n close_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n credit_index_series: Optional[str] = field(default=None, metadata=field_metadata)\r\n reference_entity: Optional[str] = field(default=None, metadata=field_metadata)\r\n restructuring_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n underlying_type: Optional[str] = field(default=None, metadata=field_metadata)\r\n underlier: Optional[str] = field(default=None, metadata=field_metadata)\r\n next_rebalance_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n last_rebalance_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n last_rebalance_approval_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n target_notional: Optional[float] = field(default=None, metadata=field_metadata)\r\n rebalance_frequency: Optional[EqBasketRebalanceFrequency] = field(default=None, metadata=field_metadata)\r\n historical_methodology: Optional[EqBasketHistoryMethodology] = field(default=None, metadata=field_metadata)\r\n cash_reinvestment_treatment: Optional[CashReinvestmentTreatment] = field(default=None, metadata=field_metadata)\r\n benchmark: Optional[str] = field(default=None, metadata=field_metadata)\r\n attribution_dataset_id: Optional[str] = field(init=False, default='STSATTR', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CSLScheduleArray(Base):\r\n schedule_values: Optional[Tuple[CSLSchedule, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass EqBasketRebalanceCalendar(Base):\r\n frequency_rule: Optional[EqBasketRebalanceFrequency] = field(default=None, metadata=field_metadata)\r\n date_override: Optional[EqBasketRebalanceDateOverride] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MarketDataShockBasedScenario(Scenario):\r\n shocks: Tuple[MarketDataPatternAndShock, ...] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='MarketDataShockBasedScenario', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass OverlayMarket(SingleMarket):\r\n base_market: BaseMarket = field(default=None, metadata=field_metadata)\r\n market_data: Tuple[MarketDataCoordinateValue, ...] = field(default=None, metadata=field_metadata)\r\n market_model_data: Optional[str] = field(default=None, metadata=field_metadata)\r\n market_type: Optional[str] = field(init=False, default='OverlayMarket', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOExposure(Base):\r\n last_data_updated_date_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n nav_includes_fx_hedges: Optional[bool] = field(default=None, metadata=field_metadata)\r\n use_fx_rate_on_base_fx_forward: Optional[bool] = field(default=None, metadata=field_metadata)\r\n last_generate_orders_date_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n legs: Optional[Tuple[PCOExposureLeg, ...]] = field(default=None, metadata=field_metadata)\r\n adjustments: Optional[PCOExposureAdjustments] = field(default=None, metadata=field_metadata)\r\n ratio_mode: Optional[str] = field(default=None, metadata=field_metadata)\r\n hedge_calc_currency: Optional[PCOCurrencyType] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOSecurity(Base):\r\n weights: Optional[PCOSecurityWeight] = field(default=None, metadata=field_metadata)\r\n base_currency_exposure_limits: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n hedge_ratio: Optional[str] = field(default=None, metadata=field_metadata)\r\n local_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n base_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n base_currency_exposure: Optional[str] = field(default=None, metadata=field_metadata)\r\n local_currency_exposure: Optional[str] = field(default=None, metadata=field_metadata)\r\n security_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass RiskMeasure(Base):\r\n asset_class: Optional[AssetClass] = field(default=None, metadata=field_metadata)\r\n measure_type: Optional[RiskMeasureType] = field(default=None, metadata=field_metadata)\r\n unit: Optional[RiskMeasureUnit] = field(default=None, metadata=field_metadata)\r\n parameters: Optional[RiskMeasureParameter] = field(default=None, metadata=field_metadata)\r\n value: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass DataSetFieldMap(Base):\r\n data_set_id: str = field(default=None, metadata=field_metadata)\r\n field_: str = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\r\n results_field: str = field(default=None, metadata=field_metadata)\r\n risk_measure: RiskMeasure = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass EntityQuery(Base):\r\n format_: Optional[Format] = field(default=None, metadata=config(field_name='format', exclude=exclude_none))\r\n where: Optional[FieldFilterMap] = field(default=None, metadata=field_metadata)\r\n as_of_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n last_updated_since: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n delay: Optional[int] = field(default=None, metadata=field_metadata)\r\n order_by: Optional[Tuple[Union[DictBase, str], ...]] = field(default=None, metadata=field_metadata)\r\n scroll: Optional[str] = field(default=None, metadata=field_metadata)\r\n scroll_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n fields: Optional[Tuple[Union[DictBase, str], ...]] = field(default=None, metadata=field_metadata)\r\n limit: Optional[int] = field(default=None, metadata=field_metadata)\r\n offset: Optional[int] = field(default=None, metadata=field_metadata)\r\n vendor: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MeasureScenario(Scenario):\r\n risk_measure: RiskMeasure = field(default=None, metadata=field_metadata)\r\n value: float = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='MeasureScenario', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PCOSecurityBreakdown(Base):\r\n breakdown_currency_type: Optional[PCOCurrencyType] = field(default=None, metadata=field_metadata)\r\n last_security_breakdown_update_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n securities: Optional[Tuple[PCOSecurity, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass Position(Base):\r\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\r\n weight: Optional[float] = field(default=None, metadata=field_metadata)\r\n notional: Optional[float] = field(default=None, metadata=field_metadata)\r\n party_to: Optional[SimpleParty] = field(default=None, metadata=field_metadata)\r\n party_from: Optional[SimpleParty] = field(default=None, metadata=field_metadata)\r\n trade_on: Optional[BasketPositionTradeOn] = field(default=None, metadata=field_metadata)\r\n external_ids: Optional[Tuple[DictBase, ...]] = field(default=None, metadata=field_metadata)\r\n margin_ids: Optional[Tuple[DictBase, ...]] = field(default=None, metadata=field_metadata)\r\n tags: Optional[Tuple[PositionTag, ...]] = field(default=None, metadata=field_metadata)\r\n instrument: Optional[InstrumentBase] = field(default=None, metadata=field_metadata)\r\n description: Optional[str] = field(default=None, metadata=field_metadata)\r\n error: Optional[str] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass RelativeMarket(Base):\r\n from_market: SingleMarket = field(default=None, metadata=field_metadata)\r\n to_market: SingleMarket = field(default=None, metadata=field_metadata)\r\n market_type: Optional[str] = field(init=False, default='RelativeMarket', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass CompositeScenario(Scenario):\r\n scenarios: Optional[Tuple[Scenario, ...]] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='CompositeScenario', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass MultiScenario(Scenario):\r\n scenarios: Optional[Tuple[Scenario, ...]] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[str] = field(init=False, default='MultiScenario', metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PositionSetRequest(Base):\r\n date: datetime.date = field(default=None, metadata=field_metadata)\r\n positions: Tuple[PositionsRequest, ...] = field(default=None, metadata=field_metadata)\r\n target_notional: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PricingDateAndMarketDataAsOf(Base):\r\n pricing_date: datetime.date = field(default=None, metadata=field_metadata)\r\n market_data_as_of: Optional[Union[datetime.date, datetime.datetime]] = field(default=None, metadata=field_metadata)\r\n market: Optional[Market] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass LiquidityRequest(Base):\r\n notional: Optional[float] = field(default=None, metadata=field_metadata)\r\n positions: Optional[Union[Tuple[Position, ...], Tuple[WeightedPosition, ...]]] = field(default=None, metadata=field_metadata)\r\n risk_model: Optional[str] = field(default=None, metadata=field_metadata)\r\n date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n participation_rate: Optional[float] = field(default=None, metadata=field_metadata)\r\n execution_horizon: Optional[float] = field(default=None, metadata=field_metadata)\r\n execution_start_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n execution_end_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n benchmark_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n measures: Optional[Tuple[LiquidityMeasure, ...]] = field(default=None, metadata=field_metadata)\r\n time_series_benchmark_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n time_series_start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n time_series_end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n format_: Optional[Format] = field(default=None, metadata=config(field_name='format', exclude=exclude_none))\r\n report_parameters: Optional[LiquidityReportParameters] = field(default=None, metadata=field_metadata)\r\n explode_positions: Optional[bool] = field(default=False, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=field_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass PositionSet(Base):\r\n positions: Tuple[Position, ...] = field(default=None, metadata=field_metadata)\r\n position_date: datetime.date = field(default=None, metadata=field_metadata)\r\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\r\n last_update_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n type_: Optional[str] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\r\n divisor: Optional[float] = field(default=None, metadata=field_metadata)\r\n weighting_strategy: Optional[PositionSetWeightingStrategy] = field(default=None, metadata=field_metadata)\r\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass RiskPosition(Base):\r\n instrument: Priceable = field(default=None, metadata=field_metadata)\r\n instrument_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass RiskRequest(Base):\r\n positions: Tuple[RiskPosition, ...] = field(default=None, metadata=field_metadata)\r\n measures: Tuple[RiskMeasure, ...] = field(default=None, metadata=field_metadata)\r\n pricing_and_market_data_as_of: Optional[Tuple[PricingDateAndMarketDataAsOf, ...]] = field(default=None, metadata=field_metadata)\r\n pricing_location: Optional[PricingLocation] = field(default=None, metadata=field_metadata)\r\n wait_for_results: Optional[bool] = field(default=False, metadata=field_metadata)\r\n scenario: Optional[MarketDataScenario] = field(default=None, metadata=field_metadata)\r\n parameters: Optional[RiskRequestParameters] = field(default=None, metadata=field_metadata)\r\n request_visible_to_gs: Optional[bool] = field(default=False, metadata=field_metadata)\r\n use_cache: Optional[bool] = field(default=False, metadata=field_metadata)\r\n priority: Optional[int] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass ReportParameters(Base):\r\n approval_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n asset_class: Optional[AssetClass] = field(default=None, metadata=field_metadata)\r\n transaction_cost_model: Optional[str] = field(default=None, metadata=field_metadata)\r\n trading_cost: Optional[float] = field(default=None, metadata=field_metadata)\r\n servicing_cost_long: Optional[float] = field(default=None, metadata=field_metadata)\r\n servicing_cost_short: Optional[float] = field(default=None, metadata=field_metadata)\r\n region: Optional[str] = field(default=None, metadata=field_metadata)\r\n risk_model: Optional[str] = field(default=None, metadata=field_metadata)\r\n benchmark: Optional[str] = field(default=None, metadata=field_metadata)\r\n blended_benchmark: Optional[Tuple[BlendedBenchmarkAttribute, ...]] = field(default=None, metadata=field_metadata)\r\n tags: Optional[Tuple[PositionTag, ...]] = field(default=None, metadata=field_metadata)\r\n aggregate_by_tag_name: Optional[str] = field(default=None, metadata=field_metadata)\r\n fx_hedged: Optional[bool] = field(default=None, metadata=field_metadata)\r\n include_risk_free_attribution: Optional[bool] = field(default=None, metadata=field_metadata)\r\n rebalance_calendar: Optional[EqBasketRebalanceCalendar] = field(default=None, metadata=field_metadata)\r\n cash_reinvestment_treatment: Optional[CashReinvestmentTreatment] = field(default=None, metadata=field_metadata)\r\n historical_methodology: Optional[EqBasketHistoryMethodology] = field(default=None, metadata=field_metadata)\r\n backtest_parameters: Optional[EqBasketBacktestParameters] = field(default=None, metadata=field_metadata)\r\n bloomberg_publish_parameters: Optional[BloombergPublishParameters] = field(default=None, metadata=field_metadata)\r\n publish_to_bloomberg: Optional[bool] = field(default=None, metadata=field_metadata)\r\n publish_to_reuters: Optional[bool] = field(default=None, metadata=field_metadata)\r\n publish_to_factset: Optional[bool] = field(default=None, metadata=field_metadata)\r\n is_silent_update: Optional[bool] = field(default=None, metadata=field_metadata)\r\n include_price_history: Optional[bool] = field(default=None, metadata=field_metadata)\r\n index_update: Optional[bool] = field(default=None, metadata=field_metadata)\r\n index_rebalance: Optional[bool] = field(default=None, metadata=field_metadata)\r\n export_rebalance: Optional[bool] = field(default=None, metadata=field_metadata)\r\n index_source_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n basket_action: Optional[BasketAction] = field(default=None, metadata=field_metadata)\r\n api_domain: Optional[bool] = field(default=None, metadata=field_metadata)\r\n initial_price: Optional[float] = field(default=None, metadata=field_metadata)\r\n stock_level_exposures: Optional[bool] = field(default=None, metadata=field_metadata)\r\n explode_positions: Optional[bool] = field(default=None, metadata=field_metadata)\r\n scenario_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n scenario_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n scenario_group_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n scenario_type: Optional[ScenarioType] = field(default=None, metadata=field_metadata)\r\n market_model_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n risk_measures: Optional[Tuple[RiskMeasure, ...]] = field(default=None, metadata=field_metadata)\r\n initial_pricing_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n backcast: Optional[bool] = field(default=None, metadata=field_metadata)\r\n risk_request: Optional[RiskRequest] = field(default=None, metadata=field_metadata)\r\n subscription_parameters: Optional[ReportSubscriptionParameters] = field(default=None, metadata=field_metadata)\r\n thresholds: Optional[Tuple[SystematicHedgeThreshold, ...]] = field(default=None, metadata=field_metadata)\r\n frequency: Optional[str] = field(default=None, metadata=field_metadata)\r\n multi_hedge_id: Optional[str] = field(default=None, metadata=field_metadata)\r\n participation_rate: Optional[float] = field(default=None, metadata=field_metadata)\r\n approve_rebalance: Optional[bool] = field(default=None, metadata=field_metadata)\r\n auto_approved_rebalance: Optional[bool] = field(default=None, metadata=field_metadata)\r\n use_risk_request_batch_mode: Optional[bool] = field(default=None, metadata=field_metadata)\r\n limited_access_assets: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n corporate_action_restricted_assets: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n backcast_dates: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\r\n base_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n local_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\r\n fund_calendar: Optional[str] = field(default=None, metadata=field_metadata)\r\n calculation_currency: Optional[PCOCurrencyType] = field(default=None, metadata=field_metadata)\r\n hedge_settlement_interval: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\r\n hedge_settlement_day: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\r\n roll_horizon: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\r\n roll_split_days: Optional[int] = field(default=None, metadata=field_metadata)\r\n pnl_currency: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\r\n nav_publication_period: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\r\n roll_date_zero_threshold: Optional[bool] = field(default=None, metadata=field_metadata)\r\n unrealised_mark_to_market: Optional[PCOUnrealisedMarkToMarket] = field(default=None, metadata=field_metadata)\r\n target_deviation: Optional[Tuple[PCOTargetDeviation, ...]] = field(default=None, metadata=field_metadata)\r\n cash_balances: Optional[Tuple[PCOCashBalance, ...]] = field(default=None, metadata=field_metadata)\r\n exposure: Optional[PCOExposure] = field(default=None, metadata=field_metadata)\r\n pco_share_class: Optional[PCOShareClass] = field(default=None, metadata=field_metadata)\r\n settlements: Optional[Tuple[PCOSettlements, ...]] = field(default=None, metadata=field_metadata)\r\n show_cash: Optional[bool] = field(default=None, metadata=field_metadata)\r\n show_exposure: Optional[bool] = field(default=None, metadata=field_metadata)\r\n enable_rfq: Optional[bool] = field(default=None, metadata=config(field_name='enableRFQ', exclude=exclude_none))\r\n fixing_descriptions: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n pco_origin: Optional[PCOOrigin] = field(default=None, metadata=field_metadata)\r\n pco_action_type: Optional[PCOActionType] = field(default=None, metadata=field_metadata)\r\n security_breakdown: Optional[PCOSecurityBreakdown] = field(default=None, metadata=field_metadata)\r\n version: Optional[str] = field(default=None, metadata=field_metadata)\r\n roll_currency: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\r\n user_modified_fields: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n use_live_market: Optional[bool] = field(default=None, metadata=field_metadata)\r\n basket_ready_for_trade: Optional[bool] = field(default=None, metadata=field_metadata)\r\n allow_in_position_rebalance: Optional[bool] = field(default=None, metadata=field_metadata)\r\n weighting_strategy: Optional[PositionSetWeightingStrategy] = field(default=None, metadata=field_metadata)\r\n on_behalf_of: Optional[str] = field(default=None, metadata=field_metadata)\r\n dependency_report_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n\r\n\r\n@handle_camel_case_args\r\n@dataclass_json(letter_case=LetterCase.CAMEL)\r\n@dataclass(unsafe_hash=True, repr=False)\r\nclass ReportScheduleRequest(Base):\r\n parameters: Optional[ReportParameters] = field(default=None, metadata=field_metadata)\r\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\r\n use_close_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\r\n priority: Optional[ReportJobPriority] = field(default=None, metadata=field_metadata)\r\n name: Optional[str] = field(default=None, metadata=name_metadata)\r\n" }, { "path": "gs_quant/target/content.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass Division(EnumBase, Enum): \n \n SECDIV = 'SECDIV'\n IBD = 'IBD'\n RISK = 'RISK'\n GIR = 'GIR'\n EO = 'EO'\n KENSHO = 'KENSHO' \n\n\nclass InvestmentRecommendationDirection(EnumBase, Enum): \n \n Buy = 'Buy'\n Hold = 'Hold'\n Sell = 'Sell'\n Strategy = 'Strategy' \n\n\nclass Language(EnumBase, Enum): \n \n \"\"\"ISO 639-1 language code for the content piece\"\"\"\n\n an = 'an'\n ar = 'ar'\n _as = 'as'\n av = 'av'\n ay = 'ay'\n az = 'az'\n ba = 'ba'\n be = 'be'\n bg = 'bg'\n bh = 'bh'\n bi = 'bi'\n bm = 'bm'\n bn = 'bn'\n bo = 'bo'\n br = 'br'\n bs = 'bs'\n ca = 'ca'\n ce = 'ce'\n ch = 'ch'\n co = 'co'\n cr = 'cr'\n cs = 'cs'\n cu = 'cu'\n cv = 'cv'\n cy = 'cy'\n da = 'da'\n de = 'de'\n dv = 'dv'\n dz = 'dz'\n ee = 'ee'\n el = 'el'\n en = 'en'\n eo = 'eo'\n es = 'es'\n et = 'et'\n eu = 'eu'\n fa = 'fa'\n ff = 'ff'\n fi = 'fi'\n fj = 'fj'\n fo = 'fo'\n fr = 'fr'\n fy = 'fy'\n ga = 'ga'\n gd = 'gd'\n gl = 'gl'\n gn = 'gn'\n gu = 'gu'\n gv = 'gv'\n ha = 'ha'\n he = 'he'\n hi = 'hi'\n ho = 'ho'\n hr = 'hr'\n ht = 'ht'\n hu = 'hu'\n hy = 'hy'\n hz = 'hz'\n ia = 'ia'\n id = 'id'\n ie = 'ie'\n ig = 'ig'\n ii = 'ii'\n ik = 'ik'\n io = 'io'\n _is = 'is'\n it = 'it'\n iu = 'iu'\n ja = 'ja'\n jv = 'jv'\n ka = 'ka'\n kg = 'kg'\n ki = 'ki'\n kj = 'kj'\n kk = 'kk'\n kl = 'kl'\n km = 'km'\n kn = 'kn'\n ko = 'ko'\n kr = 'kr'\n ks = 'ks'\n ku = 'ku'\n kv = 'kv'\n kw = 'kw'\n ky = 'ky'\n la = 'la'\n lb = 'lb'\n lg = 'lg'\n li = 'li'\n ln = 'ln'\n lo = 'lo'\n lt = 'lt'\n lu = 'lu'\n lv = 'lv'\n mg = 'mg'\n mh = 'mh'\n mi = 'mi'\n mk = 'mk'\n ml = 'ml'\n mn = 'mn'\n mr = 'mr'\n ms = 'ms'\n mt = 'mt'\n my = 'my'\n na = 'na'\n nb = 'nb'\n nd = 'nd'\n ne = 'ne'\n ng = 'ng'\n nl = 'nl'\n nn = 'nn'\n no = 'no'\n nr = 'nr'\n nv = 'nv'\n ny = 'ny'\n oc = 'oc'\n oj = 'oj'\n om = 'om'\n _or = 'or'\n os = 'os'\n pa = 'pa'\n pi = 'pi'\n pl = 'pl'\n ps = 'ps'\n pt = 'pt'\n qu = 'qu'\n rm = 'rm'\n rn = 'rn'\n ro = 'ro'\n ru = 'ru'\n rw = 'rw'\n sa = 'sa'\n sc = 'sc'\n sd = 'sd'\n se = 'se'\n sg = 'sg'\n si = 'si'\n sk = 'sk'\n sl = 'sl'\n sm = 'sm'\n sn = 'sn'\n so = 'so'\n sq = 'sq'\n sr = 'sr'\n ss = 'ss'\n st = 'st'\n su = 'su'\n sv = 'sv'\n sw = 'sw'\n ta = 'ta'\n te = 'te'\n tg = 'tg'\n th = 'th'\n ti = 'ti'\n tk = 'tk'\n tl = 'tl'\n tn = 'tn'\n to = 'to'\n tr = 'tr'\n ts = 'ts'\n tt = 'tt'\n tw = 'tw'\n ty = 'ty'\n ug = 'ug'\n uk = 'uk'\n ur = 'ur'\n uz = 'uz'\n ve = 've'\n vi = 'vi'\n vo = 'vo'\n wa = 'wa'\n wo = 'wo'\n xh = 'xh'\n yi = 'yi'\n yo = 'yo'\n za = 'za'\n zh = 'zh'\n zu = 'zu' \n\n\nclass Origin(EnumBase, Enum): \n \n \"\"\"Where the content originated from\"\"\"\n\n WEB = 'WEB'\n API = 'API'\n EMAIL = 'EMAIL'\n BLOG = 'BLOG'\n ARTICLE = 'ARTICLE' \n\n\nclass QueryableStatus(EnumBase, Enum): \n \n \"\"\"Status/state of a content piece that can be queried by a user\"\"\"\n\n Draft = 'Draft'\n Published = 'Published'\n Replaced = 'Replaced' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChannelMetadata(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n channel_visibility: Optional[object] = field(default=None, metadata=field_metadata)\n restricted: Optional[bool] = field(default=None, metadata=field_metadata)\n streaming: Optional[bool] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Content(Base):\n body: str = field(default=None, metadata=field_metadata)\n mime_type: object = field(default=None, metadata=field_metadata)\n encoding: object = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nclass Object(DictBase):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Author(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=field_metadata)\n division: Optional[Division] = field(default=None, metadata=field_metadata)\n email: Optional[str] = field(default=None, metadata=field_metadata)\n title: Optional[str] = field(default=None, metadata=field_metadata)\n kerberos: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BulkDeleteContentResponse(Base):\n status: Optional[int] = field(default=None, metadata=field_metadata)\n message: Optional[str] = field(default=None, metadata=field_metadata)\n data: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Certification(Base):\n submission_id: str = field(default=None, metadata=field_metadata)\n version: str = field(default=None, metadata=field_metadata)\n submission_state: object = field(default=None, metadata=field_metadata)\n allowed_distribution: Tuple[Object, ...] = field(default=None, metadata=field_metadata)\n etask_process_instance_id: Optional[str] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[None, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DeleteContentResponse(Base):\n status: Optional[int] = field(default=None, metadata=field_metadata)\n message: Optional[str] = field(default=None, metadata=field_metadata)\n data: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass InvestmentRecommendationAsset(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n direction: Optional[InvestmentRecommendationDirection] = field(default=None, metadata=field_metadata)\n currency: Optional[str] = field(default=None, metadata=field_metadata)\n price: Optional[float] = field(default=None, metadata=field_metadata)\n price_target: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass InvestmentRecommendationCustomAsset(Base):\n asset_name: str = field(default=None, metadata=field_metadata)\n direction: Optional[InvestmentRecommendationDirection] = field(default=None, metadata=field_metadata)\n currency: Optional[str] = field(default=None, metadata=field_metadata)\n price: Optional[float] = field(default=None, metadata=field_metadata)\n price_target: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ContentResponse(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n version: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n channels: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n content: Optional[Content] = field(default=None, metadata=field_metadata)\n language: Optional[Language] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ContentUpdateRequest(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n content: Optional[Content] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass InvestmentRecommendations(Base):\n assets: Tuple[InvestmentRecommendationAsset, ...] = field(default=None, metadata=field_metadata)\n custom_assets: Optional[Tuple[InvestmentRecommendationCustomAsset, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BulkContentUpdateRequestItem(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n update: Optional[ContentUpdateRequest] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ContentAuditFields(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n version: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n authors: Optional[Tuple[Author, ...]] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ContentParameters(Base):\n author_ids: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n language: Language = field(default=None, metadata=field_metadata)\n status: Optional[object] = field(default=None, metadata=field_metadata)\n source: Optional[Division] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n slug: Optional[str] = field(default=None, metadata=field_metadata)\n attachments: Optional[Tuple[Content, ...]] = field(default=None, metadata=field_metadata)\n certification: Optional[Certification] = field(default=None, metadata=field_metadata)\n certification_type: Optional[object] = field(default=None, metadata=field_metadata)\n asset_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n origin: Optional[Origin] = field(default=None, metadata=field_metadata)\n investment_recommendations: Optional[InvestmentRecommendations] = field(default=None, metadata=field_metadata)\n is_flow: Optional[bool] = field(default=None, metadata=field_metadata)\n is_research_summary: Optional[bool] = field(default=None, metadata=field_metadata)\n is_restricted: Optional[bool] = field(default=None, metadata=field_metadata)\n post_sharing_type: Optional[object] = field(default=None, metadata=field_metadata)\n channels_metadata: Optional[Tuple[ChannelMetadata, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass GetManyContentsResponse(Base):\n status: Optional[int] = field(default=None, metadata=field_metadata)\n message: Optional[str] = field(default=None, metadata=field_metadata)\n data: Optional[Tuple[ContentResponse, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BulkContentUpdateResponse(Base):\n status: Optional[int] = field(default=None, metadata=field_metadata)\n message: Optional[str] = field(default=None, metadata=field_metadata)\n data: Optional[Tuple[ContentAuditFields, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ContentCreateRequest(Base):\n name: str = field(default=None, metadata=field_metadata)\n entitlements: Entitlements = field(default=None, metadata=field_metadata)\n entitlement_exclusions: EntitlementExclusions = field(default=None, metadata=field_metadata)\n content: Content = field(default=None, metadata=field_metadata)\n parameters: ContentParameters = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ContentCreateResponse(Base):\n status: Optional[int] = field(default=None, metadata=field_metadata)\n message: Optional[str] = field(default=None, metadata=field_metadata)\n data: Optional[ContentAuditFields] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ContentUpdateResponse(Base):\n status: Optional[int] = field(default=None, metadata=field_metadata)\n message: Optional[str] = field(default=None, metadata=field_metadata)\n data: Optional[ContentAuditFields] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/coordinates.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass MDAPIQueryField(EnumBase, Enum): \n \n ask = 'ask'\n bid = 'bid'\n mid = 'mid'\n expectedDataQuality = 'expectedDataQuality'\n actualDataQuality = 'actualDataQuality' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MDAPIDataQuery(Base):\n market_data_coordinates: Tuple[MarketDataCoordinate, ...] = field(default=None, metadata=field_metadata)\n format_: Optional[Format] = field(default=None, metadata=config(field_name='format', exclude=exclude_none))\n pricing_location: Optional[PricingLocation] = field(default=None, metadata=field_metadata)\n selector_function: Optional[str] = field(default=None, metadata=field_metadata)\n samples: Optional[int] = field(default=None, metadata=field_metadata)\n interval: Optional[str] = field(default=None, metadata=field_metadata)\n vendor: Optional[MarketDataVendor] = field(default=None, metadata=field_metadata)\n start_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n end_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n real_time: Optional[bool] = field(default=True, metadata=field_metadata)\n fields: Optional[Tuple[MDAPIQueryField, ...]] = field(default=None, metadata=field_metadata)\n time_filter: Optional[TimeFilter] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MDAPIDataQueryResponse(Base):\n data: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MDAPIDataBatchResponse(Base):\n request_id: Optional[str] = field(default=None, metadata=field_metadata)\n responses: Optional[Tuple[MDAPIDataQueryResponse, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/countries.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CountryXref(Base):\n alpha2: str = field(default=None, metadata=field_metadata)\n alpha3: str = field(default=None, metadata=field_metadata)\n country_code: str = field(default=None, metadata=field_metadata)\n bbid: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Country(Base):\n name: str = field(default=None, metadata=field_metadata)\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n xref: CountryXref = field(default=None, metadata=field_metadata)\n region: str = field(default=None, metadata=field_metadata)\n sub_region: str = field(default=None, metadata=field_metadata)\n region_code: str = field(default=None, metadata=field_metadata)\n sub_region_code: str = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Subdivision(Base):\n name: str = field(default=None, metadata=field_metadata)\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n country_id: str = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/data.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass DataSetType(EnumBase, Enum): \n \n \"\"\"Type of the dataset\"\"\"\n\n Alloy = 'Alloy'\n Snowflake = 'Snowflake'\n PDL = 'PDL'\n LHSnowflake = 'LHSnowflake'\n NativeSnowflake = 'NativeSnowflake'\n\n\nclass DelayExclusionType(EnumBase, Enum): \n \n \"\"\"Type of the delay exclusion\"\"\"\n\n LAST_DAY_OF_THE_MONTH = 'LAST_DAY_OF_THE_MONTH' \n\n\nclass DevelopmentStatus(EnumBase, Enum): \n \n \"\"\"The status of development of this dataset. Controls rate limit on query/upload.\"\"\"\n\n Development = 'Development'\n Production = 'Production' \n\n\nclass FieldFormat(EnumBase, Enum): \n \n \"\"\"Format to apply on field validation. Currently supports a subset of built-in\n formats (from JSON schema specification).\"\"\"\n\n date = 'date'\n date_time = 'date-time' \n\n\nclass MarketDataMeasure(EnumBase, Enum): \n \n Last = 'Last'\n Curve = 'Curve'\n Close_Change = 'Close Change'\n Previous_Close = 'Previous Close' \n\n\nclass MeasureEntityType(EnumBase, Enum): \n \n \"\"\"Entity type associated with a measure.\"\"\"\n\n ASSET = 'ASSET'\n BACKTEST = 'BACKTEST'\n KPI = 'KPI'\n COUNTRY = 'COUNTRY'\n SUBDIVISION = 'SUBDIVISION'\n REPORT = 'REPORT'\n HEDGE = 'HEDGE'\n PORTFOLIO = 'PORTFOLIO'\n RISK_MODEL = 'RISK_MODEL' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AdvancedFilter(Base):\n column: str = field(default=None, metadata=field_metadata)\n operator: str = field(default=None, metadata=field_metadata)\n value: Optional[float] = field(default=None, metadata=field_metadata)\n values: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n format_: Optional[str] = field(default=None, metadata=config(field_name='format', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AlloyService(Base):\n base_url: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ColumnEnumPair(Base):\n column: Optional[str] = field(default=None, metadata=field_metadata)\n enum_key: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetCondition(Base):\n column: str = field(default=None, metadata=field_metadata)\n operator: str = field(default=None, metadata=field_metadata)\n value: Optional[float] = field(default=None, metadata=field_metadata)\n values: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetDefaults(Base):\n start_seconds: Optional[float] = field(default=None, metadata=field_metadata)\n end_seconds: Optional[float] = field(default=None, metadata=field_metadata)\n delay_seconds: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetFieldEntityAttributes(Base):\n in_code: Optional[bool] = field(default=None, metadata=field_metadata)\n is_entity: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetFieldEntityClassifications(Base):\n groups: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetFieldEntityNumberParameters(Base):\n maximum: Optional[int] = field(default=None, metadata=field_metadata)\n minimum: Optional[int] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EntityMetadata(Base):\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ErrorInfo(Base):\n status_code: int = field(default=None, metadata=field_metadata)\n reason_phrase: str = field(default=None, metadata=field_metadata)\n title: Optional[str] = field(default=None, metadata=field_metadata)\n messages: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FieldLinkSelector(Base):\n field_selector: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n display_name: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MDAPI(Base):\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n quoting_styles: Tuple[DictBase, ...] = field(default=None, metadata=field_metadata)\n class_: Optional[str] = field(default=None, metadata=config(field_name='class', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MarketDataField(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n mapping: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MarketDataFilteredField(Base):\n field_: Optional[str] = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\n default_value: Optional[str] = field(default=None, metadata=field_metadata)\n default_numerical_value: Optional[float] = field(default=None, metadata=field_metadata)\n default_boolean_value: Optional[bool] = field(default=None, metadata=field_metadata)\n numerical_values: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n values: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n multi_measure: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@dataclass\nclass MeasureBacktest(Base):\n pass\n\n\n@dataclass\nclass MeasureKpi(Base):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MidPrice(Base):\n bid_column: Optional[str] = field(default=None, metadata=field_metadata)\n ask_column: Optional[str] = field(default=None, metadata=field_metadata)\n mid_column: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ParserEntity(Base):\n only_normalized_fields: Optional[bool] = field(default=None, metadata=field_metadata)\n quotes: Optional[bool] = field(default=None, metadata=field_metadata)\n trades: Optional[bool] = field(default=None, metadata=field_metadata)\n only_mqtick_fields: Optional[bool] = field(default=None, metadata=field_metadata)\n include_trd_flg_proc: Optional[bool] = field(default=None, metadata=field_metadata)\n keep_raw_fields: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n include_mmt: Optional[bool] = field(default=None, metadata=field_metadata)\n include_currency: Optional[bool] = field(default=None, metadata=field_metadata)\n separate_quote_exchange_codes: Optional[bool] = field(default=None, metadata=field_metadata)\n trade_ids: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass QueryProcessor(Base):\n processor_name: Optional[str] = field(default=None, metadata=field_metadata)\n manual_processor_name: Optional[str] = field(default=None, metadata=field_metadata)\n params: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RemapFieldPair(Base):\n field_: Optional[str] = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\n remap_to: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ResponseInfo(Base):\n request_id: Optional[str] = field(default=None, metadata=field_metadata)\n messages: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass StoredEntity(Base):\n stored_processor_name: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SymbolFilterLink(Base):\n entity_field: Optional[str] = field(default=None, metadata=field_metadata)\n entity_type: Optional[str] = field(init=False, default='MktCoordinate', metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AlloyConfig(Base):\n data_service: Optional[AlloyService] = field(default=None, metadata=field_metadata)\n coverage_service: Optional[AlloyService] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DBConfig(Base):\n db: Optional[str] = field(default=None, metadata=field_metadata)\n drg_id: Optional[str] = field(default=None, metadata=field_metadata)\n id_column: Optional[str] = field(default=None, metadata=config(field_name='IdColumn', exclude=exclude_none))\n date_time_column: Optional[str] = field(default=None, metadata=field_metadata)\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataFilter(Base):\n field_: str = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\n values: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n column: Optional[str] = field(default=None, metadata=field_metadata)\n where: Optional[DataSetCondition] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetCoverageProperties(Base):\n prefixes: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n prefix_type: Optional[str] = field(default=None, metadata=field_metadata)\n asset_classes: Optional[Tuple[AssetClass, ...]] = field(default=None, metadata=field_metadata)\n asset_types: Optional[Tuple[AssetType, ...]] = field(default=None, metadata=field_metadata)\n entity_types: Optional[Tuple[MeasureEntityType, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetDelay(Base):\n until_seconds: float = field(default=None, metadata=field_metadata)\n at_time_zone: str = field(default=None, metadata=field_metadata)\n when: Optional[Tuple[DelayExclusionType, ...]] = field(default=None, metadata=field_metadata)\n history_up_to_seconds: Optional[float] = field(default=None, metadata=field_metadata)\n history_up_to_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n history_up_to_months: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetFieldEntityStringParameters(Base):\n enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n format_: Optional[FieldFormat] = field(default=None, metadata=config(field_name='format', exclude=exclude_none))\n pattern: Optional[str] = field(default=r'^[\\w ]{1,256}$', metadata=field_metadata)\n max_length: Optional[int] = field(default=None, metadata=field_metadata)\n min_length: Optional[int] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetTransforms(Base):\n redact_columns: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n round_columns: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n remap_fields: Optional[Tuple[RemapFieldPair, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nclass FieldFilterMapDataQuery(DictBase):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FieldLink(Base):\n entity_identifier: Optional[str] = field(default=None, metadata=field_metadata)\n prefix: Optional[str] = field(default=None, metadata=field_metadata)\n additional_entity_fields: Optional[Tuple[FieldLinkSelector, ...]] = field(default=None, metadata=field_metadata)\n entity_type: Optional[str] = field(init=False, default='Asset', metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MarketDataMapping(Base):\n asset_class: Optional[AssetClass] = field(default=None, metadata=field_metadata)\n query_type: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n scale: Optional[float] = field(default=None, metadata=field_metadata)\n frequency: Optional[MarketDataFrequency] = field(default=None, metadata=field_metadata)\n measures: Optional[Tuple[MarketDataMeasure, ...]] = field(default=None, metadata=field_metadata)\n data_set: Optional[str] = field(default=None, metadata=field_metadata)\n vendor: Optional[MarketDataVendor] = field(default=None, metadata=field_metadata)\n fields: Optional[Tuple[MarketDataField, ...]] = field(default=None, metadata=field_metadata)\n rank: Optional[float] = field(default=None, metadata=field_metadata)\n filtered_fields: Optional[Tuple[MarketDataFilteredField, ...]] = field(default=None, metadata=field_metadata)\n asset_types: Optional[Tuple[AssetType, ...]] = field(default=None, metadata=field_metadata)\n entity_type: Optional[MeasureEntityType] = field(default=None, metadata=field_metadata)\n backtest_entity: Optional[MeasureBacktest] = field(default=None, metadata=field_metadata)\n kpi_entity: Optional[MeasureKpi] = field(default=None, metadata=field_metadata)\n multi_measure: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ProcessorEntity(Base):\n filters: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n parsers: Optional[Tuple[ParserEntity, ...]] = field(default=None, metadata=field_metadata)\n deduplicate: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n enum_type: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n enums: Optional[Tuple[ColumnEnumPair, ...]] = field(default=None, metadata=field_metadata)\n fill_fwd: Optional[str] = field(default=None, metadata=field_metadata)\n stored: Optional[Tuple[StoredEntity, ...]] = field(default=None, metadata=field_metadata)\n additional_processors: Optional[Tuple[QueryProcessor, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SymbolFilterDimension(Base):\n field_: Optional[str] = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\n field_description: Optional[str] = field(default=None, metadata=field_metadata)\n symbol_filter_link: Optional[SymbolFilterLink] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ComplexFilter(Base):\n operator: str = field(default=None, metadata=field_metadata)\n simple_filters: Tuple[DataFilter, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataGroup(Base):\n context: Optional[FieldValueMap] = field(default=None, metadata=field_metadata)\n data: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataQuery(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n format_: Optional[Format] = field(default=None, metadata=config(field_name='format', exclude=exclude_none))\n where: Optional[FieldFilterMapDataQuery] = field(default=None, metadata=field_metadata)\n vendor: Optional[MarketDataVendor] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n start_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n page: Optional[int] = field(default=None, metadata=field_metadata)\n page_size: Optional[int] = field(default=None, metadata=field_metadata)\n end_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n relative_start_date: Optional[str] = field(default=None, metadata=field_metadata)\n relative_end_date: Optional[str] = field(default=None, metadata=field_metadata)\n adjust_as_of: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n as_of_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n id_as_of_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n use_temporal_x_ref: Optional[bool] = field(default=False, metadata=field_metadata)\n restrict_secondary_identifier: Optional[bool] = field(default=False, metadata=field_metadata)\n since: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n dates: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n times: Optional[Tuple[datetime.datetime, ...]] = field(default=None, metadata=field_metadata)\n delay: Optional[int] = field(default=None, metadata=field_metadata)\n intervals: Optional[int] = field(default=None, metadata=field_metadata)\n samples: Optional[int] = field(default=None, metadata=field_metadata)\n limit: Optional[int] = field(default=None, metadata=field_metadata)\n polling_interval: Optional[int] = field(default=None, metadata=field_metadata)\n group_by: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n grouped: Optional[bool] = field(default=None, metadata=field_metadata)\n fields: Optional[Tuple[Union[DictBase, str], ...]] = field(default=None, metadata=field_metadata)\n restrict_fields: Optional[bool] = field(default=False, metadata=field_metadata)\n entity_filter: Optional[FieldFilterMapDataQuery] = field(default=None, metadata=field_metadata)\n interval: Optional[str] = field(default=None, metadata=field_metadata)\n distinct_consecutive: Optional[bool] = field(default=False, metadata=field_metadata)\n time_filter: Optional[TimeFilter] = field(default=None, metadata=field_metadata)\n use_field_alias: Optional[bool] = field(default=False, metadata=field_metadata)\n remap_schema_to_alias: Optional[bool] = field(default=False, metadata=field_metadata)\n show_linked_dimensions: Optional[bool] = field(default=True, metadata=field_metadata)\n use_project_processor: Optional[bool] = field(default=False, metadata=field_metadata)\n snapshot: Optional[bool] = field(default=False, metadata=field_metadata)\n search_until: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n markout: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n offset_to_exchange_open: Optional[str] = field(default=None, metadata=field_metadata)\n offset_to_exchange_close: Optional[str] = field(default=None, metadata=field_metadata)\n multi_trading_session: Optional[bool] = field(default=None, metadata=field_metadata)\n multi_session: Optional[bool] = field(default=None, metadata=field_metadata)\n quote_consolidation: Optional[bool] = field(default=None, metadata=field_metadata)\n consolidation: Optional[bool] = field(default=None, metadata=field_metadata)\n primary: Optional[bool] = field(default=None, metadata=field_metadata)\n time_index: Optional[str] = field(default=None, metadata=field_metadata)\n empty_intervals: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetFieldEntity(Base):\n name: str = field(default=None, metadata=field_metadata)\n description: str = field(default=None, metadata=field_metadata)\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n classifications: Optional[DataSetFieldEntityClassifications] = field(default=None, metadata=field_metadata)\n unique: Optional[bool] = field(default=False, metadata=field_metadata)\n field_java_type: Optional[str] = field(default=None, metadata=field_metadata)\n parameters: Optional[Union[DataSetFieldEntityNumberParameters, DataSetFieldEntityStringParameters]] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n metadata: Optional[EntityMetadata] = field(default=None, metadata=field_metadata)\n attributes: Optional[DataSetFieldEntityAttributes] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetParameters(Base):\n frequency: str = field(default=None, metadata=field_metadata)\n category: Optional[str] = field(default=None, metadata=field_metadata)\n sub_category: Optional[str] = field(default=None, metadata=field_metadata)\n methodology: Optional[str] = field(default=None, metadata=field_metadata)\n coverage: Optional[str] = field(default=None, metadata=field_metadata)\n coverages: Optional[Tuple[AssetType, ...]] = field(default=None, metadata=field_metadata)\n notes: Optional[str] = field(default=None, metadata=field_metadata)\n history: Optional[str] = field(default=None, metadata=field_metadata)\n sample_start: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n sample_end: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n published_date: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n history_date: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(default=None, metadata=field_metadata)\n owner_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n support_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n support_distribution_list: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n apply_market_data_entitlements: Optional[bool] = field(default=None, metadata=field_metadata)\n upload_data_policy: Optional[str] = field(default=None, metadata=field_metadata)\n logical_db: Optional[str] = field(default=None, metadata=field_metadata)\n sla: Optional[str] = field(default=None, metadata=field_metadata)\n symbol_strategy: Optional[str] = field(default=None, metadata=field_metadata)\n underlying_data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n immutable: Optional[bool] = field(default=None, metadata=field_metadata)\n include_in_catalog: Optional[bool] = field(default=False, metadata=field_metadata)\n coverage_enabled: Optional[bool] = field(default=True, metadata=field_metadata)\n use_created_time_for_upload: Optional[bool] = field(default=None, metadata=field_metadata)\n apply_entity_entitlements: Optional[bool] = field(default=None, metadata=field_metadata)\n development_status: Optional[DevelopmentStatus] = field(default=None, metadata=field_metadata)\n internal_owned: Optional[bool] = field(default=None, metadata=field_metadata)\n cr_limit_read: Optional[int] = field(default=None, metadata=field_metadata)\n cr_limit_write: Optional[int] = field(default=None, metadata=field_metadata)\n alloy_config: Optional[AlloyConfig] = field(default=None, metadata=field_metadata)\n snowflake_config: Optional[DBConfig] = field(default=None, metadata=field_metadata)\n external_distribution: Optional[bool] = field(default=None, metadata=field_metadata)\n snowflake_db: Optional[str] = field(init=False, default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetTransformation(Base):\n transforms: DataSetTransforms = field(default=None, metadata=field_metadata)\n condition: Optional[DataSetCondition] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DeleteCoverageQuery(Base):\n where: Optional[FieldFilterMapDataQuery] = field(default=None, metadata=field_metadata)\n delete_all: Optional[bool] = field(default=False, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FieldColumnPair(Base):\n field_: Optional[str] = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\n column: Optional[str] = field(default=None, metadata=field_metadata)\n field_description: Optional[str] = field(default=None, metadata=field_metadata)\n link: Optional[FieldLink] = field(default=None, metadata=field_metadata)\n aliases: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n display_alias: Optional[str] = field(default=None, metadata=field_metadata)\n resolvable: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HistoryFilter(Base):\n absolute_start: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n absolute_end: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n relative_start_seconds: Optional[float] = field(default=None, metadata=field_metadata)\n relative_end_seconds: Optional[float] = field(default=None, metadata=field_metadata)\n delay: Optional[Union[DataSetDelay, Tuple[DataSetDelay, ...]]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataQueryResponse(Base):\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n request_id: Optional[str] = field(default=None, metadata=field_metadata)\n error_message: Optional[str] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n total_pages: Optional[int] = field(default=None, metadata=field_metadata)\n data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n entity_type: Optional[MeasureEntityType] = field(default=None, metadata=field_metadata)\n delay: Optional[int] = field(default=None, metadata=field_metadata)\n data: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n groups: Optional[Tuple[DataGroup, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetCatalogEntry(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n vendor: str = field(default=None, metadata=field_metadata)\n fields: DictBase = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n short_description: Optional[str] = field(default=None, metadata=field_metadata)\n data_product: Optional[str] = field(default=None, metadata=field_metadata)\n terms: Optional[str] = field(default=None, metadata=field_metadata)\n internal_only: Optional[bool] = field(default=None, metadata=field_metadata)\n actions: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n default_start_seconds: Optional[float] = field(default=None, metadata=field_metadata)\n identifier_mapper_name: Optional[str] = field(default=None, metadata=field_metadata)\n identifier_updater_name: Optional[str] = field(default=None, metadata=field_metadata)\n default_delay_minutes: Optional[float] = field(default=None, metadata=field_metadata)\n apply_market_data_entitlements: Optional[bool] = field(default=None, metadata=field_metadata)\n sample: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)\n parameters: Optional[DataSetParameters] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n created_time: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[str] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n mdapi: Optional[MDAPI] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetDimensions(Base):\n time_field: str = field(default=None, metadata=field_metadata)\n symbol_dimensions: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n transaction_time_field: Optional[str] = field(default=None, metadata=field_metadata)\n symbol_dimension_properties: Optional[Tuple[FieldColumnPair, ...]] = field(default=None, metadata=field_metadata)\n non_symbol_dimensions: Optional[Tuple[FieldColumnPair, ...]] = field(default=None, metadata=field_metadata)\n symbol_dimension_link: Optional[FieldLink] = field(default=None, metadata=field_metadata)\n linked_dimensions: Optional[Tuple[FieldLinkSelector, ...]] = field(default=None, metadata=field_metadata)\n symbol_filter_dimensions: Optional[Tuple[SymbolFilterDimension, ...]] = field(default=None, metadata=field_metadata)\n key_dimensions: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n measures: Optional[Tuple[FieldColumnPair, ...]] = field(default=None, metadata=field_metadata)\n entity_dimension: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetFieldEntityBulkRequest(Base):\n fields: Tuple[DataSetFieldEntity, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EntityFilter(Base):\n operator: Optional[str] = field(default=None, metadata=field_metadata)\n simple_filters: Optional[Tuple[DataFilter, ...]] = field(default=None, metadata=field_metadata)\n complex_filters: Optional[Tuple[ComplexFilter, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetFilters(Base):\n entity_filter: Optional[EntityFilter] = field(default=None, metadata=field_metadata)\n row_filters: Optional[Tuple[DataFilter, ...]] = field(default=None, metadata=field_metadata)\n advanced_filters: Optional[Tuple[AdvancedFilter, ...]] = field(default=None, metadata=field_metadata)\n history_filter: Optional[HistoryFilter] = field(default=None, metadata=field_metadata)\n time_filter: Optional[TimeFilter] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataSetEntity(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n organization_id: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n short_description: Optional[str] = field(default=None, metadata=field_metadata)\n mappings: Optional[Tuple[MarketDataMapping, ...]] = field(default=None, metadata=field_metadata)\n vendor: Optional[MarketDataVendor] = field(default=None, metadata=field_metadata)\n mdapi: Optional[MDAPI] = field(default=None, metadata=field_metadata)\n data_product: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n query_processors: Optional[ProcessorEntity] = field(default=None, metadata=field_metadata)\n parameters: Optional[DataSetParameters] = field(default=None, metadata=field_metadata)\n dimensions: Optional[DataSetDimensions] = field(default=None, metadata=field_metadata)\n coverage_properties: Optional[DataSetCoverageProperties] = field(default=None, metadata=field_metadata)\n defaults: Optional[DataSetDefaults] = field(default=None, metadata=field_metadata)\n filters: Optional[DataSetFilters] = field(default=None, metadata=field_metadata)\n transformations: Optional[Tuple[DataSetTransformation, ...]] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n type_: Optional[DataSetType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n" }, { "path": "gs_quant/target/data_screen.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AnalyticsScreen(Base):\n name: str = field(default=None, metadata=field_metadata)\n filter_parameters: FilterRequest = field(default=None, metadata=field_metadata)\n base_screener: str = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n hidden_columns: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/groups.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass GroupWithMembersCount(Base):\n members_count: Optional[int] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass UpdateGroupMembershipRequest(Base):\n user_ids: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass UserCoverage(Base):\n name: str = field(default=None, metadata=field_metadata)\n email: str = field(default=None, metadata=field_metadata)\n app: Optional[str] = field(default=None, metadata=field_metadata)\n phone: Optional[str] = field(default=None, metadata=field_metadata)\n guid: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass GroupResponse(Base):\n results: Tuple[GroupWithMembersCount, ...] = field(default=None, metadata=field_metadata)\n total_results: int = field(default=None, metadata=field_metadata)\n scroll_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CreateGroupRequest(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n oe_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass UpdateGroupRequest(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n oe_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Group(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n oe_id: Optional[str] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/hedge.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass CorporateActionsTypes(EnumBase, Enum): \n \n \"\"\"Types of corporate actions in the hedge\"\"\"\n\n Mergers = 'Mergers'\n Quote_lot_adjustments = 'Quote lot adjustments'\n Rights = 'Rights'\n Spinoffs = 'Spinoffs'\n Cash_dividends = 'Cash dividends'\n Stock_splits = 'Stock splits'\n Reorganization = 'Reorganization' \n\n\nclass HedgeObjective(EnumBase, Enum): \n \n \"\"\"The objective of the hedge.\"\"\"\n\n Minimize_Factor_Risk = 'Minimize Factor Risk'\n Replicate_Performance = 'Replicate Performance' \n\n\nclass HedgeUniverseAssetType(EnumBase, Enum): \n \n \"\"\"Type of assets that will be added to the hedge universe.\"\"\"\n\n Custom_Basket = 'Custom Basket'\n ETF = 'ETF'\n Research_Basket = 'Research Basket'\n Single_Stock = 'Single Stock' \n\n\nclass HedgerComparisonType(EnumBase, Enum): \n \n Asset = 'Asset'\n Portfolio = 'Portfolio'\n Hedge = 'Hedge' \n\n\nclass HedgerConstraintPrioritySetting(EnumBase, Enum): \n \n \"\"\"Priority of the constraint from 0-5 (prioritized in that order). The\n optimization will fail if it cannot meet a constraint with 0 priority. A\n constraint with priority of 1-5 can be called a relaxed constraint, which\n means that the optimization will make its best effort to meet the\n constraint but will not fail if it cannot.\"\"\"\n\n _0 = '0'\n _1 = '1'\n _2 = '2'\n _3 = '3'\n _4 = '4'\n _5 = '5' \n\n\nclass SamplingPeriod(EnumBase, Enum): \n \n \"\"\"The length of time in between return samples.\"\"\"\n\n Daily = 'Daily'\n Weekly = 'Weekly' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetConstraint(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n max_: float = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\n min_: float = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@dataclass\nclass BasketConditions(Base):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ClassificationConstraint(Base):\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n max_: float = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\n min_: float = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ESGConstraint(Base):\n name: str = field(default=None, metadata=field_metadata)\n max_: float = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\n min_: float = field(default=None, metadata=config(field_name='min', exclude=exclude_none))\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorConstraint(Base):\n factor: str = field(default=None, metadata=field_metadata)\n exposure: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorExposure(Base):\n factor: str = field(default=None, metadata=field_metadata)\n exposure: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorMCTRByGroupConstraint(Base):\n factors: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n max_: float = field(default=None, metadata=config(field_name='max', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HedgerComparisonProperties(Base):\n hedge_value_type: str = field(default=None, metadata=field_metadata)\n hedge_value: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorExposures(Base):\n country: Tuple[FactorExposure, ...] = field(default=None, metadata=field_metadata)\n industry: Tuple[FactorExposure, ...] = field(default=None, metadata=field_metadata)\n sector: Tuple[FactorExposure, ...] = field(default=None, metadata=field_metadata)\n style: Tuple[FactorExposure, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorHedgeUniverse(Base):\n asset_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_types: Optional[Tuple[HedgeUniverseAssetType, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorHedgerConstraintPrioritySettings(Base):\n min_sector_weights: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n max_sector_weights: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n min_industry_weights: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n max_industry_weights: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n min_region_weights: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n max_region_weights: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n min_country_weights: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n max_country_weights: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n style_exposures: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n country_exposures: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n region_exposures: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n industry_exposures: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n sector_exposures: Optional[HedgerConstraintPrioritySetting] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HedgeConstituent(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n weight: float = field(default=None, metadata=field_metadata)\n currency: Currency = field(default=None, metadata=field_metadata)\n country: Optional[str] = field(default=None, metadata=field_metadata)\n correlation: Optional[float] = field(default=None, metadata=field_metadata)\n transaction_cost: Optional[float] = field(default=None, metadata=field_metadata)\n marginal_cost: Optional[float] = field(default=None, metadata=field_metadata)\n borrow_cost: Optional[float] = field(default=None, metadata=field_metadata)\n shares: Optional[float] = field(default=None, metadata=field_metadata)\n price: Optional[float] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n notional: Optional[float] = field(default=None, metadata=field_metadata)\n bbid: Optional[str] = field(default=None, metadata=field_metadata)\n adv_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n sector: Optional[str] = field(default=None, metadata=field_metadata)\n industry: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorHedgerResultPositions(Base):\n beta_exposure: float = field(default=None, metadata=field_metadata)\n daily_va_r: float = field(default=None, metadata=field_metadata)\n factor_exposures: FactorExposures = field(default=None, metadata=field_metadata)\n specific_exposure: float = field(default=None, metadata=field_metadata)\n systematic_exposure: float = field(default=None, metadata=field_metadata)\n total_risk: float = field(default=None, metadata=field_metadata)\n volatility: float = field(default=None, metadata=field_metadata)\n net_exposure: float = field(default=None, metadata=field_metadata)\n constituents: Optional[Tuple[HedgeConstituent, ...]] = field(default=None, metadata=field_metadata)\n number_of_positions: Optional[float] = field(default=None, metadata=field_metadata)\n cumulative_pnl: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n transaction_cost: Optional[float] = field(default=None, metadata=field_metadata)\n borrow_cost_bps: Optional[float] = field(default=None, metadata=field_metadata)\n max_drawdown: Optional[float] = field(default=None, metadata=field_metadata)\n gross_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n long_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n short_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n tracking_error: Optional[float] = field(default=None, metadata=field_metadata)\n correlation: Optional[float] = field(default=None, metadata=field_metadata)\n exposure_overlap_with_target: Optional[float] = field(default=None, metadata=field_metadata)\n total_pnl: Optional[float] = field(default=None, metadata=field_metadata)\n turnover_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HedgeBenchmark(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n cumulative_pnl: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HedgeGetManyRequestPathSchema(Base):\n limit: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n offset: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n scroll: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n scroll_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n order_by: Optional[Tuple[Union[DictBase, str], ...]] = field(default=None, metadata=field_metadata)\n hedge_tracking_error: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n hedge_volatility: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n target_notional: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n owner_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n hedge_annualized_volatility: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n description: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n id_: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n objective: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n hedge_notional: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PerformanceHedgeResult(Base):\n target: DictBase = field(default=None, metadata=field_metadata)\n hedge: Optional[DictBase] = field(default=None, metadata=field_metadata)\n hedged_target: Optional[DictBase] = field(default=None, metadata=field_metadata)\n benchmarks: Optional[Tuple[DictBase, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorHedgeResult(Base):\n hedge: FactorHedgerResultPositions = field(default=None, metadata=field_metadata)\n hedged_target: FactorHedgerResultPositions = field(default=None, metadata=field_metadata)\n target: Optional[FactorHedgerResultPositions] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HedgerComparison(Base):\n entity_id: str = field(default=None, metadata=field_metadata)\n entity_type: HedgerComparisonType = field(default=None, metadata=field_metadata)\n hedge_properties: HedgerComparisonProperties = field(default=None, metadata=field_metadata)\n result: Optional[Union[FactorHedgeResult, PerformanceHedgeResult]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Target(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n tags: Optional[Tuple[PositionTag, ...]] = field(default=None, metadata=field_metadata)\n parent_sts_id: Optional[str] = field(default=None, metadata=field_metadata)\n positions: Optional[Tuple[Position, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorHedgeParameters(Base):\n risk_model: str = field(default=None, metadata=field_metadata)\n target_notional: float = field(default=None, metadata=field_metadata)\n hedge_notional: float = field(default=None, metadata=field_metadata)\n hedge_target: Target = field(default=None, metadata=field_metadata)\n hedge_universe: FactorHedgeUniverse = field(default=None, metadata=field_metadata)\n hedge_date: datetime.date = field(default=None, metadata=field_metadata)\n backtest_start_date: datetime.date = field(default=None, metadata=field_metadata)\n backtest_end_date: datetime.date = field(default=None, metadata=field_metadata)\n fx_hedged: Optional[bool] = field(default=None, metadata=field_metadata)\n exclude_target_assets: Optional[bool] = field(default=None, metadata=field_metadata)\n exclude_corporate_actions: Optional[bool] = field(default=None, metadata=field_metadata)\n exclude_corporate_actions_types: Optional[Tuple[CorporateActionsTypes, ...]] = field(default=None, metadata=field_metadata)\n exclude_hard_to_borrow_assets: Optional[bool] = field(default=None, metadata=field_metadata)\n exclude_restricted_assets: Optional[bool] = field(default=None, metadata=field_metadata)\n max_adv_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n explode_universe: Optional[bool] = field(default=None, metadata=field_metadata)\n min_names: Optional[float] = field(default=None, metadata=field_metadata)\n max_names: Optional[float] = field(default=None, metadata=field_metadata)\n max_trades: Optional[float] = field(default=None, metadata=field_metadata)\n min_weight: Optional[float] = field(default=None, metadata=field_metadata)\n max_weight: Optional[float] = field(default=None, metadata=field_metadata)\n min_market_cap: Optional[float] = field(default=None, metadata=field_metadata)\n max_market_cap: Optional[float] = field(default=None, metadata=field_metadata)\n max_factor_mctr: Optional[float] = field(default=None, metadata=config(field_name='maxFactorMCTR', exclude=exclude_none))\n max_factor_mctr_by_group: Optional[Tuple[FactorMCTRByGroupConstraint, ...]] = field(default=None, metadata=config(field_name='maxFactorMCTRByGroup', exclude=exclude_none))\n market_participation_rate: Optional[float] = field(default=10, metadata=field_metadata)\n asset_constraints: Optional[Tuple[AssetConstraint, ...]] = field(default=None, metadata=field_metadata)\n constrain_assets_by_notional: Optional[bool] = field(default=None, metadata=field_metadata)\n allow_long_short: Optional[bool] = field(default=None, metadata=field_metadata)\n only_reweight_target_composition: Optional[bool] = field(default=None, metadata=field_metadata)\n factor_constraints: Optional[Tuple[FactorConstraint, ...]] = field(default=None, metadata=field_metadata)\n classification_constraints: Optional[Tuple[ClassificationConstraint, ...]] = field(default=None, metadata=field_metadata)\n esg_constraints: Optional[Tuple[ESGConstraint, ...]] = field(default=None, metadata=field_metadata)\n constraint_priority_settings: Optional[FactorHedgerConstraintPrioritySettings] = field(default=None, metadata=field_metadata)\n comparisons: Optional[Tuple[HedgerComparison, ...]] = field(default=None, metadata=field_metadata)\n turnover_portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n turnover_tags: Optional[Tuple[PositionTag, ...]] = field(default=None, metadata=field_metadata)\n max_turnover_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n turnover_notional_type: Optional[str] = field(default=None, metadata=field_metadata)\n return_type: Optional[ReturnType] = field(default=None, metadata=field_metadata)\n is_best_basket: Optional[bool] = field(default=None, metadata=field_metadata)\n is_sector_etf: Optional[bool] = field(default=None, metadata=config(field_name='isSectorETF', exclude=exclude_none))\n is_sts: Optional[bool] = field(default=None, metadata=config(field_name='isSTS', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PerformanceHedgeParameters(Base):\n hedge_target: Target = field(default=None, metadata=field_metadata)\n universe: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n notional: float = field(default=None, metadata=field_metadata)\n observation_start_date: datetime.date = field(default=None, metadata=field_metadata)\n observation_end_date: datetime.date = field(default=None, metadata=field_metadata)\n backtest_start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n backtest_end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n sampling_period: Optional[SamplingPeriod] = field(default=SamplingPeriod.Weekly, metadata=field_metadata)\n max_leverage: Optional[float] = field(default=None, metadata=field_metadata)\n percentage_in_cash: Optional[float] = field(default=None, metadata=field_metadata)\n explode_universe: Optional[bool] = field(default=None, metadata=field_metadata)\n exclude_target_assets: Optional[bool] = field(default=None, metadata=field_metadata)\n exclude_corporate_actions: Optional[bool] = field(default=None, metadata=field_metadata)\n exclude_corporate_actions_types: Optional[Tuple[CorporateActionsTypes, ...]] = field(default=None, metadata=field_metadata)\n exclude_hard_to_borrow_assets: Optional[bool] = field(default=None, metadata=field_metadata)\n exclude_restricted_assets: Optional[bool] = field(default=None, metadata=field_metadata)\n max_adv_percentage: Optional[float] = field(default=None, metadata=field_metadata)\n max_return_deviation: Optional[float] = field(default=None, metadata=field_metadata)\n max_weight: Optional[float] = field(default=None, metadata=field_metadata)\n min_weight: Optional[float] = field(default=None, metadata=field_metadata)\n min_market_cap: Optional[float] = field(default=None, metadata=field_metadata)\n max_market_cap: Optional[float] = field(default=None, metadata=field_metadata)\n market_participation_rate: Optional[float] = field(default=10, metadata=field_metadata)\n asset_constraints: Optional[Tuple[AssetConstraint, ...]] = field(default=None, metadata=field_metadata)\n classification_constraints: Optional[Tuple[ClassificationConstraint, ...]] = field(default=None, metadata=field_metadata)\n esg_constraints: Optional[Tuple[ESGConstraint, ...]] = field(default=None, metadata=field_metadata)\n benchmarks: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n use_machine_learning: Optional[bool] = field(default=False, metadata=field_metadata)\n lasso_weight: Optional[float] = field(default=None, metadata=field_metadata)\n ridge_weight: Optional[float] = field(default=None, metadata=field_metadata)\n return_type: Optional[ReturnType] = field(default=None, metadata=field_metadata)\n is_sts: Optional[bool] = field(default=None, metadata=config(field_name='isSTS', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Hedge(Base):\n name: str = field(default=None, metadata=field_metadata)\n parameters: Union[FactorHedgeParameters, PerformanceHedgeParameters] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n objective: Optional[HedgeObjective] = field(default=None, metadata=field_metadata)\n result: Optional[Union[FactorHedgeResult, PerformanceHedgeResult]] = field(default=None, metadata=field_metadata)\n comparison_results: Optional[Tuple[HedgerComparison, ...]] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/indices.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass ApprovalStatus(EnumBase, Enum): \n \n \"\"\"Current status of an approval\"\"\"\n\n Draft = 'Draft'\n Cancelled = 'Cancelled'\n Submitted = 'Submitted'\n Approved = 'Approved'\n Approving = 'Approving'\n Rejected = 'Rejected'\n Locked = 'Locked'\n Error = 'Error' \n\n\nclass ISelectActionType(EnumBase, Enum): \n \n \"\"\"Workflow actions that exist in ISelect\"\"\"\n\n GetStatus = 'GetStatus'\n ValidateRebalance = 'ValidateRebalance'\n ToggleConstraints = 'ToggleConstraints'\n GenerateSubmissionConfirm = 'GenerateSubmissionConfirm'\n GenerateSubmissionConfirmWithWaiver = 'GenerateSubmissionConfirmWithWaiver'\n SubmitRebalance = 'SubmitRebalance'\n CancelRebalance = 'CancelRebalance'\n SubmitDraft = 'SubmitDraft'\n CancelDraft = 'CancelDraft'\n AgeDraft = 'AgeDraft'\n RerunChecks = 'RerunChecks'\n TraderApproves = 'TraderApproves'\n TraderRejects = 'TraderRejects'\n ClientVerifierApproves = 'ClientVerifierApproves'\n ClientVerifierRejects = 'ClientVerifierRejects'\n ClearException = 'ClearException'\n ClientAcknowledges = 'ClientAcknowledges'\n AllowWaiver = 'AllowWaiver'\n DisableWaiver = 'DisableWaiver' \n\n\nclass IndicesCurrency(EnumBase, Enum): \n \n \"\"\"Currencies supported for Indices\"\"\"\n\n USD = 'USD'\n EUR = 'EUR'\n GBP = 'GBP'\n CAD = 'CAD'\n AUD = 'AUD'\n BRL = 'BRL'\n CHF = 'CHF'\n CNH = 'CNH'\n CNY = 'CNY'\n CNO = 'CNO'\n DKK = 'DKK'\n HKD = 'HKD'\n IDR = 'IDR'\n ILS = 'ILS'\n INR = 'INR'\n JPY = 'JPY'\n KRW = 'KRW'\n KWD = 'KWD'\n MXN = 'MXN'\n MYR = 'MYR'\n NOK = 'NOK'\n NZD = 'NZD'\n PHP = 'PHP'\n PLN = 'PLN'\n RUB = 'RUB'\n SAR = 'SAR'\n SEK = 'SEK'\n SGD = 'SGD'\n THB = 'THB'\n TRY = 'TRY'\n TWD = 'TWD'\n VND = 'VND'\n ZAR = 'ZAR' \n\n\n@dataclass\nclass IndicesConstructRequestTypes(Base):\n pass\n\n\n@dataclass\nclass IndicesConstructResponseTypes(Base):\n pass\n\n\n@dataclass\nclass IndicesRebalanceActionTypes(Base):\n pass\n\n\n@dataclass\nclass IndicesRebalanceInputTypes(Base):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ApprovalComment(Base):\n timestamp: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n message: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CryptoBasketParameters(Base):\n initial_price: float = field(default=None, metadata=field_metadata)\n target_notional: float = field(default=None, metadata=field_metadata)\n divisor: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketRiskParams(Base):\n risk_model: Optional[str] = field(default=None, metadata=field_metadata)\n fx_hedged: Optional[bool] = field(default=None, metadata=field_metadata)\n delete: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketsRebalanceAction(IndicesRebalanceActionTypes):\n comment: Optional[str] = field(default=None, metadata=field_metadata)\n action_type: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketsResponse(IndicesConstructResponseTypes):\n status: Optional[str] = field(default=None, metadata=field_metadata)\n report_id: Optional[str] = field(default=None, metadata=field_metadata)\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectActionRequest(IndicesRebalanceActionTypes):\n action_comment: str = field(default=None, metadata=field_metadata)\n trader_attestations: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n user_action: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectError(Base):\n status_code: Optional[str] = field(default=None, metadata=config(field_name='Status_Code', exclude=exclude_none))\n validation_messages: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='Validation_Messages', exclude=exclude_none))\n date_validation_errors: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='Date_Validation_Errors', exclude=exclude_none))\n index_parameters_errors: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='Index_Parameters_Errors', exclude=exclude_none))\n underlyer_validation_errors: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='Underlyer_Validation_Errors', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectIndexParameter(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n value: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectIndexParameters(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n value: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectSeries(Base):\n data: Optional[tuple] = field(default=None, metadata=field_metadata)\n identifier: Optional[str] = field(default=None, metadata=field_metadata)\n identifier_type: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IndicesPositionInput(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n weight: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Link(Base):\n title: Optional[str] = field(default=None, metadata=field_metadata)\n source: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CreditCustomBasketPricingParameters(Base):\n quote_source: BasketValuationSource = field(default=None, metadata=field_metadata)\n quote_time: str = field(default='16:00:00', metadata=field_metadata)\n quote_side: Side = field(default='Mid', metadata=field_metadata)\n quoting_type: QuoteType = field(default=None, metadata=field_metadata)\n weighting_type: WeightingType = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketsPricingParameters(Base):\n currency: Optional[IndicesCurrency] = field(default=None, metadata=field_metadata)\n asset_data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n divisor: Optional[float] = field(default=None, metadata=field_metadata)\n fx_data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n fallback_date: Optional[str] = field(default=None, metadata=field_metadata)\n initial_price: Optional[float] = field(default=None, metadata=field_metadata)\n target_notional: Optional[float] = field(default=None, metadata=field_metadata)\n pricing_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n vendor: Optional[MarketDataVendor] = field(default=None, metadata=field_metadata)\n weighting_strategy: Optional[PositionSetWeightingStrategy] = field(default=None, metadata=field_metadata)\n reweight: Optional[bool] = field(default=False, metadata=field_metadata)\n asset_overwrite_data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketsRiskScheduleInputs(Base):\n risk_models: Optional[Tuple[CustomBasketRiskParams, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass GIRDomain(Base):\n document_links: Optional[Tuple[Link, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectConstituentColumn(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n field_: str = field(default=None, metadata=config(field_name='field', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n aggregator_string: Optional[str] = field(default=None, metadata=field_metadata)\n class_: Optional[str] = field(default=None, metadata=config(field_name='class', exclude=exclude_none))\n filter_: Optional[str] = field(default=None, metadata=config(field_name='filter', exclude=exclude_none))\n formatter_string: Optional[str] = field(default=None, metadata=field_metadata)\n ID: Optional[int] = field(default=None, metadata=field_metadata)\n max_width: Optional[int] = field(default=None, metadata=field_metadata)\n min_width: Optional[int] = field(default=None, metadata=field_metadata)\n precision: Optional[int] = field(default=None, metadata=field_metadata)\n sortable: Optional[int] = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IndicesPositionSet(Base):\n positions: Tuple[IndicesPositionInput, ...] = field(default=None, metadata=field_metadata)\n position_date: datetime.date = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CryptoBasketCreateInputs(IndicesConstructRequestTypes):\n ticker: str = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n position_set: Tuple[PositionPriceInput, ...] = field(default=None, metadata=field_metadata)\n asset_class: AssetClass = field(default='Digital Asset', metadata=field_metadata)\n parameters: CryptoBasketParameters = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CryptoBasketRebalanceInputs(IndicesRebalanceInputTypes):\n asset_class: AssetClass = field(default='Digital Asset', metadata=field_metadata)\n position_set: Tuple[PositionPriceInput, ...] = field(default=None, metadata=field_metadata)\n parameters: CryptoBasketParameters = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketsBackcastInputs(Base):\n position_set: Tuple[IndicesPositionSet, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DynamicConstructionResponse(IndicesConstructResponseTypes):\n action: Optional[object] = field(default=None, metadata=field_metadata)\n columns: Optional[Tuple[ISelectConstituentColumn, ...]] = field(default=None, metadata=field_metadata)\n constituent_validations: Optional[tuple] = field(default=None, metadata=field_metadata)\n date_validation_status: Optional[str] = field(default=None, metadata=field_metadata)\n types: Optional[tuple] = field(default=None, metadata=field_metadata)\n date_validations: Optional[tuple] = field(default=None, metadata=field_metadata)\n new_parameters: Optional[Tuple[ISelectNewParameter, ...]] = field(default=None, metadata=field_metadata)\n index_type: Optional[str] = field(default=None, metadata=field_metadata)\n index_parameter_definitions: Optional[tuple] = field(default=None, metadata=field_metadata)\n index_metadata: Optional[tuple] = field(default=None, metadata=field_metadata)\n index_parameters: Optional[tuple] = field(default=None, metadata=field_metadata)\n index_parameter_validation: Optional[tuple] = field(default=None, metadata=field_metadata)\n status: Optional[object] = field(default=None, metadata=field_metadata)\n valid: Optional[int] = field(default=None, metadata=field_metadata)\n validation_messages: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectRebalance(Base):\n new_weights: Optional[Tuple[ISelectNewWeight, ...]] = field(default=None, metadata=field_metadata)\n rebalance_date: Optional[str] = field(default=None, metadata=field_metadata)\n observation_date: Optional[str] = field(default=None, metadata=field_metadata)\n new_parameters: Optional[Tuple[ISelectNewParameter, ...]] = field(default=None, metadata=field_metadata)\n index_parameters: Optional[Tuple[ISelectIndexParameters, ...]] = field(default=None, metadata=field_metadata)\n waiver_requested: Optional[bool] = field(default=None, metadata=field_metadata)\n unwind_missing_constituents: Optional[bool] = field(default=True, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectRequest(IndicesRebalanceInputTypes):\n rebalance_date: str = field(default=None, metadata=field_metadata)\n use_new_rebalance_interface: bool = field(default=None, metadata=field_metadata)\n new_parameters: Optional[Tuple[ISelectNewParameter, ...]] = field(default=None, metadata=field_metadata)\n index_parameters: Optional[Tuple[ISelectIndexParameter, ...]] = field(default=None, metadata=field_metadata)\n new_weights: Optional[Tuple[ISelectNewWeight, ...]] = field(default=None, metadata=field_metadata)\n new_units: Optional[Tuple[ISelectNewUnit, ...]] = field(default=None, metadata=field_metadata)\n observation_date: Optional[str] = field(default=None, metadata=field_metadata)\n entry_type: Optional[str] = field(default=None, metadata=field_metadata)\n request_counter: Optional[int] = field(default=0, metadata=field_metadata)\n waiver_requested: Optional[bool] = field(default=None, metadata=field_metadata)\n presubmit: Optional[bool] = field(default=None, metadata=field_metadata)\n requester_id: Optional[str] = field(default=None, metadata=field_metadata)\n custom_basket_import: Optional[str] = field(default=None, metadata=field_metadata)\n action: Optional[str] = field(default=None, metadata=field_metadata)\n unwind_missing_constituents: Optional[bool] = field(default=True, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ISelectResponse(Base):\n action: Optional[ISelectActionType] = field(default=None, metadata=config(field_name='Action', exclude=exclude_none))\n action_comment: Optional[str] = field(default=None, metadata=config(field_name='ActionComment', exclude=exclude_none))\n asset_name: Optional[str] = field(default=None, metadata=field_metadata)\n asset_short_name: Optional[str] = field(default=None, metadata=field_metadata)\n available_action_confirms: Optional[Tuple[Tuple[str, ...], ...]] = field(default=None, metadata=field_metadata)\n available_actions: Optional[Tuple[Union[DictBase, ISelectActionType], ...]] = field(default=None, metadata=field_metadata)\n available_rebalance_dates: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n constituent_validations: Optional[tuple] = field(default=None, metadata=field_metadata)\n date_validation_status: Optional[str] = field(default=None, metadata=field_metadata)\n date_validations: Optional[tuple] = field(default=None, metadata=field_metadata)\n entry_mode: Optional[str] = field(default=None, metadata=field_metadata)\n entry_type: Optional[str] = field(default=None, metadata=field_metadata)\n internal_rebalance: Optional[int] = field(default=None, metadata=field_metadata)\n index_parameter_definitions: Optional[tuple] = field(default=None, metadata=field_metadata)\n index_parameters: Optional[tuple] = field(default=None, metadata=field_metadata)\n index_parameter_validation: Optional[tuple] = field(default=None, metadata=field_metadata)\n new_units: Optional[Tuple[ISelectNewUnit, ...]] = field(default=None, metadata=field_metadata)\n new_weights: Optional[Tuple[ISelectNewWeight, ...]] = field(default=None, metadata=field_metadata)\n notification_date: Optional[str] = field(default=None, metadata=field_metadata)\n rebalance_date: Optional[str] = field(default=None, metadata=field_metadata)\n rebalance_determination_date: Optional[str] = field(default=None, metadata=field_metadata)\n reb_determination_index_level: Optional[float] = field(default=None, metadata=field_metadata)\n request_counter: Optional[int] = field(default=None, metadata=field_metadata)\n series: Optional[ISelectSeries] = field(default=None, metadata=field_metadata)\n status: Optional[object] = field(default=None, metadata=field_metadata)\n submission_data: Optional[tuple] = field(default=None, metadata=field_metadata)\n submission_data_columns: Optional[Tuple[ISelectConstituentColumn, ...]] = field(default=None, metadata=field_metadata)\n submission_text: Optional[str] = field(default=None, metadata=field_metadata)\n valid: Optional[int] = field(default=None, metadata=field_metadata)\n validation_messages: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IndicesDynamicConstructInputs(IndicesConstructRequestTypes):\n index_type: str = field(default=None, metadata=field_metadata)\n new_parameters: Tuple[ISelectNewParameter, ...] = field(default=None, metadata=field_metadata)\n index_parameters: Tuple[ISelectIndexParameters, ...] = field(default=None, metadata=field_metadata)\n index_metadata: Tuple[ISelectIndexParameters, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PublishParameters(Base):\n publish_to_bloomberg: bool = field(default=False, metadata=field_metadata)\n include_price_history: bool = field(default=False, metadata=field_metadata)\n publish_to_reuters: Optional[bool] = field(default=False, metadata=field_metadata)\n publish_to_factset: Optional[bool] = field(default=False, metadata=field_metadata)\n historical_methodology: Optional[EqBasketHistoryMethodology] = field(default=None, metadata=field_metadata)\n backtest_parameters: Optional[EqBasketBacktestParameters] = field(default=None, metadata=field_metadata)\n bloomberg_publish_parameters: Optional[BloombergPublishParameters] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CreditCustomBasketCreateInputs(IndicesConstructRequestTypes):\n ticker: str = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n pricing_parameters: CreditCustomBasketPricingParameters = field(default=None, metadata=field_metadata)\n position_set: Tuple[PositionPriceInput, ...] = field(default=None, metadata=field_metadata)\n return_type: IndexCalculationType = field(default='Price Return', metadata=field_metadata)\n styles: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(default='Credit', metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n related_content: Optional[GIRDomain] = field(default=None, metadata=field_metadata)\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n publish_parameters: Optional[PublishParameters] = field(default=None, metadata=field_metadata)\n index_notes: Optional[str] = field(default=None, metadata=field_metadata)\n flagship: Optional[bool] = field(default=False, metadata=field_metadata)\n on_behalf_of: Optional[str] = field(default=None, metadata=field_metadata)\n clone_parent_id: Optional[str] = field(default=None, metadata=field_metadata)\n hedge_id: Optional[str] = field(default=None, metadata=field_metadata)\n credit_basket_type: Optional[CreditBasketType] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CreditCustomBasketEditInputs(Base):\n name: str = field(default=None, metadata=field_metadata)\n description: str = field(default=None, metadata=field_metadata)\n publish_parameters: PublishParameters = field(default=None, metadata=field_metadata)\n pricing_parameters: CreditCustomBasketPricingParameters = field(default=None, metadata=field_metadata)\n flagship: bool = field(default=False, metadata=field_metadata)\n on_behalf_of: str = field(default=None, metadata=field_metadata)\n styles: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(default='Credit', metadata=field_metadata)\n related_content: Optional[GIRDomain] = field(default=None, metadata=field_metadata)\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n return_type: Optional[IndexCalculationType] = field(default='Price Return', metadata=field_metadata)\n position_set: Optional[Tuple[PositionPriceInput, ...]] = field(default=None, metadata=field_metadata)\n index_notes: Optional[str] = field(default=None, metadata=field_metadata)\n clone_parent_id: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CreditCustomBasketRebalanceInputs(IndicesRebalanceInputTypes):\n asset_class: AssetClass = field(default='Credit', metadata=field_metadata)\n position_set: Tuple[PositionPriceInput, ...] = field(default=None, metadata=field_metadata)\n publish_parameters: Optional[PublishParameters] = field(default=None, metadata=field_metadata)\n pricing_parameters: Optional[CreditCustomBasketPricingParameters] = field(default=None, metadata=field_metadata)\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n hedge_id: Optional[str] = field(default=None, metadata=field_metadata)\n save_as_draft: Optional[bool] = field(default=False, metadata=field_metadata)\n credit_basket_type: Optional[CreditBasketType] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IndicesBackcastInputs(Base):\n parameters: CustomBasketsBackcastInputs = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketsCreateInputs(IndicesConstructRequestTypes):\n ticker: str = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n pricing_parameters: CustomBasketsPricingParameters = field(default=None, metadata=field_metadata)\n position_set: Tuple[PositionPriceInput, ...] = field(default=None, metadata=field_metadata)\n return_type: str = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(default='Equity', metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n styles: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n related_content: Optional[GIRDomain] = field(default=None, metadata=field_metadata)\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n hedge_id: Optional[str] = field(default=None, metadata=field_metadata)\n clone_parent_id: Optional[str] = field(default=None, metadata=field_metadata)\n cash_reinvestment_treatment: Optional[CashReinvestmentTreatment] = field(default=None, metadata=field_metadata)\n publish_parameters: Optional[PublishParameters] = field(default=None, metadata=field_metadata)\n index_notes: Optional[str] = field(default=None, metadata=field_metadata)\n flagship: Optional[bool] = field(default=None, metadata=field_metadata)\n on_behalf_of: Optional[str] = field(default=None, metadata=field_metadata)\n allow_limited_access_assets: Optional[bool] = field(default=False, metadata=field_metadata)\n allow_ca_restricted_assets: Optional[bool] = field(default=False, metadata=config(field_name='allowCARestrictedAssets', exclude=exclude_none))\n vendor: Optional[str] = field(default=None, metadata=field_metadata)\n default_backcast: Optional[bool] = field(default=True, metadata=field_metadata)\n action_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n preferred_risk_model: Optional[str] = field(default=None, metadata=field_metadata)\n rebalance_calendar: Optional[EqBasketRebalanceCalendar] = field(default=None, metadata=field_metadata)\n benchmark: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketsEditInputs(Base):\n asset_class: Optional[AssetClass] = field(default='Equity', metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n styles: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n related_content: Optional[GIRDomain] = field(default=None, metadata=field_metadata)\n publish_parameters: Optional[PublishParameters] = field(default=None, metadata=field_metadata)\n index_notes: Optional[str] = field(default=None, metadata=field_metadata)\n index_not_trading_reasons: Optional[IndexNotTradingReasons] = field(default=None, metadata=field_metadata)\n flagship: Optional[bool] = field(default=None, metadata=field_metadata)\n clone_parent_id: Optional[str] = field(default=None, metadata=field_metadata)\n hedge_id: Optional[str] = field(default=None, metadata=field_metadata)\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n vendor: Optional[str] = field(default=None, metadata=field_metadata)\n action_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n preferred_risk_model: Optional[str] = field(default=None, metadata=field_metadata)\n rebalance_calendar: Optional[EqBasketRebalanceCalendar] = field(default=None, metadata=field_metadata)\n benchmark: Optional[str] = field(default=None, metadata=field_metadata)\n on_behalf_of: Optional[str] = field(default=None, metadata=field_metadata)\n cash_reinvestment_treatment: Optional[CashReinvestmentTreatment] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomBasketsRebalanceInputs(Base):\n asset_class: Optional[AssetClass] = field(default='Equity', metadata=field_metadata)\n position_set: Optional[Tuple[PositionPriceInput, ...]] = field(default=None, metadata=field_metadata)\n publish_parameters: Optional[PublishParameters] = field(default=None, metadata=field_metadata)\n pricing_parameters: Optional[CustomBasketsPricingParameters] = field(default=None, metadata=field_metadata)\n allow_limited_access_assets: Optional[bool] = field(default=False, metadata=field_metadata)\n allow_ca_restricted_assets: Optional[bool] = field(default=False, metadata=config(field_name='allowCARestrictedAssets', exclude=exclude_none))\n allow_system_approval: Optional[bool] = field(default=False, metadata=field_metadata)\n clone_parent_id: Optional[str] = field(default=None, metadata=field_metadata)\n hedge_id: Optional[str] = field(default=None, metadata=field_metadata)\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n save_as_draft: Optional[bool] = field(default=False, metadata=field_metadata)\n allow_in_position_rebalance: Optional[bool] = field(default=False, metadata=field_metadata)\n action_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n preferred_risk_model: Optional[str] = field(default=None, metadata=field_metadata)\n rebalance_calendar: Optional[EqBasketRebalanceCalendar] = field(default=None, metadata=field_metadata)\n benchmark: Optional[str] = field(default=None, metadata=field_metadata)\n on_behalf_of: Optional[str] = field(default=None, metadata=field_metadata)\n cash_reinvestment_treatment: Optional[CashReinvestmentTreatment] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IndicesEditInputTypes(Base):\n parameters: Union[CreditCustomBasketEditInputs, CustomBasketsEditInputs] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IndicesEditInputs(Base):\n parameters: CustomBasketsEditInputs = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IndicesRebalanceInputs(IndicesRebalanceInputTypes):\n parameters: Union[CustomBasketsRebalanceInputs, ISelectRebalance] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ApprovalCustomBasketResponse(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n positions_to_rebalance: PositionSet = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n status: Optional[ApprovalStatus] = field(default=None, metadata=field_metadata)\n parent_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n approved_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n approved_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n submitted_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n submitted_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n comments: Optional[Tuple[ApprovalComment, ...]] = field(default=None, metadata=field_metadata)\n notifyees: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n action_type: Optional[object] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/instrument.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom gs_quant.instrument.core import Instrument\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqSyntheticDateInfo(Instrument):\n date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.SyntheticDateInfo, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqSyntheticOETTerms(Instrument):\n client_fee: Optional[str] = field(default=None, metadata=field_metadata)\n client_notice: Optional[str] = field(default=None, metadata=field_metadata)\n client_terms: Optional[str] = field(default=None, metadata=field_metadata)\n client_fee_at: Optional[float] = field(default=None, metadata=field_metadata)\n firm_fee: Optional[str] = field(default=None, metadata=field_metadata)\n firm_notice: Optional[str] = field(default=None, metadata=field_metadata)\n firm_terms: Optional[str] = field(default=None, metadata=field_metadata)\n firm_fee_at: Optional[float] = field(default=None, metadata=field_metadata)\n oet_type: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.SyntheticOETTerms, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXCorrelationSwapLeg(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.CorrelationSwapLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetRef(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n product_code: Optional[ProductCode] = field(default=None, metadata=field_metadata)\n size: Optional[float] = field(default=None, metadata=field_metadata)\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[float] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cross_Asset, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Any, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Bond(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n identifier: Optional[str] = field(default=None, metadata=field_metadata)\n identifier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n size: Optional[float] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cross_Asset, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Bond, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Cash(Instrument):\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n payment_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cash, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Cash, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodIndexSwap(Instrument):\n index: Optional[str] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_days: Optional[int] = field(default=None, metadata=field_metadata)\n settlement_days_after: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_days_type: Optional[str] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n reset_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n reset_day: Optional[int] = field(default=None, metadata=field_metadata)\n reset_frequency_multiplier: Optional[int] = field(default=None, metadata=field_metadata)\n index_name_source: Optional[str] = field(default=None, metadata=field_metadata)\n execution_method_name: Optional[str] = field(default=None, metadata=field_metadata)\n execution_method_start_time: Optional[str] = field(default=None, metadata=field_metadata)\n execution_method_end_time: Optional[str] = field(default=None, metadata=field_metadata)\n execution_method_location: Optional[str] = field(default=None, metadata=field_metadata)\n reinvestment_flag: Optional[YesNo] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.IndexSwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodListedOptionPeriod(Instrument):\n contract: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.ListedOptionPeriod, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodListedSwapPeriod(Instrument):\n contract: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.ListedSwapPeriod, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodOTCOptionPeriod(Instrument):\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n contract: Optional[str] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.OptionPeriod, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodOTCSwapLeg(Instrument):\n fixing_currency: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\n leg_description: Optional[str] = field(default=None, metadata=field_metadata)\n contract: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency_source: Optional[str] = field(default=None, metadata=field_metadata)\n underlier: Optional[str] = field(default=None, metadata=field_metadata)\n quantity_multiplier: Optional[int] = field(default=None, metadata=field_metadata)\n fixed_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.SwapLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodOTCSwapPeriod(Instrument):\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.SwapPeriod, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodSwapData(Instrument):\n commodity: Optional[str] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n contract: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency_source: Optional[str] = field(default=None, metadata=field_metadata)\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_type: Optional[str] = field(default=None, metadata=field_metadata)\n number_of_periods: Optional[int] = field(default=None, metadata=field_metadata)\n quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_price_unit: Optional[str] = field(default=None, metadata=field_metadata)\n commodity_reference_price: Optional[str] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity_period: Optional[Period] = field(default=None, metadata=field_metadata)\n strategy: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n buy_sells: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n underlier_short_name: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_days: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_days_type: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_days_from: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n currency_summary: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\n native_quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.SwapData, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqAsianOption(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n option_style: Optional[OptionStyle] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n holiday_calendar: Optional[str] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n payment_holidays: Optional[Currency] = field(default=None, metadata=field_metadata)\n composite_ccy: Optional[Currency] = field(default=None, metadata=field_metadata)\n accumstart_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n margin_id: Optional[str] = field(default=None, metadata=field_metadata)\n fx_data_source: Optional[str] = field(default=None, metadata=field_metadata)\n termination_date_bdc: Optional[str] = field(default=None, metadata=field_metadata)\n observation_date_bdc: Optional[str] = field(default=None, metadata=field_metadata)\n averaging_date_disruption: Optional[str] = field(default=None, metadata=field_metadata)\n strike_adjust_type: Optional[str] = field(default=None, metadata=field_metadata)\n use_dividend_dates: Optional[str] = field(default=None, metadata=field_metadata)\n include_special_div: Optional[str] = field(default=None, metadata=field_metadata)\n strike_weights_customized: Optional[str] = field(default=None, metadata=field_metadata)\n asian_performance: Optional[str] = field(default=None, metadata=field_metadata)\n use_contract_div: Optional[str] = field(default=None, metadata=field_metadata)\n use_net_divs: Optional[str] = field(default=None, metadata=field_metadata)\n asian_dates_customized: Optional[str] = field(default=None, metadata=field_metadata)\n expiry_settlement_days: Optional[str] = field(default=None, metadata=field_metadata)\n strike_fixing_dates_customized: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_share: Optional[str] = field(default=None, metadata=field_metadata)\n asian_weights_customized: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.AsianOption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqAutoroll(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n first_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n last_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixing_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n trigger_level: Optional[float] = field(default=None, metadata=field_metadata)\n buffer_level: Optional[float] = field(default=None, metadata=field_metadata)\n local_return_cap: Optional[float] = field(default=None, metadata=field_metadata)\n upside_leverage: Optional[float] = field(default=None, metadata=field_metadata)\n initial_fixing_override: Optional[float] = field(default=None, metadata=field_metadata)\n apply_trigger_level_shift: Optional[str] = field(default=None, metadata=field_metadata)\n trigger_level_shift: Optional[float] = field(default=None, metadata=field_metadata)\n notional: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n business_day_calendar: Optional[str] = field(default=None, metadata=field_metadata)\n payment_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_delay: Optional[str] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Autoroll, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqBarrier(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n barrier_start_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n barrier_end_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knock_up_or_down: Optional[UpDown] = field(default=None, metadata=field_metadata)\n barrier_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[float] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n currency: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Barrier, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqBinary(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n currency: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Binary, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqCliquet(Instrument):\n return_style: Optional[ReturnStyle] = field(default=ReturnStyle.Rate_of_Return, metadata=field_metadata)\n last_valuation_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n payment_frequency: Optional[PaymentFrequency] = field(default=PaymentFrequency.Maturity, metadata=field_metadata)\n global_cap: Optional[float] = field(default=1000000.0, metadata=field_metadata)\n first_valuation_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n global_floor: Optional[float] = field(default=-1000000.0, metadata=field_metadata)\n strike_price: Optional[float] = field(default=None, metadata=field_metadata)\n return_type: Optional[ReturnType] = field(default=ReturnType.Sum, metadata=field_metadata)\n valuation_period: Optional[str] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=0.0, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Cliquet, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqContractDivOption(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n option_style: Optional[OptionStyle] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=0.0, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n trade_as: Optional[TradeAs] = field(default=None, metadata=field_metadata)\n future_contract: Optional[str] = field(default=None, metadata=field_metadata)\n underlier_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiry_settlement_days: Optional[str] = field(default=None, metadata=field_metadata)\n expiry_settle_date: Optional[str] = field(default=None, metadata=field_metadata)\n accum_start_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.ContractDivOption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.number_of_options is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.number_of_options]):\n raise RuntimeError('All specified size fields must be numeric')\n self.number_of_options *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqConvertibleBond(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n ref_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\n isin: Optional[str] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Convertible, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqDigital(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n option_style: Optional[OptionStyle] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=0.0, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n trade_as: Optional[TradeAs] = field(default=None, metadata=field_metadata)\n future_contract: Optional[str] = field(default=None, metadata=field_metadata)\n underlier_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiry_settlement_days: Optional[str] = field(default=None, metadata=field_metadata)\n expiry_settle_date: Optional[str] = field(default=None, metadata=field_metadata)\n binary_position: Optional[str] = field(default=None, metadata=field_metadata)\n binary_width_bps: Optional[float] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n payment_style: Optional[str] = field(default=None, metadata=field_metadata)\n strict_barrier: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Digital, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqForward(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n forward_price: Optional[float] = field(default=None, metadata=field_metadata)\n number_of_shares: Optional[int] = field(default=1, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Forward, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqForwardVarianceSwap(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n variance_cap: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n days_in_contract: Optional[float] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n fixing_schedule_dates: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n holiday_calendar: Optional[str] = field(default=None, metadata=field_metadata)\n initial_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n initial_spot: Optional[float] = field(default=None, metadata=field_metadata)\n expiry_settlement_days: Optional[str] = field(default=None, metadata=field_metadata)\n initial_spot_valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n force_forward_tradable: Optional[bool] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.ForwardVarianceSwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqFuture(Instrument):\n identifier: Optional[str] = field(default=None, metadata=field_metadata)\n identifier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n underlier: Optional[str] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n traded_price: Optional[float] = field(default=0.0, metadata=field_metadata)\n total_quantity: Optional[float] = field(default=None, metadata=field_metadata)\n trade_as: Optional[str] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Future, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqLockedLadder(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=0.0, metadata=field_metadata)\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n first_valuation_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n last_valuation_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n valuation_period: Optional[str] = field(default=None, metadata=field_metadata)\n strike_price: Optional[float] = field(default=None, metadata=field_metadata)\n payment_frequency: Optional[PaymentFrequency] = field(default=PaymentFrequency.Maturity, metadata=field_metadata)\n ladder_multipliers: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n weights: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.LockedLadder, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqOption(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n option_style: Optional[OptionStyle] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=0.0, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n trade_as: Optional[TradeAs] = field(default=None, metadata=field_metadata)\n future_contract: Optional[str] = field(default=None, metadata=field_metadata)\n underlier_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiry_settlement_days: Optional[str] = field(default=None, metadata=field_metadata)\n expiry_settle_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Option, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.number_of_options is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.number_of_options]):\n raise RuntimeError('All specified size fields must be numeric')\n self.number_of_options *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqOptionLeg(Instrument):\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_style: Optional[OptionStyle] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[float] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n trade_as: Optional[TradeAs] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.OptionLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqQuantoOption(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=0.0, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n future_contract: Optional[str] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiry_settlement_days: Optional[str] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.QuantoOption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqStock(Instrument):\n identifier: Optional[str] = field(default=None, metadata=field_metadata)\n identifier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n traded_price: Optional[float] = field(default=0.0, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Single_Stock, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqSyntheticSchedule(Instrument):\n period: Optional[str] = field(default=None, metadata=field_metadata)\n delay: Optional[str] = field(default=None, metadata=field_metadata)\n date_rule: Optional[str] = field(default=None, metadata=field_metadata)\n dates: Optional[Tuple[EqSyntheticDateInfo, ...]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.SyntheticSchedule, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqVarianceSwap(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n variance_cap: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n days_in_contract: Optional[float] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n fixing_schedule_dates: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n holiday_calendar: Optional[str] = field(default=None, metadata=field_metadata)\n initial_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n initial_spot: Optional[float] = field(default=None, metadata=field_metadata)\n expiry_settlement_days: Optional[str] = field(default=None, metadata=field_metadata)\n initial_spot_source_flag: Optional[float] = field(default=None, metadata=field_metadata)\n force_forward_tradable: Optional[bool] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.VarianceSwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.quantity is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.quantity]):\n raise RuntimeError('All specified size fields must be numeric')\n self.quantity *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqVolatilitySwap(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n variance_cap: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n days_in_contract: Optional[float] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n fixing_schedule_dates: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n holiday_calendar: Optional[str] = field(default=None, metadata=field_metadata)\n initial_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n initial_spot: Optional[float] = field(default=None, metadata=field_metadata)\n expiry_settlement_days: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.VolatilitySwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXAccumulatorScheduleLeg(Instrument):\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n european_knock_in: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knock_out_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n leverage_ratio: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.AccumulatorScheduleLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXBinary(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Binary, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXBinaryDoubleKnockout(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n lower_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.BinaryDoubleKnockout, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXCorrelationSwap(Instrument):\n legs: Tuple[FXCorrelationSwapLeg, ...] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n strike_corr: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n first_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n last_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n calculate_mean_return: Optional[float] = field(default=0.0, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=0, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.CorrelationSwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXDoubleKnockout(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n lower_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.DoubleKnockout, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXDoubleOneTouch(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n lower_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n payout_type: Optional[PayoutType] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n touch_or_no_touch: Optional[TouchNoTouch] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.DoubleTouch, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXDualDoubleKnockoutLeg(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n lower_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.DualDoubleKnockoutLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXEuropeanKnockout(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[str] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knock_up_or_down: Optional[UpDown] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.EuropeanKnockout, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXForward(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n forward_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount_in_other_currency: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Forward, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount, self.notional_amount_in_other_currency]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n if check_resolved or self.notional_amount_in_other_currency is not None:\n self.notional_amount_in_other_currency *= abs(scaling)\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXForwardVolatilityAgreement(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n strike_vol: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n underlying_expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n underlying_settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_type: Optional[FVAStrikeType] = field(default=None, metadata=field_metadata)\n underlying_payoff: Optional[FVAUnderlyingOptionType] = field(default=None, metadata=field_metadata)\n underlying_fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n underlying_premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=0, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.ForwardVolatilityAgreement, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXKnockout(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n knock_up_or_down: Optional[UpDown] = field(default=None, metadata=field_metadata)\n barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Knockout, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXMultiCrossBinaryLeg(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.MultiCrossBinaryLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXMultiCrossDoubleBinaryLeg(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n lower_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.MultiCrossDoubleBinaryLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXMultiCrossDoubleOneTouchLeg(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n lower_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.MultiCrossDoubleTouchLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXOneTouch(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n knock_up_or_down: Optional[UpDown] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n touch_or_no_touch: Optional[TouchNoTouch] = field(default=None, metadata=field_metadata)\n payout_type: Optional[PayoutType] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.OneTouch, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXOption(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount_in_other_currency: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n exercise_style: Optional[OptionExerciseStyle] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Option, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount, self.notional_amount_in_other_currency]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n if check_resolved or self.notional_amount_in_other_currency is not None:\n self.notional_amount_in_other_currency *= abs(scaling)\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXOptionLeg(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount_in_other_currency: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n exercise_style: Optional[OptionExerciseStyle] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.OptionLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXPivotScheduleLeg(Instrument):\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n lower_leverage_ratio: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n lower_knock_in: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n lower_strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n pivot: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_knock_in: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_leverage_ratio: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.PivotScheduleLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXShiftingBermForward(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount_in_other_currency: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n window_start_date: Optional[str] = field(default=None, metadata=field_metadata)\n exercise_decision_freq: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.ShiftingBermForward, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXTarfScheduleLeg(Instrument):\n profit_strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n loss_strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n european_knock_in: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n leverage_ratio: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.TarfScheduleLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXVarianceSwap(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n strike_vol: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n first_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n last_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n annualization_factor: Optional[float] = field(default=None, metadata=field_metadata)\n calculate_mean_return: Optional[float] = field(default=0.0, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=0, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.VarianceSwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXVolatilityKnockout(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n first_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.VolatilityKnockout, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXVolatilitySwap(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n strike_vol: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n first_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n last_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n annualization_factor: Optional[float] = field(default=None, metadata=field_metadata)\n calculate_mean_return: Optional[float] = field(default=0.0, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=0, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.VolatilitySwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n \n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXWindowDoubleKnockout(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n lower_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n upper_barrier_start_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n upper_barrier_end_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n lower_barrier_start_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n lower_barrier_end_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.WindowDoubleKnockout, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXWindowKnockout(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n knock_up_or_down: Optional[UpDown] = field(default=None, metadata=field_metadata)\n barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n barrier_start_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n barrier_end_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.WindowKnockout, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXWorstOfKOLeg(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n knock_up_or_down: Optional[UpDown] = field(default=None, metadata=field_metadata)\n knock_in_or_out: Optional[InOut] = field(default=None, metadata=field_metadata)\n barrier_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knockout_convention: Optional[KnockoutConvention] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.WorstOfKOLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXWorstOfLeg(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n notional_scale_factor: Optional[float] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.WorstOfLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Forward(Instrument):\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cash, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Forward, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRBondFuture(Instrument):\n identifier: Optional[str] = field(default=None, metadata=field_metadata)\n identifier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n traded_price: Optional[float] = field(default=None, metadata=field_metadata)\n trade_settle: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.BondFuture, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRCap(Instrument):\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n cap_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Cap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRCapFloor(Instrument):\n cap_floor: Optional[CapOrFloor] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=None, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.CapOrFloor, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRFloor(Instrument):\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n floor_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Floor, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass InflationSwap(Instrument):\n pay_or_receive: Optional[PayReceive] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n index: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fixed_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n base_cpi: Optional[float] = field(default=None, metadata=field_metadata)\n clearing_house: Optional[SwapClearingHouse] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.InflationSwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.pay_or_receive is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount, self.fee]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {PayReceive.Pay: PayReceive.Receive, PayReceive.Receive: PayReceive.Pay}\n self.pay_or_receive = flip_dict[self.pay_or_receive]\n if check_resolved or self.fee is not None:\n self.fee *= -1\n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass InstrumentsRepoIRDiscreteLock(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n spot_clean_price: Optional[float] = field(default=None, metadata=field_metadata)\n settlement: Optional[str] = field(default=None, metadata=field_metadata)\n repo_rate: Optional[float] = field(default=None, metadata=field_metadata)\n forward_clean_price: Optional[float] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Repo, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Bond_Forward, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MetalForward(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n delivery_location: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Forward, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CDIndex(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n clearinghouse: Optional[SwapClearingHouse] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n first_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n first_roll_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n index_family: Optional[str] = field(default=None, metadata=field_metadata)\n index_for_basis: Optional[str] = field(default=None, metadata=field_metadata)\n index_series: Optional[float] = field(default=None, metadata=field_metadata)\n index_version: Optional[float] = field(default=None, metadata=field_metadata)\n isda_docs: Optional[str] = field(default='2014', metadata=config(field_name='ISDADocs', exclude=exclude_none))\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Credit, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Index, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CDIndexOption(Instrument):\n automatic_exercise: Optional[float] = field(default=0.0, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n clearinghouse: Optional[SwapClearingHouse] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n earliest_exercise_time: Optional[str] = field(default=None, metadata=field_metadata)\n earliest_exercise_time_centre: Optional[str] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n exercise_date_business_day_convention: Optional[BusinessDayConvention] = field(default='Following', metadata=field_metadata)\n exercise_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n expiration_time_centre: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=0.0, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n first_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n first_roll_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n index_family: Optional[str] = field(default=None, metadata=field_metadata)\n index_for_basis: Optional[str] = field(default=None, metadata=field_metadata)\n index_series: Optional[float] = field(default=None, metadata=field_metadata)\n index_version: Optional[float] = field(default=None, metadata=field_metadata)\n isda_docs: Optional[str] = field(default='2014', metadata=config(field_name='ISDADocs', exclude=exclude_none))\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n option_type: Optional[CDOptionType] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixed_rate: Optional[float] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n strike_type: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Credit, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.IndexOption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodListedOption(Instrument):\n buy_sells: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n venue: Optional[str] = field(default=None, metadata=field_metadata)\n underlier: Optional[str] = field(default=None, metadata=field_metadata)\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n native_quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[str] = field(default=None, metadata=field_metadata)\n premium_unit: Optional[str] = field(default=None, metadata=field_metadata)\n strategy: Optional[str] = field(default=None, metadata=field_metadata)\n strikes: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n quantity_period: Optional[str] = field(default=None, metadata=field_metadata)\n option_types: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n number_of_periods: Optional[int] = field(default=None, metadata=field_metadata)\n period_details: Optional[Tuple[CommodListedOptionPeriod, ...]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.ListedOption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodListedSwap(Instrument):\n buy_sells: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n venue: Optional[str] = field(default=None, metadata=field_metadata)\n commodity: Optional[str] = field(default=None, metadata=field_metadata)\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n native_quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency: Optional[str] = field(default=None, metadata=field_metadata)\n strategy: Optional[str] = field(default=None, metadata=field_metadata)\n number_of_periods: Optional[int] = field(default=None, metadata=field_metadata)\n period_details: Optional[Tuple[CommodListedSwapPeriod, ...]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.ListedSwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodOTCOptionLeg(Instrument):\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n fixing_currency: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\n premium: Optional[CommodPrice] = field(default=None, metadata=field_metadata)\n leg_description: Optional[str] = field(default=None, metadata=field_metadata)\n contract: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency_source: Optional[str] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier: Optional[str] = field(default=None, metadata=field_metadata)\n premium_settlement: Optional[str] = field(default=None, metadata=field_metadata)\n quantity_multiplier: Optional[int] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.OptionLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodOTCSwap(Instrument):\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n legs: Optional[Tuple[CommodOTCSwapLeg, ...]] = field(default=None, metadata=field_metadata)\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n number_of_periods: Optional[int] = field(default=None, metadata=field_metadata)\n quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n quantity_period: Optional[Period] = field(default=None, metadata=field_metadata)\n strategy: Optional[str] = field(default=None, metadata=field_metadata)\n settlement: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.SwapStrategy, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodOption(Instrument):\n commodity: Optional[str] = field(default=None, metadata=field_metadata)\n number_of_periods: Optional[int] = field(default=None, metadata=field_metadata)\n quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n currency_summary: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\n option_types: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n option_type: Optional[str] = field(default=None, metadata=field_metadata)\n strike_unit: Optional[str] = field(default=None, metadata=field_metadata)\n strikes: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sells: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n underlier_short_name: Optional[str] = field(default=None, metadata=field_metadata)\n settlement: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_days: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_days_type: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_days_from: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n strike_currency: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n contract: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency_source: Optional[str] = field(default=None, metadata=field_metadata)\n strike: Optional[str] = field(default=None, metadata=field_metadata)\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_type: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency: Optional[str] = field(default=None, metadata=field_metadata)\n commodity_reference_price: Optional[str] = field(default=None, metadata=field_metadata)\n quantity_period: Optional[str] = field(default=None, metadata=field_metadata)\n strategy: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[str] = field(default=None, metadata=field_metadata)\n premium_unit: Optional[str] = field(default=None, metadata=field_metadata)\n premium_type: Optional[str] = field(default=None, metadata=field_metadata)\n period_details: Optional[Tuple[CommodOTCOptionPeriod, ...]] = field(default=None, metadata=field_metadata)\n native_quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Option, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodSwap(Instrument):\n commodity: Optional[str] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n contract: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency_source: Optional[str] = field(default=None, metadata=field_metadata)\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_type: Optional[str] = field(default=None, metadata=field_metadata)\n number_of_periods: Optional[int] = field(default=None, metadata=field_metadata)\n quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement: Optional[str] = field(default=None, metadata=field_metadata)\n fixing_currency: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_price_unit: Optional[str] = field(default=None, metadata=field_metadata)\n commodity_reference_price: Optional[str] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity_period: Optional[Period] = field(default=None, metadata=field_metadata)\n strategy: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n buy_sells: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n underlier_short_name: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_days: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_days_type: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_days_from: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n currency_summary: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\n period_details: Optional[Tuple[CommodOTCSwapPeriod, ...]] = field(default=None, metadata=field_metadata)\n native_quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Swap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodVolVarSwap(Instrument):\n notional_currency: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\n notional: Optional[float] = field(default=1.0, metadata=field_metadata)\n floating_rate_is_capped: Optional[str] = field(default=None, metadata=field_metadata)\n end_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n margined: Optional[float] = field(default=None, metadata=field_metadata)\n market_disruption_agreement: Optional[str] = field(default=None, metadata=field_metadata)\n mean_rule: Optional[CommodMeanRule] = field(default=None, metadata=field_metadata)\n divisor: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_mean: Optional[float] = field(default=None, metadata=field_metadata)\n first_fixing: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n floating_rate_cap: Optional[float] = field(default=None, metadata=field_metadata)\n fx_fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n annualization_factor: Optional[float] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n contract: Optional[str] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n swap_type: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixing_currency: Optional[CurrencyName] = field(default=None, metadata=field_metadata)\n asset_fixing_source: Optional[str] = field(default=None, metadata=field_metadata)\n sampling_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n variance_convention: Optional[VarianceConvention] = field(default=None, metadata=field_metadata)\n asset: Optional[str] = field(default=None, metadata=field_metadata)\n start_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.VolVarSwap, metadata=config(field_name='type', exclude=exclude_none))\n extra_sampling_calendars: Optional[str] = field(init=False, default='--Blank--', metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqOptionStrategy(Instrument):\n underlier: Union[float, str] = field(default=None, metadata=field_metadata)\n strategy: str = field(default=None, metadata=field_metadata)\n legs: Tuple[EqOptionLeg, ...] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n option_style: Optional[OptionStyle] = field(default=None, metadata=field_metadata)\n number_of_options: Optional[float] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium: Optional[float] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n valuation_time: Optional[ValuationTime] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n trade_as: Optional[TradeAs] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.OptionStrategy, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqSyntheticLeg(Instrument):\n schedule_type: Optional[str] = field(default=None, metadata=field_metadata)\n first_roll_date: Optional[str] = field(default=None, metadata=field_metadata)\n first_roll_date_day_of_month: Optional[float] = field(default=None, metadata=field_metadata)\n payment_schedule: Optional[Tuple[EqSyntheticSchedule, ...]] = field(default=None, metadata=field_metadata)\n reset_schedule: Optional[Tuple[EqSyntheticSchedule, ...]] = field(default=None, metadata=field_metadata)\n valuation_schedule: Optional[Tuple[EqSyntheticSchedule, ...]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.SyntheticLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FRA(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n clearing_house: Optional[SwapClearingHouse] = field(default=None, metadata=field_metadata)\n clearing_legally_binding: Optional[float] = field(default=None, metadata=field_metadata)\n day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n frequency: Optional[str] = field(default=None, metadata=field_metadata)\n calendar: Optional[str] = field(default=None, metadata=field_metadata)\n rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n maturity: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n payment_delay: Optional[str] = field(default=None, metadata=field_metadata)\n roll_convention: Optional[str] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.FRA, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXAccumulator(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n new_or_unwind: Optional[NewOrUnwind] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n leverage_ratio: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n european_knock_in: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n knock_out_level: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n accum_or_decum: Optional[AccumOrDecum] = field(default=None, metadata=field_metadata)\n accumulator_type: Optional[AccumulatorType] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n first_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n coupon_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n guaranteed_coupons: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n number_of_expiry: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixing_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n schedules: Optional[Tuple[FXAccumulatorScheduleLeg, ...]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Accumulator, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXDualDoubleKnockout(Instrument):\n legs: Tuple[FXDualDoubleKnockoutLeg, ...] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.DualDoubleKnockout, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXMultiCrossBinary(Instrument):\n legs: Tuple[FXMultiCrossBinaryLeg, ...] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.MultiCrossBinary, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXMultiCrossDoubleBinary(Instrument):\n legs: Tuple[FXMultiCrossDoubleBinaryLeg, ...] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.MultiCrossDoubleBinary, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXMultiCrossDoubleOneTouch(Instrument):\n legs: Tuple[FXMultiCrossDoubleOneTouchLeg, ...] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n payout_type: Optional[PayoutType] = field(default=None, metadata=field_metadata)\n touch_or_no_touch: Optional[TouchNoTouch] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.MultiCrossDoubleTouch, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXOptionStrategy(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n strategy_name: Optional[str] = field(default=None, metadata=field_metadata)\n legs: Optional[Tuple[FXOptionLeg, ...]] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount_in_other_currency: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n strike_price: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n expiration_time: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n exercise_style: Optional[OptionExerciseStyle] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.OptionStrategy, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXPivot(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n new_or_unwind: Optional[NewOrUnwind] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n lower_leverage_ratio: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n lower_knock_in: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n lower_strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n pivot: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_knock_in: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n upper_leverage_ratio: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fixing_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n number_of_expiry: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n coupon_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n first_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n target_type: Optional[TargetType] = field(default=None, metadata=field_metadata)\n target: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n schedules: Optional[Tuple[FXPivotScheduleLeg, ...]] = field(default=None, metadata=field_metadata)\n target_adj_notional_or_strike: Optional[NotionalOrStrike] = field(default=None, metadata=field_metadata)\n payment_on_hitting_target: Optional[TargetPaymentType] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Pivot, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXTarf(Instrument):\n pair: Optional[str] = field(default=None, metadata=field_metadata)\n new_or_unwind: Optional[NewOrUnwind] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n profit_strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n loss_strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fixing_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n long_or_short: Optional[LongShort] = field(default=None, metadata=field_metadata)\n european_knock_in: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n number_of_expiry: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n coupon_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n first_fixing_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n leverage_ratio: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n target_type: Optional[TargetType] = field(default=None, metadata=field_metadata)\n target: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n schedules: Optional[Tuple[FXTarfScheduleLeg, ...]] = field(default=None, metadata=field_metadata)\n target_adj_notional_or_strike: Optional[NotionalOrStrike] = field(default=None, metadata=field_metadata)\n payment_on_hitting_target: Optional[TargetPaymentType] = field(default=None, metadata=field_metadata)\n settlement_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Tarf, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXWorstOf(Instrument):\n legs: Tuple[FXWorstOfLeg, ...] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n best_or_worst: Optional[BestWorst] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.WorstOf, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FXWorstOfKO(Instrument):\n legs: Tuple[FXWorstOfKOLeg, ...] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n best_or_worst: Optional[BestWorst] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n method_of_settlement: Optional[OptionSettlementMethod] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.WorstOfKO, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRAssetSwapFxdFlt(Instrument):\n asw_type: Optional[AswType] = field(default=None, metadata=field_metadata)\n clearing_house: Optional[SwapClearingHouse] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=None, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n fixed_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fixed_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n floating_rate_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_fx: Optional[float] = field(default=None, metadata=field_metadata)\n floating_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n floating_maturity: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n identifier: Optional[str] = field(default=None, metadata=field_metadata)\n identifier_type: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n pay_or_receive: Optional[PayReceive] = field(default=None, metadata=field_metadata)\n roll_convention: Optional[str] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n floating_rate_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n traded_clean_price: Optional[float] = field(default=100.0, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.AssetSwapFxdFlt, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRAssetSwapFxdFxd(Instrument):\n asw_type: Optional[AswType] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=None, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n fixed_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fixed_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n coupon: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n asset_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n asset_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n asset_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n asset_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n identifier: Optional[str] = field(default=None, metadata=field_metadata)\n identifier_type: Optional[str] = field(default=None, metadata=field_metadata)\n asset_maturity: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_maturity: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n roll_convention: Optional[str] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixed_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n clean_price: Optional[float] = field(default=100.0, metadata=field_metadata)\n settlement_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.AssetSwapFxdFxd, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRBasisSwap(Instrument):\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n principal_exchange: Optional[PrincipalExchange] = field(default=None, metadata=field_metadata)\n payer_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n payer_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n payer_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n payer_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n payer_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n payer_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n receiver_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n receiver_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n receiver_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n clearing_house: Optional[SwapClearingHouse] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.BasisSwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRBondOption(Instrument):\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n option_type: Optional[OptionType] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n strike_type: Optional[BondStrikeType] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n settlement: Optional[SettlementType] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.BondOption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRCMSOption(Instrument):\n cap_floor: Optional[str] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n index: Optional[str] = field(default=None, metadata=field_metadata)\n rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n start_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.CMSOption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRCMSOptionStrip(Instrument):\n cap_floor: Optional[str] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n index: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n reset_delay: Optional[str] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.CMSOptionStrip, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRCMSSpreadOption(Instrument):\n cap_floor: Optional[str] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n index1_tenor: Optional[str] = field(default=None, metadata=field_metadata)\n index2_tenor: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.CMSSpreadOption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRCMSSpreadOptionStrip(Instrument):\n cap_floor: Optional[str] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n index1: Optional[str] = field(default=None, metadata=field_metadata)\n index2: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n reset_delay: Optional[str] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.CMSSpreadOptionStrip, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRFixedLeg(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n fixed_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n fixed_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fixed_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n principal_exchange: Optional[PrincipalExchange] = field(default=None, metadata=field_metadata)\n roll_convention: Optional[str] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.FixedLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRFloatLeg(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n floating_rate_for_the_initial_calculation_period: Optional[float] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n floating_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n floating_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n principal_exchange: Optional[PrincipalExchange] = field(default=None, metadata=field_metadata)\n roll_convention: Optional[str] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n floating_rate_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.FloatLeg, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRSwap(Instrument):\n pay_or_receive: Optional[PayReceive] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n principal_exchange: Optional[PrincipalExchange] = field(default=None, metadata=field_metadata)\n floating_rate_for_the_initial_calculation_period: Optional[float] = field(default=None, metadata=field_metadata)\n floating_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fixed_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n fixed_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n clearing_house: Optional[SwapClearingHouse] = field(default=None, metadata=field_metadata)\n fixed_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n floating_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n roll_convention: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_rate_accrual_convention: Optional[AccrualConvention] = field(default=None, metadata=field_metadata)\n floating_rate_accrual_convention: Optional[AccrualConvention] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Swap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.pay_or_receive is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount, self.fee]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {PayReceive.Pay: PayReceive.Receive, PayReceive.Receive: PayReceive.Pay}\n self.pay_or_receive = flip_dict[self.pay_or_receive]\n if check_resolved or self.fee is not None:\n self.fee *= -1\n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRSwaption(Instrument):\n pay_or_receive: Optional[PayReceive] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n expiration_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_spread: Optional[float] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fixed_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n fixed_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n clearing_house: Optional[SwapClearingHouse] = field(default=None, metadata=field_metadata)\n settlement: Optional[SwapSettlement] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Swaption, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n def scale_in_place(self, scaling: Optional[float] = None, check_resolved: bool = True):\n if scaling is None or scaling == 1:\n return\n if self.unresolved is None:\n if check_resolved:\n raise RuntimeError('Can only scale resolved instruments')\n if self.notional_amount is None or self.buy_sell is None:\n raise RuntimeError('Can only scale unresolved instruments with the buysell and primary size fields set')\n if any(a is not None and not isinstance(a, (int, float)) for a in [self.notional_amount, self.fee]):\n raise RuntimeError('All specified size fields must be numeric')\n self.notional_amount *= abs(scaling)\n\n if scaling < 0:\n flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy}\n self.buy_sell = flip_dict[self.buy_sell]\n if check_resolved or self.fee is not None:\n self.fee *= -1\n return\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRXccySwap(Instrument):\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[float] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n principal_exchange: Optional[PrincipalExchange] = field(default=None, metadata=field_metadata)\n payer_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n payer_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n payer_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n payer_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n payer_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n payer_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n payer_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n receiver_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n receiver_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n receiver_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n receiver_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n initial_fx_rate: Optional[float] = field(default=None, metadata=field_metadata)\n payer_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n receiver_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n payer_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n receiver_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n payer_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n notional_reset_side: Optional[PayReceive] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.XccySwapMTM, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRXccySwapFixFix(Instrument):\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[float] = field(default=None, metadata=field_metadata)\n receiver_notional_amount: Optional[float] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n principal_exchange: Optional[PrincipalExchange] = field(default=None, metadata=field_metadata)\n payer_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n payer_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n payer_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n payer_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n payer_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n receiver_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n receiver_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n receiver_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n receiver_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.XccySwapFixFix, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRXccySwapFixFlt(Instrument):\n pay_or_receive: Optional[PayReceive] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n principal_exchange: Optional[PrincipalExchange] = field(default=None, metadata=field_metadata)\n floating_rate_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n floating_rate_for_the_initial_calculation_period: Optional[float] = field(default=None, metadata=field_metadata)\n floating_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n floating_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n floating_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n floating_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fixed_rate_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fixed_rate: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n fixed_rate_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n fixed_rate_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n fixed_rate_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n floating_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fixed_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n floating_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.XccySwapFixFlt, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass IRXccySwapFltFlt(Instrument):\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n principal_exchange: Optional[PrincipalExchange] = field(default=None, metadata=field_metadata)\n payer_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n payer_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n payer_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n payer_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n payer_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n payer_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n payer_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n receiver_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n receiver_spread: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n receiver_rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_frequency: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_day_count_fraction: Optional[DayCountFraction] = field(default=None, metadata=field_metadata)\n receiver_business_day_convention: Optional[BusinessDayConvention] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n payer_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n receiver_first_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n payer_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n receiver_last_stub: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n payer_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_holidays: Optional[str] = field(default=None, metadata=field_metadata)\n receiver_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.XccySwap, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MacroBasket(Instrument):\n cap_floor: Optional[str] = field(default=None, metadata=field_metadata)\n termination_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n notional_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n strike: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n index: Optional[str] = field(default=None, metadata=field_metadata)\n rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n premium: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n premium_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee: Optional[float] = field(default=0.0, metadata=field_metadata)\n fee_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n start_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n fee_payment_date: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cross_Asset, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.MacroBasket, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommodOTCOption(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n quantity: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n start: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n number_of_periods: Optional[int] = field(default=None, metadata=field_metadata)\n quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n settlement: Optional[str] = field(default=None, metadata=field_metadata)\n premium_summary: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n legs: Optional[Tuple[CommodOTCOptionLeg, ...]] = field(default=None, metadata=field_metadata)\n end: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n quantity_period: Optional[Period] = field(default=None, metadata=field_metadata)\n strategy: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.OptionStrategy, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EqSynthetic(Instrument):\n underlier: Union[float, str] = field(default=None, metadata=field_metadata)\n expiry: Optional[Union[datetime.date, str]] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n swap_type: Optional[SwapType] = field(default=SwapType.Eq_Synthetic_OET, metadata=field_metadata)\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n underlier_type: Optional[UnderlierType] = field(default=None, metadata=field_metadata)\n effective_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n num_of_underlyers: Optional[float] = field(default=None, metadata=field_metadata)\n rate_tenor: Optional[str] = field(default=None, metadata=field_metadata)\n forwardstartingfixednotional: Optional[float] = field(default=None, metadata=field_metadata)\n forwardstartingnotionalprecision: Optional[float] = field(default=None, metadata=field_metadata)\n commission: Optional[float] = field(default=None, metadata=field_metadata)\n commission_units: Optional[str] = field(default=None, metadata=field_metadata)\n quantity: Optional[str] = field(default=None, metadata=field_metadata)\n strike: Optional[float] = field(default=None, metadata=field_metadata)\n initial_valuation_date: Optional[str] = field(default=None, metadata=field_metadata)\n schedule_type: Optional[str] = field(default=None, metadata=field_metadata)\n oet_terms: Optional[Tuple[EqSyntheticOETTerms, ...]] = field(default=None, metadata=field_metadata)\n dividend_pay_ratio: Optional[float] = field(default=None, metadata=field_metadata)\n trade_date: Optional[str] = field(default=None, metadata=field_metadata)\n strike_date: Optional[str] = field(default=None, metadata=field_metadata)\n fx_data_source: Optional[str] = field(default=None, metadata=field_metadata)\n rate_option: Optional[str] = field(default=None, metadata=field_metadata)\n settlement_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n settlement_delay: Optional[str] = field(default=None, metadata=field_metadata)\n eq_leg: Optional[Tuple[EqSyntheticLeg, ...]] = field(default=None, metadata=field_metadata)\n funding_leg: Optional[Tuple[EqSyntheticLeg, ...]] = field(default=None, metadata=field_metadata)\n valuation_bdc: Optional[float] = field(default=None, metadata=field_metadata)\n payment_bdc: Optional[float] = field(default=None, metadata=field_metadata)\n commission_type: Optional[str] = field(default=None, metadata=field_metadata)\n designated_maturity: Optional[str] = field(default=None, metadata=field_metadata)\n reset_delay: Optional[str] = field(default=None, metadata=field_metadata)\n payment_delay: Optional[str] = field(default=None, metadata=field_metadata)\n compounded_funding: Optional[float] = field(default=None, metadata=field_metadata)\n generate_programmed_dates: Optional[float] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.Synthetic, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass InvoiceSpread(Instrument):\n buy_sell: Optional[BuySell] = field(default=None, metadata=field_metadata)\n notional_amount: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n underlier: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n swap: Optional[IRSwap] = field(default=None, metadata=field_metadata)\n future: Optional[IRBondFuture] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.InvoiceSpread, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CSLPython(Instrument):\n class_name: Optional[str] = field(default=None, metadata=field_metadata)\n denominated: Optional[Currency] = field(default=None, metadata=field_metadata)\n double_params: Optional[Tuple[CSLDouble, ...]] = field(default=None, metadata=field_metadata)\n date_params: Optional[Tuple[CSLDate, ...]] = field(default=None, metadata=field_metadata)\n string_params: Optional[Tuple[CSLString, ...]] = field(default=None, metadata=field_metadata)\n simple_schedule_params: Optional[Tuple[CSLSimpleSchedule, ...]] = field(default=None, metadata=field_metadata)\n schedule_params: Optional[Tuple[CSLSchedule, ...]] = field(default=None, metadata=field_metadata)\n currency_params: Optional[Tuple[CSLCurrency, ...]] = field(default=None, metadata=field_metadata)\n stock_params: Optional[Tuple[CSLStock, ...]] = field(default=None, metadata=field_metadata)\n index_params: Optional[Tuple[CSLIndex, ...]] = field(default=None, metadata=field_metadata)\n fx_cross_params: Optional[Tuple[CSLFXCross, ...]] = field(default=None, metadata=field_metadata)\n double_array_params: Optional[Tuple[CSLDoubleArray, ...]] = field(default=None, metadata=field_metadata)\n date_array_params: Optional[Tuple[CSLDateArray, ...]] = field(default=None, metadata=field_metadata)\n string_array_params: Optional[Tuple[CSLStringArray, ...]] = field(default=None, metadata=field_metadata)\n simple_schedule_array_params: Optional[Tuple[CSLSimpleScheduleArray, ...]] = field(default=None, metadata=field_metadata)\n schedule_array_params: Optional[Tuple[CSLScheduleArray, ...]] = field(default=None, metadata=field_metadata)\n currency_array_params: Optional[Tuple[CSLCurrencyArray, ...]] = field(default=None, metadata=field_metadata)\n stock_array_params: Optional[Tuple[CSLStockArray, ...]] = field(default=None, metadata=field_metadata)\n index_array_params: Optional[Tuple[CSLIndexArray, ...]] = field(default=None, metadata=field_metadata)\n fx_cross_array_params: Optional[Tuple[CSLFXCrossArray, ...]] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cross_Asset, metadata=field_metadata)\n type_: Optional[AssetType] = field(init=False, default=AssetType.CSL, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/measures.py", "content": "\"\"\"\r\nCopyright 2019 Goldman Sachs.\r\nLicensed under the Apache License, Version 2.0 (the \"License\");\r\nyou may not use this file except in compliance with the License.\r\nYou may obtain a copy of the License at\r\n\r\n http://www.apache.org/licenses/LICENSE-2.0\r\n\r\nUnless required by applicable law or agreed to in writing,\r\nsoftware distributed under the License is distributed on an\r\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\r\nKIND, either express or implied. See the License for the\r\nspecific language governing permissions and limitations\r\nunder the License.\r\n\"\"\"\r\n\r\nimport copy\r\n\r\nimport pandas as pd\r\nfrom gs_quant.common import AssetClass, CurrencyParameter, FiniteDifferenceParameter, StringParameter, \\\r\n ListOfStringParameter, ListOfNumberParameter, MapParameter\r\nfrom gs_quant.common import ParameterisedRiskMeasure, RiskMeasure\r\nfrom gs_quant.target.risk import RiskMeasureType, RiskMeasureUnit\r\n\r\n\r\nclass RiskMeasureWithCurrencyParameter(ParameterisedRiskMeasure):\r\n @property\r\n def currency(self):\r\n return self.parameters.value if self.parameters else None\r\n\r\n def __call__(self, currency=None, name=None):\r\n # hack to prevent ParameterisedRiskMeasure input into pandas LocIndexer as a callable function that returns\r\n # output for indexing (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html)\r\n if isinstance(currency, (pd.Series, pd.DataFrame)):\r\n return self\r\n\r\n clone = copy.copy(self)\r\n if name:\r\n clone.name = name\r\n\r\n if currency is None and clone.parameters is not None:\r\n currency = clone.parameters.currency\r\n\r\n param = CurrencyParameter(value=currency)\r\n clone.parameters = param\r\n return clone\r\n\r\n\r\nclass RiskMeasureWithDoubleParameter(ParameterisedRiskMeasure):\r\n @property\r\n def double(self):\r\n return self.parameters.value if self.parameters else None\r\n\r\n def __call__(self, double=None, name=None):\r\n # hack to prevent ParameterisedRiskMeasure input into pandas LocIndexer as a callable function that returns\r\n # output for indexing (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html)\r\n if isinstance(double, (pd.Series, pd.DataFrame)):\r\n return self\r\n\r\n clone = copy.copy(self)\r\n if name:\r\n clone.name = name\r\n\r\n if double is None and clone.parameters is not None:\r\n double = clone.parameters.double\r\n\r\n param = DoubleParameter(value=double)\r\n clone.parameters = param\r\n return clone\r\n\r\n\r\nclass RiskMeasureWithListOfNumberParameter(ParameterisedRiskMeasure):\r\n @property\r\n def list_of_number(self):\r\n return self.parameters.values if self.parameters else None\r\n\r\n def __call__(self, list_of_number=None, name=None):\r\n # hack to prevent ParameterisedRiskMeasure input into pandas LocIndexer as a callable function that returns\r\n # output for indexing (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html)\r\n if isinstance(list_of_number, (pd.Series, pd.DataFrame)):\r\n return self\r\n\r\n clone = copy.copy(self)\r\n if name:\r\n clone.name = name\r\n\r\n if list_of_number is None and clone.parameters is not None:\r\n list_of_number = clone.parameters.list_of_number\r\n\r\n param = ListOfNumberParameter(values=list_of_number)\r\n clone.parameters = param\r\n return clone\r\n\r\n\r\nclass RiskMeasureWithListOfStringParameter(ParameterisedRiskMeasure):\r\n @property\r\n def list_of_string(self):\r\n return self.parameters.values if self.parameters else None\r\n\r\n def __call__(self, list_of_string=None, name=None):\r\n # hack to prevent ParameterisedRiskMeasure input into pandas LocIndexer as a callable function that returns\r\n # output for indexing (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html)\r\n if isinstance(list_of_string, (pd.Series, pd.DataFrame)):\r\n return self\r\n\r\n clone = copy.copy(self)\r\n if name:\r\n clone.name = name\r\n\r\n if list_of_string is None and clone.parameters is not None:\r\n list_of_string = clone.parameters.list_of_string\r\n\r\n param = ListOfStringParameter(values=list_of_string)\r\n clone.parameters = param\r\n return clone\r\n\r\n\r\nclass RiskMeasureWithMapParameter(ParameterisedRiskMeasure):\r\n @property\r\n def map(self):\r\n return self.parameters.value if self.parameters else None\r\n\r\n def __call__(self, map=None, name=None):\r\n # hack to prevent ParameterisedRiskMeasure input into pandas LocIndexer as a callable function that returns\r\n # output for indexing (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html)\r\n if isinstance(map, (pd.Series, pd.DataFrame)):\r\n return self\r\n\r\n clone = copy.copy(self)\r\n if name:\r\n clone.name = name\r\n\r\n if map is None and clone.parameters is not None:\r\n map = clone.parameters.map\r\n\r\n param = MapParameter(value=map)\r\n clone.parameters = param\r\n return clone\r\n\r\n\r\nclass RiskMeasureWithStringParameter(ParameterisedRiskMeasure):\r\n @property\r\n def string(self):\r\n return self.parameters.value if self.parameters else None\r\n\r\n def __call__(self, string=None, name=None):\r\n # hack to prevent ParameterisedRiskMeasure input into pandas LocIndexer as a callable function that returns\r\n # output for indexing (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html)\r\n if isinstance(string, (pd.Series, pd.DataFrame)):\r\n return self\r\n\r\n clone = copy.copy(self)\r\n if name:\r\n clone.name = name\r\n\r\n if string is None and clone.parameters is not None:\r\n string = clone.parameters.string\r\n\r\n param = StringParameter(value=string)\r\n clone.parameters = param\r\n return clone\r\n\r\n\r\nclass RiskMeasureWithFiniteDifferenceParameter(ParameterisedRiskMeasure):\r\n @property\r\n def aggregation_level(self):\r\n return self.parameters.aggregation_level if self.parameters else None\r\n\r\n @property\r\n def bump_size(self):\r\n return self.parameters.bump_size if self.parameters else None\r\n\r\n @property\r\n def currency(self):\r\n return self.parameters.currency if self.parameters else None\r\n\r\n @property\r\n def finite_difference_method(self):\r\n return self.parameters.finite_difference_method if self.parameters else None\r\n\r\n @property\r\n def local_curve(self):\r\n return self.parameters.local_curve if self.parameters else None\r\n\r\n @property\r\n def mkt_marking_options(self):\r\n return self.parameters.mkt_marking_options if self.parameters else None\r\n\r\n @property\r\n def scale_factor(self):\r\n return self.parameters.scale_factor if self.parameters else None\r\n\r\n def __call__(self, aggregation_level=None, bump_size=None, currency=None, finite_difference_method=None, local_curve=None, mkt_marking_options=None, scale_factor=None, name=None):\r\n # hack to prevent ParameterisedRiskMeasure input into pandas LocIndexer as a callable function that returns\r\n # output for indexing (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html)\r\n if isinstance(aggregation_level, (pd.Series, pd.DataFrame)):\r\n return self\r\n\r\n clone = copy.copy(self)\r\n if name:\r\n clone.name = name\r\n\r\n if aggregation_level is None and clone.parameters is not None:\r\n aggregation_level = clone.parameters.aggregation_level\r\n if bump_size is None and clone.parameters is not None:\r\n bump_size = clone.parameters.bump_size\r\n if currency is None and clone.parameters is not None:\r\n currency = clone.parameters.currency\r\n if finite_difference_method is None and clone.parameters is not None:\r\n finite_difference_method = clone.parameters.finite_difference_method\r\n if local_curve is None and clone.parameters is not None:\r\n local_curve = clone.parameters.local_curve\r\n if mkt_marking_options is None and clone.parameters is not None:\r\n mkt_marking_options = clone.parameters.mkt_marking_options\r\n if scale_factor is None and clone.parameters is not None:\r\n scale_factor = clone.parameters.scale_factor\r\n\r\n param = FiniteDifferenceParameter(aggregation_level=aggregation_level,bump_size=bump_size,currency=currency,finite_difference_method=finite_difference_method,local_curve=local_curve,mkt_marking_options=mkt_marking_options,scale_factor=scale_factor)\r\n clone.parameters = param\r\n return clone\r\n\r\n\r\nAnnuity = RiskMeasureWithCurrencyParameter(name=\"Annuity\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"AnnuityLocalCcy\"))\r\nAnnuity.__doc__ = \"Annuity\"\r\n\r\nBaseCPI = RiskMeasure(name=\"BaseCPI\", measure_type=RiskMeasureType(\"BaseCPI\"))\r\nBaseCPI.__doc__ = \"Base CPI\"\r\n\r\nCDATMSpread = RiskMeasure(name=\"CDATMSpread\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"ATM Spread\"))\r\nCDATMSpread.__doc__ = \"Credit ATM Spread\"\r\n\r\nCDDelta = RiskMeasure(name=\"CDDelta\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"Delta\"))\r\nCDDelta.__doc__ = \"Credit Delta\"\r\n\r\nCDFwdSpread = RiskMeasure(name=\"CDFwdSpread\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"Forward Spread\"))\r\nCDFwdSpread.__doc__ = \"Credit Forward Spread\"\r\n\r\nCDGamma = RiskMeasure(name=\"CDGamma\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"Gamma\"))\r\nCDGamma.__doc__ = \"CDIndexGamma\"\r\n\r\nCDIForward = RiskMeasure(name=\"CDIForward\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDIForward\"))\r\nCDIForward.__doc__ = \"CDS Index Forward Quote in the Prevailing Quoting Style.\"\r\n\r\nCDIIndexDelta = RiskMeasure(name=\"CDIIndexDelta\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDIIndexDelta\"))\r\nCDIIndexDelta.__doc__ = \"CDS Index Delta.\"\r\n\r\nCDIIndexVega = RiskMeasure(name=\"CDIIndexVega\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDIIndexVega\"))\r\nCDIIndexVega.__doc__ = \"CDS Index Vega.\"\r\n\r\nCDIOptionPremium = RiskMeasure(name=\"CDIOptionPremium\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDIOptionPremium\"))\r\nCDIOptionPremium.__doc__ = \"CDS Index Option Premium\"\r\n\r\nCDIOptionPremiumFlatFwd = RiskMeasure(name=\"CDIOptionPremiumFlatFwd\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDIOptionPremiumFlatFwd\"))\r\nCDIOptionPremiumFlatFwd.__doc__ = \"CDS Index Option Premium assuming Flat Forwards.\"\r\n\r\nCDIOptionPremiumFlatVol = RiskMeasure(name=\"CDIOptionPremiumFlatVol\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDIOptionPremiumFlatVol\"))\r\nCDIOptionPremiumFlatVol.__doc__ = \"CDS Index Option Premium assuming Flat Volatilities.\"\r\n\r\nCDISpot = RiskMeasure(name=\"CDISpot\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDISpot\"))\r\nCDISpot.__doc__ = \"CDS Index Spot Quote in the Prevailing Quoting Style.\"\r\n\r\nCDISpreadDV01 = RiskMeasure(name=\"CDISpreadDV01\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDISpreadDV01\"))\r\nCDISpreadDV01.__doc__ = \"CDS Index Rates DV01.\"\r\n\r\nCDIUpfrontPrice = RiskMeasure(name=\"CDIUpfrontPrice\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"CDIUpfrontPrice\"))\r\nCDIUpfrontPrice.__doc__ = \"CDS Index Upfront Price.\"\r\n\r\nCDImpliedVolatility = RiskMeasure(name=\"CDImpliedVolatility\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"Implied Volatility\"))\r\nCDImpliedVolatility.__doc__ = \"CDImpliedVolatility\"\r\n\r\nCDIndexVega = RiskMeasure(name=\"CDIndexVega\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"Vega\"))\r\nCDIndexVega.__doc__ = \"CDIndexVega\"\r\n\r\nCDTheta = RiskMeasure(name=\"CDTheta\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"Theta\"))\r\nCDTheta.__doc__ = \"CDTheta\"\r\n\r\nCDVega = RiskMeasure(name=\"CDVega\", asset_class=AssetClass(\"Credit\"), measure_type=RiskMeasureType(\"Vega\"))\r\nCDVega.__doc__ = \"Credit Vega\"\r\n\r\nCRIFIRCurve = RiskMeasure(name=\"CRIFIRCurve\", measure_type=RiskMeasureType(\"CRIF IRCurve\"))\r\nCRIFIRCurve.__doc__ = \"CRIF IR Curve\"\r\n\r\nCashflows = RiskMeasure(name=\"Cashflows\", measure_type=RiskMeasureType(\"Cashflows\"))\r\nCashflows.__doc__ = \"Cashflows\"\r\n\r\nCommodDelta = RiskMeasure(name=\"CommodDelta\", asset_class=AssetClass(\"Commod\"), measure_type=RiskMeasureType(\"Delta\"))\r\nCommodDelta.__doc__ = \"Commod Delta\"\r\n\r\nCommodImpliedVol = RiskMeasure(name=\"CommodImpliedVol\", asset_class=AssetClass(\"Commod\"), measure_type=RiskMeasureType(\"Volatility\"))\r\nCommodImpliedVol.__doc__ = \"Commod Implied Volatility\"\r\n\r\nCommodTheta = RiskMeasure(name=\"CommodTheta\", asset_class=AssetClass(\"Commod\"), measure_type=RiskMeasureType(\"Theta\"))\r\nCommodTheta.__doc__ = \"Commod Theta\"\r\n\r\nCommodVega = RiskMeasure(name=\"CommodVega\", asset_class=AssetClass(\"Commod\"), measure_type=RiskMeasureType(\"Vega\"))\r\nCommodVega.__doc__ = \"Commod Vega\"\r\n\r\nCompoundedFixedRate = RiskMeasure(name=\"CompoundedFixedRate\", measure_type=RiskMeasureType(\"Compounded Fixed Rate\"))\r\nCompoundedFixedRate.__doc__ = \"CompoundedFixedRate\"\r\n\r\nCross = RiskMeasure(name=\"Cross\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Cross\"))\r\nCross.__doc__ = \"Cross\"\r\n\r\nCrossMultiplier = RiskMeasure(name=\"CrossMultiplier\", measure_type=RiskMeasureType(\"Cross Multiplier\"))\r\nCrossMultiplier.__doc__ = \"CrossMultiplier\"\r\n\r\nDescription = RiskMeasure(name=\"Description\", measure_type=RiskMeasureType(\"Description\"))\r\nDescription.__doc__ = \"Description\"\r\n\r\nDollarPrice = RiskMeasure(name=\"DollarPrice\", measure_type=RiskMeasureType(\"Dollar Price\"))\r\nDollarPrice.__doc__ = \"Price of the instrument in US Dollars\"\r\n\r\nEqAnnualImpliedVol = RiskMeasure(name=\"EqAnnualImpliedVol\", asset_class=AssetClass(\"Equity\"), measure_type=RiskMeasureType(\"Annual Implied Volatility\"), unit=RiskMeasureUnit(\"Percent\"))\r\nEqAnnualImpliedVol.__doc__ = \"Equity Annual Implied Volatility (%)\"\r\n\r\nEqDelta = RiskMeasureWithCurrencyParameter(name=\"EqDelta\", asset_class=AssetClass(\"Equity\"), measure_type=RiskMeasureType(\"Delta\"))\r\nEqDelta.__doc__ = \"Change in Dollar Price (USD present value) due to individual 1% move in the spot price of underlying equity security\"\r\n\r\nEqGamma = RiskMeasureWithCurrencyParameter(name=\"EqGamma\", asset_class=AssetClass(\"Equity\"), measure_type=RiskMeasureType(\"Gamma\"))\r\nEqGamma.__doc__ = \"Change in EqDelta for a 1% move in the price of the underlying equity security\"\r\n\r\nEqSpot = RiskMeasure(name=\"EqSpot\", asset_class=AssetClass(\"Equity\"), measure_type=RiskMeasureType(\"Spot\"))\r\nEqSpot.__doc__ = \"Equity Spot\"\r\n\r\nEqTheta = RiskMeasureWithCurrencyParameter(name=\"EqTheta\", asset_class=AssetClass(\"Equity\"), measure_type=RiskMeasureType(\"Theta\"))\r\nEqTheta.__doc__ = \"Change in Dollar Price over one day\"\r\n\r\nEqVega = RiskMeasureWithCurrencyParameter(name=\"EqVega\", asset_class=AssetClass(\"Equity\"), measure_type=RiskMeasureType(\"Vega\"))\r\nEqVega.__doc__ = \"Change in Dollar Price (USD present value) due to individual 1bp moves in the implied volatility of the underlying equity security\"\r\n\r\nExpiryInYears = RiskMeasure(name=\"ExpiryInYears\", measure_type=RiskMeasureType(\"ExpiryInYears\"))\r\nExpiryInYears.__doc__ = \"Time to Expiry expressed in fractional Years.\"\r\n\r\nFX25DeltaButterflyVolatility = RiskMeasure(name=\"FX25DeltaButterflyVolatility\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX BF 25 Vol\"))\r\nFX25DeltaButterflyVolatility.__doc__ = \"The volatility of a 25 delta butterfly\"\r\n\r\nFX25DeltaRiskReversalVolatility = RiskMeasure(name=\"FX25DeltaRiskReversalVolatility\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX RR 25 Vol\"))\r\nFX25DeltaRiskReversalVolatility.__doc__ = \"The volatility of a 25 delta risk reversal\"\r\n\r\nFXAnnualATMImpliedVol = RiskMeasure(name=\"FXAnnualATMImpliedVol\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Annual ATM Implied Volatility\"), unit=RiskMeasureUnit(\"Percent\"))\r\nFXAnnualATMImpliedVol.__doc__ = \"FX Annual ATM Implied Volatility\"\r\n\r\nFXAnnualImpliedVol = RiskMeasure(name=\"FXAnnualImpliedVol\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Annual Implied Volatility\"), unit=RiskMeasureUnit(\"Percent\"))\r\nFXAnnualImpliedVol.__doc__ = \"FX Annual Implied Volatility\"\r\n\r\nFXBlackScholes = RiskMeasure(name=\"FXBlackScholes\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"BSPrice\"))\r\nFXBlackScholes.__doc__ = \"FXBlackScholes\"\r\n\r\nFXBlackScholesPct = RiskMeasure(name=\"FXBlackScholesPct\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"BSPricePct\"))\r\nFXBlackScholesPct.__doc__ = \"FXBlackScholes in Percent of Notional\"\r\n\r\nFXCalcDelta = RiskMeasure(name=\"FXCalcDelta\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Calculated Delta\"))\r\nFXCalcDelta.__doc__ = \"FXCalcDelta\"\r\n\r\nFXCalcDeltaNoPremAdj = RiskMeasure(name=\"FXCalcDeltaNoPremAdj\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Calculated Delta No Premium Adjustment\"))\r\nFXCalcDeltaNoPremAdj.__doc__ = \"FXCalcDeltaNoPremAdj\"\r\n\r\nFXDelta = RiskMeasureWithFiniteDifferenceParameter(name=\"FXDelta\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Delta\"))\r\nFXDelta.__doc__ = \"Dollar Price sensitivity of the instrument to a move in the underlying spot such that dSpot * FXDelta = PnL\"\r\n\r\nFXDeltaHedge = RiskMeasure(name=\"FXDeltaHedge\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Hedge Delta\"))\r\nFXDeltaHedge.__doc__ = \"Size of the spot trade in the underlying currency needed to hedge the USD delta on a per-cross basis\"\r\n\r\nFXDiscountFactorOver = RiskMeasure(name=\"FXDiscountFactorOver\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Discount Factor Over\"))\r\nFXDiscountFactorOver.__doc__ = \"Discount Factor to Maturity in the Over Currency of the FX Pair\"\r\n\r\nFXDiscountFactorUnder = RiskMeasure(name=\"FXDiscountFactorUnder\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Discount Factor Under\"))\r\nFXDiscountFactorUnder.__doc__ = \"Discount Factor to Maturity in the Under Currency of the FX Pair\"\r\n\r\nFXFwd = RiskMeasure(name=\"FXFwd\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Forward Rate\"))\r\nFXFwd.__doc__ = \"FXFwd\"\r\n\r\nFXGamma = RiskMeasure(name=\"FXGamma\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Gamma\"))\r\nFXGamma.__doc__ = \"FXDelta sensitivity of the instrument to a move in the underlying spot such that dSpot * FXGamma = dDelta\"\r\n\r\nFXImpliedCorrelation = RiskMeasure(name=\"FXImpliedCorrelation\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Correlation\"))\r\nFXImpliedCorrelation.__doc__ = \"Correlation and Vol information\"\r\n\r\nFXPoints = RiskMeasure(name=\"FXPoints\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Points\"))\r\nFXPoints.__doc__ = \"FXPoints\"\r\n\r\nFXPremium = RiskMeasure(name=\"FXPremium\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Premium\"))\r\nFXPremium.__doc__ = \"FXPremium\"\r\n\r\nFXPremiumPct = RiskMeasure(name=\"FXPremiumPct\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Premium Pct\"))\r\nFXPremiumPct.__doc__ = \"FXPremium in Percent of Notional\"\r\n\r\nFXPremiumPctFlatFwd = RiskMeasure(name=\"FXPremiumPctFlatFwd\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Premium Pct Flat Fwd\"))\r\nFXPremiumPctFlatFwd.__doc__ = \"FXPremium in Percent of Notional in a Flat Forward environment\"\r\n\r\nFXQuotedDelta = RiskMeasure(name=\"FXQuotedDelta\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"QuotedDelta\"))\r\nFXQuotedDelta.__doc__ = \"FXQuotedDelta\"\r\n\r\nFXQuotedDeltaNoPremAdj = RiskMeasure(name=\"FXQuotedDeltaNoPremAdj\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Quoted Delta No Premium Adjustment\"))\r\nFXQuotedDeltaNoPremAdj.__doc__ = \"FXQuotedDeltaNoPremAdj\"\r\n\r\nFXQuotedVega = RiskMeasure(name=\"FXQuotedVega\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Quoted Vega\"))\r\nFXQuotedVega.__doc__ = \"FXQuotedVega\"\r\n\r\nFXQuotedVegaBps = RiskMeasure(name=\"FXQuotedVegaBps\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FX Quoted Vega Bps\"))\r\nFXQuotedVegaBps.__doc__ = \"FXQuotedVegaBps\"\r\n\r\nFXSpot = RiskMeasure(name=\"FXSpot\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Spot\"))\r\nFXSpot.__doc__ = \"FX spot reference\"\r\n\r\nFXSpotVal = RiskMeasure(name=\"FXSpotVal\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FXSpotVal\"))\r\nFXSpotVal.__doc__ = \"FXSpot in trade cross convention\"\r\n\r\nFXStrikePts = RiskMeasure(name=\"FXStrikePts\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"StrikePts\"))\r\nFXStrikePts.__doc__ = \"FXPoints in trade cross convention\"\r\n\r\nFXVega = RiskMeasureWithFiniteDifferenceParameter(name=\"FXVega\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"Vega\"))\r\nFXVega.__doc__ = \"Change in Dollar Price due to a 1 vol move in the implied volatility of ATM instruments used to build the volatility surface\"\r\n\r\nFairPremium = RiskMeasureWithCurrencyParameter(name=\"FairPremium\", measure_type=RiskMeasureType(\"FairPremium\"))\r\nFairPremium.__doc__ = \"Fair Premium is the instrument present value discounted to the premium settlement date\"\r\n\r\nFairPremiumInPercent = RiskMeasure(name=\"FairPremiumInPercent\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"FairPremiumPct\"), unit=RiskMeasureUnit(\"Percent\"))\r\nFairPremiumInPercent.__doc__ = \"The instrument present value discounted to the premium settlement excluding any embedded premium date as a percentage\"\r\n\r\nFairPrice = RiskMeasure(name=\"FairPrice\", asset_class=AssetClass(\"Commod\"), measure_type=RiskMeasureType(\"Fair Price\"))\r\nFairPrice.__doc__ = \"FairPrice\"\r\n\r\nFairVarStrike = RiskMeasure(name=\"FairVarStrike\", measure_type=RiskMeasureType(\"FairVarStrike\"))\r\nFairVarStrike.__doc__ = \"Fair Variance Strike Value of a Variance Swap\"\r\n\r\nFairVolStrike = RiskMeasure(name=\"FairVolStrike\", measure_type=RiskMeasureType(\"FairVolStrike\"))\r\nFairVolStrike.__doc__ = \"Fair Volatility Strike Value of a Variance Swap\"\r\n\r\nForwardPrice = RiskMeasure(name=\"ForwardPrice\", measure_type=RiskMeasureType(\"Forward Price\"), unit=RiskMeasureUnit(\"BPS\"))\r\nForwardPrice.__doc__ = \" Price of the instrument at expiry in the local currency\"\r\n\r\nIRAnnualATMImpliedVol = RiskMeasure(name=\"IRAnnualATMImpliedVol\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Annual ATMF Implied Volatility\"), unit=RiskMeasureUnit(\"Percent\"))\r\nIRAnnualATMImpliedVol.__doc__ = \"Interest rate annual implied at-the-money volatility (in percent)\"\r\n\r\nIRAnnualImpliedVol = RiskMeasure(name=\"IRAnnualImpliedVol\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Annual Implied Volatility\"), unit=RiskMeasureUnit(\"Percent\"))\r\nIRAnnualImpliedVol.__doc__ = \"Interest rate annual implied volatility (in percent)\"\r\n\r\nIRBasis = RiskMeasureWithFiniteDifferenceParameter(name=\"IRBasis\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Basis\"))\r\nIRBasis.__doc__ = \"Change in Dollar Price (USD present value) due to individual 1bp moves in the interest rate instruments used to build the basis curve(s)\"\r\n\r\nIRDailyImpliedVol = RiskMeasure(name=\"IRDailyImpliedVol\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Daily Implied Volatility\"), unit=RiskMeasureUnit(\"BPS\"))\r\nIRDailyImpliedVol.__doc__ = \"Interest rate daily implied volatility (in basis points)\"\r\n\r\nIRDelta = RiskMeasureWithFiniteDifferenceParameter(name=\"IRDelta\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Delta\"))\r\nIRDelta.__doc__ = \"Change in Dollar Price (USD present value) due to individual 1bp moves in the interest rate instruments used to build the underlying discount curve\"\r\n\r\nIRDiscountDeltaParallel = RiskMeasure(name=\"IRDiscountDeltaParallel\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"ParallelDiscountDelta\"))\r\nIRDiscountDeltaParallel.__doc__ = \"Parallel Discount Delta\"\r\n\r\nIRDiscountDeltaParallelLocalCcy = RiskMeasure(name=\"IRDiscountDeltaParallelLocalCcy\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"ParallelDiscountDeltaLocalCcy\"))\r\nIRDiscountDeltaParallelLocalCcy.__doc__ = \"Parallel Discount Delta (Local Ccy)\"\r\n\r\nIRFwdRate = RiskMeasure(name=\"IRFwdRate\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Forward Rate\"), unit=RiskMeasureUnit(\"Percent\"))\r\nIRFwdRate.__doc__ = \"Interest rate par rate (in percent)\"\r\n\r\nIRGamma = RiskMeasure(name=\"IRGamma\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Gamma\"))\r\nIRGamma.__doc__ = \"IRGamma\"\r\n\r\nIRGammaParallel = RiskMeasure(name=\"IRGammaParallel\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"ParallelGamma\"))\r\nIRGammaParallel.__doc__ = \"Change in aggregated IRDelta for a aggregated 1bp shift in the interest rate instruments used to build the underlying discount curve\"\r\n\r\nIRGammaParallelLocalCcy = RiskMeasure(name=\"IRGammaParallelLocalCcy\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"ParallelGammaLocalCcy\"))\r\nIRGammaParallelLocalCcy.__doc__ = \"Interest Rate Parallel Gamma (Local Ccy)\"\r\n\r\nIRSpotRate = RiskMeasure(name=\"IRSpotRate\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Spot Rate\"), unit=RiskMeasureUnit(\"Percent\"))\r\nIRSpotRate.__doc__ = \"Interest rate at-the-money spot rate (in percent)\"\r\n\r\nIRVanna = RiskMeasure(name=\"IRVanna\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Vanna\"))\r\nIRVanna.__doc__ = \"Interest Rate Vanna (USD)\"\r\n\r\nIRVega = RiskMeasureWithFiniteDifferenceParameter(name=\"IRVega\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Vega\"))\r\nIRVega.__doc__ = \"Change in Dollar Price (USD present value) due to individual 1bp moves in the implied volatility (IRAnnualImpliedVol) of instruments used to build the volatility surface\"\r\n\r\nIRVolga = RiskMeasure(name=\"IRVolga\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Volga\"))\r\nIRVolga.__doc__ = \"Interest Rate Volga (USD)\"\r\n\r\nIRXccyDelta = RiskMeasureWithFiniteDifferenceParameter(name=\"IRXccyDelta\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"XccyDelta\"))\r\nIRXccyDelta.__doc__ = \"Change in Price due to 1bp move in cross currency rates.\"\r\n\r\nInflDeltaParallelLocalCcyInBps = RiskMeasure(name=\"InflDeltaParallelLocalCcyInBps\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Inflation Delta in Bps\"))\r\nInflDeltaParallelLocalCcyInBps.__doc__ = \"Inflation Delta\"\r\n\r\nInflMaturityCPI = RiskMeasure(name=\"InflMaturityCPI\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"FinalCPI\"))\r\nInflMaturityCPI.__doc__ = \"InflMaturityCPI\"\r\n\r\nInfl_CompPeriod = RiskMeasure(name=\"Infl_CompPeriod\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Inflation Compounding Period\"))\r\nInfl_CompPeriod.__doc__ = \"Infl_CompPeriod\"\r\n\r\nInflationDelta = RiskMeasureWithFiniteDifferenceParameter(name=\"InflationDelta\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"InflationDelta\"))\r\nInflationDelta.__doc__ = \"Change in Price due to 1bp move in inflation curve.\"\r\n\r\nLightningDV01 = RiskMeasure(name=\"LightningDV01\", measure_type=RiskMeasureType(\"DV01\"))\r\nLightningDV01.__doc__ = \"LightningDV01\"\r\n\r\nLightningOAS = RiskMeasure(name=\"LightningOAS\", measure_type=RiskMeasureType(\"OAS\"))\r\nLightningOAS.__doc__ = \"LightningOAS\"\r\n\r\nLocalAnnuityInCents = RiskMeasure(name=\"LocalAnnuityInCents\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Local Currency Accrual in Cents\"))\r\nLocalAnnuityInCents.__doc__ = \"Local Currency Accrual in Cents\"\r\n\r\nMarket = RiskMeasure(name=\"Market\", measure_type=RiskMeasureType(\"Market\"))\r\nMarket.__doc__ = \"Market\"\r\n\r\nMarketData = RiskMeasure(name=\"MarketData\", measure_type=RiskMeasureType(\"Market Data\"))\r\nMarketData.__doc__ = \"Market Data\"\r\n\r\nMarketDataAssets = RiskMeasure(name=\"MarketDataAssets\", measure_type=RiskMeasureType(\"Market Data Assets\"))\r\nMarketDataAssets.__doc__ = \"MarketDataAssets\"\r\n\r\nNonUSDOisDomRate = RiskMeasure(name=\"NonUSDOisDomRate\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"NonUSDOisDomesticRate\"))\r\nNonUSDOisDomRate.__doc__ = \"NonUSDOisDomRate\"\r\n\r\nOisFXSprExSpkRate = RiskMeasure(name=\"OisFXSprExSpkRate\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"OisFXSpreadRateExcludingSpikes\"))\r\nOisFXSprExSpkRate.__doc__ = \"OisFXSprExSpkRate\"\r\n\r\nOisFXSprRate = RiskMeasure(name=\"OisFXSprRate\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"OisFXSpreadRate\"))\r\nOisFXSprRate.__doc__ = \"OisFXSprRate\"\r\n\r\nParSpread = RiskMeasure(name=\"ParSpread\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Spread\"))\r\nParSpread.__doc__ = \"Par Spread\"\r\n\r\nPremiumCents = RiskMeasure(name=\"PremiumCents\", asset_class=AssetClass(\"Rates\"), measure_type=RiskMeasureType(\"Premium In Cents\"))\r\nPremiumCents.__doc__ = \"PremiumCents\"\r\n\r\nPremiumSummary = RiskMeasure(name=\"PremiumSummary\", asset_class=AssetClass(\"Commod\"), measure_type=RiskMeasureType(\"Premium\"))\r\nPremiumSummary.__doc__ = \"PremiumSummary\"\r\n\r\nPrice = RiskMeasureWithCurrencyParameter(name=\"Price\", measure_type=RiskMeasureType(\"PV\"))\r\nPrice.__doc__ = \"Present Value\"\r\n\r\nPricePips = RiskMeasureWithCurrencyParameter(name=\"PricePips\", measure_type=RiskMeasureType(\"Price\"), unit=RiskMeasureUnit(\"Pips\"))\r\nPricePips.__doc__ = \"Present value in pips\"\r\n\r\nProbabilityOfExercise = RiskMeasure(name=\"ProbabilityOfExercise\", measure_type=RiskMeasureType(\"Probability Of Exercise\"))\r\nProbabilityOfExercise.__doc__ = \"Probability Of Exercise\"\r\n\r\nRFRFXRate = RiskMeasure(name=\"RFRFXRate\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"RFRFXRate\"))\r\nRFRFXRate.__doc__ = \"RFRFXRate\"\r\n\r\nRFRFXSprExSpkRate = RiskMeasure(name=\"RFRFXSprExSpkRate\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"RFRFXSpreadRateExcludingSpikes\"))\r\nRFRFXSprExSpkRate.__doc__ = \"RFRFXSprExSpkRate\"\r\n\r\nRFRFXSprRate = RiskMeasure(name=\"RFRFXSprRate\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"RFRFXSpreadRate\"))\r\nRFRFXSprRate.__doc__ = \"RFRFXSprRate\"\r\n\r\nResolvedInstrumentValues = RiskMeasure(name=\"ResolvedInstrumentValues\", measure_type=RiskMeasureType(\"Resolved Instrument Values\"))\r\nResolvedInstrumentValues.__doc__ = \"Resolved InstrumentBase Values\"\r\n\r\nTheta = RiskMeasure(name=\"Theta\", measure_type=RiskMeasureType(\"Theta\"))\r\nTheta.__doc__ = \"Theta\"\r\n\r\nUSDOisDomRate = RiskMeasure(name=\"USDOisDomRate\", asset_class=AssetClass(\"FX\"), measure_type=RiskMeasureType(\"USDOisDomesticRate\"))\r\nUSDOisDomRate.__doc__ = \"USDOisDomRate\"\r\n\r\n" }, { "path": "gs_quant/target/monitor.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass AvailableUnitTypes(EnumBase, Enum): \n \n \"\"\"Enum listing supported unit types\"\"\"\n\n percentage = 'percentage'\n percentageWithSymbol = 'percentageWithSymbol'\n bps = 'bps'\n bp = 'bp'\n x = 'x' \n\n\nclass EntitiesSupported(EnumBase, Enum): \n \n \"\"\"Enum listing supported entities\"\"\"\n\n assets = 'assets'\n tds = 'tds' \n\n\nclass ParameterPeriod(EnumBase, Enum): \n \n \"\"\"Enum listing supported parameter periods\"\"\"\n\n _1d = '1d'\n _1w = '1w'\n _1m = '1m'\n _3m = '3m'\n _6m = '6m'\n _1y = '1y'\n _2y = '2y'\n _5y = '5y'\n _10y = '10y'\n _30y = '30y'\n mtd = 'mtd'\n ytd = 'ytd' \n\n\nclass ParameterRender(EnumBase, Enum): \n \n \"\"\"Enum listing supported column definition render types\"\"\"\n\n bar = 'bar'\n boxplot = 'boxplot'\n chart = 'chart'\n color = 'color'\n default = 'default'\n direction = 'direction'\n heatmap = 'heatmap'\n hidden = 'hidden'\n multiColumnHeatmap = 'multiColumnHeatmap'\n progress = 'progress'\n range = 'range'\n scale = 'scale'\n simpleCandlestick = 'simpleCandlestick'\n sparkline = 'sparkline'\n stackedBarChart = 'stackedBarChart'\n text = 'text'\n treemap = 'treemap'\n triColor = 'triColor' \n\n\nclass RateIds(EnumBase, Enum): \n \n \"\"\"Enum listing supported rate ids\"\"\"\n\n USD = 'USD'\n EUR = 'EUR'\n JPY = 'JPY'\n GBP = 'GBP'\n CAD = 'CAD'\n AUD = 'AUD' \n\n\nclass SortDirection(EnumBase, Enum): \n \n \"\"\"Enum with available sort directions\"\"\"\n\n asc = 'asc'\n desc = 'desc'\n default = 'default' \n\n\nclass SortType(EnumBase, Enum): \n \n \"\"\"Enum listing supported sort types\"\"\"\n\n value = 'value'\n abs = 'abs' \n\n\nclass WipiFilterOperation(EnumBase, Enum): \n \n \"\"\"Enum listing supported operations for wipi filters.\"\"\"\n\n eq = 'eq'\n ne = 'ne'\n gt = 'gt'\n lt = 'lt'\n gte = 'gte'\n lte = 'lte'\n last = 'last' \n\n\nclass WipiFilterType(EnumBase, Enum): \n \n \"\"\"Enum listing supported wipi filter types.\"\"\"\n\n AND = 'AND'\n OR = 'OR' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ColumnProperty(Base):\n column_name: Optional[str] = field(default=None, metadata=field_metadata)\n property_: Optional[str] = field(default=None, metadata=config(field_name='property', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nclass FieldMap(DictBase):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Historical(Base):\n value: Optional[Union[float, str]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MonitorResponseData(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n result: DictBase = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Movers(Base):\n column_name: str = field(default=None, metadata=field_metadata)\n top: Optional[float] = field(default=None, metadata=field_metadata)\n bottom: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ColumnFormat(Base):\n precision: float = field(default=None, metadata=field_metadata)\n unit: Optional[AvailableUnitTypes] = field(default=None, metadata=field_metadata)\n human_readable: Optional[bool] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n axis_key: Optional[str] = field(default=None, metadata=field_metadata)\n show_tooltip: Optional[bool] = field(default=None, metadata=field_metadata)\n low_color: Optional[str] = field(default=None, metadata=field_metadata)\n high_color: Optional[str] = field(default=None, metadata=field_metadata)\n mid_color: Optional[str] = field(default=None, metadata=field_metadata)\n low_value: Optional[float] = field(default=None, metadata=field_metadata)\n high_value: Optional[float] = field(default=None, metadata=field_metadata)\n mid_value: Optional[float] = field(default=None, metadata=field_metadata)\n hide_value: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ColumnMappings(Base):\n column_name: Optional[str] = field(default=None, metadata=field_metadata)\n parameters: Optional[FieldMap] = field(default=None, metadata=field_metadata)\n color: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ColumnOperation(Base):\n column_names: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n function_name: Optional[str] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n parameters: Optional[FieldMap] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ExportParameters(Base):\n tokens: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n fields: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n label: Optional[str] = field(default=None, metadata=field_metadata)\n start_date: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Function(Base):\n measure: str = field(default=None, metadata=field_metadata)\n frequency: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n start_date: Optional[str] = field(default=None, metadata=field_metadata)\n end_date: Optional[str] = field(default=None, metadata=field_metadata)\n start_time: Optional[str] = field(default=None, metadata=field_metadata)\n end_time: Optional[str] = field(default=None, metadata=field_metadata)\n should_use_search_until: Optional[bool] = field(default=None, metadata=field_metadata)\n fields: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n parameters: Optional[FieldMap] = field(default=None, metadata=field_metadata)\n where: Optional[FieldMap] = field(default=None, metadata=field_metadata)\n vendor: Optional[str] = field(default=None, metadata=field_metadata)\n data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RateRow(Base):\n period: ParameterPeriod = field(default=None, metadata=field_metadata)\n last: float = field(default=None, metadata=field_metadata)\n change: float = field(default=None, metadata=field_metadata)\n std: float = field(default=None, metadata=field_metadata)\n slope: float = field(default=None, metadata=field_metadata)\n historical: Optional[Historical] = field(default=None, metadata=field_metadata)\n percentage_change: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Sort(Base):\n column_name: str = field(default=None, metadata=field_metadata)\n type_: Optional[SortType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n direction: Optional[SortDirection] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WipiRequestFilter(Base):\n column: str = field(default=None, metadata=field_metadata)\n operation: WipiFilterOperation = field(default=None, metadata=field_metadata)\n value: Union[float, str] = field(default=None, metadata=field_metadata)\n type_: Optional[WipiFilterType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ColumnDefinition(Base):\n render: ParameterRender = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n enable_cell_flashing: Optional[bool] = field(default=None, metadata=field_metadata)\n entity_property: Optional[str] = field(default=None, metadata=field_metadata)\n function: Optional[Function] = field(default=None, metadata=field_metadata)\n format_: Optional[ColumnFormat] = field(default=None, metadata=config(field_name='format', exclude=exclude_none))\n width: Optional[float] = field(default=None, metadata=field_metadata)\n column_property: Optional[ColumnProperty] = field(default=None, metadata=field_metadata)\n column_operation: Optional[ColumnOperation] = field(default=None, metadata=field_metadata)\n expression: Optional[str] = field(default=None, metadata=field_metadata)\n expressions: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n start_date: Optional[str] = field(default=None, metadata=field_metadata)\n end_date: Optional[str] = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n parent_column_name: Optional[str] = field(default=None, metadata=field_metadata)\n primary: Optional[bool] = field(default=None, metadata=field_metadata)\n pivots: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n disable_cell_tooltips: Optional[bool] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass EntityId(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n column_mappings: Optional[Tuple[ColumnMappings, ...]] = field(default=None, metadata=field_metadata)\n color: Optional[str] = field(default=None, metadata=field_metadata)\n route_url: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RatesResponseData(Base):\n name: RateIds = field(default=None, metadata=field_metadata)\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n rows: Tuple[RateRow, ...] = field(default=None, metadata=field_metadata)\n libor_id: Optional[str] = field(default=None, metadata=config(field_name='libor_id', exclude=exclude_none))\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RowGroup(Base):\n name: str = field(default=None, metadata=field_metadata)\n entity_ids: Tuple[EntityId, ...] = field(default=None, metadata=field_metadata)\n movers: Optional[Movers] = field(default=None, metadata=field_metadata)\n sort: Optional[Sort] = field(default=None, metadata=field_metadata)\n export: Optional[ExportParameters] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MonitorParameters(Base):\n column_definitions: Tuple[ColumnDefinition, ...] = field(default=None, metadata=field_metadata)\n row_groups: Tuple[RowGroup, ...] = field(default=None, metadata=field_metadata)\n export: Optional[ExportParameters] = field(default=None, metadata=field_metadata)\n ignore_business_day_logic: Optional[bool] = field(default=None, metadata=field_metadata)\n horizontal_scroll: Optional[bool] = field(default=None, metadata=field_metadata)\n mid_value_average: Optional[bool] = field(default=None, metadata=field_metadata)\n aggregate_queries: Optional[bool] = field(default=None, metadata=field_metadata)\n row_heatmap: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Monitor(Base):\n name: str = field(default=None, metadata=field_metadata)\n type_: EntitiesSupported = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n parameters: Optional[MonitorParameters] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n folder_name: Optional[str] = field(default=None, metadata=field_metadata)\n polling_time: Optional[float] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/portfolios.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nfrom gs_quant.target.reports import Report\n\n\nclass ActiveWeightType(EnumBase, Enum): \n \n \"\"\"Weight type used to calculate active holdings.\"\"\"\n\n Net = 'Net'\n Gross = 'Gross' \n\n\nclass ClientPositionFilter(EnumBase, Enum): \n \n \"\"\"Filter used to select client positions from GRDB. 'oeId' selects all positions\n associated with the provided oeId. 'oeIdOrClientAccounts' selects\n positions associated with their the provided oeId or ClientAccounts.\n 'clientAccounts' selects positions only associated with those provided\n accounts.\"\"\"\n\n oeId = 'oeId'\n clientAccounts = 'clientAccounts'\n oeIdOrClientAccounts = 'oeIdOrClientAccounts' \n\n\nclass PortfolioType(EnumBase, Enum): \n \n \"\"\"Portfolio type differentiates the portfolio categorization\"\"\"\n\n Securities_Lending = 'Securities Lending'\n Draft_Portfolio = 'Draft Portfolio'\n Draft_Bond = 'Draft Bond'\n PCO_Portfolio = 'PCO Portfolio'\n PCO_Share_Class = 'PCO Share Class' \n\n\nclass RefreshInterval(EnumBase, Enum): \n \n \"\"\"These intervals determine how often a portfolio is refreshed\"\"\"\n\n Daily = 'Daily'\n Start_Of_Week = 'Start Of Week'\n End_Of_Week = 'End Of Week'\n Start_Of_Month = 'Start Of Month'\n End_Of_Month = 'End Of Month' \n\n\nclass RiskAumSource(EnumBase, Enum): \n \n \"\"\"Source of AUM for portfolio risk calculations.\"\"\"\n\n Gross = 'Gross'\n Long = 'Long'\n Short = 'Short'\n Custom_AUM = 'Custom AUM'\n Net = 'Net' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecDbBookDetail(Base):\n book_id: Optional[str] = field(default=None, metadata=field_metadata)\n book_type: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CreditPreTradePortfolioParameters(Base):\n date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n reference_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass GRDBPortfolioParameters(Base):\n oe_id: str = field(default=None, metadata=field_metadata)\n client_name: str = field(default=None, metadata=field_metadata)\n increment: str = field(default=None, metadata=field_metadata)\n risk_packages: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n enabled: str = field(default=None, metadata=field_metadata)\n is_live: str = field(default=None, metadata=field_metadata)\n client_account_names: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n oasis_account_names: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='OasisAccountNames', exclude=exclude_none))\n client_position_filter: Optional[ClientPositionFilter] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PCOTrade(Base):\n fill_rate: Optional[str] = field(default=None, metadata=field_metadata)\n include_in_hedge: Optional[bool] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n spot_ref: Optional[str] = field(default=None, metadata=field_metadata)\n notes: Optional[str] = field(default=None, metadata=field_metadata)\n creation_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n base_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n quote_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n base_currency_notional: Optional[str] = field(default=None, metadata=field_metadata)\n quote_currency_notional: Optional[str] = field(default=None, metadata=field_metadata)\n trade_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n fixing_ref: Optional[str] = field(default=None, metadata=field_metadata)\n side: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TemporalPortfolioParameters(Base):\n return_type: Optional[ReturnType] = field(default=None, metadata=field_metadata)\n refresh_interval: Optional[RefreshInterval] = field(default=None, metadata=field_metadata)\n active_weight_type: Optional[ActiveWeightType] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PCOPortfolioParameters(Base):\n base_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n local_currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n fund_calendar: Optional[str] = field(default=None, metadata=field_metadata)\n calculation_currency: Optional[PCOCurrencyType] = field(default=None, metadata=field_metadata)\n hedge_settlement_interval: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\n hedge_settlement_day: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\n roll_horizon: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\n pnl_currency: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\n nav_publication_period: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\n roll_date_zero_threshold: Optional[bool] = field(default=None, metadata=field_metadata)\n unrealised_mark_to_market: Optional[PCOUnrealisedMarkToMarket] = field(default=None, metadata=field_metadata)\n target_deviation: Optional[Tuple[PCOTargetDeviation, ...]] = field(default=None, metadata=field_metadata)\n cash_balances: Optional[Tuple[PCOCashBalance, ...]] = field(default=None, metadata=field_metadata)\n exposure: Optional[PCOExposure] = field(default=None, metadata=field_metadata)\n pco_share_class: Optional[PCOShareClass] = field(default=None, metadata=field_metadata)\n settlements: Optional[Tuple[PCOSettlements, ...]] = field(default=None, metadata=field_metadata)\n show_cash: Optional[bool] = field(default=None, metadata=field_metadata)\n show_exposure: Optional[bool] = field(default=None, metadata=field_metadata)\n enable_rfq: Optional[bool] = field(default=None, metadata=config(field_name='enableRFQ', exclude=exclude_none))\n fixing_descriptions: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n pco_origin: Optional[PCOOrigin] = field(default=None, metadata=field_metadata)\n version: Optional[str] = field(default=None, metadata=field_metadata)\n trades: Optional[Tuple[PCOTrade, ...]] = field(default=None, metadata=field_metadata)\n investment_ratio: Optional[str] = field(default=None, metadata=field_metadata)\n roll_currency: Optional[Tuple[PCOParameterValues, ...]] = field(default=None, metadata=field_metadata)\n param_version: Optional[str] = field(default=None, metadata=field_metadata)\n security_breakdown: Optional[PCOSecurityBreakdown] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Portfolio(Base):\n currency: Currency = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n identifiers: Optional[Tuple[Identifier, ...]] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n report_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n scenario_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n short_name: Optional[str] = field(default=None, metadata=field_metadata)\n underlying_portfolio_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n type_: Optional[PortfolioType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n parameters: Optional[Union[CreditPreTradePortfolioParameters, GRDBPortfolioParameters, LiquidityRequest, PCOPortfolioParameters, TemporalPortfolioParameters, Tuple[SecDbBookDetail, ...]]] = field(default=None, metadata=field_metadata)\n aum_source: Optional[RiskAumSource] = field(default=None, metadata=field_metadata)\n tag_name_hierarchy: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PortfolioTree(Base):\n tag_name: Optional[str] = field(default=None, metadata=field_metadata)\n tag_value: Optional[str] = field(default=None, metadata=field_metadata)\n leaf_tag_name: Optional[str] = field(default=None, metadata=field_metadata)\n reports: Optional[Tuple[Report, ...]] = field(default=None, metadata=field_metadata)\n # Using forward reference via string instead of Lazy annotations because latter doesn't work very well with\n # dataclasses - https://bugs.python.org/issue39442\n sub_portfolios: Optional[Tuple['PortfolioTree', ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/positions_v2_pricing.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PositionsPricingParameters(Base):\n currency: str = field(default=None, metadata=field_metadata)\n weighting_strategy: str = field(default=None, metadata=field_metadata)\n carryover_positions_for_missing_dates: Optional[bool] = field(default=False, metadata=field_metadata)\n should_reweight: Optional[bool] = field(default=False, metadata=field_metadata)\n allow_fractional_shares: Optional[bool] = field(default=True, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PositionsPricingRequest(Base):\n parameters: PositionsPricingParameters = field(default=None, metadata=field_metadata)\n position_sets: Tuple[PositionSetRequest, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/price.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass BasketPricingEngine(EnumBase, Enum): \n \n \"\"\"Pricing engine or baskets\"\"\"\n\n Solactive = 'Solactive'\n Midas = 'Midas' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PositionPriceResponse(Base):\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\n reference_weight: Optional[float] = field(default=None, metadata=field_metadata)\n weight: Optional[float] = field(default=None, metadata=field_metadata)\n fx_spot: Optional[float] = field(default=None, metadata=field_metadata)\n spot: Optional[float] = field(default=None, metadata=field_metadata)\n multiplier: Optional[float] = field(default=None, metadata=field_metadata)\n notional: Optional[float] = field(default=None, metadata=field_metadata)\n market_cap: Optional[float] = field(default=None, metadata=field_metadata)\n borrow_cost: Optional[float] = field(default=None, metadata=field_metadata)\n hard_to_borrow: Optional[bool] = field(default=None, metadata=field_metadata)\n composite5_day_adv: Optional[float] = field(default=None, metadata=field_metadata)\n composite10_day_adv: Optional[float] = field(default=None, metadata=field_metadata)\n composite22_day_adv: Optional[float] = field(default=None, metadata=field_metadata)\n adv5_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n adv10_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n adv22_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n median_daily_volume22_day: Optional[float] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[PositionTag, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PriceParameters(Base):\n currency: Currency = field(default=None, metadata=field_metadata)\n asset_data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n divisor: Optional[float] = field(default=None, metadata=field_metadata)\n fractional_shares: Optional[bool] = field(default=False, metadata=field_metadata)\n price_regardless_of_assets_missing_prices: Optional[bool] = field(default=False, metadata=field_metadata)\n frequency: Optional[MarketDataFrequency] = field(default=None, metadata=field_metadata)\n fx_data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n initial_price: Optional[float] = field(default=None, metadata=field_metadata)\n target_notional: Optional[float] = field(default=None, metadata=field_metadata)\n pricing_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n fallback_date: Optional[str] = field(default=None, metadata=field_metadata)\n vendor: Optional[MarketDataVendor] = field(default=None, metadata=field_metadata)\n use_unadjusted_close_price: Optional[bool] = field(default=False, metadata=field_metadata)\n use_exchange_currency: Optional[bool] = field(default=False, metadata=field_metadata)\n weighting_strategy: Optional[PositionSetWeightingStrategy] = field(default=None, metadata=field_metadata)\n notional_type: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PositionSetPriceInput(Base):\n positions: Tuple[PositionPriceInput, ...] = field(default=None, metadata=field_metadata)\n parameters: PriceParameters = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PositionSetPriceResponse(Base):\n positions: Optional[Tuple[PositionPriceResponse, ...]] = field(default=None, metadata=field_metadata)\n divisor: Optional[float] = field(default=None, metadata=field_metadata)\n initial_price: Optional[float] = field(default=None, metadata=field_metadata)\n target_notional: Optional[float] = field(default=None, metadata=field_metadata)\n actual_notional: Optional[float] = field(default=None, metadata=field_metadata)\n long_notional: Optional[float] = field(default=None, metadata=field_metadata)\n short_notional: Optional[float] = field(default=None, metadata=field_metadata)\n gross_notional: Optional[float] = field(default=None, metadata=field_metadata)\n net_notional: Optional[float] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n error_message: Optional[str] = field(default=None, metadata=field_metadata)\n asset_ids_missing_prices: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_ids_missing_fx_fixings: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_ids_missing_market_caps: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n unsupported_currencies: Optional[Tuple[Currency, ...]] = field(default=None, metadata=field_metadata)\n asset_ids_missing_multiplier: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n pricing_engine: Optional[BasketPricingEngine] = field(default=None, metadata=field_metadata)\n pricing_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/reports.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass PositionSourceType(EnumBase, Enum): \n \n \"\"\"Source object for position data\"\"\"\n\n Portfolio = 'Portfolio'\n Asset = 'Asset'\n Backtest = 'Backtest'\n RiskRequest = 'RiskRequest'\n Hedge = 'Hedge' \n\n\nclass ReportGenerateTemplateId(EnumBase, Enum): \n \n \"\"\"The Report Template ID to generate the report from.\"\"\"\n\n analytics_factor_attribution_pdf = 'analytics-factor-attribution-pdf'\n analytics_factor_risk_pdf = 'analytics-factor-risk-pdf' \n\n\nclass ReportMeasures(EnumBase, Enum): \n \n \"\"\"Enums for measures to be outputted for the report\"\"\"\n\n _ = ''\n pnl = 'pnl'\n longExposure = 'longExposure'\n shortExposure = 'shortExposure'\n assetCount = 'assetCount'\n turnover = 'turnover'\n assetCountLong = 'assetCountLong'\n assetCountShort = 'assetCountShort'\n netExposure = 'netExposure'\n grossExposure = 'grossExposure'\n tradingPnl = 'tradingPnl'\n tradingCostPnl = 'tradingCostPnl'\n servicingCostLongPnl = 'servicingCostLongPnl'\n servicingCostShortPnl = 'servicingCostShortPnl'\n exposure = 'exposure'\n sensitivity = 'sensitivity'\n mctr = 'mctr'\n annualizedMCTRisk = 'annualizedMCTRisk'\n annualizedRMCTRisk = 'annualizedRMCTRisk'\n poRisk = 'poRisk'\n price = 'price'\n basePrice = 'basePrice' \n\n\nclass ReportStatus(EnumBase, Enum): \n \n \"\"\"Status of report run\"\"\"\n\n new = 'new'\n ready = 'ready'\n executing = 'executing'\n calculating = 'calculating'\n done = 'done'\n error = 'error'\n cancelled = 'cancelled'\n waiting = 'waiting'\n queued = 'queued' \n\n\nclass ReportType(EnumBase, Enum): \n \n \"\"\"Type of report to execute\"\"\"\n\n Portfolio_Performance_Analytics = 'Portfolio Performance Analytics'\n Portfolio_Factor_Risk = 'Portfolio Factor Risk'\n Portfolio_Aging = 'Portfolio Aging'\n Portfolio_Thematic_Analytics = 'Portfolio Thematic Analytics'\n Asset_Factor_Risk = 'Asset Factor Risk'\n Asset_Thematic_Analytics = 'Asset Thematic Analytics'\n Basket_Create = 'Basket Create'\n Basket_Backcast = 'Basket Backcast'\n Basket_Rebalance_Auto_Approval = 'Basket Rebalance Auto Approval'\n Scenario = 'Scenario'\n Iselect_Backtest = 'Iselect Backtest'\n Backtest_Run = 'Backtest Run'\n Analytics = 'Analytics'\n Risk_Calculation = 'Risk Calculation'\n Factor_Overview_Email = 'Factor Overview Email'\n FactSet_Fundamentals_Analytics = 'FactSet Fundamentals Analytics'\n PCO = 'PCO'\n Systematic_Hedging = 'Systematic Hedging' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AumTimeseriesDataPoint(Base):\n date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n aum: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AumTimeseries(Base):\n data: Optional[Tuple[AumTimeseriesDataPoint, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ParametersOverrides(Base):\n csa_term: Optional[str] = field(default=None, metadata=field_metadata)\n pricing_location: Optional[PricingLocation] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ReportGenerateRequest(Base):\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n report_id: Optional[str] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n template_id: Optional[ReportGenerateTemplateId] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ReportRescheduleRequest(Base):\n report_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ReportWithParametersOverrides(Base):\n report_id: Optional[str] = field(default=None, metadata=field_metadata)\n parameters: Optional[ParametersOverrides] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ReportToggleEntityRequest(Base):\n type_: Optional[str] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n user: Optional[User] = field(default=None, metadata=field_metadata)\n is_delete: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Report(Base):\n position_source_id: str = field(default=None, metadata=field_metadata)\n position_source_type: PositionSourceType = field(default=None, metadata=field_metadata)\n type_: ReportType = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n parameters: ReportParameters = field(default=None, metadata=field_metadata)\n calculation_time: Optional[float] = field(default=None, metadata=field_metadata)\n data_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n earliest_start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n measures: Optional[Tuple[ReportMeasures, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n status: Optional[ReportStatus] = field(default=None, metadata=field_metadata)\n latest_execution_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n latest_end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n percentage_complete: Optional[float] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ReportBatchScheduleRequest(Base):\n reports: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n reports_with_parameters_overrides: Optional[Tuple[ReportWithParametersOverrides, ...]] = field(default=None, metadata=field_metadata)\n time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n use_close_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n parameters: Optional[ReportParameters] = field(default=None, metadata=field_metadata)\n priority: Optional[ReportJobPriority] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ReportJob(Base):\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n use_close_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n elapsed_time: Optional[float] = field(default=None, metadata=field_metadata)\n percentage_complete: Optional[float] = field(default=None, metadata=field_metadata)\n execution_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n measures: Optional[Tuple[ReportMeasures, ...]] = field(default=None, metadata=field_metadata)\n parameters: Optional[ReportParameters] = field(default=None, metadata=field_metadata)\n parent_id: Optional[str] = field(default=None, metadata=field_metadata)\n position_source_id: Optional[str] = field(default=None, metadata=field_metadata)\n position_source_type: Optional[PositionSourceType] = field(default=None, metadata=field_metadata)\n report_id: Optional[str] = field(default=None, metadata=field_metadata)\n report_type: Optional[ReportType] = field(default=None, metadata=field_metadata)\n status: Optional[ReportStatus] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n report_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n priority: Optional[ReportJobPriority] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/risk.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass FactorRiskTableMode(EnumBase, Enum): \n \n \"\"\"View the data in the tables endpoint in the specified mode.\"\"\"\n\n Exposure = 'Exposure'\n ZScore = 'ZScore'\n Pnl = 'Pnl'\n Mctr = 'Mctr' \n\n\nclass FactorScenarioType(EnumBase, Enum): \n \n \"\"\"Marquee factor scenario type\"\"\"\n\n Factor_Shock = 'Factor Shock'\n Factor_Historical_Simulation = 'Factor Historical Simulation' \n\n\nclass OptimizationStatus(EnumBase, Enum): \n \n \"\"\"Optimization status.\"\"\"\n\n Running = 'Running'\n Completed = 'Completed' \n\n\nclass OptimizationType(EnumBase, Enum): \n \n \"\"\"Pretrade optimization algorithm type.\"\"\"\n\n APEX = 'APEX' \n\n\nclass OptimizationUrgency(EnumBase, Enum): \n \n \"\"\"Parameter which controls the urgency of executing the basket from very low to\n very high. Very High urgency tilts the schedule towards the benchmark,\n whereas Very Low would minimise cost, carrying a relatively higher risk\n to the benchmark.\"\"\"\n\n VERY_LOW = 'VERY_LOW'\n LOW = 'LOW'\n MEDIUM = 'MEDIUM'\n HIGH = 'HIGH'\n VERY_HIGH = 'VERY_HIGH' \n\n\nclass RiskViewsUnit(EnumBase, Enum): \n \n \"\"\"View data return type.\"\"\"\n\n Notional = 'Notional'\n Percent = 'Percent' \n\n\nclass ShockUnit(EnumBase, Enum): \n \n \"\"\"The unit of the shocks passed in.\"\"\"\n\n percent = 'percent'\n standardDeviation = 'standardDeviation' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AdvCurveTick(Base):\n date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n value: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ExecutionCostForHorizon(Base):\n minutes_expired: Optional[int] = field(default=None, metadata=field_metadata)\n execution_cost: Optional[float] = field(default=None, metadata=field_metadata)\n execution_cost_long: Optional[float] = field(default=None, metadata=field_metadata)\n execution_cost_short: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorHistoricalSimulationScenarioParameters(Base):\n start_date: datetime.date = field(default=None, metadata=field_metadata)\n end_date: datetime.date = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorShock(Base):\n factor: str = field(default=None, metadata=field_metadata)\n shock: float = field(default=None, metadata=field_metadata)\n factor_category: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquidityBucket(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n net_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n gross_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n net_weight: Optional[float] = field(default=None, metadata=field_metadata)\n gross_weight: Optional[float] = field(default=None, metadata=field_metadata)\n transaction_cost: Optional[float] = field(default=None, metadata=field_metadata)\n marginal_cost: Optional[float] = field(default=None, metadata=field_metadata)\n adv22_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n number_of_positions: Optional[float] = field(default=None, metadata=field_metadata)\n beta_adjusted_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n long_weight: Optional[float] = field(default=None, metadata=field_metadata)\n long_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n long_transaction_cost: Optional[float] = field(default=None, metadata=field_metadata)\n long_marginal_cost: Optional[float] = field(default=None, metadata=field_metadata)\n long_adv22_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n long_number_of_positions: Optional[float] = field(default=None, metadata=field_metadata)\n long_beta_adjusted_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n short_weight: Optional[float] = field(default=None, metadata=field_metadata)\n short_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n short_transaction_cost: Optional[float] = field(default=None, metadata=field_metadata)\n short_marginal_cost: Optional[float] = field(default=None, metadata=field_metadata)\n short_adv22_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n short_number_of_positions: Optional[float] = field(default=None, metadata=field_metadata)\n short_beta_adjusted_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquidityFactor(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n value: Optional[float] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquiditySummarySection(Base):\n adv10_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n adv22_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n adv5_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n annualized_risk: Optional[float] = field(default=None, metadata=field_metadata)\n annualized_tracking_error: Optional[float] = field(default=None, metadata=field_metadata)\n beta: Optional[float] = field(default=None, metadata=field_metadata)\n beta_adjusted_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n beta_adjusted_net_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n bid_ask_spread: Optional[float] = field(default=None, metadata=field_metadata)\n correlation: Optional[float] = field(default=None, metadata=field_metadata)\n daily_risk: Optional[float] = field(default=None, metadata=field_metadata)\n daily_tracking_error: Optional[float] = field(default=None, metadata=field_metadata)\n est1_day_complete_pct: Optional[float] = field(default=None, metadata=field_metadata)\n five_day_price_change_bps: Optional[float] = field(default=None, metadata=field_metadata)\n gross_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n marginal_cost: Optional[float] = field(default=None, metadata=field_metadata)\n market_cap: Optional[float] = field(default=None, metadata=field_metadata)\n minutes_to_trade100_pct: Optional[float] = field(default=None, metadata=field_metadata)\n net_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n number_of_positions: Optional[float] = field(default=None, metadata=field_metadata)\n percent_in_benchmark: Optional[object] = field(default=None, metadata=field_metadata)\n transaction_cost: Optional[float] = field(default=None, metadata=field_metadata)\n weight_of_top_five_positions: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationAssetAnalyticsDaily(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n trade_day_number: int = field(default=None, metadata=field_metadata)\n total_cost: float = field(default=None, metadata=field_metadata)\n total_variance_contribution: float = field(default=None, metadata=field_metadata)\n total_portfolio_risk_on_day: float = field(default=None, metadata=field_metadata)\n total_risk: float = field(default=None, metadata=field_metadata)\n cratos: float = field(default=None, metadata=field_metadata)\n adv: float = field(default=None, metadata=field_metadata)\n cluster_id: int = field(default=None, metadata=field_metadata)\n cluster_label: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationAssetAnalyticsDayOne(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n auction_trade_percentage: float = field(default=None, metadata=field_metadata)\n auction_pov_percentage: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationAssetAnalyticsIntraday(Base):\n period_number: int = field(default=None, metadata=field_metadata)\n trade_day_number: int = field(default=None, metadata=field_metadata)\n period_start_time: datetime.datetime = field(default=None, metadata=field_metadata)\n period_end_time: datetime.datetime = field(default=None, metadata=field_metadata)\n is_trading: bool = field(default=None, metadata=field_metadata)\n buy: float = field(default=None, metadata=field_metadata)\n sell: float = field(default=None, metadata=field_metadata)\n gross: float = field(default=None, metadata=field_metadata)\n net: float = field(default=None, metadata=field_metadata)\n trade_absolute: float = field(default=None, metadata=field_metadata)\n asset_id: str = field(default=None, metadata=field_metadata)\n volume: float = field(default=None, metadata=field_metadata)\n volatility: float = field(default=None, metadata=field_metadata)\n fx: float = field(default=None, metadata=field_metadata)\n price_local: float = field(default=None, metadata=field_metadata)\n currency: str = field(default=None, metadata=field_metadata)\n total_cost_spread: float = field(default=None, metadata=field_metadata)\n total_cost_volatility: float = field(default=None, metadata=field_metadata)\n total_cost_permanent: float = field(default=None, metadata=field_metadata)\n beta_historical: float = field(default=None, metadata=field_metadata)\n mcr: float = field(default=None, metadata=field_metadata)\n total_cost: float = field(default=None, metadata=field_metadata)\n adv_percentage: float = field(default=None, metadata=field_metadata)\n country: str = field(default=None, metadata=field_metadata)\n industry: str = field(default=None, metadata=field_metadata)\n sector: str = field(default=None, metadata=field_metadata)\n spread: float = field(default=None, metadata=field_metadata)\n region: str = field(default=None, metadata=field_metadata)\n region_minor: str = field(default=None, metadata=field_metadata)\n quantity: int = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationCloseAuctionAnalytics(Base):\n exchange_city: str = field(default=None, metadata=field_metadata)\n trade_absolute: float = field(default=None, metadata=field_metadata)\n trade_net: float = field(default=None, metadata=field_metadata)\n gross: float = field(default=None, metadata=field_metadata)\n net: float = field(default=None, metadata=field_metadata)\n auction_pov_percentage: float = field(default=None, metadata=field_metadata)\n close_auction_start_time: datetime.datetime = field(default=None, metadata=field_metadata)\n number_of_assets: int = field(default=None, metadata=field_metadata)\n close_auction_trade_percentage: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationClusterAnalytics(Base):\n cluster_id: int = field(default=None, metadata=field_metadata)\n cluster_label: str = field(default=None, metadata=field_metadata)\n gross: float = field(default=None, metadata=field_metadata)\n total_cost_bps: float = field(default=None, metadata=field_metadata)\n total_risk_bps: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationClusterAnalyticsIntradayItem(Base):\n cluster_id: int = field(default=None, metadata=field_metadata)\n cluster_label: str = field(default=None, metadata=field_metadata)\n adv_percentage: float = field(default=None, metadata=field_metadata)\n gross_percentage: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationEodCashPositionsItem(Base):\n trade_day_num: str = field(default=None, metadata=field_metadata)\n net: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationExcludedAsset(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n security_type: str = field(default=None, metadata=field_metadata)\n quantity: int = field(default=None, metadata=field_metadata)\n reason: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationFactorAnalyticsItem(Base):\n period_number: int = field(default=None, metadata=field_metadata)\n trade_day_number: int = field(default=None, metadata=field_metadata)\n period_start_time: datetime.datetime = field(default=None, metadata=field_metadata)\n period_end_time: datetime.datetime = field(default=None, metadata=field_metadata)\n factors: Tuple[DictBase, ...] = field(default=None, metadata=field_metadata)\n time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationPortfolioAnalyticsDaily(Base):\n trade_day_number: int = field(default=None, metadata=field_metadata)\n estimated_cost_bps: float = field(default=None, metadata=field_metadata)\n completion_rate_percent: float = field(default=None, metadata=field_metadata)\n mean_expected_cost_versus_benchmark: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationPortfolioAnalyticsIntraday(Base):\n period_number: int = field(default=None, metadata=field_metadata)\n trade_day_number: int = field(default=None, metadata=field_metadata)\n period_start_time: datetime.datetime = field(default=None, metadata=field_metadata)\n period_end_time: datetime.datetime = field(default=None, metadata=field_metadata)\n time: datetime.datetime = field(default=None, metadata=field_metadata)\n sell: float = field(default=None, metadata=field_metadata)\n buy: float = field(default=None, metadata=field_metadata)\n gross: float = field(default=None, metadata=field_metadata)\n net: float = field(default=None, metadata=field_metadata)\n trade_absolute: float = field(default=None, metadata=field_metadata)\n total_cost_spread: float = field(default=None, metadata=field_metadata)\n total_cost_volatility: float = field(default=None, metadata=field_metadata)\n total_cost_permanent: float = field(default=None, metadata=field_metadata)\n total_cost: float = field(default=None, metadata=field_metadata)\n adv_average_percentage: float = field(default=None, metadata=field_metadata)\n total_risk: float = field(default=None, metadata=field_metadata)\n factor_risk: float = field(default=None, metadata=field_metadata)\n specific_risk: float = field(default=None, metadata=field_metadata)\n diagonal_risk: float = field(default=None, metadata=field_metadata)\n total_risk_objective: float = field(default=None, metadata=field_metadata)\n factor_risk_objective: float = field(default=None, metadata=field_metadata)\n specific_risk_objective: float = field(default=None, metadata=field_metadata)\n diagonal_risk_objective: float = field(default=None, metadata=field_metadata)\n total_risk_bps: float = field(default=None, metadata=field_metadata)\n trade_percentage_cumulative_sum: float = field(default=None, metadata=field_metadata)\n net_period_percentage: float = field(default=None, metadata=field_metadata)\n total_cost_budget_percentage: float = field(default=None, metadata=field_metadata)\n total_risk_percentage: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationPortfolioSummarySection(Base):\n position: float = field(default=None, metadata=field_metadata)\n number_of_assets: int = field(default=None, metadata=field_metadata)\n diagonal_risk: float = field(default=None, metadata=field_metadata)\n total_risk: float = field(default=None, metadata=field_metadata)\n factor_risk: float = field(default=None, metadata=field_metadata)\n specific_risk: float = field(default=None, metadata=field_metadata)\n historical_beta: float = field(default=None, metadata=field_metadata)\n spread: float = field(default=None, metadata=field_metadata)\n total_risk_bps: float = field(default=None, metadata=field_metadata)\n adv_average_percentage: float = field(default=None, metadata=field_metadata)\n adv_max_percentage: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationTradedPosition(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n quantity: int = field(default=None, metadata=field_metadata)\n position: int = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PRateForHorizon(Base):\n minutes_expired: Optional[int] = field(default=None, metadata=field_metadata)\n participation_rate: Optional[float] = field(default=None, metadata=field_metadata)\n participation_rate_long: Optional[float] = field(default=None, metadata=field_metadata)\n participation_rate_short: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskAtHorizon(Base):\n minutes_expired: Optional[int] = field(default=None, metadata=field_metadata)\n risk: Optional[int] = field(default=None, metadata=field_metadata)\n risk_long: Optional[float] = field(default=None, metadata=field_metadata)\n risk_short: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScenarioGetManyRequestPathSchema(Base):\n id_: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n limit: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n offset: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n propagate_shocks: Optional[Tuple[bool, ...]] = field(default=None, metadata=field_metadata)\n short_name: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n factor_scenario_type: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n historical_simulation_end_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n shocked_factor: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n owner_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n description: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n historical_simulation_start_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n shocked_factor_category: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n scenario_group_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TradeCompleteAtHorizon(Base):\n minutes_expired: Optional[int] = field(default=None, metadata=field_metadata)\n positions_complete: Optional[int] = field(default=None, metadata=field_metadata)\n positions_complete_pct: Optional[float] = field(default=None, metadata=field_metadata)\n notional_complete_pct: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass FactorShockScenarioParameters(Base):\n risk_model: str = field(default=None, metadata=field_metadata)\n factor_shocks: Tuple[FactorShock, ...] = field(default=None, metadata=field_metadata)\n propagate_shocks: bool = field(default=None, metadata=field_metadata)\n shock_unit: Optional[ShockUnit] = field(default=ShockUnit.percent, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquidityConstituent(Base):\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n exchange: Optional[str] = field(default=None, metadata=field_metadata)\n quantity: Optional[float] = field(default=None, metadata=field_metadata)\n gross_weight: Optional[float] = field(default=None, metadata=field_metadata)\n net_weight: Optional[float] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n gross_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n net_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n transaction_cost: Optional[float] = field(default=None, metadata=field_metadata)\n marginal_cost: Optional[float] = field(default=None, metadata=field_metadata)\n country: Optional[str] = field(default=None, metadata=field_metadata)\n region: Optional[Region] = field(default=None, metadata=field_metadata)\n type_: Optional[AssetType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n market_cap_bucket: Optional[object] = field(default=None, metadata=field_metadata)\n est1_day_complete_pct: Optional[float] = field(default=None, metadata=field_metadata)\n in_benchmark: Optional[bool] = field(default=None, metadata=field_metadata)\n in_risk_model: Optional[bool] = field(default=None, metadata=field_metadata)\n in_cost_predict_model: Optional[bool] = field(default=None, metadata=field_metadata)\n beta: Optional[float] = field(default=None, metadata=field_metadata)\n daily_risk: Optional[float] = field(default=None, metadata=field_metadata)\n annualized_risk: Optional[float] = field(default=None, metadata=field_metadata)\n one_day_price_change_pct: Optional[float] = field(default=None, metadata=field_metadata)\n beta_adjusted_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n adv_bucket: Optional[object] = field(default=None, metadata=field_metadata)\n settlement_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n transition_plan_transparency_percentile: Optional[float] = field(default=None, metadata=field_metadata)\n transition_performance_percentile: Optional[float] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquidityFactorCategory(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n sub_factors: Optional[Tuple[LiquidityFactor, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquiditySummary(Base):\n total: LiquiditySummarySection = field(default=None, metadata=field_metadata)\n long: Optional[LiquiditySummarySection] = field(default=None, metadata=field_metadata)\n short: Optional[LiquiditySummarySection] = field(default=None, metadata=field_metadata)\n long_vs_short: Optional[LiquiditySummarySection] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationClusterAnalyticsIntraday(Base):\n time: datetime.datetime = field(default=None, metadata=field_metadata)\n period_number: int = field(default=None, metadata=field_metadata)\n trade_day_number: int = field(default=None, metadata=field_metadata)\n clusters: Tuple[OptimizationClusterAnalyticsIntradayItem, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationEodCashPositions(Base):\n currency: str = field(default=None, metadata=field_metadata)\n positions: Tuple[OptimizationEodCashPositionsItem, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationPortfolioCharacteristics(Base):\n sell: OptimizationPortfolioSummarySection = field(default=None, metadata=field_metadata)\n buy: OptimizationPortfolioSummarySection = field(default=None, metadata=field_metadata)\n net: OptimizationPortfolioSummarySection = field(default=None, metadata=field_metadata)\n gross: OptimizationPortfolioSummarySection = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ShockScopeFilter(Base):\n region: Optional[Region] = field(default=None, metadata=field_metadata)\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n gics_sector: Optional[str] = field(default=None, metadata=field_metadata)\n country_code: Optional[CountryCode] = field(default=None, metadata=field_metadata)\n gics_industry_group: Optional[str] = field(default=None, metadata=field_metadata)\n gics_industry: Optional[str] = field(default=None, metadata=field_metadata)\n gics_sub_industry: Optional[str] = field(default=None, metadata=field_metadata)\n is_investment_grade: Optional[bool] = field(default=None, metadata=field_metadata)\n tenor: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquidityTableRow(Base):\n asset_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n adv22_day_pct: Optional[float] = field(default=None, metadata=field_metadata)\n shares: Optional[float] = field(default=None, metadata=field_metadata)\n net_weight: Optional[float] = field(default=None, metadata=field_metadata)\n gross_weight: Optional[float] = field(default=None, metadata=field_metadata)\n gross_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n net_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n transaction_cost: Optional[float] = field(default=None, metadata=field_metadata)\n marginal_cost: Optional[float] = field(default=None, metadata=field_metadata)\n one_day_price_change_pct: Optional[float] = field(default=None, metadata=field_metadata)\n normalized_performance: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquidityTimeSeriesItem(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n normalized_performance: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n annualized_return: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n annualized_correlation: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n annualized_volatility: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n annualized_sharp_ratio: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n annualized_tracking_error: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n max_drawdown: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n net_exposure: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n cumulative_pnl: Optional[Tuple[Tuple[Union[datetime.date, float], ...], ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationFactorAnalyticsIntraday(Base):\n country: Tuple[OptimizationFactorAnalyticsItem, ...] = field(default=None, metadata=field_metadata)\n sector: Tuple[OptimizationFactorAnalyticsItem, ...] = field(default=None, metadata=field_metadata)\n domestic_china: Tuple[OptimizationFactorAnalyticsItem, ...] = field(default=None, metadata=field_metadata)\n market: Tuple[OptimizationFactorAnalyticsItem, ...] = field(default=None, metadata=field_metadata)\n currency: Tuple[OptimizationFactorAnalyticsItem, ...] = field(default=None, metadata=field_metadata)\n industry: Tuple[OptimizationFactorAnalyticsItem, ...] = field(default=None, metadata=field_metadata)\n risk: Tuple[OptimizationFactorAnalyticsItem, ...] = field(default=None, metadata=field_metadata)\n cluster_classification: Tuple[OptimizationFactorAnalyticsItem, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationTradeSchedule(Base):\n period_number: int = field(default=None, metadata=field_metadata)\n trade_day_number: int = field(default=None, metadata=field_metadata)\n period_start_time: datetime.datetime = field(default=None, metadata=field_metadata)\n period_end_time: datetime.datetime = field(default=None, metadata=field_metadata)\n traded_positions: Tuple[OptimizationTradedPosition, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ShockScope(Base):\n asset_class: Optional[AssetClass] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n filter_: Optional[ShockScopeFilter] = field(default=None, metadata=config(field_name='filter', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LiquidityResponse(Base):\n as_of_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n risk_model: Optional[str] = field(default=None, metadata=field_metadata)\n notional: Optional[float] = field(default=None, metadata=field_metadata)\n currency: Optional[Currency] = field(default=None, metadata=field_metadata)\n report: Optional[str] = field(default=None, metadata=field_metadata)\n summary: Optional[LiquiditySummary] = field(default=None, metadata=field_metadata)\n constituent_transaction_costs: Optional[Tuple[LiquidityConstituent, ...]] = field(default=None, metadata=field_metadata)\n constituents: Optional[Tuple[LiquidityConstituent, ...]] = field(default=None, metadata=field_metadata)\n largest_holdings_by_weight: Optional[Tuple[LiquidityTableRow, ...]] = field(default=None, metadata=field_metadata)\n least_liquid_holdings: Optional[Tuple[LiquidityTableRow, ...]] = field(default=None, metadata=field_metadata)\n adv_buckets: Optional[Tuple[LiquidityBucket, ...]] = field(default=None, metadata=field_metadata)\n region_buckets: Optional[Tuple[LiquidityBucket, ...]] = field(default=None, metadata=field_metadata)\n country_buckets: Optional[Tuple[LiquidityBucket, ...]] = field(default=None, metadata=field_metadata)\n sector_buckets: Optional[Tuple[LiquidityBucket, ...]] = field(default=None, metadata=field_metadata)\n industry_buckets: Optional[Tuple[LiquidityBucket, ...]] = field(default=None, metadata=field_metadata)\n market_cap_buckets: Optional[Tuple[LiquidityBucket, ...]] = field(default=None, metadata=field_metadata)\n currency_buckets: Optional[Tuple[LiquidityBucket, ...]] = field(default=None, metadata=field_metadata)\n execution_costs_with_different_time_horizons: Optional[Tuple[ExecutionCostForHorizon, ...]] = field(default=None, metadata=field_metadata)\n time_to_trade_with_different_participation_rates: Optional[Tuple[PRateForHorizon, ...]] = field(default=None, metadata=field_metadata)\n risk_over_time: Optional[Tuple[RiskAtHorizon, ...]] = field(default=None, metadata=field_metadata)\n trade_complete_percent_over_time: Optional[Tuple[TradeCompleteAtHorizon, ...]] = field(default=None, metadata=field_metadata)\n adv_percent_over_time: Optional[Tuple[AdvCurveTick, ...]] = field(default=None, metadata=field_metadata)\n risk_buckets: Optional[Tuple[LiquidityFactor, ...]] = field(default=None, metadata=field_metadata)\n factor_risk_buckets: Optional[Tuple[LiquidityFactorCategory, ...]] = field(default=None, metadata=field_metadata)\n exposure_buckets: Optional[Tuple[LiquidityFactor, ...]] = field(default=None, metadata=field_metadata)\n factor_exposure_buckets: Optional[Tuple[LiquidityFactorCategory, ...]] = field(default=None, metadata=field_metadata)\n timeseries_data: Optional[Tuple[LiquidityTimeSeriesItem, ...]] = field(default=None, metadata=field_metadata)\n assets_not_in_risk_model: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n assets_not_in_cost_predict_model: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n assets_without_compositions: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n error_message: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationAnalytics(Base):\n portfolio_characteristics: OptimizationPortfolioCharacteristics = field(default=None, metadata=field_metadata)\n asset_analytics_daily: Tuple[OptimizationAssetAnalyticsDaily, ...] = field(default=None, metadata=field_metadata)\n portfolio_analytics_daily: Tuple[OptimizationPortfolioAnalyticsDaily, ...] = field(default=None, metadata=field_metadata)\n assets_excluded: Tuple[OptimizationExcludedAsset, ...] = field(default=None, metadata=field_metadata)\n constraints_consultations: Tuple[DictBase, ...] = field(default=None, metadata=field_metadata)\n factor_analytics_intraday: OptimizationFactorAnalyticsIntraday = field(default=None, metadata=field_metadata)\n asset_analytics_intraday: Tuple[OptimizationAssetAnalyticsIntraday, ...] = field(default=None, metadata=field_metadata)\n portfolio_analytics_intraday: Tuple[OptimizationPortfolioAnalyticsIntraday, ...] = field(default=None, metadata=field_metadata)\n cluster_analytics_intraday: Tuple[OptimizationClusterAnalyticsIntraday, ...] = field(default=None, metadata=field_metadata)\n cluster_analytics: Tuple[OptimizationClusterAnalytics, ...] = field(default=None, metadata=field_metadata)\n eod_cash_positions: Tuple[OptimizationEodCashPositions, ...] = field(default=None, metadata=field_metadata)\n asset_analytics_day_one: Optional[Tuple[OptimizationAssetAnalyticsDayOne, ...]] = field(default=None, metadata=field_metadata)\n close_auction_analytics: Optional[Tuple[OptimizationCloseAuctionAnalytics, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Shock(Base):\n scope: ShockScope = field(default=None, metadata=field_metadata)\n value: float = field(default=None, metadata=field_metadata)\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationResult(Base):\n created_by_id: str = field(default=None, metadata=field_metadata)\n created_time: datetime.datetime = field(default=None, metadata=field_metadata)\n entitlements: Entitlements = field(default=None, metadata=field_metadata)\n entitlement_exclusions: EntitlementExclusions = field(default=None, metadata=field_metadata)\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n last_updated_by_id: str = field(default=None, metadata=field_metadata)\n last_updated_time: datetime.datetime = field(default=None, metadata=field_metadata)\n owner_id: str = field(default=None, metadata=field_metadata)\n analytics: OptimizationAnalytics = field(default=None, metadata=field_metadata)\n status: OptimizationStatus = field(default=None, metadata=field_metadata)\n trade_schedule: Optional[Tuple[OptimizationTradeSchedule, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Scenario(Base):\n name: str = field(default=None, metadata=field_metadata)\n shocks: Optional[Tuple[Shock, ...]] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n identifiers: Optional[Tuple[Identifier, ...]] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n scenario_group_id: Optional[str] = field(default=None, metadata=field_metadata)\n short_name: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n type_: Optional[FactorScenarioType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n parameters: Optional[DictBase] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OptimizationRequest(Base):\n positions: Tuple[Position, ...] = field(default=None, metadata=field_metadata)\n execution_start_time: datetime.datetime = field(default=None, metadata=field_metadata)\n execution_end_time: datetime.datetime = field(default=None, metadata=field_metadata)\n parameters: DictBase = field(default=None, metadata=field_metadata)\n type_: OptimizationType = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n wait_for_results: Optional[bool] = field(default=False, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/risk_models.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass RiskModelCoverage(EnumBase, Enum): \n \n \"\"\"Allowed risk model coverages\"\"\"\n\n Country = 'Country'\n Global = 'Global'\n Market_Type = 'Market Type'\n Region = 'Region' \n\n\nclass RiskModelDataMeasure(EnumBase, Enum): \n \n \"\"\"A list of the different risk model data measures to choose from.\"\"\"\n\n Asset_Universe = 'Asset Universe'\n Historical_Beta = 'Historical Beta'\n Total_Risk = 'Total Risk'\n Specific_Risk = 'Specific Risk'\n Specific_Return = 'Specific Return'\n Daily_Return = 'Daily Return'\n Estimation_Universe_Weight = 'Estimation Universe Weight'\n Residual_Variance = 'Residual Variance'\n Predicted_Beta = 'Predicted Beta'\n Global_Predicted_Beta = 'Global Predicted Beta'\n Universe_Factor_Exposure = 'Universe Factor Exposure'\n R_Squared = 'R Squared'\n Fair_Value_Gap_Percent = 'Fair Value Gap Percent'\n Fair_Value_Gap_Standard_Deviation = 'Fair Value Gap Standard Deviation'\n Factor_Id = 'Factor Id'\n Factor_Name = 'Factor Name'\n Factor_Category_Id = 'Factor Category Id'\n Factor_Category = 'Factor Category'\n Factor_Return = 'Factor Return'\n Factor_Standard_Deviation = 'Factor Standard Deviation'\n Factor_Z_Score = 'Factor Z Score'\n Factor_Volatility = 'Factor Volatility'\n Covariance_Matrix = 'Covariance Matrix'\n Issuer_Specific_Covariance = 'Issuer Specific Covariance'\n Factor_Portfolios = 'Factor Portfolios'\n Bid_Ask_Spread = 'Bid Ask Spread'\n Bid_Ask_Spread_30d = 'Bid Ask Spread 30d'\n Bid_Ask_Spread_60d = 'Bid Ask Spread 60d'\n Bid_Ask_Spread_90d = 'Bid Ask Spread 90d'\n Trading_Volume = 'Trading Volume'\n Trading_Volume_30d = 'Trading Volume 30d'\n Trading_Volume_60d = 'Trading Volume 60d'\n Trading_Volume_90d = 'Trading Volume 90d'\n Traded_Value_30d = 'Traded Value 30d'\n Composite_Volume = 'Composite Volume'\n Composite_Volume_30d = 'Composite Volume 30d'\n Composite_Volume_60d = 'Composite Volume 60d'\n Composite_Volume_90d = 'Composite Volume 90d'\n Composite_Value_30d = 'Composite Value 30d'\n Issuer_Market_Cap = 'Issuer Market Cap'\n Price = 'Price'\n Model_Price = 'Model Price'\n Capitalization = 'Capitalization'\n Currency = 'Currency'\n Unadjusted_Specific_Risk = 'Unadjusted Specific Risk'\n Dividend_Yield = 'Dividend Yield'\n Pre_VRA_Covariance_Matrix = 'Pre VRA Covariance Matrix'\n Unadjusted_Covariance_Matrix = 'Unadjusted Covariance Matrix'\n Risk_Free_Rate = 'Risk Free Rate'\n Currency_Exchange_Rate = 'Currency Exchange Rate'\n Factor_Mean = 'Factor Mean'\n Factor_Cross_Sectional_Standard_Deviation = 'Factor Cross Sectional Standard Deviation'\n Factor_Cross_Sectional_Mean = 'Factor Cross Sectional Mean' \n\n\nclass RiskModelEventType(EnumBase, Enum): \n \n \"\"\"Event type for risk model class.\"\"\"\n\n Risk_Model = 'Risk Model'\n Risk_Model_PFP_Data = 'Risk Model PFP Data'\n Risk_Model_ISC_Data = 'Risk Model ISC Data'\n Risk_Model_AWS = 'Risk Model AWS'\n Risk_Model_PFP_Data_AWS = 'Risk Model PFP Data AWS'\n Risk_Model_ISC_Data_AWS = 'Risk Model ISC Data AWS' \n\n\nclass RiskModelLogicalDb(EnumBase, Enum): \n \n QSAR_AX_NYC = 'QSAR_AX_NYC'\n STUDIO_DAILY = 'STUDIO_DAILY' \n\n\nclass RiskModelTerm(EnumBase, Enum): \n \n \"\"\"Allowed risk model terms\"\"\"\n\n Trading = 'Trading'\n Daily = 'Daily'\n Short = 'Short'\n Medium = 'Medium'\n Long = 'Long' \n\n\nclass RiskModelUniverseIdentifier(EnumBase, Enum): \n \n \"\"\"The identifier which the risk model is uploaded by.\"\"\"\n\n sedol = 'sedol'\n bcid = 'bcid'\n cusip = 'cusip'\n gsid = 'gsid'\n isin = 'isin' \n\n\nclass RiskModelUniverseIdentifierRequest(EnumBase, Enum): \n \n \"\"\"The identifier which the risk model is queried by.\"\"\"\n\n gsid = 'gsid'\n bbid = 'bbid'\n bcid = 'bcid'\n cusip = 'cusip'\n sedol = 'sedol'\n ric = 'ric'\n ticker = 'ticker'\n primeId = 'primeId'\n isin = 'isin'\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Factor(Base):\n identifier: str = field(default=None, metadata=field_metadata)\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n description: Optional[str] = field(default=None, metadata=field_metadata)\n glossary_description: Optional[str] = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelCalendar(Base):\n business_dates: Tuple[datetime.date, ...] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelCurrencyRatesData(Base):\n currency: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n exchange_rate: Tuple[float, ...] = field(default=None, metadata=field_metadata)\n risk_free_rate: Tuple[float, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelFactorData(Base):\n factor_id: str = field(default=None, metadata=field_metadata)\n factor_name: str = field(default=None, metadata=field_metadata)\n factor_category_id: str = field(default=None, metadata=field_metadata)\n factor_category: str = field(default=None, metadata=field_metadata)\n factor_return: float = field(default=None, metadata=field_metadata)\n factor_standard_deviation: Optional[float] = field(default=None, metadata=field_metadata)\n factor_cross_sectional_standard_deviation: Optional[float] = field(default=None, metadata=field_metadata)\n factor_mean: Optional[float] = field(default=None, metadata=field_metadata)\n factor_cross_sectional_mean: Optional[float] = field(default=None, metadata=field_metadata)\n factor_z_score: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nRiskModelFactorExposure = Dict[str, float]\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelFactorPortfolio(Base):\n factor_id: str = field(default=None, metadata=field_metadata)\n weights: Tuple[float, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelIssuerSpecificCovarianceData(Base):\n universe_id1: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n universe_id2: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n covariance: Tuple[float, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelAssetData(Base):\n universe: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n specific_risk: Tuple[float, ...] = field(default=None, metadata=field_metadata)\n factor_exposure: Tuple[RiskModelFactorExposure, ...] = field(default=None, metadata=field_metadata)\n unadjusted_specific_risk: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n specific_return: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n daily_return: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n estimation_universe_weight: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n residual_variance: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n historical_beta: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n predicted_beta: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n global_predicted_beta: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n total_risk: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n bid_ask_spread: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n bid_ask_spread30d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n bid_ask_spread60d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n bid_ask_spread90d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n trading_volume: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n trading_volume30d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n trading_volume60d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n trading_volume90d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n traded_value30d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n composite_volume: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n composite_volume30d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n composite_volume60d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n composite_volume90d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n composite_value30d: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n issuer_market_cap: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n price: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n capitalization: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n currency: Optional[Tuple[Currency, ...]] = field(default=None, metadata=field_metadata)\n dividend_yield: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n r_squared: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n fair_value_gap_percent: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n fair_value_gap_standard_deviation: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n model_price: Optional[Tuple[float, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelCoverageRequest(Base):\n asset_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n portfolio_id: Optional[str] = field(default=None, metadata=field_metadata)\n as_of_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n sort_by_term: Optional[RiskModelTerm] = field(default=None, metadata=field_metadata)\n vendor: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelDataAssetsRequest(Base):\n identifier: RiskModelUniverseIdentifierRequest = field(default=None, metadata=field_metadata)\n universe: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelFactorPortfoliosData(Base):\n universe: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n portfolio: Tuple[RiskModelFactorPortfolio, ...] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModel(Base):\n coverage: RiskModelCoverage = field(default=None, metadata=field_metadata)\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n term: RiskModelTerm = field(default=None, metadata=field_metadata)\n universe_identifier: RiskModelUniverseIdentifier = field(default=None, metadata=field_metadata)\n vendor: str = field(default=None, metadata=field_metadata)\n version: float = field(default=None, metadata=field_metadata)\n type_: RiskModelType = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n expected_update_time: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n universe_size: Optional[float] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelData(Base):\n date: datetime.date = field(default=None, metadata=field_metadata)\n asset_data: Optional[RiskModelAssetData] = field(default=None, metadata=field_metadata)\n factor_data: Optional[Tuple[RiskModelFactorData, ...]] = field(default=None, metadata=field_metadata)\n covariance_matrix: Optional[Tuple[Tuple[float, ...], ...]] = field(default=None, metadata=field_metadata)\n pre_vra_covariance_matrix: Optional[Tuple[Tuple[float, ...], ...]] = field(default=None, metadata=config(field_name='preVRACovarianceMatrix', exclude=exclude_none))\n unadjusted_covariance_matrix: Optional[Tuple[Tuple[float, ...], ...]] = field(default=None, metadata=field_metadata)\n currency_rates_data: Optional[RiskModelCurrencyRatesData] = field(default=None, metadata=field_metadata)\n issuer_specific_covariance: Optional[RiskModelIssuerSpecificCovarianceData] = field(default=None, metadata=field_metadata)\n factor_portfolios: Optional[RiskModelFactorPortfoliosData] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelDataRequest(Base):\n start_date: datetime.date = field(default=None, metadata=field_metadata)\n end_date: datetime.date = field(default=None, metadata=field_metadata)\n measures: Tuple[RiskModelDataMeasure, ...] = field(default=None, metadata=field_metadata)\n assets: Optional[RiskModelDataAssetsRequest] = field(default=None, metadata=field_metadata)\n factors: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n limit_factors: Optional[bool] = field(default=True, metadata=field_metadata)\n base_currency_factor: Optional[str] = field(default=None, metadata=field_metadata)\n format_: Optional[Format] = field(default=Format.Json, metadata=config(field_name='format', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RiskModelDataResponse(Base):\n results: Tuple[RiskModelData, ...] = field(default=None, metadata=field_metadata)\n total_results: int = field(default=None, metadata=field_metadata)\n missing_dates: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/scenarios.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ByAssetPnlResult(Base):\n asset_id: str = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n bbid: Optional[str] = field(default=None, metadata=field_metadata)\n sector: Optional[str] = field(default=None, metadata=field_metadata)\n industry: Optional[str] = field(default=None, metadata=field_metadata)\n country: Optional[str] = field(default=None, metadata=field_metadata)\n direction: Optional[str] = field(default=None, metadata=field_metadata)\n exposure: Optional[float] = field(default=None, metadata=field_metadata)\n estimated_pnl: Optional[float] = field(default=None, metadata=field_metadata)\n estimated_performance: Optional[float] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ErroredScenario(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n error_message: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SummaryResult(Base):\n estimated_pnl: float = field(default=None, metadata=field_metadata)\n estimated_performance: Optional[float] = field(default=None, metadata=field_metadata)\n stressed_market_value: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PnlResult(Base):\n name: str = field(default=None, metadata=field_metadata)\n estimated_pnl: float = field(default=None, metadata=field_metadata)\n factor_exposure: Optional[float] = field(default=None, metadata=field_metadata)\n exposure: Optional[float] = field(default=None, metadata=field_metadata)\n factor_shock: Optional[float] = field(default=None, metadata=field_metadata)\n by_asset: Optional[ByAssetPnlResult] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScenarioResponse(Base):\n summary: Optional[SummaryResult] = field(default=None, metadata=field_metadata)\n factor_pnl: Optional[PnlResult] = field(default=None, metadata=field_metadata)\n by_sector_aggregations: Optional[PnlResult] = field(default=None, metadata=field_metadata)\n by_region_aggregations: Optional[PnlResult] = field(default=None, metadata=field_metadata)\n by_direction_aggregations: Optional[PnlResult] = field(default=None, metadata=field_metadata)\n by_asset: Optional[ByAssetPnlResult] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScenarioCalculationResponse(Base):\n scenarios: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n errored_scenarios: Optional[Tuple[ErroredScenario, ...]] = field(default=None, metadata=field_metadata)\n results: Optional[Tuple[ScenarioResponse, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScenarioCalculationAPIRequest(Base):\n scenario_ids: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n measures: tuple = field(default=None, metadata=field_metadata)\n date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n risk_model: Optional[str] = field(default=None, metadata=field_metadata)\n entity_id: Optional[str] = field(default=None, metadata=field_metadata)\n position_set: Optional[PositionSetRequest] = field(default=None, metadata=field_metadata)\n report_id: Optional[str] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(init=False, default='Factor Scenario', metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/screens.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScreenerQueryBuilder(Base):\n filters: Optional[AssetScreenerCreditRequestFilters] = field(default=None, metadata=field_metadata)\n asset_class: Optional[str] = field(init=False, default='Credit', metadata=field_metadata)\n type_: Optional[str] = field(init=False, default='Bond', metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Screen(Base):\n name: str = field(default=None, metadata=field_metadata)\n query_builder: ScreenerQueryBuilder = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n active: Optional[bool] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/target/secmaster.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass SecMasterAssetType(EnumBase, Enum): \n \n \"\"\"Asset type differentiates the product categorization or contract type.\"\"\"\n\n ETF = 'ETF'\n ETN = 'ETN'\n Future = 'Future'\n Index = 'Index'\n Option = 'Option'\n Preferred_Stock = 'Preferred Stock'\n Single_Stock = 'Single Stock'\n Common_Stock = 'Common Stock'\n ADR = 'ADR'\n GDR = 'GDR'\n Dutch_Cert = 'Dutch Cert'\n NY_Reg_Shrs = 'NY Reg Shrs'\n Receipt = 'Receipt'\n Unit = 'Unit'\n Mutual_Fund = 'Mutual Fund'\n Right = 'Right'\n Preferred = 'Preferred'\n Misc_ = 'Misc.'\n REIT = 'REIT'\n Private_Comp = 'Private Comp'\n Preference = 'Preference'\n Ltd_Part = 'Ltd Part'\n Tracking_Stk = 'Tracking Stk'\n Royalty_Trst = 'Royalty Trst'\n Closed_End_Fund = 'Closed-End Fund'\n Open_End_Fund = 'Open-End Fund'\n Fund_of_Funds = 'Fund of Funds'\n MLP = 'MLP'\n Stapled_Security = 'Stapled Security'\n Savings_Share = 'Savings Share'\n Equity_WRT = 'Equity WRT'\n Savings_Plan = 'Savings Plan'\n Equity_Index = 'Equity Index'\n Municipal_Bond = 'Municipal Bond'\n Corporate_Bond = 'Corporate Bond'\n Government_Bond = 'Government Bond'\n Agency_Bond = 'Agency Bond'\n Currency = 'Currency'\n Equity_Option = 'Equity Option'\n Financial_index_future_ = 'Financial index future.'\n Single_Stock_Future = 'Single Stock Future'\n Pool = 'Pool'\n Certificate_Of_Deposit = 'Certificate Of Deposit'\n Debt_Structured_Note = 'Debt Structured Note'\n Agency_CMO = 'Agency CMO'\n Future_Option = 'Future Option'\n Convertible_Bond = 'Convertible Bond'\n Austrian_Crt = 'Austrian Crt'\n BDR = 'BDR'\n Belgium_Cert = 'Belgium Cert'\n CDR = 'CDR'\n EDR = 'EDR'\n German_Cert = 'German Cert'\n IDR = 'IDR'\n RDC = 'RDC'\n Swiss_Cert = 'Swiss Cert'\n Canadian_DR = 'Canadian DR'\n Singapore_DR = 'Singapore DR'\n To_Be_Announced = 'To Be Announced'\n Basket = 'Basket'\n Physical_index_future_ = 'Physical index future.'\n\n\nclass SecMasterCorporateActionStatus(EnumBase, Enum): \n \n \"\"\"Status of corporate actions.\"\"\"\n\n Normal = 'Normal'\n Completed = 'Completed'\n Pending = 'Pending'\n Deleted = 'Deleted'\n Incomplete = 'Incomplete'\n Missing_Adj_Factor = 'Missing Adj Factor'\n Not_Quoted_By_Exchange = 'Not Quoted By Exchange'\n Missing_Terms = 'Missing Terms'\n Cancelled = 'Cancelled'\n Lapsed = 'Lapsed'\n Proposed = 'Proposed'\n Withdrawn = 'Withdrawn'\n All = 'All' \n\n\nclass SecMasterCorporateActionType(EnumBase, Enum): \n \n \"\"\"Types of corporate actions.\"\"\"\n\n Cash_Dividend = 'Cash Dividend'\n Merger = 'Merger'\n Spinoff = 'Spinoff'\n Split = 'Split'\n Rights = 'Rights'\n Reorganization = 'Reorganization'\n Special_Adjustment = 'Special Adjustment'\n Quote_Lot_Adjustment = 'Quote Lot Adjustment'\n Currency_Adjustment = 'Currency Adjustment'\n All = 'All' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterAuditFields(Base):\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nclass SecMasterIdentifiers(DictBase):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterRecord(Base):\n recordid: Optional[float] = field(default=None, metadata=field_metadata)\n action_type: Optional[str] = field(default=None, metadata=field_metadata)\n instrument_value: Optional[float] = field(default=None, metadata=field_metadata)\n data_value: Optional[float] = field(default=None, metadata=field_metadata)\n term_type: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterResourceCompany(Base):\n company_id: Optional[float] = field(default=None, metadata=field_metadata)\n company_name: Optional[str] = field(default=None, metadata=field_metadata)\n identifiers: Optional[DictBase] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n issuer_id: Optional[str] = field(default=None, metadata=field_metadata)\n\n\nSecMasterSources = Dict[str, str]\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterTemporalCompany(Base):\n gs_company_id: str = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ExchangeGetRequestPathSchema(Base):\n gs_exchange_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n mic: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n operating_mic: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ric_suffix_code: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ric_exchange_code: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n bbg_exchange_code: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n country: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n fields: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n as_of_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n limit: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n offset: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n offset_key: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterAssetSources(Base):\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n asset_class: Optional[str] = field(default=None, metadata=field_metadata)\n product: Optional[SecMasterSources] = field(default=None, metadata=field_metadata)\n exchange: Optional[SecMasterSources] = field(default=None, metadata=field_metadata)\n company: Optional[SecMasterSources] = field(default=None, metadata=field_metadata)\n classifications: Optional[SecMasterSources] = field(default=None, metadata=field_metadata)\n identifiers: Optional[SecMasterSources] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterCorporateAction(Base):\n actionid: Optional[str] = field(default=None, metadata=field_metadata)\n eventid: Optional[str] = field(default=None, metadata=field_metadata)\n gsid: Optional[str] = field(default=None, metadata=field_metadata)\n event_type: Optional[SecMasterCorporateActionType] = field(default=None, metadata=field_metadata)\n announce_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n effective_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n multiplicative_adjust: Optional[float] = field(default=None, metadata=field_metadata)\n additive_adjust: Optional[float] = field(default=None, metadata=field_metadata)\n event_status: Optional[SecMasterCorporateActionStatus] = field(default=None, metadata=field_metadata)\n records: Optional[Tuple[SecMasterRecord, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterExchange(Base):\n gs_exchange_id: str = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=field_metadata)\n country: Optional[str] = field(default=None, metadata=field_metadata)\n timezone: Optional[str] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n identifiers: Optional[SecMasterIdentifiers] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterGetActionsRequestPathSchema(Base):\n event_type: Optional[Tuple[SecMasterCorporateActionType, ...]] = field(default=None, metadata=field_metadata)\n event_status: Optional[Tuple[SecMasterCorporateActionStatus, ...]] = field(default=None, metadata=field_metadata)\n gsid: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n event_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n corp_action_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n as_of_time: Optional[Tuple[datetime.datetime, ...]] = field(default=None, metadata=field_metadata)\n effective_date_from: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n effective_date_to: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n offset_key: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n limit: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterGetCapitalStructureRequestPathSchema(Base):\n gsid: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ticker: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n bbid: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ric: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n rcic: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n cusip: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n sedol: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n isin: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n gss: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n prime_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n issuer_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n type_: Optional[Tuple[str, ...]] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n as_of_time: Optional[Tuple[datetime.datetime, ...]] = field(default=None, metadata=field_metadata)\n is_primary: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n limit: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n offset_key: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterGetRequestPathSchema(Base):\n identifier: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n gsid: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ticker: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n bbg: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n bbid: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n ric: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n rcic: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n cusip: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n cins: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n sedol: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n isin: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n gss: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n prime_id: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n type_: Optional[Tuple[SecMasterAssetType, ...]] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n country_code: Optional[Tuple[CountryCode, ...]] = field(default=None, metadata=field_metadata)\n is_primary: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n all_listings: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n effective_date: Optional[Tuple[datetime.date, ...]] = field(default=None, metadata=field_metadata)\n as_of_time: Optional[Tuple[datetime.datetime, ...]] = field(default=None, metadata=field_metadata)\n limit: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n offset_key: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterResourceExchange(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n identifiers: Optional[SecMasterIdentifiers] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterResourceProduct(Base):\n name: Optional[str] = field(default=None, metadata=field_metadata)\n identifiers: Optional[SecMasterIdentifiers] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterTemporalProduct(Base):\n gsid: str = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n country: Optional[str] = field(default=None, metadata=field_metadata)\n primary_exchange_id: Optional[str] = field(default=None, metadata=field_metadata)\n type_: Optional[SecMasterAssetType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n subtype: Optional[str] = field(default=None, metadata=field_metadata)\n source: Optional[str] = field(default=None, metadata=field_metadata)\n flag: Optional[bool] = field(default=None, metadata=field_metadata)\n update_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterAsset(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n effective_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n asset_class: Optional[AssetClass] = field(default=None, metadata=field_metadata)\n type_: Optional[SecMasterAssetType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n product: Optional[SecMasterResourceProduct] = field(default=None, metadata=field_metadata)\n exchange: Optional[SecMasterResourceExchange] = field(default=None, metadata=field_metadata)\n currency: Optional[str] = field(default=None, metadata=field_metadata)\n company: Optional[SecMasterResourceCompany] = field(default=None, metadata=field_metadata)\n issuer: Optional[SecMasterResourceCompany] = field(default=None, metadata=field_metadata)\n classifications: Optional[AssetClassifications] = field(default=None, metadata=field_metadata)\n identifiers: Optional[SecMasterIdentifiers] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n active_listing: Optional[bool] = field(default=None, metadata=field_metadata)\n last_active_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)\n audit_fields: Optional[SecMasterAuditFields] = field(default=None, metadata=field_metadata)\n field_sources: Optional[SecMasterAssetSources] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterResponseActions(Base):\n results: Optional[Tuple[SecMasterCorporateAction, ...]] = field(default=None, metadata=field_metadata)\n total_results: Optional[float] = field(default=None, metadata=field_metadata)\n offset_key: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterResponseMulti(Base):\n request_id: Optional[str] = field(default=None, metadata=field_metadata)\n results: Optional[Tuple[Union[SecMasterExchange, SecMasterTemporalCompany, SecMasterTemporalProduct], ...]] = field(default=None, metadata=field_metadata)\n total_results: Optional[float] = field(default=None, metadata=field_metadata)\n offset_key: Optional[str] = field(default=None, metadata=field_metadata)\n limit: Optional[int] = field(default=None, metadata=field_metadata)\n offset: Optional[int] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SecMasterResponseAssets(Base):\n results: Optional[Tuple[SecMasterAsset, ...]] = field(default=None, metadata=field_metadata)\n total_results: Optional[float] = field(default=None, metadata=field_metadata)\n offset_key: Optional[str] = field(default=None, metadata=field_metadata)\n limit: Optional[int] = field(default=None, metadata=field_metadata)\n offset: Optional[int] = field(default=None, metadata=field_metadata)\n update_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/trades.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass MqexsAssetClass(EnumBase, Enum): \n \n \"\"\"Asset classification of security. Assets are classified into broad groups which\n exhibit similar characteristics and behave in a consistent way under\n different market conditions\"\"\"\n\n Commod = 'Commod'\n FX = 'FX'\n Equity = 'Equity'\n Earon = 'Earon'\n FICC = 'FICC'\n Prime = 'Prime'\n IOI = 'IOI'\n Rates = 'Rates'\n PCO = 'PCO' \n\n\nclass MqexsAssetClassExt(EnumBase, Enum): \n \n \"\"\"Asset classification of security. Assets are classified into broad groups which\n exhibit similar characteristics and behave in a consistent way under\n different market conditions\"\"\"\n\n Commod = 'Commod'\n FX = 'FX'\n Equity = 'Equity' \n\n\nclass MqexsClearer(EnumBase, Enum): \n \n \"\"\"The clearer code\"\"\"\n\n CME = 'CME'\n ICE = 'ICE'\n NFX = 'NFX'\n OTC = 'OTC'\n LME = 'LME'\n CME_ICE = 'CME-ICE' \n\n\nclass MqexsCurrencyExt(EnumBase, Enum): \n \n \"\"\"Currency, ISO 4217 currency code or exchange quote modifier (e.g. GBP vs GBp)\"\"\"\n\n _ = ''\n ACU = 'ACU'\n ADP = 'ADP'\n AED = 'AED'\n AFA = 'AFA'\n ALL = 'ALL'\n AMD = 'AMD'\n ANG = 'ANG'\n AOA = 'AOA'\n AOK = 'AOK'\n AON = 'AON'\n ARA = 'ARA'\n ARS = 'ARS'\n ARZ = 'ARZ'\n ATS = 'ATS'\n AUD = 'AUD'\n AUZ = 'AUZ'\n AZM = 'AZM'\n B03 = 'B03'\n BAD = 'BAD'\n BAK = 'BAK'\n BAM = 'BAM'\n BBD = 'BBD'\n BDN = 'BDN'\n BDT = 'BDT'\n BEF = 'BEF'\n BGL = 'BGL'\n BGN = 'BGN'\n BHD = 'BHD'\n BIF = 'BIF'\n BMD = 'BMD'\n BND = 'BND'\n BR6 = 'BR6'\n BRE = 'BRE'\n BRF = 'BRF'\n BRL = 'BRL'\n BRR = 'BRR'\n BSD = 'BSD'\n BTC = 'BTC'\n BTN = 'BTN'\n BTR = 'BTR'\n BWP = 'BWP'\n BYR = 'BYR'\n BZD = 'BZD'\n C23 = 'C23'\n CAC = 'CAC'\n CAD = 'CAD'\n CAZ = 'CAZ'\n CCI = 'CCI'\n CDF = 'CDF'\n CFA = 'CFA'\n CHF = 'CHF'\n CHZ = 'CHZ'\n CLF = 'CLF'\n CLP = 'CLP'\n CLZ = 'CLZ'\n CNH = 'CNH'\n CNO = 'CNO'\n CNY = 'CNY'\n CNZ = 'CNZ'\n COP = 'COP'\n COZ = 'COZ'\n CPB = 'CPB'\n CPI = 'CPI'\n CRC = 'CRC'\n CUP = 'CUP'\n CVE = 'CVE'\n CYP = 'CYP'\n CZH = 'CZH'\n CZK = 'CZK'\n DAX = 'DAX'\n DEM = 'DEM'\n DIJ = 'DIJ'\n DJF = 'DJF'\n DKK = 'DKK'\n DOP = 'DOP'\n DZD = 'DZD'\n E51 = 'E51'\n E52 = 'E52'\n E53 = 'E53'\n E54 = 'E54'\n ECI = 'ECI'\n ECS = 'ECS'\n ECU = 'ECU'\n EEK = 'EEK'\n EF0 = 'EF0'\n EGP = 'EGP'\n ESP = 'ESP'\n ETB = 'ETB'\n EUR = 'EUR'\n EUZ = 'EUZ'\n F06 = 'F06'\n FED = 'FED'\n FIM = 'FIM'\n FJD = 'FJD'\n FKP = 'FKP'\n FRF = 'FRF'\n FT1 = 'FT1'\n GBP = 'GBP'\n GBZ = 'GBZ'\n GEK = 'GEK'\n GHC = 'GHC'\n GHS = 'GHS'\n GHY = 'GHY'\n GIP = 'GIP'\n GMD = 'GMD'\n GNF = 'GNF'\n GQE = 'GQE'\n GRD = 'GRD'\n GTQ = 'GTQ'\n GWP = 'GWP'\n GYD = 'GYD'\n HKB = 'HKB'\n HKD = 'HKD'\n HNL = 'HNL'\n HRK = 'HRK'\n HSI = 'HSI'\n HTG = 'HTG'\n HUF = 'HUF'\n IDB = 'IDB'\n IDO = 'IDO'\n IDR = 'IDR'\n IEP = 'IEP'\n IGP = 'IGP'\n ILS = 'ILS'\n INO = 'INO'\n INP = 'INP'\n INR = 'INR'\n IPA = 'IPA'\n IPX = 'IPX'\n IQD = 'IQD'\n IRR = 'IRR'\n IRS = 'IRS'\n ISI = 'ISI'\n ISK = 'ISK'\n ISO = 'ISO'\n ITL = 'ITL'\n J05 = 'J05'\n JMD = 'JMD'\n JNI = 'JNI'\n JOD = 'JOD'\n JPY = 'JPY'\n JPZ = 'JPZ'\n JZ9 = 'JZ9'\n KES = 'KES'\n KGS = 'KGS'\n KHR = 'KHR'\n KMF = 'KMF'\n KOR = 'KOR'\n KPW = 'KPW'\n KRW = 'KRW'\n KWD = 'KWD'\n KYD = 'KYD'\n KZT = 'KZT'\n LAK = 'LAK'\n LBA = 'LBA'\n LBP = 'LBP'\n LHY = 'LHY'\n LKR = 'LKR'\n LRD = 'LRD'\n LSL = 'LSL'\n LSM = 'LSM'\n LTL = 'LTL'\n LUF = 'LUF'\n LVL = 'LVL'\n LYD = 'LYD'\n MAD = 'MAD'\n MDL = 'MDL'\n MGF = 'MGF'\n MKD = 'MKD'\n MMK = 'MMK'\n MNT = 'MNT'\n MOP = 'MOP'\n MRO = 'MRO'\n MTP = 'MTP'\n MUR = 'MUR'\n MVR = 'MVR'\n MWK = 'MWK'\n MXB = 'MXB'\n MXN = 'MXN'\n MXP = 'MXP'\n MXW = 'MXW'\n MXZ = 'MXZ'\n MYO = 'MYO'\n MYR = 'MYR'\n MZM = 'MZM'\n MZN = 'MZN'\n NAD = 'NAD'\n ND3 = 'ND3'\n NGF = 'NGF'\n NGI = 'NGI'\n NGN = 'NGN'\n NIC = 'NIC'\n NLG = 'NLG'\n NOK = 'NOK'\n NOZ = 'NOZ'\n NPR = 'NPR'\n NZD = 'NZD'\n NZZ = 'NZZ'\n O08 = 'O08'\n OMR = 'OMR'\n PAB = 'PAB'\n PEI = 'PEI'\n PEN = 'PEN'\n PEZ = 'PEZ'\n PGK = 'PGK'\n PHP = 'PHP'\n PKR = 'PKR'\n PLN = 'PLN'\n PLZ = 'PLZ'\n PSI = 'PSI'\n PTE = 'PTE'\n PYG = 'PYG'\n QAR = 'QAR'\n R2K = 'R2K'\n ROL = 'ROL'\n RON = 'RON'\n RSD = 'RSD'\n RUB = 'RUB'\n RUF = 'RUF'\n RUR = 'RUR'\n RWF = 'RWF'\n SAR = 'SAR'\n SBD = 'SBD'\n SCR = 'SCR'\n SDP = 'SDP'\n SDR = 'SDR'\n SEK = 'SEK'\n SET = 'SET'\n SGD = 'SGD'\n SHP = 'SHP'\n SKK = 'SKK'\n SLL = 'SLL'\n SRG = 'SRG'\n SSI = 'SSI'\n STD = 'STD'\n SUR = 'SUR'\n SVC = 'SVC'\n SVT = 'SVT'\n SYP = 'SYP'\n SZL = 'SZL'\n T21 = 'T21'\n T51 = 'T51'\n T52 = 'T52'\n T53 = 'T53'\n T54 = 'T54'\n T55 = 'T55'\n T71 = 'T71'\n TE0 = 'TE0'\n TED = 'TED'\n TF9 = 'TF9'\n THB = 'THB'\n THO = 'THO'\n TMM = 'TMM'\n TND = 'TND'\n TNT = 'TNT'\n TOP = 'TOP'\n TPE = 'TPE'\n TPX = 'TPX'\n TRB = 'TRB'\n TRL = 'TRL'\n TRY = 'TRY'\n TRZ = 'TRZ'\n TTD = 'TTD'\n TWD = 'TWD'\n TZS = 'TZS'\n UAH = 'UAH'\n UCB = 'UCB'\n UDI = 'UDI'\n UFC = 'UFC'\n UFZ = 'UFZ'\n UGS = 'UGS'\n UGX = 'UGX'\n USB = 'USB'\n USD = 'USD'\n UVR = 'UVR'\n UYP = 'UYP'\n UYU = 'UYU'\n VAC = 'VAC'\n VEB = 'VEB'\n VEF = 'VEF'\n VES = 'VES'\n VND = 'VND'\n VUV = 'VUV'\n WST = 'WST'\n XAF = 'XAF'\n XCD = 'XCD'\n XDR = 'XDR'\n XEU = 'XEU'\n XOF = 'XOF'\n XPF = 'XPF'\n YDD = 'YDD'\n YER = 'YER'\n YUD = 'YUD'\n YUN = 'YUN'\n ZAL = 'ZAL'\n ZAR = 'ZAR'\n ZAZ = 'ZAZ'\n ZMK = 'ZMK'\n ZMW = 'ZMW'\n ZRN = 'ZRN'\n ZRZ = 'ZRZ'\n ZWD = 'ZWD'\n AUd = 'AUd'\n BWp = 'BWp'\n EUr = 'EUr'\n GBp = 'GBp'\n ILs = 'ILs'\n KWd = 'KWd'\n MWk = 'MWk'\n SGd = 'SGd'\n SZl = 'SZl'\n USd = 'USd'\n ZAr = 'ZAr' \n\n\nclass MqexsErrorSeverity(EnumBase, Enum): \n \n \"\"\"The severity of the error, which can be a warning or a fatal error\"\"\"\n\n Pass = 'Pass'\n Warning = 'Warning'\n Fatal = 'Fatal'\n FatalException = 'FatalException' \n\n\nclass MqexsOtcSettlementType(EnumBase, Enum): \n \n \"\"\"OTC settlement type\"\"\"\n\n Bullet = 'Bullet'\n Calendar_Month_Average = 'Calendar Month Average'\n Trade_Month_Average = 'Trade Month Average'\n Physical = 'Physical'\n CalMonth_Avg_No_Roll_Adjust = 'CalMonth Avg No Roll Adjust'\n Heren_Month_Ahead = 'Heren Month Ahead'\n Heren_Day_Ahead = 'Heren Day Ahead'\n Argus_Month_Ahead = 'Argus Month Ahead'\n Argus_Day_Ahead = 'Argus Day Ahead'\n Multi = 'Multi' \n\n\nclass MqexsPricingType(EnumBase, Enum): \n \n \"\"\"The source to be used to get a quote\"\"\"\n\n CommodStream = 'CommodStream'\n eAronStream = 'eAronStream'\n FXStreaming = 'FXStreaming'\n Heat = 'Heat'\n Indicative = 'Indicative'\n LiquidityStream = 'LiquidityStream'\n OneDeltaStream = 'OneDeltaStream'\n OptionChain = 'OptionChain'\n RFQ = 'RFQ'\n RFQSales = 'RFQSales'\n GstRFQ = 'GstRFQ'\n Roll = 'Roll'\n RFQRollSales = 'RFQRollSales'\n Swap = 'Swap'\n RFQSwapSales = 'RFQSwapSales'\n GstSwapRFQ = 'GstSwapRFQ'\n TradableStream = 'TradableStream'\n UnifiedRFQ = 'UnifiedRFQ'\n PCORFQ = 'PCORFQ' \n\n\nclass MqexsSide(EnumBase, Enum): \n \n \"\"\"Field represents the order or trade action.\"\"\"\n\n Buy = 'Buy'\n Sell = 'Sell'\n BuySell = 'BuySell'\n SellBuy = 'SellBuy'\n Above = 'Above'\n Below = 'Below'\n _ = '' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MqexsErrorInfo(Base):\n error_code: str = field(default=None, metadata=field_metadata)\n error_msg: str = field(default=None, metadata=field_metadata)\n error_severity: Optional[MqexsErrorSeverity] = field(default=None, metadata=field_metadata)\n asset_class: Optional[MqexsAssetClass] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MqexsProductDetails(Base):\n name: str = field(default=None, metadata=field_metadata)\n contract_code: Optional[str] = field(default=None, metadata=field_metadata)\n clearer: Optional[MqexsClearer] = field(default=None, metadata=field_metadata)\n settlement_type: Optional[MqexsOtcSettlementType] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MqexsTradeDetails(Base):\n side: MqexsSide = field(default=None, metadata=field_metadata)\n quantity: str = field(default=None, metadata=field_metadata)\n unit_price: str = field(default=None, metadata=field_metadata)\n currency: MqexsCurrencyExt = field(default=None, metadata=field_metadata)\n settlement_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n quantity_lots: Optional[str] = field(default=None, metadata=field_metadata)\n quantity_unit: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MqexsTradeExt(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n trade_details: MqexsTradeDetails = field(default=None, metadata=field_metadata)\n product_details: MqexsProductDetails = field(default=None, metadata=field_metadata)\n quote_id: str = field(default=None, metadata=field_metadata)\n asset_class: MqexsAssetClassExt = field(default=None, metadata=field_metadata)\n created_by_id: str = field(default=None, metadata=field_metadata)\n created_time: datetime.datetime = field(default=None, metadata=field_metadata)\n last_updated_time: datetime.datetime = field(default=None, metadata=field_metadata)\n last_updated_by_id: str = field(default=None, metadata=field_metadata)\n inquiry_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MqexsTradesWErrorExt(Base):\n trades: Optional[Tuple[MqexsTradeExt, ...]] = field(default=None, metadata=field_metadata)\n errors: Optional[Tuple[MqexsErrorInfo, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/workflow_quote.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass Encoding(EnumBase, Enum): \n \n HTML = 'HTML'\n URL = 'URL'\n Unicode = 'Unicode'\n Base64 = 'Base64'\n Hex = 'Hex'\n ASCII = 'ASCII' \n\n\nclass HedgeModel(EnumBase, Enum): \n \n Smile = 'Smile'\n BlackScholes = 'BlackScholes' \n\n\nclass ImgType(EnumBase, Enum): \n \n APNG = 'APNG'\n AVIF = 'AVIF'\n GIF = 'GIF'\n JPEG = 'JPEG'\n PNG = 'PNG'\n SVG = 'SVG'\n WEBP = 'WEBP' \n\n\nclass MarketRefOverrideType(EnumBase, Enum): \n \n \"\"\"Market Ref Override Type Spot or Fwd\"\"\"\n\n Spot = 'Spot'\n Fwd = 'Fwd' \n\n\nclass OverlayType(EnumBase, Enum): \n \n \"\"\"Type\"\"\"\n\n Payout = 'Payout'\n Price = 'Price'\n MtM = 'MtM'\n Delta = 'Delta'\n Vega = 'Vega'\n Theta = 'Theta'\n RelativeCheapness = 'RelativeCheapness'\n ProbabilityDistribution = 'ProbabilityDistribution'\n RealisedProbability = 'RealisedProbability'\n MacroEvents = 'MacroEvents'\n MicroEvents = 'MicroEvents'\n Gamma = 'Gamma'\n Volatility = 'Volatility'\n VolatilityDistribution = 'VolatilityDistribution'\n EarlyExercise = 'EarlyExercise'\n _None = 'None' \n\n\nclass PriceFormat(EnumBase, Enum): \n \n \"\"\"display unit for price\"\"\"\n\n Absolute = 'Absolute'\n Relative = 'Relative'\n Cents = 'Cents'\n Bps = 'Bps'\n Percentage = 'Percentage'\n\n\nclass RelativeExpiryType(EnumBase, Enum): \n \n Relative = 'Relative'\n Fixed = 'Fixed' \n\n\nclass RelativeStrikeType(EnumBase, Enum): \n \n Relative_Delta = 'Relative Delta'\n Relative_Spot = 'Relative Spot'\n Relative_Fwd = 'Relative Fwd'\n Fixed = 'Fixed' \n\n\n@dataclass\nclass HedgeTypes(Base):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CustomDeltaHedge(HedgeTypes):\n amount: float = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(init=False, default='CustomDeltaHedge', metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nclass GenericResponse(DictBase):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass HyperLinkImageComments(CustomComments):\n url: Optional[str] = field(default=None, metadata=field_metadata)\n comment_type: Optional[str] = field(init=False, default='hyperLinkImageComments', metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SalesPremiumAdjustment(Base):\n value: Optional[float] = field(default=None, metadata=field_metadata)\n unit: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SolvingTarget(Base):\n constraint: Optional[float] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkflowEntitlement(Base):\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n value: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BinaryImageComments(CustomComments):\n data: Optional[str] = field(default=None, metadata=field_metadata)\n img_type: Optional[ImgType] = field(default=None, metadata=field_metadata)\n encoding: Optional[Encoding] = field(default=None, metadata=field_metadata)\n comment_type: Optional[str] = field(init=False, default='binaryImageComments', metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartingParameters(Base):\n spot_style: Optional[str] = field(default=None, metadata=field_metadata)\n overlay: Optional[OverlayType] = field(default=None, metadata=field_metadata)\n underlay: Optional[OverlayType] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DeltaHedge(HedgeTypes):\n model: Optional[HedgeModel] = field(default=None, metadata=field_metadata)\n type_: Optional[str] = field(init=False, default='DeltaHedge', metadata=config(field_name='type', exclude=exclude_none))\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MarketRefOverride(Base):\n asset: Optional[str] = field(default=None, metadata=field_metadata)\n value: Optional[float] = field(default=None, metadata=field_metadata)\n mkt_ref_override_type: Optional[MarketRefOverrideType] = field(default=None, metadata=field_metadata)\n ref_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass OverlayParameters(Base):\n overlay_type: Optional[OverlayType] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SolvingInfo(Base):\n target: Optional[SolvingTarget] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass StrategyDescription(Base):\n strategy_type: Optional[str] = field(default=None, metadata=field_metadata)\n long_short: Optional[LongShort] = field(default=LongShort.Long, metadata=field_metadata)\n assets: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n asset_classes: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkflowEntitlements(Base):\n version: Optional[int] = field(default=None, metadata=field_metadata)\n readers: Optional[Tuple[WorkflowEntitlement, ...]] = field(default=None, metadata=field_metadata)\n writers: Optional[Tuple[WorkflowEntitlement, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MarketDataParameters(Base):\n max_history: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n timestamp: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n spot_ref: Optional[float] = field(default=None, metadata=field_metadata)\n mkt_ref_override: Optional[Tuple[MarketRefOverride, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass VisualStructuringReport(QuoteReport):\n report_type: Optional[str] = field(default='VisualStructuringReport', metadata=field_metadata)\n position_set_id: Optional[str] = field(default=None, metadata=field_metadata)\n quick_entry_text: Optional[str] = field(default=None, metadata=field_metadata)\n as_of_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)\n market_data_parameters: Optional[MarketDataParameters] = field(default=None, metadata=field_metadata)\n overlay_parameters: Optional[OverlayParameters] = field(default=None, metadata=field_metadata)\n solving_info: Optional[SolvingInfo] = field(default=None, metadata=field_metadata)\n charting_parameters: Optional[ChartingParameters] = field(default=None, metadata=field_metadata)\n comments: Optional[Tuple[CustomComments, ...]] = field(default=None, metadata=field_metadata)\n strategy_description: Optional[StrategyDescription] = field(default=None, metadata=field_metadata)\n asset_class: Optional[str] = field(default=None, metadata=field_metadata)\n hedge_instruction: Optional[HedgeTypes] = field(default=None, metadata=field_metadata)\n sales_premium_adjustment: Optional[SalesPremiumAdjustment] = field(default=None, metadata=field_metadata)\n price_format: Optional[PriceFormat] = field(default=None, metadata=field_metadata)\n strike_and_barrier_type: Optional[RelativeStrikeType] = field(default=None, metadata=field_metadata)\n expiry_type: Optional[RelativeExpiryType] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SaveQuoteRequest(Base):\n positions: Tuple[PositionSet, ...] = field(default=None, metadata=field_metadata)\n measures: Tuple[RiskMeasure, ...] = field(default=None, metadata=field_metadata)\n pricing_and_market_data_as_of: Optional[Tuple[PricingDateAndMarketDataAsOf, ...]] = field(default=None, metadata=field_metadata)\n pricing_location: Optional[PricingLocation] = field(default=None, metadata=field_metadata)\n scenario: Optional[MarketDataScenario] = field(default=None, metadata=field_metadata)\n parameters: Optional[RiskRequestParameters] = field(default=None, metadata=field_metadata)\n reports: Optional[Tuple[QuoteReport, ...]] = field(default=None, metadata=field_metadata)\n shared_users: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n comments: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n original_workflow_id: Optional[str] = field(default=None, metadata=field_metadata)\n is_sharing_parent: Optional[bool] = field(default=None, metadata=field_metadata)\n entitlements: Optional[WorkflowEntitlements] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkflowPosition(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n position_sets: Optional[Tuple[PositionSet, ...]] = field(default=None, metadata=field_metadata)\n reports: Optional[Tuple[QuoteReport, ...]] = field(default=None, metadata=field_metadata)\n comments: Optional[str] = field(default=None, metadata=field_metadata)\n original_workflow_id: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[WorkflowEntitlements] = field(default=None, metadata=field_metadata)\n creator: Optional[str] = field(default=None, metadata=field_metadata)\n originating_system: Optional[str] = field(default=None, metadata=field_metadata)\n is_read_only: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkflowPositionsResponse(Base):\n results: Optional[Tuple[WorkflowPosition, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n" }, { "path": "gs_quant/target/workspaces_markets.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.base import *\nfrom gs_quant.common import *\nimport datetime\nfrom typing import Dict, Optional, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom dataclasses_json import LetterCase, config, dataclass_json\nfrom enum import Enum\n\n\nclass ComponentType(EnumBase, Enum): \n \n \"\"\"Enum listing supported component types.\"\"\"\n\n article = 'article'\n assetPlot = 'assetPlot'\n chart = 'chart'\n barChart = 'barChart'\n commentary = 'commentary'\n commentaryPromo = 'commentaryPromo'\n container = 'container'\n datagrid = 'datagrid'\n dataviz = 'dataviz'\n empty = 'empty'\n legend = 'legend'\n market = 'market'\n monitor = 'monitor'\n plot = 'plot'\n promo = 'promo'\n rates = 'rates'\n relatedLinks = 'relatedLinks'\n research = 'research'\n screener = 'screener'\n selector = 'selector'\n separator = 'separator'\n stackedBarChart = 'stackedBarChart'\n treemap = 'treemap'\n video = 'video'\n webinar = 'webinar' \n\n\nclass WorkspaceType(EnumBase, Enum): \n \n \"\"\"Enum listing support workspace types.\"\"\"\n\n cashboard = 'cashboard'\n multiplot = 'multiplot'\n study = 'study' \n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ComponentSelection(Base):\n selector_id: str = field(default=None, metadata=field_metadata)\n tag: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ContainerComponentParameters(Base):\n component_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LegendItem(Base):\n color: str = field(default=None, metadata=field_metadata)\n icon: str = field(default=None, metadata=field_metadata)\n name: str = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MarketComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\nclass NotificationTokenBody(DictBase):\n pass\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PromoComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n transparent: Optional[bool] = field(default=None, metadata=field_metadata)\n body: Optional[str] = field(default=None, metadata=field_metadata)\n size: Optional[str] = field(default=None, metadata=field_metadata)\n hide_border: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SeparatorComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n size: Optional[str] = field(default=None, metadata=field_metadata)\n show_more_url: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass VideoComponentParameters(Base):\n replay_url: str = field(default=None, metadata=field_metadata)\n date: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n height: Optional[float] = field(default=None, metadata=field_metadata)\n title: Optional[str] = field(default=None, metadata=field_metadata)\n transparent: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WebinarProvider(Base):\n url: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WebinarSpeaker(Base):\n name: str = field(default=None, metadata=field_metadata)\n title: str = field(default=None, metadata=field_metadata)\n author_url: Optional[str] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkspaceDate(Base):\n days: float = field(default=None, metadata=field_metadata)\n text: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkspaceTab(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ArticleComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n commentary_channels: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n commentary_to_desktop_link: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass AssetPlotComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass BarChartComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n hide_legend: Optional[bool] = field(default=None, metadata=field_metadata)\n chart_name: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ChartComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n ids: Tuple[str, ...] = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n hide_legend: Optional[bool] = field(default=None, metadata=field_metadata)\n chart_name: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommentaryComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n commentary_channels: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n commentary_to_desktop_link: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass CommentaryPromoComponentParameters(Base):\n height: Optional[float] = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n commentary_channels: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n transparent: Optional[bool] = field(default=None, metadata=field_metadata)\n body: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass DataGridComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass LegendComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n items: Tuple[LegendItem, ...] = field(default=None, metadata=field_metadata)\n position: Optional[str] = field(default=None, metadata=field_metadata)\n transparent: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass MonitorComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass NotificationTokens(Base):\n tokens: NotificationTokenBody = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass PlotComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n hide_legend: Optional[bool] = field(default=None, metadata=field_metadata)\n plot_frequency_mode: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ResearchComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n commentary_channels: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n commentary_to_desktop_link: Optional[bool] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass ScreenerComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SelectorComponentOption(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass TreemapComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WebinarComponentParameters(Base):\n date: str = field(default=None, metadata=field_metadata)\n date_text: str = field(default=None, metadata=field_metadata)\n description: str = field(default=None, metadata=field_metadata)\n provider: WebinarProvider = field(default=None, metadata=field_metadata)\n replay_url: str = field(default=None, metadata=field_metadata)\n title: str = field(default=None, metadata=field_metadata)\n height: Optional[float] = field(default=None, metadata=field_metadata)\n hosts: Optional[Tuple[WebinarSpeaker, ...]] = field(default=None, metadata=field_metadata)\n password: Optional[str] = field(default=None, metadata=field_metadata)\n series: Optional[str] = field(default=None, metadata=field_metadata)\n speakers: Optional[Tuple[WebinarSpeaker, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RelatedLink(Base):\n type_: str = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n name: str = field(default=None, metadata=field_metadata)\n link: str = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n notification_properties: Optional[NotificationTokens] = field(default=None, metadata=field_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass SelectorComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n container_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n default_option_index: Optional[float] = field(default=None, metadata=field_metadata)\n options: Optional[Tuple[SelectorComponentOption, ...]] = field(default=None, metadata=field_metadata)\n title: Optional[str] = field(default=None, metadata=field_metadata)\n tooltip: Optional[str] = field(default=None, metadata=field_metadata)\n width: Optional[float] = field(default=None, metadata=field_metadata)\n parent_selector_id: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass RelatedLinksComponentParameters(Base):\n height: float = field(default=None, metadata=field_metadata)\n links: Tuple[RelatedLink, ...] = field(default=None, metadata=field_metadata)\n title: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkspaceCallToAction(Base):\n actions: Tuple[RelatedLink, ...] = field(default=None, metadata=field_metadata)\n text: str = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkspaceComponent(Base):\n id_: str = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n type_: ComponentType = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n hide: Optional[bool] = field(default=None, metadata=field_metadata)\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n selections: Optional[Tuple[ComponentSelection, ...]] = field(default=None, metadata=field_metadata)\n container_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n parameters: Optional[Union[ArticleComponentParameters, AssetPlotComponentParameters, BarChartComponentParameters, ChartComponentParameters, CommentaryComponentParameters, CommentaryPromoComponentParameters, ContainerComponentParameters, DataGridComponentParameters, LegendComponentParameters, MarketComponentParameters, MonitorComponentParameters, PlotComponentParameters, PromoComponentParameters, RelatedLinksComponentParameters, ResearchComponentParameters, ScreenerComponentParameters, SelectorComponentParameters, SeparatorComponentParameters, TreemapComponentParameters, VideoComponentParameters, WebinarComponentParameters]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass WorkspaceParameters(Base):\n layout: str = field(default=None, metadata=field_metadata)\n components: Tuple[WorkspaceComponent, ...] = field(default=None, metadata=field_metadata)\n call_to_action: Optional[WorkspaceCallToAction] = field(default=None, metadata=field_metadata)\n can_share: Optional[bool] = field(default=None, metadata=field_metadata)\n date: Optional[WorkspaceDate] = field(default=None, metadata=field_metadata)\n disclaimer: Optional[str] = field(default=None, metadata=field_metadata)\n maintainers: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n parent_collection_id: Optional[str] = field(default=None, metadata=field_metadata)\n tabs: Optional[Tuple[WorkspaceTab, ...]] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=name_metadata)\n\n\n@handle_camel_case_args\n@dataclass_json(letter_case=LetterCase.CAMEL)\n@dataclass(unsafe_hash=True, repr=False)\nclass Workspace(Base):\n parameters: WorkspaceParameters = field(default=None, metadata=field_metadata)\n id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))\n alias: Optional[str] = field(default=None, metadata=field_metadata)\n name: Optional[str] = field(default=None, metadata=field_metadata)\n type_: Optional[WorkspaceType] = field(default=None, metadata=config(field_name='type', exclude=exclude_none))\n tags: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)\n created_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)\n owner_id: Optional[str] = field(default=None, metadata=field_metadata)\n entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)\n folder_name: Optional[str] = field(default=None, metadata=field_metadata)\n description: Optional[str] = field(default=None, metadata=field_metadata)\n children_aliases: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)\n" }, { "path": "gs_quant/test/__init__.py", "content": "" }, { "path": "gs_quant/test/analytics/__init__.py", "content": "" }, { "path": "gs_quant/test/analytics/test_datagrid.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pytest\nfrom gs_quant.session import GsSession, Environment\n\nfrom gs_quant.analytics.datagrid import DataGrid, DataColumn, DataRow\nfrom gs_quant.analytics.processors import EntityProcessor, ChangeProcessor, AppendProcessor\nfrom gs_quant.data import DataCoordinate, DataMeasure, DataFrequency\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom gs_quant.test.utils.datagrid_test_utils import get_test_entity\n\n\ndef test_simple_datagrid():\n name = 'Testing'\n spx = get_test_entity('MA4B66MW5E27U8P32SB')\n rows = [\n DataRow(spx),\n ]\n columns = [DataColumn(name=\"Name\", processor=EntityProcessor(field=\"short_name\"))]\n\n datagrid = DataGrid(name=name, rows=rows, columns=columns)\n\n assert datagrid.name == name\n assert datagrid.rows == rows\n assert datagrid.columns == columns\n\n assert datagrid.is_initialized is False\n assert len(datagrid.results) == 0\n datagrid.initialize()\n assert datagrid.is_initialized is True\n assert len(datagrid.results) > 0\n\n\ndef test_rdate_datagrid(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n name = 'Testing'\n SPX = get_test_entity('MA4B66MW5E27U8P32SB')\n close = DataCoordinate(\n measure=DataMeasure.CLOSE_PRICE,\n frequency=DataFrequency.DAILY,\n )\n\n last_trade_price = DataCoordinate(\n measure=DataMeasure.TRADE_PRICE,\n frequency=DataFrequency.REAL_TIME,\n )\n rows = [\n DataRow(SPX),\n ]\n columns = [\n DataColumn(\n name=\"1d Chg (RT)\",\n processor=ChangeProcessor(\n AppendProcessor(close, last_trade_price, start=RelativeDate(\"-1d\", base_date=dt.date(2021, 1, 22)))\n ),\n )\n ]\n\n datagrid = DataGrid(name=name, rows=rows, columns=columns)\n start_date = datagrid.columns[0].processor.children['a'].start\n assert start_date.base_date == RelativeDate('-1d', base_date=dt.date(2021, 1, 22)).base_date\n assert start_date.rule == RelativeDate('-1d').rule\n\n datagrid.initialize()\n datagrid.poll()\n assert str(datagrid._data_queries[0].query.start) == '2021-01-21'\n\n as_dict = datagrid.as_dict()\n start = as_dict['parameters']['columns'][0]['parameters']['a']['parameters']['start']\n assert start['type'] == 'relativeDate'\n assert start['value'] == {'rule': '-1d', 'baseDate': '2021-01-22'}\n\n # Check that base_date is not persisted when not passed in.\n columns = [\n DataColumn(\n name=\"1d Chg (RT)\",\n processor=ChangeProcessor(AppendProcessor(close, last_trade_price, start=RelativeDate(\"-1d\"))),\n )\n ]\n datagrid = DataGrid(name=name, rows=rows, columns=columns)\n as_dict = datagrid.as_dict()\n start = as_dict['parameters']['columns'][0]['parameters']['a']['parameters']['start']\n assert start['type'] == 'relativeDate'\n assert start['type'] == 'relativeDate'\n assert start['value'] == {'rule': '-1d'}\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_datagrid.py\"])\n" }, { "path": "gs_quant/test/analytics/test_sorting_and_filtering.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport numpy as np\nimport pytest\n\nfrom gs_quant.analytics.core.processor_result import ProcessorResult\nfrom gs_quant.analytics.datagrid import DataGrid, DataColumn\nfrom gs_quant.analytics.datagrid.data_cell import DataCell\nfrom gs_quant.analytics.datagrid.utils import SortOrder, DataGridSort, DataGridFilter, FilterOperation, FilterCondition\nfrom gs_quant.test.utils.datagrid_test_utils import get_test_entity\n\n\nclass TestSortingAndFiltering:\n def get_test_entities(self):\n spx = get_test_entity('MA4B66MW5E27U8P32SB')\n aapl = get_test_entity('MARCRZHY163GQ4H3')\n amzn = get_test_entity('MASGTFZYNA7PMQ3H')\n return spx, aapl, amzn\n\n def get_test_datagrid(self):\n spx, aapl, amzn = self.get_test_entities()\n datagrid = DataGrid('Test DataGrid', rows=[], columns=[DataColumn('Name', None), DataColumn('Value', None)])\n\n results = [\n [DataCell('Name', None, None, None, 0, 0), DataCell('Value', None, None, None, 1, 0)],\n [DataCell('Name', None, None, None, 0, 1), DataCell('Value', None, None, None, 1, 1)],\n [DataCell('Name', None, None, None, 0, 2), DataCell('Value', None, None, None, 1, 2)],\n ]\n\n results[0][0].value = ProcessorResult(True, spx.name)\n results[0][1].value = ProcessorResult(True, np.float64(10))\n results[1][0].value = ProcessorResult(True, aapl.name)\n results[1][1].value = ProcessorResult(True, np.float64(0))\n results[2][0].value = ProcessorResult(True, amzn.name)\n results[2][1].value = ProcessorResult(True, np.float64(-10))\n\n datagrid.results = results\n return datagrid\n\n def test_post_processing_base_case(self):\n spx, aapl, amzn = self.get_test_entities()\n datagrid = self.get_test_datagrid()\n df = datagrid._post_process()\n\n # Check no filters/sorts\n assert df['Name'].iloc[0] == spx.name\n assert df['Name'].iloc[1] == aapl.name\n assert df['Name'].iloc[2] == amzn.name\n assert df['Value'].iloc[0] == 10\n assert df['Value'].iloc[1] == 0\n assert df['Value'].iloc[2] == -10\n\n def test_sorting(self):\n # Test 1: simple name in ascending order\n spx, aapl, amzn = self.get_test_entities()\n datagrid = self.get_test_datagrid()\n datagrid.add_sort(DataGridSort('Name'))\n df = datagrid._post_process()\n assert df['Name'].iloc[0] == amzn.name\n assert df['Name'].iloc[1] == aapl.name\n assert df['Name'].iloc[2] == spx.name\n assert df['Value'].iloc[0] == -10\n assert df['Value'].iloc[1] == 0\n assert df['Value'].iloc[2] == 10\n\n # Test 2: simple name in descending order\n spx, aapl, amzn = self.get_test_entities()\n datagrid = self.get_test_datagrid()\n datagrid.sorts = [DataGridSort('Name', order=SortOrder.DESCENDING)]\n df = datagrid._post_process()\n assert df['Name'].iloc[0] == spx.name\n assert df['Name'].iloc[1] == aapl.name\n assert df['Name'].iloc[2] == amzn.name\n assert df['Value'].iloc[0] == 10\n assert df['Value'].iloc[1] == 0\n assert df['Value'].iloc[2] == -10\n\n # Test 3: Sort values in ascending order\n spx, aapl, amzn = self.get_test_entities()\n datagrid = self.get_test_datagrid()\n datagrid.sorts = [DataGridSort('Value')]\n df = datagrid._post_process()\n assert df['Name'].iloc[0] == amzn.name\n assert df['Name'].iloc[1] == aapl.name\n assert df['Name'].iloc[2] == spx.name\n assert df['Value'].iloc[0] == -10\n assert df['Value'].iloc[1] == 0\n assert df['Value'].iloc[2] == 10\n\n def test_filtering(self):\n spx, aapl, amzn = self.get_test_entities()\n\n # Test 1: Test equals number\n datagrid = self.get_test_datagrid()\n datagrid.add_filter(DataGridFilter('Value', FilterOperation.EQUALS, 0))\n df = datagrid._post_process()\n assert df['Name'].iloc[0] == aapl.name\n assert df['Value'].iloc[0] == 0\n assert len(df) == 1\n\n # Test 2: Test equals numbers\n datagrid = self.get_test_datagrid()\n datagrid.add_filter(DataGridFilter('Value', FilterOperation.EQUALS, [0, 10]))\n df = datagrid._post_process()\n assert df['Name'].iloc[0] == spx.name\n assert df['Value'].iloc[0] == 10\n assert df['Name'].iloc[1] == aapl.name\n assert df['Value'].iloc[1] == 0\n assert len(df) == 2\n\n # Test 3: Test greater than 0 and less than 0\n datagrid = self.get_test_datagrid()\n datagrid.add_filter(DataGridFilter('Value', FilterOperation.GREATER_THAN, 0))\n datagrid.add_filter(DataGridFilter('Value', FilterOperation.LESS_THAN, 0))\n df = datagrid._post_process()\n assert len(df) == 0\n\n # Test 3: Test greater than 0 or less than 0\n datagrid = self.get_test_datagrid()\n datagrid.add_filter(DataGridFilter('Value', FilterOperation.GREATER_THAN, 0))\n datagrid.add_filter(DataGridFilter('Value', FilterOperation.LESS_THAN, 0, FilterCondition.OR))\n datagrid.sorts = [DataGridSort('Value', order=SortOrder.DESCENDING)] # Filtering can give different order\n df = datagrid._post_process()\n assert df['Name'].iloc[0] == spx.name\n assert df['Value'].iloc[0] == 10\n assert df['Name'].iloc[1] == amzn.name\n assert df['Value'].iloc[1] == -10\n assert len(df) == 2\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_sorting_and_filtering.py\"])\n" }, { "path": "gs_quant/test/analytics/test_workspace.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport pytest\n\nfrom gs_quant.analytics.workspaces import PlotComponent, WorkspaceRow, Workspace, WorkspaceColumn\n\n\ndef test_layout_creation():\n plot_component_1 = PlotComponent(200, id_='CHCHF6NW1KXKFDAG')\n plot_component_2 = PlotComponent(200, id_='CHCHF6NW1KXKFDAG')\n plot_component_3 = PlotComponent(550, id_='CHCHF6NW1KXKFDAG')\n plot_component_4 = PlotComponent(550, id_='CHCHF6NW1KXKFDAG', width=8)\n\n # Case 1: Simple layout\n rows = [WorkspaceRow(components=[plot_component_1, plot_component_2])]\n workspace = Workspace(rows=rows, alias='testing-something', name='Testing')\n workspace = workspace.as_dict()\n assert workspace['parameters']['layout'] == 'r(c6($0)c6($1))'\n\n # Case 2: Extra columns equal spaced layout\n rows = [\n WorkspaceRow(\n components=[\n WorkspaceColumn(\n components=[\n WorkspaceRow(components=[plot_component_1]),\n WorkspaceRow(components=[plot_component_2]),\n ]\n ),\n plot_component_3,\n ]\n )\n ]\n workspace = Workspace(rows=rows, alias='testing-something', name='Testing')\n workspace = workspace.as_dict()\n assert workspace['parameters']['layout'] == 'r(c6(r(c12($0))r(c12($1)))c6($2))'\n\n # Case 3: Simple non-equal layout\n rows = [WorkspaceRow(components=[plot_component_4, plot_component_2])]\n workspace = Workspace(rows=rows, alias='testing-something', name='Testing')\n workspace = workspace.as_dict()\n assert workspace['parameters']['layout'] == 'r(c8($0)c4($1))'\n\n # Case 4: Non-equal spacing\n rows = [\n WorkspaceRow(\n components=[\n WorkspaceColumn(\n width=8,\n components=[\n WorkspaceRow(components=[plot_component_1]),\n WorkspaceRow(components=[plot_component_2]),\n ],\n ),\n plot_component_3,\n ]\n )\n ]\n workspace = Workspace(rows=rows, alias='testing-something', name='Testing')\n workspace = workspace.as_dict()\n assert workspace['parameters']['layout'] == 'r(c8(r(c12($0))r(c12($1)))c4($2))'\n\n\ndef test_layout_parsing():\n plot_component_1 = PlotComponent(1, id_='CHCHF6NW1KXKFDAG') # 0 is not supported for height\n plot_component_2 = PlotComponent(2, id_='CHCHF6NW1KXKFDAG')\n plot_component_3 = PlotComponent(3, id_='CHCHF6NW1KXKFDAG')\n\n # Case 1: Single Component\n rows = [WorkspaceRow(components=[plot_component_1])]\n workspace = Workspace(rows=rows, alias='testing-something', name='Testing')\n workspace = Workspace.from_dict(workspace.as_dict())\n\n assert isinstance(workspace.rows[0], WorkspaceRow)\n assert isinstance(workspace.rows[0].components[0], PlotComponent)\n assert workspace.rows[0].components[0].width == 12\n assert workspace.rows[0].components[0].height == 1\n\n # Case 2: 2 Components\n rows = [WorkspaceRow(components=[plot_component_1, plot_component_2])]\n workspace = Workspace(rows=rows, alias='testing-something', name='Testing')\n workspace = Workspace.from_dict(workspace.as_dict())\n\n assert isinstance(workspace.rows[0], WorkspaceRow)\n assert isinstance(workspace.rows[0].components[0], PlotComponent)\n assert workspace.rows[0].components[0].width == 6\n assert workspace.rows[0].components[0].height == 1\n assert isinstance(workspace.rows[0], WorkspaceRow)\n assert isinstance(workspace.rows[0].components[1], PlotComponent)\n assert workspace.rows[0].components[1].width == 6\n assert workspace.rows[0].components[1].height == 2\n\n # Case 3: Nested Columns\n rows = [\n WorkspaceRow(\n components=[\n WorkspaceColumn(\n components=[\n WorkspaceRow(components=[plot_component_1]),\n WorkspaceRow(components=[plot_component_2]),\n ]\n ),\n plot_component_3,\n ]\n )\n ]\n workspace = Workspace(rows=rows, alias='testing-something', name='Testing')\n workspace = Workspace.from_dict(workspace.as_dict())\n\n assert isinstance(workspace.rows[0], WorkspaceRow)\n assert isinstance(workspace.rows[0].components[0], WorkspaceColumn)\n assert workspace.rows[0].components[0].width == 6\n assert len(workspace.rows[0].components[0].components) == 2\n assert workspace.rows[0].components[0].components[0].components[0].height == 1\n assert workspace.rows[0].components[0].components[1].components[0].height == 2\n assert workspace.rows[0].components[1].height == 3\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_workspace.py\"])\n" }, { "path": "gs_quant/test/api/__init__.py", "content": "" }, { "path": "gs_quant/test/api/backtests_xasset/__init__.py", "content": "" }, { "path": "gs_quant/test/api/backtests_xasset/json_encoders/__init__.py", "content": "" }, { "path": "gs_quant/test/api/backtests_xasset/json_encoders/response_datatypes/__init__.py", "content": "" }, { "path": "gs_quant/test/api/backtests_xasset/json_encoders/response_datatypes/test_risk_result_datatype_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\nfrom pandas._testing import assert_series_equal, assert_frame_equal # noqa\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.risk_result_datatype_encoders import (\n encode_series_result,\n encode_dataframe_result,\n decode_series_result,\n decode_dataframe_result,\n)\n\n\ndef test_encode_series_result():\n d = {'a': 1, 'b': 2, 'c': 3}\n s = pd.Series(data=d, index=['a', 'b', 'c'], name='test')\n enc = encode_series_result(s)\n assert enc == {'index': ('a', 'b', 'c'), 'name': 'test', 'values': (1, 2, 3)}\n\n\ndef test_encode_dataframe_result():\n df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])\n enc = encode_dataframe_result(df)\n assert enc == {'index': (0, 1, 2), 'columns': ('a', 'b', 'c'), 'values': ((1, 2, 3), (4, 5, 6), (7, 8, 9))}\n\n\ndef test_decode_series_result():\n enc = {'index': ('2024-06-12', '2024-06-13', '2024-06-14'), 'name': 'test', 'values': (1, 2, 3)}\n s = decode_series_result(enc)\n assert_series_equal(\n s,\n pd.Series(\n data=[1, 2, 3], index=[dt.date(2024, 6, 12), dt.date(2024, 6, 13), dt.date(2024, 6, 14)], name='test'\n ),\n )\n\n\ndef test_decode_dataframe_result():\n enc = {\n 'index': ('2024-06-12', '2024-06-13', '2024-06-14'),\n 'columns': ('a', 'b', 'c'),\n 'values': ((1, 2, 3), (4, 5, 6), (7, 8, 9)),\n }\n df = decode_dataframe_result(enc)\n assert_frame_equal(\n df,\n pd.DataFrame(\n np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n columns=['a', 'b', 'c'],\n index=[dt.date(2024, 6, 12), dt.date(2024, 6, 13), dt.date(2024, 6, 14)],\n dtype=np.int64,\n ),\n )\n" }, { "path": "gs_quant/test/api/backtests_xasset/json_encoders/response_datatypes/test_risk_result_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport pandas as pd\nfrom pandas._testing import assert_series_equal\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_datatypes.risk_result_encoders import (\n map_result_to_datatype,\n decode_risk_result,\n)\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result import RefType, RiskResultsByDate\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result_datatypes import (\n FloatWithData,\n StringWithData,\n VectorWithData,\n MatrixWithData,\n)\n\n\ndef test_map_result_to_datatype():\n assert map_result_to_datatype(5) == FloatWithData\n assert map_result_to_datatype(5.0) == FloatWithData\n assert map_result_to_datatype(\"abc\") == StringWithData\n assert map_result_to_datatype(pd.Series([])) == VectorWithData\n assert map_result_to_datatype(pd.DataFrame([])) == MatrixWithData\n\n\ndef test_decode_risk_result():\n encoded_series_result_1 = {'index': ('a', 'b', 'c'), 'name': 'test', 'values': (1, 2, 3)}\n encoded_series_result_2 = {'index': ('a', 'b', 'c'), 'name': 'test', 'values': (1, 2, 3)}\n risk_result = {\n 'refs': {'legId': 'uuid_1', 'riskMeasure': 'uuid_2'},\n 'result': {\n '2024-06-11': {'unit': 'EUR', 'result': encoded_series_result_1, 'type': 'vector'},\n '2024-06-12': {'unit': 'EUR', 'result': encoded_series_result_2, 'type': 'vector'},\n },\n }\n decoded = decode_risk_result(risk_result)\n assert isinstance(decoded, RiskResultsByDate)\n assert decoded.refs == {RefType.LEG_ID: 'uuid_1', RefType.RISK_MEASURE: 'uuid_2'}\n assert isinstance(decoded.result, dict)\n assert len(decoded.result) == 2\n assert isinstance(decoded.result[dt.date(2024, 6, 11)], VectorWithData)\n assert decoded.result[dt.date(2024, 6, 11)].unit == 'EUR'\n assert_series_equal(\n decoded.result[dt.date(2024, 6, 11)].result, pd.Series(data=[1, 2, 3], index=['a', 'b', 'c'], name='test')\n )\n" }, { "path": "gs_quant/test/api/backtests_xasset/json_encoders/test_request_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.request_encoders import legs_decoder, legs_encoder\nfrom gs_quant.common import AssetClass, AssetType\nfrom gs_quant.instrument import FXOption, EqOption\n\n\ndef test_legs_decoder():\n fx_leg_1 = {\"pair\": \"EURUSD\", \"assetClass\": \"FX\", \"type\": \"Option\", \"name\": \"leg_0\"}\n fx_leg_2 = {\"pair\": \"GBPUSD\", \"assetClass\": \"FX\", \"type\": \"Option\"}\n eq_leg = {\"underlier\": \".SPX\", \"assetClass\": \"Equity\", \"type\": \"Option\", \"name\": \"test_eq\"}\n [inst_1, inst_2, inst_3] = legs_decoder([fx_leg_1, fx_leg_2, eq_leg])\n assert isinstance(inst_1, FXOption)\n assert inst_1.name == \"leg_0\"\n assert inst_1.pair == \"EURUSD\"\n assert isinstance(inst_2, FXOption)\n assert inst_2.name == \"leg_1\"\n assert inst_2.pair == \"GBPUSD\"\n assert isinstance(inst_3, EqOption)\n assert inst_3.name == \"test_eq\"\n assert inst_3.underlier == \".SPX\"\n\n\ndef test_legs_encoder():\n fx_leg_1 = FXOption(pair=\"EURUSD\", name=\"leg_0\")\n fx_leg_2 = FXOption(pair=\"GBPUSD\")\n eq_leg = EqOption(underlier=\".SPX\", name=\"test_eq\")\n [inst_1, inst_2, inst_3] = legs_encoder([fx_leg_1, fx_leg_2, eq_leg])\n assert isinstance(inst_1, dict)\n assert inst_1[\"assetClass\"] == AssetClass.FX\n assert inst_1[\"type\"] == AssetType.Option\n assert inst_1[\"pair\"] == \"EURUSD\"\n assert isinstance(inst_2, dict)\n assert inst_2[\"assetClass\"] == AssetClass.FX\n assert inst_2[\"type\"] == AssetType.Option\n assert inst_2[\"pair\"] == \"GBPUSD\"\n assert isinstance(inst_3, dict)\n assert inst_3[\"assetClass\"] == AssetClass.Equity\n assert inst_3[\"type\"] == AssetType.Option\n assert inst_3[\"underlier\"] == \".SPX\"\n" }, { "path": "gs_quant/test/api/backtests_xasset/json_encoders/test_response_encoders.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.api.gs.backtests_xasset.json_encoders.response_encoders import decode_basic_bt_transactions\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import TransactionDirection\nfrom gs_quant.common import Currency\nfrom gs_quant.instrument import EqOption\nimport datetime as dt\n\n\ndef test_decode_basic_bt_transactions():\n server_response_minimum = {\n dt.date(2025, 5, 1).isoformat(): [\n {\n 'portfolio': (EqOption(underlier='.SPX', expiration_date='2025-12-19', strike_price=5000).as_dict(),),\n }\n ]\n }\n assert len(decode_basic_bt_transactions(server_response_minimum)) == 1\n assert len(decode_basic_bt_transactions(server_response_minimum, decode_instruments=False)) == 1\n\n server_response = {\n dt.date(2025, 5, 1).isoformat(): [\n {\n 'portfolio': (EqOption(underlier='.SPX', expiration_date='2025-12-19', strike_price=5000).as_dict(),),\n 'currency': 'USD',\n 'portfolio_price': 100,\n 'cost': 0.5,\n 'direction': 'Entry',\n 'quantity': 25,\n }\n ]\n }\n\n decoded_response = decode_basic_bt_transactions(server_response)\n transaction = decoded_response[dt.date(2025, 5, 1)][0]\n assert transaction.portfolio_price == 100\n assert transaction.cost == 0.5\n assert transaction.currency == Currency.USD\n assert transaction.direction == TransactionDirection.Entry\n assert transaction.quantity == 25\n" }, { "path": "gs_quant/test/api/backtests_xasset/response_datatypes/test_backtest_datatypes.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport dataclasses\nimport pytest\n\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import (\n Transaction,\n AdditionalResults,\n DateConfig,\n Trade,\n Configuration,\n TransactionCostConfig,\n FixedCostModel,\n ScaledCostModel,\n TransactionCostScalingType,\n AggregateCostModel,\n CostAggregationType,\n)\n\n\ndef test_request_types():\n cls = (Transaction, AdditionalResults, DateConfig, Trade, TransactionCostConfig, Configuration)\n for c in cls:\n assert dataclasses.is_dataclass(c)\n\n\ndef test_model_addition():\n f1 = FixedCostModel(1)\n f2 = FixedCostModel(2)\n s1 = ScaledCostModel(10, TransactionCostScalingType.Vega)\n s2 = ScaledCostModel(20, TransactionCostScalingType.Vega)\n s3 = ScaledCostModel(20, TransactionCostScalingType.Delta)\n assert f1 + f2 == FixedCostModel(3)\n assert s1 + s2 == ScaledCostModel(30, TransactionCostScalingType.Vega)\n assert f1 + s1 == AggregateCostModel((f1, s1), CostAggregationType.Sum)\n assert f2 + s2 == AggregateCostModel((f2, s2), CostAggregationType.Sum)\n assert (f1 + s1) + (f2 + s2) == AggregateCostModel((f1, s1, f2, s2), CostAggregationType.Sum)\n assert s1 + s3 == AggregateCostModel((s1, s3), CostAggregationType.Sum)\n assert AggregateCostModel((f1, s1), CostAggregationType.Min) + AggregateCostModel(\n (f2, s2), CostAggregationType.Min\n ) == AggregateCostModel((f1, s1, f2, s2), CostAggregationType.Min)\n with pytest.raises(TypeError):\n s1 + 1\n with pytest.raises(TypeError):\n s1 + 'a'\n with pytest.raises(TypeError):\n s1 + AggregateCostModel((f1,), CostAggregationType.Sum)\n with pytest.raises(TypeError):\n AggregateCostModel((f1, s1), CostAggregationType.Min) + AggregateCostModel((f2, s2), CostAggregationType.Max)\n" }, { "path": "gs_quant/test/api/backtests_xasset/response_datatypes/test_risk_result.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport dataclasses\n\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result import RiskResultsByDate, RiskResultsError\n\n\ndef test_request_types():\n cls = (RiskResultsByDate, RiskResultsError)\n for c in cls:\n assert dataclasses.is_dataclass(c)\n" }, { "path": "gs_quant/test/api/backtests_xasset/response_datatypes/test_risk_result_datatypes.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport dataclasses\n\nimport pytest\n\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.risk_result_datatypes import (\n RiskResultWithData,\n FloatWithData,\n StringWithData,\n VectorWithData,\n MatrixWithData,\n)\n\n\ndef test_request_types():\n cls = (RiskResultWithData, FloatWithData, StringWithData, VectorWithData, MatrixWithData)\n for c in cls:\n assert dataclasses.is_dataclass(c)\n assert issubclass(c, RiskResultWithData)\n\n\ndef test_arithmetics():\n assert FloatWithData(result=2) + FloatWithData(result=3) == FloatWithData(result=5)\n assert FloatWithData(result=2) - FloatWithData(result=3) == FloatWithData(result=-1)\n assert FloatWithData(result=2) * FloatWithData(result=3) == FloatWithData(result=6)\n assert FloatWithData(result=2, unit='EUR') / FloatWithData(result=3, unit='EUR') == FloatWithData(\n result=2 / 3, unit='EUR'\n )\n assert 3 + FloatWithData(result=2, unit='EUR') == FloatWithData(result=5, unit='EUR')\n with pytest.raises(TypeError):\n FloatWithData(result=2) + 'a'\n with pytest.raises(ValueError):\n FloatWithData(result=2, unit='EUR') + FloatWithData(result=3, unit='USD')\n\n assert StringWithData(result='ab') + StringWithData(result='cd') == StringWithData(result='abcd')\n assert StringWithData(result='ab') + 'cd' == StringWithData(result='abcd')\n" }, { "path": "gs_quant/test/api/backtests_xasset/test_request.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport dataclasses\n\nfrom gs_quant.api.gs.backtests_xasset.request import RiskRequest, BasicBacktestRequest, GenericBacktestRequest\n\n\ndef test_request_types():\n cls = (RiskRequest, BasicBacktestRequest, GenericBacktestRequest)\n for c in cls:\n assert dataclasses.is_dataclass(c)\n" }, { "path": "gs_quant/test/api/backtests_xasset/test_response.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport dataclasses\n\nfrom gs_quant.api.gs.backtests_xasset.response import RiskResponse, BasicBacktestResponse, GenericBacktestResponse\n\n\ndef test_response_types():\n cls = (RiskResponse, BasicBacktestResponse, GenericBacktestResponse)\n for c in cls:\n assert dataclasses.is_dataclass(c)\n" }, { "path": "gs_quant/test/api/test_assets.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport dateutil.parser as dup\nimport pytest\nimport testfixtures\nfrom gs_quant.api.gs.assets import GsAssetApi, GsAsset, GsTemporalXRef, ENABLE_ASSET_CACHING\nfrom gs_quant.session import Environment, GsSession\nfrom gs_quant.common import PositionType, XRef\nfrom gs_quant.target.assets import FieldFilterMap, Position, PositionSet, EntityQuery\n\n\ndef test_get_asset(mocker):\n marquee_id = 'MQA1234567890'\n\n mock_response = GsAsset(id=marquee_id, assetClass='Equity', type='Single Stock', name='Test Asset')\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsAssetApi.get_asset(marquee_id)\n\n GsSession.current.sync.get.assert_called_with('/assets/{id}'.format(id=marquee_id), cls=GsAsset)\n\n assert response == mock_response\n\n\ndef test_get_many_assets(mocker, monkeypatch):\n marquee_id_1 = 'MQA1234567890'\n marquee_id_2 = 'MQA4567890123'\n\n query = {'id': [marquee_id_1, marquee_id_2]}\n as_of = dt.datetime.utcnow()\n\n inputs = EntityQuery(where=FieldFilterMap(**query), fields=None, asOfTime=as_of, limit=100)\n\n mock_response = {\n 'results': (\n GsAsset.from_dict({'id': marquee_id_1, 'assetClass': 'Equity', 'type': 'Single Stock', 'name': 'Test 1'}),\n GsAsset.from_dict({'id': marquee_id_2, 'assetClass': 'Equity', 'type': 'Single Stock', 'name': 'Test 2'}),\n )\n }\n\n expected_response = (\n GsAsset(id=marquee_id_1, assetClass='Equity', type='Single Stock', name='Test 1'),\n GsAsset(id=marquee_id_2, assetClass='Equity', type='Single Stock', name='Test 2'),\n )\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_response)\n\n # run test\n monkeypatch.delenv(ENABLE_ASSET_CACHING, raising=False)\n response = GsAssetApi.get_many_assets(id=[marquee_id_1, marquee_id_2], as_of=as_of)\n GsSession.current.sync.post.assert_called_with('/assets/query', cls=GsAsset, payload=inputs)\n assert response == expected_response\n\n monkeypatch.setenv(ENABLE_ASSET_CACHING, '1') # run 2x with cache on\n response = GsAssetApi.get_many_assets(id=[marquee_id_1, marquee_id_2], as_of=as_of)\n assert response == expected_response\n response = GsAssetApi.get_many_assets(id=[marquee_id_1, marquee_id_2], as_of=as_of)\n assert response == expected_response\n\n\ndef test_get_asset_xrefs(mocker):\n marquee_id = 'MQA1234567890'\n\n mock_response = {\n 'xrefs': (\n {\n 'startDate': '1952-01-01',\n 'endDate': '2018-12-31',\n 'identifiers': {'ric': '.GSTHHOLD', 'bbid': 'GSTHHOLD', 'cusip': '9EQ24FOLD', 'ticker': 'GSTHHOLD'},\n },\n {\n 'startDate': '2019-01-01',\n 'endDate': '2952-12-31',\n 'identifiers': {\n 'ric': '.GSTHHVIP',\n 'bbid': 'GSTHHVIP',\n 'cusip': '9EQ24FPE5',\n 'ticker': 'GSTHHVIP',\n },\n },\n )\n }\n\n expected_response = (\n GsTemporalXRef(\n dt.date(1952, 1, 1),\n dt.date(2018, 12, 31),\n XRef(\n ric='.GSTHHOLD',\n bbid='GSTHHOLD',\n cusip='9EQ24FOLD',\n ticker='GSTHHOLD',\n ),\n ),\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n ric='.GSTHHVIP',\n bbid='GSTHHVIP',\n cusip='9EQ24FPE5',\n ticker='GSTHHVIP',\n ),\n ),\n )\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsAssetApi.get_asset_xrefs(marquee_id)\n\n GsSession.current.sync.get.assert_called_with('/assets/{id}/xrefs'.format(id=marquee_id))\n testfixtures.compare(response, expected_response)\n\n\ndef test_get_asset_positions_for_date(mocker):\n marquee_id = 'MQA1234567890'\n position_date = dt.date(2019, 2, 19)\n\n mock_response = {\n 'results': (\n {\n 'id': 'mock1',\n 'positionDate': '2019-02-19',\n 'lastUpdateTime': '2019-02-19T12:10:32.401Z',\n 'positions': [{'assetId': 'MQA123', 'quantity': 0.3}, {'assetId': 'MQA456', 'quantity': 0.7}],\n 'type': 'open',\n 'divisor': 100,\n },\n {\n 'id': 'mock2',\n 'positionDate': '2019-02-19',\n 'lastUpdateTime': '2019-02-20T05:04:32.981Z',\n 'positions': [{'assetId': 'MQA123', 'quantity': 0.4}, {'assetId': 'MQA456', 'quantity': 0.6}],\n 'type': 'close',\n 'divisor': 120,\n },\n )\n }\n\n expected_response = (\n PositionSet(\n id_='mock1',\n position_date=dt.date(2019, 2, 19),\n last_update_time=dup.parse('2019-02-19T12:10:32.401Z'),\n positions=(Position(assetId='MQA123', quantity=0.3), Position(assetId='MQA456', quantity=0.7)),\n type_='open',\n divisor=100,\n ),\n PositionSet(\n id_='mock2',\n position_date=dt.date(2019, 2, 19),\n last_update_time=dup.parse('2019-02-20T05:04:32.981Z'),\n positions=(Position(assetId='MQA123', quantity=0.4), Position(assetId='MQA456', quantity=0.6)),\n type_='close',\n divisor=120,\n ),\n )\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsAssetApi.get_asset_positions_for_date(marquee_id, position_date)\n\n GsSession.current.sync.get.assert_called_with(\n '/assets/{id}/positions/{date}'.format(id=marquee_id, date=position_date)\n )\n\n testfixtures.compare(response, expected_response)\n\n mock_response = {\n 'results': [\n {\n 'id': 'mock',\n 'positionDate': '2019-02-19',\n 'lastUpdateTime': '2019-02-20T05:04:32.981Z',\n 'positions': [{'assetId': 'MQA123', 'quantity': 0.4}, {'assetId': 'MQA456', 'quantity': 0.6}],\n 'type': 'close',\n 'divisor': 120,\n }\n ]\n }\n\n expected_response = (\n PositionSet(\n id_='mock',\n position_date=dt.date(2019, 2, 19),\n last_update_time=dup.parse('2019-02-20T05:04:32.981Z'),\n positions=(Position(assetId='MQA123', quantity=0.4), Position(assetId='MQA456', quantity=0.6)),\n type_='close',\n divisor=120,\n ),\n )\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n\n response = GsAssetApi.get_asset_positions_for_date(marquee_id, position_date, PositionType.CLOSE)\n\n testfixtures.compare(response, expected_response)\n\n GsSession.current.sync.get.assert_called_with(\n '/assets/{id}/positions/{date}?type=close'.format(id=marquee_id, date=position_date)\n )\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/api/test_backtests.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.api.gs.backtests import GsBacktestApi, Backtest\nfrom gs_quant.session import Environment, GsSession\n\n\ndef test_get_many_backtests(mocker):\n id_1 = 'BT1'\n id_2 = 'BT2'\n\n mock_response = {\n 'results': (\n Backtest.from_dict({'id': id_1, 'assetClass': 'Commod', 'type': 'Basket', 'name': 'Example Backtest 1'}),\n Backtest.from_dict({'id': id_2, 'assetClass': 'Commod', 'type': 'Basket', 'name': 'Example Backtest 2'}),\n ),\n 'totalResults': 2,\n }\n\n expected_response = (\n Backtest(id=id_1, assetClass=\"Commod\", type=\"Basket\", name='Example Backtest 1'),\n Backtest(id=id_2, assetClass=\"Commod\", type=\"Basket\", name='Example Backtest 2'),\n )\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsBacktestApi.get_many_backtests()\n GsSession.current.sync.get.assert_called_with('/backtests?limit=100', cls=Backtest)\n assert response == expected_response\n\n\ndef test_get_backtest(mocker):\n id_1 = 'BT1'\n mock_response = Backtest(id=id_1, assetClass=\"Commod\", type=\"Basket\", name='Example Backtest')\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsBacktestApi.get_backtest(id_1)\n GsSession.current.sync.get.assert_called_with('/backtests/{id}'.format(id=id_1), cls=Backtest)\n assert response == mock_response\n\n\ndef test_create_backtest(mocker):\n id_1 = 'BT1'\n\n backtest = Backtest(id=id_1, assetClass=\"Commod\", type=\"Basket\", name='Example Backtest')\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=backtest)\n\n # run test\n response = GsBacktestApi.create_backtest(backtest)\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n GsSession.current.sync.post.assert_called_with(\n '/backtests', backtest, request_headers=request_headers, cls=Backtest\n )\n assert response == backtest\n\n\ndef test_update_backtest(mocker):\n id_1 = 'BT1'\n\n backtest = Backtest(id=id_1, assetClass=\"Commod\", type=\"Basket\", name='Example Backtest')\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'put', return_value=backtest)\n\n # run test\n response = GsBacktestApi.update_backtest(backtest)\n request_headers = {'Content-Type': 'application/json;charset=utf-8', 'Accept': 'application/json;charset=utf-8'}\n GsSession.current.sync.put.assert_called_with(\n '/backtests/{id}'.format(id=id_1), backtest, request_headers=request_headers, cls=Backtest\n )\n assert response == backtest\n\n\ndef test_delete_backtest(mocker):\n id_1 = 'BT1'\n\n mock_response = \"Successfully deleted backtest.\"\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'delete', return_value=mock_response)\n\n # run test\n response = GsBacktestApi.delete_backtest(id_1)\n GsSession.current.sync.delete.assert_called_with('/backtests/{id}'.format(id=id_1))\n assert response == mock_response\n\n\ndef test_schedule_backtest(mocker):\n id_1 = 'BT1'\n\n mock_response = \"Successfully scheduled backtest.\"\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_response)\n\n # run test\n response = GsBacktestApi.schedule_backtest(backtest_id=id_1)\n GsSession.current.sync.post.assert_called_with('/backtests/{id}/schedule'.format(id=id_1))\n assert response == mock_response\n" }, { "path": "gs_quant/test/api/test_base_screener.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.api.gs.base_screener import GsBaseScreenerApi\nfrom gs_quant.session import Environment, GsSession\nfrom gs_quant.target.base_screener import Screener, ScreenerRow, ScreenerColumn, ScreenerParameters\n\n\ndef test_get_all_screeners(mocker):\n rows = (ScreenerRow(entity_id='MA33ZVRPT84HRK5A', entity_type='index', universe=True, expand=True),)\n\n cols1 = (ScreenerColumn(column_name='Name', entity_parameter='name'),)\n cols2 = (ScreenerColumn(column_name='BBID', entity_parameter='bbid'),)\n\n screener1_params = ScreenerParameters(rows=rows, columns=cols1)\n screener2_params = ScreenerParameters(rows=rows, columns=cols2)\n\n screener1 = Screener(name='Screener1', parameters=screener1_params)\n screener2 = Screener(name='Screener2', parameters=screener2_params)\n mock_response = {'totalResults': 2, 'results': (screener1, screener2)}\n\n expected_response = (screener1, screener2)\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsBaseScreenerApi.get_screeners()\n GsSession.current.sync.get.assert_called_with('/data/screeners', cls=Screener)\n assert response == expected_response\n\n\ndef test_get_screen(mocker):\n rows = (ScreenerRow(entity_id='MA33ZVRPT84HRK5A', entity_type='index', universe=True, expand=True),)\n cols = (ScreenerColumn(column_name='Name', entity_parameter='name'),)\n screener_params = ScreenerParameters(rows=rows, columns=cols)\n screener_id = 'id'\n\n mock_response = Screener(name='Screener', parameters=screener_params, id_=screener_id)\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsBaseScreenerApi.get_screener(screener_id)\n GsSession.current.sync.get.assert_called_with('/data/screeners/{id}'.format(id=screener_id), cls=Screener)\n assert response == mock_response\n\n\ndef test_create_screener(mocker):\n rows = (ScreenerRow(entity_id='MA33ZVRPT84HRK5A', entity_type='index', universe=True, expand=True),)\n cols = (ScreenerColumn(column_name='Name', entity_parameter='name'),)\n screener_params = ScreenerParameters(rows=rows, columns=cols)\n\n mock_input = Screener(name='Screener', parameters=screener_params, id_=None)\n mock_response = Screener(name='Screener', parameters=screener_params, id_='1')\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_response)\n\n # run test\n response = GsBaseScreenerApi.create_screener(mock_input)\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n GsSession.current.sync.post.assert_called_with(\n '/data/screeners', mock_input, request_headers=request_headers, cls=Screener\n )\n assert response == mock_response\n\n\ndef test_edit_screener(mocker):\n rows = (ScreenerRow(entity_id='MA33ZVRPT84HRK5A', entity_type='index', universe=True, expand=True),)\n new_cols = (ScreenerColumn(column_name='BBID', entity_parameter='bbid'),)\n screener_params = ScreenerParameters(rows=rows, columns=new_cols)\n screener_id = 'id'\n\n mock_screen = Screener(name='Screener', parameters=screener_params, id_=screener_id)\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'put', return_value=mock_screen)\n\n # run test\n response = GsBaseScreenerApi.edit_screener(screener_id, mock_screen)\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n GsSession.current.sync.put.assert_called_with(\n '/data/screeners/{id}'.format(id=screener_id), mock_screen, request_headers=request_headers, cls=Screener\n )\n assert response == mock_screen\n\n\ndef test_publish_to_screener(mocker):\n screener_id = 'id'\n publish_data = {'rows': [{\"Name\": \"Obalon Therapeutics Inc\", \"Entity ID\": \"ABDFDSCD\"}]}\n\n mock_response = {\n \"requestId\": \"\",\n \"timestamp\": \"2022-06-10T19:46:54.945Z\",\n \"data\": [\n {\n \"name\": \"Obalon Therapeutics Inc\",\n \"entityId\": \"ABDFDSCD\",\n \"active\": True,\n \"lastUpdatedTime\": \"2022-06-10T19:46:54.862Z\",\n }\n ],\n }\n\n expected_result = [\n {\n \"name\": \"Obalon Therapeutics Inc\",\n \"entityId\": \"ABDFDSCD\",\n \"active\": True,\n \"lastUpdatedTime\": \"2022-06-10T19:46:54.862Z\",\n }\n ]\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_response)\n\n # run test\n response = GsBaseScreenerApi.publish_to_screener(screener_id, publish_data)\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n GsSession.current.sync.post.assert_called_with(\n '/data/screeners/{id}/publish'.format(id=screener_id), publish_data, request_headers=request_headers\n )\n assert response == expected_result\n\n\ndef test_clear_screener(mocker):\n screener_id = 'id'\n\n mock_response = {'requestId': '1', 'timestamp': '2022-08-01T16:56:11.241Z', 'success': True}\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_response)\n\n # run test\n response = GsBaseScreenerApi.clear_screener(screener_id)\n request_headers = {'Content-Type': 'application/json;charset=utf-8'}\n GsSession.current.sync.post.assert_called_with(\n '/data/screeners/{id}/clear'.format(id=screener_id), {}, request_headers=request_headers\n )\n\n assert response == mock_response\n\n\ndef test_delete_screener(mocker):\n screener_id = 'id'\n mock_response = None\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'delete', return_value=mock_response)\n\n # run test\n response = GsBaseScreenerApi.delete_screener(screener_id)\n GsSession.current.sync.delete.assert_called_with('/data/screeners/{id}'.format(id=screener_id))\n\n assert response == mock_response\n" }, { "path": "gs_quant/test/api/test_cache.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest import mock\n\nimport pandas as pd\n\nimport gs_quant.risk as risk\nfrom gs_quant.api.gs.risk import GsRiskApi\nfrom gs_quant.instrument import IRSwap, IRSwaption\nfrom gs_quant.markets import HistoricalPricingContext, PricingCache, PricingContext\nfrom gs_quant.session import Environment, GsSession\n\n\ndef set_session():\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.PROD, 'client_id', 'secret')\n PricingCache.clear()\n\n\ndef test_cache_addition_removal():\n set_session()\n\n p1 = IRSwap('Pay', '10y', 'DKK')\n\n with mock.patch('gs_quant.api.gs.risk.GsRiskApi._exec') as mocker:\n mocker.return_value = [[[[{'$type': 'Risk', 'val': 0.07}]]]]\n\n with PricingContext(use_cache=True) as pc:\n p1.price()\n price_key = pc._PricingContext__risk_key(risk.Price, p1.provider)\n delta_key = pc._PricingContext__risk_key(risk.IRDelta, p1.provider)\n\n assert PricingCache.get(price_key, p1)\n\n assert not PricingCache.get(delta_key, p1)\n\n # Assert that deleting the cached instrument removes it from the PricingCache\n # N.B, this may not work when debugging tests\n del p1\n del mocker\n\n import gc\n\n gc.collect()\n\n p2 = IRSwap('Pay', '10y', 'DKK')\n with PricingContext.current as cur:\n p2_price_key = cur._PricingContext__risk_key(risk.Price, p2.provider)\n # assert not PricingCache.get(p2_price_key)\n\n with mock.patch('gs_quant.api.gs.risk.GsRiskApi._exec') as mocker:\n mocker.return_value = [[[[{'$type': 'Risk', 'val': 0.07}]]]]\n\n with PricingContext(use_cache=True):\n p2_price = p2.price()\n\n assert PricingCache.get(p2_price_key, p2) == p2_price.result()\n\n # Assert that running under a scenario does not retrieve the base result\n with mock.patch('gs_quant.api.gs.risk.GsRiskApi._exec') as mocker:\n mocker.return_value = [[[[{'$type': 'Risk', 'val': 0.08}]]]]\n\n with risk.RollFwd(date='1m'), PricingContext(use_cache=True) as spc:\n # Don't want the price without the scenario\n scenario_risk_key = spc._PricingContext__risk_key(risk.Price, p2.provider)\n assert not PricingCache.get(scenario_risk_key, p2)\n scenario_price = p2.price()\n\n assert PricingCache.get(scenario_risk_key, p2) == scenario_price.result()\n\n with PricingContext(use_cache=True) as pc, risk.RollFwd(date='1m'):\n cached_scenario_price = PricingCache.get(pc._PricingContext__risk_key(risk.Price, p2.provider), p2)\n\n # Check that we get the cached scenario price\n assert cached_scenario_price == scenario_price.result()\n\n # Check the base result is still correct\n assert PricingCache.get(p2_price_key, p2) == p2_price.result()\n\n # Assert that caching respects parameters, such as csa\n with mock.patch('gs_quant.api.gs.risk.GsRiskApi._exec') as mocker:\n mocker.return_value = [[[[{'$type': 'Risk', 'val': 0.08}]]]]\n\n with PricingContext(use_cache=True, csa_term='INVALID') as pc:\n # Don't want the price with default csa\n assert not PricingCache.get(pc._PricingContext__risk_key(risk.Price, p2.provider), p2)\n csa_price = p2.price()\n\n with PricingContext(use_cache=True, csa_term='INVALID') as pc:\n cached_csa_price = PricingCache.get(pc._PricingContext__risk_key(risk.Price, p2.provider), p2)\n\n # Check that we get the cached csa price\n assert cached_csa_price == csa_price.result()\n\n # Check the base result is still correct\n assert PricingCache.get(p2_price_key, p2) == p2_price.result()\n\n # Change a property and assert that p2 is no longer cached\n p2.notional_currency = 'EUR'\n assert not PricingCache.get(p2_price_key, p2)\n\n\n@mock.patch.object(GsRiskApi, '_exec')\ndef test_cache_subset(mocker):\n set_session()\n\n ir_swap = IRSwap('Pay', '10y', 'DKK')\n\n values = [{'$type': 'Risk', 'val': 0.01}]\n mocker.return_value = [[[values]], [[values]]]\n\n dates = (dt.date(2019, 10, 7), dt.date(2019, 10, 8))\n with HistoricalPricingContext(dates=dates, use_cache=True):\n price_f = ir_swap.price()\n price_f.result()\n\n for date in dates:\n with PricingContext(pricing_date=date, use_historical_diddles_only=True) as pc:\n risk_key = pc._PricingContext__risk_key(risk.Price, ir_swap.provider)\n cached_scalar = PricingCache.get(risk_key, ir_swap)\n assert cached_scalar\n assert isinstance(cached_scalar, float)\n\n with PricingContext(pricing_date=dt.date(2019, 10, 9)) as pc:\n risk_key = pc._PricingContext__risk_key(risk.Price, ir_swap.provider)\n cached2 = PricingCache.get(risk_key, ir_swap)\n assert cached2 is None\n\n values = [\n {\n '$type': 'RiskVector',\n 'asset': [0.01, 0.015],\n 'points': [\n {'type': 'IR', 'asset': 'USD', 'class_': 'Swap', 'point': '1y'},\n {'type': 'IR', 'asset': 'USD', 'class_': 'Swap', 'point': '2y'},\n ],\n }\n ]\n\n # Check that we can return the same values from the cache, after calculating once (with return values set to None)\n\n for return_values in ([[[values]], [[values]], [[values]]], None):\n mocker.return_value = return_values\n\n with HistoricalPricingContext(dates=dates, use_cache=True):\n risk_f = ir_swap.calc(risk.IRDelta)\n\n risk_frame = risk_f.result()\n\n assert isinstance(risk_frame, pd.DataFrame)\n assert len(risk_frame.index.unique()) == len(dates)\n\n\n@mock.patch.object(GsRiskApi, '_exec')\ndef test_multiple_measures(mocker):\n day = [\n [\n [\n {\n '$type': 'RiskVector',\n 'asset': [0.01, 0.015],\n 'points': [\n {'type': 'IR Vol', 'asset': 'USD-LIBOR-BBA', 'class_': 'Swap', 'point': '1y'},\n {'type': 'IR Vol', 'asset': 'USD-LIBOR-BBA', 'class_': 'Swap', 'point': '2y'},\n ],\n }\n ]\n ],\n [\n [\n {\n '$type': 'RiskVector',\n 'asset': [0.01, 0.015],\n 'points': [\n {'type': 'IR', 'asset': 'USD', 'class_': 'Swap', 'point': '1y'},\n {'type': 'IR', 'asset': 'USD', 'class_': 'Swap', 'point': '2y'},\n ],\n }\n ],\n ],\n [[{'$type': 'Risk', 'val': 0.01}]],\n ]\n\n mocker.return_value = [day, day, day]\n\n set_session()\n\n ir_swaption = IRSwaption('Pay', '10y', 'USD')\n\n dates = (dt.date(2019, 10, 7), dt.date(2019, 10, 8), dt.date(2019, 10, 9))\n with HistoricalPricingContext(dates=dates, use_cache=True):\n ir_swaption.price()\n ir_swaption.calc(risk.IRDelta)\n ir_swaption.calc(risk.IRVega)\n\n # make sure all the risk measures got cached correctly\n for date in dates:\n with PricingContext(pricing_date=date, use_historical_diddles_only=True) as pc:\n for risk_measure in (risk.Price, risk.IRDelta, risk.IRVega):\n val = PricingCache.get(pc._PricingContext__risk_key(risk_measure, ir_swaption.provider), ir_swaption)\n assert val is not None\n\n with PricingContext(pricing_date=dt.date(2019, 10, 11), use_historical_diddles_only=True) as pc:\n for risk_measure in (risk.Price, risk.IRDelta, risk.IRVega):\n val = PricingCache.get(pc._PricingContext__risk_key(risk_measure, ir_swaption.provider), ir_swaption)\n assert val is None\n" }, { "path": "gs_quant/test/api/test_carbon.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport pytest\n\nfrom gs_quant.api.gs.carbon import GsCarbonApi, CarbonCard\nfrom gs_quant.common import Currency\nfrom gs_quant.session import GsSession, Environment\n\n\ndef test_get_carbon_data(mocker):\n mock_response = {\n \"coverage\": {\n \"weights\": {\n \"portfolio\": {\n \"scope1\": {\n \"reportedVerified\": 27.172942402143175,\n \"reportedUnverified\": 72.82705759785682,\n \"estimated\": 0,\n \"missing\": 0,\n }\n },\n \"benchmark\": {\n \"scope1\": {\n \"reportedVerified\": 56.951789932735934,\n \"reportedUnverified\": 19.955009583898327,\n \"estimated\": 18.412744312759482,\n \"missing\": 4.680456170606256,\n }\n },\n },\n \"numberOfCompanies\": {\n \"portfolio\": {\n \"scope1\": {\"reportedVerified\": 50, \"reportedUnverified\": 50, \"estimated\": 0, \"missing\": 0}\n },\n \"benchmark\": {\n \"scope1\": {\n \"reportedVerified\": 34.257425742574256,\n \"reportedUnverified\": 29.702970297029704,\n \"estimated\": 28.91089108910891,\n \"missing\": 7.128712871287124,\n }\n },\n },\n },\n \"emissions\": {\n \"scope1\": {\n \"portfolio\": [\n {\n \"year\": \"2016\",\n \"portfolioEmissions\": 0.009388646556910485,\n \"emissionsIntensityRevenue\": 0.2706793856871246,\n \"emissionsIntensityEnterpriseValue\": 0.03388271250693313,\n \"emissionsIntensityMarketCap\": 0.11190632050536728,\n },\n {\n \"year\": \"2017\",\n \"portfolioEmissions\": 0.008619992844290039,\n \"emissionsIntensityRevenue\": 0.2412522591903448,\n \"emissionsIntensityEnterpriseValue\": 0.030906851992767197,\n \"emissionsIntensityMarketCap\": 0.1066210865476519,\n },\n {\n \"year\": \"2018\",\n \"portfolioEmissions\": 0.008893662072152794,\n \"emissionsIntensityRevenue\": 0.21887783180015072,\n \"emissionsIntensityEnterpriseValue\": 0.03144114560735717,\n \"emissionsIntensityMarketCap\": 0.16376957940863043,\n },\n {\n \"year\": \"2019\",\n \"portfolioEmissions\": 0.008896070619613242,\n \"emissionsIntensityRevenue\": 0.2225589953607619,\n \"emissionsIntensityEnterpriseValue\": 0.03199235113825498,\n \"emissionsIntensityMarketCap\": 0.13300579690144726,\n },\n {\n \"year\": \"2021\",\n \"portfolioEmissions\": 0.005462648973310796,\n \"emissionsIntensityRevenue\": 0.17671239978936054,\n \"emissionsIntensityEnterpriseValue\": 0.019184671815761833,\n \"emissionsIntensityMarketCap\": 0.08433051410609066,\n },\n ],\n \"benchmark\": [\n {\n \"year\": \"2016\",\n \"portfolioEmissions\": 10.7359742295746,\n \"emissionsIntensityRevenue\": 128.48937740484533,\n \"emissionsIntensityEnterpriseValue\": 37.874021067055146,\n \"emissionsIntensityMarketCap\": 65.55320800814903,\n },\n {\n \"year\": \"2017\",\n \"portfolioEmissions\": 10.053210673655316,\n \"emissionsIntensityRevenue\": 125.70320189825257,\n \"emissionsIntensityEnterpriseValue\": 35.24939382150576,\n \"emissionsIntensityMarketCap\": 57.499118590386026,\n },\n {\n \"year\": \"2018\",\n \"portfolioEmissions\": 9.795527406862018,\n \"emissionsIntensityRevenue\": 111.70530289875649,\n \"emissionsIntensityEnterpriseValue\": 34.343535288823446,\n \"emissionsIntensityMarketCap\": 54.01561424453475,\n },\n {\n \"year\": \"2019\",\n \"portfolioEmissions\": 8.006501437233737,\n \"emissionsIntensityRevenue\": 104.83823370420704,\n \"emissionsIntensityEnterpriseValue\": 28.07322928722559,\n \"emissionsIntensityMarketCap\": 42.148992638800664,\n },\n {\n \"year\": \"2021\",\n \"portfolioEmissions\": 7.419956698372355,\n \"emissionsIntensityRevenue\": 104.38248106531313,\n \"emissionsIntensityEnterpriseValue\": 26.429401913564423,\n \"emissionsIntensityMarketCap\": 40.70785737756474,\n },\n ],\n }\n },\n }\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsCarbonApi.get_carbon_analytics(\n entity_id='MPRE78YG4J918ERD',\n benchmark_id='MA4B66MW5E27U8P32SB',\n include_estimates=True,\n currency=Currency.GBP,\n cards=[CarbonCard.COVERAGE, CarbonCard.EMISSIONS],\n )\n GsSession.current.sync.get.assert_called_with(\n '/carbon/MPRE78YG4J918ERD?benchmark=MA4B66MW5E27U8P32SB&reportingYear=Latest¤cy=GBP'\n '&includeEstimates=true&useHistoricalData=false&normalizeEmissions=false&card=coverage&card=emissions'\n '&analyticsView=Long'\n )\n assert response == mock_response\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/api/test_content.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom base64 import b64decode\nfrom unittest import mock\n\nimport pytest\n\nfrom gs_quant.api.gs.content import GsContentApi, OrderBy\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.target.content import GetManyContentsResponse\nfrom gs_quant.test.fixtures.content import ContentFixtures\n\n\ndef set_session():\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n\n\n@pytest.mark.parametrize(\n 'description, channels, asset_ids, author_ids, tags, offset, limit, order_by, expected_uri, expected_exception',\n [\n ('Limit is too large, expect exception', set(), set(), set(), set(), 0, 1001, None, None, ValueError),\n (\"Offset is too small, expect exception\", set(), set(), set(), set(), -1, None, None, None, ValueError),\n ('Default params (none specified)', None, None, None, None, None, None, None, '/content', None),\n (\n 'Normal case, multiple parameters',\n ('channel-1', 'channel-2'),\n {'asset-id-1'},\n {'author-id-1'},\n {'tag-1'},\n None,\n None,\n None,\n '/content?channel=channel-1&channel=channel-2&asset_id=asset-id-1&author_id=author-id-1&tag=tag-1',\n None,\n ),\n (\n \"With offset, limit, and orderBy\",\n set(),\n set(),\n set(),\n set(),\n 2,\n 12,\n {'direction': OrderBy.ASC, 'field': 'some-field'},\n '/content?offset=2&limit=12&order_by= list:\n module_path = 'gs_quant.target.' + module_name\n __import__(module_path)\n module = sys.modules[module_path]\n return [m for n, m in inspect.getmembers(module) if inspect.isclass(m) and issubclass(m, Base) and m is not Base]\n\n\ndef test_enum():\n ccy = copy.deepcopy(Currency.HUF)\n assert ccy == Currency.HUF\n\n gbp_pickled = pickle.dumps(Currency.GBP)\n assert pickle.loads(gbp_pickled) == Currency.GBP\n\n\ndef test_classes():\n for module_name in (\n 'assets',\n 'backtests',\n 'charts',\n 'common',\n 'content',\n 'coordinates',\n 'countries',\n 'data',\n 'hedge',\n 'indices',\n 'instrument',\n 'monitor',\n 'portfolios',\n 'reports',\n 'risk',\n 'trades',\n 'workspaces_markets',\n ):\n for typ in classes(module_name):\n if typ.__module__ != module_name:\n continue\n\n fields_by_name = typ._fields_by_name()\n if not fields_by_name:\n continue\n\n obj = typ.default_instance()\n\n for fld in fields_by_name.values():\n _ = object.__getattribute__(obj, fld.name)\n if fld.init:\n setattr(obj, fld.name, None)\n" }, { "path": "gs_quant/test/api/test_thread_manager.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom functools import partial\n\nimport pytest\n\nfrom gs_quant.api.utils import ThreadPoolManager\nfrom gs_quant.session import GsSession\n\n\nclass NullContextManager(object):\n def __init__(self, dummy_resource=None):\n self.dummy_resource = dummy_resource\n\n def __enter__(self):\n return self.dummy_resource\n\n def __exit__(self, *args):\n pass\n\n\ndef dummy_function(number: int):\n return number\n\n\ndef test_thread_manager():\n func_0 = partial(dummy_function, 5)\n func_1 = partial(dummy_function, 0)\n func_2 = partial(dummy_function, 10)\n GsSession.current = NullContextManager()\n results = ThreadPoolManager.run_async([func_0, func_1, func_2])\n assert results[0] == 5\n assert results[1] == 0\n assert results[2] == 10\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/api/test_users.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom unittest import mock\n\nimport pytest\n\nfrom gs_quant.api.gs.users import GsUsersApi\nfrom gs_quant.session import Environment, GsSession\nfrom gs_quant.target.reports import User\n\n\ndef test_get_users(mocker):\n mock_response = {\n \"totalResults\": 1,\n \"results\": [\n {\n \"internal\": True,\n \"systemUser\": False,\n \"appUser\": False,\n \"analyticsId\": \"id123\",\n \"city\": \"New York\",\n \"company\": \"Goldman Sachs Group\",\n \"rootOEId\": \"id123\",\n \"rootOEIndustry\": {\"name\": \"Multi-Business Organization\"},\n \"oeIndustry\": {\"name\": \"Multi-Business Organization\"},\n \"oeAlias\": 17670,\n \"country\": \"US\",\n \"coverage\": [\n {\"phone\": \"+1 855-MARQUE-0\", \"name\": \"Marquee Help Desk\", \"email\": \"gs-marquee-help@gs.com\"}\n ],\n \"email\": \"jane.doe@gs.com\",\n \"kerberos\": \"doeja\",\n \"id\": \"id\",\n \"name\": \"Doe, Jane\",\n \"firstName\": \"Jane\",\n \"lastName\": \"Doe\",\n \"internalID\": \"doeja\",\n \"region\": \"Americas\",\n \"departmentCode\": \"Y362\",\n \"departmentName\": \"Marquee Engineering\",\n \"divisionName\": \"Global Banking & Markets\",\n \"businessUnit\": \"Marquee Engineering\",\n \"title\": \"Analyst\",\n \"login\": \"doeja\",\n \"tokens\": [\"active\"],\n \"roles\": [\"MarqueeResearchExperienceEligible\"],\n \"groups\": [\"3AV5PPWJBS4B89X76\"],\n \"expertiseTags\": [],\n }\n ],\n }\n\n # mock GsSession\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsUsersApi.get_users(user_ids=['doeja'], user_emails=['jane.doe@gs.com'])\n GsSession.current.sync.get.assert_called_with('/users?&id=doeja&email=jane.doe@gs.com&limit=100&offset=0', cls=User)\n assert response == mock_response['results']\n\n\ndef test_get_current_user_info(mocker):\n mock_response = {\n \"internal\": True,\n \"systemUser\": False,\n \"appUser\": False,\n \"analyticsId\": \"id123\",\n \"city\": \"New York\",\n \"company\": \"Goldman Sachs Group\",\n \"rootOEId\": \"id123\",\n \"rootOEIndustry\": {\"name\": \"Multi-Business Organization\"},\n \"oeIndustry\": {\"name\": \"Multi-Business Organization\"},\n \"oeAlias\": 17670,\n \"country\": \"US\",\n \"coverage\": [{\"phone\": \"+1 855-MARQUE-0\", \"name\": \"Marquee Help Desk\", \"email\": \"gs-marquee-help@gs.com\"}],\n \"email\": \"jane.doe@gs.com\",\n \"kerberos\": \"doeja\",\n \"id\": \"id\",\n \"name\": \"Doe, Jane\",\n \"firstName\": \"Jane\",\n \"lastName\": \"Doe\",\n \"internalID\": \"doeja\",\n \"region\": \"Americas\",\n \"departmentCode\": \"Y362\",\n \"departmentName\": \"Marquee Engineering\",\n \"divisionName\": \"Global Banking & Markets\",\n \"businessUnit\": \"Marquee Engineering\",\n \"title\": \"Analyst\",\n \"login\": \"doeja\",\n \"tokens\": [\"active\"],\n \"roles\": [\"MarqueeResearchExperienceEligible\"],\n \"groups\": [\"3AV5PPWJBS4B89X76\"],\n \"expertiseTags\": [],\n }\n\n # mock GsSession\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n # run test\n response = GsUsersApi.get_current_user_info()\n GsSession.current.sync.get.assert_called_with('/users/self')\n assert response == mock_response\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/backtest/__init__.py", "content": "" }, { "path": "gs_quant/test/backtest/test_backtest_eq_vol_engine.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom unittest import mock\n\nimport datetime as dt\nfrom gs_quant.api.gs.backtests_xasset.apis import GsBacktestXassetApi\nfrom gs_quant.api.gs.backtests_xasset.request import BasicBacktestRequest\nfrom gs_quant.api.gs.backtests_xasset.response import BasicBacktestResponse\nfrom gs_quant.api.gs.backtests_xasset.response_datatypes.backtest_datatypes import (\n DateConfig,\n Trade,\n TransactionCostConfig,\n TradingCosts,\n FixedCostModel,\n Configuration,\n RollDateMode,\n StrategyHedge,\n)\nfrom gs_quant.backtests.backtest_objects import ConstantTransactionModel\nfrom gs_quant.backtests.strategy import Strategy\nfrom gs_quant.backtests.triggers import (\n PeriodicTrigger,\n PeriodicTriggerRequirements,\n DateTriggerRequirements,\n AggregateTrigger,\n AggregateTriggerRequirements,\n PortfolioTriggerRequirements,\n TriggerDirection,\n)\nfrom gs_quant.backtests.actions import (\n EnterPositionQuantityScaledAction,\n HedgeAction,\n ExitPositionAction,\n AddTradeAction,\n AddScaledTradeAction,\n ScalingActionType,\n)\nfrom gs_quant.backtests.equity_vol_engine import EquityVolEngine\nfrom gs_quant.common import BuySell\nfrom gs_quant.instrument import EqOption\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk import EqDelta\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.target.backtests import (\n OptionStyle,\n OptionType,\n BacktestTradingQuantityType,\n FlowVolBacktestMeasure,\n EquityMarketModel,\n)\nimport pytest\nimport pandas as pd\nfrom gs_quant.common import TradeAs\n\n# Tests in this module intentionally exercise the deprecated EnterPositionQuantityScaledAction and\n# ExitPositionAction classes to verify the equity vol engine still supports them.\npytestmark = pytest.mark.filterwarnings(\"ignore::DeprecationWarning\")\n\n\ndef set_session():\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n\n\ndef api_mock_data() -> BasicBacktestResponse:\n pnl_response = {\n 'measures': {\n 'PNL': {\n '2019-02-18': {'result': 0, 'type': 'float'},\n '2019-02-19': {'result': -0.18, 'type': 'float'},\n '2019-02-20': {'result': -0.27, 'type': 'float'},\n }\n },\n 'portfolio': {\n \"2019-02-18\": [\n {\n \"expirationDate\": \"2019-05-20\",\n \"optionType\": \"Call\",\n \"strikePrice\": 3244.79,\n \"assetClass\": \"Equity\",\n \"underlier\": \".STOXX50E\",\n \"currency\": \"EUR\",\n \"type\": \"Option\",\n \"settlementType\": \"Cash\",\n \"optionStyle\": \"European\",\n \"buySell\": \"Buy\",\n \"numberOfOptions\": 1,\n },\n {\n \"assetClass\": \"Equity\",\n \"expirationDate\": \"2019-05-20\",\n \"strikePrice\": 3244.79,\n \"underlier\": \".STOXX50E\",\n \"type\": \"Synthetic Forward\",\n \"currency\": \"EUR\",\n \"buySell\": \"Sell\",\n \"quantity\": 0.38264157605861704,\n },\n ]\n },\n 'transactions': {\n \"2019-02-18\": [\n {\n \"portfolio\": [\n {\n \"expirationDate\": \"2019-05-20\",\n \"optionType\": \"Call\",\n \"strikePrice\": 3244.79,\n \"assetClass\": \"Equity\",\n \"underlier\": \".STOXX50E\",\n \"currency\": \"EUR\",\n \"type\": \"Option\",\n \"settlementType\": \"Cash\",\n \"optionStyle\": \"European\",\n \"buySell\": \"Buy\",\n \"numberOfOptions\": 1,\n }\n ],\n \"portfolio_price\": 51.49543980712991,\n \"cost\": 0,\n \"currency\": None,\n \"direction\": \"Entry\",\n \"quantity\": 1,\n },\n {\n \"portfolio\": [\n {\n \"assetClass\": \"Equity\",\n \"expirationDate\": \"2019-05-20\",\n \"strikePrice\": 3244.79,\n \"underlier\": \".STOXX50E\",\n \"type\": \"Synthetic Forward\",\n \"currency\": \"EUR\",\n \"buySell\": \"Sell\",\n \"quantity\": 0.38264157605861704,\n }\n ],\n \"portfolio_price\": -67.01178502458444,\n \"cost\": 0,\n \"currency\": None,\n \"direction\": \"Entry\",\n \"quantity\": -0.38264157605861704,\n },\n ]\n },\n 'additional_results': None,\n }\n\n return BasicBacktestResponse.from_dict_custom(pnl_response, decode_instruments=False)\n\n\ndef mock_api_response(mocker, mock_result: BasicBacktestResponse):\n mocker.return_value = mock_result\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_eq_vol_engine_result(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='option',\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n number_of_options=1,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n number_of_options=1,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n backtest_result = EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert response\n\n data = [{'date': d, 'value': v.result} for d, v in api_mock_data().measures[FlowVolBacktestMeasure.PNL].items()]\n df = pd.DataFrame.from_records(data)\n df.date = pd.to_datetime(df.date)\n expected_pnl = df.set_index('date').value\n\n assert expected_pnl.equals(backtest_result.get_measure_series(FlowVolBacktestMeasure.PNL))\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_basic(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option',\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n number_of_options=1,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n number_of_options=1,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n response = BasicBacktestResponse.from_dict_custom(\n {\n 'measures': {\n 'PNL': {\n '2019-02-18': {'result': 0, 'type': 'float'},\n '2019-02-19': {'result': -0.18, 'type': 'float'},\n '2019-02-20': {'result': -0.27, 'type': 'float'},\n }\n },\n 'portfolio': {},\n 'transactions': {},\n }\n )\n\n mock_api_response(mocker, response)\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=1,\n quantity_type=BacktestTradingQuantityType.quantity,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=StrategyHedge(frequency='1b'),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n hedge_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_trade_quantity(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option',\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n number_of_options=1,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n number_of_options=1,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(\n priceables=option,\n trade_duration='1m',\n trade_quantity=12345,\n trade_quantity_type=BacktestTradingQuantityType.notional,\n name='action',\n )\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=12345,\n quantity_type=BacktestTradingQuantityType.notional,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=StrategyHedge(frequency='1b'),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n hedge_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_with_signals(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 27)\n\n option = EqOption(\n '.STOXX50E',\n expirationDate='3m',\n strikePrice='ATM',\n optionType=OptionType.Call,\n optionStyle=OptionStyle.European,\n name='option',\n number_of_options=1,\n )\n\n entry_action = EnterPositionQuantityScaledAction(\n priceables=option,\n trade_duration='1m',\n trade_quantity=12345,\n trade_quantity_type=BacktestTradingQuantityType.notional,\n name='action',\n )\n\n entry_signal_series = pd.Series(data={dt.date(2019, 2, 19): 1})\n entry_dates = entry_signal_series[entry_signal_series > 0].keys()\n\n entry_trigger = AggregateTrigger(\n AggregateTriggerRequirements(\n triggers=[\n DateTriggerRequirements(dates=entry_dates),\n PortfolioTriggerRequirements('len', 0, TriggerDirection.EQUAL),\n ]\n ),\n actions=entry_action,\n )\n\n exit_signal_series = pd.Series(data={dt.date(2019, 2, 20): 1})\n exit_dates = exit_signal_series[exit_signal_series > 0].keys()\n\n exit_trigger = AggregateTrigger(\n AggregateTriggerRequirements(\n triggers=[\n DateTriggerRequirements(dates=exit_dates),\n PortfolioTriggerRequirements('len', 0, TriggerDirection.ABOVE),\n ]\n ),\n actions=ExitPositionAction(name='exit_action'),\n )\n\n strategy = Strategy(initial_portfolio=None, triggers=[entry_trigger, exit_trigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(entry_action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=tuple(entry_dates),\n exit_dates=tuple(exit_dates),\n quantity=12345,\n quantity_type=BacktestTradingQuantityType.notional,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=None,\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0))\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_trade_quantity_nav(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option',\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n number_of_options=1,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n number_of_options=1,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(\n priceables=option,\n trade_duration='1m',\n trade_quantity=12345,\n trade_quantity_type=BacktestTradingQuantityType.NAV,\n name='action',\n )\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=12345,\n quantity_type=BacktestTradingQuantityType.NAV,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=StrategyHedge(frequency='1b'),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n hedge_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_listed_expiry_date(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m@listed',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option',\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m@listed',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n number_of_options=1,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m@listed',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n number_of_options=1,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n action.priceables[0].expiration_date = action.priceables[0].expiration_date.replace('@listed', '')\n action.priceables[0].trade_as = TradeAs.Listed\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=1,\n quantity_type=BacktestTradingQuantityType.quantity,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=StrategyHedge(frequency='1b'),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n hedge_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_listed_roll_date(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option',\n )\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m@listed', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(\n start_date=start_date, end_date=end_date, frequency='1m@listed'\n ),\n actions=action,\n )\n strategy = Strategy(\n initial_portfolio=None,\n triggers=[\n trigger,\n ],\n )\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=1,\n quantity_type=BacktestTradingQuantityType.quantity,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0))\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK,\n cash_accrual=True,\n combine_roll_signal_entries=False,\n roll_date_mode=RollDateMode.Listed,\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_market_model(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option',\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n number_of_options=1,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n number_of_options=1,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date, market_model=EquityMarketModel.SVR)\n\n # 4. assert response\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=1,\n quantity_type=BacktestTradingQuantityType.quantity,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=StrategyHedge(frequency='1b'),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n hedge_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SVR, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_portfolio(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option_call',\n )\n\n put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option_put',\n )\n\n portfolio = Portfolio(name='portfolio', priceables=[call, put])\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(priceables=portfolio, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert response\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=1,\n quantity_type=BacktestTradingQuantityType.quantity,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=StrategyHedge(frequency='1b'),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n hedge_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\ndef test_supports_strategy():\n # 1. Valid strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='option',\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedge_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedge_trigger])\n\n assert EquityVolEngine.supports_strategy(strategy)\n\n # 2. Invalid - no trade action\n\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=None,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 3. Invalid - no trade quantity\n\n action = EnterPositionQuantityScaledAction(\n priceables=option, trade_duration='1m', trade_quantity=None, name='action'\n )\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 4. Invalid - no trade quantity type\n\n action = EnterPositionQuantityScaledAction(\n priceables=option, trade_duration='1m', trade_quantity_type=None, name='action'\n )\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 5. Invalid - mismatch trade duration and trigger period\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='2m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 6. Invalid - mismatch hedge trade duration and trigger period\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedge_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='B'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='M', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedge_trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 7. Invalid - non-daily hedge trade\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedge_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='M'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='M', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedge_trigger])\n assert EquityVolEngine.supports_strategy(strategy)\n\n # 8. Invalid - expiration date modifiers must be the same\n\n option_listed = EqOption(\n '.STOXX50E',\n expirationDate='3m@listed',\n strikePrice='ATM',\n optionType=OptionType.Call,\n optionStyle=OptionStyle.European,\n name='option',\n )\n\n action = EnterPositionQuantityScaledAction(priceables=[option, option_listed], trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 9. Invalid - expiration date modifier not in [otc, listed]\n\n option_invalid = EqOption(\n '.STOXX50E', expirationDate='3m@invalid', strikePrice='ATM', optionType=OptionType.Call, name='option'\n )\n action = EnterPositionQuantityScaledAction(priceables=[option_invalid], trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 10. Invalid - hedging without synthetic forward (not a portfolio)\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call])\n\n hedge_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedge_trigger])\n\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 11. Invalid - hedging without synthetic forward (two calls)\n\n long_call_2 = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n name='option',\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, long_call_2])\n\n hedge_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedge_trigger])\n\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 12. Invalid - hedging without synthetic forward (more than two options)\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put, long_call_2])\n\n hedge_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedge_trigger])\n\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 13. Invalid - hedging without synthetic forward (properties mismatch)\n\n long_call_2m = EqOption(\n '.STOXX50E',\n expiration_date='2m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n name='option_1',\n )\n short_put_4m = EqOption(\n '.STOXX50E',\n expiration_date='4m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n name='option_2',\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call_2m, short_put_4m])\n\n hedge_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedge_trigger])\n\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 14. Valid - AddTradeAction\n\n add_trade_action = AddTradeAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=add_trade_action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert EquityVolEngine.supports_strategy(strategy)\n\n # 15. Valid - AddScaledTradeAction\n\n add_scaled_trade_action = AddScaledTradeAction(\n priceables=option, trade_duration='1m', scaling_type=ScalingActionType.size, scaling_level=2, name='action'\n )\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=add_scaled_trade_action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert EquityVolEngine.supports_strategy(strategy)\n\n # 16. Invalid - transaction_costs not supported\n\n add_trade_action_tc = AddTradeAction(\n priceables=option, trade_duration='1m', transaction_cost=ConstantTransactionModel(100), name='action'\n )\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=add_trade_action_tc,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert EquityVolEngine.supports_strategy(strategy)\n\n # 17. Valid - transaction_costs supported if 0\n\n add_trade_action_tc0 = AddTradeAction(\n priceables=option, trade_duration='1m', transaction_cost=ConstantTransactionModel(0), name='action'\n )\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=add_trade_action_tc0,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert EquityVolEngine.supports_strategy(strategy)\n\n # 18. Invalid - instrument non unit contract size\n\n option_with_mult = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n multiplier=100,\n name='option',\n )\n\n action = AddTradeAction(priceables=option_with_mult, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n # 19. Invalid - instrument non unit contract count\n\n option_with_contracts = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=100,\n name='option',\n )\n\n action = AddTradeAction(priceables=option_with_contracts, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n assert not EquityVolEngine.supports_strategy(strategy)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_basic_leg_size(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='option',\n number_of_options=0,\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=HedgeAction(EqDelta, priceables=hedge_portfolio, trade_duration='1b', name='hedge_action'),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=1,\n quantity_type=BacktestTradingQuantityType.quantity,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=StrategyHedge(frequency='1b'),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n hedge_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_fixed_expiry(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option_call = EqOption(\n '.STOXX50E',\n expiration_date=dt.date(2020, 3, 20),\n strike_price='ATM',\n option_type=OptionType.Call,\n number_of_options=1,\n name='c',\n )\n option_put = EqOption(\n '.STOXX50E',\n expiration_date=\"2020-03-20\",\n strike_price='ATM',\n option_type=OptionType.Put,\n number_of_options=1,\n name='p',\n )\n straddle = (option_call, option_put)\n\n action = EnterPositionQuantityScaledAction(\n priceables=straddle,\n trade_duration='1m',\n trade_quantity=12345,\n trade_quantity_type=BacktestTradingQuantityType.notional,\n name='action',\n )\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n\n # 2. setup mock api response\n\n mock_api_response(mocker, api_mock_data())\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=12345,\n quantity_type=BacktestTradingQuantityType.notional,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=None,\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0))\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n\n\n@mock.patch.object(GsBacktestXassetApi, 'calculate_basic_backtest')\ndef test_engine_mapping_delta_hedge(mocker):\n # 1. setup strategy\n\n start_date = dt.date(2019, 2, 18)\n end_date = dt.date(2019, 2, 20)\n\n option = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n number_of_options=1,\n name='option',\n )\n\n long_call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Buy,\n number_of_options=1,\n )\n short_put = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n buy_sell=BuySell.Sell,\n number_of_options=1,\n )\n\n hedge_portfolio = Portfolio(name='SynFwd', priceables=[long_call, short_put])\n\n action = EnterPositionQuantityScaledAction(priceables=option, trade_duration='1m', name='action')\n trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=action,\n )\n hedgetrigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='5b'),\n actions=HedgeAction(\n EqDelta, priceables=hedge_portfolio, trade_duration='5b', risk_percentage=50, name='hedge_action'\n ),\n )\n strategy = Strategy(initial_portfolio=None, triggers=[trigger, hedgetrigger])\n\n # 2. setup mock api response\n\n response = BasicBacktestResponse.from_dict_custom(\n {\n 'measures': {\n 'PNL': {\n '2019-02-18': {'result': 0, 'type': 'float'},\n '2019-02-19': {'result': -0.18, 'type': 'float'},\n '2019-02-20': {'result': -0.27, 'type': 'float'},\n }\n },\n 'portfolio': {},\n 'transactions': {},\n }\n )\n\n mock_api_response(mocker, response)\n\n # 3. when run backtest\n\n set_session()\n EquityVolEngine.run_backtest(strategy, start_date, end_date)\n\n # 4. assert API call\n\n backtest = BasicBacktestRequest(\n dates=DateConfig(start_date=start_date, end_date=end_date),\n trades=(\n Trade(\n legs=tuple(action.priceables),\n buy_frequency='1m',\n holding_period='1m',\n buy_dates=None,\n exit_dates=None,\n quantity=1,\n quantity_type=BacktestTradingQuantityType.quantity,\n ),\n ),\n measures=(FlowVolBacktestMeasure.ALL_MEASURES,),\n delta_hedge_frequency=None,\n hedge=StrategyHedge(frequency='5b', risk_percentage=50),\n transaction_costs=TransactionCostConfig(\n trade_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n hedge_cost_model=TradingCosts(entry=FixedCostModel(0), exit=FixedCostModel(0)),\n ),\n configuration=Configuration(\n market_model=EquityMarketModel.SFK, cash_accrual=True, combine_roll_signal_entries=False\n ),\n )\n\n mocker.assert_called_with(backtest, decode_instruments=False)\n" }, { "path": "gs_quant/test/backtest/test_backtest_flow_vol.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest import mock\n\nimport gs_quant.target.backtests as backtests\nfrom gs_quant.api.gs.backtests import GsBacktestApi\nfrom gs_quant.backtests.core import Backtest\nfrom gs_quant.backtests.core import TradeInMethod\nfrom gs_quant.backtests.strategy_systematic import StrategySystematic\nfrom gs_quant.base import Base\nfrom gs_quant.common import AssetClass, Currency\nfrom gs_quant.instrument import EqOption\nfrom gs_quant.session import Environment, GsSession\nfrom gs_quant.target.backtests import DeltaHedgeParameters, BacktestTradingQuantityType, BacktestRisk\n\nunderlierList = [\n EqOption(\"MA4B66MW5E27U8P32SB\", \"3m\", 3000, 'Call', 'European'),\n EqOption(\"MA4B66MW5E27U8P32SB\", \"3m\", 3000, 'Put', 'European'),\n]\n\nhedge = DeltaHedgeParameters(frequency='Daily')\n\nstrategy = StrategySystematic(\n name=\"Mock Test\",\n underliers=underlierList,\n delta_hedge=hedge,\n quantity=1,\n quantity_type=BacktestTradingQuantityType.notional,\n trade_in_method=TradeInMethod.FixedRoll,\n roll_frequency='1m',\n use_xasset_backtesting_service=False,\n)\n\n\ndef set_session():\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n\n\n@mock.patch.object(GsBacktestApi, 'run_backtest')\ndef test_eqstrategies_backtest(mocker):\n start_date = dt.date(2019, 6, 3)\n end_date = dt.date(2019, 6, 5)\n\n data = [\n {\n 'name': 'Delta',\n 'timeseries': [\n {'date': '2019-02-18', 'price': 0},\n {'date': '2019-02-19', 'price': 0},\n {'date': '2019-02-20', 'price': -14256.398},\n ],\n }\n ]\n\n delta = BacktestRisk.from_dict(\n {\n 'name': 'Delta',\n 'timeseries': [\n {'date': '2019-02-18', 'price': 0},\n {'date': '2019-02-19', 'price': -0.000000000058},\n {'date': '2019-02-20', 'price': 0.000000000262},\n ],\n }\n )\n vega = BacktestRisk.from_dict(\n {\n 'name': 'Vega',\n 'timeseries': [\n {'date': '2019-02-18', 'price': -22225.061886411593},\n {'date': '2019-02-19', 'price': -50889.171772423098},\n {'date': '2019-02-20', 'price': 44266.899784030174},\n ],\n }\n )\n gamma = BacktestRisk.from_dict(\n {\n 'name': 'Gamma',\n 'timeseries': [\n {'date': '2019-02-18', 'price': 24834.106837453899},\n {'date': '2019-02-19', 'price': 41164.597344927679},\n {'date': '2019-02-20', 'price': 94404.04327008425},\n ],\n }\n )\n theta = BacktestRisk.from_dict(\n {\n 'name': 'Theta',\n 'timeseries': [\n {'date': '2019-02-18', 'price': -57618.719522251748},\n {'date': '2019-02-19', 'price': -113749.56356887663},\n {'date': '2019-02-20', 'price': -169875.93826522797},\n ],\n }\n )\n\n risk_data = (delta, vega, gamma, theta)\n mock_response = backtests.BacktestResult('BT1', performance=data, risks=risk_data, stats=None, backtest_version=1)\n\n set_session()\n\n mocker.return_value = mock_response\n\n result = strategy.backtest(start_date, end_date)\n\n assert result == mock_response\n\n trading_parameters = backtests.BacktestTradingParameters(\n quantity=1,\n quantity_type=BacktestTradingQuantityType.notional.value,\n trade_in_method=TradeInMethod.FixedRoll.value,\n roll_frequency='1m',\n )\n\n l1 = backtests.BacktestStrategyUnderlier(\n instrument=underlierList[0],\n notional_percentage=100,\n hedge=backtests.BacktestStrategyUnderlierHedge(risk_details=hedge),\n market_model='SFK',\n )\n\n l2 = backtests.BacktestStrategyUnderlier(\n instrument=underlierList[1],\n notional_percentage=100,\n hedge=backtests.BacktestStrategyUnderlierHedge(risk_details=hedge),\n market_model='SFK',\n )\n\n underliers = [l1, l2]\n\n backtest_parameters_class: Base = getattr(backtests, 'VolatilityFlowBacktestParameters')\n\n backtest_parameter_args = {\n 'trading_parameters': trading_parameters,\n 'underliers': underliers,\n 'trade_in_method': TradeInMethod.FixedRoll,\n 'scaling_method': None,\n 'index_initial_value': 0.0,\n }\n backtest_parameters = backtest_parameters_class.from_dict(backtest_parameter_args)\n\n params_dict = backtest_parameters.as_dict()\n params_dict[\"measures\"] = [backtests.FlowVolBacktestMeasure.ALL_MEASURES]\n params = backtest_parameters_class.from_dict(params_dict)\n\n backtest = Backtest(\n name=\"Mock Test\",\n mq_symbol=\"Mock Test\",\n parameters=params,\n start_date=start_date,\n end_date=end_date,\n type='Volatility Flow',\n asset_class=AssetClass.Equity,\n currency=Currency.USD,\n cost_netting=False,\n )\n\n mocker.assert_called_with(backtest, None)\n" }, { "path": "gs_quant/test/backtest/test_backtest_predefined.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport zoneinfo\n\nimport gs_quant.backtests.predefined_asset_engine\nimport datetime as dt\nfrom gs_quant.backtests.actions import AddTradeAction\nfrom gs_quant.backtests.strategy import Strategy\nfrom gs_quant.backtests.triggers import OrdersGeneratorTrigger, DateTriggerRequirements, DateTrigger\nfrom gs_quant.backtests.backtest_objects import PredefinedAssetBacktest\nfrom gs_quant.backtests.predefined_asset_engine import PredefinedAssetEngine\nfrom gs_quant.backtests.data_sources import DataManager\nimport pandas as pd\nimport numpy as np\nfrom gs_quant.data.core import DataFrequency\nfrom gs_quant.backtests.core import ValuationFixingType\nfrom gs_quant.instrument import IRBondFuture\nfrom gs_quant.backtests.order import OrderMarketOnClose, OrderTWAP, TimeWindow\nfrom unittest import mock\nimport gs_quant.datetime\n\n\nclass ExampleTestTrigger(OrdersGeneratorTrigger):\n def __init__(self):\n super().__init__()\n\n def generate_orders(self, time: dt.datetime, backtest: PredefinedAssetBacktest = None) -> list:\n date = time.date()\n if date == dt.date(2021, 1, 5):\n return [\n OrderMarketOnClose(\n instrument=IRBondFuture(currency='EUR', name='TestRic'),\n quantity=1,\n generation_time=time,\n execution_date=date,\n source='Test',\n )\n ]\n else:\n return []\n\n def get_trigger_times(self) -> list:\n return [dt.time(10, 0, 0)]\n\n\nclass FuturesExample(OrdersGeneratorTrigger):\n def __init__(self):\n super().__init__()\n\n def get_trigger_times(self):\n \"\"\"generate orders at 9:30am every day\"\"\"\n return [dt.time(9, 30)]\n\n def generate_orders(self, state: dt.datetime, backtest: PredefinedAssetBacktest = None) -> list:\n date = state.date()\n\n contract = IRBondFuture(currency='EUR', name='TestRic')\n orders = []\n units_to_trade = 1\n\n \"\"\" enter trade is a TWAP order between 10 and 10:30 \"\"\"\n exec_start = dt.datetime.combine(date, dt.time(10))\n exec_end = dt.datetime.combine(date, dt.time(10, 30))\n orders.append(\n OrderTWAP(\n instrument=contract,\n quantity=units_to_trade,\n generation_time=state,\n window=TimeWindow(start=exec_start, end=exec_end),\n source=str(self.__class__),\n )\n )\n\n \"\"\" exit the intraday quantity at TWAP between 14 and 14:30 \"\"\"\n twap_start = dt.datetime.combine(date, dt.time(14))\n twap_end = dt.datetime.combine(date, dt.time(14, 30))\n orders.append(\n OrderTWAP(\n instrument=contract,\n quantity=units_to_trade * -1,\n generation_time=state,\n window=TimeWindow(start=twap_start, end=twap_end),\n source=str(self.__class__),\n )\n )\n return orders\n\n\ndef test_backtest_predefined_timezone_aware():\n tz = 'Europe/London'\n start_dt = '2021-01-01T08:00'\n end_dt = '2021-12-31T17:00'\n\n states = (\n pd.bdate_range(start_dt, end_dt, freq='1h', tz=tz).to_series().between_time('08:00', '17:00').index.tolist()\n )\n trigger_dates = pd.bdate_range(start_dt, end_dt, freq='1h', tz=tz).to_series().at_time('17:00').index.tolist()\n data = np.random.default_rng().standard_normal(len(states))\n s_rt = pd.Series(index=states, data=data)\n s_eod = s_rt.at_time('17:00')\n s_eod.index = s_eod.index.date\n\n generic_bond_future = IRBondFuture(currency='EUR', name='EURBond')\n\n add_trade_action = AddTradeAction(generic_bond_future)\n simple_date_trigger_requirement = DateTriggerRequirements(dates=trigger_dates)\n simple_date_trigger = DateTrigger(trigger_requirements=simple_date_trigger_requirement, actions=[add_trade_action])\n\n data_manager = DataManager()\n data_manager.add_data_source(\n pd.Series(index=states, data=data), DataFrequency.REAL_TIME, generic_bond_future, ValuationFixingType.PRICE\n )\n data_manager.add_data_source(s_eod, DataFrequency.DAILY, generic_bond_future, ValuationFixingType.PRICE)\n\n # instantiate a new strategy\n strategy = Strategy(None, triggers=simple_date_trigger)\n\n engine = PredefinedAssetEngine(data_mgr=data_manager, tz=zoneinfo.ZoneInfo(tz))\n backtest = engine.run_backtest(strategy=strategy, start=states[0], end=states[-1], states=states)\n assert len(backtest.trade_ledger()) == 364\n\n\ndef test_backtest_predefined():\n # Test simple MOC order\n trigger = ExampleTestTrigger()\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n start = dt.date(2021, 1, 4)\n mid = dt.date(2021, 1, 5)\n end = dt.date(2021, 1, 6)\n\n # these mocks are needed as the date functions need a GSSession\n gs_quant.backtests.predefined_asset_engine.is_business_day = mock.Mock(return_value=True)\n gs_quant.backtests.predefined_asset_engine.business_day_offset = mock.Mock(return_value=mid)\n\n data_mgr = DataManager()\n underlying = IRBondFuture(currency='EUR', name='TestRic')\n close_prices = pd.Series(dtype=float)\n close_prices[start] = 1\n close_prices[mid] = 1.5\n close_prices[end] = 2\n data_mgr.add_data_source(close_prices, DataFrequency.DAILY, underlying, ValuationFixingType.PRICE)\n\n engine = PredefinedAssetEngine(data_mgr=data_mgr)\n\n backtest = engine.run_backtest(strategy, start=start, end=end)\n perf = backtest.performance\n holdings = backtest.historical_holdings\n cash_asset = backtest.cash_asset\n\n # 100 on the initial date\n assert perf[start] == 100\n # 100 on the next day as we traded MOC\n assert perf[mid] == 100\n assert holdings[mid][cash_asset] == 100 - 1.5\n assert holdings[mid][underlying] == 1\n # 100.5 = 98.5 (cash) + 2 ( test asset)\n assert holdings[end][cash_asset] == 100 - 1.5\n assert holdings[end][underlying] == 1\n assert perf[end] == 100.5\n\n # Test TWAP orders with no ON positions\n twap_entry_mid = 16\n twap_exit_mid = 25\n\n twap_entry_end = 30\n twap_exit_end = 40\n\n data_twap = {\n dt.datetime.combine(mid, dt.time(10, 30)): twap_entry_mid,\n dt.datetime.combine(mid, dt.time(14, 30)): twap_exit_mid,\n dt.datetime.combine(end, dt.time(10, 30)): twap_entry_end,\n dt.datetime.combine(end, dt.time(14, 30)): twap_exit_end,\n }\n\n data_mgr.add_data_source(pd.Series(data_twap), DataFrequency.REAL_TIME, underlying, ValuationFixingType.PRICE)\n trigger = FuturesExample()\n strategy = Strategy(initial_portfolio=None, triggers=[trigger])\n engine = PredefinedAssetEngine(data_mgr=data_mgr, tz=zoneinfo.ZoneInfo('Europe/London'))\n backtest = engine.run_backtest(strategy, start=start, end=end)\n perf = backtest.performance\n holdings = backtest.historical_holdings\n weights = backtest.historical_weights\n cash_asset = backtest.cash_asset\n\n # start: 100\n assert perf[start] == 100\n # mid: 100 + (twap_exit - twap_entry)\n assert perf[mid] == 100 - twap_entry_mid + twap_exit_mid\n assert holdings[mid][cash_asset] == perf[mid]\n assert underlying not in holdings[mid]\n assert weights[mid][cash_asset] == 1\n assert underlying not in weights[mid]\n # 100.5 = 98.5 (cash) + 2 ( test asset)\n assert perf[end] == 100 - twap_entry_mid + twap_exit_mid - twap_entry_end + twap_exit_end\n assert holdings[end][cash_asset] == perf[end]\n assert underlying not in holdings[end]\n assert weights[end][cash_asset] == 1\n assert underlying not in weights[end]\n" }, { "path": "gs_quant/test/backtest/test_generic_engine.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport json\nfrom unittest.mock import patch\n\nimport numpy as np\nimport pandas as pd\nfrom gs_quant.backtests.actions import (\n AddTradeAction,\n HedgeAction,\n ExitTradeAction,\n AddScaledTradeAction,\n ScalingActionType,\n ExitAllPositionsAction,\n AddWeightedTradeAction,\n)\nfrom gs_quant.backtests.backtest_objects import (\n ScaledTransactionModel,\n AggregateTransactionModel,\n TransactionAggType,\n ConstantTransactionModel,\n BackTest,\n)\nfrom gs_quant.backtests.data_sources import GenericDataSource, MissingDataStrategy, GsDataSource\nfrom gs_quant.backtests.generic_engine import GenericEngine\nfrom gs_quant.backtests.strategy import Strategy\nfrom gs_quant.backtests.triggers import (\n TriggerDirection,\n DateTrigger,\n DateTriggerRequirements,\n NotTrigger,\n RiskTriggerRequirements,\n StrategyRiskTrigger,\n PeriodicTriggerRequirements,\n PeriodicTrigger,\n MktTrigger,\n MktTriggerRequirements,\n IntradayPeriodicTrigger,\n IntradayTriggerRequirements,\n AggregateTrigger,\n AggregateTriggerRequirements,\n PortfolioTrigger,\n PortfolioTriggerRequirements,\n MeanReversionTrigger,\n MeanReversionTriggerRequirements,\n NotTriggerRequirements,\n)\nfrom gs_quant.common import Currency, PayReceive, OptionType, OptionStyle\nfrom gs_quant.instrument import FXOption, FXForward, IRSwaption, IRSwap, EqOption\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk import Price, FXDelta, DollarPrice, IRDelta\nfrom gs_quant.target.measures import EqDelta, EqVega\nfrom gs_quant.test.utils.mock_calc import MockCalc\n\n\ndef mock_pricing_context(self):\n context = PricingContext(set_parameters_only=True, is_batch=False, show_progress=True)\n return context\n\n\ndef instrument_name(test_name: str, leg_name: str):\n return f'{test_name}_{leg_name}'\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_generic_engine_simple(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 1)\n # end_date = dt.date(2021, 12, 3)\n\n # Define trade\n call = FXOption(\n buy_sell='Buy',\n option_type='Call',\n pair='USDJPY',\n strike_price='ATMF',\n notional_amount=1e5,\n expiration_date='2y',\n name=instrument_name('test_generic_engine_simple', '2y_call'),\n )\n\n # Periodic trigger: based on frequency\n freq = '1m'\n\n # trig_req = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency=freq)\n trig_req = DateTriggerRequirements(dates=[start_date])\n actions = AddTradeAction(call, freq, name='Action1')\n\n # starting with empty portfolio (first arg to Strategy), apply actions on trig_req\n triggers = DateTrigger(trig_req, actions)\n\n strategy = Strategy(None, triggers)\n\n # run backtest daily\n engine = GenericEngine()\n backtest = engine.run_backtest(\n strategy, states=[dt.date(2021, 12, 1), dt.date(2021, 12, 2), dt.date(2021, 12, 3)], show_progress=True\n )\n summary = backtest.result_summary\n assert len(summary) == 3\n assert round(summary[Price].sum()) == 2424\n assert round(summary['Cumulative Cash'].iloc[-1]) == 0\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_hedge_action_risk_trigger(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 1)\n # end_date = dt.date(2021, 12, 3)\n\n # Define trade\n call = FXOption(\n buy_sell='Buy',\n option_type='Call',\n pair='USDJPY',\n strike_price='ATMF',\n notional_amount=1e5,\n expiration_date='2y',\n name=instrument_name('test_hedge_action_risk_trigger', '2y_call'),\n )\n\n hedge_risk = FXDelta(aggregation_level='Type')\n fwd_hedge = FXForward(\n pair='USDJPY',\n settlement_date='2y',\n notional_amount=1e5,\n name=instrument_name('test_hedge_action_risk_trigger', '2y_forward'),\n )\n\n trig_req = RiskTriggerRequirements(risk=hedge_risk, trigger_level=0, direction=TriggerDirection.ABOVE)\n action_hedge = HedgeAction(hedge_risk, fwd_hedge, '2b', name='HedgeAction1')\n\n triggers = StrategyRiskTrigger(trig_req, action_hedge)\n\n with PricingContext(pricing_date=start_date, use_historical_diddles_only=True):\n fut = call.resolve(in_place=False)\n\n call = fut.result()\n\n strategy = Strategy(call, triggers)\n\n # run backtest daily\n engine = GenericEngine()\n # backtest = engine.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n backtest = engine.run_backtest(\n strategy, states=[dt.date(2021, 12, 1), dt.date(2021, 12, 2), dt.date(2021, 12, 3)], show_progress=True\n )\n summary = backtest.result_summary\n assert len(summary) == 3\n assert round(summary[hedge_risk].sum()) == 0\n assert round(summary['Cumulative Cash'].iloc[-1]) == -7090\n assert Price in summary.columns\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_hedge_without_risk(mocker):\n with MockCalc(mocker):\n # Define trade\n call = FXOption(\n buy_sell='Buy',\n option_type='Call',\n pair='USDJPY',\n strike_price='ATMF',\n notional_amount=1e5,\n expiration_date='2y',\n name=instrument_name('test_hedge_without_risk', '2y_call'),\n )\n\n trig_req = PeriodicTriggerRequirements(\n start_date=dt.date(2021, 12, 1), end_date=dt.date(2021, 12, 3), frequency='1b'\n )\n\n action = AddTradeAction(call, '1b', name='AddAction1')\n\n hedge_risk = FXDelta(aggregation_level='Type')\n fwd_hedge = FXForward(\n pair='USDJPY',\n settlement_date='2y',\n notional_amount=1e5,\n name=instrument_name('test_hedge_without_risk', '2y_forward'),\n )\n\n hedge_trig_req = PeriodicTriggerRequirements(\n start_date=dt.date(2021, 11, 1), end_date=dt.date(2022, 1, 1), frequency='1b'\n )\n action_hedge = HedgeAction(hedge_risk, fwd_hedge, '2b', name='HedgeAction1')\n\n triggers = [PeriodicTrigger(trig_req, action), PeriodicTrigger(hedge_trig_req, action_hedge)]\n\n strategy = Strategy(None, triggers)\n\n # run backtest daily\n engine = GenericEngine()\n # backtest = engine.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n backtest = engine.run_backtest(\n strategy, states=[dt.date(2021, 12, 1), dt.date(2021, 12, 2), dt.date(2021, 12, 3)], show_progress=True\n )\n summary = backtest.result_summary\n assert len(summary) == 3\n assert round(summary[hedge_risk].sum()) == 0\n assert round(summary['Cumulative Cash'].iloc[-1]) == -6579\n assert Price in summary.columns\n\n\ns = pd.Series(\n {\n dt.date(2021, 10, 1): 0.984274,\n dt.date(2021, 10, 4): 1.000706,\n dt.date(2021, 10, 5): 1.044055,\n dt.date(2021, 10, 6): 1.095361,\n dt.date(2021, 10, 7): 1.129336,\n dt.date(2021, 10, 8): 1.182954,\n dt.date(2021, 10, 12): 1.200108,\n dt.date(2021, 10, 13): 1.220607,\n dt.date(2021, 10, 14): 1.172837,\n dt.date(2021, 10, 15): 1.163660,\n dt.date(2021, 10, 18): 1.061084,\n dt.date(2021, 10, 19): 1.025012,\n dt.date(2021, 10, 20): 1.018035,\n dt.date(2021, 10, 21): 1.080751,\n dt.date(2021, 10, 22): 1.069340,\n dt.date(2021, 10, 25): 1.033413,\n }\n)\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_mkt_trigger_data_sources(mocker):\n with MockCalc(mocker):\n action = AddTradeAction(\n IRSwaption(notional_currency='USD', expiration_date='1y', termination_date='1y'),\n 'expiration_date',\n name='Action1',\n )\n data_source = GenericDataSource(s, MissingDataStrategy.fill_forward)\n mkt_trigger = MktTrigger(MktTriggerRequirements(data_source, 1.1, TriggerDirection.ABOVE), action)\n strategy = Strategy(None, mkt_trigger)\n\n engine = GenericEngine()\n\n backtest = engine.run_backtest(strategy, states=list(s.index), show_progress=True)\n\n summary = backtest.result_summary\n ledger = backtest.trade_ledger()\n assert len(summary) == 12\n assert len(ledger) == 6\n assert round(summary[Price].sum()) == 25163614\n assert round(summary['Cumulative Cash'].iloc[-1]) == -2153015\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_exit_action_noarg(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 6)\n end_date = dt.date(2021, 12, 10)\n\n # Define trade\n irswap = IRSwap(PayReceive.Receive, '10y', Currency.USD, notional_amount=1e5, name='swap')\n\n trig_req_add = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b')\n trig_req_exit = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='2b')\n actions_add = AddTradeAction(irswap, name='Action1')\n actions_exit = ExitTradeAction(name='ExitAction1')\n\n triggers = [PeriodicTrigger(trig_req_add, actions_add), PeriodicTrigger(trig_req_exit, actions_exit)]\n strategy = Strategy(None, triggers)\n\n # run backtest daily\n engine = GenericEngine()\n # backtest = engine.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n backtest = engine.run_backtest(\n strategy,\n states=[\n dt.date(2021, 12, 6),\n dt.date(2021, 12, 7),\n dt.date(2021, 12, 8),\n dt.date(2021, 12, 9),\n dt.date(2021, 12, 10),\n ],\n end=end_date,\n show_progress=True,\n )\n\n trade_ledger = backtest.trade_ledger().to_dict('index')\n\n assert trade_ledger['Action1_swap_2021-12-06']['Open'] == dt.date(2021, 12, 6)\n assert trade_ledger['Action1_swap_2021-12-06']['Close'] == dt.date(2021, 12, 6)\n assert trade_ledger['Action1_swap_2021-12-07']['Open'] == dt.date(2021, 12, 7)\n assert trade_ledger['Action1_swap_2021-12-07']['Close'] == dt.date(2021, 12, 8)\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_exit_action_emptyresults(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 6)\n end_date = dt.date(2021, 12, 10)\n\n # Define trade\n irswap = IRSwap(PayReceive.Receive, '10y', Currency.USD, notional_amount=1e5, name='swap')\n\n trig_req_add = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='2b')\n trig_req_exit = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b')\n actions_add = AddTradeAction(irswap, name='Action1')\n actions_exit = ExitTradeAction(name='ExitAction1')\n\n triggers = [PeriodicTrigger(trig_req_add, actions_add), PeriodicTrigger(trig_req_exit, actions_exit)]\n strategy = Strategy(None, triggers)\n\n # run backtest daily\n engine = GenericEngine()\n # backtest = engine.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n backtest = engine.run_backtest(\n strategy,\n states=[\n dt.date(2021, 12, 6),\n dt.date(2021, 12, 7),\n dt.date(2021, 12, 8),\n dt.date(2021, 12, 9),\n dt.date(2021, 12, 10),\n ],\n end=end_date,\n show_progress=True,\n )\n\n trade_ledger = backtest.trade_ledger().to_dict('index')\n\n assert trade_ledger['Action1_swap_2021-12-06']['Open'] == dt.date(2021, 12, 6)\n assert trade_ledger['Action1_swap_2021-12-06']['Close'] == dt.date(2021, 12, 6)\n assert trade_ledger['Action1_swap_2021-12-08']['Open'] == dt.date(2021, 12, 8)\n assert trade_ledger['Action1_swap_2021-12-08']['Close'] == dt.date(2021, 12, 8)\n assert trade_ledger['Action1_swap_2021-12-10']['Open'] == dt.date(2021, 12, 10)\n assert trade_ledger['Action1_swap_2021-12-10']['Close'] == dt.date(2021, 12, 10)\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_exit_action_bytradename(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 6)\n end_date = dt.date(2021, 12, 10)\n\n # Define trade\n irswap1 = IRSwap(PayReceive.Receive, '10y', Currency.USD, notional_amount=1e5, name='swap1')\n irswap2 = IRSwap(PayReceive.Pay, '5y', Currency.USD, notional_amount=1e5, name='swap2')\n\n trig_req_add = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b')\n trig_req_exit = PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='2b')\n actions_add = AddTradeAction([irswap1, irswap2], name='Action1')\n actions_exit = ExitTradeAction('swap1', name='ExitAction1')\n\n triggers = [PeriodicTrigger(trig_req_add, actions_add), PeriodicTrigger(trig_req_exit, actions_exit)]\n strategy = Strategy(None, triggers)\n\n # run backtest daily\n engine = GenericEngine()\n # backtest = engine.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n backtest = engine.run_backtest(\n strategy,\n states=[\n dt.date(2021, 12, 6),\n dt.date(2021, 12, 7),\n dt.date(2021, 12, 8),\n dt.date(2021, 12, 9),\n dt.date(2021, 12, 10),\n ],\n end=end_date,\n show_progress=True,\n )\n\n trade_ledger = backtest.trade_ledger().to_dict('index')\n\n assert trade_ledger['Action1_swap1_2021-12-06']['Open'] == dt.date(2021, 12, 6)\n assert trade_ledger['Action1_swap1_2021-12-06']['Close'] == dt.date(2021, 12, 6)\n assert trade_ledger['Action1_swap1_2021-12-07']['Open'] == dt.date(2021, 12, 7)\n assert trade_ledger['Action1_swap1_2021-12-07']['Close'] == dt.date(2021, 12, 8)\n assert trade_ledger['Action1_swap2_2021-12-06']['Status'] == 'open'\n assert trade_ledger['Action1_swap2_2021-12-07']['Status'] == 'open'\n assert trade_ledger['Action1_swap2_2021-12-10']['Status'] == 'open'\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_add_scaled_action(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 6)\n end_date = dt.date(2021, 12, 10)\n\n scale_factor = 7\n\n # Define instruments for strategy\n # Portfolio of two eq options\n call = EqOption(\n '.STOXX50E',\n expiration_date='1m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='call',\n )\n put = EqOption(\n '.STOXX50E',\n expiration_date='1m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n name='put',\n )\n portfolio = Portfolio(name='portfolio', priceables=[call, put])\n\n # Trade the position monthly without any scaling\n trade_action = AddScaledTradeAction(priceables=portfolio, trade_duration='1m', name='QuantityScaledAction1')\n trade_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action,\n )\n\n strategy = Strategy(None, trade_trigger)\n\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n summary = backtest.result_summary\n ledger = backtest.trade_ledger()\n\n assert len(summary) == 5\n assert len(ledger) == 10\n assert round(summary[Price].sum()) == 2715\n assert round(summary['Cumulative Cash'].iloc[-1]) == -922\n\n # Trade the position monthly and scale the quantity of the trade\n trade_action_scaled = AddScaledTradeAction(\n priceables=portfolio, trade_duration='1m', scaling_level=scale_factor, name='QuantityScaledAction2'\n )\n trade_trigger_scaled = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action_scaled,\n )\n\n strategy_scaled = Strategy(None, trade_trigger_scaled)\n\n backtest_scaled = GE.run_backtest(\n strategy_scaled, start=start_date, end=end_date, frequency='1b', show_progress=True\n )\n\n summary_scaled = backtest_scaled.result_summary\n ledger_scaled = backtest_scaled.trade_ledger()\n\n # Price and cash should scale linearly\n assert len(summary_scaled) == len(summary)\n assert len(ledger_scaled) == len(ledger)\n assert round(summary_scaled[Price].sum()) == round(summary[Price].sum() * scale_factor)\n assert round(summary_scaled['Cumulative Cash'].iloc[-1]) == round(\n summary['Cumulative Cash'].iloc[-1] * scale_factor\n )\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_scaled_transaction_cost(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2024, 5, 6)\n end_date = dt.date(2024, 5, 10)\n\n # notional based transaction cost. charge 1/10000th of the notional\n transaction_cost = ScaledTransactionModel('notional_amount', 0.0001)\n\n swap = IRSwap(notional_currency=Currency.GBP, notional_amount='50k', termination_date='1y', name='GBP1y')\n trade_action = AddTradeAction(\n priceables=swap, trade_duration='1m', name='Action1', transaction_cost=transaction_cost\n )\n trade_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action,\n )\n\n strategy = Strategy(None, trade_trigger)\n\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n summary = backtest.result_summary\n ledger = backtest.trade_ledger()\n\n assert len(summary) == 5\n assert len(ledger) == 5\n assert round(summary[Price].sum()) == 90\n assert round(summary['Transaction Costs'].iloc[-1]) == 50000 * 0.0001 * 5 * -1\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_agg_transaction_cost(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2024, 5, 6)\n end_date = dt.date(2024, 5, 10)\n\n # aggregation based transaction cost. charge 1/10000th of the notional + 1 for evey transaction\n models = tuple([ScaledTransactionModel('notional_amount', 0.0001), ConstantTransactionModel(1)])\n transaction_cost = AggregateTransactionModel(models, TransactionAggType.SUM)\n\n swap = IRSwap(notional_currency=Currency.GBP, notional_amount='50k', termination_date='1y', name='GBP1y')\n trade_action = AddTradeAction(\n priceables=swap, trade_duration='1m', name='Action1', transaction_cost=transaction_cost\n )\n trade_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action,\n )\n\n strategy = Strategy(None, trade_trigger)\n\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n summary = backtest.result_summary\n ledger = backtest.trade_ledger()\n\n assert len(summary) == 5\n assert len(ledger) == 5\n assert round(summary[Price].sum()) == 90\n assert round(summary['Transaction Costs'].iloc[-1]) == (50000 * 0.0001 * 5 * -1) - 5\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_risk_scaled_transaction_cost(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2023, 6, 5)\n end_date = dt.date(2023, 6, 9)\n\n # risk based transaction cost. Charge a 2 times the delta.\n transaction_cost = ScaledTransactionModel(IRDelta(aggregation_level='Type'), 2)\n\n swap = IRSwap(notional_currency=Currency.GBP, notional_amount='50k', termination_date='1y', name='GBP1y')\n trade_action = AddTradeAction(\n priceables=swap, trade_duration='1m', name='Action1', transaction_cost=transaction_cost\n )\n trade_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action,\n )\n\n strategy = Strategy(None, trade_trigger)\n\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n summary = backtest.result_summary\n ledger = backtest.trade_ledger()\n\n assert len(summary) == 5\n assert len(ledger) == 5\n assert round(summary[Price].sum()) == -64\n assert round(summary['Transaction Costs'].iloc[-1]) == -62\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_hedge_transaction_costs(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2023, 6, 5)\n end_date = dt.date(2023, 6, 9)\n\n scaled_transaction_cost = ScaledTransactionModel('number_of_options', 1)\n fixed_transaction_cost = ConstantTransactionModel(0)\n\n opt = EqOption(\n underlier='.SPX',\n option_type=OptionType.Call,\n number_of_options=1,\n expiration_date='3m',\n name=instrument_name('test_hedge_transaction_costs', 'SPX_opt'),\n )\n trade_action = AddTradeAction(\n priceables=opt, trade_duration='1m', name='Action1', transaction_cost=fixed_transaction_cost\n )\n trade_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1m'),\n actions=trade_action,\n )\n hedge_port = Portfolio(\n [\n EqOption(\n underlier='.SPX',\n option_type=OptionType.Call,\n number_of_options=1,\n expiration_date='3m',\n name=instrument_name('test_hedge_transaction_costs', 'syn_fwd_call'),\n ),\n EqOption(\n underlier='.SPX',\n option_type=OptionType.Put,\n number_of_options=1,\n expiration_date='3m',\n name=instrument_name('test_hedge_transaction_costs', 'syn_fwd_put'),\n ),\n ]\n )\n hedge_action = HedgeAction(\n risk=EqDelta,\n priceables=hedge_port,\n trade_duration='1b',\n transaction_cost=scaled_transaction_cost,\n name='HedgeAction1',\n )\n hedge_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=hedge_action,\n )\n\n strategy = Strategy(None, [trade_trigger, hedge_trigger])\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n summary = backtest.result_summary\n\n assert len(summary) == 5\n\n total_expected_transaction_costs = 0\n cur_bought_hedges = 0\n for d, port in backtest.portfolio_dict.items():\n # sale of prior hedges\n total_expected_transaction_costs += cur_bought_hedges\n cur_bought_hedges = sum(i.number_of_options for i in port.priceables if i.name.startswith('Scaled'))\n # buy next hedge\n total_expected_transaction_costs += cur_bought_hedges\n np.testing.assert_almost_equal(-summary['Transaction Costs'][d], total_expected_transaction_costs)\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_exit_transaction_costs(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2024, 6, 3)\n end_date = dt.date(2024, 6, 7)\n\n scaled_cost = 2\n scaled_transaction_cost = ScaledTransactionModel('number_of_options', scaled_cost)\n fixed_cost = 5\n fixed_transaction_cost = ConstantTransactionModel(fixed_cost)\n\n opt = EqOption(\n underlier='.SPX', option_type=OptionType.Call, number_of_options=1, expiration_date='3m', name='SPX_opt'\n )\n trade_action = AddTradeAction(\n priceables=opt,\n trade_duration='1b',\n transaction_cost=scaled_transaction_cost,\n transaction_cost_exit=fixed_transaction_cost,\n name='Action1',\n )\n trade_trigger = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action,\n )\n\n strategy = Strategy(None, trade_trigger)\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n summary = backtest.result_summary\n\n assert len(summary) == 5\n\n total_expected_transaction_costs = 0\n for d, port in backtest.portfolio_dict.items():\n # sale of prior trades\n total_expected_transaction_costs += 0 if d == start_date else fixed_cost\n cur_bought = sum(i.number_of_options for i in port.priceables)\n # buy next trade\n total_expected_transaction_costs += scaled_cost * cur_bought\n np.testing.assert_almost_equal(-summary['Transaction Costs'][d], total_expected_transaction_costs)\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_add_scaled_action_nav(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 6)\n end_date = dt.date(2021, 12, 10)\n\n initial_cash = 100\n\n # Define instruments for strategy\n call = EqOption(\n '.STOXX50E',\n expiration_date='1m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='call',\n )\n\n # NAV trading strategy with specified initial cash\n trade_action_scaled = AddScaledTradeAction(\n priceables=call,\n trade_duration='1b',\n scaling_level=initial_cash,\n scaling_type=ScalingActionType.NAV,\n name='QuantityScaledAction1',\n )\n\n trade_trigger_scaled = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action_scaled,\n )\n\n strategy = Strategy(None, trade_trigger_scaled)\n\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n summary = backtest.result_summary\n ledger = backtest.trade_ledger().to_dict('index')\n\n # Start with initial cash and only use sale proceeds to buy new options\n np.testing.assert_almost_equal(ledger['QuantityScaledAction1_call_2021-12-06']['Open Value'], -initial_cash)\n np.testing.assert_almost_equal(\n ledger['QuantityScaledAction1_call_2021-12-07']['Open Value'],\n -ledger['QuantityScaledAction1_call_2021-12-06']['Close Value'],\n )\n np.testing.assert_almost_equal(\n ledger['QuantityScaledAction1_call_2021-12-08']['Open Value'],\n -ledger['QuantityScaledAction1_call_2021-12-07']['Close Value'],\n )\n np.testing.assert_almost_equal(\n ledger['QuantityScaledAction1_call_2021-12-09']['Open Value'],\n -ledger['QuantityScaledAction1_call_2021-12-08']['Close Value'],\n )\n np.testing.assert_almost_equal(\n ledger['QuantityScaledAction1_call_2021-12-10']['Open Value'],\n -ledger['QuantityScaledAction1_call_2021-12-09']['Close Value'],\n )\n\n # Total cash spent is the initial cash throughout the entire strategy\n np.testing.assert_almost_equal(summary['Cumulative Cash'].iloc[0], -initial_cash)\n for c in summary['Cumulative Cash']:\n np.testing.assert_almost_equal(c, summary['Cumulative Cash'].iloc[0])\n\n\ndef nav_scaled_action_transaction_cost_test_for_agg_type(mocker, agg_type):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 6)\n end_date = dt.date(2021, 12, 10)\n\n initial_cash = 100\n\n # Define instruments for strategy\n call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='call',\n )\n\n scaled_transaction_cost = ScaledTransactionModel(EqVega, 1.2)\n fixed_transaction_cost = ConstantTransactionModel(1)\n agg_tc = AggregateTransactionModel((scaled_transaction_cost, fixed_transaction_cost), agg_type)\n\n # NAV trading strategy with specified initial cash\n trade_action_scaled = AddScaledTradeAction(\n priceables=call,\n trade_duration='1b',\n scaling_level=initial_cash,\n scaling_type=ScalingActionType.NAV,\n transaction_cost=agg_tc,\n name=f'QuantityScaledAction2_{agg_type}',\n )\n\n trade_trigger_scaled = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action_scaled,\n )\n\n strategy = Strategy(None, trade_trigger_scaled)\n\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n summary = backtest.result_summary\n\n ledger = backtest.trade_ledger().to_dict('index')\n tc = backtest.transaction_costs\n\n # Prior close value minus today's transaction costs equal today's value of options bought\n np.testing.assert_almost_equal(\n ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-06']['Open Value'],\n -initial_cash - tc[dt.date(2021, 12, 6)],\n )\n np.testing.assert_almost_equal(\n ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-07']['Open Value'],\n -ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-06']['Close Value'] - tc[dt.date(2021, 12, 7)],\n )\n np.testing.assert_almost_equal(\n ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-08']['Open Value'],\n -ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-07']['Close Value'] - tc[dt.date(2021, 12, 8)],\n )\n np.testing.assert_almost_equal(\n ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-09']['Open Value'],\n -ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-08']['Close Value'] - tc[dt.date(2021, 12, 9)],\n )\n np.testing.assert_almost_equal(\n ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-10']['Open Value'],\n -ledger[f'QuantityScaledAction2_{agg_type}_call_2021-12-09']['Close Value'] - tc[dt.date(2021, 12, 10)],\n )\n\n # Cash spent on trades + Transaction costs are equal to the initial cash throughout the entire strategy\n total_cash_spent = summary['Cumulative Cash'] + summary['Transaction Costs']\n np.testing.assert_almost_equal(total_cash_spent.iloc[0], -initial_cash)\n for c in total_cash_spent:\n np.testing.assert_almost_equal(c, total_cash_spent.iloc[0])\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_add_scaled_action_nav_with_transaction_costs(mocker):\n for agg_type in (TransactionAggType.SUM, TransactionAggType.MAX, TransactionAggType.MIN):\n nav_scaled_action_transaction_cost_test_for_agg_type(mocker, agg_type)\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_generic_engine_custom_price_measure(mocker):\n with MockCalc(mocker):\n trig_date = dt.date(2021, 12, 1)\n call = EqOption(\n '.STOXX50E',\n expiration_date='1y',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='call',\n )\n freq = '1m'\n trig_req = DateTriggerRequirements(dates=[trig_date])\n actions = AddTradeAction(call, freq, name='Action1')\n\n triggers = DateTrigger(trig_req, actions)\n strategy = Strategy(None, triggers)\n\n engine = GenericEngine(price_measure=DollarPrice)\n backtest = engine.run_backtest(\n strategy, states=[dt.date(2021, 12, 1), dt.date(2021, 12, 2), dt.date(2021, 12, 3)], show_progress=True\n )\n summary = backtest.result_summary\n assert len(summary) == 3\n assert round(summary[DollarPrice].sum()) == 804\n assert round(summary['Cumulative Cash'].iloc[-1]) == -291\n\n\ndef test_serialisation(mocker):\n call = FXOption(\n buy_sell='Buy',\n option_type='Call',\n pair='USDJPY',\n strike_price='ATMF',\n notional_amount=1e5,\n expiration_date='2y',\n name='2y_call',\n )\n hedge = FXForward(pair='USDJPY', settlement_date='2y', name='2y_hedge')\n d1 = dt.date(2023, 4, 11)\n d2 = dt.date(2023, 12, 25)\n d3 = dt.date(2024, 2, 29)\n dt1 = dt.datetime(2024, 4, 11, 9, 17, 34)\n dt2 = dt.datetime(2024, 4, 12, 10, 7, 57)\n sample_data_source = GsDataSource('DATASET_ABC', 'ASSET123', d1, d2)\n generic_data_source = GenericDataSource(s, MissingDataStrategy.fill_forward)\n # Actions to check\n add_trade_action_1 = AddTradeAction(call, '1m', name='Action1')\n add_trade_action_2 = AddTradeAction(call, d1, name='Action2')\n add_trade_action_3 = AddTradeAction(call, d2, name='Action3')\n hedge_action = HedgeAction(FXDelta(aggregation_level='type'), hedge, name='HedgeAction')\n exit_trade_action = ExitTradeAction('2y_call', name='ExitAction1')\n exit_all_trades_action = ExitAllPositionsAction(name='ExitEverything')\n add_scaled_trade = AddScaledTradeAction(\n priceables=call,\n trade_duration='1b',\n scaling_level=100,\n scaling_type=ScalingActionType.NAV,\n name='QuantityScaledAction1',\n )\n\n # Triggers to check (randomly assign actions to triggers to cover all above actions)\n date_trigger = DateTrigger(DateTriggerRequirements(dates=(dt.date(2021, 12, 1),)), add_trade_action_1)\n periodic_trigger = PeriodicTrigger(PeriodicTriggerRequirements(d1, d2, \"3m\", (d3,)), add_trade_action_2)\n triggers = (\n date_trigger,\n periodic_trigger,\n DateTrigger(DateTriggerRequirements(dates=(dt.date(2021, 12, 1),)), [add_trade_action_3, hedge_action]),\n PeriodicTrigger(PeriodicTriggerRequirements(d1, d2, \"3m\", (d3,))),\n IntradayPeriodicTrigger(IntradayTriggerRequirements(dt1.time(), dt2.time(), 5.0)),\n MktTrigger(MktTriggerRequirements(generic_data_source, 1.1, TriggerDirection.BELOW), exit_trade_action),\n StrategyRiskTrigger(RiskTriggerRequirements(DollarPrice, -1.4, TriggerDirection.BELOW), exit_all_trades_action),\n AggregateTrigger(AggregateTriggerRequirements((date_trigger, periodic_trigger)), add_scaled_trade),\n NotTrigger(NotTriggerRequirements(date_trigger)),\n PortfolioTrigger(PortfolioTriggerRequirements('len', 2)),\n MeanReversionTrigger(MeanReversionTriggerRequirements(sample_data_source, 1.5, 0, 5)),\n )\n strategy = Strategy(None, triggers)\n\n # Test to_dict()\n strategy_dict = strategy.to_dict()\n reconstituted_strategy = Strategy.from_dict(strategy_dict)\n assert reconstituted_strategy == strategy\n\n # Test to_json()\n strategy_json = json.dumps(strategy.to_dict(), cls=JSONEncoder)\n reconstituted_strategy = Strategy.from_json(strategy_json)\n assert reconstituted_strategy == strategy\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_initial_portfolio(mocker):\n with MockCalc(mocker):\n irswap = IRSwap(PayReceive.Pay, '10y', Currency.USD, notional_amount=10000, name='10y')\n actions = AddTradeAction(irswap, '1b')\n\n new_ir_swap = IRSwap(PayReceive.Pay, '2y', Currency.USD, notional_amount=10000, name='2y')\n newer_ir_swap = IRSwap(PayReceive.Pay, '5y', Currency.USD, notional_amount=10000, name='5y')\n\n initial_port = {\n dt.date(2024, 5, 3): irswap,\n dt.date(2024, 5, 24): [irswap, new_ir_swap],\n dt.date(2024, 6, 14): [new_ir_swap, newer_ir_swap],\n }\n\n trig_req = PeriodicTriggerRequirements(\n start_date=dt.date(2024, 5, 3), end_date=dt.date(2024, 7, 5), frequency='1b'\n )\n triggers = PeriodicTrigger(trig_req, actions)\n\n strategy = Strategy(initial_port, triggers)\n\n engine = GenericEngine()\n\n pricing_dates = [\n dt.date(2024, 5, 3),\n dt.date(2024, 5, 10),\n dt.date(2024, 5, 17),\n dt.date(2024, 5, 24),\n dt.date(2024, 5, 31),\n dt.date(2024, 6, 7),\n dt.date(2024, 6, 14),\n dt.date(2024, 6, 21),\n dt.date(2024, 6, 28),\n dt.date(2024, 7, 5),\n ]\n\n backtest = BackTest(strategy, pricing_dates, [Price], Price)\n engine._resolve_initial_portfolio(initial_port, backtest, dt.date(2024, 5, 3), pricing_dates, None)\n\n port_dict = backtest.portfolio_dict\n assert len(port_dict[dt.date(2024, 5, 3)]) == 1\n assert len(port_dict[dt.date(2024, 5, 24)]) == 2\n assert len(port_dict[dt.date(2024, 6, 14)]) == 2\n assert len(port_dict[dt.date(2024, 7, 5)]) == 2\n assert port_dict[dt.date(2024, 5, 24)][0].name == '10y_2024-05-24'\n assert port_dict[dt.date(2024, 5, 24)][1].name == '2y_2024-05-24'\n assert port_dict[dt.date(2024, 6, 14)][0].name == '2y_2024-06-14'\n assert port_dict[dt.date(2024, 6, 14)][1].name == '5y_2024-06-14'\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_add_scaled_trade_action_with_quantity_signal(mocker):\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 6)\n end_date = dt.date(2021, 12, 10)\n\n # Define instruments for strategy\n call = EqOption(\n '.STOXX50E',\n expiration_date='3m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='call',\n )\n\n trade_action_scaled = AddScaledTradeAction(\n priceables=call,\n trade_duration='1b',\n scaling_level={start_date: 13, end_date: 21},\n scaling_type=ScalingActionType.size,\n name='AddScaledTradeAction1',\n )\n\n trade_trigger_scaled = PeriodicTrigger(\n trigger_requirements=PeriodicTriggerRequirements(start_date=start_date, end_date=end_date, frequency='1b'),\n actions=trade_action_scaled,\n )\n\n strategy = Strategy(None, trade_trigger_scaled)\n\n GE = GenericEngine()\n backtest = GE.run_backtest(strategy, start=start_date, end=end_date, frequency='1b', show_progress=True)\n\n portfolio_by_date = {d: p for d, p in backtest.portfolio_dict.items() if isinstance(d, dt.date)}\n assert all(len(p) == 1 for p in portfolio_by_date.values())\n quantity_by_date = {d: p.all_instruments[0].number_of_options for d, p in portfolio_by_date.items()}\n\n expected_quantity_by_date = {\n dt.date(2021, 12, 6): 13,\n dt.date(2021, 12, 7): 13,\n dt.date(2021, 12, 8): 13,\n dt.date(2021, 12, 9): 13,\n dt.date(2021, 12, 10): 21,\n }\n\n assert quantity_by_date == expected_quantity_by_date\n\n\n@patch.object(GenericEngine, 'new_pricing_context', mock_pricing_context)\ndef test_add_weighted_trade_action(mocker):\n \"\"\"\n Test AddWeightedTradeAction which scales instruments in a portfolio\n so that each has equal risk contribution based on a specified risk measure.\n \"\"\"\n with MockCalc(mocker):\n start_date = dt.date(2021, 12, 6)\n end_date = dt.date(2021, 12, 10)\n\n # Create a portfolio of equity options with different characteristics\n # Each option will be scaled to have equal risk (EqDelta) contribution\n # Use simple names that match patterns in existing mock data\n call = EqOption(\n '.STOXX50E',\n expiration_date='1m',\n strike_price='ATM',\n option_type=OptionType.Call,\n option_style=OptionStyle.European,\n name='call',\n )\n put = EqOption(\n '.STOXX50E',\n expiration_date='1m',\n strike_price='ATM',\n option_type=OptionType.Put,\n option_style=OptionStyle.European,\n name='put',\n )\n\n # Create portfolio of instruments to be weighted\n weighted_portfolio = Portfolio([call, put])\n\n # Define the weighted trade action\n # This will scale each instrument so they all have equal EqDelta risk\n weighted_action = AddWeightedTradeAction(\n priceables=weighted_portfolio,\n trade_duration='1m', # Hold trades for 1 month\n name='WeightedAction1',\n scaling_risk=EqDelta, # Risk measure to equalize across instruments\n total_size=20000.0, # Total risk budget to distribute equally\n )\n\n # Verify action is created correctly\n assert weighted_action.scaling_risk == EqDelta\n assert weighted_action.total_size == 20000.0\n assert len(weighted_action.priceables) == 2\n\n # Verify priceables are named correctly with action prefix\n priceable_names = [p.name for p in weighted_action.priceables]\n assert all('WeightedAction1' in name for name in priceable_names)\n\n # Verify calc type is semi_path_dependent\n from gs_quant.backtests.backtest_utils import CalcType\n\n assert weighted_action.calc_type == CalcType.semi_path_dependent\n\n # Define periodic trigger - add weighted trades daily\n trigger_req = PeriodicTriggerRequirements(\n start_date=start_date,\n end_date=end_date,\n frequency='1b',\n )\n trigger = PeriodicTrigger(trigger_req, weighted_action)\n\n # Create strategy with no initial portfolio\n strategy = Strategy(None, trigger)\n\n # Verify the strategy is created correctly\n assert len(strategy.triggers) == 1\n assert len(strategy.triggers[0].actions) == 1\n assert isinstance(strategy.triggers[0].actions[0], AddWeightedTradeAction)\n" }, { "path": "gs_quant/test/backtest/test_triggers.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom gs_quant.backtests.actions import AddTradeAction\nfrom gs_quant.backtests.triggers import (\n DateTriggerRequirements,\n DateTrigger,\n AggregateTriggerRequirements,\n AggregateTrigger,\n AggType,\n NotTrigger,\n NotTriggerRequirements,\n)\nfrom gs_quant.instrument import IRSwap, IRSwaption\n\n\ndef test_date_trigger():\n # Test DateTrigger\n action = AddTradeAction(IRSwap())\n trigger = DateTrigger(\n DateTriggerRequirements([dt.date(2021, 11, 9), dt.date(2021, 11, 10), dt.date(2021, 11, 11)]), [action]\n )\n\n assert not trigger.has_triggered(dt.datetime(2021, 11, 10, 14, 0))\n assert trigger.has_triggered(dt.date(2021, 11, 10))\n\n trigger = DateTrigger(\n DateTriggerRequirements(\n [dt.datetime(2021, 11, 9, 14, 0), dt.datetime(2021, 11, 10, 14, 0), dt.datetime(2021, 11, 11, 14, 0)]\n ),\n [action],\n )\n\n assert trigger.has_triggered(dt.datetime(2021, 11, 10, 14, 0))\n assert not trigger.has_triggered(dt.date(2021, 11, 10))\n\n trigger = DateTrigger(\n DateTriggerRequirements(\n [dt.datetime(2021, 11, 9, 14, 0), dt.datetime(2021, 11, 10, 14, 0), dt.datetime(2021, 11, 11, 14, 0)],\n entire_day=True,\n ),\n [action],\n )\n\n assert trigger.has_triggered(dt.datetime(2021, 11, 10, 14, 0))\n assert trigger.has_triggered(dt.date(2021, 11, 10))\n\n\ndef test_aggregate_triggger():\n # Test Aggregate Trigger\n action_1 = AddTradeAction(IRSwap())\n action_2 = AddTradeAction(IRSwaption())\n trigger_1 = DateTrigger(\n DateTriggerRequirements([dt.date(2021, 11, 9), dt.date(2021, 11, 10), dt.date(2021, 11, 11)]), [action_1]\n )\n\n trigger_2 = DateTrigger(\n DateTriggerRequirements([dt.date(2021, 11, 8), dt.date(2021, 11, 10), dt.date(2021, 11, 12)]), [action_2]\n )\n\n agg_trigger = AggregateTrigger(AggregateTriggerRequirements([trigger_1, trigger_2], aggregate_type=AggType.ALL_OF))\n\n assert not agg_trigger.has_triggered(dt.date(2021, 11, 9))\n assert agg_trigger.has_triggered(dt.date(2021, 11, 10))\n assert len(agg_trigger.actions) == 0\n\n agg_trigger = AggregateTrigger(\n AggregateTriggerRequirements([trigger_1, trigger_2], aggregate_type=AggType.ANY_OF), [action_1, action_2]\n )\n\n assert agg_trigger.has_triggered(dt.date(2021, 11, 8))\n assert isinstance(agg_trigger.actions[1].priceables[0], IRSwaption)\n assert agg_trigger.has_triggered(dt.date(2021, 11, 10))\n assert len(agg_trigger.actions) == 2\n\n\ndef test_not_triggger():\n # Test Not Trigger\n action = AddTradeAction(IRSwap())\n not_action = AddTradeAction(IRSwaption())\n trigger = DateTrigger(\n DateTriggerRequirements([dt.date(2021, 11, 9), dt.date(2021, 11, 10), dt.date(2021, 11, 11)]), [action]\n )\n\n not_trigger = NotTrigger(NotTriggerRequirements(trigger), [not_action])\n\n assert not_trigger.has_triggered(dt.date(2021, 11, 8))\n assert isinstance(not_trigger.actions[0].priceables[0], IRSwaption)\n assert not not_trigger.has_triggered(dt.date(2021, 11, 10))\n" }, { "path": "gs_quant/test/calc_cache/request00e305681f1f021b52ef57f22d4975f1.json", "content": "{\n \"request_id\": \"4_Action1_SPX_opt_2024-06-06Price2024Jun07_Action1_SPX_opt_2024-06-05Price2024Jun06_Action1_SPX_opt_2024-06-04Price2024Jun05_Action1_SPX_opt_2024-06-03Price2024Jun04\",\n \"request_hash\": \"00e305681f1f021b52ef57f22d4975f1\",\n \"type\": \"MockCalc\",\n \"tests\": [\n \"test_exit_transaction_costs\"\n ],\n \"mocked_data\": \"[[[[{\\\"$type\\\":\\\"Risk\\\",\\\"unit\\\":{\\\"United States Dollar\\\":1.0},\\\"val\\\":16448.823232831626}]]],[[[{\\\"$type\\\":\\\"Risk\\\",\\\"unit\\\":{\\\"United States Dollar\\\":1.0},\\\"val\\\":17394.03931208903}]]],[[[{\\\"$type\\\":\\\"Risk\\\",\\\"unit\\\":{\\\"United States Dollar\\\":1.0},\\\"val\\\":20727.544149823272}]]],[[[{\\\"$type\\\":\\\"Risk\\\",\\\"unit\\\":{\\\"United States Dollar\\\":1.0},\\\"val\\\":15860.139946933508}]]]]\"\n}" }, { "path": "gs_quant/test/calc_cache/request02db37fadb8c59eb98457c277883b4af.json", "content": "{\n \"request_id\": \"4_QuantityScaledAction2_TransactionAggType.MAX_call_2021-12-09Price2021Dec10_QuantityScaledAction2_TransactionAggType.MAX_call_2021-12-08Price2021Dec09_QuantityScaledAction2_TransactionAggType.MAX_call_2021-12-07Price2021Dec08_QuantityScaledAction2_TransactionAggType.MAX_call_2021-12-06Price2021Dec07\",\n \"request_hash\": \"02db37fadb8c59eb98457c277883b4af\",\n \"type\": \"MockCalc\",\n \"tests\": [\n \"test_add_scaled_action_nav_with_transaction_costs\"\n ],\n \"mocked_data\": \"[[[[{\\\"$type\\\":\\\"Risk\\\",\\\"unit\\\":{\\\"European Euro\\\":1.0},\\\"val\\\":57.29826410449691}]]],[[[{\\\"$type\\\":\\\"Risk\\\",\\\"unit\\\":{\\\"European Euro\\\":1.0},\\\"val\\\":71.6067550722692}]]],[[[{\\\"$type\\\":\\\"Risk\\\",\\\"unit\\\":{\\\"European Euro\\\":1.0},\\\"val\\\":96.58210204775983}]]],[[[{\\\"$type\\\":\\\"Risk\\\",\\\"unit\\\":{\\\"European Euro\\\":1.0},\\\"val\\\":132.57078084954878}]]]]\"\n}" }, { "path": "gs_quant/test/calc_cache/request03cc64abac61cde741b4b5bf90fc3b0f.json", "content": "{\n \"request_id\": \"5_QuantityScaledAction1_callResolvedInstrumentValues2021Dec10_QuantityScaledAction1_callResolvedInstrumentValues2021Dec09_QuantityScaledAction1_callResolvedInstrumentValues2021Dec08_QuantityScaledAction1_callResolvedInstrumentValues2021Dec07_QuantityScaledAction1_callResolvedInstrumentValues2021Dec06\",\n \"request_hash\": \"03cc64abac61cde741b4b5bf90fc3b0f\",\n \"type\": \"MockCalc\",\n \"tests\": [\n \"test_add_scaled_action_nav\"\n ],\n \"mocked_data\": \"[[[[{\\\"$type\\\":\\\"LegDefinition\\\",\\\"assetClass\\\":\\\"Equity\\\",\\\"buySell\\\":\\\"Buy\\\",\\\"expirationDate\\\":\\\"2022-01-10\\\",\\\"expirySettleDate\\\":\\\"2022-01-12\\\",\\\"expirySettlementDays\\\":\\\"2b\\\",\\\"methodOfSettlement\\\":\\\"Cash\\\",\\\"multiplier\\\":1.0,\\\"numberOfOptions\\\":1.0,\\\"optionStyle\\\":\\\"European\\\",\\\"optionType\\\":\\\"Call\\\",\\\"premium\\\":0.0,\\\"premiumCurrency\\\":\\\"EUR\\\",\\\"premiumPaymentDate\\\":\\\"2021-12-14\\\",\\\"premiumSettlementDate\\\":\\\"2021-12-14\\\",\\\"settlementDate\\\":\\\"2021-12-14\\\",\\\"strikePrice\\\":4199.16,\\\"tradeAs\\\":\\\"OTC\\\",\\\"type\\\":\\\"Option\\\",\\\"underlier\\\":\\\".STOXX50E\\\",\\\"underlierCurrency\\\":\\\"EUR\\\",\\\"underlierType\\\":\\\"RIC\\\",\\\"valuationTime\\\":\\\"MktClose\\\"}]]],[[[{\\\"$type\\\":\\\"LegDefinition\\\",\\\"assetClass\\\":\\\"Equity\\\",\\\"buySell\\\":\\\"Buy\\\",\\\"expirationDate\\\":\\\"2022-01-10\\\",\\\"expirySettleDate\\\":\\\"2022-01-12\\\",\\\"expirySettlementDays\\\":\\\"2b\\\",\\\"methodOfSettlement\\\":\\\"Cash\\\",\\\"multiplier\\\":1.0,\\\"numberOfOptions\\\":1.0,\\\"optionStyle\\\":\\\"European\\\",\\\"optionType\\\":\\\"Call\\\",\\\"premium\\\":0.0,\\\"premiumCurrency\\\":\\\"EUR\\\",\\\"premiumPaymentDate\\\":\\\"2021-12-13\\\",\\\"premiumSettlementDate\\\":\\\"2021-12-13\\\",\\\"settlementDate\\\":\\\"2021-12-13\\\",\\\"strikePrice\\\":4208.3,\\\"tradeAs\\\":\\\"OTC\\\",\\\"type\\\":\\\"Option\\\",\\\"underlier\\\":\\\".STOXX50E\\\",\\\"underlierCurrency\\\":\\\"EUR\\\",\\\"underlierType\\\":\\\"RIC\\\",\\\"valuationTime\\\":\\\"MktClose\\\"}]]],[[[{\\\"$type\\\":\\\"LegDefinition\\\",\\\"assetClass\\\":\\\"Equity\\\",\\\"buySell\\\":\\\"Buy\\\",\\\"expirationDate\\\":\\\"2022-01-10\\\",\\\"expirySettleDate\\\":\\\"2022-01-12\\\",\\\"expirySettlementDays\\\":\\\"2b\\\",\\\"methodOfSettlement\\\":\\\"Cash\\\",\\\"multiplier\\\":1.0,\\\"numberOfOptions\\\":1.0,\\\"optionStyle\\\":\\\"European\\\",\\\"optionType\\\":\\\"Call\\\",\\\"premium\\\":0.0,\\\"premiumCurrency\\\":\\\"EUR\\\",\\\"premiumPaymentDate\\\":\\\"2021-12-10\\\",\\\"premiumSettlementDate\\\":\\\"2021-12-10\\\",\\\"settlementDate\\\":\\\"2021-12-10\\\",\\\"strikePrice\\\":4233.09,\\\"tradeAs\\\":\\\"OTC\\\",\\\"type\\\":\\\"Option\\\",\\\"underlier\\\":\\\".STOXX50E\\\",\\\"underlierCurrency\\\":\\\"EUR\\\",\\\"underlierType\\\":\\\"RIC\\\",\\\"valuationTime\\\":\\\"MktClose\\\"}]]],[[[{\\\"$type\\\":\\\"LegDefinition\\\",\\\"assetClass\\\":\\\"Equity\\\",\\\"buySell\\\":\\\"Buy\\\",\\\"expirationDate\\\":\\\"2022-01-07\\\",\\\"expirySettleDate\\\":\\\"2022-01-11\\\",\\\"expirySettlementDays\\\":\\\"2b\\\",\\\"methodOfSettlement\\\":\\\"Cash\\\",\\\"multiplier\\\":1.0,\\\"numberOfOptions\\\":1.0,\\\"optionStyle\\\":\\\"European\\\",\\\"optionType\\\":\\\"Call\\\",\\\"premium\\\":0.0,\\\"premiumCurrency\\\":\\\"EUR\\\",\\\"premiumPaymentDate\\\":\\\"2021-12-09\\\",\\\"premiumSettlementDate\\\":\\\"2021-12-09\\\",\\\"settlementDate\\\":\\\"2021-12-09\\\",\\\"strikePrice\\\":4276.2,\\\"tradeAs\\\":\\\"OTC\\\",\\\"type\\\":\\\"Option\\\",\\\"underlier\\\":\\\".STOXX50E\\\",\\\"underlierCurrency\\\":\\\"EUR\\\",\\\"underlierType\\\":\\\"RIC\\\",\\\"valuationTime\\\":\\\"SQ\\\"}]]],[[[{\\\"$type\\\":\\\"LegDefinition\\\",\\\"assetClass\\\":\\\"Equity\\\",\\\"buySell\\\":\\\"Buy\\\",\\\"expirationDate\\\":\\\"2022-01-06\\\",\\\"expirySettleDate\\\":\\\"2022-01-10\\\",\\\"expirySettlementDays\\\":\\\"2b\\\",\\\"methodOfSettlement\\\":\\\"Cash\\\",\\\"multiplier\\\":1.0,\\\"numberOfOptions\\\":1.0,\\\"optionStyle\\\":\\\"European\\\",\\\"optionType\\\":\\\"Call\\\",\\\"premium\\\":0.0,\\\"premiumCurrency\\\":\\\"EUR\\\",\\\"premiumPaymentDate\\\":\\\"2021-12-08\\\",\\\"premiumSettlementDate\\\":\\\"2021-12-08\\\",\\\"settlementDate\\\":\\\"2021-12-08\\\",\\\"strikePrice\\\":4137.11,\\\"tradeAs\\\":\\\"OTC\\\",\\\"type\\\":\\\"Option\\\",\\\"underlier\\\":\\\".STOXX50E\\\",\\\"underlierCurrency\\\":\\\"EUR\\\",\\\"underlierType\\\":\\\"RIC\\\",\\\"valuationTime\\\":\\\"MktClose\\\"}]]]]\"\n}" }, { "path": "gs_quant/test/calc_cache/request04e5386077a564977740976627ab683d.json", "content": "{\n \"request_id\": \"1_Action1_callResolvedInstrumentValues2021Dec01\",\n \"request_hash\": \"04e5386077a564977740976627ab683d\",\n \"type\": \"MockCalc\",\n \"tests\": [\n \"test_generic_engine_custom_price_measure\"\n ],\n \"mocked_data\": \"[[[[{\\\"$type\\\": \\\"LegDefinition\\\", \\\"assetClass\\\": \\\"Equity\\\", \\\"buySell\\\": \\\"Buy\\\", \\\"expirationDate\\\": \\\"2022-12-01\\\", \\\"methodOfSettlement\\\": \\\"Cash\\\", \\\"multiplier\\\": 1.0, \\\"numberOfOptions\\\": 1.0, \\\"optionStyle\\\": \\\"European\\\", \\\"optionType\\\": \\\"Call\\\", \\\"premium\\\": 0.0, \\\"premiumCurrency\\\": \\\"EUR\\\", \\\"premiumPaymentDate\\\": \\\"2021-12-03\\\", \\\"settlementDate\\\": \\\"2021-12-03\\\", \\\"strikePrice\\\": 4179.15, \\\"tradeAs\\\": \\\"OTC\\\", \\\"type\\\": \\\"Option\\\", \\\"underlier\\\": \\\".STOXX50E\\\", \\\"underlierType\\\": \\\"RIC\\\", \\\"valuationTime\\\": \\\"MktClose\\\"}]]]]\"\n}" }, { "path": "gs_quant/test/calc_cache/request093e1ad35f25cb43fcad999f9adf8c50.json", "content": "{\n \"request_id\": \"1_5yPrice(value:MYR)today\",\n \"request_hash\": \"093e1ad35f25cb43fcad999f9adf8c50\",\n \"type\": \"MockCalc\",\n \"tests\": [\n \"test_currency_params\"\n ],\n \"mocked_data\": \"[[[[{\\\"$type\\\": \\\"Risk\\\", \\\"unit\\\": {\\\"Malaysian Ringgit\\\": 1.0}, \\\"val\\\": 40400773.28824528, \\\"children\\\": {}, \\\"calculationTime\\\": 0, \\\"queueingTime\\\": 1072}]]]]\"\n}" }, { "path": "gs_quant/test/calc_cache/request0b8e0fc36f0a438251dafabc0b82b1a7.json", "content": "{\n \"request_id\": \"1_5y-10yIRDeltatoday\",\n \"request_hash\": \"0b8e0fc36f0a438251dafabc0b82b1a7\",\n \"type\": \"MockCalc\",\n \"tests\": [\n \"test_transformation\"\n ],\n \"mocked_data\": \"[[[[{\\\"$type\\\": \\\"RiskVector\\\", \\\"asset\\\": [-222.89487023773194, -4.956045434570313, -257.4838070983887, -177.14621021118165, 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\\\"class_\\\": \\\"SWAP OIS\\\", \\\"path\\\": \\\"\\\", \\\"point\\\": \\\"6Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"value\\\": -7.161747689819336}, {\\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP OIS\\\", \\\"path\\\": \\\"\\\", \\\"point\\\": \\\"7Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"value\\\": 2.076456838989258}, {\\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP OIS\\\", \\\"path\\\": \\\"\\\", \\\"point\\\": \\\"8Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"value\\\": -0.34110171508789067}, {\\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP OIS\\\", \\\"path\\\": \\\"\\\", \\\"point\\\": \\\"9Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"value\\\": 0.002102958679199219}], \\\"unit\\\": {\\\"Basis Point\\\": -1.0, \\\"United States Dollar\\\": 1.0}}], [{\\\"$type\\\": \\\"RiskVector\\\", \\\"asset\\\": [-184.73840417480469, -8.77245535583496, -207.95269998779298, 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\\\"SWAP\\\", \\\"point\\\": \\\"25Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 0.0007811740875244141}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"30Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -0.00014251861572265624}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"35Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 1.2205123901367188e-05}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"3Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -154.09162789268495}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"40Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 2.044677734375e-07}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"45Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -3.39508056640625e-08}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"4Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -218.95008361053468}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"50Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 1.1825561523437501e-08}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"5Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -269.19285474510195}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"6Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -323.1999001693726}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"7Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -376.7711307369232}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"8Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -425.6325191196442}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"9Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -518.4252589351654}], \\\"asset\\\": [-1635.2538244144441, -1.5062988487243654, -975.0654988819123, -18.27122632446289, -24.443906997680664, -10.741698770523072, -27.186864908981324, -13.22385443878174, -19.90264452056885, -9.001396529006959, 98007.49496660577, 9.152537252044679, -1.2754898479461672, 0.09130501899719239, 0.0007811740875244141, -0.00014251861572265624, 1.2205123901367188e-05, -154.09162789268495, 2.044677734375e-07, -3.39508056640625e-08, -218.95008361053468, 1.1825561523437501e-08, -269.19285474510195, -323.1999001693726, -376.7711307369232, -425.6325191196442, -518.4252589351654], \\\"calculationTime\\\": 2170, \\\"queueingTime\\\": -10164}], [{\\\"$type\\\": \\\"RiskVector\\\", \\\"points\\\": [{\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"CASH\\\", \\\"point\\\": \\\"CASH STUB\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -1630.8292216583252}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"CASH\\\", \\\"point\\\": \\\"O/N\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -1.2329848213195802}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"FRA\\\", \\\"point\\\": \\\"DEC20\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -969.4754175727845}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"FRA\\\", \\\"point\\\": \\\"DEC21\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -14.956416115188599}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"FRA\\\", \\\"point\\\": \\\"JUN21\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -20.009234772109988}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"FRA\\\", \\\"point\\\": \\\"JUN22\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -8.792913516998292}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"FRA\\\", \\\"point\\\": \\\"MAR21\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -22.254558886337282}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"FRA\\\", \\\"point\\\": \\\"MAR22\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -10.824750659942627}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"FRA\\\", \\\"point\\\": \\\"SEP21\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -16.29185904083252}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"FRA\\\", \\\"point\\\": \\\"SEP22\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -7.368341283798218}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"10Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 98077.48558723679}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"12Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 7.492060999679566}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"15Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -1.0440860725402832}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"20Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 0.07473968963623047}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"25Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 0.0006395401000976563}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"30Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -0.00011658401489257813}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"35Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 9.988784790039063e-06}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"3Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -126.13596354255677}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"40Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 1.514434814453125e-07}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"45Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -2.8228759765625e-08}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"4Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -179.2276462600708}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"50Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": 1.25885009765625e-08}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"5Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -220.35525622100832}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"6Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -264.5642174613953}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"7Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -308.4164249977112}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"8Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -348.41329239501954}, {\\\"path\\\": \\\"\\\", \\\"type\\\": \\\"IR\\\", \\\"asset\\\": \\\"USD\\\", \\\"class_\\\": \\\"SWAP\\\", \\\"point\\\": \\\"9Y\\\", \\\"quoteStyle\\\": \\\"\\\", \\\"value\\\": -424.371269336319}], \\\"asset\\\": [-1630.8292216583252, -1.2329848213195802, -969.4754175727845, -14.956416115188599, -20.009234772109988, -8.792913516998292, -22.254558886337282, -10.824750659942627, -16.29185904083252, -7.368341283798218, 98077.48558723679, 7.492060999679566, -1.0440860725402832, 0.07473968963623047, 0.0006395401000976563, -0.00011658401489257813, 9.988784790039063e-06, -126.13596354255677, 1.514434814453125e-07, -2.8228759765625e-08, -179.2276462600708, 1.25885009765625e-08, -220.35525622100832, -264.5642174613953, -308.4164249977112, -348.41329239501954, -424.371269336319], \\\"calculationTime\\\": 2170, \\\"queueingTime\\\": -10164}]]]]\"\n}" }, { "path": "gs_quant/test/config/test_options.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport gs_quant\nimport pytest\nfrom gs_quant.config import DisplayOptions\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk import IRVega, AggregationLevel\nfrom gs_quant.instrument import IRSwap\nfrom gs_quant.test.utils.mock_calc import MockCalc\n\n\ndef test_display_options(mocker):\n with MockCalc(mocker):\n res = Portfolio(IRSwap(name='swap'), name='port').calc(IRVega(aggregation_level=AggregationLevel.Asset))\n\n assert gs_quant.config.display_options.show_na is False\n gs_quant.config.display_options.show_na = True\n assert gs_quant.config.display_options.show_na is True\n o = DisplayOptions(show_na=True)\n o2 = DisplayOptions(show_na=False)\n\n with pytest.raises(TypeError):\n _ = res.to_frame(display_options=True)\n df1 = res.to_frame(display_options=o)\n df2 = res.to_frame(display_options=o2)\n\n assert df1 is not None\n assert df2 is None\n # reset to make sure test works\n gs_quant.config.display_options = DisplayOptions()\n" }, { "path": "gs_quant/test/data/__init__.py", "content": "" }, { "path": "gs_quant/test/data/test_data_caching.py", "content": "\"\"\"\nCopyright 2023 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest.mock import patch\n\nfrom pandas.testing import assert_frame_equal\n\nfrom gs_quant.api.api_cache import InMemoryApiRequestCache, CacheEvent\nfrom gs_quant.api.gs.data import GsDataApi, QueryType\nfrom gs_quant.data import Dataset, DataContext\n\n\nclass _FakeSyncAPI:\n def __init__(self, session_cls):\n self._session_cls = session_cls\n\n def get(self, url, **kwargs):\n return self._session_cls._get(url, **kwargs)\n\n def post(self, url, **kwargs):\n return self._session_cls._post(url, **kwargs)\n\n\nclass NotExpectedToBeCalledSession:\n redirect_to_mds = True\n\n @classmethod\n def _get(cls, url, **kwargs):\n if url == \"/data/datasets/FXSPOT_STANDARD\":\n return {\"id\": \"FXSPOT_STANDARD\"}\n else:\n raise Exception(\"Not expecting to be called at this point\")\n\n @classmethod\n def _post(cls, url, **kwargs):\n raise Exception(\"Not expecting to be called at this point\")\n\n @property\n def sync(self):\n return _FakeSyncAPI(NotExpectedToBeCalledSession)\n\n\nclass MarketDataErrorSession:\n redirect_to_mds = True\n\n @classmethod\n def _post(cls, url, **kwargs):\n return {\n \"requestId\": \"890\",\n \"responses\": [{\"queryResponse\": [{\"errorMessages\": [\"Test Failure\"]}]}],\n }\n\n @property\n def sync(self):\n return _FakeSyncAPI(MarketDataErrorSession)\n\n\nclass FakeSession:\n redirect_to_mds = True\n\n @classmethod\n def _get(cls, url, **kwargs):\n if url == \"/data/catalog/FXSPOT_STANDARD\":\n return {\n \"id\": \"FXSPOT_STANDARD\",\n \"fields\": {\n \"date\": {\"type\": \"string\", \"format\": \"date\"},\n \"assetId\": {\"type\": \"string\"},\n \"spot\": {\n \"type\": \"number\",\n },\n \"updateTime\": {\"type\": \"string\", \"format\": \"date-time\"},\n },\n }\n elif url == \"/data/datasets/FXSPOT_STANDARD\":\n return {\"id\": \"FXSPOT_STANDARD\"}\n else:\n raise Exception(\"Need to mock _get request here\")\n\n @classmethod\n def _post(cls, url, **kwargs):\n if url == \"/data/FXSPOT_STANDARD/last/query\":\n return {\n \"requestId\": \"1234\",\n \"data\": [\n {\n \"date\": \"2023-10-25\",\n \"assetId\": \"MATGYV0J9MPX534Z\",\n \"bbid\": \"USDJPY\",\n \"spot\": 150.123,\n \"updateTime\": \"2023-10-25T21:53:56Z\",\n }\n ],\n }\n elif url == \"/data/FXSPOT_STANDARD/query\":\n return {\n \"requestId\": \"5678\",\n \"data\": [\n {\n \"date\": \"2023-10-26\",\n \"assetId\": \"MATGYV0J9MPX534Z\",\n \"bbid\": \"USDJPY\",\n \"spot\": 152.234,\n \"updateTime\": \"2023-10-26T21:53:56Z\",\n }\n ],\n }\n elif url == \"/data/measures\":\n return {\n \"requestId\": \"890\",\n \"responses\": [\n {\n \"queryResponse\": [\n {\n \"measure\": \"Curve\",\n \"dataSetIds\": [\"DATASET_FOO\"],\n \"entityTypes\": [\"ASSET\"],\n \"response\": {\n \"data\": [\n {\n \"date\": \"2023-04-11\",\n \"assetId\": \"MATGYV0J9MPX534Z\",\n \"pricingLocation\": \"HKG\",\n \"name\": \"USDJPY\",\n \"spot\": 133.0,\n },\n {\n \"date\": \"2023-04-11\",\n \"assetId\": \"MATGYV0J9MPX534Z\",\n \"pricingLocation\": \"LDN\",\n \"name\": \"USDJPY\",\n \"spot\": 134.0,\n },\n {\n \"date\": \"2023-04-11\",\n \"assetId\": \"MATGYV0J9MPX534Z\",\n \"pricingLocation\": \"NYC\",\n \"name\": \"USDJPY\",\n \"spot\": 136.0,\n },\n ]\n },\n }\n ]\n }\n ],\n }\n\n @property\n def sync(self):\n return _FakeSyncAPI(FakeSession)\n\n\nclass TestDataApiCache:\n def setup_method(self, test_method):\n self.cache = InMemoryApiRequestCache()\n GsDataApi.set_api_request_cache(self.cache)\n\n def teardown_method(self, test_method):\n GsDataApi.set_api_request_cache(None)\n\n def test_last_data(self):\n ds = Dataset(\"FXSPOT_STANDARD\")\n with patch.object(GsDataApi, \"get_session\", return_value=FakeSession()):\n df = ds.get_data_last(as_of=dt.date(2023, 10, 25), bbid=\"USDJPY\")\n with patch.object(GsDataApi, \"get_session\", return_value=NotExpectedToBeCalledSession()):\n df2 = ds.get_data_last(dt.date(2023, 10, 25), bbid=\"USDJPY\")\n assert not df.empty\n assert_frame_equal(df, df2)\n cache_events = self.cache.get_events()\n assert len(cache_events) == 4\n assert cache_events[0][0] == CacheEvent.PUT\n assert cache_events[1][0] == CacheEvent.PUT\n assert cache_events[2][0] == CacheEvent.GET\n assert cache_events[3][0] == CacheEvent.GET\n\n def test_query_data(self):\n ds = Dataset(\"FXSPOT_STANDARD\")\n with patch.object(GsDataApi, \"get_session\", return_value=FakeSession()):\n df = ds.get_data(dt.date(2023, 10, 26), dt.date(2023, 10, 26), bbid=\"USDJPY\")\n with patch.object(GsDataApi, \"get_session\", return_value=NotExpectedToBeCalledSession()):\n df2 = ds.get_data(dt.date(2023, 10, 26), dt.date(2023, 10, 26), bbid=\"USDJPY\")\n\n assert_frame_equal(df, df2)\n cache_events = self.cache.get_events()\n assert len(cache_events) == 4\n assert cache_events[0][0] == CacheEvent.PUT\n assert cache_events[1][0] == CacheEvent.PUT\n assert cache_events[2][0] == CacheEvent.GET\n assert cache_events[3][0] == CacheEvent.GET\n\n def test_market_data(self):\n asset_id = \"MATGYV0J9MPX534Z\"\n with DataContext(dt.date(2023, 4, 11), dt.date(2023, 4, 11)):\n q = GsDataApi.build_market_data_query([asset_id], QueryType.SPOT)\n\n with patch.object(GsDataApi, \"get_session\", return_value=MarketDataErrorSession()):\n try:\n df = GsDataApi.get_market_data(q)\n except Exception:\n pass\n cache_events = self.cache.get_events()\n assert len(cache_events) == 0\n with patch.object(GsDataApi, \"get_session\", return_value=FakeSession()):\n df = GsDataApi.get_market_data(q)\n with patch.object(GsDataApi, \"get_session\", return_value=NotExpectedToBeCalledSession()):\n df2 = GsDataApi.get_market_data(q)\n\n assert_frame_equal(df, df2)\n cache_events = self.cache.get_events()\n assert len(cache_events) == 2\n assert cache_events[0][0] == CacheEvent.PUT\n assert cache_events[1][0] == CacheEvent.GET\n" }, { "path": "gs_quant/test/data/test_data_coordinate.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport pytest\n\nfrom gs_quant.data import Dataset, DataCoordinate, DataMeasure, DataDimension\n\n\ndef test_immutability():\n dimensions = {DataDimension.TENOR: '1m', DataDimension.STRIKE_REFERENCE: 'Delta', DataDimension.RELATIVE_STRIKE: 50}\n\n coord1 = DataCoordinate(Dataset.GS.EDRVOL_PERCENT_STANDARD, DataMeasure.IMPLIED_VOLATILITY, dimensions)\n coord2 = DataCoordinate(Dataset.GS.EDRVOL_PERCENT_STANDARD, DataMeasure.IMPLIED_VOLATILITY, dimensions)\n\n assert id(coord1) != id(coord2)\n assert coord1 == coord2\n\n with pytest.raises(AttributeError):\n coord1.dataset_id = 'test'\n\n with pytest.raises(AttributeError):\n coord1.dimensions = {}\n\n with pytest.raises(AttributeError):\n coord1.measure = 'test'\n\n dimensions[DataDimension.TENOR] = '2m'\n\n coord3 = DataCoordinate(Dataset.GS.EDRVOL_PERCENT_STANDARD, DataMeasure.IMPLIED_VOLATILITY, dimensions)\n\n assert id(coord1) != id(coord2)\n assert coord1 != coord3\n\n\ndef test_equals():\n dimensions_a = {\n DataDimension.TENOR: '1m',\n DataDimension.STRIKE_REFERENCE: 'spot',\n DataDimension.RELATIVE_STRIKE: 0.8,\n }\n\n # Same dimensions but different order\n dimensions_b = {\n DataDimension.RELATIVE_STRIKE: 0.8,\n DataDimension.STRIKE_REFERENCE: 'spot',\n DataDimension.TENOR: '1m',\n }\n\n coord_a = DataCoordinate(\n dataset_id='EDRVOL_PERCENT_STOCK_STANDARD', measure=DataMeasure.IMPLIED_VOLATILITY, dimensions=dimensions_a\n )\n\n coord_b = DataCoordinate(\n dataset_id='EDRVOL_PERCENT_STOCK_STANDARD', measure=DataMeasure.IMPLIED_VOLATILITY, dimensions=dimensions_b\n )\n\n assert coord_a == coord_b\n assert coord_a == coord_b\n assert hash(coord_a) == hash(coord_b)\n\n\ndef test_equals_measure_str():\n coord_a = DataCoordinate(dataset_id='EDRVOL_PERCENT_STOCK_STANDARD', measure=DataMeasure.IMPLIED_VOLATILITY)\n\n coord_b = DataCoordinate(dataset_id='EDRVOL_PERCENT_STOCK_STANDARD', measure='impliedVolatility')\n assert coord_a == coord_b\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_data_coordinate.py\"])\n" }, { "path": "gs_quant/test/data/test_dataset.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport datetime as dt\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.data import Dataset\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.target.data import Format, DataQuery\nfrom gs_quant.target.data import DataSetEntity\nfrom gs_quant.target.data import DataSetParameters\nfrom gs_quant.target.data import DataSetDimensions\n\ntest_types = {\n 'date': 'date',\n 'assetId': 'string',\n 'askPrice': 'number',\n 'adjustedAskPrice': 'number',\n 'bidPrice': 'number',\n 'adjustedBidPrice': 'number',\n 'tradePrice': 'number',\n 'adjustedTradePrice': 'number',\n 'openPrice': 'number',\n 'adjustedOpenPrice': 'number',\n 'highPrice': 'number',\n 'lowPrice': 'number',\n 'adjustedHighPrice': 'number',\n 'adjustedLowPrice': 'number',\n 'updateTime': 'date-time',\n}\ntest_data = [\n {\n 'date': dt.date(2019, 1, 2),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2529,\n 'adjustedAskPrice': 2529,\n 'bidPrice': 2442.55,\n 'adjustedBidPrice': 2442.55,\n 'tradePrice': 2510.03,\n 'adjustedTradePrice': 2510.03,\n 'openPrice': 2476.96,\n 'adjustedOpenPrice': 2476.96,\n 'highPrice': 2519.49,\n 'lowPrice': 2467.47,\n 'adjustedHighPrice': 2519.49,\n 'adjustedLowPrice': 2467.47,\n 'updateTime': dt.datetime.strptime('2019-01-03T00:53:00Z', '%Y-%m-%dT%H:%M:%SZ'),\n },\n {\n 'date': dt.date(2019, 1, 3),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2502.34,\n 'adjustedAskPrice': 2502.34,\n 'bidPrice': 2418.09,\n 'adjustedBidPrice': 2418.09,\n 'tradePrice': 2447.89,\n 'adjustedTradePrice': 2447.89,\n 'openPrice': 2491.92,\n 'adjustedOpenPrice': 2491.92,\n 'highPrice': 2493.14,\n 'lowPrice': 2443.96,\n 'adjustedHighPrice': 2493.14,\n 'adjustedLowPrice': 2443.96,\n 'updateTime': dt.datetime.strptime('2019-01-04T00:14:00Z', '%Y-%m-%dT%H:%M:%SZ'),\n },\n {\n 'date': dt.date(2019, 1, 4),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2566.52,\n 'adjustedAskPrice': 2566.52,\n 'bidPrice': 2487.8,\n 'adjustedBidPrice': 2487.8,\n 'tradePrice': 2531.94,\n 'adjustedTradePrice': 2531.94,\n 'openPrice': 2474.33,\n 'adjustedOpenPrice': 2474.33,\n 'highPrice': 2538.07,\n 'lowPrice': 2474.33,\n 'adjustedHighPrice': 2538.07,\n 'adjustedLowPrice': 2474.33,\n 'updateTime': dt.datetime.strptime('2019-01-08T00:31:00Z', '%Y-%m-%dT%H:%M:%SZ'),\n },\n {\n 'date': dt.date(2019, 1, 7),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2591.75,\n 'adjustedAskPrice': 2591.75,\n 'bidPrice': 2509.77,\n 'adjustedBidPrice': 2509.77,\n 'tradePrice': 2549.69,\n 'adjustedTradePrice': 2549.69,\n 'openPrice': 2535.61,\n 'adjustedOpenPrice': 2535.61,\n 'highPrice': 2566.16,\n 'lowPrice': 2524.56,\n 'adjustedHighPrice': 2566.16,\n 'adjustedLowPrice': 2524.56,\n 'updateTime': dt.datetime.strptime('2019-01-08T00:31:00Z', '%Y-%m-%dT%H:%M:%SZ'),\n },\n {\n 'date': dt.date(2019, 1, 8),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2610.52,\n 'adjustedAskPrice': 2610.52,\n 'bidPrice': 2531.15,\n 'adjustedBidPrice': 2531.15,\n 'tradePrice': 2574.41,\n 'adjustedTradePrice': 2574.41,\n 'openPrice': 2568.11,\n 'adjustedOpenPrice': 2568.11,\n 'highPrice': 2579.82,\n 'lowPrice': 2547.56,\n 'adjustedHighPrice': 2579.82,\n 'adjustedLowPrice': 2547.56,\n 'updateTime': dt.datetime.strptime('2019-01-09T00:50:00Z', '%Y-%m-%dT%H:%M:%SZ'),\n },\n {\n 'date': dt.date(2019, 1, 9),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2623.09,\n 'adjustedAskPrice': 2623.09,\n 'bidPrice': 2537.19,\n 'adjustedBidPrice': 2537.19,\n 'tradePrice': 2584.96,\n 'adjustedTradePrice': 2584.96,\n 'openPrice': 2580,\n 'adjustedOpenPrice': 2580,\n 'highPrice': 2595.32,\n 'lowPrice': 2568.89,\n 'adjustedHighPrice': 2595.32,\n 'adjustedLowPrice': 2568.89,\n 'updateTime': dt.datetime.strptime('2019-01-10T00:44:00Z', '%Y-%m-%dT%H:%M:%SZ'),\n },\n]\n\ntr_types = {\n 'time': 'date-time',\n 'assetId': 'string',\n 'askPrice': 'number',\n 'adjustedAskPrice': 'number',\n 'bidPrice': 'number',\n 'adjustedBidPrice': 'number',\n 'tradePrice': 'number',\n 'adjustedTradePrice': 'number',\n 'openPrice': 'number',\n 'adjustedOpenPrice': 'number',\n 'highPrice': 'number',\n 'adjustedLowPrice': 'number',\n 'lowPrice': 'number',\n 'adjustedHighPrice': 'number',\n}\ntr_data = [\n {\n 'time': dt.datetime(2023, 5, 31, 14),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2529,\n 'adjustedAskPrice': 2529,\n 'bidPrice': 2442.55,\n 'adjustedBidPrice': 2442.55,\n 'tradePrice': 2510.03,\n 'adjustedTradePrice': 2510.03,\n 'openPrice': 2476.96,\n 'adjustedOpenPrice': 2476.96,\n 'highPrice': 2519.49,\n 'lowPrice': 2467.47,\n 'adjustedHighPrice': 2519.49,\n 'adjustedLowPrice': 2467.47,\n },\n {\n 'time': dt.datetime(2023, 5, 31, 15),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2502.34,\n 'adjustedAskPrice': 2502.34,\n 'bidPrice': 2418.09,\n 'adjustedBidPrice': 2418.09,\n 'tradePrice': 2447.89,\n 'adjustedTradePrice': 2447.89,\n 'openPrice': 2491.92,\n 'adjustedOpenPrice': 2491.92,\n 'highPrice': 2493.14,\n 'lowPrice': 2443.96,\n 'adjustedHighPrice': 2493.14,\n 'adjustedLowPrice': 2443.96,\n },\n {'time': dt.datetime(2023, 5, 31, 16), 'assetId': 'MA4B66MW5E27U8P32SB'},\n {\n 'time': dt.datetime(2023, 5, 31, 17),\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'askPrice': 2566.52,\n 'adjustedAskPrice': 2566.52,\n 'bidPrice': 2487.8,\n 'adjustedBidPrice': 2487.8,\n 'tradePrice': 2531.94,\n 'adjustedTradePrice': 2531.94,\n 'openPrice': 2474.33,\n 'adjustedOpenPrice': 2474.33,\n 'highPrice': 2538.07,\n 'lowPrice': 2474.33,\n 'adjustedHighPrice': 2538.07,\n 'adjustedLowPrice': 2474.33,\n },\n]\n\ntest_coverage_data = {'results': [{'gsid': 'gsid1'}]}\n\n\ndef test_query_data(mocker):\n mock = mocker.patch(\"gs_quant.api.gs.data.GsDataApi.query_data\", return_value=test_data)\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=test_types)\n dataset = Dataset(Dataset.TR.TREOD)\n data = dataset.get_data(dt.date(2019, 1, 2), dt.date(2019, 1, 9), assetId='MA4B66MW5E27U8P32SB')\n assert data.equals(GsDataApi.construct_dataframe_with_types(str(Dataset.TR.TREOD), test_data))\n\n assert mock.call_count == 1\n query = mock.call_args[0][0]\n assert type(query) is DataQuery\n assert query.empty_intervals is None\n\n\ndef test_query_data_intervals(mocker):\n mock = mocker.patch(\"gs_quant.api.gs.data.GsDataApi.query_data\", return_value=tr_data)\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=tr_types)\n dataset = Dataset(Dataset.TR.TREOD)\n data = dataset.get_data(\n dt.datetime(2023, 5, 31, 13),\n dt.datetime(2023, 5, 31, 17),\n assetId='MA4B66MW5E27U8P32SB',\n intervals=4,\n empty_intervals=True,\n )\n assert data.equals(GsDataApi.construct_dataframe_with_types(str(Dataset.TR.TR), tr_data))\n\n assert mock.call_count == 1\n query = mock.call_args[0][0]\n assert type(query) is DataQuery\n assert query.empty_intervals is True\n\n\ndef test_query_data_types(mocker):\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.query_data\", return_value=test_data)\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=test_types)\n dataset = Dataset(Dataset.TR.TREOD)\n data = dataset.get_data(dt.date(2019, 1, 2), dt.date(2019, 1, 9), assetId='MA4B66MW5E27U8P32SB')\n assert data.equals(GsDataApi.construct_dataframe_with_types(str(Dataset.TR.TREOD), test_data))\n\n\ndef test_last_data(mocker):\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.last_data\", return_value=[test_data[-1]])\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=test_types)\n dataset = Dataset(Dataset.TR.TREOD)\n data = dataset.get_data_last(dt.date(2019, 1, 9), assetId='MA4B66MW5E27U8P32SB')\n assert data.equals(GsDataApi.construct_dataframe_with_types(str(Dataset.TR.TREOD), ([test_data[-1]])))\n\n\ndef test_get_data_series(mocker):\n field_value_maps = test_data\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=test_types)\n mocker.patch.object(GsDataApi, 'query_data', return_value=field_value_maps)\n mocker.patch.object(GsDataApi, 'symbol_dimensions', return_value=('assetId',))\n\n dataset = Dataset(Dataset.TR.TREOD)\n series = dataset.get_data_series(\n 'tradePrice', dt.date(2019, 1, 2), dt.date(2019, 1, 9), assetId='MA4B66MW5E27U8P32SB'\n )\n\n df = pd.DataFrame(test_data)\n index = pd.to_datetime(df.loc[:, 'date'].values)\n expected = pd.Series(index=index, data=df.loc[:, 'tradePrice'].values)\n expected = expected.rename_axis('date')\n\n pd.testing.assert_series_equal(series, expected)\n\n\ndef test_get_coverage(mocker):\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_coverage\", return_value=test_coverage_data)\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value={'gsid': 'string'})\n data = Dataset(Dataset.TR.TREOD).get_coverage()\n results = test_coverage_data[\"results\"]\n gsid = GsDataApi.construct_dataframe_with_types(str(Dataset.TR.TREOD), results).get('gsid').get(0)\n assert data[\"results\"][0][\"gsid\"] == gsid\n\n\ndef test_construct_dataframe_with_types(mocker):\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=test_types)\n df = GsDataApi.construct_dataframe_with_types(str(Dataset.TR.TREOD), [test_data[0]])\n assert np.issubdtype(df.index.dtype, np.datetime64)\n assert df['adjustedAskPrice'].dtype == np.int64\n assert df['adjustedBidPrice'].dtype == np.float64\n # pandas 3.0 uses StringDtype for strings, older versions use object\n assert df['assetId'].dtype == object or pd.api.types.is_string_dtype(df['assetId'])\n assert np.issubdtype(df['updateTime'].dtype, np.datetime64)\n\n\ndef test_construct_dataframe_var_schema(mocker):\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=test_types)\n var_schema = copy.deepcopy(test_data)\n for i in range(len(var_schema)):\n if i % 2 == 1:\n prev = var_schema[i - 1]\n curr = var_schema[i]\n curr['difference_tradePrice'] = curr['tradePrice'] - prev['tradePrice']\n\n df = GsDataApi.construct_dataframe_with_types(str(Dataset.TR.TREOD), var_schema, True)\n assert np.issubdtype(df['difference_tradePrice'].dtype, np.floating)\n\n\ndef test_dataframe_with_mixed_date_type(mocker):\n mocker.patch.object(GsDataApi, 'get_types', return_value={'updateTime': 'date-time'})\n\n df = GsDataApi.construct_dataframe_with_types(\n 'BBG_PER_SECURITY', [{'updateTime': '2022-02-24T19:25:28Z'}, {'updateTime': '2022-11-10T17:18:23.021494Z'}]\n )\n\n assert df.empty is False\n\n\ndef test_data_series_format(mocker):\n start = dt.date(2019, 1, 2)\n end = dt.datetime(2019, 1, 9)\n df = pd.DataFrame(test_data)\n index = pd.to_datetime(df.loc[:, 'date'].values)\n expected = pd.Series(index=index, data=df.loc[:, 'tradePrice'].values)\n expected = expected.rename_axis('date')\n\n # mock GsSession and data response\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mock_response = {'requestId': 'qwerty', 'data': test_data}\n mocker.patch.object(GsSession.current.sync, 'post', side_effect=lambda *args, **kwargs: mock_response)\n mocker.patch.object(GsSession.current.sync, 'get', return_value={\"id\": \"TREOD\"})\n mocker.patch.object(GsDataApi, 'symbol_dimensions', return_value=('assetId',))\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=test_types)\n\n actual = Dataset('TREOD').get_data_series(field='tradePrice', start=start, end=end, assetId='MA4B66MW5E27U8P32SB')\n pd.testing.assert_series_equal(actual, expected)\n assert len(GsSession.current.sync.post.mock_calls) == 1\n name, args, kwargs = GsSession.current.sync.post.mock_calls[0]\n assert kwargs['payload'].format == Format.MessagePack\n assert kwargs['request_headers'] == {'Accept': 'application/msgpack'}\n assert args[0] == '/data/TREOD/query'\n\n GsSession.current.sync.post.reset_mock()\n actual = Dataset('TREOD').get_data_series(\n field='tradePrice', start=start, end=end, assetId='MA4B66MW5E27U8P32SB', format=Format.Json\n )\n pd.testing.assert_series_equal(actual, expected)\n assert len(GsSession.current.sync.post.mock_calls) == 1\n name, args, kwargs = GsSession.current.sync.post.mock_calls[0]\n assert kwargs['payload'].format == Format.Json\n assert 'request_headers' not in kwargs\n assert args[0] == '/data/TREOD/query'\n\n\ndef test_get_data_bulk(mocker):\n df2 = pd.DataFrame()\n test_df = {\n 'date': {pd.Timestamp('20230302'): dt.date(2023, 3, 2)},\n 'clusterRegion': {pd.Timestamp('20230302'): 'Asia Pacific'},\n 'clusterClass': {pd.Timestamp('20230302'): '13'},\n 'assetId': {pd.Timestamp('20230302'): 'MA4B66MW5E27U8P4ZFX'},\n 'clusterDescription': {pd.Timestamp('20230302'): 'Small Trd Count, Hard to Complete'},\n 'updateTime': {\n pd.Timestamp('20230302'): dt.datetime.strptime('2023-03-05T00:40:54.000Z', '%Y-%m-%dT%H:%M:%S.%fZ')\n },\n }\n\n df = pd.DataFrame(test_df)\n df = df.set_index('date')\n\n coverage = pd.DataFrame({'clusterRegion': ['Asia Pacific']})\n symbol_dimension = ('clusterRegion',)\n dataset_definition = DataSetEntity()\n dataset_definition.parameters = DataSetParameters()\n dataset_definition.parameters.history_date = dt.datetime(2017, 1, 2)\n dataset_definition.dimensions = DataSetDimensions()\n dataset_definition.dimensions.time_field = 'date'\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n\n # mock fetch_data function in utilities.py\n mock = mocker.patch(\"gs_quant.data.utilities.Utilities.fetch_data\", return_value=df)\n\n # mock granular functions in get_dataset_parameter in utilities.py\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.symbol_dimensions\", return_value=symbol_dimension)\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_definition\", return_value=dataset_definition)\n\n # mock get_dataset_coverage function in utilities.py\n mocker.patch(\"gs_quant.data.dataset.Dataset.get_coverage\", return_value=coverage)\n\n def handler(data_frame):\n nonlocal df2\n df2 = data_frame.head(1)\n\n dataset_id = \"EQTRADECLUSTERS\"\n original_start = dt.datetime(2023, 3, 2, 0, 0, 0)\n final_end = dt.datetime(2023, 3, 2, 0, 0, 0)\n c = Dataset(dataset_id)\n c.get_data_bulk(\n original_start=original_start,\n final_end=final_end,\n request_batch_size=4,\n identifier=\"clusterRegion\",\n handler=handler,\n )\n\n assert mock.call_count == 1\n assert df.equals(df2)\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_dataset.py\"])\n" }, { "path": "gs_quant/test/data/test_query.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pytest\n\nfrom gs_quant.api.gs.data import GsDataApi, QueryType\nfrom gs_quant.data import DataContext\n\n\ndef test_build_market_data_query():\n asset_ids = ['MA4B66MW5E27U8P3295']\n query_type = QueryType.IMPLIED_VOLATILITY\n where = {'tenor': '1y', 'strikeReference': 'delta', 'relativeStrike': 0.1}\n source = None\n real_time = False\n measure = \"Curve\"\n with DataContext(start=dt.date(2013, 1, 1), end=dt.date(2020, 2, 1)):\n queries = GsDataApi.build_market_data_query(\n asset_ids, query_type, where, source, real_time, measure, parallelize_queries=True\n )\n payload = {\n 'entityIds': ['MA4B66MW5E27U8P3295'],\n 'queryType': 'Implied Volatility',\n 'where': {'tenor': '1y', 'strikeReference': 'delta', 'relativeStrike': 0.1},\n 'source': 'any',\n 'frequency': 'End Of Day',\n 'measures': ['Curve'],\n }\n\n dates = [\n {'startDate': dt.date(2013, 1, 1), 'endDate': dt.date(2014, 1, 1)},\n {'startDate': dt.date(2014, 1, 1), 'endDate': dt.date(2015, 1, 1)},\n {'startDate': dt.date(2015, 1, 1), 'endDate': dt.date(2016, 1, 1)},\n {'startDate': dt.date(2016, 1, 1), 'endDate': dt.date(2016, 12, 31)},\n {'startDate': dt.date(2016, 12, 31), 'endDate': dt.date(2017, 12, 31)},\n {'startDate': dt.date(2017, 12, 31), 'endDate': dt.date(2018, 12, 31)},\n {'startDate': dt.date(2018, 12, 31), 'endDate': dt.date(2019, 12, 31)},\n {'startDate': dt.date(2019, 12, 31), 'endDate': dt.date(2020, 2, 1)},\n ]\n\n expected = [{'queries': [{**payload, **date_range}]} for date_range in dates]\n assert expected == queries\n\n with DataContext(start=dt.date(2013, 1, 1), end=dt.date(2025, 1, 1)):\n queries = GsDataApi.build_market_data_query(asset_ids, query_type, where, source, real_time, measure)\n payload = {\n 'entityIds': ['MA4B66MW5E27U8P3295'],\n 'queryType': 'Implied Volatility',\n 'where': {'tenor': '1y', 'strikeReference': 'delta', 'relativeStrike': 0.1},\n 'source': 'any',\n 'frequency': 'End Of Day',\n 'measures': ['Curve'],\n 'startDate': dt.date(2013, 1, 1),\n 'endDate': dt.date(2025, 1, 1),\n }\n\n expected = {'queries': [payload]}\n assert expected == queries\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_queries.py\"])\n" }, { "path": "gs_quant/test/datetime_/__init__.py", "content": "" }, { "path": "gs_quant/test/datetime_/test_date.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nfrom pytest import approx\nfrom gs_quant.datetime import DayCountConvention, day_count_fraction, has_feb_29, today, PaymentFrequency\nfrom gs_quant.common import PricingLocation\n\n\ndef test_has_feb_29():\n assert not has_feb_29(dt.date(2019, 1, 1), dt.date(2019, 12, 31))\n assert has_feb_29(dt.date(2020, 1, 1), dt.date(2020, 12, 31))\n assert has_feb_29(dt.date(2020, 2, 28), dt.date(2020, 3, 31))\n assert not has_feb_29(dt.date(2020, 2, 29), dt.date(2020, 3, 31)) # first date is exclusive\n assert not has_feb_29(dt.date(2020, 1, 1), dt.date(2020, 2, 28))\n assert has_feb_29(dt.date(2020, 1, 1), dt.date(2020, 2, 29)) # last date is inclusive\n assert has_feb_29(dt.date(2008, 1, 1), dt.date(2020, 12, 31))\n\n\ndef test_today_with_location():\n for location in PricingLocation:\n assert today(location) is not None\n\n assert today(None) == dt.date.today()\n\n\ndef test_day_count_fraction():\n # 2017 is not a leap year\n start = dt.date(2017, 1, 1)\n end = dt.date(2017, 12, 31)\n\n assert day_count_fraction(start, end, DayCountConvention.ONE_ONE) == approx(1)\n assert day_count_fraction(start, end, DayCountConvention.ACTUAL_360) == approx(1.011111111111)\n assert day_count_fraction(start, end, DayCountConvention.ACTUAL_365F) == approx(0.997260273973)\n assert day_count_fraction(start, end, DayCountConvention.ACTUAL_365L) == approx(0.997260273973)\n\n # 2016 is a leap year\n start = dt.date(2015, 11, 12)\n end = dt.date(2017, 12, 15)\n\n assert day_count_fraction(start, end, DayCountConvention.ONE_ONE) == approx(1)\n assert day_count_fraction(start, end, DayCountConvention.ACTUAL_360) == approx(2.122222222222)\n assert day_count_fraction(start, end, DayCountConvention.ACTUAL_365F) == approx(2.093150684932)\n\n # End date is not leap year, so should use 365\n assert day_count_fraction(start, end, DayCountConvention.ACTUAL_365L) == approx(2.093150684932)\n\n # Feb 29 is within range, so should use 366\n assert day_count_fraction(start, end, DayCountConvention.ACTUAL_365L, PaymentFrequency.ANNUALLY) == approx(\n 2.087431693989\n )\n" }, { "path": "gs_quant/test/datetime_/test_gscalendar.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest import mock\n\nimport pandas as pd\n\nfrom gs_quant.common import PricingLocation\nfrom gs_quant.data import Dataset\nfrom gs_quant.datetime import GsCalendar\nfrom gs_quant.test.api.test_risk import set_session\n\nMOCK_HOLIDAY = pd.DataFrame(index=[dt.datetime(1999, 9, 12)], data={'holiday': 'Labor Day'})\n\n\n# Test GsCalendar initiated with single PricingLocation\n@mock.patch.object(Dataset, 'get_coverage', return_value=pd.DataFrame())\n@mock.patch.object(Dataset, 'get_data', return_value=MOCK_HOLIDAY)\ndef test_gs_calendar_single(mocker, _mocker_cov):\n set_session()\n nyc = PricingLocation.NYC\n GsCalendar.reset()\n days = GsCalendar(nyc).holidays\n assert days\n\n\n# Test GsCalendar initiated with tuple\n@mock.patch.object(Dataset, 'get_coverage', return_value=pd.DataFrame())\n@mock.patch.object(Dataset, 'get_data', return_value=MOCK_HOLIDAY)\ndef test_gs_calendar_tuple(mocker, _mocker_cov):\n set_session()\n locs = (PricingLocation.NYC, PricingLocation.LDN)\n days = GsCalendar(locs).holidays\n assert days\n" }, { "path": "gs_quant/test/datetime_/test_point.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom gs_quant.datetime import point_sort_order\n\n\ndef test_point_sort_order():\n check_map = {\n 'o/n': 0, # O/N\n 'Jan20': 379, # EuroOrFraReg\n 'JAN20': 379, # EuroOrFraReg\n '1y': 365, # RDatePartReg\n '1Y': 365, # RDatePartReg\n '3m': 90, # RDatePartReg\n '-1f XC': -30, # CashFXReg\n '3x3': 90, # FRAxReg\n 'QE1-2020': 425, # SpikeQEReg\n 'JAN2021': 731, # MMMYYYYReg\n '9JAN2021': 739, # DDMMMYYYYReg\n '1.1y': 1.1 * 365, # FloatingYear\n 'K19': 120, # LYYReg\n 'JAN 20': 365, # MMMYYReg\n 'FFK9': 120, # FFFutReg\n 'ON GC': 0, # RepoGCReg\n '1 Week GC': 7, # RepoGCReg\n '1 week': 7, # RelativeReg\n '01Jan20': 365, # DDMMMYYReg\n '20Y;3M': 7300.0012328767125,\n }\n\n for input, expected in check_map.items():\n day = dt.date(2019, 1, 1)\n actual = point_sort_order(input, day)\n assert expected == actual\n" }, { "path": "gs_quant/test/datetime_/test_relative_date.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest.mock import Mock\n\nimport pandas as pd\nimport pytest\nfrom testfixtures import Replacer\n\nfrom gs_quant.datetime.relative_date import RelativeDate\n\nholiday_calendar = [dt.date(2021, 1, 18)]\n\n\ndef test_rule_parsing():\n assert RelativeDate('A')._get_rules() == ['A']\n assert RelativeDate('1d')._get_rules() == ['1d']\n assert RelativeDate('-1d')._get_rules() == ['-1d']\n assert RelativeDate('1y-1d')._get_rules() == ['1y', '-1d']\n assert RelativeDate('-1y-1d')._get_rules() == ['-1y', '-1d']\n assert RelativeDate('-1y+1d')._get_rules() == ['-1y', '1d']\n\n\n# A -> first day of the year\ndef test_rule_a_():\n date: dt = RelativeDate('A', base_date=dt.date(2021, 1, 19)).apply_rule()\n assert date == dt.date(2021, 1, 1)\n\n\n# b -> Business days\ndef test_rule_b():\n date: dt = RelativeDate('-1b', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 15)\n date: dt = RelativeDate('+1b', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 20)\n\n\n# d -> gregorian calendar days\ndef test_rule_d():\n date: dt = RelativeDate('+1d', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 20)\n\n\n# e -> end of month\ndef test_rule_e():\n date: dt = RelativeDate('e', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 31)\n\n\n# M -> nth Monday\ndef test_rule_m_():\n date: dt = RelativeDate('1M', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 4)\n date: dt = RelativeDate('4M', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 25)\n\n\n# T -> nth Tuesday\ndef test_rule_t_():\n date: dt = RelativeDate('1T', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 5)\n date: dt = RelativeDate('4T', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 26)\n\n\n# W -> nth Wednesday\ndef test_rule_w_():\n date: dt = RelativeDate('1W', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 6)\n date: dt = RelativeDate('4W', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 27)\n\n\n# R -> nth Thursday\ndef test_rule_r_():\n date: dt = RelativeDate('1R', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 7)\n date: dt = RelativeDate('4R', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 28)\n\n\n# F -> nth Friday\ndef test_rule_f_():\n date: dt = RelativeDate('1F', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 1)\n date: dt = RelativeDate('4F', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 22)\n\n\n# V -> nth Saturday\ndef test_rule_v_():\n date: dt = RelativeDate('1V', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 2)\n date: dt = RelativeDate('4V', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 23)\n\n\n# Z -> nth Sunday\ndef test_rule_z_():\n date: dt = RelativeDate('1Z', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 3)\n date: dt = RelativeDate('4Z', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 24)\n\n\n# g -> nth week\ndef test_rule_g():\n date: dt = RelativeDate('1g', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 26)\n date: dt = RelativeDate('3g', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 2, 9)\n date: dt = RelativeDate('-1g', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 12)\n\n\n# N -> nth Monday relative to given date\ndef test_rule_n_():\n date: dt = RelativeDate('1N', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 25)\n\n\n# U -> nth Tuesday relative to given date\ndef test_rule_u_():\n date: dt = RelativeDate('1U', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 19)\n\n\n# X -> nth Wednesday relative to given date\ndef test_rule_x_():\n date: dt = RelativeDate('1X', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 20)\n\n\n# S -> nth Thursday relative to given date\ndef test_rule_s_():\n date: dt = RelativeDate('1S', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 21)\n\n\n# G -> nth Friday relative to given date\ndef test_rule_g_():\n date: dt = RelativeDate('1G', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 22)\n\n\n# I -> nth Saturday relative to given date\ndef test_rule_i_():\n date: dt = RelativeDate('1I', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 23)\n\n\n# P -> nth Sunday relative to given date\ndef test_rule_p_():\n date: dt = RelativeDate('1P', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 24)\n\n\n# w -> weeks offset\ndef test_rule_w():\n date: dt = RelativeDate('1w', base_date=dt.date(2022, 3, 30)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2022, 4, 6)\n date: dt = RelativeDate('1w', base_date=dt.date(2025, 10, 11)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2025, 10, 20)\n date: dt = RelativeDate('-1w', base_date=dt.date(2025, 10, 11)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2025, 10, 3)\n\n\n# k -> Relative years with no implicit USD calendar\ndef test_rule_k():\n date: dt = RelativeDate('1k', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2022, 1, 19)\n date: dt = RelativeDate('-1k', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2020, 1, 20)\n\n\n# r -> end of year\ndef test_rule_r():\n date: dt = RelativeDate('r', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 12, 31)\n\n\n# u -> add business days without implicit USD holidays\ndef test_rule_u():\n date: dt = RelativeDate('1u', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 20)\n date: dt = RelativeDate('2u', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 21)\n date: dt = RelativeDate('-2u', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 14)\n date: dt = RelativeDate('-0u', base_date=dt.date(2025, 11, 30)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2025, 11, 28)\n date: dt = RelativeDate('-1u', base_date=dt.date(2025, 11, 30)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2025, 11, 28)\n\n\n# v -> last business day of the month, gets last business day of month (number is months ahead)\ndef test_rule_v():\n date: dt = RelativeDate('2v', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 3, 31)\n\n\n# x -> gets last business day of month (ignores number)\ndef test_rule_x():\n date: dt = RelativeDate('x', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 29)\n\n\n# y -> Relative years with the result moved to the next week day if the day falls on a weekend specified by the weekmask\ndef test_rule_y():\n date: dt = RelativeDate('2y', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2023, 1, 19)\n date: dt = RelativeDate('-2y', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2019, 1, 21)\n\n\n# Returns four business days after the first business day on or after the 15th calendar day of the month\ndef test_chaining():\n date: dt = RelativeDate('J+14d+0u+4u', base_date=dt.date(2021, 1, 19)).apply_rule(holiday_calendar=holiday_calendar)\n assert date == dt.date(2021, 1, 22)\n\n\n# last calendar day of month shifted earlier to immediately preceding business day. i.e. last business day of the month\ndef test_rule_e_minus_u():\n date: dt = RelativeDate('e-0u', base_date=dt.date(2025, 11, 19)).apply_rule()\n assert date == dt.date(2025, 11, 28)\n\n\ndef test_rule_roll_convention():\n date: dt = RelativeDate('1w', base_date=dt.date(2025, 10, 11)).apply_rule()\n assert date == dt.date(2025, 10, 20)\n date: dt = RelativeDate('1w', base_date=dt.date(2025, 10, 11)).apply_rule(roll_convention='backward')\n assert date == dt.date(2025, 10, 17)\n\n\ndef mock_holiday_data(*args, **kwargs):\n dq = args[1]\n ccies = dq.as_dict()['where'].get('currency', [])\n base_holidays = [{'date': '2021-12-27', 'currency': 'USD', 'description': \"Xmas\"}]\n if 'USD' in ccies:\n base_holidays.append({'date': '2022-04-11', 'currency': 'USD', 'description': \"Birthday\"})\n return base_holidays\n\n\ntest_types = {'date': 'date', 'exchange': 'string', 'currency': 'string', 'description': 'string', 'updateTime': 'date'}\n\n\n# test holiday calendar logic\ndef test_currency_holiday_calendars(mocker):\n replace = Replacer()\n replace('gs_quant.api.gs.data.GsDataApi.query_data', Mock(side_effect=mock_holiday_data))\n mocker.patch(\n \"gs_quant.api.gs.data.GsDataApi.get_coverage\",\n return_value=pd.DataFrame([['USD'], ['GBP']], columns=['currency']),\n )\n mocker.patch(\"gs_quant.api.gs.data.GsDataApi.get_types\", return_value=test_types)\n rdate = RelativeDate('-1b', base_date=dt.date(2022, 4, 12))\n assert dt.date(2022, 4, 11) == rdate.apply_rule(currencies=[])\n assert dt.date(2022, 4, 11) == rdate.apply_rule(currencies=['GBP'])\n assert dt.date(2022, 4, 8) == rdate.apply_rule(currencies=['USD'])\n assert dt.date(2022, 4, 11) == rdate.apply_rule() # No longer using USD by default\n replace.restore()\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_relative_date.py\"])\n" }, { "path": "gs_quant/test/datetime_/test_time.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport numpy as np\n\nfrom gs_quant.datetime import time_difference_as_string, SECS_IN_YEAR\n\n\ndef test_time_difference_as_string():\n check_map = {\n '4 Seconds': np.timedelta64(4, 's'),\n '1 Minute 5 Seconds': np.timedelta64(65, 's'),\n '1 Year': np.timedelta64(int(SECS_IN_YEAR), 's'),\n '1 Day 1 Minute 5 Seconds': np.timedelta64(86465, 's'),\n }\n\n for expected, input in check_map.items():\n actual = time_difference_as_string(input)\n assert expected == actual\n" }, { "path": "gs_quant/test/entities/test_entitlements.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nfrom gs_quant.common import Entitlements as TargetEntitlements\nfrom gs_quant.entities.entitlements import Group, Entitlements, User, EntitlementBlock\nfrom gs_quant.target.groups import Group as TargetGroup\nfrom gs_quant.target.reports import User as TargetUser\n\n\ndef get_fake_user():\n user = TargetUser.from_dict(\n {'id': 'userId', 'email': 'jane.doe@gs.com', 'name': 'Jane Doe', 'company': 'Goldman Sachs Group'}\n )\n\n replace = Replacer()\n mock = replace('gs_quant.api.gs.users.GsUsersApi.get_users', Mock())\n mock.return_value = [user]\n user = User.get(email='jane.doe@gs.com')\n replace.restore()\n\n return user\n\n\ndef get_fake_group():\n group = TargetGroup.from_dict({'name': 'fakeGroup', 'id': 'groupId', 'tags': []})\n\n replace = Replacer()\n mock = replace('gs_quant.api.gs.groups.GsGroupsApi.get_group', Mock())\n mock.return_value = group\n group = Group.get(group_id='groupId')\n replace.restore()\n\n return group\n\n\ndef test_to_target():\n ent = Entitlements(edit=EntitlementBlock(users=[get_fake_user()], groups=[get_fake_group()]))\n as_target = ent.to_target()\n assert as_target.edit == ('guid:userId', 'group:groupId')\n\n\ndef test_to_dict():\n ent = Entitlements(edit=EntitlementBlock(users=[get_fake_user()], groups=[get_fake_group()]))\n as_dict = ent.to_dict()\n assert as_dict == {'edit': ('guid:userId', 'group:groupId')}\n\n\ndef test_from_target():\n replace = Replacer()\n mock = replace('gs_quant.api.gs.users.GsUsersApi.get_users', Mock())\n mock.return_value = [\n TargetUser.from_dict(\n {'id': 'userId', 'email': 'jane.doe@gs.com', 'name': 'Jane Doe', 'company': 'Goldman Sachs Group'}\n )\n ]\n ent = Entitlements.from_target(TargetEntitlements(edit=('guid:userId', 'role:roleId')))\n replace.restore()\n assert ent.edit.users == [get_fake_user()]\n assert ent.edit.roles == ['roleId']\n\n\ndef test_from_dict():\n replace = Replacer()\n mock = replace('gs_quant.api.gs.users.GsUsersApi.get_users', Mock())\n mock.return_value = [\n TargetUser.from_dict(\n {'id': 'userId', 'email': 'jane.doe@gs.com', 'name': 'Jane Doe', 'company': 'Goldman Sachs Group'}\n )\n ]\n ent = Entitlements.from_dict({'edit': ['guid:userId', 'role:roleId']})\n replace.restore()\n assert ent.edit.users == [get_fake_user()]\n assert ent.edit.roles == ['roleId']\n" }, { "path": "gs_quant/test/entities/test_group.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nfrom gs_quant.entities.entitlements import Group\nfrom gs_quant.target.groups import Group as TargetGroup\n\n\ndef test_get():\n group = TargetGroup.from_dict({'name': 'fakeGroup', 'id': 'groupId', 'tags': []})\n replace = Replacer()\n mock = replace('gs_quant.api.gs.groups.GsGroupsApi.get_group', Mock())\n mock.return_value = group\n assert Group.get('groupId').name == 'fakeGroup'\n replace.restore()\n\n\ndef test_get_many():\n groups = [\n TargetGroup.from_dict({'name': 'fakeGroup', 'id': 'groupId', 'tags': []}),\n TargetGroup.from_dict({'name': 'fakeGroup2', 'id': 'groupId2', 'tags': []}),\n ]\n replace = Replacer()\n mock = replace('gs_quant.api.gs.groups.GsGroupsApi.get_groups', Mock())\n mock.return_value = groups\n assert len(Group.get_many(group_ids=['groupId', 'groupId2'])) == 2\n replace.restore()\n\n\ndef test_save_update():\n group = TargetGroup.from_dict({'name': 'fakeGroup', 'id': 'groupId', 'tags': []})\n replace = Replacer()\n mock = replace('gs_quant.api.gs.groups.GsGroupsApi.update_group', Mock())\n mock.return_value = group\n mock = replace('gs_quant.entities.entitlements.Group._group_exists', Mock())\n mock.return_value = True\n g = Group(group_id='groupId', name='fakeGroup', tags=[])\n assert g.save().name == 'fakeGroup'\n replace.restore()\n\n\ndef test_save_create():\n group = TargetGroup.from_dict({'name': 'fakeGroup', 'id': 'groupId', 'tags': []})\n replace = Replacer()\n mock = replace('gs_quant.api.gs.groups.GsGroupsApi.create_group', Mock())\n mock.return_value = group\n mock = replace('gs_quant.entities.entitlements.Group._group_exists', Mock())\n mock.return_value = False\n g = Group(group_id='groupId', name='fakeGroup', tags=[])\n assert g.save().name == 'fakeGroup'\n replace.restore()\n" }, { "path": "gs_quant/test/entities/test_user.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nfrom gs_quant.entities.entitlements import User\nfrom gs_quant.target.reports import User as TargetUser\n\n\ndef test_get():\n user = TargetUser.from_dict(\n {'id': 'userId', 'email': 'jane.doe@gs.com', 'name': 'Doe, Jane', 'company': 'Goldman Sachs Group'}\n )\n replace = Replacer()\n mock = replace('gs_quant.api.gs.users.GsUsersApi.get_users', Mock())\n mock.return_value = [user]\n assert User.get(name='Doe, Jane').id == 'userId'\n replace.restore()\n\n\ndef test_get_many():\n users = [\n TargetUser.from_dict(\n {'id': 'userId', 'email': 'jane.doe@gs.com', 'name': 'Doe, Jane', 'company': 'Goldman Sachs Group'}\n ),\n TargetUser.from_dict(\n {'id': 'userId2', 'email': 'john.doe@gs.com', 'name': 'Doe, John', 'company': 'Goldman Sachs Group'}\n ),\n ]\n replace = Replacer()\n mock = replace('gs_quant.api.gs.users.GsUsersApi.get_users', Mock())\n mock.return_value = users\n assert len(User.get_many(user_ids=['userId', 'userId2'])) == 2\n replace.restore()\n" }, { "path": "gs_quant/test/fixtures/__init__.py", "content": "" }, { "path": "gs_quant/test/fixtures/content.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom base64 import b64encode\n\nfrom gs_quant.common import Entitlements\nfrom gs_quant.target.content import GetManyContentsResponse, ContentResponse, Content\n\n\nclass ContentFixtures:\n _data = {\n 'id': 'some-id',\n 'version': 'some-version',\n 'name': 'some-name',\n 'entitlements': {\n 'view': ['some-view-entitlement'],\n 'edit': ['some-edit-entitlement'],\n 'delete': ['some-delete-token'],\n 'admin': ['some-admin-token'],\n },\n 'createdById': 'some-created-by-id',\n 'createdTime': dt.datetime(2019, 5, 13),\n 'authors': [\n {\n 'name': 'some-author-name',\n 'firstName': 'some-author-first-name',\n 'lastName': 'some-author-last-name',\n 'id': 'some-author-id',\n 'division': 'some-author-division',\n }\n ],\n 'lastUpdatedTime': dt.datetime(2019, 5, 14),\n 'content': {'body': b64encode(b'Hello world!'), 'mimeType': 'text/plain', 'encoding': 'UTF-8'},\n 'channels': ['some-channel'],\n }\n\n @classmethod\n def _get_content_response(cls):\n return ContentResponse(\n id=cls._data['id'],\n version=cls._data['version'],\n name=cls._data['name'],\n entitlements=Entitlements(\n cls._data['entitlements']['view'],\n cls._data['entitlements']['edit'],\n cls._data['entitlements']['admin'],\n cls._data['entitlements']['delete'],\n ),\n created_by_id=cls._data['createdById'],\n created_time=cls._data['createdTime'],\n last_updated_time=cls._data['lastUpdatedTime'],\n channels=cls._data['channels'],\n content=Content(\n cls._data['content']['body'], cls._data['content']['mimeType'], cls._data['content']['encoding']\n ),\n )\n\n @classmethod\n def get_many_contents_response(cls, status: int = 200, message: str = \"Ok\"):\n content = cls._get_content_response()\n return GetManyContentsResponse(status, message, (content,))\n" }, { "path": "gs_quant/test/markets/__init__.py", "content": "" }, { "path": "gs_quant/test/markets/test_baskets.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom typing import Dict\nfrom unittest import mock\n\nimport pytest\n\nfrom gs_quant.api.gs.assets import GsAsset, GsAssetApi\nfrom gs_quant.api.gs.indices import GsIndexApi\nfrom gs_quant.api.gs.reports import GsReportApi\nfrom gs_quant.api.gs.users import GsUsersApi\nfrom gs_quant.common import (\n AssetClass,\n AssetType,\n Entitlements as TargetEntitlements,\n PositionSet as TargetPositionSet,\n Position as TargetPosition,\n ReportParameters,\n XRef,\n)\nfrom gs_quant.entities.entitlements import User\nfrom gs_quant.errors import MqError\nfrom gs_quant.markets.baskets import Basket, ErrorMessage\nfrom gs_quant.markets.indices_utils import ReturnType\nfrom gs_quant.markets.position_set import Position, PositionSet\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.target.indices import CustomBasketsResponse, CustomBasketRiskParams\nfrom gs_quant.target.reports import Report, User as TargetUser\n\n# Helper mock value constants\nasset_1 = {'name': 'asset 1', 'id': 'id1', 'bbid': 'bbid1'}\nasset_2 = {'name': 'asset 2', 'id': 'id2', 'bbid': 'bbid2'}\nassets_data = [asset_1, asset_2]\nbase_user = {\n 'name': 'First Last',\n 'email': 'ex@email.com',\n 'city': 'City A',\n 'company': 'Company A',\n 'country': 'Country A',\n 'region': 'Region A',\n}\ncb_response = CustomBasketsResponse('done', 'R1234567890', 'MA1234567890')\ngs_asset = GsAsset(\n asset_class=AssetClass.Equity,\n type_=AssetType.Custom_Basket,\n name='Test Basket',\n id_='MA1234567890',\n entitlements=TargetEntitlements(admin=['guid:user_abc']),\n xref=XRef(ticker='GSMBXXXX'),\n)\ninitial_price = {'price': 100}\nmqid = 'MA1234567890'\nname = 'Test Basket'\npositions = [Position('bbid1', asset_id='id1', quantity=100), Position('bbid2', asset_id='id2', quantity=200)]\npositions_weighted = positions = [\n Position('bbid1', asset_id='id1', weight=0.4),\n Position('bbid2', asset_id='id2', weight=0.6),\n]\nposition_set = PositionSet(positions, divisor=1000)\nreport = Report(mqid, 'asset', 'Basket Create', ReportParameters(), status='done')\nresolved_asset = {'GSMBXXXX': [{'id': mqid}]}\ntarget_positions = tuple([TargetPosition(asset_id='id1', quantity=100), TargetPosition(asset_id='id2', quantity=200)])\ntarget_position_set = TargetPositionSet(target_positions, dt.date(2021, 1, 7), divisor=1000)\nticker = 'GSMBXXXX'\nuser_ea = {**base_user, 'id': 'user_abc', 'tokens': ['external', 'guid:user_abc']} # external, admin\nuser_ena = {**base_user, 'id': 'user_xyz', 'tokens': ['external', 'guid:user_xyz']} # external, non admin\nuser_ia = {**base_user, 'id': 'user_abc', 'tokens': ['internal', 'guid:user_abc']} # internal, admin\n\n\n@mock.patch.object(GsSession.__class__, 'default_value')\ndef mock_session(mocker):\n \"\"\"Mock GsSession helper\"\"\"\n mocker.return_value = GsSession.get(Environment.QA, 'client_id', 'secret')\n\n\ndef mock_response(mocker, mock_object, mock_fn, mock_response):\n \"\"\"Mock patch helper\"\"\"\n if mock_response is not None:\n mocker.patch.object(mock_object, mock_fn, return_value=mock_response)\n\n\ndef mock_basket_init(mocker, user: Dict, existing: bool = True):\n \"\"\"Mock basket initialization helper\"\"\"\n if existing:\n mock_response(mocker, GsAssetApi, 'resolve_assets', resolved_asset)\n mock_response(mocker, GsAssetApi, 'get_asset', gs_asset)\n mock_response(mocker, GsAssetApi, 'get_latest_positions', target_position_set)\n mock_response(mocker, GsAssetApi, 'get_many_assets_data', assets_data)\n mock_response(mocker, GsIndexApi, 'initial_price', initial_price)\n mock_response(mocker, GsReportApi, 'get_reports', [report])\n mock_response(mocker, GsUsersApi, 'get_users', [TargetUser.from_dict(user)])\n mock_response(mocker, GsUsersApi, 'get_current_user_info', user)\n\n\ndef test_basket_error_messages(mocker):\n mock_session()\n\n # test non admin errors\n mock_basket_init(mocker, user_ena)\n basket = Basket.get(ticker)\n with pytest.raises(MqError, match=ErrorMessage.NON_ADMIN.value):\n basket.cancel_rebalance()\n with pytest.raises(MqError, match=ErrorMessage.NON_ADMIN.value):\n basket.allow_ca_restricted_assets = False\n\n # test non internal errors\n with pytest.raises(MqError, match=ErrorMessage.NON_INTERNAL.value):\n basket.flagship = False\n\n # test unmodifiable errors\n with pytest.raises(MqError, match=ErrorMessage.UNMODIFIABLE.value):\n basket.ticker = 'GSMBZZZZ'\n\n # test uninitialized errors\n mock_basket_init(mocker, user_ena, False)\n basket = Basket()\n with pytest.raises(MqError, match=ErrorMessage.UNINITIALIZED.value):\n basket.clone()\n with pytest.raises(MqError, match=ErrorMessage.UNINITIALIZED.value):\n basket.get_latest_rebalance_data()\n\n\ndef test_basket_create(mocker):\n mock_session()\n mock_basket_init(mocker, user_ea, False)\n mock_response(mocker, GsIndexApi, 'validate_ticker', True)\n\n basket = Basket()\n basket.name = name\n basket.ticker = ticker\n basket.position_set = position_set\n basket.return_type = ReturnType.PRICE_RETURN\n\n mock_response(mocker, GsIndexApi, 'create', cb_response)\n mock_response(mocker, GsAssetApi, 'get_asset', gs_asset)\n mock_response(mocker, GsReportApi, 'get_report', report)\n mock_basket_init(mocker, user_ea)\n\n response = basket.create()\n GsIndexApi.create.assert_called()\n assert response == cb_response.as_dict()\n\n\ndef test_basket_clone(mocker):\n mock_session()\n\n # test uninitialized errors\n mock_basket_init(mocker, user_ea, False)\n basket = Basket()\n with pytest.raises(MqError, match=ErrorMessage.UNINITIALIZED.value):\n basket.clone()\n\n # test clone\n mock_basket_init(mocker, user_ena)\n parent_basket = Basket.get(ticker)\n clone = parent_basket.clone()\n mock_basket_init(mocker, user_ea, False)\n\n parent_positions = [p.as_dict() for p in parent_basket.position_set.positions]\n clone_positions = [p.as_dict() for p in clone.position_set.positions]\n\n assert clone_positions == parent_positions\n assert clone.clone_parent_id == mqid\n assert clone.parent_basket == ticker\n\n\ndef test_basket_edit(mocker):\n mock_session()\n\n # test errors\n mock_basket_init(mocker, user_ea, False)\n basket = Basket()\n with pytest.raises(MqError, match=ErrorMessage.UNINITIALIZED.value):\n basket.update()\n\n mock_basket_init(mocker, user_ena)\n basket = Basket.get(ticker)\n with pytest.raises(MqError, match=ErrorMessage.NON_ADMIN.value):\n basket.update()\n\n # test update\n mock_basket_init(mocker, user_ia)\n basket = Basket.get(ticker)\n basket.description = 'New Basket Description'\n gs_asset.description = 'New Basket Description'\n\n mock_response(mocker, GsIndexApi, 'edit', cb_response)\n mock_response(mocker, GsAssetApi, 'get_asset', gs_asset)\n mock_response(mocker, GsReportApi, 'get_report', report)\n mock_basket_init(mocker, user_ia)\n\n response = basket.update()\n GsIndexApi.edit.assert_called()\n assert response == cb_response.as_dict()\n assert basket.description == 'New Basket Description'\n\n gs_asset.description = None\n\n\ndef test_basket_rebalance(mocker):\n mock_session()\n mock_basket_init(mocker, user_ia)\n\n basket = Basket.get(ticker)\n basket.allow_ca_restricted_assets = True\n\n mock_response(mocker, GsIndexApi, 'rebalance', cb_response)\n mock_response(mocker, GsAssetApi, 'get_asset', gs_asset)\n mock_response(mocker, GsReportApi, 'get_report', report)\n mock_basket_init(mocker, user_ia)\n\n response = basket.update()\n GsIndexApi.rebalance.assert_called()\n assert response == cb_response.as_dict()\n\n\ndef test_basket_edit_and_rebalance(mocker):\n mock_session()\n mock_basket_init(mocker, user_ia)\n\n basket = Basket.get(ticker)\n basket.description = 'New Basket Description'\n gs_asset.description = 'New Basket Description'\n basket.initial_price = 2000000\n\n mock_response(mocker, GsIndexApi, 'edit', cb_response)\n mock_response(mocker, GsReportApi, 'get_report', report)\n mock_response(mocker, GsIndexApi, 'rebalance', cb_response)\n mock_response(mocker, GsAssetApi, 'get_asset', gs_asset)\n mock_response(mocker, GsReportApi, 'get_report', report)\n mock_basket_init(mocker, user_ia)\n\n response = basket.update()\n GsIndexApi.edit.assert_called()\n GsIndexApi.rebalance.assert_called()\n assert response == cb_response.as_dict()\n assert basket.description == 'New Basket Description'\n gs_asset.description = None\n\n\ndef test_basket_update_entitlements(mocker):\n mock_session()\n\n mock_basket_init(mocker, user_ia)\n basket = Basket.get(ticker)\n\n mock_response(mocker, GsUsersApi, 'get_users', [TargetUser.from_dict(user_ena)])\n new_admin = User.get(user_id='user_xyz')\n basket.entitlements.admin.users += [new_admin]\n\n entitlements_response = TargetEntitlements(admin=['guid:user_abc', 'guid:user_xyz'])\n mock_response(mocker, GsAssetApi, 'update_asset_entitlements', entitlements_response)\n response = basket.update()\n GsAssetApi.update_asset_entitlements.assert_called()\n assert response == entitlements_response\n\n\ndef test_upload_position_history(mocker):\n mock_session()\n\n # test errors\n mock_basket_init(mocker, user_ea, False)\n basket = Basket()\n with pytest.raises(MqError, match=ErrorMessage.UNINITIALIZED.value):\n basket.upload_position_history()\n\n mock_basket_init(mocker, user_ena)\n basket = Basket.get(ticker)\n with pytest.raises(MqError, match=ErrorMessage.NON_ADMIN.value):\n basket.upload_position_history()\n\n # test backcast\n mock_basket_init(mocker, user_ia)\n basket = Basket.get(ticker)\n pos_set_1 = PositionSet(positions_weighted, dt.date(2021, 1, 1))\n pos_set_2 = PositionSet(positions_weighted, dt.date(2021, 3, 1))\n pos_set_3 = PositionSet(positions_weighted, dt.date(2021, 5, 1))\n\n mock_response(mocker, GsIndexApi, 'backcast', cb_response)\n response = basket.upload_position_history([pos_set_1, pos_set_2, pos_set_3])\n GsIndexApi.backcast.assert_called()\n assert response == cb_response.as_dict()\n\n\ndef test_update_risk_reports(mocker):\n mock_session()\n\n # test errors\n mock_basket_init(mocker, user_ea, False)\n basket = Basket()\n with pytest.raises(MqError, match=ErrorMessage.UNINITIALIZED.value):\n basket.add_factor_risk_report('AXUS4M', False)\n with pytest.raises(MqError, match=ErrorMessage.UNINITIALIZED.value):\n basket.delete_factor_risk_report('AXUS4M')\n\n mock_basket_init(mocker, user_ena)\n basket = Basket.get(ticker)\n with pytest.raises(MqError, match=ErrorMessage.NON_ADMIN.value):\n basket.add_factor_risk_report('AXUS4M', False)\n with pytest.raises(MqError, match=ErrorMessage.NON_ADMIN.value):\n basket.delete_factor_risk_report('AXUS4M')\n\n # test add/delete factor risk reports\n mock_basket_init(mocker, user_ea)\n basket = Basket.get(ticker)\n\n mock_response(mocker, GsIndexApi, 'update_risk_reports', {})\n basket.add_factor_risk_report('AXUS4M', False)\n payload = CustomBasketRiskParams(risk_model='AXUS4M', fx_hedged=False)\n GsIndexApi.update_risk_reports.assert_called_with(payload)\n\n mock_response(mocker, GsIndexApi, 'update_risk_reports', {})\n basket.delete_factor_risk_report('AXUS4M')\n payload = CustomBasketRiskParams(risk_model='AXUS4M', delete=True)\n GsIndexApi.update_risk_reports.assert_called_with(payload)\n" }, { "path": "gs_quant/test/markets/test_close_market.py", "content": "\"\"\"\nCopyright 2024 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport json\n\nfrom freezegun import freeze_time\n\nfrom gs_quant.common import PricingLocation\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.markets import CloseMarket, PricingContext\n\n\ndef test_close_market_dict():\n mkt = CloseMarket(date=dt.date(2024, 4, 11))\n assert json.dumps(mkt.to_dict(), cls=JSONEncoder) == json.dumps(mkt.market.to_dict(), cls=JSONEncoder)\n mkt = CloseMarket(date=dt.date(2024, 4, 11), location='LDN')\n assert json.dumps(mkt.to_dict(), cls=JSONEncoder) == json.dumps(mkt.market.to_dict(), cls=JSONEncoder)\n\n\n@freeze_time(\"2025-03-20 16:00:00\", tz_offset=4)\ndef test_close_market_roll():\n # 8PM in LDN, 4PM in NYC\n CloseMarket.roll_hr_and_min = (17, 30)\n test_date = dt.date(2025, 3, 20)\n with PricingContext(test_date, market_data_location=\"NYC\"):\n assert PricingContext.current.market == CloseMarket(test_date - dt.timedelta(days=1), PricingLocation.NYC)\n CloseMarket.roll_hr_and_min = (12, 00)\n with PricingContext(test_date, market_data_location=\"NYC\"):\n assert PricingContext.current.market == CloseMarket(test_date, PricingLocation.NYC)\n\n\n@freeze_time(\"2025-03-20 22:00:00\", tz_offset=4)\ndef test_close_market_roll_diff_days():\n # 2AM in LDN, 10PM yesterday in NYC\n CloseMarket.roll_hr_and_min = (23, 30)\n test_date = dt.date(2025, 3, 21)\n with PricingContext(test_date, market_data_location=\"NYC\"):\n # requested a date in the future for NYC - roll back to yesterday and do not apply the roll logic\n assert PricingContext.current.market == CloseMarket(test_date - dt.timedelta(days=1), PricingLocation.NYC)\n test_date = dt.date(2025, 3, 20)\n with PricingContext(test_date, market_data_location=\"NYC\"):\n # requested today NYC, we are not past the roll time so roll back to 2025-03-19\n assert PricingContext.current.market == CloseMarket(test_date - dt.timedelta(days=1), PricingLocation.NYC)\n" }, { "path": "gs_quant/test/markets/test_hedger.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport pytest\nimport datetime as dt\n\nfrom gs_quant.api.gs.hedges import GsHedgeApi\nfrom gs_quant.markets.hedge import (\n HedgeExclusions,\n HedgeConstraints,\n Constraint,\n Hedge,\n PerformanceHedge,\n PerformanceHedgeParameters,\n)\nfrom gs_quant.markets.position_set import PositionSet, Position\n\nmock_hedge = PerformanceHedge(parameters=None)\n\ncalculation_results = {\n 'hedgedTarget': {\n 'constituents': [\n {\n 'country': 'United States',\n 'name': 'Apple Inc',\n 'transactionCost': 1.1936100899058002,\n 'sector': 'Information Technology',\n 'shares': 26,\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'bbid': 'AAPL UW',\n 'currency': 'USD',\n 'industry': 'Technology Hardware, Storage & Peripherals',\n 'marginalCost': 0.09597615517006632,\n 'advPercentage': 0,\n 'notional': 3819.9199999999996,\n 'price': 146.92,\n 'weight': 0.08040829746809594,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Albemarle Corp',\n 'transactionCost': 5.661775350570679,\n 'sector': 'Materials',\n 'shares': -2.9210200815542167,\n 'assetId': 'MA4B66MW5E27U9XPVYM',\n 'bbid': 'ALB UN',\n 'currency': 'USD',\n 'industry': 'Chemicals',\n 'marginalCost': 0.078815239121909,\n 'advPercentage': 2e-06,\n 'notional': -661.3189464638747,\n 'price': 226.4,\n 'weight': -0.01392058749098281,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Advanced Micro Devices',\n 'transactionCost': 0.5830498039722443,\n 'sector': 'Information Technology',\n 'shares': -0.8120055824108264,\n 'assetId': 'MA4B66MW5E27U9YGMB9',\n 'bbid': 'AMD UW',\n 'currency': 'USD',\n 'industry': 'Semiconductors & Semiconductor Equipment',\n 'marginalCost': 0.0010543794559583631,\n 'advPercentage': 0,\n 'notional': -85.91019061906543,\n 'price': 105.8,\n 'weight': -0.0018083866056981923,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Best Buy Co Inc',\n 'transactionCost': 2.401727557182312,\n 'sector': 'Consumer Discretionary',\n 'shares': -4.535031177626968,\n 'assetId': 'MA4B66MW5E27UA39J2L',\n 'bbid': 'BBY UN',\n 'currency': 'USD',\n 'industry': 'Specialty Retail',\n 'marginalCost': 0.02401392704093342,\n 'advPercentage': 2e-06,\n 'notional': -474.99916554464863,\n 'price': 104.74,\n 'weight': -0.009998605782375405,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Citigroup Inc',\n 'transactionCost': 1.4510516652072596,\n 'sector': 'Financials',\n 'shares': -27.227187182634943,\n 'assetId': 'MA4B66MW5E27UA4ZCWT',\n 'bbid': 'C UN',\n 'currency': 'USD',\n 'industry': 'Banks',\n 'marginalCost': 0.05919571023849292,\n 'advPercentage': 2e-06,\n 'notional': -1938.0311836599553,\n 'price': 71.18,\n 'weight': -0.040795039665274604,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Cummins Inc',\n 'transactionCost': 3.3661617040634155,\n 'sector': 'Industrials',\n 'shares': -0.4130028393296445,\n 'assetId': 'MA4B66MW5E27UACX6RZ',\n 'bbid': 'CMI UN',\n 'currency': 'USD',\n 'industry': 'Machinery',\n 'marginalCost': 0.006662256400232119,\n 'advPercentage': 0,\n 'notional': -94.02422640178686,\n 'price': 227.66,\n 'weight': -0.0019791848954225427,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Citrix Systems Inc',\n 'transactionCost': 2.754676938056946,\n 'sector': 'Information Technology',\n 'shares': -2.204015152257958,\n 'assetId': 'MA4B66MW5E27UAE4LLL',\n 'bbid': 'CTXS UW',\n 'currency': 'USD',\n 'industry': 'Software',\n 'marginalCost': 0.014082314238438266,\n 'advPercentage': 2e-06,\n 'notional': -242.86042962730437,\n 'price': 110.19,\n 'weight': -0.005112147288085058,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Duke Realty Corp',\n 'transactionCost': 1.3627572059631348,\n 'sector': 'Real Estate',\n 'shares': -8.864060939026558,\n 'assetId': 'MA4B66MW5E27UAEP46J',\n 'bbid': 'DRE UN',\n 'currency': 'USD',\n 'industry': 'Equity Real Estate Investment Trusts (REITs)',\n 'marginalCost': 0.012421166613716166,\n 'advPercentage': 5e-06,\n 'notional': -433.0093768714474,\n 'price': 48.85,\n 'weight': -0.009114731926834648,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Davita Inc',\n 'transactionCost': 3.084942579269409,\n 'sector': 'Health Care',\n 'shares': -3.4880239796169494,\n 'assetId': 'MA4B66MW5E27UAEP4D8',\n 'bbid': 'DVA UN',\n 'currency': 'USD',\n 'industry': 'Health Care Providers & Services',\n 'marginalCost': 0.027406811709718284,\n 'advPercentage': 6e-06,\n 'notional': -422.05090153365086,\n 'price': 121,\n 'weight': -0.008884058942908718,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Electronic Arts Inc',\n 'transactionCost': 2.1669615507125854,\n 'sector': 'Communication Services',\n 'shares': -6.899047429867354,\n 'assetId': 'MA4B66MW5E27UAEP4H9',\n 'bbid': 'EA UW',\n 'currency': 'USD',\n 'industry': 'Entertainment',\n 'marginalCost': 0.040878606822697415,\n 'advPercentage': 3e-06,\n 'notional': -896.1862611397693,\n 'price': 129.9,\n 'weight': -0.01886448184060066,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Extra Space Storage Inc',\n 'transactionCost': 3.9990124702453613,\n 'sector': 'Real Estate',\n 'shares': -3.1030213327842304,\n 'assetId': 'MA4B66MW5E27UAFU2CS',\n 'bbid': 'EXR UN',\n 'currency': 'USD',\n 'industry': 'Equity Real Estate Investment Trusts (REITs)',\n 'marginalCost': 0.04508686837043359,\n 'advPercentage': 5e-06,\n 'notional': -535.6125122518861,\n 'price': 172.61,\n 'weight': -0.011274500568803494,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'General Motors Co',\n 'transactionCost': 1.0103281140327454,\n 'sector': 'Consumer Discretionary',\n 'shares': -3.0290208240423557,\n 'assetId': 'MA4B66MW5E27UAGYYVE',\n 'bbid': 'GM UN',\n 'currency': 'USD',\n 'industry': 'Automobiles',\n 'marginalCost': 0.003364583586707687,\n 'advPercentage': 0,\n 'notional': -158.20575763973224,\n 'price': 52.23,\n 'weight': -0.003330189014812112,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'The Goldman Sachs Group, Inc.',\n 'transactionCost': 3.2194154492372298,\n 'sector': 'Financials',\n 'shares': 51,\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'bbid': 'GS UN',\n 'currency': 'USD',\n 'industry': 'Capital Markets',\n 'marginalCost': 1.3508400095029636,\n 'advPercentage': 2.3e-05,\n 'notional': 19933.350000000002,\n 'price': 390.85,\n 'weight': 0.4195917025319039,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Hormel Foods Corp',\n 'transactionCost': 1.4352393746376038,\n 'sector': 'Consumer Staples',\n 'shares': -11.239077266890737,\n 'assetId': 'MA4B66MW5E27UAJ5XEZ',\n 'bbid': 'HRL UN',\n 'currency': 'USD',\n 'industry': 'Food Products',\n 'marginalCost': 0.013853571784092618,\n 'advPercentage': 4e-06,\n 'notional': -458.55435248914205,\n 'price': 40.8,\n 'weight': -0.009652446852352158,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Iron Mountain Inc',\n 'transactionCost': 2.417732357978821,\n 'sector': 'Real Estate',\n 'shares': -5.0810349313170065,\n 'assetId': 'MA4B66MW5E27UAJQF4M',\n 'bbid': 'IRM UN',\n 'currency': 'USD',\n 'industry': 'Equity Real Estate Investment Trusts (REITs)',\n 'marginalCost': 0.011447654789702045,\n 'advPercentage': 3e-06,\n 'notional': -224.93741640940388,\n 'price': 44.27,\n 'weight': -0.004734872638786235,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'JPMorgan Chase & Co',\n 'transactionCost': 1.2586397729172192,\n 'sector': 'Financials',\n 'shares': -12.604086651115832,\n 'assetId': 'MA4B66MW5E27UAKHZFD',\n 'bbid': 'JPM UN',\n 'currency': 'USD',\n 'industry': 'Banks',\n 'marginalCost': 0.054444447777798256,\n 'advPercentage': 1e-06,\n 'notional': -2054.970287597925,\n 'price': 163.04,\n 'weight': -0.043256576622880225,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Kroger Co',\n 'transactionCost': 1.2731897830963135,\n 'sector': 'Consumer Staples',\n 'shares': -1.730011893560012,\n 'assetId': 'MA4B66MW5E27UAKNZNV',\n 'bbid': 'KR UN',\n 'currency': 'USD',\n 'industry': 'Food & Staples Retailing',\n 'marginalCost': 0.0018536665905637035,\n 'advPercentage': 0,\n 'notional': -69.16587550452927,\n 'price': 39.98,\n 'weight': -0.001455923237190695,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Mosaic Co/the',\n 'transactionCost': 2.1279181241989136,\n 'sector': 'Materials',\n 'shares': -5.192035694429817,\n 'assetId': 'MA4B66MW5E27UAL9SLW',\n 'bbid': 'MOS UN',\n 'currency': 'USD',\n 'industry': 'Chemicals',\n 'marginalCost': 0.008339681547933048,\n 'advPercentage': 2e-06,\n 'notional': -186.18640000225324,\n 'price': 35.86,\n 'weight': -0.003919174075869411,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Morgan Stanley',\n 'transactionCost': 2.3784881788491172,\n 'sector': 'Financials',\n 'shares': -100.16368860962515,\n 'assetId': 'MA4B66MW5E27UAL9STW',\n 'bbid': 'MS UN',\n 'currency': 'USD',\n 'industry': 'Capital Markets',\n 'marginalCost': 0.5160781640859926,\n 'advPercentage': 1.6e-05,\n 'notional': -10307.845194816524,\n 'price': 102.91,\n 'weight': -0.2169773928982519,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'MSCI Inc',\n 'transactionCost': 7.908167362213135,\n 'sector': 'Financials',\n 'shares': -0.5740039461869635,\n 'assetId': 'MA4B66MW5E27UAL9SUH',\n 'bbid': 'MSCI UN',\n 'currency': 'USD',\n 'industry': 'Capital Markets',\n 'marginalCost': 0.06319486932947428,\n 'advPercentage': 2e-06,\n 'notional': -379.628989889672,\n 'price': 661.37,\n 'weight': -0.007991089014053052,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Nucor Corp',\n 'transactionCost': 3.182479500770569,\n 'sector': 'Materials',\n 'shares': -0.7970054792874738,\n 'assetId': 'MA4B66MW5E27UALNB48',\n 'bbid': 'NUE UN',\n 'currency': 'USD',\n 'industry': 'Metals & Mining',\n 'marginalCost': 0.005388287534627193,\n 'advPercentage': 0,\n 'notional': -80.43379296969185,\n 'price': 100.92,\n 'weight': -0.001693109895388968,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Paypal Holdings Inc',\n 'transactionCost': 1.4358487129211426,\n 'sector': 'Information Technology',\n 'shares': -0.5480037674398188,\n 'assetId': 'MA4B66MW5E27UAM94N9',\n 'bbid': 'PYPL UW',\n 'currency': 'USD',\n 'industry': 'IT Services',\n 'marginalCost': 0.004606334069169853,\n 'advPercentage': 0,\n 'notional': -152.40532776268802,\n 'price': 278.11,\n 'weight': -0.0032080915125093927,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Qualcomm Inc',\n 'transactionCost': 1.0424676537513733,\n 'sector': 'Information Technology',\n 'shares': -0.6250042968063627,\n 'assetId': 'MA4B66MW5E27UAM94Q4',\n 'bbid': 'QCOM UW',\n 'currency': 'USD',\n 'industry': 'Semiconductors & Semiconductor Equipment',\n 'marginalCost': 0.001836148888424059,\n 'advPercentage': 0,\n 'notional': -83.67557525643583,\n 'price': 133.88,\n 'weight': -0.0017613485481459143,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Tractor Supply Company',\n 'transactionCost': 3.904104232788086,\n 'sector': 'Consumer Discretionary',\n 'shares': -0.49000336869618827,\n 'assetId': 'MA4B66MW5E27UANEQ5C',\n 'bbid': 'TSCO UW',\n 'currency': 'USD',\n 'industry': 'Specialty Retail',\n 'marginalCost': 0.00848540990065989,\n 'advPercentage': 1e-06,\n 'notional': -103.2535098516608,\n 'price': 210.72,\n 'weight': -0.002173458851174191,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Twitter Inc',\n 'transactionCost': 1.2658634781837463,\n 'sector': 'Communication Services',\n 'shares': -8.874061007775461,\n 'assetId': 'MA4B66MW5E27UANLXU9',\n 'bbid': 'TWTR UN',\n 'currency': 'USD',\n 'industry': 'Interactive Media & Services',\n 'marginalCost': 0.015894775099317016,\n 'advPercentage': 1e-06,\n 'notional': -596.5143809426665,\n 'price': 67.22,\n 'weight': -0.012556468666054534,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Wells Fargo & Co',\n 'transactionCost': 1.3822112533240394,\n 'sector': 'Financials',\n 'shares': -43.79430107894055,\n 'assetId': 'MA4B66MW5E27UANT8X5',\n 'bbid': 'WFC UN',\n 'currency': 'USD',\n 'industry': 'Banks',\n 'marginalCost': 0.061059807755110544,\n 'advPercentage': 2e-06,\n 'notional': -2098.622907702831,\n 'price': 47.92,\n 'weight': -0.044175452636686034,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Dentsply Sirona Inc',\n 'transactionCost': 2.9051536321640015,\n 'sector': 'Health Care',\n 'shares': -5.308036491917078,\n 'assetId': 'MA4B66MW5E27UANZH4M',\n 'bbid': 'XRAY UW',\n 'currency': 'USD',\n 'industry': 'Health Care Equipment & Supplies',\n 'marginalCost': 0.01947929477213156,\n 'advPercentage': 5e-06,\n 'notional': -318.5352698799438,\n 'price': 60.01,\n 'weight': -0.006705082497692817,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Meta Platforms Inc-Class A',\n 'transactionCost': 0.8238705992698669,\n 'sector': 'Communication Services',\n 'shares': -0.7130049017966985,\n 'assetId': 'MA4B66MW5E3VLSECN',\n 'bbid': 'FB UW',\n 'currency': 'USD',\n 'industry': 'Interactive Media & Services',\n 'marginalCost': 0.004364390584540722,\n 'advPercentage': 0,\n 'notional': -251.6622101381627,\n 'price': 352.96,\n 'weight': -0.005297422420958517,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Tapestry Inc',\n 'transactionCost': 2.314427137374878,\n 'sector': 'Consumer Discretionary',\n 'shares': -7.397050853562664,\n 'assetId': 'MAAHJ4JYCKZ6P025',\n 'bbid': 'TPR UN',\n 'currency': 'USD',\n 'industry': 'Textiles, Apparel & Luxury Goods',\n 'marginalCost': 0.014220203034272804,\n 'advPercentage': 2e-06,\n 'notional': -291.88762668158273,\n 'price': 39.46,\n 'weight': -0.006144156713614223,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Lumen Technologies Inc',\n 'transactionCost': 3.95284104347229,\n 'sector': 'Communication Services',\n 'shares': -11.81608123370237,\n 'assetId': 'MAQQM1QHGJDFAVKN',\n 'bbid': 'LUMN UN',\n 'currency': 'USD',\n 'industry': 'Diversified Telecommunication Services',\n 'marginalCost': 0.012712411491036973,\n 'advPercentage': 1e-06,\n 'notional': -152.78193035177165,\n 'price': 12.93,\n 'weight': -0.003216018896593429,\n 'borrowCost': 40,\n },\n ],\n 'transactionCost': 2.5770571473071144,\n },\n 'hedge': {\n 'transactionCost': 2.2604819652681694,\n 'correlation': 0.936758446681233,\n 'backtestPerformance': [\n ['2020-09-24', 1],\n ['2020-09-25', 1.0104116399850593],\n ['2020-09-28', 1.0307855197014755],\n ['2020-09-29', 1.0163931905275039],\n ['2020-09-30', 1.0303780678487708],\n ['2020-10-01', 1.02803072573049],\n ['2020-10-02', 1.0338706383971734],\n ['2020-10-05', 1.052525377568271],\n ['2020-10-06', 1.0340650510786302],\n ['2020-10-07', 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Discretionary',\n 'correlation': 0.6555947816398136,\n 'shares': -7.397050853562664,\n 'assetId': 'MAAHJ4JYCKZ6P025',\n 'bbid': 'TPR UN',\n 'currency': 'USD',\n 'industry': 'Textiles, Apparel & Luxury Goods',\n 'marginalCost': 0.02844040606854561,\n 'advPercentage': 2e-06,\n 'notional': -291.88762668158273,\n 'price': 39.46,\n 'weight': 0.01228831342722845,\n 'borrowCost': 40,\n },\n ],\n 'holdingError': 0.0744447922751884,\n },\n 'benchmarks': [],\n 'target': {\n 'annualizedVolatility': 0.2245953385486387,\n 'backtestPerformance': [\n ['2020-09-24', 1],\n ['2020-09-25', 1.0076306394460186],\n ['2020-09-28', 1.0295511006061422],\n ['2020-09-29', 1.0186692133468813],\n ['2020-09-30', 1.0388739557814626],\n ['2020-10-01', 1.0312010109422116],\n ['2020-10-02', 1.028915736274446],\n ['2020-10-05', 1.04359570772614],\n ['2020-10-06', 1.0339555621015752],\n ['2020-10-07', 1.0478951891716997],\n ['2020-10-08', 1.0651714582747391],\n ['2020-10-09', 1.067487286837617],\n ['2020-10-12', 1.1089121011443612],\n ['2020-10-13', 1.0889651082352152],\n ['2020-10-14', 1.0908265455374473],\n ['2020-10-15', 1.0793406312748108],\n ['2020-10-16', 1.066348958386379],\n ['2020-10-19', 1.0580790374426237],\n ['2020-10-20', 1.070545026648481],\n ['2020-10-21', 1.0487843858627914],\n ['2020-10-22', 1.0564518466695865],\n ['2020-10-23', 1.0535672455976932],\n ['2020-10-26', 1.0380051283537797],\n ['2020-10-27', 1.0193468830718104],\n ['2020-10-28', 0.9847340205087146],\n ['2020-10-29', 0.9938053936242638],\n ['2020-10-30', 0.977050891037759],\n ['2020-11-02', 0.9815023585256727],\n ['2020-11-03', 1.0157893128742363],\n ['2020-11-04', 1.0247361201097123],\n ['2020-11-05', 1.053383922227019],\n ['2020-11-06', 1.0458990013576899],\n ['2020-11-09', 1.0956900988927742],\n ['2020-11-10', 1.1051257684500482],\n ['2020-11-11', 1.114613144599051],\n ['2020-11-12', 1.0998987020862043],\n ['2020-11-13', 1.118260026182338],\n ['2020-11-16', 1.133563610467608],\n ['2020-11-17', 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1.2914042491850333],\n ['2020-12-24', 1.292302847074621],\n ['2020-12-25', 1.292302847074621],\n ['2020-12-28', 1.315621736511042],\n ['2020-12-29', 1.3056016257805925],\n ['2020-12-30', 1.3090126939682685],\n ['2020-12-31', 1.3239355297144455],\n ['2021-01-01', 1.3239355297144455],\n ['2021-01-04', 1.3224086183920336],\n ['2021-01-05', 1.3493610710472062],\n ['2021-01-06', 1.3987925727398136],\n ['2021-01-07', 1.4319647016333785],\n ['2021-01-08', 1.4279934787019752],\n ['2021-01-11', 1.4367232749388321],\n ['2021-01-12', 1.469838996641421],\n ['2021-01-13', 1.4770128945271697],\n ['2021-01-14', 1.4926776415213325],\n ['2021-01-15', 1.4616630876826273],\n ['2021-01-18', 1.4616630876826273],\n ['2021-01-19', 1.4358591481103977],\n ['2021-01-20', 1.4295109888258917],\n ['2021-01-21', 1.4349746520185538],\n ['2021-01-22', 1.439535800155589],\n ['2021-01-25', 1.4220064821263625],\n ['2021-01-26', 1.4173810924662704],\n ['2021-01-27', 1.3814583295793816],\n ['2021-01-28', 1.3780872164853137],\n ['2021-01-29', 1.3522550733175958],\n ['2021-02-01', 1.3709195860652295],\n ['2021-02-02', 1.4215560080317566],\n ['2021-02-03', 1.4257301401640343],\n ['2021-02-04', 1.4535341847163137],\n ['2021-02-05', 1.4512520437813807],\n ['2021-02-08', 1.4781277199821679],\n ['2021-02-09', 1.4775330941772888],\n ['2021-02-10', 1.4915330456044296],\n ['2021-02-11', 1.4831722464085457],\n ['2021-02-12', 1.4996431461737505],\n ['2021-02-15', 1.4996431461737505],\n ['2021-02-16', 1.5177373195459527],\n ['2021-02-17', 1.5160670399464755],\n ['2021-02-18', 1.5024180665965226],\n ['2021-02-19', 1.5255982883551262],\n ['2021-02-22', 1.5227097701171932],\n ['2021-02-23', 1.5317740923338705],\n ['2021-02-24', 1.5764039318945835],\n ['2021-02-25', 1.5560158660893277],\n ['2021-02-26', 1.5234830186934991],\n ['2021-03-01', 1.5784972654263867],\n ['2021-03-02', 1.577134091644449],\n ['2021-03-03', 1.5848454247108927],\n ['2021-03-04', 1.56037723875832],\n ['2021-03-05', 1.5553334957651503],\n ['2021-03-08', 1.5722760223215777],\n ['2021-03-09', 1.5675166655829158],\n ['2021-03-10', 1.6109345339708374],\n ['2021-03-11', 1.6152872888745418],\n ['2021-03-12', 1.6402028151888879],\n ['2021-03-15', 1.6352045113217835],\n ['2021-03-16', 1.6210823443141926],\n ['2021-03-17', 1.6323778843072827],\n ['2021-03-18', 1.6359479894361848],\n ['2021-03-19', 1.619665113640902],\n ['2021-03-22', 1.6071325326123638],\n ['2021-03-23', 1.5751950944546225],\n ['2021-03-24', 1.5577386357137408],\n ['2021-03-25', 1.5663485666697723],\n ['2021-03-26', 1.5549856514207931],\n ['2021-03-29', 1.5487197526231282],\n ['2021-03-30', 1.5707765311614452],\n ['2021-03-31', 1.5553421135304377],\n ['2021-04-01', 1.5596306269110851],\n ['2021-04-02', 1.5596306269110851],\n ['2021-04-05', 1.5491561249199899],\n ['2021-04-06', 1.5638517650358437],\n ['2021-04-07', 1.565256460777806],\n ['2021-04-08', 1.5886066875425475],\n ['2021-04-09', 1.5926554703615445],\n ['2021-04-12', 1.593196039275071],\n ['2021-04-13', 1.5830725153611647],\n ['2021-04-14', 1.608829448940914],\n ['2021-04-15', 1.6266462869574803],\n ['2021-04-16', 1.6409768471984019],\n ['2021-04-19', 1.6454784544116277],\n ['2021-04-20', 1.5971649119068507],\n ['2021-04-21', 1.61150408991306],\n ['2021-04-22', 1.5906663334464066],\n ['2021-04-23', 1.6294760477048125],\n ['2021-04-26', 1.6469520922758942],\n ['2021-04-27', 1.6587059406956703],\n ['2021-04-28', 1.6629693842136615],\n ['2021-04-29', 1.6821838670733185],\n ['2021-04-30', 1.6600095735537992],\n ['2021-05-03', 1.6690417750089481],\n ['2021-05-04', 1.6582100274749996],\n ['2021-05-05', 1.6898043218876324],\n ['2021-05-06', 1.726507384249699],\n ['2021-05-07', 1.7471226456852775],\n ['2021-05-10', 1.7314484974926188],\n ['2021-05-11', 1.6945331247311817],\n ['2021-05-12', 1.6660819643490854],\n ['2021-05-13', 1.6881833985802592],\n ['2021-05-14', 1.7330302491395915],\n ['2021-05-17', 1.7325445205506251],\n ['2021-05-18', 1.7109131462442573],\n ['2021-05-19', 1.6858949901796618],\n ['2021-05-20', 1.6968254502978186],\n ['2021-05-21', 1.7188869294353843],\n ['2021-05-24', 1.730399480427093],\n ['2021-05-25', 1.7148890697749082],\n ['2021-05-26', 1.7263342455107282],\n ['2021-05-27', 1.738998443318212],\n ['2021-05-28', 1.7402307837544118],\n ['2021-05-31', 1.7402307837544118],\n ['2021-06-01', 1.7811918056020137],\n ['2021-06-02', 1.7867761175087098],\n ['2021-06-03', 1.8047762788959507],\n ['2021-06-04', 1.820470796352018],\n ['2021-06-07', 1.8073459398182243],\n ['2021-06-08', 1.795232495555971],\n ['2021-06-09', 1.7883555188560583],\n ['2021-06-10', 1.750957551238487],\n ['2021-06-11', 1.7699048833742117],\n ['2021-06-14', 1.7557834997998292],\n ['2021-06-15', 1.7475997565089554],\n ['2021-06-16', 1.7474399361345212],\n ['2021-06-17', 1.7128231564053855],\n ['2021-06-18', 1.6594909407700957],\n ['2021-06-21', 1.698039771770234],\n ['2021-06-22', 1.7014218529292136],\n ['2021-06-23', 1.71491570650398],\n ['2021-06-24', 1.7450904199436985],\n ['2021-06-25', 1.7445592522286677],\n ['2021-06-28', 1.7477211886561963],\n ['2021-06-29', 1.7665008660854071],\n ['2021-06-30', 1.795393099363613],\n ['2021-07-01', 1.7778849340310026],\n ['2021-07-02', 1.7800479931183184],\n ['2021-07-05', 1.7800479931183184],\n ['2021-07-06', 1.7670633711287582],\n ['2021-07-07', 1.7635073677976],\n ['2021-07-08', 1.7259174590440662],\n ['2021-07-09', 1.7809489413075301],\n ['2021-07-12', 1.8146271680534696],\n ['2021-07-13', 1.798889561770962],\n ['2021-07-14', 1.7997262684371165],\n ['2021-07-15', 1.7941662429598688],\n ['2021-07-16', 1.75574746187226],\n ['2021-07-19', 1.7074472377320196],\n ['2021-07-20', 1.755098779176028],\n ['2021-07-21', 1.7884918362342517],\n ['2021-07-22', 1.7881870807163354],\n ['2021-07-23', 1.7971260536197318],\n ['2021-07-26', 1.8053936242638626],\n ['2021-07-27', 1.7966364078647248],\n ['2021-07-28', 1.790553049004526],\n ['2021-07-29', 1.805162511467499],\n ['2021-07-30', 1.7949426252690068],\n ['2021-08-02', 1.807475206297545],\n ['2021-08-03', 1.8198934060777137],\n ['2021-08-04', 1.8090694928758457],\n ['2021-08-05', 1.835024635057221],\n ['2021-08-06', 1.8874496350376353],\n ['2021-08-09', 1.895298852348689],\n ['2021-08-10', 1.9266244291709742],\n ['2021-08-11', 1.9508074454358313],\n ['2021-08-12', 1.9614143476390804],\n ['2021-08-13', 1.9449810526678597],\n ['2021-08-16', 1.939386556129459],\n ['2021-08-17', 1.9239873929928122],\n ['2021-08-18', 1.8915336723509921],\n ['2021-08-19', 1.8713297133496192],\n ['2021-08-20', 1.8835544051274093],\n ['2021-08-23', 1.9089454753990176],\n ['2021-08-24', 1.937170223583999],\n ['2021-08-25', 1.952823219080041],\n ['2021-08-26', 1.9483161278343588],\n ['2021-08-27', 1.9795625779026347],\n ['2021-08-30', 1.964436832955583],\n ['2021-08-31', 1.96144960213344],\n ['2021-09-01', 1.9634340384493298],\n ['2021-09-02', 1.969112362340987],\n ['2021-09-03', 1.9576906896014072],\n ['2021-09-06', 1.9576906896014072],\n ['2021-09-07', 1.958603389288739],\n ['2021-09-08', 1.9341289358705023],\n ['2021-09-09', 1.9303324185444852],\n ['2021-09-10', 1.915549033909334],\n ['2021-09-13', 1.939824495292736],\n ['2021-09-14', 1.9146567034854105],\n ['2021-09-15', 1.9095581201676801],\n ['2021-09-16', 1.8880129235141936],\n ['2021-09-17', 1.8615955557400916],\n ['2021-09-20', 1.8019802057765602],\n ['2021-09-21', 1.7938285832472152],\n ['2021-09-22', 1.8372347001370162],\n ['2021-09-23', 1.8647621927668683],\n ['2021-09-24', 1.8609100516830828],\n ],\n 'constituents': [\n {\n 'country': 'United States',\n 'name': 'Apple Inc',\n 'transactionCost': 1.1936100899058002,\n 'sector': 'Information Technology',\n 'shares': 26,\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'bbid': 'AAPL UW',\n 'currency': 'USD',\n 'industry': 'Technology Hardware, Storage & Peripherals',\n 'marginalCost': 0.19195231034013271,\n 'advPercentage': 0,\n 'notional': 3819.9199999999996,\n 'price': 146.92,\n 'weight': 0.16081659493619194,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'The Goldman Sachs Group, Inc.',\n 'transactionCost': 3.2194154492372298,\n 'sector': 'Financials',\n 'shares': 51,\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'bbid': 'GS UN',\n 'currency': 'USD',\n 'industry': 'Capital Markets',\n 'marginalCost': 2.701680019005928,\n 'advPercentage': 2.3e-05,\n 'notional': 19933.350000000002,\n 'price': 390.85,\n 'weight': 0.8391834050638082,\n 'borrowCost': 40,\n },\n ],\n 'assetId': '',\n 'transactionCost': 2.893632329346061,\n 'annualizedReturn': 0.861701827815391,\n },\n}\n\nmock_results = {\n 'Portfolio': {\n 'Annualized Volatility': 0.2245953385486387,\n 'Backtest Performance': [\n ['2020-09-24', 1],\n ['2020-09-25', 1.0076306394460186],\n ['2020-09-28', 1.0295511006061422],\n ['2020-09-29', 1.0186692133468813],\n ['2020-09-30', 1.0388739557814626],\n ['2020-10-01', 1.0312010109422116],\n ['2020-10-02', 1.028915736274446],\n ['2020-10-05', 1.04359570772614],\n ['2020-10-06', 1.0339555621015752],\n ['2020-10-07', 1.0478951891716997],\n ['2020-10-08', 1.0651714582747391],\n ['2020-10-09', 1.067487286837617],\n ['2020-10-12', 1.1089121011443612],\n ['2020-10-13', 1.0889651082352152],\n ['2020-10-14', 1.0908265455374473],\n ['2020-10-15', 1.0793406312748108],\n ['2020-10-16', 1.066348958386379],\n ['2020-10-19', 1.0580790374426237],\n ['2020-10-20', 1.070545026648481],\n ['2020-10-21', 1.0487843858627914],\n ['2020-10-22', 1.0564518466695865],\n ['2020-10-23', 1.0535672455976932],\n ['2020-10-26', 1.0380051283537797],\n ['2020-10-27', 1.0193468830718104],\n ['2020-10-28', 0.9847340205087146],\n ['2020-10-29', 0.9938053936242638],\n ['2020-10-30', 0.977050891037759],\n ['2020-11-02', 0.9815023585256727],\n ['2020-11-03', 1.0157893128742363],\n ['2020-11-04', 1.0247361201097123],\n ['2020-11-05', 1.053383922227019],\n ['2020-11-06', 1.0458990013576899],\n ['2020-11-09', 1.0956900988927742],\n ['2020-11-10', 1.1051257684500482],\n ['2020-11-11', 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['2021-08-25', 1.808387370019545],\n ['2021-08-26', 1.7990128100684075],\n ['2021-08-27', 1.8230267889371115],\n ['2021-08-30', 1.8066029024558345],\n ['2021-08-31', 1.8011083700062123],\n ['2021-09-01', 1.7952270192942026],\n ['2021-09-02', 1.8042111959545828],\n ['2021-09-03', 1.7956456571882038],\n ['2021-09-06', 1.7956456571882038],\n ['2021-09-07', 1.7906973619966717],\n ['2021-09-08', 1.7790846820103143],\n ['2021-09-09', 1.782974909163946],\n ['2021-09-10', 1.774210004132321],\n ['2021-09-13', 1.7863371703894837],\n ['2021-09-14', 1.7607086022595286],\n ['2021-09-15', 1.7733155737318977],\n ['2021-09-16', 1.7661953659616862],\n ['2021-09-17', 1.75552495625101],\n ['2021-09-20', 1.7073319409830758],\n ['2021-09-21', 1.7036755701399784],\n ['2021-09-22', 1.7353221001527805],\n ['2021-09-23', 1.7749324312405845],\n ['2021-09-24', 1.772300109946803],\n ],\n 'Cash Weight': 0,\n 'R Squared': 0.8279135592848923,\n 'Annualized Return': 0.77299493701611,\n 'Annualized Volatility': 0.19788814828229934,\n 'Hedge Ratio': 1,\n 'Ridge Weight': 0,\n 'Backtest Correlation': [\n ['2020-11-26', 0.9015925151077675],\n ['2020-11-27', 0.8995307601350195],\n ['2020-11-30', 0.8976193597009464],\n ['2020-12-01', 0.899134905548527],\n ['2020-12-02', 0.8968844291011409],\n ['2020-12-03', 0.8983210353924714],\n ['2020-12-04', 0.8983135812975858],\n ['2020-12-07', 0.8981598956869762],\n ['2020-12-08', 0.896541180905539],\n ['2020-12-09', 0.896342085151086],\n ['2020-12-10', 0.8960079549884281],\n ['2020-12-11', 0.9022159150284723],\n ['2020-12-14', 0.9020984285750759],\n ['2020-12-15', 0.9108756715722117],\n ['2020-12-16', 0.9216167275658368],\n ['2020-12-17', 0.9283890799097579],\n ['2020-12-18', 0.9282606242969395],\n ['2020-12-21', 0.9168926673056201],\n ['2020-12-22', 0.9235486508155735],\n ['2020-12-23', 0.9276013093341673],\n ['2020-12-24', 0.9283372838050917],\n ['2020-12-25', 0.928171587486031],\n ['2020-12-28', 0.9116851149928049],\n ['2020-12-29', 0.9056422674201283],\n 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0.003364583586707687,\n 'advPercentage': 0,\n 'notional': -158.20575763973224,\n 'price': 52.23,\n 'weight': -0.003330189014812112,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'The Goldman Sachs Group, Inc.',\n 'transactionCost': 3.2194154492372298,\n 'sector': 'Financials',\n 'shares': 51,\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'bbid': 'GS UN',\n 'currency': 'USD',\n 'industry': 'Capital Markets',\n 'marginalCost': 1.3508400095029636,\n 'advPercentage': 2.3e-05,\n 'notional': 19933.350000000002,\n 'price': 390.85,\n 'weight': 0.4195917025319039,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Hormel Foods Corp',\n 'transactionCost': 1.4352393746376038,\n 'sector': 'Consumer Staples',\n 'shares': -11.239077266890737,\n 'assetId': 'MA4B66MW5E27UAJ5XEZ',\n 'bbid': 'HRL UN',\n 'currency': 'USD',\n 'industry': 'Food Products',\n 'marginalCost': 0.013853571784092618,\n 'advPercentage': 4e-06,\n 'notional': -458.55435248914205,\n 'price': 40.8,\n 'weight': -0.009652446852352158,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Iron Mountain Inc',\n 'transactionCost': 2.417732357978821,\n 'sector': 'Real Estate',\n 'shares': -5.0810349313170065,\n 'assetId': 'MA4B66MW5E27UAJQF4M',\n 'bbid': 'IRM UN',\n 'currency': 'USD',\n 'industry': 'Equity Real Estate Investment Trusts (REITs)',\n 'marginalCost': 0.011447654789702045,\n 'advPercentage': 3e-06,\n 'notional': -224.93741640940388,\n 'price': 44.27,\n 'weight': -0.004734872638786235,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'JPMorgan Chase & Co',\n 'transactionCost': 1.2586397729172192,\n 'sector': 'Financials',\n 'shares': -12.604086651115832,\n 'assetId': 'MA4B66MW5E27UAKHZFD',\n 'bbid': 'JPM UN',\n 'currency': 'USD',\n 'industry': 'Banks',\n 'marginalCost': 0.054444447777798256,\n 'advPercentage': 1e-06,\n 'notional': -2054.970287597925,\n 'price': 163.04,\n 'weight': -0.043256576622880225,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Kroger Co',\n 'transactionCost': 1.2731897830963135,\n 'sector': 'Consumer Staples',\n 'shares': -1.730011893560012,\n 'assetId': 'MA4B66MW5E27UAKNZNV',\n 'bbid': 'KR UN',\n 'currency': 'USD',\n 'industry': 'Food & Staples Retailing',\n 'marginalCost': 0.0018536665905637035,\n 'advPercentage': 0,\n 'notional': -69.16587550452927,\n 'price': 39.98,\n 'weight': -0.001455923237190695,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Mosaic Co/the',\n 'transactionCost': 2.1279181241989136,\n 'sector': 'Materials',\n 'shares': -5.192035694429817,\n 'assetId': 'MA4B66MW5E27UAL9SLW',\n 'bbid': 'MOS UN',\n 'currency': 'USD',\n 'industry': 'Chemicals',\n 'marginalCost': 0.008339681547933048,\n 'advPercentage': 2e-06,\n 'notional': -186.18640000225324,\n 'price': 35.86,\n 'weight': -0.003919174075869411,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Morgan Stanley',\n 'transactionCost': 2.3784881788491172,\n 'sector': 'Financials',\n 'shares': -100.16368860962515,\n 'assetId': 'MA4B66MW5E27UAL9STW',\n 'bbid': 'MS UN',\n 'currency': 'USD',\n 'industry': 'Capital Markets',\n 'marginalCost': 0.5160781640859926,\n 'advPercentage': 1.6e-05,\n 'notional': -10307.845194816524,\n 'price': 102.91,\n 'weight': -0.2169773928982519,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'MSCI Inc',\n 'transactionCost': 7.908167362213135,\n 'sector': 'Financials',\n 'shares': -0.5740039461869635,\n 'assetId': 'MA4B66MW5E27UAL9SUH',\n 'bbid': 'MSCI UN',\n 'currency': 'USD',\n 'industry': 'Capital Markets',\n 'marginalCost': 0.06319486932947428,\n 'advPercentage': 2e-06,\n 'notional': -379.628989889672,\n 'price': 661.37,\n 'weight': -0.007991089014053052,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Nucor Corp',\n 'transactionCost': 3.182479500770569,\n 'sector': 'Materials',\n 'shares': -0.7970054792874738,\n 'assetId': 'MA4B66MW5E27UALNB48',\n 'bbid': 'NUE UN',\n 'currency': 'USD',\n 'industry': 'Metals & Mining',\n 'marginalCost': 0.005388287534627193,\n 'advPercentage': 0,\n 'notional': -80.43379296969185,\n 'price': 100.92,\n 'weight': -0.001693109895388968,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Paypal Holdings Inc',\n 'transactionCost': 1.4358487129211426,\n 'sector': 'Information Technology',\n 'shares': -0.5480037674398188,\n 'assetId': 'MA4B66MW5E27UAM94N9',\n 'bbid': 'PYPL UW',\n 'currency': 'USD',\n 'industry': 'IT Services',\n 'marginalCost': 0.004606334069169853,\n 'advPercentage': 0,\n 'notional': -152.40532776268802,\n 'price': 278.11,\n 'weight': -0.0032080915125093927,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Qualcomm Inc',\n 'transactionCost': 1.0424676537513733,\n 'sector': 'Information Technology',\n 'shares': -0.6250042968063627,\n 'assetId': 'MA4B66MW5E27UAM94Q4',\n 'bbid': 'QCOM UW',\n 'currency': 'USD',\n 'industry': 'Semiconductors & Semiconductor Equipment',\n 'marginalCost': 0.001836148888424059,\n 'advPercentage': 0,\n 'notional': -83.67557525643583,\n 'price': 133.88,\n 'weight': -0.0017613485481459143,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Tractor Supply Company',\n 'transactionCost': 3.904104232788086,\n 'sector': 'Consumer Discretionary',\n 'shares': -0.49000336869618827,\n 'assetId': 'MA4B66MW5E27UANEQ5C',\n 'bbid': 'TSCO UW',\n 'currency': 'USD',\n 'industry': 'Specialty Retail',\n 'marginalCost': 0.00848540990065989,\n 'advPercentage': 1e-06,\n 'notional': -103.2535098516608,\n 'price': 210.72,\n 'weight': -0.002173458851174191,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Twitter Inc',\n 'transactionCost': 1.2658634781837463,\n 'sector': 'Communication Services',\n 'shares': -8.874061007775461,\n 'assetId': 'MA4B66MW5E27UANLXU9',\n 'bbid': 'TWTR UN',\n 'currency': 'USD',\n 'industry': 'Interactive Media & Services',\n 'marginalCost': 0.015894775099317016,\n 'advPercentage': 1e-06,\n 'notional': -596.5143809426665,\n 'price': 67.22,\n 'weight': -0.012556468666054534,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Wells Fargo & Co',\n 'transactionCost': 1.3822112533240394,\n 'sector': 'Financials',\n 'shares': -43.79430107894055,\n 'assetId': 'MA4B66MW5E27UANT8X5',\n 'bbid': 'WFC UN',\n 'currency': 'USD',\n 'industry': 'Banks',\n 'marginalCost': 0.061059807755110544,\n 'advPercentage': 2e-06,\n 'notional': -2098.622907702831,\n 'price': 47.92,\n 'weight': -0.044175452636686034,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Dentsply Sirona Inc',\n 'transactionCost': 2.9051536321640015,\n 'sector': 'Health Care',\n 'shares': -5.308036491917078,\n 'assetId': 'MA4B66MW5E27UANZH4M',\n 'bbid': 'XRAY UW',\n 'currency': 'USD',\n 'industry': 'Health Care Equipment & Supplies',\n 'marginalCost': 0.01947929477213156,\n 'advPercentage': 5e-06,\n 'notional': -318.5352698799438,\n 'price': 60.01,\n 'weight': -0.006705082497692817,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Meta Platforms Inc-Class A',\n 'transactionCost': 0.8238705992698669,\n 'sector': 'Communication Services',\n 'shares': -0.7130049017966985,\n 'assetId': 'MA4B66MW5E3VLSECN',\n 'bbid': 'FB UW',\n 'currency': 'USD',\n 'industry': 'Interactive Media & Services',\n 'marginalCost': 0.004364390584540722,\n 'advPercentage': 0,\n 'notional': -251.6622101381627,\n 'price': 352.96,\n 'weight': -0.005297422420958517,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Tapestry Inc',\n 'transactionCost': 2.314427137374878,\n 'sector': 'Consumer Discretionary',\n 'shares': -7.397050853562664,\n 'assetId': 'MAAHJ4JYCKZ6P025',\n 'bbid': 'TPR UN',\n 'currency': 'USD',\n 'industry': 'Textiles, Apparel & Luxury Goods',\n 'marginalCost': 0.014220203034272804,\n 'advPercentage': 2e-06,\n 'notional': -291.88762668158273,\n 'price': 39.46,\n 'weight': -0.006144156713614223,\n 'borrowCost': 40,\n },\n {\n 'country': 'United States',\n 'name': 'Lumen Technologies Inc',\n 'transactionCost': 3.95284104347229,\n 'sector': 'Communication Services',\n 'shares': -11.81608123370237,\n 'assetId': 'MAQQM1QHGJDFAVKN',\n 'bbid': 'LUMN UN',\n 'currency': 'USD',\n 'industry': 'Diversified Telecommunication Services',\n 'marginalCost': 0.012712411491036973,\n 'advPercentage': 1e-06,\n 'notional': -152.78193035177165,\n 'price': 12.93,\n 'weight': -0.003216018896593429,\n 'borrowCost': 40,\n },\n ],\n 'Transaction Cost': 2.5770571473071144,\n },\n}\n\n\ndef test_hedge_exclusions_to_dict():\n exclusions = HedgeExclusions(\n assets=['GS UN'], countries=['Mexico'], regions=['Europe'], sectors=['Utilities'], industries=['Airlines']\n )\n\n assert exclusions.to_dict() == {\n 'classificationConstraints': [\n {'name': 'Mexico', 'min': 0, 'max': 0, 'type': 'Country'},\n {'name': 'Europe', 'min': 0, 'max': 0, 'type': 'Region'},\n {'name': 'Utilities', 'min': 0, 'max': 0, 'type': 'Sector'},\n {'name': 'Airlines', 'min': 0, 'max': 0, 'type': 'Industry'},\n ],\n 'assetConstraints': [{'min': 0, 'max': 0, 'assetId': 'GS UN'}],\n }\n\n\ndef test_hedge_constraints_to_dict():\n constraints = HedgeConstraints(\n sectors=[Constraint(constraint_name='Software', minimum=0, maximum=20)],\n esg=[Constraint(constraint_name='gPercentile', minimum=75, maximum=100)],\n )\n assert constraints.to_dict() == {\n 'classificationConstraints': [{'name': 'Software', 'min': 0, 'max': 20, 'type': 'Sector'}],\n 'esgConstraints': [{'name': 'gPercentile', 'min': 75, 'max': 100}],\n }\n\n\ndef test_format_hedge_calculate_results():\n assert Hedge._format_hedge_calculate_results(calculation_results) == mock_results\n\n\ndef get_mock_hedge(mocker):\n mocker.patch.object(PerformanceHedgeParameters, 'to_dict', return_value={})\n mocker.patch.object(PerformanceHedgeParameters, 'resolve_identifiers_in_payload', return_value={})\n mocker.patch.object(GsHedgeApi, 'calculate_hedge', return_value={'result': calculation_results})\n hedge = PerformanceHedge(\n parameters=PerformanceHedgeParameters(\n initial_portfolio=PositionSet(\n positions=[Position(asset_id='fakeId', identifier='fakeId', quantity=1)], date=dt.date(2020, 1, 1)\n ),\n universe=[],\n exclusions=HedgeExclusions(),\n constraints=HedgeConstraints(),\n )\n )\n hedge.calculate()\n return hedge\n\n\ndef test_get_constituents(mocker):\n hedge = get_mock_hedge(mocker)\n constituents = hedge.get_constituents()\n assert constituents.shape == (28, 16)\n\n\ndef test_get_statistics(mocker):\n hedge = get_mock_hedge(mocker)\n constituents = hedge.get_statistics()\n assert constituents.shape == (7, 3)\n\n\ndef test_get_backtest_performance(mocker):\n hedge = get_mock_hedge(mocker)\n constituents = hedge.get_backtest_performance()\n assert constituents.shape == (262, 2)\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/markets/test_instrument.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.instrument import EqOption, FXMultiCrossBinary, FXMultiCrossBinaryLeg, Instrument\nfrom gs_quant.test.utils.mock_calc import MockCalc\n\n\ndef test_instrument_resolve(mocker):\n with MockCalc(mocker):\n eq_option = EqOption('.FTSE', strike_price='ATMS', name='FTSE_Call')\n assert eq_option.unresolved is None\n\n eq_option.resolve()\n assert eq_option.unresolved is not None\n assert eq_option.strike_price == 7464.8\n\n eq_option.expiration_date = '3m'\n assert eq_option.unresolved is not None\n assert eq_option.strike_price == 7464.8\n\n\ndef test_nested_leg_from_dict():\n mcb = FXMultiCrossBinary(legs=(FXMultiCrossBinaryLeg(pair='USDJPY'), FXMultiCrossBinaryLeg(pair='GBPUSD')))\n mcb_dict = mcb.to_dict()\n new_mcb = Instrument.from_dict(mcb_dict)\n assert new_mcb == mcb\n" }, { "path": "gs_quant/test/markets/test_portfolio.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest import mock\n\nimport gs_quant.risk as risk\nimport numpy as np\nimport pandas as pd\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.portfolios import GsPortfolioApi\nfrom gs_quant.common import PositionSet\nfrom gs_quant.datetime import business_day_offset\nfrom gs_quant.instrument import IRSwap, IRSwaption, CurveScenario\nfrom gs_quant.markets import (\n HistoricalPricingContext,\n PricingContext,\n BackToTheFuturePricingContext,\n historical_risk_key,\n)\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk.results import PortfolioPath, PortfolioRiskResult\nfrom gs_quant.session import Environment, GsSession\nfrom gs_quant.target.portfolios import Portfolio as MarqueePortfolio\nfrom gs_quant.test.utils.mock_calc import MockCalc\n\n\ndef set_session():\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.PROD, 'client_id', 'secret')\n\n\ndef test_portfolio(mocker):\n with MockCalc(mocker):\n with PricingContext(pricing_date=dt.date(2020, 10, 15)):\n swap1 = IRSwap('Pay', '10y', 'USD', fixed_rate=0.001, name='swap_10y@10bp')\n swap2 = IRSwap('Pay', '10y', 'USD', fixed_rate=0.002, name='swap_10y@20bp')\n swap3 = IRSwap('Pay', '10y', 'USD', fixed_rate=0.003, name='swap_10y@30bp')\n\n portfolio = Portfolio((swap1, swap2, swap3))\n\n prices: PortfolioRiskResult = portfolio.dollar_price()\n result = portfolio.calc((risk.DollarPrice, risk.IRDelta))\n\n assert tuple(sorted(map(lambda x: round(x, 0), prices))) == (4439480.0, 5423407.0, 6407334.0)\n assert round(prices.aggregate(), 2) == 16270220.58\n assert round(prices[0], 0) == 6407334.0\n assert round(prices[swap2], 0) == 5423407.0\n assert round(prices['swap_10y@30bp'], 0) == 4439480.0\n\n assert tuple(map(lambda x: round(x, 0), result[risk.DollarPrice])) == (6407334.0, 5423407.0, 4439480.0)\n assert round(result[risk.DollarPrice].aggregate(), 0) == 16270221.0\n assert round(result[risk.DollarPrice]['swap_10y@30bp'], 0) == 4439480.0\n assert round(result[risk.DollarPrice]['swap_10y@30bp'], 0) == round(\n result['swap_10y@30bp'][risk.DollarPrice], 0\n )\n\n assert round(result[risk.IRDelta].aggregate().value.sum(), 0) == 278984.0\n\n prices_only = result[risk.DollarPrice]\n assert tuple(map(lambda x: round(x, 0), prices)) == tuple(map(lambda x: round(x, 0), prices_only))\n\n swap4 = IRSwap('Pay', '10y', 'USD', fixed_rate=-0.001, name='swap_10y@-10bp')\n portfolio.append(swap4)\n assert len(portfolio.instruments) == 4\n\n extracted_swap = portfolio.pop('swap_10y@20bp')\n assert extracted_swap == swap2\n assert len(portfolio.instruments) == 3\n\n swap_dict = {'swap_5': swap1, 'swap_6': swap2, 'swap_7': swap3}\n\n portfolio = Portfolio(swap_dict)\n assert len(portfolio) == 3\n\n # extend a portfolio with a portfolio\n new_portfolio = Portfolio([IRSwap(termination_date=x, name=x) for x in ['4y', '5y']])\n portfolio.extend(new_portfolio)\n assert len(portfolio) == 5\n\n # extend a portfolio with a list of trades\n portfolio.extend([IRSwap(termination_date=x, name=x) for x in ['6y', '7y']])\n assert len(portfolio) == 7\n\n\ndef test_construction():\n swap1 = IRSwap('Pay', '10y', 'USD')\n swap2 = IRSwap('Pay', '5y', 'USD')\n my_list = [swap1, swap2]\n my_tuple = (swap1, swap2)\n my_np_arr = np.array((swap1, swap2))\n\n p1 = Portfolio(my_list)\n p2 = Portfolio(my_tuple)\n p3 = Portfolio(my_np_arr)\n\n assert len(p1) == 2\n assert len(p2) == 2\n assert len(p3) == 2\n assert p1 == p2\n assert p2 == p3\n\n\ndef test_historical_pricing(mocker):\n with MockCalc(mocker):\n swap1 = IRSwap('Pay', '10y', 'USD', fixed_rate='ATM+1', name='10y@a+1')\n swap2 = IRSwap('Pay', '10y', 'USD', fixed_rate='ATM+2', name='10y@a+2')\n swap3 = IRSwap('Pay', '10y', 'USD', fixed_rate='ATM+3', name='10y@a+3')\n\n portfolio = Portfolio((swap1, swap2, swap3))\n dates = (dt.date(2021, 2, 9), dt.date(2021, 2, 10), dt.date(2021, 2, 11))\n\n with HistoricalPricingContext(dates=dates) as hpc:\n risk_key = hpc._PricingContext__risk_key(risk.DollarPrice, swap1.provider)\n results = portfolio.calc((risk.DollarPrice, risk.IRDelta))\n\n expected = risk.SeriesWithInfo(\n pd.Series(\n data=[-580315.9786130451, -580372.2014339305, -580808.9413858932],\n index=[dt.date(2021, 2, 9), dt.date(2021, 2, 10), dt.date(2021, 2, 11)],\n ),\n risk_key=historical_risk_key(risk_key),\n )\n\n assert results.dates == dates\n actual = results[risk.DollarPrice].aggregate()\n assert actual.equals(expected)\n\n assert (\n results[dt.date(2021, 2, 9)][risk.DollarPrice]['10y@a+1']\n == results[risk.DollarPrice][dt.date(2021, 2, 9)]['10y@a+1']\n )\n assert (\n results[dt.date(2021, 2, 9)][risk.DollarPrice]['10y@a+1']\n == results[risk.DollarPrice]['10y@a+1'][dt.date(2021, 2, 9)]\n )\n assert (\n results[dt.date(2021, 2, 9)][risk.DollarPrice]['10y@a+1']\n == results['10y@a+1'][risk.DollarPrice][dt.date(2021, 2, 9)]\n )\n assert (\n results[dt.date(2021, 2, 9)][risk.DollarPrice]['10y@a+1']\n == results['10y@a+1'][dt.date(2021, 2, 9)][risk.DollarPrice]\n )\n assert (\n results[dt.date(2021, 2, 9)][risk.DollarPrice]['10y@a+1']\n == results[dt.date(2021, 2, 9)]['10y@a+1'][risk.DollarPrice]\n )\n\n\ndef test_backtothefuture_pricing(mocker):\n with MockCalc(mocker):\n swap1 = IRSwap('Pay', '10y', 'USD', fixed_rate=0.01, name='swap1')\n swap2 = IRSwap('Pay', '10y', 'USD', fixed_rate=0.02, name='swap2')\n swap3 = IRSwap('Pay', '10y', 'USD', fixed_rate=0.03, name='swap3')\n\n portfolio = Portfolio((swap1, swap2, swap3))\n pricing_date = dt.date(2021, 2, 10)\n with PricingContext(pricing_date=pricing_date):\n with BackToTheFuturePricingContext(\n dates=business_day_offset(pricing_date, [-1, 0, 1], roll='forward'), name='btf'\n ) as hpc:\n risk_key = hpc._PricingContext__risk_key(risk.DollarPrice, swap1.provider)\n results = portfolio.calc(risk.DollarPrice)\n\n expected = risk.SeriesWithInfo(\n pd.Series(\n data=[-22711963.80864744, -22655907.930484552, -21582551.58922608],\n index=business_day_offset(pricing_date, [-1, 0, 1], roll='forward'),\n ),\n risk_key=historical_risk_key(risk_key),\n )\n\n actual = results[risk.DollarPrice].aggregate()\n\n assert actual.equals(expected)\n\n\ndef test_duplicate_instrument(mocker):\n with MockCalc(mocker):\n swap1 = IRSwap('Pay', '1y', 'EUR', name='EUR1y')\n swap2 = IRSwap('Pay', '2y', 'EUR', name='EUR2y')\n swap3 = IRSwap('Pay', '3y', 'EUR', name='EUR3y')\n\n portfolio = Portfolio((swap1, swap2, swap3, swap1))\n assert portfolio.paths('EUR1y') == (PortfolioPath(0), PortfolioPath(3))\n assert portfolio.paths('EUR2y') == (PortfolioPath(1),)\n with PricingContext(pricing_date=dt.date(2020, 10, 15)):\n fwds: PortfolioRiskResult = portfolio.calc(risk.IRFwdRate)\n\n assert tuple(map(lambda x: round(x, 6), fwds)) == (-0.005378, -0.005224, -0.00519, -0.005378)\n assert round(fwds.aggregate(), 6) == -0.02117\n assert round(fwds[swap1], 6) == -0.005378\n\n\ndef test_nested_portfolios(mocker):\n swap1 = IRSwap('Pay', '10y', 'USD', name='USD-swap')\n swap2 = IRSwap('Pay', '10y', 'EUR', name='EUR-swap')\n swap3 = IRSwap('Pay', '10y', 'GBP', name='GBP-swap')\n\n swap4 = IRSwap('Pay', '10y', 'JPY', name='JPY-swap')\n swap5 = IRSwap('Pay', '10y', 'HUF', name='HUF-swap')\n swap6 = IRSwap('Pay', '10y', 'CHF', name='CHF-swap')\n\n portfolio2_1 = Portfolio((swap1, swap2, swap3), name='portfolio2_1')\n portfolio2_2 = Portfolio((swap1, swap2, swap3), name='portfolio2_2')\n portfolio1_1 = Portfolio((swap4, portfolio2_1), name='portfolio1_1')\n portfolio1_2 = Portfolio((swap5, portfolio2_2), name='USD-swap')\n portfolio = Portfolio((swap6, portfolio1_1, portfolio1_2), name='portfolio')\n\n assert portfolio.paths('USD-swap') == (PortfolioPath(2), PortfolioPath((1, 1, 0)), PortfolioPath((2, 1, 0)))\n\n\ndef test_single_instrument(mocker):\n with MockCalc(mocker):\n swap1 = IRSwap('Pay', '10y', 'USD', fixed_rate=0.0, name='10y@0')\n\n portfolio = Portfolio(swap1)\n assert portfolio.paths('10y@0') == (PortfolioPath(0),)\n\n with PricingContext(pricing_date=dt.date(2020, 10, 15)):\n prices: PortfolioRiskResult = portfolio.dollar_price()\n assert tuple(map(lambda x: round(x, 0), prices)) == (7391261.0,)\n assert round(prices.aggregate(), 0) == 7391261.0\n assert round(prices[swap1], 0) == 7391261.0\n\n\ndef test_results_with_resolution(mocker):\n with MockCalc(mocker):\n swap1 = IRSwap('Pay', '10y', 'USD', name='swap1')\n swap2 = IRSwap('Pay', '10y', 'GBP', name='swap2')\n swap3 = IRSwap('Pay', '10y', 'EUR', name='swap3')\n\n portfolio = Portfolio((swap1, swap2, swap3))\n\n with PricingContext(pricing_date=dt.date(2020, 10, 15)):\n result = portfolio.calc((risk.DollarPrice, risk.IRDelta))\n\n # Check that we've got results\n assert result[swap1][risk.DollarPrice] is not None\n\n # Now resolve portfolio and assert that we can still get the result\n\n orig_swap1 = swap1.clone()\n\n with PricingContext(pricing_date=dt.date(2020, 10, 15)):\n portfolio.resolve()\n\n # Assert that the resolved swap is indeed different and that we can retrieve results by both\n\n assert swap1 != orig_swap1\n assert result[swap1][risk.DollarPrice] is not None\n assert result[orig_swap1][risk.DollarPrice] is not None\n\n # Now reset the instruments and portfolio\n\n swap1 = IRSwap('Pay', '10y', 'USD', name='swap1')\n swap2 = IRSwap('Pay', '10y', 'GBP', name='swap2')\n swap3 = IRSwap('Pay', '10y', 'EUR', name='swap3')\n\n portfolio = Portfolio((swap1, swap2, swap3, swap1))\n\n with PricingContext(dt.date(2020, 10, 14)):\n # Resolve under a different pricing date\n portfolio.resolve()\n\n assert portfolio.instruments[0].termination_date == dt.date(2030, 10, 16)\n assert portfolio.instruments[1].termination_date == dt.date(2030, 10, 14)\n assert round(swap1.fixed_rate, 4) == 0.0075\n assert round(swap2.fixed_rate, 4) == 0.004\n assert round(swap3.fixed_rate, 4) == -0.0027\n\n # Assert that after resolution under a different context, we cannot retrieve the result\n\n try:\n _ = result[swap1][risk.DollarPrice]\n assert False\n except KeyError:\n assert True\n\n # Assert that if we resolve first in one context before pricing under a different context\n # we can slice the riskresult with the origin\n with CurveScenario(parallel_shift=5, name='parallel shift 5bp'):\n result2 = portfolio.calc((risk.DollarPrice, risk.IRDelta))\n\n assert result2[swap1][risk.DollarPrice] is not None\n assert result2[orig_swap1][risk.DollarPrice] is not None\n\n # Resolve again and check we get the same values\n\n with PricingContext(dt.date(2020, 10, 14)):\n # Resolve under a different pricing date\n portfolio.resolve()\n\n assert portfolio.instruments[0].termination_date == dt.date(2030, 10, 16)\n assert portfolio.instruments[1].termination_date == dt.date(2030, 10, 14)\n assert round(swap1.fixed_rate, 4) == 0.0075\n assert round(swap2.fixed_rate, 4) == 0.004\n assert round(swap3.fixed_rate, 4) == -0.0027\n\n\ndef test_portfolio_overrides(mocker):\n swap_1 = IRSwap(\"Pay\", \"5y\", \"EUR\", fixed_rate=-0.005, name=\"5y\")\n swap_2 = IRSwap(\"Pay\", \"10y\", \"EUR\", fixed_rate=-0.005, name=\"10y\")\n swap_3 = IRSwap(\"Pay\", \"5y\", \"GBP\", fixed_rate=-0.005, name=\"5y\")\n swap_4 = IRSwap(\"Pay\", \"10y\", \"GBP\", fixed_rate=-0.005, name=\"10y\")\n eur_port = Portfolio([swap_1, swap_2], name=\"EUR\")\n gbp_port = Portfolio([swap_3, swap_4], name=\"GBP\")\n\n # override instruments after portfolio construction\n for idx in range(len(eur_port)):\n eur_port[idx].fixed_rate = eur_port[idx].fixed_rate - 0.0005\n\n assert eur_port[swap_1] is not None\n\n with MockCalc(mocker):\n # override instruments after portfolio construction and resolution\n gbp_port.resolve()\n for idx in range(len(gbp_port)):\n gbp_port[idx].notional_amount = gbp_port[idx].notional_amount - 1\n\n with PricingContext(dt.date(2020, 1, 14)):\n r1 = eur_port.calc(risk.Price)\n r2 = eur_port.calc((risk.Price, risk.DollarPrice))\n r3 = gbp_port.calc(risk.Price)\n r4 = gbp_port.calc((risk.DollarPrice, risk.Price))\n\n assert gbp_port[swap_3] is not None\n\n assert r1[eur_port[0]] is not None\n assert r1['5y'] is not None\n assert r1.to_frame() is not None\n assert r2[eur_port[0]] is not None\n assert r2[risk.Price][0] is not None\n assert r2[0][risk.Price] is not None\n assert r3[gbp_port[0]] is not None\n assert r3.to_frame() is not None\n assert r4[gbp_port[0]] is not None\n assert r4[risk.DollarPrice][0] is not None\n assert r4[0][risk.DollarPrice] is not None\n\n\ndef test_from_frame():\n swap = IRSwap('Receive', '3m', 'USD', fixed_rate=0, notional_amount=1)\n swaption = IRSwaption(notional_currency='GBP', expiration_date='10y', effective_date='0b')\n portfolio = Portfolio((swap, swaption))\n port_df = portfolio.to_frame()\n new_port_df = Portfolio.from_frame(port_df)\n\n assert new_port_df[swap] == swap\n assert new_port_df[swaption] == swaption\n\n\ndef test_single_instrument_new_mock(mocker):\n with MockCalc(mocker):\n with PricingContext(pricing_date=dt.date(2020, 10, 15)):\n swap1 = IRSwap('Pay', '10y', 'USD', name='swap1')\n\n portfolio = Portfolio(swap1)\n fwd: PortfolioRiskResult = portfolio.calc(risk.IRFwdRate)\n\n assert portfolio.paths('swap1') == (PortfolioPath(0),)\n assert tuple(map(lambda x: round(x, 6), fwd)) == (0.007512,)\n assert round(fwd.aggregate(), 2) == 0.01\n assert round(fwd[swap1], 6) == 0.007512\n\n\ndef test_get_instruments(mocker):\n mocker.patch.object(\n GsPortfolioApi,\n 'get_position_dates',\n return_value=([dt.date(2021, 1, 2), dt.date(2021, 5, 6), dt.date(2021, 6, 1)]),\n )\n mocker.patch.object(\n GsPortfolioApi,\n 'get_positions_for_date',\n return_value=(PositionSet(position_date=dt.date(2021, 6, 1), positions=[])),\n )\n mocker.patch.object(\n GsPortfolioApi, 'get_portfolio', return_value=(MarqueePortfolio(id_='id', name='name', currency='USD'))\n )\n mocker.patch.object(GsAssetApi, 'get_instruments_for_positions', return_value=([]))\n\n port = Portfolio.get(portfolio_id='id')\n port._get_instruments(dt.date(2021, 5, 14), False)\n GsPortfolioApi.get_positions_for_date.assert_called_with('id', dt.date(2021, 5, 6))\n\n\ndef test_clone():\n old_p = Portfolio((IRSwap(name='c'), Portfolio((IRSwap(name='a'), IRSwap(name='b')))))\n old_p.priceables[1].instruments[0].name = 'changed_name'\n new_p = old_p.clone()\n assert old_p['changed_name'] == ()\n assert new_p['changed_name'] == IRSwap(name='changed_name')\n\n # Check clone is deep\n inst1 = IRSwap('Pay', '10y', 'USD', fixed_rate=0.0, name='10y@0')\n inst2 = IRSwap('Pay', '5y', 'USD', fixed_rate=0.0, name='5y@0')\n port = Portfolio([inst1, Portfolio(inst2)])\n copy = port.clone(True)\n # Make some edits to the copy\n copy[0].fixed_rate = 1.0\n copy[1][0].fixed_rate = 2.0\n\n # Check not modified in place\n assert port[0].fixed_rate == 0.0\n assert port[1][0].fixed_rate == 0.0\n" }, { "path": "gs_quant/test/markets/test_portfolio_manager.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport datetime as dt\n\nimport pytest\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.carbon import (\n GsCarbonApi,\n CarbonTargetCoverageCategory,\n CarbonScope,\n CarbonEmissionsAllocationCategory,\n CarbonEmissionsIntensityType,\n CarbonCard,\n CarbonCoverageCategory,\n CarbonEntityType,\n)\nfrom gs_quant.api.gs.esg import GsEsgApi, ESGMeasure\nfrom gs_quant.api.gs.portfolios import GsPortfolioApi\nfrom gs_quant.api.gs.reports import GsReportApi\nfrom gs_quant.api.gs.scenarios import GsFactorScenarioApi\nfrom gs_quant.entities.entitlements import Entitlements, User, EntitlementBlock\nfrom gs_quant.markets.portfolio_manager import PortfolioManager, ScenarioCalculationMeasure\nfrom gs_quant.markets.report import FactorRiskReport, PerformanceReport\nfrom gs_quant.markets.scenario import FactorScenario, HistoricalSimulationParameters, FactorScenarioType\nfrom gs_quant.models.risk_model import MacroRiskModel, FactorType, UniverseIdentifier, CoverageType, Term\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.target.portfolios import Portfolio as TargetPortfolio\nfrom gs_quant.target.reports import ReportStatus, PositionSourceType, ReportType, Report\nimport pandas as pd\nimport numpy as np\n\nesg_data = {\n 'pricingDate': '2021-08-25',\n 'quintiles': [\n {\n 'measure': 'gPercentile',\n 'results': [\n {\n 'name': 'Q1',\n 'description': '<=80-100%',\n 'gross': 91.03557560284325,\n 'long': 91.03557560284325,\n 'short': 0,\n },\n {'name': 'Q2', 'description': '<=60-80%', 'gross': 0, 'long': 0, 'short': 0},\n {'name': 'Q3', 'description': '<=40-60%', 'gross': 0, 'long': 0, 'short': 0},\n {\n 'name': 'Q4',\n 'description': '<=20-40%',\n 'gross': 8.955474904385047,\n 'long': 0,\n 'short': 8.955474904385047,\n },\n {'name': 'Q5', 'description': '<=0-20%', 'gross': 0, 'long': 0, 'short': 0},\n {\n 'name': 'Not Included',\n 'description': 'Not Included',\n 'gross': 0.008949492771697426,\n 'long': 0.008949492771697426,\n 'short': 0,\n },\n ],\n }\n ],\n 'summary': {\n 'gross': {\n 'gPercentile': 91.71440761636107,\n 'gRegionalPercentile': 84.58263751763046,\n 'esPercentile': 90.07443582510577,\n 'esDisclosurePercentage': 78.50077574047954,\n 'esMomentumPercentile': 74.20073342736248,\n },\n 'long': {\n 'gPercentile': 97.39,\n 'gRegionalPercentile': 92.62,\n 'esPercentile': 94.24,\n 'esDisclosurePercentage': 83.33,\n 'esMomentumPercentile': 76.85,\n },\n 'short': {\n 'gPercentile': 34.02,\n 'gRegionalPercentile': 2.88,\n 'esPercentile': 47.73,\n 'esDisclosurePercentage': 29.41,\n 'esMomentumPercentile': 47.27,\n },\n },\n 'weightsBySector': [\n {'name': 'Diversified Financials', 'gross': 91.03557560284325, 'long': 91.03557560284325, 'short': 0},\n {'name': 'Automobile Manufacturers', 'gross': 8.955474904385047, 'long': 0, 'short': 8.955474904385047},\n {'name': 'Total', 'gross': 99.99105050722832, 'long': 91.03557560284325, 'short': 8.955474904385047},\n {'name': 'Not Included', 'gross': 0.008949492771697426, 'long': 0.008949492771697426, 'short': 0},\n ],\n 'measuresBySector': [\n {\n 'measure': 'gPercentile',\n 'results': [\n {'name': 'Diversified Financials', 'gross': 97.39, 'long': 97.39, 'short': 0},\n {'name': 'Automobile Manufacturers', 'gross': 34.02, 'long': 0, 'short': 34.02},\n {'name': 'Total', 'gross': 91.71440761636107, 'long': 97.39, 'short': 34.02},\n ],\n }\n ],\n 'weightsByRegion': [\n {'name': 'N. America', 'gross': 99.99105050722832, 'long': 91.03557560284325, 'short': 8.955474904385047},\n {'name': 'Total', 'gross': 99.99105050722832, 'long': 91.03557560284325, 'short': 8.955474904385047},\n {'name': 'Not Included', 'gross': 0.008949492771697426, 'long': 0.008949492771697426, 'short': 0},\n ],\n 'measuresByRegion': [\n {\n 'measure': 'gPercentile',\n 'results': [\n {'name': 'N. America', 'gross': 91.71440761636107, 'long': 97.39, 'short': 34.02},\n {'name': 'Total', 'gross': 91.71440761636107, 'long': 97.39, 'short': 34.02},\n ],\n }\n ],\n 'topTenRanked': [\n {\n 'measure': 'gPercentile',\n 'results': [\n {'assetId': 'MA4B66MW5E27UAHKG34', 'name': 'The Goldman Sachs Group, Inc.', 'value': 97.39},\n {'assetId': 'MA4B66MW5E27UANEQ6R', 'name': 'Tesla Inc', 'value': 34.02},\n ],\n }\n ],\n 'bottomTenRanked': [\n {\n 'measure': 'gPercentile',\n 'results': [\n {'assetId': 'MA4B66MW5E27UANEQ6R', 'name': 'Tesla Inc', 'value': 34.02},\n {'assetId': 'MA4B66MW5E27UAHKG34', 'name': 'The Goldman Sachs Group, Inc.', 'value': 97.39},\n ],\n }\n ],\n 'noESGData': {\n 'gross': {\n 'weight': 0.008949492771697426,\n 'assets': [{'assetId': 'MA4B66MW5E27UAL9SX6', 'name': 'Mustek LTD', 'weight': 0.008949492771697426}],\n },\n 'long': {\n 'weight': 0.008949492771697426,\n 'assets': [{'assetId': 'MA4B66MW5E27UAL9SX6', 'name': 'Mustek LTD', 'weight': 0.008949492771697426}],\n },\n 'short': {'weight': 0.0, 'assets': []},\n },\n}\ncarbon_data = {\n \"coverage\": {\n \"weights\": {\n \"portfolio\": {\n \"totalGHG\": {\n \"reportedVerified\": 27.432014892432875,\n \"reportedUnverified\": 72.56798510756713,\n \"estimated\": 0,\n \"missing\": 0,\n },\n \"scope1\": {\n \"reportedVerified\": 27.432014892432875,\n \"reportedUnverified\": 72.56798510756713,\n \"estimated\": 0,\n \"missing\": 0,\n },\n \"scope2\": {\n \"reportedVerified\": 27.432014892432875,\n \"reportedUnverified\": 72.56798510756713,\n \"estimated\": 0,\n \"missing\": 0,\n },\n },\n \"benchmark\": {\n \"totalGHG\": {\n \"reportedVerified\": 27.432014892432875,\n \"reportedUnverified\": 72.56798510756713,\n \"estimated\": 0,\n \"missing\": 0,\n },\n \"scope1\": {\n \"reportedVerified\": 27.432014892432875,\n \"reportedUnverified\": 72.56798510756713,\n \"estimated\": 0,\n \"missing\": 0,\n },\n \"scope2\": {\n \"reportedVerified\": 27.432014892432875,\n \"reportedUnverified\": 72.56798510756713,\n \"estimated\": 0,\n \"missing\": 0,\n },\n },\n },\n \"numberOfCompanies\": {\n \"portfolio\": {\n \"totalGHG\": {\"reportedVerified\": 50, \"reportedUnverified\": 50, \"estimated\": 0, \"missing\": 0},\n \"scope1\": {\"reportedVerified\": 50, \"reportedUnverified\": 50, \"estimated\": 0, \"missing\": 0},\n \"scope2\": {\"reportedVerified\": 50, \"reportedUnverified\": 50, \"estimated\": 0, \"missing\": 0},\n },\n \"benchmark\": {\n \"totalGHG\": {\"reportedVerified\": 50, \"reportedUnverified\": 50, \"estimated\": 0, \"missing\": 0},\n \"scope1\": {\"reportedVerified\": 50, \"reportedUnverified\": 50, \"estimated\": 0, \"missing\": 0},\n \"scope2\": {\"reportedVerified\": 50, \"reportedUnverified\": 50, \"estimated\": 0, \"missing\": 0},\n },\n },\n },\n \"sbtiAndNetZeroTargets\": {\n \"capitalAllocated\": {\n \"scienceBasedTarget\": {\n \"portfolio\": {\"targetsSet\": 0, \"targetsNotSet\": 0, \"missing\": 100},\n \"benchmark\": {\"targetsSet\": 0, \"targetsNotSet\": 0, \"missing\": 100},\n },\n \"netZeroEmissionsTarget\": {\n \"portfolio\": {\"targetsSet\": 0, \"targetsNotSet\": 0, \"missing\": 100},\n \"benchmark\": {\"targetsSet\": 0, \"targetsNotSet\": 0, \"missing\": 100},\n },\n },\n \"portfolioEmissions\": {\n \"scienceBasedTarget\": {\n \"portfolio\": {\"targetsSet\": 0, \"targetsNotSet\": 0, \"missing\": 99.99999999999997},\n \"benchmark\": {\"targetsSet\": 0, \"targetsNotSet\": 0, \"missing\": 99.99999999999997},\n },\n \"netZeroEmissionsTarget\": {\n \"portfolio\": {\"targetsSet\": 0, \"targetsNotSet\": 0, \"missing\": 99.99999999999997},\n \"benchmark\": {\"targetsSet\": 0, \"targetsNotSet\": 0, \"missing\": 99.99999999999997},\n },\n },\n },\n \"emissions\": {\n \"totalGHG\": {\n \"portfolio\": [\n {\n \"year\": \"2016\",\n \"portfolioEmissions\": 0.03161404863641,\n \"emissionsIntensityRevenue\": 5.165017590856768,\n \"emissionsIntensityEnterpriseValue\": 0.6417863895887255,\n \"emissionsIntensityMarketCap\": 2.136864492580658,\n },\n {\n \"year\": \"2017\",\n \"portfolioEmissions\": 0.02557473780478448,\n \"emissionsIntensityRevenue\": 4.152366329764957,\n \"emissionsIntensityEnterpriseValue\": 0.5150082275283909,\n \"emissionsIntensityMarketCap\": 1.8566214684311595,\n },\n {\n \"year\": \"2018\",\n \"portfolioEmissions\": 0.025883906301863414,\n \"emissionsIntensityRevenue\": 3.6355870431327695,\n \"emissionsIntensityEnterpriseValue\": 0.514601211522207,\n \"emissionsIntensityMarketCap\": 2.7567883404035856,\n },\n {\n \"year\": \"2019\",\n \"portfolioEmissions\": 0.02488419915490657,\n \"emissionsIntensityRevenue\": 3.3508727584974998,\n \"emissionsIntensityEnterpriseValue\": 0.5058550746653679,\n \"emissionsIntensityMarketCap\": 1.915020016654778,\n },\n {\n \"year\": \"2021\",\n \"portfolioEmissions\": 0.016784770256626273,\n \"emissionsIntensityRevenue\": 3.010184293088453,\n \"emissionsIntensityEnterpriseValue\": 0.3321381204145429,\n \"emissionsIntensityMarketCap\": 1.370060537473572,\n },\n ],\n \"benchmark\": [\n {\n \"year\": \"2016\",\n \"portfolioEmissions\": 0.03161404863641,\n \"emissionsIntensityRevenue\": 5.165017590856768,\n \"emissionsIntensityEnterpriseValue\": 0.6417863895887255,\n \"emissionsIntensityMarketCap\": 2.136864492580658,\n },\n {\n \"year\": \"2017\",\n \"portfolioEmissions\": 0.02557473780478448,\n \"emissionsIntensityRevenue\": 4.152366329764957,\n \"emissionsIntensityEnterpriseValue\": 0.5150082275283909,\n \"emissionsIntensityMarketCap\": 1.8566214684311595,\n },\n {\n \"year\": \"2018\",\n \"portfolioEmissions\": 0.025883906301863414,\n \"emissionsIntensityRevenue\": 3.6355870431327695,\n \"emissionsIntensityEnterpriseValue\": 0.514601211522207,\n \"emissionsIntensityMarketCap\": 2.7567883404035856,\n },\n {\n \"year\": \"2019\",\n \"portfolioEmissions\": 0.02488419915490657,\n \"emissionsIntensityRevenue\": 3.3508727584974998,\n \"emissionsIntensityEnterpriseValue\": 0.5058550746653679,\n \"emissionsIntensityMarketCap\": 1.915020016654778,\n },\n {\n \"year\": \"2021\",\n \"portfolioEmissions\": 0.016784770256626273,\n \"emissionsIntensityRevenue\": 3.010184293088453,\n \"emissionsIntensityEnterpriseValue\": 0.3321381204145429,\n \"emissionsIntensityMarketCap\": 1.370060537473572,\n },\n ],\n },\n \"scope1\": {\n \"portfolio\": [\n {\n \"year\": \"2016\",\n \"portfolioEmissions\": 0.0016712755532904242,\n \"emissionsIntensityRevenue\": 0.27028638660867355,\n \"emissionsIntensityEnterpriseValue\": 0.0339529985038069,\n \"emissionsIntensityMarketCap\": 0.11170603861184672,\n },\n {\n \"year\": \"2017\",\n \"portfolioEmissions\": 0.0015352826311627388,\n \"emissionsIntensityRevenue\": 0.24110061666622948,\n \"emissionsIntensityEnterpriseValue\": 0.030986711463004902,\n \"emissionsIntensityMarketCap\": 0.1064282069728648,\n },\n {\n \"year\": \"2018\",\n \"portfolioEmissions\": 0.001582778525839715,\n \"emissionsIntensityRevenue\": 0.21882979028252364,\n \"emissionsIntensityEnterpriseValue\": 0.03149430343217062,\n \"emissionsIntensityMarketCap\": 0.16344626276984683,\n },\n {\n \"year\": \"2019\",\n \"portfolioEmissions\": 0.0015833905465049197,\n \"emissionsIntensityRevenue\": 0.22245885390039408,\n \"emissionsIntensityEnterpriseValue\": 0.032053991311445595,\n \"emissionsIntensityMarketCap\": 0.1326706476709381,\n },\n {\n \"year\": \"2021\",\n \"portfolioEmissions\": 0.0009713787667226322,\n \"emissionsIntensityRevenue\": 0.1766993497855956,\n \"emissionsIntensityEnterpriseValue\": 0.019200832225828832,\n \"emissionsIntensityMarketCap\": 0.08410551021114267,\n },\n ],\n \"benchmark\": [\n {\n \"year\": \"2016\",\n \"portfolioEmissions\": 0.0016712755532904242,\n \"emissionsIntensityRevenue\": 0.27028638660867355,\n \"emissionsIntensityEnterpriseValue\": 0.0339529985038069,\n \"emissionsIntensityMarketCap\": 0.11170603861184672,\n },\n {\n \"year\": \"2017\",\n \"portfolioEmissions\": 0.0015352826311627388,\n \"emissionsIntensityRevenue\": 0.24110061666622948,\n 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'Style',\n 'factorExposure': -178363.23965200002,\n 'factorShock': -0.8293589360579001,\n 'name': 'Microsoft Corp',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'bbid': 'AAPL UW',\n 'estimatedPnl': 892.9364409691684,\n 'factor': 'Profitability',\n 'factorCategory': 'Style',\n 'factorExposure': 201297.377697,\n 'factorShock': 0.44359069709951626,\n 'name': 'Apple Inc',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27UAL9SUX',\n 'bbid': 'MSFT UW',\n 'estimatedPnl': -345.7748642739222,\n 'factor': 'Profitability',\n 'factorCategory': 'Style',\n 'factorExposure': -77949.0793055,\n 'factorShock': 0.44359069709951626,\n 'name': 'Microsoft Corp',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'bbid': 'AAPL UW',\n 'estimatedPnl': -2143.0994982650127,\n 'factor': 'Size',\n 'factorCategory': 'Style',\n 'factorExposure': 170372.65707780002,\n 'factorShock': -1.2578893438789152,\n 'name': 'Apple Inc',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27UAL9SUX',\n 'bbid': 'MSFT UW',\n 'estimatedPnl': 4108.920988831587,\n 'factor': 'Size',\n 'factorCategory': 'Style',\n 'factorExposure': -326652.02299599996,\n 'factorShock': -1.2578893438789152,\n 'name': 'Microsoft Corp',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'bbid': 'AAPL UW',\n 'estimatedPnl': -1293.5055312009458,\n 'factor': 'Value',\n 'factorCategory': 'Style',\n 'factorExposure': -77920.3687998,\n 'factorShock': 1.660035175814345,\n 'name': 'Apple Inc',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27UAL9SUX',\n 'bbid': 'MSFT UW',\n 'estimatedPnl': 1925.6785926035743,\n 'factor': 'Value',\n 'factorCategory': 'Style',\n 'factorExposure': 116002.276377,\n 'factorShock': 1.660035175814345,\n 'name': 'Microsoft Corp',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'bbid': 'AAPL UW',\n 'estimatedPnl': -261.8789000532033,\n 'factor': 'Volatility',\n 'factorCategory': 'Style',\n 'factorExposure': -74956.7850228,\n 'factorShock': 0.34937317545509217,\n 'name': 'Apple Inc',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27UAL9SUX',\n 'bbid': 'MSFT UW',\n 'estimatedPnl': 130.12211685969444,\n 'factor': 'Volatility',\n 'factorCategory': 'Style',\n 'factorExposure': 37244.449775,\n 'factorShock': 0.34937317545509217,\n 'name': 'Microsoft Corp',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'bbid': 'AAPL UW',\n 'estimatedPnl': 6403.244317025364,\n 'factor': 'United States',\n 'factorCategory': 'Country',\n 'factorExposure': 189690.0,\n 'factorShock': 3.375636204873933,\n 'name': 'Apple Inc',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n {\n 'assetId': 'MA4B66MW5E27UAL9SUX',\n 'bbid': 'MSFT UW',\n 'estimatedPnl': -12484.79050372624,\n 'factor': 'United States',\n 'factorCategory': 'Country',\n 'factorExposure': -369850.0,\n 'factorShock': 3.375636204873933,\n 'name': 'Microsoft Corp',\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n },\n ],\n 'summary': [\n {\n 'estimatedPerformance': 5.867062741998796,\n 'estimatedPnl': -10570.100235985032,\n 'scenarioId': 'MS0VH86TEJGWDK8V',\n 'scenarioName': 'US Presidential Election 2016',\n 'scenarioType': 'Factor Historical Simulation',\n 'stressedMarketValue': -190730.10023598504,\n }\n ],\n}\n\n\ndef test_get_reports(mocker):\n mock_portfolio = TargetPortfolio(id='MP', currency='USD', name='Example Port')\n mock_reports = (\n Report.from_dict(\n {\n 'id': 'PPAID',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP',\n 'type': 'Portfolio Performance Analytics',\n 'parameters': {'transactionCostModel': 'FIXED'},\n 'percentageComplete': 0,\n 'status': 'new',\n }\n ),\n Report.from_dict(\n {\n 'id': 'PFRID',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP',\n 'type': 'Portfolio Factor Risk',\n 'parameters': {'riskModel': 'AXUS4M', 'transactionCostModel': 'FIXED'},\n 'percentageComplete': 0,\n 'status': 'new',\n }\n ),\n )\n\n mocker.patch.object(GsPortfolioApi, 'get_portfolio', return_value=mock_portfolio)\n mocker.patch.object(GsPortfolioApi, 'get_position_dates', return_value=[dt.date(2020, 1, 1)])\n mocker.patch.object(GsPortfolioApi, 'get_positions_for_date', return_value=None)\n mocker.patch.object(GsPortfolioApi, 'get_reports', return_value=mock_reports)\n\n # run test\n pm = PortfolioManager('MP')\n reports = pm.get_reports()\n assert len(reports) == 2\n assert isinstance(reports[0], PerformanceReport)\n assert isinstance(reports[1], FactorRiskReport)\n\n\ndef test_get_schedule_dates(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(\n GsSession.current.sync, 'get', return_value={'startDate': '2019-01-01', 'endDate': '2020-01-02'}\n )\n\n # run test\n pm = PortfolioManager('MP')\n dates = pm.get_schedule_dates()\n assert dates[1] == dt.date(2020, 1, 2)\n\n\ndef test_set_entitlements(mocker):\n mock_portfolio = TargetPortfolio(id='MP', currency='USD', name='Example Port')\n entitlements = Entitlements(\n view=EntitlementBlock(\n users=[User(user_id='fakeId', name='Fake User', email='fake@gs.com', company='Goldman Sachs')]\n )\n )\n\n mocker.patch.object(GsSession, '_get', return_value=mock_portfolio)\n mocker.patch.object(\n GsPortfolioApi,\n 'get_position_dates',\n return_value=[dt.date(2020, 1, 2), dt.date(2020, 2, 1), dt.date(2020, 3, 1)],\n )\n mocker.patch.object(GsPortfolioApi, 'get_positions_for_date', return_value=None)\n mocker.patch.object(GsPortfolioApi, 'update_portfolio', return_value='')\n\n # run test\n pm = PortfolioManager('MP')\n pm.set_entitlements(entitlements)\n\n\ndef test_run_reports(mocker):\n mock_portfolio = TargetPortfolio(id='MP', currency='USD', name='Example Port')\n mock_reports = (\n Report.from_dict(\n {\n 'id': 'PPAID',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP',\n 'type': 'Portfolio Performance Analytics',\n 'parameters': {'transactionCostModel': 'FIXED'},\n 'percentageComplete': 0,\n 'status': 'new',\n }\n ),\n )\n mock_report_jobs = (\n {\n 'startDate': '2020-01-01',\n 'endDate': '2020-03-02',\n 'id': 'jobId1',\n 'createdTime': '2021-05-18T17:08:18.72Z',\n 'reportType': 'Portfolio Factor Risk',\n },\n {\n 'startDate': '2020-05-01',\n 'endDate': '2020-07-02',\n 'id': 'jobId1',\n 'createdTime': '2020-05-18T17:08:18.72Z',\n 'reportType': 'Portfolio Factor Risk',\n },\n )\n mock_report_job = {\n 'startDate': '2020-01-01',\n 'endDate': '2020-03-02',\n 'id': 'jobId1',\n 'createdTime': '2021-05-18T17:08:18.72Z',\n 'reportType': 'Portfolio Factor Risk',\n 'status': 'done',\n }\n mock_results = [\n {\n 'date': \"2019-08-27\",\n 'factor': \"United States\",\n 'factorCategory': \"Country\",\n 'pnl': -162.93571064768423,\n 'exposure': 19878.043518298073,\n 'sensitivity': 52.7507947211687,\n 'proportionOfRisk': 0.050898995661382604,\n }\n ]\n\n mocker.patch.object(GsPortfolioApi, 'get_portfolio', return_value=mock_portfolio)\n mocker.patch.object(GsPortfolioApi, 'schedule_reports', return_value='')\n mocker.patch.object(GsPortfolioApi, 'get_position_dates', return_value=[dt.date(2020, 1, 1)])\n mocker.patch.object(GsPortfolioApi, 'get_positions_for_date', return_value=None)\n mocker.patch.object(GsPortfolioApi, 'get_reports', return_value=mock_reports)\n mocker.patch.object(GsReportApi, 'get_report_jobs', return_value=mock_report_jobs)\n mocker.patch.object(GsReportApi, 'get_report_job', return_value=mock_report_job)\n mocker.patch.object(GsReportApi, 'get_factor_risk_report_results', return_value=mock_results)\n\n # run test\n pm = PortfolioManager('MP')\n pm.run_reports(is_async=False)\n\n\n# noinspection DuplicatedCode\ndef test_batched_schedule_reports(mocker):\n mocker.patch.object(\n GsPortfolioApi,\n 'get_position_dates',\n return_value=[dt.date(2022, 2, 1), dt.date(2022, 8, 1), dt.date(2022, 10, 1), dt.date(2022, 11, 1)],\n )\n\n mocker.patch.object(GsPortfolioApi, 'schedule_reports')\n schedule_spy = mocker.spy(GsPortfolioApi, 'schedule_reports')\n\n pid = 'MP'\n pm = PortfolioManager(pid)\n\n pm.schedule_reports(\n start_date=dt.date(2022, 2, 1), end_date=dt.date(2022, 11, 10), backcast=False, months_per_batch=2\n )\n assert schedule_spy.call_count == 3, 'For the given positions, batch period of 2 months should result in 3 batches'\n batch_boundaries = [dt.date(2022, 2, 1), dt.date(2022, 8, 1), dt.date(2022, 10, 1), dt.date(2022, 11, 10)]\n for i in range(len(batch_boundaries) - 1):\n schedule_spy.assert_any_call(pid, batch_boundaries[i], batch_boundaries[i + 1], backcast=False)\n\n schedule_spy.reset_mock()\n pm.schedule_reports(\n start_date=dt.date(2022, 2, 1), end_date=dt.date(2022, 11, 10), backcast=False, months_per_batch=4\n )\n assert schedule_spy.call_count == 2, 'For the given positions, batch period of 4 months should result in 2 batches'\n batch_boundaries = [dt.date(2022, 2, 1), dt.date(2022, 8, 1), dt.date(2022, 11, 10)]\n for i in range(len(batch_boundaries) - 1):\n schedule_spy.assert_any_call(pid, batch_boundaries[i], batch_boundaries[i + 1], backcast=False)\n\n\n# noinspection DuplicatedCode\ndef test_batched_schedule_reports_wo_dates(mocker):\n mocker.patch.object(\n GsPortfolioApi,\n 'get_position_dates',\n return_value=[dt.date(2022, 2, 1), dt.date(2022, 8, 1), dt.date(2022, 10, 1), dt.date(2022, 11, 1)],\n )\n mocker.patch.object(GsPortfolioApi, 'get_schedule_dates', return_value=[dt.date(2022, 2, 1), dt.date(2022, 11, 10)])\n\n mocker.patch.object(GsPortfolioApi, 'schedule_reports')\n schedule_spy = mocker.spy(GsPortfolioApi, 'schedule_reports')\n\n pid = 'MP'\n pm = PortfolioManager(pid)\n\n pm.schedule_reports(backcast=False, months_per_batch=2)\n assert schedule_spy.call_count == 3, 'For the given positions, batch period of 2 months should result in 3 batches'\n batch_boundaries = [dt.date(2022, 2, 1), dt.date(2022, 8, 1), dt.date(2022, 10, 1), dt.date(2022, 11, 10)]\n for i in range(len(batch_boundaries) - 1):\n schedule_spy.assert_any_call(pid, batch_boundaries[i], batch_boundaries[i + 1], backcast=False)\n\n\ndef test_batched_schedule_validations(mocker):\n pm = PortfolioManager('PM')\n\n # start date should be before end date\n with pytest.raises(Exception):\n pm.schedule_reports(backcast=False, start_date=dt.date(2022, 2, 1), end_date=dt.date(2022, 1, 1))\n\n mocker.patch.object(GsPortfolioApi, 'get_schedule_dates', return_value=[dt.date(2022, 2, 1), dt.date(2022, 11, 5)])\n\n # start date should be before maximum possible end date\n with pytest.raises(Exception):\n pm.schedule_reports(backcast=False, start_date=dt.date(2022, 12, 1))\n\n # end date should be after first possible start date\n with pytest.raises(Exception):\n pm.schedule_reports(backcast=False, end_date=dt.date(2022, 1, 1))\n\n mocker.patch.object(\n GsPortfolioApi,\n 'get_position_dates',\n return_value=[dt.date(2022, 2, 1), dt.date(2022, 8, 1), dt.date(2022, 10, 1), dt.date(2022, 11, 1)],\n )\n # start date should be on a position date\n with pytest.raises(Exception):\n pm.schedule_reports(backcast=False, start_date=dt.date(2022, 3, 1))\n\n\ndef test_esg_summary(mocker):\n mocker.patch.object(GsEsgApi, 'get_esg', return_value=esg_data)\n\n # run test\n pm = PortfolioManager('MP')\n summary = pm.get_esg_summary()\n assert all(summary.columns.values == ['gross', 'long', 'short'])\n\n\ndef test_esg_quintiles(mocker):\n mocker.patch.object(GsEsgApi, 'get_esg', return_value=esg_data)\n\n # run test\n pm = PortfolioManager('MP')\n quintiles = pm.get_esg_quintiles(measure=ESGMeasure.G_PERCENTILE)\n assert all(quintiles.columns.values == ['description', 'gross', 'long', 'short'])\n\n\ndef test_esg_by_sector(mocker):\n mocker.patch.object(GsEsgApi, 'get_esg', return_value=esg_data)\n\n # run test\n pm = PortfolioManager('MP')\n breakdown = pm.get_esg_by_region(measure=ESGMeasure.G_PERCENTILE)\n assert all(breakdown.columns.values == ['name', 'gross', 'long', 'short'])\n\n\ndef test_esg_by_region(mocker):\n mocker.patch.object(GsEsgApi, 'get_esg', return_value=esg_data)\n\n # run test\n pm = PortfolioManager('MP')\n breakdown = pm.get_esg_by_region(measure=ESGMeasure.G_PERCENTILE)\n assert all(breakdown.columns.values == ['name', 'gross', 'long', 'short'])\n\n\ndef test_esg_top_ten(mocker):\n mocker.patch.object(GsEsgApi, 'get_esg', return_value=esg_data)\n\n # run test\n pm = PortfolioManager('MP')\n ranked = pm.get_esg_top_ten(measure=ESGMeasure.G_PERCENTILE)\n assert ranked.size == 6\n\n\ndef test_esg_bottom_ten(mocker):\n mocker.patch.object(GsEsgApi, 'get_esg', return_value=esg_data)\n\n # run test\n pm = PortfolioManager('MP')\n ranked = pm.get_esg_bottom_ten(measure=ESGMeasure.G_PERCENTILE)\n assert ranked.size == 6\n\n\ndef test_carbon_coverage(mocker):\n mocker.patch.object(GsCarbonApi, 'get_carbon_analytics', return_value=carbon_data)\n\n pm = PortfolioManager('MP')\n coverage = pm.get_carbon_coverage(coverage_category=CarbonCoverageCategory.NUMBER_OF_COMPANIES)\n assert coverage.to_dict() == carbon_data.get(CarbonCard.COVERAGE.value).get(\n CarbonCoverageCategory.NUMBER_OF_COMPANIES.value\n ).get(CarbonEntityType.PORTFOLIO.value)\n\n\ndef test_carbon_sbti_netzero_coverage(mocker):\n mocker.patch.object(GsCarbonApi, 'get_carbon_analytics', return_value=carbon_data)\n\n pm = PortfolioManager('MP')\n coverage = pm.get_carbon_sbti_netzero_coverage(\n target_coverage_category=CarbonTargetCoverageCategory.CAPITAL_ALLOCATED\n )\n assert coverage.to_dict().get('scienceBasedTarget') == carbon_data.get(\n CarbonCard.SBTI_AND_NET_ZERO_TARGETS.value\n ).get(CarbonTargetCoverageCategory.CAPITAL_ALLOCATED.value).get('scienceBasedTarget').get(\n CarbonEntityType.PORTFOLIO.value\n )\n\n\ndef test_carbon_emissions(mocker):\n mocker.patch.object(GsCarbonApi, 'get_carbon_analytics', return_value=carbon_data)\n\n pm = PortfolioManager('MP')\n emissions = pm.get_carbon_emissions(scope=CarbonScope.SCOPE1)\n assert emissions.to_dict('records') == carbon_data.get(CarbonCard.EMISSIONS.value).get(\n CarbonScope.SCOPE1.value\n ).get(CarbonEntityType.PORTFOLIO.value)\n\n\ndef test_carbon_emissions_allocation(mocker):\n mocker.patch.object(GsCarbonApi, 'get_carbon_analytics', return_value=carbon_data)\n\n pm = PortfolioManager('MP')\n emissions = pm.get_carbon_emissions_allocation(classification=CarbonEmissionsAllocationCategory.GICS_INDUSTRY)\n assert emissions.rename(columns={CarbonEmissionsAllocationCategory.GICS_INDUSTRY.value: 'name'}).to_dict(\n 'records'\n ) == carbon_data.get(CarbonCard.ALLOCATIONS.value).get(CarbonScope.TOTAL_GHG.value).get(\n CarbonEntityType.PORTFOLIO.value\n ).get(CarbonEmissionsAllocationCategory.GICS_INDUSTRY.value)\n\n\ndef test_carbon_attribution_table(mocker):\n mocker.patch.object(GsCarbonApi, 'get_carbon_analytics', return_value=carbon_data)\n\n pm = PortfolioManager('MP')\n attribution = pm.get_carbon_attribution_table(\n benchmark_id='MA', scope=CarbonScope.SCOPE2, intensity_metric=CarbonEmissionsIntensityType.EI_REVENUE\n )\n attribution = attribution.to_dict('records')\n for entry in attribution:\n entry[CarbonEmissionsIntensityType.EI_REVENUE.value] = {\n 'emissionsIntensityPortfolio': entry.pop('emissionsIntensityPortfolio'),\n 'emissionsIntensityBenchmark': entry.pop('emissionsIntensityBenchmark'),\n 'emissionsIntensityComparison': entry.pop('emissionsIntensityComparison'),\n 'sectorAllocation': entry.pop('sectorAllocation'),\n 'securityAllocation': entry.pop('securityAllocation'),\n }\n actual_table = []\n for entry in carbon_data.get(CarbonCard.ATTRIBUTION.value).get(CarbonScope.SCOPE2.value):\n new_entry = copy.deepcopy(entry)\n new_entry.pop(CarbonEmissionsIntensityType.EI_ENTERPRISE_VALUE.value)\n new_entry.pop(CarbonEmissionsIntensityType.EI_MARKETCAP.value)\n actual_table.append(new_entry)\n assert attribution == actual_table\n\n\ndef test_get_macro_exposure(mocker):\n portfolio_constituents = {\n \"date\": [\"2022-05-02\", \"2022-05-02\"],\n \"assetId\": [\"mq1\", \"mq2\"],\n \"direction\": [\"LONG\", \"LONG\"],\n \"netExposure\": [1000, 1000],\n }\n assets_data = [{\"id\": \"mq1\", \"name\": \"asset1\", \"gsid\": \"1\"}, {\"id\": \"mq2\", \"name\": \"asset2\", \"gsid\": \"2\"}]\n factor_sens = {\n \"factor1\": {(\"1\", \"2022-05-02\"): 1, (\"2\", \"2022-05-02\"): 1},\n \"factor2\": {(\"1\", \"2022-05-02\"): 5, (\"2\", \"2022-05-02\"): 5},\n }\n factor_data = {\n \"identifier\": [\"15\", \"17\"],\n \"name\": [\"factor1\", \"factor2\"],\n \"type\": [\"Factor\", \"Factor\"],\n \"factorCategory\": [\"category1\", \"category2\"],\n \"factorCategoryId\": [\"c1\", \"c2\"],\n }\n\n result = {\n (\"Asset Information\", \"Asset Name\"): {\"1\": \"asset1\", \"2\": \"asset2\", \"Total Factor Exposure\": np.nan},\n (\"Asset Information\", \"Notional\"): {\"1\": 1000, \"2\": 1000, \"Total Factor Exposure\": 2000},\n (\"category2\", \"factor2\"): {\"1\": 50.0, \"2\": 50.0, \"Total Factor Exposure\": 100.0},\n (\"category1\", \"factor1\"): {\"1\": 10.0, \"2\": 10.0, \"Total Factor Exposure\": 20.0},\n }\n\n pm = PortfolioManager(\"portfolioId\")\n macro_model = MacroRiskModel(\n id_=\"fake_macro_model\",\n name=\"fake_model\",\n coverage=CoverageType.Region,\n vendor=\"fake_vendor\",\n term=Term.Long,\n universe_identifier=UniverseIdentifier.sedol,\n version=0.1,\n )\n fake_ppa = PerformanceReport(\n report_id='id',\n position_source_type=PositionSourceType.Portfolio,\n position_source_id='PORTFOLIOID',\n report_type=ReportType.Portfolio_Performance_Analytics,\n parameters=None,\n status=ReportStatus.done,\n )\n\n mocker.patch.object(PortfolioManager, \"get_performance_report\", return_value=fake_ppa)\n mocker.patch.object(\n fake_ppa, \"get_portfolio_constituents\", return_value=pd.DataFrame.from_dict(portfolio_constituents)\n )\n mocker.patch.object(GsAssetApi, \"get_many_assets_data_scroll\", return_value=assets_data)\n mocker.patch.object(macro_model, \"get_factor_data\", return_value=pd.DataFrame.from_dict(factor_data))\n mocker.patch.object(macro_model, \"get_universe_sensitivity\", return_value=pd.DataFrame.from_dict(factor_sens))\n\n exposure_df = pm.get_macro_exposure(model=macro_model, date=dt.date(2022, 5, 2), factor_type=FactorType.Factor)\n\n assert exposure_df.equals(pd.DataFrame.from_dict(result))\n\n\ndef test_get_factor_scenario_analytics(mocker):\n risk_report = FactorRiskReport(report_id='id', risk_model='RISK_MODEL')\n\n mocker.patch.object(PortfolioManager, \"get_factor_risk_report\", return_value=risk_report)\n mocker.patch.object(GsFactorScenarioApi, \"calculate_scenario\", return_value=scenario_data)\n\n pm = PortfolioManager(\"portfolioId\")\n\n factor_scenario = FactorScenario(\n name='US Presidential Election 2016',\n description='Presidential Elections in 2016 when Donald Trump won',\n id_=\"MS0VH86TEJGWDK8V\",\n type=FactorScenarioType.Factor_Historical_Simulation,\n parameters=HistoricalSimulationParameters(start_date=dt.date(2016, 1, 1), end_date=dt.date(2020, 1, 1)),\n )\n\n scenario_analytics_data = pm.get_factor_scenario_analytics(\n scenarios=[factor_scenario],\n date=dt.date(2024, 3, 5),\n measures=[\n ScenarioCalculationMeasure.SUMMARY,\n ScenarioCalculationMeasure.ESTIMATED_FACTOR_PNL,\n ScenarioCalculationMeasure.ESTIMATED_PNL_BY_SECTOR,\n ScenarioCalculationMeasure.ESTIMATED_PNL_BY_REGION,\n ScenarioCalculationMeasure.ESTIMATED_PNL_BY_DIRECTION,\n ScenarioCalculationMeasure.ESTIMATED_PNL_BY_ASSET,\n ],\n risk_model=\"RISK_MODEL\",\n )\n\n assert (\n len(\n set(scenario_analytics_data.keys())\n - {\n \"summary\",\n \"factorPnl\",\n \"bySectorAggregations\",\n \"byRegionAggregations\",\n \"byDirectionAggregations\",\n \"byAsset\",\n }\n )\n == 0\n )\n for key, value in scenario_analytics_data.items():\n result_df = pd.DataFrame(scenario_results[key])\n result_df.columns = result_df.columns.map(\n lambda x: {\n \"factorCategory\": \"Factor Category\",\n \"factor\": \"Factor\",\n \"sector\": \"Sector\",\n \"country\": \"Country\",\n \"industry\": \"Industry\",\n \"direction\": \"Direction\",\n \"scenarioId\": \"Scenario ID\",\n \"scenarioName\": \"Scenario Name\",\n \"scenarioType\": \"Scenario Type\",\n \"assetId\": \"Asset ID\",\n \"name\": \"Asset Name\",\n \"bbid\": \"BBID\",\n \"factorExposure\": \"Factor Exposure\",\n \"factorShock\": \"Factor Shock (%)\",\n \"exposure\": \"Exposure\",\n \"estimatedPnl\": \"Estimated Pnl\",\n \"estimatedPerformance\": \"Estimated Performance (%)\",\n \"stressedMarketValue\": \"Stressed Market Value\",\n }.get(x, x)\n )\n result_df = result_df.reindex(columns=value.columns.tolist())\n pd.testing.assert_frame_equal(value, result_df)\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/markets/test_position_set.py", "content": "\"\"\"\nCopyright 2024 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pandas as pd\nimport pytest\n\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.position_set import PositionSet, Position, GsPriceApi, PositionTag, PositionSetWeightingStrategy\nimport gs_quant.markets.position_set as position_set_module\nfrom copy import deepcopy\n\n\ndef test_position_resolve_many(mocker):\n unresolved_position_sets = [\n PositionSet(\n date=dt.date(2024, 4, 30),\n reference_notional=1000,\n positions=[\n Position(identifier='GS UN', weight=0.5, tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")]),\n Position(identifier='AAPL UW', weight=0.5, tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")]),\n ],\n ),\n PositionSet(\n date=dt.date(2024, 5, 1),\n reference_notional=1000,\n positions=[\n Position(identifier='GS UN', weight=0.5, tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")]),\n Position(identifier='AAPL UW', weight=0.5, tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")]),\n ],\n ),\n ]\n\n resolved_positions = [\n {\n \"assetId\": \"MA4B66MW5E27UAHKG34\",\n \"name\": \"GS\",\n \"bbid\": \"GS UN\",\n \"tradingRestriction\": True,\n \"asOfDate\": dt.datetime.strptime(\"2952-12-31\", '%Y-%m-%d'),\n \"startDate\": dt.datetime.strptime(\"1952-01-01\", '%Y-%m-%d'),\n \"endDate\": dt.datetime.strptime(\"2952-12-31\", '%Y-%m-%d'),\n },\n {\n \"assetId\": \"MA4B66MW5E27U9VBB94\",\n \"name\": \"Apple\",\n \"bbid\": \"AAPL UW\",\n \"tradingRestriction\": False,\n \"asOfDate\": dt.datetime.strptime(\"2952-12-31\", '%Y-%m-%d'),\n \"startDate\": dt.datetime.strptime(\"1952-01-01\", '%Y-%m-%d'),\n \"endDate\": dt.datetime.strptime(\"2952-12-31\", '%Y-%m-%d'),\n },\n ]\n\n xref_results = [\n {\n \"assetId\": \"MA4B66MW5E27UAHKG34\",\n \"bbid\": \"GS UN\",\n \"delisted\": 'no',\n \"startDate\": \"1952-01-01\",\n \"endDate\": \"2952-12-31\",\n },\n {\n \"assetId\": \"MA4B66MW5E27U9VBB94\",\n \"bbid\": \"AAPL UW\",\n \"delisted\": 'no',\n \"startDate\": \"1952-01-01\",\n \"endDate\": \"2952-12-31\",\n },\n ]\n\n mocker.patch.object(\n position_set_module, \"_get_asset_temporal_xrefs\", return_value=(pd.DataFrame(xref_results), \"bbid\")\n )\n mocker.patch.object(position_set_module, \"_resolve_many_assets\", return_value=pd.DataFrame(resolved_positions))\n\n unresolved_position_sets_copy = deepcopy(unresolved_position_sets)\n PositionSet.resolve_many(unresolved_position_sets_copy)\n\n resolved_position_sets = [\n PositionSet(\n date=dt.date(2024, 4, 30),\n reference_notional=1000,\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.5,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.5,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n ],\n ),\n PositionSet(\n date=dt.date(2024, 5, 1),\n reference_notional=1000,\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.5,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.5,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n ],\n ),\n ]\n\n for idx, pos_set in enumerate(unresolved_position_sets_copy):\n assert pos_set == resolved_position_sets[idx]\n\n # There are unresolved positions\n mocker.patch.object(\n position_set_module, \"_get_asset_temporal_xrefs\", return_value=(pd.DataFrame([xref_results[1]]), \"bbid\")\n )\n mocker.patch.object(position_set_module, \"_resolve_many_assets\", return_value=pd.DataFrame([resolved_positions[1]]))\n\n unresolved_position_sets_copy = deepcopy(unresolved_position_sets)\n\n PositionSet.resolve_many(unresolved_position_sets_copy)\n\n expected_position_set = [\n PositionSet(\n date=dt.date(2024, 4, 30),\n reference_notional=1000,\n positions=[\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.5,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n )\n ],\n unresolved_positions=[\n Position(identifier='GS UN', name=\"GS\", weight=0.5, tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")])\n ],\n ),\n PositionSet(\n date=dt.date(2024, 5, 1),\n reference_notional=1000,\n positions=[\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.5,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n )\n ],\n unresolved_positions=[\n Position(identifier='GS UN', name=\"GS\", weight=0.5, tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")])\n ],\n ),\n ]\n\n unresolved_position_sets_copy.sort(key=lambda x: x.date)\n expected_position_set.sort(key=lambda x: x.date)\n\n for idx, position_set in enumerate(unresolved_position_sets_copy):\n assert position_set == expected_position_set[idx]\n assert len(position_set.unresolved_positions) == 1\n assert len(expected_position_set[idx].unresolved_positions) == 1\n\n actual_unresolved_position = position_set.unresolved_positions[0]\n expected_unresolved_position = expected_position_set[idx].unresolved_positions[0]\n assert actual_unresolved_position.__eq__(expected_unresolved_position)\n\n\n@pytest.fixture()\ndef position_sets_with_tags_and_notional():\n return [\n PositionSet(\n date=dt.date(2024, 4, 30),\n reference_notional=1000,\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.5,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.5,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n ],\n ),\n PositionSet(\n date=dt.date(2024, 5, 1),\n reference_notional=1000,\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.5,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.5,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n ],\n ),\n ]\n\n\n@pytest.fixture()\ndef expected_position_pricing_result():\n return [\n {\n 'date': '2024-04-30',\n 'positions': [\n {\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'weight': 0.42671,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': 1,\n 'notional': 1000,\n 'referenceWeight': 0.5,\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'weight': 0.51099,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': 1,\n 'notional': 1000,\n 'referenceWeight': 0.5,\n },\n ],\n 'targetNotional': 1000,\n },\n {\n 'date': '2024-05-01',\n 'positions': [\n {\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'weight': 0.42671,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': 1,\n 'notional': 1000,\n 'referenceWeight': 0.5,\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'weight': 0.51099,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': 1,\n 'notional': 1000,\n 'referenceWeight': 0.5,\n },\n ],\n 'targetNotional': 1000,\n },\n ]\n\n\ndef test_position_price_many(mocker, position_sets_with_tags_and_notional, expected_position_pricing_result):\n # All positions on each holding date are priced\n\n mocker.patch.object(GsPriceApi, \"price_many_positions\", return_value=expected_position_pricing_result)\n new_position_set_list = deepcopy(position_sets_with_tags_and_notional)\n PositionSet.price_many(new_position_set_list)\n\n expected_positions = {\n dt.date(2024, 4, 30): [\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.42671,\n quantity=1,\n notional=1000,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.51099,\n quantity=1,\n notional=1000,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n ],\n dt.date(2024, 5, 1): [\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.42671,\n quantity=1,\n notional=1000,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.51099,\n quantity=1,\n notional=1000,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n ],\n }\n\n [pos_set.positions.sort(key=lambda position: position.asset_id) for pos_set in new_position_set_list]\n [positions.sort(key=lambda position: position.asset_id) for positions in expected_positions.values()]\n\n for pos_set in new_position_set_list:\n assert pos_set.positions == expected_positions.get(pos_set.date)\n\n # weighting strategy passed is 'quantity' but positions do not have quantities\n with pytest.raises(\n MqValueError,\n match=\"Unable to price positions without position weights and daily reference notional or position quantities\",\n ):\n PositionSet.price_many(\n position_sets_with_tags_and_notional, weighting_strategy=PositionSetWeightingStrategy.Quantity\n )\n\n # Invalid weighting strategies\n with pytest.raises(MqValueError, match=\"Can only specify a weighting strategy of weight, notional, or quantity\"):\n PositionSet.price_many(\n position_sets_with_tags_and_notional, weighting_strategy=PositionSetWeightingStrategy.Market_Capitalization\n )\n\n # There are unpriced results\n mocker.patch.object(GsPriceApi, \"price_many_positions\", return_value=[expected_position_pricing_result[1]])\n new_position_set_list = deepcopy(position_sets_with_tags_and_notional)\n PositionSet.price_many(new_position_set_list)\n\n first_position_set = new_position_set_list[0]\n positions = first_position_set.positions\n unpriced_positions = first_position_set.unpriced_positions\n\n assert not positions\n for position in unpriced_positions:\n if position.identifier == \"GS UN\":\n assert position.asset_id == \"MA4B66MW5E27UAHKG34\"\n assert position.weight == 0.5\n assert not position.quantity\n assert not position.notional\n assert len(position.tags) == 1\n tag = position.tags[0]\n assert tag.name == \"tag1\"\n assert tag.value == \"tagvalue1\"\n\n else:\n assert position.asset_id == \"MA4B66MW5E27U9VBB94\"\n assert position.weight == 0.5\n assert not position.quantity\n assert not position.notional\n assert len(position.tags) == 1\n tag = position.tags[0]\n assert tag.name == \"tag2\"\n assert tag.value == \"tagvalue2\"\n\n second_position_set = new_position_set_list[1]\n positions = second_position_set.positions\n unpriced_positions = second_position_set.unpriced_positions\n\n assert not unpriced_positions\n for position in positions:\n if position.identifier == \"GS UN\":\n assert position.asset_id == \"MA4B66MW5E27UAHKG34\"\n assert position.weight == 0.42671\n assert position.quantity == 1\n assert position.notional == 1000\n assert len(position.tags) == 1\n tag = position.tags[0]\n assert tag.name == \"tag1\"\n assert tag.value == \"tagvalue1\"\n\n else:\n assert position.asset_id == \"MA4B66MW5E27U9VBB94\"\n assert position.weight == 0.51099\n assert position.quantity == 1\n assert position.notional == 1000\n assert len(position.tags) == 1\n tag = position.tags[0]\n assert tag.name == \"tag2\"\n assert tag.value == \"tagvalue2\"\n\n # Positions do not have tags\n new_position_set_list = deepcopy(position_sets_with_tags_and_notional)\n position_sets_no_tags = [deepcopy(pos_set) for pos_set in new_position_set_list]\n for position_set_no_tag in position_sets_no_tags:\n [setattr(position, \"tags\", None) for position in position_set_no_tag.positions]\n\n mocker.patch.object(GsPriceApi, \"price_many_positions\", return_value=expected_position_pricing_result)\n PositionSet.price_many(position_sets_no_tags)\n\n for position_set in position_sets_no_tags:\n priced_positions = position_set.positions\n for position in priced_positions:\n if position.identifier == \"GS UN\":\n assert position.asset_id == \"MA4B66MW5E27UAHKG34\"\n assert position.weight == 0.42671\n assert position.quantity == 1\n assert position.notional == 1000\n assert not position.tags\n\n else:\n assert position.asset_id == \"MA4B66MW5E27U9VBB94\"\n assert position.weight == 0.51099\n assert position.quantity == 1\n assert position.notional == 1000\n assert not position.tags\n\n # Positions with different tags and common underlying asset ID\n different_tags_common_asset_id_positions = [\n PositionSet(\n date=dt.date(2024, 4, 30),\n reference_notional=1000,\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.5,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=-0.8,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n Position(identifier='AAPL UW', asset_id=\"MA4B66MW5E27U9VBB94\", name=\"Apple\", weight=0.5, tags=None),\n ],\n ),\n PositionSet(\n date=dt.date(2024, 5, 1),\n reference_notional=1000,\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.5,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=-0.8,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.5,\n tags=[PositionTag(name=\"tag3\", value=\"tagvalue3\")],\n ),\n ],\n ),\n ]\n\n same_assets_different_weights_pricing_results = [\n {\n 'date': '2024-04-30',\n 'positions': [\n {\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'weight': 0.38,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': 1,\n 'notional': 1000,\n 'referenceWeight': 0.5,\n },\n {\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'weight': -0.68,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': -1.5,\n 'notional': -1000,\n 'referenceWeight': -0.8,\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'weight': 0.41,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': 1,\n 'notional': 1000,\n 'referenceWeight': 0.5,\n },\n ],\n 'targetNotional': 1000,\n },\n {\n 'date': '2024-05-01',\n 'positions': [\n {\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'weight': 0.42671,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': 1,\n 'notional': 1000,\n 'referenceWeight': 0.5,\n },\n {\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'weight': -0.72,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': -1.5,\n 'notional': -1000,\n 'referenceWeight': -0.8,\n },\n {\n 'assetId': 'MA4B66MW5E27U9VBB94',\n 'weight': 0.51099,\n 'closePrice': 1000,\n 'fxClosePrice': 1,\n 'quantity': 1,\n 'notional': 1000,\n 'referenceWeight': 0.5,\n },\n ],\n 'targetNotional': 1000,\n },\n ]\n mocker.patch.object(GsPriceApi, \"price_many_positions\", return_value=same_assets_different_weights_pricing_results)\n PositionSet.price_many(different_tags_common_asset_id_positions)\n\n for position_set in different_tags_common_asset_id_positions:\n if position_set.date == dt.date(2024, 4, 30):\n for position in position_set.positions:\n if position.identifier == \"GS UN\":\n assert position.asset_id == \"MA4B66MW5E27UAHKG34\"\n assert position.name == \"GS\"\n assert position.weight in [0.38, -0.68]\n assert position.quantity in [1, -1.5]\n assert position.notional in [-1000, 1000]\n\n if position.weight == 0.38:\n assert position.tags[0] == PositionTag(name=\"tag1\", value=\"tagvalue1\")\n else:\n assert position.tags[0] == PositionTag(name=\"tag2\", value=\"tagvalue2\")\n else:\n assert position.asset_id == \"MA4B66MW5E27U9VBB94\"\n assert position.name == \"Apple\"\n assert position.weight == 0.41\n assert position.notional == 1000\n assert not position.tags\n\n else:\n for position in position_set.positions:\n if position.identifier == \"GS UN\":\n assert position.asset_id == \"MA4B66MW5E27UAHKG34\"\n assert position.name == \"GS\"\n assert position.weight in [0.42671, -0.72]\n assert position.quantity in [1, -1.5]\n assert position.notional in [-1000, 1000]\n\n if position.weight == 0.42671:\n assert position.tags[0] == PositionTag(name=\"tag1\", value=\"tagvalue1\")\n else:\n assert position.tags[0] == PositionTag(name=\"tag2\", value=\"tagvalue2\")\n else:\n assert position.asset_id == \"MA4B66MW5E27U9VBB94\"\n assert position.name == \"Apple\"\n assert position.weight == 0.51099\n assert position.notional == 1000\n assert position.tags[0] == PositionTag(name=\"tag3\", value=\"tagvalue3\")\n\n # There are empty positions in some position sets\n different_tags_common_asset_id_positions = [\n PositionSet(date=dt.date(2024, 4, 30), reference_notional=1000, positions=[]),\n PositionSet(\n date=dt.date(2024, 5, 1),\n reference_notional=1000,\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=0.5,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n weight=-0.8,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n weight=0.5,\n tags=[PositionTag(name=\"tag3\", value=\"tagvalue3\")],\n ),\n ],\n ),\n ]\n\n mocker.patch.object(\n GsPriceApi, \"price_many_positions\", return_value=[same_assets_different_weights_pricing_results[1]]\n )\n\n PositionSet.price_many(different_tags_common_asset_id_positions)\n\n for position_set in different_tags_common_asset_id_positions:\n if position_set.date == dt.date(2024, 4, 30):\n assert not position_set.positions\n assert not position_set.unpriced_positions\n\n else:\n for position in position_set.positions:\n if position.identifier == \"GS UN\":\n assert position.asset_id == \"MA4B66MW5E27UAHKG34\"\n assert position.name == \"GS\"\n assert position.weight in [0.42671, -0.72]\n assert position.quantity in [1, -1.5]\n assert position.notional in [-1000, 1000]\n\n if position.weight == 0.42671:\n assert position.tags[0] == PositionTag(name=\"tag1\", value=\"tagvalue1\")\n else:\n assert position.tags[0] == PositionTag(name=\"tag2\", value=\"tagvalue2\")\n else:\n assert position.asset_id == \"MA4B66MW5E27U9VBB94\"\n assert position.name == \"Apple\"\n assert position.weight == 0.51099\n assert position.notional == 1000\n assert position.tags[0] == PositionTag(name=\"tag3\", value=\"tagvalue3\")\n\n # No positions have tags\n position_set_list_no_tags = deepcopy(position_sets_with_tags_and_notional)\n\n for pos_set in position_set_list_no_tags:\n [setattr(position, 'tags', None) for position in pos_set.positions]\n mocker.patch.object(GsPriceApi, \"price_many_positions\", return_value=expected_position_pricing_result)\n PositionSet.price_many(position_set_list_no_tags)\n for position_set in position_set_list_no_tags:\n priced_positions = position_set.positions\n for position in priced_positions:\n if position.identifier == \"GS UN\":\n assert position.asset_id == \"MA4B66MW5E27UAHKG34\"\n assert position.name == \"GS\"\n assert position.weight == 0.42671\n assert position.quantity == 1\n assert position.notional == 1000\n assert not position.tags\n else:\n assert position.asset_id == \"MA4B66MW5E27U9VBB94\"\n assert position.name == \"Apple\"\n assert position.weight == 0.51099\n assert position.quantity == 1\n assert position.notional == 1000\n assert not position.tags\n\n # Positions with notional quantities\n positions_with_notional_quantities = [\n PositionSet(\n date=dt.date(2024, 4, 30),\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n notional=15000,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n notional=20000,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n ],\n ),\n PositionSet(\n date=dt.date(2024, 5, 1),\n positions=[\n Position(\n identifier='GS UN',\n asset_id=\"MA4B66MW5E27UAHKG34\",\n name=\"GS\",\n notional=10000,\n tags=[PositionTag(name=\"tag1\", value=\"tagvalue1\")],\n ),\n Position(\n identifier='AAPL UW',\n asset_id=\"MA4B66MW5E27U9VBB94\",\n name=\"Apple\",\n notional=25000,\n tags=[PositionTag(name=\"tag2\", value=\"tagvalue2\")],\n ),\n ],\n ),\n ]\n\n expected_position_notionals_result = [\n {\n \"date\": \"2024-04-30\",\n \"positions\": [\n {\n \"assetId\": \"MA4B66MW5E27UAHKG34\",\n \"notional\": 15000.0,\n \"closePrice\": 1000,\n \"fxClosePrice\": 1,\n \"quantity\": 1,\n \"referenceWeight\": 0.7826,\n \"weight\": 0.7826,\n },\n {\n \"assetId\": \"MA4B66MW5E27U9VBB94\",\n \"notional\": 20000.0,\n \"closePrice\": 1000,\n \"fxClosePrice\": 1,\n \"quantity\": 1,\n \"referenceWeight\": 0.26753,\n \"weight\": 0.26753,\n },\n ],\n \"targetNotional\": 35000.0,\n },\n {\n \"date\": \"2024-05-01\",\n \"positions\": [\n {\n \"assetId\": \"MA4B66MW5E27UAHKG34\",\n \"notional\": 10000.0,\n \"closePrice\": 1000,\n \"fxClosePrice\": 1,\n \"quantity\": 1,\n \"referenceWeight\": 0.745637,\n \"weight\": 0.745637,\n },\n {\n \"assetId\": \"MA4B66MW5E27U9VBB94\",\n \"notional\": 25000.0,\n \"closePrice\": 1000,\n \"fxClosePrice\": 1,\n \"quantity\": 1,\n \"referenceWeight\": 0.276364,\n \"weight\": 0.276364,\n },\n ],\n \"targetNotional\": 35000.0,\n },\n ]\n\n mocker.patch.object(GsPriceApi, \"price_many_positions\", return_value=expected_position_notionals_result)\n PositionSet.price_many(positions_with_notional_quantities)\n for position_set in positions_with_notional_quantities:\n priced_positions = position_set.positions\n for position in priced_positions:\n if position.identifier == \"GS UN\":\n assert position.asset_id == \"MA4B66MW5E27UAHKG34\"\n assert position.weight in [0.7826, 0.745637]\n assert position.quantity == 1\n assert round(position.notional, 2) in [10000.0, 15000.0]\n assert position.tags[0] == PositionTag(name=\"tag1\", value=\"tagvalue1\")\n\n else:\n assert position.asset_id == \"MA4B66MW5E27U9VBB94\"\n assert position.weight in [0.26753, 0.276364]\n assert position.quantity == 1\n assert round(position.notional, 2) in [20000.0, 25000.0]\n assert position.tags[0] == PositionTag(name=\"tag2\", value=\"tagvalue2\")\n" }, { "path": "gs_quant/test/markets/test_pricing_context.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom time import sleep\nfrom unittest.mock import patch, ANY\n\nimport pytest\nfrom freezegun import freeze_time\n\nfrom gs_quant import risk\nfrom gs_quant.api.gs.risk import GsRiskApi\nfrom gs_quant.api.risk import RiskApi\nfrom gs_quant.base import RiskKey\nfrom gs_quant.common import PayReceive, Currency, PricingLocation\nfrom gs_quant.datetime import business_day_offset, today\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.instrument import EqOption, IRSwap\nfrom gs_quant.markets import PricingContext, CloseMarket, OverlayMarket, MarketDataCoordinate\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk import RollFwd\nfrom gs_quant.test.utils.mock_calc import MockCalc\n\nWEEKEND_DATE = dt.date(2022, 3, 19)\n\n\nclass TestProvider:\n pass\n\n\ndef test_pricing_context(mocker):\n swap1 = IRSwap(PayReceive.Pay, '1y', Currency.EUR, name='EUR1y')\n future_date = business_day_offset(dt.date.today(), 10, roll='forward')\n with MockCalc(mocker):\n with RollFwd(date='10b', realise_fwd=True):\n market = swap1.market()\n\n with pytest.raises(ValueError):\n # cannot pass in future date into pricing context, use RollFwd instead\n with PricingContext(pricing_date=future_date):\n _ = swap1.calc(risk.Price)\n\n # cannot pass in market dated in the future into pricing context, use RollFwd instead\n with PricingContext(market=CloseMarket(date=future_date)):\n _ = swap1.calc(risk.Price)\n\n with PricingContext(\n market=OverlayMarket(base_market=CloseMarket(date=future_date, location='NYC'), market_data=market.result())\n ):\n _ = swap1.calc(risk.Price)\n\n\ndef test_pricing_dates():\n # May be on weekend but doesn't matter for basic test\n future_date = dt.date.today() + dt.timedelta(10)\n yesterday = dt.date.today() - dt.timedelta(1)\n pc = PricingContext(pricing_date=future_date, market=CloseMarket(yesterday))\n assert pc is not None\n with pytest.raises(ValueError, match=\"pricing_date in the future\"):\n PricingContext(pricing_date=future_date)\n\n\n@freeze_time(WEEKEND_DATE)\ndef test_weekend_dates():\n assert dt.date.today() == WEEKEND_DATE # Check mock worked\n next_monday = WEEKEND_DATE + dt.timedelta(2)\n prev_friday = WEEKEND_DATE - dt.timedelta(1)\n pc = PricingContext(pricing_date=next_monday)\n with pc:\n assert pc.market == CloseMarket(prev_friday, PricingLocation.LDN)\n\n\ndef test_market_data_object():\n coord_val_pair = [\n {\n 'coordinate': {\n 'mkt_type': 'IR',\n 'mkt_asset': 'USD',\n 'mkt_class': 'Swap',\n 'mkt_point': ('5y',),\n 'mkt_quoting_style': 'ATMRate',\n },\n 'value': 0.9973194889,\n },\n {\n 'coordinate': {\n 'mkt_type': 'IR',\n 'mkt_asset': 'USD',\n 'mkt_class': 'Swap',\n 'mkt_point': ('40y',),\n 'mkt_quoting_style': 'ATMRate',\n },\n 'value': 'redacted',\n },\n ]\n coordinates = {MarketDataCoordinate.from_dict(dic['coordinate']): dic['value'] for dic in coord_val_pair}\n overlay_market = OverlayMarket(base_market=CloseMarket(), market_data=coordinates)\n\n assert overlay_market.coordinates[0] == MarketDataCoordinate.from_dict(coord_val_pair[0]['coordinate'])\n assert overlay_market.redacted_coordinates[0] == MarketDataCoordinate.from_dict(coord_val_pair[1]['coordinate'])\n\n\ndef test_pricing_context_metadata():\n assert len(PricingContext.path) == 0\n assert not PricingContext.has_prior\n\n c1 = PricingContext(pricing_date=dt.date(2022, 6, 15))\n c2 = PricingContext(pricing_date=dt.date(2022, 6, 16))\n c3 = PricingContext(pricing_date=dt.date(2022, 6, 17))\n\n PricingContext.current = PricingContext()\n assert len(PricingContext.path) == 1\n\n PricingContext.current = c1\n assert len(PricingContext.path) == 1\n\n with c2:\n with pytest.raises(MqValueError):\n PricingContext.current = PricingContext()\n\n assert PricingContext.current == c2\n assert PricingContext.has_prior and PricingContext.prior == c1\n assert len(PricingContext.path) == 2\n\n with c3:\n assert PricingContext.current == c3\n assert PricingContext.has_prior and PricingContext.prior == c2\n assert len(PricingContext.path) == 3\n\n PricingContext.pop()\n assert len(PricingContext.path) == 0\n\n\ndef test_creation():\n c1 = PricingContext(pricing_date=dt.date(2022, 6, 15))\n\n # All props except for the initialised one are defaulted. Context is not useable as-is\n assert c1.market == CloseMarket(c1.pricing_date, 'LDN')\n assert c1.market_data_location == PricingLocation.LDN\n assert c1.is_batch is False\n assert c1.use_cache is False\n assert c1._max_concurrent == 1000\n assert c1.provider is None\n assert c1._dates_per_batch == 1\n\n assert c1.pricing_date == dt.date(2022, 6, 15)\n\n\ndef test_inheritance():\n c1 = PricingContext(pricing_date=dt.date(2022, 6, 16), market_data_location='NYC', provider=TestProvider)\n c2 = PricingContext(pricing_date=dt.date(2022, 7, 1))\n c3 = PricingContext(use_historical_diddles_only=True)\n\n with c1:\n with c2:\n # pricing date is set\n assert c2.pricing_date == dt.date(2022, 7, 1)\n # market data location is inherited from c1 (the active context)\n assert c2.market_data_location == c1.market_data_location\n # provider is inherited\n assert c2.provider == c1.provider\n # all other props have default values\n assert c2.is_batch is False\n assert c2.use_cache is False\n assert c2._max_concurrent == 1000\n assert not c2.use_historical_diddles_only\n assert c2._dates_per_batch == 1\n with c3:\n # market data location is inherited from c1 (the active context)\n assert c3.market_data_location == c1.market_data_location\n # pricing date is inherited from c2 (the prior context)\n assert c3.pricing_date == c2.pricing_date\n # all other props have default values\n assert c3.is_batch is False\n assert c3.use_cache is False\n assert c3._max_concurrent == 1000\n assert c3.use_historical_diddles_only\n assert c3._dates_per_batch == 1\n\n\ndef test_max_concurrent():\n a = PricingContext()\n assert a._max_concurrent == 1000 # Default value\n\n b = PricingContext()\n b._max_concurrent = 2000\n\n c = PricingContext()\n c._max_concurrent = 3000\n\n assert b._max_concurrent == 2000 # setter working?\n with b:\n assert a._max_concurrent == 2000 # a was unset so defaults to inheriting b's value\n assert c._max_concurrent == 3000 # c was explicitly set so keep's it's value\n with a:\n assert PricingContext.current._max_concurrent == 2000 # should be same as above property accessor\n with c:\n assert PricingContext.current._max_concurrent == 3000 # should be same as above property accessor\n\n\ndef test_dates_per_batch():\n a = PricingContext()\n assert a._dates_per_batch == 1\n\n b = PricingContext()\n b._dates_per_batch = 2\n\n c = PricingContext()\n c._dates_per_batch = 3\n\n assert b._dates_per_batch == 2\n with b:\n assert a._dates_per_batch == 2\n assert c._dates_per_batch == 3\n with a:\n assert PricingContext.current._dates_per_batch == 2\n with c:\n assert PricingContext.current._dates_per_batch == 3\n\n\ndef test_current_inheritance():\n cur = PricingContext.current\n assert cur.market_data_location == PricingLocation.LDN\n\n with PricingContext() as pc:\n assert pc.market_data_location == PricingLocation.LDN\n\n PricingContext.current = PricingContext(market_data_location='TKO')\n\n # We can set props on the current so that props are inherited globally\n new_cur = PricingContext.current\n assert new_cur.market_data_location == PricingLocation.TKO\n\n assert PricingContext().market_data_location == new_cur.market_data_location\n\n with PricingContext():\n with PricingContext() as pc2:\n assert pc2.market_data_location == new_cur.market_data_location\n\n # Exit the current\n PricingContext.pop()\n\n\ndef test_cleanup():\n c1 = PricingContext(pricing_date=dt.date(2022, 4, 6))\n c2 = PricingContext(request_priority=5000)\n default_date = c2.pricing_date\n\n with c1:\n with c2:\n assert c2.request_priority == 5000\n # pricing_date is inherited from c1\n assert c2.pricing_date is not None\n assert c2.pricing_date == c1.pricing_date\n # on exit from c2, the pricing date would still show what it would be as inside c1 still\n assert c2.pricing_date == c1.pricing_date\n # pricing date is cleaned up on exit from c1. request priority remains set\n assert c2.request_priority == 5000\n assert c2.pricing_date == default_date\n\n\ndef test_market_props():\n PricingContext.current = PricingContext() # Reset\n # market_data_location cannot conflict with market.location\n with pytest.raises(ValueError):\n PricingContext(\n market=CloseMarket(date=dt.date(2022, 4, 6), location='NYC'),\n pricing_date=dt.date(2022, 7, 4),\n market_data_location='TKO',\n )\n\n # Default pricing date and market location are today and LDN, respectively\n pc = PricingContext()\n with pc:\n assert pc.market_data_location == PricingLocation.LDN\n assert pc.pricing_date == business_day_offset(today(PricingLocation.LDN), 0, roll='preceding')\n\n # pricing_date and market.date can be different\n pc = PricingContext(market=CloseMarket(date=dt.date(2022, 4, 6), location='NYC'), pricing_date=dt.date(2022, 7, 4))\n\n with pc:\n assert pc.pricing_date == dt.date(2022, 7, 4)\n assert pc.market.date == dt.date(2022, 4, 6)\n\n # if market is not specified, it is inferred from pricing_date and market_data_location\n pc = PricingContext(pricing_date=dt.date(2022, 7, 4), market_data_location='TKO')\n cm = CloseMarket(date=pc.pricing_date, location=pc.market_data_location)\n assert pc.market.date == cm.date\n assert pc.market.location == cm.location\n\n # market is not inherited\n pc = PricingContext(market=CloseMarket(date=dt.date(2022, 4, 6), location='NYC'), pricing_date=dt.date(2022, 7, 4))\n with pc:\n # pc gets market_data_location from its market\n assert pc.market_data_location == PricingLocation.NYC\n with PricingContext() as inner:\n # Inner inherits pricing_date\n assert inner.pricing_date == pc.pricing_date\n # Inner initialises Market with its own pricing_date\n cm = CloseMarket(date=inner.pricing_date)\n assert inner.market.date == cm.date\n # Inner also inherits market_data_location\n assert inner.market_data_location == pc.market_data_location\n # Inner uses its own market_data_location to build its market\n assert inner.market.location == inner.market_data_location\n\n\ndef test_pricing_does_not_affect_context(mocker):\n # Pricing instruments and portfolios uses the current pricing context. This should not affect its properties\n swap1 = IRSwap(PayReceive.Pay, '1y', name='EUR1y')\n swap2 = IRSwap(PayReceive.Pay, '1y', name='EUR2y')\n port = Portfolio([swap1, swap2])\n with MockCalc(mocker):\n cm = CloseMarket(date=dt.date(2022, 7, 5), location='TKO')\n pc = PricingContext(market_data_location='TKO', pricing_date=dt.date(2022, 4, 6), market=cm)\n assert pc.market_data_location == PricingLocation.TKO\n assert pc.pricing_date == dt.date(2022, 4, 6)\n assert pc.market == cm\n assert pc.is_batch is False\n\n with pc:\n assert pc.is_batch is False\n swap1.resolve()\n swap1.dollar_price()\n swap2.calc(risk.IRDelta)\n port.resolve()\n port.price()\n port.calc(risk.IRDelta)\n assert pc.market_data_location == PricingLocation.TKO\n assert pc.pricing_date == dt.date(2022, 4, 6)\n assert pc.market == cm\n assert pc.is_batch is False\n\n assert pc.market_data_location == PricingLocation.TKO\n assert pc.pricing_date == dt.date(2022, 4, 6)\n assert pc.market == cm\n assert pc.is_batch is False\n\n\ndef test_different_nested_locations(mocker):\n s = IRSwap(name='location_test_swap')\n with MockCalc(mocker):\n with PricingContext(market_data_location='TKO', pricing_date=dt.date(2022, 7, 5)):\n with PricingContext(market_data_location='NYC', pricing_date=dt.date(2022, 7, 11)):\n # Outer context should make no difference in pricing\n nyc_price_nested = s.price()\n\n with PricingContext(market_data_location='NYC', pricing_date=dt.date(2022, 7, 11)):\n nyc_price = s.price()\n\n assert nyc_price_nested.result() == nyc_price.result()\n\n\n@patch.object(RiskApi, 'run')\n@patch.object(RiskApi, 'drain_queue')\ndef test_async_behaviour(queue_mock, run_mock):\n s = IRSwap()\n # we do not actually run requests from the RiskApi\n run_mock.return_value = None\n # queue to drain should hence be empty\n queue_mock.return_value = True, []\n\n with PricingContext(is_async=True):\n s.price()\n # sleep to give spawned threads time to call RiskApi.run\n sleep(1)\n # threads should see the _max_concurrent property of the PricingContext as 1000 even though it's exited\n run_mock.assert_called_with(ANY, ANY, 1000, ANY, timeout=ANY, span=ANY)\n\n\ndef test_use_context_for_inheritance():\n cur = PricingContext.current\n assert cur.active_context == cur\n\n with PricingContext(set_parameters_only=True) as pc1:\n # If we do price anything in a context meant to be used for inheritance, it will be used as active\n assert pc1.active_context == pc1\n with PricingContext(set_parameters_only=True) as pc2:\n # This happens at any level\n assert pc2.active_context == pc2\n with PricingContext() as pc3:\n # pc3 is the outermost entered context not meant to be used for inheritance only, so it becomes active\n assert pc3.active_context == pc3\n with PricingContext() as pc4:\n assert pc4.active_context == pc3\n\n\n@patch.object(PricingContext, '_calc')\ndef test_provider(calc_mock):\n calc_mock.return_value = None\n pc = PricingContext(provider=None)\n inst = EqOption()\n inst.PROVIDER = TestProvider\n pc.calc(inst, None)\n calc_mock.assert_called_with(inst, RiskKey(TestProvider, None, None, ANY, None, None))\n pc = PricingContext(provider=GsRiskApi)\n inst = EqOption()\n inst.PROVIDER = TestProvider\n pc.calc(inst, None)\n calc_mock.assert_called_with(inst, RiskKey(GsRiskApi, None, None, ANY, None, None))\n" }, { "path": "gs_quant/test/markets/test_report.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pytest\n\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.api.gs.portfolios import Portfolio\nfrom gs_quant.api.gs.thematics import GsThematicApi\nfrom gs_quant.markets.report import FactorRiskReport, PerformanceReport, ThematicReport, flatten_results_into_df\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.common import ReportParameters\nfrom gs_quant.target.portfolios import RiskAumSource\nfrom gs_quant.target.reports import PositionSourceType, ReportType, ReportStatus, Report\n\nfake_pfr = FactorRiskReport(\n risk_model_id='AXUS4M',\n fx_hedged=True,\n report_id='PFRID',\n position_source_type=PositionSourceType.Portfolio,\n position_source_id='PORTFOLIOID',\n report_type=ReportType.Portfolio_Factor_Risk,\n status=ReportStatus.done,\n)\n\nfake_ppa = PerformanceReport(\n report_id='PPAID',\n position_source_type=PositionSourceType.Portfolio,\n position_source_id='PORTFOLIOID',\n report_type=ReportType.Portfolio_Performance_Analytics,\n parameters=None,\n status=ReportStatus.done,\n)\nfake_pta = ThematicReport(\n report_id='PTAID',\n position_source_type=PositionSourceType.Portfolio,\n position_source_id='PORTFOLIOID',\n report_type=ReportType.Portfolio_Thematic_Analytics,\n parameters=None,\n status=ReportStatus.done,\n)\n\nfactor_risk_results = [\n {\n 'date': '2021-01-02',\n 'factor': 'factor1',\n 'pnl': 123,\n 'proportionOfRisk': 100,\n 'exposure': 200,\n 'annualRisk': 3928,\n 'dailyRisk': 202,\n },\n {\n 'date': '2021-01-03',\n 'factor': 'factor1',\n 'pnl': 124,\n 'proportionOfRisk': 200,\n 'exposure': 100,\n 'annualRisk': 392,\n 'dailyRisk': 21,\n },\n {\n 'date': '2021-01-04',\n 'factor': 'factor1',\n 'pnl': 125,\n 'proportionOfRisk': 300,\n 'exposure': 150,\n 'annualRisk': 39,\n 'dailyRisk': 22,\n },\n]\n\nthematic_results = [\n {\n \"date\": \"2021-07-12\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.448370345015856e8,\n \"thematicExposure\": 1.1057087573594835e8,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-13\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.375772519907556e8,\n \"thematicExposure\": 1.0511196135243121e8,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-14\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.321189950666118e8,\n \"thematicExposure\": 1.0089556961211234e8,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-15\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.274071805135091e8,\n \"thematicExposure\": 9.706991264825605e7,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n]\n\ncustom_aum = {\n 'data': [\n {'date': '2023-06-01', 'aum': 100},\n {'date': '2023-06-02', 'aum': 100},\n {'date': '2023-06-05', 'aum': 100},\n {'date': '2023-06-06', 'aum': 100},\n ]\n}\n\nppa_data = [\n {'date': \"2023-06-01\", 'reportId': \"PPAID\", 'netExposure': 2414214.74},\n {'date': \"2023-06-02\", 'reportId': \"PPAID\", 'netExposure': 2414214.74},\n {'date': \"2023-06-05\", 'reportId': \"PPAID\", 'netExposure': 2414214.74},\n {'date': \"2023-06-06\", 'reportId': \"PPAID\", 'netExposure': 2414214.74},\n {'date': \"2023-06-07\", 'reportId': \"PPAID\", 'netExposure': 2414214.74},\n]\n\n\ndef test_get_performance_report(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(\n GsSession.current,\n '_get',\n return_value=Report(\n id='PPAID',\n position_source_type=PositionSourceType.Portfolio,\n position_source_id='PORTFOLIOID',\n parameters=None,\n type=ReportType.Portfolio_Performance_Analytics,\n status=ReportStatus.done,\n ),\n )\n # run test\n response = PerformanceReport.get('PPAID')\n assert response.type == ReportType.Portfolio_Performance_Analytics\n\n\ndef test_get_aum_source(mocker):\n portfolio = Portfolio(id='MP123', currency='USD', name='Example Port', aum_source=RiskAumSource.Gross)\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=portfolio)\n\n # run test\n response = fake_ppa.get_aum_source()\n assert response == RiskAumSource.Gross\n\n\ndef test_get_custom_aum(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=custom_aum)\n\n # run test\n response = fake_ppa.get_custom_aum()\n assert len(response) == 4\n\n\ndef test_get_aum(mocker):\n portfolio = Portfolio(id='MP123', currency='USD', name='Example Port', aum_source=RiskAumSource.Net)\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=portfolio)\n mocker.patch.object(GsDataApi, 'query_data', return_value=ppa_data)\n\n # run test\n response = fake_ppa.get_aum(start_date=dt.date(2023, 6, 1), end_date=dt.date(2023, 6, 7))\n assert len(response) == 5\n\n\ndef test_get_risk_model_id():\n assert fake_pfr.get_risk_model_id() == 'AXUS4M'\n\n\ndef test_set_position_target():\n factor_report = FactorRiskReport(\n report_id='PFRID',\n position_source_type=PositionSourceType.Portfolio,\n position_source_id='PORTFOLIOID',\n report_type=ReportType.Portfolio_Factor_Risk,\n parameters=ReportParameters(fx_hedged=True, risk_model='AXUS4M'),\n status=ReportStatus.done,\n )\n factor_report.set_position_source('MA3FMSN9VNMD')\n assert factor_report.position_source_type == PositionSourceType.Asset\n assert factor_report.type == ReportType.Asset_Factor_Risk\n\n\ndef test_get_factor_risk_report(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(\n GsSession.current,\n '_get',\n return_value=Report(\n id='PFRID',\n position_source_type=PositionSourceType.Portfolio,\n position_source_id='PORTFOLIOID',\n parameters=ReportParameters(risk_model='AXUS4M', fx_hedged=True),\n type=ReportType.Portfolio_Factor_Risk,\n status=ReportStatus.done,\n ),\n )\n # run test\n response = FactorRiskReport.get('PFRID')\n assert response.type == ReportType.Portfolio_Factor_Risk\n\n\ndef test_get_factor_pnl(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=factor_risk_results)\n\n # run test\n response = fake_pfr.get_factor_pnl(factor_names=['factor1'])\n assert len(response) == 3\n\n\ndef test_get_factor_proportion_of_risk(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=factor_risk_results)\n\n # run test\n response = fake_pfr.get_factor_proportion_of_risk(factor_names=['factor1'])\n assert len(response) == 3\n\n\ndef test_get_factor_exposure(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=factor_risk_results)\n\n # run test\n response = fake_pfr.get_factor_exposure(factor_names=['factor1'])\n assert len(response) == 3\n\n\ndef test_get_annual_risk(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=factor_risk_results)\n\n # run test\n response = fake_pfr.get_annual_risk('factor1')\n assert len(response) == 3\n\n\ndef test_get_daily_risk(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=factor_risk_results)\n\n # run test\n response = fake_pfr.get_daily_risk('factor1')\n assert len(response) == 3\n\n\ndef test_get_measures(mocker):\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsDataApi, 'query_data', return_value=thematic_results)\n\n # run test\n response = fake_pta._get_measures([\"grossExposure\", \"thematicExposure\"])\n assert len(response) == 4\n\n\ndef test_flatten_results_into_df():\n thematics_endpoint_results = [\n {\n \"date\": \"2020-09-14\",\n \"topFiveThematicExposures\": [\n {\n \"basketName\": \"GS TMT MegaCap Tech\",\n \"basketTicker\": \"GSTMTMEG\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA459FTE58S22HY4\",\n \"thematicExposure\": 11251933.154873407,\n \"thematicBeta\": 1.438066493862124,\n },\n {\n \"basketName\": \"Hedge Fund VIP\",\n \"basketTicker\": \"GSTHHVIP\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAMGDVSWVXWHEHFQ\",\n \"thematicExposure\": 5158237.284525287,\n \"thematicBeta\": 0.6592545568983715,\n },\n {\n \"basketName\": \"GS Asia Stay at Home\",\n \"basketTicker\": \"GSXASTAY\",\n \"basketRegion\": \"Asia\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA1C2Q2AAAT57AX7\",\n \"thematicExposure\": 3878730.3538401644,\n \"thematicBeta\": 0.49572567520698857,\n },\n {\n \"basketName\": \"GS Secular Growth\",\n \"basketTicker\": \"GSXUSGRO\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAHQEJG82N4DNBCH\",\n \"thematicExposure\": 3642083.749962656,\n \"thematicBeta\": 0.46548077886443323,\n },\n {\n \"basketName\": \"GS China 14th 5Year Plan\",\n \"basketTicker\": \"GSXAC5YP\",\n \"basketRegion\": \"Asia\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAGP7WNPCMN5MJWQ\",\n \"thematicExposure\": 3347766.929109541,\n \"thematicBeta\": 0.42786527290441334,\n },\n ],\n },\n {\n \"date\": \"2020-09-15\",\n \"topFiveThematicExposures\": [\n {\n \"basketName\": \"GS TMT MegaCap Tech\",\n \"basketTicker\": \"GSTMTMEG\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA459FTE58S22HY4\",\n \"thematicExposure\": 11304916.702270757,\n \"thematicBeta\": 1.4050953516801092,\n },\n {\n \"basketName\": \"Hedge Fund VIP\",\n \"basketTicker\": \"GSTHHVIP\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAMGDVSWVXWHEHFQ\",\n \"thematicExposure\": 5241527.529878033,\n \"thematicBeta\": 0.651472820357501,\n },\n {\n \"basketName\": \"GS Asia Stay at Home\",\n \"basketTicker\": \"GSXASTAY\",\n \"basketRegion\": \"Asia\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA1C2Q2AAAT57AX7\",\n \"thematicExposure\": 3868608.7685798025,\n \"thematicBeta\": 0.4808318664664322,\n },\n {\n \"basketName\": \"GS Secular Growth\",\n \"basketTicker\": \"GSXUSGRO\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAHQEJG82N4DNBCH\",\n \"thematicExposure\": 3661450.0674766554,\n \"thematicBeta\": 0.4550839785654401,\n },\n {\n \"basketName\": \"GS China 14th 5Year Plan\",\n \"basketTicker\": \"GSXAC5YP\",\n \"basketRegion\": \"Asia\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAGP7WNPCMN5MJWQ\",\n \"thematicExposure\": 3344638.716916008,\n \"thematicBeta\": 0.41570729249548893,\n },\n ],\n },\n {\n \"date\": \"2020-09-16\",\n \"topFiveThematicExposures\": [\n {\n \"basketName\": \"GS TMT MegaCap Tech\",\n \"basketTicker\": \"GSTMTMEG\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA459FTE58S22HY4\",\n \"thematicExposure\": 11173613.76263776,\n \"thematicBeta\": 1.487220141961805,\n },\n {\n \"basketName\": \"Hedge Fund VIP\",\n \"basketTicker\": \"GSTHHVIP\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAMGDVSWVXWHEHFQ\",\n \"thematicExposure\": 4800709.171521289,\n \"thematicBeta\": 0.6389796110065071,\n },\n {\n \"basketName\": \"GS Asia Stay at Home\",\n \"basketTicker\": \"GSXASTAY\",\n \"basketRegion\": \"Asia\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA1C2Q2AAAT57AX7\",\n \"thematicExposure\": 3767199.2638762216,\n \"thematicBeta\": 0.501418318463317,\n },\n {\n \"basketName\": \"GS Secular Growth\",\n \"basketTicker\": \"GSXUSGRO\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAHQEJG82N4DNBCH\",\n \"thematicExposure\": 3645814.9989552647,\n \"thematicBeta\": 0.4852619408094451,\n },\n {\n \"basketName\": \"GS US Stay at Home\",\n \"basketTicker\": \"GSXUSTAY\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA9B9TEMQ2RW16K9\",\n \"thematicExposure\": 3354419.7766576866,\n \"thematicBeta\": 0.4464769198593304,\n },\n ],\n },\n ]\n df = flatten_results_into_df(thematics_endpoint_results)\n assert df.shape == (15, 8)\n thematics_endpoint_results = [\n {\n \"date\": \"2020-09-14\",\n \"allThematicExposures\": [\n {\n \"basketName\": \"GS TMT MegaCap Tech\",\n \"basketTicker\": \"GSTMTMEG\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA459FTE58S22HY4\",\n \"thematicExposure\": 11251933.154873407,\n \"thematicBeta\": 1.438066493862124,\n },\n {\n \"basketName\": \"Hedge Fund VIP\",\n \"basketTicker\": \"GSTHHVIP\",\n \"basketRegion\": \"Americas\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MAMGDVSWVXWHEHFQ\",\n \"thematicExposure\": 5158237.284525287,\n \"thematicBeta\": 0.6592545568983715,\n },\n {\n \"basketName\": \"GS Asia Stay at Home\",\n \"basketTicker\": \"GSXASTAY\",\n \"basketRegion\": \"Asia\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA1C2Q2AAAT57AX7\",\n \"thematicExposure\": 3878730.3538401644,\n \"thematicBeta\": 0.49572567520698857,\n },\n ],\n }\n ]\n df = flatten_results_into_df(thematics_endpoint_results)\n assert df.shape == (3, 8)\n\n\ndef test_get_thematic_breakdown(mocker):\n thematics_endpoint_results = [\n {\n \"date\": \"2020-09-14\",\n \"thematicBreakdownByAsset\": [\n {\n \"basketName\": \"GS Asia Stay at Home\",\n \"basketTicker\": \"GSXASTAY\",\n \"basketRegion\": \"Asia\",\n \"basketCurrency\": \"USD\",\n \"basketId\": \"MA1C2Q2AAAT57AX7\",\n \"thematicBeta\": 0.49572567520698857,\n \"thematicBreakdownByAsset\": [\n {\n \"name\": \"Allstate Corp\",\n \"bbid\": \"ALL UN\",\n \"sector\": \"Financials\",\n \"industry\": \"Insurance\",\n \"region\": \"Americas\",\n \"country\": \"United States\",\n \"assetId\": \"MA4B66MW5E27U9YGM27\",\n \"beta\": -0.19208866590832985,\n \"thematicExposure\": 38530.05709547743,\n },\n {\n \"name\": \"Allergan PLC\",\n \"bbid\": \"AGN UN\",\n \"sector\": \"None\",\n \"industry\": \"None\",\n \"region\": \"Europe\",\n \"country\": \"United States\",\n \"assetId\": \"MA4B66MW5E27U9XPVMG\",\n \"beta\": 0.10085184726854478,\n \"thematicExposure\": -41210.418676042646,\n },\n {\n \"name\": \"Bristol-Myers Squibb Co\",\n \"bbid\": \"BMY UN\",\n \"sector\": \"Health Care\",\n \"industry\": \"Pharmaceuticals\",\n \"region\": \"Americas\",\n \"country\": \"United States\",\n \"assetId\": \"MA4B66MW5E27UA4479J\",\n \"beta\": 0.02101418329131077,\n \"thematicExposure\": 19108.02476936883,\n },\n {\n \"name\": \"Bank of New York Mellon Corp\",\n \"bbid\": \"BK UN\",\n \"sector\": \"Financials\",\n \"industry\": \"Capital Markets\",\n \"region\": \"Americas\",\n \"country\": \"United States\",\n \"assetId\": \"MA4B66MW5E27UA39JL8\",\n \"beta\": -0.060160353375185896,\n \"thematicExposure\": 11806.1366204509,\n },\n ],\n }\n ],\n }\n ]\n mocker.patch.object(GsThematicApi, 'get_thematics', return_value=thematics_endpoint_results)\n df = fake_pta.get_thematic_breakdown(date=dt.date(2020, 9, 14), basket_id='MA1C2Q2AAAT57AX7')\n assert df.shape == (4, 9)\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/markets/test_scenarios.py", "content": "\"\"\"\nCopyright 2024 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom unittest import mock\n\nimport pytest\n\nfrom gs_quant.common import Entitlements as TargetEntitlements\nfrom gs_quant.entities.entitlements import Entitlements, EntitlementBlock\nfrom gs_quant.markets.scenario import FactorScenario, FactorScenarioType, FactorShockParameters, FactorShock\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.target.risk import Scenario\n\ndefault_entitlements = TargetEntitlements(edit=(), view=(), admin=())\n\ndefault_scenario_parameters = {\n \"riskModel\": \"MODEL_ID\",\n \"propagateShocks\": True,\n \"factorShocks\": [{\"factor\": \"Factor 1\", \"shock\": 5}, {\"factor\": \"Factor 2\", \"shock\": -5}],\n}\n\nmock_scenario_obj = Scenario(\n name=\"Scenario 1\",\n description=\"Scenario 1\",\n entitlements=default_entitlements,\n id_=\"MSCENARIO\",\n parameters=default_scenario_parameters,\n type_=FactorScenarioType.Factor_Shock,\n)\n\n\ndef mock_factor_scenario(mocker):\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_scenario_obj)\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_scenario_obj)\n mocker.patch.object(GsSession.current.sync, 'put', return_value=mock_scenario_obj)\n return FactorScenario.get('MSCENARIO')\n\n\ndef test_create_factor_scenario(mocker):\n mock_factor_scenario(mocker)\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_scenario_obj)\n new_scenario = FactorScenario(\n name=\"Scenario 1\",\n type=FactorScenarioType.Factor_Shock,\n parameters=FactorShockParameters(\n factor_shocks=[FactorShock(factor=\"Factor 1\", shock=5), FactorShock(factor=\"Factor 2\", shock=-5)],\n propagate_shocks=True,\n risk_model=\"MODEL_ID\",\n ),\n entitlements=Entitlements(view=EntitlementBlock(), edit=EntitlementBlock(), admin=EntitlementBlock()),\n id_=\"MSCENARIO\",\n description=\"Scenario 1\",\n )\n\n new_scenario.save()\n assert new_scenario.id == mock_scenario_obj.id\n assert new_scenario.name == mock_scenario_obj.name\n assert new_scenario.description == mock_scenario_obj.description\n assert new_scenario.entitlements == Entitlements.from_target(mock_scenario_obj.entitlements)\n assert new_scenario.parameters == FactorShockParameters.from_dict(mock_scenario_obj.parameters)\n assert new_scenario.type == mock_scenario_obj.type\n\n\ndef test_update_scenario_entitlements(mocker):\n scenario = mock_factor_scenario(mocker)\n\n new_entitlements = Entitlements(\n view=EntitlementBlock(roles=[\"role:A\"]), edit=EntitlementBlock(roles=[\"role:B\"]), admin=EntitlementBlock()\n )\n\n scenario.entitlements = new_entitlements\n scenario.save()\n\n mocker.patch.object(GsSession.current.sync, 'get', return_value=scenario)\n\n assert 'role:A' in scenario.entitlements.view.roles\n assert 'role:B' in scenario.entitlements.edit.roles\n\n\nif __name__ == \"__main__\":\n pytest.main([__file__])\n" }, { "path": "gs_quant/test/markets/test_securities.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport datetime as dt\nimport json\nfrom enum import Enum\n\nimport pytest\n\nfrom gs_quant.base import EnumBase\nfrom gs_quant.api.gs.assets import GsAsset, GsAssetApi\nfrom gs_quant.errors import MqTypeError, MqValueError, MqRequestError\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.securities import (\n SecurityMaster,\n AssetIdentifier,\n AssetType,\n ExchangeCode,\n SecurityMasterSource,\n SecurityIdentifier,\n Asset,\n SecMasterAsset,\n)\nfrom gs_quant.common import AssetClass, AssetType as GsAssetType\nfrom gs_quant.session import GsSession, Environment\n\n\ndef test_get_asset(mocker):\n marquee_id = 'MA1234567890'\n mock_response = GsAsset(asset_class=AssetClass.Equity, type_=GsAssetType.Single_Stock, name='Test Asset')\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.MARQUEE_ID)\n\n assert asset.name == \"Test Asset\"\n assert asset.get_type() == AssetType.STOCK\n\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.MARQUEE_ID, as_of=dt.date.today())\n\n assert asset.name == \"Test Asset\"\n assert asset.get_type() == AssetType.STOCK\n\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.MARQUEE_ID, as_of=dt.datetime.utcnow())\n\n assert asset.name == \"Test Asset\"\n assert asset.get_type() == AssetType.STOCK\n\n mock_response = GsAsset(asset_class=AssetClass.Equity, type_=GsAssetType.Index, name='Test Asset')\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.MARQUEE_ID)\n\n assert asset.name == \"Test Asset\"\n assert asset.get_type() == AssetType.INDEX\n\n mock_response = GsAsset(asset_class=AssetClass.Equity, type_=GsAssetType.Future, name='Test Asset')\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.MARQUEE_ID)\n\n assert asset.name == \"Test Asset\"\n assert asset.get_type() == AssetType.FUTURE\n\n mock_response = GsAsset(asset_class=AssetClass.Equity, type_=GsAssetType.ETF, name='Test Asset')\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.MARQUEE_ID)\n\n assert asset.name == \"Test Asset\"\n assert asset.get_type() == AssetType.ETF\n\n mock_response = GsAsset(\n asset_class=AssetClass.Equity, type_=GsAssetType.Custom_Basket, name='Test Asset', id_=marquee_id\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.MARQUEE_ID)\n\n assert asset.name == \"Test Asset\"\n assert asset.get_type() == AssetType.CUSTOM_BASKET\n\n mock_response = {\n 'results': (GsAsset(id=marquee_id, assetClass='Equity', type='Single Stock', name='Test 1'),),\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_response)\n asset = SecurityMaster.get_asset('GS.N', AssetIdentifier.REUTERS_ID)\n assert asset.name == \"Test 1\"\n assert asset.get_type() == AssetType.STOCK\n\n asset = SecurityMaster.get_asset('GS', AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE)\n assert asset.name == \"Test 1\"\n assert asset.get_type() == AssetType.STOCK\n\n asset = SecurityMaster.get_asset('GS', AssetIdentifier.TICKER, asset_type=AssetType.STOCK)\n assert asset.name == \"Test 1\"\n assert asset.get_type() == AssetType.STOCK\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value={'results': ()})\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.REUTERS_ID)\n assert asset is None\n\n\ndef test_asset_identifiers(mocker):\n marquee_id = 'MA1234567890'\n\n mocker.patch.object(GsSession, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret'))\n mock_response = GsAsset(\n asset_class=AssetClass.Equity, type_=GsAssetType.Custom_Basket, name='Test Asset', id_=marquee_id\n )\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n asset = SecurityMaster.get_asset(marquee_id, AssetIdentifier.MARQUEE_ID)\n\n mock_response = {\n 'xrefs': (\n {\n 'startDate': '1952-01-01',\n 'endDate': '2018-12-31',\n 'identifiers': {'ric': '.GSTHHOLD', 'bbid': 'GSTHHOLD', 'cusip': '9EQ24FOLD', 'ticker': 'GSTHHOLD'},\n },\n {\n 'startDate': '2019-01-01',\n 'endDate': '2952-12-31',\n 'identifiers': {\n 'ric': '.GSTHHVIP',\n 'bbid': 'GSTHHVIP',\n 'cusip': '9EQ24FPE5',\n 'ticker': 'GSTHHVIP',\n },\n },\n )\n }\n\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_response)\n\n identifiers = asset.get_identifiers(dt.date.today())\n\n assert identifiers[AssetIdentifier.REUTERS_ID.value] == '.GSTHHVIP'\n assert identifiers[AssetIdentifier.BLOOMBERG_ID.value] == 'GSTHHVIP'\n assert identifiers[AssetIdentifier.CUSIP.value] == '9EQ24FPE5'\n assert identifiers[AssetIdentifier.TICKER.value] == 'GSTHHVIP'\n\n assert asset.get_identifier(AssetIdentifier.REUTERS_ID, as_of=dt.date.today()) == '.GSTHHVIP'\n assert asset.get_identifier(AssetIdentifier.BLOOMBERG_ID, as_of=dt.date.today()) == 'GSTHHVIP'\n assert asset.get_identifier(AssetIdentifier.CUSIP, as_of=dt.date.today()) == '9EQ24FPE5'\n assert asset.get_identifier(AssetIdentifier.TICKER, as_of=dt.date.today()) == 'GSTHHVIP'\n\n market = PricingContext(dt.date(2018, 3, 1))\n\n with market:\n identifiers = asset.get_identifiers()\n\n assert identifiers[AssetIdentifier.REUTERS_ID.value] == '.GSTHHOLD'\n assert identifiers[AssetIdentifier.BLOOMBERG_ID.value] == 'GSTHHOLD'\n assert identifiers[AssetIdentifier.CUSIP.value] == '9EQ24FOLD'\n assert identifiers[AssetIdentifier.TICKER.value] == 'GSTHHOLD'\n\n market = PricingContext(dt.date(2018, 3, 1))\n\n with market:\n identifiers = asset.get_identifiers()\n\n assert identifiers[AssetIdentifier.REUTERS_ID.value] == '.GSTHHOLD'\n assert identifiers[AssetIdentifier.BLOOMBERG_ID.value] == 'GSTHHOLD'\n assert identifiers[AssetIdentifier.CUSIP.value] == '9EQ24FOLD'\n assert identifiers[AssetIdentifier.TICKER.value] == 'GSTHHOLD'\n\n\ndef test_asset_types(mocker):\n class MockType(EnumBase, Enum):\n Foo = \"Bar\"\n\n ata = getattr(SecurityMaster, '_SecurityMaster__gs_asset_to_asset')\n assert ata is not None\n asset = GsAsset(AssetClass.Equity, None, 'Test Asset')\n\n mocker.patch.object(json, 'dumps', return_value='{}')\n # with pytest.raises(ValueError) as exc_info:\n # setattr(asset, 'type', MockType.Foo)\n # assert 'is not a valid AssetType' in str(exc_info.value) # reached exception at end of function\n\n with pytest.raises(AttributeError) as exc_info:\n ata(asset)\n assert \"has no attribute 'value'\" in str(exc_info.value) # reached exception at end of function\n\n\nclass SecMasterContext:\n def __enter__(self):\n SecurityMaster.set_source(SecurityMasterSource.SECURITY_MASTER)\n\n def __exit__(self, exc_type, exc_value, traceback):\n SecurityMaster.set_source(SecurityMasterSource.ASSET_SERVICE)\n\n\nclass AssetContext:\n def __enter__(self):\n self.previous = SecurityMaster._source\n SecurityMaster.set_source(SecurityMasterSource.ASSET_SERVICE)\n\n def __exit__(self, exc_type, exc_value, traceback):\n SecurityMaster.set_source(self.previous)\n\n\ndef test_get_security(mocker):\n mocker.patch.object(GsSession, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret'))\n\n mock_response = {\n \"results\": [\n {\n \"name\": \"GOLDMAN SACHS GROUP INC (New York Stock)\",\n \"type\": \"Common Stock\",\n \"currency\": \"USD\",\n \"tags\": [],\n \"id\": \"GSPD901026E154\",\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 901026,\n \"ric\": \"GS.N\",\n \"id\": \"GSPD901026E154\",\n \"cusip\": \"38141G10\",\n \"sedol\": \"2407966\",\n \"isin\": \"US38141G1040\",\n \"ticker\": \"GS\",\n \"bbid\": \"GS UN\",\n \"bcid\": \"GS US\",\n \"gss\": \"GS\",\n \"primeId\": \"1003232152\",\n \"assetId\": \"MA4B66MW5E27UAHKG34\",\n },\n \"company\": {\"name\": \"GOLDMAN SACHS GROUP INC\", \"identifiers\": {\"gsCompanyId\": 25998}},\n \"product\": {\"name\": \"GOLDMAN SACHS GROUP INC\", \"identifiers\": {\"gsid\": 901026}},\n \"exchange\": {\"name\": \"New York Stock\", \"identifiers\": {\"gsExchangeId\": 154}},\n }\n ],\n \"totalResults\": 1,\n }\n\n mock_identifier_history_response = {\n \"results\": [\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"GS\",\n \"updateTime\": \"2002-02-09T17:58:27.58Z\",\n \"type\": \"bbg\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"GS\",\n \"updateTime\": \"2002-02-09T17:57:14.546Z\",\n \"type\": \"ticker\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"assetId\",\n \"value\": \"MA4B66MW5E27UAHKG34\",\n \"updateTime\": \"2002-10-30T21:30:29.993Z\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"ric\",\n \"value\": \"GS.N\",\n \"updateTime\": \"2002-10-30T21:30:29.993Z\",\n \"gsExchangeId\": 154,\n },\n ]\n }\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock_response, mock_identifier_history_response])\n\n with SecMasterContext():\n asset = SecurityMaster.get_asset('GS UN', SecurityIdentifier.BBID)\n assert isinstance(asset, SecMasterAsset)\n assert asset.get_marquee_id() == 'MA4B66MW5E27UAHKG34'\n ids = asset.get_identifiers()\n assert ids[SecurityIdentifier.BBG.value] == 'GS'\n assert ids[SecurityIdentifier.RIC.value] == 'GS.N'\n assert ids[SecurityIdentifier.GSID.value] == 901026\n\n\ndef test_get_security_fields(mocker):\n mock_response = {\n \"results\": [\n {\n \"name\": \"GOLDMAN SACHS GROUP INC (New York Stock)\",\n \"id\": \"GSPD901026E154\",\n \"type\": \"Common Stock\",\n \"currency\": \"USD\",\n \"tags\": [],\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 901026,\n \"ric\": \"GS.N\",\n \"id\": \"GSPD901026E154\",\n \"cusip\": \"38141G10\",\n \"sedol\": \"2407966\",\n \"isin\": \"US38141G1040\",\n \"ticker\": \"GS\",\n \"bbid\": \"GS UN\",\n \"bcid\": \"GS US\",\n \"gss\": \"GS\",\n \"primeId\": \"1003232152\",\n \"assetId\": \"MA4B66MW5E27UAHKG34\",\n },\n \"exchange\": {\"name\": \"New York Stock\", \"identifiers\": {\"gsExchangeId\": 154}},\n }\n ],\n \"totalResults\": 1,\n }\n\n mock_identifiers_response = {\n \"results\": [\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"GS\",\n \"updateTime\": \"2002-02-09T17:58:27.58Z\",\n \"type\": \"bbg\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"1003232152\",\n \"updateTime\": \"2003-01-16T15:22:54.1Z\",\n \"type\": \"primeId\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"assetId\",\n \"value\": \"MA4B66MW5E27UAHKG34\",\n \"updateTime\": \"2002-10-30T21:30:29.993Z\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"ric\",\n \"value\": \"GS.N\",\n \"updateTime\": \"2002-10-30T21:30:29.993Z\",\n \"gsExchangeId\": 154,\n },\n ],\n }\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock_response, mock_identifiers_response])\n\n with SecMasterContext():\n asset = SecurityMaster.get_asset('GS UN', SecurityIdentifier.BBID, fields=['name', 'id'])\n assert isinstance(asset, SecMasterAsset)\n assert asset.get_marquee_id() == 'MA4B66MW5E27UAHKG34'\n assert asset.name == 'GOLDMAN SACHS GROUP INC (New York Stock)'\n ids = asset.get_identifiers()\n assert ids[SecurityIdentifier.BBG.value] == 'GS'\n assert ids[SecurityIdentifier.RIC.value] == 'GS.N'\n assert ids[SecurityIdentifier.PRIMEID.value] == '1003232152'\n assert ids[SecurityIdentifier.ID.value] == 'GSPD901026E154'\n\n\ndef test_get_identifiers(mocker):\n assets = {\n \"results\": [\n {\"id\": \"GSPD901026E154\", \"identifiers\": {\"bbid\": \"GS UN\"}},\n {\"id\": \"GSPD14593E459\", \"identifiers\": {\"bbid\": \"AAPL UW\"}},\n ],\n \"totalResults\": 2,\n }\n ids_gs = {\n \"results\": [\n {\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"38141G10\",\n \"updateTime\": \"2002-02-09T17:54:27.99Z\",\n \"type\": \"cusip\",\n },\n {\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"2407966\",\n \"updateTime\": \"2002-02-09T17:54:47.77Z\",\n \"type\": \"sedol\",\n },\n ]\n }\n ids_ap = {\n \"results\": [\n {\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"03783310\",\n \"updateTime\": \"2003-04-15T22:36:17.593Z\",\n \"type\": \"cusip\",\n },\n {\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"2046251\",\n \"updateTime\": \"2003-04-15T22:36:17.6Z\",\n \"type\": \"sedol\",\n },\n ]\n }\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[assets, ids_gs, ids_ap])\n with SecMasterContext():\n identifiers = SecurityMaster.get_identifiers(['GS UN', 'AAPL UW'], SecurityIdentifier.BBID)\n assert 'GS UN' in identifiers\n assert 'AAPL UW' in identifiers\n assert identifiers['GS UN'] == ids_gs['results']\n assert identifiers['AAPL UW'] == ids_ap['results']\n\n\ndef test_get_all_identifiers(mocker):\n p1 = {\n \"results\": [\n {\n \"type\": \"Common Stock\",\n \"id\": \"GSPD901026E154\",\n \"assetClass\": \"Equity\",\n \"identifiers\": {\"gsid\": 901026, \"ric\": \"GS.N\", \"id\": \"GSPD901026E154\", \"bbid\": \"GS UN\"},\n }\n ],\n \"totalResults\": 1,\n }\n p2 = {\n \"results\": [\n {\n \"type\": \"Common Stock\",\n \"id\": \"GSPD14593E459\",\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 14593,\n \"ric\": \"AAPL.OQ\",\n \"id\": \"GSPD14593E459\",\n \"bbid\": \"AAPL UW\",\n },\n }\n ],\n \"totalResults\": 1,\n }\n p3 = {\"results\": [], \"totalResults\": 0}\n\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[p1, p2, p3])\n with SecMasterContext():\n output = SecurityMaster.get_all_identifiers(use_offset_key=False)\n assert len(output) == 2\n assert output['GSPD901026E154'] == p1['results'][0]['identifiers']\n assert output['GSPD14593E459'] == p2['results'][0]['identifiers']\n\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[p1, p2, p3])\n with SecMasterContext():\n output = SecurityMaster.get_all_identifiers(id_type=SecurityIdentifier.BBID, use_offset_key=False)\n assert len(output) == 2\n assert output['GS UN'] == p1['results'][0]['identifiers']\n assert output['AAPL UW'] == p2['results'][0]['identifiers']\n\n\ndef test_get_all_identifiers_with_assetTypes_not_none(mocker):\n mock_etf = {\n \"results\": [\n {\n \"type\": \"ETF\",\n \"id\": \"mock_ETF_id\",\n \"assetClass\": \"Equity\",\n \"identifiers\": {\"gsid\": 1111111, \"ric\": \"mock_ETF_ric\", \"id\": \"mock_ETF_id\", \"bbid\": \"mock_ETF_bbid\"},\n }\n ],\n \"totalResults\": 1,\n }\n mock_stock = {\n \"results\": [\n {\n \"type\": \"Common Stock\",\n \"id\": \"mock_stock_id\",\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 222222,\n \"ric\": \"mock_stock_ric\",\n \"bbid\": \"mock_stock_bbid\",\n # id omitted from nested dict for testing\n },\n }\n ],\n \"totalResults\": 1,\n }\n mock_etf_and_stock = {\"results\": mock_stock['results'] + mock_etf['results'], \"totalResults\": 2}\n\n def get_identifiers_byte(*args, **kwargs):\n types = kwargs['payload']['type']\n stock_str = SecurityMaster.asset_type_to_str(asset_class=AssetClass.Equity, asset_type=AssetType.STOCK)\n if len(types) == 1 and AssetType.ETF.value in types:\n return mock_etf\n elif len(types) == 1 and stock_str in types:\n return mock_stock\n elif len(types) == 2 and stock_str in types and AssetType.ETF.value in types:\n return mock_etf_and_stock\n\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=get_identifiers_byte)\n with SecMasterContext():\n output = SecurityMaster.get_all_identifiers(AssetClass.Equity, types=[AssetType.ETF])\n assert len(output) == 1\n assert output['mock_ETF_id'] == mock_etf['results'][0]['identifiers']\n\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=get_identifiers_byte)\n with SecMasterContext():\n output = SecurityMaster.get_all_identifiers(AssetClass.Equity, types=[AssetType.STOCK])\n assert len(output) == 1\n assert output['mock_stock_id'] == mock_stock['results'][0]['identifiers']\n\n with SecMasterContext():\n output = SecurityMaster.get_all_identifiers(AssetClass.Equity, types=[AssetType.STOCK, AssetType.ETF])\n assert len(output) == 2\n assert output['mock_ETF_id'] == mock_etf['results'][0]['identifiers']\n assert output['mock_stock_id'] == mock_stock['results'][0]['identifiers']\n\n\ndef test_offset_key(mocker):\n p1 = {\n \"results\": [\n {\n \"type\": \"Common Stock\",\n \"id\": \"GSPD901026E154\",\n \"assetClass\": \"Equity\",\n \"identifiers\": {\"gsid\": 901026, \"ric\": \"GS.N\", \"id\": \"GSPD901026E154\", \"bbid\": \"GS UN\"},\n }\n ],\n \"offsetKey\": \"qwerty\",\n \"totalResults\": 1,\n }\n p2 = {\n \"results\": [\n {\n \"type\": \"Common Stock\",\n \"id\": \"GSPD14593E459\",\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 14593,\n \"ric\": \"AAPL.OQ\",\n \"id\": \"GSPD14593E459\",\n \"bbid\": \"AAPL UW\",\n },\n }\n ],\n \"offsetKey\": \"azerty\",\n \"totalResults\": 1,\n }\n p3 = {\"results\": [], \"totalResults\": 0}\n\n limited = False\n hits = [0] * 3\n\n def fetch(*args, **kwargs):\n nonlocal limited\n if not limited:\n limited = True\n raise MqRequestError(429, 'too many requests')\n offset_key = kwargs['payload'].get('offsetKey')\n if offset_key is None:\n hits[0] += 1\n return p1\n if offset_key == \"qwerty\":\n hits[1] += 1\n return p2\n if offset_key == \"azerty\":\n hits[2] += 1\n return p3\n\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=fetch)\n with SecMasterContext():\n output = SecurityMaster.get_all_identifiers(sleep=0)\n assert len(output) == 2\n assert output['GSPD901026E154'] == p1['results'][0]['identifiers']\n assert output['GSPD14593E459'] == p2['results'][0]['identifiers']\n assert all(map(lambda x: x == 1, hits))\n\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=fetch)\n with SecMasterContext():\n output = SecurityMaster.get_all_identifiers(id_type=SecurityIdentifier.BBID, sleep=0)\n assert len(output) == 2\n assert output['GS UN'] == p1['results'][0]['identifiers']\n assert output['AAPL UW'] == p2['results'][0]['identifiers']\n assert all(map(lambda x: x == 2, hits))\n\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=fetch)\n with SecMasterContext():\n gen = SecurityMaster.get_all_identifiers_gen(id_type=SecurityIdentifier.BBID, sleep=0)\n page = next(gen)\n assert len(page) == 1\n assert page['GS UN'] == p1['results'][0]['identifiers']\n page = next(gen)\n assert len(page) == 1\n assert page['AAPL UW'] == p2['results'][0]['identifiers']\n\n with pytest.raises(StopIteration):\n next(gen)\n\n assert all(map(lambda x: x == 3, hits))\n\n\ndef test_map_identifiers(mocker):\n mock1 = {\n \"results\": [\n {\n \"assetId\": \"MA4B66MW5E27U9VBB93\",\n \"outputType\": \"rcic\",\n \"outputValue\": \"AAPL.O\",\n \"startDate\": \"2021-10-11\",\n \"endDate\": \"2021-10-12\",\n \"input\": \"AAPL UN\",\n },\n {\n \"assetId\": \"MARCRZHY163GQ4H3\",\n \"outputType\": \"ric\",\n \"outputValue\": \"AAPL.N\",\n \"startDate\": \"2021-10-11\",\n \"endDate\": \"2021-10-12\",\n \"input\": \"AAPL UN\",\n },\n {\n \"assetId\": \"MA4B66MW5E27UAHKG34\",\n \"outputType\": \"ric\",\n \"outputValue\": \"GS.N\",\n \"startDate\": \"2021-10-11\",\n \"endDate\": \"2021-10-12\",\n \"input\": \"GS UN\",\n },\n {\n \"outputType\": \"rcic\",\n \"outputValue\": \"GS\",\n \"startDate\": \"2021-10-11\",\n \"endDate\": \"2021-10-12\",\n \"input\": \"GS UN\",\n },\n {\n \"outputType\": \"gsid\",\n \"outputValue\": 14593,\n \"startDate\": \"2021-10-11\",\n \"endDate\": \"2021-10-12\",\n \"input\": \"AAPL UN\",\n },\n {\n \"outputType\": \"gsid\",\n \"outputValue\": 901026,\n \"startDate\": \"2021-10-11\",\n \"endDate\": \"2021-10-12\",\n \"input\": \"GS UN\",\n },\n ]\n }\n mock2 = copy.deepcopy(mock1)\n mock2[\"results\"].extend(\n [\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"AAPL\",\n \"exchange\": \"UN\",\n \"compositeExchange\": \"US\",\n \"startDate\": \"2021-10-11\",\n \"endDate\": \"2021-10-12\",\n \"input\": \"AAPL UN\",\n },\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"GS\",\n \"exchange\": \"UN\",\n \"compositeExchange\": \"US\",\n \"startDate\": \"2021-10-11\",\n \"endDate\": \"2021-10-12\",\n \"input\": \"GS UN\",\n },\n ]\n )\n\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock2, mock2])\n start = dt.date(2021, 10, 11)\n end = dt.date(2021, 10, 12)\n\n expected = {\n \"2021-10-11\": {\"AAPL UN\": {\"ric\": [\"AAPL.N\"], \"gsid\": [14593]}, \"GS UN\": {\"ric\": [\"GS.N\"], \"gsid\": [901026]}},\n \"2021-10-12\": {\"AAPL UN\": {\"ric\": [\"AAPL.N\"], \"gsid\": [14593]}, \"GS UN\": {\"ric\": [\"GS.N\"], \"gsid\": [901026]}},\n }\n with SecMasterContext():\n actual = SecurityMaster.map_identifiers(\n SecurityIdentifier.BBID, ['GS UN', 'AAPL UN'], [SecurityIdentifier.RIC, SecurityIdentifier.GSID], start, end\n )\n assert actual == expected\n\n expected = {\n \"2021-10-11\": {\n \"AAPL UN\": {\"assetId\": [\"MARCRZHY163GQ4H3\"], \"gsid\": [14593], \"bbid\": [\"AAPL UN\"]},\n \"GS UN\": {\"assetId\": [\"MA4B66MW5E27UAHKG34\"], \"gsid\": [901026], \"bbid\": [\"GS UN\"]},\n },\n \"2021-10-12\": {\n \"AAPL UN\": {\"assetId\": [\"MARCRZHY163GQ4H3\"], \"gsid\": [14593], \"bbid\": [\"AAPL UN\"]},\n \"GS UN\": {\"assetId\": [\"MA4B66MW5E27UAHKG34\"], \"gsid\": [901026], \"bbid\": [\"GS UN\"]},\n },\n }\n targets = [SecurityIdentifier.ASSET_ID, SecurityIdentifier.GSID, SecurityIdentifier.BBID]\n with SecMasterContext():\n actual = SecurityMaster.map_identifiers(SecurityIdentifier.BBID, ['GS UN', 'AAPL UN'], targets, start, end)\n assert actual == expected\n\n\ndef test_map_identifiers_change(mocker):\n mock = {\n \"results\": [\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"USAT\",\n \"exchange\": \"UW\",\n \"compositeExchange\": \"US\",\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"2021-04-18\",\n \"input\": \"104563\",\n },\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"CTLP\",\n \"exchange\": \"UW\",\n \"compositeExchange\": \"US\",\n \"startDate\": \"2021-04-19\",\n \"endDate\": \"2021-11-01\",\n \"input\": \"104563\",\n },\n {\n \"assetId\": \"MAY8Z19T2WE6RVHG\",\n \"outputType\": \"rcic\",\n \"outputValue\": \"USAT.O\",\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"2021-04-17\",\n \"input\": \"104563\",\n },\n {\n \"assetId\": \"MA4B66MW5E27UANLYDS\",\n \"outputType\": \"ric\",\n \"outputValue\": \"USAT.OQ\",\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"2021-04-17\",\n \"input\": \"104563\",\n },\n {\n \"assetId\": \"MA2640YQADTHYZ4M\",\n \"outputType\": \"rcic\",\n \"outputValue\": \"CTLP.O\",\n \"startDate\": \"2021-04-19\",\n \"endDate\": \"2021-11-01\",\n \"input\": \"104563\",\n },\n {\n \"assetId\": \"MAR754Z5RQYZ3V8E\",\n \"outputType\": \"ric\",\n \"outputValue\": \"CTLP.OQ\",\n \"startDate\": \"2021-04-19\",\n \"endDate\": \"2021-11-01\",\n \"input\": \"104563\",\n },\n # additional RICs omitted from test\n {\n \"outputType\": \"gsid\",\n \"outputValue\": 104563,\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"2021-04-18\",\n \"input\": \"104563\",\n },\n {\n \"outputType\": \"gsid\",\n \"outputValue\": 104563,\n \"startDate\": \"2021-04-19\",\n \"endDate\": \"2021-11-01\",\n \"input\": \"104563\",\n },\n {\n \"outputType\": \"isin\",\n \"outputValue\": \"US90328S5001\",\n \"startDate\": \"2021-01-01\",\n \"endDate\": \"2021-04-18\",\n \"input\": \"104563\",\n },\n {\n \"outputType\": \"isin\",\n \"outputValue\": \"US1381031061\",\n \"startDate\": \"2021-04-19\",\n \"endDate\": \"2021-11-01\",\n \"input\": \"104563\",\n },\n ]\n }\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock])\n start = dt.date(2021, 1, 1)\n end = dt.date(2021, 11, 1)\n\n expected = {\n \"2021-04-16\": {\"104563\": {\"ric\": [\"USAT.OQ\"], \"gsid\": [104563], \"isin\": [\"US90328S5001\"], \"bcid\": [\"USAT US\"]}},\n \"2021-04-19\": {\"104563\": {\"ric\": [\"CTLP.OQ\"], \"gsid\": [104563], \"isin\": [\"US1381031061\"], \"bcid\": [\"CTLP US\"]}},\n }\n targets = [SecurityIdentifier.RIC, SecurityIdentifier.GSID, SecurityIdentifier.ISIN, SecurityIdentifier.BCID]\n with SecMasterContext():\n actual = SecurityMaster.map_identifiers(SecurityIdentifier.GSID, ['104563'], targets, start, end)\n for k, v in expected.items():\n assert actual[k] == v\n\n\ndef test_map_identifiers_empty(mocker):\n mock = {\"results\": []}\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock])\n\n with SecMasterContext():\n actual = SecurityMaster.map_identifiers(SecurityIdentifier.BBID, ['invalid id'], [SecurityIdentifier.RIC])\n assert actual == {}\n\n\ndef test_map_identifiers_eq_index(mocker):\n \"\"\"\n Test to ensure that gsq result does not append exchange or compositeExchange to Bcid and Bbid if secmaster api\n does not respond any (Mainly from mapping equity indices).\n \"\"\"\n mock = {\n \"results\": [\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"SPX\",\n \"startDate\": \"2022-03-17\",\n \"endDate\": \"2022-03-17\",\n \"input\": \"100\",\n }\n ]\n }\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock])\n\n with SecMasterContext():\n actual = SecurityMaster.map_identifiers(\n SecurityIdentifier.GSID, ['100'], [SecurityIdentifier.BBID, SecurityIdentifier.BCID]\n )\n assert actual == {'2022-03-17': {'100': {'bbid': ['SPX']}}}\n\n\ndef test_secmaster_map_identifiers_with_passed_input_types(mocker):\n start = str(dt.date(2021, 10, 11))\n end = str(dt.date(2021, 10, 12))\n\n def mock_mapping_service_response_by_input_type(*args, **kwargs):\n '''\n Mocks Secmaster api's response json based on payload's input_type, output_type, and ids provided\n '''\n input_type = None\n for enum in SecurityIdentifier:\n if enum.value in kwargs['payload']:\n input_type = enum.value\n break\n output_types = kwargs['payload']['toIdentifiers']\n\n mock_output = {'results': []}\n for id in kwargs['payload'][input_type]:\n for output_type in output_types:\n row = {\n \"outputType\": output_type,\n \"outputValue\": \"mock output for \" + id,\n \"startDate\": start,\n \"endDate\": end,\n \"input\": id,\n }\n if output_type in (SecurityIdentifier.BBID, SecurityIdentifier.BBG, SecurityIdentifier.BCID):\n row['exchange'] = 'mock-exchange'\n row['compositeExchange'] = 'mock-comp'\n mock_output['results'].append(row)\n return mock_output\n\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_mapping_service_response_by_input_type)\n\n with SecMasterContext():\n mock_any_ids = [\"mock-any-1\", \"mock-any-2\"]\n any_to_cusip_results = SecurityMaster.map_identifiers(\n input_type=SecurityIdentifier.ANY, ids=mock_any_ids, output_types=[SecurityIdentifier.CUSIP]\n )\n assert start in any_to_cusip_results.keys()\n for input_id in mock_any_ids:\n assert input_id in any_to_cusip_results[start].keys()\n assert SecurityIdentifier.CUSIP.value in any_to_cusip_results[start][input_id].keys()\n assert any_to_cusip_results == {\n \"2021-10-11\": {\n \"mock-any-1\": {\"cusip\": [\"mock output for mock-any-1\"]},\n \"mock-any-2\": {\"cusip\": [\"mock output for mock-any-2\"]},\n },\n \"2021-10-12\": {\n \"mock-any-1\": {\"cusip\": [\"mock output for mock-any-1\"]},\n \"mock-any-2\": {\"cusip\": [\"mock output for mock-any-2\"]},\n },\n }\n\n mock_cusip_ids = [\"mock-cusip-input1\", \"mock-cusip-input2\"]\n cusip_to_isin_result = SecurityMaster.map_identifiers(\n input_type=SecurityIdentifier.CUSIP, ids=mock_cusip_ids, output_types=[SecurityIdentifier.ISIN]\n )\n assert start in cusip_to_isin_result.keys()\n for cusip_input_id in mock_cusip_ids:\n assert cusip_input_id in cusip_to_isin_result[start].keys()\n assert SecurityIdentifier.ISIN.value in cusip_to_isin_result[start][cusip_input_id].keys()\n assert cusip_to_isin_result == {\n \"2021-10-11\": {\n \"mock-cusip-input1\": {\"isin\": [\"mock output for mock-cusip-input1\"]},\n \"mock-cusip-input2\": {\"isin\": [\"mock output for mock-cusip-input2\"]},\n },\n \"2021-10-12\": {\n \"mock-cusip-input1\": {\"isin\": [\"mock output for mock-cusip-input1\"]},\n \"mock-cusip-input2\": {\"isin\": [\"mock output for mock-cusip-input2\"]},\n },\n }\n\n\ndef test_secmaster_map_identifiers_return_array_results(mocker):\n \"\"\"\n Check if map endpoint returns multi-valued response in arrays\n \"\"\"\n mock = {\n \"results\": [\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"GS\",\n \"exchange\": \"UN\",\n \"compositeExchange\": \"US\",\n \"startDate\": \"2022-03-21\",\n \"endDate\": \"2022-03-21\",\n \"input\": \"38141G104\",\n },\n {\n \"outputType\": \"cusip\",\n \"outputValue\": \"38141G104\",\n \"startDate\": \"2022-03-21\",\n \"endDate\": \"2022-03-21\",\n \"input\": \"38141G104\",\n },\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"GOS\",\n \"exchange\": \"TH\",\n \"startDate\": \"2022-03-21\",\n \"endDate\": \"2022-03-21\",\n \"input\": \"38141G104\",\n },\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"GSCHF\",\n \"exchange\": \"EU\",\n \"startDate\": \"2022-03-21\",\n \"endDate\": \"2022-03-21\",\n \"input\": \"38141G104\",\n },\n {\n \"outputType\": \"bbg\",\n \"outputValue\": \"GSUSD\",\n \"exchange\": \"SE\",\n \"compositeExchange\": \"SW\",\n \"startDate\": \"2022-03-21\",\n \"endDate\": \"2022-03-21\",\n \"input\": \"38141G104\",\n },\n ]\n }\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock])\n with SecMasterContext():\n actual = SecurityMaster.map_identifiers(\n input_type=SecurityIdentifier.CUSIP,\n ids=['38141G104'],\n output_types=[SecurityIdentifier.BBID, SecurityIdentifier.BCID, SecurityIdentifier.CUSIP],\n )\n assert actual == {\n '2022-03-21': {\n '38141G104': {\n 'bbid': ['GS UN', 'GOS TH', 'GSCHF EU', 'GSUSD SE'],\n 'bcid': ['GS US', 'GSUSD SW'],\n 'cusip': ['38141G104'],\n }\n }\n }\n\n\ndef test_secmaster_get_asset_no_asset_id_response_should_fail(mocker):\n mock_response = {\n \"results\": [\n {\n \"name\": \"GOLDMAN SACHS GROUP INC (US Stock Exchange Composite)\",\n \"type\": \"Common Stock\",\n \"currency\": \"USD\",\n \"tags\": [],\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 901026,\n \"cusip\": \"38141G104\",\n \"cusip8\": \"38141G10\",\n \"sedol\": \"2407966\",\n \"isin\": \"US38141G1040\",\n \"ticker\": \"GS\",\n \"bcid\": \"GS US\",\n \"primeId\": \"1003232152\",\n \"factSetRegionalId\": \"JLJ0VZ-R\",\n \"rcic\": \"GS\",\n },\n \"exchange\": {\"name\": \"US Stock Exchange Composite\", \"identifiers\": {\"gsExchangeId\": 161}},\n \"id\": \"GSPD901026E161\",\n }\n ],\n \"totalResults\": 1,\n }\n\n mock_no_asset_id_response = {\"results\": []}\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock_response, mock_no_asset_id_response])\n\n with SecMasterContext():\n asset = SecurityMaster.get_asset(id_value=\"GS\", id_type=SecurityIdentifier.TICKER)\n with pytest.raises(MqValueError):\n asset.get_marquee_id()\n\n\ndef test_secmaster_get_asset_returning_secmasterassets(mocker):\n def assert_asset_common(asset: Asset) -> None:\n with pytest.raises(MqTypeError):\n asset.get_identifier(id_type=AssetIdentifier.BLOOMBERG_ID)\n\n # get_asset() should return Stock instance when type: Common Stock\n mock_equity_response = {\n \"results\": [\n {\n \"name\": \"GOLDMAN SACHS GROUP INC (New York Stock)\",\n \"type\": \"Common Stock\",\n \"currency\": \"USD\",\n \"tags\": [],\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 901026,\n \"cusip\": \"38141G104\",\n \"cusip8\": \"38141G10\",\n \"sedol\": \"2407966\",\n \"isin\": \"US38141G1040\",\n \"ticker\": \"GS\",\n \"bbid\": \"GS UN\",\n \"bcid\": \"GS US\",\n \"primeId\": \"1003232152\",\n \"factSetRegionalId\": \"JLJ0VZ-R\",\n \"rcic\": \"GS\",\n \"ric\": \"GS.N\",\n \"assetId\": \"MA4B66MW5E27UAHKG34\",\n },\n \"exchange\": {\"name\": \"New York Stock\", \"identifiers\": {\"gsExchangeId\": 154}},\n \"id\": \"GSPD901026E154\",\n }\n ],\n \"totalResults\": 1,\n }\n\n mock_eq_id_history_response = {\n \"results\": [\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"GS\",\n \"updateTime\": \"2002-02-09T17:58:27.58Z\",\n \"type\": \"bbg\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"38141G104\",\n \"updateTime\": \"2002-02-09T17:54:27.99Z\",\n \"type\": \"cusip\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"38141G10\",\n \"updateTime\": \"2002-02-09T17:54:27.99Z\",\n \"type\": \"cusip8\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"JLJ0VZ-R\",\n \"updateTime\": \"2021-08-16T08:41:43.586Z\",\n \"type\": \"factSetRegionalId\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"US38141G1040\",\n \"updateTime\": \"2002-02-09T17:55:18.513Z\",\n \"type\": \"isin\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"1003232152\",\n \"updateTime\": \"2003-01-16T15:22:54.1Z\",\n \"type\": \"primeId\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"2407966\",\n \"updateTime\": \"2002-02-09T17:54:47.77Z\",\n \"type\": \"sedol\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"GS\",\n \"updateTime\": \"2002-02-09T17:57:14.546Z\",\n \"type\": \"ticker\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"assetId\",\n \"value\": \"MA4B66MW5E27UAHKG34\",\n \"updateTime\": \"2002-10-30T21:30:29.993Z\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"ric\",\n \"value\": \"GS.N\",\n \"updateTime\": \"2002-10-30T21:30:29.993Z\",\n \"gsExchangeId\": 154,\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"1003232152\",\n \"updateTime\": \"2003-01-16T15:22:54.1Z\",\n \"type\": \"primeId\",\n },\n ]\n }\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock_equity_response, mock_eq_id_history_response])\n with SecMasterContext():\n stock = SecurityMaster.get_asset(id_value=901026, id_type=SecurityIdentifier.GSID)\n assert isinstance(stock, SecMasterAsset)\n assert stock.get_type() == AssetType.COMMON_STOCK\n assert stock.get_marquee_id() == \"MA4B66MW5E27UAHKG34\"\n assert stock.get_identifier(id_type=SecurityIdentifier.BBG) == \"GS\"\n assert stock.get_identifier(id_type=SecurityIdentifier.ID) == \"GSPD901026E154\"\n assert stock.get_identifier(id_type=SecurityIdentifier.ASSET_ID) == \"MA4B66MW5E27UAHKG34\"\n assert stock.currency == \"USD\"\n assert stock.get_identifiers() == {\n 'assetId': 'MA4B66MW5E27UAHKG34',\n 'bbg': 'GS',\n 'cusip': '38141G104',\n 'cusip8': '38141G10',\n 'gsid': 901026,\n 'id': 'GSPD901026E154',\n 'isin': 'US38141G1040',\n 'primeId': '1003232152',\n 'ric': 'GS.N',\n 'sedol': '2407966',\n 'ticker': 'GS',\n }\n assert_asset_common(stock)\n\n # get_asset() should return Index instance when type: Equity Index\n mock_index_response = {\n \"results\": [\n {\n \"name\": \"S&P 500 INDEX\",\n \"type\": \"Equity Index\",\n \"currency\": \"USD\",\n \"tags\": [],\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 100,\n \"ticker\": \"SPX\",\n \"bbid\": \"SPX\",\n \"ric\": \".SPX\",\n \"assetId\": \"MA4B66MW5E27U8P32SB\",\n },\n \"company\": {\"name\": \"S&P 500 Index\", \"identifiers\": {\"gsCompanyId\": 10756}},\n \"id\": \"GSPD100\",\n }\n ],\n \"totalResults\": 1,\n }\n mock_index_id_history_response = {\n \"results\": [\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"2012-08-24\",\n \"value\": \"SPX\",\n \"updateTime\": \"2012-08-25T23:27:53.44Z\",\n \"type\": \"bbg\",\n },\n {\n \"startDate\": \"2012-08-25\",\n \"endDate\": \"2012-08-25\",\n \"value\": \"SPX\",\n \"updateTime\": \"2020-12-10T21:07:06.26Z\",\n \"type\": \"bbg\",\n },\n {\n \"startDate\": \"2012-08-26\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"SPX\",\n \"updateTime\": \"2012-08-27T01:48:07.046Z\",\n \"type\": \"bbg\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"2012-08-24\",\n \"value\": \"SPX\",\n \"updateTime\": \"2012-08-25T23:27:53.4Z\",\n \"type\": \"ticker\",\n },\n {\n \"startDate\": \"2012-08-25\",\n \"endDate\": \"2012-08-25\",\n \"value\": \"SPX\",\n \"updateTime\": \"2020-12-10T21:06:44.82Z\",\n \"type\": \"ticker\",\n },\n {\n \"startDate\": \"2012-08-26\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"SPX\",\n \"updateTime\": \"2012-08-27T01:48:07.043Z\",\n \"type\": \"ticker\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"assetId\",\n \"value\": \"MA4B66MW5E27U8P32SB\",\n \"updateTime\": \"2003-01-14T17:28:15.29Z\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"ric\",\n \"value\": \".SPX\",\n \"updateTime\": \"2003-01-14T17:28:15.29Z\",\n \"gsExchangeId\": 0,\n },\n ]\n }\n mocker.patch.object(\n GsSession.current.sync, 'get', side_effect=[mock_index_response, mock_index_id_history_response]\n )\n with SecMasterContext():\n index = SecurityMaster.get_asset(id_value=100, id_type=SecurityIdentifier.GSID)\n assert isinstance(index, SecMasterAsset)\n assert index.get_type() == AssetType.EQUITY_INDEX\n assert index.get_marquee_id() == \"MA4B66MW5E27U8P32SB\"\n assert index.get_identifier(id_type=SecurityIdentifier.RIC) == \".SPX\"\n assert index.get_identifier(id_type=SecurityIdentifier.ID) == \"GSPD100\"\n assert index.get_identifier(id_type=SecurityIdentifier.ASSET_ID) == \"MA4B66MW5E27U8P32SB\"\n assert index.currency == \"USD\"\n assert index.get_identifiers() == {\n 'assetId': 'MA4B66MW5E27U8P32SB',\n 'bbg': 'SPX',\n 'gsid': 100,\n 'id': 'GSPD100',\n 'ric': '.SPX',\n 'ticker': 'SPX',\n }\n assert_asset_common(index)\n\n # get_asset() should return ETF instance when type: ETF\n mock_ETF_response = {\n \"results\": [\n {\n \"name\": \"ISHARES US TRANSPORTATION ET (BATS US Trading)\",\n \"type\": \"ETF\",\n \"currency\": \"USD\",\n \"tags\": [],\n \"assetClass\": \"Equity\",\n \"identifiers\": {\n \"gsid\": 159943,\n \"primeId\": \"355769575\",\n \"bbid\": \"IYT UF\",\n \"bcid\": \"IYT US\",\n \"ticker\": \"IYT\",\n \"isin\": \"US4642871929\",\n \"sedol\": \"2012423\",\n \"cusip\": \"464287192\",\n \"cusip8\": \"46428719\",\n \"rcic\": \"IYT\",\n \"ric\": \"IYT.Z\",\n \"assetId\": \"MAZ08H8QPDQ4T7SE\",\n },\n \"exchange\": {\"name\": \"BATS US Trading\", \"identifiers\": {\"gsExchangeId\": 535}},\n \"id\": \"GSPD159943E535\",\n }\n ],\n \"totalResults\": 1,\n }\n\n mock_etf_id_history_response = {\n \"results\": [\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"IYT\",\n \"updateTime\": \"2003-09-16T22:00:44.586Z\",\n \"type\": \"bbg\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"464287192\",\n \"updateTime\": \"2003-09-16T22:00:44.506Z\",\n \"type\": \"cusip\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"46428719\",\n \"updateTime\": \"2003-09-16T22:00:44.506Z\",\n \"type\": \"cusip8\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"US4642871929\",\n \"updateTime\": \"2003-09-16T22:00:44.52Z\",\n \"type\": \"isin\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"355769575\",\n \"updateTime\": \"2003-10-02T05:12:03.51Z\",\n \"type\": \"primeId\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"2012423\",\n \"updateTime\": \"2003-10-10T23:49:00.12Z\",\n \"type\": \"sedol\",\n },\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"IYT\",\n \"updateTime\": \"2003-09-16T22:00:44.56Z\",\n \"type\": \"ticker\",\n },\n {\n \"startDate\": \"2008-11-09\",\n \"endDate\": \"2017-08-01\",\n \"type\": \"assetId\",\n \"value\": \"MAZ08H8QPDQ4T7SE\",\n \"updateTime\": \"2017-08-02T05:36:56.823Z\",\n },\n {\n \"startDate\": \"2017-08-03\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"assetId\",\n \"value\": \"MAZ08H8QPDQ4T7SE\",\n \"updateTime\": \"2017-08-02T16:09:56.146Z\",\n },\n {\n \"startDate\": \"2008-11-09\",\n \"endDate\": \"2017-08-01\",\n \"type\": \"ric\",\n \"value\": \"IYT.Z\",\n \"updateTime\": \"2017-08-02T05:36:56.823Z\",\n \"gsExchangeId\": 535,\n },\n {\n \"startDate\": \"2017-08-03\",\n \"endDate\": \"9999-99-99\",\n \"type\": \"ric\",\n \"value\": \"IYT.Z\",\n \"updateTime\": \"2017-08-02T16:09:56.146Z\",\n \"gsExchangeId\": 535,\n },\n ]\n }\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock_ETF_response, mock_etf_id_history_response])\n with SecMasterContext():\n etf = SecurityMaster.get_asset(id_value=159943, id_type=SecurityIdentifier.GSID)\n assert isinstance(etf, SecMasterAsset)\n assert etf.get_type() == AssetType.ETF\n assert etf.get_marquee_id() == \"MAZ08H8QPDQ4T7SE\"\n assert etf.get_identifier(id_type=SecurityIdentifier.RIC) == \"IYT.Z\"\n assert etf.get_identifier(id_type=SecurityIdentifier.ID) == \"GSPD159943E535\"\n assert etf.get_identifier(id_type=SecurityIdentifier.ASSET_ID) == \"MAZ08H8QPDQ4T7SE\"\n assert etf.currency == \"USD\"\n assert etf.get_identifiers() == {\n \"gsid\": 159943,\n \"primeId\": \"355769575\",\n \"bbg\": \"IYT\",\n \"ticker\": \"IYT\",\n \"isin\": \"US4642871929\",\n \"sedol\": \"2012423\",\n \"cusip\": \"464287192\",\n \"cusip8\": \"46428719\",\n \"ric\": \"IYT.Z\",\n \"assetId\": \"MAZ08H8QPDQ4T7SE\",\n 'id': 'GSPD159943E535',\n }\n assert_asset_common(etf)\n\n # get_asset() should return Currency instance when type: Currency\n mock_currency_response = {\n \"results\": [\n {\n \"name\": \"USD U.S. DOLLAR\",\n \"type\": \"Currency\",\n \"currency\": \"USD\",\n \"tags\": [],\n \"assetClass\": \"Cash\",\n \"identifiers\": {\"gsid\": 4007, \"assetId\": \"MAZ7RWC904JYHYPS\", \"ticker\": \"USD\"},\n \"id\": \"GSPD4007\",\n }\n ],\n \"totalResults\": 1,\n }\n\n mock_currency_id_history_response = {\n \"results\": [\n {\n \"startDate\": \"2007-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"USD\",\n \"updateTime\": \"2003-05-01T16:20:44.47Z\",\n \"type\": \"ticker\",\n }\n ]\n }\n mocker.patch.object(\n GsSession.current.sync, 'get', side_effect=[mock_currency_response, mock_currency_id_history_response]\n )\n with SecMasterContext():\n currency = SecurityMaster.get_asset(id_value=4007, id_type=SecurityIdentifier.GSID)\n assert isinstance(currency, SecMasterAsset)\n assert currency.get_type() == AssetType.CURRENCY\n assert currency.get_marquee_id() == \"MAZ7RWC904JYHYPS\"\n assert currency.get_identifier(id_type=SecurityIdentifier.TICKER) == \"USD\"\n assert currency.get_identifier(id_type=SecurityIdentifier.ID) == \"GSPD4007\"\n assert currency.get_identifier(id_type=SecurityIdentifier.ASSET_ID) == \"MAZ7RWC904JYHYPS\"\n assert currency.get_identifiers() == {\n \"gsid\": 4007,\n \"assetId\": \"MAZ7RWC904JYHYPS\",\n \"ticker\": \"USD\",\n \"id\": \"GSPD4007\",\n }\n assert_asset_common(currency)\n\n\ndef test_get_asset_get_data_series_with_range_over_many_asset_id_should_throw_mqerror(mocker):\n mock_asset = {\n \"results\": [\n {\n \"name\": \"ISHARES US TRANSPORTATION ET (BATS US Trading)\",\n \"type\": \"ETF\",\n \"currency\": \"USD\",\n \"tags\": [],\n \"assetClass\": \"Equity\",\n \"identifiers\": {\"assetId\": \"MAZ08H8QPDQ4T7SE\"},\n \"exchange\": {\"name\": \"BATS US Trading\", \"identifiers\": {\"gsExchangeId\": 535}},\n \"id\": \"GSPD159943E535\",\n }\n ],\n \"totalResults\": 1,\n }\n mock_id_history_response = {\n \"results\": [\n {\n \"startDate\": \"2020-01-01\",\n \"endDate\": \"9999-99-99\",\n \"value\": \"marqueid 1\",\n \"updateTime\": \"2003-05-01T16:20:44.47Z\",\n \"type\": \"assetId\",\n },\n {\n \"startDate\": \"2007-12-30\",\n \"endDate\": \"2019-12-31\",\n \"value\": \"marqueid 2\",\n \"updateTime\": \"2003-05-01T16:20:44.47Z\",\n \"type\": \"assetId\",\n },\n ]\n }\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=[mock_asset, mock_id_history_response])\n\n with SecMasterContext():\n asset = SecurityMaster.get_asset(id_value=4007, id_type=SecurityIdentifier.GSID)\n with pytest.raises(MqValueError):\n asset.get_hloc_prices(start=dt.date(2007, 1, 1), end=dt.date(2022, 1, 1))\n\n\ndef test_map_identifiers_asset_service(mocker):\n response = {'AAPL UN': ['AAPL.N'], 'GS UN': ['GS.N']}\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=lambda *arg, **kwargs: response)\n expected = {\"2021-10-11\": {\"AAPL UN\": {\"ric\": [\"AAPL.N\"]}, \"GS UN\": {\"ric\": [\"GS.N\"]}}}\n with AssetContext():\n actual = SecurityMaster.map_identifiers(\n SecurityIdentifier.BBID, ['GS UN', 'AAPL UN'], [SecurityIdentifier.RIC], as_of_date=dt.date(2021, 10, 11)\n )\n assert actual == expected\n\n date_string = dt.date.today().strftime('%Y-%m-%d')\n expected2 = {date_string: expected[\"2021-10-11\"]}\n with AssetContext():\n actual2 = SecurityMaster.map_identifiers(\n SecurityIdentifier.BBID, ['GS UN', 'AAPL UN'], [SecurityIdentifier.RIC]\n )\n assert actual2 == expected2\n\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=lambda *arg, **kwargs: {})\n with AssetContext():\n actual = SecurityMaster.map_identifiers(\n SecurityIdentifier.BBID, ['invalid id'], [SecurityIdentifier.RIC], as_of_date=dt.date(2021, 10, 11)\n )\n assert actual == {}\n\n\ndef test_map_identifiers_asset_service_exceptions():\n with pytest.raises(MqValueError):\n # multiple output types\n with AssetContext():\n SecurityMaster.map_identifiers(\n SecurityIdentifier.BBID,\n ['GS UN', 'AAPL UN'],\n [SecurityIdentifier.RIC, SecurityIdentifier.GSID],\n as_of_date=dt.date(2021, 10, 11),\n )\n\n with pytest.raises(MqValueError):\n # start date\n with AssetContext():\n SecurityMaster.map_identifiers(\n SecurityIdentifier.BBID,\n ['GS UN', 'AAPL UN'],\n [SecurityIdentifier.RIC],\n start_date=dt.date(2021, 10, 11),\n )\n\n with pytest.raises(MqValueError):\n # end date\n with AssetContext():\n SecurityMaster.map_identifiers(\n SecurityIdentifier.BBID, ['GS UN', 'AAPL UN'], [SecurityIdentifier.RIC], end_date=dt.date(2021, 10, 11)\n )\n\n with pytest.raises(MqValueError):\n # unsupported output type\n with AssetContext():\n SecurityMaster.map_identifiers(SecurityIdentifier.BBID, ['GS UN', 'AAPL UN'], [SecurityIdentifier.BBG])\n\n\nif __name__ == \"__main__\":\n pytest.main([__file__])\n" }, { "path": "gs_quant/test/mock_data_test_utils.py", "content": "\"\"\"\nCopyright 2024 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport pytest\n\nFAILED_TESTS = []\nRUN_TESTS = []\nLOGGED_EVENTS = []\n\n\ndef did_anything_fail():\n return len(FAILED_TESTS) > 0\n\n\ndef did_anything_run():\n return len(RUN_TESTS) > 0\n\n\ndef log_mock_data_event(event: str):\n LOGGED_EVENTS.append(event)\n\n\ndef pytest_addoption(parser):\n parser.addoption('--fixmockdata', action='store_true')\n\n\ndef pytest_configure(config):\n config.addinivalue_line(\"markers\", \"fixmockdata: marker to update mock file data\")\n\n\n@pytest.hookimpl(tryfirst=True)\ndef pytest_runtest_makereport(item, call) -> None:\n \"\"\"Called to make report for the test for test item (the call phase).\n\n :param item:\n The item.\n \"\"\"\n if call.when == 'call':\n RUN_TESTS.append(item.nodeid)\n if call.excinfo:\n FAILED_TESTS.append((item.nodeid, item))\n\n\ndef pytest_terminal_summary(terminalreporter, exitstatus, config):\n terminalreporter.ensure_newline()\n terminalreporter.section(\"Mock File Summary\")\n if not len(LOGGED_EVENTS):\n terminalreporter.write_line(\"Nothing Changed\")\n for event in LOGGED_EVENTS:\n terminalreporter.write_line(event)\n\n\ndef pytest_collection_modifyitems(items):\n LOGGED_EVENTS.clear()\n FAILED_TESTS.clear()\n for item in items:\n # Skip the integration tests unless explicitly asked for\n run_fix_mock_data = item.config.getoption(\"fixmockdata\") # run with pytest . --fixmockdata\n if any(marker.name == 'fixmockdata' for marker in item.own_markers) and not run_fix_mock_data:\n item.add_marker(pytest.mark.skip(reason='Skipping Mock Data updating'))\n" }, { "path": "gs_quant/test/models/__init__.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n" }, { "path": "gs_quant/test/models/test_epidemiology.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport numpy as np\n\nfrom gs_quant.models.epidemiology import SIR, EpidemicModel, SEIR\n\n\ndef test_SIR():\n # Initialize a model\n beta_0 = 0.1\n gamma_0 = 0.2\n parameters, initial_conditions = SIR.get_parameters(\n 99,\n 1,\n 0,\n 100,\n beta=beta_0,\n gamma=gamma_0,\n S0_fixed=True,\n I0_fixed=False,\n R0_fixed=True,\n S0_max=10e6,\n I0_max=5e6,\n R0_max=10e6,\n )\n sir = EpidemicModel(SIR, parameters=parameters, initial_conditions=initial_conditions)\n\n # Solve a problem with an independently checkable solution\n N = 100\n S0 = 99\n I0 = 1\n R0 = 0\n beta = 0.5\n gamma = 0.25\n days_ahead = 40 # days ahead to look at\n T = np.arange(days_ahead)\n forecast = sir.solve(T, (S0, I0, R0), (beta, gamma, N))\n\n # get the curve data as if it were our data\n S_data = forecast[:, 0]\n I_data = forecast[:, 1]\n R_data = forecast[:, 2]\n data = np.array([S_data, I_data, R_data]).T\n\n # slightly perturbed parameters, to check if the fitting finds the original (optimal) parameters\n parameters, initial_conditions = SIR.get_parameters(\n S_data[0],\n I_data[0],\n R_data[0],\n N,\n beta=beta + 0.4,\n gamma=gamma + 0.2,\n S0_fixed=True,\n I0_fixed=True,\n R0_fixed=True,\n S0_max=10e6,\n I0_max=5e6,\n R0_max=10e6,\n )\n sir = EpidemicModel(SIR, parameters=parameters, data=data, initial_conditions=initial_conditions)\n sir.fit()\n\n # check fitted parameters\n beta_fitted = sir.fitted_parameters['beta']\n gamma_fitted = sir.fitted_parameters['gamma']\n\n assert np.isclose(beta, beta_fitted)\n assert np.isclose(gamma, gamma_fitted)\n\n sir = EpidemicModel(SIR, parameters=parameters, data=data, initial_conditions=initial_conditions, fit_period=10)\n sir.fit()\n\n # check fitted parameters\n beta_fitted = sir.fitted_parameters['beta']\n gamma_fitted = sir.fitted_parameters['gamma']\n\n assert np.isclose(beta, beta_fitted)\n assert np.isclose(gamma, gamma_fitted)\n\n\ndef test_SEIR():\n # Initialize a model\n beta_0 = 0.1\n gamma_0 = 0.2\n sigma_0 = 0.1\n parameters, initial_conditions = SEIR.get_parameters(\n 99,\n 1,\n 1,\n 0,\n 100,\n beta=beta_0,\n gamma=gamma_0,\n sigma=sigma_0,\n S0_fixed=True,\n E0_fixed=True,\n I0_fixed=False,\n R0_fixed=True,\n S0_max=10e6,\n I0_max=5e6,\n R0_max=10e6,\n )\n sir = EpidemicModel(SEIR, parameters=parameters, initial_conditions=initial_conditions)\n\n # Solve a problem with an independently checkable solution\n N = 100\n S0 = 99\n E0 = 1\n I0 = 1\n R0 = 0\n beta = 0.5\n gamma = 0.25\n sigma = 0.2\n days_ahead = 40 # days ahead to look at\n T = np.arange(days_ahead)\n forecast = sir.solve(T, (S0, E0, I0, R0), (beta, gamma, sigma, N))\n\n # get the curve data as if it were our data\n S_data = forecast[:, 0]\n E_data = forecast[:, 1]\n I_data = forecast[:, 2]\n R_data = forecast[:, 3]\n data = np.array([S_data, E_data, I_data, R_data]).T\n\n # slightly perturbed parameters, to check if the fitting finds the original (optimal) parameters\n parameters, initial_conditions = SEIR.get_parameters(\n S_data[0],\n E_data[0],\n I_data[0],\n R_data[0],\n N,\n beta=beta + 0.4,\n gamma=gamma + 0.2,\n sigma=sigma + 0.1,\n S0_fixed=True,\n E0_fixed=True,\n I0_fixed=True,\n R0_fixed=True,\n S0_max=10e6,\n I0_max=5e6,\n R0_max=10e6,\n )\n seir = EpidemicModel(SEIR, parameters=parameters, data=data, initial_conditions=initial_conditions)\n seir.fit()\n\n # check fitted parameters\n beta_fitted = seir.fitted_parameters['beta']\n gamma_fitted = seir.fitted_parameters['gamma']\n sigma_fitted = seir.fitted_parameters['sigma']\n\n assert np.isclose(beta, beta_fitted)\n assert np.isclose(gamma, gamma_fitted)\n assert np.isclose(sigma, sigma_fitted)\n\n seir = EpidemicModel(SEIR, parameters=parameters, data=data, initial_conditions=initial_conditions, fit_period=10)\n seir.fit()\n\n # check fitted parameters\n beta_fitted = seir.fitted_parameters['beta']\n gamma_fitted = seir.fitted_parameters['gamma']\n sigma_fitted = seir.fitted_parameters['sigma']\n\n assert np.isclose(beta, beta_fitted)\n assert np.isclose(gamma, gamma_fitted)\n assert np.isclose(sigma, sigma_fitted)\n" }, { "path": "gs_quant/test/models/test_risk_model.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest import mock\n\nimport pytest\nfrom pandas._testing import assert_frame_equal\n\nfrom gs_quant.common import Currency\nfrom gs_quant.models.risk_model import FactorRiskModel, MacroRiskModel, ReturnFormat, Unit\nfrom gs_quant.models.risk_model_utils import get_optional_data_as_dataframe, _map_measure_to_field_name\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.target.risk_models import (\n RiskModel as Risk_Model,\n RiskModelCoverage,\n RiskModelTerm,\n RiskModelUniverseIdentifier,\n RiskModelType,\n RiskModelDataAssetsRequest as DataAssetsRequest,\n RiskModelDataMeasure as Measure,\n RiskModelUniverseIdentifierRequest as UniverseIdentifier,\n)\n\nempty_entitlements = {\"execute\": [], \"edit\": [], \"view\": [], \"admin\": [], \"query\": [], \"upload\": []}\n\nmock_risk_model_obj = Risk_Model(\n RiskModelCoverage.Country,\n 'model_id',\n 'Fake Risk Model',\n RiskModelTerm.Long,\n RiskModelUniverseIdentifier.gsid,\n 'GS',\n 1.0,\n universe_size=10000,\n entitlements=empty_entitlements,\n description='Test',\n expected_update_time='00:00:00',\n type=RiskModelType.Factor,\n)\n\nmock_macro_risk_model_obj = Risk_Model(\n coverage=RiskModelCoverage.Country,\n id='macro_model_id',\n name='Fake Risk Model',\n term=RiskModelTerm.Long,\n universe_identifier=RiskModelUniverseIdentifier.gsid,\n vendor='GS',\n version=1.0,\n entitlements=empty_entitlements,\n description='Test',\n expected_update_time='00:00:00',\n type=RiskModelType.Macro,\n)\n\n\ndef mock_risk_model(mocker):\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_risk_model_obj)\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_risk_model_obj)\n mocker.patch.object(GsSession.current.sync, 'put', return_value=mock_risk_model_obj)\n return FactorRiskModel.get('model_id')\n\n\ndef mock_macro_risk_model(mocker):\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_macro_risk_model_obj)\n mocker.patch.object(GsSession.current.sync, 'get', return_value=mock_macro_risk_model_obj)\n mocker.patch.object(GsSession.current.sync, 'put', return_value=mock_macro_risk_model_obj)\n return MacroRiskModel.get('macro_model_id')\n\n\ndef test_create_risk_model(mocker):\n mock_risk_model(mocker)\n risk_model_id = 'model_id'\n mocker.patch.object(GsSession.current.sync, 'post', return_value=mock_risk_model_obj)\n new_model = FactorRiskModel(\n risk_model_id,\n 'Fake Risk Model',\n RiskModelCoverage.Country,\n RiskModelTerm.Long,\n RiskModelUniverseIdentifier.gsid,\n 'GS',\n 0.1,\n universe_size=10000,\n entitlements={},\n description='Test',\n expected_update_time=dt.datetime.strptime('00:00:00', '%H:%M:%S').time(),\n )\n new_model.save()\n assert new_model.id == mock_risk_model_obj.id\n assert new_model.name == mock_risk_model_obj.name\n assert new_model.description == mock_risk_model_obj.description\n assert new_model.term == mock_risk_model_obj.term\n assert new_model.universe_size == mock_risk_model_obj.universe_size\n assert new_model.coverage == mock_risk_model_obj.coverage\n assert new_model.universe_identifier == mock_risk_model_obj.universe_identifier\n assert (\n new_model.expected_update_time\n == dt.datetime.strptime(mock_risk_model_obj.expected_update_time, '%H:%M:%S').time()\n )\n\n\ndef test_update_risk_model_entitlements(mocker):\n new_model = mock_risk_model(mocker)\n new_entitlements = {\"execute\": ['guid:X'], \"edit\": [], \"view\": [], \"admin\": [], \"query\": [], \"upload\": []}\n\n new_model.entitlements = new_entitlements\n new_model.save()\n assert 'guid:X' in new_model.entitlements.get('execute')\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n new_model.entitlements = empty_entitlements\n new_model.save()\n new_entitlements = {\n \"execute\": ['guid:X'],\n \"edit\": [],\n \"view\": [],\n \"admin\": ['guid:XX'],\n \"query\": [],\n \"upload\": ['guid:XXX'],\n }\n new_model.entitlements = new_entitlements\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert 'guid:X' in new_model.entitlements.get('execute')\n assert 'guid:XX' in new_model.entitlements.get('admin')\n assert 'guid:XXX' in new_model.entitlements.get('upload')\n\n\ndef test_update_risk_model(mocker):\n new_model = mock_risk_model(mocker)\n\n new_model.term = RiskModelTerm.Short\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.term == RiskModelTerm.Short\n\n new_model.description = 'Test risk model'\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.description == 'Test risk model'\n\n new_model.vendor = 'GS'\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.vendor == 'GS'\n\n new_model.term = RiskModelTerm.Medium\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.term == RiskModelTerm.Medium\n\n new_model.version = 0.1\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.version == 0.1\n\n new_model.universe_size = 10000\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.universe_size == 10000\n\n new_model.coverage = RiskModelCoverage.Global\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.coverage == RiskModelCoverage.Global\n\n new_model.name = 'TEST RISK MODEL'\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.name == 'TEST RISK MODEL'\n\n new_model.expected_update_time = dt.time(1, 0, 0)\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.expected_update_time == dt.time(1, 0, 0)\n\n new_model.type = RiskModelType.Thematic\n new_model.save()\n mocker.patch.object(GsSession.current.sync, 'get', return_value=new_model)\n assert new_model.type == RiskModelType.Thematic\n\n\ndef test_get_r_squared(mocker):\n macro_model = mock_macro_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [Measure.R_Squared, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\"universe\": [\"904026\", \"232128\", \"24985\", \"160444\"], \"rSquared\": [89.0, 45.0, 12.0, 5.0]},\n }\n ],\n 'totalResults': 1,\n }\n\n r_squared_response = {\n '160444': {'2022-04-05': 5.0},\n '232128': {'2022-04-05': 45.0},\n '24985': {'2022-04-05': 12.0},\n '904026': {'2022-04-05': 89.0},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = macro_model.get_r_squared(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n format=ReturnFormat.JSON,\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='macro_model_id'), query, timeout=200\n )\n assert response == r_squared_response\n\n\ndef test_get_fair_value_gap_standard_deviation(mocker):\n macro_model = mock_macro_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [Measure.Fair_Value_Gap_Standard_Deviation, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"904026\", \"232128\", \"24985\", \"160444\"],\n \"fairValueGapStandardDeviation\": [4.0, 5.0, 1.0, 7.0],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n fvg_response = {\n '160444': {'2022-04-05': 7.0},\n '232128': {'2022-04-05': 5.0},\n '24985': {'2022-04-05': 1.0},\n '904026': {'2022-04-05': 4.0},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n response = macro_model.get_fair_value_gap(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n format=ReturnFormat.JSON,\n )\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='macro_model_id'), query, timeout=200\n )\n assert response == fvg_response\n\n\ndef test_get_fair_value_gap_percent(mocker):\n macro_model = mock_macro_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [Measure.Fair_Value_Gap_Percent, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"904026\", \"232128\", \"24985\", \"160444\"],\n \"fairValueGapPercent\": [90.0, 34.0, 8.0, 34.0],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n fvg_response = {\n '160444': {'2022-04-05': 34.0},\n '232128': {'2022-04-05': 34.0},\n '24985': {'2022-04-05': 8.0},\n '904026': {'2022-04-05': 90.0},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n response = macro_model.get_fair_value_gap(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n fair_value_gap_unit=Unit.PERCENT,\n format=ReturnFormat.JSON,\n )\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='macro_model_id'), query, timeout=200\n )\n assert response == fvg_response\n\n\n@pytest.mark.parametrize(\n \"statistical_measure, fieldKey\",\n [\n (Measure.Factor_Mean, _map_measure_to_field_name(Measure.Factor_Mean)),\n (Measure.Factor_Cross_Sectional_Mean, _map_measure_to_field_name(Measure.Factor_Cross_Sectional_Mean)),\n (Measure.Factor_Standard_Deviation, _map_measure_to_field_name(Measure.Factor_Standard_Deviation)),\n (\n Measure.Factor_Cross_Sectional_Standard_Deviation,\n _map_measure_to_field_name(Measure.Factor_Cross_Sectional_Standard_Deviation),\n ),\n ],\n)\ndef test_get_statistical_factor_data(mocker, statistical_measure, fieldKey):\n risk_model = mock_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [\n statistical_measure,\n Measure.Factor_Name,\n Measure.Factor_Id,\n Measure.Universe_Factor_Exposure,\n Measure.Asset_Universe,\n ],\n 'limitFactors': True,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"factorData\": [\n {\"factorId\": \"1\", f\"{fieldKey}\": 89.0, \"factorName\": \"Factor1\"},\n {\"factorId\": \"2\", f\"{fieldKey}\": 0.67, \"factorName\": \"Factor2\"},\n ],\n 'assetData': {\n 'factorExposure': [\n {'1': 0.2, '2': 0.3},\n {'1': 0.02, '2': 0.03},\n {'1': 6.2, '2': 3.0},\n {'1': -6.2, '2': 0.3},\n ],\n 'universe': ['904026', '232128', '24985', '160444'],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n expected_response = {'Factor1': {'2022-04-05': 89.0}, 'Factor2': {'2022-04-05': 0.67}}\n\n kwargs = {\n \"start_date\": dt.date(2022, 4, 4),\n \"end_date\": dt.date(2022, 4, 6),\n \"assets\": DataAssetsRequest(UniverseIdentifier.gsid, universe),\n \"format\": ReturnFormat.JSON,\n }\n\n field_key_to_getter_ref = {\n _map_measure_to_field_name(Measure.Factor_Mean): risk_model.get_factor_mean,\n _map_measure_to_field_name(Measure.Factor_Cross_Sectional_Mean): risk_model.get_factor_cross_sectional_mean,\n _map_measure_to_field_name(Measure.Factor_Standard_Deviation): risk_model.get_factor_standard_deviation,\n _map_measure_to_field_name(\n Measure.Factor_Cross_Sectional_Standard_Deviation\n ): risk_model.get_factor_cross_sectional_standard_deviation,\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n actual_response = field_key_to_getter_ref[fieldKey](**kwargs)\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n\n assert actual_response == expected_response\n\n\ndef test_get_factor_z_score(mocker):\n macro_model = mock_macro_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [\n Measure.Factor_Z_Score,\n Measure.Factor_Name,\n Measure.Factor_Id,\n Measure.Universe_Factor_Exposure,\n Measure.Asset_Universe,\n ],\n 'limitFactors': True,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"factorData\": [\n {\"factorId\": \"1\", \"factorZScore\": 1.5, \"factorName\": \"Factor1\"},\n {\"factorId\": \"2\", \"factorZScore\": -1.0, \"factorName\": \"Factor2\"},\n ],\n 'assetData': {\n 'factorExposure': [\n {'1': 0.2, '2': 0.3},\n {'1': 0.02, '2': 0.03},\n {'1': 6.2, '2': 3.0},\n {'1': -6.2, '2': 0.3},\n ],\n 'universe': ['904026', '232128', '24985', '160444'],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n factor_z_score_response = {'Factor1': {'2022-04-05': 1.5}, 'Factor2': {'2022-04-05': -1.0}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = macro_model.get_factor_z_score(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n format=ReturnFormat.JSON,\n )\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='macro_model_id'), query, timeout=200\n )\n assert response == factor_z_score_response\n\n\ndef test_get_predicted_beta(mocker):\n model = mock_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [Measure.Predicted_Beta, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"904026\", \"232128\", \"24985\", \"160444\"],\n \"predictedBeta\": [0.4, 1.5, 1.2, 0.5],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n predicted_beta_response = {\n '160444': {'2022-04-05': 0.5},\n '232128': {'2022-04-05': 1.5},\n '24985': {'2022-04-05': 1.2},\n '904026': {'2022-04-05': 0.4},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_predicted_beta(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n format=ReturnFormat.JSON,\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == predicted_beta_response\n\n\ndef test_get_global_predicted_beta(mocker):\n model = mock_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [Measure.Global_Predicted_Beta, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"904026\", \"232128\", \"24985\", \"160444\"],\n \"globalPredictedBeta\": [0.4, 1.5, 1.2, 0.5],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n global_predicted_beta_response = {\n '160444': {'2022-04-05': 0.5},\n '232128': {'2022-04-05': 1.5},\n '24985': {'2022-04-05': 1.2},\n '904026': {'2022-04-05': 0.4},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_global_predicted_beta(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n format=ReturnFormat.JSON,\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == global_predicted_beta_response\n\n\ndef test_get_estimation_universe_weights(mocker):\n model = mock_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [Measure.Estimation_Universe_Weight, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"904026\", \"232128\", \"24985\", \"160444\"],\n \"estimationUniverseWeight\": [0.4, None, None, 0.6],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n estu_response = {\n '160444': {'2022-04-05': 0.6},\n '232128': {'2022-04-05': None},\n '24985': {'2022-04-05': None},\n '904026': {'2022-04-05': 0.4},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_estimation_universe_weights(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n format=ReturnFormat.JSON,\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == estu_response\n\n\ndef test_get_daily_return(mocker):\n model = mock_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [Measure.Daily_Return, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"904026\", \"232128\", \"24985\", \"160444\"],\n \"dailyReturn\": [-0.4, -1.5, 1.2, 0.5],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n daily_return_response = {\n '160444': {'2022-04-05': 0.5},\n '232128': {'2022-04-05': -1.5},\n '24985': {'2022-04-05': 1.2},\n '904026': {'2022-04-05': -0.4},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_daily_return(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n format=ReturnFormat.JSON,\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == daily_return_response\n\n\ndef test_get_specific_return(mocker):\n model = mock_risk_model(mocker)\n universe = [\"904026\", \"232128\", \"24985\", \"160444\"]\n query = {\n 'startDate': '2022-04-04',\n 'endDate': '2022-04-06',\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, universe),\n 'measures': [Measure.Specific_Return, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'missingDates': ['2022-04-04', '2022-04-06'],\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"904026\", \"232128\", \"24985\", \"160444\"],\n \"specificReturn\": [0.5, 1.6, 1.4, 0.7],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n specific_return_response = {\n '160444': {'2022-04-05': 0.7},\n '232128': {'2022-04-05': 1.6},\n '24985': {'2022-04-05': 1.4},\n '904026': {'2022-04-05': 0.5},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_specific_return(\n start_date=dt.date(2022, 4, 4),\n end_date=dt.date(2022, 4, 6),\n assets=DataAssetsRequest(UniverseIdentifier.gsid, universe),\n format=ReturnFormat.JSON,\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == specific_return_response\n\n\n@pytest.mark.parametrize(\"aws_upload\", [True, False])\ndef test_upload_risk_model_data(mocker, aws_upload):\n model = mock_risk_model(mocker)\n risk_model_data = {\n 'date': '2023-04-14',\n 'assetData': {\n 'universe': ['2407966', '2046251', 'USD'],\n 'specificRisk': [12.09, 45.12, 3.09],\n 'factorExposure': [{'1': 0.23, '2': 0.023}, {'1': 0.23}, {'3': 0.23, '2': 0.023}],\n 'totalRisk': [0.12, 0.45, 1.2],\n },\n 'factorData': [\n {'factorId': '1', 'factorName': 'USD', 'factorCategory': 'Currency', 'factorCategoryId': 'CUR'},\n {'factorId': '2', 'factorName': 'ST', 'factorCategory': 'ST', 'factorCategoryId': 'ST'},\n {'factorId': '3', 'factorName': 'IND', 'factorCategory': 'IND', 'factorCategoryId': 'IND'},\n ],\n 'covarianceMatrix': [[0.089, 0.0123, 0.345], [0.0123, 3.45, 0.345], [0.345, 0.345, 1.23]],\n 'issuerSpecificCovariance': {'universeId1': ['2407966'], 'universeId2': ['2046251'], 'covariance': [0.03754]},\n 'factorPortfolios': {\n 'universe': ['2407966', '2046251'],\n 'portfolio': [\n {'factorId': 1, 'weights': [0.25, 0.75]},\n {'factorId': 2, 'weights': [0.25, 0.75]},\n {'factorId': 3, 'weights': [0.25, 0.75]},\n ],\n },\n }\n\n base_url = f\"/risk/models/data/{model.id}?partialUpload=true\"\n date = risk_model_data.get(\"date\")\n max_asset_batch_size = 2\n\n batched_asset_data = [\n {\n \"assetData\": {\n key: value[i : i + max_asset_batch_size] for key, value in risk_model_data.get(\"assetData\").items()\n },\n \"date\": date,\n }\n for i in range(0, len(risk_model_data.get(\"assetData\").get(\"universe\")), max_asset_batch_size)\n ]\n\n max_asset_batch_size //= 2\n batched_factor_portfolios = [\n {\n \"factorPortfolios\": {\n key: (\n value[i : i + max_asset_batch_size]\n if key in \"universe\"\n else [\n {\n \"factorId\": factor_weights.get(\"factorId\"),\n \"weights\": factor_weights.get(\"weights\")[i : i + max_asset_batch_size],\n }\n for factor_weights in value\n ]\n )\n for key, value in risk_model_data.get(\"factorPortfolios\").items()\n },\n \"date\": date,\n }\n for i in range(0, len(risk_model_data.get(\"factorPortfolios\").get(\"universe\")), max_asset_batch_size)\n ]\n\n expected_factor_data_calls = [\n mock.call(\n f\"{base_url}{'&awsUpload=true' if aws_upload else ''}\",\n {\n \"date\": date,\n \"factorData\": risk_model_data.get(\"factorData\"),\n \"covarianceMatrix\": risk_model_data.get(\"covarianceMatrix\"),\n },\n timeout=200,\n )\n ]\n\n expected_asset_data_calls = []\n for batch_num, batch_asset_payload in enumerate(batched_asset_data):\n final_upload_flag = 'true' if batch_num == len(batched_asset_data) - 1 else 'false'\n expected_asset_data_calls.append(\n mock.call(\n f\"{base_url}&finalUpload={final_upload_flag}{'&awsUpload=true' if aws_upload else ''}\",\n batch_asset_payload,\n timeout=200,\n )\n )\n\n expected_factor_portfolios_data_calls = []\n for batch_num, batched_fp_payload in enumerate(batched_factor_portfolios):\n final_upload_flag = 'true' if batch_num == len(batched_factor_portfolios) - 1 else 'false'\n expected_factor_portfolios_data_calls.append(\n mock.call(\n f\"{base_url}&finalUpload={final_upload_flag}{'&awsUpload=true' if aws_upload else ''}\",\n batched_fp_payload,\n timeout=200,\n )\n )\n\n expected_isc_data_calls = [\n mock.call(\n f\"{base_url}&finalUpload=true{'&awsUpload=true' if aws_upload else ''}\",\n {\"issuerSpecificCovariance\": risk_model_data.get(\"issuerSpecificCovariance\"), \"date\": date},\n timeout=200,\n )\n ]\n\n expected_calls = (\n expected_factor_data_calls\n + expected_asset_data_calls\n + expected_isc_data_calls\n + expected_factor_portfolios_data_calls\n )\n\n # mock GsSession\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'post', return_value='Upload Successful')\n\n max_asset_batch_size = 2\n model.upload_data(risk_model_data, max_asset_batch_size=max_asset_batch_size, aws_upload=aws_upload)\n\n call_args_list = GsSession.current.sync.post.call_args_list\n\n assert len(call_args_list) == len(expected_calls)\n assert call_args_list == expected_calls\n\n GsSession.current.sync.post.assert_has_calls(expected_calls, any_order=False)\n\n\n@pytest.mark.parametrize(\"days\", [0, 30, 60, 90])\ndef test_get_bid_ask_spread(mocker, days):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n measure_to_query = Measure.Bid_Ask_Spread if not days else Measure[f'Bid_Ask_Spread_{days}d']\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [measure_to_query, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"2046251\", \"2588173\"],\n f\"bidAskSpread{'' if days == 0 else f'{days}d'}\": [0.5, 1.6],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n bid_ask_spread_response = {'2588173': {'2022-04-05': 1.6}, '2046251': {'2022-04-05': 0.5}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_bid_ask_spread(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), days=days, assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == bid_ask_spread_response\n\n\n@pytest.mark.parametrize(\"days\", [0, 30, 60, 90])\ndef test_get_trading_volume(mocker, days):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n measure_to_query = Measure.Trading_Volume if not days else Measure[f'Trading_Volume_{days}d']\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [measure_to_query, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"2046251\", \"2588173\"],\n f\"tradingVolume{'' if days == 0 else f'{days}d'}\": [0.5, 1.6],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n trading_volume_response = {'2588173': {'2022-04-05': 1.6}, '2046251': {'2022-04-05': 0.5}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_trading_volume(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), days=days, assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == trading_volume_response\n\n\n@pytest.mark.parametrize(\"days\", [0, 30])\ndef test_get_traded_value(mocker, days):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n measure_to_query = Measure.Traded_Value_30d if not days else Measure[f'Traded_Value_{days}d']\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [measure_to_query, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\"date\": \"2022-04-05\", \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"tradedValue30d\": [0.5, 1.6]}}\n ],\n 'totalResults': 1,\n }\n\n trading_value_30d_response = {'2588173': {'2022-04-05': 1.6}, '2046251': {'2022-04-05': 0.5}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_traded_value(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), days=days, assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == trading_value_30d_response\n\n\n@pytest.mark.parametrize(\"days\", [0, 30, 60, 90])\ndef test_get_composite_volume(mocker, days):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n measure_to_query = Measure.Composite_Volume if not days else Measure[f'Composite_Volume_{days}d']\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [measure_to_query, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\n \"universe\": [\"2046251\", \"2588173\"],\n f\"compositeVolume{'' if days == 0 else f'{days}d'}\": [0.5, 1.6],\n },\n }\n ],\n 'totalResults': 1,\n }\n\n composite_volume_response = {'2588173': {'2022-04-05': 1.6}, '2046251': {'2022-04-05': 0.5}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_composite_volume(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), days=days, assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == composite_volume_response\n\n\n@pytest.mark.parametrize(\"days\", [0, 30])\ndef test_get_composite_value(mocker, days):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n measure_to_query = Measure.Composite_Value_30d if not days else Measure[f'Composite_Value_{days}d']\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [measure_to_query, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\"date\": \"2022-04-05\", \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"compositeValue30d\": [0.5, 1.6]}}\n ],\n 'totalResults': 1,\n }\n\n composite_value_response = {'2588173': {'2022-04-05': 1.6}, '2046251': {'2022-04-05': 0.5}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_composite_value(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), days=days, assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == composite_value_response\n\n\ndef test_get_issuer_market_cap(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [Measure.Issuer_Market_Cap, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"issuerMarketCap\": [1000000000, 2000000000]},\n }\n ],\n 'totalResults': 1,\n }\n\n issuer_market_cap_response = {'2588173': {'2022-04-05': 2000000000}, '2046251': {'2022-04-05': 1000000000}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_issuer_market_cap(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == issuer_market_cap_response\n\n\ndef test_get_asset_price(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [Measure.Price, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [{\"date\": \"2022-04-05\", \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"price\": [100, 200]}}],\n 'totalResults': 1,\n }\n\n price_response = {'2588173': {'2022-04-05': 200}, '2046251': {'2022-04-05': 100}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_asset_price(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == price_response\n\n\ndef test_get_asset_capitalization(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [Measure.Capitalization, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"capitalization\": [1000000000, 2000000000]},\n }\n ],\n 'totalResults': 1,\n }\n\n capitalization_response = {'2588173': {'2022-04-05': 2000000000}, '2046251': {'2022-04-05': 1000000000}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_asset_capitalization(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == capitalization_response\n\n\ndef test_get_currency(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [Measure.Currency, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\"date\": \"2022-04-05\", \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"currency\": [\"USD\", \"GBP\"]}}\n ],\n 'totalResults': 1,\n }\n\n currency_response = {'2588173': {'2022-04-05': \"GBP\"}, '2046251': {'2022-04-05': \"USD\"}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_currency(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == currency_response\n\n\ndef test_get_unadjusted_specific_risk(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [Measure.Unadjusted_Specific_Risk, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"unadjustedSpecificRisk\": [0.5, 1.6]},\n }\n ],\n 'totalResults': 1,\n }\n\n unadjusted_specific_risk_response = {'2588173': {'2022-04-05': 1.6}, '2046251': {'2022-04-05': 0.5}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_unadjusted_specific_risk(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == unadjusted_specific_risk_response\n\n\ndef test_get_dividend_yield(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [Measure.Dividend_Yield, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\"date\": \"2022-04-05\", \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"dividendYield\": [0.5, 1.6]}}\n ],\n 'totalResults': 1,\n }\n\n dividend_yield_response = {'2588173': {'2022-04-05': 1.6}, '2046251': {'2022-04-05': 0.5}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_dividend_yield(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == dividend_yield_response\n\n\ndef test_get_model_price(mocker):\n model = mock_macro_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [Measure.Model_Price, Measure.Asset_Universe],\n 'limitFactors': False,\n }\n\n results = {\n 'results': [\n {\"date\": \"2022-04-05\", \"assetData\": {\"universe\": [\"2046251\", \"2588173\"], \"modelPrice\": [100, 200]}}\n ],\n 'totalResults': 1,\n }\n\n model_price_response = {'2588173': {'2022-04-05': 200}, '2046251': {'2022-04-05': 100}}\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_model_price(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='macro_model_id'), query, timeout=200\n )\n assert response == model_price_response\n\n\ndef test_get_covariance_matrix(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [\n Measure.Covariance_Matrix,\n Measure.Factor_Name,\n Measure.Factor_Id,\n Measure.Universe_Factor_Exposure,\n Measure.Asset_Universe,\n ],\n 'limitFactors': True,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"factorData\": [\n {\n \"factorId\": \"1\",\n \"factorName\": \"factor1\",\n },\n {\n \"factorId\": \"2\",\n \"factorName\": \"factor2\",\n },\n {\n \"factorId\": \"3\",\n \"factorName\": \"factor3\",\n },\n {\n \"factorId\": \"4\",\n \"factorName\": \"factor4\",\n },\n ],\n \"covarianceMatrix\": [\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n ],\n }\n ],\n 'totalResults': 1,\n }\n\n covariance_matrix_response = [\n {\n \"date\": \"2022-04-05\",\n \"factorData\": [\n {\n \"factorId\": \"1\",\n \"factorName\": \"factor1\",\n },\n {\n \"factorId\": \"2\",\n \"factorName\": \"factor2\",\n },\n {\n \"factorId\": \"3\",\n \"factorName\": \"factor3\",\n },\n {\n \"factorId\": \"4\",\n \"factorName\": \"factor4\",\n },\n ],\n \"covarianceMatrix\": [\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n ],\n }\n ]\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_covariance_matrix(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == covariance_matrix_response\n\n\ndef test_get_unadjusted_covariance_matrix(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [\n Measure.Unadjusted_Covariance_Matrix,\n Measure.Factor_Name,\n Measure.Factor_Id,\n Measure.Universe_Factor_Exposure,\n Measure.Asset_Universe,\n ],\n 'limitFactors': True,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"factorData\": [\n {\n \"factorId\": \"1\",\n \"factorName\": \"factor1\",\n },\n {\n \"factorId\": \"2\",\n \"factorName\": \"factor2\",\n },\n {\n \"factorId\": \"3\",\n \"factorName\": \"factor3\",\n },\n {\n \"factorId\": \"4\",\n \"factorName\": \"factor4\",\n },\n ],\n \"unadjustedCovarianceMatrix\": [\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n ],\n }\n ],\n 'totalResults': 1,\n }\n\n unadjusted_covariance_matrix_response = [\n {\n \"date\": \"2022-04-05\",\n \"factorData\": [\n {\n \"factorId\": \"1\",\n \"factorName\": \"factor1\",\n },\n {\n \"factorId\": \"2\",\n \"factorName\": \"factor2\",\n },\n {\n \"factorId\": \"3\",\n \"factorName\": \"factor3\",\n },\n {\n \"factorId\": \"4\",\n \"factorName\": \"factor4\",\n },\n ],\n \"unadjustedCovarianceMatrix\": [\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n ],\n }\n ]\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_unadjusted_covariance_matrix(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == unadjusted_covariance_matrix_response\n\n\ndef test_get_pre_vra_covariance_matrix(mocker):\n model = mock_risk_model(mocker)\n\n universe = [\"2046251\", \"2588173\"]\n assets = DataAssetsRequest(UniverseIdentifier.sedol, universe)\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'assets': assets,\n 'measures': [\n Measure.Pre_VRA_Covariance_Matrix,\n Measure.Factor_Name,\n Measure.Factor_Id,\n Measure.Universe_Factor_Exposure,\n Measure.Asset_Universe,\n ],\n 'limitFactors': True,\n }\n\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"factorData\": [\n {\n \"factorId\": \"1\",\n \"factorName\": \"factor1\",\n },\n {\n \"factorId\": \"2\",\n \"factorName\": \"factor2\",\n },\n {\n \"factorId\": \"3\",\n \"factorName\": \"factor3\",\n },\n {\n \"factorId\": \"4\",\n \"factorName\": \"factor4\",\n },\n ],\n \"preVRACovarianceMatrix\": [\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n ],\n }\n ],\n 'totalResults': 1,\n }\n\n pre_vra_covariance_matrix_response = [\n {\n \"date\": \"2022-04-05\",\n \"factorData\": [\n {\n \"factorId\": \"1\",\n \"factorName\": \"factor1\",\n },\n {\n \"factorId\": \"2\",\n \"factorName\": \"factor2\",\n },\n {\n \"factorId\": \"3\",\n \"factorName\": \"factor3\",\n },\n {\n \"factorId\": \"4\",\n \"factorName\": \"factor4\",\n },\n ],\n \"preVRACovarianceMatrix\": [\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n [0.5, 0.6, 0.7, 0.8],\n [0.3, 0.7, 0.8, 0.9],\n ],\n }\n ]\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n # run test\n response = model.get_pre_vra_covariance_matrix(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), assets=assets, format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == pre_vra_covariance_matrix_response\n\n\ndef test_get_risk_free_rate(mocker):\n model = mock_risk_model(mocker)\n\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'measures': [Measure.Risk_Free_Rate],\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, []),\n 'limitFactors': False,\n }\n currencies = [\"EUR\", \"INR\"]\n risk_free_rate = [1.08, 0.012]\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"currencyRatesData\": {\n \"riskFreeRate\": risk_free_rate,\n \"currency\": currencies,\n },\n }\n ],\n 'totalResults': 1,\n }\n\n risk_free_rate_response = {\n \"currency\": {('2022-04-05', 0): 'EUR', ('2022-04-05', 1): 'INR'},\n \"riskFreeRate\": {('2022-04-05', 0): 1.08, ('2022-04-05', 1): 0.012},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n response = model.get_risk_free_rate(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == risk_free_rate_response\n\n # fitler risk free rates by currency\n expected_filtered_rates = {\"currency\": {('2022-04-05', 1): 'INR'}, \"riskFreeRate\": {('2022-04-05', 1): 0.012}}\n actual_filtered_rates = model.get_risk_free_rate(\n start_date=dt.date(2022, 4, 5),\n end_date=dt.date(2022, 4, 5),\n currencies=[Currency.INR],\n format=ReturnFormat.JSON,\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n\n assert actual_filtered_rates == expected_filtered_rates\n\n # test DataFrame return format\n expected_data_frame = get_optional_data_as_dataframe(\n [\n {\n \"date\": \"2022-04-05\",\n \"currencyRatesData\": {\n \"riskFreeRate\": risk_free_rate,\n \"currency\": currencies,\n },\n }\n ],\n \"currencyRatesData\",\n )\n\n expected_data_frame = expected_data_frame.loc[expected_data_frame['currency'].isin(['INR'])]\n actual_data_frame = model.get_risk_free_rate(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), currencies=[Currency.INR]\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n\n assert_frame_equal(expected_data_frame, actual_data_frame, check_like=True)\n\n\ndef test_get_currency_exchange_rate(mocker):\n model = mock_risk_model(mocker)\n\n query = {\n 'startDate': '2022-04-05',\n 'endDate': '2022-04-05',\n 'measures': [Measure.Currency_Exchange_Rate],\n 'assets': DataAssetsRequest(UniverseIdentifier.gsid, []),\n 'limitFactors': False,\n }\n currencies = [\"EUR\", \"INR\"]\n currency_exchange_rate = [1.08, 0.012]\n results = {\n 'results': [\n {\n \"date\": \"2022-04-05\",\n \"currencyRatesData\": {\n \"exchangeRate\": currency_exchange_rate,\n \"currency\": currencies,\n },\n }\n ],\n 'totalResults': 1,\n }\n\n currency_exchange_rate_response = {\n \"currency\": {('2022-04-05', 0): 'EUR', ('2022-04-05', 1): 'INR'},\n \"exchangeRate\": {('2022-04-05', 0): 1.08, ('2022-04-05', 1): 0.012},\n }\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value=results)\n\n response = model.get_currency_exchange_rate(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), format=ReturnFormat.JSON\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n assert response == currency_exchange_rate_response\n\n # fitler risk free rates by currency\n expected_filtered_rates = {\"currency\": {('2022-04-05', 1): 'INR'}, \"exchangeRate\": {('2022-04-05', 1): 0.012}}\n actual_filtered_rates = model.get_currency_exchange_rate(\n start_date=dt.date(2022, 4, 5),\n end_date=dt.date(2022, 4, 5),\n currencies=[Currency.INR],\n format=ReturnFormat.JSON,\n )\n\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n\n assert actual_filtered_rates == expected_filtered_rates\n\n # test DataFrame return format\n\n expected_data_frame = get_optional_data_as_dataframe(\n [\n {\n \"date\": \"2022-04-05\",\n \"currencyRatesData\": {\n \"exchangeRate\": currency_exchange_rate,\n \"currency\": currencies,\n },\n }\n ],\n \"currencyRatesData\",\n )\n\n expected_data_frame = expected_data_frame.loc[expected_data_frame['currency'].isin(['INR'])]\n\n actual_data_frame = model.get_currency_exchange_rate(\n start_date=dt.date(2022, 4, 5), end_date=dt.date(2022, 4, 5), currencies=[Currency.INR]\n )\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/{id}/query'.format(id='model_id'), query, timeout=200\n )\n\n assert_frame_equal(expected_data_frame, actual_data_frame, check_like=True)\n\n\nif __name__ == \"__main__\":\n pytest.main([__file__])\n" }, { "path": "gs_quant/test/models/test_risk_model_utils.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom unittest.mock import ANY\n\nimport pytest\nfrom unittest import mock\n\nfrom gs_quant.session import GsSession, Environment\n\nfrom gs_quant.models.risk_model_utils import _upload_factor_data_if_present\n\n\n@pytest.mark.parametrize('total_factors', [100])\ndef test__upload_factor_data_if_present(mocker, total_factors: int):\n from gs_quant.session import OAuth2Session\n\n OAuth2Session.init = mock.MagicMock(return_value=None)\n GsSession.use(Environment.QA, 'client_id', 'secret')\n\n date = \"2024-03-28\"\n factor_data = [\n {\n 'factorCategory': '1',\n 'factorName': f'Factor {i + 1}',\n 'factorCategoryId': 'z',\n 'factorReturn': 0.001 * (i + 1),\n }\n for i in range(total_factors)\n ]\n\n covariance_matrix = [[0 for i in range(total_factors)] for j in range(total_factors)]\n pre_vra_covariance_matrix = [[0 for i in range(total_factors)] for j in range(total_factors)]\n unadjusted_covariance_matrix = [[0 for i in range(total_factors)] for j in range(total_factors)]\n\n risk_model_data = {\n \"factorData\": factor_data,\n \"covarianceMatrix\": covariance_matrix,\n \"preVRACovarianceMatrix\": pre_vra_covariance_matrix,\n \"unadjustedCovarianceMatrix\": unadjusted_covariance_matrix,\n }\n\n # Ensuring GsSession._post receives the values that we pass to _upload_factor_data_if_present method\n def match_dictionaries(*args, **kwargs):\n url = args[0]\n actual_data = args[1]\n\n for key in risk_model_data:\n if key in ['preVRACovarianceMatrix', 'unadjustedCovarianceMatrix'] and 'awsUpload' not in url:\n # making sure new covariance matrices are only uploaded to AWS\n assert key not in actual_data\n continue\n assert key in actual_data\n assert risk_model_data[key] == actual_data[key]\n\n mocker.patch.object(GsSession.current.sync, 'post', return_value='success', side_effect=match_dictionaries)\n\n _upload_factor_data_if_present('TEST_RISK_MODEL', risk_model_data, date)\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/TEST_RISK_MODEL?partialUpload=true', ANY, timeout=200\n )\n\n _upload_factor_data_if_present('TEST_RISK_MODEL', risk_model_data, date, aws_upload=True)\n GsSession.current.sync.post.assert_called_with(\n '/risk/models/data/TEST_RISK_MODEL?partialUpload=true&awsUpload=true', ANY, timeout=200\n )\n" }, { "path": "gs_quant/test/resources/MA4B66MW5E27U8P32SB-xrefs.json", "content": "{\n \"xrefs\": [\n {\n \"startDate\": \"1952-01-01\",\n \"endDate\": \"2012-08-24\",\n \"identifiers\": {\n \"ric\": \".SPX\",\n \"eid\": \"0\",\n \"gsid\": \"100\",\n \"cid\": \"10756\",\n \"bbid\": \"SPX\",\n \"ticker\": \"SPX\",\n \"secName\": \".SPX\"\n }\n },\n {\n \"startDate\": \"2012-08-25\",\n \"endDate\": \"2012-08-25\",\n \"identifiers\": {\n \"ric\": \".SPX\",\n \"eid\": \"0\",\n \"gsid\": \"100\",\n \"cid\": \"10756\",\n \"ticker\": \"SPX\",\n \"secName\": \".SPX\"\n }\n },\n {\n \"startDate\": \"2012-08-26\",\n \"endDate\": \"2019-03-03\",\n \"identifiers\": {\n \"ric\": \".SPX\",\n \"eid\": \"0\",\n \"gsid\": \"100\",\n \"cid\": \"10756\",\n \"bbid\": \"SPX\",\n \"cusip\": \"648815108\",\n \"gss\": \"SPX\",\n \"isin\": \"US78378X1072\",\n \"primeId\": \"1000043264\",\n \"ticker\": \"SPX\",\n \"wpk\": \"969003\",\n \"gsn\": \"152850\",\n \"secName\": \".SPX\",\n \"mdapi\": \".SPX\"\n }\n },\n {\n \"startDate\": \"2019-03-04\",\n \"endDate\": \"2019-03-04\",\n \"identifiers\": {\n \"ric\": \".SPX\",\n \"rcic\": \".SPX\",\n \"eid\": \"0\",\n \"gsid\": \"100\",\n \"bbid\": \"SPX\",\n \"cusip\": \"648815108\",\n \"gss\": \"SPX\",\n \"isin\": \"US78378X1072\",\n \"primeId\": \"1000043264\",\n \"ticker\": \"SPX\",\n \"wpk\": \"969003\",\n \"gsn\": \"152850\",\n \"secName\": \".SPX\",\n \"mdapi\": \".SPX\"\n }\n },\n {\n \"startDate\": \"2019-03-05\",\n \"endDate\": \"2019-05-16\",\n \"identifiers\": {\n \"ric\": \".SPX\",\n \"rcic\": \".SPX\",\n \"eid\": \"0\",\n \"gsid\": \"100\",\n \"bbid\": \"SPX\",\n \"delisted\": \"no\",\n \"cusip\": \"648815108\",\n \"gss\": \"SPX\",\n \"isin\": \"US78378X1072\",\n \"primeId\": \"1000043264\",\n \"ticker\": \"SPX\",\n \"wpk\": \"969003\",\n \"gsn\": \"152850\",\n \"secName\": \".SPX\",\n \"mdapi\": \".SPX\"\n }\n },\n {\n \"startDate\": \"2019-05-17\",\n \"endDate\": \"2952-12-31\",\n \"identifiers\": {\n \"ric\": \".SPX\",\n \"rcic\": \".SPX\",\n \"eid\": \"0\",\n \"gsid\": \"100\",\n \"bbid\": \"SPX\",\n \"delisted\": \"no\",\n \"cusip\": \"648815108\",\n \"gss\": \"SPX\",\n \"isin\": \"US6488151084\",\n \"primeId\": \"1000043264\",\n \"ticker\": \"SPX\",\n \"wpk\": \"969003\",\n \"gsn\": \"152850\",\n \"secName\": \".SPX\",\n \"mdapi\": \".SPX\"\n }\n }\n ]\n}" }, { "path": "gs_quant/test/resources/MA4B66MW5E27U8P32SB.json", "content": "{\n \"assetClass\": \"Equity\",\n \"classifications\": {\n \"riskCountryName\": \"United States\",\n \"riskCountryCode\": \"US\",\n \"countryName\": \"United States\",\n 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\"value\": \"0\"\n }\n ],\n \"lastUpdatedById\": \"3549cd2c46c3468bae0d90b1f2b9e609\",\n \"lastUpdatedTime\": \"2019-08-09T23:12:43.11Z\",\n \"listed\": true,\n \"name\": \"S&P 500 Index\",\n \"ownerId\": \"3549cd2c46c3468bae0d90b1f2b9e609\",\n \"parameters\": {\n \"exchangeCurrency\": \"USD\",\n \"indexCalculationType\": \"Price Return\",\n \"indexDivisor\": 8.32473196588017E9,\n \"pricingLocation\": \"NYC\"\n },\n \"rank\": 0.9,\n \"region\": \"Americas\",\n \"reportIds\": [\n \"RP5QC00HE4XJTHHN\",\n \"RP7ERDJBAY0M8Z16\",\n \"RP7X1WWKWQD855E3\",\n \"RPFR7WPWZ6G8H8X7\",\n \"RP2SBMPYCQTHXVH3\",\n \"RPDR3N2F81716JV8\",\n \"RPVYX74EMDHY2ZM7\"\n ],\n \"shortName\": \"SPX\",\n \"styles\": [],\n \"tags\": [\n \"Americas\",\n \"Equity\",\n \"Equity Option::LAL\",\n \"Equity Option::Listed\",\n \"Equity Option::OTC\",\n \"Index\",\n \"New York Stock Exchange\",\n \"NYC\",\n \"NYSE\",\n \"United States\",\n \"US\"\n ],\n \"type\": \"Index\",\n \"underliers\": [\n \"MA4B66MW5E27U9VBB6A\",\n 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"Date,SPX,USD,MIDASER\n2-Jan-19,2510.03,192.533473,2510.03\n3-Jan-19,2447.8899,192.5463086,2447.7226\n4-Jan-19,2531.9399,192.559145,2531.6037\n7-Jan-19,2549.6899,192.5976569,2548.845\n8-Jan-19,2574.4099,192.6104967,2573.3869\n9-Jan-19,2584.96,192.6233374,2583.7612\n10-Jan-19,2596.6399,192.636179,2595.2634\n11-Jan-19,2596.26,192.6490214,2594.7107\n14-Jan-19,2582.6101,192.6875512,2580.55\n15-Jan-19,2610.3,192.700397,2608.0458\n16-Jan-19,2616.1001,192.7132437,2613.667\n17-Jan-19,2635.96,192.7260913,2633.3342\n18-Jan-19,2670.71,192.7389397,2667.874\n22-Jan-19,2632.8999,192.7903367,2629.3926\n23-Jan-19,2638.7,192.8031894,2635.0097\n24-Jan-19,2642.3301,192.8160429,2638.4591\n25-Jan-19,2664.76,192.8288973,2660.6802\n28-Jan-19,2643.8501,192.8674631,2639.2702\n29-Jan-19,2640,192.880321,2635.2508\n30-Jan-19,2681.05,192.8931796,2676.0513\n31-Jan-19,2704.1001,192.9060392,2698.88\n1-Feb-19,2706.53,192.9188996,2701.1253\n4-Feb-19,2724.8701,192.9574834,2718.8886\n5-Feb-19,2737.7,192.9703472,2731.509\n6-Feb-19,2731.6101,192.9832119,2725.2508\n7-Feb-19,2706.05,192.9960774,2699.5686\n8-Feb-19,2707.8799,193.0089438,2701.214\n11-Feb-19,2709.8,193.0475456,2702.5892\n12-Feb-19,2744.73,193.0604155,2737.246\n13-Feb-19,2753.03,193.0732862,2745.341\n14-Feb-19,2745.73,193.0861577,2737.8783\n15-Feb-19,2775.6001,193.0990301,2767.4805\n19-Feb-19,2779.76,193.1505232,2770.8902\n20-Feb-19,2784.7,193.1633999,2775.6297\n21-Feb-19,2774.8799,193.1762775,2765.6565\n22-Feb-19,2792.6699,193.1891559,2783.2031\n25-Feb-19,2796.1101,193.2277937,2786.075\n26-Feb-19,2793.8999,193.2406756,2783.687\n27-Feb-19,2792.3799,193.2535583,2781.9869\n28-Feb-19,2784.49,193.2664419,2773.9409\n1-Mar-19,2803.6899,193.2793263,2792.8832\n4-Mar-19,2792.8101,193.3179821,2781.4867\n5-Mar-19,2789.6499,193.33087,2778.1539\n6-Mar-19,2771.45,193.3437587,2759.8437\n7-Mar-19,2748.9299,193.3566483,2737.234\n8-Mar-19,2743.0701,193.3695388,2731.2166\n11-Mar-19,2783.3,193.4082127,2770.7265\n12-Mar-19,2791.52,193.4211066,2778.7246\n13-Mar-19,2810.9199,193.4340013,2797.8504\n14-Mar-19,2808.48,193.4468969,2795.2353\n15-Mar-19,2822.48,193.4597934,2808.9829\n18-Mar-19,2832.9399,193.4984853,2818.831\n19-Mar-19,2832.5701,193.5113852,2818.2751\n20-Mar-19,2824.23,193.524286,2809.7892\n21-Mar-19,2854.8799,193.5372413,2840.0943\n22-Mar-19,2800.71,193.5501976,2786.0148\n25-Mar-19,2798.3601,193.5890689,2783.1177\n26-Mar-19,2818.46,193.6019749,2802.9226\n27-Mar-19,2805.3701,193.6148817,2789.718\n28-Mar-19,2815.4399,193.6278431,2799.5449\n29-Mar-19,2834.3999,193.6408054,2818.2104\n1-Apr-19,2867.1899,193.6800177,2850.2425\n2-Apr-19,2867.24,193.6929835,2850.1014\n3-Apr-19,2873.3999,193.7059501,2856.0337\n4-Apr-19,2879.3899,193.7189177,2861.7963\n5-Apr-19,2892.74,193.7318861,2874.8732\n8-Apr-19,2895.77,193.7707939,2877.3072\n9-Apr-19,2878.2,193.7837658,2859.6565\n10-Apr-19,2888.21,193.7967385,2869.4106\n11-Apr-19,2888.3201,193.8097121,2869.3279\n12-Apr-19,2907.4099,193.8226866,2888.1001\n15-Apr-19,2905.5801,193.8616127,2885.7024\n16-Apr-19,2907.0601,193.8745906,2886.9791\n17-Apr-19,2900.45,193.8875695,2880.2214\n18-Apr-19,2905.03,193.900603,2884.5759\n22-Apr-19,2907.97,193.9531204,2886.7139\n23-Apr-19,2933.6799,193.9662661,2912.0402\n24-Apr-19,2927.25,193.9794127,2905.4604\n25-Apr-19,2926.1699,193.9925602,2904.1914\n26-Apr-19,2939.8799,194.0057086,2917.6015\n29-Apr-19,2943.03,194.0451564,2920.1346\n30-Apr-19,2945.8301,194.0583623,2922.7141\n1-May-19,2923.73,194.071569,2900.5885\n2-May-19,2917.52,194.0847767,2894.2303\n3-May-19,2945.6399,194.0977696,2921.932\n6-May-19,2932.47,194.1365891,2908.2837\n7-May-19,2884.05,194.1495316,2860.0692\n8-May-19,2879.4199,194.1624749,2855.2869\n9-May-19,2870.72,194.1753651,2846.4703\n10-May-19,2881.3999,194.1882023,2856.8718\n13-May-19,2811.8701,194.2267163,2787.3673\n14-May-19,2834.4099,194.2395568,2809.5264\n15-May-19,2850.96,194.2523982,2825.7454\n16-May-19,2876.3201,194.2653484,2850.6929\n17-May-19,2859.53,194.2782454,2833.8632\n20-May-19,2840.23,194.3169392,2814.1719\n21-May-19,2864.3601,194.3298397,2837.8939\n22-May-19,2856.27,194.342741,2829.6901\n23-May-19,2822.24,194.3555892,2795.7897\n24-May-19,2826.0601,194.3684383,2799.3891\n28-May-19,2802.3899,194.4198379,2775.2021\n29-May-19,2783.02,194.4327452,2755.8359\n30-May-19,2788.8601,194.4456534,2761.436\n31-May-19,2752.0601,194.4585625,2724.8144\n3-Jun-19,2744.45,194.4974542,2716.7347\n4-Jun-19,2803.27,194.5103126,2774.7812\n5-Jun-19,2826.1499,194.5231719,2797.2451\n6-Jun-19,2843.49,194.536032,2814.2229\n7-Jun-19,2873.3401,194.548839,2843.5805\n10-Jun-19,2886.73,194.5872624,2856.2701\n11-Jun-19,2885.72,194.6000727,2855.0827\n12-Jun-19,2879.8401,194.6128839,2849.0773\n13-Jun-19,2891.6399,194.6256959,2860.5635\n14-Jun-19,2886.98,194.6385088,2855.7653\n17-Jun-19,2889.6699,194.6767877,2857.8646\n18-Jun-19,2917.75,194.689658,2885.4466\n19-Jun-19,2926.46,194.702475,2893.8702\n20-Jun-19,2954.1799,194.715293,2921.091\n21-Jun-19,2950.46,194.7281117,2917.2204\n24-Jun-19,2945.3501,194.7667328,2911.5895\n25-Jun-19,2917.3799,194.779609,2883.7474\n26-Jun-19,2913.78,194.7924861,2879.9984\n27-Jun-19,2924.9199,194.8053641,2890.8187\n28-Jun-19,2941.76,194.8182429,2907.2714\n1-Jul-19,2964.3301,194.8572065,2928.9954\n2-Jul-19,2973.01,194.8701429,2937.3774\n3-Jul-19,2995.8201,194.8831342,2959.7182\n5-Jul-19,2990.4099,194.9092269,2953.977\n8-Jul-19,2975.95,194.9485336,2939.0975\n9-Jul-19,2979.6299,194.9615843,2942.5351\n10-Jul-19,2993.0701,194.9746359,2955.611\n11-Jul-19,2999.9099,194.9876884,2962.1673\n12-Jul-19,3013.77,195.0006876,2975.6556\n15-Jul-19,3014.3,195.0393627,2975.5887\n16-Jul-19,3004.04,195.0523653,2965.2621\n17-Jul-19,2984.4199,195.065423,2945.6968\n18-Jul-19,2995.1101,195.0784815,2956.0511\n19-Jul-19,2976.6101,195.091541,2937.5944\n22-Jul-19,2985.03,195.1307218,2945.314\n23-Jul-19,3005.47,195.1437306,2965.2856\n24-Jul-19,3019.5601,195.1567401,2978.9897\n25-Jul-19,3003.6699,195.1697506,2963.1144\n26-Jul-19,3025.8601,195.1827619,2984.8075\n29-Jul-19,3020.97,195.2217985,2979.3867\n30-Jul-19,3013.1799,195.2348132,2971.5053\n31-Jul-19,2980.3799,195.2477747,2938.9616\n1-Aug-19,2953.5601,195.2607912,2912.3186\n2-Aug-19,2932.05,195.2723984,2890.9358\n5-Aug-19,2844.74,195.3072219,2804.3345\n6-Aug-19,2881.77,195.3187776,2840.6726\n7-Aug-19,2883.98,195.330334,2842.683\n8-Aug-19,2938.0901,195.3418368,2895.8509\n9-Aug-19,2918.6499,195.3533402,2876.5196\n12-Aug-19,2883.0901,195.3878526,2840.9649\n13-Aug-19,2926.3201,195.3993588,2883.396\n14-Aug-19,2840.6001,195.4108657,2798.7635\n15-Aug-19,2847.6001,195.4223732,2805.4956\n16-Aug-19,2888.6799,195.4339357,2845.8021\n19-Aug-19,2923.6499,195.4686252,2879.7478\n20-Aug-19,2900.51,195.4801904,2856.785\n21-Aug-19,2924.4299,195.4917564,2880.1753\n22-Aug-19,2922.95,195.5032686,2878.5481\n23-Aug-19,2847.1101,195.5147816,2803.6909\n26-Aug-19,2878.3799,195.5493226,2833.9884\n27-Aug-19,2869.1599,195.5608382,2824.7438\n28-Aug-19,2887.9399,195.5723546,2843.0667\n29-Aug-19,2924.5801,195.5838716,2878.9701\n30-Aug-19,2926.46,195.5953894,2880.6511\n3-Sep-19,2906.27,195.6416803,2860.0955\n4-Sep-19,2937.78,195.6532557,2890.9356\n5-Sep-19,2976,195.6648319,2928.3751\n6-Sep-19,2978.71,195.6764087,2930.8685\n9-Sep-19,2978.4299,195.7109782,2930.0751\n10-Sep-19,2979.3899,195.7225578,2930.8462\n11-Sep-19,3000.9299,195.734138,2951.8618\n12-Sep-19,3009.5701,195.745719,2960.186\n13-Sep-19,3007.3899,195.7573006,2957.8665\n16-Sep-19,2997.96,195.7922106,2948.0644\n17-Sep-19,3005.7,195.8044477,2955.4913\n18-Sep-19,3006.73,195.8169574,2956.3153\n19-Sep-19,3006.79,195.8291959,2956.1896\n20-Sep-19,2992.0701,195.8395314,2941.5613\n23-Sep-19,2991.78,195.8705393,2940.8104\n24-Sep-19,2966.6001,195.8808769,2915.9042\n25-Sep-19,2984.8701,195.8912151,2933.7082\n26-Sep-19,2977.6201,195.9015538,2926.4276\n27-Sep-19,2961.79,195.9116209,2910.7193\n30-Sep-19,2976.74,195.9414975,2924.9676\n1-Oct-19,2940.25,195.9518388,2888.9578\n2-Oct-19,2887.6101,195.9620719,2837.0854\n3-Oct-19,2910.6299,195.9721421,2859.5566\n4-Oct-19,2952.01,195.9821041,2900.0652\n7-Oct-19,2938.79,196.011828,2886.6381\n8-Oct-19,2893.0601,196.0217375,2841.5737\n9-Oct-19,2919.3999,196.0316475,2867.3011\n10-Oct-19,2938.1299,196.041558,2885.5519\n11-Oct-19,2970.27,196.051469,2916.971\n14-Oct-19,2966.1499,196.0812034,2912.4824\n15-Oct-19,2995.6799,196.0911149,2941.3309\n16-Oct-19,2989.6899,196.1014642,2935.2943\n17-Oct-19,2997.95,196.111814,2943.2491\n18-Oct-19,2986.2,196.1218919,2931.5623\n21-Oct-19,3006.72,196.1521274,2951.2549\n22-Oct-19,2995.99,196.1622074,2940.5712\n23-Oct-19,3004.52,196.172288,2948.7923\n24-Oct-19,3010.29,196.1823691,2954.3038\n25-Oct-19,3022.55,196.1924507,2966.184\n28-Oct-19,3039.4199,196.22237,2982.2869\n29-Oct-19,3036.8899,196.2323447,2979.6528\n30-Oct-19,3046.77,196.2422653,2989.1961\n31-Oct-19,3037.5601,196.2521864,2980.0091\n1-Nov-19,3066.9099,196.2607997,3008.6721\n4-Nov-19,3078.27,196.2864772,3019.4228\n5-Nov-19,3074.6201,196.2949829,3015.7118\n6-Nov-19,3076.78,196.303489,3017.6997\n7-Nov-19,3085.1799,196.311941,3025.8084\n8-Nov-19,3093.0801,196.3203933,3033.4262\n11-Nov-19,3087.01,196.3457514,3027.0814\n12-Nov-19,3091.8401,196.354204,3031.6874\n13-Nov-19,3094.04,196.3626582,3033.714\n14-Nov-19,3096.6299,196.3711127,3036.1227\n15-Nov-19,3120.46,196.3795676,3059.3565\n18-Nov-19,3122.03,196.4049332,3060.5006\n19-Nov-19,3120.1799,196.4133896,3058.5552\n20-Nov-19,3108.46,196.4218463,3046.935\n21-Nov-19,3103.54,196.4303033,3041.9813\n22-Nov-19,3110.29,196.4387607,3048.4664\n25-Nov-19,3133.6399,196.4641341,3070.9584\n26-Nov-19,3140.52,196.4725929,3077.5687\n27-Nov-19,3153.6299,196.4810522,3090.2832\n29-Nov-19,3140.98,196.4979714,3077.6213\n2-Dec-19,3113.8701,196.5235161,3050.6582\n3-Dec-19,3093.2,196.5320321,3030.2755\n4-Dec-19,3112.76,196.5404939,3049.3072\n5-Dec-19,3117.4299,196.5489561,3053.7506\n6-Dec-19,3145.9099,196.5574186,3081.5173\n9-Dec-19,3135.96,196.5828073,3071.373\n10-Dec-19,3132.52,196.5912713,3067.8717\n11-Dec-19,3141.6299,196.5997356,3076.6615\n12-Dec-19,3168.5701,196.6082003,3102.9121\n13-Dec-19,3168.8,196.6166654,3103.0037\n16-Dec-19,3191.45,196.6420617,3124.7825\n17-Dec-19,3192.52,196.6505829,3125.6948\n18-Dec-19,3191.1399,196.6590498,3124.209\n19-Dec-19,3205.3701,196.667517,3138.0062\n20-Dec-19,3221.22,196.6759847,3153.3878\n23-Dec-19,3224.01,196.7013886,3155.7118\n24-Dec-19,3223.3799,196.7098577,3154.9592\n26-Dec-19,3239.9099,196.7267966,3170.8667\n27-Dec-19,3240.02,196.7352668,3170.8379\n30-Dec-19,3221.29,196.7606785,3152.0983\n31-Dec-19,3230.78,196.7691501,3161.2487\n" }, { "path": "gs_quant/test/resources/SPX_50_icorr_in.csv", "content": "date,assetId,impliedVolatility\n2020-08-03,MA4B66MW5E27UADJ5VJ,32.059328320199995\n2020-08-04,MA4B66MW5E27UADJ5VJ,31.1381564321\n2020-08-05,MA4B66MW5E27UADJ5VJ,29.2136129051\n2020-08-05,MA4B66MW5E27UADJ5VJ,-1\n2020-08-06,MA4B66MW5E27UADJ5VJ,30.3667884444\n2020-08-07,MA4B66MW5E27UADJ5VJ,32.4291242869\n2020-08-10,MA4B66MW5E27UADJ5VJ,32.9822266526\n2020-08-11,MA4B66MW5E27UADJ5VJ,34.2772999432\n2020-08-12,MA4B66MW5E27UADJ5VJ,30.2372996096\n2020-08-13,MA4B66MW5E27UADJ5VJ,27.605939028299996\n2020-08-14,MA4B66MW5E27UADJ5VJ,24.428182949099998\n2020-08-17,MA4B66MW5E27UADJ5VJ,23.8755262187\n2020-08-18,MA4B66MW5E27UADJ5VJ,22.5281636941\n2020-08-19,MA4B66MW5E27UADJ5VJ,22.108805182399998\n2020-08-20,MA4B66MW5E27UADJ5VJ,21.538459422699997\n2020-08-21,MA4B66MW5E27UADJ5VJ,21.4757360101\n2020-08-24,MA4B66MW5E27UADJ5VJ,22.6950218659\n2020-08-25,MA4B66MW5E27UADJ5VJ,22.247796161100002\n2020-08-26,MA4B66MW5E27UADJ5VJ,23.3344421284\n2020-08-27,MA4B66MW5E27UADJ5VJ,24.7550947989\n2020-08-28,MA4B66MW5E27UADJ5VJ,24.4952536669\n2020-08-31,MA4B66MW5E27UADJ5VJ,25.7882698628\n2020-08-03,MA4B66MW5E27UAM94Q4,36.7219978252\n2020-08-04,MA4B66MW5E27UAM94Q4,36.0504251494\n2020-08-05,MA4B66MW5E27UAM94Q4,33.4754821133\n2020-08-06,MA4B66MW5E27UAM94Q4,33.7053260305\n2020-08-07,MA4B66MW5E27UAM94Q4,35.221356445000005\n2020-08-10,MA4B66MW5E27UAM94Q4,36.226518267500005\n2020-08-11,MA4B66MW5E27UAM94Q4,37.759121398\n2020-08-12,MA4B66MW5E27UAM94Q4,34.762575207000005\n2020-08-13,MA4B66MW5E27UAM94Q4,34.378342167300005\n2020-08-14,MA4B66MW5E27UAM94Q4,33.7138370863\n2020-08-17,MA4B66MW5E27UAM94Q4,34.2377308066\n2020-08-18,MA4B66MW5E27UAM94Q4,33.856144619599995\n2020-08-19,MA4B66MW5E27UAM94Q4,33.055326742000005\n2020-08-20,MA4B66MW5E27UAM94Q4,33.0824224171\n2020-08-21,MA4B66MW5E27UAM94Q4,32.6930826992\n2020-08-24,MA4B66MW5E27UAM94Q4,34.071619079\n2020-08-25,MA4B66MW5E27UAM94Q4,33.8970763465\n2020-08-26,MA4B66MW5E27UAM94Q4,32.9884027106\n2020-08-27,MA4B66MW5E27UAM94Q4,35.1474442816\n2020-08-28,MA4B66MW5E27UAM94Q4,35.101873706\n2020-08-31,MA4B66MW5E27UAM94Q4,37.4891468519\n2020-08-03,MA4B66MW5E27UAGYZ49,29.1456583948\n2020-08-04,MA4B66MW5E27UAGYZ49,26.9608117279\n2020-08-05,MA4B66MW5E27UAGYZ49,25.6071079962\n2020-08-06,MA4B66MW5E27UAGYZ49,25.0612661871\n2020-08-07,MA4B66MW5E27UAGYZ49,26.62766323\n2020-08-10,MA4B66MW5E27UAGYZ49,26.8936255337\n2020-08-11,MA4B66MW5E27UAGYZ49,28.232559675699996\n2020-08-12,MA4B66MW5E27UAGYZ49,25.291533856199997\n2020-08-13,MA4B66MW5E27UAGYZ49,26.1217764764\n2020-08-14,MA4B66MW5E27UAGYZ49,24.9849848264\n2020-08-17,MA4B66MW5E27UAGYZ49,25.567022689699996\n2020-08-18,MA4B66MW5E27UAGYZ49,25.3354011802\n2020-08-19,MA4B66MW5E27UAGYZ49,26.154226908099997\n2020-08-20,MA4B66MW5E27UAGYZ49,25.8813754409\n2020-08-21,MA4B66MW5E27UAGYZ49,26.986504313300003\n2020-08-24,MA4B66MW5E27UAGYZ49,29.2637307663\n2020-08-25,MA4B66MW5E27UAGYZ49,29.1791387387\n2020-08-26,MA4B66MW5E27UAGYZ49,31.5275620317\n2020-08-27,MA4B66MW5E27UAGYZ49,34.2235967129\n2020-08-28,MA4B66MW5E27UAGYZ49,34.3869695032\n2020-08-31,MA4B66MW5E27UAGYZ49,34.5570456695\n2020-08-03,MA4B66MW5E27UAL9SUX,34.1680490617\n2020-08-04,MA4B66MW5E27UAL9SUX,32.8075487555\n2020-08-05,MA4B66MW5E27UAL9SUX,32.0615452497\n2020-08-06,MA4B66MW5E27UAL9SUX,31.218561953000002\n2020-08-07,MA4B66MW5E27UAL9SUX,32.3056351071\n2020-08-10,MA4B66MW5E27UAL9SUX,33.6702978619\n2020-08-11,MA4B66MW5E27UAL9SUX,34.1252902831\n2020-08-12,MA4B66MW5E27UAL9SUX,30.6693127289\n2020-08-13,MA4B66MW5E27UAL9SUX,30.926053258000003\n2020-08-14,MA4B66MW5E27UAL9SUX,30.354150672699998\n2020-08-17,MA4B66MW5E27UAL9SUX,29.076861884\n2020-08-18,MA4B66MW5E27UAL9SUX,28.205456857599998\n2020-08-19,MA4B66MW5E27UAL9SUX,27.797024015100003\n2020-08-20,MA4B66MW5E27UAL9SUX,27.395934708800002\n2020-08-21,MA4B66MW5E27UAL9SUX,28.0160622736\n2020-08-24,MA4B66MW5E27UAL9SUX,29.2955355307\n2020-08-25,MA4B66MW5E27UAL9SUX,29.804381783400004\n2020-08-26,MA4B66MW5E27UAL9SUX,33.4782620755\n2020-08-27,MA4B66MW5E27UAL9SUX,39.0260854309\n2020-08-28,MA4B66MW5E27UAL9SUX,38.2732494829\n2020-08-31,MA4B66MW5E27UAL9SUX,37.0371481421\n2020-08-03,MA4B66MW5E27UADJ5GE,20.401557227799998\n2020-08-04,MA4B66MW5E27UADJ5GE,20.2195046835\n2020-08-05,MA4B66MW5E27UADJ5GE,19.5893138288\n2020-08-06,MA4B66MW5E27UADJ5GE,18.945448659700002\n2020-08-07,MA4B66MW5E27UADJ5GE,19.6405853843\n2020-08-10,MA4B66MW5E27UADJ5GE,19.0604368141\n2020-08-11,MA4B66MW5E27UADJ5GE,20.659754315100002\n2020-08-12,MA4B66MW5E27UADJ5GE,19.2148923255\n2020-08-13,MA4B66MW5E27UADJ5GE,19.6469173267\n2020-08-14,MA4B66MW5E27UADJ5GE,18.9680919495\n2020-08-17,MA4B66MW5E27UADJ5GE,19.080799558200003\n2020-08-18,MA4B66MW5E27UADJ5GE,18.0389663406\n2020-08-19,MA4B66MW5E27UADJ5GE,17.2436853667\n2020-08-20,MA4B66MW5E27UADJ5GE,18.023913295099998\n2020-08-21,MA4B66MW5E27UADJ5GE,17.9412626181\n2020-08-24,MA4B66MW5E27UADJ5GE,18.317182605\n2020-08-25,MA4B66MW5E27UADJ5GE,22.5756406281\n2020-08-26,MA4B66MW5E27UADJ5GE,22.6475106995\n2020-08-27,MA4B66MW5E27UADJ5GE,25.536643363099998\n2020-08-28,MA4B66MW5E27UADJ5GE,25.8082212341\n2020-08-31,MA4B66MW5E27UADJ5GE,27.8059472778\n2020-08-03,MA4B66MW5E27U9YGMCG,28.750922812000002\n2020-08-04,MA4B66MW5E27U9YGMCG,27.8417855464\n2020-08-05,MA4B66MW5E27U9YGMCG,26.1711340309\n2020-08-06,MA4B66MW5E27U9YGMCG,25.1480231243\n2020-08-07,MA4B66MW5E27U9YGMCG,25.044349535\n2020-08-10,MA4B66MW5E27U9YGMCG,25.5365731857\n2020-08-11,MA4B66MW5E27U9YGMCG,25.8161128186\n2020-08-12,MA4B66MW5E27U9YGMCG,24.517971221099998\n2020-08-13,MA4B66MW5E27U9YGMCG,26.1501389167\n2020-08-14,MA4B66MW5E27U9YGMCG,25.1309204841\n2020-08-17,MA4B66MW5E27U9YGMCG,23.6885894292\n2020-08-18,MA4B66MW5E27U9YGMCG,24.2647261087\n2020-08-19,MA4B66MW5E27U9YGMCG,23.1890781007\n2020-08-20,MA4B66MW5E27U9YGMCG,22.9788667607\n2020-08-21,MA4B66MW5E27U9YGMCG,22.5607911428\n2020-08-24,MA4B66MW5E27U9YGMCG,24.6505958822\n2020-08-25,MA4B66MW5E27U9YGMCG,26.2388496468\n2020-08-26,MA4B66MW5E27U9YGMCG,24.696970594699998\n2020-08-27,MA4B66MW5E27U9YGMCG,26.4963583283\n2020-08-28,MA4B66MW5E27U9YGMCG,24.723809343200003\n2020-08-31,MA4B66MW5E27U9YGMCG,27.4663329506\n2020-08-03,MA4B66MW5E27UAM94N9,35.5762578374\n2020-08-04,MA4B66MW5E27UAM94N9,34.6581451204\n2020-08-05,MA4B66MW5E27UAM94N9,33.7279642642\n2020-08-06,MA4B66MW5E27UAM94N9,32.3511735104\n2020-08-07,MA4B66MW5E27UAM94N9,33.9350573662\n2020-08-10,MA4B66MW5E27UAM94N9,36.020310728\n2020-08-11,MA4B66MW5E27UAM94N9,36.6462112203\n2020-08-12,MA4B66MW5E27UAM94N9,34.5540036974\n2020-08-13,MA4B66MW5E27UAM94N9,35.8718509147\n2020-08-14,MA4B66MW5E27UAM94N9,34.068479867200004\n2020-08-17,MA4B66MW5E27UAM94N9,34.159764939\n2020-08-18,MA4B66MW5E27UAM94N9,33.870072737099996\n2020-08-19,MA4B66MW5E27UAM94N9,33.7571612558\n2020-08-20,MA4B66MW5E27UAM94N9,32.2171786412\n2020-08-21,MA4B66MW5E27UAM94N9,32.0128401092\n2020-08-24,MA4B66MW5E27UAM94N9,33.1426632605\n2020-08-25,MA4B66MW5E27UAM94N9,33.0084643372\n2020-08-26,MA4B66MW5E27UAM94N9,33.853333242400005\n2020-08-27,MA4B66MW5E27UAM94N9,34.6099481196\n2020-08-28,MA4B66MW5E27UAM94N9,35.5220916582\n2020-08-31,MA4B66MW5E27UAM94N9,38.132732167\n2020-08-03,MA4B66MW5E27U9YGMGY,34.5578566661\n2020-08-04,MA4B66MW5E27U9YGMGY,35.5214192081\n2020-08-05,MA4B66MW5E27U9YGMGY,34.2422098496\n2020-08-06,MA4B66MW5E27U9YGMGY,33.2989087816\n2020-08-07,MA4B66MW5E27U9YGMGY,34.0442026669\n2020-08-10,MA4B66MW5E27U9YGMGY,35.342590358100004\n2020-08-11,MA4B66MW5E27U9YGMGY,35.2655857605\n2020-08-12,MA4B66MW5E27U9YGMGY,31.7945415052\n2020-08-13,MA4B66MW5E27U9YGMGY,33.2471989772\n2020-08-14,MA4B66MW5E27U9YGMGY,31.5522337503\n2020-08-17,MA4B66MW5E27U9YGMGY,31.888449962499998\n2020-08-18,MA4B66MW5E27U9YGMGY,36.856801710300005\n2020-08-19,MA4B66MW5E27U9YGMGY,35.4813341276\n2020-08-20,MA4B66MW5E27U9YGMGY,34.0230280386\n2020-08-21,MA4B66MW5E27U9YGMGY,35.015184809400004\n2020-08-24,MA4B66MW5E27U9YGMGY,37.653164841999995\n2020-08-25,MA4B66MW5E27U9YGMGY,38.9461283263\n2020-08-26,MA4B66MW5E27U9YGMGY,40.5169251595\n2020-08-27,MA4B66MW5E27U9YGMGY,40.6188899796\n2020-08-28,MA4B66MW5E27U9YGMGY,39.4578448807\n2020-08-31,MA4B66MW5E27U9YGMGY,39.6204268274\n2020-08-03,MA4B66MW5E27U9XPV7X,31.7637510144\n2020-08-04,MA4B66MW5E27U9XPV7X,31.729475946\n2020-08-05,MA4B66MW5E27U9XPV7X,29.552933519000003\n2020-08-06,MA4B66MW5E27U9XPV7X,28.3363353031\n2020-08-07,MA4B66MW5E27U9XPV7X,31.627824818599997\n2020-08-10,MA4B66MW5E27U9XPV7X,32.970957378600005\n2020-08-11,MA4B66MW5E27U9XPV7X,31.7505290281\n2020-08-12,MA4B66MW5E27U9XPV7X,30.0187164171\n2020-08-13,MA4B66MW5E27U9XPV7X,30.276149750300004\n2020-08-14,MA4B66MW5E27U9XPV7X,30.696568036\n2020-08-17,MA4B66MW5E27U9XPV7X,36.2464071687\n2020-08-18,MA4B66MW5E27U9XPV7X,35.1648187833\n2020-08-19,MA4B66MW5E27U9XPV7X,35.3170811102\n2020-08-20,MA4B66MW5E27U9XPV7X,35.1923072587\n2020-08-21,MA4B66MW5E27U9XPV7X,36.9930326676\n2020-08-24,MA4B66MW5E27U9XPV7X,40.7113665259\n2020-08-25,MA4B66MW5E27U9XPV7X,40.0483053886\n2020-08-26,MA4B66MW5E27U9XPV7X,49.2506555083\n2020-08-27,MA4B66MW5E27U9XPV7X,48.4916363854\n2020-08-28,MA4B66MW5E27U9XPV7X,49.144642165\n2020-08-31,MA4B66MW5E27U9XPV7X,49.571312741\n2020-08-03,MA4B66MW5E27UAGYZ4D,29.2417705031\n2020-08-04,MA4B66MW5E27UAGYZ4D,27.0928538432\n2020-08-05,MA4B66MW5E27UAGYZ4D,25.7215033896\n2020-08-06,MA4B66MW5E27UAGYZ4D,25.163137567299998\n2020-08-07,MA4B66MW5E27UAGYZ4D,26.6871658682\n2020-08-10,MA4B66MW5E27UAGYZ4D,26.9875788221\n2020-08-11,MA4B66MW5E27UAGYZ4D,27.9653812736\n2020-08-12,MA4B66MW5E27UAGYZ4D,25.317280800000002\n2020-08-13,MA4B66MW5E27UAGYZ4D,26.2410915389\n2020-08-14,MA4B66MW5E27UAGYZ4D,25.0654286598\n2020-08-17,MA4B66MW5E27UAGYZ4D,25.6465943257\n2020-08-18,MA4B66MW5E27UAGYZ4D,25.393898211799996\n2020-08-19,MA4B66MW5E27UAGYZ4D,26.1762826497\n2020-08-20,MA4B66MW5E27UAGYZ4D,26.042070348200003\n2020-08-21,MA4B66MW5E27UAGYZ4D,27.206719116099997\n2020-08-24,MA4B66MW5E27UAGYZ4D,29.445558499300002\n2020-08-25,MA4B66MW5E27UAGYZ4D,29.212714042299996\n2020-08-26,MA4B66MW5E27UAGYZ4D,31.3985535068\n2020-08-27,MA4B66MW5E27UAGYZ4D,34.0320744273\n2020-08-28,MA4B66MW5E27UAGYZ4D,34.2097353203\n2020-08-31,MA4B66MW5E27UAGYZ4D,34.3990043394\n2020-08-03,MAMDDXVJYX77V2K9,20.0559973983\n2020-08-04,MAMDDXVJYX77V2K9,19.1882402243\n2020-08-05,MAMDDXVJYX77V2K9,18.9826647551\n2020-08-06,MAMDDXVJYX77V2K9,18.8985025041\n2020-08-07,MAMDDXVJYX77V2K9,18.644528184000002\n2020-08-10,MAMDDXVJYX77V2K9,19.1852593875\n2020-08-11,MAMDDXVJYX77V2K9,19.6258501179\n2020-08-12,MAMDDXVJYX77V2K9,18.7599981537\n2020-08-13,MAMDDXVJYX77V2K9,18.9027056041\n2020-08-14,MAMDDXVJYX77V2K9,18.6918580502\n2020-08-17,MAMDDXVJYX77V2K9,18.8202033654\n2020-08-18,MAMDDXVJYX77V2K9,17.484061347999997\n2020-08-19,MAMDDXVJYX77V2K9,17.3812125228\n2020-08-20,MAMDDXVJYX77V2K9,16.4681256794\n2020-08-21,MAMDDXVJYX77V2K9,16.5347880198\n2020-08-24,MAMDDXVJYX77V2K9,16.3560645283\n2020-08-25,MAMDDXVJYX77V2K9,16.4482772289\n2020-08-26,MAMDDXVJYX77V2K9,17.0461712593\n2020-08-27,MAMDDXVJYX77V2K9,18.7092109856\n2020-08-28,MAMDDXVJYX77V2K9,18.7202910524\n2020-08-31,MAMDDXVJYX77V2K9,19.8119432133\n2020-08-03,MA4B66MW5E27U9ZPD6K,36.4867121817\n2020-08-04,MA4B66MW5E27U9ZPD6K,36.9225727592\n2020-08-05,MA4B66MW5E27U9ZPD6K,36.240892953300005\n2020-08-06,MA4B66MW5E27U9ZPD6K,36.2600225168\n2020-08-07,MA4B66MW5E27U9ZPD6K,36.577072672999996\n2020-08-10,MA4B66MW5E27U9ZPD6K,37.277453833\n2020-08-11,MA4B66MW5E27U9ZPD6K,38.3123213112\n2020-08-12,MA4B66MW5E27U9ZPD6K,36.0881969522\n2020-08-13,MA4B66MW5E27U9ZPD6K,36.2786288816\n2020-08-14,MA4B66MW5E27U9ZPD6K,35.8483698081\n2020-08-17,MA4B66MW5E27U9ZPD6K,35.7536470334\n2020-08-18,MA4B66MW5E27U9ZPD6K,34.0484475231\n2020-08-19,MA4B66MW5E27U9ZPD6K,35.391019102499996\n2020-08-20,MA4B66MW5E27U9ZPD6K,34.648079290599995\n2020-08-21,MA4B66MW5E27U9ZPD6K,35.5718386798\n2020-08-24,MA4B66MW5E27U9ZPD6K,36.608911018200004\n2020-08-25,MA4B66MW5E27U9ZPD6K,35.1995840517\n2020-08-26,MA4B66MW5E27U9ZPD6K,35.3762560996\n2020-08-27,MA4B66MW5E27U9ZPD6K,37.9244541985\n2020-08-28,MA4B66MW5E27U9ZPD6K,39.5686299963\n2020-08-31,MA4B66MW5E27U9ZPD6K,39.199405642200006\n2020-08-03,MA4B66MW5E27UACX6QV,31.843997685499996\n2020-08-04,MA4B66MW5E27UACX6QV,32.0034067603\n2020-08-05,MA4B66MW5E27UACX6QV,25.020124978600002\n2020-08-06,MA4B66MW5E27UACX6QV,25.0151206543\n2020-08-07,MA4B66MW5E27UACX6QV,25.119677436499998\n2020-08-10,MA4B66MW5E27UACX6QV,26.622269666300003\n2020-08-11,MA4B66MW5E27UACX6QV,25.597900407500003\n2020-08-12,MA4B66MW5E27UACX6QV,24.4059790784\n2020-08-13,MA4B66MW5E27UACX6QV,24.069803634\n2020-08-14,MA4B66MW5E27UACX6QV,23.6187288551\n2020-08-17,MA4B66MW5E27UACX6QV,24.1328338197\n2020-08-18,MA4B66MW5E27UACX6QV,22.4751137539\n2020-08-19,MA4B66MW5E27UACX6QV,21.956449105\n2020-08-20,MA4B66MW5E27UACX6QV,21.8415198682\n2020-08-21,MA4B66MW5E27UACX6QV,21.541181253300003\n2020-08-24,MA4B66MW5E27UACX6QV,22.591761142\n2020-08-25,MA4B66MW5E27UACX6QV,22.8356618939\n2020-08-26,MA4B66MW5E27UACX6QV,22.9075616186\n2020-08-27,MA4B66MW5E27UACX6QV,23.9738902808\n2020-08-28,MA4B66MW5E27UACX6QV,24.5889014779\n2020-08-31,MA4B66MW5E27UACX6QV,26.3081430677\n2020-08-03,MA4B66MW5E27UAJQETU,31.2958779849\n2020-08-04,MA4B66MW5E27UAJQETU,31.1774888957\n2020-08-05,MA4B66MW5E27UAJQETU,29.717673099800002\n2020-08-06,MA4B66MW5E27UAJQETU,29.1845887952\n2020-08-07,MA4B66MW5E27UAJQETU,29.818009539700004\n2020-08-10,MA4B66MW5E27UAJQETU,29.7932355428\n2020-08-11,MA4B66MW5E27UAJQETU,31.1385090925\n2020-08-12,MA4B66MW5E27UAJQETU,29.069061797200003\n2020-08-13,MA4B66MW5E27UAJQETU,28.932624282699997\n2020-08-14,MA4B66MW5E27UAJQETU,28.639665327899998\n2020-08-17,MA4B66MW5E27UAJQETU,27.3364607789\n2020-08-18,MA4B66MW5E27UAJQETU,26.0934184394\n2020-08-19,MA4B66MW5E27UAJQETU,25.7851791305\n2020-08-20,MA4B66MW5E27UAJQETU,26.845933223099998\n2020-08-21,MA4B66MW5E27UAJQETU,26.071731001699998\n2020-08-24,MA4B66MW5E27UAJQETU,27.388305253900004\n2020-08-25,MA4B66MW5E27UAJQETU,26.8343895983\n2020-08-26,MA4B66MW5E27UAJQETU,27.1502517246\n2020-08-27,MA4B66MW5E27UAJQETU,28.20586302\n2020-08-28,MA4B66MW5E27UAJQETU,28.916651795999996\n2020-08-31,MA4B66MW5E27UAJQETU,31.0803676915\n2020-08-03,MA4B66MW5E27UANLXV6,29.754443238400004\n2020-08-04,MA4B66MW5E27UANLXV6,28.771766027\n2020-08-05,MA4B66MW5E27UANLXV6,26.691992675199998\n2020-08-06,MA4B66MW5E27UANLXV6,26.4810209992\n2020-08-07,MA4B66MW5E27UANLXV6,28.0820986721\n2020-08-10,MA4B66MW5E27UANLXV6,26.8129507068\n2020-08-11,MA4B66MW5E27UANLXV6,27.7313839781\n2020-08-12,MA4B66MW5E27UANLXV6,26.571767940199997\n2020-08-13,MA4B66MW5E27UANLXV6,26.633450403199998\n2020-08-14,MA4B66MW5E27UANLXV6,25.9943995142\n2020-08-17,MA4B66MW5E27UANLXV6,25.8289047108\n2020-08-18,MA4B66MW5E27UANLXV6,24.6726829411\n2020-08-19,MA4B66MW5E27UANLXV6,24.9603068877\n2020-08-20,MA4B66MW5E27UANLXV6,24.6844680825\n2020-08-21,MA4B66MW5E27UANLXV6,24.0102128403\n2020-08-24,MA4B66MW5E27UANLXV6,25.738151163\n2020-08-25,MA4B66MW5E27UANLXV6,24.6283719672\n2020-08-26,MA4B66MW5E27UANLXV6,24.9287537292\n2020-08-27,MA4B66MW5E27UANLXV6,26.6345255095\n2020-08-28,MA4B66MW5E27UANLXV6,27.8917257758\n2020-08-31,MA4B66MW5E27UANLXV6,29.074584828\n2020-08-03,MA4B66MW5E27U9VBB94,37.468100511500005\n2020-08-04,MA4B66MW5E27U9VBB94,36.1883608826\n2020-08-05,MA4B66MW5E27U9VBB94,32.3832866741\n2020-08-06,MA4B66MW5E27U9VBB94,34.4964729831\n2020-08-07,MA4B66MW5E27U9VBB94,35.4857458694\n2020-08-10,MA4B66MW5E27U9VBB94,37.6298824643\n2020-08-11,MA4B66MW5E27U9VBB94,38.2465802305\n2020-08-12,MA4B66MW5E27U9VBB94,34.248123887800006\n2020-08-13,MA4B66MW5E27U9VBB94,34.8111188129\n2020-08-14,MA4B66MW5E27U9VBB94,34.835343484999996\n2020-08-17,MA4B66MW5E27U9VBB94,35.3589344361\n2020-08-18,MA4B66MW5E27U9VBB94,33.7631287033\n2020-08-19,MA4B66MW5E27U9VBB94,33.0732333554\n2020-08-20,MA4B66MW5E27U9VBB94,33.946000602\n2020-08-21,MA4B66MW5E27U9VBB94,42.1643940429\n2020-08-24,MA4B66MW5E27U9VBB94,48.3583861664\n2020-08-25,MA4B66MW5E27U9VBB94,45.2850552494\n2020-08-26,MA4B66MW5E27U9VBB94,51.5901744123\n2020-08-27,MA4B66MW5E27U9VBB94,51.809533946200006\n2020-08-28,MA4B66MW5E27U9VBB94,47.5321270835\n2020-08-31,MA4B66MW5E27U9VBB94,50.6165924077\n2020-08-03,MA4B66MW5E27UALG33R,38.7239086454\n2020-08-04,MA4B66MW5E27UALG33R,39.667337611\n2020-08-05,MA4B66MW5E27UALG33R,37.3513540516\n2020-08-06,MA4B66MW5E27UALG33R,36.6628776956\n2020-08-07,MA4B66MW5E27UALG33R,38.5055696354\n2020-08-10,MA4B66MW5E27UALG33R,38.640515226400005\n2020-08-11,MA4B66MW5E27UALG33R,38.463823716200004\n2020-08-12,MA4B66MW5E27UALG33R,35.8626995341\n2020-08-13,MA4B66MW5E27UALG33R,35.333865636700004\n2020-08-14,MA4B66MW5E27UALG33R,34.5106016957\n2020-08-17,MA4B66MW5E27UALG33R,35.3142380325\n2020-08-18,MA4B66MW5E27UALG33R,35.950621869100004\n2020-08-19,MA4B66MW5E27UALG33R,35.2450472964\n2020-08-20,MA4B66MW5E27UALG33R,35.2626699539\n2020-08-21,MA4B66MW5E27UALG33R,36.045116842199995\n2020-08-24,MA4B66MW5E27UALG33R,38.1173292286\n2020-08-25,MA4B66MW5E27UALG33R,37.5986691812\n2020-08-26,MA4B66MW5E27UALG33R,45.600495480199996\n2020-08-27,MA4B66MW5E27UALG33R,46.2809934675\n2020-08-28,MA4B66MW5E27UALG33R,48.088613627\n2020-08-31,MA4B66MW5E27UALG33R,51.3712150268\n2020-08-03,MA4B66MW5E27UALNB5V,51.396189203999995\n2020-08-04,MA4B66MW5E27UALNB5V,51.9240540815\n2020-08-05,MA4B66MW5E27UALNB5V,49.825516613699996\n2020-08-06,MA4B66MW5E27UALNB5V,49.144508203\n2020-08-07,MA4B66MW5E27UALNB5V,49.8829989145\n2020-08-10,MA4B66MW5E27UALNB5V,51.524084640199995\n2020-08-11,MA4B66MW5E27UALNB5V,53.1189094272\n2020-08-12,MA4B66MW5E27UALNB5V,48.5769572062\n2020-08-13,MA4B66MW5E27UALNB5V,48.9581254987\n2020-08-14,MA4B66MW5E27UALNB5V,49.3537609474\n2020-08-17,MA4B66MW5E27UALNB5V,51.514128738800004\n2020-08-18,MA4B66MW5E27UALNB5V,52.165929915099994\n2020-08-19,MA4B66MW5E27UALNB5V,49.644179013400006\n2020-08-20,MA4B66MW5E27UALNB5V,42.2523439124\n2020-08-21,MA4B66MW5E27UALNB5V,43.785864562\n2020-08-24,MA4B66MW5E27UALNB5V,44.7914874659\n2020-08-25,MA4B66MW5E27UALNB5V,44.2846139161\n2020-08-26,MA4B66MW5E27UALNB5V,45.5856932102\n2020-08-27,MA4B66MW5E27UALNB5V,46.028710601200004\n2020-08-28,MA4B66MW5E27UALNB5V,48.136116465099995\n2020-08-31,MA4B66MW5E27UALNB5V,50.934381928899995\n2020-08-03,MA4B66MW5E3VLSECN,33.9928973676\n2020-08-04,MA4B66MW5E3VLSECN,33.3814489368\n2020-08-05,MA4B66MW5E3VLSECN,32.0293720404\n2020-08-06,MA4B66MW5E3VLSECN,32.934977587000006\n2020-08-07,MA4B66MW5E3VLSECN,35.380882954600004\n2020-08-10,MA4B66MW5E3VLSECN,36.1823219553\n2020-08-11,MA4B66MW5E3VLSECN,38.1554977693\n2020-08-12,MA4B66MW5E3VLSECN,34.348157395399994\n2020-08-13,MA4B66MW5E3VLSECN,34.2755859528\n2020-08-14,MA4B66MW5E3VLSECN,32.9239926865\n2020-08-17,MA4B66MW5E3VLSECN,32.115436391\n2020-08-18,MA4B66MW5E3VLSECN,33.8428080462\n2020-08-19,MA4B66MW5E3VLSECN,33.4221750488\n2020-08-20,MA4B66MW5E3VLSECN,32.6232184369\n2020-08-21,MA4B66MW5E3VLSECN,33.6482212241\n2020-08-24,MA4B66MW5E3VLSECN,37.000303153800004\n2020-08-25,MA4B66MW5E3VLSECN,38.089247678\n2020-08-26,MA4B66MW5E3VLSECN,48.0859662453\n2020-08-27,MA4B66MW5E3VLSECN,47.6446817255\n2020-08-28,MA4B66MW5E3VLSECN,46.3252311785\n2020-08-31,MA4B66MW5E3VLSECN,45.1785348792\n2020-08-03,MA4B66MW5E27UANZH3T,35.6411527063\n2020-08-04,MA4B66MW5E27UANZH3T,34.7524080393\n2020-08-05,MA4B66MW5E27UANZH3T,32.9936615519\n2020-08-06,MA4B66MW5E27UANZH3T,32.9235647577\n2020-08-07,MA4B66MW5E27UANZH3T,32.330212198699996\n2020-08-10,MA4B66MW5E27UANZH3T,33.506727977400004\n2020-08-11,MA4B66MW5E27UANZH3T,33.4961302475\n2020-08-12,MA4B66MW5E27UANZH3T,32.400005296799996\n2020-08-13,MA4B66MW5E27UANZH3T,32.8898624712\n2020-08-14,MA4B66MW5E27UANZH3T,32.152383006700006\n2020-08-17,MA4B66MW5E27UANZH3T,32.5652729273\n2020-08-18,MA4B66MW5E27UANZH3T,31.996800811900002\n2020-08-19,MA4B66MW5E27UANZH3T,31.089815583500002\n2020-08-20,MA4B66MW5E27UANZH3T,31.8035570157\n2020-08-21,MA4B66MW5E27UANZH3T,31.8473613806\n2020-08-24,MA4B66MW5E27UANZH3T,31.581647526899996\n2020-08-25,MA4B66MW5E27UANZH3T,31.719156476299997\n2020-08-26,MA4B66MW5E27UANZH3T,34.4734184335\n2020-08-27,MA4B66MW5E27UANZH3T,34.5536886541\n2020-08-28,MA4B66MW5E27UANZH3T,32.9437371361\n2020-08-31,MA4B66MW5E27UANZH3T,35.613643340399996\n2020-08-03,MA4B66MW5E27UANEPLL,25.139089122\n2020-08-04,MA4B66MW5E27UANEPLL,25.150698356899998\n2020-08-05,MA4B66MW5E27UANEPLL,24.1584027738\n2020-08-06,MA4B66MW5E27UANEPLL,24.9641618596\n2020-08-07,MA4B66MW5E27UANEPLL,25.341412918400003\n2020-08-10,MA4B66MW5E27UANEPLL,25.3513411558\n2020-08-11,MA4B66MW5E27UANEPLL,25.8658692829\n2020-08-12,MA4B66MW5E27UANEPLL,23.482260213700002\n2020-08-13,MA4B66MW5E27UANEPLL,23.346236611000002\n2020-08-14,MA4B66MW5E27UANEPLL,23.1744797766\n2020-08-17,MA4B66MW5E27UANEPLL,24.5398209532\n2020-08-18,MA4B66MW5E27UANEPLL,22.5665064017\n2020-08-19,MA4B66MW5E27UANEPLL,22.364983256400002\n2020-08-20,MA4B66MW5E27UANEPLL,23.082373145000002\n2020-08-21,MA4B66MW5E27UANEPLL,22.2379473742\n2020-08-24,MA4B66MW5E27UANEPLL,23.2663973258\n2020-08-25,MA4B66MW5E27UANEPLL,23.285434237300002\n2020-08-26,MA4B66MW5E27UANEPLL,23.4152877664\n2020-08-27,MA4B66MW5E27UANEPLL,26.5942269425\n2020-08-28,MA4B66MW5E27UANEPLL,26.6252728622\n2020-08-31,MA4B66MW5E27UANEPLL,27.342388446900003\n2020-08-03,MA4B66MW5E27UAE4MDU,24.505095851100002\n2020-08-04,MA4B66MW5E27UAE4MDU,25.0329102903\n2020-08-05,MA4B66MW5E27UAE4MDU,24.0596781373\n2020-08-06,MA4B66MW5E27UAE4MDU,23.288780716599998\n2020-08-07,MA4B66MW5E27UAE4MDU,23.465785323400002\n2020-08-10,MA4B66MW5E27UAE4MDU,24.4330040471\n2020-08-11,MA4B66MW5E27UAE4MDU,25.5651388817\n2020-08-12,MA4B66MW5E27UAE4MDU,22.8813215259\n2020-08-13,MA4B66MW5E27UAE4MDU,22.8079565536\n2020-08-14,MA4B66MW5E27UAE4MDU,23.0479463262\n2020-08-17,MA4B66MW5E27UAE4MDU,22.2693035253\n2020-08-18,MA4B66MW5E27UAE4MDU,22.095031107900002\n2020-08-19,MA4B66MW5E27UAE4MDU,22.2446632405\n2020-08-20,MA4B66MW5E27UAE4MDU,21.6534913037\n2020-08-21,MA4B66MW5E27UAE4MDU,21.427762053\n2020-08-24,MA4B66MW5E27UAE4MDU,22.5262361986\n2020-08-25,MA4B66MW5E27UAE4MDU,22.2819609043\n2020-08-26,MA4B66MW5E27UAE4MDU,22.978499809200002\n2020-08-27,MA4B66MW5E27UAE4MDU,25.3602626772\n2020-08-28,MA4B66MW5E27UAE4MDU,25.282556955300002\n2020-08-31,MA4B66MW5E27UAE4MDU,26.805582766300002\n2020-08-03,MA4B66MW5E27UANLY8B,28.5228367629\n2020-08-04,MA4B66MW5E27UANLY8B,29.0592454822\n2020-08-05,MA4B66MW5E27UANLY8B,26.6638178971\n2020-08-06,MA4B66MW5E27UANLY8B,26.8480519955\n2020-08-07,MA4B66MW5E27UANLY8B,25.5358153919\n2020-08-10,MA4B66MW5E27UANLY8B,26.5836308594\n2020-08-11,MA4B66MW5E27UANLY8B,27.2995611877\n2020-08-12,MA4B66MW5E27UANLY8B,24.6692363016\n2020-08-13,MA4B66MW5E27UANLY8B,24.7485727198\n2020-08-14,MA4B66MW5E27UANLY8B,24.345126311\n2020-08-17,MA4B66MW5E27UANLY8B,24.8039814369\n2020-08-18,MA4B66MW5E27UANLY8B,24.969633882100002\n2020-08-19,MA4B66MW5E27UANLY8B,24.6092396475\n2020-08-20,MA4B66MW5E27UANLY8B,24.1993665316\n2020-08-21,MA4B66MW5E27UANLY8B,23.6044185056\n2020-08-24,MA4B66MW5E27UANLY8B,25.990883329699997\n2020-08-25,MA4B66MW5E27UANLY8B,24.4653568898\n2020-08-26,MA4B66MW5E27UANLY8B,26.0116211536\n2020-08-27,MA4B66MW5E27UANLY8B,27.110237876\n2020-08-28,MA4B66MW5E27UANLY8B,28.090477961299996\n2020-08-31,MA4B66MW5E27UANLY8B,30.3762004837\n2020-08-03,MA4B66MW5E27UAL3HZC,23.5823586025\n2020-08-04,MA4B66MW5E27UAL3HZC,22.9006838729\n2020-08-05,MA4B66MW5E27UAL3HZC,21.0717859389\n2020-08-06,MA4B66MW5E27UAL3HZC,21.842555391599998\n2020-08-07,MA4B66MW5E27UAL3HZC,20.8658220148\n2020-08-10,MA4B66MW5E27UAL3HZC,21.9350080918\n2020-08-11,MA4B66MW5E27UAL3HZC,21.7178012013\n2020-08-12,MA4B66MW5E27UAL3HZC,20.239683142500002\n2020-08-13,MA4B66MW5E27UAL3HZC,20.5630997462\n2020-08-14,MA4B66MW5E27UAL3HZC,19.9036298606\n2020-08-17,MA4B66MW5E27UAL3HZC,20.6888440331\n2020-08-18,MA4B66MW5E27UAL3HZC,18.964981709699998\n2020-08-19,MA4B66MW5E27UAL3HZC,19.2873246915\n2020-08-20,MA4B66MW5E27UAL3HZC,18.8466240242\n2020-08-21,MA4B66MW5E27UAL3HZC,19.2509020117\n2020-08-24,MA4B66MW5E27UAL3HZC,19.1507536355\n2020-08-25,MA4B66MW5E27UAL3HZC,19.1640011469\n2020-08-26,MA4B66MW5E27UAL3HZC,19.1244248922\n2020-08-27,MA4B66MW5E27UAL3HZC,21.1390399298\n2020-08-28,MA4B66MW5E27UAL3HZC,21.3412101671\n2020-08-31,MA4B66MW5E27UAL3HZC,23.075075317499998\n2020-08-03,MA4B66MW5E27UALG2XD,26.7674395023\n2020-08-04,MA4B66MW5E27UALG2XD,24.7267350319\n2020-08-05,MA4B66MW5E27UALG2XD,24.3604730578\n2020-08-06,MA4B66MW5E27UALG2XD,24.0914133816\n2020-08-07,MA4B66MW5E27UALG2XD,24.1266652282\n2020-08-10,MA4B66MW5E27UALG2XD,24.9156446474\n2020-08-11,MA4B66MW5E27UALG2XD,25.744241676500003\n2020-08-12,MA4B66MW5E27UALG2XD,23.3606851368\n2020-08-13,MA4B66MW5E27UALG2XD,23.9814849659\n2020-08-14,MA4B66MW5E27UALG2XD,23.8306298558\n2020-08-17,MA4B66MW5E27UALG2XD,24.413215838499998\n2020-08-18,MA4B66MW5E27UALG2XD,23.2287693023\n2020-08-19,MA4B66MW5E27UALG2XD,22.1373117554\n2020-08-20,MA4B66MW5E27UALG2XD,22.2776889636\n2020-08-21,MA4B66MW5E27UALG2XD,21.562953987100002\n2020-08-24,MA4B66MW5E27UALG2XD,22.180796536\n2020-08-25,MA4B66MW5E27UALG2XD,21.9615484325\n2020-08-26,MA4B66MW5E27UALG2XD,22.0708755496\n2020-08-27,MA4B66MW5E27UALG2XD,23.4057656619\n2020-08-28,MA4B66MW5E27UALG2XD,24.7083501038\n2020-08-31,MA4B66MW5E27UALG2XD,25.6887668733\n2020-08-03,MA4B66MW5E27UALUL3R,18.4741481258\n2020-08-04,MA4B66MW5E27UALUL3R,17.797150016499998\n2020-08-05,MA4B66MW5E27UALUL3R,17.5807326822\n2020-08-06,MA4B66MW5E27UALUL3R,17.1277428042\n2020-08-07,MA4B66MW5E27UALUL3R,16.608196793999998\n2020-08-10,MA4B66MW5E27UALUL3R,16.6104951717\n2020-08-11,MA4B66MW5E27UALUL3R,17.8284357076\n2020-08-12,MA4B66MW5E27UALUL3R,16.2645218723\n2020-08-13,MA4B66MW5E27UALUL3R,17.4443682423\n2020-08-14,MA4B66MW5E27UALUL3R,15.7958838075\n2020-08-17,MA4B66MW5E27UALUL3R,16.474003555899998\n2020-08-18,MA4B66MW5E27UALUL3R,15.782797406599999\n2020-08-19,MA4B66MW5E27UALUL3R,15.458176316300001\n2020-08-20,MA4B66MW5E27UALUL3R,15.5540339526\n2020-08-21,MA4B66MW5E27UALUL3R,14.648782648600001\n2020-08-24,MA4B66MW5E27UALUL3R,14.6924542067\n2020-08-25,MA4B66MW5E27UALUL3R,14.5957329661\n2020-08-26,MA4B66MW5E27UALUL3R,16.134286435699998\n2020-08-27,MA4B66MW5E27UALUL3R,19.2698102812\n2020-08-28,MA4B66MW5E27UALUL3R,17.5722900584\n2020-08-31,MA4B66MW5E27UALUL3R,19.9886195223\n2020-08-03,MA4B66MW5E27UAE4LQY,36.3024000067\n2020-08-04,MA4B66MW5E27UAE4LQY,35.838158459\n2020-08-05,MA4B66MW5E27UAE4LQY,32.9574461355\n2020-08-06,MA4B66MW5E27UAE4LQY,32.7940476067\n2020-08-07,MA4B66MW5E27UAE4LQY,32.6716410653\n2020-08-10,MA4B66MW5E27UAE4LQY,31.524406645800003\n2020-08-11,MA4B66MW5E27UAE4LQY,32.6780861162\n2020-08-12,MA4B66MW5E27UAE4LQY,30.9870352807\n2020-08-13,MA4B66MW5E27UAE4LQY,31.118571339899997\n2020-08-14,MA4B66MW5E27UAE4LQY,30.6190578456\n2020-08-17,MA4B66MW5E27UAE4LQY,30.7698312555\n2020-08-18,MA4B66MW5E27UAE4LQY,31.730474000199997\n2020-08-19,MA4B66MW5E27UAE4LQY,31.0714736909\n2020-08-20,MA4B66MW5E27UAE4LQY,31.9261769097\n2020-08-21,MA4B66MW5E27UAE4LQY,31.4681907851\n2020-08-24,MA4B66MW5E27UAE4LQY,30.848221583\n2020-08-25,MA4B66MW5E27UAE4LQY,31.045989782400003\n2020-08-26,MA4B66MW5E27UAE4LQY,31.9361614137\n2020-08-27,MA4B66MW5E27UAE4LQY,32.9621040749\n2020-08-28,MA4B66MW5E27UAE4LQY,32.4920199695\n2020-08-31,MA4B66MW5E27UAE4LQY,35.2689287702\n2020-08-03,MA4B66MW5E27UAN8F94,22.4036255061\n2020-08-04,MA4B66MW5E27UAN8F94,21.6133590936\n2020-08-05,MA4B66MW5E27UAN8F94,21.265036998\n2020-08-06,MA4B66MW5E27UAN8F94,21.062446003999998\n2020-08-07,MA4B66MW5E27UAN8F94,21.0775131915\n2020-08-10,MA4B66MW5E27UAN8F94,21.0826663872\n2020-08-11,MA4B66MW5E27UAN8F94,20.942080422100002\n2020-08-12,MA4B66MW5E27UAN8F94,20.1868293281\n2020-08-13,MA4B66MW5E27UAN8F94,20.3317560994\n2020-08-14,MA4B66MW5E27UAN8F94,19.2271290754\n2020-08-17,MA4B66MW5E27UAN8F94,19.8119446167\n2020-08-18,MA4B66MW5E27UAN8F94,19.8975026926\n2020-08-19,MA4B66MW5E27UAN8F94,19.0150142628\n2020-08-20,MA4B66MW5E27UAN8F94,19.223531327\n2020-08-21,MA4B66MW5E27UAN8F94,16.6208159404\n2020-08-24,MA4B66MW5E27UAN8F94,17.6179262944\n2020-08-25,MA4B66MW5E27UAN8F94,17.906143431\n2020-08-26,MA4B66MW5E27UAN8F94,18.035044802\n2020-08-27,MA4B66MW5E27UAN8F94,18.3256686211\n2020-08-28,MA4B66MW5E27UAN8F94,18.4730921361\n2020-08-31,MA4B66MW5E27UAN8F94,19.322078052200002\n2020-08-03,MA4B66MW5E27UA447M6,21.4894692479\n2020-08-04,MA4B66MW5E27UA447M6,20.9414316194\n2020-08-05,MA4B66MW5E27UA447M6,20.2947606177\n2020-08-06,MA4B66MW5E27UA447M6,20.7917334953\n2020-08-07,MA4B66MW5E27UA447M6,20.9421847986\n2020-08-10,MA4B66MW5E27UA447M6,19.313511427599998\n2020-08-11,MA4B66MW5E27UA447M6,19.998069791\n2020-08-12,MA4B66MW5E27UA447M6,19.2484790361\n2020-08-13,MA4B66MW5E27UA447M6,18.2138903643\n2020-08-14,MA4B66MW5E27UA447M6,18.2548992505\n2020-08-17,MA4B66MW5E27UA447M6,17.8738186159\n2020-08-18,MA4B66MW5E27UA447M6,17.421443186999998\n2020-08-19,MA4B66MW5E27UA447M6,17.2270412059\n2020-08-20,MA4B66MW5E27UA447M6,17.2809844367\n2020-08-21,MA4B66MW5E27UA447M6,17.0496380858\n2020-08-24,MA4B66MW5E27UA447M6,18.062397313\n2020-08-25,MA4B66MW5E27UA447M6,18.0358645929\n2020-08-26,MA4B66MW5E27UA447M6,18.6675150585\n2020-08-27,MA4B66MW5E27UA447M6,20.3467679317\n2020-08-28,MA4B66MW5E27UA447M6,19.0504969773\n2020-08-31,MA4B66MW5E27UA447M6,20.137281891700002\n2020-08-03,MA4B66MW5E27UAKHZDF,19.9454962029\n2020-08-04,MA4B66MW5E27UAKHZDF,20.1585503679\n2020-08-05,MA4B66MW5E27UAKHZDF,20.0076011567\n2020-08-06,MA4B66MW5E27UAKHZDF,19.4002195135\n2020-08-07,MA4B66MW5E27UAKHZDF,19.2087737469\n2020-08-10,MA4B66MW5E27UAKHZDF,18.8727693245\n2020-08-11,MA4B66MW5E27UAKHZDF,20.3621613292\n2020-08-12,MA4B66MW5E27UAKHZDF,18.0389599485\n2020-08-13,MA4B66MW5E27UAKHZDF,19.0979309806\n2020-08-14,MA4B66MW5E27UAKHZDF,18.7637469535\n2020-08-17,MA4B66MW5E27UAKHZDF,18.5477915214\n2020-08-18,MA4B66MW5E27UAKHZDF,17.929260668999998\n2020-08-19,MA4B66MW5E27UAKHZDF,17.339471187\n2020-08-20,MA4B66MW5E27UAKHZDF,17.6429490391\n2020-08-21,MA4B66MW5E27UAKHZDF,17.547851514999998\n2020-08-24,MA4B66MW5E27UAKHZDF,17.8161956543\n2020-08-25,MA4B66MW5E27UAKHZDF,17.0150457507\n2020-08-26,MA4B66MW5E27UAKHZDF,17.3423072617\n2020-08-27,MA4B66MW5E27UAKHZDF,18.9377196739\n2020-08-28,MA4B66MW5E27UAKHZDF,19.1913961485\n2020-08-31,MA4B66MW5E27UAKHZDF,20.2893506367\n2020-08-03,MA4B66MW5E27UALUL23,26.5448274577\n2020-08-04,MA4B66MW5E27UALUL23,26.1022782303\n2020-08-05,MA4B66MW5E27UALUL23,25.3577981602\n2020-08-06,MA4B66MW5E27UALUL23,24.8376179052\n2020-08-07,MA4B66MW5E27UALUL23,24.0732237402\n2020-08-10,MA4B66MW5E27UALUL23,23.8129337777\n2020-08-11,MA4B66MW5E27UALUL23,23.3450276628\n2020-08-12,MA4B66MW5E27UALUL23,21.8552219363\n2020-08-13,MA4B66MW5E27UALUL23,21.7213835639\n2020-08-14,MA4B66MW5E27UALUL23,21.497699708000003\n2020-08-17,MA4B66MW5E27UALUL23,21.942549028200002\n2020-08-18,MA4B66MW5E27UALUL23,21.663491859\n2020-08-19,MA4B66MW5E27UALUL23,20.9729282382\n2020-08-20,MA4B66MW5E27UALUL23,21.1176966499\n2020-08-21,MA4B66MW5E27UALUL23,21.477425198299997\n2020-08-24,MA4B66MW5E27UALUL23,22.1056341557\n2020-08-25,MA4B66MW5E27UALUL23,22.4447824493\n2020-08-26,MA4B66MW5E27UALUL23,22.656046648\n2020-08-27,MA4B66MW5E27UALUL23,22.8775383698\n2020-08-28,MA4B66MW5E27UALUL23,25.641954427\n2020-08-31,MA4B66MW5E27UALUL23,26.6658748762\n2020-08-03,MA4B66MW5E27UAL9SRL,24.9173619643\n2020-08-04,MA4B66MW5E27UAL9SRL,24.126289175900002\n2020-08-05,MA4B66MW5E27UAL9SRL,23.8841194908\n2020-08-06,MA4B66MW5E27UAL9SRL,23.7063995774\n2020-08-07,MA4B66MW5E27UAL9SRL,23.3118307732\n2020-08-10,MA4B66MW5E27UAL9SRL,23.5050134228\n2020-08-11,MA4B66MW5E27UAL9SRL,24.5261028019\n2020-08-12,MA4B66MW5E27UAL9SRL,22.2677843293\n2020-08-13,MA4B66MW5E27UAL9SRL,21.4280714845\n2020-08-14,MA4B66MW5E27UAL9SRL,21.8661350748\n2020-08-17,MA4B66MW5E27UAL9SRL,21.0849401218\n2020-08-18,MA4B66MW5E27UAL9SRL,21.6488210145\n2020-08-19,MA4B66MW5E27UAL9SRL,20.4852583651\n2020-08-20,MA4B66MW5E27UAL9SRL,21.109056659100002\n2020-08-21,MA4B66MW5E27UAL9SRL,20.6586070209\n2020-08-24,MA4B66MW5E27UAL9SRL,20.0260930283\n2020-08-25,MA4B66MW5E27UAL9SRL,20.6342759558\n2020-08-26,MA4B66MW5E27UAL9SRL,21.7335557338\n2020-08-27,MA4B66MW5E27UAL9SRL,22.833762025400002\n2020-08-28,MA4B66MW5E27UAL9SRL,22.252730314399997\n2020-08-31,MA4B66MW5E27UAL9SRL,23.9221363685\n2020-08-03,MA4B66MW5E27UANLY9V,29.0926614664\n2020-08-04,MA4B66MW5E27UANLY9V,27.1753803422\n2020-08-05,MA4B66MW5E27UANLY9V,26.340227126299997\n2020-08-06,MA4B66MW5E27UANLY9V,26.492642310100003\n2020-08-07,MA4B66MW5E27UANLY9V,26.091224021\n2020-08-10,MA4B66MW5E27UANLY9V,28.2296175738\n2020-08-11,MA4B66MW5E27UANLY9V,29.104670586199997\n2020-08-12,MA4B66MW5E27UANLY9V,26.582752685\n2020-08-13,MA4B66MW5E27UANLY9V,26.0813260655\n2020-08-14,MA4B66MW5E27UANLY9V,26.571681709599996\n2020-08-17,MA4B66MW5E27UANLY9V,27.1694201692\n2020-08-18,MA4B66MW5E27UANLY9V,26.4298465606\n2020-08-19,MA4B66MW5E27UANLY9V,25.9025990591\n2020-08-20,MA4B66MW5E27UANLY9V,26.0831680534\n2020-08-21,MA4B66MW5E27UANLY9V,24.7827442528\n2020-08-24,MA4B66MW5E27UANLY9V,25.8037822051\n2020-08-25,MA4B66MW5E27UANLY9V,25.4856423664\n2020-08-26,MA4B66MW5E27UANLY9V,26.113407316900002\n2020-08-27,MA4B66MW5E27UANLY9V,28.759383235399998\n2020-08-28,MA4B66MW5E27UANLY9V,29.0188941318\n2020-08-31,MA4B66MW5E27UANLY9V,29.2813205783\n2020-08-03,MA4B66MW5E27UANLYJN,26.522416179199997\n2020-08-04,MA4B66MW5E27UANLYJN,25.627622172\n2020-08-05,MA4B66MW5E27UANLYJN,24.0553617128\n2020-08-06,MA4B66MW5E27UANLYJN,24.7123721814\n2020-08-07,MA4B66MW5E27UANLYJN,24.201565530099998\n2020-08-10,MA4B66MW5E27UANLYJN,25.233288695\n2020-08-11,MA4B66MW5E27UANLYJN,26.5285911264\n2020-08-12,MA4B66MW5E27UANLYJN,24.42572098\n2020-08-13,MA4B66MW5E27UANLYJN,24.8993763837\n2020-08-14,MA4B66MW5E27UANLYJN,23.7200915663\n2020-08-17,MA4B66MW5E27UANLYJN,24.2252571056\n2020-08-18,MA4B66MW5E27UANLYJN,23.3454236427\n2020-08-19,MA4B66MW5E27UANLYJN,23.426330695799997\n2020-08-20,MA4B66MW5E27UANLYJN,23.0537084218\n2020-08-21,MA4B66MW5E27UANLYJN,22.2429885492\n2020-08-24,MA4B66MW5E27UANLYJN,22.4167580447\n2020-08-25,MA4B66MW5E27UANLYJN,22.207621656900002\n2020-08-26,MA4B66MW5E27UANLYJN,22.5319139279\n2020-08-27,MA4B66MW5E27UANLYJN,24.0522377902\n2020-08-28,MA4B66MW5E27UANLYJN,25.039721702\n2020-08-31,MA4B66MW5E27UANLYJN,26.1145299188\n2020-08-03,MA4B66MW5E27UANT8PM,17.4841313043\n2020-08-04,MA4B66MW5E27UANT8PM,17.1679184877\n2020-08-05,MA4B66MW5E27UANT8PM,17.1517443195\n2020-08-06,MA4B66MW5E27UANT8PM,16.8935571186\n2020-08-07,MA4B66MW5E27UANT8PM,17.0387727098\n2020-08-10,MA4B66MW5E27UANT8PM,16.5122885854\n2020-08-11,MA4B66MW5E27UANT8PM,17.2307832813\n2020-08-12,MA4B66MW5E27UANT8PM,16.2537391163\n2020-08-13,MA4B66MW5E27UANT8PM,16.3346361241\n2020-08-14,MA4B66MW5E27UANT8PM,15.8300689407\n2020-08-17,MA4B66MW5E27UANT8PM,15.5068766036\n2020-08-18,MA4B66MW5E27UANT8PM,14.994667931399999\n2020-08-19,MA4B66MW5E27UANT8PM,14.494235133899998\n2020-08-20,MA4B66MW5E27UANT8PM,14.699328337199999\n2020-08-21,MA4B66MW5E27UANT8PM,14.334550328599999\n2020-08-24,MA4B66MW5E27UANT8PM,14.8881520697\n2020-08-25,MA4B66MW5E27UANT8PM,15.153158962\n2020-08-26,MA4B66MW5E27UANT8PM,15.125791469700001\n2020-08-27,MA4B66MW5E27UANT8PM,17.244082383000002\n2020-08-28,MA4B66MW5E27UANT8PM,17.2467389346\n2020-08-31,MA4B66MW5E27UANT8PM,17.7692622778\n2020-08-03,MA4B66MW5E27UAKHZFD,32.0013813641\n2020-08-04,MA4B66MW5E27UAKHZFD,32.0674497802\n2020-08-05,MA4B66MW5E27UAKHZFD,30.7995594829\n2020-08-06,MA4B66MW5E27UAKHZFD,31.252053921799998\n2020-08-07,MA4B66MW5E27UAKHZFD,31.970455897599997\n2020-08-10,MA4B66MW5E27UAKHZFD,33.2128520859\n2020-08-11,MA4B66MW5E27UAKHZFD,33.828479250499996\n2020-08-12,MA4B66MW5E27UAKHZFD,32.991925051799996\n2020-08-13,MA4B66MW5E27UAKHZFD,32.239652466\n2020-08-14,MA4B66MW5E27UAKHZFD,32.6004525173\n2020-08-17,MA4B66MW5E27UAKHZFD,31.5323569378\n2020-08-18,MA4B66MW5E27UAKHZFD,31.442703661\n2020-08-19,MA4B66MW5E27UAKHZFD,30.432373397899998\n2020-08-20,MA4B66MW5E27UAKHZFD,30.0640658198\n2020-08-21,MA4B66MW5E27UAKHZFD,29.189220876700002\n2020-08-24,MA4B66MW5E27UAKHZFD,30.2725114044\n2020-08-25,MA4B66MW5E27UAKHZFD,30.199499089699998\n2020-08-26,MA4B66MW5E27UAKHZFD,31.191791129800002\n2020-08-27,MA4B66MW5E27UAKHZFD,33.029916972\n2020-08-28,MA4B66MW5E27UAKHZFD,33.1894097001\n2020-08-31,MA4B66MW5E27UAKHZFD,33.9868911836\n2020-08-03,MA4B66MW5E27UAKVAB4,28.781383963399996\n2020-08-04,MA4B66MW5E27UAKVAB4,26.6038152489\n2020-08-05,MA4B66MW5E27UAKVAB4,25.768417273100003\n2020-08-06,MA4B66MW5E27UAKVAB4,26.082219415300003\n2020-08-07,MA4B66MW5E27UAKVAB4,25.6673962918\n2020-08-10,MA4B66MW5E27UAKVAB4,27.1726035902\n2020-08-11,MA4B66MW5E27UAKVAB4,26.9311787078\n2020-08-12,MA4B66MW5E27UAKVAB4,25.043404455700003\n2020-08-13,MA4B66MW5E27UAKVAB4,25.816433333599996\n2020-08-14,MA4B66MW5E27UAKVAB4,24.7640550573\n2020-08-17,MA4B66MW5E27UAKVAB4,26.0022839799\n2020-08-18,MA4B66MW5E27UAKVAB4,24.2462128325\n2020-08-19,MA4B66MW5E27UAKVAB4,24.7474109221\n2020-08-20,MA4B66MW5E27UAKVAB4,24.554559168\n2020-08-21,MA4B66MW5E27UAKVAB4,23.331782268199998\n2020-08-24,MA4B66MW5E27UAKVAB4,24.785529566799998\n2020-08-25,MA4B66MW5E27UAKVAB4,24.1568496774\n2020-08-26,MA4B66MW5E27UAKVAB4,24.1868912823\n2020-08-27,MA4B66MW5E27UAKVAB4,26.071463805400004\n2020-08-28,MA4B66MW5E27UAKVAB4,27.749229735300002\n2020-08-31,MA4B66MW5E27UAKVAB4,30.2017582846\n2020-08-03,MA4B66MW5E27UAE4MG6,35.2729116598\n2020-08-04,MA4B66MW5E27UAE4MG6,34.9123036438\n2020-08-05,MA4B66MW5E27UAE4MG6,27.380079367999997\n2020-08-06,MA4B66MW5E27UAE4MG6,29.5305282344\n2020-08-07,MA4B66MW5E27UAE4MG6,28.2699388053\n2020-08-10,MA4B66MW5E27UAE4MG6,27.3122574453\n2020-08-11,MA4B66MW5E27UAE4MG6,28.236848257699997\n2020-08-12,MA4B66MW5E27UAE4MG6,27.5595159232\n2020-08-13,MA4B66MW5E27UAE4MG6,26.8530473576\n2020-08-14,MA4B66MW5E27UAE4MG6,27.310062821800003\n2020-08-17,MA4B66MW5E27UAE4MG6,27.485745907499997\n2020-08-18,MA4B66MW5E27UAE4MG6,27.134961102100004\n2020-08-19,MA4B66MW5E27UAE4MG6,26.530397354500003\n2020-08-20,MA4B66MW5E27UAE4MG6,25.8547756287\n2020-08-21,MA4B66MW5E27UAE4MG6,25.6588190278\n2020-08-24,MA4B66MW5E27UAE4MG6,26.2548276226\n2020-08-25,MA4B66MW5E27UAE4MG6,25.657303145100002\n2020-08-26,MA4B66MW5E27UAE4MG6,27.5801695031\n2020-08-27,MA4B66MW5E27UAE4MG6,28.1085757496\n2020-08-28,MA4B66MW5E27UAE4MG6,31.455273661499998\n2020-08-31,MA4B66MW5E27UAE4MG6,32.0857962524\n2020-08-03,MA4B66MW5E27U8P32SB,19.6120683137\n2020-08-04,MA4B66MW5E27U8P32SB,18.986084133200002\n2020-08-05,MA4B66MW5E27U8P32SB,17.9700836303\n2020-08-06,MA4B66MW5E27U8P32SB,17.350952918500003\n2020-08-07,MA4B66MW5E27U8P32SB,17.047662540399998\n2020-08-10,MA4B66MW5E27U8P32SB,17.1335019934\n2020-08-11,MA4B66MW5E27U8P32SB,19.5649846142\n2020-08-12,MA4B66MW5E27U8P32SB,17.204233235300002\n2020-08-13,MA4B66MW5E27U8P32SB,17.269626231\n2020-08-14,MA4B66MW5E27U8P32SB,16.9673806164\n2020-08-17,MA4B66MW5E27U8P32SB,16.7517135406\n2020-08-18,MA4B66MW5E27U8P32SB,16.6814846898\n2020-08-19,MA4B66MW5E27U8P32SB,17.3067401359\n2020-08-20,MA4B66MW5E27U8P32SB,16.879793488\n2020-08-21,MA4B66MW5E27U8P32SB,16.279648874899998\n2020-08-24,MA4B66MW5E27U8P32SB,16.5472866995\n2020-08-25,MA4B66MW5E27U8P32SB,16.4731881277\n2020-08-26,MA4B66MW5E27U8P32SB,17.368938294\n2020-08-27,MA4B66MW5E27U8P32SB,18.0331747448\n2020-08-28,MA4B66MW5E27U8P32SB,16.7728713201\n2020-08-31,MA4B66MW5E27U8P32SB,19.0377074698\n2020-08-03,MA4B66MW5E27UAL3J7Z,29.522037417000004\n2020-08-04,MA4B66MW5E27UAL3J7Z,29.5875348207\n2020-08-05,MA4B66MW5E27UAL3J7Z,27.739803232\n2020-08-06,MA4B66MW5E27UAL3J7Z,28.533850546900002\n2020-08-07,MA4B66MW5E27UAL3J7Z,26.7789422314\n2020-08-10,MA4B66MW5E27UAL3J7Z,28.496861884399998\n2020-08-11,MA4B66MW5E27UAL3J7Z,27.8182491112\n2020-08-12,MA4B66MW5E27UAL3J7Z,25.587331712\n2020-08-13,MA4B66MW5E27UAL3J7Z,25.691881750399997\n2020-08-14,MA4B66MW5E27UAL3J7Z,25.722516444600004\n2020-08-17,MA4B66MW5E27UAL3J7Z,25.9950531386\n2020-08-18,MA4B66MW5E27UAL3J7Z,27.084381421699998\n2020-08-19,MA4B66MW5E27UAL3J7Z,26.408104674799997\n2020-08-20,MA4B66MW5E27UAL3J7Z,27.0437847683\n2020-08-21,MA4B66MW5E27UAL3J7Z,26.4664725931\n2020-08-24,MA4B66MW5E27UAL3J7Z,27.572856640299996\n2020-08-25,MA4B66MW5E27UAL3J7Z,23.0187035177\n2020-08-26,MA4B66MW5E27UAL3J7Z,23.3821571199\n2020-08-27,MA4B66MW5E27UAL3J7Z,25.564617289399997\n2020-08-28,MA4B66MW5E27UAL3J7Z,26.3750349449\n2020-08-31,MA4B66MW5E27UAL3J7Z,27.838417302200003\n2020-08-03,MA4B66MW5E27UA4479J,36.8515643866\n2020-08-04,MA4B66MW5E27UA4479J,35.5768504565\n2020-08-05,MA4B66MW5E27UA4479J,33.0013192613\n2020-08-06,MA4B66MW5E27UA4479J,25.8477456603\n2020-08-07,MA4B66MW5E27UA4479J,24.1762054367\n2020-08-10,MA4B66MW5E27UA4479J,24.5585804745\n2020-08-11,MA4B66MW5E27UA4479J,23.6851495488\n2020-08-12,MA4B66MW5E27UA4479J,23.2822669772\n2020-08-13,MA4B66MW5E27UA4479J,23.6913186193\n2020-08-14,MA4B66MW5E27UA4479J,22.8614304917\n2020-08-17,MA4B66MW5E27UA4479J,23.575437483\n2020-08-18,MA4B66MW5E27UA4479J,22.1248692397\n2020-08-19,MA4B66MW5E27UA4479J,21.8819002517\n2020-08-20,MA4B66MW5E27UA4479J,21.4932973019\n2020-08-21,MA4B66MW5E27UA4479J,21.6668401141\n2020-08-24,MA4B66MW5E27UA4479J,22.166180894900002\n2020-08-25,MA4B66MW5E27UA4479J,21.7683245556\n2020-08-26,MA4B66MW5E27UA4479J,22.1012543658\n2020-08-27,MA4B66MW5E27UA4479J,23.5386101637\n2020-08-28,MA4B66MW5E27UA4479J,23.1035916776\n2020-08-31,MA4B66MW5E27UA4479J,24.0455426465\n2020-08-03,MA4B66MW5E27UAKNZKD,23.2218970204\n2020-08-04,MA4B66MW5E27UAKNZKD,22.316324836\n2020-08-05,MA4B66MW5E27UAKNZKD,21.2185623646\n2020-08-06,MA4B66MW5E27UAKNZKD,20.4046607436\n2020-08-07,MA4B66MW5E27UAKNZKD,20.4044401526\n2020-08-10,MA4B66MW5E27UAKNZKD,19.8225091254\n2020-08-11,MA4B66MW5E27UAKNZKD,20.6910845046\n2020-08-12,MA4B66MW5E27UAKNZKD,19.2118632386\n2020-08-13,MA4B66MW5E27UAKNZKD,18.8800908739\n2020-08-14,MA4B66MW5E27UAKNZKD,19.111484910199998\n2020-08-17,MA4B66MW5E27UAKNZKD,19.1693999063\n2020-08-18,MA4B66MW5E27UAKNZKD,18.1817459798\n2020-08-19,MA4B66MW5E27UAKNZKD,18.914242957800003\n2020-08-20,MA4B66MW5E27UAKNZKD,18.3895652361\n2020-08-21,MA4B66MW5E27UAKNZKD,18.1808882649\n2020-08-24,MA4B66MW5E27UAKNZKD,18.258989743500003\n2020-08-25,MA4B66MW5E27UAKNZKD,18.0800489587\n2020-08-26,MA4B66MW5E27UAKNZKD,18.4664483006\n2020-08-27,MA4B66MW5E27UAKNZKD,20.2423448332\n2020-08-28,MA4B66MW5E27UAKNZKD,21.2845331545\n2020-08-31,MA4B66MW5E27UAKNZKD,22.0725595114\n2020-08-03,MA4B66MW5E27U9VBBKA,24.5167052958\n2020-08-04,MA4B66MW5E27U9VBBKA,23.7062539117\n2020-08-05,MA4B66MW5E27U9VBBKA,22.778865899299998\n2020-08-06,MA4B66MW5E27U9VBBKA,22.2284453573\n2020-08-07,MA4B66MW5E27U9VBBKA,23.132197661600003\n2020-08-10,MA4B66MW5E27U9VBBKA,23.5933897217\n2020-08-11,MA4B66MW5E27U9VBBKA,24.708010054499997\n2020-08-12,MA4B66MW5E27U9VBBKA,22.6719293562\n2020-08-13,MA4B66MW5E27U9VBBKA,21.74205607\n2020-08-14,MA4B66MW5E27U9VBBKA,21.709832590799998\n2020-08-17,MA4B66MW5E27U9VBBKA,20.7930612617\n2020-08-18,MA4B66MW5E27U9VBBKA,19.9863307931\n2020-08-19,MA4B66MW5E27U9VBBKA,19.7233421376\n2020-08-20,MA4B66MW5E27U9VBBKA,19.3782642288\n2020-08-21,MA4B66MW5E27U9VBBKA,18.4870661174\n2020-08-24,MA4B66MW5E27U9VBBKA,23.9847351572\n2020-08-25,MA4B66MW5E27U9VBBKA,23.3307263997\n2020-08-26,MA4B66MW5E27U9VBBKA,23.865798701400003\n2020-08-27,MA4B66MW5E27U9VBBKA,26.4321641803\n2020-08-28,MA4B66MW5E27U9VBBKA,27.268310842299996\n2020-08-31,MA4B66MW5E27U9VBBKA,30.075632134699998\n2020-08-03,MA4B66MW5E27UADJ5RL,38.6828764577\n2020-08-04,MA4B66MW5E27UADJ5RL,37.6287945276\n2020-08-05,MA4B66MW5E27UADJ5RL,35.7546241289\n2020-08-06,MA4B66MW5E27UADJ5RL,36.3234296335\n2020-08-07,MA4B66MW5E27UADJ5RL,38.457154027\n2020-08-10,MA4B66MW5E27UADJ5RL,38.926381812600006\n2020-08-11,MA4B66MW5E27UADJ5RL,38.9903740438\n2020-08-12,MA4B66MW5E27UADJ5RL,37.7985935874\n2020-08-13,MA4B66MW5E27UADJ5RL,37.3619495631\n2020-08-14,MA4B66MW5E27UADJ5RL,37.2004577292\n2020-08-17,MA4B66MW5E27UADJ5RL,37.481224407\n2020-08-18,MA4B66MW5E27UADJ5RL,36.357387609199996\n2020-08-19,MA4B66MW5E27UADJ5RL,36.0420502\n2020-08-20,MA4B66MW5E27UADJ5RL,36.4537452442\n2020-08-21,MA4B66MW5E27UADJ5RL,37.53083899\n2020-08-24,MA4B66MW5E27UADJ5RL,36.9393476933\n2020-08-25,MA4B66MW5E27UADJ5RL,37.721539033700004\n2020-08-26,MA4B66MW5E27UADJ5RL,44.941530323399995\n2020-08-27,MA4B66MW5E27UADJ5RL,41.125937932599996\n2020-08-28,MA4B66MW5E27UADJ5RL,44.2859643073\n2020-08-31,MA4B66MW5E27UADJ5RL,43.6471308342\n2020-08-03,MA4B66MW5E27UAKV9P7,28.8953678439\n2020-08-04,MA4B66MW5E27UAKV9P7,28.325966735699996\n2020-08-05,MA4B66MW5E27UAKV9P7,26.820907762500003\n2020-08-06,MA4B66MW5E27UAKV9P7,27.089465618800002\n2020-08-07,MA4B66MW5E27UAKV9P7,26.5174348339\n2020-08-10,MA4B66MW5E27UAKV9P7,27.0456335597\n2020-08-11,MA4B66MW5E27UAKV9P7,26.344580903999997\n2020-08-12,MA4B66MW5E27UAKV9P7,25.530829630200003\n2020-08-13,MA4B66MW5E27UAKV9P7,25.376469015300003\n2020-08-14,MA4B66MW5E27UAKV9P7,25.504923085199998\n2020-08-17,MA4B66MW5E27UAKV9P7,24.6162467839\n2020-08-18,MA4B66MW5E27UAKV9P7,24.7067403256\n2020-08-19,MA4B66MW5E27UAKV9P7,24.0082105888\n2020-08-20,MA4B66MW5E27UAKV9P7,23.8571038861\n2020-08-21,MA4B66MW5E27UAKV9P7,25.527812667599996\n2020-08-24,MA4B66MW5E27UAKV9P7,26.1235291484\n2020-08-25,MA4B66MW5E27UAKV9P7,25.8839639961\n2020-08-26,MA4B66MW5E27UAKV9P7,25.184185739800004\n2020-08-27,MA4B66MW5E27UAKV9P7,26.826878958\n2020-08-28,MA4B66MW5E27UAKV9P7,26.261889011\n2020-08-31,MA4B66MW5E27UAKV9P7,28.2961856342\n2020-08-03,MA4B66MW5E27UALG396,28.6420366942\n2020-08-04,MA4B66MW5E27UALG396,28.3860315473\n2020-08-05,MA4B66MW5E27UALG396,27.2579271858\n2020-08-06,MA4B66MW5E27UALG396,25.8341743828\n2020-08-07,MA4B66MW5E27UALG396,26.461773833000002\n2020-08-10,MA4B66MW5E27UALG396,27.0520849503\n2020-08-11,MA4B66MW5E27UALG396,27.902806770100003\n2020-08-12,MA4B66MW5E27UALG396,25.712690541900002\n2020-08-13,MA4B66MW5E27UALG396,25.8044767219\n2020-08-14,MA4B66MW5E27UALG396,25.9516535964\n2020-08-17,MA4B66MW5E27UALG396,25.606249968400004\n2020-08-18,MA4B66MW5E27UALG396,25.9321753648\n2020-08-19,MA4B66MW5E27UALG396,24.7489082977\n2020-08-20,MA4B66MW5E27UALG396,25.5350399992\n2020-08-21,MA4B66MW5E27UALG396,25.128699151099998\n2020-08-24,MA4B66MW5E27UALG396,31.780435922099997\n2020-08-25,MA4B66MW5E27UALG396,32.1351585416\n2020-08-26,MA4B66MW5E27UALG396,32.3187515969\n2020-08-27,MA4B66MW5E27UALG396,34.4450884251\n2020-08-28,MA4B66MW5E27UALG396,35.8476130979\n2020-08-31,MA4B66MW5E27UALG396,38.2314033276\n2020-08-03,MA4B66MW5E27U9ZPDGH,36.4634340825\n2020-08-04,MA4B66MW5E27U9ZPDGH,35.6284997172\n2020-08-05,MA4B66MW5E27U9ZPDGH,33.690094776\n2020-08-06,MA4B66MW5E27U9ZPDGH,33.7807513474\n2020-08-07,MA4B66MW5E27U9ZPDGH,34.8606192219\n2020-08-10,MA4B66MW5E27U9ZPDGH,35.0214062178\n2020-08-11,MA4B66MW5E27U9ZPDGH,36.8608659\n2020-08-12,MA4B66MW5E27U9ZPDGH,35.2844861532\n2020-08-13,MA4B66MW5E27U9ZPDGH,34.7825178672\n2020-08-14,MA4B66MW5E27U9ZPDGH,35.1299951075\n2020-08-17,MA4B66MW5E27U9ZPDGH,34.2677849987\n2020-08-18,MA4B66MW5E27U9ZPDGH,34.4807544186\n2020-08-19,MA4B66MW5E27U9ZPDGH,34.0514173531\n2020-08-20,MA4B66MW5E27U9ZPDGH,33.2009048206\n2020-08-21,MA4B66MW5E27U9ZPDGH,32.7262830003\n2020-08-24,MA4B66MW5E27U9ZPDGH,33.3316560273\n2020-08-25,MA4B66MW5E27U9ZPDGH,33.4496640284\n2020-08-26,MA4B66MW5E27U9ZPDGH,34.1213603817\n2020-08-27,MA4B66MW5E27U9ZPDGH,35.7707413799\n2020-08-28,MA4B66MW5E27U9ZPDGH,35.1081835219\n2020-08-31,MA4B66MW5E27U9ZPDGH,36.8876027715\n2020-08-03,MA4B66MW5E27U9VBBAX,31.260357395299998\n2020-08-04,MA4B66MW5E27U9VBBAX,26.0936365688\n2020-08-05,MA4B66MW5E27U9VBBAX,24.6196077191\n2020-08-06,MA4B66MW5E27U9VBBAX,24.546401258299998\n2020-08-07,MA4B66MW5E27U9VBBAX,25.1285285107\n2020-08-10,MA4B66MW5E27U9VBBAX,24.7350512911\n2020-08-11,MA4B66MW5E27U9VBBAX,24.8184094375\n2020-08-12,MA4B66MW5E27U9VBBAX,22.632214275000003\n2020-08-13,MA4B66MW5E27U9VBBAX,23.6393876038\n2020-08-14,MA4B66MW5E27U9VBBAX,23.897435550500003\n2020-08-17,MA4B66MW5E27U9VBBAX,24.1647206105\n2020-08-18,MA4B66MW5E27U9VBBAX,23.6976432913\n2020-08-19,MA4B66MW5E27U9VBBAX,22.2240126079\n2020-08-20,MA4B66MW5E27U9VBBAX,22.488756502500003\n2020-08-21,MA4B66MW5E27U9VBBAX,22.2374798711\n2020-08-24,MA4B66MW5E27U9VBBAX,23.3315947951\n2020-08-25,MA4B66MW5E27U9VBBAX,22.654831956\n2020-08-26,MA4B66MW5E27U9VBBAX,21.979275453\n2020-08-27,MA4B66MW5E27U9VBBAX,23.4438523485\n2020-08-28,MA4B66MW5E27U9VBBAX,23.740756985799997\n2020-08-31,MA4B66MW5E27U9VBBAX,23.4223256277\n2020-08-03,MA4B66MW5E27UAHKGMA,30.1542378983\n2020-08-04,MA4B66MW5E27UAHKGMA,30.433256756600002\n2020-08-05,MA4B66MW5E27UAHKGMA,29.0914363385\n2020-08-06,MA4B66MW5E27UAHKGMA,28.7880634405\n2020-08-07,MA4B66MW5E27UAHKGMA,27.9976423079\n2020-08-10,MA4B66MW5E27UAHKGMA,28.925173450000003\n2020-08-11,MA4B66MW5E27UAHKGMA,30.9941118229\n2020-08-12,MA4B66MW5E27UAHKGMA,28.1766883044\n2020-08-13,MA4B66MW5E27UAHKGMA,28.5872002091\n2020-08-14,MA4B66MW5E27UAHKGMA,28.4888422982\n2020-08-17,MA4B66MW5E27UAHKGMA,28.135337739799997\n2020-08-18,MA4B66MW5E27UAHKGMA,24.7316562446\n2020-08-19,MA4B66MW5E27UAHKGMA,23.863452576700002\n2020-08-20,MA4B66MW5E27UAHKGMA,23.2854876392\n2020-08-21,MA4B66MW5E27UAHKGMA,22.994885721899998\n2020-08-24,MA4B66MW5E27UAHKGMA,22.6913644248\n2020-08-25,MA4B66MW5E27UAHKGMA,22.1297081507\n2020-08-26,MA4B66MW5E27UAHKGMA,23.491386334\n2020-08-27,MA4B66MW5E27UAHKGMA,25.2427555611\n2020-08-28,MA4B66MW5E27UAHKGMA,25.751678132400002\n2020-08-31,MA4B66MW5E27UAHKGMA,26.780812421900002\n2020-08-03,MA4B66MW5E27U9VBBEQ,28.4866496536\n2020-08-04,MA4B66MW5E27U9VBBEQ,26.785937735799997\n2020-08-05,MA4B66MW5E27U9VBBEQ,25.1941256132\n2020-08-06,MA4B66MW5E27U9VBBEQ,24.4256425466\n2020-08-07,MA4B66MW5E27U9VBBEQ,24.6911628101\n2020-08-10,MA4B66MW5E27U9VBBEQ,25.3075075414\n2020-08-11,MA4B66MW5E27U9VBBEQ,25.431832457499997\n2020-08-12,MA4B66MW5E27U9VBBEQ,23.3706756569\n2020-08-13,MA4B66MW5E27U9VBBEQ,23.1788581929\n2020-08-14,MA4B66MW5E27U9VBBEQ,23.4568359033\n2020-08-17,MA4B66MW5E27U9VBBEQ,22.6981725766\n2020-08-18,MA4B66MW5E27U9VBBEQ,22.0643225642\n2020-08-19,MA4B66MW5E27U9VBBEQ,22.0456489946\n2020-08-20,MA4B66MW5E27U9VBBEQ,21.710599677799998\n2020-08-21,MA4B66MW5E27U9VBBEQ,21.4003711446\n2020-08-24,MA4B66MW5E27U9VBBEQ,22.3438842509\n2020-08-25,MA4B66MW5E27U9VBBEQ,22.5854035905\n2020-08-26,MA4B66MW5E27U9VBBEQ,22.5950804007\n2020-08-27,MA4B66MW5E27U9VBBEQ,43.945140995100004\n2020-08-28,MA4B66MW5E27U9VBBEQ,29.2151068566\n2020-08-31,MA4B66MW5E27U9VBBEQ,30.0205474315\n2020-08-03,MA4B66MW5E27UANZGNN,28.229177490700003\n2020-08-04,MA4B66MW5E27UANZGNN,29.251230388\n2020-08-05,MA4B66MW5E27UANZGNN,30.127297906\n2020-08-06,MA4B66MW5E27UANZGNN,29.1916094284\n2020-08-07,MA4B66MW5E27UANZGNN,29.1649460408\n2020-08-10,MA4B66MW5E27UANZGNN,29.834239376499998\n2020-08-11,MA4B66MW5E27UANZGNN,31.093700697799996\n2020-08-12,MA4B66MW5E27UANZGNN,28.383376047600002\n2020-08-13,MA4B66MW5E27UANZGNN,28.7139446633\n2020-08-14,MA4B66MW5E27UANZGNN,28.5594233626\n2020-08-17,MA4B66MW5E27UANZGNN,27.755634650900003\n2020-08-18,MA4B66MW5E27UANZGNN,23.380512787\n2020-08-19,MA4B66MW5E27UANZGNN,22.0844164537\n2020-08-20,MA4B66MW5E27UANZGNN,21.7478212678\n2020-08-21,MA4B66MW5E27UANZGNN,21.883413668099998\n2020-08-24,MA4B66MW5E27UANZGNN,20.867506033399998\n2020-08-25,MA4B66MW5E27UANZGNN,21.0423677695\n2020-08-26,MA4B66MW5E27UANZGNN,20.7405510655\n2020-08-27,MA4B66MW5E27UANZGNN,27.954401563300003\n2020-08-28,MA4B66MW5E27UANZGNN,30.948469751500003\n2020-08-31,MA4B66MW5E27UANZGNN,31.1491983756\n" }, { "path": "gs_quant/test/resources/SPX_50_icorr_out.csv", "content": "date,value\n2020-08-03,35.19184134437535\n2020-08-04,34.34380735730206\n2020-08-05,34.51726555117047\n2020-08-06,31.714363153066717\n2020-08-07,28.83708015224759\n2020-08-08,28.257227332726938\n2020-08-09,27.690625332698072\n2020-08-10,27.13683289930194\n2020-08-11,35.583091602280895\n2020-08-12,32.37930333026746\n2020-08-13,31.78960465198087\n2020-08-14,31.891869451537243\n2020-08-15,31.493413463437463\n2020-08-16,31.09733269839318\n2020-08-17,30.70360073605032\n2020-08-18,30.85501330034845\n2020-08-19,35.20715394703207\n2020-08-20,34.01112123042673\n2020-08-21,27.390717591341733\n2020-08-22,26.06998932098279\n2020-08-23,24.81851952820634\n2020-08-24,23.6309974390261\n2020-08-25,24.188718812527714\n2020-08-26,22.078901463497903\n2020-08-27,21.316055437176637\n2020-08-28,18.728841000182346\n2020-08-29,20.553251809774125\n2020-08-30,22.396530345623557\n2020-08-31,24.256525645155662\n" }, { "path": 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"date,assetId,spot\n2020-07-01,MAMDDXVJYX77V2K9,132.36\n2020-07-02,MAMDDXVJYX77V2K9,132.85\n2020-07-06,MAMDDXVJYX77V2K9,133.3\n2020-07-07,MAMDDXVJYX77V2K9,133.57\n2020-07-08,MAMDDXVJYX77V2K9,134.22\n2020-07-09,MAMDDXVJYX77V2K9,132.71\n2020-07-10,MAMDDXVJYX77V2K9,134.46\n2020-07-13,MAMDDXVJYX77V2K9,134.91\n2020-07-14,MAMDDXVJYX77V2K9,135.52\n2020-07-15,MAMDDXVJYX77V2K9,133.69\n2020-07-16,MAMDDXVJYX77V2K9,133.88\n2020-07-17,MAMDDXVJYX77V2K9,134.66\n2020-07-20,MAMDDXVJYX77V2K9,133.11\n2020-07-21,MAMDDXVJYX77V2K9,134.43\n2020-07-22,MAMDDXVJYX77V2K9,136.01\n2020-07-23,MAMDDXVJYX77V2K9,137.08\n2020-07-24,MAMDDXVJYX77V2K9,136.06\n2020-07-27,MAMDDXVJYX77V2K9,137.67\n2020-07-28,MAMDDXVJYX77V2K9,137.38\n2020-07-29,MAMDDXVJYX77V2K9,137.93\n2020-07-30,MAMDDXVJYX77V2K9,137.69\n2020-07-31,MAMDDXVJYX77V2K9,137.66\n2020-08-03,MAMDDXVJYX77V2K9,136.7\n2020-08-04,MAMDDXVJYX77V2K9,137.47\n2020-08-05,MAMDDXVJYX77V2K9,136.25\n2020-08-06,MAMDDXVJYX77V2K9,135.86\n2020-08-07,MAMDDXVJYX77V2K9,136.74\n2020-08-10,MAMDDXVJYX77V2K9,135.98\n2020-08-11,MAMDDXVJYX77V2K9,135.12\n2020-08-12,MAMDDXVJYX77V2K9,137.8\n2020-08-13,MAMDDXVJYX77V2K9,138.1\n2020-08-14,MAMDDXVJYX77V2K9,137.56\n2020-08-17,MAMDDXVJYX77V2K9,137.92\n2020-08-18,MAMDDXVJYX77V2K9,138.11\n2020-08-19,MAMDDXVJYX77V2K9,136.45\n2020-08-20,MAMDDXVJYX77V2K9,136.72\n2020-08-21,MAMDDXVJYX77V2K9,136.46\n2020-08-24,MAMDDXVJYX77V2K9,137.64\n2020-08-25,MAMDDXVJYX77V2K9,137.3\n2020-08-26,MAMDDXVJYX77V2K9,138.47\n2020-08-27,MAMDDXVJYX77V2K9,138.4\n2020-08-28,MAMDDXVJYX77V2K9,139.94\n2020-08-31,MAMDDXVJYX77V2K9,140.06\n2020-07-01,MA4B66MW5E27U9YGMGY,2878.7\n2020-07-02,MA4B66MW5E27U9YGMGY,2890.3\n2020-07-06,MA4B66MW5E27U9YGMGY,3057.04\n2020-07-07,MA4B66MW5E27U9YGMGY,3000.12\n2020-07-08,MA4B66MW5E27U9YGMGY,3081.11\n2020-07-09,MA4B66MW5E27U9YGMGY,3182.63\n2020-07-10,MA4B66MW5E27U9YGMGY,3200.0\n2020-07-13,MA4B66MW5E27U9YGMGY,3104.0\n2020-07-14,MA4B66MW5E27U9YGMGY,3084.0\n2020-07-15,MA4B66MW5E27U9YGMGY,3008.87\n2020-07-16,MA4B66MW5E27U9YGMGY,2999.9\n2020-07-17,MA4B66MW5E27U9YGMGY,2961.97\n2020-07-20,MA4B66MW5E27U9YGMGY,3196.84\n2020-07-21,MA4B66MW5E27U9YGMGY,3138.29\n2020-07-22,MA4B66MW5E27U9YGMGY,3099.91\n2020-07-23,MA4B66MW5E27U9YGMGY,2986.55\n2020-07-24,MA4B66MW5E27U9YGMGY,3008.91\n2020-07-27,MA4B66MW5E27U9YGMGY,3055.21\n2020-07-28,MA4B66MW5E27U9YGMGY,3000.33\n2020-07-29,MA4B66MW5E27U9YGMGY,3033.53\n2020-07-30,MA4B66MW5E27U9YGMGY,3051.88\n2020-07-31,MA4B66MW5E27U9YGMGY,3164.68\n2020-08-03,MA4B66MW5E27U9YGMGY,3111.89\n2020-08-04,MA4B66MW5E27U9YGMGY,3138.83\n2020-08-05,MA4B66MW5E27U9YGMGY,3205.03\n2020-08-06,MA4B66MW5E27U9YGMGY,3225.0\n2020-08-07,MA4B66MW5E27U9YGMGY,3167.46\n2020-08-10,MA4B66MW5E27U9YGMGY,3148.16\n2020-08-11,MA4B66MW5E27U9YGMGY,3080.67\n2020-08-12,MA4B66MW5E27U9YGMGY,3162.24\n2020-08-13,MA4B66MW5E27U9YGMGY,3161.02\n2020-08-14,MA4B66MW5E27U9YGMGY,3148.02\n2020-08-17,MA4B66MW5E27U9YGMGY,3182.41\n2020-08-18,MA4B66MW5E27U9YGMGY,3312.49\n2020-08-19,MA4B66MW5E27U9YGMGY,3260.48\n2020-08-20,MA4B66MW5E27U9YGMGY,3297.37\n2020-08-21,MA4B66MW5E27U9YGMGY,3284.72\n2020-08-24,MA4B66MW5E27U9YGMGY,3307.46\n2020-08-25,MA4B66MW5E27U9YGMGY,3346.49\n2020-08-26,MA4B66MW5E27U9YGMGY,3441.85\n2020-08-27,MA4B66MW5E27U9YGMGY,3400.0\n2020-08-28,MA4B66MW5E27U9YGMGY,3401.8\n2020-08-31,MA4B66MW5E27U9YGMGY,3450.96\n2020-07-01,MA4B66MW5E27UAJQETU,58.81\n2020-07-02,MA4B66MW5E27UAJQETU,59.13\n2020-07-06,MA4B66MW5E27UAJQETU,59.54\n2020-07-07,MA4B66MW5E27UAJQETU,58.31\n2020-07-08,MA4B66MW5E27UAJQETU,58.61\n2020-07-09,MA4B66MW5E27UAJQETU,58.42\n2020-07-10,MA4B66MW5E27UAJQETU,59.53\n2020-07-13,MA4B66MW5E27UAJQETU,58.58\n2020-07-14,MA4B66MW5E27UAJQETU,58.98\n2020-07-15,MA4B66MW5E27UAJQETU,59.03\n2020-07-16,MA4B66MW5E27UAJQETU,59.14\n2020-07-17,MA4B66MW5E27UAJQETU,60.0\n2020-07-20,MA4B66MW5E27UAJQETU,61.15\n2020-07-21,MA4B66MW5E27UAJQETU,60.7\n2020-07-22,MA4B66MW5E27UAJQETU,61.05\n2020-07-23,MA4B66MW5E27UAJQETU,60.4\n2020-07-24,MA4B66MW5E27UAJQETU,50.59\n2020-07-27,MA4B66MW5E27UAJQETU,49.57\n2020-07-28,MA4B66MW5E27UAJQETU,49.24\n2020-07-29,MA4B66MW5E27UAJQETU,48.07\n2020-07-30,MA4B66MW5E27UAJQETU,47.99\n2020-07-31,MA4B66MW5E27UAJQETU,47.73\n2020-08-03,MA4B66MW5E27UAJQETU,48.3\n2020-08-04,MA4B66MW5E27UAJQETU,49.13\n2020-08-05,MA4B66MW5E27UAJQETU,48.92\n2020-08-06,MA4B66MW5E27UAJQETU,48.57\n2020-08-07,MA4B66MW5E27UAJQETU,48.03\n2020-08-10,MA4B66MW5E27UAJQETU,49.22\n2020-08-11,MA4B66MW5E27UAJQETU,48.19\n2020-08-12,MA4B66MW5E27UAJQETU,49.19\n2020-08-13,MA4B66MW5E27UAJQETU,48.56\n2020-08-14,MA4B66MW5E27UAJQETU,48.89\n2020-08-17,MA4B66MW5E27UAJQETU,48.93\n2020-08-18,MA4B66MW5E27UAJQETU,48.65\n2020-08-19,MA4B66MW5E27UAJQETU,48.33\n2020-08-20,MA4B66MW5E27UAJQETU,49.17\n2020-08-21,MA4B66MW5E27UAJQETU,49.28\n2020-08-24,MA4B66MW5E27UAJQETU,49.14\n2020-08-25,MA4B66MW5E27UAJQETU,49.43\n2020-08-26,MA4B66MW5E27UAJQETU,49.55\n2020-08-27,MA4B66MW5E27UAJQETU,49.4\n2020-08-28,MA4B66MW5E27UAJQETU,50.43\n2020-08-31,MA4B66MW5E27UAJQETU,50.95\n2020-07-01,MA4B66MW5E27UALNB5V,381.2\n2020-07-02,MA4B66MW5E27UALNB5V,384.49\n2020-07-06,MA4B66MW5E27UALNB5V,393.57\n2020-07-07,MA4B66MW5E27UALNB5V,394.87\n2020-07-08,MA4B66MW5E27UALNB5V,408.64\n2020-07-09,MA4B66MW5E27UALNB5V,420.36\n2020-07-10,MA4B66MW5E27UALNB5V,419.17\n2020-07-13,MA4B66MW5E27UALNB5V,402.09\n2020-07-14,MA4B66MW5E27UALNB5V,415.08\n2020-07-15,MA4B66MW5E27UALNB5V,409.09\n2020-07-16,MA4B66MW5E27UALNB5V,405.39\n2020-07-17,MA4B66MW5E27UALNB5V,408.06\n2020-07-20,MA4B66MW5E27UALNB5V,420.43\n2020-07-21,MA4B66MW5E27UALNB5V,413.14\n2020-07-22,MA4B66MW5E27UALNB5V,417.55\n2020-07-23,MA4B66MW5E27UALNB5V,405.19\n2020-07-24,MA4B66MW5E27UALNB5V,407.78\n2020-07-27,MA4B66MW5E27UALNB5V,416.86\n2020-07-28,MA4B66MW5E27UALNB5V,408.62\n2020-07-29,MA4B66MW5E27UALNB5V,418.62\n2020-07-30,MA4B66MW5E27UALNB5V,424.56\n2020-07-31,MA4B66MW5E27UALNB5V,424.59\n2020-08-03,MA4B66MW5E27UALNB5V,440.41\n2020-08-04,MA4B66MW5E27UALNB5V,449.11\n2020-08-05,MA4B66MW5E27UALNB5V,451.47\n2020-08-06,MA4B66MW5E27UALNB5V,453.42\n2020-08-07,MA4B66MW5E27UALNB5V,447.98\n2020-08-10,MA4B66MW5E27UALNB5V,446.6\n2020-08-11,MA4B66MW5E27UALNB5V,434.0\n2020-08-12,MA4B66MW5E27UALNB5V,457.61\n2020-08-13,MA4B66MW5E27UALNB5V,457.72\n2020-08-14,MA4B66MW5E27UALNB5V,462.56\n2020-08-17,MA4B66MW5E27UALNB5V,493.48\n2020-08-18,MA4B66MW5E27UALNB5V,490.43\n2020-08-19,MA4B66MW5E27UALNB5V,485.54\n2020-08-20,MA4B66MW5E27UALNB5V,485.64\n2020-08-21,MA4B66MW5E27UALNB5V,507.34\n2020-08-24,MA4B66MW5E27UALNB5V,508.81\n2020-08-25,MA4B66MW5E27UALNB5V,510.0\n2020-08-26,MA4B66MW5E27UALNB5V,510.92\n2020-08-27,MA4B66MW5E27UALNB5V,505.13\n2020-08-28,MA4B66MW5E27UALNB5V,525.91\n2020-08-31,MA4B66MW5E27UALNB5V,534.98\n2020-07-01,MA4B66MW5E27UAM94Q4,89.78\n2020-07-02,MA4B66MW5E27UAM94Q4,91.87\n2020-07-06,MA4B66MW5E27UAM94Q4,92.92\n2020-07-07,MA4B66MW5E27UAM94Q4,92.14\n2020-07-08,MA4B66MW5E27UAM94Q4,93.53\n2020-07-09,MA4B66MW5E27UAM94Q4,93.25\n2020-07-10,MA4B66MW5E27UAM94Q4,92.51\n2020-07-13,MA4B66MW5E27UAM94Q4,91.33\n2020-07-14,MA4B66MW5E27UAM94Q4,92.38\n2020-07-15,MA4B66MW5E27UAM94Q4,92.55\n2020-07-16,MA4B66MW5E27UAM94Q4,91.2\n2020-07-17,MA4B66MW5E27UAM94Q4,92.23\n2020-07-20,MA4B66MW5E27UAM94Q4,93.17\n2020-07-21,MA4B66MW5E27UAM94Q4,92.31\n2020-07-22,MA4B66MW5E27UAM94Q4,92.6\n2020-07-23,MA4B66MW5E27UAM94Q4,89.67\n2020-07-24,MA4B66MW5E27UAM94Q4,88.89\n2020-07-27,MA4B66MW5E27UAM94Q4,92.7\n2020-07-28,MA4B66MW5E27UAM94Q4,91.45\n2020-07-29,MA4B66MW5E27UAM94Q4,93.03\n2020-07-30,MA4B66MW5E27UAM94Q4,107.19\n2020-07-31,MA4B66MW5E27UAM94Q4,105.61\n2020-08-03,MA4B66MW5E27UAM94Q4,109.59\n2020-08-04,MA4B66MW5E27UAM94Q4,110.95\n2020-08-05,MA4B66MW5E27UAM94Q4,111.39\n2020-08-06,MA4B66MW5E27UAM94Q4,111.06\n2020-08-07,MA4B66MW5E27UAM94Q4,108.25\n2020-08-10,MA4B66MW5E27UAM94Q4,106.36\n2020-08-11,MA4B66MW5E27UAM94Q4,108.83\n2020-08-12,MA4B66MW5E27UAM94Q4,115.79\n2020-08-13,MA4B66MW5E27UAM94Q4,113.41\n2020-08-14,MA4B66MW5E27UAM94Q4,113.74\n2020-08-17,MA4B66MW5E27UAM94Q4,112.18\n2020-08-18,MA4B66MW5E27UAM94Q4,112.03\n2020-08-19,MA4B66MW5E27UAM94Q4,111.04\n2020-08-20,MA4B66MW5E27UAM94Q4,110.66\n2020-08-21,MA4B66MW5E27UAM94Q4,113.0\n2020-08-24,MA4B66MW5E27UAM94Q4,115.91\n2020-08-25,MA4B66MW5E27UAM94Q4,115.95\n2020-08-26,MA4B66MW5E27UAM94Q4,116.04\n2020-08-27,MA4B66MW5E27UAM94Q4,116.02\n2020-08-28,MA4B66MW5E27UAM94Q4,118.2\n2020-08-31,MA4B66MW5E27UAM94Q4,119.1\n2020-07-01,MA4B66MW5E27U9XPV7X,439.81\n2020-07-02,MA4B66MW5E27U9XPV7X,442.95\n2020-07-06,MA4B66MW5E27U9XPV7X,452.59\n2020-07-07,MA4B66MW5E27U9XPV7X,449.36\n2020-07-08,MA4B66MW5E27U9XPV7X,457.68\n2020-07-09,MA4B66MW5E27U9XPV7X,460.84\n2020-07-10,MA4B66MW5E27U9XPV7X,466.2\n2020-07-13,MA4B66MW5E27U9XPV7X,442.47\n2020-07-14,MA4B66MW5E27U9XPV7X,433.78\n2020-07-15,MA4B66MW5E27U9XPV7X,433.01\n2020-07-16,MA4B66MW5E27U9XPV7X,426.29\n2020-07-17,MA4B66MW5E27U9XPV7X,432.42\n2020-07-20,MA4B66MW5E27U9XPV7X,455.27\n2020-07-21,MA4B66MW5E27U9XPV7X,444.28\n2020-07-22,MA4B66MW5E27U9XPV7X,445.05\n2020-07-23,MA4B66MW5E27U9XPV7X,431.74\n2020-07-24,MA4B66MW5E27U9XPV7X,430.31\n2020-07-27,MA4B66MW5E27U9XPV7X,437.1\n2020-07-28,MA4B66MW5E27U9XPV7X,432.26\n2020-07-29,MA4B66MW5E27U9XPV7X,436.3\n2020-07-30,MA4B66MW5E27U9XPV7X,438.88\n2020-07-31,MA4B66MW5E27U9XPV7X,444.32\n2020-08-03,MA4B66MW5E27U9XPV7X,447.97\n2020-08-04,MA4B66MW5E27U9XPV7X,446.92\n2020-08-05,MA4B66MW5E27U9XPV7X,449.51\n2020-08-06,MA4B66MW5E27U9XPV7X,464.11\n2020-08-07,MA4B66MW5E27U9XPV7X,449.57\n2020-08-10,MA4B66MW5E27U9XPV7X,443.29\n2020-08-11,MA4B66MW5E27U9XPV7X,435.23\n2020-08-12,MA4B66MW5E27U9XPV7X,445.36\n2020-08-13,MA4B66MW5E27U9XPV7X,450.02\n2020-08-14,MA4B66MW5E27U9XPV7X,447.6\n2020-08-17,MA4B66MW5E27U9XPV7X,451.58\n2020-08-18,MA4B66MW5E27U9XPV7X,463.51\n2020-08-19,MA4B66MW5E27U9XPV7X,462.01\n2020-08-20,MA4B66MW5E27U9XPV7X,478.48\n2020-08-21,MA4B66MW5E27U9XPV7X,473.22\n2020-08-24,MA4B66MW5E27U9XPV7X,476.3\n2020-08-25,MA4B66MW5E27U9XPV7X,484.43\n2020-08-26,MA4B66MW5E27U9XPV7X,528.49\n2020-08-27,MA4B66MW5E27U9XPV7X,510.32\n2020-08-28,MA4B66MW5E27U9XPV7X,516.44\n2020-08-31,MA4B66MW5E27U9XPV7X,513.39\n2020-07-01,MA4B66MW5E27UADJ5GE,304.75\n2020-07-02,MA4B66MW5E27UADJ5GE,305.74\n2020-07-06,MA4B66MW5E27UADJ5GE,311.49\n2020-07-07,MA4B66MW5E27UADJ5GE,316.23\n2020-07-08,MA4B66MW5E27UADJ5GE,316.32\n2020-07-09,MA4B66MW5E27UADJ5GE,325.54\n2020-07-10,MA4B66MW5E27UADJ5GE,326.23\n2020-07-13,MA4B66MW5E27UADJ5GE,322.92\n2020-07-14,MA4B66MW5E27UADJ5GE,328.0\n2020-07-15,MA4B66MW5E27UADJ5GE,326.7\n2020-07-16,MA4B66MW5E27UADJ5GE,326.27\n2020-07-17,MA4B66MW5E27UADJ5GE,324.79\n2020-07-20,MA4B66MW5E27UADJ5GE,326.51\n2020-07-21,MA4B66MW5E27UADJ5GE,327.74\n2020-07-22,MA4B66MW5E27UADJ5GE,328.3\n2020-07-23,MA4B66MW5E27UADJ5GE,326.11\n2020-07-24,MA4B66MW5E27UADJ5GE,325.78\n2020-07-27,MA4B66MW5E27UADJ5GE,327.6\n2020-07-28,MA4B66MW5E27UADJ5GE,327.57\n2020-07-29,MA4B66MW5E27UADJ5GE,326.14\n2020-07-30,MA4B66MW5E27UADJ5GE,324.82\n2020-07-31,MA4B66MW5E27UADJ5GE,325.53\n2020-08-03,MA4B66MW5E27UADJ5GE,329.32\n2020-08-04,MA4B66MW5E27UADJ5GE,339.79\n2020-08-05,MA4B66MW5E27UADJ5GE,339.97\n2020-08-06,MA4B66MW5E27UADJ5GE,343.31\n2020-08-07,MA4B66MW5E27UADJ5GE,340.91\n2020-08-10,MA4B66MW5E27UADJ5GE,340.0\n2020-08-11,MA4B66MW5E27UADJ5GE,332.43\n2020-08-12,MA4B66MW5E27UADJ5GE,336.76\n2020-08-13,MA4B66MW5E27UADJ5GE,335.7\n2020-08-14,MA4B66MW5E27UADJ5GE,336.28\n2020-08-17,MA4B66MW5E27UADJ5GE,339.96\n2020-08-18,MA4B66MW5E27UADJ5GE,340.75\n2020-08-19,MA4B66MW5E27UADJ5GE,340.9\n2020-08-20,MA4B66MW5E27UADJ5GE,340.87\n2020-08-21,MA4B66MW5E27UADJ5GE,344.61\n2020-08-24,MA4B66MW5E27UADJ5GE,344.13\n2020-08-25,MA4B66MW5E27UADJ5GE,344.71\n2020-08-26,MA4B66MW5E27UADJ5GE,344.27\n2020-08-27,MA4B66MW5E27UADJ5GE,346.36\n2020-08-28,MA4B66MW5E27UADJ5GE,348.37\n2020-08-31,MA4B66MW5E27UADJ5GE,347.66\n2020-07-01,MA4B66MW5E27UADJ5VJ,46.06\n2020-07-02,MA4B66MW5E27UADJ5VJ,45.63\n2020-07-06,MA4B66MW5E27UADJ5VJ,46.42\n2020-07-07,MA4B66MW5E27UADJ5VJ,45.63\n2020-07-08,MA4B66MW5E27UADJ5VJ,45.81\n2020-07-09,MA4B66MW5E27UADJ5VJ,46.7\n2020-07-10,MA4B66MW5E27UADJ5VJ,46.66\n2020-07-13,MA4B66MW5E27UADJ5VJ,45.93\n2020-07-14,MA4B66MW5E27UADJ5VJ,46.26\n2020-07-15,MA4B66MW5E27UADJ5VJ,46.4\n2020-07-16,MA4B66MW5E27UADJ5VJ,45.78\n2020-07-17,MA4B66MW5E27UADJ5VJ,46.75\n2020-07-20,MA4B66MW5E27UADJ5VJ,46.97\n2020-07-21,MA4B66MW5E27UADJ5VJ,47.02\n2020-07-22,MA4B66MW5E27UADJ5VJ,46.9\n2020-07-23,MA4B66MW5E27UADJ5VJ,47.41\n2020-07-24,MA4B66MW5E27UADJ5VJ,46.4\n2020-07-27,MA4B66MW5E27UADJ5VJ,47.19\n2020-07-28,MA4B66MW5E27UADJ5VJ,46.28\n2020-07-29,MA4B66MW5E27UADJ5VJ,46.71\n2020-07-30,MA4B66MW5E27UADJ5VJ,46.44\n2020-07-31,MA4B66MW5E27UADJ5VJ,47.1\n2020-08-03,MA4B66MW5E27UADJ5VJ,47.16\n2020-08-04,MA4B66MW5E27UADJ5VJ,47.67\n2020-08-05,MA4B66MW5E27UADJ5VJ,47.33\n2020-08-06,MA4B66MW5E27UADJ5VJ,47.77\n2020-08-07,MA4B66MW5E27UADJ5VJ,47.43\n2020-08-10,MA4B66MW5E27UADJ5VJ,47.73\n2020-08-11,MA4B66MW5E27UADJ5VJ,47.19\n2020-08-12,MA4B66MW5E27UADJ5VJ,48.1\n2020-08-13,MA4B66MW5E27UADJ5VJ,42.72\n2020-08-14,MA4B66MW5E27UADJ5VJ,42.5\n2020-08-17,MA4B66MW5E27UADJ5VJ,42.09\n2020-08-18,MA4B66MW5E27UADJ5VJ,41.98\n2020-08-19,MA4B66MW5E27UADJ5VJ,41.87\n2020-08-20,MA4B66MW5E27UADJ5VJ,42.31\n2020-08-21,MA4B66MW5E27UADJ5VJ,42.25\n2020-08-24,MA4B66MW5E27UADJ5VJ,42.18\n2020-08-25,MA4B66MW5E27UADJ5VJ,41.96\n2020-08-26,MA4B66MW5E27UADJ5VJ,42.25\n2020-08-27,MA4B66MW5E27UADJ5VJ,42.29\n2020-08-28,MA4B66MW5E27UADJ5VJ,42.2\n2020-08-31,MA4B66MW5E27UADJ5VJ,42.22\n2020-07-01,MA4B66MW5E3VLSECN,237.55\n2020-07-02,MA4B66MW5E3VLSECN,233.42\n2020-07-06,MA4B66MW5E3VLSECN,240.28\n2020-07-07,MA4B66MW5E3VLSECN,240.86\n2020-07-08,MA4B66MW5E3VLSECN,243.58\n2020-07-09,MA4B66MW5E3VLSECN,244.5\n2020-07-10,MA4B66MW5E3VLSECN,245.07\n2020-07-13,MA4B66MW5E3VLSECN,239.0\n2020-07-14,MA4B66MW5E3VLSECN,239.73\n2020-07-15,MA4B66MW5E3VLSECN,240.28\n2020-07-16,MA4B66MW5E3VLSECN,240.93\n2020-07-17,MA4B66MW5E3VLSECN,242.03\n2020-07-20,MA4B66MW5E3VLSECN,245.42\n2020-07-21,MA4B66MW5E3VLSECN,241.75\n2020-07-22,MA4B66MW5E3VLSECN,239.87\n2020-07-23,MA4B66MW5E3VLSECN,232.6\n2020-07-24,MA4B66MW5E3VLSECN,230.71\n2020-07-27,MA4B66MW5E3VLSECN,233.5\n2020-07-28,MA4B66MW5E3VLSECN,230.12\n2020-07-29,MA4B66MW5E3VLSECN,233.29\n2020-07-30,MA4B66MW5E3VLSECN,234.5\n2020-07-31,MA4B66MW5E3VLSECN,253.67\n2020-08-03,MA4B66MW5E3VLSECN,251.96\n2020-08-04,MA4B66MW5E3VLSECN,249.83\n2020-08-05,MA4B66MW5E3VLSECN,249.12\n2020-08-06,MA4B66MW5E3VLSECN,265.28\n2020-08-07,MA4B66MW5E3VLSECN,268.44\n2020-08-10,MA4B66MW5E3VLSECN,263.0\n2020-08-11,MA4B66MW5E3VLSECN,256.13\n2020-08-12,MA4B66MW5E3VLSECN,259.89\n2020-08-13,MA4B66MW5E3VLSECN,261.3\n2020-08-14,MA4B66MW5E3VLSECN,261.24\n2020-08-17,MA4B66MW5E3VLSECN,261.16\n2020-08-18,MA4B66MW5E3VLSECN,262.34\n2020-08-19,MA4B66MW5E3VLSECN,262.59\n2020-08-20,MA4B66MW5E3VLSECN,269.01\n2020-08-21,MA4B66MW5E3VLSECN,267.01\n2020-08-24,MA4B66MW5E3VLSECN,271.39\n2020-08-25,MA4B66MW5E3VLSECN,280.82\n2020-08-26,MA4B66MW5E3VLSECN,303.91\n2020-08-27,MA4B66MW5E3VLSECN,293.22\n2020-08-28,MA4B66MW5E3VLSECN,293.66\n2020-08-31,MA4B66MW5E3VLSECN,293.2\n2020-07-01,MA4B66MW5E27U9ZPD6K,312.73\n2020-07-02,MA4B66MW5E27U9ZPD6K,315.47\n2020-07-06,MA4B66MW5E27U9ZPD6K,318.79\n2020-07-07,MA4B66MW5E27U9ZPD6K,313.12\n2020-07-08,MA4B66MW5E27U9ZPD6K,319.57\n2020-07-09,MA4B66MW5E27U9ZPD6K,321.5\n2020-07-10,MA4B66MW5E27U9ZPD6K,319.44\n2020-07-13,MA4B66MW5E27U9ZPD6K,311.31\n2020-07-14,MA4B66MW5E27U9ZPD6K,315.08\n2020-07-15,MA4B66MW5E27U9ZPD6K,313.78\n2020-07-16,MA4B66MW5E27U9ZPD6K,311.34\n2020-07-17,MA4B66MW5E27U9ZPD6K,312.71\n2020-07-20,MA4B66MW5E27U9ZPD6K,317.13\n2020-07-21,MA4B66MW5E27U9ZPD6K,314.34\n2020-07-22,MA4B66MW5E27U9ZPD6K,313.8\n2020-07-23,MA4B66MW5E27U9ZPD6K,309.67\n2020-07-24,MA4B66MW5E27U9ZPD6K,305.79\n2020-07-27,MA4B66MW5E27U9ZPD6K,312.68\n2020-07-28,MA4B66MW5E27U9ZPD6K,307.35\n2020-07-29,MA4B66MW5E27U9ZPD6K,308.85\n2020-07-30,MA4B66MW5E27U9ZPD6K,312.1\n2020-07-31,MA4B66MW5E27U9ZPD6K,316.75\n2020-08-03,MA4B66MW5E27U9ZPD6K,321.23\n2020-08-04,MA4B66MW5E27U9ZPD6K,328.39\n2020-08-05,MA4B66MW5E27U9ZPD6K,329.54\n2020-08-06,MA4B66MW5E27U9ZPD6K,329.16\n2020-08-07,MA4B66MW5E27U9ZPD6K,325.93\n2020-08-10,MA4B66MW5E27U9ZPD6K,327.36\n2020-08-11,MA4B66MW5E27U9ZPD6K,324.03\n2020-08-12,MA4B66MW5E27U9ZPD6K,333.64\n2020-08-13,MA4B66MW5E27U9ZPD6K,329.42\n2020-08-14,MA4B66MW5E27U9ZPD6K,327.82\n2020-08-17,MA4B66MW5E27U9ZPD6K,330.32\n2020-08-18,MA4B66MW5E27U9ZPD6K,328.64\n2020-08-19,MA4B66MW5E27U9ZPD6K,329.21\n2020-08-20,MA4B66MW5E27U9ZPD6K,328.83\n2020-08-21,MA4B66MW5E27U9ZPD6K,330.02\n2020-08-24,MA4B66MW5E27U9ZPD6K,334.0\n2020-08-25,MA4B66MW5E27U9ZPD6K,336.14\n2020-08-26,MA4B66MW5E27U9ZPD6K,339.67\n2020-08-27,MA4B66MW5E27U9ZPD6K,339.14\n2020-08-28,MA4B66MW5E27U9ZPD6K,344.82\n2020-08-31,MA4B66MW5E27U9ZPD6K,347.15\n2020-07-01,MA4B66MW5E27UAGYZ49,1438.04\n2020-07-02,MA4B66MW5E27UAGYZ49,1464.7\n2020-07-06,MA4B66MW5E27UAGYZ49,1495.7\n2020-07-07,MA4B66MW5E27UAGYZ49,1485.18\n2020-07-08,MA4B66MW5E27UAGYZ49,1496.0\n2020-07-09,MA4B66MW5E27UAGYZ49,1510.99\n2020-07-10,MA4B66MW5E27UAGYZ49,1541.74\n2020-07-13,MA4B66MW5E27UAGYZ49,1511.34\n2020-07-14,MA4B66MW5E27UAGYZ49,1520.58\n2020-07-15,MA4B66MW5E27UAGYZ49,1513.64\n2020-07-16,MA4B66MW5E27UAGYZ49,1518.0\n2020-07-17,MA4B66MW5E27UAGYZ49,1515.55\n2020-07-20,MA4B66MW5E27UAGYZ49,1565.72\n2020-07-21,MA4B66MW5E27UAGYZ49,1558.42\n2020-07-22,MA4B66MW5E27UAGYZ49,1568.49\n2020-07-23,MA4B66MW5E27UAGYZ49,1515.68\n2020-07-24,MA4B66MW5E27UAGYZ49,1511.87\n2020-07-27,MA4B66MW5E27UAGYZ49,1530.2\n2020-07-28,MA4B66MW5E27UAGYZ49,1500.34\n2020-07-29,MA4B66MW5E27UAGYZ49,1522.02\n2020-07-30,MA4B66MW5E27UAGYZ49,1531.45\n2020-07-31,MA4B66MW5E27UAGYZ49,1482.96\n2020-08-03,MA4B66MW5E27UAGYZ49,1474.45\n2020-08-04,MA4B66MW5E27UAGYZ49,1464.97\n2020-08-05,MA4B66MW5E27UAGYZ49,1473.61\n2020-08-06,MA4B66MW5E27UAGYZ49,1500.1\n2020-08-07,MA4B66MW5E27UAGYZ49,1494.49\n2020-08-10,MA4B66MW5E27UAGYZ49,1496.1\n2020-08-11,MA4B66MW5E27UAGYZ49,1480.32\n2020-08-12,MA4B66MW5E27UAGYZ49,1506.62\n2020-08-13,MA4B66MW5E27UAGYZ49,1518.45\n2020-08-14,MA4B66MW5E27UAGYZ49,1507.73\n2020-08-17,MA4B66MW5E27UAGYZ49,1517.98\n2020-08-18,MA4B66MW5E27UAGYZ49,1558.6\n2020-08-19,MA4B66MW5E27UAGYZ49,1547.53\n2020-08-20,MA4B66MW5E27UAGYZ49,1581.75\n2020-08-21,MA4B66MW5E27UAGYZ49,1580.42\n2020-08-24,MA4B66MW5E27UAGYZ49,1588.2\n2020-08-25,MA4B66MW5E27UAGYZ49,1608.22\n2020-08-26,MA4B66MW5E27UAGYZ49,1652.38\n2020-08-27,MA4B66MW5E27UAGYZ49,1634.33\n2020-08-28,MA4B66MW5E27UAGYZ49,1644.41\n2020-08-31,MA4B66MW5E27UAGYZ49,1634.18\n2020-07-01,MA4B66MW5E27UANLXV6,124.8\n2020-07-02,MA4B66MW5E27UANLXV6,125.81\n2020-07-06,MA4B66MW5E27UANLXV6,129.53\n2020-07-07,MA4B66MW5E27UANLXV6,128.6\n2020-07-08,MA4B66MW5E27UANLXV6,129.26\n2020-07-09,MA4B66MW5E27UANLXV6,130.43\n2020-07-10,MA4B66MW5E27UANLXV6,130.53\n2020-07-13,MA4B66MW5E27UANLXV6,128.82\n2020-07-14,MA4B66MW5E27UANLXV6,131.89\n2020-07-15,MA4B66MW5E27UANLXV6,132.15\n2020-07-16,MA4B66MW5E27UANLXV6,132.18\n2020-07-17,MA4B66MW5E27UANLXV6,133.89\n2020-07-20,MA4B66MW5E27UANLXV6,136.58\n2020-07-21,MA4B66MW5E27UANLXV6,135.48\n2020-07-22,MA4B66MW5E27UANLXV6,132.53\n2020-07-23,MA4B66MW5E27UANLXV6,129.04\n2020-07-24,MA4B66MW5E27UANLXV6,129.63\n2020-07-27,MA4B66MW5E27UANLXV6,132.12\n2020-07-28,MA4B66MW5E27UANLXV6,130.13\n2020-07-29,MA4B66MW5E27UANLXV6,131.64\n2020-07-30,MA4B66MW5E27UANLXV6,128.89\n2020-07-31,MA4B66MW5E27UANLXV6,127.55\n2020-08-03,MA4B66MW5E27UANLXV6,129.32\n2020-08-04,MA4B66MW5E27UANLXV6,132.23\n2020-08-05,MA4B66MW5E27UANLXV6,132.7\n2020-08-06,MA4B66MW5E27UANLXV6,133.74\n2020-08-07,MA4B66MW5E27UANLXV6,133.56\n2020-08-10,MA4B66MW5E27UANLXV6,135.41\n2020-08-11,MA4B66MW5E27UANLXV6,136.04\n2020-08-12,MA4B66MW5E27UANLXV6,138.64\n2020-08-13,MA4B66MW5E27UANLXV6,137.15\n2020-08-14,MA4B66MW5E27UANLXV6,137.35\n2020-08-17,MA4B66MW5E27UANLXV6,138.28\n2020-08-18,MA4B66MW5E27UANLXV6,139.31\n2020-08-19,MA4B66MW5E27UANLXV6,138.48\n2020-08-20,MA4B66MW5E27UANLXV6,138.57\n2020-08-21,MA4B66MW5E27UANLXV6,140.33\n2020-08-24,MA4B66MW5E27UANLXV6,141.02\n2020-08-25,MA4B66MW5E27UANLXV6,141.66\n2020-08-26,MA4B66MW5E27UANLXV6,142.45\n2020-08-27,MA4B66MW5E27UANLXV6,141.49\n2020-08-28,MA4B66MW5E27UANLXV6,143.49\n2020-08-31,MA4B66MW5E27UANLXV6,142.15\n2020-07-01,MA4B66MW5E27UAM94N9,177.43\n2020-07-02,MA4B66MW5E27UAM94N9,177.21\n2020-07-06,MA4B66MW5E27UAM94N9,176.18\n2020-07-07,MA4B66MW5E27UAM94N9,177.28\n2020-07-08,MA4B66MW5E27UAM94N9,181.57\n2020-07-09,MA4B66MW5E27UAM94N9,183.23\n2020-07-10,MA4B66MW5E27UAM94N9,178.4\n2020-07-13,MA4B66MW5E27UAM94N9,171.51\n2020-07-14,MA4B66MW5E27UAM94N9,172.3\n2020-07-15,MA4B66MW5E27UAM94N9,172.06\n2020-07-16,MA4B66MW5E27UAM94N9,169.81\n2020-07-17,MA4B66MW5E27UAM94N9,173.92\n2020-07-20,MA4B66MW5E27UAM94N9,178.82\n2020-07-21,MA4B66MW5E27UAM94N9,175.58\n2020-07-22,MA4B66MW5E27UAM94N9,178.42\n2020-07-23,MA4B66MW5E27UAM94N9,173.86\n2020-07-24,MA4B66MW5E27UAM94N9,172.56\n2020-07-27,MA4B66MW5E27UAM94N9,177.98\n2020-07-28,MA4B66MW5E27UAM94N9,176.27\n2020-07-29,MA4B66MW5E27UAM94N9,184.6\n2020-07-30,MA4B66MW5E27UAM94N9,192.51\n2020-07-31,MA4B66MW5E27UAM94N9,196.07\n2020-08-03,MA4B66MW5E27UAM94N9,197.07\n2020-08-04,MA4B66MW5E27UAM94N9,197.33\n2020-08-05,MA4B66MW5E27UAM94N9,202.91\n2020-08-06,MA4B66MW5E27UAM94N9,204.09\n2020-08-07,MA4B66MW5E27UAM94N9,198.63\n2020-08-10,MA4B66MW5E27UAM94N9,193.32\n2020-08-11,MA4B66MW5E27UAM94N9,189.03\n2020-08-12,MA4B66MW5E27UAM94N9,191.32\n2020-08-13,MA4B66MW5E27UAM94N9,193.07\n2020-08-14,MA4B66MW5E27UAM94N9,191.46\n2020-08-17,MA4B66MW5E27UAM94N9,196.29\n2020-08-18,MA4B66MW5E27UAM94N9,194.57\n2020-08-19,MA4B66MW5E27UAM94N9,192.44\n2020-08-20,MA4B66MW5E27UAM94N9,198.18\n2020-08-21,MA4B66MW5E27UAM94N9,196.79\n2020-08-24,MA4B66MW5E27UAM94N9,198.88\n2020-08-25,MA4B66MW5E27UAM94N9,201.67\n2020-08-26,MA4B66MW5E27UAM94N9,203.48\n2020-08-27,MA4B66MW5E27UAM94N9,204.34\n2020-08-28,MA4B66MW5E27UAM94N9,204.48\n2020-08-31,MA4B66MW5E27UAM94N9,204.14\n2020-07-01,MA4B66MW5E27UACX6QV,39.7\n2020-07-02,MA4B66MW5E27UACX6QV,39.25\n2020-07-06,MA4B66MW5E27UACX6QV,40.33\n2020-07-07,MA4B66MW5E27UACX6QV,40.07\n2020-07-08,MA4B66MW5E27UACX6QV,39.74\n2020-07-09,MA4B66MW5E27UACX6QV,39.29\n2020-07-10,MA4B66MW5E27UACX6QV,40.51\n2020-07-13,MA4B66MW5E27UACX6QV,40.54\n2020-07-14,MA4B66MW5E27UACX6QV,41.18\n2020-07-15,MA4B66MW5E27UACX6QV,41.96\n2020-07-16,MA4B66MW5E27UACX6QV,42.14\n2020-07-17,MA4B66MW5E27UACX6QV,42.18\n2020-07-20,MA4B66MW5E27UACX6QV,41.95\n2020-07-21,MA4B66MW5E27UACX6QV,42.57\n2020-07-22,MA4B66MW5E27UACX6QV,42.57\n2020-07-23,MA4B66MW5E27UACX6QV,42.49\n2020-07-24,MA4B66MW5E27UACX6QV,43.35\n2020-07-27,MA4B66MW5E27UACX6QV,43.56\n2020-07-28,MA4B66MW5E27UACX6QV,43.21\n2020-07-29,MA4B66MW5E27UACX6QV,43.9\n2020-07-30,MA4B66MW5E27UACX6QV,43.67\n2020-07-31,MA4B66MW5E27UACX6QV,42.8\n2020-08-03,MA4B66MW5E27UACX6QV,42.88\n2020-08-04,MA4B66MW5E27UACX6QV,43.21\n2020-08-05,MA4B66MW5E27UACX6QV,42.29\n2020-08-06,MA4B66MW5E27UACX6QV,42.87\n2020-08-07,MA4B66MW5E27UACX6QV,42.81\n2020-08-10,MA4B66MW5E27UACX6QV,43.02\n2020-08-11,MA4B66MW5E27UACX6QV,42.99\n2020-08-12,MA4B66MW5E27UACX6QV,43.34\n2020-08-13,MA4B66MW5E27UACX6QV,43.39\n2020-08-14,MA4B66MW5E27UACX6QV,43.77\n2020-08-17,MA4B66MW5E27UACX6QV,43.53\n2020-08-18,MA4B66MW5E27UACX6QV,43.54\n2020-08-19,MA4B66MW5E27UACX6QV,43.32\n2020-08-20,MA4B66MW5E27UACX6QV,43.27\n2020-08-21,MA4B66MW5E27UACX6QV,43.07\n2020-08-24,MA4B66MW5E27UACX6QV,43.8\n2020-08-25,MA4B66MW5E27UACX6QV,43.41\n2020-08-26,MA4B66MW5E27UACX6QV,44.2\n2020-08-27,MA4B66MW5E27UACX6QV,44.54\n2020-08-28,MA4B66MW5E27UACX6QV,44.56\n2020-08-31,MA4B66MW5E27UACX6QV,44.81\n2020-07-01,MA4B66MW5E27U9YGMCG,255.12\n2020-07-02,MA4B66MW5E27U9YGMCG,258.24\n2020-07-06,MA4B66MW5E27U9YGMCG,256.25\n2020-07-07,MA4B66MW5E27U9YGMCG,253.15\n2020-07-08,MA4B66MW5E27U9YGMCG,251.59\n2020-07-09,MA4B66MW5E27U9YGMCG,251.66\n2020-07-10,MA4B66MW5E27U9YGMCG,249.04\n2020-07-13,MA4B66MW5E27U9YGMCG,250.04\n2020-07-14,MA4B66MW5E27U9YGMCG,253.09\n2020-07-15,MA4B66MW5E27U9YGMCG,253.31\n2020-07-16,MA4B66MW5E27U9YGMCG,255.2\n2020-07-17,MA4B66MW5E27U9YGMCG,258.46\n2020-07-20,MA4B66MW5E27U9YGMCG,260.95\n2020-07-21,MA4B66MW5E27U9YGMCG,257.91\n2020-07-22,MA4B66MW5E27U9YGMCG,256.98\n2020-07-23,MA4B66MW5E27U9YGMCG,252.24\n2020-07-24,MA4B66MW5E27U9YGMCG,247.98\n2020-07-27,MA4B66MW5E27U9YGMCG,253.65\n2020-07-28,MA4B66MW5E27U9YGMCG,255.27\n2020-07-29,MA4B66MW5E27U9YGMCG,248.85\n2020-07-30,MA4B66MW5E27U9YGMCG,246.14\n2020-07-31,MA4B66MW5E27U9YGMCG,244.67\n2020-08-03,MA4B66MW5E27U9YGMCG,247.36\n2020-08-04,MA4B66MW5E27U9YGMCG,243.59\n2020-08-05,MA4B66MW5E27U9YGMCG,241.47\n2020-08-06,MA4B66MW5E27U9YGMCG,241.55\n2020-08-07,MA4B66MW5E27U9YGMCG,240.69\n2020-08-10,MA4B66MW5E27U9YGMCG,238.17\n2020-08-11,MA4B66MW5E27U9YGMCG,234.65\n2020-08-12,MA4B66MW5E27U9YGMCG,241.72\n2020-08-13,MA4B66MW5E27U9YGMCG,240.46\n2020-08-14,MA4B66MW5E27U9YGMCG,239.71\n2020-08-17,MA4B66MW5E27U9YGMCG,243.01\n2020-08-18,MA4B66MW5E27U9YGMCG,241.5\n2020-08-19,MA4B66MW5E27U9YGMCG,240.49\n2020-08-20,MA4B66MW5E27U9YGMCG,238.73\n2020-08-21,MA4B66MW5E27U9YGMCG,237.64\n2020-08-24,MA4B66MW5E27U9YGMCG,235.57\n2020-08-25,MA4B66MW5E27U9YGMCG,248.22\n2020-08-26,MA4B66MW5E27U9YGMCG,250.18\n2020-08-27,MA4B66MW5E27U9YGMCG,252.81\n2020-08-28,MA4B66MW5E27U9YGMCG,253.12\n2020-08-31,MA4B66MW5E27U9YGMCG,253.32\n2020-07-01,MA4B66MW5E27UALG33R,485.64\n2020-07-02,MA4B66MW5E27UALG33R,476.89\n2020-07-06,MA4B66MW5E27UALG33R,493.81\n2020-07-07,MA4B66MW5E27UALG33R,493.16\n2020-07-08,MA4B66MW5E27UALG33R,502.78\n2020-07-09,MA4B66MW5E27UALG33R,507.76\n2020-07-10,MA4B66MW5E27UALG33R,548.73\n2020-07-13,MA4B66MW5E27UALG33R,525.5\n2020-07-14,MA4B66MW5E27UALG33R,524.88\n2020-07-15,MA4B66MW5E27UALG33R,523.26\n2020-07-16,MA4B66MW5E27UALG33R,527.39\n2020-07-17,MA4B66MW5E27UALG33R,492.99\n2020-07-20,MA4B66MW5E27UALG33R,502.41\n2020-07-21,MA4B66MW5E27UALG33R,490.1\n2020-07-22,MA4B66MW5E27UALG33R,489.82\n2020-07-23,MA4B66MW5E27UALG33R,477.58\n2020-07-24,MA4B66MW5E27UALG33R,480.45\n2020-07-27,MA4B66MW5E27UALG33R,495.65\n2020-07-28,MA4B66MW5E27UALG33R,488.51\n2020-07-29,MA4B66MW5E27UALG33R,484.48\n2020-07-30,MA4B66MW5E27UALG33R,485.8\n2020-07-31,MA4B66MW5E27UALG33R,488.88\n2020-08-03,MA4B66MW5E27UALG33R,498.62\n2020-08-04,MA4B66MW5E27UALG33R,509.64\n2020-08-05,MA4B66MW5E27UALG33R,502.11\n2020-08-06,MA4B66MW5E27UALG33R,509.08\n2020-08-07,MA4B66MW5E27UALG33R,494.73\n2020-08-10,MA4B66MW5E27UALG33R,483.38\n2020-08-11,MA4B66MW5E27UALG33R,466.93\n2020-08-12,MA4B66MW5E27UALG33R,475.47\n2020-08-13,MA4B66MW5E27UALG33R,481.33\n2020-08-14,MA4B66MW5E27UALG33R,482.68\n2020-08-17,MA4B66MW5E27UALG33R,482.35\n2020-08-18,MA4B66MW5E27UALG33R,491.87\n2020-08-19,MA4B66MW5E27UALG33R,484.53\n2020-08-20,MA4B66MW5E27UALG33R,497.9\n2020-08-21,MA4B66MW5E27UALG33R,492.31\n2020-08-24,MA4B66MW5E27UALG33R,488.81\n2020-08-25,MA4B66MW5E27UALG33R,490.58\n2020-08-26,MA4B66MW5E27UALG33R,547.53\n2020-08-27,MA4B66MW5E27UALG33R,526.27\n2020-08-28,MA4B66MW5E27UALG33R,523.89\n2020-08-31,MA4B66MW5E27UALG33R,529.56\n2020-07-01,MA4B66MW5E27UAGYZ4D,1442.0\n2020-07-02,MA4B66MW5E27UAGYZ4D,1469.93\n2020-07-06,MA4B66MW5E27UAGYZ4D,1499.65\n2020-07-07,MA4B66MW5E27UAGYZ4D,1489.92\n2020-07-08,MA4B66MW5E27UAGYZ4D,1503.6\n2020-07-09,MA4B66MW5E27UAGYZ4D,1518.66\n2020-07-10,MA4B66MW5E27UAGYZ4D,1539.01\n2020-07-13,MA4B66MW5E27UAGYZ4D,1512.23\n2020-07-14,MA4B66MW5E27UAGYZ4D,1520.86\n2020-07-15,MA4B66MW5E27UAGYZ4D,1516.88\n2020-07-16,MA4B66MW5E27UAGYZ4D,1514.92\n2020-07-17,MA4B66MW5E27UAGYZ4D,1516.85\n2020-07-20,MA4B66MW5E27UAGYZ4D,1563.84\n2020-07-21,MA4B66MW5E27UAGYZ4D,1555.92\n2020-07-22,MA4B66MW5E27UAGYZ4D,1564.85\n2020-07-23,MA4B66MW5E27UAGYZ4D,1516.75\n2020-07-24,MA4B66MW5E27UAGYZ4D,1508.21\n2020-07-27,MA4B66MW5E27UAGYZ4D,1529.43\n2020-07-28,MA4B66MW5E27UAGYZ4D,1503.65\n2020-07-29,MA4B66MW5E27UAGYZ4D,1523.51\n2020-07-30,MA4B66MW5E27UAGYZ4D,1538.37\n2020-07-31,MA4B66MW5E27UAGYZ4D,1487.95\n2020-08-03,MA4B66MW5E27UAGYZ4D,1482.76\n2020-08-04,MA4B66MW5E27UAGYZ4D,1473.3\n2020-08-05,MA4B66MW5E27UAGYZ4D,1479.09\n2020-08-06,MA4B66MW5E27UAGYZ4D,1504.95\n2020-08-07,MA4B66MW5E27UAGYZ4D,1498.37\n2020-08-10,MA4B66MW5E27UAGYZ4D,1496.82\n2020-08-11,MA4B66MW5E27UAGYZ4D,1480.54\n2020-08-12,MA4B66MW5E27UAGYZ4D,1507.24\n2020-08-13,MA4B66MW5E27UAGYZ4D,1516.65\n2020-08-14,MA4B66MW5E27UAGYZ4D,1504.63\n2020-08-17,MA4B66MW5E27UAGYZ4D,1516.24\n2020-08-18,MA4B66MW5E27UAGYZ4D,1555.78\n2020-08-19,MA4B66MW5E27UAGYZ4D,1544.61\n2020-08-20,MA4B66MW5E27UAGYZ4D,1576.25\n2020-08-21,MA4B66MW5E27UAGYZ4D,1575.57\n2020-08-24,MA4B66MW5E27UAGYZ4D,1585.15\n2020-08-25,MA4B66MW5E27UAGYZ4D,1605.85\n2020-08-26,MA4B66MW5E27UAGYZ4D,1644.13\n2020-08-27,MA4B66MW5E27UAGYZ4D,1628.52\n2020-08-28,MA4B66MW5E27UAGYZ4D,1639.43\n2020-08-31,MA4B66MW5E27UAGYZ4D,1629.53\n2020-07-01,MA4B66MW5E27U9VBB94,91.0275\n2020-07-02,MA4B66MW5E27U9VBB94,91.0275\n2020-07-06,MA4B66MW5E27U9VBB94,93.4625\n2020-07-07,MA4B66MW5E27U9VBB94,93.1725\n2020-07-08,MA4B66MW5E27U9VBB94,95.3425\n2020-07-09,MA4B66MW5E27U9VBB94,95.6825\n2020-07-10,MA4B66MW5E27U9VBB94,95.92\n2020-07-13,MA4B66MW5E27U9VBB94,95.4775\n2020-07-14,MA4B66MW5E27U9VBB94,97.0575\n2020-07-15,MA4B66MW5E27U9VBB94,97.725\n2020-07-16,MA4B66MW5E27U9VBB94,96.5225\n2020-07-17,MA4B66MW5E27U9VBB94,96.3275\n2020-07-20,MA4B66MW5E27U9VBB94,98.3575\n2020-07-21,MA4B66MW5E27U9VBB94,97.0\n2020-07-22,MA4B66MW5E27U9VBB94,97.2725\n2020-07-23,MA4B66MW5E27U9VBB94,92.845\n2020-07-24,MA4B66MW5E27U9VBB94,92.615\n2020-07-27,MA4B66MW5E27U9VBB94,94.81\n2020-07-28,MA4B66MW5E27U9VBB94,93.2525\n2020-07-29,MA4B66MW5E27U9VBB94,95.04\n2020-07-30,MA4B66MW5E27U9VBB94,96.19\n2020-07-31,MA4B66MW5E27U9VBB94,106.26\n2020-08-03,MA4B66MW5E27U9VBB94,108.9375\n2020-08-04,MA4B66MW5E27U9VBB94,109.665\n2020-08-05,MA4B66MW5E27U9VBB94,110.0625\n2020-08-06,MA4B66MW5E27U9VBB94,113.9025\n2020-08-07,MA4B66MW5E27U9VBB94,111.1125\n2020-08-10,MA4B66MW5E27U9VBB94,112.7275\n2020-08-11,MA4B66MW5E27U9VBB94,109.375\n2020-08-12,MA4B66MW5E27U9VBB94,113.01\n2020-08-13,MA4B66MW5E27U9VBB94,115.01\n2020-08-14,MA4B66MW5E27U9VBB94,114.9075\n2020-08-17,MA4B66MW5E27U9VBB94,114.6075\n2020-08-18,MA4B66MW5E27U9VBB94,115.5625\n2020-08-19,MA4B66MW5E27U9VBB94,115.7075\n2020-08-20,MA4B66MW5E27U9VBB94,118.275\n2020-08-21,MA4B66MW5E27U9VBB94,124.37\n2020-08-24,MA4B66MW5E27U9VBB94,125.8575\n2020-08-25,MA4B66MW5E27U9VBB94,124.825\n2020-08-26,MA4B66MW5E27U9VBB94,126.5225\n2020-08-27,MA4B66MW5E27U9VBB94,125.01\n2020-08-28,MA4B66MW5E27U9VBB94,124.8075\n2020-08-31,MA4B66MW5E27U9VBB94,129.04\n2020-07-01,MA4B66MW5E27UAL9SUX,204.7\n2020-07-02,MA4B66MW5E27UAL9SUX,206.26\n2020-07-06,MA4B66MW5E27UAL9SUX,210.7\n2020-07-07,MA4B66MW5E27UAL9SUX,208.25\n2020-07-08,MA4B66MW5E27UAL9SUX,212.83\n2020-07-09,MA4B66MW5E27UAL9SUX,214.32\n2020-07-10,MA4B66MW5E27UAL9SUX,213.67\n2020-07-13,MA4B66MW5E27UAL9SUX,207.07\n2020-07-14,MA4B66MW5E27UAL9SUX,208.35\n2020-07-15,MA4B66MW5E27UAL9SUX,208.04\n2020-07-16,MA4B66MW5E27UAL9SUX,203.92\n2020-07-17,MA4B66MW5E27UAL9SUX,202.88\n2020-07-20,MA4B66MW5E27UAL9SUX,211.6\n2020-07-21,MA4B66MW5E27UAL9SUX,208.75\n2020-07-22,MA4B66MW5E27UAL9SUX,211.75\n2020-07-23,MA4B66MW5E27UAL9SUX,202.54\n2020-07-24,MA4B66MW5E27UAL9SUX,201.3\n2020-07-27,MA4B66MW5E27UAL9SUX,203.85\n2020-07-28,MA4B66MW5E27UAL9SUX,202.02\n2020-07-29,MA4B66MW5E27UAL9SUX,204.06\n2020-07-30,MA4B66MW5E27UAL9SUX,203.9\n2020-07-31,MA4B66MW5E27UAL9SUX,205.01\n2020-08-03,MA4B66MW5E27UAL9SUX,216.54\n2020-08-04,MA4B66MW5E27UAL9SUX,213.29\n2020-08-05,MA4B66MW5E27UAL9SUX,212.94\n2020-08-06,MA4B66MW5E27UAL9SUX,216.35\n2020-08-07,MA4B66MW5E27UAL9SUX,212.48\n2020-08-10,MA4B66MW5E27UAL9SUX,208.25\n2020-08-11,MA4B66MW5E27UAL9SUX,203.38\n2020-08-12,MA4B66MW5E27UAL9SUX,209.19\n2020-08-13,MA4B66MW5E27UAL9SUX,208.7\n2020-08-14,MA4B66MW5E27UAL9SUX,208.9\n2020-08-17,MA4B66MW5E27UAL9SUX,210.28\n2020-08-18,MA4B66MW5E27UAL9SUX,211.49\n2020-08-19,MA4B66MW5E27UAL9SUX,209.7\n2020-08-20,MA4B66MW5E27UAL9SUX,214.58\n2020-08-21,MA4B66MW5E27UAL9SUX,213.02\n2020-08-24,MA4B66MW5E27UAL9SUX,213.69\n2020-08-25,MA4B66MW5E27UAL9SUX,216.47\n2020-08-26,MA4B66MW5E27UAL9SUX,221.15\n2020-08-27,MA4B66MW5E27UAL9SUX,226.58\n2020-08-28,MA4B66MW5E27UAL9SUX,228.91\n2020-08-31,MA4B66MW5E27UAL9SUX,225.53\n2020-07-01,MA4B66MW5E27UAKV9P7,214.77\n2020-07-02,MA4B66MW5E27UAKV9P7,221.26\n2020-07-06,MA4B66MW5E27UAKV9P7,224.65\n2020-07-07,MA4B66MW5E27UAKV9P7,224.53\n2020-07-08,MA4B66MW5E27UAKV9P7,221.52\n2020-07-09,MA4B66MW5E27UAKV9P7,221.95\n2020-07-10,MA4B66MW5E27UAKV9P7,225.95\n2020-07-13,MA4B66MW5E27UAKV9P7,225.82\n2020-07-14,MA4B66MW5E27UAKV9P7,233.13\n2020-07-15,MA4B66MW5E27UAKV9P7,237.63\n2020-07-16,MA4B66MW5E27UAKV9P7,238.89\n2020-07-17,MA4B66MW5E27UAKV9P7,242.6\n2020-07-20,MA4B66MW5E27UAKV9P7,240.32\n2020-07-21,MA4B66MW5E27UAKV9P7,244.0\n2020-07-22,MA4B66MW5E27UAKV9P7,245.34\n2020-07-23,MA4B66MW5E27UAKV9P7,245.98\n2020-07-24,MA4B66MW5E27UAKV9P7,244.07\n2020-07-27,MA4B66MW5E27UAKV9P7,246.86\n2020-07-28,MA4B66MW5E27UAKV9P7,243.7\n2020-07-29,MA4B66MW5E27UAKV9P7,244.71\n2020-07-30,MA4B66MW5E27UAKV9P7,242.02\n2020-07-31,MA4B66MW5E27UAKV9P7,245.11\n2020-08-03,MA4B66MW5E27UAKV9P7,244.31\n2020-08-04,MA4B66MW5E27UAKV9P7,243.85\n2020-08-05,MA4B66MW5E27UAKV9P7,248.27\n2020-08-06,MA4B66MW5E27UAKV9P7,246.66\n2020-08-07,MA4B66MW5E27UAKV9P7,245.31\n2020-08-10,MA4B66MW5E27UAKV9P7,243.34\n2020-08-11,MA4B66MW5E27UAKV9P7,244.85\n2020-08-12,MA4B66MW5E27UAKV9P7,248.63\n2020-08-13,MA4B66MW5E27UAKV9P7,249.24\n2020-08-14,MA4B66MW5E27UAKV9P7,248.5\n2020-08-17,MA4B66MW5E27UAKV9P7,248.43\n2020-08-18,MA4B66MW5E27UAKV9P7,246.88\n2020-08-19,MA4B66MW5E27UAKV9P7,247.24\n2020-08-20,MA4B66MW5E27UAKV9P7,245.31\n2020-08-21,MA4B66MW5E27UAKV9P7,244.3\n2020-08-24,MA4B66MW5E27UAKV9P7,248.91\n2020-08-25,MA4B66MW5E27UAKV9P7,250.08\n2020-08-26,MA4B66MW5E27UAKV9P7,252.88\n2020-08-27,MA4B66MW5E27UAKV9P7,250.93\n2020-08-28,MA4B66MW5E27UAKV9P7,253.57\n2020-08-31,MA4B66MW5E27UAKV9P7,249.74\n2020-07-01,MA4B66MW5E27U9VBBEQ,91.64\n2020-07-02,MA4B66MW5E27U9VBBEQ,92.23\n2020-07-06,MA4B66MW5E27U9VBBEQ,92.97\n2020-07-07,MA4B66MW5E27U9VBBEQ,92.46\n2020-07-08,MA4B66MW5E27U9VBBEQ,92.58\n2020-07-09,MA4B66MW5E27U9VBBEQ,93.67\n2020-07-10,MA4B66MW5E27U9VBBEQ,93.04\n2020-07-13,MA4B66MW5E27U9VBBEQ,93.09\n2020-07-14,MA4B66MW5E27U9VBBEQ,94.82\n2020-07-15,MA4B66MW5E27U9VBBEQ,96.73\n2020-07-16,MA4B66MW5E27U9VBBEQ,96.4\n2020-07-17,MA4B66MW5E27U9VBBEQ,99.25\n2020-07-20,MA4B66MW5E27U9VBBEQ,99.08\n2020-07-21,MA4B66MW5E27U9VBBEQ,98.19\n2020-07-22,MA4B66MW5E27U9VBBEQ,100.19\n2020-07-23,MA4B66MW5E27U9VBBEQ,100.82\n2020-07-24,MA4B66MW5E27U9VBBEQ,98.95\n2020-07-27,MA4B66MW5E27U9VBBEQ,99.99\n2020-07-28,MA4B66MW5E27U9VBBEQ,99.86\n2020-07-29,MA4B66MW5E27U9VBBEQ,102.52\n2020-07-30,MA4B66MW5E27U9VBBEQ,101.53\n2020-07-31,MA4B66MW5E27U9VBBEQ,100.64\n2020-08-03,MA4B66MW5E27U9VBBEQ,100.68\n2020-08-04,MA4B66MW5E27U9VBBEQ,99.93\n2020-08-05,MA4B66MW5E27U9VBBEQ,102.14\n2020-08-06,MA4B66MW5E27U9VBBEQ,101.75\n2020-08-07,MA4B66MW5E27U9VBBEQ,101.23\n2020-08-10,MA4B66MW5E27U9VBBEQ,100.04\n2020-08-11,MA4B66MW5E27U9VBBEQ,99.06\n2020-08-12,MA4B66MW5E27U9VBBEQ,100.65\n2020-08-13,MA4B66MW5E27U9VBBEQ,101.1\n2020-08-14,MA4B66MW5E27U9VBBEQ,99.99\n2020-08-17,MA4B66MW5E27U9VBBEQ,100.71\n2020-08-18,MA4B66MW5E27U9VBBEQ,100.96\n2020-08-19,MA4B66MW5E27U9VBBEQ,100.48\n2020-08-20,MA4B66MW5E27U9VBBEQ,101.63\n2020-08-21,MA4B66MW5E27U9VBBEQ,102.4\n2020-08-24,MA4B66MW5E27U9VBBEQ,101.39\n2020-08-25,MA4B66MW5E27U9VBBEQ,102.47\n2020-08-26,MA4B66MW5E27U9VBBEQ,103.19\n2020-08-27,MA4B66MW5E27U9VBBEQ,111.29\n2020-08-28,MA4B66MW5E27U9VBBEQ,110.79\n2020-08-31,MA4B66MW5E27U9VBBEQ,109.47\n2020-07-01,MA4B66MW5E27U9ZPDGH,23.26\n2020-07-02,MA4B66MW5E27U9ZPDGH,23.29\n2020-07-06,MA4B66MW5E27U9ZPDGH,23.66\n2020-07-07,MA4B66MW5E27U9ZPDGH,23.01\n2020-07-08,MA4B66MW5E27U9ZPDGH,23.1\n2020-07-09,MA4B66MW5E27U9ZPDGH,22.77\n2020-07-10,MA4B66MW5E27U9ZPDGH,24.02\n2020-07-13,MA4B66MW5E27U9ZPDGH,24.19\n2020-07-14,MA4B66MW5E27U9ZPDGH,24.14\n2020-07-15,MA4B66MW5E27U9ZPDGH,24.6\n2020-07-16,MA4B66MW5E27U9ZPDGH,23.93\n2020-07-17,MA4B66MW5E27U9ZPDGH,23.22\n2020-07-20,MA4B66MW5E27U9ZPDGH,23.58\n2020-07-21,MA4B66MW5E27U9ZPDGH,24.42\n2020-07-22,MA4B66MW5E27U9ZPDGH,24.31\n2020-07-23,MA4B66MW5E27U9ZPDGH,24.54\n2020-07-24,MA4B66MW5E27U9ZPDGH,24.35\n2020-07-27,MA4B66MW5E27U9ZPDGH,24.14\n2020-07-28,MA4B66MW5E27U9ZPDGH,24.36\n2020-07-29,MA4B66MW5E27U9ZPDGH,25.27\n2020-07-30,MA4B66MW5E27U9ZPDGH,24.84\n2020-07-31,MA4B66MW5E27U9ZPDGH,24.88\n2020-08-03,MA4B66MW5E27U9ZPDGH,24.99\n2020-08-04,MA4B66MW5E27U9ZPDGH,25.01\n2020-08-05,MA4B66MW5E27U9ZPDGH,25.39\n2020-08-06,MA4B66MW5E27U9ZPDGH,25.47\n2020-08-07,MA4B66MW5E27U9ZPDGH,26.11\n2020-08-10,MA4B66MW5E27U9ZPDGH,26.56\n2020-08-11,MA4B66MW5E27U9ZPDGH,26.92\n2020-08-12,MA4B66MW5E27U9ZPDGH,26.73\n2020-08-13,MA4B66MW5E27U9ZPDGH,26.35\n2020-08-14,MA4B66MW5E27U9ZPDGH,26.47\n2020-08-17,MA4B66MW5E27U9ZPDGH,25.9\n2020-08-18,MA4B66MW5E27U9ZPDGH,25.53\n2020-08-19,MA4B66MW5E27U9ZPDGH,25.5\n2020-08-20,MA4B66MW5E27U9ZPDGH,25.1\n2020-08-21,MA4B66MW5E27U9ZPDGH,24.98\n2020-08-24,MA4B66MW5E27U9ZPDGH,25.69\n2020-08-25,MA4B66MW5E27U9ZPDGH,26.0\n2020-08-26,MA4B66MW5E27U9ZPDGH,25.56\n2020-08-27,MA4B66MW5E27U9ZPDGH,26.05\n2020-08-28,MA4B66MW5E27U9ZPDGH,26.3\n2020-08-31,MA4B66MW5E27U9ZPDGH,25.74\n2020-07-01,MA4B66MW5E27UAL9SRL,78.12\n2020-07-02,MA4B66MW5E27UAL9SRL,78.78\n2020-07-06,MA4B66MW5E27UAL9SRL,79.58\n2020-07-07,MA4B66MW5E27UAL9SRL,78.72\n2020-07-08,MA4B66MW5E27UAL9SRL,77.92\n2020-07-09,MA4B66MW5E27UAL9SRL,76.69\n2020-07-10,MA4B66MW5E27UAL9SRL,76.73\n2020-07-13,MA4B66MW5E27UAL9SRL,77.35\n2020-07-14,MA4B66MW5E27UAL9SRL,78.25\n2020-07-15,MA4B66MW5E27UAL9SRL,79.44\n2020-07-16,MA4B66MW5E27UAL9SRL,79.4\n2020-07-17,MA4B66MW5E27UAL9SRL,79.87\n2020-07-20,MA4B66MW5E27UAL9SRL,79.41\n2020-07-21,MA4B66MW5E27UAL9SRL,78.89\n2020-07-22,MA4B66MW5E27UAL9SRL,78.78\n2020-07-23,MA4B66MW5E27UAL9SRL,78.06\n2020-07-24,MA4B66MW5E27UAL9SRL,77.1\n2020-07-27,MA4B66MW5E27UAL9SRL,78.93\n2020-07-28,MA4B66MW5E27UAL9SRL,79.69\n2020-07-29,MA4B66MW5E27UAL9SRL,79.35\n2020-07-30,MA4B66MW5E27UAL9SRL,78.99\n2020-07-31,MA4B66MW5E27UAL9SRL,80.24\n2020-08-03,MA4B66MW5E27UAL9SRL,82.54\n2020-08-04,MA4B66MW5E27UAL9SRL,81.67\n2020-08-05,MA4B66MW5E27UAL9SRL,81.64\n2020-08-06,MA4B66MW5E27UAL9SRL,81.05\n2020-08-07,MA4B66MW5E27UAL9SRL,81.02\n2020-08-10,MA4B66MW5E27UAL9SRL,80.91\n2020-08-11,MA4B66MW5E27UAL9SRL,80.91\n2020-08-12,MA4B66MW5E27UAL9SRL,82.68\n2020-08-13,MA4B66MW5E27UAL9SRL,83.54\n2020-08-14,MA4B66MW5E27UAL9SRL,83.48\n2020-08-17,MA4B66MW5E27UAL9SRL,84.76\n2020-08-18,MA4B66MW5E27UAL9SRL,84.56\n2020-08-19,MA4B66MW5E27UAL9SRL,85.03\n2020-08-20,MA4B66MW5E27UAL9SRL,85.03\n2020-08-21,MA4B66MW5E27UAL9SRL,84.98\n2020-08-24,MA4B66MW5E27UAL9SRL,85.42\n2020-08-25,MA4B66MW5E27UAL9SRL,85.68\n2020-08-26,MA4B66MW5E27UAL9SRL,85.54\n2020-08-27,MA4B66MW5E27UAL9SRL,85.82\n2020-08-28,MA4B66MW5E27UAL9SRL,85.65\n2020-08-31,MA4B66MW5E27UAL9SRL,85.27\n2020-07-01,MA4B66MW5E27UA4479J,59.43\n2020-07-02,MA4B66MW5E27UA4479J,59.14\n2020-07-06,MA4B66MW5E27UA4479J,60.19\n2020-07-07,MA4B66MW5E27UA4479J,60.29\n2020-07-08,MA4B66MW5E27UA4479J,59.56\n2020-07-09,MA4B66MW5E27UA4479J,58.11\n2020-07-10,MA4B66MW5E27UA4479J,57.43\n2020-07-13,MA4B66MW5E27UA4479J,57.75\n2020-07-14,MA4B66MW5E27UA4479J,57.89\n2020-07-15,MA4B66MW5E27UA4479J,59.27\n2020-07-16,MA4B66MW5E27UA4479J,59.32\n2020-07-17,MA4B66MW5E27UA4479J,60.39\n2020-07-20,MA4B66MW5E27UA4479J,59.46\n2020-07-21,MA4B66MW5E27UA4479J,59.8\n2020-07-22,MA4B66MW5E27UA4479J,59.85\n2020-07-23,MA4B66MW5E27UA4479J,59.87\n2020-07-24,MA4B66MW5E27UA4479J,57.85\n2020-07-27,MA4B66MW5E27UA4479J,58.05\n2020-07-28,MA4B66MW5E27UA4479J,59.43\n2020-07-29,MA4B66MW5E27UA4479J,59.15\n2020-07-30,MA4B66MW5E27UA4479J,58.87\n2020-07-31,MA4B66MW5E27UA4479J,58.66\n2020-08-03,MA4B66MW5E27UA4479J,59.1\n2020-08-04,MA4B66MW5E27UA4479J,59.47\n2020-08-05,MA4B66MW5E27UA4479J,59.69\n2020-08-06,MA4B66MW5E27UA4479J,61.33\n2020-08-07,MA4B66MW5E27UA4479J,61.02\n2020-08-10,MA4B66MW5E27UA4479J,61.82\n2020-08-11,MA4B66MW5E27UA4479J,63.13\n2020-08-12,MA4B66MW5E27UA4479J,63.64\n2020-08-13,MA4B66MW5E27UA4479J,63.24\n2020-08-14,MA4B66MW5E27UA4479J,63.16\n2020-08-17,MA4B66MW5E27UA4479J,63.62\n2020-08-18,MA4B66MW5E27UA4479J,63.03\n2020-08-19,MA4B66MW5E27UA4479J,63.09\n2020-08-20,MA4B66MW5E27UA4479J,62.22\n2020-08-21,MA4B66MW5E27UA4479J,62.19\n2020-08-24,MA4B66MW5E27UA4479J,62.41\n2020-08-25,MA4B66MW5E27UA4479J,62.71\n2020-08-26,MA4B66MW5E27UA4479J,62.28\n2020-08-27,MA4B66MW5E27UA4479J,62.51\n2020-08-28,MA4B66MW5E27UA4479J,62.37\n2020-08-31,MA4B66MW5E27UA4479J,62.2\n2020-07-01,MA4B66MW5E27UADJ5RL,191.89\n2020-07-02,MA4B66MW5E27UADJ5RL,192.53\n2020-07-06,MA4B66MW5E27UADJ5RL,197.72\n2020-07-07,MA4B66MW5E27UADJ5RL,196.38\n2020-07-08,MA4B66MW5E27UADJ5RL,200.28\n2020-07-09,MA4B66MW5E27UADJ5RL,200.74\n2020-07-10,MA4B66MW5E27UADJ5RL,198.88\n2020-07-13,MA4B66MW5E27UADJ5RL,188.34\n2020-07-14,MA4B66MW5E27UADJ5RL,189.56\n2020-07-15,MA4B66MW5E27UADJ5RL,188.09\n2020-07-16,MA4B66MW5E27UADJ5RL,185.51\n2020-07-17,MA4B66MW5E27UADJ5RL,187.78\n2020-07-20,MA4B66MW5E27UADJ5RL,195.09\n2020-07-21,MA4B66MW5E27UADJ5RL,190.8\n2020-07-22,MA4B66MW5E27UADJ5RL,191.31\n2020-07-23,MA4B66MW5E27UADJ5RL,188.54\n2020-07-24,MA4B66MW5E27UADJ5RL,188.49\n2020-07-27,MA4B66MW5E27UADJ5RL,190.96\n2020-07-28,MA4B66MW5E27UADJ5RL,189.5\n2020-07-29,MA4B66MW5E27UADJ5RL,193.61\n2020-07-30,MA4B66MW5E27UADJ5RL,191.7\n2020-07-31,MA4B66MW5E27UADJ5RL,194.85\n2020-08-03,MA4B66MW5E27UADJ5RL,203.19\n2020-08-04,MA4B66MW5E27UADJ5RL,201.41\n2020-08-05,MA4B66MW5E27UADJ5RL,202.64\n2020-08-06,MA4B66MW5E27UADJ5RL,207.79\n2020-08-07,MA4B66MW5E27UADJ5RL,201.05\n2020-08-10,MA4B66MW5E27UADJ5RL,197.16\n2020-08-11,MA4B66MW5E27UADJ5RL,191.99\n2020-08-12,MA4B66MW5E27UADJ5RL,192.62\n2020-08-13,MA4B66MW5E27UADJ5RL,195.14\n2020-08-14,MA4B66MW5E27UADJ5RL,193.46\n2020-08-17,MA4B66MW5E27UADJ5RL,196.74\n2020-08-18,MA4B66MW5E27UADJ5RL,204.01\n2020-08-19,MA4B66MW5E27UADJ5RL,205.11\n2020-08-20,MA4B66MW5E27UADJ5RL,209.51\n2020-08-21,MA4B66MW5E27UADJ5RL,207.53\n2020-08-24,MA4B66MW5E27UADJ5RL,208.46\n2020-08-25,MA4B66MW5E27UADJ5RL,216.05\n2020-08-26,MA4B66MW5E27UADJ5RL,272.32\n2020-08-27,MA4B66MW5E27UADJ5RL,276.32\n2020-08-28,MA4B66MW5E27UADJ5RL,271.1\n2020-08-31,MA4B66MW5E27UADJ5RL,272.65\n2020-07-01,MA4B66MW5E27UANT8PM,54.67\n2020-07-02,MA4B66MW5E27UANT8PM,54.79\n2020-07-06,MA4B66MW5E27UANT8PM,55.24\n2020-07-07,MA4B66MW5E27UANT8PM,55.04\n2020-07-08,MA4B66MW5E27UANT8PM,55.38\n2020-07-09,MA4B66MW5E27UANT8PM,53.94\n2020-07-10,MA4B66MW5E27UANT8PM,54.49\n2020-07-13,MA4B66MW5E27UANT8PM,54.45\n2020-07-14,MA4B66MW5E27UANT8PM,55.44\n2020-07-15,MA4B66MW5E27UANT8PM,55.06\n2020-07-16,MA4B66MW5E27UANT8PM,55.78\n2020-07-17,MA4B66MW5E27UANT8PM,56.3\n2020-07-20,MA4B66MW5E27UANT8PM,55.87\n2020-07-21,MA4B66MW5E27UANT8PM,55.84\n2020-07-22,MA4B66MW5E27UANT8PM,55.75\n2020-07-23,MA4B66MW5E27UANT8PM,55.85\n2020-07-24,MA4B66MW5E27UANT8PM,56.85\n2020-07-27,MA4B66MW5E27UANT8PM,56.87\n2020-07-28,MA4B66MW5E27UANT8PM,57.48\n2020-07-29,MA4B66MW5E27UANT8PM,57.45\n2020-07-30,MA4B66MW5E27UANT8PM,57.3\n2020-07-31,MA4B66MW5E27UANT8PM,57.48\n2020-08-03,MA4B66MW5E27UANT8PM,57.24\n2020-08-04,MA4B66MW5E27UANT8PM,57.91\n2020-08-05,MA4B66MW5E27UANT8PM,57.54\n2020-08-06,MA4B66MW5E27UANT8PM,57.83\n2020-08-07,MA4B66MW5E27UANT8PM,58.53\n2020-08-10,MA4B66MW5E27UANT8PM,58.99\n2020-08-11,MA4B66MW5E27UANT8PM,58.51\n2020-08-12,MA4B66MW5E27UANT8PM,58.6\n2020-08-13,MA4B66MW5E27UANT8PM,58.52\n2020-08-14,MA4B66MW5E27UANT8PM,58.79\n2020-08-17,MA4B66MW5E27UANT8PM,58.78\n2020-08-18,MA4B66MW5E27UANT8PM,59.18\n2020-08-19,MA4B66MW5E27UANT8PM,59.05\n2020-08-20,MA4B66MW5E27UANT8PM,58.96\n2020-08-21,MA4B66MW5E27UANT8PM,58.99\n2020-08-24,MA4B66MW5E27UANT8PM,59.57\n2020-08-25,MA4B66MW5E27UANT8PM,59.42\n2020-08-26,MA4B66MW5E27UANT8PM,59.46\n2020-08-27,MA4B66MW5E27UANT8PM,59.43\n2020-08-28,MA4B66MW5E27UANT8PM,59.26\n2020-08-31,MA4B66MW5E27UANT8PM,59.27\n2020-07-01,MA4B66MW5E27UAE4MDU,177.16\n2020-07-02,MA4B66MW5E27UAE4MDU,179.68\n2020-07-06,MA4B66MW5E27UAE4MDU,183.0\n2020-07-07,MA4B66MW5E27UAE4MDU,182.04\n2020-07-08,MA4B66MW5E27UAE4MDU,184.42\n2020-07-09,MA4B66MW5E27UAE4MDU,185.47\n2020-07-10,MA4B66MW5E27UAE4MDU,183.67\n2020-07-13,MA4B66MW5E27UAE4MDU,182.41\n2020-07-14,MA4B66MW5E27UAE4MDU,185.45\n2020-07-15,MA4B66MW5E27UAE4MDU,188.34\n2020-07-16,MA4B66MW5E27UAE4MDU,188.81\n2020-07-17,MA4B66MW5E27UAE4MDU,190.95\n2020-07-20,MA4B66MW5E27UAE4MDU,193.37\n2020-07-21,MA4B66MW5E27UAE4MDU,195.0\n2020-07-22,MA4B66MW5E27UAE4MDU,195.61\n2020-07-23,MA4B66MW5E27UAE4MDU,199.22\n2020-07-24,MA4B66MW5E27UAE4MDU,195.27\n2020-07-27,MA4B66MW5E27UAE4MDU,198.98\n2020-07-28,MA4B66MW5E27UAE4MDU,199.01\n2020-07-29,MA4B66MW5E27UAE4MDU,202.13\n2020-07-30,MA4B66MW5E27UAE4MDU,202.14\n2020-07-31,MA4B66MW5E27UAE4MDU,203.8\n2020-08-03,MA4B66MW5E27UAE4MDU,206.83\n2020-08-04,MA4B66MW5E27UAE4MDU,206.07\n2020-08-05,MA4B66MW5E27UAE4MDU,205.54\n2020-08-06,MA4B66MW5E27UAE4MDU,205.69\n2020-08-07,MA4B66MW5E27UAE4MDU,205.4\n2020-08-10,MA4B66MW5E27UAE4MDU,202.05\n2020-08-11,MA4B66MW5E27UAE4MDU,200.01\n2020-08-12,MA4B66MW5E27UAE4MDU,205.92\n2020-08-13,MA4B66MW5E27UAE4MDU,207.19\n2020-08-14,MA4B66MW5E27UAE4MDU,204.86\n2020-08-17,MA4B66MW5E27UAE4MDU,205.57\n2020-08-18,MA4B66MW5E27UAE4MDU,207.68\n2020-08-19,MA4B66MW5E27UAE4MDU,205.93\n2020-08-20,MA4B66MW5E27UAE4MDU,205.66\n2020-08-21,MA4B66MW5E27UAE4MDU,207.8\n2020-08-24,MA4B66MW5E27UAE4MDU,205.56\n2020-08-25,MA4B66MW5E27UAE4MDU,207.57\n2020-08-26,MA4B66MW5E27UAE4MDU,208.19\n2020-08-27,MA4B66MW5E27UAE4MDU,204.39\n2020-08-28,MA4B66MW5E27UAE4MDU,205.64\n2020-08-31,MA4B66MW5E27UAE4MDU,206.47\n2020-07-01,MA4B66MW5E27UAKHZDF,140.38\n2020-07-02,MA4B66MW5E27UAKHZDF,140.97\n2020-07-06,MA4B66MW5E27UAKHZDF,142.98\n2020-07-07,MA4B66MW5E27UAKHZDF,142.85\n2020-07-08,MA4B66MW5E27UAKHZDF,143.27\n2020-07-09,MA4B66MW5E27UAKHZDF,142.49\n2020-07-10,MA4B66MW5E27UAKHZDF,142.37\n2020-07-13,MA4B66MW5E27UAKHZDF,145.21\n2020-07-14,MA4B66MW5E27UAKHZDF,147.92\n2020-07-15,MA4B66MW5E27UAKHZDF,148.26\n2020-07-16,MA4B66MW5E27UAKHZDF,149.25\n2020-07-17,MA4B66MW5E27UAKHZDF,149.35\n2020-07-20,MA4B66MW5E27UAKHZDF,149.6\n2020-07-21,MA4B66MW5E27UAKHZDF,149.74\n2020-07-22,MA4B66MW5E27UAKHZDF,150.01\n2020-07-23,MA4B66MW5E27UAKHZDF,149.61\n2020-07-24,MA4B66MW5E27UAKHZDF,148.12\n2020-07-27,MA4B66MW5E27UAKHZDF,147.18\n2020-07-28,MA4B66MW5E27UAKHZDF,146.83\n2020-07-29,MA4B66MW5E27UAKHZDF,146.54\n2020-07-30,MA4B66MW5E27UAKHZDF,146.84\n2020-07-31,MA4B66MW5E27UAKHZDF,145.76\n2020-08-03,MA4B66MW5E27UAKHZDF,147.35\n2020-08-04,MA4B66MW5E27UAKHZDF,147.22\n2020-08-05,MA4B66MW5E27UAKHZDF,148.4\n2020-08-06,MA4B66MW5E27UAKHZDF,147.55\n2020-08-07,MA4B66MW5E27UAKHZDF,148.6\n2020-08-10,MA4B66MW5E27UAKHZDF,148.03\n2020-08-11,MA4B66MW5E27UAKHZDF,146.97\n2020-08-12,MA4B66MW5E27UAKHZDF,149.66\n2020-08-13,MA4B66MW5E27UAKHZDF,148.0\n2020-08-14,MA4B66MW5E27UAKHZDF,148.24\n2020-08-17,MA4B66MW5E27UAKHZDF,148.99\n2020-08-18,MA4B66MW5E27UAKHZDF,150.09\n2020-08-19,MA4B66MW5E27UAKHZDF,150.39\n2020-08-20,MA4B66MW5E27UAKHZDF,151.42\n2020-08-21,MA4B66MW5E27UAKHZDF,152.76\n2020-08-24,MA4B66MW5E27UAKHZDF,152.15\n2020-08-25,MA4B66MW5E27UAKHZDF,152.06\n2020-08-26,MA4B66MW5E27UAKHZDF,152.3\n2020-08-27,MA4B66MW5E27UAKHZDF,152.98\n2020-08-28,MA4B66MW5E27UAKHZDF,153.64\n2020-08-31,MA4B66MW5E27UAKHZDF,153.41\n2020-07-01,MA4B66MW5E27UAL3HZC,184.66\n2020-07-02,MA4B66MW5E27UAL3HZC,183.52\n2020-07-06,MA4B66MW5E27UAL3HZC,188.5\n2020-07-07,MA4B66MW5E27UAL3HZC,185.82\n2020-07-08,MA4B66MW5E27UAL3HZC,185.85\n2020-07-09,MA4B66MW5E27UAL3HZC,184.33\n2020-07-10,MA4B66MW5E27UAL3HZC,184.88\n2020-07-13,MA4B66MW5E27UAL3HZC,184.92\n2020-07-14,MA4B66MW5E27UAL3HZC,190.72\n2020-07-15,MA4B66MW5E27UAL3HZC,191.77\n2020-07-16,MA4B66MW5E27UAL3HZC,190.92\n2020-07-17,MA4B66MW5E27UAL3HZC,191.48\n2020-07-20,MA4B66MW5E27UAL3HZC,191.61\n2020-07-21,MA4B66MW5E27UAL3HZC,192.98\n2020-07-22,MA4B66MW5E27UAL3HZC,198.62\n2020-07-23,MA4B66MW5E27UAL3HZC,197.55\n2020-07-24,MA4B66MW5E27UAL3HZC,198.72\n2020-07-27,MA4B66MW5E27UAL3HZC,201.25\n2020-07-28,MA4B66MW5E27UAL3HZC,196.24\n2020-07-29,MA4B66MW5E27UAL3HZC,196.21\n2020-07-30,MA4B66MW5E27UAL3HZC,195.41\n2020-07-31,MA4B66MW5E27UAL3HZC,194.28\n2020-08-03,MA4B66MW5E27UAL3HZC,194.4\n2020-08-04,MA4B66MW5E27UAL3HZC,199.36\n2020-08-05,MA4B66MW5E27UAL3HZC,199.26\n2020-08-06,MA4B66MW5E27UAL3HZC,203.18\n2020-08-07,MA4B66MW5E27UAL3HZC,204.6\n2020-08-10,MA4B66MW5E27UAL3HZC,204.12\n2020-08-11,MA4B66MW5E27UAL3HZC,205.0\n2020-08-12,MA4B66MW5E27UAL3HZC,206.02\n2020-08-13,MA4B66MW5E27UAL3HZC,206.49\n2020-08-14,MA4B66MW5E27UAL3HZC,207.03\n2020-08-17,MA4B66MW5E27UAL3HZC,208.67\n2020-08-18,MA4B66MW5E27UAL3HZC,210.32\n2020-08-19,MA4B66MW5E27UAL3HZC,209.51\n2020-08-20,MA4B66MW5E27UAL3HZC,209.88\n2020-08-21,MA4B66MW5E27UAL3HZC,211.57\n2020-08-24,MA4B66MW5E27UAL3HZC,212.62\n2020-08-25,MA4B66MW5E27UAL3HZC,212.65\n2020-08-26,MA4B66MW5E27UAL3HZC,213.76\n2020-08-27,MA4B66MW5E27UAL3HZC,212.19\n2020-08-28,MA4B66MW5E27UAL3HZC,214.91\n2020-08-31,MA4B66MW5E27UAL3HZC,213.52\n2020-07-01,MA4B66MW5E27UANEPLL,359.77\n2020-07-02,MA4B66MW5E27UANEPLL,365.89\n2020-07-06,MA4B66MW5E27UANEPLL,382.27\n2020-07-07,MA4B66MW5E27UANEPLL,380.25\n2020-07-08,MA4B66MW5E27UANEPLL,386.63\n2020-07-09,MA4B66MW5E27UANEPLL,387.67\n2020-07-10,MA4B66MW5E27UANEPLL,382.57\n2020-07-13,MA4B66MW5E27UANEPLL,378.93\n2020-07-14,MA4B66MW5E27UANEPLL,387.46\n2020-07-15,MA4B66MW5E27UANEPLL,392.7\n2020-07-16,MA4B66MW5E27UANEPLL,392.42\n2020-07-17,MA4B66MW5E27UANEPLL,395.4\n2020-07-20,MA4B66MW5E27UANEPLL,402.97\n2020-07-21,MA4B66MW5E27UANEPLL,403.09\n2020-07-22,MA4B66MW5E27UANEPLL,410.76\n2020-07-23,MA4B66MW5E27UANEPLL,410.48\n2020-07-24,MA4B66MW5E27UANEPLL,406.38\n2020-07-27,MA4B66MW5E27UANEPLL,413.44\n2020-07-28,MA4B66MW5E27UANEPLL,407.36\n2020-07-29,MA4B66MW5E27UANEPLL,409.26\n2020-07-30,MA4B66MW5E27UANEPLL,410.56\n2020-07-31,MA4B66MW5E27UANEPLL,413.95\n2020-08-03,MA4B66MW5E27UANEPLL,419.09\n2020-08-04,MA4B66MW5E27UANEPLL,415.72\n2020-08-05,MA4B66MW5E27UANEPLL,419.9\n2020-08-06,MA4B66MW5E27UANEPLL,415.73\n2020-08-07,MA4B66MW5E27UANEPLL,414.33\n2020-08-10,MA4B66MW5E27UANEPLL,410.16\n2020-08-11,MA4B66MW5E27UANEPLL,407.24\n2020-08-12,MA4B66MW5E27UANEPLL,416.17\n2020-08-13,MA4B66MW5E27UANEPLL,416.6\n2020-08-14,MA4B66MW5E27UANEPLL,413.76\n2020-08-17,MA4B66MW5E27UANEPLL,416.8\n2020-08-18,MA4B66MW5E27UANEPLL,417.3\n2020-08-19,MA4B66MW5E27UANEPLL,420.62\n2020-08-20,MA4B66MW5E27UANEPLL,422.11\n2020-08-21,MA4B66MW5E27UANEPLL,428.57\n2020-08-24,MA4B66MW5E27UANEPLL,419.76\n2020-08-25,MA4B66MW5E27UANEPLL,422.29\n2020-08-26,MA4B66MW5E27UANEPLL,425.65\n2020-08-27,MA4B66MW5E27UANEPLL,420.53\n2020-08-28,MA4B66MW5E27UANEPLL,424.48\n2020-08-31,MA4B66MW5E27UANEPLL,428.98\n2020-07-01,MA4B66MW5E27UAKVAB4,301.17\n2020-07-02,MA4B66MW5E27UAKVAB4,302.42\n2020-07-06,MA4B66MW5E27UAKVAB4,305.57\n2020-07-07,MA4B66MW5E27UAKVAB4,299.91\n2020-07-08,MA4B66MW5E27UAKVAB4,300.15\n2020-07-09,MA4B66MW5E27UAKVAB4,294.59\n2020-07-10,MA4B66MW5E27UAKVAB4,295.68\n2020-07-13,MA4B66MW5E27UAKVAB4,290.18\n2020-07-14,MA4B66MW5E27UAKVAB4,298.95\n2020-07-15,MA4B66MW5E27UAKVAB4,305.72\n2020-07-16,MA4B66MW5E27UAKVAB4,299.35\n2020-07-17,MA4B66MW5E27UAKVAB4,304.06\n2020-07-20,MA4B66MW5E27UAKVAB4,311.96\n2020-07-21,MA4B66MW5E27UAKVAB4,307.6\n2020-07-22,MA4B66MW5E27UAKVAB4,313.37\n2020-07-23,MA4B66MW5E27UAKVAB4,309.9\n2020-07-24,MA4B66MW5E27UAKVAB4,306.92\n2020-07-27,MA4B66MW5E27UAKVAB4,308.86\n2020-07-28,MA4B66MW5E27UAKVAB4,306.33\n2020-07-29,MA4B66MW5E27UAKVAB4,309.3\n2020-07-30,MA4B66MW5E27UAKVAB4,309.0\n2020-07-31,MA4B66MW5E27UAKVAB4,308.53\n2020-08-03,MA4B66MW5E27UAKVAB4,312.59\n2020-08-04,MA4B66MW5E27UAKVAB4,314.39\n2020-08-05,MA4B66MW5E27UAKVAB4,328.0\n2020-08-06,MA4B66MW5E27UAKVAB4,330.14\n2020-08-07,MA4B66MW5E27UAKVAB4,328.02\n2020-08-10,MA4B66MW5E27UAKVAB4,322.88\n2020-08-11,MA4B66MW5E27UAKVAB4,328.54\n2020-08-12,MA4B66MW5E27UAKVAB4,327.63\n2020-08-13,MA4B66MW5E27UAKVAB4,326.19\n2020-08-14,MA4B66MW5E27UAKVAB4,326.8\n2020-08-17,MA4B66MW5E27UAKVAB4,330.23\n2020-08-18,MA4B66MW5E27UAKVAB4,331.0\n2020-08-19,MA4B66MW5E27UAKVAB4,332.4\n2020-08-20,MA4B66MW5E27UAKVAB4,337.32\n2020-08-21,MA4B66MW5E27UAKVAB4,337.1\n2020-08-24,MA4B66MW5E27UAKVAB4,343.64\n2020-08-25,MA4B66MW5E27UAKVAB4,347.66\n2020-08-26,MA4B66MW5E27UAKVAB4,351.02\n2020-08-27,MA4B66MW5E27UAKVAB4,356.0\n2020-08-28,MA4B66MW5E27UAKVAB4,366.12\n2020-08-31,MA4B66MW5E27UAKVAB4,358.19\n2020-07-01,MA4B66MW5E27UAKHZFD,93.26\n2020-07-02,MA4B66MW5E27UAKHZFD,92.66\n2020-07-06,MA4B66MW5E27UAKHZFD,95.0\n2020-07-07,MA4B66MW5E27UAKHZFD,92.32\n2020-07-08,MA4B66MW5E27UAKHZFD,93.3\n2020-07-09,MA4B66MW5E27UAKHZFD,91.28\n2020-07-10,MA4B66MW5E27UAKHZFD,96.27\n2020-07-13,MA4B66MW5E27UAKHZFD,97.65\n2020-07-14,MA4B66MW5E27UAKHZFD,98.21\n2020-07-15,MA4B66MW5E27UAKHZFD,99.73\n2020-07-16,MA4B66MW5E27UAKHZFD,100.01\n2020-07-17,MA4B66MW5E27UAKHZFD,98.16\n2020-07-20,MA4B66MW5E27UAKHZFD,97.3\n2020-07-21,MA4B66MW5E27UAKHZFD,99.41\n2020-07-22,MA4B66MW5E27UAKHZFD,98.69\n2020-07-23,MA4B66MW5E27UAKHZFD,98.98\n2020-07-24,MA4B66MW5E27UAKHZFD,98.28\n2020-07-27,MA4B66MW5E27UAKHZFD,96.9\n2020-07-28,MA4B66MW5E27UAKHZFD,97.32\n2020-07-29,MA4B66MW5E27UAKHZFD,99.68\n2020-07-30,MA4B66MW5E27UAKHZFD,97.02\n2020-07-31,MA4B66MW5E27UAKHZFD,96.64\n2020-08-03,MA4B66MW5E27UAKHZFD,96.1\n2020-08-04,MA4B66MW5E27UAKHZFD,95.55\n2020-08-05,MA4B66MW5E27UAKHZFD,97.21\n2020-08-06,MA4B66MW5E27UAKHZFD,97.24\n2020-08-07,MA4B66MW5E27UAKHZFD,99.38\n2020-08-10,MA4B66MW5E27UAKHZFD,100.64\n2020-08-11,MA4B66MW5E27UAKHZFD,103.82\n2020-08-12,MA4B66MW5E27UAKHZFD,102.94\n2020-08-13,MA4B66MW5E27UAKHZFD,102.37\n2020-08-14,MA4B66MW5E27UAKHZFD,102.41\n2020-08-17,MA4B66MW5E27UAKHZFD,99.71\n2020-08-18,MA4B66MW5E27UAKHZFD,98.32\n2020-08-19,MA4B66MW5E27UAKHZFD,98.55\n2020-08-20,MA4B66MW5E27UAKHZFD,97.37\n2020-08-21,MA4B66MW5E27UAKHZFD,97.32\n2020-08-24,MA4B66MW5E27UAKHZFD,100.06\n2020-08-25,MA4B66MW5E27UAKHZFD,100.5\n2020-08-26,MA4B66MW5E27UAKHZFD,99.09\n2020-08-27,MA4B66MW5E27UAKHZFD,102.35\n2020-08-28,MA4B66MW5E27UAKHZFD,102.77\n2020-08-31,MA4B66MW5E27UAKHZFD,100.19\n2020-07-01,MA4B66MW5E27UAL3J7Z,92.32\n2020-07-02,MA4B66MW5E27UAL3J7Z,93.16\n2020-07-06,MA4B66MW5E27UAL3J7Z,93.21\n2020-07-07,MA4B66MW5E27UAL3J7Z,91.67\n2020-07-08,MA4B66MW5E27UAL3J7Z,91.61\n2020-07-09,MA4B66MW5E27UAL3J7Z,90.13\n2020-07-10,MA4B66MW5E27UAL3J7Z,90.81\n2020-07-13,MA4B66MW5E27UAL3J7Z,92.77\n2020-07-14,MA4B66MW5E27UAL3J7Z,94.65\n2020-07-15,MA4B66MW5E27UAL3J7Z,97.16\n2020-07-16,MA4B66MW5E27UAL3J7Z,95.75\n2020-07-17,MA4B66MW5E27UAL3J7Z,98.58\n2020-07-20,MA4B66MW5E27UAL3J7Z,95.73\n2020-07-21,MA4B66MW5E27UAL3J7Z,96.81\n2020-07-22,MA4B66MW5E27UAL3J7Z,97.49\n2020-07-23,MA4B66MW5E27UAL3J7Z,96.57\n2020-07-24,MA4B66MW5E27UAL3J7Z,96.35\n2020-07-27,MA4B66MW5E27UAL3J7Z,96.43\n2020-07-28,MA4B66MW5E27UAL3J7Z,96.35\n2020-07-29,MA4B66MW5E27UAL3J7Z,98.72\n2020-07-30,MA4B66MW5E27UAL3J7Z,96.47\n2020-07-31,MA4B66MW5E27UAL3J7Z,96.48\n2020-08-03,MA4B66MW5E27UAL3J7Z,97.33\n2020-08-04,MA4B66MW5E27UAL3J7Z,96.29\n2020-08-05,MA4B66MW5E27UAL3J7Z,97.02\n2020-08-06,MA4B66MW5E27UAL3J7Z,96.73\n2020-08-07,MA4B66MW5E27UAL3J7Z,100.05\n2020-08-10,MA4B66MW5E27UAL3J7Z,100.38\n2020-08-11,MA4B66MW5E27UAL3J7Z,100.72\n2020-08-12,MA4B66MW5E27UAL3J7Z,102.09\n2020-08-13,MA4B66MW5E27UAL3J7Z,101.29\n2020-08-14,MA4B66MW5E27UAL3J7Z,100.71\n2020-08-17,MA4B66MW5E27UAL3J7Z,101.86\n2020-08-18,MA4B66MW5E27UAL3J7Z,101.56\n2020-08-19,MA4B66MW5E27UAL3J7Z,100.82\n2020-08-20,MA4B66MW5E27UAL3J7Z,100.28\n2020-08-21,MA4B66MW5E27UAL3J7Z,98.73\n2020-08-24,MA4B66MW5E27UAL3J7Z,100.13\n2020-08-25,MA4B66MW5E27UAL3J7Z,102.59\n2020-08-26,MA4B66MW5E27UAL3J7Z,104.08\n2020-08-27,MA4B66MW5E27UAL3J7Z,106.86\n2020-08-28,MA4B66MW5E27UAL3J7Z,107.86\n2020-08-31,MA4B66MW5E27UAL3J7Z,107.47\n2020-07-01,MA4B66MW5E27UA447M6,177.99\n2020-07-02,MA4B66MW5E27UA447M6,178.83\n2020-07-06,MA4B66MW5E27UA447M6,182.72\n2020-07-07,MA4B66MW5E27UA447M6,181.15\n2020-07-08,MA4B66MW5E27UA447M6,181.2\n2020-07-09,MA4B66MW5E27UA447M6,178.8\n2020-07-10,MA4B66MW5E27UA447M6,182.9\n2020-07-13,MA4B66MW5E27UA447M6,184.13\n2020-07-14,MA4B66MW5E27UA447M6,189.71\n2020-07-15,MA4B66MW5E27UA447M6,190.56\n2020-07-16,MA4B66MW5E27UA447M6,191.01\n2020-07-17,MA4B66MW5E27UA447M6,190.76\n2020-07-20,MA4B66MW5E27UA447M6,190.48\n2020-07-21,MA4B66MW5E27UA447M6,192.27\n2020-07-22,MA4B66MW5E27UA447M6,192.1\n2020-07-23,MA4B66MW5E27UA447M6,193.25\n2020-07-24,MA4B66MW5E27UA447M6,194.52\n2020-07-27,MA4B66MW5E27UA447M6,192.14\n2020-07-28,MA4B66MW5E27UA447M6,194.12\n2020-07-29,MA4B66MW5E27UA447M6,196.41\n2020-07-30,MA4B66MW5E27UA447M6,194.3\n2020-07-31,MA4B66MW5E27UA447M6,195.78\n2020-08-03,MA4B66MW5E27UA447M6,199.26\n2020-08-04,MA4B66MW5E27UA447M6,200.24\n2020-08-05,MA4B66MW5E27UA447M6,203.62\n2020-08-06,MA4B66MW5E27UA447M6,205.02\n2020-08-07,MA4B66MW5E27UA447M6,209.48\n2020-08-10,MA4B66MW5E27UA447M6,212.58\n2020-08-11,MA4B66MW5E27UA447M6,212.66\n2020-08-12,MA4B66MW5E27UA447M6,213.24\n2020-08-13,MA4B66MW5E27UA447M6,211.98\n2020-08-14,MA4B66MW5E27UA447M6,210.96\n2020-08-17,MA4B66MW5E27UA447M6,206.78\n2020-08-18,MA4B66MW5E27UA447M6,208.33\n2020-08-19,MA4B66MW5E27UA447M6,207.21\n2020-08-20,MA4B66MW5E27UA447M6,206.12\n2020-08-21,MA4B66MW5E27UA447M6,207.43\n2020-08-24,MA4B66MW5E27UA447M6,212.61\n2020-08-25,MA4B66MW5E27UA447M6,213.35\n2020-08-26,MA4B66MW5E27UA447M6,214.66\n2020-08-27,MA4B66MW5E27UA447M6,216.87\n2020-08-28,MA4B66MW5E27UA447M6,218.55\n2020-08-31,MA4B66MW5E27UA447M6,218.04\n2020-07-01,MA4B66MW5E27UANLY9V,166.88\n2020-07-02,MA4B66MW5E27UANLY9V,169.08\n2020-07-06,MA4B66MW5E27UANLY9V,172.16\n2020-07-07,MA4B66MW5E27UANLY9V,169.78\n2020-07-08,MA4B66MW5E27UANLY9V,169.04\n2020-07-09,MA4B66MW5E27UANLY9V,165.99\n2020-07-10,MA4B66MW5E27UANLY9V,166.39\n2020-07-13,MA4B66MW5E27UANLY9V,169.17\n2020-07-14,MA4B66MW5E27UANLY9V,171.89\n2020-07-15,MA4B66MW5E27UANLY9V,174.39\n2020-07-16,MA4B66MW5E27UANLY9V,176.87\n2020-07-17,MA4B66MW5E27UANLY9V,181.25\n2020-07-20,MA4B66MW5E27UANLY9V,177.39\n2020-07-21,MA4B66MW5E27UANLY9V,178.66\n2020-07-22,MA4B66MW5E27UANLY9V,179.38\n2020-07-23,MA4B66MW5E27UANLY9V,175.0\n2020-07-24,MA4B66MW5E27UANLY9V,176.12\n2020-07-27,MA4B66MW5E27UANLY9V,176.82\n2020-07-28,MA4B66MW5E27UANLY9V,174.41\n2020-07-29,MA4B66MW5E27UANLY9V,177.98\n2020-07-30,MA4B66MW5E27UANLY9V,172.8\n2020-07-31,MA4B66MW5E27UANLY9V,173.35\n2020-08-03,MA4B66MW5E27UANLY9V,172.06\n2020-08-04,MA4B66MW5E27UANLY9V,173.68\n2020-08-05,MA4B66MW5E27UANLY9V,177.47\n2020-08-06,MA4B66MW5E27UANLY9V,179.61\n2020-08-07,MA4B66MW5E27UANLY9V,182.93\n2020-08-10,MA4B66MW5E27UANLY9V,186.85\n2020-08-11,MA4B66MW5E27UANLY9V,190.35\n2020-08-12,MA4B66MW5E27UANLY9V,191.83\n2020-08-13,MA4B66MW5E27UANLY9V,189.72\n2020-08-14,MA4B66MW5E27UANLY9V,191.92\n2020-08-17,MA4B66MW5E27UANLY9V,193.87\n2020-08-18,MA4B66MW5E27UANLY9V,190.7\n2020-08-19,MA4B66MW5E27UANLY9V,190.95\n2020-08-20,MA4B66MW5E27UANLY9V,189.92\n2020-08-21,MA4B66MW5E27UANLY9V,191.57\n2020-08-24,MA4B66MW5E27UANLY9V,192.56\n2020-08-25,MA4B66MW5E27UANLY9V,194.9\n2020-08-26,MA4B66MW5E27UANLY9V,194.32\n2020-08-27,MA4B66MW5E27UANLY9V,194.44\n2020-08-28,MA4B66MW5E27UANLY9V,195.13\n2020-08-31,MA4B66MW5E27UANLY9V,192.44\n2020-07-01,MA4B66MW5E27UALG2XD,246.26\n2020-07-02,MA4B66MW5E27UALG2XD,246.4\n2020-07-06,MA4B66MW5E27UALG2XD,246.96\n2020-07-07,MA4B66MW5E27UALG2XD,246.72\n2020-07-08,MA4B66MW5E27UALG2XD,253.27\n2020-07-09,MA4B66MW5E27UALG2XD,251.99\n2020-07-10,MA4B66MW5E27UALG2XD,259.6\n2020-07-13,MA4B66MW5E27UALG2XD,256.24\n2020-07-14,MA4B66MW5E27UALG2XD,262.55\n2020-07-15,MA4B66MW5E27UALG2XD,259.0\n2020-07-16,MA4B66MW5E27UALG2XD,268.38\n2020-07-17,MA4B66MW5E27UALG2XD,275.38\n2020-07-20,MA4B66MW5E27UALG2XD,274.33\n2020-07-21,MA4B66MW5E27UALG2XD,276.33\n2020-07-22,MA4B66MW5E27UALG2XD,280.42\n2020-07-23,MA4B66MW5E27UALG2XD,280.97\n2020-07-24,MA4B66MW5E27UALG2XD,280.25\n2020-07-27,MA4B66MW5E27UALG2XD,277.63\n2020-07-28,MA4B66MW5E27UALG2XD,280.04\n2020-07-29,MA4B66MW5E27UALG2XD,281.44\n2020-07-30,MA4B66MW5E27UALG2XD,282.34\n2020-07-31,MA4B66MW5E27UALG2XD,280.7\n2020-08-03,MA4B66MW5E27UALG2XD,277.59\n2020-08-04,MA4B66MW5E27UALG2XD,285.05\n2020-08-05,MA4B66MW5E27UALG2XD,283.78\n2020-08-06,MA4B66MW5E27UALG2XD,282.81\n2020-08-07,MA4B66MW5E27UALG2XD,287.78\n2020-08-10,MA4B66MW5E27UALG2XD,283.64\n2020-08-11,MA4B66MW5E27UALG2XD,277.79\n2020-08-12,MA4B66MW5E27UALG2XD,284.59\n2020-08-13,MA4B66MW5E27UALG2XD,284.4\n2020-08-14,MA4B66MW5E27UALG2XD,280.5\n2020-08-17,MA4B66MW5E27UALG2XD,282.99\n2020-08-18,MA4B66MW5E27UALG2XD,283.05\n2020-08-19,MA4B66MW5E27UALG2XD,282.34\n2020-08-20,MA4B66MW5E27UALG2XD,282.11\n2020-08-21,MA4B66MW5E27UALG2XD,282.41\n2020-08-24,MA4B66MW5E27UALG2XD,282.63\n2020-08-25,MA4B66MW5E27UALG2XD,281.31\n2020-08-26,MA4B66MW5E27UALG2XD,279.0\n2020-08-27,MA4B66MW5E27UALG2XD,278.88\n2020-08-28,MA4B66MW5E27UALG2XD,279.55\n2020-08-31,MA4B66MW5E27UALG2XD,279.17\n2020-07-01,MA4B66MW5E27UALG396,97.4\n2020-07-02,MA4B66MW5E27UALG396,98.43\n2020-07-06,MA4B66MW5E27UALG396,99.95\n2020-07-07,MA4B66MW5E27UALG396,97.07\n2020-07-08,MA4B66MW5E27UALG396,98.84\n2020-07-09,MA4B66MW5E27UALG396,96.99\n2020-07-10,MA4B66MW5E27UALG396,97.99\n2020-07-13,MA4B66MW5E27UALG396,96.46\n2020-07-14,MA4B66MW5E27UALG396,96.76\n2020-07-15,MA4B66MW5E27UALG396,98.54\n2020-07-16,MA4B66MW5E27UALG396,97.26\n2020-07-17,MA4B66MW5E27UALG396,96.28\n2020-07-20,MA4B66MW5E27UALG396,95.65\n2020-07-21,MA4B66MW5E27UALG396,98.36\n2020-07-22,MA4B66MW5E27UALG396,98.91\n2020-07-23,MA4B66MW5E27UALG396,98.3\n2020-07-24,MA4B66MW5E27UALG396,98.43\n2020-07-27,MA4B66MW5E27UALG396,97.21\n2020-07-28,MA4B66MW5E27UALG396,96.27\n2020-07-29,MA4B66MW5E27UALG396,96.97\n2020-07-30,MA4B66MW5E27UALG396,96.82\n2020-07-31,MA4B66MW5E27UALG396,97.61\n2020-08-03,MA4B66MW5E27UALG396,98.33\n2020-08-04,MA4B66MW5E27UALG396,97.33\n2020-08-05,MA4B66MW5E27UALG396,100.94\n2020-08-06,MA4B66MW5E27UALG396,100.45\n2020-08-07,MA4B66MW5E27UALG396,101.86\n2020-08-10,MA4B66MW5E27UALG396,105.41\n2020-08-11,MA4B66MW5E27UALG396,105.12\n2020-08-12,MA4B66MW5E27UALG396,105.22\n2020-08-13,MA4B66MW5E27UALG396,106.52\n2020-08-14,MA4B66MW5E27UALG396,106.43\n2020-08-17,MA4B66MW5E27UALG396,105.66\n2020-08-18,MA4B66MW5E27UALG396,106.97\n2020-08-19,MA4B66MW5E27UALG396,108.39\n2020-08-20,MA4B66MW5E27UALG396,108.01\n2020-08-21,MA4B66MW5E27UALG396,109.75\n2020-08-24,MA4B66MW5E27UALG396,111.83\n2020-08-25,MA4B66MW5E27UALG396,111.51\n2020-08-26,MA4B66MW5E27UALG396,111.53\n2020-08-27,MA4B66MW5E27UALG396,110.84\n2020-08-28,MA4B66MW5E27UALG396,112.29\n2020-08-31,MA4B66MW5E27UALG396,111.89\n2020-07-01,MA4B66MW5E27UAKNZKD,44.82\n2020-07-02,MA4B66MW5E27UAKNZKD,44.88\n2020-07-06,MA4B66MW5E27UAKNZKD,45.23\n2020-07-07,MA4B66MW5E27UAKNZKD,45.21\n2020-07-08,MA4B66MW5E27UAKNZKD,45.07\n2020-07-09,MA4B66MW5E27UAKNZKD,43.91\n2020-07-10,MA4B66MW5E27UAKNZKD,45.15\n2020-07-13,MA4B66MW5E27UAKNZKD,45.25\n2020-07-14,MA4B66MW5E27UAKNZKD,45.87\n2020-07-15,MA4B66MW5E27UAKNZKD,46.4\n2020-07-16,MA4B66MW5E27UAKNZKD,46.15\n2020-07-17,MA4B66MW5E27UAKNZKD,46.82\n2020-07-20,MA4B66MW5E27UAKNZKD,46.12\n2020-07-21,MA4B66MW5E27UAKNZKD,47.2\n2020-07-22,MA4B66MW5E27UAKNZKD,48.48\n2020-07-23,MA4B66MW5E27UAKNZKD,48.28\n2020-07-24,MA4B66MW5E27UAKNZKD,48.49\n2020-07-27,MA4B66MW5E27UAKNZKD,48.48\n2020-07-28,MA4B66MW5E27UAKNZKD,48.18\n2020-07-29,MA4B66MW5E27UAKNZKD,48.02\n2020-07-30,MA4B66MW5E27UAKNZKD,47.69\n2020-07-31,MA4B66MW5E27UAKNZKD,47.24\n2020-08-03,MA4B66MW5E27UAKNZKD,46.3\n2020-08-04,MA4B66MW5E27UAKNZKD,46.69\n2020-08-05,MA4B66MW5E27UAKNZKD,47.22\n2020-08-06,MA4B66MW5E27UAKNZKD,47.48\n2020-08-07,MA4B66MW5E27UAKNZKD,47.8\n2020-08-10,MA4B66MW5E27UAKNZKD,47.72\n2020-08-11,MA4B66MW5E27UAKNZKD,47.93\n2020-08-12,MA4B66MW5E27UAKNZKD,48.43\n2020-08-13,MA4B66MW5E27UAKNZKD,48.38\n2020-08-14,MA4B66MW5E27UAKNZKD,48.45\n2020-08-17,MA4B66MW5E27UAKNZKD,48.21\n2020-08-18,MA4B66MW5E27UAKNZKD,48.42\n2020-08-19,MA4B66MW5E27UAKNZKD,47.37\n2020-08-20,MA4B66MW5E27UAKNZKD,47.35\n2020-08-21,MA4B66MW5E27UAKNZKD,47.28\n2020-08-24,MA4B66MW5E27UAKNZKD,47.97\n2020-08-25,MA4B66MW5E27UAKNZKD,47.91\n2020-08-26,MA4B66MW5E27UAKNZKD,48.16\n2020-08-27,MA4B66MW5E27UAKNZKD,48.23\n2020-08-28,MA4B66MW5E27UAKNZKD,49.83\n2020-08-31,MA4B66MW5E27UAKNZKD,49.53\n2020-07-01,MA4B66MW5E27UALUL23,33.74\n2020-07-02,MA4B66MW5E27UALUL23,34.51\n2020-07-06,MA4B66MW5E27UALUL23,34.51\n2020-07-07,MA4B66MW5E27UALUL23,34.03\n2020-07-08,MA4B66MW5E27UALUL23,33.75\n2020-07-09,MA4B66MW5E27UALUL23,33.46\n2020-07-10,MA4B66MW5E27UALUL23,33.83\n2020-07-13,MA4B66MW5E27UALUL23,35.21\n2020-07-14,MA4B66MW5E27UALUL23,35.23\n2020-07-15,MA4B66MW5E27UALUL23,35.72\n2020-07-16,MA4B66MW5E27UALUL23,35.6\n2020-07-17,MA4B66MW5E27UALUL23,36.25\n2020-07-20,MA4B66MW5E27UALUL23,36.5\n2020-07-21,MA4B66MW5E27UALUL23,36.69\n2020-07-22,MA4B66MW5E27UALUL23,38.56\n2020-07-23,MA4B66MW5E27UALUL23,38.41\n2020-07-24,MA4B66MW5E27UALUL23,37.66\n2020-07-27,MA4B66MW5E27UALUL23,37.54\n2020-07-28,MA4B66MW5E27UALUL23,39.02\n2020-07-29,MA4B66MW5E27UALUL23,39.26\n2020-07-30,MA4B66MW5E27UALUL23,38.74\n2020-07-31,MA4B66MW5E27UALUL23,38.48\n2020-08-03,MA4B66MW5E27UALUL23,38.35\n2020-08-04,MA4B66MW5E27UALUL23,38.39\n2020-08-05,MA4B66MW5E27UALUL23,38.45\n2020-08-06,MA4B66MW5E27UALUL23,38.27\n2020-08-07,MA4B66MW5E27UALUL23,38.45\n2020-08-10,MA4B66MW5E27UALUL23,38.39\n2020-08-11,MA4B66MW5E27UALUL23,37.79\n2020-08-12,MA4B66MW5E27UALUL23,38.33\n2020-08-13,MA4B66MW5E27UALUL23,38.17\n2020-08-14,MA4B66MW5E27UALUL23,38.06\n2020-08-17,MA4B66MW5E27UALUL23,38.35\n2020-08-18,MA4B66MW5E27UALUL23,38.36\n2020-08-19,MA4B66MW5E27UALUL23,38.26\n2020-08-20,MA4B66MW5E27UALUL23,38.72\n2020-08-21,MA4B66MW5E27UALUL23,38.88\n2020-08-24,MA4B66MW5E27UALUL23,38.84\n2020-08-25,MA4B66MW5E27UALUL23,38.41\n2020-08-26,MA4B66MW5E27UALUL23,38.05\n2020-08-27,MA4B66MW5E27UALUL23,37.86\n2020-08-28,MA4B66MW5E27UALUL23,37.91\n2020-08-31,MA4B66MW5E27UALUL23,37.79\n2020-07-01,MA4B66MW5E27UANZH3T,43.71\n2020-07-02,MA4B66MW5E27UANZH3T,44.08\n2020-07-06,MA4B66MW5E27UANZH3T,44.39\n2020-07-07,MA4B66MW5E27UANZH3T,43.24\n2020-07-08,MA4B66MW5E27UANZH3T,43.14\n2020-07-09,MA4B66MW5E27UANZH3T,41.36\n2020-07-10,MA4B66MW5E27UANZH3T,42.65\n2020-07-13,MA4B66MW5E27UANZH3T,42.66\n2020-07-14,MA4B66MW5E27UANZH3T,44.07\n2020-07-15,MA4B66MW5E27UANZH3T,44.63\n2020-07-16,MA4B66MW5E27UANZH3T,44.28\n2020-07-17,MA4B66MW5E27UANZH3T,43.52\n2020-07-20,MA4B66MW5E27UANZH3T,42.5\n2020-07-21,MA4B66MW5E27UANZH3T,44.65\n2020-07-22,MA4B66MW5E27UANZH3T,43.61\n2020-07-23,MA4B66MW5E27UANZH3T,43.7\n2020-07-24,MA4B66MW5E27UANZH3T,43.43\n2020-07-27,MA4B66MW5E27UANZH3T,44.07\n2020-07-28,MA4B66MW5E27UANZH3T,43.55\n2020-07-29,MA4B66MW5E27UANZH3T,44.03\n2020-07-30,MA4B66MW5E27UANZH3T,41.87\n2020-07-31,MA4B66MW5E27UANZH3T,42.08\n2020-08-03,MA4B66MW5E27UANZH3T,42.25\n2020-08-04,MA4B66MW5E27UANZH3T,43.47\n2020-08-05,MA4B66MW5E27UANZH3T,43.85\n2020-08-06,MA4B66MW5E27UANZH3T,43.64\n2020-08-07,MA4B66MW5E27UANZH3T,43.44\n2020-08-10,MA4B66MW5E27UANZH3T,44.51\n2020-08-11,MA4B66MW5E27UANZH3T,44.97\n2020-08-12,MA4B66MW5E27UANZH3T,44.09\n2020-08-13,MA4B66MW5E27UANZH3T,43.01\n2020-08-14,MA4B66MW5E27UANZH3T,43.2\n2020-08-17,MA4B66MW5E27UANZH3T,42.64\n2020-08-18,MA4B66MW5E27UANZH3T,42.43\n2020-08-19,MA4B66MW5E27UANZH3T,41.96\n2020-08-20,MA4B66MW5E27UANZH3T,41.32\n2020-08-21,MA4B66MW5E27UANZH3T,41.01\n2020-08-24,MA4B66MW5E27UANZH3T,42.22\n2020-08-25,MA4B66MW5E27UANZH3T,40.88\n2020-08-26,MA4B66MW5E27UANZH3T,40.01\n2020-08-27,MA4B66MW5E27UANZH3T,39.74\n2020-08-28,MA4B66MW5E27UANZH3T,40.69\n2020-08-31,MA4B66MW5E27UANZH3T,39.94\n2020-07-01,MA4B66MW5E27UAE4MG6,113.01\n2020-07-02,MA4B66MW5E27UAE4MG6,112.18\n2020-07-06,MA4B66MW5E27UAE4MG6,114.43\n2020-07-07,MA4B66MW5E27UAE4MG6,113.63\n2020-07-08,MA4B66MW5E27UAE4MG6,116.66\n2020-07-09,MA4B66MW5E27UAE4MG6,116.81\n2020-07-10,MA4B66MW5E27UAE4MG6,119.34\n2020-07-13,MA4B66MW5E27UAE4MG6,116.22\n2020-07-14,MA4B66MW5E27UAE4MG6,118.66\n2020-07-15,MA4B66MW5E27UAE4MG6,120.9\n2020-07-16,MA4B66MW5E27UAE4MG6,119.43\n2020-07-17,MA4B66MW5E27UAE4MG6,118.65\n2020-07-20,MA4B66MW5E27UAE4MG6,117.79\n2020-07-21,MA4B66MW5E27UAE4MG6,118.62\n2020-07-22,MA4B66MW5E27UAE4MG6,119.03\n2020-07-23,MA4B66MW5E27UAE4MG6,118.12\n2020-07-24,MA4B66MW5E27UAE4MG6,117.61\n2020-07-27,MA4B66MW5E27UAE4MG6,116.31\n2020-07-28,MA4B66MW5E27UAE4MG6,116.18\n2020-07-29,MA4B66MW5E27UAE4MG6,115.61\n2020-07-30,MA4B66MW5E27UAE4MG6,115.66\n2020-07-31,MA4B66MW5E27UAE4MG6,116.94\n2020-08-03,MA4B66MW5E27UAE4MG6,116.35\n2020-08-04,MA4B66MW5E27UAE4MG6,117.29\n2020-08-05,MA4B66MW5E27UAE4MG6,127.61\n2020-08-06,MA4B66MW5E27UAE4MG6,130.82\n2020-08-07,MA4B66MW5E27UAE4MG6,129.93\n2020-08-10,MA4B66MW5E27UAE4MG6,128.79\n2020-08-11,MA4B66MW5E27UAE4MG6,130.49\n2020-08-12,MA4B66MW5E27UAE4MG6,131.79\n2020-08-13,MA4B66MW5E27UAE4MG6,130.96\n2020-08-14,MA4B66MW5E27UAE4MG6,130.53\n2020-08-17,MA4B66MW5E27UAE4MG6,129.37\n2020-08-18,MA4B66MW5E27UAE4MG6,128.92\n2020-08-19,MA4B66MW5E27UAE4MG6,127.77\n2020-08-20,MA4B66MW5E27UAE4MG6,128.12\n2020-08-21,MA4B66MW5E27UAE4MG6,127.44\n2020-08-24,MA4B66MW5E27UAE4MG6,130.69\n2020-08-25,MA4B66MW5E27UAE4MG6,129.79\n2020-08-26,MA4B66MW5E27UAE4MG6,132.18\n2020-08-27,MA4B66MW5E27UAE4MG6,133.73\n2020-08-28,MA4B66MW5E27UAE4MG6,135.54\n2020-08-31,MA4B66MW5E27UAE4MG6,131.87\n2020-07-01,MA4B66MW5E27UANZGNN,119.69\n2020-07-02,MA4B66MW5E27UANZGNN,119.21\n2020-07-06,MA4B66MW5E27UANZGNN,118.89\n2020-07-07,MA4B66MW5E27UANZGNN,126.95\n2020-07-08,MA4B66MW5E27UANZGNN,124.44\n2020-07-09,MA4B66MW5E27UANZGNN,127.75\n2020-07-10,MA4B66MW5E27UANZGNN,130.68\n2020-07-13,MA4B66MW5E27UANZGNN,129.52\n2020-07-14,MA4B66MW5E27UANZGNN,132.01\n2020-07-15,MA4B66MW5E27UANZGNN,132.0\n2020-07-16,MA4B66MW5E27UANZGNN,132.2\n2020-07-17,MA4B66MW5E27UANZGNN,131.74\n2020-07-20,MA4B66MW5E27UANZGNN,131.47\n2020-07-21,MA4B66MW5E27UANZGNN,132.33\n2020-07-22,MA4B66MW5E27UANZGNN,132.66\n2020-07-23,MA4B66MW5E27UANZGNN,131.64\n2020-07-24,MA4B66MW5E27UANZGNN,131.24\n2020-07-27,MA4B66MW5E27UANZGNN,131.21\n2020-07-28,MA4B66MW5E27UANZGNN,131.76\n2020-07-29,MA4B66MW5E27UANZGNN,130.69\n2020-07-30,MA4B66MW5E27UANZGNN,130.12\n2020-07-31,MA4B66MW5E27UANZGNN,129.4\n2020-08-03,MA4B66MW5E27UANZGNN,129.3\n2020-08-04,MA4B66MW5E27UANZGNN,131.64\n2020-08-05,MA4B66MW5E27UANZGNN,129.81\n2020-08-06,MA4B66MW5E27UANZGNN,129.35\n2020-08-07,MA4B66MW5E27UANZGNN,129.97\n2020-08-10,MA4B66MW5E27UANZGNN,131.88\n2020-08-11,MA4B66MW5E27UANZGNN,130.2\n2020-08-12,MA4B66MW5E27UANZGNN,131.89\n2020-08-13,MA4B66MW5E27UANZGNN,131.85\n2020-08-14,MA4B66MW5E27UANZGNN,132.6\n2020-08-17,MA4B66MW5E27UANZGNN,135.6\n2020-08-18,MA4B66MW5E27UANZGNN,134.71\n2020-08-19,MA4B66MW5E27UANZGNN,132.41\n2020-08-20,MA4B66MW5E27UANZGNN,130.57\n2020-08-21,MA4B66MW5E27UANZGNN,131.63\n2020-08-24,MA4B66MW5E27UANZGNN,131.33\n2020-08-25,MA4B66MW5E27UANZGNN,130.63\n2020-08-26,MA4B66MW5E27UANZGNN,130.7\n2020-08-27,MA4B66MW5E27UANZGNN,136.63\n2020-08-28,MA4B66MW5E27UANZGNN,140.3\n2020-08-31,MA4B66MW5E27UANZGNN,138.85\n2020-07-01,MA4B66MW5E27UALUL3R,119.98\n2020-07-02,MA4B66MW5E27UALUL3R,120.88\n2020-07-06,MA4B66MW5E27UALUL3R,121.63\n2020-07-07,MA4B66MW5E27UALUL3R,122.22\n2020-07-08,MA4B66MW5E27UALUL3R,122.89\n2020-07-09,MA4B66MW5E27UALUL3R,122.48\n2020-07-10,MA4B66MW5E27UALUL3R,123.89\n2020-07-13,MA4B66MW5E27UALUL3R,124.05\n2020-07-14,MA4B66MW5E27UALUL3R,125.09\n2020-07-15,MA4B66MW5E27UALUL3R,124.5\n2020-07-16,MA4B66MW5E27UALUL3R,124.76\n2020-07-17,MA4B66MW5E27UALUL3R,125.63\n2020-07-20,MA4B66MW5E27UALUL3R,125.24\n2020-07-21,MA4B66MW5E27UALUL3R,125.07\n2020-07-22,MA4B66MW5E27UALUL3R,126.14\n2020-07-23,MA4B66MW5E27UALUL3R,126.16\n2020-07-24,MA4B66MW5E27UALUL3R,125.96\n2020-07-27,MA4B66MW5E27UALUL3R,126.32\n2020-07-28,MA4B66MW5E27UALUL3R,127.88\n2020-07-29,MA4B66MW5E27UALUL3R,128.31\n2020-07-30,MA4B66MW5E27UALUL3R,131.42\n2020-07-31,MA4B66MW5E27UALUL3R,131.12\n2020-08-03,MA4B66MW5E27UALUL3R,131.29\n2020-08-04,MA4B66MW5E27UALUL3R,133.79\n2020-08-05,MA4B66MW5E27UALUL3R,133.44\n2020-08-06,MA4B66MW5E27UALUL3R,132.71\n2020-08-07,MA4B66MW5E27UALUL3R,133.55\n2020-08-10,MA4B66MW5E27UALUL3R,134.1\n2020-08-11,MA4B66MW5E27UALUL3R,133.23\n2020-08-12,MA4B66MW5E27UALUL3R,135.46\n2020-08-13,MA4B66MW5E27UALUL3R,135.78\n2020-08-14,MA4B66MW5E27UALUL3R,135.1\n2020-08-17,MA4B66MW5E27UALUL3R,135.5\n2020-08-18,MA4B66MW5E27UALUL3R,136.51\n2020-08-19,MA4B66MW5E27UALUL3R,135.77\n2020-08-20,MA4B66MW5E27UALUL3R,136.85\n2020-08-21,MA4B66MW5E27UALUL3R,137.44\n2020-08-24,MA4B66MW5E27UALUL3R,138.51\n2020-08-25,MA4B66MW5E27UALUL3R,139.06\n2020-08-26,MA4B66MW5E27UALUL3R,138.39\n2020-08-27,MA4B66MW5E27UALUL3R,138.21\n2020-08-28,MA4B66MW5E27UALUL3R,138.77\n2020-08-31,MA4B66MW5E27UALUL3R,138.33\n2020-07-01,MA4B66MW5E27UAN8F94,29.9\n2020-07-02,MA4B66MW5E27UAN8F94,30.08\n2020-07-06,MA4B66MW5E27UAN8F94,30.49\n2020-07-07,MA4B66MW5E27UAN8F94,30.32\n2020-07-08,MA4B66MW5E27UAN8F94,30.46\n2020-07-09,MA4B66MW5E27UAN8F94,29.54\n2020-07-10,MA4B66MW5E27UAN8F94,30.13\n2020-07-13,MA4B66MW5E27UAN8F94,29.76\n2020-07-14,MA4B66MW5E27UAN8F94,29.96\n2020-07-15,MA4B66MW5E27UAN8F94,30.01\n2020-07-16,MA4B66MW5E27UAN8F94,30.39\n2020-07-17,MA4B66MW5E27UAN8F94,30.25\n2020-07-20,MA4B66MW5E27UAN8F94,29.85\n2020-07-21,MA4B66MW5E27UAN8F94,30.25\n2020-07-22,MA4B66MW5E27UAN8F94,30.16\n2020-07-23,MA4B66MW5E27UAN8F94,29.9\n2020-07-24,MA4B66MW5E27UAN8F94,29.57\n2020-07-27,MA4B66MW5E27UAN8F94,29.29\n2020-07-28,MA4B66MW5E27UAN8F94,29.69\n2020-07-29,MA4B66MW5E27UAN8F94,29.56\n2020-07-30,MA4B66MW5E27UAN8F94,29.57\n2020-07-31,MA4B66MW5E27UAN8F94,29.58\n2020-08-03,MA4B66MW5E27UAN8F94,29.62\n2020-08-04,MA4B66MW5E27UAN8F94,30.01\n2020-08-05,MA4B66MW5E27UAN8F94,29.85\n2020-08-06,MA4B66MW5E27UAN8F94,29.84\n2020-08-07,MA4B66MW5E27UAN8F94,30.02\n2020-08-10,MA4B66MW5E27UAN8F94,30.2\n2020-08-11,MA4B66MW5E27UAN8F94,30.2\n2020-08-12,MA4B66MW5E27UAN8F94,30.18\n2020-08-13,MA4B66MW5E27UAN8F94,29.91\n2020-08-14,MA4B66MW5E27UAN8F94,30.01\n2020-08-17,MA4B66MW5E27UAN8F94,29.85\n2020-08-18,MA4B66MW5E27UAN8F94,29.79\n2020-08-19,MA4B66MW5E27UAN8F94,29.73\n2020-08-20,MA4B66MW5E27UAN8F94,29.67\n2020-08-21,MA4B66MW5E27UAN8F94,29.69\n2020-08-24,MA4B66MW5E27UAN8F94,30.03\n2020-08-25,MA4B66MW5E27UAN8F94,29.9\n2020-08-26,MA4B66MW5E27UAN8F94,29.99\n2020-08-27,MA4B66MW5E27UAN8F94,29.9\n2020-08-28,MA4B66MW5E27UAN8F94,30.04\n2020-08-31,MA4B66MW5E27UAN8F94,29.81\n2020-07-01,MA4B66MW5E27UANLYJN,193.78\n2020-07-02,MA4B66MW5E27UANLYJN,195.67\n2020-07-06,MA4B66MW5E27UANLYJN,197.76\n2020-07-07,MA4B66MW5E27UANLYJN,194.2\n2020-07-08,MA4B66MW5E27UANLYJN,195.07\n2020-07-09,MA4B66MW5E27UANLYJN,192.21\n2020-07-10,MA4B66MW5E27UANLYJN,192.55\n2020-07-13,MA4B66MW5E27UANLYJN,189.02\n2020-07-14,MA4B66MW5E27UANLYJN,193.33\n2020-07-15,MA4B66MW5E27UANLYJN,196.55\n2020-07-16,MA4B66MW5E27UANLYJN,193.5\n2020-07-17,MA4B66MW5E27UANLYJN,195.09\n2020-07-20,MA4B66MW5E27UANLYJN,198.47\n2020-07-21,MA4B66MW5E27UANLYJN,196.48\n2020-07-22,MA4B66MW5E27UANLYJN,198.86\n2020-07-23,MA4B66MW5E27UANLYJN,197.43\n2020-07-24,MA4B66MW5E27UANLYJN,195.15\n2020-07-27,MA4B66MW5E27UANLYJN,196.91\n2020-07-28,MA4B66MW5E27UANLYJN,196.74\n2020-07-29,MA4B66MW5E27UANLYJN,198.58\n2020-07-30,MA4B66MW5E27UANLYJN,194.06\n2020-07-31,MA4B66MW5E27UANLYJN,190.4\n2020-08-03,MA4B66MW5E27UANLYJN,190.69\n2020-08-04,MA4B66MW5E27UANLYJN,192.29\n2020-08-05,MA4B66MW5E27UANLYJN,196.1\n2020-08-06,MA4B66MW5E27UANLYJN,198.77\n2020-08-07,MA4B66MW5E27UANLYJN,196.36\n2020-08-10,MA4B66MW5E27UANLYJN,196.79\n2020-08-11,MA4B66MW5E27UANLYJN,197.77\n2020-08-12,MA4B66MW5E27UANLYJN,198.74\n2020-08-13,MA4B66MW5E27UANLYJN,197.58\n2020-08-14,MA4B66MW5E27UANLYJN,196.64\n2020-08-17,MA4B66MW5E27UANLYJN,199.43\n2020-08-18,MA4B66MW5E27UANLYJN,199.01\n2020-08-19,MA4B66MW5E27UANLYJN,200.99\n2020-08-20,MA4B66MW5E27UANLYJN,204.15\n2020-08-21,MA4B66MW5E27UANLYJN,204.13\n2020-08-24,MA4B66MW5E27UANLYJN,206.41\n2020-08-25,MA4B66MW5E27UANLYJN,208.1\n2020-08-26,MA4B66MW5E27UANLYJN,210.26\n2020-08-27,MA4B66MW5E27UANLYJN,211.03\n2020-08-28,MA4B66MW5E27UANLYJN,215.71\n2020-08-31,MA4B66MW5E27UANLYJN,211.99\n2020-07-01,MA4B66MW5E27U9VBBAX,99.14\n2020-07-02,MA4B66MW5E27U9VBBAX,98.88\n2020-07-06,MA4B66MW5E27U9VBBAX,99.01\n2020-07-07,MA4B66MW5E27U9VBBAX,99.35\n2020-07-08,MA4B66MW5E27U9VBBAX,99.28\n2020-07-09,MA4B66MW5E27U9VBBAX,97.94\n2020-07-10,MA4B66MW5E27U9VBBAX,96.83\n2020-07-13,MA4B66MW5E27U9VBBAX,97.86\n2020-07-14,MA4B66MW5E27U9VBBAX,98.87\n2020-07-15,MA4B66MW5E27U9VBBAX,100.48\n2020-07-16,MA4B66MW5E27U9VBBAX,99.92\n2020-07-17,MA4B66MW5E27U9VBBAX,100.83\n2020-07-20,MA4B66MW5E27U9VBBAX,99.54\n2020-07-21,MA4B66MW5E27U9VBBAX,97.4\n2020-07-22,MA4B66MW5E27U9VBBAX,97.7\n2020-07-23,MA4B66MW5E27U9VBBAX,98.03\n2020-07-24,MA4B66MW5E27U9VBBAX,97.11\n2020-07-27,MA4B66MW5E27U9VBBAX,97.16\n2020-07-28,MA4B66MW5E27U9VBBAX,96.71\n2020-07-29,MA4B66MW5E27U9VBBAX,97.01\n2020-07-30,MA4B66MW5E27U9VBBAX,96.04\n2020-07-31,MA4B66MW5E27U9VBBAX,94.91\n2020-08-03,MA4B66MW5E27U9VBBAX,95.94\n2020-08-04,MA4B66MW5E27U9VBBAX,94.29\n2020-08-05,MA4B66MW5E27U9VBBAX,93.25\n2020-08-06,MA4B66MW5E27U9VBBAX,92.57\n2020-08-07,MA4B66MW5E27U9VBBAX,92.92\n2020-08-10,MA4B66MW5E27U9VBBAX,92.38\n2020-08-11,MA4B66MW5E27U9VBBAX,92.67\n2020-08-12,MA4B66MW5E27U9VBBAX,95.51\n2020-08-13,MA4B66MW5E27U9VBBAX,94.68\n2020-08-14,MA4B66MW5E27U9VBBAX,95.07\n2020-08-17,MA4B66MW5E27U9VBBAX,96.35\n2020-08-18,MA4B66MW5E27U9VBBAX,95.65\n2020-08-19,MA4B66MW5E27U9VBBAX,96.1\n2020-08-20,MA4B66MW5E27U9VBBAX,95.2\n2020-08-21,MA4B66MW5E27U9VBBAX,94.86\n2020-08-24,MA4B66MW5E27U9VBBAX,94.51\n2020-08-25,MA4B66MW5E27U9VBBAX,94.06\n2020-08-26,MA4B66MW5E27U9VBBAX,94.35\n2020-08-27,MA4B66MW5E27U9VBBAX,94.3\n2020-08-28,MA4B66MW5E27U9VBBAX,94.17\n2020-08-31,MA4B66MW5E27U9VBBAX,95.77\n2020-07-01,MA4B66MW5E27U8P32SB,3115.86\n2020-07-02,MA4B66MW5E27U8P32SB,3130.01\n2020-07-06,MA4B66MW5E27U8P32SB,3179.72\n2020-07-07,MA4B66MW5E27U8P32SB,3145.32\n2020-07-08,MA4B66MW5E27U8P32SB,3169.94\n2020-07-09,MA4B66MW5E27U8P32SB,3152.05\n2020-07-10,MA4B66MW5E27U8P32SB,3185.04\n2020-07-13,MA4B66MW5E27U8P32SB,3155.22\n2020-07-14,MA4B66MW5E27U8P32SB,3197.52\n2020-07-15,MA4B66MW5E27U8P32SB,3226.56\n2020-07-16,MA4B66MW5E27U8P32SB,3215.57\n2020-07-17,MA4B66MW5E27U8P32SB,3224.73\n2020-07-20,MA4B66MW5E27U8P32SB,3251.84\n2020-07-21,MA4B66MW5E27U8P32SB,3257.3\n2020-07-22,MA4B66MW5E27U8P32SB,3276.02\n2020-07-23,MA4B66MW5E27U8P32SB,3235.66\n2020-07-24,MA4B66MW5E27U8P32SB,3215.63\n2020-07-27,MA4B66MW5E27U8P32SB,3239.41\n2020-07-28,MA4B66MW5E27U8P32SB,3218.44\n2020-07-29,MA4B66MW5E27U8P32SB,3258.44\n2020-07-30,MA4B66MW5E27U8P32SB,3246.22\n2020-07-31,MA4B66MW5E27U8P32SB,3271.12\n2020-08-03,MA4B66MW5E27U8P32SB,3294.61\n2020-08-04,MA4B66MW5E27U8P32SB,3306.51\n2020-08-05,MA4B66MW5E27U8P32SB,3327.77\n2020-08-06,MA4B66MW5E27U8P32SB,3349.16\n2020-08-07,MA4B66MW5E27U8P32SB,3351.28\n2020-08-10,MA4B66MW5E27U8P32SB,3360.47\n2020-08-11,MA4B66MW5E27U8P32SB,3333.69\n2020-08-12,MA4B66MW5E27U8P32SB,3380.35\n2020-08-13,MA4B66MW5E27U8P32SB,3373.43\n2020-08-14,MA4B66MW5E27U8P32SB,3372.85\n2020-08-17,MA4B66MW5E27U8P32SB,3381.99\n2020-08-18,MA4B66MW5E27U8P32SB,3389.78\n2020-08-19,MA4B66MW5E27U8P32SB,3374.85\n2020-08-20,MA4B66MW5E27U8P32SB,3385.51\n2020-08-21,MA4B66MW5E27U8P32SB,3397.16\n2020-08-24,MA4B66MW5E27U8P32SB,3431.28\n2020-08-25,MA4B66MW5E27U8P32SB,3443.62\n2020-08-26,MA4B66MW5E27U8P32SB,3478.73\n2020-08-27,MA4B66MW5E27U8P32SB,3484.55\n2020-08-28,MA4B66MW5E27U8P32SB,3508.01\n2020-08-31,MA4B66MW5E27U8P32SB,3500.31\n2020-07-01,MA4B66MW5E27UAHKGMA,248.15\n2020-07-02,MA4B66MW5E27UAHKGMA,248.5\n2020-07-06,MA4B66MW5E27UAHKGMA,249.55\n2020-07-07,MA4B66MW5E27UAHKGMA,247.35\n2020-07-08,MA4B66MW5E27UAHKGMA,249.17\n2020-07-09,MA4B66MW5E27UAHKGMA,247.96\n2020-07-10,MA4B66MW5E27UAHKGMA,250.11\n2020-07-13,MA4B66MW5E27UAHKGMA,249.62\n2020-07-14,MA4B66MW5E27UAHKGMA,257.79\n2020-07-15,MA4B66MW5E27UAHKGMA,257.8\n2020-07-16,MA4B66MW5E27UAHKGMA,258.08\n2020-07-17,MA4B66MW5E27UAHKGMA,260.38\n2020-07-20,MA4B66MW5E27UAHKGMA,260.17\n2020-07-21,MA4B66MW5E27UAHKGMA,262.42\n2020-07-22,MA4B66MW5E27UAHKGMA,265.17\n2020-07-23,MA4B66MW5E27UAHKGMA,263.81\n2020-07-24,MA4B66MW5E27UAHKGMA,265.31\n2020-07-27,MA4B66MW5E27UAHKGMA,267.42\n2020-07-28,MA4B66MW5E27UAHKGMA,265.28\n2020-07-29,MA4B66MW5E27UAHKGMA,264.66\n2020-07-30,MA4B66MW5E27UAHKGMA,266.31\n2020-07-31,MA4B66MW5E27UAHKGMA,265.49\n2020-08-03,MA4B66MW5E27UAHKGMA,266.18\n2020-08-04,MA4B66MW5E27UAHKGMA,267.87\n2020-08-05,MA4B66MW5E27UAHKGMA,267.48\n2020-08-06,MA4B66MW5E27UAHKGMA,269.37\n2020-08-07,MA4B66MW5E27UAHKGMA,271.64\n2020-08-10,MA4B66MW5E27UAHKGMA,274.73\n2020-08-11,MA4B66MW5E27UAHKGMA,274.92\n2020-08-12,MA4B66MW5E27UAHKGMA,281.58\n2020-08-13,MA4B66MW5E27UAHKGMA,281.66\n2020-08-14,MA4B66MW5E27UAHKGMA,280.55\n2020-08-17,MA4B66MW5E27UAHKGMA,288.24\n2020-08-18,MA4B66MW5E27UAHKGMA,285.0\n2020-08-19,MA4B66MW5E27UAHKGMA,282.86\n2020-08-20,MA4B66MW5E27UAHKGMA,280.68\n2020-08-21,MA4B66MW5E27UAHKGMA,283.23\n2020-08-24,MA4B66MW5E27UAHKGMA,286.75\n2020-08-25,MA4B66MW5E27UAHKGMA,286.13\n2020-08-26,MA4B66MW5E27UAHKGMA,291.93\n2020-08-27,MA4B66MW5E27UAHKGMA,288.63\n2020-08-28,MA4B66MW5E27UAHKGMA,286.29\n2020-08-31,MA4B66MW5E27UAHKGMA,285.04\n2020-07-01,MA4B66MW5E27UANLY8B,297.73\n2020-07-02,MA4B66MW5E27UANLY8B,298.26\n2020-07-06,MA4B66MW5E27UANLY8B,302.81\n2020-07-07,MA4B66MW5E27UANLY8B,296.65\n2020-07-08,MA4B66MW5E27UANLY8B,298.36\n2020-07-09,MA4B66MW5E27UANLY8B,291.16\n2020-07-10,MA4B66MW5E27UANLY8B,291.23\n2020-07-13,MA4B66MW5E27UANLY8B,299.71\n2020-07-14,MA4B66MW5E27UANLY8B,308.52\n2020-07-15,MA4B66MW5E27UANLY8B,304.07\n2020-07-16,MA4B66MW5E27UANLY8B,307.15\n2020-07-17,MA4B66MW5E27UANLY8B,306.53\n2020-07-20,MA4B66MW5E27UANLY8B,303.46\n2020-07-21,MA4B66MW5E27UANLY8B,305.11\n2020-07-22,MA4B66MW5E27UANLY8B,306.71\n2020-07-23,MA4B66MW5E27UANLY8B,302.97\n2020-07-24,MA4B66MW5E27UANLY8B,300.79\n2020-07-27,MA4B66MW5E27UANLY8B,298.6\n2020-07-28,MA4B66MW5E27UANLY8B,299.93\n2020-07-29,MA4B66MW5E27UANLY8B,306.68\n2020-07-30,MA4B66MW5E27UANLY8B,305.23\n2020-07-31,MA4B66MW5E27UANLY8B,302.78\n2020-08-03,MA4B66MW5E27UANLY8B,303.61\n2020-08-04,MA4B66MW5E27UANLY8B,304.5\n2020-08-05,MA4B66MW5E27UANLY8B,312.47\n2020-08-06,MA4B66MW5E27UANLY8B,314.06\n2020-08-07,MA4B66MW5E27UANLY8B,317.03\n2020-08-10,MA4B66MW5E27UANLY8B,319.1\n2020-08-11,MA4B66MW5E27UANLY8B,315.55\n2020-08-12,MA4B66MW5E27UANLY8B,322.27\n2020-08-13,MA4B66MW5E27UANLY8B,321.52\n2020-08-14,MA4B66MW5E27UANLY8B,323.7\n2020-08-17,MA4B66MW5E27UANLY8B,320.51\n2020-08-18,MA4B66MW5E27UANLY8B,316.81\n2020-08-19,MA4B66MW5E27UANLY8B,315.4\n2020-08-20,MA4B66MW5E27UANLY8B,313.33\n2020-08-21,MA4B66MW5E27UANLY8B,314.14\n2020-08-24,MA4B66MW5E27UANLY8B,308.84\n2020-08-25,MA4B66MW5E27UANLY8B,312.22\n2020-08-26,MA4B66MW5E27UANLY8B,308.82\n2020-08-27,MA4B66MW5E27UANLY8B,311.67\n2020-08-28,MA4B66MW5E27UANLY8B,314.37\n2020-08-31,MA4B66MW5E27UANLY8B,312.55\n2020-07-01,MA4B66MW5E27UAE4LQY,87.62\n2020-07-02,MA4B66MW5E27UAE4LQY,88.31\n2020-07-06,MA4B66MW5E27UAE4LQY,88.57\n2020-07-07,MA4B66MW5E27UAE4LQY,86.31\n2020-07-08,MA4B66MW5E27UAE4LQY,86.35\n2020-07-09,MA4B66MW5E27UAE4LQY,82.74\n2020-07-10,MA4B66MW5E27UAE4LQY,85.23\n2020-07-13,MA4B66MW5E27UAE4LQY,85.4\n2020-07-14,MA4B66MW5E27UAE4LQY,88.35\n2020-07-15,MA4B66MW5E27UAE4LQY,88.89\n2020-07-16,MA4B66MW5E27UAE4LQY,88.36\n2020-07-17,MA4B66MW5E27UAE4LQY,87.19\n2020-07-20,MA4B66MW5E27UAE4LQY,85.27\n2020-07-21,MA4B66MW5E27UAE4LQY,91.39\n2020-07-22,MA4B66MW5E27UAE4LQY,91.04\n2020-07-23,MA4B66MW5E27UAE4LQY,91.01\n2020-07-24,MA4B66MW5E27UAE4LQY,90.13\n2020-07-27,MA4B66MW5E27UAE4LQY,91.04\n2020-07-28,MA4B66MW5E27UAE4LQY,89.11\n2020-07-29,MA4B66MW5E27UAE4LQY,90.07\n2020-07-30,MA4B66MW5E27UAE4LQY,86.27\n2020-07-31,MA4B66MW5E27UAE4LQY,83.94\n2020-08-03,MA4B66MW5E27UAE4LQY,84.81\n2020-08-04,MA4B66MW5E27UAE4LQY,86.49\n2020-08-05,MA4B66MW5E27UAE4LQY,87.2\n2020-08-06,MA4B66MW5E27UAE4LQY,87.47\n2020-08-07,MA4B66MW5E27UAE4LQY,86.8\n2020-08-10,MA4B66MW5E27UAE4LQY,89.73\n2020-08-11,MA4B66MW5E27UAE4LQY,89.62\n2020-08-12,MA4B66MW5E27UAE4LQY,90.72\n2020-08-13,MA4B66MW5E27UAE4LQY,89.82\n2020-08-14,MA4B66MW5E27UAE4LQY,90.35\n2020-08-17,MA4B66MW5E27UAE4LQY,90.77\n2020-08-18,MA4B66MW5E27UAE4LQY,87.63\n2020-08-19,MA4B66MW5E27UAE4LQY,86.39\n2020-08-20,MA4B66MW5E27UAE4LQY,84.81\n2020-08-21,MA4B66MW5E27UAE4LQY,85.08\n2020-08-24,MA4B66MW5E27UAE4LQY,87.2\n2020-08-25,MA4B66MW5E27UAE4LQY,86.13\n2020-08-26,MA4B66MW5E27UAE4LQY,84.78\n2020-08-27,MA4B66MW5E27UAE4LQY,84.91\n2020-08-28,MA4B66MW5E27UAE4LQY,85.63\n2020-08-31,MA4B66MW5E27UAE4LQY,83.93\n2020-07-01,MA4B66MW5E27U9VBBKA,214.59\n2020-07-02,MA4B66MW5E27U9VBBKA,215.72\n2020-07-06,MA4B66MW5E27U9VBBKA,217.55\n2020-07-07,MA4B66MW5E27U9VBBKA,216.33\n2020-07-08,MA4B66MW5E27U9VBBKA,218.89\n2020-07-09,MA4B66MW5E27U9VBBKA,219.31\n2020-07-10,MA4B66MW5E27U9VBBKA,220.26\n2020-07-13,MA4B66MW5E27U9VBBKA,215.74\n2020-07-14,MA4B66MW5E27U9VBBKA,219.16\n2020-07-15,MA4B66MW5E27U9VBBKA,219.76\n2020-07-16,MA4B66MW5E27U9VBBKA,219.11\n2020-07-17,MA4B66MW5E27U9VBBKA,221.94\n2020-07-20,MA4B66MW5E27U9VBBKA,223.43\n2020-07-21,MA4B66MW5E27U9VBBKA,222.75\n2020-07-22,MA4B66MW5E27U9VBBKA,224.23\n2020-07-23,MA4B66MW5E27U9VBBKA,222.3\n2020-07-24,MA4B66MW5E27U9VBBKA,221.44\n2020-07-27,MA4B66MW5E27U9VBBKA,223.32\n2020-07-28,MA4B66MW5E27U9VBBKA,222.18\n2020-07-29,MA4B66MW5E27U9VBBKA,224.38\n2020-07-30,MA4B66MW5E27U9VBBKA,223.98\n2020-07-31,MA4B66MW5E27U9VBBKA,224.78\n2020-08-03,MA4B66MW5E27U9VBBKA,227.18\n2020-08-04,MA4B66MW5E27U9VBBKA,227.15\n2020-08-05,MA4B66MW5E27U9VBBKA,228.7\n2020-08-06,MA4B66MW5E27U9VBBKA,230.89\n2020-08-07,MA4B66MW5E27U9VBBKA,231.56\n2020-08-10,MA4B66MW5E27U9VBBKA,230.34\n2020-08-11,MA4B66MW5E27U9VBBKA,228.57\n2020-08-12,MA4B66MW5E27U9VBBKA,229.75\n2020-08-13,MA4B66MW5E27U9VBBKA,231.73\n2020-08-14,MA4B66MW5E27U9VBBKA,230.25\n2020-08-17,MA4B66MW5E27U9VBBKA,232.62\n2020-08-18,MA4B66MW5E27U9VBBKA,234.23\n2020-08-19,MA4B66MW5E27U9VBBKA,234.63\n2020-08-20,MA4B66MW5E27U9VBBKA,236.61\n2020-08-21,MA4B66MW5E27U9VBBKA,237.49\n2020-08-24,MA4B66MW5E27U9VBBKA,237.75\n2020-08-25,MA4B66MW5E27U9VBBKA,238.21\n2020-08-26,MA4B66MW5E27U9VBBKA,240.45\n2020-08-27,MA4B66MW5E27U9VBBKA,240.87\n2020-08-28,MA4B66MW5E27U9VBBKA,242.99\n2020-08-31,MA4B66MW5E27U9VBBKA,239.93\n" 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}, { "path": "gs_quant/test/resources/asset-query-USDJPY.json", "content": "{\n \"totalResults\": 1,\n \"results\": [\n {\n \"assetClass\": \"FX\",\n \"classifications\": {},\n \"createdById\": \"3549cd2c46c3468bae0d90b1f2b9e609\",\n \"createdTime\": \"2017-06-16T13:54:38.452Z\",\n \"currency\": \"JPY\",\n \"description\": \"Japan Yen / United States Dollar\",\n \"exchange\": \"\",\n \"id\": \"MATGYV0J9MPX534Z\",\n \"identifiers\": [\n {\n \"type\": \"CROSS\",\n \"value\": \"JPY/USD\"\n },\n {\n \"type\": \"SECNAME\",\n \"value\": \"JPY/USD\"\n },\n {\n \"type\": \"BID\",\n \"value\": \"USDJPY\"\n },\n {\n \"type\": \"MDAPI\",\n \"value\": \"JPY/USD\"\n }\n ],\n \"lastUpdatedById\": \"3549cd2c46c3468bae0d90b1f2b9e609\",\n \"lastUpdatedTime\": \"2019-08-20T14:07:02.45Z\",\n \"listed\": true,\n \"name\": \"JPY/USD\",\n \"ownerId\": \"3549cd2c46c3468bae0d90b1f2b9e609\",\n \"parameters\": {},\n \"rank\": 1,\n \"region\": \"Global\",\n \"shortName\": \"JPY/USD\",\n \"styles\": [],\n \"tags\": [\n \"JPYUSD\",\n \"USDJPY\",\n \"Execution\"\n ],\n \"type\": \"Cross\",\n \"xref\": {\n \"bbid\": \"USDJPY\",\n \"secName\": \"JPY/USD\",\n \"cross\": \"JPY/USD\",\n \"mdapi\": \"JPY/USD\"\n }\n }\n ]\n}" }, { "path": "gs_quant/test/resources/nobbid-xrefs.json", "content": "{\n \"xrefs\": [\n {\n \"startDate\": \"1952-01-01\",\n \"endDate\": \"2952-12-31\",\n \"identifiers\": {\n \"secName\": \"USD/JPY\",\n \"cross\": \"USD/JPY\",\n \"mdapi\": \"USD/JPY\"\n }\n }\n ]\n}" }, { "path": "gs_quant/test/resources/nobbid.json", "content": "{\n \"assetClass\": \"FX\",\n \"classifications\": {},\n \"createdById\": \"3549cd2c46c3468bae0d90b1f2b9e609\",\n \"createdTime\": \"2018-03-22T14:09:20.124Z\",\n \"currency\": \"USD\",\n \"description\": \"United States Dollar / Japan Yen\",\n \"exchange\": \"\",\n \"id\": \"nobbid\",\n \"identifiers\": [\n {\n \"type\": \"SECNAME\",\n \"value\": \"USD/JPY\"\n },\n {\n \"type\": \"MDAPI\",\n \"value\": \"USD/JPY\"\n },\n {\n \"type\": \"CROSS\",\n \"value\": \"USD/JPY\"\n }\n ],\n \"lastUpdatedById\": \"3549cd2c46c3468bae0d90b1f2b9e609\",\n \"lastUpdatedTime\": \"2019-08-15T14:09:53.942Z\",\n \"listed\": true,\n \"name\": \"USD/JPY\",\n \"ownerId\": \"3549cd2c46c3468bae0d90b1f2b9e609\",\n \"parameters\": {},\n \"rank\": 1,\n \"region\": \"Global\",\n \"shortName\": \"USD/JPY\",\n \"styles\": [],\n \"tags\": [\n \"USDJPY\",\n \"JPYUSD\"\n ],\n \"type\": \"Cross\",\n \"xref\": {\n \"secName\": \"USD/JPY\",\n \"cross\": \"USD/JPY\",\n \"mdapi\": \"USD/JPY\"\n }\n}" }, { "path": "gs_quant/test/risk/test_measures.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant import risk\nfrom gs_quant.instrument import IRSwap\nfrom gs_quant.risk import FloatWithInfo, DataFrameWithInfo, AggregationLevel\nfrom gs_quant.test.utils.mock_calc import MockCalc\n\nswap_1 = IRSwap(\"Pay\", \"5y\", \"EUR\", fixed_rate=-0.005, name=\"5y\")\n\n\ndef test_currency_params(mocker):\n price = risk.Price\n myr_price = risk.Price(currency=\"MYR\")\n\n with MockCalc(mocker):\n res1 = swap_1.calc(myr_price)\n res2 = swap_1.calc(price)\n res3 = swap_1.calc((price, myr_price))\n\n assert res1 != res2\n assert res3[price] == res2\n assert res3[myr_price] == res1\n\n\ndef test_finite_difference_params(mocker):\n nok_delta = risk.IRDelta(currency=\"NOK\")\n local_ccy_delta = risk.IRDelta(currency=\"local\")\n local_aggregated_delta = risk.IRDelta(currency=\"local\", aggregation_level=AggregationLevel.Type)\n\n with MockCalc(mocker):\n res3 = swap_1.calc(risk.IRDelta)\n res5 = swap_1.calc((risk.Price, nok_delta))\n res6 = swap_1.calc((local_ccy_delta, nok_delta))\n res7 = swap_1.calc(risk.IRDelta(aggregation_level=AggregationLevel.Asset))\n res8 = swap_1.calc((local_ccy_delta, local_aggregated_delta))\n\n assert isinstance(res5[risk.Price], FloatWithInfo)\n assert isinstance(res5[nok_delta], DataFrameWithInfo)\n assert res6[local_ccy_delta][\"value\"].size != res6[nok_delta][\"value\"].size\n assert res6[local_ccy_delta][\"value\"].size != res3[\"value\"].size\n assert res6[nok_delta][\"value\"].size != res3[\"value\"].size\n assert res7[\"mkt_asset\"].size == 2\n assert not isinstance(res8[local_ccy_delta], type(res8[local_aggregated_delta]))\n\n\ndef test_risk_measure_setters():\n base_delta = risk.IRDelta\n\n usd_delta = risk.IRDelta(currency=\"USD\")\n\n assert usd_delta.parameters is not None\n assert usd_delta.parameters.currency == \"USD\"\n\n local_usd_delta = usd_delta(local_curve=True)\n\n assert local_usd_delta.parameters.local_curve\n assert usd_delta != local_usd_delta\n\n assert risk.IRDelta.parameters is None\n assert base_delta.parameters is None\n" }, { "path": "gs_quant/test/risk/test_results.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport gs_quant.risk as risk\nimport numpy as np\nimport pytest\nfrom gs_quant.base import RiskKey\nfrom gs_quant.common import MarketDataPattern, RiskRequestParameters\nfrom gs_quant.instrument import IRSwap, IRBasisSwap, IRSwaption, FXMultiCrossBinary, FXMultiCrossBinaryLeg, CommodSwap\nfrom gs_quant.markets import HistoricalPricingContext, PricingContext, CloseMarket, MarketDataCoordinate\nfrom gs_quant.markets.portfolio import Portfolio\nfrom gs_quant.risk import MultiScenario, ResolvedInstrumentValues\nfrom gs_quant.risk import Price, RollFwd, CurveScenario, ErrorValue, DataFrameWithInfo, AggregationLevel, PnlExplain\nfrom gs_quant.risk.core import aggregate_risk, SeriesWithInfo, FloatWithInfo, StringWithInfo\nfrom gs_quant.risk.results import MultipleScenarioFuture\nfrom gs_quant.risk.results import MultipleScenarioResult\nfrom gs_quant.risk.transform import ResultWithInfoAggregator\nfrom gs_quant.test.utils.mock_calc import MockCalc\n\ncurvescen1 = CurveScenario(\n market_data_pattern=MarketDataPattern('IR', 'USD'), parallel_shift=5, name='parallel shift5bp'\n)\ncurvescen2 = CurveScenario(\n market_data_pattern=MarketDataPattern('IR', 'USD'),\n curve_shift=1,\n tenor_start=5,\n tenor_end=30,\n name='curve shift1bp',\n)\nrollfwd = RollFwd(date=dt.date(2020, 11, 3), name='roll fwd scenario')\nmultiscenario = MultiScenario(scenarios=tuple((curvescen1, curvescen2)), name='multiscenario')\n\n\ndef get_attributes(p, risks, ctx='PricingCtx1', resolve=False, no_frame=False):\n contexts = {\n 'Multiple': HistoricalPricingContext(dt.date(2020, 1, 14), dt.date(2020, 1, 15), market_data_location='LDN'),\n 'PricingCtx1': PricingContext(dt.date(2020, 1, 14), market_data_location='LDN'),\n 'Multiple2': HistoricalPricingContext(dt.date(2020, 1, 16), dt.date(2020, 1, 17), market_data_location='LDN'),\n 'PricingCtx2': PricingContext(dt.date(2020, 1, 16), market_data_location='NYC'),\n 'PricingCtx3': PricingContext(dt.date(2020, 1, 16), market_data_location='LDN'),\n 'RollFwd': rollfwd,\n 'CurveScen1': curvescen1,\n 'CurveScen2': curvescen2,\n 'MultiScen': multiscenario,\n }\n if resolve:\n p.resolve()\n if contexts.get(ctx):\n with contexts.get(ctx):\n res = p.calc(risks)\n elif ctx == 'Composite':\n with rollfwd, multiscenario:\n res = p.calc(risks)\n\n if not no_frame:\n frame = res.to_frame(None, None, None)\n return [col for col in frame.columns], res, frame\n else:\n return res\n\n\nswap_1 = IRSwap(\"Pay\", \"5y\", \"EUR\", fixed_rate=-0.005, name=\"5y\")\nswap_2 = IRSwap(\"Pay\", \"10y\", \"EUR\", fixed_rate=-0.005, name=\"10y\")\nswap_3 = IRSwap(\"Pay\", \"5y\", \"USD\", fixed_rate=-0.005, name=\"5y\")\nswap_4 = IRSwap(\"Pay\", \"10y\", \"USD\", fixed_rate=-0.005, name=\"10y\")\nswap_5 = IRSwap(\"Pay\", \"5y\", \"GBP\", fixed_rate=-0.005, name=\"5y\")\nswap_6 = IRSwap(\"Pay\", \"10y\", \"GBP\", fixed_rate=-0.005, name=\"10y\")\nswap_7 = IRSwap(\"Pay\", \"5y\", \"JPY\", fixed_rate=-0.005, name=\"5y\")\nswap_8 = IRSwap(\"Pay\", \"10y\", \"JPY\", fixed_rate=-0.005, name=\"10y\")\ncommod_swap = CommodSwap(name=\"Test\")\neur_port = Portfolio([swap_1, swap_2], name=\"EUR\")\nusd_port = Portfolio([swap_3, swap_4], name=\"USD\")\ngbp_port = Portfolio([swap_5, swap_6], name=\"GBP\")\njpy_port = Portfolio([swap_7, swap_8], name='JPY')\nport1 = Portfolio([eur_port, gbp_port], name='EURGBP')\nport2 = Portfolio([jpy_port, usd_port], name='USDJPY')\ncommod_port = Portfolio([commod_swap])\nport = Portfolio([port1, port2])\nswaption_port = Portfolio(\n [\n IRSwaption(\"Receive\", '5y', 'USD', expiration_date='2m', strike='atm', name='Swaption1'),\n IRSwaption(\"Receive\", '10y', 'USD', expiration_date='3m', strike='atm', name='Swaption2'),\n ]\n)\n\nbs = IRBasisSwap(\n termination_date=\"2y\",\n notional_currency=\"GBP\",\n notional_amount=\"$405392/bp\",\n effective_date=\"10y\",\n payer_rate_option=\"OIS\",\n receiver_frequency=\"3m\",\n name='IRBasisSwap',\n)\n\nbs_port = Portfolio([bs])\nmixed_port = Portfolio([bs_port, gbp_port])\n\nswaption_1 = IRSwaption('Pay', '5y', 'USD', expiration_date='1y', name='1y')\nswaption_2 = IRSwaption('Pay', '5y', 'USD', expiration_date='3m', name='3m')\nswaption_3 = IRSwaption('Pay', '5y', 'USD', expiration_date='6m', name='6m')\n\nswaption_port1 = Portfolio((swaption_1, swaption_2, swaption_3))\nswaption_port2 = Portfolio([swaption_1, swaption_2])\nswap_port1 = Portfolio([swaption_port1, usd_port])\nswap_port2 = Portfolio([swaption_port2, usd_port])\n# Portfolio of swaption with an unmarked correlation\nswaption_port3 = Portfolio(IRSwaption('Pay', termination_date='6m', effective_date='2y', expiration_date='3y'))\n\nmcb = Portfolio(FXMultiCrossBinary(name='mcb', legs=(FXMultiCrossBinaryLeg(),)), name='mcb_port')\n\n\ndef default_pivot_table_test(res, with_dates=''):\n port_depth = len(max(res.portfolio.all_paths, key=len))\n pivot_df = res.to_frame()\n if with_dates == 'dated':\n assert pivot_df.index.name == 'dates'\n else:\n if with_dates == 'has_bucketed':\n if res.dates:\n assert pivot_df.index.names[-3:] == ['instrument_name', 'risk_measure', 'dates']\n else:\n assert pivot_df.index.names[-2:] == ['instrument_name', 'risk_measure']\n assert pivot_df.columns.values[-1] == 'value'\n else:\n if port_depth == 1:\n assert pivot_df.index.nlevels == port_depth\n\n if len(res._multi_scen_key) > 1:\n if len(res.risk_measures) > 1:\n assert pivot_df.columns.names == ['risk_measure', 'scenario']\n else:\n assert pivot_df.columns.name == 'scenario'\n else:\n if port_depth == 1:\n assert pivot_df.columns.name == 'risk_measure'\n else:\n assert pivot_df.columns.names[-1] == 'risk_measure'\n\n\ndef price_values_test(res, f, with_dates=''):\n port_depth = len(max(res.portfolio.all_paths, key=len)) + 1 # +1 for risk measure\n if with_dates == 'dated':\n port_depth += 1 # +1 for dates\n if len(res.risk_measures) > 1:\n res_val_map = [n for r in res for n in r[Price].values] if with_dates == 'dated' else [r[Price] for r in res]\n else:\n res_val_map = [n for r in res for n in r.values] if with_dates == 'dated' else list(res)\n\n f = f.replace('N/A', np.nan)[f['risk_measure'] == risk.Price].dropna(axis='columns')\n df_val_map = f['value'].values\n f = f.drop('value', axis=1)\n\n assert all(res_val_map == df_val_map) # check if price values are correctly tabulated\n assert port_depth == f.columns.size # check if index setting is correct\n\n\ndef test_multi_scenario(mocker):\n with MockCalc(mocker):\n _, r1, f1 = get_attributes(usd_port, risk.Price, resolve=True, ctx='MultiScen')\n _, r2, f2 = get_attributes(usd_port, (risk.Price, risk.DollarPrice), resolve=True, ctx='MultiScen')\n _, r3, f3 = get_attributes(port1, (risk.IRFwdRate, risk.Price), resolve=True, ctx='MultiScen')\n _, r4, f4 = get_attributes(port1, risk.Price, resolve=True, ctx='MultiScen')\n\n default_pivot_table_test(r1)\n default_pivot_table_test(r2)\n default_pivot_table_test(r3)\n default_pivot_table_test(r4)\n\n # test slicing\n swap_res = r1[swap_3]\n swap_res_idx = r1[0]\n assert isinstance(swap_res, MultipleScenarioResult)\n assert swap_res == swap_res_idx\n\n multi_rm_and_scen_res = r3[curvescen1]\n multi_scen_res = r4[curvescen1]\n assert multi_rm_and_scen_res._multi_scen_key[0] == multi_scen_res._multi_scen_key[0] == curvescen1\n\n # test futures\n futures = r1.futures\n assert len(futures) == 2\n assert isinstance(futures[0], MultipleScenarioFuture)\n assert isinstance(futures[0].result(), MultipleScenarioResult)\n\n\ndef test_historical_multi_scenario(mocker):\n with MockCalc(mocker):\n with HistoricalPricingContext(dt.date(2020, 1, 14), dt.date(2020, 1, 15), market_data_location='LDN'):\n with multiscenario:\n res = Portfolio(swap_3).price()\n res_multi_rm = Portfolio(swap_3).calc((risk.Price, risk.IRFwdRate))\n\n default_pivot_table_test(res, with_dates='dated')\n default_pivot_table_test(res_multi_rm, with_dates='dated')\n\n # test slicing\n date_res = res[dt.date(2020, 1, 14)]\n assert all([isinstance(r, FloatWithInfo) for r in date_res.futures[0].result().values()])\n date_res_2 = res_multi_rm[swap_3][risk.Price][dt.date(2020, 1, 14)]\n assert all([isinstance(r, FloatWithInfo) for r in date_res_2.values()])\n\n scen_slice_res = res[curvescen2]\n assert all([isinstance(r, SeriesWithInfo) for r in scen_slice_res.futures[0].result().values()])\n assert isinstance(res_multi_rm[swap_3][risk.Price][curvescen2], SeriesWithInfo)\n\n # test futures\n futures = res.futures\n assert isinstance(futures[0], MultipleScenarioFuture)\n assert isinstance(futures[0].result(), MultipleScenarioResult)\n\n\ndef test_series_with_info_arithmetics(mocker):\n series_info = SeriesWithInfo([2.0, 4.0], [dt.date(2021, 4, 11), dt.date(2022, 4, 11)])\n scaled = series_info * 100\n assert isinstance(scaled, SeriesWithInfo)\n assert tuple(scaled.values) == (200.0, 400.0)\n\n\ndef test_composite_multi_scenario(mocker):\n with MockCalc(mocker):\n c, res1, _ = get_attributes(usd_port, risk.Price, resolve=True, ctx='Composite')\n c2, res2, _ = get_attributes(eur_port, (risk.Price, risk.IRFwdRate), resolve=True, ctx='Composite')\n\n assert 'scenario' in c\n assert 'scenario' in c2\n assert 'risk_measure' in c2\n assert len(res1.risk_measures) == 1\n assert len(res2.risk_measures) == 2\n\n\ndef test_one_portfolio(mocker):\n with MockCalc(mocker):\n _, r1, f1 = get_attributes(eur_port, risk.Price)\n _, r2, f2 = get_attributes(eur_port, (risk.Price, risk.DollarPrice))\n _, _, f3 = get_attributes(Portfolio(swap_1, name='swap_1'), (risk.Price, risk.DollarPrice))\n price_values_test(r1, f1)\n price_values_test(r2, f2)\n\n default_pivot_table_test(r1)\n default_pivot_table_test(r2)\n\n # test slicing\n # slice one risk measure\n sub_r1 = r1[risk.Price]\n assert sub_r1 == r1\n # slice one instrument\n sub_r2 = r2[swap_1].to_frame().values[0]\n assert all(sub_r2 == f3['value'].values)\n\n # test aggregate\n agg_r1 = r1.aggregate().to_frame()\n agg_r2 = r2.aggregate().to_frame().values[0]\n assert agg_r1 == sum(f1['value'].values)\n assert all(\n agg_r2 == [sum(f2.loc[f2['risk_measure'] == rm]['value'].values) for rm in [risk.Price, risk.DollarPrice]]\n )\n\n\ndef test_dated_risk_values(mocker):\n with MockCalc(mocker):\n _, res1, frame1 = get_attributes(port, risk.Price, 'Multiple')\n _, res2, frame2 = get_attributes(port1, (risk.DollarPrice, risk.Price), 'Multiple')\n _, res3, frame3 = get_attributes(port1, risk.Price)\n _, res4, frame4 = get_attributes(port1, (risk.DollarPrice, risk.Price))\n _, res5, frame5 = get_attributes(gbp_port, (risk.DollarPrice, risk.Price), 'Multiple')\n _, res6, frame6 = get_attributes(jpy_port, risk.Price, 'Multiple')\n\n price_values_test(res1, frame1, 'dated')\n price_values_test(res2, frame2, 'dated')\n price_values_test(res5, frame5, 'dated')\n price_values_test(res6, frame6, 'dated')\n\n default_pivot_table_test(res1, 'dated')\n default_pivot_table_test(res2, 'dated')\n default_pivot_table_test(res5, 'dated')\n default_pivot_table_test(res6, 'dated')\n\n # test slicing\n sub_res1 = res1[risk.Price]\n assert sub_res1 == res1\n\n # slice one date\n slice_date_res2 = res2[dt.date(2020, 1, 14)]\n assert all(slice_date_res2.to_frame(None, None, None) == frame4)\n slice_date_res3 = slice_date_res2[risk.Price]\n assert all(slice_date_res3.to_frame(None, None, None) == frame3)\n\n # slice dates\n slice_date_res2 = res2[[dt.date(2020, 1, 14), dt.date(2020, 1, 15)]]\n assert all(slice_date_res2.to_frame(None, None, None) == frame2)\n\n # test aggregate\n agg_res5 = res5.aggregate().to_frame(None, None, None)\n\n def filter_lambda(x):\n return (x['risk_measure'] == risk.DollarPrice) & (x['dates'] == dt.date(2020, 1, 14))\n\n manual_agg_r5 = frame5.loc[frame5.apply(filter_lambda, axis=1)]['value'].values.sum()\n filter_agg_res5 = agg_res5.loc[agg_res5.apply(filter_lambda, axis=1)]['value'].values[0]\n assert filter_agg_res5 == manual_agg_r5\n\n sub_res6 = res6.aggregate().to_frame().loc[dt.date(2020, 1, 14)].values[0]\n manual_agg_r6 = frame6.loc[frame6['dates'] == dt.date(2020, 1, 14)]['value'].values.sum()\n assert sub_res6 == manual_agg_r6\n\n\ndef test_bucketed_risks(mocker):\n with MockCalc(mocker):\n _, res1, frame1 = get_attributes(eur_port, risk.IRDelta)\n _, res2, frame2 = get_attributes(port, risk.IRDelta)\n _, res3, frame3 = get_attributes(gbp_port, risk.IRDelta)\n _, res4, frame4 = get_attributes(port1, risk.IRDelta, 'Multiple')\n _, res5, frame5 = get_attributes(bs_port, risk.IRBasis(aggregation_level=AggregationLevel.Asset))\n _, res6, frame6 = get_attributes(jpy_port, risk.IRBasis(aggregation_level=AggregationLevel.Asset), 'Multiple')\n _, res7, frame7 = get_attributes(commod_port, risk.CommodDelta, \"Multiple\")\n\n def check_depth(res, f, with_dates=''):\n temp_res = list(res)[0].drop('value', axis=1)\n port_depth = len(max(res.portfolio.all_paths, key=len)) + temp_res.columns.size\n port_depth = port_depth + 2 if with_dates == 'dated' else port_depth + 1\n f_temp = f.drop('value', axis=1)\n assert port_depth == f_temp.columns.size\n\n check_depth(res1, frame1)\n check_depth(res2, frame2)\n check_depth(res3, frame3)\n check_depth(res4, frame4, 'dated')\n check_depth(res5, frame5)\n check_depth(res6, frame6, 'dated')\n check_depth(res7, frame7, 'dated')\n\n default_pivot_table_test(res1, 'has_bucketed')\n default_pivot_table_test(res2, 'has_bucketed')\n default_pivot_table_test(res3, 'has_bucketed')\n default_pivot_table_test(res4, 'has_bucketed')\n default_pivot_table_test(res5, 'has_bucketed')\n default_pivot_table_test(res6, 'has_bucketed')\n default_pivot_table_test(res7, 'has_bucketed')\n\n # test slicing\n # slice one portfolio\n sub_res2 = res2[gbp_port]\n assert all(sub_res2.to_frame() == res1.to_frame())\n\n # slice one date\n sub_res4 = res4[dt.date(2020, 1, 14)]\n assert all(sub_res4[gbp_port].to_frame(None, None, None) == frame3)\n # slice dates\n sub_res4b = res4[[dt.date(2020, 1, 14), dt.date(2020, 1, 15)]]\n assert all(sub_res4b.to_frame(None, None, None) == frame4)\n\n # test aggregate\n agg_r1 = res1.aggregate().to_frame()\n manual_agg_f1 = frame1.loc[frame1['mkt_point'] == '5Y']['value'].sum()\n np.testing.assert_almost_equal(agg_r1.loc[agg_r1['mkt_point'] == '5Y']['value'].values[0], manual_agg_f1, 8)\n\n def filter_lambda(x):\n return (x['dates'] == dt.date(2020, 1, 14)) & (x['mkt_asset'] == 'JPY OIS/JPY-3M')\n\n agg_r6 = res6.aggregate().to_frame()\n filter_agg_r6 = agg_r6.loc[agg_r6.apply(filter_lambda, axis=1)]['value'].values[0]\n manual_agg_f6 = frame6.loc[frame6.apply(filter_lambda, axis=1)]['value'].values.sum()\n np.testing.assert_almost_equal(filter_agg_r6, manual_agg_f6, 8)\n\n assert isinstance(res7[dt.date(2020, 1, 14)].result().futures[0].result(), DataFrameWithInfo)\n assert res7[dt.date(2020, 1, 14)].to_frame()[\"mkt_type\"].iloc[0] == \"CMD NRG\"\n\n\ndef test_cashflows_risk(mocker):\n with MockCalc(mocker):\n _, _, frame1 = get_attributes(eur_port, risk.Cashflows)\n _, _, frame2 = get_attributes(port1, risk.Cashflows)\n\n assert 'payment_date' in frame1.columns.values\n assert 'payment_date' in frame2.columns.values\n\n assert np.unique(frame1.risk_measure.values)[0] == risk.Cashflows\n assert np.unique(frame2.risk_measure.values)[0] == risk.Cashflows\n\n assert 'instrument_name' in frame1.columns.values\n assert 'instrument_name' in frame2.columns.values\n assert 'portfolio_name_0' in frame2.columns.values\n\n\ndef test_nested_portfolio(mocker):\n with MockCalc(mocker):\n cols1, res1, frame1 = get_attributes(port1, (risk.DollarPrice, risk.Price))\n cols2, res2, frame2 = get_attributes(port, (risk.DollarPrice, risk.Price))\n _, swap1_6_res, frame3 = get_attributes(Portfolio((swap_1, swap_6), name='swap_1_6'), risk.DollarPrice)\n _, res4, frame4 = get_attributes(port1, (risk.DollarPrice, risk.Price, risk.Theta))\n\n price_values_test(res1, frame1)\n price_values_test(res2, frame2)\n dollar_eur_frame1 = frame1[(frame1['portfolio_name_0'] == 'EUR') & (frame1['risk_measure'] == risk.DollarPrice)][\n 'value'\n ].values\n dollar_eur_frame2 = frame2[\n (frame2['portfolio_name_0'] == 'EURGBP')\n & (frame2['portfolio_name_1'] == 'EUR')\n & (frame2['risk_measure'] == risk.DollarPrice)\n ]['value'].values\n\n default_pivot_table_test(res1)\n default_pivot_table_test(res2)\n\n # test slicing\n # slice multiple instruments\n slice_res2 = res1[[swap_1, swap_6]][risk.DollarPrice].to_frame(None, None, None)['value'].values\n assert all(slice_res2 == swap1_6_res.to_frame(None, None, None)['value'].values)\n\n sub_frame1 = res1[risk.DollarPrice][swap_1].to_frame()\n assert sub_frame1 == dollar_eur_frame1[0]\n assert sub_frame1 == dollar_eur_frame2[0]\n\n sub_frame2 = res2[eur_port][risk.DollarPrice].to_frame(None, None, None)['value'].values\n assert all(dollar_eur_frame1 == sub_frame2)\n assert all(dollar_eur_frame2 == sub_frame2)\n # slice multiple risk measures\n sub_res4 = res4[[risk.Price, risk.DollarPrice]]\n assert all(sub_res4.to_frame() == res1.to_frame())\n\n\ndef test_diff_types_risk_measures(mocker):\n # when risk results from scalar and bucketed risk measures are be to tabulated together\n with MockCalc(mocker):\n _, res1, frame1 = get_attributes(eur_port, (risk.Price, risk.IRDelta))\n _, res2, frame2 = get_attributes(mixed_port, (risk.IRBasis, risk.Price))\n _, res3, frame3 = get_attributes(\n mixed_port, (risk.IRBasis(aggregation_level=AggregationLevel.Asset), risk.Price)\n )\n _, res4, frame4 = get_attributes(eur_port, (risk.IRDelta, risk.Price), 'Multiple')\n _, res5, frame5 = get_attributes(mixed_port, (risk.Price, risk.IRBasis), 'Multiple')\n _, res6, frame6 = get_attributes(\n mixed_port, (risk.IRBasis(aggregation_level=AggregationLevel.Asset), risk.Price), 'Multiple'\n )\n\n price_values_test(res1, frame1)\n price_values_test(res2, frame2)\n price_values_test(res3, frame3)\n price_values_test(res4, frame4, 'dated')\n price_values_test(res5, frame5, 'dated')\n price_values_test(res6, frame6, 'dated')\n\n default_pivot_table_test(res1, 'has_bucketed')\n default_pivot_table_test(res2, 'has_bucketed')\n default_pivot_table_test(res3, 'has_bucketed')\n default_pivot_table_test(res4, 'has_bucketed')\n default_pivot_table_test(res5, 'has_bucketed')\n default_pivot_table_test(res6, 'has_bucketed')\n\n # test aggregate\n sub_res1 = res1.aggregate().to_frame()\n assert all(sub_res1.loc[risk.IRDelta]['value'].values == res1[risk.IRDelta].aggregate().to_frame()['value'].values)\n assert sub_res1.loc[risk.Price]['value'] == res1[risk.Price].aggregate().to_frame()\n\n\ndef test_empty_calc_request(mocker):\n # when calc req is sent for rm that inst is insensitive to\n with MockCalc(mocker):\n _, r1, f1 = get_attributes(swap_port1, (risk.IRVega, risk.Price))\n _, r2, f2 = get_attributes(swap_port2, (risk.IRVega(aggregation_level=AggregationLevel.Asset), risk.Price))\n _, r3, f3 = get_attributes(\n swap_port1, (risk.IRVega(aggregation_level=AggregationLevel.Asset, currency='local'), risk.Price)\n )\n _, r4, f4 = get_attributes(swap_port2, (risk.Price, risk.IRVega(currency='local')))\n _, r5, f5 = get_attributes(swap_port2, (risk.Price, risk.IRVega), 'Multiple')\n _, r6, f6 = get_attributes(\n swap_port1, (risk.IRVega(aggregation_level=AggregationLevel.Asset), risk.Price), 'Multiple'\n )\n _, r7, f7 = get_attributes(\n swap_port2,\n (risk.IRVega(aggregation_level=AggregationLevel.Asset, currency='local'), risk.Price),\n 'Multiple',\n )\n _, r8, f8 = get_attributes(swap_port1, (risk.IRVega(currency='local'), risk.Price), 'Multiple')\n\n price_values_test(r1, f1)\n price_values_test(r2, f2)\n price_values_test(r3, f3)\n price_values_test(r4, f4)\n price_values_test(r5, f5, 'dated')\n price_values_test(r6, f6, 'dated')\n price_values_test(r7, f7, 'dated')\n price_values_test(r8, f8, 'dated')\n\n default_pivot_table_test(r1, 'has_bucketed')\n default_pivot_table_test(r2, 'has_bucketed')\n default_pivot_table_test(r3, 'has_bucketed')\n default_pivot_table_test(r4, 'has_bucketed')\n default_pivot_table_test(r5, 'has_bucketed')\n default_pivot_table_test(r6, 'has_bucketed')\n default_pivot_table_test(r7, 'has_bucketed')\n default_pivot_table_test(r8, 'has_bucketed')\n\n\ndef test_adding_risk_results(mocker):\n with MockCalc(mocker):\n result1 = get_attributes(eur_port, risk.Price, no_frame=True)\n result2 = get_attributes(\n eur_port,\n (risk.IRDelta(aggregation_level=AggregationLevel.Asset, currency='local'), risk.Price),\n no_frame=True,\n )\n result3 = get_attributes(\n swaption_port,\n (risk.IRDelta(aggregation_level=AggregationLevel.Asset, currency='local'), risk.Price),\n no_frame=True,\n )\n result4 = get_attributes(port1, risk.Price, no_frame=True)\n result5 = get_attributes(\n swaption_port,\n risk.IRVega(\n aggregation_level=AggregationLevel.Asset,\n ),\n no_frame=True,\n )\n result6 = get_attributes(jpy_port, (risk.Price,), 'RollFwd', no_frame=True)\n result7 = get_attributes(jpy_port, risk.Price, 'CurveScen1', no_frame=True)\n result8 = get_attributes(jpy_port, (risk.DollarPrice, risk.Price), 'CurveScen2', no_frame=True)\n\n # (2020, 1, 14) to (2020, 1, 15)\n result9 = get_attributes(port1, risk.Price, 'Multiple', no_frame=True)\n # (2020, 1, 16) to (2020, 1, 17)\n result10 = get_attributes(port1, risk.Price, 'Multiple2', no_frame=True)\n # (2020, 1, 14)\n result11 = get_attributes(port1, risk.Price, no_frame=True)\n # (2020, 1, 16), market_data_location='NYC'\n result12 = get_attributes(port1, risk.Price, 'PricingCtx2', no_frame=True)\n # (2020, 1, 16), market_data_location='LDN'\n result13 = get_attributes(port1, risk.Price, 'PricingCtx3', no_frame=True)\n\n # adding results with same portfolio but different risk measures\n add_1 = result3 + result5\n # adding results with different portfolio but same risk measures\n add_2 = result2 + result3\n # adding results with different portfolios and overlapping risk measures\n add_3 = result1 + result3\n # adding results with different portfolios and different risk measures\n add_4 = result1 + result5\n add_5 = result3 + result4\n\n # adding dates\n\n add_6 = result9 + result13\n add_7 = result11 + result13\n add_8 = result10 + result11\n add_9 = result9 + result10\n\n default_pivot_table_test(add_1, 'has_bucketed')\n default_pivot_table_test(add_2, 'has_bucketed')\n default_pivot_table_test(add_3, 'has_bucketed')\n default_pivot_table_test(add_4, 'has_bucketed')\n default_pivot_table_test(add_5, 'has_bucketed')\n default_pivot_table_test(add_6, 'dated')\n default_pivot_table_test(add_7, 'dated')\n default_pivot_table_test(add_8, 'dated')\n default_pivot_table_test(add_9, 'dated')\n\n # throw value error when adding results where at least one particular value is being calculated twice\n\n # adding results with same portfolio but overlapping risk measures\n with pytest.raises(ValueError):\n _ = result1 + result2\n\n # adding results with overlapping portfolios and different risk measures\n with pytest.raises(ValueError):\n _ = result2 + result4\n\n # adding results with overlapping portfolios and same risk measures\n with pytest.raises(ValueError):\n _ = result1 + result4\n\n # adding results with different scenarios\n with pytest.raises(ValueError):\n _ = result6 + result7\n with pytest.raises(ValueError):\n _ = result7 + result8\n\n # overlapping dates\n with pytest.raises(ValueError):\n _ = result9 + result11\n with pytest.raises(ValueError):\n _ = result10 + result13\n\n # adding results with different market locations\n with pytest.raises(ValueError):\n _ = result10 + result12\n with pytest.raises(ValueError):\n _ = result9 + result12\n with pytest.raises(ValueError):\n _ = result12 + result13\n with pytest.raises(ValueError):\n _ = result11 + result12\n\n\ndef test_unsupported_error_datums(mocker):\n with MockCalc(mocker):\n f1 = eur_port.calc(risk.IRAnnualImpliedVol).to_frame()\n _, _, f2 = get_attributes(swap_port1, risk.IRAnnualImpliedVol)\n _, _, f3 = get_attributes(swaption_port1, risk.IRAnnualImpliedVol)\n _, _, f4 = get_attributes(swaption_port3, risk.IRAnnualImpliedVol)\n\n # assert that unsupported datums do not appear to_frame()\n assert f1 is None\n assert all(f2['value'] == f3['value'])\n\n # assert that errorvalue appears in to_frame()\n assert isinstance(f4['value'].values[0], ErrorValue)\n\n\ndef test_resolution_of_error_trade(mocker):\n with MockCalc(mocker):\n error_trade = IRSwap(notional_currency='EUR', termination_date='10y', fixed_rate='bob')\n resolved_trade = error_trade.calc(ResolvedInstrumentValues)\n assert isinstance(resolved_trade, ErrorValue)\n\n try:\n _ = resolved_trade.fixed_rate # this should fail\n assert 1 == 2\n except AttributeError as e:\n assert 'Error was' in str(e)\n\n\ndef test_resolve_to_frame(mocker):\n # makes sure resolving portfolio doesn't break to_frame\n with MockCalc(mocker):\n _, r1, f1 = get_attributes(eur_port, risk.Price, resolve=True)\n _, r2, f2 = get_attributes(port1, risk.Price, resolve=True)\n _, r3, f3 = get_attributes(jpy_port, risk.Price, 'RollFwd', resolve=True)\n _, r4, f4 = get_attributes(port1, risk.Price, 'CurveScen1', resolve=True)\n\n\ndef test_unnamed_portfolio(mocker):\n unnamed_1 = Portfolio((swap_1, swap_2))\n unnamed_2 = Portfolio((swap_3, swap_4))\n unnamed = Portfolio((unnamed_1, unnamed_2))\n with MockCalc(mocker):\n res = unnamed.calc(risk.IRFwdRate)\n df = res.to_frame()\n assert len(df) == 2\n assert list(df.index) == ['Portfolio_0', 'Portfolio_1']\n assert list(df.columns) == ['5y', '10y']\n\n\ndef test_leg_valuations(mocker):\n with MockCalc(mocker):\n # children legs return values\n _, r1, f1 = get_attributes(mcb, risk.FXSpot)\n\n assert isinstance(r1.futures[0].result(), DataFrameWithInfo)\n assert 'path' in f1.columns\n\n\ndef test_aggregation_with_heterogeous_types(mocker):\n with MockCalc(mocker):\n portfolio1 = Portfolio([IRSwaption('Pay', '10y', 'EUR', expiration_date='3m', name='EUR3m10ypayer')])\n portfolio2 = Portfolio([IRSwaption('Pay', '10y', 'EUR', expiration_date='6m', name='EUR6m10ypayer')])\n\n with PricingContext(csa_term='EUR-OIS', visible_to_gs=True):\n r1 = portfolio1.price()\n with PricingContext(csa_term='EUR-EuroSTR'):\n r2 = portfolio2.price()\n\n combined_result = r1 + r2\n\n with pytest.raises(ValueError):\n combined_result.aggregate()\n\n assert isinstance(combined_result.aggregate(allow_mismatch_risk_keys=True), float)\n\n\ndef test_aggregation_with_empty_measures(mocker):\n with MockCalc(mocker):\n swaptions = (\n IRSwaption(\n notional_currency='EUR',\n termination_date='7y',\n expiration_date='1y',\n pay_or_receive='Receive',\n strike='ATM+35',\n name='EUR 1y7y',\n ),\n IRSwaption(\n notional_currency='EUR',\n termination_date='10y',\n expiration_date='2w',\n pay_or_receive='Receive',\n strike='ATM+50',\n name='EUR 2w10y',\n ),\n )\n portfolio = Portfolio(swaptions)\n\n from_date = dt.date(2021, 11, 18)\n to_date = dt.date(2021, 11, 19)\n explain_2d = PnlExplain(CloseMarket(date=to_date))\n\n with PricingContext(pricing_date=from_date, visible_to_gs=True):\n portfolio.resolve()\n result_explain = portfolio.calc(explain_2d)\n\n total_risk = aggregate_risk(result_explain[explain_2d])['value'].sum()\n risk_swaption_1 = result_explain[0]['value'].sum()\n risk_swaption_2 = result_explain[1]['value'].sum()\n\n assert total_risk == risk_swaption_1 + risk_swaption_2\n\n\ndef test_filter_risk(mocker):\n with MockCalc(mocker):\n result = swap_1.calc(risk.IRDelta)\n\n coord = MarketDataCoordinate.from_string('IR_EUR_SWAP_5Y')\n\n df = result.filter_by_coord(coord)\n assert len(result) > 1\n assert len(df) == 1\n\n\ndef test_transformation(mocker):\n with MockCalc(mocker):\n ladder_res = usd_port.calc(risk.IRDelta)\n\n transformed_res = ladder_res.transform(ResultWithInfoAggregator())\n np.testing.assert_almost_equal(transformed_res.aggregate(), ladder_res.to_frame()['value'].sum())\n\n\ndef test_aggregation_with_identical_trades(mocker):\n with MockCalc(mocker):\n swaptions = (\n IRSwaption(\n notional_currency='EUR',\n termination_date='7y',\n expiration_date='1y',\n pay_or_receive='Receive',\n strike='ATM+35',\n name='trade_1',\n ),\n IRSwaption(\n notional_currency='EUR',\n termination_date='7y',\n expiration_date='1y',\n pay_or_receive='Receive',\n strike='ATM+35',\n name='trade_2',\n ),\n )\n portfolio = Portfolio(swaptions)\n\n delta = portfolio.calc(risk.IRDelta)\n transformed_res = delta.transform(ResultWithInfoAggregator())\n np.testing.assert_almost_equal(transformed_res.aggregate(), delta.to_frame()['value'].sum())\n\n\ndef test_scalar_with_info_on_instrument():\n # Historically there was a problem with setting risk results that were a scalar with info on an instrument\n # This was because of how copy.deepcopy would try and pickle/unpickle the class. This test checks that we can set\n # properties and still to_dict and _to_json the class\n risk_key = RiskKey(\"provider\", \"the_date\", \"mkt\", RiskRequestParameters(), None, None)\n fwi = FloatWithInfo(\n risk_key,\n 1.56,\n )\n swi = StringWithInfo(risk_key, 'USD')\n\n swap = IRSwap(floating_rate_option=swi, fixed_rate=fwi)\n swap_dict = swap.to_dict()\n\n assert swap_dict[\"floatingRateOption\"] == \"USD\"\n assert swap_dict[\"fixedRate\"] == 1.56\n\n assert swap.to_json() is not None\n\n\ndef test_display_unit():\n unit = {'A': 1, 'B': -1}\n risk_key = RiskKey(\"provider\", \"the_date\", \"mkt\", RiskRequestParameters(), None, None)\n value = 1.0\n float_with_unit = FloatWithInfo(risk_key, value, unit)\n assert float_with_unit.__repr__() == '1.0 (A/B)'\n\n unit = {'A': 1, 'B': -2}\n float_with_unit = FloatWithInfo(risk_key, value, unit)\n assert float_with_unit.__repr__() == '1.0 (A/B^2)'\n\n unit = {'A': 2}\n float_with_unit = FloatWithInfo(risk_key, value, unit)\n assert float_with_unit.__repr__() == '1.0 (A^2)'\n\n assert str(float_with_unit) == '1.0'\n" }, { "path": "gs_quant/test/test_base.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom dataclasses import field, dataclass\nfrom enum import Enum\nfrom typing import Union, Tuple, Optional\n\nimport gs_quant.base as base\nfrom gs_quant.base import handle_camel_case_args, Base, EnumBase\nfrom gs_quant.instrument.core import Security\n\n\nclass TestEnum(EnumBase, Enum):\n Enum_1 = 'Enum_1'\n Enum_2 = 'Enum_2'\n\n\n@handle_camel_case_args\n@dataclass\nclass BaseSubclass(Base):\n instance_attr: str = field(default=None)\n attr_1: Optional[str] = field(default=None)\n attr_2: Tuple[Union[float, str], ...] = field(default=())\n attr_3: Union[Tuple[Optional[str], ...], str] = field(default=None)\n attr_4: Tuple[int] = field(default=None)\n attr_5: Tuple[int, str] = field(default=None)\n attr_6: TestEnum = field(default=None)\n\n\ndef test_handle_camel_case_args():\n # Handling camelcase args on init\n obj = BaseSubclass(instanceAttr=\"test\")\n assert obj.instance_attr == \"test\"\n\n\ndef test_base_getter():\n obj = BaseSubclass(instance_attr=\"test\")\n\n # Handling camelcase getter\n assert obj.instanceAttr == \"test\"\n\n\ndef test_base_setter():\n obj = BaseSubclass()\n\n # Handling camelcase setter\n obj.instanceAttr = \"test\"\n assert obj.instance_attr == \"test\"\n\n\ndef test_setter_coercion():\n base._is_supported_generic_cache = {}\n obj = BaseSubclass(\n instance_attr='test',\n attr_1=None,\n attr_2=('test', 1.0, 1),\n attr_3='test',\n attr_4=(1,),\n attr_5=(3, 'test'),\n attr_6=TestEnum.Enum_1,\n )\n obj.attr_1 = 'test'\n # str with type hint str is unchanged\n obj.attr_1 = 'Enum_1'\n assert isinstance(obj.attr_1, str)\n # Enum with type hint str is unchanged\n obj.attr_1 = TestEnum.Enum_1\n assert isinstance(obj.attr_1, TestEnum)\n obj.attr_2 = (1, 2, 3)\n obj.attr_3 = (None, 'test', None)\n obj.attr_4 = (1, 1, 1)\n obj.attr_5 = (0, 'test')\n # Enum with type hint Enum is unchanged\n obj.attr_6 = TestEnum.Enum_2\n assert isinstance(obj.attr_6, TestEnum)\n # all handled as type matches, we do not get to the generic coercion/default case\n assert not base._is_supported_generic_cache\n # str with type hint Enum gets cast to Enum\n obj.attr_6 = 'Enum_1'\n assert isinstance(obj.attr_6, TestEnum)\n assert TestEnum in base._is_supported_generic_cache\n\n\ndef test_security_from_dict():\n # Input dictionary\n input_dict = {\n '$type': 'LegDefinition',\n 'bbid': 'TYU5 Comdty',\n 'calculationTime': 13379,\n 'properties': {'buysell': 'Buy', 'identifier': 'TYU5 Comdty', 'identifiertype': 'BloombergID', 'quantity': 1.0},\n 'queueingTime': 157,\n }\n\n # Construct Security object\n security = Security.from_dict(input_dict)\n\n # Assertions\n assert security.bbid == 'TYU5 Comdty'\n" }, { "path": "gs_quant/test/test_session.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport pickle\n\nfrom gs_quant.session import GsSession, Environment\n\n\ndef test_session_pickle():\n session = GsSession.get(Environment.PROD, 'fake_client_id', 'fake_secret')\n pk = pickle.dumps(session)\n unpk = pickle.loads(pk)\n assert unpk is not None\n" }, { "path": "gs_quant/test/timeseries/__init__.py", "content": "" }, { "path": "gs_quant/test/timeseries/multi_measure/__init__.py", "content": "" }, { "path": "gs_quant/test/timeseries/multi_measure/test_commod.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport pandas as pd\nimport pytest\nfrom gs_quant.timeseries import USE_DISPLAY_NAME\nfrom pandas.testing import assert_series_equal\nfrom testfixtures import Replacer\n\nfrom gs_quant.api.gs.data import MarketDataResponseFrame\nfrom gs_quant.data import DataContext\nfrom gs_quant.errors import MqError\nfrom gs_quant.markets.securities import CommodityNaturalGasHub, Cross\nfrom gs_quant import timeseries as tm\n\n_test_datasets = ('TEST_DATASET',)\n\n\n@pytest.mark.skipif(not USE_DISPLAY_NAME, reason=\"requires certain evnvar to run\")\ndef test_forward_price():\n # Tests for US NG assets\n def mock_natgas_forward_price(_cls, _q, ignore_errors=False):\n d = {\n 'forwardPrice': [\n 2.880,\n 2.844,\n 2.726,\n ],\n 'contract': [\n \"F21\",\n \"G21\",\n \"H21\",\n ],\n }\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 3))\n df.dataset_ids = _test_datasets\n return df\n\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_natgas_forward_price)\n mock = CommodityNaturalGasHub('MA001', 'AGT')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = pd.Series(tm.forward_price(mock, price_method='GDD', contract_range='F21'))\n expected = pd.Series([2.880], index=[dt.date(2019, 1, 2)], name='price')\n assert_series_equal(expected, actual)\n\n with pytest.raises(MqError):\n tm.forward_price(Cross('MA002', 'USD/EUR'), price_method='GDD', contract_range='F21')\n\n replace.restore()\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_commod.py\"])\n" }, { "path": "gs_quant/test/timeseries/multi_measure/test_measure_registry.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport inspect\nfrom typing import Callable\n\nimport pytest\n\nfrom gs_quant.timeseries.helper import USE_DISPLAY_NAME\nfrom gs_quant.timeseries.measure_registry import MultiMeasure\n\n\n@pytest.mark.skipif(not USE_DISPLAY_NAME, reason=\"requires certain evnvar to run\")\ndef test_registry():\n from gs_quant.timeseries.measure_registry import registry\n\n assert len(registry) > 0\n for name, mm in registry.items():\n assert name == mm.display_name, 'registry key {} does not match with MultiMeasure display_name {}'.format(\n name, mm.display_name\n )\n for cls, fns in mm.measure_map.items():\n types = [t for fn in fns if fn.asset_type is not None for t in fn.asset_type]\n assert len(set(types)) == len(types), (\n 'duplicate measures are defined for the same asset type in class ' + cls.value\n )\n\n fn_types_excluded = [fn.asset_type_excluded for fn in fns if fn.asset_type_excluded is not None]\n assert len(fn_types_excluded) <= 1, (\n 'more than one measure with asset_type_excluded is defined for asset class ' + cls.value\n )\n types_excluded = [t for types_excluded in fn_types_excluded for t in types_excluded]\n assert len(types_excluded) == 0 or set(types).issubset(set(types_excluded)), (\n 'the asset type scope overlaps in class ' + cls.value\n )\n\n\ndef test_no_duplicate_plot_measure_function_names():\n # The 'plot_measure' decorator re-defines functions as 'MultiMeasures'\n import gs_quant.timeseries as timeseries\n\n members: list[tuple[str, MultiMeasure]] = inspect.getmembers(timeseries, lambda o: isinstance(o, MultiMeasure))\n\n fns: set[Callable] = {\n fn for (_, multi_measure) in members for fns in multi_measure.measure_map.values() for fn in fns\n }\n\n fn_name_count: dict[str, int] = {}\n for fn in fns:\n fn_name_count[fn.__name__] = fn_name_count.get(fn.__name__, 0) + 1\n\n dups: dict[str, int] = {fn_name: count for (fn_name, count) in fn_name_count.items() if count > 1}\n\n assert len(dups) == 0, (\n f\"The decorated functions' names should be unique! The following function names appeared more than once: {dups}.\"\n )\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_measure_registry.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_algebra.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport math\n\nimport pytest\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nfrom pandas.testing import assert_series_equal\n\nfrom gs_quant.errors import MqValueError, MqError\nfrom gs_quant.timeseries import (\n algebra,\n Interpolate,\n filter_,\n FilterOperator,\n smooth_spikes,\n ThresholdType,\n repeat,\n and_,\n or_,\n not_,\n if_,\n weighted_sum,\n geometrically_aggregate,\n)\n\n\ndef test_add():\n dates1 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n dates2 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 1.0, 1.0, 1.0], index=dates1)\n y = pd.Series([1.0, 1.0, 1.0], index=dates2)\n\n result = algebra.add(x, y, Interpolate.INTERSECT)\n expected = pd.Series([2.0, 2.0, 2.0], index=dates2)\n assert_series_equal(result, expected, obj=\"Add intersect left\")\n\n result = algebra.add(y, x, Interpolate.INTERSECT)\n expected = pd.Series([2.0, 2.0, 2.0], index=dates2)\n assert_series_equal(result, expected, obj=\"Add intersect right\")\n\n result = algebra.add(x, y, Interpolate.NAN)\n expected = pd.Series([2.0, 2.0, 2.0, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Add NaN left\")\n\n result = algebra.add(y, x, Interpolate.NAN)\n expected = pd.Series([2.0, 2.0, 2.0, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Add NaN right\")\n\n result = algebra.add(x, y, Interpolate.ZERO)\n expected = pd.Series([2.0, 2.0, 2.0, 1.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Add zero left\")\n\n result = algebra.add(x, y, Interpolate.STEP)\n expected = pd.Series([2.0, 2.0, 2.0, 2.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Add step right\")\n\n result = algebra.add(x, 1)\n expected = pd.Series([2.0, 2.0, 2.0, 2.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Add scalar left\")\n\n result = algebra.add(1, x)\n expected = pd.Series([2.0, 2.0, 2.0, 2.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Add scalar right\")\n\n assert algebra.add(1, 2) == 3\n\n\ndef test_subtract():\n dates1 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n dates2 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 1.0, 1.0, 1.0], index=dates1)\n y = pd.Series([1.0, 1.0, 1.0], index=dates2)\n\n result = algebra.subtract(x, y, Interpolate.INTERSECT)\n expected = pd.Series([0.0, 0.0, 0.0], index=dates2)\n assert_series_equal(result, expected, obj=\"Subtract intersect left\")\n\n result = algebra.subtract(y, x, Interpolate.INTERSECT)\n expected = pd.Series([0.0, 0.0, 0.0], index=dates2)\n assert_series_equal(result, expected, obj=\"Subtract intersect right\")\n\n result = algebra.subtract(x, y, Interpolate.NAN)\n expected = pd.Series([0.0, 0.0, 0.0, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Subtract NaN left\")\n\n result = algebra.subtract(y, x, Interpolate.NAN)\n expected = pd.Series([0.0, 0.0, 0.0, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Subtract NaN right\")\n\n result = algebra.subtract(x, y, Interpolate.ZERO)\n expected = pd.Series([0.0, 0.0, 0.0, 1.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Subtract zero left\")\n\n result = algebra.subtract(x, y, Interpolate.STEP)\n expected = pd.Series([0.0, 0.0, 0.0, 0.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Subtract step right\")\n\n result = algebra.subtract(x, 1)\n expected = pd.Series([0.0, 0.0, 0.0, 0.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Subtract scalar left\")\n\n result = algebra.subtract(1, x)\n expected = pd.Series([0.0, 0.0, 0.0, 0.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Subtract scalar right\")\n\n assert algebra.subtract(1, 2) == -1\n\n\ndef test_multiply():\n dates1 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n dates2 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates1)\n y = pd.Series([2.0, 1.5, 2.0], index=dates2)\n\n result = algebra.multiply(x, y, Interpolate.INTERSECT)\n expected = pd.Series([2.0, 3.0, 6.0], index=dates2)\n assert_series_equal(result, expected, obj=\"Multiply intersect left\")\n\n result = algebra.multiply(y, x, Interpolate.INTERSECT)\n expected = pd.Series([2.0, 3.0, 6.0], index=dates2)\n assert_series_equal(result, expected, obj=\"Multiply intersect right\")\n\n result = algebra.multiply(x, y, Interpolate.NAN)\n expected = pd.Series([2.0, 3.0, 6.0, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Multiply NaN left\")\n\n result = algebra.multiply(y, x, Interpolate.NAN)\n expected = pd.Series([2.0, 3.0, 6.0, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Multiply NaN right\")\n\n result = algebra.multiply(x, y, Interpolate.ZERO)\n expected = pd.Series([2.0, 3.0, 6.0, 0.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Multiply zero left\")\n\n result = algebra.multiply(x, y, Interpolate.STEP)\n expected = pd.Series([2.0, 3.0, 6.0, 8.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Multiply step left\")\n\n result = algebra.multiply(x, 2.0)\n expected = pd.Series([2.0, 4.0, 6.0, 8.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Multiply scalar left\")\n\n result = algebra.multiply(2.0, x)\n expected = pd.Series([2.0, 4.0, 6.0, 8.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Multiply scalar right\")\n\n assert algebra.multiply(1, 2) == 2\n\n\ndef test_divide():\n dates1 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n dates2 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates1)\n y = pd.Series([2.0, 1.0, 2.0], index=dates2)\n\n result = algebra.divide(x, y, Interpolate.INTERSECT)\n expected = pd.Series([0.5, 2.0, 1.5], index=dates2)\n assert_series_equal(result, expected, obj=\"Divide intersect left\")\n\n result = algebra.divide(y, x, Interpolate.INTERSECT)\n expected = pd.Series([2.0, 0.5, 2 / 3], index=dates2)\n assert_series_equal(result, expected, obj=\"Divide intersect right\")\n\n result = algebra.divide(x, y, Interpolate.NAN)\n expected = pd.Series([0.5, 2.0, 1.5, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Divide NaN left\")\n\n result = algebra.divide(y, x, Interpolate.NAN)\n expected = pd.Series([2.0, 0.5, 2 / 3, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Divide NaN right\")\n\n result = algebra.divide(x, y, Interpolate.ZERO)\n expected = pd.Series([0.5, 2.0, 1.5, np.inf], index=dates1)\n assert_series_equal(result, expected, obj=\"Divide zero left\")\n\n result = algebra.divide(x, y, Interpolate.STEP)\n expected = pd.Series([0.5, 2.0, 1.5, 2.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Divide step left\")\n\n result = algebra.divide(x, 2)\n expected = pd.Series([0.5, 1.0, 1.5, 2.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Divide scalar left\")\n\n result = algebra.divide(2, x)\n expected = pd.Series([2.0, 1.0, 2 / 3, 0.5], index=dates1)\n assert_series_equal(result, expected, obj=\"Divide scalar right\")\n\n assert algebra.divide(1, 2) == 0.5\n\n with pytest.raises(ZeroDivisionError):\n algebra.divide(1, 0)\n\n\ndef test_floordiv():\n dates1 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n dates2 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates1)\n y = pd.Series([2.0, 1.0, 2.0], index=dates2)\n\n result = algebra.floordiv(x, y, Interpolate.INTERSECT)\n expected = pd.Series([0.0, 2.0, 1.0], index=dates2)\n assert_series_equal(result, expected, obj=\"Floor divide intersect left\")\n\n result = algebra.floordiv(y, x, Interpolate.INTERSECT)\n expected = pd.Series([2.0, 0.0, 0.0], index=dates2)\n assert_series_equal(result, expected, obj=\"Floor divide intersect right\")\n\n result = algebra.floordiv(x, y, Interpolate.NAN)\n expected = pd.Series([0.0, 2.0, 1.0, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Floor divide NaN left\")\n\n result = algebra.floordiv(y, x, Interpolate.NAN)\n expected = pd.Series([2.0, 0.0, 0.0, np.nan], index=dates1)\n assert_series_equal(result, expected, obj=\"Floor divide NaN right\")\n\n result = algebra.floordiv(x, y, Interpolate.ZERO)\n expected = pd.Series([0.0, 2.0, 1.0, np.floor_divide(1.0, 0.0)], index=dates1)\n assert_series_equal(result, expected, obj=\"Floor divide zero left\")\n\n result = algebra.floordiv(x, y, Interpolate.STEP)\n expected = pd.Series([0.0, 2.0, 1.0, 2.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Floor divide step left\")\n\n result = algebra.floordiv(x, 2, Interpolate.STEP)\n expected = pd.Series([0.0, 1.0, 1.0, 2.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Floor divide scalar left\")\n\n result = algebra.floordiv(2, x, Interpolate.STEP)\n expected = pd.Series([2.0, 1.0, 0.0, 0.0], index=dates1)\n assert_series_equal(result, expected, obj=\"Floor divide scalar right\")\n\n assert algebra.floordiv(3, 2) == 1.0\n\n with pytest.raises(ZeroDivisionError):\n algebra.floordiv(1, 0)\n\n\ndef test_exp():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0], index=dates)\n\n result = algebra.exp(x)\n expected = pd.Series([np.exp(1), np.exp(2), np.exp(3)], index=dates)\n assert_series_equal(result, expected, obj=\"Exp\")\n\n\ndef test_log():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0], index=dates)\n\n result = algebra.log(x)\n expected = pd.Series([np.log(1), np.log(2), np.log(3)], index=dates)\n assert_series_equal(result, expected, obj=\"Log\")\n\n\ndef test_power():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0], index=dates)\n\n result = algebra.power(x, 2)\n expected = pd.Series([1.0, 4.0, 9.0], index=dates)\n assert_series_equal(result, expected, obj=\"Pow\")\n\n\ndef test_sqrt():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 4.0, 9.0], index=dates)\n\n result = algebra.sqrt(x)\n expected = pd.Series([1.0, 2.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Sqrt\")\n\n actual = algebra.sqrt(9)\n assert isinstance(actual, int)\n assert 3 == actual\n\n assert math.sqrt(10) == algebra.sqrt(10)\n\n\ndef test_abs():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([-1.0, 2.0, -3.0], index=dates)\n\n result = algebra.abs_(x)\n expected = pd.Series([1.0, 2.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Abs\")\n\n\ndef test_floor():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0], index=dates)\n\n result = algebra.floor(x, 2.0)\n expected = pd.Series([2.0, 2.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Floor\")\n\n\ndef test_ceil():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0], index=dates)\n\n result = algebra.ceil(x, 2.0)\n expected = pd.Series([1.0, 2.0, 2.0], index=dates)\n assert_series_equal(result, expected, obj=\"Ceil\")\n\n\ndef test_filter():\n dates1 = [dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3), dt.date(2019, 1, 4)]\n\n all_pos = pd.Series([1.0, 1.0, 1.0, 1.0], index=dates1)\n with_null = pd.Series([1.0, np.nan, 1.0, 1.0], index=dates1)\n zero_neg_pos = pd.Series([-1.0, 0.0, 10.0, 1.0], index=dates1)\n\n result = filter_(all_pos)\n expected = all_pos\n assert_series_equal(result, expected, obj=\"zap: remove nulls when no nulls are in TS\")\n\n result = filter_(all_pos, FilterOperator.EQUALS, 0)\n expected = all_pos\n assert_series_equal(result, expected, obj=\"zap: remove 0s when no 0s are in TS\")\n\n result = filter_(with_null)\n expected = pd.Series([1.0, 1.0, 1.0], index=[dt.date(2019, 1, 1), dt.date(2019, 1, 3), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove nulls in TS\")\n\n result = filter_(zero_neg_pos, FilterOperator.EQUALS, 0)\n expected = pd.Series([-1.0, 10.0, 1.0], index=[dt.date(2019, 1, 1), dt.date(2019, 1, 3), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove 0s in TS\")\n\n result = filter_(zero_neg_pos, FilterOperator.GREATER, 0)\n expected = pd.Series([-1.0, 0.0], index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2)])\n assert_series_equal(result, expected, obj=\"zap: remove positive values in TS\")\n\n result = filter_(zero_neg_pos, FilterOperator.LESS, 0)\n expected = pd.Series([0.0, 10.0, 1.0], index=[dt.date(2019, 1, 2), dt.date(2019, 1, 3), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove negative values in TS\")\n\n result = filter_(zero_neg_pos, FilterOperator.L_EQUALS, 0)\n expected = pd.Series([10.0, 1.0], index=[dt.date(2019, 1, 3), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove values less than or eq to 0 in TS\")\n\n result = filter_(zero_neg_pos, FilterOperator.G_EQUALS, 0)\n expected = pd.Series([-1.0], index=[dt.date(2019, 1, 1)])\n assert_series_equal(result, expected, obj=\"zap: remove values greater than or eq to 0 in TS\")\n\n result = filter_(zero_neg_pos, FilterOperator.N_EQUALS, 0)\n expected = pd.Series([0.0], index=[dt.date(2019, 1, 2)])\n assert_series_equal(result, expected, obj=\"zap: remove all values but 0 in TS\")\n\n with pytest.raises(MqValueError):\n filter_(zero_neg_pos, 0, 0)\n with pytest.raises(MqValueError):\n filter_(zero_neg_pos, 0)\n\n\ndef test_filter_dates():\n dates = [dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3), dt.date(2019, 1, 4)]\n\n all_pos = pd.Series([1.0, 1.0, 1.0, 1.0], index=dates)\n with_null = pd.Series([1.0, np.nan, 1.0, 1.0], index=dates)\n\n result = algebra.filter_dates(all_pos)\n expected = all_pos\n assert_series_equal(result, expected, obj=\"zap: remove nulls when no nulls are in TS\")\n\n result = algebra.filter_dates(with_null)\n expected = pd.Series([1.0, 1.0, 1.0], index=[dt.date(2019, 1, 1), dt.date(2019, 1, 3), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove nulls in TS\")\n\n result = algebra.filter_dates(all_pos, FilterOperator.EQUALS, dt.date(2019, 1, 2))\n expected = pd.Series([1.0, 1.0, 1.0], index=[dt.date(2019, 1, 1), dt.date(2019, 1, 3), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove date in TS\")\n\n result = algebra.filter_dates(all_pos, FilterOperator.EQUALS, [dt.date(2019, 1, 2), dt.date(2019, 1, 4)])\n expected = pd.Series([1.0, 1.0], index=[dt.date(2019, 1, 1), dt.date(2019, 1, 3)])\n assert_series_equal(result, expected, obj=\"zap: remove dates in TS\")\n\n result = algebra.filter_dates(all_pos, FilterOperator.GREATER, dt.date(2019, 1, 2))\n expected = pd.Series([1.0, 1.0], index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2)])\n assert_series_equal(result, expected, obj=\"zap: remove dates after certain date in TS\")\n\n result = algebra.filter_dates(all_pos, FilterOperator.LESS, dt.date(2019, 1, 3))\n expected = pd.Series([1.0, 1.0], index=[dt.date(2019, 1, 3), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove dates before certain date in TS\")\n\n result = algebra.filter_dates(all_pos, FilterOperator.L_EQUALS, dt.date(2019, 1, 2))\n expected = pd.Series([1.0, 1.0], index=[dt.date(2019, 1, 3), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove dates on or before certain date in TS\")\n\n result = algebra.filter_dates(all_pos, FilterOperator.G_EQUALS, dt.date(2019, 1, 3))\n expected = pd.Series([1.0, 1.0], index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2)])\n assert_series_equal(result, expected, obj=\"zap: remove dates on or after certain date in TS\")\n\n result = algebra.filter_dates(all_pos, FilterOperator.N_EQUALS, dt.date(2019, 1, 2))\n expected = pd.Series([1.0], index=[dt.date(2019, 1, 2)])\n assert_series_equal(result, expected, obj=\"zap: remove all dates other than certain date in TS\")\n\n result = algebra.filter_dates(all_pos, FilterOperator.N_EQUALS, [dt.date(2019, 1, 2), dt.date(2019, 1, 4)])\n expected = pd.Series([1.0, 1.0], index=[dt.date(2019, 1, 2), dt.date(2019, 1, 4)])\n assert_series_equal(result, expected, obj=\"zap: remove all dates other than certain dates in TS\")\n\n with pytest.raises(MqValueError):\n algebra.filter_dates(all_pos, 0, 0)\n with pytest.raises(MqValueError):\n algebra.filter_dates(all_pos, 0)\n with pytest.raises(MqValueError):\n algebra.filter_dates(all_pos, FilterOperator.GREATER, [dt.date(2019, 1, 2), dt.date(2019, 1, 4)])\n\n\ndef test_smooth_spikes():\n s = pd.Series([1, 3])\n actual = smooth_spikes(s, 0.5)\n assert actual.empty\n\n sparse_index = pd.to_datetime(['2020-01-01', '2020-01-02', '2020-01-04', '2020-01-07'])\n s = pd.Series([8, 10.0, 8, 6.4], index=sparse_index)\n actual = smooth_spikes(s, 0.25)\n expected = pd.Series([10.0, 8], index=sparse_index[1:3])\n assert_series_equal(actual, expected)\n\n s = pd.Series([8, 10.1, 8, 6.4], index=sparse_index)\n actual = smooth_spikes(s, 0.25)\n expected = pd.Series([8.0, 8], index=sparse_index[1:3])\n assert_series_equal(actual, expected)\n\n s = pd.Series([0.1, 1.5, 0.2, 2], index=sparse_index)\n actual = smooth_spikes(s, threshold=1, threshold_type=ThresholdType.absolute)\n expected = pd.Series([0.15, 1.75], index=sparse_index[1:3])\n assert_series_equal(actual, expected)\n\n s = pd.Series([10.1, 8, 12, 10], index=sparse_index)\n actual = smooth_spikes(s, 1, ThresholdType.absolute)\n expected = pd.Series([11.05, 9], index=sparse_index[1:3])\n assert_series_equal(actual, expected)\n\n s = pd.Series([1, 2, 3, 4], index=sparse_index)\n actual = smooth_spikes(s, 0.25, ThresholdType.absolute)\n expected = pd.Series([2.0, 3.0], index=sparse_index[1:3])\n assert_series_equal(actual, expected)\n\n s = pd.Series([1, 3, 2, 4], index=sparse_index)\n actual = smooth_spikes(s, 0.5, ThresholdType.absolute)\n expected = pd.Series([1.5, 3.5], index=sparse_index[1:3])\n assert_series_equal(actual, expected)\n\n\ndef test_repeat():\n with pytest.raises(MqError):\n repeat(pd.Series, 0)\n with pytest.raises(MqError):\n repeat(pd.Series, 367)\n\n sparse_index = pd.to_datetime(['2020-01-01', '2020-01-02', '2020-01-04', '2020-01-07'])\n s = pd.Series([1, 2, 3, 4], index=sparse_index)\n\n actual = repeat(s)\n expected = pd.Series([1, 2, 2, 3, 3, 3, 4], index=pd.date_range(start='2020-01-01', end='2020-01-07', freq='D'))\n assert_series_equal(actual, expected)\n\n actual = repeat(s, 2)\n expected = pd.Series([1, 2, 3, 4], index=pd.date_range(start='2020-01-01', end='2020-01-07', freq='2D'))\n assert_series_equal(actual, expected)\n\n\ndef test_and():\n with pytest.raises(MqError):\n and_()\n with pytest.raises(MqError):\n and_(pd.Series(dtype=float))\n with pytest.raises(MqError):\n and_(pd.Series(dtype=float), 1)\n with pytest.raises(MqError):\n and_(pd.Series([2]), pd.Series(dtype=float))\n assert and_(pd.Series(dtype=float), pd.Series(dtype=float)).shape[0] == 0\n\n a = pd.Series([0, 0, 0, 0, 1, 1, 1, 1])\n b = pd.Series([0, 0, 1, 1, 0, 0, 1, 1])\n c = pd.Series([0, 1, 0, 1, 0, 1, 0, 1])\n assert_series_equal(and_(a, b), pd.Series([0] * 6 + [1] * 2), check_dtype=False)\n assert_series_equal(and_(a, b, c), pd.Series([0] * 7 + [1]), check_dtype=False)\n assert_series_equal(and_(pd.Series([0, 1]), pd.Series(dtype=float)), pd.Series([0] * 2), check_dtype=False)\n\n\ndef test_or():\n with pytest.raises(MqError):\n or_()\n with pytest.raises(MqError):\n or_(pd.Series(dtype=float))\n with pytest.raises(MqError):\n or_(pd.Series(dtype=float), 1)\n with pytest.raises(MqError):\n or_(pd.Series([2]), pd.Series(dtype=float))\n assert or_(pd.Series(dtype=float), pd.Series(dtype=float)).shape[0] == 0\n\n a = pd.Series([0, 0, 0, 0, 1, 1, 1, 1])\n b = pd.Series([0, 0, 1, 1, 0, 0, 1, 1])\n c = pd.Series([0, 1, 0, 1, 0, 1, 0, 1])\n assert_series_equal(or_(a, b), pd.Series([0] * 2 + [1] * 6), check_dtype=False)\n assert_series_equal(or_(a, b, c), pd.Series([0] + [1] * 7), check_dtype=False)\n assert_series_equal(or_(pd.Series([0, 1]), pd.Series(dtype=float)), pd.Series([0, 1]), check_dtype=False)\n\n\ndef test_not():\n with pytest.raises(MqError):\n not_(pd.Series([2]))\n assert not_(pd.Series(dtype=float)).shape[0] == 0\n assert_series_equal(not_(pd.Series([0, 1])), pd.Series([1, 0]), check_dtype=False)\n\n\ndef test_if():\n with pytest.raises(MqError):\n if_(pd.Series([-1, 0]), 5, 6)\n with pytest.raises(MqError):\n if_(pd.Series([1, 0]), 5, '6')\n\n flags = pd.Series([0, 1])\n truths = pd.Series([2, 2])\n\n assert_series_equal(if_(flags, 2, 3), pd.Series([3, 2]))\n assert_series_equal(if_(flags, truths, pd.Series([3, 3])), pd.Series([3, 2]))\n assert_series_equal(if_(flags, truths, pd.Series([3], index=[100])), pd.Series([np.nan, 2]), check_dtype=False)\n\n\ndef test_weighted_average():\n empty = pd.Series(dtype=float)\n with pytest.raises(MqError):\n weighted_sum([empty, 3], [0.4, 0.6])\n with pytest.raises(MqError):\n weighted_sum([empty, empty], [0.4, '.6'])\n with pytest.raises(MqError):\n weighted_sum([empty, empty], [0.4])\n\n a = pd.Series([1, 2, 3, 4], index=pd.date_range('2020-01-01', periods=4, freq='D'))\n b = pd.Series([24, 27, 30], index=(pd.date_range('2020-01-01', periods=3, freq='D')))\n actual = weighted_sum([a, b], [0.3, 0.6])\n expected = pd.Series([16.333333, 18.666666, 21], index=pd.date_range('2020-01-01', periods=3))\n expected.index.freq = None\n assert_series_equal(actual, expected)\n\n\ndef test_geometrically_aggregate():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n ]\n\n x = pd.Series([None, 0.05, 0.04, -0.03, 0.12], index=dates)\n\n result = geometrically_aggregate(x)\n expected = pd.Series([None, 0.05, 0.09200000000000008, 0.05923999999999996, 0.18634879999999998], index=dates)\n assert_series_equal(result, expected)\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_algebra.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_analysis.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.timeseries import first, last, last_value, count, Interpolate, compare, diff, lag, LagMode, repeat\nfrom gs_quant.timeseries.helper import FREQ_SECOND\nfrom pandas.testing import assert_series_equal\n\n\ndef _normalize_index(idx):\n return idx.as_unit('ns') if hasattr(idx, 'as_unit') else idx\n\n\ndef test_first():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = first(x)\n expected = pd.Series([1.0, 1.0, 1.0, 1.0], index=dates)\n assert_series_equal(result, expected, obj=\"First\")\n\n\ndef test_last():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = last(x)\n expected = pd.Series([4.0, 4.0, 4.0, 4.0], index=dates)\n assert_series_equal(result, expected, obj=\"First\")\n\n y = pd.Series([1.0, 2.0, 3.0, np.nan], index=dates)\n result = last(y)\n expected = pd.Series([3.0, 3.0, 3.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Last non-NA\")\n\n\ndef test_last_value():\n with pytest.raises(MqValueError):\n last_value(pd.Series(dtype=float))\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=(pd.date_range(\"2020-01-01\", periods=4, freq=\"D\")))\n assert last_value(x) == 4.0\n\n y = pd.Series([5])\n assert last_value(y) == 5\n\n y = pd.Series([1.0, 2.0, 3.0, np.nan], index=(pd.date_range(\"2020-01-01\", periods=4, freq=\"D\")))\n assert last_value(y) == 3.0\n\n\ndef test_count():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = count(x)\n expected = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n assert_series_equal(result, expected, obj=\"Count\")\n\n\ndef test_compare():\n dates1 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n dates2 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n ]\n\n x = pd.Series([1.0, 2.0, 2.0, 4.0], index=dates1)\n y = pd.Series([2.0, 1.0, 2.0], index=dates2)\n\n expected = pd.Series([-1.0, 1.0, 0.0], index=dates2)\n result = compare(x, y, method=Interpolate.INTERSECT)\n assert_series_equal(expected, result, obj=\"Compare series intersect\")\n\n expected = pd.Series([1.0, -1.0, 0], index=dates2)\n result = compare(y, x, method=Interpolate.INTERSECT)\n assert_series_equal(expected, result, obj=\"Compare series intersect 2\")\n\n expected = pd.Series([-1.0, 1.0, 0, 0], index=dates1)\n result = compare(x, y, method=Interpolate.NAN)\n assert_series_equal(expected, result, obj=\"Compare series nan\")\n\n expected = pd.Series([-1.0, 1.0, 0, 1.0], index=dates1)\n result = compare(x, y, method=Interpolate.ZERO)\n assert_series_equal(expected, result, obj=\"Compare series zero\")\n\n expected = pd.Series([-1.0, 1.0, 0, 1.0], index=dates1)\n result = compare(x, y, method=Interpolate.STEP)\n assert_series_equal(expected, result, obj=\"Compare series step\")\n\n dates2 = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 6),\n ]\n\n dates1.append(dt.date(2019, 1, 5))\n xp = pd.Series([1, 2, 3, 4, 5], index=pd.to_datetime(dates1))\n yp = pd.Series([1, 4, 0], index=pd.to_datetime(dates2))\n result = compare(xp, yp, Interpolate.TIME)\n dates1.append(dt.date(2019, 1, 6))\n expected = pd.Series([0.0, 1.0, 1.0, 0.0, 1.0, 0.0], index=pd.to_datetime(dates1))\n assert_series_equal(result, expected, obj=\"Compare series greater time\")\n\n\ndef test_diff():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = diff(x)\n expected = pd.Series([np.nan, 1.0, 1.0, 1.0], index=dates)\n assert_series_equal(result, expected, obj=\"Diff\")\n\n result = diff(x, 2)\n expected = pd.Series([np.nan, np.nan, 2.0, 2.0], index=dates)\n assert_series_equal(result, expected, obj=\"Diff\")\n\n empty = pd.Series(dtype=float)\n result = diff(empty)\n assert len(result) == 0\n\n\ndef test_lag():\n dates = pd.date_range(\"2019-01-01\", periods=4, freq=\"D\")\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = lag(x, '1m')\n expected = pd.Series([1.0, 2.0, 3.0, 4.0], index=_normalize_index(pd.date_range(\"2019-01-31\", periods=4, freq=\"D\")))\n expected.index.freq = None\n assert_series_equal(result, expected, obj=\"Lag 1m\")\n\n result = lag(x, '2d', LagMode.TRUNCATE)\n expected = pd.Series([1.0, 2.0], index=_normalize_index(pd.date_range(\"2019-01-03\", periods=2, freq=\"D\")))\n expected.index.freq = None\n assert_series_equal(result, expected, obj=\"Lag 2d truncate\")\n\n result = lag(x, mode=LagMode.TRUNCATE)\n expected = pd.Series([np.nan, 1.0, 2.0, 3.0], index=dates)\n expected.index.freq = None\n assert_series_equal(result, expected, obj=\"Lag\")\n\n result = lag(x, 2, LagMode.TRUNCATE)\n expected = pd.Series([np.nan, np.nan, 1.0, 2.0], index=dates)\n expected.index.freq = None\n assert_series_equal(result, expected, obj=\"Lag 2\")\n\n result = lag(x, 2, LagMode.EXTEND)\n expected = pd.Series(\n [np.nan, np.nan, 1.0, 2.0, 3.0, 4.0], index=_normalize_index(pd.date_range(\"2019-01-01\", periods=6, freq=\"D\"))\n )\n assert_series_equal(result, expected, obj=\"Lag 2 Extend\")\n\n result = lag(x, -2, LagMode.EXTEND)\n expected = pd.Series(\n [1.0, 2.0, 3.0, 4.0, np.nan, np.nan], index=_normalize_index(pd.date_range(\"2018-12-30\", periods=6, freq=\"D\"))\n )\n assert_series_equal(result, expected, obj=\"Lag Negative 2 Extend\")\n\n result = lag(x, 2)\n expected = pd.Series(\n [np.nan, np.nan, 1.0, 2.0, 3.0, 4.0], index=_normalize_index(pd.date_range(\"2019-01-01\", periods=6, freq=\"D\"))\n )\n assert_series_equal(result, expected, obj=\"Lag 2 Default\")\n\n y = pd.Series([0] * 4, index=pd.date_range('2020-01-01T00:00:00Z', periods=4, freq=FREQ_SECOND))\n with pytest.raises(Exception):\n lag(y, 5, LagMode.EXTEND)\n\n z = pd.Series([10, 11, 12], index=pd.date_range('2020-02-28', periods=3, freq='D'))\n result = lag(z, '2y')\n expected = pd.Series([10, 12], index=_normalize_index(pd.date_range('2022-02-28', periods=2, freq='D')))\n expected.index.freq = None\n assert_series_equal(result, expected, obj=\"Lag RDate 2y\")\n\n # Test that business day offsets raise an error\n with pytest.raises(MqValueError, match=\"Business day offset '1b' is not supported\"):\n lag(x, '1b')\n\n with pytest.raises(MqValueError, match=\"Business day offset '-2B' is not supported\"):\n lag(x, '-2B')\n\n\ndef test_repeat_empty_series():\n # Test case for an empty series\n empty_series = pd.Series(dtype=float)\n result = repeat(empty_series)\n assert result.empty, \"The result should be an empty series when input is empty.\"\n\n\ndef test_lag_empty_series():\n empty_series = pd.Series(dtype=float)\n result = lag(empty_series)\n assert result.empty, \"The result should be an empty series when input is empty.\"\n" }, { "path": "gs_quant/test/timeseries/test_backtesting.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas.testing import assert_series_equal\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nfrom gs_quant.timeseries import VolReference\nfrom gs_quant.timeseries.backtesting import Basket, basket_series, MqValueError, MqTypeError, RebalFreq, DataContext\n\n\ndef test_basket_series():\n dates = [\n dt.datetime(2019, 1, 1),\n dt.datetime(2019, 1, 2),\n dt.datetime(2019, 1, 3),\n dt.datetime(2019, 1, 4),\n dt.datetime(2019, 1, 5),\n dt.datetime(2019, 1, 6),\n ]\n\n x = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=dates)\n y = pd.Series([100.0, 100, 100, 100, 100, 100], index=dates)\n\n assert_series_equal(x, basket_series([x], [1]))\n assert_series_equal(x, basket_series([x, x], [0.5, 0.5]))\n assert_series_equal(x, basket_series([x, x, x], [1 / 3, 1 / 3, 1 / 3]))\n assert_series_equal(x, basket_series([x, y], [1, 0]))\n assert_series_equal(y, basket_series([x, y], [0, 1]))\n with pytest.raises(MqValueError):\n basket_series([x, y], [1])\n with pytest.raises(MqTypeError):\n basket_series([1, 2, 3], [1])\n\n dates = [\n dt.datetime(2019, 1, 1),\n dt.datetime(2019, 1, 2),\n dt.datetime(2019, 1, 3),\n dt.datetime(2019, 1, 4),\n dt.datetime(2019, 1, 5),\n dt.datetime(2019, 1, 6),\n dt.datetime(2019, 2, 1),\n dt.datetime(2019, 2, 2),\n dt.datetime(2019, 2, 3),\n dt.datetime(2019, 2, 4),\n dt.datetime(2019, 2, 5),\n dt.datetime(2019, 2, 6),\n ]\n mreb = pd.Series(\n [100.0, 101, 103.02, 100.9596, 100.9596, 102.978792, 100.0, 101, 103.02, 100.9596, 100.9596, 102.978792],\n index=dates,\n )\n assert_series_equal(mreb, basket_series([mreb], [1], rebal_freq=RebalFreq.MONTHLY))\n\n dates = [\n dt.datetime(2019, 1, 1),\n dt.datetime(2019, 1, 2),\n dt.datetime(2019, 1, 3),\n dt.datetime(2019, 1, 4),\n dt.datetime(2019, 1, 5),\n dt.datetime(2019, 1, 8),\n dt.datetime(2019, 1, 9),\n dt.datetime(2019, 1, 10),\n dt.datetime(2019, 1, 11),\n dt.datetime(2019, 1, 12),\n dt.datetime(2019, 1, 13),\n ]\n wreb = pd.Series([100.0, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150], index=dates)\n\n wreb_2 = pd.Series([100.0, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150], index=dates)\n\n ret_wreb = pd.Series([100.0, 110.0, 120.0, 130.0, 140.0, 150.0, 162.0, 174.0, 186.0, 198.0, 210.0], index=dates)\n\n assert_series_equal(ret_wreb, basket_series([wreb, wreb_2], [1, 1], rebal_freq=RebalFreq.WEEKLY))\n\n\ndef _mock_spot_data():\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n x = pd.DataFrame({'spot': [100.0, 101, 103.02, 100.9596, 100.9596, 102.978792]}, index=dates)\n x['assetId'] = 'MA4B66MW5E27U9VBB94'\n y = pd.DataFrame({'spot': [100.0, 100, 100, 100, 100, 100]}, index=dates)\n y['assetId'] = 'MA4B66MW5E27UAL9SUX'\n return pd.concat([x, y])\n\n\ndef _mock_spot_data_feb():\n dates_feb = pd.DatetimeIndex(\n [\n dt.date(2021, 2, 1),\n dt.date(2021, 2, 2),\n dt.date(2021, 2, 3),\n dt.date(2021, 2, 4),\n dt.date(2021, 2, 5),\n dt.date(2021, 2, 6),\n ]\n )\n x = pd.DataFrame({'spot': [100.0, 101.5, 106.02, 100.1, 105.3, 102.9]}, index=dates_feb)\n x['assetId'] = 'MA4B66MW5E27U9VBB94'\n y = pd.DataFrame({'spot': [100.0, 101.5, 100.02, 98.1, 95.3, 93.9]}, index=dates_feb)\n y['assetId'] = 'MA4B66MW5E27UAL9SUX'\n return pd.concat([x, y])\n\n\ndef test_basket_price():\n with pytest.raises(MqValueError):\n Basket(['AAPL UW'], [0.1, 0.9], RebalFreq.MONTHLY)\n\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n dates_feb = pd.DatetimeIndex(\n [\n dt.date(2021, 2, 1),\n dt.date(2021, 2, 2),\n dt.date(2021, 2, 3),\n dt.date(2021, 2, 4),\n dt.date(2021, 2, 5),\n dt.date(2021, 2, 6),\n ]\n )\n\n replace = Replacer()\n\n mock_data = replace('gs_quant.timeseries.backtesting.ts.get_historical_and_last_for_measure', Mock())\n mock_data.side_effect = [_mock_spot_data(), _mock_spot_data_feb()]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n ]\n\n a_basket = Basket(['AAPL UW', 'MSFT UW'], [0.1, 0.9], RebalFreq.MONTHLY)\n expected = pd.Series([100.0, 100.1, 100.302, 100.09596, 100.09596, 100.297879], index=dates)\n with DataContext('2021-01-01', '2021-01-06'):\n actual = a_basket.price()\n assert_series_equal(actual, expected)\n\n expected = pd.Series([100.00, 101.50, 100.62, 98.30, 96.30, 94.80], index=dates_feb)\n with DataContext('2021-02-01', '2021-02-06'):\n actual = a_basket.price()\n assert_series_equal(actual, expected)\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [{'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'}]\n with pytest.raises(MqValueError):\n Basket(['AAPL UW', 'ABC'], [0.1, 0.9], RebalFreq.MONTHLY).price()\n\n with pytest.raises(NotImplementedError):\n a_basket.price(real_time=True)\n\n replace.restore()\n\n\ndef test_basket_average_implied_vol():\n replace = Replacer()\n\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n\n x = pd.DataFrame({'impliedVolatility': [30.0, 30.2, 29.8, 30.6, 30.1, 30.0]}, index=dates)\n x['assetId'] = 'MA4B66MW5E27U9VBB94'\n y = pd.DataFrame({'impliedVolatility': [20.0, 20.2, 20.3, 20.6, 21.1, 20.0]}, index=dates)\n y['assetId'] = 'MA4B66MW5E27UAL9SUX'\n implied_vol = pd.concat([x, y])\n implied_vol.index.name = 'date'\n\n mock_spot = replace('gs_quant.timeseries.backtesting.ts.get_historical_and_last_for_measure', Mock())\n mock_spot.side_effect = [implied_vol.rename(columns={'impliedVolatility': 'spot'})]\n\n mock_data = replace('gs_quant.api.utils.ThreadPoolManager.run_async', Mock())\n mock_data.return_value = [implied_vol]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n ]\n\n a_basket = Basket(['AAPL UW', 'MSFT UW'], [0.1, 0.9], RebalFreq.DAILY)\n expected = pd.Series([21.0, 21.2, 21.25, 21.6, 22.0, 21.0], index=dates)\n actual = a_basket.average_implied_volatility('6m', VolReference.DELTA_CALL, 50)\n assert_series_equal(actual, expected)\n\n with pytest.raises(NotImplementedError):\n a_basket.average_implied_volatility('6m', VolReference.DELTA_CALL, 50, real_time=True)\n\n mock_data.return_value = [pd.DataFrame(), pd.DataFrame()]\n expected = pd.Series(dtype=float)\n actual = a_basket.average_implied_volatility('3m', VolReference.FORWARD, 20) # no data for this\n assert_series_equal(expected, actual)\n\n replace.restore()\n\n\ndef test_basket_average_realized_vol():\n replace = Replacer()\n\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n dates_feb = pd.DatetimeIndex(\n [\n dt.date(2021, 2, 1),\n dt.date(2021, 2, 2),\n dt.date(2021, 2, 3),\n dt.date(2021, 2, 4),\n dt.date(2021, 2, 5),\n dt.date(2021, 2, 6),\n ]\n )\n\n mock_data = replace('gs_quant.timeseries.backtesting.ts.get_historical_and_last_for_measure', Mock())\n mock_data.side_effect = [_mock_spot_data(), _mock_spot_data_feb(), _mock_spot_data_feb()]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n ]\n\n a_basket = Basket(['AAPL UW', 'MSFT UW'], [0.1, 0.9], RebalFreq.DAILY)\n\n expected = pd.Series([1.1059, 4.4906, 2.2677, 2.2228], index=dates[2:])\n with DataContext('2021-01-01', '2021-01-06'):\n actual = a_basket.average_realized_volatility('2d')\n assert_series_equal(actual, expected, atol=10e-3)\n\n expected = pd.Series([3.3088, 3.1754, 3.1754], index=dates[3:])\n with DataContext('2021-01-01', '2021-01-06'):\n actual = a_basket.average_realized_volatility('3d')\n assert_series_equal(actual, expected)\n mock_data.assert_called_once()\n\n expected = pd.Series([34.81054014068537, 19.98982339010735, 19.08853721611424], index=dates_feb[3:])\n with DataContext('2021-02-01', '2021-02-06'):\n actual = a_basket.average_realized_volatility('3d')\n assert_series_equal(actual, expected)\n\n with pytest.raises(NotImplementedError):\n a_basket.average_realized_volatility('2d', real_time=True)\n\n mock_get_last = replace('gs_quant.timeseries.measures.get_last_for_measure', Mock())\n mock_get_last.return_value = None\n # Test case where ts.get_last_for_measure returns none\n with DataContext('2021-02-01', dt.date.today() + dt.timedelta(days=2)):\n a_basket.average_realized_volatility('2d')\n\n replace.restore()\n\n\ndef _mock_vol_simple():\n return pd.Series([1 for i in range(5)], index=pd.date_range('2021-09-01', '2021-09-05'))\n\n\ndef _mock_data_simple():\n a = pd.Series([1 for i in range(5)], index=pd.date_range('2021-09-01', '2021-09-05'))\n x = pd.DataFrame({'spot': a.tolist()}, index=a.index)\n x['assetId'] = 'XLC_MOCK_MQID'\n y = pd.DataFrame({'spot': a.tolist()}, index=a.index)\n y['assetId'] = 'XLB_MOCK_MQID'\n z = pd.DataFrame({'spot': (a**3).tolist()}, index=a.index)\n z['assetId'] = 'SPX_MOCK_MQID'\n return pd.concat([x, y, z])\n\n\ndef _mock_spot_data_identical():\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n x = pd.DataFrame({'spot': [100.0, 101, 103.02, 100.9596, 100.9596, 102.978792]}, index=dates)\n x['assetId'] = 'MA4B66MW5E27U9VBB94'\n y = pd.DataFrame({'spot': [100.0, 101, 103.02, 100.9596, 100.9596, 102.978792]}, index=dates)\n y['assetId'] = 'MA4B66MW5E27UAL9SUX'\n return pd.concat([x, y])\n\n\ndef _mock_spot_data_corr():\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n x = pd.DataFrame({'spot': [78, 9, 1003, 17, -12, 5], 'assetId': 'MA4B66MW5E27U9VBB94'}, index=dates)\n y = pd.DataFrame({'spot': [-33, 33, 15, 21, -3, 2], 'assetId': 'MA4B66MW5E27UAL9SUX'}, index=dates)\n z = pd.DataFrame({'spot': [86, 86, 56, 86, 86, 9], 'assetId': 'MA4B66MW5E27UANZH2M'}, index=dates)\n return pd.concat([x, y, z])\n\n\ndef test_basket_average_realized_vol_wts():\n replace = Replacer()\n\n mock_data = replace('gs_quant.timeseries.backtesting.ts.get_historical_and_last_for_measure', Mock())\n mock_data.side_effect = [_mock_data_simple()]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'XLB_MOCK_MQID', 'bbid': 'XLP UP'},\n {'id': 'XLC_MOCK_MQID', 'bbid': 'XLC UP'},\n {'id': 'SPX_MOCK_MQID', 'bbid': 'SPX'},\n ]\n\n mock_vol = replace('gs_quant.timeseries.backtesting.volatility', Mock())\n mock_vol.side_effect = [_mock_vol_simple(), _mock_vol_simple() * 2, _mock_vol_simple() * 3, _mock_vol_simple() * 4]\n\n a_basket = Basket(['XLC UP', 'XLP UP', 'SPX'], [0.2, 0.3, 0.5], RebalFreq.DAILY)\n\n with DataContext(start=dt.date(2021, 9, 1), end=dt.date(2021, 9, 25)):\n av_realized_vol = a_basket.average_realized_volatility('2d')\n np.testing.assert_approx_equal(av_realized_vol.iloc[0], 2.3)\n\n replace.restore()\n\n\ndef test_basket_average_realized_vol_intraday():\n replace = Replacer()\n\n end_date = dt.date.today()\n start_date = end_date - dt.timedelta(days=4)\n\n a = pd.Series([1 for i in range(5)], index=pd.date_range(start_date, end_date))\n z = pd.DataFrame({'spot': (a**3).tolist()}, index=a.index)\n z['assetId'] = 'SPX_MOCK_MQID'\n\n mock_data = replace('gs_quant.timeseries.backtesting.ts.get_historical_and_last_for_measure', Mock())\n mock_data.side_effect = [z]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [{'id': 'SPX_MOCK_MQID', 'bbid': 'SPX'}]\n\n mock_today = replace('gs_quant.timeseries.get_last_for_measure', Mock())\n mock_today.side_effect = [pd.DataFrame({'assetId': 'SPX_MOCK_MQID', 'spot': 5001.0}, index=[dt.datetime.now()])]\n\n a_basket = Basket(['SPX'], [1], RebalFreq.DAILY)\n\n with DataContext(start=start_date, end=dt.date.today()):\n avg_vol = a_basket.average_realized_volatility('2d')\n assert avg_vol.index[-1].date() == dt.date.today()\n\n replace.restore()\n\n\ndef test_basket_average_realized_corr():\n replace = Replacer()\n\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n\n mock_data = replace('gs_quant.timeseries.backtesting.ts.get_historical_and_last_for_measure', Mock())\n mock_data.side_effect = [_mock_spot_data_identical(), _mock_spot_data_corr(), _mock_spot_data_identical()]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n {'id': 'ID of a dUpLiCaTe AAPL', 'bbid': 'AAPL UW'},\n ]\n\n a_basket = Basket(['AAPL UW', 'MSFT UW'], [0.1, 0.9], RebalFreq.DAILY)\n\n # Equal series have correlation of 1\n with DataContext('2021-01-01', '2021-01-06'):\n expected = pd.Series([np.nan, np.nan, 1.0, 1.0, 1.0, 1.0], index=dates)\n result = a_basket.average_realized_correlation('2d')\n assert_series_equal(result, expected)\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n {'id': 'MA4B66MW5E27UANZH2M', 'bbid': 'XLP UP'},\n {'id': 'ID of a dUpLiCaTe XLP UP', 'bbid': 'XLP UP'},\n ]\n\n b_basket = Basket(['AAPL UW', 'MSFT UW', 'XLP UP'], [0.2, 0.3, 0.5], RebalFreq.DAILY)\n\n # Test with two different series\n with DataContext('2021-01-01', '2021-01-06'):\n result = b_basket.average_realized_correlation('5d')\n expected = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan, 0.26872959922887607], index=dates)\n assert_series_equal(result, expected)\n\n # Test correct error being thrown\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n {'id': 'MA4B66MW5E27UANZH2M', 'bbid': 'XLP UP'},\n {'id': 'ID of a DuPlIcAtE MSFT UW', 'bbid': 'MSFT UW'},\n ]\n\n with pytest.raises(NotImplementedError):\n with DataContext('2021-01-01', '2021-01-09'):\n result = b_basket.average_realized_correlation('5d', real_time=True)\n replace.restore()\n\n\ndef test_basket_without_weights():\n replace = Replacer()\n\n mock_data = replace('gs_quant.timeseries.backtesting.ts.get_historical_and_last_for_measure', Mock())\n mock_data.side_effect = [_mock_spot_data(), _mock_spot_data(), _mock_spot_data(), _mock_spot_data()]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n ]\n\n a_basket = Basket(['AAPL UW', 'MSFT UW'], [0.5, 0.5], RebalFreq.DAILY)\n b_basket = Basket(['AAPL UW', 'MSFT UW'])\n\n with DataContext('2021-01-01', '2021-01-06'):\n a_price = a_basket.price()\n b_price = b_basket.price()\n a_vol = a_basket.average_realized_volatility('2d')\n b_vol = b_basket.average_realized_volatility('2d')\n assert_series_equal(a_price, b_price)\n assert_series_equal(a_vol, b_vol)\n\n with pytest.raises(MqValueError):\n c_basket = Basket(['AAPL UW'], [])\n c_basket.price()\n\n replace.restore()\n\n\ndef test_basket_avg_fwd_vol():\n replace = Replacer()\n\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n\n x_1m = pd.DataFrame({'impliedVolatility': [30.0, 30.2, 29.8, 30.6, 30.1, 30.0]}, index=dates)\n x_1m['assetId'] = 'MA4B66MW5E27U9VBB94'\n x_1m['tenor'] = '1m'\n x_2m = pd.DataFrame({'impliedVolatility': [40.0, 41.2, 35.8, 31.6, 36.1, 37.0]}, index=dates)\n x_2m['assetId'] = 'MA4B66MW5E27U9VBB94'\n x_2m['tenor'] = '2m'\n y_1m = pd.DataFrame({'impliedVolatility': [20.0, 20.2, 20.3, 20.6, 21.1, 20.0]}, index=dates)\n y_1m['assetId'] = 'MA4B66MW5E27UAL9SUX'\n y_1m['tenor'] = '1m'\n y_2m = pd.DataFrame({'impliedVolatility': [21.0, 22.2, 23.3, 21.6, 23.3, 21.0]}, index=dates)\n y_2m['assetId'] = 'MA4B66MW5E27UAL9SUX'\n y_2m['tenor'] = '2m'\n implied_vol = pd.concat([x_1m, x_2m, y_1m, y_2m])\n spot = implied_vol.rename(columns={'impliedVolatility': 'spot'})\n\n mock_data = replace('gs_quant.timeseries.backtesting.ts.get_historical_and_last_for_measure', Mock())\n mock_data.side_effect = [implied_vol, spot]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n ]\n\n a_basket = Basket(['AAPL UW', 'MSFT UW'], [0.1, 0.9], RebalFreq.DAILY)\n expected = pd.Series([24.55488, 26.61354, 27.45295, 23.557069, 26.90214, 24.046239], index=dates)\n actual = a_basket.average_forward_vol('1m', '1m', VolReference.DELTA_CALL, 50)\n assert_series_equal(actual, expected)\n\n with pytest.raises(NotImplementedError):\n a_basket.average_forward_vol('1m', '1m', VolReference.DELTA_CALL, 50, real_time=True)\n\n mock_data.side_effect = [pd.DataFrame(), spot]\n expected = pd.Series(dtype=float)\n actual = a_basket.average_forward_vol('1m', '1m', VolReference.DELTA_CALL, 50)\n assert_series_equal(expected, actual)\n\n implied_vol = pd.concat([x_1m, x_2m, y_1m])\n implied_vol.index.name = 'date'\n mock_data.side_effect = [implied_vol, spot]\n expected = pd.Series([np.nan] * 6, index=dates)\n actual = a_basket.average_forward_vol('1m', '1m', VolReference.DELTA_CALL, 50)\n assert_series_equal(expected, actual)\n\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/timeseries/test_datetime.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest import mock\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas.testing import assert_series_equal\n\nfrom gs_quant.data import Dataset\nfrom gs_quant.datetime import DayCountConvention, GsCalendar\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom gs_quant.errors import MqValueError, MqTypeError\nfrom gs_quant.test.api.test_risk import set_session\nfrom gs_quant.timeseries import (\n Interpolate,\n align,\n interpolate,\n value,\n day,\n weekday,\n quarter,\n year,\n date_range,\n day_count_fractions,\n append,\n prepend,\n union,\n bucketize,\n AggregateFunction,\n day_count,\n align_calendar,\n AggregatePeriod,\n month,\n day_countdown,\n)\n\n\ndef test_basic():\n assert type(RelativeDate('0d').apply_rule()) is dt.date\n\n\ndef test_align():\n dates1 = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n ]\n\n dates2 = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0], index=dates1)\n y = pd.Series([20.0, 40.0, 60.0], index=dates2)\n\n expectedl = pd.Series([2.0, 4.0], index=[dt.date(2019, 1, 2), dt.date(2019, 1, 4)])\n expectedr = pd.Series([20.0, 40.0], index=[dt.date(2019, 1, 2), dt.date(2019, 1, 4)])\n\n result = align(x, y, Interpolate.INTERSECT)\n assert_series_equal(result[0], expectedl, obj=\"Align intersect left\")\n assert_series_equal(result[1], expectedr, obj=\"Align intersect left\")\n\n result = align(y, x, Interpolate.INTERSECT)\n assert_series_equal(result[0], expectedr, obj=\"Align intersect right\")\n assert_series_equal(result[1], expectedl, obj=\"Align intersect right\")\n\n union_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n expected1 = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, np.nan], index=union_dates)\n expected2 = pd.Series([np.nan, 20.0, np.nan, 40.0, np.nan, 60.0], index=union_dates)\n\n result = align(x, y, Interpolate.NAN)\n assert_series_equal(result[0], expected1, obj=\"Align NaN left\")\n assert_series_equal(result[1], expected2, obj=\"Align NaN left\")\n\n result = align(y, x, Interpolate.NAN)\n assert_series_equal(result[0], expected2, obj=\"Align NaN right\")\n assert_series_equal(result[1], expected1, obj=\"Align NaN right\")\n\n expected1 = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 0.0], index=union_dates)\n expected2 = pd.Series([0.0, 20.0, 0.0, 40.0, 0.0, 60.0], index=union_dates)\n\n result = align(x, y, Interpolate.ZERO)\n assert_series_equal(result[0], expected1, obj=\"Align zero left\")\n assert_series_equal(result[1], expected2, obj=\"Align zero left\")\n\n result = align(y, x, Interpolate.ZERO)\n assert_series_equal(result[0], expected2, obj=\"Align zero right\")\n assert_series_equal(result[1], expected1, obj=\"Align zero right\")\n\n expected1 = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 5.0], index=union_dates)\n expected2 = pd.Series([20.0, 20.0, 20.0, 40.0, 40.0, 60.0], index=union_dates)\n\n result = align(x, y, Interpolate.STEP)\n assert_series_equal(result[0], expected1, obj=\"Align step left\")\n assert_series_equal(result[1], expected2, obj=\"Align step left\")\n\n result = align(y, x, Interpolate.STEP)\n assert_series_equal(result[0], expected2, obj=\"Align step left\")\n assert_series_equal(result[1], expected1, obj=\"Align step left\")\n\n xp = x.copy()\n yp = y.copy()\n xp.index = pd.to_datetime(xp.index)\n yp.index = pd.to_datetime(yp.index)\n up = pd.to_datetime(union_dates)\n\n expected1 = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, np.nan], index=up)\n expected2 = pd.Series([np.nan, 20.0, 30.0, 40.0, 50.0, 60.0], index=up)\n\n result = align(xp, yp, Interpolate.TIME)\n assert_series_equal(result[0], expected1, obj=\"Align time left\")\n assert_series_equal(result[1], expected2, obj=\"Align time left\")\n\n result = align(yp, xp, Interpolate.TIME)\n assert_series_equal(result[0], expected2, obj=\"Align time right\")\n assert_series_equal(result[1], expected1, obj=\"Align time right\")\n\n a = pd.Series([0, 100, 110], index=pd.DatetimeIndex(['2019-07-01', '2019-07-08', '2019-07-10']))\n b = pd.Series([20, 60, 70], index=pd.DatetimeIndex(['2019-07-02', '2019-07-10', '2019-07-11']))\n result = align(a, b, Interpolate.TIME)\n\n u_index = a.index.union(b.index)\n assert_series_equal(result[0], pd.Series([0, 100 / 7, 100, 110, np.nan], index=u_index))\n assert_series_equal(result[1], pd.Series([np.nan, 20, 50, 60, 70], index=u_index))\n\n result = align(x, 3)\n assert_series_equal(result[0], x, obj=\"Align scalar left\")\n assert_series_equal(result[1], pd.Series(3, index=dates1), obj=\"Align scalar left\")\n\n result = align(3, x)\n assert_series_equal(result[0], pd.Series(3, index=dates1), obj=\"Align scalar left\")\n assert_series_equal(result[1], x, obj=\"Align scalar right\")\n\n result = align(1, 2)\n assert result[0] == 1\n assert result[1] == 2\n\n with pytest.raises(MqValueError):\n align(x, x, \"None\")\n\n\ndef test_interpolate():\n dates = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 7),\n ]\n\n x = pd.Series([2.0, 3.0, 5.0, 7.0], index=dates)\n\n result = interpolate(x, dates)\n assert_series_equal(result, x, obj=\"Interpolate series by dates\")\n\n result = interpolate(x, x)\n assert_series_equal(result, x, obj=\"Interpolate series by series dates\")\n\n result = interpolate(x)\n assert_series_equal(result, x, obj=\"Interpolate series default\")\n\n select_dates = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 7),\n ]\n\n result = interpolate(x, select_dates)\n expected = pd.Series([2.0, 3.0, 7.0], index=select_dates)\n assert_series_equal(result, expected, obj=\"Interpolate subset of dates\")\n\n select_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n intersect_dates = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 7),\n ]\n\n result = interpolate(x, select_dates, Interpolate.INTERSECT)\n expected = pd.Series([2.0, 5.0, 7.0], index=intersect_dates)\n assert_series_equal(result, expected, obj=\"Interpolate intersect\")\n\n result = interpolate(x, select_dates, Interpolate.NAN)\n expected = pd.Series([np.nan, 2.0, np.nan, 5.0, np.nan, 7.0, np.nan], index=select_dates)\n assert_series_equal(result, expected, obj=\"Interpolate nan\")\n\n result = interpolate(x, select_dates, Interpolate.ZERO)\n expected = pd.Series([0.0, 2.0, 0.0, 5.0, 0.0, 7.0, 0.0], index=select_dates)\n assert_series_equal(result, expected, obj=\"Interpolate zero\")\n\n result = interpolate(x, select_dates, Interpolate.STEP)\n expected = pd.Series([2.0, 2.0, 2.0, 5.0, 5.0, 7.0, 7.0], index=select_dates)\n assert_series_equal(result, expected, obj=\"Interpolate step dates\")\n\n result = interpolate(x, pd.Series(np.nan, select_dates), Interpolate.STEP)\n expected = pd.Series([2.0, 2.0, 2.0, 5.0, 5.0, 7.0, 7.0], index=select_dates)\n assert_series_equal(result, expected, obj=\"Interpolate step series\")\n\n xnan = pd.Series([np.nan, 3.0, 5.0, 7.0], index=dates)\n\n result = interpolate(xnan, select_dates, Interpolate.STEP)\n expected = pd.Series([np.nan, np.nan, np.nan, 5.0, 5.0, 7.0, 7.0], index=select_dates)\n assert_series_equal(result, expected, obj=\"Interpolate flat nan start\")\n\n x = pd.Series([2.0, 3.0, 5.0, 7.0], index=pd.DatetimeIndex(dates))\n result = interpolate(x, select_dates, Interpolate.STEP)\n expected = pd.Series([2.0, 2.0, 2.0, 5.0, 5.0, 7.0, 7.0], index=pd.DatetimeIndex(select_dates))\n assert_series_equal(result, expected, obj=\"Interpolate step dates to series with timestamps\")\n\n with pytest.raises(MqValueError, match=\"Unknown intersection type: None\"):\n interpolate(x, x, \"None\")\n\n with pytest.raises(MqValueError, match=\"Cannot perform step interpolation on an empty series\"):\n interpolate(pd.Series(dtype=float), select_dates, Interpolate.STEP)\n\n\ndef test_value():\n dates = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 7),\n ]\n\n x = pd.Series([2.0, 3.0, 5.0, 7.0], index=dates)\n\n result = value(x, dt.date(2019, 1, 3))\n assert result == 3.0\n\n result = value(x, dt.date(2019, 1, 5))\n assert result == 5.0\n\n result = value(x, dt.date(2019, 1, 4))\n assert result == 3.0\n\n result = value(x, dt.date(2019, 1, 4), Interpolate.INTERSECT)\n assert result is None\n\n result = value(x, dt.date(2019, 1, 4), Interpolate.STEP)\n assert result == 3.0\n\n result = value(x, dt.date(2019, 1, 4), Interpolate.ZERO)\n assert result == 0.0\n\n result = value(x, dt.date(2019, 1, 4), Interpolate.NAN)\n assert np.isnan(result)\n\n\ndef test_day():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = day(x)\n expected = pd.Series([1, 2, 3, 4], index=dates)\n assert_series_equal(result, expected, obj=\"Day\")\n\n\ndef test_weekday():\n dates = [\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n dt.date(2019, 1, 9),\n dt.date(2019, 1, 10),\n dt.date(2019, 1, 11),\n dt.date(2019, 1, 12),\n dt.date(2019, 1, 13),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], index=dates)\n\n result = weekday(x)\n expected = pd.Series([0, 1, 2, 3, 4, 5, 6], index=dates)\n assert_series_equal(result, expected, obj=\"Weekday\")\n\n\ndef test_month():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 2, 1),\n dt.date(2019, 3, 1),\n dt.date(2019, 4, 1),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = month(x)\n expected = pd.Series([1, 2, 3, 4], index=dates)\n assert_series_equal(result, expected, obj=\"Month\")\n\n\ndef test_year():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2020, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2022, 1, 4),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = year(x)\n expected = pd.Series([2019, 2020, 2021, 2022], index=dates)\n assert_series_equal(result, expected, obj=\"Year\")\n\n\ndef test_quarter():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 4, 1),\n dt.date(2019, 7, 1),\n dt.date(2019, 10, 1),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0], index=dates)\n\n result = quarter(x)\n expected = pd.Series([1, 2, 3, 4], index=dates)\n assert_series_equal(result, expected, obj=\"Quarter\")\n\n\ndef test_day_count_fractions():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series(dtype=float)\n assert_series_equal(x, day_count_fractions(x))\n\n x = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=dates)\n\n result = day_count_fractions(x, DayCountConvention.ACTUAL_360)\n result2 = day_count_fractions(x.index, DayCountConvention.ACTUAL_360)\n dcf = 1 / 360\n expected = pd.Series([np.nan, dcf, dcf, dcf, dcf, dcf], index=dates)\n assert_series_equal(result, expected, obj=\"ACT/360\")\n assert_series_equal(result2, expected, obj=\"ACT/360\")\n\n result = day_count_fractions(x, DayCountConvention.ACTUAL_365F)\n dcf = 1 / 365\n expected = pd.Series([np.nan, dcf, dcf, dcf, dcf, dcf], index=dates)\n assert_series_equal(result, expected, obj=\"ACT/365\")\n\n\ndef test_date_range():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n values = [1.0, 2.0, 3.0, 4.0, 5.0, 7.0]\n s0 = pd.Series(values, index=dates)\n s1 = pd.Series(values, index=pd.date_range('2019-01-01', periods=6, freq='D').date)\n\n for x in [s0, s1]:\n assert (date_range(x, 0, 0) == x).all()\n assert (date_range(x, 0, 0, True) == x.iloc[:-2]).all()\n\n assert date_range(x, 0, dt.date(2019, 1, 3)).index[-1] == dt.date(2019, 1, 3)\n assert (date_range(x, 0, dt.date(2019, 1, 3)) == x.iloc[:3]).all()\n\n assert date_range(x, dt.date(2019, 1, 3), dt.date(2019, 1, 6)).index[0] == dt.date(2019, 1, 3)\n assert date_range(x, dt.date(2019, 1, 3), dt.date(2019, 1, 6)).index[-1] == dt.date(2019, 1, 6)\n assert (date_range(x, dt.date(2019, 1, 3), dt.date(2019, 1, 6)) == x.iloc[2:6]).all()\n\n y = pd.Series(values, index=pd.date_range('2020-10-23', periods=6, freq='D'))\n assert (date_range(y, 1, 1, True) == y.iloc[3:5]).all()\n\n with pytest.raises(MqValueError):\n date_range(pd.Series([1]), 0, 0)\n\n with pytest.raises(MqTypeError):\n date_range(pd.Series([1]), 0, 0, 'string')\n\n\ndef test_append():\n x = pd.Series([3.1, 4.1, 5.1], index=pd.date_range('2019-01-03', '2019-01-05'))\n y = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 7.0], index=pd.date_range('2019-01-01', \"2019-01-06\"))\n\n assert_series_equal(append([]), pd.Series(dtype='float64'), obj='append empty')\n\n assert_series_equal(append([x]), x, obj='append one series')\n\n actual = append([x, y])\n expected = pd.Series([3.1, 4.1, 5.1, 7.0], index=pd.date_range('2019-01-03', '2019-01-06'))\n assert_series_equal(actual, expected, obj='append two series')\n\n x = pd.Series([3.1, 4.1, 5.1], index=pd.date_range('2019-01-01 02:00', periods=3, freq='h'))\n y = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 7.0], index=pd.date_range('2019-01-01', periods=6, freq='h'))\n\n actual = append([x, y])\n expected = pd.Series([3.1, 4.1, 5.1, 7.0], index=pd.date_range('2019-01-01 02:00', periods=4, freq='h'))\n assert_series_equal(actual, expected, obj='append two real-time series')\n\n\ndef test_prepend():\n x = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 7.0], index=pd.date_range('2019-01-01', \"2019-01-06\"))\n y = pd.Series([3.1, 4.1, 5.1], index=pd.date_range('2019-01-03', '2019-01-05'))\n\n assert_series_equal(prepend([]), pd.Series(dtype='float64'), obj='prepend empty')\n\n assert_series_equal(prepend([x]), x, obj='prepend one series')\n\n actual = prepend([x, y])\n expected = pd.Series([1.0, 2.0, 3.1, 4.1, 5.1], index=pd.date_range('2019-01-01', '2019-01-05'))\n assert_series_equal(actual, expected, obj='prepend two series')\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 7.0], index=pd.date_range('2019-01-01', periods=6, freq='h'))\n y = pd.Series([3.1, 4.1, 5.1], index=pd.date_range('2019-01-01 02:00', periods=3, freq='h'))\n\n actual = prepend([x, y])\n expected = pd.Series([1.0, 2.0, 3.1, 4.1, 5.1], index=pd.date_range('2019-01-01', periods=5, freq='h'))\n assert_series_equal(actual, expected, obj='prepend two real-time series')\n\n\ndef test_union():\n x = pd.Series([3.1, 4.1, np.nan], index=pd.date_range('2019-01-03', '2019-01-05'))\n y = pd.Series([1.0, np.nan, 3.0, 4.0, 5.0, 6.0], index=pd.date_range('2019-01-01', \"2019-01-06\"))\n z = pd.Series([60.0, 70.0], index=pd.date_range('2019-01-06', \"2019-01-07\"))\n\n assert_series_equal(union([]), pd.Series(dtype='float64'), obj='union empty')\n\n x.index.freq = None\n assert_series_equal(union([x]), x, obj='union of one series')\n\n actual = union([x, y, z])\n expected = pd.Series([1.0, np.nan, 3.1, 4.1, 5.0, 6.0, 70], index=pd.date_range('2019-01-01', '2019-01-07'))\n assert_series_equal(actual, expected, check_freq=False, obj='union of three series')\n\n x = pd.Series([3.1, 4.1, np.nan], index=pd.date_range('2019-01-01 02:00', periods=3, freq='h'))\n y = pd.Series([1.0, np.nan, 3.0, 4.0, 5.0, 6.0], index=pd.date_range('2019-01-01', periods=6, freq='h'))\n\n actual = union([x, y])\n expected = pd.Series([1.0, np.nan, 3.1, 4.1, 5.0, 6.0], index=pd.date_range('2019-01-01', periods=6, freq='h'))\n assert_series_equal(actual, expected, obj='union of two real-time series')\n\n\ndef test_bucketize():\n dates = pd.bdate_range(start='1/1/2021', end='4/23/2021')\n series = pd.Series(range(len(dates)), index=dates)\n\n actual = bucketize(series, AggregateFunction.MAX, AggregatePeriod.MONTH)\n expected_index = pd.DatetimeIndex(\n [dt.date(2021, 1, 31), dt.date(2021, 2, 28), dt.date(2021, 3, 31), dt.date(2021, 4, 23)]\n )\n expected = pd.Series([20, 40, 63, 80], index=expected_index)\n actual.index.freq = None # Ignore the index freq\n assert_series_equal(actual, expected, check_index_type=False)\n\n\ndef test_day_count():\n assert day_count(dt.date(2021, 5, 7), dt.date(2021, 5, 10)) == 1\n assert day_count(dt.date(2021, 5, 10), dt.date(2021, 5, 14)) == 4\n assert day_count(dt.date(2021, 5, 10), dt.date(2021, 5, 17)) == 5\n\n with pytest.raises(MqValueError):\n day_count(dt.date(2021, 5, 7), '2021-05-10')\n\n\ndef test_day_countdown():\n start_dt = dt.date(2021, 5, 7)\n end_dt = dt.date(2021, 5, 17)\n\n # calendar days (default)\n actual = day_countdown(end_dt, start_dt)\n expected_idx = pd.date_range(start=start_dt, end=end_dt, freq='D')\n expected = pd.Series(list(range(10, -1, -1)), index=expected_idx, dtype=np.int64)\n assert_series_equal(actual, expected, obj='day_countdown calendar days')\n\n # business days (Mon-Fri)\n actual_bus = day_countdown(end_dt, start_dt, business_days=True)\n expected_bus_idx = pd.bdate_range(start=start_dt, end=end_dt)\n expected_bus = pd.Series([6, 5, 4, 3, 2, 1, 0], index=expected_bus_idx, dtype=np.int64)\n assert_series_equal(actual_bus, expected_bus, obj='day_countdown business days')\n\n # start_date after end_date => empty\n actual_empty = day_countdown(end_dt, dt.date(2021, 5, 18))\n assert_series_equal(actual_empty, pd.Series(dtype=np.int64), obj='day_countdown empty range')\n\n # type validation\n with pytest.raises(MqValueError):\n day_countdown(end_dt, '2021-05-07')\n\n with pytest.raises(MqValueError):\n day_countdown('2021-05-07', start_dt)\n\n # calendar single-day\n actual_single = day_countdown(dt.date(2021, 5, 17), dt.date(2021, 5, 17))\n expected_single_idx = pd.date_range(start=dt.date(2021, 5, 17), end=dt.date(2021, 5, 17), freq='D')\n expected_single = pd.Series([0], index=expected_single_idx, dtype=np.int64)\n assert_series_equal(actual_single, expected_single, obj='day_countdown single day calendar')\n\n # business single-day on weekend\n weekend = dt.date(2021, 5, 15) # Saturday\n actual_weekend = day_countdown(weekend, weekend, business_days=True)\n expected_weekend = pd.Series(dtype=np.int64)\n assert_series_equal(actual_weekend, expected_weekend, obj='day_countdown single day business weekend')\n\n # business countdown over a weekend: Fri -> Mon\n fri = dt.date(2021, 5, 14)\n mon = dt.date(2021, 5, 17)\n actual_fri_mon = day_countdown(mon, fri, business_days=True)\n expected_fri_mon_idx = pd.bdate_range(start=fri, end=mon)\n expected_fri_mon = pd.Series([1, 0], index=expected_fri_mon_idx, dtype=np.int64)\n assert_series_equal(actual_fri_mon, expected_fri_mon, obj='day_countdown business cross weekend')\n\n # default start_date\n class _PatchedDate(dt.date):\n @classmethod\n def today(cls):\n return cls(2021, 5, 15)\n\n class _DtShim:\n date = _PatchedDate\n\n with mock.patch.dict(day_countdown.__globals__, {'dt': _DtShim}):\n actual_default = day_countdown(_PatchedDate(end_dt.year, end_dt.month, end_dt.day))\n\n expected_default_idx = pd.date_range(start=dt.date(2021, 5, 15), end=dt.date(2021, 5, 17), freq='D')\n expected_default = pd.Series([2, 1, 0], index=expected_default_idx, dtype=np.int64)\n assert_series_equal(actual_default, expected_default, obj='day_countdown default start_date')\n\n\n@mock.patch.object(Dataset, 'get_coverage')\n@mock.patch.object(Dataset, 'get_data')\ndef test_align_calendar(mocker_data, mocker_cov):\n dates = pd.date_range(start='1/1/2023', end='1/31/2023')\n series = pd.Series(range(len(dates)), index=dates)\n\n set_session()\n mocker_data.return_value = pd.DataFrame(index=[dt.datetime(2023, 1, 3)], data={'holiday': 'New Year'})\n mocker_cov.return_value = pd.DataFrame()\n\n GsCalendar.reset()\n\n aligned_series = align_calendar(series, 'NYC')\n # Check if the holiday was removed\n assert dt.datetime(2023, 1, 3) not in aligned_series\n # Check the first saturday was removed\n assert dt.datetime(2023, 1, 7) not in aligned_series\n # Check full series length is correct\n assert aligned_series.size == 21\n\n\ndef test_bucketize_empty_series():\n empty_series = pd.Series(dtype=float)\n result = bucketize(empty_series, AggregateFunction.MAX, AggregatePeriod.MONTH)\n assert result.empty, \"The result should be an empty series when the input series is empty.\"\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_datetime.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_econometrics.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport math\nimport os\nfrom unittest.mock import Mock\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas.testing import assert_series_equal\nfrom testfixtures import Replacer\n\nfrom gs_quant.common import Currency as CurrencyEnum\nfrom gs_quant.datetime import DayCountConvention\nfrom gs_quant.errors import MqValueError, MqTypeError, MqError\nfrom gs_quant.markets.securities import Cash\nfrom gs_quant.timeseries import (\n returns,\n prices,\n index,\n change,\n annualize,\n volatility,\n correlation,\n beta,\n max_drawdown,\n Returns,\n Window,\n Direction,\n generate_series,\n SeriesType,\n Interpolate,\n CurveType,\n)\nfrom gs_quant.timeseries.econometrics import (\n _get_ratio,\n excess_returns,\n RiskFreeRateCurrency,\n sharpe_ratio,\n excess_returns_,\n corr_swap_correlation,\n vol_swap_volatility,\n)\n\n\ndef test_returns():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series(dtype=float)\n assert_series_equal(x, returns(x))\n\n x = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=dates)\n\n result = returns(x)\n expected = pd.Series([np.nan, 0.01, 0.02, -0.02, 0.0, 0.02], index=dates)\n assert_series_equal(result, expected, obj=\"Simple returns default\")\n\n result = returns(x, 1, Returns.SIMPLE)\n expected = pd.Series([np.nan, 0.01, 0.02, -0.02, 0.0, 0.02], index=dates)\n assert_series_equal(result, expected, obj=\"Simple returns\")\n\n result = returns(x, 2, Returns.SIMPLE)\n expected = pd.Series([np.nan, np.nan, 0.0302, -0.0004, -0.0200, 0.0200], index=dates)\n assert_series_equal(result, expected, obj=\"Simple returns\")\n\n result = returns(x, 1, Returns.LOGARITHMIC)\n expected = pd.Series([np.nan, 0.009950, 0.019803, -0.020203, 0.0, 0.019803], index=dates)\n assert_series_equal(result, expected, obj=\"Logarithmic returns\", atol=1e-5)\n\n result = returns(x, 2, Returns.LOGARITHMIC)\n expected = pd.Series([np.nan, np.nan, 0.029753, -0.0004, -0.020203, 0.019803], index=dates)\n assert_series_equal(result, expected, obj=\"Logarithmic returns\", atol=1e-5)\n\n result = returns(x, 1, Returns.ABSOLUTE)\n expected = pd.Series([np.nan, 1.0, 2.02, -2.0604, 0.0, 2.019192], index=dates)\n assert_series_equal(result, expected, obj=\"Absolute returns\", atol=1e-5)\n\n result = returns(x, 2, Returns.ABSOLUTE)\n expected = pd.Series([np.nan, np.nan, 3.02, -0.0404, -2.0604, 2.019192], index=dates)\n assert_series_equal(result, expected, obj=\"Absolute returns\", atol=1e-5)\n\n result = returns(x, '2d', Returns.ABSOLUTE)\n expected = pd.Series([np.nan, np.nan, 3.02, -0.0404, -2.0604, 2.019192], index=pd.DatetimeIndex(dates))\n assert_series_equal(result, expected, obj=\"Absolute returns with relative date\")\n\n with pytest.raises(MqValueError):\n returns(x, 1, \"None\")\n\n\ndef test_prices():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n r = pd.Series(dtype=float)\n assert_series_equal(r, prices(r))\n\n r = pd.Series([np.nan, 0.01, 0.02, -0.02, 0.0, 0.02], index=dates)\n\n result = prices(r)\n expected = pd.Series([1.0, 1.01, 1.0302, 1.009596, 1.009596, 1.02978792], index=dates)\n assert_series_equal(result, expected, obj=\"Simple price series default\")\n\n result = prices(r, 100)\n expected = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=dates)\n assert_series_equal(result, expected, obj=\"Simple price series default\")\n\n result = prices(r, 100, Returns.SIMPLE)\n expected = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=dates)\n assert_series_equal(result, expected, obj=\"Simple price series\")\n\n r = pd.Series([np.nan, 0.009950, 0.019803, -0.020203, 0.0, 0.019803], index=dates)\n\n result = prices(r, 100, Returns.LOGARITHMIC)\n expected = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=dates)\n assert_series_equal(result, expected, obj=\"Logarithmic prices series\")\n\n r = pd.Series([np.nan, 1.0, 2.02, -2.0604, 0.0, 2.019192], index=dates)\n\n result = prices(r, 100, Returns.ABSOLUTE)\n expected = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=dates)\n assert_series_equal(result, expected, obj=\"Absolute prices series\")\n\n with pytest.raises(MqValueError):\n prices(r, 1, \"None\")\n\n\ndef test_index():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([200, 202, 201, 203, 202, 201], index=dates)\n\n result = index(x)\n expected = pd.Series([1.000, 1.010, 1.005, 1.015, 1.010, 1.005], index=dates)\n assert_series_equal(result, expected, obj=\"Index series\")\n\n with pytest.raises(MqValueError):\n x = pd.Series(range(6), index=dates)\n result = index(x)\n\n\ndef test_change():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([200, 202, 201, 203, 202, 201.5], index=dates)\n\n result = change(x)\n expected = pd.Series([0.0, 2.0, 1.0, 3.0, 2.0, 1.5], index=dates)\n assert_series_equal(result, expected, obj=\"Change of series\", check_series_type=False)\n\n\ndef test_annualize():\n daily_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 1),\n ]\n\n daily_series = pd.Series([0.01, 0.02, -0.01], index=daily_dates)\n\n with pytest.raises(MqValueError):\n annualize(daily_series)\n\n daily_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n daily_series = pd.Series([0.01, 0.02, -0.01, 0.03, 0, -0.01], index=daily_dates)\n\n result = annualize(daily_series)\n assert_series_equal(result, daily_series * math.sqrt(252), obj=\"Annualize daily\")\n\n weekly_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 8),\n dt.date(2019, 1, 15),\n dt.date(2019, 1, 22),\n dt.date(2019, 1, 29),\n dt.date(2019, 2, 6),\n ]\n\n weekly_series = pd.Series([0.01, 0.02, -0.01, 0.03, 0, -0.01], index=weekly_dates)\n\n result = annualize(weekly_series)\n assert_series_equal(result, weekly_series * math.sqrt(52), obj=\"Annualize weekly\")\n\n semi_monthly_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 15),\n dt.date(2019, 2, 1),\n dt.date(2019, 2, 15),\n dt.date(2019, 3, 1),\n dt.date(2019, 3, 15),\n ]\n\n semi_monthly_series = pd.Series([0.01, 0.02, -0.01, 0.03, 0, -0.01], index=semi_monthly_dates)\n\n result = annualize(semi_monthly_series)\n assert_series_equal(result, semi_monthly_series * math.sqrt(26), obj=\"Annualize semi-monthly\")\n\n monthly_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 2, 1),\n dt.date(2019, 3, 1),\n dt.date(2019, 4, 1),\n dt.date(2019, 5, 1),\n dt.date(2019, 6, 1),\n ]\n\n monthly_series = pd.Series([0.01, 0.02, -0.01, 0.03, 0, -0.01], index=monthly_dates)\n\n result = annualize(monthly_series)\n assert_series_equal(result, monthly_series * math.sqrt(12), obj=\"Annualize monthly\")\n\n quarterly_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 3, 1),\n dt.date(2019, 6, 1),\n dt.date(2019, 9, 1),\n dt.date(2020, 1, 1),\n dt.date(2020, 3, 1),\n ]\n\n quarterly_series = pd.Series([0.01, 0.02, -0.01, 0.03, 0, -0.01], index=quarterly_dates)\n\n result = annualize(quarterly_series)\n assert_series_equal(result, quarterly_series * math.sqrt(4), obj=\"Annualize quarterly\")\n\n annual_dates = [\n dt.date(2019, 1, 1),\n dt.date(2020, 1, 1),\n dt.date(2021, 1, 1),\n dt.date(2022, 1, 1),\n dt.date(2023, 1, 1),\n dt.date(2024, 1, 1),\n ]\n\n annual_series = pd.Series([0.01, 0.02, -0.01, 0.03, 0, -0.01], index=annual_dates)\n\n result = annualize(annual_series)\n assert_series_equal(result, annual_series, obj=\"Annualize annually\")\n\n invalid_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 6),\n dt.date(2019, 1, 9),\n dt.date(2019, 1, 12),\n dt.date(2019, 1, 13),\n ]\n\n invalid_series = pd.Series([0.01, 0.02, -0.01, 0.03, 0, -0.01], index=invalid_dates)\n\n with pytest.raises(MqValueError):\n annualize(invalid_series)\n\n\ndef test_volatility():\n x = pd.Series(dtype=float)\n assert_series_equal(x, volatility(x))\n\n daily_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=daily_dates)\n\n std = 0.016733200530681527\n vol = std * math.sqrt(252) * 100\n\n real_vol = volatility(x)\n assert real_vol.iloc[-1] == vol\n\n vol_already_returns = volatility(returns(x), returns_type=None)\n assert_series_equal(vol_already_returns, real_vol, obj=\"Volatility strdate\")\n\n result = volatility(x, w=\"3d\")\n expected = pd.Series([33.04542, 31.74902, 31.74902], index=daily_dates[3:])\n assert_series_equal(result, expected, obj=\"Volatility strdate\")\n\n result = volatility(x, w=\"3m\")\n expected = pd.Series(dtype=float)\n assert_series_equal(pd.Series(dtype=float), expected, obj=\"Volatility strdate too large for series\")\n\n\ndef test_volatility_assume_zero_mean():\n dates = pd.date_range(start='2020-01-01', periods=50, freq='D')\n prices = pd.Series(\n [\n 100,\n 102,\n 101,\n 103,\n 102,\n 104,\n 103,\n 105,\n 104,\n 106,\n 105,\n 107,\n 106,\n 108,\n 107,\n 109,\n 108,\n 110,\n 109,\n 111,\n 110,\n 112,\n 111,\n 113,\n 112,\n 114,\n 113,\n 115,\n 114,\n 116,\n 115,\n 117,\n 116,\n 118,\n 117,\n 119,\n 118,\n 120,\n 119,\n 121,\n 120,\n 122,\n 121,\n 123,\n 122,\n 124,\n 123,\n 125,\n 124,\n 126,\n ],\n index=dates,\n )\n\n window_size = 10\n\n # zero-mean should give different result than standard volatility\n vol_zero_mean = volatility(prices, w=window_size, assume_zero_mean=True)\n vol_standard = volatility(prices, w=window_size, assume_zero_mean=False)\n assert not np.allclose(vol_zero_mean.dropna(), vol_standard.dropna()), (\n \"Zero-mean and standard volatility should differ\"\n )\n\n # manual calc check\n result = volatility(prices, w=window_size, returns_type=Returns.LOGARITHMIC, assume_zero_mean=True)\n\n log_returns = np.log(prices / prices.shift(1)).dropna()\n last_window_returns = log_returns.iloc[-window_size:].values\n expected_vol = np.sqrt(np.mean(last_window_returns**2)) * np.sqrt(252) * 100\n\n assert abs(result.iloc[-1] - expected_vol) < 0.01, (\n f\"Manual calculation mismatch: {result.iloc[-1]} vs {expected_vol}\"\n )\n\n # single value window\n vol_single = volatility(prices, w=1, assume_zero_mean=True)\n assert not vol_single.empty, \"Should handle window size of 1\"\n\n # full series (no window)\n vol_full = volatility(prices, returns_type=Returns.LOGARITHMIC, assume_zero_mean=True)\n all_returns = np.log(prices / prices.shift(1)).dropna().values\n expected_full = np.sqrt(np.mean(all_returns**2)) * np.sqrt(252) * 100\n\n assert abs(vol_full.iloc[-1] - expected_full) < 0.01, (\n f\"Full series calculation incorrect: {vol_full.iloc[-1]} vs {expected_full}\"\n )\n\n\ndef test_volatility_annualization_factor():\n rng = np.random.default_rng(100)\n dates = pd.date_range(start='2025-01-01', periods=252, freq='B')\n\n # Generate returns with ~20% annual volatility\n daily_vol = 0.20 / np.sqrt(252)\n returns = rng.normal(0, daily_vol, 252)\n prices = pd.Series(100 * np.exp(np.cumsum(returns)), index=dates)\n\n vol_auto = volatility(prices, returns_type=Returns.LOGARITHMIC)\n vol_252 = volatility(prices, returns_type=Returns.LOGARITHMIC, annualization_factor=252)\n vol_255 = volatility(prices, returns_type=Returns.LOGARITHMIC, annualization_factor=255)\n\n assert_series_equal(vol_auto, vol_252)\n\n expected_ratio_255 = np.sqrt(255 / 252)\n actual_ratio_255 = vol_255.iloc[-1] / vol_252.iloc[-1]\n np.testing.assert_almost_equal(actual_ratio_255, expected_ratio_255, decimal=5)\n\n vol_swap = volatility(prices, returns_type=Returns.LOGARITHMIC, annualization_factor=255, assume_zero_mean=True)\n\n assert not np.allclose(vol_swap.iloc[-1], vol_255.iloc[-1]), (\n \"Zero-mean volatility should differ from standard volatility\"\n )\n\n final_vol = vol_252.iloc[-1]\n assert 15 < final_vol < 25, \"Volatility should be around 20%\"\n\n\ndef test_vol_swap_volatility():\n rng = np.random.default_rng(100)\n dates = pd.date_range(start='2025-01-01', periods=50, freq='D')\n returns = rng.normal(0, 0.01, 49)\n prices = pd.Series(100 * np.exp(np.cumsum(np.concatenate([[0], returns]))), index=dates)\n\n # ramp-up must be w - 1\n with pytest.raises(MqTypeError):\n vol_swap_volatility(prices, n_days=Window(22, 0))\n\n for n_days in [3, 25, 49, 50]:\n # Windows and numbers get treated identically\n pd.testing.assert_series_equal(\n vol_swap_volatility(prices, n_days=n_days), vol_swap_volatility(prices, n_days=Window(n_days, n_days - 1))\n )\n\n result_wrapper = vol_swap_volatility(prices, n_days=22)\n result_direct = volatility(prices, Window(22, 21), Returns.LOGARITHMIC, 252, True)\n\n assert_series_equal(result_wrapper, result_direct)\n\n result_wrapper = vol_swap_volatility(prices, n_days=22, annualization_factor=365)\n result_direct = volatility(prices, Window(22, 21), Returns.LOGARITHMIC, 365, True)\n\n assert_series_equal(result_wrapper, result_direct)\n\n result_wrapper = vol_swap_volatility(prices, n_days=22, assume_zero_mean=False)\n result_direct = volatility(prices, Window(22, 21), Returns.LOGARITHMIC, 252, False)\n\n assert_series_equal(result_wrapper, result_direct)\n\n result_wrapper = vol_swap_volatility(prices)\n result_direct = volatility(prices, Window(50, 49), Returns.LOGARITHMIC, 252, True)\n\n assert_series_equal(result_wrapper, result_direct)\n\n\ndef test_correlation():\n x = pd.Series(dtype=float)\n assert_series_equal(pd.Series(dtype=float), correlation(x, x))\n assert_series_equal(pd.Series(dtype=float), correlation(x, x, 1))\n\n daily_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=daily_dates)\n y = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=daily_dates)\n\n result = correlation(x, y)\n expected = pd.Series([np.nan, np.nan, 1.0, 1.0, 1.0, 1.0], index=daily_dates)\n\n assert_series_equal(result, expected)\n\n y = pd.Series([100.0, 102.0, 104.0, 101.0, 100.95, 100.0], index=daily_dates)\n\n result = correlation(x, y)\n expected = pd.Series([np.nan, np.nan, -1.0, 0.969025, 0.969254, 0.706042], index=daily_dates)\n\n assert_series_equal(result, expected)\n\n result = correlation(x, y, Window(2, 0))\n expected = pd.Series(\n [np.nan, np.nan, -1.0000000000000435, 1.0, 0.9999999999999994, -1.0000000000000007], index=daily_dates\n )\n\n assert_series_equal(result, expected)\n\n ret_x = returns(x)\n ret_y = returns(y)\n\n result = correlation(ret_x, ret_y, Window(2, 0), False)\n values = [np.nan, np.nan, -1.0000000000000435, 1.0, 0.9999999999999994, -1.0000000000000007]\n expected = pd.Series(values, index=daily_dates)\n\n assert_series_equal(result, expected)\n\n result = correlation(x, y, Window('2d', 0))\n expected = pd.Series([np.nan, np.nan, -1.0, 1.0, np.nan, -1.0], index=daily_dates)\n assert_series_equal(result, expected)\n\n result = correlation(x, y, \"2d\")\n expected = pd.Series([-1, 1, np.nan, -1], index=daily_dates[2:])\n assert_series_equal(result, expected, obj=\"Correlation strdate as window\")\n\n result = correlation(x, y, \"3m\")\n expected = pd.Series(dtype=float, index=[])\n assert_series_equal(result, expected, obj=\"Correlation strdate as window with too large of window\")\n\n\ndef test_correlation_returns():\n x = generate_series(50, Direction.END_TODAY)\n y = generate_series(50, Direction.END_TODAY)\n base_corr = correlation(x, y)\n corr_simple = correlation(x, y, returns_type=Returns.SIMPLE)\n assert_series_equal(base_corr, corr_simple)\n\n # Log returns\n corr_log = correlation(x, y, returns_type=Returns.LOGARITHMIC)\n returns_x = returns(x, type=Returns.LOGARITHMIC)\n returns_y = returns(y, type=Returns.LOGARITHMIC)\n corr_log_manual = correlation(returns_x, returns_y, type_=SeriesType.RETURNS)\n assert_series_equal(corr_log, corr_log_manual)\n\n # Mixed returns type\n corr_log_abs = correlation(x, y, returns_type=(Returns.LOGARITHMIC, Returns.ABSOLUTE))\n returns_y = returns(y, type=Returns.ABSOLUTE)\n corr_log_manual = correlation(returns_x, returns_y, type_=SeriesType.RETURNS)\n assert_series_equal(corr_log_abs, corr_log_manual)\n\n # Error cases\n with pytest.raises(MqValueError):\n correlation(x, y, returns_type=[Returns.SIMPLE])\n with pytest.raises(MqTypeError):\n correlation(x, y, returns_type=[\"simple\", \"logarithmic\"])\n\n\ndef test_corr_swap_correlation():\n assert corr_swap_correlation(pd.Series(), pd.Series()).empty\n\n num_dates = 10\n dates = pd.to_datetime(pd.date_range(start='2025-01-01', periods=num_dates))\n rng = np.random.default_rng()\n x = pd.Series(rng.random(num_dates), index=dates)\n y = pd.Series(rng.random(num_dates), index=dates)\n\n # ramp-up must be w - 1\n with pytest.raises(MqTypeError):\n corr_swap_correlation(x, y, n_days=Window(3, 0))\n\n for n_days in [3, 5, 9, 10]:\n # Windows and numbers get treated identically\n pd.testing.assert_series_equal(\n corr_swap_correlation(x, y, n_days=n_days),\n corr_swap_correlation(x, y, n_days=Window(n_days - 1, n_days - 2)),\n )\n\n # n_days left unspecified defaults to the number of price dates\n pd.testing.assert_series_equal(\n corr_swap_correlation(x, y, n_days=num_dates),\n corr_swap_correlation(\n x,\n y,\n ),\n )\n\n for window_size in [3, 5, 9]:\n # assume_zero_mean=False should match correlation function\n corr = correlation(x, y, returns_type=Returns.LOGARITHMIC, w=Window(window_size, window_size - 1))\n corr_swap_corr = corr_swap_correlation(x, y, n_days=window_size + 1, assume_zero_mean=False)\n pd.testing.assert_series_equal(corr, corr_swap_corr)\n\n # assume_zero_mean=True yields correct numbers that are different from assume_zero_mean=False\n zero_mean_corr_swap_corr = corr_swap_correlation(x, y, n_days=window_size + 1)\n assert not np.allclose(corr_swap_corr.values, zero_mean_corr_swap_corr.values, rtol=1e-10)\n\n zero_mean_corr_swap_corr = zero_mean_corr_swap_corr.iloc[1:] # drop the first NaN\n assert np.all(zero_mean_corr_swap_corr.between(-1.0, 1.0))\n # manually verify the first valid correlation (originally at index 1, now at iloc[0])\n ret_x = np.log(x / x.shift(1)).dropna()\n ret_y = np.log(y / y.shift(1)).dropna()\n window_x = ret_x.iloc[0:window_size].values\n window_y = ret_y.iloc[0:window_size].values\n denom = len(window_x)\n var_x = np.sum(window_x**2) / denom\n var_y = np.sum(window_y**2) / denom\n covar = np.sum(window_x * window_y) / denom\n if var_x > 0 and var_y > 0:\n expected_zero_mean_corr = covar / (np.sqrt(var_x) * np.sqrt(var_y))\n actual_zero_mean_corr = zero_mean_corr_swap_corr.iloc[0]\n np.testing.assert_almost_equal(\n actual_zero_mean_corr,\n expected_zero_mean_corr,\n decimal=10,\n err_msg=f\"Zero-mean correlation mismatch for window_size={window_size}\",\n )\n\n\ndef test_beta():\n x = pd.Series(dtype=float)\n assert_series_equal(pd.Series(dtype=float), beta(x, x))\n assert_series_equal(pd.Series(dtype=float), beta(x, x, 1))\n\n daily_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=daily_dates)\n y = pd.Series([100.0, 101, 103.02, 100.9596, 100.9596, 102.978792], index=daily_dates)\n\n result = beta(x, y)\n expected = pd.Series([np.nan, np.nan, np.nan, 1.0, 1.0, 1.0], index=daily_dates)\n assert_series_equal(result, expected)\n\n y = pd.Series([100.0, 102.0, 104.0, 101.0, 100.95, 100.0], index=daily_dates)\n\n result = beta(x, y)\n expected = pd.Series([np.nan, np.nan, np.nan, 0.718146, 0.718919, 0.572201], index=daily_dates)\n assert_series_equal(result, expected)\n\n result = beta(x, y, Window(2, 0))\n expected = pd.Series(\n [np.nan, np.nan, np.nan, 0.8255252918287954, 0.7054398925453326, -2.24327163719368], index=daily_dates\n )\n assert_series_equal(result, expected)\n\n ret_x = returns(x)\n ret_y = returns(y)\n\n result = beta(ret_x, ret_y, Window(2, 0), False)\n expected = pd.Series(\n [np.nan, np.nan, np.nan, 0.8255252918287954, 0.7054398925453326, -2.24327163719368], index=daily_dates\n )\n assert_series_equal(result, expected)\n\n result = beta(x, y, Window('2d', 0))\n expected = pd.Series([np.nan, np.nan, np.nan, 0.8255252918287954, np.nan, -2.24327163719368], index=daily_dates)\n assert_series_equal(result, expected)\n\n result = beta(x, y, '2d')\n expected = pd.Series([np.nan, 0.8255252918287954, np.nan, -2.24327163719368], index=daily_dates[2:])\n assert_series_equal(result, expected, obj=\"beta with strdate window\")\n\n with pytest.raises(MqTypeError):\n beta(x, y, '2d', 1)\n\n\ndef test_max_drawdown():\n daily_dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n daily_dates = pd.to_datetime(daily_dates)\n\n series = pd.Series([1, 5, 5, 4, 4, 1], index=daily_dates)\n\n result = max_drawdown(series)\n expected = pd.Series([0.0, 0.0, 0.0, -0.2, -0.2, -0.8], index=daily_dates)\n assert_series_equal(result, expected, obj=\"Max drawdown\")\n\n result = max_drawdown(series, Window(2, 0))\n expected = pd.Series([0.0, 0.0, 0.0, -0.2, -0.2, -0.75], index=daily_dates)\n assert_series_equal(result, expected, obj=\"Max drawdown window 2\")\n\n with pytest.raises(TypeError):\n max_drawdown(pd.Series([1, 5, 5, 4, 4, 1]), Window('2d', 0))\n result = max_drawdown(series, Window('2d', 0))\n expected = pd.Series([0.0, 0.0, 0.0, -0.2, 0.0, -0.75], index=daily_dates)\n assert_series_equal(result, expected, obj=\"Max drawdown window 2d\")\n\n\ndef test_excess_returns():\n replace = Replacer()\n file = os.path.join(os.path.dirname(__file__), '..', 'resources', 'MIDASER_SPX_USD.csv')\n df = pd.read_csv(file)\n df.index = pd.to_datetime(df['Date'])\n\n market_data = replace('gs_quant.timeseries.econometrics.GsDataApi.get_market_data', Mock())\n data = df.loc[:, ['USD']]\n data = data.rename(columns={'USD': 'spot'})\n market_data.return_value = data\n\n with pytest.raises(Exception):\n excess_returns(df['SPX'], CurrencyEnum.AED)\n\n actual = excess_returns(df['SPX'], CurrencyEnum.USD)\n expected = df['MIDASER']\n assert_series_equal(actual, expected, check_names=False)\n\n plot = excess_returns_(df['SPX'], RiskFreeRateCurrency.USD)\n assert_series_equal(plot, expected, check_names=False)\n\n actual = excess_returns(df['SPX'], Cash('MABCDE', 'T_SHARPE_USD'))\n assert_series_equal(actual, expected, check_names=False)\n\n actual = excess_returns(df['SPX'], 0.0175)\n file = os.path.join(os.path.dirname(__file__), '..', 'resources', 'Sharpe_SPX_0175.csv')\n expected = pd.read_csv(file).loc[:, 'ER']\n np.testing.assert_array_almost_equal(actual.values, expected.values)\n\n market_data.return_value = pd.DataFrame()\n with pytest.raises(MqError):\n excess_returns(df['SPX'], CurrencyEnum.USD)\n\n replace.restore()\n\n\ndef test_sharpe_ratio():\n file = os.path.join(os.path.dirname(__file__), '..', 'resources', 'MIDASER_SPX_USD.csv')\n price_df = pd.read_csv(file)\n price_df.index = pd.to_datetime(price_df['Date'])\n\n file = os.path.join(os.path.dirname(__file__), '..', 'resources', 'Sharpe_SPX_0175.csv')\n er_df = pd.read_csv(file)\n er_df.index = pd.to_datetime(er_df['Date'])\n\n replace = Replacer()\n er = replace('gs_quant.timeseries.econometrics.excess_returns', Mock())\n er.return_value = er_df['ER']\n actual = _get_ratio(price_df['SPX'], 0.0175, 0, day_count_convention=DayCountConvention.ACTUAL_360)\n np.testing.assert_almost_equal(actual.values, er_df['SR'].values, decimal=5)\n actual = sharpe_ratio(price_df['SPX'], RiskFreeRateCurrency.USD)\n np.testing.assert_almost_equal(actual.values, er_df['SR'].values, decimal=5)\n actual = sharpe_ratio(price_df['SPX'][:], RiskFreeRateCurrency.USD, '1m')\n expected = pd.Series(\n [np.nan, np.nan, np.nan, 8.266434, 6.731811], index=pd.date_range(dt.date(2019, 2, 4), periods=5)\n )\n np.testing.assert_almost_equal(actual[:5].values, expected.values, decimal=5)\n with pytest.raises(MqValueError):\n actual = sharpe_ratio(price_df['SPX'][:], RiskFreeRateCurrency.USD, 22, method=Interpolate.INTERSECT)\n\n replace.restore()\n\n actual = _get_ratio(price_df['SPX'], 0.0175, 10, day_count_convention=DayCountConvention.ACTUAL_360)\n np.testing.assert_almost_equal(actual.values, er_df['SR10'].values[10:], decimal=5)\n\n actual = _get_ratio(\n er_df['ER'], 0.0175, 0, day_count_convention=DayCountConvention.ACTUAL_360, curve_type=CurveType.EXCESS_RETURNS\n )\n np.testing.assert_almost_equal(actual.values, er_df['SR'].values, decimal=5)\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_econometrics.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_helper.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom enum import IntEnum\nfrom unittest import mock\nfrom unittest.mock import MagicMock\nfrom unittest.mock import Mock\n\nimport pandas as pd\nimport pytest\nfrom testfixtures import Replace, Replacer\n\nimport gs_quant.timeseries as ts\nfrom gs_quant.data import DataContext\nfrom gs_quant.data import Dataset\nfrom gs_quant.errors import MqError, MqRequestError\nfrom gs_quant.session import GsSession\nfrom gs_quant.test.api.test_thread_manager import NullContextManager\nfrom gs_quant.timeseries.helper import (\n _create_int_enum,\n _tenor_to_month,\n _month_to_tenor,\n plot_function,\n plot_measure,\n plot_method,\n normalize_window,\n Window,\n apply_ramp,\n check_forward_looking,\n get_df_with_retries,\n get_dataset_data_with_retries,\n _split_where_conditions,\n get_dataset_with_many_assets,\n)\n\n# TODO test the instance of IntEnum when we have any.\n\nWeekDay = _create_int_enum(\n 'WeekDay', {'SUNDAY': 1, 'Monday': 2, 'TUESDAY': 3, 'WEDNESDAY': 4, 'THURSDAY': 5, 'Friday': 6, 'SATURDAY': 7}\n)\n\n\ndef test_int_enum():\n assert ['a', 'b', 'c'][WeekDay.MONDAY] == 'c'\n\n assert len(list(WeekDay)) == 7\n assert len(WeekDay) == 7\n target = 'SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY'\n target = target.split()\n for i, weekday in enumerate(target, 1):\n e = WeekDay(i)\n assert isinstance(e, IntEnum)\n assert e.name == weekday\n assert e.value == i\n\n\ndef test_tenor_to_month():\n with pytest.raises(MqError):\n _tenor_to_month('1d')\n with pytest.raises(MqError):\n _tenor_to_month('2w')\n assert _tenor_to_month('3m') == 3\n assert _tenor_to_month('4y') == 48\n\n\ndef test_month_to_tenor():\n assert _month_to_tenor(36) == '3y'\n assert _month_to_tenor(18) == '18m'\n\n\ndef test_get_dataset_with_many_assets():\n # Mock the Dataset object\n mock_dataset = Mock(spec=Dataset)\n\n def foo(**kwargs) -> pd.DataFrame:\n return pd.DataFrame({\"assetId\": list(kwargs[\"assetId\"]), \"column2\": [0] * len(kwargs[\"assetId\"])})\n\n mock_dataset.get_data = foo\n\n # Test parameters\n assets = [f'Asset{i}' for i in range(150)]\n start = dt.date(2023, 1, 1)\n end = dt.date(2023, 1, 31)\n\n # Call the function and inspect the result\n with (\n mock.patch(\"gs_quant.api.utils.DataContext\", MagicMock(spec=DataContext)) as mock_data_context,\n mock.patch(\"gs_quant.api.utils.GsSession\", MagicMock(spec=GsSession)) as mock_gs_session,\n ):\n mock_data_context.current = mock_data_context\n mock_gs_session.current = mock_gs_session\n\n result = get_dataset_with_many_assets(ds=mock_dataset, assets=assets, start=start, end=end, batch_limit=100)\n\n # Assertions\n assert len(result) == 150\n assert result['assetId'].tolist() == assets\n\n\n@plot_function\ndef pf():\n pass\n\n\n@plot_measure(('xyz',), asset_type=('abc',))\ndef pm():\n pass\n\n\n@plot_method\ndef pmt(arg):\n return arg\n\n\ndef test_decorators():\n assert pf.plot_function\n assert pm.plot_measure\n assert pmt.plot_method\n assert pm.asset_class == ('xyz',)\n assert pm.asset_type == ('abc',)\n assert pm.asset_type_excluded is None\n assert pmt(1, real_time=True) == 1\n\n\ndef test_normalize_window_defaults_window_if_none_passed():\n x = ts.generate_series(10)\n w = normalize_window(x, None)\n assert w.w == 10\n assert w.r == 0\n\n\ndef test_normalize_window_defaults_window_if_passed():\n x = ts.generate_series(10)\n w = normalize_window(x, None, default_window=2)\n assert w.w == 2\n assert w.r == 0\n\n\ndef test_normalize_window_handles_int():\n x = ts.generate_series(10)\n w = normalize_window(x, 5)\n assert w.w == 5\n assert w.r == 5\n\n\ndef test_normalize_window_handles_window_with_no_ramp():\n x = ts.generate_series(10)\n w = normalize_window(x, Window(2, None))\n assert w.w == 2\n assert w.r == 2\n\n\ndef test_normalize_window_handles_window_with_no_size():\n x = ts.generate_series(10)\n w = normalize_window(x, Window(None, 2))\n assert w.w == 10\n assert w.r == 2\n\n\ndef test_normalize_window_handles_ramp_greater_than_series_length():\n with pytest.raises(ValueError):\n x = ts.generate_series(10)\n normalize_window(x, Window(2, 11))\n\n\ndef test_normalize_window_raises_error_on_window_of_size_zero():\n with pytest.raises(ValueError):\n x = ts.generate_series(10)\n normalize_window(x, 0)\n with pytest.raises(ValueError):\n x = ts.generate_series(10)\n normalize_window(x, Window(0, 0))\n\n\ndef test_normalize_window_handles_ramp_of_size_zero():\n x = ts.generate_series(10)\n w = normalize_window(x, Window(2, 0))\n assert w.w == 2\n assert w.r == 0\n\n\ndef test_normalize_window_str():\n x = ts.generate_series(10)\n w = normalize_window(x, Window('1w', '2d'))\n assert w.w == pd.DateOffset(weeks=1)\n assert w.r == pd.DateOffset(days=2)\n\n\ndef test_normalize_window_single_str():\n x = ts.generate_series(10)\n w = normalize_window(x, \"2d\")\n assert w.w == pd.DateOffset(days=2)\n assert w.r == pd.DateOffset(days=2)\n x = ts.generate_series(90)\n w = normalize_window(x, \"3m\")\n assert w.w == pd.DateOffset(months=3)\n assert w.r == pd.DateOffset(months=3)\n x = ts.generate_series(10)\n w = normalize_window(x, \"1h\")\n assert w.w == pd.DateOffset(hours=1)\n assert w.r == pd.DateOffset(hours=1)\n\n\ndef test_apply_ramp():\n x = ts.generate_series(10)\n y = apply_ramp(x, Window(2, 2))\n assert len(y) == 8\n\n\ndef test_apply_ramp_with_window_greater_than_series_length():\n x = ts.generate_series(10)\n y = apply_ramp(x, Window(11, 2))\n assert len(y) == 0\n\n\ndef test_apply_ramp_dateoffset():\n x = pd.Series(range(10), index=pd.bdate_range('2020-02-17', freq='B', periods=10))\n y = apply_ramp(x, Window(pd.DateOffset(weeks=1), pd.DateOffset(days=1)))\n assert len(y) == 9\n\n\ndef test_apply_ramp_raises_on_edge_cases():\n with pytest.raises(ValueError):\n x = ts.generate_series(10)\n apply_ramp(x, Window(0, 0))\n with pytest.raises(ValueError):\n x = ts.generate_series(10)\n apply_ramp(x, Window(-1, 0))\n with pytest.raises(ValueError):\n x = ts.generate_series(10)\n apply_ramp(x, Window(2, -1))\n with pytest.raises(ValueError):\n x = ts.generate_series(10)\n apply_ramp(x, Window(2, 11))\n\n\ndef test_get_df_with_retries():\n start = dt.date(2020, 12, 1)\n end = dt.date(2020, 12, 2)\n counter = 0\n\n def fetch0():\n return pd.DataFrame()\n\n def fetch1():\n nonlocal counter\n counter += 1\n if counter > 1:\n return pd.DataFrame([['foo'], ['bar']])\n return pd.DataFrame()\n\n def mock_apply(self, *_args, **_kwargs):\n return self.base_date - dt.timedelta(days=1)\n\n with Replace('gs_quant.timeseries.helper.RelativeDate.apply_rule', mock_apply):\n df = get_df_with_retries(fetch0, start, end, 'NYSE')\n assert df.empty\n df = get_df_with_retries(fetch1, start, end, 'NYSE', 0)\n assert df.empty\n df = get_df_with_retries(fetch1, start, end, 'NYSE', 1)\n assert not df.empty\n df = get_df_with_retries(fetch1, start, end, 'NYSE', 2)\n assert not df.empty\n\n\ndef test_forward_looking():\n today = dt.date.today()\n source = 'plottool'\n with DataContext(today, today + dt.timedelta(days=1)):\n assert check_forward_looking('1b', None) is None\n assert check_forward_looking(today, None) is None\n assert check_forward_looking(None, None) is None\n assert check_forward_looking('1b', source) is None\n assert check_forward_looking(today, source) is None\n assert check_forward_looking(None, source) is None\n with DataContext(today - dt.timedelta(days=1), today):\n assert check_forward_looking('1b', None) is None\n assert check_forward_looking(today, None) is None\n assert check_forward_looking(None, None) is None\n assert check_forward_looking('1b', source) is None\n assert check_forward_looking(today, source) is None\n with pytest.raises(MqError):\n check_forward_looking(None, source)\n\n\ndef test_get_dataset_data_with_retries():\n replace = Replacer()\n GsSession.current = MagicMock(return_calue=NullContextManager())\n mock = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock.side_effect = [\n MqRequestError(400, message='Some other error'),\n MqRequestError(400, message='Some other error'),\n MqRequestError(400, message='Some other error'),\n ]\n dataset = Dataset(Dataset.TR.TREOD)\n with pytest.raises(MqRequestError):\n get_dataset_data_with_retries(\n dataset, start=dt.date(2000, 1, 2), end=dt.date(2019, 1, 9), assetId='MA4B66MW5E27U8P32SB', max_retries=0\n )\n\n replace.restore()\n\n\ndef test_split_where_conditions():\n where = dict(tenor=['1m', '2m'], strikeReference='delta_call', relativeStrike=25)\n expected = [\n dict(tenor=['1m'], strikeReference='delta_call', relativeStrike=25),\n dict(tenor=['2m'], strikeReference='delta_call', relativeStrike=25),\n ]\n actual = _split_where_conditions(where)\n assert actual == expected\n\n where = dict(tenor='1m', strikeReference='delta_call', relativeStrike=25)\n expected = [dict(tenor='1m', strikeReference='delta_call', relativeStrike=25)]\n actual = _split_where_conditions(where)\n assert actual == expected\n\n where = dict()\n expected = [dict()]\n actual = _split_where_conditions(where)\n assert actual == expected\n\n where = dict(tenor=['1m', '2m'], strikeReference='delta_call', relativeStrike=[25, 50])\n expected = [\n dict(tenor=['1m'], strikeReference='delta_call', relativeStrike=[25]),\n dict(tenor=['1m'], strikeReference='delta_call', relativeStrike=[50]),\n dict(tenor=['2m'], strikeReference='delta_call', relativeStrike=[25]),\n dict(tenor=['2m'], strikeReference='delta_call', relativeStrike=[50]),\n ]\n actual = _split_where_conditions(where)\n assert actual == expected\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_helper.py\"])\n\n\ndef test_get_dataset_data_with_retries_recursive_split():\n dataset = Mock()\n dataset.get_data = Mock(\n side_effect=[\n MqRequestError(400, message=\"Some error occurred\"),\n pd.DataFrame({\"data\": [1, 2]}),\n pd.DataFrame({\"data\": [3, 4]}),\n ]\n )\n\n start = dt.date(2023, 1, 1)\n end = dt.date(2023, 1, 10)\n\n result = get_dataset_data_with_retries(dataset, start=start, end=end, max_retries=2)\n\n # Verify the recursive splitting logic\n assert not result.empty\n assert len(result) == 4\n dataset.get_data.assert_called()\n" }, { "path": "gs_quant/test/timeseries/test_measures.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport json\nimport os\nfrom typing import Union, Dict\nfrom unittest import mock\nfrom unittest.mock import patch\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom numpy.testing import assert_equal\nfrom pandas.testing import assert_series_equal\nfrom pandas.tseries.offsets import CustomBusinessDay\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock, MagicMock\n\nimport gs_quant.timeseries.measures as tm\nimport gs_quant.timeseries.measures_rates as tm_rates\nfrom gs_quant.api.gs.assets import GsTemporalXRef, GsAssetApi, GsIdType, IdList, GsAsset\nfrom gs_quant.api.gs.data import GsDataApi, MarketDataResponseFrame\nfrom gs_quant.api.gs.data import QueryType\nfrom gs_quant.common import AssetType, XRef, PricingLocation, Currency as CurrEnum\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.errors import MqError, MqValueError, MqTypeError\nfrom gs_quant.json_encoder import JSONEncoder\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.baskets import Basket as CustomBasket\nfrom gs_quant.markets.index import Index\nfrom gs_quant.markets.securities import (\n AssetClass,\n Cross,\n Currency,\n SecurityMaster,\n Stock,\n Swap,\n CommodityNaturalGasHub,\n CommodityEUNaturalGasHub,\n AssetIdentifier,\n CommodityPowerAggregatedNodes,\n FutureMarket,\n DefaultSwap,\n)\nfrom gs_quant.session import GsSession, Environment, OAuth2Session\nfrom gs_quant.test.timeseries.utils import mock_request\nfrom gs_quant.timeseries import Returns, ExtendedSeries, BenchmarkType\n\n_index = [pd.Timestamp('2019-01-01')]\n_index2 = [pd.Timestamp('2019-08-02')]\n_test_datasets = ('TEST_DATASET',)\n_test_datasets2 = ('TEST_DATASET2',)\n_test_datasets_rt = ('TEST_DATASET_RT',)\n\n\ndef _make_index(dates, name=None):\n idx = pd.DatetimeIndex(dates)\n if hasattr(idx, 'as_unit'):\n idx = idx.as_unit('ns')\n if name is not None:\n idx.name = name\n return idx\n\n\ndef mock_empty_market_data_response():\n df = MarketDataResponseFrame()\n df.dataset_ids = ()\n return df\n\n\ndef map_identifiers_default_mocker(\n input_type: Union[GsIdType, str],\n output_type: Union[GsIdType, str],\n ids: IdList,\n as_of: dt.datetime = None,\n multimap: bool = False,\n limit: int = None,\n **kwargs,\n) -> dict:\n if \"USD-LIBOR-BBA\" in ids:\n return {\"USD-LIBOR-BBA\": \"MAPDB7QNB2TZVQ0E\"}\n elif \"EUR-EURIBOR-TELERATE\" in ids:\n return {\"EUR-EURIBOR-TELERATE\": \"MAJNQPFGN1EBDHAE\"}\n elif \"GBP-LIBOR-BBA\" in ids:\n return {\"GBP-LIBOR-BBA\": \"MAFYB8Z4R1377A19\"}\n elif \"JPY-LIBOR-BBA\" in ids:\n return {\"JPY-LIBOR-BBA\": \"MABMVE27EM8YZK33\"}\n elif \"EUR OIS\" in ids:\n return {\"EUR OIS\": \"MARFAGXDQRWM07Y2\"}\n\n\ndef map_identifiers_ois_mocker(\n input_type: Union[GsIdType, str],\n output_type: Union[GsIdType, str],\n ids: IdList,\n as_of: dt.datetime = None,\n multimap: bool = False,\n limit: int = None,\n **kwargs,\n) -> dict:\n if \"USD OIS\" in ids:\n return {\"USD OIS\": \"MA29GN9VZE0WK56V\"}\n elif \"EUR OIS\" in ids:\n return {\"EUR OIS\": \"MARFAGXDQRWM07Y2\"}\n elif \"SEK OIS\" in ids:\n return {\"SEK OIS\": \"MARSF2Y8GV5GHXYZ\"}\n elif \"AUD OIS\" in ids:\n return {\"AUD OIS\": \"MA2XT17D0P77A84G\"}\n elif \"JPY OIS\" in ids:\n return {\"JPY OIS\": \"MAY3XC3279QN7SHW\"}\n elif \"GBP OIS\" in ids:\n return {\"GBP OIS\": \"MAGP76C36BX2Q0YA\"}\n elif \"NOK OIS\" in ids:\n return {\"NOK OIS\": \"MAS7EYBPYZ4SQ4GG\"}\n elif \"CAD OIS\" in ids:\n return {\"CAD OIS\": \"MAX229TKVEZMZ4WT\"}\n elif \"NZD OIS\" in ids:\n return {\"NZD OIS\": \"MA48BHGWY71R4SAJ\"}\n elif \"CHF OIS\" in ids:\n return {\"CHF OIS\": \"MAK0CRRF9DPMECSJ\"}\n\n\ndef map_identifiers_swap_rate_mocker(\n input_type: Union[GsIdType, str],\n output_type: Union[GsIdType, str],\n ids: IdList,\n as_of: dt.datetime = None,\n multimap: bool = False,\n limit: int = None,\n **kwargs,\n) -> dict:\n if \"USD-3m\" in ids:\n return {\"USD-3m\": \"MAAXGV0GZTW4GFNC\"}\n elif \"EUR-6m\" in ids:\n return {\"EUR-6m\": \"MA5WM2QWRVMYKDK0\"}\n elif \"KRW\" in ids:\n return {\"KRW\": 'MAJ6SEQH3GT0GA2Z'}\n\n\ndef map_identifiers_inflation_mocker(\n input_type: Union[GsIdType, str],\n output_type: Union[GsIdType, str],\n ids: IdList,\n as_of: dt.datetime = None,\n multimap: bool = False,\n limit: int = None,\n **kwargs,\n) -> dict:\n if \"CPI-UKRPI\" in ids:\n return {\"CPI-UKRPI\": \"MAQ7ND0MBP2AVVQW\"}\n elif \"CPI-CPXTEMU\" in ids:\n return {\"CPI-CPXTEMU\": \"MAK1FHKH5P5GJSHH\"}\n\n\ndef map_identifiers_cross_basis_mocker(\n input_type: Union[GsIdType, str],\n output_type: Union[GsIdType, str],\n ids: IdList,\n as_of: dt.datetime = None,\n multimap: bool = False,\n limit: int = None,\n **kwargs,\n) -> dict:\n if \"USD-3m/JPY-3m\" in ids:\n return {\"USD-3m/JPY-3m\": \"MA99N6C1KF9078NM\"}\n elif \"EUR-3m/USD-3m\" in ids:\n return {\"EUR-3m/USD-3m\": \"MAXPKTXW2D4X6MFQ\"}\n elif \"GBP-3m/USD-3m\" in ids:\n return {\"GBP-3m/USD-3m\": \"MA8BZHQV3W32V63B\"}\n\n\ndef test_currency_to_default_ois_asset(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=map_identifiers_ois_mocker)\n\n asset_id_list = [\"MAJNQPFGN1EBDHAE\"]\n correct_mapping = [\"MARFAGXDQRWM07Y2\"]\n with PricingContext(dt.date.today()):\n for i in range(len(asset_id_list)):\n correct_id = tm.currency_to_default_ois_asset(asset_id_list[i])\n assert correct_id == correct_mapping[i]\n\n # Test that the same id is returned when a TypeError is raised\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=TypeError('Test'))\n assert tm.currency_to_default_ois_asset('MAJNQPFGN1EBDHAE') == 'MAJNQPFGN1EBDHAE'\n\n\ndef test_currency_to_default_benchmark_rate(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=map_identifiers_default_mocker)\n\n asset_id_list = [\n \"MAZ7RWC904JYHYPS\",\n \"MAJNQPFGN1EBDHAE\",\n \"MA66CZBQJST05XKG\",\n \"MAK1FHKH5P5GJSHH\",\n \"MA4J1YB8XZP2BPT8\",\n \"MA4B66MW5E27U8P32SB\",\n ]\n correct_mapping = [\n \"MAPDB7QNB2TZVQ0E\",\n \"MAJNQPFGN1EBDHAE\",\n \"MAFYB8Z4R1377A19\",\n \"MABMVE27EM8YZK33\",\n \"MA4J1YB8XZP2BPT8\",\n \"MA4B66MW5E27U8P32SB\",\n ]\n with PricingContext(dt.date.today()):\n for i in range(len(asset_id_list)):\n correct_id = tm.currency_to_default_benchmark_rate(asset_id_list[i])\n assert correct_id == correct_mapping[i]\n\n\ndef test_currency_to_default_swap_rate_asset(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=map_identifiers_swap_rate_mocker)\n\n asset_id_list = ['MAZ7RWC904JYHYPS', 'MAJNQPFGN1EBDHAE', 'MAJ6SEQH3GT0GA2Z']\n correct_mapping = ['MAAXGV0GZTW4GFNC', 'MA5WM2QWRVMYKDK0', 'MAJ6SEQH3GT0GA2Z']\n with PricingContext(dt.date.today()):\n for i in range(len(asset_id_list)):\n correct_id = tm.currency_to_default_swap_rate_asset(asset_id_list[i])\n assert correct_id == correct_mapping[i]\n\n\ndef test_currency_to_inflation_benchmark_rate(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=map_identifiers_inflation_mocker)\n\n asset_id_list = [\"MA66CZBQJST05XKG\", \"MAK1FHKH5P5GJSHH\", \"MA4J1YB8XZP2BPT8\"]\n correct_mapping = [\"MAQ7ND0MBP2AVVQW\", \"MAK1FHKH5P5GJSHH\", \"MA4J1YB8XZP2BPT8\"]\n with PricingContext(dt.date.today()):\n for i in range(len(asset_id_list)):\n correct_id = tm.currency_to_inflation_benchmark_rate(asset_id_list[i])\n assert correct_id == correct_mapping[i]\n\n # Test that the same id is returned when a TypeError is raised\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=TypeError('Test'))\n assert tm.currency_to_inflation_benchmark_rate('MA66CZBQJST05XKG') == 'MA66CZBQJST05XKG'\n\n\ndef test_cross_to_basis(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=map_identifiers_cross_basis_mocker)\n\n asset_id_list = [\"MAYJPCVVF2RWXCES\", \"MA4B66MW5E27U8P32SB\", \"nobbid\"]\n correct_mapping = [\"MA99N6C1KF9078NM\", \"MA4B66MW5E27U8P32SB\", \"nobbid\"]\n with PricingContext(dt.date.today()):\n for i in range(len(asset_id_list)):\n correct_id = tm.cross_to_basis(asset_id_list[i])\n assert correct_id == correct_mapping[i]\n\n # Test that the same id is returned when a TypeError is raised\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=TypeError('Test'))\n assert tm.cross_to_basis('MAYJPCVVF2RWXCES') == 'MAYJPCVVF2RWXCES'\n\n\ndef test_currency_to_tdapi_swap_rate_asset(mocker):\n replace = Replacer()\n mocker.patch.object(\n GsSession.__class__, 'current', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=mock_request)\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n with PricingContext(dt.date.today()):\n asset = Currency('MA25DW5ZGC1BSC8Y', 'NOK')\n bbid_mock.return_value = 'NOK'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n asset = Currency('MAZ7RWC904JYHYPS', 'USD')\n bbid_mock.return_value = 'USD'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAFRSWPAF5QPNTP2' == correct_id\n bbid_mock.return_value = 'CHF'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAW25BGQJH9P6DPT' == correct_id\n bbid_mock.return_value = 'EUR'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAA9MVX15AJNQCVG' == correct_id\n bbid_mock.return_value = 'GBP'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MA6QCAP9B7ABS9HA' == correct_id\n bbid_mock.return_value = 'JPY'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAEE219J5ZP0ZKRK' == correct_id\n bbid_mock.return_value = 'SEK'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAETMVTPNP3199A5' == correct_id\n\n bbid_mock.return_value = 'HKD'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MABRNGY8XRFVC36N' == correct_id\n bbid_mock.return_value = 'NZD'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAH16NHE1HBN0FBZ' == correct_id\n bbid_mock.return_value = 'AUD'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAY8147CRK0ZP53B' == correct_id\n bbid_mock.return_value = 'CAD'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MANJ8SS88WJ6N28Q' == correct_id\n bbid_mock.return_value = 'KRW'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAP55AXG5SQVS6C5' == correct_id\n\n bbid_mock.return_value = 'INR'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MA20JHJXN1PD5HGE' == correct_id\n\n bbid_mock.return_value = 'CNY'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MA4K1D8HH2R0RQY5' == correct_id\n\n bbid_mock.return_value = 'SGD'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MA5CQFHYBPH9E5BS' == correct_id\n\n bbid_mock.return_value = 'DKK'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n assert 'MAF131NKWVRESFYA' == correct_id\n\n asset = Currency('MA890', 'PLN')\n bbid_mock.return_value = 'PLN'\n assert 'MA890' == tm_rates._currency_to_tdapi_swap_rate_asset(asset)\n replace.restore()\n\n\ndef test_currency_to_tdapi_basis_swap_rate_asset(mocker):\n replace = Replacer()\n mocker.patch.object(\n GsSession.__class__, 'current', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=mock_request)\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n with PricingContext(dt.date.today()):\n asset = Currency('MA890', 'EGP')\n bbid_mock.return_value = 'EGP'\n assert 'MA890' == tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n asset = Currency('MAZ7RWC904JYHYPS', 'USD')\n bbid_mock.return_value = 'USD'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MAQB1PGEJFCET3GG' == correct_id\n bbid_mock.return_value = 'EUR'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MAGRG2VT11GQ2RQ9' == correct_id\n bbid_mock.return_value = 'GBP'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MAHCYNB3V75JC5Q8' == correct_id\n bbid_mock.return_value = 'JPY'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MAXVRBEZCJVH0C4V' == correct_id\n bbid_mock.return_value = 'AUD'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MAY8H7HCNZ85FJKM' == correct_id\n bbid_mock.return_value = 'NZD'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MAWK15C0P3SM6C7Q' == correct_id\n bbid_mock.return_value = 'CHF'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MA7ZHB9T0PF1SB96' == correct_id\n bbid_mock.return_value = 'DKK'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MA2164KK5DMYA561' == correct_id\n bbid_mock.return_value = 'NOK'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MAPXC5YBPZJZXYMZ' == correct_id\n bbid_mock.return_value = 'SEK'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MAS2NJCYHDHP8P0X' == correct_id\n bbid_mock.return_value = 'CAD'\n correct_id = tm_rates._currency_to_tdapi_basis_swap_rate_asset(asset)\n assert 'MARVD2E65AWEXXBA' == correct_id\n replace.restore()\n\n\ndef test_currency_to_tdapi_swap_rate_asset_for_intraday(mocker):\n replace = Replacer()\n mocker.patch.object(\n GsSession.__class__, 'current', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=mock_request)\n\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n\n with PricingContext(dt.date.today()):\n asset = Currency('MAZ7RWC904JYHYPS', 'USD')\n bbid_mock.return_value = 'USD'\n correct_id = tm_rates._currency_to_tdapi_swap_rate_asset_for_intraday(asset)\n assert 'MACF6R4J5FY4KGBZ' == correct_id\n replace.restore()\n\n\ndef my_mocked_mxapi_backtest(\n cls=None,\n builder=None,\n start_time=None,\n end_time=None,\n num_samples=60,\n csa=None,\n request_id=None,\n close_location=None,\n real_time=None,\n):\n d = {'column_name': [], 'timeStamp': []}\n df = MarketDataResponseFrame(pd.DataFrame(data=d))\n df = df.set_index('timeStamp')\n return df\n\n\ndef test_swap_rate_calc(mocker):\n replace = Replacer()\n\n mocker.patch.object(\n GsDataApi,\n 'get_mxapi_backtest_data',\n )\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n\n mocker.patch.object(GsDataApi, 'get_mxapi_backtest_data', side_effect=my_mocked_mxapi_backtest)\n\n asset = Currency('MAZ7RWC904JYHYPS', 'USD')\n bbid_mock.return_value = 'USD'\n val = tm_rates.swap_rate_calc(asset, swap_tenor='10y', benchmark_type='SOFR', real_time=True, forward_tenor='0b')\n assert len(val.keys()) == 0\n\n try:\n val = tm_rates.swap_rate_calc(asset, swap_tenor='10y', benchmark_type='SOFR', real_time=False)\n assert True\n except NotImplementedError:\n assert False\n\n try:\n val = tm_rates.swap_rate_calc(asset, swap_tenor='10x', benchmark_type='SOFR', real_time=True)\n assert False\n except MqValueError:\n assert True\n\n try:\n val = tm_rates.swap_rate_calc(asset, swap_tenor='10y', benchmark_type='SOFR', csa='CME', real_time=True)\n assert True\n except MqValueError:\n assert False\n\n try:\n # rate option with no defaults specified\n val = tm_rates.swap_rate_calc(asset, swap_tenor='10y', benchmark_type='NGN-LIBOR-SYNTHETIC', real_time=True)\n assert False\n except MqValueError:\n assert True\n\n try:\n # rate option with defaults specified\n val = tm_rates.swap_rate_calc(asset, swap_tenor='10y', benchmark_type='USD-SOFR-COMPOUND', real_time=True)\n assert True\n except MqValueError:\n assert False\n\n replace.restore()\n\n\ndef my_mocked_mxapi_measure(\n cls,\n curve_type=None,\n curve_asset=None,\n curve_point=None,\n curve_tags=None,\n measure=None,\n start_time=None,\n end_time=None,\n request_id=None,\n close_location=None,\n real_time=None,\n):\n d = {'column_name': [], 'timeStamp': []}\n df = MarketDataResponseFrame(pd.DataFrame(data=d))\n df = df.set_index('timeStamp')\n return df\n\n\ndef test_curve_measures(mocker):\n replace = Replacer()\n\n mocker.patch.object(GsDataApi, 'get_mxapi_curve_measure')\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n\n mocker.patch.object(GsDataApi, 'get_mxapi_curve_measure', side_effect=my_mocked_mxapi_measure)\n\n asset = Currency('MAZ7RWC904JYHYPS', 'USD')\n bbid_mock.return_value = 'USD'\n val = tm_rates.forward_rate(asset, forward_term='1m')\n assert len(val.keys()) == 0\n val = tm_rates.discount_factor(asset, tenor='1m')\n assert len(val.keys()) == 0\n val = tm_rates.instantaneous_forward_rate(asset, tenor='1m')\n assert len(val.keys()) == 0\n val = tm_rates.index_forward_rate(asset, forward_start_tenor='1m', benchmark_type='LIBOR')\n assert len(val.keys()) == 0\n\n try:\n val = tm_rates.forward_rate(asset)\n assert False\n except MqValueError:\n assert True\n\n try:\n val = tm_rates.discount_factor(asset)\n assert False\n except MqValueError:\n assert True\n\n try:\n val = tm_rates.index_forward_rate(asset)\n assert False\n except MqValueError:\n assert True\n\n try:\n val = tm_rates.instantaneous_forward_rate(asset)\n assert False\n except MqValueError:\n assert True\n\n try:\n # rate option with defaults specified\n val = tm_rates.index_forward_rate(\n asset, forward_start_tenor='10y', benchmark_type='USD-SOFR-COMPOUND', real_time=True\n )\n assert True\n except MqValueError:\n assert False\n\n try:\n # rate option with no defaults specified\n val = tm_rates.index_forward_rate(\n asset, forward_start_tenor='10y', benchmark_type='NGN-LIBOR-SYNTHETIC', real_time=True\n )\n assert False\n except MqValueError:\n assert True\n\n asset = Currency('MA890', 'EGP')\n bbid_mock.return_value = 'EGP'\n val = tm_rates.forward_rate(asset, forward_term='1m')\n assert len(val.keys()) == 0\n\n replace.restore()\n\n\ndef test_check_clearing_house():\n assert tm_rates._ClearingHouse.LCH == tm_rates._check_clearing_house('lch')\n assert tm_rates._ClearingHouse.CME == tm_rates._check_clearing_house(tm_rates._ClearingHouse.CME)\n assert tm_rates._ClearingHouse.LCH == tm_rates._check_clearing_house(None)\n invalid_ch = ['NYSE']\n for ch in invalid_ch:\n with pytest.raises(MqError):\n tm_rates._check_clearing_house(ch)\n\n\ndef test_get_swap_csa_terms():\n euribor_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EURIBOR.value]\n usd_libor_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD'][BenchmarkType.LIBOR.value]\n fed_funds_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD'][BenchmarkType.Fed_Funds.value]\n estr_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EUROSTR.value]\n assert dict(csaTerms='USD-1') == tm_rates._get_swap_csa_terms('USD', fed_funds_index)\n assert dict(csaTerms='EUR-EuroSTR') == tm_rates._get_swap_csa_terms('EUR', estr_index)\n assert {} == tm_rates._get_swap_csa_terms('EUR', euribor_index)\n assert {} == tm_rates._get_swap_csa_terms('USD', usd_libor_index)\n\n\ndef test_get_basis_swap_csa_terms():\n euribor_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EURIBOR.value]\n usd_libor_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD'][BenchmarkType.LIBOR.value]\n fed_funds_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD'][BenchmarkType.Fed_Funds.value]\n sofr_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD'][BenchmarkType.SOFR.value]\n estr_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EUROSTR.value]\n eonia_index = tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EONIA.value]\n assert dict(csaTerms='USD-1') == tm_rates._get_basis_swap_csa_terms('USD', fed_funds_index, sofr_index)\n assert dict(csaTerms='EUR-EuroSTR') == tm_rates._get_basis_swap_csa_terms('EUR', estr_index, eonia_index)\n assert {} == tm_rates._get_basis_swap_csa_terms('EUR', eonia_index, euribor_index)\n assert {} == tm_rates._get_basis_swap_csa_terms('USD', fed_funds_index, usd_libor_index)\n\n\ndef test_match_floating_tenors():\n swap_args = dict(\n asset_parameters_payer_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD']['LIBOR'],\n asset_parameters_payer_designated_maturity='12m',\n asset_parameters_receiver_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD']['SOFR'],\n asset_parameters_receiver_designated_maturity='1y',\n )\n\n assert '1y' == tm_rates._match_floating_tenors(swap_args)['asset_parameters_receiver_designated_maturity']\n\n swap_args = dict(\n asset_parameters_payer_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD']['SOFR'],\n asset_parameters_payer_designated_maturity='1y',\n asset_parameters_receiver_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD']['LIBOR'],\n asset_parameters_receiver_designated_maturity='12m',\n )\n assert '1y' == tm_rates._match_floating_tenors(swap_args)['asset_parameters_payer_designated_maturity']\n\n swap_args = dict(\n asset_parameters_payer_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['GBP']['SONIA'],\n asset_parameters_payer_designated_maturity='1y',\n asset_parameters_receiver_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['GBP']['LIBOR'],\n asset_parameters_receiver_designated_maturity='3m',\n )\n assert '3m' == tm_rates._match_floating_tenors(swap_args)['asset_parameters_payer_designated_maturity']\n\n swap_args = dict(\n asset_parameters_payer_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['GBP']['LIBOR'],\n asset_parameters_payer_designated_maturity='3m',\n asset_parameters_receiver_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['GBP']['SONIA'],\n asset_parameters_receiver_designated_maturity='1y',\n )\n assert '3m' == tm_rates._match_floating_tenors(swap_args)['asset_parameters_receiver_designated_maturity']\n\n swap_args = dict(\n asset_parameters_payer_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD']['LIBOR'],\n asset_parameters_payer_designated_maturity='3m',\n asset_parameters_receiver_rate_option=tm_rates.CURRENCY_TO_SWAP_RATE_BENCHMARK['USD']['LIBOR'],\n asset_parameters_receiver_designated_maturity='6m',\n )\n\n assert swap_args == tm_rates._match_floating_tenors(swap_args)\n\n\ndef test_get_term_struct_date(mocker):\n today = dt.datetime.today()\n biz_day = CustomBusinessDay()\n assert today == tm_rates._get_term_struct_date(tenor=today, index=today, business_day=biz_day)\n date_index = dt.datetime(2020, 7, 31, 0, 0)\n assert date_index == tm_rates._get_term_struct_date(tenor='2020-07-31', index=date_index, business_day=biz_day)\n assert date_index == tm_rates._get_term_struct_date(tenor='0b', index=date_index, business_day=biz_day)\n assert dt.datetime(2021, 7, 30, 0, 0) == tm_rates._get_term_struct_date(\n tenor='1y', index=date_index, business_day=biz_day\n )\n\n\ndef test_cross_stored_direction_for_fx_vol(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsSession.current.sync, 'post', side_effect=mock_request)\n asset_id_list = [\"MAYJPCVVF2RWXCES\", \"MATGYV0J9MPX534Z\"]\n correct_mapping = [\"MATGYV0J9MPX534Z\", \"MATGYV0J9MPX534Z\"]\n with PricingContext(dt.date.today()):\n for i in range(len(asset_id_list)):\n correct_id = tm.cross_stored_direction_for_fx_vol(asset_id_list[i])\n assert correct_id == correct_mapping[i]\n\n\ndef test_cross_to_usd_based_cross_for_fx_forecast(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsSession.current.sync, 'post', side_effect=mock_request)\n asset_id_list = [\"MAYJPCVVF2RWXCES\", \"MATGYV0J9MPX534Z\"]\n correct_mapping = [\"MATGYV0J9MPX534Z\", \"MATGYV0J9MPX534Z\"]\n with PricingContext(dt.date.today()):\n for i in range(len(asset_id_list)):\n correct_id = tm.cross_to_usd_based_cross(asset_id_list[i])\n assert correct_id == correct_mapping[i]\n\n\ndef test_cross_to_used_based_cross(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsSession.current.sync, 'post', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=TypeError('unsupported'))\n\n replace = Replacer()\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'HELLO'\n\n assert 'FUN' == tm.cross_to_usd_based_cross(Cross('FUN', 'EURUSD'))\n replace.restore()\n\n\ndef test_cross_stored_direction(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(GsSession.current.sync, 'post', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=TypeError('unsupported'))\n\n replace = Replacer()\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'HELLO'\n\n assert 'FUN' == tm.cross_stored_direction_for_fx_vol(Cross('FUN', 'EURUSD'))\n replace.restore()\n\n\ndef test_get_tdapi_rates_assets(mocker):\n mock_asset_1 = GsAsset(asset_class='Rates', id='MAW25BGQJH9P6DPT', type_='Swap', name='Test_asset')\n mock_asset_2 = GsAsset(asset_class='Rates', id='MAA9MVX15AJNQCVG', type_='Swap', name='Test_asset')\n mock_asset_3 = GsAsset(asset_class='Rates', id='MANQHVYC30AZFT7R', type_='BasisSwap', name='Test_asset')\n\n replace = Replacer()\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1]\n assert 'MAW25BGQJH9P6DPT' == tm_rates._get_tdapi_rates_assets()\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict(asset_parameters_termination_date='10y', asset_parameters_effective_date='0b')\n with pytest.raises(MqValueError):\n tm_rates._get_tdapi_rates_assets(**kwargs)\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = []\n with pytest.raises(MqValueError):\n tm_rates._get_tdapi_rates_assets()\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict()\n assert ['MAW25BGQJH9P6DPT', 'MAA9MVX15AJNQCVG'] == tm_rates._get_tdapi_rates_assets(**kwargs)\n replace.restore()\n\n # test case will test matching sofr maturity with libor leg and flipping legs to get right asset\n kwargs = dict(\n type='BasisSwap',\n asset_parameters_termination_date='10y',\n asset_parameters_payer_rate_option=BenchmarkType.LIBOR,\n asset_parameters_payer_designated_maturity='3m',\n asset_parameters_receiver_rate_option=BenchmarkType.SOFR,\n asset_parameters_receiver_designated_maturity='1y',\n asset_parameters_clearing_house='lch',\n asset_parameters_effective_date='Spot',\n asset_parameters_notional_currency='USD',\n pricing_location='NYC',\n )\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_3]\n assert 'MANQHVYC30AZFT7R' == tm_rates._get_tdapi_rates_assets(**kwargs)\n replace.restore()\n\n\ndef test_get_swap_leg_defaults():\n result_dict = dict(\n currency=CurrEnum.JPY,\n benchmark_type='JPY-LIBOR-BBA',\n floating_rate_tenor='6m',\n pricing_location=PricingLocation.TKO,\n )\n defaults = tm_rates._get_swap_leg_defaults(CurrEnum.JPY)\n assert result_dict == defaults\n\n result_dict = dict(\n currency=CurrEnum.USD,\n benchmark_type='USD-LIBOR-BBA',\n floating_rate_tenor='3m',\n pricing_location=PricingLocation.NYC,\n )\n defaults = tm_rates._get_swap_leg_defaults(CurrEnum.USD)\n assert result_dict == defaults\n\n result_dict = dict(\n currency=CurrEnum.EUR,\n benchmark_type='EUR-EURIBOR-TELERATE',\n floating_rate_tenor='6m',\n pricing_location=PricingLocation.LDN,\n )\n defaults = tm_rates._get_swap_leg_defaults(CurrEnum.EUR)\n assert result_dict == defaults\n\n result_dict = dict(\n currency=CurrEnum.SEK,\n benchmark_type='SEK-STIBOR-SIDE',\n floating_rate_tenor='6m',\n pricing_location=PricingLocation.LDN,\n )\n defaults = tm_rates._get_swap_leg_defaults(CurrEnum.SEK)\n assert result_dict == defaults\n\n\ndef test_check_forward_tenor():\n valid_tenors = [dt.date(2020, 1, 1), '1y', 'imm2', 'frb2', '1m', '0b']\n for tenor in valid_tenors:\n assert tenor == tm_rates._check_forward_tenor(tenor)\n\n invalid_tenors = ['5yr', 'imm5', 'frb0']\n for tenor in invalid_tenors:\n with pytest.raises(MqError):\n tm_rates._check_forward_tenor(tenor)\n\n\ndef mock_commod(_cls, _q, ignore_errors=False):\n d = {\n 'price': [\n 30,\n 30,\n 30,\n 30,\n 35.929686,\n 35.636039,\n 27.307498,\n 23.23177,\n 19.020833,\n 18.827291,\n 17.823749,\n 17.393958,\n 17.824999,\n 20.307603,\n 24.311249,\n 25.160103,\n 25.245728,\n 25.736873,\n 28.425206,\n 28.779789,\n 30.519996,\n 34.896348,\n 33.966973,\n 33.95489,\n 33.686348,\n 34.840307,\n 32.674163,\n 30.261665,\n 30,\n 30,\n 30,\n ]\n }\n df = MarketDataResponseFrame(data=d, index=pd.date_range('2019-05-01', periods=31, freq='h', tz=dt.timezone.utc))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_commod_dup(_cls, _q, ignore_errors=False):\n d = {'price': [35.929686, 35]}\n idx = pd.date_range('2019-05-01', periods=1, freq='h', tz=dt.timezone.utc)\n df = MarketDataResponseFrame(data=d, index=idx.repeat(2))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_forward_price(_cls, _q, ignore_errors=False):\n d = {\n 'forwardPrice': [\n 22.0039,\n 24.8436,\n 24.8436,\n 11.9882,\n 14.0188,\n 11.6311,\n 18.9234,\n 21.3654,\n 21.3654,\n ],\n 'quantityBucket': [\n \"PEAK\",\n \"PEAK\",\n \"PEAK\",\n \"7X8\",\n \"7X8\",\n \"7X8\",\n \"2X16H\",\n \"2X16H\",\n \"2X16H\",\n ],\n 'contract': [\n \"J20\",\n \"K20\",\n \"M20\",\n \"J20\",\n \"K20\",\n \"M20\",\n \"J20\",\n \"K20\",\n \"M20\",\n ],\n }\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 9))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_implied_volatility_elec():\n d = {\n 'impliedVolatility': [\n 0.3424,\n 0.3624,\n 0.4424,\n 0.4424,\n 0.4224,\n 0.5324,\n 0.3224,\n 0.3324,\n 0.3924,\n ],\n 'quantityBucket': [\n \"PEAK\",\n \"PEAK\",\n \"PEAK\",\n \"7X8\",\n \"7X8\",\n \"7X8\",\n \"2X16H\",\n \"2X16H\",\n \"2X16H\",\n ],\n 'contract': [\n \"J20\",\n \"K20\",\n \"M20\",\n \"J20\",\n \"K20\",\n \"M20\",\n \"J20\",\n \"K20\",\n \"M20\",\n ],\n }\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 9))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fair_price(_cls, _q, ignore_errors=False):\n d = {\n 'fairPrice': [\n 2.880,\n 2.844,\n 2.726,\n ],\n 'contract': [\n \"F21\",\n \"G21\",\n \"H21\",\n ],\n }\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 3))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_eu_natgas_forward_price(_cls, _q, ignore_errors=False):\n d = {'forwardPrice': [15.65], 'contract': [\"H21\"]}\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2021, 1, 1)]))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_natgas_forward_price(_cls, _q, ignore_errors=False):\n d = {\n 'forwardPrice': [\n 2.880,\n 2.844,\n 2.726,\n ],\n 'contract': [\n \"F21\",\n \"G21\",\n \"H21\",\n ],\n }\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 3))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_natgas_implied_volatility(_cls, _q, ignore_errors=False):\n d = {\n 'impliedVolatility': [\n 2.880,\n 2.844,\n 2.726,\n ],\n 'contract': [\n \"F21\",\n \"G21\",\n \"H21\",\n ],\n }\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 3))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fair_price_swap(_cls, _q, ignore_errors=False):\n d = {'fairPrice': [2.880]}\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)]))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_implied_volatility(_cls, _q, ignore_errors=False):\n d = {\n 'impliedVolatility': [\n 2.880,\n 2.844,\n 2.726,\n ],\n 'contract': [\n \"F21\",\n \"G21\",\n \"H21\",\n ],\n }\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 3))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_missing_bucket_forward_price(_cls, _q, ignore_errors=False):\n d = {\n 'forwardPrice': [\n 22.0039,\n 24.8436,\n 24.8436,\n 11.9882,\n 14.0188,\n 18.9234,\n 21.3654,\n 21.3654,\n ],\n 'quantityBucket': [\n \"PEAK\",\n \"PEAK\",\n \"PEAK\",\n \"7X8\",\n \"7X8\",\n \"2X16H\",\n \"2X16H\",\n \"2X16H\",\n ],\n 'contract': [\n \"J20\",\n \"K20\",\n \"M20\",\n \"J20\",\n \"K20\",\n \"J20\",\n \"K20\",\n \"M20\",\n ],\n }\n return pd.DataFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 8))\n\n\ndef mock_missing_bucket_implied_volatility():\n d = {\n 'impliedVolatility': [\n 0.3424,\n 0.3624,\n 0.4424,\n 0.4424,\n 0.4224,\n 0.3224,\n 0.3324,\n 0.3924,\n ],\n 'quantityBucket': [\n \"PEAK\",\n \"PEAK\",\n \"PEAK\",\n \"7X8\",\n \"7X8\",\n \"2X16H\",\n \"2X16H\",\n \"2X16H\",\n ],\n 'contract': [\n \"J20\",\n \"K20\",\n \"M20\",\n \"J20\",\n \"K20\",\n \"J20\",\n \"K20\",\n \"M20\",\n ],\n }\n return pd.DataFrame(data=d, index=pd.to_datetime([dt.date(2019, 1, 2)] * 8))\n\n\ndef mock_fx_vol(\n _cls,\n asset_ids=None,\n query_type=None,\n where=None,\n source=None,\n real_time=None,\n request_id=None,\n parallelize_queries=False,\n):\n d = {\n 'strikeReference': ['delta', 'spot', 'forward'],\n 'relativeStrike': [25, 100, 100],\n 'impliedVolatility': [5, 1, 2],\n 'forecast': [1.1, 1.1, 1.1],\n }\n if DataContext.current.end_date >= dt.date.today():\n periods = 4\n d['impliedVolatility'].append(3)\n d['strikeReference'].append('delta')\n d['relativeStrike'].append(100)\n d['forecast'].append(1.1)\n else:\n periods = 3\n df = MarketDataResponseFrame(data=d, index=pd.date_range('2019-01-01', periods=periods, freq='D'))\n df.dataset_ids = _test_datasets\n df.index.freq = None\n return df\n\n\ndef mock_fx_spot_fwd_3m(*args, **kwargs):\n d = pd.DataFrame(\n {\n 'spot': [1.18250, 1.18566, 1.18511],\n 'forwardPoint': [0.00234, 0.00234, 0.00235],\n 'tenor': ['3m', '3m', '3m'],\n 'date': [pd.Timestamp(2020, 9, 2), pd.Timestamp(2020, 9, 3), pd.Timestamp(2020, 9, 4)],\n 'settlementDate': [pd.Timestamp('2020-12-04'), pd.Timestamp('2020-12-08'), pd.Timestamp('2020-12-08')],\n }\n )\n d = d.set_index('date')\n df = MarketDataResponseFrame(d)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_spot_fwd_2y(*args, **kwargs):\n d = pd.DataFrame(\n {\n 'spot': [1.18250, 1.18566, 1.18511],\n 'forwardPoint': [0.02009, 0.02015, 0.02064],\n 'tenor': ['2y', '2y', '2y'],\n 'date': [pd.Timestamp(2020, 9, 2), pd.Timestamp(2020, 9, 3), pd.Timestamp(2020, 9, 4)],\n 'settlementDate': [pd.Timestamp('2020-12-04'), pd.Timestamp('2020-12-08'), pd.Timestamp('2020-12-08')],\n }\n )\n d = d.set_index('date')\n df = MarketDataResponseFrame(d)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_spot_fwd_3m_rt(*args, **kwargs):\n d = pd.DataFrame(\n {\n 'spot': [1.18250, 1.18566, 1.18511],\n 'forwardPoint': [0.00234, 0.00234, 0.00235],\n 'tenor': ['3m', '3m', '3m'],\n 'time': [pd.Timestamp(2020, 9, 2), pd.Timestamp(2020, 9, 3), pd.Timestamp(2020, 9, 4)],\n 'settlementDate': [pd.Timestamp('2020-12-04'), pd.Timestamp('2020-12-08'), pd.Timestamp('2020-12-08')],\n }\n )\n d = d.set_index('time')\n df = MarketDataResponseFrame(d)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_spot_fwd_2y_rt(*args, **kwargs):\n d = pd.DataFrame(\n {\n 'spot': [1.18250, 1.18566, 1.18511],\n 'forwardPoint': [0.02009, 0.02015, 0.02064],\n 'tenor': ['2y', '2y', '2y'],\n 'time': [pd.Timestamp(2020, 9, 2), pd.Timestamp(2020, 9, 3), pd.Timestamp(2020, 9, 4)],\n 'settlementDate': [pd.Timestamp('2020-12-04'), pd.Timestamp('2020-12-08'), pd.Timestamp('2020-12-08')],\n }\n )\n d = d.set_index('time')\n df = MarketDataResponseFrame(d)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_correlation(*args, **kwargs):\n d = pd.DataFrame(\n {\n 'impliedVolatility': [\n 7.943208,\n 8.042599,\n 7.875325,\n 8.304180,\n 8.353483,\n 8.268724,\n 8.267971,\n 8.395843,\n 8.355239,\n ],\n 'bbid': ['EURUSD', 'EURUSD', 'EURUSD', 'EURJPY', 'EURJPY', 'EURJPY', 'USDJPY', 'USDJPY', 'USDJPY'],\n 'assetId': [\n 'MAA0NE9QX2ABETG6',\n 'MAA0NE9QX2ABETG6',\n 'MAA0NE9QX2ABETG6',\n 'MAYPHS80JRWDJ8RC',\n 'MAYPHS80JRWDJ8RC',\n 'MAYPHS80JRWDJ8RC',\n 'MATGYV0J9MPX534Z',\n 'MATGYV0J9MPX534Z',\n 'MATGYV0J9MPX534Z',\n ],\n 'relativeStrike': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n 'strikeReference': ['delta', 'delta', 'delta', 'delta', 'delta', 'delta', 'delta', 'delta', 'delta'],\n 'tenor': ['3m', '3m', '3m', '3m', '3m', '3m', '3m', '3m', '3m'],\n 'date': [\n pd.Timestamp(2020, 9, 2),\n pd.Timestamp(2020, 9, 3),\n pd.Timestamp(2020, 9, 4),\n pd.Timestamp(2020, 9, 2),\n pd.Timestamp(2020, 9, 3),\n pd.Timestamp(2020, 9, 4),\n pd.Timestamp(2020, 9, 2),\n pd.Timestamp(2020, 9, 3),\n pd.Timestamp(2020, 9, 4),\n ],\n }\n )\n d = d.set_index('date')\n df = MarketDataResponseFrame(d)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_forecast(_cls, _q, ignore_errors=False):\n d = {'fxForecast': [1.1, 1.1, 1.1]}\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_forecast_time_series(_cls, _q, ignore_errors=False):\n d = {'relativePeriod': ['3m', '6m', 'EOY1', 'EOY2'], 'fxForecast': [1.18, 1.20, 1.25, 1.23]}\n df = MarketDataResponseFrame(\n data=d,\n index=pd.to_datetime([dt.date(2027, 1, 1), dt.date(2028, 1, 1), dt.date(2029, 1, 1), dt.date(2030, 1, 1)]),\n )\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_delta(_cls, _q, ignore_errors=False):\n d = {\n 'relativeStrike': [25, -25, 0],\n 'impliedVolatility': [1, 5, 2],\n 'forecast': [1.1, 1.1, 1.1],\n 'forwardPoint': [1, 1.1, 1.2],\n }\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_empty(_cls, _q, ignore_errors=False):\n d = {'strikeReference': [], 'relativeStrike': [], 'impliedVolatility': []}\n df = MarketDataResponseFrame(data=d, index=[])\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_switch(_cls, _q, _n, ignore_errors=False):\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_fx_empty)\n replace.restore()\n return Cross('MA1889', 'ABC/XYZ')\n\n\ndef mock_curr(_cls, _q, ignore_errors=False):\n d = {\n 'swapAnnuity': [1, 2, 3],\n 'swapRate': [1, 2, 3],\n 'basisSwapRate': [1, 2, 3],\n 'swaptionVol': [1, 2, 3],\n 'atmFwdRate': [1, 2, 3],\n 'midcurveVol': [1, 2, 3],\n 'capFloorVol': [1, 2, 3],\n 'spreadOptionVol': [1, 2, 3],\n 'inflationSwapRate': [1, 2, 3],\n 'midcurveAtmFwdRate': [1, 2, 3],\n 'capFloorAtmFwdRate': [1, 2, 3],\n 'spreadOptionAtmFwdRate': [1, 2, 3],\n 'strike': [0.25, 0.5, 0.75],\n }\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_cross(_cls, _q, ignore_errors=False):\n d = {\n 'basis': [1, 2, 3],\n }\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_eq(_cls, _q, ignore_errors=False):\n d = {\n 'relativeStrike': [0.75, 0.25, 0.5],\n 'impliedVolatility': [5, 1, 2],\n 'impliedCorrelation': [5, 1, 2],\n 'realizedCorrelation': [3.14, 2.71828, 1.44],\n 'averageImpliedVolatility': [5, 1, 2],\n 'averageImpliedVariance': [5, 1, 2],\n 'averageRealizedVolatility': [5, 1, 2],\n 'impliedVolatilityByDeltaStrike': [5, 1, 2],\n 'optionPremium': [5, 1, 2],\n 'absoluteStrike': [5, 1, 2],\n 'fundamentalMetric': [5, 1, 2],\n }\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_eq_vol(\n _cls,\n asset_ids=None,\n query_type=None,\n where=None,\n source=None,\n real_time=None,\n request_id=None,\n parallelize_queries=False,\n):\n d = {\n 'impliedVolatility': [5, 1, 2],\n }\n if DataContext.current.end_date >= dt.date.today():\n end = dt.datetime.now(dt.timezone.utc).date()\n periods = 4\n d['impliedVolatility'].append(3)\n else:\n end = dt.datetime.now(dt.timezone.utc).date() - dt.timedelta(days=1)\n periods = 3\n df = MarketDataResponseFrame(data=d, index=pd.date_range(end=end, periods=periods, freq='D'))\n df.dataset_ids = _test_datasets\n df.index.freq = None\n return df\n\n\ndef mock_eq_vol_last_empty(\n _cls,\n asset_ids=None,\n query_type=None,\n where=None,\n source=None,\n real_time=None,\n request_id=None,\n parallelize_queries=False,\n):\n d = {\n 'impliedVolatility': [5, 1, 2],\n }\n end = dt.date.today() - dt.timedelta(days=1)\n df = MarketDataResponseFrame(data=d, index=pd.date_range(end=end, periods=3, freq='D'))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_eq_norm(_cls, _q, ignore_errors=False):\n d = {'relativeStrike': [-4.0, 4.0, 0], 'impliedVolatility': [5, 1, 2]}\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_eq_spot(_cls, _q, ignore_errors=False):\n d = {'relativeStrike': [0.75, 1.25, 1.0], 'impliedVolatility': [5, 1, 2]}\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_inc(_cls, _q, ignore_errors=False):\n d = {'relativeStrike': [0.25, 0.75], 'impliedVolatility': [5, 1]}\n df = MarketDataResponseFrame(data=d, index=_index * 2)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_esg(_cls, _q, ignore_errors=False):\n d = {\n \"esNumericScore\": [2, 4, 6],\n \"esNumericPercentile\": [81.2, 75.4, 65.7],\n \"esPolicyScore\": [2, 4, 6],\n \"esPolicyPercentile\": [81.2, 75.4, 65.7],\n \"esScore\": [2, 4, 6],\n \"esPercentile\": [81.2, 75.4, 65.7],\n \"esProductImpactScore\": [2, 4, 6],\n \"esProductImpactPercentile\": [81.2, 75.4, 65.7],\n \"gScore\": [2, 4, 6],\n \"gPercentile\": [81.2, 75.4, 65.7],\n \"esMomentumScore\": [2, 4, 6],\n \"esMomentumPercentile\": [81.2, 75.4, 65.7],\n \"gRegionalScore\": [2, 4, 6],\n \"gRegionalPercentile\": [81.2, 75.4, 65.7],\n \"controversyScore\": [2, 4, 6],\n \"controversyPercentile\": [81.2, 75.4, 65.7],\n \"esDisclosurePercentage\": [49.2, 55.7, 98.4],\n }\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_index_positions_data(asset_id, start_date, end_date, fields=None, position_type=None):\n return [\n {\n 'underlyingAssetId': 'MA3',\n 'netWeight': 0.1,\n 'positionType': 'close',\n 'assetId': 'MA890',\n 'positionDate': '2020-01-01',\n },\n {\n 'underlyingAssetId': 'MA1',\n 'netWeight': 0.6,\n 'positionType': 'close',\n 'assetId': 'MA890',\n 'positionDate': '2020-01-01',\n },\n {\n 'underlyingAssetId': 'MA2',\n 'netWeight': 0.3,\n 'positionType': 'close',\n 'assetId': 'MA890',\n 'positionDate': '2020-01-01',\n },\n ]\n\n\ndef mock_rating(_cls, _q, ignore_errors=False):\n d = {'rating': ['Buy', 'Sell', 'Buy', 'Neutral'], 'convictionList': [1, 0, 0, 0]}\n df = MarketDataResponseFrame(\n data=d,\n index=pd.to_datetime([dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]),\n )\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_gsdeer_gsfeer(_cls, assetId, start_date):\n d = {'gsdeer': [1, 1.2, 1.1], 'gsfeer': [2, 1.8, 1.9], 'year': [2000, 2010, 2020], 'quarter': ['Q1', 'Q2', 'Q3']}\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n return df\n\n\ndef mock_factor_profile(_cls, _q, ignore_errors=False):\n d = {\n 'growthScore': [0.238, 0.234, 0.234, 0.230],\n 'financialReturnsScore': [0.982, 0.982, 0.982, 0.982],\n 'multipleScore': [0.204, 0.192, 0.190, 0.190],\n 'integratedScore': [0.672, 0.676, 0.676, 0.674],\n }\n df = MarketDataResponseFrame(\n data=d,\n index=pd.to_datetime([dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]),\n )\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_commodity_forecast(_cls, _q, ignore_errors=False):\n d = {\n 'forecastPeriod': ['3m', '3m', '3m', '3m'],\n 'forecastType': ['spotReturn', 'spotReturn', 'spotReturn', 'spotReturn'],\n 'commodityForecast': [1700, 1400, 1500, 1600],\n }\n df = MarketDataResponseFrame(\n data=d,\n index=pd.to_datetime([dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]),\n )\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_commodity_forecast_time_series(_cls, _q, ignore_errors=False):\n d = {\n 'forecastFrequency': ['Annual', 'Monthly', 'Quarterly', '3/6/12-Month Rolling'],\n 'forecastType': ['spot', 'spot', 'spot', 'spot'],\n 'commodityForecast': [56, 63, 77.75, 80],\n }\n df = MarketDataResponseFrame(\n data=d,\n index=pd.to_datetime([dt.date(2027, 1, 1), dt.date(2027, 1, 1), dt.date(2028, 1, 1), dt.date(2029, 1, 1)]),\n )\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_cds_spread(_cls, _q, ignore_errors=False):\n d = {\n \"spreadAt100\": [0.000836],\n \"spreadAt250\": [0.000436],\n \"spreadAt500\": [0.00036],\n }\n df = MarketDataResponseFrame(d, index=_index2)\n return df\n\n\ndef test_skew():\n replace = Replacer()\n\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.skew(mock_spx, '1m', tm.SkewReference.DELTA, 25)\n assert_series_equal(pd.Series([2.0], index=_index, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.skew(mock_spx, '1m', tm.SkewReference.DELTA, 25, tm.NormalizationMode.NORMALIZED)\n assert_series_equal(pd.Series([2.0], index=_index, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.skew(mock_spx, '1m', tm.SkewReference.DELTA, 25, tm.NormalizationMode.OUTRIGHT)\n assert_series_equal(pd.Series([4], index=_index, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq_norm)\n actual = tm.skew(mock_spx, '1m', tm.SkewReference.NORMALIZED, 4)\n assert_series_equal(pd.Series([2.0], index=_index, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq_spot)\n actual = tm.skew(mock_spx, '1m', tm.SkewReference.SPOT, 25)\n assert_series_equal(pd.Series([2.0], index=_index, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n mock.return_value = mock_empty_market_data_response()\n actual = tm.skew(mock_spx, '1m', tm.SkewReference.SPOT, 25)\n assert actual.empty\n\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_inc)\n with pytest.raises(MqError):\n tm.skew(mock_spx, '1m', tm.SkewReference.DELTA, 25)\n replace.restore()\n\n with pytest.raises(MqError):\n tm.skew(mock_spx, '1m', None, 25)\n\n\ndef test_skew_fx():\n replace = Replacer()\n cross = Cross('MAA0NE9QX2ABETG6', 'USD/EUR')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='EURUSD',\n ),\n )\n ]\n replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock()).return_value = cross\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_fx_delta)\n mock = cross\n\n actual = tm.skew(mock, '1m', tm.SkewReference.DELTA, 25)\n assert_series_equal(pd.Series([4], index=_index, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.skew(mock, '1m', tm.SkewReference.DELTA, 25, tm.NormalizationMode.NORMALIZED)\n assert_series_equal(pd.Series([2.0], index=_index, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.skew(mock, '1m', tm.SkewReference.DELTA, 25, tm.NormalizationMode.OUTRIGHT)\n assert_series_equal(pd.Series([4], index=_index, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n with pytest.raises(MqError):\n tm.skew(mock, '1m', tm.SkewReference.DELTA, 25, real_time=True)\n with pytest.raises(MqError):\n tm.skew(mock, '1m', tm.SkewReference.SPOT, 25)\n with pytest.raises(MqError):\n tm.skew(mock, '1m', tm.SkewReference.FORWARD, 25)\n with pytest.raises(MqError):\n tm.skew(mock, '1m', tm.SkewReference.NORMALIZED, 25)\n with pytest.raises(MqError):\n tm.skew(mock, '1m', None, 25)\n\n replace.restore()\n\n\ndef test_implied_vol():\n replace = Replacer()\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n replace('gs_quant.timeseries.measures.get_historical_and_last_for_measure', mock_eq_vol)\n idx = pd.date_range(end=dt.datetime.now(dt.timezone.utc).date(), periods=4, freq='D')\n idx.freq = None\n\n actual = tm.implied_volatility(mock_spx, '1m', tm.VolReference.DELTA_CALL, 25)\n assert_series_equal(pd.Series([5, 1, 2, 3], index=idx, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.implied_volatility(mock_spx, '1m', tm.VolReference.DELTA_PUT, 75)\n assert_series_equal(pd.Series([5, 1, 2, 3], index=idx, name='impliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(MqError):\n tm.implied_volatility(mock_spx, '1m', tm.VolReference.DELTA_NEUTRAL)\n with pytest.raises(MqError):\n tm.implied_volatility(mock_spx, '1m', tm.VolReference.DELTA_CALL)\n with DataContext(dt.date(2020, 1, 11), dt.date(2021, 1, 11)):\n actual = tm.implied_volatility(mock_spx, '1m', tm.VolReference.DELTA_CALL, 25)\n actual.index.freq = None\n assert_series_equal(pd.Series([5, 1, 2], index=idx[:-1], name='impliedVolatility'), pd.Series(actual))\n replace.restore()\n\n\ndef test_merge_dataframes():\n a = pd.DataFrame({'a': [1, 2, 3]})\n b = pd.DataFrame({'a': [1, 5, 6]})\n c = tm.merge_dataframes([a, b])\n assert not len(c.dataset_ids)\n assert len(c) == 5\n c = tm.merge_dataframes([a, None])\n assert_series_equal(a['a'], c['a'])\n assert tm.merge_dataframes(None).empty\n\n\ndef test_get_last_for_measure():\n replace = Replacer()\n mock_market = replace('gs_quant.timeseries.measures._market_data_timed', Mock())\n mock_market.return_value = pd.DataFrame(\n [1], index=pd.date_range('2020-01-01', periods=1, freq='D', tz=dt.timezone.utc)\n )\n\n a = tm.get_last_for_measure(['blah'], QueryType.IMPLIED_VOLATILITY, {})\n assert len(a) == 1\n\n mock_market.return_value = pd.DataFrame()\n a = tm.get_last_for_measure(['blah'], QueryType.IMPLIED_VOLATILITY, {})\n assert a is None\n\n mock_market.side_effect = [MqValueError()]\n a = tm.get_last_for_measure(['blah'], QueryType.IMPLIED_VOLATILITY, {})\n assert a is None\n replace.restore()\n\n\ndef test_ignore_errors():\n with mock.patch.object(GsSession.current, '_post') as mocker:\n mocker.return_value = {\n 'requestId': 'rq1234',\n 'responses': [{'queryResponse': [{'dataSetIds': ['DS1'], 'errorMessages': ['this failed']}]}],\n }\n last_data = tm.get_last_for_measure(['blah'], QueryType.IMPLIED_VOLATILITY, {}, ignore_errors=True)\n assert last_data is None\n\n\ndef test_tenor_month_to_year():\n assert tm._tenor_month_to_year('1y') == '1y'\n assert tm._tenor_month_to_year('11m') == '11m'\n assert tm._tenor_month_to_year('12m') == '1y'\n assert tm._tenor_month_to_year('24m') == '2y'\n\n\ndef test_implied_vol_no_last():\n replace = Replacer()\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n idx = pd.date_range(end=dt.date.today() - dt.timedelta(days=1), periods=3, freq='D')\n\n replace('gs_quant.timeseries.measures.get_historical_and_last_for_measure', mock_eq_vol_last_empty)\n actual = tm.implied_volatility(mock_spx, '1m', tm.VolReference.DELTA_CALL, 25)\n assert_series_equal(pd.Series([5, 1, 2], index=idx, name='impliedVolatility'), pd.Series(actual))\n actual = tm.implied_volatility(mock_spx, '1m', tm.VolReference.DELTA_PUT, 75)\n assert_series_equal(pd.Series([5, 1, 2], index=idx, name='impliedVolatility'), pd.Series(actual))\n replace.restore()\n\n\ndef test_implied_vol_fx():\n replace = Replacer()\n\n mock = Cross('MAA0NE9QX2ABETG6', 'USD/EUR')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='EURUSD',\n ),\n )\n ]\n replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock()).return_value = mock\n\n # for different delta strikes\n replace('gs_quant.timeseries.measures.get_historical_and_last_for_measure', mock_fx_vol)\n actual = tm.implied_volatility(mock, '1m', tm.VolReference.DELTA_CALL, 25)\n expected = pd.Series([5, 1, 2, 3], index=pd.date_range('2019-01-01', periods=4, freq='D'), name='impliedVolatility')\n expected.index.freq = None\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.implied_volatility(mock, '1m', tm.VolReference.DELTA_PUT, 25)\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.implied_volatility(mock, '1m', tm.VolReference.DELTA_NEUTRAL)\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.implied_volatility(mock, '1m', tm.VolReference.FORWARD, 100)\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.implied_volatility(mock, '1m', tm.VolReference.SPOT, 100)\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n # NORMALIZED not supported\n with pytest.raises(MqError):\n tm.implied_volatility(mock, '1m', tm.VolReference.DELTA_CALL)\n with pytest.raises(MqError):\n tm.implied_volatility(mock, '1m', tm.VolReference.NORMALIZED, 25)\n with pytest.raises(MqError):\n tm.implied_volatility(mock, '1m', tm.VolReference.SPOT, 25)\n with pytest.raises(MqError):\n tm.implied_volatility(mock, '1m', tm.VolReference.FORWARD, 25)\n replace.restore()\n\n\ndef test_fx_forecast():\n replace = Replacer()\n mock = Cross('MAA0NE9QX2ABETG6', 'USD/EUR')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='EURUSD',\n ),\n )\n ]\n replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock()).return_value = mock\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_fx_forecast)\n\n actual = tm.fx_forecast(mock, '12m')\n assert_series_equal(pd.Series([1.1, 1.1, 1.1], index=_index * 3, name='fxForecast'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.fx_forecast(mock, '3m')\n assert_series_equal(pd.Series([1.1, 1.1, 1.1], index=_index * 3, name='fxForecast'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.fx_forecast(mock, '3m', real_time=True)\n replace.restore()\n\n\ndef test_fx_forecast_inverse():\n replace = Replacer()\n get_cross = replace('gs_quant.timeseries.measures.cross_to_usd_based_cross', Mock())\n get_cross.return_value = \"MATGYV0J9MPX534Z\"\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_fx_forecast)\n\n mock = Cross(\"MAYJPCVVF2RWXCES\", 'USD/JPY')\n actual = tm.fx_forecast(mock, '3m')\n assert_series_equal(pd.Series([1 / 1.1, 1 / 1.1, 1 / 1.1], index=_index * 3, name='fxForecast'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n replace.restore()\n\n\ndef test_fx_forecast_time_series():\n replace = Replacer()\n mock = Cross('MAA0NE9QX2ABETG6', 'USD/EUR')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='EURUSD',\n ),\n )\n ]\n replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock()).return_value = mock\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_fx_forecast_time_series)\n\n with pytest.raises(ValueError):\n tm.fx_forecast_time_series(100, tm._FxForecastTimeSeriesPeriodType.ANNUAL.value)\n with pytest.raises(ValueError):\n tm.fx_forecast_time_series(mock, 'Monthly')\n actual = tm.fx_forecast_time_series('MATGYV0J9MPX534Z', tm._FxForecastTimeSeriesPeriodType.ANNUAL.value)\n assert not pd.Series(actual).empty\n actual = tm.fx_forecast_time_series(mock, tm._FxForecastTimeSeriesPeriodType.SHORT_TERM.value)\n assert not pd.Series(actual).empty\n actual = tm.fx_forecast_time_series(mock, tm._FxForecastTimeSeriesPeriodType.ANNUAL.value)\n assert not pd.Series(actual).empty\n actual = tm.fx_forecast_time_series(mock, tm._FxForecastTimeSeriesPeriodType.ANNUAL)\n assert not pd.Series(actual).empty\n actual = tm.fx_forecast_time_series(mock, 'Annual')\n assert not pd.Series(actual).empty\n with pytest.raises(NotImplementedError):\n tm.fx_forecast_time_series(mock, tm._FxForecastTimeSeriesPeriodType.SHORT_TERM.value, real_time=True)\n replace.restore()\n\n\ndef test_vol_smile():\n replace = Replacer()\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.vol_smile(mock_spx, '1m', tm.VolSmileReference.FORWARD, '5d')\n assert_series_equal(pd.Series([5, 1, 2], index=[0.75, 0.25, 0.5], name='vol_smile'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n assert actual.name == 'vol_smile'\n actual = tm.vol_smile(mock_spx, '1m', tm.VolSmileReference.SPOT, '5d')\n assert_series_equal(pd.Series([5, 1, 2], index=[0.75, 0.25, 0.5], name='vol_smile'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n assert actual.name == 'vol_smile'\n\n market_mock = replace('gs_quant.timeseries.measures.get_df_with_retries', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n actual = tm.vol_smile(mock_spx, '1m', tm.VolSmileReference.SPOT, '1d')\n assert actual.empty\n assert actual.dataset_ids == ()\n market_mock.assert_called_once()\n with pytest.raises(NotImplementedError):\n tm.vol_smile(mock_spx, '1m', tm.VolSmileReference.SPOT, '1d', real_time=True)\n replace.restore()\n\n\ndef test_impl_corr():\n replace = Replacer()\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.implied_correlation(mock_spx, '1m', tm.EdrDataReference.DELTA_CALL, 25)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='impliedCorrelation'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.implied_correlation(mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='impliedCorrelation'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.implied_correlation(..., '1m', tm.EdrDataReference.DELTA_PUT, 75, real_time=True)\n with pytest.raises(MqError):\n tm.implied_correlation(..., '1m', tm.EdrDataReference.DELTA_CALL, 50, '')\n replace.restore()\n\n\ndef test_impl_corr_n(mocker):\n spx = Index('MA4B66MW5E27U8P32SB', AssetClass.Equity, 'SPX')\n with pytest.raises(MqValueError):\n tm.implied_correlation(spx, '1m', tm.EdrDataReference.DELTA_CALL, 0.5, composition_date=dt.date.today())\n with pytest.raises(MqValueError):\n tm.implied_correlation(spx, '1m', tm.EdrDataReference.DELTA_CALL, 0.5, 200)\n\n resources = os.path.join(os.path.dirname(__file__), '..', 'resources')\n i_vol = pd.read_csv(os.path.join(resources, 'SPX_50_icorr_in.csv'))\n i_vol.index = pd.to_datetime(i_vol['date'])\n weights = pd.read_csv(os.path.join(resources, 'SPX_50_weights.csv'))\n weights = weights.set_index('underlyingAssetId')\n\n replace = Replacer()\n market_data = replace('gs_quant.timeseries.econometrics.GsDataApi.get_market_data', Mock())\n market_data.return_value = i_vol\n constituents = replace('gs_quant.timeseries.measures._get_index_constituent_weights', Mock())\n constituents.return_value = weights\n last_mock = replace('gs_quant.timeseries.measures.get_last_for_measure', Mock())\n last_mock.return_value = None\n\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(OAuth2Session, '_authenticate', return_value=None)\n\n expected = pd.read_csv(os.path.join(resources, 'SPX_50_icorr_out.csv'))\n expected.index = pd.to_datetime(expected['date'])\n expected = expected['value']\n actual = tm.implied_correlation(\n spx, '1m', tm.EdrDataReference.DELTA_CALL, 0.5, 50, dt.date(2020, 8, 31), source='PlotTool'\n )\n expected = ExtendedSeries(expected)\n assert_series_equal(actual, expected, check_names=False)\n\n mask = i_vol.index < '2020-08-31'\n assert not all(mask)\n market_data.return_value = i_vol[mask]\n last_mock.return_value = i_vol[~mask]\n actual = tm.implied_correlation(\n spx, '1m', tm.EdrDataReference.DELTA_CALL, 0.5, 50, dt.date(2020, 8, 31), source='PlotTool'\n )\n assert_series_equal(actual, expected, check_names=False)\n\n market_data.return_value = pd.DataFrame()\n last_mock.return_value = pd.DataFrame()\n actual = tm.implied_correlation(\n spx, '1m', tm.EdrDataReference.DELTA_CALL, 0.5, 50, dt.date(2020, 8, 31), source='PlotTool'\n )\n assert actual.empty\n\n replace.restore()\n\n\ndef test_implied_corr_basket():\n replace = Replacer()\n\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n x = pd.DataFrame({'impliedVolatility': [0.1, 0.11, 0.12, 0.13, 0.14, 0.15]}, index=dates)\n x['assetId'] = 'MA4B66MW5E27U9VBB94'\n y = pd.DataFrame({'impliedVolatility': [0.2, 0.21, 0.22, 0.23, 0.24, 0.25]}, index=dates)\n y['assetId'] = 'MA4B66MW5E27UAL9SUX'\n z = pd.DataFrame({'impliedVolatility': [0.13, 0.21, 0.3, 0.31, 0.23, 0.24]}, index=dates)\n z['assetId'] = 'MA4B66MW5E27U8P32SB'\n implied_vol = pd.concat([x, y, z])\n\n x = pd.DataFrame({'spot': [100.0, 101, 103.02, 100.9596, 100.9596, 102.978792]}, index=dates)\n x['assetId'] = 'MA4B66MW5E27U9VBB94'\n y = pd.DataFrame({'spot': [100.0, 100, 100, 100, 100, 100]}, index=dates)\n y['assetId'] = 'MA4B66MW5E27UAL9SUX'\n spot_data = pd.concat([x, y])\n\n mock_data = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n mock_data.side_effect = [implied_vol, spot_data]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n ]\n\n spx = Index('MA4B66MW5E27U8P32SB', AssetClass.Equity, 'SPX')\n a_basket = tm.Basket(['AAPL UW', 'MSFT UW'], [0.1, 0.9])\n mock_data = replace('gs_quant.timeseries.get_historical_and_last_for_measure', Mock())\n mock_data.return_value = spot_data\n actual = tm.implied_correlation_with_basket(spx, '1y', tm.EdrDataReference.DELTA_PUT, 25, a_basket)\n expected = pd.Series([-433.33333, 198.60510, 1065.90909, 986.28763, 100.0, 100.0], index=dates)\n expected = ExtendedSeries(expected)\n assert_series_equal(actual, expected)\n\n with pytest.raises(NotImplementedError):\n tm.implied_correlation_with_basket(spx, '1y', tm.EdrDataReference.DELTA_PUT, 25, a_basket, real_time=True)\n\n replace.restore()\n\n\ndef test_realized_corr_basket():\n replace = Replacer()\n\n dates = pd.DatetimeIndex(\n [\n dt.date(2021, 1, 1),\n dt.date(2021, 1, 2),\n dt.date(2021, 1, 3),\n dt.date(2021, 1, 4),\n dt.date(2021, 1, 5),\n dt.date(2021, 1, 6),\n ]\n )\n x = pd.DataFrame({'spot': [100.0, 101, 103.02, 100.9596, 100.9596, 102.978792]}, index=dates)\n x['assetId'] = 'MA4B66MW5E27U9VBB94'\n y = pd.DataFrame({'spot': [100.0, 99.5, 100.1, 101, 100.7, 100.6]}, index=dates)\n y['assetId'] = 'MA4B66MW5E27UAL9SUX'\n constituents_spot = pd.concat([x, y])\n\n index_spot = pd.DataFrame({'spot': [100.0, 101, 102, 103, 103.3, 104]}, index=dates)\n index_spot['assetId'] = 'MA4B66MW5E27U8P32SB'\n\n mock_data = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n mock_data.side_effect = [index_spot, constituents_spot]\n\n mock_asset = replace('gs_quant.timeseries.backtesting.GsAssetApi.get_many_assets_data', Mock())\n mock_asset.return_value = [\n {'id': 'MA4B66MW5E27U9VBB94', 'bbid': 'AAPL UW'},\n {'id': 'MA4B66MW5E27UAL9SUX', 'bbid': 'MSFT UW'},\n ]\n mock_data = replace('gs_quant.timeseries.get_historical_and_last_for_measure', Mock())\n mock_data.return_value = constituents_spot\n\n spx = Index('MA4B66MW5E27U8P32SB', AssetClass.Equity, 'SPX')\n a_basket = tm.Basket(['AAPL UW', 'MSFT UW'], [0.1, 0.9])\n actual = tm.realized_correlation_with_basket(spx, '2d', a_basket)\n expected = pd.Series([np.nan, np.nan, -501.344109, -108.318770, -168.132382, 109.044958], index=dates)\n expected = ExtendedSeries(expected)\n assert_series_equal(actual, expected, check_dtype=False)\n\n with pytest.raises(NotImplementedError):\n tm.realized_correlation_with_basket(spx, '2d', a_basket, real_time=True)\n\n replace.restore()\n\n\ndef test_real_corr():\n spx = Index('MA4B66MW5E27U8P32SB', AssetClass.Equity, 'SPX')\n with pytest.raises(NotImplementedError):\n tm.realized_correlation(spx, '1m', real_time=True)\n\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.realized_correlation(spx, '1m')\n assert_series_equal(pd.Series([3.14, 2.71828, 1.44], index=_index * 3), pd.Series(actual), check_names=False)\n assert actual.dataset_ids == _test_datasets\n replace.restore()\n\n\ndef test_real_corr_missing():\n spx = Index('MA4B66MW5E27U8P32SB', AssetClass.Equity, 'SPX')\n d = {\n 'assetId': ['MA4B66MW5E27U8P32SB'] * 3,\n 'spot': [3000, 3100, 3050],\n }\n df = MarketDataResponseFrame(data=d, index=pd.date_range('2020-08-01', periods=3, freq='D'))\n\n resources = os.path.join(os.path.dirname(__file__), '..', 'resources')\n weights = pd.read_csv(os.path.join(resources, 'SPX_50_weights.csv')).set_index('underlyingAssetId')\n\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', lambda *args, **kwargs: df)\n constituents = replace('gs_quant.timeseries.measures._get_index_constituent_weights', Mock())\n constituents.return_value = weights\n last_mock = replace('gs_quant.timeseries.measures.get_last_for_measure', Mock())\n last_mock.return_value = None\n\n with pytest.raises(MqValueError):\n tm.realized_correlation(spx, '1m', 50)\n replace.restore()\n\n\ndef test_real_corr_n():\n spx = Index('MA4B66MW5E27U8P32SB', AssetClass.Equity, 'SPX')\n with pytest.raises(MqValueError):\n tm.realized_correlation(spx, '1m', composition_date=dt.date.today())\n with pytest.raises(MqValueError):\n tm.realized_correlation(spx, '1m', 200)\n\n mock_asset = GsAsset(asset_class='Equity', id='MA1234567890', type_='Custom Basket', name='test')\n mock_basket = CustomBasket(gs_asset=mock_asset, _finish_init=False)\n\n # Test baskets must have top n\n with pytest.raises(MqValueError):\n tm.realized_correlation(mock_basket, '1m', composition_date=dt.date.today())\n\n mock_asset = GsAsset(asset_class='Equity', id='MA1234567890', type_='Custom Basket', name='test')\n mock_basket = CustomBasket(gs_asset=mock_asset, _finish_init=False)\n\n # Test baskets must have top n\n with pytest.raises(MqValueError):\n tm.realized_correlation(mock_basket, '1m', composition_date=None)\n\n resources = os.path.join(os.path.dirname(__file__), '..', 'resources')\n r_vol = pd.read_csv(os.path.join(resources, 'SPX_50_rcorr_in.csv'))\n r_vol.index = pd.to_datetime(r_vol['date'])\n weights = pd.read_csv(os.path.join(resources, 'SPX_50_weights.csv')).set_index('underlyingAssetId')\n\n replace = Replacer()\n market_data = replace('gs_quant.timeseries.econometrics.GsDataApi.get_market_data', Mock())\n market_data.return_value = r_vol\n constituents = replace('gs_quant.timeseries.measures._get_index_constituent_weights', Mock())\n constituents.return_value = weights\n last_mock = replace('gs_quant.timeseries.measures.get_last_for_measure', Mock())\n last_mock.return_value = None\n\n expected = pd.read_csv(os.path.join(resources, 'SPX_50_rcorr_out.csv'))\n expected.index = pd.to_datetime(expected['date'])\n expected = ExtendedSeries(expected['value'])\n actual = tm.realized_correlation(spx, '1m', 50, dt.date(2020, 8, 31), source='PlotTool')\n assert_series_equal(actual, expected, check_names=False)\n\n mask = r_vol.index < '2020-08-31'\n assert not all(mask)\n market_data.return_value = r_vol[mask]\n last_mock.return_value = r_vol[~mask]\n actual = tm.realized_correlation(spx, '1m', 50, dt.date(2020, 8, 31), source='PlotTool')\n assert_series_equal(actual, expected, check_names=False)\n\n replace.restore()\n\n\ndef test_cds_implied_vol():\n replace = Replacer()\n mock_cds = Index('MA890', AssetClass.Equity, 'CDS')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.cds_implied_volatility(mock_cds, '1m', '5y', tm.CdsVolReference.DELTA_CALL, 10)\n assert_series_equal(\n pd.Series([5, 1, 2], index=_index * 3, name='impliedVolatilityByDeltaStrike'), pd.Series(actual)\n )\n assert actual.dataset_ids == _test_datasets\n actual = tm.cds_implied_volatility(mock_cds, '1m', '5y', tm.CdsVolReference.FORWARD, 100)\n assert_series_equal(\n pd.Series([5, 1, 2], index=_index * 3, name='impliedVolatilityByDeltaStrike'), pd.Series(actual)\n )\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.cds_implied_volatility(..., '1m', '5y', tm.CdsVolReference.DELTA_PUT, 75, real_time=True)\n replace.restore()\n\n\ndef test_implied_vol_credit():\n replace = Replacer()\n mock_cds = Index('MA890', AssetClass.Equity, 'CDS')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.implied_volatility_credit(mock_cds, '1m', tm.CdsVolReference.DELTA_CALL, 10)\n assert_series_equal(\n pd.Series([5, 1, 2], index=_index * 3, name='impliedVolatilityByDeltaStrike'), pd.Series(actual)\n )\n assert actual.dataset_ids == _test_datasets\n actual = tm.implied_volatility_credit(mock_cds, '1m', tm.CdsVolReference.DELTA_PUT, 10)\n assert_series_equal(\n pd.Series([5, 1, 2], index=_index * 3, name='impliedVolatilityByDeltaStrike'), pd.Series(actual)\n )\n assert actual.dataset_ids == _test_datasets\n actual = tm.implied_volatility_credit(mock_cds, '1m', tm.CdsVolReference.FORWARD, 100)\n assert_series_equal(\n pd.Series([5, 1, 2], index=_index * 3, name='impliedVolatilityByDeltaStrike'), pd.Series(actual)\n )\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.implied_volatility_credit(..., '1m', tm.CdsVolReference.DELTA_PUT, 75, real_time=True)\n with pytest.raises(NotImplementedError):\n tm.implied_volatility_credit(..., '1m', \"\", 75)\n replace.restore()\n\n\ndef test_absolute_strike_credit():\n replace = Replacer()\n mock_cds = Index('MA890', AssetClass.Equity, 'CDS')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.absolute_strike_credit(mock_cds, '1m', tm.CdsVolReference.DELTA_CALL, 10)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='absoluteStrike'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.absolute_strike_credit(mock_cds, '1m', tm.CdsVolReference.DELTA_PUT, 10)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='absoluteStrike'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.absolute_strike_credit(mock_cds, '1m', tm.CdsVolReference.FORWARD, 100)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='absoluteStrike'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.absolute_strike_credit(..., '1m', tm.CdsVolReference.DELTA_PUT, 75, real_time=True)\n with pytest.raises(NotImplementedError):\n tm.absolute_strike_credit(..., '1m', \"\", 75)\n replace.restore()\n\n\ndef test_option_premium_credit():\n replace = Replacer()\n mock_cds = Index('MA890', AssetClass.Equity, 'CDS')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.option_premium_credit(mock_cds, '1m', tm.CdsVolReference.DELTA_CALL, 10)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='optionPremium'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.option_premium_credit(mock_cds, '1m', tm.CdsVolReference.DELTA_PUT, 10)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='optionPremium'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n actual = tm.option_premium_credit(mock_cds, '1m', tm.CdsVolReference.FORWARD, 100)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='optionPremium'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.option_premium_credit(..., '1m', tm.CdsVolReference.DELTA_PUT, 75, real_time=True)\n with pytest.raises(NotImplementedError):\n tm.option_premium_credit(..., '1m', \"\", 75)\n replace.restore()\n\n\ndef test_cds_spreads():\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_cds_spread)\n cds_asset = DefaultSwap('MAD6V3NP8ZB7HZTY', 'Lockheed_C_PFA_2y_USD')\n actual = tm.cds_spread(cds_asset, 100)\n assert_series_equal(pd.Series([0.000836], index=_index2, name='spreadAt100'), pd.Series(actual))\n actual = tm.cds_spread(cds_asset, 250)\n assert_series_equal(pd.Series([0.000436], index=_index2, name='spreadAt250'), pd.Series(actual))\n actual = tm.cds_spread(cds_asset, 500)\n assert_series_equal(pd.Series([0.00036], index=_index2, name='spreadAt500'), pd.Series(actual))\n with pytest.raises(NotImplementedError):\n tm.cds_spread(cds_asset, 200)\n with pytest.raises(NotImplementedError):\n tm.cds_spread(cds_asset, 200, real_time=True)\n replace.restore()\n\n\ndef test_avg_impl_vol(mocker):\n replace = Replacer()\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n last_mock = replace('gs_quant.timeseries.measures.get_last_for_measure', Mock())\n last_mock.return_value = None\n\n actual = tm.average_implied_volatility(mock_spx, '1m', tm.EdrDataReference.DELTA_CALL, 25)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='averageImpliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.average_implied_volatility(mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='averageImpliedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n\n df1 = pd.DataFrame(\n data={'impliedVolatility': [1, 2, 3], 'assetId': ['MA1', 'MA1', 'MA1']},\n index=pd.date_range(start='2020-01-01', periods=3),\n )\n df2 = pd.DataFrame(\n data={'impliedVolatility': [2, 3, 4], 'assetId': ['MA2', 'MA2', 'MA2']},\n index=pd.date_range(start='2020-01-01', periods=3),\n )\n df3 = pd.DataFrame(\n data={'impliedVolatility': [2, 5], 'assetId': ['MA3', 'MA3']},\n index=pd.date_range(start='2020-01-01', periods=2),\n )\n\n replace('gs_quant.api.gs.indices.GsIndexApi.get_positions_data', mock_index_positions_data)\n market_data_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n mock_implied_vol = MarketDataResponseFrame(pd.concat([df1, df2, df3], join='inner'))\n mock_implied_vol.dataset_ids = _test_datasets\n market_data_mock.return_value = mock_implied_vol\n last_mock = replace('gs_quant.timeseries.measures.get_last_for_measure', Mock())\n last_mock.return_value = None\n\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(OAuth2Session, '_authenticate', return_value=None)\n\n actual = tm.average_implied_volatility(mock_spx, '1m', tm.EdrDataReference.DELTA_CALL, 25, 3, '1d')\n expected = pd.Series([1.4, 2.6, 3.33333], index=pd.date_range(start='2020-01-01', periods=3))\n expected.index.freq = None\n assert_series_equal(expected, pd.Series(actual), check_names=False)\n assert actual.dataset_ids == _test_datasets\n\n last_mock.return_value = pd.DataFrame(\n data={'impliedVolatility': [2], 'assetId': ['MA3']}, index=pd.date_range(start='2020-01-03', periods=1)\n )\n actual = tm.average_implied_volatility(mock_spx, '1m', tm.EdrDataReference.DELTA_CALL, 25, 3, '1d')\n expected = pd.Series([1.4, 2.6, 3.2], index=pd.date_range(start='2020-01-01', periods=3))\n expected.index.freq = None\n assert_series_equal(expected, pd.Series(actual), check_names=False)\n\n # Test weight_threshold paths\n\n mock_implied_vol = MarketDataResponseFrame(pd.concat([df2, df3], join='inner'))\n mock_implied_vol.dataset_ids = _test_datasets\n market_data_mock.return_value = mock_implied_vol\n with pytest.raises(MqValueError):\n tm.average_implied_volatility(mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75, top_n_of_index=3)\n\n # Working case where MA3 is dropped\n mock_implied_vol = MarketDataResponseFrame(pd.concat([df1, df2], join='inner'))\n mock_implied_vol.dataset_ids = _test_datasets\n market_data_mock.return_value = mock_implied_vol\n last_mock.return_value = None\n tm.average_implied_volatility(\n mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75, top_n_of_index=3, weight_threshold=0.11\n )\n\n mock_implied_vol = MarketDataResponseFrame(pd.concat([df1, df2], join='inner'))\n mock_implied_vol.dataset_ids = _test_datasets\n market_data_mock.return_value = mock_implied_vol\n with pytest.raises(MqValueError):\n tm.average_implied_volatility(\n mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75, top_n_of_index=3, weight_threshold=0.01\n )\n\n with pytest.raises(MqValueError):\n tm.average_implied_volatility(mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75, top_n_of_index=3)\n\n with pytest.raises(NotImplementedError):\n tm.average_implied_volatility(..., '1m', tm.EdrDataReference.DELTA_PUT, 75, real_time=True)\n\n with pytest.raises(MqValueError):\n tm.average_implied_volatility(\n mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75, top_n_of_index=None, composition_date='1d'\n )\n\n with pytest.raises(NotImplementedError):\n tm.average_implied_volatility(mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75, top_n_of_index=101)\n\n replace.restore()\n\n\ndef test_avg_realized_vol():\n replace = Replacer()\n\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n\n actual = tm.average_realized_volatility(mock_spx, '1m')\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='averageRealizedVolatility'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n\n df1 = pd.DataFrame(\n data={'spot': [1, 2, 3], 'assetId': ['MA1', 'MA1', 'MA1']}, index=pd.date_range(start='2020-01-01', periods=3)\n )\n df2 = pd.DataFrame(\n data={'spot': [2, 3, 4], 'assetId': ['MA2', 'MA2', 'MA2']}, index=pd.date_range(start='2020-01-01', periods=3)\n )\n df3 = pd.DataFrame(\n data={'spot': [2, 2, 2], 'assetId': ['MA3', 'MA3', 'MA3']}, index=pd.date_range(start='2020-01-01', periods=3)\n )\n mock_spot = MarketDataResponseFrame(pd.concat([df1, df2, df3], join='inner'))\n mock_spot.dataset_ids = _test_datasets\n\n replace('gs_quant.api.gs.indices.GsIndexApi.get_positions_data', mock_index_positions_data)\n last_mock = replace('gs_quant.timeseries.measures.get_last_for_measure', Mock())\n last_mock.return_value = None\n market_data_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_data_mock.return_value = mock_spot\n\n actual = tm.average_realized_volatility(mock_spx, '2d', Returns.SIMPLE, 3, '1d')\n expected = pd.Series(\n [392.874026], index=pd.date_range(start='2020-01-03', periods=1), name='averageRealizedVolatility'\n )\n expected.index.freq = None\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n dfl = pd.DataFrame(\n data={'spot': [5, 5, 5], 'assetId': ['MA1', 'MA2', 'MA3']}, index=[pd.to_datetime('2020-01-04')] * 3\n )\n last_mock.return_value = dfl\n actual = tm.average_realized_volatility(mock_spx, '2d', Returns.SIMPLE, 3, '1d')\n expected = pd.Series(\n [392.874026, 308.686734], index=pd.to_datetime(['2020-01-03', '2020-01-04']), name='averageRealizedVolatility'\n )\n assert_series_equal(expected, pd.Series(actual))\n\n last_mock.return_value = None\n df4 = pd.DataFrame(\n data={'spot': [2, 2], 'assetId': ['MA3', 'MA3']}, index=pd.date_range(start='2020-01-01', periods=2)\n )\n mock_spot_2 = MarketDataResponseFrame(pd.concat([df1, df2, df4], join='inner'))\n market_data_mock.return_value = mock_spot_2\n actual = tm.average_realized_volatility(mock_spx, '2d', Returns.SIMPLE, 3, '1d')\n assert actual.dropna().empty\n\n with pytest.raises(NotImplementedError):\n tm.average_realized_volatility(mock_spx, '1w', real_time=True)\n\n with pytest.raises(MqValueError):\n tm.average_realized_volatility(mock_spx, '1w', composition_date='1d')\n\n with pytest.raises(MqValueError):\n tm.average_realized_volatility(mock_spx, '1w', Returns.SIMPLE)\n\n with pytest.raises(MqValueError):\n tm.average_realized_volatility(mock_spx, '1w', Returns.SIMPLE, 201)\n\n replace.restore()\n\n empty_positions_data_mock = replace('gs_quant.api.gs.indices.GsIndexApi.get_positions_data', Mock())\n empty_positions_data_mock.return_value = []\n with pytest.raises(MqValueError):\n tm.average_realized_volatility(mock_spx, '1w', Returns.SIMPLE, 5)\n\n replace.restore()\n\n\ndef test_avg_impl_var():\n replace = Replacer()\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n actual = tm.average_implied_variance(mock_spx, '1m', tm.EdrDataReference.DELTA_CALL, 25)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='averageImpliedVariance'), pd.Series(actual))\n actual = tm.average_implied_variance(mock_spx, '1m', tm.EdrDataReference.DELTA_PUT, 75)\n assert actual.dataset_ids == _test_datasets\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='averageImpliedVariance'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.average_implied_variance(..., '1m', tm.EdrDataReference.DELTA_PUT, 75, real_time=True)\n replace.restore()\n\n\ndef test_basis_swap_spread(mocker):\n replace = Replacer()\n args = dict(\n swap_tenor='10y',\n spread_benchmark_type=None,\n spread_tenor=None,\n reference_benchmark_type=None,\n reference_tenor=None,\n forward_tenor='0b',\n real_time=False,\n )\n\n mock_nok = Currency('MA891', 'EGP')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EGP'\n args['asset'] = mock_nok\n with pytest.raises(NotImplementedError):\n tm_rates.basis_swap_spread(**args)\n\n mock_usd = Currency('MAZ7RWC904JYHYPS', 'USD')\n args['asset'] = mock_usd\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n with pytest.raises(NotImplementedError):\n tm_rates.basis_swap_spread(..., '1y', real_time=True)\n\n args['swap_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_spread(**args)\n args['swap_tenor'] = '6y'\n\n args['spread_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_spread(**args)\n args['spread_tenor'] = '3m'\n\n args['reference_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_spread(**args)\n args['reference_tenor'] = '6m'\n\n args['forward_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_spread(**args)\n args['forward_tenor'] = None\n\n args['spread_benchmark_type'] = BenchmarkType.STIBOR\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_spread(**args)\n args['spread_benchmark_type'] = BenchmarkType.LIBOR\n\n args['reference_benchmark_type'] = 'libor_3m'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_spread(**args)\n args['reference_benchmark_type'] = BenchmarkType.LIBOR\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n identifiers = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n identifiers.return_value = {'MAQB1PGEJFCET3GG'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_rates.basis_swap_spread(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='basisSwapRate')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == expected.dataset_ids\n\n args['reference_benchmark_type'] = BenchmarkType.SOFR\n args['reference_tenor'] = '1y'\n args['reference_benchmark_type'] = BenchmarkType.LIBOR\n args['reference_tenor'] = '3m'\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n identifiers = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n identifiers.return_value = {'MA06ATQ9CM0DCZFC'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n args['location'] = PricingLocation.NYC\n actual = tm_rates.basis_swap_spread(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='basisSwapRate')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == expected.dataset_ids\n\n replace.restore()\n\n\ndef test_swap_rate(mocker):\n replace = Replacer()\n args = dict(swap_tenor='10y', benchmark_type=None, floating_rate_tenor=None, forward_tenor='0b', real_time=False)\n\n mock_usd = Currency('MAZ7RWC904JYHYPS', 'USD')\n args['asset'] = mock_usd\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n\n args['swap_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_rate(**args)\n args['swap_tenor'] = '10y'\n\n args['floating_rate_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_rate(**args)\n args['floating_rate_tenor'] = '1y'\n\n args['forward_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_rate(**args)\n args['forward_tenor'] = None\n\n args['benchmark_type'] = BenchmarkType.STIBOR\n with pytest.raises(MqValueError):\n tm_rates.swap_rate(**args)\n\n args['benchmark_type'] = 'sonia'\n with pytest.raises(MqValueError):\n tm_rates.swap_rate(**args)\n args['benchmark_type'] = 'fed_funds'\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n identifiers = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n identifiers.return_value = {'MAZ7RWC904JYHYPS'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_rates.swap_rate(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='swapRate')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EUR'\n identifiers = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n identifiers.return_value = {'MAJNQPFGN1EBDHAE'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n args['asset'] = Currency('MAJNQPFGN1EBDHAE', 'EUR')\n args['benchmark_type'] = 'estr'\n args['location'] = PricingLocation.LDN\n actual = tm_rates.swap_rate(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='swapRate')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n replace.restore()\n\n\ndef test_swap_annuity(mocker):\n replace = Replacer()\n args = dict(swap_tenor='10y', benchmark_type=None, floating_rate_tenor=None, forward_tenor='0b', real_time=False)\n\n mock_nok = Currency('MA891', 'ACU')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'ACU'\n args['asset'] = mock_nok\n with pytest.raises(NotImplementedError):\n tm_rates.swap_annuity(**args)\n\n mock_usd = Currency('MAZ7RWC904JYHYPS', 'USD')\n args['asset'] = mock_usd\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n with pytest.raises(NotImplementedError):\n tm_rates.swap_annuity(..., '1y', real_time=True)\n\n args['swap_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_annuity(**args)\n args['swap_tenor'] = '10y'\n\n args['floating_rate_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_annuity(**args)\n args['floating_rate_tenor'] = '1y'\n\n args['forward_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_annuity(**args)\n args['forward_tenor'] = None\n\n args['benchmark_type'] = BenchmarkType.STIBOR\n with pytest.raises(MqValueError):\n tm_rates.swap_annuity(**args)\n args['benchmark_type'] = BenchmarkType.SOFR\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n identifiers = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n identifiers.return_value = {'MAZ7RWC904JYHYPS'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_rates.swap_annuity(**args)\n expected = abs(tm.ExtendedSeries([1.0, 2.0, 3.0], index=_index * 3, name='swapAnnuity') * 1e4 / 1e8)\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == expected.dataset_ids\n replace.restore()\n\n\ndef test_swap_term_structure():\n replace = Replacer()\n args = dict(\n benchmark_type=None,\n floating_rate_tenor=None,\n tenor_type=tm_rates._SwapTenorType.FORWARD_TENOR,\n tenor='0b',\n real_time=False,\n )\n\n mock_nok = Currency('MA891', 'ACU')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'ACU'\n args['asset'] = mock_nok\n with pytest.raises(NotImplementedError):\n tm_rates.swap_term_structure(**args)\n\n mock_usd = Currency('MAZ7RWC904JYHYPS', 'USD')\n args['asset'] = mock_usd\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n with pytest.raises(NotImplementedError):\n tm_rates.swap_term_structure(..., '1y', real_time=True)\n\n args['floating_rate_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_term_structure(**args)\n args['floating_rate_tenor'] = '3m'\n\n args['tenor_type'] = 'expiry'\n with pytest.raises(MqValueError):\n tm_rates.swap_term_structure(**args)\n args['tenor_type'] = None\n\n args['tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_term_structure(**args)\n args['tenor'] = None\n\n args['benchmark_type'] = BenchmarkType.STIBOR\n with pytest.raises(MqValueError):\n tm_rates.swap_term_structure(**args)\n args['benchmark_type'] = BenchmarkType.LIBOR\n\n bd_mock = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n bd_mock.return_value = pd.DataFrame(\n data=dict(date=\"2020-04-10\", exchange=\"NYC\", description=\"Good Friday\"), index=[pd.Timestamp('2020-04-10')]\n )\n args['pricing_date'] = dt.date(2020, 4, 10)\n bd_mock = replace('gs_quant.datetime.gscalendar.GsCalendar.holidays', MagicMock())\n bd_mock.__iter__.return_value = (dt.date(2020, 4, 10),)\n with pytest.raises(MqValueError):\n tm_rates.swap_term_structure(**args)\n args['pricing_date'] = None\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n identifiers_empty = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n identifiers_empty.return_value = {}\n with pytest.raises(MqValueError):\n tm_rates.swap_term_structure(**args)\n\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n mock_asset = Currency('USD', name='USD')\n mock_asset.id = 'MAEMPCXQG3T716EX'\n mock_asset.exchange = 'OTC'\n identifiers.return_value = [mock_asset]\n\n d = {\n 'terminationTenor': ['1y', '2y', '3y', '4y'],\n 'swapRate': [1, 2, 3, 4],\n 'assetId': ['MAEMPCXQG3T716EX', 'MAFRSWPAF5QPNTP2', 'MA88BXZ3TCTXTFW1', 'MAC4KAG9B9ZAZHFT'],\n }\n\n pricing_date_mock = replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock())\n pricing_date_mock.return_value = [dt.date(2019, 1, 1), dt.date(2019, 1, 1)]\n bd_mock.return_value = pd.DataFrame()\n market_data_mock = replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock())\n\n market_data_mock.return_value = pd.DataFrame()\n df = pd.DataFrame(data=d, index=_index * 4)\n assert tm_rates.swap_term_structure(**args).empty\n\n market_data_mock.return_value = df\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.swap_term_structure(**args)\n actual.dataset_ids = _test_datasets\n expected = tm.ExtendedSeries(\n [1, 2, 3, 4], index=pd.to_datetime(['2020-01-01', '2021-01-01', '2021-12-31', '2022-12-30'])\n )\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual, check_names=False)\n assert actual.dataset_ids == expected.dataset_ids\n\n df = pd.DataFrame(data={'effectiveTenor': ['1y'], 'swapRate': [1], 'assetId': ['MAEMPCXQG3T716EX']}, index=_index)\n market_data_mock.return_value = df\n args['tenor_type'] = 'swap_tenor'\n args['tenor'] = '5y'\n args['location'] = PricingLocation.NYC\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.swap_term_structure(**args)\n actual.dataset_ids = _test_datasets\n expected = tm.ExtendedSeries([1], index=pd.to_datetime(['2020-01-01']))\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual, check_names=False)\n assert actual.dataset_ids == expected.dataset_ids\n\n d = {\n 'effectiveTenor': ['1y', '2y', '3y', '4y'],\n 'swapRate': [1, 2, 3, 4],\n 'assetId': ['MAEMPCXQG3T716EX', 'MAFRSWPAF5QPNTP2', 'MA88BXZ3TCTXTFW1', 'MAC4KAG9B9ZAZHFT'],\n }\n\n df = pd.DataFrame(data=d, index=_index * 4)\n market_data_mock.return_value = df\n args['tenor_type'] = 'swap_tenor'\n\n args['tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.swap_term_structure(**args)\n args['tenor'] = '5y'\n\n market_data_mock.return_value = pd.DataFrame()\n df = pd.DataFrame(data=d, index=_index * 4)\n assert tm_rates.swap_term_structure(**args).empty\n\n market_data_mock.return_value = df\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.swap_term_structure(**args)\n actual.dataset_ids = _test_datasets\n expected = tm.ExtendedSeries(\n [1, 2, 3, 4], index=pd.to_datetime(['2020-01-01', '2021-01-01', '2021-12-31', '2022-12-30'])\n )\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual, check_names=False)\n assert actual.dataset_ids == expected.dataset_ids\n\n with patch('gs_quant.datetime.GsCalendar.holidays', (dt.date(2023, 1, 1),)):\n args['pricing_date'] = dt.date(2023, 1, 1)\n with pytest.raises(MqValueError):\n tm_rates.swap_term_structure(**args)\n\n replace.restore()\n\n\ndef test_basis_swap_term_structure():\n replace = Replacer()\n range_mock = replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock())\n range_mock.return_value = [dt.date(2019, 1, 1), dt.date(2019, 1, 1)]\n\n args = dict(\n spread_benchmark_type=None,\n spread_tenor=None,\n reference_benchmark_type=None,\n reference_tenor=None,\n tenor_type=tm_rates._SwapTenorType.FORWARD_TENOR,\n tenor='0b',\n real_time=False,\n )\n\n mock_nok = Currency('MA891', 'EGP')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EGP'\n args['asset'] = mock_nok\n with pytest.raises(NotImplementedError):\n tm_rates.basis_swap_term_structure(**args)\n\n mock_usd = Currency('MAZ7RWC904JYHYPS', 'USD')\n args['asset'] = mock_usd\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n with pytest.raises(NotImplementedError):\n tm_rates.basis_swap_term_structure(..., '1y', real_time=True)\n\n args['spread_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n args['spread_tenor'] = '3m'\n\n args['reference_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n args['reference_tenor'] = '6m'\n\n args['tenor_type'] = 'expiry'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n args['tenor_type'] = 'forward_tenor'\n\n args['tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n args['tenor'] = None\n\n args['spread_benchmark_type'] = BenchmarkType.STIBOR\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n args['spread_benchmark_type'] = BenchmarkType.LIBOR\n\n args['reference_benchmark_type'] = BenchmarkType.STIBOR\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n args['reference_benchmark_type'] = BenchmarkType.LIBOR\n\n bd_mock = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n bd_mock.return_value = pd.DataFrame(\n data=dict(date=\"2020-04-10\", exchange=\"NYC\", description=\"Good Friday\"), index=[pd.Timestamp('2020-04-10')]\n )\n args['pricing_date'] = dt.date(2020, 4, 10)\n bd_mock = replace('gs_quant.datetime.gscalendar.GsCalendar.holidays', MagicMock())\n bd_mock.__iter__.return_value = (dt.date(2020, 4, 10),)\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n args['pricing_date'] = None\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n identifiers_empty = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n identifiers_empty.return_value = {}\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n mock_asset = Currency('USD', name='USD')\n mock_asset.id = 'MAEMPCXQG3T716EX'\n mock_asset.exchange = 'OTC'\n identifiers.return_value = [mock_asset]\n\n d = {\n 'terminationTenor': ['1y', '2y', '3y', '4y'],\n 'basisSwapRate': [1, 2, 3, 4],\n 'assetId': ['MAEMPCXQG3T716EX', 'MAFRSWPAF5QPNTP2', 'MA88BXZ3TCTXTFW1', 'MAC4KAG9B9ZAZHFT'],\n }\n\n pricing_date_mock = replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock())\n pricing_date_mock.return_value = [dt.date(2019, 1, 1), dt.date(2019, 1, 1)]\n bd_mock.return_value = pd.DataFrame()\n market_data_mock = replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock())\n\n market_data_mock.return_value = pd.DataFrame()\n assert tm_rates.basis_swap_term_structure(**args).empty\n\n df = pd.DataFrame(data=d, index=_index * 4)\n market_data_mock.return_value = df\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.basis_swap_term_structure(**args)\n actual.dataset_ids = _test_datasets\n expected = tm.ExtendedSeries(\n [1, 2, 3, 4], index=pd.to_datetime(['2020-01-01', '2021-01-01', '2021-12-31', '2022-12-30'])\n )\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual, check_names=False)\n assert actual.dataset_ids == expected.dataset_ids\n\n d = {\n 'effectiveTenor': ['1y', '2y', '3y', '4y'],\n 'basisSwapRate': [1, 2, 3, 4],\n 'assetId': ['MAEMPCXQG3T716EX', 'MAFRSWPAF5QPNTP2', 'MA88BXZ3TCTXTFW1', 'MAC4KAG9B9ZAZHFT'],\n }\n bd_mock.return_value = pd.DataFrame()\n market_data_mock = replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock())\n\n df = pd.DataFrame(data=d, index=_index * 4)\n market_data_mock.return_value = df\n args['tenor_type'] = tm_rates._SwapTenorType.SWAP_TENOR\n args['tenor'] = '5y'\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.basis_swap_term_structure(**args)\n actual.dataset_ids = _test_datasets\n expected = tm.ExtendedSeries(\n [1, 2, 3, 4], index=pd.to_datetime(['2020-01-01', '2021-01-01', '2021-12-31', '2022-12-30'])\n )\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual, check_names=False)\n assert actual.dataset_ids == expected.dataset_ids\n\n df = pd.DataFrame(\n data={'effectiveTenor': ['1y'], 'basisSwapRate': [1], 'assetId': ['MAEMPCXQG3T716EX']}, index=_index\n )\n market_data_mock.return_value = df\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.basis_swap_term_structure(**args)\n actual.dataset_ids = _test_datasets\n expected = tm.ExtendedSeries([1], index=pd.to_datetime(['2020-01-01']))\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual, check_names=False)\n assert actual.dataset_ids == expected.dataset_ids\n\n with patch('gs_quant.datetime.GsCalendar.holidays', (dt.date(2023, 1, 1),)):\n args['pricing_date'] = dt.date(2023, 1, 1)\n with pytest.raises(MqValueError):\n tm_rates.basis_swap_term_structure(**args)\n\n replace.restore()\n\n\ndef test_cap_floor_vol():\n replace = Replacer()\n mock_usd = Currency('MA890', 'USD')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='USD',\n ),\n )\n ]\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.map_identifiers', Mock())\n identifiers.return_value = {'USD-LIBOR-BBA': 'MA123'}\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_curr)\n actual = tm.cap_floor_vol(mock_usd, '5y', 50)\n assert_series_equal(pd.Series([1, 2, 3], index=_index * 3, name='capFloorVol'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.cap_floor_vol(..., '5y', 50, real_time=True)\n replace.restore()\n\n\ndef test_cap_floor_atm_fwd_rate():\n replace = Replacer()\n mock_usd = Currency('MA890', 'USD')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='USD',\n ),\n )\n ]\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.map_identifiers', Mock())\n identifiers.return_value = {'USD-LIBOR-BBA': 'MA123'}\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_curr)\n actual = tm.cap_floor_atm_fwd_rate(mock_usd, '5y')\n assert_series_equal(pd.Series([1, 2, 3], index=_index * 3, name='capFloorAtmFwdRate'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.cap_floor_atm_fwd_rate(..., '5y', real_time=True)\n replace.restore()\n\n\ndef test_spread_option_vol():\n replace = Replacer()\n mock_usd = Currency('MA890', 'USD')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='USD',\n ),\n )\n ]\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.map_identifiers', Mock())\n identifiers.return_value = {'USD-LIBOR-BBA': 'MA123'}\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_curr)\n actual = tm.spread_option_vol(mock_usd, '3m', '10y', '5y', 50)\n assert_series_equal(pd.Series([1, 2, 3], index=_index * 3, name='spreadOptionVol'), pd.Series(actual))\n\n # Check null/no identifier cases\n identifiers.return_value = {None: 'MA123'}\n actual = tm.spread_option_vol(mock_usd, '3m', '10y', '5y', 50)\n assert_series_equal(pd.Series([1, 2, 3], index=_index * 3, name='spreadOptionVol'), pd.Series(actual))\n identifiers.return_value = {}\n with pytest.raises(MqValueError):\n tm.spread_option_vol(mock_usd, '3m', '10y', '5y', 50)\n\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.spread_option_vol(..., '3m', '10y', '5y', 50, real_time=True)\n replace.restore()\n\n\ndef test_spread_option_atm_fwd_rate():\n replace = Replacer()\n mock_usd = Currency('MA890', 'USD')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='USD',\n ),\n )\n ]\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.map_identifiers', Mock())\n identifiers.return_value = {'USD-LIBOR-BBA': 'MA123'}\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_curr)\n actual = tm.spread_option_atm_fwd_rate(mock_usd, '3m', '10y', '5y')\n assert_series_equal(pd.Series([1, 2, 3], index=_index * 3, name='spreadOptionAtmFwdRate'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.spread_option_atm_fwd_rate(..., '3m', '10y', '5y', real_time=True)\n replace.restore()\n\n\ndef test_zc_inflation_swap_rate():\n replace = Replacer()\n mock_gbp = Currency('MA890', 'GBP')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='GBP',\n ),\n )\n ]\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.map_identifiers', Mock())\n identifiers.return_value = {'CPI-UKRPI': 'MA123'}\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_curr)\n actual = tm.zc_inflation_swap_rate(mock_gbp, '1y')\n assert_series_equal(pd.Series([1, 2, 3], index=_index * 3, name='inflationSwapRate'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.zc_inflation_swap_rate(..., '1y', real_time=True)\n replace.restore()\n\n\ndef test_basis():\n replace = Replacer()\n mock_jpyusd = Cross('MA890', 'USD/JPY')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='JPYUSD',\n ),\n )\n ]\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.map_identifiers', Mock())\n identifiers.return_value = {'USD-3m/JPY-3m': 'MA123'}\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_cross)\n actual = tm.basis(mock_jpyusd, '1y')\n assert_series_equal(pd.Series([1, 2, 3], index=_index * 3, name='basis'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.basis(..., '1y', real_time=True)\n replace.restore()\n\n\ndef test_td():\n cases = {\n '3d': pd.DateOffset(days=3),\n '9w': pd.DateOffset(weeks=9),\n '2m': pd.DateOffset(months=2),\n '10y': pd.DateOffset(years=10),\n }\n for k, v in cases.items():\n actual = tm._to_offset(k)\n assert v == actual, f'expected {v}, got actual {actual}'\n\n with pytest.raises(ValueError):\n tm._to_offset('5z')\n\n\ndef test_pricing_range():\n given = dt.date(2019, 4, 20)\n s, e = tm._range_from_pricing_date('NYSE', given)\n assert s == e == given\n\n class MockDate(dt.date):\n @classmethod\n def today(cls):\n return cls(2019, 5, 25)\n\n # mock\n replace = Replacer()\n cbd = replace('gs_quant.timeseries.measures._get_custom_bd', Mock())\n cbd.return_value = pd.tseries.offsets.BusinessDay()\n today = replace('gs_quant.timeseries.measures.pd.Timestamp.today', Mock())\n today.return_value = pd.Timestamp(2019, 5, 25)\n gold = dt.date\n dt.date = MockDate\n\n # cases\n s, e = tm._range_from_pricing_date('ANY')\n assert s == pd.Timestamp(2019, 5, 24)\n assert e == pd.Timestamp(2019, 5, 24)\n\n s, e = tm._range_from_pricing_date('ANY', '3m')\n assert s == pd.Timestamp(2019, 2, 22)\n assert e == pd.Timestamp(2019, 2, 24)\n\n s, e = tm._range_from_pricing_date('ANY', '3b')\n assert s == e == pd.Timestamp(2019, 5, 22)\n\n # restore\n dt.date = gold\n replace.restore()\n\n\ndef test_var_swap_tenors():\n session = GsSession.get(Environment.DEV, token='faux')\n\n replace = Replacer()\n get_mock = replace('gs_quant.session.GsSession._get', Mock())\n get_mock.return_value = {\n 'data': [{'dataField': 'varSwap', 'filteredFields': [{'field': 'tenor', 'values': ['abc', 'xyc']}]}]\n }\n\n with session:\n actual = tm._var_swap_tenors(Index('MAXXX', AssetClass.Equity, 'XXX'))\n assert actual == ['abc', 'xyc']\n\n get_mock.return_value = {'data': []}\n with pytest.raises(MqError):\n with session:\n tm._var_swap_tenors(Index('MAXXX', AssetClass.Equity, 'XXX'))\n replace.restore()\n\n\ndef test_forward_var_term():\n data = {\n 'varSwap': [21, 20.5, 20, 19.7, 20.3],\n 'date': [\n dt.date(2023, 2, 15),\n dt.date(2023, 2, 22),\n dt.date(2023, 3, 1),\n dt.date(2023, 3, 8),\n dt.date(2023, 3, 15),\n ],\n }\n out = ExtendedSeries(data=data['varSwap'], name='varSwap', index=[pd.Timestamp(d) for d in data['date']])\n out.dataset_ids = _test_datasets\n out.attrs = dict(latest=dt.date(2023, 1, 31))\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.var_term', Mock())\n market_mock.return_value = out\n\n # Equity\n expected = pd.Series(\n [np.nan, 19.05194455628533, 18.02476051953111, 18.088723159586152, 22.74345522665404],\n name='forwardVarTerm',\n index=_make_index(['2023-02-15', '2023-02-22', '2023-03-01', '2023-03-08', '2023-03-15']),\n )\n with DataContext('2023-01-31', '2024-07-31'):\n actual = tm.forward_var_term(Index('MA123', AssetClass.Equity, '123'))\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n market_mock.assert_called_once()\n\n # FX\n cross_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n cross_mock.return_value = 'EURUSD'\n\n with DataContext('2023-01-31', '2024-07-31'):\n actual_fx = tm.forward_var_term(Cross('ABCDE', 'EURUSD'))\n assert_series_equal(expected, pd.Series(actual_fx))\n assert actual_fx.dataset_ids == _test_datasets\n\n # no data\n market_mock.reset_mock()\n market_mock.return_value = mock_empty_market_data_response()\n actual = tm.forward_var_term(Index('MA123', AssetClass.Equity, '123'))\n assert actual.empty\n\n replace.restore()\n # real-time\n with pytest.raises(NotImplementedError):\n tm.forward_var_term(..., real_time=True)\n\n\ndef _mock_var_swap_data(_cls, q, ignore_errors=False):\n queries = q.get('queries', [])\n if len(queries) > 0 and 'Last' in queries[0]['measures']:\n return MarketDataResponseFrame({'varSwap': [4]}, index=[pd.Timestamp('2019-01-04T12:00:00Z')])\n\n idx = pd.date_range(start=\"2019-01-01\", periods=3, freq=\"D\")\n data = {'varSwap': [1, 2, 3]}\n out = MarketDataResponseFrame(data=data, index=idx)\n out.dataset_ids = _test_datasets\n return out\n\n\ndef test_var_swap():\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', _mock_var_swap_data)\n\n expected = pd.Series([1, 2, 3, 4], name='varSwap', index=pd.date_range(\"2019-01-01\", periods=4, freq=\"D\"))\n expected.index.freq = None\n actual = tm.var_swap(Index('MA123', AssetClass.Equity, '123'), '1m')\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n actual = tm.var_swap(Index('MA123', AssetClass.Equity, '123'), '1m')\n assert actual.empty\n replace.restore()\n\n\ndef _mock_var_swap_fwd(_cls, q, ignore_errors=False):\n queries = q.get('queries', [])\n if len(queries) > 0 and 'Last' in queries[0]['measures']:\n return MarketDataResponseFrame(\n {'varSwap': [4, 4.5], 'tenor': ['1y', '13m']}, index=[pd.Timestamp('2019-01-04T12:00:00Z')] * 2\n )\n\n idx = pd.date_range(start=\"2019-01-01\", periods=3, freq=\"D\")\n d1 = {'varSwap': [1, 2, 3], 'tenor': ['1y'] * 3}\n d2 = {'varSwap': [1.5, 2.5, 3.5], 'tenor': ['13m'] * 3}\n df1 = MarketDataResponseFrame(data=d1, index=idx)\n df2 = MarketDataResponseFrame(data=d2, index=idx)\n out = pd.concat([df1, df2])\n out.dataset_ids = _test_datasets\n return out\n\n\ndef _mock_var_swap_1t(_cls, q, ignore_errors=False):\n queries = q.get('queries', [])\n if len(queries) > 0 and 'Last' in queries[0]['measures']:\n return MarketDataResponseFrame(\n {'varSwap': [4, 4.5], 'tenor': ['1y', '13m']}, index=[pd.Timestamp('2019-01-04T12:00:00Z')]\n )\n\n idx = pd.date_range(start=\"2019-01-01\", periods=3, freq=\"D\")\n d1 = {'varSwap': [1, 2, 3], 'tenor': ['1y'] * 3}\n df1 = MarketDataResponseFrame(data=d1, index=idx)\n df1.dataset_ids = _test_datasets\n return df1\n\n\ndef test_var_swap_fwd():\n # bad input\n with pytest.raises(MqError):\n tm.var_swap(Index('MA123', AssetClass.Equity, '123'), '1m', 500)\n\n # regular\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', _mock_var_swap_fwd)\n\n tenors_mock = replace('gs_quant.timeseries.measures._var_swap_tenors', Mock())\n tenors_mock.return_value = ['1m', '1y', '13m']\n\n expected = pd.Series(\n [4.1533, 5.7663, 7.1589, 8.4410], name='varSwap', index=pd.date_range(start=\"2019-01-01\", periods=4, freq=\"D\")\n )\n expected.index.freq = None\n actual = tm.var_swap(Index('MA123', AssetClass.Equity, '123'), '1m', '1y')\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n # no data\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n actual = tm.var_swap(Index('MA123', AssetClass.Equity, '123'), '1m', '1y')\n assert actual.empty\n assert actual.dataset_ids == ()\n\n # no data for a tenor\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', _mock_var_swap_1t)\n actual = tm.var_swap(Index('MA123', AssetClass.Equity, '123'), '1m', '1y')\n assert actual.empty\n assert actual.dataset_ids == ()\n\n # no such tenors\n tenors_mock.return_value = []\n actual = tm.var_swap(Index('MA123', AssetClass.Equity, '123'), '1m', '1y')\n assert actual.empty\n assert actual.dataset_ids == ()\n\n # finish\n replace.restore()\n\n\ndef _var_term_typical():\n assert DataContext.current_is_set\n data = {'tenor': ['1w', '2w', '1y', '2y'], 'varSwap': [1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=data, index=pd.DatetimeIndex(['2018-01-01'] * 4))\n out.dataset_ids = _test_datasets\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = [pd.DataFrame(), out]\n\n actual = tm.var_term(Index('MA123', AssetClass.Equity, '123'))\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='varSwap')\n expected = pd.Series([1, 2, 3, 4], name='varSwap', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual), check_names=False)\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n\n return actual\n\n\ndef _var_term_empty():\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n\n actual = tm.var_term(Index('MAXYZ', AssetClass.Equity, 'XYZ'))\n assert actual.empty\n assert actual.dataset_ids == ()\n replace.restore()\n\n\ndef _var_term_fwd():\n idx = pd.date_range('2018-01-01', periods=2, freq='D')\n\n def mock_var_swap(_asset, tenor, _forward_start_date, **_kwargs):\n if tenor == '1m':\n series = tm.ExtendedSeries([1, 2], idx, name='varSwap')\n series.dataset_ids = _test_datasets\n elif tenor == '2m':\n series = tm.ExtendedSeries([3, 4], idx, name='varSwap')\n series.dataset_ids = _test_datasets\n else:\n series = tm.ExtendedSeries(dtype=float)\n series.dataset_ids = ()\n return series\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.var_swap', Mock())\n market_mock.side_effect = mock_var_swap\n tenors_mock = replace('gs_quant.timeseries.measures._var_swap_tenors', Mock())\n tenors_mock.return_value = ['1m', '2m', '3m']\n\n actual = tm.var_term(Index('MA123', AssetClass.Equity, '123'), forward_start_date='1m')\n idx = _make_index(['2018-02-02', '2018-03-02'], name='varSwap')\n expected = pd.Series([2, 4], name='varSwap', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual), check_names=False)\n assert actual.dataset_ids == _test_datasets\n market_mock.assert_called()\n\n replace.restore()\n return actual\n\n\ndef _var_term_latest():\n assert DataContext.current_is_set\n data = {'tenor': ['1w', '2w', '1y', '2y', '1w', '2w', '1y', '2y'], 'varSwap': [10, 20, 30, 40, 1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=data, index=pd.date_range(\"2018-01-01\", periods=8, freq=\"min\"))\n out.dataset_ids = _test_datasets\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = [out.iloc[-1:], out]\n\n actual = tm.var_term(Index('MA123', AssetClass.Equity, '123'))\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([1, 2, 3, 4], name='varSwap', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n\n\ndef test_var_term():\n with DataContext('2018-01-01', '2019-01-01'):\n _var_term_typical()\n _var_term_empty()\n _var_term_fwd()\n _var_term_latest()\n with DataContext('2019-01-01', '2019-07-04'):\n _var_term_fwd()\n with DataContext('2018-01-16', '2018-12-31'):\n out = _var_term_typical()\n assert out.empty\n assert out.dataset_ids == _test_datasets\n with pytest.raises(MqError):\n tm.var_term(..., pricing_date=300)\n with pytest.raises(NotImplementedError):\n tm.var_term(..., real_time=True)\n\n\ndef _mock_forward_helper():\n idx = pd.DatetimeIndex([dt.date(2020, 5, 1), dt.date(2020, 5, 2)] * 4)\n data = {\n 'impliedVolatility': [2.1, 2, 3.1, 3, 4.1, 4, 5.1, 5],\n 'tenor': ['1m', '1m', '2m', '2m', '3m', '3m', '4m', '4m'],\n }\n out = MarketDataResponseFrame(data=data, index=idx)\n out.dataset_ids = _test_datasets\n return out\n\n\ndef _mock_forward_vol_data(_cls, q, ignore_errors=False):\n queries = q.get('queries', [])\n if len(queries) > 0 and 'Last' in queries[0]['measures']:\n return MarketDataResponseFrame()\n\n return _mock_forward_helper()\n\n\ndef _mock_forward_vol_data_with_last(_cls, q, ignore_errors=False):\n queries = q.get('queries', [])\n if len(queries) > 0 and 'Last' in queries[0]['measures']:\n idx = [pd.Timestamp('2020-05-03T12:00:00Z')] * 4\n data = {'impliedVolatility': [2, 3, 4, 5], 'tenor': ['1m', '2m', '3m', '4m']}\n ids = _test_datasets_rt\n out = MarketDataResponseFrame(data=data, index=idx)\n out.dataset_ids = ids\n return out\n\n return _mock_forward_helper()\n\n\ndef _mock_forward_vol_data_error(_cls, q, ignore_errors=False):\n queries = q.get('queries', [])\n if len(queries) > 0 and 'Last' in queries[0]['measures']:\n raise MqValueError('something happened')\n\n return _mock_forward_helper()\n\n\ndef test_forward_vol():\n idx = pd.DatetimeIndex([dt.date(2020, 5, 1), dt.date(2020, 5, 2)] * 4)\n data = {\n 'impliedVolatility': [2.1, 2, 3.1, 3, 4.1, 4, 5.1, 5],\n 'tenor': ['1m', '1m', '2m', '2m', '3m', '3m', '4m', '4m'],\n }\n out = MarketDataResponseFrame(data=data, index=idx)\n out.dataset_ids = _test_datasets\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = _mock_forward_vol_data\n\n # Equity\n expected = pd.Series([5.58659, 5.47723], name='forwardVol', index=_make_index(['2020-05-01', '2020-05-02']))\n with DataContext('2020-01-01', '2020-09-01'):\n actual = tm.forward_vol(Index('MA123', AssetClass.Equity, '123'), '1m', '2m', tm.VolReference.SPOT, 100)\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n market_mock.assert_called_once()\n\n # FX\n cross_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n cross_mock.return_value = 'EURUSD'\n\n with DataContext('2020-01-01', '2020-09-01'):\n actual_fx = tm.forward_vol(Cross('ABCDE', 'EURUSD'), '1m', '2m', tm.VolReference.SPOT, 100)\n assert_series_equal(expected, pd.Series(actual_fx))\n assert actual_fx.dataset_ids == _test_datasets\n\n # EQ with last\n market_mock.reset_mock()\n market_mock.side_effect = _mock_forward_vol_data_with_last\n expected = pd.Series(\n [5.58659, 5.47723, 5.47723], name='forwardVol', index=_make_index(['2020-05-01', '2020-05-02', '2020-05-03'])\n )\n actual = tm.forward_vol(Index('MA123', AssetClass.Equity, '123'), '1m', '2m', tm.VolReference.SPOT, 100)\n assert_series_equal(expected, pd.Series(actual))\n for t in _test_datasets + _test_datasets_rt:\n assert t in actual.dataset_ids\n assert market_mock.call_count == 2\n\n # EQ with exception on last\n market_mock.reset_mock()\n market_mock.side_effect = _mock_forward_vol_data_error\n expected = pd.Series([5.58659, 5.47723], name='forwardVol', index=_make_index(['2020-05-01', '2020-05-02']))\n actual = tm.forward_vol(Index('MA123', AssetClass.Equity, '123'), '1m', '2m', tm.VolReference.SPOT, 100)\n assert_series_equal(expected, pd.Series(actual))\n assert market_mock.call_count == 2\n\n # no data\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n actual = tm.forward_vol(Index('MA123', AssetClass.Equity, '123'), '1m', '2m', tm.VolReference.SPOT, 100)\n assert actual.empty\n\n # no data for required tenor\n market_mock.reset_mock()\n market_mock.return_value = MarketDataResponseFrame(\n data={'impliedVolatility': [2.1, 3.1, 5.1], 'tenor': ['1m', '2m', '4m']}, index=[dt.date(2020, 5, 1)] * 3\n )\n with DataContext('2020-01-01', '2020-09-01'):\n actual = tm.forward_vol(Index('MA123', AssetClass.Equity, '123'), '1m', '2m', tm.VolReference.SPOT, 100)\n assert actual.empty\n\n # real-time\n with pytest.raises(NotImplementedError):\n tm.forward_vol(..., '1m', '2m', tm.VolReference.SPOT, 100, real_time=True)\n\n replace.restore()\n\n\ndef test_forward_vol_term():\n data = {\n 'impliedVolatility': [31, 27, 28, 21, 21.5],\n 'date': [\n dt.date(2023, 2, 1),\n dt.date(2023, 2, 2),\n dt.date(2023, 2, 3),\n dt.date(2023, 2, 6),\n dt.date(2023, 2, 7),\n ],\n }\n out = ExtendedSeries(\n data=data['impliedVolatility'], name='impliedVolatility', index=[pd.Timestamp(d) for d in data['date']]\n )\n out.dataset_ids = _test_datasets\n out.attrs = dict(latest=dt.date(2023, 1, 31))\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.vol_term', Mock())\n market_mock.return_value = out\n\n # Equity\n expected = pd.Series(\n [np.nan, 18.51754260110053, 24.835557737474247, 14.242257163702778, 20.171543580777623],\n name='forwardVolTerm',\n index=_make_index(['2023-02-01', '2023-02-02', '2023-02-03', '2023-02-06', '2023-02-07']),\n )\n with DataContext('2023-01-31', '2024-07-31'):\n actual = tm.forward_vol_term(\n Index('MA123', AssetClass.Equity, '123'), tm.VolReference.SPOT, 100, dt.date(2023, 1, 31)\n )\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n market_mock.assert_called_once()\n\n # FX\n cross_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n cross_mock.return_value = 'EURUSD'\n\n with DataContext('2023-01-31', '2024-07-31'):\n actual_fx = tm.forward_vol_term(Cross('ABCDE', 'EURUSD'), tm.VolReference.SPOT, 100)\n assert_series_equal(expected, pd.Series(actual_fx))\n assert actual_fx.dataset_ids == _test_datasets\n\n # no data\n market_mock.reset_mock()\n market_mock.return_value = mock_empty_market_data_response()\n actual = tm.forward_vol_term(Index('MA123', AssetClass.Equity, '123'), tm.VolReference.SPOT, 100)\n assert actual.empty\n\n replace.restore()\n # real-time\n with pytest.raises(NotImplementedError):\n tm.forward_vol_term(..., tm.VolReference.SPOT, 100, real_time=True)\n\n\ndef test_get_latest_term_structure_data():\n # Test latest_term_structure_data where no data is returned\n replace = Replacer()\n data = {'tenor': ['1w', '2w', '1y', '2y'], 'impliedVolatility': [1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=data, index=pd.DatetimeIndex(['2018-01-01'] * 4))\n out.dataset_ids = _test_datasets\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = [out, pd.DataFrame()]\n\n # test that the function runs without error when nothing is returned by second market_data_timed call\n tm._get_latest_term_structure_data('MA123', tm.QueryType.IMPLIED_VOLATILITY, {}, None, '', '')\n\n\ndef _vol_term_typical(reference, value):\n assert DataContext.current_is_set\n data = {'tenor': ['1w', '2w', '1y', '2y'], 'impliedVolatility': [1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=data, index=pd.DatetimeIndex(['2018-01-01'] * 4))\n out.dataset_ids = _test_datasets\n\n data_expiry = {'expirationDate': ['2018-01-06', '2018-01-08'], 'impliedVolatilityByExpiration': [1.2, 1.1]}\n out_expiry = MarketDataResponseFrame(data=data_expiry, index=pd.DatetimeIndex(['2018-01-01'] * 2))\n out_expiry.dataset_ids = _test_datasets2\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = [pd.DataFrame(), pd.DataFrame(), out, out_expiry]\n\n actual = tm.vol_term(Index('MA123', AssetClass.Equity, '123'), reference, value)\n idx = _make_index(['2018-01-06', '2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([1.2, 1, 2, 3, 4], name='impliedVolatility', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual))\n assert set(actual.dataset_ids) == set(_test_datasets + _test_datasets2)\n\n replace.restore()\n return actual\n\n\ndef _skew_term_typical(reference, value, normalization_mode=None):\n assert DataContext.current_is_set\n data = {\n 'tenor': ['10y', '10y', '10y', '13m', '13m', '13m'],\n 'impliedVolatility': [5, 10, 15, 50, 55, 60],\n 'relativeStrike': [0.25, 0.5, 0.75, 0.25, 0.5, 0.75],\n }\n out = MarketDataResponseFrame(data=data, index=pd.DatetimeIndex(['2022-01-11'] * 6))\n out.dataset_ids = _test_datasets\n\n data_expiry = {\n 'expirationDate': ['2022-01-12', '2022-01-12', '2022-01-12'],\n 'impliedVolatilityByExpiration': [1000, 1500, 2000],\n 'relativeStrike': [0.25, 0.5, 0.75],\n }\n out_expiry = MarketDataResponseFrame(data=data_expiry, index=pd.DatetimeIndex(['2022-01-11'] * 3))\n out_expiry.dataset_ids = _test_datasets2\n\n replace = Replacer()\n gltsd_mock = replace('gs_quant.api.utils.ThreadPoolManager.run_async', Mock())\n gltsd_mock.side_effect = [[pd.DataFrame(), pd.DataFrame()], [out, out_expiry]]\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = [out, out_expiry]\n\n actual = tm.skew_term(\n Index('MA123', AssetClass.Equity, '123'), reference, value, normalization_mode=normalization_mode\n )\n idx = _make_index(['2022-01-12', '2023-02-13', '2032-01-12'])\n\n if normalization_mode is None:\n normalization_mode = tm.NormalizationMode.NORMALIZED\n\n expected = (\n pd.Series([0.666667, 0.181818, 1], name='impliedVolatility', index=idx)\n if normalization_mode == tm.NormalizationMode.NORMALIZED\n else pd.Series([1000, 10, 10], name='impliedVolatility', index=idx)\n )\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual))\n assert set(actual.dataset_ids) == set(_test_datasets + _test_datasets2)\n\n replace.restore()\n return actual\n\n\ndef _skew_term_typical_fx(reference, value, normalization_mode=None):\n assert DataContext.current_is_set\n data = {\n 'tenor': ['10y', '10y', '10y', '13m', '13m', '13m'],\n 'impliedVolatility': [5, 10, 15, 50, 55, 60],\n 'relativeStrike': [0.25, 0.5, 0.75, 0.25, 0.5, 0.75],\n }\n out = MarketDataResponseFrame(data=data, index=pd.DatetimeIndex(['2022-01-11'] * 6))\n out.dataset_ids = _test_datasets\n\n data_expiry = {\n 'expirationDate': ['2022-01-12', '2022-01-12', '2022-01-12'],\n 'impliedVolatilityByExpiration': [1000, 1500, 2000],\n 'relativeStrike': [0.25, 0.5, 0.75],\n }\n out_expiry = MarketDataResponseFrame(data=data_expiry, index=pd.DatetimeIndex(['2022-01-11'] * 3))\n out_expiry.dataset_ids = _test_datasets2\n\n replace = Replacer()\n gltsd_mock = replace('gs_quant.api.utils.ThreadPoolManager.run_async', Mock())\n gltsd_mock.side_effect = [[pd.DataFrame(), pd.DataFrame()], [out, out_expiry]]\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n fx_asset_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n fx_asset_mock.return_value = 'MA123'\n market_mock.side_effect = [out, out_expiry]\n\n actual = tm.skew_term(Index('MA123', AssetClass.FX, '123'), reference, value, normalization_mode=normalization_mode)\n idx = _make_index(['2022-01-12', '2023-02-13', '2032-01-12'])\n\n if normalization_mode is None:\n normalization_mode = tm.NormalizationMode.OUTRIGHT\n\n expected = (\n pd.Series([0.666667, 0.181818, 1], name='impliedVolatility', index=idx)\n if normalization_mode == tm.NormalizationMode.NORMALIZED\n else pd.Series([1000, 10, 10], name='impliedVolatility', index=idx)\n )\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual))\n assert set(actual.dataset_ids) == set(_test_datasets + _test_datasets2)\n\n replace.restore()\n return actual\n\n\ndef _vol_term_empty():\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = pd.DataFrame()\n\n mock = replace('gs_quant.timeseries.measures.get_df_with_retries', Mock())\n mock.return_value = MarketDataResponseFrame()\n\n actual = tm.vol_term(Index('MAXYZ', AssetClass.Equity, 'XYZ'), tm.VolReference.DELTA_CALL, 777)\n assert actual.empty\n assert actual.dataset_ids == ()\n replace.restore()\n\n\ndef _skew_term_empty():\n replace = Replacer()\n market_mock = replace('gs_quant.api.utils.ThreadPoolManager.run_async', Mock())\n market_mock.side_effect = [[pd.DataFrame(), pd.DataFrame()], [pd.DataFrame(), pd.DataFrame()]]\n\n actual = tm.skew_term(Index('MAXYZ', AssetClass.Equity, 'XYZ'), tm.SkewReference.DELTA, 777)\n assert actual.empty\n assert actual.dataset_ids == ()\n replace.restore()\n\n\ndef _vol_term_latest():\n assert DataContext.current_is_set\n data = {\n 'tenor': ['1w', '2w', '1y', '2y', '1w', '2w', '1y', '2y'],\n 'impliedVolatility': [10, 20, 30, 40, 1, 2, 3, 4],\n }\n out = MarketDataResponseFrame(data=data, index=pd.date_range(\"2018-01-01\", periods=8, freq=\"min\"))\n out.dataset_ids = _test_datasets\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = [out.iloc[-1:], out, pd.DataFrame(), pd.DataFrame()]\n\n actual = tm.vol_term(Index('MA123', AssetClass.Equity, '123'), tm.VolReference.SPOT, 100)\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([1, 2, 3, 4], name='impliedVolatility', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n return actual\n\n\ndef test_vol_term():\n with DataContext('2018-01-01', '2019-01-01'):\n _vol_term_typical(tm.VolReference.SPOT, 100)\n _vol_term_typical(tm.VolReference.NORMALIZED, 4)\n _vol_term_typical(tm.VolReference.DELTA_PUT, 50)\n _vol_term_empty()\n _vol_term_latest()\n with DataContext('2018-01-16', '2018-12-31'):\n out = _vol_term_typical(tm.VolReference.SPOT, 100)\n assert out.empty\n assert set(out.dataset_ids) == set(_test_datasets + _test_datasets2)\n with pytest.raises(NotImplementedError):\n tm.vol_term(..., tm.VolReference.SPOT, 100, real_time=True)\n with pytest.raises(MqError):\n tm.vol_term(Index('MA123', AssetClass.Equity, '123'), tm.VolReference.DELTA_NEUTRAL, 0)\n with DataContext('2020-01-01', '2021-01-01'):\n with pytest.raises(MqError, match='forward looking date range'):\n tm.vol_term(Index('MA123', AssetClass.Equity, '123'), tm.VolReference.SPOT, 100, source='plottool')\n with DataContext(dt.date.today(), dt.date.today()):\n with pytest.raises(MqError, match='forward looking date range'):\n tm.vol_term(Index('MA123', AssetClass.Equity, '123'), tm.VolReference.SPOT, 100, source='plottool')\n\n\ndef test__get_skew_strikes():\n # Test FX\n asset = Index('MA123', AssetClass.FX, '123')\n skew_strikes, buf = tm._get_skew_strikes(asset, tm.SkewReference.DELTA, 25)\n assert skew_strikes == [-25, 25, 0] and buf == 1\n with pytest.raises(MqValueError):\n tm._get_skew_strikes(asset, tm.SkewReference.NORMALIZED, 50)\n\n # Test skew reference types\n asset = Index('MA123', AssetClass.Equity, '123')\n skew_strikes, _ = tm._get_skew_strikes(asset, tm.SkewReference.DELTA, 25)\n assert skew_strikes == [0.75, 0.25, 0.50]\n skew_strikes, _ = tm._get_skew_strikes(asset, tm.SkewReference.NORMALIZED, 25)\n assert skew_strikes == [-25, 25, 0]\n skew_strikes, _ = tm._get_skew_strikes(asset, tm.SkewReference.SPOT, 25)\n assert skew_strikes == [0.75, 1.25, 1.0]\n with pytest.raises(MqTypeError):\n tm._get_skew_strikes(asset, None, 50)\n\n\ndef test__skew():\n df = pd.DataFrame(\n {'relativeStrike': [0.25, 0.5, 0.75, 0.25, 0.5, 0.75], 'iv': [5, 10, 15, 100, 150, 200]},\n index=['2021-01-01'] * 3 + ['2021-02-01'] * 3,\n )\n q_strikes = [0.75, 0.25, 0.5]\n res = tm._skew(df, 'relativeStrike', 'iv', q_strikes, tm.NormalizationMode.OUTRIGHT)\n expected = tm.ExtendedSeries(pd.Series([10, 100], name='iv', index=_make_index(['2021-01-01', '2021-02-01'])))\n assert_series_equal(res, expected)\n\n res = tm._skew(df, 'relativeStrike', 'iv', q_strikes, tm.NormalizationMode.NORMALIZED)\n expected = tm.ExtendedSeries(\n pd.Series([1.0, 0.6666666666666666], name='iv', index=_make_index(['2021-01-01', '2021-02-01']))\n )\n assert_series_equal(res, expected)\n with pytest.raises(MqValueError):\n tm._skew(\n df.loc[df['relativeStrike'].isin([0.25, 0.5])],\n 'relativeStrike',\n 'iv',\n q_strikes,\n tm.NormalizationMode.NORMALIZED,\n )\n\n\ndef _skew_term_no_data():\n replace = Replacer()\n market_mock = replace('gs_quant.api.utils.ThreadPoolManager.run_async', Mock())\n market_mock.side_effect = [[pd.DataFrame(), pd.DataFrame()], [pd.DataFrame(), MarketDataResponseFrame()]]\n\n actual = tm.skew_term(Index('MAXYZ', AssetClass.Equity, 'XYZ'), tm.SkewReference.DELTA, 777)\n assert actual.empty\n assert actual.dataset_ids == ()\n replace.restore()\n\n\ndef test__skew_term_fetcher():\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures._market_data_timed', Mock())\n market_mock.side_effect = [MarketDataResponseFrame(), MqValueError(), MqValueError]\n actual = tm._skew_fetcher('ABC123', tm.QueryType.IMPLIED_VOLATILITY, {}, 'any', True, allow_exception=False)\n assert isinstance(actual, MarketDataResponseFrame) and actual.empty\n\n actual = tm._skew_fetcher('ABC123', tm.QueryType.IMPLIED_VOLATILITY, {}, 'any', True, allow_exception=True)\n assert isinstance(actual, MarketDataResponseFrame) and actual.empty\n\n with pytest.raises(MqValueError):\n tm._skew_fetcher('ABC123', tm.QueryType.IMPLIED_VOLATILITY, {}, 'any', True, allow_exception=False)\n replace.restore()\n\n\ndef test_skew_term():\n with DataContext('2018-01-01', '2040-01-01'):\n _skew_term_typical(tm.SkewReference.DELTA, 25)\n _skew_term_empty()\n _skew_term_no_data()\n with DataContext('2018-01-16', '2018-12-31'):\n out = _skew_term_typical(tm.SkewReference.DELTA, 25)\n assert out.empty\n assert set(out.dataset_ids) == set(_test_datasets + _test_datasets2)\n with DataContext('2018-01-16', '2018-12-31'):\n out = _skew_term_typical_fx(tm.SkewReference.DELTA, 25)\n assert out.empty\n with pytest.raises(NotImplementedError):\n tm.skew_term(..., tm.SkewReference.SPOT, 100, real_time=True)\n with DataContext('2020-01-01', '2021-01-01'):\n with pytest.raises(MqError, match='forward looking date range'):\n tm.skew_term(Index('MA123', AssetClass.Equity, '123'), tm.SkewReference.SPOT, 100, source='plottool')\n with DataContext(dt.date.today(), dt.date.today()):\n with pytest.raises(MqError, match='forward looking date range'):\n tm.skew_term(Index('MA123', AssetClass.Equity, '123'), tm.SkewReference.SPOT, 100, source='plottool')\n\n\ndef _vol_term_fx(reference, value):\n assert DataContext.current_is_set\n data = {'tenor': ['1w', '2w', '1y', '2y'], 'impliedVolatility': [1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=data, index=pd.DatetimeIndex(['2018-01-01'] * 4))\n out.dataset_ids = _test_datasets\n\n data_expiry = {'expirationDate': ['2018-01-06', '2018-01-08'], 'impliedVolatilityByExpiration': [1.2, 1.1]}\n out_expiry = MarketDataResponseFrame(data=data_expiry, index=pd.DatetimeIndex(['2018-01-01'] * 2))\n out_expiry.dataset_ids = _test_datasets2\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = [out, out_expiry]\n cross_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n cross_mock.return_value = 'EURUSD'\n\n actual = tm.vol_term(Cross('ABCDE', 'EURUSD'), reference, value)\n idx = _make_index(['2018-01-06', '2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([1.2, 1, 2, 3, 4], name='impliedVolatility', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual))\n assert set(actual.dataset_ids) == set(_test_datasets + _test_datasets2)\n\n replace.restore()\n return actual\n\n\ndef _vol_term_fx_no_expiry(reference, value):\n assert DataContext.current_is_set\n data = {'tenor': ['1w', '2w', '1y', '2y'], 'impliedVolatility': [1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=data, index=pd.DatetimeIndex(['2018-01-01'] * 4))\n out.dataset_ids = _test_datasets\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.side_effect = [out, MqValueError('a')]\n cross_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n cross_mock.return_value = 'EURUSD'\n\n actual = tm.vol_term(Cross('ABCDE', 'EURUSD'), reference, value)\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01'], name='expirationDate')\n expected = pd.Series([1, 2, 3], name='impliedVolatility', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n return actual\n\n\ndef test_vol_term_fx():\n with pytest.raises(MqError):\n tm.vol_term(Cross('MABLUE', 'BLUE'), tm.VolReference.SPOT, 50)\n with pytest.raises(MqError):\n tm.vol_term(Cross('MABLUE', 'BLUE'), tm.VolReference.NORMALIZED, 1)\n with pytest.raises(MqError):\n tm.vol_term(Cross('MABLUE', 'BLUE'), tm.VolReference.DELTA_NEUTRAL, 1)\n with DataContext('2018-01-01', '2019-01-01'):\n _vol_term_fx(tm.VolReference.DELTA_CALL, 50)\n with DataContext('2018-01-01', '2019-01-01'):\n _vol_term_fx(tm.VolReference.DELTA_PUT, 50)\n with DataContext('2018-01-01', '2019-01-01'):\n _vol_term_fx_no_expiry(tm.VolReference.DELTA_PUT, 50)\n\n\ndef _fwd_term_typical():\n assert DataContext.current_is_set\n data = {'tenor': ['1w', '2w', '1y', '2y'], 'forward': [1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=data, index=pd.DatetimeIndex(['2018-01-01'] * 4))\n out.dataset_ids = _test_datasets\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = out\n\n actual = tm.fwd_term(Index('MA123', AssetClass.Equity, '123'))\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([1, 2, 3, 4], name='forward', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n if expected.empty:\n assert actual.empty\n else:\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n market_mock.assert_called_once()\n\n replace.restore()\n return actual\n\n\ndef _fx_fwd_term_typical():\n assert DataContext.current_is_set\n fwd_data = {'tenor': ['1w', '2w', '1y', '2y'], 'forwardPoint': [1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=fwd_data, index=pd.DatetimeIndex(['2018-01-01'] * 4))\n out.dataset_ids = _test_datasets\n\n spot_data = {'spot': [100], 'assetId': 'MA123'}\n spot = MarketDataResponseFrame(data=spot_data, index=pd.DatetimeIndex(['2018-01-01']))\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = out\n\n fx_asset_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n fx_asset_mock.return_value = 'MA123'\n\n thread_mock = replace('gs_quant.api.utils.ThreadPoolManager.run_async', Mock())\n thread_mock.return_value = [out, spot]\n\n actual = tm.fx_fwd_term(Index('MA123', AssetClass.FX, '123'))\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([101, 102, 103, 104], name='forwardPoint', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n assert_series_equal(pd.Series(actual), expected)\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.fx_fwd_term(Index('MA123', AssetClass.FX, '123'), fwd_type=tm.FXForwardType.POINTS)\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([1, 2, 3, 4], name='forwardPoint', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n assert_series_equal(pd.Series(actual), expected)\n assert actual.dataset_ids == _test_datasets\n\n with pytest.raises(NotImplementedError):\n tm.fx_fwd_term(..., real_time=True)\n\n replace.restore()\n\n\ndef _fwd_term_empty():\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n\n actual = tm.fwd_term(Index('MAXYZ', AssetClass.Equity, 'XYZ'))\n assert actual.empty\n assert actual.dataset_ids == ()\n market_mock.assert_called_once()\n\n replace.restore()\n\n\ndef _fx_fwd_term_empty():\n replace = Replacer()\n fx_asset_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n fx_asset_mock.return_value = 'MA123'\n\n thread_mock = replace('gs_quant.api.utils.ThreadPoolManager.run_async', Mock())\n thread_mock.return_value = [pd.DataFrame(), pd.DataFrame()]\n\n actual = tm.fx_fwd_term(Index('MAXYZ', AssetClass.FX, 'XYZ'))\n assert actual.empty\n assert actual.dataset_ids == ()\n replace.restore()\n\n\ndef _fx_fwd_term():\n with DataContext('2018-01-01', '2019-01-01'):\n _fx_fwd_term_typical()\n _fx_fwd_term_empty()\n\n\ndef _fwd_term_equity():\n with DataContext('2018-01-01', '2019-01-01'):\n _fwd_term_typical()\n _fwd_term_empty()\n with DataContext('2018-01-16', '2018-12-31'):\n out = _fwd_term_typical()\n assert out.empty\n assert out.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.fwd_term(..., real_time=True)\n\n\ndef test_fwd_term():\n _fx_fwd_term()\n _fwd_term_equity()\n\n\ndef _carry_term_typical():\n assert DataContext.current_is_set\n fwd_data = {'tenor': ['1w', '2w', '1y', '2y'], 'forwardPoint': [1, 2, 3, 4]}\n out = MarketDataResponseFrame(data=fwd_data, index=pd.DatetimeIndex(['2018-01-01'] * 4))\n out.dataset_ids = _test_datasets\n\n spot_data = {'spot': [100], 'assetId': 'MA123'}\n spot = MarketDataResponseFrame(data=spot_data, index=pd.DatetimeIndex(['2018-01-01']))\n\n replace = Replacer()\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = out\n\n fx_asset_mock = replace('gs_quant.timeseries.measures.cross_stored_direction_for_fx_vol', Mock())\n fx_asset_mock.return_value = 'MA123'\n\n thread_mock = replace('gs_quant.api.utils.ThreadPoolManager.run_async', Mock())\n thread_mock.return_value = [out, spot]\n\n actual = tm.carry_term(Index('MA123', AssetClass.FX, '123'), annualized=tm.FXSpotCarry.DAILY)\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([0.01, 0.02, 0.03, 0.04], name='carry', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.carry_term(Index('MA123', AssetClass.FX, '123'))\n idx = _make_index(['2018-01-08', '2018-01-15', '2019-01-01', '2020-01-01'], name='expirationDate')\n expected = pd.Series([0.001667, 0.004714, 0.036105, 0.05621], name='carry', index=idx)\n expected = expected.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n\n assert_series_equal(expected, pd.Series(actual), check_exact=False, atol=0.00001)\n assert actual.dataset_ids == _test_datasets\n\n with pytest.raises(NotImplementedError):\n tm.carry_term(Index('MA123', AssetClass.FX, '123'), real_time=True)\n\n thread_mock.return_value = [pd.DataFrame(), pd.DataFrame()]\n actual = tm.carry_term(Index('MA123', AssetClass.FX, '123'))\n assert actual.empty\n replace.restore()\n\n\ndef test_carry_term():\n with DataContext('2018-01-01', '2019-01-01'):\n _carry_term_typical()\n\n\ndef test_measure_request_safe():\n def error(raise_error):\n if raise_error:\n raise MqValueError()\n\n with pytest.raises(MqValueError):\n tm.measure_request_safe('error', Index('MA123', AssetClass.Equity, '123'), error, None, True)\n tm.measure_request_safe('error', Index('MA123', AssetClass.Equity, '123'), error, None, False)\n\n\ndef test_bucketize_price():\n target = {\n '7x24': [27.323461],\n 'offpeak': [26.004816],\n 'peak': [27.982783],\n '7x8': [26.004816],\n '2x16h': [],\n 'monthly': [],\n 'CAISO 7x24': [26.953743375],\n 'CAISO peak': [29.547952562499997],\n 'MISO 7x24': [27.076390749999998],\n 'MISO offpeak': [25.263605624999997],\n }\n\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_commod)\n mock_pjm = Index('MA001', AssetClass.Commod, 'PJM')\n mock_caiso = Index('MA002', AssetClass.Commod, 'CAISO')\n mock_miso = Index('MA003', AssetClass.Commod, 'MISO')\n\n with DataContext(dt.date(2019, 5, 1), dt.date(2019, 5, 1)):\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'MISO'\n\n actual = tm.bucketize_price(mock_miso, 'LMP', bucket='7x24')\n assert_series_equal(\n pd.Series(target['MISO 7x24'], index=[dt.date(2019, 5, 1)], name='price'), pd.Series(actual)\n )\n\n actual = tm.bucketize_price(mock_miso, 'LMP', bucket='offpeak')\n assert_series_equal(\n pd.Series(target['MISO offpeak'], index=[dt.date(2019, 5, 1)], name='price'), pd.Series(actual)\n )\n\n bbid_mock.return_value = 'CAISO'\n\n actual = tm.bucketize_price(mock_caiso, 'LMP', bucket='7x24')\n assert_series_equal(\n pd.Series(target['CAISO 7x24'], index=[dt.date(2019, 5, 1)], name='price'), pd.Series(actual)\n )\n\n actual = tm.bucketize_price(mock_caiso, 'LMP', bucket='peak')\n assert_series_equal(\n pd.Series(target['CAISO peak'], index=[dt.date(2019, 5, 1)], name='price'), pd.Series(actual)\n )\n\n bbid_mock.return_value = 'PJM'\n\n actual = tm.bucketize_price(mock_pjm, 'LMP', bucket='7x24')\n assert_series_equal(pd.Series(target['7x24'], index=[dt.date(2019, 5, 1)], name='price'), pd.Series(actual))\n\n actual = tm.bucketize_price(mock_pjm, 'LMP', bucket='offpeak')\n assert_series_equal(pd.Series(target['offpeak'], index=[dt.date(2019, 5, 1)], name='price'), pd.Series(actual))\n\n actual = tm.bucketize_price(mock_pjm, 'LMP', bucket='peak')\n assert_series_equal(pd.Series(target['peak'], index=[dt.date(2019, 5, 1)], name='price'), pd.Series(actual))\n\n actual = tm.bucketize_price(mock_pjm, 'LMP', bucket='7x8')\n assert_series_equal(pd.Series(target['7x8'], index=[dt.date(2019, 5, 1)], name='price'), pd.Series(actual))\n\n actual = tm.bucketize_price(mock_pjm, 'LMP', bucket='2x16h')\n assert_series_equal(pd.Series(target['2x16h'], index=[], dtype=float, name='price'), pd.Series(actual))\n\n actual = tm.bucketize_price(mock_pjm, 'LMP', granularity='m', bucket='7X24')\n assert_series_equal(pd.Series(target['monthly'], index=[], dtype=float, name='price'), pd.Series(actual))\n\n with pytest.raises(ValueError):\n tm.bucketize_price(mock_pjm, 'LMP', bucket='7X24', real_time=True)\n\n with pytest.raises(ValueError):\n tm.bucketize_price(mock_pjm, 'LMP', bucket='weekday')\n\n with pytest.raises(ValueError):\n tm.bucketize_price(mock_caiso, 'LMP', bucket='weekday')\n\n with pytest.raises(ValueError):\n tm.bucketize_price(mock_pjm, 'LMP', granularity='yearly')\n\n replace.restore()\n\n # No market data\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'MISO'\n actual = tm.bucketize_price(mock_miso, 'LMP', bucket='7x24')\n assert_series_equal(pd.Series(dtype='float64'), pd.Series(actual))\n\n # Duplicate data in dataset\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_commod_dup)\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'MISO'\n with pytest.raises(ValueError):\n tm.bucketize_price(mock_miso, 'LMP', bucket='peak')\n\n replace.restore()\n\n\ndef test_forward_price():\n # US Power\n target = {\n '7x24': [19.46101],\n 'peak': [23.86745],\n 'J20 7x24': [18.11768888888889],\n 'J20-K20 7x24': [19.283921311475414],\n 'J20-K20 offpeak': [15.82870707070707],\n 'J20-K20 7x8': [13.020144262295084],\n }\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_forward_price)\n mock_spp = Index('MA001', AssetClass.Commod, 'SPP')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'SPP'\n\n # Should return empty series as mark for '7x8' bucket is missing\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='2Q20', bucket='7x24')\n assert_series_equal(pd.Series(target['7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='J20', bucket='7x24')\n assert_series_equal(pd.Series(target['J20 7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='2Q20', bucket='PEAK')\n assert_series_equal(pd.Series(target['peak'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='J20-K20', bucket='7x24')\n assert_series_equal(\n pd.Series(target['J20-K20 7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual)\n )\n\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='J20-K20', bucket='offpeak')\n assert_series_equal(\n pd.Series(target['J20-K20 offpeak'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual)\n )\n\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='J20-K20', bucket='7x8')\n assert_series_equal(\n pd.Series(target['J20-K20 7x8'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual)\n )\n\n actual = tm.forward_price(mock_spp, price_method='lmp', contract_range='2Q20', bucket='7x24')\n assert_series_equal(pd.Series(target['7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n with pytest.raises(ValueError):\n tm.forward_price(mock_spp, price_method='LMP', contract_range='5Q20', bucket='PEAK')\n\n with pytest.raises(ValueError):\n tm.forward_price(mock_spp, price_method='LMP', contract_range='Invalid', bucket='PEAK')\n\n with pytest.raises(ValueError):\n tm.forward_price(mock_spp, price_method='LMP', contract_range='3H20', bucket='7x24')\n\n with pytest.raises(ValueError):\n tm.forward_price(mock_spp, price_method='LMP', contract_range='F20-I20', bucket='7x24')\n\n with pytest.raises(ValueError):\n tm.forward_price(mock_spp, price_method='LMP', contract_range='2H20', bucket='7x24', real_time=True)\n\n replace.restore()\n\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_missing_bucket_forward_price)\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'SPP'\n\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='2Q20', bucket='7x24')\n\n assert_series_equal(pd.Series(dtype=float, index=[]), pd.Series(actual), check_names=False)\n\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='2Q20', bucket='PEAK')\n assert_series_equal(pd.Series(target['peak'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='J20-K20', bucket='7x24')\n assert_series_equal(\n pd.Series(target['J20-K20 7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual)\n )\n replace.restore()\n\n # No market data\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'SPP'\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = tm.forward_price(mock_spp, price_method='LMP', contract_range='2Q20', bucket='7x24')\n assert_series_equal(pd.Series(dtype='float64'), pd.Series(actual))\n replace.restore()\n\n # Query for asset with no bbid\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_forward_price)\n mock_ice = CommodityPowerAggregatedNodes('MA001', 'ICE Mid C')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n bbId_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbId_mock.return_value = None\n ISO_mock = replace('gs_quant.entities.entity.Entity.get_entity', Mock())\n ISO_mock.return_value = {'parameters': {'ISO': 'PJM'}}\n\n # Should work as other usecases where assetId has bbid\n actual = tm.forward_price(mock_ice, price_method='LMP', contract_range='2Q20', bucket='7x24')\n assert_series_equal(pd.Series(target['7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n replace.restore()\n\n\ndef test_implied_volatility_elec():\n # US Power\n target = {\n '7x24': [0.403352],\n 'peak': [0.383025],\n 'J20 7x24': [0.372178],\n 'J20-K20 7x24': [0.3737661202185792],\n 'J20-K20 offpeak': [0.3922989898989899],\n 'J20-K20 7x8': [0.4322360655737705],\n }\n replace = Replacer()\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = mock_implied_volatility_elec()\n mock_spp = Index('MA001', AssetClass.Commod, 'SPP')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'SPP'\n\n # Should return empty series as mark for '7x8' bucket is missing\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='2Q20', bucket='7x24')\n assert_series_equal(pd.Series(target['7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='J20', bucket='7x24')\n assert_series_equal(pd.Series(target['J20 7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='2Q20', bucket='PEAK')\n assert_series_equal(pd.Series(target['peak'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='J20-K20', bucket='7x24')\n assert_series_equal(\n pd.Series(target['J20-K20 7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual)\n )\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='J20-K20', bucket='offpeak')\n assert_series_equal(\n pd.Series(target['J20-K20 offpeak'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual)\n )\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='J20-K20', bucket='7x8')\n assert_series_equal(\n pd.Series(target['J20-K20 7x8'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual)\n )\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='lmp', contract_range='2Q20', bucket='7x24')\n assert_series_equal(pd.Series(target['7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n with pytest.raises(ValueError):\n tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='5Q20', bucket='PEAK')\n\n with pytest.raises(ValueError):\n tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='Invalid', bucket='PEAK')\n\n with pytest.raises(ValueError):\n tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='3H20', bucket='7x24')\n\n with pytest.raises(ValueError):\n tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='F20-I20', bucket='7x24')\n\n with pytest.raises(ValueError):\n tm.implied_volatility_elec(\n mock_spp, price_method='LMP', contract_range='2H20', bucket='7x24', real_time=True\n )\n\n replace.restore()\n\n replace = Replacer()\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = mock_missing_bucket_implied_volatility()\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'SPP'\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='2Q20', bucket='7x24')\n\n assert_series_equal(pd.Series(dtype=float, index=[]), pd.Series(actual), check_names=False)\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='2Q20', bucket='PEAK')\n assert_series_equal(pd.Series(target['peak'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='J20-K20', bucket='7x24')\n assert_series_equal(\n pd.Series(target['J20-K20 7x24'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual)\n )\n replace.restore()\n\n # No market data\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = mock_empty_market_data_response()\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'SPP'\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = tm.implied_volatility_elec(mock_spp, price_method='LMP', contract_range='2Q20', bucket='7x24')\n assert_series_equal(pd.Series(dtype='float64'), pd.Series(actual))\n replace.restore()\n\n\ndef test_forward_price_ng():\n # Tests for US NG assets\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_natgas_forward_price)\n mock = CommodityNaturalGasHub('MA001', 'AGT')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = pd.Series(tm.forward_price_ng(mock, price_method='GDD', contract_range='F21'))\n expected = pd.Series([2.880], index=[dt.date(2019, 1, 2)], name='price')\n assert_series_equal(expected, actual)\n\n actual = pd.Series(tm.forward_price_ng(mock, price_method='GDD', contract_range='F21-G21'))\n expected = pd.Series([2.8629152542372878], index=[dt.date(2019, 1, 2)], name='price')\n assert_series_equal(expected, actual)\n\n with pytest.raises(ValueError):\n tm.forward_price_ng(mock, price_method='GDD', contract_range='F21-I21')\n\n with pytest.raises(ValueError):\n tm.forward_price_ng(mock, price_method='GDD', contract_range='I21')\n\n with pytest.raises(MqTypeError):\n wrong_mock = Index('MA001', AssetClass.Commod, 'SPP')\n tm.forward_price_ng(wrong_mock, price_method='GDD', contract_range='I21')\n with pytest.raises(ValueError):\n tm.forward_price_ng(mock, price_method='GDD', contract_range='I21', real_time=True)\n\n # No market data\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = tm.forward_price_ng(mock, price_method='LMP', contract_range='2Q20')\n assert_series_equal(pd.Series(dtype='float64'), pd.Series(actual))\n replace.restore()\n\n # Tests for EU NG assets\n mock_EU_asset = CommodityEUNaturalGasHub('MA001', 'TTF')\n mock_EU_swap_asset = Swap('MA002', AssetClass.Commod, 'Swap NatGas TTF')\n mock_EU_swap_asset.id = \"Mock_Id\"\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_EU_swap_asset]\n\n # Test for forward price fetch using instrument id\n with DataContext(dt.date(2021, 1, 1), dt.date(2021, 1, 1)):\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eu_natgas_forward_price)\n expected = pd.Series([15.65], name='price', index=[dt.date(2021, 1, 1)])\n actual = tm.forward_price_ng(mock_EU_asset, contract_range='H21')\n assert_series_equal(expected, pd.Series(actual))\n\n # Test for empty market response\n with DataContext(dt.date(2021, 1, 1), dt.date(2021, 1, 1)):\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n actual = tm.forward_price_ng(mock_EU_asset, contract_range='H21', price_method='USD')\n assert actual.empty\n\n # Test for no instruments found\n with DataContext(dt.date(2021, 1, 1), dt.date(2021, 1, 1)):\n assets.return_value = []\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n actual = tm.forward_price_ng(mock_EU_asset, contract_range='H21')\n assert actual.empty\n\n replace.restore()\n\n\ndef test_implied_volatility_ng():\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_natgas_implied_volatility)\n mock = CommodityNaturalGasHub('MA001', 'AGT')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = pd.Series(tm.implied_volatility_ng(mock, price_method='GDD', contract_range='F21'))\n expected = pd.Series([2.880], index=[dt.date(2019, 1, 2)], name='price')\n assert_series_equal(expected, actual)\n\n actual = pd.Series(tm.implied_volatility_ng(mock, price_method='GDD', contract_range='F21-G21'))\n expected = pd.Series([2.8629152542372878], index=[dt.date(2019, 1, 2)], name='price')\n assert_series_equal(expected, actual)\n\n with pytest.raises(ValueError):\n tm.implied_volatility_ng(mock, price_method='GDD', contract_range='F21-I21')\n\n with pytest.raises(ValueError):\n tm.implied_volatility_ng(mock, price_method='GDD', contract_range='I21')\n\n with pytest.raises(ValueError):\n tm.implied_volatility_ng(mock, price_method='GDD', contract_range='I21', real_time=True)\n\n # No market data\n market_mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n market_mock.return_value = mock_empty_market_data_response()\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = tm.implied_volatility_ng(mock, price_method='LMP', contract_range='2Q20')\n assert_series_equal(pd.Series(dtype='float64'), pd.Series(actual))\n replace.restore()\n\n\ndef test_get_iso_data():\n tz_map = {'MISO': 'US/Central', 'CAISO': 'US/Pacific'}\n for key in tz_map:\n assert tm._get_iso_data(key)[0] == tz_map[key]\n\n\ndef test_string_to_date_interval():\n assert tm._string_to_date_interval(\"K20\")['start_date'] == dt.date(2020, 5, 1)\n assert tm._string_to_date_interval(\"K20\")['end_date'] == dt.date(2020, 5, 31)\n\n assert tm._string_to_date_interval(\"k20\")['start_date'] == dt.date(2020, 5, 1)\n assert tm._string_to_date_interval(\"k20\")['end_date'] == dt.date(2020, 5, 31)\n\n assert tm._string_to_date_interval(\"Cal22\")['start_date'] == dt.date(2022, 1, 1)\n assert tm._string_to_date_interval(\"Cal22\")['end_date'] == dt.date(2022, 12, 31)\n\n assert tm._string_to_date_interval(\"Cal2012\")['start_date'] == dt.date(2012, 1, 1)\n assert tm._string_to_date_interval(\"Cal2012\")['end_date'] == dt.date(2012, 12, 31)\n\n assert tm._string_to_date_interval(\"Cal53\")['start_date'] == dt.date(1953, 1, 1)\n assert tm._string_to_date_interval(\"Cal53\")['end_date'] == dt.date(1953, 12, 31)\n\n assert tm._string_to_date_interval(\"2010\")['start_date'] == dt.date(2010, 1, 1)\n assert tm._string_to_date_interval(\"2010\")['end_date'] == dt.date(2010, 12, 31)\n\n assert tm._string_to_date_interval(\"3Q20\")['start_date'] == dt.date(2020, 7, 1)\n assert tm._string_to_date_interval(\"3Q20\")['end_date'] == dt.date(2020, 9, 30)\n\n assert tm._string_to_date_interval(\"2h2021\")['start_date'] == dt.date(2021, 7, 1)\n assert tm._string_to_date_interval(\"2h2021\")['end_date'] == dt.date(2021, 12, 31)\n\n assert tm._string_to_date_interval(\"3q20\")['start_date'] == dt.date(2020, 7, 1)\n assert tm._string_to_date_interval(\"3q20\")['end_date'] == dt.date(2020, 9, 30)\n\n assert tm._string_to_date_interval(\"2H2021\")['start_date'] == dt.date(2021, 7, 1)\n assert tm._string_to_date_interval(\"2H2021\")['end_date'] == dt.date(2021, 12, 31)\n\n assert tm._string_to_date_interval(\"Mar2021\")['start_date'] == dt.date(2021, 3, 1)\n assert tm._string_to_date_interval(\"Mar2021\")['end_date'] == dt.date(2021, 3, 31)\n\n assert tm._string_to_date_interval(\"March2021\")['start_date'] == dt.date(2021, 3, 1)\n assert tm._string_to_date_interval(\"March2021\")['end_date'] == dt.date(2021, 3, 31)\n\n assert tm._string_to_date_interval(\"5Q20\") == \"Invalid Quarter\"\n\n assert tm._string_to_date_interval(\"HH2021\") == \"Invalid num\"\n\n assert tm._string_to_date_interval(\"3H2021\") == \"Invalid Half Year\"\n\n assert tm._string_to_date_interval(\"Cal2a\") == \"Invalid year\"\n assert tm._string_to_date_interval(\"Marc201\") == \"Invalid date code\"\n\n assert tm._string_to_date_interval(\"M1a2021\") == \"Invalid date code\"\n\n assert tm._string_to_date_interval(\"Marcha2021\") == \"Invalid date code\"\n\n assert tm._string_to_date_interval(\"I20\") == \"Invalid month\"\n\n assert tm._string_to_date_interval(\"20\") == \"Unknown date code\"\n\n\ndef test_implied_vol_commod():\n target = {\n 'F21': [2.880],\n 'F21-H21': [2.815756],\n }\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_implied_volatility)\n mock = Index('MA001', AssetClass.Commod, 'Option NG Exchange')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = tm.implied_volatility(mock, tenor='F21-H21')\n assert_series_equal(pd.Series(target['F21-H21'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n replace.restore()\n\n\ndef test_fair_price():\n target = {\n 'F21': [2.880],\n 'F21-H21': [2.815756],\n }\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_fair_price)\n mock = Index('MA001', AssetClass.Commod, 'Swap NG Exchange')\n mock2 = Swap('MA002', AssetClass.Commod, 'Swap Oil')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = tm.fair_price(mock, tenor='F21')\n assert_series_equal(pd.Series(target['F21'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n with pytest.raises(ValueError):\n tm.fair_price(mock, tenor=None)\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_fair_price_swap)\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = tm.fair_price(mock2)\n\n assert_series_equal(\n pd.Series([2.880], index=_make_index(['2019-01-02']), name='fairPrice'),\n pd.Series(actual),\n )\n\n replace.restore()\n\n\ndef test_weighted_average_valuation_curve_for_calendar_strip():\n target = {\n 'F21': [2.880],\n 'F21-H21': [2.815756],\n }\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_fair_price)\n mock = Index('MA001', AssetClass.Commod, 'Swap NG Exchange')\n\n with DataContext(dt.date(2019, 1, 2), dt.date(2019, 1, 2)):\n actual = tm._weighted_average_valuation_curve_for_calendar_strip(\n mock, contract_range='F21', query_type=QueryType.FAIR_PRICE, measure_field='fairPrice'\n )\n assert_series_equal(pd.Series(target['F21'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n actual = tm._weighted_average_valuation_curve_for_calendar_strip(\n mock, contract_range='F21-H21', query_type=QueryType.FAIR_PRICE, measure_field='fairPrice'\n )\n assert_series_equal(pd.Series(target['F21-H21'], index=[dt.date(2019, 1, 2)], name='price'), pd.Series(actual))\n\n with pytest.raises(ValueError):\n tm._weighted_average_valuation_curve_for_calendar_strip(\n mock, contract_range='Invalid', query_type=QueryType.FAIR_PRICE, measure_field='fairPrice'\n )\n with pytest.raises(ValueError):\n tm._weighted_average_valuation_curve_for_calendar_strip(\n mock, contract_range='F20-I20', query_type=QueryType.FAIR_PRICE, measure_field='fairPrice'\n )\n with pytest.raises(ValueError):\n tm._weighted_average_valuation_curve_for_calendar_strip(\n mock, contract_range='3H20', query_type=QueryType.PRICE, measure_field='fairPrice'\n )\n\n replace.restore()\n\n\ndef test_fundamental_metrics():\n replace = Replacer()\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n mock_rb = CustomBasket(\n GsAsset(\n AssetClass.Equity,\n AssetType.Research_Basket,\n 'GSTEST01',\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n {'flagship': True},\n )\n )\n mock_non_flagship = CustomBasket(\n GsAsset(\n AssetClass.Equity,\n AssetType.Research_Basket,\n 'GSTEST01',\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n {'flagship': False},\n )\n )\n mock_basket_no_parameters = CustomBasket(GsAsset(AssetClass.Equity, AssetType.Research_Basket, 'GSTEST01'))\n mock_basket_no_flagship_parameter = CustomBasket(\n GsAsset(\n AssetClass.Equity,\n AssetType.Research_Basket,\n 'GSTEST01',\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n {},\n )\n )\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_eq)\n period = '1y'\n direction = tm.FundamentalMetricPeriodDirection.FORWARD\n\n actual = tm.dividend_yield(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.dividend_yield(..., period, direction, real_time=True)\n\n actual = tm.earnings_per_share(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.earnings_per_share(..., period, direction, real_time=True)\n\n actual = tm.earnings_per_share_positive(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.earnings_per_share_positive(..., period, direction, real_time=True)\n\n actual = tm.net_debt_to_ebitda(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.net_debt_to_ebitda(..., period, direction, real_time=True)\n\n actual = tm.price_to_book(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.price_to_book(..., period, direction, real_time=True)\n\n actual = tm.price_to_cash(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.price_to_cash(..., period, direction, real_time=True)\n\n actual = tm.price_to_earnings(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.price_to_earnings(..., period, direction, real_time=True)\n\n actual = tm.price_to_earnings_positive(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.price_to_earnings_positive(..., period, direction, real_time=True)\n\n actual = tm.price_to_sales(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.price_to_sales(..., period, direction, real_time=True)\n\n actual = tm.price_to_earnings_positive_exclusive(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.price_to_earnings_positive_exclusive(..., period, direction, real_time=True)\n\n actual = tm.return_on_equity(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.return_on_equity(..., period, direction, real_time=True)\n\n actual = tm.sales_per_share(mock_spx, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.sales_per_share(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_dividend_yield(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_dividend_yield(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_earnings_per_share(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_earnings_per_share(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_earnings_per_share_positive(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_earnings_per_share_positive(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_net_debt_to_ebitda(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_net_debt_to_ebitda(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_price_to_book(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_book(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_price_to_cash(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_cash(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_price_to_earnings(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_earnings(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_price_to_earnings_positive(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_earnings_positive(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_price_to_sales(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_sales(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_return_on_equity(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_return_on_equity(..., period, direction, real_time=True)\n\n actual = tm.current_constituents_sales_per_share(mock_rb, period, direction)\n assert_series_equal(pd.Series([5, 1, 2], index=_index * 3, name='fundamentalMetric'), pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n with pytest.raises(NotImplementedError):\n tm.current_constituents_sales_per_share(..., period, direction, real_time=True)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_dividend_yield(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_earnings_per_share(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_earnings_per_share_positive(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_net_debt_to_ebitda(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_book(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_cash(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_earnings(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_earnings_positive(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_sales(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_return_on_equity(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_sales_per_share(mock_non_flagship, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_dividend_yield(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_earnings_per_share(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_earnings_per_share_positive(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_net_debt_to_ebitda(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_book(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_cash(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_earnings(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_earnings_positive(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_sales(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_return_on_equity(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_sales_per_share(mock_basket_no_parameters, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_dividend_yield(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_earnings_per_share(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_earnings_per_share_positive(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_net_debt_to_ebitda(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_book(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_cash(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_earnings(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_earnings_positive(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_price_to_sales(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_return_on_equity(mock_basket_no_flagship_parameter, period, direction)\n\n with pytest.raises(NotImplementedError):\n tm.current_constituents_sales_per_share(mock_basket_no_flagship_parameter, period, direction)\n\n replace.restore()\n\n\ndef test_realized_volatility():\n from gs_quant.timeseries.econometrics import volatility, Returns\n from gs_quant.timeseries.statistics import generate_series\n\n random = generate_series(100).rename('spot')\n window = 10\n type_ = Returns.SIMPLE\n\n replace = Replacer()\n mock_data = replace('gs_quant.timeseries.measures.get_historical_and_last_for_measure', Mock())\n return_value = MarketDataResponseFrame(random)\n return_value.dataset_ids = _test_datasets\n mock_data.return_value = return_value\n\n expected = volatility(random, window, type_)\n actual = tm.realized_volatility(Cross('MA123', 'ABCXYZ'), window, type_)\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.realized_volatility(Stock('MA4B66MW5E27U9VBB94', 'AAPL'), window, type_)\n assert_series_equal(expected, pd.Series(actual))\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n\n\ndef test_esg_headline_metric():\n replace = Replacer()\n\n mock_aapl = Stock('MA4B66MW5E27U9VBB94', 'AAPL')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_esg)\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_NUMERIC_SCORE)\n assert_series_equal(pd.Series([2, 4, 6], index=_index * 3, name='esNumericScore'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_POLICY_SCORE)\n assert_series_equal(pd.Series([2, 4, 6], index=_index * 3, name='esPolicyScore'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_AGGREGATE_SCORE)\n assert_series_equal(pd.Series([2, 4, 6], index=_index * 3, name='esScore'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_PRODUCT_IMPACT_SCORE)\n assert_series_equal(pd.Series([2, 4, 6], index=_index * 3, name='esProductImpactScore'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.GOVERNANCE_AGGREGATE_SCORE)\n assert_series_equal(pd.Series([2, 4, 6], index=_index * 3, name='gScore'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_MOMENTUM_SCORE)\n assert_series_equal(pd.Series([2, 4, 6], index=_index * 3, name='esMomentumScore'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.GOVERNANCE_REGIONAL_SCORE)\n assert_series_equal(pd.Series([2, 4, 6], index=_index * 3, name='gRegionalScore'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.CONTROVERSY_SCORE)\n assert_series_equal(pd.Series([2, 4, 6], index=_index * 3, name='controversyScore'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_NUMERIC_PERCENTILE)\n assert_series_equal(pd.Series([81.2, 75.4, 65.7], index=_index * 3, name='esNumericPercentile'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_POLICY_PERCENTILE)\n assert_series_equal(pd.Series([81.2, 75.4, 65.7], index=_index * 3, name='esPolicyPercentile'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_AGGREGATE_PERCENTILE)\n assert_series_equal(pd.Series([81.2, 75.4, 65.7], index=_index * 3, name='esPercentile'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_PRODUCT_IMPACT_PERCENTILE)\n assert_series_equal(\n pd.Series([81.2, 75.4, 65.7], index=_index * 3, name='esProductImpactPercentile'), pd.Series(actual)\n )\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.GOVERNANCE_AGGREGATE_PERCENTILE)\n assert_series_equal(pd.Series([81.2, 75.4, 65.7], index=_index * 3, name='gPercentile'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_MOMENTUM_PERCENTILE)\n assert_series_equal(pd.Series([81.2, 75.4, 65.7], index=_index * 3, name='esMomentumPercentile'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.GOVERNANCE_REGIONAL_PERCENTILE)\n assert_series_equal(pd.Series([81.2, 75.4, 65.7], index=_index * 3, name='gRegionalPercentile'), pd.Series(actual))\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.CONTROVERSY_PERCENTILE)\n assert_series_equal(\n pd.Series([81.2, 75.4, 65.7], index=_index * 3, name='controversyPercentile'), pd.Series(actual)\n )\n\n actual = tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_DISCLOSURE)\n assert_series_equal(\n pd.Series([49.2, 55.7, 98.4], index=_index * 3, name='esDisclosurePercentage'), pd.Series(actual)\n )\n\n with pytest.raises(NotImplementedError):\n tm.esg_headline_metric(mock_aapl, tm.EsgMetric.ENVIRONMENTAL_SOCIAL_NUMERIC_SCORE, real_time=True)\n\n replace.restore()\n\n\ndef test_rating():\n replace = Replacer()\n\n mock_aapl = Stock('MA4B66MW5E27U9VBB94', 'AAPL')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_rating)\n actual = tm.rating(mock_aapl, tm._RatingMetric.RATING)\n assert_series_equal(\n pd.Series(\n [1, -1, 1, 0],\n index=pd.to_datetime(\n [dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]\n ),\n name='rating',\n ),\n pd.Series(actual),\n )\n\n actual = tm.rating(mock_aapl, tm._RatingMetric.CONVICTION_LIST)\n assert_series_equal(\n pd.Series(\n [1, 0, 0, 0],\n index=pd.to_datetime(\n [dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]\n ),\n name='convictionList',\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == _test_datasets\n\n with pytest.raises(NotImplementedError):\n tm.rating(mock_aapl, tm._RatingMetric.RATING, real_time=True)\n replace.restore()\n\n\ndef test_fair_value(mocker):\n mocker.patch.object(\n GsSession.__class__, 'default_value', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n replace = Replacer()\n mock_usdeur = Cross('MAQB05GD31BA5HWV', 'USDEUR')\n mock_eurusd = Cross('MAA0NE9QX2ABETG6', \"EURUSD\")\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='EURUSD',\n ),\n )\n ]\n asset = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n asset.return_value = mock_usdeur\n replace('gs_quant.timeseries.measures.Dataset.get_data', mock_gsdeer_gsfeer)\n\n index = [dt.date(2000, 1, 1), dt.date(2010, 4, 1), dt.date(2020, 7, 1)]\n actual = tm.fair_value(mock_usdeur, tm.EquilibriumExchangeRateMetric.GSDEER)\n assert_series_equal(pd.Series([1, 1.2, 1.1], index=index, name='gsdeer'), pd.Series(actual))\n\n actual = tm.fair_value(mock_usdeur, tm.EquilibriumExchangeRateMetric.GSFEER)\n assert_series_equal(pd.Series([2, 1.8, 1.9], index=index, name='gsfeer'), pd.Series(actual))\n\n actual = tm.fair_value(mock_eurusd, tm.EquilibriumExchangeRateMetric.GSDEER)\n assert_series_equal(pd.Series([1 / 1, 1 / 1.2, 1 / 1.1], index=index, name='gsdeer'), pd.Series(actual))\n\n actual = tm.fair_value(mock_eurusd, tm.EquilibriumExchangeRateMetric.GSFEER)\n assert_series_equal(pd.Series([1 / 2, 1 / 1.8, 1 / 1.9], index=index, name='gsfeer'), pd.Series(actual))\n with pytest.raises(NotImplementedError):\n tm.fair_value(mock_usdeur, tm.EquilibriumExchangeRateMetric.GSDEER, real_time=True)\n replace.restore()\n\n\ndef test_factor_profile():\n replace = Replacer()\n\n mock_aapl = Stock('MA4B66MW5E27U9VBB94', 'AAPL')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_factor_profile)\n actual = tm.factor_profile(mock_aapl, tm._FactorProfileMetric.GROWTH_SCORE)\n assert_series_equal(\n pd.Series(\n [0.238, 0.234, 0.234, 0.230],\n index=pd.to_datetime(\n [dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]\n ),\n name='growthScore',\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.factor_profile(mock_aapl, tm._FactorProfileMetric.FINANCIAL_RETURNS_SCORE)\n assert_series_equal(\n pd.Series(\n [0.982, 0.982, 0.982, 0.982],\n index=pd.to_datetime(\n [dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]\n ),\n name='financialReturnsScore',\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.factor_profile(mock_aapl, tm._FactorProfileMetric.MULTIPLE_SCORE)\n assert_series_equal(\n pd.Series(\n [0.204, 0.192, 0.190, 0.190],\n index=pd.to_datetime(\n [dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]\n ),\n name='multipleScore',\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == _test_datasets\n\n actual = tm.factor_profile(mock_aapl, tm._FactorProfileMetric.INTEGRATED_SCORE)\n assert_series_equal(\n pd.Series(\n [0.672, 0.676, 0.676, 0.674],\n index=pd.to_datetime(\n [dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]\n ),\n name='integratedScore',\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == _test_datasets\n\n with pytest.raises(NotImplementedError):\n tm.factor_profile(mock_aapl, tm._FactorProfileMetric.GROWTH_SCORE, real_time=True)\n\n replace.restore()\n\n\ndef test_commodity_forecast():\n replace = Replacer()\n\n mock_spgcsb = Index('MA74Y70Z4D4TBX9H', 'SPGCSB', 'GSCI Sugar')\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_commodity_forecast)\n actual = tm.commodity_forecast(mock_spgcsb, '3m', tm._CommodityForecastType.SPOT_RETURN)\n assert_series_equal(\n pd.Series(\n [1700, 1400, 1500, 1600],\n index=pd.to_datetime(\n [dt.date(2020, 8, 13), dt.date(2020, 8, 14), dt.date(2020, 8, 17), dt.date(2020, 8, 18)]\n ),\n name='commodityForecast',\n ),\n pd.Series(actual),\n )\n\n with pytest.raises(NotImplementedError):\n tm.commodity_forecast(mock_spgcsb, '3m', tm._CommodityForecastType.SPOT_RETURN, real_time=True)\n replace.restore()\n\n\ndef test_commodity_forecast_time_series():\n mock_brent = Index('MA8MBQN6VHKZMW92', 'CO', 'Brent')\n replace = Replacer()\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_commodity_forecast_time_series)\n\n with pytest.raises(ValueError):\n tm.commodity_forecast_time_series(\n 100,\n forecastFrequency=tm._CommodityForecastTimeSeriesPeriodType.ANNUAL,\n forecastType=tm._CommodityForecastType.SPOT,\n )\n\n actual = tm.commodity_forecast_time_series(\n 'MA8MBQN6VHKZMW92',\n forecastFrequency=tm._CommodityForecastTimeSeriesPeriodType.ANNUAL.value,\n forecastType=tm._CommodityForecastType.SPOT,\n )\n assert not pd.Series(actual).empty\n actual = tm.commodity_forecast_time_series(\n mock_brent,\n forecastFrequency=tm._CommodityForecastTimeSeriesPeriodType.QUARTERLY.value,\n forecastType=tm._CommodityForecastType.SPOT,\n )\n assert not pd.Series(actual).empty\n actual = tm.commodity_forecast_time_series(\n mock_brent,\n forecastFrequency=tm._CommodityForecastTimeSeriesPeriodType.MONTHLY.value,\n forecastType=tm._CommodityForecastType.SPOT,\n )\n assert not pd.Series(actual).empty\n actual = tm.commodity_forecast_time_series(\n mock_brent,\n forecastFrequency=tm._CommodityForecastTimeSeriesPeriodType.SHORT_TERM.value,\n forecastType=tm._CommodityForecastType.SPOT,\n )\n assert not pd.Series(actual).empty\n actual = tm.commodity_forecast_time_series(\n mock_brent,\n forecastFrequency=tm._CommodityForecastTimeSeriesPeriodType.ANNUAL,\n forecastType=tm._CommodityForecastType.SPOT,\n )\n assert not pd.Series(actual).empty\n with pytest.raises(ValueError):\n tm.commodity_forecast_time_series(\n mock_brent, forecastFrequency='Annually', forecastType=tm._CommodityForecastType.SPOT\n )\n with pytest.raises(NotImplementedError):\n tm.commodity_forecast_time_series(\n mock_brent,\n forecastFrequency=tm._CommodityForecastTimeSeriesPeriodType.ANNUAL,\n forecastType=tm._CommodityForecastType.SPOT,\n real_time=True,\n )\n\n replace.restore()\n\n\ndef test_fx_implied_correlation():\n replace = Replacer()\n\n e1 = {\n 'last_updated_time': '2021-02-03T15:11:06.409Z',\n 'owner_id': 'qwerty',\n 'classifications': {},\n 'description': 'Europe Euro to United States Dollar',\n 'last_updated_by_id': 'qwerty',\n 'id': 'MAA0NE9QX2ABETG6',\n 'listed': True,\n 'created_time': '2017-06-16T13:59:23.617Z',\n 'exchange': '',\n 'parameters': {},\n 'type': 'Cross',\n 'entitlements': {\n 'edit': ['guid:qwerty'],\n 'view': ['internal', 'guid:qwerty', 'external'],\n 'admin': ['guid:qwerty'],\n },\n 'name': 'EURUSD',\n 'region': 'Global',\n 'styles': [],\n 'short_name': 'EURUSD',\n 'identifiers': [\n {'type': 'SECNAME', 'value': 'USD/EUR'},\n {'type': 'BID', 'value': 'EURUSD'},\n {'type': 'MDAPI', 'value': 'USD/EUR'},\n {'type': 'CROSS', 'value': 'USD/EUR'},\n ],\n 'rank': 1.0,\n 'asset_class': 'FX',\n 'xref': {'bbid': 'EURUSD', 'cross': 'USD/EUR', 'secName': 'USD/EUR', 'mdapi': 'USD/EUR'},\n 'currency': 'USD',\n 'tags': ['USDEUR', 'EURUSD', 'Execution'],\n 'created_by_id': 'qwerty',\n }\n e2 = {\n 'last_updated_time': '2021-02-03T15:05:20.993Z',\n 'owner_id': 'azerty',\n 'classifications': {},\n 'description': 'United States Dollar to Europe Euro',\n 'last_updated_by_id': 'azerty',\n 'id': 'MAQB05GD31BA5HWV',\n 'listed': True,\n 'created_time': '2017-06-16T13:52:21.396Z',\n 'exchange': '',\n 'parameters': {},\n 'type': 'Cross',\n 'entitlements': {\n 'edit': ['guid:azerty'],\n 'view': ['internal', 'guid:azerty', 'external'],\n 'admin': ['guid:azerty'],\n },\n 'name': 'USDEUR',\n 'region': 'Global',\n 'styles': [],\n 'short_name': 'USDEUR',\n 'rank': 1.0,\n 'asset_class': 'FX',\n 'xref': {'bbid': 'USDEUR', 'cross': 'EUR/USD', 'secName': 'EUR/USD', 'mdapi': 'EUR/USD'},\n 'currency': 'EUR',\n 'tags': ['EURUSD', 'USDEUR'],\n 'created_by_id': 'azerty',\n }\n\n EURUSD = Cross('MAA0NE9QX2ABETG6', 'USD/EUR', entity=e1)\n USDEUR = Cross('MAQB05GD31BA5HWV', 'EUR/USD', entity=e2)\n USDJPY = Cross('MATGYV0J9MPX534Z', 'JPY/USD')\n JPYUSD = Cross('MAYJPCVVF2RWXCES', 'USD/JPY')\n EURGBP = Cross('MA3AMDKY4YJ83G1E', 'GBP/EUR')\n EURJPY = Cross('MAYPHS80JRWDJ8RC', 'JPY/EUR')\n JPYEUR = Cross('MAWVKC8Q6405ZWNE', 'EUR/JPY')\n bbid_to_asset_dict = {\n 'EURUSD': EURUSD,\n 'USDEUR': USDEUR,\n 'USDJPY': USDJPY,\n 'JPYUSD': JPYUSD,\n 'EURJPY': EURJPY,\n 'JPYEUR': JPYEUR,\n 'EURGBP': EURGBP,\n }\n mqid_to_asset_dict = {\n 'MAA0NE9QX2ABETG6': EURUSD,\n 'MAQB05GD31BA5HWV': USDEUR,\n 'MATGYV0J9MPX534Z': USDJPY,\n 'MAYJPCVVF2RWXCES': JPYUSD,\n 'MAYPHS80JRWDJ8RC': EURJPY,\n 'MAWVKC8Q6405ZWNE': JPYEUR,\n 'MA3AMDKY4YJ83G1E': EURGBP,\n }\n df = pd.DataFrame(\n {\n 'EURUSD USDJPY': [-0.47579170795443204, -0.4842168280011238, -0.48220935681227195],\n 'USDEUR USDJPY': [0.47579175, 0.48421678, 0.48220939],\n 'EURUSD JPYUSD': [0.47579175, 0.48421678, 0.48220939],\n 'USDEUR JPYUSD': [-0.47579175, -0.48421678, -0.48220939],\n 'USDJPY EURUSD': [-0.47579175, -0.48421678, -0.48220939],\n 'date': [pd.Timestamp('2020-09-02'), pd.Timestamp('2020-09-03'), pd.Timestamp('2020-09-04')],\n }\n )\n df = df.set_index('date')\n\n def mock_get_asset(cls, asset_id, id_type: dict) -> Cross:\n if id_type == AssetIdentifier.BLOOMBERG_ID:\n return bbid_to_asset_dict[asset_id]\n else:\n return mqid_to_asset_dict[asset_id]\n\n def cross_to_bbid(asset, id_type) -> str:\n if asset == EURUSD:\n return 'EURUSD'\n elif asset == USDEUR:\n return 'USDEUR'\n elif asset == USDJPY:\n return 'USDJPY'\n elif asset == JPYUSD:\n return 'JPYUSD'\n elif asset == EURJPY:\n return 'EURJPY'\n elif asset == JPYEUR:\n return 'JPYEUR'\n elif asset == EURGBP:\n return 'EURGBP'\n\n replace('gs_quant.markets.securities.SecurityMaster.get_asset', mock_get_asset)\n replace('gs_quant.markets.securities.Asset.get_identifier', cross_to_bbid)\n replace('gs_quant.timeseries.measures._market_data_timed', mock_fx_correlation)\n\n with DataContext(dt.date(2020, 9, 2), dt.date(2020, 9, 4)):\n # supported\n q_1 = tm.fx_implied_correlation(EURUSD, USDJPY, '3m')\n q_2 = tm.fx_implied_correlation(USDEUR, USDJPY, '3m')\n q_3 = tm.fx_implied_correlation(EURUSD, JPYUSD, '3m')\n q_4 = tm.fx_implied_correlation(USDEUR, JPYUSD, '3m')\n q_5 = tm.fx_implied_correlation(USDJPY, EURUSD, '3m')\n assert_series_equal(df['EURUSD USDJPY'], pd.Series(q_1, name='EURUSD USDJPY'))\n assert_series_equal(df['USDEUR USDJPY'], pd.Series(q_2, name='USDEUR USDJPY'))\n assert_series_equal(df['EURUSD JPYUSD'], pd.Series(q_3, name='EURUSD JPYUSD'))\n assert_series_equal(df['USDEUR JPYUSD'], pd.Series(q_4, name='USDEUR JPYUSD'))\n assert_series_equal(df['USDJPY EURUSD'], pd.Series(q_5, name='USDJPY EURUSD'))\n\n replace('gs_quant.timeseries.measures._market_data_timed', lambda q, request_id=None: pd.DataFrame())\n assert tm.fx_implied_correlation(USDJPY, EURUSD, '3m').empty\n\n # not supported\n with pytest.raises(NotImplementedError):\n tm.fx_implied_correlation(EURUSD, EURGBP, '3m', real_time=True)\n with pytest.raises(MqError):\n tm.fx_implied_correlation(USDJPY, EURGBP, '3m', real_time=False)\n with pytest.raises(MqError):\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n tm.fx_implied_correlation(USDJPY, mock_spx, '3m', real_time=False)\n\n replace.restore()\n\n\ndef mock_forward_curve_peak(_cls, _q, ignore_errors=False):\n d = {'forwardPrice': [40.05], 'quantityBucket': [\"PEAK\"], 'contract': [\"U20\"]}\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2020, 8, 20)]))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_forward_curve_peak_holiday(_cls, _q, ignore_errors=False):\n d = {'forwardPrice': [26.567302], 'quantityBucket': [\"PEAK\"], 'contract': [\"U20\"]}\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2020, 9, 4)]))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_forward_curve_offpeak(_cls, _q, ignore_errors=False):\n d = {'forwardPrice': [30.9692, 44.9868], 'quantityBucket': [\"7X8\", \"SUH1X16\"], 'contract': [\"U20\", \"U20\"]}\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([dt.date(2020, 8, 20)] * 2))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_empty_forward_curve(_cls, _q, ignore_errors=False):\n df = MarketDataResponseFrame()\n df.dataset_ids = ()\n return df\n\n\ndef test_forward_curve():\n # Set output and mock attributes for test\n target = {'Peak_CAISO': [40.05], 'OffPeak_CAISO': [34.4736], 'Peak_CAISO_holiday': [26.567302]}\n replace = Replacer()\n mock_CAISO = Index('MA001', AssetClass.Commod, 'CAISO')\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'CAISO'\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data_last', Mock())\n\n with DataContext(dt.date(2020, 8, 20), dt.date(2020, 9, 20)):\n # Test for term structure for bucket: peak\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_forward_curve_peak)\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2020, 8, 20)])\n actual = tm.forward_curve(mock_CAISO, bucket='Peak', market_date='20200820')\n assert_series_equal(\n pd.Series(target['Peak_CAISO'], index=[dt.date(2020, 9, 1)], name='forwardPrice'), pd.Series(actual)\n )\n\n # Test for holiday date query, which would fetch value for prev date\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_forward_curve_peak_holiday)\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2020, 9, 4)])\n actual = tm.forward_curve(mock_CAISO, bucket='Peak', market_date='20200907')\n assert_series_equal(\n pd.Series(target['Peak_CAISO_holiday'], index=[dt.date(2020, 9, 1)], name='forwardPrice'), pd.Series(actual)\n )\n\n # Test for term structure for bucket: Off peak, which requires wtd avg computation\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_forward_curve_offpeak)\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2020, 8, 25)])\n actual = tm.forward_curve(mock_CAISO, bucket='OffPeak', market_date='20200825')\n assert_series_equal(\n pd.Series(target['OffPeak_CAISO'], index=[dt.date(2020, 9, 1)], name='forwardPrice'), pd.Series(actual)\n )\n\n # Test for an empty data query result\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_empty_forward_curve)\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2020, 8, 25)])\n actual = tm.forward_curve(mock_CAISO, bucket='OffPeak', market_date='20200825')\n assert actual.empty\n\n # Test for intra day frequency error\n with pytest.raises(MqValueError):\n tm.forward_curve(mock_CAISO, bucket='7x24', market_date='20200820', real_time=True)\n\n # Test for future date query\n with pytest.raises(MqValueError):\n tm.forward_curve(mock_CAISO, bucket='7x24', market_date='20400820')\n\n # Test for weekend date query\n with pytest.raises(MqValueError):\n tm.forward_curve(mock_CAISO, bucket='7x24', market_date='20200822')\n\n # Test for term structure for market date beyond end date which is an invalid scenario\n with pytest.raises(MqValueError):\n tm.forward_curve(mock_CAISO, bucket='OffPeak', market_date='20201001')\n\n # Test for empty market date in parameters, will fail as end date for this test is in past\n with pytest.raises(MqValueError):\n actual = tm.forward_curve(mock_CAISO, bucket='Peak')\n\n # Test for invalid market_date data type\n with pytest.raises(MqTypeError):\n actual = tm.forward_curve(mock_CAISO, bucket='Peak', market_date=20201001)\n\n # Test for invalid market_date string format\n with pytest.raises(MqValueError):\n actual = tm.forward_curve(mock_CAISO, bucket='Peak', market_date='9, Jan 2020')\n\n # Test for default date always returning a weekday\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_forward_curve_peak)\n mock_todate = replace('pandas.Timestamp.today', Mock())\n mock_todate.return_value = pd.Timestamp('2020-08-22')\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2020, 8, 20)])\n actual = tm.forward_curve(mock_CAISO, bucket='Peak')\n assert_series_equal(\n pd.Series(target['Peak_CAISO'], index=[dt.date(2020, 9, 1)], name='forwardPrice'), pd.Series(actual)\n )\n\n replace.restore()\n\n\ndef mock_us_gas_forward_curve(_cls, _q, ignore_errors=False):\n d = {'forwardPrice': [1.614, 1.685, 1.814], 'priceMethod': [\"GDD\", \"GDD\", \"GDD\"], 'contract': [\"H24\", \"J24\", \"K24\"]}\n date = dt.date(2020, 8, 20)\n df = MarketDataResponseFrame(data=d, index=pd.to_datetime([date, date, date]))\n df.dataset_ids = _test_datasets\n return df\n\n\ndef test_us_gas_forward_curve():\n replace = Replacer()\n asset = CommodityNaturalGasHub('MAA66', 'HENRY')\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data_last', Mock())\n expeted_output = [1.614, 1.685, 1.814]\n expected_dates = [dt.date(2024, 3, 1), dt.date(2024, 4, 1), dt.date(2024, 5, 1)]\n with DataContext(dt.date(2023, 12, 20), dt.date(2024, 5, 20)): # G24, H24, J24, K24\n # Normal case with range contracts between (2023, 12, 20) - (2024, 5, 20)\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_us_gas_forward_curve)\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2024, 2, 16)])\n actual = tm.forward_curve_ng(asset, market_date='20240216')\n assert_series_equal(pd.Series(expeted_output, index=expected_dates, name='forwardPrice'), pd.Series(actual))\n\n # market date is a holiday (2024, 2, 19) -> Washington's Birthday\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_us_gas_forward_curve)\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2024, 2, 16)])\n actual = tm.forward_curve_ng(asset, market_date='20240219')\n assert_series_equal(pd.Series(expeted_output, index=expected_dates, name='forwardPrice'), pd.Series(actual))\n\n # Test for an empty data query result\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_empty_forward_curve)\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2024, 2, 16)])\n actual = tm.forward_curve_ng(asset, market_date='20240219')\n assert actual.empty\n\n # Test for default date always returning a weekday\n replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', mock_us_gas_forward_curve)\n mock_todate = replace('pandas.Timestamp.today', Mock())\n mock_todate.return_value = pd.Timestamp('2024-02-18')\n mock_get_data.return_value = pd.DataFrame(data=[0], index=[dt.date(2020, 8, 20)])\n actual = tm.forward_curve_ng(asset)\n assert_series_equal(pd.Series(expeted_output, index=expected_dates, name='forwardPrice'), pd.Series(actual))\n\n # Test for intra day frequency error\n with pytest.raises(MqValueError):\n tm.forward_curve_ng(asset, market_date='20240216', real_time=True)\n\n # Test for future date query\n with pytest.raises(MqValueError):\n tm.forward_curve_ng(asset, market_date='20400820')\n\n # Test for weekend date query\n with pytest.raises(MqValueError):\n tm.forward_curve_ng(asset, market_date='20240217')\n\n # Test for term structure for market date beyond end date which is an invalid scenario\n with pytest.raises(MqValueError):\n tm.forward_curve_ng(asset, market_date='20240320')\n\n # Test for empty market date in parameters, will fail as end date for this test is in past\n with pytest.raises(MqValueError):\n mock_todate = replace('pandas.Timestamp.today', Mock())\n mock_todate.return_value = pd.Timestamp('2024-10-18')\n actual = tm.forward_curve_ng(asset)\n\n # Test for invalid market_date data type\n with pytest.raises(MqTypeError):\n actual = tm.forward_curve_ng(asset, market_date=20201001)\n\n # Test for invalid market_date string format\n with pytest.raises(MqValueError):\n actual = tm.forward_curve_ng(asset, market_date='9, Jan 2020')\n\n\ndef test_eu_ng_hub_to_swap():\n # Test for Id provider to return an instrument\n replace = Replacer()\n mock_EU_asset = CommodityEUNaturalGasHub('MA001', 'TTF')\n mock_EU_swap_asset = Swap('MA002', AssetClass.Commod, 'Swap NatGas TTF')\n mock_EU_swap_asset.id = 'MA002'\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_EU_swap_asset]\n actual = tm.eu_ng_hub_to_swap(mock_EU_asset)\n assert_equal(actual, 'MA002')\n\n # Test for Id provider when no instrument found, returns original asset id\n mock_EU_asset = CommodityEUNaturalGasHub('MA001', 'TTF')\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = []\n actual = tm.eu_ng_hub_to_swap(mock_EU_asset)\n assert_equal(actual, 'MA001')\n replace.restore()\n\n\ndef test_settlement_price():\n # Tests for settlement price function\n replace = Replacer()\n\n with DataContext(dt.date(2021, 6, 2), dt.date(2021, 6, 2)):\n # Test for EEX Asset\n Asset_Mock = FutureMarket('MA001', AssetClass.Commod, 'DEBM')\n EEX_mock = replace('gs_quant.entities.entity.Entity.get_entity', Mock())\n EEX_mock.return_value = {'parameters': {'exchange': 'EEX', 'productGroup': 'PowerFutures'}}\n EEX_ds = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n EEX_ds.return_value = pd.DataFrame(\n data=dict(settlementPrice=20.20, contract='K21'), index=[dt.date(2021, 6, 2)]\n )\n actual = pd.Series(tm.settlement_price(Asset_Mock, contract='K21'))\n expected = pd.Series([20.20], index=[dt.date(2021, 6, 2)], name='settlementPrice')\n assert_series_equal(expected, actual)\n\n # Test for CarbonCredit Asset\n Asset_Mock = FutureMarket('MA001', AssetClass.Commod, 'RGGI V19')\n CC_mock = replace('gs_quant.entities.entity.Entity.get_entity', Mock())\n CC_mock.return_value = {'parameters': {'exchange': 'ICE', 'productGroup': 'Physical Environment'}}\n CC_ds = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n CC_ds.return_value = pd.DataFrame(data=dict(settlementPrice=21.21, contract='K21'), index=[dt.date(2021, 6, 2)])\n actual = pd.Series(tm.settlement_price(Asset_Mock, contract='K21'))\n expected = pd.Series([21.21], index=[dt.date(2021, 6, 2)], name='settlementPrice')\n assert_series_equal(expected, actual)\n\n # Test for ICE Power Asset\n Asset_Mock = FutureMarket('MA001', AssetClass.Commod, 'GAB')\n CC_mock = replace('gs_quant.entities.entity.Entity.get_entity', Mock())\n CC_mock.return_value = {'parameters': {'exchange': 'ICE', 'productGroup': 'PowerFutures'}}\n CC_ds = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n CC_ds.return_value = pd.DataFrame(data=dict(settlementPrice=22.22, contract='K21'), index=[dt.date(2021, 6, 2)])\n actual = pd.Series(tm.settlement_price(Asset_Mock, contract='K21'))\n expected = pd.Series([22.22], index=[dt.date(2021, 6, 2)], name='settlementPrice')\n assert_series_equal(expected, actual)\n\n # Test for NASDAQ Power Asset\n Asset_Mock = FutureMarket('MA001', AssetClass.Commod, 'ENOFUTBL')\n CC_mock = replace('gs_quant.entities.entity.Entity.get_entity', Mock())\n CC_mock.return_value = {'parameters': {'exchange': 'NASDAQ', 'productGroup': 'PowerFutures'}}\n CC_ds = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n CC_ds.return_value = pd.DataFrame(data=dict(settlementPrice=23.23, contract='K21'), index=[dt.date(2021, 6, 2)])\n actual = pd.Series(tm.settlement_price(Asset_Mock, contract='K21'))\n expected = pd.Series([23.23], index=[dt.date(2021, 6, 2)], name='settlementPrice')\n assert_series_equal(expected, actual)\n\n # Test for empty result\n CC_ds.return_value = pd.DataFrame()\n actual = pd.Series(tm.settlement_price(Asset_Mock, contract='K21'))\n expected = pd.Series(dtype=float)\n assert_series_equal(expected, actual)\n\n # Test for asset with no exchange info\n Asset_Mock = FutureMarket('MA001', AssetClass.Commod, 'XYZ')\n empty_mock = replace('gs_quant.entities.entity.Entity.get_entity', Mock())\n empty_mock.return_value = {'parameters': {}}\n\n with pytest.raises(MqTypeError):\n tm.settlement_price(Asset_Mock, contract='F21')\n\n # Test for asset with any only some asset params filled\n Asset_Mock = FutureMarket('MA001', AssetClass.Commod, 'XYZ')\n empty_mock = replace('gs_quant.entities.entity.Entity.get_entity', Mock())\n empty_mock.return_value = {'parameters': {'exchange': 'OTHER_EXCHANGE'}}\n\n with pytest.raises(MqTypeError):\n tm.settlement_price(Asset_Mock, contract='F21')\n\n # Test for asset with any other exchange, currently not covered\n Asset_Mock = FutureMarket('MA001', AssetClass.Commod, 'XYZ')\n empty_mock = replace('gs_quant.entities.entity.Entity.get_entity', Mock())\n empty_mock.return_value = {'parameters': {'exchange': 'OTHER_EXCHANGE', 'productGroup': 'OTHER_PRODUCT'}}\n\n with pytest.raises(MqTypeError):\n tm.settlement_price(Asset_Mock, contract='F21')\n\n # Test for real time data setting on query\n with pytest.raises(MqValueError):\n tm.settlement_price(Asset_Mock, contract='F21', real_time=True)\n\n replace.restore()\n\n\ndef test_hloc_prices():\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX')\n with pytest.raises(MqValueError):\n tm.hloc_prices(mock_spx, real_time=True)\n\n replace = Replacer()\n empty_df = replace('gs_quant.timeseries.measures.Asset.get_hloc_prices', Mock())\n empty_df.return_value = pd.DataFrame()\n with DataContext(dt.date(2021, 6, 2), dt.date(2021, 6, 2)):\n tm.hloc_prices(mock_spx, real_time=False)\n\n replace.restore()\n\n\ndef test_thematic_model_exposure():\n mock_asset = GsAsset(asset_class='Equity', id='MA1234567890', type_='Custom Basket', name='test')\n mock_basket = CustomBasket(gs_asset=mock_asset, _finish_init=False)\n with pytest.raises(MqValueError):\n tm.thematic_model_exposure(mock_basket, 'TICKER', real_time=True)\n\n replace = Replacer()\n\n empty_df = replace('gs_quant.timeseries.measures.PositionedEntity.get_thematic_exposure', Mock())\n empty_df.return_value = pd.DataFrame()\n with DataContext(dt.date(2021, 6, 2), dt.date(2021, 6, 2)):\n tm.thematic_model_exposure(mock_basket, 'TICKER')\n\n replace.restore()\n\n\ndef test_thematic_model_beta():\n mock_asset = GsAsset(asset_class='Equity', id='MA1234567890', type_='Custom Basket', name='test')\n mock_basket = CustomBasket(gs_asset=mock_asset, _finish_init=False)\n with pytest.raises(MqValueError):\n tm.thematic_model_beta(mock_basket, 'TICKER', real_time=True)\n\n replace = Replacer()\n\n empty_df = replace('gs_quant.timeseries.measures.PositionedEntity.get_thematic_beta', Mock())\n empty_df.return_value = pd.DataFrame()\n with DataContext(dt.date(2021, 6, 2), dt.date(2021, 6, 2)):\n tm.thematic_model_beta(mock_basket, 'TICKER')\n\n replace.restore()\n\n\ndef test_thematic_model_beta_single_stock():\n mock_asset = GsAsset(asset_class='Equity', id='MA4B66MW5E27U9VBB94', type_='Single Stock', name='test')\n mock_asset_entity: Dict = json.loads(json.dumps(mock_asset.as_dict(), cls=JSONEncoder))\n mock_stock = Stock(\n mock_asset.id, mock_asset.name, mock_asset.exchange, mock_asset.currency, entity=mock_asset_entity\n )\n with pytest.raises(MqValueError):\n tm.thematic_model_beta(mock_stock, 'TICKER', real_time=True)\n\n replace = Replacer()\n\n empty_df = replace('gs_quant.timeseries.measures.Stock.get_thematic_beta', Mock())\n empty_df.return_value = pd.DataFrame()\n with DataContext(dt.date(2021, 6, 2), dt.date(2021, 6, 2)):\n tm.thematic_model_beta(mock_stock, 'TICKER')\n\n replace.restore()\n\n\ndef test_retail_interest_agg():\n mock_spx = Index('MA890', AssetClass.Equity, 'SPX', entity={'type': 'Index', 'underlying_asset_ids': []})\n retail_df = pd.DataFrame(\n data={\n 'shares': [10.0, 10.0],\n 'notional': [100.0, 100.0],\n 'underlyingSourceCategory': ['All', 'All'],\n 'impliedRetailShares': [5.0, 3.0],\n 'impliedRetailNotional': [60.0, 30.0],\n 'impliedRetailPctShares': [50.0, 30.0],\n 'impliedRetailPctNotional': [60.0, 30.0],\n 'assetId': ['MA4B66MW5E27UALNBLL', 'MA4B66MW5E27UALNBLL'],\n },\n index=[pd.Timestamp('2021-12-20'), pd.Timestamp('2021-12-20')],\n )\n retail_df_energy = pd.DataFrame(\n data={\n 'shares': [10.0],\n 'notional': [100.0],\n 'underlyingSourceCategory': ['All'],\n 'impliedRetailShares': [5.0],\n 'impliedRetailNotional': [60.0],\n 'impliedRetailPctShares': [50.0],\n 'impliedRetailPctNotional': [60.0],\n 'assetId': ['MA4B66MW5E27UALNBLL'],\n },\n index=[pd.Timestamp('2021-12-20')],\n )\n\n replace = Replacer()\n\n mock_retail = replace('gs_quant.timeseries.measures.get_dataset_with_many_assets', Mock())\n mock_retail.return_value = retail_df\n\n with pytest.raises(NotImplementedError):\n tm.retail_interest_agg(\n ..., data_source=tm.UnderlyingSourceCategory.ALL, sector=tm.GICSSector.ALL, real_time=True\n )\n with pytest.raises(MqError):\n tm.retail_interest_agg(mock_spx)\n\n mock_spx.entity['underlying_asset_ids'] = ['MA4B66MW5E27U9VBB6A', 'MA4B66MW5E27U9VBB86', 'MA4B66MW5E27U9VBB8U']\n\n positions_df = replace('gs_quant.timeseries.measures.PositionedEntity.get_positions_data', Mock())\n positions_df.return_value = {\n 'id': ['MA4B66MW5E27UALNBLL', 'MA4B66MW5E27UALNBLL'],\n 'assetClassificationsGicsSector': ['Energy', 'Health Care'],\n 'positionDate': ['2012-12-20', '2012-12-20'],\n }\n\n expected_df = pd.DataFrame(\n data={'shares': [20.0], 'impliedRetailPctShares': [40.0], 'impliedRetailPctNotional': [60.0]},\n index=[pd.Timestamp('2021-12-20')],\n )\n\n assert_series_equal(\n ExtendedSeries(expected_df['shares']),\n tm.retail_interest_agg(mock_spx, tm.RetailMeasures.SHARES, tm.UnderlyingSourceCategory.ALL, tm.GICSSector.ALL),\n )\n assert_series_equal(\n ExtendedSeries(expected_df['impliedRetailPctShares']),\n tm.retail_interest_agg(\n mock_spx, tm.RetailMeasures.RETAIL_PCT_SHARES, tm.UnderlyingSourceCategory.ALL, tm.GICSSector.ALL\n ),\n )\n mock_retail.return_value = retail_df_energy\n assert_series_equal(\n ExtendedSeries(expected_df['impliedRetailPctNotional']),\n tm.retail_interest_agg(\n mock_spx, tm.RetailMeasures.RETAIL_PCT_NOTIONAL, tm.UnderlyingSourceCategory.ALL, tm.GICSSector.ENERGY\n ),\n )\n mock_retail.return_value = pd.DataFrame(dtype=float)\n assert_series_equal(\n ExtendedSeries(dtype=float),\n tm.retail_interest_agg(\n mock_spx, tm.RetailMeasures.RETAIL_PCT_NOTIONAL, tm.UnderlyingSourceCategory.ALL, tm.GICSSector.ENERGY\n ),\n )\n replace.restore()\n\n\ndef test_s3_long_short_concentration():\n replace = Replacer()\n\n # Mock Returns\n mock_asset = replace('gs_quant.markets.securities.Asset.get_marquee_id', Mock())\n mock_asset.return_value = \"MAF1701E17G167B0\"\n\n mock_dataset = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_dataset.return_value = pd.DataFrame(\n {\n 'date': ['2025-02-12', '2025-02-13', '2025-02-14'],\n 'assetId': ['MAF1701E17G167B0', 'MAF1701E17G167B0', 'MAF1701E17G167B0'],\n 's3Metric': ['Long Crowding', 'Long Crowding', 'Long Crowding'],\n 'value': [0.337286, 0.337629, 0.338065],\n 'updateTime': ['2025-02-18 11:23:41+00:00', '2025-02-18 11:23:41+00:00', '2025-02-18 11:23:41+00:00'],\n }\n )\n\n # Calling Tested Function with DataContext (since only available in PTP)\n with DataContext(dt.date(2025, 2, 10), dt.date(2025, 2, 17)):\n actual = tm.s3_long_short_concentration(mock_asset, tm.S3Metrics.LONG_CROWDING)\n\n assert_series_equal(ExtendedSeries(pd.Series([0.337286, 0.337629, 0.338065]), name='value'), actual)\n\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_measures.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_measures_countries.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pandas as pd\nimport pytest\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nimport gs_quant.timeseries.measures_countries as mc\nfrom gs_quant.api.gs.data import MarketDataResponseFrame\nfrom gs_quant.data.core import DataContext\n\n\ndef test_fci():\n with pytest.raises(NotImplementedError):\n mc.fci('IN', real_time=True)\n\n data = {'fci': [101, 102, 103], 'realFCI': [100, 99, 98], 'realTWIContribution': [100, 100, 98]}\n idx = pd.date_range('2020-01-01', freq='D', periods=3)\n df = MarketDataResponseFrame(data=data, index=idx)\n df.dataset_ids = ('FCI',)\n replace = Replacer()\n mock = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n mock.return_value = df\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mc.fci('IN')\n assert actual.index.equals(idx)\n assert all(actual.values == data['fci'])\n\n mock = replace('gs_quant.timeseries.measures.Dataset.get_data', Mock())\n mock.return_value = df\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mc.fci('IN', mc._FCI_MEASURE.REAL_FCI)\n assert actual.index.equals(idx)\n assert all(actual.values == data['realFCI'])\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mc.fci('IN', mc._FCI_MEASURE.REAL_TWI_CONTRIBUTION)\n assert actual.index.equals(idx)\n assert all(actual.values == data['realTWIContribution'])\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/timeseries/test_measures_factset.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom unittest.mock import Mock\n\nimport pandas as pd\nimport pytest\nfrom pandas.testing import assert_series_equal\nfrom testfixtures import Replacer\n\nimport gs_quant.timeseries.measures_factset as tm\nfrom gs_quant.api.gs.data import MarketDataResponseFrame\nfrom gs_quant.data import DataContext\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.index import Index\nfrom gs_quant.timeseries.measures_factset import (\n EstimateItem,\n EstimateStatistic,\n EstimateBasis,\n FiscalPeriod,\n FundamentalMetric,\n FundamentalFormat,\n FundamentalBasis,\n RatingType,\n)\n\n_index = [pd.Timestamp('2021-03-30')]\n_test_datasets = ('TEST_DATASET',)\n\n\ndef mock_fe_estimate_af(_cls, bbid, start, end, feItem):\n d = {\n 'date': [dt.date(2022, 1, 8), dt.date(2022, 1, 12), dt.date(2022, 1, 13)],\n 'isin': ['US0378331005'] * 3,\n 'feItem': ['EPS'] * 3,\n 'fePerRel': [1.0] * 3,\n 'adjDate': [dt.date(2020, 8, 31)] * 3,\n 'currency': ['USD'] * 3,\n 'consEndDate': [dt.date(2022, 1, 11)] * 3,\n 'feItemDesc': ['Earnings Per Share'] * 3,\n 'feFpEnd': [dt.date(2022, 9, 30)] * 3,\n 'feDownAf': [10.0, 3.0, 3.0],\n 'feHighAf': [6.353628] * 3,\n 'feLowAf': [5.2] * 3,\n 'feMeanAf': [5.749689, 5.748439, 5.756439],\n 'feMedianAf': [5.745] * 3,\n 'feNumEstAf': [40] * 3,\n 'feStdDevAf': [0.259250, 0.258130, 0.258687],\n 'feUpAf': [15.0, 5.0, 6.0],\n 'fsymId': ['MH33D6-R'] * 3,\n 'bbid': ['AAPL UW'] * 3,\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FE_BASIC_CONH_AF_GLOBAL'\n\n return df\n\n\ndef mock_fe_estimate_qf(_cls, bbid, start, end, feItem):\n d = {\n 'date': [dt.date(2022, 1, 8), dt.date(2022, 1, 12), dt.date(2022, 1, 13)],\n 'isin': ['US0378331005'] * 3,\n 'feItem': ['EPS'] * 3,\n 'fePerRel': [1.0] * 3,\n 'adjDate': [dt.date(2020, 8, 31)] * 3,\n 'currency': ['USD'] * 3,\n 'consEndDate': [dt.date(2022, 1, 11)] * 3,\n 'feItemDesc': ['Earnings Per Share'] * 3,\n 'feFpEnd': [dt.date(2022, 9, 30)] * 3,\n 'feDownQf': [10.0, 3.0, 3.0],\n 'feHighQf': [6.353628] * 3,\n 'feLowQf': [5.2] * 3,\n 'feMeanQf': [5.749689, 5.748439, 5.756439],\n 'feMedianQf': [5.745] * 3,\n 'feNumEstQf': [40] * 3,\n 'feStdDevQf': [0.259250, 0.258130, 0.258687],\n 'feUpQf': [15.0, 5.0, 6.0],\n 'fsymId': ['MH33D6-R'] * 3,\n 'bbid': ['AAPL UW'] * 3,\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FE_BASIC_CONH_QF_GLOBAL'\n return df\n\n\ndef mock_fe_estimate_saf(_cls, bbid, start, end, feItem):\n d = {\n 'date': [dt.date(2022, 1, 8), dt.date(2022, 1, 12), dt.date(2022, 1, 13)],\n 'isin': ['US0378331005'] * 3,\n 'feItem': ['EPS'] * 3,\n 'fePerRel': [1.0] * 3,\n 'adjDate': [dt.date(2020, 8, 31)] * 3,\n 'currency': ['USD'] * 3,\n 'consEndDate': [dt.date(2022, 1, 11)] * 3,\n 'feItemDesc': ['Earnings Per Share'] * 3,\n 'feFpEnd': [dt.date(2022, 9, 30)] * 3,\n 'feDownSaf': [10.0, 3.0, 3.0],\n 'feHighSaf': [6.353628] * 3,\n 'feLowSaf': [5.2] * 3,\n 'feMeanSaf': [5.749689, 5.748439, 5.756439],\n 'feMedianSaf': [5.745] * 3,\n 'feNumEstSaf': [40] * 3,\n 'feStdDevSaf': [0.259250, 0.258130, 0.258687],\n 'feUpSaf': [15.0, 5.0, 6.0],\n 'fsymId': ['MH33D6-R'] * 3,\n 'bbid': ['AAPL UW'] * 3,\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FE_BASIC_CONH_SAF_GLOBAL'\n return df\n\n\ndef mock_fe_estimate_ntm(_cls, bbid, start, end, feItem):\n d = {\n 'date': [dt.date(2022, 1, 8), dt.date(2022, 1, 9), dt.date(2022, 1, 10)],\n 'isin': ['US0378331005'] * 3,\n 'feItem': ['EPS'] * 3,\n 'feHighNtm': [6.353628] * 3,\n 'feLowNtm': [5.2] * 3,\n 'feMeanNtm': [5.749689, 5.748439, 5.756439],\n 'feMedianNtm': [5.745] * 3,\n 'feHighStm': [6.353628] * 3,\n 'feLowStm': [5.2] * 3,\n 'feMeanStm': [5.749689, 5.748439, 5.756439],\n 'feMedianStm': [5.745] * 3,\n 'fsymId': ['MH33D6-R'] * 3,\n 'bbid': ['AAPL UW'] * 3,\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FE_NTM'\n return df\n\n\ndef mock_fe_estimate_lt(_cls, bbid, start, end, feItem):\n d = {\n 'date': [dt.date(2022, 1, 8), dt.date(2022, 1, 12), dt.date(2022, 1, 13)],\n 'isin': ['US0378331005'] * 3,\n 'feItem': ['PRICE_TGT'] * 3,\n 'adjDate': [dt.date(2020, 8, 31)] * 3,\n 'consEndDate': [dt.date(2022, 1, 11)] * 3,\n 'feDownLt': [10.0, 3.0, 3.0],\n 'feHighLt': [6.353628] * 3,\n 'feLowLt': [5.2] * 3,\n 'feMeanLt': [5.749689, 5.748439, 5.756439],\n 'feMedianLt': [5.745] * 3,\n 'feNumEstLt': [40] * 3,\n 'feStdDevLt': [0.259250, 0.258130, 0.258687],\n 'feUpLt': [15.0, 5.0, 6.0],\n 'fsymId': ['MH33D6-R'] * 3,\n 'bbid': ['AAPL UW'] * 3,\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FE_BASIC_CONH_LT_GLOBAL'\n return df\n\n\ndef mock_fe_actual(_cls, bbid, start, end, feItem):\n d = {\n 'date': [dt.date(2020, 1, 8), dt.date(2021, 1, 12), dt.date(2022, 1, 13)],\n 'isin': ['US0378331005'] * 3,\n 'feItem': ['EPS'] * 3,\n 'adjDate': [dt.date(2020, 8, 31)] * 3,\n 'currency': ['USD'] * 3,\n 'feFpEnd': [dt.date(2022, 9, 30), dt.date(2021, 9, 30), dt.date(2020, 9, 30)],\n 'feValue': [15.0, 5.0, 6.0],\n 'fsymId': ['MH33D6-R'] * 3,\n 'bbid': ['AAPL UW'] * 3,\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FE_BASIC_ACT_AF_GLOBAL'\n\n return df\n\n\ndef mock_fe_estimate_empty(_cls, bbid, start, end, feItem):\n df = MarketDataResponseFrame()\n df.dataset_id = 'FE_BASIC_CONH_SAF_GLOBAL'\n return df\n\n\ndef mock_factset_fundamentals_empty(_cls, bbid, start, end):\n df = MarketDataResponseFrame()\n df.dataset_id = 'FF_BASIC_R_AF_GLOBAL'\n return df\n\n\ndef mock_factset_fundamentals_basic(_cls, bbid, start, end):\n d = {\n 'date': [pd.Timestamp('2024-09-30 00:00:00')],\n 'isin': ['US0378331005'],\n 'adjDate': [pd.Timestamp('2020-08-31 00:00:00')],\n 'currency': ['USD'],\n 'ffEpsBasic': [6.109],\n 'fsymId': ['MH33D6-R'],\n 'bbid': ['AAPL UW'],\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FF_BASIC_AF_GLOBAL'\n return df\n\n\ndef mock_factset_fundamentals_basic_derived(_cls, bbid, start, end):\n d = {\n 'date': [pd.Timestamp('2024-09-30 00:00:00')],\n 'isin': ['US0378331005'],\n 'adjDate': [pd.Timestamp('2020-08-31 00:00:00')],\n 'currency': ['USD'],\n 'ffEbitdaOper': [6.109],\n 'fsymId': ['MH33D6-R'],\n 'bbid': ['AAPL UW'],\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FF_BASIC_DER_AF_GLOBAL'\n return df\n\n\ndef mock_factset_fundamentals_basic_restated(_cls, bbid, start, end):\n d = {\n 'date': [pd.Timestamp('2024-09-30 00:00:00')],\n 'isin': ['US0378331005'],\n 'adjDate': [pd.Timestamp('2020-08-31 00:00:00')],\n 'currency': ['USD'],\n 'ffEpsBasic': [6.109],\n 'fsymId': ['MH33D6-R'],\n 'bbid': ['AAPL UW'],\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FF_BASIC_R_AF_GLOBAL'\n return df\n\n\ndef mock_factset_ratings(_cls, bbid, start, end):\n d = {\n 'date': [pd.Timestamp('2024-12-31 00:00:00')] * 3,\n 'isin': ['US0378331005'] * 3,\n 'feItem': ['REC'] * 3,\n 'adjDate': [dt.date(2024, 12, 31)] * 3,\n 'consEndDate': [dt.date(2024, 12, 31)] * 3,\n 'feItemDesc': ['Recommendation'] * 3,\n 'feFpEnd': [dt.date(2024, 12, 31)] * 3,\n 'feBuy': [21.0, 3.0, 3.0],\n 'feHold': [14.0, 3.0, 3.0],\n 'feNoRec': [1.0, 3.0, 3.0],\n 'feOver': [10.0, 3.0, 3.0],\n 'feSell': [4.0, 3.0, 3.0],\n 'feUnder': [1.0, 3.0, 3.0],\n 'feTotal': [10.0, 3.0, 3.0],\n 'feMark': [1.57] * 3,\n 'fsymId': ['MH33D6-R'] * 3,\n 'bbid': ['AAPL UW'] * 3,\n }\n df = MarketDataResponseFrame(data=d)\n df.dataset_id = 'FE_BASIC_CONH_REC_GLOBAL'\n return df\n\n\nmock_asset = Index(\n id_='MA000',\n asset_class='Equity',\n name='test',\n currency='USD',\n exchange='NASD',\n)\n\n\ndef test_factset_estimates():\n replace = Replacer()\n bbid_mock = replace('gs_quant.markets.securities.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'AAPL UW'\n with DataContext(start=dt.date(2022, 1, 1), end=dt.date(2025, 1, 31)):\n replace('gs_quant.data.Dataset.get_data', mock_fe_estimate_af)\n\n actual = tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.ANN,\n period=FiscalPeriod(2022),\n )\n assert_series_equal(\n pd.Series(\n [5.745] * 4,\n index=pd.DatetimeIndex(\n ['2022-01-08', '2022-01-09', '2022-01-10', '2022-01-11'],\n dtype='datetime64[ns]',\n name='date',\n freq=None,\n ),\n name=EstimateStatistic.MEDIAN.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_BASIC_CONH_AF_GLOBAL'\n\n replace('gs_quant.data.Dataset.get_data', mock_fe_estimate_qf)\n\n actual = tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.QTR,\n period=FiscalPeriod(2022, 3),\n )\n assert_series_equal(\n pd.Series(\n [5.745] * 4,\n index=pd.DatetimeIndex(\n ['2022-01-08', '2022-01-09', '2022-01-10', '2022-01-11'],\n dtype='datetime64[ns]',\n name='date',\n freq=None,\n ),\n name=EstimateStatistic.MEDIAN.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_BASIC_CONH_QF_GLOBAL'\n\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.QTR,\n period=FiscalPeriod(2022, 1),\n )\n\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.QTR,\n period=FiscalPeriod(2022, -1),\n )\n\n replace('gs_quant.data.Dataset.get_data', mock_fe_estimate_saf)\n\n actual = tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.SEMI,\n period=FiscalPeriod(2022, 2),\n )\n assert_series_equal(\n pd.Series(\n [5.745] * 4,\n index=pd.DatetimeIndex(\n ['2022-01-08', '2022-01-09', '2022-01-10', '2022-01-11'],\n dtype='datetime64[ns]',\n name='date',\n freq=None,\n ),\n name=EstimateStatistic.MEDIAN.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_BASIC_CONH_SAF_GLOBAL'\n\n replace('gs_quant.data.Dataset.get_data', mock_fe_estimate_ntm)\n\n actual = tm.factset_estimates(\n mock_asset, metric=EstimateItem.EPS, statistic=EstimateStatistic.MEDIAN, report_basis=EstimateBasis.NTM\n )\n assert_series_equal(\n pd.Series(\n [5.745] * 3,\n index=pd.DatetimeIndex(\n ['2022-01-08', '2022-01-09', '2022-01-10'], dtype='datetime64[ns]', name='date', freq=None\n ),\n name=EstimateStatistic.MEDIAN.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_NTM'\n\n actual = tm.factset_estimates(\n mock_asset, metric=EstimateItem.EPS, statistic=EstimateStatistic.MEDIAN, report_basis=EstimateBasis.STM\n )\n assert_series_equal(\n pd.Series(\n [5.745] * 3,\n index=pd.DatetimeIndex(\n ['2022-01-08', '2022-01-09', '2022-01-10'], dtype='datetime64[ns]', name='date', freq=None\n ),\n name=EstimateStatistic.MEDIAN.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_NTM'\n\n replace('gs_quant.data.Dataset.get_data', mock_fe_estimate_lt)\n\n actual = tm.factset_estimates(mock_asset, metric=EstimateItem.PRICE_TGT, statistic=EstimateStatistic.MEDIAN)\n assert_series_equal(\n pd.Series(\n [5.745] * 4,\n index=pd.DatetimeIndex(\n ['2022-01-08', '2022-01-09', '2022-01-10', '2022-01-11'],\n dtype='datetime64[ns]',\n name='date',\n freq=None,\n ),\n name=EstimateStatistic.MEDIAN.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_BASIC_CONH_LT_GLOBAL'\n\n replace('gs_quant.data.Dataset.get_data', mock_fe_actual)\n\n actual = tm.factset_estimates(\n mock_asset, metric=EstimateItem.EPS, statistic=EstimateStatistic.ACTUAL, report_basis=EstimateBasis.ANN\n )\n assert_series_equal(\n pd.Series(\n [15.0],\n index=pd.DatetimeIndex(['2022-09-30'], dtype='datetime64[ns]', name='date', freq=None),\n name=EstimateStatistic.ACTUAL.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_BASIC_ACT_AF_GLOBAL'\n\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.SEMI,\n period=FiscalPeriod(2022),\n )\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.SEMI,\n period=FiscalPeriod(2022, -1),\n )\n\n replace('gs_quant.data.Dataset.get_data', None)\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.SEMI,\n period=FiscalPeriod(2022, 1),\n )\n\n replace('gs_quant.data.Dataset.get_data', mock_fe_estimate_af)\n actual = tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.ANN,\n period=1,\n )\n assert_series_equal(\n pd.Series(\n [5.745] * 4,\n index=pd.DatetimeIndex(\n ['2022-01-08', '2022-01-09', '2022-01-10', '2022-01-11'],\n dtype='datetime64[ns]',\n name='date',\n freq=None,\n ),\n name=EstimateStatistic.MEDIAN.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_BASIC_CONH_AF_GLOBAL'\n\n # Get quarterly data without specifying period\n replace('gs_quant.data.Dataset.get_data', mock_fe_estimate_qf)\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.QTR,\n period=FiscalPeriod(2022),\n )\n\n # Invalid estimate basis\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis='INV',\n period=FiscalPeriod(2022, 1),\n )\n # Invalid report basis for actuals\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.ACTUAL,\n report_basis=EstimateBasis.NTM,\n )\n # Invalid metric for actuals\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.PRICE_TGT,\n statistic=EstimateStatistic.ACTUAL,\n report_basis=EstimateBasis.ANN,\n )\n\n # Get empty data response\n replace('gs_quant.data.Dataset.get_data', mock_fe_estimate_empty)\n with pytest.raises(MqValueError):\n tm.factset_estimates(\n mock_asset,\n metric=EstimateItem.EPS,\n statistic=EstimateStatistic.MEDIAN,\n report_basis=EstimateBasis.SEMI,\n period=FiscalPeriod(2022, 1),\n )\n\n replace.restore()\n\n\ndef test_factset_fundamentals():\n replace = Replacer()\n bbid_mock = replace('gs_quant.markets.securities.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'AAPL UW'\n with DataContext(start=dt.date(2024, 9, 30), end=dt.date(2024, 9, 30)):\n replace('gs_quant.data.Dataset.get_data', mock_factset_fundamentals_basic)\n\n actual = tm.factset_fundamentals(\n mock_asset,\n metric=FundamentalMetric.EPS_BASIC,\n report_basis=FundamentalBasis.ANN,\n report_format=FundamentalFormat.NON_RESTATED,\n )\n assert_series_equal(\n pd.Series(\n [6.109],\n index=pd.DatetimeIndex(['2024-09-30'], dtype='datetime64[ns]', name='date', freq=None),\n name=FundamentalMetric.EPS_BASIC.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FF_BASIC_AF_GLOBAL'\n\n replace('gs_quant.data.Dataset.get_data', mock_factset_fundamentals_basic_derived)\n\n actual = tm.factset_fundamentals(\n mock_asset,\n metric=FundamentalMetric.EBITDA_OPER,\n report_basis=FundamentalBasis.ANN,\n report_format=FundamentalFormat.NON_RESTATED,\n )\n assert_series_equal(\n pd.Series(\n [6.109],\n index=pd.DatetimeIndex(['2024-09-30'], dtype='datetime64[ns]', name='date', freq=None),\n name=FundamentalMetric.EBITDA_OPER.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FF_BASIC_DER_AF_GLOBAL'\n\n replace('gs_quant.data.Dataset.get_data', mock_factset_fundamentals_basic_restated)\n\n actual = tm.factset_fundamentals(\n mock_asset,\n metric=FundamentalMetric.EPS_BASIC,\n report_basis=FundamentalBasis.ANN,\n report_format=FundamentalFormat.RESTATED,\n )\n assert_series_equal(\n pd.Series(\n [6.109],\n index=pd.DatetimeIndex(['2024-09-30'], dtype='datetime64[ns]', name='date', freq=None),\n name=FundamentalMetric.EPS_BASIC.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FF_BASIC_R_AF_GLOBAL'\n\n # Get empty data response\n replace('gs_quant.data.Dataset.get_data', mock_factset_fundamentals_empty)\n with pytest.raises(MqValueError):\n tm.factset_fundamentals(\n mock_asset,\n metric=FundamentalMetric.EPS_BASIC,\n report_basis=FundamentalBasis.ANN,\n report_format=FundamentalFormat.RESTATED,\n )\n\n replace.restore()\n\n\ndef test_factset_ratings():\n replace = Replacer()\n bbid_mock = replace('gs_quant.markets.securities.Asset.get_identifier', Mock())\n bbid_mock.return_value = 'AAPL UW'\n with DataContext(start=dt.date(2024, 9, 30), end=dt.date(2024, 9, 30)):\n replace('gs_quant.data.Dataset.get_data', mock_factset_ratings)\n\n actual = tm.factset_ratings(mock_asset, rating_type=RatingType.BUY)\n assert_series_equal(\n pd.Series(\n [21.0, 3.0, 3.0],\n index=pd.DatetimeIndex([dt.date(2024, 12, 31)] * 3, dtype='datetime64[ns]', name='date', freq=None),\n name=RatingType.BUY.value,\n ),\n pd.Series(actual),\n )\n assert actual.dataset_ids == 'FE_BASIC_CONH_REC_GLOBAL'\n\n replace.restore()\n\n\ndef test_fiscal_period():\n fp = FiscalPeriod.from_dict({'y': 2022, 'p': 1})\n assert fp.y == 2022\n assert fp.p == 1\n\n assert fp.as_dict() == {'y': 2022, 'p': 1}\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_measures_factset.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_measures_fx_vol.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pandas as pd\nimport pytest\nfrom pandas.testing import assert_series_equal\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nimport gs_quant.timeseries\nimport gs_quant.timeseries.measures as tm_rates\nimport gs_quant.timeseries.measures_fx_vol as tm_fxo\nimport gs_quant.timeseries.measures_xccy as tm_xccy\nfrom gs_quant.api.gs.assets import GsAsset\nfrom gs_quant.api.gs.data import GsDataApi, MarketDataResponseFrame, QueryType\nfrom gs_quant.common import PricingLocation\nfrom gs_quant.data import DataContext\nfrom gs_quant.errors import MqError, MqValueError\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.securities import Bond, Cross, Currency\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.test.timeseries.utils import mock_request\nfrom gs_quant.timeseries import Currency as CurrencyEnum, SecurityMaster, measures as tm\nfrom gs_quant.timeseries.measures_fx_vol import _currencypair_to_tdapi_fxo_asset, _currencypair_to_tdapi_fxfwd_asset\nfrom gs_quant.timeseries.measures_helper import VolReference\n\n_index = [pd.Timestamp('2021-03-30')]\n_test_datasets = ('TEST_DATASET',)\n\n\ndef test_currencypair_to_tdapi_fxfwd_asset():\n mock_eur = Cross('MA8RY265Q34P7TWZ', 'EURUSD')\n replace = Replacer()\n xrefs = replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n xrefs.return_value = 'MA8RY265Q34P7TWZ'\n bbid_mock = replace('gs_quant.timeseries.measures_fx_vol.Asset.get_identifier', Mock())\n bbid_mock.return_value = {'EURUSD'}\n assert _currencypair_to_tdapi_fxfwd_asset(mock_eur) == \"MA8RY265Q34P7TWZ\"\n replace.restore()\n\n\ndef test_currencypair_to_tdapi_fxo_asset(mocker):\n replace = Replacer()\n mocker.patch.object(\n GsSession.__class__, 'current', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=mock_request)\n bbid_mock = replace('gs_quant.timeseries.measures_fx_vol.Asset.get_identifier', Mock())\n\n with PricingContext(dt.date.today()):\n cur = [\n {\"currency_assetId\": \"MAK1FHKH5P5GJSHH\", \"currency\": \"USDJPY\", \"id\": \"MAQ7YRZ4P94M9N9C\"},\n {\"currency_assetId\": \"MA66CZBQJST05XKG\", \"currency\": \"GBPUSD\", \"id\": \"MAEHA6WVHJ2S3JY9\"},\n {\"currency_assetId\": \"MAJNQPFGN1EBDHAE\", \"currency\": \"EURUSD\", \"id\": \"MAT1J37C9ZPMANFP\"},\n ]\n for c in cur:\n print(c)\n asset = Currency(c.get(\"currency_assetId\"), c.get(\"currency\"))\n bbid_mock.return_value = c.get(\"currency\")\n mqid = _currencypair_to_tdapi_fxo_asset(asset)\n assert mqid == c.get(\"id\")\n\n bbid_mock.return_value = None\n assert _currencypair_to_tdapi_fxo_asset(asset) == c.get(\"currency_assetId\")\n replace.restore()\n\n\ndef test_get_fxo_defaults():\n result_dict = dict(under=\"AUD\", over=\"JPY\", expirationTime=\"NYC\", premiumPaymentDate=\"Fwd Settle\")\n defaults = tm_fxo._get_fxo_defaults(\"AUDJPY\")\n assert result_dict == defaults\n\n result_dict = dict(under=\"CAD\", over=\"JPY\", expirationTime=\"NYC\", premiumPaymentDate=\"Fwd Settle\")\n defaults = tm_fxo._get_fxo_defaults(\"CADJPY\")\n assert result_dict == defaults\n\n result_dict = dict(under=\"EUR\", over=\"NOK\", expirationTime=\"NYC\", premiumPaymentDate=\"Fwd Settle\")\n defaults = tm_fxo._get_fxo_defaults(\"EURNOK\")\n assert result_dict == defaults\n\n result_dict = dict(under=\"GBP\", over=\"USD\", expirationTime=\"NYC\", premiumPaymentDate=\"Fwd Settle\")\n defaults = tm_fxo._get_fxo_defaults(\"GBPUSD\")\n assert result_dict == defaults\n\n result_dict = dict(under=\"EUR\", over=\"NZD\", expirationTime=\"NYC\", premiumPaymentDate=\"Fwd Settle\")\n defaults = tm_fxo._get_fxo_defaults(\"EURNZD\")\n assert result_dict == defaults\n\n\ndef test_get_fxo_csa_terms():\n assert dict(csaTerms='USD-1') == tm_fxo._get_fx_csa_terms()\n\n\ndef test_check_valid_indices():\n valid_indices = ['LIBOR']\n for index in valid_indices:\n expected_rate_option_type = tm_xccy.CrossCurrencyRateOptionType[index]\n actual_rate_option_type = tm_xccy._check_crosscurrency_rateoption_type(CurrencyEnum.GBP, index)\n assert expected_rate_option_type == actual_rate_option_type\n\n invalid_indices = ['LIBORED', 'TestRateOption']\n for index in invalid_indices:\n with pytest.raises(MqError):\n tm_xccy._check_crosscurrency_rateoption_type(CurrencyEnum.GBP, index)\n\n\ndef test_cross_stored_direction_for_fx_vol():\n replace = Replacer()\n mock_gbp = Cross('MA26QSMPX9990G66', 'GBPUSD')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBPUSD'\n assert \"MAEHA6WVHJ2S3JY9\" == tm_fxo.cross_stored_direction_for_fx_vol(mock_gbp)\n replace.restore()\n mock_gbp_reversed = Cross('MA26QSMPX9990G67', 'USDGBP')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.side_effect = ['USDGBP', 'GBPUSD']\n assets = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n assets.return_value = mock_gbp\n assert \"MAEHA6WVHJ2S3JY9\" == tm_fxo.cross_stored_direction_for_fx_vol(mock_gbp_reversed)\n replace.restore()\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.side_effect = [mock_gbp, 'GBPUSD']\n assets = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n assets.return_value = mock_gbp\n assert \"MAEHA6WVHJ2S3JY9\" == tm_fxo.cross_stored_direction_for_fx_vol(mock_gbp)\n replace.restore()\n\n\ndef test_get_tdapi_fxo_assets():\n mock_asset_1 = GsAsset(asset_class='FX', id='MAW8SAXPSKYA94E2', type_='Option', name='FX Forward Test_asset_1')\n mock_asset_2 = GsAsset(asset_class='FX', id='MATDD783JM1C2GGD', type_='Option', name='Test_asset')\n\n replace = Replacer()\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1]\n kwargs = dict(\n asset_parameters_expiration_date='5y', asset_parameters_call_currency='USD', asset_parameters_put_currency='EUR'\n )\n assert 'MAW8SAXPSKYA94E2' == tm_fxo._get_tdapi_fxo_assets(**kwargs)\n replace.restore()\n\n # Test case: Multiple assets, one with name starting with \"FX Forward\"\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict(\n asset_parameters_expiration_date='5y',\n asset_parameters_call_currency='USD',\n asset_parameters_put_currency='EUR',\n name_prefix='FX Forward',\n )\n assert 'MAW8SAXPSKYA94E2' == tm_fxo._get_tdapi_fxo_assets(**kwargs)\n replace.restore()\n\n # Test case: Multiple assets, none with name starting with \"FX Forward\"\n mock_asset_1.name = \"Test_asset_1\"\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict(\n asset_parameters_expiration_date='5y', asset_parameters_call_currency='USD', asset_parameters_put_currency='EUR'\n )\n with pytest.raises(MqValueError):\n tm_fxo._get_tdapi_fxo_assets(**kwargs)\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = []\n kwargs = dict(\n asset_parameters_expiration_date='5y', asset_parameters_call_currency='USD', asset_parameters_put_currency='EUR'\n )\n with pytest.raises(MqValueError):\n tm_fxo._get_tdapi_fxo_assets(**kwargs)\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict()\n assert ['MAW8SAXPSKYA94E2', 'MATDD783JM1C2GGD'] == tm_xccy._get_tdapi_crosscurrency_rates_assets(**kwargs)\n replace.restore()\n\n # test case will test matching sofr maturity with libor leg and flipping legs to get right asset\n kwargs = dict(\n Asset_class='FX',\n type='Option',\n asset_parameters_call_currency='USD',\n asset_parameters_put_currency='EUR',\n asset_parameters_expiration_date='1m',\n asset_parameters_expiration_time='NYC',\n asset_parameters_option_type='Put',\n asset_parameters_premium_payment_date='Fwd Settle',\n asset_parameters_strike_price_relative='10d',\n pricing_location='NYC',\n )\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1]\n assert 'MAW8SAXPSKYA94E2' == tm_fxo._get_tdapi_fxo_assets(**kwargs)\n replace.restore()\n\n\ndef mock_curr(_cls, _q):\n d = {'impliedVolatility': [1, 2, 3], 'fwdPoints': [4, 5, 6], 'forwardPoint': [7, 8, 9]}\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_spot_carry_3m(*args, **kwargs):\n \"\"\"Mock Dataset.get_data for 3m tenor with fwdPoints column\"\"\"\n d = pd.DataFrame(\n {\n 'spot': [1.18250, 1.18566, 1.18511],\n 'fwdPoints': [0.00234, 0.00234, 0.00235],\n 'tenor': ['3m', '3m', '3m'],\n 'date': [pd.Timestamp(2020, 9, 2), pd.Timestamp(2020, 9, 3), pd.Timestamp(2020, 9, 4)],\n 'settlementDate': [pd.Timestamp('2020-12-04'), pd.Timestamp('2020-12-08'), pd.Timestamp('2020-12-08')],\n }\n )\n d = d.set_index('date')\n df = MarketDataResponseFrame(d)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef mock_fx_spot_carry_2y(*args, **kwargs):\n \"\"\"Mock Dataset.get_data for 2y tenor with fwdPoints column\"\"\"\n d = pd.DataFrame(\n {\n 'spot': [1.18250, 1.18566, 1.18511],\n 'fwdPoints': [0.02009, 0.02015, 0.02064],\n 'tenor': ['2y', '2y', '2y'],\n 'date': [pd.Timestamp(2020, 9, 2), pd.Timestamp(2020, 9, 3), pd.Timestamp(2020, 9, 4)],\n 'settlementDate': [pd.Timestamp('2020-12-04'), pd.Timestamp('2020-12-08'), pd.Timestamp('2020-12-08')],\n }\n )\n d = d.set_index('date')\n df = MarketDataResponseFrame(d)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef test_fx_vol_measure(mocker):\n replace = Replacer()\n args = dict(\n expiry_tenor='1m',\n strike='ATMF',\n option_type='Put',\n expiration_location=None,\n location=None,\n premium_payment_date=None,\n )\n\n mock_gbp = Cross('MA26QSMPX9990G66', 'GBPUSD')\n args['asset'] = mock_gbp\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBPUSD'\n\n args['expiry_tenor'] = '1yr'\n with pytest.raises(MqValueError):\n tm_fxo.implied_volatility_new(**args)\n args['expiry_tenor'] = '1m'\n\n args['real_time'] = True\n with pytest.raises(NotImplementedError):\n tm_fxo.implied_volatility_new(**args)\n args['real_time'] = False\n\n args['asset'] = Cross('MA666', 'USDAED')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'AEDUSD'\n with pytest.raises(NotImplementedError):\n tm_fxo.implied_volatility_new(**args)\n args['asset'] = mock_gbp\n\n args['asset'] = Bond('MA667', 'TEST')\n with pytest.raises(NotImplementedError):\n tm_fxo.implied_volatility_new(**args)\n args['asset'] = mock_gbp\n\n args['asset'] = Cross('MA667', 'GBPUSD')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBPUSD'\n identifiers = replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n identifiers.return_value = {'MA7F5P92330NGKAR'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_fxo.implied_volatility_new(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='impliedVolatility')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n args['asset'] = Cross('MA667', 'USDCAD')\n args['location'] = PricingLocation.LDN\n args['premium_payment_date'] = 'Fwd Settle'\n args['expiration_location'] = 'NYC'\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USDCAD'\n identifiers = replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n identifiers.return_value = {'MA7F5P92330NGKAR'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_fxo.implied_volatility_new(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='impliedVolatility')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n args['option_type'] = 'Call'\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBPUSD'\n identifiers = replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n identifiers.return_value = {'MA7F5P92330NGKAR'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_fxo.implied_volatility_new(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='impliedVolatility')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n\n\ndef test_fwd_points(mocker):\n replace = Replacer()\n args = dict(settlement_date=\"6m\", location='LDN')\n mock_eur = Cross('MAGZMXVM0J282ZTR', 'EURUSD')\n args['asset'] = mock_eur\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n\n args['settlement_date'] = '1yr'\n with pytest.raises(MqValueError):\n tm_fxo.fwd_points(**args)\n args['settlement_date'] = '6m'\n\n args['real_time'] = True\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n identifiers = replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n identifiers.return_value = {'MAGZMXVM0J282ZTR'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_fxo.fwd_points(**args)\n expected = tm.ExtendedSeries([7, 8, 9], index=_index * 3, name='forwardPoint')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n args['real_time'] = False\n\n args['asset'] = Cross('MAGZMXVM0J282ZTR', 'EURUSD')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n identifiers = replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n identifiers.return_value = {'MAGZMXVM0J282ZTR'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_fxo.fwd_points(**args)\n expected = tm.ExtendedSeries([4, 5, 6], index=_index * 3, name='fwdPoints')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n args['location'] = None\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n identifiers = replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n identifiers.return_value = {'MAGZMXVM0J282ZTR'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm_fxo.fwd_points(**args)\n expected = tm.ExtendedSeries([4, 5, 6], index=_index * 3, name='fwdPoints')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n replace.restore()\n\n\ndef mock_df():\n d = {\n 'strikeVol': [5, 1, 2],\n }\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = \"FX_VOLATILITY_SWAP\"\n return df\n\n\ndef test_vol_swap_strike_raises_exception():\n with pytest.raises(NotImplementedError):\n mock_asset_1 = GsAsset(asset_class='FX', id='MAW8SAXPSKYA94E2', type_='Option', name='Test_asset')\n tm_fxo.vol_swap_strike(mock_asset_1, \"none\", \"none\", location=PricingLocation.LDN, real_time=True)\n\n\ndef test_vol_swap_strike_unsupported_cross():\n with pytest.raises(NotImplementedError):\n replace = Replacer()\n mock_asset_1 = Cross('MA667', 'USDPLN')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USDPLN'\n\n tm_fxo.vol_swap_strike(mock_asset_1, \"none\", \"none\", location=PricingLocation.LDN, real_time=False)\n replace.restore()\n\n\ndef test_vol_swap_strike_matches_multiple_assets():\n with pytest.raises(MqValueError):\n replace = Replacer()\n base = Cross('MA667', 'EURUSD')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n mock_asset_1 = GsAsset(asset_class='FX', id='MA123', type_='VolatilitySwap', name='Test_asset')\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_1]\n tm_fxo.vol_swap_strike(base, None, location=PricingLocation.LDN, real_time=False)\n replace.restore()\n\n\ndef test_vol_swap_strike_matches_no_assets():\n with pytest.raises(MqValueError):\n replace = Replacer()\n base = Cross('MA667', 'EURUSD')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = []\n tm_fxo.vol_swap_strike(base, None, location=PricingLocation.LDN, real_time=False)\n replace.restore()\n\n\ndef test_vol_swap_strike_matches_no_assets_when_expiry_tenor_is_not_none():\n with pytest.raises(MqValueError):\n replace = Replacer()\n base = Cross('MA667', 'EURUSD')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n\n mock_asset_1 = GsAsset(\n asset_class='FX', id='MA123', type_='VolatilitySwap', name='Test_asset', parameters={\"lastFixingDate\": \"1y\"}\n )\n\n mock_asset_2 = GsAsset(\n asset_class='FX', id='MA123', type_='VolatilitySwap', name='Test_asset', parameters={\"lastFixingDate\": \"1y\"}\n )\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n\n tm_fxo.vol_swap_strike(base, \"10m\", location=PricingLocation.LDN, real_time=False)\n replace.restore()\n\n\ndef test_currencypair_to_tdapi_fx_vol_swap_asset():\n replace = Replacer()\n base = Cross('MA667', 'EURUSD')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n\n asset = tm_fxo._currencypair_to_tdapi_fx_vol_swap_asset(base)\n assert asset == \"MA66A4X4PRTC3N7B\"\n replace.restore()\n\n\ndef test_vol_swap_strike():\n replace = Replacer()\n base = Cross('MA667', 'EURUSD')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n\n mock_asset_1 = GsAsset(\n asset_class='FX', id='MA123', type_='VolatilitySwap', name='Test_asset', parameters={\"lastFixingDate\": \"1y\"}\n )\n mock_asset_2 = GsAsset(\n asset_class='FX', id='MA123', type_='VolatilitySwap', name='Test_asset', parameters={\"lastFixingDate\": \"2y\"}\n )\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n mock_data = replace('gs_quant.timeseries.measures.GsDataApi.get_market_data', Mock())\n mock_data.return_value = mock_df()\n actual = tm_fxo.vol_swap_strike(base, \"1y\", location=PricingLocation.LDN, real_time=False)\n assert_series_equal(tm_rates._extract_series_from_df(mock_df(), QueryType.STRIKE_VOL), actual)\n\n actual = tm_fxo.vol_swap_strike(base, \"1y\", None, real_time=False)\n assert_series_equal(tm_rates._extract_series_from_df(mock_df(), QueryType.STRIKE_VOL), actual)\n replace.restore()\n\n\ndef test_implied_volatility_fxvol(mocker):\n replace = Replacer()\n\n args = dict(tenor=\"1y\", location=None)\n mock_eur = Cross('MAGZMXVM0J282ZTR', 'EURUSD')\n args['asset'] = mock_eur\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n xrefs = replace('gs_quant.timeseries.measures_fx_vol._cross_stored_direction_helper', Mock())\n xrefs.return_value = 'GBPUSD'\n assets = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n assets.return_value = mock_eur\n\n preprocess_impl_vol = replace('gs_quant.timeseries.measures_fx_vol._preprocess_implied_vol_strikes_fx', Mock())\n replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='impliedVolatility')\n\n args['strike_reference'] = VolReference.DELTA_CALL\n args['relative_strike'] = 50\n preprocess_impl_vol.return_value = ['abc', 50]\n with pytest.raises(MqValueError):\n tm_fxo.implied_volatility_fxvol(**args)\n\n args['strike_reference'] = VolReference.DELTA_CALL\n args['relative_strike'] = 50\n preprocess_impl_vol.return_value = ['delta', 50]\n actual = tm_fxo.implied_volatility_fxvol(**args)\n assert_series_equal(expected, actual)\n\n args['strike_reference'] = VolReference.DELTA_PUT\n args['relative_strike'] = 25\n preprocess_impl_vol.return_value = ['delta', -25]\n actual = tm_fxo.implied_volatility_fxvol(**args)\n assert_series_equal(expected, actual)\n\n args['strike_reference'] = VolReference.DELTA_CALL\n args['relative_strike'] = 0\n preprocess_impl_vol.return_value = ['delta', 0]\n actual = tm_fxo.implied_volatility_fxvol(**args)\n assert_series_equal(expected, actual)\n\n args['strike_reference'] = VolReference.SPOT\n args['relative_strike'] = 100\n preprocess_impl_vol.return_value = ['spot', 100]\n actual = tm_fxo.implied_volatility_fxvol(**args)\n assert_series_equal(expected, actual)\n\n args['strike_reference'] = VolReference.FORWARD\n args['relative_strike'] = 100\n preprocess_impl_vol.return_value = ['forward', 100]\n actual = tm_fxo.implied_volatility_fxvol(**args)\n assert_series_equal(expected, actual)\n\n args['strike_reference'] = VolReference.NORMALIZED\n args['relative_strike'] = 100\n preprocess_impl_vol = replace('gs_quant.timeseries.measures_fx_vol._preprocess_implied_vol_strikes_fx', Mock())\n preprocess_impl_vol.return_value = ['normalized', 0]\n with pytest.raises(MqValueError):\n tm_fxo.implied_volatility_fxvol(**args)\n\n xrefs = replace('gs_quant.timeseries.measures_fx_vol._cross_stored_direction_helper', Mock())\n xrefs.return_value = 'EURUSD'\n args['strike_reference'] = VolReference.DELTA_CALL\n args['relative_strike'] = 50\n preprocess_impl_vol.return_value = ['delta', 50]\n actual = tm_fxo.implied_volatility_fxvol(**args)\n assert_series_equal(expected, actual)\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = None\n with pytest.raises(MqValueError):\n tm_fxo.implied_volatility_fxvol(**args)\n\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_measures_fx_vol.py\"])\n\n\ndef test_spot_carry(mocker):\n replace = Replacer()\n mock = Cross('MAA0NE9QX2ABETG6', 'USD/EUR')\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USDEUR'\n\n identifiers = replace('gs_quant.timeseries.measures_fx_vol._get_tdapi_fxo_assets', Mock())\n identifiers.return_value = 'MAA0NE9QX2ABETG6'\n\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n\n mock_tdapi_asset_func = replace('gs_quant.timeseries.measures_fx_vol._currencypair_to_tdapi_fxfwd_asset', Mock())\n mock_tdapi_asset_func.return_value = mock.get_marquee_id()\n df = pd.DataFrame(\n {\n '3m': [-0.001978858350951374, -0.0019735843327766817, -0.00198293829264794],\n '2y': [-0.016989429175475686, -0.016994753976688093, -0.017416104834150414],\n '3m_ann': [-0.007660096842392416, -0.007400941247912555, -0.0075142924774027195],\n 'date': [pd.Timestamp('2020-09-02'), pd.Timestamp('2020-09-03'), pd.Timestamp('2020-09-04')],\n 'settlementDate': [pd.Timestamp('2020-12-04'), pd.Timestamp('2020-12-08'), pd.Timestamp('2020-12-08')],\n }\n )\n df = df.set_index('date')\n\n with DataContext(dt.date(2020, 9, 2), dt.date(2020, 9, 4)):\n replace('gs_quant.data.dataset.Dataset.get_data', mock_fx_spot_carry_3m)\n actual_3m = gs_quant.timeseries.measures_fx_vol.spot_carry(mock, '3m')\n assert_series_equal(df['3m'], pd.Series(actual_3m, name='3m'))\n\n actual_3m_ann = gs_quant.timeseries.measures_fx_vol.spot_carry(mock, '3m', tm.FXSpotCarry.ANNUALIZED)\n assert_series_equal(df['3m_ann'], pd.Series(actual_3m_ann, name='3m_ann'))\n\n with pytest.raises(MqValueError):\n gs_quant.timeseries.measures_fx_vol.spot_carry(mock, '13m')\n\n replace('gs_quant.data.dataset.Dataset.get_data', mock_fx_spot_carry_2y)\n actual_2y = gs_quant.timeseries.measures_fx_vol.spot_carry(mock, '2y')\n assert_series_equal(df['2y'], pd.Series(actual_2y, name='2y'))\n\n with pytest.raises(NotImplementedError):\n tm_fxo.spot_carry(mock, '3m', real_time=True)\n\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=MarketDataResponseFrame())\n with DataContext(dt.date(2020, 9, 2), dt.date(2020, 9, 4)):\n result = tm_fxo.spot_carry(mock, '3m')\n assert result.empty\n assert isinstance(result, pd.Series)\n\n def mock_empty_dataset(*args, **kwargs):\n return pd.DataFrame()\n\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n with DataContext(dt.date(2020, 9, 2), dt.date(2020, 9, 4)):\n replace('gs_quant.data.dataset.Dataset.get_data', mock_empty_dataset)\n result = tm_fxo.spot_carry(mock, '3m')\n assert result.empty\n assert isinstance(result, pd.Series)\n\n replace.restore()\n" }, { "path": "gs_quant/test/timeseries/test_measures_inflation.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pandas as pd\nimport pytest\nfrom pandas._libs.tslibs.offsets import CustomBusinessDay\nfrom pandas.testing import assert_series_equal\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nimport gs_quant.timeseries.measures_inflation as tm\nfrom gs_quant.api.gs.assets import GsAsset\nfrom gs_quant.api.gs.data import GsDataApi, MarketDataResponseFrame\nfrom gs_quant.common import PricingLocation, Currency as CurrEnum\nfrom gs_quant.data import DataContext\nfrom gs_quant.errors import MqError, MqValueError\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.securities import Currency\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.test.timeseries.utils import mock_request\nfrom gs_quant.timeseries import CurrencyEnum, SecurityMaster\nfrom gs_quant.timeseries.measures_inflation import (\n _currency_to_tdapi_inflation_swap_rate_asset,\n INFLATION_RATES_DEFAULTS,\n TdapiInflationRatesDefaultsProvider,\n)\nfrom gs_quant.timeseries.measures_rates import _ClearingHouse\n\n_index = [pd.Timestamp('2021-03-30')]\n_test_datasets = ('TEST_DATASET',)\n\n\ndef test_get_floating_rate_option_for_benchmark_retuns_rate():\n provider = TdapiInflationRatesDefaultsProvider(INFLATION_RATES_DEFAULTS)\n value = provider.get_index_for_benchmark(CurrencyEnum.GBP, \"UKRPI\")\n assert value == \"CPI-UKRPI\"\n\n\ndef test_get_floating_rate_option_for_benchmark_retuns_rate_usd():\n provider = TdapiInflationRatesDefaultsProvider(INFLATION_RATES_DEFAULTS)\n value = provider.get_index_for_benchmark(CurrencyEnum.USD, \"CPURNSA\")\n assert value == \"CPI-CPURNSA\"\n\n\ndef test_currency_to_tdapi_inflation_swap_rate_asset(mocker):\n replace = Replacer()\n mocker.patch.object(\n GsSession.__class__, 'current', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=mock_request)\n bbid_mock = replace('gs_quant.timeseries.measures_inflation.Asset.get_identifier', Mock())\n\n with PricingContext(dt.date.today()):\n cur = [\n {\"currency_assetId\": \"MAK1FHKH5P5GJSHH\", \"currency\": \"JPY\", \"inflation_id\": \"MA1CENMCA88VXJ28\"},\n {\"currency_assetId\": \"MA66CZBQJST05XKG\", \"currency\": \"GBP\", \"inflation_id\": \"MAW75DV9777630QN\"},\n {\"currency_assetId\": \"MAJNQPFGN1EBDHAE\", \"currency\": \"EUR\", \"inflation_id\": \"MAJTD8XDA8EJZYRG\"},\n {\"currency_assetId\": \"MAZ7RWC904JYHYPS\", \"currency\": \"USD\", \"inflation_id\": \"MA4016GCT3MDRYVY\"},\n ]\n for c in cur:\n print(c)\n asset = Currency(c.get(\"currency_assetId\"), c.get(\"currency\"))\n bbid_mock.return_value = c.get(\"currency\")\n mqid = _currency_to_tdapi_inflation_swap_rate_asset(asset)\n assert mqid == c.get(\"inflation_id\")\n\n bbid_mock.return_value = None\n assert _currency_to_tdapi_inflation_swap_rate_asset(asset) == c.get(\"currency_assetId\")\n replace.restore()\n\n\ndef test_get_inflation_swap_leg_defaults():\n result_dict = dict(currency=CurrEnum.JPY, index_type='CPI-JCPNGENF', pricing_location=PricingLocation.TKO)\n defaults = tm._get_inflation_swap_leg_defaults(CurrEnum.JPY)\n assert result_dict == defaults\n\n result_dict = dict(currency=CurrEnum.USD, index_type='CPI-CPURNSA', pricing_location=PricingLocation.NYC)\n defaults = tm._get_inflation_swap_leg_defaults(CurrEnum.USD)\n assert result_dict == defaults\n\n result_dict = dict(currency=CurrEnum.EUR, index_type='CPI-CPXTEMU', pricing_location=PricingLocation.LDN)\n defaults = tm._get_inflation_swap_leg_defaults(CurrEnum.EUR)\n assert result_dict == defaults\n\n result_dict = dict(currency=CurrEnum.GBP, index_type='CPI-UKRPI', pricing_location=PricingLocation.LDN)\n defaults = tm._get_inflation_swap_leg_defaults(CurrEnum.GBP)\n assert result_dict == defaults\n\n\ndef test_get_inflation_swap_csa_terms():\n valid_ccy = ['EUR', 'GBP', 'USD']\n for ccy in valid_ccy:\n assert dict(csaTerms=ccy + '-1') == tm._get_inflation_swap_csa_terms(ccy, tm.InflationIndexType.UKRPI.value)\n\n\ndef test_check_valid_indices():\n valid_indices = ['UKRPI']\n for index in valid_indices:\n assert tm.InflationIndexType[index] == tm._check_inflation_index_type(CurrencyEnum.GBP, index)\n\n invalid_indices = ['UKHPI', 'TestCPI']\n for index in invalid_indices:\n with pytest.raises(MqError):\n tm._check_inflation_index_type(CurrencyEnum.GBP, index)\n\n\ndef test_get_tdapi_inflation_rates_assets(mocker):\n mock_asset_1 = GsAsset(asset_class='Rates', id='MA26QSMPX9990G66', type_='InflationSwap', name='Test_asset')\n mock_asset_2 = GsAsset(asset_class='Rates', id='MA44SBCHF192S6FR', type_='InflationSwap', name='Test_asset')\n\n replace = Replacer()\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1]\n assert 'MA26QSMPX9990G66' == tm._get_tdapi_inflation_rates_assets()\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict(asset_parameters_termination_date='5y', asset_parameters_effective_date='0b')\n with pytest.raises(MqValueError):\n tm._get_tdapi_inflation_rates_assets(**kwargs)\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = []\n kwargs = dict(asset_parameters_clearing_house='NONE', pricing_location='LDN')\n with pytest.raises(MqValueError):\n tm._get_tdapi_inflation_rates_assets(**kwargs)\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict()\n assert ['MA26QSMPX9990G66', 'MA44SBCHF192S6FR'] == tm._get_tdapi_inflation_rates_assets(**kwargs)\n replace.restore()\n\n # test case will test matching sofr maturity with libor leg and flipping legs to get right asset\n kwargs = dict(\n type='InflationSwap',\n asset_parameters_termination_date='5y',\n asset_parameters_index=tm.InflationIndexType.UKRPI,\n asset_parameters_clearing_house='None',\n asset_parameters_effective_date='5y',\n asset_parameters_notional_currency='GBP',\n pricing_location='LDN',\n )\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1]\n assert 'MA26QSMPX9990G66' == tm._get_tdapi_inflation_rates_assets(**kwargs)\n replace.restore()\n\n\ndef mock_curr(_cls, _q):\n d = {\n 'swapRate': [1, 2, 3],\n }\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef test_inflation_swap_rate(mocker):\n replace = Replacer()\n args = dict(swap_tenor='5y', index_type='UKRPI', clearing_house='LCH', forward_tenor='5y', real_time=False)\n\n mock_gbp = Currency('MA26QSMPX9990G66', 'GBP')\n args['asset'] = mock_gbp\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBP'\n\n args['swap_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm.inflation_swap_rate(**args)\n args['swap_tenor'] = '5y'\n\n args['forward_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm.inflation_swap_rate(**args)\n args['forward_tenor'] = '5y'\n\n args['real_time'] = True\n with pytest.raises(NotImplementedError):\n tm.inflation_swap_rate(**args)\n args['real_time'] = False\n\n args['asset'] = Currency('MA666', 'AED')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'AED'\n with pytest.raises(NotImplementedError):\n tm.inflation_swap_rate(**args)\n args['asset'] = mock_gbp\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBP'\n\n args['index_type'] = tm.InflationIndexType.TESTCPI\n with pytest.raises(MqValueError):\n tm.inflation_swap_rate(**args)\n args['index_type'] = tm.InflationIndexType.UKRPI\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBP'\n identifiers = replace('gs_quant.timeseries.measures_inflation._get_tdapi_inflation_rates_assets', Mock())\n identifiers.return_value = {'MA26QSMPX9990G66'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm.inflation_swap_rate(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='swapRate')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EUR'\n identifiers = replace('gs_quant.timeseries.measures_inflation._get_tdapi_inflation_rates_assets', Mock())\n identifiers.return_value = {'MAZBW57ZPS54ET7K'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n args['asset'] = Currency('MAZBW57ZPS54ET7K', 'EUR')\n args['index_type'] = 'FRCPXTOB'\n args['location'] = PricingLocation.LDN\n actual = tm.inflation_swap_rate(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='swapRate')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n replace.restore()\n\n\ndef test_inflation_swap_term(mocker):\n replace = Replacer()\n args = dict(forward_tenor='1y', pricing_date='0d', clearing_house=_ClearingHouse.LCH, real_time=False)\n\n class ObjectView(object):\n def __init__(self, d):\n self.__dict__ = d\n\n holidays = replace('gs_quant.datetime.GsCalendar.get', Mock(holidays=[]))\n holidays.return_value = ObjectView({'holidays': []})\n\n bd_calendar = replace('gs_quant.timeseries.measures_inflation._get_custom_bd', Mock())\n bd_calendar.return_value = CustomBusinessDay()\n pricing_date_mock = replace('gs_quant.timeseries.measures_inflation._range_from_pricing_date', Mock())\n pricing_date_mock.return_value = [dt.date(2019, 1, 1), dt.date(2019, 1, 1)]\n\n mock_nok = Currency('MA891', 'PLN')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'ACU'\n args['asset'] = mock_nok\n with pytest.raises(NotImplementedError):\n tm.inflation_swap_term(**args)\n\n mock_usd = Currency('MAZ7RWC904JYHYPS', 'USD')\n args['asset'] = mock_usd\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n args['real_time'] = True\n with pytest.raises(NotImplementedError):\n tm.inflation_swap_term(**args)\n args['real_time'] = False\n\n args['forward_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm.inflation_swap_term(**args)\n args['forward_tenor'] = '1y'\n\n bd_mock = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n bd_mock.return_value = pd.DataFrame(\n data=dict(date=\"2020-04-10\", exchange=\"NYC\", description=\"Good Friday\"), index=[pd.Timestamp('2020-04-10')]\n )\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'USD'\n identifiers_empty = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n identifiers_empty.return_value = {}\n with pytest.raises(MqValueError):\n tm.inflation_swap_term(**args)\n\n identifiers = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n mock_asset = Currency('USD', name='USD')\n mock_asset.id = 'MAEMPCXQG3T716EX'\n mock_asset.exchange = 'OTC'\n identifiers.return_value = [mock_asset]\n\n d = {\n 'terminationTenor': ['1y', '2y', '3y', '4y'],\n 'swapRate': [1, 2, 3, 4],\n 'assetId': ['MAEMPCXQG3T716EX', 'MAFRSWPAF5QPNTP2', 'MA88BXZ3TCTXTFW1', 'MAC4KAG9B9ZAZHFT'],\n }\n\n bd_mock.return_value = pd.DataFrame()\n market_data_mock = replace('gs_quant.timeseries.measures_inflation._get_inflation_swap_data', Mock())\n\n market_data_mock.return_value = pd.DataFrame()\n df = pd.DataFrame(data=d, index=_index * 4)\n actual = tm.inflation_swap_term(**args)\n assert actual.empty\n\n series_apply_mock = replace('gs_quant.timeseries.measures_inflation.pd.Series.apply', Mock())\n series_apply_mock.return_value = pd.Series(\n [dt.date(2022, 3, 30), dt.date(2023, 3, 30), dt.date(2024, 3, 30), dt.date(2025, 3, 30)], index=df.index\n )\n\n market_data_mock.return_value = df\n with DataContext('2019-01-01', '2026-01-01'):\n actual = tm.inflation_swap_term(**args)\n actual.dataset_ids = _test_datasets\n expected = tm.ExtendedSeries(\n [1, 2, 3, 4], index=[dt.date(2022, 3, 30), dt.date(2023, 3, 30), dt.date(2024, 3, 30), dt.date(2025, 3, 30)]\n )\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual, check_names=False)\n assert actual.dataset_ids == expected.dataset_ids\n\n args['location'] = PricingLocation.NYC\n with DataContext('2019-01-01', '2026-01-01'):\n tm.inflation_swap_term(**args)\n\n holidays.return_value = ObjectView({'holidays': ['0d']})\n with pytest.raises(MqValueError):\n tm.inflation_swap_term(**args)\n\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_measures_inflation.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_measures_portfolios.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport datetime as dt\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nimport gs_quant.timeseries.measures_portfolios as mp\nfrom gs_quant.api.gs.assets import GsTemporalXRef\nfrom gs_quant.api.gs.data import MarketDataResponseFrame\nfrom gs_quant.data import DataCoordinate\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.index import Index\nfrom gs_quant.markets.report import PerformanceReport, ThematicReport\nfrom gs_quant.markets.securities import Stock, Bond\nfrom gs_quant.models.risk_model import FactorRiskModel as Factor_Risk_Model\nfrom gs_quant.common import ReportParameters, XRef\nfrom gs_quant.target.reports import Report, PositionSourceType, ReportType\nfrom gs_quant.target.risk_models import RiskModel, RiskModelCoverage, RiskModelTerm, RiskModelUniverseIdentifier\n\nrisk_model = RiskModel(\n coverage=RiskModelCoverage.Country,\n id_='model_id',\n name='Fake Risk Model',\n term=RiskModelTerm.Long,\n universe_identifier=RiskModelUniverseIdentifier.gsid,\n vendor='GS',\n version=1.0,\n)\n\nfactor_risk_report = Report(\n position_source_id='position source id',\n position_source_type=PositionSourceType.Portfolio,\n type_=ReportType.Portfolio_Factor_Risk,\n id_='report_id',\n parameters=ReportParameters(risk_model='risk_model_id'),\n status='new',\n)\n\nasset_factor_risk_report = Report(\n position_source_id='position source id',\n position_source_type=PositionSourceType.Asset,\n type_=ReportType.Portfolio_Factor_Risk,\n id_='report_id',\n parameters=ReportParameters(risk_model='risk_model_id'),\n status='new',\n)\n\nppa_report = PerformanceReport(\n position_source_id='position source id',\n position_source_type=PositionSourceType.Portfolio,\n type_=ReportType.Portfolio_Performance_Analytics,\n id_='report_id',\n parameters=ReportParameters(risk_model='risk_model_id'),\n status='new',\n)\n\nfactor_data = [\n {\n 'date': '2020-11-23',\n 'reportId': 'report_id',\n 'factor': 'Factor Name',\n 'factorCategory': 'CNT',\n 'pnl': 11.23,\n 'exposure': -11.23,\n 'proportionOfRisk': 1,\n },\n {\n 'date': '2020-11-24',\n 'reportId': 'report_id',\n 'factor': 'Factor Name',\n 'factorCategory': 'CNT',\n 'pnl': 11.24,\n 'exposure': -11.24,\n 'proportionOfRisk': 2,\n },\n {\n 'date': '2020-11-25',\n 'reportId': 'report_id',\n 'factor': 'Factor Name',\n 'factorCategory': 'CNT',\n 'pnl': 11.25,\n 'exposure': -11.25,\n 'proportionOfRisk': 3,\n },\n {\n 'date': '2020-11-23',\n 'reportId': 'report_id',\n 'factor': 'Total',\n 'factorCategory': 'CNT',\n 'pnl': 19.23,\n 'exposure': -11.23,\n 'proportionOfRisk': 1,\n },\n {\n 'date': '2020-11-24',\n 'reportId': 'report_id',\n 'factor': 'Total',\n 'factorCategory': 'CNT',\n 'pnl': 14.24,\n 'exposure': -11.24,\n 'proportionOfRisk': 2,\n },\n {\n 'date': '2020-11-25',\n 'reportId': 'report_id',\n 'factor': 'Total',\n 'factorCategory': 'CNT',\n 'pnl': 21.25,\n 'exposure': -11.25,\n 'proportionOfRisk': 3,\n },\n]\n\naum_data = {'2020-01-01': 1, '2020-01-02': 2, '2020-01-03': 1, '2020-01-04': 1, '2020-01-05': 1, '2020-01-06': 3}\n\nyield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.04,\n}\n\nthematic_data = [\n {\n \"date\": \"2021-07-12\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.448370345015856e8,\n \"thematicExposure\": 2,\n \"thematicBeta\": 1,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-13\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.375772519907556e8,\n \"thematicExposure\": 2,\n \"thematicBeta\": 1,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-14\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.321189950666118e8,\n \"thematicExposure\": 2,\n \"thematicBeta\": 1,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-15\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.274071805135091e8,\n \"thematicExposure\": 2,\n \"thematicBeta\": 1,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n]\n\naggregate_factor_data = [\n {\n 'date': '2020-11-23',\n 'reportId': 'report_id',\n 'factor': 'Factor',\n 'factorCategory': 'CNT',\n 'pnl': 11.23,\n 'exposure': -11.23,\n 'proportionOfRisk': 1,\n 'dailyRisk': 1,\n 'annualRisk': 1,\n },\n {\n 'date': '2020-11-24',\n 'reportId': 'report_id',\n 'factor': 'Factor',\n 'factorCategory': 'CNT',\n 'pnl': 11.24,\n 'exposure': -11.24,\n 'proportionOfRisk': 2,\n 'dailyRisk': 2,\n 'annualRisk': 2,\n },\n {\n 'date': '2020-11-25',\n 'reportId': 'report_id',\n 'factor': 'Factor',\n 'factorCategory': 'CNT',\n 'pnl': 11.25,\n 'exposure': -11.25,\n 'proportionOfRisk': 3,\n 'dailyRisk': 3,\n 'annualRisk': 3,\n },\n]\n\n\ndef mock_risk_model():\n risk_model = RiskModel(\n coverage=RiskModelCoverage.Country,\n id_='model_id',\n name='Fake Risk Model',\n term=RiskModelTerm.Long,\n universe_identifier=RiskModelUniverseIdentifier.gsid,\n vendor='GS',\n version=1.0,\n )\n\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n actual = Factor_Risk_Model.get(model_id='model_id')\n replace.restore()\n return actual\n\n\ndef test_portfolio_factor_exposure():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_reports', Mock())\n mock.return_value = [factor_risk_report]\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mp.portfolio_factor_exposure('report_id', 'risk_model_id', 'Factor Name')\n assert all(actual.values == [-11.23, -11.24, -11.25])\n\n with pytest.raises(MqValueError):\n mp.portfolio_factor_exposure('report_id', 'risk_model_id', 'Wrong Factor Name')\n replace.restore()\n\n\ndef test_portfolio_factor_pnl():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_reports', Mock())\n mock.return_value = [factor_risk_report]\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n actual = mp.portfolio_factor_pnl('report_id', 'risk_model_id', 'Factor Name')\n assert all(actual.values == [11.23, 11.24, 11.25])\n\n with DataContext(dt.date(2020, 11, 22), dt.date(2020, 11, 25)):\n # mock getting report factor data with first day set to 0\n factor_data_copy = copy.copy(factor_data)\n factor_data_copy.insert(\n 0,\n {\n 'date': '2020-11-22',\n 'reportId': 'report_id',\n 'factor': 'Factor Name',\n 'factorCategory': 'CNT',\n 'pnl': 0,\n 'exposure': -11.23,\n 'proportionOfRisk': 1,\n },\n )\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data_copy\n\n actual = mp.portfolio_factor_pnl('report_id', 'risk_model_id', 'Factor Name')\n assert all(actual.values == [0.0, 11.23, 11.24, 11.25])\n\n with pytest.raises(MqValueError):\n mp.portfolio_factor_pnl('report_id', 'risk_model_id', 'Wrong Factor Name')\n replace.restore()\n\n\ndef test_portfolio_factor_proportion_of_risk():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_reports', Mock())\n mock.return_value = [factor_risk_report]\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mp.portfolio_factor_proportion_of_risk('report_id', 'risk_model_id', 'Factor Name')\n assert all(actual.values == [1, 2, 3])\n\n with pytest.raises(MqValueError):\n mp.portfolio_factor_proportion_of_risk('report_id', 'risk_model_id', 'Wrong Factor Name')\n replace.restore()\n\n\ndef test_portfolio_thematic_exposure():\n replace = Replacer()\n\n # mock getting PTA report\n mock = replace('gs_quant.markets.report.ThematicReport.get', Mock())\n mock.return_value = ThematicReport(id='report_id')\n\n mock = replace('gs_quant.entities.entity.PositionedEntity.get_thematic_report', Mock())\n mock.return_value = ThematicReport(id='report_id')\n\n # mock getting thematic exposure\n mock = replace('gs_quant.markets.report.ThematicReport.get_thematic_exposure', Mock())\n mock.return_value = pd.DataFrame(thematic_data)\n\n # mock getting asset\n mock = Stock('MAA0NE9QX2ABETG6', 'Test Asset')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n ticker='basket_ticker',\n ),\n )\n ]\n replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock()).return_value = mock\n\n with DataContext(dt.date(2020, 7, 12), dt.date(2020, 7, 15)):\n actual = mp.portfolio_thematic_exposure('report_id', 'basket_ticker')\n assert all(actual.values == [2, 2, 2, 2])\n\n replace.restore()\n\n\ndef test_portfolio_pnl():\n replace = Replacer()\n\n performance_report = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = performance_report\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = performance_report\n\n # mock PerformanceReport.get_pnl()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame.from_dict(\n {'date': ['2022-07-01', '2022-07-02', '2022-07-03'], 'pnl': [200, 400, 600]}\n )\n\n with DataContext(dt.date(2022, 7, 1), dt.date(2022, 7, 3)):\n actual = mp.portfolio_pnl('RP1')\n assert all(actual.values == [200, 400, 600])\n\n replace.restore()\n\n\ndef test_aggregate_factor_support():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.entities.entity.PositionedEntity.get_factor_risk_report', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = aggregate_factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mp.portfolio_factor_proportion_of_risk('portfolio_id', 'report_id', 'Factor')\n assert all(actual.values == [1, 2, 3])\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mp.portfolio_daily_risk('portfolio_id', 'report_id', 'Factor')\n assert all(actual.values == [1, 2, 3])\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mp.portfolio_annual_risk('portfolio_id', 'report_id', 'Factor')\n assert all(actual.values == [1, 2, 3])\n\n with pytest.raises(MqValueError):\n mp.portfolio_daily_risk('portfolio_id', 'report_id', 'Factor Name')\n\n with pytest.raises(MqValueError):\n mp.portfolio_annual_risk('portfolio_id', 'report_id', 'Factor Name')\n replace.restore()\n\n\ndef test_hit_rate():\n replace = Replacer()\n data = {\n 'pnl': [0.2, 0, 1, -0.97],\n 'date': ['2020-01-01', '2020-01-01', '2020-01-02', '2020-01-02'],\n 'entryType': ['Holding', 'Holding', 'Holding', 'Holding'],\n 'assetId': ['asset1', 'asset2', 'asset1', 'asset2'],\n }\n\n timeseries = {'2020-01-02': 0.5}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_portfolio_constituents', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_hit_rate(\"test\", 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_max_drawdown():\n replace = Replacer()\n data = {'2020-01-01': 1, '2020-01-02': 2, '2020-01-03': 1, '2020-01-04': 0}\n\n timeseries = {\n pd.Timestamp('2020-01-01 00:00:00'): np.nan,\n pd.Timestamp('2020-01-02 00:00:00'): np.nan,\n pd.Timestamp('2020-01-03 00:00:00'): np.nan,\n pd.Timestamp('2020-01-04 00:00:00'): -1,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = data\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n max_drawdown_series = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 4)):\n returned = mp.portfolio_max_drawdown(\"test\", 4)\n assert max_drawdown_series.equals(returned)\n replace.restore()\n\n\ndef test_max_recovery_period():\n replace = Replacer()\n\n timeseries = {\n pd.Timestamp('2020-01-01 00:00:00'): np.nan,\n pd.Timestamp('2020-01-02 00:00:00'): np.nan,\n pd.Timestamp('2020-01-03 00:00:00'): np.nan,\n pd.Timestamp('2020-01-04 00:00:00'): np.nan,\n pd.Timestamp('2020-01-05 00:00:00'): np.nan,\n pd.Timestamp('2020-01-06 00:00:00'): 3,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n max_drawdown_series = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 6)):\n returned = mp.portfolio_max_recovery_period(\"test\", 6)\n assert max_drawdown_series.equals(returned)\n replace.restore()\n\n\ndef test_drawdown_length():\n replace = Replacer()\n\n timeseries = {\n pd.Timestamp('2020-01-01 00:00:00'): np.nan,\n pd.Timestamp('2020-01-02 00:00:00'): np.nan,\n pd.Timestamp('2020-01-03 00:00:00'): np.nan,\n pd.Timestamp('2020-01-04 00:00:00'): np.nan,\n pd.Timestamp('2020-01-05 00:00:00'): np.nan,\n pd.Timestamp('2020-01-06 00:00:00'): 3,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n max_drawdown_series = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 6)):\n returned = mp.portfolio_drawdown_length(\"test\", 6)\n assert max_drawdown_series.equals(returned)\n replace.restore()\n\n\ndef test_standard_deviation():\n replace = Replacer()\n data = {'pnl': [4, 5, 6], 'date': ['2020-01-01', '2020-01-02', '2020-01-03']}\n\n timeseries = {'2020-01-01': np.nan, '2020-01-02': np.nan, '2020-01-03': 1.41421}\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_standard_deviation(\"test\", '2d')\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_downside_risk():\n replace = Replacer()\n data = {'pnl': [4, -7, 6], 'date': ['2020-01-01', '2020-01-02', '2020-01-03']}\n\n timeseries = {'2020-01-01': np.nan, '2020-01-02': np.nan, '2020-01-03': 5}\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_downside_risk(\"test\", 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_semi_variance():\n replace = Replacer()\n data = {'pnl': [4, -7, 6, 7], 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04']}\n\n timeseries = {'2020-01-01': np.nan, '2020-01-02': np.nan, '2020-01-04': 8}\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_semi_variance(\"test\", 3)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_kurtosis():\n replace = Replacer()\n data = {\n 'pnl': [\n -1,\n 0,\n 1,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03'],\n }\n\n timeseries = {'2020-01-01': np.nan, '2020-01-02': np.nan, '2020-01-03': -2}\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_kurtosis(\"test\", 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_skewness():\n replace = Replacer()\n data = {\n 'pnl': [\n -1,\n 0,\n 2,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03'],\n }\n\n timeseries = {'2020-01-01': np.nan, '2020-01-02': np.nan, '2020-01-03': 0}\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_skewness(\"test\", 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_realized_var():\n replace = Replacer()\n data = {\n 'pnl': [\n 1,\n 1,\n 1,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03'],\n }\n\n timeseries = {'2020-01-01': np.nan, '2020-01-02': np.nan, '2020-01-03': -0.52500}\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_realized_var(\"test\", 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_bad_date():\n replace = Replacer()\n data = {\n 'pnl': [\n 1,\n 1,\n 1,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03'],\n }\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n try:\n mp.portfolio_realized_var(\"test\", \"12p\")\n except MqValueError:\n pass\n else:\n assert False\n\n replace.restore()\n\n\ndef test_tracking_error():\n replace = Replacer()\n\n data = {\n 'pnl': [0, 0.02, 0.01, 0.01, 0.02],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.02,\n pd.Timestamp('2020-01-03'): 1.03,\n pd.Timestamp('2020-01-04'): 1.04,\n pd.Timestamp('2020-01-05'): 1.045,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-01'): np.nan,\n pd.Timestamp('2020-01-02'): np.nan,\n pd.Timestamp('2020-01-03'): 0.00340,\n pd.Timestamp('2020-01-04'): 0.003603,\n pd.Timestamp('2020-01-05'): 0.010537,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Index(\"test\", 'test', \"test\")\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace('gs_quant.data.coordinate.DataCoordinate.get_series', Mock())\n mock.return_value = pd.Series(benchmark_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_tracking_error(\"test\", 'test', 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_tracking_error_bull():\n replace = Replacer()\n data = {\n 'pnl': [0, 0.02, 0.01, 0.01, 0.02],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.02,\n pd.Timestamp('2020-01-03'): 1.03,\n pd.Timestamp('2020-01-04'): 1.04,\n pd.Timestamp('2020-01-05'): 1.045,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-01'): np.nan,\n pd.Timestamp('2020-01-02'): np.nan,\n pd.Timestamp('2020-01-03'): 0.71771,\n pd.Timestamp('2020-01-04'): 0.70357,\n pd.Timestamp('2020-01-05'): 0.34648,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Index(\"test\", 'test', \"test\")\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace('gs_quant.data.coordinate.DataCoordinate.get_series', Mock())\n mock.return_value = pd.Series(benchmark_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_tracking_error_bull(\"test\", 'test', 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_tracking_error_bear():\n replace = Replacer()\n data = {\n 'pnl': [0, 0.02, 0.01, 0.01, 0.02],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.02,\n pd.Timestamp('2020-01-03'): 1.03,\n pd.Timestamp('2020-01-04'): 1.04,\n pd.Timestamp('2020-01-05'): 1.045,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-01'): np.nan,\n pd.Timestamp('2020-01-02'): np.nan,\n pd.Timestamp('2020-01-03'): np.nan,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Index(\"test\", 'test', \"test\")\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace('gs_quant.data.coordinate.DataCoordinate.get_series', Mock())\n mock.return_value = pd.Series(benchmark_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_tracking_error_bear(\"test\", 'test', 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_sharpe_ratio():\n replace = Replacer()\n data = {\n 'pnl': [0, 0.02, 0.02, 0.01, 0.02],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 0.02,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.03,\n pd.Timestamp('2020-01-04'): 0.04,\n pd.Timestamp('2020-01-05'): 0.045,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-01'): np.nan,\n pd.Timestamp('2020-01-02'): np.nan,\n pd.Timestamp('2020-01-03'): np.nan,\n pd.Timestamp('2020-01-04'): 32.60231,\n pd.Timestamp('2020-01-05'): 33.77863,\n }\n aum_data = {'2020-01-01': 1.00, '2020-01-02': 1.035, '2020-01-03': 1.05, '2020-01-04': 1.06, '2020-01-05': 1.065}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Index(\"test\", 'test', \"test\")\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace('gs_quant.data.coordinate.DataCoordinate.get_series', Mock())\n mock.return_value = pd.Series(benchmark_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_sharpe_ratio(\"test\", 'test', 2)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_calmar_ratio():\n replace = Replacer()\n data = {\n 'pnl': [0, 0.02, 0.01, 0.04, 0.03],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.03,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.03,\n }\n\n timeseries = {pd.Timestamp('2020-01-04 00:00:00'): 16.20658, pd.Timestamp('2020-01-05 00:00:00'): 30.31192}\n\n aum_data = {'2020-01-01': 1.00, '2020-01-02': 0.95, '2020-01-03': 1.05, '2020-01-04': 1.03, '2020-01-05': 1.065}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace('gs_quant.data.coordinate.DataCoordinate.get_series', Mock())\n mock.return_value = pd.Series(yield_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_calmar_ratio(\"test\", 3)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_sortino_ratio():\n replace = Replacer()\n data = {\n 'pnl': [0.02, 0.02, -0.01, 0.02, 0.01],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.001,\n pd.Timestamp('2020-01-02'): 0.07,\n pd.Timestamp('2020-01-03'): 0.08,\n pd.Timestamp('2020-01-04'): 0.075,\n pd.Timestamp('2020-01-05'): 0.02,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-04 00:00:00'): 15.063383,\n pd.Timestamp('2020-01-05 00:00:00'): 10.128712,\n }\n\n aum_data = {'2020-01-01': 1.00, '2020-01-02': 0.95, '2020-01-03': 0.9, '2020-01-04': 1.03, '2020-01-05': 1.065}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace('gs_quant.data.coordinate.DataCoordinate.get_series', Mock())\n mock.return_value = pd.Series(yield_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_sortino_ratio(\"test\", \"test\", 3)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_sortino_ratio_index():\n replace = Replacer()\n data = {\n 'pnl': [0.02, 0.02, -0.01, 0.02, 0.01],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n pricing_data = {\n pd.Timestamp('2020-01-01'): 0.001,\n pd.Timestamp('2020-01-02'): 0.07,\n pd.Timestamp('2020-01-03'): 0.07,\n pd.Timestamp('2020-01-04'): 0.001,\n pd.Timestamp('2020-01-05'): 0.02,\n }\n\n timeseries = {pd.Timestamp('2020-01-05 00:00:00'): -8.355985}\n\n aum_data = {'2020-01-01': 1.00, '2020-01-02': 0.95, '2020-01-03': 0.9, '2020-01-04': 1.03, '2020-01-05': 1.065}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Index(\"test\", 'test', \"test\")\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace('gs_quant.data.coordinate.DataCoordinate.get_series', Mock())\n mock.return_value = pd.Series(pricing_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2020, 1, 2)):\n returned = mp.portfolio_sortino_ratio(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_jensen_alpha():\n replace = Replacer()\n data = {\n 'pnl': [0.02, 0.02, 0.01, 0.02, 0.01],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.04,\n }\n\n timeseries = {pd.Timestamp('2020-01-04 00:00:00'): 0.01814, pd.Timestamp('2020-01-05 00:00:00'): 0.00661}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace('gs_quant.data.coordinate.DataCoordinate.get_series', Mock())\n mock.return_value = pd.Series(yield_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_jensen_alpha(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_jensen_bull():\n replace = Replacer()\n\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 6,\n pd.Timestamp('2020-01-02'): 5,\n pd.Timestamp('2020-01-03'): 7,\n pd.Timestamp('2020-01-04'): 9,\n pd.Timestamp('2020-01-08'): 10,\n }\n\n data = {\n 'pnl': [0.02, 0.02, 0.01, 0.02, 0.01],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.04,\n }\n\n timeseries = {pd.Timestamp('2020-01-04 00:00:00'): 0.02530}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_jensen_alpha_bull(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_jensen_alpha_bear():\n replace = Replacer()\n\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 6,\n pd.Timestamp('2020-01-02'): 5,\n pd.Timestamp('2020-01-03'): 7,\n pd.Timestamp('2020-01-04'): 9,\n pd.Timestamp('2020-01-08'): 10,\n }\n\n data = {\n 'pnl': [0.02, -0.02, -0.01, -0.02, -0.01],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.04,\n }\n\n timeseries = {pd.Timestamp('2020-01-04 00:00:00'): -0.88850}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_jensen_alpha_bear(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_information_ratio():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 6,\n pd.Timestamp('2020-01-02'): 5,\n pd.Timestamp('2020-01-03'): 7,\n pd.Timestamp('2020-01-04'): 9,\n }\n\n data = {'pnl': [0.02, 0.02, 0.01, 0.02], 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04']}\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-02 00:00:00'): 0.748532,\n pd.Timestamp('2020-01-03 00:00:00'): -1.583948,\n pd.Timestamp('2020-01-04 00:00:00'): -1.065513,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_information_ratio(\"test\", \"test\")\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_information_ratio_bull():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 6,\n pd.Timestamp('2020-01-02'): 5,\n pd.Timestamp('2020-01-03'): 7,\n pd.Timestamp('2020-01-04'): 9,\n }\n\n data = {'pnl': [0.02, 0.02, 0.01, 0.02], 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04']}\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n }\n\n timeseries = {pd.Timestamp('2020-01-03 00:00:00'): -6.11050, pd.Timestamp('2020-01-04 00:00:00'): -4.11050}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_information_ratio_bull(\"test\", \"test\")\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_information_ratio_bear():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 6,\n pd.Timestamp('2020-01-02'): 5,\n pd.Timestamp('2020-01-03'): 4,\n pd.Timestamp('2020-01-04'): 9,\n }\n\n data = {'pnl': [0.02, 0.02, 0.01, 0.02], 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04']}\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n }\n\n timeseries = {pd.Timestamp('2020-01-02 00:00:00'): 20.36364, pd.Timestamp('2020-01-03 00:00:00'): 22.36364}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_information_ratio_bear(\"test\", \"test\")\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_modigliani_ratio():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.03,\n pd.Timestamp('2020-01-03'): 1.04,\n pd.Timestamp('2020-01-04'): 0.96,\n pd.Timestamp('2020-01-05'): 1.01,\n pd.Timestamp('2020-01-06'): 1.06,\n pd.Timestamp('2020-01-07'): 1.04,\n }\n\n data = {\n 'pnl': [\n 0.02,\n 0.02,\n -0.01,\n -0.002,\n 0.003,\n 0.01,\n 0.002,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.028,\n pd.Timestamp('2020-01-06'): 0.031,\n pd.Timestamp('2020-01-07'): 0.032,\n }\n\n timeseries = {pd.Timestamp('2020-01-05 00:00:00'): 0.31288, pd.Timestamp('2020-01-06 00:00:00'): 0.20278}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n mock = replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(\n side_effect=[\n pd.Series(benchmark_data),\n pd.Series(yield_data),\n pd.Series(yield_data),\n pd.Series(yield_data),\n pd.Series(yield_data),\n ]\n ),\n )\n mock.return_value = pd.Series(benchmark_data)\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_modigliani_ratio(\"test\", \"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_treynor_measure():\n replace = Replacer()\n data = {\n 'pnl': [\n 0.02,\n 0.02,\n 0.01,\n 0.002,\n 0.003,\n 0.01,\n 0.002,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.028,\n pd.Timestamp('2020-01-06'): 0.031,\n pd.Timestamp('2020-01-07'): 0.032,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-04 00:00:00'): -0.01426,\n }\n\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 6,\n pd.Timestamp('2020-01-02'): 5,\n pd.Timestamp('2020-01-03'): 4,\n pd.Timestamp('2020-01-04'): 9,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_treynor_measure(\"test\", 'test', 'test')\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_alpha():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.03,\n pd.Timestamp('2020-01-03'): 1.04,\n pd.Timestamp('2020-01-04'): 0.96,\n pd.Timestamp('2020-01-05'): 1.01,\n pd.Timestamp('2020-01-06'): 1.06,\n pd.Timestamp('2020-01-07'): 1.04,\n }\n\n data = {\n 'pnl': [\n 0.02,\n 0.02,\n 0.01,\n 0.002,\n 0.003,\n 0.01,\n 0.002,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.028,\n pd.Timestamp('2020-01-06'): 0.031,\n pd.Timestamp('2020-01-07'): 0.032,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-05 00:00:00'): -1.61319,\n pd.Timestamp('2020-01-06 00:00:00'): -3.73679,\n pd.Timestamp('2020-01-07 00:00:00'): -1.38878,\n }\n\n aum_data = {\n '2020-01-01': 1.00,\n '2020-01-02': 0.95,\n '2020-01-03': 0.9,\n '2020-01-04': 1.03,\n '2020-01-05': 1.065,\n '2020-01-06': 1.08,\n '2020-01-07': 1.072,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_alpha(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-6)\n replace.restore()\n\n\ndef test_beta():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.03,\n pd.Timestamp('2020-01-03'): 1.04,\n pd.Timestamp('2020-01-04'): 0.96,\n pd.Timestamp('2020-01-05'): 1.01,\n pd.Timestamp('2020-01-06'): 1.06,\n pd.Timestamp('2020-01-07'): 1.04,\n }\n\n data = {\n 'pnl': [\n 0.02,\n 0.02,\n 0.01,\n 0.002,\n 0.003,\n 0.01,\n 0.002,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.028,\n pd.Timestamp('2020-01-06'): 0.031,\n pd.Timestamp('2020-01-07'): 0.032,\n }\n\n timeseries = {\n pd.Timestamp('2020-01-05 00:00:00'): 0.00118,\n pd.Timestamp('2020-01-06 00:00:00'): 0.00070,\n pd.Timestamp('2020-01-07 00:00:00'): 0.00106,\n }\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_beta(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_correlation():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.03,\n pd.Timestamp('2020-01-03'): 1.04,\n pd.Timestamp('2020-01-04'): 0.96,\n pd.Timestamp('2020-01-05'): 1.01,\n pd.Timestamp('2020-01-06'): 1.06,\n pd.Timestamp('2020-01-07'): 1.04,\n }\n\n data = {\n 'pnl': [\n 0.02,\n 0.02,\n 0.01,\n 0.002,\n 0.003,\n 0.01,\n 0.002,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.028,\n pd.Timestamp('2020-01-06'): 0.031,\n pd.Timestamp('2020-01-07'): 0.032,\n }\n\n timeseries = {pd.Timestamp('2020-01-06 00:00:00'): 0.21846, pd.Timestamp('2020-01-07 00:00:00'): 0.40573}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_correlation(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_r_squared():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.03,\n pd.Timestamp('2020-01-03'): 1.04,\n pd.Timestamp('2020-01-04'): 0.96,\n pd.Timestamp('2020-01-05'): 1.01,\n pd.Timestamp('2020-01-06'): 1.06,\n pd.Timestamp('2020-01-07'): 1.04,\n }\n\n data = {\n 'pnl': [\n 0.02,\n 0.02,\n 0.01,\n 0.002,\n 0.003,\n 0.01,\n 0.002,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.028,\n pd.Timestamp('2020-01-06'): 0.031,\n pd.Timestamp('2020-01-07'): 0.032,\n }\n\n timeseries = {pd.Timestamp('2020-01-05 00:00:00'): 0.04772, pd.Timestamp('2020-01-06 00:00:00'): 0.16462}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_r_squared(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_capture_ratio():\n replace = Replacer()\n benchmark_data = {\n pd.Timestamp('2020-01-01'): 1,\n pd.Timestamp('2020-01-02'): 1.03,\n pd.Timestamp('2020-01-03'): 1.04,\n pd.Timestamp('2020-01-04'): 1.06,\n pd.Timestamp('2020-01-05'): 1.06,\n pd.Timestamp('2020-01-06'): 1.06,\n pd.Timestamp('2020-01-07'): 1.04,\n }\n\n data = {\n 'pnl': [\n 0.02,\n 0.02,\n 0.01,\n 0.002,\n 0.003,\n 0.01,\n 0.002,\n ],\n 'date': ['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07'],\n }\n\n yield_data = {\n pd.Timestamp('2020-01-01'): 0.01,\n pd.Timestamp('2020-01-02'): 0.02,\n pd.Timestamp('2020-01-03'): 0.025,\n pd.Timestamp('2020-01-04'): 0.03,\n pd.Timestamp('2020-01-05'): 0.028,\n pd.Timestamp('2020-01-06'): 0.031,\n pd.Timestamp('2020-01-07'): 0.032,\n }\n\n timeseries = {pd.Timestamp('2020-01-05 00:00:00'): 0.50900, pd.Timestamp('2020-01-06 00:00:00'): 0.69110}\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum_data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame(data)\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport()\n\n mock = replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock())\n mock.return_value = Bond(\n \"test\",\n 'test',\n )\n mock = replace('gs_quant.entities.entity.Entity.get_data_coordinate', Mock())\n mock.return_value = DataCoordinate(\"test\")\n replace(\n 'gs_quant.data.coordinate.DataCoordinate.get_series',\n Mock(side_effect=[pd.Series(benchmark_data), pd.Series(yield_data)]),\n )\n\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport()\n\n expected = pd.Series(timeseries)\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 2)):\n returned = mp.portfolio_capture_ratio(\"test\", \"test\", 4)\n assert np.allclose(expected.dropna(), returned.dropna(), atol=1e-5)\n replace.restore()\n\n\ndef test_custom_aum():\n data = {'aum': [101, 102, 103], 'date': ['2020-01-01', '2020-01-02', '2020-01-03']}\n idx = pd.date_range('2020-01-01', freq='D', periods=3)\n df = MarketDataResponseFrame(data=data, index=idx)\n df.dataset_ids = ('AUM',)\n replace = Replacer()\n\n # mock GsPortfolioApi.get_reports()\n mock = replace('gs_quant.api.gs.portfolios.GsPortfolioApi.get_custom_aum', Mock())\n mock.return_value = df\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mp.aum('MP1')\n assert actual.index.equals(idx)\n assert all(actual.values == data['aum'])\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/timeseries/test_measures_rates.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom typing import Union\n\nimport pandas as pd\nimport pytest\nfrom numpy.testing import assert_allclose\nfrom pandas._testing import assert_series_equal\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nimport gs_quant.timeseries.measures as tm\nimport gs_quant.timeseries.measures_rates as tm_rates\nfrom gs_quant.api.gs.assets import GsTemporalXRef, GsAssetApi\nfrom gs_quant.api.gs.data import MarketDataResponseFrame, QueryType\nfrom gs_quant.common import PricingLocation, XRef\nfrom gs_quant.data import Fields, Dataset\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.errors import MqValueError, MqError\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.securities import Cross, Currency\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.test.timeseries.test_measures import (\n _test_datasets,\n map_identifiers_default_mocker,\n mock_empty_market_data_response,\n)\nfrom gs_quant.test.timeseries.utils import mock_request\nfrom gs_quant.timeseries import (\n TdapiRatesDefaultsProvider,\n SWAPTION_DEFAULTS,\n CurrencyEnum,\n SecurityMaster,\n ExtendedSeries,\n)\nfrom gs_quant.timeseries.measures_rates import (\n _swaption_build_asset_query,\n _currency_to_tdapi_swaption_rate_asset,\n _check_strike_reference,\n _pricing_location_normalized,\n _default_pricing_location,\n)\n\n_index = [pd.Timestamp('2019-01-01')]\n\n\ndef test_parse_meeting_date():\n with pytest.raises(MqValueError):\n tm_rates.parse_meeting_date(5)\n assert tm_rates.parse_meeting_date('2019-09-01') == dt.date(2019, 9, 1)\n assert tm_rates.parse_meeting_date(dt.date(2019, 9, 1)) == dt.date(2019, 9, 1)\n replace = Replacer()\n # mock\n cbd = replace('gs_quant.timeseries.measures._get_custom_bd', Mock())\n cbd.return_value = pd.tseries.offsets.BusinessDay()\n today = replace('gs_quant.timeseries.measures.pd.Timestamp.today', Mock())\n today.return_value = pd.Timestamp(2019, 5, 25)\n # cases\n assert tm_rates.parse_meeting_date() == dt.date(2019, 5, 24)\n assert tm_rates.parse_meeting_date('3m') == dt.date(2019, 2, 24)\n assert tm_rates.parse_meeting_date('3b') == dt.date(2019, 5, 22)\n # restore\n replace.restore()\n\n\ndef test_get_swaption_parameter_floating_rate_option_returns_default():\n provider = TdapiRatesDefaultsProvider(SWAPTION_DEFAULTS)\n value = provider.get_swaption_parameter(CurrencyEnum.GBP, \"floatingRateTenor\")\n assert value == \"6m\"\n\n\ndef test_get_swaption_parameter_floating_rate_option_returns_given_value():\n provider = TdapiRatesDefaultsProvider(SWAPTION_DEFAULTS)\n value = provider.get_swaption_parameter(CurrencyEnum.GBP, \"floatingRateTenor\", \"66m\")\n assert value == \"66m\"\n\n\ndef test_get_swaption_parameter_strike_reference_option_returns_default():\n provider = TdapiRatesDefaultsProvider(SWAPTION_DEFAULTS)\n value = provider.get_swaption_parameter(CurrencyEnum.GBP, \"strikeReference\")\n assert value == \"ATM\"\n\n\ndef test_get_swaption_parameter_strike_reference_option_returns_given_value():\n provider = TdapiRatesDefaultsProvider(SWAPTION_DEFAULTS)\n value = provider.get_swaption_parameter(CurrencyEnum.GBP, \"strikeReference\", \"ATM+666\")\n assert value == \"ATM+666\"\n\n\ndef test_get_floating_rate_option_for_benchmark_retuns_rate():\n provider = TdapiRatesDefaultsProvider(SWAPTION_DEFAULTS)\n value = provider.get_floating_rate_option_for_benchmark(CurrencyEnum.GBP, \"LIBOR\")\n assert value == \"GBP-LIBOR-BBA\"\n\n\ndef test_get_floating_rate_option_for_benchmark_retuns_rate_usd():\n provider = TdapiRatesDefaultsProvider(SWAPTION_DEFAULTS)\n value = provider.get_floating_rate_option_for_benchmark(CurrencyEnum.USD, \"LIBOR\")\n assert value == \"USD-LIBOR-BBA\"\n\n\ndef test_check_strike_reference_string():\n data_input = \"ATM\"\n output = _check_strike_reference(data_input)\n assert data_input == output\n\n\ndef test_check_strike_reference_zero():\n data_input = 0\n output = _check_strike_reference(data_input)\n assert output == \"ATM\"\n\n\ndef test_check_strike_reference_spot():\n data_input = \"spot\"\n output = _check_strike_reference(data_input)\n assert output == \"ATM\"\n\n\ndef test_check_strike_reference_string_positive():\n data_input = \"ATM+25\"\n output = _check_strike_reference(data_input)\n assert output == \"ATM+25\"\n\n\ndef test_check_strike_reference_string_negtive():\n data_input = \"ATM-25\"\n output = _check_strike_reference(data_input)\n assert output == \"ATM-25\"\n\n\ndef test_check_strike_reference_string_fractional():\n data_input = \"ATM-25.5\"\n output = _check_strike_reference(data_input)\n assert output == \"ATM-25.5\"\n\n\ndef test_check_strike_reference_numeric_fractional():\n data_input = 12.5\n output = _check_strike_reference(data_input)\n assert output == \"ATM+12.5\"\n\n\ndef test_check_strike_reference_numeric():\n data_input = -20\n output = _check_strike_reference(data_input)\n assert output == \"ATM-20\"\n\n\ndef test_check_strike_reference_throws():\n data_input = \"DUXA\"\n with pytest.raises(MqValueError):\n _check_strike_reference(data_input)\n\n\ndef test_check_strike_reference_list():\n data_input = [\"ATM+20\", \"ATM-20\"]\n output = _check_strike_reference(data_input)\n assert output == data_input\n\n\ndef test_check_strike_reference_invalid_list():\n data_input = [\"ATM+20\", \"MTM-20\"]\n with pytest.raises(MqValueError):\n _check_strike_reference(data_input)\n\n\ndef test_pricing_location_normalized():\n assert _pricing_location_normalized(PricingLocation.LDN, CurrencyEnum.USD) == PricingLocation.LDN\n assert _pricing_location_normalized(PricingLocation.TKO, CurrencyEnum.USD) == PricingLocation.TKO\n assert _pricing_location_normalized(PricingLocation.LDN, CurrencyEnum.HKD) == PricingLocation.LDN\n assert _pricing_location_normalized(PricingLocation.TKO, CurrencyEnum.HKD) == PricingLocation.HKG\n\n\ndef test_default_pricing_location():\n assert _default_pricing_location(CurrencyEnum.USD) == PricingLocation.NYC\n with pytest.raises(MqValueError):\n _default_pricing_location(CurrencyEnum.EGP)\n\n\ndef test_currency_to_tdapi_swaption_rate_asset_retuns_throws():\n replace = Replacer()\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"ZAR\"\n asset = Currency(\"MA1\", \"ZAR\")\n\n assert _currency_to_tdapi_swaption_rate_asset(asset) == \"MA1\"\n replace.restore()\n\n\ndef test_currency_to_tdapi_midcurve_asset():\n replace = Replacer()\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = 'ZAR'\n asset = Currency('MA1', 'ZAR')\n assert tm_rates._currency_to_tdapi_midcurve_asset(asset) == 'MA1'\n replace.restore()\n\n\ndef test_currency_to_tdapi_swaption_rate_asset_retuns_asset_id(mocker):\n replace = Replacer()\n mocker.patch.object(\n GsSession.__class__, 'current', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=mock_request)\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n\n with PricingContext(dt.date.today()):\n cur = [\n {\"currency_assetId\": \"MAK1FHKH5P5GJSHH\", \"currency\": \"JPY\", \"swaption_id\": \"MATT7CA7PRA4B8YB\"},\n {\"currency_assetId\": \"MA66CZBQJST05XKG\", \"currency\": \"GBP\", \"swaption_id\": \"MAX2SBXZRPYR3NTY\"},\n {\"currency_assetId\": \"MAPSDDS072PHYMVQ\", \"currency\": \"AUD\", \"swaption_id\": \"MAQHSC1PAF4X5H4B\"},\n {\"currency_assetId\": \"MAJNQPFGN1EBDHAE\", \"currency\": \"EUR\", \"swaption_id\": \"MAZB3PAH8JFVVT80\"},\n {\"currency_assetId\": \"MAZ7RWC904JYHYPS\", \"currency\": \"USD\", \"swaption_id\": \"MAY0X3KRD4AN77E2\"},\n ]\n for c in cur:\n print(c)\n asset = Currency(c.get(\"currency_assetId\"), c.get(\"currency\"))\n bbid_mock.return_value = c.get(\"currency\")\n mqid = _currency_to_tdapi_swaption_rate_asset(asset)\n assert mqid == c.get(\"swaption_id\")\n\n bbid_mock.return_value = None\n assert _currency_to_tdapi_swaption_rate_asset(asset) == c.get(\"currency_assetId\")\n replace.restore()\n\n\ndef test_swaption_build_asset_query_throws_on_invalid_tenor():\n with pytest.raises(MqValueError):\n _swaption_build_asset_query(CurrencyEnum.USD, expiration_tenor=\"Abc\")\n\n\ndef test_swaption_build_asset_query_usd():\n defautls = _swaption_build_asset_query(CurrencyEnum.USD)\n assert defautls[\"asset_parameters_floating_rate_option\"] == \"USD-LIBOR-BBA\"\n assert defautls[\"asset_parameters_clearing_house\"] == \"LCH\"\n assert defautls[\"asset_parameters_termination_date\"] == \"5y\"\n assert defautls[\"asset_parameters_expiration_date\"] == \"1y\"\n assert defautls[\"asset_parameters_effective_date\"] == \"0b\"\n assert defautls[\"asset_parameters_strike\"] == \"ATM\"\n\n\ndef test_swaption_build_asset_query_strike_reference():\n defautls = _swaption_build_asset_query(CurrencyEnum.USD, None, None, None, None, \"ATM+50\")\n assert defautls[\"asset_parameters_floating_rate_option\"] == \"USD-LIBOR-BBA\"\n assert defautls[\"asset_parameters_clearing_house\"] == \"LCH\"\n assert defautls[\"asset_parameters_floating_rate_designated_maturity\"] == \"3m\"\n assert defautls[\"asset_parameters_termination_date\"] == \"5y\"\n assert defautls[\"asset_parameters_expiration_date\"] == \"1y\"\n assert defautls[\"asset_parameters_effective_date\"] == \"0b\"\n assert defautls[\"asset_parameters_strike\"] == \"ATM+50\"\n\n\ndef test_swaption_build_asset_query_clearing_house():\n defautls = _swaption_build_asset_query(CurrencyEnum.USD, None, None, None, \"12m\", \"ATM+50\", None, \"ABC\")\n assert defautls[\"asset_parameters_floating_rate_option\"] == \"USD-LIBOR-BBA\"\n assert defautls[\"asset_parameters_floating_rate_designated_maturity\"] == \"12m\"\n assert defautls[\"asset_parameters_clearing_house\"] == \"ABC\"\n assert defautls[\"asset_parameters_termination_date\"] == \"5y\"\n assert defautls[\"asset_parameters_expiration_date\"] == \"1y\"\n assert defautls[\"asset_parameters_effective_date\"] == \"0b\"\n assert defautls[\"asset_parameters_strike\"] == \"ATM+50\"\n\n\ndef test_swaption_build_asset_query_custom():\n defautls = _swaption_build_asset_query(CurrencyEnum.USD, \"LIBOR\", \"12y\", \"66y\", \"12m\", \"ATM+50\", \"120y\", \"ABC\")\n assert defautls[\"asset_parameters_floating_rate_option\"] == \"USD-LIBOR-BBA\"\n assert defautls[\"asset_parameters_floating_rate_designated_maturity\"] == \"12m\"\n assert defautls[\"asset_parameters_clearing_house\"] == \"ABC\"\n assert defautls[\"asset_parameters_termination_date\"] == \"120y\"\n assert defautls[\"asset_parameters_expiration_date\"] == \"66y\"\n assert defautls[\"asset_parameters_effective_date\"] == \"12y\"\n assert defautls[\"asset_parameters_strike\"] == \"ATM+50\"\n\n\ndef test_swaption_build_asset_query_custom_throws():\n with pytest.raises(MqValueError):\n _swaption_build_asset_query(CurrencyEnum.USD, \"NIBOR\", \"12y\", \"66y\", \"12m\", \"ATM+50\", \"120y\", \"ABC\")\n\n\ndef test_swaption_swaption_vol_term2_returns_data():\n replace = Replacer()\n df = MarketDataResponseFrame(\n data=dict(expirationTenor=['1m', '6m', '1y'], terminationTenor=['1y', '2y', '3y'], swaptionVol=[1, 2, 3]),\n index=_index * 3,\n )\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2020, 1, 2),\n dt.date(2020, 1, 2),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.swaption_vol_term(Currency(\"GBP\", name=\"GBP\"), tm.SwaptionTenorType.SWAP_MATURITY, '5y', 0)\n expected = pd.Series([1, 2, 3], index=pd.to_datetime(['2019-02-01', '2019-07-01', '2020-01-01']))\n assert_series_equal(expected, pd.Series(actual), check_names=False)\n\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.swaption_vol_term(Currency(\"GBP\", name=\"GBP\"), tm.SwaptionTenorType.OPTION_EXPIRY, '5y', 0)\n expected = pd.Series([1, 2, 3], index=pd.to_datetime(['2020-01-01', '2021-01-01', '2021-12-31']))\n assert_series_equal(expected, pd.Series(actual), check_names=False)\n replace.restore()\n\n\ndef test_swaption_swaption_vol_term2_returns_empty():\n replace = Replacer()\n df = ExtendedSeries(dtype=float)\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2020, 1, 2),\n dt.date(2020, 1, 2),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.swaption_vol_term(Currency(\"GBP\", name=\"GBP\"), tm.SwaptionTenorType.SWAP_MATURITY, '5y', 0)\n\n assert_series_equal(ExtendedSeries(dtype=float), actual, check_names=False)\n replace.restore()\n\n\ndef test_swaption_swaption_vol_term2_throws():\n with pytest.raises(NotImplementedError):\n tm_rates.swaption_vol_term(\n Currency(\"GBP\", name=\"GBP\"), tm.SwaptionTenorType.SWAP_MATURITY, '5y', 0, real_time=True\n )\n\n\ndef test_swaption_vol_smile2_returns_data():\n replace = Replacer()\n test_data = dict(strikeRelative=[\"ATM\", \"ATM+50\", \"ATM+100\"], swaptionVol=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=_index * 3)\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2020, 1, 2),\n dt.date(2020, 1, 2),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.swaption_vol_smile(Currency(\"GBP\", name=\"GBP\"), '3m', '10y')\n assert_series_equal(pd.Series([1, 2, 3], index=[0.0, 50.0, 100.0]), pd.Series(actual))\n replace.restore()\n\n\ndef test_swaption_vol_smile2_returns_no_data():\n replace = Replacer()\n df = ExtendedSeries(dtype=float)\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2020, 1, 2),\n dt.date(2020, 1, 2),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n with DataContext('2019-01-01', '2025-01-01'):\n actual = tm_rates.swaption_vol_smile(Currency(\"GBP\", name=\"GBP\"), '3m', '10y')\n assert_series_equal(ExtendedSeries(dtype=float), actual)\n replace.restore()\n\n\ndef test_swaption_vol_smile2_returns_throws():\n with pytest.raises(NotImplementedError):\n tm_rates.swaption_vol_smile(Currency(\"GBP\", name=\"GBP\"), \"1m\", \"1m\", real_time=True)\n\n\ndef test_swaption_vol2_return_data():\n replace = Replacer()\n test_data = dict(swaptionVol=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)])\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.swaption_vol(Currency(\"GBP\", name=\"GBP\"))\n assert_series_equal(tm._extract_series_from_df(df, QueryType.SWAPTION_VOL), actual)\n replace.restore()\n\n\ndef test_swaption_vol2_return__empty_data():\n replace = Replacer()\n df = ExtendedSeries(dtype=float)\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.swaption_vol(Currency(\"GBP\", name=\"GBP\"))\n assert_series_equal(ExtendedSeries(dtype=float), actual)\n replace.restore()\n\n\ndef test_swaption_annuity_return_data():\n replace = Replacer()\n test_data = dict(swaptionAnnuity=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)])\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.swaption_annuity(Currency(\"GBP\", name=\"GBP\"))\n assert_series_equal(tm._extract_series_from_df(df, QueryType.SWAPTION_ANNUITY), actual)\n replace.restore()\n\n\ndef test_swaption_premium_return_data():\n replace = Replacer()\n test_data = dict(swaptionPremium=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)])\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace(\n 'gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()\n ).return_value = \"MADWG3WHCKNE1DJA\"\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.swaption_premium(Currency(\"GBP\", name=\"GBP\"))\n assert_series_equal(tm._extract_series_from_df(df, QueryType.SWAPTION_PREMIUM), actual)\n replace.restore()\n\n\ndef test_swaption_premium_throws_for_realtime():\n with pytest.raises(NotImplementedError):\n tm_rates.swaption_premium(Currency(\"GBP\", name=\"GBP\"), real_time=True)\n\n\ndef test__check_forward_tenor_returns_None():\n assert tm_rates._check_forward_tenor(None) is None\n\n\ndef test__check_forward_tenor_returns_0b():\n assert tm_rates._check_forward_tenor(\"spot\") == '0b'\n\n\ndef test_swaption_premium_throws_for_unsupported_ccy():\n replace = Replacer()\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"ZAR\"\n with pytest.raises(NotImplementedError):\n tm_rates.swaption_premium(Currency(\"KRW\", name=\"KRW\"))\n replace.restore()\n\n\ndef test_swaption_atmFwdRate_return_data():\n replace = Replacer()\n test_data = dict(atmFwdRate=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)])\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.swaption_atm_fwd_rate(Currency(\"GBP\", name=\"GBP\"))\n assert_series_equal(tm._extract_series_from_df(df, QueryType.ATM_FWD_RATE), actual)\n replace.restore()\n\n\ndef test_midcurve_atmFwdRate_return_data():\n replace = Replacer()\n test_data = dict(midcurveAtmFwdRate=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)])\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.midcurve_atm_fwd_rate(Currency(\"GBP\", name=\"GBP\"), \"1y\", \"1y\", \"1y\")\n assert_series_equal(tm._extract_series_from_df(df, QueryType.MIDCURVE_ATM_FWD_RATE), actual)\n replace.restore()\n\n\ndef test_midcurve_annuity_return_data():\n replace = Replacer()\n test_data = dict(midcurveAnnuity=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)])\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.midcurve_annuity(Currency(\"GBP\", name=\"GBP\"), \"1y\", \"1y\", \"1y\", 0)\n assert_series_equal(tm._extract_series_from_df(df, QueryType.MIDCURVE_ANNUITY), actual)\n replace.restore()\n\n\ndef test_midcurve_premium_return_data():\n replace = Replacer()\n test_data = dict(midcurvePremium=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)])\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.midcurve_premium(Currency(\"GBP\", name=\"GBP\"), \"1y\", \"1y\", \"1y\", 0)\n assert_series_equal(tm._extract_series_from_df(df, QueryType.MIDCURVE_PREMIUM), actual)\n replace.restore()\n\n\ndef test_midcurve_vol_return_data():\n replace = Replacer()\n test_data = dict(midcurveVol=[1, 2, 3])\n df = MarketDataResponseFrame(data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)])\n\n replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock()).return_value = \"GBP\"\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock(), Mock()).return_value = [\n \"MADWG3WHCKNE1DJA\",\n \"MAH6JK3TZJJGFQ65\",\n ]\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n actual = tm_rates.midcurve_vol(Currency(\"GBP\", name=\"GBP\"), \"1y\", \"1y\", \"1y\", 0)\n assert_series_equal(tm._extract_series_from_df(df, QueryType.MIDCURVE_VOL), actual)\n replace.restore()\n\n\ndef test_cross_to_fxfwd_xcswp_asset(mocker):\n replace = Replacer()\n mocker.patch.object(\n GsSession.__class__, 'current', return_value=GsSession.get(Environment.DEV, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=mock_request)\n bbid_mock = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n correct_mapping = {\n 'EURUSD': {\"id\": 'MA1VJC1E3SZW8E4S', \"crossID\": \"MAA9MVX15AJNQCVG\"},\n 'GBPUSD': {\"id\": 'MA3JTR4HSC63H4V6', \"crossID\": \"MA58R87SPRMKKE7Z\"},\n 'AUDUSD': {\"id\": 'MAD4VBRWYXFSY1N4', \"crossID\": \"MAJTN2XJVF97SYJK\"},\n 'NZDUSD': {\"id\": 'MA1YHQMZVTM3VBWT', \"crossID\": \"MANQ8REB1VJ4JFTR\"},\n 'USDSEK': {\"id\": 'MA2APZREBGDMME83', \"crossID\": \"MA4ZD5CZGC3Y6JZD\"},\n 'USDNOK': {\"id\": 'MA0K3W6FKH6K1KJE', \"crossID\": \"MAZ0P16RPG5P0MVF\"},\n 'USDDKK': {\"id\": 'MA328HZB86DYSWSJ', \"crossID\": \"MABCHYGJ1TCBCQE4\"},\n 'USDCAD': {\"id\": 'MAT8JNEE2GN5NES6', \"crossID\": \"MAP8G81B2KHTYR07\"},\n 'USDCHF': {\"id\": 'MABNGGTNB9A0TKCG', \"crossID\": \"MAMPQHNP1A26RS1C\"},\n 'USDJPY': {\"id\": 'MAMZ9YG8AF3HQ18C', \"crossID\": \"MAYJPCVVF2RWXCES\"},\n }\n\n with PricingContext(dt.date.today()):\n for cross in correct_mapping:\n asset = Cross(correct_mapping[cross][\"crossID\"], cross)\n bbid_mock.return_value = cross\n correct_id = tm_rates._cross_to_fxfwd_xcswp_asset(asset)\n assert correct_mapping[cross][\"id\"] == correct_id\n replace.restore()\n\n\ndef test_ois_fxfwd_xcswap_measures(mocker):\n replace = Replacer()\n dict_fns = {\n \"oisXccy\": {\"fn\": tm_rates.ois_xccy, \"queryType\": QueryType.OIS_XCCY},\n \"oisXccyExSpike\": {\"fn\": tm_rates.ois_xccy_ex_spike, \"queryType\": QueryType.OIS_XCCY_EX_SPIKE},\n \"usdOis\": {\"fn\": tm_rates.usd_ois, \"queryType\": QueryType.USD_OIS},\n \"nonUsdOis\": {\"fn\": tm_rates.non_usd_ois, \"queryType\": QueryType.NON_USD_OIS},\n }\n args = dict(tenor='3y', real_time=False)\n for key in dict_fns:\n fn = dict_fns[key][\"fn\"]\n mock_jpy = Cross('MAYJPCVVF2RWXCES', 'USD/JPY')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'JPYUSD'\n args['asset'] = mock_jpy\n with pytest.raises(NotImplementedError):\n fn(**args)\n\n mock_eur = Cross('MAA0NE9QX2ABETG6', 'EUR/USD')\n args['asset'] = mock_eur\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EURUSD'\n with pytest.raises(NotImplementedError):\n fn(..., real_time=True)\n\n args['tenor'] = '5yr'\n with pytest.raises(MqValueError):\n fn(**args)\n args['tenor'] = '3y'\n\n test_data = {key: [1, 2, 3]}\n df = MarketDataResponseFrame(\n data=test_data, index=[dt.date(2019, 1, 1), dt.date(2019, 1, 2), dt.date(2019, 1, 3)]\n )\n identifiers = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n identifiers.return_value = {'MAA9MVX15AJNQCVG'} # Test on EURUSD only\n replace('gs_quant.timeseries.measures_rates._range_from_pricing_date', Mock(), Mock()).return_value = [\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 5),\n ]\n replace('gs_quant.timeseries.measures_rates._market_data_timed', Mock()).return_value = df\n\n expected = tm_rates._extract_series_from_df(df, dict_fns[key][\"queryType\"])\n actual = fn(**args)\n assert_series_equal(expected, actual)\n\n replace.restore()\n\n\ndef get_data_policy_rate_expectation_mocker(\n start: Union[dt.date, dt.datetime] = None,\n end: Union[dt.date, dt.datetime] = None,\n as_of: dt.datetime = None,\n since: dt.datetime = None,\n fields: Union[str, Fields] = None,\n asset_id_type: str = None,\n **kwargs,\n) -> pd.DataFrame:\n if 'meetingNumber' in kwargs:\n if kwargs['meetingNumber'] == 0:\n return mock_meeting_spot()\n elif 'meetingDate' in kwargs:\n if kwargs['meetingDate'] == dt.date(2019, 10, 24):\n return mock_meeting_spot()\n return mock_meeting_expectation()\n\n\ndef mock_meeting_expectation():\n data_dict = MarketDataResponseFrame(\n {\n 'date': [dt.date(2019, 12, 6)],\n 'assetId': ['MARFAGXDQRWM07Y2'],\n 'location': ['NYC'],\n 'rateType': ['Meeting Forward'],\n 'startingDate': [dt.date(2020, 1, 29)],\n 'endingDate': [dt.date(2020, 1, 29)],\n 'meetingNumber': [2],\n 'valuationDate': [dt.date(2019, 12, 6)],\n 'meetingDate': [dt.date(2020, 1, 23)],\n 'value': [-0.004550907771],\n }\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef mock_meeting_spot():\n data_dict = MarketDataResponseFrame(\n {\n 'date': [dt.date(2019, 12, 6)],\n 'assetId': ['MARFAGXDQRWM07Y2'],\n 'location': ['NYC'],\n 'rateType': ['Meeting Forward'],\n 'startingDate': [dt.date(2019, 10, 30)],\n 'endingDate': [dt.date(2019, 12, 18)],\n 'meetingNumber': [0],\n 'valuationDate': [dt.date(2019, 12, 6)],\n 'meetingDate': [dt.date(2019, 10, 24)],\n 'value': [-0.004522570525],\n }\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef mock_meeting_absolute():\n data_dict = MarketDataResponseFrame(\n {\n 'date': [dt.date(2019, 12, 6), dt.date(2019, 12, 6)],\n 'assetId': ['MARFAGXDQRWM07Y2', 'MARFAGXDQRWM07Y2'],\n 'location': ['NYC', 'NYC'],\n 'rateType': ['Meeting Forward', 'Meeting Forward'],\n 'startingDate': [dt.date(2019, 10, 30), dt.date(2020, 1, 29)],\n 'endingDate': [dt.date(2019, 10, 30), dt.date(2020, 1, 29)],\n 'meetingNumber': [0, 2],\n 'valuationDate': [dt.date(2019, 12, 6), dt.date(2019, 12, 6)],\n 'meetingDate': [dt.date(2019, 10, 24), dt.date(2020, 1, 23)],\n 'value': [-0.004522570525, -0.004550907771],\n }\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef mock_ois_spot():\n data_dict = MarketDataResponseFrame(\n {\n 'date': [dt.date(2019, 12, 6)],\n 'assetId': ['MARFAGXDQRWM07Y2'],\n 'location': ['NYC'],\n 'rateType': ['Spot'],\n 'startingDate': [dt.date(2019, 12, 6)],\n 'endingDate': [dt.date(2019, 12, 7)],\n 'meetingNumber': [-1],\n 'valuationDate': [dt.date(2019, 12, 6)],\n 'meetingDate': [dt.date(2019, 12, 6)],\n 'value': [-0.00455],\n }\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef test_get_default_ois_benchmark(mocker):\n assert tm_rates._get_default_ois_benchmark(CurrencyEnum.USD) == tm_rates.BenchmarkTypeCB.Fed_Funds\n assert tm_rates._get_default_ois_benchmark(CurrencyEnum.EUR) == tm_rates.BenchmarkTypeCB.EUROSTR\n assert tm_rates._get_default_ois_benchmark(CurrencyEnum.GBP) == tm_rates.BenchmarkTypeCB.SONIA\n\n\ndef test_policy_rate_term_structure(mocker):\n target = {\n 'meeting_absolute': -0.004550907771,\n 'meeting_relative': -0.00002833724599999969,\n 'eoy_absolute': -0.003359767756,\n 'eoy_relative': 0.001162802769,\n 'spot': -0.00455,\n }\n mock_eur = Currency('MARFAGXDQRWM07Y2', 'EUR')\n\n with DataContext(dt.date(2019, 12, 6), dt.date(2019, 12, 6)):\n replace = Replacer()\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='EUR',\n ),\n )\n ]\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=map_identifiers_default_mocker)\n\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = mock_meeting_absolute()\n\n actual_abs = tm_rates.policy_rate_term_structure(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, dt.date(2019, 12, 6)\n )\n assert target['meeting_absolute'] == actual_abs.loc[dt.date(2020, 1, 23)]\n assert actual_abs.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n actual_rel = tm_rates.policy_rate_term_structure(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE, dt.date(2019, 12, 6)\n )\n assert target['meeting_relative'] == actual_rel.loc[dt.date(2020, 1, 23)]\n assert actual_rel.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n mock_get_data.return_value = mock_ois_spot()\n actual_spot = tm_rates.policy_rate_term_structure(\n mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.ABSOLUTE, dt.date(2019, 12, 6)\n )\n assert target['spot'] == actual_spot.loc[dt.date(2019, 12, 6)]\n assert actual_spot.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure(mock_eur, 'meeting')\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure(mock_eur, tm_rates.EventType.MEETING, 'normalized', '2019-09-01')\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, 5)\n\n with pytest.raises(ValueError):\n tm_rates.policy_rate_term_structure(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, '01-09-2019'\n )\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.RELATIVE)\n\n with pytest.raises(NotImplementedError):\n tm_rates.policy_rate_term_structure(\n mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.ABSOLUTE, real_time=True\n )\n\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = pd.DataFrame()\n\n assert_series_equal(\n tm_rates.policy_rate_term_structure(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE),\n pd.Series(dtype=float, name='value'),\n )\n\n mock_get_data = replace('gs_quant.timeseries.measures_rates.policy_rate_term_structure_rt', Mock())\n mock_get_data.return_value = pd.DataFrame()\n assert tm_rates.policy_rate_term_structure(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, \"Intraday\"\n ).empty\n\n df_without_meeting_date = pd.DataFrame({'value': [1.5, 2.0], 'rateType': ['Meeting', 'Meeting']})\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = df_without_meeting_date\n\n result = tm_rates.policy_rate_term_structure(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, dt.date(2019, 12, 6)\n )\n assert_series_equal(result, pd.Series(dtype=float, name='value'))\n\n replace.restore()\n\n\ndef test_policy_rate_expectation(mocker):\n target = {\n 'meeting_number_absolute': -0.004550907771,\n 'meeting_number_relative': -0.000028337246,\n 'meeting_date_relative': -0.000028337246,\n 'meeting_number_spot': -0.004522570525,\n }\n mock_eur = Currency('MARFAGXDQRWM07Y2', 'EUR')\n\n with DataContext(dt.date(2019, 12, 6), dt.date(2019, 12, 6)):\n replace = Replacer()\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n bbid='EUR',\n ),\n )\n ]\n mocker.patch.object(GsAssetApi, 'map_identifiers', side_effect=map_identifiers_default_mocker)\n\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = mock_meeting_spot()\n\n actual_num = tm_rates.policy_rate_expectation(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.ABSOLUTE, 0)\n assert target['meeting_number_spot'] == actual_num.loc[dt.date(2019, 12, 6)]\n assert actual_num.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n mocker.patch.object(Dataset, 'get_data', side_effect=get_data_policy_rate_expectation_mocker)\n actual_num = tm_rates.policy_rate_expectation(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, 2\n )\n assert target['meeting_number_absolute'] == actual_num.loc[dt.date(2019, 12, 6)]\n assert actual_num.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n actual_date = tm_rates.policy_rate_expectation(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, dt.date(2020, 1, 23)\n )\n assert target['meeting_number_absolute'] == actual_date.loc[dt.date(2019, 12, 6)]\n assert actual_date.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n actual_num = tm_rates.policy_rate_expectation(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE, 2\n )\n assert_allclose(\n [target['meeting_number_relative']], [actual_num.loc[dt.date(2019, 12, 6)]], rtol=1e-9, atol=1e-15\n )\n assert actual_num.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n actual_num = tm_rates.policy_rate_expectation(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, 0\n )\n assert target['meeting_number_spot'] == actual_num.loc[dt.date(2019, 12, 6)]\n assert actual_num.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n actual_date = tm_rates.policy_rate_expectation(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, '2019-10-24'\n )\n assert target['meeting_number_spot'] == actual_date.loc[dt.date(2019, 12, 6)]\n assert actual_date.dataset_ids == (Dataset.GS.CENTRAL_BANK_WATCH,)\n\n mocker.patch.object(\n Dataset, 'get_data', side_effect=[mock_meeting_expectation(), mock_empty_market_data_response()]\n )\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE, 2)\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, 5.5)\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation(mock_eur, 'meeting', tm_rates.RateType.ABSOLUTE, 5)\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation(mock_eur, tm_rates.EventType.MEETING, 'normalized', dt.date(2019, 9, 1))\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE, -2)\n\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = pd.DataFrame()\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, 2)\n\n mock_get_data = replace('gs_quant.timeseries.measures_rates.policy_rate_expectation_rt', Mock())\n mock_get_data.return_value = pd.DataFrame()\n assert tm_rates.policy_rate_expectation(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, 2, real_time=True\n ).empty\n\n replace.restore()\n\n\ndef mock_policy_rt_spot():\n data_dict = MarketDataResponseFrame(\n {\n 'assetId': ['MAV2B3A3D0R369QE'],\n 'pricingLocation': ['LDN'],\n 'csaTerms': ['EUR-EuroSTR'],\n 'rate': [-0.0049],\n 'effectiveDate': [dt.date(2022, 3, 23)],\n 'terminationDate': [dt.date(2022, 4, 20)],\n 'annuity': [-778.1652098],\n 'updateTime': [dt.datetime(2022, 4, 14, 0, 0, 0)],\n 'currency': ['EUR'],\n },\n index=pd.Index([dt.datetime(2022, 4, 14, 0, 0, 0)], name='time'),\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef mock_policy_rate_expectation_rt_meeting():\n data_dict = MarketDataResponseFrame(\n {\n 'assetId': ['MA9PXTY5DC1JHV5Y'],\n 'pricingLocation': ['LDN'],\n 'csaTerms': ['EUR-EuroSTR'],\n 'rate': [-0.005337848],\n 'effectiveDate': [dt.date(2022, 7, 27)],\n 'terminationDate': [dt.date(2022, 9, 14)],\n 'annuity': [-1169.016923],\n 'updateTime': [dt.datetime(2022, 4, 14, 0, 0, 0)],\n 'currency': ['EUR'],\n },\n index=pd.Index([dt.datetime(2022, 4, 14, 0, 0, 0)], name='time'),\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef mock_policy_term_rt_meeting():\n data_dict = MarketDataResponseFrame(\n {\n 'assetId': ['MANRCY4GA0WWT1QC', 'MA9PXTY5DC1JHV5Y'],\n 'pricingLocation': ['LDN', 'LDN'],\n 'csaTerms': ['EUR-EuroSTR', 'EUR-EuroSTR'],\n 'rate': [-0.004585702, -0.005337848],\n 'effectiveDate': [dt.date(2022, 6, 15), dt.date(2022, 7, 27)],\n 'terminationDate': [dt.date(2022, 7, 27), dt.date(2022, 9, 14)],\n 'annuity': [-1364.124069, -1169.016923],\n 'updateTime': [dt.datetime(2022, 4, 14, 0, 0, 0), dt.datetime(2022, 4, 14, 0, 0, 0)],\n 'currency': ['EUR', 'EUR'],\n },\n index=pd.Index([dt.datetime(2022, 4, 14, 0, 0, 0), dt.datetime(2022, 4, 14, 0, 0, 0)], name='time'),\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef mock_policy_term_rt_meeting_series():\n data_dict = MarketDataResponseFrame(\n {\n 'assetId': ['MA9PXTY5DC1JHV5Y'],\n 'pricingLocation': ['LDN'],\n 'csaTerms': ['EUR-EuroSTR'],\n 'rate': [-0.005337848],\n 'effectiveDate': [dt.date(2022, 7, 27)],\n 'terminationDate': [dt.date(2022, 9, 14)],\n 'annuity': [-1169.016923],\n 'updateTime': [dt.datetime(2022, 4, 14, 0, 0, 0)],\n 'currency': ['EUR'],\n },\n index=pd.Index([dt.datetime(2022, 4, 14, 0, 0, 0), dt.datetime(2022, 4, 14, 0, 0, 0)], name='time'),\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef mock_policy_rate_empty_join_df():\n data_dict = MarketDataResponseFrame(\n {\n 'assetId': ['MA000', 'MA000'],\n 'pricingLocation': ['USD', 'USD'],\n 'csaTerms': ['USD-SOFR-1', 'USD-SOFR-1'],\n 'rate': [-0.004585702, -0.005337848],\n 'effectiveDate': [dt.date(2022, 6, 15), dt.date(2022, 7, 27)],\n 'terminationDate': [dt.date(2022, 7, 27), dt.date(2022, 9, 14)],\n 'annuity': [-1364.124069, -1169.016923],\n 'updateTime': [dt.datetime(2022, 4, 14, 0, 0, 0), dt.datetime(2022, 4, 14, 0, 0, 0)],\n 'currency': ['EUR', 'EUR'],\n },\n index=pd.Index([dt.datetime(2022, 4, 14, 0, 0, 0), dt.datetime(2022, 4, 14, 0, 0, 0)], name='time'),\n )\n data_dict.dataset_ids = _test_datasets\n return data_dict\n\n\ndef get_data_policy_rate_term_rt_series_mocker(**kwargs) -> pd.DataFrame:\n if len(kwargs['assetId']) == 1:\n return mock_policy_rt_spot()\n else:\n return mock_policy_term_rt_meeting_series()\n\n\ndef get_data_policy_rate_term_rt_mocker(**kwargs) -> pd.DataFrame:\n if len(kwargs['assetId']) == 1:\n return mock_policy_rt_spot()\n else:\n return mock_policy_term_rt_meeting()\n\n\ndef get_data_empty_spot_mocker(**kwargs) -> pd.DataFrame:\n if len(kwargs['assetId']) == 1:\n return pd.DataFrame()\n else:\n return mock_policy_term_rt_meeting()\n\n\ndef get_data_empty_join_mocker(**kwargs) -> pd.DataFrame:\n if len(kwargs['assetId']) == 1:\n return mock_policy_rate_empty_join_df()\n else:\n return mock_policy_term_rt_meeting()\n\n\ndef get_cb_swap_assets_mocker(allow_many=True, **kwargs) -> list:\n if isinstance(kwargs['asset_parameters_termination_date'], str) and isinstance(\n kwargs['asset_parameters_effective_date'], str\n ):\n return ['MAV2B3A3D0R369QE']\n else:\n return [\n 'MANRCY4GA0WWT1QC',\n 'MA9PXTY5DC1JHV5Y',\n 'MAACR86DGE1CMK9Y',\n 'MASSRBDHKSSDAS36',\n 'MAZAH4WR524BQ7ZN',\n 'MAY2RN50CWETF7HV',\n 'MAV2B3A3D0R369QE',\n ]\n\n\ndef test_policy_rate_term_structure_rt(mocker):\n target = {'meeting_absolute': -0.005337848, 'meeting_relative': -0.00043784800000000023, 'spot': -0.0049}\n mock_eur = Currency('MARFAGXDQRWM07Y2', 'EUR')\n mock_zar = Currency(\"MA1\", \"ZAR\")\n with DataContext(dt.date(2020, 2, 14), dt.date(2020, 3, 14)):\n replace = Replacer()\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'ZAR'\n cbd = replace('gs_quant.timeseries.measures._get_custom_bd', Mock())\n cbd.return_value = pd.tseries.offsets.BusinessDay()\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', get_cb_swap_assets_mocker)\n mocker.patch.object(Dataset, 'get_data', side_effect=get_data_policy_rate_term_rt_mocker)\n with pytest.raises(MqValueError):\n tm_rates.policy_rate_term_structure_rt(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE, None\n )\n replace.restore()\n\n with DataContext(dt.date(2021, 4, 14), dt.date(2024, 4, 14)):\n replace = Replacer()\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'ZAR'\n cbd = replace('gs_quant.timeseries.measures._get_custom_bd', Mock())\n cbd.return_value = pd.tseries.offsets.BusinessDay()\n replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', get_cb_swap_assets_mocker)\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure_rt(\n mock_zar,\n tm_rates.EventType.MEETING,\n tm_rates.RateType.ABSOLUTE,\n )\n\n xrefs.return_value = 'EUR'\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure_rt(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, tm_rates.BenchmarkTypeCB.SOFR\n )\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure_rt(mock_eur, tm_rates.EventType.EOY, tm_rates.RateType.ABSOLUTE)\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure_rt(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.RELATIVE)\n\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = mock_policy_term_rt_meeting()\n mock_get_data_coverage = replace('gs_quant.data.dataset.Dataset.get_coverage', Mock())\n mock_get_data_coverage.return_value = pd.DataFrame(columns=['exchange'])\n\n actual_abs = tm_rates.policy_rate_term_structure_rt(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, None\n )\n assert target['meeting_absolute'] == actual_abs.loc[pd.Timestamp(dt.date(2022, 7, 27))]\n\n mocker.patch.object(Dataset, 'get_data', side_effect=get_data_policy_rate_term_rt_mocker)\n actual_rel = tm_rates.policy_rate_term_structure_rt(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE, None\n )\n assert target['meeting_relative'] == actual_rel.loc[pd.Timestamp(dt.date(2022, 7, 27))]\n\n mock_get_data.return_value = mock_policy_rt_spot()\n actual_spot = tm_rates.policy_rate_term_structure_rt(\n mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.ABSOLUTE, None\n )\n assert target['spot'] == actual_spot.loc[dt.datetime(2022, 4, 14).isoformat()]\n\n mocker.patch.object(Dataset, 'get_data', side_effect=get_data_empty_join_mocker)\n assert tm_rates.policy_rate_term_structure_rt(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE\n ).empty\n\n mocker.patch.object(Dataset, 'get_data', side_effect=get_data_empty_spot_mocker)\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure_rt(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE)\n\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = pd.DataFrame()\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_term_structure_rt(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.ABSOLUTE)\n\n assert tm_rates.policy_rate_term_structure_rt(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE\n ).empty\n replace.restore()\n\n\ndef get_data_policy_rate_exp_rt_mocker(**kwargs) -> pd.DataFrame:\n if kwargs['assetId'] == 'MA000SPOT':\n return mock_policy_rt_spot()\n else:\n return mock_policy_rate_expectation_rt_meeting()\n\n\ndef policy_exp_empty_spot_mocker(**kwargs) -> pd.DataFrame:\n if kwargs['assetId'] == 'MA000SPOT':\n return pd.DataFrame()\n else:\n return mock_policy_rate_expectation_rt_meeting()\n\n\ndef test_get_swap_from_meeting_date(mocker):\n eur = CurrencyEnum.EUR\n benchmark = tm_rates.BenchmarkTypeCB.EUROSTR\n\n with pytest.raises(MqValueError):\n tm_rates._get_swap_from_meeting_date(eur, benchmark, 'ecb30')\n with pytest.raises(MqValueError):\n tm_rates._get_swap_from_meeting_date(eur, benchmark, 'frb1')\n\n replace = Replacer()\n cbd = replace('gs_quant.timeseries.measures_rates.get_cb_meeting_swap', Mock())\n cbd.return_value = 'MA123'\n\n assert 'MA123' == tm_rates._get_swap_from_meeting_date(eur, benchmark, 0)\n assert 'MA123' == tm_rates._get_swap_from_meeting_date(eur, benchmark, 1)\n assert 'MA123' == tm_rates._get_swap_from_meeting_date(eur, benchmark, 'ecb0')\n assert 'MA123' == tm_rates._get_swap_from_meeting_date(eur, benchmark, 'ecb1')\n replace.restore()\n\n\ndef test_policy_rate_expectation_rt(mocker):\n target = {'meeting_absolute': -0.005337848, 'meeting_relative': -0.00043784800000000023, 'spot': -0.0049}\n mock_eur = Currency('MARFAGXDQRWM07Y2', 'EUR')\n mock_zar = Currency(\"MA1\", \"ZAR\")\n with DataContext(dt.date(2020, 2, 14), dt.date(2020, 3, 14)):\n replace = Replacer()\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'ZAR'\n cbd = replace('gs_quant.timeseries.measures._get_custom_bd', Mock())\n cbd.return_value = pd.tseries.offsets.BusinessDay()\n mock_swap = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n mock_swap.return_value = 'MA000SPOT'\n\n mock_swap = replace('gs_quant.timeseries.measures_rates._get_swap_from_meeting_date', Mock())\n mock_swap.return_value = 'MAV2B3A3D0R369QE'\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.ABSOLUTE)\n replace.restore()\n\n with DataContext(dt.date(2021, 4, 14), dt.date(2024, 4, 14)):\n replace = Replacer()\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'ZAR'\n cbd = replace('gs_quant.timeseries.measures._get_custom_bd', Mock())\n cbd.return_value = pd.tseries.offsets.BusinessDay()\n mock_swap = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n mock_swap.return_value = 'MA000SPOT'\n\n mock_swap = replace('gs_quant.timeseries.measures_rates._get_swap_from_meeting_date', Mock())\n mock_swap.return_value = 'MAV2B3A3D0R369QE'\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(\n mock_zar,\n tm_rates.EventType.MEETING,\n tm_rates.RateType.ABSOLUTE,\n )\n\n xrefs.return_value = 'EUR'\n mocker.patch.object(Dataset, 'get_data', side_effect=policy_exp_empty_spot_mocker)\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE)\n\n mocker.patch.object(Dataset, 'get_data', side_effect=get_data_policy_rate_exp_rt_mocker)\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.EOY, tm_rates.RateType.ABSOLUTE)\n\n actual_abs = tm_rates.policy_rate_expectation_rt(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.ABSOLUTE, 2\n )\n assert target['meeting_absolute'] == actual_abs.loc[dt.datetime(2022, 4, 14)]\n\n actual_rel = tm_rates.policy_rate_expectation_rt(\n mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE, 2\n )\n assert target['meeting_relative'] == actual_rel.loc[dt.datetime(2022, 4, 14)]\n\n mock_swap.return_value = 'MA000SPOT'\n actual_spot = tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.ABSOLUTE)\n assert target['spot'] == actual_spot.loc[dt.datetime(2022, 4, 14).isoformat()]\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.RELATIVE)\n\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = pd.DataFrame()\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.ABSOLUTE)\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE)\n\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.SPOT, tm_rates.RateType.RELATIVE)\n\n mock_get_data = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_get_data.return_value = pd.Series(dtype=float)\n with pytest.raises(MqError):\n tm_rates.policy_rate_expectation_rt(mock_eur, tm_rates.EventType.MEETING, tm_rates.RateType.RELATIVE)\n\n replace.restore()\n\n\ndef test_get_cb_meeting_swap(mocker):\n replace = Replacer()\n mock_swap = replace('gs_quant.timeseries.measures_rates._get_tdapi_rates_assets', Mock())\n mock_swap.return_value = 'MA000SPOT'\n\n with pytest.raises(MqValueError):\n tm_rates.get_cb_meeting_swap(CurrencyEnum.USD, tm_rates.BenchmarkTypeCB.SOFR, '1y', '125y')\n\n assert tm_rates.get_cb_meeting_swap(CurrencyEnum.USD, tm_rates.BenchmarkTypeCB.SOFR, '0b', '1y') == 'MA000SPOT'\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_measures_rates.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_measures_reports.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport copy\nimport datetime as dt\nimport math\n\nimport pandas as pd\nimport pytest\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nimport gs_quant.timeseries.measures_reports as mr\nfrom gs_quant.api.gs.assets import GsTemporalXRef\nfrom gs_quant.api.gs.data import MarketDataResponseFrame\nfrom gs_quant.common import ReportParameters, XRef\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.report import PerformanceReport, ThematicReport, CustomAUMDataPoint\nfrom gs_quant.markets.securities import Stock\nfrom gs_quant.models.risk_model import FactorRiskModel as Factor_Risk_Model\nfrom gs_quant.target.portfolios import RiskAumSource\nfrom gs_quant.target.reports import Report, PositionSourceType, ReportType\nfrom gs_quant.target.risk_models import RiskModel, RiskModelCoverage, RiskModelTerm, RiskModelUniverseIdentifier\n\nrisk_model = RiskModel(\n coverage=RiskModelCoverage.Country,\n id_='model_id',\n name='Fake Risk Model',\n term=RiskModelTerm.Long,\n universe_identifier=RiskModelUniverseIdentifier.gsid,\n vendor='GS',\n version=1.0,\n)\n\nfactor_risk_report = Report(\n position_source_id='position source id',\n position_source_type=PositionSourceType.Portfolio,\n type_=ReportType.Portfolio_Factor_Risk,\n id_='report_id',\n parameters=ReportParameters(risk_model='risk_model_id'),\n status='new',\n)\n\nasset_factor_risk_report = Report(\n position_source_id='position source id',\n position_source_type=PositionSourceType.Asset,\n type_=ReportType.Portfolio_Factor_Risk,\n id_='report_id',\n parameters=ReportParameters(risk_model='risk_model_id'),\n status='new',\n)\n\nppa_report = PerformanceReport(\n position_source_id='position source id',\n position_source_type=PositionSourceType.Portfolio,\n type_=ReportType.Portfolio_Performance_Analytics,\n id_='report_id',\n parameters=ReportParameters(risk_model='risk_model_id'),\n status='new',\n)\n\nfactor_data = [\n {\n 'date': '2020-11-23',\n 'reportId': 'report_id',\n 'factor': 'Factor Name',\n 'factorCategory': 'CNT',\n 'pnl': 11.23,\n 'exposure': -11.23,\n 'proportionOfRisk': 1,\n },\n {\n 'date': '2020-11-24',\n 'reportId': 'report_id',\n 'factor': 'Factor Name',\n 'factorCategory': 'CNT',\n 'pnl': 11.24,\n 'exposure': -11.24,\n 'proportionOfRisk': 2,\n },\n {\n 'date': '2020-11-25',\n 'reportId': 'report_id',\n 'factor': 'Factor Name',\n 'factorCategory': 'CNT',\n 'pnl': 11.25,\n 'exposure': -11.25,\n 'proportionOfRisk': 3,\n },\n {\n 'date': '2020-11-23',\n 'reportId': 'report_id',\n 'factor': 'Total',\n 'factorCategory': 'CNT',\n 'pnl': 19.23,\n 'exposure': -11.23,\n 'proportionOfRisk': 1,\n },\n {\n 'date': '2020-11-24',\n 'reportId': 'report_id',\n 'factor': 'Total',\n 'factorCategory': 'CNT',\n 'pnl': 14.24,\n 'exposure': -11.24,\n 'proportionOfRisk': 2,\n },\n {\n 'date': '2020-11-25',\n 'reportId': 'report_id',\n 'factor': 'Total',\n 'factorCategory': 'CNT',\n 'pnl': 21.25,\n 'exposure': -11.25,\n 'proportionOfRisk': 3,\n },\n]\n\n\nfactor_exposure_data = [\n {\n 'date': '2024-03-14',\n 'reportId': 'report_id',\n 'factor': 'Factor_1',\n 'factorCategory': 'Style',\n 'pnl': 19.23,\n 'exposure': 100,\n 'proportionOfRisk': 1,\n },\n {\n 'date': '2024-03-14',\n 'reportId': 'report_id',\n 'factor': 'Factor_2',\n 'factorCategory': 'Style',\n 'pnl': 14.24,\n 'exposure': 100,\n 'proportionOfRisk': 2,\n },\n {\n 'date': '2024-03-14',\n 'reportId': 'report_id',\n 'factor': 'Factor_3',\n 'factorCategory': 'Style',\n 'pnl': 21.25,\n 'exposure': 100,\n 'proportionOfRisk': 3,\n },\n]\n\n\nfactor_return_data = [\n {\"date\": \"2024-03-13\", \"factor\": \"Factor_1\", \"return\": 2},\n {\"date\": \"2024-03-13\", \"factor\": \"Factor_2\", \"return\": 3},\n {\"date\": \"2024-03-13\", \"factor\": \"Factor_3\", \"return\": 2},\n {\"date\": \"2024-03-14\", \"factor\": \"Factor_1\", \"return\": 2},\n {\"date\": \"2024-03-14\", \"factor\": \"Factor_2\", \"return\": 3},\n {\"date\": \"2024-03-14\", \"factor\": \"Factor_3\", \"return\": 2},\n]\n\naggregate_factor_data = [\n {\n 'date': '2020-11-23',\n 'reportId': 'report_id',\n 'factor': 'Factor',\n 'factorCategory': 'CNT',\n 'pnl': 11.23,\n 'exposure': -11.23,\n 'proportionOfRisk': 1,\n 'dailyRisk': 1,\n 'annualRisk': 1,\n },\n {\n 'date': '2020-11-24',\n 'reportId': 'report_id',\n 'factor': 'Factor',\n 'factorCategory': 'CNT',\n 'pnl': 11.24,\n 'exposure': -11.24,\n 'proportionOfRisk': 2,\n 'dailyRisk': 2,\n 'annualRisk': 2,\n },\n {\n 'date': '2020-11-25',\n 'reportId': 'report_id',\n 'factor': 'Factor',\n 'factorCategory': 'CNT',\n 'pnl': 11.25,\n 'exposure': -11.25,\n 'proportionOfRisk': 3,\n 'dailyRisk': 3,\n 'annualRisk': 3,\n },\n]\n\nconstituents_data_l_s = {\n 'assetId': [\"MA1\", \"MA1\", \"MA1\", \"MA2\", \"MA2\", \"MA2\"],\n 'quantity': [-1.0, -2.0, -3.0, 1.0, 2.0, 3.0],\n 'netExposure': [-1.0, -2.0, -3.0, 1.0, 2.0, 3.0],\n 'pnl': [0.0, -1.0, -1.0, 0.0, 1.0, 1.0],\n 'date': ['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-02', '2020-01-03', '2020-01-04'],\n}\n\npnl_data_l_s = {\n 'quantity': [-1.0, -2.0, -3.0, -1.0, -2.0, -3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0],\n 'pnl': [0.0, -1.0, -1.0, 0.0, -1.0, -1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0],\n 'date': [\n '2020-01-02',\n '2020-01-03',\n '2020-01-04',\n '2020-01-02',\n '2020-01-03',\n '2020-01-04',\n '2020-01-02',\n '2020-01-03',\n '2020-01-04',\n '2020-01-02',\n '2020-01-03',\n '2020-01-04',\n ],\n}\n\nconstituents_data = {\n 'netExposure': [1.0, 2.0, 3.0],\n 'assetId': [\"MA\", \"MA\", \"MA\"],\n 'quantity': [1.0, 1.0, 1.0],\n 'pnl': [0.0, 1.0, 1.0],\n 'date': ['2020-01-02', '2020-01-03', '2020-01-04'],\n}\n\nconstituents_data_s = {\n 'netExposure': [-1.0, -2.0, -3.0],\n 'assetId': [\"MA\", \"MA\", \"MA\"],\n 'quantity': [-1.0, -1.0, -1.0],\n 'pnl': [0.0, 1.0, 1.0],\n 'date': ['2020-01-02', '2020-01-03', '2020-01-04'],\n}\n\nthematic_data = [\n {\n \"date\": \"2021-07-12\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.448370345015856e8,\n \"thematicExposure\": 2,\n \"thematicBeta\": 1,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-13\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.375772519907556e8,\n \"thematicExposure\": 2,\n \"thematicBeta\": 1,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-14\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.321189950666118e8,\n \"thematicExposure\": 2,\n \"thematicBeta\": 1,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n {\n \"date\": \"2021-07-15\",\n \"reportId\": \"PTAID\",\n \"basketId\": \"MA01GPR89HZF1FZ5\",\n \"region\": \"Asia\",\n \"grossExposure\": 3.274071805135091e8,\n \"thematicExposure\": 2,\n \"thematicBeta\": 1,\n \"updateTime\": \"2021-07-20T23:43:38Z\",\n },\n]\n\n\ndef compute_geometric_aggregation_calculations(aum: dict, pnl: dict, dates: list):\n daily_returns = [0]\n for i in range(2, len(dates)):\n daily_returns.append(pnl[dates[i]] / aum[dates[i - 1]])\n\n expected_values = [daily_returns[0]]\n for i in range(1, len(daily_returns)):\n expected_values.append((1 + expected_values[i - 1]) * (1 + daily_returns[i]) - 1)\n\n return [v * 100 for v in expected_values]\n\n\ndef mock_risk_model():\n risk_model = RiskModel(\n coverage=RiskModelCoverage.Country,\n id_='model_id',\n name='Fake Risk Model',\n term=RiskModelTerm.Long,\n universe_identifier=RiskModelUniverseIdentifier.gsid,\n vendor='GS',\n version=1.0,\n )\n\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n actual = Factor_Risk_Model.get(model_id='model_id')\n replace.restore()\n return actual\n\n\ndef test_factor_exposure():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mr.factor_exposure('report_id', 'Factor Name')\n assert all(actual.values == [-11.23, -11.24, -11.25])\n\n with pytest.raises(MqValueError):\n mr.factor_exposure('report_id', 'Wrong Factor Name')\n replace.restore()\n\n\ndef test_factor_exposure_percent():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n # mock getting performance report\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = ppa_report\n\n # mock getting aum source\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum_source', Mock())\n mock.return_value = RiskAumSource.Custom_AUM\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n # mock getting aum\n mock = replace('gs_quant.markets.report.PerformanceReport.get_custom_aum', Mock())\n mock.return_value = [\n CustomAUMDataPoint(date=dt.date(2020, 11, 23), aum=2),\n CustomAUMDataPoint(date=dt.date(2020, 11, 24), aum=2),\n CustomAUMDataPoint(date=dt.date(2020, 11, 25), aum=2),\n ]\n actual = mr.factor_exposure('report_id', 'Factor Name', 'Percent')\n assert all(actual.values == [-11.23 * 50, -11.24 * 50, -11.25 * 50])\n\n with pytest.raises(MqValueError):\n # mock getting aum with missing data\n mock = replace('gs_quant.markets.report.PerformanceReport.get_custom_aum', Mock())\n mock.return_value = [\n CustomAUMDataPoint(date=dt.date(2020, 11, 23), aum=2),\n CustomAUMDataPoint(date=dt.date(2020, 11, 25), aum=2),\n ]\n mr.factor_exposure('report_id', 'Factor Name', 'Percent')\n\n replace.restore()\n\n\ndef test_factor_pnl():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n actual = mr.factor_pnl('report_id', 'Factor Name')\n assert all(actual.values == [11.23, 11.24, 11.25])\n\n with DataContext(dt.date(2020, 11, 22), dt.date(2020, 11, 25)):\n # mock getting report factor data with first day set to 0\n factor_data_copy = copy.copy(factor_data)\n factor_data_copy.insert(\n 0,\n {\n 'date': '2020-11-22',\n 'reportId': 'report_id',\n 'factor': 'Factor Name',\n 'factorCategory': 'CNT',\n 'pnl': 0,\n 'exposure': -11.23,\n 'proportionOfRisk': 1,\n },\n )\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data_copy\n\n actual = mr.factor_pnl('report_id', 'Factor Name')\n assert all(actual.values == [0.0, 11.23, 11.24, 11.25])\n\n with pytest.raises(MqValueError):\n mr.factor_pnl('report_id', 'Wrong Factor Name')\n replace.restore()\n\n\ndef test_factor_pnl_percent():\n replace = Replacer()\n\n aum = {'2020-11-22': 200, '2020-11-23': 400, '2020-11-24': 400, '2020-11-25': 400}\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum\n\n # Each team uses pnl today/aum yesterday\n expected_values = [0, 0, 2.8844318, 5.7471343]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n # mock getting aum source\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum_source', Mock())\n mock.return_value = RiskAumSource.Long\n\n # mock getting performance report\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport(id='ID')\n\n # mock getting aum\n mock = replace('gs_quant.markets.report.PerformanceReport.get_long_exposure', Mock())\n mock.return_value = pd.DataFrame.from_dict({'date': aum.keys(), 'longExposure': aum.values()})\n actual = mr.factor_pnl('report_id', 'Factor Name', 'Percent')\n assert all([a == pytest.approx(e) for a, e in zip(actual.values, expected_values)])\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n # mock getting aum source\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum_source', Mock())\n mock.return_value = RiskAumSource.Short\n\n # mock getting performance report\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport(id='ID')\n\n # mock getting aum\n mock = replace('gs_quant.markets.report.PerformanceReport.get_short_exposure', Mock())\n mock.return_value = pd.DataFrame.from_dict({'date': aum.keys(), 'shortExposure': aum.values()})\n actual = mr.factor_pnl('report_id', 'Factor Name', 'Percent')\n assert all([a == pytest.approx(e) for a, e in zip(actual.values, expected_values)])\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n # mock getting aum source\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum_source', Mock())\n mock.return_value = RiskAumSource.Gross\n\n # mock getting performance report\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport(id='ID')\n\n # mock getting aum\n mock = replace('gs_quant.markets.report.PerformanceReport.get_gross_exposure', Mock())\n mock.return_value = pd.DataFrame.from_dict({'date': aum.keys(), 'grossExposure': aum.values()})\n actual = mr.factor_pnl('report_id', 'Factor Name', 'Percent')\n assert all([a == pytest.approx(e) for a, e in zip(actual.values, expected_values)])\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n # mock getting aum source\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum_source', Mock())\n mock.return_value = RiskAumSource.Net\n\n # mock getting performance report\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport(id='ID')\n\n # mock getting aum\n mock = replace('gs_quant.markets.report.PerformanceReport.get_net_exposure', Mock())\n mock.return_value = pd.DataFrame.from_dict({'date': aum.keys(), 'netExposure': aum.values()})\n actual = mr.factor_pnl('report_id', 'Factor Name', 'Percent')\n assert all([a == pytest.approx(e) for a, e in zip(actual.values, expected_values)])\n\n replace.restore()\n\n\ndef test_asset_factor_pnl_percent():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = asset_factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with pytest.raises(MqValueError):\n mr.factor_pnl('report_id', 'Factor Name', 'Percent')\n\n replace.restore()\n\n\ndef test_factor_proportion_of_risk():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mr.factor_proportion_of_risk('report_id', 'Factor Name')\n assert all(actual.values == [1, 2, 3])\n\n with pytest.raises(MqValueError):\n mr.factor_proportion_of_risk('report_id', 'Wrong Factor Name')\n replace.restore()\n\n\ndef test_get_factor_data():\n replace = Replacer()\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = ppa_report\n\n with pytest.raises(MqValueError):\n mr.factor_proportion_of_risk('report_id', 'Factor Name')\n replace.restore()\n\n\ndef test_aggregate_factor_support():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = risk_model\n\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = aggregate_factor_data\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2010-01-01']\n\n # mock getting risk model factor category\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = {'results': [{'factorData': [{'factorId': 'factor_id', 'factorCategory': 'Factor Name'}]}]}\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = [\n {'identifier': 'factor_id', 'type': 'Factor', 'name': 'Factor Name', 'factorCategory': 'Factor Name'}\n ]\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mr.factor_proportion_of_risk('report_id', 'Factor')\n assert all(actual.values == [1, 2, 3])\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mr.daily_risk('report_id', 'Factor')\n assert all(actual.values == [1, 2, 3])\n\n with DataContext(dt.date(2020, 11, 23), dt.date(2020, 11, 25)):\n actual = mr.annual_risk('report_id', 'Factor')\n assert all(actual.values == [1, 2, 3])\n\n with pytest.raises(MqValueError):\n mr.daily_risk('report_id', 'Factor Name')\n\n with pytest.raises(MqValueError):\n mr.annual_risk('report_id', 'Factor Name')\n replace.restore()\n\n\ndef test_normalized_performance():\n idx = pd.date_range('2020-01-02', freq='D', periods=3)\n replace = Replacer()\n expected = {\n None: pd.Series(data=[1, 2, 3], index=idx, name='normalizedPerformance', dtype='float64'),\n \"Long\": pd.Series(data=[1, 2, 3], index=idx, name='normalizedPerformance', dtype='float64'),\n }\n\n mock = replace('gs_quant.api.gs.portfolios.GsPortfolioApi.get_reports', Mock())\n mock.return_value = [\n Report.from_dict(\n {\n 'id': 'RP1',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP1',\n 'type': 'Portfolio Performance Analytics',\n 'parameters': {'transactionCostModel': 'FIXED'},\n }\n )\n ]\n # mock PerformanceReport.get_portfolio_constituents()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_portfolio_constituents', Mock())\n mock.return_value = MarketDataResponseFrame(data=constituents_data)\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n for k, v in expected.items():\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mr.normalized_performance('MP1', k)\n assert all(actual.values == v.values)\n replace.restore()\n\n\ndef test_normalized_performance_short():\n idx = pd.date_range('2020-01-02', freq='D', periods=3)\n replace = Replacer()\n expected = {\n \"Short\": pd.Series(data=[1, 1 / 2, 1 / 3], index=idx, name='normalizedPerformance', dtype='float64'),\n \"Long\": pd.Series(data=[1, 2, 3], index=idx, name='normalizedPerformance', dtype='float64'),\n None: pd.Series(\n data=[1, (2 + 1 / 2) / 2, (3 + 1 / 3) / 2], index=idx, name='normalizedPerformance', dtype='float64'\n ),\n }\n\n mock = replace('gs_quant.api.gs.portfolios.GsPortfolioApi.get_reports', Mock())\n mock.return_value = [\n Report.from_dict(\n {\n 'id': 'RP1',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP1',\n 'type': 'Portfolio Performance Analytics',\n 'parameters': {'transactionCostModel': 'FIXED'},\n }\n )\n ]\n # mock PerformanceReport.get_portfolio_constituents()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_portfolio_constituents', Mock())\n mock.return_value = MarketDataResponseFrame(data=constituents_data_l_s)\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n for k, v in expected.items():\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mr.normalized_performance('MP1', k)\n assert all((actual.values - v.values) < 0.01)\n replace.restore()\n\n\ndef test_get_long_pnl():\n idx = pd.date_range('2020-01-02', freq='D', periods=3)\n replace = Replacer()\n expected = pd.Series(data=[0, 2, 2], index=idx, name='longPnl', dtype='float64')\n\n mock = replace('gs_quant.api.gs.portfolios.GsPortfolioApi.get_reports', Mock())\n mock.return_value = [\n Report.from_dict(\n {\n 'id': 'RP1',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP1',\n 'type': 'Portfolio Performance Analytics',\n 'parameters': {'transactionCostModel': 'FIXED'},\n }\n )\n ]\n # mock PerformanceReport.get_portfolio_constituents()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_portfolio_constituents', Mock())\n mock.return_value = MarketDataResponseFrame(data=pnl_data_l_s)\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mr.long_pnl('MP1')\n assert all(actual.values == expected.values)\n replace.restore()\n\n\ndef test_get_short_pnl():\n idx = pd.date_range('2020-01-02', freq='D', periods=3)\n replace = Replacer()\n expected = pd.Series(data=[0, -2, -2], index=idx, name='shortPnl', dtype='float64')\n\n mock = replace('gs_quant.api.gs.portfolios.GsPortfolioApi.get_reports', Mock())\n mock.return_value = [\n Report.from_dict(\n {\n 'id': 'RP1',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP1',\n 'type': 'Portfolio Performance Analytics',\n 'parameters': {'transactionCostModel': 'FIXED'},\n }\n )\n ]\n # mock PerformanceReport.get_portfolio_constituents()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_portfolio_constituents', Mock())\n mock.return_value = MarketDataResponseFrame(data=pnl_data_l_s)\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mr.short_pnl('MP1')\n assert all(actual.values == expected.values)\n replace.restore()\n\n\ndef test_get_short_pnl_empty():\n replace = Replacer()\n expected = pd.Series(dtype=float)\n\n mock = replace('gs_quant.api.gs.portfolios.GsPortfolioApi.get_reports', Mock())\n mock.return_value = [\n Report.from_dict(\n {\n 'id': 'RP1',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP1',\n 'type': 'Portfolio Performance Analytics',\n 'parameters': {'transactionCostModel': 'FIXED'},\n }\n )\n ]\n # mock PerformanceReport.get_portfolio_constituents()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_portfolio_constituents', Mock())\n mock.return_value = MarketDataResponseFrame(data=constituents_data)\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mr.short_pnl('MP1')\n assert all(actual.values == expected.values)\n replace.restore()\n\n\ndef test_get_long_pnl_empty():\n replace = Replacer()\n expected = pd.Series(dtype=float)\n\n mock = replace('gs_quant.api.gs.portfolios.GsPortfolioApi.get_reports', Mock())\n mock.return_value = [\n Report.from_dict(\n {\n 'id': 'RP1',\n 'positionSourceType': 'Portfolio',\n 'positionSourceId': 'MP1',\n 'type': 'Portfolio Performance Analytics',\n 'parameters': {'transactionCostModel': 'FIXED'},\n }\n )\n ]\n # mock PerformanceReport.get_portfolio_constituents()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_portfolio_constituents', Mock())\n mock.return_value = MarketDataResponseFrame(data=constituents_data_s)\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2019, 1, 3)):\n actual = mr.long_pnl('MP1')\n assert all(actual.values == expected.values)\n replace.restore()\n\n\ndef test_thematic_exposure():\n replace = Replacer()\n\n # mock getting PTA report\n mock = replace('gs_quant.markets.report.ThematicReport.get', Mock())\n mock.return_value = ThematicReport(id='report_id')\n\n # mock getting thematic exposure\n mock = replace('gs_quant.markets.report.ThematicReport.get_thematic_exposure', Mock())\n mock.return_value = pd.DataFrame(thematic_data)\n\n # mock getting asset\n mock = Stock('MAA0NE9QX2ABETG6', 'Test Asset')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n ticker='basket_ticker',\n ),\n )\n ]\n replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock()).return_value = mock\n\n with DataContext(dt.date(2020, 7, 12), dt.date(2020, 7, 15)):\n actual = mr.thematic_exposure('report_id', 'basket_ticker')\n assert all(actual.values == [2, 2, 2, 2])\n\n replace.restore()\n\n\ndef test_thematic_beta():\n replace = Replacer()\n\n # mock getting PTA report\n mock = replace('gs_quant.markets.report.ThematicReport.get', Mock())\n mock.return_value = ThematicReport(id='report_id')\n\n # mock getting thematic exposure\n mock = replace('gs_quant.markets.report.ThematicReport.get_thematic_betas', Mock())\n mock.return_value = pd.DataFrame(thematic_data)\n\n # mock getting asset\n mock = Stock('MAA0NE9QX2ABETG6', 'Test Asset')\n xrefs = replace('gs_quant.timeseries.measures.GsAssetApi.get_asset_xrefs', Mock())\n xrefs.return_value = [\n GsTemporalXRef(\n dt.date(2019, 1, 1),\n dt.date(2952, 12, 31),\n XRef(\n ticker='basket_ticker',\n ),\n )\n ]\n replace('gs_quant.markets.securities.SecurityMaster.get_asset', Mock()).return_value = mock\n\n with DataContext(dt.date(2020, 7, 12), dt.date(2020, 7, 15)):\n actual = mr.thematic_beta('report_id', 'basket_ticker')\n assert all(actual.values == [1, 1, 1, 1])\n\n replace.restore()\n\n\ndef test_aum():\n replace = Replacer()\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = {'2022-07-01': 100, '2022-07-02': 200}\n\n with DataContext(dt.date(2022, 7, 1), dt.date(2022, 7, 3)):\n actual = mr.aum('RP1')\n assert all(actual.values == [100, 200])\n\n replace.restore()\n\n\ndef test_pnl():\n replace = Replacer()\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n # mock PerformanceReport.get_pnl()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame.from_dict(\n {'date': ['2022-07-01', '2022-07-02', '2022-07-03'], 'pnl': [200, 400, 600]}\n )\n\n with DataContext(dt.date(2022, 7, 1), dt.date(2022, 7, 3)):\n actual = mr.pnl('RP1')\n assert all(actual.values == [200, 400, 600])\n\n replace.restore()\n\n\ndef test_pnl_percent():\n replace = Replacer()\n\n # mock PerformanceReport.get()\n mock = replace('gs_quant.markets.report.PerformanceReport.get', Mock())\n mock.return_value = PerformanceReport(\n report_id='RP1',\n position_source_type='Portfolio',\n position_source_id='MP1',\n report_type='Portfolio Performance Analytics',\n parameters=ReportParameters(transaction_cost_model='FIXED'),\n )\n\n pnl = {'2022-07-01': 200, '2022-07-02': 400, '2022-07-03': 600}\n aum = {'2022-06-30': 2000, '2022-07-01': 3000, '2022-07-02': 3000, '2022-07-03': 4000}\n\n # mock PerformanceReport.get_pnl()\n mock = replace('gs_quant.markets.report.PerformanceReport.get_pnl', Mock())\n mock.return_value = pd.DataFrame.from_dict({'date': list(pnl.keys()), 'pnl': list(pnl.values())})\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum_source', Mock())\n mock.return_value = RiskAumSource.Long\n\n # mock getting performance report\n mock = replace('gs_quant.markets.portfolio_manager.PortfolioManager.get_performance_report', Mock())\n mock.return_value = PerformanceReport(id='ID')\n\n mock = replace('gs_quant.markets.report.PerformanceReport.get_aum', Mock())\n mock.return_value = aum\n\n # Each team uses pnl today/aum yesterday\n expected_values = compute_geometric_aggregation_calculations(aum, pnl, list(aum.keys()))\n\n with DataContext(dt.date(2022, 7, 1), dt.date(2022, 7, 3)):\n # mock getting aum\n mock = replace('gs_quant.markets.report.PerformanceReport.get_long_exposure', Mock())\n mock.return_value = pd.DataFrame.from_dict({'date': aum.keys(), 'longExposure': aum.values()})\n\n actual = mr.pnl('report_id', 'Percent')\n assert all([a == pytest.approx(e) for a, e in zip(actual.values, expected_values)])\n\n # with one day's aum missed to test forward filling logic\n with DataContext(dt.date(2022, 7, 1), dt.date(2022, 7, 3)):\n # mock getting aum with one day missed to test forward filling logic\n aum = {'2022-06-30': 2000, '2022-07-01': 3000, '2022-07-03': 4000}\n mock = replace('gs_quant.markets.report.PerformanceReport.get_long_exposure', Mock())\n mock.return_value = pd.DataFrame.from_dict({'date': aum.keys(), 'longExposure': aum.values()})\n\n actual = mr.pnl('report_id', 'Percent')\n assert all([a == pytest.approx(e) for a, e in zip(actual.values, expected_values)])\n\n replace.restore()\n\n\ndef test_historical_simulation_estimated_pnl():\n replace = Replacer()\n\n # mock getting report\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_exposure_data\n\n # mock sending data request\n mock = replace('gs_quant.api.gs.data.GsDataApi.execute_query', Mock())\n mock.return_value = {\"data\": factor_return_data}\n\n with DataContext(dt.date(2024, 3, 13), dt.date(2024, 3, 14)):\n actual = mr.historical_simulation_estimated_pnl('report_id')\n actual_values = actual.to_list()\n assert all([math.isclose(x, actual_values[i]) for i, x in enumerate([7.0, 14.17])])\n\n replace.restore()\n\n\ndef test_historical_simulation_estimated_factor_attribution():\n replace = Replacer()\n\n # mock getting report\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_report', Mock())\n mock.return_value = factor_risk_report\n\n # mock getting report factor data\n mock = replace('gs_quant.api.gs.reports.GsReportApi.get_factor_risk_report_results', Mock())\n mock.return_value = factor_exposure_data\n\n # mock sending data request\n mock = replace('gs_quant.api.gs.data.GsDataApi.execute_query', Mock())\n mock.return_value = {\"data\": [data for data in factor_return_data if data['factor'] == 'Factor_1']}\n\n with DataContext(dt.date(2024, 3, 13), dt.date(2024, 3, 14)):\n actual = mr.historical_simulation_estimated_factor_attribution('report_id', \"Factor_1\")\n actual_values = actual.to_list()\n assert all([math.isclose(x, actual_values[i]) for i, x in enumerate([2.0, 4.04])])\n\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/timeseries/test_measures_risk_models.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom math import sqrt\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nimport gs_quant.timeseries.measures_risk_models as mrm\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.models.risk_model import FactorRiskModel as Factor_Risk_Model\nfrom gs_quant.markets.securities import Stock\nfrom gs_quant.target.risk_models import (\n RiskModel,\n RiskModelCoverage,\n RiskModelTerm,\n RiskModelUniverseIdentifier,\n RiskModelType,\n)\n\nmock_risk_model_obj = RiskModel(\n id_='model_id',\n name='Fake Risk Model',\n coverage=RiskModelCoverage.Country,\n term=RiskModelTerm.Long,\n universe_identifier=RiskModelUniverseIdentifier.gsid,\n vendor='GS',\n version=1.0,\n type_=RiskModelType.Factor,\n)\n\nmock_risk_model_data = {\n 'totalResults': 2,\n 'missingDates': [],\n 'results': [\n {\n 'date': '2020-01-01',\n 'factorData': [\n {'factorId': '1', 'factorCategory': 'Style'},\n {'factorId': '2', 'factorCategory': 'Style'},\n {'factorId': '3', 'factorCategory': 'Style'},\n ],\n },\n {\n 'date': '2020-01-02',\n 'factorData': [\n {'factorId': '1', 'factorCategory': 'Style'},\n {'factorId': '2', 'factorCategory': 'Style'},\n {'factorId': '3', 'factorCategory': 'Style'},\n ],\n },\n {\n 'date': '2020-01-03',\n 'factorData': [\n {'factorId': '1', 'factorCategory': 'Style'},\n {'factorId': '2', 'factorCategory': 'Style'},\n {'factorId': '3', 'factorCategory': 'Style'},\n ],\n },\n ],\n}\n\nmock_risk_model_factor_data_intraday = {\n 'totalResults': 2,\n 'missingDates': [],\n 'results': [\n {\n 'time': '2020-01-01T10:10:10Z',\n 'factor': 'Factor Name',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id\",\n \"factorReturn\": 1.022,\n },\n {\n 'time': '2020-01-01T10:10:20Z',\n 'factor': 'Factor Name 1',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id_1\",\n \"factorReturn\": 1.033,\n },\n ],\n}\n\nmock_risk_model_factor_data = [\n {\n 'identifier': 'factor_id',\n 'type': 'Factor',\n 'name': \"Factor Name\",\n }\n]\n\nmock_covariance_curve = {'2020-01-01': 1.01, '2020-01-02': 1.02, '2020-01-03': 1.03}\n\nmock_volatility_curve = {'2020-01-01': sqrt(1.01), '2020-01-02': sqrt(1.02), '2020-01-03': sqrt(1.03)}\n\nmock_correlation_curve = {'2020-01-01': 1.0, '2020-01-02': 1.0, '2020-01-03': 1.0}\n\n\ndef mock_risk_model():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n actual = Factor_Risk_Model.get(model_id='model_id')\n replace.restore()\n return actual\n\n\ndef test_risk_model_measure():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = mock_risk_model_data\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = mock_risk_model_factor_data\n\n # mock getting asset gsid\n mock = replace('gs_quant.markets.securities.Asset.get_identifier', Mock())\n mock.return_value = '14593'\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2020-01-01', '2020-01-02', '2020-01-03']\n\n # mock getting risk model data\n mock = replace('gs_quant.models.risk_model.MarqueeRiskModel.get_data', Mock())\n mock.return_value = {\n 'totalResults': 3,\n 'missingDates': [],\n 'results': [\n {'date': '2024-08-19', 'assetData': {'universe': ['14593'], 'bidAskSpread30d': [0.1]}},\n {'date': '2024-08-20', 'assetData': {'universe': ['14593'], 'bidAskSpread30d': [0.2]}},\n {'date': '2024-08-21', 'assetData': {'universe': ['14593'], 'bidAskSpread30d': [0.3]}},\n ],\n }\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2020, 1, 3)):\n actual = mrm.risk_model_measure(\n Stock(id_='id', name='Fake Asset'), 'model_id', mrm.ModelMeasureString.BID_AKS_SPREAD_30D\n )\n assert all(actual.values == [0.1, 0.2, 0.3])\n\n with pytest.raises(AttributeError):\n mrm.risk_model_measure(Stock(id_='id', name='Fake Asset'), 'model_id', 'Wrong Factor Name')\n replace.restore()\n\n\ndef test_factor_zscore():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = mock_risk_model_data\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = mock_risk_model_factor_data\n\n # mock getting asset gsid\n mock = replace('gs_quant.markets.securities.Asset.get_identifier', Mock())\n mock.return_value = '12345'\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2020-01-01', '2020-01-02', '2020-01-03']\n\n # mock getting risk model data\n mock = replace('gs_quant.models.risk_model.MarqueeRiskModel.get_data', Mock())\n mock.return_value = {\n 'results': [\n {'date': '2020-01-01', 'assetData': {'factorExposure': [{'factor_id': 1.01, 'factor_id_1': 1.23}]}},\n {'date': '2020-01-02', 'assetData': {'factorExposure': [{'factor_id': 1.02, 'factor_id_1': 1.23}]}},\n {'date': '2020-01-03', 'assetData': {'factorExposure': [{'factor_id': 1.03, 'factor_id_1': 1.23}]}},\n ]\n }\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2020, 1, 3)):\n actual = mrm.factor_zscore(Stock(id_='id', name='Fake Asset'), 'model_id', 'Factor Name')\n assert all(actual.values == [1.01, 1.02, 1.03])\n\n with pytest.raises(MqValueError):\n mrm.factor_zscore(Stock(id_='id', name='Fake Asset'), 'model_id', 'Wrong Factor Name')\n replace.restore()\n\n\ndef test_factor_covariance():\n replace = Replacer()\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2020-01-01', '2020-01-02', '2020-01-03']\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = mock_risk_model_data\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = mock_risk_model_factor_data\n\n # mock getting covariances\n mock = replace('gs_quant.markets.factor.Factor.covariance', Mock())\n mock.return_value = mock_covariance_curve\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2020, 1, 3)):\n actual = mrm.factor_covariance(mock_risk_model(), 'Factor Name', 'Factor Name')\n assert all(actual.values == [1.01, 1.02, 1.03])\n\n with pytest.raises(MqValueError):\n mrm.factor_covariance(mock_risk_model(), 'Wrong Factor Name', 'Factor Name')\n replace.restore()\n\n\ndef test_factor_volatility():\n replace = Replacer()\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = mock_risk_model_data\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = mock_risk_model_factor_data\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2020-01-01', '2020-01-02', '2020-01-03']\n\n # mock getting covariances\n mock = replace('gs_quant.markets.factor.Factor.volatility', Mock())\n mock.return_value = mock_volatility_curve\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2020, 1, 3)):\n actual = mrm.factor_volatility(mock_risk_model(), 'Factor Name')\n assert all(actual.values == [sqrt(1.01) * 100, sqrt(1.02) * 100, sqrt(1.03) * 100])\n\n with pytest.raises(MqValueError):\n mrm.factor_covariance(mock_risk_model(), 'Wrong Factor Name', 'Factor Name')\n replace.restore()\n\n\ndef test_factor_correlation():\n replace = Replacer()\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = mock_risk_model_data\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = mock_risk_model_factor_data\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2020-01-01', '2020-01-02', '2020-01-03']\n\n # mock getting covariances\n mock = replace('gs_quant.markets.factor.Factor.correlation', Mock())\n mock.return_value = mock_correlation_curve\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2020, 1, 3)):\n actual = mrm.factor_correlation(mock_risk_model(), 'Factor Name', 'Factor Name')\n assert all(actual.values == [1, 1, 1])\n replace.restore()\n\n\ndef test_factor_performance():\n replace = Replacer()\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = mock_risk_model_data\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = mock_risk_model_factor_data\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n # mock getting risk model dates\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model_dates', Mock())\n mock.return_value = ['2020-01-01', '2020-01-02', '2020-01-03']\n\n # mock getting factor returns\n mock = replace('gs_quant.markets.factor.Factor.returns', Mock())\n mock.return_value = pd.DataFrame.from_dict(mock_covariance_curve, orient='index', columns=['return'])\n\n with DataContext(dt.date(2020, 1, 1), dt.date(2020, 1, 3)):\n actual = mrm.factor_performance(mock_risk_model(), 'Factor Name')\n assert len(actual.values) == 3\n replace.restore()\n\n\ndef test_factor_returns_intraday():\n replace = Replacer()\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = mock_risk_model_factor_data\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n # mock getting factor returns\n mock = replace('gs_quant.markets.factor.Factor.intraday_returns', Mock())\n mock.return_value = (\n pd.DataFrame(mock_risk_model_factor_data_intraday.get('results'))\n .set_index('time')\n .drop(columns=[\"factorCategory\", \"factor\", \"factorId\"], errors='ignore')\n )\n\n with DataContext(dt.datetime(2025, 1, 1, 0, 0, 0), dt.datetime(2025, 1, 1, 23, 59, 59)):\n actual = mrm.factor_returns_intraday(mock_risk_model(), 'Factor Name')\n assert len(actual.values) == 2\n replace.restore()\n\n\ndef test_factor_returns_percentile():\n replace = Replacer()\n\n # mock getting risk model factor entity\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_factor_data', Mock())\n mock.return_value = mock_risk_model_factor_data\n\n # mock getting risk model entity()\n mock = replace('gs_quant.api.gs.risk_models.GsRiskModelApi.get_risk_model', Mock())\n mock.return_value = mock_risk_model_obj\n\n mock_risk_model_factor_returns_data = {\n \"results\": [\n {\n 'date': '2025-01-10',\n \"factorData\": [\n {\n 'factorName': 'Factor Name',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id\",\n \"factorReturn\": 1.022,\n }\n ],\n },\n {\n 'date': '2025-01-09',\n \"factorData\": [\n {\n 'factorName': 'Factor Name',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id\",\n \"factorReturn\": 2.033,\n }\n ],\n },\n {\n 'date': '2025-01-08',\n \"factorData\": [\n {\n 'factorName': 'Factor Name',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id\",\n \"factorReturn\": 3.044,\n }\n ],\n },\n {\n 'date': '2025-01-07',\n \"factorData\": [\n {\n 'factorName': 'Factor Name',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id\",\n \"factorReturn\": 4.055,\n }\n ],\n },\n {\n 'date': '2025-01-06',\n \"factorData\": [\n {\n 'factorName': 'Factor Name',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id\",\n \"factorReturn\": 5.066,\n }\n ],\n },\n {\n 'date': '2025-01-05',\n \"factorData\": [\n {\n 'factorName': 'Factor Name',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id\",\n \"factorReturn\": 6.077,\n }\n ],\n },\n {\n 'date': '2025-01-04',\n \"factorData\": [\n {\n 'factorName': 'Factor Name',\n 'factorCategory': 'Style',\n \"factorId\": \"factor_id\",\n \"factorReturn\": 7.088,\n }\n ],\n },\n ]\n }\n expected_percentile_value = np.percentile(\n np.array(\n [f['factorReturn'] for data in mock_risk_model_factor_returns_data['results'] for f in data['factorData']]\n ),\n 90,\n )\n mock = replace('gs_quant.api.gs.risk_models.GsFactorRiskModelApi.get_risk_model_data', Mock())\n mock.return_value = mock_risk_model_factor_returns_data\n\n start_time = dt.datetime(2025, 1, 10, 0, 0, 0)\n end_time = dt.datetime(2025, 1, 10, 23, 59, 59)\n\n expected_series = pd.Series(\n expected_percentile_value, index=pd.date_range(start=start_time, end=end_time, freq='2h')\n )\n with DataContext(start_time, end_time):\n actual = mrm.factor_returns_percentile(mock_risk_model(), 'Factor Name', n_percentile=90)\n assert expected_series.equals(actual)\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/timeseries/test_measures_xccy.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pandas as pd\nimport pytest\nfrom pandas.testing import assert_series_equal\nfrom testfixtures import Replacer\nfrom testfixtures.mock import Mock\n\nimport gs_quant.timeseries.measures_xccy as tm\nfrom gs_quant.api.gs.assets import GsAsset\nfrom gs_quant.api.gs.data import GsDataApi, MarketDataResponseFrame\nfrom gs_quant.common import PricingLocation, Currency as CurrEnum\nfrom gs_quant.errors import MqError, MqValueError\nfrom gs_quant.markets import PricingContext\nfrom gs_quant.markets.securities import Bond, Cross, Currency\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.test.timeseries.utils import mock_request\nfrom gs_quant.timeseries import CurrencyEnum, SecurityMaster, CrossCurrencyRateOptionType\nfrom gs_quant.timeseries.measures_xccy import (\n _currency_to_tdapi_crosscurrency_swap_rate_asset,\n CROSSCURRENCY_RATES_DEFAULTS,\n TdapiCrossCurrencyRatesDefaultsProvider,\n)\n\n_index = [pd.Timestamp('2021-03-30')]\n_test_datasets = ('TEST_DATASET',)\n\n\ndef test_get_floating_rate_option_for_benchmark_retuns_rate():\n provider = TdapiCrossCurrencyRatesDefaultsProvider(CROSSCURRENCY_RATES_DEFAULTS)\n value = provider.get_rateoption_for_benchmark(CurrencyEnum.GBP, \"LIBOR\")\n assert value == \"GBP-LIBOR-BBA\"\n\n\ndef test_get_floating_rate_option_for_benchmark_retuns_rate_usd():\n provider = TdapiCrossCurrencyRatesDefaultsProvider(CROSSCURRENCY_RATES_DEFAULTS)\n value = provider.get_rateoption_for_benchmark(CurrencyEnum.USD, \"LIBOR\")\n assert value == \"USD-LIBOR-BBA\"\n\n\ndef test_currency_to_tdapi_xccy_swap_rate_asset(mocker):\n replace = Replacer()\n mocker.patch.object(\n GsSession.__class__, 'current', return_value=GsSession.get(Environment.QA, 'client_id', 'secret')\n )\n mocker.patch.object(GsSession.current.sync, 'get', side_effect=mock_request)\n mocker.patch.object(SecurityMaster, 'get_asset', side_effect=mock_request)\n bbid_mock = replace('gs_quant.timeseries.measures_xccy.Asset.get_identifier', Mock())\n\n with PricingContext(dt.date.today()):\n cur = [\n {\"currency_assetId\": \"MAK1FHKH5P5GJSHH\", \"currency\": \"JPY\", \"xccy_id\": \"MAFMW4HJC5TDE51H\"},\n {\"currency_assetId\": \"MA66CZBQJST05XKG\", \"currency\": \"GBP\", \"xccy_id\": \"MATDD783JM1C2GGD\"},\n {\"currency_assetId\": \"MAJNQPFGN1EBDHAE\", \"currency\": \"EUR\", \"xccy_id\": \"MAW8SAXPSKYA94E2\"},\n ]\n for c in cur:\n print(c)\n asset = Currency(c.get(\"currency_assetId\"), c.get(\"currency\"))\n bbid_mock.return_value = c.get(\"currency\")\n mqid = _currency_to_tdapi_crosscurrency_swap_rate_asset(asset)\n assert mqid == c.get(\"xccy_id\")\n\n bbid_mock.return_value = None\n assert _currency_to_tdapi_crosscurrency_swap_rate_asset(asset) == c.get(\"currency_assetId\")\n replace.restore()\n\n\ndef test_get_crosscurrency_swap_leg_defaults():\n result_dict = dict(\n currency=CurrEnum.JPY, rateOption=\"JPY-LIBOR-BBA\", designatedMaturity=\"3m\", pricing_location=PricingLocation.TKO\n )\n defaults = tm._get_crosscurrency_swap_leg_defaults(CurrEnum.JPY, tm.CrossCurrencyRateOptionType.LIBOR)\n assert result_dict == defaults\n\n result_dict = dict(\n currency=CurrEnum.EUR,\n rateOption=\"EUR-EURIBOR-TELERATE\",\n designatedMaturity=\"3m\",\n pricing_location=PricingLocation.LDN,\n )\n defaults = tm._get_crosscurrency_swap_leg_defaults(CurrEnum.EUR, tm.CrossCurrencyRateOptionType.LIBOR)\n assert result_dict == defaults\n\n result_dict = dict(\n currency=CurrEnum.EUR,\n rateOption=\"EUR-EONIA-OIS-COMPOUND\",\n designatedMaturity=\"3m\",\n pricing_location=PricingLocation.LDN,\n )\n defaults = tm._get_crosscurrency_swap_leg_defaults(CurrEnum.EUR, tm.CrossCurrencyRateOptionType.OIS)\n assert result_dict == defaults\n\n result_dict = dict(\n currency=CurrEnum.GBP, rateOption=\"GBP-LIBOR-BBA\", designatedMaturity=\"3m\", pricing_location=PricingLocation.LDN\n )\n defaults = tm._get_crosscurrency_swap_leg_defaults(CurrEnum.GBP, tm.CrossCurrencyRateOptionType.LIBOR)\n assert result_dict == defaults\n\n result_dict = dict(\n currency=CurrEnum.GBP, rateOption=\"GBP-LIBOR-BBA\", designatedMaturity=\"3m\", pricing_location=PricingLocation.LDN\n )\n defaults = tm._get_crosscurrency_swap_leg_defaults(CurrEnum.GBP, None)\n assert result_dict == defaults\n\n\ndef test_get_crosscurrency_swap_csa_terms():\n valid_ccy = ['EUR', 'GBP', 'JPY']\n for ccy in valid_ccy:\n assert dict(csaTerms=ccy + '-1') == tm._get_crosscurrency_swap_csa_terms(\n ccy, tm.CrossCurrencyRateOptionType.LIBOR.value\n )\n\n\ndef test_check_valid_indices():\n valid_indices = ['LIBOR']\n for index in valid_indices:\n assert tm.CrossCurrencyRateOptionType[index] == tm._check_crosscurrency_rateoption_type(CurrencyEnum.GBP, index)\n\n invalid_indices = ['LIBORED', 'TestRateOption']\n for index in invalid_indices:\n with pytest.raises(MqError):\n tm._check_crosscurrency_rateoption_type(CurrencyEnum.GBP, index)\n\n\ndef test_get_tdapi_crosscurrency_rates_assets(mocker):\n mock_asset_1 = GsAsset(asset_class='Rates', id='MAW8SAXPSKYA94E2', type_='XccySwapMTM', name='Test_asset')\n mock_asset_2 = GsAsset(asset_class='Rates', id='MATDD783JM1C2GGD', type_='XccySwapMTM', name='Test_asset')\n\n replace = Replacer()\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1]\n assert 'MAW8SAXPSKYA94E2' == tm._get_tdapi_crosscurrency_rates_assets()\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict(asset_parameters_termination_date='5y', asset_parameters_effective_date='0b')\n with pytest.raises(MqValueError):\n tm._get_tdapi_crosscurrency_rates_assets(**kwargs)\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = []\n kwargs = dict(\n asset_parameters_clearing_house='NONE',\n asset_parameters_payer_rate_option=\"EUR-EURIBOR-TELERATE\",\n asset_parameters_payer_currency='EUR',\n asset_parameters_payer_designated_maturity='3m',\n asset_parameters_receiver_rate_option=\"USD-LIBOR-BBA\",\n asset_parameters_receiver_currency='USD',\n asset_parameters_receiver_designated_maturity='3m',\n pricing_location='LDN',\n )\n with pytest.raises(MqValueError):\n tm._get_tdapi_crosscurrency_rates_assets(**kwargs)\n replace.restore()\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1, mock_asset_2]\n kwargs = dict()\n assert ['MAW8SAXPSKYA94E2', 'MATDD783JM1C2GGD'] == tm._get_tdapi_crosscurrency_rates_assets(**kwargs)\n replace.restore()\n\n # test case will test matching sofr maturity with libor leg and flipping legs to get right asset\n kwargs = dict(\n type='XccySwapMTM',\n asset_parameters_termination_date='5y',\n asset_parameters_payer_rate_option=\"EUR-EURIBOR-TELERATE\",\n asset_parameters_payer_currency=\"EUR\",\n asset_parameters_payer_designated_maturity='3m',\n asset_parameters_receiver_rate_option=\"USD-LIBOR-BBA\",\n asset_parameters_receiver_currency=\"USD\",\n asset_parameters_receiver_designated_maturity='3m',\n asset_parameters_clearing_house='None',\n asset_parameters_effective_date='5y',\n pricing_location='LDN',\n )\n\n assets = replace('gs_quant.timeseries.measures.GsAssetApi.get_many_assets', Mock())\n assets.return_value = [mock_asset_1]\n assert 'MAW8SAXPSKYA94E2' == tm._get_tdapi_crosscurrency_rates_assets(**kwargs)\n replace.restore()\n\n\ndef mock_curr(_cls, _q):\n d = {\n 'xccySwapSpread': [1, 2, 3],\n }\n df = MarketDataResponseFrame(data=d, index=_index * 3)\n df.dataset_ids = _test_datasets\n return df\n\n\ndef test_crosscurrency_swap_rate(mocker):\n replace = Replacer()\n args = dict(swap_tenor='5y', rateoption_type='LIBOR', clearing_house='LCH', forward_tenor='5y', real_time=False)\n\n mock_gbp = Currency('MA26QSMPX9990G66', 'GBP')\n args['asset'] = mock_gbp\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBP'\n xrefs = replace('gs_quant.timeseries.measures.SecurityMaster.get_asset', Mock())\n mock_usd = Currency('MA26QSMPX9990G63', 'USD')\n xrefs.return_value = mock_usd\n\n args['swap_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm.crosscurrency_swap_rate(**args)\n args['swap_tenor'] = '5y'\n\n args['forward_tenor'] = '5yr'\n with pytest.raises(MqValueError):\n tm.crosscurrency_swap_rate(**args)\n args['forward_tenor'] = '5y'\n\n args['real_time'] = True\n with pytest.raises(NotImplementedError):\n tm.crosscurrency_swap_rate(**args)\n args['real_time'] = False\n\n args['asset'] = Currency('MA666', 'AED')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'AED'\n with pytest.raises(NotImplementedError):\n tm.crosscurrency_swap_rate(**args)\n args['asset'] = mock_gbp\n\n args['asset'] = Bond('MA667', 'TEST')\n with pytest.raises(MqValueError):\n tm.crosscurrency_swap_rate(**args)\n args['asset'] = mock_gbp\n\n args['asset'] = Cross('MA667', 'USDAED')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.side_effect = ['AED', 'USD']\n with pytest.raises(NotImplementedError):\n tm.crosscurrency_swap_rate(**args)\n\n args['asset'] = Cross('MA667', 'USDAED')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.side_effect = ['USD', 'AED']\n with pytest.raises(NotImplementedError):\n tm.crosscurrency_swap_rate(**args)\n\n args['asset'] = Cross('MA667', 'USDGBP')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBP'\n xrefs = replace('gs_quant.timeseries.measures_xccy._check_crosscurrency_rateoption_type', Mock())\n xrefs.side_effect = [tm.CrossCurrencyRateOptionType.LIBOR, tm.CrossCurrencyRateOptionType.OIS]\n with pytest.raises(MqValueError):\n tm.crosscurrency_swap_rate(**args)\n replace.restore()\n\n xrefs = replace('gs_quant.timeseries.measures.SecurityMaster.get_asset', Mock())\n xrefs.return_value = mock_usd\n\n args['asset'] = Cross('MA667', 'USDGBP')\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBP'\n identifiers = replace('gs_quant.timeseries.measures_xccy._get_tdapi_crosscurrency_rates_assets', Mock())\n identifiers.return_value = {'MA26QSMPX9990G66'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm.crosscurrency_swap_rate(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='xccySwapSpread')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n args['asset'] = Cross('MA667', 'USDCAD')\n args['location'] = PricingLocation.LDN\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'CAD'\n identifiers = replace('gs_quant.timeseries.measures_xccy._get_tdapi_crosscurrency_rates_assets', Mock())\n identifiers.return_value = {'MA26QSMPX9990G66'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm.crosscurrency_swap_rate(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='xccySwapSpread')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n args['asset'] = mock_gbp\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBP'\n\n args['rateoption_type'] = CrossCurrencyRateOptionType.TestRateOption\n with pytest.raises(MqValueError):\n tm.crosscurrency_swap_rate(**args)\n args['rateoption_type'] = CrossCurrencyRateOptionType.LIBOR\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'GBP'\n identifiers = replace('gs_quant.timeseries.measures_xccy._get_tdapi_crosscurrency_rates_assets', Mock())\n identifiers.return_value = {'MA26QSMPX9990G66'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n actual = tm.crosscurrency_swap_rate(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='xccySwapSpread')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n\n xrefs = replace('gs_quant.timeseries.measures.Asset.get_identifier', Mock())\n xrefs.return_value = 'EUR'\n identifiers = replace('gs_quant.timeseries.measures_xccy._get_tdapi_crosscurrency_rates_assets', Mock())\n identifiers.return_value = {'MAZBW57ZPS54ET7K'}\n mocker.patch.object(GsDataApi, 'get_market_data', return_value=mock_curr(None, None))\n args['asset'] = Currency('MAZBW57ZPS54ET7K', 'EUR')\n args['rateoption_type'] = 'OIS'\n actual = tm.crosscurrency_swap_rate(**args)\n expected = tm.ExtendedSeries([1, 2, 3], index=_index * 3, name='xccySwapSpread')\n expected.dataset_ids = _test_datasets\n assert_series_equal(expected, actual)\n assert actual.dataset_ids == _test_datasets\n replace.restore()\n\n\nif __name__ == '__main__':\n pytest.main(args=[\"test_measures_xccy.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_rolling.py", "content": "\"\"\"\nCopyright 2022 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport pytest\nimport time\nimport numpy as np\nimport pandas as pd\n\nfrom gs_quant.timeseries.helper import rolling_offset\n\n\nclass Timer:\n def __init__(self, name):\n self.name = name\n\n def __enter__(self):\n self.start = time.perf_counter()\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n print(f'{self.name} ran in {(time.perf_counter() - self.start) * 1000:.2f}ms')\n\n\n@pytest.mark.parametrize(\n \"frequency,count,unit\",\n [('D', 22, 'days'), ('D', 4, 'weeks'), ('D', 3, 'months'), ('D', 1, 'years'), ('s', 12, 'hours')],\n)\ndef test_rolling_date_offset(frequency, count, unit):\n length = 1000\n rng = np.random.default_rng()\n values = [rng.random() if rng.random() > 0.1 else np.nan for _ in range(length)]\n s = pd.Series(values, index=pd.date_range(end=dt.datetime.now(), freq=frequency, periods=length))\n offset = pd.DateOffset(**{unit: count})\n\n print(f'\\nseries frequency: {frequency}, offset: {count}{unit}')\n with Timer('simple rolling'):\n expected = pd.Series(\n [np.nanmean(s.loc[(s.index > idx - offset) & (s.index <= idx)]) for idx in s.index], index=s.index\n )\n\n with Timer('rolling with method name'):\n a1 = rolling_offset(s, offset, np.nanmean, 'mean')\n pd.testing.assert_series_equal(expected, a1, obj=\"Pandas mean\")\n\n with Timer('rolling without method name'):\n a2 = rolling_offset(s, offset, np.nanmean)\n pd.testing.assert_series_equal(expected, a2, obj=\"generic mean\")\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_rolling.py\", \"-s\"])\n" }, { "path": "gs_quant/test/timeseries/test_statistics.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport scipy.stats.mstats as stats\nfrom pandas.testing import assert_series_equal\nfrom scipy.integrate import odeint\n\nfrom gs_quant.data import DataContext\nfrom gs_quant.errors import MqTypeError, MqValueError, MqError\nfrom gs_quant.timeseries import normalize_window, Window, Returns, returns\nfrom gs_quant.timeseries.statistics import (\n Direction,\n IntradayDirection,\n generate_series,\n generate_series_intraday,\n LinearRegression,\n RollingLinearRegression,\n min_,\n max_,\n range_,\n mean,\n median,\n mode,\n sum_,\n product,\n std,\n exponential_std,\n var,\n cov,\n zscores,\n winsorize,\n percentiles,\n percentile,\n SIRModel,\n SEIRModel,\n MeanType,\n)\n\n\ndef _random_series(days=365, nans=10):\n assert nans < days\n rng = np.random.default_rng()\n values = rng.random(days)\n nan_indexes = np.floor(rng.random(nans) * len(values)).astype(np.int_)\n for i in nan_indexes:\n values[i] = np.nan\n\n return pd.Series(values, index=pd.date_range(start=\"2021-01-01\", periods=days, freq='D'))\n\n\ndef _rolling_1m_test(fn, pandas_fn: str):\n x = _random_series()\n window = normalize_window(x, Window('1m', 0))\n result = fn(x, window)\n values = (getattr(x.loc[(x.index > idx - window.w) & (x.index <= idx)], pandas_fn)() for idx in x.index)\n expected = pd.Series(values, index=x.index)\n assert_series_equal(result, expected, obj=f\"{pandas_fn} with date window 1m\")\n\n\ndef test_generate_series():\n x = generate_series(100)\n\n assert len(x) == 100\n assert x.index[0] == dt.date.today()\n assert x.iloc[0] == 100\n\n x = generate_series(100, Direction.END_TODAY)\n assert len(x) == 100\n assert x.index[-1] == dt.date.today()\n assert x.iloc[0] == 100\n\n\ndef test_generate_series_intraday():\n # Test START_INTRADAY_NOW (default)\n x = generate_series_intraday(100)\n assert len(x) == 100\n assert isinstance(x.index, pd.DatetimeIndex)\n assert x.iloc[0] == 100\n # Check that first timestamp is close to now (within 1 minute)\n now = pd.Timestamp.now().floor('min')\n assert abs((x.index[0] - now).total_seconds()) < 60\n # Check that observations are 1 minute apart\n assert (x.index[-1] - x.index[0]).total_seconds() == 99 * 60\n\n # Test END_INTRADAY_NOW\n x = generate_series_intraday(100, IntradayDirection.END_INTRADAY_NOW)\n assert len(x) == 100\n assert isinstance(x.index, pd.DatetimeIndex)\n assert x.iloc[0] == 100\n # Check that last timestamp is close to now (within 1 minute)\n now = pd.Timestamp.now().floor('min')\n assert abs((x.index[-1] - now).total_seconds()) < 60\n # Check that observations are 1 minute apart\n assert (x.index[-1] - x.index[0]).total_seconds() == 99 * 60\n\n\ndef test_min():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n z = pd.Series([2.0, 5.0, 4.0, 0.0, 1.0, 3.0], index=dates)\n result = min_([x, z], Window('2d', 0))\n expected = pd.Series([2.0, 2.0, 2.0, 0.0, 1.0, 1.0], index=dates)\n assert_series_equal(result, expected, obj=\"Minimum list 2d\")\n\n result = min_(x)\n expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 1.0], index=dates)\n assert_series_equal(result, expected, obj=\"Minimum\")\n\n result = min_(x, Window(1, 0))\n expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Minimum window 1\")\n\n result = min_(x, Window(2, 0))\n expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Minimum window 2\")\n\n result = min_(x, Window('1w', 0))\n expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 1.0], index=dates)\n assert_series_equal(result, expected, obj=\"Minimum with window 1w\")\n\n y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)\n result = min_([x, y], Window(2, 0))\n expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 2.0], index=dates)\n assert_series_equal(result, expected, obj=\"Minimum of multiple series\")\n\n result = min_(x, \"2d\")\n expected = pd.Series([2.0, 1.0, 3.0, 3.0], index=dates[2:])\n assert_series_equal(result, expected, obj=\"Minimum with strdate window\")\n\n result = min_(x, \"1d\")\n expected = pd.Series([2.0, 3.0, 1.0, 3.0, 6.0], index=dates[1:])\n assert_series_equal(result, expected, obj=\"Minimum with strdate window 2\")\n\n ranges = pd.date_range('20220101', periods=6, freq='40min')\n y = pd.Series([4.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=ranges)\n result = min_(y, '2h')\n expected = pd.Series([1.0, 1.0, 1.0], index=ranges[3:])\n assert_series_equal(result, expected, obj=\"Minimum with string window 2h\")\n\n _rolling_1m_test(min_, 'min')\n\n\ndef test_max():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n z = pd.Series([1.0, 0.0, 4.0, 3.5, 7.0, 8.0], index=dates)\n\n result = max_([x, z], Window('2d', 0))\n expected = pd.Series([3.0, 3.0, 4.0, 4.0, 7.0, 8.0], index=dates)\n assert_series_equal(result, expected, obj=\"Maximum list 2d\")\n\n result = max_(x)\n expected = pd.Series([3.0, 3.0, 3.0, 3.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Maximum\")\n\n result = max_(x, Window(1, 0))\n expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Maximum window 1\")\n\n result = max_(x, Window(2, 0))\n expected = pd.Series([3.0, 3.0, 3.0, 3.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Maximum window 2\")\n\n result = max_(x, Window('2d', 0))\n expected = pd.Series([3.0, 3.0, 3.0, 3.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Maximum window 1w\")\n\n y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)\n result = max_([x, y], Window(2, 0))\n expected = pd.Series([4.0, 4.0, 4.0, 4.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Maximum of multiple series\")\n\n s = pd.Series([-3.0, -2.0, 3.0, -1.0, -3.0, 6.0], index=dates)\n t = pd.Series([0, 0], index=dates[0:2])\n result = max_([s, t], 1)\n expected = pd.Series([0.0, 3, 0, 0, 6], index=dates[1:])\n assert_series_equal(result, expected, obj=\"Maximum with constant\")\n\n ranges = pd.date_range('20220101', periods=6, freq='30min')\n y = pd.Series([4.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=ranges)\n result = max_(y, '1h')\n expected = pd.Series([3.0, 3.0, 3.0, 6.0], index=ranges[2:])\n assert_series_equal(result, expected, obj=\"Maximum with string window 1h\")\n\n _rolling_1m_test(max_, 'max')\n\n\ndef test_range():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = range_(x)\n expected = pd.Series([0.0, 1.0, 1.0, 2.0, 2.0, 5.0], index=dates)\n assert_series_equal(result, expected, obj=\"Range\")\n\n result = range_(x, Window(1, 0))\n expected = pd.Series([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], index=dates)\n assert_series_equal(result, expected, obj=\"Range window 1\")\n\n result = range_(x, Window(2, 0))\n expected = pd.Series([0.0, 1.0, 1.0, 2.0, 2.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Range window 2\")\n\n result = range_(x, Window('1w', 0))\n expected = pd.Series([0.0, 1.0, 1.0, 2.0, 2.0, 5.0], index=dates)\n assert_series_equal(result, expected, obj=\"Range window 1w\")\n\n\ndef test_mean():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = mean(x)\n expected = pd.Series([3.0, 2.5, 8 / 3, 2.25, 2.4, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Mean\")\n\n result = mean(x, Window(1, 0))\n expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Mean window 1\")\n\n result = mean(x, Window(2, 0))\n expected = pd.Series([3.0, 2.5, 2.5, 2.0, 2.0, 4.5], index=dates)\n assert_series_equal(result, expected, obj=\"Mean window 2\")\n\n result = mean(x, Window('1w', 0))\n expected = pd.Series([3.0, 2.5, 8 / 3, 2.25, 2.4, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Mean window 1w\")\n\n y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)\n result = mean([x, y], Window(2, 0))\n expected = pd.Series([3.5, 3.0, 3.0, 2.5, 2.0, 4.0], index=dates)\n assert_series_equal(result, expected, obj=\"Mean of multiple series\")\n\n result = mean([x, y], Window('2d', 0))\n expected = pd.Series([3.5, 3, 3, 2.5, 2.5, 4], index=dates)\n assert_series_equal(result, expected, obj=\"Mean of multiple series by date offset\")\n\n result = mean(y, Window(2, 0))\n expected = pd.Series([4.0, 4.0, 4.0, 3.0, 2.0, 3.5], index=dates)\n assert_series_equal(result, expected, obj=\"Mean of single series with nan\")\n\n\ndef test_quadratic_mean():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = mean(x, mean_type=MeanType.QUADRATIC)\n expected = pd.Series(\n [\n np.sqrt(9.0),\n np.sqrt((9.0 + 4.0) / 2),\n np.sqrt((9.0 + 4.0 + 9.0) / 3),\n np.sqrt((9.0 + 4.0 + 9.0 + 1.0) / 4),\n np.sqrt((9.0 + 4.0 + 9.0 + 1.0 + 9.0) / 5),\n np.sqrt((9.0 + 4.0 + 9.0 + 1.0 + 9.0 + 36.0) / 6),\n ],\n index=dates,\n )\n assert_series_equal(result, expected, obj=\"Quadratic mean\")\n\n result = mean(x, Window(1, 0), mean_type=MeanType.QUADRATIC)\n expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Quadratic mean window 1\")\n\n result = mean(x, Window(2, 0), mean_type=MeanType.QUADRATIC)\n expected = pd.Series(\n [\n np.sqrt(9.0),\n np.sqrt((9.0 + 4.0) / 2),\n np.sqrt((4.0 + 9.0) / 2),\n np.sqrt((9.0 + 1.0) / 2),\n np.sqrt((1.0 + 9.0) / 2),\n np.sqrt((9.0 + 36.0) / 2),\n ],\n index=dates,\n )\n assert_series_equal(result, expected, obj=\"Quadratic mean window 2\")\n\n result = mean(x, Window('1w', 0), mean_type=MeanType.QUADRATIC)\n expected = pd.Series(\n [\n np.sqrt(9.0),\n np.sqrt((9.0 + 4.0) / 2),\n np.sqrt((9.0 + 4.0 + 9.0) / 3),\n np.sqrt((9.0 + 4.0 + 9.0 + 1.0) / 4),\n np.sqrt((9.0 + 4.0 + 9.0 + 1.0 + 9.0) / 5),\n np.sqrt((4.0 + 9.0 + 1.0 + 9.0 + 36.0) / 5),\n ],\n index=dates,\n )\n assert_series_equal(result, expected, obj=\"Quadratic mean window 1w\")\n\n y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)\n result = mean([x, y], Window(2, 0), mean_type=MeanType.QUADRATIC)\n expected = pd.Series(\n [\n np.sqrt((9.0 + 16.0) / 2),\n np.sqrt((9.0 + 4.0 + 16.0) / 3),\n np.sqrt((4.0 + 9.0 + 16.0) / 3),\n np.sqrt((9.0 + 1.0 + 16.0 + 4.0) / 4),\n np.sqrt((1.0 + 9.0 + 4.0 + 4.0) / 4),\n np.sqrt((9.0 + 36.0 + 4.0 + 25.0) / 4),\n ],\n index=dates,\n )\n assert_series_equal(result, expected, obj=\"Quadratic mean of multiple series\")\n\n result = mean([x, y], Window('2d', 0), mean_type=MeanType.QUADRATIC)\n expected = pd.Series(\n [\n np.sqrt((9.0 + 16.0) / 2),\n np.sqrt((9.0 + 4.0 + 16.0) / 3),\n np.sqrt((4.0 + 9.0 + 16.0) / 3),\n np.sqrt((9.0 + 1.0 + 16.0 + 4.0) / 4),\n np.sqrt((9.0 + 4.0) / 2),\n np.sqrt((9.0 + 36.0 + 4.0 + 25.0) / 4),\n ],\n index=dates,\n )\n assert_series_equal(result, expected, obj=\"Quadratic mean of multiple series by date offset\")\n\n result = mean(y, Window(2, 0), mean_type=MeanType.QUADRATIC)\n expected = pd.Series(\n [\n np.sqrt(16.0),\n np.sqrt(16.0),\n np.sqrt(16.0),\n np.sqrt((16.0 + 4.0) / 2),\n np.sqrt((4.0 + 4.0) / 2),\n np.sqrt((4.0 + 25.0) / 2),\n ],\n index=dates,\n )\n assert_series_equal(result, expected, obj=\"Quadratic mean of single series with nan\")\n\n z = pd.Series([-3.0, 4.0, -5.0], index=dates[:3])\n result = mean(z, Window(2, 0), mean_type=MeanType.QUADRATIC)\n expected = pd.Series([np.sqrt(9.0), np.sqrt((9.0 + 16.0) / 2), np.sqrt((16.0 + 25.0) / 2)], index=dates[:3])\n assert_series_equal(result, expected, obj=\"Quadratic mean with negative values\")\n\n\ndef test_median():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = median(x)\n expected = pd.Series([3.0, 2.5, 3.0, 2.5, 3.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Median\")\n\n result = median(x, Window(1, 0))\n expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Median window 1\")\n\n result = median(x, Window(2, 0))\n expected = pd.Series([3.0, 2.5, 2.5, 2.0, 2.0, 4.5], index=dates)\n assert_series_equal(result, expected, obj=\"Median window 2\")\n\n result = median(x, Window('1w', 0))\n expected = pd.Series([3.0, 2.5, 3.0, 2.5, 3.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Median window 1w\")\n\n _rolling_1m_test(median, 'median')\n\n\ndef test_mode():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = mode(x)\n expected = pd.Series([3.0, 2.0, 3.0, 3.0, 3.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"mode\")\n\n result = mode(x, Window(1, 0))\n expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"mode window 1\")\n\n result = mode(x, Window(2, 0))\n expected = pd.Series([3.0, 2.0, 2.0, 1.0, 1.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"mode window 2\")\n\n result = mode(x, Window('1w', 0))\n expected = pd.Series([3.0, 2.0, 3.0, 3.0, 3.0, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Mode window 1w\")\n\n x = _random_series()\n window = normalize_window(x, Window('1m', 0))\n result = mode(x, window)\n values = (stats.mode(x.loc[(x.index > idx - window.w) & (x.index <= idx)]).mode[0] for idx in x.index)\n expected = pd.Series(values, index=x.index)\n assert_series_equal(result, expected, obj=\"Mode window 1m\")\n\n\ndef test_sum():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], index=dates)\n\n result = sum_(x)\n expected = pd.Series([1.0, 3.0, 6.0, 10, 15, 21], index=dates)\n assert_series_equal(result, expected, obj=\"Summation\")\n\n result = sum_(x, Window(2, 0))\n expected = pd.Series([1.0, 3.0, 5.0, 7.0, 9.0, 11.0], index=dates)\n assert_series_equal(result, expected, obj=\"Summation\")\n\n result = sum_(x, Window('1w', 0))\n expected = pd.Series([1.0, 3.0, 6.0, 10.0, 15.0, 20.0], index=dates)\n assert_series_equal(result, expected, obj=\"Sum window 1w\")\n\n y = pd.Series([4.0, np.nan, 4.0, 2.0, 2.0, 5.0], index=dates)\n result = sum_([x, y], Window(2, 0))\n expected = pd.Series([5.0, 7.0, 9.0, 13.0, 13.0, 18.0], index=dates)\n assert_series_equal(result, expected, obj=\"Sum of multiple series\")\n\n result = sum_(y, Window('2d', 0))\n expected = pd.Series([4, 4, 4, 6, 2, 7], index=dates, dtype=np.double)\n assert_series_equal(result, expected, obj=\"Sum with nan input\")\n\n\ndef test_product():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], index=dates)\n\n result = product(x)\n expected = pd.Series([1.0, 2.0, 6.0, 24, 120, 720], index=dates)\n assert_series_equal(result, expected, obj=\"Product\")\n\n result = product(x, Window(2, 0))\n expected = pd.Series([1.0, 2.0, 6.0, 12.0, 20.0, 30.0], index=dates)\n assert_series_equal(result, expected, obj=\"Product\")\n\n result = product(x, Window('1w', 0))\n expected = pd.Series([1.0, 2.0, 6.0, 24.0, 120.0, 720.0], index=dates)\n assert_series_equal(result, expected, obj=\"Product window 1w\")\n\n _rolling_1m_test(product, 'prod')\n\n\ndef test_std():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = std(x)\n expected = pd.Series([np.nan, 0.707106, 0.577350, 0.957427, 0.894427, 1.673320], index=dates)\n assert_series_equal(result, expected, obj=\"std\")\n\n result = std(x, Window(2, 0))\n expected = pd.Series([np.nan, 0.707106, 0.707106, 1.414214, 1.414214, 2.121320], index=dates)\n assert_series_equal(result, expected, obj=\"std window 2\")\n\n result = std(x, Window('1w', 0))\n expected = pd.Series([np.nan, 0.707106, 0.577350, 0.957427, 0.894427, 1.870828], index=dates)\n assert_series_equal(result, expected, obj=\"std window 1w\")\n\n assert std(pd.Series(dtype=float)).empty\n\n\ndef test_exponential_std():\n def exp_std_calc(ts, alpha=0.75):\n std = ts * 0\n for i in range(1, len(ts)):\n weights = (1 - alpha) * alpha ** np.arange(i, -1, -1)\n weights[0] /= 1 - alpha\n x = ts.to_numpy()[: i + 1]\n ema = sum(weights * x) / sum(weights)\n debias_fact = sum(weights) ** 2 / (sum(weights) ** 2 - sum(weights**2))\n var = debias_fact * sum(weights * (x - ema) ** 2) / sum(weights)\n std.iloc[i] = np.sqrt(var)\n std.iloc[0] = np.nan\n return std\n\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = exponential_std(x)\n expected = exp_std_calc(x)\n assert_series_equal(result, expected, obj=\"Exponentially weighted standard deviation\")\n\n result = exponential_std(x, 0.8)\n expected = exp_std_calc(x, 0.8)\n assert_series_equal(result, expected, obj=\"Exponentially weighted standard deviation weight 1\")\n\n\ndef test_var():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = var(x)\n expected = pd.Series([np.nan, 0.500000, 0.333333, 0.916667, 0.800000, 2.800000], index=dates)\n assert_series_equal(result, expected, obj=\"var\")\n\n result = var(x, Window(2, 0))\n expected = pd.Series([np.nan, 0.5, 0.5, 2.0, 2.0, 4.5], index=dates)\n assert_series_equal(result, expected, obj=\"var window 2\")\n\n result = var(x, Window('1w', 0))\n expected = pd.Series([np.nan, 0.500000, 0.333333, 0.916666, 0.800000, 3.500000], index=dates)\n assert_series_equal(result, expected, obj=\"var window 1w\")\n\n _rolling_1m_test(var, 'var')\n\n\ndef test_cov():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n y = pd.Series([3.5, 1.8, 2.9, 1.2, 3.1, 5.9], index=dates)\n\n result = cov(x, y)\n expected = pd.Series([np.nan, 0.850000, 0.466667, 0.950000, 0.825000, 2.700000], index=dates)\n assert_series_equal(result, expected, obj=\"cov\")\n\n result = cov(x, y, Window(2, 0))\n expected = pd.Series([np.nan, 0.850000, 0.549999, 1.7000000, 1.900000, 4.200000], index=dates)\n assert_series_equal(result, expected, obj=\"cov window 2\")\n\n result = cov(x, y, Window('1w', 0))\n expected = pd.Series([np.nan, 0.850000, 0.466667, 0.950000, 0.825000, 3.375000], index=dates)\n assert_series_equal(result, expected, obj=\"cov window 1w\")\n\n\ndef test_zscores():\n with pytest.raises(MqValueError):\n zscores(pd.Series(range(5)), \"2d\")\n\n assert_series_equal(zscores(pd.Series(dtype=float)), pd.Series(dtype=float))\n assert_series_equal(zscores(pd.Series(dtype=float), 1), pd.Series(dtype=float))\n\n assert_series_equal(zscores(pd.Series([1])), pd.Series([0.0]))\n assert_series_equal(zscores(pd.Series([1]), Window(1, 0)), pd.Series([0.0]))\n\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = zscores(x)\n expected = pd.Series([0.000000, -0.597614, 0.000000, -1.195229, 0.000000, 1.792843], index=dates)\n assert_series_equal(result, expected, obj=\"z-score\")\n\n assert_series_equal(result, (x - x.mean()) / x.std(), obj=\"full series zscore\")\n\n result = zscores(x, Window(2, 0))\n expected = pd.Series([0.0, -0.707107, 0.707107, -0.707107, 0.707107, 0.707107], index=dates)\n assert_series_equal(result, expected, obj=\"z-score window 2\")\n assert_series_equal(zscores(x, Window(5, 5)), zscores(x, 5))\n\n result = zscores(x, Window('1w', 0))\n expected = pd.Series([0.0, -0.707106, 0.577350, -1.305582, 0.670820, 1.603567], index=dates)\n assert_series_equal(result, expected, obj=\"z-score window 1w\")\n\n result = zscores(x, '1w')\n expected = pd.Series([1.603567], index=dates[-1:])\n assert_series_equal(result, expected, obj='z-score window string 1w')\n\n result = zscores(x, '1m')\n expected = pd.Series(dtype=float, index=[])\n assert_series_equal(result, expected, obj=\"z-score window too large\")\n\n\ndef test_winsorize():\n assert_series_equal(winsorize(pd.Series(dtype=float)), pd.Series(dtype=float))\n\n x = generate_series(10000)\n # You must use absolute returns here, generate_series uses random absolute returns and as such has a decent chance\n # of going negative on a sample of 10k, if it goes negative the relative return will be garbage and test can fail\n r = returns(x, type=Returns.ABSOLUTE)\n\n for limit in [1.0, 2.0]:\n mu = r.mean()\n sigma = r.std()\n\n b_upper = mu + sigma * limit * 1.001\n b_lower = mu - sigma * limit * 1.001\n\n assert True in r.ge(b_upper).values\n assert True in r.le(b_lower).values\n\n wr = winsorize(r, limit)\n\n assert True not in wr.ge(b_upper).values\n assert True not in wr.le(b_lower).values\n\n\ndef test_percentiles():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 7),\n dt.date(2019, 1, 8),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n y = pd.Series([3.5, 1.8, 2.9, 1.2, 3.1, 6.0], index=dates)\n y_with_mismatched_index = pd.Series([3.5, 1.8, 2.9, 1.2, 3.1], index=dates[:-1])\n\n assert_series_equal(percentiles(pd.Series(dtype=float), y), pd.Series(dtype=float))\n assert_series_equal(percentiles(x, pd.Series(dtype=float)), pd.Series(dtype=float))\n assert_series_equal(percentiles(x, y, Window(7, 0)), pd.Series(dtype=float))\n\n result = percentiles(x, y, 2)\n expected = pd.Series([50.0, 50.0, 100.0, 75.0], index=dates[2:])\n assert_series_equal(result, expected, obj=\"percentiles with window length 2\")\n\n result = percentiles(x, y, Window(2, 0))\n expected = pd.Series([100.0, 0.0, 50.0, 50.0, 100.0, 75.0], index=dates)\n assert_series_equal(result, expected, obj=\"percentiles with window 2 and ramp 0\")\n\n result = percentiles(x, y_with_mismatched_index, Window(2, 0))\n expected = pd.Series([100.0, 0.0, 50.0, 50.0, 100.0], index=dates[:-1])\n assert_series_equal(result, expected, obj=\"percentiles with mismatched y index\")\n\n result = percentiles(x, y, Window('1w', 0))\n expected = pd.Series([100.0, 0.0, 33.333333, 25.0, 100.0, 90.0], index=dates)\n assert_series_equal(result, expected, obj=\"percentiles with window 1w\")\n\n result = percentiles(x, y, Window('1w', '3d'))\n expected = pd.Series([25.0, 100.0, 90.0], index=dates[3:])\n assert_series_equal(result, expected, obj=\"percentiles with window 1w and ramp 3d\")\n\n result = percentiles(x, y, Window(5, '3d'))\n expected = pd.Series([25.0, 100.0, 90.0], index=dates[3:])\n assert_series_equal(result, expected, obj=\"percentiles with window 5 and ramp 3d\")\n\n result = percentiles(x)\n expected = pd.Series([50.0, 25.0, 66.667, 12.500, 70.0, 91.667], index=dates)\n assert_series_equal(result, expected, obj=\"percentiles over historical values\")\n\n result = percentiles(x, y)\n expected = pd.Series([100.0, 0.0, 33.333, 25.0, 100.0, 91.667], index=dates)\n assert_series_equal(result, expected, obj=\"percentiles without window length\", rtol=1e-3)\n\n with pytest.raises(ValueError):\n percentiles(x, pd.Series(dtype=float), Window(6, 1))\n\n\ndef test_percentile():\n with pytest.raises(MqError):\n percentile(pd.Series(dtype=float), -1)\n with pytest.raises(MqError):\n percentile(pd.Series(dtype=float), 100.1)\n with pytest.raises(MqTypeError):\n percentile(pd.Series(range(5), index=range(5)), 90, \"2d\")\n\n for n in range(0, 101, 5):\n assert percentile(pd.Series(x * 10 for x in range(0, 11)), n) == n\n\n x = percentile(pd.Series(x for x in range(0, 5)), 50, 2)\n assert_series_equal(x, pd.Series([1.5, 2.5, 3.5], index=pd.RangeIndex(2, 5)))\n\n x = percentile(pd.Series(dtype=float), 90, \"1d\")\n assert_series_equal(x, pd.Series(dtype=float), obj=\"Percentile with empty series\")\n\n\ndef test_percentile_str():\n today = dt.datetime.now()\n days = pd.date_range(today, periods=12, freq='D')\n start = pd.Series([29, 56, 82, 13, 35, 53, 25, 23, 21, 12, 15, 9], index=days)\n actual = percentile(start, 2, '10d')\n expected = pd.Series([12.18, 9.54], index=pd.date_range(today + dt.timedelta(days=10), periods=2, freq='D'))\n assert_series_equal(actual, expected)\n\n actual = percentile(start, 50, '1w')\n expected = percentile(start, 50, 7)\n assert_series_equal(actual, expected)\n\n\ndef test_regression():\n x1 = pd.Series([0.0, 1.0, 4.0, 9.0, 16.0, 25.0, np.nan], index=pd.date_range('2019-1-1', periods=7), name='x1')\n x2 = pd.Series([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], index=pd.date_range('2019-1-1', periods=8))\n y = pd.Series([10.0, 14.0, 20.0, 28.0, 38.0, 50.0, 60.0], index=pd.date_range('2019-1-1', periods=7))\n\n with pytest.raises(MqTypeError):\n LinearRegression([x1, x2], y, 1)\n\n regression = LinearRegression([x1, x2], y, True)\n\n np.testing.assert_almost_equal(regression.coefficient(0), 10.0)\n np.testing.assert_almost_equal(regression.coefficient(1), 1.0)\n np.testing.assert_almost_equal(regression.coefficient(2), 3.0)\n\n np.testing.assert_almost_equal(regression.r_squared(), 1.0)\n\n expected = pd.Series([10.0, 14.0, 20.0, 28.0, 38.0, 50.0], index=pd.date_range('2019-1-1', periods=6))\n assert_series_equal(regression.fitted_values(), expected)\n\n dates_predict = [dt.date(2019, 2, 1), dt.date(2019, 2, 2)]\n predicted = regression.predict(\n [pd.Series([2.0, 3.0], index=dates_predict), pd.Series([6.0, 7.0], index=dates_predict)]\n )\n expected = pd.Series([30.0, 34.0], index=dates_predict)\n assert_series_equal(predicted, expected)\n\n np.testing.assert_almost_equal(regression.standard_deviation_of_errors(), 0)\n\n\ndef test_rolling_linear_regression():\n x1 = pd.Series([0.0, 1.0, 4.0, 9.0, 16.0, 25.0, np.nan], index=pd.date_range('2019-1-1', periods=7), name='x1')\n x2 = pd.Series([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], index=pd.date_range('2019-1-1', periods=8))\n y = pd.Series([10.0, 14.0, 20.0, 28.0, 28.0, 40.0, 60.0], index=pd.date_range('2019-1-1', periods=7))\n\n with pytest.raises(MqValueError):\n RollingLinearRegression([x1, x2], y, 3, True)\n\n with pytest.raises(MqTypeError):\n RollingLinearRegression([x1, x2], y, 4, 1)\n\n regression = RollingLinearRegression([x1, x2], y, 4, True)\n\n expected = pd.Series([np.nan, np.nan, np.nan, 10.0, 2.5, 19.0], index=pd.date_range('2019-1-1', periods=6))\n assert_series_equal(regression.coefficient(0), expected, check_names=False)\n\n expected = pd.Series([np.nan, np.nan, np.nan, 1.0, -1.5, 1.0], index=pd.date_range('2019-1-1', periods=6))\n assert_series_equal(regression.coefficient(1), expected, check_names=False)\n\n expected = pd.Series([np.nan, np.nan, np.nan, 3.0, 12.5, -1.0], index=pd.date_range('2019-1-1', periods=6))\n assert_series_equal(regression.coefficient(2), expected, check_names=False)\n\n expected = pd.Series([np.nan, np.nan, np.nan, 1.0, 0.964029, 0.901961], index=pd.date_range('2019-1-1', periods=6))\n assert_series_equal(regression.r_squared(), expected, check_names=False)\n\n expected = pd.Series([np.nan, np.nan, np.nan, 28.0, 28.5, 39.0], index=pd.date_range('2019-1-1', periods=6))\n assert_series_equal(regression.fitted_values(), expected, check_names=False)\n\n expected = pd.Series([np.nan, np.nan, np.nan, 0.0, 2.236068, 4.472136], index=pd.date_range('2019-1-1', periods=6))\n assert_series_equal(regression.standard_deviation_of_errors(), expected, check_names=False)\n\n\ndef test_sir_model():\n n = 1000\n d = 100\n i0 = 100\n r0 = 0\n s0 = n\n beta = 0.5\n gamma = 0.25\n\n t = np.linspace(0, d, d)\n\n def deriv(y, t_loc, n_loc, beta_loc, gamma_loc):\n s, i, r = y\n dsdt = -beta_loc * s * i / n_loc\n didt = beta_loc * s * i / n_loc - gamma_loc * i\n drdt = gamma_loc * i\n\n return dsdt, didt, drdt\n\n def get_series(beta_loc, gamma_loc):\n # Initial conditions vector\n y0 = s0, i0, r0\n # Integrate the SIR equations over the time grid, t.\n ret = odeint(deriv, y0, t, args=(n, beta_loc, gamma_loc))\n s, i, r = ret.T\n\n dr = pd.date_range(dt.date.today(), dt.date.today() + dt.timedelta(days=d - 1))\n return pd.Series(s, dr), pd.Series(i, dr), pd.Series(r, dr)\n\n (s, i, r) = get_series(beta, gamma)\n\n sir = SIRModel(beta, gamma, s, i, r, n)\n\n assert abs(sir.beta() - beta) < 0.01\n assert abs(sir.gamma() - gamma) < 0.01\n\n beta = 0.4\n gamma = 0.25\n\n (s, i, r) = get_series(0.4, 0.25)\n\n s_predict = sir.predict_s()\n i_predict = sir.predict_i()\n r_predict = sir.predict_r()\n\n assert s_predict.size == d\n assert i_predict.size == d\n assert r_predict.size == d\n\n with pytest.raises(MqTypeError):\n SIRModel(beta, gamma, s, i, r, n, fit=0)\n\n sir = SIRModel(beta, gamma, s, i, r, n, fit=False)\n\n assert sir.beta() == beta\n assert sir.gamma() == gamma\n\n sir1 = SIRModel(beta, gamma, s, i, r, n, fit=False)\n\n with DataContext(end=dt.date.today() + dt.timedelta(days=d - 1)):\n sir2 = SIRModel(beta, gamma, s.iloc[0], i, r.iloc[0], n, fit=False)\n\n assert sir1.beta() == sir1.beta()\n assert sir2.gamma() == sir2.gamma()\n assert (sir1.predict_i() == sir2.predict_i()).all()\n assert (sir1.predict_r() == sir2.predict_r()).all()\n assert (sir1.predict_s() == sir2.predict_s()).all()\n\n\ndef test_seir_model():\n n = 1000\n d = 100\n e0 = 1\n i0 = 1\n r0 = 0\n s0 = n\n beta = 0.5\n gamma = 0.2\n sigma = 1\n\n t = np.linspace(0, d, d)\n\n def deriv(y, t_loc, n_loc, beta_loc, gamma_loc, sigma_loc):\n s, e, i, r = y\n dsdt = -beta_loc * s * i / n_loc\n dedt = beta_loc * s * i / n_loc - sigma_loc * e\n didt = sigma_loc * e - gamma * i\n drdt = gamma_loc * i\n\n return dsdt, dedt, didt, drdt\n\n def get_series(beta_loc, gamma_loc, sigma_loc):\n # Initial conditions vector\n y0 = s0, e0, i0, r0\n # Integrate the SEIR equations over the time grid, t.\n ret = odeint(deriv, y0, t, args=(n, beta_loc, gamma_loc, sigma_loc))\n s, e, i, r = ret.T\n\n dr = pd.date_range(dt.date.today(), dt.date.today() + dt.timedelta(days=d - 1))\n return pd.Series(s, dr), pd.Series(e, dr), pd.Series(i, dr), pd.Series(r, dr)\n\n (s, e, i, r) = get_series(beta, gamma, sigma)\n\n seir = SEIRModel(beta, gamma, sigma, s, e, i, r, n)\n\n assert abs(seir.beta() - beta) < 0.01\n assert abs(seir.gamma() - gamma) < 0.01\n assert abs(seir.sigma() - sigma) < 0.01\n\n s_predict = seir.predict_s()\n e_predict = seir.predict_e()\n i_predict = seir.predict_i()\n r_predict = seir.predict_i()\n\n assert s_predict.size == d\n assert e_predict.size == d\n assert i_predict.size == d\n assert r_predict.size == d\n\n with pytest.raises(MqTypeError):\n SEIRModel(beta, gamma, sigma, s, e, i, r, n, fit=0)\n\n seir = SEIRModel(beta, gamma, sigma, s, e, i, r, n, fit=False)\n\n assert seir.beta() == beta\n assert seir.gamma() == gamma\n assert seir.sigma() == sigma\n\n seir1 = SEIRModel(beta, gamma, sigma, s, e, i, r, n, fit=False)\n\n with DataContext(end=dt.date.today() + dt.timedelta(days=d - 1)):\n seir2 = SEIRModel(beta, gamma, sigma, s.iloc[0], e.iloc[0], i, r.iloc[0], n, fit=False)\n\n assert seir1.beta() == seir1.beta()\n assert seir2.gamma() == seir2.gamma()\n assert seir2.sigma() == seir2.sigma()\n assert (seir1.predict_i() == seir2.predict_i()).all()\n assert (seir1.predict_e() == seir2.predict_e()).all()\n assert (seir1.predict_r() == seir2.predict_r()).all()\n assert (seir1.predict_s() == seir2.predict_s()).all()\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_statistics.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_tca.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on ans\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom unittest.mock import Mock\n\nimport pandas as pd\nfrom testfixtures import Replacer\n\nimport gs_quant.timeseries.tca as tm\nfrom gs_quant.common import AssetClass\nfrom gs_quant.markets.index import Index\n\n\ndef test_covariance():\n mock_spx_1 = Index('MA890', AssetClass.Equity, 'SPX', entity={'type': 'Index', 'underlying_asset_ids': []})\n mock_spx_2 = Index('MA890', AssetClass.Equity, 'SPX', entity={'type': 'Index', 'underlying_asset_ids': []})\n\n replace = Replacer()\n mock_data = pd.DataFrame(\n data={\n \"assetId\": [\"MA00S9PEKCD2NQBD\"],\n \"bucketStart\": [\"19:30:00\"],\n \"bucketEnd\": [\"20:00:00\"],\n \"asset2Id\": [\"MAZYGB8GAYB9ZFQ1\"],\n \"covariance\": [5.0717e-6],\n },\n index=[pd.Timestamp('2021-12-20'), pd.Timestamp('2021-12-20')],\n )\n mock_request = replace('gs_quant.data.dataset.Dataset.get_data', Mock())\n mock_request.return_value = mock_data\n\n tm.covariance(mock_spx_1, mock_spx_2, '19:30:00', '20:00:00')\n replace.restore()\n" }, { "path": "gs_quant/test/timeseries/test_technicals.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas.testing import assert_series_equal\n\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.timeseries import (\n moving_average,\n Window,\n smoothed_moving_average,\n macd,\n bollinger_bands,\n relative_strength_index,\n exponential_moving_average,\n exponential_volatility,\n exponential_spread_volatility,\n trend,\n SeasonalModel,\n Frequency,\n seasonally_adjusted,\n)\n\n\ndef test_moving_average():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = moving_average(x)\n expected = pd.Series([3.0, 2.5, 8 / 3, 2.25, 2.4, 3.0], index=dates)\n assert_series_equal(result, expected, obj=\"Moving average\")\n\n result = moving_average(x, Window(1, 0))\n expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Moving average window 1\")\n\n result = moving_average(x, Window(2, 0))\n expected = pd.Series([3.0, 2.5, 2.5, 2.0, 2.0, 4.5], index=dates)\n assert_series_equal(result, expected, obj=\"Moving average window 2\")\n\n result = moving_average(x, \"2d\")\n expected = pd.Series([2.5, 2, 2, 4.5], index=dates[2:])\n assert_series_equal(result, expected, obj=\"Moving average strdate window\")\n\n\ndef test_smoothed_moving_average():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = smoothed_moving_average(x)\n expected = pd.Series([3.00000, 2.83333, 2.86111, 2.55093, 2.62577, 3.18814], index=dates)\n assert_series_equal(result, expected, obj=\"Smoothed moving average\")\n\n result = smoothed_moving_average(x, Window(1, 0))\n expected = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert_series_equal(result, expected, obj=\"Smoothed moving average window 1\")\n\n result = smoothed_moving_average(x, Window(2, 0))\n expected = pd.Series([3.00000, 2.50000, 2.75000, 1.87500, 2.43750, 4.21875], index=dates)\n assert_series_equal(result, expected, obj=\"Smoothed moving average window 2\")\n\n result = smoothed_moving_average(x, \"2d\")\n expected = pd.Series([2.5, 1.75, 2.375, 4.1875], index=dates[2:])\n assert_series_equal(result, expected, obj=\"Smoothed moving average window strdate\")\n\n result = smoothed_moving_average(x, \"1m\")\n expected = pd.Series(dtype=float)\n assert_series_equal(result, expected, obj=\"Smoothed moving average with wider window than series\")\n\n\ndef test_macd():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n assert (macd(x, 10, 10) == 0).all()\n\n assert (macd(x, 10, 12, s=1) == macd(x, 10, 12)).all()\n\n expected = pd.Series([0, -0.166667, 0.027778, -0.282407, 0.093364, 0.624871], index=dates)\n assert_series_equal(macd(x, 2, 3), expected)\n\n\ndef test_bollinger_bands():\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n expected_low = pd.Series([np.nan, 1.085786, 1.511966, 0.335146, 0.611146, -0.346640], index=dates)\n expected_high = pd.Series([np.nan, 3.914214, 3.821367, 4.164854, 4.188854, 6.346640], index=dates)\n\n result = bollinger_bands(x)\n\n low = result[0].squeeze()\n high = result[1].squeeze()\n\n assert_series_equal(low, expected_low, check_names=False, obj=\"Bollinger bands low\")\n assert_series_equal(high, expected_high, check_names=False, obj=\"Bollinger bands high\")\n\n result = bollinger_bands(x, \"2d\")\n print(result)\n\n\ndef test_relative_strength_index():\n dates = [\n dt.date(2020, 1, 2),\n dt.date(2020, 1, 3),\n dt.date(2020, 1, 6),\n dt.date(2020, 1, 7),\n dt.date(2020, 1, 8),\n dt.date(2020, 1, 9),\n dt.date(2020, 1, 10),\n dt.date(2020, 1, 13),\n dt.date(2020, 1, 14),\n dt.date(2020, 1, 15),\n dt.date(2020, 1, 16),\n dt.date(2020, 1, 17),\n dt.date(2020, 1, 21),\n dt.date(2020, 1, 22),\n dt.date(2020, 1, 23),\n dt.date(2020, 1, 24),\n dt.date(2020, 1, 27),\n dt.date(2020, 1, 28),\n dt.date(2020, 1, 29),\n dt.date(2020, 1, 30),\n dt.date(2020, 1, 31),\n dt.date(2020, 2, 3),\n ]\n\n SPX_values = [\n 3257.8501,\n 3234.8501,\n 3246.28,\n 3237.1799,\n 3253.05,\n 3274.7,\n 3265.3501,\n 3288.1299,\n 3283.1499,\n 3289.29,\n 3316.8101,\n 3329.6201,\n 3320.79,\n 3321.75,\n 3325.54,\n 3295.47,\n 3243.6299,\n 3276.24,\n 3273.3999,\n 3283.6599,\n 3225.52,\n 3248.9199,\n ]\n\n target_vals = [66.35899, 50.99377, 57.63855, 56.91475, 58.92162, 45.88014, 50.61763]\n\n w = 14\n SPX = pd.Series(data=SPX_values, index=dates)\n expected = pd.Series(data=target_vals, index=dates[w + 1 :])\n result = relative_strength_index(SPX, w)\n assert_series_equal(result, expected, check_names=False, obj=\"Relative Strength Index\")\n\n increasing_series = pd.Series(np.arange(1, 23, 1), index=dates)\n expected = pd.Series(data=np.ones(7) * 100, index=dates[15:])\n result = relative_strength_index(increasing_series, w)\n assert_series_equal(result, expected, check_names=False, obj=\"Relative Strength Index\")\n result = relative_strength_index(SPX, \"2w\")\n print(result)\n\n\ndef test_exponential_moving_average():\n def ema_by_hand(ts, alpha=0.75):\n R = ts.copy()\n R *= 0\n R.iloc[0] = ts.iloc[0]\n for i in range(1, len(ts)):\n R.iloc[i] = alpha * R.iloc[i - 1] + (1 - alpha) * ts.iloc[i]\n return R\n\n dates = [\n dt.date(2019, 1, 1),\n dt.date(2019, 1, 2),\n dt.date(2019, 1, 3),\n dt.date(2019, 1, 4),\n dt.date(2019, 1, 5),\n dt.date(2019, 1, 6),\n ]\n\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n\n result = exponential_moving_average(x)\n expected = ema_by_hand(x)\n assert_series_equal(result, expected, obj=\"Exponential moving average\")\n\n result = exponential_moving_average(x, 0.6)\n expected = ema_by_hand(x, 0.6)\n assert_series_equal(result, expected, obj=\"Exponential moving average weight 1\")\n\n result = exponential_moving_average(x, 0)\n expected = x\n assert_series_equal(result, expected, obj=\"Exponential moving average weight 2\")\n\n\ndef test_exponential_volatility():\n dates = pd.date_range('2019-1-1', periods=6)\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n result = exponential_volatility(x)\n expected = pd.Series([np.nan, np.nan, 935.41, 810.31, 1958.56, 1710.02], index=dates)\n assert_series_equal(result, expected, obj=\"Exponential volatility\")\n\n\ndef test_exponential_spread_volatility():\n dates = pd.date_range('2019-1-1', periods=6)\n x = pd.Series([3.0, 2.0, 3.0, 1.0, 3.0, 6.0], index=dates)\n result = exponential_spread_volatility(x)\n expected = pd.Series([np.nan, np.nan, 22.4499, 20.5757, 28.6067, 34.2183], index=dates)\n assert_series_equal(result, expected, obj=\"Exponential spread volatility\")\n\n\ndef test_trend():\n short_dates = pd.date_range('2021-01-01', '2021-01-05', freq='D')\n short_x = pd.Series(range(len(short_dates)), index=short_dates)\n with pytest.raises(MqValueError):\n trend(short_x) # Too few datapoints\n long_dates = pd.date_range('2017-01-01', '2021-01-01', freq='D')\n long_x = pd.Series(range(len(long_dates)), index=long_dates)\n res = trend(long_x) # Should not be all NaN, make sure it's correct length\n assert len(res) == len(long_x)\n assert np.isclose(res.iloc[int(len(res) / 2)], long_x.iloc[int(len(res) / 2)], 0.1)\n assert res.notna().any()\n with pytest.raises(ValueError):\n trend(long_x, SeasonalModel.MULTIPLICATIVE) # Should not be all NaN, make sure it's correct length\n res = trend(long_x + 1, SeasonalModel.MULTIPLICATIVE) # Should not be all NaN, make sure it's correct length\n assert len(res) == len(long_x)\n assert np.isclose((res.iloc[int(len(res) / 2)] - 1), long_x.iloc[int(len(res) / 2)], 0.1)\n assert res.notna().any()\n with pytest.raises(MqValueError):\n x = pd.Series(range(10))\n trend(x)\n\n\ndef test_seasonality_adjusted():\n # Test that correctly runs with different frequencies\n for pfreq in ['B', 'D', 'W', 'ME', 'QE', 'YE']:\n for freq in [Frequency.YEAR, Frequency.QUARTER, Frequency.MONTH, Frequency.WEEK]:\n dates = pd.date_range('2019-01-01', '2021-01-05', freq=pfreq)\n series = pd.Series(range(len(dates)), index=dates) + 1\n try:\n seasonally_adjusted(series, SeasonalModel.ADDITIVE, freq)\n seasonally_adjusted(series, SeasonalModel.MULTIPLICATIVE, freq)\n except MqValueError:\n pass\n with pytest.raises(MqValueError):\n x = pd.Series(range(10))\n seasonally_adjusted(x)\n\n\nif __name__ == \"__main__\":\n pytest.main(args=[\"test_technicals.py\"])\n" }, { "path": "gs_quant/test/timeseries/test_timeseries.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport inspect\nimport re\nimport types\n\nimport pandas as pd\nimport pytest\nfrom gs_quant.markets.securities import Asset\nfrom gs_quant.timeseries import Window\n\ndummy_series = pd.Series(\n [\n 191.63,\n 184.31,\n 184.09,\n 179.67,\n 178.83,\n 176.8,\n 176.7,\n 175.92,\n 172.77,\n 168.01,\n 171.5,\n 169.25,\n 168.41,\n 160.05,\n 156.35,\n 162.93,\n 165.41,\n 163.03,\n 167.05,\n 172.03,\n 169.51,\n 175.05,\n 176.02,\n 175.37,\n 176.47,\n 176.0,\n 176.93,\n 178.72,\n 179.91,\n 197.08,\n 199.09,\n 202.54,\n ]\n)\n\ndummy_series2 = pd.Series(\n [\n 2790.37,\n 2700.06,\n 2695.95,\n 2633.08,\n 2637.72,\n 2636.78,\n 2651.07,\n 2650.54,\n 2599.95,\n 2545.94,\n 2546.16,\n 2506.96,\n 2467.42,\n 2416.62,\n 2351.1,\n 2467.7,\n 2488.83,\n 2485.74,\n 2506.85,\n 2510.03,\n 2447.89,\n 2531.94,\n 2549.69,\n 2574.41,\n 2584.96,\n 2596.64,\n 2596.26,\n 2582.61,\n 2610.3,\n 2616.1,\n 2635.96,\n 2670.71,\n ]\n)\n\n\n@pytest.fixture(scope='module')\ndef ts_map():\n return {\n k: v\n for k, v in globals().items()\n if isinstance(v, types.FunctionType)\n and (hasattr(v, 'plot_function') or hasattr(v, 'plot_measure') or hasattr(v, 'plot_measure_entity'))\n }\n\n\ndef test_have_docstrings(ts_map):\n for k, v in ts_map.items():\n assert v.__doc__\n\n\ndef test_window_to_from_dict():\n window = Window(w=1, r=2)\n window_dict = window.as_dict()\n\n assert window_dict['w'] == 1\n assert window_dict['r'] == 2\n\n window_dict = {'w': 1, 'r': 2}\n\n window = Window.from_dict(window_dict)\n assert window.w == 1\n assert window.r == 2\n\n\ndef test_docstrings(ts_map):\n for k, v in ts_map.items():\n print('testing function', k)\n params = set()\n has_return = False\n others = 0\n\n lines = [x.strip() for x in v.__doc__.splitlines()]\n for line in lines:\n if not line:\n continue\n print(line)\n if line.startswith(':param'):\n params.add(re.split('[:\\\\s]+', line)[2])\n elif line.startswith(':return:'):\n has_return = True\n else:\n others += 1\n\n assert params == set(inspect.signature(v).parameters.keys()), 'all parameters documented'\n assert has_return, 'return value is documented'\n assert others >= 1, 'at least one line description'\n\n\ndef test_annotations(ts_map):\n for k, v in ts_map.items():\n print('testing function', k)\n annotations = v.__annotations__\n assert annotations, 'has annotations'\n assert 'return' in annotations, 'specifies return type'\n assert set(inspect.signature(v).parameters.keys()) | {'return'} == set(annotations.keys()), (\n 'specifies parameter types'\n )\n\n\ndef _check_measure_args(params, request_required, fn_name):\n param = params.popitem()\n name = param[1].name\n if request_required:\n assert name == 'request_id'\n if request_required or name == 'request_id':\n assert param[1].kind == inspect.Parameter.KEYWORD_ONLY\n param = params.popitem()\n\n assert param[1].name == 'real_time'\n assert param[1].kind == inspect.Parameter.KEYWORD_ONLY\n param = params.popitem()\n assert param[1].name == 'source'\n assert param[1].kind == inspect.Parameter.KEYWORD_ONLY\n\n counter = 0\n while len(params) > 0:\n param = params.popitem()\n assert param[1].kind == inspect.Parameter.POSITIONAL_OR_KEYWORD, f'wrong parameter type on {fn_name}'\n if param[1].annotation == Asset:\n counter += 1\n\n assert counter < 2, 'no more than 1 extra asset parameter allowed'\n\n\ndef test_measures(ts_map):\n for k, v in ts_map.items():\n if not hasattr(v, 'plot_measure'):\n continue\n params = inspect.signature(v).parameters.copy()\n param = params.popitem(last=False)\n assert param[1].name == 'asset'\n assert param[1].annotation == Asset\n assert param[1].kind == inspect.Parameter.POSITIONAL_OR_KEYWORD\n _check_measure_args(params, False, v.__name__)\n\n\ndef test_measures_on_entities(ts_map):\n for k, v in ts_map.items():\n if not hasattr(v, 'plot_measure_entity'):\n continue\n params = inspect.signature(v).parameters.copy()\n param = params.popitem(last=False)\n assert param[1].name == f'{v.entity_type.value}_id'\n assert param[1].annotation is str\n assert param[1].kind == inspect.Parameter.POSITIONAL_OR_KEYWORD\n _check_measure_args(params, True, v.__name__)\n\n\nif __name__ == '__main__':\n pytest.main(args=[__file__])\n" }, { "path": "gs_quant/test/timeseries/utils.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport json\nimport pathlib\nfrom typing import Union\n\nfrom gs_quant.base import Base\n\n\ndef __unpack(results: Union[dict, list], cls: type) -> Union[Base, tuple, dict]:\n if issubclass(cls, Base):\n if isinstance(results, list):\n return tuple(None if r is None else cls.from_dict(r) for r in results)\n else:\n return None if results is None else cls.from_dict(results)\n else:\n if isinstance(results, list):\n return tuple(cls(**r) for r in results)\n else:\n return cls(**results)\n\n\ndef handle_response(res, cls=None):\n if cls:\n if isinstance(res, dict) and 'results' in res:\n res['results'] = __unpack(res['results'], cls)\n else:\n res = __unpack(res, cls)\n\n return res\n\n\ndef mock_request(path, payload=None, cls=None):\n response = {}\n \"/assets/{id}/xrefs\"\n if '/assets' in path:\n if path == \"/assets/query\":\n bbid = payload.where.bbid\n with open(pathlib.Path(__file__).parents[1] / f'resources/asset-query-{bbid}.json') as response:\n response = handle_response(json.load(response), cls)\n else:\n split = path.split(\"/\")\n asset_id = split[2]\n if len(split) == 3:\n with open(pathlib.Path(__file__).parents[1] / f'resources/{asset_id}.json') as response:\n response = handle_response(json.load(response), cls)\n else:\n if \"xrefs\" in path:\n with open(pathlib.Path(__file__).parents[1] / f'resources/{asset_id}-xrefs.json') as response:\n response = handle_response(json.load(response), cls)\n return response\n" }, { "path": "gs_quant/test/tracing/__init__.py", "content": "" }, { "path": "gs_quant/test/tracing/test_tracing.py", "content": "\"\"\"\nCopyright 2025 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\n\nimport pytest\nfrom plotly.graph_objs import Figure\n\nfrom gs_quant.errors import MqError\nfrom gs_quant.tracing import Tracer\n\n\ndef make_zero_duration(spans):\n \"\"\"\n Helper function to set the duration of a span to zero\n \"\"\"\n for span in spans:\n span.unwrap()._end_time = span.start_time\n\n\ndef test_tracer_tags():\n Tracer.reset()\n with Tracer('Some work') as scope:\n scope.span.set_tag('user', 'martin')\n\n spans = Tracer.get_spans()\n assert len(spans) == 1\n assert 'user' in spans[0].tags\n assert spans[0].tags['user'] == 'martin'\n\n\ndef test_tracer_events():\n Tracer.reset()\n with Tracer('Some work') as scope:\n scope.span.log_kv({\"my_event\": \"yikes\"})\n scope.span.add_event(\"Woo hoo!\")\n spans = Tracer.get_spans()\n assert len(spans) == 1\n assert len(spans[0].events) == 2\n e1 = spans[0].events[0]\n e2 = spans[0].events[1]\n assert e1.name == \"log\" # default name if \"event\" not specified\n assert \"my_event\" in e1.attributes\n assert abs(e1.timestamp_sec - dt.datetime.now().timestamp()) < 2 # to make sure we're used right scale factor\n assert e2.name == \"Woo hoo!\"\n assert e2.attributes == {}\n\n\ndef test_tracer_print():\n Tracer.reset()\n with Tracer('A'):\n with Tracer('B'):\n pass\n with Tracer('C'):\n with Tracer('D'):\n pass\n try:\n with Tracer('E'):\n raise ValueError(\"test error handle\")\n except Exception:\n pass\n with Tracer('F'):\n pass\n # Force elapsed time to 0 to make sure no spurious tiny times\n make_zero_duration(Tracer.get_spans())\n tracer_str, _ = Tracer.print(reset=True)\n expected = '\\n'.join(\n [\n 'A 0.0 ms',\n '* B 0.0 ms',\n '* C 0.0 ms',\n '* * D 0.0 ms',\n '* * E 0.0 ms [Error]',\n 'F 0.0 ms',\n ]\n )\n assert tracer_str == expected\n\n\ndef test_tracer_plot():\n Tracer.reset()\n with Tracer(\"A\") as scope:\n scope.span.set_tag(\"hello\", \"world\")\n with Tracer(\"B\"):\n Tracer.record_exception(ValueError(\"ah\"))\n fig = Tracer.plot(True, False)\n assert isinstance(fig, Figure)\n\n\ndef test_gather_when_multi_traces():\n Tracer.reset()\n with Tracer(\"A\") as first_scope:\n first_scope.span.set_tag(\"hello\", \"world\")\n first_trace_id = first_scope.span.trace_id\n with Tracer(\"B1\"):\n pass\n with Tracer(\"B2\"):\n pass\n with Tracer(\"C\") as second_scope:\n second_trace_id = second_scope.span.trace_id\n with Tracer(\"B1\"):\n pass\n\n data1, total = Tracer.gather_data(False, trace_id=first_trace_id)\n data2, total = Tracer.gather_data(False, trace_id=second_trace_id)\n assert len(data1) == 3\n assert len(data2) == 2\n\n\ndef test_tracer_wrapped_error():\n Tracer.reset()\n with pytest.raises(MqError, match='Unable to calculate: Outer Thing'):\n with Tracer('Outer Thing', wrap_exceptions=True):\n with Tracer(\n 'Inner Thing',\n ):\n raise KeyError('meaningless error')\n spans = Tracer.get_spans()\n assert 'error' in spans[0].tags\n assert 'error' in spans[1].tags\n Tracer.reset()\n\n with pytest.raises(MqError, match='Unable to calculate: Inner Thing'):\n with Tracer('Outer Thing', wrap_exceptions=True):\n with Tracer('Inner Thing', wrap_exceptions=True):\n raise KeyError('meaningless error')\n assert 'error' in spans[0].tags\n assert 'error' in spans[1].tags\n Tracer.reset()\n\n\ndef test_active_span():\n Tracer.reset()\n inactive = Tracer.active_span()\n # We get a noop/non-recording span\n assert inactive is not None\n assert inactive.span_id is not None\n assert inactive.trace_id is not None\n assert inactive.is_recording() is False\n inactive.set_tag(\"dummy\", \"tag\") # should be a no-op, but not throw\n inactive.add_event(\"Dummy event\") # should be a no-op, but not throw\n\n with Tracer.activate_span(inactive) as scope:\n # This shouldn't have done anything material, since it's a non-recording span\n assert scope.span is not None\n assert scope.span.span_id is not None\n assert scope.span.trace_id is not None\n assert scope.span.is_recording() is False\n\n with Tracer('Outer') as scope:\n scoped_span = scope.span\n active_span = Tracer.active_span() # should be same as scoped_span, just another way to get it\n assert active_span.span_id == scoped_span.span_id\n assert active_span.trace_id == scoped_span.trace_id\n\n assert len(Tracer.get_spans()) == 1 # only \"Outer\" did anything\n\n\ndef test_span_activation():\n Tracer.reset()\n with Tracer('parent') as parent_scope:\n outer_span = parent_scope.span\n with Tracer('child-1') as child1_scope:\n with Tracer.activate_span(outer_span):\n with Tracer('child-2') as inner_scope:\n # Since we re-activated the outer span, child-2's parent should be the outer \"parent\" span\n assert inner_scope.span is not None\n assert inner_scope.span.parent_id == outer_span.span_id\n # Now that we've exited the re-activated context, this span will be nested under child-1\n with Tracer('nested-child') as nested_child:\n assert nested_child.span.parent_id == child1_scope.span.span_id\n # This is a shortcut of with Tracer.activate_span(outer_span), Tracer('child-3')\n with Tracer('child-3', parent_span=outer_span) as another_inner_scope:\n assert another_inner_scope.span is not None\n assert another_inner_scope.span.parent_id == outer_span.span_id\n # Again we should be back in the \"child-1\" context\n with Tracer('another-nested-child') as nested_child:\n assert nested_child.span.parent_id == child1_scope.span.span_id\n\n # Force elapsed time to 0 to make sure no spurious tiny times\n make_zero_duration(Tracer.get_spans())\n tracer_str, _ = Tracer.print(reset=True)\n expected = '\\n'.join(\n [\n 'parent 0.0 ms',\n '* child-2 0.0 ms',\n '* child-3 0.0 ms',\n '* child-1 0.0 ms',\n '* * nested-child 0.0 ms',\n '* * another-nested-child 0.0 ms',\n ]\n )\n assert tracer_str == expected\n\n\ndef test_inject_extract():\n Tracer.reset()\n with Tracer('A') as scope:\n span_a = scope.span\n scope.span.set_tag('user', 'bob')\n fake_http_headers = {}\n Tracer.inject(fake_http_headers)\n spans = Tracer.get_spans()\n assert len(spans) == 1\n assert 'user' in spans[0].tags\n assert spans[0].tags['user'] == 'bob'\n assert len(fake_http_headers) > 0 # we're agnostic to the inject/extractor, so long as it's done something\n\n ctx = Tracer.extract(fake_http_headers)\n with Tracer.start_active_span('B', child_of=ctx) as scope:\n assert scope.span.parent_id == span_a.span_id\n\n with Tracer('C', parent_span=ctx) as scope:\n assert scope.span.parent_id == span_a.span_id\n\n\ndef test_ignore_active_span():\n with Tracer('A') as scope_a:\n with Tracer.start_active_span('B', ignore_active_span=True) as scope_b:\n assert scope_b.span.parent_id is None\n with Tracer.start_active_span('C') as scope_c:\n assert scope_c.span.parent_id == scope_a.span.span_id\n\n\n@pytest.mark.asyncio\nasync def test_callback_in_scope():\n import asyncio\n\n Tracer.reset()\n\n finished = asyncio.Future()\n\n def my_callback_on_complete(future):\n with Tracer('Callback Work') as scope:\n scope.span.set_tag('callback', 'yes')\n assert future.result() == \"done\"\n finished.set_result(True)\n\n async def some_async_work():\n with Tracer('Async Work') as scope:\n scope.span.set_tag('async', 'yes')\n return \"done\"\n\n with Tracer('Main Work') as main_scope:\n main_scope.span.set_tag('main', 'yes')\n\n task = asyncio.create_task(some_async_work())\n task.add_done_callback(Tracer.in_scope(my_callback_on_complete))\n await task\n await finished\n assert finished.result()\n\n spans = Tracer.get_spans()\n assert len(spans) == 4\n Tracer.print()\n main_span = next(span for span in spans if span.operation_name == 'Main Work')\n async_span = next(span for span in spans if span.operation_name == 'Async Work')\n callback_span = next(span for span in spans if span.operation_name == 'callback')\n callback_work_span = next(span for span in spans if span.operation_name == 'Callback Work')\n assert main_span.tags['main'] == 'yes'\n assert async_span.tags['async'] == 'yes'\n assert async_span.parent_id == main_span.span_id\n assert callback_span.parent_id == main_span.span_id\n assert callback_work_span.parent_id == callback_span.span_id\n" }, { "path": "gs_quant/test/utils/__init__.py", "content": "from .mock_calc import *\nfrom .mock_data import *\n" }, { "path": "gs_quant/test/utils/datagrid_test_utils.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport json\nimport pathlib\n\nfrom gs_quant.common import Currency\nfrom gs_quant.markets.securities import Stock\n\n\ndef _read_entity(entity):\n with open(pathlib.Path(__file__).parents[1] / f'resources/{entity}.json') as entity:\n return json.loads(entity.read())\n\n\ndef get_test_entity(entity_id: str):\n entity = _read_entity(entity_id)\n return Stock(id_=entity_id, name=entity['name'], currency=Currency.USD, entity=entity)\n" }, { "path": "gs_quant/test/utils/mock_calc.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport inspect\nfrom pathlib import Path\n\nfrom gs_quant.api.gs.risk import GsRiskApi\nfrom gs_quant.common import CompositeScenario\nfrom gs_quant.datetime import business_day_offset\nfrom gs_quant.test.utils.mock_request import MockRequest\n\n\ndef get_risk_request_id(requests):\n \"\"\"\n This is not a formal equality of the risk request as it covers only the names of core components. When a formal\n eq function is provided on risk_request then this should be replaced with something derived from that.\n :param requests: a collection of RiskRequests\n :type requests: tuple of RiskRequest\n :return: hash\n :rtype: str\n \"\"\"\n identifier = str(len(requests))\n for request in requests:\n for pos in request.positions:\n if pos.instrument.name is None:\n raise ValueError('Positions must have names to be mocked')\n identifier += '_'\n identifier += '-'.join([pos.instrument.name for pos in request.positions])\n identifier += '-'.join([r.__repr__() for r in request.measures])\n date = request.pricing_and_market_data_as_of[0].pricing_date.strftime('%Y%b%d')\n today_pre = business_day_offset(dt.date.today(), 0, roll='preceding').strftime('%Y%b%d')\n today_post = business_day_offset(dt.date.today(), 0, roll='following').strftime('%Y%b%d')\n identifier += 'today' if date in (today_post, today_pre) else date\n if request.scenario is not None:\n if isinstance(request.scenario.scenario, CompositeScenario):\n underlying_scenarios = request.scenario.scenario.scenarios\n if any([scen.name is None for scen in underlying_scenarios]):\n raise RuntimeError('Please provide unique names for your scenarios for testing')\n identifier += '+'.join(sorted([r.name for r in underlying_scenarios]))\n else:\n if request.scenario.scenario.name is None:\n raise RuntimeError('Please provide unique names for your scenarios for testing')\n identifier += request.scenario.scenario.name\n return identifier\n\n\nclass MockCalc(MockRequest):\n api = GsRiskApi\n method = '_exec'\n api_method = GsRiskApi._exec\n\n def __init__(self, mocker, save_files=False, paths=None, application='gs-quant'):\n super().__init__(mocker, save_files, paths, application)\n self.paths = (\n paths\n if paths\n else Path(\n next(\n filter(lambda x: x.code_context and self.__class__.__name__ in x.code_context[0], inspect.stack())\n ).filename\n ).parents[1]\n )\n\n def mock_calc_create_files(self, *args, **kwargs):\n import orjson\n\n # never leave a side_effect calling this function. Call it once to create the files, check them in\n # and switch to mock_calc\n def get_json(*i_args, **i_kwargs):\n this_json = self.api_method(*i_args, **i_kwargs)\n # Post process the json a bit to remove timing info that makes spurious diffs\n for d in [d for a in this_json for b in a for c in b for d in c]:\n for key, val in list(d.items()):\n if val is None or key in ['queueingTime', 'calculationTime']:\n del d[key]\n return this_json\n\n result = get_json(*args, **kwargs)\n result_json = orjson.dumps(\n result, option=orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_NON_STR_KEYS | orjson.OPT_SORT_KEYS\n )\n request_id = self.get_request_id(args, kwargs)\n self.create_files(request_id, result_json)\n return result\n\n def get_request_id(self, args, kwargs):\n request = kwargs.get('request') or args[0]\n request_id = get_risk_request_id(request)\n return request_id\n" }, { "path": "gs_quant/test/utils/mock_data.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport inspect\nfrom pathlib import Path\n\nfrom gs_quant.api.gs.data import GsDataApi\nfrom gs_quant.test.utils.mock_request import MockRequest\n\n\nclass MockData(MockRequest):\n api = GsDataApi\n method = 'query_data'\n api_method = GsDataApi.query_data\n\n def __init__(self, mocker, save_files=False, paths=None, application='gs-quant'):\n super().__init__(mocker, save_files, paths, application)\n self.paths = (\n paths\n if paths\n else Path(\n next(\n filter(lambda x: x.code_context and self.__class__.__name__ in x.code_context[0], inspect.stack())\n ).filename\n ).parents[1]\n )\n\n def mock_calc_create_files(self, *args, **kwargs):\n import orjson\n\n def get_json(*i_args, **i_kwargs):\n this_json = self.api_method(*i_args, **i_kwargs)\n\n return this_json\n\n result = get_json(*args, **kwargs)\n result_json = orjson.dumps(\n result, option=orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_NON_STR_KEYS | orjson.OPT_SORT_KEYS\n )\n request_id = self.get_request_id(args, kwargs)\n self.create_files(request_id, result_json)\n\n return result\n\n def get_request_id(self, args, kwargs):\n query = args[0].as_dict()\n parts = []\n relevant_data_query_params = [\n k\n for k in [\"start_date\", \"start_time\", \"end_date\", \"end_time\", \"as_of_time\", \"since\", \"dates\", \"where\"]\n if k in query.keys()\n ]\n\n def stringify_values(v):\n return ','.join(v) if isinstance(v, list) else str(v)\n\n for k in sorted(relevant_data_query_params):\n v = query[k]\n if k != 'where':\n v_str = stringify_values(v)\n else:\n v_str = '_'.join([f'{where_k}:{stringify_values(where_v)}' for where_k, where_v in v.items()])\n parts.append(f'{k}:{v_str}')\n query_str = '_'.join(parts)\n return f'{args[1]}_{query_str}'\n" }, { "path": "gs_quant/test/utils/mock_request.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport hashlib\nimport json\nimport logging\nimport os\nfrom abc import abstractmethod\nfrom os.path import exists\nfrom pathlib import Path\nfrom typing import List, Dict, Tuple, Any, NamedTuple\nfrom unittest import mock\n\nfrom gs_quant.errors import MqUninitialisedError\nfrom gs_quant.session import GsSession, Environment\nfrom gs_quant.test.mock_data_test_utils import log_mock_data_event\n\nlogger = logging.getLogger(\"mock_request\")\n\n\nclass MockFileKey:\n def __init__(self, request_id):\n self.request_id = request_id.decode('utf-8') if isinstance(request_id, bytes) else request_id\n self.request_hash = self.get_hash(self.request_id)\n self.file_name = f'request{self.request_hash}.json'\n\n @staticmethod\n def get_hash(request_id: str):\n id_bytes = request_id.encode('utf-8')\n return hashlib.md5(id_bytes).hexdigest()\n\n def __eq__(self, other):\n return (\n self.file_name == other.file_name\n and self.request_id == other.request_id\n and self.request_hash == other.request_hash\n )\n\n def __hash__(self):\n return hash((self.file_name, self.request_id, self.request_hash))\n\n def __repr__(self):\n return f\"MockFileKey({self.request_hash})\"\n\n\nclass MockFileRecord(NamedTuple):\n \"\"\"a docstring\"\"\"\n\n foo: int\n bar: str\n used_in_tests: set\n\n\nclass MockRequest:\n __looked_at_files = {}\n __cached_test_data = {}\n __saved_files = set()\n api = None\n method = None\n api_method = None\n\n def __init__(self, mocker, save_files=False, paths=None, application='gs-quant'):\n # do not save tests with save_files = True\n self.save_files = save_files\n self.mocker = mocker\n self.paths = paths\n self.application = application\n if any([attr is None for attr in [self.api, self.method, self.api_method]]):\n raise NotImplementedError('Mock Subclasses must implement api and method properties')\n\n def __enter__(self):\n if self.save_files:\n GsSession.use(Environment.PROD, None, None, application=self.application)\n self.mocker.patch.object(\n self.api,\n self.method,\n side_effect=self.mock_calc_create_new_files\n if str(self.save_files).casefold() == 'new'\n else self.mock_calc_check_and_create_files,\n )\n else:\n try:\n _ = GsSession.current\n except MqUninitialisedError:\n from gs_quant.session import OAuth2Session\n\n OAuth2Session._authenticate = mock.MagicMock(return_value=None)\n GsSession.use(Environment.PROD, 'fake_client_id', 'fake_secret', application=self.application)\n self.mocker.patch.object(self.api, self.method, side_effect=self.mock_calc)\n\n def full_file_path(self, mock_file_key: MockFileKey):\n return self.build_file_path(self.paths, mock_file_key)\n\n @staticmethod\n def build_file_path(base_path, mock_file_key: MockFileKey):\n return base_path / 'calc_cache' / mock_file_key.file_name\n\n def mock_calc(self, *args, **kwargs):\n request_id = self.get_request_id(args, kwargs)\n mfk = MockFileKey(request_id)\n file_path = self.full_file_path(mfk)\n if not exists(file_path):\n logger.error(\n f'Unable to find mock test date for {self.__class__.__name__}:\\n'\n f'File {file_path} not found. Key: {request_id}'\n )\n raise FileNotFoundError(f'File {mfk.file_name} not found in {self.paths / \"calc_cache\"}')\n existing_meta, mock_data = self._load_mock_data_from_file(file_path)\n self._record_file_used_in_test(mfk)\n return mock_data\n\n def _record_file_used_in_test(self, mfk: MockFileKey):\n test_name = self._get_current_test_name()\n file_deets = MockRequest.__looked_at_files.setdefault(self.paths, dict())\n file_deets.setdefault(mfk, (self.__class__.__name__, set()))[1].add(test_name)\n\n @staticmethod\n def _get_current_test_name():\n return os.environ.get('PYTEST_CURRENT_TEST').split(':')[-1].split(' ')[0]\n\n @classmethod\n def _load_mock_data_from_file(cls, file_path):\n meta = {}\n data = cls.__cached_test_data.get(file_path)\n if data is None:\n with open(file_path) as json_data:\n data = json.load(json_data)\n cls.__cached_test_data[file_path] = data\n if isinstance(data, dict) and 'mocked_data' in data:\n meta = data\n data = json.loads(data['mocked_data'])\n return meta, data\n\n @classmethod\n def _make_mock_file_data(cls, mfk: MockFileKey, cls_name, mock_data_str, used_in_tests):\n return {\n 'request_id': mfk.request_id,\n 'request_hash': mfk.request_hash,\n 'type': cls_name,\n 'tests': used_in_tests,\n 'mocked_data': mock_data_str,\n }\n\n def create_files(self, request_id, result_json):\n cls_name = self.__class__.__name__\n ct = self._get_current_test_name()\n mfk = MockFileKey(request_id)\n file_path = self.full_file_path(mfk)\n mock_data = self._make_mock_file_data(mfk, cls_name, result_json.decode('utf-8'), [ct])\n with open(file_path, 'w') as file_data:\n MockRequest.__saved_files.add(request_id)\n file_data.write(json.dumps(mock_data, indent=4))\n log_mock_data_event(f'Created {mfk.file_name}.')\n self._record_file_used_in_test(mfk)\n\n @abstractmethod\n def mock_calc_create_files(self, *args, **kwargs): ...\n\n def mock_calc_check_and_create_files(self, *args, **kwargs):\n # Check if we have already saved this file in this test run, in which case skip re-saving it\n request_id = self.get_request_id(args, kwargs)\n if request_id in MockRequest.__saved_files:\n res = self.mock_calc(*args, **kwargs)\n # This file was remade this session so we need to reindex it here\n self._reindex_single_file(MockFileKey(request_id))\n return res\n return self.mock_calc_create_files(*args, **kwargs)\n\n def mock_calc_create_new_files(self, *args, **kwargs):\n request_id = self.get_request_id(args, kwargs)\n full_file_path = self.full_file_path(MockFileKey(request_id))\n file_exists = exists(full_file_path)\n if file_exists:\n return self.mock_calc(*args, **kwargs)\n else:\n return self.mock_calc_create_files(*args, **kwargs)\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n pass\n\n @staticmethod\n def get_file_summary() -> Dict[Path, Dict[MockFileKey, Tuple[List[str], Tuple[str, str, str]]]]:\n return MockRequest.__looked_at_files\n\n @classmethod\n def get_unused_files(cls, log=False) -> Tuple[Dict[str, Any], ...]:\n file_summary = cls.get_file_summary()\n unused_files = []\n for path, files_used in file_summary.items():\n files_names_used = set(m.file_name for m in files_used.keys())\n for test_file in os.listdir(path / 'calc_cache'):\n if test_file.endswith('.json') and test_file not in files_names_used:\n full_path = path / 'calc_cache' / test_file\n try:\n unused_meta, _ = cls._load_mock_data_from_file(full_path)\n except Exception:\n unused_meta = {'tests': ['UNKNOWN']}\n if log:\n log_mock_data_event(f'Noticed UNUSED {test_file}.')\n unused_details = {\n 'file': test_file,\n 'full_path': full_path,\n 'tests': tuple(unused_meta.get('tests', [])),\n }\n unused_files.append(unused_details)\n return tuple(unused_files)\n\n @classmethod\n def remove_unused_files(cls):\n unused_files = cls.get_unused_files()\n for file in unused_files:\n try:\n log_mock_data_event(f'Removing {file[\"file\"]}. As not used.')\n os.remove(file['full_path'])\n except Exception:\n continue\n\n @classmethod\n def reindex_test_files(cls, report_only=False, log=False) -> Tuple[str, ...]:\n reindex_files = []\n file_summary = cls.get_file_summary()\n for path, files_used in file_summary.items():\n for mock_file_key, (cls_name, now_used) in files_used.items():\n updated = cls._update_single_file_with_new_tests(path, mock_file_key, now_used, report_only, log)\n if updated:\n reindex_files.append(mock_file_key.file_name)\n return tuple(reindex_files)\n\n @classmethod\n def _update_single_file_with_new_tests(cls, base_path, mock_file_key, new_tests, report_only, log):\n full_file_path = cls.build_file_path(base_path, mock_file_key)\n existing_meta, data = cls._load_mock_data_from_file(full_file_path)\n existing_used = tuple(existing_meta.get('tests', []))\n sorted_tests = tuple(sorted(new_tests))\n if existing_used == sorted_tests:\n return False\n if report_only:\n if log:\n missing = set(existing_used) - set(sorted_tests)\n extra = set(sorted_tests) - set(existing_used)\n missing_txt = (\n f\" {len(missing)} test(s) are listed in the file but weren't used: {missing}.\"\n if len(missing)\n else \"\"\n )\n extra_txt = (\n f\" {len(extra)} test(s) that accessed this file were NOT listed in the file : {extra}.\"\n if len(extra)\n else \"\"\n )\n log_mock_data_event(f'Noticed BAD INDEX {mock_file_key.file_name}. {missing_txt}{extra_txt}')\n return True\n\n new_data = cls._make_mock_file_data(mock_file_key, existing_meta['type'], json.dumps(data), sorted_tests)\n # update the cached in memory test data with new contents\n cls.__cached_test_data[full_file_path] = new_data\n # Write the updated mock file\n with open(full_file_path, 'w') as out_file:\n out_file.write(json.dumps(new_data, indent=4))\n log_mock_data_event(f'Updated {mock_file_key.file_name}. As {existing_used} != {sorted_tests}')\n return True\n\n def _reindex_single_file(self, mock_file_key: MockFileKey):\n file_summary = self.get_file_summary()\n by_path = file_summary.get(self.paths, dict())\n mock_file_usage = by_path.get(mock_file_key)\n if mock_file_usage is None:\n return\n test_used = mock_file_usage[1]\n self._update_single_file_with_new_tests(self.paths, mock_file_key, test_used, report_only=False, log=True)\n\n @staticmethod\n def get_saved_files() -> List[str]:\n return list(MockRequest.__saved_files)\n\n @abstractmethod\n def get_request_id(self, args, kwargs): ...\n" }, { "path": "gs_quant/test/utils/test_utilities.py", "content": "\"\"\"\nCopyright 2023 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport unittest\nimport datetime as dt\nfrom gs_quant.data.utilities import SecmasterXrefFormatter\n\n\nclass TestSecmasterXrefFormatter(unittest.TestCase):\n def setUp(self):\n \"\"\"Set up test fixtures before each test method.\"\"\"\n self.formatter = SecmasterXrefFormatter()\n\n def test_event_creation_and_priority(self):\n \"\"\"Test Event dataclass creation and priority assignment.\"\"\"\n start_event = SecmasterXrefFormatter.Event(\n date=\"2023-01-01\",\n event_type=SecmasterXrefFormatter.EventType.START,\n record={\"type\": \"CUSIP\", \"value\": \"12345\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-12-31\"},\n )\n\n end_event = SecmasterXrefFormatter.Event(\n date=\"2023-01-01\",\n event_type=SecmasterXrefFormatter.EventType.END,\n record={\"type\": \"ISIN\", \"value\": \"US12345\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-12-31\"},\n )\n\n self.assertEqual(start_event.priority, 0)\n self.assertEqual(end_event.priority, 1)\n\n def test_date_sort_key(self):\n \"\"\"Test date sorting functionality.\"\"\"\n normal_date = \"2023-01-01\"\n infinity_date = SecmasterXrefFormatter.INFINITY_DATE\n\n normal_key = SecmasterXrefFormatter._date_sort_key(normal_date)\n infinity_key = SecmasterXrefFormatter._date_sort_key(infinity_date)\n\n self.assertEqual(normal_key, dt.datetime(2023, 1, 1))\n self.assertEqual(infinity_key, dt.datetime(9999, 12, 31))\n self.assertTrue(infinity_key > normal_key)\n\n def test_add_one_day(self):\n \"\"\"Test adding one day to various date scenarios.\"\"\"\n # Normal date\n result = SecmasterXrefFormatter._add_one_day(\"2023-01-01\")\n self.assertEqual(result, \"2023-01-02\")\n\n # End of month\n result = SecmasterXrefFormatter._add_one_day(\"2023-01-31\")\n self.assertEqual(result, \"2023-02-01\")\n\n # End of year\n result = SecmasterXrefFormatter._add_one_day(\"2023-12-31\")\n self.assertEqual(result, \"2024-01-01\")\n\n # Leap year\n result = SecmasterXrefFormatter._add_one_day(\"2024-02-28\")\n self.assertEqual(result, \"2024-02-29\")\n\n # Infinity date\n result = SecmasterXrefFormatter._add_one_day(SecmasterXrefFormatter.INFINITY_DATE)\n self.assertIsNone(result)\n\n # Invalid date\n result = SecmasterXrefFormatter._add_one_day(\"invalid-date\")\n self.assertIsNone(result)\n\n def test_subtract_one_day(self):\n \"\"\"Test subtracting one day from various date scenarios.\"\"\"\n # Normal date\n result = SecmasterXrefFormatter._subtract_one_day(\"2023-01-02\")\n self.assertEqual(result, \"2023-01-01\")\n\n # Beginning of month\n result = SecmasterXrefFormatter._subtract_one_day(\"2023-02-01\")\n self.assertEqual(result, \"2023-01-31\")\n\n # Beginning of year\n result = SecmasterXrefFormatter._subtract_one_day(\"2024-01-01\")\n self.assertEqual(result, \"2023-12-31\")\n\n # Invalid date (should return original)\n result = SecmasterXrefFormatter._subtract_one_day(\"invalid-date\")\n self.assertEqual(result, \"invalid-date\")\n\n def test_create_events(self):\n \"\"\"Test event creation from records.\"\"\"\n records = [\n {\"type\": \"CUSIP\", \"value\": \"12345\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-06-30\"},\n {\n \"type\": \"ISIN\",\n \"value\": \"US12345\",\n \"startDate\": \"2023-07-01\",\n \"endDate\": SecmasterXrefFormatter.INFINITY_DATE,\n },\n ]\n\n events = SecmasterXrefFormatter._create_events(records)\n\n # Should have 3 events: 2 start events + 1 end event (no end event for infinity)\n self.assertEqual(len(events), 3)\n\n # Check start events\n start_events = [e for e in events if e.event_type == SecmasterXrefFormatter.EventType.START]\n self.assertEqual(len(start_events), 2)\n\n # Check end events\n end_events = [e for e in events if e.event_type == SecmasterXrefFormatter.EventType.END]\n self.assertEqual(len(end_events), 1)\n self.assertEqual(end_events[0].date, \"2023-07-01\") # Day after end date\n\n def test_convert_empty_data(self):\n \"\"\"Test conversion with empty data.\"\"\"\n result = SecmasterXrefFormatter.convert({})\n self.assertEqual(result, {})\n\n result = SecmasterXrefFormatter.convert({\"entity1\": []})\n self.assertEqual(result, {\"entity1\": {\"xrefs\": []}})\n\n def test_convert_simple_case(self):\n \"\"\"Test conversion with a simple single identifier case.\"\"\"\n data = {\"entity1\": [{\"type\": \"CUSIP\", \"value\": \"12345\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-12-31\"}]}\n\n result = SecmasterXrefFormatter.convert(data)\n expected = {\n \"entity1\": {\n \"xrefs\": [{\"startDate\": \"2023-01-01\", \"endDate\": \"2023-12-31\", \"identifiers\": {\"CUSIP\": \"12345\"}}]\n }\n }\n\n self.assertEqual(result, expected)\n\n def test_convert_infinity_marker_normalization(self):\n \"\"\"Test that infinity markers are converted to infinity dates.\"\"\"\n data = {\n \"entity1\": [\n {\n \"type\": \"CUSIP\",\n \"value\": \"12345\",\n \"startDate\": \"2023-01-01\",\n \"endDate\": SecmasterXrefFormatter.INFINITY_MARKER,\n }\n ]\n }\n\n result = SecmasterXrefFormatter.convert(data)\n expected = {\n \"entity1\": {\n \"xrefs\": [\n {\n \"startDate\": \"2023-01-01\",\n \"endDate\": SecmasterXrefFormatter.INFINITY_DATE,\n \"identifiers\": {\"CUSIP\": \"12345\"},\n }\n ]\n }\n }\n\n self.assertEqual(result, expected)\n\n def test_convert_overlapping_periods(self):\n \"\"\"Test conversion with overlapping identifier periods.\"\"\"\n data = {\n \"entity1\": [\n {\"type\": \"CUSIP\", \"value\": \"12345\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-06-30\"},\n {\"type\": \"ISIN\", \"value\": \"US12345\", \"startDate\": \"2023-03-01\", \"endDate\": \"2023-09-30\"},\n ]\n }\n\n result = SecmasterXrefFormatter.convert(data)\n xrefs = result[\"entity1\"][\"xrefs\"]\n\n # Should have 3 periods\n self.assertEqual(len(xrefs), 3)\n\n # Period 1: Only CUSIP (Jan 1 - Feb 28)\n self.assertEqual(xrefs[0][\"startDate\"], \"2023-01-01\")\n self.assertEqual(xrefs[0][\"endDate\"], \"2023-02-28\")\n self.assertEqual(xrefs[0][\"identifiers\"], {\"CUSIP\": \"12345\"})\n\n # Period 2: Both CUSIP and ISIN (Mar 1 - Jun 30)\n self.assertEqual(xrefs[1][\"startDate\"], \"2023-03-01\")\n self.assertEqual(xrefs[1][\"endDate\"], \"2023-06-30\")\n self.assertEqual(xrefs[1][\"identifiers\"], {\"CUSIP\": \"12345\", \"ISIN\": \"US12345\"})\n\n # Period 3: Only ISIN (Jul 1 - Sep 30)\n self.assertEqual(xrefs[2][\"startDate\"], \"2023-07-01\")\n self.assertEqual(xrefs[2][\"endDate\"], \"2023-09-30\")\n self.assertEqual(xrefs[2][\"identifiers\"], {\"ISIN\": \"US12345\"})\n\n def test_convert_adjacent_periods(self):\n \"\"\"Test conversion with adjacent periods (no gaps).\"\"\"\n data = {\n \"entity1\": [\n {\"type\": \"CUSIP\", \"value\": \"12345\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-06-30\"},\n {\"type\": \"CUSIP\", \"value\": \"67890\", \"startDate\": \"2023-07-01\", \"endDate\": \"2023-12-31\"},\n ]\n }\n\n result = SecmasterXrefFormatter.convert(data)\n xrefs = result[\"entity1\"][\"xrefs\"]\n\n # Should have 2 periods with no gaps\n self.assertEqual(len(xrefs), 2)\n\n self.assertEqual(xrefs[0][\"startDate\"], \"2023-01-01\")\n self.assertEqual(xrefs[0][\"endDate\"], \"2023-06-30\")\n self.assertEqual(xrefs[0][\"identifiers\"], {\"CUSIP\": \"12345\"})\n\n self.assertEqual(xrefs[1][\"startDate\"], \"2023-07-01\")\n self.assertEqual(xrefs[1][\"endDate\"], \"2023-12-31\")\n self.assertEqual(xrefs[1][\"identifiers\"], {\"CUSIP\": \"67890\"})\n\n def test_convert_same_date_start_end(self):\n \"\"\"Test conversion when identifiers start and end on the same date.\"\"\"\n data = {\n \"entity1\": [\n {\"type\": \"CUSIP\", \"value\": \"12345\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-06-30\"},\n {\"type\": \"ISIN\", \"value\": \"US12345\", \"startDate\": \"2023-06-30\", \"endDate\": \"2023-12-31\"},\n ]\n }\n\n result = SecmasterXrefFormatter.convert(data)\n xrefs = result[\"entity1\"][\"xrefs\"]\n\n # Should handle the overlap correctly\n self.assertTrue(len(xrefs) >= 1)\n\n def test_convert_multiple_entities(self):\n \"\"\"Test conversion with multiple entities.\"\"\"\n data = {\n \"entity1\": [{\"type\": \"CUSIP\", \"value\": \"12345\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-12-31\"}],\n \"entity2\": [{\"type\": \"ISIN\", \"value\": \"US67890\", \"startDate\": \"2023-01-01\", \"endDate\": \"2023-12-31\"}],\n }\n\n result = SecmasterXrefFormatter.convert(data)\n\n self.assertIn(\"entity1\", result)\n self.assertIn(\"entity2\", result)\n self.assertEqual(len(result[\"entity1\"][\"xrefs\"]), 1)\n self.assertEqual(len(result[\"entity2\"][\"xrefs\"]), 1)\n self.assertEqual(result[\"entity1\"][\"xrefs\"][0][\"identifiers\"], {\"CUSIP\": \"12345\"})\n self.assertEqual(result[\"entity2\"][\"xrefs\"][0][\"identifiers\"], {\"ISIN\": \"US67890\"})\n\n def test_event_sorting(self):\n \"\"\"Test that events are sorted correctly by date and priority.\"\"\"\n events = [\n SecmasterXrefFormatter.Event(\"2023-01-02\", SecmasterXrefFormatter.EventType.START, {}),\n SecmasterXrefFormatter.Event(\"2023-01-01\", SecmasterXrefFormatter.EventType.END, {}),\n SecmasterXrefFormatter.Event(\"2023-01-01\", SecmasterXrefFormatter.EventType.START, {}),\n SecmasterXrefFormatter.Event(\"2023-01-02\", SecmasterXrefFormatter.EventType.END, {}),\n ]\n\n events.sort(key=lambda e: (SecmasterXrefFormatter._date_sort_key(e.date), e.priority))\n\n # Should be ordered: 2023-01-01 START, 2023-01-01 END, 2023-01-02 START, 2023-01-02 END\n self.assertEqual(events[0].date, \"2023-01-01\")\n self.assertEqual(events[0].event_type, SecmasterXrefFormatter.EventType.START)\n self.assertEqual(events[1].date, \"2023-01-01\")\n self.assertEqual(events[1].event_type, SecmasterXrefFormatter.EventType.END)\n self.assertEqual(events[2].date, \"2023-01-02\")\n self.assertEqual(events[2].event_type, SecmasterXrefFormatter.EventType.START)\n self.assertEqual(events[3].date, \"2023-01-02\")\n self.assertEqual(events[3].event_type, SecmasterXrefFormatter.EventType.END)\n\n\nif __name__ == '__main__':\n # Run the tests\n unittest.main()\n" }, { "path": "gs_quant/test/utils/test_utils.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport json\nimport pathlib\nfrom json.encoder import JSONEncoder\n\nimport pytest\n\nfrom gs_quant.test.mock_data_test_utils import did_anything_fail, did_anything_run\nfrom gs_quant.test.utils.mock_request import MockRequest\n\n\n@pytest.mark.second_to_last\n@pytest.mark.fixmockdata\ndef test_fix_mock_data():\n # This will only be run if you use \"--fixmockdata\" option to pytest\n if did_anything_fail():\n pytest.skip(\"Skipping test as another test failed\")\n if not did_anything_run():\n pytest.skip(\"Skipping test as nothing ran or not setup correctly\")\n MockRequest.reindex_test_files()\n MockRequest.remove_unused_files()\n\n\n@pytest.mark.order(\"last\")\ndef test_mock_data_file_sanity():\n # Important that this test runs last, it asserts all the test files are used so we can cleanup unused ones\n saved_files = MockRequest.get_saved_files()\n assert [] == saved_files, 'Did you accidentally commit with save_files=True?!'\n\n tests_passed = not did_anything_fail()\n\n suffix = (\n '(Other tests FAILED this is probably a red herring)'\n if not tests_passed\n else 'Run pytest with --fixmockdata to fix this!'\n )\n\n bad_index = MockRequest.reindex_test_files(report_only=True, log=tests_passed)\n assert bad_index == tuple(), f'Files with bad index. {suffix}'\n\n unused_files = MockRequest.get_unused_files(log=tests_passed)\n assert unused_files == tuple(), f'Cleanup your unused test files! {suffix}'\n\n\ndef _remove_unwanted(json_text):\n json_dict = json.loads(json_text)\n if \"asOfTime\" in json_dict:\n del json_dict[\"asOfTime\"]\n return json_dict\n\n\ndef load_json_from_resource(test_file_name, json_file_name):\n with open(pathlib.Path(__file__).parents[1] / f'resources/{test_file_name}/{json_file_name}') as json_data:\n return json.load(json_data)\n\n\ndef mock_request(method, path, payload, test_file_name):\n queries = {\n 'assetsDataGSNWithRic': '{\"asOfTime\": \"2019-05-16T21:18:18.294Z\", \"limit\": 4, \"where\": {\"ric\": [\"GS.N\"]}, \"fields\": [\"ric\", \"id\"]}',\n 'assetsDataGSNWithId': '{\"limit\": 4, \"fields\": [\"id\", \"ric\"], \"where\": {\"id\": [\"123456MW5E27U123456\"]}}',\n 'assetsDataSPXWithRic': '{\"where\": {\"ric\": [\".SPX\"]}, \"limit\": 4, \"fields\": [\"ric\", \"id\"]}',\n 'assetsDataSPXWithId': '{\"limit\": 4, \"fields\": [\"id\", \"ric\"], \"where\": {\"id\": [\"456123MW5E27U123456\"]}}',\n 'dataQueryRic': '{\"fields\": [\"adjustedTradePrice\"],'\n ' \"format\": \"MessagePack\", \"where\": {\"assetId\": [\"123456MW5E27U123456\"]}}',\n 'dataQuerySPX': '{\"fields\": [\"adjustedTradePrice\"], \"format\": \"MessagePack\", \"where\": {\"assetId\": [\"456123MW5E27U123456\"]}}',\n }\n payload = _remove_unwanted(json.dumps(payload, cls=JSONEncoder) if payload else '{}')\n if method == 'GET':\n if path == '/data/datasets/TREOD':\n return load_json_from_resource(test_file_name, 'datasets_treod_response.json')\n elif method == 'POST':\n if path == '/assets/data/query':\n if payload == _remove_unwanted(queries['assetsDataGSNWithRic']) or payload == _remove_unwanted(\n queries['assetsDataGSNWithId']\n ):\n return load_json_from_resource(test_file_name, 'assets_data_query_response_gsn.json')\n elif payload == _remove_unwanted(queries['assetsDataSPXWithRic']) or payload == _remove_unwanted(\n queries['assetsDataSPXWithId']\n ):\n return load_json_from_resource(test_file_name, 'assets_data_query_response_spx.json')\n elif path == '/data/TREOD/query':\n if payload == _remove_unwanted(queries['dataQueryRic']):\n return load_json_from_resource(test_file_name, 'treod_query_response_gsn.json')\n elif payload == _remove_unwanted(queries['dataQuerySPX']):\n return load_json_from_resource(test_file_name, 'treod_query_response_spx.json')\n raise Exception(f'Unhandled request. Method: {method}, Path: {path}, payload: {payload} not recognized.')\n" }, { "path": "gs_quant/timeseries/__init__.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .algebra import *\nfrom .analysis import *\nfrom .backtesting import *\nfrom .datetime import *\nfrom .econometrics import *\nfrom .helper import *\nfrom .measures import *\nfrom .measures_countries import *\nfrom .measures_fx_vol import *\nfrom .measures_inflation import *\nfrom .measures_portfolios import *\nfrom .measures_rates import *\nfrom .measures_reports import *\nfrom .measures_risk_models import *\nfrom .measures_xccy import *\nfrom .measures_factset import *\nfrom .statistics import *\nfrom .tca import (\n covariance\n)\nfrom .technicals import *\n\n__name__ = 'timeseries'\n" }, { "path": "gs_quant/timeseries/algebra.py", "content": "# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Chart Service will attempt to make public functions (not prefixed with _) from this module available. Such functions\n# should be fully documented: docstrings should describe parameters and the return value, and provide a 1-line\n# description. Type annotations should be provided for parameters.\n\n\nimport datetime as dt\nimport math\nfrom enum import Enum\nfrom functools import reduce\nfrom numbers import Real\nfrom typing import Union, Optional, List\n\nimport numpy as np\nimport pandas as pd\n\nfrom .datetime import align\nfrom .helper import plot_function, Interpolate\nfrom ..errors import MqValueError, MqTypeError\n\n\"\"\"\nAlgebra library contains basic numerical and algebraic operations, including addition, division, multiplication,\ndivision and other functions on timeseries\n\"\"\"\n\n\nclass FilterOperator(Enum):\n LESS = 'less_than'\n GREATER = 'greater_than'\n L_EQUALS = 'l_equals'\n G_EQUALS = 'g_equals'\n EQUALS = 'equals'\n N_EQUALS = 'not_equals'\n\n\n@plot_function\ndef add(\n x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP\n) -> Union[pd.Series, Real]:\n \"\"\"\n Add two series or scalars\n\n :param x: timeseries or scalar\n :param y: timeseries or scalar\n :param method: interpolation method (default: step). Only used when both x and y are timeseries\n :return: timeseries of x + y or sum of the given real numbers\n\n **Usage**\n\n Add two series or scalar variables with the given interpolation method\n\n :math:`R_t = X_t + Y_t`\n\n Alignment operators:\n\n ========= ========================================================================\n Method Behavior\n ========= ========================================================================\n intersect Resultant series only has values on the intersection of dates. Values\n for dates present in only one series will be ignored\n nan Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as nan in the\n other series, and therefore in the resultant series\n zero Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as zero in the\n other series\n step Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be interpolated via step\n function in the other series\n time Resultant series have values on the union of dates / times. Missing\n values surrounded by valid values will be interpolated given length of\n interval. Input series must use DateTimeIndex.\n ========= ========================================================================\n\n **Examples**\n\n Add two series:\n\n >>> a = generate_series(100)\n >>> b = generate_series(100)\n >>> add(a, b, Interpolate.STEP)\n\n **See also**\n\n :func:`subtract`\n \"\"\"\n\n if isinstance(x, Real) and isinstance(y, Real):\n return x + y\n\n [x_align, y_align] = align(x, y, method)\n return x_align.add(y_align)\n\n\n@plot_function\ndef subtract(\n x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP\n) -> Union[pd.Series, Real]:\n \"\"\"\n Add two series or scalars\n\n :param x: timeseries or scalar\n :param y: timeseries or scalar\n :param method: index alignment operator (default: intersect). Only used when both x and y are timeseries\n :return: timeseries of x - y or difference between the given real numbers\n\n **Usage**\n\n Subtracts one series or scalar from another applying the given interpolation method\n\n :math:`R_t = X_t - Y_t`\n\n Alignment operators:\n\n ========= ========================================================================\n Method Behavior\n ========= ========================================================================\n intersect Resultant series only has values on the intersection of dates.\n Values for dates present in only one series will be ignored\n nan Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as nan in the\n other series, and therefore in the resultant series\n zero Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as zero in the\n other series\n step Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be interpolated via step\n function in the other series\n time Resultant series have values on the union of dates / times. Missing\n values surrounded by valid values will be interpolated given length of\n interval. Input series must use DateTimeIndex.\n ========= ========================================================================\n\n **Examples**\n\n Subtract one series from another:\n\n >>> a = generate_series(100)\n >>> b = generate_series(100)\n >>> subtract(a, b, Interpolate.STEP)\n\n **See also**\n\n :func:`add`\n \"\"\"\n\n # Determine how we want to handle observations prior to start date\n\n if isinstance(x, Real) and isinstance(y, Real):\n return x - y\n\n [x_align, y_align] = align(x, y, method)\n return x_align.subtract(y_align)\n\n\n@plot_function\ndef multiply(\n x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP\n) -> Union[pd.Series, Real]:\n \"\"\"\n Multiply two series or scalars\n\n :param x: timeseries or scalar\n :param y: timeseries or scalar\n :param method: interpolation method (default: step). Only used when both x and y are timeseries\n :return: timeseries of x * y or product of the given real numbers\n\n **Usage**\n\n Multiply two series or scalar variables applying the given interpolation method\n\n :math:`R_t = X_t \\\\times Y_t`\n\n Alignment operators:\n\n ========= ========================================================================\n Method Behavior\n ========= ========================================================================\n intersect Resultant series only has values on the intersection of dates. Values\n for dates present in only one series will be ignored\n nan Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as nan in the\n other series, and therefore in the resultant series\n zero Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as zero in the\n other series\n step Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be interpolated via step\n function in the other series\n time Resultant series have values on the union of dates / times. Missing\n values surrounded by valid values will be interpolated given length of\n interval. Input series must use DateTimeIndex.\n ========= ========================================================================\n\n **Examples**\n\n Multiply two series:\n\n >>> a = generate_series(100)\n >>> b = generate_series(100)\n >>> multiply(a, b, Interpolate.STEP)\n\n **See also**\n\n :func:`divide`\n \"\"\"\n\n if isinstance(x, Real) and isinstance(y, Real):\n return x * y\n\n [x_align, y_align] = align(x, y, method)\n return x_align.multiply(y_align)\n\n\n@plot_function\ndef divide(\n x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP\n) -> Union[pd.Series, Real]:\n \"\"\"\n Divide two series or scalars\n\n :param x: timeseries or scalar\n :param y: timeseries or scalar\n :param method: interpolation method (default: step). Only used when both x and y are timeseries\n :return: timeseries of x / y or quotient of the given real numbers\n\n **Usage**\n\n Divide two series or scalar variables applying the given interpolation method\n\n :math:`R_t = X_t / Y_t`\n\n Alignment operators:\n\n ========= ========================================================================\n Method Behavior\n ========= ========================================================================\n intersect Resultant series only has values on the intersection of dates.\n Values for dates present in only one series will be ignored\n nan Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as nan in the\n other series, and therefore in the resultant series\n zero Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as zero in the\n other series\n step Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be interpolated via step\n function in the other series\n time Resultant series have values on the union of dates / times. Missing\n values surrounded by valid values will be interpolated given length of\n interval. Input series must use DateTimeIndex.\n ========= ========================================================================\n\n **Examples**\n\n Divide two series:\n\n >>> a = generate_series(100)\n >>> b = generate_series(100)\n >>> divide(a, b, Interpolate.STEP)\n\n **See also**\n\n :func:`multiply`\n \"\"\"\n\n if isinstance(x, Real) and isinstance(y, Real):\n return x / y\n\n [x_align, y_align] = align(x, y, method)\n return x_align.divide(y_align)\n\n\n@plot_function\ndef floordiv(\n x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP\n) -> Union[pd.Series, Real]:\n \"\"\"\n Floor divide two series or scalars\n\n :param x: timeseries or scalar\n :param y: timeseries or scalar\n :param method: interpolation method (default: step). Only used for operating two series\n :return: timeseries of x // y or quotient of the floor division of the given real numbers\n\n **Usage**\n\n Divide two series or scalar variables applying the given interpolation method\n\n :math:`R_t = X_t / Y_t`\n\n Alignment operators:\n\n ========= ========================================================================\n Method Behavior\n ========= ========================================================================\n intersect Resultant series only has values on the intersection of dates.\n Values for dates present in only one series will be ignored\n nan Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as nan in the\n other series, and therefore in the resultant series\n zero Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as zero in the\n other series\n step Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be interpolated via step\n function in the other series\n ========= ========================================================================\n\n **Examples**\n\n Floor divide two series:\n\n >>> a = generate_series(100)\n >>> b = generate_series(100)\n >>> floordiv(a, b, Interpolate.STEP)\n\n **See also**\n\n :func:`divide`\n \"\"\"\n\n if isinstance(x, Real) and isinstance(y, Real):\n return x // y\n\n [x_align, y_align] = align(x, y, method)\n return x_align.floordiv(y_align)\n\n\n@plot_function\ndef exp(x: pd.Series) -> pd.Series:\n \"\"\"\n Exponential of series\n\n :param x: timeseries\n :return: exponential of each element\n\n **Usage**\n\n For each element in the series, :math:`X_t`, raise :math:`e` (Euler's number) to the power of :math:`X_t`.\n Euler's number is the base of the natural logarithm, :math:`ln`.\n\n :math:`R_t = e^{X_t}`\n\n **Examples**\n\n Raise :math:`e` to the power :math:`1`. Returns Euler's number, approximately 2.71828\n\n >>> exp(1)\n\n **See also**\n\n :func:`log`\n\n \"\"\"\n return np.exp(x)\n\n\n@plot_function\ndef log(x: pd.Series) -> pd.Series:\n \"\"\"\n Natural logarithm of series\n\n :param x: timeseries\n :return: series with exponential of each element\n\n **Usage**\n\n For each element in the series, :math:`X_t`, return the natural logarithm :math:`ln` of :math:`X_t`\n The natural logarithm is the logarithm in base :math:`e`.\n\n :math:`R_t = log(X_t)`\n\n This function is the inverse of the exponential function.\n\n More information on `logarithms `_\n\n **Examples**\n\n Take natural logarithm of 3\n\n >>> log(3)\n\n **See also**\n\n :func:`exp`\n\n \"\"\"\n return np.log(x)\n\n\n@plot_function\ndef power(x: pd.Series, y: float = 1) -> pd.Series:\n \"\"\"\n Raise each element in series to power\n\n :param x: timeseries\n :param y: value\n :return: date-based time series of square roots\n\n **Usage**\n\n Raise each value in time series :math:`X_t` to the power :math:`y`:\n\n :math:`R_t = X_t^{y}`\n\n **Examples**\n\n Generate price series and raise each value to the power 2:\n\n >>> prices = generate_series(100)\n >>> power(prices, 2)\n\n **See also**\n\n :func:`sqrt`\n\n \"\"\"\n return np.power(x, y)\n\n\n@plot_function\ndef sqrt(x: Union[Real, pd.Series]) -> Union[Real, pd.Series]:\n \"\"\"\n Square root of (a) each element in a series or (b) a real number\n\n :param x: date-based time series of prices or real number\n :return: date-based time series of square roots or square root of given number\n\n **Usage**\n\n Return the square root of each value in time series :math:`X_t`:\n\n :math:`R_t = \\\\sqrt{X_t}`\n\n **Examples**\n\n Generate price series and take square root of each value:\n\n >>> prices = generate_series(100)\n >>> sqrt(prices)\n\n **See also**\n\n :func:`pow`\n\n \"\"\"\n if isinstance(x, pd.Series):\n return np.sqrt(x)\n\n result = math.sqrt(x)\n # return int if result is integral (should work for values up to 2**53)\n return round(result) if round(result) == result else result\n\n\n@plot_function\ndef abs_(x: pd.Series) -> pd.Series:\n \"\"\"\n Absolute value of each element in series\n\n :param x: date-based time series of prices\n :return: date-based time series of absolute value\n\n **Usage**\n\n Return the absolute value of :math:`X`. For each value in time series :math:`X_t`, return :math:`X_t` if :math:`X_t`\n is greater than or equal to 0; otherwise return :math:`-X_t`:\n\n :math:`R_t = |X_t|`\n\n Equivalent to :math:`R_t = sqrt{X_t^2}`\n\n **Examples**\n\n Generate price series and take absolute value of :math:`X_t-100`\n\n >>> prices = generate_series(100) - 100\n >>> abs_(prices)\n\n **See also**\n\n :func:`exp` :func:`sqrt`\n\n \"\"\"\n return abs(x)\n\n\n@plot_function\ndef floor(x: pd.Series, value: float = 0) -> pd.Series:\n \"\"\"\n Floor series at minimum value\n\n :param x: date-based time series of prices\n :param value: minimum value\n :return: date-based time series of maximum value\n\n **Usage**\n\n Returns series where all values are greater than or equal to the minimum value.\n\n :math:`R_t = max(X_t, value)`\n\n See `Floor and Ceil functions `_ for more details\n\n **Examples**\n\n Generate price series and floor all values at 100\n\n >>> prices = generate_series(100)\n >>> floor(prices, 100)\n\n **See also**\n\n :func:`ceil`\n\n \"\"\"\n assert x.index.is_monotonic_increasing\n return x.apply(lambda y: max(y, value))\n\n\n@plot_function\ndef ceil(x: pd.Series, value: float = 0) -> pd.Series:\n \"\"\"\n Cap series at maximum value\n\n :param x: date-based time series of prices\n :param value: maximum value\n :return: date-based time series of maximum value\n\n **Usage**\n\n Returns series where all values are less than or equal to the maximum value.\n\n :math:`R_t = min(X_t, value)`\n\n See `Floor and Ceil functions `_ for more details\n\n **Examples**\n\n Generate price series and floor all values at 100\n\n >>> prices = generate_series(100)\n >>> floor(prices, 100)\n\n **See also**\n\n :func:`floor`\n\n \"\"\"\n assert x.index.is_monotonic_increasing\n return x.apply(lambda y: min(y, value))\n\n\n@plot_function\ndef filter_(x: pd.Series, operator: Optional[FilterOperator] = None, value: Optional[Real] = None) -> pd.Series:\n \"\"\"\n Removes values where comparison with the operator and value combination results in true, defaults to removing\n missing values from the series\n\n :param x: timeseries\n :param operator: FilterOperator describing logic for value removal, e.g 'less_than'\n :param value: number indicating value(s) to remove from the series\n :return: timeseries with specified values removed\n\n\n **Usage**\n\n Remove each value determined by operator and value from timeseries where that expression yields true\n\n **Examples**\n\n Remove 0 from time series\n\n >>> prices = generate_series(100)\n >>> filter_(prices, FilterOperator.EQUALS, 0)\n\n Remove positive numbers from time series\n\n >>> prices = generate_series(100)\n >>> filter_(prices, FilterOperator.GREATER, 0)\n\n Remove missing values from time series\n\n >>> prices = generate_series(100)\n >>> filter_(prices)\n\n \"\"\"\n\n if value is None and operator is None:\n x = x.dropna(axis=0, how='any')\n elif value is None:\n raise MqValueError('No value is specified for the operator')\n else:\n if operator == FilterOperator.EQUALS:\n remove = x == value\n elif operator == FilterOperator.GREATER:\n remove = x > value\n elif operator == FilterOperator.LESS:\n remove = x < value\n elif operator == FilterOperator.L_EQUALS:\n remove = x <= value\n elif operator == FilterOperator.G_EQUALS:\n remove = x >= value\n elif operator == FilterOperator.N_EQUALS:\n remove = x != value\n else:\n if not isinstance(operator, str):\n operator = str(operator)\n raise MqValueError('Unexpected operator: ' + operator)\n x = x.drop(x[remove].index)\n return x\n\n\n@plot_function\ndef filter_dates(\n x: pd.Series, operator: Optional[FilterOperator] = None, dates: Union[List[dt.date], dt.date] = None\n) -> pd.Series:\n \"\"\"\n Removes dates where comparison with the operator and dates combination results in true, defaults to removing\n missing values from the series\n\n :param x: timeseries\n :param operator: FilterOperator describing logic for date removal, e.g 'less_than'\n :param dates: date or list of dates to remove from the series\n :return: timeseries with specified dates removed\n\n\n **Usage**\n\n Remove each date determined by operator and date from timeseries where that expression yields true\n\n **Examples**\n\n Remove today from time series\n\n >>> prices = generate_series(100)\n >>> filter_dates(prices, FilterOperator.EQUALS, date.today())\n\n Remove dates before today from time series\n\n >>> prices = generate_series(100)\n >>> filter_dates(prices, FilterOperator.LESS, date.today())\n\n \"\"\"\n\n if dates is None and operator is None:\n x = x.dropna(axis=0, how='any')\n elif dates is None:\n raise MqValueError('No date is specified for the operator')\n elif isinstance(dates, list) and operator not in [FilterOperator.EQUALS, FilterOperator.N_EQUALS]:\n raise MqValueError('Operator does not work for list of dates')\n else:\n if operator == FilterOperator.EQUALS:\n dates = dates if isinstance(dates, list) else [dates]\n x = x.loc[~x.index.isin(dates)]\n elif operator == FilterOperator.N_EQUALS:\n dates = dates if isinstance(dates, list) else [dates]\n x = x.loc[x.index.isin(dates)]\n elif operator == FilterOperator.GREATER:\n x = x.loc[x.index <= dates]\n elif operator == FilterOperator.LESS:\n x = x.loc[x.index >= dates]\n elif operator == FilterOperator.L_EQUALS:\n x = x.loc[x.index > dates]\n elif operator == FilterOperator.G_EQUALS:\n x = x.loc[x.index < dates]\n else:\n if not isinstance(operator, str):\n operator = str(operator)\n raise MqValueError('Unexpected operator: ' + operator)\n return x\n\n\ndef _sum_boolean_series(*series):\n if not 2 <= len(series) <= 100:\n raise MqValueError('expected between 2 and 100 arguments')\n\n for s in series:\n if not isinstance(s, pd.Series):\n raise MqTypeError('all arguments must be series')\n if not all(map(lambda a: a in (0, 1), s.values)):\n raise MqValueError(f'cannot perform operation on series with value(s) other than 1 and 0: {s.values}')\n\n current = series[0].add(series[1], fill_value=0)\n for s in series[2:]:\n current = current.add(s, fill_value=0)\n return current\n\n\n@plot_function\ndef and_(*series: pd.Series) -> pd.Series:\n \"\"\"\n Logical \"and\" of two or more boolean series.\n\n :param series: input series\n :return: result series (of numeric type, with booleans represented as 1s and 0s)\n \"\"\"\n s = _sum_boolean_series(*series)\n return (s == len(series)).astype(int)\n\n\n@plot_function\ndef or_(*series: pd.Series) -> pd.Series:\n \"\"\"\n Logical \"or\" of two or more boolean series.\n\n :param series: input series\n :return: result series (of numeric type, with booleans represented as 1s and 0s)\n \"\"\"\n s = _sum_boolean_series(*series)\n return (s > 0).astype(int)\n\n\n@plot_function\ndef not_(series: pd.Series) -> pd.Series:\n \"\"\"\n Logical negation of a single boolean series.\n\n :param series: single input series\n :return: result series (of numeric type, with booleans represented as 1s and 0s)\n \"\"\"\n if not all(map(lambda a: a in (0, 1), series.values)):\n raise MqValueError(f'cannot negate series with value(s) other than 1 and 0: {series.values}')\n return series.replace([0, 1], [1, 0])\n\n\n@plot_function\ndef if_(flags: pd.Series, x: Union[pd.Series, float], y: Union[pd.Series, float]) -> pd.Series:\n \"\"\"\n Returns a series s. For i in the index of flags, s[i] = x[i] if flags[i] == 1 else y[i].\n\n :param flags: series of 1s and 0s\n :param x: values to use when flag is 1\n :param y: values to use when flag is 0\n :return: result series\n\n **Usage**\n\n Returns a series based off the given conditional series. If the condition is true it shows the first series's value\n else it shows the second series's value.\n\n **PlotTool Example**\n\n if(SPX.spot() > 4000, SPX.spot(), GSTHHVIP.spot())\n\n The above expression would show SPX.spot() if the spot price is above 4000 else it shows GSTHHVIP.spot().\n\n \"\"\"\n if not all(map(lambda a: a in (0, 1), flags.values)):\n raise MqValueError(f'cannot perform \"if\" on series with value(s) other than 1 and 0: {flags.values}')\n\n def ensure_series(s):\n if isinstance(s, (float, int)):\n return flags, pd.Series([s] * flags.shape[0], index=flags.index)\n elif isinstance(s, pd.Series):\n return flags.align(s)\n else:\n raise MqTypeError('expected a number or series')\n\n x_flags, x = ensure_series(x)\n y_flags, y = ensure_series(y)\n return pd.concat([x[x_flags == 1], y[y_flags == 0]]).sort_index()\n\n\n@plot_function\ndef weighted_sum(series: List[pd.Series], weights: list) -> pd.Series:\n \"\"\"\n Calculate a weighted sum.\n\n :param series: list of time series\n :param weights: list of weights\n :return: time series of weighted average\n\n **Usage**\n\n Calculate a weighted sum e.g. for a basket.\n\n **Examples**\n\n Generate price series and get a sum (weights 70%/30%).\n\n >>> prices1 = generate_series(100)\n >>> prices2 = generate_series(100)\n >>> mybasket = weighted_sum([prices1, prices2], [0.7, 0.3])\n\n **See also**\n\n :func:`basket`\n \"\"\"\n if not all(isinstance(x, pd.Series) for x in series):\n raise MqTypeError(\"expected a list of time series\")\n if not all(isinstance(y, (float, int)) for y in weights):\n raise MqTypeError(\"expected a list of number for weights\")\n if len(weights) != len(series):\n raise MqValueError(\"must have one weight for each time series\")\n\n # for input series, get the intersection of their calendars\n cal = pd.DatetimeIndex(\n reduce(\n np.intersect1d,\n (curve.index for curve in series),\n )\n )\n\n # reindex inputs and calculate\n series = [s.reindex(cal) for s in series]\n weights = [pd.Series(w, index=cal) for w in weights]\n return sum(series[i] * weights[i] for i in range(len(series))) / sum(weights)\n\n\n@plot_function\ndef geometrically_aggregate(series: pd.Series) -> pd.Series:\n \"\"\"\n Geometrically aggregate a series.\n\n :param series: list of time series\n :return: time series of geometrically aggregated results\n\n **Usage**\n\n Used to aggregate daily returns when expressed as weights\n \"\"\"\n return series.add(1).cumprod() - 1\n" }, { "path": "gs_quant/timeseries/analysis.py", "content": "# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Chart Service will attempt to make public functions (not prefixed with _) from this module available. Such functions\n# should be fully documented: docstrings should describe parameters and the return value, and provide a 1-line\n# description. Type annotations should be provided for parameters.\nimport re\nfrom enum import Enum\nfrom numbers import Real\nfrom typing import Union\n\nimport pandas as pd\n\nfrom gs_quant.datetime import relative_date_add\nfrom . import align\nfrom .helper import plot_function, Window, Interpolate\nfrom ..errors import MqValueError\n\n\"\"\"\nTimeseries analysis library contains functions used to analyze properties of timeseries, including laging, differencing,\nautocorrelation, co-integration and other operations\n\"\"\"\n\n\nclass ThresholdType(str, Enum):\n percentage = \"percentage\"\n absolute = \"absolute\"\n\n\n@plot_function\ndef smooth_spikes(\n x: pd.Series, threshold: float, threshold_type: ThresholdType = ThresholdType.percentage\n) -> pd.Series:\n \"\"\"\n Smooth out the spikes of a series. If a point is larger/smaller than (1 +/- threshold) times both neighbors, replace\n it with the average of those neighbours. Note: the first and last points in the input series are dropped.\n\n :param x: timeseries\n :param threshold: minimum increment to trigger filter\n :param threshold_type: type of threshold check\n :return: smoothed timeseries\n\n **Usage**\n\n Returns series where values that exceed the threshold relative to both neighbors are replaced.\n\n **Examples**\n\n Generate price series and smooth spikes over a threshold of 0.5.\n\n >>> prices = generate_series(100)\n >>> smooth_spikes(prices, 0.5)\n\n **See also**\n\n :func:`exponential_moving_average`\n \"\"\"\n\n def check_percentage(previous, current, next_, multiplier) -> bool:\n current_higher = current > previous * multiplier and current > next_ * multiplier\n current_lower = previous > current * multiplier and next_ > current * multiplier\n return current_higher or current_lower\n\n def check_absolute(previous, current, next_, absolute) -> bool:\n current_higher = current > previous + absolute and current > next_ + absolute\n current_lower = previous > current + absolute and next_ > current + absolute\n return current_higher or current_lower\n\n if len(x) < 3:\n return pd.Series(dtype=float)\n\n threshold_value, check_spike = (\n (threshold, check_absolute) if threshold_type == ThresholdType.absolute else ((1 + threshold), check_percentage)\n )\n\n result = x.astype(float)\n current, next_ = x.iloc[0:2]\n for i in range(1, len(x) - 1):\n previous = current\n current = next_\n next_ = x.iloc[i + 1]\n\n if check_spike(previous, current, next_, threshold_value):\n result.iloc[i] = (previous + next_) / 2\n\n return result[1:-1]\n\n\n@plot_function\ndef repeat(x: pd.Series, n: int = 1) -> pd.Series:\n \"\"\"\n Repeats values for days where data is missing. For any date with missing data, the last recorded value is used.\n Optionally downsamples the result such that there are data points every n days.\n\n :param x: date-based timeseries\n :param n: desired frequency of output\n :return: a timeseries that has been forward-filled, and optionally downsampled\n\n **Usage**\n\n Fill missing values with last seen value e.g. to combine daily with weekly or monthly data.\n \"\"\"\n if not 0 < n < 367:\n raise MqValueError('n must be between 0 and 367')\n if x.empty:\n return x\n index = pd.date_range(freq=f'{n}D', start=x.index[0], end=x.index[-1])\n return x.reindex(index, method='ffill')\n\n\n@plot_function\ndef first(x: pd.Series) -> pd.Series:\n \"\"\"\n First value of series\n\n :param x: time series\n :return: time series of first value\n\n **Usage**\n\n Return series with first value of `X` for all dates:\n\n :math:`R_t = X_0`\n\n where :math:`X_0` is the first value in the series\n\n **Examples**\n\n Last value of series:\n\n >>> series = generate_series(100)\n >>> returns = first(series)\n\n **See also**\n\n :func:`last`\n\n \"\"\"\n return pd.Series(x.iloc[0], x.index)\n\n\n@plot_function\ndef last(x: pd.Series) -> pd.Series:\n \"\"\"\n Last value of series (as a series)\n\n :param x: time series\n :return: time series of last value\n\n **Usage**\n\n Return series with last value of `X` for all dates:\n\n :math:`R_t = X_T`\n\n where :math:`X_T` is the last value in the series\n\n **Examples**\n\n Last value of series:\n\n >>> series = generate_series(100)\n >>> returns = last(series)\n\n **See also**\n\n :func:`first`\n\n \"\"\"\n return pd.Series(x.dropna().iloc[-1], x.index)\n\n\n@plot_function\ndef last_value(x: pd.Series) -> Union[int, float]:\n \"\"\"\n Last value of series (as a scalar)\n\n :param x: time series\n :return: last value\n\n **Usage**\n\n Return a scalar value :math:`X_T` where T is the last index value in the series.\n\n **Examples**\n\n Last value of series:\n\n >>> series = generate_series(100)\n >>> lv = last_value(series)\n\n **See also**\n\n :func:`last`\n\n \"\"\"\n if x.empty:\n raise MqValueError(\"cannot get last value of an empty series\")\n return x.dropna().iloc[-1]\n\n\n@plot_function\ndef count(x: pd.Series) -> pd.Series:\n \"\"\"\n Count observations in series\n\n :param x: time series\n :return: number of observations\n\n **Usage**\n\n Count the number of valid observations in a series:\n\n :math:`R_t = R_{t-1} + 1`\n\n if :math:`X_t` is not NaN, and\n\n :math:`R_t = R_{t-1} + 0`\n\n if :math:`X_t` is NaN\n\n **Examples**\n\n Count observations in series:\n\n >>> series = generate_series(100)\n >>> count = count(series)\n\n **See also**\n\n :func:`sum`\n\n \"\"\"\n return x.rolling(x.size, 0).count()\n\n\n@plot_function\ndef diff(x: pd.Series, obs: Union[Window, int, str] = 1) -> pd.Series:\n \"\"\"\n Diff observations with given lag\n\n :param x: time series of prices\n :param obs: number of observations to lag or relative date e.g. 3d, 1w, 1m\n :return: date-based time series of return\n\n **Usage**\n\n Compute the difference in series values over a given lag:\n\n :math:`R_t = X_t - X_{t-obs}`\n\n where :math:`obs` is the number of observations to lag series in diff function\n\n **Examples**\n\n Diff prices levels:\n\n >>> series = generate_series(100)\n >>> returns = diff(series)\n\n **See also**\n\n :func:`lag`\n\n \"\"\"\n ret_series = x - lag(x, obs, LagMode.TRUNCATE)\n\n return ret_series\n\n\n@plot_function\ndef compare(\n x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.STEP\n) -> Union[pd.Series, Real]:\n \"\"\"\n Compare two series or scalars against each other\n\n :param x: timeseries or scalar\n :param y: timeseries or scalar\n :param method: interpolation method (default: step). Only used when both x and y are timeseries\n :return: binary timeseries with the result of x relation y or the comparison of the given real numbers\n\n **Usage**\n\n Compare two series or scalar variables applying the given interpolation method in case indices of :math:`x`\n and :math:`y` differ. Returns a signal with values of 1, 0, or -1.\n\n If :math:`X_t > Y_t`, then :math:`R_t = 1`.\n If :math:`X_t = Y_t`, then :math:`R_t = 0`.\n If :math:`X_t < Y_t`, then :math:`R_t = -1`.\n\n Alignment operators:\n\n ========= ========================================================================\n Method Behavior\n ========= ========================================================================\n intersect Resultant series only has values on the intersection of dates.\n Values for dates present in only one series will be ignored\n nan Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as nan in the\n other series, and therefore in the resultant series\n zero Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be treated as zero in the\n other series\n step Resultant series has values on the union of dates in both series. Values\n for dates only available in one series will be interpolated via step\n function in the other series\n time Resultant series have values on the union of dates / times. Missing\n values surrounded by valid values will be interpolated given length of\n interval. Input series must use DateTimeIndex.\n ========= ========================================================================\n \"\"\"\n x, y = align(x, y, method)\n\n return (x > y) * 1.0 + (x < y) * -1.0\n\n\nclass LagMode(Enum):\n TRUNCATE = \"truncate\"\n EXTEND = \"extend\"\n\n\n@plot_function\ndef lag(x: pd.Series, obs: Union[Window, int, str] = 1, mode: LagMode = LagMode.EXTEND) -> pd.Series:\n \"\"\"\n Lag timeseries by a number of observations or a relative date.\n\n :param x: timeseries of prices\n :param obs: non-zero integer (number of observations) or relative date e.g. \"-90d\", \"1d\", \"1m\", \"1y\"\n :param mode: whether to extend series index (into the future)\n :return: date-based time series of return\n\n **Usage**\n\n Shift the series backwards by a specified number of observations:\n\n :math:`R_t = X_{t-obs}`\n\n where :math:`obs` is the number of observations to lag series\n\n **Examples**\n\n Lag series by 2 observations:\n\n >>> prices = generate_series(100)\n >>> lagged = lag(prices, 2)\n\n Lag series by 1 year:\n\n >>> prices = generate_series(100)\n >>> lagged = lag(prices, '1y')\n\n **See also**\n\n :func:`diff`\n \"\"\"\n if isinstance(obs, str):\n # Check for business day pattern and raise an error\n if re.search('[bB]', obs):\n raise MqValueError(\n f\"Business day offset '{obs}' is not supported. \"\n \"If your dataset is indexed on business days, use an integer offset instead (e.g., 1, 2, -1).\"\n )\n\n end = pd.Timestamp(x.index[-1])\n y = x.copy() # avoid mutating the provided series\n\n match = re.fullmatch('(\\\\d+)y', obs)\n if match:\n y.index += pd.DateOffset(years=int(match.group(1)))\n y = y.groupby(y.index).first()\n if hasattr(y.index, 'as_unit'):\n y.index = y.index.as_unit('ns')\n else:\n _idx = pd.DatetimeIndex([(i + pd.DateOffset(relative_date_add(obs))).date() for i in y.index])\n y.index = _idx.as_unit('ns') if hasattr(_idx, 'as_unit') else _idx\n\n if mode == LagMode.EXTEND:\n return y\n return y[:end]\n\n obs = getattr(obs, 'w', obs)\n # Determine how we want to handle observations prior to start date\n if mode == LagMode.EXTEND:\n if x.empty:\n return x\n if x.index.resolution != 'day':\n raise MqValueError(f'unable to extend index with resolution {x.index.resolution}')\n kwargs = {'periods': abs(obs) + 1, 'freq': 'D'}\n if obs > 0:\n kwargs['start'] = x.index[-1]\n else:\n kwargs['end'] = x.index[0]\n x = x.reindex(x.index.union(pd.date_range(**kwargs)))\n if hasattr(x.index, 'as_unit'):\n x.index = x.index.as_unit('ns')\n return x.shift(obs)\n" }, { "path": "gs_quant/timeseries/backtesting.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom typing import Optional, Union\n\nimport numpy as np\nimport pandas as pd\nimport datetime as dt\nfrom functools import partial, reduce\nfrom numbers import Real\n\nfrom dateutil.relativedelta import relativedelta as rdelta\nfrom pydash import chunk\n\nfrom gs_quant.timeseries.econometrics import volatility, correlation\n\nfrom gs_quant.timeseries.measures_helper import VolReference, preprocess_implied_vol_strikes_eq\nfrom gs_quant import timeseries as ts\nfrom .algebra import sqrt\nfrom .helper import (\n _create_enum,\n _tenor_to_month,\n _month_to_tenor,\n requires_session,\n Returns,\n plot_function,\n plot_method,\n Window,\n)\nfrom ..api.gs.assets import GsAssetApi\nfrom ..api.gs.data import GsDataApi, MarketDataResponseFrame, QueryType\nfrom ..api.utils import ThreadPoolManager\nfrom ..data import DataContext\nfrom ..data.log import log_debug\nfrom ..errors import MqTypeError, MqValueError\n\n_logger = logging.getLogger(__name__)\n\nRebalFreq = _create_enum('RebalFreq', ['Daily', 'Weekly', 'Monthly'])\nReturnType = _create_enum('ReturnType', ['excess_return'])\n\n\ndef backtest_basket(\n series: list,\n weights: list,\n costs: list = None,\n rebal_freq: RebalFreq = RebalFreq.DAILY,\n):\n num_assets = len(series)\n costs = costs or [0] * num_assets\n weights = weights or [1 / num_assets] * num_assets\n\n if not all(isinstance(x, pd.Series) for x in series):\n raise MqTypeError(\"expected a list of series\")\n\n if len(weights) != num_assets or len(weights) != len(costs):\n raise MqValueError(\"series, weights, and cost lists must have the same length\")\n\n # For all inputs which are Pandas series, get the intersection of their calendars\n cal = pd.DatetimeIndex(\n reduce(\n np.intersect1d,\n (curve.index for curve in series + weights + costs if isinstance(curve, pd.Series)),\n )\n )\n\n # Reindex inputs and convert to pandas dataframes\n series = pd.concat([curve.reindex(cal) for curve in series], axis=1)\n weights = pd.concat([pd.Series(w, index=cal) for w in weights], axis=1)\n costs = pd.concat([pd.Series(c, index=cal) for c in costs], axis=1)\n\n if rebal_freq == RebalFreq.DAILY:\n rebal_dates = cal\n else:\n if rebal_freq == RebalFreq.WEEKLY:\n # Get hypothetical weekly rebalances\n num_rebals = ((cal[-1] - cal[0]).days) // 7\n rebal_dates = [cal[0] + i * rdelta(weeks=1) for i in range(num_rebals + 1)]\n else:\n # Get hypothetical monthly rebalances\n num_rebals = (cal[-1].year - cal[0].year) * 12 + cal[-1].month - cal[0].month\n rebal_dates = [cal[0] + i * rdelta(months=1) for i in range(num_rebals + 1)]\n\n # Convert the hypothetical weekly/monthly rebalance dates to actual calendar days\n rebal_dates = [min(cal[cal >= date]) for date in rebal_dates if date < max(cal)]\n\n n = len(cal)\n output_arr = np.zeros(n)\n units_arr = np.zeros((n, num_assets))\n actual_weights_arr = np.zeros((n, num_assets))\n\n # Initialize backtest\n output_arr[0] = 100\n units_arr[0,] = output_arr[0] * weights.values[0,] / series.values[0,]\n actual_weights_arr[0,] = weights.values[0,]\n\n # Run backtest\n prev_rebal = 0\n for i, date in enumerate(cal[1:], 1):\n # Update performance\n output_arr[i] = output_arr[i - 1] + np.dot(units_arr[i - 1,], series.values[i,] - series.values[i - 1,])\n\n actual_weights_arr[i,] = (\n weights.values[prev_rebal,]\n * (series.values[i,] / series.values[prev_rebal,])\n * (output_arr[prev_rebal] / output_arr[i])\n )\n\n # Rebalance on rebal_dates\n if date in rebal_dates:\n # Compute costs\n output_arr[i] -= (\n np.dot(costs.values[i,], np.abs(weights.values[i,] - actual_weights_arr[i,])) * output_arr[i]\n )\n\n # Rebalance\n units_arr[i,] = output_arr[i] * weights.values[i,] / series.values[i,]\n prev_rebal = i\n\n actual_weights_arr[i,] = weights.values[i,]\n else:\n units_arr[i,] = units_arr[i - 1,]\n\n # Copy results back to pandas objects\n output = pd.Series(output_arr, index=cal)\n actual_weights = pd.DataFrame(actual_weights_arr, index=cal, columns=series.columns)\n return output, actual_weights\n\n\n@plot_function\ndef basket_series(\n series: list,\n weights: list = None,\n costs: list = None,\n rebal_freq: RebalFreq = RebalFreq.DAILY,\n return_type: ReturnType = ReturnType.EXCESS_RETURN,\n) -> pd.Series:\n \"\"\"\n Calculates a basket return series.\n\n :param series: list of time series of instrument prices\n :param weights: list of weights (defaults to evenly weight series)\n :param costs: list of execution costs in decimal (defaults to costs of 0)\n :param rebal_freq: rebalancing frequency - Daily, Weekly or Monthly (defaults to Daily)\n :param return_type: return type of underlying instruments - only excess return is supported\n :return: time series of the resulting basket\n\n **Usage**\n\n Calculates a basket return series.\n\n **Examples**\n\n Generate price series and combine them in a basket (weights 70%/30%) which rebalances monthly and assumes\n execution costs 5bps and 10bps each time the constituents are traded.\n\n >>> prices1 = generate_series(100)\n >>> prices2 = generate_series(100)\n >>> mybasket = basket_series([prices1, prices2], [0.7, 0.3], [0.0005, 0.001], monthly)\n\n **See also**\n\n :func:`prices`\n \"\"\"\n\n return backtest_basket(series, weights, costs, rebal_freq)[0]\n\n\nclass Basket:\n \"\"\"\n Construct a basket of stocks\n\n :param stocks: list of stock bloomberg ids\n :param weights: list of weights (defaults to evenly weight stocks)\n :param rebal_freq: rebalancing frequency - Daily or Monthly (defaults to daily)\n\n \"\"\"\n\n def __init__(self, stocks: list, weights: list = None, rebal_freq: RebalFreq = RebalFreq.DAILY):\n if weights and len(weights) and len(stocks) != len(weights): # make sure that they passed in an array for weig\n raise MqValueError(\"Stocks and weights must have the same length if both specified.\")\n\n self.bbids = stocks\n self.rebal_freq = rebal_freq\n self.weights = weights\n self._marquee_ids = None\n\n self.start = DataContext.current.start_date\n self.end = DataContext.current.end_date\n\n self._spot_data = None\n self._returns = None\n self._actual_weights = None\n\n def _reset(self):\n self.start = DataContext.current.start_date\n self.end = DataContext.current.end_date\n self._spot_data = None\n self._returns = None\n self._actual_weights = None\n\n def get_marquee_ids(self):\n if self._marquee_ids is None:\n # Assets sorted by increasing rank\n assets = reversed(\n GsAssetApi.get_many_assets_data(\n bbid=self.bbids, fields=('id', 'bbid', 'rank'), limit=2 * len(self.bbids), order_by=['>rank']\n )\n )\n # If duplicate assets exist, asset_dict will contain a entry for the one with the higher rank (relevant)\n assets_dict = {entry['bbid']: entry['id'] for entry in assets}\n if len(assets_dict) != len(set(self.bbids)):\n not_found = set(assets_dict).symmetric_difference(self.bbids)\n raise MqValueError(f'Unable to find stocks: {\", \".join(not_found)}')\n self._marquee_ids = [assets_dict[bbid] for bbid in self.bbids]\n\n return self._marquee_ids\n\n def _ensure_spot_data(self, request_id: Optional[str] = None):\n if self.start != DataContext.current.start_date or self.end != DataContext.current.end_date:\n self._reset()\n\n if self._spot_data is None:\n q = GsDataApi.build_market_data_query(self.get_marquee_ids(), QueryType.SPOT)\n log_debug(request_id, _logger, 'q %s', q)\n spot_data = ts.get_historical_and_last_for_measure(\n self.get_marquee_ids(), QueryType.SPOT, {}, request_id=request_id\n )\n\n dataset_ids = getattr(spot_data, 'dataset_ids', ())\n\n df = spot_data.set_index([spot_data.index, 'assetId'])\n df = df[~df.index.duplicated(keep='last')].spot.unstack()\n df = df[self._marquee_ids]\n self._spot_data = MarketDataResponseFrame(df)\n self._spot_data.dataset_ids = dataset_ids\n\n def _ensure_backtest(self, request_id: Optional[str] = None):\n if self.start != DataContext.current.start_date or self.end != DataContext.current.end_date:\n self._reset()\n\n if self._returns is None or self._actual_weights is None:\n spot_df = self.get_spot_data(request_id=request_id)\n spot_df = spot_df.dropna()\n spot_series = [spot_df[asset_id] for asset_id in spot_df]\n results = backtest_basket(spot_series, self.weights, rebal_freq=self.rebal_freq)\n\n self._returns = results[0]\n actual_weights = results[1]\n actual_weights.columns = self.get_marquee_ids()\n self._actual_weights = actual_weights\n\n def get_returns(self, request_id: Optional[str] = None):\n self._ensure_backtest(request_id=request_id)\n return self._returns\n\n def get_actual_weights(self, request_id: Optional[str] = None):\n self._ensure_backtest(request_id=request_id)\n return self._actual_weights\n\n def get_spot_data(self, request_id: Optional[str] = None):\n self._ensure_spot_data(request_id=request_id)\n return self._spot_data\n\n @requires_session\n @plot_method\n def price(self, *, real_time: bool = False, request_id: Optional[str] = None) -> pd.Series:\n \"\"\"\n Weighted average price\n\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: time series of the average price\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time basket price not implemented')\n\n return self.get_returns(request_id=request_id)\n\n @requires_session\n @plot_method\n def average_implied_volatility(\n self,\n tenor: str,\n strike_reference: VolReference,\n relative_strike: Real,\n *,\n real_time: bool = False,\n request_id: Optional[str] = None,\n source: Optional[str] = None,\n ) -> pd.Series:\n \"\"\"\n Weighted average implied volatility\n\n :param tenor: relative date representation of expiration date e.g. 1m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :param source: name of function caller\n :return: time series of the average implied volatility\n \"\"\"\n\n if real_time:\n raise NotImplementedError('real-time basket implied vol not implemented')\n\n ref_string, relative_strike = preprocess_implied_vol_strikes_eq(strike_reference, relative_strike)\n\n log_debug(\n request_id,\n _logger,\n 'where tenor=%s, strikeReference=%s, relativeStrike=%s',\n tenor,\n ref_string,\n relative_strike,\n )\n where = dict(tenor=tenor, strikeReference=ref_string, relativeStrike=relative_strike)\n tasks = []\n for i, chunked_assets in enumerate(chunk(self.get_marquee_ids(), 3)):\n query = GsDataApi.build_market_data_query(\n chunked_assets, QueryType.IMPLIED_VOLATILITY, where=where, source=source, real_time=real_time\n )\n\n tasks.append(partial(GsDataApi.get_market_data, query, request_id))\n\n results = ThreadPoolManager.run_async(tasks)\n vol_data = pd.concat(results)\n\n actual_weights = self.get_actual_weights(request_id)\n\n # Add in today's data\n today = dt.date.today()\n if (\n not real_time\n and DataContext.current.end_date >= today\n and (vol_data.empty or today not in vol_data.index.date)\n ):\n vol_data = ts.append_last_for_measure(\n vol_data,\n self.get_marquee_ids(),\n QueryType.IMPLIED_VOLATILITY,\n where,\n source=source,\n request_id=request_id,\n )\n vol_data.index = vol_data.index.rename('date')\n\n # Below transformations will throw errors if vol_data is empty\n if vol_data.empty:\n return pd.Series(dtype=float)\n\n vols = vol_data.pivot_table('impliedVolatility', ['date'], 'assetId')\n vols.reindex(self.get_marquee_ids(), axis=1)\n vols.index.name = None\n\n # Necessary when current values appended - set weights index to match vols index\n actual_weights = actual_weights.reindex(vols.index).ffill()\n\n return actual_weights.mul(vols).sum(axis=1, skipna=False)\n\n @requires_session\n @plot_method\n def average_realized_volatility(\n self,\n tenor: str,\n returns_type: Returns = Returns.LOGARITHMIC,\n *,\n real_time: bool = False,\n request_id: Optional[str] = None,\n ) -> pd.Series:\n \"\"\"\n Weighted average realized volatility\n\n :param tenor: relative date representation of expiration date e.g. 1m\n :param returns_type: returns type\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: time series of the average realized volatility\n \"\"\"\n\n if real_time:\n raise NotImplementedError('real-time basket realized vol not implemented')\n\n spot_df = self.get_spot_data(request_id=request_id)\n actual_weights = self.get_actual_weights(request_id=request_id)\n\n vols = [volatility(spot_df[asset_id], Window(tenor, tenor), returns_type) for asset_id in spot_df]\n vols = pd.concat(vols, axis=1)\n vols.columns = list(spot_df)\n # Necessary when current values appended - set weights index to match vols index\n actual_weights = actual_weights.reindex(vols.index).ffill()\n\n return actual_weights.mul(vols, axis=1).sum(axis=1, skipna=False)\n\n @requires_session\n @plot_method\n def average_realized_correlation(\n self, w: Union[Window, int, str] = Window(None, 0), *, real_time: bool = False, request_id: Optional[str] = None\n ) -> pd.Series:\n \"\"\"\n Weighted average realized correlation. Computes the correlation between each of the basket's constituents and\n returns their average, weighted by their representation in the basket.\n\n :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window\n size and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request ID, if any\n :return: time series of the average realized correlation\n \"\"\"\n # Calculated using method (a) from here: http://www.nematrian.com/MeasuringAverageStockCorrelation\n # Could be sped-up by using another estimate method, such as (b)\n if real_time:\n raise NotImplementedError('real-time basket realized corr not implemented')\n\n spot_df = self.get_spot_data(request_id=request_id)\n actual_weights = self.get_actual_weights(request_id=request_id)\n\n tot = pd.Series(np.zeros_like(spot_df.iloc[:, 0]), index=spot_df.index)\n tot_wt = pd.Series(np.zeros_like(spot_df.iloc[:, 0]), index=spot_df.index)\n # Iterate through and compute pairwise correlation between spot price series,\n # averaging at the end.\n for i in range(len(spot_df.columns)):\n for j in range(i + 1, len(spot_df.columns)):\n corr = correlation(spot_df.iloc[:, i], spot_df.iloc[:, j], w)\n wt = actual_weights.iloc[:, i] * actual_weights.iloc[:, j]\n tot += corr * wt\n tot_wt += wt\n return pd.to_numeric(tot / tot_wt, errors='coerce')\n\n @requires_session\n @plot_method\n def average_forward_vol(\n self,\n tenor: str,\n forward_start_date: str,\n strike_reference: VolReference,\n relative_strike: Real,\n *,\n real_time: bool = False,\n request_id: Optional[str] = None,\n source: Optional[str] = None,\n ) -> pd.Series:\n \"\"\"\n Weighted average forward volatility.\n\n :param tenor: relative date representation of expiration date e.g. 1m\n :param forward_start_date: forward start date e.g. 2m, 1y\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :param source: name of function caller\n :return: average forward volatility\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time basket forward vol not implemented')\n\n ref_string, relative_strike = preprocess_implied_vol_strikes_eq(strike_reference, relative_strike)\n\n t1_month = _tenor_to_month(forward_start_date)\n t2_month = _tenor_to_month(tenor) + t1_month\n t1 = _month_to_tenor(t1_month)\n t2 = _month_to_tenor(t2_month)\n\n log_debug(\n request_id,\n _logger,\n 'where tenor=%s, strikeReference=%s, relativeStrike=%s',\n f'{t1},{t2}',\n ref_string,\n relative_strike,\n )\n where = dict(tenor=[t1, t2], strikeReference=[ref_string], relativeStrike=[relative_strike])\n asset_ids = self.get_marquee_ids()\n\n vol_data = ts.get_historical_and_last_for_measure(\n asset_ids, QueryType.IMPLIED_VOLATILITY, where, source=source, request_id=request_id\n )\n\n # Below transformations will throw errors if vol_data is empty\n if vol_data.empty:\n return pd.Series(dtype=float)\n\n grouped_by_asset_ids = vol_data.groupby('assetId')\n s = {}\n for asset_id, df in grouped_by_asset_ids:\n grouped_by_tenor = df.groupby('tenor')\n try:\n sg = grouped_by_tenor.get_group(t1)['impliedVolatility']\n lg = grouped_by_tenor.get_group(t2)['impliedVolatility']\n except KeyError:\n log_debug(request_id, _logger, 'no data for one or more tenors')\n series = pd.Series(dtype=float, name='forwardVol')\n else:\n series = pd.Series(\n sqrt((t2_month * lg**2 - t1_month * sg**2) / _tenor_to_month(tenor)), name='forwardVol'\n )\n s[asset_id] = series\n\n vols = pd.DataFrame(s)\n actual_weights = self.get_actual_weights(request_id)\n\n # Necessary when current values appended - set weights index to match vols index\n actual_weights = actual_weights.reindex(pd.DatetimeIndex(vols.index)).ffill()\n\n return actual_weights.mul(vols).sum(axis=1, skipna=False)\n" }, { "path": "gs_quant/timeseries/datetime.py", "content": "# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Chart Service will attempt to make public functions (not prefixed with _) from this module available. Such functions\n# should be fully documented: docstrings should describe parameters and the return value, and provide a 1-line\n# description. Type annotations should be provided for parameters.\nimport datetime as dt\nfrom enum import Enum\nfrom numbers import Real\nfrom typing import Any, Union, List\n\nimport numpy as np\nimport pandas as pd\n\nfrom .helper import (\n _create_enum,\n Interpolate,\n plot_function,\n requires_session,\n FREQ_MONTH_END,\n FREQ_QUARTER_END,\n FREQ_YEAR_END,\n)\nfrom ..datetime import GsCalendar\nfrom ..datetime.date import DayCountConvention, PaymentFrequency, day_count_fraction\nfrom ..datetime.date import date_range as _date_range\nfrom ..errors import MqValueError, MqTypeError\n\n\"\"\"\nDate and time manipulation for timeseries, including date or time shifting, calendar operations, curve alignment and\ninterpolation operations. Includes sampling operations based on daif dates[0]te or time manipulation\n\"\"\"\n\nAggregateFunction: Union[Union[type, Enum], Any] = _create_enum(\n 'AggregateFunction', ['max', 'min', 'mean', 'sum', 'first', 'last']\n)\nAggregatePeriod = _create_enum('AggregatePeriod', ['week', 'month', 'quarter', 'year'])\n\n\ndef __interpolate_step(x: pd.Series, dates: pd.Series = None) -> pd.Series:\n if x.empty:\n raise MqValueError('Cannot perform step interpolation on an empty series')\n\n first_date = pd.Timestamp(dates.index[0]) if isinstance(x.index[0], pd.Timestamp) else dates.index[0]\n\n # locate previous valid date or take first value from series\n prev = x.index[0] if first_date < x.index[0] else x.index[x.index.get_indexer([first_date], method='pad')]\n\n current = x[prev]\n\n curve = x.align(\n dates,\n 'right',\n )[0] # only need values from dates\n\n for knot in curve.items():\n if np.isnan(knot[1]):\n curve[knot[0]] = current\n else:\n current = knot[1]\n return curve\n\n\n@plot_function\ndef align(\n x: Union[pd.Series, Real], y: Union[pd.Series, Real], method: Interpolate = Interpolate.INTERSECT\n) -> Union[List[pd.Series], List[Real]]:\n \"\"\"\n Align dates of two series or scalars\n\n :param x: first timeseries or scalar\n :param y: second timeseries or scalar\n :param method: interpolation method (default: intersect). Only used when both x and y are timeseries\n :return: timeseries with specified dates or two scalars from the input\n\n **Usage**\n\n Align the dates of two series using the specified interpolation method. Returns two series with dates based on the\n method of interpolation, for example, can be used to intersect the dates of two series, union dates with a defined\n manner to compute missing values.\n\n Interpolation methods:\n\n ========= ========================================================================\n Type Behavior\n ========= ========================================================================\n intersect Resultant series only have values on the intersection of dates /times.\n nan Resultant series have values on the union of dates /times. Values will\n be NaN for dates or times only present in the other series\n zero Resultant series have values on the union of dates / times. Values will\n be zero for dates or times only present in the other series\n step Resultant series have values on the union of dates / times. Each series\n will use the value of the previous valid point if requested date does\n not exist. Values prior to the first date will be equivalent to the\n first available value\n time Resultant series have values on the union of dates / times. Missing\n values surrounded by valid values will be interpolated given length of\n interval. Input series must use DateTimeIndex.\n ========= ========================================================================\n\n **Examples**\n\n Stepwize interpolation of series based on dates in second series:\n\n >>> a = generate_series(100)\n >>> b = generate_series(100)\n >>> align(a, b)\n\n **See also**\n\n :func:`sub`\n \"\"\"\n if isinstance(x, Real) and isinstance(y, Real):\n return [x, y]\n if isinstance(x, Real):\n return [pd.Series(x, index=y.index), y]\n if isinstance(y, Real):\n return [x, pd.Series(y, index=x.index)]\n\n if method == Interpolate.INTERSECT:\n return x.align(y, 'inner')\n if method == Interpolate.NAN:\n return x.align(y, 'outer')\n if method == Interpolate.ZERO:\n return x.align(y, 'outer', fill_value=0)\n if method == Interpolate.TIME:\n new_x, new_y = x.align(y, 'outer')\n new_x = new_x.interpolate('time', limit_area='inside')\n new_y = new_y.interpolate('time', limit_area='inside')\n return [new_x, new_y]\n if method == Interpolate.STEP:\n new_x, new_y = x.align(y, 'outer')\n new_x = new_x.ffill().bfill()\n new_y = new_y.ffill().bfill()\n return [new_x, new_y]\n else:\n raise MqValueError('Unknown intersection type: ' + method)\n\n\n@plot_function\ndef interpolate(\n x: pd.Series,\n dates: Union[List[dt.date], List[dt.time], pd.Series] = None,\n method: Interpolate = Interpolate.INTERSECT,\n) -> pd.Series:\n \"\"\"\n Interpolate over specified dates or times\n\n :param x: timeseries to interpolate\n :param dates: array of dates/times or another series to interpolate\n :param method: interpolation method (default: intersect)\n :return: timeseries with specified dates\n\n **Usage**\n\n Interpolate the series X over the dates specified by the dates parameter. This can be an array of dates or another\n series, in which case the index of the series will be used to specify dates\n\n Interpolation methods:\n\n ========= ========================================================================\n Type Behavior\n ========= ========================================================================\n intersect Resultant series only has values on the intersection of dates /times.\n Will only contain intersection of valid dates / times in the series\n nan Resultant series only has values on the intersection of dates /times.\n Value will be NaN for dates not present in the series\n zero Resultant series has values on all requested dates / times. The series\n will have a value of zero where the requested date or time was not\n present in the series\n step Resultant series has values on all requested dates / times. The series\n will use the value of the previous valid point if requested date does\n not exist. Values prior to the first date will be equivalent to the\n first available value\n ========= ========================================================================\n\n **Examples**\n\n Stepwize interpolation of series based on dates in second series:\n\n >>> a = generate_series(100)\n >>> b = generate_series(100)\n >>> interpolate(a, b, Interpolate.INTERSECT)\n\n **See also**\n\n :func:`sub`\n \"\"\"\n if dates is None:\n dates = x\n\n if isinstance(dates, pd.Series):\n align_series = dates\n else:\n align_series = pd.Series(np.nan, dates)\n\n if method == Interpolate.INTERSECT:\n return x.align(align_series, 'inner')[0]\n if method == Interpolate.NAN:\n return x.align(align_series, 'right')[0]\n if method == Interpolate.ZERO:\n align_series = pd.Series(0.0, dates)\n return x.align(align_series, 'right', fill_value=0)[0]\n if method == Interpolate.STEP:\n return __interpolate_step(x, align_series)\n else:\n raise MqValueError('Unknown intersection type: ' + method)\n\n\n@plot_function\ndef value(x: pd.Series, date: Union[dt.date, dt.time], method: Interpolate = Interpolate.STEP) -> pd.Series:\n \"\"\"\n Value at specified date or time\n\n :param x: timeseries\n :param date: requested date or time\n :param method: interpolation method (default: step)\n :return: value at specified date or time\n\n **Usage**\n\n Returns the value of series X at the specified date:\n\n :math:`Y_t = X_{date}`\n\n If the requested date or time is not present in the series, the value function will return the value from the\n previous available date or time by default. Caller can specify other interpolation styles via the method param:\n\n Interpolation methods:\n\n ========= ========================================================================\n Type Behavior\n ========= ========================================================================\n intersect Only returns a value for valid dates\n nan Value will be NaN for dates not present in the series\n zero Value will be zero for dates not present in the series\n step Value of the previous valid point if requested date does not exist.\n Values prior to the first date will be equivalent to the first available\n value\n ========= ========================================================================\n\n **Examples**\n\n Value of series on 5Mar18:\n\n >>> a = generate_series(100)\n >>> value(a, date(2019, 1, 3)\n\n **See also**\n\n :func:`interpolate`\n \"\"\"\n\n values = interpolate(x, [date], method)\n return None if values.empty else values.iloc[0]\n\n\n@plot_function\ndef day(x: pd.Series) -> pd.Series:\n \"\"\"\n Day of each value in series\n\n :param x: time series\n :return: day of observations\n\n **Usage**\n\n Returns the day as a numeric value for each observation in the series:\n\n :math:`Y_t = day(t)`\n\n Day of the time or date is the integer day number within the month, e.g. 1-31\n\n **Examples**\n\n Day for observations in series:\n\n >>> series = generate_series(100)\n >>> days = day(series)\n\n **See also**\n\n :func:`month` :func:`year`\n\n \"\"\"\n return pd.Series(pd.to_datetime(x.index.to_series()).dt.day, dtype=np.int64)\n\n\n@plot_function\ndef month(x: pd.Series) -> pd.Series:\n \"\"\"\n Month of each value in series\n\n :param x: time series\n :return: month of observations\n\n **Usage**\n\n Returns the month as a numeric value for each observation in the series:\n\n :math:`Y_t = month(t)`\n\n Month of the time or date is the integer month number, e.g. 1-12\n\n **Examples**\n\n Day for observations in series:\n\n >>> series = generate_series(100)\n >>> days = month(series)\n\n **See also**\n\n :func:`day` :func:`year`\n\n \"\"\"\n return pd.Series(pd.to_datetime(x.index.to_series()).dt.month, dtype=np.int64)\n\n\n@plot_function\ndef year(x: pd.Series) -> pd.Series:\n \"\"\"\n Year of each value in series\n\n :param x: time series\n :return: year of observations\n\n **Usage**\n\n Returns the year as a numeric value for each observation in the series:\n\n :math:`Y_t = year(t)`\n\n Year of the time or date is the integer year number, e.g. 2019, 2020\n\n **Examples**\n\n Year for observations in series:\n\n >>> series = generate_series(100)\n >>> days = year(series)\n\n **See also**\n\n :func:`day` :func:`month`\n\n \"\"\"\n return pd.Series(pd.to_datetime(x.index.to_series()).dt.year, dtype=np.int64)\n\n\n@plot_function\ndef quarter(x: pd.Series) -> pd.Series:\n \"\"\"\n Quarter of each value in series\n\n :param x: time series\n :return: quarter of observations\n\n **Usage**\n\n Returns the quarter as a numeric value for each observation in the series:\n\n :math:`Y_t = quarter(t)`\n\n Quarter of the time or date is the integer quarter number, e.g. 1, 2, 3, 4\n\n **Examples**\n\n Quarter for observations in series:\n\n >>> series = generate_series(100)\n >>> days = quarter(series)\n\n **See also**\n\n :func:`day` :func:`month`\n\n \"\"\"\n return pd.Series(pd.to_datetime(x.index.to_series()).dt.quarter, dtype=np.int64)\n\n\n@plot_function\ndef weekday(x: pd.Series) -> pd.Series:\n \"\"\"\n Weekday of each value in series\n\n :param x: time series\n :return: weekday of observations\n\n **Usage**\n\n Returns the weekday as a numeric value for each observation in the series:\n\n :math:`Y_t = weekday(t)`\n\n Weekday of the time or date is the integer day of the week, e.g. 0-6, where 0 represents Monday\n\n **Examples**\n\n Weekday for observations in series:\n\n >>> series = generate_series(100)\n >>> days = weekday(series)\n\n **See also**\n\n :func:`day` :func:`month`\n\n \"\"\"\n return pd.Series(pd.to_datetime(x.index.to_series()).dt.weekday, dtype=np.int64)\n\n\n@plot_function\ndef day_count_fractions(\n dates: Union[List[dt.date], pd.Series],\n convention: DayCountConvention = DayCountConvention.ACTUAL_360,\n frequency: PaymentFrequency = PaymentFrequency.MONTHLY,\n) -> pd.Series:\n \"\"\"\n Day count fractions between dates in series\n\n :param dates: time series or array of dates\n :param convention: day count convention (default: Actual/360 ISDA)\n :param frequency: payment frequency of instrument (optional)\n :return: series of day count fractions\n\n **Usage**\n\n Returns the day count fraction between dates in the series\n\n :math:`Y_t = DCF(t_{-1}, t)`\n\n Default is Actual/360 per ISDA specification:\n\n :math:`Y_t = \\frac{Days(t_{-1}, t)}{360}`\n\n For a full list of available conventions, see\n `Day Count Conventions `_.\n For more information on day count conventions, see the\n `day count conventions `_ page on Wikipedia\n\n **Examples**\n\n Weekday for observations in series:\n\n >>> series = generate_series(100)\n >>> days = day_count_fractions(series)\n\n **See also**\n\n :func:`day` :func:`month` :func:`year`\n\n \"\"\"\n if isinstance(dates, pd.Series):\n date_list = list(dates.index)\n else:\n date_list = dates\n\n if len(date_list) < 2:\n return pd.Series(dtype=float)\n\n start_dates = date_list[0:-1]\n end_dates = date_list[1 : len(date_list)]\n\n dcfs = map(lambda a, b: day_count_fraction(a, b, convention, frequency), start_dates, end_dates)\n return pd.Series(data=[np.nan] + list(dcfs), index=date_list[0 : len(date_list)])\n\n\n@plot_function\ndef date_range(\n x: pd.Series, start_date: Union[dt.date, int], end_date: Union[dt.date, int], weekdays_only: bool = False\n) -> pd.Series:\n \"\"\"\n Create a time series from a (sub-)range of dates in an existing time series.\n\n :param x: time series\n :param start_date: start date for the sliced time series. If integer, number of observations after the first\n :param end_date: end date for the sliced time series. If integer, number of observations before the last\n :param weekdays_only: whether to include only weekdays in the sliced ranges\n :return: sliced time series\n\n **Usage**\n\n Returns a restricted (\"sliced\") time series based on start and end dates:\n\n :math:`Y_t = R_t |_{start < t < end}`\n\n **Examples**\n\n Slice the first and last week of a time series:\n\n >>> series = generate_series(100)\n >>> sliced_series = date_range(series,7,7)\n\n create time series with the absolute date:\n\n >>> date_range(series, dt.date(2021,1,1), dt.date(2021,12,30))\n\n **See also**\n\n :func:`day` :func: `lag`\n\n \"\"\"\n if not isinstance(weekdays_only, bool):\n raise MqTypeError('expected a boolean value for \"weekdays_only\"')\n if not (isinstance(x.index, pd.DatetimeIndex) or all(map(lambda a: isinstance(a, dt.date), x.index.values))):\n raise MqValueError('input is not a time series')\n\n if isinstance(start_date, int):\n start_date = x.index[start_date]\n if isinstance(end_date, int):\n end_date = x.index[-(1 + end_date)]\n\n try:\n start_date = start_date.date()\n end_date = end_date.date()\n except AttributeError:\n pass\n\n if weekdays_only:\n week_mask = None\n wd = start_date.weekday()\n if wd > 4:\n start_date += dt.timedelta(days=7 - wd)\n else:\n week_mask = tuple([True] * 7)\n\n date_list = list(_date_range(start_date, end_date, week_mask=week_mask))\n if isinstance(x.index, pd.DatetimeIndex):\n date_list = [pd.Timestamp(d) for d in date_list]\n return x.loc[x.index.isin(date_list)]\n\n\n@plot_function\ndef append(series: List[pd.Series]) -> pd.Series:\n \"\"\"\n Append data series\n\n :param series: an array of timeseries\n :return: concatenated timeseries\n\n **Usage**\n\n For input series [:math:`x_1`, :math:`x_2`, ... , :math:`x_n`], takes data from series :math:`x_i` until\n that series runs out, then appends data from `x_{i+1}` until that series runs out.\n\n **Examples**\n\n Append two series:\n\n >>> x = generate_series(100)\n >>> y = generate_series(100)\n >>> append([x, y])\n\n **See also**\n\n :func:`prepend`\n\n \"\"\"\n if not len(series):\n return pd.Series(dtype='float64')\n res = series[0].copy()\n for i in range(1, len(series)):\n cur = series[i]\n start = res.index[-1]\n res = pd.concat([res, cur.loc[cur.index > start]])\n return res\n\n\n@plot_function\ndef prepend(x: List[pd.Series]) -> pd.Series:\n \"\"\"\n Prepend data series\n\n :param x: an array of timeseries\n :return: concatenated timeseries\n\n **Usage**\n\n For input series [:math:`x_1`, :math:`x_2`, ... , :math:`X_n`], takes data from series :math:`X_i` until\n the first date for which :math:`X_{i+1}` has data, useful when a higher quality series has a shorter history\n than a lower quality series.\n\n **Examples**\n\n Prepend two series:\n\n >>> x = generate_series(100)\n >>> y = generate_series(100)\n >>> prepend([x, y])\n\n **See also**\n\n :func:`union`\n\n \"\"\"\n if not x:\n return pd.Series(dtype='float64')\n if len(x) == 1:\n return x[0].copy()\n parts = []\n for i in range(len(x)):\n this = x[i]\n if i == len(x) - 1:\n parts.append(this)\n else:\n end = x[i + 1].index[0]\n parts.append(this.loc[this.index < end])\n return pd.concat(parts)\n\n\n@plot_function\ndef union(x: List[pd.Series]) -> pd.Series:\n \"\"\"\n Fill in missing dates or times of one series with another\n\n :param x: an array of timeseries\n :return: combined series\n\n **Usage**\n\n Starting from :math:`i=1`, takes points from series :math:`x_i`. Where points are missing from :math:`x_i`,\n returns points from :math:`x_{i+1}`.\n\n **Examples**\n\n Union of two series:\n\n >>> x = generate_series(100)\n >>> y = generate_series(100)\n >>> union([x, y])\n\n **See also**\n\n :func:`prepend`\n\n \"\"\"\n if len(x):\n res = pd.Series(dtype='float64', index=x[0].index)\n for series in x:\n res = res.combine_first(series)\n else:\n res = pd.Series(dtype='float64')\n return res\n\n\n@plot_function\ndef bucketize(series: pd.Series, aggregate_function: AggregateFunction, period: AggregatePeriod) -> pd.Series:\n \"\"\"\n Bucketize a series and apply aggregate function to each bucket\n\n :param series: input series\n :param aggregate_function: function to use for aggregating data in each bucket\n :param period: size of each bucket\n :return: output series\n\n **Usage**\n\n Bucketize a series and apply aggregate function to each bucket. The result will be indexed by the end date of each\n bucket.\n\n **Examples**\n\n Monthly mean of a series:\n\n >>> x = generate_series(100)\n >>> bucketize(x, AggregateFunction.MEAN, AggregatePeriod.MONTH)\n\n **See also**\n\n :func:`moving_average`\n \"\"\"\n series.index = pd.to_datetime(series.index)\n period_char = period.value[0].upper()\n frequency_map = {'W': 'W', 'M': FREQ_MONTH_END, 'Q': FREQ_QUARTER_END, 'Y': FREQ_YEAR_END}\n frequency = frequency_map.get(period_char, period_char)\n agg = aggregate_function.value\n result = series.resample(frequency).apply(agg)\n\n if result.empty:\n return result\n last_timestamp = min(series.index[-1], result.index[-1])\n idx = result.index[:-1].insert(len(result) - 1, last_timestamp)\n result.index = idx\n return result\n\n\n@plot_function\ndef day_count(first: dt.date, second: dt.date) -> int:\n \"\"\"\n Counts the number of business days between two dates. Monday through Friday are considered to be business days.\n\n :param first: first date\n :param second: second date\n :return: number of business days between first and second\n \"\"\"\n if not (isinstance(first, dt.date) and isinstance(second, dt.date)):\n raise MqValueError('inputs must be dates')\n return np.busday_count(first, second)\n\n\n@plot_function\ndef day_countdown(end_date: dt.date, start_date: dt.date = None, business_days: bool = False) -> pd.Series:\n \"\"\"Create a series counting down the number of days from each date to ``end_date``.\n\n :param end_date: last date / countdown target\n :param start_date: first date in the generated series (default: today)\n :param business_days: whether to use business days (default False)\n :return: timeseries of day counts\n\n **Usage**\n\n The returned series is indexed by dates from ``start_date`` to ``end_date`` (inclusive). Values are integers giving\n the number of days between the index date and ``end_date``.\n\n If ``business_days`` is True, the index will include only Monday through Friday as business days and the\n counts will be business-day counts. If False (default), the index will include all\n calendar days and the counts will be calendar-day counts.\n\n **Examples**\n\n Calendar-day countdown from 7May21 to 17May21:\n >>> day_countdown(Date(2035, 5, 17), Date(2025, 5, 7))\n\n Business-day countdown (Mon-Fri only):\n >>> day_countdown(Date(2035, 5, 17), Date(2025, 5, 7), True)\n\n Calendar-day countdown starting today:\n >>> day_countdown(Date(2035, 5, 17))\n \"\"\"\n if start_date is None:\n start_date = dt.date.today()\n\n if not isinstance(start_date, dt.date):\n raise MqValueError('start_date must be a date')\n if not isinstance(end_date, dt.date):\n raise MqValueError('end_date must be a date')\n\n if start_date > end_date:\n return pd.Series(dtype=np.int64)\n\n if business_days:\n idx = pd.bdate_range(start=start_date, end=end_date)\n if idx.empty:\n return pd.Series(dtype=np.int64)\n # busday_count is [start, end) so end_date itself is 0\n start_days = idx.values.astype('datetime64[D]')\n end_day = np.datetime64(end_date).astype('datetime64[D]')\n values = np.busday_count(start_days, end_day).astype(np.int64)\n else:\n idx = pd.date_range(start=start_date, end=end_date, freq='D')\n start_days = idx.values.astype('datetime64[D]')\n end_day = np.datetime64(end_date).astype('datetime64[D]')\n values = (end_day - start_days).astype(np.int64)\n\n return pd.Series(values, index=idx)\n\n\n@requires_session\n@plot_function\ndef align_calendar(series: pd.Series, calendar: str) -> pd.Series:\n \"\"\"\n Aligns a series to a given calendar removing values in the holiday calendar and the week mask (i.e. remove Saturday\n and Sunday)\n\n :param series: data series to align\n :param calendar: calendar to align the series (i.e. NYC, NYSE, USD)\n\n :return: timeseries of the data aligned to a calendar\n\n **Examples**\n\n >>> x = generate_series(100)\n >>> align_calendar(x, 'NYC')\n \"\"\"\n gs_calendar = GsCalendar.get(calendar)\n cbd = pd.tseries.offsets.CustomBusinessDay(calendar=gs_calendar.business_day_calendar())\n filtered_series = series[\n series.index.isin(pd.date_range(start=series.first_valid_index(), end=series.last_valid_index(), freq=cbd))\n ]\n return filtered_series\n" }, { "path": "gs_quant/timeseries/econometrics.py", "content": "# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Chart Service will attempt to make public functions (not prefixed with _) from this module available. Such functions\n# should be fully documented: docstrings should describe parameters and the return value, and provide a 1-line\n# description. Type annotations should be provided for parameters.\nimport math\nfrom enum import IntEnum, Enum\nfrom typing import Union, Optional\n\nimport numpy as np\nimport pandas as pd\n\nfrom gs_quant.api.gs.data import GsDataApi, QueryType\nfrom gs_quant.common import Currency\nfrom gs_quant.markets.securities import Asset\nfrom .analysis import LagMode, lag\nfrom .datetime import align, interpolate\nfrom .helper import (\n CurveType,\n Interpolate,\n Window,\n normalize_window,\n apply_ramp,\n plot_session_function,\n plot_function,\n Returns,\n SeriesType,\n)\nfrom .statistics import std, product, sum_, mean, MeanType\nfrom ..data import DataContext\nfrom ..datetime import DayCountConvention, day_count_fraction\nfrom ..errors import MqValueError, MqTypeError\n\n\"\"\"\nEconometrics timeseries library is for standard economic and time series analytics operations, including returns,\ndiffs, lags, volatilities and other numerical operations which are generally finance-oriented\n\"\"\"\n\n\nclass AnnualizationFactor(IntEnum):\n DAILY = 252\n WEEKLY = 52\n SEMI_MONTHLY = 26\n MONTHLY = 12\n QUARTERLY = 4\n ANNUALLY = 1\n\n\nclass SharpeAssets(Enum):\n USD = 'MAP35DA6K5B1YXGX'\n AUD = 'MAFRZWJ790MQY0EW'\n CHF = 'MAS0NN4ZX7NYXB36'\n EUR = 'MA95W0N1214395N8'\n GBP = 'MA41ZEFTWR8Q7HBM'\n JPY = 'MA8GXV3SJ0TXH1JV'\n SEK = 'MAGNZZY0GJ4TATNG'\n\n\ndef excess_returns_pure(price_series: pd.Series, spot_curve: pd.Series) -> pd.Series:\n curve, bench_curve = align(price_series, spot_curve, Interpolate.INTERSECT)\n\n e_returns = [curve.iloc[0]]\n for i in range(1, len(curve)):\n multiplier = 1 + curve.iloc[i] / curve.iloc[i - 1] - bench_curve.iloc[i] / bench_curve.iloc[i - 1]\n e_returns.append(e_returns[-1] * multiplier)\n return pd.Series(e_returns, index=curve.index)\n\n\ndef excess_returns(\n price_series: pd.Series,\n benchmark_or_rate: Union[Asset, Currency, float],\n *,\n day_count_convention=DayCountConvention.ACTUAL_360,\n) -> pd.Series:\n if isinstance(benchmark_or_rate, float):\n er = [price_series.iloc[0]]\n for j in range(1, len(price_series)):\n fraction = day_count_fraction(price_series.index[j - 1], price_series.index[j], day_count_convention)\n er.append(er[-1] + price_series.iloc[j] - price_series.iloc[j - 1] * (1 + benchmark_or_rate * fraction))\n return pd.Series(er, index=price_series.index)\n\n if isinstance(benchmark_or_rate, Currency):\n try:\n marquee_id = SharpeAssets[benchmark_or_rate.value].value\n except KeyError:\n raise MqValueError(f\"unsupported currency {benchmark_or_rate}\")\n else:\n marquee_id = benchmark_or_rate.get_marquee_id()\n\n with DataContext(price_series.index[0], price_series.index[-1]):\n q = GsDataApi.build_market_data_query([marquee_id], QueryType.SPOT)\n df = GsDataApi.get_market_data(q)\n if df.empty:\n raise MqValueError(f'could not retrieve risk-free rate {marquee_id}')\n df = df[~df.index.duplicated(keep='first')] # handle bad data (duplicate rows)\n\n return excess_returns_pure(price_series, df['spot'])\n\n\ndef _annualized_return(\n levels: pd.Series, rolling: Union[int, pd.DateOffset], interpolation_method: Interpolate = Interpolate.NAN\n) -> pd.Series:\n if isinstance(rolling, pd.DateOffset):\n starting = [tstamp - rolling for tstamp in levels.index]\n levels = interpolate(levels, method=interpolation_method)\n points = list(\n map(\n lambda d, v, i: pow(v / levels.get(i, np.nan), 365.25 / (d - i).days) - 1,\n levels.index[1:],\n levels.values[1:],\n starting[1:],\n )\n )\n else:\n if interpolation_method is not Interpolate.NAN:\n raise MqValueError(\n f'If w is not a relative date, method must be nan. You specified method: {interpolation_method.value}.'\n )\n starting = [0] * rolling\n starting.extend([a for a in range(1, len(levels) - rolling + 1)])\n points = list(\n map(\n lambda d, v, i: pow(v / levels.iloc[i], 365.25 / (d - levels.index[i]).days) - 1,\n levels.index[1:],\n levels.values[1:],\n starting[1:],\n )\n )\n points.insert(0, 0)\n return pd.Series(points, index=levels.index)\n\n\ndef get_ratio_pure(\n er: pd.Series, w: Union[Window, int, str], interpolation_method: Interpolate = Interpolate.NAN\n) -> pd.Series:\n w = normalize_window(er, w or None) # continue to support 0 as an input for window\n ann_return = _annualized_return(er, w.w, interpolation_method=interpolation_method)\n long_enough = (er.index[-1] - w.w) >= er.index[0] if isinstance(w.w, pd.DateOffset) else w.w < len(er)\n ann_vol = volatility(er, w).iloc[1:] if long_enough else volatility(er)\n result = ann_return / ann_vol * 100\n return apply_ramp(result, w)\n\n\ndef _get_ratio(\n input_series: pd.Series,\n benchmark_or_rate: Union[Asset, float, str],\n w: Union[Window, int, str],\n *,\n day_count_convention: DayCountConvention,\n curve_type: CurveType = CurveType.PRICES,\n interpolation_method: Interpolate = Interpolate.NAN,\n) -> pd.Series:\n if curve_type == CurveType.PRICES:\n er = excess_returns(input_series, benchmark_or_rate, day_count_convention=day_count_convention)\n else:\n assert curve_type == CurveType.EXCESS_RETURNS\n er = input_series\n\n return get_ratio_pure(er, w, interpolation_method)\n\n\nclass RiskFreeRateCurrency(Enum):\n USD = \"USD\"\n AUD = \"AUD\"\n CHF = \"CHF\"\n EUR = \"EUR\"\n GBP = \"GBP\"\n JPY = \"JPY\"\n SEK = \"SEK\"\n _USD = \"usd\"\n _AUD = \"aud\"\n _CHF = \"chf\"\n _EUR = \"eur\"\n _GBP = \"gbp\"\n _JPY = \"jpy\"\n _SEK = \"sek\"\n\n\n@plot_session_function\ndef excess_returns_(price_series: pd.Series, currency: RiskFreeRateCurrency = RiskFreeRateCurrency.USD) -> pd.Series:\n \"\"\"\n Calculate excess returns\n\n :param price_series: price series\n :param currency: currency for risk-free rate, defaults to USD\n :return: excess returns\n\n **Usage**\n\n Given a price series P and risk-free rate R, excess returns E are defined as:\n\n :math:`E_t = E_{t-1} + P_t - P_{t-1} * (1 + R * (D_t - D_{t-1}) / 360)`\n\n The `Actual/360 `_ day count convention is used.\n\n **Examples**\n\n Get excess returns from a price series.\n\n >>> er = excess_returns(generate_series(100), USD)\n \"\"\"\n return excess_returns(price_series, Currency(currency.value), day_count_convention=DayCountConvention.ACTUAL_360)\n\n\n@plot_session_function\ndef sharpe_ratio(\n series: pd.Series,\n currency: RiskFreeRateCurrency = RiskFreeRateCurrency.USD,\n w: Union[Window, int, str] = None,\n curve_type: CurveType = CurveType.PRICES,\n method: Interpolate = Interpolate.NAN,\n) -> pd.Series:\n \"\"\"\n Calculate Sharpe ratio\n\n :param series: series of prices or excess returns for an asset\n :param currency: currency for risk-free rate, defaults to USD\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param curve_type: whether input series is of prices or excess returns, defaults to prices\n :param method: interpolation method (default: nan). Used to calculate returns on dates without data (i.e. weekends)\n when window is a relative date. Defaults to no interpolation.\n :return: Sharpe ratio\n\n **Usage**\n\n Given a price series P, risk-free rate R, and window of size w returns the rolling\n `Sharpe ratio `_ S:\n\n :math:`S_t = \\\\frac{(E_t / E_{t-w+1})^{365.25 / (D_t - D_{t-w})}-1}{volatility(E, w)_t}`\n\n Excess returns E are defined as:\n\n :math:`E_t = E_{t-1} + P_t - P_{t-1} * (1 + R * (D_t - D_{t-1}) / 360)`\n\n where D is the date for a data point. The\n `Actual/360 `_ day count convention is used.\n\n **Examples**\n\n Get rolling sharpe ratio of a price series (with window of 22).\n\n >>> sr = sharpe_ratio(generate_series(365, END_TODAY), USD, 22, PRICES)\n\n **See also**\n\n :func:`volatility`\n \"\"\"\n return _get_ratio(\n series,\n Currency(currency.value),\n w,\n day_count_convention=DayCountConvention.ACTUAL_360,\n curve_type=curve_type,\n interpolation_method=method,\n )\n\n\n@plot_function\ndef returns(series: pd.Series, obs: Union[Window, int, str] = 1, type: Returns = Returns.SIMPLE) -> pd.Series:\n \"\"\"\n Calculate returns from price series\n\n :param series: time series of prices\n :param obs: number of observations or relative date e.g. 3d, 1w, 1m ( relative date should be of pattern \\d+[dmywDMYW] ).\n :param type: returns type: simple, logarithmic or absolute\n :return: date-based time series of return\n\n **Usage**\n\n Compute returns series from price levels, based on the value of *type*:\n\n =========== =============================\n Type Description\n =========== =============================\n simple Simple arithmetic returns\n logarithmic Logarithmic returns\n absolute Absolute returns\n =========== =============================\n\n *Simple*\n\n Simple geometric change in asset prices, which can be aggregated across assets\n\n :math:`Y_t = \\\\frac{X_t}{X_{t-obs}} - 1`\n\n where :math:`X_t` is the asset price at time :math:`t`\n\n *Logarithmic*\n\n Natural logarithm of asset price changes, which can be aggregated through time\n\n :math:`Y_t = log(X_t) - log(X_{t-obs})`\n\n where :math:`X_t` is the asset price at time :math:`t`\n\n *Absolute*\n\n Absolute change in asset prices\n\n :math:`Y_t = X_t - X_{t-obs}`\n\n where :math:`X_t` is the asset price at time :math:`t`\n\n **Examples**\n\n Generate price series and take compute returns\n\n >>> prices = generate_series(100)\n >>> returns = returns(prices)\n\n **See also**\n\n :func:`prices`\n \"\"\"\n\n if series.size < 1:\n return series\n\n shifted_series = lag(series, obs, LagMode.TRUNCATE)\n\n if isinstance(obs, str) and not isinstance(series.index, pd.DatetimeIndex):\n series = series.copy()\n series.index = pd.DatetimeIndex(series.index)\n\n if type == Returns.SIMPLE:\n ret_series = series / shifted_series - 1\n elif type == Returns.LOGARITHMIC:\n ret_series = series.apply(math.log) - shifted_series.apply(math.log)\n elif type == Returns.ABSOLUTE:\n ret_series = series - shifted_series\n else:\n raise MqValueError('Unknown returns type (use simple / logarithmic / absolute)')\n\n return ret_series\n\n\n@plot_function\ndef prices(series: pd.Series, initial: int = 1, type: Returns = Returns.SIMPLE) -> pd.Series:\n \"\"\"\n Calculate price levels from returns series\n\n :param series: time series of returns\n :param initial: initial price level\n :param type: returns type: simple, logarithmic or absolute\n :return: date-based time series of return\n\n **Usage**\n\n Compute price levels from returns series, based on the value of *type*:\n\n =========== =============================\n Type Description\n =========== =============================\n simple Simple arithmetic returns\n logarithmic Logarithmic returns\n absolute Absolute returns\n =========== =============================\n\n *Simple*\n\n Compute asset price series from simple returns:\n\n :math:`Y_t = (1 + X_{t-1}) Y_{t-1}`\n\n where :math:`X_t` is the asset price at time :math:`t` and :math:`Y_0 = initial`\n\n *Logarithmic*\n\n Compute asset price series from logarithmic returns:\n\n :math:`Y_t = e^{X_{t-1}} Y_{t-1}`\n\n where :math:`X_t` is the asset price at time :math:`t` and :math:`Y_0 = initial`\n\n *Absolute*\n\n Compute asset price series from absolute returns:\n\n :math:`Y_t = X_{t-1} + Y_{t-1}`\n\n where :math:`X_t` is the asset price at time :math:`t` and :math:`Y_0 = initial`\n\n **Examples**\n\n Generate price series and take compute returns\n\n >>> series = generate_series(100)\n >>> returns = prices(returns(series))\n\n **See also**\n\n :func:`returns` :func:`product` :func:`exp`\n \"\"\"\n\n if series.size < 1:\n return series\n\n if type == Returns.SIMPLE:\n return product(1 + series) * initial\n elif type == Returns.LOGARITHMIC:\n return product(series.apply(math.exp)) * initial\n elif type == Returns.ABSOLUTE:\n return sum_(series) + initial\n else:\n raise MqValueError('Unknown returns type (use simple / Logarithmic / absolute)')\n\n\n@plot_function\ndef index(x: pd.Series, initial: int = 1) -> pd.Series:\n \"\"\"\n Geometric series normalization\n\n :param x: time series\n :param initial: initial value\n :return: normalized time series\n\n **Usage**\n\n Divides every value in x by the initial value of x:\n\n :math:`Y_t = initial * X_t / X_0`\n\n where :math:`X_0` is the first value in the series\n\n **Examples**\n\n Normalize series to 1:\n\n >>> series = generate_series(100)\n >>> returns = index(series)\n\n **See also**\n\n :func:`returns`\n\n \"\"\"\n i = x.first_valid_index()\n if not x[i]:\n raise MqValueError(\n 'Divide by zero error. Ensure that the first value of series passed to index(...) is non-zero'\n )\n return pd.Series(dtype=float) if i is None else initial * x / x[i]\n\n\n@plot_function\ndef change(x: pd.Series) -> pd.Series:\n \"\"\"\n Arithmetic series normalization\n\n :param x: time series\n :return: normalized time series\n\n **Usage**\n\n Compute difference of every value from the initial value of x:\n\n :math:`Y_t = X_t - X_0`\n\n where :math:`X_0` is the first value in the series\n\n **Examples**\n\n Change in level from initial value:\n\n >>> series = generate_series(100)\n >>> returns = change(series)\n\n **See also**\n\n :func:`index`\n\n \"\"\"\n return x - x.iloc[0]\n\n\ndef _get_annualization_factor(x):\n prev_idx = x.index[0]\n distances = []\n\n for idx, value in x.iloc[1:].items():\n d = (idx - prev_idx).days\n if d == 0:\n raise MqValueError('multiple data points on same date')\n distances.append(d)\n prev_idx = idx\n\n average_distance = np.average(distances)\n if average_distance < 2.1:\n factor = AnnualizationFactor.DAILY\n elif 6 <= average_distance < 8:\n factor = AnnualizationFactor.WEEKLY\n elif 14 <= average_distance < 17:\n factor = AnnualizationFactor.SEMI_MONTHLY\n elif 25 <= average_distance < 35:\n factor = AnnualizationFactor.MONTHLY\n elif 85 <= average_distance < 97:\n factor = AnnualizationFactor.QUARTERLY\n elif 360 <= average_distance < 386:\n factor = AnnualizationFactor.ANNUALLY\n else:\n raise MqValueError('Cannot infer annualization factor, average distance: ' + str(average_distance))\n return factor\n\n\n@plot_function\ndef annualize(x: pd.Series) -> pd.Series:\n \"\"\"\n Annualize series based on sample observation frequency\n\n :param x: time series of prices\n :return: date-based time series of annualized values\n\n **Usage**\n\n Based on number of days between observations, will determine an annualization factor and then adjust values\n accordingly. Useful for annualizing daily or monthly returns\n\n :math:`Y_t = X_t * \\\\sqrt{F}`\n\n Annualization factors as follows, based on period implied by observations:\n\n ========= =============================\n Period Annualization Factor (F)\n ========= =============================\n Daily :math:`252`\n Weekly :math:`52`\n Bi-Weekly :math:`26`\n Monthly :math:`12`\n Quarterly :math:`4`\n Annually :math:`1`\n ========= =============================\n\n **Examples**\n\n Annualize daily returns series:\n\n >>> prices = generate_series(100)\n >>> ann = annualize(returns(prices))\n\n **See also**\n\n :func:`returns`\n \"\"\"\n\n factor: int = _get_annualization_factor(x)\n return x * math.sqrt(factor)\n\n\n@plot_function\ndef volatility(\n x: pd.Series,\n w: Union[Window, int, str] = Window(None, 0),\n returns_type: Optional[Returns] = Returns.SIMPLE,\n annualization_factor: Optional[int] = None,\n assume_zero_mean: bool = False,\n) -> pd.Series:\n \"\"\"\n Realized volatility of price or return series\n\n :param x: time series of prices or returns (see returns_type parameter)\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param returns_type: returns type: simple, logarithmic or absolute. If None, x is assumed to be a returns series.\n :param annualization_factor: Annualization factor for volatility scaling. If None (default),\n auto-detects based on data frequency (252 for daily, 52 for weekly, etc.).\n :param assume_zero_mean: if True, assumes zero mean for returns (uses population formula with N denominator).\n if False (default), uses sample statistics (N-1 denominator).\n :return: date-based time series of annualized volatility (in percent)\n\n **Usage**\n\n Calculate rolling annualized realized volatility of a price series over a given window. Annual volatility of 20% is\n returned as 20.0:\n\n **Standard volatility (assume_zero_mean=False, default):**\n\n :math:`Y_t = \\\\sqrt{\\\\frac{1}{N-1} \\\\sum_{i=t-w+1}^t (R_t - \\\\overline{R_t})^2} * \\\\sqrt{252} * 100`\n\n where N is the number of observations in each rolling window :math:`w`, :math:`R_t` is the return on time\n :math:`t` based on *returns_type*\n\n **Zero-mean volatility (assume_zero_mean=True):**\n\n Uses root-mean-square (RMS) with population formula:\n\n :math:`Y_t = \\\\sqrt{\\\\frac{1}{N} \\\\sum_{i=t-w+1}^t R_i^2} \\\\times \\\\sqrt{252} \\\\times 100`\n\n This assumes $$\\\\overline{R} = 0$$ and uses $$N$$ (not $$N-1$$) in the denominator. This is the standard\n convention for:\n\n - Correlation swap volatility calculations (to match zero-mean covariance)\n - Variance swap realized volatility\n - Pre-demeaned returns or zero-drift processes\n\n **Returns types:**\n\n =========== =======================================================\n Type Description\n =========== =======================================================\n simple Simple geometric change in asset prices:\n :math:`R_t = \\\\frac{X_t}{X_{t-1}} - 1`\n where :math:`X_t` is the asset price at time :math:`t`\n logarithmic Natural logarithm of asset price changes:\n :math:`R_t = log(X_t) - log(X_{t-1})`\n where :math:`X_t` is the asset price at time :math:`t`\n absolute Absolute change in asset prices:\n :math:`Y_t = X_t - X_{t-obs}`\n where :math:`X_t` is the asset price at time :math:`t`\n NULL Input x is already a returns series; no conversion\n is performed\n =========== =======================================================\n\n and :math:`\\\\overline{R_t}` is the mean value over the same window:\n\n :math:`\\\\overline{R_t} = \\\\frac{\\\\sum_{i=t-w+1}^{t} R_t}{N}`\n\n If window is not provided, computes realized volatility over the full series.\n\n **Annualization:**\n\n If `annualization_factor` is None, the factor is auto-detected based on average time between observations:\n\n ================== =============================\n Data Frequency Annualization Factor (F)\n ================== =============================\n Daily $$252$$\n Weekly $$52$$\n Semi-Monthly $$26$$\n Monthly $$12$$\n Quarterly $$4$$\n Annually $$1$$\n ================== =============================\n\n **Examples**\n\n Compute rolling :math:`1` month (:math:`22` business day) annualized volatility of price series\n\n >>> series = generate_series(100)\n >>> vol_series = volatility(series, 22)\n >>> vol_series = volatility(series, Window(22, 30))\n\n Compute volatility with logarithmic returns:\n\n >>> vol_log = volatility(series, 22, returns_type=Returns.LOGARITHMIC)\n\n Compute volatility from a returns series directly:\n\n >>> returns_series = returns(series, Returns.SIMPLE)\n >>> vol_from_returns = volatility(returns_series, 22, returns_type=None)\n\n Compute zero-mean volatility:\n\n >>> vol_zero_mean = volatility(series, 22, assume_zero_mean=True)\n\n Compute FX volatility with 365-day annualization:\n\n >>> vol_fx = volatility(fx_series, 22, annualization_factor=365)\n\n **See also**\n\n :func:`std` :func:`annualize` :func:`returns` :func:`mean` :func:`corr_swap_correlation`\n\n \"\"\"\n w = normalize_window(x, w)\n\n if x.size < 1:\n return x\n\n ret = returns(x, type=returns_type) if returns_type is not None else x\n window = Window(w.w, 0)\n vol = mean(ret, window, MeanType.QUADRATIC) if assume_zero_mean else std(ret, window)\n\n annualized_vol = vol * math.sqrt(annualization_factor) if annualization_factor is not None else annualize(vol)\n\n return apply_ramp(annualized_vol.mul(100), w)\n\n\n@plot_function\ndef vol_swap_volatility(\n prices: pd.Series, n_days: Union[int, Window] = None, annualization_factor: int = 252, assume_zero_mean: bool = True\n) -> pd.Series:\n \"\"\"\n Rolling volatility of a price series for volatility swap pricing\n\n :param prices: price series\n :param n_days: number of price days in the rolling window. Defaults to length of series if not provided.\n :param annualization_factor: number of periods per year for annualization. Default is 252\n :param assume_zero_mean: if False, uses sample mean in volatility calculation (standard volatility).\n if True (default), assumes zero mean for returns (volatility swap convention)\n :return: date-based time series of annualized volatility in percentage terms\n\n **Usage**\n\n Calculate rolling realized volatility, :math:`\\\\sigma_t` of a price series over a given window\n using logarithmic returns.\n\n **When assume_zero_mean=True (default):**\n\n Computes zero-mean volatility (volatility swap convention):\n\n :math:`\\\\sigma_t = \\\\sqrt{AF \\\\times \\\\frac{1}{N}\\\\sum_{i=1}^{N} R_i^2}`\n\n where :math:`AF` is the annualization factor (default 252), :math:`N` is the number of returns\n (n_days - 1), and :math:`R_i` are the logarithmic returns:\n\n :math:`R_i = \\\\ln\\\\left(\\\\frac{P_i}{P_{i-1}}\\\\right)`\n\n This assumes :math:`\\\\overline{R} = 0`, which is the standard convention for volatility swap\n payoff calculations. The divisor is :math:`N` (not :math:`N-1`).\n\n **When assume_zero_mean=False:**\n\n Computes standard sample volatility:\n\n :math:`\\\\sigma_t = \\\\sqrt{AF \\\\times \\\\frac{1}{N-1}\\\\sum_{i=1}^{N} (R_i - \\\\overline{R})^2}`\n\n where :math:`\\\\overline{R}` is the sample mean over the window, and the divisor is :math:`N-1`\n (Bessel's correction for unbiased estimation).\n\n **Examples**\n\n Compute rolling 22 price day (21 return) volatility swap volatility:\n\n >>> series = generate_series(100)\n >>> vol_swap = vol_swap_volatility(series, n_days=22)\n\n Compute with 365-day annualization instead of default 252:\n\n >>> vol_252 = vol_swap_volatility(series, n_days=22, annualization_factor=365)\n\n Compute volatility over the full series:\n\n >>> full_vol = vol_swap_volatility(series) # n_days defaults to series length\n\n **See also**\n\n :func:`volatility` :func:`returns`\n\n \"\"\"\n if n_days is None:\n n_days = len(prices)\n if isinstance(n_days, Window):\n if n_days.r != n_days.w - 1:\n raise MqTypeError('Ramp-up must be the size of the window minus 1, e.g. Window(4, 3).')\n window = n_days\n else:\n window = Window(n_days, n_days - 1)\n return volatility(prices, window, Returns.LOGARITHMIC, annualization_factor, assume_zero_mean)\n\n\n@plot_function\ndef correlation(\n x: pd.Series,\n y: pd.Series,\n w: Union[Window, int, str] = Window(None, 0),\n type_: SeriesType = SeriesType.PRICES,\n returns_type: Returns = Returns.SIMPLE,\n assume_zero_mean: bool = False,\n) -> pd.Series:\n \"\"\"\n Rolling correlation of two price series\n\n :param x: price series\n :param y: price series\n :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param type_: type of both input series: prices or returns\n :param returns_type: Method to calculate returns when type_ is PRICES, simple, logarithmic or absolute. You can\n also pass two methods to calculate returns for x and y respectively.\n :param assume_zero_mean: if False (default), uses sample mean in correlation calculation\n (standard Pearson correlation).\n if True, assumes zero mean for returns (correlation swap convention)\n :return: date-based time series of correlation\n\n **Usage**\n\n Calculate rolling `realized correlation `_,\n :math:`\\\\rho_t` of two price series over a given window:\n\n :math:`\\\\rho_t =\n \\\\frac{\\\\sum_{i=t-w+1}^t (R_t - \\\\overline{R_t})(Y_t - \\\\overline{S_t})}{(N-1)\\\\sigma R_t\\\\sigma S_t}`\n\n where N is the number of observations in each rolling window, :math:`w`, and :math:`R_t` and :math:`S_t` are the\n simple returns for each series on time :math:`t`\n\n If prices are provided then returns are calculated based on the returns_type e.g. for simple returns:\n\n :math:`R_t = \\\\frac{X_t}{X_{t-1}} - 1` and :math:`S_t = \\\\frac{Y_t}{Y_{t-1}} - 1`\n\n If returns are provided:\n\n :math:`R_t = X_t` and :math:`S_t = Y_t`\n\n :math:`\\\\overline{R_t}`, :math:`\\\\overline{S_t}` are the mean values, and :math:`\\\\sigma R_{t}` and\n :math:`\\\\sigma S_{t}` are the sample standard deviations, of series\n :math:`R_t` and :math:`S_t` over the same window\n\n If window is not provided, computes realized correlation over the full series\n\n **Examples**\n\n Compute rolling :math:`1` month (:math:`22` business day) correlation of price series\n\n >>> series1 = generate_series(100)\n >>> series2 = generate_series(100)\n >>> corr = correlation(series1, series2, 22)\n\n **See also**\n\n :func:`std` :func:`returns`\n\n \"\"\"\n w = normalize_window(x, w)\n\n if x.size < 1:\n return x\n\n given_prices = type_ == SeriesType.PRICES\n if given_prices:\n if isinstance(returns_type, (tuple, list)):\n if len(returns_type) != 2:\n raise MqValueError('Expected a list of length 2 for \"returns_type\"')\n if not all(isinstance(r, Returns) for r in returns_type):\n raise MqTypeError('Expected a list of Returns for \"returns_type\"')\n returns_type_x, returns_type_y = returns_type\n else:\n returns_type_x = returns_type_y = returns_type\n ret_1 = returns(x, type=returns_type_x)\n ret_2 = returns(y, type=returns_type_y)\n else:\n ret_1 = x\n ret_2 = y\n\n aligned = pd.DataFrame({'r1': ret_1, 'r2': ret_2}).dropna()\n clean_ret1 = aligned['r1']\n clean_ret2 = aligned['r2']\n\n if assume_zero_mean:\n\n def daily_vols(returns):\n vols = volatility(returns, w, returns_type=None, assume_zero_mean=True)\n annualization_factor = _get_annualization_factor(returns)\n result = vols.div(math.sqrt(annualization_factor) * 100)\n return result\n\n zero_mean_cov = mean(clean_ret1 * clean_ret2, w)\n vols_1 = daily_vols(clean_ret1)\n vols_2 = daily_vols(clean_ret2)\n corr = pd.Series(zero_mean_cov / (vols_1 * vols_2), index=clean_ret1.index)\n else:\n if isinstance(w.w, pd.DateOffset):\n if isinstance(clean_ret1.index, pd.DatetimeIndex):\n values = [\n clean_ret1.loc[(clean_ret1.index > idx - w.w) & (clean_ret1.index <= idx)].corr(clean_ret2)\n for idx in clean_ret1.index\n ]\n else:\n values = [\n clean_ret1.loc[(clean_ret1.index > (idx - w.w).date()) & (clean_ret1.index <= idx)].corr(clean_ret2)\n for idx in clean_ret1.index\n ]\n corr = pd.Series(values, index=clean_ret1.index)\n else:\n corr = clean_ret1.rolling(w.w, 0).corr(clean_ret2)\n\n return apply_ramp(interpolate(corr, x, Interpolate.NAN), w)\n\n\n@plot_function\ndef corr_swap_correlation(\n x: pd.Series, y: pd.Series, n_days: Union[int, Window] = None, assume_zero_mean: bool = True\n) -> pd.Series:\n \"\"\"\n Rolling correlation of two price series for correlation swap pricing\n\n :param x: price series\n :param y: price series\n :param n_days: number of price days in the rolling window. Defaults to length of series if not provided.\n Note: the returns window size will be n_days - 1\n :param assume_zero_mean: if False, uses sample mean in correlation calculation (Pearson correlation).\n if True (default), assumes zero mean for returns (correlation swap convention)\n :return: date-based time series of correlation\n\n **Usage**\n\n Calculate rolling `realized correlation `_,\n :math:`\\\\rho_t` of two price series over a given window using logarithmic returns.\n\n **When assume_zero_mean=False (default):**\n\n Computes standard Pearson correlation:\n\n :math:`\\\\rho_t =\n \\\\frac{\\\\sum_{i=t-w+1}^t (R_i - \\\\overline{R})(S_i - \\\\overline{S})}{(w-1)\\\\sigma_R \\\\sigma_S}`\n\n where :math:`w` is the window size (n_days - 1), :math:`R_i` and :math:`S_i` are the logarithmic returns:\n\n :math:`R_i = \\\\ln\\\\left(\\\\frac{X_i}{X_{i-1}}\\\\right)` and :math:`S_i = \\\\ln\\\\left(\\\\frac{Y_i}{Y_{i-1}}\\\\right)`\n\n :math:`\\\\overline{R}` and :math:`\\\\overline{S}` are the sample means, and :math:`\\\\sigma_R` and\n :math:`\\\\sigma_S` are the sample standard deviations over the window.\n\n **When assume_zero_mean=True:**\n\n Computes zero-mean correlation (correlation swap convention):\n\n :math:`\\\\rho_t =\n \\\\frac{\\\\sum_{i=t-w+1}^t R_i S_i}{(w-1)\\\\sqrt{\\\\sum_{i=t-w+1}^t R_i^2} \\\\sqrt{\\\\sum_{i=t-w+1}^t S_i^2}}`\n\n This assumes :math:`\\\\overline{R} = \\\\overline{S} = 0`, which is the standard convention for\n correlation swap payoff calculations.\n\n **Examples**\n\n Compute rolling :math:`22` price day (21 return) standard Pearson correlation:\n\n >>> series1 = generate_series(100)\n >>> series2 = generate_series(100)\n >>> corr = corr_swap_correlation(series1, series2, n_days=22, assume_zero_mean=False)\n\n Compute zero-mean correlation for correlation swap pricing:\n\n >>> corr_swap = corr_swap_correlation(series1, series2, n_days=22)\n\n Compute correlation over the full series:\n\n >>> full_corr = corr_swap_correlation(series1, series2) # n_days defaults to series length\n\n **See also**\n\n :func:`correlation` :func:`returns` :func:`volatility`\n\n \"\"\"\n if n_days is None:\n n_days = min(len(x), len(y))\n if isinstance(n_days, Window):\n if n_days.r != n_days.w - 1:\n raise MqTypeError('Ramp-up must be the size of the window minus 1, e.g. Window(4, 3).')\n window = n_days\n else:\n window = Window(n_days - 1, n_days - 2)\n return correlation(x, y, window, SeriesType.PRICES, Returns.LOGARITHMIC, assume_zero_mean)\n\n\n@plot_function\ndef beta(x: pd.Series, b: pd.Series, w: Union[Window, int, str] = Window(None, 0), prices: bool = True) -> pd.Series:\n \"\"\"\n Rolling beta of price series and benchmark\n\n :param x: time series of prices\n :param b: time series of benchmark prices\n :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param prices: True if input series are prices, False if they are returns\n :return: date-based time series of beta\n\n **Usage**\n\n Calculate rolling `beta `_,\n :math:`\\\\beta_t` of a series to a benchmark over a given window:\n\n :math:`R_t = \\\\alpha_t + \\\\beta S_t + \\\\epsilon_t`\n\n Calculated as:\n\n :math:`\\\\beta_t = \\\\frac{\\\\sum_{i=t-w+1}^t Cov(R_t, S_t)}{Var(S_t)}`\n\n where N is the number of observations in each rolling window, :math:`w`, and :math:`R_t` and :math:`S_t` are the\n simple returns for each series on time :math:`t`:\n\n :math:`R_t = \\\\frac{X_t}{X_{t-1}} - 1` and :math:`S_t = \\\\frac{b_t}{b_{t-1}} - 1`\n\n If prices = False, assumes returns are provided:\n\n :math:`R_t = X_t` and :math:`S_t = b_t`\n\n :math:`Cov(R_t, S_t)` and :math:`Var(S_t)` are the covariance and variance of the series\n :math:`R_t` and :math:`S_t` over the same window\n\n If window is not provided, computes beta over the full series\n\n **Examples**\n\n Compute rolling :math:`1` month (:math:`22` business day) beta of two price series\n\n >>> series = generate_series(100)\n >>> benchmark = generate_series(100)\n >>> b = beta(series, benchmark, 22)\n\n **See also**\n\n :func:`var` :func:`cov` :func:`correlation` :func:`returns`\n \"\"\"\n if not isinstance(prices, bool):\n raise MqTypeError('expected a boolean value for \"prices\"')\n\n w = normalize_window(x, w)\n\n ret_series = returns(x) if prices else x\n ret_benchmark = returns(b) if prices else b\n\n if isinstance(w.w, pd.DateOffset):\n series_index = ret_series.index.intersection(ret_benchmark.index)\n size = len(series_index)\n ret_series = ret_series.loc[series_index]\n benchmark_series = ret_benchmark.loc[series_index]\n\n ret_values = np.array(ret_series.values, dtype=np.double)\n benchmark_values = np.array(benchmark_series.values, dtype=np.double)\n\n cov_results = np.empty(size, dtype=np.double)\n var_results = np.empty(size, dtype=np.double)\n\n offset = w.w\n start = 0\n for i in range(1, size):\n min_index_value = (series_index[i] - offset).date()\n for idx in range(start, i + 1):\n if series_index[idx] > min_index_value:\n start = idx\n break\n\n sub_benchmark_values = benchmark_values[start : i + 1]\n var_results[i] = np.var(sub_benchmark_values, ddof=1)\n cov_results[i] = np.cov(ret_values[start : i + 1], sub_benchmark_values, ddof=1)[0][1]\n\n result = pd.Series(cov_results / var_results, index=series_index, dtype=np.double)\n else:\n cov = ret_series.rolling(w.w, 0).cov(ret_benchmark)\n result = cov / ret_benchmark.rolling(w.w, 0).var()\n\n # do not compute initial values as they may be extreme when sample size is small\n result[0:3] = np.nan\n return apply_ramp(interpolate(result, x, Interpolate.NAN), w)\n\n\n@plot_function\ndef max_drawdown(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Compute the maximum peak to trough drawdown over a rolling window as a ratio.\n\n i.e. if the max drawdown for a period is 20%, this function will return -0.2.\n\n :param x: time series\n :param w: Window, int, or str: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: time series of rolling maximum drawdown\n\n **Examples**\n\n Compute the maximum peak to trough `drawdown `_\n\n >>> series = generate_series(100)\n >>> max_drawdown(series)\n\n **See also**\n\n :func:`returns`\n\n \"\"\"\n w = normalize_window(x, w)\n if isinstance(w.w, pd.DateOffset):\n if pd.api.types.is_datetime64_dtype(x.index):\n scores = pd.Series(\n [x[idx] / x.loc[(x.index > (idx - w.w)) & (x.index <= idx)].max() - 1 for idx in x.index], index=x.index\n )\n result = pd.Series(\n [scores.loc[(scores.index > (idx - w.w)) & (scores.index <= idx)].min() for idx in scores.index],\n index=scores.index,\n )\n else:\n raise TypeError('Please pass in list of dates as index')\n else:\n rolling_max = x.rolling(w.w, 0).max()\n result = (x / rolling_max - 1).rolling(w.w, 0).min()\n return apply_ramp(result, w)\n" }, { "path": "gs_quant/timeseries/helper.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport inspect\nimport logging\nimport os\nimport re\nfrom enum import Enum, IntEnum\nfrom functools import wraps, partial\nfrom typing import Optional, Union, List, Iterable, Callable\n\nimport numpy as np\nimport pandas as pd\n\nfrom gs_quant.api.gs.data import QueryType\nfrom gs_quant.api.utils import ThreadPoolManager\nfrom gs_quant.data import DataContext, Dataset\nfrom gs_quant.datetime.relative_date import RelativeDate\nfrom gs_quant.entities.entity import EntityType\nfrom gs_quant.errors import MqValueError, MqRequestError\nfrom gs_quant.timeseries.measure_registry import register_measure\n\n_PD_VERSION_MATCH = re.match(r'^(\\d+)\\.(\\d+)', pd.__version__)\n_PD_VERSION = (int(_PD_VERSION_MATCH.group(1)), int(_PD_VERSION_MATCH.group(2))) if _PD_VERSION_MATCH else (0, 0)\n\nif _PD_VERSION >= (2, 2):\n FREQ_MONTH_END = 'ME'\n FREQ_QUARTER_END = 'QE'\n FREQ_YEAR_END = 'YE'\n FREQ_HOUR = 'h'\n FREQ_SECOND = 's'\n FREQ_BUSINESS_MONTH_END = 'BME'\n FREQ_BUSINESS_QUARTER_END = 'BQE'\n FREQ_BUSINESS_YEAR_END = 'BYE'\nelse: # pragma: no cover\n FREQ_MONTH_END = 'M'\n FREQ_QUARTER_END = 'Q'\n FREQ_YEAR_END = 'Y'\n FREQ_HOUR = 'H'\n FREQ_SECOND = 'S'\n FREQ_BUSINESS_MONTH_END = 'BM'\n FREQ_BUSINESS_QUARTER_END = 'BQ'\n FREQ_BUSINESS_YEAR_END = 'BY'\n\nENABLE_DISPLAY_NAME = 'GSQ_ENABLE_MEASURE_DISPLAY_NAME'\nUSE_DISPLAY_NAME = os.environ.get(ENABLE_DISPLAY_NAME) == \"1\"\n_logger = logging.getLogger(__name__)\n\n\nclass Entitlement(Enum):\n INTERNAL = 'internal'\n\n\ntry:\n from quant_extensions.timeseries.rolling import rolling_apply\nexcept ImportError as e:\n _logger.debug('unable to import rolling_apply extension: %s', e)\n\n def rolling_apply(s: pd.Series, offset: pd.DateOffset, function: Callable[[np.ndarray], float]) -> pd.Series:\n if isinstance(s.index, pd.DatetimeIndex):\n values = [function(s.loc[(s.index > (idx - offset)) & (s.index <= idx)]) for idx in s.index]\n else:\n values = [function(s.loc[(s.index > (idx - offset).date()) & (s.index <= idx)]) for idx in s.index]\n return pd.Series(values, index=s.index, dtype=np.double)\n\n\ndef _create_enum(name, members):\n return Enum(name, {n.upper(): n.lower() for n in members}, module=__name__)\n\n\ndef _create_int_enum(name, mappings):\n return IntEnum(name, {k.upper(): v for k, v in mappings.items()})\n\n\ndef _to_offset(tenor: str) -> pd.DateOffset:\n import re\n\n matcher = re.fullmatch('(\\\\d+)([hdwmy])', tenor)\n if not matcher:\n raise MqValueError('invalid tenor ' + tenor)\n\n ab = matcher.group(2)\n if ab == 'h':\n name = 'hours'\n elif ab == 'd':\n name = 'days'\n elif ab == 'w':\n name = 'weeks'\n elif ab == 'm':\n name = 'months'\n else:\n assert ab == 'y'\n name = 'years'\n\n kwarg = {name: int(matcher.group(1))}\n return pd.DateOffset(**kwarg)\n\n\ndef _tenor_to_month(relative_date: str) -> int:\n matcher = re.fullmatch('([1-9]\\\\d*)([my])', relative_date)\n if matcher:\n mag = int(matcher.group(1))\n return mag if matcher.group(2) == 'm' else mag * 12\n raise MqValueError('invalid input: relative date must be in months or years')\n\n\nInterpolate = _create_enum('Interpolate', ['intersect', 'step', 'nan', 'zero', 'time'])\nReturns = _create_enum('Returns', ['simple', 'logarithmic', 'absolute'])\nSeriesType = _create_enum('SeriesType', ['prices', 'returns'])\nCurveType = _create_enum('CurveType', ['prices', 'excess_returns'])\n\n\nclass Window:\n \"\"\"\n Create a Window with size and ramp up to use.\n\n :param w: window size\n :param r: ramp up value. Defaults to the window size.\n :return: new window object\n\n **Usage**\n\n The window size and ramp up value can either the number of observations or a string representation of the time\n period.\n\n **Examples**\n\n Window size is :math:`22` observations and the ramp up value is :math:`10`:\n\n >>> Window(22, 10)\n\n Window size is one month and the ramp up size is one week:\n\n >>> Window('1m', '1w')\n\n \"\"\"\n\n def __init__(self, w: Union[int, str, None] = None, r: Union[int, str, None] = None):\n self.w = w\n self.r = w if r is None else r\n\n def as_dict(self):\n return {'w': self.w, 'r': self.r}\n\n @classmethod\n def from_dict(cls, obj):\n return Window(w=obj.get('w'), r=obj.get('r'))\n\n\ndef _check_window(series_length: int, window: Window):\n if series_length > 0 and isinstance(window.w, int) and isinstance(window.r, int):\n if window.w <= 0:\n raise MqValueError('Window value must be greater than zero.')\n if window.r > series_length or window.r < 0:\n raise MqValueError('Ramp value must be less than the length of the series and greater than zero.')\n\n\ndef apply_ramp(x: pd.Series, window: Window) -> pd.Series:\n _check_window(len(x), window)\n if isinstance(window.w, int) and window.w > len(x): # does not restrict window size when it is a DataOffset\n return pd.Series(dtype=float)\n if isinstance(window.r, pd.DateOffset):\n if np.issubdtype(x.index, dt.date):\n return x.loc[(x.index[0] + window.r).date() :]\n else:\n return x.loc[(x.index[0] + window.r).to_pydatetime() :]\n else:\n return x[window.r :]\n\n\ndef normalize_window(\n x: Union[pd.Series, pd.DataFrame], window: Union[Window, int, str, None], default_window: int = None\n) -> Window:\n if default_window is None:\n default_window = len(x)\n\n if isinstance(window, int):\n window = Window(window, window)\n elif isinstance(window, str):\n window = Window(_to_offset(window), _to_offset(window))\n else:\n if window is None:\n window = Window(default_window, 0)\n else:\n if isinstance(window.w, str):\n window = Window(_to_offset(window.w), window.r)\n if isinstance(window.r, str):\n window = Window(window.w, _to_offset(window.r))\n if window.w is None:\n window = Window(default_window, window.r)\n\n _check_window(default_window, window)\n return window\n\n\ndef plot_function(fn):\n # Indicates that fn should be exported to plottool as a pure function.\n fn.plot_function = True\n return fn\n\n\ndef plot_session_function(fn):\n fn.plot_function = True\n fn.requires_session = True\n return fn\n\n\ndef check_forward_looking(pricing_date, source, name=\"function\"):\n if pricing_date is not None or source != 'plottool':\n return\n if DataContext.current.end_date <= dt.date.today():\n msg = (\n f'{name}() requires a forward looking date range e.g. [0d, 3y]. '\n 'Please update the date range via the date picker.'\n )\n raise MqValueError(msg)\n\n\ndef plot_measure(\n asset_class: tuple,\n asset_type: Optional[tuple] = None,\n dependencies: Optional[List[QueryType]] = tuple(),\n asset_type_excluded: Optional[tuple] = None,\n display_name: Optional[str] = None,\n entitlements: Optional[List[Entitlement]] = [],\n):\n # Indicates that fn should be exported to plottool as a member function / pseudo-measure.\n # Set category to None for no restrictions, else provide a tuple of allowed values.\n def decorator(fn):\n assert isinstance(asset_class, tuple)\n assert len(asset_class) >= 1\n assert asset_type is None or isinstance(asset_type, tuple)\n assert asset_type_excluded is None or isinstance(asset_type_excluded, tuple)\n assert asset_type is None or asset_type_excluded is None\n\n fn.plot_measure = True\n fn.entity_type = EntityType.ASSET\n fn.asset_class = asset_class\n fn.asset_type = asset_type\n fn.asset_type_excluded = asset_type_excluded\n fn.dependencies = dependencies\n fn.entitlements = entitlements\n\n if USE_DISPLAY_NAME:\n fn.display_name = display_name\n\n multi_measure = register_measure(fn)\n multi_measure.entity_type = EntityType.ASSET\n return multi_measure\n else:\n return fn\n\n return decorator\n\n\ndef plot_measure_entity(entity_type: EntityType, dependencies: Optional[Iterable[QueryType]] = tuple()):\n def decorator(fn):\n assert isinstance(entity_type, EntityType)\n if dependencies is not None:\n assert isinstance(dependencies, Iterable)\n assert all(isinstance(x, QueryType) for x in dependencies)\n\n fn.plot_measure_entity = True\n fn.entity_type = entity_type\n fn.dependencies = tuple(dependencies) # immutable\n\n return fn\n\n return decorator\n\n\ndef requires_session(fn):\n fn.requires_session = True\n return fn\n\n\ndef plot_method(fn):\n # Indicates that fn should be exported to plottool as a method.\n fn.plot_method = True\n\n # Allows fn to accept and ignore real_time argument even if it is not defined in the signature\n @wraps(fn)\n def ignore_extra_argument(*args, **kwargs):\n for arg in ('real_time', 'interval', 'time_filter'):\n if arg not in inspect.signature(fn).parameters:\n kwargs.pop(arg, None)\n return fn(*args, **kwargs)\n\n return ignore_extra_argument\n\n\ndef log_return(logger: logging.Logger, message):\n def outer(fn):\n @wraps(fn)\n def inner(*args, **kwargs):\n response = fn(*args, **kwargs)\n logger.debug('%s: %s', message, response)\n return response\n\n return inner\n\n return outer\n\n\ndef get_df_with_retries(fetcher, start_date, end_date, exchange, retries=1):\n \"\"\"\n Loads results from Data Service by calling fetcher function. Shifts query date range back by business days until\n result is not empty or retry limit reached. This is a fallback feature in case a data upload is late. Measure\n implementations should be written such that retries are usually not required.\n\n :param fetcher: a no-argument function runs a data query and returns a DataFrame\n :param start_date: initial start date for query\n :param end_date: initial end date for query\n :param exchange: exchange to use for holiday calendar\n :param retries: maximum number of retries\n :return: DataFrame\n \"\"\"\n retries = max(retries, 0)\n while retries > -1:\n with DataContext(start_date, end_date):\n result = fetcher()\n if not result.empty:\n break\n kwargs = {'exchanges': [exchange]} if exchange else {}\n # no need to include any part of the previous date range since it's known to be empty\n end_date = RelativeDate('-1b', base_date=start_date).apply_rule(**kwargs)\n start_date = end_date\n retries -= 1\n return result\n\n\ndef get_dataset_data_with_retries(\n dataset: Dataset, *, start: dt.date, end: dt.date, count: int = 0, max_retries: int = 5, **kwargs\n) -> pd.DataFrame:\n try:\n data = dataset.get_data(start=start, end=end, **kwargs)\n except MqRequestError as e:\n if count < max_retries:\n mid = start + (end - start) / 2\n count += 1\n first_half = partial(get_dataset_data_with_retries, dataset, start=start, end=mid, count=count, **kwargs)\n mid = mid + dt.timedelta(days=1)\n second_half = partial(get_dataset_data_with_retries, dataset, start=mid, end=end, count=count, **kwargs)\n results = ThreadPoolManager.run_async([first_half, second_half])\n first_half_results, second_half_results = results[0], results[1]\n data = pd.concat([first_half_results, second_half_results]).sort_index()\n else:\n raise e\n return data\n\n\ndef get_dataset_with_many_assets(\n ds: Dataset, *, assets: List[str], start: dt.date, end: dt.date, batch_limit: int = 100, **kwargs\n) -> pd.DataFrame:\n tasks = [\n partial(ds.get_data, assetId=assets[i : i + batch_limit], start=start, end=end, return_type=None, **kwargs)\n for i in range(0, len(assets), batch_limit)\n ]\n results = ThreadPoolManager.run_async(tasks)\n return pd.concat(results)\n\n\ndef _month_to_tenor(months: int) -> str:\n return f'{months // 12}y' if months % 12 == 0 else f'{months}m'\n\n\ndef _split_where_conditions(where):\n la = [dict()]\n for k, v in where.items():\n lb = []\n while len(la) > 0:\n temp = la.pop()\n if isinstance(v, list):\n for cv in v:\n clone = temp.copy()\n clone[k] = [cv]\n lb.append(clone)\n else:\n lb.append(dict(**temp, **{k: v}))\n la = lb\n return la\n\n\ndef _pandas_roll(s: pd.Series, window_str: str, method_name: str):\n return getattr(s.rolling(window_str), method_name)()\n\n\ndef rolling_offset(\n s: pd.Series, offset: pd.DateOffset, function: Callable[[np.ndarray], float], method_name: str = None\n) -> pd.Series:\n \"\"\"\n Perform rolling window calculations. If offset has a fixed frequency and method name is provided, will use\n `Series.rolling< https://pandas.pydata.org/docs/reference/api/pandas.Series.rolling.html>`_ for best performance.\n\n :param s: time series\n :param offset: window size as a date offset\n :param function: function to call on each window\n :param method_name: name of method to call on each window (must be a method on Rolling object)\n :return: result time series\n \"\"\"\n # frequencies that can be passed to Series.rolling\n fixed = {'hour': 'h', 'hours': 'h', 'day': 'D', 'days': 'D'}\n\n if method_name and len(offset.kwds) == 1:\n freq, count = offset.kwds.popitem()\n if freq in fixed:\n window_str = f'{count}{fixed[freq]}'\n if np.issubdtype(s.index, np.datetime64):\n return _pandas_roll(s, window_str, method_name)\n else:\n t = s.copy(deep=False)\n t.index = pd.to_datetime(t.index) # needed for Series.rolling\n return pd.Series(_pandas_roll(t, window_str, method_name), index=s.index)\n\n return rolling_apply(s, offset, function)\n" }, { "path": "gs_quant/timeseries/measure_registry.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport re\n\nfrom gs_quant.errors import MqError\n\nregistry = {}\n\n\nclass MultiMeasure:\n def __init__(self, display_name):\n self.display_name = display_name\n self.measure_map = {}\n\n def get_fn(self, asset):\n asset_class = asset.asset_class\n asset_type = asset.get_type()\n fns = self.measure_map.get(asset_class, ())\n\n def canonicalize(word):\n pruned = re.sub(r\"[^\\w]\", \"\", word)\n return pruned.casefold()\n\n canonicalized = canonicalize(asset_type.value)\n\n for fn in fns:\n if (fn.asset_type is None or canonicalized in map(lambda x: canonicalize(x.value), fn.asset_type)) and (\n fn.asset_type_excluded is None\n or canonicalized not in map(lambda x: canonicalize(x.value), fn.asset_type_excluded)\n ):\n return fn\n\n raise MqError(\n \"No measure {} defined for asset class {} and type {}\".format(self.display_name, asset_class, asset_type)\n )\n\n def __call__(self, asset, *args, **kwargs):\n fn = self.get_fn(asset)\n return fn(asset, *args, **kwargs)\n\n def register(self, function):\n for asset_class in function.asset_class:\n self.measure_map[asset_class] = self.measure_map.get(asset_class, ()) + (function,)\n\n\ndef register_measure(fn):\n display_name = fn.display_name if fn.display_name else fn.__name__\n multi_measure = registry.get(display_name)\n if multi_measure is None:\n multi_measure = registry[display_name] = MultiMeasure(display_name)\n multi_measure.register(fn)\n return multi_measure\n" }, { "path": "gs_quant/timeseries/measures.py", "content": "# Copyright 2019 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n# Plot Service will make use of appropriately decorated functions in this module.\nimport calendar\nimport datetime as dt\nimport logging\nimport re\nfrom collections import namedtuple\nfrom enum import Enum, auto\nfrom functools import partial\nfrom numbers import Real\nfrom typing import Union, Optional, Tuple, List\n\nimport cachetools.func\nimport inflection\nimport numpy as np\nimport pandas as pd\nfrom dateutil import tz\nfrom dateutil.relativedelta import relativedelta\nfrom gs_quant.api.gs.assets import GsAssetApi, GsIdType\nfrom gs_quant.api.gs.data import MarketDataResponseFrame, QueryType, GsDataApi\nfrom gs_quant.api.gs.indices import GsIndexApi\nfrom gs_quant.api.utils import ThreadPoolManager\nfrom gs_quant.common import AssetClass, AssetType, PricingLocation\nfrom gs_quant.data import Dataset\nfrom gs_quant.data.core import DataContext, IntervalFrequency\nfrom gs_quant.data.fields import Fields, DataMeasure\nfrom gs_quant.data.log import log_debug, log_warning\nfrom gs_quant.datetime import DAYS_IN_YEAR\nfrom gs_quant.datetime.gscalendar import GsCalendar\nfrom gs_quant.datetime.point import relative_date_add\nfrom gs_quant.entities.entity import PositionedEntity\nfrom gs_quant.errors import MqValueError, MqTypeError\nfrom gs_quant.markets.securities import Asset, AssetIdentifier, AssetType as SecAssetType, SecurityMaster, Stock\nfrom gs_quant.timeseries import Basket, RelativeDate, Returns, Window, sqrt, volatility\nfrom gs_quant.timeseries.helper import (\n FREQ_MONTH_END,\n _month_to_tenor,\n _split_where_conditions,\n _tenor_to_month,\n _to_offset,\n check_forward_looking,\n get_dataset_with_many_assets,\n get_df_with_retries,\n log_return,\n plot_measure,\n)\nfrom gs_quant.timeseries.measures_helper import EdrDataReference, VolReference, preprocess_implied_vol_strikes_eq\nfrom pandas.tseries.holiday import (\n AbstractHolidayCalendar,\n Holiday,\n USLaborDay,\n USMemorialDay,\n USThanksgivingDay,\n sunday_to_monday,\n)\nfrom pydash import chunk, flatten, get\n\nGENERIC_DATE = Union[dt.date, str]\nASSET_SPEC = Union[Asset, str]\nTD_ONE = dt.timedelta(days=1)\n\n\ndef _normalize_dtidx(idx):\n if not isinstance(idx, pd.DatetimeIndex):\n idx = pd.DatetimeIndex(idx)\n return idx.as_unit('ns') if hasattr(idx, 'as_unit') else idx\n\n\n_logger = logging.getLogger(__name__)\n\nMeasureDependency: namedtuple = namedtuple(\"MeasureDependency\", [\"id_provider\", \"query_type\"])\n\n\nclass ExtendedSeries(pd.Series):\n _internal_names = pd.Series._internal_names + ['dataset_ids']\n _internal_names_set = set(_internal_names)\n\n @property\n def _constructor(self):\n return ExtendedSeries\n\n def __finalize__(self, other, method=None, **kwargs):\n # Call the parent class's __finalize__ method\n super().__finalize__(other, method)\n # Copy custom attributes from the other DataFrame\n if isinstance(other, ExtendedSeries) and hasattr(other, 'dataset_ids'):\n self.dataset_ids = getattr(other, 'dataset_ids', None)\n return self\n\n\n# TODO: get NERC Calendar from SecDB\nclass NercCalendar(AbstractHolidayCalendar):\n rules = [\n Holiday('New Years Day', month=1, day=1, observance=sunday_to_monday),\n USMemorialDay,\n Holiday('July 4th', month=7, day=4, observance=sunday_to_monday),\n USLaborDay,\n USThanksgivingDay,\n Holiday('Christmas', month=12, day=25, observance=sunday_to_monday),\n ]\n\n\nclass UnderlyingSourceCategory(Enum):\n ALL = 'All'\n EDGX = 'EDGX'\n TRF = 'TRF'\n\n\nclass GICSSector(Enum):\n INFORMATION_TECHNOLOGY = 'Information Technology'\n ENERGY = 'Energy'\n MATERIALS = 'Materials'\n INDUSTRIALS = 'Industrials'\n CONSUMER_DISCRETIONARY = 'Consumer Discretionary'\n CONSUMER_STAPLES = 'Consumer Staples'\n HEALTH_CARE = 'Health Care'\n FINANCIALS = 'Financials'\n COMMUNICATION_SERVICES = 'Communication Services'\n REAL_ESTATE = 'Real Estate'\n UTILITIES = 'Utilities'\n ALL = 'All'\n\n\nclass RetailMeasures(Enum):\n RETAIL_PCT_SHARES = 'impliedRetailPctShares'\n RETAIL_PCT_NOTIONAL = 'impliedRetailPctNotional'\n RETAIL_SHARES = 'impliedRetailShares'\n RETAIL_NOTIONAL = 'impliedRetailNotional'\n SHARES = 'shares'\n NOTIONAL = 'notional'\n RETAIL_BUY_NOTIONAL = 'impliedRetailBuyNotional'\n RETAIL_BUY_PCT_NOTIONAL = 'impliedRetailBuyPctNotional'\n RETAIL_BUY_PCT_SHARES = 'impliedRetailBuyPctShares'\n RETAIL_BUY_SHARES = 'impliedRetailBuyShares'\n RETAIL_SELL_NOTIONAL = 'impliedRetailSellNotional'\n RETAIL_SELL_PCT_NOTIONAL = 'impliedRetailSellPctNotional'\n RETAIL_SELL_PCT_SHARES = 'impliedRetailSellPctShares'\n RETAIL_SELL_SHARES = 'impliedRetailSellShares'\n\n\nclass SkewReference(Enum):\n DELTA = 'delta'\n NORMALIZED = 'normalized'\n SPOT = 'spot'\n FORWARD = 'forward'\n\n\nclass NormalizationMode(Enum):\n NORMALIZED = 'normalized'\n OUTRIGHT = 'outright'\n\n\nclass CdsVolReference(Enum):\n DELTA_CALL = 'delta_call'\n DELTA_PUT = 'delta_put'\n FORWARD = 'forward'\n\n\nclass VolSmileReference(Enum):\n SPOT = 'spot'\n FORWARD = 'forward'\n DELTA = 'delta'\n NORMALIZED = 'normalized'\n\n\nclass FXForwardType(Enum):\n POINTS = 'points'\n OUTRIGHT = 'outright'\n\n\nclass FxForecastHorizon(Enum):\n THREE_MONTH = '3m'\n SIX_MONTH = '6m'\n TWELVE_MONTH = '12m'\n EOY1 = 'EOY1'\n EOY2 = 'EOY2'\n EOY3 = 'EOY3'\n EOY4 = 'EOY4'\n\n\nclass _FxForecastTimeSeriesPeriodType(Enum):\n SHORT_TERM = '3/6/12-Month'\n ANNUAL = 'Annual'\n\n\nclass FundamentalMetricPeriodDirection(Enum):\n FORWARD = 'forward'\n TRAILING = 'trailing'\n\n\nclass RatesConversionType(Enum):\n DEFAULT_BENCHMARK_RATE = auto()\n DEFAULT_SWAP_RATE_ASSET = auto()\n INFLATION_BENCHMARK_RATE = auto()\n CROSS_CURRENCY_BASIS = auto()\n OIS_BENCHMARK_RATE = auto()\n\n\nclass EsgMetric(Enum):\n ENVIRONMENTAL_SOCIAL_AGGREGATE_SCORE = 'es_score'\n ENVIRONMENTAL_SOCIAL_AGGREGATE_PERCENTILE = 'es_percentile'\n ENVIRONMENTAL_SOCIAL_DISCLOSURE = 'es_disclosure_percentage'\n ENVIRONMENTAL_SOCIAL_NUMERIC_SCORE = 'es_numeric_score'\n ENVIRONMENTAL_SOCIAL_NUMERIC_PERCENTILE = 'es_numeric_percentile'\n ENVIRONMENTAL_SOCIAL_POLICY_SCORE = 'es_policy_score'\n ENVIRONMENTAL_SOCIAL_POLICY_PERCENTILE = 'es_policy_percentile'\n ENVIRONMENTAL_SOCIAL_PRODUCT_IMPACT_SCORE = 'es_product_impact_score'\n ENVIRONMENTAL_SOCIAL_PRODUCT_IMPACT_PERCENTILE = 'es_product_impact_percentile'\n GOVERNANCE_AGGREGATE_SCORE = 'g_score'\n GOVERNANCE_AGGREGATE_PERCENTILE = 'g_percentile'\n GOVERNANCE_REGIONAL_SCORE = 'g_regional_score'\n GOVERNANCE_REGIONAL_PERCENTILE = 'g_regional_percentile'\n ENVIRONMENTAL_SOCIAL_MOMENTUM_SCORE = 'es_momentum_score'\n ENVIRONMENTAL_SOCIAL_MOMENTUM_PERCENTILE = 'es_momentum_percentile'\n CONTROVERSY_SCORE = 'controversy_score'\n CONTROVERSY_PERCENTILE = 'controversy_percentile'\n\n\nclass SwaptionTenorType(Enum):\n OPTION_EXPIRY = 'option_expiry'\n SWAP_MATURITY = 'swap_maturity'\n\n\nclass EquilibriumExchangeRateMetric(Enum):\n GSDEER = 'gsdeer'\n GSFEER = 'gsfeer'\n\n\nclass _FactorProfileMetric(Enum):\n GROWTH_SCORE = 'growthScore'\n FINANCIAL_RETURNS_SCORE = 'financialReturnsScore'\n MULTIPLE_SCORE = 'multipleScore'\n INTEGRATED_SCORE = 'integratedScore'\n\n\nclass _CommodityForecastType(Enum):\n SPOT = 'spot'\n SPOT_RETURN = 'spotReturn'\n ROLL_RETURN = 'rollReturn'\n TOTAL_RETURN = 'totalReturn'\n\n\nclass _CommodityForecastTimeSeriesPeriodType(Enum):\n SHORT_TERM = '3/6/12-Month Rolling'\n MONTHLY = 'Monthly'\n QUARTERLY = 'Quarterly'\n ANNUAL = 'Annual'\n\n\nclass _RatingMetric(Enum):\n RATING = 'rating'\n CONVICTION_LIST = 'convictionList'\n\n\nclass EUNatGasDataReference(Enum):\n # Reference data as on the asset parameters, will distinguish Heren(USD) and ICE(EUR/ GBP) Swaps\n TTF_ICE_CommodityReferencePrice = 'ICE Data: FUTURES REPORT: ICE Endex Dutch TTF Gas Base Load Futures'\n NBP_ICE_CommodityReferencePrice = 'UK Monthly-Ice'\n TTF_Heren_CommodityReferencePrice = 'TTF-Day Ahead and Weekend Unweighted Average Price-Heren'\n NBP_Heren_CommodityReferencePrice = 'NBP-Day Ahead and Weekend Unweighted Average Price-Heren'\n # Add map for currency to priceMethod\n USD = \"Heren\"\n ICE = \"ICE\"\n\n\nclass EUPowerDataReference(Enum):\n ICE_EXCHANGE = 'ICE'\n EEX_EXCHANGE = 'EEX'\n NASDAQ_EXCHANGE = 'NASDAQ'\n CARBON_CREDIT_DATASET = 'COMMOD_US_ELEC_CARBON_CREDITS_FUTURES'\n CARBON_CREDIT_PRODUCT = 'Physical Environment'\n EU_POWER_EXCHANGE_DATASET = 'COMMOD_EU_POWER_EXCHANGE_DATA'\n EU_POWER_PRODUCT = 'PowerFutures'\n\n\nclass FXSpotCarry(Enum):\n ANNUALIZED = 'annualized'\n DAILY = 'daily'\n\n\nESG_METRIC_TO_QUERY_TYPE = {\n \"esNumericScore\": QueryType.ES_NUMERIC_SCORE,\n \"esNumericPercentile\": QueryType.ES_NUMERIC_PERCENTILE,\n \"esPolicyScore\": QueryType.ES_POLICY_SCORE,\n \"esPolicyPercentile\": QueryType.ES_POLICY_PERCENTILE,\n \"esScore\": QueryType.ES_SCORE,\n \"esPercentile\": QueryType.ES_PERCENTILE,\n \"esProductImpactScore\": QueryType.ES_PRODUCT_IMPACT_SCORE,\n \"esProductImpactPercentile\": QueryType.ES_PRODUCT_IMPACT_PERCENTILE,\n \"gScore\": QueryType.G_SCORE,\n \"gPercentile\": QueryType.G_PERCENTILE,\n \"esMomentumScore\": QueryType.ES_MOMENTUM_SCORE,\n \"esMomentumPercentile\": QueryType.ES_MOMENTUM_PERCENTILE,\n \"gRegionalScore\": QueryType.G_REGIONAL_SCORE,\n \"gRegionalPercentile\": QueryType.G_REGIONAL_PERCENTILE,\n \"esDisclosurePercentage\": QueryType.ES_DISCLOSURE_PERCENTAGE,\n \"controversyScore\": QueryType.CONTROVERSY_SCORE,\n \"controversyPercentile\": QueryType.CONTROVERSY_PERCENTILE,\n}\n\nCURRENCY_TO_OIS_RATE_BENCHMARK = {\n 'AUD': 'AUD OIS',\n 'USD': 'USD OIS',\n 'EUR': 'EUR OIS',\n 'GBP': 'GBP OIS',\n 'JPY': 'JPY OIS',\n 'CAD': 'CAD OIS',\n 'NOK': 'NOK OIS',\n 'NZD': 'NZD OIS',\n 'SEK': 'SEK OIS',\n 'CHF': 'CHF OIS',\n}\n\nCURRENCY_TO_DEFAULT_RATE_BENCHMARK = {\n 'USD': 'USD-LIBOR-BBA',\n 'EUR': 'EUR-EURIBOR-Telerate',\n 'GBP': 'GBP-LIBOR-BBA',\n 'JPY': 'JPY-LIBOR-BBA',\n}\n\nCURRENCY_TO_INFLATION_RATE_BENCHMARK = {'GBP': 'CPI-UKRPI', 'EUR': 'CPI-CPXTEMU'}\n\nCROSS_TO_CROSS_CURRENCY_BASIS = {\n 'JPYUSD': 'USD-3m/JPY-3m',\n 'USDJPY': 'USD-3m/JPY-3m',\n 'USDEUR': 'EUR-3m/USD-3m',\n 'EURUSD': 'EUR-3m/USD-3m',\n 'USDGBP': 'GBP-3m/USD-3m',\n 'GBPUSD': 'GBP-3m/USD-3m',\n}\n\n_FACTOR_PROFILE_METRIC_TO_QUERY_TYPE = {\n 'growthScore': QueryType.GROWTH_SCORE,\n 'financialReturnsScore': QueryType.FINANCIAL_RETURNS_SCORE,\n 'multipleScore': QueryType.MULTIPLE_SCORE,\n 'integratedScore': QueryType.INTEGRATED_SCORE,\n}\n\n_RATING_METRIC_TO_QUERY_TYPE = {'rating': QueryType.RATING, 'convictionList': QueryType.CONVICTION_LIST}\n\n_COMMOD_CONTRACT_MONTH_CODES = \"FGHJKMNQUVXZ\"\n_COMMOD_CONTRACT_MONTH_CODES_DICT = {k: v for k, v in enumerate(_COMMOD_CONTRACT_MONTH_CODES)}\n\n\ndef _asset_from_spec(asset_spec: ASSET_SPEC) -> Asset:\n return (\n asset_spec\n if isinstance(asset_spec, Asset)\n else SecurityMaster.get_asset(asset_spec, AssetIdentifier.MARQUEE_ID)\n )\n\n\ndef _cross_stored_direction_helper(bbid):\n if not re.fullmatch('[A-Z]{6}', bbid):\n raise TypeError('not a cross that can be reversed')\n legit_usd_cross = str.startswith(bbid, \"USD\") and not str.endswith(bbid, (\"EUR\", \"GBP\", \"NZD\", \"AUD\"))\n legit_eur_cross = str.startswith(bbid, \"EUR\")\n legit_jpy_cross = str.endswith(bbid, \"JPY\") and not str.startswith(bbid, (\"KRW\", \"IDR\", \"CLP\", \"COP\"))\n odd_cross = bbid in (\"EURUSD\", \"GBPUSD\", \"NZDUSD\", \"AUDUSD\", \"JPYKRW\", \"JPYIDR\", \"JPYCLP\", \"JPYCOP\")\n return bbid if any([legit_usd_cross, legit_eur_cross, legit_jpy_cross, odd_cross]) else bbid[3:] + bbid[:3]\n\n\ndef cross_stored_direction_for_fx_vol(asset_spec: ASSET_SPEC, *, return_asset=False) -> Union[str, Asset]:\n asset = _asset_from_spec(asset_spec)\n result = asset\n try:\n if asset.asset_class is AssetClass.FX:\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n if bbid is not None:\n cross = _cross_stored_direction_helper(bbid)\n if cross != bbid:\n cross_asset = SecurityMaster.get_asset(cross, AssetIdentifier.BLOOMBERG_ID)\n result = cross_asset\n except TypeError:\n result = asset\n return result if return_asset else result.get_marquee_id()\n\n\ndef cross_to_usd_based_cross(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n asset_id = asset.get_marquee_id()\n result_id = asset_id\n try:\n if asset.asset_class is AssetClass.FX:\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n if bbid is not None and not str.startswith(bbid, \"USD\"):\n if not re.fullmatch('[A-Z]{6}', bbid):\n raise TypeError('not a cross that can be reversed')\n cross = bbid[3:] + bbid[:3]\n cross_asset = SecurityMaster.get_asset(cross, AssetIdentifier.BLOOMBERG_ID)\n result_id = cross_asset.get_marquee_id()\n except TypeError:\n result_id = asset_id\n return result_id\n\n\ndef currency_to_default_benchmark_rate(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n asset_id = asset.get_marquee_id()\n try:\n result = convert_asset_for_rates_data_set(asset, RatesConversionType.DEFAULT_BENCHMARK_RATE)\n except TypeError:\n result = asset_id\n return result\n\n\ndef currency_to_default_ois_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n try:\n result = convert_asset_for_rates_data_set(asset, RatesConversionType.OIS_BENCHMARK_RATE)\n except TypeError:\n result = asset.get_marquee_id()\n return result\n\n\ndef currency_to_default_swap_rate_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n return convert_asset_for_rates_data_set(asset, RatesConversionType.DEFAULT_SWAP_RATE_ASSET)\n\n\ndef currency_to_inflation_benchmark_rate(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n try:\n result = convert_asset_for_rates_data_set(asset, RatesConversionType.INFLATION_BENCHMARK_RATE)\n except TypeError:\n result = asset.get_marquee_id()\n return result\n\n\ndef cross_to_basis(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n try:\n result = convert_asset_for_rates_data_set(asset, RatesConversionType.CROSS_CURRENCY_BASIS)\n except TypeError:\n result = asset.get_marquee_id()\n return result\n\n\ndef convert_asset_for_rates_data_set(from_asset: Asset, c_type: RatesConversionType) -> str:\n try:\n bbid = from_asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n if bbid is None:\n return from_asset.get_marquee_id()\n\n if c_type is RatesConversionType.DEFAULT_BENCHMARK_RATE:\n to_asset = CURRENCY_TO_DEFAULT_RATE_BENCHMARK[bbid]\n elif c_type is RatesConversionType.DEFAULT_SWAP_RATE_ASSET:\n to_asset = (\n (bbid + '-3m') if bbid == \"USD\" else (bbid + '-6m') if bbid in ['GBP', 'EUR', 'CHF', 'SEK'] else bbid\n )\n elif c_type is RatesConversionType.INFLATION_BENCHMARK_RATE:\n to_asset = CURRENCY_TO_INFLATION_RATE_BENCHMARK[bbid]\n elif c_type is RatesConversionType.OIS_BENCHMARK_RATE:\n to_asset = CURRENCY_TO_OIS_RATE_BENCHMARK[bbid]\n else:\n to_asset = CROSS_TO_CROSS_CURRENCY_BASIS[bbid]\n\n identifiers = GsAssetApi.map_identifiers(GsIdType.mdapi, GsIdType.id, [to_asset])\n if to_asset in identifiers:\n return identifiers[to_asset]\n if None in identifiers:\n return identifiers[None]\n raise MqValueError('Unable to map identifier.')\n\n except KeyError:\n logging.info('Unsupported currency or cross')\n return from_asset.get_marquee_id()\n\n\ndef _get_custom_bd(exchange):\n from pandas.tseries.offsets import CustomBusinessDay\n\n calendar = GsCalendar.get(exchange).business_day_calendar()\n return CustomBusinessDay(calendar=calendar)\n\n\n@log_return(_logger, 'trying pricing dates')\ndef _range_from_pricing_date(exchange, pricing_date: Optional[GENERIC_DATE] = None, buffer: int = 0):\n if isinstance(pricing_date, dt.date):\n return pricing_date, pricing_date\n\n today = pd.Timestamp.today().normalize()\n bd = _get_custom_bd(exchange)\n if pricing_date is None:\n t1 = today - bd\n return t1 - (buffer * bd), t1\n\n assert isinstance(pricing_date, str)\n matcher = re.fullmatch('(\\\\d+)b', pricing_date)\n if matcher:\n start = end = today - bd * int(matcher.group(1))\n else:\n end = today - dt.timedelta(days=relative_date_add(pricing_date, True))\n start = end - bd\n return start, end\n\n\ndef _market_data_timed(q, request_id=None, ignore_errors: bool = False):\n args = [q, request_id] if request_id else [q]\n return GsDataApi.get_market_data(*args, ignore_errors=ignore_errors)\n\n\ndef _extract_series_from_df(df: pd.DataFrame, query_type: QueryType, handle_missing_column=False):\n col_name = query_type.value.replace(' ', '')\n col_name = col_name[0].lower() + col_name[1:]\n if df.empty or (handle_missing_column and col_name not in df.columns):\n series = ExtendedSeries(dtype=float)\n else:\n series = ExtendedSeries(df[col_name], index=df.index)\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\ndef _fundamentals_md_query(\n mqid: str,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n metric: str,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure(\n (AssetClass.FX, AssetClass.Equity, AssetClass.Commod),\n None,\n [MeasureDependency(id_provider=cross_stored_direction_for_fx_vol, query_type=QueryType.IMPLIED_VOLATILITY)],\n asset_type_excluded=(AssetType.CommodityNaturalGasHub,),\n)\ndef skew(\n asset: Asset,\n tenor: str,\n strike_reference: SkewReference,\n distance: Real,\n normalization_mode: NormalizationMode = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Difference in implied volatility of equidistant out-of-the-money put and call options.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param strike_reference: reference for strike level (for equities)\n :param distance: distance from at-the-money option\n :param normalization_mode: \"normalized\" to divide skew by ATM implied volatility or \"outright\" to leave\n off this normalization. By default, we normalize skew for equities\n and commodities but not for FX assets.\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: skew curve\n \"\"\"\n asset_id = asset.get_marquee_id()\n kwargs = {}\n\n if real_time and asset.asset_class in (AssetClass.FX, AssetClass.Commod):\n raise MqValueError(f'real-time skew not supported for asset {asset_id}')\n\n if asset.asset_class == AssetClass.FX:\n asset_id = cross_stored_direction_for_fx_vol(asset_id)\n if normalization_mode is None:\n normalization_mode = NormalizationMode.OUTRIGHT\n if strike_reference == SkewReference.DELTA:\n q_strikes = [0 - distance, distance, 0]\n else:\n raise MqValueError('strike_reference has to be delta to get skew for FX options')\n else:\n assert asset.asset_class in (AssetClass.Equity, AssetClass.Commod)\n if normalization_mode is None:\n normalization_mode = NormalizationMode.NORMALIZED\n if strike_reference in (SkewReference.DELTA, None):\n b = 50\n elif strike_reference == SkewReference.NORMALIZED:\n b = 0\n else:\n b = 100\n\n if strike_reference in (SkewReference.DELTA, None):\n # using delta call strikes so X DP is represented as (100 - X) DC for Equity options\n q_strikes = [100 - distance, distance, b]\n else:\n q_strikes = [b - distance, b + distance, b]\n\n if not strike_reference:\n raise MqTypeError('strike reference required for equities')\n if strike_reference != SkewReference.NORMALIZED:\n q_strikes = [x / 100 for x in q_strikes]\n\n kwargs['strikeReference'] = strike_reference.value\n column = 'relativeStrike'\n kwargs[column] = q_strikes\n log_debug(request_id, _logger, 'where tenor=%s and %s', tenor, kwargs)\n where = dict(tenor=tenor, **kwargs)\n q = GsDataApi.build_market_data_query(\n [asset_id], QueryType.IMPLIED_VOLATILITY, where=where, source=source, real_time=real_time\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n dataset_ids = getattr(df, 'dataset_ids', ())\n\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n curves = {k: v for k, v in df.groupby(column)}\n if len(curves) < 3:\n raise MqValueError('skew not available for given inputs')\n series = [curves[qs]['impliedVolatility'] for qs in q_strikes]\n ext_series = (\n ((series[0] - series[1]) / series[2])\n if normalization_mode == NormalizationMode.NORMALIZED\n else (series[0] - series[1])\n )\n series = ExtendedSeries(ext_series)\n series.dataset_ids = dataset_ids\n return series\n\n\n@plot_measure((AssetClass.Credit,), (AssetType.Index,), [QueryType.IMPLIED_VOLATILITY_BY_DELTA_STRIKE])\ndef cds_implied_volatility(\n asset: Asset,\n expiry: str,\n tenor: str,\n strike_reference: CdsVolReference,\n relative_strike: Real,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of a cds index implied by observations of market prices.\n\n :param asset: asset object loaded from security master\n :param expiry: relative date representation of expiration date on the option e.g. 3m\n :param tenor: relative date representation of expiration date e.g. 1m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: implied volatility curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime cds_implied_volatility not implemented')\n\n delta_strike = \"ATMF\" if strike_reference is CdsVolReference.FORWARD else \"{}DC\".format(relative_strike)\n option_type = \"payer\" if strike_reference is CdsVolReference.DELTA_CALL else \"receiver\"\n\n _logger.debug(\n 'where expiry=%s, tenor=%s, deltaStrike=%s, optionType=%s, location=NYC',\n expiry,\n tenor,\n delta_strike,\n option_type,\n )\n\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()],\n QueryType.IMPLIED_VOLATILITY_BY_DELTA_STRIKE,\n where=dict(expiry=expiry, tenor=tenor, deltaStrike=delta_strike, optionType=option_type, location='NYC'),\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.IMPLIED_VOLATILITY_BY_DELTA_STRIKE)\n\n\n@plot_measure((AssetClass.Credit,), (AssetType.Index,), [QueryType.OPTION_PREMIUM], display_name='option_premium')\ndef option_premium_credit(\n asset: Asset,\n expiry: str,\n strike_reference: CdsVolReference,\n relative_strike: Real,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Option premium of a cds index for a given expiry and delta strike.\n\n :param asset: asset object loaded from security master\n :param expiry: relative date representation of expiration date on the option e.g. 3m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: option premium curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime option premium not implemented in credit options')\n\n if strike_reference is CdsVolReference.FORWARD:\n delta_strike = 'ATMF'\n elif strike_reference is CdsVolReference.DELTA_CALL:\n delta_strike = \"{}DC\".format(relative_strike)\n elif strike_reference is CdsVolReference.DELTA_PUT:\n delta_strike = \"{}DP\".format(relative_strike)\n else:\n raise NotImplementedError('Option Type: {} not implemented in credit', strike_reference)\n\n _logger.debug('where expiry=%s, deltaStrike=%s', expiry, delta_strike)\n\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()],\n QueryType.OPTION_PREMIUM,\n where=dict(\n expiry=expiry,\n deltaStrike=delta_strike,\n ),\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.OPTION_PREMIUM)\n\n\n@plot_measure((AssetClass.Credit,), (AssetType.Index,), [QueryType.ABSOLUTE_STRIKE], display_name='absolute_strike')\ndef absolute_strike_credit(\n asset: Asset,\n expiry: str,\n strike_reference: CdsVolReference,\n relative_strike: Real,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Absolute strike of a cds index for a given expiry and delta strike.\n\n :param asset: asset object loaded from security master\n :param expiry: relative date representation of expiration date on the option e.g. 3m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: absolute strike curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime absolute strike not implemented in credit options')\n\n if strike_reference is CdsVolReference.FORWARD:\n delta_strike = 'ATMF'\n elif strike_reference is CdsVolReference.DELTA_CALL:\n delta_strike = \"{}DC\".format(relative_strike)\n elif strike_reference is CdsVolReference.DELTA_PUT:\n delta_strike = \"{}DP\".format(relative_strike)\n else:\n raise NotImplementedError('Option Type: {} not implemented in credit', strike_reference)\n\n _logger.debug('where expiry=%s, deltaStrike=%s', expiry, delta_strike)\n\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()],\n QueryType.ABSOLUTE_STRIKE,\n where=dict(\n expiry=expiry,\n deltaStrike=delta_strike,\n ),\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.ABSOLUTE_STRIKE)\n\n\n@plot_measure(\n (AssetClass.Credit,),\n (AssetType.Index,),\n [QueryType.IMPLIED_VOLATILITY_BY_DELTA_STRIKE],\n display_name='implied_volatility',\n)\ndef implied_volatility_credit(\n asset: Asset,\n expiry: str,\n strike_reference: CdsVolReference,\n relative_strike: Real,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of a cds index implied by observations of market prices.\n\n :param asset: asset object loaded from security master\n :param expiry: relative date representation of expiration date on the option e.g. 3m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: implied volatility curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime implied_volatility not implemented in credit options')\n\n if strike_reference is CdsVolReference.FORWARD:\n delta_strike = 'ATMF'\n elif strike_reference is CdsVolReference.DELTA_CALL:\n delta_strike = \"{}DC\".format(relative_strike)\n elif strike_reference is CdsVolReference.DELTA_PUT:\n delta_strike = \"{}DP\".format(relative_strike)\n else:\n raise NotImplementedError('Option Type: {} not implemented in credit', strike_reference)\n\n _logger.debug('where expiry=%s, deltaStrike=%s', expiry, delta_strike)\n\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()],\n QueryType.IMPLIED_VOLATILITY_BY_DELTA_STRIKE,\n where=dict(\n expiry=expiry,\n deltaStrike=delta_strike,\n ),\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.IMPLIED_VOLATILITY_BY_DELTA_STRIKE)\n\n\n@plot_measure((AssetClass.Credit,), (AssetType.Default_Swap,), [QueryType.CDS_SPREAD_100], display_name='spread')\ndef cds_spread(\n asset: Asset, spread: int, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n CDS Spread levels.\n\n :param asset: asset object loaded from security master\n :param spread: in bps 100, 250, 500\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: spread curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime spread not implemented for cds spreads')\n\n _logger.debug('where spread=%s', spread)\n\n if spread == 100:\n qt = QueryType.CDS_SPREAD_100\n elif spread == 250:\n qt = QueryType.CDS_SPREAD_250\n elif spread == 500:\n qt = QueryType.CDS_SPREAD_500\n else:\n raise NotImplementedError(\"Spread {} not implemented\".format(spread))\n\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], qt, where=dict(pricingLocation=\"NYC\"), source=source, real_time=real_time\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, qt)\n\n\n@plot_measure(\n (\n AssetClass.Equity,\n AssetClass.Commod,\n ),\n None,\n [MeasureDependency(id_provider=cross_stored_direction_for_fx_vol, query_type=QueryType.IMPLIED_VOLATILITY)],\n asset_type_excluded=(AssetType.CommodityNaturalGasHub,),\n)\ndef implied_volatility(\n asset: Asset,\n tenor: str,\n strike_reference: VolReference = None,\n relative_strike: Real = None,\n parallelize_queries: bool = True,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of an asset implied by observations of market prices.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n or absolute calendar strips e.g. 'Cal20', 'F20-G20'\n :param strike_reference: reference for strike level. Forward is used for ATMF. Default market convention\n for Equity implied vols is forward\n :param relative_strike: strike relative to reference\n :param parallelize_queries: send parallel queries to the measures API\n chunked over an interval of a year based on the date context\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: implied volatility curve\n \"\"\"\n\n if asset.asset_class == AssetClass.Commod and asset.get_type() not in (SecAssetType.ETF, SecAssetType.STOCK):\n return _weighted_average_valuation_curve_for_calendar_strip(\n asset, tenor, QueryType.IMPLIED_VOLATILITY, \"impliedVolatility\"\n )\n\n if asset.asset_class == AssetClass.FX:\n asset_id = cross_stored_direction_for_fx_vol(asset.get_marquee_id())\n ref_string, relative_strike = _preprocess_implied_vol_strikes_fx(strike_reference, relative_strike)\n\n else:\n asset_id = asset.get_marquee_id()\n ref_string, relative_strike = preprocess_implied_vol_strikes_eq(strike_reference, relative_strike)\n\n log_debug(\n request_id, _logger, 'where tenor=%s, strikeReference=%s, relativeStrike=%s', tenor, ref_string, relative_strike\n )\n tenor = _tenor_month_to_year(tenor)\n where = dict(tenor=tenor, strikeReference=ref_string, relativeStrike=relative_strike)\n # Parallel calls when fetching / appending last results\n df = get_historical_and_last_for_measure(\n [asset_id],\n QueryType.IMPLIED_VOLATILITY,\n where,\n source=source,\n real_time=real_time,\n request_id=request_id,\n parallelize_queries=parallelize_queries,\n )\n\n s = _extract_series_from_df(df, QueryType.IMPLIED_VOLATILITY)\n return s\n\n\ndef _tenor_month_to_year(tenor: str):\n matched = re.fullmatch('(\\\\d+)m', tenor)\n if matched:\n month = int(matched[1])\n if month % 12 == 0:\n return str(int(month / 12)) + 'y'\n return tenor\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (AssetType.CommodityNaturalGasHub,),\n [QueryType.IMPLIED_VOLATILITY],\n display_name='implied_volatility',\n)\ndef implied_volatility_ng(\n asset: Asset,\n contract_range: str = 'F20',\n price_method: str = 'GDD',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of an asset implied by observations of market prices.\n\n :param asset: asset object loaded from security master\n :param price_method: price method - GDD/FERC/Exchange for US NG assets: Default value = GDD\n :param contract_range: e.g. inputs - 'Cal20', 'F20-G20', '2Q20', '2H20', 'Cal20-Cal21': Default Value = F20\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :param request_id: service request id, if any\n :return: implied volatility curve\n \"\"\"\n if real_time:\n raise MqValueError('Use daily frequency instead of intraday')\n\n (start_contract_range, end_contract_range) = _get_start_and_end_dates(contract_range=contract_range)\n\n dates_contract_range = get_contract_range(start_contract_range, end_contract_range, None)\n\n weights = dates_contract_range.groupby('contract_month').size()\n weights = pd.DataFrame({'contract': weights.index, 'weight': weights.values})\n\n start, end = DataContext.current.start_date, DataContext.current.end_date\n\n contracts_to_query = weights['contract'].unique().tolist()\n where = dict(contract=contracts_to_query, priceMethod=price_method)\n\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], QueryType.IMPLIED_VOLATILITY, where=where, source=source, real_time=False\n )\n data = _market_data_timed(q, request_id)\n dataset_ids = getattr(data, 'dataset_ids', ())\n\n if data.empty:\n result = ExtendedSeries(dtype='float64')\n else:\n data['dates'] = data.index.date\n keys = ['contract', 'dates']\n result = _merge_curves_by_weighted_average(data, weights, keys, \"impliedVolatility\")\n result.dataset_ids = dataset_ids\n\n return result\n\n\ndef _preprocess_implied_vol_strikes_fx(strike_reference: VolReference = None, relative_strike: Real = None):\n if relative_strike is None and strike_reference != VolReference.DELTA_NEUTRAL:\n raise MqValueError('Relative strike must be provided if your strike reference is not delta_neutral')\n\n if strike_reference in (VolReference.FORWARD, VolReference.SPOT) and relative_strike != 100:\n raise MqValueError('Relative strike must be 100 for Spot or Forward strike reference')\n if strike_reference not in VolReference or strike_reference == VolReference.NORMALIZED:\n raise MqValueError('strikeReference: ' + strike_reference.value + ' not supported for FX')\n if strike_reference == VolReference.DELTA_NEUTRAL:\n if relative_strike is None:\n relative_strike = 0\n elif relative_strike != 0:\n raise MqValueError('relative_strike must be 0 for delta_neutral')\n\n if strike_reference == VolReference.DELTA_PUT:\n relative_strike *= -1\n\n ref_string = (\n \"delta\"\n if strike_reference in (VolReference.DELTA_CALL, VolReference.DELTA_PUT, VolReference.DELTA_NEUTRAL)\n else strike_reference.value\n )\n return ref_string, relative_strike\n\n\ndef _check_top_n(top_n):\n if top_n is None:\n return\n try:\n float(top_n)\n except (ValueError, TypeError):\n raise MqValueError(f'top_n should be a number, not a {type(top_n).__name__}')\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Index,\n AssetType.ETF,\n ),\n [QueryType.IMPLIED_CORRELATION],\n)\ndef implied_correlation(\n asset: Asset,\n tenor: str,\n strike_reference: EdrDataReference,\n relative_strike: Real,\n top_n_of_index: Optional[int] = None,\n composition_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Correlation of an asset implied by observations of market prices.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param top_n_of_index: the number of top constituents to take into account\n :param composition_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y; defaults to most recent date\n available\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: implied correlation curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime implied_correlation not implemented')\n\n if top_n_of_index is None and composition_date is not None:\n raise MqValueError('specify top_n_of_index to get the implied correlation of top constituents')\n\n _check_top_n(top_n_of_index)\n if top_n_of_index is not None and top_n_of_index > 100:\n raise MqValueError('maximum number of constituents exceeded while using top_n_of_index')\n\n if strike_reference == EdrDataReference.DELTA_PUT:\n relative_strike = abs(100 - relative_strike)\n\n relative_strike = relative_strike / 100\n\n delta_types = (EdrDataReference.DELTA_CALL, EdrDataReference.DELTA_PUT)\n strike_ref = \"delta\" if strike_reference in delta_types else strike_reference.value\n\n log_debug(\n request_id, _logger, 'where tenor=%s, strikeReference=%s, relativeStrike=%s', tenor, strike_ref, relative_strike\n )\n\n mqid = asset.get_marquee_id()\n where = dict(tenor=tenor, strikeReference=strike_ref, relativeStrike=relative_strike)\n\n if top_n_of_index is None:\n q = GsDataApi.build_market_data_query(\n [mqid], QueryType.IMPLIED_CORRELATION, where=where, source=source, real_time=real_time\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.IMPLIED_CORRELATION)\n\n # results for top n\n constituents = _get_index_constituent_weights(asset, top_n_of_index, composition_date)\n\n asset_ids = constituents.index.to_list() + [mqid]\n\n df = get_historical_and_last_for_measure(\n asset_ids=asset_ids,\n query_type=QueryType.IMPLIED_VOLATILITY,\n where=where,\n source=source,\n real_time=real_time,\n request_id=request_id,\n )\n\n if df.empty:\n return pd.Series(dtype=float)\n\n dataset_ids = getattr(df, 'dataset_ids', ())\n df = df[['assetId', 'impliedVolatility']]\n constituents_weights = pd.DataFrame(constituents.to_dict()['netWeight'], index=df.index.unique())\n series = _calculate_implied_correlation(mqid, df, constituents_weights, request_id)\n series.dataset_ids = dataset_ids\n return series\n\n\ndef _calculate_implied_correlation(index_mqid, vol_df, constituents_weights, request_id):\n full_index = pd.date_range(start=vol_df.index.min(), end=vol_df.index.max(), freq='D')\n\n w = constituents_weights.reindex(full_index, method='ffill')\n\n all_groups = []\n for name, group in vol_df.groupby('assetId'):\n before = group.shape[0]\n group = group[~group.index.duplicated(keep='first')]\n after = group.shape[0]\n if before > after:\n log_debug(request_id, _logger, 'removed %d duplicates for %s', before - after, name)\n g = group.reindex(full_index)\n g['assetId'] = name\n g['impliedVolatility'] = g['impliedVolatility'].interpolate()\n all_groups.append(g)\n df = pd.concat(all_groups)\n\n df['impliedVolatility'] /= 100\n match = df['assetId'] == index_mqid\n df_asset = df.loc[match]\n df_rest = df.loc[~match]\n\n def calculate_vol(group):\n assets, vols = group['assetId'], group['impliedVolatility']\n weights_on_date = w.loc[group.name].to_dict()\n weights = np.empty(len(vols.index))\n for i, asset_id in enumerate(assets):\n weights[i] = weights_on_date[asset_id]\n total_weight = weights.sum()\n parts = np.array([vol * weight / total_weight for vol, weight in zip(vols, weights)])\n return pd.Series([parts.sum(), np.power(parts, 2).sum()], index=['first', 'second'])\n\n inter = df_rest.groupby(df_rest.index).apply(calculate_vol)\n values = (pow(df_asset['impliedVolatility'], 2) - inter['second']) / (pow(inter['first'], 2) - inter['second'])\n series = ExtendedSeries(values * 100)\n return series\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Index,\n AssetType.ETF,\n ),\n [QueryType.IMPLIED_CORRELATION],\n)\ndef implied_correlation_with_basket(\n asset: Asset,\n tenor: str,\n strike_reference: EdrDataReference,\n relative_strike: Real,\n basket: Basket,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Implied correlation between index and stock basket.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param basket: a basket of stocks. Basket rebalancing does not apply to the calculation\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: implied correlation curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime implied_correlation not implemented')\n\n if strike_reference == EdrDataReference.DELTA_PUT:\n relative_strike = abs(100 - relative_strike)\n\n relative_strike = relative_strike / 100\n\n delta_types = (EdrDataReference.DELTA_CALL, EdrDataReference.DELTA_PUT)\n strike_ref = \"delta\" if strike_reference in delta_types else strike_reference.value\n\n log_debug(\n request_id, _logger, 'where tenor=%s, strikeReference=%s, relativeStrike=%s', tenor, strike_ref, relative_strike\n )\n\n index_mqid = asset.get_marquee_id()\n where = dict(tenor=tenor, strikeReference=strike_ref, relativeStrike=relative_strike)\n\n query = GsDataApi.build_market_data_query(\n basket.get_marquee_ids() + [index_mqid],\n QueryType.IMPLIED_VOLATILITY,\n where=where,\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', query)\n\n df = _market_data_timed(query, request_id)\n dataset_ids = getattr(df, 'dataset_ids', ())\n df = df[['assetId', 'impliedVolatility']]\n\n actual_weights = basket.get_actual_weights(request_id=request_id)\n series = _calculate_implied_correlation(index_mqid, df, actual_weights, request_id)\n series.dataset_ids = dataset_ids\n return series\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Index,\n AssetType.ETF,\n ),\n [QueryType.IMPLIED_CORRELATION],\n)\ndef realized_correlation_with_basket(\n asset: Asset,\n tenor: str,\n basket: Basket,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Realized correlation between index and stock basket.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param basket: a basket of stocks. Basket rebalancing does not apply to the calculation\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: realized correlation curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime implied_correlation not implemented')\n\n dataset_ids = set()\n\n index_mqid = asset.get_marquee_id()\n query = GsDataApi.build_market_data_query([index_mqid], QueryType.SPOT, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', query)\n index_spot = _market_data_timed(query, request_id)\n dataset_ids.update(getattr(index_spot, 'dataset_ids', ()))\n\n actual_weights = basket.get_actual_weights(request_id=request_id)\n constituents_spot = basket.get_spot_data(request_id=request_id)\n dataset_ids.update(getattr(constituents_spot, 'dataset_ids', ()))\n\n vols = constituents_spot.apply(lambda col: volatility(constituents_spot[col.name], Window(tenor, tenor)) / 100)\n weighted_vols = actual_weights.mul(vols)\n idx_vol = volatility(index_spot['spot'], Window(tenor, tenor)) / 100\n s1 = weighted_vols.sum(axis=1, skipna=False)\n s2 = weighted_vols.apply(lambda x: x * x).sum(axis=1, skipna=False)\n values = (idx_vol * idx_vol - s2) / (s1 * s1 - s2) * 100\n series = ExtendedSeries(values)\n series.dataset_ids = dataset_ids\n return series\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Index,\n AssetType.ETF,\n ),\n [QueryType.AVERAGE_IMPLIED_VOLATILITY, QueryType.IMPLIED_VOLATILITY],\n)\ndef average_implied_volatility(\n asset: Asset,\n tenor: str,\n strike_reference: EdrDataReference,\n relative_strike: Real,\n top_n_of_index: Optional[int] = None,\n composition_date: Optional[GENERIC_DATE] = None,\n weight_threshold: Optional[Real] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Historical weighted average implied volatility of the top constituents of an equity index. If top_n_of_index and\n composition_date are not provided, the average is of all constituents based on the weights at each evaluation date.\n If implied volatility is missing for a constituent, that is ignored. The average result is normalized by dividing\n by the sum of weights of the constituents that have implied volatility data.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param top_n_of_index: the number of top constituents to take into account\n :param composition_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y; defaults to the most recent date\n available\n :param weight_threshold threshold of total constituent weights to drop if data is missing and top_n_of_index is\n passed in\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: average implied volatility curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime average_implied_volatility not implemented')\n\n if top_n_of_index is None and composition_date is not None:\n raise MqValueError('Specify top_n_of_index to get the average implied volatility of top constituents')\n\n _check_top_n(top_n_of_index)\n if top_n_of_index is not None and top_n_of_index > 100:\n raise NotImplementedError(\n 'Maximum number of constituents exceeded. Do not use top_n_of_index to calculate on the full list'\n )\n\n if top_n_of_index is not None:\n constituents = _get_index_constituent_weights(asset, top_n_of_index, composition_date)\n\n ref_string, relative_strike = preprocess_implied_vol_strikes_eq(\n VolReference(strike_reference.value), relative_strike\n )\n tenor = _tenor_month_to_year(tenor)\n log_debug(\n request_id,\n _logger,\n 'where tenor=%s, strikeReference=%s, relativeStrike=%s',\n tenor,\n ref_string,\n relative_strike,\n )\n where = dict(tenor=tenor, strikeReference=ref_string, relativeStrike=relative_strike)\n\n asset_ids = constituents.index.to_list()\n df = get_historical_and_last_for_measure(\n asset_ids=asset_ids,\n query_type=QueryType.IMPLIED_VOLATILITY,\n where=where,\n source=source,\n real_time=real_time,\n request_id=request_id,\n ignore_errors=True,\n )\n\n if not df.empty:\n asset_to_weight = constituents['netWeight'].to_dict()\n missing_assets = set(asset_ids).difference(set(df['assetId'].unique()))\n if len(missing_assets) and not weight_threshold:\n msg = ''\n for asset in missing_assets:\n msg += f'{asset} ({asset_to_weight[asset]:.2f})'\n raise MqValueError(\n f'Unable to calculate average_implied_volatility due to missing implied vols for assets {msg}. '\n f'Try adding a weight_threshold to skip these assets.'\n )\n\n if weight_threshold:\n total_missing_weight = sum(asset_to_weight[missing_asset] for missing_asset in missing_assets)\n if total_missing_weight > weight_threshold:\n msg = ''\n for asset in missing_assets:\n msg += f'{asset} ({asset_to_weight[asset]:.2f})'\n raise MqValueError(\n f'Unable to calculate average_implied_volatility due to missing implied vols for assets {msg}. '\n f'Try increasing the weight_threshold to skip these assets.'\n )\n\n constituents.drop(index=list(missing_assets))\n\n def calculate_avg_vol(group, weights):\n assets, vols = group['assetId'], group['impliedVolatility']\n asset_weights = [weights[asset_id] for asset_id in assets]\n total_weight = sum(asset_weights)\n return sum([vol * weight / total_weight for vol, weight in zip(vols, asset_weights)])\n\n weights = constituents.to_dict()['netWeight']\n average_vol = df.groupby(df.index).apply(calculate_avg_vol, weights)\n\n series = ExtendedSeries(average_vol, name='averageImpliedVolatility')\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n if strike_reference == EdrDataReference.DELTA_PUT:\n relative_strike = abs(100 - relative_strike)\n\n relative_strike = relative_strike / 100\n\n delta_types = (EdrDataReference.DELTA_CALL, EdrDataReference.DELTA_PUT)\n strike_ref = \"delta\" if strike_reference in delta_types else strike_reference.value\n\n _logger.debug('where tenor=%s, strikeReference=%s, relativeStrike=%s', tenor, strike_ref, relative_strike)\n\n mqid = asset.get_marquee_id()\n where = dict(tenor=tenor, strikeReference=strike_ref, relativeStrike=relative_strike)\n # no append last here, since there is no real-time avg implied vol dataset\n q = GsDataApi.build_market_data_query(\n [mqid], QueryType.AVERAGE_IMPLIED_VOLATILITY, where=where, source=source, real_time=real_time\n )\n\n _logger.debug('q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.AVERAGE_IMPLIED_VOLATILITY)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Index,\n AssetType.ETF,\n ),\n [QueryType.AVERAGE_IMPLIED_VARIANCE],\n)\ndef average_implied_variance(\n asset: Asset,\n tenor: str,\n strike_reference: EdrDataReference,\n relative_strike: Real,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Historical weighted average implied variance for the underlying assets of an equity index.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: average implied variance curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime average_implied_variance not implemented')\n\n if strike_reference == EdrDataReference.DELTA_PUT:\n relative_strike = abs(100 - relative_strike)\n\n relative_strike = relative_strike / 100\n\n delta_types = (EdrDataReference.DELTA_CALL, EdrDataReference.DELTA_PUT)\n strike_ref = \"delta\" if strike_reference in delta_types else strike_reference.value\n\n log_debug(\n request_id, _logger, 'where tenor=%s, strikeReference=%s, relativeStrike=%s', tenor, strike_ref, relative_strike\n )\n\n mqid = asset.get_marquee_id()\n where = dict(tenor=tenor, strikeReference=strike_ref, relativeStrike=relative_strike)\n q = GsDataApi.build_market_data_query(\n [mqid], QueryType.AVERAGE_IMPLIED_VARIANCE, where=where, source=source, real_time=real_time\n )\n\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.AVERAGE_IMPLIED_VARIANCE)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Index,\n AssetType.ETF,\n ),\n [QueryType.AVERAGE_REALIZED_VOLATILITY, QueryType.SPOT],\n)\ndef average_realized_volatility(\n asset: Asset,\n tenor: str,\n returns_type: Returns = Returns.LOGARITHMIC,\n top_n_of_index: int = None,\n composition_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Historical weighted average realized volatility for the underlying assets of an equity index. If top_n_of_index and\n composition_date are not provided, the average is of all constituents based on the weights at each evaluation date\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param returns_type: returns type (default logarithmic)\n :param top_n_of_index: the number of top constituents to take into account (must be specified when returns_type\n is not simple)\n :param composition_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y; defaults to the most recent date\n available\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: average realized volatility curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime average_realized_volatility not implemented')\n\n if top_n_of_index is None and composition_date is not None:\n raise MqValueError('Specify top_n_of_index to get the average realized volatility of top constituents')\n\n if top_n_of_index is None and returns_type is not Returns.LOGARITHMIC:\n raise MqValueError(f'top_n_of_index argument must be specified when using returns type {returns_type.value}')\n\n _check_top_n(top_n_of_index)\n if top_n_of_index is not None and top_n_of_index > 200:\n raise MqValueError('Maximum number of 200 constituents exceeded')\n\n if top_n_of_index is not None:\n constituents = _get_index_constituent_weights(asset, top_n_of_index, composition_date)\n assets = constituents.index.to_list()\n q = GsDataApi.build_market_data_query(assets, QueryType.SPOT, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n if not real_time and DataContext.current.end_date >= dt.date.today():\n df = append_last_for_measure(df, assets, QueryType.SPOT, None, source=source, request_id=request_id)\n\n grouped = df.groupby('assetId')\n weighted_vols = []\n for underlying_id, weight in zip(constituents.index, constituents['netWeight']):\n filtered = grouped.get_group(underlying_id) if underlying_id in grouped.indices else pd.DataFrame()\n filtered = filtered.loc[~filtered.index.duplicated(keep='last')]\n vol = (\n ExtendedSeries(dtype=float)\n if filtered.empty\n else ExtendedSeries(volatility(filtered['spot'], Window(tenor, tenor), returns_type))\n )\n weighted_vols.append(vol * weight)\n\n vol_df = pd.concat(weighted_vols, axis=1).ffill()\n series = (\n ExtendedSeries(vol_df.sum(axis=1, min_count=top_n_of_index), name='averageRealizedVolatility')\n if len(weighted_vols)\n else ExtendedSeries(dtype=float)\n )\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()],\n QueryType.AVERAGE_REALIZED_VOLATILITY,\n where={'tenor': tenor},\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.AVERAGE_REALIZED_VOLATILITY)\n\n\ndef _get_index_constituent_weights(\n asset: Asset, top_n_of_index: Optional[int] = None, composition_date: Optional[GENERIC_DATE] = None\n):\n start, end = _range_from_pricing_date(asset.exchange, composition_date, buffer=1)\n mqid = asset.get_marquee_id()\n positions_data = GsIndexApi.get_positions_data(mqid, start, end, ['netWeight'])\n if not len(positions_data):\n raise MqValueError('Unable to get constituents of {} between {} and {}'.format(mqid, start, end))\n\n constituents = pd.DataFrame(positions_data)[['underlyingAssetId', 'netWeight', 'positionDate']]\n constituents = constituents.set_index('positionDate')\n latest = constituents.index.max()\n _logger.info('selected composition date %s', latest)\n constituents = constituents.loc[latest]\n constituents = constituents.sort_values(by=['netWeight'], ascending=False)\n constituents = constituents[:top_n_of_index] if top_n_of_index is not None else constituents\n total_weight = constituents['netWeight'].sum()\n constituents['netWeight'] = constituents['netWeight'] / total_weight\n\n constituents = constituents.set_index('underlyingAssetId')\n return constituents\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=currency_to_default_benchmark_rate, query_type=QueryType.CAP_FLOOR_VOL)],\n)\ndef cap_floor_vol(\n asset: Asset,\n expiration_tenor: str,\n relative_strike: float,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n GS end-of-day implied normal volatility for cap and floor vol matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param relative_strike: strike level relative to at the money e.g. 10\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: cap and floor implied normal volatility curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime cap_floor_vol not implemented')\n\n rate_benchmark_mqid = convert_asset_for_rates_data_set(asset, RatesConversionType.DEFAULT_BENCHMARK_RATE)\n\n _logger.debug('where expiry=%s, strike=%s', expiration_tenor, relative_strike)\n\n q = GsDataApi.build_market_data_query(\n [rate_benchmark_mqid],\n QueryType.CAP_FLOOR_VOL,\n where=dict(expiry=expiration_tenor, strike=relative_strike),\n source=source,\n real_time=real_time,\n )\n\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.CAP_FLOOR_VOL)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=currency_to_default_benchmark_rate, query_type=QueryType.CAP_FLOOR_ATM_FWD_RATE)],\n)\ndef cap_floor_atm_fwd_rate(\n asset: Asset,\n expiration_tenor: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n GS end-of-day at-the-money forward rate for cap and floor matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: cap and floor atm forward rate curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime cap_floor_atm_fwd_rate not implemented')\n\n q = GsDataApi.build_market_data_query(\n [convert_asset_for_rates_data_set(asset, RatesConversionType.DEFAULT_BENCHMARK_RATE)],\n QueryType.CAP_FLOOR_ATM_FWD_RATE,\n where=dict(expiry=expiration_tenor, strike=0),\n source=source,\n real_time=real_time,\n )\n\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.CAP_FLOOR_ATM_FWD_RATE)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=currency_to_default_benchmark_rate, query_type=QueryType.SPREAD_OPTION_VOL)],\n)\ndef spread_option_vol(\n asset: Asset,\n expiration_tenor: str,\n long_tenor: str,\n short_tenor: str,\n relative_strike: float,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n GS end-of-day implied normal volatility for spread option vol matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param long_tenor: relative date representation of the instrument's tenor date e.g. 1y\n :param short_tenor: relative date representation of the instrument's tenor date e.g. 1y\n :param relative_strike: strike level relative to at the money e.g. 10\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: spread option implied normal volatility curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime spread_option_vol not implemented')\n\n rate_benchmark_mqid = convert_asset_for_rates_data_set(asset, RatesConversionType.DEFAULT_BENCHMARK_RATE)\n\n _logger.debug(\n 'where expiry=%s, longTenor=%s, shortTenor=%s, strike=%s',\n expiration_tenor,\n long_tenor,\n short_tenor,\n relative_strike,\n )\n\n q = GsDataApi.build_market_data_query(\n [rate_benchmark_mqid],\n QueryType.SPREAD_OPTION_VOL,\n where=dict(expiry=expiration_tenor, longTenor=long_tenor, shortTenor=short_tenor, strike=relative_strike),\n source=source,\n real_time=real_time,\n )\n\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.SPREAD_OPTION_VOL)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [\n MeasureDependency(\n id_provider=currency_to_default_benchmark_rate, query_type=QueryType.SPREAD_OPTION_ATM_FWD_RATE\n )\n ],\n)\ndef spread_option_atm_fwd_rate(\n asset: Asset,\n expiration_tenor: str,\n long_tenor: str,\n short_tenor: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n GS end-of-day At-the-money forward rate for spread option vol matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param long_tenor: relative date representation of the instrument's tenor date e.g. 1y\n :param short_tenor: relative date representation of the instrument's tenor date e.g. 1y\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: spread option at-the-money forward rate curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime spread_option_atm_fwd_rate not implemented')\n\n q = GsDataApi.build_market_data_query(\n [convert_asset_for_rates_data_set(asset, RatesConversionType.DEFAULT_BENCHMARK_RATE)],\n QueryType.SPREAD_OPTION_ATM_FWD_RATE,\n where=dict(expiry=expiration_tenor, longTenor=long_tenor, shortTenor=short_tenor, strike=0),\n source=source,\n real_time=real_time,\n )\n\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.SPREAD_OPTION_ATM_FWD_RATE)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=currency_to_inflation_benchmark_rate, query_type=QueryType.INFLATION_SWAP_RATE)],\n)\ndef zc_inflation_swap_rate(\n asset: Asset,\n termination_tenor: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n GS end-of-day zero coupon inflation swap break-even rate.\n\n :param asset: asset object loaded from security master\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: zero coupon inflation swap break-even rate curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime zc_inflation_swap_rate not implemented')\n\n infl_rate_benchmark_mqid = convert_asset_for_rates_data_set(asset, RatesConversionType.INFLATION_BENCHMARK_RATE)\n\n _logger.debug('where tenor=%s', termination_tenor)\n\n q = GsDataApi.build_market_data_query(\n [infl_rate_benchmark_mqid],\n QueryType.INFLATION_SWAP_RATE,\n where=dict(tenor=termination_tenor),\n source=source,\n real_time=real_time,\n )\n\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.INFLATION_SWAP_RATE)\n\n\n@plot_measure(\n (AssetClass.FX,), (AssetType.Cross,), [MeasureDependency(id_provider=cross_to_basis, query_type=QueryType.BASIS)]\n)\ndef basis(\n asset: Asset,\n termination_tenor: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n GS end-of-day cross-currency basis swap spread.\n\n :param asset: asset object loaded from security master\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: cross-currency basis swap spread curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime basis not implemented')\n\n basis_mqid = convert_asset_for_rates_data_set(asset, RatesConversionType.CROSS_CURRENCY_BASIS)\n\n _logger.debug('where tenor=%s', termination_tenor)\n\n q = GsDataApi.build_market_data_query(\n [basis_mqid], QueryType.BASIS, where=dict(tenor=termination_tenor), source=source, real_time=real_time\n )\n\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.BASIS)\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Cross,),\n [MeasureDependency(id_provider=cross_to_usd_based_cross, query_type=QueryType.FX_FORECAST)],\n)\ndef fx_forecast(\n asset: Asset,\n relativePeriod: FxForecastHorizon = FxForecastHorizon.THREE_MONTH,\n relative_period: Optional[FxForecastHorizon] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n FX forecasts made by Global Investment Research (GIR) macro analysts.\n\n :param asset: asset object loaded from security master\n :param relativePeriod: (deprecated) Forecast horizon. One of: 3m, 6m, 12m, EOY1, EOY2, EOY3, EOY4\n :param relative_period: Forecast horizon. One of: 3m, 6m, 12m, EOY1, EOY2, EOY3, EOY4\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: FX forecast curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime fx_forecast not implemented')\n\n if relativePeriod:\n log_warning(request_id, _logger, \"'relativePeriod' is deprecated, please use 'relative_period' instead.\")\n cross_mqid = asset.get_marquee_id()\n usd_based_cross_mqid = cross_to_usd_based_cross(cross_mqid)\n query_type = QueryType.FX_FORECAST\n\n q = GsDataApi.build_market_data_query(\n [usd_based_cross_mqid],\n query_type,\n where=dict(relativePeriod=relative_period or relativePeriod),\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n series = _extract_series_from_df(df, query_type)\n\n if cross_mqid != usd_based_cross_mqid:\n series = 1 / series\n\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Cross,),\n [MeasureDependency(id_provider=cross_to_usd_based_cross, query_type=QueryType.FX_FORECAST)],\n)\ndef fx_forecast_time_series(\n asset: Union[Asset, str],\n forecastFrequency: Union[str, _FxForecastTimeSeriesPeriodType] = _FxForecastTimeSeriesPeriodType.ANNUAL,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Short and long-term FX forecast time series.\n\n :param asset: Asset object or string representing the asset ID\n :param forecastFrequency: Forecast period type (e.g., Annual)\n :param source: Name of the function caller\n :param real_time: Whether to retrieve intraday data instead of EOD\n :param request_id: Service request ID, if any\n :return: Forecast time series for the Cross Asset\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime fx_forecast_time_series not implemented')\n if isinstance(asset, str):\n cross_mqid = asset\n elif isinstance(asset, Asset):\n cross_mqid = asset.get_marquee_id()\n else:\n raise ValueError(\"Asset must be of type Asset or str\")\n if isinstance(forecastFrequency, Enum):\n forecastFrequency = forecastFrequency.value\n usd_based_cross_mqid = cross_to_usd_based_cross(cross_mqid)\n query_type = QueryType.FX_FORECAST\n col_name = query_type.value.replace(' ', '')\n col_name = col_name[0].lower() + col_name[1:]\n\n q = GsDataApi.build_market_data_query([usd_based_cross_mqid], query_type, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n df.index.name = 'date'\n df = df.reset_index().sort_values(by='date').groupby('relativePeriod')[['date', col_name]].last()\n df = df.reset_index()\n if forecastFrequency == _FxForecastTimeSeriesPeriodType.SHORT_TERM.value:\n df = df[df['relativePeriod'].isin(['3m', '6m', '12m'])].drop('date', axis=1)\n df['date'] = df['relativePeriod'].apply(lambda x: pd.Timestamp.today() + relativedelta(months=int(x[:-1])))\n df = df[['date', col_name]].sort_values(by='date').set_index('date')\n elif forecastFrequency == _FxForecastTimeSeriesPeriodType.ANNUAL.value:\n df = df[df['relativePeriod'].str.startswith('EOY')].drop('date', axis=1)\n df['date'] = df['relativePeriod'].apply(\n lambda x: pd.Timestamp(f\"{pd.Timestamp.today().year + int(x[3:])}-01-01\")\n )\n df = df[['date', col_name]].sort_values(by='date').set_index('date')\n else:\n raise ValueError(\"Invalid forecastFrequency. Must be one of '3/6/12-Month', 'Annual'.\")\n series = _extract_series_from_df(df, query_type)\n if cross_mqid != usd_based_cross_mqid:\n series = 1 / series\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure((AssetClass.Equity, AssetClass.FX), None, [QueryType.IMPLIED_VOLATILITY])\ndef forward_vol(\n asset: Asset,\n tenor: str,\n forward_start_date: str,\n strike_reference: VolReference,\n relative_strike: Real,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Forward volatility.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param forward_start_date: forward start date e.g. 2m, 1y\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: forward volatility curve\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time forward vol not implemented')\n\n if asset.asset_class == AssetClass.FX:\n sr_string, relative_strike = _preprocess_implied_vol_strikes_fx(strike_reference, relative_strike)\n asset_id = cross_stored_direction_for_fx_vol(asset)\n else:\n sr_string, relative_strike = preprocess_implied_vol_strikes_eq(strike_reference, relative_strike)\n asset_id = asset.get_marquee_id()\n\n t1_month = _tenor_to_month(forward_start_date)\n t2_month = _tenor_to_month(tenor) + t1_month\n t1 = _month_to_tenor(t1_month)\n t2 = _month_to_tenor(t2_month)\n\n log_debug(\n request_id,\n _logger,\n 'where strikeReference=%s, relativeStrike=%s, tenor=%s',\n sr_string,\n relative_strike,\n f'{t1},{t2}',\n )\n where = dict(tenor=[t1, t2], strikeReference=[sr_string], relativeStrike=[relative_strike])\n q = GsDataApi.build_market_data_query(\n [asset_id], QueryType.IMPLIED_VOLATILITY, where=where, source=source, real_time=real_time\n )\n dataset_ids = []\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n dataset_ids.extend(getattr(df, 'dataset_ids', ()))\n\n now = dt.date.today()\n if not real_time and DataContext.current.end_date >= now:\n where_list = _split_where_conditions(where)\n delta = dt.timedelta(days=1)\n with DataContext(now - delta, now + delta):\n net = {\"queries\": []}\n for wl in where_list:\n last_q = GsDataApi.build_market_data_query(\n [asset_id], QueryType.IMPLIED_VOLATILITY, where=wl, source=source, real_time=True, measure='Last'\n )\n net[\"queries\"].extend(last_q[\"queries\"])\n\n log_debug(request_id, _logger, 'last q %s', net)\n try:\n df_l = _market_data_timed(net, request_id)\n dataset_ids.extend(getattr(df_l, 'dataset_ids', ()))\n except Exception as e:\n _logger.warning('error loading Last of implied vol', exc_info=e)\n else:\n if df_l.empty:\n _logger.warning('no data for Last of implied vol')\n else:\n df_l.index = df_l.index.tz_convert(None).normalize()\n if df_l.index.max() > df.index.max():\n df = pd.concat([df, df_l])\n\n if df.empty:\n series = ExtendedSeries(dtype=float, name='forwardVol')\n else:\n grouped = df.groupby(Fields.TENOR.value)\n try:\n sg = grouped.get_group(t1)['impliedVolatility']\n lg = grouped.get_group(t2)['impliedVolatility']\n except KeyError:\n log_debug(request_id, _logger, 'no data for one or more tenors')\n series = ExtendedSeries(dtype=float, name='forwardVol')\n else:\n series = ExtendedSeries(\n sqrt((t2_month * lg**2 - t1_month * sg**2) / _tenor_to_month(tenor)), name='forwardVol'\n )\n series.index = _normalize_dtidx(series.index)\n series.dataset_ids = tuple(dataset_ids)\n return series\n\n\ndef _process_forward_vol_term(asset: Asset, vol_series: pd.Series, vol_col: str, series_name: str) -> pd.Series:\n if vol_series.empty:\n return ExtendedSeries(dtype=float, name=series_name)\n else:\n cbd = _get_custom_bd(asset.exchange)\n vol_df = pd.DataFrame(vol_series)\n latest = (\n vol_series.attrs['latest'].date()\n if isinstance(vol_series.attrs['latest'], pd.Timestamp)\n else vol_series.attrs['latest']\n )\n vol_df['calTimeToExp'] = vol_df.apply(lambda row: (row.name.date() - latest).days / DAYS_IN_YEAR, axis=1)\n vol_df['timeToExp'] = vol_df.apply(\n lambda row: np.busday_count(latest, row.name.date(), weekmask=cbd.weekmask, holidays=cbd.holidays) / 252,\n axis=1,\n )\n vol_df['multiplier'] = sqrt(vol_df['calTimeToExp'] / vol_df['timeToExp'])\n vol_df['fwdVol'] = sqrt(\n (\n vol_df['timeToExp'] * (vol_df[vol_col] * vol_df['multiplier']) ** 2\n - vol_df['timeToExp'].shift(1) * (vol_df[vol_col].shift(1) * vol_df['multiplier'].shift(1)) ** 2\n )\n / (vol_df['timeToExp'] - vol_df['timeToExp'].shift(1))\n )\n ext_series = ExtendedSeries(vol_df['fwdVol'], name=series_name)[\n pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)\n ]\n ext_series.index = _normalize_dtidx(ext_series.index)\n ext_series.dataset_ids = getattr(vol_series, 'dataset_ids', ())\n ext_series = ext_series.sort_index()\n return ext_series\n\n\n@plot_measure((AssetClass.Equity, AssetClass.FX), None, [QueryType.IMPLIED_VOLATILITY])\ndef forward_vol_term(\n asset: Asset,\n strike_reference: VolReference,\n relative_strike: Real,\n pricing_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Forward volatility term structure.\n\n :param asset: asset object loaded from security master\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: forward volatility term structure\n \"\"\"\n vt = vol_term(\n asset=asset,\n strike_reference=strike_reference,\n relative_strike=relative_strike,\n pricing_date=pricing_date,\n source=source,\n real_time=real_time,\n request_id=request_id,\n )\n series = _process_forward_vol_term(asset, vt, 'impliedVolatility', 'forwardVolTerm')\n return series\n\n\ndef _get_skew_strikes(asset: Asset, strike_reference: SkewReference, distance: Real) -> Tuple[list, int]:\n \"\"\"\n Calculates strike references necessary for calculating skew.\n\n :param asset: asset object loaded from security master.\n :param strike_reference: strike reference type.\n :param distance: strike relative to reference.\n :return: list of relative strikes for computation, in specific order.\n \"\"\"\n if asset.asset_class == AssetClass.FX:\n buffer = 1 # FX vol data is loaded later\n if strike_reference == SkewReference.DELTA:\n q_strikes = [0 - distance, distance, 0]\n else:\n raise MqValueError('strike_reference has to be delta to get skew for FX options')\n else:\n assert asset.asset_class in (AssetClass.Equity, AssetClass.Commod)\n buffer = 0\n if strike_reference == SkewReference.DELTA:\n b = 50\n q_strikes = [100 - distance, distance, b]\n elif strike_reference == SkewReference.NORMALIZED:\n b = 0\n q_strikes = [b - distance, b + distance, b]\n elif strike_reference:\n b = 100\n q_strikes = [b - distance, b + distance, b]\n else:\n raise MqTypeError(\"strike_reference required for equities\")\n\n if strike_reference != SkewReference.NORMALIZED:\n q_strikes = [x / 100 for x in q_strikes]\n\n return q_strikes, buffer\n\n\ndef _skew(\n df: MarketDataResponseFrame, relative_strike_col, iv_col, q_strikes, normalization_mode: NormalizationMode\n) -> ExtendedSeries:\n \"\"\"\n Calculates skew using the data in df and returns it as a series.\n\n :param df: Dataframe with a column of relative strikes, a column of implied volatility, and a datetime index.\n :param relative_strike_col: name of the column in df with relative strikes.\n :param iv_col: name of the column in df with implied volatilities.\n :param q_strikes: length-three array of floats returned by _get_skew_strikes\n :param normalization_mode: \"normalized\" to divide skew by ATM implied volatility or \"outright\" to leave\n off this normalization. By default, we normalize skew for equities\n and commodities but not for FX assets.\n \"\"\"\n curves = {k: v for k, v in df.groupby(relative_strike_col)}\n if len(curves) < 3:\n raise MqValueError('Skew not available for given inputs')\n series = [curves[qs][iv_col] for qs in q_strikes]\n ext_series = (\n ((series[0] - series[1]) / series[2])\n if normalization_mode == NormalizationMode.NORMALIZED\n else (series[0] - series[1])\n )\n series = ExtendedSeries(ext_series)\n series.index = _normalize_dtidx(series.index)\n return series\n\n\ndef _skew_fetcher(asset_id, query_type, where, source, real_time, request_id=None, allow_exception=False):\n \"\"\"\n Helper function to get skew data. Called by the skew function and placed here to be able to be tested to ensure\n 100% test coverage.\n :param asset_id: Marquee asset ID\n :param query_type: QueryType to be sent to measures service\n :param where: dictionary containing query\n :param source: string source\n :param real_time: whether we want real time tdata\n :param request_id: for debugging purposes\n \"\"\"\n try:\n q = GsDataApi.build_market_data_query([asset_id], query_type, where=where, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n return _market_data_timed(q, request_id)\n except MqValueError as m:\n if allow_exception:\n log_warning(request_id, _logger, \"Ignoring expiry data fetching since there's no data for the givenasset\")\n return MarketDataResponseFrame()\n raise m\n\n\n@plot_measure(\n (AssetClass.FX, AssetClass.Equity, AssetClass.Commod),\n None,\n [MeasureDependency(id_provider=cross_stored_direction_for_fx_vol, query_type=QueryType.IMPLIED_VOLATILITY)],\n asset_type_excluded=(AssetType.CommodityNaturalGasHub,),\n)\ndef skew_term(\n asset: Asset,\n strike_reference: SkewReference,\n distance: Real,\n pricing_date: Optional[GENERIC_DATE] = None,\n normalization_mode: NormalizationMode = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Skew term structure. Uses most recent date available if pricing_date is not provided.\n\n If pricing_date falls on a weekend or a holiday, uses the previous business day as the pricing date.\n\n :param asset: asset object loaded from security master\n :param strike_reference: reference for strike level\n :param distance: strike relative to reference\n :param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y\n :param normalization_mode: \"normalized\" to divide skew by ATM implied volatility or \"outright\" to leave\n off this normalization. By default, we normalize skew for equities\n and commodities but not for FX assets.\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: volatility term structure\n \"\"\"\n # Validate params\n if real_time:\n raise NotImplementedError('realtime skew term not implemented') # TODO\n check_forward_looking(pricing_date, source, 'skew_term')\n\n if asset.asset_class == AssetClass.FX:\n asset_id = cross_stored_direction_for_fx_vol(asset)\n if normalization_mode is None:\n normalization_mode = NormalizationMode.OUTRIGHT\n else:\n asset_id = asset.get_marquee_id()\n if normalization_mode is None:\n normalization_mode = NormalizationMode.NORMALIZED\n\n q_strikes, buffer = _get_skew_strikes(asset, strike_reference, distance)\n\n start, end = _range_from_pricing_date(asset.exchange, pricing_date, buffer=buffer)\n df = df_expiry = pd.DataFrame()\n dataset_ids = set()\n where = {'strikeReference': strike_reference.value, 'relativeStrike': q_strikes}\n\n if asset.asset_class == AssetClass.Equity and (pricing_date is None or end >= dt.date.today()): # intraday\n data_requests = [\n partial(\n _get_latest_term_structure_data,\n asset_id,\n QueryType.IMPLIED_VOLATILITY,\n where=where,\n groupby=['tenor', 'relativeStrike'],\n source=source,\n request_id=request_id,\n ),\n partial(\n _get_latest_term_structure_data,\n asset_id,\n QueryType.IMPLIED_VOLATILITY_BY_EXPIRATION,\n where=where,\n groupby=['expirationDate', 'relativeStrike'],\n source=source,\n request_id=request_id,\n ),\n ]\n df, df_expiry = ThreadPoolManager.run_async(data_requests)\n dataset_ids.update(getattr(df, 'dataset_ids', ()))\n dataset_ids.update(getattr(df_expiry, 'dataset_ids', ()))\n\n if df.empty and df_expiry.empty: # historical\n data_requests = [\n partial(\n get_df_with_retries,\n partial(_skew_fetcher, asset_id, QueryType.IMPLIED_VOLATILITY, where, source, real_time, request_id),\n start_date=start,\n end_date=end,\n exchange=asset.exchange,\n ),\n partial(\n _skew_fetcher,\n asset_id,\n QueryType.IMPLIED_VOLATILITY_BY_EXPIRATION,\n where,\n source,\n real_time,\n request_id,\n allow_exception=True,\n ),\n ]\n df, df_expiry = ThreadPoolManager.run_async(data_requests)\n\n dataset_ids.update(getattr(df, 'dataset_ids', ()))\n # only if df_expiry not empty\n if not df_expiry.empty:\n dataset_ids.update(getattr(df_expiry, 'dataset_ids', ()))\n\n p_date = df.index.max() if not df.empty else pricing_date\n _logger.info('selected pricing date %s', p_date)\n\n # Handle tenor DF\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n df = df.loc[p_date]\n df.index = pd.DatetimeIndex(df.index) if not isinstance(df.index, pd.DatetimeIndex) else df.index\n df.index = pd.DatetimeIndex(\n [\n RelativeDate(df['tenor'].iloc[i], df.index.date[i]).apply_rule(exchange=asset.exchange)\n for i in range(len(df))\n ]\n )\n series = _skew(df, 'relativeStrike', 'impliedVolatility', q_strikes, normalization_mode)\n\n # Add additional data from expiry DF\n if not df_expiry.empty:\n df_expiry = df_expiry.loc[p_date]\n df_expiry.index = df_expiry['expirationDate']\n series_expiry = _skew(\n df_expiry, 'relativeStrike', 'impliedVolatilityByExpiration', q_strikes, normalization_mode\n )\n series_expiry = series_expiry[~series_expiry.index.isin(series.index)]\n series = pd.concat([series, series_expiry])\n\n series.name = \"impliedVolatility\"\n series = series.sort_index()\n series = ExtendedSeries(\n series.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n )\n series.dataset_ids = tuple(dataset_ids)\n return series\n\n\n@plot_measure(\n (AssetClass.Equity, AssetClass.Commod, AssetClass.FX),\n None,\n [QueryType.IMPLIED_VOLATILITY],\n asset_type_excluded=(AssetType.CommodityNaturalGasHub,),\n)\ndef vol_term(\n asset: Asset,\n strike_reference: VolReference,\n relative_strike: Real,\n pricing_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility term structure. Defaults to use the most recent date with live data if available. A specific pricing_date\n can also be provided.\n :param asset: asset object loaded from security master\n :param strike_reference: reference for strike level\n :param relative_strike: strike relative to reference\n :param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: volatility term structure\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime forward term not implemented') # TODO\n\n check_forward_looking(pricing_date, source, 'vol_term')\n if asset.asset_class == AssetClass.FX:\n sr_string, relative_strike = _preprocess_implied_vol_strikes_fx(strike_reference, relative_strike)\n asset_id = cross_stored_direction_for_fx_vol(asset)\n buffer = 1 # FX vol data is loaded later\n else:\n sr_string, relative_strike = preprocess_implied_vol_strikes_eq(strike_reference, relative_strike)\n asset_id = asset.get_marquee_id()\n buffer = 0\n\n start, end = _range_from_pricing_date(asset.exchange, pricing_date, buffer=buffer)\n _logger.debug('where strikeReference=%s, relativeStrike=%s', sr_string, relative_strike)\n where = dict(strikeReference=sr_string, relativeStrike=relative_strike)\n\n df = df_expiry = pd.DataFrame()\n dataset_ids = set()\n today = dt.date.today()\n if asset.asset_class == AssetClass.Equity and (pricing_date is None or end >= today): # use intraday data\n df = _get_latest_term_structure_data(asset_id, QueryType.IMPLIED_VOLATILITY, where, 'tenor', source, request_id)\n df_expiry = _get_latest_term_structure_data(\n asset_id, QueryType.IMPLIED_VOLATILITY_BY_EXPIRATION, where, 'expirationDate', source, request_id\n )\n dataset_ids.update(getattr(df, 'dataset_ids', ()))\n dataset_ids.update(getattr(df_expiry, 'dataset_ids', ()))\n\n if df.empty and df_expiry.empty:\n\n def fetcher(query_type):\n q = GsDataApi.build_market_data_query(\n [asset_id], query_type, where=where, source=source, real_time=real_time\n )\n log_debug(request_id, _logger, 'q %s', q)\n return _market_data_timed(q, request_id)\n\n df = get_df_with_retries(\n partial(fetcher, QueryType.IMPLIED_VOLATILITY), start_date=start, end_date=end, exchange=asset.exchange\n )\n try:\n df_expiry = get_df_with_retries(\n partial(fetcher, QueryType.IMPLIED_VOLATILITY_BY_EXPIRATION),\n start_date=start,\n end_date=end,\n exchange=asset.exchange,\n )\n except MqValueError:\n log_warning(request_id, _logger, \"Ignoring expiry data fetching since there's no data for the given asset\")\n pass # Allow to fail since it's not required to compute end result\n\n dataset_ids.update(getattr(df, 'dataset_ids', ()))\n # only if df_expiry not empty\n if not df_expiry.empty:\n dataset_ids.update(getattr(df_expiry, 'dataset_ids', ()))\n\n latest = df.index.union(df_expiry.index).max() if not df_expiry.empty else df.index.max()\n _logger.info('selected pricing date %s', latest)\n\n if df.empty:\n series = ExtendedSeries(dtype='float64')\n else:\n df = df.loc[latest]\n cbd = _get_custom_bd(asset.exchange)\n df = df.assign(\n expirationDate=pd.DatetimeIndex([ts + _to_offset(t) + cbd - cbd for ts, t in zip(df.index, df['tenor'])])\n )\n series = df.set_index('expirationDate')['impliedVolatility']\n series.index = _normalize_dtidx(series.index)\n\n if df_expiry.empty:\n series_expiry = pd.Series(dtype='float64')\n else:\n df_expiry = df_expiry.loc[latest]\n series_expiry = df_expiry.set_index('expirationDate')['impliedVolatilityByExpiration']\n series_expiry.index = _normalize_dtidx(series_expiry.index)\n\n extra = series_expiry[~series_expiry.index.isin(series.index)]\n if not extra.empty:\n series = pd.concat([series, extra])\n\n series.name = \"impliedVolatility\"\n series = series.sort_index()\n series = series.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n series = ExtendedSeries(series)\n series.attrs = dict(latest=latest)\n series.dataset_ids = tuple(dataset_ids)\n return series\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.IMPLIED_VOLATILITY])\ndef vol_smile(\n asset: Asset,\n tenor: str,\n strike_reference: VolSmileReference,\n pricing_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility smile of an asset implied by observations of market prices.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param strike_reference: reference for strike level\n :param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: implied volatility smile\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime vol_smile not implemented')\n\n mqid = asset.get_marquee_id()\n start, end = _range_from_pricing_date(asset.exchange, pricing_date)\n\n def fetcher():\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.IMPLIED_VOLATILITY,\n where=dict(tenor=tenor, strikeReference=strike_reference.value),\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n return _market_data_timed(q, request_id)\n\n df = get_df_with_retries(fetcher, start_date=start, end_date=end, exchange=asset.exchange)\n dataset_ids = getattr(df, 'dataset_ids', ())\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n\n vols = df['impliedVolatility'].values\n strikes = df['relativeStrike'].values\n series = ExtendedSeries(vols, index=strikes)\n series.dataset_ids = dataset_ids\n series.name = 'vol_smile' # used to indicate returned series has a float index\n return series\n\n\ndef measure_request_safe(parent_fn_name, asset, fn, request_id, *args, **kwargs):\n try:\n return fn(*args, **kwargs)\n except MqValueError as e:\n error_msg = e.args[0] if len(e.args) else 'No error message provided.'\n log_debug(request_id, _logger, f'measure_request_safe: {error_msg}')\n raise MqValueError(f'{parent_fn_name} not available for {asset.name}.')\n\n\n@plot_measure((AssetClass.Equity, AssetClass.Commod), None, [QueryType.FORWARD])\ndef fwd_term(\n asset: Asset,\n pricing_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Forward term structure. Uses most recent date available if pricing_date is not provided. If pricing_date falls on\n a weekend or holiday, uses the previous business day as pricing date.\n\n :param asset: asset object loaded from security master\n :param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y (default today)\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: forward term structure\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime forward term not implemented') # TODO\n\n asset_id = asset.get_marquee_id()\n query_type, where = QueryType.FORWARD, dict(strikeReference='forward', relativeStrike=1)\n forward_col_name = 'forward'\n\n check_forward_looking(pricing_date, source, 'fwd_term')\n cbd = _get_custom_bd(asset.exchange)\n start, end = _range_from_pricing_date(asset.exchange, pricing_date)\n start = start + cbd - cbd # get previous business day's pricing if start, end on a non-BD\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query([asset_id], query_type, where=where, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n df = measure_request_safe('fwd_term', asset, _market_data_timed, request_id, q, request_id)\n dataset_ids = getattr(df, 'dataset_ids', ())\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n df.loc[:, 'expirationDate'] = pd.DatetimeIndex(\n [ts + _to_offset(t) + cbd - cbd for ts, t in zip(df.index, df['tenor'])]\n )\n df = df.set_index('expirationDate')\n df.index = _normalize_dtidx(df.index)\n df = df.sort_index()\n df = df.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[forward_col_name])\n series.dataset_ids = dataset_ids\n return series\n\n\n@plot_measure((AssetClass.FX,), None, [QueryType.SPOT, QueryType.FORWARD_POINT], display_name='fwd_term')\ndef fx_fwd_term(\n asset: Asset,\n pricing_date: Optional[GENERIC_DATE] = None,\n fwd_type: FXForwardType = FXForwardType.OUTRIGHT,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Forward term structure. Uses most recent date available if pricing_date is not provided. If pricing_date falls on\n a weekend or holiday, uses the previous business day as pricing date.\n\n :param asset: asset object loaded from security master\n :param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y (default today)\n :param fwd_type: applicable only to FX forward term. Either \"points\" for forward points or \"outright\"\n (default outright)\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: forward term structure\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime forward term not implemented') # TODO\n\n assert asset.asset_class == AssetClass.FX\n asset_id = cross_stored_direction_for_fx_vol(asset)\n forward_col_name = 'forwardPoint'\n query_type, where = QueryType.FORWARD_POINT, {}\n get_spot = fwd_type == FXForwardType.OUTRIGHT\n\n check_forward_looking(pricing_date, source, 'fwd_term')\n cbd = _get_custom_bd(asset.exchange)\n start, end = _range_from_pricing_date(asset.exchange, pricing_date)\n start = start + cbd - cbd # get previous business day's pricing if start, end on a non-BD\n dataset_ids = set()\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query([asset_id], query_type, where=where, source=source, real_time=real_time)\n if get_spot: # Add in spot data for FX outright mode\n q_spot = GsDataApi.build_market_data_query(\n [asset_id], QueryType.SPOT, where={}, source=source, real_time=real_time\n )\n data_requests = [\n partial(_market_data_timed, q, request_id),\n partial(_market_data_timed, q_spot, request_id),\n ]\n df, spot_df = measure_request_safe(\n 'fwd_term', asset, ThreadPoolManager.run_async, request_id, data_requests\n )\n dataset_ids.update(getattr(df, 'dataset_ids', ()) + getattr(spot_df, 'dataset_ids', ()))\n else:\n log_debug(request_id, _logger, 'q %s', q)\n df = measure_request_safe('fwd_term', asset, _market_data_timed, request_id, q, request_id)\n dataset_ids.update(getattr(df, 'dataset_ids', ()))\n dataset_ids = getattr(df, 'dataset_ids', ())\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n df.loc[:, 'expirationDate'] = pd.DatetimeIndex(\n [ts + _to_offset(t) + cbd - cbd for ts, t in zip(df.index, df['tenor'])]\n )\n df = df.set_index('expirationDate')\n df.index = _normalize_dtidx(df.index)\n df = df.sort_index()\n df = df.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n if get_spot:\n spot = spot_df.loc[latest]['spot']\n df[forward_col_name] = df[forward_col_name] + spot\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[forward_col_name])\n series.dataset_ids = tuple(dataset_ids)\n return series\n\n\n@plot_measure((AssetClass.FX,), None, [QueryType.FORWARD_POINT, QueryType.SPOT])\ndef carry_term(\n asset: Asset,\n pricing_date: Optional[GENERIC_DATE] = None,\n annualized: FXSpotCarry = FXSpotCarry.ANNUALIZED,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Carry term structure. Uses most recent date available if pricing_date is not provided. If pricing_date falls on\n a weekend or holiday, uses the previous business day as pricing date.\n\n Carry is FORWARD POINT / SPOT at pricing date for FX assets.\n\n :param asset: asset object loaded from security master\n :param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y (default today)\n :param annualized: whether or not to annualize returned carry\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: forward term structure\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime forward term not implemented') # TODO\n\n asset_id = cross_stored_direction_for_fx_vol(asset)\n forward_col_name = 'forwardPoint'\n\n check_forward_looking(pricing_date, source, 'fwd_term')\n cbd = _get_custom_bd(asset.exchange)\n start, end = _range_from_pricing_date(asset.exchange, pricing_date)\n start = start + cbd - cbd # get previous business day's pricing if start, end on a non-BD\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query(\n [asset_id], QueryType.FORWARD_POINT, where={}, source=source, real_time=real_time\n )\n # setting pricing location as NYC as we have forward points only for NYC close\n q_spot = GsDataApi.build_market_data_query(\n [asset_id], QueryType.SPOT, where={'pricingLocation': 'NYC'}, source=source, real_time=real_time\n )\n data_requests = [partial(_market_data_timed, q, request_id), partial(_market_data_timed, q_spot, request_id)]\n df, spot_df = measure_request_safe('fwd_term', asset, ThreadPoolManager.run_async, request_id, data_requests)\n dataset_ids = set(getattr(df, 'dataset_ids', ()) + getattr(spot_df, 'dataset_ids', ()))\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n df = df.assign(\n expirationDate=pd.DatetimeIndex([ts + _to_offset(t) + cbd - cbd for ts, t in zip(df.index, df['tenor'])])\n )\n\n df = df.set_index('expirationDate')\n df.index = _normalize_dtidx(df.index)\n df = df.sort_index()\n df = df.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n spot = spot_df.loc[latest]['spot']\n\n if annualized == FXSpotCarry.ANNUALIZED:\n df['carry'] = df[forward_col_name] * np.sqrt((df.index.to_series() - latest).dt.days / 252) / spot\n else:\n df['carry'] = df[forward_col_name] / spot\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['carry'])\n series.name = 'carry'\n series.dataset_ids = tuple(dataset_ids)\n return series\n\n\n@cachetools.func.ttl_cache() # fine as long as availability is not different between users\ndef _var_swap_tenors(asset: Asset, request_id=None):\n from gs_quant.session import GsSession\n\n aid = asset.get_marquee_id()\n body = GsSession.current.sync.get(f\"/data/markets/{aid}/availability\")\n log_debug(request_id, _logger, 'Queried market availability (%s) for %s', body.get('requestId'), aid)\n for r in body['data']:\n if r['dataField'] == Fields.VAR_SWAP.value:\n for f in r['filteredFields']:\n if f['field'] == Fields.TENOR.value:\n return f['values']\n raise MqValueError(\"var swap is not available for \" + aid)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.VAR_SWAP])\ndef forward_var_term(\n asset: Asset,\n pricing_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Forward variance swap term structure.\n\n :param asset: asset object loaded from security master\n :param pricing_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: forward variance swap term structure\n \"\"\"\n vt = var_term(asset=asset, pricing_date=pricing_date, source=source, real_time=real_time, request_id=request_id)\n series = _process_forward_vol_term(asset, vt, Fields.VAR_SWAP.value, 'forwardVarTerm')\n return series\n\n\ndef _get_latest_term_structure_data(asset_id, query_type, where, groupby, source, request_id):\n today = dt.date.today()\n query_end = today + dt.timedelta(days=1)\n with DataContext(today, query_end):\n q_l = GsDataApi.build_market_data_query(\n [asset_id], query_type, where=where, source=source, real_time=True, measure='Last'\n )\n\n log_debug(request_id, _logger, 'q_l %s', q_l)\n df_l = _market_data_timed(q_l, request_id)\n\n if df_l.empty:\n _logger.warning('no data for last of %s', query_type.value)\n return df_l\n\n with DataContext(df_l.index[-1] - dt.timedelta(hours=1), query_end):\n q_r = GsDataApi.build_market_data_query([asset_id], query_type, where=where, source=source, real_time=True)\n\n log_debug(request_id, _logger, 'q_r %s', q_r)\n df_r = _market_data_timed(q_r, request_id)\n\n if df_r.empty:\n _logger.warning('no data for last of %s', query_type.value)\n return df_r\n\n dataset_ids = getattr(df_r, 'dataset_ids', ())\n df_r['date'] = df_r.index.date\n df_r = df_r.groupby(groupby, as_index=False).last()\n df_r = df_r.set_index('date')\n df_r.dataset_ids = dataset_ids\n return df_r\n\n\n@plot_measure((AssetClass.Equity, AssetClass.Commod), None, [QueryType.VAR_SWAP])\ndef var_term(\n asset: Asset,\n pricing_date: Optional[str] = None,\n forward_start_date: Optional[str] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Variance swap term structure. Uses most recent date available if pricing_date is not provided.\n\n :param asset: asset object loaded from security master\n :param pricing_date: relative days before today e.g. 3d, 2m, 1y\n :param forward_start_date: forward start date e.g. 2m, 1y; defaults to none\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: variance swap term structure\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time var term not implemented')\n\n if not (pricing_date is None or isinstance(pricing_date, str) or isinstance(pricing_date, dt.date)):\n raise MqTypeError('pricing_date should be a relative date')\n\n check_forward_looking(pricing_date, source, 'var_term')\n start, end = _range_from_pricing_date(asset.exchange, pricing_date)\n dataset_ids = set()\n\n if forward_start_date:\n with DataContext(start, end):\n tenors = _var_swap_tenors(asset, request_id)\n sub_frames = []\n for t in tenors:\n diff = _tenor_to_month(t) - _tenor_to_month(forward_start_date)\n if diff < 1:\n continue\n t1 = _month_to_tenor(diff)\n c = var_swap(asset, t1, forward_start_date, source=source, real_time=real_time)\n dataset_ids.update(getattr(c, 'dataset_ids', ()))\n c = c.to_frame()\n if not c.empty:\n c['tenor'] = t1\n sub_frames.append(c)\n df = pd.concat(sub_frames)\n else:\n asset_id = asset.get_marquee_id()\n today = dt.date.today()\n df = pd.DataFrame()\n\n if asset.asset_class == AssetClass.Equity and (pricing_date is None or end >= today): # try intraday data\n df = _get_latest_term_structure_data(asset_id, QueryType.VAR_SWAP, None, 'tenor', source, request_id)\n dataset_ids.update(getattr(df, 'dataset_ids', ()))\n\n if df.empty:\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query([asset_id], QueryType.VAR_SWAP, source=source, real_time=False)\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n dataset_ids.update(getattr(df, 'dataset_ids', ()))\n\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n cbd = _get_custom_bd(asset.exchange)\n df.loc[:, Fields.EXPIRATION_DATE.value] = pd.DatetimeIndex(\n [ts + _to_offset(t) + cbd - cbd for ts, t in zip(df.index, df[Fields.TENOR.value])]\n )\n df = df.set_index(Fields.EXPIRATION_DATE.value)\n df.index = _normalize_dtidx(df.index)\n df = df.sort_index()\n df = df.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[Fields.VAR_SWAP.value])\n series.attrs = dict(latest=latest)\n\n series.dataset_ids = tuple(dataset_ids)\n return series\n\n\ndef _get_var_swap_df(asset: Asset, where, source, real_time):\n ids = []\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], QueryType.VAR_SWAP, where=where, source=source, real_time=real_time\n )\n _logger.debug('q %s', q)\n result = _market_data_timed(q)\n ids.extend(getattr(result, 'dataset_ids', tuple()))\n\n now = dt.date.today()\n if not real_time and DataContext.current.end_date >= now:\n where_list = _split_where_conditions(where)\n delta = dt.timedelta(days=1)\n with DataContext(now - delta, now + delta):\n net = {\"queries\": []}\n for wl in where_list:\n qp = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()],\n QueryType.VAR_SWAP,\n where=wl,\n source=source,\n real_time=True,\n measure='Last',\n )\n net[\"queries\"].extend(qp[\"queries\"])\n _logger.debug('last q %s', net)\n\n try:\n df_l = _market_data_timed(net)\n ids.extend(getattr(df_l, 'dataset_ids', tuple()))\n except Exception as e:\n _logger.warning('error loading Last of var_swap', exc_info=e)\n else:\n if df_l.empty:\n _logger.warning('no data for Last of var_swap')\n else:\n df_l.index = df_l.index.tz_convert(None).normalize()\n if df_l.index.max() > result.index.max():\n result = pd.concat([result, df_l])\n\n result.dataset_ids = tuple(ids)\n return result\n\n\n@plot_measure(\n (\n AssetClass.Equity,\n AssetClass.Commod,\n ),\n None,\n [QueryType.VAR_SWAP],\n)\ndef var_swap(\n asset: Asset, tenor: str, forward_start_date: Optional[str] = None, *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"\n Strike such that the price of an uncapped variance swap on the underlying index is zero at inception. If\n forward start date is provided, then the result is a forward starting variance swap.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param forward_start_date: forward start date e.g. 2m, 1y; defaults to none\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: implied volatility curve\n \"\"\"\n\n if forward_start_date is None:\n _logger.debug('where tenor=%s', tenor)\n where = dict(tenor=[tenor])\n df = _get_var_swap_df(asset, where, source, real_time)\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[Fields.VAR_SWAP.value])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n else:\n if not isinstance(forward_start_date, str):\n raise MqTypeError('forward_start_date must be a relative date')\n\n x = _tenor_to_month(tenor)\n y = _tenor_to_month(forward_start_date)\n z = x + y\n yt = _month_to_tenor(y)\n zt = _month_to_tenor(z)\n\n tenors = _var_swap_tenors(asset)\n if yt not in tenors or zt not in tenors:\n series = ExtendedSeries(dtype=float)\n series.dataset_ids = ()\n return series\n\n _logger.debug('where tenor=%s', f'{yt},{zt}')\n where = dict(tenor=[yt, zt])\n df = _get_var_swap_df(asset, where, source, real_time)\n dataset_ids = getattr(df, 'dataset_ids', ())\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n grouped = df.groupby(Fields.TENOR.value)\n try:\n yg = grouped.get_group(yt)[Fields.VAR_SWAP.value]\n zg = grouped.get_group(zt)[Fields.VAR_SWAP.value]\n except KeyError:\n _logger.debug('no data for one or more tenors')\n series = ExtendedSeries(dtype=float)\n series.dataset_ids = ()\n return series\n series = ExtendedSeries(sqrt((z * zg**2 - y * yg**2) / x))\n series.dataset_ids = dataset_ids\n return series\n\n\ndef _get_iso_data(region: str):\n timezone = 'US/Eastern'\n peak_start = 7\n peak_end = 23\n weekends = [5, 6]\n\n if region in ['MISO', 'ERCOT', 'SPP']:\n timezone = 'US/Central'\n peak_start = 6\n peak_end = 22\n if region == 'CAISO':\n timezone = 'US/Pacific'\n weekends = [6]\n peak_start = 6\n peak_end = 22\n\n return timezone, peak_start, peak_end, weekends\n\n\ndef _get_qbt_mapping(bucket, region):\n # Finds combo bucket for power by mapping of the quantity bucket\n weekend_offpeak = \"SUH1X16\" if region == 'CAISO' else \"2X16H\"\n QBT_mapping = {\n \"OFFPEAK\": [weekend_offpeak, \"7X8\"],\n \"7X16\": [\"PEAK\", weekend_offpeak],\n \"7X24\": [\"PEAK\", \"7X8\", weekend_offpeak],\n }\n return QBT_mapping[bucket.upper()] if bucket.upper() in QBT_mapping else [bucket.upper()]\n\n\ndef _get_weight_for_bucket(asset, start_contract_range, end_contract_range, bucket):\n # Find contracts and holidays in the date range\n bbid = Asset.get_identifier(asset, AssetIdentifier.BLOOMBERG_ID)\n if bbid is None:\n # Get ISO from parameters\n region = asset.get_entity()['parameters']['ISO']\n else:\n region = bbid.split(\" \")[0]\n timezone = _get_iso_data(region)[0]\n dates_contract_range = get_contract_range(start_contract_range, end_contract_range, timezone)\n holidays = NercCalendar().holidays(start=start_contract_range, end=end_contract_range).date\n\n # Get number of hours for a given bucket for each contract\n weights = []\n buckets_QBT = _get_qbt_mapping(bucket, region)\n for bucket_QBT in buckets_QBT:\n df_bucket = _filter_by_bucket(dates_contract_range, bucket_QBT, holidays, region)\n weights_df = df_bucket.groupby('contract_month').size()\n weights_df = pd.DataFrame({'contract': weights_df.index, 'weight': weights_df.values})\n weights_df['quantityBucket'] = bucket_QBT\n weights.append(weights_df)\n\n weights = pd.concat(weights)\n return weights\n\n\ndef _filter_by_bucket(df, bucket, holidays, region):\n # TODO: get frequency definition from SecDB\n timezone, peak_start, peak_end, weekends = _get_iso_data(region)\n if bucket.lower() == '7x24':\n pass\n # offpeak: 11pm-7am & weekend & holiday\n elif bucket.lower() == 'offpeak':\n df = df.loc[\n df['date'].isin(holidays)\n | df['day'].isin(weekends)\n | (\n ~df['date'].isin(holidays)\n & ~df['day'].isin(weekends)\n & ((df['hour'] < peak_start) | (df['hour'] > peak_end - 1))\n )\n ]\n # peak: 7am to 11pm on weekdays\n elif bucket.lower() == 'peak':\n df = df.loc[\n (~df['date'].isin(holidays))\n & (~df['day'].isin(weekends))\n & (df['hour'] > peak_start - 1)\n & (df['hour'] < peak_end)\n ]\n # 7x8: 11pm to 7am\n elif bucket.lower() == '7x8':\n df = df.loc[(df['hour'] < peak_start) | (df['hour'] > peak_end - 1)]\n # 2x16h: weekends & holidays\n elif bucket.lower() == '2x16h' or bucket.lower() == 'suh1x16':\n df = df.loc[\n ((df['date'].isin(holidays)) | df['day'].isin(weekends))\n & ((df['hour'] > peak_start - 1) & (df['hour'] < peak_end))\n ]\n else:\n raise MqValueError('Invalid bucket: ' + bucket + '. Expected Value: peak, offpeak, 7x24, 7x8, 2x16h.')\n return df\n\n\ndef _string_to_date_interval(interval: str):\n \"\"\"\n Slang Date::Interval implementation\n Accept months (e.g. F07), quarters (e.g. 4Q06), half-years (e.g. 1H07), and years (e.g. Cal07 or 2007)\n :param interval: date-interval\n :return: start and end date\n \"\"\"\n if interval[-2:].isdigit():\n YS = interval[-2:]\n year = int(\"20\" + YS) if int(YS) <= 51 else int(\"19\" + YS)\n else:\n return \"Invalid year\"\n\n if len(interval) > 4 and interval[-4:].isdigit():\n YS = interval[-4:]\n year = int(YS)\n\n start_year = dt.date(year, 1, 1)\n if len(interval) == 1 + len(YS):\n if interval[0].upper() in _COMMOD_CONTRACT_MONTH_CODES:\n month_index = _COMMOD_CONTRACT_MONTH_CODES.index(interval[0].upper()) + 1\n start_date = dt.date(year, month_index, 1)\n end_date = dt.date(year, month_index, calendar.monthrange(year, month_index)[1])\n else:\n return \"Invalid month\"\n elif (len(interval) == 2 + len(YS) and interval.isdigit()) or (\n interval.casefold().startswith(\"Cal\".casefold()) and len(interval) == 3 + len(YS)\n ):\n start_date = dt.date(year, 1, 1)\n end_date = dt.date(year, 12, 31)\n elif len(interval) == 2 + len(YS):\n if interval[0].isdigit():\n num = int(interval[0])\n else:\n return \"Invalid num\"\n if interval[1].upper() == \"Q\":\n if 1 <= num <= 4:\n start_date = start_year + relativedelta(months=+(3 * (num - 1)))\n end_date = start_year + relativedelta(months=+(3 * num), days=-1)\n else:\n return \"Invalid Quarter\"\n if interval[1].upper() == \"H\":\n if 1 <= num <= 2:\n start_date = start_year + relativedelta(months=+(6 * (num - 1)))\n end_date = start_year + relativedelta(months=+(6 * num), days=-1)\n else:\n return \"Invalid Half Year\"\n elif len(interval) >= 3 + len(YS):\n left = interval[0 : len(interval) - len(YS)]\n if left.isalpha():\n if left in calendar.month_name:\n month_index = {v: k for k, v in enumerate(calendar.month_name)}[left]\n elif left in calendar.month_abbr:\n month_index = {v: k for k, v in enumerate(calendar.month_abbr)}[left]\n else:\n return \"Invalid date code\"\n start_date = dt.date(year, month_index, 1)\n end_date = dt.date(year, month_index, calendar.monthrange(year, month_index)[1])\n else:\n return \"Invalid date code\"\n else:\n return \"Unknown date code\"\n return {'start_date': start_date, 'end_date': end_date}\n\n\ndef _merge_curves_by_weighted_average(forwards_data, weights, keys, measure_column):\n \"\"\"\n Merges price curve with weights curve\n :param forwards_data: Pricing data\n :param weights: dataframe with each pricing data point associated with a weight\n :param keys: keys to join dataframes - columns that uniquely identify each datapoint\n :param measure_column: dataset measure column name\n :return:\n \"\"\"\n # Using cartesian product of the date range and weights dataframe with keys\n # as this gives us an exhaustive list of data points required for the data to be complete\n # i.e a composite key comprised of date and keys\n dates = pd.DataFrame(data=forwards_data['dates'].unique(), columns=[\"dates\"])\n weights = pd.merge(weights.assign(key=0), dates.assign(key=0), on='key').drop('key', axis=1)\n\n # Left join on the weights dataframe using the composite key\n result_df = pd.merge(weights, forwards_data, on=keys, how='left')\n\n result_df['weighted_price'] = result_df['weight'] * result_df[measure_column]\n\n # Filtering dates that have missing buckets or contracts.\n # Dates with any null values due to unmatched rows in the right table with raw data will be removed as a result.\n null_dates = set(result_df[result_df[measure_column].isna()]['dates'])\n result_df = result_df[~result_df['dates'].isin(null_dates)]\n\n result_df = result_df.groupby('dates').agg({'weight': 'sum', 'weighted_price': 'sum'})\n result_df['price'] = result_df['weighted_price'] / result_df['weight']\n\n result = ExtendedSeries(result_df['price'], index=result_df.index)\n result = result.rename_axis(None, axis='index')\n return result\n\n\ndef _weighted_average_valuation_curve_for_calendar_strip(asset, contract_range, query_type, measure_field):\n \"\"\"\n :param asset: marquee asset\n :param contract_range: tenor in case of instruments, calendar strip for underliers\n :param query_type: dataset query type\n :param measure_field: dataset measure column name\n :return:\n \"\"\"\n start_date_interval = _string_to_date_interval(contract_range.split(\"-\")[0])\n if isinstance(start_date_interval, str):\n raise MqValueError(start_date_interval)\n start_contract_range = start_date_interval['start_date']\n if \"-\" in contract_range:\n end_date_interval = _string_to_date_interval(contract_range.split(\"-\")[1])\n if isinstance(end_date_interval, str):\n raise MqValueError(end_date_interval)\n end_contract_range = end_date_interval['end_date']\n else:\n end_contract_range = start_date_interval['end_date']\n\n dates_contract_range = get_contract_range(start_contract_range, end_contract_range, None)\n\n weights = dates_contract_range.groupby('contract_month').size()\n weights = pd.DataFrame({'contract': weights.index, 'weight': weights.values})\n\n start, end = DataContext.current.start_date, DataContext.current.end_date\n\n contracts_to_query = weights['contract'].unique().tolist()\n where = dict(contract=contracts_to_query)\n\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], query_type, where=where, source=None, real_time=False\n )\n data = _market_data_timed(q)\n dataset_ids = getattr(data, 'dataset_ids', ())\n\n data['dates'] = data.index.date\n print('q %s', q)\n\n keys = ['contract', 'dates']\n result = _merge_curves_by_weighted_average(data, weights, keys, measure_field)\n result.dataset_ids = dataset_ids\n return result\n\n\n@plot_measure((AssetClass.Commod,), None, [QueryType.FAIR_PRICE])\ndef fair_price(\n asset: Asset, tenor: str = None, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"'\n Fair Price for Swap Instrument\n\n :param asset: asset object loaded from security master\n :param tenor: e.g. inputs - 'Cal20', 'F20-G20', '2Q20', '2H20', 'Cal20-Cal21': Default Value = F20\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :param request_id: service request id, if any\n :return: Fair Price\n \"\"\"\n\n asset_type = asset.get_type()\n\n if asset_type == SecAssetType.INDEX:\n if tenor is None:\n raise MqValueError('Contract month range query input is not specified')\n return _weighted_average_valuation_curve_for_calendar_strip(asset, tenor, QueryType.FAIR_PRICE, \"fairPrice\")\n\n asset_id = asset.get_marquee_id()\n q = GsDataApi.build_market_data_query([asset_id], QueryType.FAIR_PRICE, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n series = _extract_series_from_df(df, QueryType.FAIR_PRICE)\n series.index = _normalize_dtidx(series.index)\n return series\n\n\ndef _get_start_and_end_dates(contract_range: str) -> (int, int):\n start_date_interval = _string_to_date_interval(contract_range.split(\"-\")[0])\n if isinstance(start_date_interval, str):\n raise MqValueError(start_date_interval)\n start_contract_range = start_date_interval['start_date']\n if \"-\" in contract_range:\n end_date_interval = _string_to_date_interval(contract_range.split(\"-\")[1])\n if isinstance(end_date_interval, str):\n raise MqValueError(end_date_interval)\n end_contract_range = end_date_interval['end_date']\n else:\n end_contract_range = start_date_interval['end_date']\n return (start_contract_range, end_contract_range)\n\n\ndef get_weights_for_contracts(contract_range: str) -> dict:\n (start_contract_range, end_contract_range) = _get_start_and_end_dates(contract_range=contract_range)\n dates_contract_range = get_contract_range(start_contract_range, end_contract_range, None)\n weights = dates_contract_range.groupby('contract_month').size()\n return weights\n\n\ndef _forward_price_natgas(\n asset: Asset, price_method: str = 'GDD', contract_range: str = None, *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"'\n US NG Forward Price\n\n :param asset: asset object loaded from security master\n :param price_method: type of index settled against - GDD/FERC\n :param contract_range: e.g. inputs - 'Cal20', 'F20-G20', '2Q20', '2H20', 'Cal20-Cal21': Default Value = F20\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Forward Price\n \"\"\"\n weights = get_weights_for_contracts(contract_range)\n weights = pd.DataFrame({'contract': weights.index, 'weight': weights.values})\n contracts_to_query = weights['contract'].unique().tolist()\n\n start, end = DataContext.current.start_date, DataContext.current.end_date\n where = dict(contract=contracts_to_query, priceMethod=price_method.upper())\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], QueryType.FORWARD_PRICE, where=where, source=source, real_time=False\n )\n _logger.debug('q %s', q)\n data = _market_data_timed(q)\n dataset_ids = getattr(data, 'dataset_ids', ())\n\n if data.empty:\n result = ExtendedSeries(dtype='float64')\n else:\n data['dates'] = data.index.date\n keys = ['contract', 'dates']\n result = _merge_curves_by_weighted_average(data, weights, keys, \"forwardPrice\")\n result.dataset_ids = dataset_ids\n return result\n\n\ndef _forward_price_elec(\n asset: Asset,\n price_method: str = 'LMP',\n bucket: str = 'PEAK',\n contract_range: str = 'F20',\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n Us Power Forward Prices\n\n :param asset: asset object loaded from security master\n :param price_method: price method between LMP, MCP, SPP, Physical: Default value = LMP\n :param bucket: bucket type among '7x24', 'peak', 'offpeak', '2x16h', '7x16' and '7x8': Default value = 7x24\n :param contract_range: e.g. inputs - 'Cal20', 'F20-G20', '2Q20', '2H20', 'Cal20-Cal21': Default Value = F20\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Us Power Forward Prices\n \"\"\"\n\n (start_contract_range, end_contract_range) = _get_start_and_end_dates(contract_range=contract_range)\n\n weights = _get_weight_for_bucket(asset, start_contract_range, end_contract_range, bucket)\n start, end = DataContext.current.start_date, DataContext.current.end_date\n\n contracts_to_query = weights['contract'].unique().tolist()\n quantitybuckets_to_query = weights['quantityBucket'].unique().tolist()\n\n where = dict(priceMethod=price_method.upper(), quantityBucket=quantitybuckets_to_query, contract=contracts_to_query)\n with DataContext(start, end):\n\n def _query_fwd(asset_, where_, source_=None):\n q = GsDataApi.build_market_data_query(\n [asset_.get_marquee_id()], QueryType.FORWARD_PRICE, where=where_, source=source_, real_time=False\n )\n _logger.debug('q %s', q)\n return _market_data_timed(q)\n\n forwards_data = _query_fwd(asset, where, source)\n if forwards_data.empty:\n # For cases where uppercase priceMethod dont fetch data, try user input as is\n where['priceMethod'] = price_method\n forwards_data = _query_fwd(asset, where, source)\n\n dataset_ids = getattr(forwards_data, 'dataset_ids', ())\n\n if forwards_data.empty:\n result = ExtendedSeries(dtype='float64')\n else:\n forwards_data['dates'] = forwards_data.index.date\n keys = ['quantityBucket', 'contract', 'dates']\n measure_column = 'forwardPrice'\n result = _merge_curves_by_weighted_average(forwards_data, weights, keys, measure_column)\n result.dataset_ids = dataset_ids\n return result\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (\n AssetType.Index,\n AssetType.CommodityPowerAggregatedNodes,\n AssetType.CommodityPowerNode,\n ),\n [QueryType.FORWARD_PRICE],\n)\ndef forward_price(\n asset: Asset,\n price_method: str = 'LMP',\n bucket: str = 'PEAK',\n contract_range: str = 'F20',\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n Forward Prices\n\n :param asset: asset object loaded from security master\n :param price_method: price method between LMP, MCP, SPP for US Power assets and GDD, FERC for US NG assets:\n Default value = LMP\n :param bucket: bucket type among '7x24', 'peak', 'offpeak', '2x16h', '7x16' and '7x8': Default value = 7x24\n :param contract_range: e.g. inputs - 'Cal20', 'F20-G20', '2Q20', '2H20', 'Cal20-Cal21': Default Value = F20\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Forward Prices\n \"\"\"\n if real_time:\n raise MqValueError('Use daily frequency instead of intraday')\n\n return _forward_price_elec(asset, price_method, bucket, contract_range, source=source)\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (\n AssetType.Index,\n AssetType.CommodityPowerAggregatedNodes,\n AssetType.CommodityPowerNode,\n ),\n [QueryType.IMPLIED_VOLATILITY],\n)\ndef implied_volatility_elec(\n asset: Asset,\n price_method: str = 'LMP',\n bucket: str = 'PEAK',\n contract_range: str = 'F20',\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n Implied Volatility\n\n :param asset: asset object loaded from security master\n :param price_method: price method between LMP, MCP, SPP for US Power assets\n :param bucket: bucket type among '7x24', 'peak', 'offpeak', '2x16h', '7x16' and '7x8': Default value = 7x24\n :param contract_range: e.g. inputs - 'Cal20', 'F20-G20', '2Q20', '2H20', 'Cal20-Cal21': Default Value = F20\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Forward Prices\n \"\"\"\n if real_time:\n raise MqValueError('Use daily frequency instead of intraday')\n\n (start_contract_range, end_contract_range) = _get_start_and_end_dates(contract_range=contract_range)\n\n weights = _get_weight_for_bucket(asset, start_contract_range, end_contract_range, bucket)\n start, end = DataContext.current.start_date, DataContext.current.end_date\n\n contracts_to_query = weights['contract'].unique().tolist()\n quantitybuckets_to_query = weights['quantityBucket'].unique().tolist()\n\n where = dict(priceMethod=price_method.upper(), quantityBucket=quantitybuckets_to_query, contract=contracts_to_query)\n ds = Dataset('COMMOD_US_ELEC_ENERGY_IMPLIED_VOLATILITY')\n implied_vols_data = ds.get_data(assetId=[asset.get_marquee_id()], where=where, start=start, end=end)\n _logger.debug('query for %s', where)\n\n if implied_vols_data.empty:\n result = ExtendedSeries(dtype='float64')\n else:\n implied_vols_data['dates'] = implied_vols_data.index.date\n keys = ['quantityBucket', 'contract', 'dates']\n measure_column = 'impliedVolatility'\n result = _merge_curves_by_weighted_average(implied_vols_data, weights, keys, measure_column)\n\n result.dataset_ids = ds.id\n return result\n\n\ndef eu_ng_hub_to_swap(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n asset_ref_price = EUNatGasDataReference[asset.name + \"_ICE_CommodityReferencePrice\"].value\n result_id = ''\n try:\n # Search for atleast one instrument for the given asset using asset parameter\n if asset.asset_class is AssetClass.Commod and asset.get_type() == SecAssetType.COMMODITY_EU_NATURAL_GAS_HUB:\n kwargs = dict(asset_parameters_commodity_reference_price=asset_ref_price)\n instruments = GsAssetApi.get_many_assets(**kwargs)\n result_id = instruments[0].id\n except IndexError:\n result_id = asset.get_marquee_id()\n\n return result_id\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (AssetType.CommodityNaturalGasHub, AssetType.CommodityEUNaturalGasHub),\n [MeasureDependency(id_provider=eu_ng_hub_to_swap, query_type=QueryType.FORWARD_PRICE)],\n display_name='forward_price',\n)\ndef forward_price_ng(\n asset: Asset, contract_range: str = 'F20', price_method: str = 'GDD', *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"\n Forward Prices\n\n :param asset: asset object loaded from security master\n :param price_method: For US NG assets:GDD/FERC/EXCHANGE. For EU NG assets:USD/None. Ignore for native currency.\n :param contract_range: e.g. inputs - 'Cal20', 'F20-G20', '2Q20', '2H20', 'Cal20-Cal21': Default Value = F20\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Forward Prices\n \"\"\"\n if real_time:\n raise MqValueError('Use daily frequency instead of intraday')\n\n if asset.get_type() == SecAssetType.COMMODITY_NATURAL_GAS_HUB:\n return _forward_price_natgas(asset, price_method, contract_range, source=source)\n if asset.get_type() == SecAssetType.COMMODITY_EU_NATURAL_GAS_HUB:\n # If no currency mentioned, defaults to GDD from parent method\n if price_method == 'GDD':\n price_method = 'ICE'\n return _forward_price_eu_natgas(asset, contract_range, price_method)\n else:\n raise MqTypeError('The forward_price_ng is not supported for this asset')\n\n\ndef get_contract_range(start_contract_range, end_contract_range, timezone):\n if timezone:\n df = pd.date_range(\n start_contract_range, end_contract_range + dt.timedelta(days=1), None, 'h', timezone, False, None, 'left'\n ).to_frame()\n\n df['hour'] = df.index.hour\n df['day'] = df.index.dayofweek\n else:\n df = pd.date_range(\n start=start_contract_range,\n end=end_contract_range,\n ).to_frame()\n\n df['date'] = df.index.date\n df['month'] = df.index.month - 1\n df['year'] = df.index.year\n df['contract_month'] = df['month'].map(_COMMOD_CONTRACT_MONTH_CODES_DICT) + df['year'].astype(str).str[2:]\n\n return df\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (\n AssetType.Index,\n AssetType.CommodityPowerAggregatedNodes,\n AssetType.CommodityPowerNode,\n ),\n [QueryType.PRICE],\n)\ndef bucketize_price(\n asset: Asset,\n price_method: str,\n bucket: str = '7x24',\n granularity: str = 'daily',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"'\n Bucketized COMMOD_US_ELEC_ENERGY_PRICES\n\n :param asset: asset object loaded from security master\n :param price_method: price method between LMP, MCP, SPP, energy, loss, congestion: Default value = LMP\n :param bucket: bucket type among '7x24', 'peak', 'offpeak', '2x16h' and '7x8': Default value = 7x24\n :param granularity: daily or monthly: default value = daily\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :param request_id: service request id, if any\n :return: Bucketized elec energy prices\n \"\"\"\n if real_time:\n raise MqValueError('Bucketize function returns aggregated daily data')\n\n # create granularity indicator\n if granularity.lower() in ['daily', 'd']:\n granularity = 'D'\n elif granularity.lower() in ['monthly', 'm']:\n granularity = FREQ_MONTH_END\n else:\n raise MqValueError('Invalid granularity: ' + granularity + '. Expected Value: daily or monthly.')\n\n bbid = Asset.get_identifier(asset, AssetIdentifier.BLOOMBERG_ID)\n region = bbid.split(\" \")[0]\n timezone = _get_iso_data(region)[0]\n\n to_zone = tz.gettz('UTC')\n from_zone = tz.gettz(timezone)\n\n # Start date and end date are considered to be in ISO's local timezone\n start_date, end_date = DataContext.current.start_date, DataContext.current.end_date\n holidays = NercCalendar().holidays(start=start_date, end=end_date).date\n # Start time is constructed by combining start date with 00:00:00 timestamp\n # in local time and then converted to UTC time\n # End time is constructed by combining end date with 23:59:59 timestamp\n # in local time and then converted to UTC time\n start_time = dt.datetime.combine(start_date, dt.datetime.min.time(), tzinfo=from_zone).astimezone(to_zone)\n end_time = dt.datetime.combine(end_date, dt.datetime.max.time(), tzinfo=from_zone).astimezone(to_zone)\n\n where = dict(priceMethod=price_method.upper())\n with DataContext(start_time, end_time):\n\n def _query_prices(asset_, where_):\n q = GsDataApi.build_market_data_query(\n [asset_.get_marquee_id()], QueryType.PRICE, where=where_, source=source, real_time=True\n )\n log_debug(request_id, _logger, 'q %s', q)\n return _market_data_timed(q, request_id)\n\n df = _query_prices(asset, where)\n if df.empty:\n # For cases where uppercase priceMethod don't fetch data, try user input as is\n where['priceMethod'] = price_method\n df = _query_prices(asset, where)\n\n dataset_ids = getattr(df, 'dataset_ids', ())\n\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n df = df.tz_convert(timezone)\n\n df['month'] = df.index.to_period('M')\n df['date'] = df.index.date\n df['day'] = df.index.dayofweek\n df['hour'] = df.index.hour\n df['timestamp'] = df.index\n\n # This will remove any duplicate prices uploaded with the same timestamp\n df = df.drop_duplicates()\n # freq is the frequency at which the ISO publishes data for e.g. 15 min, 1hr\n freq = int(min(np.diff(df.index).astype('timedelta64[s]') / np.timedelta64(1, 's')))\n if freq == 0:\n raise MqValueError('Duplicate data rows probable for this period')\n # checking missing data points\n ref_hour_range = pd.date_range(\n str(start_date), str(end_date + dt.timedelta(days=1)), None, str(freq) + \"s\", timezone, False, None, 'left'\n )\n\n missing_hours = ref_hour_range[~ref_hour_range.isin(df.index)]\n missing_dates = np.unique(missing_hours.date)\n missing_months = np.unique(np.array(missing_dates, dtype='M8[D]').astype('M8[M]')).astype('str')\n\n # drop dates and months which have missing data\n df = df.loc[(~df['date'].isin(missing_dates))]\n if granularity == FREQ_MONTH_END:\n df = df.loc[(~df['month'].astype('str').isin(missing_months))]\n\n df = _filter_by_bucket(df, bucket, holidays, region)\n df = df['price'].resample(granularity).mean()\n df.index = df.index.date\n df = df.loc[start_date:end_date]\n series = ExtendedSeries(df)\n\n series.dataset_ids = dataset_ids\n return series\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef dividend_yield(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Dividend Yield of the single stock or the asset-weighted average of dividend yields of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: dividend yield\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time dividend_yield not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Dividend Yield\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (AssetType.Research_Basket, AssetType.Custom_Basket, AssetType.Equity_Basket, AssetType.ETF, AssetType.Index),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef earnings_per_share(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Earnings Per Share (EPS) of the single stock or the asset-weighted average EPS of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: earnings per share\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time earnings_per_share not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Earnings per Share\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef earnings_per_share_positive(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Earnings Per Share Positive of the single stock or the asset-weighted average EPSP of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: earnings per share positive\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time earnings_per_share_positive not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Earnings per Share Positive\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef net_debt_to_ebitda(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Net Debt to EBITDA of the single stock or the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Net Debt to EBITDA\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time net_debt_to_ebitda not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Net Debt to EBITDA\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef price_to_book(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Price to Book of the single stock or the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Price to Book\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time price_to_book not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Price to Book\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef price_to_cash(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Price to Cash of the single stock or the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Price to Cash\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time price_to_cash not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Price to Cash\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef price_to_earnings(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Price to Earnings of the single stock or the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Price to Earnings\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time price_to_earnings not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Price to Earnings\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef price_to_earnings_positive(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Price to Earnings Positive of the single stock or the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Price to Earnings Positive\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time price_to_earnings_positive not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Price to Earnings Positive\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef price_to_earnings_positive_exclusive(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Price to Earnings Positive of the single stock or the asset-weighted average value of a composite's underliers\n excluding negative EPS stocks.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Price to Earnings Positive Exclusive\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time price_to_earnings_positive_exclusive not implemented')\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.PRICE_TO_EARNINGS_POSITIVE_EXCLUSIVE.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef price_to_sales(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Price to Sales of the single stock or the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Price to Sales\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time price_to_sales not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Price to Sales\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef return_on_equity(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Return on Equity of the single stock or the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Return on Equity\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time return_on_equity not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Return on Equity\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.FUNDAMENTAL_METRIC])\ndef sales_per_share(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Sales per Share of the single stock or the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Sales per Share\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time sales_per_share not implemented')\n\n mqid = asset.get_marquee_id()\n metric = \"Sales per Share\"\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n QueryType.FUNDAMENTAL_METRIC,\n where=dict(metric=metric, period=period, periodDirection=period_direction.value),\n source=source,\n real_time=real_time,\n )\n\n q['queries'][0]['vendor'] = 'Goldman Sachs'\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.FUNDAMENTAL_METRIC)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_dividend_yield(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Dividend Yield of the asset-weighted average of dividend yields of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: current constituents dividend yield\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_dividend_yield not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_dividend_yield not implemented for this basket. Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_DIVIDEND_YIELD.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_earnings_per_share(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Earnings Per Share (EPS) of the asset-weighted average EPS of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: current constituents earnings per share\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_earnings_per_share not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_earnings_per_share not implemented for this basket. '\n 'Only available for flagship baskets'\n )\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_EARNINGS_PER_SHARE.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_earnings_per_share_positive(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Earnings Per Share Positive of the asset-weighted average EPSP of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: current constituents earnings per share positive\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_earnings_per_share_positive not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_earnings_per_share_positive not implemented for this basket. '\n 'Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_EARNINGS_PER_SHARE_POSITIVE.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_net_debt_to_ebitda(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Net Debt to EBITDA of the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Current Constituents Net Debt to EBITDA\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_net_debt_to_ebitda not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_net_debt_to_ebitda not implemented for this basket. '\n 'Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_NET_DEBT_TO_EBITDA.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_price_to_book(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Price to Book of the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Current Constituents Price to Book\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_price_to_book not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_price_to_book not implemented for this basket. Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_PRICE_TO_BOOK.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_price_to_cash(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Price to Cash of the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Current Constituents Price to Cash\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_price_to_cash not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_price_to_cash not implemented for this basket. Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_PRICE_TO_CASH.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_price_to_earnings(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Price to Earnings of the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Current Constituents Price to Earnings\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_price_to_earnings not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_price_to_earnings not implemented for this basket. '\n 'Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_PRICE_TO_EARNINGS.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_price_to_earnings_positive(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Price to Earnings Positive of the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Current Constituents Price to Earnings Positive\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_price_to_earnings_positive not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_price_to_earnings_positive not implemented for this basket. '\n 'Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_PRICE_TO_EARNINGS_POSITIVE.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_price_to_sales(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Price to Sales of the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Current Constituents Price to Sales\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_price_to_sales not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_price_to_sales not implemented for this basket. Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_PRICE_TO_SALES.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_return_on_equity(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Return on Equity of the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Current Constituents Return on Equity\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_return_on_equity not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_return_on_equity not implemented for this basket. Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_RETURN_ON_EQUITY.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Research_Basket,\n AssetType.Custom_Basket,\n ),\n [QueryType.FUNDAMENTAL_METRIC],\n)\ndef current_constituents_sales_per_share(\n asset: Asset,\n period: str,\n period_direction: FundamentalMetricPeriodDirection,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Current Constituents Sales per Share of the asset-weighted average value of a composite's underliers.\n\n 1y forward: time-weighted average of one fiscal year (FY1) and two fiscal year (FY2) fwd-looking estimates.\n 2y forward: time-weighted average of two fiscal year (FY2) and three fiscal year (FY3) fwd-looking estimates.\n 3y forward: time-weighted average of three fiscal year (FY3) and four fiscal year (FY4) fwd-looking estimates.\n 1y trailing: time-weighted average of latest reported fiscal year (FY0) data and one fiscal year (FY1) fwd-looking\n estimate.\n\n :param asset: asset object loaded from security master\n :param period: the relative fiscal period from now. e.g. 1y\n :param period_direction: whether the period is forward-looking or backward-looking e.g. forward\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Current Constituents Sales per Share\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time current_constituents_sales_per_share not implemented')\n\n if not asset.get_entity().get('parameters', {'flagship': False}).get('flagship', False):\n raise NotImplementedError(\n 'current_constituents_sales_per_share not implemented for this basket. Only available for flagship baskets'\n )\n\n mqid = asset.get_marquee_id()\n metric = DataMeasure.CURRENT_CONSTITUENTS_SALES_PER_SHARE.value\n\n _logger.debug('where assetId=%s, metric=%s, period=%s, periodDirection=%s', mqid, metric, period, period_direction)\n\n return _fundamentals_md_query(mqid, period, period_direction, metric, source, real_time, request_id)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (AssetType.Index, AssetType.ETF, AssetType.Custom_Basket, AssetType.Research_Basket),\n [QueryType.REALIZED_CORRELATION],\n)\ndef realized_correlation(\n asset: Asset,\n tenor: str,\n top_n_of_index: Optional[int] = None,\n composition_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Realized correlation of an asset.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param top_n_of_index: the number of top constituents to take into account\n :param composition_date: YYYY-MM-DD or relative days before today e.g. 1d, 1m, 1y; defaults to most recent date\n available\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: realized correlation curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime realized_correlation not implemented')\n\n if top_n_of_index is None:\n if composition_date is not None:\n raise MqValueError('specify top_n_of_index to get realized correlation of top constituents')\n if asset.get_type() in (SecAssetType.CUSTOM_BASKET, SecAssetType.RESEARCH_BASKET):\n raise MqValueError('Baskets must have top_n_of_index set')\n\n _check_top_n(top_n_of_index)\n if top_n_of_index is not None and top_n_of_index > 100:\n raise MqValueError('number of constituents (top_n_of_index) must be <= 100')\n\n mqid = asset.get_marquee_id()\n _logger.debug('where tenor=%s', tenor)\n where = dict(tenor=tenor)\n\n if top_n_of_index is None:\n # no append last b/c there is no real-time Realized Correlation dataset\n q = GsDataApi.build_market_data_query(\n [mqid], QueryType.REALIZED_CORRELATION, where=where, source=source, real_time=real_time\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, QueryType.REALIZED_CORRELATION)\n\n # results for top n\n constituents = _get_index_constituent_weights(asset, top_n_of_index, composition_date)\n head_start = DataContext.current.start_date - dt.timedelta(days=relative_date_add(tenor, True) + 1)\n with DataContext(head_start, DataContext.current.end_date):\n asset_ids = constituents.index.to_list() + [mqid]\n q = GsDataApi.build_market_data_query(asset_ids, QueryType.SPOT, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n\n if not real_time and DataContext.current.end_date >= dt.date.today():\n df = append_last_for_measure(df, asset_ids, QueryType.SPOT, None, source=source, request_id=request_id)\n\n match = df['assetId'] == mqid\n df_asset = df.loc[match]\n df_rest = df.loc[~match]\n\n grouped = df_rest.groupby('assetId')\n weighted_vols = []\n for underlying_id, weight in zip(constituents.index, constituents['netWeight']):\n filtered = grouped.get_group(underlying_id) if underlying_id in grouped.indices else pd.DataFrame()\n filtered = filtered.loc[~filtered.index.duplicated(keep='last')]\n if not filtered.empty:\n vol = volatility(filtered['spot'], Window(tenor, tenor)) / 100\n assert not vol.empty, f'insufficient history to calculate for {tenor}'\n weighted_vols.append(vol * weight)\n\n if len(weighted_vols) == 0:\n raise MqValueError(f'no data for constituents of {asset}')\n\n # expected number of data points for each pricing date. All assets required, i.e. do not calculate\n # if constituent is missing on that day\n min_no_of_assets = top_n_of_index\n s1 = pd.concat(weighted_vols, axis=1).sum(axis=1, min_count=min_no_of_assets).dropna()\n s2 = pd.concat(map(lambda x: x * x, weighted_vols), axis=1).sum(axis=1, min_count=min_no_of_assets).dropna()\n\n idx_vol = volatility(df_asset['spot'], Window(tenor, tenor)) / 100\n idx_vol = idx_vol[idx_vol.index.isin(s1.index)]\n\n values = (idx_vol * idx_vol - s2) / (s1 * s1 - s2) * 100\n series = ExtendedSeries(values)\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.Commod, AssetClass.Equity, AssetClass.FX, AssetClass.Credit, AssetClass.Rates),\n None,\n [QueryType.SPOT],\n asset_type_excluded=(\n AssetType.CommodityEUNaturalGasHub,\n AssetType.CommodityNaturalGasHub,\n ),\n)\ndef realized_volatility(\n asset: Asset,\n w: Union[Window, int, str] = Window(None, 0),\n returns_type: Returns = Returns.LOGARITHMIC,\n pricing_location: Optional[PricingLocation] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Realized volatility for an asset.\n\n :param asset: asset object loaded from security master\n :param w: number of observations to use; defaults to length of series. If string is provided, should be\n in relative date form (e.g. \"1d\", \"1w\", \"1m\", \"1y\", etc).\n :param returns_type: returns type: logarithmic or simple\n :param pricing_location: EOD Location if multiple available Example - \"HKG\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: realized volatility curve\n \"\"\"\n\n if asset.asset_class != AssetClass.FX or real_time:\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], QueryType.SPOT, source=source, real_time=real_time\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = get_historical_and_last_for_measure(\n [asset.get_marquee_id()], QueryType.SPOT, {}, source=source, real_time=real_time, request_id=request_id\n )\n else:\n location = pricing_location if pricing_location else PricingLocation.NYC\n where = dict(pricingLocation=location.value)\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], QueryType.SPOT, where=where, source=source, real_time=real_time\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = get_historical_and_last_for_measure(\n [asset.get_marquee_id()], QueryType.SPOT, where, source=source, real_time=real_time, request_id=request_id\n )\n\n spot = df['spot']\n spot = spot[~spot.index.duplicated(keep='first')]\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(volatility(spot, w, returns_type))\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure((AssetClass.Equity,), None, [QueryType.ES_SCORE])\ndef esg_headline_metric(\n asset: Asset,\n metricName: EsgMetric = EsgMetric.ENVIRONMENTAL_SOCIAL_AGGREGATE_SCORE,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Environmental, Social, and Governance (ESG) scores and percentiles for a broad set of companies across the globe,\n including, but not limited to, companies covered by Global Investment Research (GIR) analysts.\n :param asset: asset object loaded from security master\n :param metricName: Name of the metric\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: esg data of the asset for the field requested\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time esg_headline_metric not implemented')\n mqid = asset.get_marquee_id()\n query_metric = inflection.camelize(metricName.value, False)\n query_type = ESG_METRIC_TO_QUERY_TYPE.get(query_metric)\n _logger.debug('where assetId=%s, metric=%s, query_type=%s', mqid, query_metric, query_type.value)\n q = GsDataApi.build_market_data_query([mqid], query_type, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[query_metric])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure((AssetClass.Equity,), (AssetType.Single_Stock,), [QueryType.RATING])\ndef rating(\n asset: Asset,\n metric: _RatingMetric = _RatingMetric.RATING,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Analyst Rating, which may take on the following values\n {'Sell': -1, 'Neutral': 0, 'Buy': 1}\n Conviction List, which is true if the security is on the Conviction Buy List or false otherwise.\n Securities with a convictionList value equal to true are by definition a subset of the securities\n with a rating equal to Buy.\n *Note that there may be periods when stock coverage has been suspended,\n or a stock is not actively rated by GIR for legal or policy reasons. The lack of an active rating\n for those dates may not be visible when graphed because the default visualization connects individual\n data points where an active rating did exist and does not indicate whether there was a time gap in\n active coverage between such points. Please refer to the Data Sheet tab for the specific dates and\n values that are available, or access the relevant research reports on the research portal to see\n rating history.\n\n :param asset: asset object loaded from security master\n :param metric: Name of metric. Either rating or conviction list\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: ratings in numeric form ( 'Buy' : 1, 'Neutral' : 0, 'Sell' : -1 )\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time rating not implemented')\n mqid = asset.get_marquee_id()\n query_type = _RATING_METRIC_TO_QUERY_TYPE[metric.value]\n _logger.debug('where assetId=%s, metric=%s, query_type=%s', mqid, metric.value, query_type.value)\n q = GsDataApi.build_market_data_query([mqid], query_type, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n series = _extract_series_from_df(df, query_type)\n if query_type == QueryType.RATING:\n series = series.replace(['Buy', 'Sell', 'Neutral'], [1, -1, 0]).astype('int64')\n return series\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Cross,),\n [MeasureDependency(id_provider=cross_to_usd_based_cross, query_type=QueryType.FAIR_VALUE)],\n)\ndef fair_value(\n asset: Asset,\n metric: EquilibriumExchangeRateMetric = EquilibriumExchangeRateMetric.GSDEER,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n GSDEER and GSFEER quarterly estimates for currency fair values made by Global Investment Research (GIR)\n macro analysts.\n :param asset: asset object loaded from security master\n :param metric: Name of metric. One of gsdeer, gsfeer\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: gsdeer/gsfeer data of the asset for the field requested\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time fair_value not implemented')\n mqid = asset.get_marquee_id()\n usd_based_cross_mqid = cross_to_usd_based_cross(mqid)\n ds = Dataset('GSDEER_GSFEER')\n start_date = dt.date.today() - pd.tseries.offsets.BDay(1)\n df = ds.get_data(assetId=usd_based_cross_mqid, start_date=start_date)\n latest_date = df.index.max()\n df = df.loc[latest_date]\n df.index = df.apply(lambda x: dt.date(int(x.year), (int(x.quarter[-1]) - 1) * 3 + 1, 1), axis=1)\n series = _extract_series_from_df(df, metric)\n if mqid != usd_based_cross_mqid:\n series = 1 / series\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure((AssetClass.Equity,), (AssetType.Single_Stock,), [QueryType.GROWTH_SCORE])\ndef factor_profile(\n asset: Asset,\n metric: _FactorProfileMetric = _FactorProfileMetric.GROWTH_SCORE,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n This dataset consists of Goldman Sachs Investment Profile (\"IP\") percentiles for US and Canadian securities\n covered by Goldman Sachs GIR analysts. Beginning in mid-2017, IP was renamed GS Factor Profile;\n the dataset provided hereunder refers to both IP and GS Factor Profile percentiles, and is referred\n to herein as \"GS Factor Profile\".\n Percentiles are generated daily, and historical values are available going back to January 1, 2009.\n *Note that there may be periods when stock coverage has been suspended, or a stock is not actively\n rated by GIR for legal or policy reasons. The lack of an active rating for those dates may not be\n visible when graphed because the default visualization connects individual data points where an active\n rating did exist and does not indicate whether there was a time gap in active coverage between such points.\n Please refer to the Data Sheet tab for the specific dates and values that are available, or access the\n relevant research reports on the research portal to see rating history.\n :param asset: asset object loaded from security master\n :param metric: Growth Score, i.e. Growth percentile relative to Americas coverage universe\n (a higher score means faster growth);\n Financial Returns Score, i.e. Financial Returns percentile relative to Americas coverage universe\n (a higher score means stronger financial returns);\n Multiple Score, i.e. Multiple percentile relative to Americas coverage universe\n (a higher score means more expensive);\n Integrated Score, i.e. Average of the Growth, Financial Returns and (1-Multiple) percentile\n (a higher score means more attractive)\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Factor Profile data of the asset for the field requested\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time factor_profile not implemented')\n\n mqid = asset.get_marquee_id()\n\n query_type = _FACTOR_PROFILE_METRIC_TO_QUERY_TYPE.get(metric.value)\n _logger.debug('where assetId=%s, metric=%s, query=%s', mqid, metric.value, query_type.value)\n q = GsDataApi.build_market_data_query([mqid], query_type, source=source, real_time=real_time)\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n series = _extract_series_from_df(df, query_type)\n\n return series\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (\n AssetType.Commodity,\n AssetType.Index,\n ),\n [QueryType.COMMODITY_FORECAST],\n)\ndef commodity_forecast(\n asset: Asset,\n forecastPeriod: str = \"3m\",\n forecastType: _CommodityForecastType = _CommodityForecastType.SPOT_RETURN,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Short and long-term commodities forecast.\n :param asset: asset object loaded from security master\n :param forecastPeriod: Relative, e.g. 3m, 6m, 12m; Quarterly (up to next two years), e.g. 2020Q3;\n Annual (up to next ten years), e.g. 2023\n :param forecastType: Type of return for commodity indices, spot for individual commodities.\n One of spotReturn/totalReturn/rollReturn/spot\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Forecast Value of the commodity or index as requested\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time commodity_forecast not implemented')\n mqid = asset.get_marquee_id()\n query_type = QueryType.COMMODITY_FORECAST\n _logger.debug(\n 'where assetId=%s, forecastPeriod=%s, forecastType=%s, query_type=%s',\n mqid,\n forecastPeriod,\n forecastType,\n query_type.value,\n )\n q = GsDataApi.build_market_data_query(\n [mqid],\n query_type,\n where=dict(forecastPeriod=str(forecastPeriod), forecastType=forecastType),\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id)\n series = _extract_series_from_df(df, query_type)\n return series\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (\n AssetType.Commodity,\n AssetType.Index,\n ),\n [QueryType.COMMODITY_FORECAST],\n)\ndef commodity_forecast_time_series(\n asset: Union[Asset, str],\n forecastFrequency: Union[\n str, _CommodityForecastTimeSeriesPeriodType\n ] = _CommodityForecastTimeSeriesPeriodType.ANNUAL,\n forecastType: _CommodityForecastType = _CommodityForecastType.SPOT,\n forecastHorizonYears: int = 12,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Short and long-term commodities forecast time series.\n\n :param asset: Asset object or string representing the asset ID\n :param forecastFrequency: Forecast period type (e.g., annual, quarterly)\n :param forecastType: Type of return for commodity indices\n :param forecastHorizonYears: Length of the forecast period in years\n :param source: Name of the function caller\n :param real_time: Whether to retrieve intraday data instead of EOD\n :param request_id: Service request ID, if any\n :return: Forecast time series for the commodity or index\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time commodity_forecast not implemented')\n if isinstance(asset, str):\n mqid = asset\n elif isinstance(asset, Asset):\n mqid = asset.get_marquee_id()\n else:\n raise ValueError(\"Asset must be of type Asset or str\")\n if isinstance(forecastFrequency, Enum):\n forecastFrequency = forecastFrequency.value\n query_type = QueryType.COMMODITY_FORECAST\n col_name = query_type.value.replace(' ', '')\n col_name = col_name[0].lower() + col_name[1:]\n\n today = dt.date.today()\n start_year = today.year\n end_year = start_year + forecastHorizonYears\n if forecastFrequency == _CommodityForecastTimeSeriesPeriodType.SHORT_TERM.value:\n periods = [\"3m\", \"6m\", \"12m\"]\n elif forecastFrequency == _CommodityForecastTimeSeriesPeriodType.MONTHLY.value:\n periods = [\n f\"{date.year}M{date.month}\"\n for date in pd.date_range(start=f\"{start_year}-01-01\", end=f\"{end_year}-01-01\", freq=\"MS\")\n ]\n elif forecastFrequency == _CommodityForecastTimeSeriesPeriodType.QUARTERLY.value:\n periods = pd.period_range(start=f\"{start_year}-01-01\", end=f\"{end_year}-01-01\", freq=\"Q\").strftime(\"%YQ%q\")\n elif forecastFrequency == _CommodityForecastTimeSeriesPeriodType.ANNUAL.value:\n periods = pd.date_range(start=f\"{start_year}-01-01\", end=f\"{end_year}-01-01\", freq=\"YS\").strftime(\"%Y\")\n else:\n raise ValueError(\n \"Invalid forecastFrequency. Must be one of '3/6/12-Month Rolling', 'Monthly', 'Quarterly', 'Annual'.\"\n )\n results = []\n for period in periods:\n _logger.debug(\n 'where assetId=%s, forecastPeriod=%s, forecastType=%s, query_type=%s',\n mqid,\n period,\n forecastType,\n query_type.value,\n )\n\n q = GsDataApi.build_market_data_query(\n [mqid],\n query_type,\n where=dict(forecastPeriod=period, forecastType=forecastType),\n source=source,\n real_time=real_time,\n )\n log_debug(request_id, _logger, 'q %s', q)\n\n df = _market_data_timed(q, request_id)\n if not df.empty:\n last_value = df[col_name].iloc[-1]\n if 'm' in period:\n months = int(period[:-1])\n start_of_period = pd.Timestamp.today() + relativedelta(months=months)\n elif \"Q\" in period:\n year = int(period[:4])\n quarter = int(period[-1])\n start_of_period = pd.Timestamp(f\"{year}-{(quarter - 1) * 3 + 1:02d}-01\")\n elif \"M\" in period:\n month = int(period[5:])\n start_of_period = pd.Timestamp(period[:4] + \"-01-01\")\n start_of_period = start_of_period.replace(month=month)\n else:\n start_of_period = pd.Timestamp(period[:4] + \"-01-01\")\n results.append({'date': start_of_period.strftime('%Y-%m-%d'), col_name: last_value})\n df = pd.DataFrame(results).set_index('date')\n series = _extract_series_from_df(df, query_type)\n return series\n\n\ndef _get_marketdate_validation(market_date, start_date, end_date, timezone=None):\n \"\"\"\n Validates the market date provided by the user\n @param market_date: date string provided by the user\n @param start_date: passed form data context\n @param end_date: passed form data context\n @param timezone: passed for power forward curve\n @return: modifies market_date, start_date if applied\n \"\"\"\n if not isinstance(market_date, str):\n raise MqTypeError('Market date should be of string data type as \\'YYYYMMDD\\'')\n # If no date entered by user, assign last weekday or convert entered string format to date object\n if len(market_date) == 0:\n market_date = pd.Timestamp.today().date()\n while market_date.weekday() > 4:\n market_date -= dt.timedelta(days=1)\n else:\n try:\n market_date = dt.datetime.strptime(market_date, \"%Y%m%d\").date()\n except ValueError:\n raise MqValueError('Market date should be of format as \\'YYYYMMDD\\'')\n\n # Check if market date is future date for the given asset\n if timezone:\n today_date = pd.Timestamp.today(tz=timezone).date()\n\n else:\n today_date = pd.Timestamp.today().date()\n if market_date > today_date:\n raise MqValueError('Market date cannot be a future date')\n # Check if market date is weekend\n if market_date.weekday() > 4:\n raise MqValueError('Market date cannot be a weekend')\n # Check if market date is within end and start date ranges\n if market_date > end_date:\n raise MqValueError('Market date should be within end date for query')\n if market_date > start_date:\n start_date = market_date\n\n return market_date, start_date\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (\n AssetType.Index,\n AssetType.CommodityPowerAggregatedNodes,\n AssetType.CommodityPowerNode,\n ),\n [QueryType.FORWARD_PRICE],\n)\ndef forward_curve(\n asset: Asset, bucket: str = 'PEAK', market_date: str = \"\", *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"\n Forward Curve for Futures Data\n\n :param asset: asset object loaded from security master\n :param bucket: bucket type among '7x24', 'Peak', 'Offpeak', '2x16h', '7x16' and '7x8': Default value = Peak\n :param market_date: Date for which forward curve to be plotted, format-YYYYMMDD: default value = today\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Term structure or price projection for future contracts for a given date\n \"\"\"\n if real_time:\n raise MqValueError('Forward Curve uses daily frequency instead of intraday')\n\n # Validations for date inputs\n start, end = DataContext.current.start_date, DataContext.current.end_date\n timezone = _get_iso_data(asset.name.split()[0])[0]\n market_date, start = _get_marketdate_validation(market_date, start, end, timezone)\n\n # Check if market date do not have an entry, a holiday or value not entered yet, if so get prev date value\n ds = Dataset('COMMOD_US_ELEC_ENERGY_FORWARD_PRICES')\n last_data = ds.get_data_last(as_of=market_date)\n last_date = pd.Timestamp(last_data.index.unique().values[0])\n if market_date > last_date.date():\n market_date = last_date\n\n # Find valid contracts and buckets for query, use until end of month for end date\n weights = _get_weight_for_bucket(asset, start, end + pd.tseries.offsets.MonthEnd(0), bucket)\n contracts_to_query = weights['contract'].unique().tolist()\n quantitybuckets_to_query = weights['quantityBucket'].unique().tolist()\n\n # Get data for given asset for given market date for valid contracts and buckets\n where = dict(quantityBucket=quantitybuckets_to_query, contract=contracts_to_query)\n with DataContext(market_date, market_date):\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], QueryType.FORWARD_PRICE, where=where, source=None, real_time=False\n )\n forward_price = _market_data_timed(q)\n dataset_ids = getattr(forward_price, 'dataset_ids', ())\n\n # If no data received\n if forward_price.empty:\n return ExtendedSeries(dtype=float)\n\n # Get plot dates for valid contract_months\n forward_dates_to_plot = dict()\n for month in contracts_to_query:\n start_date_month = _string_to_date_interval(month)[\"start_date\"]\n forward_dates_to_plot[month] = start_date_month\n\n # Create a dataframe with required columns\n forward_curve_plot = {\n 'contract': forward_price['contract'],\n 'forwardPrice': forward_price['forwardPrice'],\n 'quantityBucket': forward_price['quantityBucket'],\n }\n df = pd.DataFrame(forward_curve_plot)\n df = df.reset_index(drop=True)\n\n # Compute wtd average if required for combo buckets\n is_wtd_avg_required = True if len(quantitybuckets_to_query) > 1 else False\n if is_wtd_avg_required:\n df = pd.merge(df, weights, how='left', on=['contract', 'quantityBucket'])\n df['forwardPrice'] = df['forwardPrice'] * df['weight']\n df = df.groupby('contract').agg({'weight': 'sum', 'forwardPrice': 'sum'})\n df['forwardPrice'] = df['forwardPrice'] / df['weight']\n df = df.reset_index()\n\n # Map contract to plot date and format output\n df['contract'] = df['contract'].map(forward_dates_to_plot)\n df = df.sort_values(by=['contract'])\n df = df.set_index('contract')\n\n # Create Extended Series for return\n result = ExtendedSeries(df['forwardPrice'], index=df.index)\n result = result.rename_axis(None, axis='index')\n result.dataset_ids = dataset_ids\n\n return result\n\n\n@plot_measure(\n (AssetClass.Commod,), (AssetType.CommodityNaturalGasHub,), [QueryType.FORWARD_PRICE], display_name='forward_curve'\n)\ndef forward_curve_ng(\n asset: Asset, price_method: str = 'GDD', market_date: str = \"\", *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"\n Forward Curve for Us gas Data\n\n :param asset: asset object loaded from security master\n :param price_method: Price method for forward curve for us gas eg: GDD\n :param market_date: Date for which forward curve to be plotted, format-YYYYMMDD: default value = today\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Term structure or price projection for future contracts for a given date\n \"\"\"\n if real_time:\n raise MqValueError('Forward Curve uses daily frequency instead of intraday')\n\n # Validations for date inputs\n start, end = DataContext.current.start_date, DataContext.current.end_date\n market_date, start = _get_marketdate_validation(market_date=market_date, start_date=start, end_date=end)\n\n # Check if market date do not have an entry, a holiday or value not entered yet, if so get prev date value\n ds = Dataset('COMMOD_US_NG_FORWARD_PRICES')\n last_data = ds.get_data_last(as_of=market_date)\n last_date = pd.Timestamp(last_data.index.unique().values[0])\n if market_date > last_date.date():\n market_date = last_date\n\n # Find valid contracts, use until end of month for end date\n contracts_to_query_df = get_contract_range(start, end, timezone=None)\n contracts_to_query = contracts_to_query_df['contract_month'].unique().tolist()\n # Get data for given asset for given market date for valid contracts and buckets\n where = dict(contract=contracts_to_query, priceMethod=price_method.upper())\n with DataContext(market_date, market_date):\n q = GsDataApi.build_market_data_query(\n [asset.get_marquee_id()], QueryType.FORWARD_PRICE, where=where, source=None, real_time=False\n )\n forward_price = _market_data_timed(q)\n dataset_ids = getattr(forward_price, 'dataset_ids', ())\n\n # If no data received\n if forward_price.empty:\n return ExtendedSeries(dtype=float)\n\n # Get plot dates for valid contract_months\n forward_dates_to_plot = dict()\n for month in contracts_to_query:\n start_date_month = _string_to_date_interval(month)[\"start_date\"]\n forward_dates_to_plot[month] = start_date_month\n\n # Create a dataframe with required columns\n forward_curve_plot = {\n 'contract': forward_price['contract'],\n 'forwardPrice': forward_price['forwardPrice'],\n 'priceMethod': forward_price['priceMethod'],\n }\n df = pd.DataFrame(forward_curve_plot)\n df = df.reset_index(drop=True)\n\n # Map contract to plot date and format output\n df['contract'] = df['contract'].map(forward_dates_to_plot)\n\n df = df.sort_values(by=['contract'])\n df = df.set_index('contract')\n\n # Create Extended Series for return\n result = ExtendedSeries(df['forwardPrice'], index=df.index)\n result = result.rename_axis(None, axis='index')\n result.dataset_ids = dataset_ids\n\n return result\n\n\ndef _forward_price_eu_natgas(\n asset: Asset, contract_range: str = 'F20', price_method: str = 'ICE', *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"'\n EU NG Forward Price - function to map the assets to right instruments and fetch the forward\n prices for given hub for required contracts\n\n :param asset: asset object loaded from security master\n :param contract_range: e.g. inputs - Default Value = F20\n :param price_method: Fixing source for the prices - ICE/ Heren\n User to enter USD for the Heren prices. Whereas ICE prices are in EUR /GBP for diff assets so will be default\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Forward Price\n \"\"\"\n # Get weights for each contract month within range\n weights = get_weights_for_contracts(contract_range)\n weights = pd.DataFrame({'contract': weights.index, 'weight': weights.values})\n contracts_to_query = weights['contract'].unique().tolist()\n assets_to_query = []\n price_method = EUNatGasDataReference[price_method].value\n ref_price_key = '_'.join([asset.name, price_method, \"CommodityReferencePrice\"])\n asset_commod_ref_price = EUNatGasDataReference[ref_price_key].value\n\n # Find all assets to query for each of these contract months\n for contract in contracts_to_query:\n kwargs = dict(\n asset_parameters_commodity_reference_price=asset_commod_ref_price, asset_parameters_start=contract\n )\n try:\n instruments = GsAssetApi.get_many_assets(**kwargs)\n if len(instruments) == 1:\n assets_to_query.append(instruments[0].id)\n else:\n raise ValueError\n except ValueError:\n _logger.debug('Cannot find asset or found multiple assets for: %s', contract)\n\n # Get data for each asset for the specified duration\n q = GsDataApi.build_market_data_query(assets_to_query, QueryType.FORWARD_PRICE, source=source, real_time=False)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n dataset_ids = getattr(df, 'dataset_ids', ())\n\n # Get weighted average of each contract month\n if df.empty:\n result = ExtendedSeries(dtype='float64')\n else:\n df['dates'] = df.index.date\n result = _merge_curves_by_weighted_average(df, weights, ['contract', 'dates'], \"forwardPrice\")\n\n result.dataset_ids = dataset_ids\n\n return result\n\n\n@plot_measure((AssetClass.FX,), None, [QueryType.IMPLIED_VOLATILITY])\ndef fx_implied_correlation(\n asset: Asset,\n asset_2: Asset,\n tenor: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n FX implied correlation. Uses most recent date available if pricing_date is not provided.\n\n :param asset: asset object loaded from security master\n :param asset_2: FX cross used to calculate implied correlation eg. EURUSD.fx_implied_correlation(USDJPY, 3m)\n :param tenor: relative date representation of expiration date e.g. 1m\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: FX implied correlation curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime fx_implied_correlation not implemented')\n\n if asset_2.asset_class != AssetClass.FX:\n raise MqValueError('Only FX crosses supported for fx_implied_correlation')\n\n def get_bbid(asset):\n try:\n return next(o for o in asset._Entity__entity['identifiers'] if o['type'] == 'BID')['value']\n except Exception as e:\n log_debug(request_id, _logger, 'could not get BBID from asset', exc_info=e)\n return asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n\n cross_1 = get_bbid(asset)\n cross_2 = get_bbid(asset_2)\n invert_factor = 1\n\n # checks\n ccys = [cross_1[:3], cross_1[3:], cross_2[:3], cross_2[3:]]\n if len(ccys) <= len(set(ccys)):\n raise MqValueError('crosses must have common currency')\n\n # corr cross\n cross_3, base_ccy = '', ''\n for ccy in ccys:\n if ccys.count(ccy) == 1:\n cross_3 += ccy\n if ccys.count(ccy) == 2:\n base_ccy = ccy\n\n # marquee ids\n new_asset_1 = cross_stored_direction_for_fx_vol(asset, return_asset=True)\n new_asset_2 = cross_stored_direction_for_fx_vol(asset_2, return_asset=True)\n id_1 = new_asset_1.get_marquee_id()\n id_2 = new_asset_2.get_marquee_id()\n id_3 = SecurityMaster.get_asset(\n _cross_stored_direction_helper(cross_3), AssetIdentifier.BLOOMBERG_ID\n ).get_marquee_id()\n\n # normal crosses\n c_1 = get_bbid(new_asset_1)\n if c_1 != cross_1:\n invert_factor *= -1\n c_2 = get_bbid(new_asset_2)\n if c_2 != cross_2:\n invert_factor *= -1\n if c_1[:3] == base_ccy and c_2[:3] != base_ccy:\n invert_factor *= -1\n if c_1[3:] == base_ccy and c_2[3:] != base_ccy:\n invert_factor *= -1\n\n where = dict(tenor=tenor, strikeReference='delta', relativeStrike=0)\n q = GsDataApi.build_market_data_query(\n [id_1, id_2, id_3], QueryType.IMPLIED_VOLATILITY, where=where, source=source, real_time=real_time\n )\n log_debug(request_id, _logger, 'q %s', q)\n df = _market_data_timed(q, request_id=request_id)\n if df.empty:\n return pd.Series(dtype=float)\n\n df1 = df.loc[df['assetId'] == id_1]\n df2 = df.loc[df['assetId'] == id_2]\n df3 = df.loc[df['assetId'] == id_3]\n col = 'impliedVolatility'\n corr = invert_factor * (df1[col] * df1[col] + df2[col] * df2[col] - df3[col] * df3[col]) / (2 * df1[col] * df2[col])\n series = ExtendedSeries(corr, name='impCorr')\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\ndef get_last_for_measure(\n asset_ids: List[str],\n query_type,\n where,\n *,\n source: str = None,\n request_id: Optional[str] = None,\n ignore_errors: bool = False,\n):\n now = dt.date.today()\n delta = dt.timedelta(days=2)\n with DataContext(now - delta, now + delta):\n q_l = GsDataApi.build_market_data_query(\n asset_ids, query_type, where=where, source=source, real_time=True, measure='Last'\n )\n log_debug(request_id, _logger, 'q_l %s', q_l)\n\n try:\n df_l = _market_data_timed(q_l, request_id, ignore_errors=ignore_errors)\n except Exception as e:\n log_warning(request_id, _logger, f'unable to get last of {query_type}', exc_info=e)\n else:\n if df_l.empty:\n log_warning(request_id, _logger, f'no data for last of {query_type}')\n else:\n df_l.index = df_l.index.tz_convert(None).normalize()\n return df_l\n\n return None\n\n\ndef merge_dataframes(dataframes: List[pd.DataFrame]):\n if dataframes is None:\n return pd.DataFrame()\n result = pd.concat(dataframes)\n result[\"dummy\"] = result.index # drop_duplicates ignores indexes, so we need to include it as a column first\n result = result.drop_duplicates().drop(columns=[\"dummy\"])\n result = result.sort_index(kind='mergesort') # mergesort for its \"stable\" sort functionality\n setattr(result, 'dataset_ids', tuple(set(flatten(get(df, 'dataset_ids', ()) for df in dataframes))))\n return result\n\n\ndef append_last_for_measure(\n df: pd.DataFrame, asset_ids: List[str], query_type, where, *, source: str = None, request_id: Optional[str] = None\n):\n df_l = get_last_for_measure(asset_ids, query_type, where, source=source, request_id=request_id)\n if df_l is None:\n return df\n\n result = pd.concat([df, df_l])\n result[\"dummy\"] = result.index # drop_duplicates ignores indexes, so we need to include it as a column first\n result = result.drop_duplicates().drop(columns=[\"dummy\"])\n result.dataset_ids = tuple(set(getattr(df, 'dataset_ids', ())).union(getattr(df_l, 'dataset_ids', ())))\n return result\n\n\ndef get_market_data_tasks(\n asset_ids: List[str],\n query_type,\n where: dict,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n chunk_size: int = 5,\n ignore_errors: bool = False,\n parallelize_queries: bool = False,\n) -> list:\n tasks = []\n for i, chunked_assets in enumerate(chunk(asset_ids, chunk_size)):\n queries = GsDataApi.build_market_data_query(\n chunked_assets,\n query_type,\n where=where,\n source=source,\n real_time=real_time,\n parallelize_queries=parallelize_queries,\n )\n\n queries = [queries] if not isinstance(queries, list) else queries\n tasks.extend(partial(_market_data_timed, query, request_id, ignore_errors=ignore_errors) for query in queries)\n return tasks\n\n\ndef get_historical_and_last_for_measure(\n asset_ids: List[str],\n query_type,\n where: dict,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n chunk_size: int = 5,\n ignore_errors: bool = False,\n parallelize_queries: bool = False,\n):\n tasks = get_market_data_tasks(\n asset_ids,\n query_type,\n where,\n source=source,\n real_time=real_time,\n request_id=request_id,\n chunk_size=chunk_size,\n ignore_errors=ignore_errors,\n parallelize_queries=parallelize_queries,\n )\n\n if not real_time and DataContext.current.end_date >= dt.date.today():\n where_list = _split_where_conditions(where)\n for w in where_list:\n tasks.append(\n partial(\n get_last_for_measure,\n asset_ids=asset_ids,\n query_type=query_type,\n where=w,\n source=source,\n request_id=request_id,\n ignore_errors=ignore_errors,\n )\n )\n\n results = ThreadPoolManager.run_async(tasks)\n return merge_dataframes(results)\n\n\n@plot_measure(\n (AssetClass.Commod,),\n (AssetType.FutureMarket,),\n [QueryType.SETTLEMENT_PRICE],\n)\ndef settlement_price(asset: Asset, contract: str = 'F22', *, source: str = None, real_time: bool = False) -> pd.Series:\n \"\"\"\n Settlement price from exchanges for Commod FutureMarket\n\n :param asset: asset object loaded from security master\n :param contract: contract month for price. Ex: F22\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Settlement_Price from Dataset\n \"\"\"\n if real_time:\n raise MqValueError('Use daily frequency instead of intraday')\n\n # Find the right dataset based on the asset\n try:\n asset_parameters = asset.get_entity()['parameters']\n if len(asset_parameters):\n asset_exchange = asset_parameters['exchange']\n asset_product = asset_parameters['productGroup']\n\n if (\n asset_exchange == EUPowerDataReference.ICE_EXCHANGE.value\n and asset_product == EUPowerDataReference.CARBON_CREDIT_PRODUCT.value\n ):\n ds = Dataset(EUPowerDataReference.CARBON_CREDIT_DATASET.value)\n elif (\n asset_exchange == EUPowerDataReference.EEX_EXCHANGE.value\n or asset_exchange == EUPowerDataReference.ICE_EXCHANGE.value\n or asset_exchange == EUPowerDataReference.NASDAQ_EXCHANGE.value\n ) and asset_product == EUPowerDataReference.EU_POWER_PRODUCT.value:\n ds = Dataset(EUPowerDataReference.EU_POWER_EXCHANGE_DATASET.value)\n else:\n raise MqTypeError(\n 'Assets of exchange %s and group %s not handled currently', asset_exchange, asset_product\n )\n else:\n raise MqTypeError(\"No required parameter information on asset schema\")\n except (TypeError, ValueError, KeyError) as e:\n _logger.warning('Error while fetching asset information to choose dataset', exc_info=e)\n raise MqTypeError('Error: %s', str(e))\n\n where = dict(assetId=asset.get_marquee_id(), contract=contract)\n start, end = DataContext.current.start_date, DataContext.current.end_date\n result = ds.get_data(where=where, start=start, end=end)\n _logger.debug('q - Data queried from %s', ds.id)\n\n if result.empty:\n result = ExtendedSeries(dtype='float64')\n else:\n result = ExtendedSeries(result['settlementPrice'])\n\n result.dataset_ids = ds.id\n return result\n\n\n@plot_measure((AssetClass.Equity,), (AssetType.Index, AssetType.Single_Stock), [QueryType.SPOT])\ndef hloc_prices(\n asset: Asset,\n interval_frequency: IntervalFrequency = IntervalFrequency.DAILY,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.DataFrame:\n \"\"\"\n Get the hloc (high, low, open, and close) prices of the given asset.\n :param asset: asset object loaded from security master\n :param interval_frequency: frequency of the hloc\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD, real time is currently not supported\n :return: DataFrame with high, low, open, close columns\n \"\"\"\n if real_time:\n raise MqValueError('Use daily frequency instead of intraday.')\n start, end = DataContext.current.start_date, DataContext.current.end_date\n return asset.get_hloc_prices(start, end, interval_frequency)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (AssetType.Custom_Basket, AssetType.Research_Basket, AssetType.Index, AssetType.ETF),\n [QueryType.THEMATIC_MODEL_BETA],\n)\ndef thematic_model_exposure(\n asset: Asset, basket_identifier: str, notional: int = 10000000, *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"\n Thematic exposure of an asset to a requested GS thematic flagship basket\n\n :param asset: asset object loaded from security master\n :param basket_identifier: identifer of the requested basket (ticker, bbid, etc.)\n :param notional: Optional notional, will default to $10mm for if not entered\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD, real time is currently not supported\n :return: Timeseries of daily thematic exposure of asset to requested flagship basket\n \"\"\"\n if real_time:\n raise MqValueError('Use daily frequency instead of intraday.')\n start, end = DataContext.current.start_date, DataContext.current.end_date\n thematic_exposures = PositionedEntity.get_thematic_exposure(asset, basket_identifier, notional, start, end)\n return _extract_series_from_df(thematic_exposures, QueryType.THEMATIC_EXPOSURE)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (AssetType.Custom_Basket, AssetType.Research_Basket, AssetType.Index, AssetType.ETF, AssetType.Single_Stock),\n [QueryType.THEMATIC_MODEL_BETA],\n)\ndef thematic_model_beta(\n asset: Asset, basket_identifier: str, *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"\n Thematic beta values of an asset to a requested GS thematic flagship basket\n\n :param asset: asset object loaded from security master\n :param basket_identifier: identifer of the requested basket (ticker, bbid, etc.)\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD, real time is currently not supported\n :return: Timeseries of daily thematic beta of asset to requested flagship basket\n \"\"\"\n if real_time:\n raise MqValueError('Use daily frequency instead of intraday.')\n start, end = DataContext.current.start_date, DataContext.current.end_date\n\n if asset.get_type().value == AssetType.Single_Stock.value:\n thematic_betas = Stock.get_thematic_beta(asset, basket_identifier, start, end)\n else:\n thematic_betas = PositionedEntity.get_thematic_beta(asset, basket_identifier, start, end)\n return _extract_series_from_df(thematic_betas, QueryType.THEMATIC_BETA)\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (AssetType.Custom_Basket, AssetType.Research_Basket, AssetType.Index, AssetType.ETF),\n [QueryType.SPOT],\n)\ndef retail_interest_agg(\n asset: Asset,\n measure: RetailMeasures = RetailMeasures.RETAIL_PCT_SHARES,\n data_source: UnderlyingSourceCategory = UnderlyingSourceCategory.ALL,\n sector: GICSSector = GICSSector.ALL,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n Aggregates retail interest for specified Equity asset with underliers.\n :param sector: Any valid GICSSector like Information Technology, Energy etc. or All\n :param asset: Any index, basket, ETF with underliers.\n :param measure: Measures listed on https://marquee.gs.com/s/developer/datasets/RETAIL_FLOW_DAILY_V2_PREMIUM.\n :param data_source: One of EDGX (on Exchange), TRF (Off Exchange), ALL.\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD, real time is currently not supported\n :return: historical retail interest\n \"\"\"\n\n if real_time:\n raise NotImplementedError('realtime retail_interest_agg not implemented')\n\n start, end = DataContext.current.start_date, DataContext.current.end_date\n underliers = asset.entity['underlying_asset_ids']\n if len(underliers) == 0:\n raise MqValueError('Specified asset does not have underliers, single names have separate retail measures')\n\n if sector != GICSSector.ALL:\n data = pd.DataFrame(\n PositionedEntity.get_positions_data(\n asset, RelativeDate('-2b', base_date=end).apply_rule(), end, fields=['assetClassificationsGicsSector']\n )\n )\n underliers = list(data[data['assetClassificationsGicsSector'] == sector.value]['id'].drop_duplicates().values)\n\n ds = Dataset(Dataset.GS.RETAIL_FLOW_DAILY_V2_PREMIUM.value)\n retail_data = get_dataset_with_many_assets(\n ds, assets=underliers, start=start, end=end, underlyingSourceCategory=data_source\n )\n\n if retail_data.empty:\n return ExtendedSeries(dtype=float)\n\n if 'Pct' in measure.value:\n measures_to_sum = [measure.value.replace('Pct', '')]\n if 'Shares' in measure.value:\n measures_to_sum.append(RetailMeasures.SHARES.value)\n else:\n measures_to_sum.append(RetailMeasures.NOTIONAL.value)\n category_sums = retail_data.groupby([retail_data.index])[measures_to_sum].agg(\"sum\")\n category_sums[measure.value] = 100 * category_sums[measures_to_sum[0]] / category_sums[measures_to_sum[1]]\n category_sums.drop(columns=measures_to_sum)\n else:\n category_sums = retail_data.groupby([retail_data.index])[[measure.value]].agg(sum)\n series = ExtendedSeries(category_sums[measure.value], name=measure.value)\n series.dataset_ids = ds.id\n return series\n\n\nclass S3Metrics(Enum):\n LONG_CROWDING = (\"Long Crowding\",)\n SHORT_CROWDING = (\"Short Crowding\",)\n LONG_SHORT_CROWDING_RATION = (\"Long Short Crowding Ratio\",)\n CURRENT_CONSTITUENTS_LONG_CROWDING = (\"Current Constituents Long Crowding\",)\n CURRENT_CONSTITUENTS_SHORT_CROWDING = (\"Current Constituents Short Crowding\",)\n CURRENT_CONSTITUENTS_LONG_SHORT_CROWDING = (\"Current Constituents Long Short Crowding Ratio\",)\n CROWDING_NET_EXPOSURE = (\"Crowding Net Exposure\",)\n CURRENT_CONSTITUENTS_CROWDING_NET_EXPOSURES = \"Current Constituents Crowding Net Exposure\"\n\n\n@plot_measure(\n (AssetClass.Equity,),\n (\n AssetType.Custom_Basket,\n AssetType.Research_Basket,\n ),\n)\ndef s3_long_short_concentration(\n asset: Asset,\n s3Metric: S3Metrics = S3Metrics.LONG_CROWDING,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Retrieves the specified metric from the S3 Partners Long/Short Concentration Aggregated Data dataset\n for a given asset and plots it.\n\n :param asset: asset object loaded from security master\n :param s3Metric: The aggregated S3 metric to retrieve (e.g., \"Long Crowding\", \"Short Crowding\").\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: time series of the specified metric values\n \"\"\"\n ds = Dataset('S3_BASKETS_AGG')\n\n start = DataContext.current.start_date\n end = DataContext.current.end_date\n\n # Query the timeseries from the dataset:\n df = ds.get_data(start, end, assetId=asset.get_marquee_id(), s3Metric=s3Metric)\n\n # Extract the timeseries and format it for PTP\n return _extract_series_from_df(df, QueryType.S3_AGGREGATE_DATA)\n" }, { "path": "gs_quant/timeseries/measures_countries.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom enum import Enum\nfrom typing import Optional\n\nimport pandas as pd\nimport inflection\n\nfrom gs_quant.api.gs.data import QueryType, GsDataApi\nfrom gs_quant.data import Dataset\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.entities.entity import EntityType\nfrom gs_quant.timeseries import plot_measure_entity\nfrom gs_quant.timeseries.measures import _market_data_timed, _extract_series_from_df, ExtendedSeries\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass _FCI_MEASURE(Enum):\n FCI = \"fci\"\n LONG_RATES_CONTRIBUTION = \"long_rates_contribution\"\n SHORT_RATES_CONTRIBUTION = \"short_rates_contribution\"\n CORPORATE_SPREAD_CONTRIBUTION = \"corporate_spread_contribution\"\n SOVEREIGN_SPREAD_CONTRIBUTION = \"sovereign_spread_contribution\"\n EQUITIES_CONTRIBUTION = \"equities_contribution\"\n REAL_LONG_RATES_CONTRIBUTION = \"real_long_rates_contribution\"\n REAL_SHORT_RATES_CONTRIBUTION = \"real_short_rates_contribution\"\n DWI_CONTRIBUTION = \"dwi_contribution\"\n REAL_FCI = \"real_fci\"\n REAL_TWI_CONTRIBUTION = \"real_twi_contribution\"\n TWI_CONTRIBUTION = \"twi_contribution\"\n\n\n@plot_measure_entity(EntityType.COUNTRY, [QueryType.FCI])\ndef fci(\n country_id: str,\n measure: _FCI_MEASURE = _FCI_MEASURE.FCI,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Daily Financial Conditions Index (FCI) for each of the world's large economies and many smaller ones,\n as well as aggregate FCIs for regions.\n\n :param country_id: id of country/region\n :param measure: FCI metric to retrieve\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: FCI metric value\n \"\"\"\n if real_time:\n raise NotImplementedError('real-time FCI data is not available')\n\n type_ = QueryType(inflection.titleize(measure.value))\n start, end = DataContext.current.start_date, DataContext.current.end_date\n if measure == _FCI_MEASURE.REAL_FCI or measure == _FCI_MEASURE.REAL_TWI_CONTRIBUTION:\n ds = Dataset('FCI')\n df = ds.get_data(geographyId=country_id, start=start, end=end)\n if measure == _FCI_MEASURE.REAL_FCI:\n measure = 'realFCI'\n else:\n measure = 'realTWIContribution'\n series = ExtendedSeries(dtype=float) if (measure not in df.columns) else ExtendedSeries(df[measure])\n series.dataset_ids = ('FCI',)\n return series\n\n q = GsDataApi.build_market_data_query([country_id], query_type=type_, source=source, real_time=real_time)\n df = _market_data_timed(q, request_id)\n return _extract_series_from_df(df, type_, True)\n" }, { "path": "gs_quant/timeseries/measures_factset.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nfrom enum import Enum\nfrom typing import Union, Optional\n\nimport pandas as pd\n\nfrom gs_quant.common import AssetClass, AssetType\nfrom gs_quant.data import DataContext, Dataset\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.securities import AssetIdentifier, Asset\nfrom gs_quant.timeseries import RelativeDate\nfrom gs_quant.timeseries.helper import plot_measure\nfrom gs_quant.timeseries.measures import ExtendedSeries\n\n\nclass EstimateItem(Enum):\n EPS = 'Earnings Per Share'\n EPS_C = 'Consolidated EPS'\n EPS_P = 'Standalone Earnings Per Share'\n SALES = 'Sales'\n SALES_C = 'Consolidated Sales'\n SALES_P = 'Standalone Sales'\n DPS = 'Declared Dividends Per Share'\n CFPS = 'Cash Flow Per Share'\n PRICE_TGT = 'Target Price'\n EPS_LTG = 'Long Term Growth'\n AFFO = 'Adjusted Funds From Operations'\n ASSETS = 'Total Assets'\n BFNG = 'Net Income Reported'\n BPS = 'Book Value Per Share'\n BPS_TANG = 'Tangible Book Value Per Share'\n CAPEX = 'Capital Expenditure'\n CF_FIN = 'Cash Flow From Financing'\n CF_INV = 'Cash Flow From Investing'\n CF_OP = 'Cash Flow From Operations'\n CURRENT_ASSETS = 'Current Assets'\n CURRENT_LIABILITIES = 'Current Liabilities'\n DEFREVENUE_LT = 'Deferred Revenue Long Term'\n DEFREVENUE_ST = 'Deferred Revenue Short Term'\n DEP_AMORT = 'Depreciation and Amortization'\n EBIT = 'EBIT'\n EBIT_ADJ = 'BEIT Adjusted'\n EBIT_C = 'Consolidated EBIT'\n EBIT_P = 'Standalone EBIT'\n EBITA = 'EBITA'\n EBITDA = 'EBITDA'\n EBITDA_ADJ = 'EBITDA Adjusted'\n EBITDA_REP = 'EBITDA Reported'\n EBITDAR = 'EBITDAR'\n EBITR = 'EBIT Reported'\n EPS_EX_XORD = 'Earnings Per Share Excluding Exceptionals'\n EPS_GAAP = 'Reported Earnings Per Share'\n EPS_NONGAAP = 'Earnings Per Share Non GAAP'\n EPSAD = 'Diluted Adjusted EPS'\n EPSRD = 'Diluted Reported EPS'\n FCF = 'Free Cash Flow'\n FCFPS = 'Free Cash Flow Per Share'\n FFO = 'Funds From Operations'\n G_A_EXP = 'General and Administrative Expense'\n GW_TOT = 'Total Goodwill'\n HEPSB = 'Headline Basic EPS'\n HEPSD = 'Headline Diluted EPS'\n INC_GROSS = 'Gross Income'\n INT_EXP = 'Interest Expense'\n INVENTORIES = 'Inventories'\n MAINT_CAPEX = 'Maintenance CAPEX'\n NDT = 'Net Debt'\n NET = 'Net Profit'\n NET_C = 'Consolidated Net Income'\n NET_P = 'Standalone Net Income'\n NET_SALES = 'Net Sales'\n NETBG = 'Net Profit Adjusted'\n ORGANICGROWTH = 'Organic Growth'\n PTI = 'Pre Tax Profit'\n PTI_C = 'Consolidated Pretax Income'\n PTIAG = 'Pre Tax Profit Reported'\n PTP_P = 'Standalone Pretax Income'\n PTPA = 'Pre Tax Income Adjusted'\n RD_EXP = 'Research and Development'\n REV_TOT = 'Total Revenues'\n S_M_EXP = 'Selling and Marketing Expense'\n SGA = 'Selling General and Administrative Expense'\n SH_EQUITY = 'Shareholders Equity'\n SOE = 'Stock Option Expense'\n TAX_EXPENSE = 'Tax Expense'\n TOTAL_DEBT = 'Total Debt'\n\n\nclass EstimateStatistic(Enum):\n MEAN = 'Mean'\n MEDIAN = 'Median'\n HIGH = 'High'\n LOW = 'Low'\n ACTUAL = 'Actual'\n\n\nclass EstimateBasis(Enum):\n ANN = 'Annual'\n QTR = 'Quarterly'\n SEMI = 'Semi annual'\n NTM = 'Next Twelve Months'\n STM = 'Second Twelve Months'\n\n\nclass FundamentalBasis(Enum): # todo: add LTM as a choice and routing logic to LTM global datasets\n ANN = 'Annual'\n QTR = 'Quarterly'\n SEMI = 'Semi annual'\n\n\nclass FundamentalBasicItem(Enum):\n SALES = 'SalesRevenue'\n NON_INT_INC = 'Non Interest Income'\n OPER_EXP_OTH = 'Other Operating Expense'\n OPER_INC = 'Operating Income'\n LOAN_LOSS_PROV = 'Loan Loss Provision'\n PTX_XORD_CHRG = 'Extraordinary Charge Pretax'\n PTX_INC = 'Pretax Income'\n MIN_INT_EXP = 'Minority Interest Expense'\n NET_INCOME = 'Net Income incl Discontinued Operations'\n CASH_ST = 'Cash Short Term Investments'\n CASH_DUE_FR_BK = 'Total Cash Due from Banks'\n PREM_RECEIV = 'Premium Balance Receivables'\n INVEN = 'Inventories'\n ASSETS_OTH_INTANG = 'Other Assets Intangible'\n ASSETS = 'Total Assets'\n LIABS_CURR = 'Total Current Liabilities'\n PAY_ACCT = 'Accounts Payable'\n INS_LIABS_POL = 'Insurance Policy Liabilities Insurance'\n PROV_RISK = 'Provision for Risks Charges'\n LIABS_XMIN_INT_ACCUM = 'Liabilities excl Minority Interest'\n COM_EQ = 'Common Equity Total'\n DEBT_LT_CONV = 'Convertible Debt'\n TIER1_CAP = 'Tier 1 Capital'\n INT_PAY = 'Interest Payable'\n SECS_INVEST = 'Investment Securities'\n DEP_EXP_CF = 'Depreciation Depletion Amortization Cash Flow'\n NON_CASH = 'Other Funds'\n XORD_CF = 'Extraordinary Item'\n ACQ_BUS_CF = 'Net Assets from Acquisitions'\n LOAN_DECR_CF = 'Decrease in Loans'\n INVEST_ACTIV_CF = 'Investing Activities Other Uses'\n DIV_CF = 'Cash Dividends Paid'\n DEPS_INCR_CF = 'Increase in Deposits'\n CHG_CASH_CF = 'Net Change in Cash'\n EBIT_OPER_PS = 'Operating Profit Per Share'\n BPS = 'Book Value Per Share'\n COM_SHS_OUT_EPS_DIL = 'Common Shares Used to Calculate EPS Fully Diluted'\n PRICE_CLOSE_FP = 'Market Price Fiscal Period Close'\n ACTG_STANDARD = 'Code indicating the accounting standards that the company follows'\n CONSOL_NET_INC = 'Consolidated Net Income'\n INCR_FED_HOME_ADV_CF = 'Federal Home Loan Advances Increase Decrease'\n COM_SHS_OUT_EPS = 'Common Shares Used to Calculate EPS'\n NOTES_RECEIV_LT = 'Long Term Note Receivable'\n DFD_TAX = 'Deferred Taxes'\n LIABS_SHLDRS_EQ = 'Liabilities Stockholders Equity Total'\n INVEST_AFF = 'Investment in Unconsolidated Subsidiaries'\n INVEST_RE = 'Real Estate Assets'\n RSRV_NONEQ = 'Non Equity Reserves'\n RECEIV_ST = 'Total Accounts Receivable Short Term'\n DPS_GROSS = 'Dividends Per Share Gross'\n RSRV_CHG = 'Reserves Increase Decrease'\n DFD_TAX_CR = 'Deferred Tax Liabilities'\n COM_EQ_RETAIN_EARN = 'Retained Earnings'\n INVEN_LIFO = 'Inventories LIFO Reserve'\n LOAN_CHG_CF = 'Proceeds from Loans'\n DEP_CHG_CF = 'Change in Deposits'\n DPS_ALL = 'Dividends Per Share Normal Extra and Special Dividends'\n MISC_NET_OTH = 'Other After Tax Income Expense'\n INT_INC = 'Interest Income'\n GROSS_INC = 'Gross Income'\n OPER_EXP_TOT = 'Operating Expenses Total'\n INT_EXP_TOT = 'Total Interest Expense Banks'\n NON_INT_EXP = 'Non Interest Expense'\n PTX_XORD_CR = 'Extraordinary Credit Pretax'\n INC_TAX = 'Income Taxes'\n EQ_AFF_INC = 'Equity in Earnings of Affiliates'\n DIV_PFD = 'Preferred Dividends'\n CASH_ONLY = 'Cash Only'\n BK_INVEST_TOT = 'Total Investments Banks'\n RECEIV_TOT = 'Total Accounts Receivable'\n ASSETS_CURR = 'Total Current Assets'\n INTANG = 'Intangible Assets'\n DEPS = 'Total Deposits'\n DEBT_ST = 'Short Term Debt incl Current Portion of LTD'\n ACCR_EXP_XPAYR = 'Accrued Expenses Excl Accrued Payroll'\n DEBT_LT = 'Long Term Debt'\n DFD_TAX_ITC = 'Deferred Taxes Investment Tax Credits'\n PFD_STK = 'Preferred Stock Carrying Value'\n DEBT = 'Total Debt'\n SHLDRS_EQ = 'Total Shareholders Equity'\n TIER2_CAP = 'Tier 2 Capital'\n SECS_CUSTODY = 'Securities In Custody'\n NET_INC_CF = 'Net Income Cash Flow'\n DFD_TAX_CF = 'Deferred Taxes Investment Tax Credit'\n FUNDS_OPER_GROSS = 'Funds from Operations'\n WKCAP_CHG = 'Changes in Working Capital'\n LOAN_INCR_CF = 'Increase in Loans'\n SALE_ASSETS_BUS_CF = 'Sale of Fixed Assets Businesses'\n INVEST_SOURCES_CF = 'Investing Activities Other Sources'\n DEPS_DECR_CF = 'Decrease in Deposits'\n FOR_EXCH_CF = 'Exchange Rate Effect'\n DPS = 'Dividends Per Share'\n EPS_REPORTED = 'Earnings Per Share As Reported'\n COM_SHS_OUT_EPS_BASIC = 'Common Shares Used to Calculate EPS Basic'\n COM_SHS_OUT = 'Common Shares Outstanding'\n INVEST_YLD_5YAVG = 'Yield On Investment 5 Year Average'\n FY_LENGTH_DAYS = 'Fiscal Period Length Days'\n EQ_TOT = 'Total Equity'\n EPS_BASIC = 'EPS Basic Before Extraordinaries'\n EXP_TOT = 'Total Expense'\n PAY_TAX = 'Income Tax Payable'\n MIN_INT_ACCUM = 'Accumulated Minority Interest Total'\n LOAN_NET = 'Net Loans'\n CUST_ACCEPT = 'Customer Liability on Acceptances'\n PPE_NET = 'Net Property Plant Equipment'\n DEPS_CUST = 'Total Customer Deposits'\n ADJ_NET_OTH = 'Other After Tax Adjustments'\n DIV_PAY_OUT_PS = 'Dividend Payout Per Share'\n DFD_TAX_DB = 'Deferred Tax Assets'\n RECEIV_INT = 'Interest Receivables'\n CASH_RESTR = 'Restricted Cash'\n RENT_INC = 'Rental Income'\n INVEST_ACTIV_OTH = 'Investing Activities Other Funds'\n ASSETS_NONPERF = 'Total Non Performing Assets'\n BK_COM_EQ_TIER1_TOT = 'Common Equity Tier 1 Total'\n\n\nclass FundamentalBasicDerivedItem(Enum):\n COGS = 'Cost of Goods Sold COGS including Depreciation Amortization'\n INT_EXP_NET = 'Interest Expense'\n NET_INC_BASIC = 'Net Income Avail to Common Basic'\n NON_OPER_INC = 'Non Operating Income Expense'\n SGA_OTH_EXP = 'Selling General Admin Expense Other'\n OPER_INC_AFT_INT = 'Operating Income After Interest Expense'\n ASSETS_CURR_OTH = 'Other Current Assets'\n ASSETS_OTH = 'Other Assets'\n LIABS = 'Total Liabilities'\n CAPEX = 'Capital Expenditures Total'\n INVEST_USES_CF = 'Other Uses'\n STK_CHG_CF = 'Change in Capital Stock'\n FIN_ACTIV_OTH_CF = 'Other Funds Financing Cashflow'\n MISC_FUNDS_CF = 'Miscellaneous Funds'\n EPS_DIL_BEF_UNUSUAL = 'EPS Diluted Before Unusual Expense'\n COM_EQ_ASSETS = 'Common Equity Total Assets'\n DEBT_ASSETS = 'Total Debt Total Assets'\n GROSS_MGN = 'Gross Profit Margin'\n NET_MGN = 'Net Margin'\n PAY_OUT_RATIO = 'Dividend Payout Earnings Total Dollar'\n ROA = 'Return On Average Assets'\n ROTC = 'Return On Average Total Capital'\n NET_DEBT = 'Net Debt'\n INT_INC_NET = 'Net Interest Income'\n TURN_RATE = 'Turnover Rate'\n EPS_DIL = 'EPS Fully Diluted'\n LIABS_CURR_OTH = 'Other Current Liabilities'\n PBK = 'Price To Book Value Closing Price As Of Last Trading Day'\n PE = 'Price To Earnings Closing Price As Of Fiscal Period End'\n EBIT_OPER = 'EBIT Operating Income'\n ENTRPR_VAL = 'Enterprise Value Using Diluted Shares'\n FREE_CF_MINUS_DIV = 'Free Cash Flow to Equity Minus Dividends'\n EARN_PERST = 'Earnings Persistence'\n UNUSUAL_EXP = 'Unusual Expense'\n NET_INC = 'Net Income'\n INT_INC_AFT_PROV = 'Net Interest Income after Loan Loss Provision'\n LOSS_CLAIM_RSRV = 'Losses Claims Reserves'\n OPER_INC_BEF_INT = 'Operating Income Before Interest Expense'\n ASSET_TURN = 'Asset Turnover'\n INVEST_ADV = 'Total Investments and Advances'\n LIABS_OTH = 'Other Liabilities'\n OPER_CF = 'Net Operating Cash Flow'\n INVEST_PURCH_SALE_CF = 'PurchaseSale of Investments'\n INVEST_CF = 'Net Investing Cash Flow'\n DEBT_CF = 'IssuanceReduction of Debt Net'\n FIN_CF = 'Net Financing Cash Flow'\n FREE_CF_FCFE = 'Free Cash Flow to Equity'\n CAPEX_SALES = 'Capital Expenditure Total Sales'\n CURR_RATIO = 'Current Ratio'\n EBITDA_OPER = 'EBITDA'\n MKT_VAL = 'Market Value using Period End Price'\n OPER_MGN = 'Operating Margin'\n QUICK_RATIO = 'Quick Ratio'\n ROE = 'Return On Average Total Equity'\n TCAP = 'Total Capital'\n PTX_MGN = 'Pretax Margin'\n INT_MGN = 'Net Interest Margin'\n BPS_TANG = 'Book Value Per Share Tangible'\n SGA = 'Selling General Administrative Expense'\n SALES_PS = 'Sales Per Share'\n PCF = 'Price to Cash Flow Ratio Closing Price as of Fiscal Period End'\n PSALES = 'Price To Sales Closing Price As Of Last Trading Day'\n NET_INC_AFT_XORD = 'Net Income After Extraordinary Items'\n\n\nclass FundamentalAdvancedItem(Enum):\n ACCR_EXP_CF = 'Other Accruals'\n AMORT_CF = 'Amortization of Intangible Assets'\n AMORT_INTANG = 'Amortization of Intangibles'\n ASSETS_OTH_TANG = 'Tangible Other Assets'\n AUD_FEES = 'Auditor Fees'\n BONDS_BELOW_INVGR = 'Bonds Below Investment Grade'\n COM_EQ_APIC = 'Additional Paid In Capital Capital Surplus incl Deferred Compensation'\n COM_EQ_GL = 'Unrealized GainLoss Marketable Securities'\n COM_EQ_OTH_COMPR_ADJ_OTH = 'Accumulated Other Comp Income Other Adjustments'\n COM_EQ_UNEARN_COMP = 'Comprehensive Income Unearned Compensation'\n COM_SHS_OUT_SECS = 'Common Shares Outstanding Security'\n COM_SHS_TRADE_WK = 'Common Shares Traded Weekly'\n COMMISS_INC = 'Commission Fee Income'\n CURN_DOC = 'Code representing the currency in which the companys financial statements are presented'\n DEBT_LT_NON_CONV = 'Non Convertible Debt'\n DEP_EXP = 'Depreciation'\n DEP_EXP_XAMORT_CF = 'DepreciationDepletion excl Amortization'\n DEPS_FOR = 'Foreign Office Deposits'\n DEPS_UNSPEC = 'Unspecified Deposits'\n DFD_TAX_ASSETS_ST = 'Deferred Income Tax Current Asset'\n DISC_OPER_INC = 'Discontinued Operations Income'\n DIV_PFD_CF = 'Preferred Dividends Cash Flow'\n DPS_SECS = 'Dividends Per Share Security'\n EPS = 'Earnings Per Share Fiscal Period'\n EPS_CONTIN_OPER = 'Earnings Per Share from Continuing Operations Fiscal'\n EPS_SECS = 'Earnings Per Share Security'\n EPS_XORD = 'Earnings Per Share incl Extraordinary Items Fiscal'\n FED_FUNDS = 'Federal Funds Sold'\n FOR_EXCH_BS = 'Exchange Rate Used For Translating Balance Sheet'\n FOR_EXCH_IS = 'Exchange Rate used for Translating Income Statement Cash Flow'\n GW = 'Net Goodwill'\n GW_GROSS = 'Goodwill Gross'\n INC_TAX_CURR_FOR = 'Income Tax Current Foreign'\n INC_TAX_DFD_FOR = 'Income Tax Deferred Foreign'\n INS_INVEST_INC = 'Total Investment Income Insurance'\n INS_LIABS_OTH = 'Other Insurance Liabilities'\n INS_RSRV = 'Insurance Reserves Insurance'\n INT_EXP_FED_REPOS = 'Interest Expense Federal Funds'\n INT_INC_DEPS = 'Interest Income Bank Deposits'\n INT_INC_FED_REPOS = 'Interest Income Government Securities'\n INT_INC_OTH = 'Other Interest or Dividend Income'\n INTANG_OTH_GROSS = 'Other Intangible Assets Gross'\n INVEN_FG = 'Inventories Finished Goods'\n INVEN_WIP = 'Inventories Work In Progress'\n INVEST_BONDS = 'Bonds'\n INVEST_EQ = 'Total Equity Securities Investment'\n INVEST_INC = 'Investment Income'\n INVEST_MTGE = 'Mortgage Policy Other Loans'\n INVEST_PFD_NRED = 'Non Redeemable Preferred Stock Investment'\n INVEST_SUB = 'Investment In Unconsolidated Subsidiaries'\n LABOR_EXP = 'Labor Related Expense'\n LIABS_PS = 'Long Term Liabilities Per Share'\n LOAN_BK = 'Interbank Loans'\n LOAN_COMML_INDL = 'Commercial Industrial Loans'\n LOAN_FOR = 'Foreign Loans'\n LOAN_LOSS_ACT = 'Net Loan Losses'\n LOAN_NONPERF = 'Non Performing Loans'\n MATL_EXP = 'Material Expense'\n MIN_PENS_LIABS_ADJ = 'Minimum Pension Liability Adjustment'\n NET_CAP_REQUIRE = 'Net Capital Requirement'\n OPER_EXP = 'Total Operating Expense Financial Services'\n OPER_INC_INTL = 'International Operating Income'\n OPT_WRNT_CF = 'Proceeds from Stock Options'\n PAY_ACCT_CF = 'Accounts Payable Cash Flow'\n PAY_TAX_DFD_TAX = 'Income Tax Payable Plus Short Term Deferred Tax'\n PFD_STK_ESOP_GTD = 'ESOP Guarantees Preferred Stock'\n PFD_STK_RED = 'Redeemable Preferred Stock'\n PPE_DEP_BLDG = 'Accumulated Depreciation Buildings'\n PPE_DEP_EQUIP = 'Accumulated Depreciation Machinery Equipment'\n PPE_DEP_LEASED_PROP = 'Accumulated Depreciation Leased Property'\n PPE_DEP_OTH = 'Accumulated Depreciation Other Property Plant Equip'\n PPE_DEP_TRANS_EQUIP = 'Accumulated Depreciation Transportation Equipment'\n PPE_GROSS_CONSTR = 'Property Plant Equipment Construction in Progress'\n PPE_GROSS_LAND = 'Property Plant Equipment Land Improvements'\n PPE_GROSS_LEASES = 'Property Plant Equipment Leases'\n PPE_GROSS_SOFT_EQUIP = 'Property Plant Equipment Computer Software and Equipment'\n PREM_UNEARN = 'Unearned Premiums'\n REAL_GAIN = 'Realized Securities Gain'\n RECEIV_NET = 'Accounts Receivables Net'\n RESTRUCT_PS = 'Restructuring Exp Per Share Net Of Tax Fiscal'\n RSRV_APPR_OTH = 'Other Appropriated Reserves'\n RSRV_UNAPPR = 'Unappropriated Free Reserves'\n SALES_UNCON = 'UnconsolidatedParent Company Sales'\n SECS_GAIN = 'Securities Gain'\n SECS_OTH = 'Other Securities'\n SECS_TREAS = 'Treasury Securities'\n SHS_CLOSELY_HELD_PCT = 'Closely Held Shares'\n STK_SPLIT_RATIO = 'Stock SplitDividend Ratio'\n TOT_INVEST_RET = 'Total Investment Return'\n TRADE_INC = 'Trading Account Income'\n TREAS_STK = 'Treasury Stock Common incl ESOP'\n TRUST_INC = 'Trust Income'\n VOLUME_TRADE = 'Trading Volume'\n WKCAP_ASSETS_OTH = 'Other AssetLiabilities'\n XORD_PS = 'Extraordinary CreditCharge Per Share'\n CASH_GENERIC = 'Cash Equivalents Generic'\n REINS_ADJ_RSRV = 'Reinsurance Adjustment Reserves'\n COMPR_INC_PENS_LIABS = 'Comprehensive Income Pension Liability'\n GW_IMPAIR = 'Impairment Of Goodwill'\n OPER_LEASE_EXP = 'RentalOperating Lease Expense'\n PENS_ASSETS_BONDS = 'Pension Plan Asset Allocation Bonds'\n PENS_ASSETS_LT = 'Pension Long Term Assets'\n PENS_ASSETS_PROP = 'Pension Plan Asset Allocation Property'\n PENS_BNFIT_OBLIG_PROJ = 'Pension Projected Benefit Obligation'\n PENS_COST_INT = 'Pension Interest Cost'\n PENS_COST_SERV_PRIOR = 'Pension Prior Service Costs'\n PENS_DISCOUNT_RATE = 'Pension Discount Rate'\n PENS_EXP_NPP_NET_OTH = 'Pension Other Periodic Pension IncomeExpense'\n PENS_GAIN_SETTLE = 'Pension GainsLosses from Settlements'\n PENS_LIABS_NET = 'Pension Net LiabilityAsset'\n PENS_ROA = 'Pension Expected Return on Plan Assets for Period'\n PENS_ROA_LT = 'Pension Expected Long Term Rate of Return on Assets'\n PPE_IMPAIR = 'Property Plant Equipment Impairment'\n ITC = 'Income Tax Credits'\n CAP_RATIO_TIER1 = 'Capital Adequacy Ratio Tier 1 Capital'\n COMP_SOFT = 'Computer Software'\n OPER_LEASE_COMMIT_5YR = 'Lease Commitments Over 5 Years'\n OPER_LEASE_COMMIT_YR2 = 'Lease Commitments Year 2'\n OPER_LEASE_COMMIT_YR4 = 'Lease Commitments Year 4'\n STK_OPT_EXP = 'Stock Option Compensation Expense'\n PRICE_SECS = 'Market Price Year End'\n PBK_SECS = 'Price to Book Security Level'\n BPS_SECS = 'Books Value Per Share Security Level'\n HLTH_FUNDED_STATUS = 'Healthcare Data Funded Status'\n DEBT_BONDS_CONV_SR = 'Bonds Senior Convertible'\n DEBT_BONDS_CONV_SUBORD_JR = 'Bonds Junior Subordinated Convertible'\n DEBT_BONDS_COVERED = 'Bonds Covered'\n DEBT_BONDS_MTGE1 = 'Bonds First Mortgage'\n DEBT_BONDS_OTH = 'Bonds Other Borrowings'\n DEBT_BONDS_SECURE_LIEN2 = 'Bonds Sec 2nd Lien'\n DEBT_BONDS_SUBORD = 'Bonds Subordinated'\n DEBT_BONDS_SUBORD_SR = 'Bonds Senior Subordinated'\n DEBT_CDO = 'Collateralized Debt Obligations'\n DEBT_LOAN_ADV_FFCB = 'FFCB Advances'\n DEBT_LOAN_FACIL_DIP = 'Term Loans DIP Facility'\n DEBT_LOAN_OTH = 'Loans Other Borrowings'\n DEBT_LOAN_SECURE_LIEN1 = 'Term Loans Sec 1st Lien'\n DEBT_LOAN_SECURE_LIEN3 = 'Term Loans Sec 3rd Lien'\n DEBT_LOAN_TOT = 'Total Term Loans'\n DEBT_LOAN_UNSEC_DELAY_DRAW = 'Term Loans Unsecured Delayed Drawdown'\n DEBT_LT_CONV_PFD = 'Preferreds Convertible LT Debt'\n DEBT_LT_REVOLV_FACIL_AB = 'Asset Backed Facility'\n DEBT_LT_REVOLV_SECURE = 'Revolvers Sec Line'\n DEBT_LT_REVOLV_SECURE_CURR = 'Short Term Revolver LT Debt Secured'\n DEBT_LT_REVOLV_TOT = 'Total Revolving Credit'\n DEBT_LT_REVOLV_UNSECURE_CP = 'Commercial Paper LT Debt Unsecured'\n DEBT_LT_REVLV_UNSEC_FCL_OTH = 'Revolvers Other Unsecured Facility'\n DEBT_NOTES_BONDS_TOT = 'Total NotesBonds'\n DEBT_NOTES_SURPLUS_SUBORD = 'Bonds Subordinate Surplus Notes'\n DEBT_OTH_CAPL = 'Capital Leases'\n DEBT_OTH_TOT = 'Total Other Debt'\n DEBT_RECEIV_CR_CARD = 'Credit Card Receivables'\n DEBT_RECEIV_MTGE_COMML = 'Commercial Mortgage Receivables'\n DEBT_RECEIV_OTH = 'Other Receivables'\n DEBT_REVOLV_UNSECURE_CURR = 'Short Term Borrowings from LT Unsecured Revolvers'\n DEBT_ST_ADJ = 'Adjustments Short Term'\n DEBT_ST_ADV_FHLB = 'ST FHLB Advances'\n DEBT_ST_CURR_PORT = 'Current Portion of LTD'\n DEBT_ST_NOTES_PAY = 'Notes Payable'\n DEBT_ST_REPOS = 'Repurchase Agreements'\n DEBT_ST_SECURE_OTH = 'Other Borrowings Secured'\n DEBT_ST_STN_BANK = 'Bank STNs'\n DEBT_ST_UNSECURE_OTH = 'Other Borrowings Unsecured'\n DEBT_TRUST_PFD = 'Preferreds Trust'\n IMPAIR_PPE = 'Fixed Asset Impairment'\n XCEPT_PROV = 'Exceptional Provisions'\n GW_WDOWN = 'Goodwill Write Off'\n IMPAIR_INTANG_OTH = 'Other Intangible Asset Impairment'\n OTH_UNUSUAL_EXP = 'Other Unusual Expense'\n INT_INC_NON_OPER = 'Non Operating Interest Income'\n MISC_EXP_NON_OPER = 'Other Income Expense'\n INT_CAP = 'Interest Capitalized'\n PREP_EXP = 'Prepaid Expenses'\n PPE_GROSS = 'Property Plant Equipment Gross'\n PAY_DIV = 'Dividends Payable'\n LIABS_CURR_MISC = 'Miscellaneous Current Liabilities'\n INVEST_SALE_CF = 'SaleMaturity of Investments'\n STK_PURCH_CF = 'Repurchase of Common Preferred Stock'\n DEBT_ST_CF = 'Change in Current Debt'\n CAPEX_OTH = 'Capital Expenditures Other Assets'\n DEBT_LT_REDUCT_CF = 'Reduction of LT Debt'\n FIN_SOURCES_CF = 'Other Financing Activities Sources'\n DFD_INC = 'Deferred Income'\n INVEST_PURCH_CF = 'Purchase of Investments'\n DEP_AMORT_EXP = 'Depreciation Amortization'\n OPER_INC_OTH = 'Other Operating Income'\n ORDINARY_INC = 'Ordinary Income Japan'\n HLTH_PBO = 'Healthcare Projected Benefit Obligation'\n HLTH_UNREC_SERV_COST = 'Healthcare Unrecognized Prior Service Cost'\n HLTH_ROA_EXPECTED = 'Healthcare Expected Return on Plan Assets'\n HLTH_ROA_LT = 'Healthcare Expected Long Term Return Rate on Pension Assets'\n HLTH_INT_COST = 'Healthcare Interest Cost'\n HLTH_GL_SETTLE = 'Healthcare GainLoss from Settlements'\n HLTH_NPP_EXP = 'Health Care Expense'\n DEBT_ST_LT_TOT = 'Total Short Term and Long Term Debt'\n DEBT_ST_TOT = 'Total Short Term Debt'\n LOAN_LOSS_RECOV = 'Loan Losses Recoveries'\n PREM_EARN = 'Premiums Earned'\n STK_OPT_CF = 'Stock Based Compensation Cash Flow'\n PENS_ASSETS_BEG_BAL = 'Beg Balance Assets'\n PENS_ASSETS_INT_INC = 'Interest IncomeExpected Return on Pension Assets'\n PENS_BNFIT_GL_ACTRL = 'Actuarial LSGN'\n PENS_ASSETS_CONTR = 'Contributions'\n PENS_BNFIT_PTCP_CONTR = 'Benefits Participant Contributions'\n PENS_BNFIT_PAID = 'Benefits Paid'\n PENS_BNFIT_ACQ_DVST = 'Pension Benefits AcquisitionDivestiture'\n PENS_EXCH_RATE_DIFF = 'Pension Assets Exchange Rate Difference'\n PENS_BNFIT_EXCHRATE_DIFF = 'Benefits Exchange Rate Diff'\n PENS_BNFIT_OTH_CHG = 'Other Benefits Changes'\n PENS_ASSETS_TOTAL = 'Asset Allocation Total'\n PENS_BNFT_SRVCST_PRIOR_CURR = 'Pension Benefits CurrentPrior Service Cost'\n PENS_BNFIT_SERVCOST_PRIOR = 'Benefits Prior Service Cost'\n PENS_BNFIT_INT_COST = 'Benefits Interest Cost'\n APBO_ALLOC_CCE = 'APBO Allocation Cash Cash Equivalents'\n APBO_ALLOC_DEBT = 'APBO Asset Allocation Debt'\n APBO_ALLOC_OTH = 'APBO Asset Allocation Other'\n HLTH_CONTR = 'APBO Contributions'\n HLTH_PTCP_CONTR = 'APBO Health Participant Contributions'\n HLTH_BNFIT_PAID = 'APBO Health Benefits Paid'\n HLTH_ACQ_DVST = 'APBO Health AcquisitionDivestiture'\n HLTH_CRTL_SETTLE_AMEND = 'APBO Health CurtailSettleAmend'\n HLTH_EXCH_RATE_DIFF = 'APBO Health Exchange Rate Difference'\n HLTH_OTH_CHG = 'APBO Health Other Changes'\n HLTH_ACTRL_GL = 'APBO Health Actuarial GNLS'\n HLTH_ACT_RET_ASSET = 'APBO Actual Return Assets'\n HLTH_APBO_SRVCST_PRIOR_CURR = 'APBO CurrentPrior Service Cost'\n HLTH_APBO_SERVCOST_PRIOR = 'APBO Prior Service Cost'\n HLTH_SERV_COST_TOT = 'APBO Service Cost Total'\n HLTH_GL_ACTRL = 'Healthcare Actuarial LSGN'\n UNREAL_GL_TOT = 'Unrealized Investment GainLoss Total'\n DFD_INC_CURR = 'Deferred Income Current'\n TAX_CHG_EFF = 'Effect of Tax Code Changes'\n GA_EXP = 'General Administrative Expense'\n MKT_EXP = 'Marketing Expense'\n LIABS_OPER_LEASE_CURR = 'Operating Lease Liabilities Current Portion'\n PPE_NET_OWNED = 'Property Plant and Equipment Owned net'\n DEBT_LT_XLEASE = 'Long Term Debt Excluding Lease Liabilities'\n AMORT_EXP_LEASE = 'Amortization of Leased Assets'\n FRANK_BAL_CF = 'Franking Balance for Australian Companies'\n OPER_LEASE_COMMIT_2YR_5YR = 'Operating Lease Commitments 2 to 5 Years'\n DIR_FIN_LEASE = 'Sales and Direct Financing Leases'\n ACCEL_DEP = 'Accelerated Depreciation'\n EARLY_TERM_CONTRACT = 'Early Termination of Contracts'\n UNREAL_GL_PROP = 'Unrealized GainLoss Investment Property'\n UNREAL_GL_DERIV = 'Unrealized GainLoss HedgesDerivatives'\n UNREAL_GL_OTH = 'Unrealized GainLoss Other Intangibles'\n ACTG_CHG_PS = 'Accounting Change Per Share Cum Effect Fiscal'\n AMORT_DFD_CHRG = 'Amortization of Deferred Charges'\n ASSETS_INTL = 'International Assets'\n ASSETS_SEP_ACCTS = 'Separate Variable Account Assets'\n BDEBT = 'Bad Debt Doubtful Accounts'\n CAP_LEASE = 'Capitalized Lease Obligations'\n COM_EQ_FOR_EXCH = 'Cumulative Translation Adjustment'\n COM_EQ_HEDG_GL = 'Comprehensive Income Hedging GainLoss'\n COM_EQ_PAR = 'Common Stock ParCarry Value'\n COM_SHS_AUTH = 'Number Of Shares Authorized'\n COM_SHS_TRADE = 'Shares Total Common Traded'\n COM_STK_ESOP = 'ESOP Debt Guarantee'\n COST_DEBT = 'Weighted Cost Of Debt'\n DEBT_LT_CURR = 'Current Portion Of Long Term Debt'\n DEBT_LT_XCAP = 'Long Term Debt Excluding Capitalized Leases'\n DEP_EXP_UNCON = 'UnconsolidatedParent Company Depreciation'\n DEPS_DEMAND = 'Demand Deposits'\n DEPS_SAV = 'SavingsTime Deposits'\n DFD_CHRG = 'Deferred Charges'\n DFD_TAX_XITC_CF = 'Deferred Taxes excl Investment Tax Credits'\n DISC_OPER_PS = 'Discontinued Operations Per Share Total'\n EMP_NUM = 'Number of employees'\n EPS_AFT_XORD = 'Earnings Per Share After Extraordinary Items'\n EPS_HEADLINE_UK = 'Headline Earnings Per Share UK'\n EPS_UNCON = 'UnconsolidatedParent Company Earnings Per Share'\n EQUIP_EXP = 'Equipment Expense'\n FOR_CURN_ADJ = 'Foreign Currency Adjustment Net'\n FOR_EXCH_INC = 'Foreign Exchange Gains'\n GAIN_SALE_ASSETS_NET = 'GainsLoss On Disposal Of Assets'\n GW_AMORT = 'Accumulated Goodwill Amortization'\n INC_TAX_CURR_DOM = 'Income Tax Current Domestic'\n INC_TAX_DFD_DOM = 'Income Tax Deferred Domestic incl local'\n INC_UNEARN = 'Unearned Income'\n INS_INVEST_TOT = 'Total Investment Assets Insurance'\n INS_LT_RSRV = 'Long Term Insurance Reserves'\n INT_EXP_DEPS = 'Interest Expense On Bank Deposits'\n INT_EXP_OTH_BORR = 'Other Borrowed Funds'\n INT_INC_FED_FUNDS = 'Interest Income Federal Funds'\n INT_INC_LOAN = 'Interest Income Loans'\n INTANG_OTH_AMORT = 'Accumulated Other Intangible Amortization'\n INVEN_CF = 'Inventories Cash Flow'\n INVEN_MATL = 'Inventories Raw Materials'\n INVEST = 'Investments Total'\n INVEST_COM_EQ = 'Common Stocks'\n INVEST_FIX_INC = 'Total Fixed Income Securities Investment'\n INVEST_LT_OTH = 'Other Investments Long Term'\n INVEST_OTH = 'Other Investments'\n INVEST_PFD_RED = 'Redeemable Preferred Stock Investment'\n ITC_CF = 'Investment Tax Credits'\n LIABS_CURR_DFD_TAX = 'Short Term Deferred Income Tax'\n LIFE_INS = 'Life Insurance In Force'\n LOAN_BRKR = 'Broker Financial Institution Loans'\n LOAN_CONS = 'Consumer Installment Loans'\n LOAN_LEASE_FIN = 'Lease Financing Loans'\n LOAN_MTGE = 'Real Estate Mortgage Loans'\n LOAN_OTH = 'UnspecifiedOther Loans'\n MBS = 'Mortgage Backed Securities'\n MON_CORRECT = 'Monetary Correction'\n NUM_SHRHLDRS = 'Number of Shareholders'\n OPER_INC_BEF_DEP = 'Operating Income Before Depreciation And Amortization'\n OPER_PROV = 'Operating Provisions'\n PAR_PS = 'Par Value'\n PAY_TAX_CF = 'Income Taxes Payable'\n PFD_STK_ESOP = 'Preferred Stock Issues for ESOP'\n PFD_STK_NRED = 'Non Redeemable Preferred Stock'\n POL_CLAIMS = 'Policy Claims'\n PPE_DEP_CONSTR = 'Accumulated Depreciation Construction in Progress'\n PPE_DEP_LAND = 'Accumulated Depreciation Land Improvements'\n PPE_DEP_LEASES = 'Accumulated Depreciation Leases'\n PPE_DEP_SOFT_EQUIP = 'Accumulated Depreciation Computer Software and Equipment'\n PPE_GROSS_BLDG = 'Property Plant Equipment Buildings'\n PPE_GROSS_EQUIP = 'Property Plant Equipment Machinery Equipment'\n PPE_GROSS_LEASED_PROP = 'Property Plant Equipment Leased Property'\n PPE_GROSS_OTH = 'Other Property Plant Equipment'\n PPE_GROSS_TRANS_EQUIP = 'Property Plant Equipment Transportation Equipment'\n PREM_WRITTEN = 'Net Premiums Written'\n RECEIV_CF = 'Accounts Receivable'\n RESTRUCT_EXP = 'Restructuring Expense'\n RESTRUCT_PS_PTAX = 'Restructuring Expense Per Share Pretax'\n RSRV_REVAL = 'Revaluation Reserves'\n SALES_INTL = 'International Sales'\n SECS_FED = 'Federal Agency Securities'\n SECS_MUNI = 'State Municipal Securities'\n SECS_RESALE = 'Securities Bought Under Resale Agreements'\n SHS_CLOSELY_HELD = 'Shares Number Of Closely Held'\n STK_SALE_XOPT_CF = 'Other Proceeds from SaleIssuance of Stock'\n TAX_NON_INC = 'Taxes Other than Income Taxes'\n TRADE_ACCT = 'Trading Account Securities'\n TREAS_SHS = 'Treasury Shares Number of Common Reacquired'\n TRUST_COMMISS_INC = 'Trust Fiduciary Income Commissions Fees'\n US_GAAP_AVAIL = 'US Information Available GAAP'\n VOLUME_WK_AVG = 'Trading Volume Weekly Average'\n WKCAP_PS = 'Working Capital Per Share'\n XORD_PS_PTAX = 'Extraordinary CreditCharge Per Share Pretax'\n ASSETS_RISK_WGHT = 'Risk Weighted Assets'\n INVEN_PROG_PAYMT = 'Inventories Progress Payments Other'\n FIX_ASSETS_IMPAIR = 'Impairment Of Financial Fixed Assets'\n INTANG_OTH_IMPAIR = 'Impairment Of Other Intangibles'\n RESTATE_IND = 'Flag indicating whether restated data exists'\n PENS_ABO = 'Pension Accumulated Benefit Obligation'\n PENS_ASSETS_EQ = 'Pension Plan Asset Allocation Equities'\n PENS_ASSETS_OTH = 'Pension Plan Asset Allocation Other'\n PENS_ASSETS_VAL = 'Pension Fair Value of Plan Assets'\n PENS_BNFIT_RETIR_POST = 'PensionPost Retirement Benefits'\n PENS_COST_SERV = 'Pension Service Costs'\n PENS_COST_SERV_PRIOR_UNREC = 'Pension Unrecognized Prior Service Cost'\n PENS_EXP_NPP_NET = 'Pension Expense'\n PENS_FUND_ADJ_OTH = 'Pension Other Adjustments To Funded Status For Net PensionPost'\n PENS_GAIN_UNREC = 'Pension Unrecognized Net Actuarial GainLoss'\n PENS_LIABS_UNFUNDED = 'Unfunded Pension Liability Supplementary'\n PENS_ROA_ACT = 'Pension Actual Return on Plan Assets'\n PENS_FUNDED_STATUS = 'Pension Funded Status'\n TAX_CF = 'Cash Taxes Paid'\n ACTG_CHG = 'Cumulative Effect of Accounting Change'\n CAP_RATIO_TOT = 'Capital Adequacy Ratio Total Capital'\n INT_CF = 'Interest Paid Cash Flow'\n OPER_LEASE_COMMIT_YR1 = 'Lease Commitments Year 1'\n OPER_LEASE_COMMIT_YR3 = 'Lease Commitments Year 3'\n OPER_LEASE_COMMIT_YR5 = 'Lease Commitments Year 5'\n STK_OPT_EXP_ADJ = 'Stock Option Based Compensation Exp Adjusted To Net Income'\n MKT_VAL_SECS = 'Market Value Security Level'\n PE_SECS = 'Price to Earnings Security Level'\n HLTH_BNFIT_UNFUNDED = 'Healthcare Data Unfunded Defined Benefits'\n DEBT_BONDS_CONV_JR = 'Bonds Junior Convertible'\n DEBT_BONDS_CONV_SUBORD = 'Bonds Subordinated Convertible'\n DEBT_BONDS_CONV_SUBORD_SR = 'Bonds Senior Subordinated Convertible'\n DEBT_BONDS_JR = 'Bonds Junior'\n DEBT_BONDS_MUNI = 'Bonds Municipal Taxable'\n DEBT_BONDS_SECURE_LIEN1 = 'Bonds Sec 1st Lien'\n DEBT_BONDS_SECURE_LIEN3 = 'Bonds Sec 3rd Lien'\n DEBT_BONDS_SUBORD_JR = 'Bonds Junior Subordinated'\n DEBT_BONDS_UNSECURE_SR = 'Bonds Senior Unsecured'\n DEBT_GTD_FDIC = 'FDIC Guaranteed Debt TLGP'\n DEBT_LOAN_ADV_FHLB = 'FHLB Advances'\n DEBT_LOAN_MTGE = 'Mortgage Loans'\n DEBT_LOAN_SECURE_DELAY_DRAW = 'Term Loans Sec Delayed Drawdown'\n DEBT_LOAN_SECURE_LIEN2 = 'Term Loans Sec 2nd Lien'\n DEBT_LOAN_SECURE_PLACE_PRIV = 'Private Placement Notes Secured'\n DEBT_LOAN_UNSECURE = 'Term Loans Unsecured'\n DEBT_LOAN_UNSEC_PLACE_PRIV = 'Private Placement Notes Unsecured'\n DEBT_LT_REPOS = 'Repurchase Agreement LT Debt'\n DEBT_LT_REVOLV_FACIL_DIP = 'Revolvers DIP Facility'\n DEBT_LT_REVOLV_SECURE_CP = 'Commercial Paper LT Debt Secured'\n DEBT_LT_REVLV_SEC_FACIL_OTH = 'Revolvers Other Sec Facility'\n DEBT_LT_REVOLV_UNSECURE = 'Revolvers Unsecured'\n DEBT_LT_REVLV_UNSEC_CURR = 'Short Term Revolver LT Debt Unsecured'\n DEBT_LT_STRAIGHT_PFD = 'Preferreds Straight LT Debt'\n DEBT_NOTES_SURPLUS_SR = 'Bonds Senior Surplus Notes'\n DEBT_OTH_ADJ = 'Adjustments Other'\n DEBT_OTH_LT_CURR = 'Current Portion of Other LTD'\n DEBT_RECEIV_AUTO = 'Auto Receivables'\n DEBT_RECEIV_EQUIP_TRUST = 'Equipment Trust Receivables'\n DEBT_RECEIV_MTGE_RES = 'Residential Mortgage Receivables'\n DEBT_REVOLV_SECURE_CURR = 'Short Term Borrowings from LT Sec Rev'\n DEBT_SEC_FACIL_SWING_SBRD = 'Swing Line Sub Facility Secured'\n DEBT_ST_ADV_FFCB = 'ST FFCB Advances'\n DEBT_ST_BORR_FED_RSRV = 'Federal Reserve Borrowings TAF'\n DEBT_ST_GTD_FDIC_CP = 'Commercial Paper FDIC Gtd TLGP'\n DEBT_ST_PURCH_FED_FUNDS = 'Fed Funds Purchased'\n DEBT_ST_SECURE_ABS_CP = 'Commercial Paper Secured ABS'\n DEBT_ST_SECURE_REVOLV = 'Short Term Revolver Secured'\n DEBT_ST_UNSECURE_CP = 'Commercial Paper Unsecured'\n DEBT_ST_UNSECURE_REVOLV = 'Short Term Revolver Unsecured'\n DEBT_UNSEC_FACIL_SWING_SBRD = 'Swing Line Sub Facility Unsecured'\n FIN_ASSETS_IMPAIR = 'Financial Asset Impairment'\n REORG_RESTRUCT_EXP = 'Reorganization and Restructuring Expense'\n LEGAL_CLAIM_EXP = 'Legal Claim Expense'\n UNREAL_INVEST_GL = 'Unrealized Investment GainLoss'\n DISC_OPER = 'Discontinued Operations'\n EQ_AFF_INC_PTX = 'Pretax Equity in Earnings of Affiliates'\n INT_EXP_DEBT = 'Gross Interest Expense'\n XORD_ITEMS = 'Extraordinary GainsLosses from Sale of Asset'\n ASSETS_CURR_MISC = 'Miscellaneous Current Assets'\n PPE_DEP = 'Accumulated Depreciation'\n ACCR_PAYR = 'Accrued Payroll'\n DFD_TAX_RSRV = 'Deferred Tax Liability Untaxed Reserves'\n DIV_COM_CF = 'Common Dividends'\n STK_SALE_CF = 'Sale of Common Preferred Stock'\n CAPEX_FIX = 'Capital Expenditures Fixed Assets'\n DEBT_LT_ISS_CF = 'Issuance of LT Debt'\n FIN_USES_CF = 'Other Financing Activities Uses'\n LIABS_OTH_XDFD_REV = 'Other Liabilities excluding Deferred Revenue'\n LOAN_RSRV = 'Loan Loss Allowances Reserves'\n COGS_XDEP = 'COGS excluding DA'\n RD_EXP = 'Research Development'\n INVEST_ST_OTH = 'Other Short Term Investments'\n HLTH_ASSETS_PLAN_FAIR_VAL = 'Healthcare Fair Value of Plan Assets'\n HLTH_ABO = 'Healthcare Accumulated Benefit Obligation'\n HLTH_ROA_ACT = 'Healthcare Actual Return on Plan Assets'\n HLTH_DISCOUNT_RATE = 'Healthcare Discount Rate'\n HLTH_SERV_COST = 'Healthcare Service Costs'\n HLTH_AMORT_SERV_COST = 'Healthcare Amortization of Prior Service Costs'\n HLTH_OTH_NPP_EXP = 'Healthcare Net Periodic Plan Expense Other'\n DEBT_LT_NET = 'Net Long Term Debt'\n DEBT_LT_TOT = 'Total Long Term Debt'\n LOAN_LOSS_NET = 'Loan Losses Net'\n LOSS_CLAIM_EXP = 'Losses Benefits Adjustments'\n UNDERWRITING_EXP = 'Underwriting Commissions'\n PENS_BNFIT_PBO_BB = 'Beg Balance PBO'\n PENS_ASSETS_ACTRL_GL = 'Actuarial GNLS'\n PENS_ASSETS_ACT_RET = 'Actual Return Assets'\n PENS_ASSETS_EMPLR_CONTR = 'Assets Employer Contributions'\n PENS_ASSETS_BNFIT_PAID = 'Assets Benefits Paid'\n PENS_ASSETS_ACQ_DVST = 'Pension Assets AcquisitionDivestiture'\n PENS_ASSETS_AMEND = 'Pension Assets CurtailmentSettlementAmendment'\n PENS_COST_EXCH_RATE_DIFF = 'Pension Benefit Cost Exchange Rate Difference'\n PENS_ASSETS_OTH_CHG = 'Other Changes'\n PENS_BNFIT_SETTLE = 'CurtailmentSettlement'\n PENS_ASSETS_CCE = 'Asset Allocation CCE'\n PENS_BNFIT_SERVCOST_CURR = 'Pension Benefits Current Service Cost'\n PENS_COST_SERV_TOT = 'Service Cost Total'\n PENS_COST_FIN = 'Finance CostIncome'\n APBO_ALLOC_EQ = 'APBO Asset Allocation Equity'\n APBO_ALLOC_PROP = 'APBO Asset Allocation Property'\n APBO_ALLOC_TOT = 'APBO Asset Allocation Total'\n HLTH_EMPLR_CONTR = 'APBO Health Employer Contributions'\n HLTH_APBO_PTCP_CONTR = 'APBO Participant Contribution'\n HLTH_APBO_BNFIT_PAID = 'APBO Benefits Paid'\n HLTH_APBO_ACQ_DIV = 'APBO AcquisitionDivestiture'\n HLTH_APBO_CURTAILM_SETTLEM = 'APBO CurtailmentsSettlements'\n HLTH_APBO_EXCH_RATE_DIFF = 'APBO Exchange Rate Difference'\n HLTH_APBO_OTH_CHG = 'APBO Other Changes'\n HLTH_APBO_ACTRL_GL = 'APBO Actuarial LSGN'\n HLTH_APBO_LIAB_BB = 'APBO Beg Balance Liabilities'\n HLTH_APBO_SERVCOST_CURR = 'APBO Current Service Cost'\n HLTH_APBO_INT_COST = 'APBO Interest Cost'\n HLTH_FIN_COST = 'APBO Finance CostInc'\n HLTH_PERIOD_EXCH_RATE_DIFF = 'APBO Exchange Rate Difference Period'\n ADV_EXP = 'Advertising Expense'\n SALE_PPE_CF = 'Sale of Property Plant and Equipment'\n ACQ_PROCESS_RD = 'Acquired In Process RD'\n SELL_EXP = 'Selling Marketing Expense'\n LIABS_OPER_LEASE = 'Operating Lease Liabilities'\n ASSETS_LEASE_NET = 'Operating Lease Right of Use Assets'\n DEBT_ST_XOPER = 'Short Term Debt and Current Portion of Long Term Debt Excluding Lease Liabilities'\n DEBT_LT_XOPER = 'Long Term Debt excl Operating Lease Liabilities'\n INT_EXP_LEASE = 'Interest Expense on Leased Assets'\n ASSETS_NONPERF_OTH = 'Other Non Performing Assets'\n OPER_LEASE_COMMIT_TOT = 'Total Operating Lease Commitments'\n DEBT_OTH_CF = 'Other Cash Flow Debt'\n CALAMITOUS_EVENT = 'Calamitous Event'\n EPS_HEADLINE_UK_DIL = 'Diluted Headline Earnings Per Share'\n UNREAL_GL_BIO_ASSETS = 'Unrealized GainLoss Biological Assets'\n UNREAL_GL_INVEST = 'Unrealized GainLoss Investments'\n\n\nclass FundamentalAdvancedDerivedItem(Enum):\n ACCR_EXP = 'Accrued Expenses'\n ASSETS_EQ = 'Assets To Equity End Of Period'\n ASSETS_OTH_TOT = 'Other Assets Total'\n BNFIT_LOSS_RSRV_TCAP = 'Benefit Loss Reserves Of Total Capital'\n CAPEX_5YGR = 'Capital Spending 5Yr Growth Rate'\n CAPEX_FIX_ASSETS = 'Capital Expenditure Gross Fixed Assets'\n CASH_CURR_ASSETS = 'Cash Equivalents Total Current Assets'\n CASH_DIV_COVG_RATIO = 'Cash Dividend Coverage Ratio'\n CASH_SECS_DEPS = 'Cash Securities Total Deposits'\n CF_SALES = 'Cash FlowSales'\n COGS_SALES = 'Cost of Goods Sold Sales'\n COM_EQ_GR = 'Equity 1 Year Annual Growth'\n DEBT_COM_EQ = 'Debt Total Common Equity'\n DEBT_EQ = 'Total Debt Total Equity'\n DEBT_ST_X_CURR_PORT = 'Short Term Debt'\n DEP_ACCUM_FIX_ASSETS = 'Accumulated Depreciation Gross Fixed Assets'\n DFD_TAX_ASSETS_LT = 'Deferred Income Tax Non Current Asset'\n DIV_YLD = 'Dividend Yield Close'\n DPS_GR = 'Dividends Per Share 1 Year Annual Growth'\n EARN_ASSETS_EFF = 'Efficiency Of Earning Assets'\n EARN_YLD = 'Earnings Yield Close'\n EBIT_OPER_ROA = 'Return On Average Assets EBIT'\n EBITDA_OPER_MGN = 'EBITDA Margin'\n EMP_GR = 'Employees 1 Year Annual Growth'\n ENTRPR_VAL_EBITDA_OPER = 'Enterprise Value To EBITDA'\n EPS_BASIC_GR = 'EPS Basic Before Extras Change'\n FED_REPOS_ASSET = 'Federal Funds Sold Securities Purchased'\n EBIT_OPER_FIX_CHRG_COVG = 'Fixed Charge Coverage Ratio'\n FOR_ASSETS_PCT = 'Foreign Assets Total Assets'\n FOR_INC_PCT = 'Foreign Income Total Income'\n FOR_ROA = 'Foreign Return On Assets'\n FREE_PS_CF = 'Cash Flow Per Share Diluted Free'\n INC_ADJ = 'Income Adjustment'\n INC_TAX_CURR = 'Income Tax Current Total'\n INS_RSRV_GR = 'Insurance Reserves 1 Year Annual Growth'\n INT_EXP_IB_LIABS = 'Interest Expense Total Interest Bearing Liabilities'\n INT_INC_AVG_DEPS = 'Net Interest Income Average Deposits'\n INTANG_OTH = 'Net Other Intangibles'\n INVEN_DAYS = 'Inventories Days Held'\n INVEST_ASSETS_DEPS = 'Invested Assets Total Deposits'\n INVEST_ASSETS_LOAN_DEPS = 'Invested Assets Loans Total Deposits'\n INVEST_CAP = 'Invested Capital Total'\n INVEST_LT = 'Long Term Investments'\n INVEST_ST_TOT = 'Total Short Term Investments'\n INVEST_YLD = 'Yield On Investment'\n LOAN_LOSS_ACTUAL_RSRV = 'Actual Loan Losses Reserves For Loan Losses'\n LOAN_LOSS_PCT = 'Net Loan Losses Total Loans'\n LOAN_LOSS_RSRV_ASSETS = 'Loan Loss Reserves Total Assets'\n LOAN_LOSS_RSRV_TCAP = 'Loan Loss Reserves Total Capital'\n LOSS_EXP_RATIO = 'Loss Expense Ratios Combined'\n LTD_COM_EQ = 'Long Term Debt Common Equity'\n MIN_INT_TCAP = 'Minority Interest Total Capital'\n MKT_VAL_PUBLIC = 'Market Capitalization Public'\n NET_INC_BASIC_AFT_XORD = 'Net Income Avail To Common Basic After Extraordinaries'\n NET_INC_BEF_XORD_GR = 'Net Income Before Extras 1 Year Growth'\n NET_INC_DIL_AFT_XORD = 'Net Income Avail To Common Fully Diluted After Extraordinaries'\n NET_INT_INC_EARN_ASSETS = 'Net Interest Income Earning Assets'\n NON_INT_INC_REV = 'Non Interest Income Total Revs'\n NONPERF_LOAN_COM_EQ = 'Non Performing Loans Common Equity'\n NONPERF_LOAN_PCT = 'Non Performing Loans Total Loans'\n OPER_INC_AFT_UNUSUAL = 'Operating Income After Unusual Items'\n OPER_INC_PREM_EARN = 'Operating Income Premiums Earned'\n OPER_INC_TCAP = 'Operating Income Total Capital'\n PAY_ACCT_SALES = 'Accounts PayableSales'\n PPE_NET_BLDG = 'Net Property Plant Equipment Buildings'\n PPE_NET_EQUIP = 'Net Property Plant Equipment Machinery Equipment'\n PPE_NET_LEASED_PROP = 'Net Property Plant Equipment Leased Property'\n PPE_NET_OTH = 'Net Property Plant Equipment Other Property Plant Equipment'\n PPE_NET_TRANS_EQUIP = 'Net Property Plant Equipment Transportation Equipment'\n RD_SALES = 'Research Development Sales'\n RECEIV_GROSS = 'Accounts Receivables Gross'\n RECEIV_TURN = 'Receivables Turnover'\n REINVEST_RATE = 'Reinvestment Rate'\n ROCE = 'Return On Common Equity'\n ROIC = 'Return On Average Invested Capital'\n SALES_GR = 'Sales Net 1 Year Growth'\n SALES_PER_EMP = 'Sales Per Employee'\n SALES_WKCAP = 'Sales Net Working Capital'\n SECS_RE_CAP = 'Equity Securities Real Estate Capital'\n SGA_OTH = 'Other Selling General Administrative Expense'\n SHS_FLOAT = 'Shares Outstanding Less Closely Held Shares Float'\n TAX_RATE = 'Tax Rate'\n TCAP_DEPS = 'Capital Total Total Deposits'\n UNEARN_PREM_TCAP = 'Unearned Premium Total Capital'\n UT_GROSS_INC = 'Gross Income Income Before Interest Charges'\n UT_OPERATION_EXP = 'Operation Expenses Total'\n WKCAP_PCT = 'Working Capital Total Capital'\n XORD_DISC = 'Extraordinaries Discontinued Operations'\n INVEST_RECEIV_LT = 'Investments Long Term Receivables'\n EBIT_BEF_UNUSUAL = 'Earnings Before Interest Taxes Unusual Expense'\n EPS_DIL_AFT_XORD = 'EPS Diluted After Extraordinaries'\n PBK_TANG = 'Price To Tangible Book Value'\n PFCF = 'Price To Free Cash Flow'\n PSALES_DIL = 'Price To Sales Diluted'\n TANG_ASSETS_DEBT = 'Tangible Assets To Total Debt'\n NET_INC_DIL_BEF_UNUSUAL = 'Net Income Before Unusual Expense'\n LENDING_MISC = 'Miscellaneous Lending'\n TANG_FIX_ASSETS_XRE = 'Tangible Fixed Assets'\n TRADE_INC_NET = 'Net Trading Income'\n BK_OPER_EXP_OTH = 'Bank Other Operating Expense'\n BK_OPER_INC_TOT = 'Bank Total Operating Income'\n COMMISS_INC_NET = 'Net Commission Income'\n LOAN_GROSS = 'Gross Loans'\n LOAN_DEPS = 'Loans Total Total Deposits'\n CF_ROIC = 'Cash Flow Return on Average Invested Capital'\n LIABS_DISC_OPER = 'Total Liabilities of Discontinued Operations'\n FSCORE = 'Piotroski F Score'\n IMPAIR = 'Impairments Total'\n RESTRUCT_DEBT = 'Restructuring of Debt'\n ASSETS_COM_EQ = 'Assets To Common Equity'\n ASSETS_GR = 'Assets Total 1 Year Growth'\n ASSETS_PER_EMP = 'Assets Per Employee'\n BPS_GR = 'Book Value Per Share 1 Year Annual Growth'\n CAPEX_ASSETS = 'Capital Expenditure Total Assets'\n CAPEX_PS_CF = 'Capital Expenditures Per Share'\n CASH_DIV_CF = 'Cash DividendsCash Flow'\n CASH_ROCE = 'Cash Earnings Return On Equity'\n CF_PS_GR = 'Cash Flow Per Share 1 Year Growth'\n CLAIMS_NET_PREM = 'Claims Claim Expense Net Premiums Written'\n COM_EQ_DEPS = 'Equity Total Deposits'\n COM_EQ_TCAP = 'Equity Total Capital'\n DEBT_ENTRPR_VAL = 'Total Debt To Enterprise Value'\n DEBT_LT_CF = 'Change in Long Term Debt'\n DEMAND_DEPS_PCT = 'Demand Deposits Total Deposits'\n DEPS_ASSETS = 'Deposits Total Total Assets'\n DIL_ADJ = 'Dilution Adjustment'\n DIV_YLD_SECS = 'Dividend Yield Security'\n EARN_ASSETS_AVAIL_FUNDS = 'Earning Assets Total Available Funds'\n EARN_ASSETS_PCT = 'Earning Assets Total Assets'\n EBIT_OPER_MGN = 'EBIT Margin'\n EBITDA_CF = 'EBITDA From Cash Flow'\n EFF_INT_RATE = 'Interest Rate Estimated Average'\n ENTRPR_VAL_EBIT_OPER = 'Enterprise Value To EBIT'\n ENTRPR_VAL_SALES = 'Enterprise Value To Sales'\n EXP_RATIO = 'Expense Ratio'\n FIX_ASSETS_COM_EQ = 'Fixed Assets Common Equity'\n FOR_ASSET_TURN = 'Foreign Asset Turnover'\n FOR_DEPS_PCT = 'Foreign Office Deposits Total Deposits'\n FOR_NET_MGN = 'Foreign Income Margin'\n FOR_SALES_PCT = 'Foreign Sales Total Sales'\n GROSS_CF_DEBT = 'Cash Flow Gross Total Debt'\n INC_SUND = 'Sundry RevenueIncome'\n INC_TAX_DFD = 'Deferred Income Taxes'\n EBIT_OPER_INT_COVG = 'Pretax Interest Coverage Ratio'\n INT_EXP_OTH = 'Other Interest Expense Banks'\n INT_INC_EARN_ASSETS = 'Interest Income Total Earning Assets'\n INVEN_CURR_ASSETS = 'Inventories Total Current Assets'\n INVEN_TURN = 'Inventory Turnover'\n INVEST_ASSETS_LIABS = 'Invested Assets Liabilities'\n INVEST_ASSETS_PCT = 'Invested Assets Total Assets'\n INVEST_INC_INVEST_ASSETS = 'Investment Income Invested Assets'\n INVEST_PROP = 'Investment Property'\n INVEST_TCAP = 'Investments Total Total Capital'\n LOAN_GR = 'Loans 1 Year Growth Rate'\n LOAN_LOSS_COVG = 'Loan Loss Coverage Ratio'\n LOAN_LOSS_PROV_PCT = 'Loan Losses Provision Total Loans'\n LOAN_LOSS_RSRV_PCT = 'Loan Losses Reserves Total Loans'\n LOAN_TCAP = 'Loans Total Total Capital'\n LOSS_RATIO = 'Loss Ratio'\n LTD_TCAP = 'Long Term Debt Total Capital'\n MKT_VAL_GR = 'Market Value 1 Year Growth'\n NET_CF_DEBT = 'Cash Flow Net Total Debt'\n NET_INC_BASIC_BEFT_XORD = 'Income Before Extraordinary Items Available For Common'\n NET_INC_DIL = 'Net Income Avail To Common Fully Diluted'\n NET_INC_PER_EMP = 'Net IncomeEmployee Most Recent Fy'\n NET_MGN_GR = 'Net Margin 1 Year Growth Rate'\n NON_OPER_EXP = 'Non Operating Expense'\n NONPERF_LOAN_LOSS_RSRV = 'Non Performing Loans Loan Loss Reserves'\n OPER_CF_FIX_CHRG = 'Operating Cash Flow To Fixed Charges Interest Dividend'\n OPER_INC_GR = 'Operating Income 1 Year Growth'\n OPER_INC_PREM_WRITTEN = 'Operating Income Net Premiums Written'\n OPER_PS_NET_CF = 'Cash Flow From Operations Per Share Net'\n PFD_STK_TCAP = 'Preferred Stock Total Capital'\n PPE_NET_CONSTR = 'Net Property Plant Equipment Construction In Progress'\n PPE_NET_LAND = 'Net Property Plant Equipment Land Improvements'\n PPE_NET_LEASES = 'Net Property Plant Equipment Leases'\n PPE_NET_SOFT_EQUIP = 'Net Property Plant Equipment Computer Software And Equipment'\n PREM_WRITTEN_COM_EQ = 'Net Premium Written Equity'\n RECEIV_CURR_ASSETS = 'Receivables Current Assets'\n RECEIV_ST_OTH = 'Other Receivables Short Term'\n RECEIV_TURN_DAYS = 'Accounts Receivables Days'\n ROA_PTX = 'Return On Average Assets Pretax'\n ROEA = 'Return On Earning Assets'\n SALES_FIX_ASSETS = 'Sales Net Gross Fixed Assets'\n SALES_INVEN_TURN = 'Sales To Inventory Turnover'\n SALES_PS_GR = 'Sales Per Share 1 Year Growth'\n SAV_DEPS_PCT = 'Savings Deposits Total Deposits'\n SECS_RE_INVEST_ASSETS = 'Equity Securities Real Estate Invested Assets'\n SGA_SALES = 'Selling General Admin Expense Sales'\n SPEC_ITEMS = 'Special Items'\n TCAP_ASSETS = 'Capital Total Total Assets'\n TOT_DEBT_TCAP_STD = 'Total Debt Total Capital'\n US_GAAP_ADJ = 'US GAAP Adjustment'\n UT_NON_OPER_INC_OTH = 'Nonoperating Income Net Other'\n WKCAP = 'Working Capital'\n XORD = 'Extraordinary Items Including Accounting Change'\n ZSCORE = 'ALTMANS Z SCORE'\n DEBT_SERV = 'Debt Service Ratio Including ST Debt Pretax'\n EBITDA_BEF_UNUSUAL = 'EBITDA Before Unusual Expenses'\n EPS_DIL_GR = 'EPS Diluted Before Extras Change'\n PE_DIL = 'Price To Earnings Diluted'\n PFCF_DIL = 'Diluted Price To Free Cash Flow'\n STD_DEBT = 'Short Term Debt Total Debt'\n PAY_TURN_DAYS = 'Days of Payables Outstanding'\n INT_INC_MISC = 'Miscellaneous Interest Income'\n RE_INVEST = 'Real Estate Investment'\n FIN_INVEST_TOT = 'Total Investments for Financial'\n BK_OPER_INC_OTH = 'Bank Other Operating Income'\n BK_OPER_EXP_TOT = 'Bank Total Operating Expense'\n BK_NON_OPER_INC = 'Bank Non Operating Income Expense'\n ACCT_PAY_OPER = 'Creditors'\n LOAN_ASSETS = 'Loans Total Total Assets'\n ASSETS_DISC_OPER = 'Total Assets of Discontinued Operations'\n LIABS_LEASE = 'Capital and Operating Lease Obligations'\n FCF_YLD = 'Free Cash Flow Yield'\n OTH_XCEPT_CHRG = 'Exceptional Charges Other'\n\n\nfundamental_basic_dict = {item.name: item.value for item in FundamentalBasicItem}\nfundamental_basic_derived_dict = {item.name: item.value for item in FundamentalBasicDerivedItem}\nfundamental_advanced_dict = {item.name: item.value for item in FundamentalAdvancedItem}\nfundamental_advanced_derived_dict = {item.name: item.value for item in FundamentalAdvancedDerivedItem}\n\nFundamentalMetric = Enum(\n 'FundamentalMetric',\n {\n **fundamental_basic_dict,\n **fundamental_basic_derived_dict,\n **fundamental_advanced_dict,\n **fundamental_advanced_derived_dict,\n },\n)\n\n\nclass FundamentalFormat(Enum):\n RESTATED = 'Restated'\n NON_RESTATED = 'Non Restated'\n\n\nclass RatingType(Enum):\n BUY = 'Buy'\n OVERWEIGHT = 'Overweight'\n HOLD = 'Hold'\n UNDERWEIGHT = 'Underweight'\n SELL = 'Sell'\n NONE = 'No Recommendations'\n TOTAL = 'Total'\n SCORE = 'Numeric Score'\n\n\nclass FiscalPeriod:\n \"\"\"\n Create an abosolute fiscal period with year and period.\n\n :param y: year\n :param p: period\n :return: fiscal period object\n\n **Usage**\n\n The year combined with period can determine the fiscal period of the estimate.\n\n **Examples**\n\n Year is :math:`2025` and period is :math:`2`:\n\n >>> FiscalPeriod(2025, 2)\n\n \"\"\"\n\n def __init__(self, y: Union[int, str, None] = None, p: Union[int, str, None] = None):\n self.y = y\n self.p = p\n\n def as_dict(self):\n return {'y': self.y, 'p': self.p}\n\n @classmethod\n def from_dict(cls, obj):\n return FiscalPeriod(y=obj.get('y'), p=obj.get('p'))\n\n\nBASIC_MEASURES = [\n EstimateItem.EPS,\n EstimateItem.EPS_C,\n EstimateItem.EPS_P,\n EstimateItem.SALES,\n EstimateItem.SALES_C,\n EstimateItem.SALES_P,\n EstimateItem.DPS,\n EstimateItem.CFPS,\n EstimateItem.PRICE_TGT,\n EstimateItem.EPS_LTG,\n]\n\nLT_MEASURES = [EstimateItem.PRICE_TGT, EstimateItem.EPS_LTG]\n\nBASIS_TO_DATASET = {\n EstimateBasis.ANN: 'AF',\n EstimateBasis.QTR: 'QF',\n EstimateBasis.SEMI: 'SAF',\n EstimateBasis.NTM: 'NTM',\n EstimateBasis.STM: 'NTM',\n}\n\nBASIS_TO_FIELD = {\n EstimateBasis.ANN: 'Af',\n EstimateBasis.QTR: 'Qf',\n EstimateBasis.SEMI: 'Saf',\n EstimateBasis.NTM: 'Ntm',\n EstimateBasis.STM: 'Stm',\n}\n\nFF_BASIS_TO_DATASET = {FundamentalBasis.ANN: 'AF', FundamentalBasis.QTR: 'QF', FundamentalBasis.SEMI: 'SAF'}\n\nFF_BASIS_TO_FIELD = {FundamentalBasis.ANN: 'Af', FundamentalBasis.QTR: 'Qf', FundamentalBasis.SEMI: 'Saf'}\n\nRATING_TO_FIELD = {\n RatingType.BUY: 'feBuy',\n RatingType.OVERWEIGHT: 'feOver',\n RatingType.HOLD: 'feHold',\n RatingType.UNDERWEIGHT: 'feUnder',\n RatingType.SELL: 'feSell',\n RatingType.NONE: 'feNoRec',\n RatingType.TOTAL: 'feTotal',\n RatingType.SCORE: 'feMark',\n}\n\n\n@plot_measure((AssetClass.Equity,), (AssetType.Single_Stock,)) # TO DO add query type\ndef factset_estimates(\n asset: Asset,\n metric: EstimateItem = EstimateItem.EPS,\n statistic: EstimateStatistic = EstimateStatistic.MEAN,\n report_basis: EstimateBasis = EstimateBasis.ANN,\n period: Union[int, FiscalPeriod, None] = 1,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n FactSet estimates for single stocks.\n\n :param asset: asset object loaded from security master\n :param metric: metric for estimate e.g. EPS\n :param statistic: type of estimate to show e.g. Mean\n :param report_basis: reporting basis of the estimate e.g. Annual\n :param period: rolling window for the estimate e.g. 1 period ahead or fixed non-rolling window e.g.\n FiscalPeriod(2025, 2)\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD, real time is currently not supported\n :param request_id: service request id, if any\n :return: FactSet Estimates\n \"\"\"\n if not isinstance(report_basis, EstimateBasis):\n raise MqValueError('Invalid Estimate Basis argument')\n\n start, end = DataContext.current.start_date, DataContext.current.end_date\n start_new = RelativeDate('-1y', base_date=start).apply_rule()\n basic = 'BASIC' if metric in BASIC_MEASURES else 'ADVANCED'\n column_prefix = '' if metric in BASIC_MEASURES else 'Adv'\n consensus = 'CONH' if statistic != EstimateStatistic.ACTUAL else 'ACT'\n basis_ds = 'LT' if metric in LT_MEASURES else BASIS_TO_DATASET[report_basis]\n basis_cl = 'Lt' if metric in LT_MEASURES else BASIS_TO_FIELD[report_basis]\n if report_basis in [EstimateBasis.NTM, EstimateBasis.STM]:\n ds_id = f'FE_{basis_ds}'\n else:\n ds_id = f'FE_{basic}_{consensus}_{basis_ds}_GLOBAL'\n ds = Dataset(ds_id)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n try:\n df = ds.get_data(bbid=bbid, start=start_new, end=end, feItem=metric.name)\n except Exception as e:\n raise MqValueError(f'Could not query dataset {ds_id} because of {e}')\n if df.empty:\n raise MqValueError(f'No data found for {metric.value} for {asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)}')\n\n df = df.reset_index()\n if statistic == EstimateStatistic.ACTUAL:\n if report_basis in [EstimateBasis.NTM, EstimateBasis.STM]:\n raise MqValueError('NTM and STM are not supported for actual values')\n elif metric in LT_MEASURES:\n raise MqValueError(f'No actual data for {metric.value}')\n else:\n df = df[['feFpEnd', 'feValue']]\n df = df.rename(columns={'feFpEnd': 'date'})\n df['date'] = pd.to_datetime(df['date'])\n column = 'feValue'\n elif report_basis not in [EstimateBasis.NTM, EstimateBasis.STM]:\n if metric in LT_MEASURES:\n pass\n elif isinstance(period, int):\n df = df[df['fePerRel'] == period]\n else:\n if report_basis == EstimateBasis.ANN:\n fiscal_period_start = dt.datetime(period.y, 1, 1)\n fiscal_period_end = dt.datetime(period.y, 12, 31)\n elif report_basis == EstimateBasis.QTR:\n if isinstance(period.p, int) and period.p not in [1, 2, 3, 4]:\n raise MqValueError('Period number has to be one of 1,2,3 or 4 for quarterly basis')\n elif period.p is None:\n raise MqValueError(\n 'Please specify the period as an integer between 1 and 4 like FiscalPeriod(2022, 4) '\n 'for 2022Q4 estimate'\n )\n fiscal_period_start = dt.datetime(period.y, (period.p - 1) * 3 + 1, 1)\n fiscal_period_end = fiscal_period_start + pd.DateOffset(months=3) - pd.DateOffset(days=1)\n fiscal_period_end = pd.to_datetime(fiscal_period_end)\n elif report_basis == EstimateBasis.SEMI:\n if isinstance(period.p, int) and period.p not in [1, 2]:\n raise MqValueError('Period number has to be 1 or 2 for semi-annual basis')\n elif period.p is None:\n raise MqValueError(\n 'Please specify the period as 1 or 2 like FiscalPeriod(2022, 2) for 2022H2 estimate'\n )\n fiscal_period_start = dt.datetime(period.y, (period.p - 1) * 6 + 1, 1)\n fiscal_period_end = fiscal_period_start + pd.DateOffset(months=6) - pd.DateOffset(days=1)\n fiscal_period_end = pd.to_datetime(fiscal_period_end)\n df['feFpEnd'] = pd.to_datetime(df['feFpEnd'])\n df = df[(df['feFpEnd'] >= fiscal_period_start) & (df['feFpEnd'] <= fiscal_period_end)]\n if df.empty:\n raise MqValueError('No Data returned for selected fiscal period')\n\n df = df.fillna({'consEndDate': end})\n df['date_range'] = df.apply(lambda row: pd.date_range(row['date'], row['consEndDate']), axis=1)\n df = df.explode('date_range').drop(columns=['date', 'consEndDate']).rename(columns={'date_range': 'date'})\n column = f'fe{column_prefix}{statistic.value}{basis_cl}'\n else:\n df['date'] = pd.to_datetime(df['date'])\n column = f'fe{statistic.value}{basis_cl}'\n\n df = df[df['date'] >= pd.to_datetime(start)]\n df = df.sort_values(by='date', ascending=True).set_index('date')\n series = ExtendedSeries(df[column], name=statistic.value)\n _idx = pd.DatetimeIndex(series.index)\n series.index = _idx.as_unit('ns') if hasattr(_idx, 'as_unit') else _idx\n series.dataset_ids = ds.id\n\n return series\n\n\n@plot_measure((AssetClass.Equity,), (AssetType.Single_Stock,)) # TO DO add query type\ndef factset_fundamentals(\n asset: Asset,\n metric: FundamentalMetric = FundamentalMetric.EPS_BASIC,\n report_basis: FundamentalBasis = FundamentalBasis.ANN,\n report_format: FundamentalFormat = FundamentalFormat.NON_RESTATED,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n FactSet fundamentals for single stocks.\n\n :param asset: asset object loaded from security master\n :param metric: metric for estimate e.g. EPS\n :param report_basis: reporting basis of the estimate e.g. Annual\n :param report_format: restated or non-restated\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD, real time is currently not supported\n :param request_id: service request id, if any\n :return: FactSet Fundamentals\n \"\"\"\n start, end = DataContext.current.start_date, DataContext.current.end_date\n start_new = RelativeDate('-1y', base_date=start).apply_rule()\n\n basic = 'BASIC' if metric.name in {**fundamental_basic_dict, **fundamental_basic_derived_dict} else 'ADVANCED'\n derived = '_DER' if metric.name in {**fundamental_basic_derived_dict, **fundamental_advanced_derived_dict} else ''\n\n if report_format == FundamentalFormat.RESTATED:\n restated = '_R'\n else:\n restated = ''\n ds_id = f'FF_{basic}{derived}{restated}_{FF_BASIS_TO_DATASET[report_basis]}_GLOBAL'\n ds = Dataset(ds_id)\n df = ds.get_data(bbid=asset.get_identifier(AssetIdentifier.BLOOMBERG_ID), start=start_new, end=end)\n if df.empty:\n raise MqValueError(f'No data found for {metric.value} for {asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)}')\n df = df.reset_index()\n column = 'ff' + metric.name.replace('_', ' ').title().replace(' ', '')\n df = df[['date', column]]\n date_range = pd.date_range(start=start_new, end=end, freq='D')\n date_df = pd.DataFrame({'date': date_range})\n df = pd.merge(date_df, df, on='date', how='left')\n df[column] = df[column].ffill()\n df = df[df['date'] >= pd.to_datetime(start)]\n df = df.sort_values(by='date', ascending=True).set_index('date')\n series = ExtendedSeries(df[column], name=metric.value)\n _idx = pd.DatetimeIndex(series.index)\n series.index = _idx.as_unit('ns') if hasattr(_idx, 'as_unit') else _idx\n series.dataset_ids = ds.id\n\n return series\n\n\n@plot_measure((AssetClass.Equity,), (AssetType.Single_Stock,)) # TO DO add query type\ndef factset_ratings(\n asset: Asset,\n rating_type: RatingType = RatingType.BUY,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n FactSet consensus ratings for single stocks, shows number of brokers for the rating type\n or a standardized numeric value representing the consensus of broker recommendations.\n\n :param asset: asset object loaded from security master\n :param rating_type: type of broker rating e.g. Buy\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD, real time is currently not supported\n :param request_id: service request id, if any\n :return: FactSet Ratings\n\n \"\"\"\n start, end = DataContext.current.start_date, DataContext.current.end_date\n start_new = RelativeDate('-1y', base_date=start).apply_rule()\n ds_id = 'FE_BASIC_CONH_REC_GLOBAL'\n ds = Dataset(ds_id)\n df = ds.get_data(bbid=asset.get_identifier(AssetIdentifier.BLOOMBERG_ID), start=start_new, end=end)\n df = df.reset_index().fillna({'consEndDate': end})\n df['date_range'] = df.apply(lambda row: pd.date_range(row['date'], row['consEndDate']), axis=1)\n df = df.explode('date_range').drop(columns=['date', 'consEndDate']).rename(columns={'date_range': 'date'})\n df = df[df['date'] >= pd.to_datetime(start)].sort_values(by='date', ascending=True).set_index('date')\n series = ExtendedSeries(df[RATING_TO_FIELD[rating_type]], name=rating_type.value)\n _idx = pd.DatetimeIndex(series.index)\n series.index = _idx.as_unit('ns') if hasattr(_idx, 'as_unit') else _idx\n series.dataset_ids = ds.id\n\n return series\n" }, { "path": "gs_quant/timeseries/measures_fx_vol.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom enum import Enum\nfrom numbers import Real\nfrom typing import Union, Optional\n\nimport pandas as pd\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.data import QueryType, GsDataApi\nfrom gs_quant.common import AssetClass, AssetType, PricingLocation\nfrom gs_quant.data import DataContext, Dataset\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.securities import AssetIdentifier, Asset, SecurityMaster\nfrom gs_quant.timeseries import ASSET_SPEC, plot_measure, MeasureDependency, FXSpotCarry\nfrom gs_quant.timeseries import ExtendedSeries, measures_rates as tm_rates\nfrom gs_quant.timeseries.measures import (\n _asset_from_spec,\n _market_data_timed,\n _cross_stored_direction_helper,\n _preprocess_implied_vol_strikes_fx,\n _tenor_month_to_year,\n)\nfrom gs_quant.timeseries.measures_helper import VolReference\n\n_logger = logging.getLogger(__name__)\n\n\nclass OptionType(Enum):\n CALL = 'Call'\n PUT = 'Put'\n STRADDLE = 'Straddle'\n\n\nclass TdapiFXDefaultsProvider:\n # flag to indicate that a given property should not be included in asset query\n EMPTY_PROPERTY = \"null\"\n\n def __init__(self, defaults: dict):\n self.defaults = defaults\n\n def get_defaults_for_cross(self, cross: str):\n return dict(self.defaults.get(cross))\n\n\nFX_DEFAULTS = {\n \"AUDCAD\": {\"under\": \"AUD\", \"over\": \"CAD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"AUDJPY\": {\"under\": \"AUD\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"AUDUSD\": {\"under\": \"AUD\", \"over\": \"USD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"BRLJPY\": {\"under\": \"BRL\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"CADJPY\": {\"under\": \"CAD\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"CADMXN\": {\"under\": \"CAD\", \"over\": \"MXN\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"CHFJPY\": {\"under\": \"CHF\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"CNHJPY\": {\"under\": \"CNH\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURAUD\": {\"under\": \"EUR\", \"over\": \"AUD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURBRL\": {\"under\": \"EUR\", \"over\": \"BRL\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURCAD\": {\"under\": \"EUR\", \"over\": \"CAD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURCHF\": {\"under\": \"EUR\", \"over\": \"CHF\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURCLP\": {\"under\": \"EUR\", \"over\": \"CLP\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURCNH\": {\"under\": \"EUR\", \"over\": \"CNH\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURCZK\": {\"under\": \"EUR\", \"over\": \"CZK\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURGBP\": {\"under\": \"EUR\", \"over\": \"GBP\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURHUF\": {\"under\": \"EUR\", \"over\": \"HUF\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURILS\": {\"under\": \"EUR\", \"over\": \"ILS\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURINR\": {\"under\": \"EUR\", \"over\": \"INR\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURJPY\": {\"under\": \"EUR\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURKRW\": {\"under\": \"EUR\", \"over\": \"KRW\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURMXN\": {\"under\": \"EUR\", \"over\": \"MXN\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURNOK\": {\"under\": \"EUR\", \"over\": \"NOK\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURNZD\": {\"under\": \"EUR\", \"over\": \"NZD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURPLN\": {\"under\": \"EUR\", \"over\": \"PLN\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURRUB\": {\"under\": \"EUR\", \"over\": \"RUB\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURSEK\": {\"under\": \"EUR\", \"over\": \"SEK\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURTRY\": {\"under\": \"EUR\", \"over\": \"TRY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURUSD\": {\"under\": \"EUR\", \"over\": \"USD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"EURZAR\": {\"under\": \"EUR\", \"over\": \"ZAR\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"GBPJPY\": {\"under\": \"GBP\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"GBPUSD\": {\"under\": \"GBP\", \"over\": \"USD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"HUFJPY\": {\"under\": \"HUF\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"ILSJPY\": {\"under\": \"ILS\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"INRJPY\": {\"under\": \"INR\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"JPYKRW\": {\"under\": \"JPY\", \"over\": \"KRW\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"MXNJPY\": {\"under\": \"MXN\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"NOKJPY\": {\"under\": \"NOK\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"NZDJPY\": {\"under\": \"NZD\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"NZDUSD\": {\"under\": \"NZD\", \"over\": \"USD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"PLNJPY\": {\"under\": \"PLN\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"RUBJPY\": {\"under\": \"RUB\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"SEKJPY\": {\"under\": \"SEK\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDBRL\": {\"under\": \"USD\", \"over\": \"BRL\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDCAD\": {\"under\": \"USD\", \"over\": \"CAD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDCHF\": {\"under\": \"USD\", \"over\": \"CHF\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDCLP\": {\"under\": \"USD\", \"over\": \"CLP\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDCNH\": {\"under\": \"USD\", \"over\": \"CNH\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDCOP\": {\"under\": \"USD\", \"over\": \"COP\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDHUF\": {\"under\": \"USD\", \"over\": \"HUF\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDIDR\": {\"under\": \"USD\", \"over\": \"IDR\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDILS\": {\"under\": \"USD\", \"over\": \"ILS\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDINR\": {\"under\": \"USD\", \"over\": \"INR\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDJPY\": {\"under\": \"USD\", \"over\": \"JPY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDKRW\": {\"under\": \"USD\", \"over\": \"KRW\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDMXN\": {\"under\": \"USD\", \"over\": \"MXN\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDNOK\": {\"under\": \"USD\", \"over\": \"NOK\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDPHP\": {\"under\": \"USD\", \"over\": \"PHP\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDPLN\": {\"under\": \"USD\", \"over\": \"PLN\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDRUB\": {\"under\": \"USD\", \"over\": \"RUB\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDSEK\": {\"under\": \"USD\", \"over\": \"SEK\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDSGD\": {\"under\": \"USD\", \"over\": \"SGD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDTRY\": {\"under\": \"USD\", \"over\": \"TRY\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDTWD\": {\"under\": \"USD\", \"over\": \"TWD\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n \"USDZAR\": {\"under\": \"USD\", \"over\": \"ZAR\", \"expirationTime\": \"NYC\", \"premiumPaymentDate\": \"Fwd Settle\"},\n}\nfx_defaults_provider = TdapiFXDefaultsProvider(FX_DEFAULTS)\n\nFX_VOL_SWAP_DEFAULTS = [\n \"EURUSD\",\n \"GBPUSD\",\n \"USDCHF\",\n \"DKKUSD\",\n \"NOKUSD\",\n \"SEKUSD\",\n \"USDCAD\",\n \"USDJPY\",\n \"AUDUSD\",\n \"NZDUSD\",\n \"USDCNH\",\n \"INRUSD\",\n \"USDSGD\",\n]\n\nCURRENCY_TO_DUMMY_FFO_BBID = {\n 'AUDCAD': 'MA4TJRN9MBET5NKS',\n 'AUDJPY': 'MAH5X6NHGRBKMWPE',\n 'AUDUSD': 'MAN0G5EB00H14W0S',\n 'BRLJPY': 'MA1WP7QREXS9WZVQ',\n 'CADJPY': 'MAVHN1MPE1X9JW52',\n 'CADMXN': 'MAE2415EP4XX6ACZ',\n 'CHFJPY': 'MAJ7VVAY60D3E2WE',\n 'CNHJPY': 'MAEF5B5SYGCADZQJ',\n 'EURAUD': 'MAN5S7K3N5K7TAYQ',\n 'EURBRL': 'MA9CQ07TPZFWR37M',\n 'EURCAD': 'MA46Z26SSKTC26X0',\n 'EURCHF': 'MAX77PXQC2B09S30',\n 'EURCLP': 'MAYYHB7XGPS49B5P',\n 'EURCNH': 'MA6S2DN8YER1ZWNK',\n 'EURCZK': 'MAH68Y664M87S2WK',\n 'EURGBP': 'MAXGQ18G9245FMFN',\n 'EURHUF': 'MAQY2AFQGAGMWA86',\n 'EURILS': 'MA80D8MWZJA884M7',\n 'EURINR': 'MA9M0AD4XF4PZDJ4',\n 'EURJPY': 'MATCYWGHQCM89K72',\n 'EURKRW': 'MATRZZCW5H78ZMNX',\n 'EURMXN': 'MAH2P631H1JXERTK',\n 'EURNOK': 'MA3WQVT62D4C0ZQ8',\n 'EURNZD': 'MA28186ESNB3HR6A',\n 'EURPLN': 'MAFEVSPC80GP6PJX',\n 'EURRUB': 'MANABN4EAX6YYB3D',\n 'EURSEK': 'MA0FCJ0QXH4F73VE',\n 'EURTRY': 'MAY54D9K3PYCT18A',\n 'EURUSD': 'MAT1J37C9ZPMANFP',\n 'EURZAR': 'MAPNZ5Z1X18YNKN3',\n 'GBPJPY': 'MATJS76YB4JSCSCA',\n 'GBPUSD': 'MAEHA6WVHJ2S3JY9',\n 'HUFJPY': 'MA98M1V3YEMS5XEB',\n 'ILSJPY': 'MA6S47WRX6F498JB',\n 'INRJPY': 'MAJEZ70FKX95F5M7',\n 'JPYKRW': 'MA3WF2A5FHGBVJ6X',\n 'MXNJPY': 'MABS05Q0848GQCQW',\n 'NOKJPY': 'MAF5TG7GKB5ZCWDY',\n 'NZDJPY': 'MABZBJSAPM9KHASD',\n 'NZDUSD': 'MA18GEASNDN55AHS',\n 'PLNJPY': 'MAN2CFHW1Z4XFXQ3',\n 'RUBJPY': 'MAHT6HYTYXEK2QY1',\n 'SEKJPY': 'MAA0W4P56NZEGZSC',\n 'USDBRL': 'MA2CKWJWZXA3A3QQ',\n 'USDCAD': 'MAXWBP82QZ3B2304',\n 'USDCHF': 'MANPCKXE2H0FQMFC',\n 'USDCLP': 'MAS2G31EZJDWEXQ9',\n 'USDCNH': 'MA141AAE2NWP5BHN',\n 'USDCOP': 'MAPWBD5FQP0W303V',\n 'USDHUF': 'MAQEDRH3T69WCA8J',\n 'USDIDR': 'MAFKA004WSQEZ6KR',\n 'USDILS': 'MAS2AM5ENGQ00MP5',\n 'USDINR': 'MA8JP0HYF8CQZCYG',\n 'USDJPY': 'MAQ7YRZ4P94M9N9C',\n 'USDKRW': 'MAVZG5BFM5VVTYMD',\n 'USDMXN': 'MAPAVBP6KQMQJZP3',\n 'USDNOK': 'MAFT0XXQASPQ7X1J',\n 'USDPHP': 'MA5N37TB77VX7RCR',\n 'USDPLN': 'MACNEGC1Q81HK2QP',\n 'USDRUB': 'MAXMSS4B5YXE935S',\n 'USDSEK': 'MAE4YZ9B2KKVM67J',\n 'USDSGD': 'MA9DWW9GPFT7H1A9',\n 'USDTRY': 'MA4YKBCXZ4W341YC',\n 'USDTWD': 'MAANPDQCDNKV6G2X',\n 'USDZAR': 'MA9T8EZ1GXEGY8C3',\n}\n\nCURRENCY_TO_DUMMY_FFO_BBID_VOL_SWAPS = {\n \"EURUSD\": \"MA66A4X4PRTC3N7B\",\n \"GBPUSD\": \"MA808D9BQ4E396C4\",\n \"USDCHF\": \"MAPGXTC189B2111M\",\n \"DKKUSD\": \"MABCHYGJ1TCBCQE4\",\n \"NOKUSD\": \"MAZ0P16RPG5P0MVF\",\n \"SEKUSD\": \"MA4ZD5CZGC3Y6JZD\",\n \"USDCAD\": \"MAZPF9SV1M6ZSGFH\",\n \"USDJPY\": \"MA3Y28M97R55G16Y\",\n \"AUDUSD\": \"MAAEVQXHFBKNDYRP\",\n \"NZDUSD\": \"MA9RP21MYV18TW3E\",\n \"USDCNH\": \"MAQEB18HP88FEP5G\",\n \"INRUSD\": \"MAMC996297G5GEAM\",\n \"USDSGD\": \"MAFCHSQN3NH77G17\",\n}\n\n\ndef _currencypair_to_tdapi_fxfwd_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n\n kwargs = dict(\n asset_class='FX',\n type='Forward',\n asset_parameters_pair=bbid,\n asset_parameters_settlement_date='1y',\n )\n\n mqid = _get_tdapi_fxo_assets(**kwargs)\n return mqid\n\n\ndef _currencypair_to_tdapi_fxo_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n # for each currency, get a dummy asset for checking availability\n result = CURRENCY_TO_DUMMY_FFO_BBID.get(bbid, asset.get_marquee_id())\n return result\n\n\ndef _currencypair_to_tdapi_fx_vol_swap_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n # for each currency, get a dummy asset for checking availability\n result = CURRENCY_TO_DUMMY_FFO_BBID_VOL_SWAPS.get(bbid, asset.get_marquee_id())\n return result\n\n\ndef _get_tdapi_fxo_assets(**kwargs) -> Union[str, list]:\n # sanitize input for asset query.\n if \"pricing_location\" in kwargs:\n del kwargs[\"pricing_location\"]\n name_prefix = kwargs.pop(\"name_prefix\", None)\n assets = GsAssetApi.get_many_assets(**kwargs)\n\n if len(assets) > 1:\n if name_prefix:\n for asset in assets:\n if asset.name.startswith(name_prefix):\n return asset.id\n raise MqValueError('Specified arguments match multiple assets' + str(kwargs))\n elif len(assets) == 0:\n raise MqValueError('Specified arguments did not match any asset in the dataset' + str(kwargs))\n else:\n return assets[0].id\n\n\ndef get_fxo_asset(\n asset: Asset,\n expiry_tenor: str,\n strike: str,\n option_type: str = None,\n expiration_location: str = None,\n premium_payment_date: str = None,\n) -> str:\n cross = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n\n if cross not in FX_DEFAULTS.keys():\n raise NotImplementedError('Data not available for {} FX Vanilla options'.format(cross))\n\n defaults = _get_fxo_defaults(cross)\n\n if not (tm_rates._is_valid_relative_date_tenor(expiry_tenor)):\n raise MqValueError('invalid expiry ' + expiry_tenor)\n\n if expiration_location is None:\n _ = defaults[\"expirationTime\"]\n else:\n _ = expiration_location\n\n if premium_payment_date is None:\n premium_date = defaults[\"premiumPaymentDate\"]\n else:\n premium_date = premium_payment_date\n\n if option_type == \"Put\":\n call_ccy = defaults[\"over\"]\n put_ccy = defaults[\"under\"]\n else:\n call_ccy = defaults[\"under\"]\n put_ccy = defaults[\"over\"]\n\n kwargs = dict(\n asset_class='FX',\n type='Option',\n asset_parameters_call_currency=call_ccy,\n asset_parameters_put_currency=put_ccy,\n asset_parameters_expiration_date=expiry_tenor,\n asset_parameters_option_type=option_type,\n asset_parameters_premium_payment_date=premium_date,\n asset_parameters_strike_price_relative=strike,\n )\n\n return _get_tdapi_fxo_assets(**kwargs)\n\n\ndef _get_tdapi_fxo_assets_vol_swaps(**kwargs) -> Union[str, list]:\n # sanitize input for asset query.\n\n expiry_tenor = kwargs.get(\"expiry_tenor\")\n ignore_list = [\"expiry_tenor\", \"pricing_location\"]\n inputs = {k: v for k, v in kwargs.items() if k not in ignore_list}\n\n assets = GsAssetApi.get_many_assets(**inputs)\n\n # For vol swaps we are not restricting assets using a filter\n # as asset service isn't setup for the parameters we pass in\n # instead query all assets and apply the filter in code here\n\n if len(assets) == 0:\n raise MqValueError('No assets found matching search criteria' + str(kwargs))\n\n if expiry_tenor is not None:\n for asset in assets:\n if asset.parameters[\"lastFixingDate\"].lower() == expiry_tenor.lower():\n return asset.id\n raise MqValueError('Specified arguments did not match any asset in the dataset' + str(kwargs))\n\n\ndef cross_stored_direction_for_fx_vol(asset_spec: ASSET_SPEC) -> Union[str, Asset]:\n asset = _asset_from_spec(asset_spec)\n result = asset\n try:\n if asset.asset_class is AssetClass.FX:\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n if bbid is not None:\n cross = _cross_stored_direction_helper(bbid)\n if cross != bbid:\n cross_asset = SecurityMaster.get_asset(cross, AssetIdentifier.BLOOMBERG_ID)\n result = cross_asset\n except TypeError:\n result = asset\n return _currencypair_to_tdapi_fxo_asset(result)\n\n\ndef _get_fxo_defaults(cross: str) -> dict:\n return fx_defaults_provider.get_defaults_for_cross(cross)\n\n\ndef _get_fx_csa_terms() -> dict:\n return dict(csaTerms='USD-1')\n\n\ndef _get_fx_vol_swap_data(\n asset: Asset,\n expiry_tenor: str,\n strike_type: str = None,\n location: PricingLocation = None,\n source: str = None,\n real_time: bool = False,\n query_type: QueryType = QueryType.STRIKE_VOL,\n) -> pd.DataFrame:\n if real_time:\n raise NotImplementedError('realtime FX Vol swap data not implemented')\n\n cross = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n\n if cross not in FX_VOL_SWAP_DEFAULTS:\n raise NotImplementedError('Data not available for {} FX Vol Swaps'.format(cross))\n\n kwargs = dict(\n asset_class='FX',\n type='VolatilitySwap',\n expiry_tenor=expiry_tenor,\n asset_parameters_pair=cross,\n # asset_parameters_strike_vol=strike_type\n )\n fxv_mqid = _get_tdapi_fxo_assets_vol_swaps(**kwargs)\n\n if location is None:\n pricing_location = PricingLocation.NYC\n else:\n pricing_location = PricingLocation(location)\n\n where = dict(pricingLocation=pricing_location.value)\n\n q = GsDataApi.build_market_data_query([fxv_mqid], query_type, where=where, source=source, real_time=real_time)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n return df\n\n\ndef _get_fxfwd_data(\n asset: Asset,\n settlement_date: str,\n location: str = None,\n source: str = None,\n real_time: bool = False,\n query_type: QueryType = QueryType.FWD_POINTS,\n) -> pd.DataFrame:\n if real_time:\n mqid = asset.get_identifier(AssetIdentifier.MARQUEE_ID)\n q = GsDataApi.build_market_data_query(\n [mqid], QueryType.FORWARD_POINT, source=source, real_time=real_time, where={'tenor': settlement_date}\n )\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n return df\n\n cross = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n\n if not (tm_rates._is_valid_relative_date_tenor(settlement_date)):\n raise MqValueError('invalid settlements date ' + settlement_date)\n\n kwargs = dict(\n asset_class='FX',\n type='Forward',\n asset_parameters_pair=cross,\n asset_parameters_settlement_date=settlement_date,\n name_prefix='FX Forward',\n )\n\n mqid = _get_tdapi_fxo_assets(**kwargs)\n\n if location is None:\n pricing_location = PricingLocation.NYC\n else:\n pricing_location = PricingLocation(location)\n\n where = dict(pricingLocation=pricing_location.value)\n\n q = GsDataApi.build_market_data_query([mqid], query_type, where=where, source=source, real_time=real_time)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n return df\n\n\ndef _get_fxo_data(\n asset: Asset,\n expiry_tenor: str,\n strike: str,\n option_type: str = None,\n expiration_location: str = None,\n location: PricingLocation = None,\n premium_payment_date: str = None,\n source: str = None,\n real_time: bool = False,\n query_type: QueryType = QueryType.IMPLIED_VOLATILITY,\n) -> pd.DataFrame:\n if real_time:\n raise NotImplementedError('realtime FX Option data not implemented')\n\n asset_mqid = get_fxo_asset(\n asset=asset,\n expiry_tenor=expiry_tenor,\n strike=strike,\n option_type=option_type,\n expiration_location=expiration_location,\n premium_payment_date=premium_payment_date,\n )\n\n if location is None:\n pricing_location = PricingLocation.NYC\n else:\n pricing_location = PricingLocation(location)\n\n where = dict(pricingLocation=pricing_location.value)\n\n # _logger.debug(f'where asset= {rate_mqid}, swap_tenor={swap_tenor}, index={defaults[\"index_type\"]}, '\n # f'forward_tenor={forward_tenor}, pricing_location={pricing_location.value}, '\n # f'clearing_house={clearing_house.value}, notional_currency={currency.name}')\n q = GsDataApi.build_market_data_query([asset_mqid], query_type, where=where, source=source, real_time=real_time)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n return df\n\n\ndef implied_volatility_new(\n asset: Asset,\n expiry_tenor: str,\n strike: str,\n option_type: str = None,\n expiration_location: str = None,\n location: PricingLocation = None,\n premium_payment_date: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day FX vanilla implied volatilities across major crosses.\n\n :param asset: asset object loaded from security master\n :param expiry_tenor: relative date representation of expiration date e.g. 1m\n :param strike: option strike\n :param option_type: option type (e.g. Put, Call or Straddle )\n :param expiration_location: location indicating the time of the option expiry\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param premium_payment_date: payment date of the premium Example: Fwd Settle vs Spot\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swap rate curve\n \"\"\"\n\n df = _get_fxo_data(\n asset=asset,\n expiry_tenor=expiry_tenor,\n strike=strike,\n option_type=option_type,\n expiration_location=expiration_location,\n location=location,\n premium_payment_date=premium_payment_date,\n source=source,\n real_time=real_time,\n query_type=QueryType.IMPLIED_VOLATILITY,\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['impliedVolatility'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n\"\"\"\nNew Implementation\n\"\"\"\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Cross,),\n [MeasureDependency(id_provider=cross_stored_direction_for_fx_vol, query_type=QueryType.IMPLIED_VOLATILITY)],\n display_name=\"implied_volatility\",\n)\ndef implied_volatility_fxvol(\n asset: Asset,\n tenor: str,\n strike_reference: VolReference = None,\n relative_strike: Real = None,\n location: Optional[PricingLocation] = None,\n legacy_implementation: bool = False,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n Volatility of an asset implied by observations of market prices.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n or absolute calendar strips e.g. 'Cal20', 'F20-G20'\n :param strike_reference: reference for strike level. Forward is used for ATMF. Default market convention\n for FX implied vols is delta neutral\n :param relative_strike: strike relative to reference\n :param location: location of the data snapshot Example - \"HKG\", \"LDN\", \"NYC\"\n :param legacy_implementation: Deprecated (supplied values are ignored)\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: implied volatility curve\n \"\"\"\n\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n if bbid is not None:\n cross = _cross_stored_direction_helper(bbid)\n if cross != bbid:\n cross_asset = SecurityMaster.get_asset(cross, AssetIdentifier.BLOOMBERG_ID)\n if strike_reference.value == VolReference.DELTA_CALL.value:\n strike_reference = VolReference.DELTA_PUT\n elif strike_reference.value == VolReference.DELTA_PUT.value:\n strike_reference = VolReference.DELTA_CALL\n else:\n cross_asset = asset\n else:\n raise MqValueError('Badly setup cross ' + asset.name)\n\n ref_string, relative_strike = _preprocess_implied_vol_strikes_fx(strike_reference, relative_strike)\n\n if ref_string == 'delta':\n if relative_strike == 0:\n strike = 'DN'\n option_type = OptionType.CALL.value\n else:\n if relative_strike > 0:\n strike = str(relative_strike) + 'D'\n option_type = OptionType.CALL.value\n else:\n strike = str(-relative_strike) + 'D'\n option_type = OptionType.PUT.value\n elif ref_string == VolReference.SPOT.value:\n strike = 'Spot'\n option_type = OptionType.CALL.value\n elif ref_string == VolReference.FORWARD.value:\n strike = 'ATMF'\n option_type = OptionType.CALL.value\n else:\n raise MqValueError('unknown strike_reference and relative_strike combination')\n\n tenor = _tenor_month_to_year(tenor)\n s = implied_volatility_new(\n cross_asset, tenor, strike, option_type, location=location, source=source, real_time=real_time\n )\n return s\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Cross,),\n [MeasureDependency(id_provider=_currencypair_to_tdapi_fxfwd_asset, query_type=QueryType.FWD_POINTS)],\n display_name=\"forward_point\",\n)\ndef fwd_points(\n asset: Asset, settlement_date: str, location: str = None, *, source: str = None, real_time: bool = False\n) -> pd.Series:\n \"\"\"\n GS End-of-day FX forward points for G3, G10, and EM crosses.\n\n :param asset: asset object loaded from security master\n :param settlement_date: relative date representation of expiration date e.g. 1m\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: fwd points\n \"\"\"\n df = _get_fxfwd_data(\n asset=asset,\n settlement_date=settlement_date,\n location=location,\n source=source,\n real_time=real_time,\n query_type=QueryType.FWD_POINTS,\n )\n if real_time:\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['forwardPoint'])\n else:\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['fwdPoints'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Cross,),\n [MeasureDependency(id_provider=_currencypair_to_tdapi_fx_vol_swap_asset, query_type=QueryType.STRIKE_VOL)],\n)\ndef vol_swap_strike(\n asset: Asset,\n expiry_tenor: str,\n strike_type: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day FX Vol Swaps volatilities across major crosses.\n\n :param asset: asset object loaded from security master\n :param expiry_tenor: relative date representation of expiration date e.g. 1m\n :param strike_type: option type (e.g. Put, Call or Straddle )\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: curve of vol swap strike\n \"\"\"\n df = _get_fx_vol_swap_data(\n asset=asset,\n expiry_tenor=expiry_tenor,\n strike_type=strike_type,\n location=location,\n source=source,\n real_time=real_time,\n query_type=QueryType.STRIKE_VOL,\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['strikeVol'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure((AssetClass.FX,), None, [QueryType.FORWARD_POINT])\ndef spot_carry(\n asset: Asset,\n tenor: str,\n annualized: FXSpotCarry = FXSpotCarry.DAILY,\n pricing_location: Optional[PricingLocation] = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n Calculates carry using forward term structure i.e forwardpoints/spot with option to return it in annualized terms.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1m\n :param annualized: whether to annualize carry, one of annualized or daily\n :param pricing_location: EOD location if multiple available example - \"HKG\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: FX spot carry curve\n \"\"\"\n\n if real_time:\n raise NotImplementedError('realtime spot_carry not implemented')\n\n if tenor not in [\n '1m',\n '2m',\n '3m',\n '4m',\n '5m',\n '6m',\n '7m',\n '8m',\n '9m',\n '10m',\n '11m',\n '1y',\n '15m',\n '18m',\n '21m',\n '2y',\n ]:\n raise MqValueError('tenor not included in dataset')\n cross = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n kwargs = dict(\n asset_class='FX',\n type='Forward',\n asset_parameters_pair=cross,\n asset_parameters_settlement_date=tenor,\n name_prefix='FX Forward',\n )\n mqid = _get_tdapi_fxo_assets(**kwargs)\n q = GsDataApi.build_market_data_query([mqid], QueryType.FWD_POINTS, source=source, real_time=real_time)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n if df.empty:\n return pd.Series(dtype=float)\n dataset_ids = getattr(df, 'dataset_ids', ())\n ds = Dataset(dataset_ids[0])\n location = pricing_location if pricing_location else PricingLocation.NYC\n start, end = DataContext.current.start_date, DataContext.current.end_date\n mq_df = ds.get_data(asset_id=mqid, start=start, end=end, pricingLocation=location.value)\n if mq_df.empty:\n return pd.Series(dtype=float)\n mq_df = mq_df.reset_index()\n mq_df['ann_factor'] = mq_df.apply(lambda x: 360 / (x.settlementDate - x.date).days, axis=1)\n mq_df = mq_df.set_index('date')\n\n if annualized == FXSpotCarry.ANNUALIZED:\n mq_df['carry'] = -1 * mq_df['ann_factor'] * mq_df['fwdPoints'] / mq_df['spot']\n else:\n mq_df['carry'] = -1 * mq_df['fwdPoints'] / mq_df['spot']\n series = ExtendedSeries(mq_df['carry'], name='spotCarry')\n series.dataset_ids = dataset_ids\n return series\n" }, { "path": "gs_quant/timeseries/measures_helper.py", "content": "\"\"\"\nCopyright 2021 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom enum import Enum\nfrom numbers import Real\n\nfrom gs_quant.errors import MqValueError\n\n\nclass EdrDataReference(Enum):\n DELTA_CALL = 'delta_call'\n DELTA_PUT = 'delta_put'\n SPOT = 'spot'\n\n\nclass VolReference(Enum):\n DELTA_CALL = 'delta_call'\n DELTA_PUT = 'delta_put'\n DELTA_NEUTRAL = 'delta_neutral'\n NORMALIZED = 'normalized'\n SPOT = 'spot'\n FORWARD = 'forward'\n\n\ndef preprocess_implied_vol_strikes_eq(strike_reference: VolReference = None, relative_strike: Real = None):\n if relative_strike is None and strike_reference != VolReference.DELTA_NEUTRAL:\n raise MqValueError('Relative strike must be provided if your strike reference is not delta_neutral')\n\n if strike_reference == VolReference.DELTA_NEUTRAL:\n raise MqValueError('delta_neutral strike reference is not supported for equities.')\n\n if strike_reference == VolReference.DELTA_PUT:\n relative_strike = abs(100 - relative_strike)\n relative_strike = relative_strike if strike_reference == VolReference.NORMALIZED else relative_strike / 100\n\n ref_string = (\n \"delta\"\n if strike_reference in (VolReference.DELTA_CALL, VolReference.DELTA_PUT, VolReference.DELTA_NEUTRAL)\n else strike_reference.value\n )\n\n return ref_string, relative_strike\n" }, { "path": "gs_quant/timeseries/measures_inflation.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom collections import OrderedDict\nfrom enum import Enum\nfrom typing import Optional, Union\n\nimport pandas as pd\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.data import QueryType, GsDataApi\nfrom gs_quant.common import Currency as CurrencyEnum, AssetClass, AssetType, PricingLocation\nfrom gs_quant.data import DataContext\nfrom gs_quant.datetime import GsCalendar\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.securities import AssetIdentifier, Asset\nfrom gs_quant.timeseries import ASSET_SPEC, plot_measure, MeasureDependency, GENERIC_DATE\nfrom gs_quant.timeseries import ExtendedSeries, measures_rates as tm_rates, check_forward_looking\nfrom gs_quant.timeseries.measures import _asset_from_spec, _market_data_timed, _range_from_pricing_date, _get_custom_bd\nfrom gs_quant.timeseries.measures_rates import _get_term_struct_date\n\n_logger = logging.getLogger(__name__)\n\n\nclass InflationIndexType(Enum):\n AUCPI = 'AUCPI'\n BECPHLTH = 'BECPHLTH'\n CACPI = 'CACPI'\n CPALBE = 'CPALBE'\n CPALEMU = 'CPALEMU'\n CPUPAXFE = 'CPUPAXFE'\n CPURNSA = 'CPURNSA'\n CPUS = 'CPUS'\n CPXTEMU = 'CPXTEMU'\n DNCPINEW = 'DNCPINEW'\n FRCPXTOB = 'FRCPXTOB'\n GKCPIUHL = 'GKCPIUHL'\n GKCPNEWL = 'GKCPNEWL'\n GRCP2010 = 'GRCP2010'\n GRCPTK = 'GRCPTK'\n IECPALL = 'IECPALL'\n IECPEUI = 'IECPEUI'\n IECPINEW = 'IECPINEW'\n ILCPI = 'ILCPI'\n INFINFY = 'INFINFY'\n ISCPIL = 'ISCPIL'\n ITCPI = 'ITCPI'\n ITCPNICT = 'ITCPNICT'\n JCPNGENF = 'JCPNGENF'\n KRCPI = 'KRCPI'\n MXNInfl = 'MXNInfl'\n NECPIND = 'NECPIND'\n NOCPI = 'NOCPI'\n POCPILB = 'POCPILB'\n PPFXFDE = 'PPFXFDE'\n RUCP2000 = 'RUCP2000'\n SACPI = 'SACPI'\n SPCPEU = 'SPCPEU'\n SPIPC = 'SPIPC'\n SWCPI = 'SWCPI'\n UKCPI = 'UKCPI'\n UKCPIH = 'UKCPIH'\n UKRPI = 'UKRPI'\n TESTCPI = 'TESTCPI'\n\n\nclass TdapiInflationRatesDefaultsProvider:\n # flag to indicate that a given property should not be included in asset query\n EMPTY_PROPERTY = \"null\"\n\n def __init__(self, defaults: dict):\n self.defaults = defaults\n benchmark_mappings = {}\n for k, v in defaults.get(\"CURRENCIES\").items():\n for e in v:\n benchmark_mappings[k] = {e.get(\"IndexType\"): e.get('Index')}\n self.defaults['MAPPING'] = benchmark_mappings\n\n def get_index_for_benchmark(self, currency: CurrencyEnum, benchmark: str):\n return self.defaults.get(\"MAPPING\").get(currency.value).get(benchmark)\n\n\nINFLATION_RATES_DEFAULTS = {\n \"CURRENCIES\": {\n \"EUR\": [\n {\"IndexType\": \"CPXTEMU\", \"Index\": \"CPI-CPXTEMU\", \"pricingLocation\": [\"LDN\"]},\n {\"IndexType\": \"FRCPXTOB\", \"Index\": \"CPI-FRCPXTOB\", \"pricingLocation\": [\"LDN\"]},\n ],\n \"USD\": [{\"IndexType\": \"CPURNSA\", \"Index\": \"CPI-CPURNSA\", \"pricingLocation\": [\"NYC\"]}],\n \"GBP\": [{\"IndexType\": \"UKRPI\", \"Index\": \"CPI-UKRPI\", \"pricingLocation\": [\"LDN\"]}],\n \"JPY\": [{\"IndexType\": \"JCPNGENF\", \"Index\": \"CPI-JCPNGENF\", \"pricingLocation\": [\"TKO\"]}],\n },\n \"COMMON\": {\"fixedRate\": \"ATM\", \"clearingHouse\": \"LCH\", \"terminationDate\": \"5y\", \"effectiveDate\": \"0b\"},\n}\ninflationRates_defaults_provider = TdapiInflationRatesDefaultsProvider(INFLATION_RATES_DEFAULTS)\n\nCURRENCY_TO_INDEX_BENCHMARK = {\n 'AUD': OrderedDict([('AUCPI', 'CPI-AUCPI')]),\n 'CAD': OrderedDict([('CACPI', 'CPI-CACPI')]),\n 'DKK': OrderedDict([('DNCPINEW', 'CPI-DNCPINEW')]),\n 'EUR': OrderedDict(\n [\n ('CPXTEMU', 'CPI-CPXTEMU'),\n ('BECPHLTH', 'CPI-BECPHLTH'),\n ('CPALBE', 'CPI-CPALBE'),\n ('CPALEMU', 'CPI-CPALEMU'),\n ('FRCPXTOB', 'CPI-FRCPXTOB'),\n ('GKCPIUHL', 'CPI-GKCPIUHL'),\n ('GKCPNEWL', 'CPI-GKCPNEWL'),\n ('GRCP2010', 'CPI-GRCP2010'),\n ('GRCPTK', 'CPI-GRCPTK'),\n ('IECPALL', 'CPI-IECPALL'),\n ('IECPEUI', 'CPI-IECPEUI'),\n ('IECPINEW', 'CPI-IECPINEW'),\n ('ITCPI', 'CPI-ITCPI'),\n ('ITCPNICT', 'CPI-ITCPNICT'),\n ('MXNInfl', 'CPI-MXNInfl'),\n ('NECPIND', 'CPI-NECPIND'),\n ('SPCPEU', 'CPI-SPCPEU'),\n ('SPIPC', 'CPI-SPIPC'),\n ]\n ),\n 'GBP': OrderedDict([('UKRPI', 'CPI-UKRPI'), ('UKCPI', 'CPI-UKCPI'), ('UKCPIH', 'CPI-UKCPIH')]),\n 'ILS': OrderedDict([('ILCPI', 'CPI-ILCPI'), ('ISCPIL', 'CPI-ISCPIL')]),\n 'INR': OrderedDict([('INFINFY', 'CPI-INFINFY')]),\n 'JPY': OrderedDict([('JCPNGENF', 'CPI-JCPNGENF')]),\n 'KOR': OrderedDict([('KRCPI', 'CPI-KRCPI')]),\n 'NOK': OrderedDict([('NOCPI', 'CPI-NOCPI')]),\n 'PLN': OrderedDict([('POCPILB', 'CPI-POCPILB')]),\n 'RUB': OrderedDict([('RUCP2000', 'CPI-RUCP2000')]),\n 'SEK': OrderedDict([('SWCPI', 'CPI-SWCPI')]),\n 'USD': OrderedDict(\n [('CPURNSA', 'CPI-CPURNSA'), ('CPUPAXFE', 'CPI-CPUPAXFE'), ('CPUS', 'CPI-CPUS'), ('PPFXFDE', 'CPI-PPFXFDE')]\n ),\n 'ZAR': OrderedDict([('SACPI', 'CPI-SACPI')]),\n}\n\nCURRENCY_TO_DUMMY_INFLATION_SWAP_BBID = {\n 'EUR': 'MAJTD8XDA8EJZYRG',\n 'GBP': 'MAW75DV9777630QN',\n 'JPY': 'MA1CENMCA88VXJ28',\n 'USD': 'MA4016GCT3MDRYVY',\n}\n\n\ndef _currency_to_tdapi_inflation_swap_rate_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n # for each currency, get a dummy asset for checking availability\n result = CURRENCY_TO_DUMMY_INFLATION_SWAP_BBID.get(bbid, asset.get_marquee_id())\n return result\n\n\ndef _get_tdapi_inflation_rates_assets(allow_many=False, **kwargs) -> Union[str, list]:\n # sanitize input for asset query.\n if \"pricing_location\" in kwargs:\n del kwargs[\"pricing_location\"]\n assets = GsAssetApi.get_many_assets(**kwargs)\n\n if len(assets) == 0 and ('asset_parameters_clearing_house' in kwargs): # test without the clearing house\n if kwargs['asset_parameters_clearing_house'] == tm_rates._ClearingHouse.NONE.value:\n del kwargs['asset_parameters_clearing_house']\n assets = GsAssetApi.get_many_assets(**kwargs)\n\n if len(assets) > 1:\n # term structure measures need multiple assets\n if (\n ('asset_parameters_termination_date' not in kwargs)\n or ('asset_parameters_effective_date' not in kwargs)\n or allow_many\n ):\n return [asset.id for asset in assets]\n else:\n raise MqValueError('Specified arguments match multiple assets')\n elif len(assets) == 0:\n raise MqValueError('Specified arguments did not match any asset in the dataset' + str(kwargs))\n else:\n return assets[0].id\n\n\ndef _check_inflation_index_type(currency, benchmark_type: Union[InflationIndexType, str]) -> InflationIndexType:\n if isinstance(benchmark_type, str):\n if benchmark_type.upper() in InflationIndexType.__members__:\n benchmark_type = InflationIndexType[benchmark_type.upper()]\n else:\n raise MqValueError(\n benchmark_type\n + ' is not a valid index, pick one among '\n + ', '.join([x.value for x in InflationIndexType])\n )\n\n if (\n isinstance(benchmark_type, InflationIndexType)\n and benchmark_type.value not in CURRENCY_TO_INDEX_BENCHMARK[currency.value].keys()\n ):\n raise MqValueError('%s is not supported for %s', benchmark_type.value, currency.value)\n else:\n return benchmark_type\n\n\ndef _get_inflation_swap_leg_defaults(currency: CurrencyEnum, benchmark_type: InflationIndexType = None) -> dict:\n pricing_location = tm_rates.CURRENCY_TO_PRICING_LOCATION.get(currency, PricingLocation.LDN)\n # default benchmark types\n if benchmark_type is None:\n benchmark_type = InflationIndexType(str(list(CURRENCY_TO_INDEX_BENCHMARK[currency.value].keys())[0]))\n benchmark_type_input = CURRENCY_TO_INDEX_BENCHMARK[currency.value].get(\n benchmark_type.value, \"CPI-\" + benchmark_type.value\n )\n return dict(currency=currency, index_type=benchmark_type_input, pricing_location=pricing_location)\n\n\ndef _get_inflation_swap_csa_terms(curr: str, inflationindextype: str) -> dict:\n return dict(csaTerms=curr + '-1')\n\n\ndef _get_inflation_swap_data(\n asset: Asset,\n swap_tenor: str,\n index_type: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: tm_rates._ClearingHouse = None,\n source: str = None,\n real_time: bool = False,\n query_type: QueryType = QueryType.SWAP_RATE,\n location: PricingLocation = None,\n allow_many=False,\n request_id: Optional[str] = None,\n) -> pd.DataFrame:\n if real_time:\n raise NotImplementedError('realtime inflation swap data not implemented')\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n\n if currency.value not in CURRENCY_TO_INDEX_BENCHMARK.keys():\n raise NotImplementedError('Data not available for {} inflation swap rates'.format(currency.value))\n index_type = _check_inflation_index_type(currency, index_type)\n\n clearing_house = tm_rates._check_clearing_house(clearing_house)\n\n defaults = _get_inflation_swap_leg_defaults(currency, index_type)\n\n if not (tm_rates._is_valid_relative_date_tenor(swap_tenor)):\n raise MqValueError('invalid swap tenor ' + swap_tenor)\n\n forward_tenor = tm_rates._check_forward_tenor(forward_tenor)\n\n fixed_rate = 'ATM'\n kwargs = dict(\n asset_class='Rates',\n type='InflationSwap',\n asset_parameters_termination_date=swap_tenor,\n asset_parameters_index=defaults['index_type'],\n asset_parameters_fixed_rate=fixed_rate,\n asset_parameters_clearing_house=clearing_house.value,\n asset_parameters_effective_date=forward_tenor,\n asset_parameters_notional_currency=currency.name,\n )\n\n rate_mqids = _get_tdapi_inflation_rates_assets(allow_many=allow_many, **kwargs)\n\n if location is None:\n pricing_location = tm_rates._default_pricing_location(currency)\n else:\n pricing_location = PricingLocation(location)\n pricing_location = tm_rates._pricing_location_normalized(pricing_location, currency)\n where = dict(pricingLocation=pricing_location.value)\n entity_ids = [rate_mqids] if not isinstance(rate_mqids, list) else rate_mqids\n\n _logger.debug(\n f'where asset= {rate_mqids}, swap_tenor={swap_tenor}, index={defaults[\"index_type\"]}, '\n f'forward_tenor={forward_tenor}, pricing_location={pricing_location.value}, '\n f'clearing_house={clearing_house.value}, notional_currency={currency.name}, '\n f'request_id={request_id}'\n )\n q = GsDataApi.build_market_data_query(entity_ids, query_type, where=where, source=source, real_time=real_time)\n _logger.debug(f'q: {q}, request_id: {request_id}')\n df = _market_data_timed(q)\n return df\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_inflation_swap_rate_asset, query_type=QueryType.SWAP_RATE)],\n)\ndef inflation_swap_rate(\n asset: Asset,\n swap_tenor: str,\n index_type: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: tm_rates._ClearingHouse = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day Zero Coupon Inflation Swap curves across major currencies.\n\n :param asset: asset object loaded from security master\n :param swap_tenor: relative date representation of expiration date e.g. 1m\n :param index_type: benchmark type e.g. UKRPI\n :param forward_tenor: absolute / relative date representation of forward starting point eg: '1y' or 'Spot' for\n spot starting swaps, 'imm1' or 'frb1'\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swap rate curve\n \"\"\"\n df = _get_inflation_swap_data(\n asset=asset,\n swap_tenor=swap_tenor,\n index_type=index_type,\n forward_tenor=forward_tenor,\n clearing_house=clearing_house,\n source=source,\n real_time=real_time,\n query_type=QueryType.SWAP_RATE,\n location=location,\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['swapRate'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_inflation_swap_rate_asset, query_type=QueryType.SWAP_RATE)],\n)\ndef inflation_swap_term(\n asset: Asset,\n index_type: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n pricing_date: Optional[GENERIC_DATE] = None,\n clearing_house: tm_rates._ClearingHouse = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Forward term structure of GS end-of-day inflation swaps.\n\n :param asset: asset object loaded from security master\n :param index_type: benchmark type e.g. UKRPI\n :param forward_tenor: absolute / relative date representation of forward starting point. Only 'Spot' for spot\n starting swaps currently supported.\n :param pricing_date: YYYY-MM-DD or relative date\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: inflation swap forward term curve\n \"\"\"\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n\n if currency.value not in CURRENCY_TO_INDEX_BENCHMARK.keys():\n raise NotImplementedError('Data not available for {} inflation swap rates'.format(currency.value))\n if location is None:\n location = tm_rates._default_pricing_location(currency)\n else:\n location = PricingLocation(location)\n calendar = location.value\n if pricing_date is not None and pricing_date in list(GsCalendar.get(calendar).holidays):\n raise MqValueError('Specified pricing date is a holiday in {} calendar'.format(calendar))\n start, end = _range_from_pricing_date(calendar, pricing_date)\n with DataContext(start, end):\n df = _get_inflation_swap_data(\n asset=asset,\n swap_tenor=None,\n index_type=index_type,\n forward_tenor=forward_tenor,\n clearing_house=clearing_house,\n source=source,\n real_time=real_time,\n query_type=QueryType.SWAP_RATE,\n location=location,\n allow_many=True,\n request_id=request_id,\n )\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n # TODO: add forward tenor to latest. Technically series index should be latest + forwardTenor + terminationTenor\n # TODO: but that would make it hard to compare term structure btwn different forwardTenors\n # TODO: may be implemented some day when plot can handle different x-axes\n # TODO: As-is, this is consistent with other swap term measures axis handling\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n biz_day = _get_custom_bd(calendar)\n df['expirationDate'] = df['terminationTenor'].apply(_get_term_struct_date, args=(latest, biz_day))\n df = df.set_index('expirationDate')\n df = df.sort_index()\n df = df.loc[DataContext.current.start_date : DataContext.current.end_date]\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['swapRate'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n if series.empty: # Raise descriptive error if no data returned + historical date context\n check_forward_looking(None, source, 'inflation_swap_term')\n return series\n" }, { "path": "gs_quant/timeseries/measures_portfolios.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nfrom typing import Optional, Union\n\nimport pandas as pd\n\nimport gs_quant.timeseries.measures_reports as ReportMeasures\nfrom gs_quant.api.gs.data import QueryType\nfrom gs_quant.api.gs.portfolios import GsPortfolioApi\nfrom gs_quant.entities.entity import EntityType\nfrom gs_quant.markets.portfolio_manager import PortfolioManager\nfrom gs_quant.timeseries import plot_measure_entity\nfrom gs_quant.timeseries.measures import _extract_series_from_df\n\nLOGGER = logging.getLogger(__name__)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.AUM])\ndef aum(\n portfolio_id: str,\n start_date: dt.date = None,\n end_date: dt.date = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Returns the Custom AUM uploaded for the portfolio\n :param portfolio_id: id of portfolio\n :param start_date: start date for getting aum\n :param end_date: end date for getting aum\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio aum values\n \"\"\"\n data = GsPortfolioApi.get_custom_aum(portfolio_id, start_date, end_date)\n df = data if isinstance(data, pd.DataFrame) else pd.DataFrame.from_records(data)\n df = df.set_index(pd.DatetimeIndex(df['date']))\n return _extract_series_from_df(df, QueryType.AUM, True)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.FACTOR_EXPOSURE])\ndef portfolio_factor_exposure(\n portfolio_id: str,\n risk_model_id: str,\n factor_name: str,\n unit: str = 'Notional',\n benchmark_id: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Factor exposure data associated with a factor in a factor risk report\n\n :param portfolio_id: portfolio id\n :param risk_model_id: factor risk model id\n :param factor_name: factor name\n :param unit: unit in which the timeseries is returned (Notional or Percent)\n :param benchmark_id: optional benchmark id\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of factor exposure for requested factor\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n report = pm.get_factor_risk_report(risk_model_id=risk_model_id, benchmark_id=benchmark_id)\n return ReportMeasures.factor_exposure(report.id, factor_name, unit)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.FACTOR_PNL])\ndef portfolio_factor_pnl(\n portfolio_id: str,\n risk_model_id: str,\n factor_name: str,\n unit: str = 'Notional',\n benchmark_id: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Factor PnL data associated with a factor in a factor risk report\n\n :param portfolio_id: portfolio id\n :param risk_model_id: factor risk model id\n :param factor_name: factor name\n :param unit: unit in which the timeseries is returned (Notional or Percent)\n :param benchmark_id: optional benchmark id\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of factor pnl for requested factor\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n report = pm.get_factor_risk_report(risk_model_id=risk_model_id, benchmark_id=benchmark_id)\n return ReportMeasures.factor_pnl(report.id, factor_name, unit)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.FACTOR_PROPORTION_OF_RISK])\ndef portfolio_factor_proportion_of_risk(\n portfolio_id: str,\n risk_model_id: str,\n factor_name: str,\n benchmark_id: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Factor proportion of risk data associated with a factor in a factor risk report\n\n :param portfolio_id: portfolio id\n :param risk_model_id: factor risk model id\n :param factor_name: factor name\n :param benchmark_id: optional benchmark id\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of factor proportion of risk for requested factor\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n report = pm.get_factor_risk_report(risk_model_id=risk_model_id, benchmark_id=benchmark_id)\n return ReportMeasures.factor_proportion_of_risk(report.id, factor_name)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.DAILY_RISK])\ndef portfolio_daily_risk(\n portfolio_id: str,\n risk_model_id: str,\n factor_name: str = 'Total',\n benchmark_id: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Daily risk data associated with a factor in a factor risk report\n\n :param portfolio_id: portfolio id\n :param risk_model_id: factor risk model id\n :param factor_name: factor name (must be \"Factor\", \"Specific\", or \"Total\")\n :param benchmark_id: optional benchmark id\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of daily risk for requested factor\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n report = pm.get_factor_risk_report(risk_model_id=risk_model_id, benchmark_id=benchmark_id)\n return ReportMeasures.daily_risk(report.id, factor_name)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.ANNUAL_RISK])\ndef portfolio_annual_risk(\n portfolio_id: str,\n risk_model_id: str,\n factor_name: str = 'Total',\n benchmark_id: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Annual risk data associated with a factor in a factor risk report\n\n :param portfolio_id: portfolio id\n :param risk_model_id: factor risk model id\n :param factor_name: factor name (must be \"Factor\", \"Specific\", or \"Total\")\n :param benchmark_id: optional benchmark id\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of daily risk for requested factor\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n report = pm.get_factor_risk_report(risk_model_id=risk_model_id, benchmark_id=benchmark_id)\n return ReportMeasures.annual_risk(report.id, factor_name)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.THEMATIC_EXPOSURE])\ndef portfolio_thematic_exposure(\n portfolio_id: str,\n basket_ticker: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Thematic exposure of a portfolio to a requested GS thematic flagship basket\n\n :param portfolio_id: portfolio id\n :param basket_ticker: ticker for thematic basket\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of daily thematic beta of portfolio to requested flagship basket\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n thematic_report = pm.get_thematic_report()\n return ReportMeasures.thematic_exposure(thematic_report.id, basket_ticker)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.PNL])\ndef portfolio_pnl(\n portfolio_id: str,\n unit: str = 'Notional',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n PNL from all holdings, if unit is Percent, geometrically aggregate over time frame\n\n :param portfolio_id: id of portfolio\n :param unit: by default uses mode as Notional, but can also be set to Percent\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio pnl\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.pnl(performance_report.id, unit)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.HIT_RATE])\ndef portfolio_hit_rate(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Create a time series of the hit rate of a portfolio over a given rolling period\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio hit rate\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.hit_rate(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.MAX_DRAWDOWN])\ndef portfolio_max_drawdown(\n portfolio_id: str,\n rolling_window: int,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Create a time series of the max drawdown of a portfolio over a given rolling period\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of portfolio hit rate\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.portfolio_max_drawdown(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.MAX_DRAWDOWN])\ndef portfolio_drawdown_length(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The length in days between the peak and the trough of the maximum drawdown\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the drawdown length\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.drawdown_length(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.MAX_DRAWDOWN])\ndef portfolio_max_recovery_period(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The maximum number of days used to reach a previously broken price level over the stated timeframe.\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of max recovery period\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.max_recovery_period(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.STANDARD_DEVIATION])\ndef portfolio_standard_deviation(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of the total return over the stated time frame.\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of standard deviation\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.standard_deviation(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.DOWNSIDE_RISK])\ndef portfolio_downside_risk(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of the periodic returns that are lower than the mean return over the stated time frame.\n Standard deviation is calculated using all returns, downside risk is calculated using only the returns\n that are below the mean.\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of downside_risk\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.downside_risk(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.DOWNSIDE_RISK])\ndef portfolio_semi_variance(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of the periodic returns that are lower than the mean return over the stated time frame.\n Standard deviation is calculated using all returns, semi variance is calculated using only the returns\n that are below 0.\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of downside_risk\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.semi_variance(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.KURTOSIS])\ndef portfolio_kurtosis(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Kurtosis measure the peakedness or flatness of the return distribution over the state time frame.\n In a flat distribution the average value is more likely to occur.\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of kurtosis\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.kurtosis(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.SKEWNESS])\ndef portfolio_skewness(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Skewness measures the degree of symmetry of the return distribution over the stated time frame.\n If the left tail is more pronounced than the right tail, it is said the return have negative skewness.\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of skewness\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n performance_report = pm.get_performance_report()\n return ReportMeasures.skewness(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.REALIZED_VAR])\ndef portfolio_realized_var(\n portfolio_id: str,\n rolling_window: Union[int, str],\n confidence_interval: float = 0.95,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The maximum expected loss of the portfolios, calculated using\n the natural distribution of returns over a stated time frame and is based on a confidence level.\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param confidence_interval: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of realized var\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.realized_var(performance_report.id, rolling_window, confidence_interval)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.TRACKING_ERROR])\ndef portfolio_tracking_error(\n portfolio_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The standard deviation of the excess return relative to the benchmark over the state time frame.\n\n Tracking Error = sqrt( sum((ExcessRi - ExcessRmean)^2) / (N-1) )\n\n Where:\n\n ExcessRi = excess return in period i\n\n ExcessRmean = mean excess return during bull markets\n\n N = number of periods\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param benchmark_id: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of tracking error\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.tracking_error(performance_report.id, benchmark_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.TRACKING_ERROR_BEAR])\ndef portfolio_tracking_error_bear(\n portfolio_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The standard deviation of the excess return relative to the benchmark over the state time frame,\n only counting the periods when benchmark returns were negative.\n\n Bear Tracking Error = sqrt( sum((ExcessRi - ExcessRmeanBear)^2) / (N-1) ) for all when Benchmark Return < 0\n\n Where:\n\n ExcessRi = excess return in period i\n\n ExcessRmeanBear = mean excess return during bull markets\n\n N = number of periods\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param benchmark_id: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of tracking error\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.tracking_error_bear(performance_report.id, benchmark_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.TRACKING_ERROR_BULL])\ndef portfolio_tracking_error_bull(\n portfolio_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The standard deviation of the excess return relative to the benchmark over the state time frame,\n only counting the periods when benchmark returns were positive.\n\n Bull Tracking Error = sqrt( sum((ExcessRi - ExcessRmeanBull)^2) / (N-1) ) for all when Benchmark Return > 0\n\n Where:\n\n ExcessRi = excess return in period i\n\n ExcessRmeanBull = mean excess return during bull markets\n\n N = number of periods\n\n :param portfolio_id: id of portfolio\n :param rolling_window: the rolling window to look back on\n :param benchmark_id: the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of tracking error\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.tracking_error_bull(performance_report.id, benchmark_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.SHARPE_RATIO])\ndef portfolio_sharpe_ratio(\n portfolio_id: str,\n risk_free_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the risk free rate per unit of volatility\n\n Sharpe Ratio = (Rp - Rf) / σp\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n σp = Standard Deviation of Portfolio Return.\n\n :param portfolio_id: id of portfolio\n :param risk_free_id: id of risk-free asset\n :param rolling_window: size of the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of sharpe ratio\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.portfolio_sharpe_ratio(performance_report.id, risk_free_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.CALMAR_RATIO])\ndef portfolio_calmar_ratio(\n portfolio_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted performance metric that divides an investment strategy’s\n annualized rate of return by its maximum drawdown, focusing specifically\n on the strategy’s return per unit of downside risk.\n\n Calmar Ratio = Annualized Return / Max Drawdown\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of calmar ratio\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.calmar_ratio(performance_report.id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.SORTINO_RATIO])\ndef portfolio_sortino_ratio(\n portfolio_id: str,\n comparison_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the benchmark per unit of semi-variance\n\n Sortino Ratio = (Rp - Rb) / Downside Deviation\n\n Where:\n\n Rp = Portfolio Return\n\n Rb = Benchmark Return\n\n Downside Deviation = Standard Deviation of negative asset returns.\n\n :param portfolio_id: id of portfolio\n :param comparison_id: id of security to compare to\n :param rolling_window: size of the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the sortino ratio\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.sortino_ratio(performance_report.id, comparison_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.INFORMATION_RATIO])\ndef portfolio_information_ratio(\n portfolio_id: str,\n benchmark_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the benchmark,\n per unit of tracking error volatility. It measures the consistency with\n which the portfolio is beating the benchmark.\n\n Information Ratio = (Rp - Rb) / Tracking Error\n\n Where:\n\n Rp = Portfolio Return\n\n Rb = Benchmark Return\n\n Tracking Error = Standard Deviation of the difference between the portfolio and benchmark returns.\n\n :param portfolio_id: id of portfolio\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the information ratio\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.information_ratio(performance_report.id, benchmark_id)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.INFORMATION_RATIO])\ndef portfolio_information_ratio_bull(\n portfolio_id: str,\n benchmark_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the benchmark,\n per unit of tracking error volatility, counting only the periods when the benchmark return was positive.\n\n Information Ratio = (Rp - Rb) / Tracking Error\n\n Where:\n\n Rp = Portfolio Return\n\n Rb = Benchmark Return\n\n Tracking Error = Standard Deviation of the difference between the portfolio and benchmark returns.\n\n :param portfolio_id: id of portfolio\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the information ratio\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.information_ratio_bull(performance_report.id, benchmark_id)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.INFORMATION_RATIO])\ndef portfolio_information_ratio_bear(\n portfolio_id: str,\n benchmark_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the benchmark,\n per unit of tracking error volatility, counting only the periods when the benchmark return was negative\n\n\n Information Ratio = (Rp - Rb) / Tracking Error\n\n Where:\n\n Rp = Portfolio Return\n\n Rb = Benchmark Return\n\n Tracking Error = Standard Deviation of the difference between the portfolio and benchmark returns.\n\n\n :param portfolio_id: id of portfolio\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the information ratio\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.information_ratio_bear(performance_report.id, benchmark_id)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.MODIGLIANI_RATIO])\ndef portfolio_modigliani_ratio(\n portfolio_id: str,\n benchmark_id: str,\n risk_free_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Modigliani RAP measures how much the portfolio would have returns if it had had\n the same risk as the benchmark. It is a linear transformation of the Sharpe Ratio,\n but the results are expressed in terms of performance.\n\n M2 = (Sharpe Ratio * σb) + Rf\n\n Where:\n\n Sharpe Ratio = Portfolio Sharpe Ratio\n\n σb = Standard Deviation of the Benchmark Return\n\n Rf = Risk Free Rate.\n\n :param portfolio_id: id of portfolio\n :param benchmark_id: id of benchmark asset\n :param risk_free_id: id of risk free asset\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the modigliani ratio\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.modigliani_ratio(performance_report.id, benchmark_id, risk_free_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.TREYNOR_RATIO])\ndef portfolio_treynor_measure(\n portfolio_id: str,\n risk_free_id: str,\n benchmark_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the risk free rate\n per unit of beta relative to the benchmark. This is useful for assessing the excess\n return from each unit of systematic risk.\n\n Treynor Ratio = (Rp - Rf) / Beta\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n Beta = Portfolio Beta\n\n :param portfolio_id: id of portfolio\n :param risk_free_id: id of risk-free asset\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the treynor measure\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.treynor_measure(performance_report.id, risk_free_id, benchmark_id)\n\n\ndef portfolio_jensen_alpha(\n portfolio_id: str,\n benchmark_id: str,\n risk_free_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-Adjusted measure that calculates the actual return of the portfolio\n over and above the return predicted by the CAPM, given the portfolios beta\n and benchmark returns.\n\n Jensen's Alpha = Rp - [Rf + Beta * (Rb - Rf)]\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n Beta = Portfolio Beta\n\n Rb = Benchmark Return\n\n :param portfolio_id: id of portfolio\n :param risk_free_id: id of risk-free asset\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of jensen alpha\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.jensen_alpha(performance_report.id, benchmark_id, risk_free_id)\n\n\ndef portfolio_jensen_alpha_bear(\n portfolio_id: str,\n benchmark_id: str,\n risk_free_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-Adjusted measure that calculates the actual return of the portfolio\n over and above the return predicted by the CAPM, given the portfolios beta\n and benchmark returns. calculated based only on the periods when the benchmark return was negative\n\n Jensen's Alpha = Rp - [Rf + Beta * (Rb - Rf)]\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n Beta = Portfolio Beta\n\n Rb = Benchmark Return\n\n :param portfolio_id: id of portfolio\n :param risk_free_id: id of risk-free asset\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of jensen alpha\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.jensen_alpha_bear(performance_report.id, benchmark_id, risk_free_id)\n\n\ndef portfolio_jensen_alpha_bull(\n portfolio_id: str,\n benchmark_id: str,\n risk_free_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-Adjusted measure that calculates the actual return of the portfolio\n over and above the return predicted by the CAPM, given the portfolios beta\n and benchmark returns. calculated based only on the periods when the benchmark return was positive\n\n Jensen's Alpha = Rp - [Rf + Beta * (Rb - Rf)]\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n Beta = Portfolio Beta\n\n Rb = Benchmark Return\n\n :param portfolio_id: id of portfolio\n :param risk_free_id: id of risk-free asset\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of jensen alpha\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.jensen_alpha_bull(performance_report.id, benchmark_id, risk_free_id)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.ALPHA])\ndef portfolio_alpha(\n portfolio_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n This is the intercept from a regression analysis, where the portfolio's returns are regressed\n against the benchmark's returns. It represents the portion of the portfolio's return that is\n not explained by the benchmark's performance\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of the rolling window to look back on\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the beta\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.alpha(performance_report.id, benchmark_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.BETA])\ndef portfolio_beta(\n portfolio_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Beta defined as the slope of the regression between report pnl and benchmark pnl\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of the rolling window to look back on\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the beta\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.portfolio_beta(performance_report.id, benchmark_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.CORRELATION])\ndef portfolio_correlation(\n portfolio_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Correlation coefficient between the portfolio and the benchmark returns over the stated time frame\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of the rolling window to look back on\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the correlation coefficient\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.portfolio_correlation(performance_report.id, benchmark_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.R_SQUARED])\ndef portfolio_r_squared(\n portfolio_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n A measure of how well the portfolio's performance correlates with the performance of the benchmark,\n and thus a measure of what portion of its performance may be explained by the performance of the benchmark.\n\n :param portfolio_id: id of portfolio\n :param rolling_window: size of the rolling window to look back on\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio r squared\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.r_squared(performance_report.id, benchmark_id, rolling_window)\n\n\n@plot_measure_entity(EntityType.PORTFOLIO, [QueryType.CAPTURE_RATIO])\ndef portfolio_capture_ratio(\n portfolio_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n A measure of how well portfolio is doing relative to the benchmark,\n defined as the ratio of the portfolio return to the benchmark return calculated\n on a daily basis and averaged over the stated time period.\n\n :param portfolio_id: id of portfolio\n :param benchmark_id: id of benchmark asset\n :param rolling_window: size of the rolling window to look back on\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio capture ratio\n \"\"\"\n pm = PortfolioManager(portfolio_id)\n\n performance_report = pm.get_performance_report()\n return ReportMeasures.capture_ratio(performance_report.id, benchmark_id, rolling_window)\n" }, { "path": "gs_quant/timeseries/measures_rates.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport re\nfrom collections import OrderedDict\nfrom enum import Enum\nfrom typing import Optional, Union, Dict, List\n\nimport pandas as pd\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.data import QueryType, GsDataApi\nfrom gs_quant.common import Currency as CurrencyEnum, AssetClass, AssetType, PricingLocation, SwapClearingHouse\nfrom gs_quant.data import DataContext, Dataset\nfrom gs_quant.datetime.gscalendar import GsCalendar\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.instrument import IRSwap\nfrom gs_quant.markets.securities import AssetIdentifier, Asset\nfrom gs_quant.timeseries import currency_to_default_ois_asset, convert_asset_for_rates_data_set, RatesConversionType\nfrom gs_quant.timeseries.helper import _to_offset, check_forward_looking, plot_measure, Entitlement\nfrom gs_quant.timeseries.measures import (\n _market_data_timed,\n _range_from_pricing_date,\n _get_custom_bd,\n ExtendedSeries,\n SwaptionTenorType,\n _extract_series_from_df,\n GENERIC_DATE,\n _asset_from_spec,\n ASSET_SPEC,\n MeasureDependency,\n _logger,\n)\n\n\n# TODO: Use gs_quant object\nclass _ClearingHouse(Enum):\n LCH = 'LCH'\n EUREX = 'EUREX'\n JSCC = 'JSCC'\n CME = 'CME'\n NONE = 'NONE'\n\n\nclass _SwapTenorType(Enum):\n FORWARD_TENOR = 'forward_tenor'\n SWAP_TENOR = 'swap_tenor'\n\n\nclass EventType(Enum):\n MEETING = 'Meeting Forward'\n EOY = 'EOY Forward'\n SPOT = 'Spot'\n\n\nclass RateType(Enum):\n ABSOLUTE = 'absolute'\n RELATIVE = 'relative'\n\n\nCCY_TO_CB = {'EUR': 'ecb', 'USD': 'frb', 'GBP': 'mpc'}\n\nCENTRAL_BANK_WATCH_START_DATE = dt.date(2016, 1, 1)\n\n\nclass TdapiRatesDefaultsProvider:\n # flag to indicate that a given property should not be included in asset query\n EMPTY_PROPERTY = \"null\"\n\n def __init__(self, defaults: dict):\n self.defaults = defaults\n benchmark_mappings = {}\n for k, v in defaults.get(\"CURRENCIES\").items():\n benchmark_mappings[k] = {e.get(\"benchmarkType\"): e.get('floatingRateOption') for e in v}\n self.defaults['MAPPING'] = benchmark_mappings\n\n def is_supported(self, currency: CurrencyEnum):\n return currency.value in self.defaults.get(\"CURRENCIES\").keys()\n\n def get_floating_rate_option_for_benchmark(self, currency: CurrencyEnum, benchmark: str):\n return self.defaults.get(\"MAPPING\").get(currency.value).get(benchmark)\n\n def get_swaption_parameter(self, currency, field, value=None):\n if value == self.EMPTY_PROPERTY:\n return None\n if value is not None:\n return value\n if isinstance(currency, str):\n currency_name = currency\n else:\n currency_name = currency.value\n for entry in [self.defaults.get(\"CURRENCIES\").get(currency_name)[0], self.defaults.get(\"COMMON\")]:\n if field in entry:\n value = entry[field][0] if isinstance(entry[field], list) else entry[field]\n return value\n\n\nSWAPTION_DEFAULTS = {\n \"CURRENCIES\": {\n \"AUD\": [\n {\n \"benchmarkType\": \"BBR\",\n \"floatingRateOption\": \"AUD-BBR-BBSW\",\n \"floatingRateTenor\": [\"6m\", \"3m\"],\n \"assetIdForAvailabilityCheck\": \"MAQHSC1PAF4X5H4B\",\n \"pricingLocation\": [\"TKO\"],\n }\n ],\n \"EUR\": [\n {\n \"benchmarkType\": \"LIBOR\",\n \"floatingRateOption\": \"EUR-EURIBOR-TELERATE\",\n \"floatingRateTenor\": [\"6m\", \"3m\"],\n \"assetIdForAvailabilityCheck\": \"MAZB3PAH8JFVVT80\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"benchmarkType\": \"EURIBOR\",\n \"floatingRateOption\": \"EUR-EURIBOR-TELERATE\",\n \"floatingRateTenor\": [\"6m\", \"3m\"],\n \"assetIdForAvailabilityCheck\": \"MAZB3PAH8JFVVT80\",\n \"pricingLocation\": [\"LDN\"],\n },\n ],\n \"GBP\": [\n {\n \"benchmarkType\": \"LIBOR\",\n \"floatingRateOption\": \"GBP-LIBOR-BBA\",\n \"floatingRateTenor\": [\"6m\", \"3m\"],\n \"assetIdForAvailabilityCheck\": \"MAX2SBXZRPYR3NTY\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"benchmarkType\": \"SONIA\",\n \"floatingRateOption\": \"GBP-SONIA-COMPOUND\",\n \"floatingRateTenor\": [\"1y\", \"6m\", \"3m\"],\n \"assetIdForAvailabilityCheck\": \"MAQC2E5J9X6WGGCJ\",\n \"pricingLocation\": [\"LDN\"],\n },\n ],\n \"JPY\": [\n {\n \"benchmarkType\": \"LIBOR\",\n \"floatingRateOption\": \"JPY-LIBOR-BBA\",\n \"floatingRateTenor\": [\"6m\"],\n \"assetIdForAvailabilityCheck\": \"MATT7CA7PRA4B8YB\",\n \"pricingLocation\": [\"TKO\"],\n }\n ],\n \"KRW\": [\n {\n \"benchmarkType\": \"KSDA\",\n \"floatingRateOption\": \"KRW-CD-KSDA-BLOOMBERG\",\n \"floatingRateTenor\": [\"3m\"],\n \"assetIdForAvailabilityCheck\": \"MAMNSGB00G4ZCWMP\",\n \"pricingLocation\": [\"TKO\"],\n }\n ],\n \"NZD\": [\n {\n \"benchmarkType\": \"BBR\",\n \"floatingRateOption\": \"NZD-BBR-FRA\",\n \"floatingRateTenor\": [\"3m\"],\n \"assetIdForAvailabilityCheck\": \"MAHGK129ZCWCEG33\",\n \"pricingLocation\": [\"TKO\"],\n }\n ],\n \"USD\": [\n {\n \"benchmarkType\": \"LIBOR\",\n \"floatingRateOption\": \"USD-LIBOR-BBA\",\n \"floatingRateTenor\": [\"3m\", \"6m\"],\n \"assetIdForAvailabilityCheck\": \"MAY0X3KRD4AN77E2\",\n \"strikeReference\": [\"ATM\"],\n \"pricingLocation\": [\"NYC\"],\n },\n {\n \"benchmarkType\": \"SOFR\",\n \"floatingRateOption\": \"USD-SOFR-COMPOUND\",\n \"floatingRateTenor\": [\"1y\", \"6m\", \"3m\"],\n \"assetIdForAvailabilityCheck\": \"MANYJ1AWNEX5C7FY\",\n \"strikeReference\": [\"ATM\"],\n \"pricingLocation\": [\"NYC\"],\n },\n ],\n },\n \"COMMON\": {\n \"strikeReference\": \"ATM\",\n \"clearingHouse\": \"LCH\",\n \"terminationTenor\": \"5y\",\n \"expirationTenor\": \"1y\",\n \"effectiveDate\": \"0b\",\n },\n}\nswaptions_defaults_provider = TdapiRatesDefaultsProvider(SWAPTION_DEFAULTS)\n\nCURRENCY_TO_SWAP_RATE_BENCHMARK = {\n 'AUD': OrderedDict([('BBR', 'AUD-BBR-BBSW'), ('AONIA', 'AUD-AONIA-OIS-COMPOUND')]),\n 'BRL': {'CDI': 'BRR-CDI-COMPOUNDED'},\n 'CAD': OrderedDict([('CDOR', 'CAD-BA-CDOR'), ('CORRA', 'CAD-CORRA-OIS-COMP')]),\n 'CHF': OrderedDict([('LIBOR', 'CHF-LIBOR-BBA'), ('SARON', 'CHF-SARON-OIS-COMPOUND')]),\n 'CLP': {'TNA': 'CLP-ICP-CAMARA'},\n 'CNY': {'REPO': 'CNY-REPO RATE'},\n 'COP': {'IBR': 'COP-IBR-ON'},\n 'CZK': {'PRIBOR': 'CZK-PRIBOR-PRBO'},\n 'DKK': OrderedDict([('CIBOR', 'DKK-CIBOR2-DKNA13'), ('OIS', 'DKK-DKKOIS-OIS-COMPOUND')]),\n 'EUR': OrderedDict(\n [('EURIBOR', 'EUR-EURIBOR-TELERATE'), ('EONIA', 'EUR-EONIA-OIS-COMPOUND'), ('EUROSTR', 'EUR-EUROSTR-COMPOUND')]\n ),\n 'GBP': OrderedDict([('LIBOR', 'GBP-LIBOR-BBA'), ('SONIA', 'GBP-SONIA-COMPOUND')]),\n 'HKD': {'HIBOR': 'HKD-HIBOR-HKAB'},\n 'HUF': {'BIBOR': 'HUF-BIBOR-BUB'},\n 'ILS': {'TELBOR': 'ILS-TELBOR-FCI'},\n 'INR': {'MIBOR': 'INR-MIBOR-OIS-COMPOUND'},\n 'JPY': OrderedDict([('LIBOR', 'JPY-LIBOR-BBA'), ('TONA', 'JPY-TONA-OIS-COMPOUND')]),\n 'KRW': {'KSDA': 'KRW-CD-KSDA-BLOOMBERG'},\n 'MXN': {'TIIE': 'MXN-TIIE-FX'},\n 'NOK': OrderedDict([('NIBOR', 'NOK-NIBOR-BBA'), ('NOWA', 'NOK-NOWA-OIS-COMPOUND')]),\n 'NZD': OrderedDict([('BBR', 'NZD-BBR-FRA'), ('NZIONA', 'NZD-NZIONA-OIS-COMPOUND')]),\n 'PLN': {'WIBOR': 'PLZ-WIBOR-WIBO'},\n 'RUB': {'MOSPRIME': 'RUB-MOSPRIME-NFEA'},\n 'SEK': OrderedDict([('STIBOR', 'SEK-STIBOR-SIDE'), ('SIOR', 'SEK-SIOR-OIS-COMPOUND')]),\n 'SGD': OrderedDict([('SOR', 'SGD-SOR-VWAP'), ('SORA', 'SGD-SORA-COMPOUND')]),\n 'THB': {'THOR': 'THB-THOR-COMPOUND'},\n 'USD': OrderedDict(\n [('LIBOR', 'USD-LIBOR-BBA'), ('Fed_Funds', 'USD-Federal Funds-H.15-OIS-COMP'), ('SOFR', 'USD-SOFR-COMPOUND')]\n ),\n 'ZAR': {'JIBAR': 'ZAR-JIBAR-SAFEX'},\n}\n# TODO Join into single object.\nBENCHMARK_TO_DEFAULT_FLOATING_RATE_TENORS = {\n 'BRR-CDI-COMPOUNDED': '1b',\n 'AUD-BBR-BBSW': '6m',\n 'AUD-AONIA-OIS-COMPOUND': '1y',\n 'CAD-BA-CDOR': '3m',\n 'CAD-CORRA-OIS-COMP': '3m',\n 'CHF-LIBOR-BBA': '6m',\n 'CHF-SARON-OIS-COMPOUND': '1y',\n 'CLP-ICP-CAMARA': '1b',\n 'CNY-REPO RATE': '1w',\n 'COP-IBR-ON': '1b',\n 'CZK-PRIBOR-PRBO': '6m',\n 'DKK-CIBOR2-DKNA13': '6m',\n 'DKK-DKKOIS-OIS-COMPOUND': '1y',\n 'EUR-EURIBOR-TELERATE': '6m',\n 'EUR-EUROSTR-COMPOUND': '1y',\n 'EUR-EONIA-OIS-COMPOUND': '1y',\n 'GBP-LIBOR-BBA': '6m',\n 'GBP-SONIA-COMPOUND': '1y',\n 'HKD-HIBOR-HKAB': '3m',\n 'HUF-BIBOR-BUB': '6m',\n 'INR-MIBOR-OIS-COMPOUND': '6m',\n 'ILS-TELBOR-FCI': '3m',\n 'JPY-LIBOR-BBA': '6m',\n 'JPY-TONA-OIS-COMPOUND': '1y',\n 'KRW-CD-KSDA-BLOOMBERG': '3m',\n 'MXN-TIIE-FX': '28d',\n 'NOK-NIBOR-BBA': '6m',\n 'NOK-NOWA-OIS-COMPOUND': '1y',\n 'NZD-BBR-FRA': '3m',\n 'NZD-NZIONA-OIS-COMPOUND': '1y',\n 'PLZ-WIBOR-WIBO': '6m',\n 'RUB-MOSPRIME-NFEA': '3m',\n 'SEK-STIBOR-SIDE': '6m',\n 'SEK-SIOR-OIS-COMPOUND': '1y',\n 'SGD-SOR-VWAP': '6m',\n 'SGD-SORA-COMPOUND': '3m',\n 'THB-THOR-COMPOUND': '3m',\n 'USD-LIBOR-BBA': '3m',\n 'USD-Federal Funds-H.15-OIS-COMP': '1y',\n 'USD-SOFR-COMPOUND': '1y',\n 'ZAR-JIBAR-SAFEX': '3m',\n}\nCURRENCY_TO_PRICING_LOCATION = {\n CurrencyEnum.JPY: PricingLocation.TKO,\n CurrencyEnum.USD: PricingLocation.NYC,\n CurrencyEnum.AUD: PricingLocation.TKO,\n CurrencyEnum.NZD: PricingLocation.TKO,\n CurrencyEnum.CNY: PricingLocation.HKG,\n CurrencyEnum.HKD: PricingLocation.HKG,\n CurrencyEnum.INR: PricingLocation.HKG,\n CurrencyEnum.KRW: PricingLocation.HKG,\n CurrencyEnum.SGD: PricingLocation.HKG,\n CurrencyEnum.CAD: PricingLocation.NYC,\n CurrencyEnum.EUR: PricingLocation.LDN,\n CurrencyEnum.GBP: PricingLocation.LDN,\n CurrencyEnum.CHF: PricingLocation.LDN,\n CurrencyEnum.DKK: PricingLocation.LDN,\n CurrencyEnum.NOK: PricingLocation.LDN,\n CurrencyEnum.SEK: PricingLocation.LDN,\n CurrencyEnum.BRL: PricingLocation.NYC,\n CurrencyEnum.COP: PricingLocation.NYC,\n CurrencyEnum.CLP: PricingLocation.NYC,\n CurrencyEnum.MXN: PricingLocation.NYC,\n}\n\nCURRENCY_TO_DUMMY_SWAP_BBID = {\n 'CHF': 'MAW25BGQJH9P6DPT',\n 'EUR': 'MAA9MVX15AJNQCVG',\n 'GBP': 'MA6QCAP9B7ABS9HA',\n 'JPY': 'MAEE219J5ZP0ZKRK',\n 'SEK': 'MAETMVTPNP3199A5',\n 'USD': 'MAFRSWPAF5QPNTP2',\n 'DKK': 'MAF131NKWVRESFYA',\n 'NOK': 'MA25DW5ZGC1BSC8Y',\n 'HKD': 'MABRNGY8XRFVC36N',\n 'NZD': 'MAH16NHE1HBN0FBZ',\n 'AUD': 'MAY8147CRK0ZP53B',\n 'CNY': 'MA4K1D8HH2R0RQY5',\n 'CAD': 'MANJ8SS88WJ6N28Q',\n 'KRW': 'MAP55AXG5SQVS6C5',\n 'INR': 'MA20JHJXN1PD5HGE',\n 'SGD': 'MA5CQFHYBPH9E5BS',\n 'BRL': 'MATPPVN02HJ4M9NS',\n 'COP': 'MADA0AGFQ65CTMWF',\n 'CLP': 'MAP8A0SHH9Q86SXC',\n 'MXN': 'MAAJ9RAHYBAXGYD2',\n}\n\nSUPPORTED_INTRADAY_CURRENCY_TO_DUMMY_SWAP_BBID = {\n 'CHF': 'MACF6R4J5FY4KGBZ',\n 'EUR': 'MACF6R4J5FY4KGBZ',\n 'GBP': 'MACF6R4J5FY4KGBZ',\n 'JPY': 'MACF6R4J5FY4KGBZ',\n 'SEK': 'MACF6R4J5FY4KGBZ',\n 'USD': 'MACF6R4J5FY4KGBZ',\n 'DKK': 'MACF6R4J5FY4KGBZ',\n 'NOK': 'MACF6R4J5FY4KGBZ',\n 'NZD': 'MACF6R4J5FY4KGBZ',\n 'AUD': 'MACF6R4J5FY4KGBZ',\n 'CAD': 'MACF6R4J5FY4KGBZ',\n}\n\n# FXFwd XCCYSwap rates Defaults\nCROSS_BBID_TO_DUMMY_OISXCCY_ASSET = {\n 'EURUSD': 'MA1VJC1E3SZW8E4S',\n 'GBPUSD': 'MA3JTR4HSC63H4V6',\n 'AUDUSD': 'MAD4VBRWYXFSY1N4',\n 'NZDUSD': 'MA1YHQMZVTM3VBWT',\n 'USDSEK': 'MA2APZREBGDMME83',\n 'USDNOK': 'MA0K3W6FKH6K1KJE',\n 'USDDKK': 'MA328HZB86DYSWSJ',\n 'USDCAD': 'MAT8JNEE2GN5NES6',\n 'USDCHF': 'MABNGGTNB9A0TKCG',\n 'USDJPY': 'MAMZ9YG8AF3HQ18C',\n}\n\nCURRENCY_TO_CSA_DEFAULT_MAP = {'USD': 'USD-SOFR', 'EUR': 'EUR-EUROSTR'}\n\n\ndef _pricing_location_normalized(location: PricingLocation, ccy: CurrencyEnum) -> PricingLocation:\n if location == PricingLocation.HKG or location == PricingLocation.TKO:\n if ccy in CURRENCY_TO_PRICING_LOCATION.keys() and PricingLocation.HKG == CURRENCY_TO_PRICING_LOCATION.get(\n ccy, PricingLocation.LDN\n ):\n return PricingLocation.HKG\n else:\n return PricingLocation.TKO\n else:\n return location\n\n\ndef _default_pricing_location(ccy: CurrencyEnum) -> PricingLocation:\n if ccy in CURRENCY_TO_PRICING_LOCATION.keys():\n return CURRENCY_TO_PRICING_LOCATION.get(ccy, PricingLocation.LDN)\n else:\n raise MqValueError('No default location set for currency ' + ccy.value + ', please provide one.')\n\n\ndef _cross_to_fxfwd_xcswp_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n # for each currency, get a dummy asset for checking availability\n result = CROSS_BBID_TO_DUMMY_OISXCCY_ASSET.get(bbid, asset.get_marquee_id())\n return result\n\n\ndef _currency_to_tdapi_swap_rate_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n # for each currency, get a dummy asset for checking availability\n result = CURRENCY_TO_DUMMY_SWAP_BBID.get(bbid, asset.get_marquee_id())\n return result\n\n\ndef _currency_to_tdapi_swap_rate_asset_for_intraday(asset_spec: ASSET_SPEC) -> str:\n return 'MACF6R4J5FY4KGBZ'\n\n\ndef _currency_to_tdapi_asset_base(asset_spec: ASSET_SPEC, allowed_bbids=None) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n if bbid is None or (allowed_bbids and bbid not in allowed_bbids):\n return asset.get_marquee_id()\n try:\n result = swaptions_defaults_provider.get_swaption_parameter(bbid, \"assetIdForAvailabilityCheck\")\n except TypeError:\n logging.info(\"No assetIdForAvailabilityCheck for\" + bbid)\n return asset.get_marquee_id()\n return result\n\n\ndef _currency_to_tdapi_midcurve_asset(asset_spec: ASSET_SPEC) -> str:\n return _currency_to_tdapi_asset_base(asset_spec, ['GBP', 'EUR', 'USD'])\n\n\ndef _currency_to_tdapi_swaption_rate_asset(asset_spec: ASSET_SPEC) -> str:\n return _currency_to_tdapi_asset_base(asset_spec)\n\n\ndef _currency_to_tdapi_basis_swap_rate_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n # for each currency, get a dummy asset for checking availability\n if bbid == 'EUR':\n result = 'MAGRG2VT11GQ2RQ9'\n elif bbid == 'GBP':\n result = 'MAHCYNB3V75JC5Q8'\n elif bbid == 'JPY':\n result = 'MAXVRBEZCJVH0C4V'\n elif bbid == 'USD':\n result = 'MAQB1PGEJFCET3GG'\n elif bbid == 'CAD':\n result = 'MARVD2E65AWEXXBA'\n elif bbid == 'AUD':\n result = 'MAY8H7HCNZ85FJKM'\n elif bbid == 'NZD':\n result = 'MAWK15C0P3SM6C7Q'\n elif bbid == 'SEK':\n result = 'MAS2NJCYHDHP8P0X'\n elif bbid == 'NOK':\n result = 'MAPXC5YBPZJZXYMZ'\n elif bbid == 'DKK':\n result = 'MA2164KK5DMYA561'\n elif bbid == 'CHF':\n result = 'MA7ZHB9T0PF1SB96'\n else:\n return asset.get_marquee_id()\n return result\n\n\ndef _match_floating_tenors(swap_args) -> dict:\n payer_index = swap_args['asset_parameters_payer_rate_option']\n receiver_index = swap_args['asset_parameters_receiver_rate_option']\n\n if payer_index != receiver_index:\n if 'SOFR' in payer_index:\n swap_args['asset_parameters_payer_designated_maturity'] = swap_args[\n 'asset_parameters_receiver_designated_maturity'\n ]\n if swap_args['asset_parameters_payer_designated_maturity'] == \"12m\":\n swap_args['asset_parameters_payer_designated_maturity'] = \"1y\"\n elif 'SOFR' in receiver_index:\n swap_args['asset_parameters_receiver_designated_maturity'] = swap_args[\n 'asset_parameters_payer_designated_maturity'\n ]\n if swap_args['asset_parameters_receiver_designated_maturity'] == \"12m\":\n swap_args['asset_parameters_receiver_designated_maturity'] = \"1y\"\n elif 'LIBOR' in payer_index or 'EURIBOR' in payer_index or 'STIBOR' in payer_index:\n swap_args['asset_parameters_receiver_designated_maturity'] = swap_args[\n 'asset_parameters_payer_designated_maturity'\n ]\n elif 'LIBOR' in receiver_index or 'EURIBOR' in receiver_index or 'STIBOR' in receiver_index:\n swap_args['asset_parameters_payer_designated_maturity'] = swap_args[\n 'asset_parameters_receiver_designated_maturity'\n ]\n return swap_args\n\n\ndef _get_tdapi_rates_assets(allow_many=False, **kwargs) -> Union[str, list]:\n # sanitize input for asset query.\n if \"pricing_location\" in kwargs:\n del kwargs[\"pricing_location\"]\n assets = GsAssetApi.get_many_assets(**kwargs)\n # change order of basis swap legs and check if swap in dataset\n if len(assets) == 0 and ('asset_parameters_payer_rate_option' in kwargs): # flip legs\n kwargs['asset_parameters_payer_rate_option'], kwargs['asset_parameters_receiver_rate_option'] = (\n kwargs['asset_parameters_receiver_rate_option'],\n kwargs['asset_parameters_payer_rate_option'],\n )\n\n (\n kwargs['asset_parameters_payer_designated_maturity'],\n kwargs['asset_parameters_receiver_designated_maturity'],\n ) = (\n kwargs['asset_parameters_receiver_designated_maturity'],\n kwargs['asset_parameters_payer_designated_maturity'],\n )\n\n assets = GsAssetApi.get_many_assets(**kwargs)\n\n if len(assets) > 1:\n # term structure measures need multiple assets\n if (\n ('asset_parameters_termination_date' not in kwargs)\n or ('asset_parameters_effective_date' not in kwargs)\n or allow_many\n ):\n return [asset.id for asset in assets]\n else:\n raise MqValueError('Specified arguments match multiple assets')\n elif len(assets) == 0:\n raise MqValueError('Specified arguments did not match any asset in the dataset' + str(kwargs))\n else:\n return assets[0].id\n\n\ndef _check_forward_tenor(forward_tenor) -> GENERIC_DATE:\n if isinstance(forward_tenor, dt.date):\n return forward_tenor\n elif forward_tenor in ['Spot', 'spot', 'SPOT']:\n return '0b'\n elif not (\n _is_valid_relative_date_tenor(forward_tenor) or re.fullmatch('(imm[1-4]|frb[1-9]|ecb[1-9])', forward_tenor)\n ):\n raise MqValueError('invalid forward tenor ' + forward_tenor)\n else:\n return forward_tenor\n\n\nclass BenchmarkType(Enum):\n LIBOR = 'LIBOR'\n EURIBOR = 'EURIBOR'\n EUROSTR = 'EUROSTR'\n STIBOR = 'STIBOR'\n OIS = 'OIS'\n CDKSDA = 'CDKSDA'\n SOFR = 'SOFR'\n SARON = 'SARON'\n EONIA = 'EONIA'\n SONIA = 'SONIA'\n TONA = 'TONA'\n Fed_Funds = 'Fed_Funds'\n NIBOR = 'NIBOR'\n CIBOR = 'CIBOR'\n BBR = 'BBR'\n BA = 'BA'\n KSDA = 'KSDA'\n REPO = 'REPO'\n SOR = 'SOR'\n HIBOR = 'HIBOR'\n MIBOR = 'MIBOR'\n CDOR = 'CDOR'\n CDI = 'CDI'\n TNA = 'TNA'\n IBR = 'IBR'\n TIIE = 'TIIE'\n AONIA = 'AONIA'\n NZIONA = 'NZIONA'\n NOWA = 'NOWA'\n CORRA = 'CORRA'\n SIOR = 'SIOR'\n SORA = 'SORA'\n\n\ndef _check_benchmark_type(\n currency, benchmark_type: Union[BenchmarkType, str], nothrow: bool = False\n) -> Union[BenchmarkType, str]:\n if isinstance(benchmark_type, str):\n if benchmark_type.upper() in BenchmarkType.__members__:\n benchmark_type = BenchmarkType[benchmark_type.upper()]\n elif benchmark_type in ['fed_funds', 'Fed_Funds', 'FED_FUNDS']:\n benchmark_type = BenchmarkType.Fed_Funds\n elif benchmark_type in ['estr', 'ESTR', 'eurostr', 'EuroStr']:\n benchmark_type = BenchmarkType.EUROSTR\n elif not nothrow:\n raise MqValueError(\n f'{benchmark_type} is not valid, pick one among ' + ', '.join([x.value for x in BenchmarkType])\n )\n else:\n return benchmark_type\n\n if (\n isinstance(benchmark_type, BenchmarkType)\n and benchmark_type.value not in CURRENCY_TO_SWAP_RATE_BENCHMARK[currency.value].keys()\n ):\n raise MqValueError(f'{benchmark_type.value} is not supported for {currency.value}')\n else:\n return benchmark_type\n\n\ndef _check_clearing_house(clearing_house: Union[_ClearingHouse, str]) -> _ClearingHouse:\n if isinstance(clearing_house, str) and clearing_house.upper() in _ClearingHouse.__members__:\n clearing_house = _ClearingHouse[clearing_house.upper()]\n\n if clearing_house is None:\n return _ClearingHouse.LCH\n elif isinstance(clearing_house, _ClearingHouse):\n return clearing_house\n else:\n raise MqValueError(\n 'invalid clearing house: '\n + clearing_house\n + ' choose one among '\n + ', '.join([ch.value for ch in _ClearingHouse])\n )\n\n\ndef _check_tenor_type(tenor_type: _SwapTenorType) -> _SwapTenorType:\n if isinstance(tenor_type, str) and tenor_type.upper() in _SwapTenorType.__members__:\n tenor_type = _SwapTenorType[tenor_type.upper()]\n\n if tenor_type is None:\n return _SwapTenorType.FORWARD_TENOR\n elif isinstance(tenor_type, _SwapTenorType):\n return tenor_type\n else:\n raise MqValueError(\n 'invalid tenor_type: ' + tenor_type + ' choose one among ' + ', '.join([ch.value for ch in _SwapTenorType])\n )\n\n\ndef _check_term_structure_tenor(tenor_type: _SwapTenorType, tenor: str) -> Dict:\n if tenor_type == _SwapTenorType.FORWARD_TENOR:\n tenor = _check_forward_tenor(tenor)\n tenor_to_plot = 'terminationTenor'\n tenor_dataset_field = 'asset_parameters_effective_date'\n elif not re.fullmatch('(\\\\d+)([bdwmy])', tenor) or re.fullmatch('(frb[1-9])', tenor):\n raise MqValueError('invalid swap tenor ' + tenor)\n else:\n tenor_to_plot = 'effectiveTenor'\n tenor_dataset_field = 'asset_parameters_termination_date'\n return dict(tenor=tenor, tenor_to_plot=tenor_to_plot, tenor_dataset_field=tenor_dataset_field)\n\n\ndef _get_benchmark_type(currency: CurrencyEnum, benchmark_type: BenchmarkType = None):\n if benchmark_type is None:\n if currency == CurrencyEnum.EUR:\n benchmark_type = BenchmarkType.EURIBOR\n elif currency == CurrencyEnum.SEK:\n benchmark_type = BenchmarkType.STIBOR\n else:\n benchmark_type = BenchmarkType(str(list(CURRENCY_TO_SWAP_RATE_BENCHMARK[currency.value].keys())[0]))\n benchmark_type_input = CURRENCY_TO_SWAP_RATE_BENCHMARK[currency.value][benchmark_type.value]\n\n return benchmark_type_input\n\n\ndef _get_swap_leg_defaults(\n currency: CurrencyEnum, benchmark_type: Union[BenchmarkType, str] = None, floating_rate_tenor: str = None\n) -> dict:\n pricing_location = CURRENCY_TO_PRICING_LOCATION.get(currency, PricingLocation.LDN)\n # default benchmark types\n if not isinstance(benchmark_type, str):\n benchmark_type_input = _get_benchmark_type(currency, benchmark_type)\n else:\n benchmark_type_input = benchmark_type\n # default floating index\n if floating_rate_tenor is None:\n if benchmark_type_input in BENCHMARK_TO_DEFAULT_FLOATING_RATE_TENORS:\n floating_rate_tenor = BENCHMARK_TO_DEFAULT_FLOATING_RATE_TENORS[benchmark_type_input]\n else:\n raise MqValueError(f\"{benchmark_type_input} has no default fixing tenor, please specify one\")\n\n return dict(\n currency=currency,\n benchmark_type=benchmark_type_input,\n floating_rate_tenor=floating_rate_tenor,\n pricing_location=pricing_location,\n )\n\n\ndef _get_swap_csa_terms(curr: str, benchmark_type: str) -> dict:\n euribor_index = CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EURIBOR.value]\n usd_libor_index = CURRENCY_TO_SWAP_RATE_BENCHMARK['USD'][BenchmarkType.LIBOR.value]\n estr_index = CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EUROSTR.value]\n if benchmark_type in [euribor_index, usd_libor_index]:\n return {}\n elif benchmark_type == estr_index:\n return dict(csaTerms=curr + '-EuroSTR')\n else:\n return dict(csaTerms=curr + '-1')\n\n\ndef _get_basis_swap_csa_terms(curr: str, payer_benchmark: str, receiver_benchmark: str) -> dict:\n benchmarks = [payer_benchmark, receiver_benchmark]\n euribor_index: str = CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EURIBOR.value]\n usd_libor_index: str = CURRENCY_TO_SWAP_RATE_BENCHMARK['USD'][BenchmarkType.LIBOR.value]\n estr_index: str = CURRENCY_TO_SWAP_RATE_BENCHMARK['EUR'][BenchmarkType.EUROSTR.value]\n if (euribor_index in benchmarks) or (usd_libor_index in benchmarks):\n return {} # different csaTerms after SOFR and ESTR transitions for a given asset\n elif estr_index in benchmarks:\n return dict(csaTerms=curr + '-EuroSTR')\n else:\n return dict(csaTerms=curr + '-1')\n\n\ndef _get_swap_data(\n asset: Asset,\n swap_tenor: str,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: _ClearingHouse = None,\n source: str = None,\n real_time: bool = False,\n location: PricingLocation = None,\n query_type: QueryType = QueryType.SWAP_RATE,\n) -> pd.DataFrame:\n if real_time and not (query_type == QueryType.SWAP_RATE):\n raise NotImplementedError('realtime swap_rate not implemented for anything but rates')\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n\n if currency.value not in CURRENCY_TO_SWAP_RATE_BENCHMARK.keys():\n raise NotImplementedError('Data not available for {} swap rates'.format(currency.value))\n benchmark_type = _check_benchmark_type(currency, benchmark_type)\n\n clearing_house = _check_clearing_house(clearing_house)\n\n defaults = _get_swap_leg_defaults(currency, benchmark_type, floating_rate_tenor)\n\n if not (re.fullmatch('(\\\\d+)([bdwmy])', swap_tenor) or re.fullmatch('(frb[1-9]|ecb[1-6])', forward_tenor)):\n raise MqValueError('invalid swap tenor ' + swap_tenor)\n\n if not re.fullmatch('(\\\\d+)([bdwmy])', defaults['floating_rate_tenor']):\n raise MqValueError(\n 'invalid floating rate tenor '\n + defaults['floating_rate_tenor']\n + ' for index: '\n + defaults['benchmark_type']\n )\n\n forward_tenor = _check_forward_tenor(forward_tenor)\n fixed_rate = 'ATM'\n\n if location is None:\n pricing_location = _default_pricing_location(currency)\n else:\n pricing_location = PricingLocation(location)\n\n kwargs = dict(\n asset_class='Rates',\n type='Swap',\n asset_parameters_termination_date=swap_tenor,\n asset_parameters_floating_rate_option=defaults['benchmark_type'],\n asset_parameters_fixed_rate=fixed_rate,\n asset_parameters_clearing_house=clearing_house.value,\n asset_parameters_floating_rate_designated_maturity=defaults['floating_rate_tenor'],\n asset_parameters_effective_date=forward_tenor,\n asset_parameters_notional_currency=currency.name,\n )\n\n rate_mqid = _get_tdapi_rates_assets(**kwargs)\n\n _logger.debug(\n 'where asset= %s, swap_tenor=%s, benchmark_type=%s, floating_rate_tenor=%s, forward_tenor=%s, '\n 'pricing_location=%s',\n rate_mqid,\n swap_tenor,\n defaults['benchmark_type'],\n defaults['floating_rate_tenor'],\n forward_tenor,\n pricing_location.value,\n )\n\n pricing_location = _pricing_location_normalized(pricing_location, currency)\n where = dict(pricingLocation=pricing_location.value)\n q = GsDataApi.build_market_data_query([rate_mqid], query_type, where=where, source=source, real_time=real_time)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n return df\n\n\ndef _get_swap_data_calc(\n asset: Asset,\n swap_tenor: str,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n csa: str = None,\n real_time: bool = False,\n location: PricingLocation = None,\n) -> pd.DataFrame:\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n\n benchmark_type = _check_benchmark_type(currency, benchmark_type, True)\n\n clearing_house = SwapClearingHouse.LCH\n if csa in ['EUREX', 'JSCC', 'CME']:\n clearing_house = SwapClearingHouse(csa)\n\n defaults = _get_swap_leg_defaults(currency, benchmark_type, floating_rate_tenor)\n\n if not re.fullmatch('(\\\\d+)([bdwmy])', swap_tenor):\n raise MqValueError('invalid swap tenor ' + swap_tenor)\n\n forward_tenor = _check_forward_tenor(forward_tenor)\n\n builder = IRSwap(\n notional_currency=currency,\n clearing_house=clearing_house,\n floating_rate_designated_maturity=defaults['floating_rate_tenor'],\n floating_rate_option=defaults['benchmark_type'],\n fixed_rate=0.0,\n termination_date=swap_tenor,\n )\n\n if forward_tenor:\n builder.effective_date = forward_tenor\n\n _logger.debug(f'where builder={builder.as_dict()}')\n\n location = location or PricingLocation.NYC\n q = GsDataApi.get_mxapi_backtest_data(builder, close_location=location.value, real_time=real_time, csa=csa)\n return q\n\n\ndef _get_term_struct_date(tenor: Union[str, dt.datetime], index: dt.datetime, business_day) -> dt.datetime:\n if isinstance(tenor, (dt.datetime, dt.date)):\n return tenor\n try:\n year, month, day = tenor.split('-')\n return dt.datetime(int(year), int(month), int(day))\n except ValueError:\n if tenor == '0b':\n return index + business_day - business_day\n else:\n return index + _to_offset(tenor) + business_day - business_day\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swap_rate_asset, query_type=QueryType.SWAP_ANNUITY)],\n)\ndef swap_annuity(\n asset: Asset,\n swap_tenor: str,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: _ClearingHouse = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day Fixed-Floating interest rate swap(IRS) annuity values in years for paying leg across major currencies.\n\n\n :param asset: asset object loaded from security master\n :param swap_tenor: relative date representation of expiration date e.g. 1m\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param forward_tenor: absolute / relative date representation of forward starting point eg: '1y' or 'Spot' for\n spot starting swaps, 'imm1' or 'frb1'\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: annuity of swap\n \"\"\"\n df = _get_swap_data(\n asset=asset,\n swap_tenor=swap_tenor,\n benchmark_type=benchmark_type,\n floating_rate_tenor=floating_rate_tenor,\n forward_tenor=forward_tenor,\n clearing_house=clearing_house,\n source=source,\n real_time=real_time,\n query_type=QueryType.SWAP_ANNUITY,\n location=location,\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(abs(df['swapAnnuity'] * 1e4 / 1e8))\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swaption_rate_asset, query_type=QueryType.SWAPTION_PREMIUM)],\n)\ndef swaption_premium(\n asset: Asset,\n expiration_tenor: str = None,\n termination_tenor: str = None,\n relative_strike: str = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day premium for swaption.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param relative_strike: strike level relative to at the money e.g. 10 or ATM+10\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n df = _get_swaption_measure(\n asset,\n benchmark_type,\n floating_rate_tenor,\n \"0b\",\n expiration_tenor,\n termination_tenor,\n relative_strike,\n clearing_house,\n source=source,\n real_time=real_time,\n start=DataContext.current.start_date,\n end=DataContext.current.end_date,\n query_type=QueryType.SWAPTION_PREMIUM,\n location=location,\n )\n\n return _extract_series_from_df(df, QueryType.SWAPTION_PREMIUM)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swaption_rate_asset, query_type=QueryType.SWAPTION_ANNUITY)],\n)\ndef swaption_annuity(\n asset: Asset,\n expiration_tenor: str = None,\n termination_tenor: str = None,\n relative_strike: float = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day annuity for swaption.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param relative_strike: strike level relative to at the money e.g. 10 or ATM+10\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n\n df = _get_swaption_measure(\n asset,\n benchmark_type,\n floating_rate_tenor,\n \"0b\",\n expiration_tenor,\n termination_tenor,\n relative_strike,\n clearing_house,\n source=source,\n real_time=real_time,\n start=DataContext.current.start_date,\n end=DataContext.current.end_date,\n query_type=QueryType.SWAPTION_ANNUITY,\n location=location,\n )\n return _extract_series_from_df(df, QueryType.SWAPTION_ANNUITY)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_midcurve_asset, query_type=QueryType.MIDCURVE_PREMIUM)],\n)\ndef midcurve_premium(\n asset: Asset,\n expiration_tenor: str,\n forward_tenor: str,\n termination_tenor: str,\n relative_strike: float = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day premium for midcurve\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param forward_tenor: relative date representation of swap's start date after option expiry e.g. 2y\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param relative_strike: strike level relative to at the money e.g. 10 or ATM+10\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n\n df = _get_swaption_measure(\n asset,\n benchmark_type,\n floating_rate_tenor,\n forward_tenor,\n expiration_tenor,\n termination_tenor,\n relative_strike,\n clearing_house,\n source=source,\n real_time=real_time,\n start=DataContext.current.start_date,\n end=DataContext.current.end_date,\n query_type=QueryType.MIDCURVE_PREMIUM,\n location=location,\n )\n return _extract_series_from_df(df, QueryType.MIDCURVE_PREMIUM)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_midcurve_asset, query_type=QueryType.MIDCURVE_ANNUITY)],\n)\ndef midcurve_annuity(\n asset: Asset,\n expiration_tenor: str,\n forward_tenor: str,\n termination_tenor: str,\n relative_strike: float = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day annuity for midcurve.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param forward_tenor: relative date representation of swap's start date after option expiry e.g. 2y\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param relative_strike: strike level relative to at the money e.g. 10 or ATM+10\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n\n df = _get_swaption_measure(\n asset,\n benchmark_type,\n floating_rate_tenor,\n forward_tenor,\n expiration_tenor,\n termination_tenor,\n relative_strike,\n clearing_house,\n source=source,\n real_time=real_time,\n start=DataContext.current.start_date,\n end=DataContext.current.end_date,\n query_type=QueryType.MIDCURVE_ANNUITY,\n location=location,\n )\n return _extract_series_from_df(df, QueryType.MIDCURVE_ANNUITY)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swaption_rate_asset, query_type=QueryType.ATM_FWD_RATE)],\n)\ndef swaption_atm_fwd_rate(\n asset: Asset,\n expiration_tenor: str = None,\n termination_tenor: str = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day atm forward rate for swaption vol matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n df = _get_swaption_measure(\n asset,\n benchmark_type=benchmark_type,\n floating_rate_tenor=floating_rate_tenor,\n effective_date=\"0b\",\n expiration_tenor=expiration_tenor,\n termination_tenor=termination_tenor,\n clearing_house=clearing_house,\n source=source,\n real_time=real_time,\n start=DataContext.current.start_date,\n end=DataContext.current.end_date,\n query_type=QueryType.ATM_FWD_RATE,\n location=location,\n )\n return _extract_series_from_df(df, QueryType.ATM_FWD_RATE)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swaption_rate_asset, query_type=QueryType.SWAPTION_VOL)],\n)\ndef swaption_vol(\n asset: Asset,\n expiration_tenor: str = None,\n termination_tenor: str = None,\n relative_strike: float = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day implied normal volatility for swaption vol matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param relative_strike: strike level relative to at the money e.g. 10 or ATM+10\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n df = _get_swaption_measure(\n asset,\n benchmark_type,\n floating_rate_tenor,\n \"0b\",\n expiration_tenor,\n termination_tenor,\n relative_strike,\n clearing_house,\n source=source,\n real_time=real_time,\n query_type=QueryType.SWAPTION_VOL,\n start=DataContext.current.start_date,\n end=DataContext.current.end_date,\n location=location,\n )\n return _extract_series_from_df(df, QueryType.SWAPTION_VOL)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_midcurve_asset, query_type=QueryType.MIDCURVE_VOL)],\n)\ndef midcurve_vol(\n asset: Asset,\n expiration_tenor: str,\n forward_tenor: str,\n termination_tenor: str,\n relative_strike: float = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day implied normal volatility for swaption vol matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param forward_tenor: relative date representation of swap's start date after option expiry e.g. 2y\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param relative_strike: strike level relative to at the money e.g. 10 or ATM+10\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n df = _get_swaption_measure(\n asset,\n benchmark_type,\n floating_rate_tenor,\n forward_tenor,\n expiration_tenor,\n termination_tenor,\n relative_strike,\n clearing_house,\n source=source,\n real_time=real_time,\n query_type=QueryType.MIDCURVE_VOL,\n start=DataContext.current.start_date,\n end=DataContext.current.end_date,\n location=location,\n )\n return _extract_series_from_df(df, QueryType.MIDCURVE_VOL)\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_midcurve_asset, query_type=QueryType.MIDCURVE_ATM_FWD_RATE)],\n)\ndef midcurve_atm_fwd_rate(\n asset: Asset,\n expiration_tenor: str,\n forward_tenor: str,\n termination_tenor: str,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day atm forward rate for swaption vol matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param forward_tenor: relative date representation of swap's start date after option expiry e.g. 2y\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n df = _get_swaption_measure(\n asset,\n benchmark_type=benchmark_type,\n floating_rate_tenor=floating_rate_tenor,\n effective_date=forward_tenor,\n expiration_tenor=expiration_tenor,\n termination_tenor=termination_tenor,\n clearing_house=clearing_house,\n source=source,\n real_time=real_time,\n start=DataContext.current.start_date,\n end=DataContext.current.end_date,\n query_type=QueryType.MIDCURVE_ATM_FWD_RATE,\n location=location,\n )\n return _extract_series_from_df(df, QueryType.MIDCURVE_ATM_FWD_RATE)\n\n\ndef _get_swaption_measure(\n asset: Asset,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n effective_date: str = None,\n expiration_tenor: str = None,\n termination_tenor: str = None,\n strike_reference: [str, int] = None,\n clearing_house: str = None,\n start: str = DataContext.current.start_date,\n end: str = DataContext.current.end_date,\n source: str = None,\n real_time: bool = False,\n allow_many: bool = False,\n query_type: QueryType = QueryType.SWAPTION_PREMIUM,\n location: PricingLocation = None,\n) -> pd.Series:\n if real_time:\n raise NotImplementedError(f'realtime {query_type.value} not implemented')\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n\n if not swaptions_defaults_provider.is_supported(currency):\n raise NotImplementedError(f'Data not available for {currency.value} {query_type.value}')\n\n query = _swaption_build_asset_query(\n currency,\n benchmark_type,\n effective_date,\n expiration_tenor,\n floating_rate_tenor,\n strike_reference,\n termination_tenor,\n clearing_house,\n )\n\n _logger.debug(query)\n\n rate_mqid = _get_tdapi_rates_assets(**query, allow_many=allow_many)\n if isinstance(rate_mqid, str):\n rate_mqid = [rate_mqid]\n\n if location is None:\n pricing_location = _default_pricing_location(currency)\n else:\n pricing_location = PricingLocation(location)\n pricing_location = _pricing_location_normalized(pricing_location, currency)\n\n where = dict(pricingLocation=pricing_location.value)\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query(rate_mqid, query_type, where=where, source=source, real_time=real_time)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n return df\n\n\ndef _swaption_build_asset_query(\n currency,\n benchmark_type=None,\n effective_date=None,\n expiration_tenor=None,\n floating_rate_tenor=None,\n strike_reference=None,\n termination_tenor=None,\n clearinghouse=None,\n):\n benchmark_type = swaptions_defaults_provider.get_swaption_parameter(currency, 'benchmarkType', benchmark_type)\n\n floating_rate_option = swaptions_defaults_provider.get_floating_rate_option_for_benchmark(currency, benchmark_type)\n if floating_rate_option is None:\n raise MqValueError(\n \"Invalid benchmark type {}: cannot map benchmark_type with underlying rate for {}\".format(\n benchmark_type, currency\n )\n )\n floating_rate_tenor = swaptions_defaults_provider.get_swaption_parameter(\n currency, \"floatingRateTenor\", floating_rate_tenor\n )\n strike_reference = swaptions_defaults_provider.get_swaption_parameter(currency, 'strikeReference', strike_reference)\n\n termination_tenor = swaptions_defaults_provider.get_swaption_parameter(\n currency, 'terminationTenor', termination_tenor\n )\n effective_date = swaptions_defaults_provider.get_swaption_parameter(currency, 'effectiveDate', effective_date)\n expiration_tenor = swaptions_defaults_provider.get_swaption_parameter(currency, 'expirationTenor', expiration_tenor)\n for tenor in [termination_tenor, expiration_tenor]:\n if not _is_valid_relative_date_tenor(tenor):\n raise MqValueError('invalid tenor ' + tenor + ' for index: ' + benchmark_type)\n forward_tenor = _check_forward_tenor(effective_date)\n strike_reference = _check_strike_reference(strike_reference)\n clearinghouse = swaptions_defaults_provider.get_swaption_parameter(currency, 'clearingHouse', clearinghouse)\n query = dict(asset_class='Rates', type='Swaption', asset_parameters_notional_currency=currency.name)\n if termination_tenor is not None:\n query[\"asset_parameters_termination_date\"] = termination_tenor\n if floating_rate_option is not None:\n query[\"asset_parameters_floating_rate_option\"] = floating_rate_option\n if floating_rate_tenor is not None:\n query[\"asset_parameters_floating_rate_designated_maturity\"] = floating_rate_tenor\n if expiration_tenor is not None:\n query[\"asset_parameters_expiration_date\"] = expiration_tenor\n if strike_reference is not None:\n query[\"asset_parameters_strike\"] = strike_reference\n if clearinghouse is not None:\n query[\"asset_parameters_clearing_house\"] = clearinghouse\n if forward_tenor is not None:\n query[\"asset_parameters_effective_date\"] = forward_tenor\n if expiration_tenor is not None:\n query[\"asset_parameters_notional_currency\"] = currency.name\n\n return query\n\n\ndef _check_strike_reference(strike_reference):\n if strike_reference is None:\n return None\n if isinstance(strike_reference, float) or isinstance(strike_reference, int):\n if strike_reference == 0:\n strike_reference = \"ATM\"\n else:\n strike_reference = ('ATM%+f' % strike_reference).rstrip('0').rstrip(\".\")\n elif isinstance(strike_reference, str) and strike_reference.upper() == \"SPOT\":\n strike_reference = \"ATM\"\n\n if isinstance(strike_reference, list):\n to_check = strike_reference\n else:\n to_check = [strike_reference]\n\n for s in to_check:\n if not re.fullmatch(r\"ATM|ATM[-+]?([0-9]*\\.[0-9]+|[0-9]+)\", s):\n raise MqValueError('invalid strike reference ' + s)\n return strike_reference\n\n\ndef _is_valid_relative_date_tenor(tenor):\n if tenor is None:\n return True\n if re.fullmatch('(\\\\d+)([bdwmy])', tenor):\n return True\n else:\n return False\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swaption_rate_asset, query_type=QueryType.SWAPTION_VOL)],\n)\ndef swaption_vol_smile(\n asset: Asset,\n expiration_tenor: str,\n termination_tenor: str,\n pricing_date: Optional[GENERIC_DATE] = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day implied normal volatility for swaption vol matrices.\n\n :param asset: asset object loaded from security master\n :param expiration_tenor: relative date representation of expiration date on the option e.g. 3m\n :param termination_tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param pricing_date: YYYY-MM-DD or relative date\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime swaption_vol not implemented')\n\n _logger.debug('where expiry=%s, tenor=%s', expiration_tenor, termination_tenor)\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n if location is None:\n location = PricingLocation(swaptions_defaults_provider.get_swaption_parameter(currency, \"pricingLocation\"))\n\n start, end = _range_from_pricing_date(location, pricing_date)\n df = _get_swaption_measure(\n asset,\n expiration_tenor=expiration_tenor,\n termination_tenor=termination_tenor,\n strike_reference=TdapiRatesDefaultsProvider.EMPTY_PROPERTY,\n source=source,\n query_type=QueryType.SWAPTION_VOL,\n benchmark_type=benchmark_type,\n floating_rate_tenor=floating_rate_tenor,\n clearing_house=clearing_house,\n location=location,\n start=start,\n end=end,\n allow_many=True,\n )\n\n dataset_ids = getattr(df, 'dataset_ids', ())\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n # convert string ATM+20 to numerical value 20\n df[\"strikeRelative\"] = df[\"strikeRelative\"].apply(lambda d: float(d.split(\"ATM\")[1] if d != \"ATM\" else 0))\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n df = df.set_index('strikeRelative')\n df = df.sort_index()\n series = ExtendedSeries(df['swaptionVol'].values, index=df.index.values)\n series.dataset_ids = dataset_ids\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swaption_rate_asset, query_type=QueryType.SWAPTION_VOL)],\n)\ndef swaption_vol_term(\n asset: Asset,\n tenor_type: SwaptionTenorType,\n tenor: str,\n relative_strike: float,\n pricing_date: Optional[GENERIC_DATE] = None,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n clearing_house: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n Term structure of GS end-of-day implied normal volatility for swaption vol matrices.\n\n :param asset: an asset\n :param tenor_type: specifies which type of tenor will be fixed, one of OPTION_EXPIRATION or SWAP_MATURITY\n :param tenor: relative date representation of the instrument's expiration date e.g. 1y\n :param relative_strike: strike level relative to at the money e.g. 10\n :param pricing_date: YYYY-MM-DD or relative date\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swaption implied normal volatility term structure\n \"\"\"\n\n if real_time:\n raise NotImplementedError('realtime swaption_vol not implemented')\n\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n if location is None:\n location = PricingLocation(swaptions_defaults_provider.get_swaption_parameter(currency, \"pricingLocation\"))\n start, end = _range_from_pricing_date(location.value, pricing_date)\n if tenor_type == SwaptionTenorType.OPTION_EXPIRY:\n tenor_to_plot = 'terminationTenor'\n df = _get_swaption_measure(\n asset,\n expiration_tenor=tenor,\n termination_tenor=TdapiRatesDefaultsProvider.EMPTY_PROPERTY,\n strike_reference=relative_strike,\n query_type=QueryType.SWAPTION_VOL,\n benchmark_type=benchmark_type,\n floating_rate_tenor=floating_rate_tenor,\n clearing_house=clearing_house,\n location=location,\n source=source,\n start=start,\n end=end,\n allow_many=True,\n )\n else:\n tenor_to_plot = 'expirationTenor'\n df = _get_swaption_measure(\n asset,\n expiration_tenor=TdapiRatesDefaultsProvider.EMPTY_PROPERTY,\n termination_tenor=tenor,\n strike_reference=relative_strike,\n benchmark_type=benchmark_type,\n floating_rate_tenor=floating_rate_tenor,\n clearing_house=clearing_house,\n query_type=QueryType.SWAPTION_VOL,\n source=source,\n start=start,\n end=end,\n allow_many=True,\n )\n\n dataset_ids = getattr(df, 'dataset_ids', ())\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n business_day = _get_custom_bd(asset.exchange)\n df = df.assign(\n expirationDate=pd.DatetimeIndex(\n [ts + _to_offset(t) + business_day - business_day for ts, t in zip(df.index, df[tenor_to_plot])]\n )\n )\n df = df.set_index('expirationDate')\n df.index = pd.DatetimeIndex(df.index)\n df = df.sort_index()\n df = df.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['swaptionVol'])\n series.dataset_ids = dataset_ids\n if series.empty: # Raise descriptive error if no data returned + historical date context\n check_forward_looking(None, source, 'swaption_vol_term')\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swap_rate_asset, query_type=QueryType.SWAP_RATE)],\n)\ndef swap_rate(\n asset: Asset,\n swap_tenor: str,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: _ClearingHouse = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day Fixed-Floating interest rate swap (IRS) curves across major currencies.\n\n\n :param asset: asset object loaded from security master\n :param swap_tenor: relative date representation of expiration date e.g. 1m\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param forward_tenor: absolute / relative date representation of forward starting point eg: '1y' or 'Spot' for\n spot starting swaps, 'imm1' or 'frb1'\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swap rate curve\n \"\"\"\n df = _get_swap_data(\n asset=asset,\n swap_tenor=swap_tenor,\n benchmark_type=benchmark_type,\n floating_rate_tenor=floating_rate_tenor,\n forward_tenor=forward_tenor,\n clearing_house=clearing_house,\n source=source,\n real_time=real_time,\n query_type=QueryType.SWAP_RATE,\n location=location,\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['swapRate'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swap_rate_asset_for_intraday, query_type=QueryType.SPOT)],\n)\ndef swap_rate_calc(\n asset: Asset,\n swap_tenor: str,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n csa: str = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS intra-day Fixed-Floating interest rate swap (IRS) curves across major currencies.\n This API runs on-the-fly calculations\n\n\n :param asset: asset object loaded from security master\n :param swap_tenor: relative date representation of expiration date e.g. 1m\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param forward_tenor: absolute / relative date representation of forward starting point eg: '1y' or 'Spot' for\n spot starting swaps, 'imm1' or 'frb1'\n :param csa: Collateral type or clearing house for cleared swaps - set to Default for standard domestic funding.\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swap rate curve\n \"\"\"\n\n df = _get_swap_data_calc(\n asset=asset,\n swap_tenor=swap_tenor,\n benchmark_type=benchmark_type,\n floating_rate_tenor=floating_rate_tenor,\n forward_tenor=forward_tenor,\n csa=csa,\n real_time=real_time,\n location=location,\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['ATMRate'])\n series.dataset_ids = ()\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swap_rate_asset_for_intraday, query_type=QueryType.SPOT)],\n)\ndef forward_rate(\n asset: Asset,\n forward_start_tenor: str = None,\n forward_term: str = None,\n csa: str = None,\n close_location: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS Forward Rate across major currencies.\n This API computes forward rates off stored forward/discount curves\n\n\n :param asset: asset object loaded from security master\n :param forward_start_tenor: Relative date of start of forward e.g. 1y\n :param forward_term: Term of forward, e.g. 3m\n :param csa: Collateral code of curve, e.g. GBP-1. If set to default, default CSA is chosen\n :param close_location: For EOD data, gives location of close\n :param source: name of function caller\n :param real_time: whether to retrieve intra-day data instead of EOD\n :return: annualised instantaneous forward rate\n \"\"\"\n\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n csa = csa or 'Default'\n close_location = close_location or 'NYC'\n if not forward_term:\n raise MqValueError(\"Forward rate term not specified\")\n\n if not forward_start_tenor:\n forward_start_tenor = '0d'\n\n measure = f'FR:{forward_start_tenor}:{forward_term}'\n df = GsDataApi.get_mxapi_curve_measure(\n 'DISCOUNT CURVE', currency.value, [], [csa], measure, close_location=close_location, real_time=real_time\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[measure])\n series.dataset_ids = ()\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swap_rate_asset_for_intraday, query_type=QueryType.SPOT)],\n entitlements=[Entitlement.INTERNAL],\n)\ndef discount_factor(\n asset: Asset,\n tenor: str = None,\n csa: str = None,\n close_location: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS Discount Factor across major currencies.\n This API computes discount factor off stored forward/discount curves\n\n\n :param asset: asset object loaded from security master\n :param tenor: tenor of discount factor e.g. 1m\n :param csa: Collateral code of curve to fetch discount factor, e.g. GBP-1. If not specified, default CSA is chosen\n :param close_location: For EOD data, gives location of close\n :param source: name of function caller\n :param real_time: whether to retrieve intra-day data instead of EOD\n :return: annualised instantaneous forward rate\n \"\"\"\n\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n close_location = close_location or 'NYC'\n if not tenor:\n raise MqValueError(\"Discount Curve start and end date not specified\")\n\n csa = csa or 'Default'\n\n measure = f'DF:{tenor}'\n df = GsDataApi.get_mxapi_curve_measure(\n 'DISCOUNT CURVE', currency.value, [], [csa], measure, close_location=close_location, real_time=real_time\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[measure])\n series.dataset_ids = ()\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swap_rate_asset_for_intraday, query_type=QueryType.SPOT)],\n)\ndef instantaneous_forward_rate(\n asset: Asset,\n tenor: str = None,\n csa: str = None,\n close_location: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS Floating Rate Benchmark annualised instantaneous forward rates across major currencies.\n This API computes IFR off stored forward/discount curves\n\n\n :param asset: asset object loaded from security master\n :param tenor: tenor of IFR e.g. 1m\n :param csa: Collateral code of curve to fetch IFR. Set to default for default collateral\n :param close_location: For EOD data, gives location of close\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: annualised instantaneous forward rate\n \"\"\"\n\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n close_location = close_location or 'NYC'\n if not tenor:\n raise MqValueError(\"Forward rate tenor not specified\")\n\n csa = csa or 'Default'\n\n measure = f'IFR:{tenor}'\n df = GsDataApi.get_mxapi_curve_measure(\n 'DISCOUNT CURVE', currency.value, [], [csa], measure, close_location=close_location, real_time=real_time\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[measure])\n series.dataset_ids = ()\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swap_rate_asset_for_intraday, query_type=QueryType.SPOT)],\n)\ndef index_forward_rate(\n asset: Asset,\n forward_start_tenor: str = None,\n benchmark_type: str = None,\n fixing_tenor: str = None,\n close_location: str = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS annualised forward rates across floating rate benchmark\n This API computes index forward rate off stored index forward curves\n\n\n :param asset: asset object loaded from security master\n :param forward_start_tenor: relative start rate of forward e.g. 1m\n :param benchmark_type: benchmark type of floating rate option e.g. LIBOR\n :param fixing_tenor: Fixing tenor of the given benchmark type. Leave empty to use default e.g. 3m\n :param close_location: For EOD data, gives location of close\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: annualised forward rate\n \"\"\"\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n close_location = close_location or 'NYC'\n if not forward_start_tenor:\n raise MqValueError(\"Forward rate start date not specified\")\n\n benchmark_type = _check_benchmark_type(currency, benchmark_type, True)\n if not isinstance(benchmark_type, str):\n benchmark_type_input = _get_benchmark_type(currency, benchmark_type)\n else:\n benchmark_type_input = benchmark_type\n\n if fixing_tenor is None:\n if benchmark_type_input not in BENCHMARK_TO_DEFAULT_FLOATING_RATE_TENORS:\n raise MqValueError(\n \"Please provide fixing tenor: \" + f\"default fixing tenor not specified for {benchmark_type_input}\"\n )\n fixing_tenor = BENCHMARK_TO_DEFAULT_FLOATING_RATE_TENORS[benchmark_type_input]\n\n measure = f'FR:{forward_start_tenor}:{fixing_tenor}'\n df = GsDataApi.get_mxapi_curve_measure(\n 'INDEX CURVE',\n benchmark_type_input,\n [fixing_tenor],\n [f'{currency.value}-1'],\n measure,\n close_location=close_location,\n real_time=real_time,\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df[measure])\n series.dataset_ids = ()\n return series\n\n\ndef _get_basis_swap_kwargs(\n asset: Asset,\n spread_benchmark_type: str = None,\n spread_tenor: str = None,\n reference_benchmark_type: str = None,\n reference_tenor: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: _ClearingHouse = None,\n location: PricingLocation = None,\n) -> dict:\n currency = CurrencyEnum(asset.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n if currency.value not in ['JPY', 'EUR', 'USD', 'GBP', 'CHF', 'DKK', 'NOK', 'SEK', 'CAD', 'AUD', 'NZD']:\n raise NotImplementedError('Data not available for {} basis swap rates'.format(currency.value))\n\n clearing_house = _check_clearing_house(clearing_house)\n spread_benchmark_type = _check_benchmark_type(currency, spread_benchmark_type)\n reference_benchmark_type = _check_benchmark_type(currency, reference_benchmark_type)\n\n # default benchmark types\n legs_w_defaults = dict()\n legs_w_defaults['spread'] = _get_swap_leg_defaults(currency, spread_benchmark_type, spread_tenor)\n legs_w_defaults['reference'] = _get_swap_leg_defaults(currency, reference_benchmark_type, reference_tenor)\n\n for key, leg in legs_w_defaults.items():\n if not re.fullmatch('(\\\\d+)([bdwmy])', leg['floating_rate_tenor']):\n raise MqValueError(\n 'invalid floating rate tenor ' + leg['floating_rate_tenor'] + ' index: ' + leg['benchmark_type']\n )\n\n forward_tenor = _check_forward_tenor(forward_tenor)\n\n if location is None:\n pricing_location = PricingLocation(legs_w_defaults['spread']['pricing_location'].value)\n else:\n pricing_location = PricingLocation(location)\n pricing_location = _pricing_location_normalized(pricing_location, currency)\n\n kwargs = dict(\n type='BasisSwap',\n asset_parameters_payer_rate_option=legs_w_defaults['spread']['benchmark_type'],\n asset_parameters_payer_designated_maturity=legs_w_defaults['spread']['floating_rate_tenor'],\n asset_parameters_receiver_rate_option=legs_w_defaults['reference']['benchmark_type'],\n asset_parameters_receiver_designated_maturity=legs_w_defaults['reference']['floating_rate_tenor'],\n asset_parameters_clearing_house=clearing_house.value,\n asset_parameters_effective_date=forward_tenor,\n asset_parameters_notional_currency=currency.name,\n pricing_location=pricing_location,\n )\n kwargs = _match_floating_tenors(kwargs)\n return kwargs\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_basis_swap_rate_asset, query_type=QueryType.BASIS_SWAP_RATE)],\n)\ndef basis_swap_spread(\n asset: Asset,\n swap_tenor: str = '1y',\n spread_benchmark_type: str = None,\n spread_tenor: str = None,\n reference_benchmark_type: str = None,\n reference_tenor: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: _ClearingHouse = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day Floating-Floating interest rate swap (IRS) curves across major currencies.\n\n\n :param asset: asset object loaded from security master\n :param swap_tenor: relative date representation of expiration date e.g. 1m\n :param spread_benchmark_type: benchmark type of spread leg on which basis spread is added e.g. LIBOR\n :param spread_tenor: relative date representation of expiration date of paying leg e.g. 1m\n :param reference_benchmark_type: benchmark type of reference leg e.g. LIBOR\n :param reference_tenor: relative date representation of expiration date of reference leg e.g. 1m\n :param forward_tenor: absolute / relative date representation of forward starting point eg: '1y' or 'Spot' for\n spot starting swaps, 'imm1' or 'frb1'\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swap rate curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime basis_swap_rate not implemented')\n\n if not (re.fullmatch('(\\\\d+)([bdwmy])', swap_tenor) or re.fullmatch('(frb[1-9])', forward_tenor)):\n raise MqValueError('invalid swap tenor ' + swap_tenor)\n\n kwargs = _get_basis_swap_kwargs(\n asset=asset,\n spread_benchmark_type=spread_benchmark_type,\n spread_tenor=spread_tenor,\n reference_benchmark_type=reference_benchmark_type,\n reference_tenor=reference_tenor,\n forward_tenor=forward_tenor,\n clearing_house=clearing_house,\n location=location,\n )\n kwargs['asset_parameters_termination_date'] = swap_tenor\n\n rate_mqid = _get_tdapi_rates_assets(**kwargs)\n _logger.debug(\n 'where asset=%s, swap_tenor=%s, spread_benchmark_type=%s, spread_tenor=%s, '\n 'reference_benchmark_type=%s, reference_tenor=%s, forward_tenor=%s, pricing_location=%s ',\n rate_mqid,\n swap_tenor,\n kwargs['asset_parameters_payer_rate_option'],\n kwargs['asset_parameters_payer_designated_maturity'],\n kwargs['asset_parameters_receiver_rate_option'],\n kwargs['asset_parameters_receiver_designated_maturity'],\n kwargs['asset_parameters_effective_date'],\n kwargs['pricing_location'],\n )\n where = {'pricingLocation': kwargs['pricing_location'].value}\n\n q = GsDataApi.build_market_data_query(\n [rate_mqid], QueryType.BASIS_SWAP_RATE, where=where, source=source, real_time=real_time\n )\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['basisSwapRate'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_swap_rate_asset, query_type=QueryType.SWAP_RATE)],\n)\ndef swap_term_structure(\n asset: Asset,\n benchmark_type: str = None,\n floating_rate_tenor: str = None,\n tenor_type: _SwapTenorType = None,\n tenor: Optional[GENERIC_DATE] = None,\n clearing_house: _ClearingHouse = None,\n location: PricingLocation = None,\n pricing_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day Fixed-Floating interest rate swap (IRS) term structure across major currencies.\n\n :param asset: asset object loaded from security master\n :param benchmark_type: benchmark type e.g. LIBOR\n :param floating_rate_tenor: floating index rate\n :param tenor_type: specify which tenor should be fixed, SWAP_TENOR or FORWARD_TENOR\n :param tenor: absolute / relative date representation of forward starting point or swap maturity\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param pricing_date: YYYY-MM-DD or relative date\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swap rate term structure\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime swap_rate not implemented')\n\n currency = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n currency = CurrencyEnum(currency)\n if currency.value not in CURRENCY_TO_SWAP_RATE_BENCHMARK.keys():\n raise NotImplementedError('Data not available for {} swap rates'.format(currency.value))\n\n clearing_house = _check_clearing_house(clearing_house)\n benchmark_type = _check_benchmark_type(currency, benchmark_type)\n tenor_type = _check_tenor_type(tenor_type)\n tenor_dict = _check_term_structure_tenor(tenor_type=tenor_type, tenor=tenor)\n defaults = _get_swap_leg_defaults(currency, benchmark_type, floating_rate_tenor)\n\n if not re.fullmatch('(\\\\d+)([bdwmy])', defaults['floating_rate_tenor']):\n raise MqValueError(\n 'invalid floating rate tenor '\n + defaults['floating_rate_tenor']\n + ' for index: '\n + defaults['benchmark_type']\n )\n\n if location is None:\n pricing_location = _default_pricing_location(currency)\n else:\n pricing_location = location\n pricing_location = _pricing_location_normalized(pricing_location, currency)\n\n calendar = pricing_location.value\n if pricing_date is not None and pricing_date in list(GsCalendar.get(calendar).holidays):\n raise MqValueError('Specified pricing date is a holiday in {} calendar'.format(calendar))\n\n fixed_rate = 'ATM'\n\n where = dict(pricingLocation=pricing_location.value)\n\n kwargs = dict(\n type='Swap',\n asset_parameters_floating_rate_option=defaults['benchmark_type'],\n asset_parameters_fixed_rate=fixed_rate,\n asset_parameters_clearing_house=clearing_house.value,\n asset_parameters_floating_rate_designated_maturity=defaults['floating_rate_tenor'],\n asset_parameters_notional_currency=currency.name,\n pricing_location=pricing_location.value,\n )\n kwargs[tenor_dict['tenor_dataset_field']] = tenor_dict['tenor']\n rate_mqids = _get_tdapi_rates_assets(allow_many=True, **kwargs)\n if isinstance(rate_mqids, str):\n rate_mqids = [rate_mqids]\n _logger.debug('assets returned %s', ', '.join(rate_mqids))\n _logger.debug(\n 'where benchmark_type=%s, floating_rate_tenor=%s, %s=%s, pricing_location=%s',\n defaults['benchmark_type'],\n defaults['floating_rate_tenor'],\n tenor_type.value,\n tenor_dict['tenor'],\n defaults['pricing_location'].value,\n )\n start, end = _range_from_pricing_date(calendar, pricing_date)\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query(\n rate_mqids, QueryType.SWAP_RATE, where=where, source=source, real_time=real_time\n )\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n biz_day = _get_custom_bd(calendar)\n col_to_plot = tenor_dict['tenor_to_plot']\n if isinstance(df, pd.Series):\n series = ExtendedSeries(\n pd.Series(\n df['swapRate'], index=pd.DatetimeIndex([_get_term_struct_date(df[col_to_plot], latest, biz_day)])\n )\n )\n series = series.loc[\n pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)\n ]\n else:\n if col_to_plot == 'effectiveTenor':\n df = df[~df[col_to_plot].isin(['imm1', 'imm2', 'imm3', 'imm4'])]\n df['expirationDate'] = df[col_to_plot].apply(_get_term_struct_date, args=(latest, biz_day))\n df = df.set_index('expirationDate')\n df.index = pd.DatetimeIndex(df.index)\n df = df.sort_index()\n df = df.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['swapRate'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n if series.empty: # Raise descriptive error if no data returned + date context is in the past\n check_forward_looking(None, source, 'swaption_vol_term')\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=_currency_to_tdapi_basis_swap_rate_asset, query_type=QueryType.BASIS_SWAP_RATE)],\n)\ndef basis_swap_term_structure(\n asset: Asset,\n spread_benchmark_type: str = None,\n spread_tenor: str = None,\n reference_benchmark_type: str = None,\n reference_tenor: str = None,\n tenor_type: _SwapTenorType = None,\n tenor: Optional[GENERIC_DATE] = None,\n clearing_house: _ClearingHouse = None,\n location: PricingLocation = None,\n pricing_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day Floating-Floating interest rate swap (IRS) term structure across major currencies.\n\n\n :param asset: asset object loaded from security master\n :param spread_benchmark_type: benchmark type of spread leg on which basis spread is added e.g. LIBOR\n :param spread_tenor: relative date representation of expiration date of spread leg e.g. 1m\n :param reference_benchmark_type: benchmark type of reference leg e.g. LIBOR\n :param reference_tenor: relative date representation of expiration date of reference leg e.g. 1m\n :param tenor_type: specify which tenor should be fixed, SWAP_TENOR or FORWARD_TENOR\n :param tenor: absolute / relative date representation of forward starting point or swap maturity\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param pricing_date: YYYY-MM-DD or relative date\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swap rate curve\n \"\"\"\n if real_time:\n raise NotImplementedError('realtime basis_swap_rate not implemented')\n\n tenor_type = _check_tenor_type(tenor_type)\n tenor_dict = _check_term_structure_tenor(tenor_type=tenor_type, tenor=tenor)\n kwargs = _get_basis_swap_kwargs(\n asset=asset,\n spread_benchmark_type=spread_benchmark_type,\n spread_tenor=spread_tenor,\n reference_benchmark_type=reference_benchmark_type,\n reference_tenor=reference_tenor,\n clearing_house=clearing_house,\n location=location,\n )\n kwargs[tenor_dict['tenor_dataset_field']] = tenor_dict['tenor']\n calendar = kwargs['pricing_location'].value\n if pricing_date is not None and pricing_date in list(GsCalendar.get(calendar).holidays):\n raise MqValueError('Specified pricing date is a holiday in {} calendar'.format(calendar))\n\n rate_mqids = _get_tdapi_rates_assets(allow_many=True, **kwargs)\n if isinstance(rate_mqids, str): # single asset returned\n rate_mqids = [rate_mqids]\n _logger.debug('assets returned %s', ', '.join(rate_mqids))\n _logger.debug(\n 'where spread_benchmark_type=%s, spread_tenor=%s, reference_benchmark_type=%s, '\n 'reference_tenor=%s, %s=%s, pricing_location=%s ',\n kwargs['asset_parameters_payer_rate_option'],\n kwargs['asset_parameters_payer_designated_maturity'],\n kwargs['asset_parameters_receiver_rate_option'],\n kwargs['asset_parameters_receiver_designated_maturity'],\n kwargs[tenor_dict['tenor_dataset_field']],\n tenor_dict['tenor'],\n kwargs['pricing_location'].value,\n )\n\n where = {'pricingLocation': kwargs['pricing_location'].value}\n start, end = _range_from_pricing_date(calendar, pricing_date)\n with DataContext(start, end):\n q = GsDataApi.build_market_data_query(\n rate_mqids, QueryType.BASIS_SWAP_RATE, where=where, source=source, real_time=real_time\n )\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n\n if df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = df.index.max()\n _logger.info('selected pricing date %s', latest)\n df = df.loc[latest]\n biz_day = _get_custom_bd(calendar)\n col_to_plot = tenor_dict['tenor_to_plot']\n if isinstance(df, pd.Series): # single asset returned\n series = ExtendedSeries(\n pd.Series(\n df['basisSwapRate'],\n index=pd.DatetimeIndex([_get_term_struct_date(df[col_to_plot], latest, biz_day)]),\n )\n )\n series = series.loc[\n pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)\n ]\n else:\n if col_to_plot == 'effectiveTenor': # for forward term structure imm date assets\n df = df[~df[col_to_plot].isin(['imm1', 'imm2', 'imm3', 'imm4'])]\n df['expirationDate'] = df[col_to_plot].apply(_get_term_struct_date, args=(latest, biz_day))\n df = df.set_index('expirationDate')\n df.index = pd.DatetimeIndex(df.index)\n df = df.sort_index()\n df = df.loc[pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)]\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['basisSwapRate'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n if series.empty: # Raise descriptive error if no data returned + historical date context\n check_forward_looking(None, source, 'swaption_vol_term')\n return series\n\n\ndef _get_fxfwd_xccy_swp_rates_data(\n asset: Asset, tenor: str, real_time: bool = False, source: str = None, query_type: QueryType = None\n) -> pd.DataFrame:\n if real_time:\n raise NotImplementedError('realtime not implemented')\n pair = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n\n if pair not in CROSS_BBID_TO_DUMMY_OISXCCY_ASSET.keys():\n raise NotImplementedError('Data not available for pair: ' + str(pair))\n\n if not (re.fullmatch('(\\\\d+)([wfmy])', tenor)):\n raise MqValueError('invalid tenor: ' + tenor)\n\n remap_tenor = tenor.replace('m', 'f')\n currency = pair.replace('USD', '')\n price_location_defaults = CURRENCY_TO_PRICING_LOCATION.get(currency, PricingLocation.LDN)\n kwargs = dict(type='Forward', asset_parameters_settlement_date=remap_tenor, asset_parameters_pair=pair)\n\n rate_mqid = _get_tdapi_rates_assets(**kwargs)\n\n _logger.debug(\n 'where asset= %s (%s), ois_xccy_tenor=%s, pricing_location=%s', rate_mqid, pair, tenor, price_location_defaults\n )\n\n q = GsDataApi.build_market_data_query([rate_mqid], query_type, source=source, real_time=real_time)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n return df\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Forward, AssetType.Cross),\n [MeasureDependency(id_provider=_cross_to_fxfwd_xcswp_asset, query_type=QueryType.OIS_XCCY)],\n)\ndef ois_xccy(asset: Asset, tenor: str = None, *, source: str = None, real_time: bool = False) -> pd.Series:\n \"\"\"\n GS end-of-day OIS Xccy spreads curves across G10 cross currencies.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1w\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: ois xccy spread curve\n \"\"\"\n df = _get_fxfwd_xccy_swp_rates_data(\n asset=asset, tenor=tenor, query_type=QueryType.OIS_XCCY, source=source, real_time=real_time\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['oisXccy'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Forward, AssetType.Cross),\n [MeasureDependency(id_provider=_cross_to_fxfwd_xcswp_asset, query_type=QueryType.OIS_XCCY_EX_SPIKE)],\n)\ndef ois_xccy_ex_spike(asset: Asset, tenor: str = None, *, source: str = None, real_time: bool = False) -> pd.Series:\n \"\"\"\n GS end-of-day OIS Xccy spreads curves excluding spikes across G10 cross currencies.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1w\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: ois xccy spread curve excluding spikes\n \"\"\"\n df = _get_fxfwd_xccy_swp_rates_data(\n asset=asset, tenor=tenor, query_type=QueryType.OIS_XCCY_EX_SPIKE, source=source, real_time=real_time\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['oisXccyExSpike'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Forward, AssetType.Cross),\n [MeasureDependency(id_provider=_cross_to_fxfwd_xcswp_asset, query_type=QueryType.NON_USD_OIS)],\n)\ndef non_usd_ois(asset: Asset, tenor: str = None, *, source: str = None, real_time: bool = False) -> pd.Series:\n \"\"\"\n GS end-of-day non domestic USD ois rate curve for G10 cross currencies.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1w\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: non usd ois domestic rate curve (from cross)\n \"\"\"\n df = _get_fxfwd_xccy_swp_rates_data(\n asset=asset, tenor=tenor, query_type=QueryType.NON_USD_OIS, source=source, real_time=real_time\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['nonUsdOis'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\n@plot_measure(\n (AssetClass.FX,),\n (AssetType.Forward, AssetType.Cross),\n [MeasureDependency(id_provider=_cross_to_fxfwd_xcswp_asset, query_type=QueryType.USD_OIS)],\n)\ndef usd_ois(asset: Asset, tenor: str = None, *, source: str = None, real_time: bool = False) -> pd.Series:\n \"\"\"\n GS end-of-day USD domestic ois rates curves across G10 cross currencies.\n\n :param asset: asset object loaded from security master\n :param tenor: relative date representation of expiration date e.g. 1w\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: usd ois domestic rate curve (from cross)\n \"\"\"\n df = _get_fxfwd_xccy_swp_rates_data(\n asset=asset, tenor=tenor, query_type=QueryType.USD_OIS, source=source, real_time=real_time\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['usdOis'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n\n\nclass BenchmarkTypeCB(Enum):\n EUROSTR = 'EUROSTR'\n SOFR = 'SOFR'\n EONIA = 'EONIA'\n SONIA = 'SONIA'\n Fed_Funds = 'Fed_Funds'\n\n\ndef get_cb_swaps_kwargs(currency: CurrencyEnum, benchmark_type: BenchmarkTypeCB) -> Dict:\n benchmark_type = _check_benchmark_type(currency, benchmark_type)\n clearing_house = _check_clearing_house(None)\n defaults = _get_swap_leg_defaults(currency, benchmark_type)\n possible_swap_tenors = [f\"{CCY_TO_CB[currency.value]}{i}\" for i in range(0, 20)]\n possible_fwd_tenors = [f\"{CCY_TO_CB[currency.value]}{i}\" for i in range(0, 20)]\n possible_fwd_tenors.append('0b')\n fixed_rate = 'ATM'\n kwargs = dict(\n asset_class='Rates',\n type='Swap',\n asset_parameters_floating_rate_option=defaults['benchmark_type'],\n asset_parameters_fixed_rate=fixed_rate,\n asset_parameters_clearing_house=clearing_house.value,\n # asset_parameters_floating_rate_designated_maturity=defaults['floating_rate_tenor'],\n asset_parameters_termination_date=possible_swap_tenors,\n asset_parameters_effective_date=possible_fwd_tenors,\n asset_parameters_notional_currency=currency.value,\n )\n return kwargs\n\n\ndef get_cb_meeting_swaps(currency: CurrencyEnum, benchmark_type: BenchmarkTypeCB) -> List:\n kwargs = get_cb_swaps_kwargs(currency=currency, benchmark_type=benchmark_type)\n return _get_tdapi_rates_assets(allow_many=True, **kwargs)\n\n\ndef get_cb_meeting_swap(\n currency: CurrencyEnum, benchmark_type: BenchmarkTypeCB, forward_tenor: str, swap_tenor: str\n) -> str:\n kwargs = get_cb_swaps_kwargs(currency=currency, benchmark_type=benchmark_type)\n if not (\n re.fullmatch(f\"({CCY_TO_CB[currency.value]}[0-9]|1[0-9])\", swap_tenor)\n or re.fullmatch(f\"({CCY_TO_CB[currency.value]}[0-9]|1[0-9]|0b)\", forward_tenor)\n ):\n raise MqValueError('invalid swap tenor ' + swap_tenor)\n kwargs['asset_parameters_termination_date'] = swap_tenor\n kwargs['asset_parameters_effective_date'] = forward_tenor\n return _get_tdapi_rates_assets(**kwargs)\n\n\ndef get_cb_swap_data(currency: CurrencyEnum, rate_mqids: list = None):\n ds = Dataset(Dataset.GS.IR_SWAP_RATES_INTRADAY_CALC_BANK)\n pricing_location = _default_pricing_location(currency)\n pricing_location = _pricing_location_normalized(pricing_location, currency)\n cbw_df = ds.get_data(\n assetId=rate_mqids,\n pricingLocation=pricing_location.value,\n startTime=DataContext.current.start_time,\n endTime=DataContext.current.end_time,\n )\n return cbw_df\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=currency_to_default_ois_asset, query_type=QueryType.CENTRAL_BANK_SWAP_RATE)],\n)\ndef policy_rate_term_structure(\n asset: Asset,\n event_type: EventType = EventType.MEETING,\n rate_type: RateType = RateType.ABSOLUTE,\n valuation_date: Optional[GENERIC_DATE] = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n Forward Policy Rate expectations for future Central Bank meetings or End Of Year Dates as of specified date.\n\n :param asset: Currency\n :param event_type: Spot= Effective OIS/Policy rate, Meeting = Forward Expectations across all future CB meetings,\n EOY = Forward Expectations at End of Year Dates\n :param rate_type: One of (absolute, relative), where relative = forward - spot rate to show what\n hikes/cuts are priced in by the market.\n :param valuation_date: reference date on which all future expectations are calculated, Eg. 3m: for 3 months ago,\n 2022-05-02 : for expectations as of 02May22, Intraday: how expectations are changing intraday.\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: OIS Swap Rate Term structure for swaps structured between consecutive CB meeting dates\n \"\"\"\n check_forward_looking(valuation_date, source, 'policy_rate_expectation')\n if real_time:\n raise NotImplementedError(\n 'change end date to +10y in date picker and specify '\n 'valuation_date = \"Intraday\" for real-time policy rate term structure'\n )\n if not isinstance(event_type, EventType):\n raise MqValueError('event_type must be one of Spot, Meeting Forward and EOY Forward')\n if not isinstance(rate_type, RateType):\n raise MqValueError('event_type must be either absolute or relative')\n\n if isinstance(valuation_date, str) and valuation_date.lower() == \"intraday\":\n return policy_rate_term_structure_rt(\n asset=asset, event_type=event_type, rate_type=rate_type, benchmark_type=None, source=source\n )\n else:\n valuation_date = parse_meeting_date(valuation_date)\n mqid = convert_asset_for_rates_data_set(asset, RatesConversionType.OIS_BENCHMARK_RATE)\n\n _logger.debug(\n 'where assetId=%s, metric=Central Bank Swap Rate, event_type=%s, event_type=%s, valuation date=%s',\n mqid,\n event_type,\n rate_type,\n str(valuation_date),\n )\n\n ds = Dataset(Dataset.GS.CENTRAL_BANK_WATCH)\n if event_type == EventType.SPOT:\n if rate_type == RateType.RELATIVE:\n raise MqValueError('rate_type must be absolute for event_type = Spot')\n else:\n df = ds.get_data(assetId=[mqid], rateType=event_type, start=CENTRAL_BANK_WATCH_START_DATE)\n else:\n df = ds.get_data(\n assetId=[mqid], rateType=event_type, valuationDate=valuation_date, start=CENTRAL_BANK_WATCH_START_DATE\n )\n\n if df.empty:\n return pd.Series(dtype=float, name='value')\n\n if rate_type == RateType.RELATIVE:\n # df = remove_dates_with_null_entries(df)\n spot = df[df['meetingNumber'] == 0]['value'][0]\n df['value'] = df['value'] - spot\n\n try:\n df = df.reset_index()\n df = df.set_index('meetingDate')\n series = ExtendedSeries(df['value'])\n series.dataset_ids = (Dataset.GS.CENTRAL_BANK_WATCH,)\n except KeyError: # No data returned from ds.get_data\n series = pd.Series(dtype=float, name='value')\n return series\n\n\n@plot_measure(\n (AssetClass.Cash,),\n (AssetType.Currency,),\n [MeasureDependency(id_provider=currency_to_default_ois_asset, query_type=QueryType.POLICY_RATE_EXPECTATION)],\n)\ndef policy_rate_expectation(\n asset: Asset,\n event_type: EventType = EventType.MEETING,\n rate_type: RateType = RateType.ABSOLUTE,\n meeting_date: Union[dt.date, int, str] = 0,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"'\n Evolution of OIS/policy rate expectations for a given meeting date or end of year date.\n\n :param asset: asset object loaded from security master\n :param meeting_date: Actual meeting date eg. Date(2022-04-02) or meeting number standing today : 0 for last,\n 1 for next , 2 for meeting after next and so on\n :param rate_type: One of (absolute, relative), where relative = forward - spot rate to show what hikes/cuts are\n priced in by the market for specified meeting date.\n :param event_type: Spot= Effective OIS/Policy rate, Meeting = Evolution of Rate Expectations for a given meeting\n date, EOY = Evolution of Rate Expectations for specified End of Year Date.\n :param source: name of function caller: default source = None\n :param real_time: whether to retrieve intraday data instead of EOD: default value = False\n :return: Historical policy rate expectations for a given CB meeting date or an End of Year date.\n \"\"\"\n\n if not isinstance(event_type, EventType):\n raise MqValueError('invalid event_type specified, Meeting Forward, Spot or EOY Forward allowed')\n if not isinstance(rate_type, RateType):\n raise MqValueError('event_type must be either absolute or relative')\n if not isinstance(meeting_date, (dt.date, str, int)):\n raise MqValueError('valuation_date must be of type datetime.date or string YYYY-MM-DD or integer')\n\n if real_time:\n return policy_rate_expectation_rt(asset, event_type, rate_type, meeting_date=meeting_date, benchmark_type=None)\n else:\n mqid = convert_asset_for_rates_data_set(asset, RatesConversionType.OIS_BENCHMARK_RATE)\n _logger.debug(\n 'where assetId=%s, metric=Policy Rate Expectation, meeting_date=%s, event_type=%s',\n mqid,\n str(meeting_date),\n rate_type,\n )\n\n ds = Dataset(Dataset.GS.CENTRAL_BANK_WATCH)\n if event_type == EventType.SPOT:\n cbw_df = ds.get_data(assetId=[mqid], rateType=event_type, start=CENTRAL_BANK_WATCH_START_DATE)\n elif isinstance(meeting_date, int):\n meeting_number = meeting_date\n if meeting_number < 0 or meeting_number > 20:\n raise MqValueError(\n 'meeting_number has to be an integer between 0 and 20 where 0 is the '\n 'last meeting and 1 is the next meeting'\n )\n cbw_df = ds.get_data(\n assetId=[mqid], rateType=event_type, meetingNumber=meeting_number, start=CENTRAL_BANK_WATCH_START_DATE\n )\n else:\n meeting_date = parse_meeting_date(meeting_date)\n cbw_df = ds.get_data(\n assetId=[mqid], rateType=event_type, meetingDate=meeting_date, start=CENTRAL_BANK_WATCH_START_DATE\n )\n\n if cbw_df.empty:\n raise MqValueError('meeting date specified returned no data')\n\n if rate_type == RateType.RELATIVE:\n spot_df = ds.get_data(\n assetId=[mqid], rateType=event_type, meetingNumber=0, start=CENTRAL_BANK_WATCH_START_DATE\n ).rename(columns={'value': 'spotValue'})\n if spot_df.empty:\n raise MqValueError('no spot data returned to rebase')\n joined_df = cbw_df.merge(spot_df, on=['date', 'assetId', 'rateType', 'location', 'valuationDate'], how='inner')\n joined_df = joined_df.set_index('valuationDate')\n joined_df['relValue'] = joined_df['value'] - joined_df['spotValue']\n series = ExtendedSeries(joined_df['relValue'])\n else:\n cbw_df = cbw_df.set_index('valuationDate')\n series = ExtendedSeries(cbw_df['value'])\n series.dataset_ids = (Dataset.GS.CENTRAL_BANK_WATCH,)\n return series\n\n\ndef parse_meeting_date(valuation_date: Optional[GENERIC_DATE] = None):\n if isinstance(valuation_date, str):\n if len(valuation_date.split('-')) == 3:\n year, month, day = valuation_date.split('-')\n return dt.date(int(year), int(month), int(day))\n else:\n start, valuation_date = _range_from_pricing_date('USD', valuation_date)\n return valuation_date.date() if isinstance(valuation_date, pd.Timestamp) else valuation_date\n elif isinstance(valuation_date, dt.date) or valuation_date is None:\n start, valuation_date = _range_from_pricing_date(None, valuation_date)\n return valuation_date.date() if isinstance(valuation_date, pd.Timestamp) else valuation_date\n else:\n raise MqValueError(\n 'valuation_date must be of type datetime.date or string YYYY-MM-DD or relative date strings like 1m, 1y'\n )\n\n\ndef _get_default_ois_benchmark(currency: CurrencyEnum) -> BenchmarkTypeCB:\n if currency == CurrencyEnum.USD:\n return BenchmarkTypeCB.Fed_Funds\n elif currency == CurrencyEnum.GBP:\n return BenchmarkTypeCB.SONIA\n elif currency == CurrencyEnum.EUR:\n return BenchmarkTypeCB.EUROSTR\n\n\ndef _check_cb_ccy_benchmark_rt(asset: Asset, benchmark_type: BenchmarkTypeCB) -> tuple:\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n currency = CurrencyEnum(bbid)\n if currency not in [CurrencyEnum.EUR, CurrencyEnum.GBP, CurrencyEnum.USD]:\n raise MqValueError('Only EUR, GBP and USD are supported for real time Central Bank swap data')\n if benchmark_type is None:\n benchmark_type = _get_default_ois_benchmark(currency)\n if (\n isinstance(benchmark_type, BenchmarkTypeCB)\n and benchmark_type.value not in CURRENCY_TO_SWAP_RATE_BENCHMARK[currency.value].keys()\n ):\n raise MqValueError('%s is not supported for %s', benchmark_type.value, currency.value)\n return currency, benchmark_type\n\n\ndef _get_swap_from_meeting_date(\n currency: CurrencyEnum, benchmark_type: BenchmarkTypeCB, meeting_date: Union[dt.date, int, str]\n) -> str:\n if isinstance(meeting_date, int):\n if meeting_date == 0:\n forward_tenor = '0b'\n else:\n forward_tenor = f\"{CCY_TO_CB[currency.value]}{meeting_date}\"\n swap_tenor = f\"{CCY_TO_CB[currency.value]}{str(meeting_date + 1)}\"\n elif isinstance(meeting_date, str) and re.fullmatch(f\"({CCY_TO_CB[currency.value]}[0-9]|1[0-9])\", meeting_date):\n if meeting_date[-1] == '0':\n forward_tenor = '0b'\n else:\n forward_tenor = f\"{CCY_TO_CB[currency.value]}{meeting_date[-1]}\"\n swap_tenor = f\"{CCY_TO_CB[currency.value]}{str(int(meeting_date[-1]) + 1)}\"\n else:\n raise MqValueError(\n 'only meeting dates of the type ecb1, mpc1, frb1 or an integer 1,2...20 for next'\n ' meeting are available for real time central bank data'\n )\n rate_mqid = get_cb_meeting_swap(currency, benchmark_type, forward_tenor=forward_tenor, swap_tenor=swap_tenor)\n return rate_mqid\n\n\ndef policy_rate_expectation_rt(\n asset: Asset,\n event_type: EventType = EventType.MEETING,\n rate_type: RateType = RateType.ABSOLUTE,\n meeting_date: Union[dt.date, int, str] = 0,\n benchmark_type: BenchmarkTypeCB = None,\n):\n currency, benchmark_type = _check_cb_ccy_benchmark_rt(asset, benchmark_type)\n rate_mqid = _get_swap_from_meeting_date(currency, benchmark_type, meeting_date)\n cbw_df = get_cb_swap_data(currency=currency, rate_mqids=rate_mqid)\n if cbw_df.empty:\n raise MqValueError('meeting date specified returned no data')\n\n if event_type == EventType.SPOT:\n if rate_type == RateType.ABSOLUTE:\n series = ExtendedSeries(cbw_df['rate'])\n series.dataset_ids = (Dataset.GS.IR_SWAP_RATES_INTRADAY_CALC_BANK,)\n return series\n else:\n raise MqValueError('rate_type must be absolute for event_type = Spot')\n elif event_type == EventType.MEETING:\n if rate_type == RateType.RELATIVE:\n spot_id = get_cb_meeting_swap(currency, benchmark_type, '0b', f\"{CCY_TO_CB[currency.value]}1\")\n spot_df = get_cb_swap_data(currency, spot_id)\n if spot_df.empty:\n raise MqValueError('no spot data returned to rebase')\n joined_df = cbw_df.merge(\n spot_df,\n on=['time', 'pricingLocation', 'csaTerms', 'currency'],\n how='inner',\n suffixes=['_meeting', '_spot'],\n )\n joined_df['rate'] = joined_df['rate_meeting'] - joined_df['rate_spot']\n series = ExtendedSeries(joined_df['rate'])\n else:\n series = ExtendedSeries(cbw_df['rate'])\n series.dataset_ids = (Dataset.GS.IR_SWAP_RATES_INTRADAY_CALC_BANK,)\n return series\n else:\n raise MqValueError(\"Real Time End of Year pricing is not supported\")\n\n\ndef policy_rate_term_structure_rt(\n asset: Asset,\n event_type: EventType = EventType.MEETING,\n rate_type: RateType = RateType.ABSOLUTE,\n benchmark_type: BenchmarkTypeCB = None,\n source: str = None,\n):\n currency, benchmark_type = _check_cb_ccy_benchmark_rt(asset, benchmark_type)\n\n if event_type == EventType.SPOT:\n if rate_type == RateType.RELATIVE:\n raise MqValueError('rate_type must be absolute for event_type = Spot')\n else:\n mqid = get_cb_meeting_swap(\n currency, benchmark_type=benchmark_type, forward_tenor='0b', swap_tenor=f\"{CCY_TO_CB[currency.value]}1\"\n )\n spot_df = get_cb_swap_data(currency, [mqid])\n if spot_df.empty:\n raise MqValueError('no spot data returned')\n series = ExtendedSeries(spot_df['rate'])\n series.dataset_ids = (Dataset.GS.IR_SWAP_RATES_INTRADAY_CALC_BANK,)\n return series\n elif event_type == EventType.MEETING:\n mqids = get_cb_meeting_swaps(currency, benchmark_type=benchmark_type)\n cbw_df = get_cb_swap_data(currency, rate_mqids=mqids)\n if cbw_df.empty:\n return ExtendedSeries(dtype=float)\n else:\n raise MqValueError(\"Real Time End of Year pricing is not supported\")\n\n if rate_type == RateType.RELATIVE:\n spot_id = get_cb_meeting_swap(\n currency, benchmark_type=benchmark_type, forward_tenor='0b', swap_tenor=f\"{CCY_TO_CB[currency.value]}1\"\n )\n spot_df = get_cb_swap_data(currency, spot_id)\n if spot_df.empty:\n raise MqValueError('no spot data returned to rebase')\n joined_df = cbw_df.merge(\n spot_df, on=['time', 'pricingLocation', 'csaTerms', 'currency'], how='inner', suffixes=['_meeting', '_spot']\n )\n joined_df['rate'] = joined_df['rate_meeting'] - joined_df['rate_spot']\n joined_df = joined_df.rename(columns={'effectiveDate_meeting': 'effectiveDate'})\n else:\n joined_df = cbw_df\n\n if joined_df.empty:\n series = ExtendedSeries(dtype=float)\n else:\n latest = joined_df.index.max()\n _logger.info('selected pricing date %s', latest)\n joined_df = joined_df.loc[latest]\n biz_day = _get_custom_bd(_default_pricing_location(currency).value)\n # col_to_plot = 'effectiveTenor'\n col_to_plot = 'effectiveDate'\n joined_df.loc[:, 'expirationDate'] = joined_df[col_to_plot].apply(_get_term_struct_date, args=(latest, biz_day))\n joined_df = joined_df.set_index('expirationDate')\n joined_df.index = pd.DatetimeIndex(joined_df.index)\n joined_df = joined_df.sort_index()\n joined_df = joined_df.loc[\n pd.Timestamp(DataContext.current.start_date) : pd.Timestamp(DataContext.current.end_date)\n ]\n series = ExtendedSeries(dtype=float) if joined_df.empty else ExtendedSeries(joined_df['rate'])\n series.dataset_ids = getattr(joined_df, 'dataset_ids', ())\n if series.empty: # Raise descriptive error if no data returned + date context is in the past\n check_forward_looking(None, source, 'policy_rate_term_structure')\n return series\n" }, { "path": "gs_quant/timeseries/measures_reports.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport re\nfrom enum import Enum\nfrom typing import Optional, Union, List\n\nimport pandas as pd\nimport numpy as np\nimport math\nimport scipy.stats as stats\nfrom pandas.tseries.offsets import BDay\nfrom pydash import decapitalize\n\nfrom gs_quant.api.gs.data import QueryType, GsDataApi, DataQuery\nfrom gs_quant.data import DataMeasure\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.entities.entity import EntityType\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.portfolio_manager import PortfolioManager\nfrom gs_quant.markets.report import (\n FactorRiskReport,\n PerformanceReport,\n ThematicReport,\n ReturnFormat,\n format_aum_for_return_calculation,\n get_pnl_percent,\n get_factor_pnl_percent_for_single_factor,\n)\nfrom gs_quant.markets.securities import Bond\nfrom gs_quant.models.risk_model import FactorRiskModel\nfrom gs_quant.target.reports import PositionSourceType\nfrom gs_quant.timeseries import plot_measure_entity, beta, correlation, max_drawdown\nfrom gs_quant.timeseries.algebra import geometrically_aggregate\nfrom gs_quant.timeseries.measures import _extract_series_from_df, SecurityMaster, AssetIdentifier\n\n\nclass Unit(Enum):\n NOTIONAL = 'Notional'\n PERCENT = 'Percent'\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.FACTOR_EXPOSURE])\ndef factor_exposure(\n report_id: str,\n factor_name: str,\n unit: str = 'Notional',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Factor exposure data associated with a factor in a factor risk report\n\n :param report_id: factor risk report id\n :param factor_name: factor name\n :param unit: unit in which the timeseries is returned (Notional or Percent)\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of factor exposure for requested factor\n \"\"\"\n return _get_factor_data(report_id, factor_name, QueryType.FACTOR_EXPOSURE, Unit(unit))\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.FACTOR_PNL])\ndef factor_pnl(\n report_id: str,\n factor_name: str,\n unit: str = 'Notional',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Factor PnL data associated with a factor in a factor risk report\n\n :param report_id: factor risk report id\n :param factor_name: factor name\n :param unit: unit in which the timeseries is returned (Notional or Percent)\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of factor pnl for requested factor\n \"\"\"\n return _get_factor_data(report_id, factor_name, QueryType.FACTOR_PNL, Unit(unit))\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.FACTOR_PROPORTION_OF_RISK])\ndef factor_proportion_of_risk(\n report_id: str, factor_name: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Factor proportion of risk data associated with a factor in a factor risk report\n\n :param report_id: factor risk report id\n :param factor_name: factor name\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of factor proportion of risk for requested factor\n \"\"\"\n return _get_factor_data(report_id, factor_name, QueryType.FACTOR_PROPORTION_OF_RISK)\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.DAILY_RISK])\ndef daily_risk(\n report_id: str,\n factor_name: str = 'Total',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Daily risk data associated with a factor in a factor risk report\n\n :param report_id: factor risk report id\n :param factor_name: factor name (must be \"Factor\", \"Specific\", or \"Total\")\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of daily risk for requested factor\n \"\"\"\n return _get_factor_data(report_id, factor_name, QueryType.DAILY_RISK)\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.ANNUAL_RISK])\ndef annual_risk(\n report_id: str,\n factor_name: str = 'Total',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Annual risk data associated with a factor in a factor risk report\n\n :param report_id: factor risk report id\n :param factor_name: factor name (must be \"Factor\", \"Specific\", or \"Total\")\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of daily risk for requested factor\n \"\"\"\n return _get_factor_data(report_id, factor_name, QueryType.ANNUAL_RISK)\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.PNL])\ndef normalized_performance(\n report_id: str, leg: str = None, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Returns the Normalized Performance of a performance report based on AUM source\n :param report_id: id of performance report\n :param leg: short or long\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio normalized performance\n\n **Usage**\n\n Returns the normalized performance of the portfolio.\n\n :math:`NP(L/S)_{t} = SUM( PNL(L/S)_{t}/ ( EXP(L/S)_{t} ) - cPNL(L/S)_{t-1) )\n if ( EXP(L/S)_{t} ) > 0\n else:\n 1/ SUM( PNL(L/S)_{t}/ ( EXP(L/S)_{t} ) - cPNL(L/S)_{t-1) )`\n For each leg, short and long, then:\n :math:`NP_{t} = NP(L)_{t} * SUM(EXP(L)) / SUM(GROSS_EXP) + NP(S)_{t} * SUM(EXP(S)) / SUM(GROSS_EXP) + 1`\n\n If leg is short, set SUM(EXP(L)) to 0, if leg is long, set SUM(EXP(S)) to 0\n\n where :math:`cPNL(L/S)_{t-1}` is your performance reports cumulative long or short PNL at date t-1\n where :math:`PNL(L/S)_{t}` is your performance reports long or short pnl at date t\n where :math:`GROSS_EXP_{t}` is portfolio gross exposure on date t\n where :math:`EXP(L/S)_{t}` is the long or short exposure on date t\n\n \"\"\"\n start_date = DataContext.current.start_time\n end_date = DataContext.current.end_time\n\n start_date = (start_date - BDay(1)).date()\n end_date = end_date.date()\n\n performance_report = PerformanceReport.get(report_id)\n\n constituent_data = performance_report.get_portfolio_constituents(\n fields=['assetId', 'pnl', 'quantity', 'netExposure'], start_date=start_date, end_date=end_date\n ).set_index('date')\n\n if leg:\n if leg.lower() == \"long\":\n constituent_data = constituent_data[constituent_data['quantity'] > 0]\n if leg.lower() == \"short\":\n constituent_data = constituent_data[constituent_data['quantity'] < 0]\n\n # Split into long and short and aggregate across dates\n long_side = _return_metrics(\n constituent_data[constituent_data['quantity'] > 0], list(constituent_data.index.unique()), \"long\"\n )\n short_side = _return_metrics(\n constituent_data[constituent_data['quantity'] < 0], list(constituent_data.index.unique()), \"short\"\n )\n\n short_exposure = sum(abs(short_side['exposure']))\n long_exposure = sum(long_side['exposure'])\n gross_exposure = short_exposure + long_exposure\n\n long_side['longRetWeighted'] = (long_side['longMetrics'] - 1) * (long_exposure / gross_exposure)\n short_side['shortRetWeighted'] = (short_side['shortMetrics'] - 1) * (short_exposure / gross_exposure)\n\n combined = long_side[['longRetWeighted']].join(short_side[['shortRetWeighted']], how='inner')\n combined['normalizedPerformance'] = combined['longRetWeighted'] + combined['shortRetWeighted'] + 1\n return pd.Series(combined['normalizedPerformance'], name=\"normalizedPerformance\").dropna()\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.PNL])\ndef long_pnl(\n report_id: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n PNL from long holdings\n\n :param report_id: id of performance report\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio long pnl\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n performance_report = PerformanceReport.get(report_id)\n\n constituent_data = performance_report.get_portfolio_constituents(\n fields=['pnl', 'quantity'], start_date=start_date, end_date=end_date\n ).set_index('date')\n long_leg = constituent_data[constituent_data['quantity'] > 0]['pnl']\n long_leg = long_leg.groupby(level=0).sum()\n return pd.Series(long_leg, name=\"longPnl\")\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.PNL])\ndef short_pnl(\n report_id: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n\n PNL from short holdings\n :param report_id: id of performance report\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio short pnl\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n performance_report = PerformanceReport.get(report_id)\n\n constituent_data = performance_report.get_portfolio_constituents(\n fields=['pnl', 'quantity'], start_date=start_date, end_date=end_date\n ).set_index('date')\n short_leg = constituent_data[constituent_data['quantity'] < 0]['pnl']\n short_leg = short_leg.groupby(level=0).sum()\n return pd.Series(short_leg, name=\"shortPnl\")\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.THEMATIC_EXPOSURE])\ndef thematic_exposure(\n report_id: str, basket_ticker: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Thematic exposure of a portfolio to a requested GS thematic flagship basket\n\n :param report_id: portfolio thematic analytics report id\n :param basket_ticker: ticker for thematic basket\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of daily thematic beta of portfolio to requested flagship basket\n \"\"\"\n thematic_report = ThematicReport.get(report_id)\n asset = SecurityMaster.get_asset(basket_ticker, AssetIdentifier.TICKER)\n df = thematic_report.get_thematic_exposure(\n start_date=DataContext.current.start_date,\n end_date=DataContext.current.end_date,\n basket_ids=[asset.get_marquee_id()],\n )\n if not df.empty:\n df = df.set_index('date')\n df.index = pd.to_datetime(df.index)\n return _extract_series_from_df(df, QueryType.THEMATIC_EXPOSURE)\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.THEMATIC_EXPOSURE])\ndef thematic_beta(\n report_id: str, basket_ticker: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Thematic beta values of a portfolio to a requested GS thematic flagship basket\n\n :param report_id: portfolio thematic analytics report id\n :param basket_ticker: ticker for thematic basket\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Timeseries of daily thematic beta of portfolio to requested flagship basket\n \"\"\"\n thematic_report = ThematicReport.get(report_id)\n asset = SecurityMaster.get_asset(basket_ticker, AssetIdentifier.TICKER)\n df = thematic_report.get_thematic_betas(\n start_date=DataContext.current.start_date,\n end_date=DataContext.current.end_date,\n basket_ids=[asset.get_marquee_id()],\n )\n if not df.empty:\n df = df.set_index('date')\n df.index = pd.to_datetime(df.index)\n return _extract_series_from_df(df, QueryType.THEMATIC_BETA)\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.AUM])\ndef aum(report_id: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None) -> pd.Series:\n \"\"\"\n AUM of the portfolio\n\n :param report_id: id of performance report\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio long pnl\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n performance_report = PerformanceReport.get(report_id)\n aum_curve = performance_report.get_aum(start_date=start_date, end_date=end_date)\n aum_dict = [{'date': key, 'aum': aum_curve[key]} for key in aum_curve]\n\n # Create and return timeseries\n df = pd.DataFrame(aum_dict)\n if not df.empty:\n df = df.set_index('date')\n df.index = pd.to_datetime(df.index)\n return _extract_series_from_df(df, QueryType.AUM)\n\n\n@plot_measure_entity(EntityType.REPORT, [QueryType.PNL])\ndef pnl(\n report_id: str,\n unit: str = 'Notional',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n PNL from all holdings, if unit is Percent, geometrically aggregate over time frame\n\n :param report_id: id of performance report\n :param unit: by default uses mode as Notional, but can also be set to Percent\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio pnl\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n performance_report = PerformanceReport.get(report_id)\n pnl_df = performance_report.get_pnl(start_date, end_date)\n\n if unit == Unit.PERCENT.value:\n return get_pnl_percent(performance_report, pnl_df, 'pnl', start_date, end_date)\n else:\n pnl_df = pnl_df.set_index('date')\n return pd.Series(pnl_df['pnl'], name=\"pnl\")\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef historical_simulation_estimated_pnl(\n report_id: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Estimated Pnl from replaying a historical simulation scenario on your latest positions\n :param report_id: id of performance report\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio estimated pnl\n \"\"\"\n\n factor_attributed_pnl = _replay_historical_factor_moves_on_latest_positions(report_id, [])\n total_factor_attributed_pnl = factor_attributed_pnl.apply(np.sum, axis=1).to_frame(\"estimatedPnl\")\n\n total_factor_attributed_pnl.index = pd.to_datetime(total_factor_attributed_pnl.index)\n return total_factor_attributed_pnl.squeeze()\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef historical_simulation_estimated_factor_attribution(\n report_id: str, factor_name: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Estimated Pnl attributed to the factor after replaying a historical simulation scenario on a portfolio's latest\n positions\n :param report_id: id of performance report\n :param factor_name: name of the factor\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio estimated pnl\n \"\"\"\n\n factor_attributed_pnl = _replay_historical_factor_moves_on_latest_positions(report_id, [factor_name])\n factor_attributed_pnl.index = pd.to_datetime(factor_attributed_pnl.index)\n\n return factor_attributed_pnl.squeeze()\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef hit_rate(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The hit rate of a portfolio is a percentage of positions that have generated positive returns over a given period.\n\n :param report_id: id of performance report\n :param rolling_window: size of rolling window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the hit rate\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n performance_report = PerformanceReport.get(report_id)\n window = _parse_window(rolling_window)\n portfolio_constituents = performance_report.get_portfolio_constituents(\n fields=['date', 'pnl'], start_date=start_date, end_date=end_date\n ).set_index('date')\n\n portfolio_constituents = portfolio_constituents[portfolio_constituents['entryType'] == 'Holding']\n portfolio_constituents = portfolio_constituents.sort_values('date')\n pivot_constituents = portfolio_constituents.pivot_table(index='date', columns='assetId', values='pnl')\n\n rolling_compound = (pivot_constituents).rolling(window=window).sum()\n positive_count = (rolling_compound > 0).sum(axis=1)\n valid_assets = rolling_compound.count(axis=1)\n\n return positive_count / valid_assets\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef portfolio_max_drawdown(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The largest drop from peak to trough in a sub-period over the stated timeframe\n\n :param report_id: id of performance report\n :param rolling_window: size of rolling window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio estimated pnl\n \"\"\"\n aum_series = aum(report_id)\n rolling_window = _parse_window(rolling_window)\n max_drawdown_series = aum_series.rolling(window=rolling_window).apply(_max_drawdown, raw=False)\n\n return max_drawdown_series\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef drawdown_length(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The length in days between the peak and the trough of the maximum drawdown\n\n :param report_id: id of performance report\n :param rolling_window: size of rolling window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the drawdown length\n \"\"\"\n aum_series = aum(report_id)\n rolling_window = _parse_window(rolling_window)\n drawdown_length_series = aum_series.rolling(window=rolling_window).apply(_drawdown_length, raw=False)\n\n return drawdown_length_series\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef max_recovery_period(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The maximum number of days used to reach a previously broken price level over the stated timeframe.\n\n :param report_id: id of performance report\n :param rolling_window: size of rolling window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of max recovery period\n \"\"\"\n aum_series = aum(report_id)\n rolling_window = _parse_window(rolling_window)\n recovery_period_series = aum_series.rolling(window=rolling_window).apply(_max_recovery_period, raw=False)\n\n return recovery_period_series\n\n\ndef _max_recovery_period(series):\n peak = series.iloc[0]\n recovery_periods = []\n current_drawdown = 0\n in_drawdown = False\n\n for val in series[1:]:\n if val < peak:\n in_drawdown = True\n current_drawdown += 1\n else:\n if in_drawdown:\n recovery_periods.append(current_drawdown)\n in_drawdown = False\n peak = val\n current_drawdown = 0\n return max(recovery_periods) if recovery_periods else 0\n\n\ndef _drawdown_length(series):\n peak = series.iloc[0]\n drawdown_start = None\n max_drawdown_length = 0\n current_length = 0\n\n for value in series:\n if value >= peak:\n # Reset when a new peak is found\n peak = value\n drawdown_start = None\n current_length = 0\n else:\n # Track the drawdown length\n if drawdown_start is None:\n drawdown_start = value\n current_length += 1\n max_drawdown_length = max(max_drawdown_length, current_length)\n\n return max_drawdown_length\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef standard_deviation(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of the total return over the stated time frame.\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of standard deviation\n \"\"\"\n portfolio_pnl = _get_daily_pnl(report_id)\n\n rolling_window = _parse_window(rolling_window)\n rolling_std = portfolio_pnl.rolling(window=rolling_window).std()\n\n return rolling_std\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef downside_risk(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of the periodic returns that are lower than the mean return over the stated time frame.\n Standard deviation is calculated using all returns, downside risk is calculated using only the returns\n that are below the mean.\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of downside_risk\n \"\"\"\n portfolio_pnl = _get_daily_pnl(report_id)\n rolling_window = _parse_window(rolling_window)\n downside_risk_series = portfolio_pnl.rolling(window=rolling_window).apply(_rolling_downside_risk, raw=False)\n\n return downside_risk_series\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef semi_variance(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility of the periodic returns that are lower than the mean return over the stated time frame.\n Standard deviation is calculated using all returns, semi variance is calculated using only the returns\n that are below 0.\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of downside_risk\n \"\"\"\n portfolio_pnl = _get_daily_pnl(report_id)\n rolling_window = _parse_window(rolling_window)\n semi_variance_series = portfolio_pnl.rolling(window=rolling_window).apply(_rolling_semi_variance, raw=False)\n\n return semi_variance_series\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef kurtosis(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Kurtosis measure the peakedness or flatness of the return distribution over the state time frame.\n In a flat distribution the average value is more likely to occur.\n\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of kurtosis\n \"\"\"\n portfolio_pnl = _get_daily_pnl(report_id)\n rolling_window = _parse_window(rolling_window)\n rolling_kurtosis = portfolio_pnl.rolling(window=rolling_window).apply(\n lambda x: stats.kurtosis(x, fisher=True, bias=False), raw=False\n )\n\n return rolling_kurtosis\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef skewness(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Skewness measures the degree of symmetry of the return distribution over the stated time frame.\n If the left tail is more pronounced than the right tail, it is said the return have negative skewness.\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of skewness\n \"\"\"\n portfolio_pnl = _get_daily_pnl(report_id)\n rolling_window = _parse_window(rolling_window)\n rolling_skewness = portfolio_pnl.rolling(window=rolling_window).apply(\n lambda x: stats.skew(x, bias=False), raw=False\n )\n\n return rolling_skewness\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef realized_var(\n report_id: str,\n rolling_window: Union[int, str],\n confidence_interval: float = 0.95,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The maximum expected loss of the portfolios, calculated using\n the natural distribution of returns over a stated time frame and is based on a confidence level.\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param confidence_interval: confidence interval for variance\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of realized var\n \"\"\"\n portfolio_pnl = _get_daily_pnl(report_id)\n rolling_window = _parse_window(rolling_window)\n realized_variance = portfolio_pnl.rolling(window=rolling_window).quantile(1 - confidence_interval)\n realized_variance = realized_variance * -1\n\n return realized_variance\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef tracking_error(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The standard deviation of the excess return relative to the benchmark over the state time frame.\n\n Tracking Error = sqrt( sum((ExcessRi - ExcessRmean)^2) / (N-1) )\n\n Where:\n\n ExcessRi = excess return in period i\n\n ExcessRmean = mean excess return during bull markets\n\n N = number of periods\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param benchmark_id: id of benchmark\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of tracking error\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n benchmark_returns = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n portfolio_returns = _get_daily_pnl(report_id)\n active_return = portfolio_returns - benchmark_returns\n rolling_error = active_return.rolling(window=rolling_window).std()\n return rolling_error\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef tracking_error_bear(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The standard deviation of the excess return relative to the benchmark over the state time frame,\n only counting the periods when benchmark returns were negative.\n\n Bear Tracking Error = sqrt( sum((ExcessRi - ExcessRmeanBear)^2) / (N-1) ) for all when Benchmark Return < 0\n\n Where:\n\n ExcessRi = excess return in period i\n\n ExcessRmeanBear = mean excess return during bull markets\n\n N = number of periods\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param benchmark_id: id of benchmark\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of tracking error\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n benchmark_returns = _get_benchmark_return(benchmark_id, start_date, end_date)\n portfolio_returns = _get_daily_pnl(report_id)\n is_bear = benchmark_returns <= 0\n active_return = portfolio_returns - benchmark_returns\n rolling_error_bear = active_return.rolling(window=rolling_window).apply(\n lambda x: np.std(x, ddof=1) if is_bear.loc[x.index[-1]] else np.nan, raw=False\n )\n return rolling_error_bear\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef tracking_error_bull(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n The standard deviation of the excess return relative to the benchmark over the state time frame,\n only counting the periods when benchmark returns were positive.\n\n Bull Tracking Error = sqrt( sum((ExcessRi - ExcessRmeanBull)^2) / (N-1) ) for all when Benchmark Return > 0\n\n Where:\n\n ExcessRi = excess return in period i\n\n ExcessRmeanBull = mean excess return during bull markets\n\n N = number of periods\n\n :param report_id: id of performance report\n :param rolling_window: length of window\n :param benchmark_id: id of benchmark\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of tracking error\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n benchmark_returns = _get_benchmark_return(benchmark_id, start_date, end_date)\n portfolio_pnl = _get_daily_pnl(report_id)\n is_bull = benchmark_returns > 0\n active_return = portfolio_pnl - benchmark_returns\n rolling_error_bull = active_return.rolling(window=rolling_window).apply(\n lambda x: np.std(x, ddof=1) if is_bull.loc[x.index[-1]] else np.nan, raw=False\n )\n return rolling_error_bull\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef portfolio_sharpe_ratio(\n report_id: str,\n risk_free_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the risk free rate per unit of volatility\n\n Sharpe Ratio = (Rp - Rf) / σp\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n σp = Standard Deviation of Portfolio Return.\n\n :param report_id: id of performance report\n :param risk_free_id: id of risk-free asset\n :param rolling_window: length of window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of sharpe ratio\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n window = _parse_window(rolling_window)\n portfolio_pnl = _get_daily_pnl(report_id)\n risk_free_rate_daily = (1 + _get_risk_free_rate(risk_free_id, start_date, end_date)) ** (1 / 252) - 1\n\n daily_excess = (portfolio_pnl - risk_free_rate_daily).dropna()\n rolling_mean = daily_excess.rolling(window).mean()\n rolling_std = daily_excess.rolling(window).std()\n\n portfolio_sharpe_ratio_series = (rolling_mean / rolling_std) * np.sqrt(252)\n return portfolio_sharpe_ratio_series\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef calmar_ratio(\n report_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted performance metric that divides an investment strategy’s\n annualized rate of return by its maximum drawdown, focusing specifically\n on the strategy’s return per unit of downside risk.\n\n Calmar Ratio = Annualized Return / Max Drawdown\n\n :param report_id: id of performance report\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the calmar ratio\n \"\"\"\n rolling_window = _parse_window(rolling_window)\n portfolio_pnl = _get_daily_pnl(report_id)\n cumulative_return = (1 + portfolio_pnl) ** np.sqrt(252) - 1\n cumulative_return.index = pd.to_datetime(cumulative_return.index)\n aum_series = aum(report_id)\n max_drawdown_series = max_drawdown(aum_series, rolling_window)\n calmar = cumulative_return / max_drawdown_series.abs()\n\n return calmar\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef sortino_ratio(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the benchmark per unit of semi-variance\n\n Sortino Ratio = (Rp - Rb) / Downside Deviation\n\n Where:\n\n Rp = Portfolio Return\n\n Rb = Benchmark Return\n\n Downside Deviation = Standard Deviation of negative asset returns.\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset, can be security or risk free\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio estimated pnl\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n portfolio_pnl = _get_daily_pnl(report_id)\n\n security = SecurityMaster.get_asset(benchmark_id, AssetIdentifier.MARQUEE_ID)\n\n if isinstance(security, Bond):\n benchmark_pnl = (1 + _get_risk_free_rate(benchmark_id, start_date, end_date)) ** (1 / 252) - 1\n else:\n benchmark_pnl = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n\n excess = (portfolio_pnl - benchmark_pnl).dropna()\n sortino = excess.rolling(window=rolling_window).apply(lambda x: _compute_sortino(x), raw=False)\n return sortino\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef jensen_alpha(\n report_id: str,\n benchmark_id: str,\n risk_free_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-Adjusted measure that calculates the actual return of the portfolio\n over and above the return predicted by the CAPM, given the portfolios beta\n and benchmark returns.\n\n Jensen's Alpha = Rp - [Rf + Beta * (Rb - Rf)]\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n Beta = Portfolio Beta\n\n Rb = Benchmark Return\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param risk_free_id: id of risk-free asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of jensen alpha\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n portfolio_pnl = _get_daily_pnl(report_id)\n benchmark_pnl = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n\n risk_free_rate = (1 + _get_risk_free_rate(risk_free_id, start_date, end_date)) ** (1 / 252) - 1\n\n portfolio_beta_series = beta(portfolio_pnl, benchmark_pnl, prices=False)\n jensen = portfolio_pnl - (risk_free_rate + portfolio_beta_series * (benchmark_pnl - risk_free_rate))\n return jensen\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef jensen_alpha_bear(\n report_id: str,\n benchmark_id: str,\n risk_free_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-Adjusted measure that calculates the actual return of the portfolio\n over and above the return predicted by the CAPM, given the portfolios beta\n and benchmark returns. calculated based only on the periods when the benchmark return was negative\n\n Jensen's Alpha = Rp - [Rf + Beta * (Rb - Rf)]\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n Beta = Portfolio Beta\n\n Rb = Benchmark Return\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param risk_free_id: id of risk-free asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of jensen alpha\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n portfolio_pnl = _get_daily_pnl(report_id)\n benchmark_pnl = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n risk_free_rate = (1 + _get_risk_free_rate(risk_free_id, start_date, end_date)) ** (1 / 252) - 1\n portfolio_beta_series = beta(portfolio_pnl, benchmark_pnl, prices=False)\n\n benchmark_pnl = benchmark_pnl[benchmark_pnl < 0]\n\n jensen = portfolio_pnl - (risk_free_rate + portfolio_beta_series * (benchmark_pnl - risk_free_rate))\n return jensen\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef jensen_alpha_bull(\n report_id: str,\n benchmark_id: str,\n risk_free_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-Adjusted measure that calculates the actual return of the portfolio\n over and above the return predicted by the CAPM, given the portfolios beta\n and benchmark returns. calculated based only on the periods when the benchmark return was positive\n\n Jensen's Alpha = Rp - [Rf + Beta * (Rb - Rf)]\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n Beta = Portfolio Beta\n\n Rb = Benchmark Return\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param risk_free_id: id of risk-free asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of jensen alpha\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n portfolio_pnl = _get_daily_pnl(report_id)\n benchmark_pnl = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n risk_free_rate = (1 + _get_risk_free_rate(risk_free_id, start_date, end_date)) ** (1 / 252) - 1\n\n portfolio_beta_series = beta(portfolio_pnl, benchmark_pnl, prices=False)\n benchmark_pnl = benchmark_pnl[benchmark_pnl > 0]\n jensen = portfolio_pnl - (risk_free_rate + portfolio_beta_series * (benchmark_pnl - risk_free_rate))\n return jensen\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef information_ratio(\n report_id: str, benchmark_id: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the benchmark,\n per unit of tracking error volatility. It measures the consistency with\n which the portfolio is beating the benchmark.\n\n Information Ratio = (Rp - Rb) / Tracking Error\n\n Where:\n\n Rp = Portfolio Return\n\n Rb = Benchmark Return\n\n Tracking Error = Standard Deviation of the difference between the portfolio and benchmark returns.\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the information ratio\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n portfolio_pnl = _get_daily_pnl(report_id)\n benchmark_pnl = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n\n portfolio_excess = portfolio_pnl - benchmark_pnl\n portfolio_std = np.std(portfolio_excess)\n information = portfolio_excess / portfolio_std\n\n return information\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef information_ratio_bear(\n report_id: str, benchmark_id: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the benchmark,\n per unit of tracking error volatility, counting only the periods when the benchmark return was negative\n\n\n Information Ratio = (Rp - Rb) / Tracking Error\n\n Where:\n\n Rp = Portfolio Return\n\n Rb = Benchmark Return\n\n Tracking Error = Standard Deviation of the difference between the portfolio and benchmark returns.\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the information ratio\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n portfolio_pnl = _get_daily_pnl(report_id)\n benchmark_pnl = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n benchmark_pnl = benchmark_pnl[benchmark_pnl < 0]\n portfolio_excess = portfolio_pnl - benchmark_pnl\n portfolio_std = np.std(portfolio_excess)\n information = portfolio_excess / portfolio_std\n\n return information\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef information_ratio_bull(\n report_id: str, benchmark_id: str, *, source: str = None, real_time: bool = False, request_id: Optional[str] = None\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the benchmark,\n per unit of tracking error volatility, counting only the periods when the benchmark return was positive.\n\n Information Ratio = (Rp - Rb) / Tracking Error\n\n Where:\n\n Rp = Portfolio Return\n\n Rb = Benchmark Return\n\n Tracking Error = Standard Deviation of the difference between the portfolio and benchmark returns.\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio estimated pnl\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n portfolio_pnl = _get_daily_pnl(report_id)\n benchmark_pnl = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n benchmark_pnl = benchmark_pnl[benchmark_pnl > 0]\n portfolio_excess = portfolio_pnl - benchmark_pnl\n portfolio_std = np.std(portfolio_excess)\n information = portfolio_excess / portfolio_std\n\n return information\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef modigliani_ratio(\n report_id: str,\n benchmark_id: str,\n risk_free_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Modigliani RAP measures how much the portfolio would have returns if it had had\n the same risk as the benchmark. It is a linear transformation of the Sharpe Ratio,\n but the results are expressed in terms of performance.\n\n M2 = (Sharpe Ratio * σb) + Rf\n\n Where:\n\n Sharpe Ratio = Portfolio Sharpe Ratio\n\n σb = Standard Deviation of the Benchmark Return\n\n Rf = Risk Free Rate.\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param risk_free_id: id of risk free asset\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the modigliani ratio\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n\n benchmark_return = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n risk_free_rates = (1 + _get_risk_free_rate(risk_free_id, start_date, end_date)) ** (1 / 252) - 1\n\n sharpe_series = portfolio_sharpe_ratio(report_id, risk_free_id, rolling_window)\n\n benchmark_excess = benchmark_return - risk_free_rates\n\n std_benchmark = benchmark_excess.dropna().rolling(window=rolling_window).std()\n\n modigliani = (sharpe_series * std_benchmark) + risk_free_rates\n\n return modigliani\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef treynor_measure(\n report_id: str,\n risk_free_id: str,\n benchmark_id: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Risk-adjusted measure that calculates the excess return over the risk free rate\n per unit of beta relative to the benchmark. This is useful for assessing the excess\n return from each unit of systematic risk.\n\n Treynor Ratio = (Rp - Rf) / Beta\n\n Where:\n\n Rp = Portfolio Return\n\n Rf = Risk-Free Rate\n\n Beta = Portfolio Beta\n\n :param report_id: id of performance report\n :param risk_free_id: id of risk-free asset\n :param benchmark_id: id of benchmark asset\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio estimated pnl\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n portfolio_return = _get_daily_pnl(report_id)\n security = SecurityMaster.get_asset(benchmark_id, AssetIdentifier.MARQUEE_ID)\n spot_data_coordinate = security.get_data_coordinate(DataMeasure.SPOT_PRICE)\n benchmark_pricing = spot_data_coordinate.get_series(start=start_date, end=end_date)\n benchmark_pricing = benchmark_pricing.sort_index()\n portfolio_return = (1 + portfolio_return) ** np.sqrt(252) - 1\n risk_free_rate = _get_risk_free_rate(risk_free_id, start_date, end_date)\n\n beta_series = beta(aum(report_id), benchmark_pricing)\n\n treynor_series = (portfolio_return - risk_free_rate) / beta_series\n return treynor_series\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef alpha(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n This is the intercept from a regression analysis, where the portfolio's returns are regressed\n against the benchmark's returns. It represents the portion of the portfolio's return that is\n not explained by the benchmark's performance.\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the beta\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n portfolio_pnl = _get_daily_pnl(report_id)\n\n benchmark_pnl = _get_benchmark_return(benchmark_id, start_date, end_date)\n\n intercepts = {}\n benchmark_pnl = benchmark_pnl.reindex(portfolio_pnl.index)\n\n for i in range(rolling_window, len(portfolio_pnl) + 1):\n portfolio_window = portfolio_pnl.iloc[i - rolling_window : i]\n benchmark_window = benchmark_pnl.iloc[i - rolling_window : i]\n\n slope, intercept, *_ = stats.linregress(portfolio_window, benchmark_window)\n intercepts[portfolio_pnl.index[i - 1]] = intercept\n\n alpha_series = pd.Series(intercepts)\n\n return alpha_series\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef portfolio_beta(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Beta defined as the slope of the regression between report pnl and benchmark pnl\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the beta\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n portfolio_return = _get_daily_pnl(report_id)\n benchmark_pnl = _get_benchmark_return(benchmark_id, start_date, end_date)\n\n return beta(portfolio_return, benchmark_pnl, rolling_window, prices=False)\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef portfolio_correlation(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Correlation coefficient between the portfolio and the benchmark returns over the stated time frame\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: time series of the correlation coefficient\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n portfolio_pnl = aum(report_id)\n portfolio_pnl.index = pd.to_datetime(portfolio_pnl.index)\n\n security = SecurityMaster.get_asset(benchmark_id, AssetIdentifier.MARQUEE_ID)\n spot_data_coordinate = security.get_data_coordinate(DataMeasure.SPOT_PRICE)\n benchmark_pricing = spot_data_coordinate.get_series(start=start_date, end=end_date)\n benchmark_pricing = benchmark_pricing.sort_index()\n\n return correlation(portfolio_pnl, benchmark_pricing, rolling_window)\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef capture_ratio(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n A measure of how well portfolio is doing relative to the benchmark,\n defined as the ratio of the portfolio return to the benchmark return calculated\n on a daily basis and averaged over the stated time period.\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark asset\n :param rolling_window: size of window to use\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio capture ratio\n \"\"\"\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n rolling_window = _parse_window(rolling_window)\n benchmark_return = _get_benchmark_daily_return(benchmark_id, start_date, end_date)\n portfolio_return = _get_daily_pnl(report_id)\n\n avg_portfolio = portfolio_return.rolling(window=rolling_window).mean()\n avg_benchmark = benchmark_return.rolling(window=rolling_window).mean()\n\n capture_ratio_series = avg_portfolio / avg_benchmark\n return capture_ratio_series\n\n\n@plot_measure_entity(EntityType.REPORT)\ndef r_squared(\n report_id: str,\n benchmark_id: str,\n rolling_window: Union[int, str],\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n A measure of how well the portfolio's performance correlates with the performance of the benchmark,\n and thus a measure of what portion of its performance may be explained by the performance of the benchmark.\n\n :param report_id: id of performance report\n :param benchmark_id: id of benchmark\n :param rolling_window: size of rolling window\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: portfolio r squared\n \"\"\"\n rolling_window = _parse_window(rolling_window)\n r_values = portfolio_correlation(report_id, benchmark_id, rolling_window)\n r_values_squared = r_values**2\n return r_values_squared\n\n\ndef _get_benchmark_return(benchmark_id, start_date, end_date):\n security = SecurityMaster.get_asset(benchmark_id, AssetIdentifier.MARQUEE_ID)\n spot_data_coordinate = security.get_data_coordinate(DataMeasure.SPOT_PRICE)\n benchmark_pricing = spot_data_coordinate.get_series(start=start_date, end=end_date)\n benchmark_pricing = benchmark_pricing.sort_index()\n\n start_price = benchmark_pricing.iloc[0]\n\n benchmark_returns = (benchmark_pricing / start_price) - 1\n return benchmark_returns * 100\n\n\ndef _get_benchmark_daily_return(benchmark_id, start_date, end_date):\n security = SecurityMaster.get_asset(benchmark_id, AssetIdentifier.MARQUEE_ID)\n spot_data_coordinate = security.get_data_coordinate(DataMeasure.SPOT_PRICE)\n benchmark_pricing = spot_data_coordinate.get_series(start=start_date, end=end_date)\n benchmark_pricing = benchmark_pricing.sort_index()\n return benchmark_pricing.pct_change()\n\n\ndef _max_drawdown(series):\n running_max = series.cummax()\n drawdown = (series - running_max) / running_max\n max_drawdown_ts = drawdown.cummin()\n return max_drawdown_ts.iloc[-1]\n\n\ndef _compute_sortino(series):\n downside = series[series < 0]\n downside_std = np.sqrt((downside**2).mean()) * np.sqrt(252)\n mean_excess = series.mean() * 252\n return mean_excess / downside_std if downside_std != 0 else np.nan\n\n\ndef _rolling_downside_risk(series):\n negative_devs = series[series < series.mean()] - series.mean()\n semi_variance_series = (negative_devs**2).mean()\n return np.sqrt(semi_variance_series)\n\n\ndef _rolling_semi_variance(series):\n negative_devs = series[series < 0] - series.mean()\n semi_variance_series = (negative_devs**2).mean()\n return np.sqrt(semi_variance_series)\n\n\ndef _get_daily_pnl(report_id: str) -> pd.Series:\n portfolio_return = pnl(report_id)\n portfolio_return.index = pd.to_datetime(portfolio_return.index)\n portfolio_return = portfolio_return / aum(report_id).shift(1)\n\n return portfolio_return\n\n\ndef _get_risk_free_rate(risk_free_id, start_date, end_date):\n risk_free = SecurityMaster.get_asset(risk_free_id, AssetIdentifier.MARQUEE_ID)\n risk_free_pricing_data = risk_free.get_data_coordinate('yield')\n risk_free_rate = risk_free_pricing_data.get_series(start=start_date, end=end_date)\n return risk_free_rate.drop_duplicates()\n\n\ndef _replay_historical_factor_moves_on_latest_positions(\n report_id: str, factors: List[str]\n) -> Union[pd.Series, pd.DataFrame]:\n start_date = DataContext.current.start_time.date()\n end_date = DataContext.current.end_time.date()\n risk_report = FactorRiskReport.get(report_id)\n risk_model_id = risk_report.get_risk_model_id()\n\n # Get data in batches of 365 days\n date_range = pd.bdate_range(start_date, end_date)\n batches = np.array_split([d.date() for d in date_range.tolist()], math.ceil(len(date_range.tolist()) / 365))\n data_query_results = []\n query = {\"riskModel\": risk_model_id}\n if factors:\n query.update({\"factor\": factors})\n for batch in batches:\n data_query_results += GsDataApi.execute_query(\n 'RISK_MODEL_FACTOR', DataQuery(where=query, start_date=batch[0], end_date=batch[-1])\n ).get('data', [])\n\n return_data = pd.DataFrame(data_query_results).pivot(columns=\"factor\", index=\"date\", values=\"return\").sort_index()\n return_data_aggregated = (return_data / 100).apply(geometrically_aggregate)\n\n latest_report_date = risk_report.latest_end_date\n factor_exposures = risk_report.get_results(\n start_date=latest_report_date, end_date=latest_report_date, return_format=ReturnFormat.JSON\n )\n factor_exposure_df = (\n pd.DataFrame(factor_exposures).pivot(columns=\"factor\", index=\"date\", values=\"exposure\").sort_index()\n )\n\n factor_exposure_df = factor_exposure_df.reindex(columns=return_data_aggregated.columns)\n factor_attributed_pnl_values = return_data_aggregated.values * factor_exposure_df.values\n factor_attributed_pnl = pd.DataFrame(\n factor_attributed_pnl_values, index=return_data_aggregated.index, columns=return_data_aggregated.columns\n )\n\n return factor_attributed_pnl\n\n\ndef _parse_window(window: Union[int, str]):\n if isinstance(window, int):\n return window\n match = re.fullmatch(r'(\\d+)([dwmy])', window.strip().lower())\n if match is None:\n raise MqValueError('Invalid window format, please end with one of: [\"d\", \"w\", \"m\", \"y\"]')\n value, unit = match.groups()\n value = int(value)\n unit_multipliers = {'d': 1, 'w': 5, 'm': 22, 'y': 252}\n return value * unit_multipliers[unit]\n\n\ndef _get_factor_data(report_id: str, factor_name: str, query_type: QueryType, unit: Unit = Unit.NOTIONAL) -> pd.Series:\n # Check params\n report = FactorRiskReport.get(report_id)\n if factor_name not in ['Factor', 'Specific', 'Total']:\n if query_type in [QueryType.DAILY_RISK, QueryType.ANNUAL_RISK]:\n raise MqValueError('Please pick a factor name from the following: [\"Total\", \"Factor\", \"Specific\"]')\n model = FactorRiskModel.get(report.get_risk_model_id())\n factor = model.get_factor(factor_name)\n factor_name = factor.name\n\n # Extract relevant data for each date\n col_name = query_type.value.replace(' ', '')\n col_name = decapitalize(col_name)\n data_type = decapitalize(col_name[6:]) if col_name.startswith('factor') else col_name\n include_total_factor = factor_name != 'Total' and unit == Unit.PERCENT and query_type == QueryType.FACTOR_PNL\n factors_to_query = [factor_name, 'Total'] if include_total_factor else [factor_name]\n factor_data = report.get_results(\n factors=factors_to_query,\n start_date=DataContext.current.start_date,\n end_date=DataContext.current.end_date,\n return_format=ReturnFormat.JSON,\n )\n total_data = [d for d in factor_data if d.get(data_type) is not None and d.get('factor') == 'Total']\n factor_data = [d for d in factor_data if d.get(data_type) is not None and d.get('factor') == factor_name]\n if unit == Unit.PERCENT:\n if report.position_source_type != PositionSourceType.Portfolio:\n raise MqValueError('Unit can only be set to percent for portfolio reports')\n pm = PortfolioManager(report.position_source_id)\n tags = dict((tag.name, tag.value) for tag in report.parameters.tags) if report.parameters.tags else None\n performance_report = pm.get_performance_report(tags=tags)\n start_date = dt.datetime.strptime(min([d['date'] for d in factor_data]), '%Y-%m-%d').date()\n end_date = dt.datetime.strptime(max([d['date'] for d in factor_data]), '%Y-%m-%d').date()\n if query_type == QueryType.FACTOR_PNL:\n # Factor Pnl needs to be geometrically aggregated if unit is %\n aum_df = format_aum_for_return_calculation(performance_report, start_date, end_date)\n return get_factor_pnl_percent_for_single_factor(factor_data, total_data, aum_df, start_date)\n else:\n aum = performance_report.get_aum(start_date=start_date, end_date=end_date)\n for data in factor_data:\n if aum.get(data['date']) is None:\n raise MqValueError('Cannot convert to percent: Missing AUM on some dates in the date range')\n factor_exposures = [\n {'date': d['date'], col_name: d[data_type] / aum.get(d['date']) * 100} for d in factor_data\n ]\n else:\n factor_exposures = [{'date': d['date'], col_name: d[data_type]} for d in factor_data]\n\n # Create and return timeseries\n df = pd.DataFrame(factor_exposures)\n if not df.empty:\n df = df.set_index('date')\n df.index = pd.to_datetime(df.index)\n return _extract_series_from_df(df, query_type)\n\n\ndef _return_metrics(one_leg: pd.DataFrame, dates: list, name: str):\n if one_leg.empty:\n return pd.DataFrame(index=dates, data={f'{name}Metrics': [0 for d in dates], \"exposure\": [0 for d in dates]})\n one_leg = one_leg.groupby(one_leg.index).agg(pnl=('pnl', 'sum'), exposure=('netExposure', 'sum'))\n\n one_leg['cumulativePnl'] = one_leg['pnl'].cumsum(axis=0)\n\n one_leg['normalizedExposure'] = one_leg['exposure'] - one_leg['cumulativePnl']\n one_leg.iloc[0, one_leg.columns.get_loc('cumulativePnl')] = 0\n one_leg[f'{name}Metrics'] = one_leg['cumulativePnl'] / one_leg['normalizedExposure'] + 1\n\n one_leg[f'{name}Metrics'] = (\n 1 / one_leg[f'{name}Metrics'] if one_leg['exposure'].iloc[-1] < 0 else one_leg[f'{name}Metrics']\n )\n return one_leg\n" }, { "path": "gs_quant/timeseries/measures_risk_models.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom enum import Enum\nfrom typing import Dict, Optional, Union\n\nimport pandas as pd\nfrom pydash import decapitalize\n\nfrom gs_quant.api.gs.data import QueryType\nfrom gs_quant.api.gs.risk_models import IntradayFactorDataSource\nfrom gs_quant.common import AssetClass, AssetType\nfrom gs_quant.data.core import DataContext\nfrom gs_quant.entities.entity import EntityType\nfrom gs_quant.markets.factor import ReturnFormat\nfrom gs_quant.markets.securities import Asset, AssetIdentifier\nfrom gs_quant.models.risk_model import FactorRiskModel, MarqueeRiskModel\nfrom gs_quant.target.risk_models import (\n RiskModelDataMeasure,\n RiskModelDataAssetsRequest,\n RiskModelUniverseIdentifierRequest,\n)\nfrom gs_quant.timeseries import plot_measure_entity, plot_measure, prices\nfrom .statistics import percentile\nfrom gs_quant.timeseries.measures import _extract_series_from_df\nimport datetime as dt\n\nModelMeasureStr = {\n 'Asset Universe': RiskModelDataMeasure.Asset_Universe,\n 'Historical Beta': RiskModelDataMeasure.Historical_Beta,\n 'Total Risk': RiskModelDataMeasure.Total_Risk,\n 'Specific Risk': RiskModelDataMeasure.Specific_Risk,\n 'Specific Return': RiskModelDataMeasure.Specific_Return,\n 'Daily Returns': RiskModelDataMeasure.Daily_Return,\n 'Estimation Universe Weight': RiskModelDataMeasure.Estimation_Universe_Weight,\n 'Residual Variance': RiskModelDataMeasure.Residual_Variance,\n 'Predicted Beta': RiskModelDataMeasure.Predicted_Beta,\n 'Global Predicted Beta': RiskModelDataMeasure.Global_Predicted_Beta,\n 'Universe Factor Exposure': RiskModelDataMeasure.Universe_Factor_Exposure,\n 'R Squared': RiskModelDataMeasure.R_Squared,\n 'Fair Value Gap Percent': RiskModelDataMeasure.Fair_Value_Gap_Percent,\n 'Fair Value Gap Standard Deviation': RiskModelDataMeasure.Fair_Value_Gap_Standard_Deviation,\n 'Bid Ask Spread': RiskModelDataMeasure.Bid_Ask_Spread,\n 'Bid Ask Spread 30d': RiskModelDataMeasure.Bid_Ask_Spread_30d,\n 'Bid Ask Spread 60d': RiskModelDataMeasure.Bid_Ask_Spread_60d,\n 'Bid Ask Spread 90d': RiskModelDataMeasure.Bid_Ask_Spread_90d,\n 'Trading Volume': RiskModelDataMeasure.Trading_Volume,\n 'Trading Volume 30d': RiskModelDataMeasure.Trading_Volume_30d,\n 'Trading Volume 60d': RiskModelDataMeasure.Trading_Volume_60d,\n 'Trading Volume 90d': RiskModelDataMeasure.Trading_Volume_90d,\n 'Trading Value 30d': RiskModelDataMeasure.Traded_Value_30d,\n 'Composite Volume': RiskModelDataMeasure.Composite_Volume,\n 'Composite Volume 30d': RiskModelDataMeasure.Composite_Volume_30d,\n 'Composite Volume 60d': RiskModelDataMeasure.Composite_Volume_60d,\n 'Composite Volume 90d': RiskModelDataMeasure.Composite_Volume_90d,\n 'Composite Value 30d': RiskModelDataMeasure.Composite_Value_30d,\n 'Composite Issuer Market Cap': RiskModelDataMeasure.Issuer_Market_Cap,\n 'Composite Price': RiskModelDataMeasure.Price,\n 'Composite Model Price': RiskModelDataMeasure.Model_Price,\n 'Composite Capitalization': RiskModelDataMeasure.Capitalization,\n 'Composite Currency': RiskModelDataMeasure.Currency,\n 'Composite Unadjusted Specific Risk': RiskModelDataMeasure.Unadjusted_Specific_Risk,\n 'Dividend Yield': RiskModelDataMeasure.Dividend_Yield,\n}\n\n\nclass ModelMeasureString(Enum):\n ASSET_UNIVERSE = 'Asset Universe'\n HISTORICAL_BETA = 'Historical Beta'\n TOTAL_RISK = 'Total Risk'\n SPECIFIC_RISK = 'Specific Risk'\n SPECIFIC_RETURNS = 'Specific Return'\n DAILY_RETURNS = 'Daily Returns'\n ESTIMATION_UNVERSE_WEIGHT = 'Estimation Universe Weight'\n RESIDUAL_VARIANCE = 'Residual Variance'\n PREDICTED_BETA = 'Predicted Beta'\n GLOBAL_PREDICTED_BETA = 'Global Predicted Beta'\n UNIVERSE_FACTOR_EXPOSURE = 'Universe Factor Exposure'\n R_SQUARED = 'R Squared'\n FAIR_VALUE_GAP_PERCENT = 'Fair Value Gap Percent'\n FAIR_VALUE_GAP_STANDARD_DEVIATION = 'Fair Value Gap Standard Deviation'\n BID_ASK_SPREAD = 'Bid Ask Spread'\n BID_AKS_SPREAD_30D = 'Bid Ask Spread 30d'\n BID_AKS_SPREAD_60D = 'Bid Ask Spread 60d'\n BID_AKS_SPREAD_90D = 'Bid Ask Spread 90d'\n TRADING_VOLUME = 'Trading Volume'\n TRADING_VOLUME_30D = 'Trading Volume 30d'\n TRADING_VOLUME_60D = 'Trading Volume 60d'\n TRADING_VOLUME_90D = 'Trading Volume 90d'\n TRADING_VALUE_30D = 'Trading Value 30d'\n COMPOSITE_VOLUME = 'Composite Volume'\n COMPOSITE_VOLUME_30D = 'Composite Volume 30d'\n COMPOSITE_VOLUME_60D = 'Composite Volume 60d'\n COMPOSITE_VOLUME_90D = 'Composite Volume 90d'\n COMPOSITE_VALUE_30d = 'Composite Value 30d'\n COMPOSITE_ISSUER_MARKET_CAP = 'Composite Issuer Market Cap'\n COMPOSITE_PRICE = 'Composite Price'\n COMPOSITE_MODEL_PRICE = 'Composite Model Price'\n COMPOSITE_CAPITALIZATION = 'Composite Capitalization'\n COMPOSITE_CURRENCY = 'Composite Currency'\n COMPOSITE_UNADJUSTED_SPECIFIC_RISK = 'Composite Unadjusted Specific Risk'\n DIVIDEND_YIELD = 'Dividend Yield'\n\n\n@plot_measure((AssetClass.Equity,), (AssetType.Single_Stock,))\ndef risk_model_measure(\n asset: Asset,\n risk_model_id: str,\n risk_model_measure_selected: ModelMeasureString = ModelMeasureString.HISTORICAL_BETA,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Retrieve risk model measures for a given asset.\n\n :param asset: Asset object loaded from security master\n :param risk_model_id: ID of the risk model\n :param risk_model_measure_selected: Selected risk model measure\n :param source: Name of function caller\n :param real_time: Whether to retrieve intraday data instead of EOD\n :param request_id: Service request ID, if any\n :return: Risk model measure data for the asset\n \"\"\"\n model = MarqueeRiskModel.get(risk_model_id)\n gsid = asset.get_identifier(AssetIdentifier.GSID)\n risk_model_measure_selected = ModelMeasureStr[risk_model_measure_selected.value]\n\n query_results = model.get_data(\n measures=[risk_model_measure_selected, RiskModelDataMeasure.Asset_Universe],\n start_date=DataContext.current.start_time,\n end_date=DataContext.current.end_time,\n assets=RiskModelDataAssetsRequest(identifier=RiskModelUniverseIdentifierRequest.gsid, universe=[gsid]),\n limit_factors=False,\n ).get('results', [])\n\n measures = {}\n for result in query_results:\n if result:\n measure_name = set(result.get('assetData', {}).keys()).difference({'universe'})\n exposures = result.get('assetData', {}).get(measure_name.pop(), [])\n if exposures:\n measures[result['date']] = exposures[0]\n\n return __format_plot_measure_results(measures, QueryType.FACTOR_EXPOSURE)\n\n\n@plot_measure((AssetClass.Equity,), (AssetType.Single_Stock,))\ndef factor_zscore(\n asset: Asset,\n risk_model_id: str,\n factor_name: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Asset factor exposure (in the form of z-scores) for a factor using specified risk model\n\n :param asset: asset object loaded from security master\n :param risk_model_id: requested risk model id\n :param factor_name: requested factor name\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Time-series of asset factor exposure across available risk model dates\n \"\"\"\n model = MarqueeRiskModel.get(risk_model_id)\n factor = model.get_factor(factor_name)\n gsid = asset.get_identifier(AssetIdentifier.GSID)\n\n # Query risk model data\n query_results = model.get_data(\n measures=[\n RiskModelDataMeasure.Factor_Name,\n RiskModelDataMeasure.Universe_Factor_Exposure,\n RiskModelDataMeasure.Asset_Universe,\n ],\n start_date=DataContext.current.start_time,\n end_date=DataContext.current.end_time,\n assets=RiskModelDataAssetsRequest(identifier=RiskModelUniverseIdentifierRequest.gsid, universe=[gsid]),\n factors=[factor],\n ).get('results', [])\n\n # Get the factor data from query results\n z_scores = {}\n for result in query_results:\n exposures = result.get('assetData', {}).get('factorExposure', [])\n if exposures:\n z_scores[result['date']] = exposures[0].get(factor.id)\n\n return __format_plot_measure_results(z_scores, QueryType.FACTOR_EXPOSURE)\n\n\n@plot_measure_entity(EntityType.RISK_MODEL, [QueryType.FACTOR_RETURN])\ndef factor_covariance(\n risk_model_id: str,\n factor_name_1: str,\n factor_name_2: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Covariance time-series between two factors in a risk model\n\n :param risk_model_id: risk model entity\n :param factor_name_1: first factor name\n :param factor_name_2: second factor name\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Time-series of covariances between the two factors across available risk model dates\n \"\"\"\n\n model = FactorRiskModel.get(risk_model_id)\n factor_1 = model.get_factor(factor_name_1)\n factor_2 = model.get_factor(factor_name_2)\n covariance_curve = factor_1.covariance(\n factor_2, DataContext.current.start_date, DataContext.current.end_date, ReturnFormat.JSON\n )\n return __format_plot_measure_results(covariance_curve, QueryType.COVARIANCE)\n\n\n@plot_measure_entity(EntityType.RISK_MODEL, [QueryType.FACTOR_RETURN])\ndef factor_volatility(\n risk_model_id: str,\n factor_name: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Volatility timeseries for a factor in a risk model\n\n :param risk_model_id: risk model entity\n :param factor_name: factor name\n :param source: name of function caller\n :param real_time: whether to retrieve intra-day data instead of EOD\n :param request_id: server request id\n :return: Time-series of a factor's volatility across available risk model dates\n \"\"\"\n model = FactorRiskModel.get(risk_model_id)\n factor = model.get_factor(factor_name)\n volatility = factor.volatility(DataContext.current.start_date, DataContext.current.end_date, ReturnFormat.JSON)\n return __format_plot_measure_results(volatility, QueryType.VOLATILITY, multiplier=100)\n\n\n@plot_measure_entity(EntityType.RISK_MODEL, [QueryType.FACTOR_RETURN])\ndef factor_correlation(\n risk_model_id: str,\n factor_name_1: str,\n factor_name_2: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Correlation time-series between two factors in a risk model\n\n :param risk_model_id: risk model entity\n :param factor_name_1: first factor name\n :param factor_name_2: second factor name\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Time-series of correlations between the two factors across available risk model dates\n \"\"\"\n model = FactorRiskModel.get(risk_model_id)\n factor_1 = model.get_factor(factor_name_1)\n factor_2 = model.get_factor(factor_name_2)\n correlation = factor_1.correlation(\n factor_2, DataContext.current.start_date, DataContext.current.end_date, ReturnFormat.JSON\n )\n return __format_plot_measure_results(correlation, QueryType.CORRELATION)\n\n\n@plot_measure_entity(EntityType.RISK_MODEL, [QueryType.FACTOR_RETURN])\ndef factor_performance(\n risk_model_id: str,\n factor_name: str,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Factor returns as a price time-series for a factor in a risk model\n\n :param risk_model_id: risk model entity\n :param factor_name: factor name\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Time-series of factor returns as a price series across available risk model dates\n \"\"\"\n\n model = FactorRiskModel.get(risk_model_id)\n factor = model.get_factor(factor_name)\n factor_returns_df = factor.returns(DataContext.current.start_date, DataContext.current.end_date)\n factor_returns_series = factor_returns_df.squeeze() / 100\n return prices(factor_returns_series, 100)\n\n\n@plot_measure_entity(EntityType.RISK_MODEL, [])\ndef factor_returns_intraday(\n risk_model_id: str,\n factor_name: str,\n data_source: Union[IntradayFactorDataSource, str] = None,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Factor returns as a time-series for a factor in a risk model\n\n :param risk_model_id: risk model entity\n :param factor_name: factor name\n :param data_source: requested data source\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: server request id\n :return: Time-series of factor returns as a series across different timestamps\n \"\"\"\n\n model = FactorRiskModel.get(risk_model_id)\n factor = model.get_factor(factor_name)\n factor_returns_df = factor.intraday_returns(\n DataContext.current.start_time, DataContext.current.end_time, data_source\n )\n return factor_returns_df.squeeze()\n\n\n@plot_measure_entity(EntityType.RISK_MODEL, [])\ndef factor_returns_percentile(\n risk_model_id: str,\n factor_name: str,\n lookback_days: int = 10,\n n_percentile: float = 90.0,\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Percentile of factor returns over a lookback period for a factor in a risk model\n \"\"\"\n start_date = (DataContext.current.start_time - dt.timedelta(days=lookback_days)).date()\n end_date = DataContext.current.end_time.date()\n\n model = FactorRiskModel.get(risk_model_id)\n factor = model.get_factor(factor_name)\n factor_returns_df = factor.returns(start_date=start_date, end_date=end_date, format=ReturnFormat.DATA_FRAME)\n percentile_value = percentile(factor_returns_df.squeeze(), n_percentile)\n # Return the percentile value as pandas series for the entire time range from start_time to end_time\n return pd.Series(\n percentile_value,\n index=pd.date_range(start=DataContext.current.start_time, end=DataContext.current.end_time, freq='2h'),\n )\n\n\ndef __format_plot_measure_results(time_series: Dict, query_type: QueryType, multiplier=1, handle_missing_column=False):\n \"\"\"Create and return panda series expected for a plot measure\"\"\"\n col_name = query_type.value.replace(' ', '')\n col_name = decapitalize(col_name)\n time_series_list = [{'date': k, col_name: v * multiplier} for k, v in time_series.items()]\n df = pd.DataFrame(time_series_list)\n if not df.empty:\n df = df.set_index('date')\n df.index = pd.to_datetime(df.index)\n return _extract_series_from_df(df, query_type, handle_missing_column)\n" }, { "path": "gs_quant/timeseries/measures_xccy.py", "content": "\"\"\"\nCopyright 2020 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport logging\nfrom collections import OrderedDict\nfrom enum import Enum\nfrom typing import Optional, Union, Dict\n\nimport pandas as pd\n\nfrom gs_quant.api.gs.assets import GsAssetApi\nfrom gs_quant.api.gs.data import QueryType, GsDataApi\nfrom gs_quant.errors import MqValueError\nfrom gs_quant.markets.securities import AssetIdentifier, Asset, SecurityMaster\nfrom gs_quant.common import Currency as CurrencyEnum, AssetClass, AssetType, PricingLocation\nfrom gs_quant.timeseries import ASSET_SPEC, plot_measure, MeasureDependency, GENERIC_DATE\nfrom gs_quant.timeseries.measures import _asset_from_spec, _market_data_timed, ExtendedSeries\nfrom gs_quant.timeseries import measures_rates as tm_rates\n\n_logger = logging.getLogger(__name__)\n\n\nclass CrossCurrencyRateOptionType(Enum):\n LIBOR = 'LIBOR'\n OIS = 'OIS'\n EUROSTR = 'EUROSTR'\n SOFR = 'SOFR'\n SOFRVLIBOR = 'SOFRVLIBOR'\n TestRateOption = 'TestRateOption'\n\n\nclass TdapiCrossCurrencyRatesDefaultsProvider:\n # flag to indicate that a given property should not be included in asset query\n EMPTY_PROPERTY = \"null\"\n\n def __init__(self, defaults: dict):\n self.defaults = defaults\n benchmark_mappings = {}\n maturity_mappings = {}\n for k, v in defaults.get(\"CURRENCIES\").items():\n benchmark_mappings[k] = {}\n maturity_mappings[k] = {}\n for e in v:\n benchmark_mappings[k][e.get(\"BenchmarkType\")] = e.get('rateOption')\n maturity_mappings[k][e.get(\"BenchmarkType\")] = e.get('designatedMaturity')\n self.defaults['MAPPING'] = benchmark_mappings\n self.defaults['MATURITIES'] = maturity_mappings\n\n def get_rateoption_for_benchmark(self, currency: CurrencyEnum, benchmark: str):\n return self.defaults.get(\"MAPPING\").get(currency.value).get(benchmark)\n\n def get_maturity_for_benchmark(self, currency: CurrencyEnum, benchmark: str):\n return self.defaults.get(\"MATURITIES\").get(currency.value).get(benchmark)\n\n\nCROSSCURRENCY_RATES_DEFAULTS = {\n \"CURRENCIES\": {\n \"AUD\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"AUD-BBR-BBSW\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"AUD-AONIA-OIS-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n {\n \"BenchmarkType\": \"SOFRVLIBOR\",\n \"rateOption\": \"AUD-BBR-BBSW\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n ],\n \"CAD\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"CAD-BA-CDOR\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"NYC\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"CAD-BA-CDOR\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"NYC\"],\n },\n ],\n \"CHF\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"CHF-LIBOR-BBA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"CHF-SARON-OIS-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"SOFRVLIBOR\",\n \"rateOption\": \"CHF-LIBOR-BBA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n ],\n \"DKK\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"DKK-CIBOR2-DKNA13\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"DKK-CIBOR2-DKNA13\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n ],\n \"EUR\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"EUR-EURIBOR-TELERATE\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"OIS\",\n \"rateOption\": \"EUR-EONIA-OIS-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"EUR-EUROSTR-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n ],\n \"GBP\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"GBP-LIBOR-BBA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"OIS\",\n \"rateOption\": \"GBP-SONIA-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"GBP-SONIA-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n ],\n \"JPY\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"JPY-LIBOR-BBA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n {\n \"BenchmarkType\": \"OIS\",\n \"rateOption\": \"JPY-TONA-OIS-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"JPY-TONA-OIS-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n ],\n \"NOK\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"NOK-NIBOR-BBA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"NOK-NIBOR-BBA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n ],\n \"NZD\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"NZD-BBR-FRA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"NZD-NZIONA-OIS-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n {\n \"BenchmarkType\": \"SOFRVLIBOR\",\n \"rateOption\": \"NZD-BBR-FRA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n ],\n \"SEK\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"SEK-STIBOR-SIDE\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"SEK-STIBOR-SIDE\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"LDN\"],\n },\n ],\n \"SGD\": [\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"SGD-SORA-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"TKO\"],\n },\n ],\n \"USD\": [\n {\n \"BenchmarkType\": \"LIBOR\",\n \"rateOption\": \"USD-LIBOR-BBA\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"NYC\"],\n },\n {\n \"BenchmarkType\": \"OIS\",\n \"rateOption\": \"USD-FEDERAL FUNDS-H.15-OIS-COMP\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"NYC\"],\n },\n {\n \"BenchmarkType\": \"SOFR\",\n \"rateOption\": \"USD-SOFR-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"NYC\"],\n },\n {\n \"BenchmarkType\": \"SOFRVLIBOR\",\n \"rateOption\": \"USD-SOFR-COMPOUND\",\n \"designatedMaturity\": \"3m\",\n \"pricingLocation\": [\"NYC\"],\n },\n ],\n },\n \"COMMON\": {\"payerSpread\": \"ATM\", \"clearingHouse\": \"NONE\", \"terminationTenor\": \"5y\", \"effectiveDate\": \"0b\"},\n}\ncrossCurrencyRates_defaults_provider = TdapiCrossCurrencyRatesDefaultsProvider(CROSSCURRENCY_RATES_DEFAULTS)\n\nCURRENCY_TO_XCCY_SWAP_RATE_BENCHMARK = {\n 'AUD': OrderedDict([('LIBOR', 'AUD-BBR-BBSW'), ('SOFR', 'AUD-AONIA-OIS-COMPOUND'), ('SOFRVLIBOR', 'AUD-BBR-BBSW')]),\n 'CAD': OrderedDict([('LIBOR', 'CAD-BA-CDOR'), ('SOFR', 'CAD-BA-CDOR')]),\n 'CHF': OrderedDict(\n [\n ('LIBOR', 'CHF-LIBOR-BBA'),\n ('OIS', 'CHF-SARON-OIS-COMPOUND'),\n ('SOFR', 'CHF-SARON-OIS-COMPOUND'),\n ('SOFRVLIBOR', 'CHF-LIBOR-BBA'),\n ]\n ),\n 'DKK': OrderedDict([('LIBOR', 'DKK-CIBOR2-DKNA13'), ('SOFR', 'DKK-CIBOR2-DKNA13')]),\n 'EUR': OrderedDict(\n [('LIBOR', 'EUR-EURIBOR-TELERATE'), ('OIS', 'EUR-EONIA-OIS-COMPOUND'), ('SOFR', 'EUR-EUROSTR-COMPOUND')]\n ),\n 'GBP': OrderedDict([('LIBOR', 'GBP-LIBOR-BBA'), ('OIS', 'GBP-SONIA-COMPOUND'), ('SOFR', 'GBP-SONIA-COMPOUND')]),\n 'JPY': OrderedDict(\n [('LIBOR', 'JPY-LIBOR-BBA'), ('OIS', 'JPY-TONA-OIS-COMPOUND'), ('SOFR', 'JPY-TONA-OIS-COMPOUND')]\n ),\n 'NOK': OrderedDict([('LIBOR', 'NOK-NIBOR-BBA'), ('SOFR', 'NOK-NIBOR-BBA')]),\n 'NZD': OrderedDict([('LIBOR', 'NZD-BBR-FRA'), ('SOFR', 'NZD-NZIONA-OIS-COMPOUND'), ('SOFRVLIBOR', 'NZD-BBR-FRA')]),\n 'SEK': OrderedDict([('LIBOR', 'SEK-STIBOR-SIDE'), ('SOFR', 'SEK-STIBOR-SIDE')]),\n 'SGD': OrderedDict([('SOFR', 'SGD-SORA-COMPOUND')]),\n 'USD': OrderedDict(\n [\n ('LIBOR', 'USD-LIBOR-BBA'),\n ('OIS', 'USD-FEDERAL FUNDS-H.15-OIS-COMP'),\n ('SOFR', 'USD-SOFR-COMPOUND'),\n ('SOFRVLIBOR', 'USD-SOFR-COMPOUND'),\n ]\n ),\n}\n\nCURRENCY_TO_DUMMY_CROSSCURRENCY_SWAP_BBID = {\n 'EUR': 'MAW8SAXPSKYA94E2',\n 'GBP': 'MATDD783JM1C2GGD',\n 'JPY': 'MAFMW4HJC5TDE51H',\n 'CHF': 'MA5YSMR2VN8A5T0N',\n 'NOK': 'MA44T4N363CWAQWN',\n 'DKK': 'MAF8ZEM0Q7R2D0R3',\n 'SEK': 'MADQDFZH81GX4NDP',\n 'AUD': 'MAHN46BVXFAZ354E',\n 'NZD': 'MAW1MNKPQWYJPX3A',\n 'CAD': 'MAPRDGWMZSQJ8CZ0',\n 'SGD': 'MA7B3JT80Q617KGQ',\n}\n\n\ndef _currency_to_tdapi_crosscurrency_swap_rate_asset(asset_spec: ASSET_SPEC) -> str:\n asset = _asset_from_spec(asset_spec)\n bbid = asset.get_identifier(AssetIdentifier.BLOOMBERG_ID)\n # for each currency, get a dummy asset for checking availability\n result = CURRENCY_TO_DUMMY_CROSSCURRENCY_SWAP_BBID.get(bbid, asset.get_marquee_id())\n return result\n\n\ndef _get_tdapi_crosscurrency_rates_assets(allow_many=False, **kwargs) -> Union[str, list]:\n # sanitize input for asset query.\n if \"pricing_location\" in kwargs:\n del kwargs[\"pricing_location\"]\n assets = GsAssetApi.get_many_assets(**kwargs)\n\n # change order of basis swap legs and check if swap in dataset\n if len(assets) == 0 and ('asset_parameters_payer_rate_option' in kwargs): # flip legs\n kwargs['asset_parameters_payer_rate_option'], kwargs['asset_parameters_receiver_rate_option'] = (\n kwargs['asset_parameters_receiver_rate_option'],\n kwargs['asset_parameters_payer_rate_option'],\n )\n if 'asset_parameters_payer_designated_maturity' in kwargs:\n (\n kwargs['asset_parameters_payer_designated_maturity'],\n kwargs['asset_parameters_receiver_designated_maturity'],\n ) = (\n kwargs['asset_parameters_receiver_designated_maturity'],\n kwargs['asset_parameters_payer_designated_maturity'],\n )\n if 'asset_parameters_payer_currency' in kwargs:\n kwargs['asset_parameters_payer_currency'], kwargs['asset_parameters_receiver_currency'] = (\n kwargs['asset_parameters_receiver_currency'],\n kwargs['asset_parameters_payer_currency'],\n )\n assets = GsAssetApi.get_many_assets(**kwargs)\n\n if len(assets) == 0 and ('asset_parameters_clearing_house' in kwargs): # test without the clearing house\n if kwargs['asset_parameters_clearing_house'] == tm_rates._ClearingHouse.NONE.value:\n del kwargs['asset_parameters_clearing_house']\n assets = GsAssetApi.get_many_assets(**kwargs)\n\n # change order of basis swap legs and check if swap in dataset\n if len(assets) == 0 and ('asset_parameters_payer_rate_option' in kwargs): # flip legs\n kwargs['asset_parameters_payer_rate_option'], kwargs['asset_parameters_receiver_rate_option'] = (\n kwargs['asset_parameters_receiver_rate_option'],\n kwargs['asset_parameters_payer_rate_option'],\n )\n if 'asset_parameters_payer_designated_maturity' in kwargs:\n (\n kwargs['asset_parameters_payer_designated_maturity'],\n kwargs['asset_parameters_receiver_designated_maturity'],\n ) = (\n kwargs['asset_parameters_receiver_designated_maturity'],\n kwargs['asset_parameters_payer_designated_maturity'],\n )\n if 'asset_parameters_payer_currency' in kwargs:\n kwargs['asset_parameters_payer_currency'], kwargs['asset_parameters_receiver_currency'] = (\n kwargs['asset_parameters_receiver_currency'],\n kwargs['asset_parameters_payer_currency'],\n )\n assets = GsAssetApi.get_many_assets(**kwargs)\n\n if len(assets) > 1:\n # term structure measures need multiple assets\n if (\n ('asset_parameters_termination_date' not in kwargs)\n or ('asset_parameters_effective_date' not in kwargs)\n or allow_many\n ):\n return [asset.id for asset in assets]\n else:\n raise MqValueError('Specified arguments match multiple assets')\n elif len(assets) == 0:\n raise MqValueError('Specified arguments did not match any asset in the dataset' + str(kwargs))\n else:\n return assets[0].id\n\n\ndef _check_crosscurrency_rateoption_type(\n currency, benchmark_type: Union[CrossCurrencyRateOptionType, str]\n) -> CrossCurrencyRateOptionType:\n if isinstance(benchmark_type, str):\n if benchmark_type.upper() in CrossCurrencyRateOptionType.__members__:\n benchmark_type = CrossCurrencyRateOptionType[benchmark_type.upper()]\n else:\n raise MqValueError(\n benchmark_type.upper()\n + ' is not valid, pick one among '\n + ', '.join([x.value for x in CrossCurrencyRateOptionType])\n )\n\n if (\n isinstance(benchmark_type, CrossCurrencyRateOptionType)\n and benchmark_type.value not in CURRENCY_TO_XCCY_SWAP_RATE_BENCHMARK[currency.value].keys()\n ):\n raise MqValueError('%s is not supported for %s', benchmark_type.value, currency.value)\n else:\n return benchmark_type\n\n\ndef _get_crosscurrency_swap_leg_defaults(\n currency: CurrencyEnum, benchmark_type: CrossCurrencyRateOptionType = None\n) -> Dict:\n pricing_location = tm_rates.CURRENCY_TO_PRICING_LOCATION.get(currency, PricingLocation.LDN)\n # default benchmark types\n if benchmark_type is None:\n benchmark_type = CrossCurrencyRateOptionType(\n str(list(CURRENCY_TO_XCCY_SWAP_RATE_BENCHMARK[currency.value].keys())[0])\n )\n benchmark_type_input = CURRENCY_TO_XCCY_SWAP_RATE_BENCHMARK[currency.value].get(benchmark_type.value, \"\")\n designated_maturity = crossCurrencyRates_defaults_provider.get_maturity_for_benchmark(\n currency, benchmark_type.value\n )\n return dict(\n currency=currency,\n rateOption=benchmark_type_input,\n designatedMaturity=designated_maturity,\n pricing_location=pricing_location,\n )\n\n\ndef _get_crosscurrency_swap_csa_terms(curr: str, crosscurrencyindextype: str) -> dict:\n return dict(csaTerms=curr + '-1')\n\n\ndef _get_crosscurrency_swap_data(\n asset1: Asset,\n asset2: Asset,\n swap_tenor: str,\n rateoption_type: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: tm_rates._ClearingHouse = None,\n source: str = None,\n real_time: bool = False,\n query_type: QueryType = QueryType.SWAP_RATE,\n location: PricingLocation = None,\n) -> pd.DataFrame:\n if real_time:\n raise NotImplementedError('realtime swap_rate not implemented for anything but rates')\n\n currency1 = CurrencyEnum(asset1.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n currency2 = CurrencyEnum(asset2.get_identifier(AssetIdentifier.BLOOMBERG_ID))\n\n if currency1.value not in CURRENCY_TO_XCCY_SWAP_RATE_BENCHMARK.keys():\n raise NotImplementedError('Data not available for {} crosscurrency swap rates'.format(currency1.value))\n if currency2.value not in CURRENCY_TO_XCCY_SWAP_RATE_BENCHMARK.keys():\n raise NotImplementedError('Data not available for {} crosscurrency swap rates'.format(currency2.value))\n\n rateoption_type1 = _check_crosscurrency_rateoption_type(currency1, rateoption_type)\n rateoption_type2 = _check_crosscurrency_rateoption_type(currency2, rateoption_type)\n\n if rateoption_type1 != rateoption_type2:\n raise MqValueError('The two currencies do not both support the rate Option type ' + rateoption_type)\n rateoption_type = rateoption_type1\n\n clearing_house = tm_rates._check_clearing_house(clearing_house)\n\n defaults1 = _get_crosscurrency_swap_leg_defaults(currency1, rateoption_type)\n defaults2 = _get_crosscurrency_swap_leg_defaults(currency2, rateoption_type)\n\n if not (tm_rates._is_valid_relative_date_tenor(swap_tenor)):\n raise MqValueError('invalid swap tenor ' + swap_tenor)\n\n if defaults1[\"pricing_location\"] == PricingLocation.NYC:\n default_location = defaults2[\"pricing_location\"]\n currency = currency2\n else:\n default_location = defaults1[\"pricing_location\"]\n currency = currency1\n\n if location is None:\n pricing_location = PricingLocation(default_location)\n else:\n pricing_location = PricingLocation(location)\n pricing_location = tm_rates._pricing_location_normalized(pricing_location, currency)\n where = dict(pricingLocation=pricing_location.value)\n\n forward_tenor = tm_rates._check_forward_tenor(forward_tenor)\n fixed_rate = 'ATM'\n kwargs = dict(\n asset_class='Rates',\n type='XccySwapMTM',\n asset_parameters_termination_date=swap_tenor,\n asset_parameters_effective_date=forward_tenor,\n asset_parameters_payer_spread=fixed_rate,\n # asset_parameters_payer_currency=defaults1['currency'].value,\n asset_parameters_payer_rate_option=defaults1['rateOption'],\n # asset_parameters_payer_designated_maturity=defaults1['designatedMaturity'],\n # asset_parameters_receiver_currency=defaults2['currency'].value,\n asset_parameters_receiver_rate_option=defaults2['rateOption'],\n # asset_parameters_receiver_designated_maturity=defaults2['designatedMaturity'],\n asset_parameters_clearing_house=clearing_house.value,\n pricing_location=pricing_location,\n )\n\n rate_mqid = _get_tdapi_crosscurrency_rates_assets(**kwargs)\n\n _logger.debug(\n f'where asset= {rate_mqid}, swap_tenor={swap_tenor}, forward_tenor={forward_tenor}, '\n f'payer_currency={defaults1[\"currency\"].value}, payer_rate_option={defaults1[\"rateOption\"]}, '\n f'payer_designated_maturity={defaults1[\"designatedMaturity\"]}, '\n f'receiver_currency={defaults2[\"currency\"].value}, receiver_rate_option={defaults2[\"rateOption\"]}, '\n f'receiver_designated_maturity={defaults2[\"designatedMaturity\"]}, '\n f'clearing_house={clearing_house.value}, pricing_location={pricing_location.value}'\n )\n q = GsDataApi.build_market_data_query([rate_mqid], query_type, where=where, source=source, real_time=real_time)\n _logger.debug('q %s', q)\n df = _market_data_timed(q)\n return df\n\n\n@plot_measure(\n (\n AssetClass.Cash,\n AssetClass.FX,\n ),\n (\n AssetType.Currency,\n AssetType.Cross,\n ),\n [\n MeasureDependency(\n id_provider=_currency_to_tdapi_crosscurrency_swap_rate_asset, query_type=QueryType.XCCY_SWAP_SPREAD\n )\n ],\n)\ndef crosscurrency_swap_rate(\n asset: Asset,\n swap_tenor: str,\n rateoption_type: str = None,\n forward_tenor: Optional[GENERIC_DATE] = None,\n clearing_house: tm_rates._ClearingHouse = None,\n location: PricingLocation = None,\n *,\n source: str = None,\n real_time: bool = False,\n) -> pd.Series:\n \"\"\"\n GS end-of-day Zero Coupon CrossCurrency Swap curves across major currencies.\n\n :param asset: asset object loaded from security master\n :param swap_tenor: relative date representation of expiration date e.g. 1m\n :param rateoption_type: benchmark type e.g. LIBOR\n :param forward_tenor: absolute / relative date representation of forward starting point eg: '1y' or 'Spot' for\n spot starting swaps, 'imm1' or 'frb1'\n :param clearing_house: Example - \"LCH\", \"EUREX\", \"JSCC\", \"CME\"\n :param location: Example - \"TKO\", \"LDN\", \"NYC\"\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :return: swap rate curve\n \"\"\"\n\n if asset.get_type().value == AssetType.Cross.value:\n pair = asset.name\n [under, over] = [pair[i : i + 3] for i in range(0, 6, 3)]\n asset1 = SecurityMaster.get_asset(under, AssetIdentifier.BLOOMBERG_ID)\n asset2 = SecurityMaster.get_asset(over, AssetIdentifier.BLOOMBERG_ID)\n elif asset.get_type().value == AssetType.Currency.value:\n asset1 = asset\n asset2 = SecurityMaster.get_asset(\"USD\", AssetIdentifier.BLOOMBERG_ID)\n else:\n raise MqValueError('Asset type not supported ' + asset.get_type().value)\n\n df = _get_crosscurrency_swap_data(\n asset1=asset1,\n asset2=asset2,\n swap_tenor=swap_tenor,\n rateoption_type=rateoption_type,\n forward_tenor=forward_tenor,\n clearing_house=clearing_house,\n source=source,\n real_time=real_time,\n query_type=QueryType.XCCY_SWAP_SPREAD,\n location=location,\n )\n\n series = ExtendedSeries(dtype=float) if df.empty else ExtendedSeries(df['xccySwapSpread'])\n series.dataset_ids = getattr(df, 'dataset_ids', ())\n return series\n" }, { "path": "gs_quant/timeseries/statistics.py", "content": "# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Marquee Plot Service will attempt to make public functions (not prefixed with _) from this module available.\n# Such functions should be fully documented: docstrings should describe parameters and the return value, and provide\n# a 1-line description. Type annotations should be provided for parameters.\n\nimport datetime as dt\nfrom enum import Enum\nfrom typing import List, Union, Optional\n\nimport numpy as np\nimport pandas as pd\nimport scipy.stats.mstats as stats\nimport statsmodels.api as sm\nfrom scipy.stats import percentileofscore\nfrom statsmodels.regression.rolling import RollingOLS\n\nfrom .algebra import ceil, floor\nfrom .datetime import interpolate\nfrom .helper import (\n Window,\n normalize_window,\n rolling_offset,\n apply_ramp,\n plot_function,\n rolling_apply,\n Interpolate,\n plot_method,\n)\nfrom ..data import DataContext\nfrom ..errors import MqValueError, MqTypeError\nfrom ..models.epidemiology import SIR, SEIR, EpidemicModel\n\n\"\"\"\nStats library is for basic arithmetic and statistical operations on timeseries.\nThese include basic algebraic operations, probability and distribution analysis.\nGenerally not finance-specific routines.\n\"\"\"\n\ntry:\n from quant_extensions.timeseries.statistics import rolling_std\nexcept ImportError:\n\n def rolling_std(x: pd.Series, offset: pd.DateOffset) -> pd.Series:\n size = len(x)\n index = x.index\n results = np.empty(size, dtype=np.double)\n results[0] = np.nan\n values = np.array(x.values, dtype=np.double) # put data into np arrays to save time on slicing later\n\n start = 0\n for i in range(1, size):\n for j in range(start, i + 1):\n if pd.Timestamp(index[j]) > index[i] - offset:\n start = j\n break\n section = values[start : i + 1]\n results[i] = np.std(section[section == section], ddof=1)\n return pd.Series(results, index=index, dtype=np.double)\n\n\ndef _concat_series(series: List[pd.Series]):\n curves = []\n constants = {}\n k = 0\n for s in series:\n if s.min() != s.max():\n curves.append(s)\n else:\n constants[f'temp{k}'] = s.min()\n k += 1\n return pd.concat(curves, axis=1).assign(**constants)\n\n\n@plot_function\ndef min_(x: Union[pd.Series, List[pd.Series]], w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Minimum value of series over given window\n\n :param x: series: a timeseries or an array of timeseries\n\n :param w: window: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative time duration like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of minimum value\n\n **Usage**\n\n Returns the minimum value of the series over each window.\n\n If :math:`x` is a series:\n\n :math:`R_t = min(X_{t-w+1}:X_t)`\n\n where :math:`w` is the size of the rolling window.\n\n\n If :math:`x` is an array of series:\n\n :math:`R_t = min(X_{1, t-w+1}:X_{n, t})`\n\n where :math:`w` is the size of the rolling window, and :math:`n` is the number of series.\n\n If window is not provided, returns the minimum value over the\n full series. If the window size is greater than the available data, will return minimum of available values.\n\n If :math:`w` is a string, it should be a relative time duration such as '1m', '5d', etc. The available frequency\n strings can be found below:\n\n ============== ================\n Frequency Description\n ============== ================\n y one year\n m one month\n w one week\n d one day\n h one hour\n ============== ================\n\n **Examples**\n\n Minimum value of price series over the last :math:`22` observations:\n\n >>> prices = generate_series(100)\n >>> min_(prices, 22)\n\n **See also**\n\n :func:`max_`\n\n \"\"\"\n if isinstance(x, list):\n x = _concat_series(x).min(axis=1)\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n if isinstance(w.w, pd.DateOffset):\n values = (\n rolling_offset(x, w.w, np.nanmin, 'min')\n if isinstance(x, pd.Series)\n else [x.loc[(x.index > (idx - w.w).datetime()) & (x.index <= idx)].min() for idx in x.index]\n )\n return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).min(), w)\n\n\n@plot_function\ndef max_(x: Union[pd.Series, List[pd.Series]], w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Maximum value of series over given window\n\n :param x: series: a timeseries or an array of timeseries\n :param w: window: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative time duration like '1m', '5d', etc.\n Window size defaults to length of series.\n :return: timeseries of maximum value\n\n **Usage**\n\n Returns the maximum value of the series over each window.\n\n If :math:`x` is a series:\n\n :math:`R_t = max(X_{t-w+1}:X_t)`\n\n where :math:`w` is the size of the rolling window.\n\n If :math:`x` is an array of series:\n\n :math:`R_t = max(X_{1, t-w+1}:X_{n, t})`\n\n where :math:`w` is the size of the rolling window, and :math:`n` is the number of series.\n\n If window is not provided, returns the maximum value over the full series. If the window size is greater than the\n available data, will return maximum of available values.\n\n If :math:`w` is a string, it should be a relative time duration such as '1m', '5d', etc. The available frequency\n strings can be found below:\n\n ============== ================\n Frequency Description\n ============== ================\n y one year\n m one month\n w one week\n d one day\n h one hour\n ============== ================\n\n **Examples**\n\n Maximum value of price series over the last :math:`22` observations:\n\n >>> prices = generate_series(100)\n >>> max_(prices, 22)\n\n **See also**\n\n :func:`min_`\n\n \"\"\"\n if isinstance(x, list):\n x = _concat_series(x).max(axis=1)\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n if isinstance(w.w, pd.DateOffset):\n values = (\n rolling_offset(x, w.w, np.nanmax, 'max')\n if isinstance(x, pd.Series)\n else [x.loc[(x.index > idx - w.w) & (x.index <= idx)].max() for idx in x.index]\n )\n return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).max(), w)\n\n\n@plot_function\ndef range_(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Range of series over given window\n\n :param x: series: timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of range\n\n **Usage**\n\n Returns the range of the series (max - min) over rolling window:\n\n :math:`R_t = max(X_{t-w+1}:X_t) - min(X_{t-w+1}:X_t)`\n\n where :math:`w` is the size of the rolling window. If window is not provided, returns the range over the\n full series. If the window size is greater than the available data, will return range of all available values.\n\n **Examples**\n\n Range of price series over the last :math:`22` observations:\n\n >>> prices = generate_series(100)\n >>> range_(prices, 22)\n\n **See also**\n\n :func:`min_` :func:`max_`\n\n \"\"\"\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n\n max_v = max_(x, Window(w.w, 0))\n min_v = min_(x, Window(w.w, 0))\n\n return apply_ramp(max_v - min_v, w)\n\n\nclass MeanType(Enum):\n ARITHMETIC = 'arithmetic'\n QUADRATIC = 'quadratic'\n\n\n@plot_function\ndef mean(\n x: Union[pd.Series, List[pd.Series]],\n w: Union[Window, int, str] = Window(None, 0),\n mean_type: MeanType = MeanType.ARITHMETIC,\n) -> pd.Series:\n \"\"\"\n Arithmetic mean of series over given window\n\n :param x: series: a timeseries or an array of timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of mean value\n :param mean_type: type of mean to compute - arithmetic or quadratic, arithmetic by default\n\n **Usage**\n\n Calculates `arithmetic mean `_ of the series over a rolling window\n\n If a timeseries is provided:\n\n :math:`R_t = \\\\frac{\\\\sum_{i=t-w+1}^{t} X_i}{N}`\n\n where :math:`N` is the number of observations in each rolling window, :math:`w`.\n\n If an array of timeseries is provided:\n\n :math:`R_t = \\\\frac{\\\\sum_{i=t-w+1}^{t} {\\\\sum_{j=1}^{n}} X_{ij}}{N}`\n\n where :math:`n` is the number of series, and :math:`N` is the number of observations in each rolling window,\n :math:`w`.\n\n If window is not provided, computes rolling mean over the full series. If the window size is greater than the\n available data, will return mean of available values.\n\n **Examples**\n\n Generate price series and compute mean over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> mean(prices, 22)\n\n **See also**\n\n :func:`median` :func:`mode`\n\n \"\"\"\n if isinstance(x, list):\n x = pd.concat(x, axis=1)\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n\n if mean_type is MeanType.QUADRATIC:\n x = x**2\n\n if isinstance(w.w, pd.DateOffset):\n if isinstance(x, pd.Series):\n values = rolling_offset(x, w.w, np.nanmean, 'mean')\n else:\n values = [np.nanmean(x.loc[(x.index > (idx - w.w).date()) & (x.index <= idx)]) for idx in x.index]\n else:\n if isinstance(x, pd.Series):\n values = x.rolling(w.w, 0).mean() # faster than slicing in Python\n else:\n values = [np.nanmean(x.iloc[max(idx - w.w + 1, 0) : idx + 1]) for idx in range(0, len(x))]\n result = pd.Series(values, index=x.index, dtype=np.dtype(float))\n if mean_type is MeanType.QUADRATIC:\n result = np.sqrt(result)\n return apply_ramp(result, w)\n\n\n@plot_function\ndef median(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Median value of series over given window\n\n :param x: series: timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of median value\n\n **Usage**\n\n Computes the `median `_ value over a given window. For each window, this\n function will return the middle value when all elements in the window are sorted. If the number of observations in\n the window is even, will return the average of the middle two values. If the window size is greater than the\n available data, will return median of available values:\n\n :math:`d = \\\\frac{w-1}{2}`\n\n :math:`R_t = \\\\frac{X_{\\\\lfloor t-d \\\\rfloor} + X_{\\\\lceil t-d \\\\rceil}}{2}`\n\n where :math:`w` is the size of the rolling window. If window is not provided, computes median over the full series\n\n **Examples**\n\n Generate price series and compute median over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> median(prices, 22)\n\n **See also**\n\n :func:`mean` :func:`mode`\n \"\"\"\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n if isinstance(w.w, pd.DateOffset):\n values = (\n rolling_offset(x, w.w, np.nanmedian, 'median')\n if isinstance(x, pd.Series)\n else [x.loc[(x.index > (idx - w.w).date()) & (x.index <= idx)].median() for idx in x.index]\n )\n return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).median(), w)\n\n\n@plot_function\ndef mode(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Most common value in series over given window\n\n :param x: series: timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of mode value\n\n **Usage**\n\n Computes the `mode `_ over a given window. For each window, this\n function will return the most common value of all elements in the window. If there are multiple values with the same\n frequency of occurrence, will return the smallest value.\n\n If window is not provided, computes mode over the full series.\n\n **Examples**\n\n Generate price series and compute mode over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> mode(prices, 22)\n\n **See also**\n\n :func:`mean` :func:`median`\n \"\"\"\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n if isinstance(w.w, pd.DateOffset):\n values = (\n rolling_apply(x, w.w, lambda a: stats.mode(a).mode[0])\n if isinstance(x, pd.Series)\n else [stats.mode(x.loc[(x.index > (idx - w.w).date()) & (x.index <= idx)]).mode[0] for idx in x.index]\n )\n return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).apply(lambda y: stats.mode(y).mode, raw=True), w)\n\n\n@plot_function\ndef sum_(x: Union[pd.Series, List[pd.Series]], w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Rolling sum of series over given window\n\n :param x: series: a timeseries or an array of timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of rolling sum\n\n **Usage**\n\n Calculate the sum of observations over a given rolling window.\n\n If :math:`x` is a series:\n\n :math:`R_t = \\\\sum_{i=t-w+1}^{t} X_i`\n\n where :math:`w` is the size of the rolling window.\n\n If :math:`x` is an array of series:\n\n :math:`R_t = \\\\sum_{i=t-w+1}^{t} \\\\sum_{j=1}^{n} X_{ij}`\n\n where :math:`w` is the size of the rolling window and :math:`n` is the number of series\n\n If window is not provided, computes sum over the full series. If the window size is greater than the available data,\n will return sum of available values.\n\n **Examples**\n\n Generate price series and compute rolling sum over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> sum_(prices, 22)\n\n **See also**\n\n :func:`product`\n\n \"\"\"\n if isinstance(x, list):\n x = pd.concat(x, axis=1).sum(axis=1)\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing\n if isinstance(w.w, pd.DateOffset):\n assert isinstance(x, pd.Series), 'expected a series'\n values = rolling_offset(x, w.w, np.nansum, 'sum')\n return apply_ramp(values, w)\n else:\n return apply_ramp(x.rolling(w.w, 0).sum(), w)\n\n\n@plot_function\ndef product(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Rolling product of series over given window\n\n :param x: series: timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of rolling product\n\n **Usage**\n\n Calculate the product of observations over a given rolling window. For each time, :math:`t`, returns the value\n of all observations from :math:`t-w+1` to :math:`t` multiplied together:\n\n :math:`R_t = \\\\prod_{i=t-w+1}^{t} X_i`\n\n where :math:`w` is the size of the rolling window. If window is not provided, computes product over the full series\n\n **Examples**\n\n Generate price series and compute rolling sum over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> product(1+returns(prices))\n\n **See also**\n\n :func:`sum_`\n \"\"\"\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing\n if isinstance(w.w, pd.DateOffset):\n values = (\n rolling_offset(x, w.w, np.nanprod, 'prod')\n if isinstance(x, pd.Series)\n else [x.loc[(x.index > (idx - w.w).date()) & (x.index <= idx)].prod() for idx in x.index]\n )\n return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).agg(pd.Series.prod), w)\n\n\n@plot_function\ndef std(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Rolling standard deviation of series over given window\n\n :param x: series: timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of standard deviation\n\n **Usage**\n\n Provides `unbiased estimator `_ of sample\n `standard deviation `_ over a rolling window:\n\n :math:`R_t = \\\\sqrt{\\\\frac{1}{N-1} \\\\sum_{i=t-w+1}^t (X_i - \\\\overline{X_t})^2}`\n\n where :math:`N` is the number of observations in each rolling window, :math:`w`, and :math:`\\\\overline{X_t}` is the\n mean value over the same window:\n\n :math:`\\\\overline{X_t} = \\\\frac{\\\\sum_{i=t-w+1}^{t} X_i}{N}`\n\n If window is not provided, computes standard deviation over the full series\n\n **Examples**\n\n Generate price series and compute standard deviation of returns over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> std(returns(prices), 22)\n\n **See also**\n\n :func:`sum` :func:`mean` :func:`var`\n\n \"\"\"\n if x.empty:\n return x\n\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n if isinstance(w.w, pd.DateOffset):\n return apply_ramp(rolling_std(x, w.w), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).std(), w)\n\n\n@plot_function\ndef exponential_std(x: pd.Series, beta: float = 0.75) -> pd.Series:\n \"\"\"\n Exponentially weighted standard deviation\n\n :param x: time series\n :param beta: how much to weigh the previous price in the time series, thus controlling how much importance we\n place on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)\n :return: time series of standard deviation of the input series\n\n **Usage**\n\n Provides an unbiased estimator of `exponentially weighted standard deviation\n `_ of\n a series [:math:`X_0`, :math:`X_1`, :math:`X_2`, ...]:\n\n :math:`S_t = \\\\sqrt{[EWMA(X_t^2) - EWMA(X_t)^2] * DF_t}`\n\n where :math:`EWMA(X_t)` is the `exponential moving average\n `_ at :math:`t` (see function\n :func:`exponential_moving_average`), :math:`DF_t` is the debiasing factor (see\n `Weighted sample variance `_\n for further details):\n\n :math:`DF_t = \\\\frac{(\\\\sum_{i=0}^t w_i)^2} {(\\\\sum_{i=0}^t w_i)^2 - \\\\sum_{i=0}^t w_i^2}`\n\n where :math:`w_i` is the weight assigned to :math:`i` th observation:\n\n :math:`w_i = (1-\\\\beta)\\\\beta^i` for i>> prices = generate_series(100)\n >>> exponential_std(returns(prices), 0.9)\n\n **See also**\n\n :func:`std` :func:`var` :func:`exponential_moving_average`\n\n \"\"\"\n return x.ewm(alpha=1 - beta, adjust=False).std()\n\n\n@plot_function\ndef var(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Rolling variance of series over given window\n\n :param x: series: timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of variance\n\n **Usage**\n\n Provides `unbiased estimator `_ of sample\n `variance `_ over a rolling window:\n\n :math:`R_t = \\\\frac{1}{N-1} \\\\sum_{i=t-w+1}^t (X_i - \\\\overline{X_t})^2`\n\n where :math:`N` is the number of observations in each rolling window, :math:`w`, and :math:`\\\\overline{X_t}` is the\n mean value over the same window:\n\n :math:`\\\\overline{X_t} = \\\\frac{\\\\sum_{i=t-w+1}^{t} X_i}{N}`\n\n If window is not provided, computes variance over the full series\n\n **Examples**\n\n Generate price series and compute variance of returns over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> var(returns(prices), 22)\n\n **See also**\n\n :func:`var` :func:`mean` :func:`std`\n\n \"\"\"\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n if isinstance(w.w, pd.DateOffset):\n values = (\n rolling_offset(x, w.w, lambda a: np.nanvar(a, ddof=1), 'var')\n if isinstance(x, pd.Series)\n else [x.loc[(x.index > (idx - w.w).date()) & (x.index <= idx)].var() for idx in x.index]\n )\n return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).var(), w)\n\n\n@plot_function\ndef cov(x: pd.Series, y: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Rolling co-variance of series over given window\n\n :param x: series: timeseries\n :param y: series: timeseries\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of covariance\n\n **Usage**\n\n Provides `unbiased estimator `_ of sample\n `co-variance `_ over a rolling window:\n\n :math:`R_t = \\\\frac{1}{N-1} \\\\sum_{i=t-w+1}^t (X_i - \\\\overline{X_t}) (Y_i - \\\\overline{Y_t})`\n\n where :math:`N` is the number of observations in each rolling window, :math:`w`, and :math:`\\\\overline{X_t}` and\n :math:`\\\\overline{Y_t}` represent the sample mean of series :math:`X_t` and :math:`Y_t` over the same window:\n\n :math:`\\\\overline{X_t} = \\\\frac{\\\\sum_{i=t-w+1}^{t} X_i}{N}` and\n :math:`\\\\overline{Y_t} = \\\\frac{\\\\sum_{i=t-w+1}^{t} Y_i}{N}`\n\n If window is not provided, computes variance over the full series\n\n **Examples**\n\n Generate price series and compute variance of returns over :math:`22` observations\n\n >>> prices_x = generate_series(100)\n >>> prices_y = generate_series(100)\n >>> cov(returns(prices_x) returns(prices_y), 22)\n\n **See also**\n\n :func:`sum` :func:`mean` :func:`var`\n \"\"\"\n w = normalize_window(x, w)\n assert x.index.is_monotonic_increasing, \"series index is monotonic increasing\"\n if isinstance(w.w, pd.DateOffset):\n values = [x.loc[(x.index > (idx - w.w).date()) & (x.index <= idx)].cov(y) for idx in x.index]\n return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).cov(y), w)\n\n\ndef _zscore(x):\n if x.size == 1:\n return 0\n\n return stats.zscore(x, ddof=1)[-1]\n\n\n@plot_function\ndef zscores(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Rolling z-scores over a given window\n\n :param x: time series of prices\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of z-scores\n\n **Usage**\n\n Calculate `standard score `_ of each value in series over given\n window. Standard deviation and sample mean are computed over the specified rolling window, then element is\n normalized to provide a rolling z-score:\n\n :math:`R_t = \\\\frac { X_t - \\\\mu }{ \\\\sigma }`\n\n Where :math:`\\\\mu` and :math:`\\\\sigma` are sample mean and standard deviation over the given window\n\n If window is not provided, computes z-score relative to mean and standard deviation over the full series\n\n **Examples**\n\n Generate price series and compute z-score of returns over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> zscores(returns(prices), 22)\n\n **See also**\n\n :func:`mean` :func:`std`\n\n \"\"\"\n if x.size < 1:\n return x\n\n if isinstance(w, int):\n w = normalize_window(x, w)\n elif isinstance(w, str):\n if not (isinstance(x.index, pd.DatetimeIndex) or isinstance(x.index[0], dt.date)):\n raise MqValueError(\"When string is passed window index must be a DatetimeIndex or of type datetime.date\")\n w = normalize_window(x, w)\n if not w.w:\n if x.size == 1:\n return pd.Series([0.0], index=x.index, dtype=np.dtype(float))\n\n clean_series = x.dropna()\n zscore_series = pd.Series(stats.zscore(clean_series, ddof=1), clean_series.index, dtype=np.dtype(float))\n return interpolate(zscore_series, x, Interpolate.NAN)\n if not isinstance(w.w, int):\n w = normalize_window(x, w)\n dt_idx = pd.DatetimeIndex(x.index).date\n values = [_zscore(x.loc[(dt_idx > (idx - w.w).date()) & (dt_idx <= idx)]) for idx in dt_idx]\n return apply_ramp(pd.Series(values, index=x.index, dtype=np.dtype(float)), w)\n else:\n return apply_ramp(x.rolling(w.w, 0).apply(_zscore, raw=False), w)\n\n\n@plot_function\ndef winsorize(x: pd.Series, limit: float = 2.5, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Limit extreme values in series\n\n :param x: time series of prices\n :param limit: max z-score of values\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of winsorized values\n\n **Usage**\n\n Cap and floor values in the series which have a z-score greater or less than provided value. This function will\n restrict the distribution of values. Calculates the sample standard deviation and adjusts values which\n fall outside the specified range to be equal to the upper or lower limits\n\n Lower and upper limits are defined as:\n\n :math:`upper = \\\\mu + \\\\sigma \\\\times limit`\n\n :math:`lower = \\\\mu - \\\\sigma \\\\times limit`\n\n Where :math:`\\\\mu` and :math:`\\\\sigma` are sample mean and standard deviation. The series is restricted by:\n\n :math:`R_t = max( min( X_t, upper), lower )`\n\n See `winsorising `_ for additional information\n\n **Examples**\n\n Generate price series and winsorize z-score of returns over :math:`22` observations\n\n >>> prices = generate_series(100)\n >>> winsorize(zscore(returns(prices), 22))\n\n **See also**\n\n :func:`zscore` :func:`mean` :func:`std`\n\n \"\"\"\n w = normalize_window(x, w)\n\n if x.size < 1:\n return x\n\n assert w.w, \"window is not 0\"\n\n mu = x.mean()\n sigma = x.std()\n\n high = mu + sigma * limit\n low = mu - sigma * limit\n\n ret = ceil(x, high)\n ret = floor(ret, low)\n\n return apply_ramp(ret, w)\n\n\nclass Direction(Enum):\n START_TODAY = 'start_today'\n END_TODAY = 'end_today'\n\n\n@plot_function\ndef generate_series(length: int, direction: Direction = Direction.START_TODAY) -> pd.Series:\n \"\"\"\n Generate sample timeseries\n\n :param length: number of observations\n :param direction: whether generated series should start from today or end on today\n :return: date-based time series of randomly generated prices\n\n **Usage**\n\n Create timeseries from returns generated from a normally distributed random variables (IDD). Length determines the\n number of observations to be generated.\n\n Assume random variables :math:`R` which follow a normal distribution with mean :math:`0` and standard deviation\n of :math:`1`\n\n :math:`R \\\\sim N(0, 1)`\n\n The timeseries is generated from these random numbers through:\n\n :math:`X_t = (1 + R)X_{t-1}`\n\n **Examples**\n\n Generate price series with 100 observations starting from today's date:\n\n >>> prices = generate_series(100)\n\n **See also**\n\n :func:`numpy.random.normal()`\n\n \"\"\"\n levels = [100]\n first = dt.date.today()\n if direction == Direction.END_TODAY:\n first -= dt.timedelta(days=length - 1)\n dates = [first]\n\n rng = np.random.default_rng()\n for i in range(length - 1):\n levels.append(levels[i] * 1 + rng.standard_normal())\n dates.append(dt.date.fromordinal(dates[i].toordinal() + 1))\n\n return pd.Series(data=levels, index=dates, dtype=np.dtype(float))\n\n\nclass IntradayDirection(Enum):\n START_INTRADAY_NOW = 'start_intraday_now'\n END_INTRADAY_NOW = 'end_intraday_now'\n\n\n@plot_function\ndef generate_series_intraday(\n length: int, direction: IntradayDirection = IntradayDirection.START_INTRADAY_NOW\n) -> pd.Series:\n \"\"\"\n Generate sample intraday timeseries with minute-level granularity\n\n :param length: number of observations (minutes)\n :param direction: whether generated series should start from or end at current timestamp\n :return: datetime-based time series of randomly generated prices at 1-minute intervals\n\n **Usage**\n\n Create intraday timeseries from returns generated from normally distributed random variables (IID). Length\n determines the number of minute-level observations to be generated. The series can either start from the\n current timestamp (START_INTRADAY_NOW) or end at the current timestamp (END_INTRADAY_NOW).\n\n Assume random variables :math:`R` which follow a normal distribution with mean :math:`0` and standard deviation\n of :math:`0.001` (scaled for intraday volatility):\n\n :math:`R \\\\sim N(0, 0.001)`\n\n The timeseries is generated from these random numbers through:\n\n :math:`X_t = (1 + R)X_{t-1}`\n\n where each observation is spaced 1 minute apart.\n\n **Examples**\n\n Generate intraday price series with 120 observations (2 hours) starting from current time:\n\n >>> prices = generate_series_intraday(120)\n\n Generate intraday price series for a full day (24 hours) ending at current time:\n\n >>> prices = generate_series_intraday(1440, IntradayDirection.END_INTRADAY_NOW)\n\n **See also**\n\n :func:`generate_series` :func:`numpy.random.normal()`\n\n \"\"\"\n levels = [100]\n first = pd.Timestamp.now().floor('min')\n if direction == IntradayDirection.END_INTRADAY_NOW:\n first -= pd.Timedelta(minutes=length - 1)\n times = [first]\n rng = np.random.default_rng()\n\n for i in range(length - 1):\n levels.append(levels[i] * 1 + rng.standard_normal())\n times.append(times[i] + pd.Timedelta(minutes=1))\n return pd.Series(data=levels, index=times, dtype=np.dtype(float))\n\n\n@plot_function\ndef percentiles(x: pd.Series, y: Optional[pd.Series] = None, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Rolling percentiles over given window\n\n :param x: value series\n :param y: distribution series\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: timeseries of percentiles\n\n **Usage**\n\n Calculate `percentile rank `_ of :math:`y` in the sample distribution\n of :math:`x` over a rolling window of length :math:`w`:\n\n :math:`R_t = \\\\frac{\\\\sum_{i=t-N+1}^{t}{[X_i<{Y_t}]}+0.5\\\\sum_{i=t-N+1}^{t}{[X_i={Y_t}]}}{N}\\\\times100\\\\%`\n\n Where :math:`N` is the number of observations in a rolling window. If :math:`y` is not provided (or is NULL),\n calculates percentiles of :math:`x` over its historical values. If window length :math:`w` is not provided, uses an\n ever-growing history of values. If :math:`w` is greater than the available data size, returns empty.\n\n **Examples**\n\n Compute percentile ranks of a series in the sample distribution of a second series over :math:`22` observations\n\n >>> a = generate_series(100)\n >>> b = generate_series(100)\n >>> percentiles(a, b, 22)\n\n **See also**\n\n :func:`zscores`\n\n \"\"\"\n if x.empty:\n return x\n\n if y is None:\n y = x.copy()\n w = normalize_window(y, w)\n\n if isinstance(w.r, int) and w.r > len(y):\n raise ValueError('Ramp value must be less than the length of the series y.')\n\n if isinstance(w.w, int) and w.w > len(x):\n return pd.Series(dtype=float)\n\n res = pd.Series(dtype=np.dtype(float))\n convert_to_date = not isinstance(x.index, pd.DatetimeIndex)\n\n if isinstance(w.w, pd.DateOffset):\n for idx, val in y.items():\n sample = x.loc[(x.index > ((idx - w.w).date() if convert_to_date else idx - w.w)) & (x.index <= idx)]\n res.loc[idx] = percentileofscore(sample, val, kind='mean')\n elif not y.empty:\n min_periods = 0 if isinstance(w.r, pd.DateOffset) else w.r\n rolling_window = x[: y.index[-1]].rolling(w.w, min_periods)\n percentile_on_x_index = rolling_window.apply(\n lambda a: percentileofscore(a, y[a.index[-1] :].iloc[0], kind=\"mean\")\n )\n joined_index = pd.concat([x, y], axis=1).index\n res = percentile_on_x_index.reindex(joined_index, method=\"ffill\")[y.index]\n return apply_ramp(res, w)\n\n\n@plot_function\ndef percentile(x: pd.Series, n: float, w: Union[Window, int, str] = None) -> Union[pd.Series, float]:\n \"\"\"\n Returns the nth percentile of a series.\n\n :param x: series\n :param n: percentile\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n :return: nth percentile\n\n **Usage**\n\n Calculates the `nth percentile rank `_ of :math:`x`. Rolling nth\n percentile is returned if a window is specified, else a scalar for nth percentile over the entire series.\n\n **Example**\n\n Compute the 90th percentile of a series.\n\n >>> a = generate_series(100)\n >>> percentile(a, 90)\n\n \"\"\"\n if not 0 <= n <= 100:\n raise MqValueError('percentile must be in range [0, 100]')\n x = x.dropna()\n if x.size < 1:\n return x\n if w is None:\n return np.percentile(x.values, n)\n\n n /= 100\n w = normalize_window(x, w)\n if isinstance(w.w, pd.DateOffset):\n try:\n if isinstance(x.index, pd.DatetimeIndex):\n values = [x.loc[(x.index > (idx - w.w)) & (x.index <= idx)].quantile(n) for idx in x.index]\n else:\n values = [x.loc[(x.index > (idx - w.w).date()) & (x.index <= idx)].quantile(n) for idx in x.index]\n except TypeError:\n raise MqTypeError(f'cannot use relative dates with index {x.index}')\n res = pd.Series(values, index=x.index, dtype=np.dtype(float))\n else:\n res = x.rolling(w.w, 0).quantile(n)\n return apply_ramp(res, w)\n\n\nclass LinearRegression:\n \"\"\"\n Fit an Ordinary least squares (OLS) linear regression model.\n\n :param X: observations of the explanatory variable(s)\n :param y: observations of the dependent variable\n :param fit_intercept: whether to calculate intercept in the model\n\n **Usage**\n\n Fit `OLS Model `_ based on observations of the explanatory\n variables(s) X and the dependent variable y. If X and y are not aligned, only use the intersection of dates/times.\n\n **Examples**\n\n Run a linear regression on y vs. x1 and x2 and compute the R squared:\n\n >>> x1 = generate_series(100)\n >>> x2 = generate_series(100)\n >>> y = generate_series(100)\n >>> r = LinearRegression([x1, x2], y, True)\n >>> r.r_squared()\n\n \"\"\"\n\n def __init__(self, X: Union[pd.Series, List[pd.Series]], y: pd.Series, fit_intercept: bool = True):\n if not isinstance(fit_intercept, bool):\n raise MqTypeError('expected a boolean value for \"fit_intercept\"')\n\n df = pd.concat(X, axis=1) if isinstance(X, list) else X.to_frame()\n df = sm.add_constant(df) if fit_intercept else df\n df.columns = range(len(df.columns)) if fit_intercept else range(1, len(df.columns) + 1)\n\n df = df[~df.isin([np.nan, np.inf, -np.inf]).any(axis=1)] # filter out nan and inf\n y = y[~y.isin([np.nan, np.inf, -np.inf])]\n df_aligned, y_aligned = df.align(y, 'inner', axis=0) # align series\n\n self._index_scope = range(0, len(df.columns)) if fit_intercept else range(1, len(df.columns) + 1)\n self._res = sm.OLS(y_aligned, df_aligned).fit()\n self._fit_intercept = fit_intercept\n\n @plot_method\n def coefficient(self, i: int) -> float:\n \"\"\"\n Estimated coefficient.\n\n :param i: 0 for intercept (available if intercept is used), 1 for regression slope\n :return: estimated coefficient of the i-th predictor\n \"\"\"\n return self._res.params[i]\n\n @plot_method\n def r_squared(self) -> float:\n \"\"\"\n Coefficient of determination (R Squared)\n\n :return: R Squared\n \"\"\"\n return self._res.rsquared\n\n @plot_method\n def fitted_values(self) -> pd.Series:\n \"\"\"\n Fitted values computed by evaluating the regression model on the original input X.\n\n :return: fitted values\n \"\"\"\n return self._res.fittedvalues\n\n @plot_method\n def predict(self, X_predict: Union[pd.Series, List[pd.Series]]) -> pd.Series:\n \"\"\"\n Use the model for prediction.\n\n :param X_predict: the values for which to predict\n :return: predicted values\n \"\"\"\n df = pd.concat(X_predict, axis=1) if isinstance(X_predict, list) else X_predict.to_frame()\n return self._res.predict(sm.add_constant(df) if self._fit_intercept else df)\n\n @plot_method\n def standard_deviation_of_errors(self) -> float:\n \"\"\"\n Standard deviation of the error term.\n\n :return: standard deviation of the error term\n \"\"\"\n return np.sqrt(self._res.mse_resid)\n\n\nclass RollingLinearRegression:\n \"\"\"\n Fit a rolling ordinary least squares (OLS) linear regression model.\n\n :param X: observations of the explanatory variable(s)\n :param y: observations of the dependant variable\n :param w: number of observations in each rolling window. Must be larger than the number of observations or\n explanatory variables\n :param fit_intercept: whether to calculate intercept in the model\n\n **Usage**\n\n Fit `OLS Model `_ based on observations of the explanatory\n variables(s) X and the dependant variable y across a rolling window with fixed number of observations.\n The parameters of each rolling window are stored at the end of each window.\n If X and y are not aligned, only use the intersection of dates/times.\n\n **Examples**\n\n Run linear regressions on y vs. x1 and x2 in a rolling window of 22 observations and compute the R Squared:\n\n >>> x1 = generate_series(100)\n >>> x2 = generate_series(100)\n >>> y = generate_series(100)\n >>> r = RollingLinearRegression([x1, x2], y, 22)\n >>> r.r_squared()\n\n \"\"\"\n\n def __init__(self, X: Union[pd.Series, List[pd.Series]], y: pd.Series, w: int, fit_intercept: bool = True):\n if not isinstance(fit_intercept, bool):\n raise MqTypeError('expected a boolean value for \"fit_intercept\"')\n\n df = pd.concat(X, axis=1) if isinstance(X, list) else X.to_frame()\n df = sm.add_constant(df) if fit_intercept else df\n df.columns = range(len(df.columns)) if fit_intercept else range(1, len(df.columns) + 1)\n\n if w <= len(df.columns):\n raise MqValueError('Window length must be larger than the number of explanatory variables')\n\n df = df[~df.isin([np.nan, np.inf, -np.inf]).any(axis=1)] # filter out nan and inf\n y = y[~y.isin([np.nan, np.inf, -np.inf])]\n df_aligned, y_aligned = df.align(y, 'inner', axis=0) # align series\n\n self._X = df_aligned.copy()\n self._res = RollingOLS(y_aligned, df_aligned, w).fit()\n\n @plot_method\n def coefficient(self, i: int) -> pd.Series:\n \"\"\"\n Estimated coefficients.\n\n :param i: 0 for intercept (available if intercept is used), 1 for regression slope.\n :return: estimated coefficients of the i-th predictor\n \"\"\"\n return self._res.params[i]\n\n @plot_method\n def r_squared(self) -> pd.Series:\n \"\"\"\n Coefficients of determination (R Squared) of rolling regressions.\n\n :return: R Squared\n \"\"\"\n return self._res.rsquared\n\n @plot_method\n def fitted_values(self) -> pd.Series:\n \"\"\"\n Fitted values at the end of each rolling window.\n\n :return: fitted values\n \"\"\"\n comp = self._X.mul(self._res.params.values)\n return comp.sum(axis=1, min_count=len(comp.columns))\n\n @plot_method\n def standard_deviation_of_errors(self) -> pd.Series:\n \"\"\"\n Standard deviations of the error terms.\n\n :return: standard deviations of the error terms\n \"\"\"\n return np.sqrt(self._res.mse_resid)\n\n\nclass SIRModel:\n \"\"\"SIR Compartmental model for transmission of infectious disease\n\n :param beta: transmission rate of the infection\n :param gamma: recovery rate of the infection\n :param s: number of susceptible individuals in population\n :param i: number of infectious individuals in population\n :param r: number of recovered individuals in population\n :param n: total population size\n :param end_date: end date for the evolution of the model\n :param fit: whether to fit the model to the data\n :param fit_period: on how many days back to fit the model\n\n **Usage**\n\n Fit `SIR Model `_ based on the\n population in each compartment over a given time period.\n\n The SIR models the movement of individuals between three compartments: susceptible (S), infected (I), and resistant\n (R). The model calibrates parameters :\n\n =========== =======================================================\n Parameter Description\n =========== =======================================================\n S0 initial susceptible individuals\n I0 initial infected individuals\n R0 initial recovered individuals\n beta Transmission rate from susceptible to infected\n gamma Immunity rate from infected to resistant\n =========== =======================================================\n\n The parameters beta and gamma model how fast people move from being susceptible to infected (beta), and\n subsequently from infected to resistant (gamma). This model can be used to forecast the populations of each\n compartment once calibrated\n\n \"\"\"\n\n def __init__(\n self,\n beta: float = None,\n gamma: float = None,\n s: Union[pd.Series, float] = None,\n i: Union[pd.Series, float] = None,\n r: Union[pd.Series, float] = None,\n n: Union[pd.Series, float] = None,\n fit: bool = True,\n fit_period: int = None,\n ):\n if not isinstance(fit, bool):\n raise MqTypeError('expected a boolean value for \"fit\"')\n\n n = n.dropna().iloc[0] if isinstance(n, pd.Series) else n\n n = 100 if n is None else n\n fit = False if s is None and i is None and r is None else fit\n s = n if s is None else s\n i = 1 if i is None else i\n r = 0 if r is None else r\n\n data_start = [ts.index.min().date() for ts in [s, i, r] if isinstance(ts, pd.Series)]\n data_start.append(DataContext.current.start_date)\n start_date = max(data_start)\n\n data_end = [ts.index.max().date() for ts in [s, i, r] if isinstance(ts, pd.Series)]\n data_end.append(DataContext.current.end_date)\n end_date = max(data_end)\n\n self.s = s if isinstance(s, pd.Series) else pd.Series([s])\n self.i = i if isinstance(i, pd.Series) else pd.Series([i])\n self.r = r if isinstance(r, pd.Series) else pd.Series([r])\n self.n = n\n self.beta_init = beta\n self.gamma_init = gamma\n self.fit = fit\n self.fit_period = fit_period\n self.beta_fixed = not (self.fit or (self.beta_init is None))\n self.gamma_fixed = not (self.fit or (self.gamma_init is None))\n\n lens = [len(x) for x in (self.s, self.i, self.r)]\n dtype = float if max(lens) == min(lens) else object\n data = np.array([self.s, self.i, self.r], dtype=dtype).T\n\n beta_init = self.beta_init if self.beta_init is not None else 0.9\n gamma_init = self.gamma_init if self.gamma_init is not None else 0.01\n\n parameters, initial_conditions = SIR.get_parameters(\n self.s.iloc[0],\n self.i.iloc[0],\n self.r.iloc[0],\n n,\n beta=beta_init,\n gamma=gamma_init,\n beta_fixed=self.beta_fixed,\n gamma_fixed=self.gamma_fixed,\n S0_fixed=True,\n I0_fixed=True,\n R0_fixed=True,\n )\n self.parameters = parameters\n\n self._model = EpidemicModel(\n SIR, parameters=parameters, data=data, initial_conditions=initial_conditions, fit_period=self.fit_period\n )\n if self.fit:\n self._model.fit(verbose=False)\n\n t = np.arange((end_date - start_date).days + 1)\n predict = self._model.solve(t, (self.s0(), self.i0(), self.r0()), (self.beta(), self.gamma(), n))\n\n predict_dates = pd.date_range(start_date, end_date)\n\n self._model.s_predict = pd.Series(predict[:, 0], predict_dates)\n self._model.i_predict = pd.Series(predict[:, 1], predict_dates)\n self._model.r_predict = pd.Series(predict[:, 2], predict_dates)\n\n @plot_method\n def s0(self) -> float:\n \"\"\"\n Model calibration for initial susceptible individuals\n\n :return: initial susceptible individuals\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['S0']\n return self.parameters['S0'].value\n\n @plot_method\n def i0(self) -> float:\n \"\"\"\n Model calibration for initial infectious individuals\n\n :return: initial infectious individuals\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['I0']\n return self.parameters['I0'].value\n\n @plot_method\n def r0(self) -> float:\n \"\"\"\n Model calibration for initial recovered individuals\n\n :return: initial recovered individuals\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['R0']\n return self.parameters['R0'].value\n\n @plot_method\n def beta(self) -> float:\n \"\"\"\n Model calibration for transmission rate (susceptible to infected)\n\n :return: beta\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['beta']\n return self.parameters['beta'].value\n\n @plot_method\n def gamma(self) -> float:\n \"\"\"\n Model calibration for immunity (infected to resistant)\n\n :return: beta\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['gamma']\n return self.parameters['gamma'].value\n\n @plot_method\n def predict_s(self) -> pd.Series:\n \"\"\"\n Model calibration for susceptible individuals through time\n\n :return: susceptible predict\n \"\"\"\n return self._model.s_predict\n\n @plot_method\n def predict_i(self) -> pd.Series:\n \"\"\"\n Model calibration for infected individuals through time\n\n :return: infected predict\n \"\"\"\n return self._model.i_predict\n\n @plot_method\n def predict_r(self) -> pd.Series:\n \"\"\"\n Model calibration for recovered individuals through time\n\n :return: infected predict\n \"\"\"\n return self._model.r_predict\n\n\nclass SEIRModel(SIRModel):\n \"\"\"SEIR Compartmental model for transmission of infectious disease\n\n :param beta: transmission rate of the infection\n :param gamma: recovery rate of the infection\n :param sigma: immunity rate from exposed to infected\n :param s: number of susceptible individuals in population\n :param e: number of exposed individuals in population\n :param i: number of infectious individuals in population\n :param r: number of recovered individuals in population\n :param n: total population size\n :param end_date: end date for the evolution of the model\n :param fit: whether to fit the model to the data\n :param fit_period: on how many days back to fit the model\n\n **Usage**\n\n Fit `SEIR Model `_ based on the\n population in each compartment over a given time period.\n\n The SEIR models the movement of individuals between four compartments: susceptible (S), exposed (E), infected (I),\n and resistant (R). The model calibrates parameters :\n\n =========== =======================================================\n Parameter Description\n =========== =======================================================\n S0 initial susceptible individuals\n E0 initial exposed individuals\n I0 initial infected individuals\n R0 initial recovered individuals\n beta Transmission rate from susceptible to exposed\n gamma Immunity rate from infected to resistant\n sigma Immunity rate from exposed to infected\n =========== =======================================================\n\n The parameters beta, gamma, and sigma, model how fast people move from being susceptible to exposed (beta),\n from exposed to infected (sigma), and subsequently from infected to resistant (gamma). This model can be used to\n predict the populations of each compartment once calibrated.\n\n \"\"\"\n\n def __init__(\n self,\n beta: float = None,\n gamma: float = None,\n sigma: float = None,\n s: Union[pd.Series, float] = None,\n e: Union[pd.Series, float] = None,\n i: Union[pd.Series, float] = None,\n r: Union[pd.Series, float] = None,\n n: Union[pd.Series, float] = None,\n fit: bool = True,\n fit_period: int = None,\n ):\n if not isinstance(fit, bool):\n raise MqTypeError('expected a boolean value for \"fit\"')\n\n n = n.dropna().iloc[0] if isinstance(n, pd.Series) else n\n n = 100 if n is None else n\n fit = False if all(state is None for state in (s, e, i, r)) else fit\n s = n if s is None else s\n e = 1 if e is None else e\n i = 1 if i is None else i\n r = 0 if r is None else r\n\n data_start = [ts.index.min().date() for ts in [s, i, r] if isinstance(ts, pd.Series)]\n data_start.append(DataContext.current.start_date)\n start_date = max(data_start)\n\n data_end = [ts.index.max().date() for ts in [s, i, r] if isinstance(ts, pd.Series)]\n data_end.append(DataContext.current.end_date)\n end_date = max(data_end)\n\n self.s = s if isinstance(s, pd.Series) else pd.Series([s])\n self.e = e if isinstance(e, pd.Series) else pd.Series([e])\n self.i = i if isinstance(i, pd.Series) else pd.Series([i])\n self.r = r if isinstance(r, pd.Series) else pd.Series([r])\n self.n = n\n self.beta_init = beta\n self.gamma_init = gamma\n self.sigma_init = sigma\n self.fit = fit\n self.fit_period = fit_period\n self.beta_fixed = not (self.fit or (self.beta is None))\n self.gamma_fixed = not (self.fit or (self.gamma is None))\n self.sigma_fixed = not (self.fit or (self.sigma is None))\n\n lens = [len(x) for x in (self.s, self.e, self.i, self.r)]\n dtype = float if max(lens) == min(lens) else object\n data = np.array([self.s, self.e, self.i, self.r], dtype=dtype).T\n\n beta_init = self.beta_init if self.beta_init is not None else 0.9\n gamma_init = self.gamma_init if self.gamma_init is not None else 0.01\n sigma_init = self.sigma_init if self.sigma_init is not None else 0.2\n\n parameters, initial_conditions = SEIR.get_parameters(\n self.s.iloc[0],\n self.e.iloc[0],\n self.i.iloc[0],\n self.r.iloc[0],\n n,\n beta=beta_init,\n gamma=gamma_init,\n sigma=sigma_init,\n beta_fixed=self.beta_fixed,\n gamma_fixed=self.gamma_fixed,\n sigma_fixed=self.sigma_fixed,\n S0_fixed=True,\n I0_fixed=True,\n R0_fixed=True,\n E0_fixed=True,\n S0_max=5e6,\n I0_max=5e6,\n E0_max=10e6,\n R0_max=10e6,\n )\n self.parameters = parameters\n\n self._model = EpidemicModel(\n SEIR, parameters=parameters, data=data, initial_conditions=initial_conditions, fit_period=self.fit_period\n )\n if self.fit:\n self._model.fit(verbose=False)\n\n t = np.arange((end_date - start_date).days + 1)\n predict = self._model.solve(\n t, (self.s0(), self.e0(), self.i0(), self.r0()), (self.beta(), self.gamma(), self.sigma(), n)\n )\n\n predict_dates = pd.date_range(start_date, end_date)\n\n self._model.s_predict = pd.Series(predict[:, 0], predict_dates)\n self._model.e_predict = pd.Series(predict[:, 1], predict_dates)\n self._model.i_predict = pd.Series(predict[:, 2], predict_dates)\n self._model.r_predict = pd.Series(predict[:, 3], predict_dates)\n\n @plot_method\n def e0(self) -> float:\n \"\"\"\n Model calibration for initial exposed individuals\n\n :return: initial exposed individuals\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['E0']\n return self.parameters['E0'].value\n\n @plot_method\n def beta(self) -> float:\n \"\"\"\n Model calibration for transmission rate (susceptible to exposed)\n\n :return: beta\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['beta']\n return self.parameters['beta'].value\n\n @plot_method\n def gamma(self) -> float:\n \"\"\"\n Model calibration for immunity (infected to resistant)\n\n :return: gamma\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['gamma']\n return self.parameters['gamma'].value\n\n @plot_method\n def sigma(self) -> float:\n \"\"\"\n Model calibration for infection rate (exposed to infected)\n\n :return: sigma\n \"\"\"\n if self.fit:\n return self._model.fitted_parameters['sigma']\n return self.parameters['sigma'].value\n\n @plot_method\n def predict_e(self) -> pd.Series:\n \"\"\"\n Model calibration for exposed individuals through time\n\n :return: exposed predict\n \"\"\"\n return self._model.e_predict\n" }, { "path": "gs_quant/timeseries/tca.py", "content": "\"\"\"\nCopyright 2019 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Optional\n\nimport pandas as pd\n\nfrom gs_quant.api.gs.data import QueryType\nfrom gs_quant.common import AssetClass\nfrom gs_quant.data import Dataset, DataContext\nfrom gs_quant.markets.securities import Asset\nfrom gs_quant.timeseries import plot_measure\nfrom gs_quant.timeseries.measures import _extract_series_from_df\n\n\n@plot_measure((AssetClass.Equity,), None, [])\ndef covariance(\n asset: Asset,\n asset_2: Asset,\n bucket_start: str = '\"08:00:00\"',\n bucket_end: str = '\"08:30:00\"',\n *,\n source: str = None,\n real_time: bool = False,\n request_id: Optional[str] = None,\n) -> pd.Series:\n \"\"\"\n Provides an estimates of the covariances between stocks in the three major equity markets - US, EMEA and Japan -\n using an advanced machine learning technique.\n\n :param asset: asset to calculate covariance\n :param asset_2: asset to calculate covariance\n :param bucket_start: start time of bucket i.e. '08:00:00'\n :param bucket_end: end time of bucket i.e. '08:30:00'\n :param source: name of function caller\n :param real_time: whether to retrieve intraday data instead of EOD\n :param request_id: service request id, if any\n :return: Series of the covariance between two stocks\n \"\"\"\n start, end = DataContext.current.start_date, DataContext.current.end_date\n ds = Dataset(Dataset.GS.QES_INTRADAY_COVARIANCE)\n where = dict(\n assetId=asset.get_marquee_id(),\n asset2Id=asset_2.get_marquee_id(),\n bucketStart=bucket_start,\n bucketEnd=bucket_end,\n )\n\n data = ds.get_data(start=start, end=end, where=where)\n series = _extract_series_from_df(data, QueryType.COVARIANCE)\n series.dataset_ids = (Dataset.GS.QES_INTRADAY_COVARIANCE.value,)\n return series\n" }, { "path": "gs_quant/timeseries/technicals.py", "content": "# Copyright 2018 Goldman Sachs.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n#\n# Marquee Plot Service will attempt to make public functions (not prefixed with _) from this module available.\n# Such functions should be fully documented: docstrings should describe parameters and the return value, and provide\n# a 1-line description. Type annotations should be provided for parameters.\nfrom enum import Enum\nfrom typing import Union\n\nimport pandas as pd\nimport statsmodels.tsa.seasonal\n\nfrom gs_quant.timeseries import diff, annualize, returns\nfrom .algebra import subtract\nfrom .helper import Window, plot_function, normalize_window, apply_ramp\nfrom .statistics import mean, std, exponential_std\nfrom ..errors import MqValueError\n\n\"\"\"\nTechnicals library is for technical analysis functions on timeseries, including moving averages,\nvolatility indicators and and other numerical operations which are finance-oriented for analyzing\nstatistical properties of trading activity, such as price movement and volume changes\n\"\"\"\n\n\nclass Seasonality(Enum):\n MONTH = 'month'\n QUARTER = 'quarter'\n\n\nclass SeasonalModel(Enum):\n ADDITIVE = 'additive'\n MULTIPLICATIVE = 'multiplicative'\n\n\nclass Frequency(Enum):\n WEEK = 'week'\n MONTH = 'month'\n QUARTER = 'quarter'\n YEAR = 'year'\n\n\n@plot_function\ndef moving_average(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Moving average over specified window\n\n :param x: time series of prices\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: date-based time series of return\n\n **Usage**\n\n Simple arithmetic moving average over the specified window (number of observations). Shorter windows will be more\n reactive to changes in the asset price, but more volatile. Larger windows will be smoother but less reactive to\n near term changes in asset prices.\n\n :math:`R_t = \\\\frac{\\\\sum_{i=t-w+1}^{t} X_t}{N}`\n\n where N is the number of observations in each rolling window, :math:`w`. If window is not provided, computes\n rolling mean over the full series\n\n Equivalent to ``mean``\n\n **Examples**\n\n Generate price series with 100 observations starting from today's date:\n\n >>> prices = generate_series(100)\n >>> moving_average(prices, 22)\n\n **See also**\n\n :func:`mean`\n\n \"\"\"\n w = normalize_window(x, w)\n return apply_ramp(mean(x, Window(w.w, 0)), w)\n\n\n@plot_function\ndef bollinger_bands(x: pd.Series, w: Union[Window, int, str] = Window(None, 0), k: float = 2) -> pd.DataFrame:\n \"\"\"\n Bollinger bands with given window and width\n\n :param x: time series of prices\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :param k: band width in standard deviations (default: 2)\n :return: date-based time series of return\n\n **Usage**\n\n Standard deviation bands around the moving average of asset price level. Bollinger bands can be used to determine\n a range around the price level which responds to local volatility changes. Returns two series,\n upper, :math:`u_t` and lower, :math:`l_t`\n\n :math:`u_t = \\\\bar{X_t} + k\\\\sigma_t`\n\n :math:`l_t = \\\\bar{X_t} - k\\\\sigma_t`\n\n where :math:`\\\\bar{X_t}` is the moving average over specified window, and :math:`\\\\sigma_t` is the rolling\n standard deviation over the specified window\n\n See `Bollinger Bands `_ for more information\n\n **Examples**\n\n Compute bollinger bands around :math:`20` day moving average at :math:`2` standard deviations:\n\n >>> prices = generate_series(100)\n >>> bollinger_bands(prices, 20, 2)\n\n **See also**\n\n :func:`moving_average` :func:`std`\n \"\"\"\n w = normalize_window(x, w)\n avg = moving_average(x, w)\n sigma_t = std(x, w)\n\n upper = avg + k * sigma_t\n lower = avg - k * sigma_t\n\n return pd.concat([lower, upper], axis=1)\n\n\n@plot_function\ndef smoothed_moving_average(x: pd.Series, w: Union[Window, int, str] = Window(None, 0)) -> pd.Series:\n \"\"\"\n Smoothed moving average over specified window\n\n :param x: time series of prices\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: date-based time series of return\n\n **Usage**\n\n A modified moving average (MMA), running moving average (RMA), or smoothed moving average (SMMA) is defined as:\n\n :math:`P_{MM,today} = \\\\frac{(N-1)P_{MM,yesterday} + P_today}{N}`\n\n where N is the number of observations in each rolling window, :math:`w`. If window is not provided, computes\n rolling mean over the full series\n\n See `Modified moving average `_ for more\n information\n\n\n **Examples**\n\n Generate price series with 100 observations starting from today's date:\n\n >>> prices = generate_series(100)\n >>> smoothed_moving_average(prices, 22)\n\n **See also**\n\n :func:`mean` :func:'moving_average'\n\n \"\"\"\n w = normalize_window(x, w)\n window_size = w.w\n ramp = w.r\n means = apply_ramp(mean(x, Window(window_size, 0)), w)\n if means.size < 1:\n return pd.Series(dtype=float)\n initial_moving_average = means.iloc[0]\n if (isinstance(ramp, int) and ramp > 0) or isinstance(ramp, pd.DateOffset):\n x = apply_ramp(x, w)\n\n smoothed_moving_averages = x.copy()\n smoothed_moving_averages *= 0\n smoothed_moving_averages.iloc[0] = initial_moving_average\n for i in range(1, len(x)):\n if isinstance(window_size, int):\n window_num_elem = window_size\n else:\n window_num_elem = len(x[(x.index > (x.index[i] - window_size).date()) & (x.index <= x.index[i])])\n smoothed_moving_averages.iloc[i] = (\n (window_num_elem - 1) * smoothed_moving_averages.iloc[i - 1] + x.iloc[i]\n ) / window_num_elem\n return smoothed_moving_averages\n\n\n@plot_function\ndef relative_strength_index(x: pd.Series, w: Union[Window, int, str] = 14) -> pd.DataFrame:\n \"\"\"\n Relative Strength Index\n\n :param x: time series of prices\n :param w: Window or int: size of window and ramp up to use. e.g. Window(22, 10) where 22 is the window size\n and 10 the ramp up value. If w is a string, it should be a relative date like '1m', '1d', etc.\n Window size defaults to length of series.\n :return: date-based time series of RSI\n\n **Usage**\n\n The RSI computes momentum as the ratio of higher closes to lower closes: stocks which have had more or stronger\n positive changes have a higher RSI than stocks which have had more or stronger negative changes.\n\n See `RSI `_ for more information\n\n **Examples**\n\n Compute relative strength index over a :math:`14` day window:\n\n >>> prices = generate_series(100)\n >>> relative_strength_index(prices, 14)\n\n **See also**\n\n :func:`moving_average` :func:`std` :func:`smoothed_moving_average`\n \"\"\"\n w = normalize_window(x, w)\n one_period_change = diff(x, 1)[1:]\n gains = one_period_change.copy()\n losses = one_period_change.copy()\n gains[gains < 0] = 0\n losses[losses > 0] = 0\n losses[losses < 0] *= -1\n\n moving_avg_gains = smoothed_moving_average(gains, w)\n moving_avg_losses = smoothed_moving_average(losses, w)\n\n rsi_len = len(moving_avg_gains)\n rsi = moving_avg_gains.copy()\n rsi *= 0\n\n for index in range(0, rsi_len):\n if moving_avg_losses.iloc[index] == 0:\n rsi.iloc[index] = 100\n else:\n relative_strength = moving_avg_gains.iloc[index] / moving_avg_losses.iloc[index]\n rsi.iloc[index] = 100 - (100 / (1 + relative_strength))\n\n return rsi\n\n\n@plot_function\ndef exponential_moving_average(x: pd.Series, beta: float = 0.75) -> pd.Series:\n \"\"\"\n Exponentially weighted moving average\n\n :param x: time series of prices\n :param beta: how much to weigh the previous observations in the time series, thus controlling how much importance we\n place on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)\n :return: date-based time series of return\n\n **Usage**\n\n The exponential(ly weighted) moving average (EMA) of a series [:math:`X_0`, :math:`X_1`, :math:`X_2`, ...],\n is defined as:\n\n :math:`Y_0 = X_0`\n\n :math:`Y_t = \\\\beta \\\\cdot Y_{t-1} + (1 - \\\\beta) \\\\cdot X_t`\n\n where :math:`\\\\beta` is the weight we place on the previous average.\n\n See `Exponential moving average `_ for\n more information\n\n **Examples**\n\n Generate price series with 100 observations starting from today's date:\n\n >>> prices = generate_series(100)\n >>> exponential_moving_average(prices, 0.9)\n\n **See also**\n\n :func:`mean` :func:`moving_average` :func:`smoothed_moving_average`\n\n \"\"\"\n return x.ewm(alpha=1 - beta, adjust=False).mean()\n\n\n@plot_function\ndef macd(x: pd.Series, m: int = 12, n: int = 26, s: int = 1) -> pd.Series:\n \"\"\"\n Moving average convergence divergence (MACD).\n\n Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship\n between two moving averages of a timeseries. It is the result of subtracting the exponential moving average of `x`\n with a period of :math:`m` from the exponential moving average of :math:`x` with a period of :math:`n`.\n\n Optionally, specify :math:`s` to apply an exponential moving average to the resulting series with a period of\n :math:`s` (default 1, equivalent to no exponential moving average).\n\n :param x: time series\n :param m: period of first, short exponential moving average (default 12)\n :param n: period of second, long exponential moving average (default 26)\n :param s: optional smoothing parameter (default 1)\n :return: date-based time series of return\n\n **Usage**\n\n The exponential(ly weighted) moving average (EMA) of a series [:math:`X_0`, :math:`X_1`, :math:`X_2`, ...],\n is defined as:\n\n :math:`Y_0 = X_0`\n\n :math:`Y_t = \\\\beta \\\\cdot Y_{t-1} + (1 - \\\\beta) \\\\cdot X_t`\n\n where :math:`\\\\beta = \\\\frac{2}{\\\\text{period} + 1}` is the weight we place on the previous average.\n\n The MACD of a series is defined as :math:`\\\\text{EMA}(\\\\text{EMA}(X, M) - \\\\text{EMA}(X, N), S)`\n\n **Examples**\n\n Generate price series with 100 observations starting from today's date:\n\n >>> prices = generate_series(100)\n >>> macd(prices, 12, 26)\n\n **See also**\n\n :func:`exponential_moving_average` :func:`moving_average` :func:`smoothed_moving_average`\n\n \"\"\"\n a = x.ewm(adjust=False, span=m).mean()\n b = x.ewm(adjust=False, span=n).mean()\n return subtract(a, b).ewm(adjust=False, span=s).mean()\n\n\n@plot_function\ndef exponential_volatility(x: pd.Series, beta: float = 0.75) -> pd.Series:\n \"\"\"\n Exponentially weighted volatility\n\n :param x: time series of prices\n :param beta: how much to weigh the previous price in the time series, thus controlling how much importance we\n place on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)\n :return: date-based time series of exponential volatility of the input series\n\n **Usage**\n\n Calculates the exponentially weighted standard deviation of the return of the input series, and annualizes the\n standard deviation\n\n **Examples**\n\n Generate price series and compute exponentially weighted standard deviation of returns\n\n >>> prices = generate_series(100)\n >>> exponential_volatility(prices, 0.9)\n\n The above is equivalent to\n\n >>> annualize(exponential_std(returns(prices), 0.9)) * 100\n\n **See also**\n\n :func:`volatility` :func:`exponential_std` :func:`exponential_spread_volatility`\n\n \"\"\"\n return annualize(exponential_std(returns(x), beta)).mul(100)\n\n\n@plot_function\ndef exponential_spread_volatility(x: pd.Series, beta: float = 0.75) -> pd.Series:\n \"\"\"\n Exponentially weighted spread volatility\n\n :param x: time series of prices\n :param beta: how much to weigh the previous price in the time series, thus controlling how much importance we\n place on the (more distant) past. Must be between 0 (inclusive) and 1 (exclusive)\n :return: date-based time series of exponential spread volatility of the input series\n\n **Usage**\n\n Exponentially weights the daily differences of the input series, calculates the annualized standard deviation\n\n **Examples**\n\n Generate price series and compute exponentially weighted standard deviation of returns\n\n >>> prices = generate_series(100)\n >>> exponential_volatility(prices, 0.9)\n\n The above is equivalent to\n\n >>> annualize(exponential_std(diff(prices, 1), 0.9))\n\n **See also**\n\n :func:`volatility` :func:`exponential_std` :func:`exponential_volatility`\n\n \"\"\"\n return annualize(exponential_std(diff(x, 1), beta))\n\n\ndef _freq_to_period(x: pd.Series, freq: Frequency = Frequency.YEAR):\n \"\"\"\n Given input series x with a DateTimeIndex and a desired temporal frequency (period), returns x with all NaNs\n forward-filled (according to x's index's DateTime frequency) and the number of data points in a period.\n\n freq should be the length of time in which x's cycles repeat. For example: yearly retail sales cycle, yearly\n temperature fluctuation cycle.\n\n For example: 1) If x is a daily series and freq = YEARLY, then there are 365 data points in a period; 2) If x is\n a monthly series and freq = QUARTERLY, then there are 3 data points in a period.\n\n Freq parameter only applies when data frequency is:\n 'B' and frequency == Weekly --> period = 5\n 'B' and frequency == Monthly --> convert to 'D' and period = 30\n 'D' and frequency == Weekly --> period = 7\n 'D' and frequency == Monthly --> period = 30\n 'M' and frequency == Quarterly --> Period = 3\n 'W' and frequency == Quarterly --> period = 13\n \"\"\"\n if not isinstance(x.index, pd.DatetimeIndex):\n raise MqValueError(\"Series must have a pandas.DateTimeIndex.\")\n pfreq = getattr(getattr(x, 'index', None), 'inferred_freq', None)\n # Some older versions of statsmodels don't handle some of the newer pandas frequencies, so we manually adjust them\n pfreq = 'MS' if pfreq in ('ME', 'M') else pfreq # Convert Month[End] into MonthlyStart\n pfreq = 'QS' if pfreq in ('QE-DEC', 'QE') else pfreq # Convert Quarter[End] into QuarterlyStart\n period = None if pfreq is None else statsmodels.tsa.seasonal.freq_to_period(pfreq)\n if period in [7, None]: # daily\n x = x.asfreq('D', method='ffill')\n if freq == Frequency.YEAR:\n return x, 365\n elif freq == Frequency.QUARTER:\n return x, 91\n elif freq == Frequency.MONTH:\n return x, 30\n else:\n return x, 7\n elif period == 5: # business day\n if freq == Frequency.YEAR:\n return x.asfreq('D', method='ffill'), 365\n if freq == Frequency.QUARTER:\n return x.asfreq('D', method='ffill'), 91\n elif freq == Frequency.MONTH:\n return x.asfreq('D', method='ffill'), 30\n else: # freq == Frequency.WEEKLY:\n return x.asfreq('B', method='ffill'), 5\n elif period == 52: # weekly frequency\n x = x.asfreq('W', method='ffill')\n if freq == Frequency.YEAR:\n return x, period\n elif freq == Frequency.QUARTER:\n return x, 13\n elif freq == Frequency.MONTH:\n return x, 4\n else:\n raise MqValueError(f'Frequency {freq.value} not compatible with series with frequency {pfreq}.')\n elif period == 12: # monthly frequency\n x = x.asfreq('ME', method='ffill')\n if freq == Frequency.YEAR:\n return x, period\n elif freq == Frequency.QUARTER:\n return x, 3\n else:\n raise MqValueError(f'Frequency {freq.value} not compatible with series with frequency {pfreq}.')\n return x, period\n\n\ndef _seasonal_decompose(x: pd.Series, method: SeasonalModel = SeasonalModel.ADDITIVE, freq: Frequency = Frequency.YEAR):\n x, period = _freq_to_period(x, freq)\n if x.shape[0] < 2 * period:\n # Replace ValueError in seasonal_decompose with more descriptive error\n raise MqValueError(f\"Series must have two complete cycles to be analyzed. Series has only {x.shape[0]} dpts.\")\n decompose_obj = statsmodels.tsa.seasonal.seasonal_decompose(x, period=period, model=method.value)\n return decompose_obj\n\n\n@plot_function\ndef seasonally_adjusted(\n x: pd.Series, method: SeasonalModel = SeasonalModel.ADDITIVE, freq: Frequency = Frequency.YEAR\n) -> pd.Series:\n \"\"\"\n Seasonally adjusted series\n\n :param x: time series with at least two years worth of data.\n :param method: 'additive' or 'multiplicative'. Type of seasonal model to use. 'multiplicative' is appropriate when\n the magnitude of the series's values affect the magnitude of seasonal swings; 'additive' is appropriate\n when seasonal swings' sizes are independent of the series's values.\n :param freq: 'year', 'quarter', 'month', or 'week'. Period in which full cycle occurs (i.e. the \"period\" of\n a wave).\n :return: date-based time series of seasonally-adjusted input series.\n\n **Usage**\n\n Uses a centered moving average and convolution to decompose the input series into seasonal, trend, and\n residual components. This function returns the series with the seasonal component removed.\n\n If using the default additive model:\n\n :math:`Y_t = X_t - S_t`\n\n If using the multiplicative model:\n\n :math:`Y_t = X_t / S_t`\n\n **Examples**\n\n Generate price series and compute seasonally-adjusted series.\n\n >>> prices = generate_series(1000)\n >>> seasonally_adjusted(prices)\n\n **See also**\n\n :func:`trend`\n\n \"\"\"\n decompose_obj = _seasonal_decompose(x, method, freq)\n if method == SeasonalModel.ADDITIVE:\n return decompose_obj.trend + decompose_obj.resid\n else:\n return decompose_obj.trend * decompose_obj.resid\n\n\n@plot_function\ndef trend(x: pd.Series, method: SeasonalModel = SeasonalModel.ADDITIVE, freq: Frequency = Frequency.YEAR) -> pd.Series:\n \"\"\"\n Trend of series with seasonality and residuals removed.\n\n :param x: time series with at least two years worth of data.\n :param method: 'additive' or 'multiplicative'. Type of seasonal model to use. 'multiplicative' is appropriate when\n the magnitude of the series's values affect the magnitude of seasonal swings; 'additive' is appropriate\n when seasonal swings' sizes are independent of the series's values.\n :param freq: 'year', 'quarter', 'month', or 'week'. Period in which full cycle occurs (i.e. the \"period\" of\n a wave).\n :return: date-based time series with trend of input series.\n\n **Usage**\n\n Uses a centered moving average and convolution to decompose the input series into seasonal, trend, and\n residual components. This function returns the trend component.\n\n If using the default additive model:\n\n :math:`Y_t = X_t - S_t - R_t`\n\n If using the multiplicative model:\n\n :math:`Y_t = X_t / (S_t * R_t)`\n\n **Examples**\n\n Generate price series and compute its trend.\n\n >>> prices = generate_series(1000)\n >>> trend(prices)\n\n **See also**\n\n :func:`seasonally_adjusted`\n\n \"\"\"\n decompose_obj = _seasonal_decompose(x, method, freq)\n return decompose_obj.trend\n" }, { "path": "gs_quant/tracing/__init__.py", "content": "\"\"\"\nCopyright 2022 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom .tracing import *\n\n__name__ = 'tracing'\n" }, { "path": "gs_quant/tracing/tracing.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport traceback\nfrom contextlib import ContextDecorator\nfrom enum import Enum\nfrom typing import Tuple, Optional, Sequence, Mapping, Union, Callable\n\nimport pandas as pd\nfrom opentelemetry import trace, context\nfrom opentelemetry.context import Context\nfrom opentelemetry.propagate import extract, inject, set_global_textmap\nfrom opentelemetry.propagators.textmap import TextMapPropagator\nfrom opentelemetry.sdk.trace import (\n TracerProvider,\n SynchronousMultiSpanProcessor,\n ReadableSpan,\n Span,\n Event,\n SpanProcessor,\n)\nfrom opentelemetry.sdk.trace.export import SimpleSpanProcessor, SpanExporter\nfrom opentelemetry.trace import Tracer as OtelTracer, SpanContext, INVALID_SPAN\nfrom opentelemetry.trace import format_trace_id, format_span_id\n\nfrom gs_quant.errors import MqWrappedError\n\n_logger = logging.getLogger(__name__)\n\n\nclass Tags(Enum):\n HTTP_METHOD = 'http.method'\n HTTP_URL = 'http.url'\n HTTP_STATUS_CODE = 'http.status_code'\n CONTENT_LENGTH = 'content.length'\n\n\nclass SpanConsumer(SpanExporter):\n _instance = None\n\n @staticmethod\n def get_instance():\n if SpanConsumer._instance is None:\n SpanConsumer._instance = SpanConsumer()\n return SpanConsumer._instance\n\n @staticmethod\n def get_spans() -> Sequence['TracingSpan']:\n return SpanConsumer.get_instance()._collected_spans\n\n @staticmethod\n def reset():\n SpanConsumer.get_instance()._collected_spans = []\n\n @staticmethod\n def manually_record(spans: Sequence['TracingSpan']):\n SpanConsumer.get_instance()._collected_spans.extend(spans)\n\n def __init__(self):\n self._collected_spans = []\n\n def export(self, spans: Sequence[ReadableSpan]) -> None:\n self._collected_spans.extend(TracingSpan(span) for span in spans)\n\n\nclass TracingContext:\n def __init__(self, ctx: SpanContext):\n self._context = ctx\n\n\nclass TracingEvent:\n def __init__(self, ot_event: Event):\n self._event = ot_event\n\n @property\n def name(self) -> str:\n return self._event.name\n\n @property\n def timestamp(self) -> int:\n \"\"\"\n Timestamp in ns\n \"\"\"\n return self._event.timestamp\n\n @property\n def timestamp_sec(self) -> float:\n \"\"\"\n Timestamp in seconds\n \"\"\"\n return self._event.timestamp / 1e9\n\n @property\n def attributes(self) -> Mapping[str, any]:\n return self._event.attributes\n\n\nclass TracingScope:\n def __init__(self, token, span: Optional[Span], finish_on_close: bool = True):\n self._token = token\n self._span = TracingSpan(span) if span else NonRecordingTracingSpan(INVALID_SPAN)\n self._finish_on_close = finish_on_close\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if exc_val:\n Tracer.record_exception(exc_val, self._span, exc_tb)\n self.close()\n\n def close(self):\n if self._token:\n context.detach(self._token)\n if self._finish_on_close:\n return self._span.end()\n\n @property\n def span(self) -> 'TracingSpan':\n return self._span\n\n\nclass TracingSpan:\n def __init__(self, span: Span, endpoint: Optional[str] = None):\n self._span = span\n self._endpoint = endpoint\n\n def unwrap(self):\n return self._span\n\n @property\n def context(self):\n return self\n\n def end(self):\n self._span.end()\n\n def is_recording(self):\n return self._span.is_recording()\n\n @property\n def operation_name(self) -> str:\n return self._span.name\n\n def transportable(self, endpoint_override: Optional[str] = None) -> 'TransportableSpan':\n return TransportableSpan(self, endpoint_override or self._endpoint)\n\n @property\n def endpoint(self) -> str:\n return self._endpoint\n\n @endpoint.setter\n def endpoint(self, endpoint):\n self._endpoint = endpoint\n\n @property\n def trace_id(self) -> str:\n return format_trace_id(self._span.get_span_context().trace_id)\n\n def is_error(self) -> bool:\n return self._span.attributes.get('error', False)\n\n @property\n def start_time(self) -> int:\n \"\"\"\n Start time in ns\n \"\"\"\n return self._span.start_time\n\n @property\n def end_time(self) -> Optional[int]:\n \"\"\"\n End time in ns\n \"\"\"\n return self._span.end_time\n\n @property\n def duration(self) -> float:\n \"\"\"\n Duration of the span in milliseconds, or None if the span is not finished.\n \"\"\"\n unwrapped = self._span\n return (unwrapped.end_time - unwrapped.start_time) / 1e6 if unwrapped.end_time else None\n\n @property\n def span_id(self) -> str:\n return format_span_id(self._span.get_span_context().span_id)\n\n @property\n def parent_id(self) -> Optional[str]:\n parent = self._span.parent\n return format_span_id(parent.span_id) if parent else None\n\n @property\n def tags(self) -> Mapping[str, any]:\n return self._span.attributes\n\n @property\n def events(self) -> Sequence[TracingEvent]:\n return tuple(TracingEvent(event) for event in self._span.events)\n\n def set_tag(self, key: Union[Enum, str], value: Union[bool, str, bytes, int, float, dt.date]) -> 'TracingSpan':\n if value is None:\n return self\n if isinstance(value, dt.date):\n value = value.isoformat()\n elif isinstance(value, Enum):\n value = value.value\n if isinstance(key, Enum):\n key = key.value\n self._span.set_attribute(key, value)\n return self\n\n def add_event(\n self, name: str, attributes: Optional[Mapping[str, any]] = None, timestamp: Optional[float] = None\n ) -> 'TracingSpan':\n converted_timestamp = int(timestamp * 1e9) if timestamp else None\n self._span.add_event(name, attributes, converted_timestamp)\n return self\n\n def log_kv(self, key_values: Mapping[str, any], timestamp=None) -> 'TracingSpan':\n converted_timestamp = int(timestamp * 1e9) if timestamp else None\n event_name = \"log\" if key_values is None or \"event\" not in key_values else key_values[\"event\"]\n self._span.add_event(event_name, key_values, converted_timestamp)\n return self\n\n\nclass NonRecordingTracingSpan(TracingSpan):\n def __init__(self, span: Span):\n super().__init__(span)\n self._span = span\n\n def end(self):\n pass\n\n @property\n def operation_name(self) -> str:\n return \"Non-Recording Span\"\n\n @property\n def parent_id(self) -> Optional[str]:\n return None\n\n @property\n def tags(self) -> Mapping[str, any]:\n return dict()\n\n @property\n def start_time(self) -> int:\n return 0\n\n @property\n def end_time(self) -> Optional[int]:\n return None\n\n @property\n def duration(self) -> float:\n return 0\n\n\nNOOP_TRACING_SCOPE = TracingScope(None, None)\n\n\nclass TransportableSpan(TracingSpan):\n \"\"\"\n A transportable span is a representation of a finished TracingSpan that can be pickled.\n \"\"\"\n\n def __init__(self, span: TracingSpan, endpoint: Optional[str] = None):\n super().__init__(None, endpoint or span.endpoint)\n self._operation_name = span.operation_name\n self._trace_id = span.trace_id\n self._span_id = span.span_id\n self._parent_id = span.parent_id\n self._tags = dict(span.tags)\n self._start_time = span.start_time\n self._end_time = span.end_time\n self._events = tuple(TransportableTracingEvent(event) for event in span.events)\n\n def transportable(self, endpoint_override: Optional[str] = None) -> 'TransportableSpan':\n if not endpoint_override or endpoint_override == self._endpoint:\n return self\n else:\n return TransportableSpan(self, endpoint_override or self._endpoint)\n\n def end(self):\n pass\n\n def is_recording(self):\n return self._end_time is None\n\n @property\n def operation_name(self) -> str:\n return self._operation_name\n\n @property\n def trace_id(self) -> str:\n return self._trace_id\n\n def is_error(self) -> bool:\n return self._tags.get('error', False)\n\n @property\n def start_time(self) -> int:\n \"\"\"\n Start time in ns\n \"\"\"\n return self._start_time\n\n @property\n def end_time(self) -> Optional[int]:\n \"\"\"\n End time in ns\n \"\"\"\n return self._end_time\n\n @property\n def duration(self) -> float:\n \"\"\"\n Duration of the span in milliseconds, or None if the span is not finished.\n \"\"\"\n return (self._end_time - self._start_time) / 1e6 if self._end_time else None\n\n @property\n def span_id(self) -> str:\n return self._span_id\n\n @property\n def parent_id(self) -> Optional[str]:\n return self._parent_id\n\n @property\n def tags(self) -> Mapping[str, any]:\n return self._tags\n\n @property\n def events(self) -> Sequence[TracingEvent]:\n return self._events\n\n def set_tag(self, key: Union[Enum, str], value: Union[bool, str, bytes, int, float, dt.date]) -> 'TracingSpan':\n return self\n\n def add_event(\n self, name: str, attributes: Optional[Mapping[str, any]] = None, timestamp: Optional[float] = None\n ) -> 'TracingSpan':\n return self\n\n def log_kv(self, key_values: Mapping[str, any], timestamp=None) -> 'TracingSpan':\n return self\n\n\nclass TransportableTracingEvent(TracingEvent):\n def __init__(self, event: TracingEvent):\n super().__init__(None)\n self._name = event.name\n self._timestamp = event.timestamp\n self._attributes = dict(event.attributes)\n\n @property\n def name(self) -> str:\n return self._name\n\n @property\n def timestamp(self) -> int:\n \"\"\"\n Timestamp in ns\n \"\"\"\n return self._timestamp\n\n @property\n def timestamp_sec(self) -> float:\n \"\"\"\n Timestamp in seconds\n \"\"\"\n return self._timestamp / 1e9\n\n @property\n def attributes(self) -> Mapping[str, any]:\n return self._attributes\n\n\nclass TracerFactory:\n __tracer_instance = None\n _extra_span_processors = []\n\n def get(self) -> OtelTracer:\n if TracerFactory.__tracer_instance is None:\n # Define which OpenTelemetry Tracer provider implementation to use.\n span_processor = SynchronousMultiSpanProcessor()\n span_processor.add_span_processor(SimpleSpanProcessor(SpanConsumer.get_instance()))\n for extra_processor in TracerFactory._extra_span_processors:\n span_processor.add_span_processor(extra_processor)\n trace.set_tracer_provider(TracerProvider(active_span_processor=span_processor))\n\n # Create an OpenTelemetry Tracer.\n otel_tracer = trace.get_tracer(__name__)\n TracerFactory.__tracer_instance = otel_tracer\n return TracerFactory.__tracer_instance\n\n @staticmethod\n def preregister_span_processor(processor: SpanProcessor):\n if TracerFactory.__tracer_instance is not None:\n _logger.error(\"Can't add span consumer after tracer has been created\")\n else:\n TracerFactory._extra_span_processors.append(processor)\n\n\nclass Tracer(ContextDecorator):\n __factory = TracerFactory()\n\n def __init__(\n self,\n label: str = 'Execution',\n print_on_exit: bool = False,\n threshold: int = None,\n wrap_exceptions=False,\n parent_span: Optional[Union[TracingSpan, TracingContext]] = None,\n ):\n self.__print_on_exit = print_on_exit\n self.__label = label\n self.__threshold = threshold\n self.wrap_exceptions = wrap_exceptions\n self._parent_span = parent_span if isinstance(parent_span, TracingSpan) else None\n self._parent_ctx = parent_span if isinstance(parent_span, TracingContext) else None\n\n def __enter__(self):\n if self._parent_span:\n self.__parent_scope = Tracer.activate_span(self._parent_span)\n self.__scope = Tracer.start_active_span(self.__label, child_of=self._parent_ctx)\n return self.__scope\n\n def __exit__(self, exc_type, exc_value, exc_tb):\n if exc_value:\n self.record_exception(exc_value, self.__scope.span, exc_tb)\n self.__scope.close()\n if self._parent_span:\n self.__parent_scope.close()\n if self.wrap_exceptions and exc_type is not None and not exc_type == MqWrappedError:\n raise MqWrappedError(f'Unable to calculate: {self.__label}') from exc_value\n\n @staticmethod\n def get_instance() -> OtelTracer:\n return Tracer.__factory.get()\n\n @staticmethod\n def set_factory(factory: TracerFactory):\n Tracer.__factory = factory\n\n @staticmethod\n def active_span():\n current_span = trace.get_current_span()\n if current_span is None or not current_span.is_recording():\n return NonRecordingTracingSpan(INVALID_SPAN)\n else:\n return TracingSpan(current_span)\n\n @staticmethod\n def set_propagator_format(propagator_format: TextMapPropagator):\n set_global_textmap(propagator_format)\n\n @staticmethod\n def inject(carrier):\n span = trace.get_current_span()\n if span is not None and span.is_recording():\n try:\n inject(carrier)\n except Exception:\n _logger.error(\"Error injecting trace context\", exc_info=True)\n\n @staticmethod\n def extract(carrier) -> TracingContext:\n try:\n return TracingContext(extract(carrier))\n except Exception:\n _logger.error(\"Error extracting trace context\", exc_info=True)\n\n @staticmethod\n def activate_span(span: TracingSpan = None, finish_on_close: bool = False) -> Optional[TracingScope]:\n \"\"\"\n Activates the current span\n :return: The current span\n \"\"\"\n if span is None or not span.is_recording():\n return NOOP_TRACING_SCOPE\n ctx = trace.set_span_in_context(span.unwrap())\n token = context.attach(ctx)\n return TracingScope(token, span.unwrap(), finish_on_close=finish_on_close)\n\n @staticmethod\n def start_active_span(\n operation_name: str,\n child_of: Optional[TracingContext] = None,\n ignore_active_span: bool = False,\n finish_on_close: bool = True,\n ) -> TracingScope:\n ctx = Context() if ignore_active_span else child_of._context if child_of else None\n span = Tracer.get_instance().start_span(operation_name, context=ctx)\n # Set as the implicit current context\n # Creates a Context object with parent set as current span\n ctx = trace.set_span_in_context(span)\n token = context.attach(ctx)\n return TracingScope(token, span, finish_on_close)\n\n @staticmethod\n def record_exception(e, span: TracingSpan = None, exc_tb=None):\n span = span or TracingSpan(trace.get_current_span())\n if span is not None:\n try:\n span.set_tag('error', True)\n span.log_kv(\n {\n 'event': 'error',\n 'message': str(e),\n 'error.object': str(e),\n 'error.kind': type(e).__name__,\n 'stack': Tracer.__format_traceback(type(e), e, exc_tb),\n }\n )\n except Exception:\n pass\n\n @staticmethod\n def __format_traceback(exc_type, exc_value, exc_tb=None):\n if exc_value is None:\n return ''\n try:\n return ''.join(traceback.format_exception(exc_type, exc_value, exc_tb, limit=10))\n except Exception:\n return ''\n\n @staticmethod\n def reset():\n SpanConsumer.reset()\n\n @staticmethod\n def get_spans() -> Sequence[TracingSpan]:\n return SpanConsumer.get_spans()\n\n @staticmethod\n def plot(reset=False, show=True):\n try:\n import plotly.express as px\n except ImportError:\n _logger.warning('Package \"plotly\" required to visualise the trace, printing instead')\n Tracer.print(reset)\n return\n color_list = ('#2b76f7', '#5a8efb', '#79aaff', '#89bbff', '#cdddff')\n error_color = 'rgb(244, 127, 114)'\n ordered_spans, _ = Tracer.gather_data(False)\n span_df = pd.DataFrame.from_records(\n [\n (\n f'#{i}',\n f'{s.operation_name} {int(s.duration):,.0f}ms',\n dt.datetime.fromtimestamp(s.start_time / 1e9),\n dt.datetime.fromtimestamp(s.end_time / 1e9),\n '\\n '.join([f'{k}={v}' for k, v in s.tags.items()]) if s.tags else '',\n )\n for i, (depth, s) in enumerate(ordered_spans)\n ],\n columns=['id', 'operation', 'start', 'end', 'tags'],\n )\n color_map = {\n f'#{i}': error_color if 'error' in s.tags else color_list[depth % len(color_list)]\n for i, (depth, s) in enumerate(ordered_spans)\n }\n fig = px.timeline(\n data_frame=span_df,\n width=1000,\n height=40 + 30 * len(span_df),\n x_start=\"start\",\n x_end=\"end\",\n y=\"id\",\n hover_data='tags',\n text='operation',\n color='id',\n color_discrete_map=color_map,\n )\n fig.update_layout(\n showlegend=False,\n yaxis_visible=False,\n yaxis_showticklabels=False,\n paper_bgcolor='rgba(0,0,0,0)',\n plot_bgcolor='rgba(0,0,0,0)',\n margin={'l': 0, 't': 0, 'b': 0, 'r': 0},\n )\n if reset:\n Tracer.reset()\n if show:\n fig.show()\n else:\n return fig\n\n @staticmethod\n def gather_data(as_string: bool = True, root_id: Optional[str] = None, trace_id: Optional[str] = None):\n spans = Tracer.get_spans()\n spans_by_parent = {}\n\n for span in reversed(spans):\n if trace_id and span.trace_id != trace_id:\n continue\n spans_by_parent.setdefault(span.parent_id, []).append(span)\n\n def _build_tree(parent_span, depth):\n if as_string:\n name = f'{\"* \" * depth}{parent_span.operation_name}'\n elapsed = (parent_span.end_time - parent_span.start_time) / 1000000\n error = \" [Error]\" if parent_span.is_error() else \"\"\n lines.append(f'{name:<50}{elapsed:>8.1f} ms{error}')\n else:\n lines.append((depth, parent_span))\n for child_span in reversed(spans_by_parent.get(parent_span.span_id, [])):\n _build_tree(child_span, depth + 1)\n\n total = 0\n lines = []\n # By default, we look for the span with no parent, but this might not always be what we want\n for span in reversed(spans_by_parent.get(root_id, [])):\n _build_tree(span, 0)\n total += (span.end_time - span.start_time) / 1000000\n\n if as_string:\n tracing_str = '\\n'.join(lines)\n return tracing_str, total\n else:\n return lines, total\n\n @staticmethod\n def print(reset=True, root_id=None, trace_id=None):\n tracing_str, total = Tracer.gather_data(root_id=root_id, trace_id=trace_id)\n str_id = trace_id or root_id or \"\"\n _logger.warning(f'Tracing Info: {str_id}\\n{tracing_str}\\n{\"-\" * 61}\\nTOTAL:{total:>52.1f} ms')\n if reset:\n Tracer.reset()\n return tracing_str, total\n\n @staticmethod\n def in_scope(func: Callable, operation_name='callback') -> Callable:\n \"\"\"\n for using with futures or callbacks that would otherwise lose the tracing context\n e.g.\n >>>my_future.add_done_callback(my_callback)\n >>>my_other_future.add_done_callback(lambda fut: my_callback(fut.result())\n `\n becomes:\n\n >>>my_future.add_done_callback(Tracer.in_scope(my_callback))\n >>>my_other_future.add_done_callback(Tracer.in_scope(lambda fut: my_callback(fut.result()) )\n \"\"\"\n span_carrier = {}\n Tracer.inject(span_carrier)\n\n def wrapper(*args, **kwargs):\n span_ctx = Tracer.extract(span_carrier)\n with Tracer(operation_name, parent_span=span_ctx):\n return func(*args, **kwargs)\n\n return wrapper\n\n\ndef parse_tracing_line_args(line: str) -> Tuple[Optional[str], bool]:\n stripped = tuple(s for s in line.split(' ') if s != '')\n if len(stripped) > 0 and stripped[0] in ('chart', 'plot', 'graph'):\n return tuple(stripped[1:]) if len(stripped[1:]) else None, True\n return stripped if len(stripped) else None, False\n\n\ntry:\n # Attempt to import/register some jupyter magic\n import gs_quant_internal.tracing.jupyter # noqa\nexcept ImportError:\n try:\n from IPython.core.magic import register_cell_magic\n from IPython import get_ipython\n\n @register_cell_magic(\"trace\")\n def trace_ipython_cell(line, cell):\n \"\"\"Wraps the execution of a cell in a tracer call and prints\"\"\"\n span_name, show_chart = parse_tracing_line_args(line)\n if cell is None:\n return line\n with Tracer(label=span_name):\n res = get_ipython().run_cell(cell)\n if res.error_in_exec:\n Tracer.record_exception(res.error_in_exec)\n if show_chart:\n Tracer.plot(True)\n else:\n Tracer.print(True)\n return None\n except Exception:\n pass\n" }, { "path": "gs_quant/workflow/__init__.py", "content": "\"\"\"\nCopyright 2023 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom gs_quant.target.workflow_quote import * # noqa\nfrom .workflow import * # noqa" }, { "path": "gs_quant/workflow/workflow.py", "content": "\"\"\"\nCopyright 2023 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nfrom typing import Optional, Tuple\n\nfrom dataclasses_json import global_config\n\nfrom gs_quant.json_convertors import decode_hedge_type, decode_hedge_types\nfrom gs_quant.target.workflow_quote import HedgeTypes # noqa - We need to import this one from target\n\nglobal_config.decoders[Optional[HedgeTypes]] = decode_hedge_type\nglobal_config.decoders[HedgeTypes] = decode_hedge_type\nglobal_config.decoders[Optional[Tuple[HedgeTypes, ...]]] = decode_hedge_types\n" }, { "path": "pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools>=61.0\", \"versioneer[toml]==0.28\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"gs_quant\"\ndynamic = [\"version\"]\nauthors = [\n {name = \"Goldman Sachs\", email = \"developer@gs.com\"}\n]\ndescription = \"Goldman Sachs Quant\"\nreadme = \"README.md\"\nlicense = {text = \"Apache-2.0\"}\nrequires-python = \">=3.9\"\nclassifiers = [\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n \"Programming Language :: Python :: 3.13\",\n \"Operating System :: OS Independent\",\n \"License :: OSI Approved :: Apache Software License\",\n]\ndependencies = [\n \"aenum\",\n \"backoff\",\n \"backports.zoneinfo; python_version < '3.9'\",\n \"cachetools\",\n \"certifi\",\n \"dataclasses_json\",\n \"deprecation\",\n \"inflection\",\n \"lmfit\",\n \"more_itertools\",\n \"msgpack\",\n \"nest-asyncio\",\n \"numpy>1.17.0,<2.4.0\",\n \"opentelemetry-api\",\n \"opentelemetry-sdk\",\n \"pandas>=1.4\",\n \"pydash<7.0.0\",\n \"python-dateutil>=2.7.0\",\n \"requests\",\n \"httpx>=0.28.1\",\n \"scipy>=1.2.0\",\n \"statsmodels>=0.13.0\",\n \"tqdm\",\n \"websockets\",\n]\n\n[project.optional-dependencies]\nturbo = [\n \"quant-extensions\",\n]\nnotebook = [\n \"jupyter\",\n \"matplotlib\",\n \"seaborn\",\n \"treelib\",\n]\ntest = [\n \"pytest\",\n \"pytest-cov\",\n \"pytest-mock\",\n \"pytest-order\",\n \"pytest-asyncio\",\n \"testfixtures\",\n \"nbconvert\",\n \"nbformat\",\n \"plotly\",\n \"freezegun\",\n \"ruff\",\n]\ndevelop = [\n \"wheel\",\n \"sphinx\",\n \"sphinx_rtd_theme\",\n \"sphinx_autodoc_typehints\",\n \"pytest\",\n \"pytest-cov\",\n \"pytest-mock\",\n \"pytest-order\",\n \"testfixtures\",\n]\n\n[project.urls]\nHomepage = \"https://marquee.gs.com\"\n\n[dependency-groups]\ntest = [\n \"pytest\",\n \"pytest-cov\",\n \"pytest-mock\",\n \"pytest-order\",\n \"pytest-asyncio\",\n \"testfixtures\",\n \"nbconvert\",\n \"nbformat\",\n \"plotly\",\n \"freezegun\",\n \"ruff\",\n]\ndev = [\n \"wheel\",\n \"sphinx\",\n \"sphinx_rtd_theme\",\n \"sphinx_autodoc_typehints\",\n \"pytest\",\n \"pytest-cov\",\n \"pytest-mock\",\n \"pytest-order\",\n \"testfixtures\",\n]\n\n[tool.setuptools]\ninclude-package-data = true\n\n[tool.setuptools.dynamic]\nversion = {attr = \"gs_quant.__version__\"}\n\n[tool.setuptools.packages.find]\ninclude = [\"gs_quant*\"]\n\n[tool.setuptools.package-data]\ngs_quant = [\n \"config.ini\",\n \"resources/asset-service.json\",\n \"resources/data-service.json\",\n \"resources/risk-service.json\",\n \"resources/certificates/*\",\n \"examples/*\",\n \"guides/*\",\n \"tutorials/*\",\n \"docs/*\",\n \"_version.py\",\n]\n\n[tool.versioneer]\nVCS = \"git\"\nstyle = \"pep440\"\nversionfile_source = \"gs_quant/_version.py\"\nversionfile_build = \"gs_quant/_version.py\"\ntag_prefix = \"release-\"\nparentdir_prefix = \"gs_quant-\"\n\n\n[tool.ruff]\n# Setting can be seen here\n# https://docs.astral.sh/ruff/settings/\n\n# Line lenght override\nline-length = 120\nindent-width = 4\n\n# In addition to the standard set of exclusions, omit all tests, plus a specific file.\nextend-exclude = [\"_version.py\", \"versioneer.py\", \"*.json\", \"target\", \"__init__.py\", \"content\", \"documentation\", \"datacube\"]\n\n# Assume at least Python 3.10, code will be compatible with 3.10 or later\ntarget-version = \"py310\"\n\n[tool.ruff.format]\n# Keep the quotes as they are\nquote-style = \"preserve\"\nskip-magic-trailing-comma = false\n\n[tool.ruff.lint.isort]\ncombine-as-imports = true\n\n[tool.ruff.lint]\n# We add the tidy import selection to pickup banned-api\nextend-select = [\n \"TID251\", # see flake8-tidy-imports.banned-api below\n \"ICN\", # import conventions, e.g. import pandas as pd\n \"NPY\", # Mainly for numpy 2.0 deprecation check\n]\n\n# e.g. block\n# from pandas import DataFrame, Series\n# instead do\n# import pandas as pd\n# pd.DataFrame(), pd.Series()\n[tool.ruff.lint.flake8-import-conventions]\nbanned-from = [\n \"pandas\",\n \"numpy\",\n \"datetime\"\n]\n# The default list include \"pandas as pd\", \"numpy as np\"\nextend-aliases = { \"datetime\" = \"dt\" }\n\n[tool.ruff.lint.flake8-tidy-imports.banned-api]\n\"gs_quant.target.common\".msg = \"Use gs_quant.common instead of gs_quant.target.common\"\n\"gs_quant.target.instrument\".msg = \"Use gs_quant.instrument instead of gs_quant.target.instrument\"\n\"gs_quant.target.workflow_quote\".msg = \"Use gs_quant.workflow instead of gs_quant.target.workflow_quote\"\n\"pytz\".msg = \"Use zoneinfo package or datetime.timezone instead of pytz\"\n# When we support a minimium version of python 3.10\n#\"typing.List\".msg = \"Use built-in list instead of typing.List\"\n#\"typing.Tuple\".msg = \"Use built-in tuple instead of typing.Tuple\"\n#\"typing.Dict\".msg = \"Use built-in dict instead of typing.Dict\"\n#\"typing.Set\".msg = \"Use built-in set instead of typing.set\"\n\n[tool.uv]\ndefault-groups = [\"test\", \"dev\"]\nnative-tls = true\n\n[[tool.uv.index]]\nname = \"aws\"\nurl = \"https://pypi.aws.site.gs.com/repository/pypi-group/simple\"\ndefault = true\n\n[tool.uv.pip]\nindex-url = \"https://pypi.aws.site.gs.com/repository/pypi-group/simple\"\nemit-index-url = true\nuniversal = true\n" }, { "path": "requirements.txt", "content": ".\ngs_quant[notebook]\n" }, { "path": "setup.cfg", "content": "# See the docstring in versioneer.py for instructions. Note that you must\n# re-run 'versioneer.py setup' after changing this section, and commit the\n# resulting files.\n\n[flake8]\nmax-line-length = 120\nignore = F403,F405,W605,W504\nexclude =\n holodeck\n target\n __init__.py\n common.py\n core.py\n base.py\n assets.py\n datacube\n" }, { "path": "setup.py", "content": "\"\"\"\nCopyright 2018 Goldman Sachs.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing,\nsoftware distributed under the License is distributed on an\n\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\nKIND, either express or implied. See the License for the\nspecific language governing permissions and limitations\nunder the License.\n\"\"\"\n\nimport setuptools\nimport versioneer\n\nsetuptools.setup(\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n)\n" }, { "path": "versioneer.py", "content": "\n# Version: 0.28\n\n\"\"\"The Versioneer - like a rocketeer, but for versions.\n\nThe Versioneer\n==============\n\n* like a rocketeer, but for versions!\n* https://github.com/python-versioneer/python-versioneer\n* Brian Warner\n* License: Public Domain (Unlicense)\n* Compatible with: Python 3.7, 3.8, 3.9, 3.10 and pypy3\n* [![Latest Version][pypi-image]][pypi-url]\n* [![Build Status][travis-image]][travis-url]\n\nThis is a tool for managing a recorded version number in setuptools-based\npython projects. The goal is to remove the tedious and error-prone \"update\nthe embedded version string\" step from your release process. Making a new\nrelease should be as easy as recording a new tag in your version-control\nsystem, and maybe making new tarballs.\n\n\n## Quick Install\n\nVersioneer provides two installation modes. The \"classic\" vendored mode installs\na copy of versioneer into your repository. The experimental build-time dependency mode\nis intended to allow you to skip this step and simplify the process of upgrading.\n\n### Vendored mode\n\n* `pip install versioneer` to somewhere in your $PATH\n * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is\n available, so you can also use `conda install -c conda-forge versioneer`\n* add a `[tool.versioneer]` section to your `pyproject.toml` or a\n `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md))\n * Note that you will need to add `tomli; python_version < \"3.11\"` to your\n build-time dependencies if you use `pyproject.toml`\n* run `versioneer install --vendor` in your source tree, commit the results\n* verify version information with `python setup.py version`\n\n### Build-time dependency mode\n\n* `pip install versioneer` to somewhere in your $PATH\n * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is\n available, so you can also use `conda install -c conda-forge versioneer`\n* add a `[tool.versioneer]` section to your `pyproject.toml` or a\n `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md))\n* add `versioneer` (with `[toml]` extra, if configuring in `pyproject.toml`)\n to the `requires` key of the `build-system` table in `pyproject.toml`:\n ```toml\n [build-system]\n requires = [\"setuptools\", \"versioneer[toml]\"]\n build-backend = \"setuptools.build_meta\"\n ```\n* run `versioneer install --no-vendor` in your source tree, commit the results\n* verify version information with `python setup.py version`\n\n## Version Identifiers\n\nSource trees come from a variety of places:\n\n* a version-control system checkout (mostly used by developers)\n* a nightly tarball, produced by build automation\n* a snapshot tarball, produced by a web-based VCS browser, like github's\n \"tarball from tag\" feature\n* a release tarball, produced by \"setup.py sdist\", distributed through PyPI\n\nWithin each source tree, the version identifier (either a string or a number,\nthis tool is format-agnostic) can come from a variety of places:\n\n* ask the VCS tool itself, e.g. \"git describe\" (for checkouts), which knows\n about recent \"tags\" and an absolute revision-id\n* the name of the directory into which the tarball was unpacked\n* an expanded VCS keyword ($Id$, etc)\n* a `_version.py` created by some earlier build step\n\nFor released software, the version identifier is closely related to a VCS\ntag. Some projects use tag names that include more than just the version\nstring (e.g. \"myproject-1.2\" instead of just \"1.2\"), in which case the tool\nneeds to strip the tag prefix to extract the version identifier. For\nunreleased software (between tags), the version identifier should provide\nenough information to help developers recreate the same tree, while also\ngiving them an idea of roughly how old the tree is (after version 1.2, before\nversion 1.3). Many VCS systems can report a description that captures this,\nfor example `git describe --tags --dirty --always` reports things like\n\"0.7-1-g574ab98-dirty\" to indicate that the checkout is one revision past the\n0.7 tag, has a unique revision id of \"574ab98\", and is \"dirty\" (it has\nuncommitted changes).\n\nThe version identifier is used for multiple purposes:\n\n* to allow the module to self-identify its version: `myproject.__version__`\n* to choose a name and prefix for a 'setup.py sdist' tarball\n\n## Theory of Operation\n\nVersioneer works by adding a special `_version.py` file into your source\ntree, where your `__init__.py` can import it. This `_version.py` knows how to\ndynamically ask the VCS tool for version information at import time.\n\n`_version.py` also contains `$Revision$` markers, and the installation\nprocess marks `_version.py` to have this marker rewritten with a tag name\nduring the `git archive` command. As a result, generated tarballs will\ncontain enough information to get the proper version.\n\nTo allow `setup.py` to compute a version too, a `versioneer.py` is added to\nthe top level of your source tree, next to `setup.py` and the `setup.cfg`\nthat configures it. This overrides several distutils/setuptools commands to\ncompute the version when invoked, and changes `setup.py build` and `setup.py\nsdist` to replace `_version.py` with a small static file that contains just\nthe generated version data.\n\n## Installation\n\nSee [INSTALL.md](./INSTALL.md) for detailed installation instructions.\n\n## Version-String Flavors\n\nCode which uses Versioneer can learn about its version string at runtime by\nimporting `_version` from your main `__init__.py` file and running the\n`get_versions()` function. From the \"outside\" (e.g. in `setup.py`), you can\nimport the top-level `versioneer.py` and run `get_versions()`.\n\nBoth functions return a dictionary with different flavors of version\ninformation:\n\n* `['version']`: A condensed version string, rendered using the selected\n style. This is the most commonly used value for the project's version\n string. The default \"pep440\" style yields strings like `0.11`,\n `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the \"Styles\" section\n below for alternative styles.\n\n* `['full-revisionid']`: detailed revision identifier. For Git, this is the\n full SHA1 commit id, e.g. \"1076c978a8d3cfc70f408fe5974aa6c092c949ac\".\n\n* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the\n commit date in ISO 8601 format. This will be None if the date is not\n available.\n\n* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that\n this is only accurate if run in a VCS checkout, otherwise it is likely to\n be False or None\n\n* `['error']`: if the version string could not be computed, this will be set\n to a string describing the problem, otherwise it will be None. It may be\n useful to throw an exception in setup.py if this is set, to avoid e.g.\n creating tarballs with a version string of \"unknown\".\n\nSome variants are more useful than others. Including `full-revisionid` in a\nbug report should allow developers to reconstruct the exact code being tested\n(or indicate the presence of local changes that should be shared with the\ndevelopers). `version` is suitable for display in an \"about\" box or a CLI\n`--version` output: it can be easily compared against release notes and lists\nof bugs fixed in various releases.\n\nThe installer adds the following text to your `__init__.py` to place a basic\nversion in `YOURPROJECT.__version__`:\n\n from ._version import get_versions\n __version__ = get_versions()['version']\n del get_versions\n\n## Styles\n\nThe setup.cfg `style=` configuration controls how the VCS information is\nrendered into a version string.\n\nThe default style, \"pep440\", produces a PEP440-compliant string, equal to the\nun-prefixed tag name for actual releases, and containing an additional \"local\nversion\" section with more detail for in-between builds. For Git, this is\nTAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags\n--dirty --always`. For example \"0.11+2.g1076c97.dirty\" indicates that the\ntree is like the \"1076c97\" commit but has uncommitted changes (\".dirty\"), and\nthat this commit is two revisions (\"+2\") beyond the \"0.11\" tag. For released\nsoftware (exactly equal to a known tag), the identifier will only contain the\nstripped tag, e.g. \"0.11\".\n\nOther styles are available. See [details.md](details.md) in the Versioneer\nsource tree for descriptions.\n\n## Debugging\n\nVersioneer tries to avoid fatal errors: if something goes wrong, it will tend\nto return a version of \"0+unknown\". To investigate the problem, run `setup.py\nversion`, which will run the version-lookup code in a verbose mode, and will\ndisplay the full contents of `get_versions()` (including the `error` string,\nwhich may help identify what went wrong).\n\n## Known Limitations\n\nSome situations are known to cause problems for Versioneer. This details the\nmost significant ones. More can be found on Github\n[issues page](https://github.com/python-versioneer/python-versioneer/issues).\n\n### Subprojects\n\nVersioneer has limited support for source trees in which `setup.py` is not in\nthe root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are\ntwo common reasons why `setup.py` might not be in the root:\n\n* Source trees which contain multiple subprojects, such as\n [Buildbot](https://github.com/buildbot/buildbot), which contains both\n \"master\" and \"slave\" subprojects, each with their own `setup.py`,\n `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI\n distributions (and upload multiple independently-installable tarballs).\n* Source trees whose main purpose is to contain a C library, but which also\n provide bindings to Python (and perhaps other languages) in subdirectories.\n\nVersioneer will look for `.git` in parent directories, and most operations\nshould get the right version string. However `pip` and `setuptools` have bugs\nand implementation details which frequently cause `pip install .` from a\nsubproject directory to fail to find a correct version string (so it usually\ndefaults to `0+unknown`).\n\n`pip install --editable .` should work correctly. `setup.py install` might\nwork too.\n\nPip-8.1.1 is known to have this problem, but hopefully it will get fixed in\nsome later version.\n\n[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking\nthis issue. The discussion in\n[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the\nissue from the Versioneer side in more detail.\n[pip PR#3176](https://github.com/pypa/pip/pull/3176) and\n[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve\npip to let Versioneer work correctly.\n\nVersioneer-0.16 and earlier only looked for a `.git` directory next to the\n`setup.cfg`, so subprojects were completely unsupported with those releases.\n\n### Editable installs with setuptools <= 18.5\n\n`setup.py develop` and `pip install --editable .` allow you to install a\nproject into a virtualenv once, then continue editing the source code (and\ntest) without re-installing after every change.\n\n\"Entry-point scripts\" (`setup(entry_points={\"console_scripts\": ..})`) are a\nconvenient way to specify executable scripts that should be installed along\nwith the python package.\n\nThese both work as expected when using modern setuptools. When using\nsetuptools-18.5 or earlier, however, certain operations will cause\n`pkg_resources.DistributionNotFound` errors when running the entrypoint\nscript, which must be resolved by re-installing the package. This happens\nwhen the install happens with one version, then the egg_info data is\nregenerated while a different version is checked out. Many setup.py commands\ncause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into\na different virtualenv), so this can be surprising.\n\n[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes\nthis one, but upgrading to a newer version of setuptools should probably\nresolve it.\n\n\n## Updating Versioneer\n\nTo upgrade your project to a new release of Versioneer, do the following:\n\n* install the new Versioneer (`pip install -U versioneer` or equivalent)\n* edit `setup.cfg` and `pyproject.toml`, if necessary,\n to include any new configuration settings indicated by the release notes.\n See [UPGRADING](./UPGRADING.md) for details.\n* re-run `versioneer install --[no-]vendor` in your source tree, to replace\n `SRC/_version.py`\n* commit any changed files\n\n## Future Directions\n\nThis tool is designed to make it easily extended to other version-control\nsystems: all VCS-specific components are in separate directories like\nsrc/git/ . The top-level `versioneer.py` script is assembled from these\ncomponents by running make-versioneer.py . In the future, make-versioneer.py\nwill take a VCS name as an argument, and will construct a version of\n`versioneer.py` that is specific to the given VCS. It might also take the\nconfiguration arguments that are currently provided manually during\ninstallation by editing setup.py . Alternatively, it might go the other\ndirection and include code from all supported VCS systems, reducing the\nnumber of intermediate scripts.\n\n## Similar projects\n\n* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time\n dependency\n* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of\n versioneer\n* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools\n plugin\n\n## License\n\nTo make Versioneer easier to embed, all its code is dedicated to the public\ndomain. The `_version.py` that it creates is also in the public domain.\nSpecifically, both are released under the \"Unlicense\", as described in\nhttps://unlicense.org/.\n\n[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg\n[pypi-url]: https://pypi.python.org/pypi/versioneer/\n[travis-image]:\nhttps://img.shields.io/travis/com/python-versioneer/python-versioneer.svg\n[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer\n\n\"\"\"\n# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring\n# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements\n# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error\n# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with\n# pylint:disable=attribute-defined-outside-init,too-many-arguments\n\nimport configparser\nimport errno\nimport json\nimport os\nimport re\nimport subprocess\nimport sys\nfrom pathlib import Path\nfrom typing import Callable, Dict\nimport functools\n\nhave_tomllib = True\nif sys.version_info >= (3, 11):\n import tomllib\nelse:\n try:\n import tomli as tomllib\n except ImportError:\n have_tomllib = False\n\n\nclass VersioneerConfig:\n \"\"\"Container for Versioneer configuration parameters.\"\"\"\n\n\ndef get_root():\n \"\"\"Get the project root directory.\n\n We require that all commands are run from the project root, i.e. the\n directory that contains setup.py, setup.cfg, and versioneer.py .\n \"\"\"\n root = os.path.realpath(os.path.abspath(os.getcwd()))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n # allow 'python path/to/setup.py COMMAND'\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n err = (\"Versioneer was unable to run the project root directory. \"\n \"Versioneer requires setup.py to be executed from \"\n \"its immediate directory (like 'python setup.py COMMAND'), \"\n \"or in a way that lets it use sys.argv[0] to find the root \"\n \"(like 'python path/to/setup.py COMMAND').\")\n raise VersioneerBadRootError(err)\n try:\n # Certain runtime workflows (setup.py install/develop in a setuptools\n # tree) execute all dependencies in a single python process, so\n # \"versioneer\" may be imported multiple times, and python's shared\n # module-import table will cache the first one. So we can't use\n # os.path.dirname(__file__), as that will find whichever\n # versioneer.py was first imported, even in later projects.\n my_path = os.path.realpath(os.path.abspath(__file__))\n me_dir = os.path.normcase(os.path.splitext(my_path)[0])\n vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\n if me_dir != vsr_dir and \"VERSIONEER_PEP518\" not in globals():\n print(\"Warning: build in %s is using versioneer.py from %s\"\n % (os.path.dirname(my_path), versioneer_py))\n except NameError:\n pass\n return root\n\n\ndef get_config_from_root(root):\n \"\"\"Read the project setup.cfg file to determine Versioneer config.\"\"\"\n # This might raise OSError (if setup.cfg is missing), or\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\n # configparser.NoOptionError (if it lacks \"VCS=\"). See the docstring at\n # the top of versioneer.py for instructions on writing your setup.cfg .\n root = Path(root)\n pyproject_toml = root / \"pyproject.toml\"\n setup_cfg = root / \"setup.cfg\"\n section = None\n if pyproject_toml.exists() and have_tomllib:\n try:\n with open(pyproject_toml, 'rb') as fobj:\n pp = tomllib.load(fobj)\n section = pp['tool']['versioneer']\n except (tomllib.TOMLDecodeError, KeyError):\n pass\n if not section:\n parser = configparser.ConfigParser()\n with open(setup_cfg) as cfg_file:\n parser.read_file(cfg_file)\n parser.get(\"versioneer\", \"VCS\") # raise error if missing\n\n section = parser[\"versioneer\"]\n\n cfg = VersioneerConfig()\n cfg.VCS = section['VCS']\n cfg.style = section.get(\"style\", \"\")\n cfg.versionfile_source = section.get(\"versionfile_source\")\n cfg.versionfile_build = section.get(\"versionfile_build\")\n cfg.tag_prefix = section.get(\"tag_prefix\")\n if cfg.tag_prefix in (\"''\", '\"\"', None):\n cfg.tag_prefix = \"\"\n cfg.parentdir_prefix = section.get(\"parentdir_prefix\")\n cfg.verbose = section.get(\"verbose\")\n return cfg\n\n\nclass NotThisMethod(Exception):\n \"\"\"Exception raised if a method is not valid for the current scenario.\"\"\"\n\n\n# these dictionaries contain VCS-specific tools\nLONG_VERSION_PY: Dict[str, str] = {}\nHANDLERS: Dict[str, Dict[str, Callable]] = {}\n\n\ndef register_vcs_handler(vcs, method): # decorator\n \"\"\"Create decorator to mark a method as the handler of a VCS.\"\"\"\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n HANDLERS.setdefault(vcs, {})[method] = f\n return f\n return decorate\n\n\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,\n env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n process = None\n\n popen_kwargs = {}\n if sys.platform == \"win32\":\n # This hides the console window if pythonw.exe is used\n startupinfo = subprocess.STARTUPINFO()\n startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n popen_kwargs[\"startupinfo\"] = startupinfo\n\n for command in commands:\n try:\n dispcmd = str([command] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n process = subprocess.Popen([command] + args, cwd=cwd, env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr\n else None), **popen_kwargs)\n break\n except OSError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None, None\n stdout = process.communicate()[0].strip().decode()\n if process.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, process.returncode\n return stdout, process.returncode\n\n\nLONG_VERSION_PY['git'] = r'''\n# This file helps to compute a version number in source trees obtained from\n# git-archive tarball (such as those provided by githubs download-from-tag\n# feature). Distribution tarballs (built by setup.py sdist) and build\n# directories (produced by setup.py build) will contain a much shorter file\n# that just contains the computed version number.\n\n# This file is released into the public domain.\n# Generated by versioneer-0.28\n# https://github.com/python-versioneer/python-versioneer\n\n\"\"\"Git implementation of _version.py.\"\"\"\n\nimport errno\nimport os\nimport re\nimport subprocess\nimport sys\nfrom typing import Callable, Dict\nimport functools\n\n\ndef get_keywords():\n \"\"\"Get the keywords needed to look up the version information.\"\"\"\n # these strings will be replaced by git during git-archive.\n # setup.py/versioneer.py will grep for the variable names, so they must\n # each be defined on a line of their own. _version.py will just call\n # get_keywords().\n git_refnames = \"%(DOLLAR)sFormat:%%d%(DOLLAR)s\"\n git_full = \"%(DOLLAR)sFormat:%%H%(DOLLAR)s\"\n git_date = \"%(DOLLAR)sFormat:%%ci%(DOLLAR)s\"\n keywords = {\"refnames\": git_refnames, \"full\": git_full, \"date\": git_date}\n return keywords\n\n\nclass VersioneerConfig:\n \"\"\"Container for Versioneer configuration parameters.\"\"\"\n\n\ndef get_config():\n \"\"\"Create, populate and return the VersioneerConfig() object.\"\"\"\n # these strings are filled in when 'setup.py versioneer' creates\n # _version.py\n cfg = VersioneerConfig()\n cfg.VCS = \"git\"\n cfg.style = \"%(STYLE)s\"\n cfg.tag_prefix = \"%(TAG_PREFIX)s\"\n cfg.parentdir_prefix = \"%(PARENTDIR_PREFIX)s\"\n cfg.versionfile_source = \"%(VERSIONFILE_SOURCE)s\"\n cfg.verbose = False\n return cfg\n\n\nclass NotThisMethod(Exception):\n \"\"\"Exception raised if a method is not valid for the current scenario.\"\"\"\n\n\nLONG_VERSION_PY: Dict[str, str] = {}\nHANDLERS: Dict[str, Dict[str, Callable]] = {}\n\n\ndef register_vcs_handler(vcs, method): # decorator\n \"\"\"Create decorator to mark a method as the handler of a VCS.\"\"\"\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n return decorate\n\n\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,\n env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n process = None\n\n popen_kwargs = {}\n if sys.platform == \"win32\":\n # This hides the console window if pythonw.exe is used\n startupinfo = subprocess.STARTUPINFO()\n startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW\n popen_kwargs[\"startupinfo\"] = startupinfo\n\n for command in commands:\n try:\n dispcmd = str([command] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n process = subprocess.Popen([command] + args, cwd=cwd, env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr\n else None), **popen_kwargs)\n break\n except OSError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %%s\" %% dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %%s\" %% (commands,))\n return None, None\n stdout = process.communicate()[0].strip().decode()\n if process.returncode != 0:\n if verbose:\n print(\"unable to run %%s (error)\" %% dispcmd)\n print(\"stdout was %%s\" %% stdout)\n return None, process.returncode\n return stdout, process.returncode\n\n\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for _ in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\"version\": dirname[len(parentdir_prefix):],\n \"full-revisionid\": None,\n \"dirty\": False, \"error\": None, \"date\": None}\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\"Tried directories %%s but none started with prefix %%s\" %%\n (str(rootdirs), parentdir_prefix))\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")\n\n\n@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n with open(versionfile_abs, \"r\") as fobj:\n for line in fobj:\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n except OSError:\n pass\n return keywords\n\n\n@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n \"\"\"Get version information from git keywords.\"\"\"\n if \"refnames\" not in keywords:\n raise NotThisMethod(\"Short version file found\")\n date = keywords.get(\"date\")\n if date is not None:\n # Use only the last line. Previous lines may contain GPG signature\n # information.\n date = date.splitlines()[-1]\n\n # git-2.2.0 added \"%%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = {r.strip() for r in refnames.strip(\"()\").split(\",\")}\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %%d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = {r for r in refs if re.search(r'\\d', r)}\n if verbose:\n print(\"discarding '%%s', no digits\" %% \",\".join(refs - tags))\n if verbose:\n print(\"likely tags: %%s\" %% \",\".join(sorted(tags)))\n for ref in sorted(tags):\n # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n if ref.startswith(tag_prefix):\n r = ref[len(tag_prefix):]\n # Filter out refs that exactly match prefix or that don't start\n # with a number once the prefix is stripped (mostly a concern\n # when prefix is '')\n if not re.match(r'\\d', r):\n continue\n if verbose:\n print(\"picking %%s\" %% r)\n return {\"version\": r,\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": None,\n \"date\": date}\n # no suitable tags, so version is \"0+unknown\", but full hex is still there\n if verbose:\n print(\"no suitable tags, using unknown + full revision id\")\n return {\"version\": \"0+unknown\",\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": \"no suitable tags\", \"date\": None}\n\n\n@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n # GIT_DIR can interfere with correct operation of Versioneer.\n # It may be intended to be passed to the Versioneer-versioned project,\n # but that should not change where we get our version from.\n env = os.environ.copy()\n env.pop(\"GIT_DIR\", None)\n runner = functools.partial(runner, env=env)\n\n _, rc = runner(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root,\n hide_stderr=not verbose)\n if rc != 0:\n if verbose:\n print(\"Directory %%s not under git control\" %% root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = runner(GITS, [\n \"describe\", \"--tags\", \"--dirty\", \"--always\", \"--long\",\n \"--match\", f\"{tag_prefix}[[:digit:]]*\"\n ], cwd=root)\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = runner(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n branch_name, rc = runner(GITS, [\"rev-parse\", \"--abbrev-ref\", \"HEAD\"],\n cwd=root)\n # --abbrev-ref was added in git-1.6.3\n if rc != 0 or branch_name is None:\n raise NotThisMethod(\"'git rev-parse --abbrev-ref' returned error\")\n branch_name = branch_name.strip()\n\n if branch_name == \"HEAD\":\n # If we aren't exactly on a branch, pick a branch which represents\n # the current commit. If all else fails, we are on a branchless\n # commit.\n branches, rc = runner(GITS, [\"branch\", \"--contains\"], cwd=root)\n # --contains was added in git-1.5.4\n if rc != 0 or branches is None:\n raise NotThisMethod(\"'git branch --contains' returned error\")\n branches = branches.split(\"\\n\")\n\n # Remove the first line if we're running detached\n if \"(\" in branches[0]:\n branches.pop(0)\n\n # Strip off the leading \"* \" from the list of branches.\n branches = [branch[2:] for branch in branches]\n if \"master\" in branches:\n branch_name = \"master\"\n elif not branches:\n branch_name = None\n else:\n # Pick the first branch that is returned. Good or bad.\n branch_name = branches[0]\n\n pieces[\"branch\"] = branch_name\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[:git_describe.rindex(\"-dirty\")]\n\n # now we have TAG-NUM-gHEX or HEX\n\n if \"-\" in git_describe:\n # TAG-NUM-gHEX\n mo = re.search(r'^(.+)-(\\d+)-g([0-9a-f]+)$', git_describe)\n if not mo:\n # unparsable. Maybe git-describe is misbehaving?\n pieces[\"error\"] = (\"unable to parse git-describe output: '%%s'\"\n %% describe_out)\n return pieces\n\n # tag\n full_tag = mo.group(1)\n if not full_tag.startswith(tag_prefix):\n if verbose:\n fmt = \"tag '%%s' doesn't start with prefix '%%s'\"\n print(fmt %% (full_tag, tag_prefix))\n pieces[\"error\"] = (\"tag '%%s' doesn't start with prefix '%%s'\"\n %% (full_tag, tag_prefix))\n return pieces\n pieces[\"closest-tag\"] = full_tag[len(tag_prefix):]\n\n # distance: number of commits since tag\n pieces[\"distance\"] = int(mo.group(2))\n\n # commit: short hex revision ID\n pieces[\"short\"] = mo.group(3)\n\n else:\n # HEX: no tags\n pieces[\"closest-tag\"] = None\n out, rc = runner(GITS, [\"rev-list\", \"HEAD\", \"--left-right\"], cwd=root)\n pieces[\"distance\"] = len(out.split()) # total number of commits\n\n # commit date: see ISO-8601 comment in git_versions_from_keywords()\n date = runner(GITS, [\"show\", \"-s\", \"--format=%%ci\", \"HEAD\"], cwd=root)[0].strip()\n # Use only the last line. Previous lines may contain GPG signature\n # information.\n date = date.splitlines()[-1]\n pieces[\"date\"] = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n\n return pieces\n\n\ndef plus_or_dot(pieces):\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"\n\n\ndef render_pep440(pieces):\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%%d.g%%s\" %% (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%%d.g%%s\" %% (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_branch(pieces):\n \"\"\"TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .\n\n The \".dev0\" means not master branch. Note that .dev0 sorts backwards\n (a feature branch will appear \"older\" than the master branch).\n\n Exceptions:\n 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"%%d.g%%s\" %% (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0\"\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += \"+untagged.%%d.g%%s\" %% (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef pep440_split_post(ver):\n \"\"\"Split pep440 version string at the post-release segment.\n\n Returns the release segments before the post-release and the\n post-release version number (or -1 if no post-release segment is present).\n \"\"\"\n vc = str.split(ver, \".post\")\n return vc[0], int(vc[1] or 0) if len(vc) == 2 else None\n\n\ndef render_pep440_pre(pieces):\n \"\"\"TAG[.postN.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post0.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n if pieces[\"distance\"]:\n # update the post release segment\n tag_version, post_version = pep440_split_post(pieces[\"closest-tag\"])\n rendered = tag_version\n if post_version is not None:\n rendered += \".post%%d.dev%%d\" %% (post_version + 1, pieces[\"distance\"])\n else:\n rendered += \".post0.dev%%d\" %% (pieces[\"distance\"])\n else:\n # no commits, use the tag as the version\n rendered = pieces[\"closest-tag\"]\n else:\n # exception #1\n rendered = \"0.post0.dev%%d\" %% pieces[\"distance\"]\n return rendered\n\n\ndef render_pep440_post(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%%d\" %% pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%%s\" %% pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%%d\" %% pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%%s\" %% pieces[\"short\"]\n return rendered\n\n\ndef render_pep440_post_branch(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .\n\n The \".dev0\" means not master branch.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%%d\" %% pieces[\"distance\"]\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%%s\" %% pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0.post%%d\" %% pieces[\"distance\"]\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += \"+g%%s\" %% pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_old(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%%d\" %% pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%%d\" %% pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered\n\n\ndef render_git_describe(pieces):\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%%d-g%%s\" %% (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered\n\n\ndef render_git_describe_long(pieces):\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%%d-g%%s\" %% (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered\n\n\ndef render(pieces, style):\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None}\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-branch\":\n rendered = render_pep440_branch(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-post-branch\":\n rendered = render_pep440_post_branch(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%%s'\" %% style)\n\n return {\"version\": rendered, \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"], \"error\": None,\n \"date\": pieces.get(\"date\")}\n\n\ndef get_versions():\n \"\"\"Get version information or return default if unable to do so.\"\"\"\n # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have\n # __file__, we can work backwards from there to the root. Some\n # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which\n # case we can only use expanded keywords.\n\n cfg = get_config()\n verbose = cfg.verbose\n\n try:\n return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,\n verbose)\n except NotThisMethod:\n pass\n\n try:\n root = os.path.realpath(__file__)\n # versionfile_source is the relative path from the top of the source\n # tree (where the .git directory might live) to this file. Invert\n # this to find the root from __file__.\n for _ in cfg.versionfile_source.split('/'):\n root = os.path.dirname(root)\n except NameError:\n return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to find root of source tree\",\n \"date\": None}\n\n try:\n pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)\n return render(pieces, cfg.style)\n except NotThisMethod:\n pass\n\n try:\n if cfg.parentdir_prefix:\n return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)\n except NotThisMethod:\n pass\n\n return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n \"dirty\": None,\n \"error\": \"unable to compute version\", \"date\": None}\n'''\n\n\n@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n with open(versionfile_abs, \"r\") as fobj:\n for line in fobj:\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n except OSError:\n pass\n return keywords\n\n\n@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n \"\"\"Get version information from git keywords.\"\"\"\n if \"refnames\" not in keywords:\n raise NotThisMethod(\"Short version file found\")\n date = keywords.get(\"date\")\n if date is not None:\n # Use only the last line. Previous lines may contain GPG signature\n # information.\n date = date.splitlines()[-1]\n\n # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = {r.strip() for r in refnames.strip(\"()\").split(\",\")}\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = {r for r in refs if re.search(r'\\d', r)}\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n if verbose:\n print(\"likely tags: %s\" % \",\".join(sorted(tags)))\n for ref in sorted(tags):\n # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n if ref.startswith(tag_prefix):\n r = ref[len(tag_prefix):]\n # Filter out refs that exactly match prefix or that don't start\n # with a number once the prefix is stripped (mostly a concern\n # when prefix is '')\n if not re.match(r'\\d', r):\n continue\n if verbose:\n print(\"picking %s\" % r)\n return {\"version\": r,\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": None,\n \"date\": date}\n # no suitable tags, so version is \"0+unknown\", but full hex is still there\n if verbose:\n print(\"no suitable tags, using unknown + full revision id\")\n return {\"version\": \"0+unknown\",\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": \"no suitable tags\", \"date\": None}\n\n\n@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.exe\", r\"I:\\sw\\gs\\git-1.16\\dist\\win\\cmd\\git.exe\"]\n\n # GIT_DIR can interfere with correct operation of Versioneer.\n # It may be intended to be passed to the Versioneer-versioned project,\n # but that should not change where we get our version from.\n env = os.environ.copy()\n env.pop(\"GIT_DIR\", None)\n runner = functools.partial(runner, env=env)\n\n _, rc = runner(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root,\n hide_stderr=not verbose)\n if rc != 0:\n if verbose:\n print(\"Directory %s not under git control\" % root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = runner(GITS, [\n \"describe\", \"--tags\", \"--dirty\", \"--always\", \"--long\",\n \"--match\", f\"{tag_prefix}[[:digit:]]*\"\n ], cwd=root)\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = runner(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n branch_name, rc = runner(GITS, [\"rev-parse\", \"--abbrev-ref\", \"HEAD\"],\n cwd=root)\n # --abbrev-ref was added in git-1.6.3\n if rc != 0 or branch_name is None:\n raise NotThisMethod(\"'git rev-parse --abbrev-ref' returned error\")\n branch_name = branch_name.strip()\n\n if branch_name == \"HEAD\":\n # If we aren't exactly on a branch, pick a branch which represents\n # the current commit. If all else fails, we are on a branchless\n # commit.\n branches, rc = runner(GITS, [\"branch\", \"--contains\"], cwd=root)\n # --contains was added in git-1.5.4\n if rc != 0 or branches is None:\n raise NotThisMethod(\"'git branch --contains' returned error\")\n branches = branches.split(\"\\n\")\n\n # Remove the first line if we're running detached\n if \"(\" in branches[0]:\n branches.pop(0)\n\n # Strip off the leading \"* \" from the list of branches.\n branches = [branch[2:] for branch in branches]\n if \"master\" in branches:\n branch_name = \"master\"\n elif not branches:\n branch_name = None\n else:\n # Pick the first branch that is returned. Good or bad.\n branch_name = branches[0]\n\n pieces[\"branch\"] = branch_name\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[:git_describe.rindex(\"-dirty\")]\n\n # now we have TAG-NUM-gHEX or HEX\n\n if \"-\" in git_describe:\n # TAG-NUM-gHEX\n mo = re.search(r'^(.+)-(\\d+)-g([0-9a-f]+)$', git_describe)\n if not mo:\n # unparsable. Maybe git-describe is misbehaving?\n pieces[\"error\"] = (\"unable to parse git-describe output: '%s'\"\n % describe_out)\n return pieces\n\n # tag\n full_tag = mo.group(1)\n if not full_tag.startswith(tag_prefix):\n if verbose:\n fmt = \"tag '%s' doesn't start with prefix '%s'\"\n print(fmt % (full_tag, tag_prefix))\n pieces[\"error\"] = (\"tag '%s' doesn't start with prefix '%s'\"\n % (full_tag, tag_prefix))\n return pieces\n pieces[\"closest-tag\"] = full_tag[len(tag_prefix):]\n\n # distance: number of commits since tag\n pieces[\"distance\"] = int(mo.group(2))\n\n # commit: short hex revision ID\n pieces[\"short\"] = mo.group(3)\n\n else:\n # HEX: no tags\n pieces[\"closest-tag\"] = None\n out, rc = runner(GITS, [\"rev-list\", \"HEAD\", \"--left-right\"], cwd=root)\n pieces[\"distance\"] = len(out.split()) # total number of commits\n\n # commit date: see ISO-8601 comment in git_versions_from_keywords()\n date = runner(GITS, [\"show\", \"-s\", \"--format=%ci\", \"HEAD\"], cwd=root)[0].strip()\n # Use only the last line. Previous lines may contain GPG signature\n # information.\n date = date.splitlines()[-1]\n pieces[\"date\"] = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n\n return pieces\n\n\ndef do_vcs_install(versionfile_source, ipy):\n \"\"\"Git-specific installation logic for Versioneer.\n\n For Git, this means creating/changing .gitattributes to mark _version.py\n for export-subst keyword substitution.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.exe\", r\"I:\\sw\\gs\\git-1.16\\dist\\win\\cmd\\git.exe\"]\n files = [versionfile_source]\n if ipy:\n files.append(ipy)\n if \"VERSIONEER_PEP518\" not in globals():\n try:\n my_path = __file__\n if my_path.endswith((\".pyc\", \".pyo\")):\n my_path = os.path.splitext(my_path)[0] + \".py\"\n versioneer_file = os.path.relpath(my_path)\n except NameError:\n versioneer_file = \"versioneer.py\"\n files.append(versioneer_file)\n present = False\n try:\n with open(\".gitattributes\", \"r\") as fobj:\n for line in fobj:\n if line.strip().startswith(versionfile_source):\n if \"export-subst\" in line.strip().split()[1:]:\n present = True\n break\n except OSError:\n pass\n if not present:\n with open(\".gitattributes\", \"a+\") as fobj:\n fobj.write(f\"{versionfile_source} export-subst\\n\")\n files.append(\".gitattributes\")\n run_command(GITS, [\"add\", \"--\"] + files)\n\n\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for _ in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\"version\": dirname[len(parentdir_prefix):],\n \"full-revisionid\": None,\n \"dirty\": False, \"error\": None, \"date\": None}\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\"Tried directories %s but none started with prefix %s\" %\n (str(rootdirs), parentdir_prefix))\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")\n\n\nSHORT_VERSION_PY = \"\"\"\n# This file was generated by 'versioneer.py' (0.28) from\n# revision-control system data, or from the parent directory name of an\n# unpacked source archive. Distribution tarballs contain a pre-generated copy\n# of this file.\n\nimport json\n\nversion_json = '''\n%s\n''' # END VERSION_JSON\n\n\ndef get_versions():\n return json.loads(version_json)\n\"\"\"\n\n\ndef versions_from_file(filename):\n \"\"\"Try to determine the version from _version.py if present.\"\"\"\n try:\n with open(filename) as f:\n contents = f.read()\n except OSError:\n raise NotThisMethod(\"unable to read _version.py\")\n mo = re.search(r\"version_json = '''\\n(.*)''' # END VERSION_JSON\",\n contents, re.M | re.S)\n if not mo:\n mo = re.search(r\"version_json = '''\\r\\n(.*)''' # END VERSION_JSON\",\n contents, re.M | re.S)\n if not mo:\n raise NotThisMethod(\"no version_json in _version.py\")\n return json.loads(mo.group(1))\n\n\ndef write_to_version_file(filename, versions):\n \"\"\"Write the given version number to the given _version.py file.\"\"\"\n os.unlink(filename)\n contents = json.dumps(versions, sort_keys=True,\n indent=1, separators=(\",\", \": \"))\n with open(filename, \"w\") as f:\n f.write(SHORT_VERSION_PY % contents)\n\n print(\"set %s to '%s'\" % (filename, versions[\"version\"]))\n\n\ndef plus_or_dot(pieces):\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"\n\n\ndef render_pep440(pieces):\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_branch(pieces):\n \"\"\"TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .\n\n The \".dev0\" means not master branch. Note that .dev0 sorts backwards\n (a feature branch will appear \"older\" than the master branch).\n\n Exceptions:\n 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0\"\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += \"+untagged.%d.g%s\" % (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef pep440_split_post(ver):\n \"\"\"Split pep440 version string at the post-release segment.\n\n Returns the release segments before the post-release and the\n post-release version number (or -1 if no post-release segment is present).\n \"\"\"\n vc = str.split(ver, \".post\")\n return vc[0], int(vc[1] or 0) if len(vc) == 2 else None\n\n\ndef render_pep440_pre(pieces):\n \"\"\"TAG[.postN.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post0.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n if pieces[\"distance\"]:\n # update the post release segment\n tag_version, post_version = pep440_split_post(pieces[\"closest-tag\"])\n rendered = tag_version\n if post_version is not None:\n rendered += \".post%d.dev%d\" % (post_version + 1, pieces[\"distance\"])\n else:\n rendered += \".post0.dev%d\" % (pieces[\"distance\"])\n else:\n # no commits, use the tag as the version\n rendered = pieces[\"closest-tag\"]\n else:\n # exception #1\n rendered = \"0.post0.dev%d\" % pieces[\"distance\"]\n return rendered\n\n\ndef render_pep440_post(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered\n\n\ndef render_pep440_post_branch(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .\n\n The \".dev0\" means not master branch.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"branch\"] != \"master\":\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered\n\n\ndef render_pep440_old(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered\n\n\ndef render_git_describe(pieces):\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered\n\n\ndef render_git_describe_long(pieces):\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered\n\n\ndef render(pieces, style):\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None}\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-branch\":\n rendered = render_pep440_branch(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-post-branch\":\n rendered = render_pep440_post_branch(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\"version\": rendered, \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"], \"error\": None,\n \"date\": pieces.get(\"date\")}\n\n\nclass VersioneerBadRootError(Exception):\n \"\"\"The project root directory is unknown or missing key files.\"\"\"\n\n\ndef get_versions(verbose=False):\n \"\"\"Get the project version from whatever source is available.\n\n Returns dict with two keys: 'version' and 'full'.\n \"\"\"\n if \"versioneer\" in sys.modules:\n # see the discussion in cmdclass.py:get_cmdclass()\n del sys.modules[\"versioneer\"]\n\n root = get_root()\n cfg = get_config_from_root(root)\n\n assert cfg.VCS is not None, \"please set [versioneer]VCS= in setup.cfg\"\n handlers = HANDLERS.get(cfg.VCS)\n assert handlers, \"unrecognized VCS '%s'\" % cfg.VCS\n verbose = verbose or cfg.verbose\n assert cfg.versionfile_source is not None, \\\n \"please set versioneer.versionfile_source\"\n assert cfg.tag_prefix is not None, \"please set versioneer.tag_prefix\"\n\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\n\n # extract version from first of: _version.py, VCS command (e.g. 'git\n # describe'), parentdir. This is meant to work for developers using a\n # source checkout, for users of a tarball created by 'setup.py sdist',\n # and for users of a tarball/zipball created by 'git archive' or github's\n # download-from-tag feature or the equivalent in other VCSes.\n\n get_keywords_f = handlers.get(\"get_keywords\")\n from_keywords_f = handlers.get(\"keywords\")\n if get_keywords_f and from_keywords_f:\n try:\n keywords = get_keywords_f(versionfile_abs)\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\n if verbose:\n print(\"got version from expanded keyword %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n try:\n ver = versions_from_file(versionfile_abs)\n if verbose:\n print(\"got version from file %s %s\" % (versionfile_abs, ver))\n return ver\n except NotThisMethod:\n pass\n\n from_vcs_f = handlers.get(\"pieces_from_vcs\")\n if from_vcs_f:\n try:\n pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\n ver = render(pieces, cfg.style)\n if verbose:\n print(\"got version from VCS %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n try:\n if cfg.parentdir_prefix:\n ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)\n if verbose:\n print(\"got version from parentdir %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n if verbose:\n print(\"unable to compute version\")\n\n return {\"version\": \"0+unknown\", \"full-revisionid\": None,\n \"dirty\": None, \"error\": \"unable to compute version\",\n \"date\": None}\n\n\ndef get_version():\n \"\"\"Get the short version string for this project.\"\"\"\n return get_versions()[\"version\"]\n\n\ndef get_cmdclass(cmdclass=None):\n \"\"\"Get the custom setuptools subclasses used by Versioneer.\n\n If the package uses a different cmdclass (e.g. one from numpy), it\n should be provide as an argument.\n \"\"\"\n if \"versioneer\" in sys.modules:\n del sys.modules[\"versioneer\"]\n # this fixes the \"python setup.py develop\" case (also 'install' and\n # 'easy_install .'), in which subdependencies of the main project are\n # built (using setup.py bdist_egg) in the same python process. Assume\n # a main project A and a dependency B, which use different versions\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\n # sys.modules by the time B's setup.py is executed, causing B to run\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\n # sandbox that restores sys.modules to it's pre-build state, so the\n # parent is protected against the child's \"import versioneer\". By\n # removing ourselves from sys.modules here, before the child build\n # happens, we protect the child from the parent's versioneer too.\n # Also see https://github.com/python-versioneer/python-versioneer/issues/52\n\n cmds = {} if cmdclass is None else cmdclass.copy()\n\n # we add \"version\" to setuptools\n from setuptools import Command\n\n class cmd_version(Command):\n description = \"report generated version string\"\n user_options = []\n boolean_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n print(\" date: %s\" % vers.get(\"date\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])\n cmds[\"version\"] = cmd_version\n\n # we override \"build_py\" in setuptools\n #\n # most invocation pathways end up running build_py:\n # distutils/build -> build_py\n # distutils/install -> distutils/build ->..\n # setuptools/bdist_wheel -> distutils/install ->..\n # setuptools/bdist_egg -> distutils/install_lib -> build_py\n # setuptools/install -> bdist_egg ->..\n # setuptools/develop -> ?\n # pip install:\n # copies source tree to a tempdir before running egg_info/etc\n # if .git isn't copied too, 'git describe' will fail\n # then does setup.py bdist_wheel, or sometimes setup.py install\n # setup.py egg_info -> ?\n\n # pip install -e . and setuptool/editable_wheel will invoke build_py\n # but the build_py command is not expected to copy any files.\n\n # we override different \"build_py\" commands for both environments\n if 'build_py' in cmds:\n _build_py = cmds['build_py']\n else:\n from setuptools.command.build_py import build_py as _build_py\n\n class cmd_build_py(_build_py):\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n _build_py.run(self)\n if getattr(self, \"editable_mode\", False):\n # During editable installs `.py` and data files are\n # not copied to build_lib\n return\n # now locate _version.py in the new build/ directory and replace\n # it with an updated value\n if cfg.versionfile_build:\n target_versionfile = os.path.join(self.build_lib,\n cfg.versionfile_build)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)\n cmds[\"build_py\"] = cmd_build_py\n\n if 'build_ext' in cmds:\n _build_ext = cmds['build_ext']\n else:\n from setuptools.command.build_ext import build_ext as _build_ext\n\n class cmd_build_ext(_build_ext):\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n _build_ext.run(self)\n if self.inplace:\n # build_ext --inplace will only build extensions in\n # build/lib<..> dir with no _version.py to write to.\n # As in place builds will already have a _version.py\n # in the module dir, we do not need to write one.\n return\n # now locate _version.py in the new build/ directory and replace\n # it with an updated value\n if not cfg.versionfile_build:\n return\n target_versionfile = os.path.join(self.build_lib,\n cfg.versionfile_build)\n if not os.path.exists(target_versionfile):\n print(f\"Warning: {target_versionfile} does not exist, skipping \"\n \"version update. This can happen if you are running build_ext \"\n \"without first running build_py.\")\n return\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)\n cmds[\"build_ext\"] = cmd_build_ext\n\n if \"cx_Freeze\" in sys.modules: # cx_freeze enabled?\n from cx_Freeze.dist import build_exe as _build_exe\n # nczeczulin reports that py2exe won't like the pep440-style string\n # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.\n # setup(console=[{\n # \"version\": versioneer.get_version().split(\"+\", 1)[0], # FILEVERSION\n # \"product_version\": versioneer.get_version(),\n # ...\n\n class cmd_build_exe(_build_exe):\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n target_versionfile = cfg.versionfile_source\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)\n\n _build_exe.run(self)\n os.unlink(target_versionfile)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(LONG %\n {\"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n })\n cmds[\"build_exe\"] = cmd_build_exe\n del cmds[\"build_py\"]\n\n if 'py2exe' in sys.modules: # py2exe enabled?\n try:\n from py2exe.setuptools_buildexe import py2exe as _py2exe\n except ImportError:\n from py2exe.distutils_buildexe import py2exe as _py2exe\n\n class cmd_py2exe(_py2exe):\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n target_versionfile = cfg.versionfile_source\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)\n\n _py2exe.run(self)\n os.unlink(target_versionfile)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(LONG %\n {\"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n })\n cmds[\"py2exe\"] = cmd_py2exe\n\n # sdist farms its file list building out to egg_info\n if 'egg_info' in cmds:\n _egg_info = cmds['egg_info']\n else:\n from setuptools.command.egg_info import egg_info as _egg_info\n\n class cmd_egg_info(_egg_info):\n def find_sources(self):\n # egg_info.find_sources builds the manifest list and writes it\n # in one shot\n super().find_sources()\n\n # Modify the filelist and normalize it\n root = get_root()\n cfg = get_config_from_root(root)\n self.filelist.append('versioneer.py')\n if cfg.versionfile_source:\n # There are rare cases where versionfile_source might not be\n # included by default, so we must be explicit\n self.filelist.append(cfg.versionfile_source)\n self.filelist.sort()\n self.filelist.remove_duplicates()\n\n # The write method is hidden in the manifest_maker instance that\n # generated the filelist and was thrown away\n # We will instead replicate their final normalization (to unicode,\n # and POSIX-style paths)\n from setuptools import unicode_utils\n normalized = [unicode_utils.filesys_decode(f).replace(os.sep, '/')\n for f in self.filelist.files]\n\n manifest_filename = os.path.join(self.egg_info, 'SOURCES.txt')\n with open(manifest_filename, 'w') as fobj:\n fobj.write('\\n'.join(normalized))\n\n cmds['egg_info'] = cmd_egg_info\n\n # we override different \"sdist\" commands for both environments\n if 'sdist' in cmds:\n _sdist = cmds['sdist']\n else:\n from setuptools.command.sdist import sdist as _sdist\n\n class cmd_sdist(_sdist):\n def run(self):\n versions = get_versions()\n self._versioneer_generated_versions = versions\n # unless we update this, the command will keep using the old\n # version\n self.distribution.metadata.version = versions[\"version\"]\n return _sdist.run(self)\n\n def make_release_tree(self, base_dir, files):\n root = get_root()\n cfg = get_config_from_root(root)\n _sdist.make_release_tree(self, base_dir, files)\n # now locate _version.py in the new base_dir directory\n # (remembering that it may be a hardlink) and replace it with an\n # updated value\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile,\n self._versioneer_generated_versions)\n cmds[\"sdist\"] = cmd_sdist\n\n return cmds\n\n\nCONFIG_ERROR = \"\"\"\nsetup.cfg is missing the necessary Versioneer configuration. You need\na section like:\n\n [versioneer]\n VCS = git\n style = pep440\n versionfile_source = src/myproject/_version.py\n versionfile_build = myproject/_version.py\n tag_prefix =\n parentdir_prefix = myproject-\n\nYou will also need to edit your setup.py to use the results:\n\n import versioneer\n setup(version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(), ...)\n\nPlease read the docstring in ./versioneer.py for configuration instructions,\nedit setup.cfg, and re-run the installer or 'python versioneer.py setup'.\n\"\"\"\n\nSAMPLE_CONFIG = \"\"\"\n# See the docstring in versioneer.py for instructions. Note that you must\n# re-run 'versioneer.py setup' after changing this section, and commit the\n# resulting files.\n\n[versioneer]\n#VCS = git\n#style = pep440\n#versionfile_source =\n#versionfile_build =\n#tag_prefix =\n#parentdir_prefix =\n\n\"\"\"\n\nOLD_SNIPPET = \"\"\"\nfrom ._version import get_versions\n__version__ = get_versions()['version']\ndel get_versions\n\"\"\"\n\nINIT_PY_SNIPPET = \"\"\"\nfrom . import {0}\n__version__ = {0}.get_versions()['version']\n\"\"\"\n\n\ndef do_setup():\n \"\"\"Do main VCS-independent setup function for installing Versioneer.\"\"\"\n root = get_root()\n try:\n cfg = get_config_from_root(root)\n except (OSError, configparser.NoSectionError,\n configparser.NoOptionError) as e:\n if isinstance(e, (OSError, configparser.NoSectionError)):\n print(\"Adding sample versioneer config to setup.cfg\",\n file=sys.stderr)\n with open(os.path.join(root, \"setup.cfg\"), \"a\") as f:\n f.write(SAMPLE_CONFIG)\n print(CONFIG_ERROR, file=sys.stderr)\n return 1\n\n print(\" creating %s\" % cfg.versionfile_source)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(LONG % {\"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n })\n\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source),\n \"__init__.py\")\n if os.path.exists(ipy):\n try:\n with open(ipy, \"r\") as f:\n old = f.read()\n except OSError:\n old = \"\"\n module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0]\n snippet = INIT_PY_SNIPPET.format(module)\n if OLD_SNIPPET in old:\n print(\" replacing boilerplate in %s\" % ipy)\n with open(ipy, \"w\") as f:\n f.write(old.replace(OLD_SNIPPET, snippet))\n elif snippet not in old:\n print(\" appending to %s\" % ipy)\n with open(ipy, \"a\") as f:\n f.write(snippet)\n else:\n print(\" %s unmodified\" % ipy)\n else:\n print(\" %s doesn't exist, ok\" % ipy)\n ipy = None\n\n # Make VCS-specific changes. For git, this means creating/changing\n # .gitattributes to mark _version.py for export-subst keyword\n # substitution.\n do_vcs_install(cfg.versionfile_source, ipy)\n return 0\n\n\ndef scan_setup_py():\n \"\"\"Validate the contents of setup.py against Versioneer's expectations.\"\"\"\n found = set()\n setters = False\n errors = 0\n with open(\"setup.py\", \"r\") as f:\n for line in f.readlines():\n if \"import versioneer\" in line:\n found.add(\"import\")\n if \"versioneer.get_cmdclass()\" in line:\n found.add(\"cmdclass\")\n if \"versioneer.get_version()\" in line:\n found.add(\"get_version\")\n if \"versioneer.VCS\" in line:\n setters = True\n if \"versioneer.versionfile_source\" in line:\n setters = True\n if len(found) != 3:\n print(\"\")\n print(\"Your setup.py appears to be missing some important items\")\n print(\"(but I might be wrong). Please make sure it has something\")\n print(\"roughly like the following:\")\n print(\"\")\n print(\" import versioneer\")\n print(\" setup( version=versioneer.get_version(),\")\n print(\" cmdclass=versioneer.get_cmdclass(), ...)\")\n print(\"\")\n errors += 1\n if setters:\n print(\"You should remove lines like 'versioneer.VCS = ' and\")\n print(\"'versioneer.versionfile_source = ' . This configuration\")\n print(\"now lives in setup.cfg, and should be removed from setup.py\")\n print(\"\")\n errors += 1\n return errors\n\n\ndef setup_command():\n \"\"\"Set up Versioneer and exit with appropriate error code.\"\"\"\n errors = do_setup()\n errors += scan_setup_py()\n sys.exit(1 if errors else 0)\n\n\nif __name__ == \"__main__\":\n cmd = sys.argv[1]\n if cmd == \"setup\":\n setup_command()\n" } ]